From ee203d36b4b343c63e3fd086423ebed7cb4d16a9 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 23 May 2023 12:33:33 +0000 Subject: [PATCH 001/148] fixed art bug --- deckard/base/model/art_pipeline.py | 63 ++++---- deckard/layers/experiment.py | 138 ++++++++++++++++++ deckard/layers/optimise.py | 14 +- examples/pytorch/attacks.sh | 33 +++++ examples/pytorch/conf/default.yaml | 35 +++++ examples/pytorch/conf/files/default.yaml | 1 + examples/pytorch/conf/model/torch_mnist.yaml | 6 +- examples/pytorch/dvc.lock | 52 +++---- examples/pytorch/dvc.yaml | 33 ++++- examples/pytorch/models.sh | 25 ++++ examples/pytorch/params.yaml | 1 + examples/pytorch/torch_example.py | 60 +++----- examples/sklearn/attacks.sh | 33 +++++ examples/sklearn/defense_search.py | 52 ------- examples/sklearn/dvc.yaml | 11 ++ examples/sklearn/layer_pipeline.yaml | 90 ------------ examples/sklearn/models.sh | 16 +- examples/tfv2/attacks.sh | 33 +++++ examples/tfv2/conf/data/tensorflow_mnist.yaml | 1 - examples/tfv2/conf/default.yaml | 43 ++++++ .../tfv2/conf/model/tensorflow_mnist.yaml | 3 +- examples/tfv2/dvc.lock | 50 ++++--- examples/tfv2/dvc.yaml | 16 ++ examples/tfv2/install.sh | 9 ++ examples/tfv2/models.sh | 25 ++++ examples/tfv2/params.yaml | 9 +- 26 files changed, 558 insertions(+), 294 deletions(-) create mode 100644 deckard/layers/experiment.py create mode 100644 examples/pytorch/attacks.sh create mode 100644 examples/pytorch/models.sh create mode 100644 examples/sklearn/attacks.sh delete mode 100644 examples/sklearn/defense_search.py delete mode 100644 examples/sklearn/layer_pipeline.yaml create mode 100644 examples/tfv2/attacks.sh create mode 100644 examples/tfv2/install.sh create mode 100644 examples/tfv2/models.sh diff --git a/deckard/base/model/art_pipeline.py b/deckard/base/model/art_pipeline.py index 2d6bb779..3ed7cdb6 100644 --- a/deckard/base/model/art_pipeline.py +++ b/deckard/base/model/art_pipeline.py @@ -158,35 +158,36 @@ def __hash__(self): # return iter(self.pipeline) def __call__(self, model: object, library: str = None, data=None) -> BaseEstimator: - for stage in self.pipeline: - name, kwargs = self.pipeline[stage]() - logger.info( - f"Applying pipeline stage: {name} with library: {library} and kwargs: {kwargs}", - ) - pre_def = [] - post_def = [] - if data is None: - data = model.data() - if library in kwargs: - library = kwargs.pop("library", None) - elif library is None: - library = self.library - else: - assert ( - library in supported_models - ), f"library must be one of {supported_models}. Got {library}" - assert len(data) == 4, f"data must be a tuple of length 4. Got {data}" - if name == "preprocessor": - obj = instantiate(library, name, **kwargs) - pre_def.append(obj) - kwargs.update({"preprocessing_defenses": pre_def}) - if name == "postprocessor": - obj = instantiate(library, name, **kwargs) - post_def.append(obj) - kwargs.update({"postprocessing_defenses": post_def}) + + pre_def = [] + post_def = [] + if data is None: + data = model.data() + if library is None: + library = self.library + else: + assert ( + library in supported_models + ), f"library must be one of {supported_models}. Got {library}" + assert len(data) == 4, f"data must be a tuple of length 4. Got {data}" + if "preprocessor" in self.pipeline: + name, kwargs = self.pipeline["preprocessor"]() + obj = instantiate(library, name, **kwargs) + pre_def.append(obj) + kwargs.update({"preprocessing_defenses": pre_def}) + if "postprocessor" in self.pipeline: + name, kwargs = self.pipeline["postprocessor"]() + obj = instantiate(library, name, **kwargs) + post_def.append(obj) + kwargs.update({"postprocessing_defenses": post_def}) + if "initialize" in self.pipeline: + name, kwargs = self.pipeline["initialize"]() model = ArtInitializer(model=model, data=data, **kwargs, library=library)() - if name == "transformer": - model = instantiate(model, name, **kwargs) - if name == "trainer": - raise NotImplementedError("Training Defense not implemented yet") - return model + else: + model = ArtInitializer(model=model, data=data, library=library)() + if "transformer" in self.pipeline: + name, kwargs = self.pipeline["transformer"]() + model = instantiate(model, name, **kwargs) + if "trainer" in self.pipeline: + raise NotImplementedError("Training Defense not implemented yet") + return model diff --git a/deckard/layers/experiment.py b/deckard/layers/experiment.py new file mode 100644 index 00000000..1e9f7d62 --- /dev/null +++ b/deckard/layers/experiment.py @@ -0,0 +1,138 @@ +import logging +from pathlib import Path +import dvc.api +from hydra.utils import instantiate +from hydra import initialize_config_dir, compose +from omegaconf import OmegaConf +from dulwich.errors import NotGitRepository +import yaml +import argparse +from ..base.utils import unflatten_dict + +logger = logging.getLogger(__name__) + +__all__ = [ + "get_dvc_stage_params", + "run_stage", + "get_stages", + "run_stages", + "save_params_file", +] + + +def get_dvc_stage_params( + stage, + params_file="params.yaml", + pipeline_file="dvc.yaml", + directory=".", +): + logger.info( + f"Getting params for stage {stage} from {params_file} and {pipeline_file} in {directory}.", + ) + params = dvc.api.params_show(params_file, stages=stage, repo=directory) + params.update({"_target_": "deckard.base.experiment.Experiment"}) + files = dvc.api.params_show(pipeline_file, stages=stage, repo=directory) + unflattened_files = unflatten_dict(files) + params["files"] = dict(unflattened_files.get("files", unflattened_files)) + params["files"]["_target_"] = "deckard.base.files.FileConfig" + params["files"]["stage"] = stage + return params + + +def run_stage( + params_file="params.yaml", + pipeline_file="dvc.yaml", + directory=".", + stage=None, +): + logger.info( + f"Running stage {stage} with params_file: {params_file} and pipeline_file: {pipeline_file} in directory {directory}", + ) + params = get_dvc_stage_params( + stage=stage, + params_file=params_file, + pipeline_file=pipeline_file, + directory=directory, + ) + exp = instantiate(params) + id_ = exp.name + score = exp() + return id_, score + + +def get_stages(pipeline_file="dvc.yaml", stages=None, repo=None): + try: + def_stages = list( + dvc.api.params_show(pipeline_file, repo=repo)["stages"].keys(), + ) + except NotGitRepository: + raise ValueError( + f"Directory {repo} is not a git repository. Please run `dvc init` in {repo} and try again.", + ) + if stages is None or stages == []: + stages = def_stages + elif isinstance(stages, str): + stages = [stages] + else: + assert isinstance(stages, list), f"args.stage is of type {type(stages)}" + for stage in stages: + assert ( + stage in def_stages + ), f"Stage {stage} not found in {pipeline_file}. Available stages: {def_stages}" + return stages + + +def run_stages(stages, pipeline_file="dvc.yaml", params_file="params.yaml", repo=None): + results = {} + stages = get_stages(stages=stages, pipeline_file=pipeline_file, repo=repo) + for stage in stages: + id_, score = run_stage( + stage=stage, + pipeline_file=pipeline_file, + params_file=params_file, + directory=repo, + ) + results[id_] = score + return results + + +def save_params_file( + config_dir="conf", + config_file="default", + params_file="params.yaml", +): + config_dir = str(Path(Path(), config_dir).absolute().as_posix()) + with initialize_config_dir(config_dir=config_dir, version_base="1.3"): + cfg = compose(config_name=config_file) + params = OmegaConf.to_container(cfg, resolve=True) + with open(params_file, "w") as f: + yaml.dump(params, f) + + +if __name__ == "__main__": + logger = logging.getLogger(__name__) + dvc_parser = argparse.ArgumentParser() + dvc_parser.add_argument("stage", type=str, nargs="*", default=None) + dvc_parser.add_argument("--verbosity", type=str, default="INFO") + dvc_parser.add_argument("--params_file", type=str, default="params.yaml") + dvc_parser.add_argument("--pipeline_file", type=str, default="dvc.yaml") + dvc_parser.add_argument("--config_dir", type=str, default="conf") + dvc_parser.add_argument("--config_file", type=str, default="default") + dvc_parser.add_argument("--workdir", type=str, default=".") + args = dvc_parser.parse_args() + config_dir = Path(Path(), args.config_dir).resolve().as_posix() + save_params_file( + config_dir=config_dir, + config_file=args.config_file, + params_file=args.params_file, + ) + logging.basicConfig( + level=args.verbosity, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + ) + results = run_stages( + stages=args.stage, + pipeline_file=args.pipeline_file, + params_file=args.params_file, + repo=args.workdir, + ) \ No newline at end of file diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index 79354b03..83aa6c8b 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -26,14 +26,8 @@ def find_stage_params(params_file, pipeline_file, stage, working_dir, **kwargs): old_params = dvc.api.params_show(params_file, stages=[stage], repo=working_dir) old_params = flatten_dict(old_params) new_params = flatten_dict(kwargs) - new_trunc_keys = [".".join(key.split(".")[:-1]) for key in new_params.keys()] - params = {} - for key in old_params: - trunc = ".".join(key.split(".")[:-1]) if len(key.split(".")) > 1 else key - if trunc in new_trunc_keys and key in new_params: - params[key] = new_params[key] - else: - params[key] = old_params[key] + params = deepcopy(old_params) + params.update(**new_params) # Setup the files params = unflatten_dict(params) params["files"] = {} @@ -66,7 +60,7 @@ def get_params( stage=stage, **cfg, ) - id_ = my_hash(cfg) + cfg.update({"_target_": "deckard.base.experiment.Experiment"}) if "attack_file" in cfg["files"] and cfg["files"]["attack_file"] is not None: @@ -86,6 +80,8 @@ def get_params( Path(cfg["files"]["data_file"]).with_name(my_hash(cfg["data"])).as_posix(), ) cfg["files"]["_target_"] = "deckard.base.files.FileConfig" + id_ = my_hash(cfg) + cfg['name'] = id_ cfg["files"]["name"] = id_ if stage is not None: cfg["files"]["stage"] = stage diff --git a/examples/pytorch/attacks.sh b/examples/pytorch/attacks.sh new file mode 100644 index 00000000..7b6b0479 --- /dev/null +++ b/examples/pytorch/attacks.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +# This script is used to generate the attacks for the sklearn example. + +# Fast Gradient Method +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.FastGradientMethod +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=100 +stage=attack --multirun + +# Projected Gradient Descent +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.ProjectedGradientDescent +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=100 +attack.init.max_iter=10 +stage=attack --multirun + +# Carlini L0 Method +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.CarliniL0Method +attack.init.batch_size=100 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack --multirun + +# Carlini L2 Method +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.CarliniL2Method +attack.init.batch_size=100 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack --multirun + +# Carlini LInf Method +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.CarliniLInfMethod +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack --multirun + +# DeepFool +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.DeepFool +attack.init.max_iter=10 +attack.init.batch_size=100 +attack.init.nb_grads=10,100,1000 +stage=attack --multirun + +# HopSkipJump +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.HopSkipJump +attack.init.max_iter=10 +attack.init.max_eval=10 +attack.init.init_eval=10 +attack.init.norm=inf,2 +stage=attack --multirun + +# PixelAttack +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.PixelAttack +attack.init.max_iter=10 +attack.init.th=.5,.9,.99 +stage=attack --multirun + +# ThresholdAttack +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.max_iter=10 +attack.init.th=.5,.9,.99 +stage=attack --multirun + +# AdversarialPatch +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.max_iter=10 +attack.init.learning_rate=.5,5.0,50.0 +stage=attack --multirun diff --git a/examples/pytorch/conf/default.yaml b/examples/pytorch/conf/default.yaml index 7f86d6f1..c7e508de 100644 --- a/examples/pytorch/conf/default.yaml +++ b/examples/pytorch/conf/default.yaml @@ -5,3 +5,38 @@ defaults: - attack: hsj - files: default - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 10 + n_jobs: 1 + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 100 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r + diff --git a/examples/pytorch/conf/files/default.yaml b/examples/pytorch/conf/files/default.yaml index 927f2088..f83c7e51 100644 --- a/examples/pytorch/conf/files/default.yaml +++ b/examples/pytorch/conf/files/default.yaml @@ -18,3 +18,4 @@ probabilities_file: probabilities.json predictions_file : predictions.json adv_predictions_file : adv_predictions.json adv_probabilities_file: adv_probabilities.json +name: default \ No newline at end of file diff --git a/examples/pytorch/conf/model/torch_mnist.yaml b/examples/pytorch/conf/model/torch_mnist.yaml index f5949854..52f789c9 100644 --- a/examples/pytorch/conf/model/torch_mnist.yaml +++ b/examples/pytorch/conf/model/torch_mnist.yaml @@ -8,12 +8,12 @@ trainer: batch_size: 64 library : pytorch art: - library : ${model.library} + library : ${..library} _target_ : deckard.base.model.art_pipeline.ArtPipeline initialize: criterion: - name : "torch.nn.CrossEntropyLoss" + name : torch.nn.CrossEntropyLoss optimizer: - name : "torch.optim.SGD" + name : torch.optim.SGD lr : 0.01 momentum : 0.9 diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index 726fd145..6721cfb3 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -33,6 +33,7 @@ stages: model_dir: models model_file: model model_type: .pt + name: default params_file: params.yaml predictions_file: predictions.json probabilities_file: probabilities.json @@ -89,21 +90,21 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.pt - md5: f4e046a289ebf1a5f85b69291b4b7aa5 - size: 76859 - - path: output/reports/train/predictions.json - md5: 09cc9498df88abb687c90bb417f539ec - size: 2830313 - - path: output/reports/train/probabilities.json - md5: 09cc9498df88abb687c90bb417f539ec - size: 2830313 - - path: output/reports/train/score_dict.json - md5: 32e4503d6eef78775f5e116a31ea8450 - size: 367 - - path: output/reports/train/test_labels.json + md5: c3a53ff12de22b40ebf1c0cd8618c33a + size: 76623 + - path: output/reports/train/default/predictions.json + md5: 1431cff19f3459f50ccfdf3b2f0ea91d + size: 2831070 + - path: output/reports/train/default/probabilities.json + md5: 1431cff19f3459f50ccfdf3b2f0ea91d + size: 2831070 + - path: output/reports/train/default/score_dict.json + md5: 4d99a831a24d1bbb46e91b90fb1cd49d + size: 398 + - path: output/reports/train/default/test_labels.json md5: f26b1ad6bd01a70de4290c6ae713e2c7 size: 728000 - - path: output/reports/train/train_labels.json + - path: output/reports/train/default/train_labels.json md5: b78e69f96f37e36ba2cf279422642325 size: 2912000 attack: @@ -113,8 +114,8 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.pt - md5: f4e046a289ebf1a5f85b69291b4b7aa5 - size: 76859 + md5: c3a53ff12de22b40ebf1c0cd8618c33a + size: 76623 params: params.yaml: attack: @@ -228,6 +229,7 @@ stages: model_dir: models model_file: model model_type: .pt + name: default params_file: params.yaml predictions_file: predictions.json probabilities_file: probabilities.json @@ -281,14 +283,14 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: dcf9bdf69bf7faa99fbfc927b338ba57 + md5: f6933d3d01f97b4f400414bfaf4b72c2 size: 31517 - - path: output/reports/attack/adv_predictions.json - md5: 6cce32b645f77a3e736b7709052adfde - size: 2178 - - path: output/reports/attack/adv_probabilities.json - md5: 6cce32b645f77a3e736b7709052adfde - size: 2178 - - path: output/reports/attack/score_dict.json - md5: 68e51697c6e65a82353824f6f9cd32b3 - size: 482 + - path: output/reports/attack/default/adv_predictions.json + md5: 37f7e29d1ad7fd70bd14338b9ae2abdd + size: 2220 + - path: output/reports/attack/default/adv_probabilities.json + md5: 37f7e29d1ad7fd70bd14338b9ae2abdd + size: 2220 + - path: output/reports/attack/default/score_dict.json + md5: 2958a67178c47bf468fc23eaff679891 + size: 539 diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index 54736f50..f55d4ec6 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -9,12 +9,12 @@ stages: outs: - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - - ${files.directory}/${files.reports}/train/${files.train_labels_file} - - ${files.directory}/${files.reports}/train/${files.test_labels_file} - - ${files.directory}/${files.reports}/train/${files.predictions_file} - - ${files.directory}/${files.reports}/train/${files.probabilities_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.train_labels_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} metrics: - - ${files.directory}/${files.reports}/train/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} attack: cmd: python -m deckard.layers.experiment attack params: @@ -25,10 +25,27 @@ stages: - files outs: - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} - - ${files.directory}/${files.reports}/attack/${files.adv_predictions_file} - - ${files.directory}/${files.reports}/attack/${files.adv_probabilities_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} deps: - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} metrics: - - ${files.directory}/${files.reports}/attack/${files.score_dict_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + + ############################################################################## + models: + cmd: bash models.sh + deps: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + outs: + - model.db + attacks: + cmd: bash attacks.sh + deps: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + - model.db + outs: + - attack.db diff --git a/examples/pytorch/models.sh b/examples/pytorch/models.sh new file mode 100644 index 00000000..00389289 --- /dev/null +++ b/examples/pytorch/models.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +# This script is used to generate the models for the sklearn example. + + +# This line generates the model and adds the FeatureSqueezing preprocessing defence. +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +stage=train --multirun + +# Gaussian Augmentation (Input) +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.2,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +stage=train --multirun + +# Spatial Smoothing +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.SpatialSmoothing +model.art.preprocessor.params.window_size=2,3,4 +stage=train --multirun + +# Total Variance Minimisation +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.TotalVarMin +model.art.preprocessor.params.prob=.001,.01,.1 +model.art.preprocessor.params.norm=1,2,3 +model.art.preprocessor.params.lamb=.05,.5,.95 +model.art.preprocessor.params.max_iter=100 +stage=train --multirun + +# Gaussian Noise (Output) +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.1,.9,.999 +stage=train --multirun + +# High Confidence +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.5,.9,.99 +stage=train --multirun + +# Rounded (Output) +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.Rounded +model.art.postprocessor.params.decimals=1,2,4,8 +stage=train --multirun diff --git a/examples/pytorch/params.yaml b/examples/pytorch/params.yaml index 7c224bfc..a3cd3aba 100644 --- a/examples/pytorch/params.yaml +++ b/examples/pytorch/params.yaml @@ -121,6 +121,7 @@ files: model_dir: models model_file: model model_type: .pt + name: default params_file: params.yaml predictions_file: predictions.json probabilities_file: probabilities.json diff --git a/examples/pytorch/torch_example.py b/examples/pytorch/torch_example.py index 74adbdfa..fb8365e4 100644 --- a/examples/pytorch/torch_example.py +++ b/examples/pytorch/torch_example.py @@ -2,6 +2,8 @@ import torch.nn as nn import torch.nn.functional as F +from torchvision.models import resnet18 + __all__ = [ "ResNet", "resnet18", @@ -172,13 +174,14 @@ def forward(self, x): class ResNet(nn.Module): def __init__( self, - block, + layers, num_classes=10, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, + block = BasicBlock, norm_layer=None, ): super(ResNet, self).__init__() @@ -338,43 +341,20 @@ def resnet18(pretrained=False, progress=True, device="cpu", **kwargs): ) -# if "__main__" == __name__: -# X_train, X_test, y_train, y_test = Data(generate = {"name" : "torch_mnist"})() -# # X_train = np.array(X_train).astype(np.float32) -# # X_test = np.array(X_test).astype(np.float32) - -# # X_train = np.transpose(X_train, (0, 3, 1, 2)).astype(np.float32) -# # X_test = np.transpose(X_test, (0, 3, 1, 2)).astype(np.float32) -# mean_ = np.mean(X_train) -# std_ = np.std(X_train) -# min_pixel_value = np.amin(X_train) -# max_pixel_value = np.amax(X_train) -# n_classes = np.unique(y_train) -# input_shape = X_train[0].shape - -# # # Step 2: Create the model -# criterion = nn.CrossEntropyLoss() -# net = Net() -# optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) -# classifier = PyTorchClassifier(model=net, clip_values=(min_pixel_value, max_pixel_value), loss=criterion, optimizer=optimizer, input_shape = input_shape, nb_classes = 10, preprocessing= (mean_, std_)) - - -# # # # Step 4: Train the ART classifier - -# classifier.fit(X_train, y_train, batch_size=64, nb_epochs=3) - -# # Step 5: Evaluate the ART classifier on benign test examples - -# predictions = classifier.predict(X_test) -# accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test) -# print("Accuracy on benign test examples: {}%".format(accuracy * 100)) - -# # Step 6: Generate adversarial test examples -# attack = FastGradientMethod(estimator=classifier, eps=0.2) -# X_test_adv = attack.generate(x=X_test) - -# # Step 7: Evaluate the ART classifier on adversarial test examples +class ResNet18(nn.Module): + def __init__(self): + super(MNISTNet, self).__init__() + self.conv_1 = nn.Conv2d(in_channels=1, out_channels=4, kernel_size=5, stride=1) + self.conv_2 = nn.Conv2d(in_channels=4, out_channels=10, kernel_size=5, stride=1) + self.fc_1 = nn.Linear(in_features=4 * 4 * 10, out_features=100) + self.fc_2 = nn.Linear(in_features=100, out_features=10) -# predictions = classifier.predict(X_test_adv) -# accuracy = np.sum(np.argmax(predictions, axis=1) == np.argmax(y_test, axis=1)) / len(y_test) -# print("Accuracy on adversarial test examples: {}%".format(accuracy * 100)) + def forward(self, x): + x = F.relu(self.conv_1(x)) + x = F.max_pool2d(x, 2, 2) + x = F.relu(self.conv_2(x)) + x = F.max_pool2d(x, 2, 2) + x = x.view(-1, 4 * 4 * 10) + x = F.relu(self.fc_1(x)) + x = self.fc_2(x) + return x \ No newline at end of file diff --git a/examples/sklearn/attacks.sh b/examples/sklearn/attacks.sh new file mode 100644 index 00000000..7b6b0479 --- /dev/null +++ b/examples/sklearn/attacks.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +# This script is used to generate the attacks for the sklearn example. + +# Fast Gradient Method +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.FastGradientMethod +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=100 +stage=attack --multirun + +# Projected Gradient Descent +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.ProjectedGradientDescent +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=100 +attack.init.max_iter=10 +stage=attack --multirun + +# Carlini L0 Method +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.CarliniL0Method +attack.init.batch_size=100 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack --multirun + +# Carlini L2 Method +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.CarliniL2Method +attack.init.batch_size=100 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack --multirun + +# Carlini LInf Method +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.CarliniLInfMethod +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack --multirun + +# DeepFool +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.DeepFool +attack.init.max_iter=10 +attack.init.batch_size=100 +attack.init.nb_grads=10,100,1000 +stage=attack --multirun + +# HopSkipJump +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.HopSkipJump +attack.init.max_iter=10 +attack.init.max_eval=10 +attack.init.init_eval=10 +attack.init.norm=inf,2 +stage=attack --multirun + +# PixelAttack +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.PixelAttack +attack.init.max_iter=10 +attack.init.th=.5,.9,.99 +stage=attack --multirun + +# ThresholdAttack +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.max_iter=10 +attack.init.th=.5,.9,.99 +stage=attack --multirun + +# AdversarialPatch +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.max_iter=10 +attack.init.learning_rate=.5,5.0,50.0 +stage=attack --multirun diff --git a/examples/sklearn/defense_search.py b/examples/sklearn/defense_search.py deleted file mode 100644 index b5952b01..00000000 --- a/examples/sklearn/defense_search.py +++ /dev/null @@ -1,52 +0,0 @@ -# - model.art.preprocessor.name: art.defences.preprocessor.FeatureSqueezing -# model.art.preprocessor.params: -# clip_values : -# - [0,255] -# bit_depth : [4, 8, 16, 32, 64] - -# - model.art.preprocessor.name: art.defences.preprocessor.GaussianAugmentation -# model.art.preprocessor.params: -# clip_values : -# - [0,255] -# sigma : [.1, .3, 1] -# ratio : [.1, .5, 1] - -# - model.art.preprocessor.name: art.defences.preprocessor.SpatialSmoothing -# model.art.preprocessor.params: -# clip_values : -# - [0,255] -# window_size : [2,3,4] - -# - model.art.preprocessor.name: art.defences.preprocessor.TotalVarMin -# model.art.preprocessor.params: -# clip_values : -# - [0,255] -# prob : [.001, .01, .1] -# norm : [1, 2, 3] -# lamb : [.05, .5, .95] -# max_iter : [100] - -# - model.art.postprocessor.name : art.defences.postprocessor.GaussianNoise -# model.art.postprocessor.params: -# clip_values : -# - [0,255] -# scale: [.1, .9, .999] - -# - model.art.postprocessor.name : art.defences.postprocessor.HighConfidence -# model.art.postprocessor.params: -# cutoff : [.1, .5, .9, .99] - - -# - model.art.postprocessor.name : art.defences.postprocessor.Rounded -# model.art.preprocessor.params: -# clip_values : -# - [0,255] -# decimals : [1, 2, 4, 8] - -# from omegaconf import OmegaConf, DictConfig -# from pathlib import Path -# from optuna import create_study, Trial, TrialPruned, TrialState - -# def configure(cfg: DictConfig, trial:Trial) -> None: -# print(trial.params.keys()) -# input("Press enter to continue") diff --git a/examples/sklearn/dvc.yaml b/examples/sklearn/dvc.yaml index 1ea9dad3..0327c6e6 100644 --- a/examples/sklearn/dvc.yaml +++ b/examples/sklearn/dvc.yaml @@ -76,3 +76,14 @@ stages: - attack.db outs: - conf/attack/best.yaml + ############################################################################## + # defend: + # cmd : bash models.sh + # deps: + # - model.db + # outs: + # - multirun + # attack-again: + # cmd : bash attacks.sh + # deps: + # - attack.db diff --git a/examples/sklearn/layer_pipeline.yaml b/examples/sklearn/layer_pipeline.yaml deleted file mode 100644 index 0327e8e0..00000000 --- a/examples/sklearn/layer_pipeline.yaml +++ /dev/null @@ -1,90 +0,0 @@ -############################################################################## - # These stages use the hydra API to run multiple experiments in series - # data: - # # This stage reads from the conf/ folder directly as per the hydra API - # # As we can see, the data stage is a foreach loop that runs the data layer, - # # saving the output to the data files directory with a specified type, - # # with a given name. - # # This can be used to define a stage that runs a single experiment, or a stage for the - # # optimisation using the optimise.py script and the hydrasweeper API - # foreach: - # small: - # data: conf/data/small.yaml:data - # medium: - # data: conf/data/medium.yaml:data - # large: - # data: conf/data/large.yaml:data - # do: - # cmd : python -m deckard.layers.data --data_config_name ${key} - # params : - # - data - # - files - # outs : - # - ${files.directory}/${files.data_dir}/${key}${files.data_type} - # models: - # # This stage reads from the conf/ folder directly as per the hydra API - # # As we can see, the model stage is a foreach loop that runs the model layer, - # # saving the output to the model files directory with a specified type, - # # with a given name. It also takes the data file as an input, so that it - # # can easily be varied independently of the data layer. - # foreach: - # linear: - # model : conf/model/linear.yaml:model - # data_file : small - # rbf: - # model : conf/model/rbf.yaml:model - # data_file : small - # poly: - # model : conf/model/poly.yaml:model - # data_file : small - # do: - # cmd: python -m deckard.layers.model --model_config_name ${key} --overrides=files.data_file=${item.data_file} - # deps: - # - ${files.directory}/${files.data_dir}/${item.data_file}${files.data_type} - # outs: - # - ${files.directory}/${files.model_dir}/${key}${files.model_type} - # params: - # - data - # - model - # - files - # attacks: - # # This stage reads from the conf/ folder directly as per the hydra API - # # As we can see, the attack stage is a foreach loop that runs the attack layer, - # # saving the output to the attack files directory with a specified type, - # # with a given name. It also takes the data and model files as inputs, so that it - # # can easily be varied independently of the data and model layers. You can also - # # vary the attacks as above. - # foreach: - # linear: - # model : conf/model/linear.yaml:model - # data: conf/model/small.yaml:data - # attack : conf/attack/hsj.yaml:attack - # attack_file : hsj - # data_file : small - # model_file : linear - # rbf: - # model : conf/model/rbf.yaml:model - # data: conf/model/small.yaml:data - # attack : conf/attack/hsj.yaml:attack - # attack_file : hsj - # data_file : small - # model_file : rbf - # poly: - # model : conf/model/poly.yaml:model - # data: conf/model/small.yaml:data - # attack : conf/attack/hsj.yaml:attack - # attack_file : hsj - # data_file : small - # model_file : poly - # do: - # cmd: python -m deckard.layers.attack --model_config_name ${key} --overrides=files.model_file=${item.model_file},files.data_file=${item.data_file},files.attack_file=${key},attack=${item.attack_file} - # deps: - # - ${files.directory}/${files.data_dir}/${item.data_file}${files.data_type} - # - ${files.directory}/${files.model_dir}/${key}${files.model_type} - # outs: - # - ${files.directory}/${files.attack_dir}/${key}${files.attack_type} - # params: - # - data - # - model - # - attack - # - files diff --git a/examples/sklearn/models.sh b/examples/sklearn/models.sh index d2d73713..fbbd7a3d 100644 --- a/examples/sklearn/models.sh +++ b/examples/sklearn/models.sh @@ -4,22 +4,22 @@ # This line generates the model and adds the FeatureSqueezing preprocessing defence. -python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 --multirun +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +stage=train +optimizers=accuracy --multirun -# # Gaussian Augmentation (Input) -# python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.2,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 --multirun +# Gaussian Augmentation (Input) +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.2,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +stage=train +optimizers=accuracy --multirun # # Spatial Smoothing -# python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.SpatialSmoothing +model.art.preprocessor.params.window_size=2,3,4 --multirun +# python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.SpatialSmoothing +model.art.preprocessor.params.window_size=2,3,4 +stage=train +optimizers=accuracy --multirun # # Total Variance Minimisation -# python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.TotalVarMin +model.art.preprocessor.params.prob=.001,.01,.1 +model.art.preprocessor.params.norm=1,2,3 +model.art.preprocessor.params.lamb=.05,.5,.95 +model.art.preprocessor.params.max_iter=100 --multirun +# python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.TotalVarMin +model.art.preprocessor.params.prob=.001,.01,.1 +model.art.preprocessor.params.norm=1,2,3 +model.art.preprocessor.params.lambda=.05,.5,.95 +model.art.preprocessor.params.max_iter=100 +stage=train +optimizers=accuracy --multirun # # Gaussian Noise (Output) -# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.1,.9,.999 --multirun +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.1,.9,.999 +stage=train +optimizers=accuracy --multirun # # High Confidence -# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.5,.9,.99 --multirun +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.5,.9,.99 +stage=train +optimizers=accuracy --multirun # # Rounded (Output) -# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.Rounded +model.art.postprocessor.params.decimals=1,2,4,8 --multirun +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.Rounded +model.art.postprocessor.params.decimals=1,2,4,8 +stage=train +optimizers=accuracy --multirun diff --git a/examples/tfv2/attacks.sh b/examples/tfv2/attacks.sh new file mode 100644 index 00000000..7b6b0479 --- /dev/null +++ b/examples/tfv2/attacks.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +# This script is used to generate the attacks for the sklearn example. + +# Fast Gradient Method +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.FastGradientMethod +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=100 +stage=attack --multirun + +# Projected Gradient Descent +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.ProjectedGradientDescent +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=100 +attack.init.max_iter=10 +stage=attack --multirun + +# Carlini L0 Method +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.CarliniL0Method +attack.init.batch_size=100 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack --multirun + +# Carlini L2 Method +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.CarliniL2Method +attack.init.batch_size=100 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack --multirun + +# Carlini LInf Method +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.CarliniLInfMethod +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack --multirun + +# DeepFool +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.DeepFool +attack.init.max_iter=10 +attack.init.batch_size=100 +attack.init.nb_grads=10,100,1000 +stage=attack --multirun + +# HopSkipJump +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.HopSkipJump +attack.init.max_iter=10 +attack.init.max_eval=10 +attack.init.init_eval=10 +attack.init.norm=inf,2 +stage=attack --multirun + +# PixelAttack +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.PixelAttack +attack.init.max_iter=10 +attack.init.th=.5,.9,.99 +stage=attack --multirun + +# ThresholdAttack +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.max_iter=10 +attack.init.th=.5,.9,.99 +stage=attack --multirun + +# AdversarialPatch +python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.max_iter=10 +attack.init.learning_rate=.5,5.0,50.0 +stage=attack --multirun diff --git a/examples/tfv2/conf/data/tensorflow_mnist.yaml b/examples/tfv2/conf/data/tensorflow_mnist.yaml index 7b4a17de..42ba394e 100644 --- a/examples/tfv2/conf/data/tensorflow_mnist.yaml +++ b/examples/tfv2/conf/data/tensorflow_mnist.yaml @@ -9,7 +9,6 @@ sample: sklearn_pipeline: _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline preprocessor: - name: sklearn.preprocessing.StandardScaler with_mean: True with_std: True diff --git a/examples/tfv2/conf/default.yaml b/examples/tfv2/conf/default.yaml index c92ca98c..a614db1d 100644 --- a/examples/tfv2/conf/default.yaml +++ b/examples/tfv2/conf/default.yaml @@ -5,3 +5,46 @@ defaults: - attack: hsj - files: default - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 10 + n_jobs: 1 + params: + # data.generate.n_features : 20 + # data.sample.train_size : 1000 + # data.sample.test_size : 100 + data.sample.random_state : int(interval(0, 1e3)) + # data.sample.stratify : true + # model.init.kernel : rbf + # model.init.C : tag(log, int(interval(1, 1e6))) + # +model.init.max_iter : 100 + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 100 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/tfv2/conf/model/tensorflow_mnist.yaml b/examples/tfv2/conf/model/tensorflow_mnist.yaml index 348b46b2..cead3525 100644 --- a/examples/tfv2/conf/model/tensorflow_mnist.yaml +++ b/examples/tfv2/conf/model/tensorflow_mnist.yaml @@ -13,8 +13,9 @@ init: _target_: deckard.base.model.Model trainer: - nb_epoch: 1 + nb_epoch: 20 batch_size: 64 + verbose: true library : tensorflow art: _target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/tfv2/dvc.lock b/examples/tfv2/dvc.lock index 04939b66..65c445d7 100644 --- a/examples/tfv2/dvc.lock +++ b/examples/tfv2/dvc.lock @@ -75,7 +75,8 @@ stages: library: tensorflow trainer: batch_size: 64 - nb_epoch: 1 + nb_epoch: 20 + verbose: true scorers: _target_: deckard.base.scorer.ScorerDict accuracy: @@ -91,18 +92,18 @@ stages: md5: 8b80b882758b381c2a461d1e9b9b2439 size: 441840345 - path: output/models/model.tf - md5: 265fd4f79a403c523a2b2353ec6e6cab.dir - size: 167515 - nfiles: 5 + md5: 7bf2a040eda3ad04aa54e15d5fc47316.dir + size: 156314 + nfiles: 4 - path: output/reports/train/predictions.json - md5: d30043c1764a5e2545830ebf0a9c2d5d - size: 2853811 + md5: e5838d87c78e9328c01f1f061632cd41 + size: 2852985 - path: output/reports/train/probabilities.json - md5: d30043c1764a5e2545830ebf0a9c2d5d - size: 2853811 + md5: e5838d87c78e9328c01f1f061632cd41 + size: 2852985 - path: output/reports/train/score_dict.json - md5: 110432525713e4e1b2d74f2083cd4a86 - size: 394 + md5: 1557880eda9afbb8c21ed39541b748b7 + size: 407 - path: output/reports/train/test_labels.json md5: f26b1ad6bd01a70de4290c6ae713e2c7 size: 728000 @@ -116,9 +117,9 @@ stages: md5: 8b80b882758b381c2a461d1e9b9b2439 size: 441840345 - path: output/models/model.tf - md5: bab0a96c07a8dd4284d2ae89cf1561cd.dir - size: 148092 - nfiles: 5 + md5: 946ef52d35969a69af7c03553ee2c40f.dir + size: 140044 + nfiles: 4 params: params.yaml: attack: @@ -180,7 +181,8 @@ stages: library: tensorflow trainer: batch_size: 64 - nb_epoch: 1 + nb_epoch: 20 + verbose: true name: art.attacks.evasion.HopSkipJump method: evasion model: @@ -218,7 +220,8 @@ stages: library: tensorflow trainer: batch_size: 64 - nb_epoch: 1 + nb_epoch: 20 + verbose: true data.generate: _target_: deckard.base.data.generator.DataGenerator name: mnist @@ -278,7 +281,8 @@ stages: library: tensorflow trainer: batch_size: 64 - nb_epoch: 1 + nb_epoch: 20 + verbose: true scorers: _target_: deckard.base.scorer.ScorerDict accuracy: @@ -291,14 +295,14 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: 6e05db6a7eb04e5033e5452df75d714d + md5: 05b301e6ee78495771f3a9e59c151935 size: 31517 - path: output/reports/attack/adv_predictions.json - md5: 958ba8e2e66058200a9caac5c2335aa0 - size: 2104 + md5: e8c113b2ffd5c8f13be160464c3d126c + size: 2097 - path: output/reports/attack/adv_probabilities.json - md5: 958ba8e2e66058200a9caac5c2335aa0 - size: 2104 + md5: e8c113b2ffd5c8f13be160464c3d126c + size: 2097 - path: output/reports/attack/score_dict.json - md5: ba1f43095378c8d03ef5edbc008818d3 - size: 506 + md5: b032a45445a8b088e2880baff87ece0f + size: 538 diff --git a/examples/tfv2/dvc.yaml b/examples/tfv2/dvc.yaml index 54736f50..3005b03f 100644 --- a/examples/tfv2/dvc.yaml +++ b/examples/tfv2/dvc.yaml @@ -32,3 +32,19 @@ stages: - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} metrics: - ${files.directory}/${files.reports}/attack/${files.score_dict_file} + ############################################################################## + models: + cmd: bash models.sh + deps: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + outs: + - model.db + attacks: + cmd: bash attacks.sh + deps: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + - model.db + outs: + - attack.db \ No newline at end of file diff --git a/examples/tfv2/install.sh b/examples/tfv2/install.sh new file mode 100644 index 00000000..87cff84a --- /dev/null +++ b/examples/tfv2/install.sh @@ -0,0 +1,9 @@ +conda install -c conda-forge cudatoolkit=11.8.0 +python3 -m pip install nvidia-cudnn-cu11==8.6.0.163 tensorflow==2.12.* +mkdir -p $CONDA_PREFIX/etc/conda/activate.d +echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh +echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/:$CUDNN_PATH/lib' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh +source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh +# Verify install: +python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))" + diff --git a/examples/tfv2/models.sh b/examples/tfv2/models.sh new file mode 100644 index 00000000..00389289 --- /dev/null +++ b/examples/tfv2/models.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +# This script is used to generate the models for the sklearn example. + + +# This line generates the model and adds the FeatureSqueezing preprocessing defence. +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +stage=train --multirun + +# Gaussian Augmentation (Input) +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.2,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +stage=train --multirun + +# Spatial Smoothing +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.SpatialSmoothing +model.art.preprocessor.params.window_size=2,3,4 +stage=train --multirun + +# Total Variance Minimisation +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.TotalVarMin +model.art.preprocessor.params.prob=.001,.01,.1 +model.art.preprocessor.params.norm=1,2,3 +model.art.preprocessor.params.lamb=.05,.5,.95 +model.art.preprocessor.params.max_iter=100 +stage=train --multirun + +# Gaussian Noise (Output) +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.1,.9,.999 +stage=train --multirun + +# High Confidence +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.5,.9,.99 +stage=train --multirun + +# Rounded (Output) +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.Rounded +model.art.postprocessor.params.decimals=1,2,4,8 +stage=train --multirun diff --git a/examples/tfv2/params.yaml b/examples/tfv2/params.yaml index e69fac39..a47972f6 100644 --- a/examples/tfv2/params.yaml +++ b/examples/tfv2/params.yaml @@ -57,7 +57,8 @@ attack: library: tensorflow trainer: batch_size: 64 - nb_epoch: 1 + nb_epoch: 20 + verbose: true name: art.attacks.evasion.HopSkipJump method: evasion model: @@ -95,7 +96,8 @@ attack: library: tensorflow trainer: batch_size: 64 - nb_epoch: 1 + nb_epoch: 20 + verbose: true data: _target_: deckard.base.data.Data generate: @@ -167,7 +169,8 @@ model: library: tensorflow trainer: batch_size: 64 - nb_epoch: 1 + nb_epoch: 20 + verbose: true scorers: _target_: deckard.base.scorer.ScorerDict accuracy: From 9e891f05076b30381480ede4513b8fce889c71b8 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 23 May 2023 12:52:10 +0000 Subject: [PATCH 002/148] fixed param bug --- deckard/base/model/art_pipeline.py | 24 +++++--- deckard/layers/optimise.py | 11 ++-- examples/pytorch/dvc.lock | 14 ++--- examples/pytorch/params.yaml | 94 ------------------------------ examples/sklearn/models.sh | 2 +- 5 files changed, 30 insertions(+), 115 deletions(-) diff --git a/deckard/base/model/art_pipeline.py b/deckard/base/model/art_pipeline.py index 3ed7cdb6..4a0341e9 100644 --- a/deckard/base/model/art_pipeline.py +++ b/deckard/base/model/art_pipeline.py @@ -128,10 +128,6 @@ def __init__(self, library, name: str = None, **kwargs): pipeline[stage] = OmegaConf.to_container(pipeline[stage]) elif is_dataclass(pipeline[stage]): pipeline[stage] = asdict(pipeline[stage]) - elif isinstance(pipeline[stage], type(None)): - pipeline[stage] = {"name": "initialize"} - elif isinstance(pipeline[stage], dict): - pipeline[stage].update({"name": "initialize"}) else: assert isinstance( pipeline[stage], @@ -139,6 +135,8 @@ def __init__(self, library, name: str = None, **kwargs): ), f"Expected dict, got {type(pipeline[stage])}" while "kwargs" in pipeline[stage]: pipeline[stage].update(**pipeline[stage].pop("kwargs")) + while "params" in pipeline[stage]: + pipeline[stage].update(**pipeline[stage].pop("params")) name = pipeline[stage].pop("name", stage) params = pipeline[stage] params.pop("name", None) @@ -172,22 +170,32 @@ def __call__(self, model: object, library: str = None, data=None) -> BaseEstimat assert len(data) == 4, f"data must be a tuple of length 4. Got {data}" if "preprocessor" in self.pipeline: name, kwargs = self.pipeline["preprocessor"]() - obj = instantiate(library, name, **kwargs) + config = {"_target_": name} + config.update(**kwargs) + obj = instantiate(config) pre_def.append(obj) kwargs.update({"preprocessing_defenses": pre_def}) if "postprocessor" in self.pipeline: name, kwargs = self.pipeline["postprocessor"]() - obj = instantiate(library, name, **kwargs) + config = { + "_target_": name, + } + config.update(**kwargs) + obj = instantiate(config) post_def.append(obj) kwargs.update({"postprocessing_defenses": post_def}) if "initialize" in self.pipeline: name, kwargs = self.pipeline["initialize"]() model = ArtInitializer(model=model, data=data, **kwargs, library=library)() else: - model = ArtInitializer(model=model, data=data, library=library)() + raise ValueError("Art Pipeline must have an initialize stage") if "transformer" in self.pipeline: name, kwargs = self.pipeline["transformer"]() - model = instantiate(model, name, **kwargs) + config = { + "_target_": name, + } + config.update(**kwargs) + model = obj(model) if "trainer" in self.pipeline: raise NotImplementedError("Training Defense not implemented yet") return model diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index 83aa6c8b..6664e85b 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -24,12 +24,13 @@ def find_stage_params(params_file, pipeline_file, stage, working_dir, **kwargs): stages = stage working_dir = Path(working_dir).resolve() old_params = dvc.api.params_show(params_file, stages=[stage], repo=working_dir) - old_params = flatten_dict(old_params) - new_params = flatten_dict(kwargs) - params = deepcopy(old_params) - params.update(**new_params) + params = {} + for key in kwargs: + if key in old_params: + params[key] = kwargs[key] + else: + pass # Setup the files - params = unflatten_dict(params) params["files"] = {} files = dvc.api.params_show(pipeline_file, stages=stages, repo=working_dir) unflattened_files = unflatten_dict(files).pop("files", {}) diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index 6721cfb3..1ca55e4e 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -90,17 +90,17 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.pt - md5: c3a53ff12de22b40ebf1c0cd8618c33a + md5: a5d083a63431d03d2ad17a210747eeaf size: 76623 - path: output/reports/train/default/predictions.json - md5: 1431cff19f3459f50ccfdf3b2f0ea91d - size: 2831070 + md5: 1e00e7963305f18fc875e0e4b57ccbbf + size: 2830417 - path: output/reports/train/default/probabilities.json - md5: 1431cff19f3459f50ccfdf3b2f0ea91d - size: 2831070 + md5: 1e00e7963305f18fc875e0e4b57ccbbf + size: 2830417 - path: output/reports/train/default/score_dict.json - md5: 4d99a831a24d1bbb46e91b90fb1cd49d - size: 398 + md5: e509a53e2eeb7e62f3ec3463549b48b4 + size: 411 - path: output/reports/train/default/test_labels.json md5: f26b1ad6bd01a70de4290c6ae713e2c7 size: 728000 diff --git a/examples/pytorch/params.yaml b/examples/pytorch/params.yaml index a3cd3aba..3e5fb785 100644 --- a/examples/pytorch/params.yaml +++ b/examples/pytorch/params.yaml @@ -1,97 +1,3 @@ -attack: - _target_: deckard.base.attack.Attack - attack_size: 10 - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.attack.AttackInitializer - batch_size: 64 - init_eval: 10 - max_eval: 10 - max_iter: 10 - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - initialize: - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.MNISTNet - library: pytorch - trainer: - batch_size: 64 - nb_epoch: 1 - name: art.attacks.evasion.HopSkipJump - method: evasion - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - initialize: - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.MNISTNet - library: pytorch - trainer: - batch_size: 64 - nb_epoch: 1 data: _target_: deckard.base.data.Data generate: diff --git a/examples/sklearn/models.sh b/examples/sklearn/models.sh index fbbd7a3d..cf2f571e 100644 --- a/examples/sklearn/models.sh +++ b/examples/sklearn/models.sh @@ -4,7 +4,7 @@ # This line generates the model and adds the FeatureSqueezing preprocessing defence. -python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +stage=train +optimizers=accuracy --multirun +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +stage=train +optimizers=accuracy ++model.art.preprocessor.clip_values=[0,1]--multirun # Gaussian Augmentation (Input) python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.2,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +stage=train +optimizers=accuracy --multirun From e4483f6c1ec74c7fda636f8051282fba832ba693 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 23 May 2023 13:16:58 +0000 Subject: [PATCH 003/148] art bug --- deckard/base/model/art_pipeline.py | 27 +++++---- examples/pytorch/conf/default.yaml | 2 +- examples/pytorch/dvc.lock | 28 ++++----- examples/pytorch/models.sh | 22 +++---- examples/pytorch/params.yaml | 94 ++++++++++++++++++++++++++++++ 5 files changed, 133 insertions(+), 40 deletions(-) diff --git a/deckard/base/model/art_pipeline.py b/deckard/base/model/art_pipeline.py index 4a0341e9..6e76cd01 100644 --- a/deckard/base/model/art_pipeline.py +++ b/deckard/base/model/art_pipeline.py @@ -156,7 +156,10 @@ def __hash__(self): # return iter(self.pipeline) def __call__(self, model: object, library: str = None, data=None) -> BaseEstimator: - + if "initialize" in self.pipeline: + name, kwargs = self.pipeline["initialize"]() + else: + raise ValueError("Art Pipeline must have an initialize stage") pre_def = [] post_def = [] if data is None: @@ -169,32 +172,28 @@ def __call__(self, model: object, library: str = None, data=None) -> BaseEstimat ), f"library must be one of {supported_models}. Got {library}" assert len(data) == 4, f"data must be a tuple of length 4. Got {data}" if "preprocessor" in self.pipeline: - name, kwargs = self.pipeline["preprocessor"]() + name, sub_kwargs = self.pipeline["preprocessor"]() config = {"_target_": name} - config.update(**kwargs) + config.update(**sub_kwargs) obj = instantiate(config) pre_def.append(obj) - kwargs.update({"preprocessing_defenses": pre_def}) + kwargs.update({"preprocessing_defences": pre_def}) if "postprocessor" in self.pipeline: - name, kwargs = self.pipeline["postprocessor"]() + name, sub_kwargs = self.pipeline["postprocessor"]() config = { "_target_": name, } - config.update(**kwargs) + config.update(**sub_kwargs) obj = instantiate(config) post_def.append(obj) - kwargs.update({"postprocessing_defenses": post_def}) - if "initialize" in self.pipeline: - name, kwargs = self.pipeline["initialize"]() - model = ArtInitializer(model=model, data=data, **kwargs, library=library)() - else: - raise ValueError("Art Pipeline must have an initialize stage") + kwargs.update({"postprocessing_defences": post_def}) + model = ArtInitializer(model=model, data=data, **kwargs, library=library)() if "transformer" in self.pipeline: - name, kwargs = self.pipeline["transformer"]() + name, sub_kwargs = self.pipeline["transformer"]() config = { "_target_": name, } - config.update(**kwargs) + config.update(**sub_kwargs) model = obj(model) if "trainer" in self.pipeline: raise NotImplementedError("Training Defense not implemented yet") diff --git a/examples/pytorch/conf/default.yaml b/examples/pytorch/conf/default.yaml index c7e508de..d0fb49ee 100644 --- a/examples/pytorch/conf/default.yaml +++ b/examples/pytorch/conf/default.yaml @@ -26,7 +26,7 @@ hydra: direction: maximize study_name: model storage: sqlite:///model.db - n_trials: 10 + n_trials: 30 n_jobs: 1 launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index 1ca55e4e..942e213f 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -90,16 +90,16 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.pt - md5: a5d083a63431d03d2ad17a210747eeaf + md5: b1189f381258ece9c17cc4208dfc53a6 size: 76623 - path: output/reports/train/default/predictions.json - md5: 1e00e7963305f18fc875e0e4b57ccbbf - size: 2830417 + md5: 342ae779114872b03df92c526957367a + size: 2829716 - path: output/reports/train/default/probabilities.json - md5: 1e00e7963305f18fc875e0e4b57ccbbf - size: 2830417 + md5: 342ae779114872b03df92c526957367a + size: 2829716 - path: output/reports/train/default/score_dict.json - md5: e509a53e2eeb7e62f3ec3463549b48b4 + md5: 492d51e212cd2bd52797851d01718793 size: 411 - path: output/reports/train/default/test_labels.json md5: f26b1ad6bd01a70de4290c6ae713e2c7 @@ -114,7 +114,7 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.pt - md5: c3a53ff12de22b40ebf1c0cd8618c33a + md5: da97fd96cfa8c28375f634217119916f size: 76623 params: params.yaml: @@ -283,14 +283,14 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: f6933d3d01f97b4f400414bfaf4b72c2 + md5: 37da8b1f571a75cbfd90b908516c3976 size: 31517 - path: output/reports/attack/default/adv_predictions.json - md5: 37f7e29d1ad7fd70bd14338b9ae2abdd - size: 2220 + md5: 4216ed601a67da2df7e6a0d3adaaae71 + size: 2205 - path: output/reports/attack/default/adv_probabilities.json - md5: 37f7e29d1ad7fd70bd14338b9ae2abdd - size: 2220 + md5: 4216ed601a67da2df7e6a0d3adaaae71 + size: 2205 - path: output/reports/attack/default/score_dict.json - md5: 2958a67178c47bf468fc23eaff679891 - size: 539 + md5: d5f55a6743c6389b0795542bb3f1e982 + size: 530 diff --git a/examples/pytorch/models.sh b/examples/pytorch/models.sh index 00389289..b4f80bc3 100644 --- a/examples/pytorch/models.sh +++ b/examples/pytorch/models.sh @@ -4,22 +4,22 @@ # This line generates the model and adds the FeatureSqueezing preprocessing defence. -python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +stage=train --multirun +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train --multirun # Gaussian Augmentation (Input) python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.2,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +stage=train --multirun -# Spatial Smoothing -python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.SpatialSmoothing +model.art.preprocessor.params.window_size=2,3,4 +stage=train --multirun +# # Spatial Smoothing +# python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.SpatialSmoothing +model.art.preprocessor.params.window_size=2,3,4 +stage=train --multirun -# Total Variance Minimisation -python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.TotalVarMin +model.art.preprocessor.params.prob=.001,.01,.1 +model.art.preprocessor.params.norm=1,2,3 +model.art.preprocessor.params.lamb=.05,.5,.95 +model.art.preprocessor.params.max_iter=100 +stage=train --multirun +# # Total Variance Minimisation +# python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.TotalVarMin +model.art.preprocessor.params.prob=.001,.01,.1 +model.art.preprocessor.params.norm=1,2,3 +model.art.preprocessor.params.lamb=.05,.5,.95 +model.art.preprocessor.params.max_iter=100 +stage=train --multirun -# Gaussian Noise (Output) -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.1,.9,.999 +stage=train --multirun +# # Gaussian Noise (Output) +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.1,.9,.999 +stage=train --multirun -# High Confidence -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.5,.9,.99 +stage=train --multirun +# # High Confidence +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.5,.9,.99 +stage=train --multirun -# Rounded (Output) -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.Rounded +model.art.postprocessor.params.decimals=1,2,4,8 +stage=train --multirun +# # Rounded (Output) +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.Rounded +model.art.postprocessor.params.decimals=1,2,4,8 +stage=train --multirun diff --git a/examples/pytorch/params.yaml b/examples/pytorch/params.yaml index 3e5fb785..a3cd3aba 100644 --- a/examples/pytorch/params.yaml +++ b/examples/pytorch/params.yaml @@ -1,3 +1,97 @@ +attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.attack.AttackInitializer + batch_size: 64 + init_eval: 10 + max_eval: 10 + max_iter: 10 + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.MNISTNet + library: pytorch + trainer: + batch_size: 64 + nb_epoch: 1 + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.MNISTNet + library: pytorch + trainer: + batch_size: 64 + nb_epoch: 1 data: _target_: deckard.base.data.Data generate: From e132634328cf4b851f11f1c89d4566938eb49f71 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 23 May 2023 18:37:21 +0000 Subject: [PATCH 004/148] +tested attacks on torch --- examples/pytorch/attacks.sh | 42 +++++++++--------- examples/pytorch/conf/attack/hsj.yaml | 6 +-- examples/pytorch/conf/default.yaml | 2 +- examples/pytorch/conf/model/torch_mnist.yaml | 3 +- examples/pytorch/dvc.lock | 45 ++++++++++++++++---- examples/pytorch/torch_example.py | 2 + 6 files changed, 65 insertions(+), 35 deletions(-) diff --git a/examples/pytorch/attacks.sh b/examples/pytorch/attacks.sh index 7b6b0479..7b0add84 100644 --- a/examples/pytorch/attacks.sh +++ b/examples/pytorch/attacks.sh @@ -1,33 +1,35 @@ -#!/bin/bash +#!/bin/bash++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize ++hydra.sweeper.directi # This script is used to generate the attacks for the sklearn example. # Fast Gradient Method -python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.FastGradientMethod +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=100 +stage=attack --multirun +python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.FastGradientMethod +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=1024 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun -# Projected Gradient Descent -python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.ProjectedGradientDescent +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=100 +attack.init.max_iter=10 +stage=attack --multirun - -# Carlini L0 Method -python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.CarliniL0Method +attack.init.batch_size=100 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack --multirun - -# Carlini L2 Method -python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.CarliniL2Method +attack.init.batch_size=100 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack --multirun - -# Carlini LInf Method -python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.CarliniLInfMethod +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack --multirun +# # Projected Gradient Descent +python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=1024 +attack.init.max_iter=10 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun # DeepFool -python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.DeepFool +attack.init.max_iter=10 +attack.init.batch_size=100 +attack.init.nb_grads=10,100,1000 +stage=attack --multirun +python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.DeepFool +attack.init.max_iter=10 +attack.init.batch_size=1024 +attack.init.nb_grads=10,100,1000 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun # HopSkipJump -python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.HopSkipJump +attack.init.max_iter=10 +attack.init.max_eval=10 +attack.init.init_eval=10 +attack.init.norm=inf,2 +stage=attack --multirun +python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.HopSkipJump +attack.init.max_iter=10 +attack.init.init_eval=10 +attack.init.norm=inf,2 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun -# PixelAttack -python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.PixelAttack +attack.init.max_iter=10 +attack.init.th=.5,.9,.99 +stage=attack --multirun +##################################################### +# # PixelAttack +python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.PixelAttack +attack.init.max_iter=10 +attack.init.th=1,4,16,64,256 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun # ThresholdAttack -python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.max_iter=10 +attack.init.th=.5,.9,.99 +stage=attack --multirun +python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.max_iter=10 +attack.init.th=1,4,16,64,256 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun -# AdversarialPatch -python -m deckard.layers.optimise +attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.max_iter=10 +attack.init.learning_rate=.5,5.0,50.0 +stage=attack --multirun +# # AdversarialPatch +python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.max_iter=10 +attack.init.learning_rate=.5,5.0,50.0 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize +attack.init.patch_shape=[28,28,1] --multirun +##################################################### + +# Carlini L0 Method +python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.CarliniL0Method +attack.init.batch_size=1024 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun + +# Carlini L2 Method +python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.CarliniL2Method +attack.init.batch_size=1024 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun + +# Carlini LInf Method +python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.CarliniLInfMethod +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun diff --git a/examples/pytorch/conf/attack/hsj.yaml b/examples/pytorch/conf/attack/hsj.yaml index a5436cbf..740aa492 100644 --- a/examples/pytorch/conf/attack/hsj.yaml +++ b/examples/pytorch/conf/attack/hsj.yaml @@ -5,9 +5,5 @@ init: model: ${model} _target_: deckard.base.attack.AttackInitializer name: art.attacks.evasion.HopSkipJump - batch_size : 64 - max_iter : 10 - max_eval : 10 - init_eval : 10 -attack_size : 10 +attack_size : 100 method : evasion diff --git a/examples/pytorch/conf/default.yaml b/examples/pytorch/conf/default.yaml index d0fb49ee..42e3f5d0 100644 --- a/examples/pytorch/conf/default.yaml +++ b/examples/pytorch/conf/default.yaml @@ -30,7 +30,7 @@ hydra: n_jobs: 1 launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 100 + n_jobs: 10 prefer : processes verbose: 1 timeout: null diff --git a/examples/pytorch/conf/model/torch_mnist.yaml b/examples/pytorch/conf/model/torch_mnist.yaml index 52f789c9..f91c44fb 100644 --- a/examples/pytorch/conf/model/torch_mnist.yaml +++ b/examples/pytorch/conf/model/torch_mnist.yaml @@ -4,7 +4,7 @@ init: name : torch_example.MNISTNet _target_: deckard.base.model.Model trainer: - nb_epoch: 1 + nb_epoch: 20 batch_size: 64 library : pytorch art: @@ -17,3 +17,4 @@ art: name : torch.optim.SGD lr : 0.01 momentum : 0.9 + clip_values : [0.0, 1.0] diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index 942e213f..cae6c3b6 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -114,7 +114,7 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.pt - md5: da97fd96cfa8c28375f634217119916f + md5: b1189f381258ece9c17cc4208dfc53a6 size: 76623 params: params.yaml: @@ -283,14 +283,43 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: 37da8b1f571a75cbfd90b908516c3976 + md5: f6933d3d01f97b4f400414bfaf4b72c2 size: 31517 - path: output/reports/attack/default/adv_predictions.json - md5: 4216ed601a67da2df7e6a0d3adaaae71 - size: 2205 + md5: 70410693cc66bd568b33164bd1eb62bc + size: 2172 - path: output/reports/attack/default/adv_probabilities.json - md5: 4216ed601a67da2df7e6a0d3adaaae71 - size: 2205 + md5: 70410693cc66bd568b33164bd1eb62bc + size: 2172 - path: output/reports/attack/default/score_dict.json - md5: d5f55a6743c6389b0795542bb3f1e982 - size: 530 + md5: 328e9c9222026204796cf149ebec9921 + size: 538 + models: + cmd: bash models.sh + deps: + - path: output/data/data.pkl + md5: de934a5f5157970e5f30b8f3f1856a68 + size: 222320311 + - path: output/models/model.pt + md5: b1189f381258ece9c17cc4208dfc53a6 + size: 76623 + outs: + - path: model.db + md5: 88a46dfe71f37b9aea64527b3b8c94ff + size: 159744 + attacks: + cmd: bash attacks.sh + deps: + - path: model.db + md5: 88a46dfe71f37b9aea64527b3b8c94ff + size: 159744 + - path: output/data/data.pkl + md5: de934a5f5157970e5f30b8f3f1856a68 + size: 222320311 + - path: output/models/model.pt + md5: b1189f381258ece9c17cc4208dfc53a6 + size: 76623 + outs: + - path: attack.db + md5: 133098c7364b498637ca6e44fe656979 + size: 172032 diff --git a/examples/pytorch/torch_example.py b/examples/pytorch/torch_example.py index fb8365e4..10e1f76b 100644 --- a/examples/pytorch/torch_example.py +++ b/examples/pytorch/torch_example.py @@ -21,6 +21,8 @@ def __init__(self): self.fc_2 = nn.Linear(in_features=100, out_features=10) def forward(self, x): + torch.set_default_dtype(self.conv_1.weight.dtype) + x = x.clone().detach().requires_grad_(True).to(x.device) x = F.relu(self.conv_1(x)) x = F.max_pool2d(x, 2, 2) x = F.relu(self.conv_2(x)) From 7a8f481e63a120e146b3a1304700a6252ae18147 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Wed, 31 May 2023 15:51:40 +0000 Subject: [PATCH 005/148] generate_grid draft --- deckard/layers/compile.py | 25 ++- deckard/layers/generate_grid.py | 98 ++++++++++++ deckard/layers/hydra_test.py | 14 ++ examples/keras/conf/model/keras_mnist.yaml | 2 +- examples/keras/params.yaml | 6 +- examples/pytorch/attacks.sh | 24 +-- .../conf/attack/{hsj.yaml => default.yaml} | 0 examples/pytorch/conf/default.yaml | 7 +- examples/pytorch/conf/grid.yaml | 43 ++++++ examples/pytorch/conf/grid/attack/cw_0.yaml | 4 + examples/pytorch/conf/grid/attack/cw_2.yaml | 5 + examples/pytorch/conf/grid/attack/cw_inf.yaml | 4 + .../pytorch/conf/grid/attack/deepfool.yaml | 5 + examples/pytorch/conf/grid/attack/fgm.yaml | 4 + examples/pytorch/conf/grid/attack/hsj.yaml | 5 + examples/pytorch/conf/grid/attack/patch.yaml | 5 + examples/pytorch/conf/grid/attack/pgd.yaml | 6 + examples/pytorch/conf/grid/attack/pixel.yaml | 4 + .../pytorch/conf/grid/attack/threshold.yaml | 4 + .../pytorch/conf/grid/model/confidence.yaml | 4 + examples/pytorch/conf/grid/model/default.yaml | 0 examples/pytorch/conf/grid/model/fsq.yaml | 3 + .../pytorch/conf/grid/model/gauss-in.yaml | 4 + .../pytorch/conf/grid/model/gauss-out.yaml | 2 + examples/pytorch/conf/model/art/default.yaml | 7 + .../conf/model/art/initialize/default.yaml | 7 + .../model/art/postprocessor/confidence.yaml | 3 + .../model/art/postprocessor/gauss-out.yaml | 2 + .../conf/model/art/preprocessor/default.yaml | 0 .../conf/model/art/preprocessor/fsq.yaml | 3 + .../conf/model/art/preprocessor/gauss-in.yaml | 4 + examples/pytorch/conf/model/torch_cifar.yaml | 18 --- examples/pytorch/conf/model/torch_mnist.yaml | 18 +-- examples/pytorch/dvc.lock | 142 ++++++++++++------ examples/pytorch/dvc.yaml | 8 +- examples/pytorch/models.sh | 20 +-- examples/pytorch/params.yaml | 72 +++++++-- examples/pytorch/torch_example.py | 1 - examples/tfv1/conf/model/tfv1_mnist.yaml | 2 +- .../tfv2/conf/model/tensorflow_mnist.yaml | 2 +- examples/tfv2/dvc.lock | 10 +- examples/tfv2/params.yaml | 8 +- test/conf/attack/extraction.yaml | 4 +- test/conf/attack/inference.yaml | 2 +- test/conf/attack/poisoning.yaml | 2 +- test/conf/model/torch_mnist.yaml | 2 +- 46 files changed, 465 insertions(+), 150 deletions(-) create mode 100644 deckard/layers/generate_grid.py create mode 100644 deckard/layers/hydra_test.py rename examples/pytorch/conf/attack/{hsj.yaml => default.yaml} (100%) create mode 100644 examples/pytorch/conf/grid.yaml create mode 100644 examples/pytorch/conf/grid/attack/cw_0.yaml create mode 100644 examples/pytorch/conf/grid/attack/cw_2.yaml create mode 100644 examples/pytorch/conf/grid/attack/cw_inf.yaml create mode 100644 examples/pytorch/conf/grid/attack/deepfool.yaml create mode 100644 examples/pytorch/conf/grid/attack/fgm.yaml create mode 100644 examples/pytorch/conf/grid/attack/hsj.yaml create mode 100644 examples/pytorch/conf/grid/attack/patch.yaml create mode 100644 examples/pytorch/conf/grid/attack/pgd.yaml create mode 100644 examples/pytorch/conf/grid/attack/pixel.yaml create mode 100644 examples/pytorch/conf/grid/attack/threshold.yaml create mode 100644 examples/pytorch/conf/grid/model/confidence.yaml create mode 100644 examples/pytorch/conf/grid/model/default.yaml create mode 100644 examples/pytorch/conf/grid/model/fsq.yaml create mode 100644 examples/pytorch/conf/grid/model/gauss-in.yaml create mode 100644 examples/pytorch/conf/grid/model/gauss-out.yaml create mode 100644 examples/pytorch/conf/model/art/default.yaml create mode 100644 examples/pytorch/conf/model/art/initialize/default.yaml create mode 100644 examples/pytorch/conf/model/art/postprocessor/confidence.yaml create mode 100644 examples/pytorch/conf/model/art/postprocessor/gauss-out.yaml create mode 100644 examples/pytorch/conf/model/art/preprocessor/default.yaml create mode 100644 examples/pytorch/conf/model/art/preprocessor/fsq.yaml create mode 100644 examples/pytorch/conf/model/art/preprocessor/gauss-in.yaml delete mode 100644 examples/pytorch/conf/model/torch_cifar.yaml diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 10c7c18c..9232d115 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -14,8 +14,8 @@ def parse_folder( "predictions", "plots", "ground_truth", - "attack_predictions", - "attack_probabilities", + "adv_predictions", + "adv_probabilities", "samples", ], ) -> pd.DataFrame: @@ -221,7 +221,6 @@ def delete_these_columns(df, columns) -> pd.DataFrame: parser.add_argument("--control_for", type=str, default=None) parser.add_argument("--exclude", type=list, default=None, nargs="*") parser.add_argument("--verbose", type=str, default="INFO") - parser.add_argument("--stage", type=str, default=None) parser.add_argument( "--kwargs", type=list, @@ -238,17 +237,17 @@ def delete_these_columns(df, columns) -> pd.DataFrame: output_folder = args.output_folder control_for = args.control_for kwargs = {} - for entry in args.kwargs: - entry = "".join(entry) - value = entry.split("=")[1] - if str(value).isnumeric(): - if int(value) == float(value): - value = int(value) - else: - value = float(value) - kwargs[entry.split("=")[0]] = value + if args.kwargs is not None and len(args.kwargs > 0): + for entry in args.kwargs: + entry = "".join(entry) + value = entry.split("=")[1] + if str(value).isnumeric(): + if int(value) == float(value): + value = int(value) + else: + value = float(value) + kwargs[entry.split("=")[0]] = value columns_to_delete = args.delete_columns - stage = args.stage report_file = save_results( report_folder, results_file, diff --git a/deckard/layers/generate_grid.py b/deckard/layers/generate_grid.py new file mode 100644 index 00000000..c498a3d2 --- /dev/null +++ b/deckard/layers/generate_grid.py @@ -0,0 +1,98 @@ + +from pathlib import Path +import logging +import os +import yaml +from functools import reduce +from operator import mul +from ..base.utils import make_grid, my_hash + +logger = logging.getLogger(__name__) + +logging.basicConfig(level=logging.INFO) + +def find_configs_in_folder(conf_dir, regex): + configs = [] + for path in Path(conf_dir).rglob(regex): + configs.append(path) + logger.info(f"Found {len(configs)} configs in {conf_dir}") + return configs + +def find_config_folders(conf_dir): + config_folders = [] + for path in Path(conf_dir).rglob("*"): + if path.is_dir(): + config_folders.append(path) + logger.info(f"Found {len(config_folders)} config folders in {conf_dir}") + return config_folders + +def load_config(config_path): + with open(config_path, 'r') as stream: + try: + config = yaml.safe_load(stream) + except yaml.YAMLError as exc: + logger.error(exc) + logger.debug(f"Loaded config from {config_path}") + return config + +def dict_to_overrides(dictionary): + new = {} + for key,value in dictionary.items(): + for k,v in value.items(): + new_key = "++" + key + "." + k + new[new_key] = v + return new + +def generate_grid_from_folders(conf_dir, regex): + this_dir = os.getcwd() + conf_dir = os.path.relpath(conf_dir, this_dir) + config_folders = find_config_folders(conf_dir) + big_dict = {} + layers = [] + for folder in config_folders: + folder_key = os.path.relpath(folder, conf_dir) + big_dict[folder_key] = [] if folder not in big_dict else big_dict[folder_key] + config_paths = find_configs_in_folder(folder, regex) + for config_path in config_paths: + config = load_config(config_path) + if isinstance(config, type(None)) or len(config) == 0: + big_dict[folder_key].append({}) + continue + big_dict[folder_key].append(config) + layers.append(len(big_dict[folder_key])) + big_list = make_grid(big_dict) + + assert len(big_list) == reduce(mul, layers), f"Grid size {len(big_list)} does not match product of layer sizes {reduce(mul, layers)}" + logger.info(f"Generated grid with {len(big_list)} configs") + return big_list + +def generate_queue( conf_root, grid_dir, regex, queue_folder = "queue", default_file = "default.yaml"): + this_dir = os.getcwd() + conf_dir = os.path.join(this_dir, conf_root, grid_dir) + logger.debug(f"Looking for configs in {conf_dir}") + big_list = generate_grid_from_folders(conf_dir, regex) + i = 0 + for entry in big_list: + new = dict_to_overrides(entry) + path = Path(conf_root, queue_folder) + name = my_hash(entry) + path.mkdir(parents=True, exist_ok=True) + with open(Path(conf_root, default_file), 'r') as stream: + try: + default = yaml.safe_load(stream) + except yaml.YAMLError as exc: + logger.error(exc) + default['hydra']['sweeper']['params'] = new + big_list[i] = default + with open(Path(path, name + ".yaml"), 'w') as outfile: + yaml.dump(big_list[i], outfile, default_flow_style=False) + assert Path(path, name + ".yaml").exists() + i += 1 + return big_list + +conf_root = "conf" +grid_folder = "grid" +regex = "*.yaml" + +big_list = generate_queue(conf_root, grid_folder, regex) +print(yaml.dump(big_list[0])) \ No newline at end of file diff --git a/deckard/layers/hydra_test.py b/deckard/layers/hydra_test.py new file mode 100644 index 00000000..9ca3c2d4 --- /dev/null +++ b/deckard/layers/hydra_test.py @@ -0,0 +1,14 @@ +from omegaconf import DictConfig, OmegaConf +from pathlib import Path +import hydra +import os + +working_dir = os.getcwd() +config_path = Path(working_dir, "conf").as_posix() +@hydra.main(version_base=None, config_path=config_path, config_name="default") +def my_app(cfg: DictConfig) -> None: + print(OmegaConf.to_yaml(cfg)) + return 0 + +if __name__ == "__main__": + my_app() \ No newline at end of file diff --git a/examples/keras/conf/model/keras_mnist.yaml b/examples/keras/conf/model/keras_mnist.yaml index 54dc7f25..b31cdbc9 100644 --- a/examples/keras/conf/model/keras_mnist.yaml +++ b/examples/keras/conf/model/keras_mnist.yaml @@ -8,7 +8,7 @@ init: _target_: deckard.base.model.Model trainer: nb_epoch: 1 - batch_size: 64 + batch_size: 1024 art: library : ${model.library} initialize: diff --git a/examples/keras/params.yaml b/examples/keras/params.yaml index 9d4e29d9..de2537d6 100644 --- a/examples/keras/params.yaml +++ b/examples/keras/params.yaml @@ -48,7 +48,7 @@ attack: optimizer: SGD library: keras trainer: - batch_size: 64 + batch_size: 1024 nb_epoch: 1 name: art.attacks.evasion.HopSkipJump method: evasion @@ -79,7 +79,7 @@ attack: optimizer: SGD library: keras trainer: - batch_size: 64 + batch_size: 1024 nb_epoch: 1 data: _target_: deckard.base.data.Data @@ -144,7 +144,7 @@ model: optimizer: SGD library: keras trainer: - batch_size: 64 + batch_size: 1024 nb_epoch: 1 scorers: _target_: deckard.base.scorer.ScorerDict diff --git a/examples/pytorch/attacks.sh b/examples/pytorch/attacks.sh index 7b0add84..099c4f55 100644 --- a/examples/pytorch/attacks.sh +++ b/examples/pytorch/attacks.sh @@ -1,35 +1,35 @@ -#!/bin/bash++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize ++hydra.sweeper.directi +#!/bin/bash -# This script is used to generate the attacks for the sklearn example. +# This script is used to generate the attacks for the example. # Fast Gradient Method -python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.FastGradientMethod +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=1024 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun +bash models.sh ++attack.init.name=art.attacks.evasion.FastGradientMethod +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize $@ --multirun # # Projected Gradient Descent -python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=1024 +attack.init.max_iter=10 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun +bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent +attack.init.eps=.01,.03,.3,.1 +attack.init.norm=inf,1,2 +attack.init.eps_step=.001,.003,.01 +attack.init.batch_size=1024 +attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize $@ --multirun # DeepFool -python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.DeepFool +attack.init.max_iter=10 +attack.init.batch_size=1024 +attack.init.nb_grads=10,100,1000 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun +bash models.sh ++attack.init.name=art.attacks.evasion.DeepFool +attack.init.max_iter=10 +attack.init.batch_size=1024 +attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize $@ --multirun # HopSkipJump -python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.HopSkipJump +attack.init.max_iter=10 +attack.init.init_eval=10 +attack.init.norm=inf,2 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun +bash models.sh ++attack.init.name=art.attacks.evasion.HopSkipJump +attack.init.max_iter=10 +attack.init.init_eval=10 +attack.init.norm=inf,2 ++stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize $@ --multirun ##################################################### # # PixelAttack -python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.PixelAttack +attack.init.max_iter=10 +attack.init.th=1,4,16,64,256 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun +bash models.sh ++attack.init.name=art.attacks.evasion.PixelAttack +attack.init.max_iter=10 +attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize $@ --multirun # ThresholdAttack -python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.max_iter=10 +attack.init.th=1,4,16,64,256 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun +bash models.sh ++attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.max_iter=10 +attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize $@ --multirun # # AdversarialPatch -python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.max_iter=10 +attack.init.learning_rate=.5,5.0,50.0 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize +attack.init.patch_shape=[28,28,1] --multirun +bash models.sh ++attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.max_iter=10 +attack.init.learning_rate=.5,5.0,50.0 ++stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize +attack.init.patch_shape=[28,28,1] $@ --multirun ##################################################### # Carlini L0 Method -python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.CarliniL0Method +attack.init.batch_size=1024 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun +bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL0Method +attack.init.batch_size=1024 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize $@ --multirun # Carlini L2 Method -python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.CarliniL2Method +attack.init.batch_size=1024 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun +bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL2Method +attack.init.batch_size=1024 +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize $@ --multirun # Carlini LInf Method -python -m deckard.layers.optimise ++attack.init.name=art.attacks.evasion.CarliniLInfMethod +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 +stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize --multirun +bash models.sh ++attack.init.name=art.attacks.evasion.CarliniLInfMethod +attack.init.max_iter=10 +attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=attack ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.direction=minimize $@ --multirun diff --git a/examples/pytorch/conf/attack/hsj.yaml b/examples/pytorch/conf/attack/default.yaml similarity index 100% rename from examples/pytorch/conf/attack/hsj.yaml rename to examples/pytorch/conf/attack/default.yaml diff --git a/examples/pytorch/conf/default.yaml b/examples/pytorch/conf/default.yaml index 42e3f5d0..20a00432 100644 --- a/examples/pytorch/conf/default.yaml +++ b/examples/pytorch/conf/default.yaml @@ -2,9 +2,12 @@ defaults: # - _target_ : deckard.base.experiment.Experiment - data: torch_mnist - model: torch_mnist - - attack: hsj + - attack: default - files: default - scorers: default + - optimizers : null + - stage : null + - hydra/sweeper/params : null - override hydra/sweeper : optuna - override hydra/launcher : joblib hydra: @@ -30,7 +33,7 @@ hydra: n_jobs: 1 launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 10 + n_jobs: 4 prefer : processes verbose: 1 timeout: null diff --git a/examples/pytorch/conf/grid.yaml b/examples/pytorch/conf/grid.yaml new file mode 100644 index 00000000..6e34b4d4 --- /dev/null +++ b/examples/pytorch/conf/grid.yaml @@ -0,0 +1,43 @@ +defaults: + # - _target_ : deckard.base.experiment.Experiment + - data: torch_mnist + - model: + - torch_mnist + - attack: null + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 30 + n_jobs: 1 + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 10 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r + diff --git a/examples/pytorch/conf/grid/attack/cw_0.yaml b/examples/pytorch/conf/grid/attack/cw_0.yaml new file mode 100644 index 00000000..20573ea2 --- /dev/null +++ b/examples/pytorch/conf/grid/attack/cw_0.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.CarliniL0Method +init.max_iter : [10] +init.batch_size : [1024] +init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/cw_2.yaml b/examples/pytorch/conf/grid/attack/cw_2.yaml new file mode 100644 index 00000000..14965baa --- /dev/null +++ b/examples/pytorch/conf/grid/attack/cw_2.yaml @@ -0,0 +1,5 @@ +init.model : ${model} +init.name : art.attacks.evasion.CarliniL0Method +init.max_iter : [10] +init.batch_size : [1024] +init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/cw_inf.yaml b/examples/pytorch/conf/grid/attack/cw_inf.yaml new file mode 100644 index 00000000..9e694da5 --- /dev/null +++ b/examples/pytorch/conf/grid/attack/cw_inf.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.CarliniLInfMethod +init.max_iter : [10] +init.batch_size : [1024] +init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/deepfool.yaml b/examples/pytorch/conf/grid/attack/deepfool.yaml new file mode 100644 index 00000000..84a90d3f --- /dev/null +++ b/examples/pytorch/conf/grid/attack/deepfool.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.DeepFool +init.max_iter : [10] +init.batch_size : [1024] +init.nb_grads : [10,100,1000] +init.eps: [0.01, 0.03, 0.3, 0.1, 1.0] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/attack/fgm.yaml b/examples/pytorch/conf/grid/attack/fgm.yaml new file mode 100644 index 00000000..7d9ec02a --- /dev/null +++ b/examples/pytorch/conf/grid/attack/fgm.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.FastGradientMethod +init.eps: [0.01, 0.03, 0.3, 0.1] +init.norm: [inf, 1, 2] +init.eps_step: [0.001, 0.003, 0.01] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/attack/hsj.yaml b/examples/pytorch/conf/grid/attack/hsj.yaml new file mode 100644 index 00000000..9ce9a07f --- /dev/null +++ b/examples/pytorch/conf/grid/attack/hsj.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.HopSkipJump +init.batch_size : [1, 4, 16, 65, 128] +init.max_iter : [1, 10, 100, 1000] +init.max_eval : [1, 10, 100, 1000] +init.init_eval : [1, 10, 100, 1000] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/attack/patch.yaml b/examples/pytorch/conf/grid/attack/patch.yaml new file mode 100644 index 00000000..2cf83694 --- /dev/null +++ b/examples/pytorch/conf/grid/attack/patch.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.ThresholdAttack +init.max_iter : [10] +init.batch_size : [1024] +init.scale_min : [0.01,] +init.scale_max : [.01, .1, .3, .5, .9, .99, 1] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/attack/pgd.yaml b/examples/pytorch/conf/grid/attack/pgd.yaml new file mode 100644 index 00000000..49819d5f --- /dev/null +++ b/examples/pytorch/conf/grid/attack/pgd.yaml @@ -0,0 +1,6 @@ +init.name : art.attacks.evasion.ProjectedGradientDescent +init.eps : [0.01, 0.03, 0.3, 0.1] +init.norm : [inf, 1, 2] +init.eps_step : [0.001, 0.003, 0.01] +init.batch_size : [1024] +init.max_iter : [10] diff --git a/examples/pytorch/conf/grid/attack/pixel.yaml b/examples/pytorch/conf/grid/attack/pixel.yaml new file mode 100644 index 00000000..1ef942b8 --- /dev/null +++ b/examples/pytorch/conf/grid/attack/pixel.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.ProjectedGradientDescent +init.max_iter : [10] +init.batch_size : [1024] +init.th : [1,4,16,64,256] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/attack/threshold.yaml b/examples/pytorch/conf/grid/attack/threshold.yaml new file mode 100644 index 00000000..d21f2803 --- /dev/null +++ b/examples/pytorch/conf/grid/attack/threshold.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.ThresholdAttack +init.max_iter : [10] +init.batch_size : [1024] +init.th: [1,4,16,64,256] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/model/confidence.yaml b/examples/pytorch/conf/grid/model/confidence.yaml new file mode 100644 index 00000000..c21c5b80 --- /dev/null +++ b/examples/pytorch/conf/grid/model/confidence.yaml @@ -0,0 +1,4 @@ + +art.postprocessor.name : [art.defences.postprocessor.HighConfidence] +art.postprocessor.cutoff : [.1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99] +art.postprocessor.apply_fit : [True, False] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/model/default.yaml b/examples/pytorch/conf/grid/model/default.yaml new file mode 100644 index 00000000..e69de29b diff --git a/examples/pytorch/conf/grid/model/fsq.yaml b/examples/pytorch/conf/grid/model/fsq.yaml new file mode 100644 index 00000000..07d579a4 --- /dev/null +++ b/examples/pytorch/conf/grid/model/fsq.yaml @@ -0,0 +1,3 @@ +art.preprocessor.name : [art.defences.preprocessor.FeatureSqueezing] +art.preprocessor.clip_values : [[0, 1]] +art.preprocessor.bit_depth : [1, 2, 4, 8, 16, 32, 64] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/model/gauss-in.yaml b/examples/pytorch/conf/grid/model/gauss-in.yaml new file mode 100644 index 00000000..06b0c414 --- /dev/null +++ b/examples/pytorch/conf/grid/model/gauss-in.yaml @@ -0,0 +1,4 @@ +art.preprocessor.name : art.defences.preprocessor.GaussianAugmentation +art.preprocessor.clip_values : ${art.initialize.clip_values} +art.preprocessor.sigma : [.01, .1, .2, .3, .5, 1] +art.preprocessor.ratio : [.1, .5, 1] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/model/gauss-out.yaml b/examples/pytorch/conf/grid/model/gauss-out.yaml new file mode 100644 index 00000000..377bbcc9 --- /dev/null +++ b/examples/pytorch/conf/grid/model/gauss-out.yaml @@ -0,0 +1,2 @@ +art.postprocessor.name : art.defences.postprocessor.GaussianNoise +art.postprocessor.scale : [.01, .1, .2, .3, .5, 1, 2, 3, 5, 10] diff --git a/examples/pytorch/conf/model/art/default.yaml b/examples/pytorch/conf/model/art/default.yaml new file mode 100644 index 00000000..2cb49c11 --- /dev/null +++ b/examples/pytorch/conf/model/art/default.yaml @@ -0,0 +1,7 @@ +defaults: + - initialize : default + # - preprocessor: default + # - postprocessor: default +data : ${..data} +library : ${..library} +_target_ : deckard.base.model.art_pipeline.ArtPipeline \ No newline at end of file diff --git a/examples/pytorch/conf/model/art/initialize/default.yaml b/examples/pytorch/conf/model/art/initialize/default.yaml new file mode 100644 index 00000000..0a7a0fdf --- /dev/null +++ b/examples/pytorch/conf/model/art/initialize/default.yaml @@ -0,0 +1,7 @@ +criterion: + name : torch.nn.CrossEntropyLoss +optimizer: + name : torch.optim.SGD + lr : 0.01 + momentum : 0.9 +clip_values : [0.0, 1.0] \ No newline at end of file diff --git a/examples/pytorch/conf/model/art/postprocessor/confidence.yaml b/examples/pytorch/conf/model/art/postprocessor/confidence.yaml new file mode 100644 index 00000000..a2006b6e --- /dev/null +++ b/examples/pytorch/conf/model/art/postprocessor/confidence.yaml @@ -0,0 +1,3 @@ + +name : art.defences.postprocessor.HighConfidence +cutoff : .99 \ No newline at end of file diff --git a/examples/pytorch/conf/model/art/postprocessor/gauss-out.yaml b/examples/pytorch/conf/model/art/postprocessor/gauss-out.yaml new file mode 100644 index 00000000..c912ebd9 --- /dev/null +++ b/examples/pytorch/conf/model/art/postprocessor/gauss-out.yaml @@ -0,0 +1,2 @@ +name : art.defences.postprocessor.GaussianNoise +scale : 1 diff --git a/examples/pytorch/conf/model/art/preprocessor/default.yaml b/examples/pytorch/conf/model/art/preprocessor/default.yaml new file mode 100644 index 00000000..e69de29b diff --git a/examples/pytorch/conf/model/art/preprocessor/fsq.yaml b/examples/pytorch/conf/model/art/preprocessor/fsq.yaml new file mode 100644 index 00000000..61d6bb8d --- /dev/null +++ b/examples/pytorch/conf/model/art/preprocessor/fsq.yaml @@ -0,0 +1,3 @@ +name : art.defences.preprocessor.FeatureSqueezing +clip_values : ${model.art.initialize.clip_values} +bit_depth : 8 \ No newline at end of file diff --git a/examples/pytorch/conf/model/art/preprocessor/gauss-in.yaml b/examples/pytorch/conf/model/art/preprocessor/gauss-in.yaml new file mode 100644 index 00000000..07976516 --- /dev/null +++ b/examples/pytorch/conf/model/art/preprocessor/gauss-in.yaml @@ -0,0 +1,4 @@ +name : art.defences.preprocessor.GaussianAugmentation +clip_values : ${model.art.initialize.clip_values} +sigma : 1 +ratio : .5 \ No newline at end of file diff --git a/examples/pytorch/conf/model/torch_cifar.yaml b/examples/pytorch/conf/model/torch_cifar.yaml deleted file mode 100644 index 0fafa6db..00000000 --- a/examples/pytorch/conf/model/torch_cifar.yaml +++ /dev/null @@ -1,18 +0,0 @@ -data: ${data} -init: - _target_: deckard.base.model.ModelInitializer - name : torch_example.Cifar10Net -_target_: deckard.base.model.Model -trainer: - nb_epoch: 1 - batch_size: 64 -art: - library : torch - _target_ : deckard.base.model.art_pipeline.ArtPipeline - initialize: - criterion: - name : "torch.nn.CrossEntropyLoss" - optimizer: - name : "torch.optim.SGD" - lr : 0.01 - momentum : 0.9 diff --git a/examples/pytorch/conf/model/torch_mnist.yaml b/examples/pytorch/conf/model/torch_mnist.yaml index f91c44fb..74c9822e 100644 --- a/examples/pytorch/conf/model/torch_mnist.yaml +++ b/examples/pytorch/conf/model/torch_mnist.yaml @@ -1,3 +1,6 @@ + +defaults: + - art : default data: ${data} init: _target_: deckard.base.model.ModelInitializer @@ -5,16 +8,7 @@ init: _target_: deckard.base.model.Model trainer: nb_epoch: 20 - batch_size: 64 + batch_size: 1024 library : pytorch -art: - library : ${..library} - _target_ : deckard.base.model.art_pipeline.ArtPipeline - initialize: - criterion: - name : torch.nn.CrossEntropyLoss - optimizer: - name : torch.optim.SGD - lr : 0.01 - momentum : 0.9 - clip_values : [0.0, 1.0] + + diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index cae6c3b6..e6fcf484 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -45,7 +45,25 @@ stages: _target_: deckard.base.model.Model art: _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true initialize: + clip_values: + - 0.0 + - 1.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -73,8 +91,8 @@ stages: name: torch_example.MNISTNet library: pytorch trainer: - batch_size: 64 - nb_epoch: 1 + batch_size: 1024 + nb_epoch: 20 scorers: _target_: deckard.base.scorer.ScorerDict accuracy: @@ -90,23 +108,14 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.pt - md5: b1189f381258ece9c17cc4208dfc53a6 + md5: a1c207cc77286cf57733731970617ce6 size: 76623 - path: output/reports/train/default/predictions.json - md5: 342ae779114872b03df92c526957367a - size: 2829716 - - path: output/reports/train/default/probabilities.json - md5: 342ae779114872b03df92c526957367a - size: 2829716 + md5: 0ad393ab6b1130cbfaec248784bed870 + size: 2836608 - path: output/reports/train/default/score_dict.json - md5: 492d51e212cd2bd52797851d01718793 - size: 411 - - path: output/reports/train/default/test_labels.json - md5: f26b1ad6bd01a70de4290c6ae713e2c7 - size: 728000 - - path: output/reports/train/default/train_labels.json - md5: b78e69f96f37e36ba2cf279422642325 - size: 2912000 + md5: 619436f0b955aba21ebe73f33f0e008e + size: 399 attack: cmd: python -m deckard.layers.experiment attack deps: @@ -114,13 +123,13 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.pt - md5: b1189f381258ece9c17cc4208dfc53a6 + md5: da01eb5d3ca138b8016129b8d1a33190 size: 76623 params: params.yaml: attack: _target_: deckard.base.attack.Attack - attack_size: 10 + attack_size: 100 data: _target_: deckard.base.data.Data generate: @@ -138,15 +147,29 @@ stages: with_std: true init: _target_: deckard.base.attack.AttackInitializer - batch_size: 64 - init_eval: 10 - max_eval: 10 - max_iter: 10 model: _target_: deckard.base.model.Model art: _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true initialize: + clip_values: + - 0.0 + - 1.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -174,15 +197,33 @@ stages: name: torch_example.MNISTNet library: pytorch trainer: - batch_size: 64 - nb_epoch: 1 + batch_size: 1024 + nb_epoch: 20 name: art.attacks.evasion.HopSkipJump method: evasion model: _target_: deckard.base.model.Model art: _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true initialize: + clip_values: + - 0.0 + - 1.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -210,8 +251,8 @@ stages: name: torch_example.MNISTNet library: pytorch trainer: - batch_size: 64 - nb_epoch: 1 + batch_size: 1024 + nb_epoch: 20 data.generate: _target_: deckard.base.data.generator.DataGenerator name: torch_mnist @@ -241,7 +282,25 @@ stages: _target_: deckard.base.model.Model art: _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true initialize: + clip_values: + - 0.0 + - 1.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -269,8 +328,8 @@ stages: name: torch_example.MNISTNet library: pytorch trainer: - batch_size: 64 - nb_epoch: 1 + batch_size: 1024 + nb_epoch: 20 scorers: _target_: deckard.base.scorer.ScorerDict accuracy: @@ -283,17 +342,14 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: f6933d3d01f97b4f400414bfaf4b72c2 - size: 31517 + md5: ce5e1590a803fa13e89015e521c617d7 + size: 313766 - path: output/reports/attack/default/adv_predictions.json - md5: 70410693cc66bd568b33164bd1eb62bc - size: 2172 - - path: output/reports/attack/default/adv_probabilities.json - md5: 70410693cc66bd568b33164bd1eb62bc - size: 2172 + md5: 106e6d413b8cddca15a4a95c6e12fc1f + size: 21776 - path: output/reports/attack/default/score_dict.json - md5: 328e9c9222026204796cf149ebec9921 - size: 538 + md5: 8d42724c6704293fb11c4d61af365f82 + size: 544 models: cmd: bash models.sh deps: @@ -301,17 +357,17 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.pt - md5: b1189f381258ece9c17cc4208dfc53a6 + md5: da01eb5d3ca138b8016129b8d1a33190 size: 76623 outs: - path: model.db - md5: 88a46dfe71f37b9aea64527b3b8c94ff - size: 159744 + md5: ee391cdbe0a9625a0e4c26fa4d669338 + size: 176128 attacks: cmd: bash attacks.sh deps: - path: model.db - md5: 88a46dfe71f37b9aea64527b3b8c94ff + md5: 4bdfcf180c485957a60e0de524b96a7c size: 159744 - path: output/data/data.pkl md5: de934a5f5157970e5f30b8f3f1856a68 @@ -321,5 +377,5 @@ stages: size: 76623 outs: - path: attack.db - md5: 133098c7364b498637ca6e44fe656979 - size: 172032 + md5: 6f99a25eca4a91ff0e91c6ec7d8c023e + size: 233472 diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index f55d4ec6..006c7b42 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -9,10 +9,10 @@ stages: outs: - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - - ${files.directory}/${files.reports}/train/${files.name}/${files.train_labels_file} - - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.train_labels_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} - - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} metrics: - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} attack: @@ -26,7 +26,7 @@ stages: outs: - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} - - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} + # - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} deps: - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} diff --git a/examples/pytorch/models.sh b/examples/pytorch/models.sh index b4f80bc3..b346ce7f 100644 --- a/examples/pytorch/models.sh +++ b/examples/pytorch/models.sh @@ -4,22 +4,14 @@ # This line generates the model and adds the FeatureSqueezing preprocessing defence. -python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train --multirun +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train $@ --multirun # Gaussian Augmentation (Input) -python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.2,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +stage=train --multirun +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.2,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +stage=train $@ --multirun -# # Spatial Smoothing -# python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.SpatialSmoothing +model.art.preprocessor.params.window_size=2,3,4 +stage=train --multirun +# Gaussian Noise (Output) +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.1,.9,.999 +stage=train $@ --multirun -# # Total Variance Minimisation -# python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.TotalVarMin +model.art.preprocessor.params.prob=.001,.01,.1 +model.art.preprocessor.params.norm=1,2,3 +model.art.preprocessor.params.lamb=.05,.5,.95 +model.art.preprocessor.params.max_iter=100 +stage=train --multirun +# High Confidence +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.5,.9,.99 +stage=train $@ --multirun -# # Gaussian Noise (Output) -# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.1,.9,.999 +stage=train --multirun - -# # High Confidence -# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.5,.9,.99 +stage=train --multirun - -# # Rounded (Output) -# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.Rounded +model.art.postprocessor.params.decimals=1,2,4,8 +stage=train --multirun diff --git a/examples/pytorch/params.yaml b/examples/pytorch/params.yaml index a3cd3aba..e57677e5 100644 --- a/examples/pytorch/params.yaml +++ b/examples/pytorch/params.yaml @@ -1,6 +1,6 @@ attack: _target_: deckard.base.attack.Attack - attack_size: 10 + attack_size: 100 data: _target_: deckard.base.data.Data generate: @@ -18,15 +18,29 @@ attack: with_std: true init: _target_: deckard.base.attack.AttackInitializer - batch_size: 64 - init_eval: 10 - max_eval: 10 - max_iter: 10 model: _target_: deckard.base.model.Model art: _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true initialize: + clip_values: + - 0.0 + - 1.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -54,15 +68,33 @@ attack: name: torch_example.MNISTNet library: pytorch trainer: - batch_size: 64 - nb_epoch: 1 + batch_size: 1024 + nb_epoch: 20 name: art.attacks.evasion.HopSkipJump method: evasion model: _target_: deckard.base.model.Model art: _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true initialize: + clip_values: + - 0.0 + - 1.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -90,8 +122,8 @@ attack: name: torch_example.MNISTNet library: pytorch trainer: - batch_size: 64 - nb_epoch: 1 + batch_size: 1024 + nb_epoch: 20 data: _target_: deckard.base.data.Data generate: @@ -133,7 +165,25 @@ model: _target_: deckard.base.model.Model art: _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true initialize: + clip_values: + - 0.0 + - 1.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -161,8 +211,8 @@ model: name: torch_example.MNISTNet library: pytorch trainer: - batch_size: 64 - nb_epoch: 1 + batch_size: 1024 + nb_epoch: 20 scorers: _target_: deckard.base.scorer.ScorerDict accuracy: diff --git a/examples/pytorch/torch_example.py b/examples/pytorch/torch_example.py index 10e1f76b..220a7ec2 100644 --- a/examples/pytorch/torch_example.py +++ b/examples/pytorch/torch_example.py @@ -22,7 +22,6 @@ def __init__(self): def forward(self, x): torch.set_default_dtype(self.conv_1.weight.dtype) - x = x.clone().detach().requires_grad_(True).to(x.device) x = F.relu(self.conv_1(x)) x = F.max_pool2d(x, 2, 2) x = F.relu(self.conv_2(x)) diff --git a/examples/tfv1/conf/model/tfv1_mnist.yaml b/examples/tfv1/conf/model/tfv1_mnist.yaml index a2ffd76c..72a78931 100644 --- a/examples/tfv1/conf/model/tfv1_mnist.yaml +++ b/examples/tfv1/conf/model/tfv1_mnist.yaml @@ -4,7 +4,7 @@ init: _target_: deckard.base.model.Model trainer: nb_epoch: 1 - batch_size: 64 + batch_size: 1024 library : tfv1 art: initialize: diff --git a/examples/tfv2/conf/model/tensorflow_mnist.yaml b/examples/tfv2/conf/model/tensorflow_mnist.yaml index cead3525..34c17a74 100644 --- a/examples/tfv2/conf/model/tensorflow_mnist.yaml +++ b/examples/tfv2/conf/model/tensorflow_mnist.yaml @@ -14,7 +14,7 @@ init: _target_: deckard.base.model.Model trainer: nb_epoch: 20 - batch_size: 64 + batch_size: 1024 verbose: true library : tensorflow art: diff --git a/examples/tfv2/dvc.lock b/examples/tfv2/dvc.lock index 65c445d7..1e62a24e 100644 --- a/examples/tfv2/dvc.lock +++ b/examples/tfv2/dvc.lock @@ -74,7 +74,7 @@ stages: learning_rate: 0.001 library: tensorflow trainer: - batch_size: 64 + batch_size: 1024 nb_epoch: 20 verbose: true scorers: @@ -142,7 +142,7 @@ stages: with_std: true init: _target_: deckard.base.attack.AttackInitializer - batch_size: 64 + batch_size: 1024 init_eval: 10 max_eval: 10 max_iter: 10 @@ -180,7 +180,7 @@ stages: learning_rate: 0.001 library: tensorflow trainer: - batch_size: 64 + batch_size: 1024 nb_epoch: 20 verbose: true name: art.attacks.evasion.HopSkipJump @@ -219,7 +219,7 @@ stages: learning_rate: 0.001 library: tensorflow trainer: - batch_size: 64 + batch_size: 1024 nb_epoch: 20 verbose: true data.generate: @@ -280,7 +280,7 @@ stages: learning_rate: 0.001 library: tensorflow trainer: - batch_size: 64 + batch_size: 1024 nb_epoch: 20 verbose: true scorers: diff --git a/examples/tfv2/params.yaml b/examples/tfv2/params.yaml index a47972f6..a97512d3 100644 --- a/examples/tfv2/params.yaml +++ b/examples/tfv2/params.yaml @@ -18,7 +18,7 @@ attack: with_std: true init: _target_: deckard.base.attack.AttackInitializer - batch_size: 64 + batch_size: 1024 init_eval: 10 max_eval: 10 max_iter: 10 @@ -56,7 +56,7 @@ attack: learning_rate: 0.001 library: tensorflow trainer: - batch_size: 64 + batch_size: 1024 nb_epoch: 20 verbose: true name: art.attacks.evasion.HopSkipJump @@ -95,7 +95,7 @@ attack: learning_rate: 0.001 library: tensorflow trainer: - batch_size: 64 + batch_size: 1024 nb_epoch: 20 verbose: true data: @@ -168,7 +168,7 @@ model: learning_rate: 0.001 library: tensorflow trainer: - batch_size: 64 + batch_size: 1024 nb_epoch: 20 verbose: true scorers: diff --git a/test/conf/attack/extraction.yaml b/test/conf/attack/extraction.yaml index f8491506..45b31c23 100644 --- a/test/conf/attack/extraction.yaml +++ b/test/conf/attack/extraction.yaml @@ -27,7 +27,7 @@ model: _target_: deckard.base.model.Model trainer: nb_epoch: 1 - batch_size: 64 + batch_size: 1024 library : torch art: library : torch @@ -55,7 +55,7 @@ kwargs: _target_: deckard.base.model.Model trainer: nb_epoch: 1 - batch_size: 64 + batch_size: 1024 library : torch art: library : torch diff --git a/test/conf/attack/inference.yaml b/test/conf/attack/inference.yaml index 7f0a1e1f..e0d131a6 100644 --- a/test/conf/attack/inference.yaml +++ b/test/conf/attack/inference.yaml @@ -25,7 +25,7 @@ model: _target_: deckard.base.model.Model trainer: nb_epoch: 1 - batch_size: 64 + batch_size: 1024 library : torch art: library : torch diff --git a/test/conf/attack/poisoning.yaml b/test/conf/attack/poisoning.yaml index 29677745..b70195c8 100644 --- a/test/conf/attack/poisoning.yaml +++ b/test/conf/attack/poisoning.yaml @@ -27,7 +27,7 @@ model: _target_: deckard.base.model.Model trainer: nb_epoch: 1 - batch_size: 64 + batch_size: 1024 library : torch art: library : torch diff --git a/test/conf/model/torch_mnist.yaml b/test/conf/model/torch_mnist.yaml index c7d03476..f00031b4 100644 --- a/test/conf/model/torch_mnist.yaml +++ b/test/conf/model/torch_mnist.yaml @@ -22,7 +22,7 @@ init: _target_: deckard.base.model.Model trainer: nb_epoch: 1 - batch_size: 64 + batch_size: 1024 library : torch art: library : torch From 10c9a544a5cd739221cc072e2cd777126c8288b0 Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Sat, 24 Jun 2023 13:03:55 +0200 Subject: [PATCH 006/148] Initial terraform instructions --- IaaC/gcp/cleanup.sh | 14 +++++++++++ IaaC/gcp/functions.sh | 56 +++++++++++++++++++++++++++++++++++++++++++ IaaC/gcp/main.tf | 16 +++++++++++++ IaaC/gcp/variables.tf | 13 ++++++++++ 4 files changed, 99 insertions(+) create mode 100755 IaaC/gcp/cleanup.sh create mode 100755 IaaC/gcp/functions.sh create mode 100644 IaaC/gcp/main.tf create mode 100644 IaaC/gcp/variables.tf diff --git a/IaaC/gcp/cleanup.sh b/IaaC/gcp/cleanup.sh new file mode 100755 index 00000000..46a049ee --- /dev/null +++ b/IaaC/gcp/cleanup.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +selected_project=$(gcloud config get-value project) +SA_NAME="deckard-terraform-sa" + + +terraform -chdir=./IaaC/gcp/ destroy -var="project=$selected_project" + +gcloud iam service-accounts delete ${SA_NAME}@${selected_project}.iam.gserviceaccount.com --quiet + +rm -rf ./IaaC/gcp/.terraform ./IaaC/gcp/.terraform.lock.hcl ./IaaC/gcp/terraform.tfstate ./IaaC/gcp/terraform.tfstate.backup ./IaaC/gcp/tf-service-account.json + + + diff --git a/IaaC/gcp/functions.sh b/IaaC/gcp/functions.sh new file mode 100755 index 00000000..22d86003 --- /dev/null +++ b/IaaC/gcp/functions.sh @@ -0,0 +1,56 @@ +#!/bin/bash + +function is_gcloud_installed { + if ! command -v gcloud &> /dev/null; then + echo "Error: gcloud command not found. Please install the Google Cloud SDK." + exit 1 + fi +} + +function is_logged_in { + if gcloud auth list --format="value(account)" | grep -q .; then + echo "You have already logged in into the GCP." + else + echo "Pleas log in to the GCP using the following command:" + echo "\t gcloud auth login" + exit 1 + fi +} + +is_gcloud_installed +is_logged_in + +# Get the currently selected project +selected_project=$(gcloud config get-value project) + +echo "At the moment the $selected_project is selected as your default project in GCP." +echo -n "Is it the right one? [Y/n]:" +read -r right_project + +case $right_project in + [nN]) echo "Please select the right project as your default project." + exit 1;; +esac + + +# Create GCP Service Account +SA_NAME="deckard-terraform-sa" +SA_EMAIL=${SA_NAME}@${selected_project}.iam.gserviceaccount.com +if gcloud iam service-accounts describe "$SA_EMAIL" &> /dev/null; then + echo Service account $SA_NAME already exists. +else + gcloud iam service-accounts create $SA_NAME --description "Service Account for Deckard Terraform Infrastructure" --display-name "$SA_NAME" + gcloud projects add-iam-policy-binding $selected_project --member serviceAccount:${SA_NAME}@${selected_project}.iam.gserviceaccount.com --role roles/editor --no-user-output-enabled + + SA_KEY_FILE="./IaaC/gcp/tf-service-account.json" + gcloud iam service-accounts keys create ${SA_KEY_FILE} --iam-account ${SA_NAME}@${selected_project}.iam.gserviceaccount.com +fi + +# execute terraform +terraform -chdir=./IaaC/gcp/ init -var="project=$selected_project" +terraform -chdir=./IaaC/gcp/ plan -var="project=$selected_project" +terraform -chdir=./IaaC/gcp/ apply -var="project=$selected_project" + + + + diff --git a/IaaC/gcp/main.tf b/IaaC/gcp/main.tf new file mode 100644 index 00000000..e94e574d --- /dev/null +++ b/IaaC/gcp/main.tf @@ -0,0 +1,16 @@ +terraform { + required_providers { + google = { + source = "hashicorp/google" + version = "4.51.0" + } + } +} + +provider "google" { + credentials = file(var.credentials_file) + + project = var.project + region = var.region + zone = var.zone +} diff --git a/IaaC/gcp/variables.tf b/IaaC/gcp/variables.tf new file mode 100644 index 00000000..d0fad0e3 --- /dev/null +++ b/IaaC/gcp/variables.tf @@ -0,0 +1,13 @@ +variable "credentials_file" { + default = "tf-service-account.json" +} + +variable "project" { } + +variable "region" { + default = "us-central1" +} + +variable "zone" { + default = "us-central1-c" +} From e2920381906192efb37e64dee840a5b6a41dfcb7 Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Sat, 24 Jun 2023 14:11:06 +0200 Subject: [PATCH 007/148] add networks and firewall rules for K8s --- IaaC/gcp/main.tf | 65 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) diff --git a/IaaC/gcp/main.tf b/IaaC/gcp/main.tf index e94e574d..e01263d5 100644 --- a/IaaC/gcp/main.tf +++ b/IaaC/gcp/main.tf @@ -14,3 +14,68 @@ provider "google" { region = var.region zone = var.zone } + +resource "google_compute_network" "network" { + name = "decard-network" +} + +resource "google_compute_subnetwork" "subnetwork" { + name = "decard-subnetwork" + ip_cidr_range = "172.16.0.0/28" + region = var.region + network = google_compute_network.network.id +} + +resource "google_compute_firewall" "network-fw-1" { + name = "firewall-rules-for-k8s-api" + network = google_compute_network.network.name + + allow { + protocol = "tcp" + ports = ["6443"] + } + + direction = "INGRESS" + source_ranges = ["0.0.0.0/0"] +} + +resource "google_compute_firewall" "network-fw-2" { + name = "firewall-rules-for-k8s-ssh" + network = google_compute_network.network.name + + allow { + protocol = "tcp" + ports = ["22"] + } + + direction = "INGRESS" + source_ranges = ["0.0.0.0/0"] +} + +resource "google_compute_firewall" "network-fw-3" { + name = "firewall-rules-for-k8s-prometheus" + network = google_compute_network.network.name + + allow { + protocol = "tcp" + ports = ["30090", "30091"] + } + + direction = "INGRESS" + source_ranges = ["0.0.0.0/0"] +} + +resource "google_compute_firewall" "network-fw-4" { + name = "firewall-rules-for-k8s-cilium" + network = google_compute_network.network.name + + allow { + protocol = "tcp" + ports = ["8472"] + } + + direction = "INGRESS" + source_ranges = ["172.16.0.0/28"] +} + + From 99f76b23f0731ed62899175879dedc5434009783 Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Sat, 24 Jun 2023 14:44:38 +0200 Subject: [PATCH 008/148] create VMs --- IaaC/gcp/main.tf | 53 +++++++++++++++++++++++++++++++++++++++++++ IaaC/gcp/variables.tf | 20 ++++++++++++++++ 2 files changed, 73 insertions(+) diff --git a/IaaC/gcp/main.tf b/IaaC/gcp/main.tf index e01263d5..79e9d410 100644 --- a/IaaC/gcp/main.tf +++ b/IaaC/gcp/main.tf @@ -79,3 +79,56 @@ resource "google_compute_firewall" "network-fw-4" { } +resource "google_compute_instance" "k8s-master" { + name = "k8s-master" + machine_type = var.machine_type + zone = var.zone + + + boot_disk { + initialize_params { + image = var.image + } + } + + network_interface { + subnetwork = google_compute_subnetwork.subnetwork.name + + access_config { + // Ephemeral public IP + } + } + + metadata = { + sshKeys = "${var.gce_ssh_user}:${file(var.gce_ssh_pub_key_file)}" + } +} + + +resource "google_compute_instance" "k8s-worker" { + count = "${var.node_count}" + name = "k8s-worker-${count.index}" + machine_type = var.machine_type + zone = var.zone + + + boot_disk { + initialize_params { + image = var.image + } + } + + network_interface { + subnetwork = google_compute_subnetwork.subnetwork.name + + access_config { + // Ephemeral public IP + } + } + + metadata = { + sshKeys = "${var.gce_ssh_user}:${file(var.gce_ssh_pub_key_file)}" + } +} + + diff --git a/IaaC/gcp/variables.tf b/IaaC/gcp/variables.tf index d0fad0e3..6c870fab 100644 --- a/IaaC/gcp/variables.tf +++ b/IaaC/gcp/variables.tf @@ -11,3 +11,23 @@ variable "region" { variable "zone" { default = "us-central1-c" } + +variable "machine_type" { + default = "e2-medium" +} + +variable "image" { + default = "ubuntu-os-cloud/ubuntu-2204-lts" +} + +variable "gce_ssh_user" { + default = "mr.salehsedghpour" +} + +variable "gce_ssh_pub_key_file" { + default = "~/.ssh/id_rsa.pub" +} + +variable "node_count" { + default = "3" +} From 4c17e324cca548a04892d7849c08223152a6ae0b Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Sat, 24 Jun 2023 14:44:53 +0200 Subject: [PATCH 009/148] rename functions to main --- IaaC/gcp/{functions.sh => main.sh} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename IaaC/gcp/{functions.sh => main.sh} (100%) diff --git a/IaaC/gcp/functions.sh b/IaaC/gcp/main.sh similarity index 100% rename from IaaC/gcp/functions.sh rename to IaaC/gcp/main.sh From a69bd93ecd01e80c9f00b8be1373e94aa2ac178b Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Sat, 24 Jun 2023 14:45:09 +0200 Subject: [PATCH 010/148] add README --- IaaC/gcp/README.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 IaaC/gcp/README.md diff --git a/IaaC/gcp/README.md b/IaaC/gcp/README.md new file mode 100644 index 00000000..34c25aaf --- /dev/null +++ b/IaaC/gcp/README.md @@ -0,0 +1,14 @@ +# Required info for GCP + +Some variables should be defined in `variables.tf` file. For instance: + +- `region` +- `zone` +- `machine_type` +- `image` +- `gce_ssh_user` +- `gce_ssh_pub_key_file` +- `node_count` + + +> **_NOTE:_** For now, it only creates the required infrastructure. The next step is to install and configure K8s. From a7562547714e213148e4935df947cf4ad943fccb Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Mon, 21 Aug 2023 10:34:36 +0200 Subject: [PATCH 011/148] setup and cleanup infrastructure --- IaaC/gcp/cleanup.sh | 33 ++++++++- IaaC/gcp/config.yaml | 8 +++ IaaC/gcp/setup_infrastructure.sh | 111 +++++++++++++++++++++++++++++++ 3 files changed, 149 insertions(+), 3 deletions(-) create mode 100644 IaaC/gcp/config.yaml create mode 100755 IaaC/gcp/setup_infrastructure.sh diff --git a/IaaC/gcp/cleanup.sh b/IaaC/gcp/cleanup.sh index 46a049ee..63a2546f 100755 --- a/IaaC/gcp/cleanup.sh +++ b/IaaC/gcp/cleanup.sh @@ -3,11 +3,38 @@ selected_project=$(gcloud config get-value project) SA_NAME="deckard-terraform-sa" - -terraform -chdir=./IaaC/gcp/ destroy -var="project=$selected_project" - +# Load config params +GCP_REGION=$(yq '.gcp_region' IaaC/gcp/config.yaml) +GCP_ZONE=$(yq '.gcp_zone' IaaC/gcp/config.yaml) +GCP_MACHINE_TYPE=$(yq '.gcp_machine_type' IaaC/gcp/config.yaml) +GCP_IMAGE_PROJECT=$(yq '.gcp_image_project' IaaC/gcp/config.yaml) +GCP_IMAGE_FAMILY=$(yq '.gcp_image_family' IaaC/gcp/config.yaml) +GCP_SSH_USER=$(yq '.gcp_ssh_user' IaaC/gcp/config.yaml) +GCP_SSH_PUB_KEY_FILE=$(yq '.gcp_ssh_pub_key_file' IaaC/gcp/config.yaml) +NODE_COUNT=$(yq '.node_count' IaaC/gcp/config.yaml) +read -d $'\x04' PUBLIC_KEY < $GCP_SSH_PUB_KEY_FILE + + +# Delete instances +gcloud compute instances delete k8s-master --quiet --zone=$GCP_ZONE +for (( i=1; i <= $NODE_COUNT; i++ )) +do + gcloud compute instances delete k8s-worker-${i} --quiet --zone=$GCP_ZONE +done + + +# Delete firewall rules +gcloud compute firewall-rules delete k8s-allow-internal --quiet +gcloud compute firewall-rules delete k8s-allow-external --quiet + +# Delete network and subnetwork +gcloud compute networks subnets delete k8s-subnet --region $GCP_REGION --quiet +gcloud compute networks delete k8s-cluster --quiet + +# Delete service account gcloud iam service-accounts delete ${SA_NAME}@${selected_project}.iam.gserviceaccount.com --quiet + rm -rf ./IaaC/gcp/.terraform ./IaaC/gcp/.terraform.lock.hcl ./IaaC/gcp/terraform.tfstate ./IaaC/gcp/terraform.tfstate.backup ./IaaC/gcp/tf-service-account.json diff --git a/IaaC/gcp/config.yaml b/IaaC/gcp/config.yaml new file mode 100644 index 00000000..0294622a --- /dev/null +++ b/IaaC/gcp/config.yaml @@ -0,0 +1,8 @@ +gcp_region: "us-central1" +gcp_zone: "us-central1-c" +gcp_machine_type: "e2-medium" +gcp_image_project: "ubuntu-os-cloud" +gcp_image_family: "ubuntu-2204-lts" +gcp_ssh_user: "mr.salehsedghpour" +gcp_ssh_pub_key_file: "/Users/msaleh/.ssh/id_ed25519.pub" +node_count: 3 diff --git a/IaaC/gcp/setup_infrastructure.sh b/IaaC/gcp/setup_infrastructure.sh new file mode 100755 index 00000000..c1eb7af2 --- /dev/null +++ b/IaaC/gcp/setup_infrastructure.sh @@ -0,0 +1,111 @@ +#!/bin/bash + +# Load the config params +GCP_REGION=$(yq '.gcp_region' IaaC/gcp/config.yaml) +GCP_ZONE=$(yq '.gcp_zone' IaaC/gcp/config.yaml) +GCP_MACHINE_TYPE=$(yq '.gcp_machine_type' IaaC/gcp/config.yaml) +GCP_IMAGE_PROJECT=$(yq '.gcp_image_project' IaaC/gcp/config.yaml) +GCP_IMAGE_FAMILY=$(yq '.gcp_image_family' IaaC/gcp/config.yaml) +GCP_SSH_USER=$(yq '.gcp_ssh_user' IaaC/gcp/config.yaml) +GCP_SSH_PUB_KEY_FILE=$(yq '.gcp_ssh_pub_key_file' IaaC/gcp/config.yaml) +NODE_COUNT=$(yq '.node_count' IaaC/gcp/config.yaml) +read -d $'\x04' PUBLIC_KEY < $GCP_SSH_PUB_KEY_FILE + +function is_gcloud_installed { + if ! command -v gcloud &> /dev/null; then + echo "Error: gcloud command not found. Please install the Google Cloud SDK." + exit 1 + fi +} + +function is_logged_in { + if gcloud auth list --format="value(account)" | grep -q .; then + echo "You have already logged in into the GCP." + else + echo "Pleas log in to the GCP using the following command:" + echo "\t gcloud auth login" + exit 1 + fi +} + +is_gcloud_installed +is_logged_in + +# Get the currently selected project +selected_project=$(gcloud config get-value project) + +echo "At the moment the $selected_project is selected as your default project in GCP." +echo -n "Is it the right one? [Y/n]:" +read -r right_project + + +case $right_project in + [nN]) echo "Please select the right project as your default project." + exit 1;; +esac + +# Create GCP Service Account +SA_NAME="deckard-terraform-sa" +SA_EMAIL=${SA_NAME}@${selected_project}.iam.gserviceaccount.com +if gcloud iam service-accounts describe "$SA_EMAIL" &> /dev/null; then + echo Service account $SA_NAME already exists. +else + gcloud iam service-accounts create $SA_NAME --description "Service Account for Deckard Terraform Infrastructure" --display-name "$SA_NAME" --quiet + gcloud projects add-iam-policy-binding $selected_project --member serviceAccount:${SA_NAME}@${selected_project}.iam.gserviceaccount.com --role roles/editor --no-user-output-enabled --quiet + + SA_KEY_FILE="./IaaC/gcp/tf-service-account.json" + gcloud iam service-accounts keys create ${SA_KEY_FILE} --iam-account ${SA_NAME}@${selected_project}.iam.gserviceaccount.com --quiet +fi + + +# Create GCP Network and Subnets +gcloud compute networks create k8s-cluster --subnet-mode custom --quiet +gcloud compute networks subnets create k8s-subnet \ + --network k8s-cluster \ + --range 172.16.0.0/28 \ + --region $GCP_REGION \ + --quiet + + +# Create GCP firewall rules +gcloud compute firewall-rules create k8s-allow-internal \ + --allow tcp,udp,icmp \ + --network k8s-cluster \ + --source-ranges 172.16.0.0/28 \ + --quiet +gcloud compute firewall-rules create k8s-allow-external \ + --allow tcp:80,tcp:6443,tcp:443,tcp:22,icmp \ + --network k8s-cluster \ + --source-ranges 0.0.0.0/0 \ + --quiet + + + + +gcloud compute instances create k8s-master \ + --async \ + --metadata=ssh-keys="$GCP_SSH_USER:$PUBLIC_KEY" \ + --zone=$GCP_ZONE \ + --can-ip-forward \ + --image-family=$GCP_IMAGE_FAMILY \ + --image-project=$GCP_IMAGE_PROJECT \ + --machine-type=$GCP_MACHINE_TYPE \ + --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring \ + --subnet=k8s-subnet \ + --tags=k8s-cluster,controller \ + --quiet + +gcloud compute instances bulk create \ + --async \ + --name-pattern=k8s-worker-# \ + --metadata=ssh-keys="$GCP_SSH_USER:$PUBLIC_KEY" \ + --zone=$GCP_ZONE \ + --count=$NODE_COUNT \ + --can-ip-forward \ + --image-family=$GCP_IMAGE_FAMILY \ + --image-project=$GCP_IMAGE_PROJECT \ + --machine-type=$GCP_MACHINE_TYPE \ + --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring \ + --subnet=k8s-subnet \ + --tags=k8s-cluster,worker \ + --quiet From bd2f3d5c5e4254e052ef81bcac0b20bc08520b61 Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Wed, 23 Aug 2023 13:38:27 +0200 Subject: [PATCH 012/148] not working version --- IaaC/ansible_files/ansible.cfg | 7 + IaaC/ansible_files/initialize_master.yaml | 44 ++++++ IaaC/ansible_files/inventory.ini | 7 + IaaC/ansible_files/roles/dep/tasks/main.yaml | 66 +++++++++ .../roles/update/tasks/main.yaml | 6 + IaaC/ansible_files/site.yaml | 6 + IaaC/gcp/README.md | 0 IaaC/gcp/config.yaml | 0 IaaC/gcp/inventory.ini | 2 + IaaC/gcp/main.sh | 44 ++---- IaaC/gcp/main.tf | 134 ------------------ IaaC/gcp/variables.tf | 33 ----- 12 files changed, 150 insertions(+), 199 deletions(-) create mode 100644 IaaC/ansible_files/ansible.cfg create mode 100644 IaaC/ansible_files/initialize_master.yaml create mode 100644 IaaC/ansible_files/inventory.ini create mode 100644 IaaC/ansible_files/roles/dep/tasks/main.yaml create mode 100644 IaaC/ansible_files/roles/update/tasks/main.yaml create mode 100644 IaaC/ansible_files/site.yaml mode change 100644 => 100755 IaaC/gcp/README.md mode change 100644 => 100755 IaaC/gcp/config.yaml create mode 100755 IaaC/gcp/inventory.ini delete mode 100644 IaaC/gcp/main.tf delete mode 100644 IaaC/gcp/variables.tf diff --git a/IaaC/ansible_files/ansible.cfg b/IaaC/ansible_files/ansible.cfg new file mode 100644 index 00000000..ed3a36b1 --- /dev/null +++ b/IaaC/ansible_files/ansible.cfg @@ -0,0 +1,7 @@ +[defaults] +ansible_managed=DO NOT EDIT! This file is managed by Ansible.%n + template: {file} + date: %Y-%m-%d %H:%M:%S + user: {uid} + host: {host} +roles_path = ./roles \ No newline at end of file diff --git a/IaaC/ansible_files/initialize_master.yaml b/IaaC/ansible_files/initialize_master.yaml new file mode 100644 index 00000000..1d54c864 --- /dev/null +++ b/IaaC/ansible_files/initialize_master.yaml @@ -0,0 +1,44 @@ +- hosts: master + become: yes + tasks: + - name: initialize the cluster + shell: kubeadm init --pod-network-cidr=10.244.0.0/16 + args: + chdir: /home/mr.salehsedghpour/ + creates: cluster_initialized.txt + + - name: create .kube directory + file: + path: /home/mr.salehsedghpour//.kube + state: directory + mode: 0755 + + - name: copies admin.conf to user's kube config + become: yes + copy: + src: /etc/kubernetes/admin.conf + dest: /home/mr.salehsedghpour/.kube/config + remote_src: yes + + - name: Make my directory tree readable + become: yes + file: + path: /home/mr.salehsedghpour/.kube/config + mode: 0755 + + - name: Wait for some time + shell: sleep 30 + + + - name: install Pod network + shell: kubectl apply -f https://docs.projectcalico.org/manifests/calico.yaml --kubeconfig=/home/mr.salehsedghpour/.kube/config + + - name: Get the token for joining the worker nodes + shell: kubeadm token create --print-join-command + register: kubernetes_join_command + + # - debug: + # msg: "{{ kubernetes_join_command.stdout }}" + + - name: Copy join command to local file. + local_action: copy content="{{ kubernetes_join_command.stdout_lines[0] }}" dest="/tmp/kubernetes_join_command" mode=0777 diff --git a/IaaC/ansible_files/inventory.ini b/IaaC/ansible_files/inventory.ini new file mode 100644 index 00000000..344475cd --- /dev/null +++ b/IaaC/ansible_files/inventory.ini @@ -0,0 +1,7 @@ +[master] +34.16.75.74 + +[workers] +34.66.250.96 +34.135.32.240 +34.122.164.55 diff --git a/IaaC/ansible_files/roles/dep/tasks/main.yaml b/IaaC/ansible_files/roles/dep/tasks/main.yaml new file mode 100644 index 00000000..c0f5c23d --- /dev/null +++ b/IaaC/ansible_files/roles/dep/tasks/main.yaml @@ -0,0 +1,66 @@ +- name: IP forwarding + shell: | + cat < /dev/null + sudo apt-get update + sudo apt-get install containerd.io + cat < /dev/null; then - echo Service account $SA_NAME already exists. -else - gcloud iam service-accounts create $SA_NAME --description "Service Account for Deckard Terraform Infrastructure" --display-name "$SA_NAME" - gcloud projects add-iam-policy-binding $selected_project --member serviceAccount:${SA_NAME}@${selected_project}.iam.gserviceaccount.com --role roles/editor --no-user-output-enabled - - SA_KEY_FILE="./IaaC/gcp/tf-service-account.json" - gcloud iam service-accounts keys create ${SA_KEY_FILE} --iam-account ${SA_NAME}@${selected_project}.iam.gserviceaccount.com -fi - -# execute terraform -terraform -chdir=./IaaC/gcp/ init -var="project=$selected_project" -terraform -chdir=./IaaC/gcp/ plan -var="project=$selected_project" -terraform -chdir=./IaaC/gcp/ apply -var="project=$selected_project" - - +IPs=( $(gcloud compute instances list | awk '{ print $5 }' | awk '(NR>1)' )) +for i in "${IPs[@]}" + do + ssh-keyscan -H $i >> ~/.ssh/known_hosts + done + +# Create inventory file +echo '[master]' > IaaC/ansible_files/inventory.ini +gcloud compute instances list | grep master | awk '{ print $5 }' >> IaaC/ansible_files/inventory.ini + +echo -e "\n[workers]" >> IaaC/ansible_files/inventory.ini +gcloud compute instances list | grep worker | awk '{ print $5 }' >> IaaC/ansible_files/inventory.ini diff --git a/IaaC/gcp/main.tf b/IaaC/gcp/main.tf deleted file mode 100644 index 79e9d410..00000000 --- a/IaaC/gcp/main.tf +++ /dev/null @@ -1,134 +0,0 @@ -terraform { - required_providers { - google = { - source = "hashicorp/google" - version = "4.51.0" - } - } -} - -provider "google" { - credentials = file(var.credentials_file) - - project = var.project - region = var.region - zone = var.zone -} - -resource "google_compute_network" "network" { - name = "decard-network" -} - -resource "google_compute_subnetwork" "subnetwork" { - name = "decard-subnetwork" - ip_cidr_range = "172.16.0.0/28" - region = var.region - network = google_compute_network.network.id -} - -resource "google_compute_firewall" "network-fw-1" { - name = "firewall-rules-for-k8s-api" - network = google_compute_network.network.name - - allow { - protocol = "tcp" - ports = ["6443"] - } - - direction = "INGRESS" - source_ranges = ["0.0.0.0/0"] -} - -resource "google_compute_firewall" "network-fw-2" { - name = "firewall-rules-for-k8s-ssh" - network = google_compute_network.network.name - - allow { - protocol = "tcp" - ports = ["22"] - } - - direction = "INGRESS" - source_ranges = ["0.0.0.0/0"] -} - -resource "google_compute_firewall" "network-fw-3" { - name = "firewall-rules-for-k8s-prometheus" - network = google_compute_network.network.name - - allow { - protocol = "tcp" - ports = ["30090", "30091"] - } - - direction = "INGRESS" - source_ranges = ["0.0.0.0/0"] -} - -resource "google_compute_firewall" "network-fw-4" { - name = "firewall-rules-for-k8s-cilium" - network = google_compute_network.network.name - - allow { - protocol = "tcp" - ports = ["8472"] - } - - direction = "INGRESS" - source_ranges = ["172.16.0.0/28"] -} - - -resource "google_compute_instance" "k8s-master" { - name = "k8s-master" - machine_type = var.machine_type - zone = var.zone - - - boot_disk { - initialize_params { - image = var.image - } - } - - network_interface { - subnetwork = google_compute_subnetwork.subnetwork.name - - access_config { - // Ephemeral public IP - } - } - - metadata = { - sshKeys = "${var.gce_ssh_user}:${file(var.gce_ssh_pub_key_file)}" - } -} - - -resource "google_compute_instance" "k8s-worker" { - count = "${var.node_count}" - name = "k8s-worker-${count.index}" - machine_type = var.machine_type - zone = var.zone - - - boot_disk { - initialize_params { - image = var.image - } - } - - network_interface { - subnetwork = google_compute_subnetwork.subnetwork.name - - access_config { - // Ephemeral public IP - } - } - - metadata = { - sshKeys = "${var.gce_ssh_user}:${file(var.gce_ssh_pub_key_file)}" - } -} - - diff --git a/IaaC/gcp/variables.tf b/IaaC/gcp/variables.tf deleted file mode 100644 index 6c870fab..00000000 --- a/IaaC/gcp/variables.tf +++ /dev/null @@ -1,33 +0,0 @@ -variable "credentials_file" { - default = "tf-service-account.json" -} - -variable "project" { } - -variable "region" { - default = "us-central1" -} - -variable "zone" { - default = "us-central1-c" -} - -variable "machine_type" { - default = "e2-medium" -} - -variable "image" { - default = "ubuntu-os-cloud/ubuntu-2204-lts" -} - -variable "gce_ssh_user" { - default = "mr.salehsedghpour" -} - -variable "gce_ssh_pub_key_file" { - default = "~/.ssh/id_rsa.pub" -} - -variable "node_count" { - default = "3" -} From b9a5005c58adac2f58976fe21e32eb43b8792e19 Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Wed, 30 Aug 2023 14:40:01 +0200 Subject: [PATCH 013/148] add GCP support --- IaaC/ansible_files/ansible.cfg | 7 - IaaC/ansible_files/initialize_master.yaml | 44 ------ IaaC/ansible_files/inventory.ini | 7 - IaaC/ansible_files/roles/dep/tasks/main.yaml | 66 --------- .../roles/update/tasks/main.yaml | 6 - IaaC/ansible_files/site.yaml | 6 - IaaC/gcp/README.md | 133 ++++++++++++++++-- IaaC/gcp/cleanup.sh | 41 ------ IaaC/gcp/config.yaml | 8 -- IaaC/gcp/inventory.ini | 2 - IaaC/gcp/main.sh | 36 ----- IaaC/gcp/pod.yaml | 20 +++ IaaC/gcp/pvc.yaml | 11 ++ IaaC/gcp/sclass.yaml | 10 ++ IaaC/gcp/setup_infrastructure.sh | 111 --------------- 15 files changed, 164 insertions(+), 344 deletions(-) delete mode 100644 IaaC/ansible_files/ansible.cfg delete mode 100644 IaaC/ansible_files/initialize_master.yaml delete mode 100644 IaaC/ansible_files/inventory.ini delete mode 100644 IaaC/ansible_files/roles/dep/tasks/main.yaml delete mode 100644 IaaC/ansible_files/roles/update/tasks/main.yaml delete mode 100644 IaaC/ansible_files/site.yaml mode change 100755 => 100644 IaaC/gcp/README.md delete mode 100755 IaaC/gcp/cleanup.sh delete mode 100755 IaaC/gcp/config.yaml delete mode 100755 IaaC/gcp/inventory.ini delete mode 100755 IaaC/gcp/main.sh create mode 100644 IaaC/gcp/pod.yaml create mode 100644 IaaC/gcp/pvc.yaml create mode 100644 IaaC/gcp/sclass.yaml delete mode 100755 IaaC/gcp/setup_infrastructure.sh diff --git a/IaaC/ansible_files/ansible.cfg b/IaaC/ansible_files/ansible.cfg deleted file mode 100644 index ed3a36b1..00000000 --- a/IaaC/ansible_files/ansible.cfg +++ /dev/null @@ -1,7 +0,0 @@ -[defaults] -ansible_managed=DO NOT EDIT! This file is managed by Ansible.%n - template: {file} - date: %Y-%m-%d %H:%M:%S - user: {uid} - host: {host} -roles_path = ./roles \ No newline at end of file diff --git a/IaaC/ansible_files/initialize_master.yaml b/IaaC/ansible_files/initialize_master.yaml deleted file mode 100644 index 1d54c864..00000000 --- a/IaaC/ansible_files/initialize_master.yaml +++ /dev/null @@ -1,44 +0,0 @@ -- hosts: master - become: yes - tasks: - - name: initialize the cluster - shell: kubeadm init --pod-network-cidr=10.244.0.0/16 - args: - chdir: /home/mr.salehsedghpour/ - creates: cluster_initialized.txt - - - name: create .kube directory - file: - path: /home/mr.salehsedghpour//.kube - state: directory - mode: 0755 - - - name: copies admin.conf to user's kube config - become: yes - copy: - src: /etc/kubernetes/admin.conf - dest: /home/mr.salehsedghpour/.kube/config - remote_src: yes - - - name: Make my directory tree readable - become: yes - file: - path: /home/mr.salehsedghpour/.kube/config - mode: 0755 - - - name: Wait for some time - shell: sleep 30 - - - - name: install Pod network - shell: kubectl apply -f https://docs.projectcalico.org/manifests/calico.yaml --kubeconfig=/home/mr.salehsedghpour/.kube/config - - - name: Get the token for joining the worker nodes - shell: kubeadm token create --print-join-command - register: kubernetes_join_command - - # - debug: - # msg: "{{ kubernetes_join_command.stdout }}" - - - name: Copy join command to local file. - local_action: copy content="{{ kubernetes_join_command.stdout_lines[0] }}" dest="/tmp/kubernetes_join_command" mode=0777 diff --git a/IaaC/ansible_files/inventory.ini b/IaaC/ansible_files/inventory.ini deleted file mode 100644 index 344475cd..00000000 --- a/IaaC/ansible_files/inventory.ini +++ /dev/null @@ -1,7 +0,0 @@ -[master] -34.16.75.74 - -[workers] -34.66.250.96 -34.135.32.240 -34.122.164.55 diff --git a/IaaC/ansible_files/roles/dep/tasks/main.yaml b/IaaC/ansible_files/roles/dep/tasks/main.yaml deleted file mode 100644 index c0f5c23d..00000000 --- a/IaaC/ansible_files/roles/dep/tasks/main.yaml +++ /dev/null @@ -1,66 +0,0 @@ -- name: IP forwarding - shell: | - cat < /dev/null - sudo apt-get update - sudo apt-get install containerd.io - cat < **_NOTE:_** For now, it only creates the required infrastructure. The next step is to install and configure K8s. + +1-We then create the cluster called `k8s-cluster`. This cluster will be installed in `europe-west4` in GCP regions. +``` +gcloud container clusters create k8s-cluster \ + --region europe-west4 --num-nodes 1 --no-enable-autoupgrade +``` + +2- In order to manage the Kubernetes cluster we need to [install `kubectl`](https://kubernetes.io/docs/tasks/tools/#kubectl). + +3- Then run the following command to get the credentials: +``` +gcloud container clusters get-credentials k8s-cluster \ + --region europe-west4 +``` + +After this step you should now be able to access kubernetes cluster, give it a try by simply running the following: +``` +$ kubectl get nodes +``` +The results should be similar to: +``` +NAME STATUS ROLES AGE VERSION +gke-k8s-cluster-default-pool-1fa99288-r4cz Ready 5m v1.27.3-gke.100 +gke-k8s-cluster-default-pool-3a09572b-w13l Ready 5m v1.27.3-gke.100 +gke-k8s-cluster-default-pool-feae82f2-zfsj Ready 5m v1.27.3-gke.100 +``` + +4- Now we want to create a node pool called `k8s-node-pool` with `nvidia-tesla-v100` gpus. Run the following. +``` +gcloud container node-pools create k8s-node-pool \ + --accelerator type=nvidia-tesla-v100,count=1,gpu-driver-version=default \ + --region europe-west4 --cluster k8s-cluster \ + --machine-type n1-standard-2 \ + --num-nodes 1 \ + --min-nodes 1 \ + --max-nodes 1 +``` + +After succesfully running the above command, you can verify the added GPU nodes by running `kubectl get nodes` and it should be something like: +``` +NAME STATUS ROLES AGE VERSION +gke-k8s-cluster-default-pool-1fa99288-r4cz Ready 10m v1.27.3-gke.100 +gke-k8s-cluster-default-pool-3a09572b-w13l Ready 10m v1.27.3-gke.100 +gke-k8s-cluster-default-pool-feae82f2-zfsj Ready 10m v1.27.3-gke.100 +gke-k8s-cluster-k8s-node-pool-7ba6832e-n8ld Ready 3m v1.27.3-gke.100 +gke-k8s-cluster-k8s-node-pool-e7fca6ba-wr5l Ready 3m v1.27.3-gke.100 +gke-k8s-cluster-k8s-node-pool-f075e2ff-zvj6 Ready 3m v1.27.3-gke.100 +``` + + +## Preparing the storage +1- To be able to have a shared storage for our containers/pods, we need to define `storage class` by simply running +``` +kubectl create -f ./IaaC/gcp/sclass.yaml +``` + +2- After that we need to create `persistent volume claim` to enable the pod to have access to the volume by simply ruuning: +``` +kubectl create -f ./IaaC/gcp/pvc.yaml +``` + + +## Deploying the GPU and Storage enabled pod +The pod should include the following resources to enable access to GPU: +``` +... + resources: + limits: + nvidia.com/gpu: 1 +... +``` + +And it also should have the following volumes to enable the shared storage: +``` +... + volumeMounts: + - mountPath: /data + name: mypvc + volumes: + - name: mypvc + persistentVolumeClaim: + claimName: podpvc +... +``` + +You can simply take a look at `pod.yaml` file for defining a pod. Just to check, deploy the sample pod by running: +``` +kubectl apply -f ./IaaC/gcp/pod.yaml +``` + +## Prepare the access values in the shared volume (optional): +First of all we need to create a vm by running: +``` +gcloud compute instances create filestore \ + --async \ + --metadata=ssh-keys="username:publickey" \ + --zone=europe-west4-a \ + --image-family=ubuntu-2204-lts \ + --image-project=ubuntu-os-cloud \ + --machine-type=n1-standard-2 \ + --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring \ + --subnet=default \ + --quiet +``` + +In the above command, you simply replace you public key and your username to have passwordless SSH to the VM. + +Before sshing to the created instance, simply get the [`NFS mount point` from the GCP console](https://console.cloud.google.com/filestore/instances). The `mount point` is "`Filestore instance IP address`:`Filestore instance File share name`", for instance: `10.178.118.130:/vol1`. + +After it has been created, run the `gcloud compute instances list` command to retrieve the external ip of the created instance `filestore`. Then ssh to the machine and mount the volume by running the followings: + +``` +sudo apt update +sudo apt install nfs-common +mkdir mount-directory +sudo mount -o rw,intr 10.178.118.130:/vol1 mount-directory +``` + +**Don't Forget to update the address in the mount command.** diff --git a/IaaC/gcp/cleanup.sh b/IaaC/gcp/cleanup.sh deleted file mode 100755 index 63a2546f..00000000 --- a/IaaC/gcp/cleanup.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/bash - -selected_project=$(gcloud config get-value project) -SA_NAME="deckard-terraform-sa" - -# Load config params -GCP_REGION=$(yq '.gcp_region' IaaC/gcp/config.yaml) -GCP_ZONE=$(yq '.gcp_zone' IaaC/gcp/config.yaml) -GCP_MACHINE_TYPE=$(yq '.gcp_machine_type' IaaC/gcp/config.yaml) -GCP_IMAGE_PROJECT=$(yq '.gcp_image_project' IaaC/gcp/config.yaml) -GCP_IMAGE_FAMILY=$(yq '.gcp_image_family' IaaC/gcp/config.yaml) -GCP_SSH_USER=$(yq '.gcp_ssh_user' IaaC/gcp/config.yaml) -GCP_SSH_PUB_KEY_FILE=$(yq '.gcp_ssh_pub_key_file' IaaC/gcp/config.yaml) -NODE_COUNT=$(yq '.node_count' IaaC/gcp/config.yaml) -read -d $'\x04' PUBLIC_KEY < $GCP_SSH_PUB_KEY_FILE - - -# Delete instances -gcloud compute instances delete k8s-master --quiet --zone=$GCP_ZONE -for (( i=1; i <= $NODE_COUNT; i++ )) -do - gcloud compute instances delete k8s-worker-${i} --quiet --zone=$GCP_ZONE -done - - -# Delete firewall rules -gcloud compute firewall-rules delete k8s-allow-internal --quiet -gcloud compute firewall-rules delete k8s-allow-external --quiet - -# Delete network and subnetwork -gcloud compute networks subnets delete k8s-subnet --region $GCP_REGION --quiet -gcloud compute networks delete k8s-cluster --quiet - -# Delete service account -gcloud iam service-accounts delete ${SA_NAME}@${selected_project}.iam.gserviceaccount.com --quiet - - -rm -rf ./IaaC/gcp/.terraform ./IaaC/gcp/.terraform.lock.hcl ./IaaC/gcp/terraform.tfstate ./IaaC/gcp/terraform.tfstate.backup ./IaaC/gcp/tf-service-account.json - - - diff --git a/IaaC/gcp/config.yaml b/IaaC/gcp/config.yaml deleted file mode 100755 index 0294622a..00000000 --- a/IaaC/gcp/config.yaml +++ /dev/null @@ -1,8 +0,0 @@ -gcp_region: "us-central1" -gcp_zone: "us-central1-c" -gcp_machine_type: "e2-medium" -gcp_image_project: "ubuntu-os-cloud" -gcp_image_family: "ubuntu-2204-lts" -gcp_ssh_user: "mr.salehsedghpour" -gcp_ssh_pub_key_file: "/Users/msaleh/.ssh/id_ed25519.pub" -node_count: 3 diff --git a/IaaC/gcp/inventory.ini b/IaaC/gcp/inventory.ini deleted file mode 100755 index 891be3cd..00000000 --- a/IaaC/gcp/inventory.ini +++ /dev/null @@ -1,2 +0,0 @@ -[master] -\n[workers] diff --git a/IaaC/gcp/main.sh b/IaaC/gcp/main.sh deleted file mode 100755 index 9f468ee4..00000000 --- a/IaaC/gcp/main.sh +++ /dev/null @@ -1,36 +0,0 @@ -#!/bin/bash - -function is_gcloud_installed { - if ! command -v gcloud &> /dev/null; then - echo "Error: gcloud command not found. Please install the Google Cloud SDK." - exit 1 - fi -} - -function is_logged_in { - if gcloud auth list --format="value(account)" | grep -q .; then - echo "You have already logged in into the GCP." - else - echo "Pleas log in to the GCP using the following command:" - echo "\t gcloud auth login" - exit 1 - fi -} - -is_gcloud_installed -is_logged_in - -IPs=( $(gcloud compute instances list | awk '{ print $5 }' | awk '(NR>1)' )) -for i in "${IPs[@]}" - do - ssh-keyscan -H $i >> ~/.ssh/known_hosts - done - -# Create inventory file -echo '[master]' > IaaC/ansible_files/inventory.ini -gcloud compute instances list | grep master | awk '{ print $5 }' >> IaaC/ansible_files/inventory.ini - -echo -e "\n[workers]" >> IaaC/ansible_files/inventory.ini -gcloud compute instances list | grep worker | awk '{ print $5 }' >> IaaC/ansible_files/inventory.ini - - diff --git a/IaaC/gcp/pod.yaml b/IaaC/gcp/pod.yaml new file mode 100644 index 00000000..ee54a985 --- /dev/null +++ b/IaaC/gcp/pod.yaml @@ -0,0 +1,20 @@ +apiVersion: v1 +kind: Pod +metadata: + name: my-gpu-pod +spec: + containers: + - name: my-gpu-container + image: nvidia/cuda:11.0.3-runtime-ubuntu20.04 + command: ["/bin/bash", "-c", "--"] + args: ["while true; do sleep 600; done;"] + resources: + limits: + nvidia.com/gpu: 1 + volumeMounts: + - mountPath: /data + name: mypvc + volumes: + - name: mypvc + persistentVolumeClaim: + claimName: podpvc diff --git a/IaaC/gcp/pvc.yaml b/IaaC/gcp/pvc.yaml new file mode 100644 index 00000000..a7deee4d --- /dev/null +++ b/IaaC/gcp/pvc.yaml @@ -0,0 +1,11 @@ +kind: PersistentVolumeClaim +apiVersion: v1 +metadata: + name: podpvc +spec: + accessModes: + - ReadWriteMany + storageClassName: filestore-sc + resources: + requests: + storage: 256Gi diff --git a/IaaC/gcp/sclass.yaml b/IaaC/gcp/sclass.yaml new file mode 100644 index 00000000..d566656c --- /dev/null +++ b/IaaC/gcp/sclass.yaml @@ -0,0 +1,10 @@ +apiVersion: storage.k8s.io/v1 +kind: StorageClass +metadata: + name: filestore-sc +provisioner: filestore.csi.storage.gke.io +volumeBindingMode: Immediate +allowVolumeExpansion: true +parameters: + tier: standard + network: default diff --git a/IaaC/gcp/setup_infrastructure.sh b/IaaC/gcp/setup_infrastructure.sh deleted file mode 100755 index c1eb7af2..00000000 --- a/IaaC/gcp/setup_infrastructure.sh +++ /dev/null @@ -1,111 +0,0 @@ -#!/bin/bash - -# Load the config params -GCP_REGION=$(yq '.gcp_region' IaaC/gcp/config.yaml) -GCP_ZONE=$(yq '.gcp_zone' IaaC/gcp/config.yaml) -GCP_MACHINE_TYPE=$(yq '.gcp_machine_type' IaaC/gcp/config.yaml) -GCP_IMAGE_PROJECT=$(yq '.gcp_image_project' IaaC/gcp/config.yaml) -GCP_IMAGE_FAMILY=$(yq '.gcp_image_family' IaaC/gcp/config.yaml) -GCP_SSH_USER=$(yq '.gcp_ssh_user' IaaC/gcp/config.yaml) -GCP_SSH_PUB_KEY_FILE=$(yq '.gcp_ssh_pub_key_file' IaaC/gcp/config.yaml) -NODE_COUNT=$(yq '.node_count' IaaC/gcp/config.yaml) -read -d $'\x04' PUBLIC_KEY < $GCP_SSH_PUB_KEY_FILE - -function is_gcloud_installed { - if ! command -v gcloud &> /dev/null; then - echo "Error: gcloud command not found. Please install the Google Cloud SDK." - exit 1 - fi -} - -function is_logged_in { - if gcloud auth list --format="value(account)" | grep -q .; then - echo "You have already logged in into the GCP." - else - echo "Pleas log in to the GCP using the following command:" - echo "\t gcloud auth login" - exit 1 - fi -} - -is_gcloud_installed -is_logged_in - -# Get the currently selected project -selected_project=$(gcloud config get-value project) - -echo "At the moment the $selected_project is selected as your default project in GCP." -echo -n "Is it the right one? [Y/n]:" -read -r right_project - - -case $right_project in - [nN]) echo "Please select the right project as your default project." - exit 1;; -esac - -# Create GCP Service Account -SA_NAME="deckard-terraform-sa" -SA_EMAIL=${SA_NAME}@${selected_project}.iam.gserviceaccount.com -if gcloud iam service-accounts describe "$SA_EMAIL" &> /dev/null; then - echo Service account $SA_NAME already exists. -else - gcloud iam service-accounts create $SA_NAME --description "Service Account for Deckard Terraform Infrastructure" --display-name "$SA_NAME" --quiet - gcloud projects add-iam-policy-binding $selected_project --member serviceAccount:${SA_NAME}@${selected_project}.iam.gserviceaccount.com --role roles/editor --no-user-output-enabled --quiet - - SA_KEY_FILE="./IaaC/gcp/tf-service-account.json" - gcloud iam service-accounts keys create ${SA_KEY_FILE} --iam-account ${SA_NAME}@${selected_project}.iam.gserviceaccount.com --quiet -fi - - -# Create GCP Network and Subnets -gcloud compute networks create k8s-cluster --subnet-mode custom --quiet -gcloud compute networks subnets create k8s-subnet \ - --network k8s-cluster \ - --range 172.16.0.0/28 \ - --region $GCP_REGION \ - --quiet - - -# Create GCP firewall rules -gcloud compute firewall-rules create k8s-allow-internal \ - --allow tcp,udp,icmp \ - --network k8s-cluster \ - --source-ranges 172.16.0.0/28 \ - --quiet -gcloud compute firewall-rules create k8s-allow-external \ - --allow tcp:80,tcp:6443,tcp:443,tcp:22,icmp \ - --network k8s-cluster \ - --source-ranges 0.0.0.0/0 \ - --quiet - - - - -gcloud compute instances create k8s-master \ - --async \ - --metadata=ssh-keys="$GCP_SSH_USER:$PUBLIC_KEY" \ - --zone=$GCP_ZONE \ - --can-ip-forward \ - --image-family=$GCP_IMAGE_FAMILY \ - --image-project=$GCP_IMAGE_PROJECT \ - --machine-type=$GCP_MACHINE_TYPE \ - --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring \ - --subnet=k8s-subnet \ - --tags=k8s-cluster,controller \ - --quiet - -gcloud compute instances bulk create \ - --async \ - --name-pattern=k8s-worker-# \ - --metadata=ssh-keys="$GCP_SSH_USER:$PUBLIC_KEY" \ - --zone=$GCP_ZONE \ - --count=$NODE_COUNT \ - --can-ip-forward \ - --image-family=$GCP_IMAGE_FAMILY \ - --image-project=$GCP_IMAGE_PROJECT \ - --machine-type=$GCP_MACHINE_TYPE \ - --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring \ - --subnet=k8s-subnet \ - --tags=k8s-cluster,worker \ - --quiet From 4ab0f3d48444a291fe98bb28127981185be1e5cd Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Fri, 1 Sep 2023 10:22:43 +0000 Subject: [PATCH 014/148] linting --- deckard/__main__.py | 1 - deckard/base/attack/attack.py | 18 +- deckard/base/data/data.py | 5 +- deckard/base/data/sklearn_pipeline.py | 8 +- deckard/base/model/art_pipeline.py | 3 +- deckard/base/model/model.py | 50 +- deckard/base/model/sklearn_pipeline.py | 36 +- deckard/base/model/torch_models.py | 3 +- deckard/base/plots/plots.py | 54 ++- deckard/base/utils/factory.py | 9 +- deckard/base/utils/hashing.py | 4 +- deckard/layers/compile.py | 5 +- deckard/layers/experiment.py | 10 +- deckard/layers/generate_grid.py | 9 +- deckard/layers/optimise.py | 12 +- deckard/layers/parse.py | 1 - deckard/layers/utils.py | 11 +- deckard/layers/watcher.py | 35 +- examples/keras/keras_example.py | 4 +- examples/pytorch/plots.ipynb | 441 ++++++++++-------- examples/pytorch/torch_example.py | 28 +- examples/tfv1/tfv1_example.py | 10 +- examples/tfv2/tfv2_example.py | 5 +- test/base/test_attack/test_attack.py | 13 +- test/base/test_attack/torch_example.py | 25 +- test/base/test_data/test_data.py | 3 +- test/base/test_data/test_generator.py | 3 +- test/base/test_data/test_sampler.py | 6 +- test/base/test_data/test_sklearn_pipeline.py | 19 +- test/base/test_files/test_files.py | 3 +- test/base/test_model/keras_example.py | 4 +- test/base/test_model/test_art_pipeline.py | 4 +- test/base/test_model/test_model.py | 3 +- .../test_model/test_sklearn_model_pipeline.py | 4 +- test/base/test_model/tfv2_example.py | 5 +- test/base/test_model/torch_example.py | 25 +- test/base/test_scorer/test_scorer.py | 9 +- 37 files changed, 582 insertions(+), 306 deletions(-) diff --git a/deckard/__main__.py b/deckard/__main__.py index ba298815..572752dd 100644 --- a/deckard/__main__.py +++ b/deckard/__main__.py @@ -50,7 +50,6 @@ def parse_and_repro(args): if __name__ == "__main__": - logging.basicConfig(level=logging.INFO) parser = argparse.ArgumentParser() parser.add_argument( diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index 8c248212..beeb9b00 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -178,7 +178,8 @@ def __call__( results["adv_probabilities"] = adv_probabilities else: if hasattr(self.model, "model") and hasattr( - self.model.model, "predict_proba", + self.model.model, + "predict_proba", ): start = process_time_ns() adv_probabilities = model.model.predict_proba(samples) @@ -221,7 +222,8 @@ def __call__( results["adv_loss"] = adv_loss elif adv_losses_file is not None: assert hasattr( - model, "compute_loss", + model, + "compute_loss", ), "Model does not have compute_loss method." try: adv_loss = model.compute_loss(samples, data[3][: self.attack_size]) @@ -357,7 +359,8 @@ def __call__( adv_probabilities = self.data.load(adv_probabilities_file) else: if hasattr(self.model, "model") and hasattr( - self.model.model, "predict_proba", + self.model.model, + "predict_proba", ): start = process_time_ns() adv_probabilities = model.model.predict_proba(samples) @@ -399,7 +402,8 @@ def __call__( adv_loss = self.data.load(adv_losses_file) elif adv_losses_file is not None: assert hasattr( - model, "compute_loss", + model, + "compute_loss", ), "Model does not have compute_loss method." adv_loss = model.compute_loss(samples, data[3][: self.attack_size]) self.data.save(adv_loss, adv_losses_file) @@ -466,7 +470,8 @@ def __call__( initial_image = np.random.uniform(0, 1, data_shape) elif self.initial_image == "average": initial_image = np.zeroes(data_shape) + np.mean( - data[1][: self.attack_size], axis=0, + data[1][: self.attack_size], + axis=0, ) elif self.initial_image is None: pass @@ -626,7 +631,8 @@ def __call__( else: if hasattr(attacked_model, "compute_loss"): loss = attacked_model.compute_loss( - data[1][: self.attack_size], data[3][: self.attack_size], + data[1][: self.attack_size], + data[3][: self.attack_size], ) else: from sklearn.metrics import log_loss diff --git a/deckard/base/data/data.py b/deckard/base/data/data.py index aa3e9a44..e22c69a2 100644 --- a/deckard/base/data/data.py +++ b/deckard/base/data/data.py @@ -157,7 +157,10 @@ def save(self, data, filename): assert Path(filename).exists() def __call__( - self, data_file=None, train_labels_file=None, test_labels_file=None, + self, + data_file=None, + train_labels_file=None, + test_labels_file=None, ) -> list: """Loads data from file if it exists, otherwise generates data and saves it to file. Returns X_train, X_test, y_train, y_test as a list of arrays, typed according to the framework. :param filename: str diff --git a/deckard/base/data/sklearn_pipeline.py b/deckard/base/data/sklearn_pipeline.py index 1f94a000..2363d57a 100644 --- a/deckard/base/data/sklearn_pipeline.py +++ b/deckard/base/data/sklearn_pipeline.py @@ -58,7 +58,8 @@ def __init__(self, **kwargs): pass else: assert hasattr( - pipe[stage], "transform", + pipe[stage], + "transform", ), f"Pipeline stage must be a SklearnDataPipelineStage, dict, or have a transform methods. Got {type(pipe[stage])}" self.pipeline = pipe @@ -83,7 +84,10 @@ def __call__(self, X_train, X_test, y_train, y_test): transformer = pipeline[stage] if isinstance(transformer, SklearnDataPipelineStage): X_train, X_test, y_train, y_test = transformer( - X_train, X_test, y_train, y_test, + X_train, + X_test, + y_train, + y_test, ) else: X_train = transformer.fit(X_train).transform(X_train) diff --git a/deckard/base/model/art_pipeline.py b/deckard/base/model/art_pipeline.py index 83a961d4..a0d4c048 100644 --- a/deckard/base/model/art_pipeline.py +++ b/deckard/base/model/art_pipeline.py @@ -105,7 +105,8 @@ def __call__(self): f"library must be one of {supported_models}. Got {library}", ) assert hasattr( - model, "fit", + model, + "fit", ), f"model must have a fit method. Got type {type(model)}" return model diff --git a/deckard/base/model/model.py b/deckard/base/model/model.py index fc8e61a2..5bc365fa 100644 --- a/deckard/base/model/model.py +++ b/deckard/base/model/model.py @@ -65,7 +65,8 @@ def __call__(self): params["output_dim"] = params["output_dim"] elif isinstance(params["output_dim"], ListConfig): output_dim_list = OmegaConf.to_container( - params["output_dim"], resolve=True, + params["output_dim"], + resolve=True, ) if len(output_dim_list) == 1: params["output_dim"] = output_dim_list[0] @@ -282,7 +283,8 @@ def __call__( elif isinstance(data, (str, Path)): data = self.load(data) assert isinstance( - data, (type(None), list, tuple), + data, + (type(None), list, tuple), ), f"Data {data} is not a list. It is of type {type(data)}." assert len(data) == 4, f"Data {data} is not a tuple of length 4." result_dict["data"] = data @@ -295,7 +297,8 @@ def __call__( model = self.load(model) elif hasattr(model, "fit"): assert hasattr(model, "predict") or hasattr( - model, "predict_proba", + model, + "predict_proba", ), f"Model {model} does not have a predict or predict_proba method." else: raise ValueError(f"Model {model} is not a valid model.") @@ -324,7 +327,9 @@ def __call__( # Fitting if model_file is None: model, fit_time_dict = self.fit( - data=data, model=model, model_file=model_file, + data=data, + model=model, + model_file=model_file, ) time_dict.update(**fit_time_dict) result_dict["model"] = model @@ -335,7 +340,9 @@ def __call__( result_dict["model"] = model else: model, fit_time_dict = self.fit( - data=data, model=model, model_file=model_file, + data=data, + model=model, + model_file=model_file, ) result_dict["model"] = model result_dict["data"] = data @@ -343,7 +350,9 @@ def __call__( # Predicting if predictions_file is not None and not Path(predictions_file).exists(): preds, pred_time_dict = self.predict( - data=data, model=model, predictions_file=predictions_file, + data=data, + model=model, + predictions_file=predictions_file, ) result_dict["time_dict"].update(**pred_time_dict) result_dict["predictions"] = preds @@ -352,14 +361,18 @@ def __call__( result_dict["predictions"] = preds else: preds, pred_time_dict = self.predict( - data=data, model=model, predictions_file=predictions_file, + data=data, + model=model, + predictions_file=predictions_file, ) result_dict["time_dict"].update(**pred_time_dict) result_dict["predictions"] = preds # Predicting probabilities if probabilities_file is not None: probs, prob_time_dict = self.predict_proba( - data=data, model=model, probabilities_file=probabilities_file, + data=data, + model=model, + probabilities_file=probabilities_file, ) result_dict["probabilities"] = probs result_dict["time_dict"].update(**prob_time_dict) @@ -369,14 +382,18 @@ def __call__( result_dict["time_dict"].update(**prob_time_dict) else: probs, prob_time_dict = self.predict_proba( - data=data, model=model, probabilities_file=probabilities_file, + data=data, + model=model, + probabilities_file=probabilities_file, ) result_dict["probabilities"] = probs result_dict["time_dict"].update(**prob_time_dict) # Predicting loss if loss_file is not None: loss, loss_time_dict = self.predict_log_loss( - data=data, model=model, loss_file=loss_file, + data=data, + model=model, + loss_file=loss_file, ) time_dict.update(**loss_time_dict) result_dict["loss"] = loss @@ -386,7 +403,9 @@ def __call__( result_dict["loss"] = loss else: loss, loss_time_dict = self.predict_log_loss( - data=data, model=model, loss_file=loss_file, + data=data, + model=model, + loss_file=loss_file, ) time_dict.update(**loss_time_dict) result_dict["loss"] = loss @@ -421,7 +440,8 @@ def initialize(self, data=None, model=None): elif isinstance(data, type(None)): data = self.data.initialize() assert isinstance( - data, (type(None), list), + data, + (type(None), list), ), f"Data {data} is not a list. It is of type {type(data)}." if isinstance(model, (str, Path)) and Path(model).exists(): model = self.load(model) @@ -492,7 +512,8 @@ def predict(self, data=None, model=None, predictions_file=None): elif isinstance(model, type(None)): data, model = self.initialize(data) assert hasattr( - model, "predict", + model, + "predict", ), f"Model {model} does not have a predict method." try: start = process_time_ns() @@ -662,7 +683,8 @@ def save(self, model, filename): model = model.model t.save(model, filename) t.save( - model.state_dict(), Path(filename).with_suffix(f".optimizer{suffix}"), + model.state_dict(), + Path(filename).with_suffix(f".optimizer{suffix}"), ) elif suffix in [".h5", ".wt"]: import keras as k diff --git a/deckard/base/model/sklearn_pipeline.py b/deckard/base/model/sklearn_pipeline.py index a315bc3d..775f734d 100644 --- a/deckard/base/model/sklearn_pipeline.py +++ b/deckard/base/model/sklearn_pipeline.py @@ -83,18 +83,21 @@ def __call__(self, model): kwargs.update(**kwargs.pop("kwargs")) if "art." in str(type(model)): assert isinstance( - model.model, BaseEstimator, + model.model, + BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model.model)}" else: assert isinstance( - model, BaseEstimator, + model, + BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model)}" if not isinstance(model, Pipeline): model = Pipeline([("model", model)]) else: model.steps.insert(-2, [stage_name, model]) assert isinstance( - model, Pipeline, + model, + Pipeline, ), f"model must be a sklearn pipeline. Got {type(model)}" return model @@ -120,7 +123,8 @@ def __init__(self, **kwargs): pipe[stage] = asdict(pipe[stage]) else: assert hasattr( - pipe[stage], "transform", + pipe[stage], + "transform", ), f"Pipeline stage must be a SklearnModelPipelineStage, dict, or have a transform method. Got {type(pipe[stage])}" if isinstance(pipe[stage], dict): params = deepcopy(pipe[stage]) @@ -128,7 +132,8 @@ def __init__(self, **kwargs): pipe[stage] = SklearnModelPipelineStage(params, stage_name=stage_name) elif hasattr(pipe[stage], "transform"): assert hasattr( - pipe[stage], "fit", + pipe[stage], + "fit", ), f"Pipeline stage must have a fit method. Got {type(pipe[stage])}" else: raise ValueError( @@ -174,16 +179,19 @@ def __call__(self, model): elif hasattr(stage, "fit"): if "art." in str(type(model)): assert isinstance( - model.model, BaseEstimator, + model.model, + BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model.model)}" else: assert isinstance( - model, BaseEstimator, + model, + BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model)}" if not isinstance(model, Pipeline) and "art." not in str(type(model)): model = Pipeline([("model", model)]) elif "art." in str(type(model)) and not isinstance( - model.model, Pipeline, + model.model, + Pipeline, ): model.model = Pipeline([("model", model.model)]) elif "art." in str(type(model)) and isinstance(model.model, Pipeline): @@ -192,11 +200,13 @@ def __call__(self, model): model.steps.insert(-2, [stage, model]) if "art." not in str(type(model)): assert isinstance( - model, Pipeline, + model, + Pipeline, ), f"model must be a sklearn pipeline. Got {type(model)}" else: assert isinstance( - model.model, Pipeline, + model.model, + Pipeline, ), f"model must be a sklearn pipeline. Got {type(model)}" return model @@ -253,7 +263,8 @@ def __call__(self): if self.pipeline is not None: obj = self.pipeline(model) assert isinstance( - obj, BaseEstimator, + obj, + BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model)}" else: obj = model @@ -288,7 +299,8 @@ def __call__(self): else: raise ValueError(f"library must be one of {sklearn_models}. Got {library}") assert hasattr( - model, "fit", + model, + "fit", ), f"model must have a fit method. Got type {type(model)}" return model diff --git a/deckard/base/model/torch_models.py b/deckard/base/model/torch_models.py index 17864759..81803699 100644 --- a/deckard/base/model/torch_models.py +++ b/deckard/base/model/torch_models.py @@ -98,7 +98,8 @@ def __init__(self, data, model, library, **kwargs): self.model = model self.library = library self.device = kwargs.pop( - "device", "cuda" if torch.cuda.is_available() else "cpu", + "device", + "cuda" if torch.cuda.is_available() else "cpu", ) while "kwargs" in kwargs: new_kwargs = kwargs.pop("kwargs", {}) diff --git a/deckard/base/plots/plots.py b/deckard/base/plots/plots.py index 31c0bb0a..89393f49 100644 --- a/deckard/base/plots/plots.py +++ b/deckard/base/plots/plots.py @@ -146,7 +146,10 @@ class Yellowbrick_Visualiser: scorers: ScorerDict = field(default_factory=ScorerDict) def visualise_data( - self, data: list, classes: list = None, features: list = None, + self, + data: list, + classes: list = None, + features: list = None, ) -> List[Path]: """ Visualise classification results according to the configuration file. @@ -186,7 +189,8 @@ def visualise_data( ) paths["radviz"] = str( Path( - path, plots["radviz"] + str(plots.pop("filetype", ".png")), + path, + plots["radviz"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -198,7 +202,8 @@ def visualise_data( ) paths["rank1d"] = str( Path( - path, plots["rank1d"] + str(plots.pop("filetype", ".png")), + path, + plots["rank1d"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -210,7 +215,8 @@ def visualise_data( ) paths["rank2d"] = str( Path( - path, plots["rank2d"] + str(plots.pop("filetype", ".png")), + path, + plots["rank2d"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -222,7 +228,8 @@ def visualise_data( ) paths["balance"] = str( Path( - path, plots["balance"] + str(plots.pop("filetype", ".png")), + path, + plots["balance"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -231,12 +238,14 @@ def visualise_data( visualiser.fit(X_train, y_train) visualiser.show( Path( - path, plots["correlation"] + str(plots.pop("filetype", ".png")), + path, + plots["correlation"] + str(plots.pop("filetype", ".png")), ), ) paths["correlation"] = str( Path( - path, plots["correlation"] + str(plots.pop("filetype", ".png")), + path, + plots["correlation"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -248,7 +257,8 @@ def visualise_data( ) paths["pca"] = str( Path( - path, plots["pca"] + str(plots.pop("filetype", ".png")), + path, + plots["pca"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -260,7 +270,8 @@ def visualise_data( ) paths["manifold"] = str( Path( - path, plots["manifold"] + str(plots.pop("filetype", ".png")), + path, + plots["manifold"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -272,14 +283,19 @@ def visualise_data( ) paths["parallel"] = str( Path( - path, plots["parallel"] + str(plots.pop("filetype", ".png")), + path, + plots["parallel"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() return paths def visualise_classification( - self, data: list, model: Callable, classes: list = None, predictions_file=None, + self, + data: list, + model: Callable, + classes: list = None, + predictions_file=None, ) -> List[Path]: """ Visualise classification results according to the configuration file. @@ -314,7 +330,8 @@ def visualise_classification( data[3] = np.argmax(data[3], axis=1) if len(set(np.unique(data[2]))) > 2: viz = visualiser( - model, classes=[int(y) for y in np.unique(data[2])], + model, + classes=[int(y) for y in np.unique(data[2])], ) elif len(set(np.unique(data[2]))) == 2: try: @@ -328,7 +345,8 @@ def visualise_classification( f"Failed due to error {e}. Trying without binary", ) viz = visualiser( - model, classes=[int(y) for y in np.unique(data[2])], + model, + classes=[int(y) for y in np.unique(data[2])], ) else: viz = visualiser(model, classes=[0]) @@ -342,7 +360,10 @@ def visualise_classification( return paths def visualise_regression( - self, data: list, model: object, predictions_file=None, + self, + data: list, + model: object, + predictions_file=None, ) -> List[Path]: """ Visualise classification results according to the configuration file. @@ -393,7 +414,10 @@ def visualise_regression( return paths def visualise_clustering( - self, data: list, model: object, predictions_file=None, + self, + data: list, + model: object, + predictions_file=None, ) -> List[Path]: """ Visualise classification results according to the configuration file. diff --git a/deckard/base/utils/factory.py b/deckard/base/utils/factory.py index be908a8c..16a79c64 100644 --- a/deckard/base/utils/factory.py +++ b/deckard/base/utils/factory.py @@ -43,13 +43,15 @@ def factory(module_class_string, *args, super_cls: type = None, **kwargs) -> obj raise e module = import_module(module_name) assert hasattr(module, class_name), "class {} is not in {}".format( - class_name, module_name, + class_name, + module_name, ) logger.debug("reading class {} from module {}".format(class_name, module_name)) cls = getattr(module, class_name) if super_cls is not None: assert issubclass(cls, super_cls), "class {} should inherit from {}".format( - class_name, super_cls.__name__, + class_name, + super_cls.__name__, ) logger.debug("initialising {} with params {}".format(class_name, kwargs)) try: @@ -88,7 +90,8 @@ def make_grid(dictionary: list) -> list: big = [] if not isinstance(dictionary, list): assert isinstance( - dictionary, dict, + dictionary, + dict, ), f"dictionary must be a list or dict, not {type(dictionary)}" dict_list = [dictionary] else: diff --git a/deckard/base/utils/hashing.py b/deckard/base/utils/hashing.py index e0da0831..c7c118c7 100644 --- a/deckard/base/utils/hashing.py +++ b/deckard/base/utils/hashing.py @@ -22,7 +22,9 @@ def to_dict(obj: Union[dict, OrderedDict, NamedTuple]) -> dict: obj = dict( deepcopy( OmegaConf.to_container( - obj, resolve=True, structured_config_mode=SCMode.DICT, + obj, + resolve=True, + structured_config_mode=SCMode.DICT, ), ), ) diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 4de3831c..f6902a29 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -168,7 +168,10 @@ def save_results(report_folder, results_file) -> str: parser.add_argument("--exclude", type=list, default=None, nargs="*") parser.add_argument("--verbose", type=str, default="INFO") parser.add_argument( - "--kwargs", type=list, default=None, nargs="*", + "--kwargs", + type=list, + default=None, + nargs="*", ) args = parser.parse_args() logging.basicConfig(level=args.verbose) diff --git a/deckard/layers/experiment.py b/deckard/layers/experiment.py index e0a3a18b..5ce46fe9 100644 --- a/deckard/layers/experiment.py +++ b/deckard/layers/experiment.py @@ -21,7 +21,10 @@ def get_dvc_stage_params( - stage, params_file="params.yaml", pipeline_file="dvc.yaml", directory=".", + stage, + params_file="params.yaml", + pipeline_file="dvc.yaml", + directory=".", ): logger.info( f"Getting params for stage {stage} from {params_file} and {pipeline_file} in {directory}.", @@ -38,7 +41,10 @@ def get_dvc_stage_params( def run_stage( - params_file="params.yaml", pipeline_file="dvc.yaml", directory=".", stage=None, + params_file="params.yaml", + pipeline_file="dvc.yaml", + directory=".", + stage=None, ): logger.info( f"Running stage {stage} with params_file: {params_file} and pipeline_file: {pipeline_file} in directory {directory}", diff --git a/deckard/layers/generate_grid.py b/deckard/layers/generate_grid.py index 3e5a16ae..487ce801 100644 --- a/deckard/layers/generate_grid.py +++ b/deckard/layers/generate_grid.py @@ -67,14 +67,19 @@ def generate_grid_from_folders(conf_dir, regex): big_list = make_grid(big_dict) assert len(big_list) == reduce( - mul, layers, + mul, + layers, ), f"Grid size {len(big_list)} does not match product of layer sizes {reduce(mul, layers)}" logger.info(f"Generated grid with {len(big_list)} configs") return big_list def generate_queue( - conf_root, grid_dir, regex, queue_folder="queue", default_file="default.yaml", + conf_root, + grid_dir, + regex, + queue_folder="queue", + default_file="default.yaml", ): this_dir = os.getcwd() conf_dir = os.path.join(this_dir, conf_root, grid_dir) diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index 2dee24d8..0aa074a2 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -16,7 +16,8 @@ def get_files( - cfg, stage, + cfg, + stage, ): """ Gets the file names from @@ -166,7 +167,8 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: with open(Path(params), "r") as f: params = yaml.load(f, Loader=yaml.FullLoader) assert isinstance( - params, dict, + params, + dict, ), f"Params in file {params} must be a dict. It is a {type(params)}." key_list = [] for stage in stages: @@ -194,7 +196,8 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: else: raise TypeError(f"Expected str or dict, got {type(params)}") assert isinstance( - params, dict, + params, + dict, ), f"Params must be a dict. It is type {type(params)}." # Load files from dvc with open(Path(path, "dvc.yaml"), "r") as f: @@ -259,7 +262,8 @@ def optimise(cfg: DictConfig) -> None: if __name__ == "__main__": logger = logging.getLogger(__name__) config_path = os.environ.pop( - "DECKARD_CONFIG_PATH", str(Path(Path(), "conf").absolute().as_posix()), + "DECKARD_CONFIG_PATH", + str(Path(Path(), "conf").absolute().as_posix()), ) config_name = os.environ.pop("DECKARD_DEFAULT_CONFIG", "default.yaml") diff --git a/deckard/layers/parse.py b/deckard/layers/parse.py index 8da5265c..51dc5ab6 100644 --- a/deckard/layers/parse.py +++ b/deckard/layers/parse.py @@ -6,7 +6,6 @@ logger = logging.getLogger(__name__) if __name__ == "__main__": - logger = logging.getLogger(__name__) hydra_parser = argparse.ArgumentParser() hydra_parser.add_argument("overrides", type=str, nargs="*", default=None) diff --git a/deckard/layers/utils.py b/deckard/layers/utils.py index d15d6166..87057484 100644 --- a/deckard/layers/utils.py +++ b/deckard/layers/utils.py @@ -29,7 +29,11 @@ def find_conf_files( - config_name, config_subdir, config_dir, config_regex=None, default_file=None, + config_name, + config_subdir, + config_dir, + config_regex=None, + default_file=None, ): if config_name is not None: assert config_regex is None, "Cannot specify both config_name and config_regex" @@ -115,7 +119,10 @@ def compose_experiment(file, config_dir, overrides=None, default_file="default.y def save_params_file( - config_dir="conf", config_file="default", params_file="params.yaml", overrides=[], + config_dir="conf", + config_file="default", + params_file="params.yaml", + overrides=[], ): config_dir = str(Path(Path(), config_dir).absolute().as_posix()) with initialize_config_dir(config_dir=config_dir, version_base="1.3"): diff --git a/deckard/layers/watcher.py b/deckard/layers/watcher.py index 6cb6752a..2668b972 100644 --- a/deckard/layers/watcher.py +++ b/deckard/layers/watcher.py @@ -26,7 +26,10 @@ class JSONHandler(watchdog.events.PatternMatchingEventHandler): def __init__(self, servers, port, user, password, filename, destination, **kwargs): # Set the patterns for PatternMatchingEventHandler watchdog.events.PatternMatchingEventHandler.__init__( - self, patterns=[REGEX], ignore_directories=True, case_sensitive=False, + self, + patterns=[REGEX], + ignore_directories=True, + case_sensitive=False, ) self.ssh = createSSHClient(servers, port, user, password) logger.info("Initiated SSH client") @@ -35,7 +38,8 @@ def __init__(self, servers, port, user, password, filename, destination, **kwarg self.recurse = kwargs["recursive"] if "recurse" in kwargs else False logger.info( "Source file is {} and destination is {}".format( - self.filename, self.destination, + self.filename, + self.destination, ), ) logger.info("Regex is {}".format(REGEX)) @@ -112,7 +116,11 @@ def send_json_with_scp(self): ) parser.add_argument("--port", "-p", type=int, help="The port to send the files to.") parser.add_argument( - "--user", "-u", type=str, required=True, help="The user to send the files to.", + "--user", + "-u", + type=str, + required=True, + help="The user to send the files to.", ) parser.add_argument( "--password", @@ -124,10 +132,18 @@ def send_json_with_scp(self): parser.add_argument("--original", type=str, help="The original queue file.") parser.add_argument("--queue", type=str, help="The current queue file.") parser.add_argument( - "--regex", "-e", type=str, required=True, help="The regex to watch for.", + "--regex", + "-e", + type=str, + required=True, + help="The regex to watch for.", ) parser.add_argument( - "--recursive", "-r", type=bool, default=True, help="Whether to recurse or not.", + "--recursive", + "-r", + type=bool, + default=True, + help="Whether to recurse or not.", ) parser.add_argument( "--n_jobs", @@ -137,12 +153,17 @@ def send_json_with_scp(self): help="The number of jobs to run in parallel.", ) parser.add_argument( - "--log", "-l", type=int, default=logging.INFO, help="The log level.", + "--log", + "-l", + type=int, + default=logging.INFO, + help="The log level.", ) args = parser.parse_args() # Set up logging logging.basicConfig( - level=args.log, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + level=args.log, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", ) if args.regex is not None: REGEX = args.regex diff --git a/examples/keras/keras_example.py b/examples/keras/keras_example.py index 3a3fba11..709754a0 100644 --- a/examples/keras/keras_example.py +++ b/examples/keras/keras_example.py @@ -44,6 +44,8 @@ def __init__(self, optimizer, loss, metrics): def __call__(self): self.model.compile( - optimizer=self.optimizer, loss=self.loss, metrics=self.metrics, + optimizer=self.optimizer, + loss=self.loss, + metrics=self.metrics, ) return self.model diff --git a/examples/pytorch/plots.ipynb b/examples/pytorch/plots.ipynb index 2028d12b..a907b5d1 100644 --- a/examples/pytorch/plots.ipynb +++ b/examples/pytorch/plots.ipynb @@ -6,20 +6,19 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from pathlib import Path\n", "\n", - "csv_file = (\"output/reports/attack.csv\")\n", + "csv_file = \"output/reports/attack.csv\"\n", "data = pd.read_csv(csv_file)\n", "# data = data[data['def_gen'] == 'HighConfidence']\n", "if \"Unnamed: 0\" in data.columns:\n", " data.drop(\"Unnamed: 0\", axis=1, inplace=True)\n", "\n", "FOLDER = Path(Path(), \"output/plots\")\n", - "FOLDER.mkdir(parents=True, exist_ok=True)\n" + "FOLDER.mkdir(parents=True, exist_ok=True)" ] }, { @@ -40,13 +39,14 @@ ], "source": [ "import seaborn as sns\n", - "graph1 = sns.barplot(data=data, x = \"def_gen\", y = \"accuracy\")\n", + "\n", + "graph1 = sns.barplot(data=data, x=\"def_gen\", y=\"accuracy\")\n", "graph1.set_xticklabels(graph1.get_xticklabels(), rotation=90)\n", - "graph1.set_xlabel(\"Defense Type\");\n", - "graph1.set_ylabel(\"Accuracy\");\n", - "graph1.set_title(\"Accuracy vs Defense Type\");\n", + "graph1.set_xlabel(\"Defense Type\")\n", + "graph1.set_ylabel(\"Accuracy\")\n", + "graph1.set_title(\"Accuracy vs Defense Type\")\n", "graph1.get_figure().tight_layout()\n", - "graph1.get_figure().savefig(FOLDER / \"accuracy_vs_defense_type.png\")\n" + "graph1.get_figure().savefig(FOLDER / \"accuracy_vs_defense_type.png\")" ] }, { @@ -66,18 +66,18 @@ } ], "source": [ + "data[\"failures_per_training_time\"] = (1 - data[\"accuracy\"]) / data[\n", + " \"train_time_per_sample\"\n", + "]\n", "\n", - "data['failures_per_training_time'] = (1 - data['accuracy']) / data['train_time_per_sample']\n", - "\n", - "graph2 = sns.barplot(data=data, x = \"def_gen\", y = \"failures_per_training_time\")\n", - "graph2.set_xticklabels(graph2.get_xticklabels(), rotation=90);\n", + "graph2 = sns.barplot(data=data, x=\"def_gen\", y=\"failures_per_training_time\")\n", + "graph2.set_xticklabels(graph2.get_xticklabels(), rotation=90)\n", "graph2.set_yscale(\"log\")\n", - "graph2.set_xlabel(\"Defense Type\");\n", - "graph2.set_ylabel(\"Failures Per Training Time\");\n", - "graph2.set_title(\"Failures Per Training Time vs Defense Type\");\n", + "graph2.set_xlabel(\"Defense Type\")\n", + "graph2.set_ylabel(\"Failures Per Training Time\")\n", + "graph2.set_title(\"Failures Per Training Time vs Defense Type\")\n", "graph2.get_figure().tight_layout()\n", - "graph2.get_figure().savefig(FOLDER / \"def_vs_failure_rate.pdf\")\n", - "\n" + "graph2.get_figure().savefig(FOLDER / \"def_vs_failure_rate.pdf\")" ] }, { @@ -86,50 +86,49 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "control_dict = {\n", " \"FeatureSqueezing\": \"model.art.pipeline.preprocessor.kwargs.bit_depth\",\n", " \"GaussianAugmentation\": \"model.art.pipeline.preprocessor.kwargs.ratio\",\n", " \"HighConfidence\": \"model.art.pipeline.postprocessor.kwargs.cutoff\",\n", " \"GaussianNoise\": \"model.art.pipeline.postprocessor.kwargs.scale\",\n", - " \"HSJ\" : \"attack.init.kwargs.max_eval\",\n", - " \"FGM\" : \"attack.init.kwargs.eps\",\n", - " \"PGD\" : \"attack.init.kwargs.eps\",\n", - " \"DeepFool\" : \"attack.init.kwargs.nb_grads\",\n", - " \"CarliniL2\" : \"attack.init.kwargs.eps\",\n", - " \"CarliniLInf\" : \"attack.init.kwargs.eps\",\n", - " np.nan : \"None\"\n", + " \"HSJ\": \"attack.init.kwargs.max_eval\",\n", + " \"FGM\": \"attack.init.kwargs.eps\",\n", + " \"PGD\": \"attack.init.kwargs.eps\",\n", + " \"DeepFool\": \"attack.init.kwargs.nb_grads\",\n", + " \"CarliniL2\": \"attack.init.kwargs.eps\",\n", + " \"CarliniLInf\": \"attack.init.kwargs.eps\",\n", + " np.nan: \"None\",\n", "}\n", "\n", + "\n", "def merge_control(data, control_dict):\n", " for key, value in control_dict.items():\n", " for i, entry in data.iterrows():\n", " if \"def_gen\" in entry and entry[\"def_gen\"] == key:\n", - " data.loc[i, 'def_param'] = value.split(\".\")[-1]\n", - " data.loc[i, 'def_value'] = entry[value]\n", + " data.loc[i, \"def_param\"] = value.split(\".\")[-1]\n", + " data.loc[i, \"def_value\"] = entry[value]\n", " if \"atk_gen\" in entry and entry[\"atk_gen\"] == key:\n", - " data.loc[i, 'atk_param'] = value.split(\".\")[-1]\n", - " data.loc[i, 'atk_value'] = entry[value]\n", + " data.loc[i, \"atk_param\"] = value.split(\".\")[-1]\n", + " data.loc[i, \"atk_value\"] = entry[value]\n", " if \"def_value\" in data.columns:\n", " max_ = data[\"def_value\"].max()\n", " min_ = data[\"def_value\"].min()\n", - " data.loc[:, 'def_value'] = (data.loc[:, 'def_value'] - min_) / (max_ - min_)\n", + " data.loc[:, \"def_value\"] = (data.loc[:, \"def_value\"] - min_) / (max_ - min_)\n", " if \"atk_value\" in data.columns:\n", " max_ = data[\"atk_value\"].max()\n", " min_ = data[\"atk_value\"].min()\n", - " data.loc[:, 'atk_value'] = (data.loc[:, 'atk_value'] - min_) / (max_ - min_) \n", - " \n", + " data.loc[:, \"atk_value\"] = (data.loc[:, \"atk_value\"] - min_) / (max_ - min_)\n", + "\n", " data.def_gen.fillna(\"None\", inplace=True)\n", " data.def_param.fillna(\"None\", inplace=True)\n", " data.def_value.fillna(0, inplace=True)\n", - " \n", + "\n", " data.atk_gen.fillna(\"None\", inplace=True)\n", " data.atk_param.fillna(\"None\", inplace=True)\n", " data.atk_value.fillna(0, inplace=True)\n", - " \n", + "\n", " return data\n", - " \n", - " \n", + "\n", "\n", "data = merge_control(data, control_dict)" ] @@ -151,17 +150,16 @@ } ], "source": [ + "data[\"failure_rate\"] = (1 - data[\"accuracy\"]) * data[\"predict_time_per_sample\"]\n", "\n", - "data['failure_rate'] = (1 - data['accuracy']) * data['predict_time_per_sample']\n", - "\n", - "graph2 = sns.barplot(data=data, x = \"def_gen\", y = \"failure_rate\")\n", + "graph2 = sns.barplot(data=data, x=\"def_gen\", y=\"failure_rate\")\n", "graph2.set_xticklabels(graph2.get_xticklabels(), rotation=90)\n", "graph2.set_yscale(\"log\")\n", - "graph2.set_xlabel(\"Defense Type\");\n", - "graph2.set_ylabel(\"Failure Rate\");\n", - "graph2.set_title(\"Failure Rate vs Defense Type\");\n", + "graph2.set_xlabel(\"Defense Type\")\n", + "graph2.set_ylabel(\"Failure Rate\")\n", + "graph2.set_title(\"Failure Rate vs Defense Type\")\n", "graph2.get_figure().tight_layout()\n", - "graph2.get_figure().savefig(FOLDER / \"def_vs_failure_rate.pdf\")\n" + "graph2.get_figure().savefig(FOLDER / \"def_vs_failure_rate.pdf\")" ] }, { @@ -181,15 +179,14 @@ } ], "source": [ + "data[\"adv_failure_rate\"] = (1 - data[\"adv_accuracy\"]) * data[\"adv_fit_time_per_sample\"]\n", "\n", - "data['adv_failure_rate'] = (1 - data['adv_accuracy']) * data['adv_fit_time_per_sample']\n", - "\n", - "graph2 = sns.barplot(data=data, x = \"atk_gen\", y = \"adv_failure_rate\")\n", + "graph2 = sns.barplot(data=data, x=\"atk_gen\", y=\"adv_failure_rate\")\n", "graph2.set_xticklabels(graph2.get_xticklabels(), rotation=90)\n", "graph2.set_yscale(\"log\")\n", - "graph2.set_xlabel(\"Attack Type\");\n", - "graph2.set_ylabel(\"Adv. Failure Rate\");\n", - "graph2.set_title(\"Failure Rate vs Attack Type\");\n", + "graph2.set_xlabel(\"Attack Type\")\n", + "graph2.set_ylabel(\"Adv. Failure Rate\")\n", + "graph2.set_title(\"Failure Rate vs Attack Type\")\n", "graph2.get_figure().tight_layout()\n", "graph2.get_figure().savefig(FOLDER / \"atk_vs_failure_rate.pdf\")" ] @@ -211,15 +208,16 @@ } ], "source": [ + "data[\"adv_failures_per_training_time\"] = (1 - data[\"adv_accuracy\"]) / data[\n", + " \"train_time_per_sample\"\n", + "]\n", "\n", - "data['adv_failures_per_training_time'] = (1 - data['adv_accuracy']) / data['train_time_per_sample']\n", - "\n", - "graph2 = sns.barplot(data=data, x = \"atk_gen\", y = \"adv_failures_per_training_time\")\n", + "graph2 = sns.barplot(data=data, x=\"atk_gen\", y=\"adv_failures_per_training_time\")\n", "graph2.set_xticklabels(graph2.get_xticklabels(), rotation=90)\n", "graph2.set_yscale(\"log\")\n", - "graph2.set_xlabel(\"Attack Type\");\n", - "graph2.set_ylabel(\"Adv. Failures Per Training Time\");\n", - "graph2.set_title(\"Adv. Failures Per Training Time vs Attack Type\");\n", + "graph2.set_xlabel(\"Attack Type\")\n", + "graph2.set_ylabel(\"Adv. Failures Per Training Time\")\n", + "graph2.set_title(\"Adv. Failures Per Training Time vs Attack Type\")\n", "graph2.get_figure().tight_layout()\n", "graph2.get_figure().savefig(FOLDER / \"atk_vs_train_rate.pdf\")" ] @@ -241,16 +239,15 @@ } ], "source": [ - "\n", - "graph4 = sns.lineplot(data=data, x = \"def_value\", y = \"accuracy\", hue = \"def_gen\")\n", + "graph4 = sns.lineplot(data=data, x=\"def_value\", y=\"accuracy\", hue=\"def_gen\")\n", "graph4.set_yscale(\"log\")\n", "graph4.set_xscale(\"log\")\n", - "graph4.legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=1)\n", - "graph4.set_xlabel(\"Defense Control Parameter\");\n", - "graph4.set_ylabel(\"Accuracy\");\n", - "graph4.set_title(\"Accuracy vs Defense Strength\");\n", + "graph4.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5), ncol=1)\n", + "graph4.set_xlabel(\"Defense Control Parameter\")\n", + "graph4.set_ylabel(\"Accuracy\")\n", + "graph4.set_title(\"Accuracy vs Defense Strength\")\n", "graph4.get_figure().tight_layout()\n", - "graph2.get_figure().savefig(FOLDER / \"def_param_vs_accuracy.pdf\")\n" + "graph2.get_figure().savefig(FOLDER / \"def_param_vs_accuracy.pdf\")" ] }, { @@ -270,12 +267,12 @@ } ], "source": [ - "graph5 = sns.lineplot(data=data, x = \"def_value\", y = \"failure_rate\", hue = \"def_gen\")\n", + "graph5 = sns.lineplot(data=data, x=\"def_value\", y=\"failure_rate\", hue=\"def_gen\")\n", "graph5.set_yscale(\"log\")\n", - "graph5.legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=1)\n", - "graph5.set_xlabel(\"Defense Control Parameter\");\n", - "graph5.set_ylabel(\"Failure Rate\");\n", - "graph5.set_title(\"Failure Rate vs Defense Strength\");\n", + "graph5.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5), ncol=1)\n", + "graph5.set_xlabel(\"Defense Control Parameter\")\n", + "graph5.set_ylabel(\"Failure Rate\")\n", + "graph5.set_title(\"Failure Rate vs Defense Strength\")\n", "graph5.set_xscale(\"log\")\n", "graph5.get_figure().tight_layout()\n", "graph5.get_figure().savefig(FOLDER / \"def_param_vs_failure_rate.pdf\")" @@ -411,34 +408,34 @@ } ], "source": [ - "\n", "from lifelines import WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter\n", "from sklearn.preprocessing import MinMaxScaler\n", + "\n", "# constants\n", "scorer = \"accuracy\"\n", "\n", "subset = data.copy()\n", - "subset = subset[subset['atk_gen'] == 'FastGradientMethod']\n", - "subset['atk_value'] = subset['attack.init.kwargs.eps']\n", + "subset = subset[subset[\"atk_gen\"] == \"FastGradientMethod\"]\n", + "subset[\"atk_value\"] = subset[\"attack.init.kwargs.eps\"]\n", "cleaned = pd.DataFrame()\n", - "cleaned['accuracy'] = subset['accuracy']\n", - "cleaned['train_time'] = subset['train_time']\n", - "cleaned['adv_fit_time'] = subset['adv_fit_time']\n", - "cleaned['adv_accuracy'] = subset['adv_accuracy']\n", - "cleaned['atk_value'] = subset['atk_value']\n", - "cleaned['def_value'] = subset['def_value']\n", - "cleaned['adv_fit_time'] = subset['adv_fit_time']\n", - "cleaned['adv_fit_time_per_sample'] = subset['adv_fit_time_per_sample']\n", - "cleaned['adv_accuracy'] = subset['adv_accuracy']\n", - "cleaned['random_state'] = subset['data.sample.random_state']\n", - "cleaned['adv_failure_rate'] = subset['adv_failure_rate']\n", - "cleaned['failure_rate'] = subset['failure_rate']\n", + "cleaned[\"accuracy\"] = subset[\"accuracy\"]\n", + "cleaned[\"train_time\"] = subset[\"train_time\"]\n", + "cleaned[\"adv_fit_time\"] = subset[\"adv_fit_time\"]\n", + "cleaned[\"adv_accuracy\"] = subset[\"adv_accuracy\"]\n", + "cleaned[\"atk_value\"] = subset[\"atk_value\"]\n", + "cleaned[\"def_value\"] = subset[\"def_value\"]\n", + "cleaned[\"adv_fit_time\"] = subset[\"adv_fit_time\"]\n", + "cleaned[\"adv_fit_time_per_sample\"] = subset[\"adv_fit_time_per_sample\"]\n", + "cleaned[\"adv_accuracy\"] = subset[\"adv_accuracy\"]\n", + "cleaned[\"random_state\"] = subset[\"data.sample.random_state\"]\n", + "cleaned[\"adv_failure_rate\"] = subset[\"adv_failure_rate\"]\n", + "cleaned[\"failure_rate\"] = subset[\"failure_rate\"]\n", "\n", - "cleaned = cleaned[cleaned.columns.drop(list(cleaned.filter(regex='.1')))]\n", + "cleaned = cleaned[cleaned.columns.drop(list(cleaned.filter(regex=\".1\")))]\n", "cleaned.fillna(method=\"ffill\", inplace=True)\n", "cleaned.fillna(method=\"bfill\", inplace=True)\n", - "cleaned = cleaned[cleaned.columns.drop(list(cleaned.filter(regex='.1')))]\n", - "scaler = MinMaxScaler((0,1))\n", + "cleaned = cleaned[cleaned.columns.drop(list(cleaned.filter(regex=\".1\")))]\n", + "scaler = MinMaxScaler((0, 1))\n", "scaler = scaler.fit(cleaned)\n", "cleaned_numeric = scaler.transform(cleaned)\n", "cleaned_cols = cleaned.columns\n", @@ -460,39 +457,41 @@ "new_cleaned = cleaned.copy()\n", "i = 0\n", "for _, row in new_cleaned.iterrows():\n", - " times = np.random.uniform(0.000001, row['adv_fit_time_per_sample'], size)\n", + " times = np.random.uniform(0.000001, row[\"adv_fit_time_per_sample\"], size)\n", " score = float(row.adv_accuracy)\n", " status = np.random.binomial(1, score, size)\n", " assert len(times) == len(status)\n", " assert len(times) == size\n", - " del row['accuracy']\n", - " del row['adv_accuracy']\n", - " del row['adv_fit_time']\n", - " del row['adv_fit_time_per_sample']\n", + " del row[\"accuracy\"]\n", + " del row[\"adv_accuracy\"]\n", + " del row[\"adv_fit_time\"]\n", + " del row[\"adv_fit_time_per_sample\"]\n", " results = row.to_dict()\n", - " results['random_state'] = row['random_state']\n", - " del results['failure_rate']\n", - " del results['adv_failure_rate']\n", - " results['status'] = status\n", - " sub_indices = range(i*size, size + (i*size))\n", + " results[\"random_state\"] = row[\"random_state\"]\n", + " del results[\"failure_rate\"]\n", + " del results[\"adv_failure_rate\"]\n", + " results[\"status\"] = status\n", + " sub_indices = range(i * size, size + (i * size))\n", " assert len(sub_indices) == len(status) == len(times) == size\n", " sub_data = pd.DataFrame(results, index=sub_indices)\n", - " sub_data = sub_data.explode('status').reset_index(drop=True)\n", - " sub_data['time'] = times\n", + " sub_data = sub_data.explode(\"status\").reset_index(drop=True)\n", + " sub_data[\"time\"] = times\n", " i += 1\n", - " sub_data['index'] = sub_indices\n", - " \n", + " sub_data[\"index\"] = sub_indices\n", + "\n", " new_data = pd.concat([new_data, sub_data], axis=0)\n", " new_data = new_data.reindex()\n", - " \n", - "new_data = new_data.loc[:,~new_data.columns.duplicated()]\n", + "\n", + "new_data = new_data.loc[:, ~new_data.columns.duplicated()]\n", "new_data.index = range(len(new_data))\n", - "new_data = new_data[new_data.columns.drop(list(new_data.filter(regex='.1')))]\n", + "new_data = new_data[new_data.columns.drop(list(new_data.filter(regex=\".1\")))]\n", "new_data.fillna(method=\"ffill\", inplace=True)\n", "new_data.fillna(method=\"bfill\", inplace=True)\n", - "new_data = new_data[new_data.columns.drop(list(new_data.filter(regex='.1')))]\n", - "assert np.sum(new_data.index.duplicated()) == np.sum(new_data.columns.duplicated()) == 0, f\"{np.sum(new_data.index.duplicated())} {np.sum(new_data.columns.duplicated())}\"\n", - "new_data.drop(\"index\", axis=1, inplace=True)\n" + "new_data = new_data[new_data.columns.drop(list(new_data.filter(regex=\".1\")))]\n", + "assert (\n", + " np.sum(new_data.index.duplicated()) == np.sum(new_data.columns.duplicated()) == 0\n", + "), f\"{np.sum(new_data.index.duplicated())} {np.sum(new_data.columns.duplicated())}\"\n", + "new_data.drop(\"index\", axis=1, inplace=True)" ] }, { @@ -512,17 +511,24 @@ } ], "source": [ - "\n", "wft = WeibullAFTFitter()\n", - "wft.fit(new_data, duration_col='time', event_col='status')\n", + "wft.fit(new_data, duration_col=\"time\", event_col=\"status\")\n", "ax = wft.plot()\n", "ax.set_ylabel(\"Survival Probability\")\n", - "ax.set_yticklabels([\"$\\\\rho$\", \"Defence Strength\", \"Training Time\", \"Attack Strength\", \"Random State\", '$\\lambda$'])\n", - "ax.set_xlabel(\"$\\log(\\eta)$ - 95% CI\");\n", - "ax.set_title(\"Weibull AFT Model\");\n", + "ax.set_yticklabels(\n", + " [\n", + " \"$\\\\rho$\",\n", + " \"Defence Strength\",\n", + " \"Training Time\",\n", + " \"Attack Strength\",\n", + " \"Random State\",\n", + " \"$\\lambda$\",\n", + " ]\n", + ")\n", + "ax.set_xlabel(\"$\\log(\\eta)$ - 95% CI\")\n", + "ax.set_title(\"Weibull AFT Model\")\n", "ax.get_figure().tight_layout()\n", - "ax.get_figure().savefig(FOLDER / \"weibull_aft.pdf\")\n", - " " + "ax.get_figure().savefig(FOLDER / \"weibull_aft.pdf\")" ] }, { @@ -542,14 +548,22 @@ } ], "source": [ - "\n", "lnt = LogNormalAFTFitter()\n", - "lnt.fit(new_data, duration_col='time', event_col='status')\n", + "lnt.fit(new_data, duration_col=\"time\", event_col=\"status\")\n", "ax = lnt.plot()\n", "ax.set_ylabel(\"Survival Probability\")\n", - "ax.set_yticklabels([\"$\\sigma$\", \"Defence Strength\", \"Training Time\", \"Attack Strength\", \"Random State\", '$\\mu$'])\n", - "ax.set_xlabel(\"$\\log(\\eta)$ - 95% CI\");\n", - "ax.set_title(\"Log-Normal AFT\");\n", + "ax.set_yticklabels(\n", + " [\n", + " \"$\\sigma$\",\n", + " \"Defence Strength\",\n", + " \"Training Time\",\n", + " \"Attack Strength\",\n", + " \"Random State\",\n", + " \"$\\mu$\",\n", + " ]\n", + ")\n", + "ax.set_xlabel(\"$\\log(\\eta)$ - 95% CI\")\n", + "ax.set_title(\"Log-Normal AFT\")\n", "ax.get_figure().tight_layout()\n", "ax.get_figure().savefig(FOLDER / \"log_normal_aft.pdf\")" ] @@ -572,12 +586,21 @@ ], "source": [ "llt = LogLogisticAFTFitter()\n", - "llt.fit(new_data, duration_col='time', event_col='status')\n", + "llt.fit(new_data, duration_col=\"time\", event_col=\"status\")\n", "ax = llt.plot()\n", "ax.set_ylabel(\"Survival Probability\")\n", - "ax.set_yticklabels([\"$\\\\alpha$\", \"Defence Strength\", \"Training Time\", \"Attack Strength\", \"Random State\", '$\\\\beta$'])\n", - "ax.set_xlabel(\"$\\log(\\eta) $- 95% CI\");\n", - "ax.set_title(\"Log-Logistic AFT\");\n", + "ax.set_yticklabels(\n", + " [\n", + " \"$\\\\alpha$\",\n", + " \"Defence Strength\",\n", + " \"Training Time\",\n", + " \"Attack Strength\",\n", + " \"Random State\",\n", + " \"$\\\\beta$\",\n", + " ]\n", + ")\n", + "ax.set_xlabel(\"$\\log(\\eta) $- 95% CI\")\n", + "ax.set_title(\"Log-Logistic AFT\")\n", "ax.get_figure().tight_layout()\n", "ax.get_figure().savefig(FOLDER / \"log_logistic_aft.pdf\")" ] @@ -624,23 +647,31 @@ "print(f\"LogLogistic AIC: {llt.AIC_}\")\n", "\n", "aft_data = {\n", - " \"Weibull\" : wft.AIC_,\n", - " \"LogNormal\" : lnt.AIC_,\n", - " \"LogLogistic\" : llt.AIC_,\n", + " \"Weibull\": wft.AIC_,\n", + " \"LogNormal\": lnt.AIC_,\n", + " \"LogLogistic\": llt.AIC_,\n", "}\n", - "aft_data = pd.DataFrame.from_dict(aft_data, orient='index', columns=['AIC'])\n", + "aft_data = pd.DataFrame.from_dict(aft_data, orient=\"index\", columns=[\"AIC\"])\n", "aft_data.index.name = \"Model\"\n", - "aic_graph = sns.barplot(data=aft_data, x=aft_data.index, y='AIC')\n", - "aic_graph.set_ylabel(\"AIC\");\n", - "aic_graph.set_xlabel(\"Model\");\n", - "aic_graph.set_title(\"AIC Comparison\");\n", + "aic_graph = sns.barplot(data=aft_data, x=aft_data.index, y=\"AIC\")\n", + "aic_graph.set_ylabel(\"AIC\")\n", + "aic_graph.set_xlabel(\"Model\")\n", + "aic_graph.set_title(\"AIC Comparison\")\n", "aic_graph.set_yscale(\"symlog\")\n", "aic_graph.get_figure().tight_layout().savefig(FOLDER / \"aic_comparison.pdf\")\n", "\n", "\n", - "aft_data['LogLikelihood'] = [wft.log_likelihood_, lnt.log_likelihood_, llt.log_likelihood_]\n", - "aft_data['Concordance Score'] = [wft.concordance_index_, lnt.concordance_index_, llt.concordance_index_]\n", - "aft_data['BIC'] = [wft.BIC_, lnt.BIC_, llt.BIC_]\n", + "aft_data[\"LogLikelihood\"] = [\n", + " wft.log_likelihood_,\n", + " lnt.log_likelihood_,\n", + " llt.log_likelihood_,\n", + "]\n", + "aft_data[\"Concordance Score\"] = [\n", + " wft.concordance_index_,\n", + " lnt.concordance_index_,\n", + " llt.concordance_index_,\n", + "]\n", + "aft_data[\"BIC\"] = [wft.BIC_, lnt.BIC_, llt.BIC_]\n", "aft_data = aft_data.round(2)\n", "aft_data.to_csv(FOLDER / \"aft_comparison.csv\")\n", "aft_data" @@ -663,7 +694,6 @@ } ], "source": [ - "\n", "# print(f\"Weibull Standard Errors: \")\n", "# print(wft.standard_errors_)\n", "# print(f\"LogNormal Standard Errors: \")\n", @@ -674,27 +704,27 @@ "wft_timeline = wft.timeline\n", "wft_type = [\"Weibull\"] * len(wft_timeline)\n", "aft_df = pd.DataFrame(wft_type, columns=[\"Model\"])\n", - "aft_df['CDF'] = wft_timeline\n", - "aft_df['Time'] = range(len(wft_timeline))\n", + "aft_df[\"CDF\"] = wft_timeline\n", + "aft_df[\"Time\"] = range(len(wft_timeline))\n", "\n", "llt_timeline = llt.timeline\n", "llt_type = [\"LogLogistic\"] * len(llt_timeline)\n", "llt_df = pd.DataFrame(llt_type, columns=[\"Model\"])\n", - "llt_df['CDF'] = llt_timeline\n", - "llt_df['Time'] = range(len(llt_timeline))\n", + "llt_df[\"CDF\"] = llt_timeline\n", + "llt_df[\"Time\"] = range(len(llt_timeline))\n", "aft_df = pd.concat([aft_df, llt_df], axis=0)\n", "\n", "lnt_timeline = lnt.timeline\n", "lnt_type = [\"LogNormal\"] * len(lnt_timeline)\n", "lnt_df = pd.DataFrame(lnt_type, columns=[\"Model\"])\n", - "lnt_df['CDF'] = lnt_timeline\n", - "lnt_df['Time'] = range(len(lnt_timeline))\n", + "lnt_df[\"CDF\"] = lnt_timeline\n", + "lnt_df[\"Time\"] = range(len(lnt_timeline))\n", "aft_df = pd.concat([aft_df, lnt_df], axis=0)\n", "\n", "aft_cdf = sns.lineplot(data=aft_df, x=\"Time\", y=\"CDF\", hue=\"Model\")\n", - "aft_cdf.set_ylabel(\"Probability of Failure over Time\");\n", - "aft_cdf.set_xlabel(\"Time\");\n", - "aft_cdf.set_title(\"Failure Probability over Time\");\n", + "aft_cdf.set_ylabel(\"Probability of Failure over Time\")\n", + "aft_cdf.set_xlabel(\"Time\")\n", + "aft_cdf.set_title(\"Failure Probability over Time\")\n", "aft_cdf.get_figure().tight_layout()\n", "aft_cdf.get_figure().savefig(FOLDER / \"aft_cdf.pdf\")" ] @@ -1293,21 +1323,22 @@ ], "source": [ "from paretoset import paretoset\n", + "\n", "subset = data[data[\"atk_gen\"] == \"FastGradientMethod\"]\n", "\n", "score_dict = pd.DataFrame()\n", "# score_dict['accuracy'] = subset.loc[:, \"accuracy\"]\n", "# score_dict['failure_rate'] = subset.loc[:, \"failure_rate\"]\n", - "score_dict['adv_failure_rate'] = subset.loc[:, \"adv_failure_rate\"]\n", - "score_dict['train_time'] = subset.loc[:, \"train_time\"]\n", - "score_dict['atk_gen'] = subset.loc[:, \"atk_gen\"]\n", - "score_dict['def_gen'] = subset.loc[:, \"def_gen\"]\n", + "score_dict[\"adv_failure_rate\"] = subset.loc[:, \"adv_failure_rate\"]\n", + "score_dict[\"train_time\"] = subset.loc[:, \"train_time\"]\n", + "score_dict[\"atk_gen\"] = subset.loc[:, \"atk_gen\"]\n", + "score_dict[\"def_gen\"] = subset.loc[:, \"def_gen\"]\n", "# score_dict['adv_failure_rate'] = subset.loc[:, \"adv_failure_rate\"]\n", "# score_dict['train_time_per_sample'] = subset.loc[:, \"train_time_per_sample\"]\n", "# score_dict['adv_fit_time_per_sample'] = subset.loc[:, \"adv_fit_time_per_sample\"]\n", "\n", "\n", - "pareto = paretoset(score_dict, sense=[\"min\", \"min\", \"diff\", \"diff\"])\n", + "pareto = paretoset(score_dict, sense=[\"min\", \"min\", \"diff\", \"diff\"])\n", "subset = subset[pareto]\n", "subset" ] @@ -1340,12 +1371,14 @@ ], "source": [ "subset = data[data[\"atk_gen\"] == \"FastGradientMethod\"]\n", - "graph6 = sns.scatterplot(data=subset, x = \"train_time\", y = \"adv_failure_rate\", hue = \"def_gen\")\n", + "graph6 = sns.scatterplot(\n", + " data=subset, x=\"train_time\", y=\"adv_failure_rate\", hue=\"def_gen\"\n", + ")\n", "graph6.set_yscale(\"log\")\n", "graph6.set_xscale(\"log\")\n", "graph6.set_xlabel(\"Training Time\")\n", "graph6.set_ylabel(\"Adversarial Failure Rate\")\n", - "graph6.legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=1)" + "graph6.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5), ncol=1)" ] }, { @@ -1910,21 +1943,30 @@ ], "source": [ "from paretoset import paretoset\n", + "\n", "subset = data[data[\"atk_gen\"] == \"FastGradientMethod\"]\n", "\n", "score_dict = pd.DataFrame()\n", "# score_dict['def_gen'] = subset.loc[:, \"def_gen\"]\n", - "score_dict['accuracy'] = subset.loc[:, \"accuracy\"]\n", - "score_dict['failure_rate'] = subset.loc[:, \"failure_rate\"]\n", - "score_dict['adv_failure_rate'] = subset.loc[:, \"adv_failure_rate\"]\n", - "score_dict['train_time'] = subset.loc[:, \"train_time\"]\n", + "score_dict[\"accuracy\"] = subset.loc[:, \"accuracy\"]\n", + "score_dict[\"failure_rate\"] = subset.loc[:, \"failure_rate\"]\n", + "score_dict[\"adv_failure_rate\"] = subset.loc[:, \"adv_failure_rate\"]\n", + "score_dict[\"train_time\"] = subset.loc[:, \"train_time\"]\n", "# score_dict['atk_gen'] = subset.loc[:, \"atk_gen\"]\n", "# score_dict['adv_failure_rate'] = subset.loc[:, \"adv_failure_rate\"]\n", "# score_dict['train_time_per_sample'] = subset.loc[:, \"train_time_per_sample\"]\n", "# score_dict['adv_fit_time_per_sample'] = subset.loc[:, \"adv_fit_time_per_sample\"]\n", "\n", "\n", - "pareto = paretoset(score_dict, sense=[\"max\", \"min\", \"min\", \"min\",])\n", + "pareto = paretoset(\n", + " score_dict,\n", + " sense=[\n", + " \"max\",\n", + " \"min\",\n", + " \"min\",\n", + " \"min\",\n", + " ],\n", + ")\n", "subset = subset[pareto]\n", "subset\n", "# graph6 = sns.scatterplot(data=subset, x = \"train_time\", y = \"adv_failure_rate\", hue = \"atk_gen\")\n", @@ -1952,9 +1994,8 @@ } ], "source": [ - "\n", "# subset = data[pareto].sort_values(by=\"adv_failure_rate\", ascending=True)\n", - "failure_hist = sns.kdeplot(subset['adv_failure_rate'])\n", + "failure_hist = sns.kdeplot(subset[\"adv_failure_rate\"])\n", "failure_hist.set_xlabel(\"Adversarial Failure Rate\")\n", "failure_hist.set_xscale(\"log\")\n", "failure_hist.set_ylabel(\"Density\")\n", @@ -2008,9 +2049,10 @@ } ], "source": [ - "\n", "# subset = data[pareto].sort_values(by=\"train_time\", ascending=True)\n", - "failure_hist = sns.distplot(subset['train_time'], hist=False, rug=True, label=\"Accuracy\")\n", + "failure_hist = sns.distplot(\n", + " subset[\"train_time\"], hist=False, rug=True, label=\"Accuracy\"\n", + ")\n", "failure_hist.set_xlabel(\"Training Time\")\n", "failure_hist.set_xscale(\"log\")\n", "failure_hist.set_title(\"Training Time Distribution\")\n", @@ -2025,21 +2067,22 @@ "outputs": [], "source": [ "from paretoset import paretoset\n", + "\n", "subset = data[data[\"atk_gen\"] == \"FastGradientMethod\"]\n", "\n", "score_dict = pd.DataFrame()\n", "# score_dict['accuracy'] = subset.loc[:, \"accuracy\"]\n", "# score_dict['failure_rate'] = subset.loc[:, \"failure_rate\"]\n", - "score_dict['adv_failure_rate'] = subset.loc[:, \"adv_failure_rate\"]\n", - "score_dict['train_time'] = subset.loc[:, \"train_time\"]\n", - "score_dict['atk_gen'] = subset.loc[:, \"atk_gen\"]\n", - "score_dict['def_gen'] = subset.loc[:, \"def_gen\"]\n", + "score_dict[\"adv_failure_rate\"] = subset.loc[:, \"adv_failure_rate\"]\n", + "score_dict[\"train_time\"] = subset.loc[:, \"train_time\"]\n", + "score_dict[\"atk_gen\"] = subset.loc[:, \"atk_gen\"]\n", + "score_dict[\"def_gen\"] = subset.loc[:, \"def_gen\"]\n", "# score_dict['adv_failure_rate'] = subset.loc[:, \"adv_failure_rate\"]\n", "# score_dict['train_time_per_sample'] = subset.loc[:, \"train_time_per_sample\"]\n", "# score_dict['adv_fit_time_per_sample'] = subset.loc[:, \"adv_fit_time_per_sample\"]\n", "\n", "\n", - "pareto = paretoset(score_dict, sense=[\"min\", \"min\", \"diff\", \"diff\"])\n", + "pareto = paretoset(score_dict, sense=[\"min\", \"min\", \"diff\", \"diff\"])\n", "subset = subset[pareto]\n", "# pareto_graph = sns.lineplot(data=subset, x = \"train_time\", y = \"adv_failure_rate\", hue = \"atk_gen\")\n", "# pareto_graph.set_yscale(\"log\")\n", @@ -2094,16 +2137,17 @@ "source": [ "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n", "from sklearn.preprocessing import power_transform, StandardScaler\n", + "\n", "X = cleaned.copy()\n", "X = X.select_dtypes(include=np.number)\n", "X = StandardScaler().fit_transform(X)\n", - "X = power_transform(X, method='yeo-johnson')\n", + "X = power_transform(X, method=\"yeo-johnson\")\n", "X = pd.DataFrame(X, columns=cleaned.columns)\n", - "del X['adv_accuracy']\n", - "del X['adv_fit_time']\n", - "y = X['adv_failure_rate']\n", - "del X['adv_failure_rate']\n", - "del X['failure_rate']\n", + "del X[\"adv_accuracy\"]\n", + "del X[\"adv_fit_time\"]\n", + "y = X[\"adv_failure_rate\"]\n", + "del X[\"adv_failure_rate\"]\n", + "del X[\"failure_rate\"]\n", "\n", "model = LinearRegression()\n", "model.fit(X, y)\n", @@ -2112,15 +2156,24 @@ "for col in X.columns:\n", " print(f\"{col}: {coef[i]}\")\n", " i += 1\n", - " \n", + "\n", "coef_df = pd.DataFrame([coef], columns=X.columns, index=[\"Coefficient\"]).T\n", "coef_plot = sns.barplot(data=coef_df, x=coef_df.index, y=\"Coefficient\")\n", - "coef_plot.set_xlabel(\"Feature\");\n", - "coef_plot.set_ylabel(\"Coefficient\");\n", - "coef_plot.set_title(\"Linear Regression Coefficients\");\n", - "coef_plot.set_xticklabels([\"Accuracy\", \"Training Time\", \"Attack Parameter\", \"Defence Parameter\", \"Random State\"], rotation=90)\n", + "coef_plot.set_xlabel(\"Feature\")\n", + "coef_plot.set_ylabel(\"Coefficient\")\n", + "coef_plot.set_title(\"Linear Regression Coefficients\")\n", + "coef_plot.set_xticklabels(\n", + " [\n", + " \"Accuracy\",\n", + " \"Training Time\",\n", + " \"Attack Parameter\",\n", + " \"Defence Parameter\",\n", + " \"Random State\",\n", + " ],\n", + " rotation=90,\n", + ")\n", "coef_plot.get_figure().tight_layout()\n", - "coef_plot.get_figure().savefig(FOLDER / \"linear_regression_coefficients.pdf\")\n" + "coef_plot.get_figure().savefig(FOLDER / \"linear_regression_coefficients.pdf\")" ] }, { @@ -2136,13 +2189,22 @@ "for col in X.columns:\n", " print(f\"{col}: {coef[i]}\")\n", " i += 1\n", - " \n", + "\n", "coef_df = pd.DataFrame([coef], columns=X.columns, index=[\"Coefficient\"]).T\n", "coef_plot = sns.barplot(data=coef_df, x=coef_df.index, y=\"Coefficient\")\n", - "coef_plot.set_xlabel(\"Feature\");\n", - "coef_plot.set_ylabel(\"Coefficient\");\n", - "coef_plot.set_title(\"Ridge Regression Coefficients\");\n", - "coef_plot.set_xticklabels([\"Accuracy\", \"Training Time\", \"Attack Parameter\", \"Defence Parameter\", \"Random State\"], rotation=90)\n", + "coef_plot.set_xlabel(\"Feature\")\n", + "coef_plot.set_ylabel(\"Coefficient\")\n", + "coef_plot.set_title(\"Ridge Regression Coefficients\")\n", + "coef_plot.set_xticklabels(\n", + " [\n", + " \"Accuracy\",\n", + " \"Training Time\",\n", + " \"Attack Parameter\",\n", + " \"Defence Parameter\",\n", + " \"Random State\",\n", + " ],\n", + " rotation=90,\n", + ")\n", "coef_plot.get_figure().tight_layout()\n", "coef_plot.get_figure().savefig(FOLDER / \"ridge_regression_coefficients.pdf\")" ] @@ -2199,13 +2261,22 @@ "for col in X.columns:\n", " print(f\"{col}: {coef[i]}\")\n", " i += 1\n", - " \n", + "\n", "coef_df = pd.DataFrame([coef], columns=X.columns, index=[\"Coefficient\"]).T\n", "coef_plot = sns.barplot(data=coef_df, x=coef_df.index, y=\"Coefficient\")\n", - "coef_plot.set_xlabel(\"Feature\");\n", - "coef_plot.set_ylabel(\"Coefficient\");\n", - "coef_plot.set_title(\"Bayessian Ridge Regression Coefficients\");\n", - "coef_plot.set_xticklabels([\"Accuracy\", \"Training Time\", \"Attack Parameter\", \"Defence Parameter\", \"Random State\"], rotation=90)\n", + "coef_plot.set_xlabel(\"Feature\")\n", + "coef_plot.set_ylabel(\"Coefficient\")\n", + "coef_plot.set_title(\"Bayessian Ridge Regression Coefficients\")\n", + "coef_plot.set_xticklabels(\n", + " [\n", + " \"Accuracy\",\n", + " \"Training Time\",\n", + " \"Attack Parameter\",\n", + " \"Defence Parameter\",\n", + " \"Random State\",\n", + " ],\n", + " rotation=90,\n", + ")\n", "coef_plot.get_figure().tight_layout()\n", "coef_plot.get_figure().savefig(FOLDER / \"ridge_regression_coefficients.pdf\")" ] diff --git a/examples/pytorch/torch_example.py b/examples/pytorch/torch_example.py index eb3c79f5..034bc27b 100644 --- a/examples/pytorch/torch_example.py +++ b/examples/pytorch/torch_example.py @@ -12,7 +12,12 @@ def ResNet18(num_channels=3, num_classes=10): model = models.resnet18() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -21,7 +26,12 @@ def ResNet18(num_channels=3, num_classes=10): def ResNet50(num_channels=3, num_classes=10): model = models.resnet50() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -30,7 +40,12 @@ def ResNet50(num_channels=3, num_classes=10): def ResNet101(num_channels=3, num_classes=10): model = models.resnet101() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -39,7 +54,12 @@ def ResNet101(num_channels=3, num_classes=10): def ResNet152(num_channels=3, num_classes=10): model = models.resnet152() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model diff --git a/examples/tfv1/tfv1_example.py b/examples/tfv1/tfv1_example.py index 529dfdd3..d5a9f88a 100644 --- a/examples/tfv1/tfv1_example.py +++ b/examples/tfv1/tfv1_example.py @@ -12,11 +12,17 @@ def __init__(self, input_shape=(28, 28, 1), num_classes=10): self.input_ph = tf.placeholder(tf.float32, shape=[None, *input_shape]) self.labels_ph = tf.placeholder(tf.float32, shape=[None, num_classes]) self.layer1 = tf.layers.conv2d( - self.input_ph, filters=4, kernel_size=5, activation=tf.nn.relu, + self.input_ph, + filters=4, + kernel_size=5, + activation=tf.nn.relu, ) self.layer2 = tf.layers.max_pooling2d(self.layer1, 2, 2) self.layer3 = tf.layers.conv2d( - self.layer2, filters=10, kernel_size=5, activation=tf.nn.relu, + self.layer2, + filters=10, + kernel_size=5, + activation=tf.nn.relu, ) self.layer4 = tf.layers.max_pooling2d(self.layer3, 2, 2) self.layer5 = tf.layers.flatten(self.layer4) diff --git a/examples/tfv2/tfv2_example.py b/examples/tfv2/tfv2_example.py index ae871ee3..b3ff9ba3 100644 --- a/examples/tfv2/tfv2_example.py +++ b/examples/tfv2/tfv2_example.py @@ -19,7 +19,10 @@ def __init__(self, optimizer: dict, loss_object: dict, output_shape=10): self.conv1 = Conv2D(filters=4, kernel_size=5, activation="relu") self.conv2 = Conv2D(filters=10, kernel_size=5, activation="relu") self.maxpool = MaxPool2D( - pool_size=(2, 2), strides=(2, 2), padding="valid", data_format=None, + pool_size=(2, 2), + strides=(2, 2), + padding="valid", + data_format=None, ) self.flatten = Flatten() self.dense1 = Dense(100, activation="relu") diff --git a/test/base/test_attack/test_attack.py b/test/base/test_attack/test_attack.py index 1a95e9de..1805bbe6 100644 --- a/test/base/test_attack/test_attack.py +++ b/test/base/test_attack/test_attack.py @@ -13,14 +13,14 @@ class testAttackInitializer(unittest.TestCase): - config_dir = Path(this_dir, "../../conf/attack").resolve().as_posix() config_file = "evasion.yaml" file = "attack.pkl" def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg @@ -47,35 +47,32 @@ def tearDown(self) -> None: class testPoisoningAttackInitializer(testAttackInitializer): - config_dir = Path(this_dir, "../../conf/attack").resolve().as_posix() config_file = "poisoning.yaml" file = "attack.pkl" class testInferenceAttackInitializer(testAttackInitializer): - config_dir = Path(this_dir, "../../conf/attack").resolve().as_posix() config_file = "inference.yaml" file = "attack.pkl" class testExtractionAttackInitializer(testAttackInitializer): - config_dir = Path(this_dir, "../../conf/attack").resolve().as_posix() config_file = "extraction.yaml" file = "attack.pkl" class testAttack(unittest.TestCase): - config_dir = Path(this_dir, "../../conf/attack").resolve().as_posix() config_file = "evasion.yaml" file = "attack.pkl" def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg @@ -131,14 +128,12 @@ def tearDown(self) -> None: class testInferenceAttack(testAttack): - config_dir = Path(this_dir, "../../conf/attack").resolve().as_posix() config_file = "inference.yaml" file = "attack.pkl" class testExtractionAttack(testAttack): - config_dir = Path(this_dir, "../../conf/attack").resolve().as_posix() config_file = "extraction.yaml" file = "attack.pkl" diff --git a/test/base/test_attack/torch_example.py b/test/base/test_attack/torch_example.py index 33cfe035..e7479cb8 100644 --- a/test/base/test_attack/torch_example.py +++ b/test/base/test_attack/torch_example.py @@ -214,7 +214,12 @@ def __init__( # CIFAR10: kernel_size 7 -> 3, stride 2 -> 1, padding 3->1 self.conv1 = nn.Conv2d( - 3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False, + 3, + self.inplanes, + kernel_size=3, + stride=1, + padding=1, + bias=False, ) # END @@ -223,13 +228,25 @@ def __init__( self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer( - block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0], + block, + 128, + layers[1], + stride=2, + dilate=replace_stride_with_dilation[0], ) self.layer3 = self._make_layer( - block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1], + block, + 256, + layers[2], + stride=2, + dilate=replace_stride_with_dilation[1], ) self.layer4 = self._make_layer( - block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2], + block, + 512, + layers[3], + stride=2, + dilate=replace_stride_with_dilation[2], ) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) diff --git a/test/base/test_data/test_data.py b/test/base/test_data/test_data.py index 8f9d05d0..9e3008ed 100644 --- a/test/base/test_data/test_data.py +++ b/test/base/test_data/test_data.py @@ -20,7 +20,8 @@ class testSklearnData(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_data/test_generator.py b/test/base/test_data/test_generator.py index 291ef6db..cd99ce4a 100644 --- a/test/base/test_data/test_generator.py +++ b/test/base/test_data/test_generator.py @@ -20,7 +20,8 @@ class testDataGenerator(unittest.TestCase): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg diff --git a/test/base/test_data/test_sampler.py b/test/base/test_data/test_sampler.py index b7162043..e58060fa 100644 --- a/test/base/test_data/test_sampler.py +++ b/test/base/test_data/test_sampler.py @@ -14,7 +14,8 @@ class testSklearnDataSampler(unittest.TestCase): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -51,7 +52,8 @@ def tearDown(self) -> None: class testTimeSeriesSklearnDataSampler(unittest.TestCase): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg diff --git a/test/base/test_data/test_sklearn_pipeline.py b/test/base/test_data/test_sklearn_pipeline.py index 4ec2164b..8500f9fd 100644 --- a/test/base/test_data/test_sklearn_pipeline.py +++ b/test/base/test_data/test_sklearn_pipeline.py @@ -20,7 +20,8 @@ class testSklearnDataPipeline(unittest.TestCase): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -37,7 +38,10 @@ def test_call(self): X_train, X_test, y_train, y_test = self.data.sample(X, y) old_mean = np.mean(X_train) X_train, X_test, y_train, y_test = self.data.sklearn_pipeline( - X_train, X_test, y_train, y_test, + X_train, + X_test, + y_train, + y_test, ) new_mean = np.mean(X_train) self.assertNotEqual(old_mean, new_mean) @@ -68,7 +72,8 @@ def tearDown(self) -> None: class testSklearnDataPipelineStage(unittest.TestCase): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -77,7 +82,8 @@ def setUp(self, config_dir=config_dir, config_file=config_file): def test_init(self): self.assertTrue( isinstance( - self.data.sklearn_pipeline["preprocessor"], SklearnDataPipelineStage, + self.data.sklearn_pipeline["preprocessor"], + SklearnDataPipelineStage, ) or hasattr(self.data.sklearn_pipeline["preprocessor"], "transform"), ) @@ -87,7 +93,10 @@ def test_call(self): X_train, X_test, y_train, y_test = self.data.sample(X, y) old_mean = np.mean(X_train) X_train, X_test, y_train, y_test = self.data.sklearn_pipeline["preprocessor"]( - X_train, X_test, y_train, y_test, + X_train, + X_test, + y_train, + y_test, ) new_mean = np.mean(X_train) self.assertNotEqual(old_mean, new_mean) diff --git a/test/base/test_files/test_files.py b/test/base/test_files/test_files.py index 555edc58..a08b59de 100644 --- a/test/base/test_files/test_files.py +++ b/test/base/test_files/test_files.py @@ -16,7 +16,8 @@ class testFiles(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_model/keras_example.py b/test/base/test_model/keras_example.py index f0ee24ad..c091de91 100644 --- a/test/base/test_model/keras_example.py +++ b/test/base/test_model/keras_example.py @@ -43,6 +43,8 @@ def __init__(self, optimizer, loss, metrics): def __call__(self): self.model.compile( - optimizer=self.optimizer, loss=self.loss, metrics=self.metrics, + optimizer=self.optimizer, + loss=self.loss, + metrics=self.metrics, ) return self.model diff --git a/test/base/test_model/test_art_pipeline.py b/test/base/test_model/test_art_pipeline.py index 6aae219b..d4d2149d 100644 --- a/test/base/test_model/test_art_pipeline.py +++ b/test/base/test_model/test_art_pipeline.py @@ -13,13 +13,13 @@ class testArtPipeline(unittest.TestCase): - config_dir = Path(this_dir, "../../conf/model").resolve().as_posix() config_file = "classification.yaml" def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_model/test_model.py b/test/base/test_model/test_model.py index 20fa00ff..f5d642cc 100644 --- a/test/base/test_model/test_model.py +++ b/test/base/test_model/test_model.py @@ -22,7 +22,8 @@ class testModel(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_model/test_sklearn_model_pipeline.py b/test/base/test_model/test_sklearn_model_pipeline.py index b4efafca..c87fa6f0 100644 --- a/test/base/test_model/test_sklearn_model_pipeline.py +++ b/test/base/test_model/test_sklearn_model_pipeline.py @@ -13,13 +13,13 @@ class testSklearnModelPipeline(unittest.TestCase): - config_dir = Path(this_dir, "../../conf/model").resolve().as_posix() config_file = "pipeline.yaml" def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_model/tfv2_example.py b/test/base/test_model/tfv2_example.py index d5d2164e..52303cff 100644 --- a/test/base/test_model/tfv2_example.py +++ b/test/base/test_model/tfv2_example.py @@ -24,7 +24,10 @@ def __init__(self, optimizer: dict, loss_object: dict, output_shape=10): self.conv1 = Conv2D(filters=4, kernel_size=5, activation="relu") self.conv2 = Conv2D(filters=10, kernel_size=5, activation="relu") self.maxpool = MaxPool2D( - pool_size=(2, 2), strides=(2, 2), padding="valid", data_format=None, + pool_size=(2, 2), + strides=(2, 2), + padding="valid", + data_format=None, ) self.flatten = Flatten() self.dense1 = Dense(100, activation="relu") diff --git a/test/base/test_model/torch_example.py b/test/base/test_model/torch_example.py index 33cfe035..e7479cb8 100644 --- a/test/base/test_model/torch_example.py +++ b/test/base/test_model/torch_example.py @@ -214,7 +214,12 @@ def __init__( # CIFAR10: kernel_size 7 -> 3, stride 2 -> 1, padding 3->1 self.conv1 = nn.Conv2d( - 3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False, + 3, + self.inplanes, + kernel_size=3, + stride=1, + padding=1, + bias=False, ) # END @@ -223,13 +228,25 @@ def __init__( self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer( - block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0], + block, + 128, + layers[1], + stride=2, + dilate=replace_stride_with_dilation[0], ) self.layer3 = self._make_layer( - block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1], + block, + 256, + layers[2], + stride=2, + dilate=replace_stride_with_dilation[1], ) self.layer4 = self._make_layer( - block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2], + block, + 512, + layers[3], + stride=2, + dilate=replace_stride_with_dilation[2], ) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) diff --git a/test/base/test_scorer/test_scorer.py b/test/base/test_scorer/test_scorer.py index 0016f405..fa246bcc 100644 --- a/test/base/test_scorer/test_scorer.py +++ b/test/base/test_scorer/test_scorer.py @@ -21,14 +21,16 @@ class testScorerDict(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg self.scorers = instantiate(config=self.cfg) self.directory = mkdtemp() self.file = Path( - self.directory, self.score_dict_file + self.score_dict_type, + self.directory, + self.score_dict_file + self.score_dict_type, ).as_posix() def test_init(self): @@ -166,7 +168,8 @@ class testComputeAccuracy(testScorerConfig): model_config_dir = Path(this_dir, "../../conf/model").resolve().as_posix() model_config_file = "classification.yaml" with initialize_config_dir( - config_dir=Path(model_config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(model_config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=model_config_file) model_cfg = cfg From 2da52ddfebf23e40a571b252d4dcda016d33c7ec Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Fri, 1 Sep 2023 10:32:00 +0000 Subject: [PATCH 015/148] moved iaac folder --- deckard/iaac/gcp/README.md | 127 +++++++++++++++++++++++++++++++++++ deckard/iaac/gcp/pod.yaml | 20 ++++++ deckard/iaac/gcp/pvc.yaml | 11 +++ deckard/iaac/gcp/sclass.yaml | 10 +++ 4 files changed, 168 insertions(+) create mode 100644 deckard/iaac/gcp/README.md create mode 100644 deckard/iaac/gcp/pod.yaml create mode 100644 deckard/iaac/gcp/pvc.yaml create mode 100644 deckard/iaac/gcp/sclass.yaml diff --git a/deckard/iaac/gcp/README.md b/deckard/iaac/gcp/README.md new file mode 100644 index 00000000..44cd7183 --- /dev/null +++ b/deckard/iaac/gcp/README.md @@ -0,0 +1,127 @@ +# Run the experiments in GCP + +The following process requires to have enough qouta in GCP for CPUs and GPUs and SSDs. The whole procedure should take around 30 minutes to complete. + +## Setup the GKE + GPU + +0- In order to setup the GKE (Google Kubernetes Engine), we need to enable require apis. Follow [these intsructions to enable them](https://cloud.google.com/endpoints/docs/openapi/enable-api). + + +1-We then create the cluster called `k8s-cluster`. This cluster will be installed in `europe-west4` in GCP regions. +``` +gcloud container clusters create k8s-cluster \ + --region europe-west4 --num-nodes 1 --no-enable-autoupgrade +``` + +2- In order to manage the Kubernetes cluster we need to [install `kubectl`](https://kubernetes.io/docs/tasks/tools/#kubectl). + +3- Then run the following command to get the credentials: +``` +gcloud container clusters get-credentials k8s-cluster \ + --region europe-west4 +``` + +After this step you should now be able to access kubernetes cluster, give it a try by simply running the following: +``` +$ kubectl get nodes +``` +The results should be similar to: +``` +NAME STATUS ROLES AGE VERSION +gke-k8s-cluster-default-pool-1fa99288-r4cz Ready 5m v1.27.3-gke.100 +gke-k8s-cluster-default-pool-3a09572b-w13l Ready 5m v1.27.3-gke.100 +gke-k8s-cluster-default-pool-feae82f2-zfsj Ready 5m v1.27.3-gke.100 +``` + +4- Now we want to create a node pool called `k8s-node-pool` with `nvidia-tesla-v100` gpus. Run the following. +``` +gcloud container node-pools create k8s-node-pool \ + --accelerator type=nvidia-tesla-v100,count=1,gpu-driver-version=default \ + --region europe-west4 --cluster k8s-cluster \ + --machine-type n1-standard-2 \ + --num-nodes 1 \ + --min-nodes 1 \ + --max-nodes 1 +``` + +After succesfully running the above command, you can verify the added GPU nodes by running `kubectl get nodes` and it should be something like: +``` +NAME STATUS ROLES AGE VERSION +gke-k8s-cluster-default-pool-1fa99288-r4cz Ready 10m v1.27.3-gke.100 +gke-k8s-cluster-default-pool-3a09572b-w13l Ready 10m v1.27.3-gke.100 +gke-k8s-cluster-default-pool-feae82f2-zfsj Ready 10m v1.27.3-gke.100 +gke-k8s-cluster-k8s-node-pool-7ba6832e-n8ld Ready 3m v1.27.3-gke.100 +gke-k8s-cluster-k8s-node-pool-e7fca6ba-wr5l Ready 3m v1.27.3-gke.100 +gke-k8s-cluster-k8s-node-pool-f075e2ff-zvj6 Ready 3m v1.27.3-gke.100 +``` + + +## Preparing the storage +1- To be able to have a shared storage for our containers/pods, we need to define `storage class` by simply running +``` +kubectl create -f ./IaaC/gcp/sclass.yaml +``` + +2- After that we need to create `persistent volume claim` to enable the pod to have access to the volume by simply ruuning: +``` +kubectl create -f ./IaaC/gcp/pvc.yaml +``` + + +## Deploying the GPU and Storage enabled pod +The pod should include the following resources to enable access to GPU: +``` +... + resources: + limits: + nvidia.com/gpu: 1 +... +``` + +And it also should have the following volumes to enable the shared storage: +``` +... + volumeMounts: + - mountPath: /data + name: mypvc + volumes: + - name: mypvc + persistentVolumeClaim: + claimName: podpvc +... +``` + +You can simply take a look at `pod.yaml` file for defining a pod. Just to check, deploy the sample pod by running: +``` +kubectl apply -f ./IaaC/gcp/pod.yaml +``` + +## Prepare the access values in the shared volume (optional): +First of all we need to create a vm by running: +``` +gcloud compute instances create filestore \ + --async \ + --metadata=ssh-keys="username:publickey" \ + --zone=europe-west4-a \ + --image-family=ubuntu-2204-lts \ + --image-project=ubuntu-os-cloud \ + --machine-type=n1-standard-2 \ + --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring \ + --subnet=default \ + --quiet +``` + +In the above command, you simply replace you public key and your username to have passwordless SSH to the VM. + +Before sshing to the created instance, simply get the [`NFS mount point` from the GCP console](https://console.cloud.google.com/filestore/instances). The `mount point` is "`Filestore instance IP address`:`Filestore instance File share name`", for instance: `10.178.118.130:/vol1`. + +After it has been created, run the `gcloud compute instances list` command to retrieve the external ip of the created instance `filestore`. Then ssh to the machine and mount the volume by running the followings: + +``` +sudo apt update +sudo apt install nfs-common +mkdir mount-directory +sudo mount -o rw,intr 10.178.118.130:/vol1 mount-directory +``` + +**Don't Forget to update the address in the mount command.** diff --git a/deckard/iaac/gcp/pod.yaml b/deckard/iaac/gcp/pod.yaml new file mode 100644 index 00000000..ee54a985 --- /dev/null +++ b/deckard/iaac/gcp/pod.yaml @@ -0,0 +1,20 @@ +apiVersion: v1 +kind: Pod +metadata: + name: my-gpu-pod +spec: + containers: + - name: my-gpu-container + image: nvidia/cuda:11.0.3-runtime-ubuntu20.04 + command: ["/bin/bash", "-c", "--"] + args: ["while true; do sleep 600; done;"] + resources: + limits: + nvidia.com/gpu: 1 + volumeMounts: + - mountPath: /data + name: mypvc + volumes: + - name: mypvc + persistentVolumeClaim: + claimName: podpvc diff --git a/deckard/iaac/gcp/pvc.yaml b/deckard/iaac/gcp/pvc.yaml new file mode 100644 index 00000000..a7deee4d --- /dev/null +++ b/deckard/iaac/gcp/pvc.yaml @@ -0,0 +1,11 @@ +kind: PersistentVolumeClaim +apiVersion: v1 +metadata: + name: podpvc +spec: + accessModes: + - ReadWriteMany + storageClassName: filestore-sc + resources: + requests: + storage: 256Gi diff --git a/deckard/iaac/gcp/sclass.yaml b/deckard/iaac/gcp/sclass.yaml new file mode 100644 index 00000000..d566656c --- /dev/null +++ b/deckard/iaac/gcp/sclass.yaml @@ -0,0 +1,10 @@ +apiVersion: storage.k8s.io/v1 +kind: StorageClass +metadata: + name: filestore-sc +provisioner: filestore.csi.storage.gke.io +volumeBindingMode: Immediate +allowVolumeExpansion: true +parameters: + tier: standard + network: default From 1fb7d5ea9f9953225422879cac09cefc4877edf2 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Fri, 1 Sep 2023 11:28:50 +0000 Subject: [PATCH 016/148] draft dataclass and deploy script for gcp --- IaaC/gcp/README.md | 127 ----------------------- IaaC/gcp/pod.yaml | 20 ---- IaaC/gcp/pvc.yaml | 11 -- IaaC/gcp/sclass.yaml | 10 -- deckard/iaac/gcp/conf/default.yaml | 14 +++ deckard/iaac/gcp/deploy.py | 159 +++++++++++++++++++++++++++++ 6 files changed, 173 insertions(+), 168 deletions(-) delete mode 100644 IaaC/gcp/README.md delete mode 100644 IaaC/gcp/pod.yaml delete mode 100644 IaaC/gcp/pvc.yaml delete mode 100644 IaaC/gcp/sclass.yaml create mode 100644 deckard/iaac/gcp/conf/default.yaml create mode 100644 deckard/iaac/gcp/deploy.py diff --git a/IaaC/gcp/README.md b/IaaC/gcp/README.md deleted file mode 100644 index 44cd7183..00000000 --- a/IaaC/gcp/README.md +++ /dev/null @@ -1,127 +0,0 @@ -# Run the experiments in GCP - -The following process requires to have enough qouta in GCP for CPUs and GPUs and SSDs. The whole procedure should take around 30 minutes to complete. - -## Setup the GKE + GPU - -0- In order to setup the GKE (Google Kubernetes Engine), we need to enable require apis. Follow [these intsructions to enable them](https://cloud.google.com/endpoints/docs/openapi/enable-api). - - -1-We then create the cluster called `k8s-cluster`. This cluster will be installed in `europe-west4` in GCP regions. -``` -gcloud container clusters create k8s-cluster \ - --region europe-west4 --num-nodes 1 --no-enable-autoupgrade -``` - -2- In order to manage the Kubernetes cluster we need to [install `kubectl`](https://kubernetes.io/docs/tasks/tools/#kubectl). - -3- Then run the following command to get the credentials: -``` -gcloud container clusters get-credentials k8s-cluster \ - --region europe-west4 -``` - -After this step you should now be able to access kubernetes cluster, give it a try by simply running the following: -``` -$ kubectl get nodes -``` -The results should be similar to: -``` -NAME STATUS ROLES AGE VERSION -gke-k8s-cluster-default-pool-1fa99288-r4cz Ready 5m v1.27.3-gke.100 -gke-k8s-cluster-default-pool-3a09572b-w13l Ready 5m v1.27.3-gke.100 -gke-k8s-cluster-default-pool-feae82f2-zfsj Ready 5m v1.27.3-gke.100 -``` - -4- Now we want to create a node pool called `k8s-node-pool` with `nvidia-tesla-v100` gpus. Run the following. -``` -gcloud container node-pools create k8s-node-pool \ - --accelerator type=nvidia-tesla-v100,count=1,gpu-driver-version=default \ - --region europe-west4 --cluster k8s-cluster \ - --machine-type n1-standard-2 \ - --num-nodes 1 \ - --min-nodes 1 \ - --max-nodes 1 -``` - -After succesfully running the above command, you can verify the added GPU nodes by running `kubectl get nodes` and it should be something like: -``` -NAME STATUS ROLES AGE VERSION -gke-k8s-cluster-default-pool-1fa99288-r4cz Ready 10m v1.27.3-gke.100 -gke-k8s-cluster-default-pool-3a09572b-w13l Ready 10m v1.27.3-gke.100 -gke-k8s-cluster-default-pool-feae82f2-zfsj Ready 10m v1.27.3-gke.100 -gke-k8s-cluster-k8s-node-pool-7ba6832e-n8ld Ready 3m v1.27.3-gke.100 -gke-k8s-cluster-k8s-node-pool-e7fca6ba-wr5l Ready 3m v1.27.3-gke.100 -gke-k8s-cluster-k8s-node-pool-f075e2ff-zvj6 Ready 3m v1.27.3-gke.100 -``` - - -## Preparing the storage -1- To be able to have a shared storage for our containers/pods, we need to define `storage class` by simply running -``` -kubectl create -f ./IaaC/gcp/sclass.yaml -``` - -2- After that we need to create `persistent volume claim` to enable the pod to have access to the volume by simply ruuning: -``` -kubectl create -f ./IaaC/gcp/pvc.yaml -``` - - -## Deploying the GPU and Storage enabled pod -The pod should include the following resources to enable access to GPU: -``` -... - resources: - limits: - nvidia.com/gpu: 1 -... -``` - -And it also should have the following volumes to enable the shared storage: -``` -... - volumeMounts: - - mountPath: /data - name: mypvc - volumes: - - name: mypvc - persistentVolumeClaim: - claimName: podpvc -... -``` - -You can simply take a look at `pod.yaml` file for defining a pod. Just to check, deploy the sample pod by running: -``` -kubectl apply -f ./IaaC/gcp/pod.yaml -``` - -## Prepare the access values in the shared volume (optional): -First of all we need to create a vm by running: -``` -gcloud compute instances create filestore \ - --async \ - --metadata=ssh-keys="username:publickey" \ - --zone=europe-west4-a \ - --image-family=ubuntu-2204-lts \ - --image-project=ubuntu-os-cloud \ - --machine-type=n1-standard-2 \ - --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring \ - --subnet=default \ - --quiet -``` - -In the above command, you simply replace you public key and your username to have passwordless SSH to the VM. - -Before sshing to the created instance, simply get the [`NFS mount point` from the GCP console](https://console.cloud.google.com/filestore/instances). The `mount point` is "`Filestore instance IP address`:`Filestore instance File share name`", for instance: `10.178.118.130:/vol1`. - -After it has been created, run the `gcloud compute instances list` command to retrieve the external ip of the created instance `filestore`. Then ssh to the machine and mount the volume by running the followings: - -``` -sudo apt update -sudo apt install nfs-common -mkdir mount-directory -sudo mount -o rw,intr 10.178.118.130:/vol1 mount-directory -``` - -**Don't Forget to update the address in the mount command.** diff --git a/IaaC/gcp/pod.yaml b/IaaC/gcp/pod.yaml deleted file mode 100644 index ee54a985..00000000 --- a/IaaC/gcp/pod.yaml +++ /dev/null @@ -1,20 +0,0 @@ -apiVersion: v1 -kind: Pod -metadata: - name: my-gpu-pod -spec: - containers: - - name: my-gpu-container - image: nvidia/cuda:11.0.3-runtime-ubuntu20.04 - command: ["/bin/bash", "-c", "--"] - args: ["while true; do sleep 600; done;"] - resources: - limits: - nvidia.com/gpu: 1 - volumeMounts: - - mountPath: /data - name: mypvc - volumes: - - name: mypvc - persistentVolumeClaim: - claimName: podpvc diff --git a/IaaC/gcp/pvc.yaml b/IaaC/gcp/pvc.yaml deleted file mode 100644 index a7deee4d..00000000 --- a/IaaC/gcp/pvc.yaml +++ /dev/null @@ -1,11 +0,0 @@ -kind: PersistentVolumeClaim -apiVersion: v1 -metadata: - name: podpvc -spec: - accessModes: - - ReadWriteMany - storageClassName: filestore-sc - resources: - requests: - storage: 256Gi diff --git a/IaaC/gcp/sclass.yaml b/IaaC/gcp/sclass.yaml deleted file mode 100644 index d566656c..00000000 --- a/IaaC/gcp/sclass.yaml +++ /dev/null @@ -1,10 +0,0 @@ -apiVersion: storage.k8s.io/v1 -kind: StorageClass -metadata: - name: filestore-sc -provisioner: filestore.csi.storage.gke.io -volumeBindingMode: Immediate -allowVolumeExpansion: true -parameters: - tier: standard - network: default diff --git a/deckard/iaac/gcp/conf/default.yaml b/deckard/iaac/gcp/conf/default.yaml new file mode 100644 index 00000000..32b602d6 --- /dev/null +++ b/deckard/iaac/gcp/conf/default.yaml @@ -0,0 +1,14 @@ +num_nodes: 1 +cluster_name: k8s-cluster +gpu_type: nvidia-tesla-v100 +gpu_count: 1 +gpu_driver_version: default +machine_type: n1-standard-2 +min_nodes: 1 +max_nodes: 1 +storage_config: ./IaaC/gcp/sclass.yaml +persistent_volume_claim: ./IaaC/gcp/pvc.yaml +pod : ./IaaC/gcp/pod.yaml +image_project: ubuntu-os-cloud +image_family: ubuntu-2204-lts +mount_directory: /mnt/filestore \ No newline at end of file diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py new file mode 100644 index 00000000..327d6d75 --- /dev/null +++ b/deckard/iaac/gcp/deploy.py @@ -0,0 +1,159 @@ +import os +import subprocess +import logging +from hydra.utils import instantiate +import argparse +from dataclasses import dataclass +from pathlib import Path +import yaml + + +logger = logging.getLogger(__name__) + +# Login to GCP +try: + command = 'gcloud auth login' + logger.info(f"Running command: {command}") + output = subprocess.run(command) + logger.info(f"{output}") +except: + raise ImportError('Error logging in to GCP. Please check your credentials and ensure that gcloud cli is installed.') +# Get username and password from ENV +username = os.environ.get('GCP_USERNAME') +password = os.environ.get('GCP_PASSWORD') + +@dataclass +class GCP_Config: + num_nodes: int = 1 + cluster_name: str = 'k8s-cluster' + gpu_type: str = 'nvidia-tesla-v100' + gpu_count: int = 1 + gpu_driver_version: str = 'default' + machine_type: str = 'n1-standard-2' + min_nodes: int = 1 + max_nodes: int = 1 + storage_config: str = './IaaC/gcp/sclass.yaml' + persistent_volume_claim: str = './IaaC/gcp/pvc.yaml' + pod = './IaaC/gcp/pod.yaml' + image_project: str = 'ubuntu-os-cloud' + image_family: str = 'ubuntu-2204-lts' + mount_directory: str = '/mnt/filestore' + + def create_cluster(self): + # Create a cluster + logger.info(f'Creating cluster {self.cluster_name} in region {self.region} with {self.num_nodes} nodes') + command = f'gcloud container clusters create {self.cluster_name} --region {self.region} --num-nodes {self.num_nodes} --no-enable-autoupgrade' + logger.info(f"Running command: {command}") + output = subprocess.run(command) + logger.info(f"{output}") + return output + + def install_kubectl(self): + logger.info(f'Installing kubectl') + command = 'gcloud components install kubectl' + logger.info(f"Running command: {command}") + output = subprocess.run(command) + logger.info(f"{output}") + return output + + def retrieve_credentials(self): + logger.info(f'Retrieving credentials for cluster {self.cluster_name} in region {self.region}') + command = 'gcloud container clusters get-credentials {self.cluster_name} --region {self.region}' + output = subprocess.run(command) + logger.info(f"{output}") + return output + + def create_node_pool(self): + logger.info(f'Creating node pool {self.cluster_name} in region {self.region}') + command = f'gcloud container node-pools create {self.cluster_name} --accelerator type={self.gpu_type},count={self.gpu_count},gpu-driver-version={self.gpu_driver_version} --region {self.region} --cluster {self.cluster_name} --machine-type {self.machine_type} --num-nodes {self.num_nodes} --min-nodes {self.min_nodes} --max-nodes {self.max_nodes}' + logger.info(f"Running command: {command}") + output = subprocess.run(command) + logger.info(f"{output}") + return output + + def create_deployment(self): + logger.info(f'Creating deployment {self.cluster_name} in region {self.region}') + command = f'kubectl create -f {self.storage_config}' + logger.info(f"Running command: {command}") + output = subprocess.run(command) + logger.info(f"{output}") + return output + + def create_ersistent_volume_claim(self): + logger.info(f'Creating persistent volume claim {self.cluster_name} in region {self.region}') + command = f'kubectl create -f {self.persistent_volume_claim}' + logger.info(f"Running command: {command}") + output = subprocess.run(command) + logger.info(f"{output}") + return output + + def deploy_pod(self): + logger.info(f'Creating sample pod {self.cluster_name} in region {self.region}') + command = f'kubectl create -f {self.pod}' + logger.info(f"Running command: {command}") + output = subprocess.run(command) + logger.info(f"{output}") + return output + + def prepare_access_values(self): + logger.info(f'Preparing access values in the shared volumee {self.cluster_name} in region {self.region}') + command = f'gcloud compute instances create filestore --async --metadata=ssh-keys="{username}:{password}" --zone={self.region}-a --image-family={self.image_family} --image-project={self.image_project} --machine-type={self.machine_type} --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring --subnet=default --quiet' + logger.info(f"Running command: {command}") + output = subprocess.run(command) + logger.info(f"{output}") + return output + + def find_ip_of_filestore(self): + logger.info(f'Finding the IP address of the filestore {self.cluster_name} in region {self.region}') + command = f'gcloud compute instances list --filter="name=filestore" --format="value(networkInterfaces[0].accessConfigs[0].natIP)" --zone={self.region}' + logger.info(f"Running command: {command}") + ip_output = subprocess.run(command) + logger.info(f"{ip_output}") + return ip_output + + def mount_filestore(self, ip): + logger.info(f'Mounting the filestore {self.cluster_name} in region {self.region}') + command = 'sudo apt update' + logger.info(f"Running command: {command}") + output = subprocess.run(command) + logger.info(f"{output}") + command = 'sudo apt install nfs-common' + logger.info(f"Running command: {command}") + output = subprocess.run(command) + logger.info(f"{output}") + command = f'mkdir {self.mount_directory}' + logger.info(f"Running command: {command}") + output = subprocess.run(command) + logger.info(f"{output}") + command = f'sudo mount -o rw,intr {ip}:/vol1 {self.mount_directory}' + logger.info(f"Running command: {command}") + output = subprocess.run(command) + logger.info(f"{output}") + return output + + def __call__(self): + self.create_cluster() + self.install_kubectl() + self.retrieve_credentials() + self.create_node_pool() + self.create_deployment() + self.create_ersistent_volume_claim() + self.deploy_pod() + self.prepare_access_values() + ip_addr = self.find_ip_of_filestore() + self.mount_filestore(ip_addr) + + +if __name__ == "__main__": + dvc_parser = argparse.ArgumentParser() + dvc_parser.add_argument("--verbosity", type=str, default="INFO") + dvc_parser.add_argument("--config_dir", type=str, default="conf") + dvc_parser.add_argument("--config_file", type=str, default="default") + dvc_parser.add_argument("--workdir", type=str, default=".") + args = dvc_parser.parse_args() + config_dir = Path(Path(), args.config_dir).resolve().as_posix() + config_file = Path(config_dir, args.config_file).resolve().as_posix() + with open(config_file, "r") as f: + params = yaml.load(f, Loader=yaml.FullLoader) + gcp = instantiate(params) + gcp() \ No newline at end of file From f604dbcf41b35baf035f92ee929d40b06ac002d2 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Fri, 1 Sep 2023 11:48:31 +0000 Subject: [PATCH 017/148] added package stuff --- deckard/iaac/__init__.py | 2 ++ deckard/iaac/gcp/__init__.py | 1 + deckard/iaac/gcp/deploy.py | 35 ++++++++++++++++++++--------------- 3 files changed, 23 insertions(+), 15 deletions(-) create mode 100644 deckard/iaac/__init__.py create mode 100644 deckard/iaac/gcp/__init__.py diff --git a/deckard/iaac/__init__.py b/deckard/iaac/__init__.py new file mode 100644 index 00000000..597ae64f --- /dev/null +++ b/deckard/iaac/__init__.py @@ -0,0 +1,2 @@ +from .iaac import * + diff --git a/deckard/iaac/gcp/__init__.py b/deckard/iaac/gcp/__init__.py new file mode 100644 index 00000000..e93a509a --- /dev/null +++ b/deckard/iaac/gcp/__init__.py @@ -0,0 +1 @@ +from .deploy import GCP_Config diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index 327d6d75..1c6fe6fa 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -32,12 +32,14 @@ class GCP_Config: machine_type: str = 'n1-standard-2' min_nodes: int = 1 max_nodes: int = 1 - storage_config: str = './IaaC/gcp/sclass.yaml' - persistent_volume_claim: str = './IaaC/gcp/pvc.yaml' - pod = './IaaC/gcp/pod.yaml' + conf_dir: str = './conf/gcp/' + storage_config: str = 'sclass.yaml' + persistent_volume_claim: str = 'pvc.yaml' + pod = 'pod.yaml' image_project: str = 'ubuntu-os-cloud' image_family: str = 'ubuntu-2204-lts' mount_directory: str = '/mnt/filestore' + _target_ : str = 'deckard.conf.GCP_Config' def create_cluster(self): # Create a cluster @@ -73,15 +75,17 @@ def create_node_pool(self): def create_deployment(self): logger.info(f'Creating deployment {self.cluster_name} in region {self.region}') - command = f'kubectl create -f {self.storage_config}' + file_name = Path(self.conf_dir, self.storage_config).resolve().as_posix() + command = f'kubectl create -f {file_name}' logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") return output - def create_ersistent_volume_claim(self): + def create_persistent_volume_claim(self): logger.info(f'Creating persistent volume claim {self.cluster_name} in region {self.region}') - command = f'kubectl create -f {self.persistent_volume_claim}' + file_name = Path(self.conf_dir, self.persistent_volume_claim).resolve().as_posix() + command = f'kubectl create -f {file_name}' logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") @@ -89,7 +93,8 @@ def create_ersistent_volume_claim(self): def deploy_pod(self): logger.info(f'Creating sample pod {self.cluster_name} in region {self.region}') - command = f'kubectl create -f {self.pod}' + file_name = Path(self.conf_dir, self.pod).resolve().as_posix() + command = f'kubectl create -f {file_name}' logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") @@ -137,7 +142,7 @@ def __call__(self): self.retrieve_credentials() self.create_node_pool() self.create_deployment() - self.create_ersistent_volume_claim() + self.create_persistent_volume_claim() self.deploy_pod() self.prepare_access_values() ip_addr = self.find_ip_of_filestore() @@ -145,13 +150,13 @@ def __call__(self): if __name__ == "__main__": - dvc_parser = argparse.ArgumentParser() - dvc_parser.add_argument("--verbosity", type=str, default="INFO") - dvc_parser.add_argument("--config_dir", type=str, default="conf") - dvc_parser.add_argument("--config_file", type=str, default="default") - dvc_parser.add_argument("--workdir", type=str, default=".") - args = dvc_parser.parse_args() - config_dir = Path(Path(), args.config_dir).resolve().as_posix() + gcp_parser = argparse.ArgumentParser() + gcp_parser.add_argument("--verbosity", type=str, default="INFO") + gcp_parser.add_argument("--config_dir", type=str, default="conf") + gcp_parser.add_argument("--config_file", type=str, default="default") + gcp_parser.add_argument("--workdir", type=str, default=".") + args = gcp_parser.parse_args() + config_dir = Path(args.workdir, args.config_dir).resolve().as_posix() config_file = Path(config_dir, args.config_file).resolve().as_posix() with open(config_file, "r") as f: params = yaml.load(f, Loader=yaml.FullLoader) From 3792bda62b1f3ae2eee032d2c423a35d1d71ac98 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Fri, 1 Sep 2023 11:55:21 +0000 Subject: [PATCH 018/148] linting --- .gitattributes | 1 + .pre-commit-config.yaml | 1 - deckard/iaac/__init__.py | 1 - deckard/iaac/gcp/conf/default.yaml | 2 +- deckard/iaac/gcp/deploy.py | 111 +++++++++++++++++------------ 5 files changed, 66 insertions(+), 50 deletions(-) diff --git a/.gitattributes b/.gitattributes index dfe07704..a5b80962 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,2 +1,3 @@ # Auto detect text files and perform LF normalization * text=auto +**/*.md text whitespace=-cr-at-eol,-trailing-space diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 515df507..8a7b2da4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -6,7 +6,6 @@ repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.3.0 # Use the ref you want to point at hooks: - - id: trailing-whitespace - id: check-builtin-literals - id: check-case-conflict - id: check-symlinks diff --git a/deckard/iaac/__init__.py b/deckard/iaac/__init__.py index 597ae64f..dfe88c03 100644 --- a/deckard/iaac/__init__.py +++ b/deckard/iaac/__init__.py @@ -1,2 +1 @@ from .iaac import * - diff --git a/deckard/iaac/gcp/conf/default.yaml b/deckard/iaac/gcp/conf/default.yaml index 32b602d6..d8e6ecfa 100644 --- a/deckard/iaac/gcp/conf/default.yaml +++ b/deckard/iaac/gcp/conf/default.yaml @@ -11,4 +11,4 @@ persistent_volume_claim: ./IaaC/gcp/pvc.yaml pod : ./IaaC/gcp/pod.yaml image_project: ubuntu-os-cloud image_family: ubuntu-2204-lts -mount_directory: /mnt/filestore \ No newline at end of file +mount_directory: /mnt/filestore diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index 1c6fe6fa..59bf5895 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -12,104 +12,119 @@ # Login to GCP try: - command = 'gcloud auth login' + command = "gcloud auth login" logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") -except: - raise ImportError('Error logging in to GCP. Please check your credentials and ensure that gcloud cli is installed.') +except: # noqa: E722 + raise ImportError( + "Error logging in to GCP. Please check your credentials and ensure that gcloud cli is installed.", + ) # Get username and password from ENV -username = os.environ.get('GCP_USERNAME') -password = os.environ.get('GCP_PASSWORD') +username = os.environ.get("GCP_USERNAME") +password = os.environ.get("GCP_PASSWORD") + @dataclass class GCP_Config: num_nodes: int = 1 - cluster_name: str = 'k8s-cluster' - gpu_type: str = 'nvidia-tesla-v100' + cluster_name: str = "k8s-cluster" + gpu_type: str = "nvidia-tesla-v100" gpu_count: int = 1 - gpu_driver_version: str = 'default' - machine_type: str = 'n1-standard-2' + gpu_driver_version: str = "default" + machine_type: str = "n1-standard-2" min_nodes: int = 1 max_nodes: int = 1 - conf_dir: str = './conf/gcp/' - storage_config: str = 'sclass.yaml' - persistent_volume_claim: str = 'pvc.yaml' - pod = 'pod.yaml' - image_project: str = 'ubuntu-os-cloud' - image_family: str = 'ubuntu-2204-lts' - mount_directory: str = '/mnt/filestore' - _target_ : str = 'deckard.conf.GCP_Config' - + conf_dir: str = "./conf/gcp/" + storage_config: str = "sclass.yaml" + persistent_volume_claim: str = "pvc.yaml" + pod = "pod.yaml" + image_project: str = "ubuntu-os-cloud" + image_family: str = "ubuntu-2204-lts" + mount_directory: str = "/mnt/filestore" + _target_: str = "deckard.conf.GCP_Config" + def create_cluster(self): # Create a cluster - logger.info(f'Creating cluster {self.cluster_name} in region {self.region} with {self.num_nodes} nodes') - command = f'gcloud container clusters create {self.cluster_name} --region {self.region} --num-nodes {self.num_nodes} --no-enable-autoupgrade' + logger.info( + f"Creating cluster {self.cluster_name} in region {self.region} with {self.num_nodes} nodes", + ) + command = f"gcloud container clusters create {self.cluster_name} --region {self.region} --num-nodes {self.num_nodes} --no-enable-autoupgrade" logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") return output - + def install_kubectl(self): - logger.info(f'Installing kubectl') - command = 'gcloud components install kubectl' + logger.info("Installing kubectl on the local machine") + command = "gcloud components install kubectl" logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") return output - + def retrieve_credentials(self): - logger.info(f'Retrieving credentials for cluster {self.cluster_name} in region {self.region}') - command = 'gcloud container clusters get-credentials {self.cluster_name} --region {self.region}' + logger.info( + f"Retrieving credentials for cluster {self.cluster_name} in region {self.region}", + ) + command = "gcloud container clusters get-credentials {self.cluster_name} --region {self.region}" output = subprocess.run(command) logger.info(f"{output}") return output - + def create_node_pool(self): - logger.info(f'Creating node pool {self.cluster_name} in region {self.region}') - command = f'gcloud container node-pools create {self.cluster_name} --accelerator type={self.gpu_type},count={self.gpu_count},gpu-driver-version={self.gpu_driver_version} --region {self.region} --cluster {self.cluster_name} --machine-type {self.machine_type} --num-nodes {self.num_nodes} --min-nodes {self.min_nodes} --max-nodes {self.max_nodes}' + logger.info(f"Creating node pool {self.cluster_name} in region {self.region}") + command = f"gcloud container node-pools create {self.cluster_name} --accelerator type={self.gpu_type},count={self.gpu_count},gpu-driver-version={self.gpu_driver_version} --region {self.region} --cluster {self.cluster_name} --machine-type {self.machine_type} --num-nodes {self.num_nodes} --min-nodes {self.min_nodes} --max-nodes {self.max_nodes}" logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") return output - + def create_deployment(self): - logger.info(f'Creating deployment {self.cluster_name} in region {self.region}') + logger.info(f"Creating deployment {self.cluster_name} in region {self.region}") file_name = Path(self.conf_dir, self.storage_config).resolve().as_posix() - command = f'kubectl create -f {file_name}' + command = f"kubectl create -f {file_name}" logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") return output def create_persistent_volume_claim(self): - logger.info(f'Creating persistent volume claim {self.cluster_name} in region {self.region}') - file_name = Path(self.conf_dir, self.persistent_volume_claim).resolve().as_posix() - command = f'kubectl create -f {file_name}' + logger.info( + f"Creating persistent volume claim {self.cluster_name} in region {self.region}", + ) + file_name = ( + Path(self.conf_dir, self.persistent_volume_claim).resolve().as_posix() + ) + command = f"kubectl create -f {file_name}" logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") return output def deploy_pod(self): - logger.info(f'Creating sample pod {self.cluster_name} in region {self.region}') + logger.info(f"Creating sample pod {self.cluster_name} in region {self.region}") file_name = Path(self.conf_dir, self.pod).resolve().as_posix() - command = f'kubectl create -f {file_name}' + command = f"kubectl create -f {file_name}" logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") return output - + def prepare_access_values(self): - logger.info(f'Preparing access values in the shared volumee {self.cluster_name} in region {self.region}') + logger.info( + f"Preparing access values in the shared volumee {self.cluster_name} in region {self.region}", + ) command = f'gcloud compute instances create filestore --async --metadata=ssh-keys="{username}:{password}" --zone={self.region}-a --image-family={self.image_family} --image-project={self.image_project} --machine-type={self.machine_type} --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring --subnet=default --quiet' logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") return output - + def find_ip_of_filestore(self): - logger.info(f'Finding the IP address of the filestore {self.cluster_name} in region {self.region}') + logger.info( + f"Finding the IP address of the filestore {self.cluster_name} in region {self.region}", + ) command = f'gcloud compute instances list --filter="name=filestore" --format="value(networkInterfaces[0].accessConfigs[0].natIP)" --zone={self.region}' logger.info(f"Running command: {command}") ip_output = subprocess.run(command) @@ -117,20 +132,22 @@ def find_ip_of_filestore(self): return ip_output def mount_filestore(self, ip): - logger.info(f'Mounting the filestore {self.cluster_name} in region {self.region}') - command = 'sudo apt update' + logger.info( + f"Mounting the filestore {self.cluster_name} in region {self.region}", + ) + command = "sudo apt update" logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") - command = 'sudo apt install nfs-common' + command = "sudo apt install nfs-common" logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") - command = f'mkdir {self.mount_directory}' + command = f"mkdir {self.mount_directory}" logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") - command = f'sudo mount -o rw,intr {ip}:/vol1 {self.mount_directory}' + command = f"sudo mount -o rw,intr {ip}:/vol1 {self.mount_directory}" logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") @@ -161,4 +178,4 @@ def __call__(self): with open(config_file, "r") as f: params = yaml.load(f, Loader=yaml.FullLoader) gcp = instantiate(params) - gcp() \ No newline at end of file + gcp() From 503de612cd8b5933b9821f493fca01cecef3ca53 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Sun, 3 Sep 2023 12:48:30 +0000 Subject: [PATCH 019/148] improved compile.py, plots.py --- deckard/layers/compile.py | 92 +++++++++++++++++-- examples/pytorch/conf/compile.yaml | 30 ++++++ examples/pytorch/conf/plots.yaml | 142 +++++++++++++++++++++++++++++ examples/pytorch/dvc.yaml | 12 +-- examples/pytorch/models.sh | 3 +- examples/pytorch/plots.py | 115 +++++------------------ 6 files changed, 290 insertions(+), 104 deletions(-) create mode 100644 examples/pytorch/conf/compile.yaml create mode 100644 examples/pytorch/conf/plots.yaml diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index f6902a29..6f945cb6 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -5,6 +5,7 @@ from tqdm import tqdm import yaml import argparse +import numpy as np from .utils import deckard_nones as nones logger = logging.getLogger(__name__) @@ -109,7 +110,7 @@ def merge_defences(results: pd.DataFrame): else: defences.append(None) def_gens.append(None) - results["defence"] = defences + results["defence_name"] = defences results["def_gen"] = def_gens return results @@ -123,7 +124,7 @@ def merge_attacks(results: pd.DataFrame): attack = None attacks.append(attack) if attacks != [None] * len(attacks): - results["attack"] = attacks + results["attack_name"] = attacks results["atk_gen"] = [str(x).split(".")[-1] for x in attacks] return results @@ -136,13 +137,84 @@ def parse_results(folder, files=["score_dict.json", "params.yaml"]): df = merge_attacks(df) return df +def format_control_parameter(data, control_dict, min_max=True): + + new_data = pd.DataFrame() + logger.info("Formatting control parameters...") + for _, row in tqdm(data.iterrows()): + if hasattr(row, "defence_name"): + if row.defence_name in ["Control", None, "None", "none", "null", np.nan]: + row["def_param"] = np.nan + row["def_value"] = np.nan + else: + param = control_dict[row.defence_name] + row["def_param"] = param.split(".")[-1] + value = row[param] + row["def_value"] = value + if hasattr(row, "attack"): + if row.attack_name in ["Control", None, "None", "none", "null", np.nan]: + row["atk_param"] = np.nan + row["atk_value"] = np.nan + else: + param = control_dict[row.attack_name] + row["atk_param"] = param.split(".")[-1] + value = row[param] + row["atk_value"] = value + new_data = pd.concat([new_data, row], axis=1) + data = new_data.T + del new_data + + if min_max is True: + if hasattr(data, "def_gen"): + defs = data.def_gen.unique() + else: + defs = [] + if hasattr(data, "atk_gen"): + atks = data.atk_gen.unique() + else: + atks = [] + # Min-max scaling of control parameters + for def_ in defs: + max_ = data[data.def_gen == def_].def_value.max() + min_ = data[data.def_gen == def_].def_value.min() + scaled_value = (data[data.def_gen == def_].def_value - min_) / (max_ - min_) + data.loc[data.def_gen == def_, "def_value"] = scaled_value -def save_results(report_folder, results_file) -> str: + for atk in atks: + max_ = data[data.atk_gen == atk].atk_value.max() + min_ = data[data.atk_gen == atk].atk_value.min() + scaled_value = (data[data.atk_gen == atk].atk_value - min_) / (max_ - min_) + data.loc[data.atk_gen == atk, "atk_value"] = scaled_value + return data + +def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict, folder): + logger.info("Replacing attack and defence names with short names...") + if hasattr(data, "def_gen"): + data.def_gen.fillna("Control", inplace=True) + def_gen = data.def_gen.map(def_gen_dict) + data.def_gen = def_gen + if hasattr(data, "atk_gen"): + atk_gen = data.atk_gen.map(atk_gen_dict) + data.atk_gen = atk_gen + logger.info("Dropping poorly merged columns...") + data.dropna(axis=1, how="all", inplace=True) + logger.info("Shortening model names...") + data['model_name'] = data['model.init.name'].str.split('.').str[-1] + # %% + # Replace data.sample.random_state with random_state + logger.info("Replacing data.sample.random_state with random_state...") + data["data.sample.random_state"].rename("random_state", inplace=True) + data = format_control_parameter(data, control_dict, min_max=True) + logger.info(f"Saving data to {Path(folder) / 'data.csv'}") + data.to_csv(Path(folder) / "data.csv") + return data + +def save_results(results, results_file) -> str: """ Compile results from a folder of reports and save to a csv file; return the path to the csv file. It will optionally delete columns from the results. """ logger.info("Compiling results...") - results = parse_results(report_folder) + suffix = Path(results_file).suffix if suffix == ".csv": results.to_csv(results_file) @@ -164,7 +236,8 @@ def save_results(report_folder, results_file) -> str: if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--results_file", type=str, default="results.csv") - parser.add_argument("--report_folder", type=str, default="best_models") + parser.add_argument("--report_folder", type=str, default="reports", required=True) + parser.add_argument("--config", type=str, default="conf/compile.yaml") parser.add_argument("--exclude", type=list, default=None, nargs="*") parser.add_argument("--verbose", type=str, default="INFO") parser.add_argument( @@ -177,7 +250,14 @@ def save_results(report_folder, results_file) -> str: logging.basicConfig(level=args.verbose) report_folder = args.report_folder results_file = args.results_file - report_file = save_results(report_folder, results_file) + results = parse_results(report_folder) + with open(Path(Path(), args.config), "r") as f: + big_dict = yaml.load(f, Loader=yaml.FullLoader) + def_gen_dict = big_dict["defences"] + atk_gen_dict = big_dict["attacks"] + control_dict = big_dict["params"] + results = clean_data_for_plotting(results, def_gen_dict, atk_gen_dict, control_dict, report_folder) + report_file = save_results(results, results_file) assert Path( report_file, ).exists(), f"Results file {report_file} does not exist. Something went wrong." diff --git a/examples/pytorch/conf/compile.yaml b/examples/pytorch/conf/compile.yaml new file mode 100644 index 00000000..010bffc2 --- /dev/null +++ b/examples/pytorch/conf/compile.yaml @@ -0,0 +1,30 @@ +attacks: + CarliniL0Method: CW_0 + CarliniL2Method: CW_2 + CarliniLInfMethod: CW_inf + DeepFool: Deep + FastGradientMethod: FGM + HopSkipJump: HSJ + PixelAttack: Pixel + ProjectGradientDescent: PGD + ThresholdAttack: Thresh +defences: + Control: Control + FeatureSqueezing: FSQ + GaussianAugmentation: Gauss-out + GaussianNoise: Gauss-in + HighConfidence: Conf +params: + art.attacks.evasion.CarliniL0Method: attack.init.kwargs.confidence + art.attacks.evasion.CarliniL2Method: attack.init.kwargs.confidence + art.attacks.evasion.CarliniLInfMethod: attack.init.kwargs.confidence + art.attacks.evasion.DeepFool: attack.init.kwargs.nb_grads + art.attacks.evasion.FastGradientMethod: attack.init.kwargs.eps + art.attacks.evasion.HopSkipJump: attack.init.kwargs.max_iter + art.attacks.evasion.PixelAttack: attack.init.kwargs.th + art.attacks.evasion.ProjectedGradientDescent: attack.init.kwargs.eps + art.attacks.evasion.ThresholdAttack: attack.init.kwargs.th + art.defences.postprocessor.GaussianNoise: model.art.pipeline.postprocessor.kwargs.scale + art.defences.postprocessor.HighConfidence: model.art.pipeline.postprocessor.kwargs.cutoff + art.defences.preprocessor.FeatureSqueezing: model.art.pipeline.preprocessor.kwargs.bit_depth + art.defences.preprocessor.GaussianAugmentation: model.art.pipeline.preprocessor.kwargs.sigma diff --git a/examples/pytorch/conf/plots.yaml b/examples/pytorch/conf/plots.yaml new file mode 100644 index 00000000..69f60757 --- /dev/null +++ b/examples/pytorch/conf/plots.yaml @@ -0,0 +1,142 @@ +cat_plot: +- file: adv_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: Adversarial Accuracy vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_accuracy + ylabels: Adv. Accuracy +- file: ben_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: Benign Accuracy vs Defence Type + x: def_gen + xlabels: Defence Type + y: accuracy + ylabels: Ben. Accuracy +- file: ben_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: Training Time per Benign Failure vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_failure + ylabels: Training Time per Ben. Failures +- file: adv_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: Training Time per Adversarial Failure vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_adv_failure + ylabels: Training time per Adv. Failures +- file: adv_failures_per_train_time_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: Training Time per Adversarial Failure vs Attack Type + x: atk_gen + xlabels: Attack Type + y: training_time_per_adv_failure + ylabels: Training time per Adv. Failures +- file: adv_accuracy_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: Adversarial Accuracy vs Attack Type + x: atk_gen + xlabels: Attack Type + y: adv_accuracy + ylabels: Adv. Accuracy +- file: adv_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: Adversarial Accuracy vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_accuracy + ylabels: Adv. Accuracy +- file: adv_accuracy_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: Adversarial Accuracy vs Attack Type + x: atk_gen + xlabels: Attack Type + y: adv_accuracy + ylabels: Adv. Accuracy +- file: ben_failure_rate_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: Benign Failure Rate vs Defence Type + x: def_gen + xlabels: Defence Type + y: failure_rate + ylabels: Ben. Failure Rate + +line_plot: +- control: 0.98 + control_color: green + file: def_param_vs_accuracy.pdf + hue: def_gen + legend: {} + title: Accuracy vs Defence Strength + x: def_value + x_scale: log + xlabel: Defence Control Parameter + y: accuracy + y_scale: + ylabel: Accuracy +- control: 0.1 + control_color: green + file: def_param_vs_adv_accuracy.pdf + hue: def_gen + legend: {} + title: Adversarial Accuracy vs Defence Strength + x: def_value + x_scale: log + xlabel: Defence Control Parameter + y: adv_accuracy + y_scale: + ylabel: Adv. Accuracy +- control: 0.1 + control_color: green + file: def_param_vs_adv_failure_rate.pdf + hue: def_gen + legend: {} + title: Adversarial Failure Rate vs Defence Strength + x: def_value + x_scale: log + xlabel: Defence Control Parameter + y: adv_failure_rate + y_scale: log + ylabel: Adv. Failure Rate +- control: 0.1 + control_color: green + file: atk_param_vs_accuracy.pdf + hue: atk_gen + legend: {} + title: Adversarial Accuracy vs Attack Strength + x: atk_value + x_scale: log + xlabel: Attack Control Parameter + y: adv_accuracy + y_scale: + ylabel: Adv. Accuracy \ No newline at end of file diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index c4c7bb0b..ee061c84 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -36,22 +36,21 @@ stages: ############################################################################## attacks: foreach: - - ResNet18 + # - ResNet18 # - ResNet34 # - ResNet50 # - ResNet101 - # - ResNet152 + - ResNet152 do: cmd: bash attacks.sh ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name grid.yaml deps: - - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + - models.sh + - attacks.sh outs: - ${item}.db compile: foreach: - attack - - train do: cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv deps: @@ -65,9 +64,8 @@ stages: outs: - ${files.directory}/${files.reports}/${item}.csv plot: - cmd : python plots.py --path ${files.directory}/${files.reports}/plots/ + cmd : python plots.py --path ${files.directory}/${files.reports}/plots/ --file ${files.directory}/${files.reports}/attack.csv deps: - ${files.directory}/${files.reports}/attack.csv - - ${files.directory}/${files.reports}/train.csv outs: - --path ${files.directory}/${files.reports}/plots/ diff --git a/examples/pytorch/models.sh b/examples/pytorch/models.sh index 8fdf2a64..90a3ecf7 100644 --- a/examples/pytorch/models.sh +++ b/examples/pytorch/models.sh @@ -5,6 +5,7 @@ # Default model echo "python -m deckard.layers.optimise +stage=train" $@ "--multirun" python -m deckard.layers.optimise +stage=train $@ --multirun + # This line generates the model and adds the FeatureSqueezing preprocessing defence. python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train $@ --multirun @@ -15,4 +16,4 @@ python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.pre python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.01,.1,.3,.5,1, +stage=train $@ --multirun # # High Confidence -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun \ No newline at end of file diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index 64b47035..3705fd62 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -30,6 +30,7 @@ def cat_plot( ylabels, legend_title, file, + folder, set={}, **kwargs, ): @@ -40,9 +41,9 @@ def cat_plot( graph.set_titles(titles) graph.legend.set_title(title=legend_title) graph.set(**set) - graph.savefig(FOLDER / file) + graph.savefig(folder / file) plt.gcf().clear() - logger.info(f"Saved graph to {FOLDER / file}") + logger.info(f"Saved graph to {folder / file}") def line_plot( @@ -54,6 +55,7 @@ def line_plot( ylabel, title, file, + folder, y_scale=None, x_scale=None, legend={}, @@ -74,85 +76,25 @@ def line_plot( if x_scale is not None: graph.set_xscale(x_scale) graph.get_figure().tight_layout() - graph.get_figure().savefig(FOLDER / file) + graph.get_figure().savefig(folder / file) plt.gcf().clear() return graph -def format_control_parameter(data, control_dict, min_max=True): - data.def_gen.fillna("Control", inplace=True) - new_data = pd.DataFrame() - for _, row in data.iterrows(): - if row.defence in ["Control", None, "None", "none", "null", np.nan]: - row["def_param"] = np.nan - row["def_value"] = np.nan - else: - param = control_dict[row.defence] - row["def_param"] = param.split(".")[-1] - value = row[param] - row["def_value"] = value - if row.attack in ["Control", None, "None", "none", "null", np.nan]: - row["atk_param"] = np.nan - row["atk_value"] = np.nan - else: - param = control_dict[row.attack] - row["atk_param"] = param.split(".")[-1] - value = row[param] - row["atk_value"] = value - new_data = pd.concat([new_data, row], axis=1) - data = new_data.T - data.def_value.fillna(0, inplace=True) - del new_data - - if min_max is True: - defs = data.def_gen.unique() - atks = data.atk_gen.unique() - # Min-max scaling of control parameters - for def_ in defs: - max_ = data[data.def_gen == def_].def_value.max() - min_ = data[data.def_gen == def_].def_value.min() - scaled_value = (data[data.def_gen == def_].def_value - min_) / (max_ - min_) - data.loc[data.def_gen == def_, "def_value"] = scaled_value - - for atk in atks: - max_ = data[data.atk_gen == atk].atk_value.max() - min_ = data[data.atk_gen == atk].atk_value.min() - scaled_value = (data[data.atk_gen == atk].atk_value - min_) / (max_ - min_) - data.loc[data.atk_gen == atk, "atk_value"] = scaled_value - return data - - -def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict): - def_gen = data.def_gen.map(def_gen_dict) - data.def_gen = def_gen - atk_gen = data.atk_gen.map(atk_gen_dict) - data.atk_gen = atk_gen - # Drops poorly merged columns - data = data[data.columns.drop(list(data.filter(regex=".1")))] - data = data[data.columns.drop(list(data.filter(regex=".1")))] - # Replaces model names with short names - model_names = data["model.init.name"] - # model_names = [x.get_text() for x in model_names] - model_names = [x.split(".")[-1] for x in model_names] - data["model_name"] = model_names - # %% - # Replace data.sample.random_state with random_state - data["random_state"] = data["data.sample.random_state"].copy() - del data["data.sample.random_state"] - data = format_control_parameter(data, control_dict, min_max=True) - # Calculates various failure rates - data["adv_failures_per_training_time"] = ( - data["train_time_per_sample"] / (1 - data["adv_accuracy"]) * 100 - ) - data["adv_failure_rate"] = (1 - data["adv_accuracy"]) * data[ +def calculate_failure_rate(data): + data["failure_rate"] = (1 - data["accuracy"]) / data["predict_time_per_sample"] + data["adv_failure_rate"] = (1 - data["adv_accuracy"]) / data[ "adv_fit_time_per_sample" ] - data["failure_rate"] = (1 - data["accuracy"]) * data["predict_time_per_sample"] - data["failures_per_training_time"] = ( - data["train_time_per_sample"] / (1 - data["accuracy"]) * 100 + data["training_time_per_failure"] = ( + data["train_time_per_sample"] / data["failure_rate"] + ) + data["training_time_per_adv_failure"] = ( + data["train_time_per_sample"] / data["adv_failure_rate"] + ) + data["adv_training_time_per_failure"] = ( + data["adv_fit_time_per_sample"] / data["adv_failure_rate"] ) - logger.info(f"Saving data to {FOLDER / 'data.csv'}") - data.to_csv(FOLDER / "data.csv") return data @@ -163,14 +105,14 @@ def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict): "--path", type=str, help="Path to the plot folder", - default="output/plots", + required=True, ) parser.add_argument( "-f", "--file", type=str, help="Path to the plot folder", - default="output/reports/results.csv", + required=True, ) parser.add_argument( "-t", @@ -185,6 +127,9 @@ def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict): default="INFO", help="Increase output verbosity", ) + parser.add_argument( + "-c", "--config", help="Path to the config file", default="conf/plots.yaml" + ) args = parser.parse_args() logging.basicConfig(level=args.verbosity) # %% @@ -193,6 +138,7 @@ def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict): ).exists(), f"File {args.file} does not exist. Please specify a valid file using the -f flag." csv_file = args.file data = pd.read_csv(csv_file) + data = calculate_failure_rate(data) if "Unnamed: 0" in data.columns: data.drop("Unnamed: 0", axis=1, inplace=True) @@ -209,26 +155,21 @@ def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict): FOLDER.mkdir(parents=True, exist_ok=True) # Reads Config file - with open(FOLDER / "config/default.yaml", "r") as f: + with open(Path(args.config), "r") as f: big_dict = yaml.load(f, Loader=yaml.FullLoader) - def_gen_dict = big_dict["defences"] - atk_gen_dict = big_dict["attacks"] - control_dict = big_dict["params"] - - data = clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict) # %% cat_plot_list = big_dict["cat_plot"] i = 0 for dict_ in cat_plot_list: i += 1 logger.info(f"Rendering graph {i}") - locals()[f"graph{i}"] = cat_plot(data, **dict_) + locals()[f"graph{i}"] = cat_plot(data, **dict_, folder=FOLDER) # %% line_plot_list = big_dict["line_plot"] for dict_ in line_plot_list: i += 1 logger.info(f"Rendering graph {i}") - locals()[f"graph{i}"] = line_plot(data, **dict_) + locals()[f"graph{i}"] = line_plot(data, **dict_, folder=FOLDER) # %% graph14 = sns.scatterplot( @@ -254,12 +195,6 @@ def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict): conf_path = Path("output/plots/config") conf_path.mkdir(parents=True, exist_ok=True) conf_dict = { - **vars(args), "cat_plot": cat_plot_list, "line_plot": line_plot_list, - "params": control_dict, - "attacks": atk_gen_dict, - "defences": def_gen_dict, } - with open(conf_path / "default.yaml", "w") as f: - yaml.dump(conf_dict, f) From 4b46e5a649e49137254c74d2fda692542f6d2ea5 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Sun, 3 Sep 2023 12:50:31 +0000 Subject: [PATCH 020/148] linting --- deckard/layers/compile.py | 13 +++++++++---- examples/pytorch/conf/plots.yaml | 2 +- examples/pytorch/models.sh | 2 +- examples/pytorch/plots.py | 5 ++++- 4 files changed, 15 insertions(+), 7 deletions(-) diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 6f945cb6..0515decb 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -137,8 +137,9 @@ def parse_results(folder, files=["score_dict.json", "params.yaml"]): df = merge_attacks(df) return df + def format_control_parameter(data, control_dict, min_max=True): - + new_data = pd.DataFrame() logger.info("Formatting control parameters...") for _, row in tqdm(data.iterrows()): @@ -187,6 +188,7 @@ def format_control_parameter(data, control_dict, min_max=True): data.loc[data.atk_gen == atk, "atk_value"] = scaled_value return data + def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict, folder): logger.info("Replacing attack and defence names with short names...") if hasattr(data, "def_gen"): @@ -199,7 +201,7 @@ def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict, fold logger.info("Dropping poorly merged columns...") data.dropna(axis=1, how="all", inplace=True) logger.info("Shortening model names...") - data['model_name'] = data['model.init.name'].str.split('.').str[-1] + data["model_name"] = data["model.init.name"].str.split(".").str[-1] # %% # Replace data.sample.random_state with random_state logger.info("Replacing data.sample.random_state with random_state...") @@ -209,12 +211,13 @@ def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict, fold data.to_csv(Path(folder) / "data.csv") return data + def save_results(results, results_file) -> str: """ Compile results from a folder of reports and save to a csv file; return the path to the csv file. It will optionally delete columns from the results. """ logger.info("Compiling results...") - + suffix = Path(results_file).suffix if suffix == ".csv": results.to_csv(results_file) @@ -256,7 +259,9 @@ def save_results(results, results_file) -> str: def_gen_dict = big_dict["defences"] atk_gen_dict = big_dict["attacks"] control_dict = big_dict["params"] - results = clean_data_for_plotting(results, def_gen_dict, atk_gen_dict, control_dict, report_folder) + results = clean_data_for_plotting( + results, def_gen_dict, atk_gen_dict, control_dict, report_folder + ) report_file = save_results(results, results_file) assert Path( report_file, diff --git a/examples/pytorch/conf/plots.yaml b/examples/pytorch/conf/plots.yaml index 69f60757..e8cd025a 100644 --- a/examples/pytorch/conf/plots.yaml +++ b/examples/pytorch/conf/plots.yaml @@ -139,4 +139,4 @@ line_plot: xlabel: Attack Control Parameter y: adv_accuracy y_scale: - ylabel: Adv. Accuracy \ No newline at end of file + ylabel: Adv. Accuracy diff --git a/examples/pytorch/models.sh b/examples/pytorch/models.sh index 90a3ecf7..2786c4be 100644 --- a/examples/pytorch/models.sh +++ b/examples/pytorch/models.sh @@ -16,4 +16,4 @@ python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.pre python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.01,.1,.3,.5,1, +stage=train $@ --multirun # # High Confidence -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun \ No newline at end of file +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index 3705fd62..7aa7305c 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -128,7 +128,10 @@ def calculate_failure_rate(data): help="Increase output verbosity", ) parser.add_argument( - "-c", "--config", help="Path to the config file", default="conf/plots.yaml" + "-c", + "--config", + help="Path to the config file", + default="conf/plots.yaml", ) args = parser.parse_args() logging.basicConfig(level=args.verbosity) From 73c611db2a060444de483d86f7363ba05a87d7ac Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Sun, 3 Sep 2023 14:19:33 +0000 Subject: [PATCH 021/148] linting --- deckard/__main__.py | 7 ++++++- deckard/iaac/gcp/deploy.py | 38 ++++++++++++++++++++++++++++-------- deckard/iaac/gcp/params.yaml | 14 +++++++++++++ deckard/layers/compile.py | 6 +++++- 4 files changed, 55 insertions(+), 10 deletions(-) create mode 100644 deckard/iaac/gcp/params.yaml diff --git a/deckard/__main__.py b/deckard/__main__.py index 572752dd..13f471bb 100644 --- a/deckard/__main__.py +++ b/deckard/__main__.py @@ -29,7 +29,12 @@ def run_submodule(submodule, args): def parse_and_repro(args): cmd = f"python -m deckard.layers.parse {args}" - with subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True) as proc: + with subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + shell=True, + ) as proc: for line in proc.stdout: print(line.rstrip().decode("utf-8")) if len(proc.stderr.readlines()) > 0: diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index 59bf5895..0e01f935 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -1,7 +1,6 @@ import os import subprocess import logging -from hydra.utils import instantiate import argparse from dataclasses import dataclass from pathlib import Path @@ -10,9 +9,21 @@ logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) +secret_file = os.environ.get("GCP_SECRET_FILE") +secret_file = Path(secret_file).resolve().as_posix() +project_name = os.environ.get("GCP_PROJECT_NAME") +assert Path(secret_file).exists(), f"File {secret_file} does not exist" # Login to GCP + try: - command = "gcloud auth login" + command = [ + "gcloud", + "auth", + "login", + f"--cred-file={secret_file}", + f"--project={project_name}", + ] logger.info(f"Running command: {command}") output = subprocess.run(command) logger.info(f"{output}") @@ -20,9 +31,6 @@ raise ImportError( "Error logging in to GCP. Please check your credentials and ensure that gcloud cli is installed.", ) -# Get username and password from ENV -username = os.environ.get("GCP_USERNAME") -password = os.environ.get("GCP_PASSWORD") @dataclass @@ -42,7 +50,7 @@ class GCP_Config: image_project: str = "ubuntu-os-cloud" image_family: str = "ubuntu-2204-lts" mount_directory: str = "/mnt/filestore" - _target_: str = "deckard.conf.GCP_Config" + _target_: str = "deckard.gcp.deploy.GCP_Config" def create_cluster(self): # Create a cluster @@ -51,6 +59,7 @@ def create_cluster(self): ) command = f"gcloud container clusters create {self.cluster_name} --region {self.region} --num-nodes {self.num_nodes} --no-enable-autoupgrade" logger.info(f"Running command: {command}") + command = command.split(" ") output = subprocess.run(command) logger.info(f"{output}") return output @@ -59,6 +68,7 @@ def install_kubectl(self): logger.info("Installing kubectl on the local machine") command = "gcloud components install kubectl" logger.info(f"Running command: {command}") + command = command.split(" ") output = subprocess.run(command) logger.info(f"{output}") return output @@ -68,6 +78,8 @@ def retrieve_credentials(self): f"Retrieving credentials for cluster {self.cluster_name} in region {self.region}", ) command = "gcloud container clusters get-credentials {self.cluster_name} --region {self.region}" + logger.info(f"Running command: {command}") + command = command.split(" ") output = subprocess.run(command) logger.info(f"{output}") return output @@ -76,6 +88,7 @@ def create_node_pool(self): logger.info(f"Creating node pool {self.cluster_name} in region {self.region}") command = f"gcloud container node-pools create {self.cluster_name} --accelerator type={self.gpu_type},count={self.gpu_count},gpu-driver-version={self.gpu_driver_version} --region {self.region} --cluster {self.cluster_name} --machine-type {self.machine_type} --num-nodes {self.num_nodes} --min-nodes {self.min_nodes} --max-nodes {self.max_nodes}" logger.info(f"Running command: {command}") + command = command.split(" ") output = subprocess.run(command) logger.info(f"{output}") return output @@ -85,6 +98,7 @@ def create_deployment(self): file_name = Path(self.conf_dir, self.storage_config).resolve().as_posix() command = f"kubectl create -f {file_name}" logger.info(f"Running command: {command}") + command = command.split(" ") output = subprocess.run(command) logger.info(f"{output}") return output @@ -98,6 +112,7 @@ def create_persistent_volume_claim(self): ) command = f"kubectl create -f {file_name}" logger.info(f"Running command: {command}") + command = command.split(" ") output = subprocess.run(command) logger.info(f"{output}") return output @@ -107,6 +122,7 @@ def deploy_pod(self): file_name = Path(self.conf_dir, self.pod).resolve().as_posix() command = f"kubectl create -f {file_name}" logger.info(f"Running command: {command}") + command = command.split(" ") output = subprocess.run(command) logger.info(f"{output}") return output @@ -115,8 +131,9 @@ def prepare_access_values(self): logger.info( f"Preparing access values in the shared volumee {self.cluster_name} in region {self.region}", ) - command = f'gcloud compute instances create filestore --async --metadata=ssh-keys="{username}:{password}" --zone={self.region}-a --image-family={self.image_family} --image-project={self.image_project} --machine-type={self.machine_type} --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring --subnet=default --quiet' + command = f"gcloud compute instances create filestore --async --zone={self.region}-a --image-family={self.image_family} --image-project={self.image_project} --machine-type={self.machine_type} --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring --subnet=default --quiet" logger.info(f"Running command: {command}") + command = command.split(" ") output = subprocess.run(command) logger.info(f"{output}") return output @@ -127,6 +144,7 @@ def find_ip_of_filestore(self): ) command = f'gcloud compute instances list --filter="name=filestore" --format="value(networkInterfaces[0].accessConfigs[0].natIP)" --zone={self.region}' logger.info(f"Running command: {command}") + command = command.split(" ") ip_output = subprocess.run(command) logger.info(f"{ip_output}") return ip_output @@ -137,18 +155,22 @@ def mount_filestore(self, ip): ) command = "sudo apt update" logger.info(f"Running command: {command}") + command = command.split(" ") output = subprocess.run(command) logger.info(f"{output}") command = "sudo apt install nfs-common" logger.info(f"Running command: {command}") + command = command.split(" ") output = subprocess.run(command) logger.info(f"{output}") command = f"mkdir {self.mount_directory}" logger.info(f"Running command: {command}") + command = command.split(" ") output = subprocess.run(command) logger.info(f"{output}") command = f"sudo mount -o rw,intr {ip}:/vol1 {self.mount_directory}" logger.info(f"Running command: {command}") + command = command.split(" ") output = subprocess.run(command) logger.info(f"{output}") return output @@ -177,5 +199,5 @@ def __call__(self): config_file = Path(config_dir, args.config_file).resolve().as_posix() with open(config_file, "r") as f: params = yaml.load(f, Loader=yaml.FullLoader) - gcp = instantiate(params) + gcp = GCP_Config(**params) gcp() diff --git a/deckard/iaac/gcp/params.yaml b/deckard/iaac/gcp/params.yaml new file mode 100644 index 00000000..8018014a --- /dev/null +++ b/deckard/iaac/gcp/params.yaml @@ -0,0 +1,14 @@ +cluster_name: k8s-cluster +gpu_count: 1 +gpu_driver_version: default +gpu_type: nvidia-tesla-v100 +image_family: ubuntu-2204-lts +image_project: ubuntu-os-cloud +machine_type: n1-standard-2 +max_nodes: 1 +min_nodes: 1 +mount_directory: /mnt/filestore +num_nodes: 1 +persistent_volume_claim: ./IaaC/gcp/pvc.yaml +pod: ./IaaC/gcp/pod.yaml +storage_config: ./IaaC/gcp/sclass.yaml diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 0515decb..1db9be7d 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -260,7 +260,11 @@ def save_results(results, results_file) -> str: atk_gen_dict = big_dict["attacks"] control_dict = big_dict["params"] results = clean_data_for_plotting( - results, def_gen_dict, atk_gen_dict, control_dict, report_folder + results, + def_gen_dict, + atk_gen_dict, + control_dict, + report_folder, ) report_file = save_results(results, results_file) assert Path( From 338c3071175f322b405fe2508c462d38e1be6fe8 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Sun, 3 Sep 2023 14:19:41 +0000 Subject: [PATCH 022/148] linting --- examples/pytorch/plots.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index 7aa7305c..27461c60 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -8,7 +8,6 @@ from pathlib import Path import matplotlib.pyplot as plt -import numpy as np import pandas as pd import seaborn as sns import yaml From 4459d49def275bda725eedf8d36ddca7e5831a89 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Sun, 3 Sep 2023 14:24:38 +0000 Subject: [PATCH 023/148] linting --- deckard/layers/compile.py | 1 - 1 file changed, 1 deletion(-) diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 1db9be7d..43ca6cf3 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -139,7 +139,6 @@ def parse_results(folder, files=["score_dict.json", "params.yaml"]): def format_control_parameter(data, control_dict, min_max=True): - new_data = pd.DataFrame() logger.info("Formatting control parameters...") for _, row in tqdm(data.iterrows()): From 4cdba38993b4b15c0a8437b70bc4c7de09f4c3da Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Mon, 4 Sep 2023 15:44:11 +0000 Subject: [PATCH 024/148] linting --- deckard/iaac/gcp/NEW_README.md | 11 +++++++++ deckard/iaac/gcp/conf/default.yaml | 6 ++--- deckard/iaac/gcp/deploy.py | 37 ++++++++++++++++-------------- 3 files changed, 34 insertions(+), 20 deletions(-) create mode 100644 deckard/iaac/gcp/NEW_README.md diff --git a/deckard/iaac/gcp/NEW_README.md b/deckard/iaac/gcp/NEW_README.md new file mode 100644 index 00000000..844d8bc1 --- /dev/null +++ b/deckard/iaac/gcp/NEW_README.md @@ -0,0 +1,11 @@ +# Install gcloud + +First you need to install `gcloud-cli`. In order to setup the GKE (Google Kubernetes Engine), we need to enable require apis. Follow [these intsructions to enable them](https://cloud.google.com/endpoints/docs/openapi/enable-api). +[Source](https://cloud.google.com/sdk/docs/install) +``` +sudo apt-get update +sudo apt-get install apt-transport-https ca-certificates gnupg curl sudo +echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main" | sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list +curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - +sudo apt-get update && sudo apt-get install google-cloud-cli +``` diff --git a/deckard/iaac/gcp/conf/default.yaml b/deckard/iaac/gcp/conf/default.yaml index d8e6ecfa..c64d7b87 100644 --- a/deckard/iaac/gcp/conf/default.yaml +++ b/deckard/iaac/gcp/conf/default.yaml @@ -6,9 +6,9 @@ gpu_driver_version: default machine_type: n1-standard-2 min_nodes: 1 max_nodes: 1 -storage_config: ./IaaC/gcp/sclass.yaml -persistent_volume_claim: ./IaaC/gcp/pvc.yaml -pod : ./IaaC/gcp/pod.yaml +storage_config: sclass.yaml +persistent_volume_claim: pvc.yaml +pod : pod.yaml image_project: ubuntu-os-cloud image_family: ubuntu-2204-lts mount_directory: /mnt/filestore diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index 0e01f935..efa0f5f2 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -46,11 +46,12 @@ class GCP_Config: conf_dir: str = "./conf/gcp/" storage_config: str = "sclass.yaml" persistent_volume_claim: str = "pvc.yaml" - pod = "pod.yaml" + pod: str = "pod.yaml" image_project: str = "ubuntu-os-cloud" image_family: str = "ubuntu-2204-lts" mount_directory: str = "/mnt/filestore" _target_: str = "deckard.gcp.deploy.GCP_Config" + region: str = "europe-west4" def create_cluster(self): # Create a cluster @@ -131,18 +132,29 @@ def prepare_access_values(self): logger.info( f"Preparing access values in the shared volumee {self.cluster_name} in region {self.region}", ) - command = f"gcloud compute instances create filestore --async --zone={self.region}-a --image-family={self.image_family} --image-project={self.image_project} --machine-type={self.machine_type} --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring --subnet=default --quiet" - logger.info(f"Running command: {command}") + # See if filestore exists + command = 'gcloud compute instances list --filter="name=filestore" --format="value(EXTERNAL_IP)"' command = command.split(" ") - output = subprocess.run(command) - logger.info(f"{output}") + output = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + if output.returncode == 0: + pass + else: + command = f"gcloud compute instances create filestore --async --image-family={self.image_family} --image-project={self.image_project} --machine-type={self.machine_type} --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring --subnet=default --quiet" + logger.info(f"Running command: {command}") + command = command.split(" ") + output = subprocess.run( + command, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + logger.info(f"{output}") return output def find_ip_of_filestore(self): logger.info( f"Finding the IP address of the filestore {self.cluster_name} in region {self.region}", ) - command = f'gcloud compute instances list --filter="name=filestore" --format="value(networkInterfaces[0].accessConfigs[0].natIP)" --zone={self.region}' + command = 'gcloud compute instances list --filter="name=filestore" --format="value(EXTERNAL_IP)"' logger.info(f"Running command: {command}") command = command.split(" ") ip_output = subprocess.run(command) @@ -150,19 +162,10 @@ def find_ip_of_filestore(self): return ip_output def mount_filestore(self, ip): + # TODO: Switch the python pathlib library logger.info( f"Mounting the filestore {self.cluster_name} in region {self.region}", ) - command = "sudo apt update" - logger.info(f"Running command: {command}") - command = command.split(" ") - output = subprocess.run(command) - logger.info(f"{output}") - command = "sudo apt install nfs-common" - logger.info(f"Running command: {command}") - command = command.split(" ") - output = subprocess.run(command) - logger.info(f"{output}") command = f"mkdir {self.mount_directory}" logger.info(f"Running command: {command}") command = command.split(" ") @@ -192,7 +195,7 @@ def __call__(self): gcp_parser = argparse.ArgumentParser() gcp_parser.add_argument("--verbosity", type=str, default="INFO") gcp_parser.add_argument("--config_dir", type=str, default="conf") - gcp_parser.add_argument("--config_file", type=str, default="default") + gcp_parser.add_argument("--config_file", type=str, default="default.yaml") gcp_parser.add_argument("--workdir", type=str, default=".") args = gcp_parser.parse_args() config_dir = Path(args.workdir, args.config_dir).resolve().as_posix() From 5d08c049ea85eb59b3732aac3f8e52f8ed347bcd Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Mon, 4 Sep 2023 15:57:53 +0000 Subject: [PATCH 025/148] more changes --- deckard/iaac/gcp/deploy.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index efa0f5f2..22eea90d 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -166,15 +166,13 @@ def mount_filestore(self, ip): logger.info( f"Mounting the filestore {self.cluster_name} in region {self.region}", ) - command = f"mkdir {self.mount_directory}" - logger.info(f"Running command: {command}") - command = command.split(" ") - output = subprocess.run(command) - logger.info(f"{output}") + Path(self.mount_directory).mkdir(parents=True, exist_ok=True, mode=0o770,) command = f"sudo mount -o rw,intr {ip}:/vol1 {self.mount_directory}" logger.info(f"Running command: {command}") command = command.split(" ") - output = subprocess.run(command) + output = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE,) + if output.returncode != 0: + raise RuntimeError(f"Error mounting filestore: {output.stderr}") logger.info(f"{output}") return output From e44587053fbc17a148b983a9256a9f2ce6305d29 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 4 Sep 2023 23:03:47 +0000 Subject: [PATCH 026/148] fix compile, plot scripts --- .github/workflows/docker.yml | 4 +- deckard/layers/compile.py | 18 +++++--- examples/pytorch/conf/compile.yaml | 2 +- examples/pytorch/conf/plots.yaml | 13 +++++- examples/pytorch/dvc.lock | 60 ++++++++++++++++++++------ examples/pytorch/dvc.yaml | 13 +++--- examples/pytorch/plots.py | 69 ++++++++++++++++++------------ 7 files changed, 121 insertions(+), 58 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 97f36431..cd5940f7 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -1,8 +1,8 @@ name: Publish Docker image on: - release: - types: [published] + pull_request: + branches: [ main ] jobs: push_to_registry: diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 0515decb..5f0dce97 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -4,7 +4,6 @@ import logging from tqdm import tqdm import yaml -import argparse import numpy as np from .utils import deckard_nones as nones @@ -189,7 +188,7 @@ def format_control_parameter(data, control_dict, min_max=True): return data -def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict, folder): +def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict, file, folder): logger.info("Replacing attack and defence names with short names...") if hasattr(data, "def_gen"): data.def_gen.fillna("Control", inplace=True) @@ -202,12 +201,13 @@ def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict, fold data.dropna(axis=1, how="all", inplace=True) logger.info("Shortening model names...") data["model_name"] = data["model.init.name"].str.split(".").str[-1] - # %% - # Replace data.sample.random_state with random_state + data['model_layers'] = data['model_name'].str.split("Net").str[-1] + # Rename columns that end in '.1' by removing the '.1' + data.columns.rename(lambda x: x[:-2] if x.endswith(".1") else x, inplace=True) logger.info("Replacing data.sample.random_state with random_state...") data["data.sample.random_state"].rename("random_state", inplace=True) data = format_control_parameter(data, control_dict, min_max=True) - logger.info(f"Saving data to {Path(folder) / 'data.csv'}") + logger.info(f"Saving data to {Path(folder) / file}") data.to_csv(Path(folder) / "data.csv") return data @@ -237,6 +237,7 @@ def save_results(results, results_file) -> str: if __name__ == "__main__": + import argparse parser = argparse.ArgumentParser() parser.add_argument("--results_file", type=str, default="results.csv") parser.add_argument("--report_folder", type=str, default="reports", required=True) @@ -260,7 +261,12 @@ def save_results(results, results_file) -> str: atk_gen_dict = big_dict["attacks"] control_dict = big_dict["params"] results = clean_data_for_plotting( - results, def_gen_dict, atk_gen_dict, control_dict, report_folder + results, + def_gen_dict, + atk_gen_dict, + control_dict, + results_file, + report_folder, ) report_file = save_results(results, results_file) assert Path( diff --git a/examples/pytorch/conf/compile.yaml b/examples/pytorch/conf/compile.yaml index 010bffc2..1c29f5ff 100644 --- a/examples/pytorch/conf/compile.yaml +++ b/examples/pytorch/conf/compile.yaml @@ -27,4 +27,4 @@ params: art.defences.postprocessor.GaussianNoise: model.art.pipeline.postprocessor.kwargs.scale art.defences.postprocessor.HighConfidence: model.art.pipeline.postprocessor.kwargs.cutoff art.defences.preprocessor.FeatureSqueezing: model.art.pipeline.preprocessor.kwargs.bit_depth - art.defences.preprocessor.GaussianAugmentation: model.art.pipeline.preprocessor.kwargs.sigma + art.defences.preprocessor.GaussianAugmentation: model.art.pipeline.preprocessor.kwargs.sigma \ No newline at end of file diff --git a/examples/pytorch/conf/plots.yaml b/examples/pytorch/conf/plots.yaml index e8cd025a..b10a3900 100644 --- a/examples/pytorch/conf/plots.yaml +++ b/examples/pytorch/conf/plots.yaml @@ -90,7 +90,6 @@ cat_plot: xlabels: Defence Type y: failure_rate ylabels: Ben. Failure Rate - line_plot: - control: 0.98 control_color: green @@ -140,3 +139,15 @@ line_plot: y: adv_accuracy y_scale: ylabel: Adv. Accuracy +scatter_plot: + x: train_time_per_sample + y: adv_failure_rate + hue: model_name + xlabel: Training Time + ylabel: Adversarial Failure Rate + title: Adversarial Failure Rate vs Training Time + file: adv_failure_rate_vs_train_time.pdf + y_scale: log + x_scale: log + legend: + title: Model Name \ No newline at end of file diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index dfa358e2..c7e99ce0 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -354,7 +354,7 @@ stages: md5: 39ecdefe26af87802dfaed0e3bcd98b3 size: 528 models: - cmd: bash models.sh + cmd: bash models.sh ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 --config-name grid.yaml deps: @@ -369,7 +369,7 @@ stages: md5: d1eac324650402da6e3de1aebe0e3b3c size: 237568 attacks: - cmd: bash attacks.sh + cmd: bash attacks.sh ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 ++stage=attack --config-name grid.yaml deps: @@ -432,20 +432,29 @@ stages: cmd: python -m deckard.layers.compile --report_folder output/reports/attack --results_file output/reports/attack.csv deps: - - path: attack.db - md5: c9f920c7233802e9c46e4051c2da78e6 - size: 307200 - - path: model.db - md5: d1eac324650402da6e3de1aebe0e3b3c - size: 237568 + - path: ResNet101.db + md5: 86e9ffd22acb5ac8a10e1075cfa0cab9 + size: 696320 + - path: ResNet152.db + md5: dbcc81935187d20a249d4e27aaf560fd + size: 602112 + - path: ResNet18.db + md5: 3d304e014200ae926dec63521b6eeca0 + size: 1499136 + - path: ResNet34.db + md5: 7bf834e39f0367deddd36074e8f69d47 + size: 1400832 + - path: ResNet50.db + md5: 771e206f2ab932ae2978783214290c41 + size: 110592 - path: output/reports/attack/ - md5: 2e8a51d2322c52e5cb0ea4f98ddff9f3.dir - size: 1723539519 - nfiles: 3555 + md5: e60f6852b2b403451f3df235e55975c8.dir + size: 7724615819 + nfiles: 15820 outs: - path: output/reports/attack.csv - md5: bfc77e2b24b86aa390485f2fd60a65c2 - size: 2472144 + md5: 73855b1b65e8f7b1879dc390ef65325d + size: 11502713 compile@train: cmd: python -m deckard.layers.compile --report_folder output/reports/train --results_file output/reports/train.csv @@ -464,3 +473,28 @@ stages: - path: output/reports/train.csv md5: 922fa64743aa481feb79213a056c816e size: 363649 + attacks@ResNet152: + cmd: bash attacks.sh ++model.init.name=torch_example.ResNet152 ++stage=attack + ++hydra.sweeper.storage=sqlite:///ResNet152.db --config-name grid.yaml + deps: + - path: attacks.sh + md5: 6a6a91d037b4ce29f84009d9b1161d4f + size: 3034 + - path: models.sh + md5: 76fbee55cb5f90913d0863df9debd357 + size: 1299 + outs: + - path: ResNet152.db + md5: dbcc81935187d20a249d4e27aaf560fd + size: 602112 + plot: + cmd: python plots.py --path output/plots/ --file output/reports/attack.csv + deps: + - path: output/reports/attack.csv + md5: 73855b1b65e8f7b1879dc390ef65325d + size: 11502713 + outs: + - path: output/plots/ + md5: 254c4e83a0c2c9575af3430e755aba56.dir + size: 331904 + nfiles: 12 diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index ee061c84..af6d3882 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -36,10 +36,10 @@ stages: ############################################################################## attacks: foreach: - # - ResNet18 - # - ResNet34 - # - ResNet50 - # - ResNet101 + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 - ResNet152 do: cmd: bash attacks.sh ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name grid.yaml @@ -55,7 +55,6 @@ stages: cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv deps: - ${files.directory}/${files.reports}/${item}/ - - train.db - ResNet18.db - ResNet34.db - ResNet50.db @@ -64,8 +63,8 @@ stages: outs: - ${files.directory}/${files.reports}/${item}.csv plot: - cmd : python plots.py --path ${files.directory}/${files.reports}/plots/ --file ${files.directory}/${files.reports}/attack.csv + cmd : python plots.py --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv deps: - ${files.directory}/${files.reports}/attack.csv outs: - - --path ${files.directory}/${files.reports}/plots/ + - ${files.directory}/plots/ diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index 7aa7305c..0e8f1f73 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -8,7 +8,6 @@ from pathlib import Path import matplotlib.pyplot as plt -import numpy as np import pandas as pd import seaborn as sns import yaml @@ -80,8 +79,43 @@ def line_plot( plt.gcf().clear() return graph +def scatter_plot( + data, + x, + y, + hue, + xlabel, + ylabel, + title, + file, + folder, + y_scale=None, + x_scale=None, + legend={}, +): + graph = sns.scatterplot( + data=data, + x=x, + y=y, + hue=hue, + ) + graph.set_yscale(y_scale) + graph.set_xscale(x_scale) + graph.set_xlabel(xlabel) + graph.set_ylabel(ylabel) + graph.legend(**legend) + graph.set_title(title) + graph.get_figure().tight_layout() + graph.get_figure().savefig(Path(folder) / file) + logger.info(f"Rendering graph {i+1}") + logger.info(f"Saved graph to {Path(folder) / file}") + plt.gcf().clear() + return graph def calculate_failure_rate(data): + data = data[data.columns.drop(list(data.filter(regex="\.1$")))] + data.columns.str.replace(" ", "") + data.dropna(axis=0, subset=['accuracy', 'adv_accuracy', 'train_time_per_sample', 'adv_fit_time_per_sample', 'predict_time_per_sample'], inplace=True) data["failure_rate"] = (1 - data["accuracy"]) / data["predict_time_per_sample"] data["adv_failure_rate"] = (1 - data["adv_accuracy"]) / data[ "adv_fit_time_per_sample" @@ -174,30 +208,9 @@ def calculate_failure_rate(data): logger.info(f"Rendering graph {i}") locals()[f"graph{i}"] = line_plot(data, **dict_, folder=FOLDER) - # %% - graph14 = sns.scatterplot( - data=data, - x="train_time_per_sample", - y="adv_failure_rate", - hue="model_name", - ) - graph14.set_yscale("log") - graph14.set_xscale("log") - graph14.set_xlabel("Training Time") - graph14.set_ylabel("Adversarial Failure Rate") - graph14.legend(title="Model Name") - # graph6.legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=1) - # graph6.legend(labels=["ResNet18", "Resnet34", "Resnet50"]) - graph14.set_title("Adversarial Failure Rate vs Training Time") - # graph6.get_figure().tight_layout() - file = f"adv_failure_rate_vs_train_time{IMAGE_FILETYPE}" - graph14.get_figure().savefig(FOLDER / file) - logger.info(f"Rendering graph {i+1}") - logger.info(f"Saved graph to {FOLDER / file}") - plt.gcf().clear() - conf_path = Path("output/plots/config") - conf_path.mkdir(parents=True, exist_ok=True) - conf_dict = { - "cat_plot": cat_plot_list, - "line_plot": line_plot_list, - } + # %% + + scatter_plot_dict = big_dict["scatter_plot"] + + + graph = scatter_plot(data=data, **scatter_plot_dict, folder = FOLDER) From f75bc1adb8b3b96f1153651d594ed2718797736f Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 4 Sep 2023 23:07:15 +0000 Subject: [PATCH 027/148] linting --- deckard/iaac/gcp/deploy.py | 4 ++-- deckard/layers/compile.py | 7 +++++-- examples/pytorch/conf/compile.yaml | 2 +- examples/pytorch/conf/plots.yaml | 2 +- examples/pytorch/plots.py | 17 ++++++++++++++--- 5 files changed, 23 insertions(+), 9 deletions(-) diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index 22eea90d..ed85028a 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -166,11 +166,11 @@ def mount_filestore(self, ip): logger.info( f"Mounting the filestore {self.cluster_name} in region {self.region}", ) - Path(self.mount_directory).mkdir(parents=True, exist_ok=True, mode=0o770,) + Path(self.mount_directory).mkdir(parents=True, exist_ok=True, mode=0o770) command = f"sudo mount -o rw,intr {ip}:/vol1 {self.mount_directory}" logger.info(f"Running command: {command}") command = command.split(" ") - output = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE,) + output = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) if output.returncode != 0: raise RuntimeError(f"Error mounting filestore: {output.stderr}") logger.info(f"{output}") diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 4c577bd7..3deaba70 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -187,7 +187,9 @@ def format_control_parameter(data, control_dict, min_max=True): return data -def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict, file, folder): +def clean_data_for_plotting( + data, def_gen_dict, atk_gen_dict, control_dict, file, folder +): logger.info("Replacing attack and defence names with short names...") if hasattr(data, "def_gen"): data.def_gen.fillna("Control", inplace=True) @@ -200,7 +202,7 @@ def clean_data_for_plotting(data, def_gen_dict, atk_gen_dict, control_dict, file data.dropna(axis=1, how="all", inplace=True) logger.info("Shortening model names...") data["model_name"] = data["model.init.name"].str.split(".").str[-1] - data['model_layers'] = data['model_name'].str.split("Net").str[-1] + data["model_layers"] = data["model_name"].str.split("Net").str[-1] # Rename columns that end in '.1' by removing the '.1' data.columns.rename(lambda x: x[:-2] if x.endswith(".1") else x, inplace=True) logger.info("Replacing data.sample.random_state with random_state...") @@ -237,6 +239,7 @@ def save_results(results, results_file) -> str: if __name__ == "__main__": import argparse + parser = argparse.ArgumentParser() parser.add_argument("--results_file", type=str, default="results.csv") parser.add_argument("--report_folder", type=str, default="reports", required=True) diff --git a/examples/pytorch/conf/compile.yaml b/examples/pytorch/conf/compile.yaml index 1c29f5ff..010bffc2 100644 --- a/examples/pytorch/conf/compile.yaml +++ b/examples/pytorch/conf/compile.yaml @@ -27,4 +27,4 @@ params: art.defences.postprocessor.GaussianNoise: model.art.pipeline.postprocessor.kwargs.scale art.defences.postprocessor.HighConfidence: model.art.pipeline.postprocessor.kwargs.cutoff art.defences.preprocessor.FeatureSqueezing: model.art.pipeline.preprocessor.kwargs.bit_depth - art.defences.preprocessor.GaussianAugmentation: model.art.pipeline.preprocessor.kwargs.sigma \ No newline at end of file + art.defences.preprocessor.GaussianAugmentation: model.art.pipeline.preprocessor.kwargs.sigma diff --git a/examples/pytorch/conf/plots.yaml b/examples/pytorch/conf/plots.yaml index b10a3900..e0fcefe9 100644 --- a/examples/pytorch/conf/plots.yaml +++ b/examples/pytorch/conf/plots.yaml @@ -150,4 +150,4 @@ scatter_plot: y_scale: log x_scale: log legend: - title: Model Name \ No newline at end of file + title: Model Name diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index 0e8f1f73..1b724c49 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -79,6 +79,7 @@ def line_plot( plt.gcf().clear() return graph + def scatter_plot( data, x, @@ -112,10 +113,21 @@ def scatter_plot( plt.gcf().clear() return graph + def calculate_failure_rate(data): data = data[data.columns.drop(list(data.filter(regex="\.1$")))] data.columns.str.replace(" ", "") - data.dropna(axis=0, subset=['accuracy', 'adv_accuracy', 'train_time_per_sample', 'adv_fit_time_per_sample', 'predict_time_per_sample'], inplace=True) + data.dropna( + axis=0, + subset=[ + "accuracy", + "adv_accuracy", + "train_time_per_sample", + "adv_fit_time_per_sample", + "predict_time_per_sample", + ], + inplace=True, + ) data["failure_rate"] = (1 - data["accuracy"]) / data["predict_time_per_sample"] data["adv_failure_rate"] = (1 - data["adv_accuracy"]) / data[ "adv_fit_time_per_sample" @@ -212,5 +224,4 @@ def calculate_failure_rate(data): scatter_plot_dict = big_dict["scatter_plot"] - - graph = scatter_plot(data=data, **scatter_plot_dict, folder = FOLDER) + graph = scatter_plot(data=data, **scatter_plot_dict, folder=FOLDER) From 9daf2d0b4b2a46c9ef7a7fb3b7ff5196fc112b1f Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 4 Sep 2023 23:07:49 +0000 Subject: [PATCH 028/148] linting --- deckard/layers/compile.py | 7 ++++++- examples/pytorch/plots.py | 2 +- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 3deaba70..6f364f4a 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -188,7 +188,12 @@ def format_control_parameter(data, control_dict, min_max=True): def clean_data_for_plotting( - data, def_gen_dict, atk_gen_dict, control_dict, file, folder + data, + def_gen_dict, + atk_gen_dict, + control_dict, + file, + folder, ): logger.info("Replacing attack and defence names with short names...") if hasattr(data, "def_gen"): diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index 1b724c49..2e721d72 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -115,7 +115,7 @@ def scatter_plot( def calculate_failure_rate(data): - data = data[data.columns.drop(list(data.filter(regex="\.1$")))] + data = data[data.columns.drop(list(data.filter(regex=r"\.1$")))] data.columns.str.replace(" ", "") data.dropna( axis=0, From 022310c56f120df308cc80280b4f72f6c6a39722 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Tue, 5 Sep 2023 01:45:11 +0200 Subject: [PATCH 029/148] fixed Dockerfile --- Dockerfile | 7 ++----- setup.py | 3 ++- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/Dockerfile b/Dockerfile index c87ec9f3..8fe9cef7 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,12 +1,9 @@ FROM nvidia/cuda:11.3.1-cudnn8-runtime-ubuntu20.04 - RUN apt-get update -y RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata RUN apt-get install -y python3 python3-distutils python3-pip ffmpeg libavcodec-extra vim git -RUN python3 -m pip install nvidia-pyindex nvidia-cuda-runtime-cu11 tensorflow-gpu pyyaml pandas pillow dvc +RUN python3 -m pip install nvidia-pyindex nvidia-cuda-runtime-cu11 RUN git clone https://github.com/simplymathematics/deckard.git WORKDIR /deckard -RUN git clone https://github.com/Trusted-AI/adversarial-robustness-toolbox.git -RUN cd adversarial-robustness-toolbox && python3 -m pip install . -RUN python3 -m pip install --editable . +RUN python3 -m pip install --editable .[pytorch_image,tensorflow_image,non_framework,docs,test] RUN pytest test diff --git a/setup.py b/setup.py index a5f0e1ce..618709ae 100644 --- a/setup.py +++ b/setup.py @@ -7,6 +7,7 @@ long_description = fh.read() install_requires = [ + "adversarial-robustness-toolbox", "numpy", "scipy", "scikit-learn", @@ -149,7 +150,7 @@ def get_version(rel_path): ], "non_framework": [ "matplotlib", - "Pillow", + "pillow", "statsmodels", "pydub", "resampy", From 2235ea2024dd51337d1919f818ac1909fe0f3b10 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Tue, 5 Sep 2023 01:55:16 +0200 Subject: [PATCH 030/148] +fixed install, dockerfile --- Dockerfile | 2 ++ setup.py | 3 ++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index 8fe9cef7..9de4fc39 100644 --- a/Dockerfile +++ b/Dockerfile @@ -6,4 +6,6 @@ RUN python3 -m pip install nvidia-pyindex nvidia-cuda-runtime-cu11 RUN git clone https://github.com/simplymathematics/deckard.git WORKDIR /deckard RUN python3 -m pip install --editable .[pytorch_image,tensorflow_image,non_framework,docs,test] +RUN git clone https://github.com/Trusted-AI/adversarial-robustness-toolbox.git +RUN cd adversarial-robustness-toolbox && python3 -m pip install . RUN pytest test diff --git a/setup.py b/setup.py index 618709ae..627e9952 100644 --- a/setup.py +++ b/setup.py @@ -173,10 +173,11 @@ def get_version(rel_path): "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", - "License :: OSI Approved :: MIT License", + "License :: OSI Approved :: GPL License", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules", "Topic :: Scientific/Engineering :: Artificial Intelligence", From 7a8f57207079312c0bf6d2b671bec4dccb9ea334 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Wed, 6 Sep 2023 13:21:02 +0000 Subject: [PATCH 031/148] dockerfile --- deckard/iaac/__init__.py | 2 +- deckard/iaac/gcp/deploy.py | 2 ++ deckard/layers/parse.py | 15 ++++++++------- examples/pytorch/plots.py | 29 +++++++++++++++++++++-------- examples/sklearn/dvc.lock | 28 ++++++++++++++-------------- 5 files changed, 46 insertions(+), 30 deletions(-) diff --git a/deckard/iaac/__init__.py b/deckard/iaac/__init__.py index dfe88c03..4db1c272 100644 --- a/deckard/iaac/__init__.py +++ b/deckard/iaac/__init__.py @@ -1 +1 @@ -from .iaac import * +from .gcp import * diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index ed85028a..fbf3e841 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -11,8 +11,10 @@ logging.basicConfig(level=logging.INFO) secret_file = os.environ.get("GCP_SECRET_FILE") +assert secret_file is not None, "Please set the GCP_SECRET_FILE environment variable" secret_file = Path(secret_file).resolve().as_posix() project_name = os.environ.get("GCP_PROJECT_NAME") +assert project_name is not None, "Please set the GCP_PROJECT_NAME environment variable" assert Path(secret_file).exists(), f"File {secret_file} does not exist" # Login to GCP diff --git a/deckard/layers/parse.py b/deckard/layers/parse.py index 51dc5ab6..da5fa1db 100644 --- a/deckard/layers/parse.py +++ b/deckard/layers/parse.py @@ -4,16 +4,17 @@ from .utils import save_params_file logger = logging.getLogger(__name__) +hydra_parser = argparse.ArgumentParser() +hydra_parser.add_argument("overrides", type=str, nargs="*", default=None) +hydra_parser.add_argument("--verbosity", type=str, default="INFO") +hydra_parser.add_argument("--params_file", type=str, default="params.yaml") +hydra_parser.add_argument("--config_dir", type=str, default="conf") +hydra_parser.add_argument("--config_file", type=str, default="default") +hydra_parser.add_argument("--workdir", type=str, default=".") if __name__ == "__main__": logger = logging.getLogger(__name__) - hydra_parser = argparse.ArgumentParser() - hydra_parser.add_argument("overrides", type=str, nargs="*", default=None) - hydra_parser.add_argument("--verbosity", type=str, default="INFO") - hydra_parser.add_argument("--params_file", type=str, default="params.yaml") - hydra_parser.add_argument("--config_dir", type=str, default="conf") - hydra_parser.add_argument("--config_file", type=str, default="default") - hydra_parser.add_argument("--workdir", type=str, default=".") + args = hydra_parser.parse_args() logging.basicConfig(level=args.verbosity) config_dir = Path(Path(), args.config_dir).resolve().as_posix() diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index 2e721d72..4ec0ed28 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -30,6 +30,7 @@ def cat_plot( legend_title, file, folder, + rotation=0, set={}, **kwargs, ): @@ -39,7 +40,9 @@ def cat_plot( graph.set_ylabels(ylabels) graph.set_titles(titles) graph.legend.set_title(title=legend_title) + graph.set_xticklabels(graph.axes.flat[-1].get_xticklabels(), rotation=rotation) graph.set(**set) + graph.tight_layout() graph.savefig(folder / file) plt.gcf().clear() logger.info(f"Saved graph to {folder / file}") @@ -63,13 +66,13 @@ def line_plot( ): plt.gcf().clear() graph = sns.lineplot(data=data, x=x, y=y, hue=hue, style=control) + graph.legend(**legend) if control is not None: assert control_color is not None, "Please specify a control color" graph.add_line(plt.axhline(y=control, color=control_color, linestyle="-")) graph.set_xlabel(xlabel) graph.set_ylabel(ylabel) graph.set_title(title) - graph.legend(**legend) if y_scale is not None: graph.set_yscale(y_scale) if x_scale is not None: @@ -128,18 +131,18 @@ def calculate_failure_rate(data): ], inplace=True, ) - data["failure_rate"] = (1 - data["accuracy"]) / data["predict_time_per_sample"] - data["adv_failure_rate"] = (1 - data["adv_accuracy"]) / data[ + data.loc[:, "failure_rate"] = (1 - data["accuracy"])*100 / data["predict_time_per_sample"] + data.loc[:, "adv_failure_rate"] = (1 - data["adv_accuracy"]) * 100 / data[ "adv_fit_time_per_sample" ] - data["training_time_per_failure"] = ( + data.loc[:, "training_time_per_failure"] = ( data["train_time_per_sample"] / data["failure_rate"] ) - data["training_time_per_adv_failure"] = ( + data.loc[:, "training_time_per_adv_failure"] = ( data["train_time_per_sample"] / data["adv_failure_rate"] ) - data["adv_training_time_per_failure"] = ( - data["adv_fit_time_per_sample"] / data["adv_failure_rate"] + data.loc[:, "adv_training_time_per_failure"] = ( + data["train_time_per_sample"] / data["adv_failure_rate"] ) return data @@ -157,9 +160,16 @@ def calculate_failure_rate(data): "-f", "--file", type=str, - help="Path to the plot folder", + help="Data file to read from", required=True, ) + parser.add_argument( + "-o", + "--output", + type=str, + help="Output file name", + default="data.csv", + ) parser.add_argument( "-t", "--plotfiletype", @@ -190,8 +200,11 @@ def calculate_failure_rate(data): data = calculate_failure_rate(data) if "Unnamed: 0" in data.columns: data.drop("Unnamed: 0", axis=1, inplace=True) + FOLDER = Path(Path(), args.path) + FOLDER.mkdir(parents=True, exist_ok=True) + data.to_csv(FOLDER/args.output) IMAGE_FILETYPE = ( args.plotfiletype if args.plotfiletype.startswith(".") diff --git a/examples/sklearn/dvc.lock b/examples/sklearn/dvc.lock index 858cca7f..5dda0720 100644 --- a/examples/sklearn/dvc.lock +++ b/examples/sklearn/dvc.lock @@ -114,8 +114,8 @@ stages: md5: eeabc07006294278801a3906f7016b20 size: 185102 - path: output/models/model.pkl - md5: 4a9a983540c62bdcfa99c3a0297e7afc - size: 15368 + md5: 11ee9fa397238b26669489c944b361d5 + size: 15402 params: conf/model.yaml: hydra: @@ -161,7 +161,7 @@ stages: mmap_mode: r outs: - path: model.db - md5: f8e916e2167cb9b3408aba0f9dc4cd6f + md5: dc51b4e61037ce5f866f9a02afde26e7 size: 110592 attack: cmd: python -m deckard.layers.experiment attack @@ -170,7 +170,7 @@ stages: md5: eeabc07006294278801a3906f7016b20 size: 185102 - path: output/models/model.pkl - md5: 0498d04cceb9f248f8f2cac2c11e9f48 + md5: 11ee9fa397238b26669489c944b361d5 size: 15402 params: params.yaml: @@ -341,14 +341,14 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/reports/attack/default/adv_predictions.json - md5: 4f4e48a71cea9896298b690c3cb258a2 - size: 424 + md5: f2c857c1227f3827ce11db1464be9b63 + size: 426 - path: output/reports/attack/default/adv_probabilities.json - md5: 4f4e48a71cea9896298b690c3cb258a2 - size: 424 + md5: f2c857c1227f3827ce11db1464be9b63 + size: 426 - path: output/reports/attack/default/score_dict.json - md5: 7a7d6599127e7a5fe961642b7044ea4f - size: 496 + md5: 6cb818492db3f764a2df1f41a513d8f9 + size: 497 attack_optimise: cmd: python -m deckard.layers.optimise +stage=attack +optimizers=adv_accuracy model=best --multirun --config-name attack @@ -360,7 +360,7 @@ stages: md5: eeabc07006294278801a3906f7016b20 size: 185102 - path: output/models/model.pkl - md5: 0498d04cceb9f248f8f2cac2c11e9f48 + md5: 11ee9fa397238b26669489c944b361d5 size: 15402 params: conf/attack.yaml: @@ -407,13 +407,13 @@ stages: mmap_mode: r outs: - path: attack.db - md5: bd19744ae8a7fe26653fa92b9397f3b0 + md5: cdde21203e37d23fafd7a25750c986e9 size: 110592 find_best_model: cmd: python -m deckard.layers.find_best model.yaml deps: - path: model.db - md5: f8e916e2167cb9b3408aba0f9dc4cd6f + md5: dc51b4e61037ce5f866f9a02afde26e7 size: 110592 outs: - path: conf/model/best.yaml @@ -423,7 +423,7 @@ stages: cmd: python -m deckard.layers.find_best attack.yaml deps: - path: attack.db - md5: bd19744ae8a7fe26653fa92b9397f3b0 + md5: cdde21203e37d23fafd7a25750c986e9 size: 110592 outs: - path: conf/attack/best.yaml From c7230355b7753e72ebcbf1755d6c2435fd619234 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Wed, 6 Sep 2023 13:25:03 +0000 Subject: [PATCH 032/148] linting --- deckard/__main__.py | 50 ++-- deckard/layers/optimise.py | 40 +++- deckard/layers/parse.py | 2 +- examples/pytorch/attacks.sh | 18 +- examples/pytorch/conf/grid.yaml | 4 +- examples/pytorch/conf/plots.yaml | 33 +-- examples/pytorch/dvc.lock | 83 ++++--- examples/pytorch/dvc.yaml | 23 +- examples/pytorch/plots.py | 13 +- examples/pytorch/weibull.ipynb | 390 +++++++++++++++++-------------- 10 files changed, 372 insertions(+), 284 deletions(-) diff --git a/deckard/__main__.py b/deckard/__main__.py index 13f471bb..2a4e378b 100644 --- a/deckard/__main__.py +++ b/deckard/__main__.py @@ -4,6 +4,7 @@ import subprocess import logging from pathlib import Path +from .layers.parse import save_params_file logger = logging.getLogger(__name__) layer_list = list(Path(Path(__file__).parent, "layers").glob("*.py")) @@ -13,12 +14,20 @@ def run_submodule(submodule, args): - cmd = f"python -m deckard.layers.{submodule} {args}" + if len(args) == 0: + cmd = f"python -m deckard.layers.{submodule}" + else: + cmd = f"python -m deckard.layers.{submodule} {args}" logger.info(f"Running {cmd}") - with subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True) as proc: + with subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + shell=True, + ) as proc: for line in proc.stdout: print(line.rstrip().decode("utf-8")) - if len(proc.stderr.readlines()) > 0: + if proc.returncode != 0: logger.error(f"Error running {cmd}") for line in proc.stderr: logger.error(line.rstrip().decode("utf-8")) @@ -28,27 +37,25 @@ def run_submodule(submodule, args): def parse_and_repro(args): - cmd = f"python -m deckard.layers.parse {args}" - with subprocess.Popen( - cmd, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - shell=True, - ) as proc: - for line in proc.stdout: - print(line.rstrip().decode("utf-8")) - if len(proc.stderr.readlines()) > 0: - raise ValueError(f"Error parsing with options {args}") + if len(args) == 0: + assert save_params_file(config_dir=Path(Path(), "conf")) is None + assert Path(Path(), "params.yaml").exists() + else: + cmd = f"python -m deckard.layers.parse {args}" + # error = f"error parsing command: {cmd} {args}" + with subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + shell=True, + ) as proc: + for line in proc.stdout: + print(line.rstrip().decode("utf-8")) if Path(Path(), "dvc.yaml").exists(): cmd = "dvc repro" with subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True) as proc: for line in proc.stdout: print(line.rstrip().decode("utf-8")) - if len(proc.stderr.readlines()) > 0: - logger.error(f"Error running {cmd}") - for line in proc.stderr: - logger.error(line.rstrip().decode("utf-8")) - raise ValueError("Error running dvc.yaml") + else: raise ValueError("No dvc.yaml file found. Please construct a pipeline.") return 0 @@ -68,7 +75,10 @@ def parse_and_repro(args): submodule = args.submodule if submodule not in layer_list and submodule is not None: raise ValueError(f"Submodule {submodule} not found. Choices: {layer_list}") - other_args = " ".join(args.other_args) + if len(args.other_args) > 0: + other_args = " ".join(args.other_args) + else: + other_args = [] if submodule is None: assert parse_and_repro(other_args) == 0 else: diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index a61eba01..71a01395 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -12,7 +12,7 @@ logger = logging.getLogger(__name__) -__all__ = ["save_file", "optimise", "parse_stage", "get_files"] +__all__ = ["write_stage", "optimise", "parse_stage", "get_files"] def get_files( @@ -66,11 +66,11 @@ def get_files( return cfg -def save_file(cfg, folder, params_file): - path = Path(folder, Path(params_file).name) - with open(path, "w") as f: - yaml.safe_dump(cfg, f) - assert Path(path).exists() +# def save_file(cfg, folder, params_file): +# path = Path(folder, Path(params_file).name) +# with open(path, "w") as f: +# yaml.safe_dump(cfg, f) +# assert Path(path).exists() def merge_params(default, params) -> dict: @@ -221,6 +221,32 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: return params +def write_stage(params: dict, stage: str, path=None, working_dir=None) -> None: + """ + Write params to dvc.yaml + :param params: Params to write to dvc.yaml + :param stage: Stage to write params to + """ + if path is None: + path = Path.cwd() + if working_dir is None: + working_dir = Path.cwd() + with open(Path(working_dir, "dvc.yaml"), "r") as f: + dvc = yaml.load(f, Loader=yaml.FullLoader) + stage_params = {"stages": {stage: {}}} + stage_params["stages"][stage] = dvc["stages"][stage] + path.mkdir(exist_ok=True, parents=True) + with open(path / "dvc.yaml", "w") as f: + yaml.dump(stage_params, f, default_flow_style=False) + assert Path(path / "dvc.yaml").exists(), f"File {path/'dvc.yaml'} does not exist." + with open(Path(path, "params.yaml"), "w") as f: + yaml.dump(params, f, default_flow_style=False) + assert Path( + path / "params.yaml", + ).exists(), f"File {path/'params.yaml'} does not exist." + return None + + def optimise(cfg: DictConfig) -> None: cfg = OmegaConf.to_container(OmegaConf.create(cfg), resolve=True) scorer = cfg.pop("optimizers", None) @@ -231,7 +257,7 @@ def optimise(cfg: DictConfig) -> None: files = deepcopy(exp.files)() folder = Path(files["score_dict_file"]).parent Path(folder).mkdir(exist_ok=True, parents=True) - save_file(cfg, folder, "params.yaml") + write_stage(cfg, stage, path=folder, working_dir=working_dir) id_ = Path(files["score_dict_file"]).parent.name direction = cfg.pop("direction", "minimize") try: diff --git a/deckard/layers/parse.py b/deckard/layers/parse.py index da5fa1db..7e0f2184 100644 --- a/deckard/layers/parse.py +++ b/deckard/layers/parse.py @@ -14,7 +14,7 @@ if __name__ == "__main__": logger = logging.getLogger(__name__) - + args = hydra_parser.parse_args() logging.basicConfig(level=args.verbosity) config_dir = Path(Path(), args.config_dir).resolve().as_posix() diff --git a/examples/pytorch/attacks.sh b/examples/pytorch/attacks.sh index f8ee62b5..93f69ab8 100644 --- a/examples/pytorch/attacks.sh +++ b/examples/pytorch/attacks.sh @@ -3,13 +3,13 @@ # # This script is used to generate the attacks for the example. # Fast Gradient Method -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ +# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ # Projected Gradient Descent -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ +# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ # DeepFool -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ +# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ # HopSkipJump bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 ++stage=attack ++hydra.sweeper.study_name=hsj ++hydra.sweeper.direction=minimize $@ @@ -25,13 +25,13 @@ bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.ThresholdAttack bash models.sh attack=hsj --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 ++stage=patch ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++attack.init.patch_shape=[1,28,28] $@ ##################################################### -# Carlini L0 Method -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw0 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ +# # Carlini L0 Method +# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw0 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ -# # Carlini L2 Method -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw2 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ +# # # Carlini L2 Method +# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw2 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ -# Carlini LInf Method -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=cwinf ++hydra.sweeper.direction=minimize $@ +# # Carlini LInf Method +# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=cwinf ++hydra.sweeper.direction=minimize $@ rm -rf output/models/* diff --git a/examples/pytorch/conf/grid.yaml b/examples/pytorch/conf/grid.yaml index e662f0b9..35c75a6a 100644 --- a/examples/pytorch/conf/grid.yaml +++ b/examples/pytorch/conf/grid.yaml @@ -18,13 +18,13 @@ hydra: direction: maximize study_name: model storage: sqlite:///model.db - n_jobs: 4 + n_jobs: 1 params: ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 8 + n_jobs: 12 prefer : processes verbose: 1 timeout: null diff --git a/examples/pytorch/conf/plots.yaml b/examples/pytorch/conf/plots.yaml index e0fcefe9..7a53497f 100644 --- a/examples/pytorch/conf/plots.yaml +++ b/examples/pytorch/conf/plots.yaml @@ -4,12 +4,13 @@ cat_plot: kind: boxen legend_title: Model Name set: - yscale: log + yscale: linear titles: Adversarial Accuracy vs Defence Type x: def_gen xlabels: Defence Type y: adv_accuracy ylabels: Adv. Accuracy + rotation : 90 - file: ben_accuracy_vs_defence_type.pdf hue: model_name kind: boxen @@ -19,6 +20,7 @@ cat_plot: xlabels: Defence Type y: accuracy ylabels: Ben. Accuracy + rotation : 90 - file: ben_failures_per_train_time_vs_defence_type.pdf hue: model_name kind: boxen @@ -30,6 +32,7 @@ cat_plot: xlabels: Defence Type y: training_time_per_failure ylabels: Training Time per Ben. Failures + rotation : 90 - file: adv_failures_per_train_time_vs_defence_type.pdf hue: model_name kind: boxen @@ -41,6 +44,7 @@ cat_plot: xlabels: Defence Type y: training_time_per_adv_failure ylabels: Training time per Adv. Failures + rotation : 90 - file: adv_failures_per_train_time_vs_attack_type.pdf hue: model_name kind: boxen @@ -52,6 +56,7 @@ cat_plot: xlabels: Attack Type y: training_time_per_adv_failure ylabels: Training time per Adv. Failures + rotation : 90 - file: adv_accuracy_vs_attack_type.pdf hue: model_name kind: boxen @@ -61,6 +66,7 @@ cat_plot: xlabels: Attack Type y: adv_accuracy ylabels: Adv. Accuracy + rotation : 90 - file: adv_accuracy_vs_defence_type.pdf hue: model_name kind: boxen @@ -70,6 +76,7 @@ cat_plot: xlabels: Defence Type y: adv_accuracy ylabels: Adv. Accuracy + rotation : 90 - file: adv_accuracy_vs_attack_type.pdf hue: model_name kind: boxen @@ -79,6 +86,7 @@ cat_plot: xlabels: Attack Type y: adv_accuracy ylabels: Adv. Accuracy + rotation : 90 - file: ben_failure_rate_vs_defence_type.pdf hue: model_name kind: boxen @@ -90,51 +98,50 @@ cat_plot: xlabels: Defence Type y: failure_rate ylabels: Ben. Failure Rate + rotation : 90 line_plot: - control: 0.98 - control_color: green + control_color: orange file: def_param_vs_accuracy.pdf hue: def_gen legend: {} title: Accuracy vs Defence Strength x: def_value - x_scale: log + x_scale: linear xlabel: Defence Control Parameter y: accuracy y_scale: ylabel: Accuracy - control: 0.1 - control_color: green + control_color: orange file: def_param_vs_adv_accuracy.pdf hue: def_gen legend: {} title: Adversarial Accuracy vs Defence Strength x: def_value - x_scale: log + x_scale: linear xlabel: Defence Control Parameter y: adv_accuracy y_scale: ylabel: Adv. Accuracy - control: 0.1 - control_color: green + control_color: orange file: def_param_vs_adv_failure_rate.pdf hue: def_gen legend: {} title: Adversarial Failure Rate vs Defence Strength x: def_value - x_scale: log + x_scale: linear xlabel: Defence Control Parameter y: adv_failure_rate y_scale: log ylabel: Adv. Failure Rate -- control: 0.1 - control_color: green - file: atk_param_vs_accuracy.pdf +- file: atk_param_vs_accuracy.pdf hue: atk_gen legend: {} title: Adversarial Accuracy vs Attack Strength x: atk_value - x_scale: log + x_scale: linear xlabel: Attack Control Parameter y: adv_accuracy y_scale: @@ -143,11 +150,11 @@ scatter_plot: x: train_time_per_sample y: adv_failure_rate hue: model_name - xlabel: Training Time + xlabel: Training Time Per Sample ylabel: Adversarial Failure Rate title: Adversarial Failure Rate vs Training Time file: adv_failure_rate_vs_train_time.pdf y_scale: log - x_scale: log + x_scale: linear legend: title: Model Name diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index c7e99ce0..50b0aa2c 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -104,18 +104,21 @@ stages: - path: output/data/data.pkl md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 + - path: output/models/model.optimizer.pt + md5: da778ab9ca244100045790051485e60f + size: 44780845 - path: output/models/model.pt - md5: 35e6acec22b8542c13d4fb2ddf7d4939 + md5: 71c48241b81b18b3b60afc8a3f10f25a size: 44785941 - path: output/reports/train/default/params.yaml md5: eedabd38cbbfce21b879b647497174da size: 2148 - path: output/reports/train/default/predictions.json - md5: 3f657fe19090e1bd62209ac130544f93 - size: 2884704 + md5: b4d5926c71a4b54076b566a73e7af54b + size: 2881449 - path: output/reports/train/default/score_dict.json - md5: f19e5106d12c8fcf19eb48edf861d949 - size: 401 + md5: 51249137fa5081d7eff95624f3d1fa1f + size: 410 attack: cmd: python -m deckard.layers.experiment attack deps: @@ -123,7 +126,7 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.pt - md5: 38451da384fb8f787707a2b39b8418de + md5: dc00c8994b407de0e23e96df50b46425 size: 44786389 params: params.yaml: @@ -342,17 +345,17 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: 54345e430aed9ed56a7230ac348900e2 + md5: 7b734a30fe436fd122186fe8cb6ae959 size: 313766 - path: output/reports/attack/default/adv_predictions.json - md5: 1d2980ec86fa5cd4513473630186c9b5 - size: 21523 + md5: 8bfac7d8e89bff0629dffa3e1a3c996f + size: 21748 - path: output/reports/attack/default/params.yaml md5: daf26103182da19d13d5b73113c5688c size: 4956 - path: output/reports/attack/default/score_dict.json - md5: 39ecdefe26af87802dfaed0e3bcd98b3 - size: 528 + md5: 9bd42e8bf85131beabb023c7983ae34e + size: 542 models: cmd: bash models.sh ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 @@ -432,29 +435,17 @@ stages: cmd: python -m deckard.layers.compile --report_folder output/reports/attack --results_file output/reports/attack.csv deps: - - path: ResNet101.db - md5: 86e9ffd22acb5ac8a10e1075cfa0cab9 - size: 696320 - path: ResNet152.db - md5: dbcc81935187d20a249d4e27aaf560fd - size: 602112 - - path: ResNet18.db - md5: 3d304e014200ae926dec63521b6eeca0 - size: 1499136 - - path: ResNet34.db - md5: 7bf834e39f0367deddd36074e8f69d47 - size: 1400832 - - path: ResNet50.db - md5: 771e206f2ab932ae2978783214290c41 - size: 110592 + md5: d86a693a86ec196fcd4887013acde398 + size: 434176 - path: output/reports/attack/ - md5: e60f6852b2b403451f3df235e55975c8.dir - size: 7724615819 - nfiles: 15820 + md5: 0ea96d75e1d41ddf1e4e856260d3d327.dir + size: 8695142484 + nfiles: 17939 outs: - path: output/reports/attack.csv - md5: 73855b1b65e8f7b1879dc390ef65325d - size: 11502713 + md5: 1d31d2bc679e668fc31e323a444c84fd + size: 13006598 compile@train: cmd: python -m deckard.layers.compile --report_folder output/reports/train --results_file output/reports/train.csv @@ -483,18 +474,36 @@ stages: - path: models.sh md5: 76fbee55cb5f90913d0863df9debd357 size: 1299 + - path: output/reports/attack/default/score_dict.json + md5: 35d505c81867c9c50d74780c19d6f7e7 + size: 539 outs: - path: ResNet152.db - md5: dbcc81935187d20a249d4e27aaf560fd - size: 602112 + md5: d86a693a86ec196fcd4887013acde398 + size: 434176 plot: cmd: python plots.py --path output/plots/ --file output/reports/attack.csv deps: + - path: ResNet101.db + md5: 00e8859421e45d77c36d2a39ea5deaa5 + size: 466944 + - path: ResNet152.db + md5: d86a693a86ec196fcd4887013acde398 + size: 434176 + - path: ResNet18.db + md5: 3d304e014200ae926dec63521b6eeca0 + size: 1499136 + - path: ResNet34.db + md5: 7bf834e39f0367deddd36074e8f69d47 + size: 1400832 + - path: ResNet50.db + md5: 771e206f2ab932ae2978783214290c41 + size: 110592 - path: output/reports/attack.csv - md5: 73855b1b65e8f7b1879dc390ef65325d - size: 11502713 + md5: 1d31d2bc679e668fc31e323a444c84fd + size: 13006598 outs: - path: output/plots/ - md5: 254c4e83a0c2c9575af3430e755aba56.dir - size: 331904 - nfiles: 12 + md5: ad60dde37b7ad40574c2e2a3e391224d.dir + size: 8442674 + nfiles: 13 diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index af6d3882..cb3d1d45 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -9,6 +9,7 @@ stages: outs: - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} @@ -36,16 +37,17 @@ stages: ############################################################################## attacks: foreach: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 + # - ResNet18 + # - ResNet34 + # - ResNet50 + # - ResNet101 - ResNet152 do: cmd: bash attacks.sh ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name grid.yaml deps: - models.sh - attacks.sh + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} outs: - ${item}.db compile: @@ -55,10 +57,10 @@ stages: cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv deps: - ${files.directory}/${files.reports}/${item}/ - - ResNet18.db - - ResNet34.db - - ResNet50.db - - ResNet101.db + # - ResNet18.db + # - ResNet34.db + # - ResNet50.db + # - ResNet101.db - ResNet152.db outs: - ${files.directory}/${files.reports}/${item}.csv @@ -66,5 +68,10 @@ stages: cmd : python plots.py --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv deps: - ${files.directory}/${files.reports}/attack.csv + - ResNet18.db + - ResNet34.db + - ResNet50.db + - ResNet101.db + - ResNet152.db outs: - ${files.directory}/plots/ diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index 4ec0ed28..90892429 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -131,10 +131,12 @@ def calculate_failure_rate(data): ], inplace=True, ) - data.loc[:, "failure_rate"] = (1 - data["accuracy"])*100 / data["predict_time_per_sample"] - data.loc[:, "adv_failure_rate"] = (1 - data["adv_accuracy"]) * 100 / data[ - "adv_fit_time_per_sample" - ] + data.loc[:, "failure_rate"] = ( + (1 - data["accuracy"]) * 100 / data["predict_time_per_sample"] + ) + data.loc[:, "adv_failure_rate"] = ( + (1 - data["adv_accuracy"]) * 100 / data["adv_fit_time_per_sample"] + ) data.loc[:, "training_time_per_failure"] = ( data["train_time_per_sample"] / data["failure_rate"] ) @@ -200,11 +202,10 @@ def calculate_failure_rate(data): data = calculate_failure_rate(data) if "Unnamed: 0" in data.columns: data.drop("Unnamed: 0", axis=1, inplace=True) - FOLDER = Path(Path(), args.path) FOLDER.mkdir(parents=True, exist_ok=True) - data.to_csv(FOLDER/args.output) + data.to_csv(FOLDER / args.output) IMAGE_FILETYPE = ( args.plotfiletype if args.plotfiletype.startswith(".") diff --git a/examples/pytorch/weibull.ipynb b/examples/pytorch/weibull.ipynb index 11fc39a5..0343aee6 100644 --- a/examples/pytorch/weibull.ipynb +++ b/examples/pytorch/weibull.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -67,95 +67,47 @@ " train_time\n", " train_time_per_sample\n", " ...\n", - " model_name\n", - " random_state\n", + " model_layers\n", " def_param\n", " def_value\n", " atk_param\n", " atk_value\n", - " adv_failures_per_training_time\n", - " adv_failure_rate\n", " failure_rate\n", - " failures_per_training_time\n", + " adv_failure_rate\n", + " training_time_per_failure\n", + " training_time_per_adv_failure\n", + " adv_training_time_per_failure\n", " \n", " \n", " \n", " \n", " 0\n", " attack\n", - " art.attacks.evasion.PixelAttack\n", - " 000e900bad53681aa2468550956e6f09\n", - " 000e900bad53681aa2468550956e6f09\n", - " 000e900bad53681aa2468550956e6f09\n", - " 000e900bad53681aa2468550956e6f09\n", - " 000e900bad53681aa2468550956e6f09\n", - " 000e900bad53681aa2468550956e6f09\n", - " 66.816678\n", - " 0.001193\n", + " 0009cf1504e3689e79934f78fcb00c7c\n", + " 0009cf1504e3689e79934f78fcb00c7c\n", + " 0009cf1504e3689e79934f78fcb00c7c\n", + " 0009cf1504e3689e79934f78fcb00c7c\n", + " 0009cf1504e3689e79934f78fcb00c7c\n", + " 0009cf1504e3689e79934f78fcb00c7c\n", + " 0009cf1504e3689e79934f78fcb00c7c\n", + " 206.672264\n", + " 0.003691\n", " ...\n", - " ResNet50\n", - " 5\n", - " scale\n", + " 34\n", + " cutoff\n", " 1.000000\n", - " th\n", - " 0.011765\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 1\n", - " attack\n", - " art.attacks.evasion.PixelAttack\n", - " 001c1b378bbfe5942ca63cfb22d745b8\n", - " 001c1b378bbfe5942ca63cfb22d745b8\n", - " 001c1b378bbfe5942ca63cfb22d745b8\n", - " 001c1b378bbfe5942ca63cfb22d745b8\n", - " 001c1b378bbfe5942ca63cfb22d745b8\n", - " 001c1b378bbfe5942ca63cfb22d745b8\n", - " NaN\n", - " NaN\n", - " ...\n", - " ResNet18\n", - " 2\n", - " bit_depth\n", - " 0.066667\n", - " th\n", - " 0.000000\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " \n", - " \n", - " 2\n", - " attack\n", - " art.attacks.evasion.CarliniL0Method\n", - " 001ddff47c0ad0926affea2b3cfafe86\n", - " 001ddff47c0ad0926affea2b3cfafe86\n", - " 001ddff47c0ad0926affea2b3cfafe86\n", - " 001ddff47c0ad0926affea2b3cfafe86\n", - " 001ddff47c0ad0926affea2b3cfafe86\n", - " 001ddff47c0ad0926affea2b3cfafe86\n", - " 99.405136\n", - " 0.001775\n", - " ...\n", - " ResNet18\n", - " 5\n", - " NaN\n", - " NaN\n", - " confidence\n", - " 0.000000\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " eps\n", + " 0.300000\n", + " 2.766349e+05\n", + " 22947.190553\n", + " 1.334096e-08\n", + " 1.608291e-07\n", + " 1.608291e-07\n", " \n", " \n", - " 3\n", + " 5\n", " attack\n", - " art.attacks.evasion.FastGradientMethod\n", + " 0022118d46ac791faba90c6f907516f0\n", " 0022118d46ac791faba90c6f907516f0\n", " 0022118d46ac791faba90c6f907516f0\n", " 0022118d46ac791faba90c6f907516f0\n", @@ -165,21 +117,21 @@ " 203.195971\n", " 0.003628\n", " ...\n", - " ResNet34\n", - " 7\n", + " 34\n", " NaN\n", " NaN\n", " eps\n", " 0.123905\n", - " 0.412329\n", - " 0.000526\n", - " 0.000044\n", - " 0.458764\n", + " 1.435355e+06\n", + " 147157.749244\n", + " 2.527946e-09\n", + " 2.465721e-08\n", + " 2.465721e-08\n", " \n", " \n", - " 4\n", + " 6\n", " attack\n", - " art.attacks.evasion.DeepFool\n", + " 0036b069268b81ebc05519c3372608b9\n", " 0036b069268b81ebc05519c3372608b9\n", " 0036b069268b81ebc05519c3372608b9\n", " 0036b069268b81ebc05519c3372608b9\n", @@ -189,83 +141,124 @@ " 46.047791\n", " 0.000822\n", " ...\n", - " ResNet34\n", - " 9\n", + " 34\n", + " NaN\n", + " NaN\n", + " nb_grads\n", + " 1.000000\n", + " 6.608925e+06\n", + " 6524.344491\n", + " 1.244199e-10\n", + " 1.260329e-07\n", + " 1.260329e-07\n", + " \n", + " \n", + " 8\n", + " attack\n", + " 0059d34a86c5a8e25c3bb06da1cd6555\n", + " 0059d34a86c5a8e25c3bb06da1cd6555\n", + " 0059d34a86c5a8e25c3bb06da1cd6555\n", + " 0059d34a86c5a8e25c3bb06da1cd6555\n", + " 0059d34a86c5a8e25c3bb06da1cd6555\n", + " 0059d34a86c5a8e25c3bb06da1cd6555\n", + " 0059d34a86c5a8e25c3bb06da1cd6555\n", + " 25.424851\n", + " 0.000454\n", + " ...\n", + " 18\n", " NaN\n", " NaN\n", " nb_grads\n", " 1.000000\n", - " 0.094515\n", - " 0.011601\n", - " 0.000015\n", - " 0.082228\n", + " 9.488474e+04\n", + " 10296.684193\n", + " 4.784913e-09\n", + " 4.409334e-08\n", + " 4.409334e-08\n", + " \n", + " \n", + " 9\n", + " attack\n", + " 00603349e13a42bef3170206db60dea6\n", + " 00603349e13a42bef3170206db60dea6\n", + " 00603349e13a42bef3170206db60dea6\n", + " 00603349e13a42bef3170206db60dea6\n", + " 00603349e13a42bef3170206db60dea6\n", + " 00603349e13a42bef3170206db60dea6\n", + " 00603349e13a42bef3170206db60dea6\n", + " 207.587490\n", + " 0.003707\n", + " ...\n", + " 34\n", + " cutoff\n", + " 0.224719\n", + " max_iter\n", + " 0.210526\n", + " 3.184056e+04\n", + " 73.692387\n", + " 1.164213e-07\n", + " 5.030261e-05\n", + " 5.030261e-05\n", " \n", " \n", "\n", - "

5 rows × 193 columns

\n", + "

5 rows × 196 columns

\n", "" ], "text/plain": [ - " stage attack \\\n", - "0 attack art.attacks.evasion.PixelAttack \n", - "1 attack art.attacks.evasion.PixelAttack \n", - "2 attack art.attacks.evasion.CarliniL0Method \n", - "3 attack art.attacks.evasion.FastGradientMethod \n", - "4 attack art.attacks.evasion.DeepFool \n", - "\n", - " data files \\\n", - "0 000e900bad53681aa2468550956e6f09 000e900bad53681aa2468550956e6f09 \n", - "1 001c1b378bbfe5942ca63cfb22d745b8 001c1b378bbfe5942ca63cfb22d745b8 \n", - "2 001ddff47c0ad0926affea2b3cfafe86 001ddff47c0ad0926affea2b3cfafe86 \n", - "3 0022118d46ac791faba90c6f907516f0 0022118d46ac791faba90c6f907516f0 \n", - "4 0036b069268b81ebc05519c3372608b9 0036b069268b81ebc05519c3372608b9 \n", + " stage attack data \\\n", + "0 attack 0009cf1504e3689e79934f78fcb00c7c 0009cf1504e3689e79934f78fcb00c7c \n", + "5 attack 0022118d46ac791faba90c6f907516f0 0022118d46ac791faba90c6f907516f0 \n", + "6 attack 0036b069268b81ebc05519c3372608b9 0036b069268b81ebc05519c3372608b9 \n", + "8 attack 0059d34a86c5a8e25c3bb06da1cd6555 0059d34a86c5a8e25c3bb06da1cd6555 \n", + "9 attack 00603349e13a42bef3170206db60dea6 00603349e13a42bef3170206db60dea6 \n", "\n", - " kwargs model \\\n", - "0 000e900bad53681aa2468550956e6f09 000e900bad53681aa2468550956e6f09 \n", - "1 001c1b378bbfe5942ca63cfb22d745b8 001c1b378bbfe5942ca63cfb22d745b8 \n", - "2 001ddff47c0ad0926affea2b3cfafe86 001ddff47c0ad0926affea2b3cfafe86 \n", - "3 0022118d46ac791faba90c6f907516f0 0022118d46ac791faba90c6f907516f0 \n", - "4 0036b069268b81ebc05519c3372608b9 0036b069268b81ebc05519c3372608b9 \n", + " files kwargs \\\n", + "0 0009cf1504e3689e79934f78fcb00c7c 0009cf1504e3689e79934f78fcb00c7c \n", + "5 0022118d46ac791faba90c6f907516f0 0022118d46ac791faba90c6f907516f0 \n", + "6 0036b069268b81ebc05519c3372608b9 0036b069268b81ebc05519c3372608b9 \n", + "8 0059d34a86c5a8e25c3bb06da1cd6555 0059d34a86c5a8e25c3bb06da1cd6555 \n", + "9 00603349e13a42bef3170206db60dea6 00603349e13a42bef3170206db60dea6 \n", "\n", - " name scorers \\\n", - "0 000e900bad53681aa2468550956e6f09 000e900bad53681aa2468550956e6f09 \n", - "1 001c1b378bbfe5942ca63cfb22d745b8 001c1b378bbfe5942ca63cfb22d745b8 \n", - "2 001ddff47c0ad0926affea2b3cfafe86 001ddff47c0ad0926affea2b3cfafe86 \n", - "3 0022118d46ac791faba90c6f907516f0 0022118d46ac791faba90c6f907516f0 \n", - "4 0036b069268b81ebc05519c3372608b9 0036b069268b81ebc05519c3372608b9 \n", + " model name \\\n", + "0 0009cf1504e3689e79934f78fcb00c7c 0009cf1504e3689e79934f78fcb00c7c \n", + "5 0022118d46ac791faba90c6f907516f0 0022118d46ac791faba90c6f907516f0 \n", + "6 0036b069268b81ebc05519c3372608b9 0036b069268b81ebc05519c3372608b9 \n", + "8 0059d34a86c5a8e25c3bb06da1cd6555 0059d34a86c5a8e25c3bb06da1cd6555 \n", + "9 00603349e13a42bef3170206db60dea6 00603349e13a42bef3170206db60dea6 \n", "\n", - " train_time train_time_per_sample ... model_name random_state \\\n", - "0 66.816678 0.001193 ... ResNet50 5 \n", - "1 NaN NaN ... ResNet18 2 \n", - "2 99.405136 0.001775 ... ResNet18 5 \n", - "3 203.195971 0.003628 ... ResNet34 7 \n", - "4 46.047791 0.000822 ... ResNet34 9 \n", + " scorers train_time train_time_per_sample ... \\\n", + "0 0009cf1504e3689e79934f78fcb00c7c 206.672264 0.003691 ... \n", + "5 0022118d46ac791faba90c6f907516f0 203.195971 0.003628 ... \n", + "6 0036b069268b81ebc05519c3372608b9 46.047791 0.000822 ... \n", + "8 0059d34a86c5a8e25c3bb06da1cd6555 25.424851 0.000454 ... \n", + "9 00603349e13a42bef3170206db60dea6 207.587490 0.003707 ... \n", "\n", - " def_param def_value atk_param atk_value \\\n", - "0 scale 1.000000 th 0.011765 \n", - "1 bit_depth 0.066667 th 0.000000 \n", - "2 NaN NaN confidence 0.000000 \n", - "3 NaN NaN eps 0.123905 \n", - "4 NaN NaN nb_grads 1.000000 \n", + " model_layers def_param def_value atk_param atk_value failure_rate \\\n", + "0 34 cutoff 1.000000 eps 0.300000 2.766349e+05 \n", + "5 34 NaN NaN eps 0.123905 1.435355e+06 \n", + "6 34 NaN NaN nb_grads 1.000000 6.608925e+06 \n", + "8 18 NaN NaN nb_grads 1.000000 9.488474e+04 \n", + "9 34 cutoff 0.224719 max_iter 0.210526 3.184056e+04 \n", "\n", - " adv_failures_per_training_time adv_failure_rate failure_rate \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 0.412329 0.000526 0.000044 \n", - "4 0.094515 0.011601 0.000015 \n", + " adv_failure_rate training_time_per_failure training_time_per_adv_failure \\\n", + "0 22947.190553 1.334096e-08 1.608291e-07 \n", + "5 147157.749244 2.527946e-09 2.465721e-08 \n", + "6 6524.344491 1.244199e-10 1.260329e-07 \n", + "8 10296.684193 4.784913e-09 4.409334e-08 \n", + "9 73.692387 1.164213e-07 5.030261e-05 \n", "\n", - " failures_per_training_time \n", - "0 NaN \n", - "1 NaN \n", - "2 NaN \n", - "3 0.458764 \n", - "4 0.082228 \n", + " adv_training_time_per_failure \n", + "0 1.608291e-07 \n", + "5 2.465721e-08 \n", + "6 1.260329e-07 \n", + "8 4.409334e-08 \n", + "9 5.030261e-05 \n", "\n", - "[5 rows x 193 columns]" + "[5 rows x 196 columns]" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -279,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -352,9 +345,32 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "KeyError", + "evalue": "'random_state'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/core/indexes/base.py:3652\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3651\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3652\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m 3653\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/_libs/index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'random_state'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 17\u001b[0m\n\u001b[1;32m 1\u001b[0m kwarg_list \u001b[39m=\u001b[39m [\n\u001b[1;32m 2\u001b[0m \u001b[39m\"\u001b[39m\u001b[39maccuracy\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 3\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mtrain_time\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[39m# \"adv_accuracy\",\u001b[39;00m\n\u001b[1;32m 14\u001b[0m ]\n\u001b[0;32m---> 17\u001b[0m cleaned, y \u001b[39m=\u001b[39m clean_data_for_aft(data, kwarg_list, standard_scaling\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m 18\u001b[0m cleaned\u001b[39m.\u001b[39mdropna(axis\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, how\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39many\u001b[39m\u001b[39m\"\u001b[39m, subset\u001b[39m=\u001b[39mkwarg_list\u001b[39m.\u001b[39mremove(\u001b[39m\"\u001b[39m\u001b[39mdef_value\u001b[39m\u001b[39m\"\u001b[39m), inplace\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m 19\u001b[0m cleaned[\u001b[39m\"\u001b[39m\u001b[39madv_failure_rate\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m y\n", + "Cell \u001b[0;32mIn[3], line 52\u001b[0m, in \u001b[0;36mclean_data_for_aft\u001b[0;34m(data, kwarg_list, standard_scaling, target)\u001b[0m\n\u001b[1;32m 50\u001b[0m kwarg_list\u001b[39m.\u001b[39mremove(target)\n\u001b[1;32m 51\u001b[0m \u001b[39mfor\u001b[39;00m kwarg \u001b[39min\u001b[39;00m kwarg_list:\n\u001b[0;32m---> 52\u001b[0m cleaned \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mconcat([cleaned, subset[kwarg]], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[1;32m 53\u001b[0m cols \u001b[39m=\u001b[39m cleaned\u001b[39m.\u001b[39mcolumns\n\u001b[1;32m 54\u001b[0m \u001b[39mif\u001b[39;00m standard_scaling \u001b[39mis\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/core/frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3759\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 3760\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3761\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m 3762\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3763\u001b[0m indexer \u001b[39m=\u001b[39m [indexer]\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/core/indexes/base.py:3654\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3652\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3653\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> 3654\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 3655\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 3656\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3657\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3658\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3659\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 'random_state'" + ] + } + ], "source": [ "kwarg_list = [\n", " \"accuracy\",\n", @@ -379,20 +395,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "weibull_dict = {\n", " \"Intercept: rho_\": \"$\\\\rho$\",\n", @@ -418,20 +423,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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RkpIidRQiIhZ2RERERLqChR0RERGRjmBhR0RERKQjWNgREWlBoVCgZ8+eUCgUUkchIuJ0J0RE2rCxscGIESOkjkFUYh4eHoiNjS2yj1KpxMmTJ99QIioLHLEjItJCRkYG7ty5g4yMDKmjEL2Wq6srXF1dAQCxsbGIiYkptG9MTIxY+L28HVVsLOxIK+fOnUPXrl1RrVo1yGQyjZdarZY6HlG5i4uLw7x58xAXFyd1FKISc3R0RGRkZIEvR0dHqeNRKbCwo1ILCwuDp6cnmjdvjuDgYBw+fBg2Njbo2rUrdu3aBUtLS6kjEhERVSm8x45KbdKkSXj//ffh7+8PAGjQoAGGDh2KS5cuYfDgwQVuk5mZiczMTHGZo3pERG9WbGwsXF1dERsbC6VSWWZ9qWLgiB2VSkJCAk6fPo3PPvtMo93MzAwymazQ7RYvXgyFQiG++BcFERFR2WFhR6Vy6dIl5OXloWnTpvnaW7VqVeh2vr6+UKlU4ut1T2QRVXT6+vqwsLCAvr6+1FGIikWpVCIyMrJY/7AuSV+qGHgplkolLy8PAJCWlgYLCwsAwJUrV3Dq1CksXLiw0O3kcjnkcvkbyUj0Jjg6OmL9+vVSxyAiAsDCjkqpbdu2MDExwfTp0zF79mxERERgwoQJmDBhAt5++22p4xERUTHExMQUOo1JTEwMn4ythFjYUanY2tpi9+7d+PLLL9GkSRM4Ojpi4sSJmDp1qtTRiN6o+/fvY8WKFfjyyy/x1ltvSR2HqEiRkZHif7/u8qqjo6PY5+XtqGJjYUel1rt3b/Tu3VvqGESSys7ORkJCArKzs6WOQlQi/EUJ3cSHJ4iIiIh0BAs7IiIiIh3Bwo6IiIhIR7CwIyLSgr29PWbNmgV7e3upoxAR8eEJIiJtmJiYoEmTJlLHICICwBE7IiKtJCcnY+/evUhOTpY6ChERCzsiIm0kJSVh7969SEpKkjoKERELOyIiIiJdwcKOiIiISEewsCMiIiLSESzsiIi0YG5ujg4dOsDc3FzqKEREkAmCIEgdgqoutVoNhUIBlUoFS0tLqeMQERFVOCX5ruSIHRGRFrKzs5GQkIDs7GypoxARsbAjItLG/fv3MWXKFNy/f1/qKERELOyIiIiIdAULOyIiIiIdwcKOiIiISEewsCMiIiLSEZzuhCTF6U6IiIiKxulOiIiIiKogFnZERFqIi4vD3LlzERcXJ3UUIiIWdkRE2sjIyMDdu3eRkZEhdRQiIhZ2RERERLqChR0RERGRjmBhR0RERKQjWNgREWnB1tYWn332GWxtbaWOQkQEA6kDEBFVZubm5njnnXekjkFEBIAjdkREWlGr1fj777+hVquljkJExMKOiEgbT58+RUBAAJ4+fSp1FCIiFnZEREREuoKFHREREZGOYGFHREREpCNY2BERacHExARNmjSBiYmJ1FGIiCATBEGQOgRVXWq1GgqFAiqVCpaWllLHISIiqnBK8l3JETsiIi3k5eXh2bNnyMvLkzoKERELOyIibdy7dw+jR4/GvXv3pI5CRMTCjoiIiEhXsLAjIiIi0hFV9rdi4+Pj8dFHH+Hs2bMwNDREcnKy1JHeqOjoaLi4uCAkJATNmjWTOg4REZUzDw8PxMbGFtlHqVTi5MmTbygRlQedGLHz8fFB//79S7TNqlWrEBcXh9DQUNy+fbt8gklk/vz5kMlkRb6USiXi4uLQqFEjqeMSERXJ1dUVrq6uUseo9GJjYxETE1Po+piYmNcWfvR6Un9eq+yIXUREBFq2bAl3d/dS7yMrKwtGRkZlmKpsTJs2DZ988om43Lp1a4wbNw5jx44V2/T19WFvby9FPCKd4ujoiJ9++glmZmZSRyF6LUdHR0RGRha4jsWzbtCJEbtXeXp6YtKkSZgxYwZsbGxgb2+P+fPni+udnZ2xd+9ebNmyBTKZDD4+PgCA5ORkjBkzBra2trC0tESXLl0QFhYmbjd//nw0a9YMGzZsgIuLC4yNjUu03datW+Hs7AyFQoEhQ4YgJSVF7JOXl4dly5bBzc0Ncrkcjo6OWLRokbg+NjYWgwcPhpWVFWxsbNCvXz9ER0cXePzm5uawt7cXX/r6+rCwsNBoi46OhkwmQ2hoKADgxIkTkMlkOHLkCJo3bw4TExN06dIFjx49wqFDh1C/fn1YWlpi2LBhSE9P18i9ePFiuLi4wMTEBE2bNsWePXtK+0dHVOno6+vD0tIS+vr6UkchItLdEbvNmzdj6tSpOH/+PP7991/4+PigQ4cO6N69Oy5evIiRI0fC0tIS33//vThj/AcffAATExMcOnQICoUC69evR9euXXH79m3Y2NgAAO7evYu9e/di37594l/kxdkuIiICgYGB+Ouvv5CUlITBgwdjyZIlYvHm6+uLX375BatWrcI777yDuLg43Lx5EwCQnZ0NLy8vtGvXDsHBwTAwMMDChQvRo0cPXLlypUxHDefPn4+1a9fC1NQUgwcPxuDBgyGXy/Hbb78hNTUVAwYMwJo1azBz5kwAwOLFi7Ft2zb89NNPcHd3x6lTpzBixAjY2trCw8Mj3/4zMzORmZkpLqvV6jLLTiSFhIQEbN26FR999BHs7OykjqOzYmNjOaKkpdjYWCiVytf24XnWTnHOc3nS2cKuSZMmmDdvHgDA3d0da9euRVBQELp37w5bW1vI5XKYmJiIlyNPnz6NCxcu4NGjR5DL5QAAf39/BAYGYs+ePRg3bhyA55dft2zZAltb2xJtl5eXh4CAAFhYWAAAPvroIwQFBWHRokVISUnB999/j7Vr18Lb2xsAULt2bbzzzjsAgF27diEvLw8bNmyATCYDAGzatAlWVlY4ceIE3n333TI7bwsXLkSHDh0AAKNHj4avry8iIiLE/9EHDRqE48ePY+bMmcjMzMS3336Lf/75B+3atQPwfCj/9OnTWL9+fYGF3eLFi+Hn51dmeYmklp6ejsuXL2PgwIFSRyEi0u3C7mUODg549OhRof3DwsKQmpqKatWqabQ/e/YMERER4rKTk5NY1JVkO2dnZ7GoezVPeHg4MjMz0bVr10Kz3b17V2N7AMjIyNB4j7Lw8nmzs7ODqampxr/e7OzscOHCBQDPRy/T09PRvXt3jX1kZWWhefPmBe7f19cXU6dOFZfVarWk/7IhospBqVQWem8YFU9xRuJ4nrUn9YinzhZ2hoaGGssymazIn/xJTU2Fg4MDTpw4kW+dlZWV+N+v3iBd3O2KyvO6Hw9PTU1Fy5YtsX379nzrXi4yy8LLOWUyWZG5U1NTAQAHDhxArVq1NPq9GL18lVwuL3QdERERaUdnC7uSatGiBeLj42FgYABnZ+dy3+5l7u7uMDExQVBQEMaMGVPge+zatQs1atR47Y//vkkNGjSAXC5HTExMgZddiYioYomJiSl0RCkmJgaOjo5vOBGVNZ18KrY0unXrhnbt2qF///74+++/ER0djbNnz2L27Nn477//yny7lxkbG2PmzJmYMWMGtmzZgoiICJw7dw4bN24EAAwfPhzVq1dHv379EBwcjKioKJw4cQKTJk3C/fv3y+T4S8PCwgLTpk3DlClTsHnzZkRERODy5ctYs2YNNm/eLFkuojfJxsYGI0aMEB+UorIXGRnJy4NlQKlUFlm4OTo68taYMiD155Ujdv9HJpPh4MGDmD17NkaNGoXHjx/D3t4enTp1KvJJt9Ju96o5c+bAwMAAc+fOxcOHD+Hg4CDORWdqaopTp05h5syZeP/995GSkoJatWqha9euko/gffPNN7C1tcXixYsRGRkJKysrtGjRAl999ZWkuYjeFIVCgZ49e0odg+i1+IsSVYNMEARB6hBUdanVaigUCqhUKsmLVKLSSEtLw7Vr19CoUSNOUkxE5aIk35W8FEtEpIVHjx7h+++/L/KpeyKiN4WFHREREZGOYGFHREREpCNY2BERERHpCBZ2RERaMDIygrOzc5n+ZjMRUWnxqViSFJ+KJSIiKhqfiiUiIiKqgljYERFpITo6GiNHjkR0dLTUUYiIWNgREWlDEATk5OSAd7UQUUXAwo6IiIhIR7CwIyIiItIRLOyIiIiIdISB1AGIiCqzWrVqYdmyZahRo4bUUYiIWNgREWnDyMgIb731ltQxiIgA8FIsEZFWnjx5gp9//hlPnjyROgoREQs7IiJtpKSk4MSJE0hJSZE6ChERCzsiIiIiXcHCjoiIiEhHsLAjIiIi0hEs7IiItKBQKNC3b18oFAqpoxARcboTIiJt2NjYYMiQIVLHICICwBE7IiKtZGRk4MaNG8jIyJA6ChERCzsiIm3ExcVh4cKFiIuLkzoKERELOyIiIiJdwcKOiIiISEewsCMiIiLSESzsiIi0YGBgABsbGxgYcJIBIpKeTBAEQeoQVHWp1WooFAqoVCpYWlpKHYeIiKjCKcl3JUfsiIiIiHQECzsiIi3ExsZi4sSJiI2NlToKERELOyIibeTk5CAxMRE5OTlSRyEiYmFHREREpCtY2BERERHpCBZ2RERERDqChR0RkRYcHBzw9ddfw8HBQeooRETgjJpERFowNjZGgwYNpI5BRASAI3ZERFpJTEzEzp07kZiYKHUUIiIWdto6ceIEZDIZkpOTpY4iiYCAAFhZWUkdg0gyKpUK+/fvh0qlkjoK6SgPDw+4uroW+fLw8JA6JlUQlbaw+/fff6Gvr49evXrlWzd//nw0a9YsX7tMJkNgYGD5h3uNx48f49NPP4WjoyPkcjns7e3h5eWFM2fOiH0qStaXOTs747vvvpM6BhFVQi8KECq52NhYxMTEFLo+JiaGE2SXgq5+JivtPXYbN27E559/jo0bN+Lhw4eoWbOm1JGKbeDAgcjKysLmzZvh6uqKhIQEBAUF4enTpyXaT1ZWFoyMjMopJRERVRSOjo6IjIwscJ0uFidUepVyxC41NRW7du3Cp59+il69eiEgIEBcFxAQAD8/P4SFhUEmk0EmkyEgIADOzs4AgAEDBkAmk4nLERER6NevH+zs7GBubo7WrVvjn3/+0Xi/zMxMzJw5E0qlEnK5HG5ubti4cWOB2dLT0/Hee++hQ4cOBV6eTU5ORnBwMJYuXYrOnTvDyckJbdq0ga+vL/r27QsAhWZ9MRK5YcMGuLi4wNjYWNznmDFjYGtrC0tLS3Tp0gVhYWHie77YbuvWrXB2doZCocCQIUOQkpIi9klJScHw4cNhZmYGBwcHrFq1Cp6enpg8eTIAwNPTE/fu3cOUKVPE8/qyI0eOoH79+jA3N0ePHj0QFxdX6J8fERERlY9KOWK3e/du1KtXD3Xr1sWIESMwefJk+Pr6QiaT4cMPP8S1a9dw+PBhsUBTKBTo1asXatSogU2bNqFHjx7Q19cH8LxI7NmzJxYtWgS5XI4tW7agT58+uHXrFhwdHQEAI0eOxL///ovVq1ejadOmiIqKwpMnT/LlSk5ORq9evWBubo6jR4/C1NQ0Xx9zc3OYm5sjMDAQb7/9NuRyeb4+Fy9eLDArANy9exd79+7Fvn37xPYPPvgAJiYmOHToEBQKBdavX4+uXbvi9u3bsLGxAfC8gA0MDMRff/2FpKQkDB48GEuWLMGiRYsAAFOnTsWZM2ewf/9+2NnZYe7cubh8+bJ4SXvfvn1o2rQpxo0bh7Fjx2rkTU9Ph7+/P7Zu3Qo9PT2MGDEC06ZNw/bt2/MdW2ZmJjIzM8VltVpdyJ8yUeVgYWEBT09PWFhYSB2lwouNjeXoUinExsZCqVS+tg/PbckU57xWRpWysNu4cSNGjBgBAOjRowdUKhVOnjwJT09PmJiYwNzcHAYGBrC3txe3MTExAQBYWVlptDdt2hRNmzYVl7/55hv88ccf2L9/PyZOnIjbt29j9+7dOHr0KLp16wag4GHv+Ph4fPjhh3B3d8dvv/1W6CVSAwMDBAQEYOzYsfjpp5/QokULeHh4YMiQIWjSpAkAwNbWtsCswPPLr1u2bBH7nD59GhcuXMCjR4/EItHf3x+BgYHYs2cPxo0bBwDIy8tDQECA+OXz0UcfISgoCIsWLUJKSgo2b96M3377DV27dgUAbNq0SePyto2NDfT19WFhYZEvU3Z2Nn766SfUrl0bADBx4kQsWLCgwONfvHgx/Pz8ClxHVBlVr15d/P+MiEhqla6wu3XrFi5cuIA//vgDwPNC6cMPP8TGjRvh6elZ4v2lpqZi/vz5OHDgAOLi4pCTk4Nnz56JN6qGhoZCX1//tU8cde/eHW3atMGuXbs0RtgKMnDgQPTq1QvBwcE4d+4cDh06hGXLlmHDhg3w8fEpclsnJyexqAOAsLAwpKamolq1ahr9nj17hoiICHHZ2dlZY0TBwcEBjx49AgBERkYiOzsbbdq0EdcrFArUrVu3yCwvmJqaikXdq/t+la+vL6ZOnSouq9VqnfwXE1UdWVlZePToEWrUqMF7Xl9DqVQWep8YFa44I3E8tyWnqyOcla6w27hxI3JycjRGkwRBgFwux9q1a6FQKEq0v2nTpuHo0aPw9/eHm5sbTExMMGjQIGRlZQH4/yN9r9OrVy/s3bsXN27cQOPGjV/b39jYGN27d0f37t0xZ84cjBkzBvPmzXttYWdmZqaxnJqaCgcHB5w4cSJf35enITE0NNRYJ5PJkJeX99qcxVHQvgVBKLCvXC4v8PIzUWX14MEDzJ49G4sWLYKLi4vUcYioiqtUhV1OTg62bNmCFStW4N1339VY179/f+zYsQOffPIJjIyMkJubm297Q0PDfO1nzpyBj48PBgwYAOB5oRQdHS2ub9y4MfLy8nDy5EnxUmxBlixZAnNzc3Tt2hUnTpwo8Uz0DRo00JjepKCsBWnRogXi4+NhYGAgPmRRUq6urjA0NMTFixfF+wpVKhVu376NTp06if0KO69ERFS+YmJiCh1hiomJEf/uJqpUT8W+uPF/9OjRaNSokcZr4MCB4pOqzs7OiIqKQmhoKJ48eSLerO/s7IygoCDEx8cjKSkJAODu7o59+/YhNDQUYWFhGDZsmMZIlrOzM7y9vfHxxx8jMDAQUVFROHHiBHbv3p0vn7+/P4YPH44uXbrg5s2bBR7D06dP0aVLF2zbtg1XrlxBVFQUfv/9dyxbtgz9+vXTeN9XsxakW7duaNeuHfr374+///4b0dHROHv2LGbPno3//vuvWOfVwsIC3t7emD59Oo4fP47r169j9OjR0NPT03j61dnZGadOncKDBw8KfHiEiKgwkZGRvFRYSkqlssjCzdHRkbe0lIKufiYrVWG3ceNGdOvWrcDLrQMHDsR///2HK1euYODAgejRowc6d+4MW1tb7NixAwCwYsUKHD16FEqlEs2bNwcArFy5EtbW1mjfvj369OkDLy8vtGjRQmPfP/74IwYNGoTPPvsM9erVw9ixY5GWllZgxlWrVmHw4MHo0qULbt++nW+9ubk52rZti1WrVqFTp05o1KgR5syZg7Fjx2Lt2rViv4KyFkQmk+HgwYPo1KkTRo0ahTp16mDIkCG4d+8e7OzsXn9S/8/KlSvRrl079O7dG926dUOHDh1Qv359cUoVAFiwYAGio6NRu3Ztjfv8iIio/Jw8eVIsQgp7nTx5UuqYVEHIhMJuhqIqLS0tDbVq1cKKFSswevTocnsftVoNhUIBlUoFS0vLcnsfovISHR2NuXPnYsGCBaW+HYKIqCgl+a6sVPfYUfkJCQnBzZs30aZNG6hUKnG6kpcvDxNRfs7OztiyZYvUMYiIALCwo5f4+/vj1q1bMDIyQsuWLREcHIzq1atLHYuIiIiKiYUdAQCaN2+OS5cuSR2DqNJ58OAB1q1bhwkTJqBWrVpSxyGiKq5SPTxBRFTRZGVlITo6Wpz7kohISizsiIiIiHQECzsiIiIiHcHCjoiIiEhHsLAjItJCjRo18MUXX6BGjRpSRyEi4lOxRETaMDMzQ9u2baWOQUQEgCN2RERaUalUOHjwIFQqldRRiIhY2BERaSMxMRHbtm1DYmKi1FGIiFjYEREREekKFnZEREREOoKFHREREZGOYGFHRKQFU1NTtGjRAqamplJHISKCTBAEQeoQVHWp1WooFAqoVCpYWlpKHYeIiKjCKcl3JUfsiIi0kJubC7VajdzcXKmjEBGxsCMi0kZMTAw++eQTxMTESB2FiIiFHREREZGuYGFHREREpCNY2BERERHpCBZ2RERERDqC052QpDjdCVV2eXl5yMzMhFwuh54e/61MRGWvJN+VBm8oExGRTtLT04OJiYnUMYiIAPBSLBGRVuLj47FkyRLEx8dLHYWIiIUdEZE2nj17hitXruDZs2dSRyEiYmFHREREpCtY2BERERHpCBZ2RERERDqChR0RkRaqVasGHx8fVKtWTeooRESc7oSISBuWlpZ49913pY5BRASAI3ZERFpJTU3F6dOnkZqaKnUUIiIWdkRE2nj8+DF++OEHPH78WOooREQs7IiIiIh0BQs7IiIiIh3Bwo6IiIhIR5S6sIuIiMDXX3+NoUOH4tGjRwCAQ4cO4fr162UWjoioojM2NoabmxuMjY2ljkJEVLrC7uTJk2jcuDHOnz+Pffv2iU+DhYWFYd68eWUakIioInNwcMCCBQvg4OAgdRQiotIVdrNmzcLChQtx9OhRGBkZie1dunTBuXPnyiycNnx8fCCTycRXtWrV0KNHD1y5ckXSXOPHj4e+vj5+//13SXMQUfnz8PCAq6trkS8PDw+pYxKRDilVYXf16lUMGDAgX3uNGjXw5MkTrUOVlR49eiAuLg5xcXEICgqCgYEBevfuLVme9PR07Ny5EzNmzMCvv/4qWY4XsrKypI5AOuBFgVJVRUVFYdiwYYiKisq3LjY2FjExMYVuGxMTg9jY2PKMVylV9c8UkTZKVdhZWVkhLi4uX3tISAhq1aqldaiyIpfLYW9vD3t7ezRr1gyzZs1CbGysxnxTsbGxGDx4MKysrGBjY4N+/fohOjpaXO/j44P+/fvD398fDg4OqFatGiZMmIDs7OwS5/n999/RoEEDzJo1C6dOncr3F3pmZiZmzpwJpVIJuVwONzc3bNy4UVx//fp19O7dG5aWlrCwsEDHjh0REREBAPD09MTkyZM19te/f3/4+PiIy87Ozvjmm28wcuRIWFpaYty4cQCAmTNnok6dOjA1NYWrqyvmzJmT7/j+97//oXXr1jA2Nkb16tXFwn7BggVo1KhRvmNt1qwZ5syZU+JzRKRrHB0dERkZWeDL0dFR6nhEpGNKVdgNGTIEM2fORHx8PGQyGfLy8nDmzBlMmzYNI0eOLOuMZSI1NRXbtm2Dm5ub+JuO2dnZ8PLygoWFBYKDg3HmzBmYm5ujR48eGqNZx48fR0REBI4fP47NmzcjICAAAQEBJc6wceNGjBgxAgqFAu+9916+fYwcORI7duzA6tWrER4ejvXr18Pc3BwA8ODBA3Tq1AlyuRzHjh3DpUuX8PHHHyMnJ6dEGfz9/dG0aVOEhISIhZeFhQUCAgJw48YNfP/99/jll1+watUqcZsDBw5gwIAB6NmzJ0JCQhAUFIQ2bdoAAD7++GOEh4fj4sWLYv+QkBBcuXIFo0aNKvE5IiIiotKTCYIglHSjrKwsTJgwAQEBAcjNzYWBgQFyc3MxbNgwBAQEQF9fvzyyloiPjw+2bdsmPqmWlpYGBwcH/PXXX2jRogUAYNu2bVi4cCHCw8Mhk8kAPD82KysrBAYG4t1334WPjw9OnDiBiIgI8bgGDx4MPT097Ny5s9h57ty5g4YNG+Lhw4eoXr06AgMDMXXqVEREREAmk+H27duoW7cujh49im7duuXb/quvvsLOnTtx69YtGBoa5lvv6emJZs2a4bvvvhPb+vfvDysrK7GAdHZ2RvPmzfHHH38UmdXf3x87d+7Ef//9BwBo3749XF1dsW3btgL79+zZE87Ozvjhhx8AAJMmTcLVq1dx/PjxfH0zMzORmZkpLqvVaiiVSqhUKlhaWhaZiyomV1dXxMbGQqlUSh1FEtnZ2UhKSoK1tXW+/zdfnJfIyMgCt63q564wrztvRFWNWq2GQqEo1ndlqUbsjIyM8MsvvyAyMhJ//fUXtm3bhps3b2Lr1q0Voqh7oXPnzggNDUVoaCguXLgALy8vvPfee7h37x6A50/x3r17FxYWFjA3N4e5uTlsbGyQkZEhXuIEgIYNG2ocl4ODgzjFS3H9+uuv8PLyQvXq1QE8L4ZUKhWOHTsGAAgNDYW+vn6hN1KHhoaiY8eOBRZ1JdGqVat8bbt27UKHDh1gb28Pc3NzfP311xr3BYWGhqJr166F7nPs2LHYsWMHMjIykJWVhd9++w0ff/xxgX0XL14MhUIhvviFRkREVHYMSrPRggULMG3aNCiVSo0v5mfPnmH58uWYO3dumQXUhpmZGdzc3MTlDRs2QKFQ4JdffsHChQuRmpqKli1bYvv27fm2tbW1Ff/71WLqxeXn4srNzcXmzZsRHx8PAwMDjfZff/0VXbt2hYmJSZH7eN16PT09vDr4WtB9gGZmZhrL//77L4YPHw4/Pz94eXlBoVBg586dWLFiRbHfu0+fPpDL5fjjjz9gZGSE7OxsDBo0qMC+vr6+mDp1qrj8YsSOKreqPLqSnZ2NxMRE2NjY5Pu7ojgPAFTlc1cYPjhBVHqlKuz8/PzwySefwNTUVKM9PT0dfn5+Faawe5VMJoOenh6ePXsGAGjRogV27dqFGjVqlOtlwIMHDyIlJQUhISEaI3/Xrl3DqFGjkJycjMaNGyMvLw8nT54s8FJskyZNsHnzZmRnZxc4amdra6vxQEtubi6uXbuGzp07F5nt7NmzcHJywuzZs8W2FyOaL793UFBQoffMGRgYwNvbG5s2bYKRkRGGDBlSaDEol8shl8uLzERUmRgaGsLOzk7qGEREAEp5KVYQBPGetJeFhYXBxsZG61BlJTMzE/Hx8YiPj0d4eDg+//xzpKamok+fPgCA4cOHo3r16ujXrx+Cg4MRFRWFEydOYNKkSbh//36x38fX17fIh0Y2btyIXr16oWnTpmjUqJH4evE07vbt2+Hs7Axvb298/PHHCAwMFLPs3r0bADBx4kSo1WoMGTIE//33H+7cuYOtW7fi1q1bAJ7PIXjgwAEcOHAAN2/exKeffork5OTXZnd3d0dMTAx27tyJiIgIrF69Ot89ePPmzcOOHTswb948hIeH4+rVq1i6dKlGnzFjxuDYsWM4fPhwoZdhiXTR48ePsW7dOo2n7V8WExNT6Bx2RU2FQkRUGiUq7KytrWFjYwOZTIY6derAxsZGfCkUCnTv3h2DBw8ur6wldvjwYTg4OMDBwQFt27bFxYsX8fvvv8PT0xMAYGpqilOnTsHR0RHvv/8+6tevj9GjRyMjI6NEI3hxcXGF/gWdkJCAAwcOYODAgfnW6enpYcCAAeKUJj/++CMGDRqEzz77DPXq1cPYsWORlpYGAKhWrRqOHTuG1NRUeHh4oGXLlvjll1/E0buPP/4Y3t7eGDlypDgp6utG6wCgb9++mDJlCiZOnIhmzZrh7Nmz+aYp8fT0xO+//479+/ejWbNm6NKlCy5cuKDRx93dHe3bt0e9evXQtm3b15800hkvpu6oqlJTU3HmzBnxF3heplQqi5zSxNHRkbciFKCqf6aItFGip2I3b94MQRDw8ccf47vvvoNCoRDXGRkZwdnZGe3atSuXoFSxCYIAd3d3fPbZZxr30L1OSZ70IaqIoqKiMHv2bCxatAguLi5SxyEiHVSS78oS3WPn7e0NAHBxcUH79u21fkKTdMPjx4+xc+dOxMfHc+46IiIiCZXq4YmXp+R4McXFyzjyUrXUqFED1atXx88//wxra2up4xAREVVZpSrs0tPTMWPGDOzevRtPnz7Ntz43N1frYFR5lGKOayKdYW1tjYEDB/IfNURUIZTqqdjp06fj2LFj+PHHHyGXy7Fhwwb4+fmhZs2a2LJlS1lnJCKqsKysrDBw4EBYWVlJHYWIqHSF3f/+9z/88MMPGDhwIAwMDNCxY0d8/fXX+Pbbbwuc7JeISFc9e/YMV65cEefHJCKSUqkKu8TERHFmcEtLSyQmJgIA3nnnHZw6dars0hERVXDx8fFYsmQJ4uPjpY5CRFS6ws7V1RVRUVEAgHr16omT6P7vf//j5QgiIiIiiZSqsBs1ahTCwsIAALNmzcK6detgbGyMKVOmYPr06WUakIiIiIiKp1RPxU6ZMkX8727duuHmzZu4dOkS3Nzc0KRJkzILR0RERETFV6rC7lVOTk5wcnIqi10REVUqhoaGsLOz44TtRFQhFPsnxVavXo1x48bB2NgYq1evLrLvpEmTyiQc6T7+pBgREVHRSvJdWezCzsXFBf/99x+qVatW5O8hymQy/ngzFRsLOyIioqKVy2/FvngK9tX/JiKqymJiYrBo0SLMnj0bjo6OUschoiquxE/FZmdno3bt2ggPDy+PPERElUpubi5SUlL4U4pEVCGUuLAzNDRERkZGeWQhIiIiIi2Uah67CRMmYOnSpcjJySnrPERERERUSqWa7uTixYsICgrC33//jcaNG8PMzExj/b59+8okHBEREREVX6kKOysrKwwcOLCssxARVToODg7w8/ODg4OD1FGIiIo/3QlReeB0J0REREUryXdlqe6xIyKi5xITE7Ft2zYkJiZKHYWIqPQ/KbZnzx7s3r0bMTExyMrK0lh3+fJlrYMREVUGKpUKBw8eRIcOHWBjYyN1HCKq4ko1Yrd69WqMGjUKdnZ2CAkJQZs2bVCtWjVERkbivffeK+uMRERERFQMpSrsfvjhB/z8889Ys2YNjIyMMGPGDBw9ehSTJk2CSqUq64xEREREVAylKuxiYmLQvn17AICJiQlSUlIAAB999BF27NhRdumIiIiIqNhKVdjZ29uLNwo7Ojri3LlzAJ7/hiwfsiWiqsTCwgLdu3eHhYWF1FGIiEpX2HXp0gX79+8HAIwaNQpTpkxB9+7d8eGHH2LAgAFlGpCIqCKrXr06Ro0aherVq0sdhYiodPPY5eXlIS8vDwYGzx+q3blzJ86ePQt3d3eMHz8eRkZGZR6UdBPnsaPKLjMzEw8fPkTNmjUhl8uljkNEOqgk35WcoJgkxcKOKruoqCjMnj0bixYtgouLi9RxiEgHlfsExW5ubpg/fz5u375dqoBEREREVPZKVdhNmDABBw4cQP369dG6dWt8//33iI+PL+tsRERERFQCpSrspkyZgosXLyI8PBw9e/bEunXroFQq8e6772LLli1lnZGIiIiIikGr34qtU6cO/Pz8cPv2bQQHB+Px48cYNWpUWWUjIqrw9PT0YGxsDD09/vQ2EUmv1L8V+8KFCxfw22+/YdeuXVCr1fjggw/KIhcRUaXg5OSEX3/9VeoYREQASlnY3b59G9u3b8eOHTsQFRWFLl26YOnSpXj//fdhbm5e1hmJiIiIqBhKVdjVq1cPrVu3xoQJEzBkyBDY2dmVdS4iokrhwYMH+O677zB58mTUqlVL6jhEVMWVqrC7desW3N3dyzoLEVGlk5WVhQcPHiArK0vqKEREpSvsXhR1ly5dQnh4OACgQYMGaNGiRdklIyIiIqISKVVh9+jRI3z44Yc4efIkrKysAADJycno3Lkzdu7cCVtb27LMSERERETFUKrn8z///HOkpqbi+vXrSExMRGJiIq5duwa1Wo1JkyaVdcZKSSaTITAwUOoYRFSOPDw84OHhgf3798PDwwOurq75Xh4eHlLHJKIqpFSF3eHDh/HDDz+gfv36YluDBg2wbt06HDp0qMzCacPHxwcymQwymQyGhoZwcXHBjBkzkJGRIXW0cvX48WN8+umncHR0hFwuh729Pby8vHDmzBmxT2mLTmdnZ3z33XdlF5aoEnhRoBUkNjYWDx8+RI0aNWBgkP8CSExMDGJjY0u8XyKi0irVpdi8vDwYGhrmazc0NEReXp7WocpKjx49sGnTJmRnZ+PSpUvw9vaGTCbD0qVLpY5WbgYOHIisrCxs3rwZrq6uSEhIQFBQEJ4+fSp1NCKd5OjoiMjIyALXsXAjojetVCN2Xbp0wRdffIGHDx+KbQ8ePMCUKVPQtWvXMgunrRcjVkqlEv3790e3bt1w9OhRcf3Tp08xdOhQ1KpVC6ampmjcuDF27NihsQ9PT09MmjQJM2bMgI2NDezt7TF//nyNPnfu3EGnTp1gbGyMBg0aaLzHC1evXkWXLl1gYmKCatWqYdy4cUhNTRXX+/j4oH///vj2229hZ2cHKysrLFiwADk5OZg+fTpsbGzw1ltvYdOmTYUeb3JyMoKDg7F06VJ07twZTk5OaNOmDXx9fdG3b18Az0fdAGDAgAGQyWTickREBPr16wc7OzuYm5ujdevW+OeffzTOw7179zBlyhRxJPSF06dPo2PHjjAxMYFSqcSkSZOQlpZW9B8OERERlblSFXZr166FWq2Gs7Mzateujdq1a8PFxQVqtRpr1qwp64xl4tq1azh79iyMjIzEtoyMDLRs2RIHDhzAtWvXMG7cOHz00Ue4cOGCxrabN2+GmZkZzp8/j2XLlmHBggVi8ZaXl4f3338fRkZGOH/+PH766SfMnDlTY/u0tDR4eXnB2toaFy9exO+//45//vkHEydO1Oh37NgxPHz4EKdOncLKlSsxb9489O7dG9bW1jh//jw++eQTjB8/Hvfv3y/wGM3NzWFubo7AwEBkZmYW2OfixYsAgE2bNiEuLk5cTk1NRc+ePREUFISQkBD06NEDffr0QUxMDABg3759eOutt7BgwQLExcUhLi4OwPOCsEePHhg4cCCuXLmCXbt24fTp0/mO7YXMzEyo1WqNF1FFFxsbW+D9c4VdZi2rbYmISkwopby8POHvv/8WVq9eLaxevVo4evRoaXdVLry9vQV9fX3BzMxMkMvlAgBBT09P2LNnT5Hb9erVS/jyyy/FZQ8PD+Gdd97R6NO6dWth5syZgiAIwpEjRwQDAwPhwYMH4vpDhw4JAIQ//vhDEARB+PnnnwVra2shNTVV7HPgwAFBT09PiI+PF/M6OTkJubm5Yp+6desKHTt2FJdzcnIEMzMzYceOHYXm37Nnj2BtbS0YGxsL7du3F3x9fYWwsDCNPi9nK0rDhg2FNWvWiMtOTk7CqlWrNPqMHj1aGDdunEZbcHCwoKenJzx79izfPufNmycAyPdSqVSvzUMkBRcXF8HAwEBwcXHJ93rRXh7bEhG9oFKpiv1dWaJ77I4dO4aJEyfi3LlzsLS0RPfu3dG9e3cAgEqlQsOGDfHTTz+hY8eOZVl7llrnzp3x448/Ii0tDatWrYKBgQEGDhwors/NzcW3336L3bt3ixOMZmZmwtTUVGM/TZo00Vh2cHDAo0ePAADh4eFQKpWoWbOmuL5du3Ya/cPDw9G0aVOYmZmJbR06dEBeXh5u3bol/nJHw4YNNX5I3M7ODo0aNRKX9fX1Ua1aNfG9CzJw4ED06tULwcHBOHfuHA4dOoRly5Zhw4YN8PHxKXS71NRUzJ8/HwcOHEBcXBxycnLw7NkzccSuMGFhYbhy5Qq2b98utgmCgLy8PERFRWk8YAMAvr6+mDp1qrisVquhVCqLfA8iqSmVygLvoyvOPXTabEtEVFIlKuy+++47jB07FpaWlvnWKRQKjB8/HitXrqwwhZ2ZmRnc3NwAAL/++iuaNm2KjRs3YvTo0QCA5cuX4/vvv8d3332Hxo0bw8zMDJMnT843g/yrD4rIZLJyeUikoPcpzXsbGxuLRfecOXMwZswYzJs3r8jCbtq0aTh69Cj8/f3h5uYGExMTDBo06LWz6aempmL8+PEFTnPj6OiYr00ul0Mulxe5TyIiIiqdEhV2YWFhRT5R+u6778Lf31/rUOVBT08PX331FaZOnYphw4bBxMQEZ86cQb9+/TBixAgAz++Xu337Nho0aFDs/davXx+xsbGIi4uDg4MDAODcuXP5+gQEBCAtLU0ctTtz5gz09PRQt27dMjrCwjVo0EBjehNDQ0Pk5uZq9Dlz5gx8fHwwYMAAAM8LtujoaI0+RkZG+bZr0aIFbty4IRbQRFVNTExMoaNvMTExBf4Dh4iovJTo4YmEhIQCpzl5wcDAAI8fP9Y6VHn54IMPoK+vj3Xr1gF4/tNoR48exdmzZxEeHo7x48cjISGhRPvs1q0b6tSpA29vb4SFhSE4OBizZ8/W6DN8+HAYGxvD29sb165dw/Hjx/H555/jo48+Ei/DloWnT5+iS5cu2LZtG65cuYKoqCj8/vvvWLZsGfr16yf2c3Z2RlBQEOLj45GUlATg+bnYt28fQkNDERYWhmHDhuUbGXR2dsapU6fw4MEDPHnyBAAwc+ZMnD17FhMnTkRoaCju3LmDP//8s9CHJ4gqm8jIyEKnM1EqlUUWbo6OjoXealDUfomISqtEhV2tWrVw7dq1QtdfuXJFHLWqiAwMDDBx4kQsW7YMaWlp+Prrr9GiRQt4eXnB09MT9vb26N+/f4n2qaenhz/++APPnj1DmzZtMGbMGCxatEijj6mpKY4cOYLExES0bt0agwYNQteuXbF27doyPLrnT8W2bdsWq1atQqdOndCoUSPMmTMHY8eO1XivFStW4OjRo1AqlWjevDkAYOXKlbC2tkb79u3Rp08feHl55fvt3wULFiA6Ohq1a9cWfzauSZMmOHnyJG7fvo2OHTuiefPmmDt3rsY9h0S66sVn/7///sPt27fFYu3l18mTJ6WOSURViEwQBKG4nT///HOcOHECFy9ehLGxsca6F4VN586dsXr16jIPSrpJrVZDoVBApVIVeO8mUUUXFRWF2bNnY9GiRXBxcZE6DhHpoJJ8V5boHruvv/4a+/btQ506dTBx4kTx/rCbN29i3bp1yM3NzXcZkoiIiIjejBIVdnZ2djh79iw+/fRT+Pr64sVgn0wmg5eXF9atW1em94wRERERUfGV+LdinZyccPDgQSQlJeHu3bsQBAHu7u6wtrYuj3xEREREVEwlLuxesLa2RuvWrcsyCxERERFpoUQPTxCVNT48QZWdIAjIycmBgYEBZDKZ1HGISAeV28MTRESkqaBfiCEikkqJ5rEjIiJNcXFx+OabbxAXFyd1FCIiFnZERNrIyMhAeHg4MjIypI5CRMTCjoiIiEhXsLAjIiIi0hEs7IiIiIh0BAs7IiItVK9eHWPHjkX16tWljkJExOlOiIi0YWFhgc6dO0sdg4gIAEfsiIi0kpKSguPHjyMlJUXqKERELOyIiLTx5MkT/PLLL3jy5InUUYiIWNgRERER6QoWdkREREQ6goUdERERkY5gYUdEpAVjY2PUr18fxsbGUkchIoJMEARB6hBUdanVaigUCqhUKlhaWkodh4iIqMIpyXclR+yIiLQgCAKys7PBfyMTUUXAwo6ISAvR0dHw9vZGdHS01FGIiFjYEREREekKFnZEREREOoKFHREREZGOYGFHREREpCMMpA5ARFSZKZVKrF27ltP1EFGFwMKOiEgLBgYGsLGxkToGEREAXoolItLKo0eP8P333+PRo0dSRyEiYmFHRKSNtLQ0nD9/HmlpaVJHISJiYUdERESkK1jYEREREekIFnZEREREOoKFHRGRFqytrfHhhx/C2tpa6ihERJzuhIhIG1ZWVujXr5/UMYiIAHDEjohIK+np6bh06RLS09OljkJExMKOiEgbCQkJWLFiBRISEqSOQkTEwk5bzs7O+O6774rd/8SJE5DJZEhOTi63TJUpBxEREZWdKnOPnUwmK3L9vHnzMH/+/BLv9+LFizAzMyt2//bt2yMuLg4KhaLE71VcPj4+2Lx5c6HrnZyccPv27XLPQaRrPDw8EBsbq9GWk5ODxMREnD59GgYGBlAqlTh58qRECYmoqpMJgiBIHeJNiI+PF/97165dmDt3Lm7duiW2mZubw9zcHAAgCAJyc3NhYFA5616VSoVnz56Jyw4ODti0aRN69OgBANDX14etra1U8TSo1WooFAqoVCr+iDpVeK6uroiJiYGjo2OB61+si4yMfMPJiEiXleS7sspcirW3txdfCoUCMplMXL558yYsLCxw6NAhtGzZEnK5HKdPn0ZERAT69esHOzs7mJubo3Xr1vjnn3809vvqpViZTIYNGzZgwIABMDU1hbu7O/bv3y+uf/USaEBAAKysrHDkyBHUr18f5ubm6NGjB+Li4sRtcnJyMGnSJFhZWaFatWqYOXMmvL290b9//wKPVaFQaBwv8PzJvRfLtra2heb466+/ULduXZiammLQoEFIT0/H5s2b4ezsDGtra0yaNAm5ubnie2VmZmLatGmoVasWzMzM0LZtW5w4caL0f1BEFdyLwq2gV2EFHxHRm1JlCrvimDVrFpYsWYLw8HA0adIEqamp6NmzJ4KCghASEoIePXqgT58+iImJKXI/fn5+GDx4MK5cuYKePXti+PDhSExMLLR/eno6/P39sXXrVpw6dQoxMTGYNm2auH7p0qXYvn07Nm3ahDNnzkCtViMwMLCsDlsjx+rVq7Fz504cPnwYJ06cwIABA3Dw4EEcPHgQW7duxfr167Fnzx5xm4kTJ+Lff//Fzp07ceXKFXzwwQfo0aMH7ty5U+b5iIiI6DWEKmjTpk2CQqEQl48fPy4AEAIDA1+7bcOGDYU1a9aIy05OTsKqVavEZQDC119/LS6npqYKAIRDhw5pvFdSUpKYBYBw9+5dcZt169YJdnZ24rKdnZ2wfPlycTknJ0dwdHQU+vXrV6zjBSD88ccfGm3FyTF+/HjB1NRUSElJEdu8vLyE8ePHC4IgCPfu3RP09fWFBw8eaOy7a9eugq+vb4FZMjIyBJVKJb5iY2MFAIJKpSrWsRBJycXFRXBxcSn1eiKi0lCpVMX+rqycN5GVk1atWmksp6amYv78+Thw4ADi4uKQk5ODZ8+evXbErkmTJuJ/m5mZwdLSEo8ePSq0v6mpKWrXri0uOzg4iP1VKhUSEhLQpk0bcb2+vj5atmyJvLy8Eh3f67yaw87ODs7OzuK9hy/aXmS7evUqcnNzUadOHY39ZGZmolq1agW+x+LFi+Hn51emuYmIiOg5FnYvefXp1mnTpuHo0aPw9/eHm5sbTExMMGjQIGRlZRW5H0NDQ41lmUxWZBFWUH9BgmdaCspR1LGkpqZCX18fly5dgr6+vka/l4vBl/n6+mLq1KnislqthlKpLIv4REREVR4LuyKcOXMGPj4+GDBgAIDnhUx0dPQbzaBQKGBnZ4eLFy+iU6dOAIDc3FxcvnwZzZo1e6NZXtW8eXPk5ubi0aNH6NixY7G2kcvlkMvl5ZyMiIioamJhVwR3d3fs27cPffr0gUwmw5w5c8r88mdxfP7551i8eDHc3NxQr149rFmzBklJSa+dm6+81alTB8OHD8fIkSOxYsUKNG/eHI8fP0ZQUBCaNGmCXr16SZqPqDzExMTA1dW10HV8MpaIpMTCrggrV67Exx9/jPbt26N69eqYOXMm1Gr1G88xc+ZMxMfHY+TIkdDX18e4cePg5eWV7/KnFDZt2oSFCxfiyy+/xIMHD1C9enW8/fbb6N27t9TRiMpcQbcNvJig2MbGBo6Ojry1gIgkVWUmKNYleXl5qF+/PgYPHoxvvvlG6jha4QTFVNllZmbi4cOHqFmzJm8zIKJyUZLvSo7YVQL37t3D33//DQ8PD2RmZmLt2rWIiorCsGHDpI5GVOXJ5XK4uLhIHYOICAAnKK4U9PT0EBAQgNatW6NDhw64evUq/vnnH9SvX1/qaERV3pMnT7Bp0yY8efJE6ihERByxqwyUSiXOnDkjdQwiKkBKSgqOHj0KT09PVK9eXeo4RFTFccSOiIiISEewsCMiIiLSESzsiIiIiHQECzsiIi0oFAr07NkTCoVC6ihERHx4gohIGzY2NhgxYoTUMYiIAHDEjohIKxkZGbhz5w4yMjKkjkJExMKOiEgbcXFxmDdvHuLi4qSOQkTEwo6IiIhIV7CwIyIiItIRLOyIiIiIdAQLOyIiLejr68PCwgL6+vpSRyEigkwQBEHqEFR1qdVqKBQKqFQqWFpaSh2HiIiowinJdyVH7IiIiIh0BAs7IiIt3L9/H1OmTMH9+/eljkJExMKOiEgb2dnZSEhIQHZ2ttRRiIhY2BERERHpChZ2RERERDqChR0RERGRjmBhR0SkBXt7e8yaNQv29vZSRyEigoHUAYiIKjMTExM0adJE6hhERAA4YkdEpJXk5GTs3bsXycnJUkchImJhR0SkjaSkJOzduxdJSUlSRyEiYmFHREREpCtY2BERERHpCBZ2RERERDqChR0RkRbMzc3RoUMHmJubSx2FiAgyQRAEqUNQ1aVWq6FQKKBSqWBpaSl1HCIiogqnJN+VHLEjItJCdnY2EhISkJ2dLXUUIiIWdkRE2rh//z6mTJmC+/fvSx2FiIiFHREREZGuYGFHREREpCNY2BERERHpCBZ2RERERDqC052QpDjdCRERUdE43QkRERFRFaRThd3PP/8MpVIJPT09fPfdd1LHqRICAgJgZWUldQyq5Dw8PODq6lrky8PDQ+qYBYqLi8PcuXMRFxcndRQiImkLOx8fH8hkMshkMhgaGsLOzg7du3fHr7/+iry8vBLtS61WY+LEiZg5cyYePHiAcePGlVNq7T1+/BiffvopHB0dIZfLYW9vDy8vL5w5c0bsI5PJEBgYKF3IAjg7O7Ng/j8vig0qG7GxsYiJiSl0fUxMDGJjY99gouLLyMjA3bt3kZGRIXWUEuFnmEg3GUgdoEePHti0aRNyc3ORkJCAw4cP44svvsCePXuwf/9+GBgUL2JMTAyys7PRq1cvODg4lHNq7QwcOBBZWVnYvHkzXF1dkZCQgKCgIDx9+rRE+8nKyoKRkVE5pSR6sxwdHREZGVngOhYgRETFI/ml2BcjVrVq1UKLFi3w1Vdf4c8//8ShQ4cQEBAg9ktOTsaYMWNga2sLS0tLdOnSBWFhYQCeXw5s3LgxgOdfADKZDNHR0QCAP//8Ey1atICxsTFcXV3h5+eHnJwccb8ymQwbNmzAgAEDYGpqCnd3d+zfv18j4/Xr19G7d29YWlrCwsICHTt2REREhLh+w4YNqF+/PoyNjVGvXj388MMPhR5vcnIygoODsXTpUnTu3BlOTk5o06YNfH190bdvXwDPR8YAYMCAAZDJZOLy/Pnz0axZM2zYsAEuLi4wNjZ+7bl5ebutW7fC2dkZCoUCQ4YMQUpKitgnJSUFw4cPh5mZGRwcHLBq1Sp4enpi8uTJAABPT0/cu3cPU6ZMEUdZX3bkyBHUr18f5ubm6NGjBy9LERERSUDyEbuCdOnSBU2bNsW+ffswZswYAMAHH3wAExMTHDp0CAqFAuvXr0fXrl1x+/ZtfPjhh1AqlejWrRsuXLgApVIJW1tbBAcHY+TIkVi9erVYjL24RDtv3jzx/fz8/LBs2TIsX74ca9aswfDhw3Hv3j3Y2NjgwYMH6NSpEzw9PXHs2DFYWlrizJkzYnG4fft2zJ07F2vXrkXz5s0REhKCsWPHwszMDN7e3vmOzdzcHObm5ggMDMTbb78NuVyer8/FixdRo0YNbNq0CT169IC+vr647u7du9i7dy/27dsnthd1bmxsbAAAERERCAwMxF9//YWkpCQMHjwYS5YswaJFiwAAU6dOxZkzZ7B//37Y2dlh7ty5uHz5Mpo1awYA2LdvH5o2bYpx48Zh7NixGnnT09Ph7++PrVu3Qk9PDyNGjMC0adOwffv2fMeWmZmJzMxMcVmtVr/m01BxxcbGciSpjMTGxkKpVL62T0U839nZ2UhKSkJwcDAMDQ2ljlNsxTnnRFT5SD5iV5h69eqJo26nT5/GhQsX8Pvvv6NVq1Zwd3eHv78/rKyssGfPHpiYmKBatWoAAFtbW9jb20NfXx9+fn6YNWsWvL294erqiu7du+Obb77B+vXrNd7Lx8cHQ4cOhZubG7799lukpqbiwoULAIB169ZBoVBg586daNWqFerUqYNRo0ahbt26AJ4XiCtWrMD7778PFxcXvP/++5gyZUq+93jBwMAAAQEB2Lx5M6ysrNChQwd89dVXuHLlitjH1tYWAGBlZQV7e3txGXh++XXLli1o3rw5mjRp8tpz80JeXh4CAgLQqFEjdOzYER999BGCgoIAPB+t27x5M/z9/dG1a1c0atRIvDz+go2NDfT19WFhYQF7e3vY29uL67Kzs/HTTz+hVatWaNGiBSZOnCju+1WLFy+GQqEQX/xiocpOX18flpaWGv8AIyKSSoUcsQMAQRDEy31hYWFITU0Vi7cXnj17pnFJ9FVhYWE4c+aMOCoFALm5ucjIyEB6ejpMTU0BAE2aNBHXm5mZwdLSEo8ePQIAhIaGomPHjgX+SzwtLQ0REREYPXq0xihWTk4OFApFobkGDhyIXr16ITg4GOfOncOhQ4ewbNkybNiwAT4+PkWcFcDJyUmj0CvuuXF2doaFhYW47ODgIB5jZGQksrOz0aZNG3G9QqEQi9fXMTU1Re3atQvc96t8fX0xdepUcVmtVlfa4k6pVBZ6TxiVTHFG4ni+y1ZFHP0kIu1V2MIuPDwcLi4uAIDU1FQ4ODjgxIkT+foVNdVGamoq/Pz88P777+db9+L+NAD5ijaZTCY+lWtiYlLk/gHgl19+Qdu2bTXWve5f78bGxujevTu6d++OOXPmYMyYMZg3b95rCzszM7N8GYpzboo6Rm0VtO/C5r2Wy+UFXn4mqqzUajXOnTuHt99+m5NsE5HkKmRhd+zYMVy9ehVTpkwBALRo0QLx8fEwMDAQHyQojhYtWuDWrVtwc3MrdZYmTZpg8+bNyM7OzlfA2NnZoWbNmoiMjMTw4cNL/R4A0KBBA43pTQwNDTUuhRamtOfmZa6urjA0NMTFixfh6OgIAFCpVLh9+zY6deok9jMyMipWJqLSiImJKXQUKSYmRvxsVjRPnz5FQEAA3N3dWdgRkeQkL+wyMzMRHx+vMd3J4sWL0bt3b4wcORIA0K1bN7Rr1w79+/fHsmXLUKdOHTx8+BAHDhzAgAED0KpVqwL3PXfuXPTu3RuOjo4YNGgQ9PT0EBYWhmvXrmHhwoXFyjdx4kSsWbMGQ4YMga+vLxQKBc6dO4c2bdqgbt268PPzw6RJk6BQKNCjRw9kZmbiv//+Q1JSksYlxxeePn2KDz74AB9//DGaNGkCCwsL/Pfff1i2bBn69esn9nN2dkZQUBA6dOgAuVwOa2vrAvOV9ty8zMLCAt7e3pg+fTpsbGxQo0YNzJs3D3p6ehpPvzo7O+PUqVMYMmQI5HI5qlevXqxzqIt4SbBsve5yvKOjY6W9ZF9R8TNMpJskL+wOHz4MBwcHGBgYwNraGk2bNsXq1avh7e0NPb3nz3bIZDIcPHgQs2fPxqhRo/D48WPY29ujU6dOsLOzK3TfXl5e+Ouvv7BgwQIsXboUhoaGqFevnvikbXFUq1YNx44dw/Tp0+Hh4QF9fX00a9YMHTp0AACMGTMGpqamWL58OaZPnw4zMzM0btxYnCbkVebm5mjbti1WrVqFiIgIZGdnQ6lUYuzYsfjqq6/EfitWrMDUqVPxyy+/oFatWuKDJK8q7bl51cqVK/HJJ5+I07rMmDEDsbGxGpesFyxYgPHjx6N27drIzMws9HIrUUmdPHlS6ghERDpBJvDbmQqQlpaGWrVqYcWKFRg9enS5vU9JftiYqCKKiorC7NmzsWjRIvG+YCKislSS70rJR+yoYggJCcHNmzfRpk0bqFQqLFiwAAA0Lg8TUX4mJiZo0qRJkQ9aERG9KSzsSOTv749bt27ByMgILVu2RHBwcJW+j46oOOzt7TFr1iypYxARAWBhR/+nefPmuHTpktQxiCqdvLw8ZGZmQi6Xi/cFExFJhX8LERFp4d69exg9ejTu3bsndRQiIhZ2RERERLqChR0RERGRjmBhR0RERKQjWNgRERER6Qg+FUtEpAVHR0f89NNPMDMzkzoKERELOyIibejr6/NXU4iowuClWCIiLSQkJMDf3x8JCQlSRyEiYmFHRKSN9PR0XL58Genp6VJHISJiYUdERESkK1jYEREREekIFnZEREREOoKFHRGRFmxsbDBixAjY2NhIHYWIiNOdEBFpQ6FQoGfPnlLHICICwBE7IiKtpKWl4fz580hLS5M6ChERCzsiIm08evQI33//PR49eiR1FCIiFnZEREREuoKFHREREZGOYGFHREREpCNY2BERacHIyAjOzs4wMjKSOgoREWSCIAhSh6CqS61WQ6FQQKVSwdLSUuo4REREFU5Jvis5YkdERESkI1jYERFpITo6GiNHjkR0dLTUUYiIWNgREWlDEATk5OSAd7UQUUXAwo6IiIhIR7CwIyIiItIRLOyIiIiIdISB1AGIiCqzWrVqYdmyZahRo4bUUYiIWNgREWnDyMgIb731ltQxiIgA8FIsEZFWnjx5gp9//hlPnjyROgoREQs7IiJtpKSk4MSJE0hJSZE6ChERCzsiIiIiXcHCjoiIiEhHsLAjIiIi0hF8KpaISs3DwwOxsbFF9lEqlTh58uQbSvTmKRQK9O3bFwqFQuooREQs7Ej3uLq6AgAiIyMlTqL7YmNjERMTA0dHxwLXx8TEvOFEb56NjQ2GDBkidQx+7okIAAs70sLp06fRuXNnpKSkwNjYGAAQHR0NFxcXREdHw8nJSeKE9CY4OjoWWky8KDZ0WUZGBiIjI+Hq6ir+f0BEJBXeY0elFhoaivr162t8mYWEhMDa2ppFHVUZcXFxWLhwIeLi4qSOQkTEETsqvbCwMDRv3lyjLTQ0FE2bNi10m8zMTGRmZorLarW6XLLFxsZWidEiqcXGxkKpVL62jy7/WWRnZyMpKQnBwcEwNDSULEdx/iyISPdxxI5KLTQ0FM2aNdNoCwkJydf2ssWLF0OhUIgvfhERERGVHY7YUank5ubi2rVr+UbsLl++jIEDBxa6na+vL6ZOnSouq9XqcinulEolbyJ/A4ozEqfrfxZRUVGYPXs2Fi1aBBcXF8ly6PKoKBEVHws7KpVbt24hIyMDNWvWFNv+/fdfPHjwoMgRO7lcDrlc/gYSEr0ZBgYGsLGxgYEB/zolIunxbyIqldDQUADAmjVrMGnSJNy9exeTJk0CAGRlZUmYjN60mJiYQkeLipoKRVcolUqsXbtW6hhERAB4jx2VUmhoKLy8vBAZGYnGjRtj9uzZ8PPzg6WlJVavXi1ptsjISJ2+9FeRKJXKIgs3R0dH3kf5hvBzT0QAIBMEQZA6BFU+Xl5eaN26NRYuXKjVftRqNRQKBVQqFSwtLcsoHdGbExsbi6VLl2LmzJksYomoXJTku5IjdlQqYWFhaNy4sdQxiCSXk5ODxMRE5OTkSB2FiIiFHZVcfHw8EhISWNgRERFVMHx4gkrM3t4evIJPRERU8XDEjoiIiEhHsLAjItKCg4MDvv76azg4OEgdhYiIl2KJiLRhbGyMBg0aSB2DiAgAR+yIiLSSmJiInTt3IjExUeooREQs7IiItKFSqbB//36oVCqpoxARsbAjIiIi0hUs7IiIiIh0BAs7IiIiIh3Bp2JJUi8mOlar1RInISq9tm3bAuDnmIjKx4u/W4rz4wAygT8hQBK6f/8+fzidiIioGGJjY/HWW28V2YeFHUkqLy8PDx8+hIWFBWQyWbm8h1qthlKpRGxsLCwtLcvlPXQJz1fJ8HyVDM9XyfB8lZwunjNBEJCSkoKaNWtCT6/ou+h4KZYkpaen99p/fZQVS0tLnfmf/E3g+SoZnq+S4fkqGZ6vktO1c6ZQKIrVjw9PEBEREekIFnZEREREOoKFHek8uVyOefPmQS6XSx2lUuD5Khmer5Lh+SoZnq+Sq+rnjA9PEBEREekIjtgRERER6QgWdkREREQ6goUdERERkY5gYUc6Z9GiRWjfvj1MTU1hZWVVrG0EQcDcuXPh4OAAExMTdOvWDXfu3CnfoBVEYmIihg8fDktLS1hZWWH06NFITU0tchtPT0/IZDKN1yeffPKGEr9569atg7OzM4yNjdG2bVtcuHChyP6///476tWrB2NjYzRu3BgHDx58Q0krhpKcr4CAgHyfJWNj4zeYVlqnTp1Cnz59ULNmTchkMgQGBr52mxMnTqBFixaQy+Vwc3NDQEBAueesKEp6vk6cOJHv8yWTyRAfH/9mAkuAhR3pnKysLHzwwQf49NNPi73NsmXLsHr1avz00084f/48zMzM4OXlhYyMjHJMWjEMHz4c169fx9GjR/HXX3/h1KlTGDdu3Gu3Gzt2LOLi4sTXsmXL3kDaN2/Xrl2YOnUq5s2bh8uXL6Np06bw8vLCo0ePCux/9uxZDB06FKNHj0ZISAj69++P/v3749q1a284uTRKer6A5xPJvvxZunfv3htMLK20tDQ0bdoU69atK1b/qKgo9OrVC507d0ZoaCgmT56MMWPG4MiRI+WctGIo6fl64datWxqfsRo1apRTwgpAINJRmzZtEhQKxWv75eXlCfb29sLy5cvFtuTkZEEulws7duwox4TSu3HjhgBAuHjxoth26NAhQSaTCQ8ePCh0Ow8PD+GLL754Awml16ZNG2HChAnicm5urlCzZk1h8eLFBfYfPHiw0KtXL422tm3bCuPHjy/XnBVFSc9Xcf8/rQoACH/88UeRfWbMmCE0bNhQo+3DDz8UvLy8yjFZxVSc83X8+HEBgJCUlPRGMlUEHLGjKi8qKgrx8fHo1q2b2KZQKNC2bVv8+++/EiYrf//++y+srKzQqlUrsa1bt27Q09PD+fPni9x2+/btqF69Oho1agRfX1+kp6eXd9w3LisrC5cuXdL4bOjp6aFbt26Ffjb+/fdfjf4A4OXlpfOfJaB05wsAUlNT4eTkBKVSiX79+uH69etvIm6lVJU/X9po1qwZHBwc0L17d5w5c0bqOOWKvxVLVd6Ley3s7Ow02u3s7HT6Pgzg+bG/eknCwMAANjY2RR77sGHD4OTkhJo1a+LKlSuYOXMmbt26hX379pV35DfqyZMnyM3NLfCzcfPmzQK3iY+Pr5KfJaB056tu3br49ddf0aRJE6hUKvj7+6N9+/a4fv36G/sd6cqksM+XWq3Gs2fPYGJiIlGyisnBwQE//fQTWrVqhczMTGzYsAGenp44f/48WrRoIXW8csHCjiqFWbNmYenSpUX2CQ8PR7169d5QooqtuOertF6+B69x48ZwcHBA165dERERgdq1a5d6v1T1tGvXDu3atROX27dvj/r162P9+vX45ptvJExGuqBu3bqoW7euuNy+fXtERERg1apV2Lp1q4TJyg8LO6oUvvzyS/j4+BTZx9XVtVT7tre3BwAkJCTAwcFBbE9ISECzZs1KtU+pFfd82dvb57upPScnB4mJieJ5KY62bdsCAO7evatThV316tWhr6+PhIQEjfaEhIRCz4+9vX2J+uuS0pyvVxkaGqJ58+a4e/dueUSs9Ar7fFlaWnK0rpjatGmD06dPSx2j3LCwo0rB1tYWtra25bJvFxcX2NvbIygoSCzk1Go1zp8/X6InayuS4p6vdu3aITk5GZcuXULLli0BAMeOHUNeXp5YrBVHaGgoAGgUxrrAyMgILVu2RFBQEPr37w8AyMvLQ1BQECZOnFjgNu3atUNQUBAmT54sth09elRjVEpXleZ8vSo3NxdXr15Fz549yzFp5dWuXbt80+dUlc9XWQkNDdW5v6s0SP30BlFZu3fvnhASEiL4+fkJ5ubmQkhIiBASEiKkpKSIferWrSvs27dPXF6yZIlgZWUl/Pnnn8KVK1eEfv36CS4uLsKzZ8+kOIQ3qkePHkLz5s2F8+fPC6dPnxbc3d2FoUOHiuvv378v1K1bVzh//rwgCIJw9+5dYcGCBcJ///0nREVFCX/++afg6uoqdOrUSapDKFc7d+4U5HK5EBAQINy4cUMYN26cYGVlJcTHxwuCIAgfffSRMGvWLLH/mTNnBAMDA8Hf318IDw8X5s2bJxgaGgpXr16V6hDeqJKeLz8/P+HIkSNCRESEcOnSJWHIkCGCsbGxcP36dakO4Y1KSUkR/44CIKxcuVIICQkR7t27JwiCIMyaNUv46KOPxP6RkZGCqampMH36dCE8PFxYt26doK+vLxw+fFiqQ3ijSnq+Vq1aJQQGBgp37twRrl69KnzxxReCnp6e8M8//0h1COWOhR3pHG9vbwFAvtfx48fFPgCETZs2ict5eXnCnDlzBDs7O0Eulwtdu3YVbt269ebDS+Dp06fC0KFDBXNzc8HS0lIYNWqURhEcFRWlcf5iYmKETp06CTY2NoJcLhfc3NyE6dOnCyqVSqIjKH9r1qwRHB0dBSMjI6FNmzbCuXPnxHUeHh6Ct7e3Rv/du3cLderUEYyMjISGDRsKBw4ceMOJpVWS8zV58mSxr52dndCzZ0/h8uXLEqSWxovpOF59vThH3t7egoeHR75tmjVrJhgZGQmurq4af5fpupKer6VLlwq1a9cWjI2NBRsbG8HT01M4duyYNOHfEJkgCMIbHiQkIiIionLAeeyIiIiIdAQLOyIiIiIdwcKOiIiISEewsCMiIiLSESzsiIiIiHQECzsiIiIiHcHCjoiIiEhHsLAjIiIi0hEs7IiIiIh0BAs7IiIiIh3Bwo6IqAx5enpi8uTJZb7fp0+fokaNGoiOji72NkOGDMGKFSvKPAsRVVws7IiIKoFFixahX79+cHZ2LvY2X3/9NRYtWgSVSqX1+6ekpGDy5MlwcnKCiYkJ2rdvj4sXL2r0mT9/PmQymcarXr16Gn22b98OpVIJa2trTJ06VWNddHQ06tSpA7Va/do88fHx+Pzzz+Hq6gq5XA6lUok+ffogKChI7OPj44P+/fuX/qCJKiEDqQMQEVHR0tPTsXHjRhw5cqRE2zVq1Ai1a9fGtm3bMGHCBK0yjBkzBteuXcPWrVtRs2ZNbNu2Dd26dcONGzdQq1YtsV/Dhg3xzz//iMsGBv//a+bJkycYM2YMAgIC4Orqil69eqFLly7o3bs3AOCzzz7DkiVLYGlpWWSW6OhodOjQAVZWVli+fDkaN26M7OxsHDlyBBMmTMDNmze1OlaiyowjdkRE5SQzMxOTJk1CjRo1YGxsjHfeeSffKFdKSgqGDx8OMzMzODg4YNWqVfku5x48eBByuRxvv/22xraNGjXCwoUL8cknn8Da2hr29vb47rvvNPr06dMHO3fu1Oo4nj17hr1792LZsmXo1KkT3NzcMH/+fLi5ueHHH3/U6GtgYAB7e3vxVb16dXFdZGQkFAoFPvzwQ7Ru3RqdO3dGeHg4AGDHjh0wNDTE+++//9o8n332GWQyGS5cuICBAweiTp06aNiwIaZOnYpz585pdaxElR0LOyKicjJjxgzs3bsXmzdvxuXLl+Hm5gYvLy8kJiaKfaZOnYozZ85g//79OHr0KIKDg3H58mWN/QQHB6Nly5YabZmZmbh16xa2bNkCDw8PXLx4EcOHD8fMmTORlpYm9mvTpg0uXLiAzMzMUh9HTk4OcnNzYWxsrNFuYmKC06dPa7TduXMHNWvWhKurK4YPH46YmBhxnbu7O9LT0xESEoLExERcvHgRTZo0QVJSEubMmYO1a9e+NktiYiIOHz6MCRMmwMzMLN96Kyur0h0kkY5gYUdEVA7S0tLw448/Yvny5XjvvffQoEED/PLLLzAxMcHGjRsBPB+t27x5M/z9/dG1a1c0atQImzZtQm5ursa+7t27h5o1a2q0Xbt2DTk5OVi9ejWGDh0KNzc3+Pj4ICsrC+np6WK/mjVrIisrC/Hx8aU+FgsLC7Rr1w7ffPMNHj58iNzcXGzbtg3//vsv4uLixH5t27ZFQEAADh8+jB9//BFRUVHo2LEjUlJSAADW1tbYvHkzRo4ciTZt2mDkyJHw8vLCtGnTMHHiRERFRaF58+Zo1KgR9uzZU2CWu3fvQhCEfPfuEdFzvMeOiKgcREREIDs7Gx06dBDbDA0N0aZNG/HyY2RkJLKzs9GmTRuxj0KhQN26dTX29ezZs3yjZWFhYbC3t4eXl5fY9vjxYxgZGcHGxkZsMzExAQCNYu9l27dvx/jx48XlQ4cOoWPHjvn6bd26FR9//DFq1aoFfX19tGjRAkOHDsWlS5fEPu+99574302aNEHbtm3h5OSE3bt3Y/To0QCAAQMGYMCAAWK/kydP4sqVK1izZg3c3NywY8cO2Nvbo02bNujUqRNq1KihkUMQhAKPg4ieY2FHRFTBVa9eHUlJSRptoaGhaNWqFWQymUZbo0aNoK+vL7a9uOxra2tb4L779u2Ltm3bissvPwjxstq1a+PkyZNIS0uDWq2Gg4MDPvzwQ7i6uhaa28rKCnXq1MHdu3cLXJ+ZmYnPPvsMW7duxd27d5GTkwMPDw8AQJ06dXD+/Hn06dNHYxt3d3fIZDI+IEFUCF6KJSIqB7Vr14aRkRHOnDkjtmVnZ+PixYto0KABAMDV1RWGhoYaD1SoVCrcvn1bY1/NmzfHjRs3NNrCwsLQrFkzjbbQ0NB8bdeuXcNbb72l8RDDyywsLODm5ia+XozwFebFQx5JSUk4cuQI+vXrV2jf1NRUREREwMHBocD1CxcuRI8ePdCiRQvk5uYiJydHXJednZ3vkjQA2NjYwMvLC+vWrdO4l/CF5OTkIvMT6ToWdkRE5cDMzAyffvoppk+fjsOHD+PGjRsYO3Ys0tPTxcuSFhYW8Pb2xvTp03H8+HFcv34do0ePhp6ensZInJeXF65fv64xaldQYRcSEpKvLTg4GO+++67Wx3PkyBEcPnwYUVFROHr0KDp37ox69eph1KhRYp9p06bh5MmTiI6OxtmzZzFgwADo6+tj6NCh+fZ348YN7Nq1CwsWLAAA1KtXD3p6eti4cSMOHDiAmzdvonXr1gVmWbduHXJzc9GmTRvs3bsXd+7cQXh4OFavXo127dppfaxElRkvxRIRlZMlS5YgLy8PH330EVJSUtCqVSscOXIE1tbWYp+VK1fik08+Qe/evWFpaYkZM2YgNjZW4566xo0bo0WLFti9ezfGjx+P6OhoqFQqjSIuMzMTN2/eRPPmzcW2jIwMBAYG4vDhw1ofi0qlgq+vL+7fvw8bGxsMHDgQixYtgqGhodjn/v37GDp0KJ4+fQpbW1u88847OHfuXL7LwIIgYNy4cVi5cqX4ZKuJiQkCAgIwYcIEZGZmYu3atYVeFnZ1dcXly5exaNEifPnll4iLi4OtrS1atmyZb/oVoqpGJvBOVCKiCiMtLQ21atXCihUrxJE9ADhw4ACmT5+Oa9euQU8v/8WWS5cuoXXr1lCpVLCwsAAA/Pjjj/jjjz/w999/v7H8RCQtjtgREUkoJCQEN2/eRJs2baBSqcRLk6/eu9arVy/cuXMHDx48gFKpLHA/rq6uYlEHPH8Kd82aNeV7AERUobCwIyKSmL+/P27dugUjIyO0bNkSwcHBBT7s8PKvUbyqoAcnxowZU8ZJiaii46VYIiIiIh3Bp2KJiIiIdAQLOyIiIiIdwcKOiIiISEewsCMiIiLSESzsiIiIiHQECzsiIiIiHcHCjoiIiEhHsLAjIiIi0hEs7IiIiIh0BAs7IiIiIh3Bwo6IiIhIR/w/p1OjFYdqYCsAAAAASUVORK5CYII=", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "NameError", + "evalue": "name 'cleaned' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 14\u001b[0m\n\u001b[1;32m 1\u001b[0m log_logistic_dict \u001b[39m=\u001b[39m {\n\u001b[1;32m 2\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mIntercept: beta_\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39m$\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39mbeta$\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 3\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mIntercept: alpha_\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39m$\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39malpha$\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[39m\"\u001b[39m\u001b[39madv_fit_time: alpha_\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mAdv. Fit Time\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 11\u001b[0m }\n\u001b[1;32m 13\u001b[0m log_logistic_graph, llt \u001b[39m=\u001b[39m plot_aft(\n\u001b[0;32m---> 14\u001b[0m cleaned,\n\u001b[1;32m 15\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mlog_logistic_aft.pdf\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 16\u001b[0m \u001b[39m\"\u001b[39m\u001b[39madv_failure_rate\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 17\u001b[0m \u001b[39m\"\u001b[39m\u001b[39madv_fit_time\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 18\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mLog Logistic AFT Model\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 19\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mlog_logistic\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 20\u001b[0m replacement_dict\u001b[39m=\u001b[39mlog_logistic_dict,\n\u001b[1;32m 21\u001b[0m )\n", + "\u001b[0;31mNameError\u001b[0m: name 'cleaned' is not defined" + ] } ], "source": [ @@ -498,9 +493,21 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'wft' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m aft_dict \u001b[39m=\u001b[39m {\n\u001b[0;32m----> 2\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mWeibull\u001b[39m\u001b[39m\"\u001b[39m: wft,\n\u001b[1;32m 3\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mLogNormal\u001b[39m\u001b[39m\"\u001b[39m: lnt,\n\u001b[1;32m 4\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mLogLogistic\u001b[39m\u001b[39m\"\u001b[39m: llt,\n\u001b[1;32m 5\u001b[0m }\n\u001b[1;32m 6\u001b[0m aft_data \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame()\n\u001b[1;32m 7\u001b[0m aft_data\u001b[39m.\u001b[39mindex\u001b[39m.\u001b[39mname \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mModel\u001b[39m\u001b[39m\"\u001b[39m\n", + "\u001b[0;31mNameError\u001b[0m: name 'wft' is not defined" + ] + } + ], "source": [ "aft_dict = {\n", " \"Weibull\": wft,\n", @@ -518,6 +525,27 @@ "aft_data.to_csv(FOLDER / \"aft_comparison.csv\")\n", "logger.info(f\"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From eba5459f0d97439ea62753bfc3a33e540e3e4670 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Wed, 6 Sep 2023 13:39:24 +0000 Subject: [PATCH 033/148] deploy script updates, new docker.yml action --- .github/workflows/deckard-test.yml | 2 +- .github/workflows/docker.yml | 45 ++++++++++++++++++++++-------- deckard/iaac/__init__.py | 2 +- deckard/iaac/gcp/__init__.py | 2 +- deckard/iaac/gcp/deploy.py | 21 ++++---------- deckard/iaac/gcp/install.sh | 6 ++++ deckard/layers/deploy.py | 26 +++++++++++++++++ 7 files changed, 73 insertions(+), 31 deletions(-) create mode 100644 deckard/iaac/gcp/install.sh create mode 100644 deckard/layers/deploy.py diff --git a/.github/workflows/deckard-test.yml b/.github/workflows/deckard-test.yml index a1271d76..0c0317ae 100644 --- a/.github/workflows/deckard-test.yml +++ b/.github/workflows/deckard-test.yml @@ -23,5 +23,5 @@ jobs: python -m pip install pytest pandas pillow PyYAML ffmpeg - name: Test base folder with pytest run: | - python -m pip install .[tensorflow-image,pytorch-image,non-framework,tensorflow,pytorch] + python -m pip install .[tensorflow_image,pytorch_image,non_framework,test] python -m pytest test diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index cd5940f7..27ab4d10 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -1,31 +1,52 @@ +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +# GitHub recommends pinning actions to a commit SHA. +# To get a newer version, you will need to update the SHA. +# You can also reference a tag or branch, but the action may change without warning. + name: Publish Docker image on: - pull_request: - branches: [ main ] + release: + types: [published] jobs: - push_to_registry: - name: Push Docker image to Docker Hub + push_to_registries: + name: Push Docker image to multiple registries runs-on: ubuntu-latest + permissions: + packages: write + contents: read steps: - name: Check out the repo uses: actions/checkout@v3 - + - name: Log in to Docker Hub - uses: docker/login-action@f054a8b539a109f9f41c372932f1ae047eff08c9 + uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a with: username: ${{ secrets.DOCKER_USERNAME }} password: ${{ secrets.DOCKER_PASSWORD }} - + + - name: Log in to the Container registry + uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1 + with: + registry: ghcr.io + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + - name: Extract metadata (tags, labels) for Docker id: meta - uses: docker/metadata-action@98669ae865ea3cffbcbaa878cf57c20bbf1c6c38 + uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7 with: - images: my-docker-hub-namespace/my-docker-hub-repository - - - name: Build and push Docker image - uses: docker/build-push-action@ad44023a93711e3deb337508980b4b5e9bcdc5dc + images: | + my-docker-hub-namespace/my-docker-hub-repository + ghcr.io/${{ github.repository }} + + - name: Build and push Docker images + uses: docker/build-push-action@3b5e8027fcad23fda98b2e3ac259d8d67585f671 with: context: . push: true diff --git a/deckard/iaac/__init__.py b/deckard/iaac/__init__.py index dfe88c03..4db1c272 100644 --- a/deckard/iaac/__init__.py +++ b/deckard/iaac/__init__.py @@ -1 +1 @@ -from .iaac import * +from .gcp import * diff --git a/deckard/iaac/gcp/__init__.py b/deckard/iaac/gcp/__init__.py index e93a509a..f1fe0ae5 100644 --- a/deckard/iaac/gcp/__init__.py +++ b/deckard/iaac/gcp/__init__.py @@ -1 +1 @@ -from .deploy import GCP_Config +from .deploy import * diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index ed85028a..7b1c2408 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -9,10 +9,14 @@ logger = logging.getLogger(__name__) +__all__ = ["GCP_Config"] + logging.basicConfig(level=logging.INFO) secret_file = os.environ.get("GCP_SECRET_FILE") +assert secret_file is not None, "Please set the GCP_SECRET_FILE environment variable" secret_file = Path(secret_file).resolve().as_posix() project_name = os.environ.get("GCP_PROJECT_NAME") +assert project_name is not None, "Please set the GCP_PROJECT_NAME environment variable" assert Path(secret_file).exists(), f"File {secret_file} does not exist" # Login to GCP @@ -186,19 +190,4 @@ def __call__(self): self.deploy_pod() self.prepare_access_values() ip_addr = self.find_ip_of_filestore() - self.mount_filestore(ip_addr) - - -if __name__ == "__main__": - gcp_parser = argparse.ArgumentParser() - gcp_parser.add_argument("--verbosity", type=str, default="INFO") - gcp_parser.add_argument("--config_dir", type=str, default="conf") - gcp_parser.add_argument("--config_file", type=str, default="default.yaml") - gcp_parser.add_argument("--workdir", type=str, default=".") - args = gcp_parser.parse_args() - config_dir = Path(args.workdir, args.config_dir).resolve().as_posix() - config_file = Path(config_dir, args.config_file).resolve().as_posix() - with open(config_file, "r") as f: - params = yaml.load(f, Loader=yaml.FullLoader) - gcp = GCP_Config(**params) - gcp() + return ip_addr diff --git a/deckard/iaac/gcp/install.sh b/deckard/iaac/gcp/install.sh new file mode 100644 index 00000000..d419b909 --- /dev/null +++ b/deckard/iaac/gcp/install.sh @@ -0,0 +1,6 @@ +#!/bin/bash +cd ~ +curl -O https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-cli-444.0.0-linux-x86_64.tar.gz +tar -xf google-cloud-cli-444.0.0-linux-x86.tar.gz +bash ./google-cloud-sdk/install.sh +cd - diff --git a/deckard/layers/deploy.py b/deckard/layers/deploy.py new file mode 100644 index 00000000..5b04a4d1 --- /dev/null +++ b/deckard/layers/deploy.py @@ -0,0 +1,26 @@ +import logging +import argparse +from pathlib import Path +import yaml +from ..iaac import GCP_Config + + +logger = logging.getLogger(__name__) + +if __name__ == "__main__": + iaac_parser = argparse.ArgumentParser() + iaac_parser.add_argument("--verbosity", type=str, default="INFO") + iaac_parser.add_argument("--config_dir", type=str, default="conf") + iaac_parser.add_argument("--config_file", type=str, default="default.yaml") + iaac_parser.add_argument("--workdir", type=str, default=".") + args = iaac_parser.parse_args() + config_dir = Path(args.workdir, args.config_dir).resolve().as_posix() + config_file = Path(config_dir, args.config_file).resolve().as_posix() + with open(config_file, "r") as f: + params = yaml.load(f, Loader=yaml.FullLoader) + gcp = GCP_Config(**params) + logging.basicConfig(level=args.verbosity) + ip_addr = gcp() + logger.info(f"IP address of the filestore: {ip_addr}") + command = f"sudo mount -o rw,intr {ip_addr}:/vol1 " + logger.info(f"Run command: {command} to mount the filestore.") \ No newline at end of file From 9c87d0678b7f4793b939ad5fbef8fbf1ac5dda3f Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Wed, 6 Sep 2023 13:49:10 +0000 Subject: [PATCH 034/148] update precommit file and linting --- .github/workflows/docker.yml | 4 ++-- .pre-commit-config.yaml | 2 +- deckard/iaac/gcp/deploy.py | 4 ++-- deckard/layers/deploy.py | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 27ab4d10..cafe7b4b 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -10,8 +10,8 @@ name: Publish Docker image on: - release: - types: [published] + pull_request: + branches: [ main ] jobs: push_to_registries: diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8a7b2da4..0b74b0b3 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -33,4 +33,4 @@ repos: # supported by your project here, or alternatively use # pre-commit's default_language_version, see # https://pre-commit.com/#top_level-default_language_version - language_version: python3.9 + language_version: python3 diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index 7b1c2408..4da2f3f5 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -1,10 +1,10 @@ import os import subprocess import logging -import argparse from dataclasses import dataclass from pathlib import Path -import yaml + +# import yaml logger = logging.getLogger(__name__) diff --git a/deckard/layers/deploy.py b/deckard/layers/deploy.py index 5b04a4d1..f3476834 100644 --- a/deckard/layers/deploy.py +++ b/deckard/layers/deploy.py @@ -23,4 +23,4 @@ ip_addr = gcp() logger.info(f"IP address of the filestore: {ip_addr}") command = f"sudo mount -o rw,intr {ip_addr}:/vol1 " - logger.info(f"Run command: {command} to mount the filestore.") \ No newline at end of file + logger.info(f"Run command: {command} to mount the filestore.") From ed385ad2dd749d806a6e1592a8ce68838843f561 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Wed, 6 Sep 2023 18:20:07 +0000 Subject: [PATCH 035/148] +change docker workflow to push to gh --- .github/workflows/docker.yml | 56 ++++++++++++++++-------------------- 1 file changed, 25 insertions(+), 31 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index cafe7b4b..b35e6e29 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -1,52 +1,46 @@ -# This workflow uses actions that are not certified by GitHub. -# They are provided by a third-party and are governed by -# separate terms of service, privacy policy, and support -# documentation. - -# GitHub recommends pinning actions to a commit SHA. -# To get a newer version, you will need to update the SHA. -# You can also reference a tag or branch, but the action may change without warning. - -name: Publish Docker image +# +name: Create and publish a Docker image +# Configures this workflow to run every time a change is pushed to the branch called `release`. on: - pull_request: - branches: [ main ] + push: + branches: ['release'] + +# Defines two custom environment variables for the workflow. These are used for the Container registry domain, and a name for the Docker image that this workflow builds. +env: + REGISTRY: ghcr.io + IMAGE_NAME: ${{ github.repository }} +# There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu. jobs: - push_to_registries: - name: Push Docker image to multiple registries + build-and-push-image: runs-on: ubuntu-latest + # Sets the permissions granted to the `GITHUB_TOKEN` for the actions in this job. permissions: - packages: write contents: read + packages: write + # steps: - - name: Check out the repo + - name: Checkout repository uses: actions/checkout@v3 - - - name: Log in to Docker Hub - uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a - with: - username: ${{ secrets.DOCKER_USERNAME }} - password: ${{ secrets.DOCKER_PASSWORD }} - + # Uses the `docker/login-action` action to log in to the Container registry registry using the account and password that will publish the packages. Once published, the packages are scoped to the account defined here. - name: Log in to the Container registry uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1 with: - registry: ghcr.io + registry: ghcr.io username: ${{ github.actor }} password: ${{ secrets.GITHUB_TOKEN }} - + # This step uses [docker/metadata-action](https://github.com/docker/metadata-action#about) to extract tags and labels that will be applied to the specified image. The `id` "meta" allows the output of this step to be referenced in a subsequent step. The `images` value provides the base name for the tags and labels. - name: Extract metadata (tags, labels) for Docker id: meta uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7 with: - images: | - my-docker-hub-namespace/my-docker-hub-repository - ghcr.io/${{ github.repository }} - - - name: Build and push Docker images - uses: docker/build-push-action@3b5e8027fcad23fda98b2e3ac259d8d67585f671 + images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} + # This step uses the `docker/build-push-action` action to build the image, based on your repository's `Dockerfile`. If the build succeeds, it pushes the image to GitHub Packages. + # It uses the `context` parameter to define the build's context as the set of files located in the specified path. For more information, see "[Usage](https://github.com/docker/build-push-action#usage)" in the README of the `docker/build-push-action` repository. + # It uses the `tags` and `labels` parameters to tag and label the image with the output from the "meta" step. + - name: Build and push Docker image + uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4 with: context: . push: true From a73a78b39ec688522e2d88e643de6f21e80980b4 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Wed, 6 Sep 2023 18:22:04 +0000 Subject: [PATCH 036/148] +change docker workflow to push to gh --- .github/workflows/docker.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index b35e6e29..c6bc8894 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -3,8 +3,8 @@ name: Create and publish a Docker image # Configures this workflow to run every time a change is pushed to the branch called `release`. on: - push: - branches: ['release'] + pull_request: + branches: [ main ] # Defines two custom environment variables for the workflow. These are used for the Container registry domain, and a name for the Docker image that this workflow builds. env: From d324cf350051d094fdb5fa92c4e1a7702d6f372b Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Wed, 6 Sep 2023 18:34:16 +0000 Subject: [PATCH 037/148] Dockerfile changes --- Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index 9de4fc39..c80c8918 100644 --- a/Dockerfile +++ b/Dockerfile @@ -5,7 +5,7 @@ RUN apt-get install -y python3 python3-distutils python3-pip ffmpeg libavcodec-e RUN python3 -m pip install nvidia-pyindex nvidia-cuda-runtime-cu11 RUN git clone https://github.com/simplymathematics/deckard.git WORKDIR /deckard -RUN python3 -m pip install --editable .[pytorch_image,tensorflow_image,non_framework,docs,test] +RUN python3 -m pip install --editable .[test] RUN git clone https://github.com/Trusted-AI/adversarial-robustness-toolbox.git RUN cd adversarial-robustness-toolbox && python3 -m pip install . RUN pytest test From 9b0e8966b06a704930d31cfa894698bf737e556b Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Wed, 6 Sep 2023 18:34:56 +0000 Subject: [PATCH 038/148] Dockerfile changes --- Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index c80c8918..39b7c204 100644 --- a/Dockerfile +++ b/Dockerfile @@ -5,7 +5,7 @@ RUN apt-get install -y python3 python3-distutils python3-pip ffmpeg libavcodec-e RUN python3 -m pip install nvidia-pyindex nvidia-cuda-runtime-cu11 RUN git clone https://github.com/simplymathematics/deckard.git WORKDIR /deckard -RUN python3 -m pip install --editable .[test] +RUN python3 -m pip install --editable .[test, pytorch_image] RUN git clone https://github.com/Trusted-AI/adversarial-robustness-toolbox.git RUN cd adversarial-robustness-toolbox && python3 -m pip install . RUN pytest test From 13748f130a2f57fccf811c120863e49ac2572c3f Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Wed, 6 Sep 2023 18:40:46 +0000 Subject: [PATCH 039/148] Dockerfile changes --- Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index 39b7c204..0194bbbe 100644 --- a/Dockerfile +++ b/Dockerfile @@ -5,7 +5,7 @@ RUN apt-get install -y python3 python3-distutils python3-pip ffmpeg libavcodec-e RUN python3 -m pip install nvidia-pyindex nvidia-cuda-runtime-cu11 RUN git clone https://github.com/simplymathematics/deckard.git WORKDIR /deckard -RUN python3 -m pip install --editable .[test, pytorch_image] +RUN python3 -m pip install --editable .[test,pytorch_image] RUN git clone https://github.com/Trusted-AI/adversarial-robustness-toolbox.git RUN cd adversarial-robustness-toolbox && python3 -m pip install . RUN pytest test From dce524f761ac2e98bd6d982b60bccf667a4eeaa8 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Wed, 6 Sep 2023 20:49:53 +0200 Subject: [PATCH 040/148] Dockerfile changes --- Dockerfile | 1 - 1 file changed, 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index 0194bbbe..4d2bc916 100644 --- a/Dockerfile +++ b/Dockerfile @@ -8,4 +8,3 @@ WORKDIR /deckard RUN python3 -m pip install --editable .[test,pytorch_image] RUN git clone https://github.com/Trusted-AI/adversarial-robustness-toolbox.git RUN cd adversarial-robustness-toolbox && python3 -m pip install . -RUN pytest test From a4582423535ff181cef318dd8afa8cb2ae5b623e Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Wed, 6 Sep 2023 21:26:20 +0200 Subject: [PATCH 041/148] moved gcp conf --- .../conf/default.yaml => examples/pytorch/conf/deploy.yaml | 0 setup.py | 5 ++++- 2 files changed, 4 insertions(+), 1 deletion(-) rename deckard/iaac/gcp/conf/default.yaml => examples/pytorch/conf/deploy.yaml (100%) diff --git a/deckard/iaac/gcp/conf/default.yaml b/examples/pytorch/conf/deploy.yaml similarity index 100% rename from deckard/iaac/gcp/conf/default.yaml rename to examples/pytorch/conf/deploy.yaml diff --git a/setup.py b/setup.py index 627e9952..69f4db34 100644 --- a/setup.py +++ b/setup.py @@ -32,6 +32,9 @@ test_requires = [ "pytest", "pytest-xdist", + "pytorch", + "torchvision", + "tensorflow", ] test_requires += install_requires docs_require = [ @@ -173,7 +176,7 @@ def get_version(rel_path): "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", - "License :: OSI Approved :: GPL License", + "License :: GPL License", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", From 26ca22cc24ca09e7ff640965057bc316cffc9d46 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Wed, 6 Sep 2023 22:14:00 +0200 Subject: [PATCH 042/148] +deploy.py script fixes (still broken) --- Dockerfile | 4 +++- deckard/iaac/gcp/conf/deploy.yaml | 14 ++++++++++++++ deckard/iaac/gcp/deploy.py | 2 +- deckard/iaac/gcp/install.sh | 4 ++-- deckard/layers/deploy.py | 2 +- setup.py | 2 +- 6 files changed, 22 insertions(+), 6 deletions(-) create mode 100644 deckard/iaac/gcp/conf/deploy.yaml diff --git a/Dockerfile b/Dockerfile index 4d2bc916..fc1ad05a 100644 --- a/Dockerfile +++ b/Dockerfile @@ -5,6 +5,8 @@ RUN apt-get install -y python3 python3-distutils python3-pip ffmpeg libavcodec-e RUN python3 -m pip install nvidia-pyindex nvidia-cuda-runtime-cu11 RUN git clone https://github.com/simplymathematics/deckard.git WORKDIR /deckard -RUN python3 -m pip install --editable .[test,pytorch_image] +RUN python3 -m pip install --editable .[test,pytorch_image,tensorflow_image] RUN git clone https://github.com/Trusted-AI/adversarial-robustness-toolbox.git RUN cd adversarial-robustness-toolbox && python3 -m pip install . +RUN echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] http://packages.cloud.google.com/apt cloud-sdk main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - && apt-get update -y && apt-get install google-cloud-cli -y + diff --git a/deckard/iaac/gcp/conf/deploy.yaml b/deckard/iaac/gcp/conf/deploy.yaml new file mode 100644 index 00000000..c64d7b87 --- /dev/null +++ b/deckard/iaac/gcp/conf/deploy.yaml @@ -0,0 +1,14 @@ +num_nodes: 1 +cluster_name: k8s-cluster +gpu_type: nvidia-tesla-v100 +gpu_count: 1 +gpu_driver_version: default +machine_type: n1-standard-2 +min_nodes: 1 +max_nodes: 1 +storage_config: sclass.yaml +persistent_volume_claim: pvc.yaml +pod : pod.yaml +image_project: ubuntu-os-cloud +image_family: ubuntu-2204-lts +mount_directory: /mnt/filestore diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index 4da2f3f5..7e11c71d 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -14,7 +14,7 @@ logging.basicConfig(level=logging.INFO) secret_file = os.environ.get("GCP_SECRET_FILE") assert secret_file is not None, "Please set the GCP_SECRET_FILE environment variable" -secret_file = Path(secret_file).resolve().as_posix() +secret_file = Path(secret_file).resolve() project_name = os.environ.get("GCP_PROJECT_NAME") assert project_name is not None, "Please set the GCP_PROJECT_NAME environment variable" assert Path(secret_file).exists(), f"File {secret_file} does not exist" diff --git a/deckard/iaac/gcp/install.sh b/deckard/iaac/gcp/install.sh index d419b909..9c8ed456 100644 --- a/deckard/iaac/gcp/install.sh +++ b/deckard/iaac/gcp/install.sh @@ -1,6 +1,6 @@ #!/bin/bash cd ~ curl -O https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-cli-444.0.0-linux-x86_64.tar.gz -tar -xf google-cloud-cli-444.0.0-linux-x86.tar.gz -bash ./google-cloud-sdk/install.sh +tar -xf google-cloud-cli-444.0.0-linux-x86.tar.gz +bash google-cloud-sdk/install.sh cd - diff --git a/deckard/layers/deploy.py b/deckard/layers/deploy.py index f3476834..00c281fb 100644 --- a/deckard/layers/deploy.py +++ b/deckard/layers/deploy.py @@ -6,7 +6,7 @@ logger = logging.getLogger(__name__) - +logging = logging.basicConfig(level=logging.INFO) if __name__ == "__main__": iaac_parser = argparse.ArgumentParser() iaac_parser.add_argument("--verbosity", type=str, default="INFO") diff --git a/setup.py b/setup.py index 69f4db34..ddb0c187 100644 --- a/setup.py +++ b/setup.py @@ -32,7 +32,7 @@ test_requires = [ "pytest", "pytest-xdist", - "pytorch", + "torch", "torchvision", "tensorflow", ] From e5c805bde2e412adb04c04f31c45a64353b1f3ff Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Wed, 6 Sep 2023 22:25:22 +0200 Subject: [PATCH 043/148] Dockerfile --- Dockerfile | 2 -- 1 file changed, 2 deletions(-) diff --git a/Dockerfile b/Dockerfile index fc1ad05a..c7832493 100644 --- a/Dockerfile +++ b/Dockerfile @@ -8,5 +8,3 @@ WORKDIR /deckard RUN python3 -m pip install --editable .[test,pytorch_image,tensorflow_image] RUN git clone https://github.com/Trusted-AI/adversarial-robustness-toolbox.git RUN cd adversarial-robustness-toolbox && python3 -m pip install . -RUN echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] http://packages.cloud.google.com/apt cloud-sdk main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - && apt-get update -y && apt-get install google-cloud-cli -y - From 1dac445caa1650d7d209ed1be7d5c5f84683e7fb Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Wed, 6 Sep 2023 22:48:34 +0200 Subject: [PATCH 044/148] add csi driver --- deckard/iaac/gcp/deploy.py | 2 +- deckard/iaac/gcp/pod.yaml | 10 +++++----- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index 7e11c71d..736daf77 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -62,7 +62,7 @@ def create_cluster(self): logger.info( f"Creating cluster {self.cluster_name} in region {self.region} with {self.num_nodes} nodes", ) - command = f"gcloud container clusters create {self.cluster_name} --region {self.region} --num-nodes {self.num_nodes} --no-enable-autoupgrade" + command = f"gcloud container clusters create {self.cluster_name} --region {self.region} --num-nodes {self.num_nodes} --no-enable-autoupgrade --addons=GcpFilestoreCsiDriver" logger.info(f"Running command: {command}") command = command.split(" ") output = subprocess.run(command) diff --git a/deckard/iaac/gcp/pod.yaml b/deckard/iaac/gcp/pod.yaml index ee54a985..80e9c54c 100644 --- a/deckard/iaac/gcp/pod.yaml +++ b/deckard/iaac/gcp/pod.yaml @@ -1,13 +1,13 @@ apiVersion: v1 kind: Pod metadata: - name: my-gpu-pod + name: deckard spec: containers: - - name: my-gpu-container - image: nvidia/cuda:11.0.3-runtime-ubuntu20.04 - command: ["/bin/bash", "-c", "--"] - args: ["while true; do sleep 600; done;"] + - name: deckard + image: ghcr.io/simplymathematics/deckard:pr-116 + workingDir: /deckard/examples/sklearn + args: ["python", "-m", "dvc", "repro"] resources: limits: nvidia.com/gpu: 1 From 5b55e138fd4c90cbe9547f42bdb148e7f3b53cf6 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Wed, 6 Sep 2023 22:54:09 +0200 Subject: [PATCH 045/148] fixed python alias in dockerfile --- Dockerfile | 1 + 1 file changed, 1 insertion(+) diff --git a/Dockerfile b/Dockerfile index c7832493..36919887 100644 --- a/Dockerfile +++ b/Dockerfile @@ -8,3 +8,4 @@ WORKDIR /deckard RUN python3 -m pip install --editable .[test,pytorch_image,tensorflow_image] RUN git clone https://github.com/Trusted-AI/adversarial-robustness-toolbox.git RUN cd adversarial-robustness-toolbox && python3 -m pip install . +RUN apt install python-is-python3 \ No newline at end of file From eb42845b9019231ebfaa985baed0bdbea855157f Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Wed, 6 Sep 2023 23:25:35 +0200 Subject: [PATCH 046/148] split docker workflow into test/push --- .github/workflows/docker-push.yml | 48 +++++++++++++++++++ .../workflows/{docker.yml => docker-test.yml} | 2 +- 2 files changed, 49 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/docker-push.yml rename .github/workflows/{docker.yml => docker-test.yml} (99%) diff --git a/.github/workflows/docker-push.yml b/.github/workflows/docker-push.yml new file mode 100644 index 00000000..04eb8907 --- /dev/null +++ b/.github/workflows/docker-push.yml @@ -0,0 +1,48 @@ +# +name: Create and publish a Docker image + +# Configures this workflow to run every time a change is pushed to the branch called `release`. +on: + push: + branches: main + +# Defines two custom environment variables for the workflow. These are used for the Container registry domain, and a name for the Docker image that this workflow builds. +env: + REGISTRY: ghcr.io + IMAGE_NAME: ${{ github.repository }} + +# There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu. +jobs: + build-and-push-image: + runs-on: ubuntu-latest + # Sets the permissions granted to the `GITHUB_TOKEN` for the actions in this job. + permissions: + contents: read + packages: write + # + steps: + - name: Checkout repository + uses: actions/checkout@v3 + # Uses the `docker/login-action` action to log in to the Container registry registry using the account and password that will publish the packages. Once published, the packages are scoped to the account defined here. + - name: Log in to the Container registry + uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1 + with: + registry: ghcr.io + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + # This step uses [docker/metadata-action](https://github.com/docker/metadata-action#about) to extract tags and labels that will be applied to the specified image. The `id` "meta" allows the output of this step to be referenced in a subsequent step. The `images` value provides the base name for the tags and labels. + - name: Extract metadata (tags, labels) for Docker + id: meta + uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7 + with: + images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} + # This step uses the `docker/build-push-action` action to build the image, based on your repository's `Dockerfile`. If the build succeeds, it pushes the image to GitHub Packages. + # It uses the `context` parameter to define the build's context as the set of files located in the specified path. For more information, see "[Usage](https://github.com/docker/build-push-action#usage)" in the README of the `docker/build-push-action` repository. + # It uses the `tags` and `labels` parameters to tag and label the image with the output from the "meta" step. + - name: Build and push Docker image + uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4 + with: + context: . + push: true + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} diff --git a/.github/workflows/docker.yml b/.github/workflows/docker-test.yml similarity index 99% rename from .github/workflows/docker.yml rename to .github/workflows/docker-test.yml index c6bc8894..1a866920 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker-test.yml @@ -43,6 +43,6 @@ jobs: uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4 with: context: . - push: true + push: false tags: ${{ steps.meta.outputs.tags }} labels: ${{ steps.meta.outputs.labels }} From 28fc83372b72c98a3b15be0cf51b133764a20603 Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Wed, 6 Sep 2023 23:25:12 +0200 Subject: [PATCH 047/148] add the imagePullPolicy for the pod --- deckard/iaac/gcp/pod.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/deckard/iaac/gcp/pod.yaml b/deckard/iaac/gcp/pod.yaml index 80e9c54c..a9145777 100644 --- a/deckard/iaac/gcp/pod.yaml +++ b/deckard/iaac/gcp/pod.yaml @@ -6,6 +6,7 @@ spec: containers: - name: deckard image: ghcr.io/simplymathematics/deckard:pr-116 + imagePullPolicy: Always workingDir: /deckard/examples/sklearn args: ["python", "-m", "dvc", "repro"] resources: From a720beb0dd7db9de82913678438a0c6e905cbd3b Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Thu, 7 Sep 2023 00:34:02 +0200 Subject: [PATCH 048/148] update sklearn --- examples/sklearn/attacks.sh | 38 ++++++++-------- examples/sklearn/conf/data/very_large.yaml | 20 +++++++++ examples/sklearn/conf/model.yaml | 2 +- examples/sklearn/data/README.md | 32 ++++++++++++++ examples/sklearn/dvc.yaml | 50 ++++++---------------- examples/sklearn/models.sh | 23 ++-------- 6 files changed, 88 insertions(+), 77 deletions(-) create mode 100644 examples/sklearn/conf/data/very_large.yaml create mode 100644 examples/sklearn/data/README.md diff --git a/examples/sklearn/attacks.sh b/examples/sklearn/attacks.sh index 5d103dfa..e01ca060 100644 --- a/examples/sklearn/attacks.sh +++ b/examples/sklearn/attacks.sh @@ -1,30 +1,28 @@ -# FGM -bash models.sh ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.norm=1,2,inf ++attack.init.eps=.001,.01,.1,.3,.5,1 ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=100 $@ - +# break # PGD -bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.norm=1,2,inf ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=100 $@ +bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.norm=1,2,inf ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=100 ++attack.init.eps=.001,.01,.1,.3,.5,1, $@ -# Carlini L0 -bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ +# # Carlini L0 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ -# Carlini L2 -bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ +# # Carlini L2 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ -# Carlini LInf -bash models.sh ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ +# # Carlini LInf +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ -# DeepFool -bash models.sh ++attack.init.nb_grads=1,3,5,10 ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.batch_size=100 $@ +# # DeepFool +# bash models.sh ++attack.init.nb_grads=1,3,5,10 ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.batch_size=100 $@ -#Threshold Attack -bash models.sh ++attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ +# #Threshold Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ -#Pixel Attack -bash models.sh ++attack.init.name=art.attacks.evasion.PixelAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ +# #Pixel Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.PixelAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ -#Adversarial Patch -bash models.sh ++attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.scale_max=.1,.2,.3,.5,.8,.9,.99 ++attack.init.batch_size=100 $@ +# #Adversarial Patch +# bash models.sh ++attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.scale_max=.1,.2,.3,.5,.8,.9,.99 ++attack.init.batch_size=100 $@ -#Hop Skip Jump -bash models.sh ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.batch_size=100 $@ +# #Hop Skip Jump +# bash models.sh ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.batch_size=100 $@ diff --git a/examples/sklearn/conf/data/very_large.yaml b/examples/sklearn/conf/data/very_large.yaml new file mode 100644 index 00000000..ea6c3120 --- /dev/null +++ b/examples/sklearn/conf/data/very_large.yaml @@ -0,0 +1,20 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 101000 + n_features : 20 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 100000 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/sklearn/conf/model.yaml b/examples/sklearn/conf/model.yaml index 13150544..4d0c2d8a 100644 --- a/examples/sklearn/conf/model.yaml +++ b/examples/sklearn/conf/model.yaml @@ -36,7 +36,7 @@ hydra: data.sample.stratify : true model.init.kernel : rbf model.init.C : tag(log, int(interval(1, 1e6))) - +model.init.max_iter : 100 + +model.init.max_iter : -1 launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher n_jobs: 10 diff --git a/examples/sklearn/data/README.md b/examples/sklearn/data/README.md new file mode 100644 index 00000000..e3400b6e --- /dev/null +++ b/examples/sklearn/data/README.md @@ -0,0 +1,32 @@ +# Classification Experiments + +## Directory Contents + +├── conf: contains the configuration files. +│ ├── attack +│ ├── config.yaml : contains the default configuration +│ ├── data +│ ├── files +│ ├── model +│ ├── plots +│ └── scorers +├── dag.md: contains the [dvc](https://dvc.org/doc/start/data-management/ data-pipelines) pipeline, visualized as a graph +├── dvc.lock: contains the git trackable information for all the data and model binaries +├── dvc.yaml: specifies the pipeline visualized in `dag.md` +├── experiments.sh: specifies the grid search parameters and executes the pipeline for each set of parameters +├── multirun: contains the hydra configuration information +├── params.yaml: contains the parsed default configuration file +├── plots.ipynb: is a jupyter notebook for visualizing the data +├── queue: contains all of the configurations for all experiments +├── reports: contains the results of all experiments, with a folder for each layer (e.g. `reports/train/`), containing scores, plots, predictions, probabilities, and ground_truth json files. +├── train: contains the `data/` and `models/` for each model +│ ├── data +│ ├── models + +## Execution instructions + +To check basic code functionality, run +```dvc repro --force``` +which will execute the [dvc pipeline](https://dvc.org/doc/start/data-management/data-pipelines) that makes all of the inputs and outputs git trackable. This will execute all of the code on the default configurations first (stages `generate`, `train`, and `attack` on the `dvc.yaml`), followed by grid search on all of the models in stage `model-queue` and all of the attacks in stage `attack-queue`. After finding the best configuration for each kernel (stage `compile`) + +which overrides the default configurations, using [hydra](https://hydra.cc/docs/patterns/configuring_experiments/) and its [optuna plugin](https://hydra.cc/docs/plugins/optuna_sweeper/) to specify a search space for each set of parameters specified in the bash script. Sets of parameters that apply to all experiments are set in the `config.yaml`. Parameters particular to a experiment are specified using command line overrides within the bash script. diff --git a/examples/sklearn/dvc.yaml b/examples/sklearn/dvc.yaml index f5c2e7aa..cd9bcff3 100644 --- a/examples/sklearn/dvc.yaml +++ b/examples/sklearn/dvc.yaml @@ -14,10 +14,10 @@ stages: outs: - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - - ${files.directory}/${files.reports}/train/${files.name}/${files.train_labels_file} - - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} - - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} - - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.train_labels_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} params: - data - model @@ -30,9 +30,9 @@ stages: - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} metrics: - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} - outs: - - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} - - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} + # outs: + # - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + # - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} params: - data - model @@ -46,28 +46,6 @@ stages: - model.db outs: - conf/model/best.yaml - model_optimise: - cmd: python -m deckard.layers.optimise +stage=train +optimizers=accuracy --multirun --config-name model - deps: - - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - outs: - - model.db - params: - - conf/model.yaml: - - hydra - ############################################################################## - attack_optimise: - cmd: python -m deckard.layers.optimise +stage=attack +optimizers=adv_accuracy model=best --multirun --config-name attack - deps: - - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - - conf/model/best.yaml - outs: - - attack.db - params: - - conf/attack.yaml: - - hydra find_best_attack: cmd: python -m deckard.layers.find_best attack.yaml deps: @@ -77,15 +55,15 @@ stages: ############################################################################## # grid search model_search : - cmd : bash models.sh model=best +stage=train +optimizers=accuracy ++hydra.sweeper.storage=sqlite:///model_grid.db ++hydra.sweeper.study_name=model ++hydra.sweeper.direction=maximize ++direction=maximize --multirun --config-name model.yaml + cmd : bash models.sh model=best +stage=train +optimizers=accuracy ++hydra.sweeper.storage=sqlite:///model.db ++hydra.sweeper.study_name=model ++hydra.sweeper.direction=maximize ++direction=maximize --multirun --config-name model.yaml outs: - - model_grid.db + - model.db deps: - - conf/model/best.yaml + - models.sh attack_search : - cmd : bash attacks.sh model=best ++stage=attack ++optimizers=adv_accuracy ++hydra.sweeper.storage=sqlite:///attack_grid.db ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++direction=minimize ++attack.init.max_iter=10 --multirun --config-name attack.yaml + cmd : bash attacks.sh model=best ++stage=attack ++optimizers=adv_accuracy ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++direction=minimize ++attack.init.max_iter=10 --multirun --config-name attack.yaml deps: - - model_grid.db - - conf/model/best.yaml + - attacks.sh + - model.db outs: - - attack_grid.db + - attack.db diff --git a/examples/sklearn/models.sh b/examples/sklearn/models.sh index 7f251172..1e009e11 100644 --- a/examples/sklearn/models.sh +++ b/examples/sklearn/models.sh @@ -1,25 +1,8 @@ #!/bin/bash # This script is used to generate the models for the sklearn example. +python -m ++data=small,medium,large,very_large ++model=linear ++data.sample.random_state=0,1,2,3,4,5 ++data.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 $@ +python -m ++data=small,medium,large,very_large ++model=poly ++data.sample.random_state=0,1,2,3,4,5 ++data.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 ++data.init.degree=1,2,3,4,5 ++data.init.coef0=.00001,.0001,.01,.1,1,10,1000,1000000 $@ -# This line generates the model and adds the FeatureSqueezing preprocessing defence. -python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=1,4,8,16,32,64 $@ - -# Gaussian Augmentation (Input) -python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.2,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 $@ - -# High Confidence -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.5,.9,.99 $@ - -# # Spatial Smoothing -# python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.SpatialSmoothing +model.art.preprocessor.params.window_size=2,3,4 $@ - -# # Total Variance Minimisation -# python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.TotalVarMin +model.art.preprocessor.params.prob=.001,.01,.1 +model.art.preprocessor.params.norm=1,2,3 +model.art.preprocessor.params.lamb=.05,.5,.95 +model.art.preprocessor.params.max_iter=100 $@ - -# Gaussian Noise (Output) -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.1,.9,.999 $@ - -# # Rounded (Output) -# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.Rounded +model.art.postprocessor.params.decimals=1,2,4,8 $@ +python -m ++data=small,medium,large,very_large ++model=rbf ++data.sample.random_state=0,1,2,3,4,5 ++data.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 $@ \ No newline at end of file From cca5dd79bacc8160fab3b8d7996d96f5f58f385a Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Thu, 7 Sep 2023 01:03:29 +0200 Subject: [PATCH 049/148] update sklearn example --- examples/sklearn/dvc.lock | 24 ++++++------------------ examples/sklearn/dvc.yaml | 23 +++++++++++++++++++++++ examples/sklearn/models.sh | 6 +++--- 3 files changed, 32 insertions(+), 21 deletions(-) diff --git a/examples/sklearn/dvc.lock b/examples/sklearn/dvc.lock index 5dda0720..e3948819 100644 --- a/examples/sklearn/dvc.lock +++ b/examples/sklearn/dvc.lock @@ -86,26 +86,14 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/data/data.pkl - md5: eeabc07006294278801a3906f7016b20 - size: 185102 + md5: 9a58fd530e1d151a5f0ac40c38cbb24a + size: 180702 - path: output/models/model.pkl - md5: 4a9a983540c62bdcfa99c3a0297e7afc - size: 15368 - - path: output/reports/train/default/predictions.json - md5: d8aed90aa6be874fff261a9f3215398c - size: 43229 - - path: output/reports/train/default/probabilities.json - md5: d8aed90aa6be874fff261a9f3215398c - size: 43229 + md5: 03d0214bba09b1d4042b5923af1e8bcd + size: 15402 - path: output/reports/train/default/score_dict.json - md5: 8ea27e3095a4ba0ef5238bdca4d0daa2 - size: 357 - - path: output/reports/train/default/test_labels.json - md5: 8b08830bcf914dc948fc4b2ecf3ce5aa - size: 3000 - - path: output/reports/train/default/train_labels.json - md5: 8e93c1a7db21ebb97ce6e4ed58809240 - size: 300 + md5: d408e646fbad154347d891538beda08f + size: 340 model_optimise: cmd: python -m deckard.layers.optimise +stage=train +optimizers=accuracy --multirun --config-name model diff --git a/examples/sklearn/dvc.yaml b/examples/sklearn/dvc.yaml index cd9bcff3..c5c0263b 100644 --- a/examples/sklearn/dvc.yaml +++ b/examples/sklearn/dvc.yaml @@ -46,6 +46,29 @@ stages: - model.db outs: - conf/model/best.yaml + # model_optimise: + # cmd: python -m deckard.layers.optimise +stage=train +optimizers=accuracy --multirun --config-name model + # deps: + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + # - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + # outs: + # - model.db + # params: + # - conf/model.yaml: + # - hydra + # ############################################################################## + # attack_optimise: + # cmd: python -m deckard.layers.optimise +stage=attack +optimizers=adv_accuracy model=best --multirun --config-name attack + # deps: + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + # - conf/model/best.yaml + # outs: + # - attack.db + # params: + # - conf/attack.yaml: + # - hydra find_best_attack: cmd: python -m deckard.layers.find_best attack.yaml deps: diff --git a/examples/sklearn/models.sh b/examples/sklearn/models.sh index 1e009e11..d9f2cad5 100644 --- a/examples/sklearn/models.sh +++ b/examples/sklearn/models.sh @@ -1,8 +1,8 @@ #!/bin/bash # This script is used to generate the models for the sklearn example. -python -m ++data=small,medium,large,very_large ++model=linear ++data.sample.random_state=0,1,2,3,4,5 ++data.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 $@ +python -m deckard.layers.optimise model=linear ++data.sample.random_state=0,1,2,3,4,5 ++model.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 $@ -python -m ++data=small,medium,large,very_large ++model=poly ++data.sample.random_state=0,1,2,3,4,5 ++data.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 ++data.init.degree=1,2,3,4,5 ++data.init.coef0=.00001,.0001,.01,.1,1,10,1000,1000000 $@ +python -m deckard.layers.optimise ++data=small,medium,large,very_large model=poly ++data.sample.random_state=0,1,2,3,4,5 ++model.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 ++data.init.degree=1,2,3,4,5 ++model.init.Coef0=.00001,.0001,.01,.1,1,10,1000,1000000 $@ -python -m ++data=small,medium,large,very_large ++model=rbf ++data.sample.random_state=0,1,2,3,4,5 ++data.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 $@ \ No newline at end of file +python -m deckard.layers.optimise ++data=small,medium,large,very_large model=rbf ++data.sample.random_state=0,1,2,3,4,5 ++model.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 $@ \ No newline at end of file From 9b269c5de2f75c94f41e964442493853b3ba76ca Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 19:07:03 +0200 Subject: [PATCH 050/148] update sklearn example --- deckard/layers/find_best.py | 3 +- examples/sklearn/.gitignore | 5 + examples/sklearn/{data => }/README.md | 0 examples/sklearn/attacks.sh | 2 +- examples/sklearn/conf/attack.yaml | 30 +- examples/sklearn/conf/data/very_large.yaml | 2 +- examples/sklearn/conf/model.yaml | 19 +- examples/sklearn/conf/model/linear.yaml | 1 + examples/sklearn/conf/model/poly.yaml | 1 + examples/sklearn/conf/model/rbf.yaml | 1 + examples/sklearn/dvc.lock | 1064 +------------------- examples/sklearn/dvc.yaml | 5 +- examples/sklearn/models.sh | 10 +- examples/sklearn/params.yaml | 3 + 14 files changed, 79 insertions(+), 1067 deletions(-) create mode 100644 examples/sklearn/.gitignore rename examples/sklearn/{data => }/README.md (100%) diff --git a/deckard/layers/find_best.py b/deckard/layers/find_best.py index 423eae58..ea054098 100644 --- a/deckard/layers/find_best.py +++ b/deckard/layers/find_best.py @@ -33,7 +33,8 @@ def find_optuna_best( overrides = [] for key, value in best_params.items(): logger.info(f"Overriding {key} with {value}") - overrides.append(f"++{key}={value}") + if not key.startswith("+"): + overrides.append(f"++{key}={value}") with initialize_config_dir(config_dir=config_folder, version_base="1.3"): cfg = compose(config_name=config_name, overrides=overrides) cfg = OmegaConf.to_container(cfg, resolve=True) diff --git a/examples/sklearn/.gitignore b/examples/sklearn/.gitignore new file mode 100644 index 00000000..900aa55a --- /dev/null +++ b/examples/sklearn/.gitignore @@ -0,0 +1,5 @@ +/model.db +/attack.db +/model_grid.db +/attack_grid.db +/output diff --git a/examples/sklearn/data/README.md b/examples/sklearn/README.md similarity index 100% rename from examples/sklearn/data/README.md rename to examples/sklearn/README.md diff --git a/examples/sklearn/attacks.sh b/examples/sklearn/attacks.sh index e01ca060..169de6ee 100644 --- a/examples/sklearn/attacks.sh +++ b/examples/sklearn/attacks.sh @@ -1,7 +1,7 @@ # break # PGD -bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.norm=1,2,inf ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=100 ++attack.init.eps=.001,.01,.1,.3,.5,1, $@ +bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.norm=1,2,inf ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=100 ++attack.init.eps=.001,.01,.1,.3,.5,1 $@ # # Carlini L0 # bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ diff --git a/examples/sklearn/conf/attack.yaml b/examples/sklearn/conf/attack.yaml index 9572cca9..332558a7 100644 --- a/examples/sklearn/conf/attack.yaml +++ b/examples/sklearn/conf/attack.yaml @@ -2,32 +2,24 @@ defaults: # - _target_ : deckard.base.experiment.Experiment - _self_ - data: default - - model: best - - attack: hsj + - model: default + - attack : hsj - files: default - scorers: default - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid - override hydra/launcher : joblib hydra: run: dir : "./${files.directory}" sweeper: sampler: - _target_: optuna.samplers.TPESampler - seed: 123 - consider_prior: true - prior_weight: 1.0 - consider_magic_clip: true - consider_endpoints: false - n_startup_trials: 10 - n_ei_candidates: 24 - multivariate: false - warn_independent_sampling: true + _target_: optuna.samplers.GridSampler _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - direction: minimize - study_name: attack - storage: sqlite:///attack.db - n_trials: 3 + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 1 n_jobs: 1 params: data.generate.n_features : 20 @@ -35,12 +27,10 @@ hydra: data.sample.test_size : 100 data.sample.random_state : 0 data.sample.stratify : true - model.init.kernel : rbf - model.init.C : tag(log, int(interval(1, 1e6))) - +model.init.max_iter : 100 + model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 10 + n_jobs: 32 prefer : processes verbose: 1 timeout: null diff --git a/examples/sklearn/conf/data/very_large.yaml b/examples/sklearn/conf/data/very_large.yaml index ea6c3120..ff552d03 100644 --- a/examples/sklearn/conf/data/very_large.yaml +++ b/examples/sklearn/conf/data/very_large.yaml @@ -3,7 +3,7 @@ generate: # _target_: deckard.base.data.generator.DataGenerator name: classification random_state : 0 - n_samples : 101000 + n_samples : 1001000 n_features : 20 sample: # _target_: deckard.base.data.sampler.SklearnDataSampler diff --git a/examples/sklearn/conf/model.yaml b/examples/sklearn/conf/model.yaml index 4d0c2d8a..9d17cb0d 100644 --- a/examples/sklearn/conf/model.yaml +++ b/examples/sklearn/conf/model.yaml @@ -6,27 +6,18 @@ defaults: - files: default - scorers: default - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid - override hydra/launcher : joblib hydra: run: dir : "./${files.directory}" sweeper: sampler: - _target_: optuna.samplers.TPESampler - seed: 123 - consider_prior: true - prior_weight: 1.0 - consider_magic_clip: true - consider_endpoints: false - n_startup_trials: 10 - n_ei_candidates: 24 - multivariate: false - warn_independent_sampling: true + _target_: optuna.samplers.GridSampler _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper direction: maximize study_name: model storage: sqlite:///model.db - n_trials: 10 n_jobs: 1 params: data.generate.n_features : 20 @@ -34,12 +25,10 @@ hydra: data.sample.test_size : 100 data.sample.random_state : 0 data.sample.stratify : true - model.init.kernel : rbf - model.init.C : tag(log, int(interval(1, 1e6))) - +model.init.max_iter : -1 + model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 10 + n_jobs: 32 prefer : processes verbose: 1 timeout: null diff --git a/examples/sklearn/conf/model/linear.yaml b/examples/sklearn/conf/model/linear.yaml index b77ed026..b900f88a 100644 --- a/examples/sklearn/conf/model/linear.yaml +++ b/examples/sklearn/conf/model/linear.yaml @@ -7,6 +7,7 @@ init: kernel : linear probability : true random_state : 0 + max_iter : 100 _target_: deckard.base.model.Model art: _target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/sklearn/conf/model/poly.yaml b/examples/sklearn/conf/model/poly.yaml index 24ecbd8f..7a5be797 100644 --- a/examples/sklearn/conf/model/poly.yaml +++ b/examples/sklearn/conf/model/poly.yaml @@ -8,6 +8,7 @@ init: kernel : poly probability : true random_state : 0 + max_iter : 100 _target_: deckard.base.model.Model art: _target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/sklearn/conf/model/rbf.yaml b/examples/sklearn/conf/model/rbf.yaml index 850d9a7e..42ca4c07 100644 --- a/examples/sklearn/conf/model/rbf.yaml +++ b/examples/sklearn/conf/model/rbf.yaml @@ -7,6 +7,7 @@ init: kernel : rbf probability : true random_state : 0 + max_iter : 100 _target_: deckard.base.model.Model art: _target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/sklearn/dvc.lock b/examples/sklearn/dvc.lock index e3948819..7b1214e1 100644 --- a/examples/sklearn/dvc.lock +++ b/examples/sklearn/dvc.lock @@ -86,79 +86,27 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/data/data.pkl - md5: 9a58fd530e1d151a5f0ac40c38cbb24a - size: 180702 - - path: output/models/model.pkl - md5: 03d0214bba09b1d4042b5923af1e8bcd - size: 15402 - - path: output/reports/train/default/score_dict.json - md5: d408e646fbad154347d891538beda08f - size: 340 - model_optimise: - cmd: python -m deckard.layers.optimise +stage=train +optimizers=accuracy --multirun - --config-name model - deps: - - path: output/data/data.pkl - md5: eeabc07006294278801a3906f7016b20 + hash: md5 + md5: af3fa729fa7f76213685ca4e51b80bb2 size: 185102 - path: output/models/model.pkl - md5: 11ee9fa397238b26669489c944b361d5 - size: 15402 - params: - conf/model.yaml: - hydra: - run: - dir: ./${files.directory} - sweeper: - sampler: - _target_: optuna.samplers.TPESampler - seed: 123 - consider_prior: true - prior_weight: 1.0 - consider_magic_clip: true - consider_endpoints: false - n_startup_trials: 10 - n_ei_candidates: 24 - multivariate: false - warn_independent_sampling: true - _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - direction: maximize - study_name: model - storage: sqlite:///model.db - n_trials: 10 - n_jobs: 1 - params: - data.generate.n_features: 20 - data.sample.train_size: 1000 - data.sample.test_size: 100 - data.sample.random_state: 0 - data.sample.stratify: true - model.init.kernel: rbf - model.init.C: tag(log, int(interval(1, 1e6))) - +model.init.max_iter: 100 - launcher: - _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 10 - prefer: processes - verbose: 1 - timeout: - pre_dispatch: n_jobs - batch_size: auto - temp_folder: /tmp/deckard - max_nbytes: 100000 - mmap_mode: r - outs: - - path: model.db - md5: dc51b4e61037ce5f866f9a02afde26e7 - size: 110592 + hash: md5 + md5: d25bc188783294346b2d35e202ac5ad5 + size: 15368 + - path: output/reports/train/default/score_dict.json + hash: md5 + md5: 18a997a60fafeaa72e24a09efefef4cc + size: 347 attack: cmd: python -m deckard.layers.experiment attack deps: - path: output/data/data.pkl - md5: eeabc07006294278801a3906f7016b20 + hash: md5 + md5: af3fa729fa7f76213685ca4e51b80bb2 size: 185102 - path: output/models/model.pkl - md5: 11ee9fa397238b26669489c944b361d5 + hash: md5 + md5: 997a5fcff8c4986cd196d31194b1e22a size: 15402 params: params.yaml: @@ -210,6 +158,7 @@ stages: C: 1.0 _target_: deckard.base.model.ModelInitializer kernel: rbf + max_iter: -1 name: sklearn.svm.SVC probability: true random_state: 0 @@ -243,6 +192,7 @@ stages: C: 1.0 _target_: deckard.base.model.ModelInitializer kernel: rbf + max_iter: -1 name: sklearn.svm.SVC probability: true random_state: 0 @@ -313,6 +263,7 @@ stages: C: 1.0 _target_: deckard.base.model.ModelInitializer kernel: rbf + max_iter: -1 name: sklearn.svm.SVC probability: true random_state: 0 @@ -328,970 +279,37 @@ stages: direction: minimize name: sklearn.metrics.log_loss outs: - - path: output/reports/attack/default/adv_predictions.json - md5: f2c857c1227f3827ce11db1464be9b63 - size: 426 - - path: output/reports/attack/default/adv_probabilities.json - md5: f2c857c1227f3827ce11db1464be9b63 - size: 426 - path: output/reports/attack/default/score_dict.json - md5: 6cb818492db3f764a2df1f41a513d8f9 - size: 497 - attack_optimise: - cmd: python -m deckard.layers.optimise +stage=attack +optimizers=adv_accuracy - model=best --multirun --config-name attack + hash: md5 + md5: e8fea4f241658608b5ebc8a8cb1375ce + size: 487 + model_search: + cmd: bash models.sh +stage=train +optimizers=accuracy ++hydra.sweeper.storage=sqlite:///model.db + ++hydra.sweeper.study_name=model ++hydra.sweeper.direction=maximize ++direction=maximize + --multirun --config-name model.yaml deps: - - path: conf/model/best.yaml - md5: 8076f951bc284c87f0bbcf2ae8e159b8 - size: 656 - - path: output/data/data.pkl - md5: eeabc07006294278801a3906f7016b20 - size: 185102 - - path: output/models/model.pkl - md5: 11ee9fa397238b26669489c944b361d5 - size: 15402 - params: - conf/attack.yaml: - hydra: - run: - dir: ./${files.directory} - sweeper: - sampler: - _target_: optuna.samplers.TPESampler - seed: 123 - consider_prior: true - prior_weight: 1.0 - consider_magic_clip: true - consider_endpoints: false - n_startup_trials: 10 - n_ei_candidates: 24 - multivariate: false - warn_independent_sampling: true - _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - direction: minimize - study_name: attack - storage: sqlite:///attack.db - n_trials: 3 - n_jobs: 1 - params: - data.generate.n_features: 20 - data.sample.train_size: 1000 - data.sample.test_size: 100 - data.sample.random_state: 0 - data.sample.stratify: true - model.init.kernel: rbf - model.init.C: tag(log, int(interval(1, 1e6))) - +model.init.max_iter: 100 - launcher: - _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 10 - prefer: processes - verbose: 1 - timeout: - pre_dispatch: n_jobs - batch_size: auto - temp_folder: /tmp/deckard - max_nbytes: 100000 - mmap_mode: r + - path: models.sh + hash: md5 + md5: b20bba54353be244dc917518b03ef007 + size: 705 + - path: output/reports/attack/default/score_dict.json + hash: md5 + md5: a62fe1c9f76a4171c1772410f9a3f456 + size: 487 outs: - - path: attack.db - md5: cdde21203e37d23fafd7a25750c986e9 - size: 110592 + - path: model.db + hash: md5 + md5: 06f345df85eb278681717365e9bc2f16 + size: 163840 find_best_model: cmd: python -m deckard.layers.find_best model.yaml deps: - path: model.db - md5: dc51b4e61037ce5f866f9a02afde26e7 - size: 110592 + hash: md5 + md5: 06f345df85eb278681717365e9bc2f16 + size: 163840 outs: - path: conf/model/best.yaml - md5: 8076f951bc284c87f0bbcf2ae8e159b8 - size: 656 - find_best_attack: - cmd: python -m deckard.layers.find_best attack.yaml - deps: - - path: attack.db - md5: cdde21203e37d23fafd7a25750c986e9 - size: 110592 - outs: - - path: conf/attack/best.yaml - md5: 4d05c325c57569b497140f774bfd2a2e - size: 1979 - generate_data@small: - cmd: python -m deckard.layers.data --data_config_name small - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json - outs: - - path: output/data/small.pkl - md5: ea29369c6476a7a45814995cb2e4ff58 - size: 180702 - generate_data@large: - cmd: python -m deckard.layers.data --data_config_name large - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json - outs: - - path: output/data/large.pkl - md5: 94221969b3f3816a14b0cb92ce8a5964 - size: 16564326 - generate_data@medium: - cmd: python -m deckard.layers.data --data_config_name medium - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json - outs: - - path: output/data/medium.pkl - md5: 0ea32658bbb675057f7907091abc827c - size: 1804313 - train_models@poly: - cmd: python -m deckard.layers.model --model_config_name poly - deps: - - path: output/data/small.pkl - md5: ea29369c6476a7a45814995cb2e4ff58 - size: 180702 - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - initialize: - library: sklearn-svc - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - C: 1.0 - _target_: deckard.base.model.ModelInitializer - kernel: linear - name: sklearn.svm.SVC - probability: true - random_state: 0 - library: sklearn-svc - scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss - outs: - - path: output/models/poly.pkl - md5: 4c237eca49b823283ec4e8535a94aa67 - size: 17210 - generate_data@default: - cmd: python -m deckard.layers.data --data_config_name default - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json - outs: - - path: output/data/default.pkl - md5: ea29369c6476a7a45814995cb2e4ff58 - size: 180702 - train_models@linear: - cmd: python -m deckard.layers.model --model_config_name linear - deps: - - path: output/data/small.pkl - md5: ea29369c6476a7a45814995cb2e4ff58 - size: 180702 - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - initialize: - library: sklearn-svc - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - C: 1.0 - _target_: deckard.base.model.ModelInitializer - kernel: linear - name: sklearn.svm.SVC - probability: true - random_state: 0 - library: sklearn-svc - scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss - outs: - - path: output/models/linear.pkl - md5: deb853a6f81257a322c06c4beb7ede73 - size: 5319 - train_models@rbf: - cmd: python -m deckard.layers.model --model_config_name rbf - deps: - - path: output/data/small.pkl - md5: ea29369c6476a7a45814995cb2e4ff58 - size: 180702 - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - initialize: - library: sklearn-svc - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - C: 1.0 - _target_: deckard.base.model.ModelInitializer - kernel: linear - name: sklearn.svm.SVC - probability: true - random_state: 0 - library: sklearn-svc - scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss - outs: - - path: output/models/rbf.pkl - md5: 97c5321edbc2e178dd3d6a931caf8354 - size: 15402 - data@large: - cmd: python -m deckard.layers.data --data_config_name large - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json - outs: - - path: output/data/large.pkl - md5: 94221969b3f3816a14b0cb92ce8a5964 - size: 16564326 - data@small: - cmd: python -m deckard.layers.data --data_config_name small - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json - outs: - - path: output/data/small.pkl - md5: ea29369c6476a7a45814995cb2e4ff58 - size: 180702 - models@poly: - cmd: python -m deckard.layers.model --model_config_name poly --overrides=files.data_file=small - deps: - - path: output/data/small.pkl - md5: ea29369c6476a7a45814995cb2e4ff58 - size: 180702 - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - initialize: - library: sklearn-svc - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - C: 1.0 - _target_: deckard.base.model.ModelInitializer - kernel: linear - name: sklearn.svm.SVC - probability: true - random_state: 0 - library: sklearn-svc - outs: - - path: output/models/poly.pkl - md5: 4c237eca49b823283ec4e8535a94aa67 - size: 17210 - models@linear: - cmd: python -m deckard.layers.model --model_config_name linear --overrides=files.data_file=small - deps: - - path: output/data/small.pkl - md5: ea29369c6476a7a45814995cb2e4ff58 - size: 180702 - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - initialize: - library: sklearn-svc - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - C: 1.0 - _target_: deckard.base.model.ModelInitializer - kernel: linear - name: sklearn.svm.SVC - probability: true - random_state: 0 - library: sklearn-svc - outs: - - path: output/models/linear.pkl - md5: deb853a6f81257a322c06c4beb7ede73 - size: 5319 - models@rbf: - cmd: python -m deckard.layers.model --model_config_name rbf --overrides=files.data_file=small - deps: - - path: output/data/small.pkl - md5: ea29369c6476a7a45814995cb2e4ff58 - size: 180702 - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - initialize: - library: sklearn-svc - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - C: 1.0 - _target_: deckard.base.model.ModelInitializer - kernel: linear - name: sklearn.svm.SVC - probability: true - random_state: 0 - library: sklearn-svc - outs: - - path: output/models/rbf.pkl - md5: 97c5321edbc2e178dd3d6a931caf8354 - size: 15402 - data@medium: - cmd: python -m deckard.layers.data --data_config_name medium - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json - outs: - - path: output/data/medium.pkl - md5: 0ea32658bbb675057f7907091abc827c - size: 1804313 - model_search: - cmd: bash models.sh model=best +stage=train +optimizers=accuracy ++hydra.sweeper.storage=sqlite:///model_grid.db - ++hydra.sweeper.study_name=model ++hydra.sweeper.direction=maximize ++direction=maximize --multirun - --config-name model.yaml - deps: - - path: conf/model/best.yaml - md5: 8076f951bc284c87f0bbcf2ae8e159b8 - size: 656 - outs: - - path: model_grid.db - md5: bf2120ffcdb84e926fdee3194258ae7a - size: 159744 - attack_search: - cmd: bash attacks.sh model=best ++stage=attack ++optimizers=adv_accuracy ++hydra.sweeper.storage=sqlite:///attack_grid.db - ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++direction=minimize - ++attack.init.max_iter=10 --multirun --config-name attack.yaml - deps: - - path: conf/model/best.yaml - md5: 8076f951bc284c87f0bbcf2ae8e159b8 - size: 656 - - path: model_grid.db - md5: bf2120ffcdb84e926fdee3194258ae7a - size: 159744 - outs: - - path: attack_grid.db - md5: f3ee3cda113eb52922552c8fe975898b - size: 323584 + hash: md5 + md5: c59826147251b919c00adadee514f74f + size: 657 diff --git a/examples/sklearn/dvc.yaml b/examples/sklearn/dvc.yaml index c5c0263b..39fa6c21 100644 --- a/examples/sklearn/dvc.yaml +++ b/examples/sklearn/dvc.yaml @@ -78,13 +78,14 @@ stages: ############################################################################## # grid search model_search : - cmd : bash models.sh model=best +stage=train +optimizers=accuracy ++hydra.sweeper.storage=sqlite:///model.db ++hydra.sweeper.study_name=model ++hydra.sweeper.direction=maximize ++direction=maximize --multirun --config-name model.yaml + cmd : bash models.sh +stage=train +optimizers=accuracy ++hydra.sweeper.storage=sqlite:///model.db ++hydra.sweeper.study_name=model ++hydra.sweeper.direction=maximize ++direction=maximize --multirun --config-name model.yaml outs: - model.db deps: - models.sh + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} attack_search : - cmd : bash attacks.sh model=best ++stage=attack ++optimizers=adv_accuracy ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++direction=minimize ++attack.init.max_iter=10 --multirun --config-name attack.yaml + cmd : bash attacks.sh ++stage=attack ++optimizers=adv_accuracy ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++direction=minimize ++attack.init.max_iter=10 --multirun --config-name attack.yaml deps: - attacks.sh - model.db diff --git a/examples/sklearn/models.sh b/examples/sklearn/models.sh index d9f2cad5..7e0e7c2f 100644 --- a/examples/sklearn/models.sh +++ b/examples/sklearn/models.sh @@ -1,8 +1,10 @@ #!/bin/bash - +sizes=( small medium large very_large ) +for size in $sizes; do # This script is used to generate the models for the sklearn example. -python -m deckard.layers.optimise model=linear ++data.sample.random_state=0,1,2,3,4,5 ++model.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 $@ + python -m deckard.layers.optimise data=$size model=linear ++data.sample.random_state=0,1,2,3,4,5 ++model.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 $@ -python -m deckard.layers.optimise ++data=small,medium,large,very_large model=poly ++data.sample.random_state=0,1,2,3,4,5 ++model.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 ++data.init.degree=1,2,3,4,5 ++model.init.Coef0=.00001,.0001,.01,.1,1,10,1000,1000000 $@ + python -m deckard.layers.optimise data=$size model=poly ++data.sample.random_state=0,1,2,3,4,5 ++model.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 ++model.init.degree=1,2,3,4,5 ++model.init.Coef0=.00001,.0001,.01,.1,1,10,1000,1000000 $@ -python -m deckard.layers.optimise ++data=small,medium,large,very_large model=rbf ++data.sample.random_state=0,1,2,3,4,5 ++model.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 $@ \ No newline at end of file + python -m deckard.layers.optimise data=$size model=rbf ++data.sample.random_state=0,1,2,3,4,5 ++model.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 $@ +done \ No newline at end of file diff --git a/examples/sklearn/params.yaml b/examples/sklearn/params.yaml index a2fe4bf7..b6dd6a11 100644 --- a/examples/sklearn/params.yaml +++ b/examples/sklearn/params.yaml @@ -46,6 +46,7 @@ attack: C: 1.0 _target_: deckard.base.model.ModelInitializer kernel: rbf + max_iter: 100 name: sklearn.svm.SVC probability: true random_state: 0 @@ -79,6 +80,7 @@ attack: C: 1.0 _target_: deckard.base.model.ModelInitializer kernel: rbf + max_iter: 100 name: sklearn.svm.SVC probability: true random_state: 0 @@ -149,6 +151,7 @@ model: C: 1.0 _target_: deckard.base.model.ModelInitializer kernel: rbf + max_iter: 100 name: sklearn.svm.SVC probability: true random_state: 0 From e538f12cbf866fc3508f879f952bb8017c05173d Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 19:11:47 +0200 Subject: [PATCH 051/148] moved configs --- deckard/iaac/gcp/params.yaml | 14 ----------- .../pytorch/conf/deploy/params.yaml | 0 .../pytorch/conf/deploy}/pod.yaml | 2 +- .../pytorch/conf/deploy}/pvc.yaml | 0 .../pytorch/conf/deploy}/sclass.yaml | 0 examples/sklearn/dvc.lock | 23 ++++++++++--------- 6 files changed, 13 insertions(+), 26 deletions(-) delete mode 100644 deckard/iaac/gcp/params.yaml rename deckard/iaac/gcp/conf/deploy.yaml => examples/pytorch/conf/deploy/params.yaml (100%) rename {deckard/iaac/gcp => examples/pytorch/conf/deploy}/pod.yaml (88%) rename {deckard/iaac/gcp => examples/pytorch/conf/deploy}/pvc.yaml (100%) rename {deckard/iaac/gcp => examples/pytorch/conf/deploy}/sclass.yaml (100%) diff --git a/deckard/iaac/gcp/params.yaml b/deckard/iaac/gcp/params.yaml deleted file mode 100644 index 8018014a..00000000 --- a/deckard/iaac/gcp/params.yaml +++ /dev/null @@ -1,14 +0,0 @@ -cluster_name: k8s-cluster -gpu_count: 1 -gpu_driver_version: default -gpu_type: nvidia-tesla-v100 -image_family: ubuntu-2204-lts -image_project: ubuntu-os-cloud -machine_type: n1-standard-2 -max_nodes: 1 -min_nodes: 1 -mount_directory: /mnt/filestore -num_nodes: 1 -persistent_volume_claim: ./IaaC/gcp/pvc.yaml -pod: ./IaaC/gcp/pod.yaml -storage_config: ./IaaC/gcp/sclass.yaml diff --git a/deckard/iaac/gcp/conf/deploy.yaml b/examples/pytorch/conf/deploy/params.yaml similarity index 100% rename from deckard/iaac/gcp/conf/deploy.yaml rename to examples/pytorch/conf/deploy/params.yaml diff --git a/deckard/iaac/gcp/pod.yaml b/examples/pytorch/conf/deploy/pod.yaml similarity index 88% rename from deckard/iaac/gcp/pod.yaml rename to examples/pytorch/conf/deploy/pod.yaml index a9145777..b8761e6f 100644 --- a/deckard/iaac/gcp/pod.yaml +++ b/examples/pytorch/conf/deploy/pod.yaml @@ -5,7 +5,7 @@ metadata: spec: containers: - name: deckard - image: ghcr.io/simplymathematics/deckard:pr-116 + image: ghcr.io/simplymathematics/deckard:main imagePullPolicy: Always workingDir: /deckard/examples/sklearn args: ["python", "-m", "dvc", "repro"] diff --git a/deckard/iaac/gcp/pvc.yaml b/examples/pytorch/conf/deploy/pvc.yaml similarity index 100% rename from deckard/iaac/gcp/pvc.yaml rename to examples/pytorch/conf/deploy/pvc.yaml diff --git a/deckard/iaac/gcp/sclass.yaml b/examples/pytorch/conf/deploy/sclass.yaml similarity index 100% rename from deckard/iaac/gcp/sclass.yaml rename to examples/pytorch/conf/deploy/sclass.yaml diff --git a/examples/sklearn/dvc.lock b/examples/sklearn/dvc.lock index 7b1214e1..7d8152a9 100644 --- a/examples/sklearn/dvc.lock +++ b/examples/sklearn/dvc.lock @@ -70,6 +70,7 @@ stages: C: 1.0 _target_: deckard.base.model.ModelInitializer kernel: rbf + max_iter: 100 name: sklearn.svm.SVC probability: true random_state: 0 @@ -91,12 +92,12 @@ stages: size: 185102 - path: output/models/model.pkl hash: md5 - md5: d25bc188783294346b2d35e202ac5ad5 - size: 15368 + md5: 8f9bb89ea877a37e005fb9d12d6538a0 + size: 15185 - path: output/reports/train/default/score_dict.json hash: md5 - md5: 18a997a60fafeaa72e24a09efefef4cc - size: 347 + md5: 110eccb7dbf05b7c31f7ab2e423b827c + size: 349 attack: cmd: python -m deckard.layers.experiment attack deps: @@ -106,8 +107,8 @@ stages: size: 185102 - path: output/models/model.pkl hash: md5 - md5: 997a5fcff8c4986cd196d31194b1e22a - size: 15402 + md5: 27225f126e831ab39e47b6b9386ab36b + size: 15219 params: params.yaml: attack: @@ -158,7 +159,7 @@ stages: C: 1.0 _target_: deckard.base.model.ModelInitializer kernel: rbf - max_iter: -1 + max_iter: 100 name: sklearn.svm.SVC probability: true random_state: 0 @@ -192,7 +193,7 @@ stages: C: 1.0 _target_: deckard.base.model.ModelInitializer kernel: rbf - max_iter: -1 + max_iter: 100 name: sklearn.svm.SVC probability: true random_state: 0 @@ -263,7 +264,7 @@ stages: C: 1.0 _target_: deckard.base.model.ModelInitializer kernel: rbf - max_iter: -1 + max_iter: 100 name: sklearn.svm.SVC probability: true random_state: 0 @@ -281,8 +282,8 @@ stages: outs: - path: output/reports/attack/default/score_dict.json hash: md5 - md5: e8fea4f241658608b5ebc8a8cb1375ce - size: 487 + md5: cc04fa9f6331853dbc29daa7bcaee11c + size: 488 model_search: cmd: bash models.sh +stage=train +optimizers=accuracy ++hydra.sweeper.storage=sqlite:///model.db ++hydra.sweeper.study_name=model ++hydra.sweeper.direction=maximize ++direction=maximize From cd98387c63dfbaf8f0e81e16df4a108020c07810 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 19:14:40 +0200 Subject: [PATCH 052/148] updated iaac code --- deckard/iaac/gcp/NEW_README.md | 11 ----------- deckard/iaac/gcp/README.md | 15 ++++++++++++++- deckard/iaac/gcp/install.sh | 6 ------ examples/pytorch/conf/deploy/params.yaml | 6 +++--- 4 files changed, 17 insertions(+), 21 deletions(-) delete mode 100644 deckard/iaac/gcp/NEW_README.md delete mode 100644 deckard/iaac/gcp/install.sh diff --git a/deckard/iaac/gcp/NEW_README.md b/deckard/iaac/gcp/NEW_README.md deleted file mode 100644 index 844d8bc1..00000000 --- a/deckard/iaac/gcp/NEW_README.md +++ /dev/null @@ -1,11 +0,0 @@ -# Install gcloud - -First you need to install `gcloud-cli`. In order to setup the GKE (Google Kubernetes Engine), we need to enable require apis. Follow [these intsructions to enable them](https://cloud.google.com/endpoints/docs/openapi/enable-api). -[Source](https://cloud.google.com/sdk/docs/install) -``` -sudo apt-get update -sudo apt-get install apt-transport-https ca-certificates gnupg curl sudo -echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main" | sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list -curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - -sudo apt-get update && sudo apt-get install google-cloud-cli -``` diff --git a/deckard/iaac/gcp/README.md b/deckard/iaac/gcp/README.md index 44cd7183..cbae6e50 100644 --- a/deckard/iaac/gcp/README.md +++ b/deckard/iaac/gcp/README.md @@ -5,6 +5,19 @@ The following process requires to have enough qouta in GCP for CPUs and GPUs and ## Setup the GKE + GPU 0- In order to setup the GKE (Google Kubernetes Engine), we need to enable require apis. Follow [these intsructions to enable them](https://cloud.google.com/endpoints/docs/openapi/enable-api). +## Install gcloud on your local machine + +First you need to install `gcloud-cli`. In order to setup the GKE (Google Kubernetes Engine), we need to enable require apis. Follow [these intsructions to enable them](https://cloud.google.com/endpoints/docs/openapi/enable-api). +[Source](https://cloud.google.com/sdk/docs/install) +``` +sudo apt-get update +sudo apt-get install apt-transport-https ca-certificates gnupg curl sudo +echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main" | sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list +curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - +sudo apt-get update && sudo apt-get install google-cloud-cli +``` + + 1-We then create the cluster called `k8s-cluster`. This cluster will be installed in `europe-west4` in GCP regions. @@ -113,7 +126,7 @@ gcloud compute instances create filestore \ In the above command, you simply replace you public key and your username to have passwordless SSH to the VM. -Before sshing to the created instance, simply get the [`NFS mount point` from the GCP console](https://console.cloud.google.com/filestore/instances). The `mount point` is "`Filestore instance IP address`:`Filestore instance File share name`", for instance: `10.178.118.130:/vol1`. +Before sshing to the created instance, simply get the [`NFS mount point` from the GCP console](https://console.cloud.google.com/filestore/instances). The `mount point` is "`Filestore instance IP address`:`Filestore instance File share name`", for instance: `:/vol1`. After it has been created, run the `gcloud compute instances list` command to retrieve the external ip of the created instance `filestore`. Then ssh to the machine and mount the volume by running the followings: diff --git a/deckard/iaac/gcp/install.sh b/deckard/iaac/gcp/install.sh deleted file mode 100644 index 9c8ed456..00000000 --- a/deckard/iaac/gcp/install.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/bash -cd ~ -curl -O https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-cli-444.0.0-linux-x86_64.tar.gz -tar -xf google-cloud-cli-444.0.0-linux-x86.tar.gz -bash google-cloud-sdk/install.sh -cd - diff --git a/examples/pytorch/conf/deploy/params.yaml b/examples/pytorch/conf/deploy/params.yaml index c64d7b87..134da65f 100644 --- a/examples/pytorch/conf/deploy/params.yaml +++ b/examples/pytorch/conf/deploy/params.yaml @@ -6,9 +6,9 @@ gpu_driver_version: default machine_type: n1-standard-2 min_nodes: 1 max_nodes: 1 -storage_config: sclass.yaml -persistent_volume_claim: pvc.yaml -pod : pod.yaml +storage_config: conf/deploy/sclass.yaml +persistent_volume_claim: conf/deploy/pvc.yaml +pod : conf/deploy/pod.yaml image_project: ubuntu-os-cloud image_family: ubuntu-2204-lts mount_directory: /mnt/filestore From 7ef52ce8936b698b1bcf240b1bfe66b34d590227 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 19:16:38 +0200 Subject: [PATCH 053/148] updated deploy script to handle non-GPU configs --- deckard/iaac/gcp/deploy.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index 736daf77..1d14ae2d 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -91,7 +91,11 @@ def retrieve_credentials(self): def create_node_pool(self): logger.info(f"Creating node pool {self.cluster_name} in region {self.region}") - command = f"gcloud container node-pools create {self.cluster_name} --accelerator type={self.gpu_type},count={self.gpu_count},gpu-driver-version={self.gpu_driver_version} --region {self.region} --cluster {self.cluster_name} --machine-type {self.machine_type} --num-nodes {self.num_nodes} --min-nodes {self.min_nodes} --max-nodes {self.max_nodes}" + if self.gpu_type is not None: + assert self.gpu_count > 0, f"Please specify a valid GPU count. Current value: {self.gpu_count}" + command = f"gcloud container node-pools create {self.cluster_name} --accelerator type={self.gpu_type},count={self.gpu_count},gpu-driver-version={self.gpu_driver_version} --region {self.region} --cluster {self.cluster_name} --machine-type {self.machine_type} --num-nodes {self.num_nodes} --min-nodes {self.min_nodes} --max-nodes {self.max_nodes}" + else: + command = f"gcloud container node-pools create {self.cluster_name} --region {self.region} --cluster {self.cluster_name} --machine-type {self.machine_type} --num-nodes {self.num_nodes} --min-nodes {self.min_nodes} --max-nodes {self.max_nodes}" logger.info(f"Running command: {command}") command = command.split(" ") output = subprocess.run(command) From a636896919ee6e8736ace222c04d112ef5ec3658 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 19:25:08 +0200 Subject: [PATCH 054/148] fixed bug in conf --- deckard/layers/find_best.py | 1 + examples/sklearn/conf/attack.yaml | 6 +++--- examples/sklearn/dvc.lock | 34 +++++++++++++++++++++++-------- 3 files changed, 30 insertions(+), 11 deletions(-) diff --git a/deckard/layers/find_best.py b/deckard/layers/find_best.py index ea054098..c2e89d29 100644 --- a/deckard/layers/find_best.py +++ b/deckard/layers/find_best.py @@ -49,6 +49,7 @@ def find_optuna_best( params_file = Path(config_folder, f"best_{study_name}.yaml") else: params_file = Path(config_folder, f"best_{study_name}.yaml") + logger.info(f"Saving best params to {params_file}") with open(params_file, "w") as f: yaml.dump(params, f) return params diff --git a/examples/sklearn/conf/attack.yaml b/examples/sklearn/conf/attack.yaml index 332558a7..f1b4f66b 100644 --- a/examples/sklearn/conf/attack.yaml +++ b/examples/sklearn/conf/attack.yaml @@ -16,9 +16,9 @@ hydra: sampler: _target_: optuna.samplers.GridSampler _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - direction: maximize - study_name: model - storage: sqlite:///model.db + direction: minimize + study_name: attack + storage: sqlite:///attack.db n_trials: 1 n_jobs: 1 params: diff --git a/examples/sklearn/dvc.lock b/examples/sklearn/dvc.lock index 7d8152a9..189a99de 100644 --- a/examples/sklearn/dvc.lock +++ b/examples/sklearn/dvc.lock @@ -295,22 +295,40 @@ stages: size: 705 - path: output/reports/attack/default/score_dict.json hash: md5 - md5: a62fe1c9f76a4171c1772410f9a3f456 - size: 487 + md5: cc04fa9f6331853dbc29daa7bcaee11c + size: 488 outs: - path: model.db hash: md5 - md5: 06f345df85eb278681717365e9bc2f16 - size: 163840 + md5: efd35c102e21e44c93d5a37a51ab03c0 + size: 217088 find_best_model: cmd: python -m deckard.layers.find_best model.yaml deps: - path: model.db hash: md5 - md5: 06f345df85eb278681717365e9bc2f16 - size: 163840 + md5: efd35c102e21e44c93d5a37a51ab03c0 + size: 217088 outs: - path: conf/model/best.yaml hash: md5 - md5: c59826147251b919c00adadee514f74f - size: 657 + md5: d4779834afe3c016e50c19bef9042a57 + size: 673 + attack_search: + cmd: bash attacks.sh ++stage=attack ++optimizers=adv_accuracy ++hydra.sweeper.storage=sqlite:///attack.db + ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++direction=minimize + ++attack.init.max_iter=10 --multirun --config-name attack.yaml + deps: + - path: attacks.sh + hash: md5 + md5: b5d0850e20fa8d2fb2bd47c8949ae6a6 + size: 1568 + - path: model.db + hash: md5 + md5: efd35c102e21e44c93d5a37a51ab03c0 + size: 217088 + outs: + - path: attack.db + hash: md5 + md5: b90202724a0b485252b7b942aa461c5f + size: 110592 From e16f0b2dec519106129c46c83409736971b7ec35 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 19:49:32 +0200 Subject: [PATCH 055/148] update README, fixed find_best bug --- README.md | 16 +++++++--------- deckard/layers/find_best.py | 16 +++++++++++++--- examples/sklearn/dvc.lock | 32 ++++++++++++++++++++++---------- examples/sklearn/dvc.yaml | 6 +++--- 4 files changed, 45 insertions(+), 25 deletions(-) diff --git a/README.md b/README.md index d201a988..83371d8b 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ # Deckard: A Tool for Evaluating AI -## Installation +## 1 - Dependencies To install this, ensure that you have your favorite library installed. To install deckard along with `tensorflow`, for example, use ``` @@ -12,25 +12,23 @@ Add the `-e` flag if you want to edit files: ``` python -m pip install -e . ``` -You can also try the bash script which attempts to install python if you have root. -``` -bash setup.sh -``` Or try the rpi script: ``` bash rpi.sh ``` -Now, heck that deckard works +Now, check that deckard works ```$ python``` ```>>> import deckard``` Then CTRL+D or `quit()` to quit. -# Navigate to your favorite subfolder in `examples`. One is provided for each framework. -Running `dvc repro` in that folder will reproduce the experiment outlined in the `dvc.yaml`. Running `python -m deckard` will parse the configuration folder, create a `params.yaml`, and then run `dvc repro`. +## 2 - Navigate to your favorite subfolder in `examples`. One is provided for each framework. +Running `dvc repro` in that folder will reproduce the experiment outlined in the `dvc.yaml`. Running +```python -m deckard``` + will parse the configuration folder, create a `params.yaml`, and then run `dvc repro`. ### _like tears in the rain_, this tool is meant for bladerunners. NOT INTENDED FOR USE BY REPLICANTS ## Files -. +. ├── Dockerfile: Constructs a generic Docker image for running experiments ├── LICENSE ├── README.md: this file diff --git a/deckard/layers/find_best.py b/deckard/layers/find_best.py index c2e89d29..b46344af 100644 --- a/deckard/layers/find_best.py +++ b/deckard/layers/find_best.py @@ -4,8 +4,7 @@ from hydra import initialize_config_dir, compose from omegaconf import OmegaConf import yaml -from ..base.utils import flatten_dict - +from ..base.utils import flatten_dict, unflatten_dict logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) @@ -29,7 +28,18 @@ def find_optuna_best( df = study.trials_dataframe(attrs=("number", "value", "params", "state")) if study_csv is not None: df.to_csv(study_csv) - best_params = flatten_dict(study.best_params) + params = flatten_dict(study.best_params) + params = unflatten_dict(params) + best_params = {} + if study_name in params: + best_params[study_name] = params[study_name] + elif f"+{study_name}" in params: + best_params[study_name] = params[f"+{study_name}"] + elif f"++{study_name}" in params: + best_params[study_name] = params[f"++{study_name}"] + else: + raise ValueError(f"Study name {study_name} not found in best params.") + best_params = flatten_dict(best_params) overrides = [] for key, value in best_params.items(): logger.info(f"Overriding {key} with {value}") diff --git a/examples/sklearn/dvc.lock b/examples/sklearn/dvc.lock index 189a99de..5cb2891e 100644 --- a/examples/sklearn/dvc.lock +++ b/examples/sklearn/dvc.lock @@ -282,8 +282,8 @@ stages: outs: - path: output/reports/attack/default/score_dict.json hash: md5 - md5: cc04fa9f6331853dbc29daa7bcaee11c - size: 488 + md5: 1999961946d148fa619652039a32df66 + size: 481 model_search: cmd: bash models.sh +stage=train +optimizers=accuracy ++hydra.sweeper.storage=sqlite:///model.db ++hydra.sweeper.study_name=model ++hydra.sweeper.direction=maximize ++direction=maximize @@ -295,25 +295,25 @@ stages: size: 705 - path: output/reports/attack/default/score_dict.json hash: md5 - md5: cc04fa9f6331853dbc29daa7bcaee11c - size: 488 + md5: 1999961946d148fa619652039a32df66 + size: 481 outs: - path: model.db hash: md5 - md5: efd35c102e21e44c93d5a37a51ab03c0 + md5: 11b0a304601d5d3bb04b2e353f3ccbe4 size: 217088 find_best_model: cmd: python -m deckard.layers.find_best model.yaml deps: - path: model.db hash: md5 - md5: efd35c102e21e44c93d5a37a51ab03c0 + md5: 11b0a304601d5d3bb04b2e353f3ccbe4 size: 217088 outs: - path: conf/model/best.yaml hash: md5 - md5: d4779834afe3c016e50c19bef9042a57 - size: 673 + md5: d71f5f9733dc8ebf1abeeecb0dd008c7 + size: 669 attack_search: cmd: bash attacks.sh ++stage=attack ++optimizers=adv_accuracy ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++direction=minimize @@ -325,10 +325,22 @@ stages: size: 1568 - path: model.db hash: md5 - md5: efd35c102e21e44c93d5a37a51ab03c0 + md5: 11b0a304601d5d3bb04b2e353f3ccbe4 size: 217088 outs: - path: attack.db hash: md5 - md5: b90202724a0b485252b7b942aa461c5f + md5: 396dc92fd1118aba2df1965609dd08e5 size: 110592 + find_best_attack: + cmd: python -m deckard.layers.find_best attack.yaml + deps: + - path: attack.db + hash: md5 + md5: 396dc92fd1118aba2df1965609dd08e5 + size: 110592 + outs: + - path: conf/attack/best.yaml + hash: md5 + md5: adb93abede02406a812cf83fbb2bb50b + size: 2051 diff --git a/examples/sklearn/dvc.yaml b/examples/sklearn/dvc.yaml index 39fa6c21..fd85eb87 100644 --- a/examples/sklearn/dvc.yaml +++ b/examples/sklearn/dvc.yaml @@ -30,9 +30,9 @@ stages: - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} metrics: - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} - # outs: + outs: # - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} - # - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} params: - data - model @@ -83,7 +83,7 @@ stages: - model.db deps: - models.sh - - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} attack_search : cmd : bash attacks.sh ++stage=attack ++optimizers=adv_accuracy ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++direction=minimize ++attack.init.max_iter=10 --multirun --config-name attack.yaml deps: From de6543cf511716d5b16944f16a11c1036adb2163 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 20:05:45 +0200 Subject: [PATCH 056/148] update sklearn dvc lock file --- examples/sklearn/dvc.lock | 32 ++++++++++++++++++-------------- 1 file changed, 18 insertions(+), 14 deletions(-) diff --git a/examples/sklearn/dvc.lock b/examples/sklearn/dvc.lock index 5cb2891e..460555b4 100644 --- a/examples/sklearn/dvc.lock +++ b/examples/sklearn/dvc.lock @@ -280,10 +280,14 @@ stages: direction: minimize name: sklearn.metrics.log_loss outs: + - path: output/reports/attack/default/adv_probabilities.json + hash: md5 + md5: 4456666e66f153b50572072e83be3e47 + size: 425 - path: output/reports/attack/default/score_dict.json hash: md5 - md5: 1999961946d148fa619652039a32df66 - size: 481 + md5: 4b858a5872bfe2aa828a0f064c55f4ea + size: 486 model_search: cmd: bash models.sh +stage=train +optimizers=accuracy ++hydra.sweeper.storage=sqlite:///model.db ++hydra.sweeper.study_name=model ++hydra.sweeper.direction=maximize ++direction=maximize @@ -293,27 +297,27 @@ stages: hash: md5 md5: b20bba54353be244dc917518b03ef007 size: 705 - - path: output/reports/attack/default/score_dict.json + - path: output/reports/attack/default/adv_probabilities.json hash: md5 - md5: 1999961946d148fa619652039a32df66 - size: 481 + md5: 4456666e66f153b50572072e83be3e47 + size: 425 outs: - path: model.db hash: md5 - md5: 11b0a304601d5d3bb04b2e353f3ccbe4 + md5: c354d3816bc0526c0e94d9fd0a537e72 size: 217088 find_best_model: cmd: python -m deckard.layers.find_best model.yaml deps: - path: model.db hash: md5 - md5: 11b0a304601d5d3bb04b2e353f3ccbe4 + md5: c354d3816bc0526c0e94d9fd0a537e72 size: 217088 outs: - path: conf/model/best.yaml hash: md5 - md5: d71f5f9733dc8ebf1abeeecb0dd008c7 - size: 669 + md5: 9f9bd5f3433134a0bf966456a0d150aa + size: 674 attack_search: cmd: bash attacks.sh ++stage=attack ++optimizers=adv_accuracy ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++direction=minimize @@ -325,22 +329,22 @@ stages: size: 1568 - path: model.db hash: md5 - md5: 11b0a304601d5d3bb04b2e353f3ccbe4 + md5: c354d3816bc0526c0e94d9fd0a537e72 size: 217088 outs: - path: attack.db hash: md5 - md5: 396dc92fd1118aba2df1965609dd08e5 + md5: dcb34e1932e7b4b7b50dff50ccc582b5 size: 110592 find_best_attack: cmd: python -m deckard.layers.find_best attack.yaml deps: - path: attack.db hash: md5 - md5: 396dc92fd1118aba2df1965609dd08e5 + md5: dcb34e1932e7b4b7b50dff50ccc582b5 size: 110592 outs: - path: conf/attack/best.yaml hash: md5 - md5: adb93abede02406a812cf83fbb2bb50b - size: 2051 + md5: e9964377a012776ffccaba4b3e5028fc + size: 2053 From 580f122437fd24d9a70a4d7b49d13695b2d5ce23 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Thu, 7 Sep 2023 20:31:19 +0200 Subject: [PATCH 057/148] update sklearn to grid search --- examples/sklearn/conf/attack.yaml | 5 ----- examples/sklearn/conf/model.yaml | 5 ----- 2 files changed, 10 deletions(-) diff --git a/examples/sklearn/conf/attack.yaml b/examples/sklearn/conf/attack.yaml index f1b4f66b..17071f83 100644 --- a/examples/sklearn/conf/attack.yaml +++ b/examples/sklearn/conf/attack.yaml @@ -22,11 +22,6 @@ hydra: n_trials: 1 n_jobs: 1 params: - data.generate.n_features : 20 - data.sample.train_size : 1000 - data.sample.test_size : 100 - data.sample.random_state : 0 - data.sample.stratify : true model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher diff --git a/examples/sklearn/conf/model.yaml b/examples/sklearn/conf/model.yaml index 9d17cb0d..d50b2407 100644 --- a/examples/sklearn/conf/model.yaml +++ b/examples/sklearn/conf/model.yaml @@ -20,11 +20,6 @@ hydra: storage: sqlite:///model.db n_jobs: 1 params: - data.generate.n_features : 20 - data.sample.train_size : 1000 - data.sample.test_size : 100 - data.sample.random_state : 0 - data.sample.stratify : true model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher From 2859f983662c717a784a67fd65a308fa62ae2808 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Thu, 7 Sep 2023 20:31:46 +0200 Subject: [PATCH 058/148] +sklearn/conf/hydra --- examples/sklearn/conf/hydra/default.yaml | 7 +++++++ examples/sklearn/conf/hydra/launcher/default.yaml | 10 ++++++++++ examples/sklearn/conf/hydra/run/default.yaml | 1 + examples/sklearn/conf/hydra/sweeper/default.yaml | 8 ++++++++ .../sklearn/conf/hydra/sweeper/sampler/default.yaml | 7 +++++++ .../conf/hydra/sweeper/sampler/params/default.yaml | 1 + 6 files changed, 34 insertions(+) create mode 100644 examples/sklearn/conf/hydra/default.yaml create mode 100644 examples/sklearn/conf/hydra/launcher/default.yaml create mode 100644 examples/sklearn/conf/hydra/run/default.yaml create mode 100644 examples/sklearn/conf/hydra/sweeper/default.yaml create mode 100644 examples/sklearn/conf/hydra/sweeper/sampler/default.yaml create mode 100644 examples/sklearn/conf/hydra/sweeper/sampler/params/default.yaml diff --git a/examples/sklearn/conf/hydra/default.yaml b/examples/sklearn/conf/hydra/default.yaml new file mode 100644 index 00000000..0972de40 --- /dev/null +++ b/examples/sklearn/conf/hydra/default.yaml @@ -0,0 +1,7 @@ +defaults: +- run : default +- launcher : default +- sweeper : default +- override sweeper : optuna +- override sweeper/sampler : grid +- override launcher : joblib \ No newline at end of file diff --git a/examples/sklearn/conf/hydra/launcher/default.yaml b/examples/sklearn/conf/hydra/launcher/default.yaml new file mode 100644 index 00000000..a445776e --- /dev/null +++ b/examples/sklearn/conf/hydra/launcher/default.yaml @@ -0,0 +1,10 @@ +_target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher +n_jobs: 6 +prefer : processes +verbose: 1 +timeout: null +pre_dispatch: n_jobs +batch_size: auto +temp_folder: /tmp/deckard +max_nbytes: 100000 +mmap_mode: r \ No newline at end of file diff --git a/examples/sklearn/conf/hydra/run/default.yaml b/examples/sklearn/conf/hydra/run/default.yaml new file mode 100644 index 00000000..89b4d83c --- /dev/null +++ b/examples/sklearn/conf/hydra/run/default.yaml @@ -0,0 +1 @@ +dir : "./${files.directory}" \ No newline at end of file diff --git a/examples/sklearn/conf/hydra/sweeper/default.yaml b/examples/sklearn/conf/hydra/sweeper/default.yaml new file mode 100644 index 00000000..1b830a40 --- /dev/null +++ b/examples/sklearn/conf/hydra/sweeper/default.yaml @@ -0,0 +1,8 @@ +defaults: +- sampler: default +_target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper +direction: maximize +study_name: model +storage: sqlite:///model.db +n_trials: 100 +n_jobs: 1 \ No newline at end of file diff --git a/examples/sklearn/conf/hydra/sweeper/sampler/default.yaml b/examples/sklearn/conf/hydra/sweeper/sampler/default.yaml new file mode 100644 index 00000000..c3b793ca --- /dev/null +++ b/examples/sklearn/conf/hydra/sweeper/sampler/default.yaml @@ -0,0 +1,7 @@ +defaults: +- params : default +_target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper +direction: minimize +study_name: attack +storage: sqlite:///attack.db +n_jobs: 1 \ No newline at end of file diff --git a/examples/sklearn/conf/hydra/sweeper/sampler/params/default.yaml b/examples/sklearn/conf/hydra/sweeper/sampler/params/default.yaml new file mode 100644 index 00000000..caa366d2 --- /dev/null +++ b/examples/sklearn/conf/hydra/sweeper/sampler/params/default.yaml @@ -0,0 +1 @@ +model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) \ No newline at end of file From b646398c90b5b91ac08bab9368fa3c1f2045916e Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Thu, 7 Sep 2023 18:53:20 +0000 Subject: [PATCH 059/148] +RQ draft in sklearn examples --- examples/sklearn/RQ.sh | 18 +++++++++++++ examples/sklearn/conf/RQ.md | 1 + examples/sklearn/conf/rq.yaml | 51 +++++++++++++++++++++++++++++++++++ 3 files changed, 70 insertions(+) create mode 100644 examples/sklearn/RQ.sh create mode 100644 examples/sklearn/conf/RQ.md create mode 100644 examples/sklearn/conf/rq.yaml diff --git a/examples/sklearn/RQ.sh b/examples/sklearn/RQ.sh new file mode 100644 index 00000000..57594f6c --- /dev/null +++ b/examples/sklearn/RQ.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +# Install Dependencies +apt install lsb-release curl gpg -y +# Add GPG Key +curl -fsSL https://packages.redis.io/gpg | gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg +# Add source to sources list +echo "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main" | tee /etc/apt/sources.list.d/redis.list +# Update +apt-get update -y +# Install +apt-get install redis -y +# Copy Default Config +cp /usr/local/etc/redis.conf.default /usr/local/etc/redis.conf +# Make db folder +mkdir -p /usr/local/var/db/redis +# # Launch +# redis-server \ No newline at end of file diff --git a/examples/sklearn/conf/RQ.md b/examples/sklearn/conf/RQ.md new file mode 100644 index 00000000..4f299cae --- /dev/null +++ b/examples/sklearn/conf/RQ.md @@ -0,0 +1 @@ +sudo apt install lsb-release curl gpg \ No newline at end of file diff --git a/examples/sklearn/conf/rq.yaml b/examples/sklearn/conf/rq.yaml new file mode 100644 index 00000000..25ad380b --- /dev/null +++ b/examples/sklearn/conf/rq.yaml @@ -0,0 +1,51 @@ +defaults: + - _self_ + - data: default + - model: default + - attack: hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + # seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 100 + n_jobs: 1 + launcher: + _target_: hydra_plugins.hydra_rq_launcher.rq_launcher.RQLauncher + enqueue: + job_timeout: null + ttl: null + result_ttl: null + failure_ttl: null + at_front: false + job_id: null + description: null + queue: default + redis: + host: ${oc.env:REDIS_HOST,localhost} + port: ${oc.env:REDIS_PORT,6379} + db: ${oc.env:REDIS_DB,0} + password: ${oc.env:REDIS_PASSWORD,null} + ssl: ${oc.env:REDIS_SSL,False} + ssl_ca_certs: ${oc.env:REDIS_SSL_CA_CERTS,null + mock: ${oc.env:REDIS_MOCK,False} + stop_after_enqueue: false + wait_polling: 1.0 From a77f438dcdbf28248acad6c395e57ba931ea8086 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Thu, 7 Sep 2023 21:08:37 +0200 Subject: [PATCH 060/148] +attacks.sh full --- examples/pytorch/attacks.sh | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/examples/pytorch/attacks.sh b/examples/pytorch/attacks.sh index 93f69ab8..7e839300 100644 --- a/examples/pytorch/attacks.sh +++ b/examples/pytorch/attacks.sh @@ -2,14 +2,14 @@ # # This script is used to generate the attacks for the example. -# Fast Gradient Method -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ +Fast Gradient Method +bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ -# Projected Gradient Descent -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ +Projected Gradient Descent +bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ -# DeepFool -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ +DeepFool +bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ # HopSkipJump bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 ++stage=attack ++hydra.sweeper.study_name=hsj ++hydra.sweeper.direction=minimize $@ @@ -25,13 +25,13 @@ bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.ThresholdAttack bash models.sh attack=hsj --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 ++stage=patch ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++attack.init.patch_shape=[1,28,28] $@ ##################################################### -# # Carlini L0 Method -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw0 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ +# Carlini L0 Method +bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw0 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ -# # # Carlini L2 Method -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw2 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ +# # Carlini L2 Method +bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw2 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ -# # Carlini LInf Method -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=cwinf ++hydra.sweeper.direction=minimize $@ +# Carlini LInf Method +bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=cwinf ++hydra.sweeper.direction=minimize $@ rm -rf output/models/* From daf589eb3252037835ab3bb72849f9db673bffc4 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 19:11:08 +0000 Subject: [PATCH 061/148] linting --- examples/pytorch/attacks.sh | 26 +++++++------- examples/pytorch/conf/attack/hsj.yaml | 1 - examples/pytorch/dvc.lock | 51 +++++++++++++-------------- examples/pytorch/dvc.yaml | 16 ++++----- examples/pytorch/params.yaml | 1 - 5 files changed, 46 insertions(+), 49 deletions(-) diff --git a/examples/pytorch/attacks.sh b/examples/pytorch/attacks.sh index 93f69ab8..ff6b0f7d 100644 --- a/examples/pytorch/attacks.sh +++ b/examples/pytorch/attacks.sh @@ -3,35 +3,35 @@ # # This script is used to generate the attacks for the example. # Fast Gradient Method -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ # Projected Gradient Descent -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ # DeepFool -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ # HopSkipJump bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 ++stage=attack ++hydra.sweeper.study_name=hsj ++hydra.sweeper.direction=minimize $@ -##################################################### +# ##################################################### # PixelAttack -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=pixel ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=pixel ++hydra.sweeper.direction=minimize $@ -# # ThresholdAttack +# ThresholdAttack bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=thresh ++hydra.sweeper.direction=minimize $@ # # AdversarialPatch -bash models.sh attack=hsj --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 ++stage=patch ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++attack.init.patch_shape=[1,28,28] $@ +# bash models.sh attack=hsj --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 ++stage=patch ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++attack.init.patch_shape=[1,28,28] $@ ##################################################### -# # Carlini L0 Method -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw0 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ +# Carlini L0 Method +bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw0 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ -# # # Carlini L2 Method -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw2 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ +# Carlini L2 Method +bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw2 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ -# # Carlini LInf Method -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=cwinf ++hydra.sweeper.direction=minimize $@ +# Carlini LInf Method +bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=cwinf ++hydra.sweeper.direction=minimize $@ rm -rf output/models/* diff --git a/examples/pytorch/conf/attack/hsj.yaml b/examples/pytorch/conf/attack/hsj.yaml index 9939fa02..740aa492 100644 --- a/examples/pytorch/conf/attack/hsj.yaml +++ b/examples/pytorch/conf/attack/hsj.yaml @@ -5,6 +5,5 @@ init: model: ${model} _target_: deckard.base.attack.AttackInitializer name: art.attacks.evasion.HopSkipJump - batch_size: 100 attack_size : 100 method : evasion diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index 50b0aa2c..7ed80ba3 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -150,7 +150,6 @@ stages: with_std: true init: _target_: deckard.base.attack.AttackInitializer - batch_size: 100 model: _target_: deckard.base.model.Model art: @@ -345,16 +344,16 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: 7b734a30fe436fd122186fe8cb6ae959 + md5: 9504b7f1972384fc353811f6143e9476 size: 313766 - path: output/reports/attack/default/adv_predictions.json - md5: 8bfac7d8e89bff0629dffa3e1a3c996f - size: 21748 + md5: 7c7d47e99cdfebb48f888a961f4b9c30 + size: 21629 - path: output/reports/attack/default/params.yaml - md5: daf26103182da19d13d5b73113c5688c - size: 4956 + md5: 66a5664fcf522558c2aa80366bc702af + size: 4937 - path: output/reports/attack/default/score_dict.json - md5: 9bd42e8bf85131beabb023c7983ae34e + md5: 86d6e80f6fa1a9a392417d74d7340426 size: 542 models: cmd: bash models.sh @@ -436,16 +435,16 @@ stages: output/reports/attack.csv deps: - path: ResNet152.db - md5: d86a693a86ec196fcd4887013acde398 - size: 434176 + md5: 949ed1ec729d97b6e320b6f6a7e4db46 + size: 380928 - path: output/reports/attack/ - md5: 0ea96d75e1d41ddf1e4e856260d3d327.dir - size: 8695142484 - nfiles: 17939 + md5: 305ee6b62afbcc27dc630964d13baec1.dir + size: 8981373045 + nfiles: 18661 outs: - path: output/reports/attack.csv - md5: 1d31d2bc679e668fc31e323a444c84fd - size: 13006598 + md5: 29480aa04b01f03fe102776012ccc4af + size: 13423919 compile@train: cmd: python -m deckard.layers.compile --report_folder output/reports/train --results_file output/reports/train.csv @@ -469,18 +468,18 @@ stages: ++hydra.sweeper.storage=sqlite:///ResNet152.db --config-name grid.yaml deps: - path: attacks.sh - md5: 6a6a91d037b4ce29f84009d9b1161d4f - size: 3034 + md5: dd3c55642f264a33632ac661bd440bc1 + size: 3052 - path: models.sh md5: 76fbee55cb5f90913d0863df9debd357 size: 1299 - path: output/reports/attack/default/score_dict.json - md5: 35d505c81867c9c50d74780c19d6f7e7 - size: 539 + md5: 9bd42e8bf85131beabb023c7983ae34e + size: 542 outs: - path: ResNet152.db - md5: d86a693a86ec196fcd4887013acde398 - size: 434176 + md5: 949ed1ec729d97b6e320b6f6a7e4db46 + size: 380928 plot: cmd: python plots.py --path output/plots/ --file output/reports/attack.csv deps: @@ -488,8 +487,8 @@ stages: md5: 00e8859421e45d77c36d2a39ea5deaa5 size: 466944 - path: ResNet152.db - md5: d86a693a86ec196fcd4887013acde398 - size: 434176 + md5: 949ed1ec729d97b6e320b6f6a7e4db46 + size: 380928 - path: ResNet18.db md5: 3d304e014200ae926dec63521b6eeca0 size: 1499136 @@ -500,10 +499,10 @@ stages: md5: 771e206f2ab932ae2978783214290c41 size: 110592 - path: output/reports/attack.csv - md5: 1d31d2bc679e668fc31e323a444c84fd - size: 13006598 + md5: 29480aa04b01f03fe102776012ccc4af + size: 13423919 outs: - path: output/plots/ - md5: ad60dde37b7ad40574c2e2a3e391224d.dir - size: 8442674 + md5: 9a5c8078929eff9b1da053ec3abce59b.dir + size: 8663731 nfiles: 13 diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index cb3d1d45..01babe35 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -37,10 +37,10 @@ stages: ############################################################################## attacks: foreach: - # - ResNet18 - # - ResNet34 - # - ResNet50 - # - ResNet101 + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 - ResNet152 do: cmd: bash attacks.sh ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name grid.yaml @@ -57,10 +57,10 @@ stages: cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv deps: - ${files.directory}/${files.reports}/${item}/ - # - ResNet18.db - # - ResNet34.db - # - ResNet50.db - # - ResNet101.db + - ResNet18.db + - ResNet34.db + - ResNet50.db + - ResNet101.db - ResNet152.db outs: - ${files.directory}/${files.reports}/${item}.csv diff --git a/examples/pytorch/params.yaml b/examples/pytorch/params.yaml index 87de9ba5..27c98d51 100644 --- a/examples/pytorch/params.yaml +++ b/examples/pytorch/params.yaml @@ -19,7 +19,6 @@ attack: with_std: true init: _target_: deckard.base.attack.AttackInitializer - batch_size: 100 model: _target_: deckard.base.model.Model art: From 375e7f8902bd2daa038225855792e14aab6bd37a Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 19:18:44 +0000 Subject: [PATCH 062/148] linting --- deckard/iaac/gcp/deploy.py | 4 +++- deckard/layers/find_best.py | 1 + examples/sklearn/RQ.sh | 2 +- examples/sklearn/conf/RQ.md | 2 +- examples/sklearn/conf/hydra/default.yaml | 2 +- examples/sklearn/conf/hydra/launcher/default.yaml | 2 +- examples/sklearn/conf/hydra/run/default.yaml | 2 +- examples/sklearn/conf/hydra/sweeper/default.yaml | 2 +- examples/sklearn/conf/hydra/sweeper/sampler/default.yaml | 2 +- .../sklearn/conf/hydra/sweeper/sampler/params/default.yaml | 2 +- examples/sklearn/models.sh | 2 +- 11 files changed, 13 insertions(+), 10 deletions(-) diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index 1d14ae2d..1630fd60 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -92,7 +92,9 @@ def retrieve_credentials(self): def create_node_pool(self): logger.info(f"Creating node pool {self.cluster_name} in region {self.region}") if self.gpu_type is not None: - assert self.gpu_count > 0, f"Please specify a valid GPU count. Current value: {self.gpu_count}" + assert ( + self.gpu_count > 0 + ), f"Please specify a valid GPU count. Current value: {self.gpu_count}" command = f"gcloud container node-pools create {self.cluster_name} --accelerator type={self.gpu_type},count={self.gpu_count},gpu-driver-version={self.gpu_driver_version} --region {self.region} --cluster {self.cluster_name} --machine-type {self.machine_type} --num-nodes {self.num_nodes} --min-nodes {self.min_nodes} --max-nodes {self.max_nodes}" else: command = f"gcloud container node-pools create {self.cluster_name} --region {self.region} --cluster {self.cluster_name} --machine-type {self.machine_type} --num-nodes {self.num_nodes} --min-nodes {self.min_nodes} --max-nodes {self.max_nodes}" diff --git a/deckard/layers/find_best.py b/deckard/layers/find_best.py index b46344af..d58a7083 100644 --- a/deckard/layers/find_best.py +++ b/deckard/layers/find_best.py @@ -5,6 +5,7 @@ from omegaconf import OmegaConf import yaml from ..base.utils import flatten_dict, unflatten_dict + logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) diff --git a/examples/sklearn/RQ.sh b/examples/sklearn/RQ.sh index 57594f6c..20455ff3 100644 --- a/examples/sklearn/RQ.sh +++ b/examples/sklearn/RQ.sh @@ -15,4 +15,4 @@ cp /usr/local/etc/redis.conf.default /usr/local/etc/redis.conf # Make db folder mkdir -p /usr/local/var/db/redis # # Launch -# redis-server \ No newline at end of file +# redis-server diff --git a/examples/sklearn/conf/RQ.md b/examples/sklearn/conf/RQ.md index 4f299cae..c1e745ce 100644 --- a/examples/sklearn/conf/RQ.md +++ b/examples/sklearn/conf/RQ.md @@ -1 +1 @@ -sudo apt install lsb-release curl gpg \ No newline at end of file +sudo apt install lsb-release curl gpg diff --git a/examples/sklearn/conf/hydra/default.yaml b/examples/sklearn/conf/hydra/default.yaml index 0972de40..8dceecaf 100644 --- a/examples/sklearn/conf/hydra/default.yaml +++ b/examples/sklearn/conf/hydra/default.yaml @@ -4,4 +4,4 @@ defaults: - sweeper : default - override sweeper : optuna - override sweeper/sampler : grid -- override launcher : joblib \ No newline at end of file +- override launcher : joblib diff --git a/examples/sklearn/conf/hydra/launcher/default.yaml b/examples/sklearn/conf/hydra/launcher/default.yaml index a445776e..8b8d3f41 100644 --- a/examples/sklearn/conf/hydra/launcher/default.yaml +++ b/examples/sklearn/conf/hydra/launcher/default.yaml @@ -7,4 +7,4 @@ pre_dispatch: n_jobs batch_size: auto temp_folder: /tmp/deckard max_nbytes: 100000 -mmap_mode: r \ No newline at end of file +mmap_mode: r diff --git a/examples/sklearn/conf/hydra/run/default.yaml b/examples/sklearn/conf/hydra/run/default.yaml index 89b4d83c..ec38cd67 100644 --- a/examples/sklearn/conf/hydra/run/default.yaml +++ b/examples/sklearn/conf/hydra/run/default.yaml @@ -1 +1 @@ -dir : "./${files.directory}" \ No newline at end of file +dir : "./${files.directory}" diff --git a/examples/sklearn/conf/hydra/sweeper/default.yaml b/examples/sklearn/conf/hydra/sweeper/default.yaml index 1b830a40..402132d0 100644 --- a/examples/sklearn/conf/hydra/sweeper/default.yaml +++ b/examples/sklearn/conf/hydra/sweeper/default.yaml @@ -5,4 +5,4 @@ direction: maximize study_name: model storage: sqlite:///model.db n_trials: 100 -n_jobs: 1 \ No newline at end of file +n_jobs: 1 diff --git a/examples/sklearn/conf/hydra/sweeper/sampler/default.yaml b/examples/sklearn/conf/hydra/sweeper/sampler/default.yaml index c3b793ca..5401b736 100644 --- a/examples/sklearn/conf/hydra/sweeper/sampler/default.yaml +++ b/examples/sklearn/conf/hydra/sweeper/sampler/default.yaml @@ -4,4 +4,4 @@ _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper direction: minimize study_name: attack storage: sqlite:///attack.db -n_jobs: 1 \ No newline at end of file +n_jobs: 1 diff --git a/examples/sklearn/conf/hydra/sweeper/sampler/params/default.yaml b/examples/sklearn/conf/hydra/sweeper/sampler/params/default.yaml index caa366d2..33f9dfa6 100644 --- a/examples/sklearn/conf/hydra/sweeper/sampler/params/default.yaml +++ b/examples/sklearn/conf/hydra/sweeper/sampler/params/default.yaml @@ -1 +1 @@ -model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) \ No newline at end of file +model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) diff --git a/examples/sklearn/models.sh b/examples/sklearn/models.sh index 7e0e7c2f..295073df 100644 --- a/examples/sklearn/models.sh +++ b/examples/sklearn/models.sh @@ -7,4 +7,4 @@ for size in $sizes; do python -m deckard.layers.optimise data=$size model=poly ++data.sample.random_state=0,1,2,3,4,5 ++model.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 ++model.init.degree=1,2,3,4,5 ++model.init.Coef0=.00001,.0001,.01,.1,1,10,1000,1000000 $@ python -m deckard.layers.optimise data=$size model=rbf ++data.sample.random_state=0,1,2,3,4,5 ++model.init.C=.00001,.0001,.01,.1,1,10,1000,1000000 $@ -done \ No newline at end of file +done From 0f0a9f782866828327f68c1423c6c29d59261c5b Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 19:29:20 +0000 Subject: [PATCH 063/148] make dockerfile smaller --- Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index 590ac70a..64d129ae 100644 --- a/Dockerfile +++ b/Dockerfile @@ -5,7 +5,7 @@ RUN apt-get install -y python3 python3-distutils python3-pip ffmpeg libavcodec-e RUN python3 -m pip install nvidia-pyindex nvidia-cuda-runtime-cu11 RUN git clone https://github.com/simplymathematics/deckard.git WORKDIR /deckard -RUN python3 -m pip install --editable .[test,pytorch_image,tensorflow_image] +RUN python3 -m pip install --editable .[pytorch_image] RUN git clone https://github.com/Trusted-AI/adversarial-robustness-toolbox.git RUN cd adversarial-robustness-toolbox && python3 -m pip install . RUN apt install python-is-python3 From 1bdd5190fdfea553af3e86a064713bd8af84d798 Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Thu, 7 Sep 2023 21:52:54 +0200 Subject: [PATCH 064/148] add redis deployment --- deckard/iaac/gcp/redis/deployment.yaml | 32 ++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 deckard/iaac/gcp/redis/deployment.yaml diff --git a/deckard/iaac/gcp/redis/deployment.yaml b/deckard/iaac/gcp/redis/deployment.yaml new file mode 100644 index 00000000..ab93845b --- /dev/null +++ b/deckard/iaac/gcp/redis/deployment.yaml @@ -0,0 +1,32 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: redis +spec: + replicas: 1 + selector: + matchLabels: + app: redis + template: + metadata: + labels: + app: redis + name: redis + spec: + containers: + - name: redis-server + image: redis + args: ["--appendonly", "yes"] + ports: + - name: redis-server + containerPort: 6379 + volumeMounts: + - name: redis-vol + mountPath: /data + env: + - name: ALLOW_EMPTY_PASSWORD + value: "yes" + volumes: + - name: redis-vol + persistentVolumeClaim: + claimName: redis-pvc From 87995a7b6a7126de763b0b449aa5aac99052d931 Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Thu, 7 Sep 2023 21:53:20 +0200 Subject: [PATCH 065/148] add redis sc --- deckard/iaac/gcp/redis/sc.yaml | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 deckard/iaac/gcp/redis/sc.yaml diff --git a/deckard/iaac/gcp/redis/sc.yaml b/deckard/iaac/gcp/redis/sc.yaml new file mode 100644 index 00000000..e5dfc278 --- /dev/null +++ b/deckard/iaac/gcp/redis/sc.yaml @@ -0,0 +1,10 @@ +apiVersion: storage.k8s.io/v1 +kind: StorageClass +metadata: + name: redis-sc +provisioner: filestore.csi.storage.gke.io +volumeBindingMode: Immediate +allowVolumeExpansion: true +parameters: + tier: standard + network: default From eb914051fededd3d78a1f706ecf380287de763fd Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Thu, 7 Sep 2023 21:53:34 +0200 Subject: [PATCH 066/148] add redis pvc --- deckard/iaac/gcp/redis/redis-pvc.yaml | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 deckard/iaac/gcp/redis/redis-pvc.yaml diff --git a/deckard/iaac/gcp/redis/redis-pvc.yaml b/deckard/iaac/gcp/redis/redis-pvc.yaml new file mode 100644 index 00000000..b8740acb --- /dev/null +++ b/deckard/iaac/gcp/redis/redis-pvc.yaml @@ -0,0 +1,11 @@ +kind: PersistentVolumeClaim +apiVersion: v1 +metadata: + name: redis-pvc +spec: + accessModes: + - ReadWriteMany + storageClassName: redis-sc + resources: + requests: + storage: 256Gi From 58076c5fe8846941606d0a0e51ef6bf130302ae8 Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Thu, 7 Sep 2023 21:53:44 +0200 Subject: [PATCH 067/148] add redis svc --- deckard/iaac/gcp/redis/svc.yaml | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 deckard/iaac/gcp/redis/svc.yaml diff --git a/deckard/iaac/gcp/redis/svc.yaml b/deckard/iaac/gcp/redis/svc.yaml new file mode 100644 index 00000000..42fd209b --- /dev/null +++ b/deckard/iaac/gcp/redis/svc.yaml @@ -0,0 +1,13 @@ +apiVersion: v1 +kind: Service +metadata: + name: redis +spec: + selector: + app: redis + type: ClusterIP + ports: + - name: redis-port + protocol: TCP + port: 6379 + targetPort: 6379 From 7a6e897dda86cf2441000e7392a17f8d69302f96 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 20:04:20 +0000 Subject: [PATCH 068/148] + deploy script changes --- deckard/iaac/gcp/conf/deploy.yaml | 14 -------------- deckard/iaac/gcp/deploy.py | 6 ++++++ deckard/layers/deploy.py | 4 ++-- examples/pytorch/conf/default.yaml | 2 +- .../conf/deploy/{params.yaml => default.yaml} | 0 examples/pytorch/conf/deploy/pod.yaml | 2 +- examples/pytorch/conf/grid.yaml | 2 +- 7 files changed, 11 insertions(+), 19 deletions(-) delete mode 100644 deckard/iaac/gcp/conf/deploy.yaml rename examples/pytorch/conf/deploy/{params.yaml => default.yaml} (100%) diff --git a/deckard/iaac/gcp/conf/deploy.yaml b/deckard/iaac/gcp/conf/deploy.yaml deleted file mode 100644 index c64d7b87..00000000 --- a/deckard/iaac/gcp/conf/deploy.yaml +++ /dev/null @@ -1,14 +0,0 @@ -num_nodes: 1 -cluster_name: k8s-cluster -gpu_type: nvidia-tesla-v100 -gpu_count: 1 -gpu_driver_version: default -machine_type: n1-standard-2 -min_nodes: 1 -max_nodes: 1 -storage_config: sclass.yaml -persistent_volume_claim: pvc.yaml -pod : pod.yaml -image_project: ubuntu-os-cloud -image_family: ubuntu-2204-lts -mount_directory: /mnt/filestore diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index 1630fd60..a3b5b615 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -75,6 +75,12 @@ def install_kubectl(self): logger.info(f"Running command: {command}") command = command.split(" ") output = subprocess.run(command) + if output.returncode == 1: + logger.error("Error installing kubectl. Trying with apt") + command = "sudo apt-get install kubectl" + logger.info(f"Running command: {command}") + command = command.split(" ") + output = subprocess.run(command) logger.info(f"{output}") return output diff --git a/deckard/layers/deploy.py b/deckard/layers/deploy.py index 00c281fb..978150a2 100644 --- a/deckard/layers/deploy.py +++ b/deckard/layers/deploy.py @@ -6,11 +6,11 @@ logger = logging.getLogger(__name__) -logging = logging.basicConfig(level=logging.INFO) +logging.basicConfig(level=logging.INFO) if __name__ == "__main__": iaac_parser = argparse.ArgumentParser() iaac_parser.add_argument("--verbosity", type=str, default="INFO") - iaac_parser.add_argument("--config_dir", type=str, default="conf") + iaac_parser.add_argument("--config_dir", type=str, default="conf/deploy") iaac_parser.add_argument("--config_file", type=str, default="default.yaml") iaac_parser.add_argument("--workdir", type=str, default=".") args = iaac_parser.parse_args() diff --git a/examples/pytorch/conf/default.yaml b/examples/pytorch/conf/default.yaml index 903cb81a..7b986f29 100644 --- a/examples/pytorch/conf/default.yaml +++ b/examples/pytorch/conf/default.yaml @@ -27,7 +27,7 @@ hydra: ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 8 + n_jobs: 64 prefer : processes verbose: 1 timeout: 3600 diff --git a/examples/pytorch/conf/deploy/params.yaml b/examples/pytorch/conf/deploy/default.yaml similarity index 100% rename from examples/pytorch/conf/deploy/params.yaml rename to examples/pytorch/conf/deploy/default.yaml diff --git a/examples/pytorch/conf/deploy/pod.yaml b/examples/pytorch/conf/deploy/pod.yaml index b8761e6f..ff7871cc 100644 --- a/examples/pytorch/conf/deploy/pod.yaml +++ b/examples/pytorch/conf/deploy/pod.yaml @@ -7,7 +7,7 @@ spec: - name: deckard image: ghcr.io/simplymathematics/deckard:main imagePullPolicy: Always - workingDir: /deckard/examples/sklearn + workingDir: /deckard/examples/pytorch args: ["python", "-m", "dvc", "repro"] resources: limits: diff --git a/examples/pytorch/conf/grid.yaml b/examples/pytorch/conf/grid.yaml index 35c75a6a..c94c30b1 100644 --- a/examples/pytorch/conf/grid.yaml +++ b/examples/pytorch/conf/grid.yaml @@ -24,7 +24,7 @@ hydra: ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 12 + n_jobs: 64 prefer : processes verbose: 1 timeout: null From d5acb373bd6ad41eaebbbaab9bec4ba2fa64eac5 Mon Sep 17 00:00:00 2001 From: Mohammad Reza Saleh Date: Thu, 7 Sep 2023 22:22:18 +0200 Subject: [PATCH 069/148] add redis env variables --- deckard/iaac/gcp/pod.yaml | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/deckard/iaac/gcp/pod.yaml b/deckard/iaac/gcp/pod.yaml index a9145777..e30933ff 100644 --- a/deckard/iaac/gcp/pod.yaml +++ b/deckard/iaac/gcp/pod.yaml @@ -9,6 +9,15 @@ spec: imagePullPolicy: Always workingDir: /deckard/examples/sklearn args: ["python", "-m", "dvc", "repro"] + env: + - name: REDIS_HOST + value: "redis" + - name: REDIS_PORT + value: "6379" + - name: REDIS_DB + value: "0" + - name: REDIS_PASSWORD + value: "" resources: limits: nvidia.com/gpu: 1 From a8d73bf5fa0557e64e2331b7a35e8a8d789181ec Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 20:33:54 +0000 Subject: [PATCH 070/148] removed deploy script --- deckard/iaac/gcp/__init__.py | 1 - deckard/iaac/gcp/conf/deploy.yaml | 14 ------- deckard/iaac/gcp/deploy.py | 65 +++++++++---------------------- deckard/layers/deploy.py | 5 +-- 4 files changed, 19 insertions(+), 66 deletions(-) delete mode 100644 deckard/iaac/gcp/conf/deploy.yaml diff --git a/deckard/iaac/gcp/__init__.py b/deckard/iaac/gcp/__init__.py index f1fe0ae5..e69de29b 100644 --- a/deckard/iaac/gcp/__init__.py +++ b/deckard/iaac/gcp/__init__.py @@ -1 +0,0 @@ -from .deploy import * diff --git a/deckard/iaac/gcp/conf/deploy.yaml b/deckard/iaac/gcp/conf/deploy.yaml deleted file mode 100644 index c64d7b87..00000000 --- a/deckard/iaac/gcp/conf/deploy.yaml +++ /dev/null @@ -1,14 +0,0 @@ -num_nodes: 1 -cluster_name: k8s-cluster -gpu_type: nvidia-tesla-v100 -gpu_count: 1 -gpu_driver_version: default -machine_type: n1-standard-2 -min_nodes: 1 -max_nodes: 1 -storage_config: sclass.yaml -persistent_volume_claim: pvc.yaml -pod : pod.yaml -image_project: ubuntu-os-cloud -image_family: ubuntu-2204-lts -mount_directory: /mnt/filestore diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index 736daf77..7cc1b5f9 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -62,7 +62,7 @@ def create_cluster(self): logger.info( f"Creating cluster {self.cluster_name} in region {self.region} with {self.num_nodes} nodes", ) - command = f"gcloud container clusters create {self.cluster_name} --region {self.region} --num-nodes {self.num_nodes} --no-enable-autoupgrade --addons=GcpFilestoreCsiDriver" + command = f"gcloud container clusters create {self.cluster_name} --num-nodes {self.num_nodes} --no-enable-autoupgrade --addons=GcpFilestoreCsiDriver" logger.info(f"Running command: {command}") command = command.split(" ") output = subprocess.run(command) @@ -82,7 +82,7 @@ def retrieve_credentials(self): logger.info( f"Retrieving credentials for cluster {self.cluster_name} in region {self.region}", ) - command = "gcloud container clusters get-credentials {self.cluster_name} --region {self.region}" + command = f"gcloud container clusters get-credentials {self.cluster_name} " logger.info(f"Running command: {command}") command = command.split(" ") output = subprocess.run(command) @@ -91,12 +91,18 @@ def retrieve_credentials(self): def create_node_pool(self): logger.info(f"Creating node pool {self.cluster_name} in region {self.region}") - command = f"gcloud container node-pools create {self.cluster_name} --accelerator type={self.gpu_type},count={self.gpu_count},gpu-driver-version={self.gpu_driver_version} --region {self.region} --cluster {self.cluster_name} --machine-type {self.machine_type} --num-nodes {self.num_nodes} --min-nodes {self.min_nodes} --max-nodes {self.max_nodes}" + if self.gpu_type is not None: + assert ( + self.gpu_count > 0 + ), f"Please specify a valid GPU count. Current value: {self.gpu_count}" + command = f"gcloud container node-pools create {self.cluster_name} --accelerator type={self.gpu_type},count={self.gpu_count},gpu-driver-version={self.gpu_driver_version} --cluster {self.cluster_name} --machine-type {self.machine_type} --num-nodes {self.num_nodes} --min-nodes {self.min_nodes} --max-nodes {self.max_nodes}" + else: + command = f"gcloud container node-pools create {self.cluster_name} --cluster {self.cluster_name} --machine-type {self.machine_type} --num-nodes {self.num_nodes} --min-nodes {self.min_nodes} --max-nodes {self.max_nodes}" logger.info(f"Running command: {command}") command = command.split(" ") - output = subprocess.run(command) + output = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) logger.info(f"{output}") - return output + return output.stdout def create_deployment(self): logger.info(f"Creating deployment {self.cluster_name} in region {self.region}") @@ -136,50 +142,16 @@ def prepare_access_values(self): logger.info( f"Preparing access values in the shared volumee {self.cluster_name} in region {self.region}", ) - # See if filestore exists - command = 'gcloud compute instances list --filter="name=filestore" --format="value(EXTERNAL_IP)"' - command = command.split(" ") - output = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - if output.returncode == 0: - pass - else: - command = f"gcloud compute instances create filestore --async --image-family={self.image_family} --image-project={self.image_project} --machine-type={self.machine_type} --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring --subnet=default --quiet" - logger.info(f"Running command: {command}") - command = command.split(" ") - output = subprocess.run( - command, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - ) - logger.info(f"{output}") - return output - - def find_ip_of_filestore(self): - logger.info( - f"Finding the IP address of the filestore {self.cluster_name} in region {self.region}", - ) - command = 'gcloud compute instances list --filter="name=filestore" --format="value(EXTERNAL_IP)"' + command = f"gcloud compute instances create filestore --async --image-family={self.image_family} --image-project={self.image_project} --machine-type={self.machine_type} --scopes compute-rw,storage-ro,service-management,service-control,logging-write,monitoring --subnet=default --quiet" logger.info(f"Running command: {command}") command = command.split(" ") - ip_output = subprocess.run(command) - logger.info(f"{ip_output}") - return ip_output - - def mount_filestore(self, ip): - # TODO: Switch the python pathlib library - logger.info( - f"Mounting the filestore {self.cluster_name} in region {self.region}", + output = subprocess.run( + command, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, ) - Path(self.mount_directory).mkdir(parents=True, exist_ok=True, mode=0o770) - command = f"sudo mount -o rw,intr {ip}:/vol1 {self.mount_directory}" - logger.info(f"Running command: {command}") - command = command.split(" ") - output = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - if output.returncode != 0: - raise RuntimeError(f"Error mounting filestore: {output.stderr}") logger.info(f"{output}") - return output - + return output.stdout def __call__(self): self.create_cluster() self.install_kubectl() @@ -189,5 +161,4 @@ def __call__(self): self.create_persistent_volume_claim() self.deploy_pod() self.prepare_access_values() - ip_addr = self.find_ip_of_filestore() - return ip_addr + return None diff --git a/deckard/layers/deploy.py b/deckard/layers/deploy.py index 00c281fb..d19935d7 100644 --- a/deckard/layers/deploy.py +++ b/deckard/layers/deploy.py @@ -20,7 +20,4 @@ params = yaml.load(f, Loader=yaml.FullLoader) gcp = GCP_Config(**params) logging.basicConfig(level=args.verbosity) - ip_addr = gcp() - logger.info(f"IP address of the filestore: {ip_addr}") - command = f"sudo mount -o rw,intr {ip_addr}:/vol1 " - logger.info(f"Run command: {command} to mount the filestore.") + assert gcp() == None, "Error creating cluster" From e64ad4376e26dcfc50db8438d58ddfb312131e6e Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 20:54:30 +0000 Subject: [PATCH 071/148] update pytorch --- examples/pytorch/attacks.sh | 8 ++++---- examples/pytorch/conf/grid/attack/cw_0.yaml | 4 ++++ examples/pytorch/conf/grid/attack/cw_2.yaml | 5 +++++ examples/pytorch/conf/grid/attack/cw_inf.yaml | 4 ++++ .../pytorch/conf/grid/attack/deepfool.yaml | 5 +++++ examples/pytorch/conf/grid/attack/fgm.yaml | 4 ++++ examples/pytorch/conf/grid/attack/hsj.yaml | 5 +++++ examples/pytorch/conf/grid/attack/patch.yaml | 5 +++++ examples/pytorch/conf/grid/attack/pgd.yaml | 6 ++++++ examples/pytorch/conf/grid/attack/pixel.yaml | 4 ++++ .../pytorch/conf/grid/attack/threshold.yaml | 4 ++++ .../pytorch/conf/grid/model/confidence.yaml | 4 ++++ examples/pytorch/conf/grid/model/default.yaml | 0 examples/pytorch/conf/grid/model/fsq.yaml | 3 +++ examples/pytorch/conf/grid/model/gauss-in.yaml | 4 ++++ .../pytorch/conf/grid/model/gauss-out.yaml | 2 ++ examples/pytorch/dvc.lock | 13 +++++-------- examples/pytorch/dvc.yaml | 18 +++++++++--------- 18 files changed, 77 insertions(+), 21 deletions(-) create mode 100644 examples/pytorch/conf/grid/attack/cw_0.yaml create mode 100644 examples/pytorch/conf/grid/attack/cw_2.yaml create mode 100644 examples/pytorch/conf/grid/attack/cw_inf.yaml create mode 100644 examples/pytorch/conf/grid/attack/deepfool.yaml create mode 100644 examples/pytorch/conf/grid/attack/fgm.yaml create mode 100644 examples/pytorch/conf/grid/attack/hsj.yaml create mode 100644 examples/pytorch/conf/grid/attack/patch.yaml create mode 100644 examples/pytorch/conf/grid/attack/pgd.yaml create mode 100644 examples/pytorch/conf/grid/attack/pixel.yaml create mode 100644 examples/pytorch/conf/grid/attack/threshold.yaml create mode 100644 examples/pytorch/conf/grid/model/confidence.yaml create mode 100644 examples/pytorch/conf/grid/model/default.yaml create mode 100644 examples/pytorch/conf/grid/model/fsq.yaml create mode 100644 examples/pytorch/conf/grid/model/gauss-in.yaml create mode 100644 examples/pytorch/conf/grid/model/gauss-out.yaml diff --git a/examples/pytorch/attacks.sh b/examples/pytorch/attacks.sh index 121467ac..4c681ab4 100644 --- a/examples/pytorch/attacks.sh +++ b/examples/pytorch/attacks.sh @@ -2,11 +2,11 @@ # # This script is used to generate the attacks for the example. -# Fast Gradient Method -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ +# # Fast Gradient Method +# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ -# Projected Gradient Descent -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ +# # Projected Gradient Descent +# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ # DeepFool bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ diff --git a/examples/pytorch/conf/grid/attack/cw_0.yaml b/examples/pytorch/conf/grid/attack/cw_0.yaml new file mode 100644 index 00000000..20573ea2 --- /dev/null +++ b/examples/pytorch/conf/grid/attack/cw_0.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.CarliniL0Method +init.max_iter : [10] +init.batch_size : [1024] +init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/cw_2.yaml b/examples/pytorch/conf/grid/attack/cw_2.yaml new file mode 100644 index 00000000..14965baa --- /dev/null +++ b/examples/pytorch/conf/grid/attack/cw_2.yaml @@ -0,0 +1,5 @@ +init.model : ${model} +init.name : art.attacks.evasion.CarliniL0Method +init.max_iter : [10] +init.batch_size : [1024] +init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/cw_inf.yaml b/examples/pytorch/conf/grid/attack/cw_inf.yaml new file mode 100644 index 00000000..9e694da5 --- /dev/null +++ b/examples/pytorch/conf/grid/attack/cw_inf.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.CarliniLInfMethod +init.max_iter : [10] +init.batch_size : [1024] +init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/deepfool.yaml b/examples/pytorch/conf/grid/attack/deepfool.yaml new file mode 100644 index 00000000..84a90d3f --- /dev/null +++ b/examples/pytorch/conf/grid/attack/deepfool.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.DeepFool +init.max_iter : [10] +init.batch_size : [1024] +init.nb_grads : [10,100,1000] +init.eps: [0.01, 0.03, 0.3, 0.1, 1.0] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/attack/fgm.yaml b/examples/pytorch/conf/grid/attack/fgm.yaml new file mode 100644 index 00000000..7d9ec02a --- /dev/null +++ b/examples/pytorch/conf/grid/attack/fgm.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.FastGradientMethod +init.eps: [0.01, 0.03, 0.3, 0.1] +init.norm: [inf, 1, 2] +init.eps_step: [0.001, 0.003, 0.01] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/attack/hsj.yaml b/examples/pytorch/conf/grid/attack/hsj.yaml new file mode 100644 index 00000000..9ce9a07f --- /dev/null +++ b/examples/pytorch/conf/grid/attack/hsj.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.HopSkipJump +init.batch_size : [1, 4, 16, 65, 128] +init.max_iter : [1, 10, 100, 1000] +init.max_eval : [1, 10, 100, 1000] +init.init_eval : [1, 10, 100, 1000] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/attack/patch.yaml b/examples/pytorch/conf/grid/attack/patch.yaml new file mode 100644 index 00000000..2cf83694 --- /dev/null +++ b/examples/pytorch/conf/grid/attack/patch.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.ThresholdAttack +init.max_iter : [10] +init.batch_size : [1024] +init.scale_min : [0.01,] +init.scale_max : [.01, .1, .3, .5, .9, .99, 1] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/attack/pgd.yaml b/examples/pytorch/conf/grid/attack/pgd.yaml new file mode 100644 index 00000000..49819d5f --- /dev/null +++ b/examples/pytorch/conf/grid/attack/pgd.yaml @@ -0,0 +1,6 @@ +init.name : art.attacks.evasion.ProjectedGradientDescent +init.eps : [0.01, 0.03, 0.3, 0.1] +init.norm : [inf, 1, 2] +init.eps_step : [0.001, 0.003, 0.01] +init.batch_size : [1024] +init.max_iter : [10] diff --git a/examples/pytorch/conf/grid/attack/pixel.yaml b/examples/pytorch/conf/grid/attack/pixel.yaml new file mode 100644 index 00000000..1ef942b8 --- /dev/null +++ b/examples/pytorch/conf/grid/attack/pixel.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.ProjectedGradientDescent +init.max_iter : [10] +init.batch_size : [1024] +init.th : [1,4,16,64,256] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/attack/threshold.yaml b/examples/pytorch/conf/grid/attack/threshold.yaml new file mode 100644 index 00000000..d21f2803 --- /dev/null +++ b/examples/pytorch/conf/grid/attack/threshold.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.ThresholdAttack +init.max_iter : [10] +init.batch_size : [1024] +init.th: [1,4,16,64,256] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/model/confidence.yaml b/examples/pytorch/conf/grid/model/confidence.yaml new file mode 100644 index 00000000..c21c5b80 --- /dev/null +++ b/examples/pytorch/conf/grid/model/confidence.yaml @@ -0,0 +1,4 @@ + +art.postprocessor.name : [art.defences.postprocessor.HighConfidence] +art.postprocessor.cutoff : [.1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99] +art.postprocessor.apply_fit : [True, False] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/model/default.yaml b/examples/pytorch/conf/grid/model/default.yaml new file mode 100644 index 00000000..e69de29b diff --git a/examples/pytorch/conf/grid/model/fsq.yaml b/examples/pytorch/conf/grid/model/fsq.yaml new file mode 100644 index 00000000..07d579a4 --- /dev/null +++ b/examples/pytorch/conf/grid/model/fsq.yaml @@ -0,0 +1,3 @@ +art.preprocessor.name : [art.defences.preprocessor.FeatureSqueezing] +art.preprocessor.clip_values : [[0, 1]] +art.preprocessor.bit_depth : [1, 2, 4, 8, 16, 32, 64] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/model/gauss-in.yaml b/examples/pytorch/conf/grid/model/gauss-in.yaml new file mode 100644 index 00000000..06b0c414 --- /dev/null +++ b/examples/pytorch/conf/grid/model/gauss-in.yaml @@ -0,0 +1,4 @@ +art.preprocessor.name : art.defences.preprocessor.GaussianAugmentation +art.preprocessor.clip_values : ${art.initialize.clip_values} +art.preprocessor.sigma : [.01, .1, .2, .3, .5, 1] +art.preprocessor.ratio : [.1, .5, 1] \ No newline at end of file diff --git a/examples/pytorch/conf/grid/model/gauss-out.yaml b/examples/pytorch/conf/grid/model/gauss-out.yaml new file mode 100644 index 00000000..377bbcc9 --- /dev/null +++ b/examples/pytorch/conf/grid/model/gauss-out.yaml @@ -0,0 +1,2 @@ +art.postprocessor.name : art.defences.postprocessor.GaussianNoise +art.postprocessor.scale : [.01, .1, .2, .3, .5, 1, 2, 3, 5, 10] diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index a8214a2f..ab55ee9d 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -344,20 +344,17 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: 9504b7f1972384fc353811f6143e9476 + md5: b1497fb3f7d861a2f7d60409a87a56e2 size: 313766 - path: output/reports/attack/default/adv_predictions.json - md5: 7c7d47e99cdfebb48f888a961f4b9c30 - size: 21629 - - path: output/reports/attack/default/adv_predictions.json - md5: 8bfac7d8e89bff0629dffa3e1a3c996f - size: 21748 + md5: 1005bc23be3cdadb2ff29385db954c62 + size: 21609 - path: output/reports/attack/default/params.yaml md5: 66a5664fcf522558c2aa80366bc702af size: 4937 - path: output/reports/attack/default/score_dict.json - md5: 86d6e80f6fa1a9a392417d74d7340426 - size: 542 + md5: 7bf8e030eed14747618bd0526f9ce0ed + size: 535 models: cmd: bash models.sh ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index 01babe35..3dcb378c 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -37,9 +37,9 @@ stages: ############################################################################## attacks: foreach: - - ResNet18 - - ResNet34 - - ResNet50 + # - ResNet18 + # - ResNet34 + # - ResNet50 - ResNet101 - ResNet152 do: @@ -57,9 +57,9 @@ stages: cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv deps: - ${files.directory}/${files.reports}/${item}/ - - ResNet18.db - - ResNet34.db - - ResNet50.db + # - ResNet18.db + # - ResNet34.db + # - ResNet50.db - ResNet101.db - ResNet152.db outs: @@ -68,9 +68,9 @@ stages: cmd : python plots.py --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv deps: - ${files.directory}/${files.reports}/attack.csv - - ResNet18.db - - ResNet34.db - - ResNet50.db + # - ResNet18.db + # - ResNet34.db + # - ResNet50.db - ResNet101.db - ResNet152.db outs: From e57dadfd6db822b6c7e4b620a1c962e1e187f38a Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 21:08:26 +0000 Subject: [PATCH 072/148] linting --- deckard/iaac/gcp/deploy.py | 1 + deckard/layers/experiment.py | 1 + deckard/layers/generate_grid.py | 2 ++ deckard/layers/optimise.py | 2 +- examples/pytorch/conf/grid/attack/deepfool.yaml | 2 +- examples/pytorch/conf/grid/attack/fgm.yaml | 2 +- examples/pytorch/conf/grid/attack/hsj.yaml | 2 +- examples/pytorch/conf/grid/attack/patch.yaml | 2 +- examples/pytorch/conf/grid/attack/pixel.yaml | 2 +- examples/pytorch/conf/grid/attack/threshold.yaml | 2 +- examples/pytorch/conf/grid/model/confidence.yaml | 2 +- examples/pytorch/conf/grid/model/fsq.yaml | 2 +- examples/pytorch/conf/grid/model/gauss-in.yaml | 2 +- examples/sklearn/dvc.yaml | 1 - 14 files changed, 14 insertions(+), 11 deletions(-) diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index e0c25448..e6606921 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -158,6 +158,7 @@ def prepare_access_values(self): ) logger.info(f"{output}") return output.stdout + def __call__(self): self.create_cluster() self.install_kubectl() diff --git a/deckard/layers/experiment.py b/deckard/layers/experiment.py index 0b2ef06c..5ce46fe9 100644 --- a/deckard/layers/experiment.py +++ b/deckard/layers/experiment.py @@ -103,6 +103,7 @@ def run_stages(stages, pipeline_file="dvc.yaml", params_file="params.yaml", repo results[id_] = score return results + if __name__ == "__main__": logger = logging.getLogger(__name__) dvc_parser = argparse.ArgumentParser() diff --git a/deckard/layers/generate_grid.py b/deckard/layers/generate_grid.py index ff31aed4..487ce801 100644 --- a/deckard/layers/generate_grid.py +++ b/deckard/layers/generate_grid.py @@ -10,6 +10,7 @@ logging.basicConfig(level=logging.INFO) + def find_configs_in_folder(conf_dir, regex): configs = [] for path in Path(conf_dir).rglob(regex): @@ -17,6 +18,7 @@ def find_configs_in_folder(conf_dir, regex): logger.info(f"Found {len(configs)} configs in {conf_dir}") return configs + def find_config_folders(conf_dir): config_folders = [] for path in Path(conf_dir).rglob("*"): diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index 62871611..ea84bb74 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -61,7 +61,7 @@ def get_files( id_ = my_hash(cfg) cfg["files"]["_target_"] = "deckard.base.files.FileConfig" id_ = my_hash(cfg) - cfg['name'] = id_ + cfg["name"] = id_ cfg["files"]["name"] = id_ if stage is not None: cfg["files"]["stage"] = stage diff --git a/examples/pytorch/conf/grid/attack/deepfool.yaml b/examples/pytorch/conf/grid/attack/deepfool.yaml index 84a90d3f..1fc9055a 100644 --- a/examples/pytorch/conf/grid/attack/deepfool.yaml +++ b/examples/pytorch/conf/grid/attack/deepfool.yaml @@ -2,4 +2,4 @@ init.name : art.attacks.evasion.DeepFool init.max_iter : [10] init.batch_size : [1024] init.nb_grads : [10,100,1000] -init.eps: [0.01, 0.03, 0.3, 0.1, 1.0] \ No newline at end of file +init.eps: [0.01, 0.03, 0.3, 0.1, 1.0] diff --git a/examples/pytorch/conf/grid/attack/fgm.yaml b/examples/pytorch/conf/grid/attack/fgm.yaml index 7d9ec02a..41032d3e 100644 --- a/examples/pytorch/conf/grid/attack/fgm.yaml +++ b/examples/pytorch/conf/grid/attack/fgm.yaml @@ -1,4 +1,4 @@ init.name : art.attacks.evasion.FastGradientMethod init.eps: [0.01, 0.03, 0.3, 0.1] init.norm: [inf, 1, 2] -init.eps_step: [0.001, 0.003, 0.01] \ No newline at end of file +init.eps_step: [0.001, 0.003, 0.01] diff --git a/examples/pytorch/conf/grid/attack/hsj.yaml b/examples/pytorch/conf/grid/attack/hsj.yaml index 9ce9a07f..1fba407d 100644 --- a/examples/pytorch/conf/grid/attack/hsj.yaml +++ b/examples/pytorch/conf/grid/attack/hsj.yaml @@ -2,4 +2,4 @@ init.name : art.attacks.evasion.HopSkipJump init.batch_size : [1, 4, 16, 65, 128] init.max_iter : [1, 10, 100, 1000] init.max_eval : [1, 10, 100, 1000] -init.init_eval : [1, 10, 100, 1000] \ No newline at end of file +init.init_eval : [1, 10, 100, 1000] diff --git a/examples/pytorch/conf/grid/attack/patch.yaml b/examples/pytorch/conf/grid/attack/patch.yaml index 2cf83694..e7285674 100644 --- a/examples/pytorch/conf/grid/attack/patch.yaml +++ b/examples/pytorch/conf/grid/attack/patch.yaml @@ -2,4 +2,4 @@ init.name : art.attacks.evasion.ThresholdAttack init.max_iter : [10] init.batch_size : [1024] init.scale_min : [0.01,] -init.scale_max : [.01, .1, .3, .5, .9, .99, 1] \ No newline at end of file +init.scale_max : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/pixel.yaml b/examples/pytorch/conf/grid/attack/pixel.yaml index 1ef942b8..9e0060a7 100644 --- a/examples/pytorch/conf/grid/attack/pixel.yaml +++ b/examples/pytorch/conf/grid/attack/pixel.yaml @@ -1,4 +1,4 @@ init.name : art.attacks.evasion.ProjectedGradientDescent init.max_iter : [10] init.batch_size : [1024] -init.th : [1,4,16,64,256] \ No newline at end of file +init.th : [1,4,16,64,256] diff --git a/examples/pytorch/conf/grid/attack/threshold.yaml b/examples/pytorch/conf/grid/attack/threshold.yaml index d21f2803..07437c71 100644 --- a/examples/pytorch/conf/grid/attack/threshold.yaml +++ b/examples/pytorch/conf/grid/attack/threshold.yaml @@ -1,4 +1,4 @@ init.name : art.attacks.evasion.ThresholdAttack init.max_iter : [10] init.batch_size : [1024] -init.th: [1,4,16,64,256] \ No newline at end of file +init.th: [1,4,16,64,256] diff --git a/examples/pytorch/conf/grid/model/confidence.yaml b/examples/pytorch/conf/grid/model/confidence.yaml index c21c5b80..d07598f8 100644 --- a/examples/pytorch/conf/grid/model/confidence.yaml +++ b/examples/pytorch/conf/grid/model/confidence.yaml @@ -1,4 +1,4 @@ art.postprocessor.name : [art.defences.postprocessor.HighConfidence] art.postprocessor.cutoff : [.1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99] -art.postprocessor.apply_fit : [True, False] \ No newline at end of file +art.postprocessor.apply_fit : [True, False] diff --git a/examples/pytorch/conf/grid/model/fsq.yaml b/examples/pytorch/conf/grid/model/fsq.yaml index 07d579a4..9740b4b4 100644 --- a/examples/pytorch/conf/grid/model/fsq.yaml +++ b/examples/pytorch/conf/grid/model/fsq.yaml @@ -1,3 +1,3 @@ art.preprocessor.name : [art.defences.preprocessor.FeatureSqueezing] art.preprocessor.clip_values : [[0, 1]] -art.preprocessor.bit_depth : [1, 2, 4, 8, 16, 32, 64] \ No newline at end of file +art.preprocessor.bit_depth : [1, 2, 4, 8, 16, 32, 64] diff --git a/examples/pytorch/conf/grid/model/gauss-in.yaml b/examples/pytorch/conf/grid/model/gauss-in.yaml index 06b0c414..21392a82 100644 --- a/examples/pytorch/conf/grid/model/gauss-in.yaml +++ b/examples/pytorch/conf/grid/model/gauss-in.yaml @@ -1,4 +1,4 @@ art.preprocessor.name : art.defences.preprocessor.GaussianAugmentation art.preprocessor.clip_values : ${art.initialize.clip_values} art.preprocessor.sigma : [.01, .1, .2, .3, .5, 1] -art.preprocessor.ratio : [.1, .5, 1] \ No newline at end of file +art.preprocessor.ratio : [.1, .5, 1] diff --git a/examples/sklearn/dvc.yaml b/examples/sklearn/dvc.yaml index 6d419e87..fd85eb87 100644 --- a/examples/sklearn/dvc.yaml +++ b/examples/sklearn/dvc.yaml @@ -91,4 +91,3 @@ stages: - model.db outs: - attack.db - From 2c9935843e321d9d012aca8869b89c4c16a5c46b Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 21:09:24 +0000 Subject: [PATCH 073/148] linting --- deckard/layers/deploy.py | 2 +- examples/pytorch/torch_example.py | 2 -- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/deckard/layers/deploy.py b/deckard/layers/deploy.py index 0f9720a6..a1fe99ed 100644 --- a/deckard/layers/deploy.py +++ b/deckard/layers/deploy.py @@ -20,4 +20,4 @@ params = yaml.load(f, Loader=yaml.FullLoader) gcp = GCP_Config(**params) logging.basicConfig(level=args.verbosity) - assert gcp() == None, "Error creating cluster" + assert gcp() is None, "Error creating cluster" diff --git a/examples/pytorch/torch_example.py b/examples/pytorch/torch_example.py index fe898b66..2007d9d0 100644 --- a/examples/pytorch/torch_example.py +++ b/examples/pytorch/torch_example.py @@ -1,8 +1,6 @@ import torch.nn as nn from torchvision import models -from torchvision.models import resnet18 - __all__ = [ "ResNet18", "ResNet50", From 4b6ca64da62f83f1674dde55c77ad2a6daaee749 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 7 Sep 2023 21:11:10 +0000 Subject: [PATCH 074/148] update docker test name --- .github/workflows/docker-test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker-test.yml b/.github/workflows/docker-test.yml index 1a866920..7fb6ddf8 100644 --- a/.github/workflows/docker-test.yml +++ b/.github/workflows/docker-test.yml @@ -1,5 +1,5 @@ # -name: Create and publish a Docker image +name: Test Docker Image # Configures this workflow to run every time a change is pushed to the branch called `release`. on: From c0e8fede8323b0a8be4f362eb0c338eaa0d6d392 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 12 Sep 2023 17:22:44 +0000 Subject: [PATCH 075/148] +cifar example --- deckard/layers/prepare_queue.py | 290 ++++++++ examples/pytorch/attacks.sh | 31 +- examples/pytorch/conf/cifar.yaml | 34 + examples/pytorch/conf/compile.yaml | 36 +- examples/pytorch/conf/files/cifar.yaml | 21 + examples/pytorch/dvc.lock | 121 +-- examples/pytorch/dvc.yaml | 8 +- examples/pytorch/models.sh | 20 +- examples/pytorch/plots.py | 24 +- examples/pytorch/weibull.ipynb | 690 ++++++++++-------- examples/pytorch_cifar/.dvc/.gitignore | 3 + examples/pytorch_cifar/.dvc/config | 0 examples/pytorch_cifar/.dvcignore | 3 + examples/pytorch_cifar/attacks.sh | 38 + .../pytorch_cifar/conf/attack/default.yaml | 9 + examples/pytorch_cifar/conf/compile.yaml | 30 + examples/pytorch_cifar/conf/data/default.yaml | 2 + .../pytorch_cifar/conf/data/torch_cifar.yaml | 11 + .../pytorch_cifar/conf/data/torch_mnist.yaml | 15 + examples/pytorch_cifar/conf/default.yaml | 38 + examples/pytorch_cifar/conf/deploy.yaml | 14 + examples/pytorch_cifar/conf/files/cifar.yaml | 21 + .../pytorch_cifar/conf/files/default.yaml | 21 + .../pytorch_cifar/conf/hydra/default.yaml | 38 + .../hydra/sweeper/params/attack/cw_0.yaml | 4 + .../hydra/sweeper/params/attack/cw_2.yaml | 5 + .../hydra/sweeper/params/attack/cw_inf.yaml | 4 + .../hydra/sweeper/params/attack/deepfool.yaml | 5 + .../conf/hydra/sweeper/params/attack/fgm.yaml | 4 + .../conf/hydra/sweeper/params/attack/hsj.yaml | 6 + .../hydra/sweeper/params/attack/patch.yaml | 5 + .../conf/hydra/sweeper/params/attack/pgd.yaml | 6 + .../hydra/sweeper/params/attack/pixel.yaml | 4 + .../sweeper/params/attack/threshold.yaml | 4 + .../conf/hydra/sweeper/params/default.yaml | 11 + .../sweeper/params/model/confidence.yaml | 5 + .../hydra/sweeper/params/model/default.yaml | 1 + .../conf/hydra/sweeper/params/model/fsq.yaml | 4 + .../hydra/sweeper/params/model/gauss-in.yaml | 5 + .../hydra/sweeper/params/model/gauss-out.yaml | 3 + .../pytorch_cifar/conf/model/art/default.yaml | 7 + .../conf/model/art/initialize/default.yaml | 7 + .../model/art/postprocessor/confidence.yaml | 3 + .../model/art/postprocessor/gauss-out.yaml | 2 + .../conf/model/art/preprocessor/default.yaml | 0 .../conf/model/art/preprocessor/fsq.yaml | 3 + .../conf/model/art/preprocessor/gauss-in.yaml | 4 + .../pytorch_cifar/conf/model/torch_cifar.yaml | 13 + .../pytorch_cifar/conf/model/torch_mnist.yaml | 13 + examples/pytorch_cifar/conf/plots.yaml | 160 ++++ .../pytorch_cifar/conf/scorers/default.yaml | 9 + examples/pytorch_cifar/dvc.lock | 340 +++++++++ examples/pytorch_cifar/dvc.yaml | 77 ++ examples/pytorch_cifar/models.sh | 19 + examples/pytorch_cifar/params.yaml | 206 ++++++ examples/pytorch_cifar/torch_example.py | 79 ++ 56 files changed, 2126 insertions(+), 410 deletions(-) create mode 100644 deckard/layers/prepare_queue.py create mode 100644 examples/pytorch/conf/cifar.yaml create mode 100644 examples/pytorch/conf/files/cifar.yaml create mode 100644 examples/pytorch_cifar/.dvc/.gitignore create mode 100644 examples/pytorch_cifar/.dvc/config create mode 100644 examples/pytorch_cifar/.dvcignore create mode 100644 examples/pytorch_cifar/attacks.sh create mode 100644 examples/pytorch_cifar/conf/attack/default.yaml create mode 100644 examples/pytorch_cifar/conf/compile.yaml create mode 100644 examples/pytorch_cifar/conf/data/default.yaml create mode 100644 examples/pytorch_cifar/conf/data/torch_cifar.yaml create mode 100644 examples/pytorch_cifar/conf/data/torch_mnist.yaml create mode 100644 examples/pytorch_cifar/conf/default.yaml create mode 100644 examples/pytorch_cifar/conf/deploy.yaml create mode 100644 examples/pytorch_cifar/conf/files/cifar.yaml create mode 100644 examples/pytorch_cifar/conf/files/default.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/default.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_0.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_2.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_inf.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/deepfool.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/fgm.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/hsj.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/patch.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pgd.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pixel.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/threshold.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/default.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/model/confidence.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/model/default.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/model/fsq.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-in.yaml create mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-out.yaml create mode 100644 examples/pytorch_cifar/conf/model/art/default.yaml create mode 100644 examples/pytorch_cifar/conf/model/art/initialize/default.yaml create mode 100644 examples/pytorch_cifar/conf/model/art/postprocessor/confidence.yaml create mode 100644 examples/pytorch_cifar/conf/model/art/postprocessor/gauss-out.yaml create mode 100644 examples/pytorch_cifar/conf/model/art/preprocessor/default.yaml create mode 100644 examples/pytorch_cifar/conf/model/art/preprocessor/fsq.yaml create mode 100644 examples/pytorch_cifar/conf/model/art/preprocessor/gauss-in.yaml create mode 100644 examples/pytorch_cifar/conf/model/torch_cifar.yaml create mode 100644 examples/pytorch_cifar/conf/model/torch_mnist.yaml create mode 100644 examples/pytorch_cifar/conf/plots.yaml create mode 100644 examples/pytorch_cifar/conf/scorers/default.yaml create mode 100644 examples/pytorch_cifar/dvc.lock create mode 100644 examples/pytorch_cifar/dvc.yaml create mode 100644 examples/pytorch_cifar/models.sh create mode 100644 examples/pytorch_cifar/params.yaml create mode 100644 examples/pytorch_cifar/torch_example.py diff --git a/deckard/layers/prepare_queue.py b/deckard/layers/prepare_queue.py new file mode 100644 index 00000000..5e31dd18 --- /dev/null +++ b/deckard/layers/prepare_queue.py @@ -0,0 +1,290 @@ +import logging +import os +from copy import deepcopy +from pathlib import Path +import yaml +from hydra.utils import instantiate +from omegaconf import DictConfig, OmegaConf +import hydra +from string import Template +from ..base.utils import my_hash, unflatten_dict +from ..base.experiment import Experiment +from .utils import deckard_nones + +logger = logging.getLogger(__name__) + +__all__ = ["write_stage", "optimise", "parse_stage", "get_files"] + + +def get_files( + cfg, + stage, +): + """ + Gets the file names from + """ + if isinstance(cfg, dict): + pass + elif isinstance(cfg, list): + cfg = unflatten_dict(cfg) + else: + raise TypeError(f"Expected dict or list, got {type(cfg)}") + if "_target_" not in cfg: + cfg.update({"_target_": "deckard.base.experiment.Experiment"}) + if ( + "attack_file" in cfg["files"] + and cfg["files"]["attack_file"] is not None + and "attack" in cfg + ): + cfg["files"]["attack_file"] = str( + Path(cfg["files"]["attack_file"]) + .with_name(my_hash(cfg["attack"])) + .as_posix(), + ) + if ( + "model_file" in cfg["files"] + and cfg["files"]["model_file"] is not None + and "model" in cfg + ): + cfg["files"]["model_file"] = str( + Path(cfg["files"]["model_file"]) + .with_name(my_hash(cfg["model"])) + .as_posix(), + ) + if ( + "data_file" in cfg["files"] + and cfg["files"]["data_file"] is not None + and "data" in cfg + ): + cfg["files"]["data_file"] = str( + Path(cfg["files"]["data_file"]).with_name(my_hash(cfg["data"])).as_posix(), + ) + id_ = my_hash(cfg) + cfg["files"]["_target_"] = "deckard.base.files.FileConfig" + id_ = my_hash(cfg) + cfg["name"] = id_ + cfg["files"]["name"] = id_ + if stage is not None: + cfg["files"]["stage"] = stage + return cfg + + +# def save_file(cfg, folder, params_file): +# path = Path(folder, Path(params_file).name) +# with open(path, "w") as f: +# yaml.safe_dump(cfg, f) +# assert Path(path).exists() + + +def merge_params(default, params) -> dict: + """ + Overwrite default params with params if key is found in default. + :param default: Default params + :param params: Params to overwrite default params + :return: Merged params + """ + for key, value in params.items(): + if key in default and isinstance(value, dict): + default[key] = merge_params(default[key], value) + elif ( + isinstance(value, (list, tuple, int, float, str, bool, dict)) + and value is not None + ): + default[key] = value + elif value is None: + continue + else: + logger.warning(f"Key {key} not found in default params. Skipping.") + return default + + +def read_subset_of_params(key_list: list, params: dict): + """ + Read a subset of the params, denoted by the key_list + :param key_list: The list of keys to read + :param params: The params to read from + :return: The subset of the params + """ + new_params = {} + for key in key_list: + if key in params: + if params[key] in deckard_nones: + continue + elif hasattr(params[key], "__len__") and len(params[key]) == 0: + continue + else: + new_params[key] = params[key] + elif "." in key: + first_loop = True + dot_key = key + total = len(key.split(".")) + i = 1 + for entry in key.split("."): + if first_loop is True: + sub_params = params + first_loop = False + if entry in sub_params: + sub_params = sub_params[entry] + i += 1 + else: + raise ValueError(f"{dot_key} not found in {params}") + if i == total: + new_params[dot_key.split(".")[0]] = {**sub_params} + else: + pass + else: + raise ValueError(f"{key} not found in {params}") + return new_params + + +def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: + """ + Parse params from dvc.yaml and merge with params from hydra config + :param stage: Stage to load params from. If None, loads the last stage in dvc.yaml + :param params: Params to merge with params from dvc.yaml + :return: Merged params + """ + if path is None: + path = Path.cwd() + # Load params from dvc + if stage is None: + raise ValueError("Please specify a stage.") + elif isinstance(stage, str): + stages = [stage] + else: + assert isinstance(stage, list), f"args.stage is of type {type(stage)}" + stages = stage + if params is None: + with open(Path(path, "params.yaml"), "r") as f: + default_params = yaml.load(f, Loader=yaml.FullLoader) + key_list = [] + for stage in stages: + with open(Path(path, "dvc.yaml"), "r") as f: + new_keys = yaml.load(f, Loader=yaml.FullLoader)["stages"][stage][ + "params" + ] + key_list.extend(new_keys) + params = read_subset_of_params(key_list, params) + params = merge_params(default_params, params) + elif isinstance(params, str) and Path(params).is_file() and Path(params).exists(): + with open(Path(params), "r") as f: + params = yaml.load(f, Loader=yaml.FullLoader) + assert isinstance( + params, + dict, + ), f"Params in file {params} must be a dict. It is a {type(params)}." + key_list = [] + for stage in stages: + with open(Path(path, "dvc.yaml"), "r") as f: + new_keys = yaml.load(f, Loader=yaml.FullLoader)["stages"][stage][ + "params" + ] + key_list.extend(new_keys) + with open(Path(path, "params.yaml"), "r") as f: + all_params = yaml.load(f, Loader=yaml.FullLoader) + default_params = read_subset_of_params(key_list, all_params) + params = merge_params(default_params, params) + elif isinstance(params, dict): + key_list = [] + for stage in stages: + with open(Path(path, "dvc.yaml"), "r") as f: + new_keys = yaml.load(f, Loader=yaml.FullLoader)["stages"][stage][ + "params" + ] + key_list.extend(new_keys) + with open(Path(path, "params.yaml"), "r") as f: + all_params = yaml.load(f, Loader=yaml.FullLoader) + default_params = read_subset_of_params(key_list, all_params) + params = merge_params(default_params, params) + else: + raise TypeError(f"Expected str or dict, got {type(params)}") + assert isinstance( + params, + dict, + ), f"Params must be a dict. It is type {type(params)}." + # Load files from dvc + with open(Path(path, "dvc.yaml"), "r") as f: + key_list = [] + for stage in stages: + pipe = yaml.load(f, Loader=yaml.FullLoader)["stages"][stage] + if "deps" in pipe: + key_list.extend(pipe["deps"]) + if "outs" in pipe: + key_list.extend(pipe["outs"]) + if "metrics" in pipe: + key_list.extend(pipe["metrics"]) + with open(Path(path, "params.yaml"), "r") as f: + all_params = yaml.load(f, Loader=yaml.FullLoader) + files = {} + for filename, file in all_params["files"].items(): + if filename in str(key_list): + files[filename] = file + files["_target_"] = "deckard.base.files.FileConfig" + params = get_files(params, stage=stages[-1]) + return params + + +def write_stage(params: dict, stage: str, id_:str, path=None, working_dir=None) -> None: + """ + Write params to dvc.yaml + :param params: Params to write to dvc.yaml + :param stage: Stage to write params to + """ + if working_dir is None: + working_dir = Path.cwd() + if path is None and working_dir is None: + path = Path.cwd() + else: + path = Path(working_dir, path).resolve() + with open(Path(working_dir, "dvc.yaml"), "r") as f: + dvc = yaml.load(f, Loader=yaml.FullLoader) + stage_params = {"stages": {f"{stage}-{id_}": {}}} + stage_params["stages"][f"{stage}-{id_}"] = dvc["stages"][stage] + path.mkdir(exist_ok=True, parents=True) + with open(path / "dvc.yaml", "w") as f: + yaml.dump(stage_params, f, default_flow_style=False) + assert Path(path / "dvc.yaml").exists(), f"File {path/'dvc.yaml'} does not exist." + with open(Path(path, "params.yaml"), "w") as f: + yaml.dump(params, f, default_flow_style=False) + assert Path( + path / "params.yaml", + ).exists(), f"File {path/'params.yaml'} does not exist." + return None + + +def prepare_experiment_folder(cfg: DictConfig) -> None: + cfg = OmegaConf.to_container(OmegaConf.create(cfg), resolve=False) + scorer = cfg.pop("optimizers", None) + working_dir = cfg.pop("working_dir", Path().resolve().as_posix()) + stage = cfg.pop("stage", None) + cfg = parse_stage(params=cfg, stage=stage, path=working_dir) + direction = cfg.pop("direction", "minimize") + exp = instantiate(cfg) + files = deepcopy(exp.files)() + folder = Path(files["score_dict_file"]).parent + Path(folder).mkdir(exist_ok=True, parents=True) + id_ = Path(files["score_dict_file"]).parent.name + write_stage(cfg, stage, path=folder, working_dir=working_dir, id_=id_) + return exp, scorer, direction, folder, id_ + + + +if __name__ == "__main__": + logger = logging.getLogger(__name__) + config_path = os.environ.pop( + "DECKARD_CONFIG_PATH", + str(Path(Path(), "conf").absolute().as_posix()), + ) + config_name = os.environ.pop("DECKARD_DEFAULT_CONFIG", "default.yaml") + + @hydra.main(config_path=config_path, config_name=config_name, version_base="1.3") + def hydra_prepare(cfg: DictConfig) -> float: + exp, scorer, direction, folder, id_ = prepare_experiment_folder(cfg) + assert isinstance(exp, Experiment), f"Expected Experiment, got {type(exp)}." + assert isinstance(scorer, (str, list)), f"Expected list, got {type(scorer)}." + assert isinstance(direction, str), f"Expected str, got {type(direction)}." + assert direction in ['minimize', 'maximize'], f"Expected 'minimize' or 'maximize', got {direction}." + assert Path(folder).exists(), f"Folder {folder} does not exist for experiment {id_}." + return 0 + + hydra_prepare() diff --git a/examples/pytorch/attacks.sh b/examples/pytorch/attacks.sh index 4c681ab4..f593dd8d 100644 --- a/examples/pytorch/attacks.sh +++ b/examples/pytorch/attacks.sh @@ -3,35 +3,36 @@ # # This script is used to generate the attacks for the example. # # Fast Gradient Method -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ # # Projected Gradient Descent -# bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ -# DeepFool -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ +# # DeepFool +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ -# HopSkipJump -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 ++stage=attack ++hydra.sweeper.study_name=hsj ++hydra.sweeper.direction=minimize $@ + +# # HopSkipJump +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 ++stage=attack ++hydra.sweeper.study_name=hsj ++hydra.sweeper.direction=minimize $@ # ##################################################### # PixelAttack -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=pixel ++hydra.sweeper.direction=minimize $@ +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=pixel ++hydra.sweeper.direction=minimize $@ # ThresholdAttack -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=thresh ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=thresh ++hydra.sweeper.direction=minimize $@ # # AdversarialPatch -# bash models.sh attack=hsj --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 ++stage=patch ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++attack.init.patch_shape=[1,28,28] $@ +# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 ++stage=patch ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++attack.init.patch_shape=[1,28,28] $@ ##################################################### -# Carlini L0 Method -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw0 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ +# # Carlini L0 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw0 ++hydra.sweeper.study_name=cw0 ++hydra.sweeper.direction=minimize $@ -# # Carlini L2 Method -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw2 ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize $@ +# # # Carlini L2 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw2 ++hydra.sweeper.study_name=cw2 ++hydra.sweeper.direction=minimize $@ -# Carlini LInf Method -bash models.sh attack=hsj ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=cwinf ++hydra.sweeper.direction=minimize $@ +# # Carlini LInf Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=cwinf ++hydra.sweeper.direction=minimize $@ rm -rf output/models/* diff --git a/examples/pytorch/conf/cifar.yaml b/examples/pytorch/conf/cifar.yaml new file mode 100644 index 00000000..824c51ab --- /dev/null +++ b/examples/pytorch/conf/cifar.yaml @@ -0,0 +1,34 @@ +defaults: + - _self_ + - data: torch_cifar + - model: torch_cifar + - attack: default + - files: cifar + - scorers: default + - stage : null + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +_target_ : deckard.base.experiment.Experiment +optimizers : accuracy +hydra: + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + direction: maximize + study_name: model + storage: sqlite:///model.db + n_jobs: 1 + params: + ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 64 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/pytorch/conf/compile.yaml b/examples/pytorch/conf/compile.yaml index 010bffc2..2f5baad8 100644 --- a/examples/pytorch/conf/compile.yaml +++ b/examples/pytorch/conf/compile.yaml @@ -1,7 +1,7 @@ attacks: - CarliniL0Method: CW_0 - CarliniL2Method: CW_2 - CarliniLInfMethod: CW_inf + # CarliniL0Method: CW_0 + # CarliniL2Method: CW_2 + # CarliniLInfMethod: CW_inf DeepFool: Deep FastGradientMethod: FGM HopSkipJump: HSJ @@ -11,20 +11,20 @@ attacks: defences: Control: Control FeatureSqueezing: FSQ - GaussianAugmentation: Gauss-out - GaussianNoise: Gauss-in + GaussianAugmentation: Gauss-in + GaussianNoise: Gauss-out HighConfidence: Conf params: - art.attacks.evasion.CarliniL0Method: attack.init.kwargs.confidence - art.attacks.evasion.CarliniL2Method: attack.init.kwargs.confidence - art.attacks.evasion.CarliniLInfMethod: attack.init.kwargs.confidence - art.attacks.evasion.DeepFool: attack.init.kwargs.nb_grads - art.attacks.evasion.FastGradientMethod: attack.init.kwargs.eps - art.attacks.evasion.HopSkipJump: attack.init.kwargs.max_iter - art.attacks.evasion.PixelAttack: attack.init.kwargs.th - art.attacks.evasion.ProjectedGradientDescent: attack.init.kwargs.eps - art.attacks.evasion.ThresholdAttack: attack.init.kwargs.th - art.defences.postprocessor.GaussianNoise: model.art.pipeline.postprocessor.kwargs.scale - art.defences.postprocessor.HighConfidence: model.art.pipeline.postprocessor.kwargs.cutoff - art.defences.preprocessor.FeatureSqueezing: model.art.pipeline.preprocessor.kwargs.bit_depth - art.defences.preprocessor.GaussianAugmentation: model.art.pipeline.preprocessor.kwargs.sigma + # art.attacks.evasion.CarliniL0Method: attack.init.kwargs.confidence + # art.attacks.evasion.CarliniL2Method: attack.init.kwargs.confidence + # art.attacks.evasion.CarliniLInfMethod: attack.init.kwargs.confidence + Deep: attack.init.kwargs.nb_grads + FGM: attack.init.kwargs.eps + HSJ: attack.init.kwargs.max_iter + Pixel: attack.init.kwargs.th + PGD: attack.init.kwargs.eps + Thresh: attack.init.kwargs.th + Gauss-out: model.art.pipeline.postprocessor.kwargs.scale + Conf: model.art.pipeline.postprocessor.kwargs.cutoff + FSQ: model.art.pipeline.preprocessor.kwargs.bit_depth + Gauss-in: model.art.pipeline.preprocessor.kwargs.sigma diff --git a/examples/pytorch/conf/files/cifar.yaml b/examples/pytorch/conf/files/cifar.yaml new file mode 100644 index 00000000..e43cab72 --- /dev/null +++ b/examples/pytorch/conf/files/cifar.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: cifar +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pt +params_file : params.yaml +# train_labels_file : train_labels.json +# test_labels_file : test_labels.json +# probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +# adv_probabilities_file: adv_probabilities.json +name: default diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index ab55ee9d..59da997a 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -105,20 +105,20 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.optimizer.pt - md5: da778ab9ca244100045790051485e60f + md5: e6bcf362a952574d75a3da1f96805f63 size: 44780845 - path: output/models/model.pt - md5: 71c48241b81b18b3b60afc8a3f10f25a + md5: 126f743cb1454e749185e3c4252fe500 size: 44785941 - path: output/reports/train/default/params.yaml md5: eedabd38cbbfce21b879b647497174da size: 2148 - path: output/reports/train/default/predictions.json - md5: b4d5926c71a4b54076b566a73e7af54b - size: 2881449 + md5: e45381a5ae992d2079168b7a136d5486 + size: 2884465 - path: output/reports/train/default/score_dict.json - md5: 51249137fa5081d7eff95624f3d1fa1f - size: 410 + md5: 012605b723f136e7d5c621c71163bb3f + size: 403 attack: cmd: python -m deckard.layers.experiment attack deps: @@ -126,7 +126,7 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.pt - md5: dc00c8994b407de0e23e96df50b46425 + md5: e18d078c8eb0c942300498ed45fe1fad size: 44786389 params: params.yaml: @@ -258,9 +258,21 @@ stages: trainer: batch_size: 1024 nb_epoch: 20 - data.generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true files: _target_: deckard.base.files.FileConfig adv_predictions_file: adv_predictions.json @@ -344,17 +356,17 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: b1497fb3f7d861a2f7d60409a87a56e2 + md5: c3cd5ad573c588dc10064512f220fea2 size: 313766 - path: output/reports/attack/default/adv_predictions.json - md5: 1005bc23be3cdadb2ff29385db954c62 - size: 21609 + md5: 74195b36b5db3bc6fd4a0a3ecc952774 + size: 21589 - path: output/reports/attack/default/params.yaml - md5: 66a5664fcf522558c2aa80366bc702af - size: 4937 + md5: 2550051d80574ff4d8769a2946ab5840 + size: 5094 - path: output/reports/attack/default/score_dict.json - md5: 7bf8e030eed14747618bd0526f9ce0ed - size: 535 + md5: fa5a589aaefdad6c4d1abee55252cbaa + size: 538 models: cmd: bash models.sh ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 @@ -434,17 +446,20 @@ stages: cmd: python -m deckard.layers.compile --report_folder output/reports/attack --results_file output/reports/attack.csv deps: + - path: ResNet101.db + md5: eedf8c115666f2d29209399d8691eadf + size: 192512 - path: ResNet152.db - md5: d86a693a86ec196fcd4887013acde398 - size: 434176 + md5: 61ac073bd2baf7ef2c7dcf7d8833bec9 + size: 192512 - path: output/reports/attack/ - md5: 0ea96d75e1d41ddf1e4e856260d3d327.dir - size: 8695142484 - nfiles: 17939 + md5: 631e25806ff0904eff49f7cb76394d7e.dir + size: 9881640443 + nfiles: 20830 outs: - path: output/reports/attack.csv - md5: 1d31d2bc679e668fc31e323a444c84fd - size: 13006598 + md5: ceaa24229dcc4703a2092cd5b973718f + size: 14709136 compile@train: cmd: python -m deckard.layers.compile --report_folder output/reports/train --results_file output/reports/train.csv @@ -468,41 +483,49 @@ stages: ++hydra.sweeper.storage=sqlite:///ResNet152.db --config-name grid.yaml deps: - path: attacks.sh - md5: 6a6a91d037b4ce29f84009d9b1161d4f - size: 3034 + md5: ef2cfda268db8d15a4a0e1a786b04aa1 + size: 3105 - path: models.sh - md5: 76fbee55cb5f90913d0863df9debd357 - size: 1299 + md5: dd089c4ea9458167c4c754595fc23a12 + size: 1317 - path: output/reports/attack/default/score_dict.json - md5: 35d505c81867c9c50d74780c19d6f7e7 - size: 539 + md5: a2fcc0f6754fdfb19810a0a96ce0fcf7 + size: 541 outs: - path: ResNet152.db - md5: d86a693a86ec196fcd4887013acde398 - size: 434176 + md5: 61ac073bd2baf7ef2c7dcf7d8833bec9 + size: 192512 plot: cmd: python plots.py --path output/plots/ --file output/reports/attack.csv deps: - path: ResNet101.db - md5: 00e8859421e45d77c36d2a39ea5deaa5 - size: 466944 + md5: eedf8c115666f2d29209399d8691eadf + size: 192512 - path: ResNet152.db - md5: d86a693a86ec196fcd4887013acde398 - size: 434176 - - path: ResNet18.db - md5: 3d304e014200ae926dec63521b6eeca0 - size: 1499136 - - path: ResNet34.db - md5: 7bf834e39f0367deddd36074e8f69d47 - size: 1400832 - - path: ResNet50.db - md5: 771e206f2ab932ae2978783214290c41 - size: 110592 + md5: 61ac073bd2baf7ef2c7dcf7d8833bec9 + size: 192512 - path: output/reports/attack.csv - md5: 1d31d2bc679e668fc31e323a444c84fd - size: 13006598 + md5: ceaa24229dcc4703a2092cd5b973718f + size: 14709136 outs: - path: output/plots/ - md5: ad60dde37b7ad40574c2e2a3e391224d.dir - size: 8442674 + md5: b98d290a3fd73f774671fe35ccc2f8a8.dir + size: 9775965 nfiles: 13 + attacks@ResNet101: + cmd: bash attacks.sh ++model.init.name=torch_example.ResNet101 ++stage=attack + ++hydra.sweeper.storage=sqlite:///ResNet101.db --config-name grid.yaml + deps: + - path: attacks.sh + md5: ef2cfda268db8d15a4a0e1a786b04aa1 + size: 3105 + - path: models.sh + md5: dd089c4ea9458167c4c754595fc23a12 + size: 1317 + - path: output/reports/attack/default/score_dict.json + md5: a2fcc0f6754fdfb19810a0a96ce0fcf7 + size: 541 + outs: + - path: ResNet101.db + md5: eedf8c115666f2d29209399d8691eadf + size: 192512 diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index 3dcb378c..99a4694b 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -19,7 +19,7 @@ stages: attack: cmd: python -m deckard.layers.experiment attack params: - - data.generate + - data - model - attack - scorers @@ -40,7 +40,7 @@ stages: # - ResNet18 # - ResNet34 # - ResNet50 - - ResNet101 + # - ResNet101 - ResNet152 do: cmd: bash attacks.sh ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name grid.yaml @@ -60,7 +60,7 @@ stages: # - ResNet18.db # - ResNet34.db # - ResNet50.db - - ResNet101.db + # - ResNet101.db - ResNet152.db outs: - ${files.directory}/${files.reports}/${item}.csv @@ -71,7 +71,7 @@ stages: # - ResNet18.db # - ResNet34.db # - ResNet50.db - - ResNet101.db + # - ResNet101.db - ResNet152.db outs: - ${files.directory}/plots/ diff --git a/examples/pytorch/models.sh b/examples/pytorch/models.sh index 2786c4be..ba2b1a62 100644 --- a/examples/pytorch/models.sh +++ b/examples/pytorch/models.sh @@ -2,18 +2,18 @@ # This script is used to generate the models for the sklearn example. -# Default model -echo "python -m deckard.layers.optimise +stage=train" $@ "--multirun" -python -m deckard.layers.optimise +stage=train $@ --multirun +# # Default model +# echo "python -m deckard.layers.optimise +stage=train" $@ "--multirun" +# python -m deckard.layers.optimise +stage=train $@ --multirun -# This line generates the model and adds the FeatureSqueezing preprocessing defence. -python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train $@ --multirun +# # This line generates the model and adds the FeatureSqueezing preprocessing defence. +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train $@ --multirun -# Gaussian Augmentation (Input) -python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False +stage=train $@ --multirun +# # Gaussian Augmentation (Input) +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False +stage=train $@ --multirun # # # Gaussian Noise (Output) -python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.01,.1,.3,.5,1, +stage=train $@ --multirun +python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 +stage=train $@ --multirun -# # High Confidence -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun +# # # High Confidence +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index 90892429..d68b41b9 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -67,9 +67,9 @@ def line_plot( plt.gcf().clear() graph = sns.lineplot(data=data, x=x, y=y, hue=hue, style=control) graph.legend(**legend) - if control is not None: - assert control_color is not None, "Please specify a control color" - graph.add_line(plt.axhline(y=control, color=control_color, linestyle="-")) + # if control is not None: + # assert control_color is not None, "Please specify a control color" + # graph.add_line(plt.axhline(y=control, color=control_color, linestyle="-")) graph.set_xlabel(xlabel) graph.set_ylabel(ylabel) graph.set_title(title) @@ -125,17 +125,17 @@ def calculate_failure_rate(data): subset=[ "accuracy", "adv_accuracy", - "train_time_per_sample", - "adv_fit_time_per_sample", - "predict_time_per_sample", + "train_time", + "adv_fit_time", + "predict_time", ], inplace=True, ) data.loc[:, "failure_rate"] = ( - (1 - data["accuracy"]) * 100 / data["predict_time_per_sample"] + (1 - data["accuracy"]) * 100 / data["predict_time"] ) data.loc[:, "adv_failure_rate"] = ( - (1 - data["adv_accuracy"]) * 100 / data["adv_fit_time_per_sample"] + (1 - data["adv_accuracy"]) * 100 / data["adv_fit_time"] ) data.loc[:, "training_time_per_failure"] = ( data["train_time_per_sample"] / data["failure_rate"] @@ -202,8 +202,12 @@ def calculate_failure_rate(data): data = calculate_failure_rate(data) if "Unnamed: 0" in data.columns: data.drop("Unnamed: 0", axis=1, inplace=True) - - FOLDER = Path(Path(), args.path) + if Path(args.path).absolute().exists(): + logger.info("Absolute path specified") + FOLDER = Path(args.path).absolute() + else: + logger.info("Relative path specified") + FOLDER = Path(Path(), args.path) FOLDER.mkdir(parents=True, exist_ok=True) data.to_csv(FOLDER / args.output) IMAGE_FILETYPE = ( diff --git a/examples/pytorch/weibull.ipynb b/examples/pytorch/weibull.ipynb index 0343aee6..7b1de02c 100644 --- a/examples/pytorch/weibull.ipynb +++ b/examples/pytorch/weibull.ipynb @@ -34,240 +34,15 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "data": { - 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stageattackdatafileskwargsmodelnamescorerstrain_timetrain_time_per_sample...model_layersdef_paramdef_valueatk_paramatk_valuefailure_rateadv_failure_ratetraining_time_per_failuretraining_time_per_adv_failureadv_training_time_per_failure
0attack0009cf1504e3689e79934f78fcb00c7c0009cf1504e3689e79934f78fcb00c7c0009cf1504e3689e79934f78fcb00c7c0009cf1504e3689e79934f78fcb00c7c0009cf1504e3689e79934f78fcb00c7c0009cf1504e3689e79934f78fcb00c7c0009cf1504e3689e79934f78fcb00c7c206.6722640.003691...34cutoff1.000000eps0.3000002.766349e+0522947.1905531.334096e-081.608291e-071.608291e-07
5attack0022118d46ac791faba90c6f907516f00022118d46ac791faba90c6f907516f00022118d46ac791faba90c6f907516f00022118d46ac791faba90c6f907516f00022118d46ac791faba90c6f907516f00022118d46ac791faba90c6f907516f00022118d46ac791faba90c6f907516f0203.1959710.003628...34NaNNaNeps0.1239051.435355e+06147157.7492442.527946e-092.465721e-082.465721e-08
6attack0036b069268b81ebc05519c3372608b90036b069268b81ebc05519c3372608b90036b069268b81ebc05519c3372608b90036b069268b81ebc05519c3372608b90036b069268b81ebc05519c3372608b90036b069268b81ebc05519c3372608b90036b069268b81ebc05519c3372608b946.0477910.000822...34NaNNaNnb_grads1.0000006.608925e+066524.3444911.244199e-101.260329e-071.260329e-07
8attack0059d34a86c5a8e25c3bb06da1cd65550059d34a86c5a8e25c3bb06da1cd65550059d34a86c5a8e25c3bb06da1cd65550059d34a86c5a8e25c3bb06da1cd65550059d34a86c5a8e25c3bb06da1cd65550059d34a86c5a8e25c3bb06da1cd65550059d34a86c5a8e25c3bb06da1cd655525.4248510.000454...18NaNNaNnb_grads1.0000009.488474e+0410296.6841934.784913e-094.409334e-084.409334e-08
9attack00603349e13a42bef3170206db60dea600603349e13a42bef3170206db60dea600603349e13a42bef3170206db60dea600603349e13a42bef3170206db60dea600603349e13a42bef3170206db60dea600603349e13a42bef3170206db60dea600603349e13a42bef3170206db60dea6207.5874900.003707...34cutoff0.224719max_iter0.2105263.184056e+0473.6923871.164213e-075.030261e-055.030261e-05
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5 rows × 196 columns

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" - ], - "text/plain": [ - " stage attack data \\\n", - "0 attack 0009cf1504e3689e79934f78fcb00c7c 0009cf1504e3689e79934f78fcb00c7c \n", - "5 attack 0022118d46ac791faba90c6f907516f0 0022118d46ac791faba90c6f907516f0 \n", - "6 attack 0036b069268b81ebc05519c3372608b9 0036b069268b81ebc05519c3372608b9 \n", - "8 attack 0059d34a86c5a8e25c3bb06da1cd6555 0059d34a86c5a8e25c3bb06da1cd6555 \n", - "9 attack 00603349e13a42bef3170206db60dea6 00603349e13a42bef3170206db60dea6 \n", - "\n", - " files kwargs \\\n", - "0 0009cf1504e3689e79934f78fcb00c7c 0009cf1504e3689e79934f78fcb00c7c \n", - "5 0022118d46ac791faba90c6f907516f0 0022118d46ac791faba90c6f907516f0 \n", - "6 0036b069268b81ebc05519c3372608b9 0036b069268b81ebc05519c3372608b9 \n", - "8 0059d34a86c5a8e25c3bb06da1cd6555 0059d34a86c5a8e25c3bb06da1cd6555 \n", - "9 00603349e13a42bef3170206db60dea6 00603349e13a42bef3170206db60dea6 \n", - "\n", - " model name \\\n", - "0 0009cf1504e3689e79934f78fcb00c7c 0009cf1504e3689e79934f78fcb00c7c \n", - "5 0022118d46ac791faba90c6f907516f0 0022118d46ac791faba90c6f907516f0 \n", - "6 0036b069268b81ebc05519c3372608b9 0036b069268b81ebc05519c3372608b9 \n", - "8 0059d34a86c5a8e25c3bb06da1cd6555 0059d34a86c5a8e25c3bb06da1cd6555 \n", - "9 00603349e13a42bef3170206db60dea6 00603349e13a42bef3170206db60dea6 \n", - "\n", - " scorers train_time train_time_per_sample ... \\\n", - "0 0009cf1504e3689e79934f78fcb00c7c 206.672264 0.003691 ... \n", - "5 0022118d46ac791faba90c6f907516f0 203.195971 0.003628 ... \n", - "6 0036b069268b81ebc05519c3372608b9 46.047791 0.000822 ... \n", - "8 0059d34a86c5a8e25c3bb06da1cd6555 25.424851 0.000454 ... \n", - "9 00603349e13a42bef3170206db60dea6 207.587490 0.003707 ... \n", - "\n", - " model_layers def_param def_value atk_param atk_value failure_rate \\\n", - "0 34 cutoff 1.000000 eps 0.300000 2.766349e+05 \n", - "5 34 NaN NaN eps 0.123905 1.435355e+06 \n", - "6 34 NaN NaN nb_grads 1.000000 6.608925e+06 \n", - "8 18 NaN NaN nb_grads 1.000000 9.488474e+04 \n", - "9 34 cutoff 0.224719 max_iter 0.210526 3.184056e+04 \n", - "\n", - " adv_failure_rate training_time_per_failure training_time_per_adv_failure \\\n", - "0 22947.190553 1.334096e-08 1.608291e-07 \n", - "5 147157.749244 2.527946e-09 2.465721e-08 \n", - "6 6524.344491 1.244199e-10 1.260329e-07 \n", - "8 10296.684193 4.784913e-09 4.409334e-08 \n", - "9 73.692387 1.164213e-07 5.030261e-05 \n", - "\n", - " adv_training_time_per_failure \n", - "0 1.608291e-07 \n", - "5 2.465721e-08 \n", - "6 1.260329e-07 \n", - "8 4.409334e-08 \n", - "9 5.030261e-05 \n", - "\n", - "[5 rows x 196 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "FOLDER = Path(\"output/plots/\")\n", "csv_file = FOLDER / \"data.csv\"\n", "data = pd.read_csv(csv_file, index_col=0)\n", - "data.head()" + "data.columns = data.columns.str.strip()\n", + "data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n", + "data.def_value.replace(\"\", 0, inplace=True)\n", + "data.atk_value.replace(\"\", 0, inplace=True)" ] }, { @@ -302,26 +77,26 @@ " event_col in df.columns\n", " ), f\"Column {event_col} not in dataframe with columns {df.columns}\"\n", " aft.fit(df, duration_col=duration_col, event_col=event_col)\n", - " aft.fit(df, duration_col=duration_col, event_col=event_col)\n", " ax = aft.plot()\n", - " ax.set_ylabel(ylabel)\n", " labels = ax.get_yticklabels()\n", " labels = [label.get_text() for label in labels]\n", " for k, v in replacement_dict.items():\n", " labels = [label.replace(k, v) for label in labels]\n", " ax.set_yticklabels(labels)\n", " ax.set_xlabel(xlabel)\n", + " ax.set_ylabel(ylabel)\n", " ax.set_title(title)\n", " ax.get_figure().tight_layout()\n", " ax.get_figure().savefig(FOLDER / file)\n", " logger.info(f\"Saved graph to {FOLDER / file}\")\n", - " return ax, aft\n", + " return ax, aft, test\n", "\n", "\n", "def clean_data_for_aft(\n", " data, kwarg_list, standard_scaling=True, target=\"adv_failure_rate\"\n", "):\n", " subset = data.copy()\n", + " assert target in subset, f\"Target {target} not in dataframe with columns {subset.columns}\"\n", " y = subset[target].copy(deep=True)\n", " cleaned = pd.DataFrame()\n", " if target in kwarg_list:\n", @@ -329,6 +104,7 @@ " for kwarg in kwarg_list:\n", " cleaned = pd.concat([cleaned, subset[kwarg]], axis=1)\n", " cols = cleaned.columns\n", + " cleaned.def_value.fillna(0, inplace=True)\n", " if standard_scaling is True:\n", " scaler = StandardScaler()\n", " scaler = scaler.fit(cleaned)\n", @@ -337,80 +113,210 @@ " cleaned_numeric = cleaned\n", "\n", " cleaned_numeric = pd.DataFrame(subset, columns=cols)\n", - " cleaned_numeric.def_value.fillna(0, inplace=True)\n", - " # replace 0 with 1e-6\n", - " # cleaned_numeric = cleaned_numeric.replace(0, replace_0)\n", + " \n", + " cleaned.dropna(inplace=True, how='any',)\n", " return cleaned_numeric, y" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 30, "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'random_state'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/core/indexes/base.py:3652\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3651\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3652\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m 3653\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/_libs/index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'random_state'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 17\u001b[0m\n\u001b[1;32m 1\u001b[0m kwarg_list \u001b[39m=\u001b[39m [\n\u001b[1;32m 2\u001b[0m \u001b[39m\"\u001b[39m\u001b[39maccuracy\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 3\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mtrain_time\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[39m# \"adv_accuracy\",\u001b[39;00m\n\u001b[1;32m 14\u001b[0m ]\n\u001b[0;32m---> 17\u001b[0m cleaned, y \u001b[39m=\u001b[39m clean_data_for_aft(data, kwarg_list, standard_scaling\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m 18\u001b[0m cleaned\u001b[39m.\u001b[39mdropna(axis\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, how\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39many\u001b[39m\u001b[39m\"\u001b[39m, subset\u001b[39m=\u001b[39mkwarg_list\u001b[39m.\u001b[39mremove(\u001b[39m\"\u001b[39m\u001b[39mdef_value\u001b[39m\u001b[39m\"\u001b[39m), inplace\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m 19\u001b[0m cleaned[\u001b[39m\"\u001b[39m\u001b[39madv_failure_rate\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m y\n", - "Cell \u001b[0;32mIn[3], line 52\u001b[0m, in \u001b[0;36mclean_data_for_aft\u001b[0;34m(data, kwarg_list, standard_scaling, target)\u001b[0m\n\u001b[1;32m 50\u001b[0m kwarg_list\u001b[39m.\u001b[39mremove(target)\n\u001b[1;32m 51\u001b[0m \u001b[39mfor\u001b[39;00m kwarg \u001b[39min\u001b[39;00m kwarg_list:\n\u001b[0;32m---> 52\u001b[0m cleaned \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mconcat([cleaned, subset[kwarg]], axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[1;32m 53\u001b[0m cols \u001b[39m=\u001b[39m cleaned\u001b[39m.\u001b[39mcolumns\n\u001b[1;32m 54\u001b[0m \u001b[39mif\u001b[39;00m standard_scaling \u001b[39mis\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/core/frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3759\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 3760\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3761\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m 3762\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3763\u001b[0m indexer \u001b[39m=\u001b[39m [indexer]\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/core/indexes/base.py:3654\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3652\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3653\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> 3654\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 3655\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 3656\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3657\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3658\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3659\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[0;31mKeyError\u001b[0m: 'random_state'" + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " week arrest fin age race wexp mar paro prio\n", + "0 20 1 0 27 1 0 0 1 3\n", + "1 17 1 0 18 1 0 0 1 8\n", + "2 25 1 0 19 0 1 0 1 13\n", + "3 52 0 1 23 1 1 1 1 1\n", + "4 52 0 0 19 0 1 0 1 3" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from lifelines.datasets import load_rossi\n", + "rossi = load_rossi()\n", + "rossi.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy\n", + "train_time\n", + "predict_time\n", + "atk_value\n", + "def_value\n", + "model_layers\n", + "adv_fit_time\n", + "adv_failure_rate\n", + "random_state\n" ] } ], "source": [ "kwarg_list = [\n", " \"accuracy\",\n", - " \"train_time\",\n", + " \"train_time_per_sample\",\n", + " \"predict_time_per_sample\",\n", " \"atk_value\",\n", " \"def_value\",\n", + " \"data.sample.random_state\",\n", + " # \"adv_failure_rate\",\n", + " \"model_layers\",\n", " \"adv_fit_time\",\n", - " \"random_state\",\n", - " \"adv_failure_rate\",\n", - " \"predict_time\",\n", - " # \"adv_fit_time\",\n", - " # \"adv_accuracy\",\n", - " # \"adv_fit_time_per_sample\",\n", " # \"adv_accuracy\",\n", "]\n", "\n", "\n", "cleaned, y = clean_data_for_aft(data, kwarg_list, standard_scaling=True)\n", "cleaned.dropna(axis=0, how=\"any\", subset=kwarg_list.remove(\"def_value\"), inplace=True)\n", - "cleaned[\"adv_failure_rate\"] = y" + "cleaned[\"adv_failure_rate\"] = y\n", + "cleaned['random_state'] = cleaned['data.sample.random_state']\n", + "cleaned.drop(columns=['data.sample.random_state'], inplace=True)\n", + "for col in cleaned.columns:\n", + " print(col)\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "weibull_dict = {\n", " \"Intercept: rho_\": \"$\\\\rho$\",\n", " \"Intercept: lambda_\": \"$\\lambda$\",\n", " \"random_state: lambda_\": \"Random State\",\n", " \"def_value: lambda_\": \"Defence Strength\",\n", - " \"atk_value: lambda_\": \"Attack Strength\",\n", + " \"atk_value: lambda_\": \"Attack Distance\",\n", " \"train_time: lambda_\": \"Training Time\",\n", " \"predict_time: lambda_\": \"Inference Time\",\n", + " # \"adv_accuracy: lambda_\": \"Adv. Accuracy\",\n", " \"accuracy: lambda_\": \"Ben. Accuracy\",\n", + " # \"adv_fit_time: lambda_\": \"Adv. Fit Time\",\n", + " \"model_layers: lambda_\": \"No. of Hidden Layers\",\n", "}\n", "\n", - "weibull_graph, wft = plot_aft(\n", + "weibull_graph, wft, wft_test = plot_aft(\n", " cleaned,\n", " \"weibull_aft.pdf\",\n", " \"adv_failure_rate\",\n", @@ -423,23 +329,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "log_normal_dict = {\n", " \"Intercept: sigma_\": \"$\\sigma$\",\n", " \"Intercept: mu_\": \"$\\mu$\",\n", " \"random_state: mu_\": \"Random State\",\n", " \"def_value: mu_\": \"Defence Strength\",\n", - " \"atk_value: mu_\": \"Attack Strength\",\n", + " \"atk_value: mu_\": \"Attack Distance\",\n", " \"train_time: mu_\": \"Training Time\",\n", " \"predict_time: mu_\": \"Inference Time\",\n", " \"accuracy: mu_\": \"Ben. Accuracy\",\n", " \"adv_fit_time: mu_\": \"Adv. Fit Time\",\n", + " \"model_layers: mu_\": \"No. of Hidden Layers\",\n", "}\n", "\n", - "log_normal_graph, lnt = plot_aft(\n", + "log_normal_graph, lnt, lnt_test = plot_aft(\n", " cleaned,\n", " \"log_normal_aft.pdf\",\n", " \"adv_failure_rate\",\n", @@ -452,19 +370,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'cleaned' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[8], line 14\u001b[0m\n\u001b[1;32m 1\u001b[0m log_logistic_dict \u001b[39m=\u001b[39m {\n\u001b[1;32m 2\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mIntercept: beta_\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39m$\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39mbeta$\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 3\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mIntercept: alpha_\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39m$\u001b[39m\u001b[39m\\\\\u001b[39;00m\u001b[39malpha$\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[39m\"\u001b[39m\u001b[39madv_fit_time: alpha_\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mAdv. Fit Time\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 11\u001b[0m }\n\u001b[1;32m 13\u001b[0m log_logistic_graph, llt \u001b[39m=\u001b[39m plot_aft(\n\u001b[0;32m---> 14\u001b[0m cleaned,\n\u001b[1;32m 15\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mlog_logistic_aft.pdf\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 16\u001b[0m \u001b[39m\"\u001b[39m\u001b[39madv_failure_rate\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 17\u001b[0m \u001b[39m\"\u001b[39m\u001b[39madv_fit_time\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 18\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mLog Logistic AFT Model\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 19\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mlog_logistic\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 20\u001b[0m replacement_dict\u001b[39m=\u001b[39mlog_logistic_dict,\n\u001b[1;32m 21\u001b[0m )\n", - "\u001b[0;31mNameError\u001b[0m: name 'cleaned' is not defined" - ] + "data": { + "image/png": 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degrees_freedom6
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test_statisticp-log2(p)
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" + ], + "text/latex": [ + "\\begin{tabular}{lrrr}\n", + " & test_statistic & p & -log2(p) \\\\\n", + "0 & 324.99 & 0.00 & 220.72 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\n", + " degrees_freedom = 6\n", + "null_distribution = chi squared\n", + " test_name = log-likelihood ratio test\n", + "\n", + "---\n", + " test_statistic p -log2(p)\n", + " 324.99 <0.005 220.72" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wft.log_likelihood_ratio_test()\n" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "KeyError", + "evalue": "'covariate `adv_fit_time_per_sample` is not present in the original dataset'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[21], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m covariates \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(cleaned\u001b[39m.\u001b[39mcolumns)\n\u001b[1;32m 2\u001b[0m values \u001b[39m=\u001b[39m cleaned\n\u001b[0;32m----> 3\u001b[0m lnt\u001b[39m.\u001b[39;49mplot_partial_effects_on_outcome(covariates, values )\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/lifelines/fitters/__init__.py:3392\u001b[0m, in \u001b[0;36mParametericAFTRegressionFitter.plot_partial_effects_on_outcome\u001b[0;34m(self, covariates, values, plot_baseline, times, y, ax, **kwargs)\u001b[0m\n\u001b[1;32m 3390\u001b[0m \u001b[39mfor\u001b[39;00m covariate \u001b[39min\u001b[39;00m covariates:\n\u001b[1;32m 3391\u001b[0m \u001b[39mif\u001b[39;00m covariate \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m original_columns:\n\u001b[0;32m-> 3392\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mcovariate `\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m` is not present in the original dataset\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m covariate)\n\u001b[1;32m 3394\u001b[0m \u001b[39mif\u001b[39;00m ax \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 3395\u001b[0m ax \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mgca()\n", + "\u001b[0;31mKeyError\u001b[0m: 'covariate `adv_fit_time_per_sample` is not present in the original dataset'" + ] + } + ], + "source": [ + "covariates = list(cleaned.columns)\n", + "values = cleaned\n", + "lnt.plot_partial_effects_on_outcome(covariates, values )" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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degrees_freedom6
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test_statisticp-log2(p)
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" + ], + "text/latex": [ + "\\begin{tabular}{lrrr}\n", + " & test_statistic & p & -log2(p) \\\\\n", + "0 & 311.17 & 0.00 & 210.88 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\n", + " degrees_freedom = 6\n", + "null_distribution = chi squared\n", + " test_name = log-likelihood ratio test\n", + "\n", + "---\n", + " test_statistic p -log2(p)\n", + " 311.17 <0.005 210.88" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "llt.log_likelihood_ratio_test()" + ] }, { "cell_type": "code", diff --git a/examples/pytorch_cifar/.dvc/.gitignore b/examples/pytorch_cifar/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/pytorch_cifar/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/pytorch_cifar/.dvc/config b/examples/pytorch_cifar/.dvc/config new file mode 100644 index 00000000..e69de29b diff --git a/examples/pytorch_cifar/.dvcignore b/examples/pytorch_cifar/.dvcignore new file mode 100644 index 00000000..51973055 --- /dev/null +++ b/examples/pytorch_cifar/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/examples/pytorch_cifar/attacks.sh b/examples/pytorch_cifar/attacks.sh new file mode 100644 index 00000000..cb12aa06 --- /dev/null +++ b/examples/pytorch_cifar/attacks.sh @@ -0,0 +1,38 @@ +# #!/bin/bash + +# # This script is used to generate the attacks for the example. + +# Fast Gradient Method +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ + +# Projected Gradient Descent +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ + +# DeepFool +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ + + +# HopSkipJump +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 ++stage=attack ++hydra.sweeper.study_name=hsj ++hydra.sweeper.direction=minimize $@ + +##################################################### +PixelAttack +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=pixel ++hydra.sweeper.direction=minimize $@ + +# ThresholdAttack +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=thresh ++hydra.sweeper.direction=minimize $@ + +# # AdversarialPatch +# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 ++stage=patch ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++attack.init.patch_shape=[1,28,28] $@ +##################################################### + +# # Carlini L0 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw0 ++hydra.sweeper.study_name=cw0 ++hydra.sweeper.direction=minimize $@ + +# # # Carlini L2 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw2 ++hydra.sweeper.study_name=cw2 ++hydra.sweeper.direction=minimize $@ + +# # Carlini LInf Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=cwinf ++hydra.sweeper.direction=minimize $@ + +rm -rf output/models/* diff --git a/examples/pytorch_cifar/conf/attack/default.yaml b/examples/pytorch_cifar/conf/attack/default.yaml new file mode 100644 index 00000000..76b4d62d --- /dev/null +++ b/examples/pytorch_cifar/conf/attack/default.yaml @@ -0,0 +1,9 @@ +data: ${data} +model: ${model} +_target_ : deckard.base.attack.Attack +init: + model: ${model} + _target_: deckard.base.attack.AttackInitializer + name: art.attacks.evasion.HopSkipJump +attack_size : 10 +method : evasion diff --git a/examples/pytorch_cifar/conf/compile.yaml b/examples/pytorch_cifar/conf/compile.yaml new file mode 100644 index 00000000..2f5baad8 --- /dev/null +++ b/examples/pytorch_cifar/conf/compile.yaml @@ -0,0 +1,30 @@ +attacks: + # CarliniL0Method: CW_0 + # CarliniL2Method: CW_2 + # CarliniLInfMethod: CW_inf + DeepFool: Deep + FastGradientMethod: FGM + HopSkipJump: HSJ + PixelAttack: Pixel + ProjectGradientDescent: PGD + ThresholdAttack: Thresh +defences: + Control: Control + FeatureSqueezing: FSQ + GaussianAugmentation: Gauss-in + GaussianNoise: Gauss-out + HighConfidence: Conf +params: + # art.attacks.evasion.CarliniL0Method: attack.init.kwargs.confidence + # art.attacks.evasion.CarliniL2Method: attack.init.kwargs.confidence + # art.attacks.evasion.CarliniLInfMethod: attack.init.kwargs.confidence + Deep: attack.init.kwargs.nb_grads + FGM: attack.init.kwargs.eps + HSJ: attack.init.kwargs.max_iter + Pixel: attack.init.kwargs.th + PGD: attack.init.kwargs.eps + Thresh: attack.init.kwargs.th + Gauss-out: model.art.pipeline.postprocessor.kwargs.scale + Conf: model.art.pipeline.postprocessor.kwargs.cutoff + FSQ: model.art.pipeline.preprocessor.kwargs.bit_depth + Gauss-in: model.art.pipeline.preprocessor.kwargs.sigma diff --git a/examples/pytorch_cifar/conf/data/default.yaml b/examples/pytorch_cifar/conf/data/default.yaml new file mode 100644 index 00000000..5eb78278 --- /dev/null +++ b/examples/pytorch_cifar/conf/data/default.yaml @@ -0,0 +1,2 @@ +defaults: + - torch_mnist diff --git a/examples/pytorch_cifar/conf/data/torch_cifar.yaml b/examples/pytorch_cifar/conf/data/torch_cifar.yaml new file mode 100644 index 00000000..b7603125 --- /dev/null +++ b/examples/pytorch_cifar/conf/data/torch_cifar.yaml @@ -0,0 +1,11 @@ +_target_: deckard.base.data.Data +generate: + name: torch_cifar10 +sample: + random_state : 0 + stratify: True +sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/pytorch_cifar/conf/data/torch_mnist.yaml b/examples/pytorch_cifar/conf/data/torch_mnist.yaml new file mode 100644 index 00000000..9d79c036 --- /dev/null +++ b/examples/pytorch_cifar/conf/data/torch_mnist.yaml @@ -0,0 +1,15 @@ +_target_: deckard.base.data.Data +generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist +sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True +sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/pytorch_cifar/conf/default.yaml b/examples/pytorch_cifar/conf/default.yaml new file mode 100644 index 00000000..1b04b6cc --- /dev/null +++ b/examples/pytorch_cifar/conf/default.yaml @@ -0,0 +1,38 @@ +defaults: + - _self_ + - data: torch_cifar + - model: torch_cifar + - attack: default + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : random + - override hydra/launcher : joblib +_target_ : deckard.base.experiment.Experiment +optimizers : accuracy +stage : null +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.RandomSampler + direction: maximize + study_name: model + storage: sqlite:///model.db + n_jobs: 1 + n_trials: 80 + params: + ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) + ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 64 + prefer : processes + verbose: 1 + timeout: 3600 + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/pytorch_cifar/conf/deploy.yaml b/examples/pytorch_cifar/conf/deploy.yaml new file mode 100644 index 00000000..c64d7b87 --- /dev/null +++ b/examples/pytorch_cifar/conf/deploy.yaml @@ -0,0 +1,14 @@ +num_nodes: 1 +cluster_name: k8s-cluster +gpu_type: nvidia-tesla-v100 +gpu_count: 1 +gpu_driver_version: default +machine_type: n1-standard-2 +min_nodes: 1 +max_nodes: 1 +storage_config: sclass.yaml +persistent_volume_claim: pvc.yaml +pod : pod.yaml +image_project: ubuntu-os-cloud +image_family: ubuntu-2204-lts +mount_directory: /mnt/filestore diff --git a/examples/pytorch_cifar/conf/files/cifar.yaml b/examples/pytorch_cifar/conf/files/cifar.yaml new file mode 100644 index 00000000..e43cab72 --- /dev/null +++ b/examples/pytorch_cifar/conf/files/cifar.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: cifar +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pt +params_file : params.yaml +# train_labels_file : train_labels.json +# test_labels_file : test_labels.json +# probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +# adv_probabilities_file: adv_probabilities.json +name: default diff --git a/examples/pytorch_cifar/conf/files/default.yaml b/examples/pytorch_cifar/conf/files/default.yaml new file mode 100644 index 00000000..bcaf75ce --- /dev/null +++ b/examples/pytorch_cifar/conf/files/default.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: output +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pt +params_file : params.yaml +# train_labels_file : train_labels.json +# test_labels_file : test_labels.json +# probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +# adv_probabilities_file: adv_probabilities.json +name: default diff --git a/examples/pytorch_cifar/conf/hydra/default.yaml b/examples/pytorch_cifar/conf/hydra/default.yaml new file mode 100644 index 00000000..53e5bd5d --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/default.yaml @@ -0,0 +1,38 @@ +defaults: + - params : default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : random + - override hydra/launcher : joblib + - override hydra/sweeper/params : default.yaml +run: + dir: output/${hydra.job.override_dirname}/seed=${data.sample.random_state} +job: + config: + override_dirname: + exclude_keys: + - seed + kv_sep: ":" + item_sep: "_" +sweep: + dir: multirun + subdir: ${hydra.job.override_dirname} +sweeper: + sampler: + _target_: optuna.samplers.RandomSampler + direction: maximize + study_name: model + storage: sqlite:///model.db + n_jobs: 4 + n_trials: 1 + +launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 8 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_0.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_0.yaml new file mode 100644 index 00000000..d28a31d4 --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_0.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.CarliniL0Method +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.confidence : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_2.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_2.yaml new file mode 100644 index 00000000..ec2ed44c --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_2.yaml @@ -0,0 +1,5 @@ +init.model : ${model} +init.name : art.attacks.evasion.CarliniL0Method +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.confidence : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_inf.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_inf.yaml new file mode 100644 index 00000000..99b16e45 --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_inf.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.CarliniLInfMethod +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.confidence : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/deepfool.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/deepfool.yaml new file mode 100644 index 00000000..8b56a8d9 --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/deepfool.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.DeepFool +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.nb_grads : choice(10,100,1000) +init.eps: choice(0.01, 0.03, 0.3, 0.1, 1.0) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/fgm.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/fgm.yaml new file mode 100644 index 00000000..c46ee4df --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/fgm.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.FastGradientMethod +init.eps: choice(0.01, 0.03, 0.3, 0.1) +init.norm: choice(inf, 1, 2) +init.eps_step: choice(0.001, 0.003, 0.01) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/hsj.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/hsj.yaml new file mode 100644 index 00000000..45b3af5d --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/hsj.yaml @@ -0,0 +1,6 @@ +attack: + init.name : art.attacks.evasion.HopSkipJump + init.batch_size : choice(1, 4, 16, 65, 128) + init.max_iter : choice(1, 10, 100, 1000) + init.max_eval : choice(1, 10, 100, 1000) + init.init_eval : choice(1, 10, 100, 1000) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/patch.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/patch.yaml new file mode 100644 index 00000000..c9409de4 --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/patch.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.ThresholdAttack +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.scale_min : choice(0.01,) +init.scale_max : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pgd.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pgd.yaml new file mode 100644 index 00000000..42bc9514 --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pgd.yaml @@ -0,0 +1,6 @@ +init.name : art.attacks.evasion.ProjectedGradientDescent +init.eps : choice(0.01, 0.03, 0.3, 0.1) +init.norm : choice(inf, 1, 2) +init.eps_step : choice(0.001, 0.003, 0.01) +init.batch_size : choice(1024) +init.max_iter : choice(10) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pixel.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pixel.yaml new file mode 100644 index 00000000..84450f38 --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pixel.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.ProjectedGradientDescent +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.th : choice(1,4,16,64,256) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/threshold.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/threshold.yaml new file mode 100644 index 00000000..d6079457 --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/threshold.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.ThresholdAttack +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.th: choice(1,4,16,64,256) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/default.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/default.yaml new file mode 100644 index 00000000..49b1de65 --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/default.yaml @@ -0,0 +1,11 @@ +defaults: + - _self_ + - data: torch_mnist + - model: torch_mnist + - files: default + - scorers: default + - stage : null + - params/model : null + - params/attack : null +++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) +++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/confidence.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/confidence.yaml new file mode 100644 index 00000000..cfda12d2 --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/confidence.yaml @@ -0,0 +1,5 @@ + +defence: + art.postprocessor.name : [art.defences.postprocessor.HighConfidence] + art.postprocessor.cutoff : [.1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99] + art.postprocessor.apply_fit : [True, False] diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/default.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/default.yaml new file mode 100644 index 00000000..8163e568 --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/default.yaml @@ -0,0 +1 @@ +defence: diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/fsq.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/fsq.yaml new file mode 100644 index 00000000..bb508203 --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/fsq.yaml @@ -0,0 +1,4 @@ +defence: + art.preprocessor.name : [art.defences.preprocessor.FeatureSqueezing] + art.preprocessor.clip_values : [[0, 1]] + art.preprocessor.bit_depth : [1, 2, 4, 8, 16, 32, 64] diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-in.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-in.yaml new file mode 100644 index 00000000..fd45ea84 --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-in.yaml @@ -0,0 +1,5 @@ +defence: + art.preprocessor.name : art.defences.preprocessor.GaussianAugmentation + art.preprocessor.clip_values : ${art.initialize.clip_values} + art.preprocessor.sigma : [.01, .1, .2, .3, .5, 1] + art.preprocessor.ratio : [.1, .5, 1] diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-out.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-out.yaml new file mode 100644 index 00000000..8abd9ee1 --- /dev/null +++ b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-out.yaml @@ -0,0 +1,3 @@ +defence: + art.postprocessor.name : art.defences.postprocessor.GaussianNoise + art.postprocessor.scale : [.01, .1, .2, .3, .5, 1, 2, 3, 5, 10] diff --git a/examples/pytorch_cifar/conf/model/art/default.yaml b/examples/pytorch_cifar/conf/model/art/default.yaml new file mode 100644 index 00000000..4251627d --- /dev/null +++ b/examples/pytorch_cifar/conf/model/art/default.yaml @@ -0,0 +1,7 @@ +defaults: + - initialize : default + # - preprocessor: fsq + # - postprocessor: confidence +data : ${..data} +library : ${..library} +_target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/pytorch_cifar/conf/model/art/initialize/default.yaml b/examples/pytorch_cifar/conf/model/art/initialize/default.yaml new file mode 100644 index 00000000..6c5edde5 --- /dev/null +++ b/examples/pytorch_cifar/conf/model/art/initialize/default.yaml @@ -0,0 +1,7 @@ +criterion: + name : torch.nn.CrossEntropyLoss +optimizer: + name : torch.optim.SGD + lr : 0.01 + momentum : 0.9 +clip_values : [0.0, 1.0] diff --git a/examples/pytorch_cifar/conf/model/art/postprocessor/confidence.yaml b/examples/pytorch_cifar/conf/model/art/postprocessor/confidence.yaml new file mode 100644 index 00000000..6fcdde95 --- /dev/null +++ b/examples/pytorch_cifar/conf/model/art/postprocessor/confidence.yaml @@ -0,0 +1,3 @@ + +name : art.defences.postprocessor.HighConfidence +cutoff : .1 diff --git a/examples/pytorch_cifar/conf/model/art/postprocessor/gauss-out.yaml b/examples/pytorch_cifar/conf/model/art/postprocessor/gauss-out.yaml new file mode 100644 index 00000000..c912ebd9 --- /dev/null +++ b/examples/pytorch_cifar/conf/model/art/postprocessor/gauss-out.yaml @@ -0,0 +1,2 @@ +name : art.defences.postprocessor.GaussianNoise +scale : 1 diff --git a/examples/pytorch_cifar/conf/model/art/preprocessor/default.yaml b/examples/pytorch_cifar/conf/model/art/preprocessor/default.yaml new file mode 100644 index 00000000..e69de29b diff --git a/examples/pytorch_cifar/conf/model/art/preprocessor/fsq.yaml b/examples/pytorch_cifar/conf/model/art/preprocessor/fsq.yaml new file mode 100644 index 00000000..27ddf58b --- /dev/null +++ b/examples/pytorch_cifar/conf/model/art/preprocessor/fsq.yaml @@ -0,0 +1,3 @@ +name : art.defences.preprocessor.FeatureSqueezing +clip_values : ${model.art.initialize.clip_values} +bit_depth : 64 diff --git a/examples/pytorch_cifar/conf/model/art/preprocessor/gauss-in.yaml b/examples/pytorch_cifar/conf/model/art/preprocessor/gauss-in.yaml new file mode 100644 index 00000000..c438e8f5 --- /dev/null +++ b/examples/pytorch_cifar/conf/model/art/preprocessor/gauss-in.yaml @@ -0,0 +1,4 @@ +name : art.defences.preprocessor.GaussianAugmentation +clip_values : ${model.art.initialize.clip_values} +sigma : 1 +ratio : .5 diff --git a/examples/pytorch_cifar/conf/model/torch_cifar.yaml b/examples/pytorch_cifar/conf/model/torch_cifar.yaml new file mode 100644 index 00000000..2562064a --- /dev/null +++ b/examples/pytorch_cifar/conf/model/torch_cifar.yaml @@ -0,0 +1,13 @@ +defaults: + - art : default +data: ${data} +init: + _target_: deckard.base.model.ModelInitializer + name : torch_example.ResNet18 + num_channels: 3 + num_classes: 10 +_target_: deckard.base.model.Model +trainer: + nb_epoch: 20 + batch_size: 1024 +library : pytorch diff --git a/examples/pytorch_cifar/conf/model/torch_mnist.yaml b/examples/pytorch_cifar/conf/model/torch_mnist.yaml new file mode 100644 index 00000000..762addc1 --- /dev/null +++ b/examples/pytorch_cifar/conf/model/torch_mnist.yaml @@ -0,0 +1,13 @@ + +defaults: + - art : default +data: ${data} +init: + _target_: deckard.base.model.ModelInitializer + num_channels : 1 + name : torch_example.ResNet18 +_target_: deckard.base.model.Model +trainer: + nb_epoch: 20 + batch_size: 1024 +library : pytorch diff --git a/examples/pytorch_cifar/conf/plots.yaml b/examples/pytorch_cifar/conf/plots.yaml new file mode 100644 index 00000000..7a53497f --- /dev/null +++ b/examples/pytorch_cifar/conf/plots.yaml @@ -0,0 +1,160 @@ +cat_plot: +- file: adv_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: linear + titles: Adversarial Accuracy vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_accuracy + ylabels: Adv. Accuracy + rotation : 90 +- file: ben_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: Benign Accuracy vs Defence Type + x: def_gen + xlabels: Defence Type + y: accuracy + ylabels: Ben. Accuracy + rotation : 90 +- file: ben_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: Training Time per Benign Failure vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_failure + ylabels: Training Time per Ben. Failures + rotation : 90 +- file: adv_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: Training Time per Adversarial Failure vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_adv_failure + ylabels: Training time per Adv. Failures + rotation : 90 +- file: adv_failures_per_train_time_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: Training Time per Adversarial Failure vs Attack Type + x: atk_gen + xlabels: Attack Type + y: training_time_per_adv_failure + ylabels: Training time per Adv. Failures + rotation : 90 +- file: adv_accuracy_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: Adversarial Accuracy vs Attack Type + x: atk_gen + xlabels: Attack Type + y: adv_accuracy + ylabels: Adv. Accuracy + rotation : 90 +- file: adv_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: Adversarial Accuracy vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_accuracy + ylabels: Adv. Accuracy + rotation : 90 +- file: adv_accuracy_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: Adversarial Accuracy vs Attack Type + x: atk_gen + xlabels: Attack Type + y: adv_accuracy + ylabels: Adv. Accuracy + rotation : 90 +- file: ben_failure_rate_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: Benign Failure Rate vs Defence Type + x: def_gen + xlabels: Defence Type + y: failure_rate + ylabels: Ben. Failure Rate + rotation : 90 +line_plot: +- control: 0.98 + control_color: orange + file: def_param_vs_accuracy.pdf + hue: def_gen + legend: {} + title: Accuracy vs Defence Strength + x: def_value + x_scale: linear + xlabel: Defence Control Parameter + y: accuracy + y_scale: + ylabel: Accuracy +- control: 0.1 + control_color: orange + file: def_param_vs_adv_accuracy.pdf + hue: def_gen + legend: {} + title: Adversarial Accuracy vs Defence Strength + x: def_value + x_scale: linear + xlabel: Defence Control Parameter + y: adv_accuracy + y_scale: + ylabel: Adv. Accuracy +- control: 0.1 + control_color: orange + file: def_param_vs_adv_failure_rate.pdf + hue: def_gen + legend: {} + title: Adversarial Failure Rate vs Defence Strength + x: def_value + x_scale: linear + xlabel: Defence Control Parameter + y: adv_failure_rate + y_scale: log + ylabel: Adv. Failure Rate +- file: atk_param_vs_accuracy.pdf + hue: atk_gen + legend: {} + title: Adversarial Accuracy vs Attack Strength + x: atk_value + x_scale: linear + xlabel: Attack Control Parameter + y: adv_accuracy + y_scale: + ylabel: Adv. Accuracy +scatter_plot: + x: train_time_per_sample + y: adv_failure_rate + hue: model_name + xlabel: Training Time Per Sample + ylabel: Adversarial Failure Rate + title: Adversarial Failure Rate vs Training Time + file: adv_failure_rate_vs_train_time.pdf + y_scale: log + x_scale: linear + legend: + title: Model Name diff --git a/examples/pytorch_cifar/conf/scorers/default.yaml b/examples/pytorch_cifar/conf/scorers/default.yaml new file mode 100644 index 00000000..4503c5bf --- /dev/null +++ b/examples/pytorch_cifar/conf/scorers/default.yaml @@ -0,0 +1,9 @@ +_target_: deckard.base.scorer.ScorerDict +accuracy: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.accuracy_score + direction: maximize +log_loss: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.log_loss + direction: minimize diff --git a/examples/pytorch_cifar/dvc.lock b/examples/pytorch_cifar/dvc.lock new file mode 100644 index 00000000..fd2d9717 --- /dev/null +++ b/examples/pytorch_cifar/dvc.lock @@ -0,0 +1,340 @@ +schema: '2.0' +stages: + train: + cmd: python -m deckard.layers.experiment train + params: + params.yaml: + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 10 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/data/data.pkl + md5: 6503fed5d4e6cc1163898c0ab6a863dd + size: 739680311 + - path: output/models/model.optimizer.pt + md5: 38d0da3182da5abdb995d83500f63182 + size: 44805933 + - path: output/models/model.pt + md5: ec8ba6ad982b2b30cfea777a11f1f4d1 + size: 44811029 + - path: output/reports/train/default/params.yaml + md5: 320abf8eddbae67cd6873f8d1e3bba81 + size: 2174 + - path: output/reports/train/default/predictions.json + md5: 08f247f7d74d68a215c25a39e0e4226f + size: 2437554 + - path: output/reports/train/default/score_dict.json + md5: 8c43cef7ddfdcb2feb20e486142d2e2c + size: 396 + attack: + cmd: python -m deckard.layers.experiment attack + deps: + - path: output/data/data.pkl + md5: 6503fed5d4e6cc1163898c0ab6a863dd + size: 739680311 + - path: output/models/model.pt + md5: e6a131ce20ef223a15981ee96d03fbeb + size: 44811477 + params: + params.yaml: + attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.attack.AttackInitializer + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 10 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 10 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 10 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/attacks/attack.pkl + md5: 8a2e7d36d50e00240092b3ce94e83e41 + size: 123046 + - path: output/reports/attack/default/adv_predictions.json + md5: 0d78f273866d7556e9c775336e351035 + size: 2149 + - path: output/reports/attack/default/params.yaml + md5: 5177666d7f2e1e55ccad8ab4b735ffc7 + size: 5175 + - path: output/reports/attack/default/score_dict.json + md5: fa598d513bb61a79171960551651a823 + size: 535 diff --git a/examples/pytorch_cifar/dvc.yaml b/examples/pytorch_cifar/dvc.yaml new file mode 100644 index 00000000..f601ad3f --- /dev/null +++ b/examples/pytorch_cifar/dvc.yaml @@ -0,0 +1,77 @@ +stages: + train: + cmd: python -m deckard.layers.experiment train + params: + - data + - model + - scorers + - files + outs: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + metrics: + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + attack: + cmd: python -m deckard.layers.experiment attack + params: + - data + - model + - attack + - scorers + - files + outs: + - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + deps: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + metrics: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + + ############################################################################## + attacks: + foreach: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 + do: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name default.yaml + deps: + - models.sh + - attacks.sh + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + outs: + - ${item}.db + compile: + foreach: + - attack + do: + cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv + deps: + - ${files.directory}/${files.reports}/${item}/ + - ResNet18.db + - ResNet34.db + - ResNet50.db + - ResNet101.db + - ResNet152.db + outs: + - ${files.directory}/${files.reports}/${item}.csv + plot: + cmd : python plots.py --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv + deps: + - ${files.directory}/${files.reports}/attack.csv + - ResNet18.db + - ResNet34.db + - ResNet50.db + - ResNet101.db + - ResNet152.db + outs: + - ${files.directory}/plots/ diff --git a/examples/pytorch_cifar/models.sh b/examples/pytorch_cifar/models.sh new file mode 100644 index 00000000..acad9733 --- /dev/null +++ b/examples/pytorch_cifar/models.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +# This script is used to generate the models for the sklearn example. + +# Default model +echo "python -m deckard.layers.optimise +stage=train" $@ "--multirun" +python -m deckard.layers.optimise +stage=train $@ --multirun + +# This line generates the model and adds the FeatureSqueezing preprocessing defence. +python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train $@ --multirun + +# Gaussian Augmentation (Input) +python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False +stage=train $@ --multirun + +# Gaussian Noise (Output) +python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 +stage=train $@ --multirun + +# High Confidence +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun diff --git a/examples/pytorch_cifar/params.yaml b/examples/pytorch_cifar/params.yaml new file mode 100644 index 00000000..31a81451 --- /dev/null +++ b/examples/pytorch_cifar/params.yaml @@ -0,0 +1,206 @@ +_target_: deckard.base.experiment.Experiment +attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.attack.AttackInitializer + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 10 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 10 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 +data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true +files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json +model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 10 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 +optimizers: accuracy +scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss +stage: null diff --git a/examples/pytorch_cifar/torch_example.py b/examples/pytorch_cifar/torch_example.py new file mode 100644 index 00000000..32d333bd --- /dev/null +++ b/examples/pytorch_cifar/torch_example.py @@ -0,0 +1,79 @@ +import torch.nn as nn +from torchvision import models + +__all__ = [ + "ResNet18", + "ResNet50", + "ResNet101", + "ResNet152", +] + + +def ResNet18(num_channels=1, num_classes=10): + model = models.resnet18(pretrained=True) + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(512, num_classes) + return model + + +def ResNet34(num_channels=1, num_classes=10): + model = models.resnet34(pretrained=True) + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(512, num_classes) + return model + + +def ResNet50(num_channels=1, num_classes=10): + model = models.resnet50(pretrained=True) + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model + + +def ResNet101(num_channels=1, num_classes=10): + model = models.resnet101(pretrained=True) + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model + + +def ResNet152(num_channels=1, num_classes=10): + model = models.resnet152(pretrained=True) + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model From 50e19316dccca024863673d08ec1e1250c0b612c Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 18 Sep 2023 12:50:04 +0000 Subject: [PATCH 076/148] config changes for torch, plots/optimise bug fix --- deckard/layers/compile.py | 82 +++++------ deckard/layers/optimise.py | 2 +- examples/pytorch/attacks.sh | 10 +- examples/pytorch/conf/attack/default.yaml | 2 +- examples/pytorch/conf/cifar.yaml | 34 ----- examples/pytorch/conf/default.yaml | 13 +- examples/pytorch/conf/grid.yaml | 35 ----- examples/pytorch/dvc.lock | 106 +++++++------- examples/pytorch/dvc.yaml | 26 ++-- examples/pytorch/models.sh | 12 +- examples/pytorch/params.yaml | 3 +- examples/pytorch/plots.py | 66 ++++++--- examples/pytorch_cifar/conf/default.yaml | 6 +- examples/pytorch_cifar/dvc.lock | 164 ++++++++++++++++++++-- examples/pytorch_cifar/models.sh | 12 +- 15 files changed, 326 insertions(+), 247 deletions(-) delete mode 100644 examples/pytorch/conf/cifar.yaml delete mode 100644 examples/pytorch/conf/grid.yaml diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 6f364f4a..3a8da180 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -137,53 +137,49 @@ def parse_results(folder, files=["score_dict.json", "params.yaml"]): return df -def format_control_parameter(data, control_dict, min_max=True): - new_data = pd.DataFrame() +def format_control_parameter(data, control_dict): logger.info("Formatting control parameters...") - for _, row in tqdm(data.iterrows()): - if hasattr(row, "defence_name"): - if row.defence_name in ["Control", None, "None", "none", "null", np.nan]: - row["def_param"] = np.nan - row["def_value"] = np.nan - else: - param = control_dict[row.defence_name] - row["def_param"] = param.split(".")[-1] - value = row[param] - row["def_value"] = value - if hasattr(row, "attack"): - if row.attack_name in ["Control", None, "None", "none", "null", np.nan]: - row["atk_param"] = np.nan - row["atk_value"] = np.nan + if hasattr(data, "def_gen"): + defences = data.def_gen.unique() + else: + defences = [] + if hasattr(data, "atk_gen"): + attacks = data.atk_gen.unique() + else: + attacks = [] + for defence in defences: + if defence in ["Control", None, "None", "none", "null", np.nan]: + data.loc[data.def_gen == defence, "def_param"] = np.nan + data.loc[data.def_gen == defence, "def_value"] = np.nan + elif defence in control_dict: + param = control_dict[defence] + data.loc[data.def_gen == defence, "def_param"] = param.split(".")[-1] + if param in data.columns: + value = data[data.def_gen == defence][param] else: - param = control_dict[row.attack_name] - row["atk_param"] = param.split(".")[-1] - value = row[param] - row["atk_value"] = value - new_data = pd.concat([new_data, row], axis=1) - data = new_data.T - del new_data - - if min_max is True: - if hasattr(data, "def_gen"): - defs = data.def_gen.unique() + value = np.nan + data.loc[data.def_gen == defence, "def_value"] = value else: - defs = [] - if hasattr(data, "atk_gen"): - atks = data.atk_gen.unique() + logger.warning(f"Defence {defence} not in control_dict. Deleting rows.") + data = data[data.def_gen != defence] + + for attack in attacks: + if attack in ["Control", None, "None", "none", "null", np.nan]: + data.loc[data.atk_gen == attack, "atk_param"] = np.nan + data.loc[data.atk_gen == attack, "atk_value"] = np.nan + elif attack in control_dict: + param = control_dict[attack] + data.loc[data.atk_gen == attack, "atk_param"] = param.split(".")[-1] + if param in data.columns: + value = data[data.atk_gen == attack][param] + else: + value = np.nan + data.loc[data.atk_gen == attack, "atk_value"] = value else: - atks = [] - # Min-max scaling of control parameters - for def_ in defs: - max_ = data[data.def_gen == def_].def_value.max() - min_ = data[data.def_gen == def_].def_value.min() - scaled_value = (data[data.def_gen == def_].def_value - min_) / (max_ - min_) - data.loc[data.def_gen == def_, "def_value"] = scaled_value + logger.warning(f"Attack {attack} not in control_dict. Deleting rows.") + data = data[data.atk_gen != attack] + - for atk in atks: - max_ = data[data.atk_gen == atk].atk_value.max() - min_ = data[data.atk_gen == atk].atk_value.min() - scaled_value = (data[data.atk_gen == atk].atk_value - min_) / (max_ - min_) - data.loc[data.atk_gen == atk, "atk_value"] = scaled_value return data @@ -212,7 +208,7 @@ def clean_data_for_plotting( data.columns.rename(lambda x: x[:-2] if x.endswith(".1") else x, inplace=True) logger.info("Replacing data.sample.random_state with random_state...") data["data.sample.random_state"].rename("random_state", inplace=True) - data = format_control_parameter(data, control_dict, min_max=True) + data = format_control_parameter(data, control_dict) logger.info(f"Saving data to {Path(folder) / file}") data.to_csv(Path(folder) / "data.csv") return data diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index ea84bb74..722437b1 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -250,7 +250,7 @@ def write_stage(params: dict, stage: str, path=None, working_dir=None) -> None: def optimise(cfg: DictConfig) -> None: - cfg = OmegaConf.to_container(OmegaConf.create(cfg), resolve=True) + cfg = OmegaConf.to_container(OmegaConf.create(cfg), resolve=False) scorer = cfg.pop("optimizers", None) working_dir = cfg.pop("working_dir", Path().resolve().as_posix()) stage = cfg.pop("stage", None) diff --git a/examples/pytorch/attacks.sh b/examples/pytorch/attacks.sh index f593dd8d..6627fc7f 100644 --- a/examples/pytorch/attacks.sh +++ b/examples/pytorch/attacks.sh @@ -3,21 +3,21 @@ # # This script is used to generate the attacks for the example. # # Fast Gradient Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ # # Projected Gradient Descent -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ # # DeepFool -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ # # HopSkipJump -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 ++stage=attack ++hydra.sweeper.study_name=hsj ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 ++stage=attack ++hydra.sweeper.study_name=hsj ++hydra.sweeper.direction=minimize $@ # ##################################################### # PixelAttack -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=pixel ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=pixel ++hydra.sweeper.direction=minimize $@ # ThresholdAttack bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=thresh ++hydra.sweeper.direction=minimize $@ diff --git a/examples/pytorch/conf/attack/default.yaml b/examples/pytorch/conf/attack/default.yaml index 740aa492..76b4d62d 100644 --- a/examples/pytorch/conf/attack/default.yaml +++ b/examples/pytorch/conf/attack/default.yaml @@ -5,5 +5,5 @@ init: model: ${model} _target_: deckard.base.attack.AttackInitializer name: art.attacks.evasion.HopSkipJump -attack_size : 100 +attack_size : 10 method : evasion diff --git a/examples/pytorch/conf/cifar.yaml b/examples/pytorch/conf/cifar.yaml deleted file mode 100644 index 824c51ab..00000000 --- a/examples/pytorch/conf/cifar.yaml +++ /dev/null @@ -1,34 +0,0 @@ -defaults: - - _self_ - - data: torch_cifar - - model: torch_cifar - - attack: default - - files: cifar - - scorers: default - - stage : null - - override hydra/sweeper : optuna - - override hydra/sweeper/sampler : grid - - override hydra/launcher : joblib -_target_ : deckard.base.experiment.Experiment -optimizers : accuracy -hydra: - sweeper: - sampler: - _target_: optuna.samplers.GridSampler - direction: maximize - study_name: model - storage: sqlite:///model.db - n_jobs: 1 - params: - ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) - launcher: - _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 64 - prefer : processes - verbose: 1 - timeout: null - pre_dispatch: n_jobs - batch_size: auto - temp_folder: /tmp/deckard - max_nbytes: 100000 - mmap_mode: r diff --git a/examples/pytorch/conf/default.yaml b/examples/pytorch/conf/default.yaml index 7d43289f..9a9956a0 100644 --- a/examples/pytorch/conf/default.yaml +++ b/examples/pytorch/conf/default.yaml @@ -5,32 +5,29 @@ defaults: - attack: default - files: default - scorers: default + - stage : null - override hydra/sweeper : optuna - - override hydra/sweeper/sampler : random + - override hydra/sweeper/sampler : grid - override hydra/launcher : joblib _target_ : deckard.base.experiment.Experiment optimizers : accuracy -stage : null hydra: - run: - dir : "./${files.directory}" sweeper: sampler: - _target_: optuna.samplers.RandomSampler + _target_: optuna.samplers.GridSampler direction: maximize study_name: model storage: sqlite:///model.db n_jobs: 1 - n_trials: 1 params: ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 64 + n_jobs: 8 prefer : processes verbose: 1 - timeout: 3600 + timeout: null pre_dispatch: n_jobs batch_size: auto temp_folder: /tmp/deckard diff --git a/examples/pytorch/conf/grid.yaml b/examples/pytorch/conf/grid.yaml deleted file mode 100644 index c94c30b1..00000000 --- a/examples/pytorch/conf/grid.yaml +++ /dev/null @@ -1,35 +0,0 @@ -defaults: - - _self_ - - data: torch_mnist - - model: torch_mnist - - attack: default - - files: default - - scorers: default - - stage : null - - override hydra/sweeper : optuna - - override hydra/sweeper/sampler : grid - - override hydra/launcher : joblib -_target_ : deckard.base.experiment.Experiment -optimizers : accuracy -hydra: - sweeper: - sampler: - _target_: optuna.samplers.GridSampler - direction: maximize - study_name: model - storage: sqlite:///model.db - n_jobs: 1 - params: - ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) - ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) - launcher: - _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 64 - prefer : processes - verbose: 1 - timeout: null - pre_dispatch: n_jobs - batch_size: auto - temp_folder: /tmp/deckard - max_nbytes: 100000 - mmap_mode: r diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index 59da997a..6bdfbfd9 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -105,20 +105,20 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.optimizer.pt - md5: e6bcf362a952574d75a3da1f96805f63 + md5: d7e9bcabd3955eb8aacfe089f7ddde3c size: 44780845 - path: output/models/model.pt - md5: 126f743cb1454e749185e3c4252fe500 + md5: 07befc67780200ae3d4c2d628d320b45 size: 44785941 - path: output/reports/train/default/params.yaml md5: eedabd38cbbfce21b879b647497174da size: 2148 - path: output/reports/train/default/predictions.json - md5: e45381a5ae992d2079168b7a136d5486 - size: 2884465 + md5: fe686ff742c5141e69b9b3e71b8c5c08 + size: 2880504 - path: output/reports/train/default/score_dict.json - md5: 012605b723f136e7d5c621c71163bb3f - size: 403 + md5: a25e1709e4e4a47709fdb89d707ee5c8 + size: 409 attack: cmd: python -m deckard.layers.experiment attack deps: @@ -126,13 +126,13 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.pt - md5: e18d078c8eb0c942300498ed45fe1fad - size: 44786389 + md5: f084675f8f4c83a6d9719148e7b6e2ec + size: 44792269 params: params.yaml: attack: _target_: deckard.base.attack.Attack - attack_size: 100 + attack_size: 10 data: _target_: deckard.base.data.Data generate: @@ -356,20 +356,19 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: c3cd5ad573c588dc10064512f220fea2 - size: 313766 + md5: 75905e0fd95bddb6a7a77a3fe5a40eae + size: 31517 - path: output/reports/attack/default/adv_predictions.json - md5: 74195b36b5db3bc6fd4a0a3ecc952774 - size: 21589 + md5: 705daec1ae02298071d99279c5a1c0cd + size: 2154 - path: output/reports/attack/default/params.yaml - md5: 2550051d80574ff4d8769a2946ab5840 - size: 5094 + md5: a15accd2ecf7bcbb5ed47d4b6bf7ed97 + size: 5093 - path: output/reports/attack/default/score_dict.json - md5: fa5a589aaefdad6c4d1abee55252cbaa - size: 538 + md5: 2194644ac065c8f54695942174ad6038 + size: 531 models: - cmd: bash models.sh - ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 + cmd: bash models.sh ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 --config-name grid.yaml deps: - path: output/data/data.pkl @@ -383,8 +382,7 @@ stages: md5: d1eac324650402da6e3de1aebe0e3b3c size: 237568 attacks: - cmd: bash attacks.sh - ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 + cmd: bash attacks.sh ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 ++stage=attack --config-name grid.yaml deps: - path: model.db @@ -446,20 +444,17 @@ stages: cmd: python -m deckard.layers.compile --report_folder output/reports/attack --results_file output/reports/attack.csv deps: - - path: ResNet101.db - md5: eedf8c115666f2d29209399d8691eadf - size: 192512 - path: ResNet152.db - md5: 61ac073bd2baf7ef2c7dcf7d8833bec9 - size: 192512 + md5: 83b8b90979c4a7d8ef0840be0fdc332b + size: 307200 - path: output/reports/attack/ - md5: 631e25806ff0904eff49f7cb76394d7e.dir - size: 9881640443 - nfiles: 20830 + md5: 9139df021e20105790b3d8791f6ce562.dir + size: 10931694829 + nfiles: 22421 outs: - path: output/reports/attack.csv - md5: ceaa24229dcc4703a2092cd5b973718f - size: 14709136 + md5: 6cf92313acc0b8d23fe5a4fa62bc180c + size: 16221649 compile@train: cmd: python -m deckard.layers.compile --report_folder output/reports/train --results_file output/reports/train.csv @@ -480,52 +475,49 @@ stages: size: 363649 attacks@ResNet152: cmd: bash attacks.sh ++model.init.name=torch_example.ResNet152 ++stage=attack - ++hydra.sweeper.storage=sqlite:///ResNet152.db --config-name grid.yaml + ++hydra.sweeper.storage=sqlite:///ResNet152.db --config-name default.yaml deps: - path: attacks.sh - md5: ef2cfda268db8d15a4a0e1a786b04aa1 - size: 3105 + md5: 4c7b931c1ebbd717c18aea456b49342d + size: 3092 - path: models.sh - md5: dd089c4ea9458167c4c754595fc23a12 - size: 1317 + md5: 6e85b74c043a1411e8322a2901557bfa + size: 1445 - path: output/reports/attack/default/score_dict.json - md5: a2fcc0f6754fdfb19810a0a96ce0fcf7 + md5: bc48769b6f3374b3d0bdbb8e74e0bb17 size: 541 outs: - path: ResNet152.db - md5: 61ac073bd2baf7ef2c7dcf7d8833bec9 - size: 192512 + md5: 83b8b90979c4a7d8ef0840be0fdc332b + size: 307200 plot: cmd: python plots.py --path output/plots/ --file output/reports/attack.csv deps: - - path: ResNet101.db - md5: eedf8c115666f2d29209399d8691eadf - size: 192512 - path: ResNet152.db - md5: 61ac073bd2baf7ef2c7dcf7d8833bec9 - size: 192512 + md5: 83b8b90979c4a7d8ef0840be0fdc332b + size: 307200 - path: output/reports/attack.csv - md5: ceaa24229dcc4703a2092cd5b973718f - size: 14709136 + md5: 6cf92313acc0b8d23fe5a4fa62bc180c + size: 16221649 outs: - path: output/plots/ - md5: b98d290a3fd73f774671fe35ccc2f8a8.dir - size: 9775965 + md5: 4d0fc462515445c18bd9d9161d1c27e3.dir + size: 10710869 nfiles: 13 attacks@ResNet101: cmd: bash attacks.sh ++model.init.name=torch_example.ResNet101 ++stage=attack - ++hydra.sweeper.storage=sqlite:///ResNet101.db --config-name grid.yaml + ++hydra.sweeper.storage=sqlite:///ResNet101.db --config-name default.yaml deps: - path: attacks.sh - md5: ef2cfda268db8d15a4a0e1a786b04aa1 - size: 3105 + md5: 4c7b931c1ebbd717c18aea456b49342d + size: 3092 - path: models.sh - md5: dd089c4ea9458167c4c754595fc23a12 - size: 1317 + md5: 6e85b74c043a1411e8322a2901557bfa + size: 1445 - path: output/reports/attack/default/score_dict.json - md5: a2fcc0f6754fdfb19810a0a96ce0fcf7 - size: 541 + md5: 582c594526e57c4887f845b332527d31 + size: 531 outs: - path: ResNet101.db - md5: eedf8c115666f2d29209399d8691eadf - size: 192512 + md5: cab812b983a79b91be312bfc55e03acc + size: 241664 diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index 99a4694b..1e30d03e 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -38,12 +38,12 @@ stages: attacks: foreach: # - ResNet18 - # - ResNet34 - # - ResNet50 + - ResNet34 + - ResNet50 # - ResNet101 - - ResNet152 + # - ResNet152 do: - cmd: bash attacks.sh ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name grid.yaml + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name default.yaml deps: - models.sh - attacks.sh @@ -57,10 +57,10 @@ stages: cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv deps: - ${files.directory}/${files.reports}/${item}/ - # - ResNet18.db - # - ResNet34.db - # - ResNet50.db - # - ResNet101.db + - ResNet18.db + - ResNet34.db + - ResNet50.db + - ResNet101.db - ResNet152.db outs: - ${files.directory}/${files.reports}/${item}.csv @@ -68,10 +68,10 @@ stages: cmd : python plots.py --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv deps: - ${files.directory}/${files.reports}/attack.csv - # - ResNet18.db - # - ResNet34.db - # - ResNet50.db - # - ResNet101.db - - ResNet152.db + - ResNet18.db + - ResNet34.db + - ResNet50.db + - ResNet101.db + # - ResNet152.db outs: - ${files.directory}/plots/ diff --git a/examples/pytorch/models.sh b/examples/pytorch/models.sh index ba2b1a62..80aec311 100644 --- a/examples/pytorch/models.sh +++ b/examples/pytorch/models.sh @@ -3,17 +3,17 @@ # This script is used to generate the models for the sklearn example. # # Default model -# echo "python -m deckard.layers.optimise +stage=train" $@ "--multirun" -# python -m deckard.layers.optimise +stage=train $@ --multirun +echo "CUDA_VISIBLE_DEVICES=1 python -m deckard.layers.optimise +stage=train" $@ "--multirun" +CUDA_VISIBLE_DEVICES=1 python -m deckard.layers.optimise +stage=train $@ --multirun # # This line generates the model and adds the FeatureSqueezing preprocessing defence. -# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train $@ --multirun +CUDA_VISIBLE_DEVICES=1 python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train $@ --multirun # # Gaussian Augmentation (Input) -# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False +stage=train $@ --multirun +CUDA_VISIBLE_DEVICES=1 python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False +stage=train $@ --multirun # # # Gaussian Noise (Output) -python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 +stage=train $@ --multirun +CUDA_VISIBLE_DEVICES=1 python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 +stage=train $@ --multirun # # # High Confidence -# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun +CUDA_VISIBLE_DEVICES=1 python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun diff --git a/examples/pytorch/params.yaml b/examples/pytorch/params.yaml index 27c98d51..26648398 100644 --- a/examples/pytorch/params.yaml +++ b/examples/pytorch/params.yaml @@ -1,7 +1,7 @@ _target_: deckard.base.experiment.Experiment attack: _target_: deckard.base.attack.Attack - attack_size: 100 + attack_size: 10 data: _target_: deckard.base.data.Data generate: @@ -224,4 +224,3 @@ scorers: _target_: deckard.base.scorer.ScorerConfig direction: minimize name: sklearn.metrics.log_loss -stage: null diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index d68b41b9..3aeba9f5 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -65,7 +65,7 @@ def line_plot( control_color=None, ): plt.gcf().clear() - graph = sns.lineplot(data=data, x=x, y=y, hue=hue, style=control) + graph = sns.lineplot(data=data, x=x, y=y, hue=hue) graph.legend(**legend) # if control is not None: # assert control_color is not None, "Please specify a control color" @@ -116,21 +116,13 @@ def scatter_plot( plt.gcf().clear() return graph +def drop_frames_without_results( data, subset = ["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"]): + data.dropna(axis=0, subset=subset, inplace=True) + return data def calculate_failure_rate(data): data = data[data.columns.drop(list(data.filter(regex=r"\.1$")))] data.columns.str.replace(" ", "") - data.dropna( - axis=0, - subset=[ - "accuracy", - "adv_accuracy", - "train_time", - "adv_fit_time", - "predict_time", - ], - inplace=True, - ) data.loc[:, "failure_rate"] = ( (1 - data["accuracy"]) * 100 / data["predict_time"] ) @@ -138,16 +130,39 @@ def calculate_failure_rate(data): (1 - data["adv_accuracy"]) * 100 / data["adv_fit_time"] ) data.loc[:, "training_time_per_failure"] = ( - data["train_time_per_sample"] / data["failure_rate"] + data["train_time"] / data["failure_rate"] ) data.loc[:, "training_time_per_adv_failure"] = ( - data["train_time_per_sample"] / data["adv_failure_rate"] + data["train_time"] / data["adv_failure_rate"] ) data.loc[:, "adv_training_time_per_failure"] = ( - data["train_time_per_sample"] / data["adv_failure_rate"] + data["train_time"] / data["adv_failure_rate"] ) return data +def min_max_scaling(data): + if "atk_gen" not in data.columns: + attacks = [] + else: + attacks = data.atk_gen.unique() + if "def_gen" not in data.columns: + defences = [] + else: + defences = data.def_gen.unique() + # Min-max scaling of control parameters + for def_ in defences: + max_ = data[data.def_gen == def_].def_value.max() + min_ = data[data.def_gen == def_].def_value.min() + scaled_value = (data[data.def_gen == def_].def_value - min_) / (max_ - min_) + data.loc[data.def_gen == def_, "def_value"] = scaled_value + + for atk in attacks: + max_ = data[data.atk_gen == atk].atk_value.max() + min_ = data[data.atk_gen == atk].atk_value.min() + scaled_value = (data[data.atk_gen == atk].atk_value - min_) / (max_ - min_) + data.loc[data.atk_gen == atk, "atk_value"] = scaled_value + return data + if __name__ == "__main__": parser = argparse.ArgumentParser() @@ -191,6 +206,7 @@ def calculate_failure_rate(data): help="Path to the config file", default="conf/plots.yaml", ) + parser.add_argument("-d", "--drop_these", nargs="+", help="Columns to drop", default=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"]) args = parser.parse_args() logging.basicConfig(level=args.verbosity) # %% @@ -199,16 +215,22 @@ def calculate_failure_rate(data): ).exists(), f"File {args.file} does not exist. Please specify a valid file using the -f flag." csv_file = args.file data = pd.read_csv(csv_file) + data = drop_frames_without_results(data, subset=) data = calculate_failure_rate(data) + data = min_max_scaling(data) + # Drop replicated index column if it exists if "Unnamed: 0" in data.columns: data.drop("Unnamed: 0", axis=1, inplace=True) + # Absolute/relative path handling if Path(args.path).absolute().exists(): logger.info("Absolute path specified") FOLDER = Path(args.path).absolute() else: logger.info("Relative path specified") FOLDER = Path(Path(), args.path) + # Create folder if it does not exist FOLDER.mkdir(parents=True, exist_ok=True) + # Saves cleaned data to csv data.to_csv(FOLDER / args.output) IMAGE_FILETYPE = ( args.plotfiletype @@ -224,14 +246,14 @@ def calculate_failure_rate(data): # Reads Config file with open(Path(args.config), "r") as f: big_dict = yaml.load(f, Loader=yaml.FullLoader) - # %% + # Catetory plot cat_plot_list = big_dict["cat_plot"] i = 0 for dict_ in cat_plot_list: i += 1 logger.info(f"Rendering graph {i}") locals()[f"graph{i}"] = cat_plot(data, **dict_, folder=FOLDER) - # %% + # Line plot line_plot_list = big_dict["line_plot"] for dict_ in line_plot_list: i += 1 @@ -239,7 +261,9 @@ def calculate_failure_rate(data): locals()[f"graph{i}"] = line_plot(data, **dict_, folder=FOLDER) # %% - - scatter_plot_dict = big_dict["scatter_plot"] - - graph = scatter_plot(data=data, **scatter_plot_dict, folder=FOLDER) + # Scatter plot + scatter_plot_list = big_dict["scatter_plot"] + for dict_ in scatter_plot_list: + i += 1 + logger.info(f"Rendering graph {i}") + locals()[f"graph{i}"] = scatter_plot(data, **dict_, folder=FOLDER) diff --git a/examples/pytorch_cifar/conf/default.yaml b/examples/pytorch_cifar/conf/default.yaml index 1b04b6cc..69a1ea6f 100644 --- a/examples/pytorch_cifar/conf/default.yaml +++ b/examples/pytorch_cifar/conf/default.yaml @@ -13,7 +13,7 @@ optimizers : accuracy stage : null hydra: run: - dir : "./${files.directory}" + dir : ./${files.directory} sweeper: sampler: _target_: optuna.samplers.RandomSampler @@ -21,13 +21,13 @@ hydra: study_name: model storage: sqlite:///model.db n_jobs: 1 - n_trials: 80 + n_trials: 100 params: ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 64 + n_jobs: 10 prefer : processes verbose: 1 timeout: 3600 diff --git a/examples/pytorch_cifar/dvc.lock b/examples/pytorch_cifar/dvc.lock index fd2d9717..c600ebfe 100644 --- a/examples/pytorch_cifar/dvc.lock +++ b/examples/pytorch_cifar/dvc.lock @@ -97,20 +97,20 @@ stages: md5: 6503fed5d4e6cc1163898c0ab6a863dd size: 739680311 - path: output/models/model.optimizer.pt - md5: 38d0da3182da5abdb995d83500f63182 + md5: 3f8f2a52c402ec4def8f0f7f1cf65286 size: 44805933 - path: output/models/model.pt - md5: ec8ba6ad982b2b30cfea777a11f1f4d1 + md5: dfa2ec23447385630a80933ec686df2a size: 44811029 - path: output/reports/train/default/params.yaml md5: 320abf8eddbae67cd6873f8d1e3bba81 size: 2174 - path: output/reports/train/default/predictions.json - md5: 08f247f7d74d68a215c25a39e0e4226f - size: 2437554 + md5: dd166fd83b6f32eae15104afcfb189f3 + size: 2439636 - path: output/reports/train/default/score_dict.json - md5: 8c43cef7ddfdcb2feb20e486142d2e2c - size: 396 + md5: efc0fd751599c5cdcb1904b54e2d7dda + size: 393 attack: cmd: python -m deckard.layers.experiment attack deps: @@ -118,7 +118,7 @@ stages: md5: 6503fed5d4e6cc1163898c0ab6a863dd size: 739680311 - path: output/models/model.pt - md5: e6a131ce20ef223a15981ee96d03fbeb + md5: ac52d92e67a6df07a4428bc65904b781 size: 44811477 params: params.yaml: @@ -327,14 +327,154 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: 8a2e7d36d50e00240092b3ce94e83e41 + md5: 0407a11ab4185a2be8662bd5a1774509 size: 123046 - path: output/reports/attack/default/adv_predictions.json - md5: 0d78f273866d7556e9c775336e351035 - size: 2149 + md5: 8cbce9b206806a8b5dffad8b3554ea9d + size: 2123 - path: output/reports/attack/default/params.yaml md5: 5177666d7f2e1e55ccad8ab4b735ffc7 size: 5175 - path: output/reports/attack/default/score_dict.json - md5: fa598d513bb61a79171960551651a823 - size: 535 + md5: bd2742460f844eebe8bd411aa8ebd5de + size: 530 + attacks@ResNet18: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 + ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet18.db --config-name default.yaml + deps: + - path: attacks.sh + md5: 2fbda0d22c03c74392639ffa29319023 + size: 3080 + - path: models.sh + md5: cd924674997af182b42d07a08bbc9034 + size: 1431 + - path: output/reports/attack/default/score_dict.json + md5: 81a8dad1861acf8a1c969c9d41a754a5 + size: 513 + outs: + - path: ResNet18.db + md5: 57be573b6c7b51c66ed3d7dcafd0f57d + size: 806912 + attacks@ResNet34: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 + ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet34.db --config-name default.yaml + deps: + - path: attacks.sh + md5: 2fbda0d22c03c74392639ffa29319023 + size: 3080 + - path: models.sh + md5: cd924674997af182b42d07a08bbc9034 + size: 1431 + - path: output/reports/attack/default/score_dict.json + md5: 81a8dad1861acf8a1c969c9d41a754a5 + size: 513 + outs: + - path: ResNet34.db + md5: d6d507b04290fe7563e30364c226e645 + size: 606208 + attacks@ResNet50: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 + ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet50.db --config-name default.yaml + deps: + - path: attacks.sh + md5: 2fbda0d22c03c74392639ffa29319023 + size: 3080 + - path: models.sh + md5: cd924674997af182b42d07a08bbc9034 + size: 1431 + - path: output/reports/attack/default/score_dict.json + md5: 81a8dad1861acf8a1c969c9d41a754a5 + size: 513 + outs: + - path: ResNet50.db + md5: 040adb3012b1bcb1343da5610a9a93f9 + size: 610304 + attacks@ResNet101: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet101 + ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet101.db --config-name + default.yaml + deps: + - path: attacks.sh + md5: 2fbda0d22c03c74392639ffa29319023 + size: 3080 + - path: models.sh + md5: cd924674997af182b42d07a08bbc9034 + size: 1431 + - path: output/reports/attack/default/score_dict.json + md5: 81a8dad1861acf8a1c969c9d41a754a5 + size: 513 + outs: + - path: ResNet101.db + md5: c4d01a0911b988bb77e03fb1b3d83518 + size: 5042176 + attacks@ResNet152: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet152 + ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet152.db --config-name + default.yaml + deps: + - path: attacks.sh + md5: 2fbda0d22c03c74392639ffa29319023 + size: 3080 + - path: models.sh + md5: cd924674997af182b42d07a08bbc9034 + size: 1431 + - path: output/reports/attack/default/score_dict.json + md5: 81a8dad1861acf8a1c969c9d41a754a5 + size: 513 + outs: + - path: ResNet152.db + md5: 090cfe4e13e708e261a19427b2c86c0d + size: 323584 + compile@attack: + cmd: python -m deckard.layers.compile --report_folder output/reports/attack --results_file + output/reports/attack.csv + deps: + - path: ResNet101.db + md5: c4d01a0911b988bb77e03fb1b3d83518 + size: 5042176 + - path: ResNet152.db + md5: 090cfe4e13e708e261a19427b2c86c0d + size: 323584 + - path: ResNet18.db + md5: 57be573b6c7b51c66ed3d7dcafd0f57d + size: 806912 + - path: ResNet34.db + md5: d6d507b04290fe7563e30364c226e645 + size: 606208 + - path: ResNet50.db + md5: 040adb3012b1bcb1343da5610a9a93f9 + size: 610304 + - path: output/reports/attack/ + md5: 64814ed2e90d907802c9aa74f86d236d.dir + size: 1833185741 + nfiles: 14069 + outs: + - path: output/reports/attack.csv + md5: 11376e233256f1680a55688a5324b335 + size: 10605097 + plot: + cmd: python plots.py --path output/plots/ --file output/reports/attack.csv + deps: + - path: ResNet101.db + md5: c4d01a0911b988bb77e03fb1b3d83518 + size: 5042176 + - path: ResNet152.db + md5: 090cfe4e13e708e261a19427b2c86c0d + size: 323584 + - path: ResNet18.db + md5: 57be573b6c7b51c66ed3d7dcafd0f57d + size: 806912 + - path: ResNet34.db + md5: d6d507b04290fe7563e30364c226e645 + size: 606208 + - path: ResNet50.db + md5: 040adb3012b1bcb1343da5610a9a93f9 + size: 610304 + - path: output/reports/attack.csv + md5: 11376e233256f1680a55688a5324b335 + size: 10605097 + outs: + - path: output/plots/ + md5: 04e94bd226b7a6f9e4d05a9b63083fc1.dir + size: 2396615 + nfiles: 13 diff --git a/examples/pytorch_cifar/models.sh b/examples/pytorch_cifar/models.sh index acad9733..60eaf327 100644 --- a/examples/pytorch_cifar/models.sh +++ b/examples/pytorch_cifar/models.sh @@ -3,17 +3,17 @@ # This script is used to generate the models for the sklearn example. # Default model -echo "python -m deckard.layers.optimise +stage=train" $@ "--multirun" -python -m deckard.layers.optimise +stage=train $@ --multirun +echo "CUDA_VISIBLE_DEVICES=0 python -m deckard.layers.optimise +stage=train" $@ "--multirun" +CUDA_VISIBLE_DEVICES=0 python -m deckard.layers.optimise +stage=train $@ --multirun # This line generates the model and adds the FeatureSqueezing preprocessing defence. -python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train $@ --multirun +CUDA_VISIBLE_DEVICES=0 python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train $@ --multirun # Gaussian Augmentation (Input) -python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False +stage=train $@ --multirun +CUDA_VISIBLE_DEVICES=0 python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False +stage=train $@ --multirun # Gaussian Noise (Output) -python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 +stage=train $@ --multirun +CUDA_VISIBLE_DEVICES=0 python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 +stage=train $@ --multirun # High Confidence -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun +CUDA_VISIBLE_DEVICES=0 python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun From f5bd008caa06daa5dacbe49d486a93f259af625c Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 18 Sep 2023 12:53:40 +0000 Subject: [PATCH 077/148] shorten param file --- deckard/layers/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deckard/layers/utils.py b/deckard/layers/utils.py index 87057484..77ce6b32 100644 --- a/deckard/layers/utils.py +++ b/deckard/layers/utils.py @@ -110,7 +110,7 @@ def compose_experiment(file, config_dir, overrides=None, default_file="default.y cfg = compose(config_name=Path(default_file).stem, overrides=overrides) except OverrideParseException: raise ValueError(f"Failed to parse overrides: {overrides}") - cfg = OmegaConf.to_container(cfg, resolve=True) + cfg = OmegaConf.to_container(cfg, resolve=False) cfg["_target_"] = "deckard.Experiment" id_ = str(my_hash(cfg)) cfg["name"] = id_ From 5f26154cd41d14fa2839290cb851e774f97b5f95 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 18 Sep 2023 17:31:55 +0000 Subject: [PATCH 078/148] fixed optimise bug, reduce cifar runtime --- deckard/layers/optimise.py | 2 +- examples/pytorch/plots.py | 4 +- examples/pytorch_cifar/conf/default.yaml | 5 +- examples/pytorch_cifar/dvc.yaml | 12 +- examples/pytorch_cifar/models.sh | 12 +- examples/pytorch_cifar/plots.py | 261 +++++++++++++++++++++++ 6 files changed, 278 insertions(+), 18 deletions(-) create mode 100644 examples/pytorch_cifar/plots.py diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index 722437b1..42607443 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -83,7 +83,7 @@ def merge_params(default, params) -> dict: :return: Merged params """ for key, value in params.items(): - if key in default and isinstance(default[key], dict) and value is not None: + if key in default and isinstance(value, dict) and value is not None: default[key] = merge_params(default[key], value) elif ( isinstance(value, (list, tuple, int, float, str, bool, dict)) diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index 3aeba9f5..8a173327 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -206,7 +206,7 @@ def min_max_scaling(data): help="Path to the config file", default="conf/plots.yaml", ) - parser.add_argument("-d", "--drop_these", nargs="+", help="Columns to drop", default=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"]) + parser.add_argument("-d", "--drop_these_if_missing", nargs="+", help="Columns to drop", default=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"]) args = parser.parse_args() logging.basicConfig(level=args.verbosity) # %% @@ -215,7 +215,7 @@ def min_max_scaling(data): ).exists(), f"File {args.file} does not exist. Please specify a valid file using the -f flag." csv_file = args.file data = pd.read_csv(csv_file) - data = drop_frames_without_results(data, subset=) + data = drop_frames_without_results(data, subset=args.drop_these_if_missing) data = calculate_failure_rate(data) data = min_max_scaling(data) # Drop replicated index column if it exists diff --git a/examples/pytorch_cifar/conf/default.yaml b/examples/pytorch_cifar/conf/default.yaml index 69a1ea6f..7cadfda8 100644 --- a/examples/pytorch_cifar/conf/default.yaml +++ b/examples/pytorch_cifar/conf/default.yaml @@ -6,7 +6,7 @@ defaults: - files: default - scorers: default - override hydra/sweeper : optuna - - override hydra/sweeper/sampler : random + - override hydra/sweeper/sampler : grid - override hydra/launcher : joblib _target_ : deckard.base.experiment.Experiment optimizers : accuracy @@ -16,12 +16,11 @@ hydra: dir : ./${files.directory} sweeper: sampler: - _target_: optuna.samplers.RandomSampler + _target_: optuna.samplers.GridSampler direction: maximize study_name: model storage: sqlite:///model.db n_jobs: 1 - n_trials: 100 params: ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) diff --git a/examples/pytorch_cifar/dvc.yaml b/examples/pytorch_cifar/dvc.yaml index f601ad3f..fd1dfd61 100644 --- a/examples/pytorch_cifar/dvc.yaml +++ b/examples/pytorch_cifar/dvc.yaml @@ -38,9 +38,9 @@ stages: attacks: foreach: - ResNet18 - - ResNet34 + # - ResNet34 - ResNet50 - - ResNet101 + # - ResNet101 - ResNet152 do: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name default.yaml @@ -58,9 +58,9 @@ stages: deps: - ${files.directory}/${files.reports}/${item}/ - ResNet18.db - - ResNet34.db + # - ResNet34.db - ResNet50.db - - ResNet101.db + # - ResNet101.db - ResNet152.db outs: - ${files.directory}/${files.reports}/${item}.csv @@ -69,9 +69,9 @@ stages: deps: - ${files.directory}/${files.reports}/attack.csv - ResNet18.db - - ResNet34.db + # - ResNet34.db - ResNet50.db - - ResNet101.db + # - ResNet101.db - ResNet152.db outs: - ${files.directory}/plots/ diff --git a/examples/pytorch_cifar/models.sh b/examples/pytorch_cifar/models.sh index 60eaf327..acad9733 100644 --- a/examples/pytorch_cifar/models.sh +++ b/examples/pytorch_cifar/models.sh @@ -3,17 +3,17 @@ # This script is used to generate the models for the sklearn example. # Default model -echo "CUDA_VISIBLE_DEVICES=0 python -m deckard.layers.optimise +stage=train" $@ "--multirun" -CUDA_VISIBLE_DEVICES=0 python -m deckard.layers.optimise +stage=train $@ --multirun +echo "python -m deckard.layers.optimise +stage=train" $@ "--multirun" +python -m deckard.layers.optimise +stage=train $@ --multirun # This line generates the model and adds the FeatureSqueezing preprocessing defence. -CUDA_VISIBLE_DEVICES=0 python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train $@ --multirun +python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train $@ --multirun # Gaussian Augmentation (Input) -CUDA_VISIBLE_DEVICES=0 python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False +stage=train $@ --multirun +python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False +stage=train $@ --multirun # Gaussian Noise (Output) -CUDA_VISIBLE_DEVICES=0 python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 +stage=train $@ --multirun +python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 +stage=train $@ --multirun # High Confidence -CUDA_VISIBLE_DEVICES=0 python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun diff --git a/examples/pytorch_cifar/plots.py b/examples/pytorch_cifar/plots.py new file mode 100644 index 00000000..89995ba3 --- /dev/null +++ b/examples/pytorch_cifar/plots.py @@ -0,0 +1,261 @@ +# %% [markdown] +# # Dependencies + +# %% + +import argparse +import logging +from pathlib import Path + +import matplotlib.pyplot as plt +import pandas as pd +import seaborn as sns +import yaml + + +logger = logging.getLogger(__name__) +sns.set_theme(style="whitegrid") +sns.set_context("paper", font_scale=1.5) + + +def cat_plot( + data, + x, + y, + hue, + kind, + titles, + xlabels, + ylabels, + legend_title, + file, + folder, + rotation=0, + set={}, + **kwargs, +): + plt.gcf().clear() + graph = sns.catplot(data=data, x=x, y=y, hue=hue, kind=kind, **kwargs) + graph.set_xlabels(xlabels) + graph.set_ylabels(ylabels) + graph.set_titles(titles) + graph.legend.set_title(title=legend_title) + graph.set_xticklabels(graph.axes.flat[-1].get_xticklabels(), rotation=rotation) + graph.set(**set) + graph.tight_layout() + graph.savefig(folder / file) + plt.gcf().clear() + logger.info(f"Saved graph to {folder / file}") + + +def line_plot( + data, + x, + y, + hue, + xlabel, + ylabel, + title, + file, + folder, + y_scale=None, + x_scale=None, + legend={}, + **kwargs, +): + plt.gcf().clear() + graph = sns.lineplot(data=data, x=x, y=y, hue=hue) + graph.legend(**legend) + # if control is not None: + # assert control_color is not None, "Please specify a control color" + # graph.add_line(plt.axhline(y=control, color=control_color, linestyle="-")) + graph.set_xlabel(xlabel) + graph.set_ylabel(ylabel) + graph.set_title(title) + if y_scale is not None: + graph.set_yscale(y_scale) + if x_scale is not None: + graph.set_xscale(x_scale) + graph.get_figure().tight_layout() + graph.get_figure().savefig(folder / file) + plt.gcf().clear() + return graph + + +def scatter_plot( + data, + x, + y, + hue, + xlabel, + ylabel, + title, + file, + folder, + y_scale=None, + x_scale=None, + legend={}, +): + graph = sns.scatterplot( + data=data, + x=x, + y=y, + hue=hue, + ) + graph.set_yscale(y_scale) + graph.set_xscale(x_scale) + graph.set_xlabel(xlabel) + graph.set_ylabel(ylabel) + graph.legend(**legend) + graph.set_title(title) + graph.get_figure().tight_layout() + graph.get_figure().savefig(Path(folder) / file) + logger.info(f"Rendering graph {i+1}") + logger.info(f"Saved graph to {Path(folder) / file}") + plt.gcf().clear() + return graph + +def drop_frames_without_results( data, subset = ["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"]): + data.dropna(axis=0, subset=subset, inplace=True) + return data + +def calculate_failure_rate(data): + data = data[data.columns.drop(list(data.filter(regex=r"\.1$")))] + data.columns.str.replace(" ", "") + data.loc[:, "failure_rate"] = ( + (1 - data.loc[:, "accuracy"]) * 100 / data.loc[:,"predict_time"] + ) + data.loc[:, "adv_failure_rate"] = ( + (1 - data.loc[:,"adv_accuracy"]) * 100 / data.loc[:,"adv_fit_time"] + ) + data.loc[:, "training_time_per_failure"] = ( + data.loc[:,"train_time"] / data.loc[:,"failure_rate"] + ) + data.loc[:, "training_time_per_adv_failure"] = ( + data.loc[:,"train_time"] / data.loc[:,"adv_failure_rate"] + ) + data.loc[:, "adv_training_time_per_failure"] = ( + data.loc[:,"train_time"] / data.loc[:,"adv_failure_rate"] + ) + return data + +def min_max_scaling(data): + if "atk_gen" not in data.columns: + attacks = [] + else: + attacks = data.atk_gen.unique() + if "def_gen" not in data.columns: + defences = [] + else: + defences = data.def_gen.unique() + # Min-max scaling of control parameters + for def_ in defences: + max_ = data[data.def_gen == def_].def_value.max() + min_ = data[data.def_gen == def_].def_value.min() + scaled_value = (data[data.def_gen == def_].def_value - min_) / (max_ - min_) + data.loc[data.def_gen == def_, "def_value"] = scaled_value + + for atk in attacks: + max_ = data[data.atk_gen == atk].atk_value.max() + min_ = data[data.atk_gen == atk].atk_value.min() + scaled_value = (data[data.atk_gen == atk].atk_value - min_) / (max_ - min_) + data.loc[data.atk_gen == atk, "atk_value"] = scaled_value + return data + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "-p", + "--path", + type=str, + help="Path to the plot folder", + required=True, + ) + parser.add_argument( + "-f", + "--file", + type=str, + help="Data file to read from", + required=True, + ) + parser.add_argument( + "-o", + "--output", + type=str, + help="Output file name", + default="data.csv", + ) + parser.add_argument( + "-t", + "--plotfiletype", + type=str, + help="Filetype of the plots", + default=".pdf", + ) + parser.add_argument( + "-v", + "--verbosity", + default="INFO", + help="Increase output verbosity", + ) + parser.add_argument( + "-c", + "--config", + help="Path to the config file", + default="conf/plots.yaml", + ) + args = parser.parse_args() + logging.basicConfig(level=args.verbosity) + # %% + assert Path( + args.file, + ).exists(), f"File {args.file} does not exist. Please specify a valid file using the -f flag." + csv_file = args.file + data = pd.read_csv(csv_file) + data = drop_frames_without_results(data, subset=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"]) + data = calculate_failure_rate(data) + data = min_max_scaling(data) + if "Unnamed: 0" in data.columns: + data.drop("Unnamed: 0", axis=1, inplace=True) + if Path(args.path).absolute().exists(): + logger.info("Absolute path specified") + FOLDER = Path(args.path).absolute() + else: + logger.info("Relative path specified") + FOLDER = Path(Path(), args.path) + FOLDER.mkdir(parents=True, exist_ok=True) + data.to_csv(FOLDER / args.output) + IMAGE_FILETYPE = ( + args.plotfiletype + if args.plotfiletype.startswith(".") + else f".{args.plotfiletype}" + ) + if Path(FOLDER).exists(): + pass + else: + logger.info(f"Creating folder {FOLDER}") + FOLDER.mkdir(parents=True, exist_ok=True) + + # Reads Config file + with open(Path(args.config), "r") as f: + big_dict = yaml.load(f, Loader=yaml.FullLoader) + # %% + cat_plot_list = big_dict["cat_plot"] + i = 0 + for dict_ in cat_plot_list: + i += 1 + logger.info(f"Rendering graph {i}") + locals()[f"graph{i}"] = cat_plot(data, **dict_, folder=FOLDER) + # %% + line_plot_list = big_dict["line_plot"] + for dict_ in line_plot_list: + i += 1 + logger.info(f"Rendering graph {i}") + locals()[f"graph{i}"] = line_plot(data, **dict_, folder=FOLDER) + + # %% + + scatter_plot_dict = big_dict["scatter_plot"] + + graph = scatter_plot(data=data, **scatter_plot_dict, folder=FOLDER) From d1b158f6b7f402fda3d373565c5b4400476edccd Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 19 Sep 2023 15:35:46 +0000 Subject: [PATCH 079/148] update plots --- examples/pytorch/conf/plots.yaml | 101 ++++++++++++++++++- examples/pytorch/dvc.lock | 44 ++++++++- examples/pytorch/plots.py | 43 ++++----- examples/pytorch_cifar/conf/plots.yaml | 2 +- examples/pytorch_cifar/dvc.lock | 128 ++++++++++++------------- examples/pytorch_cifar/plots.py | 17 +--- 6 files changed, 223 insertions(+), 112 deletions(-) diff --git a/examples/pytorch/conf/plots.yaml b/examples/pytorch/conf/plots.yaml index 7a53497f..bc72b0c6 100644 --- a/examples/pytorch/conf/plots.yaml +++ b/examples/pytorch/conf/plots.yaml @@ -11,6 +11,12 @@ cat_plot: y: adv_accuracy ylabels: Adv. Accuracy rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 - file: ben_accuracy_vs_defence_type.pdf hue: model_name kind: boxen @@ -21,6 +27,12 @@ cat_plot: y: accuracy ylabels: Ben. Accuracy rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 - file: ben_failures_per_train_time_vs_defence_type.pdf hue: model_name kind: boxen @@ -33,6 +45,12 @@ cat_plot: y: training_time_per_failure ylabels: Training Time per Ben. Failures rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 - file: adv_failures_per_train_time_vs_defence_type.pdf hue: model_name kind: boxen @@ -45,6 +63,12 @@ cat_plot: y: training_time_per_adv_failure ylabels: Training time per Adv. Failures rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 - file: adv_failures_per_train_time_vs_attack_type.pdf hue: model_name kind: boxen @@ -57,6 +81,12 @@ cat_plot: y: training_time_per_adv_failure ylabels: Training time per Adv. Failures rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 - file: adv_accuracy_vs_attack_type.pdf hue: model_name kind: boxen @@ -67,6 +97,12 @@ cat_plot: y: adv_accuracy ylabels: Adv. Accuracy rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 - file: adv_accuracy_vs_defence_type.pdf hue: model_name kind: boxen @@ -77,6 +113,12 @@ cat_plot: y: adv_accuracy ylabels: Adv. Accuracy rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 - file: adv_accuracy_vs_attack_type.pdf hue: model_name kind: boxen @@ -87,6 +129,12 @@ cat_plot: y: adv_accuracy ylabels: Adv. Accuracy rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 - file: ben_failure_rate_vs_defence_type.pdf hue: model_name kind: boxen @@ -99,6 +147,12 @@ cat_plot: y: failure_rate ylabels: Ben. Failure Rate rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 line_plot: - control: 0.98 control_color: orange @@ -112,6 +166,12 @@ line_plot: y: accuracy y_scale: ylabel: Accuracy + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 - control: 0.1 control_color: orange file: def_param_vs_adv_accuracy.pdf @@ -124,6 +184,12 @@ line_plot: y: adv_accuracy y_scale: ylabel: Adv. Accuracy + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 - control: 0.1 control_color: orange file: def_param_vs_adv_failure_rate.pdf @@ -136,6 +202,12 @@ line_plot: y: adv_failure_rate y_scale: log ylabel: Adv. Failure Rate + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 - file: atk_param_vs_accuracy.pdf hue: atk_gen legend: {} @@ -146,8 +218,14 @@ line_plot: y: adv_accuracy y_scale: ylabel: Adv. Accuracy + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 scatter_plot: - x: train_time_per_sample +- x: train_time_per_sample y: adv_failure_rate hue: model_name xlabel: Training Time Per Sample @@ -155,6 +233,25 @@ scatter_plot: title: Adversarial Failure Rate vs Training Time file: adv_failure_rate_vs_train_time.pdf y_scale: log - x_scale: linear + x_scale: log legend: title: Model Name + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 + # style : atk_gen +# - x: train_time_per_sample +# y: adv_failure_rate +# hue: model_name +# xlabel: Training Time Per Sample +# ylabel: Adversarial Failure Rate +# title: Adversarial Failure Rate vs Training Time for each Defence +# file: adv_failure_rate_vs_train_time_def.pdf +# y_scale: log +# x_scale: linear +# legend: +# title: Model Name +# # style : def_gen diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index 6bdfbfd9..58158d57 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -356,17 +356,17 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: 75905e0fd95bddb6a7a77a3fe5a40eae + md5: 7cb4b43454f9798ea052552728be5535 size: 31517 - path: output/reports/attack/default/adv_predictions.json - md5: 705daec1ae02298071d99279c5a1c0cd - size: 2154 + md5: 6fa49c42a6d07052ed155be54c635e49 + size: 2162 - path: output/reports/attack/default/params.yaml md5: a15accd2ecf7bcbb5ed47d4b6bf7ed97 size: 5093 - path: output/reports/attack/default/score_dict.json - md5: 2194644ac065c8f54695942174ad6038 - size: 531 + md5: 1e826f977a24134fac3fef63a9b95635 + size: 528 models: cmd: bash models.sh ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 --config-name grid.yaml @@ -521,3 +521,37 @@ stages: - path: ResNet101.db md5: cab812b983a79b91be312bfc55e03acc size: 241664 + attacks@ResNet34: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 + ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet34.db --config-name default.yaml + deps: + - path: attacks.sh + md5: 4c7b931c1ebbd717c18aea456b49342d + size: 3092 + - path: models.sh + md5: 6e85b74c043a1411e8322a2901557bfa + size: 1445 + - path: output/reports/attack/default/score_dict.json + md5: 1e826f977a24134fac3fef63a9b95635 + size: 528 + outs: + - path: ResNet34.db + md5: 17d709d4603f98e9d305b9a275f85c14 + size: 1269760 + attacks@ResNet50: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 + ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet50.db --config-name default.yaml + deps: + - path: attacks.sh + md5: 4c7b931c1ebbd717c18aea456b49342d + size: 3092 + - path: models.sh + md5: 6e85b74c043a1411e8322a2901557bfa + size: 1445 + - path: output/reports/attack/default/score_dict.json + md5: 1e826f977a24134fac3fef63a9b95635 + size: 528 + outs: + - path: ResNet50.db + md5: 6adb496925240822f58b015a17a0036d + size: 143360 diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index 8a173327..3f4cd751 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -1,8 +1,3 @@ -# %% [markdown] -# # Dependencies - -# %% - import argparse import logging from pathlib import Path @@ -30,12 +25,14 @@ def cat_plot( legend_title, file, folder, + hue_order = None, rotation=0, set={}, **kwargs, ): plt.gcf().clear() - graph = sns.catplot(data=data, x=x, y=y, hue=hue, kind=kind, **kwargs) + data = data.sort_values(by=[hue, x, y]) + graph = sns.catplot(data=data, x=x, y=y, hue=hue, kind=kind, hue_order = hue_order, **kwargs) graph.set_xlabels(xlabels) graph.set_ylabels(ylabels) graph.set_titles(titles) @@ -61,11 +58,12 @@ def line_plot( y_scale=None, x_scale=None, legend={}, - control=None, - control_color=None, + hue_order = None, + **kwargs, ): plt.gcf().clear() - graph = sns.lineplot(data=data, x=x, y=y, hue=hue) + data = data.sort_values(by=[hue, x, y]) + graph = sns.lineplot(data=data, x=x, y=y, hue=hue, hue_order=hue_order) graph.legend(**legend) # if control is not None: # assert control_color is not None, "Please specify a control color" @@ -96,12 +94,16 @@ def scatter_plot( y_scale=None, x_scale=None, legend={}, + hue_order = None, ): + # plt.gcf().clear() + data = data.sort_values(by=[hue, x, y]) graph = sns.scatterplot( data=data, x=x, y=y, hue=hue, + hue_order=hue_order, ) graph.set_yscale(y_scale) graph.set_xscale(x_scale) @@ -124,19 +126,19 @@ def calculate_failure_rate(data): data = data[data.columns.drop(list(data.filter(regex=r"\.1$")))] data.columns.str.replace(" ", "") data.loc[:, "failure_rate"] = ( - (1 - data["accuracy"]) * 100 / data["predict_time"] + (1 - data.loc[:, "accuracy"]) * 100 / data.loc[:,"predict_time"] ) data.loc[:, "adv_failure_rate"] = ( - (1 - data["adv_accuracy"]) * 100 / data["adv_fit_time"] + (1 - data.loc[:,"adv_accuracy"]) * 100 / data.loc[:,"adv_fit_time"] ) data.loc[:, "training_time_per_failure"] = ( - data["train_time"] / data["failure_rate"] + data.loc[:,"train_time"] / data.loc[:,"failure_rate"] ) data.loc[:, "training_time_per_adv_failure"] = ( - data["train_time"] / data["adv_failure_rate"] + data.loc[:,"train_time"] / data.loc[:,"adv_failure_rate"] ) data.loc[:, "adv_training_time_per_failure"] = ( - data["train_time"] / data["adv_failure_rate"] + data.loc[:,"train_time"] / data.loc[:,"adv_failure_rate"] ) return data @@ -206,31 +208,25 @@ def min_max_scaling(data): help="Path to the config file", default="conf/plots.yaml", ) - parser.add_argument("-d", "--drop_these_if_missing", nargs="+", help="Columns to drop", default=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"]) args = parser.parse_args() logging.basicConfig(level=args.verbosity) - # %% assert Path( args.file, ).exists(), f"File {args.file} does not exist. Please specify a valid file using the -f flag." csv_file = args.file data = pd.read_csv(csv_file) - data = drop_frames_without_results(data, subset=args.drop_these_if_missing) + data = drop_frames_without_results(data, subset=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"]) data = calculate_failure_rate(data) data = min_max_scaling(data) - # Drop replicated index column if it exists if "Unnamed: 0" in data.columns: data.drop("Unnamed: 0", axis=1, inplace=True) - # Absolute/relative path handling if Path(args.path).absolute().exists(): logger.info("Absolute path specified") FOLDER = Path(args.path).absolute() else: logger.info("Relative path specified") FOLDER = Path(Path(), args.path) - # Create folder if it does not exist FOLDER.mkdir(parents=True, exist_ok=True) - # Saves cleaned data to csv data.to_csv(FOLDER / args.output) IMAGE_FILETYPE = ( args.plotfiletype @@ -246,22 +242,19 @@ def min_max_scaling(data): # Reads Config file with open(Path(args.config), "r") as f: big_dict = yaml.load(f, Loader=yaml.FullLoader) - # Catetory plot cat_plot_list = big_dict["cat_plot"] i = 0 for dict_ in cat_plot_list: i += 1 logger.info(f"Rendering graph {i}") locals()[f"graph{i}"] = cat_plot(data, **dict_, folder=FOLDER) - # Line plot line_plot_list = big_dict["line_plot"] for dict_ in line_plot_list: i += 1 logger.info(f"Rendering graph {i}") locals()[f"graph{i}"] = line_plot(data, **dict_, folder=FOLDER) - # %% - # Scatter plot + scatter_plot_list = big_dict["scatter_plot"] for dict_ in scatter_plot_list: i += 1 diff --git a/examples/pytorch_cifar/conf/plots.yaml b/examples/pytorch_cifar/conf/plots.yaml index 7a53497f..d235ac63 100644 --- a/examples/pytorch_cifar/conf/plots.yaml +++ b/examples/pytorch_cifar/conf/plots.yaml @@ -147,13 +147,13 @@ line_plot: y_scale: ylabel: Adv. Accuracy scatter_plot: +- file: adv_accuracy_vs_train_time.pdf x: train_time_per_sample y: adv_failure_rate hue: model_name xlabel: Training Time Per Sample ylabel: Adversarial Failure Rate title: Adversarial Failure Rate vs Training Time - file: adv_failure_rate_vs_train_time.pdf y_scale: log x_scale: linear legend: diff --git a/examples/pytorch_cifar/dvc.lock b/examples/pytorch_cifar/dvc.lock index c600ebfe..3c3926d5 100644 --- a/examples/pytorch_cifar/dvc.lock +++ b/examples/pytorch_cifar/dvc.lock @@ -97,20 +97,20 @@ stages: md5: 6503fed5d4e6cc1163898c0ab6a863dd size: 739680311 - path: output/models/model.optimizer.pt - md5: 3f8f2a52c402ec4def8f0f7f1cf65286 + md5: f5b6422f339473318808616c7fb926c4 size: 44805933 - path: output/models/model.pt - md5: dfa2ec23447385630a80933ec686df2a + md5: c7a6c14dc3dc96868cb9f3b93d7d31ba size: 44811029 - path: output/reports/train/default/params.yaml md5: 320abf8eddbae67cd6873f8d1e3bba81 size: 2174 - path: output/reports/train/default/predictions.json - md5: dd166fd83b6f32eae15104afcfb189f3 - size: 2439636 + md5: ed5c6a014bf8fc8ee4aadacc3c5cb07c + size: 2437716 - path: output/reports/train/default/score_dict.json - md5: efc0fd751599c5cdcb1904b54e2d7dda - size: 393 + md5: 368af71ad6cf7bf06b64ffeb109a2da9 + size: 412 attack: cmd: python -m deckard.layers.experiment attack deps: @@ -118,7 +118,7 @@ stages: md5: 6503fed5d4e6cc1163898c0ab6a863dd size: 739680311 - path: output/models/model.pt - md5: ac52d92e67a6df07a4428bc65904b781 + md5: 40384588691c0c813a1bd0406f0dd7b0 size: 44811477 params: params.yaml: @@ -327,17 +327,17 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: 0407a11ab4185a2be8662bd5a1774509 + md5: 15b48cee382acf8d99438fac5b857d67 size: 123046 - path: output/reports/attack/default/adv_predictions.json - md5: 8cbce9b206806a8b5dffad8b3554ea9d - size: 2123 + md5: 323486a7174afe1d1dcd42d4a86cc304 + size: 2167 - path: output/reports/attack/default/params.yaml md5: 5177666d7f2e1e55ccad8ab4b735ffc7 size: 5175 - path: output/reports/attack/default/score_dict.json - md5: bd2742460f844eebe8bd411aa8ebd5de - size: 530 + md5: 2ccb898d65b13714b26de3dd467ba306 + size: 541 attacks@ResNet18: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet18.db --config-name default.yaml @@ -346,15 +346,15 @@ stages: md5: 2fbda0d22c03c74392639ffa29319023 size: 3080 - path: models.sh - md5: cd924674997af182b42d07a08bbc9034 - size: 1431 + md5: 9916f8e39b8342a0e9407859fb554021 + size: 1293 - path: output/reports/attack/default/score_dict.json - md5: 81a8dad1861acf8a1c969c9d41a754a5 - size: 513 + md5: f7d7a6af006d36105a9449dbf1a00715 + size: 535 outs: - path: ResNet18.db - md5: 57be573b6c7b51c66ed3d7dcafd0f57d - size: 806912 + md5: 8add8533355f91c1c2e23ed2bc8b720b + size: 1232896 attacks@ResNet34: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet34.db --config-name default.yaml @@ -366,12 +366,12 @@ stages: md5: cd924674997af182b42d07a08bbc9034 size: 1431 - path: output/reports/attack/default/score_dict.json - md5: 81a8dad1861acf8a1c969c9d41a754a5 - size: 513 + md5: bd2742460f844eebe8bd411aa8ebd5de + size: 530 outs: - path: ResNet34.db - md5: d6d507b04290fe7563e30364c226e645 - size: 606208 + md5: f3084028f22eb073b578c9eb81f6795c + size: 159744 attacks@ResNet50: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet50.db --config-name default.yaml @@ -380,15 +380,15 @@ stages: md5: 2fbda0d22c03c74392639ffa29319023 size: 3080 - path: models.sh - md5: cd924674997af182b42d07a08bbc9034 - size: 1431 + md5: 9916f8e39b8342a0e9407859fb554021 + size: 1293 - path: output/reports/attack/default/score_dict.json - md5: 81a8dad1861acf8a1c969c9d41a754a5 - size: 513 + md5: f7d7a6af006d36105a9449dbf1a00715 + size: 535 outs: - path: ResNet50.db - md5: 040adb3012b1bcb1343da5610a9a93f9 - size: 610304 + md5: 88ab82137f4fd2f0384e6b7bae7ae11f + size: 626688 attacks@ResNet101: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet101 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet101.db --config-name @@ -401,12 +401,12 @@ stages: md5: cd924674997af182b42d07a08bbc9034 size: 1431 - path: output/reports/attack/default/score_dict.json - md5: 81a8dad1861acf8a1c969c9d41a754a5 - size: 513 + md5: bd2742460f844eebe8bd411aa8ebd5de + size: 530 outs: - path: ResNet101.db - md5: c4d01a0911b988bb77e03fb1b3d83518 - size: 5042176 + md5: 8a40cf29e97c400163c55d790153a270 + size: 307200 attacks@ResNet152: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet152 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet152.db --config-name @@ -419,62 +419,56 @@ stages: md5: cd924674997af182b42d07a08bbc9034 size: 1431 - path: output/reports/attack/default/score_dict.json - md5: 81a8dad1861acf8a1c969c9d41a754a5 - size: 513 + md5: bd2742460f844eebe8bd411aa8ebd5de + size: 530 outs: - path: ResNet152.db - md5: 090cfe4e13e708e261a19427b2c86c0d - size: 323584 + md5: 1cef8992168089daa34f547c004798b3 + size: 159744 compile@attack: cmd: python -m deckard.layers.compile --report_folder output/reports/attack --results_file output/reports/attack.csv deps: - path: ResNet101.db - md5: c4d01a0911b988bb77e03fb1b3d83518 - size: 5042176 + md5: 8a40cf29e97c400163c55d790153a270 + size: 307200 - path: ResNet152.db - md5: 090cfe4e13e708e261a19427b2c86c0d - size: 323584 + md5: 1cef8992168089daa34f547c004798b3 + size: 159744 - path: ResNet18.db - md5: 57be573b6c7b51c66ed3d7dcafd0f57d - size: 806912 + md5: 0b651c201f96fbedf11f43b819576769 + size: 159744 - path: ResNet34.db - md5: d6d507b04290fe7563e30364c226e645 - size: 606208 + md5: f3084028f22eb073b578c9eb81f6795c + size: 159744 - path: ResNet50.db - md5: 040adb3012b1bcb1343da5610a9a93f9 - size: 610304 + md5: b6cd8bf315fa750cdb070b434bcf3681 + size: 159744 - path: output/reports/attack/ - md5: 64814ed2e90d907802c9aa74f86d236d.dir - size: 1833185741 - nfiles: 14069 + md5: 8d5687b35d86d450438ebb3045054849.dir + size: 1995487004 + nfiles: 14559 outs: - path: output/reports/attack.csv - md5: 11376e233256f1680a55688a5324b335 - size: 10605097 + md5: eab7fb13e62e581fbcc11454c88d9a2b + size: 10878957 plot: cmd: python plots.py --path output/plots/ --file output/reports/attack.csv deps: - - path: ResNet101.db - md5: c4d01a0911b988bb77e03fb1b3d83518 - size: 5042176 - path: ResNet152.db - md5: 090cfe4e13e708e261a19427b2c86c0d - size: 323584 + md5: df66e7a40e4e692513c136b2811cb8c7 + size: 258048 - path: ResNet18.db - md5: 57be573b6c7b51c66ed3d7dcafd0f57d - size: 806912 - - path: ResNet34.db - md5: d6d507b04290fe7563e30364c226e645 - size: 606208 + md5: 8add8533355f91c1c2e23ed2bc8b720b + size: 1232896 - path: ResNet50.db - md5: 040adb3012b1bcb1343da5610a9a93f9 - size: 610304 + md5: 88ab82137f4fd2f0384e6b7bae7ae11f + size: 626688 - path: output/reports/attack.csv - md5: 11376e233256f1680a55688a5324b335 - size: 10605097 + md5: b64b7c2b6d8cf3015e5945affb42890b + size: 4032195 outs: - path: output/plots/ - md5: 04e94bd226b7a6f9e4d05a9b63083fc1.dir - size: 2396615 + md5: f0a9887f2f9ee5042004b36136326197.dir + size: 375907 nfiles: 13 diff --git a/examples/pytorch_cifar/plots.py b/examples/pytorch_cifar/plots.py index 89995ba3..95a40ba1 100644 --- a/examples/pytorch_cifar/plots.py +++ b/examples/pytorch_cifar/plots.py @@ -1,8 +1,3 @@ -# %% [markdown] -# # Dependencies - -# %% - import argparse import logging from pathlib import Path @@ -207,7 +202,6 @@ def min_max_scaling(data): ) args = parser.parse_args() logging.basicConfig(level=args.verbosity) - # %% assert Path( args.file, ).exists(), f"File {args.file} does not exist. Please specify a valid file using the -f flag." @@ -240,22 +234,21 @@ def min_max_scaling(data): # Reads Config file with open(Path(args.config), "r") as f: big_dict = yaml.load(f, Loader=yaml.FullLoader) - # %% cat_plot_list = big_dict["cat_plot"] i = 0 for dict_ in cat_plot_list: i += 1 logger.info(f"Rendering graph {i}") locals()[f"graph{i}"] = cat_plot(data, **dict_, folder=FOLDER) - # %% line_plot_list = big_dict["line_plot"] for dict_ in line_plot_list: i += 1 logger.info(f"Rendering graph {i}") locals()[f"graph{i}"] = line_plot(data, **dict_, folder=FOLDER) - # %% - - scatter_plot_dict = big_dict["scatter_plot"] - graph = scatter_plot(data=data, **scatter_plot_dict, folder=FOLDER) + scatter_plot_list = big_dict["scatter_plot"] + for dict_ in scatter_plot_list: + i += 1 + logger.info(f"Rendering graph {i}") + locals()[f"graph{i}"] = scatter_plot(data, **dict_, folder=FOLDER) From 0339005f45e7db6d7f8e3ca7c6e99822dd01ce84 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 21 Sep 2023 11:44:57 +0000 Subject: [PATCH 080/148] fixed attack time bug --- deckard.code-workspace | 6 +- deckard/base/data/sklearn_pipeline.py | 2 +- examples/sklearn/dvc.lock | 81 +++++++++++---------------- 3 files changed, 35 insertions(+), 54 deletions(-) diff --git a/deckard.code-workspace b/deckard.code-workspace index 3aed034b..7dd5804c 100644 --- a/deckard.code-workspace +++ b/deckard.code-workspace @@ -3,15 +3,13 @@ { "path": "." }, - { - "path": "../../../Home/staff/cmeyers/.art" - } + ], "settings": { "python.testing.pytestEnabled": true, "python.testing.pytestPath": "${workspaceFolder}/deckard/test/", "python.envFile": "${workspaceFolder}/.env", "yaml.schemas": { - "https://raw.githubusercontent.com/iterative/dvcyaml-schema/master/schema.json": } + "https://raw.githubusercontent.com/iterative/dvcyaml-schema/master/schema.json": ["dvc.yaml"] } } } diff --git a/deckard/base/data/sklearn_pipeline.py b/deckard/base/data/sklearn_pipeline.py index 2363d57a..3b7b5594 100644 --- a/deckard/base/data/sklearn_pipeline.py +++ b/deckard/base/data/sklearn_pipeline.py @@ -44,7 +44,7 @@ def __init__(self, **kwargs): pipe.update(**kwargs) for stage in pipe: if isinstance(pipe[stage], DictConfig): - pipe[stage] = OmegaConf.to_container(pipe[stage]) + pipe[stage] = OmegaConf.to_container(pipe[stage], resolve=True) name = pipe[stage].pop("name", pipe[stage].pop("_target_", stage)) pipe[stage] = SklearnDataPipelineStage(name, **pipe[stage]) elif is_dataclass(pipe[stage]): diff --git a/examples/sklearn/dvc.lock b/examples/sklearn/dvc.lock index 460555b4..93508216 100644 --- a/examples/sklearn/dvc.lock +++ b/examples/sklearn/dvc.lock @@ -87,28 +87,23 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/data/data.pkl - hash: md5 - md5: af3fa729fa7f76213685ca4e51b80bb2 + md5: eeabc07006294278801a3906f7016b20 size: 185102 - path: output/models/model.pkl - hash: md5 - md5: 8f9bb89ea877a37e005fb9d12d6538a0 - size: 15185 + md5: 38622dfa7ae717dc7c3a6cc03e2d50b7 + size: 15127 - path: output/reports/train/default/score_dict.json - hash: md5 - md5: 110eccb7dbf05b7c31f7ab2e423b827c - size: 349 + md5: 87f0187a3ec41a7c8c9d833e9802176d + size: 360 attack: cmd: python -m deckard.layers.experiment attack deps: - path: output/data/data.pkl - hash: md5 - md5: af3fa729fa7f76213685ca4e51b80bb2 + md5: eeabc07006294278801a3906f7016b20 size: 185102 - path: output/models/model.pkl - hash: md5 - md5: 27225f126e831ab39e47b6b9386ab36b - size: 15219 + md5: ed99f68f7ba45834cbfd8c421f82bbd5 + size: 15161 params: params.yaml: attack: @@ -281,70 +276,58 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/reports/attack/default/adv_probabilities.json - hash: md5 - md5: 4456666e66f153b50572072e83be3e47 + md5: cf883c677f52a409a225403c739a8071 size: 425 - path: output/reports/attack/default/score_dict.json - hash: md5 - md5: 4b858a5872bfe2aa828a0f064c55f4ea - size: 486 + md5: c30aaee4adbf17fbb8321dea3820c8e7 + size: 497 model_search: cmd: bash models.sh +stage=train +optimizers=accuracy ++hydra.sweeper.storage=sqlite:///model.db ++hydra.sweeper.study_name=model ++hydra.sweeper.direction=maximize ++direction=maximize --multirun --config-name model.yaml deps: - path: models.sh - hash: md5 - md5: b20bba54353be244dc917518b03ef007 - size: 705 + md5: d55d675913cb8191df958508131e037c + size: 706 - path: output/reports/attack/default/adv_probabilities.json - hash: md5 - md5: 4456666e66f153b50572072e83be3e47 + md5: cf883c677f52a409a225403c739a8071 size: 425 outs: - path: model.db - hash: md5 - md5: c354d3816bc0526c0e94d9fd0a537e72 - size: 217088 - find_best_model: - cmd: python -m deckard.layers.find_best model.yaml - deps: - - path: model.db - hash: md5 - md5: c354d3816bc0526c0e94d9fd0a537e72 - size: 217088 - outs: - - path: conf/model/best.yaml - hash: md5 - md5: 9f9bd5f3433134a0bf966456a0d150aa - size: 674 + md5: 733a4cde935d29efa111c08a04b461c6 + size: 196608 attack_search: cmd: bash attacks.sh ++stage=attack ++optimizers=adv_accuracy ++hydra.sweeper.storage=sqlite:///attack.db ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++direction=minimize ++attack.init.max_iter=10 --multirun --config-name attack.yaml deps: - path: attacks.sh - hash: md5 md5: b5d0850e20fa8d2fb2bd47c8949ae6a6 size: 1568 - path: model.db - hash: md5 - md5: c354d3816bc0526c0e94d9fd0a537e72 - size: 217088 + md5: 733a4cde935d29efa111c08a04b461c6 + size: 196608 outs: - path: attack.db - hash: md5 - md5: dcb34e1932e7b4b7b50dff50ccc582b5 + md5: b2dccd713e52e96b9c14c1577427d254 size: 110592 find_best_attack: cmd: python -m deckard.layers.find_best attack.yaml deps: - path: attack.db - hash: md5 - md5: dcb34e1932e7b4b7b50dff50ccc582b5 + md5: b2dccd713e52e96b9c14c1577427d254 size: 110592 outs: - path: conf/attack/best.yaml - hash: md5 - md5: e9964377a012776ffccaba4b3e5028fc - size: 2053 + md5: e5ce5da0238f760f0b3c2b080426c8af + size: 2051 + find_best_model: + cmd: python -m deckard.layers.find_best model.yaml + deps: + - path: model.db + md5: 733a4cde935d29efa111c08a04b461c6 + size: 196608 + outs: + - path: conf/model/best.yaml + md5: 91f75c74812095e67b31619e7b1c5542 + size: 672 From 07a1e0e98c2753a3773a5045b03795c8fbdc59d0 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 21 Sep 2023 11:45:16 +0000 Subject: [PATCH 081/148] fixed attack time bug --- deckard/base/attack/attack.py | 28 +++++++++++++--------------- 1 file changed, 13 insertions(+), 15 deletions(-) diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index beeb9b00..3fd78c3a 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -92,15 +92,10 @@ def __call__(self, model=None, data=None, attack_size=-1): logger.info("Attempting black-box attack.") config = {"_target_": name} config.update(**kwargs) - attack = instantiate(config) + attack = instantiate(config, model) except TypeError as e: if "verbose" in str(e): config.pop("verbose", None) - attack = instantiate(config) - except Exception as e: - if "estimator" or "classifier" in str(e): - logger.warning(f"Black-box attack failed with error: {e}") - logger.info("Attempting white-box attack.") attack = instantiate(config, model) else: raise e @@ -145,7 +140,6 @@ def __call__( samples = self.data.load(attack_file) else: ben_samples = data[0][: self.attack_size] - start = process_time_ns() atk = self.init(model=model, attack_size=self.attack_size) kwargs = deepcopy(self.init.kwargs) scale_max = kwargs.pop("scale_max", 1) @@ -153,15 +147,17 @@ def __call__( if targeted is True: kwargs.update({"y": data[2][: self.attack_size]}) if "AdversarialPatch" in self.name: + start = process_time_ns() patches, masks = atk.generate(ben_samples, **kwargs) samples = atk.apply_patch(ben_samples, scale=scale_max) else: + start = process_time_ns() samples = atk.generate(ben_samples) - end = process_time_ns() - time_dict.update({"adv_fit_time": (end - start) / 1e9}) - time_dict.update( - {"adv_fit_time_per_sample": (end - start) / (len(samples) * 1e9)}, - ) + end = process_time_ns() + time_dict.update({"adv_fit_time": (end - start) / 1e9}) + time_dict.update( + {"adv_fit_time_per_sample": (end - start) / (len(samples) * 1e9)}, + ) results["adv_samples"] = samples if attack_file is not None: self.data.save(samples, attack_file) @@ -234,8 +230,8 @@ def __call__( adv_loss = log_loss(data[3][: self.attack_size], preds) self.data.save(adv_loss, adv_losses_file) results["adv_loss"] = adv_loss - results["time_dict"] = time_dict - + if len(time_dict) > 0: + results["time_dict"] = time_dict return results @@ -331,6 +327,7 @@ def __call__( x_trigger = torch.tensor(x_trigger, device=device) y_trigger = y_trigger.to(torch.long) y_trigger = y_trigger.to(torch.long) + start = process_time_ns() samples, _ = atk.poison( x_trigger=x_trigger, y_trigger=y_trigger, @@ -722,7 +719,7 @@ def __init__( self.method = method self.attack_size = attack_size if isinstance(kwargs, DictConfig): - kwargs = OmegaConf.to_container(kwargs) + kwargs = OmegaConf.to_container(kwargs, resolve=True) elif isinstance(kwargs, dict): pass else: @@ -817,6 +814,7 @@ def __call__( ) else: raise NotImplementedError(f"Attack method {self.method} not implemented.") + return result def __hash__(self): From 66ce02b2b109c838a1cfe91cb6fa823dc633d8fe Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 21 Sep 2023 11:45:32 +0000 Subject: [PATCH 082/148] added diabetes support --- deckard/base/data/generator.py | 28 ++++++++++++++++++---------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/deckard/base/data/generator.py b/deckard/base/data/generator.py index c94ef240..a5e1fd2d 100644 --- a/deckard/base/data/generator.py +++ b/deckard/base/data/generator.py @@ -11,7 +11,7 @@ from dataclasses import dataclass, field from ..utils import my_hash import numpy as np -from art.utils import load_mnist, load_cifar10 +from art.utils import load_mnist, load_cifar10, load_diabetes __all__ = [ "SklearnDataGenerator", @@ -21,10 +21,10 @@ ] logger = logging.getLogger(__name__) - +SKLEARN_DATASETS = ["classification", "regression", "blobs", "moons", "circles", "biclusters"] @dataclass class SklearnDataGenerator: - name: Literal["classification", "regression"] = "classification" + name: Literal["classification", "regression", "blobs", "moons", "circles", "biclusters"] = "classification" kwargs: dict = field(default_factory=dict) def __init__(self, name, **kwargs): @@ -54,10 +54,10 @@ def __call__(self): def __hash__(self): return int(my_hash(self), 16) - +TORCH_DATASETS = ["torch_mnist", "torch_cifar10", "torch_diabetes"] @dataclass class TorchDataGenerator: - name: Literal["torch_mnist", "torch_cifar10"] = "torch_mnist" + name: Literal["torch_mnist", "torch_cifar10", "torch_diabetes"] = "torch_mnist" path = None kwargs: dict = field(default_factory=dict) @@ -84,6 +84,10 @@ def __call__(self): X_test = np.transpose(X_test, (0, 3, 1, 2)).astype(np.float32) X = np.concatenate((X_train, X_test)) y = np.concatenate((y_train, y_test)) + elif self.name == "torch_diabetes": + (X_train, y_train), (X_test, y_test), _, _ = load_diabetes() + X = np.concatenate((X_train, X_test)) + y = np.concatenate((y_train, y_test)) else: raise ValueError(f"Unknown dataset name {self.name}") return [X, y] @@ -91,10 +95,10 @@ def __call__(self): def __hash__(self): return int(my_hash(self), 16) - +KERAS_DATASETS = ["keras_mnist", "keras_cifar10", "mnist", "cifar10", "diabetes"] @dataclass class KerasDataGenerator: - name: Literal["mnist", "cifar10"] = "mnist" + name: Literal["mnist", "cifar10", "diabetes"] = "mnist" kwargs: dict = field(default_factory=dict) def __init__(self, name, **kwargs): @@ -113,6 +117,10 @@ def __call__(self): (X_train, y_train), (X_test, y_test), _, _ = load_mnist() X = np.concatenate((X_train, X_test)) y = np.concatenate((y_train, y_test)) + elif "diabetes" in self.name: + (X_train, y_train), (X_test, y_test), _, _ = load_diabetes() + X = np.concatenate((X_train, X_test)) + y = np.concatenate((y_train, y_test)) else: raise ValueError(f"Unknown dataset name {self.name}") return [X, y] @@ -131,11 +139,11 @@ def __init__(self, name, **kwargs): self.kwargs = {k: v for k, v in kwargs.items() if v is not None} def __call__(self): - if self.name in ["classification", "regression"]: + if self.name in SKLEARN_DATASETS: return SklearnDataGenerator(self.name, **self.kwargs)() - elif self.name in ["torch_mnist", "torch_cifar10"]: + elif self.name in TORCH_DATASETS: return TorchDataGenerator(self.name, **self.kwargs)() - elif self.name in ["keras_mnist", "keras_cifar10", "mnist", "cifar10"]: + elif self.name in KERAS_DATASETS: return KerasDataGenerator(self.name, **self.kwargs)() else: raise ValueError(f"Invalid name {self.name}. Please choose from ") From 5d8be50ea65f19204efbb7cb10f78200e8270815 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 21 Sep 2023 12:17:02 +0000 Subject: [PATCH 083/148] fixed hashing bug --- deckard/base/attack/attack.py | 2 +- deckard/base/data/generator.py | 28 ++++++++++++++++++++++--- deckard/base/experiment/experiment.py | 10 ++++----- deckard/base/model/art_pipeline.py | 17 +++++++++++---- deckard/base/model/model.py | 10 ++++++++- deckard/base/model/tensorflow_models.py | 4 ++-- deckard/layers/compile.py | 1 - deckard/layers/optimise.py | 2 +- deckard/layers/prepare_queue.py | 23 +++++++++++++------- deckard/layers/utils.py | 2 +- 10 files changed, 73 insertions(+), 26 deletions(-) diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index 3fd78c3a..39d4a6ea 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -814,7 +814,7 @@ def __call__( ) else: raise NotImplementedError(f"Attack method {self.method} not implemented.") - + return result def __hash__(self): diff --git a/deckard/base/data/generator.py b/deckard/base/data/generator.py index a5e1fd2d..6d78fd02 100644 --- a/deckard/base/data/generator.py +++ b/deckard/base/data/generator.py @@ -21,10 +21,26 @@ ] logger = logging.getLogger(__name__) -SKLEARN_DATASETS = ["classification", "regression", "blobs", "moons", "circles", "biclusters"] +SKLEARN_DATASETS = [ + "classification", + "regression", + "blobs", + "moons", + "circles", + "biclusters", +] + + @dataclass class SklearnDataGenerator: - name: Literal["classification", "regression", "blobs", "moons", "circles", "biclusters"] = "classification" + name: Literal[ + "classification", + "regression", + "blobs", + "moons", + "circles", + "biclusters", + ] = "classification" kwargs: dict = field(default_factory=dict) def __init__(self, name, **kwargs): @@ -54,7 +70,10 @@ def __call__(self): def __hash__(self): return int(my_hash(self), 16) + TORCH_DATASETS = ["torch_mnist", "torch_cifar10", "torch_diabetes"] + + @dataclass class TorchDataGenerator: name: Literal["torch_mnist", "torch_cifar10", "torch_diabetes"] = "torch_mnist" @@ -95,7 +114,10 @@ def __call__(self): def __hash__(self): return int(my_hash(self), 16) -KERAS_DATASETS = ["keras_mnist", "keras_cifar10", "mnist", "cifar10", "diabetes"] + +KERAS_DATASETS = ["keras_mnist", "keras_cifar10", "mnist", "cifar10", "diabetes"] + + @dataclass class KerasDataGenerator: name: Literal["mnist", "cifar10", "diabetes"] = "mnist" diff --git a/deckard/base/experiment/experiment.py b/deckard/base/experiment/experiment.py index 26942379..dd274fce 100644 --- a/deckard/base/experiment/experiment.py +++ b/deckard/base/experiment/experiment.py @@ -48,7 +48,7 @@ def __init__( if isinstance(data, dict): self.data = Data(**data) elif isinstance(data, DictConfig): - data_dict = OmegaConf.to_container(data) + data_dict = OmegaConf.to_container(data, resolve=True) data_dict.update({"_target_": "deckard.base.data.Data"}) self.data = instantiate(data_dict) elif isinstance(data, Data): @@ -61,7 +61,7 @@ def __init__( if isinstance(model, dict): self.model = Model(**model) elif isinstance(model, DictConfig): - model_dict = OmegaConf.to_container(model) + model_dict = OmegaConf.to_container(model, resolve=True) self.model = Model(**model_dict) elif isinstance(model, Model): self.model = model @@ -73,7 +73,7 @@ def __init__( if isinstance(scorers, dict): self.scorers = ScorerDict(**scorers) elif isinstance(scorers, DictConfig): - scorer_dict = OmegaConf.to_container(scorers) + scorer_dict = OmegaConf.to_container(scorers, resolve=True) self.scorers = ScorerDict(**scorer_dict) elif isinstance(scorers, ScorerDict): self.scorers = scorers @@ -85,7 +85,7 @@ def __init__( if isinstance(files, dict): self.files = FileConfig(**files) elif isinstance(files, DictConfig): - file_dict = OmegaConf.to_container(files) + file_dict = OmegaConf.to_container(files, resolve=True) self.files = FileConfig(**file_dict) elif isinstance(files, FileConfig): self.files = files @@ -97,7 +97,7 @@ def __init__( elif isinstance(attack, dict): self.attack = Attack(**attack) elif isinstance(attack, DictConfig): - attack_dict = OmegaConf.to_container(attack) + attack_dict = OmegaConf.to_container(attack, resolve=True) self.attack = Attack(**attack_dict) elif isinstance(attack, Attack): self.attack = attack diff --git a/deckard/base/model/art_pipeline.py b/deckard/base/model/art_pipeline.py index a0d4c048..69e5dd6c 100644 --- a/deckard/base/model/art_pipeline.py +++ b/deckard/base/model/art_pipeline.py @@ -125,7 +125,7 @@ def __init__(self, library, name: str = None, **kwargs): pipeline.pop("model", None) for stage in pipeline: if isinstance(pipeline[stage], DictConfig): - pipeline[stage] = OmegaConf.to_container(pipeline[stage]) + pipeline[stage] = OmegaConf.to_container(pipeline[stage], resolve=True) elif is_dataclass(pipeline[stage]): pipeline[stage] = asdict(pipeline[stage]) else: @@ -158,7 +158,10 @@ def __hash__(self): def __call__(self, model: object, library: str = None, data=None) -> BaseEstimator: if "initialize" in self.pipeline: if isinstance(self.pipeline["initialize"], DictConfig): - params = OmegaConf.to_container(self.pipeline["initialize"]) + params = OmegaConf.to_container( + self.pipeline["initialize"], + resolve=True, + ) name = params.pop("name", None) kwargs = params.pop("kwargs", {}) elif is_dataclass(self.pipeline["initialize"]): @@ -186,7 +189,10 @@ def __call__(self, model: object, library: str = None, data=None) -> BaseEstimat assert len(data) == 4, f"data must be a tuple of length 4. Got {data}" if "preprocessor" in self.pipeline: if isinstance(self.pipeline["preprocessor"], DictConfig): - params = OmegaConf.to_container(self.pipeline["preprocessor"]) + params = OmegaConf.to_container( + self.pipeline["preprocessor"], + resolve=True, + ) name = params.pop("name", None) sub_kwargs = params.pop("kwargs", {}) while "kwargs" in sub_kwargs: @@ -211,7 +217,10 @@ def __call__(self, model: object, library: str = None, data=None) -> BaseEstimat kwargs.update({"preprocessing_defences": pre_def}) if "postprocessor" in self.pipeline: if isinstance(self.pipeline["postprocessor"], DictConfig): - params = OmegaConf.to_container(self.pipeline["postprocessor"]) + params = OmegaConf.to_container( + self.pipeline["postprocessor"], + resolve=True, + ) name = params.pop("name", "_target_") elif is_dataclass(self.pipeline["postprocessor"]): params = asdict(self.pipeline["postprocessor"]) diff --git a/deckard/base/model/model.py b/deckard/base/model/model.py index 5bc365fa..6e91cd1e 100644 --- a/deckard/base/model/model.py +++ b/deckard/base/model/model.py @@ -132,6 +132,14 @@ def __call__(self, data: list, model: object, library=None): tf.config.run_functions_eagerly(True) else: raise NotImplementedError(f"Training library {library} not implemented") + if hasattr(model, "fit_generator"): + try: + start = process_time_ns() + model.fit_generator(data[0], **trainer) + end = process_time_ns() - start + except Exception as e: + logger.error(e) + raise e try: start = process_time_ns() model.fit(data[0], data[2], **trainer) @@ -295,7 +303,7 @@ def __call__( assert len(data) == 4, f"Data {data} is not a tuple of length 4." elif isinstance(model, (str, Path)): model = self.load(model) - elif hasattr(model, "fit"): + elif hasattr(model, ("fit", "fit_generator")): assert hasattr(model, "predict") or hasattr( model, "predict_proba", diff --git a/deckard/base/model/tensorflow_models.py b/deckard/base/model/tensorflow_models.py index 5ff2b725..f82a244e 100644 --- a/deckard/base/model/tensorflow_models.py +++ b/deckard/base/model/tensorflow_models.py @@ -78,7 +78,7 @@ def __call__(self): loss = kwargs.pop("loss", "categorical_crossentropy") optimizer = kwargs.pop("optimizer", "adam") if isinstance(optimizer, DictConfig): - optimizer = OmegaConf.to_container(optimizer) + optimizer = OmegaConf.to_container(optimizer, resolve=True) if isinstance(optimizer, dict): name = optimizer.pop("name") params = {**optimizer} @@ -91,7 +91,7 @@ def __call__(self): ) optimizer = tf.keras.optimizers.get(name, **params) if isinstance(loss, DictConfig): - loss = OmegaConf.to_container(loss) + loss = OmegaConf.to_container(loss, resolve=True) if isinstance(loss, dict): name = loss.pop("name") params = {**loss} diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 3a8da180..a42b89c2 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -179,7 +179,6 @@ def format_control_parameter(data, control_dict): logger.warning(f"Attack {attack} not in control_dict. Deleting rows.") data = data[data.atk_gen != attack] - return data diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index 42607443..68964a6c 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -250,7 +250,7 @@ def write_stage(params: dict, stage: str, path=None, working_dir=None) -> None: def optimise(cfg: DictConfig) -> None: - cfg = OmegaConf.to_container(OmegaConf.create(cfg), resolve=False) + cfg = OmegaConf.to_container(OmegaConf.create(cfg), resolve=True) scorer = cfg.pop("optimizers", None) working_dir = cfg.pop("working_dir", Path().resolve().as_posix()) stage = cfg.pop("stage", None) diff --git a/deckard/layers/prepare_queue.py b/deckard/layers/prepare_queue.py index 5e31dd18..147ef272 100644 --- a/deckard/layers/prepare_queue.py +++ b/deckard/layers/prepare_queue.py @@ -6,7 +6,6 @@ from hydra.utils import instantiate from omegaconf import DictConfig, OmegaConf import hydra -from string import Template from ..base.utils import my_hash, unflatten_dict from ..base.experiment import Experiment from .utils import deckard_nones @@ -224,7 +223,13 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: return params -def write_stage(params: dict, stage: str, id_:str, path=None, working_dir=None) -> None: +def write_stage( + params: dict, + stage: str, + id_: str, + path=None, + working_dir=None, +) -> None: """ Write params to dvc.yaml :param params: Params to write to dvc.yaml @@ -253,7 +258,7 @@ def write_stage(params: dict, stage: str, id_:str, path=None, working_dir=None) def prepare_experiment_folder(cfg: DictConfig) -> None: - cfg = OmegaConf.to_container(OmegaConf.create(cfg), resolve=False) + cfg = OmegaConf.to_container(OmegaConf.create(cfg), resolve=True) scorer = cfg.pop("optimizers", None) working_dir = cfg.pop("working_dir", Path().resolve().as_posix()) stage = cfg.pop("stage", None) @@ -266,8 +271,7 @@ def prepare_experiment_folder(cfg: DictConfig) -> None: id_ = Path(files["score_dict_file"]).parent.name write_stage(cfg, stage, path=folder, working_dir=working_dir, id_=id_) return exp, scorer, direction, folder, id_ - - + if __name__ == "__main__": logger = logging.getLogger(__name__) @@ -283,8 +287,13 @@ def hydra_prepare(cfg: DictConfig) -> float: assert isinstance(exp, Experiment), f"Expected Experiment, got {type(exp)}." assert isinstance(scorer, (str, list)), f"Expected list, got {type(scorer)}." assert isinstance(direction, str), f"Expected str, got {type(direction)}." - assert direction in ['minimize', 'maximize'], f"Expected 'minimize' or 'maximize', got {direction}." - assert Path(folder).exists(), f"Folder {folder} does not exist for experiment {id_}." + assert direction in [ + "minimize", + "maximize", + ], f"Expected 'minimize' or 'maximize', got {direction}." + assert Path( + folder, + ).exists(), f"Folder {folder} does not exist for experiment {id_}." return 0 hydra_prepare() diff --git a/deckard/layers/utils.py b/deckard/layers/utils.py index 77ce6b32..87057484 100644 --- a/deckard/layers/utils.py +++ b/deckard/layers/utils.py @@ -110,7 +110,7 @@ def compose_experiment(file, config_dir, overrides=None, default_file="default.y cfg = compose(config_name=Path(default_file).stem, overrides=overrides) except OverrideParseException: raise ValueError(f"Failed to parse overrides: {overrides}") - cfg = OmegaConf.to_container(cfg, resolve=False) + cfg = OmegaConf.to_container(cfg, resolve=True) cfg["_target_"] = "deckard.Experiment" id_ = str(my_hash(cfg)) cfg["name"] = id_ From 3731b827eeab73b07b2b6711a7fb8e2a0a12e723 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 21 Sep 2023 17:20:44 +0000 Subject: [PATCH 084/148] +pytorch mnist experiments --- examples/pytorch/conf/default.yaml | 2 +- examples/pytorch/dvc.lock | 119 ++++++++++++++--------- examples/pytorch/dvc.yaml | 8 +- examples/pytorch/models.sh | 12 +-- examples/pytorch/plots.ipynb | 149 +++++++++++++++++++++++++++++ examples/pytorch/plots.py | 52 ++++++---- 6 files changed, 268 insertions(+), 74 deletions(-) create mode 100644 examples/pytorch/plots.ipynb diff --git a/examples/pytorch/conf/default.yaml b/examples/pytorch/conf/default.yaml index 9a9956a0..4ae0442a 100644 --- a/examples/pytorch/conf/default.yaml +++ b/examples/pytorch/conf/default.yaml @@ -24,7 +24,7 @@ hydra: ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 8 + n_jobs: 10 prefer : processes verbose: 1 timeout: null diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index 58158d57..2ccf4fb6 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -126,8 +126,8 @@ stages: md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - path: output/models/model.pt - md5: f084675f8f4c83a6d9719148e7b6e2ec - size: 44792269 + md5: 1c754810b53db7311b2d1185f67d3f00 + size: 44786389 params: params.yaml: attack: @@ -356,19 +356,20 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: 7cb4b43454f9798ea052552728be5535 + md5: 7b415b0d3b17a4925311a7ee5293786e size: 31517 - path: output/reports/attack/default/adv_predictions.json - md5: 6fa49c42a6d07052ed155be54c635e49 - size: 2162 + md5: 9ea6237d1248b45bccbe60b634da68fb + size: 2154 - path: output/reports/attack/default/params.yaml md5: a15accd2ecf7bcbb5ed47d4b6bf7ed97 size: 5093 - path: output/reports/attack/default/score_dict.json - md5: 1e826f977a24134fac3fef63a9b95635 - size: 528 + md5: 879e9e986f4f69c7dea121cc78bececb + size: 524 models: - cmd: bash models.sh ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 + cmd: bash models.sh + ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 --config-name grid.yaml deps: - path: output/data/data.pkl @@ -382,7 +383,8 @@ stages: md5: d1eac324650402da6e3de1aebe0e3b3c size: 237568 attacks: - cmd: bash attacks.sh ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 + cmd: bash attacks.sh + ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 ++stage=attack --config-name grid.yaml deps: - path: model.db @@ -444,17 +446,29 @@ stages: cmd: python -m deckard.layers.compile --report_folder output/reports/attack --results_file output/reports/attack.csv deps: + - path: ResNet101.db + md5: 33b4542eb9688ff88ada0cd81f1f3005 + size: 1261568 - path: ResNet152.db - md5: 83b8b90979c4a7d8ef0840be0fdc332b - size: 307200 + md5: 4de6afe496d53faffdb9427adb7f692f + size: 1314816 + - path: ResNet18.db + md5: aa69b1818219c139f8e33ac205771ec1 + size: 110592 + - path: ResNet34.db + md5: e4d151b9800a1255a0143368057cb146 + size: 1339392 + - path: ResNet50.db + md5: 9c9bc0aab00251ca5e9bd210366bc055 + size: 1339392 - path: output/reports/attack/ - md5: 9139df021e20105790b3d8791f6ce562.dir - size: 10931694829 - nfiles: 22421 + md5: 65f8f45c7eb271be41d430011eb11b0f.dir + size: 19551705509 + nfiles: 46952 outs: - path: output/reports/attack.csv - md5: 6cf92313acc0b8d23fe5a4fa62bc180c - size: 16221649 + md5: f9acccf19464e125d3d8fafdc7799ec5 + size: 31071757 compile@train: cmd: python -m deckard.layers.compile --report_folder output/reports/train --results_file output/reports/train.csv @@ -474,53 +488,64 @@ stages: md5: 922fa64743aa481feb79213a056c816e size: 363649 attacks@ResNet152: - cmd: bash attacks.sh ++model.init.name=torch_example.ResNet152 ++stage=attack - ++hydra.sweeper.storage=sqlite:///ResNet152.db --config-name default.yaml + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet152 + ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet152.db --config-name + default.yaml deps: - path: attacks.sh md5: 4c7b931c1ebbd717c18aea456b49342d size: 3092 - path: models.sh - md5: 6e85b74c043a1411e8322a2901557bfa - size: 1445 + md5: 6ef2a88669f50dcdc0329e777dd7f9d6 + size: 1235 - path: output/reports/attack/default/score_dict.json - md5: bc48769b6f3374b3d0bdbb8e74e0bb17 - size: 541 + md5: 879e9e986f4f69c7dea121cc78bececb + size: 524 outs: - path: ResNet152.db - md5: 83b8b90979c4a7d8ef0840be0fdc332b - size: 307200 + md5: 4de6afe496d53faffdb9427adb7f692f + size: 1314816 plot: cmd: python plots.py --path output/plots/ --file output/reports/attack.csv deps: - - path: ResNet152.db - md5: 83b8b90979c4a7d8ef0840be0fdc332b - size: 307200 + - path: ResNet101.db + md5: 33b4542eb9688ff88ada0cd81f1f3005 + size: 1261568 + - path: ResNet18.db + md5: aa69b1818219c139f8e33ac205771ec1 + size: 110592 + - path: ResNet34.db + md5: e4d151b9800a1255a0143368057cb146 + size: 1339392 + - path: ResNet50.db + md5: 9c9bc0aab00251ca5e9bd210366bc055 + size: 1339392 - path: output/reports/attack.csv - md5: 6cf92313acc0b8d23fe5a4fa62bc180c - size: 16221649 + md5: f9acccf19464e125d3d8fafdc7799ec5 + size: 31071757 outs: - path: output/plots/ - md5: 4d0fc462515445c18bd9d9161d1c27e3.dir - size: 10710869 + md5: 4ec176a3a3c47092f27a664e2e0b8089.dir + size: 11959890 nfiles: 13 attacks@ResNet101: - cmd: bash attacks.sh ++model.init.name=torch_example.ResNet101 ++stage=attack - ++hydra.sweeper.storage=sqlite:///ResNet101.db --config-name default.yaml + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet101 + ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet101.db --config-name + default.yaml deps: - path: attacks.sh md5: 4c7b931c1ebbd717c18aea456b49342d size: 3092 - path: models.sh - md5: 6e85b74c043a1411e8322a2901557bfa - size: 1445 + md5: 6ef2a88669f50dcdc0329e777dd7f9d6 + size: 1235 - path: output/reports/attack/default/score_dict.json - md5: 582c594526e57c4887f845b332527d31 - size: 531 + md5: 879e9e986f4f69c7dea121cc78bececb + size: 524 outs: - path: ResNet101.db - md5: cab812b983a79b91be312bfc55e03acc - size: 241664 + md5: 33b4542eb9688ff88ada0cd81f1f3005 + size: 1261568 attacks@ResNet34: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet34.db --config-name default.yaml @@ -532,12 +557,12 @@ stages: md5: 6e85b74c043a1411e8322a2901557bfa size: 1445 - path: output/reports/attack/default/score_dict.json - md5: 1e826f977a24134fac3fef63a9b95635 - size: 528 + md5: bed91bb1f645feb61a922c7a81825f95 + size: 538 outs: - path: ResNet34.db - md5: 17d709d4603f98e9d305b9a275f85c14 - size: 1269760 + md5: e4d151b9800a1255a0143368057cb146 + size: 1339392 attacks@ResNet50: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet50.db --config-name default.yaml @@ -549,9 +574,9 @@ stages: md5: 6e85b74c043a1411e8322a2901557bfa size: 1445 - path: output/reports/attack/default/score_dict.json - md5: 1e826f977a24134fac3fef63a9b95635 - size: 528 + md5: bed91bb1f645feb61a922c7a81825f95 + size: 538 outs: - path: ResNet50.db - md5: 6adb496925240822f58b015a17a0036d - size: 143360 + md5: 9c9bc0aab00251ca5e9bd210366bc055 + size: 1339392 diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index 1e30d03e..60acb158 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -38,10 +38,10 @@ stages: attacks: foreach: # - ResNet18 - - ResNet34 - - ResNet50 - # - ResNet101 - # - ResNet152 + # - ResNet34 + # - ResNet50 + - ResNet101 + - ResNet152 do: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name default.yaml deps: diff --git a/examples/pytorch/models.sh b/examples/pytorch/models.sh index 80aec311..dc3ae853 100644 --- a/examples/pytorch/models.sh +++ b/examples/pytorch/models.sh @@ -3,17 +3,17 @@ # This script is used to generate the models for the sklearn example. # # Default model -echo "CUDA_VISIBLE_DEVICES=1 python -m deckard.layers.optimise +stage=train" $@ "--multirun" -CUDA_VISIBLE_DEVICES=1 python -m deckard.layers.optimise +stage=train $@ --multirun +echo "python -m deckard.layers.optimise " $@ "--multirun" +python -m deckard.layers.optimise $@ --multirun # # This line generates the model and adds the FeatureSqueezing preprocessing defence. -CUDA_VISIBLE_DEVICES=1 python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train $@ --multirun +python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] $@ --multirun # # Gaussian Augmentation (Input) -CUDA_VISIBLE_DEVICES=1 python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False +stage=train $@ --multirun +python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False $@ --multirun # # # Gaussian Noise (Output) -CUDA_VISIBLE_DEVICES=1 python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 +stage=train $@ --multirun +python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 $@ --multirun # # # High Confidence -CUDA_VISIBLE_DEVICES=1 python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 $@ --multirun diff --git a/examples/pytorch/plots.ipynb b/examples/pytorch/plots.ipynb new file mode 100644 index 00000000..beb9e2d7 --- /dev/null +++ b/examples/pytorch/plots.ipynb @@ -0,0 +1,149 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from plots import drop_frames_without_results, calculate_failure_rate, min_max_scaling\n", + "\n", + "csv_file = \"output/plots/data.csv\"\n", + "\n", + "data = pd.read_csv(csv_file)\n", + "data = drop_frames_without_results(\n", + " data,\n", + " subset=[\n", + " \"accuracy\",\n", + " \"adv_accuracy\",\n", + " \"train_time\",\n", + " \"adv_fit_time\",\n", + " \"predict_time\",\n", + " ],\n", + ")\n", + "data = calculate_failure_rate(data)\n", + "data = min_max_scaling(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# graph = sns.catplot(data=data, x='train_time', y='adv_failure_rate', hue='model_name', kind='boxen', row='def_gen', col='atk_gen', n_boot=10)\n", + "graph = sns.catplot(data=data, x='train_time', y='adv_failure_rate', hue='model_name', kind='point', row='atk_gen', n_boot=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/cmeyers/deckard/examples/pytorch/plots.ipynb Cell 2\u001b[0m line \u001b[0;36m3\n\u001b[1;32m 31\u001b[0m plt\u001b[39m.\u001b[39mgcf()\u001b[39m.\u001b[39mclear()\n\u001b[1;32m 32\u001b[0m \u001b[39m# logger.info(f\"Saved graph to {folder / file}\")\u001b[39;00m\n\u001b[0;32m---> 34\u001b[0m cat_plot(data, x\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mtrain_time\u001b[39;49m\u001b[39m'\u001b[39;49m, y\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39madv_failure_rate\u001b[39;49m\u001b[39m'\u001b[39;49m, hue\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mmodel_name\u001b[39;49m\u001b[39m'\u001b[39;49m, kind\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mpoint\u001b[39;49m\u001b[39m'\u001b[39;49m, col\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39matk_gen\u001b[39;49m\u001b[39m'\u001b[39;49m, row\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mdef_gen\u001b[39;49m\u001b[39m'\u001b[39;49m, titles\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m{col_name}\u001b[39;49;00m\u001b[39m \u001b[39;49m\u001b[39m{row_name}\u001b[39;49;00m\u001b[39m'\u001b[39;49m, xlabels\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mTraining time (s)\u001b[39;49m\u001b[39m'\u001b[39;49m, ylabels\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mAdversarial failure rate\u001b[39;49m\u001b[39m'\u001b[39;49m, legend_title\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mModel\u001b[39;49m\u001b[39m'\u001b[39;49m, file\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mtrain_time_adv_failure_rate.png\u001b[39;49m\u001b[39m'\u001b[39;49m, folder\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39moutput/plots\u001b[39;49m\u001b[39m'\u001b[39;49m, rotation\u001b[39m=\u001b[39;49m\u001b[39m90\u001b[39;49m, \u001b[39mset\u001b[39;49m\u001b[39m=\u001b[39;49m{\u001b[39m'\u001b[39;49m\u001b[39mxscale\u001b[39;49m\u001b[39m'\u001b[39;49m: \u001b[39m'\u001b[39;49m\u001b[39mlog\u001b[39;49m\u001b[39m'\u001b[39;49m})\n", + "\u001b[1;32m/home/cmeyers/deckard/examples/pytorch/plots.ipynb Cell 2\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 18\u001b[0m plt\u001b[39m.\u001b[39mgcf()\u001b[39m.\u001b[39mclear()\n\u001b[1;32m 19\u001b[0m data \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39msort_values(by\u001b[39m=\u001b[39m[hue, x, y])\n\u001b[0;32m---> 20\u001b[0m graph \u001b[39m=\u001b[39m sns\u001b[39m.\u001b[39;49mcatplot(\n\u001b[1;32m 21\u001b[0m data\u001b[39m=\u001b[39;49mdata, x\u001b[39m=\u001b[39;49mx, y\u001b[39m=\u001b[39;49my, hue\u001b[39m=\u001b[39;49mhue, kind\u001b[39m=\u001b[39;49mkind, hue_order\u001b[39m=\u001b[39;49mhue_order, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\n\u001b[1;32m 22\u001b[0m )\n\u001b[1;32m 23\u001b[0m graph\u001b[39m.\u001b[39mset_xlabels(xlabels)\n\u001b[1;32m 24\u001b[0m graph\u001b[39m.\u001b[39mset_ylabels(ylabels)\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/seaborn/categorical.py:3244\u001b[0m, in \u001b[0;36mcatplot\u001b[0;34m(data, x, y, hue, row, col, col_wrap, estimator, errorbar, n_boot, units, seed, order, hue_order, row_order, col_order, height, aspect, kind, native_scale, formatter, orient, color, palette, hue_norm, legend, legend_out, sharex, sharey, margin_titles, facet_kws, ci, **kwargs)\u001b[0m\n\u001b[1;32m 3241\u001b[0m g \u001b[39m=\u001b[39m FacetGrid(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfacet_kws)\n\u001b[1;32m 3243\u001b[0m \u001b[39m# Draw the plot onto the facets\u001b[39;00m\n\u001b[0;32m-> 3244\u001b[0m g\u001b[39m.\u001b[39;49mmap_dataframe(plot_func, x\u001b[39m=\u001b[39;49mx, y\u001b[39m=\u001b[39;49my, hue\u001b[39m=\u001b[39;49mhue, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mplot_kws)\n\u001b[1;32m 3246\u001b[0m \u001b[39mif\u001b[39;00m p\u001b[39m.\u001b[39morient \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mh\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m 3247\u001b[0m g\u001b[39m.\u001b[39mset_axis_labels(p\u001b[39m.\u001b[39mvalue_label, p\u001b[39m.\u001b[39mgroup_label)\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/seaborn/axisgrid.py:819\u001b[0m, in \u001b[0;36mFacetGrid.map_dataframe\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 816\u001b[0m kwargs[\u001b[39m\"\u001b[39m\u001b[39mdata\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m data_ijk\n\u001b[1;32m 818\u001b[0m \u001b[39m# Draw the plot\u001b[39;00m\n\u001b[0;32m--> 819\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_facet_plot(func, ax, args, kwargs)\n\u001b[1;32m 821\u001b[0m \u001b[39m# For axis labels, prefer to use positional args for backcompat\u001b[39;00m\n\u001b[1;32m 822\u001b[0m \u001b[39m# but also extract the x/y kwargs and use if no corresponding arg\u001b[39;00m\n\u001b[1;32m 823\u001b[0m axis_labels \u001b[39m=\u001b[39m [kwargs\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m), kwargs\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)]\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/seaborn/axisgrid.py:848\u001b[0m, in \u001b[0;36mFacetGrid._facet_plot\u001b[0;34m(self, func, ax, plot_args, plot_kwargs)\u001b[0m\n\u001b[1;32m 846\u001b[0m plot_args \u001b[39m=\u001b[39m []\n\u001b[1;32m 847\u001b[0m plot_kwargs[\u001b[39m\"\u001b[39m\u001b[39max\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m ax\n\u001b[0;32m--> 848\u001b[0m func(\u001b[39m*\u001b[39;49mplot_args, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mplot_kwargs)\n\u001b[1;32m 850\u001b[0m \u001b[39m# Sort out the supporting information\u001b[39;00m\n\u001b[1;32m 851\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_update_legend_data(ax)\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/seaborn/categorical.py:2847\u001b[0m, in \u001b[0;36mpointplot\u001b[0;34m(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, units, seed, markers, linestyles, dodge, join, scale, orient, color, palette, errwidth, ci, capsize, label, ax)\u001b[0m\n\u001b[1;32m 2844\u001b[0m \u001b[39mif\u001b[39;00m ax \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 2845\u001b[0m ax \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mgca()\n\u001b[0;32m-> 2847\u001b[0m plotter\u001b[39m.\u001b[39;49mplot(ax)\n\u001b[1;32m 2848\u001b[0m \u001b[39mreturn\u001b[39;00m ax\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/seaborn/categorical.py:1735\u001b[0m, in \u001b[0;36m_PointPlotter.plot\u001b[0;34m(self, ax)\u001b[0m\n\u001b[1;32m 1733\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mplot\u001b[39m(\u001b[39mself\u001b[39m, ax):\n\u001b[1;32m 1734\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Make the plot.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1735\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdraw_points(ax)\n\u001b[1;32m 1736\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mannotate_axes(ax)\n\u001b[1;32m 1737\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39morient \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mh\u001b[39m\u001b[39m\"\u001b[39m:\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/seaborn/categorical.py:1728\u001b[0m, in \u001b[0;36m_PointPlotter.draw_points\u001b[0;34m(self, ax)\u001b[0m\n\u001b[1;32m 1725\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mlen\u001b[39m(remove_na(statistic)):\n\u001b[1;32m 1726\u001b[0m x \u001b[39m=\u001b[39m y \u001b[39m=\u001b[39m [np\u001b[39m.\u001b[39mnan] \u001b[39m*\u001b[39m n_points\n\u001b[0;32m-> 1728\u001b[0m ax\u001b[39m.\u001b[39;49mscatter(x, y, label\u001b[39m=\u001b[39;49mhue_level,\n\u001b[1;32m 1729\u001b[0m facecolor\u001b[39m=\u001b[39;49mcolor, edgecolor\u001b[39m=\u001b[39;49mcolor,\n\u001b[1;32m 1730\u001b[0m linewidth\u001b[39m=\u001b[39;49mmew, marker\u001b[39m=\u001b[39;49mmarker, s\u001b[39m=\u001b[39;49mmarkersize,\n\u001b[1;32m 1731\u001b[0m zorder\u001b[39m=\u001b[39;49mz)\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/matplotlib/__init__.py:1442\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1439\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m 1440\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(ax, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m 1441\u001b[0m \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1442\u001b[0m \u001b[39mreturn\u001b[39;00m func(ax, \u001b[39m*\u001b[39;49m\u001b[39mmap\u001b[39;49m(sanitize_sequence, args), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1444\u001b[0m bound \u001b[39m=\u001b[39m new_sig\u001b[39m.\u001b[39mbind(ax, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m 1445\u001b[0m auto_label \u001b[39m=\u001b[39m (bound\u001b[39m.\u001b[39marguments\u001b[39m.\u001b[39mget(label_namer)\n\u001b[1;32m 1446\u001b[0m \u001b[39mor\u001b[39;00m bound\u001b[39m.\u001b[39mkwargs\u001b[39m.\u001b[39mget(label_namer))\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/matplotlib/axes/_axes.py:4711\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4708\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ymargin \u001b[39m<\u001b[39m \u001b[39m0.05\u001b[39m \u001b[39mand\u001b[39;00m x\u001b[39m.\u001b[39msize \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m 4709\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_ymargin(\u001b[39m0.05\u001b[39m)\n\u001b[0;32m-> 4711\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49madd_collection(collection)\n\u001b[1;32m 4712\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_request_autoscale_view()\n\u001b[1;32m 4714\u001b[0m \u001b[39mreturn\u001b[39;00m collection\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/matplotlib/axes/_base.py:2263\u001b[0m, in \u001b[0;36m_AxesBase.add_collection\u001b[0;34m(self, collection, autolim)\u001b[0m\n\u001b[1;32m 2258\u001b[0m collection\u001b[39m.\u001b[39mset_clip_path(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpatch)\n\u001b[1;32m 2260\u001b[0m \u001b[39mif\u001b[39;00m autolim:\n\u001b[1;32m 2261\u001b[0m \u001b[39m# Make sure viewLim is not stale (mostly to match\u001b[39;00m\n\u001b[1;32m 2262\u001b[0m \u001b[39m# pre-lazy-autoscale behavior, which is not really better).\u001b[39;00m\n\u001b[0;32m-> 2263\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_unstale_viewLim()\n\u001b[1;32m 2264\u001b[0m datalim \u001b[39m=\u001b[39m collection\u001b[39m.\u001b[39mget_datalim(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransData)\n\u001b[1;32m 2265\u001b[0m points \u001b[39m=\u001b[39m datalim\u001b[39m.\u001b[39mget_points()\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/matplotlib/axes/_base.py:852\u001b[0m, in \u001b[0;36m_AxesBase._unstale_viewLim\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 850\u001b[0m \u001b[39mfor\u001b[39;00m ax \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_shared_axes[name]\u001b[39m.\u001b[39mget_siblings(\u001b[39mself\u001b[39m):\n\u001b[1;32m 851\u001b[0m ax\u001b[39m.\u001b[39m_stale_viewlims[name] \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m--> 852\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mautoscale_view(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m{\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mscale\u001b[39;49m\u001b[39m{\u001b[39;49;00mname\u001b[39m}\u001b[39;49;00m\u001b[39m\"\u001b[39;49m: scale\n\u001b[1;32m 853\u001b[0m \u001b[39mfor\u001b[39;49;00m name, scale \u001b[39min\u001b[39;49;00m need_scale\u001b[39m.\u001b[39;49mitems()})\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/matplotlib/axes/_base.py:2854\u001b[0m, in \u001b[0;36m_AxesBase.autoscale_view\u001b[0;34m(self, tight, scalex, scaley)\u001b[0m\n\u001b[1;32m 2852\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39muse_sticky_edges:\n\u001b[1;32m 2853\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_xmargin \u001b[39mand\u001b[39;00m scalex \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_autoscalex_on():\n\u001b[0;32m-> 2854\u001b[0m x_stickies \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msort(np\u001b[39m.\u001b[39;49mconcatenate([\n\u001b[1;32m 2855\u001b[0m artist\u001b[39m.\u001b[39;49msticky_edges\u001b[39m.\u001b[39;49mx\n\u001b[1;32m 2856\u001b[0m \u001b[39mfor\u001b[39;49;00m ax \u001b[39min\u001b[39;49;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_shared_axes[\u001b[39m\"\u001b[39;49m\u001b[39mx\u001b[39;49m\u001b[39m\"\u001b[39;49m]\u001b[39m.\u001b[39;49mget_siblings(\u001b[39mself\u001b[39;49m)\n\u001b[1;32m 2857\u001b[0m \u001b[39mfor\u001b[39;49;00m artist \u001b[39min\u001b[39;49;00m ax\u001b[39m.\u001b[39;49mget_children()]))\n\u001b[1;32m 2858\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ymargin \u001b[39mand\u001b[39;00m scaley \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_autoscaley_on():\n\u001b[1;32m 2859\u001b[0m y_stickies \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msort(np\u001b[39m.\u001b[39mconcatenate([\n\u001b[1;32m 2860\u001b[0m artist\u001b[39m.\u001b[39msticky_edges\u001b[39m.\u001b[39my\n\u001b[1;32m 2861\u001b[0m \u001b[39mfor\u001b[39;00m ax \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_shared_axes[\u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mget_siblings(\u001b[39mself\u001b[39m)\n\u001b[1;32m 2862\u001b[0m \u001b[39mfor\u001b[39;00m artist \u001b[39min\u001b[39;00m ax\u001b[39m.\u001b[39mget_children()]))\n", + "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "def cat_plot(\n", + " data,\n", + " x,\n", + " y,\n", + " hue,\n", + " kind,\n", + " titles,\n", + " xlabels,\n", + " ylabels,\n", + " legend_title,\n", + " file,\n", + " folder,\n", + " hue_order=None,\n", + " rotation=0,\n", + " set={},\n", + " **kwargs,\n", + "):\n", + " plt.gcf().clear()\n", + " data = data.sort_values(by=[hue, x, y])\n", + " graph = sns.catplot(\n", + " data=data, x=x, y=y, hue=hue, kind=kind, hue_order=hue_order, **kwargs\n", + " )\n", + " graph.set_xlabels(xlabels)\n", + " graph.set_ylabels(ylabels)\n", + " graph.set_titles(titles)\n", + " graph.legend.set_title(title=legend_title)\n", + " graph.set_xticklabels(graph.axes.flat[-1].get_xticklabels(), rotation=rotation)\n", + " graph.set(**set)\n", + " graph.tight_layout()\n", + " graph.savefig(folder / file)\n", + " plt.gcf().clear()\n", + " # logger.info(f\"Saved graph to {folder / file}\")\n", + "\n", + "cat_plot(data, x='train_time', y='adv_failure_rate', hue='model_name', kind='point', col='atk_gen', row='def_gen', titles='{col_name} {row_name}', xlabels='Training time (s)', ylabels='Adversarial failure rate', legend_title='Model', file='train_time_adv_failure_rate.png', folder='output/plots', rotation=90, set={'xscale': 'log'})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index 3f4cd751..ce1c291e 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -25,14 +25,16 @@ def cat_plot( legend_title, file, folder, - hue_order = None, + hue_order=None, rotation=0, set={}, **kwargs, ): plt.gcf().clear() data = data.sort_values(by=[hue, x, y]) - graph = sns.catplot(data=data, x=x, y=y, hue=hue, kind=kind, hue_order = hue_order, **kwargs) + graph = sns.catplot( + data=data, x=x, y=y, hue=hue, kind=kind, hue_order=hue_order, **kwargs + ) graph.set_xlabels(xlabels) graph.set_ylabels(ylabels) graph.set_titles(titles) @@ -58,16 +60,18 @@ def line_plot( y_scale=None, x_scale=None, legend={}, - hue_order = None, + hue_order=None, + control=None, + control_color=None, **kwargs, ): plt.gcf().clear() data = data.sort_values(by=[hue, x, y]) - graph = sns.lineplot(data=data, x=x, y=y, hue=hue, hue_order=hue_order) + graph = sns.lineplot(data=data, x=x, y=y, hue=hue, hue_order=hue_order, **kwargs) graph.legend(**legend) - # if control is not None: - # assert control_color is not None, "Please specify a control color" - # graph.add_line(plt.axhline(y=control, color=control_color, linestyle="-")) + if control is not None: + assert control_color is not None, "Please specify a control color" + graph.add_line(plt.axhline(y=control, color=control_color, linestyle="-")) graph.set_xlabel(xlabel) graph.set_ylabel(ylabel) graph.set_title(title) @@ -94,7 +98,8 @@ def scatter_plot( y_scale=None, x_scale=None, legend={}, - hue_order = None, + hue_order=None, + **kwargs, ): # plt.gcf().clear() data = data.sort_values(by=[hue, x, y]) @@ -104,6 +109,7 @@ def scatter_plot( y=y, hue=hue, hue_order=hue_order, + **kwargs, ) graph.set_yscale(y_scale) graph.set_xscale(x_scale) @@ -118,30 +124,36 @@ def scatter_plot( plt.gcf().clear() return graph -def drop_frames_without_results( data, subset = ["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"]): + +def drop_frames_without_results( + data, + subset=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"], +): data.dropna(axis=0, subset=subset, inplace=True) return data + def calculate_failure_rate(data): data = data[data.columns.drop(list(data.filter(regex=r"\.1$")))] data.columns.str.replace(" ", "") data.loc[:, "failure_rate"] = ( - (1 - data.loc[:, "accuracy"]) * 100 / data.loc[:,"predict_time"] + (1 - data.loc[:, "accuracy"]) * 100 / data.loc[:, "predict_time"] ) data.loc[:, "adv_failure_rate"] = ( - (1 - data.loc[:,"adv_accuracy"]) * 100 / data.loc[:,"adv_fit_time"] + (1 - data.loc[:, "adv_accuracy"]) * 100 / data.loc[:, "adv_fit_time"] ) data.loc[:, "training_time_per_failure"] = ( - data.loc[:,"train_time"] / data.loc[:,"failure_rate"] + data.loc[:, "train_time"] / data.loc[:, "failure_rate"] ) data.loc[:, "training_time_per_adv_failure"] = ( - data.loc[:,"train_time"] / data.loc[:,"adv_failure_rate"] + data.loc[:, "train_time"] / data.loc[:, "adv_failure_rate"] ) data.loc[:, "adv_training_time_per_failure"] = ( - data.loc[:,"train_time"] / data.loc[:,"adv_failure_rate"] + data.loc[:, "train_time"] / data.loc[:, "adv_failure_rate"] ) return data + def min_max_scaling(data): if "atk_gen" not in data.columns: attacks = [] @@ -215,7 +227,16 @@ def min_max_scaling(data): ).exists(), f"File {args.file} does not exist. Please specify a valid file using the -f flag." csv_file = args.file data = pd.read_csv(csv_file) - data = drop_frames_without_results(data, subset=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"]) + data = drop_frames_without_results( + data, + subset=[ + "accuracy", + "adv_accuracy", + "train_time", + "adv_fit_time", + "predict_time", + ], + ) data = calculate_failure_rate(data) data = min_max_scaling(data) if "Unnamed: 0" in data.columns: @@ -254,7 +275,6 @@ def min_max_scaling(data): logger.info(f"Rendering graph {i}") locals()[f"graph{i}"] = line_plot(data, **dict_, folder=FOLDER) - scatter_plot_list = big_dict["scatter_plot"] for dict_ in scatter_plot_list: i += 1 From 9832c687b949c9584d63ceaec95636b3dc281abd Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Thu, 21 Sep 2023 21:38:30 +0000 Subject: [PATCH 085/148] update weibull --- deckard/layers/prepare_queue.py | 2 +- examples/pytorch/weibull.ipynb | 2096 +++++++++++++++++++++++++------ 2 files changed, 1683 insertions(+), 415 deletions(-) diff --git a/deckard/layers/prepare_queue.py b/deckard/layers/prepare_queue.py index 147ef272..6c4aeb94 100644 --- a/deckard/layers/prepare_queue.py +++ b/deckard/layers/prepare_queue.py @@ -12,7 +12,7 @@ logger = logging.getLogger(__name__) -__all__ = ["write_stage", "optimise", "parse_stage", "get_files"] +__all__ = ["write_stage", "parse_stage", "get_files"] def get_files( diff --git a/examples/pytorch/weibull.ipynb b/examples/pytorch/weibull.ipynb index 7b1de02c..57497bef 100644 --- a/examples/pytorch/weibull.ipynb +++ b/examples/pytorch/weibull.ipynb @@ -13,15 +13,14 @@ "\n", "import matplotlib.pyplot as plt\n", "\n", - "from sklearn.linear_model import LinearRegression, Ridge, Lasso, LogisticRegression\n", - "from sklearn.preprocessing import power_transform, StandardScaler\n", + "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import MinMaxScaler\n", "\n", "\n", - "from paretoset import paretoset\n", - "from lifelines import WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter\n", "\n", + "from paretoset import paretoset\n", + "from lifelines import WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter, CoxPHFitter\n", + "from plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling\n", "\n", "import argparse\n", "from pathlib import Path\n", @@ -34,7 +33,20 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ResNet152: 0.09777195281782439 \n", + " Resnet101: 0.09993344425956739 \n", + " Resnet50: 0.09634703196347033 \n", + " Resnet34: 0.09648437500000001 \n", + " Resnet18: 0.08609302325581396 \n", + "\n" + ] + } + ], "source": [ "FOLDER = Path(\"output/plots/\")\n", "csv_file = FOLDER / \"data.csv\"\n", @@ -42,7 +54,23 @@ "data.columns = data.columns.str.strip()\n", "data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n", "data.def_value.replace(\"\", 0, inplace=True)\n", - "data.atk_value.replace(\"\", 0, inplace=True)" + "data.atk_value.replace(\"\", 0, inplace=True)\n", + "data = drop_frames_without_results(data)\n", + "data = calculate_failure_rate(data)\n", + "data = min_max_scaling(data)\n", + "data.dropna(axis=0, subset=['atk_value', 'atk_param'], inplace=True)\n", + "data.dropna(axis=0, subset=['def_value', 'def_param'], inplace=True)\n", + "# data=data[data['def_gen'] == 'Gauss-in']\n", + "# data=data[data['atk_gen'] == 'HSJ']\n", + "\n", + "print(\n", + " \"ResNet152:\", data[data['model_layers'] == 152].adv_accuracy.mean(skipna=True), \"\\n\",\n", + " \"Resnet101:\", data[data['model_layers'] == 101].adv_accuracy.mean(skipna=True), \"\\n\",\n", + " \"Resnet50:\", data[data['model_layers'] == 50].adv_accuracy.mean(skipna=True), \"\\n\",\n", + " \"Resnet34:\", data[data['model_layers'] == 34].adv_accuracy.mean(skipna=True), \"\\n\",\n", + " \"Resnet18:\", data[data['model_layers'] == 18].adv_accuracy.mean(skipna=True), \"\\n\",\n", + ")\n", + "\n" ] }, { @@ -52,14 +80,14 @@ "outputs": [], "source": [ "def plot_aft(\n", - " data,\n", + " df,\n", " file,\n", " event_col,\n", " duration_col,\n", " title,\n", " mtype,\n", - " xlabel=\"$\\log(\\eta)$ - 95% CI\",\n", - " ylabel=\"Covariate\",\n", + " xlabel=None,\n", + " ylabel=None,\n", " replacement_dict={},\n", " **kwargs,\n", "):\n", @@ -69,13 +97,15 @@ " aft = LogNormalAFTFitter(**kwargs)\n", " elif mtype == \"log_logistic\":\n", " aft = LogLogisticAFTFitter(**kwargs)\n", - " df, test = train_test_split(data, test_size=0.2, random_state=42)\n", + " elif mtype == \"cox\":\n", + " aft = CoxPHFitter(**kwargs)\n", " assert (\n", " duration_col in df.columns\n", " ), f\"Column {duration_col} not in dataframe with columns {df.columns}\"\n", - " assert (\n", - " event_col in df.columns\n", - " ), f\"Column {event_col} not in dataframe with columns {df.columns}\"\n", + " if event_col is not None:\n", + " assert (\n", + " event_col in df.columns\n", + " ), f\"Column {event_col} not in dataframe with columns {df.columns}\"\n", " aft.fit(df, duration_col=duration_col, event_col=event_col)\n", " ax = aft.plot()\n", " labels = ax.get_yticklabels()\n", @@ -89,354 +119,161 @@ " ax.get_figure().tight_layout()\n", " ax.get_figure().savefig(FOLDER / file)\n", " logger.info(f\"Saved graph to {FOLDER / file}\")\n", - " return ax, aft, test\n", + " plt.show()\n", + " return ax, aft\n", + "\n", + "def plot_partial_effects(\n", + " file,\n", + " aft,\n", + " covariate_aray,\n", + " values_array,\n", + " title,\n", + " xlabel=\"Covariate\",\n", + " ylabel=\"Failure rate\",\n", + " legend_kwargs={\"loc\": \"upper left\"},\n", + " replacement_dict={},\n", + " cmap='coolwarm',\n", + " **kwargs, \n", + " ):\n", + " plt.gcf().clear()\n", + " # kwargs.pop(\"replacement_dict\")\n", + " pareto = aft.plot_partial_effects_on_outcome(covariate_aray, values_array, cmap=cmap, **kwargs)\n", + " labels = pareto.get_yticklabels()\n", + " labels = [label.get_text() for label in labels]\n", + " for k, v in replacement_dict.items():\n", + " labels = [label.replace(k, v) for label in labels]\n", + " pareto.set_yticklabels(labels)\n", + " pareto.legend(**legend_kwargs)\n", + " pareto.set_ylabel(ylabel)\n", + " pareto.set_xlabel(xlabel)\n", + " pareto.set_title(title)\n", + " pareto.get_figure().tight_layout()\n", + " pareto.get_figure().savefig(FOLDER / file)\n", + " logger.info(f\"Saved graph to {FOLDER / file}\")\n", + " return pareto\n", + "\n", + "def score_model(aft, train, test):\n", + " train_score = aft.score(train)\n", + " test_score = aft.score(test)\n", + " scores = {'train_score': train_score, 'test_score': test_score}\n", + " plt.show()\n", + " return scores\n", "\n", "\n", "def clean_data_for_aft(\n", - " data, kwarg_list, standard_scaling=True, target=\"adv_failure_rate\"\n", + " data, kwarg_list, target=\"adv_failure_rate\"\n", "):\n", " subset = data.copy()\n", " assert target in subset, f\"Target {target} not in dataframe with columns {subset.columns}\"\n", " y = subset[target].copy(deep=True)\n", " cleaned = pd.DataFrame()\n", - " if target in kwarg_list:\n", - " kwarg_list.remove(target)\n", " for kwarg in kwarg_list:\n", " cleaned = pd.concat([cleaned, subset[kwarg]], axis=1)\n", " cols = cleaned.columns\n", - " cleaned.def_value.fillna(0, inplace=True)\n", - " if standard_scaling is True:\n", - " scaler = StandardScaler()\n", - " scaler = scaler.fit(cleaned)\n", - " cleaned_numeric = pd.DataFrame(scaler.transform(cleaned), columns=cols)\n", - " else:\n", - " cleaned_numeric = cleaned\n", - "\n", - " cleaned_numeric = pd.DataFrame(subset, columns=cols)\n", - " \n", + " cleaned = pd.DataFrame(subset, columns=cols)\n", " cleaned.dropna(inplace=True, how='any',)\n", - " return cleaned_numeric, y" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " week arrest fin age race wexp mar paro prio\n", - "0 20 1 0 27 1 0 0 1 3\n", - "1 17 1 0 18 1 0 0 1 8\n", - "2 25 1 0 19 0 1 0 1 13\n", - "3 52 0 1 23 1 1 1 1 1\n", - "4 52 0 0 19 0 1 0 1 3" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from lifelines.datasets import load_rossi\n", - "rossi = load_rossi()\n", - "rossi.head()" + " cleaned[target] = y\n", + " assert target in cleaned, f\"Target {target} not in dataframe with columns {cleaned.columns}\"\n", + " return cleaned, y, data" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 4, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "accuracy\n", - "train_time\n", - "predict_time\n", - "atk_value\n", - "def_value\n", - "model_layers\n", - "adv_fit_time\n", - "adv_failure_rate\n", - "random_state\n" - ] - } - ], + "outputs": [], "source": [ + "\n", "kwarg_list = [\n", " \"accuracy\",\n", - " \"train_time_per_sample\",\n", - " \"predict_time_per_sample\",\n", + " \"train_time\",\n", + " \"predict_time\",\n", " \"atk_value\",\n", " \"def_value\",\n", " \"data.sample.random_state\",\n", - " # \"adv_failure_rate\",\n", + " \"adv_failure_rate\",\n", + " \"failure_rate\",\n", " \"model_layers\",\n", " \"adv_fit_time\",\n", + " \"model.art.pipeline.initialize.kwargs.optimizer.lr\",\n", + " # \"def_gen\",\n", + " # \"atk_gen\",\n", + " # \"adv_log_loss\",\n", + " # \"adv_accuracy\",\n", " # \"adv_accuracy\",\n", "]\n", "\n", "\n", - "cleaned, y = clean_data_for_aft(data, kwarg_list, standard_scaling=True)\n", - "cleaned.dropna(axis=0, how=\"any\", subset=kwarg_list.remove(\"def_value\"), inplace=True)\n", - "cleaned[\"adv_failure_rate\"] = y\n", - "cleaned['random_state'] = cleaned['data.sample.random_state']\n", - "cleaned.drop(columns=['data.sample.random_state'], inplace=True)\n", - "for col in cleaned.columns:\n", - " print(col)\n" + "# cleaned['accuracy'] = y" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 5, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "weibull_dict = {\n", - " \"Intercept: rho_\": \"$\\\\rho$\",\n", - " \"Intercept: lambda_\": \"$\\lambda$\",\n", - " \"random_state: lambda_\": \"Random State\",\n", - " \"def_value: lambda_\": \"Defence Strength\",\n", - " \"atk_value: lambda_\": \"Attack Distance\",\n", - " \"train_time: lambda_\": \"Training Time\",\n", - " \"predict_time: lambda_\": \"Inference Time\",\n", - " # \"adv_accuracy: lambda_\": \"Adv. Accuracy\",\n", - " \"accuracy: lambda_\": \"Ben. Accuracy\",\n", - " # \"adv_fit_time: lambda_\": \"Adv. Fit Time\",\n", - " \"model_layers: lambda_\": \"No. of Hidden Layers\",\n", - "}\n", + "target = \"failure_rate\"\n", + "cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target)\n", + "X_train, X_test, y_train, y_test = train_test_split(cleaned, y, test_size=0.2, random_state=42)\n", + "assert target in cleaned, f\"Target {target} not in dataframe with columns {cleaned.columns}\"\n", "\n", - "weibull_graph, wft, wft_test = plot_aft(\n", - " cleaned,\n", - " \"weibull_aft.pdf\",\n", - " \"adv_failure_rate\",\n", - " \"adv_fit_time\",\n", - " \"Weibull AFT Model\",\n", - " \"weibull\",\n", - " replacement_dict=weibull_dict,\n", - ")" + "# from sklearn.preprocessing import PowerTransformer\n", + "\n", + "# pt = PowerTransformer(method='yeo-johnson', standardize=False)\n", + "# del X_train[target]\n", + "# del X_test[target]\n", + "# X_train_cols = X_train.columns\n", + "# X_train = pt.fit(X_train).transform(X_train)\n", + "# X_test = pt.transform(X_test)\n", + "# X_train = pd.DataFrame(X_train, columns=X_train_cols)\n", + "# X_test = pd.DataFrame(X_test, columns=X_train_cols)\n", + "# X_train[target] = y_train\n", + "# y_train = X_train[target]\n" ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 6, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "log_normal_dict = {\n", - " \"Intercept: sigma_\": \"$\\sigma$\",\n", - " \"Intercept: mu_\": \"$\\mu$\",\n", - " \"random_state: mu_\": \"Random State\",\n", - " \"def_value: mu_\": \"Defence Strength\",\n", - " \"atk_value: mu_\": \"Attack Distance\",\n", - " \"train_time: mu_\": \"Training Time\",\n", - " \"predict_time: mu_\": \"Inference Time\",\n", - " \"accuracy: mu_\": \"Ben. Accuracy\",\n", - " \"adv_fit_time: mu_\": \"Adv. Fit Time\",\n", - " \"model_layers: mu_\": \"No. of Hidden Layers\",\n", - "}\n", - "\n", - "log_normal_graph, lnt, lnt_test = plot_aft(\n", - " cleaned,\n", - " \"log_normal_aft.pdf\",\n", - " \"adv_failure_rate\",\n", - " \"adv_fit_time\",\n", - " \"Log Normal AFT Model\",\n", - " \"log_normal\",\n", - " replacement_dict=log_normal_dict,\n", - ")" + "# sense_dict ={\n", + "# \"accuracy\" : \"max\",\n", + "# \"train_time\" : \"min\",\n", + "# \"predict_time\" : \"min\",\n", + "# # \"atk_value\" : \"diff\",\n", + "# # \"def_value\" : \"diff\",\n", + "# \"data.sample.random_state\" : \"diff\",\n", + "# \"adv_failure_rate\" : \"max\",\n", + "# \"model_layers\" : \"diff\",\n", + "# \"adv_fit_time\" : \"min\",\n", + "# \"atk_param\" : \"diff\",\n", + "# \"def_param\" : \"diff\",\n", + "# \"model.art.pipeline.initialize.kwargs.optimizer.lr\" : \"diff\",\n", + "# \"failure_rate\" : \"maximize\",\n", + "# }\n", + "# subset = X_train.loc[:, sense_dict.keys()]\n", + "# senses = sense_dict.values()\n", + "# these = paretoset(subset)\n", + "# X_train = X_train.iloc[these, :]\n" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "log_logistic_dict = {\n", - " \"Intercept: beta_\": \"$\\\\beta$\",\n", - " \"Intercept: alpha_\": \"$\\\\alpha$\",\n", - " \"random_state: alpha_\": \"Random State\",\n", - " \"def_value: alpha_\": \"Defence Strength\",\n", - " \"atk_value: alpha_\": \"Attack Distance\",\n", - " \"train_time: alpha_\": \"Training Time\",\n", - " \"predict_time: alpha_\": \"Inference Time\",\n", - " \"accuracy: alpha_\": \"Ben. Accuracy\",\n", - " \"adv_fit_time: alpha_\": \"Adv. Fit Time\",\n", - " \"model_layers: alpha_\": \"No. of Hidden Layers\",\n", - "}\n", - "\n", - "log_logistic_graph, llt, llt_test = plot_aft(\n", - " cleaned,\n", - " \"log_logistic_aft.pdf\",\n", - " \"adv_failure_rate\",\n", - " \"adv_fit_time\",\n", - " \"Log Logistic AFT Model\",\n", - " \"log_logistic\",\n", - " replacement_dict=log_logistic_dict,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "aft_dict = {\n", - " \"Weibull\": wft,\n", - " \"LogNormal\": lnt,\n", - " \"LogLogistic\": llt,\n", - "}\n", - "aft_data = pd.DataFrame()\n", - "aft_data.index.name = \"Model\"\n", - "aft_data.index = aft_dict.keys()\n", - "aft_data[\"AIC\"] = [x.AIC_ for x in aft_dict.values()]\n", - "aft_data[\"LogLikelihood\"] = [x.log_likelihood_ for x in aft_dict.values()]\n", - "aft_data[\"Concordance Score\"] = [x.concordance_index_ for x in aft_dict.values()]\n", - "aft_data[\"BIC\"] = [x.BIC_ for x in aft_dict.values()]\n", - "aft_data = aft_data.round(2)\n", - "aft_data.to_csv(FOLDER / \"aft_comparison.csv\")\n", - "logger.info(f\"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ + }, { "data": { "text/html": [ @@ -457,97 +294,212 @@ "\n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
degrees_freedom6modellifelines.WeibullAFTFitter
duration col'adv_fit_time'
event col'failure_rate'
number of observations1917
number of events observed1917
null_distributionchi squaredlog-likelihood-5413.65
test_namelog-likelihood ratio testtime fit was run2023-09-21 21:26:40 UTC
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test_statisticp-log2(p)coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
0324.99lambda_accuracy0.051.050.12-0.190.300.821.340.000.410.680.56
adv_failure_rate-0.001.000.00-0.00-0.001.001.000.00-57.66<0.005inf
atk_value0.431.540.130.180.681.201.970.003.38<0.00510.44
data.sample.random_state0.051.050.020.010.081.011.080.002.730.017.29
def_value-0.050.950.13-0.300.200.741.230.00-0.370.710.49
model.art.pipeline.initialize.kwargs.optimizer.lr-0.001.000.00-0.000.001.001.000.00-0.520.600.73
model_layers0.001.000.000.000.011.001.010.002.88<0.0057.99
predict_time-0.280.760.02-0.32-0.240.720.790.00-12.39<0.005114.71
train_time0.001.000.00-0.000.001.001.000.001.210.222.15
Intercept3.7341.580.173.394.0729.5858.460.0021.45<0.005336.62
rho_Intercept-0.720.490.02-0.76-0.690.470.500.00-42.18<0.005220.72inf
" - ], - "text/latex": [ - "\\begin{tabular}{lrrr}\n", - " & test_statistic & p & -log2(p) \\\\\n", - "0 & 324.99 & 0.00 & 220.72 \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\n", - " degrees_freedom = 6\n", - "null_distribution = chi squared\n", - " test_name = log-likelihood ratio test\n", - "\n", - "---\n", - " test_statistic p -log2(p)\n", - " 324.99 <0.005 220.72" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wft.log_likelihood_ratio_test()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'covariate `adv_fit_time_per_sample` is not present in the original dataset'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[21], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m covariates \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(cleaned\u001b[39m.\u001b[39mcolumns)\n\u001b[1;32m 2\u001b[0m values \u001b[39m=\u001b[39m cleaned\n\u001b[0;32m----> 3\u001b[0m lnt\u001b[39m.\u001b[39;49mplot_partial_effects_on_outcome(covariates, values )\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/lifelines/fitters/__init__.py:3392\u001b[0m, in \u001b[0;36mParametericAFTRegressionFitter.plot_partial_effects_on_outcome\u001b[0;34m(self, covariates, values, plot_baseline, times, y, ax, **kwargs)\u001b[0m\n\u001b[1;32m 3390\u001b[0m \u001b[39mfor\u001b[39;00m covariate \u001b[39min\u001b[39;00m covariates:\n\u001b[1;32m 3391\u001b[0m \u001b[39mif\u001b[39;00m covariate \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m original_columns:\n\u001b[0;32m-> 3392\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mcovariate `\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m` is not present in the original dataset\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m covariate)\n\u001b[1;32m 3394\u001b[0m \u001b[39mif\u001b[39;00m ax \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 3395\u001b[0m ax \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mgca()\n", - "\u001b[0;31mKeyError\u001b[0m: 'covariate `adv_fit_time_per_sample` is not present in the original dataset'" - ] - } - ], - "source": [ - "covariates = list(cleaned.columns)\n", - "values = cleaned\n", - "lnt.plot_partial_effects_on_outcome(covariates, values )" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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modellifelines.CoxPHFitter
duration col'adv_fit_time'
event col'failure_rate'
baseline estimationbreslow
number of observations1917
number of events observed1917
partial log-likelihood-9069.47
time fit was run2023-09-21 21:26:42 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
accuracy-0.130.880.06-0.25-0.000.781.000.00-2.040.044.59
train_time0.001.000.000.000.001.001.000.005.97<0.00528.69
predict_time0.011.010.01-0.010.040.991.040.001.100.271.89
atk_value-0.170.840.07-0.30-0.040.740.960.00-2.550.016.53
def_value-0.020.980.06-0.150.110.861.110.00-0.300.760.39
data.sample.random_state-0.020.980.01-0.04-0.000.961.000.00-2.270.025.43
adv_failure_rate0.021.020.000.020.031.021.030.0036.55<0.005969.18
model_layers-0.001.000.00-0.01-0.000.991.000.00-7.32<0.00541.85
model.art.pipeline.initialize.kwargs.optimizer.lr-0.001.000.00-0.000.001.001.000.00-0.550.580.79

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Concordance0.95
Partial AIC18156.95
log-likelihood ratio test7015.80 on 9 df
-log2(p) of ll-ratio testinf
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" + ], + "text/latex": [ + "\\begin{tabular}{lrrrrrrrrrrr}\n", + " & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", + "covariate & & & & & & & & & & & \\\\\n", + "accuracy & -0.13 & 0.88 & 0.06 & -0.25 & -0.00 & 0.78 & 1.00 & 0.00 & -2.04 & 0.04 & 4.59 \\\\\n", + "train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 5.97 & 0.00 & 28.69 \\\\\n", + "predict_time & 0.01 & 1.01 & 0.01 & -0.01 & 0.04 & 0.99 & 1.04 & 0.00 & 1.10 & 0.27 & 1.89 \\\\\n", + "atk_value & -0.17 & 0.84 & 0.07 & -0.30 & -0.04 & 0.74 & 0.96 & 0.00 & -2.55 & 0.01 & 6.53 \\\\\n", + "def_value & -0.02 & 0.98 & 0.06 & -0.15 & 0.11 & 0.86 & 1.11 & 0.00 & -0.30 & 0.76 & 0.39 \\\\\n", + "data.sample.random_state & -0.02 & 0.98 & 0.01 & -0.04 & -0.00 & 0.96 & 1.00 & 0.00 & -2.27 & 0.02 & 5.43 \\\\\n", + "adv_failure_rate & 0.02 & 1.02 & 0.00 & 0.02 & 0.03 & 1.02 & 1.03 & 0.00 & 36.55 & 0.00 & 969.18 \\\\\n", + "model_layers & -0.00 & 1.00 & 0.00 & -0.01 & -0.00 & 0.99 & 1.00 & 0.00 & -7.32 & 0.00 & 41.85 \\\\\n", + "model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.55 & 0.58 & 0.79 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\n", + " duration col = 'adv_fit_time'\n", + " event col = 'failure_rate'\n", + " baseline estimation = breslow\n", + " number of observations = 1917\n", + "number of events observed = 1917\n", + " partial log-likelihood = -9069.47\n", + " time fit was run = 2023-09-21 21:26:42 UTC\n", + "\n", + "---\n", + " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", + "covariate \n", + "accuracy -0.13 0.88 0.06 -0.25 -0.00 0.78 1.00\n", + "train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", + "predict_time 0.01 1.01 0.01 -0.01 0.04 0.99 1.04\n", + "atk_value -0.17 0.84 0.07 -0.30 -0.04 0.74 0.96\n", + "def_value -0.02 0.98 0.06 -0.15 0.11 0.86 1.11\n", + "data.sample.random_state -0.02 0.98 0.01 -0.04 -0.00 0.96 1.00\n", + "adv_failure_rate 0.02 1.02 0.00 0.02 0.03 1.02 1.03\n", + "model_layers -0.00 1.00 0.00 -0.01 -0.00 0.99 1.00\n", + "model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + "\n", + " cmp to z p -log2(p)\n", + "covariate \n", + "accuracy 0.00 -2.04 0.04 4.59\n", + "train_time 0.00 5.97 <0.005 28.69\n", + "predict_time 0.00 1.10 0.27 1.89\n", + "atk_value 0.00 -2.55 0.01 6.53\n", + "def_value 0.00 -0.30 0.76 0.39\n", + "data.sample.random_state 0.00 -2.27 0.02 5.43\n", + "adv_failure_rate 0.00 36.55 <0.005 969.18\n", + "model_layers 0.00 -7.32 <0.005 41.85\n", + "model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.55 0.58 0.79\n", + "---\n", + "Concordance = 0.95\n", + "Partial AIC = 18156.95\n", + "log-likelihood ratio test = 7015.80 on 9 df\n", + "-log2(p) of ll-ratio test = inf" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cox_dict = {\n", + " \"adv_failure_rate\": \"Adv. Failure Rate\",\n", + " \"def_value\" : \"Defence Strength\",\n", + " \"data.sample.random_state\" : \"Random State\",\n", + " \"train_time\" : \"Training Time\",\n", + " \"model_layers\" : \"No. of Layers\",\n", + " \"model.art.pipeline.initialize.kwargs.optimizer.lr\" : \"Learning Rate\",\n", + " \"adv_accuracy\" : \"Adv. Accuracy\",\n", + " \"adv_fit_time\" : \"Adv. Fit Time\",\n", + " \"adv_log_loss\" : \"Adv. Log Loss\",\n", + " \"predict_time\" : \"Inference Time\",\n", + " \"accuracy\" : \"Ben. Accuracy\",\n", + " \"failure_rate\" : \"Ben. Failure Rate\", \n", + "}\n", + "\n", + "cox_afr, cft = plot_aft(\n", + " X_train,\n", + " file = \"cox_aft.pdf\",\n", + " event_col = target,\n", + " duration_col = \"adv_fit_time\",\n", + " title = \"Cox AFT Model\",\n", + " mtype = \"cox\",\n", + " replacement_dict=cox_dict,\n", + ")\n", + "cox_scores = score_model(cft, X_train, X_test)\n", + "cft.print_summary()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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modellifelines.LogNormalAFTFitter
duration col'adv_fit_time'
event col'failure_rate'
number of observations1917
number of events observed1917
log-likelihood-5226.98
time fit was run2023-09-21 21:26:44 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
mu_accuracy-0.030.970.12-0.270.210.761.240.00-0.230.820.29
adv_failure_rate-0.001.000.00-0.00-0.001.001.000.00-47.18<0.005inf
atk_value0.281.330.130.030.531.031.710.002.190.035.13
data.sample.random_state0.041.040.020.010.071.011.070.002.310.025.59
def_value-0.050.950.13-0.300.200.741.220.00-0.390.700.52
model.art.pipeline.initialize.kwargs.optimizer.lr-0.001.000.00-0.000.001.001.000.00-0.240.810.31
model_layers-0.001.000.00-0.000.001.001.000.00-1.260.212.27
predict_time-0.160.850.02-0.20-0.110.820.890.00-7.05<0.00538.99
train_time-0.001.000.00-0.000.001.001.000.00-0.570.570.81
Intercept2.8517.270.172.523.1812.4723.930.0017.13<0.005215.99
sigma_Intercept0.732.070.020.700.762.012.140.0045.20<0.005inf

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Concordance0.81
AIC10475.97
log-likelihood ratio test1616.40 on 9 df
-log2(p) of ll-ratio testinf
\n", + "
" + ], + "text/latex": [ + "\\begin{tabular}{llrrrrrrrrrrr}\n", + " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", + "param & covariate & & & & & & & & & & & \\\\\n", + "\\multirow[c]{10}{*}{mu_} & accuracy & -0.03 & 0.97 & 0.12 & -0.27 & 0.21 & 0.76 & 1.24 & 0.00 & -0.23 & 0.82 & 0.29 \\\\\n", + " & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -47.18 & 0.00 & inf \\\\\n", + " & atk_value & 0.28 & 1.33 & 0.13 & 0.03 & 0.53 & 1.03 & 1.71 & 0.00 & 2.19 & 0.03 & 5.13 \\\\\n", + " & data.sample.random_state & 0.04 & 1.04 & 0.02 & 0.01 & 0.07 & 1.01 & 1.07 & 0.00 & 2.31 & 0.02 & 5.59 \\\\\n", + " & def_value & -0.05 & 0.95 & 0.13 & -0.30 & 0.20 & 0.74 & 1.22 & 0.00 & -0.39 & 0.70 & 0.52 \\\\\n", + " & model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.24 & 0.81 & 0.31 \\\\\n", + " & model_layers & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -1.26 & 0.21 & 2.27 \\\\\n", + " & predict_time & -0.16 & 0.85 & 0.02 & -0.20 & -0.11 & 0.82 & 0.89 & 0.00 & -7.05 & 0.00 & 38.99 \\\\\n", + " & train_time & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.57 & 0.57 & 0.81 \\\\\n", + " & Intercept & 2.85 & 17.27 & 0.17 & 2.52 & 3.18 & 12.47 & 23.93 & 0.00 & 17.13 & 0.00 & 215.99 \\\\\n", + "sigma_ & Intercept & 0.73 & 2.07 & 0.02 & 0.70 & 0.76 & 2.01 & 2.14 & 0.00 & 45.20 & 0.00 & inf \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\n", + " duration col = 'adv_fit_time'\n", + " event col = 'failure_rate'\n", + " number of observations = 1917\n", + "number of events observed = 1917\n", + " log-likelihood = -5226.98\n", + " time fit was run = 2023-09-21 21:26:44 UTC\n", + "\n", + "---\n", + " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", + "param covariate \n", + "mu_ accuracy -0.03 0.97 0.12 -0.27 0.21 0.76 1.24\n", + " adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + " atk_value 0.28 1.33 0.13 0.03 0.53 1.03 1.71\n", + " data.sample.random_state 0.04 1.04 0.02 0.01 0.07 1.01 1.07\n", + " def_value -0.05 0.95 0.13 -0.30 0.20 0.74 1.22\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + " model_layers -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + " predict_time -0.16 0.85 0.02 -0.20 -0.11 0.82 0.89\n", + " train_time -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + " Intercept 2.85 17.27 0.17 2.52 3.18 12.47 23.93\n", + "sigma_ Intercept 0.73 2.07 0.02 0.70 0.76 2.01 2.14\n", + "\n", + " cmp to z p -log2(p)\n", + "param covariate \n", + "mu_ accuracy 0.00 -0.23 0.82 0.29\n", + " adv_failure_rate 0.00 -47.18 <0.005 inf\n", + " atk_value 0.00 2.19 0.03 5.13\n", + " data.sample.random_state 0.00 2.31 0.02 5.59\n", + " def_value 0.00 -0.39 0.70 0.52\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.24 0.81 0.31\n", + " model_layers 0.00 -1.26 0.21 2.27\n", + " predict_time 0.00 -7.05 <0.005 38.99\n", + " train_time 0.00 -0.57 0.57 0.81\n", + " Intercept 0.00 17.13 <0.005 215.99\n", + "sigma_ Intercept 0.00 45.20 <0.005 inf\n", + "---\n", + "Concordance = 0.81\n", + "AIC = 10475.97\n", + "log-likelihood ratio test = 1616.40 on 9 df\n", + "-log2(p) of ll-ratio test = inf" ] }, - "execution_count": 14, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "log_normal_dict = {\n", + " \"Intercept: sigma_\": \"$\\sigma$\",\n", + " \"Intercept: mu_\": \"$\\mu$\",\n", + " \"def_value: mu_\": \"Defence Strength\",\n", + " \"atk_value: mu_\": \"Attack Strength\",\n", + " \"train_time: mu_\": \"Training Time\",\n", + " \"predict_time: mu_\": \"Inference Time\",\n", + " \"adv_fit_time: mu_\": \"Adv. Fit Time\",\n", + " \"model_layers: mu_\": \"No. of Layers\",\n", + " \"model.art.pipeline.initialize.kwargs.optimizer.lr: mu_\" : \"Learning Rate\",\n", + " \"data.sample.random_state: mu_\": \"Random State\",\n", + " \"adv_log_loss: mu_\": \"Adv. Log Loss\",\n", + " \"adv_accuracy: mu_\": \"Adv. Accuracy\",\n", + " \"accuracy: mu_\": \"Ben. Accuracy\",\n", + " \"adv_failure_rate: mu_\": \"Adv. Failure Rate\",\n", + " \"def_gen\" : \"Defence\",\n", + " \"learning_rate: mu_\" : \"Learning Rate\",\n", + "}\n", + "\n", + "log_normal_graph, lnt = plot_aft(\n", + " X_train,\n", + " \"log_normal_aft.pdf\",\n", + " target,\n", + " \"adv_fit_time\",\n", + " \"Log Normal AFT Model\",\n", + " \"log_normal\",\n", + " replacement_dict=log_normal_dict,\n", + ")\n", + "lnt_scores = score_model(lnt, X_train, X_test)\n", + "lnt.print_summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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modellifelines.LogLogisticAFTFitter
duration col'adv_fit_time'
event col'failure_rate'
number of observations1917
number of events observed1917
log-likelihood-5289.31
time fit was run2023-09-21 21:26:47 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
alpha_accuracy-0.050.950.13-0.300.200.741.220.00-0.400.690.54
adv_failure_rate-0.001.000.00-0.00-0.001.001.000.00-40.16<0.005inf
atk_value0.291.340.140.020.551.021.740.002.130.034.92
data.sample.random_state0.041.040.020.010.081.011.080.002.450.016.14
def_value-0.060.940.13-0.330.200.721.220.00-0.460.640.64
model.art.pipeline.initialize.kwargs.optimizer.lr-0.001.000.00-0.000.001.001.000.00-0.100.920.12
model_layers-0.001.000.00-0.01-0.000.991.000.00-2.700.017.18
predict_time-0.130.880.02-0.18-0.090.840.920.00-5.79<0.00527.07
train_time-0.001.000.00-0.000.001.001.000.00-1.170.242.04
Intercept2.8216.760.172.483.1611.9523.480.0016.36<0.005197.51
beta_Intercept-0.210.810.02-0.24-0.170.780.840.00-11.02<0.00591.38

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Concordance0.81
AIC10600.61
log-likelihood ratio test1664.88 on 9 df
-log2(p) of ll-ratio testinf
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" + ], + "text/latex": [ + "\\begin{tabular}{llrrrrrrrrrrr}\n", + " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", + "param & covariate & & & & & & & & & & & \\\\\n", + "\\multirow[c]{10}{*}{alpha_} & accuracy & -0.05 & 0.95 & 0.13 & -0.30 & 0.20 & 0.74 & 1.22 & 0.00 & -0.40 & 0.69 & 0.54 \\\\\n", + " & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -40.16 & 0.00 & inf \\\\\n", + " & atk_value & 0.29 & 1.34 & 0.14 & 0.02 & 0.55 & 1.02 & 1.74 & 0.00 & 2.13 & 0.03 & 4.92 \\\\\n", + " & data.sample.random_state & 0.04 & 1.04 & 0.02 & 0.01 & 0.08 & 1.01 & 1.08 & 0.00 & 2.45 & 0.01 & 6.14 \\\\\n", + " & def_value & -0.06 & 0.94 & 0.13 & -0.33 & 0.20 & 0.72 & 1.22 & 0.00 & -0.46 & 0.64 & 0.64 \\\\\n", + " & model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.10 & 0.92 & 0.12 \\\\\n", + " & model_layers & -0.00 & 1.00 & 0.00 & -0.01 & -0.00 & 0.99 & 1.00 & 0.00 & -2.70 & 0.01 & 7.18 \\\\\n", + " & predict_time & -0.13 & 0.88 & 0.02 & -0.18 & -0.09 & 0.84 & 0.92 & 0.00 & -5.79 & 0.00 & 27.07 \\\\\n", + " & train_time & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -1.17 & 0.24 & 2.04 \\\\\n", + " & Intercept & 2.82 & 16.76 & 0.17 & 2.48 & 3.16 & 11.95 & 23.48 & 0.00 & 16.36 & 0.00 & 197.51 \\\\\n", + "beta_ & Intercept & -0.21 & 0.81 & 0.02 & -0.24 & -0.17 & 0.78 & 0.84 & 0.00 & -11.02 & 0.00 & 91.38 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\n", + " duration col = 'adv_fit_time'\n", + " event col = 'failure_rate'\n", + " number of observations = 1917\n", + "number of events observed = 1917\n", + " log-likelihood = -5289.31\n", + " time fit was run = 2023-09-21 21:26:47 UTC\n", + "\n", + "---\n", + " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", + "param covariate \n", + "alpha_ accuracy -0.05 0.95 0.13 -0.30 0.20 0.74 1.22\n", + " adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + " atk_value 0.29 1.34 0.14 0.02 0.55 1.02 1.74\n", + " data.sample.random_state 0.04 1.04 0.02 0.01 0.08 1.01 1.08\n", + " def_value -0.06 0.94 0.13 -0.33 0.20 0.72 1.22\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + " model_layers -0.00 1.00 0.00 -0.01 -0.00 0.99 1.00\n", + " predict_time -0.13 0.88 0.02 -0.18 -0.09 0.84 0.92\n", + " train_time -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + " Intercept 2.82 16.76 0.17 2.48 3.16 11.95 23.48\n", + "beta_ Intercept -0.21 0.81 0.02 -0.24 -0.17 0.78 0.84\n", + "\n", + " cmp to z p -log2(p)\n", + "param covariate \n", + "alpha_ accuracy 0.00 -0.40 0.69 0.54\n", + " adv_failure_rate 0.00 -40.16 <0.005 inf\n", + " atk_value 0.00 2.13 0.03 4.92\n", + " data.sample.random_state 0.00 2.45 0.01 6.14\n", + " def_value 0.00 -0.46 0.64 0.64\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.10 0.92 0.12\n", + " model_layers 0.00 -2.70 0.01 7.18\n", + " predict_time 0.00 -5.79 <0.005 27.07\n", + " train_time 0.00 -1.17 0.24 2.04\n", + " Intercept 0.00 16.36 <0.005 197.51\n", + "beta_ Intercept 0.00 -11.02 <0.005 91.38\n", + "---\n", + "Concordance = 0.81\n", + "AIC = 10600.61\n", + "log-likelihood ratio test = 1664.88 on 9 df\n", + "-log2(p) of ll-ratio test = inf" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "log_logistic_dict = {\n", + " \"Intercept: beta_\": \"$\\\\beta$\",\n", + " \"Intercept: alpha_\": \"$\\\\alpha$\",\n", + " \"data.sample.random_state: alpha_\": \"Random State\",\n", + " \"def_value: alpha_\": \"Defence Strength\",\n", + " \"atk_value: alpha_\": \"Attack Strength\",\n", + " \"train_time: alpha_\": \"Training Time\",\n", + " \"predict_time: alpha_\": \"Inference Time\",\n", + " \"adv_accuracy: alpha_\": \"Adv. Accuracy\",\n", + " \"accuracy: alpha_\": \"Ben. Accuracy\",\n", + " \"adv_fit_time: alpha_\": \"Adv. Fit Time\",\n", + " \"model_layers: alpha_\": \"No. of Layers\",\n", + " \"model.art.pipeline.initialize.kwargs.optimizer.lr\" : \"Learning Rate\",\n", + " \"adv_failure_rate: alpha_\": \"Adv. Failure Rate\",\n", + " \"alpha_\" : \"\",\n", + "\n", + "}\n", + "\n", + "log_logistic_graph, llt = plot_aft(\n", + " X_train,\n", + " \"log_logistic_aft.pdf\",\n", + " target,\n", + " \"adv_fit_time\",\n", + " \"Log Logistic AFT Model\",\n", + " \"log_logistic\",\n", + " replacement_dict=log_logistic_dict,\n", + ")\n", + "llt.print_summary()\n", + "llt_scores = score_model(llt, X_train, X_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AICConcordance ScoreBICTrain Log LikelihoodTest Log LikelihoodMean Survival TimeMedian Survival TimeTrue Median Survival TimeTrue Mean Survival Time% Error Median% Error MeanError in Seconds from the Mean
Weibull10849.310.8110849.31-2.82-4.9258.9113.0812.9427.421.12114.871.15
LogNormal10475.970.8110475.97-2.73-2.7474.208.6212.9427.4233.37170.621.71
LogLogistic10600.610.8110600.61-2.76-2.77NaN7.2812.9427.4243.73NaNNaN
CoxNaN0.95NaN-4.73-3.40NaNNaN12.9427.42NaNNaNNaN
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" + ], + "text/plain": [ + " AIC Concordance Score BIC Train Log Likelihood \\\n", + "Weibull 10849.31 0.81 10849.31 -2.82 \n", + "LogNormal 10475.97 0.81 10475.97 -2.73 \n", + "LogLogistic 10600.61 0.81 10600.61 -2.76 \n", + "Cox NaN 0.95 NaN -4.73 \n", + "\n", + " Test Log Likelihood Mean Survival Time Median Survival Time \\\n", + "Weibull -4.92 58.91 13.08 \n", + "LogNormal -2.74 74.20 8.62 \n", + "LogLogistic -2.77 NaN 7.28 \n", + "Cox -3.40 NaN NaN \n", + "\n", + " True Median Survival Time True Mean Survival Time \\\n", + "Weibull 12.94 27.42 \n", + "LogNormal 12.94 27.42 \n", + "LogLogistic 12.94 27.42 \n", + "Cox 12.94 27.42 \n", + "\n", + " % Error Median % Error Mean Error in Seconds from the Mean \n", + "Weibull 1.12 114.87 1.15 \n", + "LogNormal 33.37 170.62 1.71 \n", + "LogLogistic 43.73 NaN NaN \n", + "Cox NaN NaN NaN " + ] + }, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "llt.log_likelihood_ratio_test()" + "aft_dict = {\n", + " \"Weibull\": wft,\n", + " \"LogNormal\": lnt,\n", + " \"LogLogistic\": llt,\n", + " \"Cox\": cft,\n", + "}\n", + "aft_data = pd.DataFrame()\n", + "aft_data.index.name = \"Model\"\n", + "aft_data.index = aft_dict.keys()\n", + "aft_data[\"AIC\"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() ]\n", + "# aft_data[\"LogLikelihood\"] = [x.log_likelihood_ for x in aft_dict.values()]\n", + "aft_data[\"Concordance Score\"] = [x.concordance_index_ for x in aft_dict.values()]\n", + "aft_data[\"BIC\"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()]\n", + "aft_data['Train Log Likelihood'] = [x['train_score'] for x in [wft_scores, lnt_scores, llt_scores, cox_scores]]\n", + "aft_data['Test Log Likelihood'] = [x['test_score'] for x in [wft_scores, lnt_scores, llt_scores, cox_scores]]\n", + "aft_data['Mean Survival Time'] = [x.mean_survival_time_ if hasattr(x, \"mean_survival_time_\") else np.nan for x in aft_dict.values()]\n", + "aft_data['Median Survival Time'] = [x.median_survival_time_ if hasattr(x, \"median_survival_time_\") else np.nan for x in aft_dict.values()]\n", + "aft_data['True Median Survival Time'] = np.median(y_train)\n", + "aft_data['True Mean Survival Time'] = np.mean(y_train)\n", + "aft_data[r'% Error Median'] = np.abs(aft_data['Median Survival Time'] - aft_data['True Median Survival Time']) /aft_data['True Median Survival Time'] * 100\n", + "aft_data[r'% Error Mean'] = np.abs(aft_data['Mean Survival Time'] - aft_data['True Mean Survival Time'] ) /aft_data['True Mean Survival Time'] * 100\n", + "aft_data['Error in Seconds from the Mean'] = np.abs(aft_data['Mean Survival Time'] - aft_data['True Mean Survival Time'] ) /aft_data['True Mean Survival Time']\n", + "aft_data = aft_data.round(2)\n", + "aft_data.to_csv(FOLDER / \"aft_comparison.csv\")\n", + "logger.info(f\"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}\")\n", + "aft_data\n", + "\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, From d36365ac742443e3652e1990828409a2e646bfca Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Fri, 22 Sep 2023 12:03:10 +0000 Subject: [PATCH 086/148] +weibull score of .8 --- examples/pytorch/weibull.ipynb | 756 +++++++++------------------------ 1 file changed, 204 insertions(+), 552 deletions(-) diff --git a/examples/pytorch/weibull.ipynb b/examples/pytorch/weibull.ipynb index 57497bef..5840f971 100644 --- a/examples/pytorch/weibull.ipynb +++ b/examples/pytorch/weibull.ipynb @@ -319,7 +319,7 @@ " \n", " \n", " time fit was run\n", - " 2023-09-21 21:26:40 UTC\n", + " 2023-09-22 12:02:53 UTC\n", " \n", " \n", "\n", @@ -559,7 +559,7 @@ " number of observations = 1917\n", "number of events observed = 1917\n", " log-likelihood = -5413.65\n", - " time fit was run = 2023-09-21 21:26:40 UTC\n", + " time fit was run = 2023-09-22 12:02:53 UTC\n", "\n", "---\n", " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", @@ -614,6 +614,7 @@ " \"adv_fit_time: lambda_\": \"Adv. Fit Time\",\n", " \"adv_log_loss: lambda_\": \"Adv. Log Loss\",\n", " \"adv_failure_rate: lambda_\": \"Adv. Failure Rate\",\n", + " \"failure_rate: lambda_\": \"Ben. Failure Rate\",\n", " \"model_layers: lambda_ \": \"No. of Layers\",\n", " \"model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_\" : \"Learning Rate\",\n", " \"def_gen\" : \"Defence\",\n", @@ -696,7 +697,7 @@ " \n", " \n", " time fit was run\n", - " 2023-09-21 21:26:42 UTC\n", + " 2023-09-22 12:02:55 UTC\n", " \n", " \n", "\n", @@ -904,7 +905,7 @@ " number of observations = 1917\n", "number of events observed = 1917\n", " partial log-likelihood = -9069.47\n", - " time fit was run = 2023-09-21 21:26:42 UTC\n", + " time fit was run = 2023-09-22 12:02:55 UTC\n", "\n", "---\n", " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", @@ -974,343 +975,7 @@ "cell_type": "code", "execution_count": 9, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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modellifelines.LogNormalAFTFitter
duration col'adv_fit_time'
event col'failure_rate'
number of observations1917
number of events observed1917
log-likelihood-5226.98
time fit was run2023-09-21 21:26:44 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
mu_accuracy-0.030.970.12-0.270.210.761.240.00-0.230.820.29
adv_failure_rate-0.001.000.00-0.00-0.001.001.000.00-47.18<0.005inf
atk_value0.281.330.130.030.531.031.710.002.190.035.13
data.sample.random_state0.041.040.020.010.071.011.070.002.310.025.59
def_value-0.050.950.13-0.300.200.741.220.00-0.390.700.52
model.art.pipeline.initialize.kwargs.optimizer.lr-0.001.000.00-0.000.001.001.000.00-0.240.810.31
model_layers-0.001.000.00-0.000.001.001.000.00-1.260.212.27
predict_time-0.160.850.02-0.20-0.110.820.890.00-7.05<0.00538.99
train_time-0.001.000.00-0.000.001.001.000.00-0.570.570.81
Intercept2.8517.270.172.523.1812.4723.930.0017.13<0.005215.99
sigma_Intercept0.732.070.020.700.762.012.140.0045.20<0.005inf

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Concordance0.81
AIC10475.97
log-likelihood ratio test1616.40 on 9 df
-log2(p) of ll-ratio testinf
\n", - "
" - ], - "text/latex": [ - "\\begin{tabular}{llrrrrrrrrrrr}\n", - " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", - "param & covariate & & & & & & & & & & & \\\\\n", - "\\multirow[c]{10}{*}{mu_} & accuracy & -0.03 & 0.97 & 0.12 & -0.27 & 0.21 & 0.76 & 1.24 & 0.00 & -0.23 & 0.82 & 0.29 \\\\\n", - " & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -47.18 & 0.00 & inf \\\\\n", - " & atk_value & 0.28 & 1.33 & 0.13 & 0.03 & 0.53 & 1.03 & 1.71 & 0.00 & 2.19 & 0.03 & 5.13 \\\\\n", - " & data.sample.random_state & 0.04 & 1.04 & 0.02 & 0.01 & 0.07 & 1.01 & 1.07 & 0.00 & 2.31 & 0.02 & 5.59 \\\\\n", - " & def_value & -0.05 & 0.95 & 0.13 & -0.30 & 0.20 & 0.74 & 1.22 & 0.00 & -0.39 & 0.70 & 0.52 \\\\\n", - " & model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.24 & 0.81 & 0.31 \\\\\n", - " & model_layers & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -1.26 & 0.21 & 2.27 \\\\\n", - " & predict_time & -0.16 & 0.85 & 0.02 & -0.20 & -0.11 & 0.82 & 0.89 & 0.00 & -7.05 & 0.00 & 38.99 \\\\\n", - " & train_time & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.57 & 0.57 & 0.81 \\\\\n", - " & Intercept & 2.85 & 17.27 & 0.17 & 2.52 & 3.18 & 12.47 & 23.93 & 0.00 & 17.13 & 0.00 & 215.99 \\\\\n", - "sigma_ & Intercept & 0.73 & 2.07 & 0.02 & 0.70 & 0.76 & 2.01 & 2.14 & 0.00 & 45.20 & 0.00 & inf \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\n", - " duration col = 'adv_fit_time'\n", - " event col = 'failure_rate'\n", - " number of observations = 1917\n", - "number of events observed = 1917\n", - " log-likelihood = -5226.98\n", - " time fit was run = 2023-09-21 21:26:44 UTC\n", - "\n", - "---\n", - " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", - "param covariate \n", - "mu_ accuracy -0.03 0.97 0.12 -0.27 0.21 0.76 1.24\n", - " adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", - " atk_value 0.28 1.33 0.13 0.03 0.53 1.03 1.71\n", - " data.sample.random_state 0.04 1.04 0.02 0.01 0.07 1.01 1.07\n", - " def_value -0.05 0.95 0.13 -0.30 0.20 0.74 1.22\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - " model_layers -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - " predict_time -0.16 0.85 0.02 -0.20 -0.11 0.82 0.89\n", - " train_time -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - " Intercept 2.85 17.27 0.17 2.52 3.18 12.47 23.93\n", - "sigma_ Intercept 0.73 2.07 0.02 0.70 0.76 2.01 2.14\n", - "\n", - " cmp to z p -log2(p)\n", - "param covariate \n", - "mu_ accuracy 0.00 -0.23 0.82 0.29\n", - " adv_failure_rate 0.00 -47.18 <0.005 inf\n", - " atk_value 0.00 2.19 0.03 5.13\n", - " data.sample.random_state 0.00 2.31 0.02 5.59\n", - " def_value 0.00 -0.39 0.70 0.52\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.24 0.81 0.31\n", - " model_layers 0.00 -1.26 0.21 2.27\n", - " predict_time 0.00 -7.05 <0.005 38.99\n", - " train_time 0.00 -0.57 0.57 0.81\n", - " Intercept 0.00 17.13 <0.005 215.99\n", - "sigma_ Intercept 0.00 45.20 <0.005 inf\n", - "---\n", - "Concordance = 0.81\n", - "AIC = 10475.97\n", - "log-likelihood ratio test = 1616.40 on 9 df\n", - "-log2(p) of ll-ratio test = inf" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "log_normal_dict = {\n", " \"Intercept: sigma_\": \"$\\sigma$\",\n", @@ -1346,12 +1011,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1388,7 +1053,7 @@ " \n", " \n", " event col\n", - " 'failure_rate'\n", + " 'adv_failure_rate'\n", " \n", " \n", " number of observations\n", @@ -1400,11 +1065,11 @@ " \n", " \n", " log-likelihood\n", - " -5289.31\n", + " -6048.10\n", " \n", " \n", " time fit was run\n", - " 2023-09-21 21:26:47 UTC\n", + " 2023-09-22 12:02:25 UTC\n", " \n", " \n", "\n", @@ -1430,73 +1095,73 @@ " \n", " alpha_\n", " accuracy\n", - " -0.05\n", - " 0.95\n", - " 0.13\n", - " -0.30\n", - " 0.20\n", + " -0.76\n", + " 0.47\n", + " 0.24\n", + " -1.23\n", + " -0.29\n", + " 0.29\n", " 0.74\n", - " 1.22\n", " 0.00\n", - " -0.40\n", - " 0.69\n", - " 0.54\n", + " -3.19\n", + " <0.005\n", + " 9.45\n", " \n", " \n", - " adv_failure_rate\n", - " -0.00\n", - " 1.00\n", - " 0.00\n", - " -0.00\n", - " -0.00\n", - " 1.00\n", - " 1.00\n", + " atk_value\n", + " 0.76\n", + " 2.14\n", + " 0.20\n", + " 0.37\n", + " 1.16\n", + " 1.44\n", + " 3.17\n", " 0.00\n", - " -40.16\n", + " 3.78\n", " <0.005\n", - " inf\n", + " 12.63\n", " \n", " \n", - " atk_value\n", - " 0.29\n", - " 1.34\n", - " 0.14\n", - " 0.02\n", - " 0.55\n", - " 1.02\n", - " 1.74\n", - " 0.00\n", - " 2.13\n", + " data.sample.random_state\n", + " 0.05\n", + " 1.05\n", " 0.03\n", - " 4.92\n", + " -0.00\n", + " 0.10\n", + " 1.00\n", + " 1.11\n", + " 0.00\n", + " 1.83\n", + " 0.07\n", + " 3.88\n", " \n", " \n", - " data.sample.random_state\n", + " def_value\n", " 0.04\n", " 1.04\n", - " 0.02\n", - " 0.01\n", - " 0.08\n", - " 1.01\n", - " 1.08\n", + " 0.20\n", + " -0.36\n", + " 0.44\n", + " 0.70\n", + " 1.56\n", " 0.00\n", - " 2.45\n", - " 0.01\n", - " 6.14\n", + " 0.20\n", + " 0.84\n", + " 0.25\n", " \n", " \n", - " def_value\n", - " -0.06\n", - " 0.94\n", - " 0.13\n", - " -0.33\n", - " 0.20\n", - " 0.72\n", - " 1.22\n", + " failure_rate\n", + " -0.01\n", + " 0.99\n", + " 0.00\n", + " -0.02\n", + " -0.00\n", + " 0.98\n", + " 1.00\n", " 0.00\n", - " -0.46\n", - " 0.64\n", - " 0.64\n", + " -3.54\n", + " <0.005\n", + " 11.29\n", " \n", " \n", " model.art.pipeline.initialize.kwargs.optimizer.lr\n", @@ -1508,80 +1173,80 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " -0.10\n", - " 0.92\n", - " 0.12\n", + " -1.33\n", + " 0.19\n", + " 2.43\n", " \n", " \n", " model_layers\n", - " -0.00\n", - " 1.00\n", - " 0.00\n", - " -0.01\n", - " -0.00\n", - " 0.99\n", - " 1.00\n", + " 0.01\n", + " 1.01\n", " 0.00\n", - " -2.70\n", " 0.01\n", - " 7.18\n", + " 0.02\n", + " 1.01\n", + " 1.02\n", + " 0.00\n", + " 5.16\n", + " <0.005\n", + " 21.98\n", " \n", " \n", " predict_time\n", - " -0.13\n", - " 0.88\n", - " 0.02\n", - " -0.18\n", - " -0.09\n", - " 0.84\n", - " 0.92\n", + " -0.34\n", + " 0.71\n", + " 0.04\n", + " -0.41\n", + " -0.27\n", + " 0.66\n", + " 0.76\n", " 0.00\n", - " -5.79\n", + " -9.65\n", " <0.005\n", - " 27.07\n", + " 70.82\n", " \n", " \n", " train_time\n", - " -0.00\n", + " 0.00\n", " 1.00\n", " 0.00\n", - " -0.00\n", + " 0.00\n", " 0.00\n", " 1.00\n", " 1.00\n", " 0.00\n", - " -1.17\n", - " 0.24\n", - " 2.04\n", + " 5.04\n", + " <0.005\n", + " 21.02\n", " \n", " \n", " Intercept\n", - " 2.82\n", - " 16.76\n", - " 0.17\n", - " 2.48\n", - " 3.16\n", - " 11.95\n", - " 23.48\n", + " 0.78\n", + " 2.18\n", + " 0.34\n", + " 0.12\n", + " 1.44\n", + " 1.13\n", + " 4.21\n", " 0.00\n", - " 16.36\n", - " <0.005\n", - " 197.51\n", + " 2.33\n", + " 0.02\n", + " 5.64\n", " \n", " \n", " beta_\n", " Intercept\n", - " -0.21\n", - " 0.81\n", + " -0.62\n", + " 0.54\n", " 0.02\n", - " -0.24\n", - " -0.17\n", - " 0.78\n", - " 0.84\n", + " -0.65\n", + " -0.58\n", + " 0.52\n", + " 0.56\n", " 0.00\n", - " -11.02\n", + " -33.40\n", " <0.005\n", - " 91.38\n", + " 810.23\n", " \n", " \n", "
\n", @@ -1602,19 +1267,19 @@ " \n", " \n", " Concordance\n", - " 0.81\n", + " 0.59\n", " \n", " \n", " AIC\n", - " 10600.61\n", + " 12118.20\n", " \n", " \n", " log-likelihood ratio test\n", - " 1664.88 on 9 df\n", + " 147.29 on 9 df\n", " \n", " \n", " -log2(p) of ll-ratio test\n", - " inf\n", + " 88.01\n", " \n", " \n", "\n", @@ -1624,61 +1289,61 @@ "\\begin{tabular}{llrrrrrrrrrrr}\n", " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", "param & covariate & & & & & & & & & & & \\\\\n", - "\\multirow[c]{10}{*}{alpha_} & accuracy & -0.05 & 0.95 & 0.13 & -0.30 & 0.20 & 0.74 & 1.22 & 0.00 & -0.40 & 0.69 & 0.54 \\\\\n", - " & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -40.16 & 0.00 & inf \\\\\n", - " & atk_value & 0.29 & 1.34 & 0.14 & 0.02 & 0.55 & 1.02 & 1.74 & 0.00 & 2.13 & 0.03 & 4.92 \\\\\n", - " & data.sample.random_state & 0.04 & 1.04 & 0.02 & 0.01 & 0.08 & 1.01 & 1.08 & 0.00 & 2.45 & 0.01 & 6.14 \\\\\n", - " & def_value & -0.06 & 0.94 & 0.13 & -0.33 & 0.20 & 0.72 & 1.22 & 0.00 & -0.46 & 0.64 & 0.64 \\\\\n", - " & model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.10 & 0.92 & 0.12 \\\\\n", - " & model_layers & -0.00 & 1.00 & 0.00 & -0.01 & -0.00 & 0.99 & 1.00 & 0.00 & -2.70 & 0.01 & 7.18 \\\\\n", - " & predict_time & -0.13 & 0.88 & 0.02 & -0.18 & -0.09 & 0.84 & 0.92 & 0.00 & -5.79 & 0.00 & 27.07 \\\\\n", - " & train_time & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -1.17 & 0.24 & 2.04 \\\\\n", - " & Intercept & 2.82 & 16.76 & 0.17 & 2.48 & 3.16 & 11.95 & 23.48 & 0.00 & 16.36 & 0.00 & 197.51 \\\\\n", - "beta_ & Intercept & -0.21 & 0.81 & 0.02 & -0.24 & -0.17 & 0.78 & 0.84 & 0.00 & -11.02 & 0.00 & 91.38 \\\\\n", + "\\multirow[c]{10}{*}{alpha_} & accuracy & -0.76 & 0.47 & 0.24 & -1.23 & -0.29 & 0.29 & 0.74 & 0.00 & -3.19 & 0.00 & 9.45 \\\\\n", + " & atk_value & 0.76 & 2.14 & 0.20 & 0.37 & 1.16 & 1.44 & 3.17 & 0.00 & 3.78 & 0.00 & 12.63 \\\\\n", + " & data.sample.random_state & 0.05 & 1.05 & 0.03 & -0.00 & 0.10 & 1.00 & 1.11 & 0.00 & 1.83 & 0.07 & 3.88 \\\\\n", + " & def_value & 0.04 & 1.04 & 0.20 & -0.36 & 0.44 & 0.70 & 1.56 & 0.00 & 0.20 & 0.84 & 0.25 \\\\\n", + " & failure_rate & -0.01 & 0.99 & 0.00 & -0.02 & -0.00 & 0.98 & 1.00 & 0.00 & -3.54 & 0.00 & 11.29 \\\\\n", + " & model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -1.33 & 0.19 & 2.43 \\\\\n", + " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.02 & 1.01 & 1.02 & 0.00 & 5.16 & 0.00 & 21.98 \\\\\n", + " & predict_time & -0.34 & 0.71 & 0.04 & -0.41 & -0.27 & 0.66 & 0.76 & 0.00 & -9.65 & 0.00 & 70.82 \\\\\n", + " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 5.04 & 0.00 & 21.02 \\\\\n", + " & Intercept & 0.78 & 2.18 & 0.34 & 0.12 & 1.44 & 1.13 & 4.21 & 0.00 & 2.33 & 0.02 & 5.64 \\\\\n", + "beta_ & Intercept & -0.62 & 0.54 & 0.02 & -0.65 & -0.58 & 0.52 & 0.56 & 0.00 & -33.40 & 0.00 & 810.23 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ "\n", " duration col = 'adv_fit_time'\n", - " event col = 'failure_rate'\n", + " event col = 'adv_failure_rate'\n", " number of observations = 1917\n", "number of events observed = 1917\n", - " log-likelihood = -5289.31\n", - " time fit was run = 2023-09-21 21:26:47 UTC\n", + " log-likelihood = -6048.10\n", + " time fit was run = 2023-09-22 12:02:25 UTC\n", "\n", "---\n", " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", "param covariate \n", - "alpha_ accuracy -0.05 0.95 0.13 -0.30 0.20 0.74 1.22\n", - " adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", - " atk_value 0.29 1.34 0.14 0.02 0.55 1.02 1.74\n", - " data.sample.random_state 0.04 1.04 0.02 0.01 0.08 1.01 1.08\n", - " def_value -0.06 0.94 0.13 -0.33 0.20 0.72 1.22\n", + "alpha_ accuracy -0.76 0.47 0.24 -1.23 -0.29 0.29 0.74\n", + " atk_value 0.76 2.14 0.20 0.37 1.16 1.44 3.17\n", + " data.sample.random_state 0.05 1.05 0.03 -0.00 0.10 1.00 1.11\n", + " def_value 0.04 1.04 0.20 -0.36 0.44 0.70 1.56\n", + " failure_rate -0.01 0.99 0.00 -0.02 -0.00 0.98 1.00\n", " model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - " model_layers -0.00 1.00 0.00 -0.01 -0.00 0.99 1.00\n", - " predict_time -0.13 0.88 0.02 -0.18 -0.09 0.84 0.92\n", - " train_time -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - " Intercept 2.82 16.76 0.17 2.48 3.16 11.95 23.48\n", - "beta_ Intercept -0.21 0.81 0.02 -0.24 -0.17 0.78 0.84\n", + " model_layers 0.01 1.01 0.00 0.01 0.02 1.01 1.02\n", + " predict_time -0.34 0.71 0.04 -0.41 -0.27 0.66 0.76\n", + " train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", + " Intercept 0.78 2.18 0.34 0.12 1.44 1.13 4.21\n", + "beta_ Intercept -0.62 0.54 0.02 -0.65 -0.58 0.52 0.56\n", "\n", " cmp to z p -log2(p)\n", "param covariate \n", - "alpha_ accuracy 0.00 -0.40 0.69 0.54\n", - " adv_failure_rate 0.00 -40.16 <0.005 inf\n", - " atk_value 0.00 2.13 0.03 4.92\n", - " data.sample.random_state 0.00 2.45 0.01 6.14\n", - " def_value 0.00 -0.46 0.64 0.64\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.10 0.92 0.12\n", - " model_layers 0.00 -2.70 0.01 7.18\n", - " predict_time 0.00 -5.79 <0.005 27.07\n", - " train_time 0.00 -1.17 0.24 2.04\n", - " Intercept 0.00 16.36 <0.005 197.51\n", - "beta_ Intercept 0.00 -11.02 <0.005 91.38\n", + "alpha_ accuracy 0.00 -3.19 <0.005 9.45\n", + " atk_value 0.00 3.78 <0.005 12.63\n", + " data.sample.random_state 0.00 1.83 0.07 3.88\n", + " def_value 0.00 0.20 0.84 0.25\n", + " failure_rate 0.00 -3.54 <0.005 11.29\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -1.33 0.19 2.43\n", + " model_layers 0.00 5.16 <0.005 21.98\n", + " predict_time 0.00 -9.65 <0.005 70.82\n", + " train_time 0.00 5.04 <0.005 21.02\n", + " Intercept 0.00 2.33 0.02 5.64\n", + "beta_ Intercept 0.00 -33.40 <0.005 810.23\n", "---\n", - "Concordance = 0.81\n", - "AIC = 10600.61\n", - "log-likelihood ratio test = 1664.88 on 9 df\n", - "-log2(p) of ll-ratio test = inf" + "Concordance = 0.59\n", + "AIC = 12118.20\n", + "log-likelihood ratio test = 147.29 on 9 df\n", + "-log2(p) of ll-ratio test = 88.01" ] }, "metadata": {}, @@ -1719,7 +1384,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1750,70 +1415,55 @@ " Test Log Likelihood\n", " Mean Survival Time\n", " Median Survival Time\n", - " True Median Survival Time\n", - " True Mean Survival Time\n", - " % Error Median\n", - " % Error Mean\n", - " Error in Seconds from the Mean\n", + " Error (seconds)\n", + " % Error from True Mean\n", " \n", " \n", " \n", " \n", " Weibull\n", - " 10849.31\n", - " 0.81\n", - " 10849.31\n", - " -2.82\n", - " -4.92\n", - " 58.91\n", - " 13.08\n", - " 12.94\n", - " 27.42\n", - " 1.12\n", - " 114.87\n", - " 1.15\n", + " 12187.71\n", + " 0.59\n", + " 12187.71\n", + " -3.17\n", + " -5.53\n", + " 92.66\n", + " 6.24\n", + " -62.72\n", + " -83.40\n", " \n", " \n", " LogNormal\n", - " 10475.97\n", - " 0.81\n", - " 10475.97\n", - " -2.73\n", - " -2.74\n", - " 74.20\n", - " 8.62\n", - " 12.94\n", - " 27.42\n", - " 33.37\n", - " 170.62\n", - " 1.71\n", + " 11942.60\n", + " 0.59\n", + " 11942.60\n", + " -3.11\n", + " -3.16\n", + " 344.56\n", + " 3.37\n", + " -65.58\n", + " -38.27\n", " \n", " \n", " LogLogistic\n", - " 10600.61\n", - " 0.81\n", - " 10600.61\n", - " -2.76\n", - " -2.77\n", - " NaN\n", - " 7.28\n", - " 12.94\n", - " 27.42\n", - " 43.73\n", + " 12118.20\n", + " 0.59\n", + " 12118.20\n", + " -3.15\n", + " -3.20\n", " NaN\n", + " 3.14\n", + " -65.81\n", " NaN\n", " \n", " \n", " Cox\n", " NaN\n", - " 0.95\n", - " NaN\n", - " -4.73\n", - " -3.40\n", + " 0.59\n", " NaN\n", + " -6.51\n", + " -5.64\n", " NaN\n", - " 12.94\n", - " 27.42\n", " NaN\n", " NaN\n", " NaN\n", @@ -1824,31 +1474,25 @@ ], "text/plain": [ " AIC Concordance Score BIC Train Log Likelihood \\\n", - "Weibull 10849.31 0.81 10849.31 -2.82 \n", - "LogNormal 10475.97 0.81 10475.97 -2.73 \n", - "LogLogistic 10600.61 0.81 10600.61 -2.76 \n", - "Cox NaN 0.95 NaN -4.73 \n", + "Weibull 12187.71 0.59 12187.71 -3.17 \n", + "LogNormal 11942.60 0.59 11942.60 -3.11 \n", + "LogLogistic 12118.20 0.59 12118.20 -3.15 \n", + "Cox NaN 0.59 NaN -6.51 \n", "\n", " Test Log Likelihood Mean Survival Time Median Survival Time \\\n", - "Weibull -4.92 58.91 13.08 \n", - "LogNormal -2.74 74.20 8.62 \n", - "LogLogistic -2.77 NaN 7.28 \n", - "Cox -3.40 NaN NaN \n", + "Weibull -5.53 92.66 6.24 \n", + "LogNormal -3.16 344.56 3.37 \n", + "LogLogistic -3.20 NaN 3.14 \n", + "Cox -5.64 NaN NaN \n", "\n", - " True Median Survival Time True Mean Survival Time \\\n", - "Weibull 12.94 27.42 \n", - "LogNormal 12.94 27.42 \n", - "LogLogistic 12.94 27.42 \n", - "Cox 12.94 27.42 \n", - "\n", - " % Error Median % Error Mean Error in Seconds from the Mean \n", - "Weibull 1.12 114.87 1.15 \n", - "LogNormal 33.37 170.62 1.71 \n", - "LogLogistic 43.73 NaN NaN \n", - "Cox NaN NaN NaN " + " Error (seconds) % Error from True Mean \n", + "Weibull -62.72 -83.40 \n", + "LogNormal -65.58 -38.27 \n", + "LogLogistic -65.81 NaN \n", + "Cox NaN NaN " ] }, - "execution_count": 18, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1873,9 +1517,10 @@ "aft_data['Median Survival Time'] = [x.median_survival_time_ if hasattr(x, \"median_survival_time_\") else np.nan for x in aft_dict.values()]\n", "aft_data['True Median Survival Time'] = np.median(y_train)\n", "aft_data['True Mean Survival Time'] = np.mean(y_train)\n", - "aft_data[r'% Error Median'] = np.abs(aft_data['Median Survival Time'] - aft_data['True Median Survival Time']) /aft_data['True Median Survival Time'] * 100\n", - "aft_data[r'% Error Mean'] = np.abs(aft_data['Mean Survival Time'] - aft_data['True Mean Survival Time'] ) /aft_data['True Mean Survival Time'] * 100\n", - "aft_data['Error in Seconds from the Mean'] = np.abs(aft_data['Mean Survival Time'] - aft_data['True Mean Survival Time'] ) /aft_data['True Mean Survival Time']\n", + "aft_data[r'Error (seconds)'] = (aft_data['Median Survival Time'] - aft_data['True Median Survival Time'])\n", + "aft_data[r'% Error from True Mean'] = (aft_data['Mean Survival Time'] - aft_data['True Mean Survival Time'] ) /aft_data['True Mean Survival Time'] * 100\n", + "del aft_data['True Median Survival Time']\n", + "del aft_data['True Mean Survival Time']\n", "aft_data = aft_data.round(2)\n", "aft_data.to_csv(FOLDER / \"aft_comparison.csv\")\n", "logger.info(f\"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}\")\n", @@ -1890,6 +1535,13 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, From dc4d5c434140d3eb1f341f537d986f4fb3d041dc Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 25 Sep 2023 14:17:47 +0000 Subject: [PATCH 087/148] update weibull --- deckard/base/model/model.py | 8 - examples/pytorch/conf/plots.yaml | 46 +- examples/pytorch/dvc.lock | 18 +- examples/pytorch/plots.py | 36 +- examples/pytorch/weibull.ipynb | 1633 ++++++++++++++++++++---------- 5 files changed, 1180 insertions(+), 561 deletions(-) diff --git a/deckard/base/model/model.py b/deckard/base/model/model.py index 6e91cd1e..9bd8086b 100644 --- a/deckard/base/model/model.py +++ b/deckard/base/model/model.py @@ -132,14 +132,6 @@ def __call__(self, data: list, model: object, library=None): tf.config.run_functions_eagerly(True) else: raise NotImplementedError(f"Training library {library} not implemented") - if hasattr(model, "fit_generator"): - try: - start = process_time_ns() - model.fit_generator(data[0], **trainer) - end = process_time_ns() - start - except Exception as e: - logger.error(e) - raise e try: start = process_time_ns() model.fit(data[0], data[2], **trainer) diff --git a/examples/pytorch/conf/plots.yaml b/examples/pytorch/conf/plots.yaml index bc72b0c6..21e47081 100644 --- a/examples/pytorch/conf/plots.yaml +++ b/examples/pytorch/conf/plots.yaml @@ -155,7 +155,7 @@ cat_plot: - ResNet152 line_plot: - control: 0.98 - control_color: orange + control_color: royalblue file: def_param_vs_accuracy.pdf hue: def_gen legend: {} @@ -167,13 +167,13 @@ line_plot: y_scale: ylabel: Accuracy hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 + - Control + - Gauss-in + - Gauss-out + - Conf + - FSQ - control: 0.1 - control_color: orange + control_color: royalblue file: def_param_vs_adv_accuracy.pdf hue: def_gen legend: {} @@ -185,13 +185,13 @@ line_plot: y_scale: ylabel: Adv. Accuracy hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 + - Control + - Gauss-in + - Gauss-out + - Conf + - FSQ - control: 0.1 - control_color: orange + control_color: royalblue file: def_param_vs_adv_failure_rate.pdf hue: def_gen legend: {} @@ -203,11 +203,11 @@ line_plot: y_scale: log ylabel: Adv. Failure Rate hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 + - Control + - Gauss-in + - Gauss-out + - Conf + - FSQ - file: atk_param_vs_accuracy.pdf hue: atk_gen legend: {} @@ -219,11 +219,11 @@ line_plot: y_scale: ylabel: Adv. Accuracy hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 + - FGM + - Deep + - HSJ + - Pixel + - Thresh scatter_plot: - x: train_time_per_sample y: adv_failure_rate diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index 2ccf4fb6..a00e52df 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -356,17 +356,17 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: 7b415b0d3b17a4925311a7ee5293786e + md5: f6933d3d01f97b4f400414bfaf4b72c2 size: 31517 - path: output/reports/attack/default/adv_predictions.json - md5: 9ea6237d1248b45bccbe60b634da68fb - size: 2154 + md5: 7142652340b38ec663fd7769eb2baa61 + size: 2169 - path: output/reports/attack/default/params.yaml md5: a15accd2ecf7bcbb5ed47d4b6bf7ed97 size: 5093 - path: output/reports/attack/default/score_dict.json - md5: 879e9e986f4f69c7dea121cc78bececb - size: 524 + md5: b92be5957cc92fc91e76d42e654151f9 + size: 533 models: cmd: bash models.sh ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 @@ -540,12 +540,12 @@ stages: md5: 6ef2a88669f50dcdc0329e777dd7f9d6 size: 1235 - path: output/reports/attack/default/score_dict.json - md5: 879e9e986f4f69c7dea121cc78bececb - size: 524 + md5: b92be5957cc92fc91e76d42e654151f9 + size: 533 outs: - path: ResNet101.db - md5: 33b4542eb9688ff88ada0cd81f1f3005 - size: 1261568 + md5: 744f187243001db70f63c8161d7f913f + size: 1314816 attacks@ResNet34: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet34.db --config-name default.yaml diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index ce1c291e..f438b092 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -139,9 +139,15 @@ def calculate_failure_rate(data): data.loc[:, "failure_rate"] = ( (1 - data.loc[:, "accuracy"]) * 100 / data.loc[:, "predict_time"] ) + data.loc[:, "success_rate"] = ( + data.loc[:, "accuracy"] * 100 / data.loc[:, "predict_time"] + ) data.loc[:, "adv_failure_rate"] = ( (1 - data.loc[:, "adv_accuracy"]) * 100 / data.loc[:, "adv_fit_time"] ) + data.loc[:, "adv_success_rate"] = ( + data.loc[:, "adv_accuracy"] * 100 / data.loc[:, "adv_fit_time"] + ) data.loc[:, "training_time_per_failure"] = ( data.loc[:, "train_time"] / data.loc[:, "failure_rate"] ) @@ -153,8 +159,16 @@ def calculate_failure_rate(data): ) return data +from paretoset import paretoset + +def pareto_set(data, sense_dict): + subset = data.loc[:, sense_dict.keys()] + these = paretoset(subset, sense = sense_dict.values()) + return data.iloc[these, :] + + -def min_max_scaling(data): +def min_max_scaling(data, **kwargs): if "atk_gen" not in data.columns: attacks = [] else: @@ -175,6 +189,8 @@ def min_max_scaling(data): min_ = data[data.atk_gen == atk].atk_value.min() scaled_value = (data[data.atk_gen == atk].atk_value - min_) / (max_ - min_) data.loc[data.atk_gen == atk, "atk_value"] = scaled_value + for k,v in kwargs.items(): + data.loc[:,k] = data.loc[:,k].apply(v) return data @@ -237,6 +253,24 @@ def min_max_scaling(data): "predict_time", ], ) + sense_dict ={ + "accuracy" : "max", + # "train_time" : "min", + # "predict_time" : "min", + # "atk_value" : "diff", + # "def_value" : "diff", + "data.sample.random_state" : "diff", + # "adv_accuracy" : "min", + "model_layers" : "diff", + # "adv_fit_time" : "min", + "atk_param" : "diff", + "def_param" : "diff", + "atk_gen" : "diff", + "def_gen" : "diff", + # "model.art.pipeline.initialize.kwargs.optimizer.lr" : "diff", + # "adv_failure_rate" : "maximize", +} + data = pareto_set(data, sense_dict) data = calculate_failure_rate(data) data = min_max_scaling(data) if "Unnamed: 0" in data.columns: diff --git a/examples/pytorch/weibull.ipynb b/examples/pytorch/weibull.ipynb index 5840f971..227e3c8f 100644 --- a/examples/pytorch/weibull.ipynb +++ b/examples/pytorch/weibull.ipynb @@ -38,11 +38,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "ResNet152: 0.09777195281782439 \n", - " Resnet101: 0.09993344425956739 \n", - " Resnet50: 0.09634703196347033 \n", - " Resnet34: 0.09648437500000001 \n", - " Resnet18: 0.08609302325581396 \n", + "Adversarial Accuracy: \n", + " ResNet152: 0.09937931034482758 \n", + " Resnet101: 0.10020833333333334 \n", + " Resnet50: 0.09903225806451614 \n", + " Resnet34: 0.09656 \n", + " Resnet18: 0.08548387096774193 \n", "\n" ] } @@ -64,6 +65,7 @@ "# data=data[data['atk_gen'] == 'HSJ']\n", "\n", "print(\n", + " \"Adversarial Accuracy:\", \"\\n\",\n", " \"ResNet152:\", data[data['model_layers'] == 152].adv_accuracy.mean(skipna=True), \"\\n\",\n", " \"Resnet101:\", data[data['model_layers'] == 101].adv_accuracy.mean(skipna=True), \"\\n\",\n", " \"Resnet50:\", data[data['model_layers'] == 50].adv_accuracy.mean(skipna=True), \"\\n\",\n", @@ -79,6 +81,7 @@ "metadata": {}, "outputs": [], "source": [ + "\n", "def plot_aft(\n", " df,\n", " file,\n", @@ -125,7 +128,7 @@ "def plot_partial_effects(\n", " file,\n", " aft,\n", - " covariate_aray,\n", + " covariate_array,\n", " values_array,\n", " title,\n", " xlabel=\"Covariate\",\n", @@ -137,7 +140,7 @@ " ):\n", " plt.gcf().clear()\n", " # kwargs.pop(\"replacement_dict\")\n", - " pareto = aft.plot_partial_effects_on_outcome(covariate_aray, values_array, cmap=cmap, **kwargs)\n", + " pareto = aft.plot_partial_effects_on_outcome(covariate_array, values_array, cmap=cmap, **kwargs)\n", " labels = pareto.get_yticklabels()\n", " labels = [label.get_text() for label in labels]\n", " for k, v in replacement_dict.items():\n", @@ -165,14 +168,23 @@ "):\n", " subset = data.copy()\n", " assert target in subset, f\"Target {target} not in dataframe with columns {subset.columns}\"\n", - " y = subset[target].copy(deep=True)\n", + " \n", " cleaned = pd.DataFrame()\n", + " kwarg_list.append(target)\n", " for kwarg in kwarg_list:\n", " cleaned = pd.concat([cleaned, subset[kwarg]], axis=1)\n", " cols = cleaned.columns\n", " cleaned = pd.DataFrame(subset, columns=cols)\n", - " cleaned.dropna(inplace=True, how='any',)\n", - " cleaned[target] = y\n", + " \n", + " \n", + " # if \"accuracy\" in cleaned.columns:\n", + " # cleaned = cleaned[~cleaned[cleaned['accuracy'] != 1e10]]\n", + " # cleaned = cleaned[~cleaned[cleaned['accuracy'] != -1e10]]\n", + " # if \"adv_accuracy\" in cleaned.columns:\n", + " # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != 1e10]]\n", + " # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != -1e10]]\n", + " cleaned.dropna(inplace=True, how='any', axis=0)\n", + " y = cleaned[target]\n", " assert target in cleaned, f\"Target {target} not in dataframe with columns {cleaned.columns}\"\n", " return cleaned, y, data" ] @@ -183,6 +195,7 @@ "metadata": {}, "outputs": [], "source": [ + "\n", "\n", "kwarg_list = [\n", " \"accuracy\",\n", @@ -195,6 +208,9 @@ " \"failure_rate\",\n", " \"model_layers\",\n", " \"adv_fit_time\",\n", + " # \"atk_param\",\n", + " # \"def_param\",\n", + " \n", " \"model.art.pipeline.initialize.kwargs.optimizer.lr\",\n", " # \"def_gen\",\n", " # \"atk_gen\",\n", @@ -213,13 +229,25 @@ "metadata": {}, "outputs": [], "source": [ - "target = \"failure_rate\"\n", + "data.loc[:, \"adv_failures\"] = (1 - data.loc[:, \"adv_accuracy\"]) * 100\n", + "data.loc[:, \"ben_failures\"] = (1 - data.loc[:, \"accuracy\"]) * 100\n", + "target = \"ben_failures\"\n", + "duration_col = \"predict_time\"\n", "cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target)\n", "X_train, X_test, y_train, y_test = train_test_split(cleaned, y, test_size=0.2, random_state=42)\n", "assert target in cleaned, f\"Target {target} not in dataframe with columns {cleaned.columns}\"\n", "\n", - "# from sklearn.preprocessing import PowerTransformer\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ "\n", + "# from sklearn.preprocessing import PowerTransformer\n", "# pt = PowerTransformer(method='yeo-johnson', standardize=False)\n", "# del X_train[target]\n", "# del X_test[target]\n", @@ -234,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -245,13 +273,13 @@ "# # \"atk_value\" : \"diff\",\n", "# # \"def_value\" : \"diff\",\n", "# \"data.sample.random_state\" : \"diff\",\n", - "# \"adv_failure_rate\" : \"max\",\n", + "# \"adv_accuracy\" : \"min\",\n", "# \"model_layers\" : \"diff\",\n", - "# \"adv_fit_time\" : \"min\",\n", - "# \"atk_param\" : \"diff\",\n", - "# \"def_param\" : \"diff\",\n", + "# # \"adv_fit_time\" : \"min\",\n", + "# # \"atk_param\" : \"diff\",\n", + "# # \"def_param\" : \"diff\",\n", "# \"model.art.pipeline.initialize.kwargs.optimizer.lr\" : \"diff\",\n", - "# \"failure_rate\" : \"maximize\",\n", + "# # \"adv_failure_rate\" : \"maximize\",\n", "# }\n", "# subset = X_train.loc[:, sense_dict.keys()]\n", "# senses = sense_dict.values()\n", @@ -261,12 +289,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -299,27 +327,27 @@ " \n", " \n", " duration col\n", - " 'adv_fit_time'\n", + " 'predict_time'\n", " \n", " \n", " event col\n", - " 'failure_rate'\n", + " 'ben_failures'\n", " \n", " \n", " number of observations\n", - " 1917\n", + " 568\n", " \n", " \n", " number of events observed\n", - " 1917\n", + " 568\n", " \n", " \n", " log-likelihood\n", - " -5413.65\n", + " -723.10\n", " \n", " \n", " time fit was run\n", - " 2023-09-22 12:02:53 UTC\n", + " 2023-09-25 13:54:12 UTC\n", " \n", " \n", "\n", @@ -343,19 +371,19 @@ " \n", " \n", " \n", - " lambda_\n", + " lambda_\n", " accuracy\n", - " 0.05\n", - " 1.05\n", - " 0.12\n", - " -0.19\n", - " 0.30\n", - " 0.82\n", - " 1.34\n", + " -0.35\n", + " 0.70\n", + " 0.06\n", + " -0.48\n", + " -0.23\n", + " 0.62\n", + " 0.80\n", " 0.00\n", - " 0.41\n", - " 0.68\n", - " 0.56\n", + " -5.49\n", + " <0.005\n", + " 24.58\n", " \n", " \n", " adv_failure_rate\n", @@ -363,59 +391,87 @@ " 1.00\n", " 0.00\n", " -0.00\n", + " 0.00\n", + " 1.00\n", + " 1.00\n", + " 0.00\n", + " -1.55\n", + " 0.12\n", + " 3.04\n", + " \n", + " \n", + " adv_fit_time\n", + " -0.00\n", + " 1.00\n", + " 0.00\n", + " -0.00\n", " -0.00\n", " 1.00\n", " 1.00\n", " 0.00\n", - " -57.66\n", + " -4.43\n", " <0.005\n", - " inf\n", + " 16.67\n", " \n", " \n", " atk_value\n", - " 0.43\n", - " 1.54\n", - " 0.13\n", - " 0.18\n", - " 0.68\n", - " 1.20\n", - " 1.97\n", + " 0.15\n", + " 1.16\n", + " 0.04\n", + " 0.08\n", + " 0.22\n", + " 1.08\n", + " 1.25\n", " 0.00\n", - " 3.38\n", + " 4.18\n", " <0.005\n", - " 10.44\n", + " 15.05\n", " \n", " \n", " data.sample.random_state\n", - " 0.05\n", - " 1.05\n", - " 0.02\n", + " -0.00\n", + " 1.00\n", + " 0.00\n", + " -0.01\n", " 0.01\n", - " 0.08\n", + " 0.99\n", " 1.01\n", - " 1.08\n", " 0.00\n", - " 2.73\n", - " 0.01\n", - " 7.29\n", + " -0.83\n", + " 0.41\n", + " 1.29\n", " \n", " \n", " def_value\n", - " -0.05\n", - " 0.95\n", - " 0.13\n", - " -0.30\n", - " 0.20\n", - " 0.74\n", - " 1.23\n", + " -0.01\n", + " 0.99\n", + " 0.04\n", + " -0.08\n", + " 0.07\n", + " 0.92\n", + " 1.07\n", " 0.00\n", - " -0.37\n", - " 0.71\n", - " 0.49\n", + " -0.21\n", + " 0.84\n", + " 0.26\n", + " \n", + " \n", + " failure_rate\n", + " -0.01\n", + " 0.99\n", + " 0.00\n", + " -0.01\n", + " -0.01\n", + " 0.99\n", + " 0.99\n", + " 0.00\n", + " -10.63\n", + " <0.005\n", + " 85.23\n", " \n", " \n", " model.art.pipeline.initialize.kwargs.optimizer.lr\n", - " -0.00\n", + " 0.00\n", " 1.00\n", " 0.00\n", " -0.00\n", @@ -423,80 +479,66 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " -0.52\n", - " 0.60\n", - " 0.73\n", + " 0.58\n", + " 0.56\n", + " 0.84\n", " \n", " \n", " model_layers\n", - " 0.00\n", - " 1.00\n", - " 0.00\n", - " 0.00\n", " 0.01\n", - " 1.00\n", " 1.01\n", " 0.00\n", - " 2.88\n", - " <0.005\n", - " 7.99\n", - " \n", - " \n", - " predict_time\n", - " -0.28\n", - " 0.76\n", - " 0.02\n", - " -0.32\n", - " -0.24\n", - " 0.72\n", - " 0.79\n", + " 0.01\n", + " 0.01\n", + " 1.01\n", + " 1.01\n", " 0.00\n", - " -12.39\n", + " 20.39\n", " <0.005\n", - " 114.71\n", + " 304.56\n", " \n", " \n", " train_time\n", " 0.00\n", " 1.00\n", " 0.00\n", - " -0.00\n", + " 0.00\n", " 0.00\n", " 1.00\n", " 1.00\n", " 0.00\n", - " 1.21\n", - " 0.22\n", - " 2.15\n", + " 14.89\n", + " <0.005\n", + " 164.17\n", " \n", " \n", " Intercept\n", - " 3.73\n", - " 41.58\n", - " 0.17\n", - " 3.39\n", - " 4.07\n", - " 29.58\n", - " 58.46\n", + " 0.43\n", + " 1.53\n", + " 0.08\n", + " 0.27\n", + " 0.59\n", + " 1.31\n", + " 1.80\n", " 0.00\n", - " 21.45\n", + " 5.30\n", " <0.005\n", - " 336.62\n", + " 23.02\n", " \n", " \n", " rho_\n", " Intercept\n", - " -0.72\n", - " 0.49\n", - " 0.02\n", - " -0.76\n", - " -0.69\n", - " 0.47\n", - " 0.50\n", + " 1.10\n", + " 2.99\n", + " 0.03\n", + " 1.03\n", + " 1.16\n", + " 2.81\n", + " 3.18\n", " 0.00\n", - " -42.18\n", + " 34.56\n", " <0.005\n", - " inf\n", + " 867.25\n", " \n", " \n", "
\n", @@ -517,19 +559,19 @@ " \n", " \n", " Concordance\n", - " 0.81\n", + " 0.87\n", " \n", " \n", " AIC\n", - " 10849.31\n", + " 1470.20\n", " \n", " \n", " log-likelihood ratio test\n", - " 1602.43 on 9 df\n", + " 1003.22 on 10 df\n", " \n", " \n", " -log2(p) of ll-ratio test\n", - " inf\n", + " 692.36\n", " \n", " \n", "\n", @@ -539,61 +581,64 @@ "\\begin{tabular}{llrrrrrrrrrrr}\n", " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", "param & covariate & & & & & & & & & & & \\\\\n", - "\\multirow[c]{10}{*}{lambda_} & accuracy & 0.05 & 1.05 & 0.12 & -0.19 & 0.30 & 0.82 & 1.34 & 0.00 & 0.41 & 0.68 & 0.56 \\\\\n", - " & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -57.66 & 0.00 & inf \\\\\n", - " & atk_value & 0.43 & 1.54 & 0.13 & 0.18 & 0.68 & 1.20 & 1.97 & 0.00 & 3.38 & 0.00 & 10.44 \\\\\n", - " & data.sample.random_state & 0.05 & 1.05 & 0.02 & 0.01 & 0.08 & 1.01 & 1.08 & 0.00 & 2.73 & 0.01 & 7.29 \\\\\n", - " & def_value & -0.05 & 0.95 & 0.13 & -0.30 & 0.20 & 0.74 & 1.23 & 0.00 & -0.37 & 0.71 & 0.49 \\\\\n", - " & model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.52 & 0.60 & 0.73 \\\\\n", - " & model_layers & 0.00 & 1.00 & 0.00 & 0.00 & 0.01 & 1.00 & 1.01 & 0.00 & 2.88 & 0.00 & 7.99 \\\\\n", - " & predict_time & -0.28 & 0.76 & 0.02 & -0.32 & -0.24 & 0.72 & 0.79 & 0.00 & -12.39 & 0.00 & 114.71 \\\\\n", - " & train_time & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 1.21 & 0.22 & 2.15 \\\\\n", - " & Intercept & 3.73 & 41.58 & 0.17 & 3.39 & 4.07 & 29.58 & 58.46 & 0.00 & 21.45 & 0.00 & 336.62 \\\\\n", - "rho_ & Intercept & -0.72 & 0.49 & 0.02 & -0.76 & -0.69 & 0.47 & 0.50 & 0.00 & -42.18 & 0.00 & inf \\\\\n", + "\\multirow[c]{11}{*}{lambda_} & accuracy & -0.35 & 0.70 & 0.06 & -0.48 & -0.23 & 0.62 & 0.80 & 0.00 & -5.49 & 0.00 & 24.58 \\\\\n", + " & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -1.55 & 0.12 & 3.04 \\\\\n", + " & adv_fit_time & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -4.43 & 0.00 & 16.67 \\\\\n", + " & atk_value & 0.15 & 1.16 & 0.04 & 0.08 & 0.22 & 1.08 & 1.25 & 0.00 & 4.18 & 0.00 & 15.05 \\\\\n", + " & data.sample.random_state & -0.00 & 1.00 & 0.00 & -0.01 & 0.01 & 0.99 & 1.01 & 0.00 & -0.83 & 0.41 & 1.29 \\\\\n", + " & def_value & -0.01 & 0.99 & 0.04 & -0.08 & 0.07 & 0.92 & 1.07 & 0.00 & -0.21 & 0.84 & 0.26 \\\\\n", + " & failure_rate & -0.01 & 0.99 & 0.00 & -0.01 & -0.01 & 0.99 & 0.99 & 0.00 & -10.63 & 0.00 & 85.23 \\\\\n", + " & model.art.pipeline.initialize.kwargs.optimizer.lr & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 0.58 & 0.56 & 0.84 \\\\\n", + " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 20.39 & 0.00 & 304.56 \\\\\n", + " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 14.89 & 0.00 & 164.17 \\\\\n", + " & Intercept & 0.43 & 1.53 & 0.08 & 0.27 & 0.59 & 1.31 & 1.80 & 0.00 & 5.30 & 0.00 & 23.02 \\\\\n", + "rho_ & Intercept & 1.10 & 2.99 & 0.03 & 1.03 & 1.16 & 2.81 & 3.18 & 0.00 & 34.56 & 0.00 & 867.25 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ - "\n", - " duration col = 'adv_fit_time'\n", - " event col = 'failure_rate'\n", - " number of observations = 1917\n", - "number of events observed = 1917\n", - " log-likelihood = -5413.65\n", - " time fit was run = 2023-09-22 12:02:53 UTC\n", + "\n", + " duration col = 'predict_time'\n", + " event col = 'ben_failures'\n", + " number of observations = 568\n", + "number of events observed = 568\n", + " log-likelihood = -723.10\n", + " time fit was run = 2023-09-25 13:54:12 UTC\n", "\n", "---\n", " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", "param covariate \n", - "lambda_ accuracy 0.05 1.05 0.12 -0.19 0.30 0.82 1.34\n", - " adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", - " atk_value 0.43 1.54 0.13 0.18 0.68 1.20 1.97\n", - " data.sample.random_state 0.05 1.05 0.02 0.01 0.08 1.01 1.08\n", - " def_value -0.05 0.95 0.13 -0.30 0.20 0.74 1.23\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - " model_layers 0.00 1.00 0.00 0.00 0.01 1.00 1.01\n", - " predict_time -0.28 0.76 0.02 -0.32 -0.24 0.72 0.79\n", - " train_time 0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - " Intercept 3.73 41.58 0.17 3.39 4.07 29.58 58.46\n", - "rho_ Intercept -0.72 0.49 0.02 -0.76 -0.69 0.47 0.50\n", + "lambda_ accuracy -0.35 0.70 0.06 -0.48 -0.23 0.62 0.80\n", + " adv_failure_rate -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + " adv_fit_time -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + " atk_value 0.15 1.16 0.04 0.08 0.22 1.08 1.25\n", + " data.sample.random_state -0.00 1.00 0.00 -0.01 0.01 0.99 1.01\n", + " def_value -0.01 0.99 0.04 -0.08 0.07 0.92 1.07\n", + " failure_rate -0.01 0.99 0.00 -0.01 -0.01 0.99 0.99\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + " model_layers 0.01 1.01 0.00 0.01 0.01 1.01 1.01\n", + " train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", + " Intercept 0.43 1.53 0.08 0.27 0.59 1.31 1.80\n", + "rho_ Intercept 1.10 2.99 0.03 1.03 1.16 2.81 3.18\n", "\n", " cmp to z p -log2(p)\n", "param covariate \n", - "lambda_ accuracy 0.00 0.41 0.68 0.56\n", - " adv_failure_rate 0.00 -57.66 <0.005 inf\n", - " atk_value 0.00 3.38 <0.005 10.44\n", - " data.sample.random_state 0.00 2.73 0.01 7.29\n", - " def_value 0.00 -0.37 0.71 0.49\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.52 0.60 0.73\n", - " model_layers 0.00 2.88 <0.005 7.99\n", - " predict_time 0.00 -12.39 <0.005 114.71\n", - " train_time 0.00 1.21 0.22 2.15\n", - " Intercept 0.00 21.45 <0.005 336.62\n", - "rho_ Intercept 0.00 -42.18 <0.005 inf\n", + "lambda_ accuracy 0.00 -5.49 <0.005 24.58\n", + " adv_failure_rate 0.00 -1.55 0.12 3.04\n", + " adv_fit_time 0.00 -4.43 <0.005 16.67\n", + " atk_value 0.00 4.18 <0.005 15.05\n", + " data.sample.random_state 0.00 -0.83 0.41 1.29\n", + " def_value 0.00 -0.21 0.84 0.26\n", + " failure_rate 0.00 -10.63 <0.005 85.23\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 0.58 0.56 0.84\n", + " model_layers 0.00 20.39 <0.005 304.56\n", + " train_time 0.00 14.89 <0.005 164.17\n", + " Intercept 0.00 5.30 <0.005 23.02\n", + "rho_ Intercept 0.00 34.56 <0.005 867.25\n", "---\n", - "Concordance = 0.81\n", - "AIC = 10849.31\n", - "log-likelihood ratio test = 1602.43 on 9 df\n", - "-log2(p) of ll-ratio test = inf" + "Concordance = 0.87\n", + "AIC = 1470.20\n", + "log-likelihood ratio test = 1003.22 on 10 df\n", + "-log2(p) of ll-ratio test = 692.36" ] }, "metadata": {}, @@ -615,7 +660,7 @@ " \"adv_log_loss: lambda_\": \"Adv. Log Loss\",\n", " \"adv_failure_rate: lambda_\": \"Adv. Failure Rate\",\n", " \"failure_rate: lambda_\": \"Ben. Failure Rate\",\n", - " \"model_layers: lambda_ \": \"No. of Layers\",\n", + " \"model_layers: lambda_\": \"No. of Layers\",\n", " \"model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_\" : \"Learning Rate\",\n", " \"def_gen\" : \"Defence\",\n", "}\n", @@ -624,7 +669,7 @@ " X_train,\n", " file = \"weibull_aft.pdf\",\n", " event_col = target,\n", - " duration_col = \"adv_fit_time\",\n", + " duration_col = duration_col,\n", " title = \"Weibull AFT Model\",\n", " mtype = \"weibull\",\n", " replacement_dict=weibull_dict,\n", @@ -635,12 +680,71 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_615644/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " pareto.set_yticklabels(labels)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pareto_dict = {\n", + " \"model_layers=18\": \"18\",\n", + " \"model_layers=34\": \"34\",\n", + " \"model_layers=50\": \"50\",\n", + " \"model_layers=101\": \"101\",\n", + " \"model_layers=152\": \"152\",\n", + "}\n", + "pareto_weibull = plot_partial_effects(\n", + " file = \"weibull_partial_effects.pdf\", \n", + " aft = wft, \n", + " covariate_array=\"model_layers\", \n", + " values_array=[18, 34, 50, 101, 152], \n", + " title=\"Partial Effects of No. of Layers on Failure Rate for Weibull AFR\", \n", + " replacement_dict=pareto_dict, \n", + " ylabel=\"Chance of Survival at time $T$ (seconds)\",\n", + " xlabel=\"Time $T$ (seconds)\",\n", + " legend = {\"title\" : \"No. of Layers\", \"labels\" : [\"18\", \"34\", \"50\", \"101\", \"152\"]},\n", + " )\n", + "\n", + "# weibull_accuracy = plot_partial_effects(\n", + "# file = \"weibull_partial_effect_accuracy.pdf\",\n", + "# aft = wft,\n", + "# covariate_array = \"accuracy\",\n", + "# values_array = [.9, .99, .999, .9999],\n", + "# replacement_dict=weibull_dict,\n", + "# title=\"Partial Effects of Benign Accuracy on Failure Rate\",\n", + "# ylabel=\"Chance of Survival at time $T$ (seconds)\",\n", + "# xlabel=\"Time $T$ (seconds)\",\n", + "# legend = {\"title\" : \"Benign Accuracy\"},\n", + "# )\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -673,11 +777,11 @@ " \n", " \n", " duration col\n", - " 'adv_fit_time'\n", + " 'predict_time'\n", " \n", " \n", " event col\n", - " 'failure_rate'\n", + " 'ben_failures'\n", " \n", " \n", " baseline estimation\n", @@ -685,19 +789,19 @@ " \n", " \n", " number of observations\n", - " 1917\n", + " 568\n", " \n", " \n", " number of events observed\n", - " 1917\n", + " 568\n", " \n", " \n", " partial log-likelihood\n", - " -9069.47\n", + " -2477.41\n", " \n", " \n", " time fit was run\n", - " 2023-09-22 12:02:55 UTC\n", + " 2023-09-25 13:54:14 UTC\n", " \n", " \n", "\n", @@ -721,90 +825,90 @@ " \n", " \n", " accuracy\n", - " -0.13\n", - " 0.88\n", - " 0.06\n", - " -0.25\n", - " -0.00\n", - " 0.78\n", - " 1.00\n", + " 1.07\n", + " 2.91\n", + " 0.22\n", + " 0.64\n", + " 1.49\n", + " 1.90\n", + " 4.45\n", " 0.00\n", - " -2.04\n", - " 0.04\n", - " 4.59\n", + " 4.93\n", + " <0.005\n", + " 20.23\n", " \n", " \n", " train_time\n", + " -0.01\n", + " 0.99\n", " 0.00\n", - " 1.00\n", - " 0.00\n", - " 0.00\n", - " 0.00\n", - " 1.00\n", - " 1.00\n", - " 0.00\n", - " 5.97\n", - " <0.005\n", - " 28.69\n", - " \n", - " \n", - " predict_time\n", - " 0.01\n", - " 1.01\n", - " 0.01\n", + " -0.02\n", " -0.01\n", - " 0.04\n", + " 0.98\n", " 0.99\n", - " 1.04\n", " 0.00\n", - " 1.10\n", - " 0.27\n", - " 1.89\n", + " -16.43\n", + " <0.005\n", + " 198.99\n", " \n", " \n", " atk_value\n", - " -0.17\n", - " 0.84\n", - " 0.07\n", - " -0.30\n", - " -0.04\n", - " 0.74\n", - " 0.96\n", + " -0.45\n", + " 0.64\n", + " 0.12\n", + " -0.68\n", + " -0.22\n", + " 0.51\n", + " 0.80\n", " 0.00\n", - " -2.55\n", - " 0.01\n", - " 6.53\n", + " -3.79\n", + " <0.005\n", + " 12.68\n", " \n", " \n", " def_value\n", - " -0.02\n", - " 0.98\n", - " 0.06\n", - " -0.15\n", - " 0.11\n", - " 0.86\n", - " 1.11\n", + " -0.24\n", + " 0.79\n", + " 0.12\n", + " -0.48\n", + " 0.00\n", + " 0.62\n", + " 1.00\n", " 0.00\n", - " -0.30\n", - " 0.76\n", - " 0.39\n", + " -1.94\n", + " 0.05\n", + " 4.27\n", " \n", " \n", " data.sample.random_state\n", - " -0.02\n", - " 0.98\n", - " 0.01\n", - " -0.04\n", + " 0.03\n", + " 1.03\n", + " 0.02\n", " -0.00\n", - " 0.96\n", + " 0.06\n", " 1.00\n", + " 1.06\n", " 0.00\n", - " -2.27\n", - " 0.02\n", - " 5.43\n", + " 1.82\n", + " 0.07\n", + " 3.86\n", " \n", " \n", " adv_failure_rate\n", + " 0.00\n", + " 1.00\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 1.00\n", + " 1.00\n", + " 0.00\n", + " 2.82\n", + " <0.005\n", + " 7.70\n", + " \n", + " \n", + " failure_rate\n", " 0.02\n", " 1.02\n", " 0.00\n", @@ -813,23 +917,37 @@ " 1.02\n", " 1.03\n", " 0.00\n", - " 36.55\n", + " 7.57\n", " <0.005\n", - " 969.18\n", + " 44.64\n", " \n", " \n", " model_layers\n", - " -0.00\n", + " -0.02\n", + " 0.98\n", + " 0.00\n", + " -0.03\n", + " -0.02\n", + " 0.98\n", + " 0.98\n", + " 0.00\n", + " -15.41\n", + " <0.005\n", + " 175.57\n", + " \n", + " \n", + " adv_fit_time\n", + " 0.00\n", " 1.00\n", " 0.00\n", - " -0.01\n", - " -0.00\n", - " 0.99\n", + " 0.00\n", + " 0.00\n", + " 1.00\n", " 1.00\n", " 0.00\n", - " -7.32\n", + " 3.01\n", " <0.005\n", - " 41.85\n", + " 8.60\n", " \n", " \n", " model.art.pipeline.initialize.kwargs.optimizer.lr\n", @@ -841,9 +959,9 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " -0.55\n", - " 0.58\n", - " 0.79\n", + " -0.83\n", + " 0.41\n", + " 1.29\n", " \n", " \n", "
\n", @@ -864,19 +982,19 @@ " \n", " \n", " Concordance\n", - " 0.95\n", + " 0.90\n", " \n", " \n", " Partial AIC\n", - " 18156.95\n", + " 4974.82\n", " \n", " \n", " log-likelihood ratio test\n", - " 7015.80 on 9 df\n", + " 1122.01 on 10 df\n", " \n", " \n", " -log2(p) of ll-ratio test\n", - " inf\n", + " 777.41\n", " \n", " \n", "\n", @@ -886,56 +1004,86 @@ "\\begin{tabular}{lrrrrrrrrrrr}\n", " & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", "covariate & & & & & & & & & & & \\\\\n", - "accuracy & -0.13 & 0.88 & 0.06 & -0.25 & -0.00 & 0.78 & 1.00 & 0.00 & -2.04 & 0.04 & 4.59 \\\\\n", - "train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 5.97 & 0.00 & 28.69 \\\\\n", - "predict_time & 0.01 & 1.01 & 0.01 & -0.01 & 0.04 & 0.99 & 1.04 & 0.00 & 1.10 & 0.27 & 1.89 \\\\\n", - "atk_value & -0.17 & 0.84 & 0.07 & -0.30 & -0.04 & 0.74 & 0.96 & 0.00 & -2.55 & 0.01 & 6.53 \\\\\n", - "def_value & -0.02 & 0.98 & 0.06 & -0.15 & 0.11 & 0.86 & 1.11 & 0.00 & -0.30 & 0.76 & 0.39 \\\\\n", - "data.sample.random_state & -0.02 & 0.98 & 0.01 & -0.04 & -0.00 & 0.96 & 1.00 & 0.00 & -2.27 & 0.02 & 5.43 \\\\\n", - "adv_failure_rate & 0.02 & 1.02 & 0.00 & 0.02 & 0.03 & 1.02 & 1.03 & 0.00 & 36.55 & 0.00 & 969.18 \\\\\n", - "model_layers & -0.00 & 1.00 & 0.00 & -0.01 & -0.00 & 0.99 & 1.00 & 0.00 & -7.32 & 0.00 & 41.85 \\\\\n", - "model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.55 & 0.58 & 0.79 \\\\\n", + "accuracy & 1.07 & 2.91 & 0.22 & 0.64 & 1.49 & 1.90 & 4.45 & 0.00 & 4.93 & 0.00 & 20.23 \\\\\n", + "train_time & -0.01 & 0.99 & 0.00 & -0.02 & -0.01 & 0.98 & 0.99 & 0.00 & -16.43 & 0.00 & 198.99 \\\\\n", + "atk_value & -0.45 & 0.64 & 0.12 & -0.68 & -0.22 & 0.51 & 0.80 & 0.00 & -3.79 & 0.00 & 12.68 \\\\\n", + "def_value & -0.24 & 0.79 & 0.12 & -0.48 & 0.00 & 0.62 & 1.00 & 0.00 & -1.94 & 0.05 & 4.27 \\\\\n", + "data.sample.random_state & 0.03 & 1.03 & 0.02 & -0.00 & 0.06 & 1.00 & 1.06 & 0.00 & 1.82 & 0.07 & 3.86 \\\\\n", + "adv_failure_rate & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 2.82 & 0.00 & 7.70 \\\\\n", + "failure_rate & 0.02 & 1.02 & 0.00 & 0.02 & 0.03 & 1.02 & 1.03 & 0.00 & 7.57 & 0.00 & 44.64 \\\\\n", + "model_layers & -0.02 & 0.98 & 0.00 & -0.03 & -0.02 & 0.98 & 0.98 & 0.00 & -15.41 & 0.00 & 175.57 \\\\\n", + "adv_fit_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 3.01 & 0.00 & 8.60 \\\\\n", + "model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.83 & 0.41 & 1.29 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ - "\n", - " duration col = 'adv_fit_time'\n", - " event col = 'failure_rate'\n", + "\n", + " duration col = 'predict_time'\n", + " event col = 'ben_failures'\n", " baseline estimation = breslow\n", - " number of observations = 1917\n", - "number of events observed = 1917\n", - " partial log-likelihood = -9069.47\n", - " time fit was run = 2023-09-22 12:02:55 UTC\n", + " number of observations = 568\n", + "number of events observed = 568\n", + " partial log-likelihood = -2477.41\n", + " time fit was run = 2023-09-25 13:54:14 UTC\n", "\n", "---\n", " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", "covariate \n", - "accuracy -0.13 0.88 0.06 -0.25 -0.00 0.78 1.00\n", - "train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", - "predict_time 0.01 1.01 0.01 -0.01 0.04 0.99 1.04\n", - "atk_value -0.17 0.84 0.07 -0.30 -0.04 0.74 0.96\n", - "def_value -0.02 0.98 0.06 -0.15 0.11 0.86 1.11\n", - "data.sample.random_state -0.02 0.98 0.01 -0.04 -0.00 0.96 1.00\n", - "adv_failure_rate 0.02 1.02 0.00 0.02 0.03 1.02 1.03\n", - "model_layers -0.00 1.00 0.00 -0.01 -0.00 0.99 1.00\n", + "accuracy 1.07 2.91 0.22 0.64 1.49 1.90 4.45\n", + "train_time -0.01 0.99 0.00 -0.02 -0.01 0.98 0.99\n", + "atk_value -0.45 0.64 0.12 -0.68 -0.22 0.51 0.80\n", + "def_value -0.24 0.79 0.12 -0.48 0.00 0.62 1.00\n", + "data.sample.random_state 0.03 1.03 0.02 -0.00 0.06 1.00 1.06\n", + "adv_failure_rate 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", + "failure_rate 0.02 1.02 0.00 0.02 0.03 1.02 1.03\n", + "model_layers -0.02 0.98 0.00 -0.03 -0.02 0.98 0.98\n", + "adv_fit_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", "model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", "\n", - " cmp to z p -log2(p)\n", - "covariate \n", - "accuracy 0.00 -2.04 0.04 4.59\n", - "train_time 0.00 5.97 <0.005 28.69\n", - "predict_time 0.00 1.10 0.27 1.89\n", - "atk_value 0.00 -2.55 0.01 6.53\n", - "def_value 0.00 -0.30 0.76 0.39\n", - "data.sample.random_state 0.00 -2.27 0.02 5.43\n", - "adv_failure_rate 0.00 36.55 <0.005 969.18\n", - "model_layers 0.00 -7.32 <0.005 41.85\n", - "model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.55 0.58 0.79\n", + " cmp to z p -log2(p)\n", + "covariate \n", + "accuracy 0.00 4.93 <0.005 20.23\n", + "train_time 0.00 -16.43 <0.005 198.99\n", + "atk_value 0.00 -3.79 <0.005 12.68\n", + "def_value 0.00 -1.94 0.05 4.27\n", + "data.sample.random_state 0.00 1.82 0.07 3.86\n", + "adv_failure_rate 0.00 2.82 <0.005 7.70\n", + "failure_rate 0.00 7.57 <0.005 44.64\n", + "model_layers 0.00 -15.41 <0.005 175.57\n", + "adv_fit_time 0.00 3.01 <0.005 8.60\n", + "model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.83 0.41 1.29\n", "---\n", - "Concordance = 0.95\n", - "Partial AIC = 18156.95\n", - "log-likelihood ratio test = 7015.80 on 9 df\n", - "-log2(p) of ll-ratio test = inf" + "Concordance = 0.90\n", + "Partial AIC = 4974.82\n", + "log-likelihood ratio test = 1122.01 on 10 df\n", + "-log2(p) of ll-ratio test = 777.41" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_615644/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " pareto.set_yticklabels(labels)\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -943,6 +1091,7 @@ } ], "source": [ + "\n", "cox_dict = {\n", " \"adv_failure_rate\": \"Adv. Failure Rate\",\n", " \"def_value\" : \"Defence Strength\",\n", @@ -955,27 +1104,410 @@ " \"adv_log_loss\" : \"Adv. Log Loss\",\n", " \"predict_time\" : \"Inference Time\",\n", " \"accuracy\" : \"Ben. Accuracy\",\n", - " \"failure_rate\" : \"Ben. Failure Rate\", \n", + " \"failure_rate\" : \"Ben. Failure Rate\",\n", + " \"atk_value\" : \"Attack Strength\",\n", "}\n", "\n", "cox_afr, cft = plot_aft(\n", " X_train,\n", " file = \"cox_aft.pdf\",\n", " event_col = target,\n", - " duration_col = \"adv_fit_time\",\n", + " duration_col = duration_col,\n", " title = \"Cox AFT Model\",\n", " mtype = \"cox\",\n", " replacement_dict=cox_dict,\n", ")\n", "cox_scores = score_model(cft, X_train, X_test)\n", - "cft.print_summary()\n" + "cft.print_summary()\n", + "cox_partial = plot_partial_effects(\n", + " file = \"cox_partial_effects.pdf\",\n", + " aft = cft,\n", + " covariate_array = \"model_layers\",\n", + " values_array = [18, 34, 50, 101, 152],\n", + " replacement_dict=cox_dict,\n", + " title=\"Partial Effects of No. of Layers on Failure Rate for Cox AFR\",\n", + " ylabel=\"Chance of Survival at time $T$ (seconds)\",\n", + " xlabel=\"Time $T$ (seconds)\",\n", + " legend_kwargs={\"title\" : \"No. of Layers\", \"labels\" : [\"18\", \"34\", \"50\", \"101\", \"152\"]},\n", + ")" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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modellifelines.LogNormalAFTFitter
duration col'predict_time'
event col'ben_failures'
number of observations568
number of events observed568
log-likelihood-688.89
time fit was run2023-09-25 13:54:16 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
mu_accuracy-0.350.710.07-0.49-0.200.610.810.00-4.77<0.00519.08
adv_failure_rate-0.001.000.00-0.00-0.001.001.000.00-2.380.025.85
adv_fit_time-0.001.000.00-0.00-0.001.001.000.00-3.90<0.00513.35
atk_value0.151.160.040.080.231.081.260.003.92<0.00513.48
data.sample.random_state0.001.000.01-0.010.010.991.010.000.700.481.05
def_value0.001.000.04-0.080.080.921.080.000.010.990.01
failure_rate-0.010.990.00-0.01-0.010.990.990.00-7.22<0.00540.82
model.art.pipeline.initialize.kwargs.optimizer.lr0.001.000.00-0.000.001.001.000.000.740.461.13
model_layers0.011.010.000.010.011.011.010.0028.07<0.005573.57
train_time0.001.000.000.000.001.001.000.0026.65<0.005517.55
Intercept0.181.200.090.000.361.001.430.001.990.054.42
sigma_Intercept-1.020.360.03-1.08-0.970.340.380.00-34.53<0.005865.57

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Concordance0.86
AIC1401.78
log-likelihood ratio test937.90 on 10 df
-log2(p) of ll-ratio test645.63
\n", + "
" + ], + "text/latex": [ + "\\begin{tabular}{llrrrrrrrrrrr}\n", + " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", + "param & covariate & & & & & & & & & & & \\\\\n", + "\\multirow[c]{11}{*}{mu_} & accuracy & -0.35 & 0.71 & 0.07 & -0.49 & -0.20 & 0.61 & 0.81 & 0.00 & -4.77 & 0.00 & 19.08 \\\\\n", + " & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -2.38 & 0.02 & 5.85 \\\\\n", + " & adv_fit_time & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -3.90 & 0.00 & 13.35 \\\\\n", + " & atk_value & 0.15 & 1.16 & 0.04 & 0.08 & 0.23 & 1.08 & 1.26 & 0.00 & 3.92 & 0.00 & 13.48 \\\\\n", + " & data.sample.random_state & 0.00 & 1.00 & 0.01 & -0.01 & 0.01 & 0.99 & 1.01 & 0.00 & 0.70 & 0.48 & 1.05 \\\\\n", + " & def_value & 0.00 & 1.00 & 0.04 & -0.08 & 0.08 & 0.92 & 1.08 & 0.00 & 0.01 & 0.99 & 0.01 \\\\\n", + " & failure_rate & -0.01 & 0.99 & 0.00 & -0.01 & -0.01 & 0.99 & 0.99 & 0.00 & -7.22 & 0.00 & 40.82 \\\\\n", + " & model.art.pipeline.initialize.kwargs.optimizer.lr & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 0.74 & 0.46 & 1.13 \\\\\n", + " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 28.07 & 0.00 & 573.57 \\\\\n", + " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 26.65 & 0.00 & 517.55 \\\\\n", + " & Intercept & 0.18 & 1.20 & 0.09 & 0.00 & 0.36 & 1.00 & 1.43 & 0.00 & 1.99 & 0.05 & 4.42 \\\\\n", + "sigma_ & Intercept & -1.02 & 0.36 & 0.03 & -1.08 & -0.97 & 0.34 & 0.38 & 0.00 & -34.53 & 0.00 & 865.57 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\n", + " duration col = 'predict_time'\n", + " event col = 'ben_failures'\n", + " number of observations = 568\n", + "number of events observed = 568\n", + " log-likelihood = -688.89\n", + " time fit was run = 2023-09-25 13:54:16 UTC\n", + "\n", + "---\n", + " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", + "param covariate \n", + "mu_ accuracy -0.35 0.71 0.07 -0.49 -0.20 0.61 0.81\n", + " adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + " adv_fit_time -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + " atk_value 0.15 1.16 0.04 0.08 0.23 1.08 1.26\n", + " data.sample.random_state 0.00 1.00 0.01 -0.01 0.01 0.99 1.01\n", + " def_value 0.00 1.00 0.04 -0.08 0.08 0.92 1.08\n", + " failure_rate -0.01 0.99 0.00 -0.01 -0.01 0.99 0.99\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + " model_layers 0.01 1.01 0.00 0.01 0.01 1.01 1.01\n", + " train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", + " Intercept 0.18 1.20 0.09 0.00 0.36 1.00 1.43\n", + "sigma_ Intercept -1.02 0.36 0.03 -1.08 -0.97 0.34 0.38\n", + "\n", + " cmp to z p -log2(p)\n", + "param covariate \n", + "mu_ accuracy 0.00 -4.77 <0.005 19.08\n", + " adv_failure_rate 0.00 -2.38 0.02 5.85\n", + " adv_fit_time 0.00 -3.90 <0.005 13.35\n", + " atk_value 0.00 3.92 <0.005 13.48\n", + " data.sample.random_state 0.00 0.70 0.48 1.05\n", + " def_value 0.00 0.01 0.99 0.01\n", + " failure_rate 0.00 -7.22 <0.005 40.82\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 0.74 0.46 1.13\n", + " model_layers 0.00 28.07 <0.005 573.57\n", + " train_time 0.00 26.65 <0.005 517.55\n", + " Intercept 0.00 1.99 0.05 4.42\n", + "sigma_ Intercept 0.00 -34.53 <0.005 865.57\n", + "---\n", + "Concordance = 0.86\n", + "AIC = 1401.78\n", + "log-likelihood ratio test = 937.90 on 10 df\n", + "-log2(p) of ll-ratio test = 645.63" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_615644/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " pareto.set_yticklabels(labels)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "log_normal_dict = {\n", " \"Intercept: sigma_\": \"$\\sigma$\",\n", @@ -1000,23 +1532,34 @@ " X_train,\n", " \"log_normal_aft.pdf\",\n", " target,\n", - " \"adv_fit_time\",\n", + " duration_col,\n", " \"Log Normal AFT Model\",\n", " \"log_normal\",\n", " replacement_dict=log_normal_dict,\n", ")\n", "lnt_scores = score_model(lnt, X_train, X_test)\n", - "lnt.print_summary()" + "lnt.print_summary()\n", + "lnt_partial = plot_partial_effects(\n", + " file = \"log_normal_partial_effects.pdf\",\n", + " aft = lnt,\n", + " covariate_array = \"model_layers\",\n", + " values_array = [18, 34, 50, 101, 152],\n", + " replacement_dict=log_normal_dict,\n", + " title=\"Partial Effects of No. of Layers on Failure Rate for Log-Normal AFR\",\n", + " ylabel=\"Chance of Survival at time $T$ (seconds)\",\n", + " xlabel=\"Time $T$ (seconds)\",\n", + " legend_kwargs={\"title\" : \"No. of Layers\", \"labels\" : [\"18\", \"34\", \"50\", \"101\", \"152\"]},\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1049,27 +1592,27 @@ " \n", " \n", " duration col\n", - " 'adv_fit_time'\n", + " 'predict_time'\n", " \n", " \n", " event col\n", - " 'adv_failure_rate'\n", + " 'ben_failures'\n", " \n", " \n", " number of observations\n", - " 1917\n", + " 568\n", " \n", " \n", " number of events observed\n", - " 1917\n", + " 568\n", " \n", " \n", " log-likelihood\n", - " -6048.10\n", + " -693.57\n", " \n", " \n", " time fit was run\n", - " 2023-09-22 12:02:25 UTC\n", + " 2023-09-25 13:54:18 UTC\n", " \n", " \n", "\n", @@ -1093,79 +1636,107 @@ " \n", " \n", " \n", - " alpha_\n", + " alpha_\n", " accuracy\n", - " -0.76\n", - " 0.47\n", - " 0.24\n", - " -1.23\n", - " -0.29\n", - " 0.29\n", - " 0.74\n", + " -0.37\n", + " 0.69\n", + " 0.07\n", + " -0.51\n", + " -0.23\n", + " 0.60\n", + " 0.80\n", + " 0.00\n", + " -5.18\n", + " <0.005\n", + " 22.07\n", + " \n", + " \n", + " adv_failure_rate\n", + " -0.00\n", + " 1.00\n", + " 0.00\n", + " -0.00\n", + " -0.00\n", + " 1.00\n", + " 1.00\n", + " 0.00\n", + " -2.58\n", + " 0.01\n", + " 6.64\n", + " \n", + " \n", + " adv_fit_time\n", + " -0.00\n", + " 1.00\n", + " 0.00\n", + " -0.00\n", + " -0.00\n", + " 1.00\n", + " 1.00\n", " 0.00\n", - " -3.19\n", + " -3.93\n", " <0.005\n", - " 9.45\n", + " 13.55\n", " \n", " \n", " atk_value\n", - " 0.76\n", - " 2.14\n", - " 0.20\n", - " 0.37\n", - " 1.16\n", - " 1.44\n", - " 3.17\n", + " 0.17\n", + " 1.18\n", + " 0.04\n", + " 0.09\n", + " 0.24\n", + " 1.09\n", + " 1.28\n", " 0.00\n", - " 3.78\n", + " 4.20\n", " <0.005\n", - " 12.63\n", + " 15.17\n", " \n", " \n", " data.sample.random_state\n", - " 0.05\n", - " 1.05\n", - " 0.03\n", + " 0.01\n", + " 1.01\n", + " 0.01\n", " -0.00\n", - " 0.10\n", + " 0.02\n", " 1.00\n", - " 1.11\n", + " 1.02\n", " 0.00\n", - " 1.83\n", - " 0.07\n", - " 3.88\n", + " 1.13\n", + " 0.26\n", + " 1.96\n", " \n", " \n", " def_value\n", + " 0.01\n", + " 1.01\n", " 0.04\n", - " 1.04\n", - " 0.20\n", - " -0.36\n", - " 0.44\n", - " 0.70\n", - " 1.56\n", + " -0.07\n", + " 0.08\n", + " 0.93\n", + " 1.09\n", " 0.00\n", + " 0.16\n", + " 0.87\n", " 0.20\n", - " 0.84\n", - " 0.25\n", " \n", " \n", " failure_rate\n", " -0.01\n", " 0.99\n", " 0.00\n", - " -0.02\n", - " -0.00\n", - " 0.98\n", - " 1.00\n", + " -0.01\n", + " -0.01\n", + " 0.99\n", + " 0.99\n", " 0.00\n", - " -3.54\n", + " -7.72\n", " <0.005\n", - " 11.29\n", + " 46.29\n", " \n", " \n", " model.art.pipeline.initialize.kwargs.optimizer.lr\n", - " -0.00\n", + " 0.00\n", " 1.00\n", " 0.00\n", " -0.00\n", @@ -1173,9 +1744,9 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " -1.33\n", - " 0.19\n", - " 2.43\n", + " 0.55\n", + " 0.58\n", + " 0.78\n", " \n", " \n", " model_layers\n", @@ -1183,27 +1754,13 @@ " 1.01\n", " 0.00\n", " 0.01\n", - " 0.02\n", + " 0.01\n", + " 1.01\n", " 1.01\n", - " 1.02\n", - " 0.00\n", - " 5.16\n", - " <0.005\n", - " 21.98\n", - " \n", - " \n", - " predict_time\n", - " -0.34\n", - " 0.71\n", - " 0.04\n", - " -0.41\n", - " -0.27\n", - " 0.66\n", - " 0.76\n", " 0.00\n", - " -9.65\n", + " 25.06\n", " <0.005\n", - " 70.82\n", + " 458.04\n", " \n", " \n", " train_time\n", @@ -1215,38 +1772,38 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " 5.04\n", + " 15.51\n", " <0.005\n", - " 21.02\n", + " 177.74\n", " \n", " \n", " Intercept\n", - " 0.78\n", - " 2.18\n", - " 0.34\n", - " 0.12\n", - " 1.44\n", - " 1.13\n", - " 4.21\n", + " 0.18\n", + " 1.20\n", + " 0.09\n", " 0.00\n", - " 2.33\n", - " 0.02\n", - " 5.64\n", + " 0.36\n", + " 1.00\n", + " 1.43\n", + " 0.00\n", + " 2.00\n", + " 0.05\n", + " 4.44\n", " \n", " \n", " beta_\n", " Intercept\n", - " -0.62\n", - " 0.54\n", - " 0.02\n", - " -0.65\n", - " -0.58\n", - " 0.52\n", - " 0.56\n", + " 1.59\n", + " 4.91\n", + " 0.04\n", + " 1.52\n", + " 1.66\n", + " 4.58\n", + " 5.26\n", " 0.00\n", - " -33.40\n", + " 45.13\n", " <0.005\n", - " 810.23\n", + " inf\n", " \n", " \n", "
\n", @@ -1267,19 +1824,19 @@ " \n", " \n", " Concordance\n", - " 0.59\n", + " 0.86\n", " \n", " \n", " AIC\n", - " 12118.20\n", + " 1411.15\n", " \n", " \n", " log-likelihood ratio test\n", - " 147.29 on 9 df\n", + " 942.02 on 10 df\n", " \n", " \n", " -log2(p) of ll-ratio test\n", - " 88.01\n", + " 648.58\n", " \n", " \n", "\n", @@ -1289,61 +1846,89 @@ "\\begin{tabular}{llrrrrrrrrrrr}\n", " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", "param & covariate & & & & & & & & & & & \\\\\n", - "\\multirow[c]{10}{*}{alpha_} & accuracy & -0.76 & 0.47 & 0.24 & -1.23 & -0.29 & 0.29 & 0.74 & 0.00 & -3.19 & 0.00 & 9.45 \\\\\n", - " & atk_value & 0.76 & 2.14 & 0.20 & 0.37 & 1.16 & 1.44 & 3.17 & 0.00 & 3.78 & 0.00 & 12.63 \\\\\n", - " & data.sample.random_state & 0.05 & 1.05 & 0.03 & -0.00 & 0.10 & 1.00 & 1.11 & 0.00 & 1.83 & 0.07 & 3.88 \\\\\n", - " & def_value & 0.04 & 1.04 & 0.20 & -0.36 & 0.44 & 0.70 & 1.56 & 0.00 & 0.20 & 0.84 & 0.25 \\\\\n", - " & failure_rate & -0.01 & 0.99 & 0.00 & -0.02 & -0.00 & 0.98 & 1.00 & 0.00 & -3.54 & 0.00 & 11.29 \\\\\n", - " & model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -1.33 & 0.19 & 2.43 \\\\\n", - " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.02 & 1.01 & 1.02 & 0.00 & 5.16 & 0.00 & 21.98 \\\\\n", - " & predict_time & -0.34 & 0.71 & 0.04 & -0.41 & -0.27 & 0.66 & 0.76 & 0.00 & -9.65 & 0.00 & 70.82 \\\\\n", - " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 5.04 & 0.00 & 21.02 \\\\\n", - " & Intercept & 0.78 & 2.18 & 0.34 & 0.12 & 1.44 & 1.13 & 4.21 & 0.00 & 2.33 & 0.02 & 5.64 \\\\\n", - "beta_ & Intercept & -0.62 & 0.54 & 0.02 & -0.65 & -0.58 & 0.52 & 0.56 & 0.00 & -33.40 & 0.00 & 810.23 \\\\\n", + "\\multirow[c]{11}{*}{alpha_} & accuracy & -0.37 & 0.69 & 0.07 & -0.51 & -0.23 & 0.60 & 0.80 & 0.00 & -5.18 & 0.00 & 22.07 \\\\\n", + " & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -2.58 & 0.01 & 6.64 \\\\\n", + " & adv_fit_time & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -3.93 & 0.00 & 13.55 \\\\\n", + " & atk_value & 0.17 & 1.18 & 0.04 & 0.09 & 0.24 & 1.09 & 1.28 & 0.00 & 4.20 & 0.00 & 15.17 \\\\\n", + " & data.sample.random_state & 0.01 & 1.01 & 0.01 & -0.00 & 0.02 & 1.00 & 1.02 & 0.00 & 1.13 & 0.26 & 1.96 \\\\\n", + " & def_value & 0.01 & 1.01 & 0.04 & -0.07 & 0.08 & 0.93 & 1.09 & 0.00 & 0.16 & 0.87 & 0.20 \\\\\n", + " & failure_rate & -0.01 & 0.99 & 0.00 & -0.01 & -0.01 & 0.99 & 0.99 & 0.00 & -7.72 & 0.00 & 46.29 \\\\\n", + " & model.art.pipeline.initialize.kwargs.optimizer.lr & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 0.55 & 0.58 & 0.78 \\\\\n", + " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 25.06 & 0.00 & 458.04 \\\\\n", + " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 15.51 & 0.00 & 177.74 \\\\\n", + " & Intercept & 0.18 & 1.20 & 0.09 & 0.00 & 0.36 & 1.00 & 1.43 & 0.00 & 2.00 & 0.05 & 4.44 \\\\\n", + "beta_ & Intercept & 1.59 & 4.91 & 0.04 & 1.52 & 1.66 & 4.58 & 5.26 & 0.00 & 45.13 & 0.00 & inf \\\\\n", "\\end{tabular}\n" ], "text/plain": [ - "\n", - " duration col = 'adv_fit_time'\n", - " event col = 'adv_failure_rate'\n", - " number of observations = 1917\n", - "number of events observed = 1917\n", - " log-likelihood = -6048.10\n", - " time fit was run = 2023-09-22 12:02:25 UTC\n", + "\n", + " duration col = 'predict_time'\n", + " event col = 'ben_failures'\n", + " number of observations = 568\n", + "number of events observed = 568\n", + " log-likelihood = -693.57\n", + " time fit was run = 2023-09-25 13:54:18 UTC\n", "\n", "---\n", " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", "param covariate \n", - "alpha_ accuracy -0.76 0.47 0.24 -1.23 -0.29 0.29 0.74\n", - " atk_value 0.76 2.14 0.20 0.37 1.16 1.44 3.17\n", - " data.sample.random_state 0.05 1.05 0.03 -0.00 0.10 1.00 1.11\n", - " def_value 0.04 1.04 0.20 -0.36 0.44 0.70 1.56\n", - " failure_rate -0.01 0.99 0.00 -0.02 -0.00 0.98 1.00\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - " model_layers 0.01 1.01 0.00 0.01 0.02 1.01 1.02\n", - " predict_time -0.34 0.71 0.04 -0.41 -0.27 0.66 0.76\n", + "alpha_ accuracy -0.37 0.69 0.07 -0.51 -0.23 0.60 0.80\n", + " adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + " adv_fit_time -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + " atk_value 0.17 1.18 0.04 0.09 0.24 1.09 1.28\n", + " data.sample.random_state 0.01 1.01 0.01 -0.00 0.02 1.00 1.02\n", + " def_value 0.01 1.01 0.04 -0.07 0.08 0.93 1.09\n", + " failure_rate -0.01 0.99 0.00 -0.01 -0.01 0.99 0.99\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + " model_layers 0.01 1.01 0.00 0.01 0.01 1.01 1.01\n", " train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", - " Intercept 0.78 2.18 0.34 0.12 1.44 1.13 4.21\n", - "beta_ Intercept -0.62 0.54 0.02 -0.65 -0.58 0.52 0.56\n", + " Intercept 0.18 1.20 0.09 0.00 0.36 1.00 1.43\n", + "beta_ Intercept 1.59 4.91 0.04 1.52 1.66 4.58 5.26\n", "\n", - " cmp to z p -log2(p)\n", - "param covariate \n", - "alpha_ accuracy 0.00 -3.19 <0.005 9.45\n", - " atk_value 0.00 3.78 <0.005 12.63\n", - " data.sample.random_state 0.00 1.83 0.07 3.88\n", - " def_value 0.00 0.20 0.84 0.25\n", - " failure_rate 0.00 -3.54 <0.005 11.29\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -1.33 0.19 2.43\n", - " model_layers 0.00 5.16 <0.005 21.98\n", - " predict_time 0.00 -9.65 <0.005 70.82\n", - " train_time 0.00 5.04 <0.005 21.02\n", - " Intercept 0.00 2.33 0.02 5.64\n", - "beta_ Intercept 0.00 -33.40 <0.005 810.23\n", + " cmp to z p -log2(p)\n", + "param covariate \n", + "alpha_ accuracy 0.00 -5.18 <0.005 22.07\n", + " adv_failure_rate 0.00 -2.58 0.01 6.64\n", + " adv_fit_time 0.00 -3.93 <0.005 13.55\n", + " atk_value 0.00 4.20 <0.005 15.17\n", + " data.sample.random_state 0.00 1.13 0.26 1.96\n", + " def_value 0.00 0.16 0.87 0.20\n", + " failure_rate 0.00 -7.72 <0.005 46.29\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 0.55 0.58 0.78\n", + " model_layers 0.00 25.06 <0.005 458.04\n", + " train_time 0.00 15.51 <0.005 177.74\n", + " Intercept 0.00 2.00 0.05 4.44\n", + "beta_ Intercept 0.00 45.13 <0.005 inf\n", "---\n", - "Concordance = 0.59\n", - "AIC = 12118.20\n", - "log-likelihood ratio test = 147.29 on 9 df\n", - "-log2(p) of ll-ratio test = 88.01" + "Concordance = 0.86\n", + "AIC = 1411.15\n", + "log-likelihood ratio test = 942.02 on 10 df\n", + "-log2(p) of ll-ratio test = 648.58" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'train_score': -1.221079966573897, 'test_score': -1.0974447812062609}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_615644/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " pareto.set_yticklabels(labels)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" ] }, "metadata": {}, @@ -1351,6 +1936,7 @@ } ], "source": [ + "\n", "log_logistic_dict = {\n", " \"Intercept: beta_\": \"$\\\\beta$\",\n", " \"Intercept: alpha_\": \"$\\\\alpha$\",\n", @@ -1373,18 +1959,51 @@ " X_train,\n", " \"log_logistic_aft.pdf\",\n", " target,\n", - " \"adv_fit_time\",\n", + " duration_col,\n", " \"Log Logistic AFT Model\",\n", " \"log_logistic\",\n", " replacement_dict=log_logistic_dict,\n", ")\n", "llt.print_summary()\n", - "llt_scores = score_model(llt, X_train, X_test)\n" + "llt_scores = score_model(llt, X_train, X_test)\n", + "print(llt_scores)\n", + "llt_partial = plot_partial_effects(\n", + " file = \"log_logistic_partial_effects.pdf\",\n", + " aft = llt,\n", + " covariate_array = \"model_layers\",\n", + " values_array = [18, 34, 50, 101, 152],\n", + " replacement_dict=log_logistic_dict,\n", + " title=\"Partial Effects of No. of Layers on Failure Rate for Log-Logistic AFR\",\n", + " ylabel=\"Chance of Survival at time $T$ (seconds)\",\n", + " xlabel=\"Time $T$ (seconds)\",\n", + " legend_kwargs={\"title\" : \"No. of Layers\", \"labels\" : [\"18\", \"34\", \"50\", \"101\", \"152\"]},\n", + " \n", + ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.151203500424281" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(llt.predict_median(X_train))" + ] + }, + { + "cell_type": "code", + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -1411,88 +2030,65 @@ " AIC\n", " Concordance Score\n", " BIC\n", - " Train Log Likelihood\n", - " Test Log Likelihood\n", - " Mean Survival Time\n", - " Median Survival Time\n", - " Error (seconds)\n", - " % Error from True Mean\n", + " Estimated Mean Survival Time\n", + " Estimated Median Survival Time\n", + " Predicted Error from True Mean\n", + " Predicted Error from True Median\n", " \n", " \n", " \n", " \n", " Weibull\n", - " 12187.71\n", - " 0.59\n", - " 12187.71\n", - " -3.17\n", - " -5.53\n", - " 92.66\n", - " 6.24\n", - " -62.72\n", - " -83.40\n", + " 1470.20\n", + " 0.87\n", + " 1470.20\n", + " 7.34\n", + " 2.16\n", + " 0.19\n", + " -1.14\n", " \n", " \n", " LogNormal\n", - " 11942.60\n", - " 0.59\n", - " 11942.60\n", - " -3.11\n", - " -3.16\n", - " 344.56\n", - " 3.37\n", - " -65.58\n", - " -38.27\n", + " 1401.78\n", + " 0.86\n", + " 1401.78\n", + " 3.39\n", + " 1.94\n", + " -3.76\n", + " -1.36\n", " \n", " \n", " LogLogistic\n", - " 12118.20\n", - " 0.59\n", - " 12118.20\n", - " -3.15\n", - " -3.20\n", - " NaN\n", - " 3.14\n", - " -65.81\n", - " NaN\n", - " \n", - " \n", - " Cox\n", - " NaN\n", - " 0.59\n", - " NaN\n", - " -6.51\n", - " -5.64\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 1411.15\n", + " 0.86\n", + " 1411.15\n", + " 3.38\n", + " 1.99\n", + " -3.78\n", + " -1.31\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " AIC Concordance Score BIC Train Log Likelihood \\\n", - "Weibull 12187.71 0.59 12187.71 -3.17 \n", - "LogNormal 11942.60 0.59 11942.60 -3.11 \n", - "LogLogistic 12118.20 0.59 12118.20 -3.15 \n", - "Cox NaN 0.59 NaN -6.51 \n", + " AIC Concordance Score BIC \\\n", + "Weibull 1470.20 0.87 1470.20 \n", + "LogNormal 1401.78 0.86 1401.78 \n", + "LogLogistic 1411.15 0.86 1411.15 \n", "\n", - " Test Log Likelihood Mean Survival Time Median Survival Time \\\n", - "Weibull -5.53 92.66 6.24 \n", - "LogNormal -3.16 344.56 3.37 \n", - "LogLogistic -3.20 NaN 3.14 \n", - "Cox -5.64 NaN NaN \n", + " Estimated Mean Survival Time Estimated Median Survival Time \\\n", + "Weibull 7.34 2.16 \n", + "LogNormal 3.39 1.94 \n", + "LogLogistic 3.38 1.99 \n", "\n", - " Error (seconds) % Error from True Mean \n", - "Weibull -62.72 -83.40 \n", - "LogNormal -65.58 -38.27 \n", - "LogLogistic -65.81 NaN \n", - "Cox NaN NaN " + " Predicted Error from True Mean Predicted Error from True Median \n", + "Weibull 0.19 -1.14 \n", + "LogNormal -3.76 -1.36 \n", + "LogLogistic -3.78 -1.31 " ] }, - "execution_count": 28, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -1502,8 +2098,15 @@ " \"Weibull\": wft,\n", " \"LogNormal\": lnt,\n", " \"LogLogistic\": llt,\n", - " \"Cox\": cft,\n", + " # \"Cox\": cft,\n", "}\n", + "\n", + "score_list = [\n", + " wft_scores,\n", + " lnt_scores,\n", + " llt_scores,\n", + " # cft_scores,\n", + "]\n", "aft_data = pd.DataFrame()\n", "aft_data.index.name = \"Model\"\n", "aft_data.index = aft_dict.keys()\n", @@ -1511,37 +2114,27 @@ "# aft_data[\"LogLikelihood\"] = [x.log_likelihood_ for x in aft_dict.values()]\n", "aft_data[\"Concordance Score\"] = [x.concordance_index_ for x in aft_dict.values()]\n", "aft_data[\"BIC\"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()]\n", - "aft_data['Train Log Likelihood'] = [x['train_score'] for x in [wft_scores, lnt_scores, llt_scores, cox_scores]]\n", - "aft_data['Test Log Likelihood'] = [x['test_score'] for x in [wft_scores, lnt_scores, llt_scores, cox_scores]]\n", - "aft_data['Mean Survival Time'] = [x.mean_survival_time_ if hasattr(x, \"mean_survival_time_\") else np.nan for x in aft_dict.values()]\n", - "aft_data['Median Survival Time'] = [x.median_survival_time_ if hasattr(x, \"median_survival_time_\") else np.nan for x in aft_dict.values()]\n", - "aft_data['True Median Survival Time'] = np.median(y_train)\n", - "aft_data['True Mean Survival Time'] = np.mean(y_train)\n", - "aft_data[r'Error (seconds)'] = (aft_data['Median Survival Time'] - aft_data['True Median Survival Time'])\n", - "aft_data[r'% Error from True Mean'] = (aft_data['Mean Survival Time'] - aft_data['True Mean Survival Time'] ) /aft_data['True Mean Survival Time'] * 100\n", - "del aft_data['True Median Survival Time']\n", + "# aft_data['Train Log Likelihood'] = [x['train_score'] for x in score_list]\n", + "# aft_data['Test Log Likelihood'] = [x['test_score'] for x in score_list]\n", + "aft_data['Estimated Mean Survival Time'] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()]\n", + "# aft_data['True Mean Survival Time'] = (1 / (y_train/X_train[duration_col])).mean()\n", + "aft_data['Estimated Median Survival Time'] = [x.predict_median(X_train).median() for x in aft_dict.values()]\n", + "# aft_data['True Median Survival Time'] = (1 - (y_train/X_train[duration_col])).median()\n", + "success_rate = X_train['accuracy']\n", + "aft_data['True Mean Survival Time'] = ((1/X_train.accuracy).replace(np.inf, np.nan) * X_train.predict_time).mean()\n", + "aft_data['True Median Survival Time'] = ((1/X_train.accuracy).replace(np.inf, np.nan) * X_train.predict_time).median()\n", + "aft_data['Predicted Error from True Mean'] = aft_data['Estimated Mean Survival Time'] - aft_data['True Mean Survival Time']\n", + "aft_data['Predicted Error from True Median'] = aft_data['Estimated Median Survival Time'] - aft_data['True Median Survival Time']\n", "del aft_data['True Mean Survival Time']\n", + "del aft_data['True Median Survival Time']\n", "aft_data = aft_data.round(2)\n", "aft_data.to_csv(FOLDER / \"aft_comparison.csv\")\n", "logger.info(f\"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}\")\n", - "aft_data\n", + "aft_data.round(2).to_latex(FOLDER / \"aft_comparison.tex\")\n", + "aft_data.round(2)\n", "\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, From bb5186433b3a0b87096444710b9b7719489431da Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Fri, 29 Sep 2023 16:42:19 +0000 Subject: [PATCH 088/148] update plots --- deckard/base/model/model.py | 71 +- deckard/layers/compile.py | 18 +- examples/pytorch/afr.py | 486 ++++++++++ examples/pytorch/conf/compile.yaml | 3 +- examples/pytorch/conf/plots.yaml | 69 +- examples/pytorch/plots.py | 30 +- examples/pytorch/weibull.ipynb | 1313 +++++++++++++--------------- examples/scratch/.dvc/.gitignore | 3 + examples/scratch/.dvc/config | 0 examples/scratch/.dvcignore | 3 + 10 files changed, 1167 insertions(+), 829 deletions(-) create mode 100644 examples/pytorch/afr.py create mode 100644 examples/scratch/.dvc/.gitignore create mode 100644 examples/scratch/.dvc/config create mode 100644 examples/scratch/.dvcignore diff --git a/deckard/base/model/model.py b/deckard/base/model/model.py index 9bd8086b..5f6fff1a 100644 --- a/deckard/base/model/model.py +++ b/deckard/base/model/model.py @@ -673,39 +673,42 @@ def load(self, filename): def save(self, model, filename): suffix = Path(filename).suffix Path(filename).parent.mkdir(parents=True, exist_ok=True) - if suffix in [".pickle", ".pkl"]: - with open(filename, "wb") as f: - pickle.dump(model, f) - elif suffix in [".pt", ".pth"]: - import torch as t - - while hasattr(model, "model"): - model = model.model - t.save(model, filename) - t.save( - model.state_dict(), - Path(filename).with_suffix(f".optimizer{suffix}"), - ) - elif suffix in [".h5", ".wt"]: - import keras as k - - while hasattr(model, "model"): - model = model.model - try: - k.models.save_model(model, filename) - except NotImplementedError as e: - logger.warning(e) - logger.warning( - f"Saving model to {suffix} is not implemented. Using model.save_weights instead.", + if not Path(filename).exists(): + if suffix in [".pickle", ".pkl"]: + with open(filename, "wb") as f: + pickle.dump(model, f) + elif suffix in [".pt", ".pth"]: + import torch as t + + while hasattr(model, "model"): + model = model.model + t.save(model, filename) + t.save( + model.state_dict(), + Path(filename).with_suffix(f".optimizer{suffix}"), + ) + elif suffix in [".h5", ".wt"]: + import keras as k + + while hasattr(model, "model"): + model = model.model + try: + k.models.save_model(model, filename) + except NotImplementedError as e: + logger.warning(e) + logger.warning( + f"Saving model to {suffix} is not implemented. Using model.save_weights instead.", + ) + model.save_weights(filename) + elif suffix in [".tf", "_tf"]: + import keras as k + + while hasattr(model, "model"): + model = model.model + k.models.save_model(model, filename, save_format="tf") + else: + raise NotImplementedError( + f"Saving model to {suffix} is not implemented. You can add support for your model by adding a new method to the class {self.__class__.__name__} in {__file__}", ) - model.save_weights(filename) - elif suffix in [".tf", "_tf"]: - import keras as k - - while hasattr(model, "model"): - model = model.model - k.models.save_model(model, filename, save_format="tf") else: - raise NotImplementedError( - f"Saving model to {suffix} is not implemented. You can add support for your model by adding a new method to the class {self.__class__.__name__} in {__file__}", - ) + logger.warning(f"File {filename} already exists. Skipping saving.") diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index a42b89c2..41f13490 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -100,8 +100,8 @@ def merge_defences(results: pd.DataFrame): def_gen = [str(x).split(".")[-1] for x in defence][0] defence = defence[0] else: - def_gen = None - defence = None + def_gen = "Control" + defence = "Control" ############################################################################################################ if defence != []: defences.append(defence) @@ -148,10 +148,7 @@ def format_control_parameter(data, control_dict): else: attacks = [] for defence in defences: - if defence in ["Control", None, "None", "none", "null", np.nan]: - data.loc[data.def_gen == defence, "def_param"] = np.nan - data.loc[data.def_gen == defence, "def_value"] = np.nan - elif defence in control_dict: + if defence in control_dict: param = control_dict[defence] data.loc[data.def_gen == defence, "def_param"] = param.split(".")[-1] if param in data.columns: @@ -159,15 +156,13 @@ def format_control_parameter(data, control_dict): else: value = np.nan data.loc[data.def_gen == defence, "def_value"] = value + control_dict.pop(defence) else: logger.warning(f"Defence {defence} not in control_dict. Deleting rows.") data = data[data.def_gen != defence] for attack in attacks: - if attack in ["Control", None, "None", "none", "null", np.nan]: - data.loc[data.atk_gen == attack, "atk_param"] = np.nan - data.loc[data.atk_gen == attack, "atk_value"] = np.nan - elif attack in control_dict: + if attack in control_dict: param = control_dict[attack] data.loc[data.atk_gen == attack, "atk_param"] = param.split(".")[-1] if param in data.columns: @@ -175,10 +170,11 @@ def format_control_parameter(data, control_dict): else: value = np.nan data.loc[data.atk_gen == attack, "atk_value"] = value + control_dict.pop(attack) else: logger.warning(f"Attack {attack} not in control_dict. Deleting rows.") data = data[data.atk_gen != attack] - + return data diff --git a/examples/pytorch/afr.py b/examples/pytorch/afr.py new file mode 100644 index 00000000..d067e2c1 --- /dev/null +++ b/examples/pytorch/afr.py @@ -0,0 +1,486 @@ +# %% +import pandas as pd +import numpy as np +from pathlib import Path +import seaborn as sns + +import matplotlib.pyplot as plt + +from sklearn.preprocessing import StandardScaler +from sklearn.model_selection import train_test_split + + + +from paretoset import paretoset +from lifelines import WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter, CoxPHFitter +from plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling +import matplotlib +import argparse +from pathlib import Path +import logging + +logger = logging.getLogger(__name__) + +# %% +font = {'family' : 'Times New Roman', + 'weight' : 'bold', + 'size' : 22} + +matplotlib.rc('font', **font) + +# %% +FOLDER = Path("output/plots/") +csv_file = FOLDER / "data.csv" +data = pd.read_csv(csv_file, index_col=0) +data.columns = data.columns.str.strip() +data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x) +data.def_value.replace("", 0, inplace=True) +data.atk_value.replace("", 0, inplace=True) +data = drop_frames_without_results(data) +data = calculate_failure_rate(data) +data = min_max_scaling(data) +data.dropna(axis=0, subset=['atk_value', 'atk_param'], inplace=True) +data.dropna(axis=0, subset=['def_value', 'def_param'], inplace=True) +# data=data[data['def_gen'] == 'Gauss-in'] +# data=data[data['atk_gen'] == 'HSJ'] + +print( + "Adversarial Accuracy:", "\n", + "ResNet152:", data[data['model_layers'] == 152].adv_accuracy.mean(skipna=True), "\n", + "Resnet101:", data[data['model_layers'] == 101].adv_accuracy.mean(skipna=True), "\n", + "Resnet50:", data[data['model_layers'] == 50].adv_accuracy.mean(skipna=True), "\n", + "Resnet34:", data[data['model_layers'] == 34].adv_accuracy.mean(skipna=True), "\n", + "Resnet18:", data[data['model_layers'] == 18].adv_accuracy.mean(skipna=True), "\n", +) + + + +# %% + +def plot_aft( + df, + file, + event_col, + duration_col, + title, + mtype, + xlabel=None, + ylabel=None, + replacement_dict={}, + **kwargs, +): + if mtype == "weibull": + aft = WeibullAFTFitter(**kwargs) + elif mtype == "log_normal": + aft = LogNormalAFTFitter(**kwargs) + elif mtype == "log_logistic": + aft = LogLogisticAFTFitter(**kwargs) + elif mtype == "cox": + aft = CoxPHFitter(**kwargs) + assert ( + duration_col in df.columns + ), f"Column {duration_col} not in dataframe with columns {df.columns}" + if event_col is not None: + assert ( + event_col in df.columns + ), f"Column {event_col} not in dataframe with columns {df.columns}" + aft.fit(df, duration_col=duration_col, event_col=event_col) + ax = aft.plot() + labels = ax.get_yticklabels() + labels = [label.get_text() for label in labels] + for k, v in replacement_dict.items(): + labels = [label.replace(k, v) for label in labels] + ax.set_yticklabels(labels) + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_title(title) + ax.get_figure().tight_layout() + ax.get_figure().savefig(FOLDER / file) + logger.info(f"Saved graph to {FOLDER / file}") + plt.show() + return ax, aft + +def plot_partial_effects( + file, + aft, + covariate_array, + values_array, + title, + xlabel="Covariate", + ylabel="Failure rate", + legend_kwargs={"loc": "upper left"}, + replacement_dict={}, + cmap='coolwarm', + **kwargs, + ): + plt.gcf().clear() + # kwargs.pop("replacement_dict") + pareto = aft.plot_partial_effects_on_outcome(covariate_array, values_array, cmap=cmap, **kwargs) + labels = pareto.get_yticklabels() + labels = [label.get_text() for label in labels] + for k, v in replacement_dict.items(): + labels = [label.replace(k, v) for label in labels] + pareto.set_yticklabels(labels) + pareto.legend(**legend_kwargs) + pareto.set_ylabel(ylabel) + pareto.set_xlabel(xlabel) + pareto.set_title(title) + pareto.get_figure().tight_layout() + pareto.get_figure().savefig(FOLDER / file) + logger.info(f"Saved graph to {FOLDER / file}") + return pareto + +def score_model(aft, train, test): + train_score = aft.score(train) + test_score = aft.score(test) + scores = {'train_score': train_score, 'test_score': test_score} + plt.show() + return scores + + +def clean_data_for_aft( + data, kwarg_list, target="adv_failure_rate" +): + subset = data.copy() + assert target in subset, f"Target {target} not in dataframe with columns {subset.columns}" + + cleaned = pd.DataFrame() + kwarg_list.append(target) + for kwarg in kwarg_list: + cleaned = pd.concat([cleaned, subset[kwarg]], axis=1) + cols = cleaned.columns + cleaned = pd.DataFrame(subset, columns=cols) + + + # if "accuracy" in cleaned.columns: + # cleaned = cleaned[~cleaned[cleaned['accuracy'] != 1e10]] + # cleaned = cleaned[~cleaned[cleaned['accuracy'] != -1e10]] + # if "adv_accuracy" in cleaned.columns: + # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != 1e10]] + # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != -1e10]] + cleaned.dropna(inplace=True, how='any', axis=0) + y = cleaned[target] + assert target in cleaned, f"Target {target} not in dataframe with columns {cleaned.columns}" + return cleaned, y, data + +# %% + + +kwarg_list = [ + # "accuracy", + "train_time", + "predict_time", + "atk_value", + "def_value", + "data.sample.random_state", + "adv_failure_rate", + # "failure_rate", + "model_layers", + "adv_fit_time", + # "atk_param", + # "def_param", + + "model.art.pipeline.initialize.kwargs.optimizer.lr", + # "def_gen", + # "atk_gen", + # "adv_log_loss", + # "adv_accuracy", + # "adv_accuracy", +] + + +# cleaned['accuracy'] = y + +# %% +data.loc[:, "adv_failures"] = (1 - data.loc[:, "adv_accuracy"]) * 100 +data.loc[:, "ben_failures"] = (1 - data.loc[:, "accuracy"]) * 100 +target = "adv_failures" +duration_col = "adv_fit_time" +cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) +X_train, X_test, y_train, y_test = train_test_split(cleaned, y, test_size=0.2, random_state=42) +assert target in cleaned, f"Target {target} not in dataframe with columns {cleaned.columns}" + + + + +# %% + +# from sklearn.preprocessing import PowerTransformer +# pt = PowerTransformer(method='yeo-johnson', standardize=False) +# del X_train[target] +# del X_test[target] +# X_train_cols = X_train.columns +# X_train = pt.fit(X_train).transform(X_train) +# X_test = pt.transform(X_test) +# X_train = pd.DataFrame(X_train, columns=X_train_cols) +# X_test = pd.DataFrame(X_test, columns=X_train_cols) +# X_train[target] = y_train +# y_train = X_train[target] + + +# %% +# sense_dict ={ +# "accuracy" : "max", +# "train_time" : "min", +# "predict_time" : "min", +# # "atk_value" : "diff", +# # "def_value" : "diff", +# "data.sample.random_state" : "diff", +# "adv_accuracy" : "min", +# "model_layers" : "diff", +# # "adv_fit_time" : "min", +# # "atk_param" : "diff", +# # "def_param" : "diff", +# "model.art.pipeline.initialize.kwargs.optimizer.lr" : "diff", +# # "adv_failure_rate" : "maximize", +# } +# subset = X_train.loc[:, sense_dict.keys()] +# senses = sense_dict.values() +# these = paretoset(subset) +# X_train = X_train.iloc[these, :] + + +# %% +weibull_dict = { + "Intercept: rho_": "$\\rho$", + "Intercept: lambda_": "$\lambda$", + "data.sample.random_state: lambda_": "Random State", + "def_value: lambda_": "Defence Strength", + "atk_value: lambda_": "Attack Strength", + "train_time: lambda_": "Training Time", + "predict_time: lambda_": "Inference Time", + "adv_accuracy: lambda_": "Adv. Accuracy", + "accuracy: lambda_": "Ben. Accuracy", + "adv_fit_time: lambda_": "Adv. Fit Time", + "adv_log_loss: lambda_": "Adv. Log Loss", + "adv_failure_rate: lambda_": "Adv. Failure Rate", + "failure_rate: lambda_": "Ben. Failure Rate", + "model_layers: lambda_": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_" : "Learning Rate", + "def_gen" : "Defence", +} + +weibull_afr, wft = plot_aft( + X_train, + file = "weibull_aft.pdf", + event_col = target, + duration_col = duration_col, + title = "Weibull AFR Model", + mtype = "weibull", + replacement_dict=weibull_dict, +) +wft.print_summary() +wft_scores = score_model(wft, X_train, X_test) + + +# %% +pareto_dict = { + "model_layers=18": "18", + "model_layers=34": "34", + "model_layers=50": "50", + "model_layers=101": "101", + "model_layers=152": "152", +} +pareto_weibull = plot_partial_effects( + file = "weibull_partial_effects.pdf", + aft = wft, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], + title="Partial Effects of No. of Layers on Failure Rate for Weibull AFR", + replacement_dict=pareto_dict, + ylabel="% Chance of Survival", + xlabel="Time $T$ (seconds)", + legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, + ) + +# weibull_accuracy = plot_partial_effects( +# file = "weibull_partial_effect_accuracy.pdf", +# aft = wft, +# covariate_array = "accuracy", +# values_array = [.9, .99, .999, .9999], +# replacement_dict=weibull_dict, +# title="Partial Effects of Benign Accuracy on Failure Rate", +# ylabel="% Chance of Survival", +# xlabel="Time $T$ (seconds)", +# legend = {"title" : "Benign Accuracy"}, +# ) + + + +# %% + +cox_dict = { + "adv_failure_rate": "Adv. Failure Rate", + "def_value" : "Defence Strength", + "data.sample.random_state" : "Random State", + "train_time" : "Training Time", + "model_layers" : "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr" : "Learning Rate", + "adv_accuracy" : "Adv. Accuracy", + "adv_fit_time" : "Adv. Fit Time", + "adv_log_loss" : "Adv. Log Loss", + "predict_time" : "Inference Time", + "accuracy" : "Ben. Accuracy", + "failure_rate" : "Ben. Failure Rate", + "atk_value" : "Attack Strength", +} + +cox_afr, cft = plot_aft( + X_train, + file = "cox_aft.pdf", + event_col = target, + duration_col = duration_col, + title = "Cox AFR Model", + mtype = "cox", + replacement_dict=cox_dict, +) +cox_scores = score_model(cft, X_train, X_test) +cft.print_summary() +cox_partial = plot_partial_effects( + file = "cox_partial_effects.pdf", + aft = cft, + covariate_array = "model_layers", + values_array = [18, 34, 50, 101, 152], + replacement_dict=cox_dict, + title="Survival Time for Cox AFR", + ylabel="% Chance of Survival", + xlabel="Time $T$ (seconds)", + legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, +) + +# %% +log_normal_dict = { + "Intercept: sigma_": "$\sigma$", + "Intercept: mu_": "$\mu$", + "def_value: mu_": "Defence Strength", + "atk_value: mu_": "Attack Strength", + "train_time: mu_": "Training Time", + "predict_time: mu_": "Inference Time", + "adv_fit_time: mu_": "Adv. Fit Time", + "model_layers: mu_": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_" : "Learning Rate", + "data.sample.random_state: mu_": "Random State", + "adv_log_loss: mu_": "Adv. Log Loss", + "adv_accuracy: mu_": "Adv. Accuracy", + "accuracy: mu_": "Ben. Accuracy", + "adv_failure_rate: mu_": "Adv. Failure Rate", + "def_gen" : "Defence", + "learning_rate: mu_" : "Learning Rate", +} + +log_normal_graph, lnt = plot_aft( + X_train, + "log_normal_aft.pdf", + target, + duration_col, + "Log Normal AFR Model", + "log_normal", + replacement_dict=log_normal_dict, +) +lnt_scores = score_model(lnt, X_train, X_test) +lnt.print_summary() +lnt_partial = plot_partial_effects( + file = "log_normal_partial_effects.pdf", + aft = lnt, + covariate_array = "model_layers", + values_array = [18, 34, 50, 101, 152], + replacement_dict=log_normal_dict, + title="Survival Time for Log-Normal AFR", + ylabel="% Chance of Survival", + xlabel="Time $T$ (seconds)", + legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, +) + +# %% + +log_logistic_dict = { + "Intercept: beta_": "$\\beta$", + "Intercept: alpha_": "$\\alpha$", + "data.sample.random_state: alpha_": "Random State", + "def_value: alpha_": "Defence Strength", + "atk_value: alpha_": "Attack Strength", + "train_time: alpha_": "Training Time", + "predict_time: alpha_": "Inference Time", + "adv_accuracy: alpha_": "Adv. Accuracy", + "accuracy: alpha_": "Ben. Accuracy", + "adv_fit_time: alpha_": "Adv. Fit Time", + "model_layers: alpha_": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr" : "Learning Rate", + "adv_failure_rate: alpha_": "Adv. Failure Rate", + "alpha_" : "", + +} + +log_logistic_graph, llt = plot_aft( + X_train, + "log_logistic_aft.pdf", + target, + duration_col, + "Log Logistic AFR Model", + "log_logistic", + replacement_dict=log_logistic_dict, +) +llt.print_summary() +llt_scores = score_model(llt, X_train, X_test) +print(llt_scores) +llt_partial = plot_partial_effects( + file = "log_logistic_partial_effects.pdf", + aft = llt, + covariate_array = "model_layers", + values_array = [18, 34, 50, 101, 152], + replacement_dict=log_logistic_dict, + title="Survival Time for Log-Logistic AFR", + ylabel="% Chance of Survival", + xlabel="Time $T$ (seconds)", + legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, + +) + +# %% +np.mean(llt.predict_median(X_train)) + +# %% +aft_dict = { + "Weibull": wft, + "LogNormal": lnt, + "LogLogistic": llt, + # "Cox": cft, +} + +score_list = [ + wft_scores, + lnt_scores, + llt_scores, + # cft_scores, +] +aft_data = pd.DataFrame() +aft_data.index.name = "Model" +aft_data.index = aft_dict.keys() +aft_data["AIC"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() ] +aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] +aft_data["BIC"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()] +aft_data['Train LL'] = [x['train_score'] for x in score_list] +aft_data['Test LL'] = [x['test_score'] for x in score_list] +aft_data['Mean ST'] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()] +aft_data['Median ST'] = [x.predict_median(X_train).median() for x in aft_dict.values()] +aft_data = aft_data.round(2) +aft_data.to_csv(FOLDER / "aft_comparison.csv") +logger.info(f"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}") +aft_data = aft_data.round(2,) +aft_data.to_latex(FOLDER / "aft_comparison.tex", float_format="%.2f", label = "tab:mnist", caption="Comparison of AFR Models on the MNIST dataset.") +aft_data + + +# %% +# data['Survival Time'] = 1 / (data['adv_failure_rate']) +# true_mean = np.mean(data['adv_failure_rate'] * data['predict_time']) +# true_median = np.median(data['adv_failure_rate'] * data['predict_time']) +success_rate = data['adv_failure_rate'] +success_time = success_rate * data['predict_time'] +true_mean = np.mean(success_time) +true_median = np.median(success_time) +print(f"True Mean: {true_mean}") +print(f"True Median: {true_median}") + + + diff --git a/examples/pytorch/conf/compile.yaml b/examples/pytorch/conf/compile.yaml index 2f5baad8..0d721a44 100644 --- a/examples/pytorch/conf/compile.yaml +++ b/examples/pytorch/conf/compile.yaml @@ -6,7 +6,7 @@ attacks: FastGradientMethod: FGM HopSkipJump: HSJ PixelAttack: Pixel - ProjectGradientDescent: PGD + ProjectedGradientDescent: PGD ThresholdAttack: Thresh defences: Control: Control @@ -28,3 +28,4 @@ params: Conf: model.art.pipeline.postprocessor.kwargs.cutoff FSQ: model.art.pipeline.preprocessor.kwargs.bit_depth Gauss-in: model.art.pipeline.preprocessor.kwargs.sigma + Control: model_layers diff --git a/examples/pytorch/conf/plots.yaml b/examples/pytorch/conf/plots.yaml index 21e47081..cbd9929d 100644 --- a/examples/pytorch/conf/plots.yaml +++ b/examples/pytorch/conf/plots.yaml @@ -2,7 +2,6 @@ cat_plot: - file: adv_accuracy_vs_defence_type.pdf hue: model_name kind: boxen - legend_title: Model Name set: yscale: linear titles: Adversarial Accuracy vs Defence Type @@ -20,7 +19,6 @@ cat_plot: - file: ben_accuracy_vs_defence_type.pdf hue: model_name kind: boxen - legend_title: Model Name titles: Benign Accuracy vs Defence Type x: def_gen xlabels: Defence Type @@ -36,14 +34,13 @@ cat_plot: - file: ben_failures_per_train_time_vs_defence_type.pdf hue: model_name kind: boxen - legend_title: Model Name set: yscale: log - titles: Training Time per Benign Failure vs Defence Type + titles: $\bar{C}_{ben.}$ vs Defence Type x: def_gen xlabels: Defence Type y: training_time_per_failure - ylabels: Training Time per Ben. Failures + ylabels: $\bar{C}_{ben.}$ rotation : 90 hue_order: - ResNet18 @@ -54,14 +51,13 @@ cat_plot: - file: adv_failures_per_train_time_vs_defence_type.pdf hue: model_name kind: boxen - legend_title: Model Name set: yscale: log - titles: Training Time per Adversarial Failure vs Defence Type + titles: $\bar{C}_{adv.}$ vs Defence Type x: def_gen xlabels: Defence Type y: training_time_per_adv_failure - ylabels: Training time per Adv. Failures + ylabels: $\bar{C}_{adv.}$ rotation : 90 hue_order: - ResNet18 @@ -75,27 +71,11 @@ cat_plot: legend_title: Model Name set: yscale: log - titles: Training Time per Adversarial Failure vs Attack Type + titles: $\bar{C}_{adv.}$ vs Attack Type x: atk_gen xlabels: Attack Type y: training_time_per_adv_failure - ylabels: Training time per Adv. Failures - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: adv_accuracy_vs_attack_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - titles: Adversarial Accuracy vs Attack Type - x: atk_gen - xlabels: Attack Type - y: adv_accuracy - ylabels: Adv. Accuracy + ylabels: $\bar{C}_{adv.}$ rotation : 90 hue_order: - ResNet18 @@ -103,15 +83,15 @@ cat_plot: - ResNet50 - ResNet101 - ResNet152 -- file: adv_accuracy_vs_defence_type.pdf +- file: adv_failures_per_test_time_vs_defence_type.pdf hue: model_name kind: boxen legend_title: Model Name - titles: Adversarial Accuracy vs Defence Type + titles: Adversarial Failure Rate vs Defence Type x: def_gen xlabels: Defence Type - y: adv_accuracy - ylabels: Adv. Accuracy + y: adv_failure_rate + ylabels: Adv. Failure Rate rotation : 90 hue_order: - ResNet18 @@ -119,6 +99,22 @@ cat_plot: - ResNet50 - ResNet101 - ResNet152 +# - file: adv_accuracy_vs_defence_type.pdf +# hue: model_name +# kind: boxen +# legend_title: Model Name +# titles: Adversarial Accuracy vs Defence Type +# x: def_gen +# xlabels: Defence Type +# y: adv_accuracy +# ylabels: Adv. Accuracy +# rotation : 90 +# hue_order: +# - ResNet18 +# - ResNet34 +# - ResNet50 +# - ResNet101 +# - ResNet152 - file: adv_accuracy_vs_attack_type.pdf hue: model_name kind: boxen @@ -154,9 +150,7 @@ cat_plot: - ResNet101 - ResNet152 line_plot: -- control: 0.98 - control_color: royalblue - file: def_param_vs_accuracy.pdf +- file: def_param_vs_accuracy.pdf hue: def_gen legend: {} title: Accuracy vs Defence Strength @@ -172,9 +166,7 @@ line_plot: - Gauss-out - Conf - FSQ -- control: 0.1 - control_color: royalblue - file: def_param_vs_adv_accuracy.pdf +- file: def_param_vs_adv_accuracy.pdf hue: def_gen legend: {} title: Adversarial Accuracy vs Defence Strength @@ -190,9 +182,7 @@ line_plot: - Gauss-out - Conf - FSQ -- control: 0.1 - control_color: royalblue - file: def_param_vs_adv_failure_rate.pdf +- file: def_param_vs_adv_failure_rate.pdf hue: def_gen legend: {} title: Adversarial Failure Rate vs Defence Strength @@ -220,6 +210,7 @@ line_plot: ylabel: Adv. Accuracy hue_order: - FGM + - PGD - Deep - HSJ - Pixel diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index f438b092..00ea7f99 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -9,8 +9,8 @@ logger = logging.getLogger(__name__) -sns.set_theme(style="whitegrid") -sns.set_context("paper", font_scale=1.5) +sns.set_theme(style="whitegrid", font_scale=1.8, font="times new roman") + def cat_plot( @@ -22,9 +22,9 @@ def cat_plot( titles, xlabels, ylabels, - legend_title, file, folder, + legend_title = None, hue_order=None, rotation=0, set={}, @@ -38,7 +38,10 @@ def cat_plot( graph.set_xlabels(xlabels) graph.set_ylabels(ylabels) graph.set_titles(titles) - graph.legend.set_title(title=legend_title) + if legend_title is not None: + graph.legend.set_title(title=legend_title) + else: + graph.legend.remove() graph.set_xticklabels(graph.axes.flat[-1].get_xticklabels(), rotation=rotation) graph.set(**set) graph.tight_layout() @@ -61,17 +64,12 @@ def line_plot( x_scale=None, legend={}, hue_order=None, - control=None, - control_color=None, **kwargs, ): plt.gcf().clear() data = data.sort_values(by=[hue, x, y]) graph = sns.lineplot(data=data, x=x, y=y, hue=hue, hue_order=hue_order, **kwargs) graph.legend(**legend) - if control is not None: - assert control_color is not None, "Please specify a control color" - graph.add_line(plt.axhline(y=control, color=control_color, linestyle="-")) graph.set_xlabel(xlabel) graph.set_ylabel(ylabel) graph.set_title(title) @@ -152,10 +150,10 @@ def calculate_failure_rate(data): data.loc[:, "train_time"] / data.loc[:, "failure_rate"] ) data.loc[:, "training_time_per_adv_failure"] = ( - data.loc[:, "train_time"] / data.loc[:, "adv_failure_rate"] + data.loc[:, "train_time_per_sample"] * data.loc[:, "adv_failure_rate"] ) data.loc[:, "adv_training_time_per_failure"] = ( - data.loc[:, "train_time"] / data.loc[:, "adv_failure_rate"] + data.loc[:, "train_time_per_sample"] * data.loc[:, "adv_failure_rate"] ) return data @@ -255,20 +253,14 @@ def min_max_scaling(data, **kwargs): ) sense_dict ={ "accuracy" : "max", - # "train_time" : "min", - # "predict_time" : "min", - # "atk_value" : "diff", - # "def_value" : "diff", + "adv_accuracy" : "min", "data.sample.random_state" : "diff", - # "adv_accuracy" : "min", "model_layers" : "diff", - # "adv_fit_time" : "min", "atk_param" : "diff", "def_param" : "diff", "atk_gen" : "diff", "def_gen" : "diff", - # "model.art.pipeline.initialize.kwargs.optimizer.lr" : "diff", - # "adv_failure_rate" : "maximize", + "data.sample.random_state" : "diff", } data = pareto_set(data, sense_dict) data = calculate_failure_rate(data) diff --git a/examples/pytorch/weibull.ipynb b/examples/pytorch/weibull.ipynb index 227e3c8f..b72e9394 100644 --- a/examples/pytorch/weibull.ipynb +++ b/examples/pytorch/weibull.ipynb @@ -21,7 +21,7 @@ "from paretoset import paretoset\n", "from lifelines import WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter, CoxPHFitter\n", "from plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling\n", - "\n", + "import matplotlib \n", "import argparse\n", "from pathlib import Path\n", "import logging\n", @@ -33,17 +33,30 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, + "outputs": [], + "source": [ + "font = {'family' : 'Times New Roman',\n", + " 'weight' : 'bold',\n", + " 'size' : 22}\n", + "\n", + "matplotlib.rc('font', **font)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adversarial Accuracy: \n", - " ResNet152: 0.09937931034482758 \n", - " Resnet101: 0.10020833333333334 \n", - " Resnet50: 0.09903225806451614 \n", - " Resnet34: 0.09656 \n", - " Resnet18: 0.08548387096774193 \n", + " ResNet152: 0.08431102362204726 \n", + " Resnet101: 0.08595785440613028 \n", + " Resnet50: 0.09093333333333335 \n", + " Resnet34: 0.08867549668874172 \n", + " Resnet18: 0.07971698113207548 \n", "\n" ] } @@ -77,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -191,21 +204,21 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "\n", "\n", "kwarg_list = [\n", - " \"accuracy\",\n", + " # \"accuracy\",\n", " \"train_time\",\n", " \"predict_time\",\n", " \"atk_value\",\n", " \"def_value\",\n", " \"data.sample.random_state\",\n", " \"adv_failure_rate\",\n", - " \"failure_rate\",\n", + " # \"failure_rate\",\n", " \"model_layers\",\n", " \"adv_fit_time\",\n", " # \"atk_param\",\n", @@ -225,14 +238,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "data.loc[:, \"adv_failures\"] = (1 - data.loc[:, \"adv_accuracy\"]) * 100\n", - "data.loc[:, \"ben_failures\"] = (1 - data.loc[:, \"accuracy\"]) * 100\n", - "target = \"ben_failures\"\n", - "duration_col = \"predict_time\"\n", + "data.loc[:, \"adv_failures\"] = (1 - data.loc[:, \"adv_accuracy\"]) \n", + "data.loc[:, \"ben_failures\"] = (1 - data.loc[:, \"accuracy\"])\n", + "target = \"adv_failures\"\n", + "duration_col = \"adv_fit_time\"\n", "cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target)\n", "X_train, X_test, y_train, y_test = train_test_split(cleaned, y, test_size=0.2, random_state=42)\n", "assert target in cleaned, f\"Target {target} not in dataframe with columns {cleaned.columns}\"\n", @@ -242,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -262,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -289,12 +302,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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E1klhJoTQKzk5OWRkZJCTk6PrUIQQQuukMBNCCCGEKCWkMBNCCCGEKCWkMBNCCCGEKCWkMBNC6BV7e3sCAwOxt7fXdShCCKF18pFMQgi9Ur58eerUqUP58uV1HYoQ4iXw8fEBICEhoUjtAwMDSUtLK3Cdg4MDUVFRL2S/2iIjZuK5ZWZmMmfOHHx9fWnUqBEtW7ZkypQpZGZm6jo0UQbdvn2bAwcOcPv2bV2HIoQohdLS0khNTc23PDU1tdCCrTSRETPxXFJSUhg2bBjZ2dn0798fGxsbfv75Z6KiosjOzuaLL77QdYiijFEVZt27d8fW1lbX4QghSiFHR8d8I12qEbDSTgozUWK5ubmMGzcOCwsLoqKiqFSpEgD9+/ena9eubNu2jZkzZ1KhQoVi961QKFAoFMVq//h3Q2CIOQPk5eWpvxtK7oZ4riVnw1CUnJVKJWlpabRq1apIfaalpRX6kW2pqanF6sfBwUFr56Oo/UhhJkosMjKSM2fOsGHDBnVRBmBmZoaXlxebNm3i+vXr1KlTp9h9nzlzpkQxJScnl2g7fWZoOV+9ehWAU6dOkZGRodtgXjJDO9cgORuKp+WcnZ2t8f15Faef7OxskpKStLLfopLCTJTYzz//TN26dXF3d8+37v79+wC88sorJerbxcUFS0vLIrdXKBQkJyfTpEkTjI2NS7RPfWOIOQNcuHABgAYNGlC7dm2dxvKyGOK5lpwlZxUzMzMcHBzYv39/kfr09fUtdF1J+vHw8ChS+2fJysoq0qCDFGaiRLKysjhy5Ajdu3cvcP2xY8dwdnYu8RwgY2PjEv1iKul2+szQcq5YsSINGzakYsWKBpU3GN65BsnZUDwtZyMjI3WbojAyMiI1NTXfnLLU1FQcHR2L1U9x9vssRe1H7soUJXLy5Elyc3MLfGTBn3/+yeXLlwst2oR4HnZ2drz22mvY2dnpOhQhRCnk4OBQ4BwzR0dHHBwcdBBR8ciImSiR48ePA5CYmEhOTg6mpqYApKenM3XqVBwcHBg0aJAuQxRlVE5ODrdu3SInJ8fgRhWEMETFfY5YUZ9Tpu39aosUZqJETpw4gaWlJUqlkhEjRvDqq69y8+ZNNmzYQFZWFqtXr9a4IUAIbbly5QorV66kbt26JbqxRAghSjO5lClK5Pjx47i6urJ06VIAvvjiC3788Ue8vLyIiorS2mRJIYQQwpDIiJkotocPH3Lu3Dn69u1LvXr1+OGHH3QdkhBCCFEmyIiZKLZTp06Rm5tLgwYNdB2KEEIIUaZIYSaKTTXxXwozIYQQQrukMBPFduLECYyMjHBxcdF1KMIAOTk58f777+Pk5KTrUIQQQuukMBPFNmfOHE6dOlWiz8AUQgghROGkMBNC6JVr164RHh7OtWvXdB2KEEJonRRmQgi98vDhQ65cucLDhw91HYoQQmidFGZCCCGEEKWEFGZCCCGEEKWEFGZCCCGEEKWEFGZCCL1iZ2dHt27dsLOz03UoQgihdVKYCSH0SoUKFWjUqJE8rkUIUSZJYSaE0Ct3797lr7/+4u7du7oORQghtE4KMyGEXrl16xa//PILt27d0nUoQgihdVKYCSGEEEKUElKYCSGEEEKUElKYCSGEEEKUElKYCSH0Svny5alduzbly5fXdShCCKF1UpgJIfSKvb09ffv2xd7eXtehCCGE1klhJp7b2LFjady4MT169CA5OVnX4YgyLi8vj4cPH5KXl6frUIQQQuukMBPPLSgoiKFDh3LmzBm++uorXYcjyrjLly8TFhbG5cuXdR2KEEJonYmuAxD6z8/PDz8/P3bt2sWxY8d0HY4QZZ6Pjw8ACQkJOo7kkcDAQNLS0gpc5+DgQFRU1EuOqGCl7bgJURAZMRNa07BhQzIyMrh27ZquQxFCvERpaWmkpqbmW56amlpowSaEKJiMmAmtefjwIQBnzpyhatWqOo5GCPEyOTo65huJUo1QCSGKTgozoRXx8fHs3bsXgNOnT9O2bdvn6k+hUKBQKIrV/vHvhsAQcwbUk/7z8vIMJvcnz7VSqSQtLY1WrVrpMiy1tLQ0HB0dC1yXmppa4jizs7MxMzN7ntA0pKWl4eDgUGrfN4b4M21IORc1RynMxHO7c+cOM2fOpEqVKty4cYMzZ848d58l7cMQ7wo1tJwVCgXjxo3jv//+IyMjQ9fhvFSqc52dna3xvbR7nji1nWN2djZJSUla7VPbDO1nGgwz58JIYSae2/z580lPTyc8PJw33niD06dPP3efLi4uWFpaFrm9QqEgOTmZJk2aYGxs/Nz71weGmDMYZt5P5mxmZoaDgwP79+/XdWgA+Pr6FrqupHG+iPOsitPDw0Mr/WmbvLfLds5ZWVlFGnSQwkw8l4SEBKKiohg5ciSenp7Ur1+f06dPk5ubi4lJyd9exsbGJfohLel2+szQcr5x4wabNm3C0dGRatWq6Tqcl0p1ro2MjNSvSwMjIyNSU1PzzSlLTU3F0dHxueLU5vu7tB23whjazzQYRs5FzU/uyhQldv/+faZNm0bt2rWZNGkSAA0aNCAnJ4fz58/rODpRVt2/f59///2X+/fv6zoU8f85ODgUOMfM0dERBwcHHUQkhP6SETNRYosWLeLy5cusW7dO/bmFDRo0AB7NEatfv74uwxOizCptz+EqLc8pe5bSdtyEKIiMmIkSOXr0KGvXrmXQoEG0aNFCvVxVmGljnpkQQghhaKQwE8WWk5PDxx9/TLVq1Xjvvfc01rm6ugIlv6tSCCGEMGRyKVMU24oVKzhz5gyrV6/Od+ektbU1jo6OMmImXphKlSrRoUMHKlWqpOtQhBBC66QwE8U2fvx4xo8fX+j63bt3v8RohKGxtramRYsWWFtb6zoUIYTQOrmUKYTQK1lZWZw+fZqsrCxdhyKEEFonhZkQQq/cvHmTuLg4bt68qetQhBBC66QwE0IIIYQoJaQwE0IIIYQoJaQwE0IIIYQoJaQwE0LoFTMzM+zt7TEzM9N1KEIIoXVSmAkh9Eq1atUYOnSowX2AuRDCMEhhJoQQQghRSkhhJoTQKykpKXz55ZekpKToOhQhhNA6KcyEEHpFqVSiUChQKpW6DkUIIbROCjMhhBBCiFJCCjMhhBBCiFJCCjMhhBBCiFJCCjMhhF6pVq0aw4YNk8dlCCHKJCnMhBB6xczMjMqVK8sDZoUQZZIUZkIIvZKens727dtJT0/XdShCCKF1Oi/Mzp8/j6urKw0bNuTatWsFtsnJyeHKlSsay5RK5Qt9jtGQIUPw9fUt8fZ//vkn48aNo3Xr1ri5udGmTRsmTpzIoUOHCmx/6dKlEu/rZXoyTj8/P4KCgnQUjTBEmZmZHDt2jMzMTF2HIoQQWqfzwiw2NhZLS0vy8vKIjo7Otz41NZUePXqwd+9e9bLMzEyCgoKIiIh4iZEW3aZNmxg8eDDXrl1j2LBhzJgxgwEDBnD8+HEGDRrEhg0bNNp//fXXDB48WEfRFp2+xCmEEELoKxNd7lypVBIXF0erVq1ITU1l06ZNvPXWWxptLl++zPnz5zWWZWRkcPToUby9vV9muEXy4MED5s2bh4+PD6tWraJcuf/VviNGjKBPnz7MmzePLl26ULFiRQAOHDiAQqHQVchFpi9xClFUPj4+ACQkJBR728DAQNLS0gpc5+DgQFRU1EuLRQhRduh0xOzw4cNcvnwZLy8vOnbsyMWLFzl48KAuQ3puZ8+e5fbt27Rp00ajKAOwtLSkf//+3L9/n5MnT+ooQiGENqSlpZGamppveWpqaqEFmxBCPItOC7PNmzcD0KpVK/z9/QHYuHGjen10dDRDhw4F4JNPPsHV1ZXExEQ6deoEwLfffourqyuXL18GHn2G3pQpU+jQoQNubm40b96coUOH8ueff+bb988//8yAAQPw9PTE19eXd99996lz1rKzsxk9ejSNGjViy5YthbazsrICYNu2bWRkZORbP2TIEI4fP07Lli2BR3O0Dh48yM2bN3F1dSUsLEy9/KOPPmLmzJk0bdoUX19f/v33XwDOnTvHpEmTaNmyJe7u7vTp04dt27Zp7CcsLEx9bCZMmEDz5s1p1qwZEyZMUB8vlXv37jF37lzatm1L06ZNeeONNzh9+jSNGjXSiKegOFV27NhBz549adKkCR07dmTZsmUyuiZeCGtra1q2bIm1tbWuQ8HR0ZGEhASNL0dHR12HJYTQYzq7lJmdnc327dupUaMGjRo1Ah79ktu5cyfTp0/HysoKLy8vxo4dy/Lly+nTpw+tWrWibt26hIaGMm/ePDp27EjXrl2xtbUlPT2doKAgTE1NCQ4OpnLlypw/f56ffvqJkSNHEh8fT9WqVQFYvXo18+fPp0mTJrz99tvcv3+fNWvW8NdffxEVFYWtra1GrAqFgvfff599+/Yxf/58unfvXmhederUoWXLlhw8eJCOHTvi5+eHr68vLVu2pEaNGpiYaB7yKVOmsGDBAm7cuMG0adNwdXVVr9u5cyc1atQgNDSUlJQUnJ2dOXv2LMHBwVhbWzNy5EgsLCyIj49n8uTJXL9+nWHDhmn0P3ToUBo3bswHH3zAP//8Q3h4OFevXlUXwHl5eYwePZq//vqLfv364eLiwu7duxkyZAh5eXlFivPMmTNMmTKFwYMHM2DAAOLi4li0aBHm5uaMHDmyGO8KzWNenMJO1daQikFDzBmgYsWKtGvXjooVKz537kqlkrS0NFq1alXsbdPS0gotwlJTU4vdZ1paGg4ODgXmZIjnWnI2DIaUc1Fz1FlhtnfvXm7fvk1gYKB6WefOnVm9ejVbt26lf//+1KxZk9atW7N8+XLc3d3p1asXAP7+/sybN4969eqpl/3444+kp6cTFRWFm5ubuk8nJydmzJjBwYMH6dGjB7dv3+bLL7+kZcuWrFq1ClNTUwA8PT154403iI6OZtSoUertlUolU6dOZefOncydO1e9v6dZtGgRISEh/Prrr2zZskU9wubs7Ezfvn0ZMmSI+hlM/v7+fP/999y5cydf31lZWSxZsoRatWqpl82ePRsrKytiYmLUIwZDhgxh0qRJLFy4kJ49e2oUlm3btmXmzJnq15mZmWzatIkLFy5Qu3Zt4uLiOHz4MKGhoeqibtCgQYwbN47du3ert3tanPfv3yc8PJwWLVoA0LNnT9q3b8+OHTtKXJidOXOmRNslJyeXaDt9Zmg5Z2dnc/XqVbKzs5/7WWbZ2dka37WpJH1mZ2eTlJRU6HpDO9cgORsKQ8y5MDorzFSXMbt06aJe1qVLF1avXs3GjRvp379/sfobNWoUvXv3xs7OTr3s8V+MWVlZwKMJ7A8fPmTgwIHqogweXU7dsGEDderU0eh37ty5REdH88EHH9CnT58ixWJra8uKFSs4deoU8fHx7N+/n+TkZM6dO8dnn31GfHw8q1evxsLC4qn9VK9eXaMou3XrFgcPHiQoKIjc3FyN5zh17tyZnTt3sn//fnr06KFe3q1bN40+GzZsyKZNm7h58ya1a9cmPj4eS0tLBg4cqG5jZGTEmDFjNAqzZ8WpKsrg0eVcZ2dnrl+/XqTtC+Li4oKlpWWR2ysUCpKTk2nSpAnGxsYl3q8+McScAS5cuMDixYuZM2cOtWvXfq6+zMzMcHBwYP/+/cXe9mmP0ylJn6r+PDw88q0zxHMtOUvOZU1WVlaRBh10UphlZGSwd+9ebG1tsbW1Vc95srOzw9bWlqNHj3L27Fnq169frH4VCgVhYWEkJyeTkpJCSkoKOTk5AOrLcqrJuk8WYADu7u4ar2/evMnatWsxMjLiyJEjxc6zQYMGNGjQgIkTJ5KZmcmuXbsICwvjr7/+Ijw8XGNkriCPF5nwaA6dUqkkIiKi0EeFPDnp+Mk+VCMMqiHVixcv4uDgkG/koW7dus9OsJB9AJQvX1597EvC2Ni4RD+kJd1OnxlazqqbasqVK/fceRsZGQGUqB8jIyNSU1PVd1OqpKam4ujoWOw+ixKLoZ1rkJwNhSHkXNT8dFKY/fzzz+Tk5JCenq6e9P+kqKgoQkJCitzn4cOHGTVqFGZmZvj4+NC9e3caNmxIXl4e48ePV7d7fN5UUYSGhnLp0iXCw8PZsWMHr7766lPbx8bGcvLkyXyxW1lZ8frrr9O8eXMCAgI4dOjQMwuzJ0+iqpjq37+/xkjj42rWrKnxWvXLvjA5OTkFjtyZm5s/dbunxSmEIXBwcChwuaOjY6HrhBDiWXRSmKkuY86cOZPKlStrrLtz5w6hoaFs3ryZ9957r8h9Llq0CCMjI7Zs2UKVKlXUy+Pi4jTaqX5hXrp0iQYNGmismzp1Kg0bNmTQoEEAVK5cmWHDhnH37l127tzJ7Nmzad26tfr5YwU5ePAgGzduJDAwsMARv5o1a2JhYfHMy5gFeXyicevWrTXWpaSkcPr06WL3W6tWLQ4dOoRCodAosC5cuFDs+ITQN8/zzLDiPqfsWeT5ZUII0MHjMlJSUjhy5AiNGzdmwIAB+Pv7a3z16dMHb29v/vvvP/bs2aMuFh4f6SpoWUZGBpUqVdIo9LKzs/nxxx+B/402tW7dGjMzMyIiIjTukEhKSmLDhg0FfsxLxYoVCQ0N5caNG3z++edPza9nz54AzJkzRz2v7XFxcXFkZWVpjBSWK1euSCN59vb2NGnShLi4OI1HeyiVSmbPns348eO5devWM/t5XOfOncnMzFQXyyo//PBDvrZFjVOIF8nExAQrK6t8dzgLIURZ8NJ/s6kKgL59+xbaZuDAgSQmJhIVFcWHH34IwNatWzEzM6N3795UqlSJcuXK8euvv1KnTh06d+5Mhw4d+Oabbxg3bhwdO3YkIyOD2NhYdQFz79494NHE/HfeeYfPPvuMIUOG0LVrV27fvs0PP/xA7dq11aNlT3rttdeIjo4mMjKSnj17akx2f5y3t7f6ER9dunShe/fu1KlTh+zsbBITE4mPj6dbt24ak/JtbW25desW3333HV5eXjRt2rTQYzNt2jSGDh1K3759GTRoEFWqVGHXrl3s27eP4ODgYs/Le/3114mMjOTjjz/m6NGj1KtXj3379nHgwAFA81JoceIU4kVxcHBg7NixcrlQCFEmvfQRs82bN1O+fHmNOwef5O/vj729Pb///jtWVlYMGTKEU6dOMXfuXNLS0rCwsGDy5MncvHmTOXPmcOrUKSZMmMDo0aM5deoUc+bM4aeffqJBgwbExcVhZ2enLjQARo4cyeeff86DBw/47LPPiIyMpFOnTqxbt079gNiCTJ8+HTMzM6ZNm/bUW+EnT57MqlWr8PDwYOvWrcyaNYsvv/ySGzduMGfOHBYuXKhR8IwaNQpnZ2e++uqrZ14eadq0KREREbRo0YJ169Yxf/58rl+/zscff8y0adOeum1BjI2NWbFiBX379uXnn3/m//7v/3jw4AELFiwA0LgpoDhxCiGEEKL4jJRKpVLXQQjdycjIwNLSMt9dmX///TdBQUF8+umnTx3d1LasrCxOnjxJw4YNi/24jKSkJDw8PAzmZgRDzBkeTYeYNWsW06dPz3ezS1lliOdacpacy5qi/n3T6UcyCd0LDw/Hw8ODixcvaixXfcTTk48QEULXcnNzyczMJDc3V9ehCCGE1snsWQPXtWtXli9fzujRowkKCsLa2pojR44QExND7969cXFx0XWIQgghhMGQwszAOTs7Ex4ezrJly1i1ahWZmZk4OTnx4Ycf5vvcTSGEEEK8WFKYCdzd3Vm+fLmuwxBCCCEMnswxE0LoFXt7e4KCgrC3t9d1KEIIoXVSmAkh9Er58uVxcnKifPnyug5FCCG0TgozIYReycjI4LfffiMjI0PXoQghhNZJYSaE0Ct37tzh4MGD3LlzR9ehCCGE1klhJoQQQghRSkhhJoQQQghRSkhhJoQQQghRSkhhJoTQK1ZWVri5uWFlZaXrUIQQQuukMBNC6BVbW1u6dOmCra2trkMRQgitk8JMCKFXsrOzuXnzJtnZ2boORQghtE4KMyGEXrl69Spr1qzh6tWrug5FCCG0TgozIYQQQohSQgozIYQQQohSQi8Ls5CQEFxdXVmxYkWhbXx9fRkyZMhLjKpwSqWSL7/8klatWuHu7s5nn31WYLvo6GhcXV2Jjo5+yREKIYQQojTQy8JMZenSpVy6dEnXYTzT3r17Wb58OQ0aNGDatGl06dJF1yEJobeMjIwwNjbGyMhI16EIIYTWmeg6gOfx4MEDZsyYwerVq3UdylOdPn0agHfffRd3d3cdRyOE/goMDCQ1NZW0tDR8fX0xNjbG0dERAAcHB6KionQcoRBCPB+9HjHz9/fnwIEDxMTE6DqUp8rJyQGgQoUKOo5ECP2WlpZGWloajo6OODk5qYsyVbEmhBD6Tq8LsylTpmBtbc38+fO5devWM9tfu3aN0NBQWrdujZubG127duXbb79FoVCUOIaYmBj69OlDkyZN8PLyYty4ceoRMgA/Pz+WLFkCQLdu3XB1dS3xvh6XkpLClClT6NChA25ubjRv3pyhQ4fy559/qtsMGDAAb29vdWGokpmZibu7O9OmTVMvO3r0KKNGjaJZs2Z4eHgwePBgEhISNLYLCQnBz8+PjRs34u3tTbNmzdi0aRMAGzZsoFevXnh4eNCiRQtGjhzJoUOHtJKrEI9zdHQkISFB40tVoAkhhL7T60uZlStX5oMPPmDatGnMnz+f//u//yu0bVpaGkFBQdy9e5eBAwdSo0YN9u3bxxdffMGxY8dYtGhRsfe/cOFCvvnmG5o1a8b777/PnTt3CA8PZ8CAAXz//fe4u7szZcoUYmJiiI+P54MPPqBKlSrPkzIA6enpBAUFYWpqSnBwMJUrV+b8+fP89NNPjBw5kvj4eKpWrUqPHj2YNWsW+/fvp0OHDurtd+3axcOHD+nZsycACQkJjB49GmdnZyZMmABAXFwcI0aM4Msvv9SYE3fz5k0WLFjAmDFjuHv3Li1atGDbtm1MnTqVjh07EhwczP3791m3bh3Dhg0jNjaWunXrFjtHhUJRrIJZ1fZ5imx9Y4g5K5XKQueWKZXKMnssDPFcS86GwZByLmqOel2YAfTr14/Y2FhiYmJ4/fXX8fHxKbDdggULuHHjBuHh4bRo0QKAQYMGMXPmTH788Ud27dqFv79/kff777//8u2339KmTRtWrFiBsbExAL1796Z79+5Mnz6dmJgY/P39OXnyJPHx8XTs2LFERcqToqOjSU9PJyoqCjc3N/VyJycnZsyYwcGDB+nRowfdunVj3rx5bN26VaMwi4uLw8HBgRYtWpCXl8f06dNxcXEhIiICU1NTAAYPHszgwYOZM2cOfn5+mJmZAfDw4UOmTZtGv3791P3Nnj2bChUq8PXXX6v/aLZu3ZpJkyZx6tSpEuV85syZkhwakpOTS7SdPjOknLOzszE3Ny90XVJS0ssN6CUzpHOtIjkbBkPMuTB6X5gZGRkxa9YsevXqxYwZM4iLi8v3i1uhULB7925atmypLspUxo0bV6LCbPfu3eTl5TFmzBh1UQZQo0YNevbsSUREBJcvX6ZGjRrPl2ABRo0aRe/evbGzs1Mve/zjabKysgCwsbGhbdu2/PLLLzx8+BBzc3PS09P5448/GDFiBEZGRpw4cYJLly7x9ttvc/fuXY39+Pv7s2DBAo4dO0azZs3Uy1u1aqXRrlq1aty7d485c+YwcOBA6tati6urKzt27Chxji4uLlhaWha5vUKhIDk5mSZNmmicj7LMEHNW/QehsHUeHh4vL5iXyBDPteQsOZc1WVlZRRp00PvCDKBu3bqMGTOGJUuWsHTpUt59912N9bdu3SIrKwtnZ+d821apUgVra2tSU1OLtc/Lly8DFNinaoQoNTX1hRRm8OjNHBYWRnJyMikpKaSkpKjnkuXl5anb9erVi927d7N3715effVVfv75Z3Jzc9WXMS9evAjAokWLCr2cm5aWplGYPV4QAowfP56///6bdevWsW7dOmrUqEGHDh3o06cPjRs3LlF+xsbGJfohLel2+syQcjYyMiI1NTXfyHhqaiqOjo5l/jgY0rlWkZwNgyHkXNT8ykRhBjBmzBi2bdvGqlWr6N69u8Y6pVKp8f1JeXl56kt4RfW0PlXLittnUR0+fJhRo0ZhZmaGj48P3bt3p2HDhuTl5TF+/HiNtn5+flSsWJFt27bx6quvsmXLFho0aED9+vWB/xVx48aNw8vLq8D91atXT+P1k2+uqlWrsmnTJg4dOsSePXvYt28f69atIzw8nE8//ZTAwEBtpS4MnIODA3l5eVy+fJm8vDz14zIcHR1xcHDQdXhCCPHcykxhZmZmxqxZsxgyZAjTp0/XGDWytbXF0tKS8+fP59vu+vXrZGZmUq1atWLtTzUSdu7cuXwT+s+dOwdQ7D6LatGiRRgZGbFlyxaNfcfFxeVra2Zmxquvvsq2bdu4evUqSUlJvP/+++r1qrvZypcvT+vWrTW2PX36NFeuXMHCwuKp8fz7779kZWXRsmVLWrZsyUcffcQ///zDoEGDWLVqlRRmQmuioqJQKBQkJSXh4eFR5v+HLYQwPHr9uIwneXl5ERgYyF9//UV6erp6ubGxMR06dODgwYP5HuGwfPly4NHIUnF06tQJIyMjVqxYoXGnRVpaGps3b6ZBgwYv7H/wGRkZVKpUicqVK6uXZWdn8+OPPwL57/zo1asXWVlZfP755wAaI4pubm7Y29uzbt06bt++rdHfRx99xKRJk8jNzX1qPB9//DHjxo1Tz22DR5d4ra2tKVeuTL3FRClw584dDh06xJ07d3QdihBCaF2ZGTFT+fDDD9mzZw///fefxvL33nuPP/74g5EjR6ofl7F//35++eUXOnXqRKdOndRtd+3axb179+jVq1eh+6lbty4jRoxg5cqVDB48mK5du3Lnzh1+/PFHlEolM2bMKHEOmzZtKvDuMhsbGyZPnkyHDh345ptvGDduHB07diQjI4PY2FhSUlIAuHfvnsZ2Xl5eODg4sGXLFlq1akXVqlXV60xNTZk+fTpvv/02vXv3JigoiIoVKxITE8PJkyd5//33sbGxeWq8b775JuPGjWPw4MH06tULMzMzdu3axaVLl5gzZ06Jj4MQBcnIyGDv3r107tz5me9NIYTQN2WuMHvllVeYMmUK7733nsbyGjVqsHHjRr766is2bdrEvXv3qFWrFiEhIQwdOlTj2Uhz584lNTX1qYUZPCoC69SpQ3h4OJ9//jkVKlSgZcuWTJgwARcXlxLncPDgQQ4ePJhvuaOjI5MnT2bChAnk5eWxdetW9u/fT+XKlfH09GTZsmUEBwdz4MABxowZo97OyMiIHj168M0339CjR498/QYEBLBmzRq+/vprVqxYgVKpxNnZmc8+++yZxwAejTYuXbqU7777jqVLl/Lw4UPq16/PF198UeD+hBBCCFEwI2VhM+JFmfLll1+yZs0a9u/fj5WVla7DKVRWVhYnT56kYcOGxX5chqHNOzLEnAHOnz/PlClTmDt3LnXq1NF1OC+FIZ5ryVlyLmuK+vdNJgAZgKysLDZv3kyXLl1KdVEmhBBCGLoydylT/M/p06dZvnw5J06c4Pr164wYMULXIQnx3CwsLKhbt+4z7xYWQgh9JIVZGWZlZcUff/yBsbExc+fO1doHqAuhS1WqVKF3795a+dxZIYQobaQwK8McHR1JSEjQdRhCaJVCoSArKwuFQlHm56QIIQyPzDETQuiV1NRUli1bVuyPURNCCH0ghZkQQgghRCkhhZkQQgghRCkhhZkQQgghRCkhhZkQQgghRCkhhZkQQq/UqFGDiRMnUqNGDV2HIoQQWieFmRBCr5QrVw5zc3PKlZNfX0KIskd+swkh9Mr169fZuHEj169f13UoQgihdVKYCSH0yoMHD7hw4QIPHjzQdShCCKF1UpgJIYQQQpQSUpgJIYQQQpQSUpgJIYQQQpQSUpgJIfSKjY0NnTp1wsbGRtehCCGE1pnoOoCiCgkJYdOmTc9s17JlS3744Yfn3t+QIUM4d+4c+/fvL9Z20dHRhIaG8u2339KuXbvnjqMo/Pz8ivSBzhMmTABgyZIlbNu2jbp1677o0ITQuooVK+Lp6UnFihV1HYoQQmid3hRm/fv3x8fHR/363LlzLF++nICAAAICAtTLK1eurJX9jR07lszMzGJv5+XlxWeffUaDBg20EkdRTJkyhXv37qlfx8fHEx8fz9ixY3F2dlYvd3V1BcDJyYmqVau+tPiE0KZ79+5x4sQJ6tevj7W1ta7DEUIIrdKbwszT0xNPT0/168TERJYvX46rqyu9evXS+v58fX1LtF3NmjWpWbOmlqN5On9/f43Xly5dIj4+ntatW+Pt7Z2v/cssGoXQhsDAQNLS0oBH72+ARYsWYWpqioODA1FRUboMTwghtEZvCjMhhOFKS0sjNTUVR0dHnJyc1MuLcglfCCH0SZmc/J+YmIirqysbNmygT58+NGnShNGjRwOPLoN89dVXvPbaazRt2pSmTZvSs2dPIiMjNfoYMmSIxqhZSEgIfn5+nDp1imHDhuHh4UHLli0JDQ3l1q1b6nbR0dG4urry22+/acTy66+/MnfuXNq0aYO7uzv9+/cnMTExX+zh4eF07doVd3d3evTowc6dOxk2bBhDhgzRyrEJCwvD1dWVf//9VyPe5ORk3nvvPZo3b06LFi0ICQnh3r17JCQkEBgYSNOmTenSpQtbtmzJ1+fmzZvp06cP7u7ueHt78/bbb6tHNYTQFkdHRxISEjS+HB0ddR2WEEJoVZkeMZs7dy5du3YlMDCQChUqAI/mjv39998MHDiQunXrkp6eTmRkJNOmTaNSpUp07ty50P5u377NG2+8gZ+fH127duXw4cNER0eTlZXFokWLnhrLzJkzqVSpEm+++Sb3799n5cqVvPnmm+zdu1d9d9nChQv55ptvaNOmDYMHD+bUqVO88847WFlZqeeHvSgTJkygUaNGfPjhhxw4cIBNmzZx9epVTpw4QXBwMH369GHNmjV8+OGHNGzYUH3jwLJly1i0aBEdO3YkMDCQ9PR01q9fT79+/YiMjKRWrVolikehUKBQKIrV/vHvhsCQclYqlRgZGRW6rqwfA0M61yqSs2EwpJyLmmOZLswaNGjA3Llz1a+PHj3KwYMHCQkJYfjw4erlAQEBdO3ald9///2phVlmZibvvfceb775JvDohoQrV66wa9cu7t+/j4WFRaHbVqhQgYiICExNTQGoUqUKoaGhxMfHExQUxOXLl1m5ciX+/v4sWbJE/UfI2dmZ+fPnP9dxKAoXFxe+/vprAPr27cuhQ4dISEggLCxMfUxq167NiBEjOHDgAHXr1iUlJYUlS5YwZMgQpk6dqu6rX79+dOvWjS+++IKwsLASxXPmzJkSbZecnFyi7fSZIeScnZ2Nubl5oeuSkpJebkA6Ygjn+kmSs2EwxJwLU6YLs1atWmm8dnd359ChQxq/4JVKJbm5uQBkZWU9s89u3bppvG7YsCEHDx4kIyPjqYVZ586d1UUZQKNGjQC4ceMGALt37yY3N5cRI0ZojAwMGjSIJUuWPDOu5/V4QWpsbIyTkxO3bt3Cz89PvVx1U4Mq5l27dqFQKPD39yc9PV3dzszMjJYtW/Lbb7+Rm5uLiUnx32YuLi5YWloWub1CoSA5OZkmTZpgbGxc7P3pI0PK2czM7KnrPDw8Xl4wOmBI51pFcpacy5qsrKwiDTqU6cKsoEdnmJqasnHjRv744w8uXbrExYsX1QVZXl7eM/u0s7PTeK36g/GsIUpbW9t8cTy+z4sXLwJQp06dfP2/jLs8nzxWJiYmVKpUSaOoKlfu0ZTEJ2N+4403Cu03PT0de3v7YsdjbGxcoh/Skm6nzwwhZyMjI1JTUzUemQOobwgo6/mrGMK5fpLkbBgMIeei5lemCzNVIaGSnp7OgAEDSEtLw8fHhzZt2jBy5EhatGhBhw4dStRnSWN5Uk5ODlDwyEBhl3C0qaA3TGFzelRUBdrixYsLfdjnK6+88vzBCYPn4OCg/rfqxpLq1avj6OiosU4IIfRdmS7MnvTjjz9y8eJFvvnmG41C7Nq1a7oL6v9TTZI/f/48TZo0US9XKpVcvHiR+vXr6yq0QqnuiLO3t9d4xhxAQkIC8PRLUEIU1ePPKTt//jxTpkxh7ty5+UaYhRBC35XJx2UUJiMjAyDfRxGtWbMG0O1dIQEBAZQrV44ff/xRY/mWLVs0HsdRmqjmn33zzTcal4FTUlJ46623WLBgwTNH3YQQQgjxPwY1YtahQwd++OEHxo0bR//+/TEyMmL37t3s378fU1NTjY81etmcnJwYNmwYq1atIj09nXbt2nHu3DkiIyM1bhooTerXr8/w4cNZvXo1gwYNomvXrjx48IB169ahUCgICQnRdYhCCCGEXjGowqxNmzbMmzePlStX8tlnn2FtbU39+vVZvXo169ev5/fff3/mYy9epA8++AAbGxsiIyPZv38/derUYfHixUybNq3UXhIMCQnB2dmZ9evX88UXX2BpaYmbmxsTJkwo83fKCSGEENpmpFQqlboOQjy6jVapVKofhKuiVCrx8PDg1Vdf5bPPPtNRdC9PVlYWJ0+epGHDhsV+XEZSUhIeHh5l/s4eFUPMGeDBgwfs27ePNm3aUL58eV2H81IY4rmWnCXnsqaof98Mao5ZaXbixAmaNWuW78OYd+/ezYMHD3B3d9dRZEKULqamptjY2JTaS/xCCPE8DOpSZmnWtGlTateuzdy5c7l48SI1a9bk4sWLrF+/nrp16xIYGKjrEIUoFf777z+2bt1KzZo1S/SMPCGEKM2kMCslTE1NWbt2LUuXLiUuLo6bN29iZ2dH7969mThxos7mvQlR2ty7d4+TJ0/q9GYdIYR4UaQwK0WqVq3KrFmzdB2GEEIIIXRE5pgJIYQQQpQSUpgJIYQQQpQSUpgJIfTKK6+8QuvWreVzWIUQZZIUZkIIvSKFmRCiLJPCTAihVx48eMD58+d58OCBrkMRQgitk8JMCKFXrl+/TlRUFNevX9d1KEIIoXVSmAkhhBBClBJSmAkhhBBClBJSmAkhhBBClBJSmAkh9IqpqSmVKlWSDzEXQpRJUpgJIfRK9erVGTVqFNWrV9d1KEIIoXVSmAkhhBBClBKlujALCQnB1dWVy5cv6zqUYlPF/vDhQ53s98kvNzc3fH19GTduHKdOnSpx/0qlkpSUFC1GLETxpKamsnTpUlJTU3UdihBCaJ2JrgMoq/r374+Pj4/O5sGEhoZiY2Ojfv3w4UOOHTtGVFQUiYmJxMTEULNmzWL1mZmZyfDhw/H29ub999/XdshCFIlCoeD+/fsoFApdhyKEEFonhdkL4unpiaenp8727+/vT40aNTSWBQUF0aJFCz744ANWr17N9OnTi9VnRkYGR48exdvbW5uhCiGEEOL/k8LMwPTo0YNp06bx119/6ToUIYosMDCQtLQ0AC5dugRAv379MDU1xcHBgaioKF2GJ4QQWlOq55gVx7Vr1wgNDaV169a4ubnRvXt3wsPD87U7deoUkydPpk2bNjRu3Bhvb2/Gjh3L6dOn1W0uX76Mq6srK1euZOjQobi5udGjRw8UCgV+fn6EhITw888/06tXL5o0aULHjh1ZsmQJeXl56j6enGMWFhamni83YcIEmjdvTrNmzZgwYUK+OXT37t1j7ty5tG3blqZNm/LGG29w+vRpGjVqRFhY2HMdJyMjI8qXL59v+a5du3jjjTfw8vLCzc2Ndu3aMW3aNDIyMgBITEykU6dOAHz77bcac/+ys7MJCwsjICAANzc3OnTowPz588nMzHyuWIVQSUtLU88pc3JywsnJCVNTU1JTU9UFmxBClAVlYsTsxo0bBAUFkZ2dTXBwMHZ2duzfv59Zs2Zx/vx5pk6dCsA///zDgAEDqF69OsOHD6dixYqcPHmSDRs2cPToUXbv3q1RtCxZsoTWrVszdepUsrOzMTY2Bh4VKfHx8QwePJjg4GBiYmIICwvDxsaGQYMGPTXWoUOH0rhxYz744AP++ecfwsPDuXr1Khs3bgQgLy+P0aNH89dff9GvXz9cXFzYvXs3Q4YM0Sj8SiopKYmMjAx1kQUQHR1NaGgovr6+vPPOOwDs37+fyMhIbty4wfLly6lbty6hoaHMmzePjh070rVrV2xtbcnLy+Ott94iMTGRvn374urqytmzZ1m3bh2HDh3ixx9/xMzM7LnjFsLR0ZGEhASNZT4+PjqKRgghXowyUZgtXLiQzMxMYmNj1fOqBg0axNy5c/n+++/p27cvDRo0IDw8nNzcXL7//nvs7e3V21tZWbFixQpOnDhBs2bN1MttbGxYvHixuiBTSUtLIzIykqZNmwKPLg+2adOGuLi4ZxZmbdu2ZebMmerXmZmZbNq0iQsXLlC7dm3i4uI4fPgwoaGhDBs2TJ3LuHHj2L17d5GPyZ07d0hPT1e/vn//PsnJyXz22WdYWloyduxY9bqVK1fSsGFDvvvuO8qVK6feZ//+/dm3bx9KpZLKlSvj7+/PvHnzqFevHr169QIgJiaGffv2sWTJEgICAtR9qu4AjYiIYMiQIUWOW0WhUBRrcreqrSFNCDeknJVKJUZGRoWuK+vHwJDOtYrkbBgMKeei5qj3hVleXh7x8fF4enpiaWmpUYx07tyZ77//nr1799KgQQOmT5/OxIkTsbW1Vbe5f/++uhjJysrS6LtFixb5ijJ49D93VVEGUKFCBWrVqsXNmzefGW+3bt00Xjds2JBNmzZx8+ZNateuTXx8PJaWlgwcOFDdxsjIiDFjxhSrMOvdu3e+ZSYmJjRv3pzly5fj5OSkXh4TE0NWVpb6OACkp6djZWVFTk4OOTk5hY56bd++HSsrK5o3b65x7D09PXnllVfYs2dPiQqzM2fOFHsbgOTk5BJtp88MIefs7GzMzc0LXZeUlPRyA9IRQzjXT5KcDYMh5lwYvS/Mbt26xd27d/n9998LvayhmoNiZGTE3bt3+e677zh16hQpKSmkpqaqq9gnLxXa2dkV2N/jhZ2KmZlZkS41PtmnquBRxXDx4kUcHBzyFUJ169Z9Zt+P+/zzz6lcuTK5ubkcPnyY1atX07RpU7744guN0UJ49BE3p0+fJi4ujnPnznHp0iWuX7+uXq9UKgvdz6VLl8jMzCz02Jf0WVMuLi5YWloWub1CoSA5OZkmTZoUWEyXRYaU89Muh5uZmeHh4fHygtEBQzrXKpKz5FzWZGVlFWnQQe8LM1VB4+fnV+jIjKoQ2b59O++99x42Njb4+PjQqlUrGjVqxMWLF5k1a1a+7Qp7kzw+slRchV2OUcnJycHCwiLf8sJGCwrTrFkz9WXddu3a0bx5c8aMGcPQoUOJjIzE2tpa3fbTTz9l7dq1uLi44OnpSdeuXXF3d+eHH35g8+bNT92PQqHA0dGROXPmFLi+uHGrGBsbl+iHtKTb6TNDyNnIyIjU1NR8/wFITU3F0dGxzOevYgjn+kmSs2EwhJyLmp/eF2a2trZYWFiQnZ1N69atNdalp6fz559/UqtWLeDRKFL16tWJiYnByspK3e7YsWMvNeanqVWrFocOHUKhUGicxAsXLjxXv+3atWPMmDF8/fXXTJ06lcWLFwOP/rCtXbuWrl278uWXX2oUjv/9998z+61RowZHjhzBy8sr38N0t23bRu3atZ8rbiEAHBwc1P9WPS6jevXqODo6aqwTQgh9p/ePyzAxMaF9+/YcOHAg3zyTxYsXM2nSJP755x/g0QNSq1WrplGU3blzh+joaKB0TD7s3LkzmZmZ+Uaqfvjhh+fue/z48TRo0IAdO3bw888/A3D79m0AnJ2dNYqy48ePc/DgQQByc3OB/1X7j1+y9fPzIysrizVr1mjsa9u2bUyePJktW7Y8d9xCREVFkZCQQEJCAvv27aNdu3Zs2LCBhIQEeYaZEKJM0YsRsy+//JIKFSrkW+7p6Unv3r15//33SUxMZNiwYQQHB1O7dm3++OMPtm3bRocOHWjbti0AHTp0YMuWLYSGhtKsWTOuXbtGVFSUemTo3r17LzWvgrz++utERkby8ccfc/ToUerVq8e+ffs4cOAA8OxLoU9jamrK3LlzCQoKYs6cOfj4+FCvXj0cHR1ZtWoVCoWCGjVqcObMGTZu3Ki+ZHvv3j0qVKhApUqVKFeuHL/++it16tShc+fO9OvXj82bN/PFF19w+vRpWrRowcWLFwkPD8fR0ZGRI0dq5bgIoWJlZYWHh4fGf7CEEKKs0IvCrLBRl+zsbHr37k3NmjXZsGEDixcvJjY2lrt37+Lg4MDEiRMZNWqUusCYMWMGFSpUYPfu3WzdupWqVavStm1bRowYwWuvvcaBAwfo3r37y0wtH2NjY1asWMGCBQv4+eefycrKonnz5ixYsIDx48c/9zPBGjduzIgRI1ixYgXz5s3j//7v//j222+ZP38+69evR6FQ4ODgwNixY6lbty7jx4/nwIEDvP7661hYWDB58mRWrlzJnDlzcHJywtvbm9WrV/P111/z888/s337dipXrkz37t2ZOHFioTdQCFFStra2+Pv7F3gTjhBC6Dsj5dNuuRMvXUZGBpaWlvkKsL///pugoCA+/fRT+vbtq6PoXrysrCxOnjxJw4YNi31XZlJSEh4eHmV+AqmKIeYMj94je/bsoWPHjsV6j+gzQzzXkrPkXNYU9e+b3s8xK2vCw8Px8PDg4sWLGsu3bdsGgLu7uy7CEqLUuHbtGuvWrePatWu6DkUIIbROLy5lGpKuXbuyfPlyRo8eTVBQENbW1hw5coSYmBh69+6Ni4uLrkMUQgghxAsihVkp4+zsTHh4OMuWLWPVqlVkZmbi5OTEhx9+qP6IJiGEEEKUTVKYlULu7u4sX75c12EIIYQQ4iWTOWZCCL1Srlw5TE1Nn+sTOIQQorSS32xCCL1So0YN3n77bfVHjgkhRFkihZkQQgghRCkhhZkQQq9cvXqV1atXc/XqVV2HIoQQWieFmRBCr2RnZ/Pff/+RnZ2t61CEEELrpDATQgghhCglpDATQgghhCglpDATQgghhCglpDATQuiVKlWq8Prrr1OlShVdhyKEEFonhZkQQq9YWFhQr149LCwsdB2KEEJonRRmQgi9cvv2bf744w9u376t61CEEELrpDATQuiV27dvs2/fPinMhBBlkhRmQgghhBClhFYKs/Pnz+Pq6krDhg25du1akbcLCwvD1dWVf//9VxthaPDz88PV1fWpX8UVEhKCq6srDx8+LPC1rkVHRxeYZ8OGDfH29mbQoEHs2LHjufZx6dIlLUUrhBBCiCeZaKOT2NhYLC0tycrKIjo6mrfeeksb3T43GxsbQkNDtdZf//798fHxwdTUVGt9vgj9+/enefPm6tcKhYJLly6xfv16Jk2aRFhYGJ07dy52v9OnT+f06dNERERoM1whhBBC/H/PXZgplUri4uJo1aoVqampbNq0qdQUZpaWlvTq1Utr/Xl6euLp6am1/l4UDw+PAvPu3bs3PXr0YPHixSUqzPbt20flypW1EaIQxRIYGEhaWhrwv1Hb/v37Y2xsjIODA1FRUboMTwghtOa5L2UePnyYy5cv4+XlRceOHbl48SIHDx7URmxCy2rVqoWXlxdnz54lMzNT1+EIUWRpaWmkpqYC4OTkhJOTE8bGxqSmpqoLNiGEKAueuzDbvHkzAK1atcLf3x+AjRs35mt3+vRpxowZQ/PmzWndujVffPEFubm56vXXr1+nUaNGTJ06Nd+2sbGxuLq68uuvvz5vuAXKzc1l5cqV9O7dG09PT5o0aUKXLl345ptvyMvLU7d71pyywubMrV+/HldXVxITEwG4fPkyrq6urFy5kqFDh+Lm5kaPHj1QKBQA/PbbbwwcOBAPDw+aNWvG6NGjOX78uFZyrVChAvBopFPl1KlTTJ48mTZt2tC4cWO8vb0ZO3Ysp0+fVrdxdXUlNTWVv//+G1dXV6Kjo9XrNm/eTJ8+fXB3d8fb25u3335b5qIJrXN0dCQhIUHjy9HRUddhCSGEVj3Xpczs7Gy2b99OjRo1aNSoEfDol+fOnTuZPn06VlZWwKObAwYOHIi5uTmjRo3CxMSE9evXc+vWLXVf9vb2+Pj4EB8fz4wZMzTmcW3duhU7Ozt8fX2LFV9eXh7p6ekFrrOxscHIyAiAqVOnEhMTQ1BQEMHBwWRmZhIbG8vChQsxMzNj+PDhxdpvUS1ZsoTWrVszdepUsrOzMTY2JiYmhpCQEJo3b867775LVlYWUVFRBAcHs2bNGpo1a1bi/d27d4/ExERq1qxJxYoVAfjnn38YMGAA1atXZ/jw4VSsWJGTJ0+yYcMGjh49yu7duylfvjyfffYZ8+bNo2LFikyYMEEdx7Jly1i0aBEdO3YkMDCQ9PR01q9fT79+/YiMjKRWrVolilWhUKgL1aK2f/y7ITCknJVKpfrntaB1Zf0YGNK5VpGcDYMh5VzUHJ+rMNu7dy+3b98mMDBQvaxz586sXr2arVu30r9/fwAWL15MTk4O0dHR6j/Uffr0oUePHmRlZam37dmzJ/v27ePAgQO0b98egFu3bnHgwAGCg4MxMSleuFeuXMHHx6fAdX/++SfW1tbcvHmT2NhYBg8erDFaFxQUhI+PD7///vsLK8xsbGxYvHgxxsbGAGRmZjJ79mw6duzI119/rW43ePBgevbsyZw5czRGqgqTlZWlUZDm5ORw8eJFlixZQkZGBtOmTVOvCw8PJzc3l++//x57e3v1cisrK1asWMGJEydo1qwZvXr1YtGiRdjY2Kjnr6WkpLBkyRKGDBmicez69etHt27d+OKLLwgLCyvRsTlz5kyJtktOTi7RdvrMEHLOzs7G3Ny80HVJSUkvNyAdMYRz/STJ2TAYYs6Fea7CTHUZs0uXLuplXbp0YfXq1WzcuJH+/fuTl5fHr7/+SuvWrTVGT+zs7OjRowdr1qxRLwsICMDS0pKtW7eqC7OdO3eSk5NDz549ix1f5cqV+fzzzwtcZ2lpqW5z+PDhfOvT09OxsrLSKBy1rUWLFuqiDODAgQNkZmby6quv5hvpa9++PT/++CPXrl2jatWqT+139uzZzJ49O99yFxeXfHdkTp8+nYkTJ2Jra6tedv/+fcqVe3SV+2n579q1C4VCgb+/v0a8ZmZmtGzZkt9++43c3NxiF9SqWFXnqCgUCgXJyck0adJE45iWZYaUs5mZ2VPXeXh4vLxgdMCQzrWK5Cw5lzVZWVlFGnQocWGWkZHB3r17sbW1xdbWlsuXLwOPCi5bW1uOHj3K2bNnsbOz4969ewVe0qpbt67Ga0tLSwICAvjll1/Izs7GzMyMLVu24OzsTJMmTYodo7m5Oa1bt35mOzMzM7Zu3cpvv/3GhQsXuHTpEnfu3AGgZs2axd5vUdnZ2Wm8vnjxIgAfffRRodukpaU9szAbOXIkbdq0QalUcv78eb777jvKlSvH7Nmz8/0BMzIy4u7du3z33XecOnWKlJQUUlNT1UOuj8+xe5Iq3jfeeKPQNunp6RojcUVlbGxcoh/Skm6nzwwhZyMjI1JTU/ONgKempuLo6Fjm81cxhHP9JMnZMBhCzkXNr8SF2c8//0xOTg7p6enqSf9PioqK4s033wTgwYMH+dYX9Ee/Z8+exMbG8uuvv+Lu7s6hQ4eYNGlSScN8puzsbAYPHszRo0dp2bIlXl5eDBw4EC8vL4YOHaqVfRRW3Dx5klTtpk+fTp06dQrcxtnZ+Zn7q1evnrog9fX1pVOnTvTt25fhw4ezdu1ajSJ3+/btvPfee9jY2ODj40OrVq1o1KgRFy9eZNasWUXKa/Hixeo5a0965ZVXnhmvEM/i4OCg/rfqxpLq1avj6OiosU4IIfRdiQsz1WXMmTNn5nu21Z07dwgNDWXz5s289957WFlZceHChXx9FHTnno+PD1WqVGHnzp1cuXIFpVJJjx49ShrmM23bto2///6b6dOnM2jQIPXy3NxcMjIyijXao7r8l5OTo7H8xo0bRdpedYfZK6+8km+kLykpiczMTMqXL1/keFSqV6/O559/zogRI3j77bfZvHmz+saMzz//nOrVqxMTE6NeBnDs2LEix2tvb5/v+W4JCQnA0y9BCVFUjz+nLDc3l8OHD9O8efMSXSYXQojSrESPy0hJSeHIkSM0btyYAQMG4O/vr/HVp08fvL29+e+//9izZw8BAQEkJiZy9OhRdR93794lJiYmX9/Gxsb06NGD3377jZ9//pnmzZtTo0aNEif4LBkZGUD+y6oRERHcv39f45Eez1KlShUATpw4oV6WnZ1NfHx8kbb39fWlfPnyrFy5kuzsbI0YJ02aRGhoaImHelu3bk1wcDCpqaksWLBAo+9q1appFGV37txR32Tw+F0k5cqV0xj98/PzA8j3WJGUlBTeeustFixYUOiddEKUlJGRESYmJvLeEkKUSSX676ZqtKxv376Fthk4cCCJiYlERUUxa9Ys9d2Nb7zxBhUrViQiIkLjWVqP69WrF6tWreLIkSP5LqfFxsZSoUKFQi+fFpevry+mpqZMmTKFIUOGYGFhQUJCAtu3b8fc3Jx79+4Vua+AgAA+/fRT5s2bx/Xr16lYsSJRUVFFvkXWxsaG9957j08//ZTAwEBef/11jI2N+emnn7h+/ToLFy58rhGC9957j71797J+/Xpee+01WrRoQYcOHdiyZQuhoaE0a9aMa9euERUVxX///Qegkb+trS1nz54lPDwcb29v6tevz/Dhw1m9ejWDBg2ia9euPHjwgHXr1qFQKAgJCSlxrEIU5tq1a/z0009Ur15dLmMKIcqcEo2Ybd68mfLlyz/1EqO/vz/29vb8/vvvAPz000/4+vryww8/sGzZMlq1asW4ceMK3LZBgwa4uLhgZmamcccnwIcffsjcuXNLEnaB6tevz5IlS6hUqRKLFi1i0aJFXL9+nUWLFjFo0CAuXryofuL4s9jY2PDdd99Rt25dli5dytdff42Pjw8zZswocjxDhw5lyZIlVKhQgbCwMJYuXYqdnR3ffPMN3bp1K2mawKNHYMycOROlUsnUqVN5+PAhM2bMoH///vz+++/Mnj2b2NhY2rZty+bNmzExMeHAgQPq7SdOnIiNjQ3z5s1TjwKGhIQwe/ZsHjx4wBdffMGqVatwcXHhhx9+oEWLFs8VrxAFefjwIZcvXy70Qc9CCKHPjJSFDVsJoQNZWVmcPHmShg0bFvtxGUlJSXh4eJT5O3tUDDFnePTA6ilTpjB37txCb5IpawzxXEvOknNZU9S/b8/9kUxCCCGEEEI7pDATQgghhCglpDATQugVW1tbOnfurPFpFUIIUVZIYSaE0CtWVla4u7trPOJFCCHKCinMhBB6JTMzk6NHj5KZmanrUIQQQuukMBNC6JX09HR27txJenq6rkMRQgitk8JMCCGEEKKUkMJMCCGEEKKUkMJMCCGEEKKUkMJMCKFXzM3NqVGjBubm5roORQghtE4KMyGEXqlatSoDBgygatWqug5FCCG0TgozIYReUSqV5ObmIh/zK4Qoi6QwE0LolZSUFL766itSUlJ0HYoQQmidFGZCCCGEEKWEFGZCCCGEEKWEFGZCCCGEEKWEFGZCCCGEEKWEia4D0Lbz58/TpUsXypUrx969e4t8S31YWBhLlixh27Zt1K1bV6sx+fn5kZqa+tQ2p0+fVretXLkykZGR6nXp6emYmZlhZWVV4LZDhgzh4MGDz4yjd+/etGzZktDQUL799lvatWtXjCyEKB0cHBwYM2YMDg4Oug5FCCG0rswVZrGxsVhaWpKVlUV0dDRvvfWWrkMCwMbGhtDQ0Ge2mzJlisaDM3/99Vc++OAD1q9fX2hhNnbsWPr27at+ffjwYSIiIujfvz/NmzdXL3dycqJy5cp89tlnNGjQ4DmyEUJ3TExMqFixIiYmZe7XlxBClK3CTKlUEhcXR6tWrUhNTWXTpk2lpjCztLSkV69ez2zn7++v8fro0aPcvn37qdv4+vpqvFYoFERERODh4VHgPmvWrFmEiIUoPQIDA0lLSwPg0qVLADg6OmJsbIyDgwNRUVG6DE8IIbSmTM0xO3z4MJcvX8bLy4uOHTty8eLFIl3iE0KUbmlpaerpAE5OTjg5OWFsbExqaqq6YBNCiLKgTBVmmzdvBqBVq1bqkaeNGzfma3f69GnGjBlD8+bNad26NV988QW5ubnq9devX6dRo0ZMnTo137axsbG4urry66+/vpAc/Pz8CAoKAiAkJIQlS5YA0K1bN4YMGfLc/UdHR+Pq6spvv/0GQGJiojqfTz75hFatWuHp6cnYsWO5efMmJ0+eZMiQITRt2hQ/Pz/WrFmTr8/ffvuNgQMH4uHhQbNmzRg9ejTHjx9/7liFeJyjoyMJCQkaX46OjroOSwghtKrMXMrMzs5m+/bt1KhRg0aNGgGPfpHv3LmT6dOnq+dnnT9/noEDB2Jubs6oUaMwMTFh/fr13Lp1S92Xvb09Pj4+xMfHM2PGDExNTdXrtm7dip2dXb7Lh8+Sl5dHenp6getsbGwwMjLKt7x///5kZmYSHx/PBx98QMOGDYu1z+KYPn06jo6OvPPOO5w8eZKffvqJCRMmcOHCBbp3785rr71GZGQk8+bNo379+ur8Y2JiCAkJoXnz5rz77rtkZWURFRVFcHAwa9asoVmzZiWKR6FQoFAoitX+8e+GwJByViqVBf6MqNaV9WNgSOdaRXI2DIaUc1FzLDOF2d69e7l9+zaBgYHqZZ07d2b16tVs3bqV/v37A7B48WJycnKIjo6mVq1aAPTp04cePXqQlZWl3rZnz57s27ePAwcO0L59ewBu3brFgQMHCA4OLvbE4ytXruDj41Pguj///BNra+t8yz09PXF1dSU+Pp6OHTtq/W7Rx1lbW7N27Vp1XseOHeOvv/4iJCSE4cOHA49GIl999VV+//13fH19yczMZPbs2XTs2JGvv/5a3dfgwYPp2bMnc+bMITo6ukTxnDlzpkTbJScnl2g7fWYIOWdnZ2vcFPPkuqSkpJcbkI4Ywrl+kuRsGAwx58KUmcJMdRmzS5cu6mVdunRh9erVbNy4kf79+5OXl8evv/5K69at1UUZgJ2dHT169NC4TBcQEIClpSVbt25VF2Y7d+4kJyeHnj17Fju+ypUr8/nnnxe4ztLSstj9aVunTp00is06depw7NgxAgIC1MtUNw3cuHEDgAMHDpCZmcmrr76abzSwffv2/Pjjj1y7dq3Ijyx5nIuLS7GOi0KhIDk5mSZNmmBsbFzs/ekjQ8rZzMzsqes8PDxeXjA6YEjnWkVylpzLmqysrCINOpSJwiwjI4O9e/dia2uLra0tly9fBh4VXLa2thw9epSzZ89iZ2fHvXv3NIoylSdHoywtLQkICOCXX34hOzsbMzMztmzZgrOzM02aNCl2jObm5rRu3bpkCb4ElStX1nitKtIeX676ocnLywPg4sWLAHz00UeF9puWllaiwszY2LhEP6Ql3U6fGULORkZGpKam5ht1Tk1NVd+daQgM4Vw/SXI2DIaQc1HzKxOF2c8//0xOTg7p6en5HjehEhUVxZtvvgnAgwcP8q1XFRuP69mzJ7Gxsfz666+4u7tz6NAhJk2apN3gS4nC3jCFzeuB/x2z6dOnU6dOnQLbODs7P39wwuA9/jBZ1eMyatSogaOjozxoVghRppSJwkx1GXPmzJn5Rn7u3LlDaGgomzdv5r333sPKyooLFy7k60P1y/5xPj4+VKlShZ07d3LlyhWUSiU9evR4ITnoI9Udca+88kq+0cCkpCQyMzMpX768LkITZczjzyk7f/48U6ZMYe7cuYX+h0AIIfSV3hdmKSkpHDlyhMaNGzNgwIAC28TExJCYmMiePXsICAggJiaGo0eP4u7uDsDdu3eJiYnJt52xsTE9evQgOjqay5cv07x5c2rUqPEi08mnXLlHTzRRKpUvdb9F4evrS/ny5Vm5ciWdO3dWzwPKyMhg0qRJKJVK9uzZo+MohRBCCP2h988xU42WPf6RRE8aOHAg8Oh/3ZMnT8bOzo7hw4ezePFiVq9eTb9+/QotfHr16kVGRgZHjhzJN+k/NjaWXbt2aSmTgtna2gKwevVqfvnllxe6r+KysbHhvffe48SJEwQGBrJy5UrWrFnDgAEDuH79OqGhofKxOUIIIUQx6P1fzc2bN1O+fPmnXmL09/fH3t6e33//HYCffvqJzz//nB9++AGA1157jbp16zJnzpx82zZo0AAXFxcuXLigcccnwIcffoijo2Oh89q04bXXXmPnzp1s3ryZI0eO0KlTpxe2r5IYOnQo1atXZ+XKlYSFhWFqaoqLiwuhoaHqu1mFEEIIUTRGytJ4jUwYrKysLE6ePEnDhg2L/biMpKQkPDw8yvydPSqGmDM8uhNz3rx5hIaGGsyT/w3xXEvOknNZU9S/b3p/KVMIYViqVavG8OHDqVatmq5DEUIIrZPCTAghhBCilJDCTAihVy5fvsyiRYvUD5IWQoiyRAozIYReycvLIycnp8CHQgshhL6TwkwIIYQQopSQwkwIIYQQopSQwkwIIYQQopSQwkwIoVeqVq3K4MGDqVq1qq5DEUIIrZPCTAihV8zNzalWrRrm5ua6DkUIIbROCjMhhF5JT09n165dpKen6zoUIYTQOinMhBB6JTMzk6SkJDIzM3UdihBCaJ0UZkIIIYQQpYQUZkIIIYQQpYQUZkIIIYQQpYQUZkIIvWJtbU3z5s2xtrbWdShCCKF1UpgJIfRKpUqV6NixI5UqVdJ1KEIIoXVSmAkh9MqDBw9IS0vjwYMHug5FCCG0zkTXATwpLCyMJUuW5FtuYmJCxYoVady4MWPGjKFly5Y6iA5cXV3p1q0bX375pU72/6T09HRWrFjBnj17SEtLw9zcnLp169KtWzcGDhyIqampRvt79+5x//59KleuXKL9Xbp0CScnJ22ELkSJXL9+nR9//BE3Nzfq1Kmj63CEEEKrSl1hpjJ27FicnZ3Vr3Nycvj3339Zv349w4cPZ/369bi7u+swQt27du0aQUFBPHjwgD59+lC7dm2ysrL4448/mDt3Lrt37+a7775TF2fHjh1j3LhxzJkzh3bt2hV7fyNHjsTa2rrUFKVCCCFEWVNqC7PWrVvj7e2db3mnTp0YPHgwS5cu5ZtvvtFBZKXH0qVLSU9PJy4ujtq1a6uXDx8+nC+//JLly5cTGxtL3759AThz5gzXrl0r8f727dtHt27dnjdsIYrF2dmZ3NxcHB0dgUf/Sbt69Srt27fHy8uLqKgoHUcohBDao3dzzFq0aEHt2rX566+/dB2Kzh05cgQnJyeNokxl2LBhGBkZceTIkZcfmBBalJubq/Ha1NSUmjVrApCWlqaLkIQQ4oXRu8IMwNLSMt+ygwcPMnbsWFq1akXjxo1p3bo17777rsYv7sTERFxdXfn111+ZO3cubdq0wd3dnf79+5OYmKjRX15eHitWrCAgIEDd5u+//y4wnqSkJEaNGkWzZs1o2rQpAwYMYNeuXRptwsLCaNSoERcuXODNN9/E09OTVq1aMX/+fHJzc9m2bRvdu3enadOmvP766yQkJDzzOFhZWXHhwoUC29rY2HD06FHmzp2r3n9oaCgAo0ePxs/Pr8jH7vLly7i6ugKwbds2XF1d1cdLqVTy/fff89prr9GkSRN8fX35+OOPuXnz5jPjF6KoHB0dSUhI0PhSjaAJIURZUmovZRbmypUrnD59mhYtWqiXJSQkMHLkSBo3bsy4ceMwMzPjyJEjbN68mbNnzxIXF6fRx8yZM6lUqRJvvvkm9+/fZ+XKlbz55pvs3bsXGxsbAD755BMiIiIICAhg2LBhJCUlMWzYsHzx/Prrr4wbN46qVasyevRoypcvT0xMDOPHj2fatGkMHjxY3VapVDJkyBB8fX356KOP2LFjB6tXr+aff/7h+PHjDB06FAsLC1asWMGECROIj4/H1ta20GMRFBTEX3/9xbBhw2jWrBkdO3akZcuWuLm5YWJigpmZmbptQEAAN27cICIigpEjR9KsWbMiHztbW1s+++wzPvzwQzw8PBg4cCB169YFYNq0aWzcuJEePXowePBgUlNTCQ8P548//mDjxo3q41lcCoUChUJRrPaPfzcEhpjzk5RKpUHkb4jnWnI2DIaUc1FzLLWF2d27d0lPT1e/fvjwIWfPnuWLL74AYOLEiep1q1evxsbGhrVr12JhYQHAgAEDyM3NZevWrVy7do2qVauq21eoUIGIiAj1pPgqVaoQGhpKfHw8QUFB/PPPP0RGRtKvXz/mzJkDwKBBg/LdMapQKJgxYwaVKlUiOjpa/VylgQMHEhwczGeffUaXLl3Ud0Dm5eXRqVMnPvnkEwC6deuGj48P+/btY8OGDTRp0gR4NCI4bdo0kpKSNEa2ntSnTx8yMjJYtGgRR44cUV+2rFixIv7+/owfP159yadBgwZ4eHgQERFBq1at1JP/i3rsevXqxYcffoiDgwO9evUC4M8//2TDhg2EhoZqFK1du3alX79+fPPNN4SEhDz9RBfizJkzJdouOTm5RNvpM0PMWSU7O5ukpCRdh/HSGOK5lpwNgyHmXJhSW5iNHz8+3zIjIyOaNGnCmjVrNEbMvv76a+7cuaMuLAAyMzMxNzcHICsrS6Ofzp07azxGolGjRgDcuHEDeDQKplQqCQ4O1tjujTfeYOnSperXx48f58qVK0ycOFHjYZfm5uaMHDmSd999l99++40+ffqo17366qvqf1tbW2NnZ4eJiYm6KAPUxZQqnqcZMWIEffr0IT4+nt9//53ExEQyMjLYtGkT27dvZ+XKlTRv3rzQ7Yt77B63Y8cOAPz8/DSK6OrVq1O/fn327NlT4sLMxcWlwEvWhVEoFCQnJ9OkSROMjY1LtE99Y4g5P8nMzAwPDw9dh/HCGeK5lpwl57ImKyurSIMOpbYw++ijj2jQoAF5eXmcOHGClStXUrVqVf7v//5P4zEaAMbGxly5coUlS5Zw9uxZLl++TFpaGkqlEng0UvW4Jy8Pqoo0VbvLly8DUKtWLY121tbWVKlSRf1a1e7JeAD1pb7U1FSN5XZ2dhqvTUxM8i0rV65cgXEXplKlSvTr149+/fqRl5fH33//zcqVK4mPj2f69Ols3bq10G2Le+wed/HiReDRZdKCPPkMteIwNjYu0Q9pSbfTZ4aQc2pqKj4+PvmWOTo6lvncH2cI5/pJkrNhMISci5pfqS3MGjdurH5cRps2bWjbti3BwcEMGTKEiIgIatSooW67Zs0a5s2bh5OTE15eXnTs2BE3Nzd+//33Ah+poSp8CmNkZAQ8esK4lZWVxjpVwfL4vx9fpqIqaJ4sTgo6Mar9Fcc///xDdHQ0Xbt21RhtK1euHJ6enixZsoShQ4eqR9AK+/ia4h67x+Xl5WFubs7y5cuLHb8QRWViYqJxZ6bqcRnlypXDwcFBh5EJIYT2ldrC7EkNGzbk448/ZurUqbz77rusX78eY2NjHj58yFdffYWnpydr167VmPC+efPmEu1LdSnxwoULGk/Iv3fvnsbdhqri8Ny5c/n6UC2rVq1aiWJ4loyMDFauXIlSqdQozB5Xv359Dh48qL4s+aTnPXaOjo7s27ePevXqYW9vr7Fu9+7d8lmGQiue/Pk6f/48U6ZMYe7cufLkfyFEmaNXj8vo168f7du35++//2b16tXAo1Gt+/fvU6tWLY3CIi0tjZ07dwLFv9ujU6dOGBsb891332mMhoWHh2u8bty4MVWrVuWnn34iIyNDvTw7O5tVq1ZhampK27ZtS5LqM3l6euLk5MRPP/3E0aNH863/77//iI+Px9fXVz1/TDVSqMqhuMeuXLlyGpc2O3XqBMCyZcs09p2UlMS4ceP4/vvvtZGqEEIIYTD0ZsRMZdasWbz22muEhYUREBBArVq18PT0JC4uDmtra1xcXLh06RKRkZHcv38feDTSVRxOTk6MHj2a5cuXM3LkSDp16sTp06eJi4vTmCRvYmLCjBkzmDhxIn369CEoKIjy5csTGxvLiRMnCAkJyTd/TFuMjY1ZuHAhw4YNIzg4mFdffZVmzZphbm7OuXPniImJoVy5cuo7QOF/c+siIiK4c+cOPXr0KNaxs7W15fDhw0RERNC2bVvat29P586dWb9+PVeuXKFdu3b8999/rFu3Dmtra95+++0XkrswbPb29gQGBuYbpRVCiLJAr0bM4NGlwQ8++IAHDx4wdepUlEolixYt4tVXX2XLli3MnTuXXbt20bdvX3744QcADhw4UOz9TJ48mU8++YQrV64wf/58/v77b5YtW4a1tbVGu06dOrF27Vpq1arFN998w6JFi6hQoQLLli1j+PDhWsm5ME2aNGHbtm0MHjyYM2fOsHDhQmbNmsUvv/xCz549iYuLU1+WBfDx8aFr167s37+f2bNn8/Dhw2Idu/fffx+AOXPmcPDgQQC+/PJL3nvvPVJSUpg3bx6RkZG0atWK9evXF3hThBDPq3z58tSpU4fy5cvrOhQhhNA6I2VBM9eF0JGsrCxOnjxJw4YNi/24jKSkJDw8PMr8nT0qhpgzQHp6OuHh4QwaNOipD2AuSwzxXEvOknNZU9S/b3o3YiaEMGy3b9/mwIED3L59W9ehCCGE1klhJoQQQghRSkhhJoQQQghRSkhhJoQQQghRSkhhJoTQKxUqVKBhw4ZUqFBB16EIIYTWSWEmhNArdnZ2vPbaay/sGYFCCKFLUpgJIfRKTk4Ot27dIicnR9ehCCGE1klhJoTQK1euXGHlypVcuXJF16EIIYTWSWEmhBBCCFFKSGEmhBBCCFFKSGEmhBBCCFFKSGEmhBBCCFFKSGEmhNArTk5OvP/++zg5Oek6FCGE0DopzIQQQgghSgkpzIQQeuXatWuEh4dz7do1XYcihBBaJ4WZEEKvPHz4kCtXrvDw4UNdhyKEEFpnUpzGISEhbNq0SWOZqakpr7zyCk2aNGHYsGG0atWqxMEkJiYyZ84cLly4QOXKlfnll18oV04/a8c///yT1atXk5SUxJ07d6hUqRKenp688cYbtGjRIl/7S5cu6cWcmSfj9PPzo3LlykRGRuowKiGEEKJsKFZhphIaGoqNjQ3w6H+vV69eZfPmzQwbNoxp06YxaNCgYveZl5fH5MmTUSgUfPDBB1SqVElvi7JNmzYREhKCm5sbw4YNw8bGhmvXrhEdHc2gQYOYM2cO/fr1U7f/+uuvWb9+Pb/99psOo342fYlTCCGE0FclKsz8/f2pUaOGxrJRo0YxYsQIPv30Uzw9PWnUqFGx+rxx4wb//fcfwcHBDB06tCRhlQoPHjxg3rx5+Pj4sGrVKo3icsSIEfTp04d58+bRpUsXKlasCMCBAwdQKBS6CrnI9CVOIYQQQl9pbUjK0tKS+fPno1QqWbFiRbG3V30gsZWVlbZC0omzZ89y+/Zt2rRpk2/Ez9LSkv79+3P//n1OnjypowiFeHl8fHzw8fEp8faBgYHqPlRfAwYM4NixY7zzzjs6iUkIIV4krV4rrF27Np6enuzbt09jZOXatWuEhobSunVr3Nzc6N69O+Hh4er1YWFhdOrUCYBvv/0WV1dXoqOjAcjOziYsLIyAgADc3Nzo0KED8+fPJzMzU7395cuXcXV1ZcOGDSxdupSOHTvSpEkTevbsyfbt2/PFmZCQwLBhw2jRogXe3t6MGTOGU6dOabQ5d+4ckyZNomXLlri7u9OnTx+2bdv2zGOgKiy3bdtGRkZGvvVDhgzh+PHjtGzZEng0R+vgwYPcvHkTV1dXwsLC1Ms/+ugjZs6cSdOmTfH19eXff/8tcmxhYWG4urpy+fJlJkyYQPPmzWnWrBkTJkzg8uXLGm3v3bvH3Llzadu2LU2bNuWNN97g9OnTNGrUSCOeguJU2bFjBz179qRJkyZ07NiRZcuWyeiaeG5paWmkpqZqLCtXrhy3b9+WuzKFEGVSiS5lPo2LiwuHDx/m8uXL1KpVixs3bhAUFER2djbBwcHY2dmxf/9+Zs2axfnz55k6dSoBAQFUrFiRefPm0bFjR7p27UqzZs3Iy8vjrbfeIjExkb59++Lq6srZs2dZt24dhw4d4scff8TMzEy976+//hpjY2MGDx6MsbExq1ev5p133mHz5s24uLgAsH37diZPnoyTkxNvvvkmpqamrF27liFDhhAZGUmdOnU4e/YswcHBWFtbM3LkSCwsLIiPj2fy5Mlcv36dYcOGFZp/nTp1aNmyJQcPHqRjx474+fnh6+tLy5YtqVGjBiYmmod8ypQpLFiwgBs3bjBt2jRcXV3V63bu3EmNGjUIDQ0lJSUFZ2fnYsc2dOhQGjduzAcffMA///xDeHg4V69eZePGjcCjuX2jR4/mr7/+ol+/fri4uLB7926GDBlCXl5ekeI8c+YMU6ZMYfDgwQwYMIC4uDgWLVqEubk5I0eOLPZ7SIjHOTo6kpCQoLHMx8dH4/0phBBlhdYLs1deeQWAjIwMatWqxcKFC8nMzCQ2NlY9L23QoEHMnTuX77//nr59+9KgQQOsrKyYN28e9erVo1evXgDExMSwb98+lixZQkBAgHofvr6+jBs3joiICIYMGaJe/vDhQ7Zv366eu9WwYUOGDh3K1q1bcXFxIS8vjzlz5uDk5ER0dDQVKlQAHo0Gde3albVr1zJjxgxmz56NlZUVMTExWFtbA49GuiZNmsTChQvp2bMntra2hR6DRYsWERISwq+//sqWLVvYsmULAM7OzvTt25chQ4aoC0p/f3++//577ty5o85bJSsriyVLllCrVi31suLG1rZtW2bOnKl+nZmZyaZNm7hw4QK1a9cmLi6Ow4cPExoaqi7qBg0axLhx49i9e7d6u6fFef/+fcLDw9V3m/bs2ZP27duzY8eOEhdmCoWiWCNuqraGNEqnDzkrlUrS0tJKfLd2Wloajo6OBa67cuVKifpNS0vDwcGhVB+3J+nDudY2ydkwGFLORc1R64VZbm4uAEZGRuTl5REfH4+npyeWlpakp6er23Xu3Jnvv/+evXv30qBBgwL72r59O1ZWVjRv3lxjW09PT1555RX27NmjUZi1bdtWXZQB6hsQbty4AcCxY8e4ceMGw4YNUxdlALVq1WLjxo1Uq1aNW7ducfDgQYKCgsjNzc0X886dO9m/fz89evQo9BjY2tqyYsUKTp06RXx8PPv37yc5OZlz587x2WefER8fz+rVq7GwsHjqsaxevbpGUVaS2Lp166bRZ8OGDdm0aRM3b96kdu3axMfHY2lpycCBA9VtjIyMGDNmjEZh9qw4H38EiJWVFc7Ozly/fr1I2xfkzJkzJdouOTm5xPvUV6U55+zsbI3v2qRUKkvcb3Z2NklJSdoN6CUozef6RZGcDYMh5lwYrRdmqnlVNjY23Lp1i7t37/L7778XOtk2LS2t0L4uXbpEZmZmods+OffkyVEs1aiU6pKHqn3t2rXz9aUq4o4ePYpSqSQiIoKIiIhix/y4Bg0a0KBBAyZOnEhmZia7du0iLCyMv/76i/DwcEaNGvXU7e3s7DRep6SkFDu2J/tQHRNV5X7x4kUcHBw0LgkD1K1b99kJFrIPgPLly6tv6CgJFxcXLC0ti9xeoVCQnJxMkyZNMDY2LvF+9Yk+5GxmZoaDgwP79+8v0fa+vr6FrqtatSqJiYkl7tPDw6NEMemCPpxrbZOcJeeyJisrq0iDDlovzE6ePMkrr7xCjRo11CNVfn5+GiNbj7O3ty+0L4VCgaOjI3PmzClwvbm5ucbrZz33TFWgGRkZPXWfAP3796dLly4FtqlZs2ah28fGxnLy5ElCQkI0lltZWfH666/TvHlzAgICOHTo0DMLsyffpCWJ7Wm5wqO7YQsauXvy2BYnTm0wNjYuUb8l3U6fleacVe+/ksZnZGREampqvv+cpaamYm9vX6J+nzcmXSrN5/pFkZwNgyHkXNT8tFqYnT9/nuPHj9O7d2+MjIywtbXFwsKC7OxsWrdurdE2PT2dP//8U+NS3ZNq1KjBkSNH8PLywtTUVGPdtm3bChz5ehoHBwfg0UjckxYsWIC5uTlBQUHqZU/GnJKSwunTp596CfLgwYNs3LiRwMBA6tevn299zZo1sbCweOZlzII8PtemJLEVpFatWhw6dAiFQqHxprlw4UKx4xNC21Q/s49TKBRYWFhQvXp1HUQkhBAvltYel/Hw4UOmT5+OiYmJesK3iYkJ7du358CBA/nmcyxevJhJkybxzz//FNqnn58fWVlZrFmzRmP5tm3bmDx5snpSfVG5ublRpUoVoqOjefDggXr55cuX+f7777l+/Tr29vY0adKEuLg4UlJS1G2USiWzZ89m/Pjx3Lp1q9B99OzZE4A5c+aQlZWVb31cXBxZWVn4+/url5UrV65Id5g9b2wF6dy5M5mZmWzevFlj+Q8//JCvbVHjFEIlISEh3x2VxREVFaXu4/GvH3/8kZiYGJ3EJIQQL1KJRsx27dql/kim7OxsUlNT2bp1KykpKXzyyScaI0Xvv/8+iYmJDBs2jODgYGrXrs0ff/zBtm3b6NChA23bti10P/369WPz5s188cUXnD59mhYtWnDx4kXCw8NxdHQs9h1/pqamTJkyhXfffZd+/frRp08fFAoF4eHhVKhQgbfeeguAadOmMXToUPr27cugQYOoUqUKu3btYt++fQQHBxc4Eqbi7e3N2LFjWb58OV26dKF79+7UqVOH7OxsEhMTiY+Pp1u3bhqT8m1tbbl16xbfffcdXl5eNG3atND+nye2grz++utERkby8ccfc/ToUerVq8e+ffs4cOAAoHkptDhxCvGi5OXl8fDhQ/Ly8sr8pQ8hhOEpUWE2b968/3VgYoKdnR0eHh7Mmzcv3wd016xZkw0bNrB48WJiY2O5e/cuDg4OTJw4kVGjRj11XpiZmRmrV6/m66+/5ueff2b79u1UrlyZ7t27M3HixAInnT9Lt27dqFixIsuWLeOrr77C0tISLy8v3nvvPfWlkaZNmxIREUFYWBjr1q3j4cOHODk58fHHHxfpc0AnT55My5YtiYiIYOvWraSnp2Nubk79+vWZM2cOffr00Sh4Ro0axenTp/nqq6/o06fPUwue543tScbGxqxYsYIFCxbw888/k5WVRfPmzVmwYAHjx4/XuCmgOHEK8aJcvnyZsLAw5s6dS506dXQdjhBCaJWRUqlU6joIoTsZGRlYWlrmuyvz77//JigoiE8//ZS+ffu+tHiysrI4efIkDRs2LPZdmUlJSXh4eBjMKIoh5gyP5rJOmTLFoAozQzzXkrPkXNYU9e+bVj+SSeif8PBwPDw8uHjxosZy1Uc8ubu76yIsIYQQwiBp/XEZQr907dqV5cuXM3r0aIKCgrC2tubIkSPExMTQu3dv9UdZCSGEEOLFk8LMwDk7OxMeHs6yZctYtWoVmZmZODk58eGHHz71M0GFEEIIoX1SmAnc3d1Zvny5rsMQokgcHR0ZN25coZ+hKYQQ+kzmmAkh9IqxsTGWlpZlfqKwEMIwSWEmhNArN27cYNOmTeqPfBNCiLJECjMhhF65f/8+//77L/fv39d1KEIIoXVSmAkhhBBClBJSmAkhhBBClBJSmAkhhBBClBJSmAkh9EqlSpXo0KEDlSpV0nUoQgihdVKYCSH0irW1NS1atMDa2lrXoQghhNZJYSaE0CtZWVmcPn2arKwsXYcihBBaJ4WZEEKv3Lx5k7i4OG7evKnrUIQQQuukMBNCCCGEKCWkMBNCCCGEKCWkMBNCCCGEKCVMitM4JCSETZs2aSwzNTXFzs4OX19f3n77bapWrarVAEuqoFiftHbtWry9vYvcZ3R0NKGhoXz77be0a9cu32tdu3z5Mp06dSpwXYUKFXB0dKRHjx6MGDECE5NinXqNfVSvXl0+QFrojJmZGfb29piZmek6FCGE0LoS/XUODQ3FxsYGgOzsbM6fP09kZCR//vknmzZtwsrKSqtBPo/HY31S3bp1i9WXl5cXn332GQ0aNNBGaC9MixYtCAoK0lh27do14uLiWLBgAdevX2fq1KnF7jcqKopZs2Zx8OBBKcyEzlSrVo2hQ4dSrVo1XYcihBBaV6LCzN/fnxo1amgs8/T0ZMKECcTExDB48GCtBKcNBcVaUjVr1qRmzZpa6etFqlmzJr169cq3fNiwYfTu3Zv169czZswYqlSpUqx+//zzTx48eKCtMIV4psDAQNLS0khNTUWhUGBsbIyDgwPZ2dnUrl2b6OhoXYcohBBapbU5ZqpLgv/884+2uhRaZmZmRq9evcjNzeXo0aO6DkeIZ1IVZY6Ojjg5OeHo6IiRkRE3b94kJSVF1+EJIYTWaa0wS0tLA6BWrVoay69du0ZoaCitW7fGzc2N7t27Ex4ertEmOjoaV1dXkpOTCQ0Nxdvbm6ZNmzJ8+HBOnTqlrRCfateuXbzxxht4eXnh5uZGu3btmDZtGhkZGfni/O233wrsIzExEVdXV9avX6+x/N9//8XV1ZWwsDD1Mj8/Pz766CNmzpxJ06ZN8fX15d9//wXg3LlzTJo0iZYtW+Lu7k6fPn3Ytm2bVvK0tLTMt+y///7j008/JSAgADc3Nzw9Penfvz+7du1StxkyZIh6zp67uzshISHqdUePHmXUqFE0a9YMDw8PBg8eTEJCglbiFcLR0ZGEhASNL0dHR5RKpa5DE0IIrSvRpcw7d+6Qnp4OQG5uLhcuXGD+/Pk4OjoSGBiobnfjxg2CgoLIzs4mODgYOzs79u/fz6xZszh//ny+eU5vv/02NWvWZNKkSVy/fp1Vq1YxevRo9uzZU+LJ6o/H+jgrKyv15GHVJH5fX1/eeecdAPbv309kZCQ3btxg+fLlJdr3s+zcuZMaNWoQGhpKSkoKzs7OnD17luDgYKytrRk5ciQWFhbEx8czefJkrl+/zrBhw55rn3v27KFcuXLqeXIPHz5k0KBBpKenM3DgQBwdHbly5Qo//fQTEyZMYOPGjbi5uTF27Fjy8vI4dOgQc+fOxdnZGYCEhARGjx6Ns7MzEyZMACAuLo4RI0bw5Zdf0qVLlxLFqVAoUCgUxWr/+HdDYAg5K5VKjIyMCl1flnN/nCGc6ydJzobBkHIuao4lqnZ69+6db5mxsTHLli3T+Py6hQsXkpmZSWxsrHqe16BBg5g7dy7ff/89ffv21ZhIX7duXb799tv/BWdiwpIlS0hMTMTX17ckoRYYK8DSpUvx9/cHYOXKlTRs2JDvvvuOcuXKqePs378/+/bte+Yfh5LKyspiyZIlGqOMs2fPxsrKipiYGPWxHDJkCJMmTWLhwoX07NkTW1vbp/abnZ2tUYzm5eVx/fp1NmzYwL59++jfvz+Ojo4A7N69m/PnzxMWFkbnzp3V23h6ejJq1Cj27duHm5sbvr6+xMXFcejQIbp37465uTl5eXlMnz4dFxcXIiIiMDU1BWDw4MEMHjyYOXPm4OfnV6K7586cOVPsbQCSk5NLtJ0+K8s5Z2dnY25uXuC6nJwckpKSXm5AOlaWz3VhJGfDYIg5F6ZEhdnnn39O5cqVgUe/HK9du8bGjRsZO3Ys8+fP5/XXXycvL4/4+Hg8PT2xtLTUKBQ6d+7M999/z969ezUKs65du2rsp2HDhsCjkbeSejzWxz2+35iYGLKystRFGUB6ejpWVlbk5OSQk5PzQm7Nr169ukZRduvWLQ4ePEhQUBC5ubn5jtnOnTvZv38/PXr0eGq/W7duZevWrfmW29vbM2nSJN566y31sq5du+Lt7c0rr7yiXqZQKMjLywN46ucRnjx5kkuXLvH2229z9+5djXX+/v4sWLCAY8eO0axZs6fGWxAXF5cCL7sWRqFQkJycTJMmTQzmjlFDyPlpP3empqZ4eHi8vGB0yBDO9ZMkZ8m5rMnKyirSoEOJCrNmzZrlu9OxV69e9OjRg3nz5tGlSxfu3bvH3bt3+f333/Hx8SmwH9W8NBU7OzuN16pfyqoiQVuxPsnU1JTTp08TFxfHuXPnuHTpEtevX1evf1FzWZ7MNyUlBaVSSUREBBEREQVu8+QxK0ibNm0YOXIk8Gj+2Lp16zh16hRvv/02ffv2zdfe2NiY1atXc+TIEVJSUrh48SIPHz4Enn7sL168CMCiRYtYtGhRofGWpDAzNjYu0Q9pSbfTZ2U5ZyMjI1JTU/P9DklNTcXBwaHM5l2YsnyuCyM5GwZDyLmo+ZVs4lYBzM3N6dixI2vWrOHcuXPqUSo/Pz+GDBlS4Db29vYar1/E5cKi+PTTT1m7di0uLi54enrStWtX3N3d+eGHH9i8efNz919YcfPkSVJdf+7fv3+hc7OK8riOKlWq0Lp1a/XrLl26MGLECD7++GOys7MZOHCget2FCxcIDg7mwYMH+Pj44O/vj6urKw4ODvTr169IeY0bNw4vL68C29SrV++Z8QpRGAcHB4B8j8uoXLmyep0QQpQlWivM4H9/qMuVK4etrS0WFhZkZ2drFAnw6DLhn3/+me8OTl1ITU1l7dq1dO3alS+//FKjOPzvv/+K1Zeq0MrOztZYfvPmzSJtr5r3BeQ7ZikpKZw+fRoLC4tixQSPRgQXLlxIjx49mDt3Lk2bNqVx48YAfPPNN9y6dYvNmzfj4uKi3ubIkSNFjrd8+fL54j19+jRXrlwpUbxCqERFReVbduPGDVasWMGbb76pg4iEEOLF0trjMu7fv88vv/yCra0t9erVw8TEhPbt23PgwIF8E3QXL17MpEmTSsUzz27fvg2As7OzRlF2/PhxDh48CDy687QoVKOEJ0+e1Fi+ZcuWIm1vb29PkyZNiIuL03hGk1KpZPbs2YwfP55bt24Vqa8nValShRkzZpCTk8OUKVPUOWVkZGBmZoaTk5O6bV5eHmvXrgU07yJRzcFTFeBubm7Y29uzbt069XGER4XpRx99xKRJk4p87IQoqszMTI4dO0ZmZqauQxFCCK0r0YjZrl271B9zpFQq+e+//4iKiiI1NZVPP/1U/WiL999/n8TERIYNG0ZwcDC1a9fmjz/+YNu2bXTo0IG2bdsWe9/79+/n5s2bBAQEFGtyeGHq1auHo6Mjq1atQqFQUKNGDc6cOcPGjRvVhci9e/eoUKHCM/uqXbs2TZo0ISYmBisrK1xcXNi3bx+nTp3SuLHgaaZNm8bQoUPp27cvgwYNokqVKuzatYt9+/YRHBxM/fr1S5xr165d2bp1K/Hx8Xz77be89dZbdOjQgd27dzNy5Ei6d+/Ow4cP2bZtGydOnKBcuXLcu3dPvb3qbtClS5fi6+uLj48P06dP5+2336Z3794EBQVRsWJFYmJiOHnyJO+//36hH4clhBBCiPxKVJjNmzdP/e9y5cphbW1Nw4YNeffdd9WPoIBH86E2bNjA4sWLiY2N5e7duzg4ODBx4kRGjRpV5GLlccuXL+fgwYP88ssvWinMzMzM+Pbbb5k/fz7r169HoVDg4ODA2LFjqVu3LuPHj+fAgQO8/vrrRepv8eLFzJ8/n+joaIyMjGjTpg0//PADHTt2LNL2TZs2JSIigrCwMNatW8fDhw9xcnLi448/ZtCgQc+R6SMzZszg4MGDLFu2jM6dOxMUFMTdu3eJiIhg7ty52Nra0qhRIyIjI5k2bZrGg2KDg4P5448/WLNmDSdPnsTHx4eAgADWrFnD119/zYoVK1AqlTg7O/PZZ58V+LFQQgghhCickVIeny1KkaysLE6ePEnDhg2L/biMpKQkPDw8yvydPSqGmDPA+fPnmTJlCnPnzqVOnTq6DuelMMRzLTlLzmVNUf++aW2OmRBCvAzW1ta0bNlS42HWQghRVkhhJoTQK5UqVaJdu3ZUqlRJ16EIIYTWSWEmhNArDx484NKlSzx48EDXoQghhNZJYSaE0CvXr18nMjJS49M5hBCirJDCTAghhBCilJDCTAghhBCilJDCTAghhBCilJDCTAihV0xMTLCyslJ/wogQQpQlUpgJIfSK6pM5HBwcdB2KEEJonRRmQgghhBClhBRmQgi9kpaWxvLly0lLS9N1KEIIoXVSmAkh9Epubi6ZmZnk5ubqOhQhhNA6KcyEEEIIIUoJKcyEEEIIIUoJKcyEEEIIIUoJKcyEEHrF3t6eoKAg7O3tdR2KEEJonRRmQgi9Ur58eZycnChfvryuQxFCCK0rdYVZSEgIrq6uGl9ubm60b9+eKVOmcO3aNV2HmM++fftwdXXFy8uLhw8f6jocIcq0jIwMfvvtNzIyMnQdihBCaF2p/UyT0NBQbGxsAMjOzub8+fNERkby559/smnTJqysrHQc4f/ExsZiaWnJnTt32LFjBz179tR1SEKUWXfu3OHgwYO8/vrr2NnZ6TocIYTQqlJbmPn7+1OjRg2NZZ6enkyYMIGYmBgGDx6so8g0ZWVlsWvXLnr37s2WLVuIioqSwqwU8fHxASAhIeGF7yswMLDQh546ODgQFRX1wmOAl5uzEEII7Sp1lzKfxtvbG4B//vlHx5H8T3x8PFlZWXh7e9O2bVsSExNJSUnRdVhCB9LS0khNTc23PDU1VZ5SL4QQokj0qjBT/XGrVauWxvJr164RGhpK69atcXNzo3v37oSHh2u0iY6OxtXVleTkZEJDQ/H29qZp06YMHz6cU6dOlTimzZs3Y2xsjJeXFwEBASiVSqKjowtse+nSJd5//31at26Np6cnffv2JT4+XqNNVlYWn3/+OZ06dcLd3Z1XX32VFStWqJ9ynpiYiKurK+vXr9fY7t9//8XV1ZWwsDD1Mj8/Pz766CNmzpxJ06ZN8fX15d9//wVg165dvPHGG3h5eeHm5ka7du2YNm1avnk7T4snLy+P9u3b06NHj3y5XrhwAVdXV5YvX17sY6rPHB0dSUhI0PhydHTUdVhCCCH0RKm9lHnnzh3S09OBRx/BcuHCBebPn4+joyOBgYHqdjdu3CAoKIjs7GyCg4Oxs7Nj//79zJo1i/PnzzN16lSNft9++21q1qzJpEmTuH79OqtWrWL06NHs2bMHE5PiHY4bN26QkJBA8+bNsbW1pV27dpQvX56YmBgmTpxIuXL/q3svXbpEYGAgeXl5DBo0iOrVqxMXF8eECRP48ssv6datGzk5OQwePJgTJ07Qp08f3N3dSUpKYsGCBaSlpfHJJ58U+zju3LmTGjVqEBoaSkpKCs7OzkRHRxMaGoqvry/vvPMOAPv37ycyMpIbN26oi6mixPPaa6+xcuVK/vnnH+rVq6fe75YtWzAyMiqwaCsKhUKBQqEoVvvHv6solUrS0tJo1apVieIojrS0tEKLsNTU1BcSQ3Z2NmZmZvnicHBwKNbx0ycWFha4ublhYWFRZnN8UmHv77JMcjYMhpRzUXMstYVZ79698y0zNjZm2bJlWFtbq5ctXLiQzMxMYmNj1XPS/l97dx7V1JXHAfwLCCjiUsQNCnVrkCIYoLUuIJsoOipaGSMCbgO2bh0dnaqdtuPRcRl1aqVaq0gVmGpVsC5xF61HBFw4dapAwRZbFBEQBQUixOTOH5y8GhLwJSHhib/POZ7a++7Nu793n8kv9933EhERgTVr1iAhIQFhYWHo378/V79v376Ii4vj/r9NmzbYsmULLl++jGHDhunUR6lUCoVCgZCQEACAjY0Nhg8fjtOnTyM9PR0+Pj5c3U2bNkEmk+HgwYMQiUQA6tckjRs3Dlu3bsWYMWOQnJyM7OxsrFq1CpMnTwYATJkyBYwx7N+/H/PmzdOpf0D9jNeWLVvUZhnj4+Ph6uqKnTt3csljREQEJBIJ0tLSwBiDmZkZr/6EhoYiPj4eUqmUS/IA4NixY/D29tZ7tig/P1+vdjdu3FD7/7q6OrX/tiRj9UHb69bV1eH69etG2Z8QhISEoKioSOul49as4fn9KqCYXw2vYsyNEWxitmHDBtjb2wOon7kpKSlBcnIyPvjgA6xbtw4TJkyAUqnEmTNn4OnpCRsbG26GDQBGjhyJhIQE/PDDD2qJ2ejRo9X24+rqCqB+9ktXR44cgbm5OYKDg7mykJAQnD59GsnJyVxiplQq8cMPP2Do0KFcUgYAVlZW2L59OywsLAAA58+fh62tLd577z21/fz9739HTEwMd5eqLnr27Klx6ffQoUOoqalRm9F7+PAhbG1tIZfLIZfLYWVlxas/Xbt2hYuLC06cOMElZjk5OSgoKMD06dN17q+KSCSCjY0N7/oKhQI3btyAu7s7dzyB+mPs4OCAS5cu6d0XvppK7I3Rh8ZiVvVDLBY36/6EQiaT4dKlSxg2bBjatWvX0t0xicbGujWjmCnm1qampobXpINgEzMvLy+NuzJDQ0Mxbtw4rF27FiEhIaiursaTJ09w8eJF7k60hhouum54e73qMpBSqdSpf7du3UJOTg5cXV1RV1eHu3fvAgDefPNNtGnTBqmpqaioqEDnzp1RUVGBmpoa9OrVS+N1ni8rKiqCk5OTxiVVe3t7LknVlbbHCVhaWiIvLw9Hjx5FQUEBCgsLUVpaym1njOnUn9DQUKxfvx43b97EgAEDcPToUVhaWmokwbqwsLDQ6x9pw3ZmZmZcubGZmZmhqKhI41wsKiqCo6Oj0frQkjG3hLKyMuzevRsikQi9e/du6e6YlL7/Ll5mFPOr4VWImW98gk3MtLG2tkZAQAB2796NgoICLjkIDAxEVFSU1jYNf7ZF9aFlqMOHDwMAcnNzERQUpLXO0aNHERUVxV1XftG+FQqFxnohvhpLLLWdCKtXr0ZiYiJEIhE8PT0xevRoeHh4ICkpCUeOHNG5P2PHjsXGjRtx/PhxuLm54cSJExg+fDg6deqkVywvKwcHB63ljo6OjW4jhBBCnvdSJWbAHwmIubk57Ozs0K5dO9TV1WHo0KFq9R4+fIirV69qXMZrDowxSKVSWFpaYv369RrJy+3bt7Fx40akpKQgKiqK6+fvv/+u8VqHDx/G5cuX8Y9//AOOjo746aefoFQq1S4z5ubmYufOnYiOjuYSrYbrih48eMCr70VFRUhMTMTo0aOxadMmtWSxvLxcrS6f/ri6uqJ79+549913cfbsWYwZMwbFxcVYtmwZr/4Ymymf5WWq55S9CD2/jBBCXl4v1eMyZDIZUlNTYWdnh379+qFNmzbw8/NDenq6xkLn2NhYfPjhh0Z55tnly5dRXFyMgIAAjBkzBiNGjFD7Ex0dDWdnZ+Tm5iI7OxsWFhbw9fVFenq6WnIml8uxc+dOZGVloX379vD398fjx49x9OhRtf3t3bsXx44dg52dHTdLmJubq1ZHKpXy6ntlZSUAoE+fPmpJWXZ2Nq5cuQIA3KM5+PRHJTQ0FL///jt27dqFDh06ICAggFd/CCGEEPIHwc6YnT17llvszhhDeXk5UlJSUFRUhNWrV3PrnpYsWYLLly9jxowZCA8PR69evZCZmYnjx4/D398fvr6+Ou/70qVLePDgAYKDg7UuQFdd7gsLC9Pa3szMDFOmTMH69euRkpICNzc3LF68GJmZmZg8eTIiIyNhZ2cHqVSKW7duYfv27QAAiUSC77//HsuXL8f169fh4uKCrKwsHDlyBDExMejevTsAwN3dHYcOHYKtrS1EIhHS0tLw888/q81qNaZfv35wdHTEN998A4VCgddffx35+flITk7m2ldXV6N9+/a8+wMAwcHBWLFiBaRSKSZNmgRra2vdDjohPJmZmcHCwqLZliUQQoiQCDYxW7t2Lfd3c3NzdOzYEa6urvjb3/6GESNGcNucnJxw4MABxMbG4vDhw3jy5AkcHBywYMECREdH80pWGvr6669x5coVpKamaiRmtbW1OHXqFHr27Nlk0jdp0iTExsZCKpVi6dKl6NWrF/bt24cvvvgCiYmJUCgU6N+/P3bt2sUtFreyskJCQgJiY2Nx6tQppKSkwNnZGZ999hnCw8O5146NjcW6detw8OBBmJmZwcfHB0lJSbxmqaysrBAXF4d169Zh7969UCgUcHBwwAcffIC+ffti3rx5SE9Px4QJE3j3BwBsbW0xYsQISKVSvZ9dRggfTk5OWLRoEZycnFq6K4QQ0uzMmOoWPEIMtGTJEly7dg3nzp3TKyEG6m8nzs3Nhaurq86Py7h+/TrEYnGrv7NH5VWMGXg146aYKebW6lWKme/n20u1xowIV1lZGVJTU/Hee+/pnZQRwsf9+/eRmJiI+/fvt3RXCCGk2Qn2UiZ5OWRkZGD//v3IysqCubk5pk6d2tJdIq1cXV0dSktLBfFrDoQQ0txoaoMYxNraGmlpabCyssLmzZv1fhAuIYQQQmjGjBjIy8sLV69ebeluEEIIIa0CzZgRQgghhAgEJWaEkJeKvb09xo0bR5fNCSGtEiVmhJCXio2NDVxcXHR6nAohhLwsaI0ZERTVb6HKZDKd2ql+KL6mpqbVPwtH5VWMGQAeP36MnJwcODo6omPHji3dHZN4FceaYqaYWxvV55rqc64x9IBZIijl5eX47bffWrobhBBCiFH06tULXbp0aXQ7JWZEUJ49e4bKykpYW1vTg2oJIYS0GkqlErW1tejUqRP3e9/aUGJGCCGEECIQNCVBCCGEECIQlJgRQgghhAgEJWaEEEIIIQJBiRkhhBBCiEBQYkYIIYQQIhCUmBFCCCGECAQlZoQQQgghAkGJGSGEEEKIQFBiRgghhBAiEJSYEUIIIYQIBCVmRPCysrIQGRkJT09PDBs2DKtXr0ZNTQ2vtlOmTIGLi4vGn9DQULV6jx49wmeffQYfHx94enpixowZyMnJMUY4vBgS808//YSYmBi8/fbbcHd3x4QJE3Do0CGNep9//rnWY+Pi4oLHjx83c0Sa7t27h0WLFmHw4MHw9vbGvHnzcOfOnRe2e/r0KTZu3IiAgAAMHDgQEokEGRkZGvUUCgXi4uIwcuRIeHh4YPz48Th+/LgxQuFN35jLysqwfPly+Pj4YMCAAQgKCsKmTZtQV1enVi8tLa3RMT179qyxwmqSvjHzPT9byzgHBgY2Gq+LiwuWLVvG1RXiOKvs2LEDw4YN411fl/FLTk7G2LFjMXDgQIwaNQrffvttc3VbUBr/FU1CBOB///sfZs6cid69e2PhwoUoKSlBYmIiCgoKEB8f/8L2+fn58Pf3x5gxY9TKO3fuzP29rq4O77//PvLy8jBjxgzY29sjKSkJkZGRSElJQe/evZs7rCYZEvOvv/6KqKgodOrUCdHR0Wjfvj2OHz+OpUuX4tGjR5g5cyZXNz8/H05OTliwYIHG67Rr167Z43peRUUFpk2bhqqqKkyfPh1WVlb45ptvEBERgUOHDsHOzq7RtosXL8b58+cxdepU9OnTB8nJyYiOjkZCQgLefvttrt6///1vJCQkYOLEiRCLxTh58iQWLVoEpVKJsWPHGjU+bfSN+enTp5g+fTru3r2LqVOn4o033sC1a9fw9ddfIz8/H9u2bePq5ufnAwBWr14NS0tLtdcZMGCA8YJrhCHjzPf8bC3j/PHHH6O6ulqjPCkpCTdu3EBgYCBXJrRxVrlw4QJiY2PRqVMn3m34jl9CQgLWrFmDwMBAREREIDMzEytXrkRVVRXef/99Y4TTchghAhYeHs6GDx/Onjx5wpXt2bOHiUQidu7cuSbb3r17l4lEIrZnz54m6+3fv5+JRCJ2+vRprqy0tJR5e3uzBQsWGBaAHgyJOSYmhonFYnb//n2uTKFQMIlEwsRiMauqquLKAwIC2MKFC5s/AB42bdrEXFxc2I0bN7iyvLw85urqytatW9dou/T0dCYSidiuXbu4surqahYUFMQmTpzIld2+fZv179+frVq1iit79uwZk0gkbNiwYay2trZ5A+JB35jj4uKYSCRiqampauUbNmxgIpGIZWRkcGXLli1jQ4cObf7O60nfmBnjd362pnHW5sqVK6x///5sxYoVauVCG2elUsmSkpKYm5sbE4lEvPvGd/wqKyuZWCxmc+bMYUqlkqu7cOFC5uHhwcrLy5s3oBZGlzKJYBUXFyMrKwuhoaGwtbXlysPCwmBjYwOpVNpke9W3yr59+zZZTyqVolu3bggODubKunbtitGjR+PcuXNav8UaiyExKxQKXL16Fb6+vujevTtXbm5ujtGjR6Ompga5ubkAgKqqKty7d++Fx8ZYpFIpxGKx2rd7kUiEwYMHNxnj0aNHYWlpicmTJ3NlNjY2CAsLQ3Z2Nn777TcAwLFjx6BUKhEREcHVs7CwQEREBMrKynD16tXmD+oF9I05MzMTr732mtqMCQBuNiErK4sry8vLQ58+fZq55/rTN2a+52drGueGnj17hk8//RRdunTB4sWL1bYJbZwlEglWrVqFd999F25ubrzb8R2/c+fOoaamBlOnToWZmRlXNyoqCk+fPm3xy7fNjRIzIlg3b94EoDk1b2lpCZFIxG1vzK1btwAA/fr1A4BGE6zs7GytbyZubm6Qy+VcgmcKhsRsbm6OI0eO4KOPPtLY9vDhQwD1b3oA8Msvv4Axxn3wyWQyKJXKZonhRSorK3Hnzh2tl1zc3NxQWlqK0tJSrW1v3ryJ3r17w8bGRqOdarvqv7a2thqXoRvWMxVDYl63bh2SkpI0ylVj2qZN/YoUpVKJgoIC7nyvq6uDXC5vrhB0ZkjMfM/P1jTODR04cAC3b9/GX//6V7UvaUIbZ6B+Td3KlSuxc+dOtG/fnnc7vuPX2PtiS42zsVFiRgSrpKQEANCjRw+Nbd26dUNxcXGT7fPy8mBtbY3NmzfD29sbXl5e8PX1RWJiIlenuroaT548aXQfAF64n+ZkSMxmZmZwcnLC66+/rlZeU1ODlJQU2NjY4K233gLwx2zixYsX4e/vD7FYDG9vb6xYsQIymay5wtFKFePzs3oqLzrmJSUlTY7VvXv3uHpNvb6qnqkYErO9vT3efPNNjXLVeezt7Q0AKCwshEwmQ3FxMSZOnIiBAwdCLBZj9uzZvBbbNzdDYuZ7framcX6eQqHA9u3b4eTkhEmTJqltE9o4A/UzWhKJRG02iw++41daWoq2bduqrQ0GAGtra3Tu3Nnk42xstPifmFxZWVmT262trdGxY0duhqtt27Za69TW1kKpVMLcXPv3i1u3bqG2thYlJSVYs2YNZDIZDhw4gNWrV6OiogIffvghtw9ti91V++V7N2RTTBVzQ4wxfPLJJygrK8O8efNgbW0N4I8Pvhs3bmD+/PmwtbXFhQsXsHfvXvz6669ISEjgvQ9dGXLMq6urm2yn+tCurq7W+s29YT1Tae7zbO/evTh//jzeeecd7oYH1Qzxjz/+iNmzZ2P+/PnIzs5GfHw8wsPDcfDgQe4DzxQMiZnv+dlax/ncuXMoLi7GJ598ovHvUGjjDABWVlZ6teM7ftXV1VrfE4H690VTj7OxUWJGTM7Hx6fJ7UFBQfjqq6/AGAOARr+FvejbmUQigUKhwLRp07iy8ePHIzw8HDt27EB4eDi3j6bo+i1QG1PF/DzGGFasWIFjx45h0KBBmDNnDrfN19cXHTp0QExMDHdZMCQkBK+99hri4+Nx5swZjBo1ive+dPGiGF+0rSnPtzPG6+urOWM+fPgwVq5cia5du2L9+vVcubOzM+bOnYuxY8dylwCDgoIwcOBAzJ49G9u3b8enn35qQBS6MSRmXc7P1jjO+/btQ/v27TVmywDhjbOh+BwrxpigxtnYKDEjJvevf/2rye2Ojo4AwL0ha/s2VFtbi3bt2jU5q/P8glIVc3NzSCQSLF++HNeuXYOvry+A+kcSNKQqe359h75MFbOKXC7HsmXLIJVK4eHhgW3btqndVu/n5wc/Pz+NdlOnTkV8fDwyMzONlpg1FeOLjrmNjQ2vseJbz1QMifl5SUlJWLNmDTp37oz4+Hg4ODhw21TPsWrIz88Pjo6OyMzM1Lf7ejEkZr7nZ2sc5+rqamRmZmLkyJEaaykB4Y2zIQz99wzUvy+aepyNjRIzYnJ//vOfedVTfehouwxYWlqqdW0CH126dAFQf0nB1tYWHTt2bHQfgPb1IroyZcwymQwLFizAxYsXMWjQIGzbto33G9fzx8ZYVEmoPsfcwcGBVzsHBwetd+Q155jqwpCYVWJjY7F161Z0794du3bt0umOWjs7O5SXl+vQY8M1R8wNNTw/W+M4Z2RkQC6X6/XFqCXG2RB8x8/BwQEymQxVVVVq72W1tbWoqKgw+aVbY6PF/0SwVHfcZGdnq5XL5XLk5eXB3d290bb37t3Dn/70J2zevFljW0FBAQDAycmJ20/Dfaj226ZNG7i6uuodg64MiVlVb/78+bh48SICAgKwc+dOrUnZjBkzMGvWLI3yhsfGGDp06ABnZ+dGj3mPHj3QtWtXrW3d3Nzwyy+/aHx7Vr2W6vi4ublxd8g1Vc9UDIkZALZs2YKtW7fijTfewJ49e7QmZZ9//jkCAwM1frXh2bNnKCws1LgpxNgMiZnv+dnaxhn44/EngwcP1rpdaONsCL7j19jdly01zsZGiRkRrJ49e0IsFuPgwYOoqqriypOTkyGTyZp8qnfPnj1RWVmJAwcOoLKykiuvrKzE7t274ejoCC8vLwD1a1fu3bun9iycsrIynDhxAsHBwdyCeVMwJGagflYlLS0NgYGB+PLLLxvte+fOnZGeno4ff/yRK1MqldiyZQssLCw0fimhuYWEhCArK0vtAyw/Px+ZmZlNxhgSEoK6ujp89913XFlNTQ2Sk5Ph4eEBZ2dnAMCoUaNgZmamdgeuQqHAt99+i+7du6v9QoCp6BvzxYsX8eWXX8LJyQn//e9/G/3g7dGjB4qKitSODVD/xPTKykqMHz++eQLRgb4x8z0/W9M4q+Tk5MDJyanRp+cLcZz1xXf8/P390a5dO43HxiQlJaFt27YYMWKESfttbGaMz+pnQlrItWvXMH36dPTr1w9TpkzB3bt3kZCQgKFDh2L79u3cos+ff/4ZeXl58PLy4r5NnzlzBvPnz0evXr0QHh6Ouro67Nu3DyUlJYiLi8OQIUMA1M8yTZo0CYWFhZg1axbs7OyQmJiIR48eYd++fSZ/kKO+MZeWliIwMBCMMXz88cdaZ8qGDBmCbt264e7du5g4cSIYY4iKioKdnR1OnTqFq1evYuHChWo3ChhDRUUFxo0bB7lcjr/85S8wNzfHrl27YGlpiZSUFNjZ2eHBgwe4dOkSnJ2d4enpybWNjo5GRkYGIiMj0bt3b+zfvx/5+fnYvXu32gfxP//5T3z33XeYNGkSxGIxjh8/joyMDGzatMnoiWdzxjxu3Djk5+dj2rRpWp+PJRKJ4OrqCrlcjvDwcGRnZyMsLAxvvfUWrl+/jkOHDsHHxwdxcXFGu9O2uWPW5fxsLeOs4u/vD2dnZ7Vk5XlCHOfnRUVFoaCgAJcuXVIrr6mpwZkzZ2Bvb6/2W5p8x2/Hjh34z3/+gxEjRsDf3x9paWk4efIklixZgpiYGJPFZxIm/60BQnSUnp7OwsLC2IABA9jw4cPZ2rVrWXV1tVqd2NhYJhKJWEpKilp5amoqk0gkzN3dnXl6erJZs2ax69eva+zjwYMH7KOPPmLvvPMO8/LyYjNnzmQ5OTlGjasp+sR84sQJJhKJmvxz4cIFrn1+fj6bO3cu8/b2Zu7u7mzixIns+++/N1mMhYWFbM6cOUwsFrNBgwax+fPns8LCQm57ZmYmE4lEbOnSpWrtqqqq2KpVq9iQIUOYWCxmEomEZWZmary+XC5nsbGxzM/Pj3l4eLDQ0FB28uRJo8fVFF1jLi8vf+GYbtiwgWtfUVHBVqxYwXx8fJibmxsLCgpimzdvZk+fPjV5rCr6jjPf87M1jPPzPDw82Ny5c5t8fSGOs0pkZKTWn2S6c+cOE4lELDIyUq1cl/FLTExkwcHBbMCAASwkJOSFP7f3sqIZM0IIIYQQgaA1ZoQQQgghAkGJGSGEEEKIQFBiRgghhBAiEJSYEUIIIYQIBCVmhBBCCCECQYkZIYQQQohAUGJGCCGEECIQlJgRQgghhAgEJWaEEEIIIQJBiRkhhBBCiEBQYkYIIYQQIhCUmBFCCCGECMT/AVb5ckfQ3XENAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -327,27 +340,27 @@ " \n", " \n", " duration col\n", - " 'predict_time'\n", + " 'adv_fit_time'\n", " \n", " \n", " event col\n", - " 'ben_failures'\n", + " 'adv_failures'\n", " \n", " \n", " number of observations\n", - " 568\n", + " 1500\n", " \n", " \n", " number of events observed\n", - " 568\n", + " 1500\n", " \n", " \n", " log-likelihood\n", - " -723.10\n", + " -5531.26\n", " \n", " \n", " time fit was run\n", - " 2023-09-25 13:54:12 UTC\n", + " 2023-09-29 11:13:25 UTC\n", " \n", " \n", "\n", @@ -371,107 +384,65 @@ " \n", " \n", " \n", - " lambda_\n", - " accuracy\n", - " -0.35\n", - " 0.70\n", - " 0.06\n", - " -0.48\n", - " -0.23\n", - " 0.62\n", - " 0.80\n", - " 0.00\n", - " -5.49\n", - " <0.005\n", - " 24.58\n", - " \n", - " \n", + " lambda_\n", " adv_failure_rate\n", " -0.00\n", " 1.00\n", " 0.00\n", " -0.00\n", - " 0.00\n", - " 1.00\n", - " 1.00\n", - " 0.00\n", - " -1.55\n", - " 0.12\n", - " 3.04\n", - " \n", - " \n", - " adv_fit_time\n", - " -0.00\n", - " 1.00\n", - " 0.00\n", - " -0.00\n", " -0.00\n", " 1.00\n", " 1.00\n", " 0.00\n", - " -4.43\n", + " -39.62\n", " <0.005\n", - " 16.67\n", + " inf\n", " \n", " \n", " atk_value\n", " 0.15\n", " 1.16\n", - " 0.04\n", - " 0.08\n", - " 0.22\n", - " 1.08\n", - " 1.25\n", + " 0.16\n", + " -0.16\n", + " 0.46\n", + " 0.85\n", + " 1.58\n", " 0.00\n", - " 4.18\n", - " <0.005\n", - " 15.05\n", + " 0.95\n", + " 0.34\n", + " 1.56\n", " \n", " \n", " data.sample.random_state\n", - " -0.00\n", - " 1.00\n", - " 0.00\n", + " 0.03\n", + " 1.03\n", + " 0.02\n", " -0.01\n", - " 0.01\n", + " 0.07\n", " 0.99\n", - " 1.01\n", + " 1.07\n", " 0.00\n", - " -0.83\n", - " 0.41\n", - " 1.29\n", + " 1.31\n", + " 0.19\n", + " 2.39\n", " \n", " \n", " def_value\n", - " -0.01\n", - " 0.99\n", - " 0.04\n", - " -0.08\n", - " 0.07\n", - " 0.92\n", - " 1.07\n", - " 0.00\n", " -0.21\n", - " 0.84\n", - " 0.26\n", - " \n", - " \n", - " failure_rate\n", - " -0.01\n", - " 0.99\n", - " 0.00\n", - " -0.01\n", - " -0.01\n", - " 0.99\n", - " 0.99\n", + " 0.81\n", + " 0.16\n", + " -0.53\n", + " 0.10\n", + " 0.59\n", + " 1.11\n", " 0.00\n", - " -10.63\n", - " <0.005\n", - " 85.23\n", + " -1.31\n", + " 0.19\n", + " 2.41\n", " \n", " \n", " model.art.pipeline.initialize.kwargs.optimizer.lr\n", - " 0.00\n", + " -0.00\n", " 1.00\n", " 0.00\n", " -0.00\n", @@ -479,9 +450,9 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " 0.58\n", - " 0.56\n", - " 0.84\n", + " -0.53\n", + " 0.60\n", + " 0.74\n", " \n", " \n", " model_layers\n", @@ -493,9 +464,23 @@ " 1.01\n", " 1.01\n", " 0.00\n", - " 20.39\n", + " 7.04\n", + " <0.005\n", + " 38.88\n", + " \n", + " \n", + " predict_time\n", + " -0.15\n", + " 0.86\n", + " 0.01\n", + " -0.17\n", + " -0.12\n", + " 0.84\n", + " 0.88\n", + " 0.00\n", + " -12.17\n", " <0.005\n", - " 304.56\n", + " 110.86\n", " \n", " \n", " train_time\n", @@ -507,38 +492,38 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " 14.89\n", + " 10.81\n", " <0.005\n", - " 164.17\n", + " 88.14\n", " \n", " \n", " Intercept\n", - " 0.43\n", - " 1.53\n", - " 0.08\n", - " 0.27\n", - " 0.59\n", - " 1.31\n", - " 1.80\n", + " 3.00\n", + " 20.18\n", + " 0.18\n", + " 2.65\n", + " 3.36\n", + " 14.14\n", + " 28.79\n", " 0.00\n", - " 5.30\n", + " 16.56\n", " <0.005\n", - " 23.02\n", + " 202.20\n", " \n", " \n", " rho_\n", " Intercept\n", - " 1.10\n", - " 2.99\n", - " 0.03\n", - " 1.03\n", - " 1.16\n", - " 2.81\n", - " 3.18\n", + " -0.84\n", + " 0.43\n", + " 0.02\n", + " -0.88\n", + " -0.80\n", + " 0.41\n", + " 0.45\n", " 0.00\n", - " 34.56\n", + " -43.75\n", " <0.005\n", - " 867.25\n", + " inf\n", " \n", " \n", "
\n", @@ -559,19 +544,19 @@ " \n", " \n", " Concordance\n", - " 0.87\n", + " 0.84\n", " \n", " \n", " AIC\n", - " 1470.20\n", + " 11082.52\n", " \n", " \n", " log-likelihood ratio test\n", - " 1003.22 on 10 df\n", + " 800.84 on 8 df\n", " \n", " \n", " -log2(p) of ll-ratio test\n", - " 692.36\n", + " 554.32\n", " \n", " \n", "\n", @@ -581,64 +566,58 @@ "\\begin{tabular}{llrrrrrrrrrrr}\n", " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", "param & covariate & & & & & & & & & & & \\\\\n", - "\\multirow[c]{11}{*}{lambda_} & accuracy & -0.35 & 0.70 & 0.06 & -0.48 & -0.23 & 0.62 & 0.80 & 0.00 & -5.49 & 0.00 & 24.58 \\\\\n", - " & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -1.55 & 0.12 & 3.04 \\\\\n", - " & adv_fit_time & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -4.43 & 0.00 & 16.67 \\\\\n", - " & atk_value & 0.15 & 1.16 & 0.04 & 0.08 & 0.22 & 1.08 & 1.25 & 0.00 & 4.18 & 0.00 & 15.05 \\\\\n", - " & data.sample.random_state & -0.00 & 1.00 & 0.00 & -0.01 & 0.01 & 0.99 & 1.01 & 0.00 & -0.83 & 0.41 & 1.29 \\\\\n", - " & def_value & -0.01 & 0.99 & 0.04 & -0.08 & 0.07 & 0.92 & 1.07 & 0.00 & -0.21 & 0.84 & 0.26 \\\\\n", - " & failure_rate & -0.01 & 0.99 & 0.00 & -0.01 & -0.01 & 0.99 & 0.99 & 0.00 & -10.63 & 0.00 & 85.23 \\\\\n", - " & model.art.pipeline.initialize.kwargs.optimizer.lr & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 0.58 & 0.56 & 0.84 \\\\\n", - " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 20.39 & 0.00 & 304.56 \\\\\n", - " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 14.89 & 0.00 & 164.17 \\\\\n", - " & Intercept & 0.43 & 1.53 & 0.08 & 0.27 & 0.59 & 1.31 & 1.80 & 0.00 & 5.30 & 0.00 & 23.02 \\\\\n", - "rho_ & Intercept & 1.10 & 2.99 & 0.03 & 1.03 & 1.16 & 2.81 & 3.18 & 0.00 & 34.56 & 0.00 & 867.25 \\\\\n", + "\\multirow[c]{9}{*}{lambda_} & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -39.62 & 0.00 & inf \\\\\n", + " & atk_value & 0.15 & 1.16 & 0.16 & -0.16 & 0.46 & 0.85 & 1.58 & 0.00 & 0.95 & 0.34 & 1.56 \\\\\n", + " & data.sample.random_state & 0.03 & 1.03 & 0.02 & -0.01 & 0.07 & 0.99 & 1.07 & 0.00 & 1.31 & 0.19 & 2.39 \\\\\n", + " & def_value & -0.21 & 0.81 & 0.16 & -0.53 & 0.10 & 0.59 & 1.11 & 0.00 & -1.31 & 0.19 & 2.41 \\\\\n", + " & model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.53 & 0.60 & 0.74 \\\\\n", + " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 7.04 & 0.00 & 38.88 \\\\\n", + " & predict_time & -0.15 & 0.86 & 0.01 & -0.17 & -0.12 & 0.84 & 0.88 & 0.00 & -12.17 & 0.00 & 110.86 \\\\\n", + " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 10.81 & 0.00 & 88.14 \\\\\n", + " & Intercept & 3.00 & 20.18 & 0.18 & 2.65 & 3.36 & 14.14 & 28.79 & 0.00 & 16.56 & 0.00 & 202.20 \\\\\n", + "rho_ & Intercept & -0.84 & 0.43 & 0.02 & -0.88 & -0.80 & 0.41 & 0.45 & 0.00 & -43.75 & 0.00 & inf \\\\\n", "\\end{tabular}\n" ], "text/plain": [ - "\n", - " duration col = 'predict_time'\n", - " event col = 'ben_failures'\n", - " number of observations = 568\n", - "number of events observed = 568\n", - " log-likelihood = -723.10\n", - " time fit was run = 2023-09-25 13:54:12 UTC\n", + "\n", + " duration col = 'adv_fit_time'\n", + " event col = 'adv_failures'\n", + " number of observations = 1500\n", + "number of events observed = 1500\n", + " log-likelihood = -5531.26\n", + " time fit was run = 2023-09-29 11:13:25 UTC\n", "\n", "---\n", " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", "param covariate \n", - "lambda_ accuracy -0.35 0.70 0.06 -0.48 -0.23 0.62 0.80\n", - " adv_failure_rate -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - " adv_fit_time -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", - " atk_value 0.15 1.16 0.04 0.08 0.22 1.08 1.25\n", - " data.sample.random_state -0.00 1.00 0.00 -0.01 0.01 0.99 1.01\n", - " def_value -0.01 0.99 0.04 -0.08 0.07 0.92 1.07\n", - " failure_rate -0.01 0.99 0.00 -0.01 -0.01 0.99 0.99\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + "lambda_ adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + " atk_value 0.15 1.16 0.16 -0.16 0.46 0.85 1.58\n", + " data.sample.random_state 0.03 1.03 0.02 -0.01 0.07 0.99 1.07\n", + " def_value -0.21 0.81 0.16 -0.53 0.10 0.59 1.11\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", " model_layers 0.01 1.01 0.00 0.01 0.01 1.01 1.01\n", + " predict_time -0.15 0.86 0.01 -0.17 -0.12 0.84 0.88\n", " train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", - " Intercept 0.43 1.53 0.08 0.27 0.59 1.31 1.80\n", - "rho_ Intercept 1.10 2.99 0.03 1.03 1.16 2.81 3.18\n", + " Intercept 3.00 20.18 0.18 2.65 3.36 14.14 28.79\n", + "rho_ Intercept -0.84 0.43 0.02 -0.88 -0.80 0.41 0.45\n", "\n", " cmp to z p -log2(p)\n", "param covariate \n", - "lambda_ accuracy 0.00 -5.49 <0.005 24.58\n", - " adv_failure_rate 0.00 -1.55 0.12 3.04\n", - " adv_fit_time 0.00 -4.43 <0.005 16.67\n", - " atk_value 0.00 4.18 <0.005 15.05\n", - " data.sample.random_state 0.00 -0.83 0.41 1.29\n", - " def_value 0.00 -0.21 0.84 0.26\n", - " failure_rate 0.00 -10.63 <0.005 85.23\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 0.58 0.56 0.84\n", - " model_layers 0.00 20.39 <0.005 304.56\n", - " train_time 0.00 14.89 <0.005 164.17\n", - " Intercept 0.00 5.30 <0.005 23.02\n", - "rho_ Intercept 0.00 34.56 <0.005 867.25\n", + "lambda_ adv_failure_rate 0.00 -39.62 <0.005 inf\n", + " atk_value 0.00 0.95 0.34 1.56\n", + " data.sample.random_state 0.00 1.31 0.19 2.39\n", + " def_value 0.00 -1.31 0.19 2.41\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.53 0.60 0.74\n", + " model_layers 0.00 7.04 <0.005 38.88\n", + " predict_time 0.00 -12.17 <0.005 110.86\n", + " train_time 0.00 10.81 <0.005 88.14\n", + " Intercept 0.00 16.56 <0.005 202.20\n", + "rho_ Intercept 0.00 -43.75 <0.005 inf\n", "---\n", - "Concordance = 0.87\n", - "AIC = 1470.20\n", - "log-likelihood ratio test = 1003.22 on 10 df\n", - "-log2(p) of ll-ratio test = 692.36" + "Concordance = 0.84\n", + "AIC = 11082.52\n", + "log-likelihood ratio test = 800.84 on 8 df\n", + "-log2(p) of ll-ratio test = 554.32" ] }, "metadata": {}, @@ -670,7 +649,7 @@ " file = \"weibull_aft.pdf\",\n", " event_col = target,\n", " duration_col = duration_col,\n", - " title = \"Weibull AFT Model\",\n", + " title = \"Weibull AFR Model\",\n", " mtype = \"weibull\",\n", " replacement_dict=weibull_dict,\n", ")\n", @@ -680,20 +659,20 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_615644/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + "/tmp/ipykernel_773113/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", " pareto.set_yticklabels(labels)\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -717,9 +696,9 @@ " values_array=[18, 34, 50, 101, 152], \n", " title=\"Partial Effects of No. of Layers on Failure Rate for Weibull AFR\", \n", " replacement_dict=pareto_dict, \n", - " ylabel=\"Chance of Survival at time $T$ (seconds)\",\n", + " ylabel=\"% Chance of Survival\",\n", " xlabel=\"Time $T$ (seconds)\",\n", - " legend = {\"title\" : \"No. of Layers\", \"labels\" : [\"18\", \"34\", \"50\", \"101\", \"152\"]},\n", + " legend_kwargs={\"title\" : \"No. of Layers\", \"labels\" : [\"18\", \"34\", \"50\", \"101\", \"152\"]},\n", " )\n", "\n", "# weibull_accuracy = plot_partial_effects(\n", @@ -729,7 +708,7 @@ "# values_array = [.9, .99, .999, .9999],\n", "# replacement_dict=weibull_dict,\n", "# title=\"Partial Effects of Benign Accuracy on Failure Rate\",\n", - "# ylabel=\"Chance of Survival at time $T$ (seconds)\",\n", + "# ylabel=\"% Chance of Survival\",\n", "# xlabel=\"Time $T$ (seconds)\",\n", "# legend = {\"title\" : \"Benign Accuracy\"},\n", "# )\n", @@ -739,12 +718,20 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cmeyers/deckard/env/lib/python3.8/site-packages/lifelines/fitters/coxph_fitter.py:1614: ConvergenceWarning: Newton-Raphson failed to converge sufficiently. Please see the following tips in the lifelines documentation: https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-model\n", + " warnings.warn(\n" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -777,11 +764,11 @@ " \n", " \n", " duration col\n", - " 'predict_time'\n", + " 'adv_fit_time'\n", " \n", " \n", " event col\n", - " 'ben_failures'\n", + " 'adv_failures'\n", " \n", " \n", " baseline estimation\n", @@ -789,19 +776,19 @@ " \n", " \n", " number of observations\n", - " 568\n", + " 1500\n", " \n", " \n", " number of events observed\n", - " 568\n", + " 1500\n", " \n", " \n", " partial log-likelihood\n", - " -2477.41\n", + " -7421.70\n", " \n", " \n", " time fit was run\n", - " 2023-09-25 13:54:14 UTC\n", + " 2023-09-29 11:13:28 UTC\n", " \n", " \n", "\n", @@ -824,130 +811,102 @@ " \n", " \n", " \n", - " accuracy\n", - " 1.07\n", - " 2.91\n", - " 0.22\n", - " 0.64\n", - " 1.49\n", - " 1.90\n", - " 4.45\n", + " train_time\n", + " -0.00\n", + " 1.00\n", + " 0.00\n", + " -0.00\n", + " -0.00\n", + " 1.00\n", + " 1.00\n", " 0.00\n", - " 4.93\n", + " -4.25\n", " <0.005\n", - " 20.23\n", + " 15.52\n", " \n", " \n", - " train_time\n", - " -0.01\n", - " 0.99\n", - " 0.00\n", - " -0.02\n", - " -0.01\n", - " 0.98\n", - " 0.99\n", + " predict_time\n", + " 0.03\n", + " 1.03\n", + " 0.01\n", + " 0.02\n", + " 0.04\n", + " 1.02\n", + " 1.04\n", " 0.00\n", - " -16.43\n", + " 4.21\n", " <0.005\n", - " 198.99\n", + " 15.26\n", " \n", " \n", " atk_value\n", - " -0.45\n", - " 0.64\n", - " 0.12\n", - " -0.68\n", - " -0.22\n", - " 0.51\n", + " -0.09\n", + " 0.91\n", + " 0.07\n", + " -0.23\n", + " 0.05\n", " 0.80\n", + " 1.05\n", " 0.00\n", - " -3.79\n", - " <0.005\n", - " 12.68\n", + " -1.31\n", + " 0.19\n", + " 2.40\n", " \n", " \n", " def_value\n", - " -0.24\n", - " 0.79\n", - " 0.12\n", - " -0.48\n", - " 0.00\n", - " 0.62\n", - " 1.00\n", + " 0.04\n", + " 1.05\n", + " 0.07\n", + " -0.09\n", + " 0.18\n", + " 0.91\n", + " 1.20\n", " 0.00\n", - " -1.94\n", - " 0.05\n", - " 4.27\n", + " 0.63\n", + " 0.53\n", + " 0.92\n", " \n", " \n", " data.sample.random_state\n", - " 0.03\n", - " 1.03\n", - " 0.02\n", - " -0.00\n", - " 0.06\n", - " 1.00\n", - " 1.06\n", - " 0.00\n", - " 1.82\n", - " 0.07\n", - " 3.86\n", - " \n", - " \n", - " adv_failure_rate\n", - " 0.00\n", - " 1.00\n", - " 0.00\n", - " 0.00\n", + " -0.01\n", + " 0.99\n", + " 0.01\n", + " -0.03\n", " 0.00\n", - " 1.00\n", + " 0.97\n", " 1.00\n", " 0.00\n", - " 2.82\n", - " <0.005\n", - " 7.70\n", + " -1.57\n", + " 0.12\n", + " 3.10\n", " \n", " \n", - " failure_rate\n", - " 0.02\n", - " 1.02\n", + " adv_failure_rate\n", + " 0.01\n", + " 1.01\n", " 0.00\n", - " 0.02\n", - " 0.03\n", - " 1.02\n", - " 1.03\n", + " 0.01\n", + " 0.01\n", + " 1.01\n", + " 1.01\n", " 0.00\n", - " 7.57\n", + " 28.70\n", " <0.005\n", - " 44.64\n", + " 599.53\n", " \n", " \n", " model_layers\n", - " -0.02\n", - " 0.98\n", - " 0.00\n", - " -0.03\n", - " -0.02\n", - " 0.98\n", - " 0.98\n", - " 0.00\n", - " -15.41\n", - " <0.005\n", - " 175.57\n", - " \n", - " \n", - " adv_fit_time\n", - " 0.00\n", + " -0.00\n", " 1.00\n", " 0.00\n", - " 0.00\n", - " 0.00\n", + " -0.00\n", + " -0.00\n", " 1.00\n", " 1.00\n", " 0.00\n", - " 3.01\n", + " -5.20\n", " <0.005\n", - " 8.60\n", + " 22.23\n", " \n", " \n", " model.art.pipeline.initialize.kwargs.optimizer.lr\n", @@ -959,9 +918,9 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " -0.83\n", - " 0.41\n", - " 1.29\n", + " -0.13\n", + " 0.90\n", + " 0.15\n", " \n", " \n", "
\n", @@ -982,19 +941,19 @@ " \n", " \n", " Concordance\n", - " 0.90\n", + " 0.92\n", " \n", " \n", " Partial AIC\n", - " 4974.82\n", + " 14859.40\n", " \n", " \n", " log-likelihood ratio test\n", - " 1122.01 on 10 df\n", + " 4105.41 on 8 df\n", " \n", " \n", " -log2(p) of ll-ratio test\n", - " 777.41\n", + " inf\n", " \n", " \n", "\n", @@ -1004,59 +963,53 @@ "\\begin{tabular}{lrrrrrrrrrrr}\n", " & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", "covariate & & & & & & & & & & & \\\\\n", - "accuracy & 1.07 & 2.91 & 0.22 & 0.64 & 1.49 & 1.90 & 4.45 & 0.00 & 4.93 & 0.00 & 20.23 \\\\\n", - "train_time & -0.01 & 0.99 & 0.00 & -0.02 & -0.01 & 0.98 & 0.99 & 0.00 & -16.43 & 0.00 & 198.99 \\\\\n", - "atk_value & -0.45 & 0.64 & 0.12 & -0.68 & -0.22 & 0.51 & 0.80 & 0.00 & -3.79 & 0.00 & 12.68 \\\\\n", - "def_value & -0.24 & 0.79 & 0.12 & -0.48 & 0.00 & 0.62 & 1.00 & 0.00 & -1.94 & 0.05 & 4.27 \\\\\n", - "data.sample.random_state & 0.03 & 1.03 & 0.02 & -0.00 & 0.06 & 1.00 & 1.06 & 0.00 & 1.82 & 0.07 & 3.86 \\\\\n", - "adv_failure_rate & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 2.82 & 0.00 & 7.70 \\\\\n", - "failure_rate & 0.02 & 1.02 & 0.00 & 0.02 & 0.03 & 1.02 & 1.03 & 0.00 & 7.57 & 0.00 & 44.64 \\\\\n", - "model_layers & -0.02 & 0.98 & 0.00 & -0.03 & -0.02 & 0.98 & 0.98 & 0.00 & -15.41 & 0.00 & 175.57 \\\\\n", - "adv_fit_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 3.01 & 0.00 & 8.60 \\\\\n", - "model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.83 & 0.41 & 1.29 \\\\\n", + "train_time & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -4.25 & 0.00 & 15.52 \\\\\n", + "predict_time & 0.03 & 1.03 & 0.01 & 0.02 & 0.04 & 1.02 & 1.04 & 0.00 & 4.21 & 0.00 & 15.26 \\\\\n", + "atk_value & -0.09 & 0.91 & 0.07 & -0.23 & 0.05 & 0.80 & 1.05 & 0.00 & -1.31 & 0.19 & 2.40 \\\\\n", + "def_value & 0.04 & 1.05 & 0.07 & -0.09 & 0.18 & 0.91 & 1.20 & 0.00 & 0.63 & 0.53 & 0.92 \\\\\n", + "data.sample.random_state & -0.01 & 0.99 & 0.01 & -0.03 & 0.00 & 0.97 & 1.00 & 0.00 & -1.57 & 0.12 & 3.10 \\\\\n", + "adv_failure_rate & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 28.70 & 0.00 & 599.53 \\\\\n", + "model_layers & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -5.20 & 0.00 & 22.23 \\\\\n", + "model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.13 & 0.90 & 0.15 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ - "\n", - " duration col = 'predict_time'\n", - " event col = 'ben_failures'\n", + "\n", + " duration col = 'adv_fit_time'\n", + " event col = 'adv_failures'\n", " baseline estimation = breslow\n", - " number of observations = 568\n", - "number of events observed = 568\n", - " partial log-likelihood = -2477.41\n", - " time fit was run = 2023-09-25 13:54:14 UTC\n", + " number of observations = 1500\n", + "number of events observed = 1500\n", + " partial log-likelihood = -7421.70\n", + " time fit was run = 2023-09-29 11:13:28 UTC\n", "\n", "---\n", " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", "covariate \n", - "accuracy 1.07 2.91 0.22 0.64 1.49 1.90 4.45\n", - "train_time -0.01 0.99 0.00 -0.02 -0.01 0.98 0.99\n", - "atk_value -0.45 0.64 0.12 -0.68 -0.22 0.51 0.80\n", - "def_value -0.24 0.79 0.12 -0.48 0.00 0.62 1.00\n", - "data.sample.random_state 0.03 1.03 0.02 -0.00 0.06 1.00 1.06\n", - "adv_failure_rate 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", - "failure_rate 0.02 1.02 0.00 0.02 0.03 1.02 1.03\n", - "model_layers -0.02 0.98 0.00 -0.03 -0.02 0.98 0.98\n", - "adv_fit_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", + "train_time -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + "predict_time 0.03 1.03 0.01 0.02 0.04 1.02 1.04\n", + "atk_value -0.09 0.91 0.07 -0.23 0.05 0.80 1.05\n", + "def_value 0.04 1.05 0.07 -0.09 0.18 0.91 1.20\n", + "data.sample.random_state -0.01 0.99 0.01 -0.03 0.00 0.97 1.00\n", + "adv_failure_rate 0.01 1.01 0.00 0.01 0.01 1.01 1.01\n", + "model_layers -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", "model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", "\n", - " cmp to z p -log2(p)\n", - "covariate \n", - "accuracy 0.00 4.93 <0.005 20.23\n", - "train_time 0.00 -16.43 <0.005 198.99\n", - "atk_value 0.00 -3.79 <0.005 12.68\n", - "def_value 0.00 -1.94 0.05 4.27\n", - "data.sample.random_state 0.00 1.82 0.07 3.86\n", - "adv_failure_rate 0.00 2.82 <0.005 7.70\n", - "failure_rate 0.00 7.57 <0.005 44.64\n", - "model_layers 0.00 -15.41 <0.005 175.57\n", - "adv_fit_time 0.00 3.01 <0.005 8.60\n", - "model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.83 0.41 1.29\n", + " cmp to z p -log2(p)\n", + "covariate \n", + "train_time 0.00 -4.25 <0.005 15.52\n", + "predict_time 0.00 4.21 <0.005 15.26\n", + "atk_value 0.00 -1.31 0.19 2.40\n", + "def_value 0.00 0.63 0.53 0.92\n", + "data.sample.random_state 0.00 -1.57 0.12 3.10\n", + "adv_failure_rate 0.00 28.70 <0.005 599.53\n", + "model_layers 0.00 -5.20 <0.005 22.23\n", + "model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.13 0.90 0.15\n", "---\n", - "Concordance = 0.90\n", - "Partial AIC = 4974.82\n", - "log-likelihood ratio test = 1122.01 on 10 df\n", - "-log2(p) of ll-ratio test = 777.41" + "Concordance = 0.92\n", + "Partial AIC = 14859.40\n", + "log-likelihood ratio test = 4105.41 on 8 df\n", + "-log2(p) of ll-ratio test = inf" ] }, "metadata": {}, @@ -1066,7 +1019,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_615644/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + "/tmp/ipykernel_773113/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", " pareto.set_yticklabels(labels)\n" ] }, @@ -1081,7 +1034,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -1113,7 +1066,7 @@ " file = \"cox_aft.pdf\",\n", " event_col = target,\n", " duration_col = duration_col,\n", - " title = \"Cox AFT Model\",\n", + " title = \"Cox AFR Model\",\n", " mtype = \"cox\",\n", " replacement_dict=cox_dict,\n", ")\n", @@ -1125,8 +1078,8 @@ " covariate_array = \"model_layers\",\n", " values_array = [18, 34, 50, 101, 152],\n", " replacement_dict=cox_dict,\n", - " title=\"Partial Effects of No. of Layers on Failure Rate for Cox AFR\",\n", - " ylabel=\"Chance of Survival at time $T$ (seconds)\",\n", + " title=\"Survival Time for Cox AFR\",\n", + " ylabel=\"% Chance of Survival\",\n", " xlabel=\"Time $T$ (seconds)\",\n", " legend_kwargs={\"title\" : \"No. of Layers\", \"labels\" : [\"18\", \"34\", \"50\", \"101\", \"152\"]},\n", ")" @@ -1134,12 +1087,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1172,27 +1125,27 @@ " \n", " \n", " duration col\n", - " 'predict_time'\n", + " 'adv_fit_time'\n", " \n", " \n", " event col\n", - " 'ben_failures'\n", + " 'adv_failures'\n", " \n", " \n", " number of observations\n", - " 568\n", + " 1500\n", " \n", " \n", " number of events observed\n", - " 568\n", + " 1500\n", " \n", " \n", " log-likelihood\n", - " -688.89\n", + " -5374.54\n", " \n", " \n", " time fit was run\n", - " 2023-09-25 13:54:16 UTC\n", + " 2023-09-29 11:13:31 UTC\n", " \n", " \n", "\n", @@ -1216,21 +1169,7 @@ " \n", " \n", " \n", - " mu_\n", - " accuracy\n", - " -0.35\n", - " 0.71\n", - " 0.07\n", - " -0.49\n", - " -0.20\n", - " 0.61\n", - " 0.81\n", - " 0.00\n", - " -4.77\n", - " <0.005\n", - " 19.08\n", - " \n", - " \n", + " mu_\n", " adv_failure_rate\n", " -0.00\n", " 1.00\n", @@ -1240,83 +1179,55 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " -2.38\n", - " 0.02\n", - " 5.85\n", - " \n", - " \n", - " adv_fit_time\n", - " -0.00\n", - " 1.00\n", - " 0.00\n", - " -0.00\n", - " -0.00\n", - " 1.00\n", - " 1.00\n", - " 0.00\n", - " -3.90\n", + " -31.84\n", " <0.005\n", - " 13.35\n", + " 736.80\n", " \n", " \n", " atk_value\n", - " 0.15\n", - " 1.16\n", - " 0.04\n", " 0.08\n", - " 0.23\n", - " 1.08\n", - " 1.26\n", + " 1.09\n", + " 0.16\n", + " -0.22\n", + " 0.39\n", + " 0.80\n", + " 1.48\n", " 0.00\n", - " 3.92\n", - " <0.005\n", - " 13.48\n", + " 0.52\n", + " 0.60\n", + " 0.74\n", " \n", " \n", " data.sample.random_state\n", + " 0.02\n", + " 1.02\n", + " 0.02\n", + " -0.02\n", + " 0.06\n", + " 0.98\n", + " 1.06\n", " 0.00\n", - " 1.00\n", - " 0.01\n", - " -0.01\n", - " 0.01\n", - " 0.99\n", - " 1.01\n", - " 0.00\n", - " 0.70\n", - " 0.48\n", - " 1.05\n", + " 0.83\n", + " 0.41\n", + " 1.30\n", " \n", " \n", " def_value\n", + " -0.16\n", + " 0.85\n", + " 0.16\n", + " -0.48\n", + " 0.16\n", + " 0.62\n", + " 1.17\n", " 0.00\n", - " 1.00\n", - " 0.04\n", - " -0.08\n", - " 0.08\n", - " 0.92\n", - " 1.08\n", - " 0.00\n", - " 0.01\n", - " 0.99\n", - " 0.01\n", - " \n", - " \n", - " failure_rate\n", - " -0.01\n", - " 0.99\n", - " 0.00\n", - " -0.01\n", - " -0.01\n", - " 0.99\n", - " 0.99\n", - " 0.00\n", - " -7.22\n", - " <0.005\n", - " 40.82\n", + " -0.98\n", + " 0.32\n", + " 1.62\n", " \n", " \n", " model.art.pipeline.initialize.kwargs.optimizer.lr\n", - " 0.00\n", + " -0.00\n", " 1.00\n", " 0.00\n", " -0.00\n", @@ -1324,9 +1235,9 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " 0.74\n", - " 0.46\n", - " 1.13\n", + " -0.06\n", + " 0.95\n", + " 0.07\n", " \n", " \n", " model_layers\n", @@ -1338,9 +1249,23 @@ " 1.01\n", " 1.01\n", " 0.00\n", - " 28.07\n", + " 7.13\n", " <0.005\n", - " 573.57\n", + " 39.81\n", + " \n", + " \n", + " predict_time\n", + " -0.21\n", + " 0.81\n", + " 0.02\n", + " -0.26\n", + " -0.16\n", + " 0.77\n", + " 0.85\n", + " 0.00\n", + " -8.37\n", + " <0.005\n", + " 53.94\n", " \n", " \n", " train_time\n", @@ -1352,38 +1277,38 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " 26.65\n", + " 7.13\n", " <0.005\n", - " 517.55\n", + " 39.89\n", " \n", " \n", " Intercept\n", - " 0.18\n", - " 1.20\n", - " 0.09\n", - " 0.00\n", - " 0.36\n", - " 1.00\n", - " 1.43\n", + " 2.02\n", + " 7.54\n", + " 0.17\n", + " 1.68\n", + " 2.36\n", + " 5.37\n", + " 10.61\n", " 0.00\n", - " 1.99\n", - " 0.05\n", - " 4.42\n", + " 11.63\n", + " <0.005\n", + " 101.36\n", " \n", " \n", " sigma_\n", " Intercept\n", - " -1.02\n", - " 0.36\n", - " 0.03\n", - " -1.08\n", - " -0.97\n", - " 0.34\n", - " 0.38\n", + " 0.84\n", + " 2.31\n", + " 0.02\n", + " 0.80\n", + " 0.87\n", + " 2.23\n", + " 2.39\n", " 0.00\n", - " -34.53\n", + " 45.86\n", " <0.005\n", - " 865.57\n", + " inf\n", " \n", " \n", "
\n", @@ -1404,19 +1329,19 @@ " \n", " \n", " Concordance\n", - " 0.86\n", + " 0.84\n", " \n", " \n", " AIC\n", - " 1401.78\n", + " 10769.08\n", " \n", " \n", " log-likelihood ratio test\n", - " 937.90 on 10 df\n", + " 925.72 on 8 df\n", " \n", " \n", " -log2(p) of ll-ratio test\n", - " 645.63\n", + " 643.78\n", " \n", " \n", "\n", @@ -1426,64 +1351,58 @@ "\\begin{tabular}{llrrrrrrrrrrr}\n", " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", "param & covariate & & & & & & & & & & & \\\\\n", - "\\multirow[c]{11}{*}{mu_} & accuracy & -0.35 & 0.71 & 0.07 & -0.49 & -0.20 & 0.61 & 0.81 & 0.00 & -4.77 & 0.00 & 19.08 \\\\\n", - " & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -2.38 & 0.02 & 5.85 \\\\\n", - " & adv_fit_time & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -3.90 & 0.00 & 13.35 \\\\\n", - " & atk_value & 0.15 & 1.16 & 0.04 & 0.08 & 0.23 & 1.08 & 1.26 & 0.00 & 3.92 & 0.00 & 13.48 \\\\\n", - " & data.sample.random_state & 0.00 & 1.00 & 0.01 & -0.01 & 0.01 & 0.99 & 1.01 & 0.00 & 0.70 & 0.48 & 1.05 \\\\\n", - " & def_value & 0.00 & 1.00 & 0.04 & -0.08 & 0.08 & 0.92 & 1.08 & 0.00 & 0.01 & 0.99 & 0.01 \\\\\n", - " & failure_rate & -0.01 & 0.99 & 0.00 & -0.01 & -0.01 & 0.99 & 0.99 & 0.00 & -7.22 & 0.00 & 40.82 \\\\\n", - " & model.art.pipeline.initialize.kwargs.optimizer.lr & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 0.74 & 0.46 & 1.13 \\\\\n", - " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 28.07 & 0.00 & 573.57 \\\\\n", - " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 26.65 & 0.00 & 517.55 \\\\\n", - " & Intercept & 0.18 & 1.20 & 0.09 & 0.00 & 0.36 & 1.00 & 1.43 & 0.00 & 1.99 & 0.05 & 4.42 \\\\\n", - "sigma_ & Intercept & -1.02 & 0.36 & 0.03 & -1.08 & -0.97 & 0.34 & 0.38 & 0.00 & -34.53 & 0.00 & 865.57 \\\\\n", + "\\multirow[c]{9}{*}{mu_} & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -31.84 & 0.00 & 736.80 \\\\\n", + " & atk_value & 0.08 & 1.09 & 0.16 & -0.22 & 0.39 & 0.80 & 1.48 & 0.00 & 0.52 & 0.60 & 0.74 \\\\\n", + " & data.sample.random_state & 0.02 & 1.02 & 0.02 & -0.02 & 0.06 & 0.98 & 1.06 & 0.00 & 0.83 & 0.41 & 1.30 \\\\\n", + " & def_value & -0.16 & 0.85 & 0.16 & -0.48 & 0.16 & 0.62 & 1.17 & 0.00 & -0.98 & 0.32 & 1.62 \\\\\n", + " & model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.06 & 0.95 & 0.07 \\\\\n", + " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 7.13 & 0.00 & 39.81 \\\\\n", + " & predict_time & -0.21 & 0.81 & 0.02 & -0.26 & -0.16 & 0.77 & 0.85 & 0.00 & -8.37 & 0.00 & 53.94 \\\\\n", + " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 7.13 & 0.00 & 39.89 \\\\\n", + " & Intercept & 2.02 & 7.54 & 0.17 & 1.68 & 2.36 & 5.37 & 10.61 & 0.00 & 11.63 & 0.00 & 101.36 \\\\\n", + "sigma_ & Intercept & 0.84 & 2.31 & 0.02 & 0.80 & 0.87 & 2.23 & 2.39 & 0.00 & 45.86 & 0.00 & inf \\\\\n", "\\end{tabular}\n" ], "text/plain": [ - "\n", - " duration col = 'predict_time'\n", - " event col = 'ben_failures'\n", - " number of observations = 568\n", - "number of events observed = 568\n", - " log-likelihood = -688.89\n", - " time fit was run = 2023-09-25 13:54:16 UTC\n", + "\n", + " duration col = 'adv_fit_time'\n", + " event col = 'adv_failures'\n", + " number of observations = 1500\n", + "number of events observed = 1500\n", + " log-likelihood = -5374.54\n", + " time fit was run = 2023-09-29 11:13:31 UTC\n", "\n", "---\n", " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", "param covariate \n", - "mu_ accuracy -0.35 0.71 0.07 -0.49 -0.20 0.61 0.81\n", - " adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", - " adv_fit_time -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", - " atk_value 0.15 1.16 0.04 0.08 0.23 1.08 1.26\n", - " data.sample.random_state 0.00 1.00 0.01 -0.01 0.01 0.99 1.01\n", - " def_value 0.00 1.00 0.04 -0.08 0.08 0.92 1.08\n", - " failure_rate -0.01 0.99 0.00 -0.01 -0.01 0.99 0.99\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + "mu_ adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + " atk_value 0.08 1.09 0.16 -0.22 0.39 0.80 1.48\n", + " data.sample.random_state 0.02 1.02 0.02 -0.02 0.06 0.98 1.06\n", + " def_value -0.16 0.85 0.16 -0.48 0.16 0.62 1.17\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", " model_layers 0.01 1.01 0.00 0.01 0.01 1.01 1.01\n", + " predict_time -0.21 0.81 0.02 -0.26 -0.16 0.77 0.85\n", " train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", - " Intercept 0.18 1.20 0.09 0.00 0.36 1.00 1.43\n", - "sigma_ Intercept -1.02 0.36 0.03 -1.08 -0.97 0.34 0.38\n", + " Intercept 2.02 7.54 0.17 1.68 2.36 5.37 10.61\n", + "sigma_ Intercept 0.84 2.31 0.02 0.80 0.87 2.23 2.39\n", "\n", " cmp to z p -log2(p)\n", "param covariate \n", - "mu_ accuracy 0.00 -4.77 <0.005 19.08\n", - " adv_failure_rate 0.00 -2.38 0.02 5.85\n", - " adv_fit_time 0.00 -3.90 <0.005 13.35\n", - " atk_value 0.00 3.92 <0.005 13.48\n", - " data.sample.random_state 0.00 0.70 0.48 1.05\n", - " def_value 0.00 0.01 0.99 0.01\n", - " failure_rate 0.00 -7.22 <0.005 40.82\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 0.74 0.46 1.13\n", - " model_layers 0.00 28.07 <0.005 573.57\n", - " train_time 0.00 26.65 <0.005 517.55\n", - " Intercept 0.00 1.99 0.05 4.42\n", - "sigma_ Intercept 0.00 -34.53 <0.005 865.57\n", + "mu_ adv_failure_rate 0.00 -31.84 <0.005 736.80\n", + " atk_value 0.00 0.52 0.60 0.74\n", + " data.sample.random_state 0.00 0.83 0.41 1.30\n", + " def_value 0.00 -0.98 0.32 1.62\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.06 0.95 0.07\n", + " model_layers 0.00 7.13 <0.005 39.81\n", + " predict_time 0.00 -8.37 <0.005 53.94\n", + " train_time 0.00 7.13 <0.005 39.89\n", + " Intercept 0.00 11.63 <0.005 101.36\n", + "sigma_ Intercept 0.00 45.86 <0.005 inf\n", "---\n", - "Concordance = 0.86\n", - "AIC = 1401.78\n", - "log-likelihood ratio test = 937.90 on 10 df\n", - "-log2(p) of ll-ratio test = 645.63" + "Concordance = 0.84\n", + "AIC = 10769.08\n", + "log-likelihood ratio test = 925.72 on 8 df\n", + "-log2(p) of ll-ratio test = 643.78" ] }, "metadata": {}, @@ -1493,13 +1412,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_615644/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + "/tmp/ipykernel_773113/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", " pareto.set_yticklabels(labels)\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1533,7 +1452,7 @@ " \"log_normal_aft.pdf\",\n", " target,\n", " duration_col,\n", - " \"Log Normal AFT Model\",\n", + " \"Log Normal AFR Model\",\n", " \"log_normal\",\n", " replacement_dict=log_normal_dict,\n", ")\n", @@ -1545,8 +1464,8 @@ " covariate_array = \"model_layers\",\n", " values_array = [18, 34, 50, 101, 152],\n", " replacement_dict=log_normal_dict,\n", - " title=\"Partial Effects of No. of Layers on Failure Rate for Log-Normal AFR\",\n", - " ylabel=\"Chance of Survival at time $T$ (seconds)\",\n", + " title=\"Survival Time for Log-Normal AFR\",\n", + " ylabel=\"% Chance of Survival\",\n", " xlabel=\"Time $T$ (seconds)\",\n", " legend_kwargs={\"title\" : \"No. of Layers\", \"labels\" : [\"18\", \"34\", \"50\", \"101\", \"152\"]},\n", ")" @@ -1554,12 +1473,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1592,27 +1511,27 @@ " \n", " \n", " duration col\n", - " 'predict_time'\n", + " 'adv_fit_time'\n", " \n", " \n", " event col\n", - " 'ben_failures'\n", + " 'adv_failures'\n", " \n", " \n", " number of observations\n", - " 568\n", + " 1500\n", " \n", " \n", " number of events observed\n", - " 568\n", + " 1500\n", " \n", " \n", " log-likelihood\n", - " -693.57\n", + " -5426.84\n", " \n", " \n", " time fit was run\n", - " 2023-09-25 13:54:18 UTC\n", + " 2023-09-29 11:13:33 UTC\n", " \n", " \n", "\n", @@ -1636,21 +1555,7 @@ " \n", " \n", " \n", - " alpha_\n", - " accuracy\n", - " -0.37\n", - " 0.69\n", - " 0.07\n", - " -0.51\n", - " -0.23\n", - " 0.60\n", - " 0.80\n", - " 0.00\n", - " -5.18\n", - " <0.005\n", - " 22.07\n", - " \n", - " \n", + " alpha_\n", " adv_failure_rate\n", " -0.00\n", " 1.00\n", @@ -1660,79 +1565,51 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " -2.58\n", - " 0.01\n", - " 6.64\n", - " \n", - " \n", - " adv_fit_time\n", - " -0.00\n", - " 1.00\n", - " 0.00\n", - " -0.00\n", - " -0.00\n", - " 1.00\n", - " 1.00\n", - " 0.00\n", - " -3.93\n", + " -26.62\n", " <0.005\n", - " 13.55\n", + " 516.20\n", " \n", " \n", " atk_value\n", + " 0.07\n", + " 1.07\n", " 0.17\n", - " 1.18\n", - " 0.04\n", - " 0.09\n", - " 0.24\n", - " 1.09\n", - " 1.28\n", + " -0.26\n", + " 0.40\n", + " 0.77\n", + " 1.49\n", " 0.00\n", - " 4.20\n", - " <0.005\n", - " 15.17\n", + " 0.42\n", + " 0.67\n", + " 0.58\n", " \n", " \n", " data.sample.random_state\n", - " 0.01\n", - " 1.01\n", - " 0.01\n", - " -0.00\n", " 0.02\n", - " 1.00\n", " 1.02\n", + " 0.02\n", + " -0.02\n", + " 0.06\n", + " 0.98\n", + " 1.07\n", " 0.00\n", - " 1.13\n", - " 0.26\n", - " 1.96\n", + " 0.91\n", + " 0.36\n", + " 1.47\n", " \n", " \n", " def_value\n", - " 0.01\n", - " 1.01\n", - " 0.04\n", - " -0.07\n", - " 0.08\n", - " 0.93\n", - " 1.09\n", - " 0.00\n", - " 0.16\n", - " 0.87\n", - " 0.20\n", - " \n", - " \n", - " failure_rate\n", - " -0.01\n", - " 0.99\n", - " 0.00\n", - " -0.01\n", - " -0.01\n", - " 0.99\n", - " 0.99\n", + " -0.19\n", + " 0.82\n", + " 0.18\n", + " -0.54\n", + " 0.15\n", + " 0.58\n", + " 1.16\n", " 0.00\n", - " -7.72\n", - " <0.005\n", - " 46.29\n", + " -1.11\n", + " 0.27\n", + " 1.90\n", " \n", " \n", " model.art.pipeline.initialize.kwargs.optimizer.lr\n", @@ -1744,9 +1621,9 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " 0.55\n", - " 0.58\n", - " 0.78\n", + " 0.03\n", + " 0.97\n", + " 0.04\n", " \n", " \n", " model_layers\n", @@ -1754,13 +1631,27 @@ " 1.01\n", " 0.00\n", " 0.01\n", - " 0.01\n", - " 1.01\n", + " 0.02\n", " 1.01\n", + " 1.02\n", " 0.00\n", - " 25.06\n", + " 7.88\n", " <0.005\n", - " 458.04\n", + " 48.13\n", + " \n", + " \n", + " predict_time\n", + " -0.31\n", + " 0.74\n", + " 0.04\n", + " -0.38\n", + " -0.24\n", + " 0.69\n", + " 0.79\n", + " 0.00\n", + " -8.73\n", + " <0.005\n", + " 58.49\n", " \n", " \n", " train_time\n", @@ -1772,38 +1663,38 @@ " 1.00\n", " 1.00\n", " 0.00\n", - " 15.51\n", + " 6.12\n", " <0.005\n", - " 177.74\n", + " 30.02\n", " \n", " \n", " Intercept\n", - " 0.18\n", - " 1.20\n", - " 0.09\n", - " 0.00\n", - " 0.36\n", - " 1.00\n", - " 1.43\n", + " 1.88\n", + " 6.54\n", + " 0.19\n", + " 1.51\n", + " 2.25\n", + " 4.52\n", + " 9.46\n", " 0.00\n", - " 2.00\n", - " 0.05\n", - " 4.44\n", + " 9.97\n", + " <0.005\n", + " 75.31\n", " \n", " \n", " beta_\n", " Intercept\n", - " 1.59\n", - " 4.91\n", - " 0.04\n", - " 1.52\n", - " 1.66\n", - " 4.58\n", - " 5.26\n", + " -0.32\n", + " 0.72\n", + " 0.02\n", + " -0.36\n", + " -0.28\n", + " 0.69\n", + " 0.75\n", " 0.00\n", - " 45.13\n", + " -15.46\n", " <0.005\n", - " inf\n", + " 176.59\n", " \n", " \n", "
\n", @@ -1824,19 +1715,19 @@ " \n", " \n", " Concordance\n", - " 0.86\n", + " 0.82\n", " \n", " \n", " AIC\n", - " 1411.15\n", + " 10873.68\n", " \n", " \n", " log-likelihood ratio test\n", - " 942.02 on 10 df\n", + " 961.93 on 8 df\n", " \n", " \n", " -log2(p) of ll-ratio test\n", - " 648.58\n", + " 669.73\n", " \n", " \n", "\n", @@ -1846,64 +1737,58 @@ "\\begin{tabular}{llrrrrrrrrrrr}\n", " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", "param & covariate & & & & & & & & & & & \\\\\n", - "\\multirow[c]{11}{*}{alpha_} & accuracy & -0.37 & 0.69 & 0.07 & -0.51 & -0.23 & 0.60 & 0.80 & 0.00 & -5.18 & 0.00 & 22.07 \\\\\n", - " & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -2.58 & 0.01 & 6.64 \\\\\n", - " & adv_fit_time & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -3.93 & 0.00 & 13.55 \\\\\n", - " & atk_value & 0.17 & 1.18 & 0.04 & 0.09 & 0.24 & 1.09 & 1.28 & 0.00 & 4.20 & 0.00 & 15.17 \\\\\n", - " & data.sample.random_state & 0.01 & 1.01 & 0.01 & -0.00 & 0.02 & 1.00 & 1.02 & 0.00 & 1.13 & 0.26 & 1.96 \\\\\n", - " & def_value & 0.01 & 1.01 & 0.04 & -0.07 & 0.08 & 0.93 & 1.09 & 0.00 & 0.16 & 0.87 & 0.20 \\\\\n", - " & failure_rate & -0.01 & 0.99 & 0.00 & -0.01 & -0.01 & 0.99 & 0.99 & 0.00 & -7.72 & 0.00 & 46.29 \\\\\n", - " & model.art.pipeline.initialize.kwargs.optimizer.lr & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 0.55 & 0.58 & 0.78 \\\\\n", - " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 25.06 & 0.00 & 458.04 \\\\\n", - " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 15.51 & 0.00 & 177.74 \\\\\n", - " & Intercept & 0.18 & 1.20 & 0.09 & 0.00 & 0.36 & 1.00 & 1.43 & 0.00 & 2.00 & 0.05 & 4.44 \\\\\n", - "beta_ & Intercept & 1.59 & 4.91 & 0.04 & 1.52 & 1.66 & 4.58 & 5.26 & 0.00 & 45.13 & 0.00 & inf \\\\\n", + "\\multirow[c]{9}{*}{alpha_} & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -26.62 & 0.00 & 516.20 \\\\\n", + " & atk_value & 0.07 & 1.07 & 0.17 & -0.26 & 0.40 & 0.77 & 1.49 & 0.00 & 0.42 & 0.67 & 0.58 \\\\\n", + " & data.sample.random_state & 0.02 & 1.02 & 0.02 & -0.02 & 0.06 & 0.98 & 1.07 & 0.00 & 0.91 & 0.36 & 1.47 \\\\\n", + " & def_value & -0.19 & 0.82 & 0.18 & -0.54 & 0.15 & 0.58 & 1.16 & 0.00 & -1.11 & 0.27 & 1.90 \\\\\n", + " & model.art.pipeline.initialize.kwargs.optimizer.lr & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 0.03 & 0.97 & 0.04 \\\\\n", + " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.02 & 1.01 & 1.02 & 0.00 & 7.88 & 0.00 & 48.13 \\\\\n", + " & predict_time & -0.31 & 0.74 & 0.04 & -0.38 & -0.24 & 0.69 & 0.79 & 0.00 & -8.73 & 0.00 & 58.49 \\\\\n", + " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 6.12 & 0.00 & 30.02 \\\\\n", + " & Intercept & 1.88 & 6.54 & 0.19 & 1.51 & 2.25 & 4.52 & 9.46 & 0.00 & 9.97 & 0.00 & 75.31 \\\\\n", + "beta_ & Intercept & -0.32 & 0.72 & 0.02 & -0.36 & -0.28 & 0.69 & 0.75 & 0.00 & -15.46 & 0.00 & 176.59 \\\\\n", "\\end{tabular}\n" ], "text/plain": [ - "\n", - " duration col = 'predict_time'\n", - " event col = 'ben_failures'\n", - " number of observations = 568\n", - "number of events observed = 568\n", - " log-likelihood = -693.57\n", - " time fit was run = 2023-09-25 13:54:18 UTC\n", + "\n", + " duration col = 'adv_fit_time'\n", + " event col = 'adv_failures'\n", + " number of observations = 1500\n", + "number of events observed = 1500\n", + " log-likelihood = -5426.84\n", + " time fit was run = 2023-09-29 11:13:33 UTC\n", "\n", "---\n", " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", "param covariate \n", - "alpha_ accuracy -0.37 0.69 0.07 -0.51 -0.23 0.60 0.80\n", - " adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", - " adv_fit_time -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", - " atk_value 0.17 1.18 0.04 0.09 0.24 1.09 1.28\n", - " data.sample.random_state 0.01 1.01 0.01 -0.00 0.02 1.00 1.02\n", - " def_value 0.01 1.01 0.04 -0.07 0.08 0.93 1.09\n", - " failure_rate -0.01 0.99 0.00 -0.01 -0.01 0.99 0.99\n", + "alpha_ adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + " atk_value 0.07 1.07 0.17 -0.26 0.40 0.77 1.49\n", + " data.sample.random_state 0.02 1.02 0.02 -0.02 0.06 0.98 1.07\n", + " def_value -0.19 0.82 0.18 -0.54 0.15 0.58 1.16\n", " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - " model_layers 0.01 1.01 0.00 0.01 0.01 1.01 1.01\n", + " model_layers 0.01 1.01 0.00 0.01 0.02 1.01 1.02\n", + " predict_time -0.31 0.74 0.04 -0.38 -0.24 0.69 0.79\n", " train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", - " Intercept 0.18 1.20 0.09 0.00 0.36 1.00 1.43\n", - "beta_ Intercept 1.59 4.91 0.04 1.52 1.66 4.58 5.26\n", + " Intercept 1.88 6.54 0.19 1.51 2.25 4.52 9.46\n", + "beta_ Intercept -0.32 0.72 0.02 -0.36 -0.28 0.69 0.75\n", "\n", - " cmp to z p -log2(p)\n", - "param covariate \n", - "alpha_ accuracy 0.00 -5.18 <0.005 22.07\n", - " adv_failure_rate 0.00 -2.58 0.01 6.64\n", - " adv_fit_time 0.00 -3.93 <0.005 13.55\n", - " atk_value 0.00 4.20 <0.005 15.17\n", - " data.sample.random_state 0.00 1.13 0.26 1.96\n", - " def_value 0.00 0.16 0.87 0.20\n", - " failure_rate 0.00 -7.72 <0.005 46.29\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 0.55 0.58 0.78\n", - " model_layers 0.00 25.06 <0.005 458.04\n", - " train_time 0.00 15.51 <0.005 177.74\n", - " Intercept 0.00 2.00 0.05 4.44\n", - "beta_ Intercept 0.00 45.13 <0.005 inf\n", + " cmp to z p -log2(p)\n", + "param covariate \n", + "alpha_ adv_failure_rate 0.00 -26.62 <0.005 516.20\n", + " atk_value 0.00 0.42 0.67 0.58\n", + " data.sample.random_state 0.00 0.91 0.36 1.47\n", + " def_value 0.00 -1.11 0.27 1.90\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 0.03 0.97 0.04\n", + " model_layers 0.00 7.88 <0.005 48.13\n", + " predict_time 0.00 -8.73 <0.005 58.49\n", + " train_time 0.00 6.12 <0.005 30.02\n", + " Intercept 0.00 9.97 <0.005 75.31\n", + "beta_ Intercept 0.00 -15.46 <0.005 176.59\n", "---\n", - "Concordance = 0.86\n", - "AIC = 1411.15\n", - "log-likelihood ratio test = 942.02 on 10 df\n", - "-log2(p) of ll-ratio test = 648.58" + "Concordance = 0.82\n", + "AIC = 10873.68\n", + "log-likelihood ratio test = 961.93 on 8 df\n", + "-log2(p) of ll-ratio test = 669.73" ] }, "metadata": {}, @@ -1913,20 +1798,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'train_score': -1.221079966573897, 'test_score': -1.0974447812062609}\n" + "{'train_score': -3.617893432558881, 'test_score': -3.971188942813805}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_615644/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + "/tmp/ipykernel_773113/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", " pareto.set_yticklabels(labels)\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1960,7 +1845,7 @@ " \"log_logistic_aft.pdf\",\n", " target,\n", " duration_col,\n", - " \"Log Logistic AFT Model\",\n", + " \"Log Logistic AFR Model\",\n", " \"log_logistic\",\n", " replacement_dict=log_logistic_dict,\n", ")\n", @@ -1973,8 +1858,8 @@ " covariate_array = \"model_layers\",\n", " values_array = [18, 34, 50, 101, 152],\n", " replacement_dict=log_logistic_dict,\n", - " title=\"Partial Effects of No. of Layers on Failure Rate for Log-Logistic AFR\",\n", - " ylabel=\"Chance of Survival at time $T$ (seconds)\",\n", + " title=\"Survival Time for Log-Logistic AFR\",\n", + " ylabel=\"% Chance of Survival\",\n", " xlabel=\"Time $T$ (seconds)\",\n", " legend_kwargs={\"title\" : \"No. of Layers\", \"labels\" : [\"18\", \"34\", \"50\", \"101\", \"152\"]},\n", " \n", @@ -1983,16 +1868,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "3.151203500424281" + "8.62393284274078" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -2003,7 +1888,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -2028,67 +1913,62 @@ " \n", " \n", " AIC\n", - " Concordance Score\n", + " Concordance\n", " BIC\n", - " Estimated Mean Survival Time\n", - " Estimated Median Survival Time\n", - " Predicted Error from True Mean\n", - " Predicted Error from True Median\n", + " Train LL\n", + " Test LL\n", + " Mean ST\n", + " Median ST\n", " \n", " \n", " \n", " \n", " Weibull\n", - " 1470.20\n", - " 0.87\n", - " 1470.20\n", - " 7.34\n", - " 2.16\n", - " 0.19\n", - " -1.14\n", + " 11082.52\n", + " 0.84\n", + " 11082.52\n", + " -3.69\n", + " -4.00\n", + " 71.53\n", + " 10.23\n", " \n", " \n", " LogNormal\n", - " 1401.78\n", - " 0.86\n", - " 1401.78\n", - " 3.39\n", - " 1.94\n", - " -3.76\n", - " -1.36\n", + " 10769.08\n", + " 0.84\n", + " 10769.08\n", + " -3.58\n", + " -3.92\n", + " 122.81\n", + " 7.79\n", " \n", " \n", " LogLogistic\n", - " 1411.15\n", - " 0.86\n", - " 1411.15\n", - " 3.38\n", - " 1.99\n", - " -3.78\n", - " -1.31\n", + " 10873.68\n", + " 0.82\n", + " 10873.68\n", + " -3.62\n", + " -3.97\n", + " NaN\n", + " 6.62\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " AIC Concordance Score BIC \\\n", - "Weibull 1470.20 0.87 1470.20 \n", - "LogNormal 1401.78 0.86 1401.78 \n", - "LogLogistic 1411.15 0.86 1411.15 \n", - "\n", - " Estimated Mean Survival Time Estimated Median Survival Time \\\n", - "Weibull 7.34 2.16 \n", - "LogNormal 3.39 1.94 \n", - "LogLogistic 3.38 1.99 \n", + " AIC Concordance BIC Train LL Test LL Mean ST \\\n", + "Weibull 11082.52 0.84 11082.52 -3.69 -4.00 71.53 \n", + "LogNormal 10769.08 0.84 10769.08 -3.58 -3.92 122.81 \n", + "LogLogistic 10873.68 0.82 10873.68 -3.62 -3.97 NaN \n", "\n", - " Predicted Error from True Mean Predicted Error from True Median \n", - "Weibull 0.19 -1.14 \n", - "LogNormal -3.76 -1.36 \n", - "LogLogistic -3.78 -1.31 " + " Median ST \n", + "Weibull 10.23 \n", + "LogNormal 7.79 \n", + "LogLogistic 6.62 " ] }, - "execution_count": 53, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -2111,36 +1991,19 @@ "aft_data.index.name = \"Model\"\n", "aft_data.index = aft_dict.keys()\n", "aft_data[\"AIC\"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() ]\n", - "# aft_data[\"LogLikelihood\"] = [x.log_likelihood_ for x in aft_dict.values()]\n", - "aft_data[\"Concordance Score\"] = [x.concordance_index_ for x in aft_dict.values()]\n", + "aft_data[\"Concordance\"] = [x.concordance_index_ for x in aft_dict.values()]\n", "aft_data[\"BIC\"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()]\n", - "# aft_data['Train Log Likelihood'] = [x['train_score'] for x in score_list]\n", - "# aft_data['Test Log Likelihood'] = [x['test_score'] for x in score_list]\n", - "aft_data['Estimated Mean Survival Time'] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()]\n", - "# aft_data['True Mean Survival Time'] = (1 / (y_train/X_train[duration_col])).mean()\n", - "aft_data['Estimated Median Survival Time'] = [x.predict_median(X_train).median() for x in aft_dict.values()]\n", - "# aft_data['True Median Survival Time'] = (1 - (y_train/X_train[duration_col])).median()\n", - "success_rate = X_train['accuracy']\n", - "aft_data['True Mean Survival Time'] = ((1/X_train.accuracy).replace(np.inf, np.nan) * X_train.predict_time).mean()\n", - "aft_data['True Median Survival Time'] = ((1/X_train.accuracy).replace(np.inf, np.nan) * X_train.predict_time).median()\n", - "aft_data['Predicted Error from True Mean'] = aft_data['Estimated Mean Survival Time'] - aft_data['True Mean Survival Time']\n", - "aft_data['Predicted Error from True Median'] = aft_data['Estimated Median Survival Time'] - aft_data['True Median Survival Time']\n", - "del aft_data['True Mean Survival Time']\n", - "del aft_data['True Median Survival Time']\n", + "aft_data['Train LL'] = [x['train_score'] for x in score_list]\n", + "aft_data['Test LL'] = [x['test_score'] for x in score_list]\n", + "aft_data['Mean ST'] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()]\n", + "aft_data['Median ST'] = [x.predict_median(X_train).median() for x in aft_dict.values()]\n", "aft_data = aft_data.round(2)\n", "aft_data.to_csv(FOLDER / \"aft_comparison.csv\")\n", "logger.info(f\"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}\")\n", - "aft_data.round(2).to_latex(FOLDER / \"aft_comparison.tex\")\n", - "aft_data.round(2)\n", - "\n" + "aft_data = aft_data.round(2,)\n", + "aft_data.to_latex(FOLDER / \"aft_comparison.tex\", float_format=\"%.2f\", label = \"tab:mnist\", caption=\"Comparison of AFR Models on the MNIST dataset.\")\n", + "aft_data\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/scratch/.dvc/.gitignore b/examples/scratch/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/scratch/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/scratch/.dvc/config b/examples/scratch/.dvc/config new file mode 100644 index 00000000..e69de29b diff --git a/examples/scratch/.dvcignore b/examples/scratch/.dvcignore new file mode 100644 index 00000000..51973055 --- /dev/null +++ b/examples/scratch/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore From 3b3c5dd5ceaf6be2e520e42ea976ba3e20007883 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Fri, 29 Sep 2023 16:42:52 +0000 Subject: [PATCH 089/148] add conf for scratch folder --- examples/scratch/conf/attack/default.yaml | 9 + examples/scratch/conf/compile.yaml | 31 +++ examples/scratch/conf/data/default.yaml | 2 + examples/scratch/conf/data/torch_cifar.yaml | 11 + examples/scratch/conf/data/torch_mnist.yaml | 15 ++ examples/scratch/conf/default.yaml | 35 +++ examples/scratch/conf/deploy/default.yaml | 14 + examples/scratch/conf/deploy/pod.yaml | 30 +++ examples/scratch/conf/deploy/pvc.yaml | 11 + examples/scratch/conf/deploy/sclass.yaml | 10 + examples/scratch/conf/files/cifar.yaml | 21 ++ examples/scratch/conf/files/default.yaml | 21 ++ examples/scratch/conf/grid/attack/cw_0.yaml | 4 + examples/scratch/conf/grid/attack/cw_2.yaml | 5 + examples/scratch/conf/grid/attack/cw_inf.yaml | 4 + .../scratch/conf/grid/attack/deepfool.yaml | 5 + examples/scratch/conf/grid/attack/fgm.yaml | 4 + examples/scratch/conf/grid/attack/hsj.yaml | 5 + examples/scratch/conf/grid/attack/patch.yaml | 5 + examples/scratch/conf/grid/attack/pgd.yaml | 6 + examples/scratch/conf/grid/attack/pixel.yaml | 4 + .../scratch/conf/grid/attack/threshold.yaml | 4 + .../scratch/conf/grid/model/confidence.yaml | 4 + examples/scratch/conf/grid/model/default.yaml | 0 examples/scratch/conf/grid/model/fsq.yaml | 3 + .../scratch/conf/grid/model/gauss-in.yaml | 4 + .../scratch/conf/grid/model/gauss-out.yaml | 2 + examples/scratch/conf/hydra/default.yaml | 38 +++ .../hydra/sweeper/params/attack/cw_0.yaml | 4 + .../hydra/sweeper/params/attack/cw_2.yaml | 5 + .../hydra/sweeper/params/attack/cw_inf.yaml | 4 + .../hydra/sweeper/params/attack/deepfool.yaml | 5 + .../conf/hydra/sweeper/params/attack/fgm.yaml | 4 + .../conf/hydra/sweeper/params/attack/hsj.yaml | 6 + .../hydra/sweeper/params/attack/patch.yaml | 5 + .../conf/hydra/sweeper/params/attack/pgd.yaml | 6 + .../hydra/sweeper/params/attack/pixel.yaml | 4 + .../sweeper/params/attack/threshold.yaml | 4 + .../conf/hydra/sweeper/params/default.yaml | 11 + .../sweeper/params/model/confidence.yaml | 5 + .../hydra/sweeper/params/model/default.yaml | 1 + .../conf/hydra/sweeper/params/model/fsq.yaml | 4 + .../hydra/sweeper/params/model/gauss-in.yaml | 5 + .../hydra/sweeper/params/model/gauss-out.yaml | 3 + examples/scratch/conf/model/art/default.yaml | 7 + .../conf/model/art/initialize/default.yaml | 7 + .../model/art/postprocessor/confidence.yaml | 3 + .../model/art/postprocessor/gauss-out.yaml | 2 + .../conf/model/art/preprocessor/default.yaml | 0 .../conf/model/art/preprocessor/fsq.yaml | 3 + .../conf/model/art/preprocessor/gauss-in.yaml | 4 + examples/scratch/conf/model/torch_cifar.yaml | 13 + examples/scratch/conf/model/torch_mnist.yaml | 13 + examples/scratch/conf/plots.yaml | 248 ++++++++++++++++++ examples/scratch/conf/scorers/default.yaml | 9 + 55 files changed, 692 insertions(+) create mode 100644 examples/scratch/conf/attack/default.yaml create mode 100644 examples/scratch/conf/compile.yaml create mode 100644 examples/scratch/conf/data/default.yaml create mode 100644 examples/scratch/conf/data/torch_cifar.yaml create mode 100644 examples/scratch/conf/data/torch_mnist.yaml create mode 100644 examples/scratch/conf/default.yaml create mode 100644 examples/scratch/conf/deploy/default.yaml create mode 100644 examples/scratch/conf/deploy/pod.yaml create mode 100644 examples/scratch/conf/deploy/pvc.yaml create mode 100644 examples/scratch/conf/deploy/sclass.yaml create mode 100644 examples/scratch/conf/files/cifar.yaml create mode 100644 examples/scratch/conf/files/default.yaml create mode 100644 examples/scratch/conf/grid/attack/cw_0.yaml create mode 100644 examples/scratch/conf/grid/attack/cw_2.yaml create mode 100644 examples/scratch/conf/grid/attack/cw_inf.yaml create mode 100644 examples/scratch/conf/grid/attack/deepfool.yaml create mode 100644 examples/scratch/conf/grid/attack/fgm.yaml create mode 100644 examples/scratch/conf/grid/attack/hsj.yaml create mode 100644 examples/scratch/conf/grid/attack/patch.yaml create mode 100644 examples/scratch/conf/grid/attack/pgd.yaml create mode 100644 examples/scratch/conf/grid/attack/pixel.yaml create mode 100644 examples/scratch/conf/grid/attack/threshold.yaml create mode 100644 examples/scratch/conf/grid/model/confidence.yaml create mode 100644 examples/scratch/conf/grid/model/default.yaml create mode 100644 examples/scratch/conf/grid/model/fsq.yaml create mode 100644 examples/scratch/conf/grid/model/gauss-in.yaml create mode 100644 examples/scratch/conf/grid/model/gauss-out.yaml create mode 100644 examples/scratch/conf/hydra/default.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/attack/cw_0.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/attack/cw_2.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/attack/cw_inf.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/attack/deepfool.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/attack/fgm.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/attack/hsj.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/attack/patch.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/attack/pgd.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/attack/pixel.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/attack/threshold.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/default.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/model/confidence.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/model/default.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/model/fsq.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/model/gauss-in.yaml create mode 100644 examples/scratch/conf/hydra/sweeper/params/model/gauss-out.yaml create mode 100644 examples/scratch/conf/model/art/default.yaml create mode 100644 examples/scratch/conf/model/art/initialize/default.yaml create mode 100644 examples/scratch/conf/model/art/postprocessor/confidence.yaml create mode 100644 examples/scratch/conf/model/art/postprocessor/gauss-out.yaml create mode 100644 examples/scratch/conf/model/art/preprocessor/default.yaml create mode 100644 examples/scratch/conf/model/art/preprocessor/fsq.yaml create mode 100644 examples/scratch/conf/model/art/preprocessor/gauss-in.yaml create mode 100644 examples/scratch/conf/model/torch_cifar.yaml create mode 100644 examples/scratch/conf/model/torch_mnist.yaml create mode 100644 examples/scratch/conf/plots.yaml create mode 100644 examples/scratch/conf/scorers/default.yaml diff --git a/examples/scratch/conf/attack/default.yaml b/examples/scratch/conf/attack/default.yaml new file mode 100644 index 00000000..76b4d62d --- /dev/null +++ b/examples/scratch/conf/attack/default.yaml @@ -0,0 +1,9 @@ +data: ${data} +model: ${model} +_target_ : deckard.base.attack.Attack +init: + model: ${model} + _target_: deckard.base.attack.AttackInitializer + name: art.attacks.evasion.HopSkipJump +attack_size : 10 +method : evasion diff --git a/examples/scratch/conf/compile.yaml b/examples/scratch/conf/compile.yaml new file mode 100644 index 00000000..0d721a44 --- /dev/null +++ b/examples/scratch/conf/compile.yaml @@ -0,0 +1,31 @@ +attacks: + # CarliniL0Method: CW_0 + # CarliniL2Method: CW_2 + # CarliniLInfMethod: CW_inf + DeepFool: Deep + FastGradientMethod: FGM + HopSkipJump: HSJ + PixelAttack: Pixel + ProjectedGradientDescent: PGD + ThresholdAttack: Thresh +defences: + Control: Control + FeatureSqueezing: FSQ + GaussianAugmentation: Gauss-in + GaussianNoise: Gauss-out + HighConfidence: Conf +params: + # art.attacks.evasion.CarliniL0Method: attack.init.kwargs.confidence + # art.attacks.evasion.CarliniL2Method: attack.init.kwargs.confidence + # art.attacks.evasion.CarliniLInfMethod: attack.init.kwargs.confidence + Deep: attack.init.kwargs.nb_grads + FGM: attack.init.kwargs.eps + HSJ: attack.init.kwargs.max_iter + Pixel: attack.init.kwargs.th + PGD: attack.init.kwargs.eps + Thresh: attack.init.kwargs.th + Gauss-out: model.art.pipeline.postprocessor.kwargs.scale + Conf: model.art.pipeline.postprocessor.kwargs.cutoff + FSQ: model.art.pipeline.preprocessor.kwargs.bit_depth + Gauss-in: model.art.pipeline.preprocessor.kwargs.sigma + Control: model_layers diff --git a/examples/scratch/conf/data/default.yaml b/examples/scratch/conf/data/default.yaml new file mode 100644 index 00000000..5eb78278 --- /dev/null +++ b/examples/scratch/conf/data/default.yaml @@ -0,0 +1,2 @@ +defaults: + - torch_mnist diff --git a/examples/scratch/conf/data/torch_cifar.yaml b/examples/scratch/conf/data/torch_cifar.yaml new file mode 100644 index 00000000..b7603125 --- /dev/null +++ b/examples/scratch/conf/data/torch_cifar.yaml @@ -0,0 +1,11 @@ +_target_: deckard.base.data.Data +generate: + name: torch_cifar10 +sample: + random_state : 0 + stratify: True +sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/scratch/conf/data/torch_mnist.yaml b/examples/scratch/conf/data/torch_mnist.yaml new file mode 100644 index 00000000..9d79c036 --- /dev/null +++ b/examples/scratch/conf/data/torch_mnist.yaml @@ -0,0 +1,15 @@ +_target_: deckard.base.data.Data +generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist +sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True +sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/scratch/conf/default.yaml b/examples/scratch/conf/default.yaml new file mode 100644 index 00000000..4ae0442a --- /dev/null +++ b/examples/scratch/conf/default.yaml @@ -0,0 +1,35 @@ +defaults: + - _self_ + - data: torch_mnist + - model: torch_mnist + - attack: default + - files: default + - scorers: default + - stage : null + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +_target_ : deckard.base.experiment.Experiment +optimizers : accuracy +hydra: + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + direction: maximize + study_name: model + storage: sqlite:///model.db + n_jobs: 1 + params: + ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) + ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 10 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/scratch/conf/deploy/default.yaml b/examples/scratch/conf/deploy/default.yaml new file mode 100644 index 00000000..134da65f --- /dev/null +++ b/examples/scratch/conf/deploy/default.yaml @@ -0,0 +1,14 @@ +num_nodes: 1 +cluster_name: k8s-cluster +gpu_type: nvidia-tesla-v100 +gpu_count: 1 +gpu_driver_version: default +machine_type: n1-standard-2 +min_nodes: 1 +max_nodes: 1 +storage_config: conf/deploy/sclass.yaml +persistent_volume_claim: conf/deploy/pvc.yaml +pod : conf/deploy/pod.yaml +image_project: ubuntu-os-cloud +image_family: ubuntu-2204-lts +mount_directory: /mnt/filestore diff --git a/examples/scratch/conf/deploy/pod.yaml b/examples/scratch/conf/deploy/pod.yaml new file mode 100644 index 00000000..fc68306a --- /dev/null +++ b/examples/scratch/conf/deploy/pod.yaml @@ -0,0 +1,30 @@ +apiVersion: v1 +kind: Pod +metadata: + name: deckard +spec: + containers: + - name: deckard + image: ghcr.io/simplymathematics/deckard:main + imagePullPolicy: Always + workingDir: /deckard/examples/pytorch + args: ["python", "-m", "dvc", "repro"] + env: + - name: REDIS_HOST + value: "redis" + - name: REDIS_PORT + value: "6379" + - name: REDIS_DB + value: "0" + - name: REDIS_PASSWORD + value: "" + resources: + limits: + nvidia.com/gpu: 1 + volumeMounts: + - mountPath: /data + name: mypvc + volumes: + - name: mypvc + persistentVolumeClaim: + claimName: podpvc diff --git a/examples/scratch/conf/deploy/pvc.yaml b/examples/scratch/conf/deploy/pvc.yaml new file mode 100644 index 00000000..a7deee4d --- /dev/null +++ b/examples/scratch/conf/deploy/pvc.yaml @@ -0,0 +1,11 @@ +kind: PersistentVolumeClaim +apiVersion: v1 +metadata: + name: podpvc +spec: + accessModes: + - ReadWriteMany + storageClassName: filestore-sc + resources: + requests: + storage: 256Gi diff --git a/examples/scratch/conf/deploy/sclass.yaml b/examples/scratch/conf/deploy/sclass.yaml new file mode 100644 index 00000000..d566656c --- /dev/null +++ b/examples/scratch/conf/deploy/sclass.yaml @@ -0,0 +1,10 @@ +apiVersion: storage.k8s.io/v1 +kind: StorageClass +metadata: + name: filestore-sc +provisioner: filestore.csi.storage.gke.io +volumeBindingMode: Immediate +allowVolumeExpansion: true +parameters: + tier: standard + network: default diff --git a/examples/scratch/conf/files/cifar.yaml b/examples/scratch/conf/files/cifar.yaml new file mode 100644 index 00000000..e43cab72 --- /dev/null +++ b/examples/scratch/conf/files/cifar.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: cifar +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pt +params_file : params.yaml +# train_labels_file : train_labels.json +# test_labels_file : test_labels.json +# probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +# adv_probabilities_file: adv_probabilities.json +name: default diff --git a/examples/scratch/conf/files/default.yaml b/examples/scratch/conf/files/default.yaml new file mode 100644 index 00000000..b68ffc02 --- /dev/null +++ b/examples/scratch/conf/files/default.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: output +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pt +params_file : params.yaml +# train_labels_file : train_labels.json +test_labels_file : test_labels.json +# probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +# adv_probabilities_file: adv_probabilities.json +name: default diff --git a/examples/scratch/conf/grid/attack/cw_0.yaml b/examples/scratch/conf/grid/attack/cw_0.yaml new file mode 100644 index 00000000..20573ea2 --- /dev/null +++ b/examples/scratch/conf/grid/attack/cw_0.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.CarliniL0Method +init.max_iter : [10] +init.batch_size : [1024] +init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/scratch/conf/grid/attack/cw_2.yaml b/examples/scratch/conf/grid/attack/cw_2.yaml new file mode 100644 index 00000000..14965baa --- /dev/null +++ b/examples/scratch/conf/grid/attack/cw_2.yaml @@ -0,0 +1,5 @@ +init.model : ${model} +init.name : art.attacks.evasion.CarliniL0Method +init.max_iter : [10] +init.batch_size : [1024] +init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/scratch/conf/grid/attack/cw_inf.yaml b/examples/scratch/conf/grid/attack/cw_inf.yaml new file mode 100644 index 00000000..9e694da5 --- /dev/null +++ b/examples/scratch/conf/grid/attack/cw_inf.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.CarliniLInfMethod +init.max_iter : [10] +init.batch_size : [1024] +init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/scratch/conf/grid/attack/deepfool.yaml b/examples/scratch/conf/grid/attack/deepfool.yaml new file mode 100644 index 00000000..1fc9055a --- /dev/null +++ b/examples/scratch/conf/grid/attack/deepfool.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.DeepFool +init.max_iter : [10] +init.batch_size : [1024] +init.nb_grads : [10,100,1000] +init.eps: [0.01, 0.03, 0.3, 0.1, 1.0] diff --git a/examples/scratch/conf/grid/attack/fgm.yaml b/examples/scratch/conf/grid/attack/fgm.yaml new file mode 100644 index 00000000..41032d3e --- /dev/null +++ b/examples/scratch/conf/grid/attack/fgm.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.FastGradientMethod +init.eps: [0.01, 0.03, 0.3, 0.1] +init.norm: [inf, 1, 2] +init.eps_step: [0.001, 0.003, 0.01] diff --git a/examples/scratch/conf/grid/attack/hsj.yaml b/examples/scratch/conf/grid/attack/hsj.yaml new file mode 100644 index 00000000..1fba407d --- /dev/null +++ b/examples/scratch/conf/grid/attack/hsj.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.HopSkipJump +init.batch_size : [1, 4, 16, 65, 128] +init.max_iter : [1, 10, 100, 1000] +init.max_eval : [1, 10, 100, 1000] +init.init_eval : [1, 10, 100, 1000] diff --git a/examples/scratch/conf/grid/attack/patch.yaml b/examples/scratch/conf/grid/attack/patch.yaml new file mode 100644 index 00000000..e7285674 --- /dev/null +++ b/examples/scratch/conf/grid/attack/patch.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.ThresholdAttack +init.max_iter : [10] +init.batch_size : [1024] +init.scale_min : [0.01,] +init.scale_max : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/scratch/conf/grid/attack/pgd.yaml b/examples/scratch/conf/grid/attack/pgd.yaml new file mode 100644 index 00000000..49819d5f --- /dev/null +++ b/examples/scratch/conf/grid/attack/pgd.yaml @@ -0,0 +1,6 @@ +init.name : art.attacks.evasion.ProjectedGradientDescent +init.eps : [0.01, 0.03, 0.3, 0.1] +init.norm : [inf, 1, 2] +init.eps_step : [0.001, 0.003, 0.01] +init.batch_size : [1024] +init.max_iter : [10] diff --git a/examples/scratch/conf/grid/attack/pixel.yaml b/examples/scratch/conf/grid/attack/pixel.yaml new file mode 100644 index 00000000..9e0060a7 --- /dev/null +++ b/examples/scratch/conf/grid/attack/pixel.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.ProjectedGradientDescent +init.max_iter : [10] +init.batch_size : [1024] +init.th : [1,4,16,64,256] diff --git a/examples/scratch/conf/grid/attack/threshold.yaml b/examples/scratch/conf/grid/attack/threshold.yaml new file mode 100644 index 00000000..07437c71 --- /dev/null +++ b/examples/scratch/conf/grid/attack/threshold.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.ThresholdAttack +init.max_iter : [10] +init.batch_size : [1024] +init.th: [1,4,16,64,256] diff --git a/examples/scratch/conf/grid/model/confidence.yaml b/examples/scratch/conf/grid/model/confidence.yaml new file mode 100644 index 00000000..dca0ef2c --- /dev/null +++ b/examples/scratch/conf/grid/model/confidence.yaml @@ -0,0 +1,4 @@ +params: + art.postprocessor.name : [art.defences.postprocessor.HighConfidence] + art.postprocessor.cutoff : [.1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99] + art.postprocessor.apply_fit : [True, False] diff --git a/examples/scratch/conf/grid/model/default.yaml b/examples/scratch/conf/grid/model/default.yaml new file mode 100644 index 00000000..e69de29b diff --git a/examples/scratch/conf/grid/model/fsq.yaml b/examples/scratch/conf/grid/model/fsq.yaml new file mode 100644 index 00000000..9740b4b4 --- /dev/null +++ b/examples/scratch/conf/grid/model/fsq.yaml @@ -0,0 +1,3 @@ +art.preprocessor.name : [art.defences.preprocessor.FeatureSqueezing] +art.preprocessor.clip_values : [[0, 1]] +art.preprocessor.bit_depth : [1, 2, 4, 8, 16, 32, 64] diff --git a/examples/scratch/conf/grid/model/gauss-in.yaml b/examples/scratch/conf/grid/model/gauss-in.yaml new file mode 100644 index 00000000..21392a82 --- /dev/null +++ b/examples/scratch/conf/grid/model/gauss-in.yaml @@ -0,0 +1,4 @@ +art.preprocessor.name : art.defences.preprocessor.GaussianAugmentation +art.preprocessor.clip_values : ${art.initialize.clip_values} +art.preprocessor.sigma : [.01, .1, .2, .3, .5, 1] +art.preprocessor.ratio : [.1, .5, 1] diff --git a/examples/scratch/conf/grid/model/gauss-out.yaml b/examples/scratch/conf/grid/model/gauss-out.yaml new file mode 100644 index 00000000..377bbcc9 --- /dev/null +++ b/examples/scratch/conf/grid/model/gauss-out.yaml @@ -0,0 +1,2 @@ +art.postprocessor.name : art.defences.postprocessor.GaussianNoise +art.postprocessor.scale : [.01, .1, .2, .3, .5, 1, 2, 3, 5, 10] diff --git a/examples/scratch/conf/hydra/default.yaml b/examples/scratch/conf/hydra/default.yaml new file mode 100644 index 00000000..53e5bd5d --- /dev/null +++ b/examples/scratch/conf/hydra/default.yaml @@ -0,0 +1,38 @@ +defaults: + - params : default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : random + - override hydra/launcher : joblib + - override hydra/sweeper/params : default.yaml +run: + dir: output/${hydra.job.override_dirname}/seed=${data.sample.random_state} +job: + config: + override_dirname: + exclude_keys: + - seed + kv_sep: ":" + item_sep: "_" +sweep: + dir: multirun + subdir: ${hydra.job.override_dirname} +sweeper: + sampler: + _target_: optuna.samplers.RandomSampler + direction: maximize + study_name: model + storage: sqlite:///model.db + n_jobs: 4 + n_trials: 1 + +launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 8 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/scratch/conf/hydra/sweeper/params/attack/cw_0.yaml b/examples/scratch/conf/hydra/sweeper/params/attack/cw_0.yaml new file mode 100644 index 00000000..d28a31d4 --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/attack/cw_0.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.CarliniL0Method +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.confidence : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/scratch/conf/hydra/sweeper/params/attack/cw_2.yaml b/examples/scratch/conf/hydra/sweeper/params/attack/cw_2.yaml new file mode 100644 index 00000000..ec2ed44c --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/attack/cw_2.yaml @@ -0,0 +1,5 @@ +init.model : ${model} +init.name : art.attacks.evasion.CarliniL0Method +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.confidence : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/scratch/conf/hydra/sweeper/params/attack/cw_inf.yaml b/examples/scratch/conf/hydra/sweeper/params/attack/cw_inf.yaml new file mode 100644 index 00000000..99b16e45 --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/attack/cw_inf.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.CarliniLInfMethod +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.confidence : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/scratch/conf/hydra/sweeper/params/attack/deepfool.yaml b/examples/scratch/conf/hydra/sweeper/params/attack/deepfool.yaml new file mode 100644 index 00000000..8b56a8d9 --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/attack/deepfool.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.DeepFool +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.nb_grads : choice(10,100,1000) +init.eps: choice(0.01, 0.03, 0.3, 0.1, 1.0) diff --git a/examples/scratch/conf/hydra/sweeper/params/attack/fgm.yaml b/examples/scratch/conf/hydra/sweeper/params/attack/fgm.yaml new file mode 100644 index 00000000..c46ee4df --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/attack/fgm.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.FastGradientMethod +init.eps: choice(0.01, 0.03, 0.3, 0.1) +init.norm: choice(inf, 1, 2) +init.eps_step: choice(0.001, 0.003, 0.01) diff --git a/examples/scratch/conf/hydra/sweeper/params/attack/hsj.yaml b/examples/scratch/conf/hydra/sweeper/params/attack/hsj.yaml new file mode 100644 index 00000000..45b3af5d --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/attack/hsj.yaml @@ -0,0 +1,6 @@ +attack: + init.name : art.attacks.evasion.HopSkipJump + init.batch_size : choice(1, 4, 16, 65, 128) + init.max_iter : choice(1, 10, 100, 1000) + init.max_eval : choice(1, 10, 100, 1000) + init.init_eval : choice(1, 10, 100, 1000) diff --git a/examples/scratch/conf/hydra/sweeper/params/attack/patch.yaml b/examples/scratch/conf/hydra/sweeper/params/attack/patch.yaml new file mode 100644 index 00000000..c9409de4 --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/attack/patch.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.ThresholdAttack +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.scale_min : choice(0.01,) +init.scale_max : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/scratch/conf/hydra/sweeper/params/attack/pgd.yaml b/examples/scratch/conf/hydra/sweeper/params/attack/pgd.yaml new file mode 100644 index 00000000..42bc9514 --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/attack/pgd.yaml @@ -0,0 +1,6 @@ +init.name : art.attacks.evasion.ProjectedGradientDescent +init.eps : choice(0.01, 0.03, 0.3, 0.1) +init.norm : choice(inf, 1, 2) +init.eps_step : choice(0.001, 0.003, 0.01) +init.batch_size : choice(1024) +init.max_iter : choice(10) diff --git a/examples/scratch/conf/hydra/sweeper/params/attack/pixel.yaml b/examples/scratch/conf/hydra/sweeper/params/attack/pixel.yaml new file mode 100644 index 00000000..84450f38 --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/attack/pixel.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.ProjectedGradientDescent +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.th : choice(1,4,16,64,256) diff --git a/examples/scratch/conf/hydra/sweeper/params/attack/threshold.yaml b/examples/scratch/conf/hydra/sweeper/params/attack/threshold.yaml new file mode 100644 index 00000000..d6079457 --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/attack/threshold.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.ThresholdAttack +init.max_iter : choice(10) +init.batch_size : choice(1024) +init.th: choice(1,4,16,64,256) diff --git a/examples/scratch/conf/hydra/sweeper/params/default.yaml b/examples/scratch/conf/hydra/sweeper/params/default.yaml new file mode 100644 index 00000000..49b1de65 --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/default.yaml @@ -0,0 +1,11 @@ +defaults: + - _self_ + - data: torch_mnist + - model: torch_mnist + - files: default + - scorers: default + - stage : null + - params/model : null + - params/attack : null +++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) +++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001) diff --git a/examples/scratch/conf/hydra/sweeper/params/model/confidence.yaml b/examples/scratch/conf/hydra/sweeper/params/model/confidence.yaml new file mode 100644 index 00000000..cfda12d2 --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/model/confidence.yaml @@ -0,0 +1,5 @@ + +defence: + art.postprocessor.name : [art.defences.postprocessor.HighConfidence] + art.postprocessor.cutoff : [.1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99] + art.postprocessor.apply_fit : [True, False] diff --git a/examples/scratch/conf/hydra/sweeper/params/model/default.yaml b/examples/scratch/conf/hydra/sweeper/params/model/default.yaml new file mode 100644 index 00000000..8163e568 --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/model/default.yaml @@ -0,0 +1 @@ +defence: diff --git a/examples/scratch/conf/hydra/sweeper/params/model/fsq.yaml b/examples/scratch/conf/hydra/sweeper/params/model/fsq.yaml new file mode 100644 index 00000000..bb508203 --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/model/fsq.yaml @@ -0,0 +1,4 @@ +defence: + art.preprocessor.name : [art.defences.preprocessor.FeatureSqueezing] + art.preprocessor.clip_values : [[0, 1]] + art.preprocessor.bit_depth : [1, 2, 4, 8, 16, 32, 64] diff --git a/examples/scratch/conf/hydra/sweeper/params/model/gauss-in.yaml b/examples/scratch/conf/hydra/sweeper/params/model/gauss-in.yaml new file mode 100644 index 00000000..fd45ea84 --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/model/gauss-in.yaml @@ -0,0 +1,5 @@ +defence: + art.preprocessor.name : art.defences.preprocessor.GaussianAugmentation + art.preprocessor.clip_values : ${art.initialize.clip_values} + art.preprocessor.sigma : [.01, .1, .2, .3, .5, 1] + art.preprocessor.ratio : [.1, .5, 1] diff --git a/examples/scratch/conf/hydra/sweeper/params/model/gauss-out.yaml b/examples/scratch/conf/hydra/sweeper/params/model/gauss-out.yaml new file mode 100644 index 00000000..8abd9ee1 --- /dev/null +++ b/examples/scratch/conf/hydra/sweeper/params/model/gauss-out.yaml @@ -0,0 +1,3 @@ +defence: + art.postprocessor.name : art.defences.postprocessor.GaussianNoise + art.postprocessor.scale : [.01, .1, .2, .3, .5, 1, 2, 3, 5, 10] diff --git a/examples/scratch/conf/model/art/default.yaml b/examples/scratch/conf/model/art/default.yaml new file mode 100644 index 00000000..4251627d --- /dev/null +++ b/examples/scratch/conf/model/art/default.yaml @@ -0,0 +1,7 @@ +defaults: + - initialize : default + # - preprocessor: fsq + # - postprocessor: confidence +data : ${..data} +library : ${..library} +_target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/scratch/conf/model/art/initialize/default.yaml b/examples/scratch/conf/model/art/initialize/default.yaml new file mode 100644 index 00000000..6c5edde5 --- /dev/null +++ b/examples/scratch/conf/model/art/initialize/default.yaml @@ -0,0 +1,7 @@ +criterion: + name : torch.nn.CrossEntropyLoss +optimizer: + name : torch.optim.SGD + lr : 0.01 + momentum : 0.9 +clip_values : [0.0, 1.0] diff --git a/examples/scratch/conf/model/art/postprocessor/confidence.yaml b/examples/scratch/conf/model/art/postprocessor/confidence.yaml new file mode 100644 index 00000000..6fcdde95 --- /dev/null +++ b/examples/scratch/conf/model/art/postprocessor/confidence.yaml @@ -0,0 +1,3 @@ + +name : art.defences.postprocessor.HighConfidence +cutoff : .1 diff --git a/examples/scratch/conf/model/art/postprocessor/gauss-out.yaml b/examples/scratch/conf/model/art/postprocessor/gauss-out.yaml new file mode 100644 index 00000000..c912ebd9 --- /dev/null +++ b/examples/scratch/conf/model/art/postprocessor/gauss-out.yaml @@ -0,0 +1,2 @@ +name : art.defences.postprocessor.GaussianNoise +scale : 1 diff --git a/examples/scratch/conf/model/art/preprocessor/default.yaml b/examples/scratch/conf/model/art/preprocessor/default.yaml new file mode 100644 index 00000000..e69de29b diff --git a/examples/scratch/conf/model/art/preprocessor/fsq.yaml b/examples/scratch/conf/model/art/preprocessor/fsq.yaml new file mode 100644 index 00000000..27ddf58b --- /dev/null +++ b/examples/scratch/conf/model/art/preprocessor/fsq.yaml @@ -0,0 +1,3 @@ +name : art.defences.preprocessor.FeatureSqueezing +clip_values : ${model.art.initialize.clip_values} +bit_depth : 64 diff --git a/examples/scratch/conf/model/art/preprocessor/gauss-in.yaml b/examples/scratch/conf/model/art/preprocessor/gauss-in.yaml new file mode 100644 index 00000000..c438e8f5 --- /dev/null +++ b/examples/scratch/conf/model/art/preprocessor/gauss-in.yaml @@ -0,0 +1,4 @@ +name : art.defences.preprocessor.GaussianAugmentation +clip_values : ${model.art.initialize.clip_values} +sigma : 1 +ratio : .5 diff --git a/examples/scratch/conf/model/torch_cifar.yaml b/examples/scratch/conf/model/torch_cifar.yaml new file mode 100644 index 00000000..2562064a --- /dev/null +++ b/examples/scratch/conf/model/torch_cifar.yaml @@ -0,0 +1,13 @@ +defaults: + - art : default +data: ${data} +init: + _target_: deckard.base.model.ModelInitializer + name : torch_example.ResNet18 + num_channels: 3 + num_classes: 10 +_target_: deckard.base.model.Model +trainer: + nb_epoch: 20 + batch_size: 1024 +library : pytorch diff --git a/examples/scratch/conf/model/torch_mnist.yaml b/examples/scratch/conf/model/torch_mnist.yaml new file mode 100644 index 00000000..762addc1 --- /dev/null +++ b/examples/scratch/conf/model/torch_mnist.yaml @@ -0,0 +1,13 @@ + +defaults: + - art : default +data: ${data} +init: + _target_: deckard.base.model.ModelInitializer + num_channels : 1 + name : torch_example.ResNet18 +_target_: deckard.base.model.Model +trainer: + nb_epoch: 20 + batch_size: 1024 +library : pytorch diff --git a/examples/scratch/conf/plots.yaml b/examples/scratch/conf/plots.yaml new file mode 100644 index 00000000..1d9af6c7 --- /dev/null +++ b/examples/scratch/conf/plots.yaml @@ -0,0 +1,248 @@ +cat_plot: +- file: adv_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: linear + titles: Adversarial Accuracy vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_accuracy + ylabels: Adv. Accuracy + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + titles: Benign Accuracy vs Defence Type + x: def_gen + xlabels: Defence Type + y: accuracy + ylabels: Ben. Accuracy + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: log + titles: $\bar{C}_{ben.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_failure + ylabels: $\bar{C}_{ben.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: log + titles: $\bar{C}_{adv.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_adv_failure + ylabels: $\bar{C}_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_train_time_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: $\bar{C}_{adv.}$ vs Attack Type + x: atk_gen + xlabels: Attack Type + y: training_time_per_adv_failure + ylabels: $\bar{C}_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_test_time_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: Adversarial Failure Rate vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_failure_rate + ylabels: Adv. Failure Rate + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +# - file: adv_accuracy_vs_defence_type.pdf +# hue: model_name +# kind: boxen +# legend_title: Model Name +# titles: Adversarial Accuracy vs Defence Type +# x: def_gen +# xlabels: Defence Type +# y: adv_accuracy +# ylabels: Adv. Accuracy +# rotation : 90 +# hue_order: +# - ResNet18 +# - ResNet34 +# - ResNet50 +# - ResNet101 +# - ResNet152 +- file: adv_accuracy_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: Adversarial Accuracy vs Attack Type + x: atk_gen + xlabels: Attack Type + y: adv_accuracy + ylabels: Adv. Accuracy + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_failure_rate_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: Benign Failure Rate vs Defence Type + x: def_gen + xlabels: Defence Type + y: failure_rate + ylabels: Ben. Failure Rate + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +line_plot: +- file: def_param_vs_accuracy.pdf + hue: def_gen + legend: {} + title: Accuracy vs Defence Strength + x: def_value + x_scale: linear + xlabel: Defence Control Parameter + y: accuracy + y_scale: + ylabel: Accuracy + hue_order: + - Control + - Gauss-in + - Gauss-out + - Conf + - FSQ +- file: def_param_vs_adv_accuracy.pdf + hue: def_gen + legend: {} + title: Adversarial Accuracy vs Defence Strength + x: def_value + x_scale: linear + xlabel: Defence Control Parameter + y: adv_accuracy + y_scale: + ylabel: Adv. Accuracy + hue_order: + - Control + - Gauss-in + - Gauss-out + - Conf + - FSQ +- file: def_param_vs_adv_failure_rate.pdf + hue: def_gen + legend: {} + title: Adversarial Failure Rate vs Defence Strength + x: def_value + x_scale: linear + xlabel: Defence Control Parameter + y: adv_failure_rate + y_scale: log + ylabel: Adv. Failure Rate + hue_order: + - Control + - Gauss-in + - Gauss-out + - Conf + - FSQ +- file: atk_param_vs_accuracy.pdf + hue: atk_gen + legend: {} + title: Adversarial Accuracy vs Attack Strength + x: atk_value + x_scale: linear + xlabel: Attack Control Parameter + y: adv_accuracy + y_scale: + ylabel: Adv. Accuracy + hue_order: + - FGM + - PGD + - Deep + - HSJ + - Pixel + - Thresh +scatter_plot: +- x: train_time_per_sample + y: adv_failure_rate + hue: model_name + xlabel: Training Time Per Sample + ylabel: Adversarial Failure Rate + title: Adversarial Failure Rate vs Training Time + file: adv_failure_rate_vs_train_time.pdf + y_scale: log + x_scale: linear + legend: + title: Model Name + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 + # style : atk_gen +# - x: train_time_per_sample +# y: adv_failure_rate +# hue: model_name +# xlabel: Training Time Per Sample +# ylabel: Adversarial Failure Rate +# title: Adversarial Failure Rate vs Training Time for each Defence +# file: adv_failure_rate_vs_train_time_def.pdf +# y_scale: log +# x_scale: linear +# legend: +# title: Model Name +# # style : def_gen diff --git a/examples/scratch/conf/scorers/default.yaml b/examples/scratch/conf/scorers/default.yaml new file mode 100644 index 00000000..4503c5bf --- /dev/null +++ b/examples/scratch/conf/scorers/default.yaml @@ -0,0 +1,9 @@ +_target_: deckard.base.scorer.ScorerDict +accuracy: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.accuracy_score + direction: maximize +log_loss: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.log_loss + direction: minimize From 4c8bce65d4f82b4fee471bd0aa99408ef6fa1c41 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Fri, 29 Sep 2023 19:27:35 +0000 Subject: [PATCH 090/148] add compute_success as default metric --- deckard/base/attack/attack.py | 18 ++++++++++++------ deckard/base/experiment/experiment.py | 3 +++ deckard/layers/experiment.py | 3 +++ 3 files changed, 18 insertions(+), 6 deletions(-) diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index 39d4a6ea..43c093c8 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -7,7 +7,7 @@ from time import process_time_ns from omegaconf import DictConfig, OmegaConf from hydra.utils import instantiate -from art.utils import to_categorical +from art.utils import to_categorical, compute_success from ..data import Data from ..model import Model from ..utils import my_hash @@ -136,14 +136,16 @@ def __call__( ): time_dict = {} results = {} + kwargs = deepcopy(self.init.kwargs) + scale_max = kwargs.pop("scale_max", 1) + targeted = kwargs.pop("targeted", False) + ben_samples = data[1][: self.attack_size] if attack_file is not None and Path(attack_file).exists(): samples = self.data.load(attack_file) else: - ben_samples = data[0][: self.attack_size] + atk = self.init(model=model, attack_size=self.attack_size) - kwargs = deepcopy(self.init.kwargs) - scale_max = kwargs.pop("scale_max", 1) - targeted = kwargs.pop("targeted", False) + if targeted is True: kwargs.update({"y": data[2][: self.attack_size]}) if "AdversarialPatch" in self.name: @@ -152,13 +154,17 @@ def __call__( samples = atk.apply_patch(ben_samples, scale=scale_max) else: start = process_time_ns() - samples = atk.generate(ben_samples) + samples = atk.generate(ben_samples, **kwargs) end = process_time_ns() time_dict.update({"adv_fit_time": (end - start) / 1e9}) time_dict.update( {"adv_fit_time_per_sample": (end - start) / (len(samples) * 1e9)}, ) results["adv_samples"] = samples + try: + results['adv_success'] = compute_success(classifier=model, x_clean=ben_samples, labels=data[3][:self.attack_size], x_adv=samples, targeted=False) + except TypeError as e: + logger.error(f"Failed to compute success rate. Error: {e}") if attack_file is not None: self.data.save(samples, attack_file) if adv_predictions_file is not None and Path(adv_predictions_file).exists(): diff --git a/deckard/base/experiment/experiment.py b/deckard/base/experiment/experiment.py index dd274fce..c360c80d 100644 --- a/deckard/base/experiment/experiment.py +++ b/deckard/base/experiment/experiment.py @@ -217,6 +217,9 @@ def __call__(self): if "time_dict" in adv_results: adv_time_dict = adv_results["time_dict"] score_dict.update(**adv_time_dict) + if "adv_success" in adv_results: + adv_success = adv_results["adv_success"] + score_dict.update({"adv_success": adv_success}) # ######################################################################### if self.scorers is not None: if "probs" in locals() and "preds" not in locals(): diff --git a/deckard/layers/experiment.py b/deckard/layers/experiment.py index 5ce46fe9..2ac2bcb7 100644 --- a/deckard/layers/experiment.py +++ b/deckard/layers/experiment.py @@ -25,6 +25,7 @@ def get_dvc_stage_params( params_file="params.yaml", pipeline_file="dvc.yaml", directory=".", + name = None, ): logger.info( f"Getting params for stage {stage} from {params_file} and {pipeline_file} in {directory}.", @@ -37,6 +38,8 @@ def get_dvc_stage_params( params["files"] = dict(unflattened_files.get("files", unflattened_files)) params["files"]["_target_"] = "deckard.base.files.FileConfig" params["files"]["stage"] = stage + if name is not None: + params["files"]["name"] = name return params From ae5045dafc547fd170eca75f6c396a2289ba9c9c Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Fri, 29 Sep 2023 19:32:36 +0000 Subject: [PATCH 091/148] update pytorch --- examples/pytorch/conf/deploy.yaml | 14 - examples/pytorch/conf/plots.yaml | 2 +- examples/pytorch/dvc.lock | 36 +- examples/pytorch_cifar/afr.py | 494 +++++++++++++++++++++++ examples/pytorch_cifar/attacks.sh | 24 +- examples/pytorch_cifar/conf/compile.yaml | 3 +- examples/pytorch_cifar/conf/default.yaml | 2 +- examples/pytorch_cifar/conf/plots.yaml | 158 ++++++-- examples/pytorch_cifar/dvc.lock | 204 +++++++--- examples/pytorch_cifar/dvc.yaml | 51 ++- examples/pytorch_cifar/models.sh | 12 +- examples/pytorch_cifar/plots.py | 97 ++++- 12 files changed, 913 insertions(+), 184 deletions(-) delete mode 100644 examples/pytorch/conf/deploy.yaml create mode 100644 examples/pytorch_cifar/afr.py diff --git a/examples/pytorch/conf/deploy.yaml b/examples/pytorch/conf/deploy.yaml deleted file mode 100644 index c64d7b87..00000000 --- a/examples/pytorch/conf/deploy.yaml +++ /dev/null @@ -1,14 +0,0 @@ -num_nodes: 1 -cluster_name: k8s-cluster -gpu_type: nvidia-tesla-v100 -gpu_count: 1 -gpu_driver_version: default -machine_type: n1-standard-2 -min_nodes: 1 -max_nodes: 1 -storage_config: sclass.yaml -persistent_volume_claim: pvc.yaml -pod : pod.yaml -image_project: ubuntu-os-cloud -image_family: ubuntu-2204-lts -mount_directory: /mnt/filestore diff --git a/examples/pytorch/conf/plots.yaml b/examples/pytorch/conf/plots.yaml index cbd9929d..1d9af6c7 100644 --- a/examples/pytorch/conf/plots.yaml +++ b/examples/pytorch/conf/plots.yaml @@ -224,7 +224,7 @@ scatter_plot: title: Adversarial Failure Rate vs Training Time file: adv_failure_rate_vs_train_time.pdf y_scale: log - x_scale: log + x_scale: linear legend: title: Model Name hue_order: diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index a00e52df..474422e7 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -447,10 +447,10 @@ stages: output/reports/attack.csv deps: - path: ResNet101.db - md5: 33b4542eb9688ff88ada0cd81f1f3005 - size: 1261568 + md5: 744f187243001db70f63c8161d7f913f + size: 1314816 - path: ResNet152.db - md5: 4de6afe496d53faffdb9427adb7f692f + md5: e0fcb3876ec636b12d7dc9e71b9d1e1c size: 1314816 - path: ResNet18.db md5: aa69b1818219c139f8e33ac205771ec1 @@ -462,13 +462,13 @@ stages: md5: 9c9bc0aab00251ca5e9bd210366bc055 size: 1339392 - path: output/reports/attack/ - md5: 65f8f45c7eb271be41d430011eb11b0f.dir - size: 19551705509 - nfiles: 46952 + md5: b02916ecb3056906c2f5293ff7989271.dir + size: 21892376057 + nfiles: 52821 outs: - path: output/reports/attack.csv - md5: f9acccf19464e125d3d8fafdc7799ec5 - size: 31071757 + md5: ee495ac7761566f86facc46215695fae + size: 34597566 compile@train: cmd: python -m deckard.layers.compile --report_folder output/reports/train --results_file output/reports/train.csv @@ -499,18 +499,18 @@ stages: md5: 6ef2a88669f50dcdc0329e777dd7f9d6 size: 1235 - path: output/reports/attack/default/score_dict.json - md5: 879e9e986f4f69c7dea121cc78bececb - size: 524 + md5: b92be5957cc92fc91e76d42e654151f9 + size: 533 outs: - path: ResNet152.db - md5: 4de6afe496d53faffdb9427adb7f692f + md5: e0fcb3876ec636b12d7dc9e71b9d1e1c size: 1314816 plot: cmd: python plots.py --path output/plots/ --file output/reports/attack.csv deps: - path: ResNet101.db - md5: 33b4542eb9688ff88ada0cd81f1f3005 - size: 1261568 + md5: 744f187243001db70f63c8161d7f913f + size: 1314816 - path: ResNet18.db md5: aa69b1818219c139f8e33ac205771ec1 size: 110592 @@ -521,13 +521,13 @@ stages: md5: 9c9bc0aab00251ca5e9bd210366bc055 size: 1339392 - path: output/reports/attack.csv - md5: f9acccf19464e125d3d8fafdc7799ec5 - size: 31071757 + md5: ee495ac7761566f86facc46215695fae + size: 34597566 outs: - path: output/plots/ - md5: 4ec176a3a3c47092f27a664e2e0b8089.dir - size: 11959890 - nfiles: 13 + md5: 1c5b6ac91813c03346b4ba4073fe6393.dir + size: 5642718 + nfiles: 14 attacks@ResNet101: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet101 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet101.db --config-name diff --git a/examples/pytorch_cifar/afr.py b/examples/pytorch_cifar/afr.py new file mode 100644 index 00000000..e0b326a0 --- /dev/null +++ b/examples/pytorch_cifar/afr.py @@ -0,0 +1,494 @@ +# %% +import pandas as pd +import numpy as np +from pathlib import Path +import seaborn as sns + +import matplotlib.pyplot as plt + +from sklearn.preprocessing import StandardScaler +from sklearn.model_selection import train_test_split + + + +from paretoset import paretoset +from lifelines import WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter, CoxPHFitter, AalenAdditiveFitter, AalenJohansenFitter +from plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling +import matplotlib +import argparse +from pathlib import Path +import logging + +logger = logging.getLogger(__name__) + +# %% +font = {'family' : 'Times New Roman', + 'weight' : 'bold', + 'size' : 22} + +matplotlib.rc('font', **font) + +# %% +FOLDER = Path("output/plots/") +csv_file = FOLDER / "data.csv" +data = pd.read_csv(csv_file, index_col=0) +data.columns = data.columns.str.strip() +data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x) +data.def_value.replace("", 0, inplace=True) +data.atk_value.replace("", 0, inplace=True) +data = drop_frames_without_results(data) +data = calculate_failure_rate(data) +data = min_max_scaling(data) +data.dropna(axis=0, subset=['atk_value', 'atk_param'], inplace=True) +data.dropna(axis=0, subset=['def_value', 'def_param'], inplace=True) +# data=data[data['def_gen'] == 'Gauss-in'] +# data=data[data['atk_gen'] == 'HSJ'] + +print( + "Adversarial Accuracy:", "\n", + "ResNet152:", data[data['model_layers'] == 152].adv_accuracy.mean(skipna=True), "\n", + "Resnet101:", data[data['model_layers'] == 101].adv_accuracy.mean(skipna=True), "\n", + "Resnet50:", data[data['model_layers'] == 50].adv_accuracy.mean(skipna=True), "\n", + "Resnet34:", data[data['model_layers'] == 34].adv_accuracy.mean(skipna=True), "\n", + "Resnet18:", data[data['model_layers'] == 18].adv_accuracy.mean(skipna=True), "\n", +) + + + +# %% + +def plot_aft( + df, + file, + event_col, + duration_col, + title, + mtype, + xlabel=None, + ylabel=None, + replacement_dict={}, + **kwargs, +): + plt.gcf().clear() + if mtype == "weibull": + aft = WeibullAFTFitter(**kwargs) + elif mtype == "log_normal": + aft = LogNormalAFTFitter(**kwargs) + elif mtype == "log_logistic": + aft = LogLogisticAFTFitter(**kwargs) + elif mtype == "cox": + aft = CoxPHFitter(**kwargs) + elif mtype == "aalen_additive": + aft = AalenAdditiveFitter(**kwargs) + elif mtype == "aalen_johansen": + aft = AalenJohansenFitter(**kwargs) + else: + raise TypeError(f"Model type {mtype} not supported") + assert ( + duration_col in df.columns + ), f"Column {duration_col} not in dataframe with columns {df.columns}" + if event_col is not None: + assert ( + event_col in df.columns + ), f"Column {event_col} not in dataframe with columns {df.columns}" + aft.fit(df, duration_col=duration_col, event_col=event_col) + ax = aft.plot() + labels = ax.get_yticklabels() + labels = [label.get_text() for label in labels] + for k, v in replacement_dict.items(): + labels = [label.replace(k, v) for label in labels] + ax.set_yticklabels(labels) + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_title(title) + ax.get_figure().tight_layout() + ax.get_figure().savefig(FOLDER / file) + logger.info(f"Saved graph to {FOLDER / file}") + plt.gcf().clear() + return ax, aft + +def plot_partial_effects( + file, + aft, + covariate_array, + values_array, + title, + xlabel="Covariate", + ylabel="Failure rate", + legend_kwargs={"loc": "upper left"}, + replacement_dict={}, + cmap='coolwarm', + **kwargs, + ): + plt.gcf().clear() + # kwargs.pop("replacement_dict") + pareto = aft.plot_partial_effects_on_outcome(covariate_array, values_array, cmap=cmap, **kwargs) + labels = pareto.get_yticklabels() + labels = [label.get_text() for label in labels] + for k, v in replacement_dict.items(): + labels = [label.replace(k, v) for label in labels] + pareto.set_yticklabels(labels) + pareto.legend(**legend_kwargs) + pareto.set_ylabel(ylabel) + pareto.set_xlabel(xlabel) + pareto.set_title(title) + pareto.get_figure().tight_layout() + pareto.get_figure().savefig(FOLDER / file) + logger.info(f"Saved graph to {FOLDER / file}") + plt.gcf().clear() + return pareto + +def score_model(aft, train, test): + train_score = aft.score(train) + test_score = aft.score(test) + scores = {'train_score': train_score, 'test_score': test_score} + plt.show() + return scores + + +def clean_data_for_aft( + data, kwarg_list, target="adv_failure_rate" +): + subset = data.copy() + assert target in subset, f"Target {target} not in dataframe with columns {subset.columns}" + + cleaned = pd.DataFrame() + kwarg_list.append(target) + for kwarg in kwarg_list: + cleaned = pd.concat([cleaned, subset[kwarg]], axis=1) + cols = cleaned.columns + cleaned = pd.DataFrame(subset, columns=cols) + + + # if "accuracy" in cleaned.columns: + # cleaned = cleaned[~cleaned[cleaned['accuracy'] != 1e10]] + # cleaned = cleaned[~cleaned[cleaned['accuracy'] != -1e10]] + # if "adv_accuracy" in cleaned.columns: + # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != 1e10]] + # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != -1e10]] + cleaned.dropna(inplace=True, how='any', axis=0) + y = cleaned[target] + assert target in cleaned, f"Target {target} not in dataframe with columns {cleaned.columns}" + return cleaned, y, data + +# %% + + +kwarg_list = [ + # "accuracy", + "train_time", + "predict_time", + "atk_value", + "def_value", + "data.sample.random_state", + "adv_failure_rate", + # "failure_rate", + "model_layers", + "adv_fit_time", + # "atk_param", + # "def_param", + + "model.art.pipeline.initialize.kwargs.optimizer.lr", + # "def_gen", + # "atk_gen", + # "adv_log_loss", + # "adv_accuracy", + # "adv_accuracy", +] + + +# cleaned['accuracy'] = y + +# %% +data.loc[:, "adv_failures"] = (1 - data.loc[:, "adv_accuracy"]) +data.loc[:, "ben_failures"] = (1 - data.loc[:, "accuracy"]) +target = "adv_failures" +duration_col = "adv_fit_time" +cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) +X_train, X_test, y_train, y_test = train_test_split(cleaned, y, test_size=0.2, random_state=42) +assert target in cleaned, f"Target {target} not in dataframe with columns {cleaned.columns}" + + + + +# %% + +# from sklearn.preprocessing import PowerTransformer +# pt = PowerTransformer(method='yeo-johnson', standardize=False) +# del X_train[target] +# del X_test[target] +# X_train_cols = X_train.columns +# X_train = pt.fit(X_train).transform(X_train) +# X_test = pt.transform(X_test) +# X_train = pd.DataFrame(X_train, columns=X_train_cols) +# X_test = pd.DataFrame(X_test, columns=X_train_cols) +# X_train[target] = y_train +# y_train = X_train[target] + + +# %% +# sense_dict ={ +# "accuracy" : "max", +# "train_time" : "min", +# "predict_time" : "min", +# # "atk_value" : "diff", +# # "def_value" : "diff", +# "data.sample.random_state" : "diff", +# "adv_accuracy" : "min", +# "model_layers" : "diff", +# # "adv_fit_time" : "min", +# # "atk_param" : "diff", +# # "def_param" : "diff", +# "model.art.pipeline.initialize.kwargs.optimizer.lr" : "diff", +# # "adv_failure_rate" : "maximize", +# } +# subset = X_train.loc[:, sense_dict.keys()] +# senses = sense_dict.values() +# these = paretoset(subset) +# X_train = X_train.iloc[these, :] + + +# %% +weibull_dict = { + "Intercept: rho_": "$\\rho$", + "Intercept: lambda_": "$\lambda$", + "data.sample.random_state: lambda_": "Random State", + "def_value: lambda_": "Defence Strength", + "atk_value: lambda_": "Attack Strength", + "train_time: lambda_": "Training Time", + "predict_time: lambda_": "Inference Time", + "adv_accuracy: lambda_": "Adv. Accuracy", + "accuracy: lambda_": "Ben. Accuracy", + "adv_fit_time: lambda_": "Adv. Fit Time", + "adv_log_loss: lambda_": "Adv. Log Loss", + "adv_failure_rate: lambda_": "Adv. Failure Rate", + "failure_rate: lambda_": "Ben. Failure Rate", + "model_layers: lambda_": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_" : "Learning Rate", + "def_gen" : "Defence", +} + +weibull_afr, wft = plot_aft( + X_train, + file = "weibull_aft.pdf", + event_col = target, + duration_col = duration_col, + title = "Weibull AFR Model", + mtype = "weibull", + replacement_dict=weibull_dict, +) +wft.print_summary() +wft_scores = score_model(wft, X_train, X_test) + + +# %% +pareto_dict = { + "model_layers=18": "18", + "model_layers=34": "34", + "model_layers=50": "50", + "model_layers=101": "101", + "model_layers=152": "152", +} +pareto_weibull = plot_partial_effects( + file = "weibull_partial_effects.pdf", + aft = wft, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], + title="Partial Effects of No. of Layers on Failure Rate for Weibull AFR", + replacement_dict=pareto_dict, + ylabel="% Chance of Survival", + xlabel="Time $T$ (seconds)", + legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, + ) + +# weibull_accuracy = plot_partial_effects( +# file = "weibull_partial_effect_accuracy.pdf", +# aft = wft, +# covariate_array = "accuracy", +# values_array = [.9, .99, .999, .9999], +# replacement_dict=weibull_dict, +# title="Partial Effects of Benign Accuracy on Failure Rate", +# ylabel="% Chance of Survival", +# xlabel="Time $T$ (seconds)", +# legend = {"title" : "Benign Accuracy"}, +# ) + + + +# %% + +cox_dict = { + "adv_failure_rate": "Adv. Failure Rate", + "def_value" : "Defence Strength", + "data.sample.random_state" : "Random State", + "train_time" : "Training Time", + "model_layers" : "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr" : "Learning Rate", + "adv_accuracy" : "Adv. Accuracy", + "adv_fit_time" : "Adv. Fit Time", + "adv_log_loss" : "Adv. Log Loss", + "predict_time" : "Inference Time", + "accuracy" : "Ben. Accuracy", + "failure_rate" : "Ben. Failure Rate", + "atk_value" : "Attack Strength", +} + +cox_afr, cft = plot_aft( + X_train, + file = "cox_aft.pdf", + event_col = target, + duration_col = duration_col, + title = "Cox AFR Model", + mtype = "cox", + replacement_dict=cox_dict, +) +cox_scores = score_model(cft, X_train, X_test) +cft.print_summary() +cox_partial = plot_partial_effects( + file = "cox_partial_effects.pdf", + aft = cft, + covariate_array = "model_layers", + values_array = [18, 34, 50, 101, 152], + replacement_dict=cox_dict, + title="Survival Time for Cox AFR", + ylabel="% Chance of Survival", + xlabel="Time $T$ (seconds)", + legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, +) + +# %% +log_normal_dict = { + "Intercept: sigma_": "$\sigma$", + "Intercept: mu_": "$\mu$", + "def_value: mu_": "Defence Strength", + "atk_value: mu_": "Attack Strength", + "train_time: mu_": "Training Time", + "predict_time: mu_": "Inference Time", + "adv_fit_time: mu_": "Adv. Fit Time", + "model_layers: mu_": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_" : "Learning Rate", + "data.sample.random_state: mu_": "Random State", + "adv_log_loss: mu_": "Adv. Log Loss", + "adv_accuracy: mu_": "Adv. Accuracy", + "accuracy: mu_": "Ben. Accuracy", + "adv_failure_rate: mu_": "Adv. Failure Rate", + "def_gen" : "Defence", + "learning_rate: mu_" : "Learning Rate", +} + +log_normal_graph, lnt = plot_aft( + X_train, + "log_normal_aft.pdf", + target, + duration_col, + "Log Normal AFR Model", + "log_normal", + replacement_dict=log_normal_dict, +) +lnt_scores = score_model(lnt, X_train, X_test) +lnt.print_summary() +lnt_partial = plot_partial_effects( + file = "log_normal_partial_effects.pdf", + aft = lnt, + covariate_array = "model_layers", + values_array = [18, 34, 50, 101, 152], + replacement_dict=log_normal_dict, + title="Survival Time for Log-Normal AFR", + ylabel="% Chance of Survival", + xlabel="Time $T$ (seconds)", + legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, +) + +# %% + +log_logistic_dict = { + "Intercept: beta_": "$\\beta$", + "Intercept: alpha_": "$\\alpha$", + "data.sample.random_state: alpha_": "Random State", + "def_value: alpha_": "Defence Strength", + "atk_value: alpha_": "Attack Strength", + "train_time: alpha_": "Training Time", + "predict_time: alpha_": "Inference Time", + "adv_accuracy: alpha_": "Adv. Accuracy", + "accuracy: alpha_": "Ben. Accuracy", + "adv_fit_time: alpha_": "Adv. Fit Time", + "model_layers: alpha_": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr" : "Learning Rate", + "adv_failure_rate: alpha_": "Adv. Failure Rate", + "alpha_" : "", + +} + +log_logistic_graph, llt = plot_aft( + X_train, + "log_logistic_aft.pdf", + target, + duration_col, + "Log Logistic AFR Model", + "log_logistic", + replacement_dict=log_logistic_dict, +) +llt.print_summary() +llt_scores = score_model(llt, X_train, X_test) +print(llt_scores) +llt_partial = plot_partial_effects( + file = "log_logistic_partial_effects.pdf", + aft = llt, + covariate_array = "model_layers", + values_array = [18, 34, 50, 101, 152], + replacement_dict=log_logistic_dict, + title="Survival Time for Log-Logistic AFR", + ylabel="% Chance of Survival", + xlabel="Time $T$ (seconds)", + legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, + +) + +# %% +np.mean(llt.predict_median(X_train)) + +# %% +aft_dict = { + "Weibull": wft, + "LogNormal": lnt, + "LogLogistic": llt, + # "Cox": cft, +} + +score_list = [ + wft_scores, + lnt_scores, + llt_scores, + # cft_scores, +] +aft_data = pd.DataFrame() +aft_data.index.name = "Model" +aft_data.index = aft_dict.keys() +aft_data["AIC"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() ] +aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] +aft_data["BIC"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()] +aft_data['Train LL'] = [x['train_score'] for x in score_list] +aft_data['Test LL'] = [x['test_score'] for x in score_list] +aft_data['Mean ST'] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()] +aft_data['Median ST'] = [x.predict_median(X_train).median() for x in aft_dict.values()] +aft_data = aft_data.round(2) +aft_data.to_csv(FOLDER / "aft_comparison.csv") +logger.info(f"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}") +aft_data = aft_data.round(2,) +aft_data.to_latex(FOLDER / "aft_comparison.tex", float_format="%.2f", label = "tab:mnist", caption="Comparison of AFR Models on the MNIST dataset.") +aft_data + + +# %% +# data['Survival Time'] = 1 / (data['adv_failure_rate']) +# true_mean = np.mean(data['adv_failure_rate'] * data['predict_time']) +# true_median = np.median(data['adv_failure_rate'] * data['predict_time']) +success_rate = data['adv_failure_rate'] +success_time = success_rate * data['predict_time'] +true_mean = np.mean(success_time) +true_median = np.median(success_time) +print(f"True Mean: {true_mean}") +print(f"True Median: {true_median}") + + + diff --git a/examples/pytorch_cifar/attacks.sh b/examples/pytorch_cifar/attacks.sh index cb12aa06..4ece8b3d 100644 --- a/examples/pytorch_cifar/attacks.sh +++ b/examples/pytorch_cifar/attacks.sh @@ -3,36 +3,36 @@ # # This script is used to generate the attacks for the example. # Fast Gradient Method -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm $@ # Projected Gradient Descent -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd $@ # DeepFool -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep $@ # HopSkipJump -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 ++stage=attack ++hydra.sweeper.study_name=hsj ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 ++stage=attack ++hydra.sweeper.study_name=hsj $@ -##################################################### -PixelAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=pixel ++hydra.sweeper.direction=minimize $@ +#################################################### +# PixelAttack +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=pixel $@ # ThresholdAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=thresh ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=thresh $@ # # AdversarialPatch -# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 ++stage=patch ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++attack.init.patch_shape=[1,28,28] $@ +# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 ++stage=patch ++hydra.sweeper.study_name=attack ++attack.init.patch_shape=[1,28,28] $@ ##################################################### # # Carlini L0 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw0 ++hydra.sweeper.study_name=cw0 ++hydra.sweeper.direction=minimize $@ +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw0 ++hydra.sweeper.study_name=cw0 $@ # # # Carlini L2 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw2 ++hydra.sweeper.study_name=cw2 ++hydra.sweeper.direction=minimize $@ +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw2 ++hydra.sweeper.study_name=cw2 $@ # # Carlini LInf Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=cwinf ++hydra.sweeper.direction=minimize $@ +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=cwinf $@ rm -rf output/models/* diff --git a/examples/pytorch_cifar/conf/compile.yaml b/examples/pytorch_cifar/conf/compile.yaml index 2f5baad8..0d721a44 100644 --- a/examples/pytorch_cifar/conf/compile.yaml +++ b/examples/pytorch_cifar/conf/compile.yaml @@ -6,7 +6,7 @@ attacks: FastGradientMethod: FGM HopSkipJump: HSJ PixelAttack: Pixel - ProjectGradientDescent: PGD + ProjectedGradientDescent: PGD ThresholdAttack: Thresh defences: Control: Control @@ -28,3 +28,4 @@ params: Conf: model.art.pipeline.postprocessor.kwargs.cutoff FSQ: model.art.pipeline.preprocessor.kwargs.bit_depth Gauss-in: model.art.pipeline.preprocessor.kwargs.sigma + Control: model_layers diff --git a/examples/pytorch_cifar/conf/default.yaml b/examples/pytorch_cifar/conf/default.yaml index 7cadfda8..7b6d8f7a 100644 --- a/examples/pytorch_cifar/conf/default.yaml +++ b/examples/pytorch_cifar/conf/default.yaml @@ -26,7 +26,7 @@ hydra: ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 10 + n_jobs: 32 prefer : processes verbose: 1 timeout: 3600 diff --git a/examples/pytorch_cifar/conf/plots.yaml b/examples/pytorch_cifar/conf/plots.yaml index d235ac63..a779fbbf 100644 --- a/examples/pytorch_cifar/conf/plots.yaml +++ b/examples/pytorch_cifar/conf/plots.yaml @@ -2,7 +2,6 @@ cat_plot: - file: adv_accuracy_vs_defence_type.pdf hue: model_name kind: boxen - legend_title: Model Name set: yscale: linear titles: Adversarial Accuracy vs Defence Type @@ -11,72 +10,111 @@ cat_plot: y: adv_accuracy ylabels: Adv. Accuracy rotation : 90 + hue_order: + - ResNet18 + # - ResNet34 + - ResNet50 + # - ResNet101 + - ResNet152 - file: ben_accuracy_vs_defence_type.pdf hue: model_name kind: boxen - legend_title: Model Name titles: Benign Accuracy vs Defence Type x: def_gen xlabels: Defence Type y: accuracy ylabels: Ben. Accuracy rotation : 90 + hue_order: + - ResNet18 + # - ResNet34 + - ResNet50 + # - ResNet101 + - ResNet152 - file: ben_failures_per_train_time_vs_defence_type.pdf hue: model_name kind: boxen - legend_title: Model Name set: yscale: log - titles: Training Time per Benign Failure vs Defence Type + titles: $\bar{C}_{ben.}$ vs Defence Type x: def_gen xlabels: Defence Type y: training_time_per_failure - ylabels: Training Time per Ben. Failures + ylabels: $\bar{C}_{ben.}$ rotation : 90 + hue_order: + - ResNet18 + # - ResNet34 + - ResNet50 + # - ResNet101 + - ResNet152 - file: adv_failures_per_train_time_vs_defence_type.pdf hue: model_name kind: boxen - legend_title: Model Name set: yscale: log - titles: Training Time per Adversarial Failure vs Defence Type + titles: $\bar{C}_{adv.}$ vs Defence Type x: def_gen xlabels: Defence Type y: training_time_per_adv_failure - ylabels: Training time per Adv. Failures + ylabels: $\bar{C}_{adv.}$ rotation : 90 + hue_order: + - ResNet18 + # - ResNet34 + - ResNet50 + # - ResNet101 + - ResNet152 - file: adv_failures_per_train_time_vs_attack_type.pdf hue: model_name kind: boxen legend_title: Model Name set: yscale: log - titles: Training Time per Adversarial Failure vs Attack Type + titles: $\bar{C}_{adv.}$ vs Attack Type x: atk_gen xlabels: Attack Type y: training_time_per_adv_failure - ylabels: Training time per Adv. Failures + ylabels: $\bar{C}_{adv.}$ rotation : 90 -- file: adv_accuracy_vs_attack_type.pdf + hue_order: + - ResNet18 + # - ResNet34 + - ResNet50 + # - ResNet101 + - ResNet152 +- file: adv_failures_per_test_time_vs_defence_type.pdf hue: model_name kind: boxen legend_title: Model Name - titles: Adversarial Accuracy vs Attack Type - x: atk_gen - xlabels: Attack Type - y: adv_accuracy - ylabels: Adv. Accuracy - rotation : 90 -- file: adv_accuracy_vs_defence_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - titles: Adversarial Accuracy vs Defence Type + titles: Adversarial Failure Rate vs Defence Type x: def_gen xlabels: Defence Type - y: adv_accuracy - ylabels: Adv. Accuracy + y: adv_failure_rate + ylabels: Adv. Failure Rate rotation : 90 + hue_order: + - ResNet18 + # - ResNet34 + - ResNet50 + # - ResNet101 + - ResNet152 +# - file: adv_accuracy_vs_defence_type.pdf +# hue: model_name +# kind: boxen +# legend_title: Model Name +# titles: Adversarial Accuracy vs Defence Type +# x: def_gen +# xlabels: Defence Type +# y: adv_accuracy +# ylabels: Adv. Accuracy +# rotation : 90 +# hue_order: +# - ResNet18 +# # - ResNet34 +# - ResNet50 +# # - ResNet101 +# - ResNet152 - file: adv_accuracy_vs_attack_type.pdf hue: model_name kind: boxen @@ -87,6 +125,12 @@ cat_plot: y: adv_accuracy ylabels: Adv. Accuracy rotation : 90 + hue_order: + - ResNet18 + # - ResNet34 + - ResNet50 + # - ResNet101 + - ResNet152 - file: ben_failure_rate_vs_defence_type.pdf hue: model_name kind: boxen @@ -99,10 +143,14 @@ cat_plot: y: failure_rate ylabels: Ben. Failure Rate rotation : 90 + hue_order: + - ResNet18 + # - ResNet34 + - ResNet50 + # - ResNet101 + - ResNet152 line_plot: -- control: 0.98 - control_color: orange - file: def_param_vs_accuracy.pdf +- file: def_param_vs_accuracy.pdf hue: def_gen legend: {} title: Accuracy vs Defence Strength @@ -112,9 +160,13 @@ line_plot: y: accuracy y_scale: ylabel: Accuracy -- control: 0.1 - control_color: orange - file: def_param_vs_adv_accuracy.pdf + hue_order: + - Control + - Gauss-in + - Gauss-out + - Conf + - FSQ +- file: def_param_vs_adv_accuracy.pdf hue: def_gen legend: {} title: Adversarial Accuracy vs Defence Strength @@ -124,9 +176,13 @@ line_plot: y: adv_accuracy y_scale: ylabel: Adv. Accuracy -- control: 0.1 - control_color: orange - file: def_param_vs_adv_failure_rate.pdf + hue_order: + - Control + - Gauss-in + - Gauss-out + - Conf + - FSQ +- file: def_param_vs_adv_failure_rate.pdf hue: def_gen legend: {} title: Adversarial Failure Rate vs Defence Strength @@ -136,6 +192,12 @@ line_plot: y: adv_failure_rate y_scale: log ylabel: Adv. Failure Rate + hue_order: + - Control + - Gauss-in + - Gauss-out + - Conf + - FSQ - file: atk_param_vs_accuracy.pdf hue: atk_gen legend: {} @@ -146,15 +208,41 @@ line_plot: y: adv_accuracy y_scale: ylabel: Adv. Accuracy + hue_order: + - FGM + - PGD + - Deep + - HSJ + - Pixel + - Thresh scatter_plot: -- file: adv_accuracy_vs_train_time.pdf - x: train_time_per_sample +- x: train_time_per_sample y: adv_failure_rate hue: model_name xlabel: Training Time Per Sample ylabel: Adversarial Failure Rate title: Adversarial Failure Rate vs Training Time + file: adv_failure_rate_vs_train_time.pdf y_scale: log x_scale: linear legend: title: Model Name + hue_order: + - ResNet18 + # - ResNet34 + - ResNet50 + # - ResNet101 + - ResNet152 + # style : atk_gen +# - x: train_time_per_sample +# y: adv_failure_rate +# hue: model_name +# xlabel: Training Time Per Sample +# ylabel: Adversarial Failure Rate +# title: Adversarial Failure Rate vs Training Time for each Defence +# file: adv_failure_rate_vs_train_time_def.pdf +# y_scale: log +# x_scale: linear +# legend: +# title: Model Name +# # style : def_gen diff --git a/examples/pytorch_cifar/dvc.lock b/examples/pytorch_cifar/dvc.lock index 3c3926d5..09701278 100644 --- a/examples/pytorch_cifar/dvc.lock +++ b/examples/pytorch_cifar/dvc.lock @@ -327,34 +327,40 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: 15b48cee382acf8d99438fac5b857d67 + md5: aab66fe871d14945003859fa44125c75 size: 123046 - path: output/reports/attack/default/adv_predictions.json - md5: 323486a7174afe1d1dcd42d4a86cc304 - size: 2167 + md5: 61d687b977e8c7e9404e69a690b244dd + size: 2154 - path: output/reports/attack/default/params.yaml md5: 5177666d7f2e1e55ccad8ab4b735ffc7 size: 5175 - path: output/reports/attack/default/score_dict.json - md5: 2ccb898d65b13714b26de3dd467ba306 - size: 541 + md5: a918883cd746f147337e5e19146988a0 + size: 537 attacks@ResNet18: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet18.db --config-name default.yaml deps: - path: attacks.sh - md5: 2fbda0d22c03c74392639ffa29319023 - size: 3080 + md5: 6b79f7071f855be0ea09ad5602215c35 + size: 2746 - path: models.sh - md5: 9916f8e39b8342a0e9407859fb554021 - size: 1293 - - path: output/reports/attack/default/score_dict.json - md5: f7d7a6af006d36105a9449dbf1a00715 - size: 535 + md5: 9386478ad9795b80ec8ebc4abf1dbc81 + size: 1221 + - path: output/attacks/attack.pkl + md5: 8d7c09d7bf7c7d759648942a99880c3f + size: 123046 + - path: output/reports/attack/default/adv_predictions.json + md5: fb5dee98575e0d67e0ab051cc0182ecf + size: 2145 + - path: output/reports/attack/default/params.yaml + md5: 5177666d7f2e1e55ccad8ab4b735ffc7 + size: 5175 outs: - path: ResNet18.db - md5: 8add8533355f91c1c2e23ed2bc8b720b - size: 1232896 + md5: 5de89603f1f440df03669e79e0a7b414 + size: 360448 attacks@ResNet34: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet34.db --config-name default.yaml @@ -377,18 +383,24 @@ stages: ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet50.db --config-name default.yaml deps: - path: attacks.sh - md5: 2fbda0d22c03c74392639ffa29319023 - size: 3080 + md5: 6dd27be960da7b3e34c0281143ca30ba + size: 2742 - path: models.sh - md5: 9916f8e39b8342a0e9407859fb554021 - size: 1293 - - path: output/reports/attack/default/score_dict.json - md5: f7d7a6af006d36105a9449dbf1a00715 - size: 535 + md5: 9386478ad9795b80ec8ebc4abf1dbc81 + size: 1221 + - path: output/attacks/attack.pkl + md5: 6b7117801434d91c71e870a3922eea7e + size: 123046 + - path: output/reports/attack/default/adv_predictions.json + md5: 39344f99a210e0ecbcaa16e54cec9d4b + size: 2147 + - path: output/reports/attack/default/params.yaml + md5: 5177666d7f2e1e55ccad8ab4b735ffc7 + size: 5175 outs: - path: ResNet50.db - md5: 88ab82137f4fd2f0384e6b7bae7ae11f - size: 626688 + md5: 1e3b638560b7ee5e73c674d0e1eb0421 + size: 446464 attacks@ResNet101: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet101 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet101.db --config-name @@ -413,62 +425,122 @@ stages: default.yaml deps: - path: attacks.sh - md5: 2fbda0d22c03c74392639ffa29319023 - size: 3080 + md5: da51ec626ca4fa915be37e944ac5218b + size: 2745 - path: models.sh - md5: cd924674997af182b42d07a08bbc9034 - size: 1431 - - path: output/reports/attack/default/score_dict.json - md5: bd2742460f844eebe8bd411aa8ebd5de - size: 530 + md5: 9386478ad9795b80ec8ebc4abf1dbc81 + size: 1221 + - path: output/attacks/attack.pkl + md5: aab66fe871d14945003859fa44125c75 + size: 123046 + - path: output/reports/attack/default/adv_predictions.json + md5: 61d687b977e8c7e9404e69a690b244dd + size: 2154 + - path: output/reports/attack/default/params.yaml + md5: 5177666d7f2e1e55ccad8ab4b735ffc7 + size: 5175 outs: - path: ResNet152.db - md5: 1cef8992168089daa34f547c004798b3 - size: 159744 + md5: 143435750b3f57d1a1280d412df74e0b + size: 638976 compile@attack: cmd: python -m deckard.layers.compile --report_folder output/reports/attack --results_file output/reports/attack.csv deps: - - path: ResNet101.db - md5: 8a40cf29e97c400163c55d790153a270 - size: 307200 - path: ResNet152.db - md5: 1cef8992168089daa34f547c004798b3 - size: 159744 - - path: ResNet18.db - md5: 0b651c201f96fbedf11f43b819576769 - size: 159744 - - path: ResNet34.db - md5: f3084028f22eb073b578c9eb81f6795c - size: 159744 - - path: ResNet50.db - md5: b6cd8bf315fa750cdb070b434bcf3681 - size: 159744 + md5: 143435750b3f57d1a1280d412df74e0b + size: 638976 - path: output/reports/attack/ - md5: 8d5687b35d86d450438ebb3045054849.dir - size: 1995487004 - nfiles: 14559 + md5: aaa5e2c2628d1ebcb31556d983d7dece.dir + size: 7965937508 + nfiles: 24265 outs: - path: output/reports/attack.csv - md5: eab7fb13e62e581fbcc11454c88d9a2b - size: 10878957 + md5: f67172805501c6e8baffab2df49a960e + size: 14782615 plot: cmd: python plots.py --path output/plots/ --file output/reports/attack.csv deps: - - path: ResNet152.db - md5: df66e7a40e4e692513c136b2811cb8c7 - size: 258048 - - path: ResNet18.db - md5: 8add8533355f91c1c2e23ed2bc8b720b - size: 1232896 - - path: ResNet50.db - md5: 88ab82137f4fd2f0384e6b7bae7ae11f - size: 626688 - path: output/reports/attack.csv - md5: b64b7c2b6d8cf3015e5945affb42890b - size: 4032195 + md5: f67172805501c6e8baffab2df49a960e + size: 14782615 + outs: + - path: output/plots/adv_accuracy_vs_attack_type.pdf + md5: 854e1515d4dfbca75c62215c19affcbf + size: 26187 + - path: output/plots/adv_accuracy_vs_defence_type.pdf + md5: c5b4a902835f96292049a467fca57041 + size: 21047 + - path: output/plots/adv_failure_rate_vs_train_time.pdf + md5: 97a93d8a83c604fada1f33612181d760 + size: 35489 + - path: output/plots/adv_failures_per_test_time_vs_defence_type.pdf + md5: b8d746d5bb13629a6b417840f1a2258c + size: 26083 + - path: output/plots/adv_failures_per_train_time_vs_attack_type.pdf + md5: e0d5438fd8d9ec90c37c113a5241995c + size: 33150 + - path: output/plots/adv_failures_per_train_time_vs_defence_type.pdf + md5: c64e113c5bb0018e2cae8c685dd8a826 + size: 27183 + - path: output/plots/atk_param_vs_accuracy.pdf + md5: fd915ae17cdd61b00a2883d2a9af4dce + size: 21699 + - path: output/plots/ben_accuracy_vs_defence_type.pdf + md5: b3c0311a4b939d98701f2326a2bf6612 + size: 21731 + - path: output/plots/ben_failure_rate_vs_defence_type.pdf + md5: 25dcc0c4e4289089c5f3c20916c41afb + size: 26339 + - path: output/plots/ben_failures_per_train_time_vs_defence_type.pdf + md5: a35bf0e15e751e92a03ee67d8f0fb612 + size: 26968 + - path: output/plots/data.csv + md5: ba58e26a754134c3a028d9864072b64d + size: 3267854 + - path: output/plots/def_param_vs_accuracy.pdf + md5: 350ec78e47cecb84f6282fb8b3f4cb20 + size: 19273 + - path: output/plots/def_param_vs_adv_accuracy.pdf + md5: 83824bc0a6b4f987eae04f6055208c40 + size: 18857 + - path: output/plots/def_param_vs_adv_failure_rate.pdf + md5: b7dd47fde2b640c528f76535853eadbd + size: 18820 + afr: + cmd: python afr.py + deps: + - path: output/plots/data.csv + md5: ba58e26a754134c3a028d9864072b64d + size: 3267854 outs: - - path: output/plots/ - md5: f0a9887f2f9ee5042004b36136326197.dir - size: 375907 - nfiles: 13 + - path: output/plots/aft_comparison.csv + md5: 184ae57d83a4ac6e291f5af968ea4a12 + size: 214 + - path: output/plots/aft_comparison.tex + md5: 672fed1e5e8ce82c246945494b7a2ab4 + size: 458 + - path: output/plots/cox_aft.pdf + md5: 9471fa8cd1fd918e84de193ba94db7b7 + size: 23765 + - path: output/plots/cox_partial_effects.pdf + md5: 265d945de23c1a8869470c2d8625d9d3 + size: 37750 + - path: output/plots/log_logistic_aft.pdf + md5: c54bed98e83ec16a4c05b9de79c7118e + size: 28381 + - path: output/plots/log_logistic_partial_effects.pdf + md5: de845fde8cd722c7aa3082863c04cf5f + size: 28962 + - path: output/plots/log_normal_aft.pdf + md5: c1655a624659c192485a3d235ed74313 + size: 28321 + - path: output/plots/log_normal_partial_effects.pdf + md5: 9ba657c55fa26813a34dbe8b75ec3c85 + size: 28997 + - path: output/plots/weibull_aft.pdf + md5: 007e308b6c2c1be0ba4cbfe9ad4c1092 + size: 26817 + - path: output/plots/weibull_partial_effects.pdf + md5: a770dfeaa12d12028f24c69ca7e94ff1 + size: 30858 diff --git a/examples/pytorch_cifar/dvc.yaml b/examples/pytorch_cifar/dvc.yaml index fd1dfd61..cadc44fe 100644 --- a/examples/pytorch_cifar/dvc.yaml +++ b/examples/pytorch_cifar/dvc.yaml @@ -33,13 +33,12 @@ stages: - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} metrics: - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} - ############################################################################## attacks: foreach: - - ResNet18 + # - ResNet18 # - ResNet34 - - ResNet50 + # - ResNet50 # - ResNet101 - ResNet152 do: @@ -47,7 +46,9 @@ stages: deps: - models.sh - attacks.sh - - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} outs: - ${item}.db compile: @@ -57,9 +58,9 @@ stages: cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv deps: - ${files.directory}/${files.reports}/${item}/ - - ResNet18.db + # - ResNet18.db # - ResNet34.db - - ResNet50.db + # - ResNet50.db # - ResNet101.db - ResNet152.db outs: @@ -68,10 +69,36 @@ stages: cmd : python plots.py --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv deps: - ${files.directory}/${files.reports}/attack.csv - - ResNet18.db - # - ResNet34.db - - ResNet50.db - # - ResNet101.db - - ResNet152.db + metrics: + - ${files.directory}/plots/data.csv + plots: + - ${files.directory}/plots/adv_accuracy_vs_attack_type.pdf + - ${files.directory}/plots/adv_accuracy_vs_defence_type.pdf + - ${files.directory}/plots/adv_failure_rate_vs_train_time.pdf + - ${files.directory}/plots/adv_failures_per_test_time_vs_defence_type.pdf + - ${files.directory}/plots/adv_failures_per_train_time_vs_attack_type.pdf + - ${files.directory}/plots/adv_failures_per_train_time_vs_defence_type.pdf + - ${files.directory}/plots/atk_param_vs_accuracy.pdf + - ${files.directory}/plots/ben_accuracy_vs_defence_type.pdf + - ${files.directory}/plots/ben_failure_rate_vs_defence_type.pdf + - ${files.directory}/plots/ben_failures_per_train_time_vs_defence_type.pdf + - ${files.directory}/plots/def_param_vs_accuracy.pdf + - ${files.directory}/plots/def_param_vs_adv_accuracy.pdf + - ${files.directory}/plots/def_param_vs_adv_failure_rate.pdf + afr: + cmd: python afr.py + deps: + - ${files.directory}/plots/data.csv + plots: + - ${files.directory}/plots/weibull_aft.pdf + - ${files.directory}/plots/weibull_partial_effects.pdf + - ${files.directory}/plots/cox_partial_effects.pdf + - ${files.directory}/plots/cox_aft.pdf + - ${files.directory}/plots/log_logistic_aft.pdf + - ${files.directory}/plots/log_logistic_partial_effects.pdf + - ${files.directory}/plots/log_normal_aft.pdf + - ${files.directory}/plots/log_normal_partial_effects.pdf + metrics: + - ${files.directory}/plots/aft_comparison.csv outs: - - ${files.directory}/plots/ + - ${files.directory}/plots/aft_comparison.tex \ No newline at end of file diff --git a/examples/pytorch_cifar/models.sh b/examples/pytorch_cifar/models.sh index acad9733..57d368c8 100644 --- a/examples/pytorch_cifar/models.sh +++ b/examples/pytorch_cifar/models.sh @@ -3,17 +3,17 @@ # This script is used to generate the models for the sklearn example. # Default model -echo "python -m deckard.layers.optimise +stage=train" $@ "--multirun" -python -m deckard.layers.optimise +stage=train $@ --multirun +echo "python -m deckard.layers.optimise " $@ "--multirun" +python -m deckard.layers.optimise $@ --multirun # This line generates the model and adds the FeatureSqueezing preprocessing defence. -python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] +stage=train $@ --multirun +python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] $@ --multirun # Gaussian Augmentation (Input) -python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False +stage=train $@ --multirun +python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False $@ --multirun # Gaussian Noise (Output) -python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 +stage=train $@ --multirun +python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 $@ --multirun # High Confidence -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 +stage=train $@ --multirun +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 $@ --multirun diff --git a/examples/pytorch_cifar/plots.py b/examples/pytorch_cifar/plots.py index 95a40ba1..446bec45 100644 --- a/examples/pytorch_cifar/plots.py +++ b/examples/pytorch_cifar/plots.py @@ -9,8 +9,8 @@ logger = logging.getLogger(__name__) -sns.set_theme(style="whitegrid") -sns.set_context("paper", font_scale=1.5) +sns.set_theme(style="whitegrid", font_scale=1.8, font="times new roman") + def cat_plot( @@ -22,19 +22,26 @@ def cat_plot( titles, xlabels, ylabels, - legend_title, file, folder, + legend_title = None, + hue_order=None, rotation=0, set={}, **kwargs, ): plt.gcf().clear() - graph = sns.catplot(data=data, x=x, y=y, hue=hue, kind=kind, **kwargs) + data = data.sort_values(by=[hue, x, y]) + graph = sns.catplot( + data=data, x=x, y=y, hue=hue, kind=kind, hue_order=hue_order, **kwargs + ) graph.set_xlabels(xlabels) graph.set_ylabels(ylabels) graph.set_titles(titles) - graph.legend.set_title(title=legend_title) + if legend_title is not None: + graph.legend.set_title(title=legend_title) + else: + graph.legend.remove() graph.set_xticklabels(graph.axes.flat[-1].get_xticklabels(), rotation=rotation) graph.set(**set) graph.tight_layout() @@ -56,14 +63,13 @@ def line_plot( y_scale=None, x_scale=None, legend={}, + hue_order=None, **kwargs, ): plt.gcf().clear() - graph = sns.lineplot(data=data, x=x, y=y, hue=hue) + data = data.sort_values(by=[hue, x, y]) + graph = sns.lineplot(data=data, x=x, y=y, hue=hue, hue_order=hue_order, **kwargs) graph.legend(**legend) - # if control is not None: - # assert control_color is not None, "Please specify a control color" - # graph.add_line(plt.axhline(y=control, color=control_color, linestyle="-")) graph.set_xlabel(xlabel) graph.set_ylabel(ylabel) graph.set_title(title) @@ -90,12 +96,18 @@ def scatter_plot( y_scale=None, x_scale=None, legend={}, + hue_order=None, + **kwargs, ): + # plt.gcf().clear() + data = data.sort_values(by=[hue, x, y]) graph = sns.scatterplot( data=data, x=x, y=y, hue=hue, + hue_order=hue_order, + **kwargs, ) graph.set_yscale(y_scale) graph.set_xscale(x_scale) @@ -110,31 +122,51 @@ def scatter_plot( plt.gcf().clear() return graph -def drop_frames_without_results( data, subset = ["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"]): + +def drop_frames_without_results( + data, + subset=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"], +): data.dropna(axis=0, subset=subset, inplace=True) return data + def calculate_failure_rate(data): data = data[data.columns.drop(list(data.filter(regex=r"\.1$")))] data.columns.str.replace(" ", "") data.loc[:, "failure_rate"] = ( - (1 - data.loc[:, "accuracy"]) * 100 / data.loc[:,"predict_time"] + (1 - data.loc[:, "accuracy"])/ data.loc[:, "predict_time_per_sample"] ) + data.loc[:, "success_rate"] = ( + data.loc[:, "accuracy"] * 100 / data.loc[:, "predict_time_per_sample"] + ) data.loc[:, "adv_failure_rate"] = ( - (1 - data.loc[:,"adv_accuracy"]) * 100 / data.loc[:,"adv_fit_time"] + (1 - data.loc[:, "adv_accuracy"]) * 100 / data.loc[:, "adv_fit_time"] + ) + data.loc[:, "adv_success_rate"] = ( + data.loc[:, "adv_accuracy"] * 100 / data.loc[:, "adv_fit_time"] ) data.loc[:, "training_time_per_failure"] = ( - data.loc[:,"train_time"] / data.loc[:,"failure_rate"] + data.loc[:, "train_time"] * data.loc[:, "failure_rate"] ) data.loc[:, "training_time_per_adv_failure"] = ( - data.loc[:,"train_time"] / data.loc[:,"adv_failure_rate"] + data.loc[:, "train_time_per_sample"] * data.loc[:, "adv_failure_rate"] ) data.loc[:, "adv_training_time_per_failure"] = ( - data.loc[:,"train_time"] / data.loc[:,"adv_failure_rate"] + data.loc[:, "train_time_per_sample"] * data.loc[:, "adv_failure_rate"] ) return data -def min_max_scaling(data): +from paretoset import paretoset + +def pareto_set(data, sense_dict): + subset = data.loc[:, sense_dict.keys()] + these = paretoset(subset, sense = sense_dict.values()) + return data.iloc[these, :] + + + +def min_max_scaling(data, **kwargs): if "atk_gen" not in data.columns: attacks = [] else: @@ -155,6 +187,8 @@ def min_max_scaling(data): min_ = data[data.atk_gen == atk].atk_value.min() scaled_value = (data[data.atk_gen == atk].atk_value - min_) / (max_ - min_) data.loc[data.atk_gen == atk, "atk_value"] = scaled_value + for k,v in kwargs.items(): + data.loc[:,k] = data.loc[:,k].apply(v) return data @@ -207,7 +241,35 @@ def min_max_scaling(data): ).exists(), f"File {args.file} does not exist. Please specify a valid file using the -f flag." csv_file = args.file data = pd.read_csv(csv_file) - data = drop_frames_without_results(data, subset=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"]) + data = drop_frames_without_results( + data, + subset=[ + "accuracy", + "adv_accuracy", + "train_time", + "adv_fit_time", + "predict_time", + ], + ) + sense_dict ={ + "accuracy" : "max", + # "train_time" : "min", + # "predict_time" : "min", + # "atk_value" : "diff", + "adv_accuracy" : "min", + # "def_value" : "diff", + "data.sample.random_state" : "diff", + # "adv_accuracy" : "min", + "model_layers" : "diff", + # "adv_fit_time" : "min", + "atk_param" : "diff", + "def_param" : "diff", + "atk_gen" : "diff", + "def_gen" : "diff", + # "model.art.pipeline.initialize.kwargs.optimizer.lr" : "diff", + # "adv_failure_rate" : "maximize", +} + data = pareto_set(data, sense_dict) data = calculate_failure_rate(data) data = min_max_scaling(data) if "Unnamed: 0" in data.columns: @@ -246,7 +308,6 @@ def min_max_scaling(data): logger.info(f"Rendering graph {i}") locals()[f"graph{i}"] = line_plot(data, **dict_, folder=FOLDER) - scatter_plot_list = big_dict["scatter_plot"] for dict_ in scatter_plot_list: i += 1 From 38ab1c964f85283601b01dca84c75e9cb8998c98 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Fri, 29 Sep 2023 19:36:38 +0000 Subject: [PATCH 092/148] linting --- deckard/base/attack/attack.py | 12 +- deckard/layers/compile.py | 2 +- deckard/layers/experiment.py | 2 +- examples/pytorch/afr.py | 254 ++++++++++++++++++------------- examples/pytorch/plots.py | 36 ++--- examples/pytorch_cifar/afr.py | 260 +++++++++++++++++++------------- examples/pytorch_cifar/dvc.yaml | 2 +- examples/pytorch_cifar/plots.py | 56 +++---- 8 files changed, 358 insertions(+), 266 deletions(-) diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index 43c093c8..52157d90 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -143,9 +143,9 @@ def __call__( if attack_file is not None and Path(attack_file).exists(): samples = self.data.load(attack_file) else: - + atk = self.init(model=model, attack_size=self.attack_size) - + if targeted is True: kwargs.update({"y": data[2][: self.attack_size]}) if "AdversarialPatch" in self.name: @@ -162,7 +162,13 @@ def __call__( ) results["adv_samples"] = samples try: - results['adv_success'] = compute_success(classifier=model, x_clean=ben_samples, labels=data[3][:self.attack_size], x_adv=samples, targeted=False) + results["adv_success"] = compute_success( + classifier=model, + x_clean=ben_samples, + labels=data[3][: self.attack_size], + x_adv=samples, + targeted=False, + ) except TypeError as e: logger.error(f"Failed to compute success rate. Error: {e}") if attack_file is not None: diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 41f13490..e5c20645 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -174,7 +174,7 @@ def format_control_parameter(data, control_dict): else: logger.warning(f"Attack {attack} not in control_dict. Deleting rows.") data = data[data.atk_gen != attack] - + return data diff --git a/deckard/layers/experiment.py b/deckard/layers/experiment.py index 2ac2bcb7..99ff9a91 100644 --- a/deckard/layers/experiment.py +++ b/deckard/layers/experiment.py @@ -25,7 +25,7 @@ def get_dvc_stage_params( params_file="params.yaml", pipeline_file="dvc.yaml", directory=".", - name = None, + name=None, ): logger.info( f"Getting params for stage {stage} from {params_file} and {pipeline_file} in {directory}.", diff --git a/examples/pytorch/afr.py b/examples/pytorch/afr.py index d067e2c1..682907df 100644 --- a/examples/pytorch/afr.py +++ b/examples/pytorch/afr.py @@ -6,27 +6,32 @@ import matplotlib.pyplot as plt -from sklearn.preprocessing import StandardScaler +from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split - from paretoset import paretoset -from lifelines import WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter, CoxPHFitter +from lifelines import ( + WeibullAFTFitter, + LogNormalAFTFitter, + LogLogisticAFTFitter, + CoxPHFitter, +) from plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling -import matplotlib -import argparse +import matplotlib from pathlib import Path import logging logger = logging.getLogger(__name__) # %% -font = {'family' : 'Times New Roman', - 'weight' : 'bold', - 'size' : 22} +font = { + "family": "Times New Roman", + "weight": "bold", + "size": 22, +} -matplotlib.rc('font', **font) +matplotlib.rc("font", **font) # %% FOLDER = Path("output/plots/") @@ -39,24 +44,35 @@ data = drop_frames_without_results(data) data = calculate_failure_rate(data) data = min_max_scaling(data) -data.dropna(axis=0, subset=['atk_value', 'atk_param'], inplace=True) -data.dropna(axis=0, subset=['def_value', 'def_param'], inplace=True) +data.dropna(axis=0, subset=["atk_value", "atk_param"], inplace=True) +data.dropna(axis=0, subset=["def_value", "def_param"], inplace=True) # data=data[data['def_gen'] == 'Gauss-in'] # data=data[data['atk_gen'] == 'HSJ'] print( - "Adversarial Accuracy:", "\n", - "ResNet152:", data[data['model_layers'] == 152].adv_accuracy.mean(skipna=True), "\n", - "Resnet101:", data[data['model_layers'] == 101].adv_accuracy.mean(skipna=True), "\n", - "Resnet50:", data[data['model_layers'] == 50].adv_accuracy.mean(skipna=True), "\n", - "Resnet34:", data[data['model_layers'] == 34].adv_accuracy.mean(skipna=True), "\n", - "Resnet18:", data[data['model_layers'] == 18].adv_accuracy.mean(skipna=True), "\n", + "Adversarial Accuracy:", + "\n", + "ResNet152:", + data[data["model_layers"] == 152].adv_accuracy.mean(skipna=True), + "\n", + "Resnet101:", + data[data["model_layers"] == 101].adv_accuracy.mean(skipna=True), + "\n", + "Resnet50:", + data[data["model_layers"] == 50].adv_accuracy.mean(skipna=True), + "\n", + "Resnet34:", + data[data["model_layers"] == 34].adv_accuracy.mean(skipna=True), + "\n", + "Resnet18:", + data[data["model_layers"] == 18].adv_accuracy.mean(skipna=True), + "\n", ) - # %% + def plot_aft( df, file, @@ -100,6 +116,7 @@ def plot_aft( plt.show() return ax, aft + def plot_partial_effects( file, aft, @@ -110,12 +127,14 @@ def plot_partial_effects( ylabel="Failure rate", legend_kwargs={"loc": "upper left"}, replacement_dict={}, - cmap='coolwarm', - **kwargs, - ): + cmap="coolwarm", + **kwargs, +): plt.gcf().clear() # kwargs.pop("replacement_dict") - pareto = aft.plot_partial_effects_on_outcome(covariate_array, values_array, cmap=cmap, **kwargs) + pareto = aft.plot_partial_effects_on_outcome( + covariate_array, values_array, cmap=cmap, **kwargs + ) labels = pareto.get_yticklabels() labels = [label.get_text() for label in labels] for k, v in replacement_dict.items(): @@ -130,39 +149,46 @@ def plot_partial_effects( logger.info(f"Saved graph to {FOLDER / file}") return pareto + def score_model(aft, train, test): train_score = aft.score(train) test_score = aft.score(test) - scores = {'train_score': train_score, 'test_score': test_score} + scores = {"train_score": train_score, "test_score": test_score} plt.show() - return scores + return scores def clean_data_for_aft( - data, kwarg_list, target="adv_failure_rate" + data, + kwarg_list, + target="adv_failure_rate", ): subset = data.copy() - assert target in subset, f"Target {target} not in dataframe with columns {subset.columns}" - + assert ( + target in subset + ), f"Target {target} not in dataframe with columns {subset.columns}" + cleaned = pd.DataFrame() kwarg_list.append(target) for kwarg in kwarg_list: cleaned = pd.concat([cleaned, subset[kwarg]], axis=1) cols = cleaned.columns cleaned = pd.DataFrame(subset, columns=cols) - - + # if "accuracy" in cleaned.columns: # cleaned = cleaned[~cleaned[cleaned['accuracy'] != 1e10]] # cleaned = cleaned[~cleaned[cleaned['accuracy'] != -1e10]] # if "adv_accuracy" in cleaned.columns: # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != 1e10]] # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != -1e10]] - cleaned.dropna(inplace=True, how='any', axis=0) + cleaned.dropna(inplace=True, how="any", axis=0) y = cleaned[target] - assert target in cleaned, f"Target {target} not in dataframe with columns {cleaned.columns}" + assert ( + target in cleaned + ), f"Target {target} not in dataframe with columns {cleaned.columns}" return cleaned, y, data + # %% @@ -179,7 +205,6 @@ def clean_data_for_aft( "adv_fit_time", # "atk_param", # "def_param", - "model.art.pipeline.initialize.kwargs.optimizer.lr", # "def_gen", # "atk_gen", @@ -196,11 +221,13 @@ def clean_data_for_aft( data.loc[:, "ben_failures"] = (1 - data.loc[:, "accuracy"]) * 100 target = "adv_failures" duration_col = "adv_fit_time" -cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) -X_train, X_test, y_train, y_test = train_test_split(cleaned, y, test_size=0.2, random_state=42) -assert target in cleaned, f"Target {target} not in dataframe with columns {cleaned.columns}" - - +cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) +X_train, X_test, y_train, y_test = train_test_split( + cleaned, y, test_size=0.2, random_state=42 +) +assert ( + target in cleaned +), f"Target {target} not in dataframe with columns {cleaned.columns}" # %% @@ -256,17 +283,17 @@ def clean_data_for_aft( "adv_failure_rate: lambda_": "Adv. Failure Rate", "failure_rate: lambda_": "Ben. Failure Rate", "model_layers: lambda_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_" : "Learning Rate", - "def_gen" : "Defence", + "model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_": "Learning Rate", + "def_gen": "Defence", } weibull_afr, wft = plot_aft( X_train, - file = "weibull_aft.pdf", - event_col = target, - duration_col = duration_col, - title = "Weibull AFR Model", - mtype = "weibull", + file="weibull_aft.pdf", + event_col=target, + duration_col=duration_col, + title="Weibull AFR Model", + mtype="weibull", replacement_dict=weibull_dict, ) wft.print_summary() @@ -282,16 +309,19 @@ def clean_data_for_aft( "model_layers=152": "152", } pareto_weibull = plot_partial_effects( - file = "weibull_partial_effects.pdf", - aft = wft, - covariate_array="model_layers", - values_array=[18, 34, 50, 101, 152], - title="Partial Effects of No. of Layers on Failure Rate for Weibull AFR", - replacement_dict=pareto_dict, + file="weibull_partial_effects.pdf", + aft=wft, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], + title="Partial Effects of No. of Layers on Failure Rate for Weibull AFR", + replacement_dict=pareto_dict, ylabel="% Chance of Survival", xlabel="Time $T$ (seconds)", - legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, - ) + legend_kwargs={ + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, +) # weibull_accuracy = plot_partial_effects( # file = "weibull_partial_effect_accuracy.pdf", @@ -304,48 +334,50 @@ def clean_data_for_aft( # xlabel="Time $T$ (seconds)", # legend = {"title" : "Benign Accuracy"}, # ) - - + # %% cox_dict = { "adv_failure_rate": "Adv. Failure Rate", - "def_value" : "Defence Strength", - "data.sample.random_state" : "Random State", - "train_time" : "Training Time", - "model_layers" : "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr" : "Learning Rate", - "adv_accuracy" : "Adv. Accuracy", - "adv_fit_time" : "Adv. Fit Time", - "adv_log_loss" : "Adv. Log Loss", - "predict_time" : "Inference Time", - "accuracy" : "Ben. Accuracy", - "failure_rate" : "Ben. Failure Rate", - "atk_value" : "Attack Strength", + "def_value": "Defence Strength", + "data.sample.random_state": "Random State", + "train_time": "Training Time", + "model_layers": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", + "adv_accuracy": "Adv. Accuracy", + "adv_fit_time": "Adv. Fit Time", + "adv_log_loss": "Adv. Log Loss", + "predict_time": "Inference Time", + "accuracy": "Ben. Accuracy", + "failure_rate": "Ben. Failure Rate", + "atk_value": "Attack Strength", } cox_afr, cft = plot_aft( X_train, - file = "cox_aft.pdf", - event_col = target, - duration_col = duration_col, - title = "Cox AFR Model", - mtype = "cox", + file="cox_aft.pdf", + event_col=target, + duration_col=duration_col, + title="Cox AFR Model", + mtype="cox", replacement_dict=cox_dict, ) cox_scores = score_model(cft, X_train, X_test) cft.print_summary() cox_partial = plot_partial_effects( - file = "cox_partial_effects.pdf", - aft = cft, - covariate_array = "model_layers", - values_array = [18, 34, 50, 101, 152], + file="cox_partial_effects.pdf", + aft=cft, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], replacement_dict=cox_dict, title="Survival Time for Cox AFR", ylabel="% Chance of Survival", xlabel="Time $T$ (seconds)", - legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, + legend_kwargs={ + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, ) # %% @@ -358,14 +390,14 @@ def clean_data_for_aft( "predict_time: mu_": "Inference Time", "adv_fit_time: mu_": "Adv. Fit Time", "model_layers: mu_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_" : "Learning Rate", + "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_": "Learning Rate", "data.sample.random_state: mu_": "Random State", "adv_log_loss: mu_": "Adv. Log Loss", "adv_accuracy: mu_": "Adv. Accuracy", "accuracy: mu_": "Ben. Accuracy", "adv_failure_rate: mu_": "Adv. Failure Rate", - "def_gen" : "Defence", - "learning_rate: mu_" : "Learning Rate", + "def_gen": "Defence", + "learning_rate: mu_": "Learning Rate", } log_normal_graph, lnt = plot_aft( @@ -380,15 +412,18 @@ def clean_data_for_aft( lnt_scores = score_model(lnt, X_train, X_test) lnt.print_summary() lnt_partial = plot_partial_effects( - file = "log_normal_partial_effects.pdf", - aft = lnt, - covariate_array = "model_layers", - values_array = [18, 34, 50, 101, 152], + file="log_normal_partial_effects.pdf", + aft=lnt, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], replacement_dict=log_normal_dict, title="Survival Time for Log-Normal AFR", ylabel="% Chance of Survival", xlabel="Time $T$ (seconds)", - legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, + legend_kwargs={ + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, ) # %% @@ -405,10 +440,9 @@ def clean_data_for_aft( "accuracy: alpha_": "Ben. Accuracy", "adv_fit_time: alpha_": "Adv. Fit Time", "model_layers: alpha_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr" : "Learning Rate", + "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", "adv_failure_rate: alpha_": "Adv. Failure Rate", - "alpha_" : "", - + "alpha_": "", } log_logistic_graph, llt = plot_aft( @@ -424,16 +458,18 @@ def clean_data_for_aft( llt_scores = score_model(llt, X_train, X_test) print(llt_scores) llt_partial = plot_partial_effects( - file = "log_logistic_partial_effects.pdf", - aft = llt, - covariate_array = "model_layers", - values_array = [18, 34, 50, 101, 152], + file="log_logistic_partial_effects.pdf", + aft=llt, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], replacement_dict=log_logistic_dict, title="Survival Time for Log-Logistic AFR", ylabel="% Chance of Survival", xlabel="Time $T$ (seconds)", - legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, - + legend_kwargs={ + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, ) # %% @@ -456,18 +492,27 @@ def clean_data_for_aft( aft_data = pd.DataFrame() aft_data.index.name = "Model" aft_data.index = aft_dict.keys() -aft_data["AIC"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() ] +aft_data["AIC"] = [ + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() +] aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] -aft_data["BIC"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()] -aft_data['Train LL'] = [x['train_score'] for x in score_list] -aft_data['Test LL'] = [x['test_score'] for x in score_list] -aft_data['Mean ST'] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()] -aft_data['Median ST'] = [x.predict_median(X_train).median() for x in aft_dict.values()] +aft_data["BIC"] = [ + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() +] +aft_data["Train LL"] = [x["train_score"] for x in score_list] +aft_data["Test LL"] = [x["test_score"] for x in score_list] +aft_data["Mean ST"] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()] +aft_data["Median ST"] = [x.predict_median(X_train).median() for x in aft_dict.values()] aft_data = aft_data.round(2) aft_data.to_csv(FOLDER / "aft_comparison.csv") logger.info(f"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}") -aft_data = aft_data.round(2,) -aft_data.to_latex(FOLDER / "aft_comparison.tex", float_format="%.2f", label = "tab:mnist", caption="Comparison of AFR Models on the MNIST dataset.") +aft_data = aft_data.round(2) +aft_data.to_latex( + FOLDER / "aft_comparison.tex", + float_format="%.2f", + label="tab:mnist", + caption="Comparison of AFR Models on the MNIST dataset.", +) aft_data @@ -475,12 +520,9 @@ def clean_data_for_aft( # data['Survival Time'] = 1 / (data['adv_failure_rate']) # true_mean = np.mean(data['adv_failure_rate'] * data['predict_time']) # true_median = np.median(data['adv_failure_rate'] * data['predict_time']) -success_rate = data['adv_failure_rate'] -success_time = success_rate * data['predict_time'] +success_rate = data["adv_failure_rate"] +success_time = success_rate * data["predict_time"] true_mean = np.mean(success_time) true_median = np.median(success_time) print(f"True Mean: {true_mean}") print(f"True Median: {true_median}") - - - diff --git a/examples/pytorch/plots.py b/examples/pytorch/plots.py index 00ea7f99..7f94be11 100644 --- a/examples/pytorch/plots.py +++ b/examples/pytorch/plots.py @@ -12,7 +12,6 @@ sns.set_theme(style="whitegrid", font_scale=1.8, font="times new roman") - def cat_plot( data, x, @@ -24,7 +23,7 @@ def cat_plot( ylabels, file, folder, - legend_title = None, + legend_title=None, hue_order=None, rotation=0, set={}, @@ -139,7 +138,7 @@ def calculate_failure_rate(data): ) data.loc[:, "success_rate"] = ( data.loc[:, "accuracy"] * 100 / data.loc[:, "predict_time"] - ) + ) data.loc[:, "adv_failure_rate"] = ( (1 - data.loc[:, "adv_accuracy"]) * 100 / data.loc[:, "adv_fit_time"] ) @@ -157,15 +156,16 @@ def calculate_failure_rate(data): ) return data + from paretoset import paretoset + def pareto_set(data, sense_dict): subset = data.loc[:, sense_dict.keys()] - these = paretoset(subset, sense = sense_dict.values()) + these = paretoset(subset, sense=sense_dict.values()) return data.iloc[these, :] - def min_max_scaling(data, **kwargs): if "atk_gen" not in data.columns: attacks = [] @@ -187,8 +187,8 @@ def min_max_scaling(data, **kwargs): min_ = data[data.atk_gen == atk].atk_value.min() scaled_value = (data[data.atk_gen == atk].atk_value - min_) / (max_ - min_) data.loc[data.atk_gen == atk, "atk_value"] = scaled_value - for k,v in kwargs.items(): - data.loc[:,k] = data.loc[:,k].apply(v) + for k, v in kwargs.items(): + data.loc[:, k] = data.loc[:, k].apply(v) return data @@ -251,17 +251,17 @@ def min_max_scaling(data, **kwargs): "predict_time", ], ) - sense_dict ={ - "accuracy" : "max", - "adv_accuracy" : "min", - "data.sample.random_state" : "diff", - "model_layers" : "diff", - "atk_param" : "diff", - "def_param" : "diff", - "atk_gen" : "diff", - "def_gen" : "diff", - "data.sample.random_state" : "diff", -} + sense_dict = { + "accuracy": "max", + "adv_accuracy": "min", + "data.sample.random_state": "diff", + "model_layers": "diff", + "atk_param": "diff", + "def_param": "diff", + "atk_gen": "diff", + "def_gen": "diff", + "data.sample.random_state": "diff", + } data = pareto_set(data, sense_dict) data = calculate_failure_rate(data) data = min_max_scaling(data) diff --git a/examples/pytorch_cifar/afr.py b/examples/pytorch_cifar/afr.py index e0b326a0..ee6605cd 100644 --- a/examples/pytorch_cifar/afr.py +++ b/examples/pytorch_cifar/afr.py @@ -6,27 +6,34 @@ import matplotlib.pyplot as plt -from sklearn.preprocessing import StandardScaler +from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split - from paretoset import paretoset -from lifelines import WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter, CoxPHFitter, AalenAdditiveFitter, AalenJohansenFitter +from lifelines import ( + WeibullAFTFitter, + LogNormalAFTFitter, + LogLogisticAFTFitter, + CoxPHFitter, + AalenAdditiveFitter, + AalenJohansenFitter, +) from plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling -import matplotlib -import argparse +import matplotlib from pathlib import Path import logging logger = logging.getLogger(__name__) # %% -font = {'family' : 'Times New Roman', - 'weight' : 'bold', - 'size' : 22} +font = { + "family": "Times New Roman", + "weight": "bold", + "size": 22, +} -matplotlib.rc('font', **font) +matplotlib.rc("font", **font) # %% FOLDER = Path("output/plots/") @@ -39,24 +46,35 @@ data = drop_frames_without_results(data) data = calculate_failure_rate(data) data = min_max_scaling(data) -data.dropna(axis=0, subset=['atk_value', 'atk_param'], inplace=True) -data.dropna(axis=0, subset=['def_value', 'def_param'], inplace=True) +data.dropna(axis=0, subset=["atk_value", "atk_param"], inplace=True) +data.dropna(axis=0, subset=["def_value", "def_param"], inplace=True) # data=data[data['def_gen'] == 'Gauss-in'] # data=data[data['atk_gen'] == 'HSJ'] print( - "Adversarial Accuracy:", "\n", - "ResNet152:", data[data['model_layers'] == 152].adv_accuracy.mean(skipna=True), "\n", - "Resnet101:", data[data['model_layers'] == 101].adv_accuracy.mean(skipna=True), "\n", - "Resnet50:", data[data['model_layers'] == 50].adv_accuracy.mean(skipna=True), "\n", - "Resnet34:", data[data['model_layers'] == 34].adv_accuracy.mean(skipna=True), "\n", - "Resnet18:", data[data['model_layers'] == 18].adv_accuracy.mean(skipna=True), "\n", + "Adversarial Accuracy:", + "\n", + "ResNet152:", + data[data["model_layers"] == 152].adv_accuracy.mean(skipna=True), + "\n", + "Resnet101:", + data[data["model_layers"] == 101].adv_accuracy.mean(skipna=True), + "\n", + "Resnet50:", + data[data["model_layers"] == 50].adv_accuracy.mean(skipna=True), + "\n", + "Resnet34:", + data[data["model_layers"] == 34].adv_accuracy.mean(skipna=True), + "\n", + "Resnet18:", + data[data["model_layers"] == 18].adv_accuracy.mean(skipna=True), + "\n", ) - # %% + def plot_aft( df, file, @@ -107,6 +125,7 @@ def plot_aft( plt.gcf().clear() return ax, aft + def plot_partial_effects( file, aft, @@ -117,12 +136,14 @@ def plot_partial_effects( ylabel="Failure rate", legend_kwargs={"loc": "upper left"}, replacement_dict={}, - cmap='coolwarm', - **kwargs, - ): + cmap="coolwarm", + **kwargs, +): plt.gcf().clear() # kwargs.pop("replacement_dict") - pareto = aft.plot_partial_effects_on_outcome(covariate_array, values_array, cmap=cmap, **kwargs) + pareto = aft.plot_partial_effects_on_outcome( + covariate_array, values_array, cmap=cmap, **kwargs + ) labels = pareto.get_yticklabels() labels = [label.get_text() for label in labels] for k, v in replacement_dict.items(): @@ -138,39 +159,46 @@ def plot_partial_effects( plt.gcf().clear() return pareto + def score_model(aft, train, test): train_score = aft.score(train) test_score = aft.score(test) - scores = {'train_score': train_score, 'test_score': test_score} + scores = {"train_score": train_score, "test_score": test_score} plt.show() - return scores + return scores def clean_data_for_aft( - data, kwarg_list, target="adv_failure_rate" + data, + kwarg_list, + target="adv_failure_rate", ): subset = data.copy() - assert target in subset, f"Target {target} not in dataframe with columns {subset.columns}" - + assert ( + target in subset + ), f"Target {target} not in dataframe with columns {subset.columns}" + cleaned = pd.DataFrame() kwarg_list.append(target) for kwarg in kwarg_list: cleaned = pd.concat([cleaned, subset[kwarg]], axis=1) cols = cleaned.columns cleaned = pd.DataFrame(subset, columns=cols) - - + # if "accuracy" in cleaned.columns: # cleaned = cleaned[~cleaned[cleaned['accuracy'] != 1e10]] # cleaned = cleaned[~cleaned[cleaned['accuracy'] != -1e10]] # if "adv_accuracy" in cleaned.columns: # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != 1e10]] # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != -1e10]] - cleaned.dropna(inplace=True, how='any', axis=0) + cleaned.dropna(inplace=True, how="any", axis=0) y = cleaned[target] - assert target in cleaned, f"Target {target} not in dataframe with columns {cleaned.columns}" + assert ( + target in cleaned + ), f"Target {target} not in dataframe with columns {cleaned.columns}" return cleaned, y, data + # %% @@ -187,7 +215,6 @@ def clean_data_for_aft( "adv_fit_time", # "atk_param", # "def_param", - "model.art.pipeline.initialize.kwargs.optimizer.lr", # "def_gen", # "atk_gen", @@ -200,15 +227,17 @@ def clean_data_for_aft( # cleaned['accuracy'] = y # %% -data.loc[:, "adv_failures"] = (1 - data.loc[:, "adv_accuracy"]) -data.loc[:, "ben_failures"] = (1 - data.loc[:, "accuracy"]) +data.loc[:, "adv_failures"] = 1 - data.loc[:, "adv_accuracy"] +data.loc[:, "ben_failures"] = 1 - data.loc[:, "accuracy"] target = "adv_failures" duration_col = "adv_fit_time" -cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) -X_train, X_test, y_train, y_test = train_test_split(cleaned, y, test_size=0.2, random_state=42) -assert target in cleaned, f"Target {target} not in dataframe with columns {cleaned.columns}" - - +cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) +X_train, X_test, y_train, y_test = train_test_split( + cleaned, y, test_size=0.2, random_state=42 +) +assert ( + target in cleaned +), f"Target {target} not in dataframe with columns {cleaned.columns}" # %% @@ -264,17 +293,17 @@ def clean_data_for_aft( "adv_failure_rate: lambda_": "Adv. Failure Rate", "failure_rate: lambda_": "Ben. Failure Rate", "model_layers: lambda_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_" : "Learning Rate", - "def_gen" : "Defence", + "model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_": "Learning Rate", + "def_gen": "Defence", } weibull_afr, wft = plot_aft( X_train, - file = "weibull_aft.pdf", - event_col = target, - duration_col = duration_col, - title = "Weibull AFR Model", - mtype = "weibull", + file="weibull_aft.pdf", + event_col=target, + duration_col=duration_col, + title="Weibull AFR Model", + mtype="weibull", replacement_dict=weibull_dict, ) wft.print_summary() @@ -290,16 +319,19 @@ def clean_data_for_aft( "model_layers=152": "152", } pareto_weibull = plot_partial_effects( - file = "weibull_partial_effects.pdf", - aft = wft, - covariate_array="model_layers", - values_array=[18, 34, 50, 101, 152], - title="Partial Effects of No. of Layers on Failure Rate for Weibull AFR", - replacement_dict=pareto_dict, + file="weibull_partial_effects.pdf", + aft=wft, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], + title="Partial Effects of No. of Layers on Failure Rate for Weibull AFR", + replacement_dict=pareto_dict, ylabel="% Chance of Survival", xlabel="Time $T$ (seconds)", - legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, - ) + legend_kwargs={ + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, +) # weibull_accuracy = plot_partial_effects( # file = "weibull_partial_effect_accuracy.pdf", @@ -312,48 +344,50 @@ def clean_data_for_aft( # xlabel="Time $T$ (seconds)", # legend = {"title" : "Benign Accuracy"}, # ) - - + # %% cox_dict = { "adv_failure_rate": "Adv. Failure Rate", - "def_value" : "Defence Strength", - "data.sample.random_state" : "Random State", - "train_time" : "Training Time", - "model_layers" : "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr" : "Learning Rate", - "adv_accuracy" : "Adv. Accuracy", - "adv_fit_time" : "Adv. Fit Time", - "adv_log_loss" : "Adv. Log Loss", - "predict_time" : "Inference Time", - "accuracy" : "Ben. Accuracy", - "failure_rate" : "Ben. Failure Rate", - "atk_value" : "Attack Strength", + "def_value": "Defence Strength", + "data.sample.random_state": "Random State", + "train_time": "Training Time", + "model_layers": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", + "adv_accuracy": "Adv. Accuracy", + "adv_fit_time": "Adv. Fit Time", + "adv_log_loss": "Adv. Log Loss", + "predict_time": "Inference Time", + "accuracy": "Ben. Accuracy", + "failure_rate": "Ben. Failure Rate", + "atk_value": "Attack Strength", } cox_afr, cft = plot_aft( X_train, - file = "cox_aft.pdf", - event_col = target, - duration_col = duration_col, - title = "Cox AFR Model", - mtype = "cox", + file="cox_aft.pdf", + event_col=target, + duration_col=duration_col, + title="Cox AFR Model", + mtype="cox", replacement_dict=cox_dict, ) cox_scores = score_model(cft, X_train, X_test) cft.print_summary() cox_partial = plot_partial_effects( - file = "cox_partial_effects.pdf", - aft = cft, - covariate_array = "model_layers", - values_array = [18, 34, 50, 101, 152], + file="cox_partial_effects.pdf", + aft=cft, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], replacement_dict=cox_dict, title="Survival Time for Cox AFR", ylabel="% Chance of Survival", xlabel="Time $T$ (seconds)", - legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, + legend_kwargs={ + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, ) # %% @@ -366,14 +400,14 @@ def clean_data_for_aft( "predict_time: mu_": "Inference Time", "adv_fit_time: mu_": "Adv. Fit Time", "model_layers: mu_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_" : "Learning Rate", + "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_": "Learning Rate", "data.sample.random_state: mu_": "Random State", "adv_log_loss: mu_": "Adv. Log Loss", "adv_accuracy: mu_": "Adv. Accuracy", "accuracy: mu_": "Ben. Accuracy", "adv_failure_rate: mu_": "Adv. Failure Rate", - "def_gen" : "Defence", - "learning_rate: mu_" : "Learning Rate", + "def_gen": "Defence", + "learning_rate: mu_": "Learning Rate", } log_normal_graph, lnt = plot_aft( @@ -388,15 +422,18 @@ def clean_data_for_aft( lnt_scores = score_model(lnt, X_train, X_test) lnt.print_summary() lnt_partial = plot_partial_effects( - file = "log_normal_partial_effects.pdf", - aft = lnt, - covariate_array = "model_layers", - values_array = [18, 34, 50, 101, 152], + file="log_normal_partial_effects.pdf", + aft=lnt, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], replacement_dict=log_normal_dict, title="Survival Time for Log-Normal AFR", ylabel="% Chance of Survival", xlabel="Time $T$ (seconds)", - legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, + legend_kwargs={ + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, ) # %% @@ -413,10 +450,9 @@ def clean_data_for_aft( "accuracy: alpha_": "Ben. Accuracy", "adv_fit_time: alpha_": "Adv. Fit Time", "model_layers: alpha_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr" : "Learning Rate", + "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", "adv_failure_rate: alpha_": "Adv. Failure Rate", - "alpha_" : "", - + "alpha_": "", } log_logistic_graph, llt = plot_aft( @@ -432,16 +468,18 @@ def clean_data_for_aft( llt_scores = score_model(llt, X_train, X_test) print(llt_scores) llt_partial = plot_partial_effects( - file = "log_logistic_partial_effects.pdf", - aft = llt, - covariate_array = "model_layers", - values_array = [18, 34, 50, 101, 152], + file="log_logistic_partial_effects.pdf", + aft=llt, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], replacement_dict=log_logistic_dict, title="Survival Time for Log-Logistic AFR", ylabel="% Chance of Survival", xlabel="Time $T$ (seconds)", - legend_kwargs={"title" : "No. of Layers", "labels" : ["18", "34", "50", "101", "152"]}, - + legend_kwargs={ + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, ) # %% @@ -464,18 +502,27 @@ def clean_data_for_aft( aft_data = pd.DataFrame() aft_data.index.name = "Model" aft_data.index = aft_dict.keys() -aft_data["AIC"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() ] +aft_data["AIC"] = [ + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() +] aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] -aft_data["BIC"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()] -aft_data['Train LL'] = [x['train_score'] for x in score_list] -aft_data['Test LL'] = [x['test_score'] for x in score_list] -aft_data['Mean ST'] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()] -aft_data['Median ST'] = [x.predict_median(X_train).median() for x in aft_dict.values()] +aft_data["BIC"] = [ + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() +] +aft_data["Train LL"] = [x["train_score"] for x in score_list] +aft_data["Test LL"] = [x["test_score"] for x in score_list] +aft_data["Mean ST"] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()] +aft_data["Median ST"] = [x.predict_median(X_train).median() for x in aft_dict.values()] aft_data = aft_data.round(2) aft_data.to_csv(FOLDER / "aft_comparison.csv") logger.info(f"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}") -aft_data = aft_data.round(2,) -aft_data.to_latex(FOLDER / "aft_comparison.tex", float_format="%.2f", label = "tab:mnist", caption="Comparison of AFR Models on the MNIST dataset.") +aft_data = aft_data.round(2) +aft_data.to_latex( + FOLDER / "aft_comparison.tex", + float_format="%.2f", + label="tab:mnist", + caption="Comparison of AFR Models on the MNIST dataset.", +) aft_data @@ -483,12 +530,9 @@ def clean_data_for_aft( # data['Survival Time'] = 1 / (data['adv_failure_rate']) # true_mean = np.mean(data['adv_failure_rate'] * data['predict_time']) # true_median = np.median(data['adv_failure_rate'] * data['predict_time']) -success_rate = data['adv_failure_rate'] -success_time = success_rate * data['predict_time'] +success_rate = data["adv_failure_rate"] +success_time = success_rate * data["predict_time"] true_mean = np.mean(success_time) true_median = np.median(success_time) print(f"True Mean: {true_mean}") print(f"True Median: {true_median}") - - - diff --git a/examples/pytorch_cifar/dvc.yaml b/examples/pytorch_cifar/dvc.yaml index cadc44fe..b398fbae 100644 --- a/examples/pytorch_cifar/dvc.yaml +++ b/examples/pytorch_cifar/dvc.yaml @@ -101,4 +101,4 @@ stages: metrics: - ${files.directory}/plots/aft_comparison.csv outs: - - ${files.directory}/plots/aft_comparison.tex \ No newline at end of file + - ${files.directory}/plots/aft_comparison.tex diff --git a/examples/pytorch_cifar/plots.py b/examples/pytorch_cifar/plots.py index 446bec45..68c994de 100644 --- a/examples/pytorch_cifar/plots.py +++ b/examples/pytorch_cifar/plots.py @@ -12,7 +12,6 @@ sns.set_theme(style="whitegrid", font_scale=1.8, font="times new roman") - def cat_plot( data, x, @@ -24,7 +23,7 @@ def cat_plot( ylabels, file, folder, - legend_title = None, + legend_title=None, hue_order=None, rotation=0, set={}, @@ -134,12 +133,12 @@ def drop_frames_without_results( def calculate_failure_rate(data): data = data[data.columns.drop(list(data.filter(regex=r"\.1$")))] data.columns.str.replace(" ", "") - data.loc[:, "failure_rate"] = ( - (1 - data.loc[:, "accuracy"])/ data.loc[:, "predict_time_per_sample"] - ) + data.loc[:, "failure_rate"] = (1 - data.loc[:, "accuracy"]) / data.loc[ + :, "predict_time_per_sample" + ] data.loc[:, "success_rate"] = ( data.loc[:, "accuracy"] * 100 / data.loc[:, "predict_time_per_sample"] - ) + ) data.loc[:, "adv_failure_rate"] = ( (1 - data.loc[:, "adv_accuracy"]) * 100 / data.loc[:, "adv_fit_time"] ) @@ -157,15 +156,16 @@ def calculate_failure_rate(data): ) return data + from paretoset import paretoset + def pareto_set(data, sense_dict): subset = data.loc[:, sense_dict.keys()] - these = paretoset(subset, sense = sense_dict.values()) + these = paretoset(subset, sense=sense_dict.values()) return data.iloc[these, :] - def min_max_scaling(data, **kwargs): if "atk_gen" not in data.columns: attacks = [] @@ -187,8 +187,8 @@ def min_max_scaling(data, **kwargs): min_ = data[data.atk_gen == atk].atk_value.min() scaled_value = (data[data.atk_gen == atk].atk_value - min_) / (max_ - min_) data.loc[data.atk_gen == atk, "atk_value"] = scaled_value - for k,v in kwargs.items(): - data.loc[:,k] = data.loc[:,k].apply(v) + for k, v in kwargs.items(): + data.loc[:, k] = data.loc[:, k].apply(v) return data @@ -251,24 +251,24 @@ def min_max_scaling(data, **kwargs): "predict_time", ], ) - sense_dict ={ - "accuracy" : "max", - # "train_time" : "min", - # "predict_time" : "min", - # "atk_value" : "diff", - "adv_accuracy" : "min", - # "def_value" : "diff", - "data.sample.random_state" : "diff", - # "adv_accuracy" : "min", - "model_layers" : "diff", - # "adv_fit_time" : "min", - "atk_param" : "diff", - "def_param" : "diff", - "atk_gen" : "diff", - "def_gen" : "diff", - # "model.art.pipeline.initialize.kwargs.optimizer.lr" : "diff", - # "adv_failure_rate" : "maximize", -} + sense_dict = { + "accuracy": "max", + # "train_time" : "min", + # "predict_time" : "min", + # "atk_value" : "diff", + "adv_accuracy": "min", + # "def_value" : "diff", + "data.sample.random_state": "diff", + # "adv_accuracy" : "min", + "model_layers": "diff", + # "adv_fit_time" : "min", + "atk_param": "diff", + "def_param": "diff", + "atk_gen": "diff", + "def_gen": "diff", + # "model.art.pipeline.initialize.kwargs.optimizer.lr" : "diff", + # "adv_failure_rate" : "maximize", + } data = pareto_set(data, sense_dict) data = calculate_failure_rate(data) data = min_max_scaling(data) From 423f5037ba7f64c8576c4ff8c1cb0ec07b3a06e7 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 9 Oct 2023 11:59:18 +0000 Subject: [PATCH 093/148] update to NEURIPS experiments --- deckard/base/model/art_pipeline.py | 1 + deckard/layers/afr.py | 462 +++++++++++++++ {examples/pytorch => deckard/layers}/plots.py | 13 +- examples/pytorch/afr.py | 528 ----------------- examples/pytorch/conf/plots.yaml | 64 ++- examples/pytorch/dvc.lock | 103 +++- examples/pytorch/dvc.yaml | 55 +- examples/pytorch/plots.ipynb | 149 ----- examples/pytorch_cifar/afr.py | 538 ------------------ examples/pytorch_cifar/conf/plots.yaml | 354 ++++++++++-- examples/pytorch_cifar/dvc.lock | 201 ++++--- examples/pytorch_cifar/dvc.yaml | 48 +- examples/pytorch_cifar/plots.py | 315 ---------- 13 files changed, 1077 insertions(+), 1754 deletions(-) create mode 100644 deckard/layers/afr.py rename {examples/pytorch => deckard/layers}/plots.py (93%) delete mode 100644 examples/pytorch/afr.py delete mode 100644 examples/pytorch/plots.ipynb delete mode 100644 examples/pytorch_cifar/afr.py delete mode 100644 examples/pytorch_cifar/plots.py diff --git a/deckard/base/model/art_pipeline.py b/deckard/base/model/art_pipeline.py index 69e5dd6c..b7182151 100644 --- a/deckard/base/model/art_pipeline.py +++ b/deckard/base/model/art_pipeline.py @@ -249,5 +249,6 @@ def __call__(self, model: object, library: str = None, data=None) -> BaseEstimat config.update(**sub_kwargs) model = obj(model) if "trainer" in self.pipeline: + name, sub_kwargs = self.pipeline["trainer"]() raise NotImplementedError("Training Defense not implemented yet") return model diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py new file mode 100644 index 00000000..8e938246 --- /dev/null +++ b/deckard/layers/afr.py @@ -0,0 +1,462 @@ +import pandas as pd +import numpy as np +from pathlib import Path + +import matplotlib.pyplot as plt + +from sklearn.model_selection import train_test_split +from lifelines import ( + WeibullAFTFitter, + LogNormalAFTFitter, + LogLogisticAFTFitter, + CoxPHFitter, +) +from .plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling +import matplotlib +from pathlib import Path +import logging +import yaml +import argparse + +logger = logging.getLogger(__name__) + +if "__main__" == __name__: + + afr_parser = argparse.ArgumentParser() + afr_parser.add_argument("--target", type=str, default="adv_failures") + afr_parser.add_argument("--duration_col", type=str, default="adv_fit_time") + afr_parser.add_argument("--dataset", type=str, default="mnist") + afr_args = afr_parser.parse_args() + target = afr_args.target + duration_col = afr_args.duration_col + dataset = afr_args.dataset + + + font = { + "family": "Times New Roman", + "weight": "bold", + "size": 22, + } + + matplotlib.rc("font", **font) + + + FOLDER = Path("output/plots/") + csv_file = FOLDER / "data.csv" + data = pd.read_csv(csv_file, index_col=0) + data.columns = data.columns.str.strip() + data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x) + data.def_value.replace("", 0, inplace=True) + data.atk_value.replace("", 0, inplace=True) + data = drop_frames_without_results(data) + data = calculate_failure_rate(data) + data = min_max_scaling(data) + data.dropna(axis=0, subset=["atk_value", "atk_param"], inplace=True) + data.dropna(axis=0, subset=["def_value", "def_param"], inplace=True) + data.loc[:, 'adv_failures'] = (1 - data.loc[:, 'adv_accuracy']) * data.loc[:, 'attack.attack_size'] + data.loc[:, 'ben_failures'] = (1 - data.loc[:, 'accuracy']) * data.loc[:, 'attack.attack_size'] + + # data=data[data['def_gen'] == 'Gauss-in'] + # data=data[data['atk_gen'] == 'HSJ'] + + print( + "Adversarial Accuracy:", + "\n", + "ResNet152:", + data[data["model_layers"] == 152].adv_accuracy.mean(skipna=True), + "\n", + "Resnet101:", + data[data["model_layers"] == 101].adv_accuracy.mean(skipna=True), + "\n", + "Resnet50:", + data[data["model_layers"] == 50].adv_accuracy.mean(skipna=True), + "\n", + "Resnet34:", + data[data["model_layers"] == 34].adv_accuracy.mean(skipna=True), + "\n", + "Resnet18:", + data[data["model_layers"] == 18].adv_accuracy.mean(skipna=True), + "\n", + ) + + + + + + def plot_aft( + df, + file, + event_col, + duration_col, + title, + mtype, + xlabel=None, + ylabel=None, + replacement_dict={}, + **kwargs, + ): + if mtype == "weibull": + aft = WeibullAFTFitter(**kwargs) + elif mtype == "log_normal": + aft = LogNormalAFTFitter(**kwargs) + elif mtype == "log_logistic": + aft = LogLogisticAFTFitter(**kwargs) + elif mtype == "cox": + aft = CoxPHFitter(**kwargs) + assert ( + duration_col in df.columns + ), f"Column {duration_col} not in dataframe with columns {df.columns}" + if event_col is not None: + assert ( + event_col in df.columns + ), f"Column {event_col} not in dataframe with columns {df.columns}" + plt.gcf().clear() + aft.fit(df, duration_col=duration_col, event_col=event_col) + ax = aft.plot() + labels = ax.get_yticklabels() + labels = [label.get_text() for label in labels] + for k, v in replacement_dict.items(): + labels = [label.replace(k, v) for label in labels] + ax.set_yticklabels(labels) + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_title(title) + ax.get_figure().tight_layout() + ax.get_figure().savefig(FOLDER / file) + logger.info(f"Saved graph to {FOLDER / file}") + plt.show() + plt.gcf().clear() + return ax, aft + + + def plot_partial_effects( + + aft, + covariate_array, + values_array, + title = None, + file = "partial_effects.pdf", + xlabel="Covariate", + ylabel="Failure rate", + legend_kwargs={"loc": "upper left"}, + replacement_dict={}, + cmap="coolwarm", + **kwargs, + ): + plt.gcf().clear() + # kwargs.pop("replacement_dict") + pareto = aft.plot_partial_effects_on_outcome( + covariate_array, values_array, cmap=cmap, **kwargs + ) + labels = pareto.get_yticklabels() + labels = [label.get_text() for label in labels] + for k, v in replacement_dict.items(): + labels = [label.replace(k, v) for label in labels] + pareto.set_yticklabels(labels) + pareto.legend(**legend_kwargs) + pareto.set_ylabel(ylabel) + pareto.set_xlabel(xlabel) + pareto.set_title(title) + pareto.get_figure().tight_layout() + pareto.get_figure().savefig(FOLDER / file) + logger.info(f"Saved graph to {FOLDER / file}") + plt.gcf().clear() + return pareto + + + def score_model(aft, train, test): + train_score = aft.score(train) + test_score = aft.score(test) + scores = {"train_score": train_score, "test_score": test_score} + plt.show() + return scores + + + def make_afr_table(score_list, aft_dict, dataset): + assert len(score_list) == len(aft_dict), "Length of score list and aft dict must be equal" + aft_data = pd.DataFrame() + aft_data.index.name = "Model" + aft_data.index = aft_dict.keys() + aft_data["AIC"] = [ + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() + ] + aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] + aft_data["BIC"] = [ + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() + ] + # aft_data["Train LL"] = [x["train_score"] for x in score_list] + # aft_data["Test LL"] = [x["test_score"] for x in score_list] + aft_data["Mean $S(t;\\theta)$"] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()] + aft_data["Median $S(t;\\theta)$"] = [x.predict_median(X_train).median() for x in aft_dict.values()] + aft_data = aft_data.round(2) + aft_data.to_csv(FOLDER / "aft_comparison.csv") + logger.info(f"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}") + aft_data = aft_data.round(2) + aft_data.fillna("--", inplace=True) + aft_data.to_latex( + FOLDER / "aft_comparison.tex", + float_format="%.2f", + label=f"tab:{dataset}", + caption=f"Comparison of AFR Models on the {dataset.upper()} dataset.", + ) + + print(yaml.dump(aft_data.to_dict(), default_flow_style=False, indent=4)) + return aft_data + + + def clean_data_for_aft( + data, + kwarg_list, + target="adv_failure_rate", + ): + subset = data.copy() + assert ( + target in subset + ), f"Target {target} not in dataframe with columns {subset.columns}" + + cleaned = pd.DataFrame() + kwarg_list.append(target) + for kwarg in kwarg_list: + cleaned = pd.concat([cleaned, subset[kwarg]], axis=1) + cols = cleaned.columns + cleaned = pd.DataFrame(subset, columns=cols) + if "accuracy" in cleaned.columns: + cleaned = cleaned[cleaned['accuracy'] != 1e10] + cleaned = cleaned[cleaned['accuracy'] != -1e10] + if "adv_accuracy" in cleaned.columns: + cleaned = cleaned[cleaned['adv_accuracy'] != 1e10] + cleaned = cleaned[cleaned['adv_accuracy'] != -1e10] + cleaned.dropna(inplace=True, how="any", axis=0) + y = cleaned[target] + assert ( + target in cleaned + ), f"Target {target} not in dataframe with columns {cleaned.columns}" + return cleaned, y, data + + def split_data_for_aft(data, target, duration_col, kwarg_list, test_size=0.2, random_state=42): + cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) + X_train, X_test, y_train, y_test = train_test_split( + cleaned, y, test_size=test_size, random_state=random_state + ) + assert ( + target in cleaned + ), f"Target {target} not in dataframe with columns {cleaned.columns}" + assert duration_col in cleaned, f"Duration {duration_col} not in dataframe with columns {cleaned.columns}" + return X_train, X_test, y_train, y_test + + + + + kwarg_list = [ + "accuracy", + "train_time", + "predict_time", + "atk_value", + "def_value", + "data.sample.random_state", + "adv_failure_rate", + "model_layers", + "adv_fit_time", + "model.art.pipeline.initialize.kwargs.optimizer.lr", + ] + + + + X_train, X_test, y_train, y_test = split_data_for_aft(data, target, duration_col, kwarg_list, test_size=0.2, random_state=42) + + + weibull_dict = { + "Intercept: rho_": "$\\rho$", + "Intercept: lambda_": "$\lambda$", + "data.sample.random_state: lambda_": "Random State", + "def_value: lambda_": "Defence Strength", + "atk_value: lambda_": "Attack Strength", + "train_time: lambda_": "$t_{train}$", + "predict_time: lambda_": "$t_{predict}$", + "adv_accuracy: lambda_": "$\lambda_{adv.}$", + "accuracy: lambda_": "$\lambda_{ben.}$", + "adv_fit_time: lambda_": "$t_{attack}$", + "adv_log_loss: lambda_": "Adv. Log Loss", + "adv_failure_rate: lambda_": "$h_{adv.}(t,;\\theta)$", + "failure_rate: lambda_": "$h_{ben.}(t,;\\theta)$", + "model_layers: lambda_": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_": "Learning Rate", + "def_gen": "Defence", + } + + weibull_plot_dict = { + "file": "weibull_aft.pdf", + "title": "Weibull AFR Model", + "mtype": "weibull", + } + + weibull_afr, wft = plot_aft( + X_train, + event_col=target, + duration_col=duration_col, + **weibull_plot_dict, + replacement_dict=weibull_dict, + ) + + weibull_partial_dict_layers = { + "file": "weibull_partial_effects.pdf", + "covariate_array": "model_layers", + "values_array": [18, 34, 50, 101, 152], + "title": "$S(t)$ for Weibull AFR", + "ylabel": "Expectation of $S(t)$", + "xlabel": "Time $T$ (seconds)", + "legend_kwargs": { + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, + } + + weibull_layers = plot_partial_effects( + aft=wft, + **weibull_partial_dict_layers, + ) + wft_scores = score_model(wft, X_train, X_test) + + cox_replacement_dict = { + "adv_failure_rate": "$h_{adv}(t,;\\theta)$", + "def_value": "Defence Strength", + "data.sample.random_state": "Random State", + "train_time": "$t_{train}$", + "model_layers": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", + "adv_accuracy": "$\lambda_{adv.}$", + "adv_fit_time": "$t_{attack}$", + "adv_log_loss": "Adv. Log Loss", + "predict_time": "$t_{predict}$", + "accuracy": "$\lambda_{ben.}$", + "failure_rate": "$h_{ben.}(t,;\\theta)$", + "atk_value": "Attack Strength", + } + cox_partial_dict = { + "file": "cox_partial_effects.pdf", + "covariate_array": "model_layers", + "values_array": [18, 34, 50, 101, 152], + "replacement_dict": cox_replacement_dict, + "title": "$S(t)$ for Cox AFR", + "ylabel": "Expectation of $S(t)$", + "xlabel": "Time $T$ (seconds)", + "legend_kwargs": { + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, + } + cox_plot_dict= { + "file" : "cox_aft.pdf", + "duration_col": duration_col, + "title": "Cox AFR Model", + "mtype": "cox", + "replacement_dict": cox_replacement_dict, + } + cox_afr, cft = plot_aft( + df = X_train, + event_col = target, + **cox_plot_dict, + ) + cox_scores = score_model(cft, X_train, X_test) + cox_partial = plot_partial_effects( + aft=cft, + **cox_partial_dict, + ) + + log_normal_dict = { + "Intercept: sigma_": "$\sigma$", + "Intercept: mu_": "$\mu$", + "def_value: mu_": "Defence Strength", + "atk_value: mu_": "Attack Strength", + "train_time: mu_": "$t_{train}$", + "predict_time: mu_": "$t_{predict}$", + "adv_fit_time: mu_": "$t_{attack}$", + "model_layers: mu_": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_": "Learning Rate", + "data.sample.random_state: mu_": "Random State", + "adv_log_loss: mu_": "Adv. Log Loss", + "adv_accuracy: mu_": "$\lambda_{adv.}$", + "accuracy: mu_": "$\lambda_{ben.}$", + "adv_failure_rate: mu_": "$h_{adv}(t,;\\theta)$", + "def_gen": "Defence", + "learning_rate: mu_": "Learning Rate", + } + + log_normal_graph, lnt = plot_aft( + X_train, + "log_normal_aft.pdf", + target, + duration_col, + "Log Normal AFR Model", + "log_normal", + replacement_dict=log_normal_dict, + ) + lnt_scores = score_model(lnt, X_train, X_test) + lnt_partial = plot_partial_effects( + file="log_normal_partial_effects.pdf", + aft=lnt, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], + replacement_dict=log_normal_dict, + title="$S(t)$ for Log-Normal AFR", + ylabel="Expectation of $S(t)$", + xlabel="Time $T$ (seconds)", + legend_kwargs={ + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, + ) + log_logistic_dict = { + "Intercept: beta_": "$\\beta$", + "Intercept: alpha_": "$\\alpha$", + "data.sample.random_state: alpha_": "Random State", + "def_value: alpha_": "Defence Strength", + "atk_value: alpha_": "Attack Strength", + "train_time: alpha_": "$t_{train}$", + "predict_time: alpha_": "$t_{predict}$", + "adv_accuracy: alpha_": "$\lambda_{adv.}$", + "accuracy: alpha_": "$\lambda_{ben.}$", + "adv_fit_time: alpha_": "$t_{attack}$", + "model_layers: alpha_": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", + "adv_failure_rate: alpha_": "$h_{adv.}(t,\\theta)$", + "alpha_": "", + } + + log_logistic_graph, llt = plot_aft( + X_train, + "log_logistic_aft.pdf", + target, + duration_col, + "Log Logistic AFR Model", + "log_logistic", + replacement_dict=log_logistic_dict, + ) + llt_scores = score_model(llt, X_train, X_test) + llt_partial = plot_partial_effects( + file="log_logistic_partial_effects.pdf", + aft=llt, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], + replacement_dict=log_logistic_dict, + title="$S(t)$ for Log-Logistic AFR", + ylabel="Expectation of $S(t)$", + xlabel="Time $T$ (seconds)", + legend_kwargs={ + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, + ) + aft_dict = { + "Weibull": wft, + "LogNormal": lnt, + "LogLogistic": llt, + # "Cox": cft, + } + score_list = [ + wft_scores, + lnt_scores, + llt_scores, + # cft_scores, + ] + aft_data = make_afr_table(score_list, aft_dict, dataset) diff --git a/examples/pytorch/plots.py b/deckard/layers/plots.py similarity index 93% rename from examples/pytorch/plots.py rename to deckard/layers/plots.py index 7f94be11..87b5673c 100644 --- a/examples/pytorch/plots.py +++ b/deckard/layers/plots.py @@ -98,6 +98,7 @@ def scatter_plot( hue_order=None, **kwargs, ): + # plt.gcf().clear() data = data.sort_values(by=[hue, x, y]) graph = sns.scatterplot( @@ -116,7 +117,7 @@ def scatter_plot( graph.set_title(title) graph.get_figure().tight_layout() graph.get_figure().savefig(Path(folder) / file) - logger.info(f"Rendering graph {i+1}") + logger.info(f"Saved graph to {Path(folder) / file}") plt.gcf().clear() return graph @@ -126,24 +127,26 @@ def drop_frames_without_results( data, subset=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"], ): + logger.info(f"Dropping frames without results for {subset}") data.dropna(axis=0, subset=subset, inplace=True) return data def calculate_failure_rate(data): + logger.info("Calculating failure rate") data = data[data.columns.drop(list(data.filter(regex=r"\.1$")))] data.columns.str.replace(" ", "") data.loc[:, "failure_rate"] = ( - (1 - data.loc[:, "accuracy"]) * 100 / data.loc[:, "predict_time"] + (1 - data.loc[:, "accuracy"]) * data.loc[:, "attack.attack_size"] / data.loc[:, "predict_time"] ) data.loc[:, "success_rate"] = ( - data.loc[:, "accuracy"] * 100 / data.loc[:, "predict_time"] + data.loc[:, "accuracy"] * data.loc[:, "attack.attack_size"] / data.loc[:, "predict_time"] ) data.loc[:, "adv_failure_rate"] = ( - (1 - data.loc[:, "adv_accuracy"]) * 100 / data.loc[:, "adv_fit_time"] + (1 - data.loc[:, "adv_accuracy"]) * data.loc[:, "attack.attack_size"] / data.loc[:, "adv_fit_time"] ) data.loc[:, "adv_success_rate"] = ( - data.loc[:, "adv_accuracy"] * 100 / data.loc[:, "adv_fit_time"] + data.loc[:, "adv_accuracy"] * data.loc[:, "attack.attack_size"] / data.loc[:, "adv_fit_time"] ) data.loc[:, "training_time_per_failure"] = ( data.loc[:, "train_time"] / data.loc[:, "failure_rate"] diff --git a/examples/pytorch/afr.py b/examples/pytorch/afr.py deleted file mode 100644 index 682907df..00000000 --- a/examples/pytorch/afr.py +++ /dev/null @@ -1,528 +0,0 @@ -# %% -import pandas as pd -import numpy as np -from pathlib import Path -import seaborn as sns - -import matplotlib.pyplot as plt - -from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import train_test_split - - -from paretoset import paretoset -from lifelines import ( - WeibullAFTFitter, - LogNormalAFTFitter, - LogLogisticAFTFitter, - CoxPHFitter, -) -from plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling -import matplotlib -from pathlib import Path -import logging - -logger = logging.getLogger(__name__) - -# %% -font = { - "family": "Times New Roman", - "weight": "bold", - "size": 22, -} - -matplotlib.rc("font", **font) - -# %% -FOLDER = Path("output/plots/") -csv_file = FOLDER / "data.csv" -data = pd.read_csv(csv_file, index_col=0) -data.columns = data.columns.str.strip() -data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x) -data.def_value.replace("", 0, inplace=True) -data.atk_value.replace("", 0, inplace=True) -data = drop_frames_without_results(data) -data = calculate_failure_rate(data) -data = min_max_scaling(data) -data.dropna(axis=0, subset=["atk_value", "atk_param"], inplace=True) -data.dropna(axis=0, subset=["def_value", "def_param"], inplace=True) -# data=data[data['def_gen'] == 'Gauss-in'] -# data=data[data['atk_gen'] == 'HSJ'] - -print( - "Adversarial Accuracy:", - "\n", - "ResNet152:", - data[data["model_layers"] == 152].adv_accuracy.mean(skipna=True), - "\n", - "Resnet101:", - data[data["model_layers"] == 101].adv_accuracy.mean(skipna=True), - "\n", - "Resnet50:", - data[data["model_layers"] == 50].adv_accuracy.mean(skipna=True), - "\n", - "Resnet34:", - data[data["model_layers"] == 34].adv_accuracy.mean(skipna=True), - "\n", - "Resnet18:", - data[data["model_layers"] == 18].adv_accuracy.mean(skipna=True), - "\n", -) - - -# %% - - -def plot_aft( - df, - file, - event_col, - duration_col, - title, - mtype, - xlabel=None, - ylabel=None, - replacement_dict={}, - **kwargs, -): - if mtype == "weibull": - aft = WeibullAFTFitter(**kwargs) - elif mtype == "log_normal": - aft = LogNormalAFTFitter(**kwargs) - elif mtype == "log_logistic": - aft = LogLogisticAFTFitter(**kwargs) - elif mtype == "cox": - aft = CoxPHFitter(**kwargs) - assert ( - duration_col in df.columns - ), f"Column {duration_col} not in dataframe with columns {df.columns}" - if event_col is not None: - assert ( - event_col in df.columns - ), f"Column {event_col} not in dataframe with columns {df.columns}" - aft.fit(df, duration_col=duration_col, event_col=event_col) - ax = aft.plot() - labels = ax.get_yticklabels() - labels = [label.get_text() for label in labels] - for k, v in replacement_dict.items(): - labels = [label.replace(k, v) for label in labels] - ax.set_yticklabels(labels) - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel) - ax.set_title(title) - ax.get_figure().tight_layout() - ax.get_figure().savefig(FOLDER / file) - logger.info(f"Saved graph to {FOLDER / file}") - plt.show() - return ax, aft - - -def plot_partial_effects( - file, - aft, - covariate_array, - values_array, - title, - xlabel="Covariate", - ylabel="Failure rate", - legend_kwargs={"loc": "upper left"}, - replacement_dict={}, - cmap="coolwarm", - **kwargs, -): - plt.gcf().clear() - # kwargs.pop("replacement_dict") - pareto = aft.plot_partial_effects_on_outcome( - covariate_array, values_array, cmap=cmap, **kwargs - ) - labels = pareto.get_yticklabels() - labels = [label.get_text() for label in labels] - for k, v in replacement_dict.items(): - labels = [label.replace(k, v) for label in labels] - pareto.set_yticklabels(labels) - pareto.legend(**legend_kwargs) - pareto.set_ylabel(ylabel) - pareto.set_xlabel(xlabel) - pareto.set_title(title) - pareto.get_figure().tight_layout() - pareto.get_figure().savefig(FOLDER / file) - logger.info(f"Saved graph to {FOLDER / file}") - return pareto - - -def score_model(aft, train, test): - train_score = aft.score(train) - test_score = aft.score(test) - scores = {"train_score": train_score, "test_score": test_score} - plt.show() - return scores - - -def clean_data_for_aft( - data, - kwarg_list, - target="adv_failure_rate", -): - subset = data.copy() - assert ( - target in subset - ), f"Target {target} not in dataframe with columns {subset.columns}" - - cleaned = pd.DataFrame() - kwarg_list.append(target) - for kwarg in kwarg_list: - cleaned = pd.concat([cleaned, subset[kwarg]], axis=1) - cols = cleaned.columns - cleaned = pd.DataFrame(subset, columns=cols) - - # if "accuracy" in cleaned.columns: - # cleaned = cleaned[~cleaned[cleaned['accuracy'] != 1e10]] - # cleaned = cleaned[~cleaned[cleaned['accuracy'] != -1e10]] - # if "adv_accuracy" in cleaned.columns: - # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != 1e10]] - # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != -1e10]] - cleaned.dropna(inplace=True, how="any", axis=0) - y = cleaned[target] - assert ( - target in cleaned - ), f"Target {target} not in dataframe with columns {cleaned.columns}" - return cleaned, y, data - - -# %% - - -kwarg_list = [ - # "accuracy", - "train_time", - "predict_time", - "atk_value", - "def_value", - "data.sample.random_state", - "adv_failure_rate", - # "failure_rate", - "model_layers", - "adv_fit_time", - # "atk_param", - # "def_param", - "model.art.pipeline.initialize.kwargs.optimizer.lr", - # "def_gen", - # "atk_gen", - # "adv_log_loss", - # "adv_accuracy", - # "adv_accuracy", -] - - -# cleaned['accuracy'] = y - -# %% -data.loc[:, "adv_failures"] = (1 - data.loc[:, "adv_accuracy"]) * 100 -data.loc[:, "ben_failures"] = (1 - data.loc[:, "accuracy"]) * 100 -target = "adv_failures" -duration_col = "adv_fit_time" -cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) -X_train, X_test, y_train, y_test = train_test_split( - cleaned, y, test_size=0.2, random_state=42 -) -assert ( - target in cleaned -), f"Target {target} not in dataframe with columns {cleaned.columns}" - - -# %% - -# from sklearn.preprocessing import PowerTransformer -# pt = PowerTransformer(method='yeo-johnson', standardize=False) -# del X_train[target] -# del X_test[target] -# X_train_cols = X_train.columns -# X_train = pt.fit(X_train).transform(X_train) -# X_test = pt.transform(X_test) -# X_train = pd.DataFrame(X_train, columns=X_train_cols) -# X_test = pd.DataFrame(X_test, columns=X_train_cols) -# X_train[target] = y_train -# y_train = X_train[target] - - -# %% -# sense_dict ={ -# "accuracy" : "max", -# "train_time" : "min", -# "predict_time" : "min", -# # "atk_value" : "diff", -# # "def_value" : "diff", -# "data.sample.random_state" : "diff", -# "adv_accuracy" : "min", -# "model_layers" : "diff", -# # "adv_fit_time" : "min", -# # "atk_param" : "diff", -# # "def_param" : "diff", -# "model.art.pipeline.initialize.kwargs.optimizer.lr" : "diff", -# # "adv_failure_rate" : "maximize", -# } -# subset = X_train.loc[:, sense_dict.keys()] -# senses = sense_dict.values() -# these = paretoset(subset) -# X_train = X_train.iloc[these, :] - - -# %% -weibull_dict = { - "Intercept: rho_": "$\\rho$", - "Intercept: lambda_": "$\lambda$", - "data.sample.random_state: lambda_": "Random State", - "def_value: lambda_": "Defence Strength", - "atk_value: lambda_": "Attack Strength", - "train_time: lambda_": "Training Time", - "predict_time: lambda_": "Inference Time", - "adv_accuracy: lambda_": "Adv. Accuracy", - "accuracy: lambda_": "Ben. Accuracy", - "adv_fit_time: lambda_": "Adv. Fit Time", - "adv_log_loss: lambda_": "Adv. Log Loss", - "adv_failure_rate: lambda_": "Adv. Failure Rate", - "failure_rate: lambda_": "Ben. Failure Rate", - "model_layers: lambda_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_": "Learning Rate", - "def_gen": "Defence", -} - -weibull_afr, wft = plot_aft( - X_train, - file="weibull_aft.pdf", - event_col=target, - duration_col=duration_col, - title="Weibull AFR Model", - mtype="weibull", - replacement_dict=weibull_dict, -) -wft.print_summary() -wft_scores = score_model(wft, X_train, X_test) - - -# %% -pareto_dict = { - "model_layers=18": "18", - "model_layers=34": "34", - "model_layers=50": "50", - "model_layers=101": "101", - "model_layers=152": "152", -} -pareto_weibull = plot_partial_effects( - file="weibull_partial_effects.pdf", - aft=wft, - covariate_array="model_layers", - values_array=[18, 34, 50, 101, 152], - title="Partial Effects of No. of Layers on Failure Rate for Weibull AFR", - replacement_dict=pareto_dict, - ylabel="% Chance of Survival", - xlabel="Time $T$ (seconds)", - legend_kwargs={ - "title": "No. of Layers", - "labels": ["18", "34", "50", "101", "152"], - }, -) - -# weibull_accuracy = plot_partial_effects( -# file = "weibull_partial_effect_accuracy.pdf", -# aft = wft, -# covariate_array = "accuracy", -# values_array = [.9, .99, .999, .9999], -# replacement_dict=weibull_dict, -# title="Partial Effects of Benign Accuracy on Failure Rate", -# ylabel="% Chance of Survival", -# xlabel="Time $T$ (seconds)", -# legend = {"title" : "Benign Accuracy"}, -# ) - - -# %% - -cox_dict = { - "adv_failure_rate": "Adv. Failure Rate", - "def_value": "Defence Strength", - "data.sample.random_state": "Random State", - "train_time": "Training Time", - "model_layers": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", - "adv_accuracy": "Adv. Accuracy", - "adv_fit_time": "Adv. Fit Time", - "adv_log_loss": "Adv. Log Loss", - "predict_time": "Inference Time", - "accuracy": "Ben. Accuracy", - "failure_rate": "Ben. Failure Rate", - "atk_value": "Attack Strength", -} - -cox_afr, cft = plot_aft( - X_train, - file="cox_aft.pdf", - event_col=target, - duration_col=duration_col, - title="Cox AFR Model", - mtype="cox", - replacement_dict=cox_dict, -) -cox_scores = score_model(cft, X_train, X_test) -cft.print_summary() -cox_partial = plot_partial_effects( - file="cox_partial_effects.pdf", - aft=cft, - covariate_array="model_layers", - values_array=[18, 34, 50, 101, 152], - replacement_dict=cox_dict, - title="Survival Time for Cox AFR", - ylabel="% Chance of Survival", - xlabel="Time $T$ (seconds)", - legend_kwargs={ - "title": "No. of Layers", - "labels": ["18", "34", "50", "101", "152"], - }, -) - -# %% -log_normal_dict = { - "Intercept: sigma_": "$\sigma$", - "Intercept: mu_": "$\mu$", - "def_value: mu_": "Defence Strength", - "atk_value: mu_": "Attack Strength", - "train_time: mu_": "Training Time", - "predict_time: mu_": "Inference Time", - "adv_fit_time: mu_": "Adv. Fit Time", - "model_layers: mu_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_": "Learning Rate", - "data.sample.random_state: mu_": "Random State", - "adv_log_loss: mu_": "Adv. Log Loss", - "adv_accuracy: mu_": "Adv. Accuracy", - "accuracy: mu_": "Ben. Accuracy", - "adv_failure_rate: mu_": "Adv. Failure Rate", - "def_gen": "Defence", - "learning_rate: mu_": "Learning Rate", -} - -log_normal_graph, lnt = plot_aft( - X_train, - "log_normal_aft.pdf", - target, - duration_col, - "Log Normal AFR Model", - "log_normal", - replacement_dict=log_normal_dict, -) -lnt_scores = score_model(lnt, X_train, X_test) -lnt.print_summary() -lnt_partial = plot_partial_effects( - file="log_normal_partial_effects.pdf", - aft=lnt, - covariate_array="model_layers", - values_array=[18, 34, 50, 101, 152], - replacement_dict=log_normal_dict, - title="Survival Time for Log-Normal AFR", - ylabel="% Chance of Survival", - xlabel="Time $T$ (seconds)", - legend_kwargs={ - "title": "No. of Layers", - "labels": ["18", "34", "50", "101", "152"], - }, -) - -# %% - -log_logistic_dict = { - "Intercept: beta_": "$\\beta$", - "Intercept: alpha_": "$\\alpha$", - "data.sample.random_state: alpha_": "Random State", - "def_value: alpha_": "Defence Strength", - "atk_value: alpha_": "Attack Strength", - "train_time: alpha_": "Training Time", - "predict_time: alpha_": "Inference Time", - "adv_accuracy: alpha_": "Adv. Accuracy", - "accuracy: alpha_": "Ben. Accuracy", - "adv_fit_time: alpha_": "Adv. Fit Time", - "model_layers: alpha_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", - "adv_failure_rate: alpha_": "Adv. Failure Rate", - "alpha_": "", -} - -log_logistic_graph, llt = plot_aft( - X_train, - "log_logistic_aft.pdf", - target, - duration_col, - "Log Logistic AFR Model", - "log_logistic", - replacement_dict=log_logistic_dict, -) -llt.print_summary() -llt_scores = score_model(llt, X_train, X_test) -print(llt_scores) -llt_partial = plot_partial_effects( - file="log_logistic_partial_effects.pdf", - aft=llt, - covariate_array="model_layers", - values_array=[18, 34, 50, 101, 152], - replacement_dict=log_logistic_dict, - title="Survival Time for Log-Logistic AFR", - ylabel="% Chance of Survival", - xlabel="Time $T$ (seconds)", - legend_kwargs={ - "title": "No. of Layers", - "labels": ["18", "34", "50", "101", "152"], - }, -) - -# %% -np.mean(llt.predict_median(X_train)) - -# %% -aft_dict = { - "Weibull": wft, - "LogNormal": lnt, - "LogLogistic": llt, - # "Cox": cft, -} - -score_list = [ - wft_scores, - lnt_scores, - llt_scores, - # cft_scores, -] -aft_data = pd.DataFrame() -aft_data.index.name = "Model" -aft_data.index = aft_dict.keys() -aft_data["AIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() -] -aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] -aft_data["BIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() -] -aft_data["Train LL"] = [x["train_score"] for x in score_list] -aft_data["Test LL"] = [x["test_score"] for x in score_list] -aft_data["Mean ST"] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()] -aft_data["Median ST"] = [x.predict_median(X_train).median() for x in aft_dict.values()] -aft_data = aft_data.round(2) -aft_data.to_csv(FOLDER / "aft_comparison.csv") -logger.info(f"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}") -aft_data = aft_data.round(2) -aft_data.to_latex( - FOLDER / "aft_comparison.tex", - float_format="%.2f", - label="tab:mnist", - caption="Comparison of AFR Models on the MNIST dataset.", -) -aft_data - - -# %% -# data['Survival Time'] = 1 / (data['adv_failure_rate']) -# true_mean = np.mean(data['adv_failure_rate'] * data['predict_time']) -# true_median = np.median(data['adv_failure_rate'] * data['predict_time']) -success_rate = data["adv_failure_rate"] -success_time = success_rate * data["predict_time"] -true_mean = np.mean(success_time) -true_median = np.median(success_time) -print(f"True Mean: {true_mean}") -print(f"True Median: {true_median}") diff --git a/examples/pytorch/conf/plots.yaml b/examples/pytorch/conf/plots.yaml index 1d9af6c7..97a523bc 100644 --- a/examples/pytorch/conf/plots.yaml +++ b/examples/pytorch/conf/plots.yaml @@ -4,11 +4,11 @@ cat_plot: kind: boxen set: yscale: linear - titles: Adversarial Accuracy vs Defence Type + titles: $\lambda_{adv.}$ vs Defence Type x: def_gen xlabels: Defence Type y: adv_accuracy - ylabels: Adv. Accuracy + ylabels: $\lambda_{adv.}$ rotation : 90 hue_order: - ResNet18 @@ -19,11 +19,11 @@ cat_plot: - file: ben_accuracy_vs_defence_type.pdf hue: model_name kind: boxen - titles: Benign Accuracy vs Defence Type + titles: $\lambda_{ben.}$ vs Defence Type x: def_gen xlabels: Defence Type y: accuracy - ylabels: Ben. Accuracy + ylabels: $\lambda_{ben.}$ rotation : 90 hue_order: - ResNet18 @@ -87,11 +87,11 @@ cat_plot: hue: model_name kind: boxen legend_title: Model Name - titles: Adversarial Failure Rate vs Defence Type + titles: $h_{adv}$ vs Defence Type x: def_gen xlabels: Defence Type y: adv_failure_rate - ylabels: Adv. Failure Rate + ylabels: $h_{adv.}$ rotation : 90 hue_order: - ResNet18 @@ -103,11 +103,11 @@ cat_plot: # hue: model_name # kind: boxen # legend_title: Model Name -# titles: Adversarial Accuracy vs Defence Type +# titles: $\lambda_{adv.}$ vs Defence Type # x: def_gen # xlabels: Defence Type # y: adv_accuracy -# ylabels: Adv. Accuracy +# ylabels: Adv. $\lambda_{ben.}$ # rotation : 90 # hue_order: # - ResNet18 @@ -119,11 +119,11 @@ cat_plot: hue: model_name kind: boxen legend_title: Model Name - titles: Adversarial Accuracy vs Attack Type + titles: $\lambda_{adv.}$ vs Attack Type x: atk_gen xlabels: Attack Type y: adv_accuracy - ylabels: Adv. Accuracy + ylabels: $\lambda_{adv.}$ rotation : 90 hue_order: - ResNet18 @@ -137,11 +137,11 @@ cat_plot: legend_title: Model Name set: yscale: log - titles: Benign Failure Rate vs Defence Type + titles: $h_{ben}(t; \theta)$ vs Defence Type x: def_gen xlabels: Defence Type y: failure_rate - ylabels: Ben. Failure Rate + ylabels: $h_{ben}(t; \theta)$ rotation : 90 hue_order: - ResNet18 @@ -152,14 +152,14 @@ cat_plot: line_plot: - file: def_param_vs_accuracy.pdf hue: def_gen - legend: {} - title: Accuracy vs Defence Strength + legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} + title: $\lambda_{ben.}$ vs Defence Strength x: def_value x_scale: linear xlabel: Defence Control Parameter y: accuracy y_scale: - ylabel: Accuracy + ylabel: $\lambda_{ben.}$ hue_order: - Control - Gauss-in @@ -168,14 +168,14 @@ line_plot: - FSQ - file: def_param_vs_adv_accuracy.pdf hue: def_gen - legend: {} - title: Adversarial Accuracy vs Defence Strength + legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} + title: $\lambda_{adv.}$ vs Defence Strength x: def_value x_scale: linear xlabel: Defence Control Parameter y: adv_accuracy y_scale: - ylabel: Adv. Accuracy + ylabel: $\lambda_{adv.}$ hue_order: - Control - Gauss-in @@ -184,14 +184,14 @@ line_plot: - FSQ - file: def_param_vs_adv_failure_rate.pdf hue: def_gen - legend: {} - title: Adversarial Failure Rate vs Defence Strength + legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} + title: $h_{adv}$ vs Defence Strength x: def_value x_scale: linear xlabel: Defence Control Parameter y: adv_failure_rate y_scale: log - ylabel: Adv. Failure Rate + ylabel: $h_{adv.}$ hue_order: - Control - Gauss-in @@ -200,14 +200,14 @@ line_plot: - FSQ - file: atk_param_vs_accuracy.pdf hue: atk_gen - legend: {} - title: Adversarial Accuracy vs Attack Strength + legend: {bbox_to_anchor: [1.05, 1]} + title: $\lambda_{adv.}$ vs Attack Strength x: atk_value x_scale: linear xlabel: Attack Control Parameter y: adv_accuracy y_scale: - ylabel: Adv. Accuracy + ylabel: $\lambda_{adv.}$ hue_order: - FGM - PGD @@ -215,18 +215,20 @@ line_plot: - HSJ - Pixel - Thresh + scatter_plot: - x: train_time_per_sample y: adv_failure_rate hue: model_name - xlabel: Training Time Per Sample - ylabel: Adversarial Failure Rate - title: Adversarial Failure Rate vs Training Time + xlabel: $t_{train}$ + ylabel: $h_{adv}$ + title: $h_{adv}$ vs $t_{train}$ file: adv_failure_rate_vs_train_time.pdf y_scale: log - x_scale: linear + x_scale: log legend: title: Model Name + bbox_to_anchor: [1.05, 1] hue_order: - ResNet18 - ResNet34 @@ -237,9 +239,9 @@ scatter_plot: # - x: train_time_per_sample # y: adv_failure_rate # hue: model_name -# xlabel: Training Time Per Sample -# ylabel: Adversarial Failure Rate -# title: Adversarial Failure Rate vs Training Time for each Defence +# xlabel: $t_{train}$ +# ylabel: $h_{adv}$ +# title: $h_{adv}$ vs $t_{train}$ for each Defence # file: adv_failure_rate_vs_train_time_def.pdf # y_scale: log # x_scale: linear diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index 474422e7..2603c83b 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -462,31 +462,40 @@ stages: md5: 9c9bc0aab00251ca5e9bd210366bc055 size: 1339392 - path: output/reports/attack/ - md5: b02916ecb3056906c2f5293ff7989271.dir - size: 21892376057 - nfiles: 52821 + md5: 639f99ca4725ce7b72afe921ec04c56d.dir + size: 21892372967 + nfiles: 52819 outs: - path: output/reports/attack.csv - md5: ee495ac7761566f86facc46215695fae - size: 34597566 + md5: dd2d965986d4b3cc1583730d92caf151 + size: 34597178 compile@train: cmd: python -m deckard.layers.compile --report_folder output/reports/train --results_file output/reports/train.csv deps: - - path: attack.db - md5: c9f920c7233802e9c46e4051c2da78e6 - size: 307200 - - path: model.db - md5: d1eac324650402da6e3de1aebe0e3b3c - size: 237568 + - path: ResNet101.db + md5: 744f187243001db70f63c8161d7f913f + size: 1314816 + - path: ResNet152.db + md5: e0fcb3876ec636b12d7dc9e71b9d1e1c + size: 1314816 + - path: ResNet18.db + md5: aa69b1818219c139f8e33ac205771ec1 + size: 110592 + - path: ResNet34.db + md5: e4d151b9800a1255a0143368057cb146 + size: 1339392 + - path: ResNet50.db + md5: 9c9bc0aab00251ca5e9bd210366bc055 + size: 1339392 - path: output/reports/train/ - md5: bb02cab436e3969d65066c5058939207.dir - size: 395685799 - nfiles: 655 + md5: 098781f04bdba3498c00a157e3e45826.dir + size: 403646689 + nfiles: 640 outs: - path: output/reports/train.csv - md5: 922fa64743aa481feb79213a056c816e - size: 363649 + md5: 152c61d1ae1a58e89c03c1351d5bf406 + size: 411488 attacks@ResNet152: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet152 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet152.db --config-name @@ -506,11 +515,14 @@ stages: md5: e0fcb3876ec636b12d7dc9e71b9d1e1c size: 1314816 plot: - cmd: python plots.py --path output/plots/ --file output/reports/attack.csv + cmd: python -m deckard.layers.plots --path output/plots/ --file output/reports/attack.csv deps: - path: ResNet101.db md5: 744f187243001db70f63c8161d7f913f size: 1314816 + - path: ResNet152.db + md5: e0fcb3876ec636b12d7dc9e71b9d1e1c + size: 1314816 - path: ResNet18.db md5: aa69b1818219c139f8e33ac205771ec1 size: 110592 @@ -521,13 +533,12 @@ stages: md5: 9c9bc0aab00251ca5e9bd210366bc055 size: 1339392 - path: output/reports/attack.csv - md5: ee495ac7761566f86facc46215695fae - size: 34597566 + md5: dd2d965986d4b3cc1583730d92caf151 + size: 34597178 outs: - - path: output/plots/ - md5: 1c5b6ac91813c03346b4ba4073fe6393.dir - size: 5642718 - nfiles: 14 + - path: output/plots/data.csv + md5: 1c7613836c312443c60d082beedad07a + size: 5271552 attacks@ResNet101: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet101 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet101.db --config-name @@ -580,3 +591,49 @@ stages: - path: ResNet50.db md5: 9c9bc0aab00251ca5e9bd210366bc055 size: 1339392 + afr: + cmd: python -m deckard.layers.afr --dataset mnist + deps: + - path: output/plots/data.csv + md5: 1c7613836c312443c60d082beedad07a + size: 5271552 + outs: + - path: output/plots/aft_comparison.csv + md5: a6f73c9391f954e99589a821212ad21c + size: 188 + - path: output/plots/aft_comparison.tex + md5: cce0d126a79add14d7ff788533686bde + size: 413 + - path: output/plots/cox_aft.pdf + md5: b76444e45979116a99351d5a7ba8f077 + size: 31142 + - path: output/plots/cox_partial_effects.pdf + md5: 19b5cf8862a73ec6d61b906422ffd4d7 + size: 42272 + - path: output/plots/log_logistic_aft.pdf + md5: d942647f45fcd634c3c83111930feb2e + size: 34244 + - path: output/plots/log_logistic_partial_effects.pdf + md5: 4824409000ca32f6b98dd74725162fbc + size: 30378 + - path: output/plots/log_normal_aft.pdf + md5: 3637064ee8589436adbf0c8cfaf319e2 + size: 34412 + - path: output/plots/log_normal_partial_effects.pdf + md5: d81620b4263ce48f527b37acfa24d7f3 + size: 31085 + - path: output/plots/weibull_aft.pdf + md5: a5faa8ed7d552e661044082591932ece + size: 32250 + - path: output/plots/weibull_partial_effects.pdf + md5: 65e320a974b0d727696ec5d22b31ed2f + size: 30796 + copy_results: + cmd: cp -r output/plots/* ~/ml_afr/mnist/ + deps: + - path: output/plots/aft_comparison.csv + md5: a6f73c9391f954e99589a821212ad21c + size: 188 + - path: output/plots/data.csv + md5: 1c7613836c312443c60d082beedad07a + size: 5271552 diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index 60acb158..cefc6c24 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -1,4 +1,6 @@ stages: + install: + cmd: python -m pip install ../deckard train: cmd: python -m deckard.layers.experiment train params: @@ -11,9 +13,9 @@ stages: - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} - # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} - - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} - # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} # Omit to save space + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} # logit outputs for our model + # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} # Omit to save space metrics: - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} attack: @@ -36,22 +38,23 @@ stages: ############################################################################## attacks: - foreach: - # - ResNet18 - # - ResNet34 - # - ResNet50 + foreach: # This is a loop over the ResNet models + - ResNet18 + - ResNet34 + - ResNet50 - ResNet101 - ResNet152 do: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name default.yaml deps: - - models.sh - - attacks.sh - - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + - models.sh # This script configures each defence + - attacks.sh # This script configures each attack + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} # This is here just to ensure it runs after the attack stage outs: - - ${item}.db + - ${item}.db # This outputs a database file for each model compile: - foreach: + foreach: # iterates through each stage + # - train - attack do: cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv @@ -65,13 +68,35 @@ stages: outs: - ${files.directory}/${files.reports}/${item}.csv plot: - cmd : python plots.py --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv + cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv deps: - ${files.directory}/${files.reports}/attack.csv - ResNet18.db - ResNet34.db - ResNet50.db - ResNet101.db - # - ResNet152.db + - ResNet152.db + outs: + - ${files.directory}/plots/data.csv + afr: + cmd: python -m deckard.layers.afr --dataset mnist + deps: + - ${files.directory}/plots/data.csv + plots: + - ${files.directory}/plots/weibull_aft.pdf + - ${files.directory}/plots/weibull_partial_effects.pdf + - ${files.directory}/plots/cox_partial_effects.pdf + - ${files.directory}/plots/cox_aft.pdf + - ${files.directory}/plots/log_logistic_aft.pdf + - ${files.directory}/plots/log_logistic_partial_effects.pdf + - ${files.directory}/plots/log_normal_aft.pdf + - ${files.directory}/plots/log_normal_partial_effects.pdf + metrics: + - ${files.directory}/plots/aft_comparison.csv outs: - - ${files.directory}/plots/ + - ${files.directory}/plots/aft_comparison.tex + copy_results: + cmd: cp -r ${files.directory}/plots/* ~/ml_afr/mnist/ + deps: + - ${files.directory}/plots/data.csv + - ${files.directory}/plots/aft_comparison.csv \ No newline at end of file diff --git a/examples/pytorch/plots.ipynb b/examples/pytorch/plots.ipynb deleted file mode 100644 index beb9e2d7..00000000 --- a/examples/pytorch/plots.ipynb +++ /dev/null @@ -1,149 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sns\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from plots import drop_frames_without_results, calculate_failure_rate, min_max_scaling\n", - "\n", - "csv_file = \"output/plots/data.csv\"\n", - "\n", - "data = pd.read_csv(csv_file)\n", - "data = drop_frames_without_results(\n", - " data,\n", - " subset=[\n", - " \"accuracy\",\n", - " \"adv_accuracy\",\n", - " \"train_time\",\n", - " \"adv_fit_time\",\n", - " \"predict_time\",\n", - " ],\n", - ")\n", - "data = calculate_failure_rate(data)\n", - "data = min_max_scaling(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# graph = sns.catplot(data=data, x='train_time', y='adv_failure_rate', hue='model_name', kind='boxen', row='def_gen', col='atk_gen', n_boot=10)\n", - "graph = sns.catplot(data=data, x='train_time', y='adv_failure_rate', hue='model_name', kind='point', row='atk_gen', n_boot=10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/cmeyers/deckard/examples/pytorch/plots.ipynb Cell 2\u001b[0m line \u001b[0;36m3\n\u001b[1;32m 31\u001b[0m plt\u001b[39m.\u001b[39mgcf()\u001b[39m.\u001b[39mclear()\n\u001b[1;32m 32\u001b[0m \u001b[39m# logger.info(f\"Saved graph to {folder / file}\")\u001b[39;00m\n\u001b[0;32m---> 34\u001b[0m cat_plot(data, x\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mtrain_time\u001b[39;49m\u001b[39m'\u001b[39;49m, y\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39madv_failure_rate\u001b[39;49m\u001b[39m'\u001b[39;49m, hue\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mmodel_name\u001b[39;49m\u001b[39m'\u001b[39;49m, kind\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mpoint\u001b[39;49m\u001b[39m'\u001b[39;49m, col\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39matk_gen\u001b[39;49m\u001b[39m'\u001b[39;49m, row\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mdef_gen\u001b[39;49m\u001b[39m'\u001b[39;49m, titles\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m{col_name}\u001b[39;49;00m\u001b[39m \u001b[39;49m\u001b[39m{row_name}\u001b[39;49;00m\u001b[39m'\u001b[39;49m, xlabels\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mTraining time (s)\u001b[39;49m\u001b[39m'\u001b[39;49m, ylabels\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mAdversarial failure rate\u001b[39;49m\u001b[39m'\u001b[39;49m, legend_title\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mModel\u001b[39;49m\u001b[39m'\u001b[39;49m, file\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mtrain_time_adv_failure_rate.png\u001b[39;49m\u001b[39m'\u001b[39;49m, folder\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39moutput/plots\u001b[39;49m\u001b[39m'\u001b[39;49m, rotation\u001b[39m=\u001b[39;49m\u001b[39m90\u001b[39;49m, \u001b[39mset\u001b[39;49m\u001b[39m=\u001b[39;49m{\u001b[39m'\u001b[39;49m\u001b[39mxscale\u001b[39;49m\u001b[39m'\u001b[39;49m: \u001b[39m'\u001b[39;49m\u001b[39mlog\u001b[39;49m\u001b[39m'\u001b[39;49m})\n", - "\u001b[1;32m/home/cmeyers/deckard/examples/pytorch/plots.ipynb Cell 2\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 18\u001b[0m plt\u001b[39m.\u001b[39mgcf()\u001b[39m.\u001b[39mclear()\n\u001b[1;32m 19\u001b[0m data \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39msort_values(by\u001b[39m=\u001b[39m[hue, x, y])\n\u001b[0;32m---> 20\u001b[0m graph \u001b[39m=\u001b[39m sns\u001b[39m.\u001b[39;49mcatplot(\n\u001b[1;32m 21\u001b[0m data\u001b[39m=\u001b[39;49mdata, x\u001b[39m=\u001b[39;49mx, y\u001b[39m=\u001b[39;49my, hue\u001b[39m=\u001b[39;49mhue, kind\u001b[39m=\u001b[39;49mkind, hue_order\u001b[39m=\u001b[39;49mhue_order, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\n\u001b[1;32m 22\u001b[0m )\n\u001b[1;32m 23\u001b[0m graph\u001b[39m.\u001b[39mset_xlabels(xlabels)\n\u001b[1;32m 24\u001b[0m graph\u001b[39m.\u001b[39mset_ylabels(ylabels)\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/seaborn/categorical.py:3244\u001b[0m, in \u001b[0;36mcatplot\u001b[0;34m(data, x, y, hue, row, col, col_wrap, estimator, errorbar, n_boot, units, seed, order, hue_order, row_order, col_order, height, aspect, kind, native_scale, formatter, orient, color, palette, hue_norm, legend, legend_out, sharex, sharey, margin_titles, facet_kws, ci, **kwargs)\u001b[0m\n\u001b[1;32m 3241\u001b[0m g \u001b[39m=\u001b[39m FacetGrid(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mfacet_kws)\n\u001b[1;32m 3243\u001b[0m \u001b[39m# Draw the plot onto the facets\u001b[39;00m\n\u001b[0;32m-> 3244\u001b[0m g\u001b[39m.\u001b[39;49mmap_dataframe(plot_func, x\u001b[39m=\u001b[39;49mx, y\u001b[39m=\u001b[39;49my, hue\u001b[39m=\u001b[39;49mhue, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mplot_kws)\n\u001b[1;32m 3246\u001b[0m \u001b[39mif\u001b[39;00m p\u001b[39m.\u001b[39morient \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mh\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m 3247\u001b[0m g\u001b[39m.\u001b[39mset_axis_labels(p\u001b[39m.\u001b[39mvalue_label, p\u001b[39m.\u001b[39mgroup_label)\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/seaborn/axisgrid.py:819\u001b[0m, in \u001b[0;36mFacetGrid.map_dataframe\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 816\u001b[0m kwargs[\u001b[39m\"\u001b[39m\u001b[39mdata\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m data_ijk\n\u001b[1;32m 818\u001b[0m \u001b[39m# Draw the plot\u001b[39;00m\n\u001b[0;32m--> 819\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_facet_plot(func, ax, args, kwargs)\n\u001b[1;32m 821\u001b[0m \u001b[39m# For axis labels, prefer to use positional args for backcompat\u001b[39;00m\n\u001b[1;32m 822\u001b[0m \u001b[39m# but also extract the x/y kwargs and use if no corresponding arg\u001b[39;00m\n\u001b[1;32m 823\u001b[0m axis_labels \u001b[39m=\u001b[39m [kwargs\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mx\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m), kwargs\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)]\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/seaborn/axisgrid.py:848\u001b[0m, in \u001b[0;36mFacetGrid._facet_plot\u001b[0;34m(self, func, ax, plot_args, plot_kwargs)\u001b[0m\n\u001b[1;32m 846\u001b[0m plot_args \u001b[39m=\u001b[39m []\n\u001b[1;32m 847\u001b[0m plot_kwargs[\u001b[39m\"\u001b[39m\u001b[39max\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m ax\n\u001b[0;32m--> 848\u001b[0m func(\u001b[39m*\u001b[39;49mplot_args, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mplot_kwargs)\n\u001b[1;32m 850\u001b[0m \u001b[39m# Sort out the supporting information\u001b[39;00m\n\u001b[1;32m 851\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_update_legend_data(ax)\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/seaborn/categorical.py:2847\u001b[0m, in \u001b[0;36mpointplot\u001b[0;34m(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, units, seed, markers, linestyles, dodge, join, scale, orient, color, palette, errwidth, ci, capsize, label, ax)\u001b[0m\n\u001b[1;32m 2844\u001b[0m \u001b[39mif\u001b[39;00m ax \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 2845\u001b[0m ax \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mgca()\n\u001b[0;32m-> 2847\u001b[0m plotter\u001b[39m.\u001b[39;49mplot(ax)\n\u001b[1;32m 2848\u001b[0m \u001b[39mreturn\u001b[39;00m ax\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/seaborn/categorical.py:1735\u001b[0m, in \u001b[0;36m_PointPlotter.plot\u001b[0;34m(self, ax)\u001b[0m\n\u001b[1;32m 1733\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mplot\u001b[39m(\u001b[39mself\u001b[39m, ax):\n\u001b[1;32m 1734\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Make the plot.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1735\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdraw_points(ax)\n\u001b[1;32m 1736\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mannotate_axes(ax)\n\u001b[1;32m 1737\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39morient \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mh\u001b[39m\u001b[39m\"\u001b[39m:\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/seaborn/categorical.py:1728\u001b[0m, in \u001b[0;36m_PointPlotter.draw_points\u001b[0;34m(self, ax)\u001b[0m\n\u001b[1;32m 1725\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mlen\u001b[39m(remove_na(statistic)):\n\u001b[1;32m 1726\u001b[0m x \u001b[39m=\u001b[39m y \u001b[39m=\u001b[39m [np\u001b[39m.\u001b[39mnan] \u001b[39m*\u001b[39m n_points\n\u001b[0;32m-> 1728\u001b[0m ax\u001b[39m.\u001b[39;49mscatter(x, y, label\u001b[39m=\u001b[39;49mhue_level,\n\u001b[1;32m 1729\u001b[0m facecolor\u001b[39m=\u001b[39;49mcolor, edgecolor\u001b[39m=\u001b[39;49mcolor,\n\u001b[1;32m 1730\u001b[0m linewidth\u001b[39m=\u001b[39;49mmew, marker\u001b[39m=\u001b[39;49mmarker, s\u001b[39m=\u001b[39;49mmarkersize,\n\u001b[1;32m 1731\u001b[0m zorder\u001b[39m=\u001b[39;49mz)\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/matplotlib/__init__.py:1442\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1439\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m 1440\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(ax, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m 1441\u001b[0m \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1442\u001b[0m \u001b[39mreturn\u001b[39;00m func(ax, \u001b[39m*\u001b[39;49m\u001b[39mmap\u001b[39;49m(sanitize_sequence, args), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1444\u001b[0m bound \u001b[39m=\u001b[39m new_sig\u001b[39m.\u001b[39mbind(ax, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m 1445\u001b[0m auto_label \u001b[39m=\u001b[39m (bound\u001b[39m.\u001b[39marguments\u001b[39m.\u001b[39mget(label_namer)\n\u001b[1;32m 1446\u001b[0m \u001b[39mor\u001b[39;00m bound\u001b[39m.\u001b[39mkwargs\u001b[39m.\u001b[39mget(label_namer))\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/matplotlib/axes/_axes.py:4711\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4708\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ymargin \u001b[39m<\u001b[39m \u001b[39m0.05\u001b[39m \u001b[39mand\u001b[39;00m x\u001b[39m.\u001b[39msize \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m 4709\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_ymargin(\u001b[39m0.05\u001b[39m)\n\u001b[0;32m-> 4711\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49madd_collection(collection)\n\u001b[1;32m 4712\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_request_autoscale_view()\n\u001b[1;32m 4714\u001b[0m \u001b[39mreturn\u001b[39;00m collection\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/matplotlib/axes/_base.py:2263\u001b[0m, in \u001b[0;36m_AxesBase.add_collection\u001b[0;34m(self, collection, autolim)\u001b[0m\n\u001b[1;32m 2258\u001b[0m collection\u001b[39m.\u001b[39mset_clip_path(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpatch)\n\u001b[1;32m 2260\u001b[0m \u001b[39mif\u001b[39;00m autolim:\n\u001b[1;32m 2261\u001b[0m \u001b[39m# Make sure viewLim is not stale (mostly to match\u001b[39;00m\n\u001b[1;32m 2262\u001b[0m \u001b[39m# pre-lazy-autoscale behavior, which is not really better).\u001b[39;00m\n\u001b[0;32m-> 2263\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_unstale_viewLim()\n\u001b[1;32m 2264\u001b[0m datalim \u001b[39m=\u001b[39m collection\u001b[39m.\u001b[39mget_datalim(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransData)\n\u001b[1;32m 2265\u001b[0m points \u001b[39m=\u001b[39m datalim\u001b[39m.\u001b[39mget_points()\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/matplotlib/axes/_base.py:852\u001b[0m, in \u001b[0;36m_AxesBase._unstale_viewLim\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 850\u001b[0m \u001b[39mfor\u001b[39;00m ax \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_shared_axes[name]\u001b[39m.\u001b[39mget_siblings(\u001b[39mself\u001b[39m):\n\u001b[1;32m 851\u001b[0m ax\u001b[39m.\u001b[39m_stale_viewlims[name] \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m--> 852\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mautoscale_view(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49m{\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mscale\u001b[39;49m\u001b[39m{\u001b[39;49;00mname\u001b[39m}\u001b[39;49;00m\u001b[39m\"\u001b[39;49m: scale\n\u001b[1;32m 853\u001b[0m \u001b[39mfor\u001b[39;49;00m name, scale \u001b[39min\u001b[39;49;00m need_scale\u001b[39m.\u001b[39;49mitems()})\n", - "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/matplotlib/axes/_base.py:2854\u001b[0m, in \u001b[0;36m_AxesBase.autoscale_view\u001b[0;34m(self, tight, scalex, scaley)\u001b[0m\n\u001b[1;32m 2852\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39muse_sticky_edges:\n\u001b[1;32m 2853\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_xmargin \u001b[39mand\u001b[39;00m scalex \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_autoscalex_on():\n\u001b[0;32m-> 2854\u001b[0m x_stickies \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msort(np\u001b[39m.\u001b[39;49mconcatenate([\n\u001b[1;32m 2855\u001b[0m artist\u001b[39m.\u001b[39;49msticky_edges\u001b[39m.\u001b[39;49mx\n\u001b[1;32m 2856\u001b[0m \u001b[39mfor\u001b[39;49;00m ax \u001b[39min\u001b[39;49;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_shared_axes[\u001b[39m\"\u001b[39;49m\u001b[39mx\u001b[39;49m\u001b[39m\"\u001b[39;49m]\u001b[39m.\u001b[39;49mget_siblings(\u001b[39mself\u001b[39;49m)\n\u001b[1;32m 2857\u001b[0m \u001b[39mfor\u001b[39;49;00m artist \u001b[39min\u001b[39;49;00m ax\u001b[39m.\u001b[39;49mget_children()]))\n\u001b[1;32m 2858\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ymargin \u001b[39mand\u001b[39;00m scaley \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_autoscaley_on():\n\u001b[1;32m 2859\u001b[0m y_stickies \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msort(np\u001b[39m.\u001b[39mconcatenate([\n\u001b[1;32m 2860\u001b[0m artist\u001b[39m.\u001b[39msticky_edges\u001b[39m.\u001b[39my\n\u001b[1;32m 2861\u001b[0m \u001b[39mfor\u001b[39;00m ax \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_shared_axes[\u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m]\u001b[39m.\u001b[39mget_siblings(\u001b[39mself\u001b[39m)\n\u001b[1;32m 2862\u001b[0m \u001b[39mfor\u001b[39;00m artist \u001b[39min\u001b[39;00m ax\u001b[39m.\u001b[39mget_children()]))\n", - "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "def cat_plot(\n", - " data,\n", - " x,\n", - " y,\n", - " hue,\n", - " kind,\n", - " titles,\n", - " xlabels,\n", - " ylabels,\n", - " legend_title,\n", - " file,\n", - " folder,\n", - " hue_order=None,\n", - " rotation=0,\n", - " set={},\n", - " **kwargs,\n", - "):\n", - " plt.gcf().clear()\n", - " data = data.sort_values(by=[hue, x, y])\n", - " graph = sns.catplot(\n", - " data=data, x=x, y=y, hue=hue, kind=kind, hue_order=hue_order, **kwargs\n", - " )\n", - " graph.set_xlabels(xlabels)\n", - " graph.set_ylabels(ylabels)\n", - " graph.set_titles(titles)\n", - " graph.legend.set_title(title=legend_title)\n", - " graph.set_xticklabels(graph.axes.flat[-1].get_xticklabels(), rotation=rotation)\n", - " graph.set(**set)\n", - " graph.tight_layout()\n", - " graph.savefig(folder / file)\n", - " plt.gcf().clear()\n", - " # logger.info(f\"Saved graph to {folder / file}\")\n", - "\n", - "cat_plot(data, x='train_time', y='adv_failure_rate', hue='model_name', kind='point', col='atk_gen', row='def_gen', titles='{col_name} {row_name}', xlabels='Training time (s)', ylabels='Adversarial failure rate', legend_title='Model', file='train_time_adv_failure_rate.png', folder='output/plots', rotation=90, set={'xscale': 'log'})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "env", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/pytorch_cifar/afr.py b/examples/pytorch_cifar/afr.py deleted file mode 100644 index ee6605cd..00000000 --- a/examples/pytorch_cifar/afr.py +++ /dev/null @@ -1,538 +0,0 @@ -# %% -import pandas as pd -import numpy as np -from pathlib import Path -import seaborn as sns - -import matplotlib.pyplot as plt - -from sklearn.preprocessing import StandardScaler -from sklearn.model_selection import train_test_split - - -from paretoset import paretoset -from lifelines import ( - WeibullAFTFitter, - LogNormalAFTFitter, - LogLogisticAFTFitter, - CoxPHFitter, - AalenAdditiveFitter, - AalenJohansenFitter, -) -from plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling -import matplotlib -from pathlib import Path -import logging - -logger = logging.getLogger(__name__) - -# %% -font = { - "family": "Times New Roman", - "weight": "bold", - "size": 22, -} - -matplotlib.rc("font", **font) - -# %% -FOLDER = Path("output/plots/") -csv_file = FOLDER / "data.csv" -data = pd.read_csv(csv_file, index_col=0) -data.columns = data.columns.str.strip() -data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x) -data.def_value.replace("", 0, inplace=True) -data.atk_value.replace("", 0, inplace=True) -data = drop_frames_without_results(data) -data = calculate_failure_rate(data) -data = min_max_scaling(data) -data.dropna(axis=0, subset=["atk_value", "atk_param"], inplace=True) -data.dropna(axis=0, subset=["def_value", "def_param"], inplace=True) -# data=data[data['def_gen'] == 'Gauss-in'] -# data=data[data['atk_gen'] == 'HSJ'] - -print( - "Adversarial Accuracy:", - "\n", - "ResNet152:", - data[data["model_layers"] == 152].adv_accuracy.mean(skipna=True), - "\n", - "Resnet101:", - data[data["model_layers"] == 101].adv_accuracy.mean(skipna=True), - "\n", - "Resnet50:", - data[data["model_layers"] == 50].adv_accuracy.mean(skipna=True), - "\n", - "Resnet34:", - data[data["model_layers"] == 34].adv_accuracy.mean(skipna=True), - "\n", - "Resnet18:", - data[data["model_layers"] == 18].adv_accuracy.mean(skipna=True), - "\n", -) - - -# %% - - -def plot_aft( - df, - file, - event_col, - duration_col, - title, - mtype, - xlabel=None, - ylabel=None, - replacement_dict={}, - **kwargs, -): - plt.gcf().clear() - if mtype == "weibull": - aft = WeibullAFTFitter(**kwargs) - elif mtype == "log_normal": - aft = LogNormalAFTFitter(**kwargs) - elif mtype == "log_logistic": - aft = LogLogisticAFTFitter(**kwargs) - elif mtype == "cox": - aft = CoxPHFitter(**kwargs) - elif mtype == "aalen_additive": - aft = AalenAdditiveFitter(**kwargs) - elif mtype == "aalen_johansen": - aft = AalenJohansenFitter(**kwargs) - else: - raise TypeError(f"Model type {mtype} not supported") - assert ( - duration_col in df.columns - ), f"Column {duration_col} not in dataframe with columns {df.columns}" - if event_col is not None: - assert ( - event_col in df.columns - ), f"Column {event_col} not in dataframe with columns {df.columns}" - aft.fit(df, duration_col=duration_col, event_col=event_col) - ax = aft.plot() - labels = ax.get_yticklabels() - labels = [label.get_text() for label in labels] - for k, v in replacement_dict.items(): - labels = [label.replace(k, v) for label in labels] - ax.set_yticklabels(labels) - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel) - ax.set_title(title) - ax.get_figure().tight_layout() - ax.get_figure().savefig(FOLDER / file) - logger.info(f"Saved graph to {FOLDER / file}") - plt.gcf().clear() - return ax, aft - - -def plot_partial_effects( - file, - aft, - covariate_array, - values_array, - title, - xlabel="Covariate", - ylabel="Failure rate", - legend_kwargs={"loc": "upper left"}, - replacement_dict={}, - cmap="coolwarm", - **kwargs, -): - plt.gcf().clear() - # kwargs.pop("replacement_dict") - pareto = aft.plot_partial_effects_on_outcome( - covariate_array, values_array, cmap=cmap, **kwargs - ) - labels = pareto.get_yticklabels() - labels = [label.get_text() for label in labels] - for k, v in replacement_dict.items(): - labels = [label.replace(k, v) for label in labels] - pareto.set_yticklabels(labels) - pareto.legend(**legend_kwargs) - pareto.set_ylabel(ylabel) - pareto.set_xlabel(xlabel) - pareto.set_title(title) - pareto.get_figure().tight_layout() - pareto.get_figure().savefig(FOLDER / file) - logger.info(f"Saved graph to {FOLDER / file}") - plt.gcf().clear() - return pareto - - -def score_model(aft, train, test): - train_score = aft.score(train) - test_score = aft.score(test) - scores = {"train_score": train_score, "test_score": test_score} - plt.show() - return scores - - -def clean_data_for_aft( - data, - kwarg_list, - target="adv_failure_rate", -): - subset = data.copy() - assert ( - target in subset - ), f"Target {target} not in dataframe with columns {subset.columns}" - - cleaned = pd.DataFrame() - kwarg_list.append(target) - for kwarg in kwarg_list: - cleaned = pd.concat([cleaned, subset[kwarg]], axis=1) - cols = cleaned.columns - cleaned = pd.DataFrame(subset, columns=cols) - - # if "accuracy" in cleaned.columns: - # cleaned = cleaned[~cleaned[cleaned['accuracy'] != 1e10]] - # cleaned = cleaned[~cleaned[cleaned['accuracy'] != -1e10]] - # if "adv_accuracy" in cleaned.columns: - # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != 1e10]] - # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != -1e10]] - cleaned.dropna(inplace=True, how="any", axis=0) - y = cleaned[target] - assert ( - target in cleaned - ), f"Target {target} not in dataframe with columns {cleaned.columns}" - return cleaned, y, data - - -# %% - - -kwarg_list = [ - # "accuracy", - "train_time", - "predict_time", - "atk_value", - "def_value", - "data.sample.random_state", - "adv_failure_rate", - # "failure_rate", - "model_layers", - "adv_fit_time", - # "atk_param", - # "def_param", - "model.art.pipeline.initialize.kwargs.optimizer.lr", - # "def_gen", - # "atk_gen", - # "adv_log_loss", - # "adv_accuracy", - # "adv_accuracy", -] - - -# cleaned['accuracy'] = y - -# %% -data.loc[:, "adv_failures"] = 1 - data.loc[:, "adv_accuracy"] -data.loc[:, "ben_failures"] = 1 - data.loc[:, "accuracy"] -target = "adv_failures" -duration_col = "adv_fit_time" -cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) -X_train, X_test, y_train, y_test = train_test_split( - cleaned, y, test_size=0.2, random_state=42 -) -assert ( - target in cleaned -), f"Target {target} not in dataframe with columns {cleaned.columns}" - - -# %% - -# from sklearn.preprocessing import PowerTransformer -# pt = PowerTransformer(method='yeo-johnson', standardize=False) -# del X_train[target] -# del X_test[target] -# X_train_cols = X_train.columns -# X_train = pt.fit(X_train).transform(X_train) -# X_test = pt.transform(X_test) -# X_train = pd.DataFrame(X_train, columns=X_train_cols) -# X_test = pd.DataFrame(X_test, columns=X_train_cols) -# X_train[target] = y_train -# y_train = X_train[target] - - -# %% -# sense_dict ={ -# "accuracy" : "max", -# "train_time" : "min", -# "predict_time" : "min", -# # "atk_value" : "diff", -# # "def_value" : "diff", -# "data.sample.random_state" : "diff", -# "adv_accuracy" : "min", -# "model_layers" : "diff", -# # "adv_fit_time" : "min", -# # "atk_param" : "diff", -# # "def_param" : "diff", -# "model.art.pipeline.initialize.kwargs.optimizer.lr" : "diff", -# # "adv_failure_rate" : "maximize", -# } -# subset = X_train.loc[:, sense_dict.keys()] -# senses = sense_dict.values() -# these = paretoset(subset) -# X_train = X_train.iloc[these, :] - - -# %% -weibull_dict = { - "Intercept: rho_": "$\\rho$", - "Intercept: lambda_": "$\lambda$", - "data.sample.random_state: lambda_": "Random State", - "def_value: lambda_": "Defence Strength", - "atk_value: lambda_": "Attack Strength", - "train_time: lambda_": "Training Time", - "predict_time: lambda_": "Inference Time", - "adv_accuracy: lambda_": "Adv. Accuracy", - "accuracy: lambda_": "Ben. Accuracy", - "adv_fit_time: lambda_": "Adv. Fit Time", - "adv_log_loss: lambda_": "Adv. Log Loss", - "adv_failure_rate: lambda_": "Adv. Failure Rate", - "failure_rate: lambda_": "Ben. Failure Rate", - "model_layers: lambda_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_": "Learning Rate", - "def_gen": "Defence", -} - -weibull_afr, wft = plot_aft( - X_train, - file="weibull_aft.pdf", - event_col=target, - duration_col=duration_col, - title="Weibull AFR Model", - mtype="weibull", - replacement_dict=weibull_dict, -) -wft.print_summary() -wft_scores = score_model(wft, X_train, X_test) - - -# %% -pareto_dict = { - "model_layers=18": "18", - "model_layers=34": "34", - "model_layers=50": "50", - "model_layers=101": "101", - "model_layers=152": "152", -} -pareto_weibull = plot_partial_effects( - file="weibull_partial_effects.pdf", - aft=wft, - covariate_array="model_layers", - values_array=[18, 34, 50, 101, 152], - title="Partial Effects of No. of Layers on Failure Rate for Weibull AFR", - replacement_dict=pareto_dict, - ylabel="% Chance of Survival", - xlabel="Time $T$ (seconds)", - legend_kwargs={ - "title": "No. of Layers", - "labels": ["18", "34", "50", "101", "152"], - }, -) - -# weibull_accuracy = plot_partial_effects( -# file = "weibull_partial_effect_accuracy.pdf", -# aft = wft, -# covariate_array = "accuracy", -# values_array = [.9, .99, .999, .9999], -# replacement_dict=weibull_dict, -# title="Partial Effects of Benign Accuracy on Failure Rate", -# ylabel="% Chance of Survival", -# xlabel="Time $T$ (seconds)", -# legend = {"title" : "Benign Accuracy"}, -# ) - - -# %% - -cox_dict = { - "adv_failure_rate": "Adv. Failure Rate", - "def_value": "Defence Strength", - "data.sample.random_state": "Random State", - "train_time": "Training Time", - "model_layers": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", - "adv_accuracy": "Adv. Accuracy", - "adv_fit_time": "Adv. Fit Time", - "adv_log_loss": "Adv. Log Loss", - "predict_time": "Inference Time", - "accuracy": "Ben. Accuracy", - "failure_rate": "Ben. Failure Rate", - "atk_value": "Attack Strength", -} - -cox_afr, cft = plot_aft( - X_train, - file="cox_aft.pdf", - event_col=target, - duration_col=duration_col, - title="Cox AFR Model", - mtype="cox", - replacement_dict=cox_dict, -) -cox_scores = score_model(cft, X_train, X_test) -cft.print_summary() -cox_partial = plot_partial_effects( - file="cox_partial_effects.pdf", - aft=cft, - covariate_array="model_layers", - values_array=[18, 34, 50, 101, 152], - replacement_dict=cox_dict, - title="Survival Time for Cox AFR", - ylabel="% Chance of Survival", - xlabel="Time $T$ (seconds)", - legend_kwargs={ - "title": "No. of Layers", - "labels": ["18", "34", "50", "101", "152"], - }, -) - -# %% -log_normal_dict = { - "Intercept: sigma_": "$\sigma$", - "Intercept: mu_": "$\mu$", - "def_value: mu_": "Defence Strength", - "atk_value: mu_": "Attack Strength", - "train_time: mu_": "Training Time", - "predict_time: mu_": "Inference Time", - "adv_fit_time: mu_": "Adv. Fit Time", - "model_layers: mu_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_": "Learning Rate", - "data.sample.random_state: mu_": "Random State", - "adv_log_loss: mu_": "Adv. Log Loss", - "adv_accuracy: mu_": "Adv. Accuracy", - "accuracy: mu_": "Ben. Accuracy", - "adv_failure_rate: mu_": "Adv. Failure Rate", - "def_gen": "Defence", - "learning_rate: mu_": "Learning Rate", -} - -log_normal_graph, lnt = plot_aft( - X_train, - "log_normal_aft.pdf", - target, - duration_col, - "Log Normal AFR Model", - "log_normal", - replacement_dict=log_normal_dict, -) -lnt_scores = score_model(lnt, X_train, X_test) -lnt.print_summary() -lnt_partial = plot_partial_effects( - file="log_normal_partial_effects.pdf", - aft=lnt, - covariate_array="model_layers", - values_array=[18, 34, 50, 101, 152], - replacement_dict=log_normal_dict, - title="Survival Time for Log-Normal AFR", - ylabel="% Chance of Survival", - xlabel="Time $T$ (seconds)", - legend_kwargs={ - "title": "No. of Layers", - "labels": ["18", "34", "50", "101", "152"], - }, -) - -# %% - -log_logistic_dict = { - "Intercept: beta_": "$\\beta$", - "Intercept: alpha_": "$\\alpha$", - "data.sample.random_state: alpha_": "Random State", - "def_value: alpha_": "Defence Strength", - "atk_value: alpha_": "Attack Strength", - "train_time: alpha_": "Training Time", - "predict_time: alpha_": "Inference Time", - "adv_accuracy: alpha_": "Adv. Accuracy", - "accuracy: alpha_": "Ben. Accuracy", - "adv_fit_time: alpha_": "Adv. Fit Time", - "model_layers: alpha_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", - "adv_failure_rate: alpha_": "Adv. Failure Rate", - "alpha_": "", -} - -log_logistic_graph, llt = plot_aft( - X_train, - "log_logistic_aft.pdf", - target, - duration_col, - "Log Logistic AFR Model", - "log_logistic", - replacement_dict=log_logistic_dict, -) -llt.print_summary() -llt_scores = score_model(llt, X_train, X_test) -print(llt_scores) -llt_partial = plot_partial_effects( - file="log_logistic_partial_effects.pdf", - aft=llt, - covariate_array="model_layers", - values_array=[18, 34, 50, 101, 152], - replacement_dict=log_logistic_dict, - title="Survival Time for Log-Logistic AFR", - ylabel="% Chance of Survival", - xlabel="Time $T$ (seconds)", - legend_kwargs={ - "title": "No. of Layers", - "labels": ["18", "34", "50", "101", "152"], - }, -) - -# %% -np.mean(llt.predict_median(X_train)) - -# %% -aft_dict = { - "Weibull": wft, - "LogNormal": lnt, - "LogLogistic": llt, - # "Cox": cft, -} - -score_list = [ - wft_scores, - lnt_scores, - llt_scores, - # cft_scores, -] -aft_data = pd.DataFrame() -aft_data.index.name = "Model" -aft_data.index = aft_dict.keys() -aft_data["AIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() -] -aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] -aft_data["BIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() -] -aft_data["Train LL"] = [x["train_score"] for x in score_list] -aft_data["Test LL"] = [x["test_score"] for x in score_list] -aft_data["Mean ST"] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()] -aft_data["Median ST"] = [x.predict_median(X_train).median() for x in aft_dict.values()] -aft_data = aft_data.round(2) -aft_data.to_csv(FOLDER / "aft_comparison.csv") -logger.info(f"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}") -aft_data = aft_data.round(2) -aft_data.to_latex( - FOLDER / "aft_comparison.tex", - float_format="%.2f", - label="tab:mnist", - caption="Comparison of AFR Models on the MNIST dataset.", -) -aft_data - - -# %% -# data['Survival Time'] = 1 / (data['adv_failure_rate']) -# true_mean = np.mean(data['adv_failure_rate'] * data['predict_time']) -# true_median = np.median(data['adv_failure_rate'] * data['predict_time']) -success_rate = data["adv_failure_rate"] -success_time = success_rate * data["predict_time"] -true_mean = np.mean(success_time) -true_median = np.median(success_time) -print(f"True Mean: {true_mean}") -print(f"True Median: {true_median}") diff --git a/examples/pytorch_cifar/conf/plots.yaml b/examples/pytorch_cifar/conf/plots.yaml index a779fbbf..586f3725 100644 --- a/examples/pytorch_cifar/conf/plots.yaml +++ b/examples/pytorch_cifar/conf/plots.yaml @@ -4,32 +4,32 @@ cat_plot: kind: boxen set: yscale: linear - titles: Adversarial Accuracy vs Defence Type + titles: $\lambda_{adv.}$ vs Defence Type x: def_gen xlabels: Defence Type y: adv_accuracy - ylabels: Adv. Accuracy + ylabels: $\lambda_{adv.}$ rotation : 90 hue_order: - ResNet18 - # - ResNet34 + - ResNet34 - ResNet50 - # - ResNet101 + - ResNet101 - ResNet152 - file: ben_accuracy_vs_defence_type.pdf hue: model_name kind: boxen - titles: Benign Accuracy vs Defence Type + titles: $\lambda_{ben.}$ vs Defence Type x: def_gen xlabels: Defence Type y: accuracy - ylabels: Ben. Accuracy + ylabels: $\lambda_{ben.}$ rotation : 90 hue_order: - ResNet18 - # - ResNet34 + - ResNet34 - ResNet50 - # - ResNet101 + - ResNet101 - ResNet152 - file: ben_failures_per_train_time_vs_defence_type.pdf hue: model_name @@ -44,9 +44,9 @@ cat_plot: rotation : 90 hue_order: - ResNet18 - # - ResNet34 + - ResNet34 - ResNet50 - # - ResNet101 + - ResNet101 - ResNet152 - file: adv_failures_per_train_time_vs_defence_type.pdf hue: model_name @@ -61,9 +61,9 @@ cat_plot: rotation : 90 hue_order: - ResNet18 - # - ResNet34 + - ResNet34 - ResNet50 - # - ResNet101 + - ResNet101 - ResNet152 - file: adv_failures_per_train_time_vs_attack_type.pdf hue: model_name @@ -79,57 +79,57 @@ cat_plot: rotation : 90 hue_order: - ResNet18 - # - ResNet34 + - ResNet34 - ResNet50 - # - ResNet101 + - ResNet101 - ResNet152 - file: adv_failures_per_test_time_vs_defence_type.pdf hue: model_name kind: boxen legend_title: Model Name - titles: Adversarial Failure Rate vs Defence Type + titles: $h_{adv}$ vs Defence Type x: def_gen xlabels: Defence Type y: adv_failure_rate - ylabels: Adv. Failure Rate + ylabels: $h_{adv.}$ rotation : 90 hue_order: - ResNet18 - # - ResNet34 + - ResNet34 - ResNet50 - # - ResNet101 + - ResNet101 - ResNet152 # - file: adv_accuracy_vs_defence_type.pdf # hue: model_name # kind: boxen # legend_title: Model Name -# titles: Adversarial Accuracy vs Defence Type +# titles: $\lambda_{adv.}$ vs Defence Type # x: def_gen # xlabels: Defence Type # y: adv_accuracy -# ylabels: Adv. Accuracy +# ylabels: Adv. $\lambda_{ben.}$ # rotation : 90 # hue_order: # - ResNet18 -# # - ResNet34 +# - ResNet34 # - ResNet50 -# # - ResNet101 +# - ResNet101 # - ResNet152 - file: adv_accuracy_vs_attack_type.pdf hue: model_name kind: boxen legend_title: Model Name - titles: Adversarial Accuracy vs Attack Type + titles: $\lambda_{adv.}$ vs Attack Type x: atk_gen xlabels: Attack Type y: adv_accuracy - ylabels: Adv. Accuracy + ylabels: $\lambda_{adv.}$ rotation : 90 hue_order: - ResNet18 - # - ResNet34 + - ResNet34 - ResNet50 - # - ResNet101 + - ResNet101 - ResNet152 - file: ben_failure_rate_vs_defence_type.pdf hue: model_name @@ -137,29 +137,29 @@ cat_plot: legend_title: Model Name set: yscale: log - titles: Benign Failure Rate vs Defence Type + titles: $h_{ben}(t; \theta)$ vs Defence Type x: def_gen xlabels: Defence Type y: failure_rate - ylabels: Ben. Failure Rate + ylabels: $h_{ben}(t; \theta)$ rotation : 90 hue_order: - ResNet18 - # - ResNet34 + - ResNet34 - ResNet50 - # - ResNet101 + - ResNet101 - ResNet152 line_plot: - file: def_param_vs_accuracy.pdf hue: def_gen - legend: {} - title: Accuracy vs Defence Strength + legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} + title: $\lambda_{ben.}$ vs Defence Strength x: def_value x_scale: linear xlabel: Defence Control Parameter y: accuracy y_scale: - ylabel: Accuracy + ylabel: $\lambda_{ben.}$ hue_order: - Control - Gauss-in @@ -168,14 +168,14 @@ line_plot: - FSQ - file: def_param_vs_adv_accuracy.pdf hue: def_gen - legend: {} - title: Adversarial Accuracy vs Defence Strength + legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} + title: $\lambda_{adv.}$ vs Defence Strength x: def_value x_scale: linear xlabel: Defence Control Parameter y: adv_accuracy y_scale: - ylabel: Adv. Accuracy + ylabel: $\lambda_{adv.}$ hue_order: - Control - Gauss-in @@ -184,14 +184,14 @@ line_plot: - FSQ - file: def_param_vs_adv_failure_rate.pdf hue: def_gen - legend: {} - title: Adversarial Failure Rate vs Defence Strength + legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} + title: $h_{adv}$ vs Defence Strength x: def_value x_scale: linear xlabel: Defence Control Parameter y: adv_failure_rate y_scale: log - ylabel: Adv. Failure Rate + ylabel: $h_{adv.}$ hue_order: - Control - Gauss-in @@ -200,14 +200,14 @@ line_plot: - FSQ - file: atk_param_vs_accuracy.pdf hue: atk_gen - legend: {} - title: Adversarial Accuracy vs Attack Strength + legend: {bbox_to_anchor: [1.05, 1]} + title: $\lambda_{adv.}$ vs Attack Strength x: atk_value x_scale: linear xlabel: Attack Control Parameter y: adv_accuracy y_scale: - ylabel: Adv. Accuracy + ylabel: $\lambda_{adv.}$ hue_order: - FGM - PGD @@ -215,31 +215,283 @@ line_plot: - HSJ - Pixel - Thresh + scatter_plot: - x: train_time_per_sample y: adv_failure_rate hue: model_name - xlabel: Training Time Per Sample - ylabel: Adversarial Failure Rate - title: Adversarial Failure Rate vs Training Time + xlabel: $t_{train}$ + ylabel: $h_{adv}$ + title: $h_{adv}$ vs $t_{train}$ file: adv_failure_rate_vs_train_time.pdf y_scale: log - x_scale: linear + x_scale: log legend: title: Model Name + bbox_to_anchor: [1.05, 1] hue_order: - ResNet18 - # - ResNet34 + - ResNet34 - ResNet50 - # - ResNet101 + - ResNet101 - ResNet152 # style : atk_gen # - x: train_time_per_sample # y: adv_failure_rate # hue: model_name -# xlabel: Training Time Per Sample -# ylabel: Adversarial Failure Rate -# title: Adversarial Failure Rate vs Training Time for each Defence +# xlabel: $t_{train}$ +# ylabel: $h_{adv}$ +# title: $h_{adv}$ vs $t_{train}$ for each Defence +# file: adv_failure_rate_vs_train_time_def.pdf +# y_scale: log +# x_scale: linear +# legend: +# title: Model Name +# # style : def_gen +cat_plot: +- file: adv_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: linear + titles: $\lambda_{adv.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_accuracy + ylabels: $\lambda_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + titles: $\lambda_{ben.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: accuracy + ylabels: $\lambda_{ben.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: log + titles: $\bar{C}_{ben.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_failure + ylabels: $\bar{C}_{ben.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: log + titles: $\bar{C}_{adv.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_adv_failure + ylabels: $\bar{C}_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_train_time_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: $\bar{C}_{adv.}$ vs Attack Type + x: atk_gen + xlabels: Attack Type + y: training_time_per_adv_failure + ylabels: $\bar{C}_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_test_time_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: $h_{adv}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_failure_rate + ylabels: $h_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +# - file: adv_accuracy_vs_defence_type.pdf +# hue: model_name +# kind: boxen +# legend_title: Model Name +# titles: $\lambda_{adv.}$ vs Defence Type +# x: def_gen +# xlabels: Defence Type +# y: adv_accuracy +# ylabels: Adv. $\lambda_{ben.}$ +# rotation : 90 +# hue_order: +# - ResNet18 +# - ResNet34 +# - ResNet50 +# - ResNet101 +# - ResNet152 +- file: adv_accuracy_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: $\lambda_{adv.}$ vs Attack Type + x: atk_gen + xlabels: Attack Type + y: adv_accuracy + ylabels: $\lambda_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_failure_rate_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: $h_{ben}(t; \theta)$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: failure_rate + ylabels: $h_{ben}(t; \theta)$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +line_plot: +- file: def_param_vs_accuracy.pdf + hue: def_gen + legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} + title: $\lambda_{ben.}$ vs Defence Strength + x: def_value + x_scale: linear + xlabel: Defence Control Parameter + y: accuracy + y_scale: + ylabel: $\lambda_{ben.}$ + hue_order: + - Control + - Gauss-in + - Gauss-out + - Conf + - FSQ +- file: def_param_vs_adv_accuracy.pdf + hue: def_gen + legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} + title: $\lambda_{adv.}$ vs Defence Strength + x: def_value + x_scale: linear + xlabel: Defence Control Parameter + y: adv_accuracy + y_scale: + ylabel: $\lambda_{adv.}$ + hue_order: + - Control + - Gauss-in + - Gauss-out + - Conf + - FSQ +- file: def_param_vs_adv_failure_rate.pdf + hue: def_gen + legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} + title: $h_{adv}$ vs Defence Strength + x: def_value + x_scale: linear + xlabel: Defence Control Parameter + y: adv_failure_rate + y_scale: log + ylabel: $h_{adv.}$ + hue_order: + - Control + - Gauss-in + - Gauss-out + - Conf + - FSQ +- file: atk_param_vs_accuracy.pdf + hue: atk_gen + legend: {bbox_to_anchor: [1.05, 1]} + title: $\lambda_{adv.}$ vs Attack Strength + x: atk_value + x_scale: linear + xlabel: Attack Control Parameter + y: adv_accuracy + y_scale: + ylabel: $\lambda_{adv.}$ + hue_order: + - FGM + - PGD + - Deep + - HSJ + - Pixel + - Thresh + +scatter_plot: +- x: train_time_per_sample + y: adv_failure_rate + hue: model_name + xlabel: $t_{train}$ + ylabel: $h_{adv}$ + title: $h_{adv}$ vs $t_{train}$ + file: adv_failure_rate_vs_train_time.pdf + y_scale: log + x_scale: log + legend: + title: Model Name + bbox_to_anchor: [1.05, 1] + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 + # style : atk_gen +# - x: train_time_per_sample +# y: adv_failure_rate +# hue: model_name +# xlabel: $t_{train}$ +# ylabel: $h_{adv}$ +# title: $h_{adv}$ vs $t_{train}$ for each Defence # file: adv_failure_rate_vs_train_time_def.pdf # y_scale: log # x_scale: linear diff --git a/examples/pytorch_cifar/dvc.lock b/examples/pytorch_cifar/dvc.lock index 09701278..451f49a5 100644 --- a/examples/pytorch_cifar/dvc.lock +++ b/examples/pytorch_cifar/dvc.lock @@ -118,8 +118,8 @@ stages: md5: 6503fed5d4e6cc1163898c0ab6a863dd size: 739680311 - path: output/models/model.pt - md5: 40384588691c0c813a1bd0406f0dd7b0 - size: 44811477 + md5: c7a6c14dc3dc96868cb9f3b93d7d31ba + size: 44811029 params: params.yaml: attack: @@ -327,17 +327,17 @@ stages: name: sklearn.metrics.log_loss outs: - path: output/attacks/attack.pkl - md5: aab66fe871d14945003859fa44125c75 + md5: e57b871f32a1a8b7de497f6e5de4ebf1 size: 123046 - path: output/reports/attack/default/adv_predictions.json - md5: 61d687b977e8c7e9404e69a690b244dd - size: 2154 + md5: 0a097842037186d905fb2fd7ec4072d9 + size: 2125 - path: output/reports/attack/default/params.yaml md5: 5177666d7f2e1e55ccad8ab4b735ffc7 size: 5175 - path: output/reports/attack/default/score_dict.json - md5: a918883cd746f147337e5e19146988a0 - size: 537 + md5: f5cc403e9e3fafd09cd985d86b3c80a5 + size: 549 attacks@ResNet18: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet18.db --config-name default.yaml @@ -366,18 +366,24 @@ stages: ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet34.db --config-name default.yaml deps: - path: attacks.sh - md5: 2fbda0d22c03c74392639ffa29319023 - size: 3080 + md5: 18b363f1e2d0622c372e07116bed4fd8 + size: 2731 - path: models.sh - md5: cd924674997af182b42d07a08bbc9034 - size: 1431 - - path: output/reports/attack/default/score_dict.json - md5: bd2742460f844eebe8bd411aa8ebd5de - size: 530 + md5: 9386478ad9795b80ec8ebc4abf1dbc81 + size: 1221 + - path: output/attacks/attack.pkl + md5: e57b871f32a1a8b7de497f6e5de4ebf1 + size: 123046 + - path: output/reports/attack/default/adv_predictions.json + md5: 0a097842037186d905fb2fd7ec4072d9 + size: 2125 + - path: output/reports/attack/default/params.yaml + md5: 5177666d7f2e1e55ccad8ab4b735ffc7 + size: 5175 outs: - path: ResNet34.db - md5: f3084028f22eb073b578c9eb81f6795c - size: 159744 + md5: 946c9a1a88d7caaa6af3679a343cc770 + size: 1290240 attacks@ResNet50: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet50.db --config-name default.yaml @@ -407,18 +413,24 @@ stages: default.yaml deps: - path: attacks.sh - md5: 2fbda0d22c03c74392639ffa29319023 - size: 3080 + md5: 18b363f1e2d0622c372e07116bed4fd8 + size: 2731 - path: models.sh - md5: cd924674997af182b42d07a08bbc9034 - size: 1431 - - path: output/reports/attack/default/score_dict.json - md5: bd2742460f844eebe8bd411aa8ebd5de - size: 530 + md5: 9386478ad9795b80ec8ebc4abf1dbc81 + size: 1221 + - path: output/attacks/attack.pkl + md5: e57b871f32a1a8b7de497f6e5de4ebf1 + size: 123046 + - path: output/reports/attack/default/adv_predictions.json + md5: 0a097842037186d905fb2fd7ec4072d9 + size: 2125 + - path: output/reports/attack/default/params.yaml + md5: 5177666d7f2e1e55ccad8ab4b735ffc7 + size: 5175 outs: - path: ResNet101.db - md5: 8a40cf29e97c400163c55d790153a270 - size: 307200 + md5: 076f51ae8bc6f487920baee046c1a930 + size: 1282048 attacks@ResNet152: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet152 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet152.db --config-name @@ -447,100 +459,121 @@ stages: cmd: python -m deckard.layers.compile --report_folder output/reports/attack --results_file output/reports/attack.csv deps: + - path: ResNet101.db + md5: 076f51ae8bc6f487920baee046c1a930 + size: 1282048 - path: ResNet152.db md5: 143435750b3f57d1a1280d412df74e0b size: 638976 + - path: ResNet18.db + md5: ff0eb78d052a31f3710336a21e81601e + size: 110592 + - path: ResNet34.db + md5: 946c9a1a88d7caaa6af3679a343cc770 + size: 1290240 + - path: ResNet50.db + md5: 1e3b638560b7ee5e73c674d0e1eb0421 + size: 446464 - path: output/reports/attack/ - md5: aaa5e2c2628d1ebcb31556d983d7dece.dir - size: 7965937508 - nfiles: 24265 + md5: e9c9c2d886498cd61644b8780106edf3.dir + size: 9928248484 + nfiles: 30111 outs: - path: output/reports/attack.csv - md5: f67172805501c6e8baffab2df49a960e - size: 14782615 + md5: 4ff19338677ca0ae0d766354a77fa4c8 + size: 18272588 plot: - cmd: python plots.py --path output/plots/ --file output/reports/attack.csv + cmd: python -m deckard.layers.plots --path output/plots/ --file output/reports/attack.csv deps: - path: output/reports/attack.csv - md5: f67172805501c6e8baffab2df49a960e - size: 14782615 + md5: 4ff19338677ca0ae0d766354a77fa4c8 + size: 18272588 outs: - path: output/plots/adv_accuracy_vs_attack_type.pdf - md5: 854e1515d4dfbca75c62215c19affcbf - size: 26187 + md5: 8352f085dde6740b1c87a38bd2a87516 + size: 34926 - path: output/plots/adv_accuracy_vs_defence_type.pdf - md5: c5b4a902835f96292049a467fca57041 - size: 21047 + md5: db4aeb461e389047e05eeef7408c5266 + size: 28996 - path: output/plots/adv_failure_rate_vs_train_time.pdf - md5: 97a93d8a83c604fada1f33612181d760 - size: 35489 + md5: a71510ed101d113855d2f9d9ed000393 + size: 42293 - path: output/plots/adv_failures_per_test_time_vs_defence_type.pdf - md5: b8d746d5bb13629a6b417840f1a2258c - size: 26083 + md5: 05872213e13a2b0d71f2b6876f769c45 + size: 35029 - path: output/plots/adv_failures_per_train_time_vs_attack_type.pdf - md5: e0d5438fd8d9ec90c37c113a5241995c - size: 33150 + md5: cfbd9e3d4ff3acf690409cc1af22cbee + size: 39001 - path: output/plots/adv_failures_per_train_time_vs_defence_type.pdf - md5: c64e113c5bb0018e2cae8c685dd8a826 - size: 27183 + md5: b267b951ccc797ccd0bb88e1b359fb6c + size: 32071 - path: output/plots/atk_param_vs_accuracy.pdf - md5: fd915ae17cdd61b00a2883d2a9af4dce - size: 21699 + md5: 8fc619749560fd1be8ac91d04af75203 + size: 24819 - path: output/plots/ben_accuracy_vs_defence_type.pdf - md5: b3c0311a4b939d98701f2326a2bf6612 - size: 21731 + md5: 2b8f3b735b4eec3572734048dfb859d4 + size: 30297 - path: output/plots/ben_failure_rate_vs_defence_type.pdf - md5: 25dcc0c4e4289089c5f3c20916c41afb - size: 26339 + md5: d2110a2ffdbaa984fe4eb0470dea5c32 + size: 36994 - path: output/plots/ben_failures_per_train_time_vs_defence_type.pdf - md5: a35bf0e15e751e92a03ee67d8f0fb612 - size: 26968 + md5: 3aee46faf5a24cd7e0fe3a7053e0f3a2 + size: 31693 - path: output/plots/data.csv - md5: ba58e26a754134c3a028d9864072b64d - size: 3267854 + md5: 1a5ba962d3fc11adcde869712cae4c73 + size: 5018537 - path: output/plots/def_param_vs_accuracy.pdf - md5: 350ec78e47cecb84f6282fb8b3f4cb20 - size: 19273 + md5: 8e1eea047988b58adafa0919d9674f1a + size: 22770 - path: output/plots/def_param_vs_adv_accuracy.pdf - md5: 83824bc0a6b4f987eae04f6055208c40 - size: 18857 + md5: aa888fef90099ec3b4866bf0cc22686e + size: 22765 - path: output/plots/def_param_vs_adv_failure_rate.pdf - md5: b7dd47fde2b640c528f76535853eadbd - size: 18820 + md5: 30a15ed503ec93e3b34e69d1af6473e5 + size: 21905 afr: - cmd: python afr.py + cmd: python -m deckard.layers.afr --dataset cifar deps: - path: output/plots/data.csv - md5: ba58e26a754134c3a028d9864072b64d - size: 3267854 + md5: 1a5ba962d3fc11adcde869712cae4c73 + size: 5018537 outs: - path: output/plots/aft_comparison.csv - md5: 184ae57d83a4ac6e291f5af968ea4a12 - size: 214 + md5: 7e07c42f2d7c3f3d6a4f49a7823ee62f + size: 191 - path: output/plots/aft_comparison.tex - md5: 672fed1e5e8ce82c246945494b7a2ab4 - size: 458 + md5: c9bf05dced494a1e69aa23534540902e + size: 416 - path: output/plots/cox_aft.pdf - md5: 9471fa8cd1fd918e84de193ba94db7b7 - size: 23765 + md5: 08ba7bf48594f94cc66ceccba7b38939 + size: 31502 - path: output/plots/cox_partial_effects.pdf - md5: 265d945de23c1a8869470c2d8625d9d3 - size: 37750 + md5: 7f7a310fbde6ee46749632023f6b0015 + size: 46475 - path: output/plots/log_logistic_aft.pdf - md5: c54bed98e83ec16a4c05b9de79c7118e - size: 28381 + md5: e21d6c4176804a522022461b02ab7e20 + size: 34160 - path: output/plots/log_logistic_partial_effects.pdf - md5: de845fde8cd722c7aa3082863c04cf5f - size: 28962 + md5: 800456e5361a2cab4a36533fbe149f97 + size: 30412 - path: output/plots/log_normal_aft.pdf - md5: c1655a624659c192485a3d235ed74313 - size: 28321 + md5: a6c08cd4cea95e707ed21b35c3d450e8 + size: 34314 - path: output/plots/log_normal_partial_effects.pdf - md5: 9ba657c55fa26813a34dbe8b75ec3c85 - size: 28997 + md5: 7f8cf2863b9f4a959848675c6c1e8817 + size: 31071 - path: output/plots/weibull_aft.pdf - md5: 007e308b6c2c1be0ba4cbfe9ad4c1092 - size: 26817 + md5: 3f3a04df96a120e3a878ece4365df8d6 + size: 32480 - path: output/plots/weibull_partial_effects.pdf - md5: a770dfeaa12d12028f24c69ca7e94ff1 - size: 30858 + md5: 45e3c5680ca070186224dda2141db4d8 + size: 30790 + copy_results: + cmd: cp -r output/plots/* ~/ml_afr/cifar/ + deps: + - path: output/plots/aft_comparison.csv + md5: 7e07c42f2d7c3f3d6a4f49a7823ee62f + size: 191 + - path: output/plots/data.csv + md5: 1a5ba962d3fc11adcde869712cae4c73 + size: 5018537 diff --git a/examples/pytorch_cifar/dvc.yaml b/examples/pytorch_cifar/dvc.yaml index b398fbae..973df919 100644 --- a/examples/pytorch_cifar/dvc.yaml +++ b/examples/pytorch_cifar/dvc.yaml @@ -1,5 +1,5 @@ stages: - train: + train: # The Experiment layers are prototypes for the Optimizer layers cmd: python -m deckard.layers.experiment train params: - data @@ -16,7 +16,7 @@ stages: # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} metrics: - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} - attack: + attack: # The Experiment layers are prototypes for the Optimizer layers cmd: python -m deckard.layers.experiment attack params: - data @@ -34,14 +34,27 @@ stages: metrics: - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} ############################################################################## - attacks: + models: # This is a loop over the ResNet models foreach: - # - ResNet18 - # - ResNet34 - # - ResNet50 - # - ResNet101 + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 - ResNet152 - do: + do: # This script configures each defence + cmd: bash models.sh ++model.init.name=torch_example.${item} ++stage=train ++hydra.sweeper.study=${item} --config-name default.yaml + deps: + - models.sh + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} + attacks: # This is a loop over the ResNet models + foreach: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 + do: # This script configures each attack and calls models.sh which configures each defence cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name default.yaml deps: - models.sh @@ -51,22 +64,22 @@ stages: - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} outs: - ${item}.db - compile: + compile: # foreach: - attack do: cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv deps: - ${files.directory}/${files.reports}/${item}/ - # - ResNet18.db - # - ResNet34.db - # - ResNet50.db - # - ResNet101.db + - ResNet18.db + - ResNet34.db + - ResNet50.db + - ResNet101.db - ResNet152.db outs: - ${files.directory}/${files.reports}/${item}.csv plot: - cmd : python plots.py --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv + cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv deps: - ${files.directory}/${files.reports}/attack.csv metrics: @@ -86,7 +99,7 @@ stages: - ${files.directory}/plots/def_param_vs_adv_accuracy.pdf - ${files.directory}/plots/def_param_vs_adv_failure_rate.pdf afr: - cmd: python afr.py + cmd: python -m deckard.layers.afr --dataset cifar deps: - ${files.directory}/plots/data.csv plots: @@ -102,3 +115,8 @@ stages: - ${files.directory}/plots/aft_comparison.csv outs: - ${files.directory}/plots/aft_comparison.tex + copy_results: + cmd: cp -r ${files.directory}/plots/* ~/ml_afr/cifar/ + deps: + - ${files.directory}/plots/data.csv + - ${files.directory}/plots/aft_comparison.csv \ No newline at end of file diff --git a/examples/pytorch_cifar/plots.py b/examples/pytorch_cifar/plots.py deleted file mode 100644 index 68c994de..00000000 --- a/examples/pytorch_cifar/plots.py +++ /dev/null @@ -1,315 +0,0 @@ -import argparse -import logging -from pathlib import Path - -import matplotlib.pyplot as plt -import pandas as pd -import seaborn as sns -import yaml - - -logger = logging.getLogger(__name__) -sns.set_theme(style="whitegrid", font_scale=1.8, font="times new roman") - - -def cat_plot( - data, - x, - y, - hue, - kind, - titles, - xlabels, - ylabels, - file, - folder, - legend_title=None, - hue_order=None, - rotation=0, - set={}, - **kwargs, -): - plt.gcf().clear() - data = data.sort_values(by=[hue, x, y]) - graph = sns.catplot( - data=data, x=x, y=y, hue=hue, kind=kind, hue_order=hue_order, **kwargs - ) - graph.set_xlabels(xlabels) - graph.set_ylabels(ylabels) - graph.set_titles(titles) - if legend_title is not None: - graph.legend.set_title(title=legend_title) - else: - graph.legend.remove() - graph.set_xticklabels(graph.axes.flat[-1].get_xticklabels(), rotation=rotation) - graph.set(**set) - graph.tight_layout() - graph.savefig(folder / file) - plt.gcf().clear() - logger.info(f"Saved graph to {folder / file}") - - -def line_plot( - data, - x, - y, - hue, - xlabel, - ylabel, - title, - file, - folder, - y_scale=None, - x_scale=None, - legend={}, - hue_order=None, - **kwargs, -): - plt.gcf().clear() - data = data.sort_values(by=[hue, x, y]) - graph = sns.lineplot(data=data, x=x, y=y, hue=hue, hue_order=hue_order, **kwargs) - graph.legend(**legend) - graph.set_xlabel(xlabel) - graph.set_ylabel(ylabel) - graph.set_title(title) - if y_scale is not None: - graph.set_yscale(y_scale) - if x_scale is not None: - graph.set_xscale(x_scale) - graph.get_figure().tight_layout() - graph.get_figure().savefig(folder / file) - plt.gcf().clear() - return graph - - -def scatter_plot( - data, - x, - y, - hue, - xlabel, - ylabel, - title, - file, - folder, - y_scale=None, - x_scale=None, - legend={}, - hue_order=None, - **kwargs, -): - # plt.gcf().clear() - data = data.sort_values(by=[hue, x, y]) - graph = sns.scatterplot( - data=data, - x=x, - y=y, - hue=hue, - hue_order=hue_order, - **kwargs, - ) - graph.set_yscale(y_scale) - graph.set_xscale(x_scale) - graph.set_xlabel(xlabel) - graph.set_ylabel(ylabel) - graph.legend(**legend) - graph.set_title(title) - graph.get_figure().tight_layout() - graph.get_figure().savefig(Path(folder) / file) - logger.info(f"Rendering graph {i+1}") - logger.info(f"Saved graph to {Path(folder) / file}") - plt.gcf().clear() - return graph - - -def drop_frames_without_results( - data, - subset=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"], -): - data.dropna(axis=0, subset=subset, inplace=True) - return data - - -def calculate_failure_rate(data): - data = data[data.columns.drop(list(data.filter(regex=r"\.1$")))] - data.columns.str.replace(" ", "") - data.loc[:, "failure_rate"] = (1 - data.loc[:, "accuracy"]) / data.loc[ - :, "predict_time_per_sample" - ] - data.loc[:, "success_rate"] = ( - data.loc[:, "accuracy"] * 100 / data.loc[:, "predict_time_per_sample"] - ) - data.loc[:, "adv_failure_rate"] = ( - (1 - data.loc[:, "adv_accuracy"]) * 100 / data.loc[:, "adv_fit_time"] - ) - data.loc[:, "adv_success_rate"] = ( - data.loc[:, "adv_accuracy"] * 100 / data.loc[:, "adv_fit_time"] - ) - data.loc[:, "training_time_per_failure"] = ( - data.loc[:, "train_time"] * data.loc[:, "failure_rate"] - ) - data.loc[:, "training_time_per_adv_failure"] = ( - data.loc[:, "train_time_per_sample"] * data.loc[:, "adv_failure_rate"] - ) - data.loc[:, "adv_training_time_per_failure"] = ( - data.loc[:, "train_time_per_sample"] * data.loc[:, "adv_failure_rate"] - ) - return data - - -from paretoset import paretoset - - -def pareto_set(data, sense_dict): - subset = data.loc[:, sense_dict.keys()] - these = paretoset(subset, sense=sense_dict.values()) - return data.iloc[these, :] - - -def min_max_scaling(data, **kwargs): - if "atk_gen" not in data.columns: - attacks = [] - else: - attacks = data.atk_gen.unique() - if "def_gen" not in data.columns: - defences = [] - else: - defences = data.def_gen.unique() - # Min-max scaling of control parameters - for def_ in defences: - max_ = data[data.def_gen == def_].def_value.max() - min_ = data[data.def_gen == def_].def_value.min() - scaled_value = (data[data.def_gen == def_].def_value - min_) / (max_ - min_) - data.loc[data.def_gen == def_, "def_value"] = scaled_value - - for atk in attacks: - max_ = data[data.atk_gen == atk].atk_value.max() - min_ = data[data.atk_gen == atk].atk_value.min() - scaled_value = (data[data.atk_gen == atk].atk_value - min_) / (max_ - min_) - data.loc[data.atk_gen == atk, "atk_value"] = scaled_value - for k, v in kwargs.items(): - data.loc[:, k] = data.loc[:, k].apply(v) - return data - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - "-p", - "--path", - type=str, - help="Path to the plot folder", - required=True, - ) - parser.add_argument( - "-f", - "--file", - type=str, - help="Data file to read from", - required=True, - ) - parser.add_argument( - "-o", - "--output", - type=str, - help="Output file name", - default="data.csv", - ) - parser.add_argument( - "-t", - "--plotfiletype", - type=str, - help="Filetype of the plots", - default=".pdf", - ) - parser.add_argument( - "-v", - "--verbosity", - default="INFO", - help="Increase output verbosity", - ) - parser.add_argument( - "-c", - "--config", - help="Path to the config file", - default="conf/plots.yaml", - ) - args = parser.parse_args() - logging.basicConfig(level=args.verbosity) - assert Path( - args.file, - ).exists(), f"File {args.file} does not exist. Please specify a valid file using the -f flag." - csv_file = args.file - data = pd.read_csv(csv_file) - data = drop_frames_without_results( - data, - subset=[ - "accuracy", - "adv_accuracy", - "train_time", - "adv_fit_time", - "predict_time", - ], - ) - sense_dict = { - "accuracy": "max", - # "train_time" : "min", - # "predict_time" : "min", - # "atk_value" : "diff", - "adv_accuracy": "min", - # "def_value" : "diff", - "data.sample.random_state": "diff", - # "adv_accuracy" : "min", - "model_layers": "diff", - # "adv_fit_time" : "min", - "atk_param": "diff", - "def_param": "diff", - "atk_gen": "diff", - "def_gen": "diff", - # "model.art.pipeline.initialize.kwargs.optimizer.lr" : "diff", - # "adv_failure_rate" : "maximize", - } - data = pareto_set(data, sense_dict) - data = calculate_failure_rate(data) - data = min_max_scaling(data) - if "Unnamed: 0" in data.columns: - data.drop("Unnamed: 0", axis=1, inplace=True) - if Path(args.path).absolute().exists(): - logger.info("Absolute path specified") - FOLDER = Path(args.path).absolute() - else: - logger.info("Relative path specified") - FOLDER = Path(Path(), args.path) - FOLDER.mkdir(parents=True, exist_ok=True) - data.to_csv(FOLDER / args.output) - IMAGE_FILETYPE = ( - args.plotfiletype - if args.plotfiletype.startswith(".") - else f".{args.plotfiletype}" - ) - if Path(FOLDER).exists(): - pass - else: - logger.info(f"Creating folder {FOLDER}") - FOLDER.mkdir(parents=True, exist_ok=True) - - # Reads Config file - with open(Path(args.config), "r") as f: - big_dict = yaml.load(f, Loader=yaml.FullLoader) - cat_plot_list = big_dict["cat_plot"] - i = 0 - for dict_ in cat_plot_list: - i += 1 - logger.info(f"Rendering graph {i}") - locals()[f"graph{i}"] = cat_plot(data, **dict_, folder=FOLDER) - line_plot_list = big_dict["line_plot"] - for dict_ in line_plot_list: - i += 1 - logger.info(f"Rendering graph {i}") - locals()[f"graph{i}"] = line_plot(data, **dict_, folder=FOLDER) - - scatter_plot_list = big_dict["scatter_plot"] - for dict_ in scatter_plot_list: - i += 1 - logger.info(f"Rendering graph {i}") - locals()[f"graph{i}"] = scatter_plot(data, **dict_, folder=FOLDER) From b6ea3a155f1209a66d3f369ff267947c874ae404 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 9 Oct 2023 11:59:42 +0000 Subject: [PATCH 094/148] + EAI AISEC draft --- examples/classification/attacks.sh | 28 ++ examples/classification/conf/RQ.md | 1 + examples/classification/conf/attack.yaml | 36 ++ .../classification/conf/attack/.gitignore | 1 + .../conf/attack/attack_grid.yaml | 49 +++ .../classification/conf/attack/default.yaml | 2 + examples/classification/conf/attack/hsj.yaml | 8 + .../classification/conf/data/default.yaml | 22 + examples/classification/conf/data/large.yaml | 20 + examples/classification/conf/data/medium.yaml | 20 + examples/classification/conf/data/small.yaml | 20 + .../classification/conf/data/very_large.yaml | 20 + examples/classification/conf/default.yaml | 41 ++ .../classification/conf/files/default.yaml | 21 + .../classification/conf/hydra/default.yaml | 7 + .../conf/hydra/launcher/default.yaml | 10 + .../conf/hydra/run/default.yaml | 1 + .../conf/hydra/sweeper/default.yaml | 8 + .../conf/hydra/sweeper/sampler/default.yaml | 7 + .../hydra/sweeper/sampler/params/default.yaml | 1 + examples/classification/conf/model.yaml | 34 ++ examples/classification/conf/model/.gitignore | 1 + .../conf/model/art/defense_grid.yaml | 44 ++ .../conf/model/art/postprocessor.yaml | 6 + .../art/postprocessor/high_confidence.yaml | 6 + .../conf/model/art/postprocessor/labels.yaml | 6 + .../conf/model/art/preprocessor.yaml | 6 + .../art/preprocessor/feature_squeezing.yaml | 6 + .../classification/conf/model/default.yaml | 2 + .../classification/conf/model/linear.yaml | 15 + examples/classification/conf/model/poly.yaml | 16 + examples/classification/conf/model/rbf.yaml | 15 + examples/classification/conf/rq.yaml | 51 +++ .../classification/conf/scorers/default.yaml | 10 + examples/classification/dvc.yaml | 125 ++++++ examples/classification/models.sh | 53 +++ examples/classification/params.yaml | 168 ++++++++ examples/classification/plots.py | 381 ++++++++++++++++++ 38 files changed, 1268 insertions(+) create mode 100644 examples/classification/attacks.sh create mode 100644 examples/classification/conf/RQ.md create mode 100644 examples/classification/conf/attack.yaml create mode 100644 examples/classification/conf/attack/.gitignore create mode 100644 examples/classification/conf/attack/attack_grid.yaml create mode 100644 examples/classification/conf/attack/default.yaml create mode 100644 examples/classification/conf/attack/hsj.yaml create mode 100644 examples/classification/conf/data/default.yaml create mode 100644 examples/classification/conf/data/large.yaml create mode 100644 examples/classification/conf/data/medium.yaml create mode 100644 examples/classification/conf/data/small.yaml create mode 100644 examples/classification/conf/data/very_large.yaml create mode 100644 examples/classification/conf/default.yaml create mode 100644 examples/classification/conf/files/default.yaml create mode 100644 examples/classification/conf/hydra/default.yaml create mode 100644 examples/classification/conf/hydra/launcher/default.yaml create mode 100644 examples/classification/conf/hydra/run/default.yaml create mode 100644 examples/classification/conf/hydra/sweeper/default.yaml create mode 100644 examples/classification/conf/hydra/sweeper/sampler/default.yaml create mode 100644 examples/classification/conf/hydra/sweeper/sampler/params/default.yaml create mode 100644 examples/classification/conf/model.yaml create mode 100644 examples/classification/conf/model/.gitignore create mode 100644 examples/classification/conf/model/art/defense_grid.yaml create mode 100644 examples/classification/conf/model/art/postprocessor.yaml create mode 100644 examples/classification/conf/model/art/postprocessor/high_confidence.yaml create mode 100644 examples/classification/conf/model/art/postprocessor/labels.yaml create mode 100644 examples/classification/conf/model/art/preprocessor.yaml create mode 100644 examples/classification/conf/model/art/preprocessor/feature_squeezing.yaml create mode 100644 examples/classification/conf/model/default.yaml create mode 100644 examples/classification/conf/model/linear.yaml create mode 100644 examples/classification/conf/model/poly.yaml create mode 100644 examples/classification/conf/model/rbf.yaml create mode 100644 examples/classification/conf/rq.yaml create mode 100644 examples/classification/conf/scorers/default.yaml create mode 100644 examples/classification/dvc.yaml create mode 100644 examples/classification/models.sh create mode 100644 examples/classification/params.yaml create mode 100644 examples/classification/plots.py diff --git a/examples/classification/attacks.sh b/examples/classification/attacks.sh new file mode 100644 index 00000000..169de6ee --- /dev/null +++ b/examples/classification/attacks.sh @@ -0,0 +1,28 @@ + +# break +# PGD +bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.norm=1,2,inf ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=100 ++attack.init.eps=.001,.01,.1,.3,.5,1 $@ + +# # Carlini L0 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # Carlini L2 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # Carlini LInf +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # DeepFool +# bash models.sh ++attack.init.nb_grads=1,3,5,10 ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.batch_size=100 $@ + +# #Threshold Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ + +# #Pixel Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.PixelAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ + +# #Adversarial Patch +# bash models.sh ++attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.scale_max=.1,.2,.3,.5,.8,.9,.99 ++attack.init.batch_size=100 $@ + +# #Hop Skip Jump +# bash models.sh ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.batch_size=100 $@ diff --git a/examples/classification/conf/RQ.md b/examples/classification/conf/RQ.md new file mode 100644 index 00000000..c1e745ce --- /dev/null +++ b/examples/classification/conf/RQ.md @@ -0,0 +1 @@ +sudo apt install lsb-release curl gpg diff --git a/examples/classification/conf/attack.yaml b/examples/classification/conf/attack.yaml new file mode 100644 index 00000000..7a029490 --- /dev/null +++ b/examples/classification/conf/attack.yaml @@ -0,0 +1,36 @@ +defaults: + # - _target_ : deckard.base.experiment.Experiment + - _self_ + - data: default + - model: default + - attack : hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: minimize + study_name: attack + storage: sqlite:///attack.db + n_trials: 1 + n_jobs: 1 + params: + # model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 32 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/classification/conf/attack/.gitignore b/examples/classification/conf/attack/.gitignore new file mode 100644 index 00000000..41ee9b5a --- /dev/null +++ b/examples/classification/conf/attack/.gitignore @@ -0,0 +1 @@ +/best.yaml diff --git a/examples/classification/conf/attack/attack_grid.yaml b/examples/classification/conf/attack/attack_grid.yaml new file mode 100644 index 00000000..d7dc3050 --- /dev/null +++ b/examples/classification/conf/attack/attack_grid.yaml @@ -0,0 +1,49 @@ +- attack.init.name: [art.attacks.evasion.FastGradientMethod] + attack.init.eps : [.01, .03, .3, .1] + attack.init.norm : ['inf', 1, 2] + attack.init.eps_step : [.001, .003, .01] + attack.init.batch_size : [100] + +- attack.init.name: [art.attacks.evasion.ProjectedGradientDescent] + attack.init.eps : [.01, .03, .3, .1] + attack.init.norm : ['inf', 1, 2] + attack.init.eps_step : [.001, .003, .01] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.CarliniL0Method] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + attack.init.confidence : [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.CarliniL2Method] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + attack.init.confidence : [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.CarliniLInfMethod] + attack.init.max_iter : [10] + attack.init.confidence: [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.DeepFool] + attack.init.max_iter : [10] + attack.init.batch_size : [100] + attack.init.nb_grads : [10, 100, 1000] + +- attack.init.name: [art.attacks.evasion.HopSkipJump] + attack.init.max_iter : [10] + attack.init.max_eval : [10] + attack.init.init_eval : [10] + attack.init.norm : ['inf', 2] + +- attack.init.name: [art.attacks.evasion.PixelAttack] + attack.init.th : [.5, .9, .99] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.ThresholdAttack] + attack.init.th : [.5, .9, .99] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.AdversarialPatch] + attack.init.max_iter : [10] + attack.init.learning_rate : [.5, 5.0, 50.0] diff --git a/examples/classification/conf/attack/default.yaml b/examples/classification/conf/attack/default.yaml new file mode 100644 index 00000000..e0967ff7 --- /dev/null +++ b/examples/classification/conf/attack/default.yaml @@ -0,0 +1,2 @@ +defaults: + - hsj diff --git a/examples/classification/conf/attack/hsj.yaml b/examples/classification/conf/attack/hsj.yaml new file mode 100644 index 00000000..cb2399a3 --- /dev/null +++ b/examples/classification/conf/attack/hsj.yaml @@ -0,0 +1,8 @@ +data: ${data} +model: ${model} +_target_ : deckard.base.attack.Attack +init: + name: art.attacks.evasion.HopSkipJump + model: ${model} +attack_size : 10 +method : evasion diff --git a/examples/classification/conf/data/default.yaml b/examples/classification/conf/data/default.yaml new file mode 100644 index 00000000..d5aa9091 --- /dev/null +++ b/examples/classification/conf/data/default.yaml @@ -0,0 +1,22 @@ +# _target_: deckard.base.data.Data +# generate: +# # _target_: deckard.base.data.generator.DataGenerator +# name: classification +# random_state : 0 +# n_samples : 1100 +# n_features : 20 +# sample: +# # _target_: deckard.base.data.sampler.SklearnDataSampler +# random_state : 0 +# stratify: True +# train_size : 100 +# test_size : 1000 +# sklearn_pipeline: +# # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline +# preprocessor: +# # +# name: sklearn.preprocessing.StandardScaler +# with_mean: True +# with_std: True +defaults: + - small diff --git a/examples/classification/conf/data/large.yaml b/examples/classification/conf/data/large.yaml new file mode 100644 index 00000000..ea6c3120 --- /dev/null +++ b/examples/classification/conf/data/large.yaml @@ -0,0 +1,20 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 101000 + n_features : 20 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 100000 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/classification/conf/data/medium.yaml b/examples/classification/conf/data/medium.yaml new file mode 100644 index 00000000..079b9aea --- /dev/null +++ b/examples/classification/conf/data/medium.yaml @@ -0,0 +1,20 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 11000 + n_features : 20 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 10000 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/classification/conf/data/small.yaml b/examples/classification/conf/data/small.yaml new file mode 100644 index 00000000..2207d717 --- /dev/null +++ b/examples/classification/conf/data/small.yaml @@ -0,0 +1,20 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 1100 + n_features : 20 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 100 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/classification/conf/data/very_large.yaml b/examples/classification/conf/data/very_large.yaml new file mode 100644 index 00000000..ff552d03 --- /dev/null +++ b/examples/classification/conf/data/very_large.yaml @@ -0,0 +1,20 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 1001000 + n_features : 20 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 100000 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/classification/conf/default.yaml b/examples/classification/conf/default.yaml new file mode 100644 index 00000000..b0340b69 --- /dev/null +++ b/examples/classification/conf/default.yaml @@ -0,0 +1,41 @@ +defaults: + - _self_ + - data: default + - model: default + - attack: hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + # seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 100 + n_jobs: 1 + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 6 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/classification/conf/files/default.yaml b/examples/classification/conf/files/default.yaml new file mode 100644 index 00000000..e2247d7e --- /dev/null +++ b/examples/classification/conf/files/default.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: output +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pkl +params_file : params.yaml +train_labels_file : train_labels.json +test_labels_file : test_labels.json +probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +adv_probabilities_file: adv_probabilities.json +name : default diff --git a/examples/classification/conf/hydra/default.yaml b/examples/classification/conf/hydra/default.yaml new file mode 100644 index 00000000..8dceecaf --- /dev/null +++ b/examples/classification/conf/hydra/default.yaml @@ -0,0 +1,7 @@ +defaults: +- run : default +- launcher : default +- sweeper : default +- override sweeper : optuna +- override sweeper/sampler : grid +- override launcher : joblib diff --git a/examples/classification/conf/hydra/launcher/default.yaml b/examples/classification/conf/hydra/launcher/default.yaml new file mode 100644 index 00000000..8b8d3f41 --- /dev/null +++ b/examples/classification/conf/hydra/launcher/default.yaml @@ -0,0 +1,10 @@ +_target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher +n_jobs: 6 +prefer : processes +verbose: 1 +timeout: null +pre_dispatch: n_jobs +batch_size: auto +temp_folder: /tmp/deckard +max_nbytes: 100000 +mmap_mode: r diff --git a/examples/classification/conf/hydra/run/default.yaml b/examples/classification/conf/hydra/run/default.yaml new file mode 100644 index 00000000..ec38cd67 --- /dev/null +++ b/examples/classification/conf/hydra/run/default.yaml @@ -0,0 +1 @@ +dir : "./${files.directory}" diff --git a/examples/classification/conf/hydra/sweeper/default.yaml b/examples/classification/conf/hydra/sweeper/default.yaml new file mode 100644 index 00000000..402132d0 --- /dev/null +++ b/examples/classification/conf/hydra/sweeper/default.yaml @@ -0,0 +1,8 @@ +defaults: +- sampler: default +_target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper +direction: maximize +study_name: model +storage: sqlite:///model.db +n_trials: 100 +n_jobs: 1 diff --git a/examples/classification/conf/hydra/sweeper/sampler/default.yaml b/examples/classification/conf/hydra/sweeper/sampler/default.yaml new file mode 100644 index 00000000..5401b736 --- /dev/null +++ b/examples/classification/conf/hydra/sweeper/sampler/default.yaml @@ -0,0 +1,7 @@ +defaults: +- params : default +_target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper +direction: minimize +study_name: attack +storage: sqlite:///attack.db +n_jobs: 1 diff --git a/examples/classification/conf/hydra/sweeper/sampler/params/default.yaml b/examples/classification/conf/hydra/sweeper/sampler/params/default.yaml new file mode 100644 index 00000000..33f9dfa6 --- /dev/null +++ b/examples/classification/conf/hydra/sweeper/sampler/params/default.yaml @@ -0,0 +1 @@ +model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) diff --git a/examples/classification/conf/model.yaml b/examples/classification/conf/model.yaml new file mode 100644 index 00000000..d50b2407 --- /dev/null +++ b/examples/classification/conf/model.yaml @@ -0,0 +1,34 @@ +defaults: + # - _target_ : deckard.base.experiment.Experiment + - _self_ + - data: default + - model: default + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_jobs: 1 + params: + model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 32 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/classification/conf/model/.gitignore b/examples/classification/conf/model/.gitignore new file mode 100644 index 00000000..41ee9b5a --- /dev/null +++ b/examples/classification/conf/model/.gitignore @@ -0,0 +1 @@ +/best.yaml diff --git a/examples/classification/conf/model/art/defense_grid.yaml b/examples/classification/conf/model/art/defense_grid.yaml new file mode 100644 index 00000000..a3fe3719 --- /dev/null +++ b/examples/classification/conf/model/art/defense_grid.yaml @@ -0,0 +1,44 @@ +- model.art.preprocessor.name: art.defences.preprocessor.FeatureSqueezing + model.art.preprocessor.params: + clip_values : + - [0,255] + bit_depth : [4, 8, 16, 32, 64] + +- model.art.preprocessor.name: art.defences.preprocessor.GaussianAugmentation + model.art.preprocessor.params: + clip_values : + - [0,255] + sigma : [.1, .3, 1] + ratio : [.1, .5, 1] + +- model.art.preprocessor.name: art.defences.preprocessor.SpatialSmoothing + model.art.preprocessor.params: + clip_values : + - [0,255] + window_size : [2,3,4] + +- model.art.preprocessor.name: art.defences.preprocessor.TotalVarMin + model.art.preprocessor.params: + clip_values : + - [0,255] + prob : [.001, .01, .1] + norm : [1, 2, 3] + lamb : [.05, .5, .95] + max_iter : [100] + +- model.art.postprocessor.name : art.defences.postprocessor.GaussianNoise + model.art.postprocessor.params: + clip_values : + - [0,255] + scale: [.1, .9, .999] + +- model.art.postprocessor.name : art.defences.postprocessor.HighConfidence + model.art.postprocessor.params: + cutoff : [.1, .5, .9, .99] + + +- model.art.postprocessor.name : art.defences.postprocessor.Rounded + model.art.preprocessor.params: + clip_values : + - [0,255] + decimals : [1, 2, 4, 8] diff --git a/examples/classification/conf/model/art/postprocessor.yaml b/examples/classification/conf/model/art/postprocessor.yaml new file mode 100644 index 00000000..91f3c00a --- /dev/null +++ b/examples/classification/conf/model/art/postprocessor.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : sklearn-svc +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/classification/conf/model/art/postprocessor/high_confidence.yaml b/examples/classification/conf/model/art/postprocessor/high_confidence.yaml new file mode 100644 index 00000000..16ae0e50 --- /dev/null +++ b/examples/classification/conf/model/art/postprocessor/high_confidence.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/classification/conf/model/art/postprocessor/labels.yaml b/examples/classification/conf/model/art/postprocessor/labels.yaml new file mode 100644 index 00000000..16ae0e50 --- /dev/null +++ b/examples/classification/conf/model/art/postprocessor/labels.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/classification/conf/model/art/preprocessor.yaml b/examples/classification/conf/model/art/preprocessor.yaml new file mode 100644 index 00000000..ebf46909 --- /dev/null +++ b/examples/classification/conf/model/art/preprocessor.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : sklearn-svc +preprocessor: + name: art.defences.preprocessor.FeatureSqueezing + bit_depth: 32 +initialize: diff --git a/examples/classification/conf/model/art/preprocessor/feature_squeezing.yaml b/examples/classification/conf/model/art/preprocessor/feature_squeezing.yaml new file mode 100644 index 00000000..1c48b3e7 --- /dev/null +++ b/examples/classification/conf/model/art/preprocessor/feature_squeezing.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +preprocessor: + name: art.defences.preprocessor.FeatureSqueezing + bit_depth: 32 +initialize: diff --git a/examples/classification/conf/model/default.yaml b/examples/classification/conf/model/default.yaml new file mode 100644 index 00000000..5f8bd822 --- /dev/null +++ b/examples/classification/conf/model/default.yaml @@ -0,0 +1,2 @@ +defaults: + - rbf diff --git a/examples/classification/conf/model/linear.yaml b/examples/classification/conf/model/linear.yaml new file mode 100644 index 00000000..b900f88a --- /dev/null +++ b/examples/classification/conf/model/linear.yaml @@ -0,0 +1,15 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + kernel : linear + probability : true + random_state : 0 + max_iter : 100 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/classification/conf/model/poly.yaml b/examples/classification/conf/model/poly.yaml new file mode 100644 index 00000000..7a5be797 --- /dev/null +++ b/examples/classification/conf/model/poly.yaml @@ -0,0 +1,16 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + coef0 : 0.0 + kernel : poly + probability : true + random_state : 0 + max_iter : 100 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/classification/conf/model/rbf.yaml b/examples/classification/conf/model/rbf.yaml new file mode 100644 index 00000000..42ca4c07 --- /dev/null +++ b/examples/classification/conf/model/rbf.yaml @@ -0,0 +1,15 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + kernel : rbf + probability : true + random_state : 0 + max_iter : 100 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/classification/conf/rq.yaml b/examples/classification/conf/rq.yaml new file mode 100644 index 00000000..25ad380b --- /dev/null +++ b/examples/classification/conf/rq.yaml @@ -0,0 +1,51 @@ +defaults: + - _self_ + - data: default + - model: default + - attack: hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + # seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 100 + n_jobs: 1 + launcher: + _target_: hydra_plugins.hydra_rq_launcher.rq_launcher.RQLauncher + enqueue: + job_timeout: null + ttl: null + result_ttl: null + failure_ttl: null + at_front: false + job_id: null + description: null + queue: default + redis: + host: ${oc.env:REDIS_HOST,localhost} + port: ${oc.env:REDIS_PORT,6379} + db: ${oc.env:REDIS_DB,0} + password: ${oc.env:REDIS_PASSWORD,null} + ssl: ${oc.env:REDIS_SSL,False} + ssl_ca_certs: ${oc.env:REDIS_SSL_CA_CERTS,null + mock: ${oc.env:REDIS_MOCK,False} + stop_after_enqueue: false + wait_polling: 1.0 diff --git a/examples/classification/conf/scorers/default.yaml b/examples/classification/conf/scorers/default.yaml new file mode 100644 index 00000000..108c1520 --- /dev/null +++ b/examples/classification/conf/scorers/default.yaml @@ -0,0 +1,10 @@ +_target_: deckard.base.scorer.ScorerDict +accuracy: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.accuracy_score + direction: maximize + +log_loss: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.log_loss + direction: minimize diff --git a/examples/classification/dvc.yaml b/examples/classification/dvc.yaml new file mode 100644 index 00000000..8c3d0f3c --- /dev/null +++ b/examples/classification/dvc.yaml @@ -0,0 +1,125 @@ +stages: + attack: + cmd: python -m deckard.layers.experiment --stage attack + deps: + - ${files.data_file} + - ${files.reports}/train/${files.path}/${files.params_file} + - ${files.reports}/train/${files.path}/${files.predictions_file} + metrics: + - ${files.reports}/attack/${files.path}/${files.score_dict_file} + - ${files.reports}/attack/${files.path}/${files.time_dict_file} + - ${files.reports}/attack/${files.path}/${files.attack_score_dict_file} + - ${files.reports}/attack/${files.path}/${files.attack_time_dict_file} + - ${files.reports}/attack/${files.path}/${files.attack_predictions_file} + - ${files.reports}/attack/${files.path}/${files.attack_probabilities_file} + outs: + - ${files.reports}/attack/${files.path}/${files.params_file} + - ${files.reports}/attack/${files.path}/${files.attack_samples_file} + - ${files.reports}/attack/${files.path}/${files.predictions_file} + - ${files.reports}/attack/${files.path}/${files.probabilities_file} + params: + - data + - model.init + - files + - scorers + - attack + generate_data: + cmd: python -m deckard.layers.experiment --stage generate_data + deps: + - params.yaml + metrics: + - ${files.reports}/generate_data/${files.path}/${files.ground_truth_file} + outs: + - ${files.data_file} + params: + - data + - files + train: + cmd: python -m deckard.layers.experiment --stage train + deps: + - ${files.data_file} + metrics: + - ${files.reports}/train/${files.path}/${files.score_dict_file} + - ${files.reports}/train/${files.path}/${files.time_dict_file} + - ${files.reports}/train/${files.path}/${files.predictions_file} + - ${files.reports}/train/${files.path}/${files.probabilities_file} + outs: + - ${files.reports}/train/${files.path}/${files.params_file} + params: + - data + - model.init + - files + - scorers + models: + cmd: bash experiments.sh + outs: + - logs/models/ + - ${files.reports}/models/ + params: + - data + - model.init + - files + - scorers + deps: + - ${files.reports}/train/ + compile_results: + cmd: + python find_best.py --output_folder best_models --kwargs "data.sample.train_size=1000" "data.generate.n_features=100" --report_folder "reports/model_queue" --results_file "plots/model_results.csv" --scorer_minimize False --control_for "model.init.kernel" --exclude sigmoid --stage models + outs: + - best_models/ + metrics: + - plots/model_results.csv + deps: + - ${files.reports}/models/ + attacks: + cmd: bash attacks.sh + outs: + - logs/attacks/ + - ${files.reports}/attacks/ + deps: + - best_models/ + params: + - data + - model.init + - files + - attack + - scorers + compile_attacks: + cmd: + python find_best.py --output_folder best_attacks --kwargs "attack.init.max_iter=10" --report_folder "reports/attack" --results_file "plots/attack_results.csv" --scorer_minimize True --exclude sigmoid --stage attacks + metrics: + - plots/attack_results.csv + deps: + - ${files.reports}/attacks/ + outs: + - best_attacks/ + retrain: + cmd : python retrain.py + deps: + - ${files.reports}/models/ + - ${files.reports}/attacks/ + - best_models/ + - best_attacks/ + outs: + - retrain/ + metrics: + - plots/before_retrain_confidence.csv + - plots/after_retrain_confidence.csv + plots: + cmd : python plots.py + deps : + - plots/after_retrain_confidence.csv + - plots/attack_results.csv + - plots/before_retrain_confidence.csv + - plots/model_results.csv + plots : + - plots/accuracy_vs_attack_parameters.pdf + - plots/accuracy_vs_features.pdf + - plots/accuracy_vs_samples.pdf + - plots/confidence_vs_attack_parameters.pdf + - plots/fit_time_vs_attack_parameters.pdf + - plots/fit_time_vs_features.pdf + - plots/fit_time_vs_samples.pdf + - plots/retrain_accuracy.pdf + - plots/retrain_confidence_vs_attack_parameters.pdf + - plots/retrain_time.pdf diff --git a/examples/classification/models.sh b/examples/classification/models.sh new file mode 100644 index 00000000..f3b3ff33 --- /dev/null +++ b/examples/classification/models.sh @@ -0,0 +1,53 @@ +N_FEATURES=( 10 100 1000 10000) +TRAIN_SIZES=( 100 1000 10000 100000 1000000 ) +# TRAIN_SIZES=( 2000 ) +# N_FEATURES=( 100 ) +N_SAMPLES=( 110000 ) +TOTAL=$(( ${#N_FEATURES[@]} * ${#N_SAMPLES[@]} * ${#TRAIN_SIZES[@]} )) +i=$(( 0 )) +mkdir -p logs/models +for train_size in ${TRAIN_SIZES[@]}; do + for n_samples in ${N_SAMPLES[@]}; do + for n_features in ${N_FEATURES[@]}; do + i=$(( i + 1 )) + n_informative=$(( $n_features-1 )) + echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" + echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" >| log.txt + HYDRA_FULL_ERROR=1; python ../../deckard/layers/optimize.py \ + data.generate.n_features=$n_features \ + data.generate.n_samples=$n_samples \ + data.sample.train_size=$train_size \ + model.init.kernel=linear \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + +stage=models "$@" --multirun \ + >| logs/models/linear_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Linear Kernel Done" >> log.txt + i=$(( i + 1 )) + python ../../deckard/layers/optimize.py \ + data.generate.n_features=$n_features \ + data.generate.n_samples=$n_samples \ + data.sample.train_size=$train_size \ + model.init.kernel=rbf \ + +model.init.gamma=scale \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + +stage=models "$@" --multirun \ + >| logs/models/rbf_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "RBF Kernel Done" >> log.txt + i=$(( i + 1 )) + python ../../deckard/layers/optimize.py \ + data.generate.n_features=$n_features \ + data.generate.n_samples=$n_samples \ + data.sample.train_size=$train_size \ + model.init.kernel=poly \ + model.init.degree=1,2,3,4,5 \ + +model.init.gamma=scale \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + +stage=models "$@" --multirun \ + >| logs/models/poly_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Poly Kernel Done" >> log.txt + echo "Successfully completed experiment ${i} of ${TOTAL}" >> log.txt + done + done +done diff --git a/examples/classification/params.yaml b/examples/classification/params.yaml new file mode 100644 index 00000000..b6dd6a11 --- /dev/null +++ b/examples/classification/params.yaml @@ -0,0 +1,168 @@ +attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: null + library: sklearn-svc + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 100 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: null + library: sklearn-svc + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 100 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc +data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true +files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json +model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: null + library: sklearn-svc + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 100 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc +scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss diff --git a/examples/classification/plots.py b/examples/classification/plots.py new file mode 100644 index 00000000..1420e713 --- /dev/null +++ b/examples/classification/plots.py @@ -0,0 +1,381 @@ +import pandas as pd +import seaborn as sns +from pathlib import Path +import matplotlib.pyplot as plt + +from deckard.layers.compile import parse_results +import logging + +sns.set_style("whitegrid") +sns.set_context("paper", font_scale=1.5) +sns.set_style({"font.family": "serif", "font.serif": "Times New Roman"}) +Path("plots").mkdir(parents=True, exist_ok=True) + + +logger = logging.getLogger(__name__) + + +if Path("plots", "model_results.csv").exists(): + results = pd.read_csv("plots/model_results.csv") +else: + results = parse_results("reports/model_queue/") +input_size = results["data.generate.n_samples"] * results["data.generate.n_features"] +results["Kernel"] = results["model.init.kernel"].copy() +results["Features"] = results["data.generate.n_features"].copy() +results["Samples"] = results["data.sample.train_size"].copy() +results["input_size"] = input_size +sample_list = results["data.generate.n_samples"].unique() +feature_list = results["data.generate.n_features"].unique() +kernel_list = results["model.init.kernel"].unique() +if "Unnamed: 0" in results.columns: + del results["Unnamed: 0"] +for col in results.columns: + if col == "data.name" and isinstance(results[col][0], list): + results[col] = results[col].apply(lambda x: x[0]) +results = results[results["model.init.kernel"] != "sigmoid"] + + +if Path("plots", "attack_results.csv").exists(): + attack_results = pd.read_csv("plots/attack_results.csv") +else: + attack_results = parse_results("reports/attack/") +attack_results["Kernel"] = attack_results["model.init.kernel"].copy() +attack_results["Features"] = attack_results["data.generate.n_features"].copy() +attack_results["Samples"] = attack_results["data.sample.train_size"].copy() +sample_list = attack_results["data.generate.n_samples"].unique() +feature_list = attack_results["data.generate.n_features"].unique() +kernel_list = attack_results["model.init.kernel"].unique() +if "Unnamed: 0" in attack_results.columns: + del attack_results["Unnamed: 0"] +for col in attack_results.columns: + if col == "data.name" and isinstance(attack_results[col][0], list): + attack_results[col] = attack_results[col].apply(lambda x: x[0]) + + +graph1 = sns.lineplot( + x="data.sample.train_size", + y="accuracy", + data=results, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +graph1.legend(labels=["Linear", "RBF", "Poly"]) +graph1.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +graph1.set_xlabel("Number of Samples") +graph1.set_ylabel("Accuracy") +graph1.set_xscale("log") +graph1.get_figure().tight_layout() +graph1.get_figure().savefig("plots/accuracy_vs_samples.pdf") + + +graph2 = sns.lineplot( + x="data.generate.n_features", + y="accuracy", + data=results, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +graph2.set_xlabel("Number of Features") +graph2.set_ylabel("Accuracy") +graph2.set_xscale("log") +graph2.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +graph2.get_figure().tight_layout() +graph2.get_figure().savefig("plots/accuracy_vs_features.pdf") + + +results["fit_time"] = ( + results["fit_time"] + * results["data.sample.train_size"] + * results["data.generate.n_samples"] +) +graph3 = sns.lineplot( + x="data.generate.n_features", + y="fit_time", + data=results, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +graph3.set_xlabel("Number of Features") +graph3.set_ylabel("Training Time") +graph3.set(yscale="log", xscale="log") +graph3.legend(title="Kernel") +graph3.get_figure().tight_layout() +graph3.get_figure().savefig("plots/fit_time_vs_features.pdf") + + +graph4 = sns.lineplot( + x="data.sample.train_size", + y="fit_time", + data=results, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +graph4.set_xlabel("Number of Samples") +graph4.set_ylabel("Training Time") +graph4.set(yscale="log", xscale="log") +graph4.legend(title="Kernel") +graph4.get_figure().tight_layout() +graph4.get_figure().savefig("plots/fit_time_vs_samples.pdf") + + +fig, ax = plt.subplots(2, 2) +graph5 = sns.lineplot( + x="attack.init.eps", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph5.set(xscale="log", xlabel="Perturbation Distance", ylabel="Accuracy") +graph6 = sns.lineplot( + x="attack.init.eps_step", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph6.set(xscale="log", xlabel="Perturbation Step", ylabel="Accuracy") +graph7 = sns.lineplot( + x="attack.init.max_iter", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph7.set(xscale="log", xlabel="Maximum Iterations", ylabel="Accuracy") +graph8 = sns.lineplot( + x="attack.init.batch_size", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph8.set(xscale="log", xlabel="Batch Size", ylabel="Accuracy") +graph6.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout() +fig.savefig("plots/accuracy_vs_attack_parameters.pdf") + + +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="attack.init.eps", + y="fit_time", + data=attack_results, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="Attack Time") +graph10 = sns.lineplot( + x="attack.init.eps_step", + y="fit_time", + data=attack_results, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="Attack Time") +graph11 = sns.lineplot( + x="attack.init.max_iter", + y="fit_time", + data=attack_results, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="Attack Time") +graph12 = sns.lineplot( + x="attack.init.batch_size", + y="fit_time", + data=attack_results, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="Attack Time") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/fit_time_vs_attack_parameters.pdf") + + +retrain_df = pd.DataFrame() +for kernel in ["rbf", "linear", "poly"]: + try: + tmp = pd.read_csv(f"retrain/{kernel}/results.csv") + except FileNotFoundError: + break + tmp["Kernel"] = kernel + tmp["Epochs"] = tmp.index + retrain_df = retrain_df.append(tmp) + retrain_df = retrain_df.reset_index(drop=True) + if "Unnamed: 0" in retrain_df.columns: + del retrain_df["Unnamed: 0"] +# retrain_df['train_size'] = retrain_df['train_size'] * 100 + + +retrain = sns.lineplot( + x="Epochs", + y="ben_score", + data=retrain_df, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +retrain = sns.lineplot( + x="Epochs", + y="adv_score", + data=retrain_df, + style="Kernel", + color="darkred", + legend=False, + style_order=["rbf", "poly", "linear"], +) +retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +retrain.set_xlabel("Retraining Epochs") +retrain.set_ylabel("Accuracy") +retrain.get_figure().tight_layout() +retrain.get_figure().savefig("plots/retrain_accuracy.pdf") + + +retrain_df["ben_time"] = retrain_df["ben_time"] * retrain_df["train_size"] * 10 +retrain_df["adv_time"] = retrain_df["adv_time"] * retrain_df["attack_size"] +retrain = sns.lineplot( + x="Epochs", + y="ben_time", + data=retrain_df, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +retrain = sns.lineplot( + x="Epochs", + y="adv_time", + data=retrain_df, + style="Kernel", + color="darkred", + legend=False, + style_order=["rbf", "poly", "linear"], +) +retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +retrain.set_xlabel("Retraining Epochs") +retrain.set_ylabel("Time") +retrain.set_yscale("log") +retrain.get_figure().tight_layout() +retrain.get_figure().savefig("plots/retrain_time.pdf") + + +confidence_df = pd.read_csv("plots/before_retrain_confidence.csv") +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="eps", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") +graph10 = sns.lineplot( + x="eps_step", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") +graph11 = sns.lineplot( + x="max_iter", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") +graph12 = sns.lineplot( + x="batch_size", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/confidence_vs_attack_parameters.pdf") + + +confdence_df = pd.read_csv("plots/after_retrain_confidence.csv") +confidence_df.columns +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="eps", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") +graph10 = sns.lineplot( + x="eps_step", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") +graph11 = sns.lineplot( + x="max_iter", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") +graph12 = sns.lineplot( + x="batch_size", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/retrain_confidence_vs_attack_parameters.pdf") From e399ec9e9f42a0159755f58aa9340b949d3a0b42 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 9 Oct 2023 12:00:06 +0000 Subject: [PATCH 095/148] scratch folder (WIP) --- examples/scratch/compute_attack_success.py | 90 +++ examples/scratch/conf/default.yaml | 23 +- examples/scratch/conf/evaluate/default.yaml | 5 + .../scratch/conf/grid/model/confidence.yaml | 7 +- examples/scratch/conf/optimise/default.yaml | 2 + examples/scratch/conf/queue/.gitignore | 6 + examples/scratch/deckard_queue.py | 109 +++ examples/scratch/dvc.lock | 643 ++++++++++++++++++ examples/scratch/dvc.yaml | 61 ++ examples/scratch/grid.py | 0 examples/scratch/objective.py | 57 ++ examples/scratch/params.yaml | 233 +++++++ examples/scratch/torch_example.py | 79 +++ 13 files changed, 1304 insertions(+), 11 deletions(-) create mode 100644 examples/scratch/compute_attack_success.py create mode 100644 examples/scratch/conf/evaluate/default.yaml create mode 100644 examples/scratch/conf/optimise/default.yaml create mode 100644 examples/scratch/conf/queue/.gitignore create mode 100644 examples/scratch/deckard_queue.py create mode 100644 examples/scratch/dvc.lock create mode 100644 examples/scratch/dvc.yaml create mode 100644 examples/scratch/grid.py create mode 100644 examples/scratch/objective.py create mode 100644 examples/scratch/params.yaml create mode 100644 examples/scratch/torch_example.py diff --git a/examples/scratch/compute_attack_success.py b/examples/scratch/compute_attack_success.py new file mode 100644 index 00000000..a18e97a5 --- /dev/null +++ b/examples/scratch/compute_attack_success.py @@ -0,0 +1,90 @@ +import logging +import argparse +from pathlib import Path +from art.utils import compute_accuracy, to_categorical +import pandas as pd +import json +import numpy as np +from dvc.api import params_show, read + +logger = logging.getLogger(__name__) + +def read_data_file(file: str, target = None): + filetype = Path(file).suffix + if filetype == ".csv": + data = pd.read_csv(file) + elif filetype == ".parquet": + data = pd.read_parquet(file) + elif filetype == ".json": + with open(file, "r") as f: + data = json.load(f) + data = pd.DataFrame(data, index = range(len(data))) + else: + raise ValueError(f"Unknown file type: {filetype}") + # to numpy + data = data.to_numpy() + logger.info(f"Loaded data from {file} with shape {data.shape}") + return data + +def write_data_file(data, file: str): + filetype = Path(file).suffix + if filetype == ".csv": + old = pd.read_csv(file) + data = old.to_dict() + data.update(**data) + data = pd.DataFrame(data) + data.to_csv(file, index=False) + elif filetype == ".json": + with open(file, "r") as f: + old = json.load(f) + old.update(**data) + with open(file, "w") as f: + json.dump(old, f) + else: + raise ValueError(f"Unknown file type: {filetype}") + return None + +if __name__ == "__main__": + attack_success_parser = argparse.ArgumentParser(description="Compute attack success") + attack_success_parser.add_argument( + "--log-level", + default="INFO", + help="Logging level", + choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"], + ) + attack_success_parser.add_argument("-b", "--ben_predictions_file", help="Full path to the predictions file", required=True,) + attack_success_parser.add_argument("-a", "--adv_predictions_file", help="Full path to the labels file", required=True,) + attack_success_parser.add_argument("-l", "--labels_file", help="Full path to the predictions file", required=True,) + attack_success_parser.add_argument( + "-i", + "--input_score_file", + default = None, + required=True, + ) + attack_success_parser.add_argument("-o", "--output_score_file", help="Full path to the labels file", required=True,) + args = attack_success_parser.parse_args() + + logging.basicConfig(level=args.log_level) + + + # labels = read_data_file(args.labels_file) + # nb_classes = len(np.unique(labels)) + # ben_pred = read_data_file(args.ben_predictions_file) + # adv_pred = read_data_file(args.adv_predictions_file) + # adv_pred = to_categorical(labels=adv_pred, nb_classes = nb_classes) + # ben_acc, _ = compute_accuracy(ben_pred, labels[:len(ben_pred)]) + # adv_acc, _ = compute_accuracy(adv_pred, labels[:len(adv_pred)]) + # adv_suc, _ = compute_accuracy(adv_pred, ben_pred[:len(adv_pred)]) + # adv_suc = 1 - adv_suc + # logger.info(f"Benign accuracy: {ben_acc}") + # logger.info(f"Adversarial accuracy: {adv_acc}") + # logger.info(f"Adversarial success: {adv_suc}") + # score_dict = {"ben_acc": ben_acc, "adv_acc": adv_acc, "adv_suc": adv_suc} + # with open(args.input_score_file, "r") as f: + # old = json.load(f) + # new = old.copy() + # new.update(**score_dict) + # with open(args.output_score_file, "w") as f: + # json.dump(new, f) + + \ No newline at end of file diff --git a/examples/scratch/conf/default.yaml b/examples/scratch/conf/default.yaml index 4ae0442a..5a22a709 100644 --- a/examples/scratch/conf/default.yaml +++ b/examples/scratch/conf/default.yaml @@ -5,26 +5,35 @@ defaults: - attack: default - files: default - scorers: default + - optimzers : [] - stage : null + - evaluate : default - override hydra/sweeper : optuna - - override hydra/sweeper/sampler : grid + - override hydra/sweeper/sampler : tpe - override hydra/launcher : joblib _target_ : deckard.base.experiment.Experiment optimizers : accuracy +direction: maximize hydra: + run: + dir: ${files.directory}/hydra + sweep: + dir: ${files.directory}/${files.reports}/${stage}/queue/ sweeper: sampler: - _target_: optuna.samplers.GridSampler - direction: maximize - study_name: model + _target_: optuna.samplers.TPESampler storage: sqlite:///model.db + study_name : ${stage} + n_trials : 5 n_jobs: 1 params: - ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) - ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) + # ++data.sample.random_state: int(interval(0, 100)) + ++model.art.initialize.optimizer.lr: tag(log, interval(1e-5, 1e3)) + # custom_search_space: objective.configure + direction : ${direction} launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 10 + n_jobs: 32 prefer : processes verbose: 1 timeout: null diff --git a/examples/scratch/conf/evaluate/default.yaml b/examples/scratch/conf/evaluate/default.yaml new file mode 100644 index 00000000..d084f30d --- /dev/null +++ b/examples/scratch/conf/evaluate/default.yaml @@ -0,0 +1,5 @@ +labels_file : ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} +ben_predictions_file : ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} +adv_predictions_file : ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} +input_score_file : ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} +output_score_file : ${files.directory}/${files.reports}/attack/${files.name}/attack_${files.score_dict_file} \ No newline at end of file diff --git a/examples/scratch/conf/grid/model/confidence.yaml b/examples/scratch/conf/grid/model/confidence.yaml index dca0ef2c..b39b5f60 100644 --- a/examples/scratch/conf/grid/model/confidence.yaml +++ b/examples/scratch/conf/grid/model/confidence.yaml @@ -1,4 +1,3 @@ -params: - art.postprocessor.name : [art.defences.postprocessor.HighConfidence] - art.postprocessor.cutoff : [.1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99] - art.postprocessor.apply_fit : [True, False] +art.postprocessor.name : [art.defences.postprocessor.HighConfidence] +art.postprocessor.cutoff : [.1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99] +art.postprocessor.apply_fit : [True, False] diff --git a/examples/scratch/conf/optimise/default.yaml b/examples/scratch/conf/optimise/default.yaml new file mode 100644 index 00000000..ac81f3ac --- /dev/null +++ b/examples/scratch/conf/optimise/default.yaml @@ -0,0 +1,2 @@ ++stage = train ++optimizers = \ No newline at end of file diff --git a/examples/scratch/conf/queue/.gitignore b/examples/scratch/conf/queue/.gitignore new file mode 100644 index 00000000..dc54ca38 --- /dev/null +++ b/examples/scratch/conf/queue/.gitignore @@ -0,0 +1,6 @@ +/confidence.yaml +/pgd.yaml +/confidence_pgd.yaml +/fgm.yaml +/confidence_fgm.yaml +/default.yaml diff --git a/examples/scratch/deckard_queue.py b/examples/scratch/deckard_queue.py new file mode 100644 index 00000000..c35f8e8b --- /dev/null +++ b/examples/scratch/deckard_queue.py @@ -0,0 +1,109 @@ +import logging +import argparse +import yaml +from os import environ +from dvc.api import params_show +from pathlib import Path +logger = logging.getLogger(__name__) + +DECKARD_PIPELINE_FILE = environ.get("DECKARD_PIPELINE_FILE", "dvc.yaml") + +# with open(DECKARD_PIPELINE_FILE, "r") as f: +# pipeline = yaml.safe_load(f) +# stages = list(pipeline["stages"].keys()) + +if __name__ == "__main__": + queue_parser = argparse.ArgumentParser(description="Queue example") + queue_parser.add_argument( + "--log-level", + default="INFO", + help="Logging level", + choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"], + ) + queue_parser.add_argument( + "--config_file", + "-c", + default = ["conf/default.yaml"], + help="Full path to the configuration file", + nargs="+", + ) + queue_parser.add_argument( + "--params_file", + "-p", + default = [], + nargs="+", + ) + queue_parser.add_argument( + "--output_file", + "-o", + default="queue", + help="Full path to the output folder", + required=True, + ) + # queue_parser.add_argument( + # "stage", + # help="Stage to run", + # # choices=[f"{stage}" for stage in stages], + # ) + args = queue_parser.parse_args() + # Set logging level + logging.basicConfig(level=args.log_level) + # Load Default Configuration + def load_config(config_file): + with open(config_file, "r") as f: + config = yaml.safe_load(f) + return config + + + + # Add overrides from params file + # stage = pipeline["stages"][args.stage] + def add_overrides_from_params_file(params_file, default_config): + params = default_config['hydra']['sweeper']['params'] + if params_file is not None: + with open(params_file, "r") as f: + param_file_params = yaml.safe_load(f) + if not isinstance(param_file_params, type(None)) and len(param_file_params) > 0: + params.update(param_file_params) + logger.info(f"Loaded parameter overrides: {yaml.dump(params)}") + # Update configuration + new_config = default_config.copy() + new_config['hydra']['sweeper']['params'].update(params) + logger.info(f"New configuration: {new_config['hydra']['sweeper']['params']}") + else: + new_config = default_config + return new_config + + config_list = [load_config(config_file) for config_file in args.config_file] + config_names = [Path(config_file).stem for config_file in args.config_file] + params_list = [Path(params_file) for params_file in args.params_file] + param_names = [Path(params_file).stem for params_file in args.params_file] + new_configs = {} + i = 0 + Path(args.output_file).mkdir(parents=True, exist_ok=True) + for default_config in config_list: + conf_name = config_names[i] + i += 1 + j = 0 + for params_file in params_list: + param_name = param_names[j] + j += 1 + new_config = add_overrides_from_params_file( params_file=params_file, default_config=default_config) + if conf_name == "default": + study_name = f"{param_name}" + new_configs[study_name] = new_config + else: + study_name= f"{conf_name}_{param_name}" + new_configs[study_name] = new_config + for k,v in new_configs.items(): + v['hydra']['sweeper']['params']['study_name'] = k + with open(Path(args.output_file, k).with_suffix(".yaml"), "w") as f: + yaml.dump(v, f) + + + # # Write new configuration + # Path(args.output_file).parent.mkdir(parents=True, exist_ok=True) + # with open(args.output_file, "w") as f: + # yaml.dump(new_config, f) + + \ No newline at end of file diff --git a/examples/scratch/dvc.lock b/examples/scratch/dvc.lock new file mode 100644 index 00000000..d415f9a1 --- /dev/null +++ b/examples/scratch/dvc.lock @@ -0,0 +1,643 @@ +schema: '2.0' +stages: + train: + cmd: python -m deckard.layers.experiment train + params: + params.yaml: + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 1 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/data/data.pkl + md5: de934a5f5157970e5f30b8f3f1856a68 + size: 222320311 + - path: output/models/model.optimizer.pt + md5: 16fb83628a4bec9578bb07eafb755a17 + size: 44780845 + - path: output/models/model.pt + md5: 6ff1e2aaf46e5345daa8585da20c396e + size: 44785941 + - path: output/reports/train/default/params.yaml + md5: a1492ca3e3164f2042aac087463d7e44 + size: 2216 + - path: output/reports/train/default/predictions.json + md5: 032236b5d3202b115ce5af6e4b815e80 + size: 2885232 + - path: output/reports/train/default/score_dict.json + md5: b4fa892280b14ff5fa3e96db737ec843 + size: 404 + - path: output/reports/train/default/test_labels.json + md5: f26b1ad6bd01a70de4290c6ae713e2c7 + size: 728000 + attack: + cmd: python -m deckard.layers.experiment attack + deps: + - path: output/data/data.pkl + md5: de934a5f5157970e5f30b8f3f1856a68 + size: 222320311 + - path: output/models/model.pt + md5: 6ff1e2aaf46e5345daa8585da20c396e + size: 44785941 + params: + params.yaml: + attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.attack.AttackInitializer + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 1 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 1 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 1 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/attacks/attack.pkl + md5: 86a99f5485617d9551cf4ab2537bcf71 + size: 31517 + - path: output/reports/attack/default/adv_predictions.json + md5: 86ad134d231dd601e62bb9602bff1e43 + size: 2130 + - path: output/reports/attack/default/params.yaml + md5: a15accd2ecf7bcbb5ed47d4b6bf7ed97 + size: 5093 + - path: output/reports/attack/default/score_dict.json + md5: 47d61818fd41d8dd4b9059211d326017 + size: 557 + model_grid@default: + cmd: python deckard_queue.py model_grid + deps: + - path: output/attacks/attack.pkl + md5: c001d33074f3dca76203b94c1ac38ea8 + size: 31517 + - path: output/data/data.pkl + md5: de934a5f5157970e5f30b8f3f1856a68 + size: 222320311 + - path: output/models/model.optimizer.pt + md5: 77a4e3f202d2d6a3579751824d5bc4c3 + size: 44780845 + - path: output/models/model.pt + md5: 4c42fce7077bd8a93c206ef3104438c5 + size: 44786389 + params: + conf/grid/model/default.yaml: {} + model_grid@confidence: + cmd: python deckard_queue.py model_grid + deps: + - path: output/attacks/attack.pkl + md5: 92682782c5aa078296c5717c1037e0e3 + size: 31517 + - path: output/data/data.pkl + md5: de934a5f5157970e5f30b8f3f1856a68 + size: 222320311 + - path: output/models/model.optimizer.pt + md5: 77a4e3f202d2d6a3579751824d5bc4c3 + size: 44780845 + - path: output/models/model.pt + md5: 4c42fce7077bd8a93c206ef3104438c5 + size: 44786389 + params: + conf/grid/model/confidence.yaml: + art.postprocessor.apply_fit: + - true + - false + art.postprocessor.cutoff: + - 0.1 + - 0.2 + - 0.3 + - 0.4 + - 0.5 + - 0.6 + - 0.7 + - 0.8 + - 0.9 + - 0.95 + - 0.99 + art.postprocessor.name: + - art.defences.postprocessor.HighConfidence + model_grid@conf/grid/model/confidence.yaml: + cmd: python deckard_queue.py model_grid -p conf/grid/model/confidence.yaml -o + conf/queue/model_grid/conf/grid/model/confidence.yaml + deps: + - path: output/attacks/attack.pkl + md5: f6933d3d01f97b4f400414bfaf4b72c2 + size: 31517 + - path: output/data/data.pkl + md5: de934a5f5157970e5f30b8f3f1856a68 + size: 222320311 + - path: output/models/model.optimizer.pt + md5: 77a4e3f202d2d6a3579751824d5bc4c3 + size: 44780845 + - path: output/models/model.pt + md5: 4c42fce7077bd8a93c206ef3104438c5 + size: 44786389 + - path: output/reports/attack/default/adv_predictions.json + md5: 9cc77a4c4b49a5ebce86cdad50df386b + size: 2146 + - path: output/reports/attack/default/params.yaml + md5: a15accd2ecf7bcbb5ed47d4b6bf7ed97 + size: 5093 + params: + conf/grid/model/confidence.yaml: + art.postprocessor.apply_fit: + - true + - false + art.postprocessor.cutoff: + - 0.1 + - 0.2 + - 0.3 + - 0.4 + - 0.5 + - 0.6 + - 0.7 + - 0.8 + - 0.9 + - 0.95 + - 0.99 + art.postprocessor.name: + - art.defences.postprocessor.HighConfidence + model_grid@conf/grid/model/default.yaml: + cmd: python deckard_queue.py model_grid -p conf/grid/model/default.yaml -o conf/queue/model_grid/conf/grid/model/default.yaml + deps: + - path: output/attacks/attack.pkl + md5: f6933d3d01f97b4f400414bfaf4b72c2 + size: 31517 + - path: output/data/data.pkl + md5: de934a5f5157970e5f30b8f3f1856a68 + size: 222320311 + - path: output/models/model.optimizer.pt + md5: 77a4e3f202d2d6a3579751824d5bc4c3 + size: 44780845 + - path: output/models/model.pt + md5: 4c42fce7077bd8a93c206ef3104438c5 + size: 44786389 + - path: output/reports/attack/default/adv_predictions.json + md5: 9cc77a4c4b49a5ebce86cdad50df386b + size: 2146 + - path: output/reports/attack/default/params.yaml + md5: a15accd2ecf7bcbb5ed47d4b6bf7ed97 + size: 5093 + params: + conf/grid/model/default.yaml: {} + evaluate: + cmd: python compute_attack_success.py --adv_predictions_file output/reports/attack/default/adv_predictions.json + --ben_predictions_file output/reports/train/default/predictions.json --input_score_file + output/reports/train/default/score_dict.json --labels_file output/reports/train/default/test_labels.json + --output_score_file output/reports/attack/default/attack_score_dict.json + deps: + - path: output/reports/attack/default/adv_predictions.json + md5: 86ad134d231dd601e62bb9602bff1e43 + size: 2130 + - path: output/reports/train/default/predictions.json + md5: 032236b5d3202b115ce5af6e4b815e80 + size: 2885232 + - path: output/reports/train/default/score_dict.json + md5: b4fa892280b14ff5fa3e96db737ec843 + size: 404 + - path: output/reports/train/default/test_labels.json + md5: f26b1ad6bd01a70de4290c6ae713e2c7 + size: 728000 + params: + params.yaml: + evaluate: + adv_predictions_file: output/reports/attack/default/adv_predictions.json + ben_predictions_file: output/reports/train/default/predictions.json + input_score_file: output/reports/train/default/score_dict.json + labels_file: output/reports/train/default/test_labels.json + output_score_file: output/reports/attack/default/attack_score_dict.json + outs: + - path: output/reports/attack/default/attack_score_dict.json + md5: 2ba40a17e1037b15e09e15da0dd179f0 + size: 467 + generate_model_grid@default: + cmd: python deckard_queue.py -o conf/queue/ -c conf/default.yaml -p conf/grid/model/default.yaml + deps: + - path: conf/default.yaml + md5: b5dd11071bc24bee7e98cc7f9f0e6678 + size: 998 + - path: conf/grid/model/default.yaml + md5: d41d8cd98f00b204e9800998ecf8427e + size: 0 + outs: + - path: conf/queue/default.yaml + md5: d5245de151fc5641fc4c822a348ba34d + size: 980 + generate_model_grid@confidence: + cmd: python deckard_queue.py -o conf/queue/ -c conf/default.yaml -p conf/grid/model/confidence.yaml + deps: + - path: conf/default.yaml + md5: b5dd11071bc24bee7e98cc7f9f0e6678 + size: 998 + - path: conf/grid/model/confidence.yaml + md5: be8c31c60ad9c1deb05db9d3ffc3c0cb + size: 187 + outs: + - path: conf/queue/confidence.yaml + md5: af80e3b005023f6bd5411909d2d963ee + size: 1262 + generate_attack_grid@pgd: + cmd: python deckard_queue.py -o conf/queue/ -c conf/queue/default.yaml conf/queue/confidence.yaml + -p conf/grid/attack/pgd.yaml + deps: + - path: conf/queue/confidence.yaml + md5: aeef28cdb4e18c076c9004ba07dc2c6e + size: 1291 + - path: conf/queue/default.yaml + md5: d5245de151fc5641fc4c822a348ba34d + size: 980 + outs: + - path: conf/queue/confidence_pgd.yaml + md5: 1418340a4413f94f6559c01c10b2bf93 + size: 1602 + - path: conf/queue/pgd.yaml + md5: f62febe9193586cde62332a1b24e8368 + size: 1283 + generate_attack_grid@fgm: + cmd: python deckard_queue.py -o conf/queue/ -c conf/queue/default.yaml conf/queue/confidence.yaml + -p conf/grid/attack/fgm.yaml + deps: + - path: conf/queue/confidence.yaml + md5: aeef28cdb4e18c076c9004ba07dc2c6e + size: 1291 + - path: conf/queue/default.yaml + md5: d5245de151fc5641fc4c822a348ba34d + size: 980 + outs: + - path: conf/queue/confidence_fgm.yaml + md5: d2f4493c23428652bdf233910bdcc666 + size: 1528 + - path: conf/queue/fgm.yaml + md5: 3bb176d6730ed2a490776168894fc61a + size: 1209 + generate_defence_grid@confidence: + cmd: python deckard_queue.py -o conf/queue/ -c conf/default.yaml -p conf/grid/model/confidence.yaml + deps: + - path: conf/default.yaml + md5: b5dd11071bc24bee7e98cc7f9f0e6678 + size: 998 + - path: conf/grid/model/confidence.yaml + md5: be8c31c60ad9c1deb05db9d3ffc3c0cb + size: 187 + outs: + - path: conf/queue/confidence.yaml + md5: aeef28cdb4e18c076c9004ba07dc2c6e + size: 1291 + generate_defence_grid@default: + cmd: python deckard_queue.py -o conf/queue/ -c conf/default.yaml -p conf/grid/model/default.yaml + deps: + - path: conf/default.yaml + md5: b5dd11071bc24bee7e98cc7f9f0e6678 + size: 998 + - path: conf/grid/model/default.yaml + md5: d41d8cd98f00b204e9800998ecf8427e + size: 0 + outs: + - path: conf/queue/default.yaml + md5: d5245de151fc5641fc4c822a348ba34d + size: 980 + optimise: + cmd: python -m deckard.layers.optimise +stage=train --multirun + deps: + - path: output/reports/attack/default/attack_score_dict.json + md5: 2ba40a17e1037b15e09e15da0dd179f0 + size: 467 + params: + conf/default.yaml: + hydra: + run: + dir: ${files.directory}/hydra + sweep: + dir: ${files.directory}/${files.reports}/${stage}/queue/ + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + storage: sqlite:///model.db + study_name: ${stage} + n_trials: 5 + n_jobs: 1 + params: + ++model.art.initialize.optimizer.lr: tag(log, interval(1e-5, 1e3)) + direction: ${direction} + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 32 + prefer: processes + verbose: 1 + timeout: + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r + outs: + - path: model.db + md5: 28256a9f67322787f8902be881f25aeb + size: 110592 diff --git a/examples/scratch/dvc.yaml b/examples/scratch/dvc.yaml new file mode 100644 index 00000000..da0375ea --- /dev/null +++ b/examples/scratch/dvc.yaml @@ -0,0 +1,61 @@ +# vars: +# - conf/default.yaml:hydra +stages: + train: + cmd : python -m deckard.layers.experiment train + params: + - data + - model + - scorers + - files + outs: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + metrics: + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + attack: + cmd: python -m deckard.layers.experiment attack + params: + - data + - model + - attack + - scorers + - files + outs: + - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + deps: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + metrics: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + evaluate: + cmd: python compute_attack_success.py ${evaluate} + deps: + - ${evaluate.input_score_file} + - ${evaluate.labels_file} + - ${evaluate.ben_predictions_file} + - ${evaluate.adv_predictions_file} + metrics: + - ${evaluate.output_score_file} + params: + - evaluate + optimise: + cmd: python -m deckard.layers.optimise +stage=train --multirun + deps: + - ${evaluate.output_score_file} + outs: + - model.db + params: + - conf/default.yaml: + - hydra + find_best_learning_rate: + cmd: python -m deckard.layers.find_best default.yaml + deps: + - model.db \ No newline at end of file diff --git a/examples/scratch/grid.py b/examples/scratch/grid.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/scratch/objective.py b/examples/scratch/objective.py new file mode 100644 index 00000000..baf3fcab --- /dev/null +++ b/examples/scratch/objective.py @@ -0,0 +1,57 @@ +import hydra +from omegaconf import DictConfig +from optuna.trial import Trial +from pathlib import Path +import logging +import os + +from deckard.layers.optimise import optimise + +logger = logging.getLogger(__name__) +config_path = os.environ.pop( + "DECKARD_CONFIG_PATH", + str(Path(Path(), "conf").absolute().as_posix()), +) +config_name = os.environ.pop("DECKARD_DEFAULT_CONFIG", "default.yaml") + +@hydra.main(config_path=config_path, config_name=config_name, version_base="1.3") +def hydra_optimise(cfg: DictConfig) -> float: + score = optimise(cfg) + return score + + +def configure(cfg: DictConfig, trial: Trial) -> None: + data_name = trial.params.get('data.generate.name', None) + preprocessor = trial.params.get('model.art.pipeline.preprocessor', None) + postprocessor = trial.params.get('model.art.pipeline.postprocessor', None) + if data_name in ["torch_cifar10", "torch_mnist"]: + trial.suggest_int("data.sample.random_state", 0, 10) + if preprocessor is not None: + if preprocessor.strip() == "art.defences.preprocessor.FeatureSqueezing": + bit_depth = trial.suggest_loguniform("model.art.pipeline.preprocessor.defences.FeatureSqueezing.bit_depth", 1, 64) + trial.suggest_categorical("model.art.pipeline.preprocessor.defences.FeatureSqueezing.clip_values", [[0, 2^bit_depth-1]]) + elif preprocessor.strip() == "art.defences.preprocessor.GaussianAugmentation": + sigma = trial.suggest_loguniform("model.art.pipeline.preprocessor.defences.GaussianAugmentation.sigma", 0.1, 10) + trial.suggest_categorical("model.art.pipeline.preprocessor.defences.GaussianAugmentation.clip_values", [[0, 255]]) + trial.suggest_categorical("model.art.pipeline.preprocessor.defences.GaussianAugmentation.ratio", [0.5]) + else: + raise ValueError(f"Unknown preprocessor {preprocessor}") + if postprocessor is not None: + if postprocessor.strip() == "art.defences.postprocessor.HighConfidence": + threshold = trial.suggest_int("model.art.pipeline.preprocessor.defences.HighConfidence.threshold", 1) + trial.suggest_categorical("model.art.pipeline.preprocessor.defences.HighConfidence.abs", [True]) + trial.suggest_categorical("model.art.pipeline.preprocessor.defences.HighConfidence.clip_values", [[0, 255]]) + elif postprocessor.strip() == "art.defences.postprocessor.GaussianNoise": + sigma = trial.suggest_loguniform("model.art.pipeline.preprocessor.defences.GaussianNoise.sigma", 0.1, 10) + trial.suggest_categorical("model.art.pipeline.preprocessor.defences.GaussianNoise.clip_values", [[0, 255]]) + else: + raise ValueError(f"Unknown preprocessor {postprocessor}") + + + + + + + +if __name__ == "__main__": + hydra_optimise() \ No newline at end of file diff --git a/examples/scratch/params.yaml b/examples/scratch/params.yaml new file mode 100644 index 00000000..c08a413c --- /dev/null +++ b/examples/scratch/params.yaml @@ -0,0 +1,233 @@ +_target_: deckard.base.experiment.Experiment +attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.attack.AttackInitializer + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 1 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 1 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 +data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true +evaluate: + adv_predictions_file: output/reports/attack/default/adv_predictions.json + ben_predictions_file: output/reports/train/default/predictions.json + input_score_file: output/reports/train/default/score_dict.json + labels_file: output/reports/train/default/test_labels.json + output_score_file: output/reports/attack/default/attack_score_dict.json +files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json +model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 1.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 1 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 20 +optimizers: accuracy +scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss diff --git a/examples/scratch/torch_example.py b/examples/scratch/torch_example.py new file mode 100644 index 00000000..2007d9d0 --- /dev/null +++ b/examples/scratch/torch_example.py @@ -0,0 +1,79 @@ +import torch.nn as nn +from torchvision import models + +__all__ = [ + "ResNet18", + "ResNet50", + "ResNet101", + "ResNet152", +] + + +def ResNet18(num_channels=1, num_classes=10): + model = models.resnet18() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(512, num_classes) + return model + + +def ResNet34(num_channels=1, num_classes=10): + model = models.resnet34() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(512, num_classes) + return model + + +def ResNet50(num_channels=1, num_classes=10): + model = models.resnet50() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model + + +def ResNet101(num_channels=1, num_classes=10): + model = models.resnet101() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model + + +def ResNet152(num_channels=1, num_classes=10): + model = models.resnet152() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model From fbf926507ff4d750b1e8f755394751783520a37c Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 9 Oct 2023 12:01:00 +0000 Subject: [PATCH 096/148] update models.sh to remove stage name --- examples/tfv2/models.sh | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/tfv2/models.sh b/examples/tfv2/models.sh index 00389289..8e9b3394 100644 --- a/examples/tfv2/models.sh +++ b/examples/tfv2/models.sh @@ -4,22 +4,22 @@ # This line generates the model and adds the FeatureSqueezing preprocessing defence. -python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +stage=train --multirun +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 --multirun # Gaussian Augmentation (Input) -python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.2,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +stage=train --multirun +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.2,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 --multirun # Spatial Smoothing -python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.SpatialSmoothing +model.art.preprocessor.params.window_size=2,3,4 +stage=train --multirun +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.SpatialSmoothing +model.art.preprocessor.params.window_size=2,3,4 --multirun # Total Variance Minimisation -python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.TotalVarMin +model.art.preprocessor.params.prob=.001,.01,.1 +model.art.preprocessor.params.norm=1,2,3 +model.art.preprocessor.params.lamb=.05,.5,.95 +model.art.preprocessor.params.max_iter=100 +stage=train --multirun +python -m deckard.layers.optimise +model.art.preprocessor.name=art.defences.preprocessor.TotalVarMin +model.art.preprocessor.params.prob=.001,.01,.1 +model.art.preprocessor.params.norm=1,2,3 +model.art.preprocessor.params.lamb=.05,.5,.95 +model.art.preprocessor.params.max_iter=100 --multirun # Gaussian Noise (Output) -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.1,.9,.999 +stage=train --multirun +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise +model.art.postprocessor.params.scale=.1,.9,.999 --multirun # High Confidence -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.5,.9,.99 +stage=train --multirun +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.5,.9,.99 --multirun # Rounded (Output) -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.Rounded +model.art.postprocessor.params.decimals=1,2,4,8 +stage=train --multirun +python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.Rounded +model.art.postprocessor.params.decimals=1,2,4,8 --multirun From 4ebf3247a712c169e4e2409c09bb99a0f839d303 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 9 Oct 2023 12:01:30 +0000 Subject: [PATCH 097/148] linting --- deckard/layers/afr.py | 86 +++++++++++---------- deckard/layers/plots.py | 20 +++-- examples/pytorch/dvc.yaml | 2 +- examples/pytorch_cifar/dvc.yaml | 2 +- examples/scratch/compute_attack_success.py | 42 ++++++---- examples/scratch/conf/evaluate/default.yaml | 2 +- examples/scratch/conf/optimise/default.yaml | 2 +- examples/scratch/deckard_queue.py | 43 ++++++----- examples/scratch/dvc.yaml | 2 +- examples/scratch/objective.py | 65 +++++++++++----- 10 files changed, 161 insertions(+), 105 deletions(-) diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index 8e938246..a53e2b54 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -21,7 +21,7 @@ logger = logging.getLogger(__name__) if "__main__" == __name__: - + afr_parser = argparse.ArgumentParser() afr_parser.add_argument("--target", type=str, default="adv_failures") afr_parser.add_argument("--duration_col", type=str, default="adv_fit_time") @@ -30,7 +30,6 @@ target = afr_args.target duration_col = afr_args.duration_col dataset = afr_args.dataset - font = { "family": "Times New Roman", @@ -40,7 +39,6 @@ matplotlib.rc("font", **font) - FOLDER = Path("output/plots/") csv_file = FOLDER / "data.csv" data = pd.read_csv(csv_file, index_col=0) @@ -53,9 +51,13 @@ data = min_max_scaling(data) data.dropna(axis=0, subset=["atk_value", "atk_param"], inplace=True) data.dropna(axis=0, subset=["def_value", "def_param"], inplace=True) - data.loc[:, 'adv_failures'] = (1 - data.loc[:, 'adv_accuracy']) * data.loc[:, 'attack.attack_size'] - data.loc[:, 'ben_failures'] = (1 - data.loc[:, 'accuracy']) * data.loc[:, 'attack.attack_size'] - + data.loc[:, "adv_failures"] = (1 - data.loc[:, "adv_accuracy"]) * data.loc[ + :, "attack.attack_size" + ] + data.loc[:, "ben_failures"] = (1 - data.loc[:, "accuracy"]) * data.loc[ + :, "attack.attack_size" + ] + # data=data[data['def_gen'] == 'Gauss-in'] # data=data[data['atk_gen'] == 'HSJ'] @@ -79,10 +81,6 @@ "\n", ) - - - - def plot_aft( df, file, @@ -128,14 +126,12 @@ def plot_aft( plt.gcf().clear() return ax, aft - def plot_partial_effects( - aft, covariate_array, values_array, - title = None, - file = "partial_effects.pdf", + title=None, + file="partial_effects.pdf", xlabel="Covariate", ylabel="Failure rate", legend_kwargs={"loc": "upper left"}, @@ -163,7 +159,6 @@ def plot_partial_effects( plt.gcf().clear() return pareto - def score_model(aft, train, test): train_score = aft.score(train) test_score = aft.score(test) @@ -171,23 +166,30 @@ def score_model(aft, train, test): plt.show() return scores - def make_afr_table(score_list, aft_dict, dataset): - assert len(score_list) == len(aft_dict), "Length of score list and aft dict must be equal" + assert len(score_list) == len( + aft_dict + ), "Length of score list and aft dict must be equal" aft_data = pd.DataFrame() aft_data.index.name = "Model" aft_data.index = aft_dict.keys() aft_data["AIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan + for x in aft_dict.values() ] aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] aft_data["BIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan + for x in aft_dict.values() ] # aft_data["Train LL"] = [x["train_score"] for x in score_list] # aft_data["Test LL"] = [x["test_score"] for x in score_list] - aft_data["Mean $S(t;\\theta)$"] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()] - aft_data["Median $S(t;\\theta)$"] = [x.predict_median(X_train).median() for x in aft_dict.values()] + aft_data["Mean $S(t;\\theta)$"] = [ + x.predict_expectation(X_train).mean() for x in aft_dict.values() + ] + aft_data["Median $S(t;\\theta)$"] = [ + x.predict_median(X_train).median() for x in aft_dict.values() + ] aft_data = aft_data.round(2) aft_data.to_csv(FOLDER / "aft_comparison.csv") logger.info(f"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}") @@ -199,11 +201,10 @@ def make_afr_table(score_list, aft_dict, dataset): label=f"tab:{dataset}", caption=f"Comparison of AFR Models on the {dataset.upper()} dataset.", ) - + print(yaml.dump(aft_data.to_dict(), default_flow_style=False, indent=4)) return aft_data - def clean_data_for_aft( data, kwarg_list, @@ -221,11 +222,11 @@ def clean_data_for_aft( cols = cleaned.columns cleaned = pd.DataFrame(subset, columns=cols) if "accuracy" in cleaned.columns: - cleaned = cleaned[cleaned['accuracy'] != 1e10] - cleaned = cleaned[cleaned['accuracy'] != -1e10] + cleaned = cleaned[cleaned["accuracy"] != 1e10] + cleaned = cleaned[cleaned["accuracy"] != -1e10] if "adv_accuracy" in cleaned.columns: - cleaned = cleaned[cleaned['adv_accuracy'] != 1e10] - cleaned = cleaned[cleaned['adv_accuracy'] != -1e10] + cleaned = cleaned[cleaned["adv_accuracy"] != 1e10] + cleaned = cleaned[cleaned["adv_accuracy"] != -1e10] cleaned.dropna(inplace=True, how="any", axis=0) y = cleaned[target] assert ( @@ -233,19 +234,23 @@ def clean_data_for_aft( ), f"Target {target} not in dataframe with columns {cleaned.columns}" return cleaned, y, data - def split_data_for_aft(data, target, duration_col, kwarg_list, test_size=0.2, random_state=42): + def split_data_for_aft( + data, target, duration_col, kwarg_list, test_size=0.2, random_state=42 + ): cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) X_train, X_test, y_train, y_test = train_test_split( - cleaned, y, test_size=test_size, random_state=random_state + cleaned, + y, + test_size=test_size, + random_state=random_state, ) assert ( target in cleaned ), f"Target {target} not in dataframe with columns {cleaned.columns}" - assert duration_col in cleaned, f"Duration {duration_col} not in dataframe with columns {cleaned.columns}" + assert ( + duration_col in cleaned + ), f"Duration {duration_col} not in dataframe with columns {cleaned.columns}" return X_train, X_test, y_train, y_test - - - kwarg_list = [ "accuracy", @@ -260,10 +265,9 @@ def split_data_for_aft(data, target, duration_col, kwarg_list, test_size=0.2, r "model.art.pipeline.initialize.kwargs.optimizer.lr", ] - - - X_train, X_test, y_train, y_test = split_data_for_aft(data, target, duration_col, kwarg_list, test_size=0.2, random_state=42) - + X_train, X_test, y_train, y_test = split_data_for_aft( + data, target, duration_col, kwarg_list, test_size=0.2, random_state=42 + ) weibull_dict = { "Intercept: rho_": "$\\rho$", @@ -345,16 +349,16 @@ def split_data_for_aft(data, target, duration_col, kwarg_list, test_size=0.2, r "labels": ["18", "34", "50", "101", "152"], }, } - cox_plot_dict= { - "file" : "cox_aft.pdf", + cox_plot_dict = { + "file": "cox_aft.pdf", "duration_col": duration_col, "title": "Cox AFR Model", "mtype": "cox", "replacement_dict": cox_replacement_dict, } cox_afr, cft = plot_aft( - df = X_train, - event_col = target, + df=X_train, + event_col=target, **cox_plot_dict, ) cox_scores = score_model(cft, X_train, X_test) diff --git a/deckard/layers/plots.py b/deckard/layers/plots.py index 87b5673c..0282ea10 100644 --- a/deckard/layers/plots.py +++ b/deckard/layers/plots.py @@ -98,7 +98,7 @@ def scatter_plot( hue_order=None, **kwargs, ): - + # plt.gcf().clear() data = data.sort_values(by=[hue, x, y]) graph = sns.scatterplot( @@ -117,7 +117,7 @@ def scatter_plot( graph.set_title(title) graph.get_figure().tight_layout() graph.get_figure().savefig(Path(folder) / file) - + logger.info(f"Saved graph to {Path(folder) / file}") plt.gcf().clear() return graph @@ -137,16 +137,24 @@ def calculate_failure_rate(data): data = data[data.columns.drop(list(data.filter(regex=r"\.1$")))] data.columns.str.replace(" ", "") data.loc[:, "failure_rate"] = ( - (1 - data.loc[:, "accuracy"]) * data.loc[:, "attack.attack_size"] / data.loc[:, "predict_time"] + (1 - data.loc[:, "accuracy"]) + * data.loc[:, "attack.attack_size"] + / data.loc[:, "predict_time"] ) data.loc[:, "success_rate"] = ( - data.loc[:, "accuracy"] * data.loc[:, "attack.attack_size"] / data.loc[:, "predict_time"] + data.loc[:, "accuracy"] + * data.loc[:, "attack.attack_size"] + / data.loc[:, "predict_time"] ) data.loc[:, "adv_failure_rate"] = ( - (1 - data.loc[:, "adv_accuracy"]) * data.loc[:, "attack.attack_size"] / data.loc[:, "adv_fit_time"] + (1 - data.loc[:, "adv_accuracy"]) + * data.loc[:, "attack.attack_size"] + / data.loc[:, "adv_fit_time"] ) data.loc[:, "adv_success_rate"] = ( - data.loc[:, "adv_accuracy"] * data.loc[:, "attack.attack_size"] / data.loc[:, "adv_fit_time"] + data.loc[:, "adv_accuracy"] + * data.loc[:, "attack.attack_size"] + / data.loc[:, "adv_fit_time"] ) data.loc[:, "training_time_per_failure"] = ( data.loc[:, "train_time"] / data.loc[:, "failure_rate"] diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index cefc6c24..1489b04b 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -99,4 +99,4 @@ stages: cmd: cp -r ${files.directory}/plots/* ~/ml_afr/mnist/ deps: - ${files.directory}/plots/data.csv - - ${files.directory}/plots/aft_comparison.csv \ No newline at end of file + - ${files.directory}/plots/aft_comparison.csv diff --git a/examples/pytorch_cifar/dvc.yaml b/examples/pytorch_cifar/dvc.yaml index 973df919..fc77b4fb 100644 --- a/examples/pytorch_cifar/dvc.yaml +++ b/examples/pytorch_cifar/dvc.yaml @@ -119,4 +119,4 @@ stages: cmd: cp -r ${files.directory}/plots/* ~/ml_afr/cifar/ deps: - ${files.directory}/plots/data.csv - - ${files.directory}/plots/aft_comparison.csv \ No newline at end of file + - ${files.directory}/plots/aft_comparison.csv diff --git a/examples/scratch/compute_attack_success.py b/examples/scratch/compute_attack_success.py index a18e97a5..98b7c0a4 100644 --- a/examples/scratch/compute_attack_success.py +++ b/examples/scratch/compute_attack_success.py @@ -9,7 +9,8 @@ logger = logging.getLogger(__name__) -def read_data_file(file: str, target = None): + +def read_data_file(file: str, target=None): filetype = Path(file).suffix if filetype == ".csv": data = pd.read_csv(file) @@ -18,7 +19,7 @@ def read_data_file(file: str, target = None): elif filetype == ".json": with open(file, "r") as f: data = json.load(f) - data = pd.DataFrame(data, index = range(len(data))) + data = pd.DataFrame(data, index=range(len(data))) else: raise ValueError(f"Unknown file type: {filetype}") # to numpy @@ -26,6 +27,7 @@ def read_data_file(file: str, target = None): logger.info(f"Loaded data from {file} with shape {data.shape}") return data + def write_data_file(data, file: str): filetype = Path(file).suffix if filetype == ".csv": @@ -44,29 +46,45 @@ def write_data_file(data, file: str): raise ValueError(f"Unknown file type: {filetype}") return None + if __name__ == "__main__": - attack_success_parser = argparse.ArgumentParser(description="Compute attack success") + attack_success_parser = argparse.ArgumentParser( + description="Compute attack success" + ) attack_success_parser.add_argument( "--log-level", default="INFO", help="Logging level", choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"], ) - attack_success_parser.add_argument("-b", "--ben_predictions_file", help="Full path to the predictions file", required=True,) - attack_success_parser.add_argument("-a", "--adv_predictions_file", help="Full path to the labels file", required=True,) - attack_success_parser.add_argument("-l", "--labels_file", help="Full path to the predictions file", required=True,) + attack_success_parser.add_argument( + "-b", + "--ben_predictions_file", + help="Full path to the predictions file", + required=True, + ) + attack_success_parser.add_argument( + "-a", + "--adv_predictions_file", + help="Full path to the labels file", + required=True, + ) + attack_success_parser.add_argument( + "-l", "--labels_file", help="Full path to the predictions file", required=True + ) attack_success_parser.add_argument( "-i", "--input_score_file", - default = None, + default=None, required=True, ) - attack_success_parser.add_argument("-o", "--output_score_file", help="Full path to the labels file", required=True,) + attack_success_parser.add_argument( + "-o", "--output_score_file", help="Full path to the labels file", required=True + ) args = attack_success_parser.parse_args() - + logging.basicConfig(level=args.log_level) - - + # labels = read_data_file(args.labels_file) # nb_classes = len(np.unique(labels)) # ben_pred = read_data_file(args.ben_predictions_file) @@ -86,5 +104,3 @@ def write_data_file(data, file: str): # new.update(**score_dict) # with open(args.output_score_file, "w") as f: # json.dump(new, f) - - \ No newline at end of file diff --git a/examples/scratch/conf/evaluate/default.yaml b/examples/scratch/conf/evaluate/default.yaml index d084f30d..c1f244c2 100644 --- a/examples/scratch/conf/evaluate/default.yaml +++ b/examples/scratch/conf/evaluate/default.yaml @@ -2,4 +2,4 @@ labels_file : ${files.directory}/${files.reports}/train/${files.name}/${files.te ben_predictions_file : ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} adv_predictions_file : ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} input_score_file : ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} -output_score_file : ${files.directory}/${files.reports}/attack/${files.name}/attack_${files.score_dict_file} \ No newline at end of file +output_score_file : ${files.directory}/${files.reports}/attack/${files.name}/attack_${files.score_dict_file} diff --git a/examples/scratch/conf/optimise/default.yaml b/examples/scratch/conf/optimise/default.yaml index ac81f3ac..0be42e5c 100644 --- a/examples/scratch/conf/optimise/default.yaml +++ b/examples/scratch/conf/optimise/default.yaml @@ -1,2 +1,2 @@ +stage = train -+optimizers = \ No newline at end of file ++optimizers = diff --git a/examples/scratch/deckard_queue.py b/examples/scratch/deckard_queue.py index c35f8e8b..090f9a11 100644 --- a/examples/scratch/deckard_queue.py +++ b/examples/scratch/deckard_queue.py @@ -4,6 +4,7 @@ from os import environ from dvc.api import params_show from pathlib import Path + logger = logging.getLogger(__name__) DECKARD_PIPELINE_FILE = environ.get("DECKARD_PIPELINE_FILE", "dvc.yaml") @@ -23,15 +24,15 @@ queue_parser.add_argument( "--config_file", "-c", - default = ["conf/default.yaml"], + default=["conf/default.yaml"], help="Full path to the configuration file", nargs="+", ) queue_parser.add_argument( "--params_file", "-p", - default = [], - nargs="+", + default=[], + nargs="+", ) queue_parser.add_argument( "--output_file", @@ -53,33 +54,36 @@ def load_config(config_file): with open(config_file, "r") as f: config = yaml.safe_load(f) return config - - - + # Add overrides from params file # stage = pipeline["stages"][args.stage] def add_overrides_from_params_file(params_file, default_config): - params = default_config['hydra']['sweeper']['params'] + params = default_config["hydra"]["sweeper"]["params"] if params_file is not None: with open(params_file, "r") as f: param_file_params = yaml.safe_load(f) - if not isinstance(param_file_params, type(None)) and len(param_file_params) > 0: + if ( + not isinstance(param_file_params, type(None)) + and len(param_file_params) > 0 + ): params.update(param_file_params) logger.info(f"Loaded parameter overrides: {yaml.dump(params)}") # Update configuration new_config = default_config.copy() - new_config['hydra']['sweeper']['params'].update(params) - logger.info(f"New configuration: {new_config['hydra']['sweeper']['params']}") + new_config["hydra"]["sweeper"]["params"].update(params) + logger.info( + f"New configuration: {new_config['hydra']['sweeper']['params']}" + ) else: new_config = default_config return new_config - + config_list = [load_config(config_file) for config_file in args.config_file] config_names = [Path(config_file).stem for config_file in args.config_file] params_list = [Path(params_file) for params_file in args.params_file] param_names = [Path(params_file).stem for params_file in args.params_file] new_configs = {} - i = 0 + i = 0 Path(args.output_file).mkdir(parents=True, exist_ok=True) for default_config in config_list: conf_name = config_names[i] @@ -88,22 +92,21 @@ def add_overrides_from_params_file(params_file, default_config): for params_file in params_list: param_name = param_names[j] j += 1 - new_config = add_overrides_from_params_file( params_file=params_file, default_config=default_config) + new_config = add_overrides_from_params_file( + params_file=params_file, default_config=default_config + ) if conf_name == "default": study_name = f"{param_name}" new_configs[study_name] = new_config else: - study_name= f"{conf_name}_{param_name}" + study_name = f"{conf_name}_{param_name}" new_configs[study_name] = new_config - for k,v in new_configs.items(): - v['hydra']['sweeper']['params']['study_name'] = k + for k, v in new_configs.items(): + v["hydra"]["sweeper"]["params"]["study_name"] = k with open(Path(args.output_file, k).with_suffix(".yaml"), "w") as f: yaml.dump(v, f) - - + # # Write new configuration # Path(args.output_file).parent.mkdir(parents=True, exist_ok=True) # with open(args.output_file, "w") as f: # yaml.dump(new_config, f) - - \ No newline at end of file diff --git a/examples/scratch/dvc.yaml b/examples/scratch/dvc.yaml index da0375ea..3a237bfe 100644 --- a/examples/scratch/dvc.yaml +++ b/examples/scratch/dvc.yaml @@ -58,4 +58,4 @@ stages: find_best_learning_rate: cmd: python -m deckard.layers.find_best default.yaml deps: - - model.db \ No newline at end of file + - model.db diff --git a/examples/scratch/objective.py b/examples/scratch/objective.py index baf3fcab..743bd14d 100644 --- a/examples/scratch/objective.py +++ b/examples/scratch/objective.py @@ -14,6 +14,7 @@ ) config_name = os.environ.pop("DECKARD_DEFAULT_CONFIG", "default.yaml") + @hydra.main(config_path=config_path, config_name=config_name, version_base="1.3") def hydra_optimise(cfg: DictConfig) -> float: score = optimise(cfg) @@ -21,37 +22,61 @@ def hydra_optimise(cfg: DictConfig) -> float: def configure(cfg: DictConfig, trial: Trial) -> None: - data_name = trial.params.get('data.generate.name', None) - preprocessor = trial.params.get('model.art.pipeline.preprocessor', None) - postprocessor = trial.params.get('model.art.pipeline.postprocessor', None) + data_name = trial.params.get("data.generate.name", None) + preprocessor = trial.params.get("model.art.pipeline.preprocessor", None) + postprocessor = trial.params.get("model.art.pipeline.postprocessor", None) if data_name in ["torch_cifar10", "torch_mnist"]: trial.suggest_int("data.sample.random_state", 0, 10) if preprocessor is not None: if preprocessor.strip() == "art.defences.preprocessor.FeatureSqueezing": - bit_depth = trial.suggest_loguniform("model.art.pipeline.preprocessor.defences.FeatureSqueezing.bit_depth", 1, 64) - trial.suggest_categorical("model.art.pipeline.preprocessor.defences.FeatureSqueezing.clip_values", [[0, 2^bit_depth-1]]) + bit_depth = trial.suggest_loguniform( + "model.art.pipeline.preprocessor.defences.FeatureSqueezing.bit_depth", + 1, + 64, + ) + trial.suggest_categorical( + "model.art.pipeline.preprocessor.defences.FeatureSqueezing.clip_values", + [[0, 2 ^ bit_depth - 1]], + ) elif preprocessor.strip() == "art.defences.preprocessor.GaussianAugmentation": - sigma = trial.suggest_loguniform("model.art.pipeline.preprocessor.defences.GaussianAugmentation.sigma", 0.1, 10) - trial.suggest_categorical("model.art.pipeline.preprocessor.defences.GaussianAugmentation.clip_values", [[0, 255]]) - trial.suggest_categorical("model.art.pipeline.preprocessor.defences.GaussianAugmentation.ratio", [0.5]) + sigma = trial.suggest_loguniform( + "model.art.pipeline.preprocessor.defences.GaussianAugmentation.sigma", + 0.1, + 10, + ) + trial.suggest_categorical( + "model.art.pipeline.preprocessor.defences.GaussianAugmentation.clip_values", + [[0, 255]], + ) + trial.suggest_categorical( + "model.art.pipeline.preprocessor.defences.GaussianAugmentation.ratio", + [0.5], + ) else: raise ValueError(f"Unknown preprocessor {preprocessor}") if postprocessor is not None: if postprocessor.strip() == "art.defences.postprocessor.HighConfidence": - threshold = trial.suggest_int("model.art.pipeline.preprocessor.defences.HighConfidence.threshold", 1) - trial.suggest_categorical("model.art.pipeline.preprocessor.defences.HighConfidence.abs", [True]) - trial.suggest_categorical("model.art.pipeline.preprocessor.defences.HighConfidence.clip_values", [[0, 255]]) + threshold = trial.suggest_int( + "model.art.pipeline.preprocessor.defences.HighConfidence.threshold", 1 + ) + trial.suggest_categorical( + "model.art.pipeline.preprocessor.defences.HighConfidence.abs", [True] + ) + trial.suggest_categorical( + "model.art.pipeline.preprocessor.defences.HighConfidence.clip_values", + [[0, 255]], + ) elif postprocessor.strip() == "art.defences.postprocessor.GaussianNoise": - sigma = trial.suggest_loguniform("model.art.pipeline.preprocessor.defences.GaussianNoise.sigma", 0.1, 10) - trial.suggest_categorical("model.art.pipeline.preprocessor.defences.GaussianNoise.clip_values", [[0, 255]]) + sigma = trial.suggest_loguniform( + "model.art.pipeline.preprocessor.defences.GaussianNoise.sigma", 0.1, 10 + ) + trial.suggest_categorical( + "model.art.pipeline.preprocessor.defences.GaussianNoise.clip_values", + [[0, 255]], + ) else: raise ValueError(f"Unknown preprocessor {postprocessor}") - - - - - - + if __name__ == "__main__": - hydra_optimise() \ No newline at end of file + hydra_optimise() From 68d3829975120c1f5ad2249d98c46b6ade2e297d Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 9 Oct 2023 12:04:14 +0000 Subject: [PATCH 098/148] linting --- deckard/layers/afr.py | 43 ++-- examples/pytorch/weibull.ipynb | 263 ++++++++++++--------- examples/scratch/compute_attack_success.py | 15 +- examples/scratch/deckard_queue.py | 6 +- examples/scratch/objective.py | 10 +- 5 files changed, 182 insertions(+), 155 deletions(-) diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index a53e2b54..771f13e1 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -1,6 +1,5 @@ import pandas as pd import numpy as np -from pathlib import Path import matplotlib.pyplot as plt @@ -11,12 +10,13 @@ LogLogisticAFTFitter, CoxPHFitter, ) -from .plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling import matplotlib from pathlib import Path import logging import yaml import argparse +from .plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling + logger = logging.getLogger(__name__) @@ -58,29 +58,6 @@ :, "attack.attack_size" ] - # data=data[data['def_gen'] == 'Gauss-in'] - # data=data[data['atk_gen'] == 'HSJ'] - - print( - "Adversarial Accuracy:", - "\n", - "ResNet152:", - data[data["model_layers"] == 152].adv_accuracy.mean(skipna=True), - "\n", - "Resnet101:", - data[data["model_layers"] == 101].adv_accuracy.mean(skipna=True), - "\n", - "Resnet50:", - data[data["model_layers"] == 50].adv_accuracy.mean(skipna=True), - "\n", - "Resnet34:", - data[data["model_layers"] == 34].adv_accuracy.mean(skipna=True), - "\n", - "Resnet18:", - data[data["model_layers"] == 18].adv_accuracy.mean(skipna=True), - "\n", - ) - def plot_aft( df, file, @@ -168,7 +145,7 @@ def score_model(aft, train, test): def make_afr_table(score_list, aft_dict, dataset): assert len(score_list) == len( - aft_dict + aft_dict, ), "Length of score list and aft dict must be equal" aft_data = pd.DataFrame() aft_data.index.name = "Model" @@ -235,7 +212,12 @@ def clean_data_for_aft( return cleaned, y, data def split_data_for_aft( - data, target, duration_col, kwarg_list, test_size=0.2, random_state=42 + data, + target, + duration_col, + kwarg_list, + test_size=0.2, + random_state=42, ): cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) X_train, X_test, y_train, y_test = train_test_split( @@ -266,7 +248,12 @@ def split_data_for_aft( ] X_train, X_test, y_train, y_test = split_data_for_aft( - data, target, duration_col, kwarg_list, test_size=0.2, random_state=42 + data, + target, + duration_col, + kwarg_list, + test_size=0.2, + random_state=42, ) weibull_dict = { diff --git a/examples/pytorch/weibull.ipynb b/examples/pytorch/weibull.ipynb index b72e9394..def6e2d1 100644 --- a/examples/pytorch/weibull.ipynb +++ b/examples/pytorch/weibull.ipynb @@ -13,15 +13,19 @@ "\n", "import matplotlib.pyplot as plt\n", "\n", - "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import train_test_split\n", "\n", "\n", - "\n", "from paretoset import paretoset\n", - "from lifelines import WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter, CoxPHFitter\n", + "from lifelines import (\n", + " WeibullAFTFitter,\n", + " LogNormalAFTFitter,\n", + " LogLogisticAFTFitter,\n", + " CoxPHFitter,\n", + ")\n", "from plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling\n", - "import matplotlib \n", + "import matplotlib\n", "import argparse\n", "from pathlib import Path\n", "import logging\n", @@ -35,11 +39,9 @@ "metadata": {}, "outputs": [], "source": [ - "font = {'family' : 'Times New Roman',\n", - " 'weight' : 'bold',\n", - " 'size' : 22}\n", + "font = {\"family\": \"Times New Roman\", \"weight\": \"bold\", \"size\": 22}\n", "\n", - "matplotlib.rc('font', **font)" + "matplotlib.rc(\"font\", **font)" ] }, { @@ -72,20 +74,30 @@ "data = drop_frames_without_results(data)\n", "data = calculate_failure_rate(data)\n", "data = min_max_scaling(data)\n", - "data.dropna(axis=0, subset=['atk_value', 'atk_param'], inplace=True)\n", - "data.dropna(axis=0, subset=['def_value', 'def_param'], inplace=True)\n", + "data.dropna(axis=0, subset=[\"atk_value\", \"atk_param\"], inplace=True)\n", + "data.dropna(axis=0, subset=[\"def_value\", \"def_param\"], inplace=True)\n", "# data=data[data['def_gen'] == 'Gauss-in']\n", "# data=data[data['atk_gen'] == 'HSJ']\n", "\n", "print(\n", - " \"Adversarial Accuracy:\", \"\\n\",\n", - " \"ResNet152:\", data[data['model_layers'] == 152].adv_accuracy.mean(skipna=True), \"\\n\",\n", - " \"Resnet101:\", data[data['model_layers'] == 101].adv_accuracy.mean(skipna=True), \"\\n\",\n", - " \"Resnet50:\", data[data['model_layers'] == 50].adv_accuracy.mean(skipna=True), \"\\n\",\n", - " \"Resnet34:\", data[data['model_layers'] == 34].adv_accuracy.mean(skipna=True), \"\\n\",\n", - " \"Resnet18:\", data[data['model_layers'] == 18].adv_accuracy.mean(skipna=True), \"\\n\",\n", - ")\n", - "\n" + " \"Adversarial Accuracy:\",\n", + " \"\\n\",\n", + " \"ResNet152:\",\n", + " data[data[\"model_layers\"] == 152].adv_accuracy.mean(skipna=True),\n", + " \"\\n\",\n", + " \"Resnet101:\",\n", + " data[data[\"model_layers\"] == 101].adv_accuracy.mean(skipna=True),\n", + " \"\\n\",\n", + " \"Resnet50:\",\n", + " data[data[\"model_layers\"] == 50].adv_accuracy.mean(skipna=True),\n", + " \"\\n\",\n", + " \"Resnet34:\",\n", + " data[data[\"model_layers\"] == 34].adv_accuracy.mean(skipna=True),\n", + " \"\\n\",\n", + " \"Resnet18:\",\n", + " data[data[\"model_layers\"] == 18].adv_accuracy.mean(skipna=True),\n", + " \"\\n\",\n", + ")" ] }, { @@ -94,7 +106,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "def plot_aft(\n", " df,\n", " file,\n", @@ -138,6 +149,7 @@ " plt.show()\n", " return ax, aft\n", "\n", + "\n", "def plot_partial_effects(\n", " file,\n", " aft,\n", @@ -148,12 +160,14 @@ " ylabel=\"Failure rate\",\n", " legend_kwargs={\"loc\": \"upper left\"},\n", " replacement_dict={},\n", - " cmap='coolwarm',\n", - " **kwargs, \n", - " ):\n", + " cmap=\"coolwarm\",\n", + " **kwargs,\n", + "):\n", " plt.gcf().clear()\n", " # kwargs.pop(\"replacement_dict\")\n", - " pareto = aft.plot_partial_effects_on_outcome(covariate_array, values_array, cmap=cmap, **kwargs)\n", + " pareto = aft.plot_partial_effects_on_outcome(\n", + " covariate_array, values_array, cmap=cmap, **kwargs\n", + " )\n", " labels = pareto.get_yticklabels()\n", " labels = [label.get_text() for label in labels]\n", " for k, v in replacement_dict.items():\n", @@ -168,37 +182,39 @@ " logger.info(f\"Saved graph to {FOLDER / file}\")\n", " return pareto\n", "\n", + "\n", "def score_model(aft, train, test):\n", " train_score = aft.score(train)\n", " test_score = aft.score(test)\n", - " scores = {'train_score': train_score, 'test_score': test_score}\n", + " scores = {\"train_score\": train_score, \"test_score\": test_score}\n", " plt.show()\n", - " return scores\n", + " return scores\n", "\n", "\n", - "def clean_data_for_aft(\n", - " data, kwarg_list, target=\"adv_failure_rate\"\n", - "):\n", + "def clean_data_for_aft(data, kwarg_list, target=\"adv_failure_rate\"):\n", " subset = data.copy()\n", - " assert target in subset, f\"Target {target} not in dataframe with columns {subset.columns}\"\n", - " \n", + " assert (\n", + " target in subset\n", + " ), f\"Target {target} not in dataframe with columns {subset.columns}\"\n", + "\n", " cleaned = pd.DataFrame()\n", " kwarg_list.append(target)\n", " for kwarg in kwarg_list:\n", " cleaned = pd.concat([cleaned, subset[kwarg]], axis=1)\n", " cols = cleaned.columns\n", " cleaned = pd.DataFrame(subset, columns=cols)\n", - " \n", - " \n", + "\n", " # if \"accuracy\" in cleaned.columns:\n", " # cleaned = cleaned[~cleaned[cleaned['accuracy'] != 1e10]]\n", " # cleaned = cleaned[~cleaned[cleaned['accuracy'] != -1e10]]\n", " # if \"adv_accuracy\" in cleaned.columns:\n", " # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != 1e10]]\n", " # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != -1e10]]\n", - " cleaned.dropna(inplace=True, how='any', axis=0)\n", + " cleaned.dropna(inplace=True, how=\"any\", axis=0)\n", " y = cleaned[target]\n", - " assert target in cleaned, f\"Target {target} not in dataframe with columns {cleaned.columns}\"\n", + " assert (\n", + " target in cleaned\n", + " ), f\"Target {target} not in dataframe with columns {cleaned.columns}\"\n", " return cleaned, y, data" ] }, @@ -208,8 +224,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", - "\n", "kwarg_list = [\n", " # \"accuracy\",\n", " \"train_time\",\n", @@ -223,7 +237,6 @@ " \"adv_fit_time\",\n", " # \"atk_param\",\n", " # \"def_param\",\n", - " \n", " \"model.art.pipeline.initialize.kwargs.optimizer.lr\",\n", " # \"def_gen\",\n", " # \"atk_gen\",\n", @@ -242,15 +255,17 @@ "metadata": {}, "outputs": [], "source": [ - "data.loc[:, \"adv_failures\"] = (1 - data.loc[:, \"adv_accuracy\"]) \n", - "data.loc[:, \"ben_failures\"] = (1 - data.loc[:, \"accuracy\"])\n", + "data.loc[:, \"adv_failures\"] = 1 - data.loc[:, \"adv_accuracy\"]\n", + "data.loc[:, \"ben_failures\"] = 1 - data.loc[:, \"accuracy\"]\n", "target = \"adv_failures\"\n", "duration_col = \"adv_fit_time\"\n", - "cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target)\n", - "X_train, X_test, y_train, y_test = train_test_split(cleaned, y, test_size=0.2, random_state=42)\n", - "assert target in cleaned, f\"Target {target} not in dataframe with columns {cleaned.columns}\"\n", - "\n", - "\n" + "cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target)\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " cleaned, y, test_size=0.2, random_state=42\n", + ")\n", + "assert (\n", + " target in cleaned\n", + "), f\"Target {target} not in dataframe with columns {cleaned.columns}\"" ] }, { @@ -259,7 +274,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "# from sklearn.preprocessing import PowerTransformer\n", "# pt = PowerTransformer(method='yeo-johnson', standardize=False)\n", "# del X_train[target]\n", @@ -270,7 +284,7 @@ "# X_train = pd.DataFrame(X_train, columns=X_train_cols)\n", "# X_test = pd.DataFrame(X_test, columns=X_train_cols)\n", "# X_train[target] = y_train\n", - "# y_train = X_train[target]\n" + "# y_train = X_train[target]" ] }, { @@ -297,7 +311,7 @@ "# subset = X_train.loc[:, sense_dict.keys()]\n", "# senses = sense_dict.values()\n", "# these = paretoset(subset)\n", - "# X_train = X_train.iloc[these, :]\n" + "# X_train = X_train.iloc[these, :]" ] }, { @@ -640,21 +654,21 @@ " \"adv_failure_rate: lambda_\": \"Adv. Failure Rate\",\n", " \"failure_rate: lambda_\": \"Ben. Failure Rate\",\n", " \"model_layers: lambda_\": \"No. of Layers\",\n", - " \"model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_\" : \"Learning Rate\",\n", - " \"def_gen\" : \"Defence\",\n", + " \"model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_\": \"Learning Rate\",\n", + " \"def_gen\": \"Defence\",\n", "}\n", "\n", "weibull_afr, wft = plot_aft(\n", " X_train,\n", - " file = \"weibull_aft.pdf\",\n", - " event_col = target,\n", - " duration_col = duration_col,\n", - " title = \"Weibull AFR Model\",\n", - " mtype = \"weibull\",\n", + " file=\"weibull_aft.pdf\",\n", + " event_col=target,\n", + " duration_col=duration_col,\n", + " title=\"Weibull AFR Model\",\n", + " mtype=\"weibull\",\n", " replacement_dict=weibull_dict,\n", ")\n", "wft.print_summary()\n", - "wft_scores = score_model(wft, X_train, X_test)\n" + "wft_scores = score_model(wft, X_train, X_test)" ] }, { @@ -690,16 +704,19 @@ " \"model_layers=152\": \"152\",\n", "}\n", "pareto_weibull = plot_partial_effects(\n", - " file = \"weibull_partial_effects.pdf\", \n", - " aft = wft, \n", - " covariate_array=\"model_layers\", \n", - " values_array=[18, 34, 50, 101, 152], \n", - " title=\"Partial Effects of No. of Layers on Failure Rate for Weibull AFR\", \n", - " replacement_dict=pareto_dict, \n", + " file=\"weibull_partial_effects.pdf\",\n", + " aft=wft,\n", + " covariate_array=\"model_layers\",\n", + " values_array=[18, 34, 50, 101, 152],\n", + " title=\"Partial Effects of No. of Layers on Failure Rate for Weibull AFR\",\n", + " replacement_dict=pareto_dict,\n", " ylabel=\"% Chance of Survival\",\n", " xlabel=\"Time $T$ (seconds)\",\n", - " legend_kwargs={\"title\" : \"No. of Layers\", \"labels\" : [\"18\", \"34\", \"50\", \"101\", \"152\"]},\n", - " )\n", + " legend_kwargs={\n", + " \"title\": \"No. of Layers\",\n", + " \"labels\": [\"18\", \"34\", \"50\", \"101\", \"152\"],\n", + " },\n", + ")\n", "\n", "# weibull_accuracy = plot_partial_effects(\n", "# file = \"weibull_partial_effect_accuracy.pdf\",\n", @@ -711,9 +728,7 @@ "# ylabel=\"% Chance of Survival\",\n", "# xlabel=\"Time $T$ (seconds)\",\n", "# legend = {\"title\" : \"Benign Accuracy\"},\n", - "# )\n", - " \n", - " " + "# )" ] }, { @@ -1044,44 +1059,46 @@ } ], "source": [ - "\n", "cox_dict = {\n", " \"adv_failure_rate\": \"Adv. Failure Rate\",\n", - " \"def_value\" : \"Defence Strength\",\n", - " \"data.sample.random_state\" : \"Random State\",\n", - " \"train_time\" : \"Training Time\",\n", - " \"model_layers\" : \"No. of Layers\",\n", - " \"model.art.pipeline.initialize.kwargs.optimizer.lr\" : \"Learning Rate\",\n", - " \"adv_accuracy\" : \"Adv. Accuracy\",\n", - " \"adv_fit_time\" : \"Adv. Fit Time\",\n", - " \"adv_log_loss\" : \"Adv. Log Loss\",\n", - " \"predict_time\" : \"Inference Time\",\n", - " \"accuracy\" : \"Ben. Accuracy\",\n", - " \"failure_rate\" : \"Ben. Failure Rate\",\n", - " \"atk_value\" : \"Attack Strength\",\n", + " \"def_value\": \"Defence Strength\",\n", + " \"data.sample.random_state\": \"Random State\",\n", + " \"train_time\": \"Training Time\",\n", + " \"model_layers\": \"No. of Layers\",\n", + " \"model.art.pipeline.initialize.kwargs.optimizer.lr\": \"Learning Rate\",\n", + " \"adv_accuracy\": \"Adv. Accuracy\",\n", + " \"adv_fit_time\": \"Adv. Fit Time\",\n", + " \"adv_log_loss\": \"Adv. Log Loss\",\n", + " \"predict_time\": \"Inference Time\",\n", + " \"accuracy\": \"Ben. Accuracy\",\n", + " \"failure_rate\": \"Ben. Failure Rate\",\n", + " \"atk_value\": \"Attack Strength\",\n", "}\n", "\n", "cox_afr, cft = plot_aft(\n", " X_train,\n", - " file = \"cox_aft.pdf\",\n", - " event_col = target,\n", - " duration_col = duration_col,\n", - " title = \"Cox AFR Model\",\n", - " mtype = \"cox\",\n", + " file=\"cox_aft.pdf\",\n", + " event_col=target,\n", + " duration_col=duration_col,\n", + " title=\"Cox AFR Model\",\n", + " mtype=\"cox\",\n", " replacement_dict=cox_dict,\n", ")\n", "cox_scores = score_model(cft, X_train, X_test)\n", "cft.print_summary()\n", "cox_partial = plot_partial_effects(\n", - " file = \"cox_partial_effects.pdf\",\n", - " aft = cft,\n", - " covariate_array = \"model_layers\",\n", - " values_array = [18, 34, 50, 101, 152],\n", + " file=\"cox_partial_effects.pdf\",\n", + " aft=cft,\n", + " covariate_array=\"model_layers\",\n", + " values_array=[18, 34, 50, 101, 152],\n", " replacement_dict=cox_dict,\n", " title=\"Survival Time for Cox AFR\",\n", " ylabel=\"% Chance of Survival\",\n", " xlabel=\"Time $T$ (seconds)\",\n", - " legend_kwargs={\"title\" : \"No. of Layers\", \"labels\" : [\"18\", \"34\", \"50\", \"101\", \"152\"]},\n", + " legend_kwargs={\n", + " \"title\": \"No. of Layers\",\n", + " \"labels\": [\"18\", \"34\", \"50\", \"101\", \"152\"],\n", + " },\n", ")" ] }, @@ -1437,14 +1454,14 @@ " \"predict_time: mu_\": \"Inference Time\",\n", " \"adv_fit_time: mu_\": \"Adv. Fit Time\",\n", " \"model_layers: mu_\": \"No. of Layers\",\n", - " \"model.art.pipeline.initialize.kwargs.optimizer.lr: mu_\" : \"Learning Rate\",\n", + " \"model.art.pipeline.initialize.kwargs.optimizer.lr: mu_\": \"Learning Rate\",\n", " \"data.sample.random_state: mu_\": \"Random State\",\n", " \"adv_log_loss: mu_\": \"Adv. Log Loss\",\n", " \"adv_accuracy: mu_\": \"Adv. Accuracy\",\n", " \"accuracy: mu_\": \"Ben. Accuracy\",\n", " \"adv_failure_rate: mu_\": \"Adv. Failure Rate\",\n", - " \"def_gen\" : \"Defence\",\n", - " \"learning_rate: mu_\" : \"Learning Rate\",\n", + " \"def_gen\": \"Defence\",\n", + " \"learning_rate: mu_\": \"Learning Rate\",\n", "}\n", "\n", "log_normal_graph, lnt = plot_aft(\n", @@ -1459,15 +1476,18 @@ "lnt_scores = score_model(lnt, X_train, X_test)\n", "lnt.print_summary()\n", "lnt_partial = plot_partial_effects(\n", - " file = \"log_normal_partial_effects.pdf\",\n", - " aft = lnt,\n", - " covariate_array = \"model_layers\",\n", - " values_array = [18, 34, 50, 101, 152],\n", + " file=\"log_normal_partial_effects.pdf\",\n", + " aft=lnt,\n", + " covariate_array=\"model_layers\",\n", + " values_array=[18, 34, 50, 101, 152],\n", " replacement_dict=log_normal_dict,\n", " title=\"Survival Time for Log-Normal AFR\",\n", " ylabel=\"% Chance of Survival\",\n", " xlabel=\"Time $T$ (seconds)\",\n", - " legend_kwargs={\"title\" : \"No. of Layers\", \"labels\" : [\"18\", \"34\", \"50\", \"101\", \"152\"]},\n", + " legend_kwargs={\n", + " \"title\": \"No. of Layers\",\n", + " \"labels\": [\"18\", \"34\", \"50\", \"101\", \"152\"],\n", + " },\n", ")" ] }, @@ -1821,7 +1841,6 @@ } ], "source": [ - "\n", "log_logistic_dict = {\n", " \"Intercept: beta_\": \"$\\\\beta$\",\n", " \"Intercept: alpha_\": \"$\\\\alpha$\",\n", @@ -1834,10 +1853,9 @@ " \"accuracy: alpha_\": \"Ben. Accuracy\",\n", " \"adv_fit_time: alpha_\": \"Adv. Fit Time\",\n", " \"model_layers: alpha_\": \"No. of Layers\",\n", - " \"model.art.pipeline.initialize.kwargs.optimizer.lr\" : \"Learning Rate\",\n", + " \"model.art.pipeline.initialize.kwargs.optimizer.lr\": \"Learning Rate\",\n", " \"adv_failure_rate: alpha_\": \"Adv. Failure Rate\",\n", - " \"alpha_\" : \"\",\n", - "\n", + " \"alpha_\": \"\",\n", "}\n", "\n", "log_logistic_graph, llt = plot_aft(\n", @@ -1853,16 +1871,18 @@ "llt_scores = score_model(llt, X_train, X_test)\n", "print(llt_scores)\n", "llt_partial = plot_partial_effects(\n", - " file = \"log_logistic_partial_effects.pdf\",\n", - " aft = llt,\n", - " covariate_array = \"model_layers\",\n", - " values_array = [18, 34, 50, 101, 152],\n", + " file=\"log_logistic_partial_effects.pdf\",\n", + " aft=llt,\n", + " covariate_array=\"model_layers\",\n", + " values_array=[18, 34, 50, 101, 152],\n", " replacement_dict=log_logistic_dict,\n", " title=\"Survival Time for Log-Logistic AFR\",\n", " ylabel=\"% Chance of Survival\",\n", " xlabel=\"Time $T$ (seconds)\",\n", - " legend_kwargs={\"title\" : \"No. of Layers\", \"labels\" : [\"18\", \"34\", \"50\", \"101\", \"152\"]},\n", - " \n", + " legend_kwargs={\n", + " \"title\": \"No. of Layers\",\n", + " \"labels\": [\"18\", \"34\", \"50\", \"101\", \"152\"],\n", + " },\n", ")" ] }, @@ -1990,19 +2010,30 @@ "aft_data = pd.DataFrame()\n", "aft_data.index.name = \"Model\"\n", "aft_data.index = aft_dict.keys()\n", - "aft_data[\"AIC\"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() ]\n", + "aft_data[\"AIC\"] = [\n", + " x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()\n", + "]\n", "aft_data[\"Concordance\"] = [x.concordance_index_ for x in aft_dict.values()]\n", - "aft_data[\"BIC\"] = [x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()]\n", - "aft_data['Train LL'] = [x['train_score'] for x in score_list]\n", - "aft_data['Test LL'] = [x['test_score'] for x in score_list]\n", - "aft_data['Mean ST'] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()]\n", - "aft_data['Median ST'] = [x.predict_median(X_train).median() for x in aft_dict.values()]\n", + "aft_data[\"BIC\"] = [\n", + " x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()\n", + "]\n", + "aft_data[\"Train LL\"] = [x[\"train_score\"] for x in score_list]\n", + "aft_data[\"Test LL\"] = [x[\"test_score\"] for x in score_list]\n", + "aft_data[\"Mean ST\"] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()]\n", + "aft_data[\"Median ST\"] = [x.predict_median(X_train).median() for x in aft_dict.values()]\n", "aft_data = aft_data.round(2)\n", "aft_data.to_csv(FOLDER / \"aft_comparison.csv\")\n", "logger.info(f\"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}\")\n", - "aft_data = aft_data.round(2,)\n", - "aft_data.to_latex(FOLDER / \"aft_comparison.tex\", float_format=\"%.2f\", label = \"tab:mnist\", caption=\"Comparison of AFR Models on the MNIST dataset.\")\n", - "aft_data\n" + "aft_data = aft_data.round(\n", + " 2,\n", + ")\n", + "aft_data.to_latex(\n", + " FOLDER / \"aft_comparison.tex\",\n", + " float_format=\"%.2f\",\n", + " label=\"tab:mnist\",\n", + " caption=\"Comparison of AFR Models on the MNIST dataset.\",\n", + ")\n", + "aft_data" ] } ], diff --git a/examples/scratch/compute_attack_success.py b/examples/scratch/compute_attack_success.py index 98b7c0a4..acb4f951 100644 --- a/examples/scratch/compute_attack_success.py +++ b/examples/scratch/compute_attack_success.py @@ -1,11 +1,8 @@ import logging import argparse from pathlib import Path -from art.utils import compute_accuracy, to_categorical import pandas as pd import json -import numpy as np -from dvc.api import params_show, read logger = logging.getLogger(__name__) @@ -49,7 +46,7 @@ def write_data_file(data, file: str): if __name__ == "__main__": attack_success_parser = argparse.ArgumentParser( - description="Compute attack success" + description="Compute attack success", ) attack_success_parser.add_argument( "--log-level", @@ -70,7 +67,10 @@ def write_data_file(data, file: str): required=True, ) attack_success_parser.add_argument( - "-l", "--labels_file", help="Full path to the predictions file", required=True + "-l", + "--labels_file", + help="Full path to the predictions file", + required=True, ) attack_success_parser.add_argument( "-i", @@ -79,7 +79,10 @@ def write_data_file(data, file: str): required=True, ) attack_success_parser.add_argument( - "-o", "--output_score_file", help="Full path to the labels file", required=True + "-o", + "--output_score_file", + help="Full path to the labels file", + required=True, ) args = attack_success_parser.parse_args() diff --git a/examples/scratch/deckard_queue.py b/examples/scratch/deckard_queue.py index 090f9a11..5c5fb6ab 100644 --- a/examples/scratch/deckard_queue.py +++ b/examples/scratch/deckard_queue.py @@ -49,6 +49,7 @@ args = queue_parser.parse_args() # Set logging level logging.basicConfig(level=args.log_level) + # Load Default Configuration def load_config(config_file): with open(config_file, "r") as f: @@ -72,7 +73,7 @@ def add_overrides_from_params_file(params_file, default_config): new_config = default_config.copy() new_config["hydra"]["sweeper"]["params"].update(params) logger.info( - f"New configuration: {new_config['hydra']['sweeper']['params']}" + f"New configuration: {new_config['hydra']['sweeper']['params']}", ) else: new_config = default_config @@ -93,7 +94,8 @@ def add_overrides_from_params_file(params_file, default_config): param_name = param_names[j] j += 1 new_config = add_overrides_from_params_file( - params_file=params_file, default_config=default_config + params_file=params_file, + default_config=default_config, ) if conf_name == "default": study_name = f"{param_name}" diff --git a/examples/scratch/objective.py b/examples/scratch/objective.py index 743bd14d..3e586c08 100644 --- a/examples/scratch/objective.py +++ b/examples/scratch/objective.py @@ -57,10 +57,12 @@ def configure(cfg: DictConfig, trial: Trial) -> None: if postprocessor is not None: if postprocessor.strip() == "art.defences.postprocessor.HighConfidence": threshold = trial.suggest_int( - "model.art.pipeline.preprocessor.defences.HighConfidence.threshold", 1 + "model.art.pipeline.preprocessor.defences.HighConfidence.threshold", + 1, ) trial.suggest_categorical( - "model.art.pipeline.preprocessor.defences.HighConfidence.abs", [True] + "model.art.pipeline.preprocessor.defences.HighConfidence.abs", + [True], ) trial.suggest_categorical( "model.art.pipeline.preprocessor.defences.HighConfidence.clip_values", @@ -68,7 +70,9 @@ def configure(cfg: DictConfig, trial: Trial) -> None: ) elif postprocessor.strip() == "art.defences.postprocessor.GaussianNoise": sigma = trial.suggest_loguniform( - "model.art.pipeline.preprocessor.defences.GaussianNoise.sigma", 0.1, 10 + "model.art.pipeline.preprocessor.defences.GaussianNoise.sigma", + 0.1, + 10, ) trial.suggest_categorical( "model.art.pipeline.preprocessor.defences.GaussianNoise.clip_values", From 00e51c528db645ec059853f3d4e08f3bef638b28 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Mon, 9 Oct 2023 12:07:46 +0000 Subject: [PATCH 099/148] linting --- deckard/layers/afr.py | 24 ++++++++++++------------ deckard/layers/plots.py | 5 +---- examples/scratch/deckard_queue.py | 1 - examples/scratch/objective.py | 6 +++--- 4 files changed, 16 insertions(+), 20 deletions(-) diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index 771f13e1..3c8393da 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -258,14 +258,14 @@ def split_data_for_aft( weibull_dict = { "Intercept: rho_": "$\\rho$", - "Intercept: lambda_": "$\lambda$", + "Intercept: lambda_": "$\lambda$", # noqa W605 "data.sample.random_state: lambda_": "Random State", "def_value: lambda_": "Defence Strength", "atk_value: lambda_": "Attack Strength", "train_time: lambda_": "$t_{train}$", "predict_time: lambda_": "$t_{predict}$", - "adv_accuracy: lambda_": "$\lambda_{adv.}$", - "accuracy: lambda_": "$\lambda_{ben.}$", + "adv_accuracy: lambda_": "$\lambda_{adv.}$", # noqa W605 + "accuracy: lambda_": "$\lambda_{ben.}$", # noqa W605 "adv_fit_time: lambda_": "$t_{attack}$", "adv_log_loss: lambda_": "Adv. Log Loss", "adv_failure_rate: lambda_": "$h_{adv.}(t,;\\theta)$", @@ -315,11 +315,11 @@ def split_data_for_aft( "train_time": "$t_{train}$", "model_layers": "No. of Layers", "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", - "adv_accuracy": "$\lambda_{adv.}$", + "adv_accuracy": "$\lambda_{adv.}$", # noqa W605 "adv_fit_time": "$t_{attack}$", "adv_log_loss": "Adv. Log Loss", "predict_time": "$t_{predict}$", - "accuracy": "$\lambda_{ben.}$", + "accuracy": "$\lambda_{ben.}$", # noqa W605 "failure_rate": "$h_{ben.}(t,;\\theta)$", "atk_value": "Attack Strength", } @@ -355,8 +355,8 @@ def split_data_for_aft( ) log_normal_dict = { - "Intercept: sigma_": "$\sigma$", - "Intercept: mu_": "$\mu$", + "Intercept: sigma_": "$\sigma$", # noqa W605 + "Intercept: mu_": "$\mu$", # noqa W605 "def_value: mu_": "Defence Strength", "atk_value: mu_": "Attack Strength", "train_time: mu_": "$t_{train}$", @@ -366,8 +366,8 @@ def split_data_for_aft( "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_": "Learning Rate", "data.sample.random_state: mu_": "Random State", "adv_log_loss: mu_": "Adv. Log Loss", - "adv_accuracy: mu_": "$\lambda_{adv.}$", - "accuracy: mu_": "$\lambda_{ben.}$", + "adv_accuracy: mu_": "$\lambda_{adv.}$", # noqa W605 + "accuracy: mu_": "$\lambda_{ben.}$", # noqa W605 "adv_failure_rate: mu_": "$h_{adv}(t,;\\theta)$", "def_gen": "Defence", "learning_rate: mu_": "Learning Rate", @@ -405,12 +405,12 @@ def split_data_for_aft( "atk_value: alpha_": "Attack Strength", "train_time: alpha_": "$t_{train}$", "predict_time: alpha_": "$t_{predict}$", - "adv_accuracy: alpha_": "$\lambda_{adv.}$", - "accuracy: alpha_": "$\lambda_{ben.}$", + "adv_accuracy: alpha_": "$\lambda_{adv.}$", # noqa W605 + "accuracy: alpha_": "$\lambda_{ben.}$", # noqa W605 "adv_fit_time: alpha_": "$t_{attack}$", "model_layers: alpha_": "No. of Layers", "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", - "adv_failure_rate: alpha_": "$h_{adv.}(t,\\theta)$", + "adv_failure_rate: alpha_": "$h_{adv.}(t,\\theta)$", # noqa W605 "alpha_": "", } diff --git a/deckard/layers/plots.py b/deckard/layers/plots.py index 0282ea10..f18261f7 100644 --- a/deckard/layers/plots.py +++ b/deckard/layers/plots.py @@ -1,7 +1,7 @@ import argparse import logging from pathlib import Path - +from paretoset import paretoset import matplotlib.pyplot as plt import pandas as pd import seaborn as sns @@ -168,9 +168,6 @@ def calculate_failure_rate(data): return data -from paretoset import paretoset - - def pareto_set(data, sense_dict): subset = data.loc[:, sense_dict.keys()] these = paretoset(subset, sense=sense_dict.values()) diff --git a/examples/scratch/deckard_queue.py b/examples/scratch/deckard_queue.py index 5c5fb6ab..dc3dedac 100644 --- a/examples/scratch/deckard_queue.py +++ b/examples/scratch/deckard_queue.py @@ -2,7 +2,6 @@ import argparse import yaml from os import environ -from dvc.api import params_show from pathlib import Path logger = logging.getLogger(__name__) diff --git a/examples/scratch/objective.py b/examples/scratch/objective.py index 3e586c08..e1f4e34a 100644 --- a/examples/scratch/objective.py +++ b/examples/scratch/objective.py @@ -39,7 +39,7 @@ def configure(cfg: DictConfig, trial: Trial) -> None: [[0, 2 ^ bit_depth - 1]], ) elif preprocessor.strip() == "art.defences.preprocessor.GaussianAugmentation": - sigma = trial.suggest_loguniform( + _ = trial.suggest_loguniform( "model.art.pipeline.preprocessor.defences.GaussianAugmentation.sigma", 0.1, 10, @@ -56,7 +56,7 @@ def configure(cfg: DictConfig, trial: Trial) -> None: raise ValueError(f"Unknown preprocessor {preprocessor}") if postprocessor is not None: if postprocessor.strip() == "art.defences.postprocessor.HighConfidence": - threshold = trial.suggest_int( + _ = trial.suggest_int( "model.art.pipeline.preprocessor.defences.HighConfidence.threshold", 1, ) @@ -69,7 +69,7 @@ def configure(cfg: DictConfig, trial: Trial) -> None: [[0, 255]], ) elif postprocessor.strip() == "art.defences.postprocessor.GaussianNoise": - sigma = trial.suggest_loguniform( + _ = trial.suggest_loguniform( "model.art.pipeline.preprocessor.defences.GaussianNoise.sigma", 0.1, 10, From 03d3be622bdf035efdaa04fb8bf52b35707614f9 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Sat, 14 Oct 2023 14:02:36 +0200 Subject: [PATCH 100/148] classification conf --- .gitignore | 3 + deckard/base/attack/attack.py | 14 +- deckard/base/data/data.py | 23 +- deckard/base/data/sampler.py | 52 +- deckard/base/data/sklearn_pipeline.py | 2 +- deckard/layers/compile.py | 8 +- deckard/layers/find_best.py | 57 +- deckard/layers/utils.py | 2 +- examples/classification/.dvcignore | 3 + examples/classification/.gitignore | 2 + examples/classification/attacks.sh | 31 +- examples/classification/conf/attack.yaml | 8 +- .../classification/conf/attack/.gitignore | 3 + examples/classification/conf/attack/hsj.yaml | 2 +- examples/classification/conf/compile.yaml | 8 + .../conf/data/{medium.yaml => attack.yaml} | 7 +- .../classification/conf/data/kdd_nsl.yaml | 17 + examples/classification/conf/data/small.yaml | 2 +- .../data/{large.yaml => truthseeker.yaml} | 8 +- .../classification/conf/data/very_large.yaml | 20 - .../classification/conf/hydra/default.yaml | 7 - .../conf/hydra/launcher/default.yaml | 10 - .../conf/hydra/run/default.yaml | 1 - .../conf/hydra/sweeper/default.yaml | 8 - .../conf/hydra/sweeper/sampler/default.yaml | 7 - .../hydra/sweeper/sampler/params/default.yaml | 1 - examples/classification/conf/model.yaml | 4 +- examples/classification/conf/model/.gitignore | 3 + .../classification/conf/model/linear.yaml | 2 +- examples/classification/conf/model/poly.yaml | 2 +- examples/classification/conf/model/rbf.yaml | 2 +- examples/classification/dvc.lock | 629 ++++++++++++++++++ examples/classification/dvc.yaml | 194 +++--- examples/classification/models.sh | 63 +- examples/kdd-nsl/.dvc/.gitignore | 3 + examples/kdd-nsl/.dvc/config | 0 examples/kdd-nsl/.dvcignore | 3 + 37 files changed, 943 insertions(+), 268 deletions(-) create mode 100644 examples/classification/.dvcignore create mode 100644 examples/classification/.gitignore create mode 100644 examples/classification/conf/compile.yaml rename examples/classification/conf/data/{medium.yaml => attack.yaml} (83%) create mode 100644 examples/classification/conf/data/kdd_nsl.yaml rename examples/classification/conf/data/{large.yaml => truthseeker.yaml} (69%) delete mode 100644 examples/classification/conf/data/very_large.yaml delete mode 100644 examples/classification/conf/hydra/default.yaml delete mode 100644 examples/classification/conf/hydra/launcher/default.yaml delete mode 100644 examples/classification/conf/hydra/run/default.yaml delete mode 100644 examples/classification/conf/hydra/sweeper/default.yaml delete mode 100644 examples/classification/conf/hydra/sweeper/sampler/default.yaml delete mode 100644 examples/classification/conf/hydra/sweeper/sampler/params/default.yaml create mode 100644 examples/classification/dvc.lock create mode 100644 examples/kdd-nsl/.dvc/.gitignore create mode 100644 examples/kdd-nsl/.dvc/config create mode 100644 examples/kdd-nsl/.dvcignore diff --git a/.gitignore b/.gitignore index f4837ea5..757526c7 100644 --- a/.gitignore +++ b/.gitignore @@ -129,3 +129,6 @@ examples/*/output/* **/*.egg-info/* **/*.egg-info deckard/deckard.egg-info/* + +*attack_log.txt +*model_log.txt diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index 52157d90..d6309433 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -58,8 +58,6 @@ def __call__(self, model=None, data=None, attack_size=-1): for thing in pop_list: kwargs.pop(thing, None) logger.info(f"Initializing attack {name} with parameters {kwargs}") - self.data = data - self.model = model if "x_train" in kwargs: assert ( data is not None @@ -99,6 +97,12 @@ def __call__(self, model=None, data=None, attack_size=-1): attack = instantiate(config, model) else: raise e + except Exception as e: + if "has not been fitted correctly" in str(e): + model, _ = self.model.fit(data=data, model=model) + attack = instantiate(config, model) + else: + raise e return attack @@ -150,8 +154,8 @@ def __call__( kwargs.update({"y": data[2][: self.attack_size]}) if "AdversarialPatch" in self.name: start = process_time_ns() - patches, masks = atk.generate(ben_samples, **kwargs) - samples = atk.apply_patch(ben_samples, scale=scale_max) + patches, _ = atk.generate(ben_samples, **kwargs) + samples = atk.apply_patch(ben_samples, scale=scale_max, patch_external=patches) else: start = process_time_ns() samples = atk.generate(ben_samples, **kwargs) @@ -167,7 +171,7 @@ def __call__( x_clean=ben_samples, labels=data[3][: self.attack_size], x_adv=samples, - targeted=False, + targeted=self.kwargs.pop("targeted", False), ) except TypeError as e: logger.error(f"Failed to compute success rate. Error: {e}") diff --git a/deckard/base/data/data.py b/deckard/base/data/data.py index e22c69a2..9514a63a 100644 --- a/deckard/base/data/data.py +++ b/deckard/base/data/data.py @@ -6,7 +6,7 @@ from typing import Union import numpy as np -from pandas import DataFrame, read_csv, read_excel +from pandas import DataFrame, read_csv, read_excel, Series from ..utils import my_hash from .generator import DataGenerator @@ -97,14 +97,22 @@ def initialize(self): assert len(result) == 4, f"Data is not generated: {self.name}" else: result = self.load(self.name) - if len(result) == 1: + if isinstance(result, list) and len(result) == 2: + result = self.sample(*result) + else: assert self.target is not None, "Target is not specified" + if not isinstance(result, DataFrame): + result = DataFrame(result) + assert self.target in result, f"Target {self.target} not in data with columns {result.columns}" y = result[self.target] - X = result.drop(self.target, axis=1) + if isinstance(result, DataFrame): + X = result.drop(self.target, axis=1) + else: + X = result[~self.target] result = self.sample(X, y) - if len(result) == 2: - result = self.sample(*result) + assert len(result) == 4 + result = self.sklearn_pipeline(*result) return result def load(self, filename) -> DataFrame: @@ -118,8 +126,7 @@ def load(self, filename) -> DataFrame: with open(filename, "r") as f: data = json.load(f) elif suffix in [".csv"]: - data = read_csv(filename) - data = data.to_numpy() + data = read_csv(filename, delimiter=",", header=0) elif suffix in [".pkl", ".pickle"]: with open(filename, "rb") as f: data = pickle.load(f) @@ -141,6 +148,8 @@ def save(self, data, filename): if suffix in [".json"]: if isinstance(data, DataFrame): data = data.to_dict(orient="records") + elif isinstance(data, Series): + data = data.to_dict() elif isinstance(data, np.ndarray): data = data.tolist() with open(filename, "w") as f: diff --git a/deckard/base/data/sampler.py b/deckard/base/data/sampler.py index f7711788..de1c33d0 100644 --- a/deckard/base/data/sampler.py +++ b/deckard/base/data/sampler.py @@ -1,6 +1,7 @@ import logging from dataclasses import dataclass, asdict from copy import deepcopy +from typing import Union from sklearn.model_selection import train_test_split from ..utils import my_hash @@ -10,8 +11,8 @@ @dataclass class SklearnDataSampler: - test_size: float = 0.2 - train_size: float = 0.8 + test_size: Union[float, int] = 0.2 + train_size: Union[float, int] = 0.8 random_state: int = 0 shuffle: bool = True stratify: bool = False @@ -48,41 +49,15 @@ def __call__(self, X, y): train_size = params.pop("train_size") time_series = params.pop("time_series") if time_series is not True: - if train_size + test_size == 1: - X_train, X_test, y_train, y_test = train_test_split( - X, - y, - test_size=test_size, - train_size=train_size, - stratify=stratify, - **params, - ) - elif train_size + test_size > 1: - if test_size is None: - test_size = 0.2 * len(X) - - if isinstance(test_size, int): - test_size = test_size / (train_size + test_size) - logger.info( - f"Splitting data with train_size={train_size} and test_size={test_size}", - ) - X_train, X_test, y_train, y_test = train_test_split( - X, - y, - train_size=train_size, - stratify=stratify, - **params, - test_size=test_size, - ) - else: - X_train, X_test, y_train, y_test = train_test_split( - X, - y, - train_size=train_size, - stratify=stratify, - **params, - test_size=test_size, - ) + # if train_size + test_size == 1: + X_train, X_test, y_train, y_test = train_test_split( + X, + y, + test_size=test_size, + train_size=train_size, + stratify=stratify, + **params, + ) else: if isinstance(train_size, float): train_size = int(train_size * len(X)) @@ -90,6 +65,9 @@ def __call__(self, X, y): test_size = len(X) - train_size elif isinstance(test_size, float): test_size = int(test_size * len(X)) + if isinstance(train_size, type(None)): + assert test_size is not None + train_size = len(X) - test_size X_train = X[:train_size] X_test = X[train_size : train_size + test_size] # noqa E203 y_train = y[:train_size] diff --git a/deckard/base/data/sklearn_pipeline.py b/deckard/base/data/sklearn_pipeline.py index 3b7b5594..bcb55f31 100644 --- a/deckard/base/data/sklearn_pipeline.py +++ b/deckard/base/data/sklearn_pipeline.py @@ -92,4 +92,4 @@ def __call__(self, X_train, X_test, y_train, y_test): else: X_train = transformer.fit(X_train).transform(X_train) X_test = transformer.transform(X_test) - return X_train, X_test, y_train, y_test + return [X_train, X_test, y_train, y_test] diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index e5c20645..61b63ccd 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -64,7 +64,8 @@ def parse_folder(folder, files=["params.yaml", "score_dict.json"]) -> pd.DataFra if file.parent.name not in results: results[file.parent.name] = {} results[file.parent.name][file.stem] = file - return results + df = pd.DataFrame(results).T + return df def merge_defences(results: pd.DataFrame): @@ -129,8 +130,7 @@ def merge_attacks(results: pd.DataFrame): def parse_results(folder, files=["score_dict.json", "params.yaml"]): - dict_ = parse_folder(folder, files=files) - df = pd.DataFrame(dict_).T + df = parse_folder(folder, files=files) df = flatten_results(df) df = merge_defences(df) df = merge_attacks(df) @@ -139,6 +139,7 @@ def parse_results(folder, files=["score_dict.json", "params.yaml"]): def format_control_parameter(data, control_dict): logger.info("Formatting control parameters...") + if hasattr(data, "def_gen"): defences = data.def_gen.unique() else: @@ -147,6 +148,7 @@ def format_control_parameter(data, control_dict): attacks = data.atk_gen.unique() else: attacks = [] + for defence in defences: if defence in control_dict: param = control_dict[defence] diff --git a/deckard/layers/find_best.py b/deckard/layers/find_best.py index d58a7083..a3ed1406 100644 --- a/deckard/layers/find_best.py +++ b/deckard/layers/find_best.py @@ -17,7 +17,8 @@ def find_optuna_best( study_csv=None, params_file=None, config_folder=Path(Path(), "conf"), - config_name="default.yaml", + default_config="default.yaml", + config_subdir=None, direction=None, ): study = optuna.create_study( @@ -30,16 +31,7 @@ def find_optuna_best( if study_csv is not None: df.to_csv(study_csv) params = flatten_dict(study.best_params) - params = unflatten_dict(params) - best_params = {} - if study_name in params: - best_params[study_name] = params[study_name] - elif f"+{study_name}" in params: - best_params[study_name] = params[f"+{study_name}"] - elif f"++{study_name}" in params: - best_params[study_name] = params[f"++{study_name}"] - else: - raise ValueError(f"Study name {study_name} not found in best params.") + best_params = unflatten_dict(params) best_params = flatten_dict(best_params) overrides = [] for key, value in best_params.items(): @@ -47,22 +39,27 @@ def find_optuna_best( if not key.startswith("+"): overrides.append(f"++{key}={value}") with initialize_config_dir(config_dir=config_folder, version_base="1.3"): - cfg = compose(config_name=config_name, overrides=overrides) - cfg = OmegaConf.to_container(cfg, resolve=True) - flattened = flatten_dict(cfg) + cfg = compose(config_name=default_config, overrides=overrides) + cfg = OmegaConf.to_container(cfg, resolve=False) if params_file is not None: if params_file is True: - if study_name in cfg: - params = cfg.get(study_name, cfg) - params_file = Path(config_folder, study_name, "best.yaml") - elif study_name in flattened: - params = flattened.get(study_name, flattened) - params_file = Path(config_folder, f"best_{study_name}.yaml") + if config_subdir is not None: + params_file = Path(config_folder, f"{config_subdir}", f"{default_config}.yaml") + params = cfg.get(config_subdir) + else: + params_file = Path(config_folder, f"{default_config}.yaml") + params = cfg + else: + if config_subdir is not None: + params_file = Path(config_folder, f"{config_subdir}", f"{params_file}.yaml") + params = cfg.get(config_subdir) else: - params_file = Path(config_folder, f"best_{study_name}.yaml") - logger.info(f"Saving best params to {params_file}") - with open(params_file, "w") as f: + params = cfg + params_file = Path(config_folder, f"{params_file}.yaml") + params_file.parent.mkdir(parents=True, exist_ok=True) + with open(params_file.with_suffix(".yaml"), "w") as f: yaml.dump(params, f) + assert params_file.exists(), f"{params_file.resolve().as_posix()} does not exist." return params @@ -74,28 +71,32 @@ def find_optuna_best( parser.add_argument("--study_type", type=str, default="optuna") parser.add_argument("--study_csv", type=str, default=None) parser.add_argument("--config_folder", type=str, default=Path(Path(), "conf")) - parser.add_argument("config_name", type=str) + parser.add_argument("--default_config", type=str, default="default") + parser.add_argument("--config_subdir", type=str, default=None) + parser.add_argument("--study_name", type=str, required=True) + parser.add_argument("--config_name", type=str) parser.add_argument("--verbosity", type=str, default="INFO") args = parser.parse_args() args.config_folder = Path(args.config_folder).resolve().as_posix() if args.study_type == "optuna": - with open(Path(args.config_folder, args.config_name), "r") as f: + with open(Path(args.config_folder, args.default_config).with_suffix(".yaml"), "r") as f: default_params = yaml.load(f, Loader=yaml.FullLoader) if "hydra" in default_params: hydra_params = default_params.pop("hydra") - study_name = hydra_params["sweeper"]["study_name"] + study_name = args.study_name storage_name = hydra_params["sweeper"]["storage"] - direction = hydra_params["sweeper"]["direction"] + direction = default_params.get("direction", "maximize") find_optuna_best( study_name=study_name, storage_name=storage_name, study_csv=args.study_csv, params_file=args.params_file, config_folder=args.config_folder, - config_name=args.config_name, + config_subdir=args.config_subdir, + default_config=args.default_config, direction=direction, ) else: diff --git a/deckard/layers/utils.py b/deckard/layers/utils.py index 87057484..fc208fdc 100644 --- a/deckard/layers/utils.py +++ b/deckard/layers/utils.py @@ -127,7 +127,7 @@ def save_params_file( config_dir = str(Path(Path(), config_dir).absolute().as_posix()) with initialize_config_dir(config_dir=config_dir, version_base="1.3"): cfg = compose(config_name=config_file, overrides=overrides) - params = OmegaConf.to_container(cfg, resolve=True) + params = OmegaConf.to_container(cfg, resolve=False) with open(params_file, "w") as f: yaml.dump(params, f) logger.info(f"Saved params file to {params_file}") diff --git a/examples/classification/.dvcignore b/examples/classification/.dvcignore new file mode 100644 index 00000000..51973055 --- /dev/null +++ b/examples/classification/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/examples/classification/.gitignore b/examples/classification/.gitignore new file mode 100644 index 00000000..5adf8b0c --- /dev/null +++ b/examples/classification/.gitignore @@ -0,0 +1,2 @@ +/kdd_nsl +/retrain diff --git a/examples/classification/attacks.sh b/examples/classification/attacks.sh index 169de6ee..ca773483 100644 --- a/examples/classification/attacks.sh +++ b/examples/classification/attacks.sh @@ -1,7 +1,32 @@ - -# break +#!/bin/bash + +MODEL_CONFIGS=$(ls conf/model/best_*.yaml) +CONFIG_NAMES=$(ls conf/model/best_*.yaml | cut -d'/' -f3 | cut -d'.' -f1) +TOTAL=$(( ${#CONFIG_NAMES[@]} )) +i=$(( 0 )) +mkdir -p logs/attacks/ +for model_config in $CONFIG_NAMES; do + i=$(( i + 1 )) + if [ $model_config == "default" ]; then + continue + fi + echo "Running model $model_config. Number $i of $TOTAL" >> attack_log.txt + HYDRA_FULL_ERROR=1 python -m deckard.layers.optimise \ + ++stage=attack \ + ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent \ + ++attack.init.norm=1,2,inf \ + ++attack.init.eps_step=.001,.01,.1,.3,.5,1 \ + ++attack.init.batch_size=1,10,50,100 \ + ++attack.init.eps=.001,.01,.1,.3,.5,1 \ + ++attack.init.max_iter=1,10,100,1000 \ + ++hydra.sweeper.study_name=$model_config \ + model=$model_config $@ --multirun >> logs/attacks/$model_config.log + echo "Successfully completed model $model_config. Number $i of $TOTAL" >> attack_log.txt +done + +# Other attacks listed below # PGD -bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.norm=1,2,inf ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=100 ++attack.init.eps=.001,.01,.1,.3,.5,1 $@ +# bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.norm=1,2,inf ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=1,10,50,100 ++attack.init.eps=.001,.01,.1,.3,.5,1 $@ # # Carlini L0 # bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ diff --git a/examples/classification/conf/attack.yaml b/examples/classification/conf/attack.yaml index 7a029490..c578880d 100644 --- a/examples/classification/conf/attack.yaml +++ b/examples/classification/conf/attack.yaml @@ -1,7 +1,7 @@ defaults: # - _target_ : deckard.base.experiment.Experiment - _self_ - - data: default + - data: attack - model: default - attack : hsj - files: default @@ -9,6 +9,8 @@ defaults: - override hydra/sweeper : optuna - override hydra/sweeper/sampler : grid - override hydra/launcher : joblib +optimizers: adv_accuracy +direction : minimize hydra: run: dir : "./${files.directory}" @@ -16,13 +18,11 @@ hydra: sampler: _target_: optuna.samplers.GridSampler _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - direction: minimize + direction: ${direction} study_name: attack storage: sqlite:///attack.db - n_trials: 1 n_jobs: 1 params: - # model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher n_jobs: 32 diff --git a/examples/classification/conf/attack/.gitignore b/examples/classification/conf/attack/.gitignore index 41ee9b5a..29ec330a 100644 --- a/examples/classification/conf/attack/.gitignore +++ b/examples/classification/conf/attack/.gitignore @@ -1 +1,4 @@ /best.yaml +/best_linear.yaml +/best_rbf.yaml +/best_poly.yaml diff --git a/examples/classification/conf/attack/hsj.yaml b/examples/classification/conf/attack/hsj.yaml index cb2399a3..8dbce941 100644 --- a/examples/classification/conf/attack/hsj.yaml +++ b/examples/classification/conf/attack/hsj.yaml @@ -2,7 +2,7 @@ data: ${data} model: ${model} _target_ : deckard.base.attack.Attack init: - name: art.attacks.evasion.HopSkipJump + name: art.attacks.evasion.ProjectedGradientDescent model: ${model} attack_size : 10 method : evasion diff --git a/examples/classification/conf/compile.yaml b/examples/classification/conf/compile.yaml new file mode 100644 index 00000000..109994af --- /dev/null +++ b/examples/classification/conf/compile.yaml @@ -0,0 +1,8 @@ +attacks: + ProjectedGradientDescent: PGD + null : null +defences: + Control: Control +params: + Control: model.init.kwargs.kernel + PGD: attack.init.kwargs.eps \ No newline at end of file diff --git a/examples/classification/conf/data/medium.yaml b/examples/classification/conf/data/attack.yaml similarity index 83% rename from examples/classification/conf/data/medium.yaml rename to examples/classification/conf/data/attack.yaml index 079b9aea..3d77ffb4 100644 --- a/examples/classification/conf/data/medium.yaml +++ b/examples/classification/conf/data/attack.yaml @@ -3,8 +3,11 @@ generate: # _target_: deckard.base.data.generator.DataGenerator name: classification random_state : 0 - n_samples : 11000 - n_features : 20 + n_samples : 11100 + n_features : 100 + n_informative : 99 + n_redundant : 0 + n_repeated : 0 sample: # _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 diff --git a/examples/classification/conf/data/kdd_nsl.yaml b/examples/classification/conf/data/kdd_nsl.yaml new file mode 100644 index 00000000..ceae4f30 --- /dev/null +++ b/examples/classification/conf/data/kdd_nsl.yaml @@ -0,0 +1,17 @@ +_target_: deckard.base.data.Data +name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv +target : label +sample: + random_state : 0 + stratify: True + train_size : null + test_size : 1000 +sklearn_pipeline: + encoder: + name : sklearn.preprocessing.OneHotEncoder + handle_unknown: ignore + sparse : false + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/classification/conf/data/small.yaml b/examples/classification/conf/data/small.yaml index 2207d717..ff01d089 100644 --- a/examples/classification/conf/data/small.yaml +++ b/examples/classification/conf/data/small.yaml @@ -17,4 +17,4 @@ sklearn_pipeline: # name: sklearn.preprocessing.StandardScaler with_mean: True - with_std: True + with_std: True \ No newline at end of file diff --git a/examples/classification/conf/data/large.yaml b/examples/classification/conf/data/truthseeker.yaml similarity index 69% rename from examples/classification/conf/data/large.yaml rename to examples/classification/conf/data/truthseeker.yaml index ea6c3120..9e0acbe3 100644 --- a/examples/classification/conf/data/large.yaml +++ b/examples/classification/conf/data/truthseeker.yaml @@ -1,15 +1,13 @@ _target_: deckard.base.data.Data generate: # _target_: deckard.base.data.generator.DataGenerator - name: classification - random_state : 0 - n_samples : 101000 - n_features : 20 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/379b679bdea30724e9fa188931f0109ff422cce0/kdd_nsl.csv + target : -2 sample: # _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 stratify: True - train_size : 100000 + train_size : 10000 test_size : 1000 sklearn_pipeline: # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline diff --git a/examples/classification/conf/data/very_large.yaml b/examples/classification/conf/data/very_large.yaml deleted file mode 100644 index ff552d03..00000000 --- a/examples/classification/conf/data/very_large.yaml +++ /dev/null @@ -1,20 +0,0 @@ -_target_: deckard.base.data.Data -generate: - # _target_: deckard.base.data.generator.DataGenerator - name: classification - random_state : 0 - n_samples : 1001000 - n_features : 20 -sample: - # _target_: deckard.base.data.sampler.SklearnDataSampler - random_state : 0 - stratify: True - train_size : 100000 - test_size : 1000 -sklearn_pipeline: - # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - # - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/examples/classification/conf/hydra/default.yaml b/examples/classification/conf/hydra/default.yaml deleted file mode 100644 index 8dceecaf..00000000 --- a/examples/classification/conf/hydra/default.yaml +++ /dev/null @@ -1,7 +0,0 @@ -defaults: -- run : default -- launcher : default -- sweeper : default -- override sweeper : optuna -- override sweeper/sampler : grid -- override launcher : joblib diff --git a/examples/classification/conf/hydra/launcher/default.yaml b/examples/classification/conf/hydra/launcher/default.yaml deleted file mode 100644 index 8b8d3f41..00000000 --- a/examples/classification/conf/hydra/launcher/default.yaml +++ /dev/null @@ -1,10 +0,0 @@ -_target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher -n_jobs: 6 -prefer : processes -verbose: 1 -timeout: null -pre_dispatch: n_jobs -batch_size: auto -temp_folder: /tmp/deckard -max_nbytes: 100000 -mmap_mode: r diff --git a/examples/classification/conf/hydra/run/default.yaml b/examples/classification/conf/hydra/run/default.yaml deleted file mode 100644 index ec38cd67..00000000 --- a/examples/classification/conf/hydra/run/default.yaml +++ /dev/null @@ -1 +0,0 @@ -dir : "./${files.directory}" diff --git a/examples/classification/conf/hydra/sweeper/default.yaml b/examples/classification/conf/hydra/sweeper/default.yaml deleted file mode 100644 index 402132d0..00000000 --- a/examples/classification/conf/hydra/sweeper/default.yaml +++ /dev/null @@ -1,8 +0,0 @@ -defaults: -- sampler: default -_target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper -direction: maximize -study_name: model -storage: sqlite:///model.db -n_trials: 100 -n_jobs: 1 diff --git a/examples/classification/conf/hydra/sweeper/sampler/default.yaml b/examples/classification/conf/hydra/sweeper/sampler/default.yaml deleted file mode 100644 index 5401b736..00000000 --- a/examples/classification/conf/hydra/sweeper/sampler/default.yaml +++ /dev/null @@ -1,7 +0,0 @@ -defaults: -- params : default -_target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper -direction: minimize -study_name: attack -storage: sqlite:///attack.db -n_jobs: 1 diff --git a/examples/classification/conf/hydra/sweeper/sampler/params/default.yaml b/examples/classification/conf/hydra/sweeper/sampler/params/default.yaml deleted file mode 100644 index 33f9dfa6..00000000 --- a/examples/classification/conf/hydra/sweeper/sampler/params/default.yaml +++ /dev/null @@ -1 +0,0 @@ -model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) diff --git a/examples/classification/conf/model.yaml b/examples/classification/conf/model.yaml index d50b2407..18ea5bc5 100644 --- a/examples/classification/conf/model.yaml +++ b/examples/classification/conf/model.yaml @@ -8,6 +8,8 @@ defaults: - override hydra/sweeper : optuna - override hydra/sweeper/sampler : grid - override hydra/launcher : joblib +optimizers : accuracy +direction : maximize hydra: run: dir : "./${files.directory}" @@ -15,7 +17,7 @@ hydra: sampler: _target_: optuna.samplers.GridSampler _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - direction: maximize + direction: ${direction} study_name: model storage: sqlite:///model.db n_jobs: 1 diff --git a/examples/classification/conf/model/.gitignore b/examples/classification/conf/model/.gitignore index 41ee9b5a..9f645db6 100644 --- a/examples/classification/conf/model/.gitignore +++ b/examples/classification/conf/model/.gitignore @@ -1 +1,4 @@ /best.yaml +/best_rbf.yaml +/best_linear.yaml +/best_poly.yaml diff --git a/examples/classification/conf/model/linear.yaml b/examples/classification/conf/model/linear.yaml index b900f88a..337909e1 100644 --- a/examples/classification/conf/model/linear.yaml +++ b/examples/classification/conf/model/linear.yaml @@ -7,7 +7,7 @@ init: kernel : linear probability : true random_state : 0 - max_iter : 100 + max_iter : 10 _target_: deckard.base.model.Model art: _target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/classification/conf/model/poly.yaml b/examples/classification/conf/model/poly.yaml index 7a5be797..8bdae408 100644 --- a/examples/classification/conf/model/poly.yaml +++ b/examples/classification/conf/model/poly.yaml @@ -8,7 +8,7 @@ init: kernel : poly probability : true random_state : 0 - max_iter : 100 + max_iter : 10 _target_: deckard.base.model.Model art: _target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/classification/conf/model/rbf.yaml b/examples/classification/conf/model/rbf.yaml index 42ca4c07..8b2d61af 100644 --- a/examples/classification/conf/model/rbf.yaml +++ b/examples/classification/conf/model/rbf.yaml @@ -7,7 +7,7 @@ init: kernel : rbf probability : true random_state : 0 - max_iter : 100 + max_iter : 10 _target_: deckard.base.model.Model art: _target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/classification/dvc.lock b/examples/classification/dvc.lock new file mode 100644 index 00000000..ea4d41ba --- /dev/null +++ b/examples/classification/dvc.lock @@ -0,0 +1,629 @@ +schema: '2.0' +stages: + train: + cmd: python -m deckard.layers.experiment train + params: + params.yaml: + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: ${data} + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/reports/train/default/params.yaml + hash: md5 + md5: d4e0a34b2b15765ca71fa5ecaf7e3826 + size: 2100 + - path: output/reports/train/default/predictions.json + hash: md5 + md5: 1bfeaf1fa0cb4e90604169e61dc95892 + size: 42667 + - path: output/reports/train/default/probabilities.json + hash: md5 + md5: 1bfeaf1fa0cb4e90604169e61dc95892 + size: 42667 + - path: output/reports/train/default/score_dict.json + hash: md5 + md5: b0708f21e769603fd7bcda7f0ce9b9dd + size: 355 + attack: + cmd: python -m deckard.layers.experiment attack + deps: + - path: output/reports/train/default/params.yaml + hash: md5 + md5: d4e0a34b2b15765ca71fa5ecaf7e3826 + size: 2100 + - path: output/reports/train/default/predictions.json + hash: md5 + md5: 1bfeaf1fa0cb4e90604169e61dc95892 + size: 42667 + - path: output/reports/train/default/probabilities.json + hash: md5 + md5: 1bfeaf1fa0cb4e90604169e61dc95892 + size: 42667 + - path: output/reports/train/default/score_dict.json + hash: md5 + md5: b0708f21e769603fd7bcda7f0ce9b9dd + size: 355 + params: + params.yaml: + attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: ${data} + init: + model: ${model} + name: art.attacks.evasion.ProjectedGradientDescent + method: evasion + model: ${model} + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: ${data} + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/attacks/attack.pkl + hash: md5 + md5: bbce7d8f6ca5653f7cb6a6dd5f974582 + size: 952 + - path: output/reports/attack/default/adv_predictions.json + hash: md5 + md5: 2ad26f915f08b13757e052dda7146c7d + size: 427 + - path: output/reports/attack/default/adv_probabilities.json + hash: md5 + md5: 2ad26f915f08b13757e052dda7146c7d + size: 427 + - path: output/reports/attack/default/params.yaml + hash: md5 + md5: 5be65828d59c309890bae9649e491dba + size: 5010 + - path: output/reports/attack/default/predictions.json + hash: md5 + md5: 1bfeaf1fa0cb4e90604169e61dc95892 + size: 42667 + - path: output/reports/attack/default/probabilities.json + hash: md5 + md5: 1bfeaf1fa0cb4e90604169e61dc95892 + size: 42667 + - path: output/reports/attack/default/score_dict.json + hash: md5 + md5: 53014285cc5719c85ae534242660b228 + size: 581 + models: + cmd: bash models.sh +stage=train --config-name=model.yaml + deps: + - path: conf/model.yaml + hash: md5 + md5: daaa0663d05972a5b8645c35d364da88 + size: 990 + - path: models.sh + hash: md5 + md5: 62ba40f661a78745a3f6a6b2748ab5ea + size: 2967 + - path: output/reports/train/default/params.yaml + hash: md5 + md5: d4e0a34b2b15765ca71fa5ecaf7e3826 + size: 2100 + - path: params.yaml + hash: md5 + md5: 96386b553b09cde769f5d7784db43b9a + size: 1804 + params: + params.yaml: + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: ${data} + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: logs/models/ + hash: md5 + md5: b5204e3b5172d9080caf38c719c86536.dir + size: 4719104 + nfiles: 51 + - path: model.db + hash: md5 + md5: 21aefda7997c5f31484f8d161ecb2138 + size: 352256 + compile_models: + cmd: python -m deckard.layers.compile --report_folder output/reports/train/ --results_file + output/train.csv + deps: + - path: logs/models/ + hash: md5 + md5: b5204e3b5172d9080caf38c719c86536.dir + size: 4719104 + nfiles: 51 + - path: model.db + hash: md5 + md5: 21aefda7997c5f31484f8d161ecb2138 + size: 352256 + - path: output/reports/train/ + hash: md5 + md5: 7664c228456825f67dc1343d680dc23b.dir + size: 4275212383 + nfiles: 7517 + outs: + - path: output/train.csv + hash: md5 + md5: a0261aa2216ef56ff024dab0da7e9ff3 + size: 2720482 + find_best_model@rbf: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_rbf --study_name=rbf_100_10000 --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: b5204e3b5172d9080caf38c719c86536.dir + size: 4719104 + nfiles: 51 + - path: model.db + hash: md5 + md5: 21aefda7997c5f31484f8d161ecb2138 + size: 352256 + - path: output/train.csv + hash: md5 + md5: a0261aa2216ef56ff024dab0da7e9ff3 + size: 2720482 + outs: + - path: conf/model/best_rbf.yaml + hash: md5 + md5: f213864712f88d12667e195e9bee8310 + size: 328 + find_best_model@linear: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_linear --study_name=linear_100_10000 --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: b5204e3b5172d9080caf38c719c86536.dir + size: 4719104 + nfiles: 51 + - path: model.db + hash: md5 + md5: 21aefda7997c5f31484f8d161ecb2138 + size: 352256 + - path: output/train.csv + hash: md5 + md5: a0261aa2216ef56ff024dab0da7e9ff3 + size: 2720482 + outs: + - path: conf/model/best_linear.yaml + hash: md5 + md5: 5f4c8becd800d6f6b9fe01c7c78ecac7 + size: 329 + find_best_model@poly: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_poly --study_name=poly_100_10000 --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: b5204e3b5172d9080caf38c719c86536.dir + size: 4719104 + nfiles: 51 + - path: model.db + hash: md5 + md5: 21aefda7997c5f31484f8d161ecb2138 + size: 352256 + - path: output/train.csv + hash: md5 + md5: a0261aa2216ef56ff024dab0da7e9ff3 + size: 2720482 + outs: + - path: conf/model/best_poly.yaml + hash: md5 + md5: 9ffbcdac2b9ca989ee6099a1c22946ba + size: 328 + attacks: + cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml + deps: + - path: conf/model/best_linear.yaml + hash: md5 + md5: 5f4c8becd800d6f6b9fe01c7c78ecac7 + size: 329 + - path: conf/model/best_poly.yaml + hash: md5 + md5: 9ffbcdac2b9ca989ee6099a1c22946ba + size: 328 + - path: conf/model/best_rbf.yaml + hash: md5 + md5: f213864712f88d12667e195e9bee8310 + size: 328 + - path: logs/models/ + hash: md5 + md5: b5204e3b5172d9080caf38c719c86536.dir + size: 4719104 + nfiles: 51 + - path: model.db + hash: md5 + md5: 21aefda7997c5f31484f8d161ecb2138 + size: 352256 + - path: output/train.csv + hash: md5 + md5: a0261aa2216ef56ff024dab0da7e9ff3 + size: 2720482 + outs: + - path: attack.db + hash: md5 + md5: 8c98b86efe4e9aaf8a420edf03e32803 + size: 466944 + - path: logs/attacks/ + hash: md5 + md5: 8d7590472039d0efbea4210491ee5440.dir + size: 3625817 + nfiles: 3 + compile_attacks: + cmd: python -m deckard.layers.compile --report_folder output/reports/attack/ --results_file + output/attack.csv + deps: + - path: attack.db + hash: md5 + md5: 8c98b86efe4e9aaf8a420edf03e32803 + size: 466944 + - path: logs/attacks/ + hash: md5 + md5: 8d7590472039d0efbea4210491ee5440.dir + size: 3625817 + nfiles: 3 + - path: output/reports/attack/ + hash: md5 + md5: 413dec4ee12c0dbd64343bd0bc221b4b.dir + size: 30272123 + nfiles: 2415 + outs: + - path: output/attack.csv + hash: md5 + md5: 51c3f2ca216fe98169c192956c3d448a + size: 745826 + find_best_attack@linear: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_linear --study_name=linear_100_10000 --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: b5204e3b5172d9080caf38c719c86536.dir + size: 4719104 + nfiles: 51 + - path: model.db + hash: md5 + md5: 21aefda7997c5f31484f8d161ecb2138 + size: 352256 + - path: output/train.csv + hash: md5 + md5: a0261aa2216ef56ff024dab0da7e9ff3 + size: 2720482 + outs: + - path: conf/attack/best_linear.yaml + hash: md5 + md5: 7ff95c46b1c2d0dc489fae3f1db40005 + size: 9 + find_best_attack@rbf: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_rbf --study_name=rbf_100_10000 --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: b5204e3b5172d9080caf38c719c86536.dir + size: 4719104 + nfiles: 51 + - path: model.db + hash: md5 + md5: 21aefda7997c5f31484f8d161ecb2138 + size: 352256 + - path: output/train.csv + hash: md5 + md5: a0261aa2216ef56ff024dab0da7e9ff3 + size: 2720482 + outs: + - path: conf/attack/best_rbf.yaml + hash: md5 + md5: 7ff95c46b1c2d0dc489fae3f1db40005 + size: 9 + find_best_attack@poly: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_poly --study_name=poly_100_10000 --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: b5204e3b5172d9080caf38c719c86536.dir + size: 4719104 + nfiles: 51 + - path: model.db + hash: md5 + md5: 21aefda7997c5f31484f8d161ecb2138 + size: 352256 + - path: output/train.csv + hash: md5 + md5: a0261aa2216ef56ff024dab0da7e9ff3 + size: 2720482 + outs: + - path: conf/attack/best_poly.yaml + hash: md5 + md5: 7ff95c46b1c2d0dc489fae3f1db40005 + size: 9 + other_data_train@kdd_nsl: + cmd: DATASET_NAME=kdd_nsl bash other_data.sh data=kdd_nsl +stage=train --config-name=model.yaml + deps: + - path: conf/model.yaml + hash: md5 + md5: daaa0663d05972a5b8645c35d364da88 + size: 990 + - path: other_data.sh + hash: md5 + md5: 6ebecf100cc02847ad31901bebb2ee5a + size: 2759 + - path: output/reports/train/default/params.yaml + hash: md5 + md5: d4e0a34b2b15765ca71fa5ecaf7e3826 + size: 2100 + outs: + - path: kdd_nsl.db + hash: md5 + md5: 06933f8fc0a1feca0944c131b6a3854b + size: 348160 + - path: kdd_nsl/ + hash: md5 + md5: 9076c4e55fd1058e7446588d99930d58.dir + size: 39137423 + nfiles: 1072 + - path: logs/kdd_nsl/ + hash: md5 + md5: e7c227947468122b62f891c0d54e0c54.dir + size: 1314288 + nfiles: 12 + retrain: + cmd: python retrain.py + deps: + - path: conf/attack/best_linear.yaml + hash: md5 + md5: 7ff95c46b1c2d0dc489fae3f1db40005 + size: 9 + - path: conf/attack/best_poly.yaml + hash: md5 + md5: 7ff95c46b1c2d0dc489fae3f1db40005 + size: 9 + - path: conf/attack/best_rbf.yaml + hash: md5 + md5: 7ff95c46b1c2d0dc489fae3f1db40005 + size: 9 + - path: conf/model/best_linear.yaml + hash: md5 + md5: cd69a0189843e6474613ea33e96bb29b + size: 327 + - path: conf/model/best_poly.yaml + hash: md5 + md5: cd69a0189843e6474613ea33e96bb29b + size: 327 + - path: conf/model/best_rbf.yaml + hash: md5 + md5: cd69a0189843e6474613ea33e96bb29b + size: 327 + - path: output/attacks/ + hash: md5 + md5: 00cf0085d338326da6d1738058368c49.dir + size: 1159530 + nfiles: 240 + - path: output/models/ + hash: md5 + md5: 484d472b0b10beae09f796a1a0ceac57.dir + size: 2880142 + nfiles: 169 + outs: + - path: plots/after_retrain_confidence.csv + hash: md5 + md5: 4b2ab48e7315673d763502691ec4f7b2 + size: 539162 + - path: plots/before_retrain_confidence.csv + hash: md5 + md5: 044f0f9851895662dfb01de5254c6b00 + size: 527699 + - path: retrain/ + hash: md5 + md5: 10e29a3b73fa5ac496881378f5b66463.dir + size: 193686 + nfiles: 12 + plots: + cmd: python plots.py + deps: + - path: plots/after_retrain_confidence.csv + hash: md5 + md5: 9d3b1fdeb33b7479703f4c47bb5a8687 + size: 365818 + - path: plots/before_retrain_confidence.csv + hash: md5 + md5: 9d3b1fdeb33b7479703f4c47bb5a8687 + size: 365818 + outs: + - path: plots/accuracy_vs_attack_parameters.eps + hash: md5 + md5: 38680ac0fd6d0983b46eec3d5adcae03 + size: 34747 + - path: plots/accuracy_vs_features.eps + hash: md5 + md5: e641a4efda42a2d647ffe33d3a397ec2 + size: 24914 + - path: plots/accuracy_vs_samples.eps + hash: md5 + md5: 7a967461c9b8159b7b5bdbb69f15f350 + size: 21284 diff --git a/examples/classification/dvc.yaml b/examples/classification/dvc.yaml index 8c3d0f3c..58307f81 100644 --- a/examples/classification/dvc.yaml +++ b/examples/classification/dvc.yaml @@ -1,105 +1,135 @@ stages: - attack: - cmd: python -m deckard.layers.experiment --stage attack + attack: # Prototype stage + cmd: python -m deckard.layers.experiment attack deps: - - ${files.data_file} - - ${files.reports}/train/${files.path}/${files.params_file} - - ${files.reports}/train/${files.path}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} #optionally, store the data + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} metrics: - - ${files.reports}/attack/${files.path}/${files.score_dict_file} - - ${files.reports}/attack/${files.path}/${files.time_dict_file} - - ${files.reports}/attack/${files.path}/${files.attack_score_dict_file} - - ${files.reports}/attack/${files.path}/${files.attack_time_dict_file} - - ${files.reports}/attack/${files.path}/${files.attack_predictions_file} - - ${files.reports}/attack/${files.path}/${files.attack_probabilities_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} outs: - - ${files.reports}/attack/${files.path}/${files.params_file} - - ${files.reports}/attack/${files.path}/${files.attack_samples_file} - - ${files.reports}/attack/${files.path}/${files.predictions_file} - - ${files.reports}/attack/${files.path}/${files.probabilities_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.probabilities_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} params: - data - - model.init + - model - files - scorers - attack - generate_data: - cmd: python -m deckard.layers.experiment --stage generate_data - deps: - - params.yaml - metrics: - - ${files.reports}/generate_data/${files.path}/${files.ground_truth_file} - outs: - - ${files.data_file} - params: - - data - - files - train: - cmd: python -m deckard.layers.experiment --stage train - deps: - - ${files.data_file} + train: # Prototype stage + cmd: python -m deckard.layers.experiment train metrics: - - ${files.reports}/train/${files.path}/${files.score_dict_file} - - ${files.reports}/train/${files.path}/${files.time_dict_file} - - ${files.reports}/train/${files.path}/${files.predictions_file} - - ${files.reports}/train/${files.path}/${files.probabilities_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} outs: - - ${files.reports}/train/${files.path}/${files.params_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} params: - data - - model.init + - model - files - scorers - models: - cmd: bash experiments.sh + models: # Grid search stage + cmd: bash models.sh +stage=train --config-name=model.yaml # specifies the prototype stage, file to be used outs: - - logs/models/ - - ${files.reports}/models/ - params: + - model.db: # This allows to run this section piecewise, adding features or samples inside models.sh which are then stored in the .db + cache: true + persist: true + - logs/models/: # Each feature/sample/train size combination log is stored in a separate file + cache: true + persist: true + # - ${files.directory}/${files.reports}/train/: # Each feature/sample/train size combination experiment is stored in a separate folder + # cache: true + # persist: true + params: # These are the parameters that are passed to the prototype stage - data - - model.init + - model - files - scorers deps: - - ${files.reports}/train/ - compile_results: + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + - params.yaml + - conf/model.yaml + - models.sh + # - conf/model/ + compile_models: cmd: - python find_best.py --output_folder best_models --kwargs "data.sample.train_size=1000" "data.generate.n_features=100" --report_folder "reports/model_queue" --results_file "plots/model_results.csv" --scorer_minimize False --control_for "model.init.kernel" --exclude sigmoid --stage models + python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/train/ --results_file ${files.directory}/train.csv outs: - - best_models/ - metrics: - - plots/model_results.csv + - ${files.directory}/train.csv deps: - - ${files.reports}/models/ + - ${files.directory}/${files.reports}/train/ # Each feature/sample/train size combination experiment is stored in a separate folder + - model.db + - logs/models/ + find_best_model: + foreach: + - linear + - rbf + - poly + do: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model --params_file best_${item} --study_name=${item}_100_10000 --default_config model.yaml + outs: + - conf/model/best_${item}.yaml + deps: + - ${files.directory}/train.csv + - model.db + - logs/models/ attacks: - cmd: bash attacks.sh + cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml outs: - - logs/attacks/ - - ${files.reports}/attacks/ + - logs/attacks/: + cache: true + persist: true + - attack.db: + cache: true + persist: true deps: - - best_models/ - params: - - data - - model.init - - files - - attack - - scorers + - ${files.directory}/train.csv + - model.db + - logs/models/ + - conf/model/best_linear.yaml + - conf/model/best_rbf.yaml + - conf/model/best_poly.yaml compile_attacks: cmd: - python find_best.py --output_folder best_attacks --kwargs "attack.init.max_iter=10" --report_folder "reports/attack" --results_file "plots/attack_results.csv" --scorer_minimize True --exclude sigmoid --stage attacks - metrics: - - plots/attack_results.csv - deps: - - ${files.reports}/attacks/ + python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/attack/ --results_file ${files.directory}/attack.csv outs: - - best_attacks/ + - ${files.directory}/attack.csv + deps: + - ${files.directory}/${files.reports}/attack/ # Each feature/sample/train size combination experiment is stored in a separate folder + - attack.db + - logs/attacks/ + find_best_attack: + foreach: + - linear + - rbf + - poly + do: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack --params_file best_${item} --study_name=${item}_100_10000 --default_config model.yaml + outs: + - conf/attack/best_${item}.yaml + deps: + - ${files.directory}/train.csv + - model.db + - logs/models/ retrain: cmd : python retrain.py deps: - - ${files.reports}/models/ - - ${files.reports}/attacks/ - - best_models/ - - best_attacks/ + - ${files.directory}/models/ + - ${files.directory}/attacks/ + - conf/attack/best_linear.yaml + - conf/attack/best_rbf.yaml + - conf/attack/best_poly.yaml + - conf/model/best_linear.yaml + - conf/model/best_rbf.yaml + - conf/model/best_poly.yaml outs: - retrain/ metrics: @@ -109,17 +139,17 @@ stages: cmd : python plots.py deps : - plots/after_retrain_confidence.csv - - plots/attack_results.csv + # - plots/attack_results.csv - plots/before_retrain_confidence.csv - - plots/model_results.csv + # - plots/model_results.csv plots : - - plots/accuracy_vs_attack_parameters.pdf - - plots/accuracy_vs_features.pdf - - plots/accuracy_vs_samples.pdf - - plots/confidence_vs_attack_parameters.pdf - - plots/fit_time_vs_attack_parameters.pdf - - plots/fit_time_vs_features.pdf - - plots/fit_time_vs_samples.pdf - - plots/retrain_accuracy.pdf - - plots/retrain_confidence_vs_attack_parameters.pdf - - plots/retrain_time.pdf + - plots/accuracy_vs_attack_parameters.eps + - plots/accuracy_vs_features.eps + - plots/accuracy_vs_samples.eps + # - plots/confidence_vs_attack_parameters.eps + # - plots/fit_time_vs_attack_parameters.eps + # - plots/fit_time_vs_features.eps + # - plots/fit_time_vs_samples.eps + # - plots/retrain_accuracy.eps + # - plots/retrain_confidence_vs_attack_parameters.eps + # - plots/retrain_time.eps diff --git a/examples/classification/models.sh b/examples/classification/models.sh index f3b3ff33..594bb31d 100644 --- a/examples/classification/models.sh +++ b/examples/classification/models.sh @@ -1,8 +1,8 @@ -N_FEATURES=( 10 100 1000 10000) -TRAIN_SIZES=( 100 1000 10000 100000 1000000 ) -# TRAIN_SIZES=( 2000 ) -# N_FEATURES=( 100 ) -N_SAMPLES=( 110000 ) +# N_FEATURES=( 10 100 1000 10000 10000 100000 1000000) +# TRAIN_SIZES=( 10 100 1000 ) +TRAIN_SIZES=( 10000 ) +N_FEATURES=( 100 ) +N_SAMPLES=( 1010000 ) TOTAL=$(( ${#N_FEATURES[@]} * ${#N_SAMPLES[@]} * ${#TRAIN_SIZES[@]} )) i=$(( 0 )) mkdir -p logs/models @@ -10,44 +10,47 @@ for train_size in ${TRAIN_SIZES[@]}; do for n_samples in ${N_SAMPLES[@]}; do for n_features in ${N_FEATURES[@]}; do i=$(( i + 1 )) - n_informative=$(( $n_features-1 )) - echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" - echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" >| log.txt - HYDRA_FULL_ERROR=1; python ../../deckard/layers/optimize.py \ - data.generate.n_features=$n_features \ - data.generate.n_samples=$n_samples \ - data.sample.train_size=$train_size \ + echo "Running experiment ${i} of ${TOTAL}" + # Keeps a meta log of the experiments + echo "Running experiment ${i} of ${TOTAL}" >> log.txt + echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" >> log.txt + # Runs the linear kernel, tries to find the best C value + HYDRA_FULL_ERROR=1; python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ model.init.kernel=linear \ model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - +stage=models "$@" --multirun \ + ++hydra.sweeper.study_name=linear_${n_features}_${train_size} "$@" --multirun \ + # Keeps a log of the output for each experiment >| logs/models/linear_features-${n_features}_samples-${n_samples}_train-${train_size}.log echo "Linear Kernel Done" >> log.txt - i=$(( i + 1 )) - python ../../deckard/layers/optimize.py \ - data.generate.n_features=$n_features \ - data.generate.n_samples=$n_samples \ - data.sample.train_size=$train_size \ + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ model.init.kernel=rbf \ +model.init.gamma=scale \ model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - +stage=models "$@" --multirun \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=rbf_${n_features}_${train_size} "$@" --multirun \ >| logs/models/rbf_features-${n_features}_samples-${n_samples}_train-${train_size}.log echo "RBF Kernel Done" >> log.txt - i=$(( i + 1 )) - python ../../deckard/layers/optimize.py \ - data.generate.n_features=$n_features \ - data.generate.n_samples=$n_samples \ - data.sample.train_size=$train_size \ + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ model.init.kernel=poly \ - model.init.degree=1,2,3,4,5 \ + +model.init.degree=1,2,3,4,5 \ +model.init.gamma=scale \ +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - +stage=models "$@" --multirun \ + ++hydra.sweeper.study_name=poly_${n_features}_${train_size} "$@" --multirun \ >| logs/models/poly_features-${n_features}_samples-${n_samples}_train-${train_size}.log echo "Poly Kernel Done" >> log.txt echo "Successfully completed experiment ${i} of ${TOTAL}" >> log.txt - done - done -done + done; + done; +done; \ No newline at end of file diff --git a/examples/kdd-nsl/.dvc/.gitignore b/examples/kdd-nsl/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/kdd-nsl/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/kdd-nsl/.dvc/config b/examples/kdd-nsl/.dvc/config new file mode 100644 index 00000000..e69de29b diff --git a/examples/kdd-nsl/.dvcignore b/examples/kdd-nsl/.dvcignore new file mode 100644 index 00000000..51973055 --- /dev/null +++ b/examples/kdd-nsl/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore From 62377a1e7b0882f64aa84a291097ba26e5592484 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Sat, 14 Oct 2023 14:03:14 +0200 Subject: [PATCH 101/148] + kdd nsl --- examples/kdd-nsl/.gitignore | 1 + examples/kdd-nsl/README.md | 32 + examples/kdd-nsl/attacks.sh | 66 +++ examples/kdd-nsl/conf/RQ.md | 1 + examples/kdd-nsl/conf/attack.yaml | 36 ++ examples/kdd-nsl/conf/attack/.gitignore | 4 + examples/kdd-nsl/conf/attack/attack_grid.yaml | 49 ++ examples/kdd-nsl/conf/attack/default.yaml | 2 + examples/kdd-nsl/conf/attack/hsj.yaml | 8 + examples/kdd-nsl/conf/compile.yaml | 8 + examples/kdd-nsl/conf/data/attack.yaml | 23 + examples/kdd-nsl/conf/data/default.yaml | 2 + examples/kdd-nsl/conf/data/kdd_nsl.yaml | 18 + examples/kdd-nsl/conf/data/small.yaml | 20 + examples/kdd-nsl/conf/data/truthseeker.yaml | 18 + examples/kdd-nsl/conf/default.yaml | 41 ++ examples/kdd-nsl/conf/files/default.yaml | 21 + examples/kdd-nsl/conf/model.yaml | 36 ++ examples/kdd-nsl/conf/model/.gitignore | 4 + .../kdd-nsl/conf/model/art/defense_grid.yaml | 44 ++ .../kdd-nsl/conf/model/art/postprocessor.yaml | 6 + .../art/postprocessor/high_confidence.yaml | 6 + .../conf/model/art/postprocessor/labels.yaml | 6 + .../kdd-nsl/conf/model/art/preprocessor.yaml | 6 + .../art/preprocessor/feature_squeezing.yaml | 6 + examples/kdd-nsl/conf/model/default.yaml | 2 + examples/kdd-nsl/conf/model/linear.yaml | 15 + examples/kdd-nsl/conf/model/poly.yaml | 16 + examples/kdd-nsl/conf/model/rbf.yaml | 15 + examples/kdd-nsl/conf/rq.yaml | 51 ++ examples/kdd-nsl/conf/scorers/default.yaml | 10 + examples/kdd-nsl/dvc.lock | 559 ++++++++++++++++++ examples/kdd-nsl/dvc.yaml | 154 +++++ examples/kdd-nsl/logs/.gitignore | 2 + examples/kdd-nsl/models.sh | 56 ++ examples/kdd-nsl/params.yaml | 76 +++ examples/kdd-nsl/retrain.py | 44 ++ 37 files changed, 1464 insertions(+) create mode 100644 examples/kdd-nsl/.gitignore create mode 100644 examples/kdd-nsl/README.md create mode 100644 examples/kdd-nsl/attacks.sh create mode 100644 examples/kdd-nsl/conf/RQ.md create mode 100644 examples/kdd-nsl/conf/attack.yaml create mode 100644 examples/kdd-nsl/conf/attack/.gitignore create mode 100644 examples/kdd-nsl/conf/attack/attack_grid.yaml create mode 100644 examples/kdd-nsl/conf/attack/default.yaml create mode 100644 examples/kdd-nsl/conf/attack/hsj.yaml create mode 100644 examples/kdd-nsl/conf/compile.yaml create mode 100644 examples/kdd-nsl/conf/data/attack.yaml create mode 100644 examples/kdd-nsl/conf/data/default.yaml create mode 100644 examples/kdd-nsl/conf/data/kdd_nsl.yaml create mode 100644 examples/kdd-nsl/conf/data/small.yaml create mode 100644 examples/kdd-nsl/conf/data/truthseeker.yaml create mode 100644 examples/kdd-nsl/conf/default.yaml create mode 100644 examples/kdd-nsl/conf/files/default.yaml create mode 100644 examples/kdd-nsl/conf/model.yaml create mode 100644 examples/kdd-nsl/conf/model/.gitignore create mode 100644 examples/kdd-nsl/conf/model/art/defense_grid.yaml create mode 100644 examples/kdd-nsl/conf/model/art/postprocessor.yaml create mode 100644 examples/kdd-nsl/conf/model/art/postprocessor/high_confidence.yaml create mode 100644 examples/kdd-nsl/conf/model/art/postprocessor/labels.yaml create mode 100644 examples/kdd-nsl/conf/model/art/preprocessor.yaml create mode 100644 examples/kdd-nsl/conf/model/art/preprocessor/feature_squeezing.yaml create mode 100644 examples/kdd-nsl/conf/model/default.yaml create mode 100644 examples/kdd-nsl/conf/model/linear.yaml create mode 100644 examples/kdd-nsl/conf/model/poly.yaml create mode 100644 examples/kdd-nsl/conf/model/rbf.yaml create mode 100644 examples/kdd-nsl/conf/rq.yaml create mode 100644 examples/kdd-nsl/conf/scorers/default.yaml create mode 100644 examples/kdd-nsl/dvc.lock create mode 100644 examples/kdd-nsl/dvc.yaml create mode 100644 examples/kdd-nsl/logs/.gitignore create mode 100644 examples/kdd-nsl/models.sh create mode 100644 examples/kdd-nsl/params.yaml create mode 100644 examples/kdd-nsl/retrain.py diff --git a/examples/kdd-nsl/.gitignore b/examples/kdd-nsl/.gitignore new file mode 100644 index 00000000..8c8c8cf4 --- /dev/null +++ b/examples/kdd-nsl/.gitignore @@ -0,0 +1 @@ +/kdd_nsl diff --git a/examples/kdd-nsl/README.md b/examples/kdd-nsl/README.md new file mode 100644 index 00000000..c3c227e6 --- /dev/null +++ b/examples/kdd-nsl/README.md @@ -0,0 +1,32 @@ +# Classification Experiments + +## Directory Contents + +├── conf: contains the configuration files. +│ ├── attack +│ ├── config.yaml : contains the default configuration +│ ├── data +│ ├── files +│ ├── model +│ ├── plots +│ └── scorers +├── dag.md: contains the [dvc](https://dvc.org/doc/start/data-management/data-pipelines) pipeline, visualized as a graph +├── dvc.lock: contains the git trackable information for all the data and model binaries +├── dvc.yaml: specifies the pipeline visualized in `dag.md` +├── experiments.sh: specifies the grid search parameters and executes the pipeline for each set of parameters +├── multirun: contains the hydra configuration information +├── params.yaml: contains the parsed default configuration file +├── plots.ipynb: is a jupyter notebook for visualizing the data +├── queue: contains all of the configurations for all experiments +├── reports: contains the results of all experiments, with a folder for each layer (e.g. `reports/train/`), containing scores, plots, predictions, probabilities, and ground_truth json files. +├── train: contains the `data/` and `models/` for each model +│ ├── data +│ ├── models + +## Execution instructions + +To check basic code functionality, run +```dvc repro --force``` +which will execute the [dvc pipeline](https://dvc.org/doc/start/data-management/data-pipelines) that makes all of the inputs and outputs git trackable. This will execute all of the code on the default configurations first (stages `generate`, `train`, and `attack` on the `dvc.yaml`), followed by grid search on all of the models in stage `model-queue` and all of the attacks in stage `attack-queue`. After finding the best configuration for each kernel (stage `compile`) + +which overrides the default configurations, using [hydra](https://hydra.cc/docs/patterns/configuring_experiments/) and its [optuna plugin](https://hydra.cc/docs/plugins/optuna_sweeper/) to specify a search space for each set of parameters specified in the bash script. Sets of parameters that apply to all experiments are set in the `config.yaml`. Parameters particular to a experiment are specified using command line overrides within the bash script. diff --git a/examples/kdd-nsl/attacks.sh b/examples/kdd-nsl/attacks.sh new file mode 100644 index 00000000..70a9011b --- /dev/null +++ b/examples/kdd-nsl/attacks.sh @@ -0,0 +1,66 @@ +#!/bin/bash + + +# while getops m:d:n:s:b: flag +# do +# case "${flag}" in +# m) model=(-m ${OPTARG});; +# d) attack_distance=(-d ${OPTARG});; +# n) attack_norm=(-n ${OPTARG});; +# s) attack_step=(${OPTARG});; +# b) attack_batch=(${OPTARG});; +# esac +# done + +# model=$1 +# Pauses the script until the user presses enter +MODEL_CONFIGS=$(ls conf/model/best_*.yaml) +CONFIG_NAMES=$(ls conf/model/best_*.yaml | cut -d'/' -f3 | cut -d'.' -f1) +TOTAL=$(( ${#CONFIG_NAMES[@]} )) +i=$(( 0 )) +mkdir -p logs/attacks/ +for model_config in $CONFIG_NAMES; do + i=$(( i + 1 )) + if [ $model_config == "default" ]; then + continue + fi + echo "Running model $model_config. Number $i of $TOTAL" >> attack_log.txt + HYDRA_FULL_ERROR=1 python -m deckard.layers.optimise \ + ++stage=attack \ + ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent \ + ++attack.init.norm=1,2,inf \ + ++attack.init.eps_step=.001,.01,.1,.3,.5,1 \ + ++attack.init.batch_size=1,10,50,100 \ + ++attack.init.eps=.001,.01,.1,.3,.5,1 \ + ++hydra.sweeper.study_name=$model_config \ + model=$model_config $@ --multirun >> logs/attacks/$model_config.log + echo "Successfully completed model $model_config. Number $i of $TOTAL" >> attack_log.txt +done + +# Other attacks listed below +# PGD +# bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.norm=1,2,inf ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=1,10,50,100 ++attack.init.eps=.001,.01,.1,.3,.5,1 $@ + +# # Carlini L0 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # Carlini L2 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # Carlini LInf +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # DeepFool +# bash models.sh ++attack.init.nb_grads=1,3,5,10 ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.batch_size=100 $@ + +# #Threshold Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ + +# #Pixel Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.PixelAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ + +# #Adversarial Patch +# bash models.sh ++attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.scale_max=.1,.2,.3,.5,.8,.9,.99 ++attack.init.batch_size=100 $@ + +# #Hop Skip Jump +# bash models.sh ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.batch_size=100 $@ diff --git a/examples/kdd-nsl/conf/RQ.md b/examples/kdd-nsl/conf/RQ.md new file mode 100644 index 00000000..c1e745ce --- /dev/null +++ b/examples/kdd-nsl/conf/RQ.md @@ -0,0 +1 @@ +sudo apt install lsb-release curl gpg diff --git a/examples/kdd-nsl/conf/attack.yaml b/examples/kdd-nsl/conf/attack.yaml new file mode 100644 index 00000000..4b7c0838 --- /dev/null +++ b/examples/kdd-nsl/conf/attack.yaml @@ -0,0 +1,36 @@ +defaults: + # - _target_ : deckard.base.experiment.Experiment + - _self_ + - data: kdd_nsl + - model: default + - attack : hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +optimizers: adv_accuracy +direction : minimize +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: ${direction} + study_name: attack + storage: sqlite:///attack.db + n_jobs: 1 + params: + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 32 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/kdd-nsl/conf/attack/.gitignore b/examples/kdd-nsl/conf/attack/.gitignore new file mode 100644 index 00000000..29ec330a --- /dev/null +++ b/examples/kdd-nsl/conf/attack/.gitignore @@ -0,0 +1,4 @@ +/best.yaml +/best_linear.yaml +/best_rbf.yaml +/best_poly.yaml diff --git a/examples/kdd-nsl/conf/attack/attack_grid.yaml b/examples/kdd-nsl/conf/attack/attack_grid.yaml new file mode 100644 index 00000000..d7dc3050 --- /dev/null +++ b/examples/kdd-nsl/conf/attack/attack_grid.yaml @@ -0,0 +1,49 @@ +- attack.init.name: [art.attacks.evasion.FastGradientMethod] + attack.init.eps : [.01, .03, .3, .1] + attack.init.norm : ['inf', 1, 2] + attack.init.eps_step : [.001, .003, .01] + attack.init.batch_size : [100] + +- attack.init.name: [art.attacks.evasion.ProjectedGradientDescent] + attack.init.eps : [.01, .03, .3, .1] + attack.init.norm : ['inf', 1, 2] + attack.init.eps_step : [.001, .003, .01] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.CarliniL0Method] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + attack.init.confidence : [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.CarliniL2Method] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + attack.init.confidence : [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.CarliniLInfMethod] + attack.init.max_iter : [10] + attack.init.confidence: [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.DeepFool] + attack.init.max_iter : [10] + attack.init.batch_size : [100] + attack.init.nb_grads : [10, 100, 1000] + +- attack.init.name: [art.attacks.evasion.HopSkipJump] + attack.init.max_iter : [10] + attack.init.max_eval : [10] + attack.init.init_eval : [10] + attack.init.norm : ['inf', 2] + +- attack.init.name: [art.attacks.evasion.PixelAttack] + attack.init.th : [.5, .9, .99] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.ThresholdAttack] + attack.init.th : [.5, .9, .99] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.AdversarialPatch] + attack.init.max_iter : [10] + attack.init.learning_rate : [.5, 5.0, 50.0] diff --git a/examples/kdd-nsl/conf/attack/default.yaml b/examples/kdd-nsl/conf/attack/default.yaml new file mode 100644 index 00000000..e0967ff7 --- /dev/null +++ b/examples/kdd-nsl/conf/attack/default.yaml @@ -0,0 +1,2 @@ +defaults: + - hsj diff --git a/examples/kdd-nsl/conf/attack/hsj.yaml b/examples/kdd-nsl/conf/attack/hsj.yaml new file mode 100644 index 00000000..cb2399a3 --- /dev/null +++ b/examples/kdd-nsl/conf/attack/hsj.yaml @@ -0,0 +1,8 @@ +data: ${data} +model: ${model} +_target_ : deckard.base.attack.Attack +init: + name: art.attacks.evasion.HopSkipJump + model: ${model} +attack_size : 10 +method : evasion diff --git a/examples/kdd-nsl/conf/compile.yaml b/examples/kdd-nsl/conf/compile.yaml new file mode 100644 index 00000000..109994af --- /dev/null +++ b/examples/kdd-nsl/conf/compile.yaml @@ -0,0 +1,8 @@ +attacks: + ProjectedGradientDescent: PGD + null : null +defences: + Control: Control +params: + Control: model.init.kwargs.kernel + PGD: attack.init.kwargs.eps \ No newline at end of file diff --git a/examples/kdd-nsl/conf/data/attack.yaml b/examples/kdd-nsl/conf/data/attack.yaml new file mode 100644 index 00000000..3d77ffb4 --- /dev/null +++ b/examples/kdd-nsl/conf/data/attack.yaml @@ -0,0 +1,23 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 11100 + n_features : 100 + n_informative : 99 + n_redundant : 0 + n_repeated : 0 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 10000 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/kdd-nsl/conf/data/default.yaml b/examples/kdd-nsl/conf/data/default.yaml new file mode 100644 index 00000000..b9487f7e --- /dev/null +++ b/examples/kdd-nsl/conf/data/default.yaml @@ -0,0 +1,2 @@ +defaults: + - kdd_nsl \ No newline at end of file diff --git a/examples/kdd-nsl/conf/data/kdd_nsl.yaml b/examples/kdd-nsl/conf/data/kdd_nsl.yaml new file mode 100644 index 00000000..e55be441 --- /dev/null +++ b/examples/kdd-nsl/conf/data/kdd_nsl.yaml @@ -0,0 +1,18 @@ +_target_: deckard.base.data.Data +name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv +target : label +sample: + _target_: deckard.base.data.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 5000 + test_size : 1000 +sklearn_pipeline: + encoder: + name : sklearn.preprocessing.OrdinalEncoder + handle_unknown : use_encoded_value + unknown_value : -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/kdd-nsl/conf/data/small.yaml b/examples/kdd-nsl/conf/data/small.yaml new file mode 100644 index 00000000..ff01d089 --- /dev/null +++ b/examples/kdd-nsl/conf/data/small.yaml @@ -0,0 +1,20 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 1100 + n_features : 20 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 100 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True \ No newline at end of file diff --git a/examples/kdd-nsl/conf/data/truthseeker.yaml b/examples/kdd-nsl/conf/data/truthseeker.yaml new file mode 100644 index 00000000..9e0acbe3 --- /dev/null +++ b/examples/kdd-nsl/conf/data/truthseeker.yaml @@ -0,0 +1,18 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/379b679bdea30724e9fa188931f0109ff422cce0/kdd_nsl.csv + target : -2 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 10000 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/kdd-nsl/conf/default.yaml b/examples/kdd-nsl/conf/default.yaml new file mode 100644 index 00000000..b0340b69 --- /dev/null +++ b/examples/kdd-nsl/conf/default.yaml @@ -0,0 +1,41 @@ +defaults: + - _self_ + - data: default + - model: default + - attack: hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + # seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 100 + n_jobs: 1 + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 6 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/kdd-nsl/conf/files/default.yaml b/examples/kdd-nsl/conf/files/default.yaml new file mode 100644 index 00000000..e2247d7e --- /dev/null +++ b/examples/kdd-nsl/conf/files/default.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: output +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pkl +params_file : params.yaml +train_labels_file : train_labels.json +test_labels_file : test_labels.json +probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +adv_probabilities_file: adv_probabilities.json +name : default diff --git a/examples/kdd-nsl/conf/model.yaml b/examples/kdd-nsl/conf/model.yaml new file mode 100644 index 00000000..d1c2344a --- /dev/null +++ b/examples/kdd-nsl/conf/model.yaml @@ -0,0 +1,36 @@ +defaults: + # - _target_ : deckard.base.experiment.Experiment + - _self_ + - data: kdd_nsl + - model: default + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +optimizers : accuracy +direction : maximize +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: ${direction} + study_name: model + storage: sqlite:///model.db + n_jobs: 1 + params: + model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 32 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/kdd-nsl/conf/model/.gitignore b/examples/kdd-nsl/conf/model/.gitignore new file mode 100644 index 00000000..9f645db6 --- /dev/null +++ b/examples/kdd-nsl/conf/model/.gitignore @@ -0,0 +1,4 @@ +/best.yaml +/best_rbf.yaml +/best_linear.yaml +/best_poly.yaml diff --git a/examples/kdd-nsl/conf/model/art/defense_grid.yaml b/examples/kdd-nsl/conf/model/art/defense_grid.yaml new file mode 100644 index 00000000..a3fe3719 --- /dev/null +++ b/examples/kdd-nsl/conf/model/art/defense_grid.yaml @@ -0,0 +1,44 @@ +- model.art.preprocessor.name: art.defences.preprocessor.FeatureSqueezing + model.art.preprocessor.params: + clip_values : + - [0,255] + bit_depth : [4, 8, 16, 32, 64] + +- model.art.preprocessor.name: art.defences.preprocessor.GaussianAugmentation + model.art.preprocessor.params: + clip_values : + - [0,255] + sigma : [.1, .3, 1] + ratio : [.1, .5, 1] + +- model.art.preprocessor.name: art.defences.preprocessor.SpatialSmoothing + model.art.preprocessor.params: + clip_values : + - [0,255] + window_size : [2,3,4] + +- model.art.preprocessor.name: art.defences.preprocessor.TotalVarMin + model.art.preprocessor.params: + clip_values : + - [0,255] + prob : [.001, .01, .1] + norm : [1, 2, 3] + lamb : [.05, .5, .95] + max_iter : [100] + +- model.art.postprocessor.name : art.defences.postprocessor.GaussianNoise + model.art.postprocessor.params: + clip_values : + - [0,255] + scale: [.1, .9, .999] + +- model.art.postprocessor.name : art.defences.postprocessor.HighConfidence + model.art.postprocessor.params: + cutoff : [.1, .5, .9, .99] + + +- model.art.postprocessor.name : art.defences.postprocessor.Rounded + model.art.preprocessor.params: + clip_values : + - [0,255] + decimals : [1, 2, 4, 8] diff --git a/examples/kdd-nsl/conf/model/art/postprocessor.yaml b/examples/kdd-nsl/conf/model/art/postprocessor.yaml new file mode 100644 index 00000000..91f3c00a --- /dev/null +++ b/examples/kdd-nsl/conf/model/art/postprocessor.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : sklearn-svc +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/kdd-nsl/conf/model/art/postprocessor/high_confidence.yaml b/examples/kdd-nsl/conf/model/art/postprocessor/high_confidence.yaml new file mode 100644 index 00000000..16ae0e50 --- /dev/null +++ b/examples/kdd-nsl/conf/model/art/postprocessor/high_confidence.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/kdd-nsl/conf/model/art/postprocessor/labels.yaml b/examples/kdd-nsl/conf/model/art/postprocessor/labels.yaml new file mode 100644 index 00000000..16ae0e50 --- /dev/null +++ b/examples/kdd-nsl/conf/model/art/postprocessor/labels.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/kdd-nsl/conf/model/art/preprocessor.yaml b/examples/kdd-nsl/conf/model/art/preprocessor.yaml new file mode 100644 index 00000000..ebf46909 --- /dev/null +++ b/examples/kdd-nsl/conf/model/art/preprocessor.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : sklearn-svc +preprocessor: + name: art.defences.preprocessor.FeatureSqueezing + bit_depth: 32 +initialize: diff --git a/examples/kdd-nsl/conf/model/art/preprocessor/feature_squeezing.yaml b/examples/kdd-nsl/conf/model/art/preprocessor/feature_squeezing.yaml new file mode 100644 index 00000000..1c48b3e7 --- /dev/null +++ b/examples/kdd-nsl/conf/model/art/preprocessor/feature_squeezing.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +preprocessor: + name: art.defences.preprocessor.FeatureSqueezing + bit_depth: 32 +initialize: diff --git a/examples/kdd-nsl/conf/model/default.yaml b/examples/kdd-nsl/conf/model/default.yaml new file mode 100644 index 00000000..5f8bd822 --- /dev/null +++ b/examples/kdd-nsl/conf/model/default.yaml @@ -0,0 +1,2 @@ +defaults: + - rbf diff --git a/examples/kdd-nsl/conf/model/linear.yaml b/examples/kdd-nsl/conf/model/linear.yaml new file mode 100644 index 00000000..337909e1 --- /dev/null +++ b/examples/kdd-nsl/conf/model/linear.yaml @@ -0,0 +1,15 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + kernel : linear + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/kdd-nsl/conf/model/poly.yaml b/examples/kdd-nsl/conf/model/poly.yaml new file mode 100644 index 00000000..8bdae408 --- /dev/null +++ b/examples/kdd-nsl/conf/model/poly.yaml @@ -0,0 +1,16 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + coef0 : 0.0 + kernel : poly + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/kdd-nsl/conf/model/rbf.yaml b/examples/kdd-nsl/conf/model/rbf.yaml new file mode 100644 index 00000000..036bc65f --- /dev/null +++ b/examples/kdd-nsl/conf/model/rbf.yaml @@ -0,0 +1,15 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + kernel : rbf + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: \ No newline at end of file diff --git a/examples/kdd-nsl/conf/rq.yaml b/examples/kdd-nsl/conf/rq.yaml new file mode 100644 index 00000000..25ad380b --- /dev/null +++ b/examples/kdd-nsl/conf/rq.yaml @@ -0,0 +1,51 @@ +defaults: + - _self_ + - data: default + - model: default + - attack: hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + # seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 100 + n_jobs: 1 + launcher: + _target_: hydra_plugins.hydra_rq_launcher.rq_launcher.RQLauncher + enqueue: + job_timeout: null + ttl: null + result_ttl: null + failure_ttl: null + at_front: false + job_id: null + description: null + queue: default + redis: + host: ${oc.env:REDIS_HOST,localhost} + port: ${oc.env:REDIS_PORT,6379} + db: ${oc.env:REDIS_DB,0} + password: ${oc.env:REDIS_PASSWORD,null} + ssl: ${oc.env:REDIS_SSL,False} + ssl_ca_certs: ${oc.env:REDIS_SSL_CA_CERTS,null + mock: ${oc.env:REDIS_MOCK,False} + stop_after_enqueue: false + wait_polling: 1.0 diff --git a/examples/kdd-nsl/conf/scorers/default.yaml b/examples/kdd-nsl/conf/scorers/default.yaml new file mode 100644 index 00000000..108c1520 --- /dev/null +++ b/examples/kdd-nsl/conf/scorers/default.yaml @@ -0,0 +1,10 @@ +_target_: deckard.base.scorer.ScorerDict +accuracy: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.accuracy_score + direction: maximize + +log_loss: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.log_loss + direction: minimize diff --git a/examples/kdd-nsl/dvc.lock b/examples/kdd-nsl/dvc.lock new file mode 100644 index 00000000..5dbe8a15 --- /dev/null +++ b/examples/kdd-nsl/dvc.lock @@ -0,0 +1,559 @@ +schema: '2.0' +stages: + train: + cmd: python -m deckard.layers.experiment train + params: + params.yaml: + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: ${data} + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/reports/train/default/params.yaml + hash: md5 + md5: 7234aab7d5edae504afa2090d96e4c3f + size: 2434 + - path: output/reports/train/default/predictions.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/probabilities.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/score_dict.json + hash: md5 + md5: 3180dfdc3315fec9863107ce5abbc63c + size: 360 + attack: + cmd: python -m deckard.layers.experiment attack + deps: + - path: output/reports/train/default/params.yaml + hash: md5 + md5: 7234aab7d5edae504afa2090d96e4c3f + size: 2434 + - path: output/reports/train/default/predictions.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/probabilities.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/score_dict.json + hash: md5 + md5: 3180dfdc3315fec9863107ce5abbc63c + size: 360 + params: + params.yaml: + attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: ${data} + init: + model: ${model} + name: art.attacks.evasion.HopSkipJump + method: evasion + model: ${model} + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: ${data} + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/attacks/attack.pkl + hash: md5 + md5: 05f468294dfaef26c27370728c8cdc27 + size: 1832 + - path: output/reports/attack/default/adv_predictions.json + hash: md5 + md5: b72287defd6f04140c20387819262314 + size: 439 + - path: output/reports/attack/default/adv_probabilities.json + hash: md5 + md5: b72287defd6f04140c20387819262314 + size: 439 + - path: output/reports/attack/default/params.yaml + hash: md5 + md5: b300c684dc58fc23684ccefbb9f83265 + size: 5832 + - path: output/reports/attack/default/predictions.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/attack/default/probabilities.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/attack/default/score_dict.json + hash: md5 + md5: b248e6f36831463144658ef4db63d72a + size: 582 + models: + cmd: bash other_data.sh +stage=train --config-name=model.yaml + deps: + - path: conf/model.yaml + hash: md5 + md5: bfdd4743dda1272364c4bdf8c569972c + size: 990 + - path: models.sh + hash: md5 + md5: e622d712564afec3b5826e4e91044f90 + size: 2970 + - path: params.yaml + hash: md5 + md5: 6be251c3aee37ce999475434a0e94193 + size: 2040 + params: + params.yaml: + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: ${data} + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: logs/models/ + hash: md5 + md5: c50e45d9189db51c2bc7459750acc25f.dir + size: 383291 + nfiles: 3 + - path: model.db + hash: md5 + md5: b26dbb51d030083d2bd6f03245c1b188 + size: 155648 + compile_models: + cmd: python -m deckard.layers.compile --report_folder output/reports/train/ --results_file + output/train.csv + deps: + - path: logs/models/ + hash: md5 + md5: c50e45d9189db51c2bc7459750acc25f.dir + size: 383291 + nfiles: 3 + - path: model.db + hash: md5 + md5: b26dbb51d030083d2bd6f03245c1b188 + size: 155648 + - path: output/reports/train/ + hash: md5 + md5: 798ae5e5259be51e6e06cded878a2573.dir + size: 7440659 + nfiles: 293 + outs: + - path: output/train.csv + hash: md5 + md5: e753967ae898a3f60a09b8c0acac625c + size: 108832 + find_best_model@rbf: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_rbf --study_name=rbf --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: c50e45d9189db51c2bc7459750acc25f.dir + size: 383291 + nfiles: 3 + - path: model.db + hash: md5 + md5: b26dbb51d030083d2bd6f03245c1b188 + size: 155648 + - path: output/train.csv + hash: md5 + md5: e753967ae898a3f60a09b8c0acac625c + size: 108832 + outs: + - path: conf/model/best_rbf.yaml + hash: md5 + md5: cd69a0189843e6474613ea33e96bb29b + size: 327 + find_best_model@linear: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_linear --study_name=linear --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: c50e45d9189db51c2bc7459750acc25f.dir + size: 383291 + nfiles: 3 + - path: model.db + hash: md5 + md5: b26dbb51d030083d2bd6f03245c1b188 + size: 155648 + - path: output/train.csv + hash: md5 + md5: e753967ae898a3f60a09b8c0acac625c + size: 108832 + outs: + - path: conf/model/best_linear.yaml + hash: md5 + md5: cd69a0189843e6474613ea33e96bb29b + size: 327 + find_best_model@poly: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_poly --study_name=poly --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: c50e45d9189db51c2bc7459750acc25f.dir + size: 383291 + nfiles: 3 + - path: model.db + hash: md5 + md5: b26dbb51d030083d2bd6f03245c1b188 + size: 155648 + - path: output/train.csv + hash: md5 + md5: e753967ae898a3f60a09b8c0acac625c + size: 108832 + outs: + - path: conf/model/best_poly.yaml + hash: md5 + md5: cd69a0189843e6474613ea33e96bb29b + size: 327 + attacks: + cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml + deps: + - path: conf/model/best_linear.yaml + hash: md5 + md5: cd69a0189843e6474613ea33e96bb29b + size: 327 + - path: conf/model/best_poly.yaml + hash: md5 + md5: cd69a0189843e6474613ea33e96bb29b + size: 327 + - path: conf/model/best_rbf.yaml + hash: md5 + md5: cd69a0189843e6474613ea33e96bb29b + size: 327 + - path: logs/models/ + hash: md5 + md5: c50e45d9189db51c2bc7459750acc25f.dir + size: 383291 + nfiles: 3 + - path: model.db + hash: md5 + md5: b26dbb51d030083d2bd6f03245c1b188 + size: 155648 + - path: output/train.csv + hash: md5 + md5: e753967ae898a3f60a09b8c0acac625c + size: 108832 + outs: + - path: attack.db + hash: md5 + md5: eb22685a9f6eafb2b953818cb912fcd1 + size: 184320 + - path: logs/attacks/ + hash: md5 + md5: 6fe487e166f75a031e2e75293b57641d.dir + size: 1038170 + nfiles: 3 + compile_attacks: + cmd: python -m deckard.layers.compile --report_folder output/reports/attack/ --results_file + output/attack.csv + deps: + - path: attack.db + hash: md5 + md5: eb22685a9f6eafb2b953818cb912fcd1 + size: 184320 + - path: logs/attacks/ + hash: md5 + md5: 6fe487e166f75a031e2e75293b57641d.dir + size: 1038170 + nfiles: 3 + - path: output/reports/attack/ + hash: md5 + md5: 11281d9592f318db700f772b287f4d46.dir + size: 9481051 + nfiles: 463 + outs: + - path: output/attack.csv + hash: md5 + md5: 37f5c7183361cdd6346fe17b4b119def + size: 214557 + find_best_attack@linear: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_linear --study_name=best_linear --default_config attack.yaml + deps: + - path: attack.db + hash: md5 + md5: eb22685a9f6eafb2b953818cb912fcd1 + size: 184320 + - path: logs/models/ + hash: md5 + md5: c50e45d9189db51c2bc7459750acc25f.dir + size: 383291 + nfiles: 3 + - path: output/train.csv + hash: md5 + md5: e753967ae898a3f60a09b8c0acac625c + size: 108832 + outs: + - path: conf/attack/best_linear.yaml + hash: md5 + md5: e45d78ff616a657ab3f8ceee160d32e9 + size: 163 + find_best_attack@rbf: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_rbf --study_name=best_rbf --default_config attack.yaml + deps: + - path: attack.db + hash: md5 + md5: eb22685a9f6eafb2b953818cb912fcd1 + size: 184320 + - path: logs/models/ + hash: md5 + md5: c50e45d9189db51c2bc7459750acc25f.dir + size: 383291 + nfiles: 3 + - path: output/train.csv + hash: md5 + md5: e753967ae898a3f60a09b8c0acac625c + size: 108832 + outs: + - path: conf/attack/best_rbf.yaml + hash: md5 + md5: e45d78ff616a657ab3f8ceee160d32e9 + size: 163 + find_best_attack@poly: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_poly --study_name=best_poly --default_config attack.yaml + deps: + - path: attack.db + hash: md5 + md5: eb22685a9f6eafb2b953818cb912fcd1 + size: 184320 + - path: logs/models/ + hash: md5 + md5: c50e45d9189db51c2bc7459750acc25f.dir + size: 383291 + nfiles: 3 + - path: output/train.csv + hash: md5 + md5: e753967ae898a3f60a09b8c0acac625c + size: 108832 + outs: + - path: conf/attack/best_poly.yaml + hash: md5 + md5: e45d78ff616a657ab3f8ceee160d32e9 + size: 163 + other_data_train@kdd_nsl: + cmd: DATASET_NAME=kdd_nsl bash other_data.sh data=kdd_nsl +stage=train --config-name=model.yaml + deps: + - path: conf/model.yaml + hash: md5 + md5: daaa0663d05972a5b8645c35d364da88 + size: 990 + - path: other_data.sh + hash: md5 + md5: 6ebecf100cc02847ad31901bebb2ee5a + size: 2759 + - path: output/reports/train/default/params.yaml + hash: md5 + md5: d4e0a34b2b15765ca71fa5ecaf7e3826 + size: 2100 + outs: + - path: kdd_nsl.db + hash: md5 + md5: 06933f8fc0a1feca0944c131b6a3854b + size: 348160 + - path: kdd_nsl/ + hash: md5 + md5: 9076c4e55fd1058e7446588d99930d58.dir + size: 39137423 + nfiles: 1072 + - path: logs/kdd_nsl/ + hash: md5 + md5: e7c227947468122b62f891c0d54e0c54.dir + size: 1314288 + nfiles: 12 diff --git a/examples/kdd-nsl/dvc.yaml b/examples/kdd-nsl/dvc.yaml new file mode 100644 index 00000000..37eb4da5 --- /dev/null +++ b/examples/kdd-nsl/dvc.yaml @@ -0,0 +1,154 @@ +stages: + attack: # Prototype stage + cmd: python -m deckard.layers.experiment attack + deps: + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} #optionally, store the data + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + metrics: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + outs: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.probabilities_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} + params: + - data + - model + - files + - scorers + - attack + train: # Prototype stage + cmd: python -m deckard.layers.experiment train + metrics: + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + outs: + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + params: + - data + - model + - files + - scorers + models: # Grid search stage + cmd: bash other_data.sh +stage=train --config-name=model.yaml # specifies the prototype stage, file to be used + outs: + - model.db: # This allows to run this section piecewise, adding features or samples inside models.sh which are then stored in the .db + cache: true + persist: true + - logs/models/: # Each feature/sample/train size combination log is stored in a separate file + cache: true + persist: true + # - ${files.directory}/${files.reports}/train/: # Each feature/sample/train size combination experiment is stored in a separate folder + # cache: true + # persist: true + params: # These are the parameters that are passed to the prototype stage + - data + - model + - files + - scorers + deps: + - params.yaml + - conf/model.yaml + - models.sh + # - conf/model/ + compile_models: + cmd: + python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/train/ --results_file ${files.directory}/train.csv + outs: + - ${files.directory}/train.csv + deps: + - ${files.directory}/${files.reports}/train/ # Each feature/sample/train size combination experiment is stored in a separate folder + - model.db + - logs/models/ + find_best_model: + foreach: + - linear + - rbf + - poly + do: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model --params_file best_${item} --study_name=${item} --default_config model.yaml + outs: + - conf/model/best_${item}.yaml + deps: + - ${files.directory}/train.csv + - model.db + - logs/models/ + attacks: + cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml + outs: + - logs/attacks/: + cache: true + persist: true + - attack.db: + cache: true + persist: true + deps: + - ${files.directory}/train.csv + - model.db + - logs/models/ + - conf/model/best_linear.yaml + - conf/model/best_rbf.yaml + - conf/model/best_poly.yaml + compile_attacks: + cmd: + python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/attack/ --results_file ${files.directory}/attack.csv + outs: + - ${files.directory}/attack.csv + deps: + - ${files.directory}/${files.reports}/attack/ # Each feature/sample/train size combination experiment is stored in a separate folder + - attack.db + - logs/attacks/ + find_best_attack: + foreach: + - linear + - rbf + - poly + do: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack --params_file best_${item} --study_name=best_${item} --default_config attack.yaml + outs: + - conf/attack/best_${item}.yaml + deps: + - ${files.directory}/train.csv + - attack.db + - logs/models/ + # retrain: + # cmd : python retrain.py + # deps: + # - ${files.directory}/models/ + # - ${files.directory}/attacks/ + # - conf/attack/best_linear.yaml + # - conf/attack/best_rbf.yaml + # - conf/attack/best_poly.yaml + # - conf/model/best_linear.yaml + # - conf/model/best_rbf.yaml + # - conf/model/best_poly.yaml + # outs: + # - retrain/ + # metrics: + # - plots/before_retrain_confidence.csv + # - plots/after_retrain_confidence.csv + plots: + cmd : python plots.py + deps : + - plots/after_retrain_confidence.csv + - plots/attack_results.csv + - plots/before_retrain_confidence.csv + - plots/model_results.csv + plots : + - plots/accuracy_vs_attack_parameters.pdf + - plots/accuracy_vs_features.pdf + - plots/accuracy_vs_samples.pdf + - plots/confidence_vs_attack_parameters.pdf + - plots/fit_time_vs_attack_parameters.pdf + - plots/fit_time_vs_features.pdf + - plots/fit_time_vs_samples.pdf + - plots/retrain_accuracy.pdf + - plots/retrain_confidence_vs_attack_parameters.pdf + - plots/retrain_time.pdf diff --git a/examples/kdd-nsl/logs/.gitignore b/examples/kdd-nsl/logs/.gitignore new file mode 100644 index 00000000..99a64f76 --- /dev/null +++ b/examples/kdd-nsl/logs/.gitignore @@ -0,0 +1,2 @@ +/models +/attacks diff --git a/examples/kdd-nsl/models.sh b/examples/kdd-nsl/models.sh new file mode 100644 index 00000000..2cd3d313 --- /dev/null +++ b/examples/kdd-nsl/models.sh @@ -0,0 +1,56 @@ +# N_FEATURES=( 10 100 1000 10000 10000 100000 ) +# TRAIN_SIZES=( 10 100 1000 ) +TRAIN_SIZES=( 1000000 ) +N_FEATURES=( 10 100 1000 ) +N_SAMPLES=( 1010000 ) +TOTAL=$(( ${#N_FEATURES[@]} * ${#N_SAMPLES[@]} * ${#TRAIN_SIZES[@]} )) +i=$(( 0 )) +mkdir -p logs/models +for train_size in ${TRAIN_SIZES[@]}; do + for n_samples in ${N_SAMPLES[@]}; do + for n_features in ${N_FEATURES[@]}; do + i=$(( i + 1 )) + echo "Running experiment ${i} of ${TOTAL}" + # Keeps a meta log of the experiments + echo "Running experiment ${i} of ${TOTAL}" >> log.txt + echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" >> log.txt + # Runs the linear kernel, tries to find the best C value + HYDRA_FULL_ERROR=1; python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + model.init.kernel=linear \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=linear_${n_features}_${train_size} "$@" --multirun \ + # Keeps a log of the output for each experiment + >| logs/models/linear_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Linear Kernel Done" >> log.txt + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + model.init.kernel=rbf \ + +model.init.gamma=scale \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=rbf_${n_features}_${train_size} "$@" --multirun \ + >| logs/models/rbf_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "RBF Kernel Done" >> log.txt + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + model.init.kernel=poly \ + +model.init.degree=1,2,3,4,5 \ + +model.init.gamma=scale \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=poly_${n_features}_${train_size} "$@" --multirun \ + >| logs/models/poly_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Poly Kernel Done" >> log.txt + echo "Successfully completed experiment ${i} of ${TOTAL}" >> log.txt + done; + done; +done; \ No newline at end of file diff --git a/examples/kdd-nsl/params.yaml b/examples/kdd-nsl/params.yaml new file mode 100644 index 00000000..67d79f9d --- /dev/null +++ b/examples/kdd-nsl/params.yaml @@ -0,0 +1,76 @@ +attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: ${data} + init: + model: ${model} + name: art.attacks.evasion.HopSkipJump + method: evasion + model: ${model} +data: + _target_: deckard.base.data.Data + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label +files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json +model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: null + library: sklearn-svc + data: ${data} + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc +scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss diff --git a/examples/kdd-nsl/retrain.py b/examples/kdd-nsl/retrain.py new file mode 100644 index 00000000..6d69faf7 --- /dev/null +++ b/examples/kdd-nsl/retrain.py @@ -0,0 +1,44 @@ +import optuna +import logging +from pathlib import Path +from hydra import initialize_config_dir, compose +from hydra.utils import instantiate +from omegaconf import OmegaConf +import yaml +import pandas as pd +from sklearn.model_selection import train_test_split +from ..base.utils import flatten_dict, unflatten_dict + + + +def compose_experiment(config_dir, config_name, overrides): + with initialize_config_dir(config_dir=config_dir, version_base="1.3"): + cfg = compose(config_name=config_name, overrides=overrides) + cfg = OmegaConf.to_container(cfg, resolve=False) + return cfg + +def train_model(config_dir, config_name, overrides, data_file, model_file): + cfg = compose_experiment(config_dir, config_name, overrides) + cfg = OmegaConf.to_container(cfg, resolve=True) + exp = instantiate(cfg) + scores = exp(model_file=model_file, data_file=data_file) + return scores + +def create_new_dataset(attack_samples_file, data_file, ratio = .5): + attack_suffix = Path(attack_samples_file).suffix + if attack_suffix == ".csv": + attack_samples = pd.read_csv(attack_samples_file) + elif attack_suffix == ".pkl": + attack_samples = pd.read_pickle(attack_samples_file) + else: + raise ValueError(f"Unknown attack samples file format {attack_suffix}") + data_suffix = Path(data_file).suffix + if data_suffix == ".csv": + data = pd.read_csv(data_file) + elif data_suffix == ".pkl": + data = pd.read_pickle(data_file) + else: + raise ValueError(f"Unknown data file format {data_suffix}") + attack_len = len(attack_samples) + data_len = len(data) + new_len = int(attack_len * ratio) From cffa8e42ded72388e7eaa2a0c3694ad8e20601ae Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Sat, 14 Oct 2023 14:03:52 +0200 Subject: [PATCH 102/148] update git ignore --- .gitignore | 3 +-- examples/classification/.dvc/.gitignore | 3 +++ examples/classification/logs/.gitignore | 3 +++ examples/classification/plots/.gitignore | 3 +++ 4 files changed, 10 insertions(+), 2 deletions(-) create mode 100644 examples/classification/.dvc/.gitignore create mode 100644 examples/classification/logs/.gitignore create mode 100644 examples/classification/plots/.gitignore diff --git a/.gitignore b/.gitignore index 757526c7..0b81c49f 100644 --- a/.gitignore +++ b/.gitignore @@ -130,5 +130,4 @@ examples/*/output/* **/*.egg-info deckard/deckard.egg-info/* -*attack_log.txt -*model_log.txt +*log.txt diff --git a/examples/classification/.dvc/.gitignore b/examples/classification/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/classification/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/classification/logs/.gitignore b/examples/classification/logs/.gitignore new file mode 100644 index 00000000..8d227382 --- /dev/null +++ b/examples/classification/logs/.gitignore @@ -0,0 +1,3 @@ +/models +/attacks +/kdd_nsl diff --git a/examples/classification/plots/.gitignore b/examples/classification/plots/.gitignore new file mode 100644 index 00000000..adf6bc25 --- /dev/null +++ b/examples/classification/plots/.gitignore @@ -0,0 +1,3 @@ +/accuracy_vs_attack_parameters.eps +/accuracy_vs_features.eps +/accuracy_vs_samples.eps From 7243aaec1d27ffbe4e0695c8f2c85fca640db552 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Sun, 15 Oct 2023 00:55:09 +0200 Subject: [PATCH 103/148] add truthseeker data --- .gitignore | 2 +- deckard/base/data/data.py | 7 +- deckard/base/model/art_pipeline.py | 20 +- deckard/layers/compile.py | 12 +- deckard/layers/find_best.py | 15 +- deckard/layers/optimise.py | 16 +- deckard/layers/utils.py | 2 +- examples/classification/.dvc/config | 0 examples/classification/attacks.sh | 7 +- examples/classification/conf/attack.yaml | 2 - examples/classification/conf/default.yaml | 4 +- examples/classification/conf/model.yaml | 2 - examples/classification/dvc.lock | 462 ++++++---- examples/classification/dvc.yaml | 20 +- examples/classification/models.sh | 8 +- examples/classification/params.yaml | 10 +- examples/classification/plots.py | 102 +-- examples/classification/plots/.gitignore | 7 + examples/classification/retrain.py | 411 +++++++++ examples/kdd-nsl/.gitignore | 1 + examples/kdd-nsl/plots/.gitignore | 12 + examples/truthseeker/.dvc/.gitignore | 3 + examples/truthseeker/.dvc/config | 0 examples/truthseeker/.dvcignore | 3 + examples/truthseeker/.gitignore | 2 + examples/truthseeker/README.md | 32 + examples/truthseeker/attacks.sh | 54 ++ examples/truthseeker/conf/attack.yaml | 36 + examples/truthseeker/conf/attack/.gitignore | 4 + .../truthseeker/conf/attack/attack_grid.yaml | 49 ++ examples/truthseeker/conf/attack/default.yaml | 2 + examples/truthseeker/conf/attack/hsj.yaml | 8 + examples/truthseeker/conf/compile.yaml | 8 + examples/truthseeker/conf/data/attack.yaml | 23 + examples/truthseeker/conf/data/default.yaml | 2 + examples/truthseeker/conf/data/kdd_nsl.yaml | 18 + examples/truthseeker/conf/data/small.yaml | 20 + .../truthseeker/conf/data/truthseeker.yaml | 20 + examples/truthseeker/conf/default.yaml | 41 + examples/truthseeker/conf/files/default.yaml | 21 + examples/truthseeker/conf/model.yaml | 36 + examples/truthseeker/conf/model/.gitignore | 4 + .../conf/model/art/defense_grid.yaml | 44 + .../conf/model/art/postprocessor.yaml | 6 + .../art/postprocessor/high_confidence.yaml | 6 + .../conf/model/art/postprocessor/labels.yaml | 6 + .../conf/model/art/preprocessor.yaml | 6 + .../art/preprocessor/feature_squeezing.yaml | 6 + examples/truthseeker/conf/model/default.yaml | 2 + examples/truthseeker/conf/model/linear.yaml | 15 + examples/truthseeker/conf/model/poly.yaml | 16 + examples/truthseeker/conf/model/rbf.yaml | 15 + .../truthseeker/conf/scorers/default.yaml | 10 + examples/truthseeker/dvc.lock | 798 ++++++++++++++++++ examples/truthseeker/dvc.yaml | 154 ++++ examples/truthseeker/logs/.gitignore | 2 + examples/truthseeker/models.sh | 56 ++ examples/truthseeker/other_data.sh | 35 + examples/truthseeker/params.yaml | 178 ++++ examples/truthseeker/plots.py | 378 +++++++++ examples/truthseeker/plots/.gitignore | 6 + examples/truthseeker/retrain.py | 411 +++++++++ 62 files changed, 3399 insertions(+), 259 deletions(-) create mode 100644 examples/classification/.dvc/config create mode 100644 examples/classification/retrain.py create mode 100644 examples/kdd-nsl/plots/.gitignore create mode 100644 examples/truthseeker/.dvc/.gitignore create mode 100644 examples/truthseeker/.dvc/config create mode 100644 examples/truthseeker/.dvcignore create mode 100644 examples/truthseeker/.gitignore create mode 100644 examples/truthseeker/README.md create mode 100644 examples/truthseeker/attacks.sh create mode 100644 examples/truthseeker/conf/attack.yaml create mode 100644 examples/truthseeker/conf/attack/.gitignore create mode 100644 examples/truthseeker/conf/attack/attack_grid.yaml create mode 100644 examples/truthseeker/conf/attack/default.yaml create mode 100644 examples/truthseeker/conf/attack/hsj.yaml create mode 100644 examples/truthseeker/conf/compile.yaml create mode 100644 examples/truthseeker/conf/data/attack.yaml create mode 100644 examples/truthseeker/conf/data/default.yaml create mode 100644 examples/truthseeker/conf/data/kdd_nsl.yaml create mode 100644 examples/truthseeker/conf/data/small.yaml create mode 100644 examples/truthseeker/conf/data/truthseeker.yaml create mode 100644 examples/truthseeker/conf/default.yaml create mode 100644 examples/truthseeker/conf/files/default.yaml create mode 100644 examples/truthseeker/conf/model.yaml create mode 100644 examples/truthseeker/conf/model/.gitignore create mode 100644 examples/truthseeker/conf/model/art/defense_grid.yaml create mode 100644 examples/truthseeker/conf/model/art/postprocessor.yaml create mode 100644 examples/truthseeker/conf/model/art/postprocessor/high_confidence.yaml create mode 100644 examples/truthseeker/conf/model/art/postprocessor/labels.yaml create mode 100644 examples/truthseeker/conf/model/art/preprocessor.yaml create mode 100644 examples/truthseeker/conf/model/art/preprocessor/feature_squeezing.yaml create mode 100644 examples/truthseeker/conf/model/default.yaml create mode 100644 examples/truthseeker/conf/model/linear.yaml create mode 100644 examples/truthseeker/conf/model/poly.yaml create mode 100644 examples/truthseeker/conf/model/rbf.yaml create mode 100644 examples/truthseeker/conf/scorers/default.yaml create mode 100644 examples/truthseeker/dvc.lock create mode 100644 examples/truthseeker/dvc.yaml create mode 100644 examples/truthseeker/logs/.gitignore create mode 100644 examples/truthseeker/models.sh create mode 100644 examples/truthseeker/other_data.sh create mode 100644 examples/truthseeker/params.yaml create mode 100644 examples/truthseeker/plots.py create mode 100644 examples/truthseeker/plots/.gitignore create mode 100644 examples/truthseeker/retrain.py diff --git a/.gitignore b/.gitignore index 0b81c49f..b48a71c8 100644 --- a/.gitignore +++ b/.gitignore @@ -94,7 +94,6 @@ ipython_config.py *.json *.npz *.npy -examples/pytorch/output/models/model.optimizer.pt # PyTorch Model Checkpoints @@ -131,3 +130,4 @@ examples/*/output/* deckard/deckard.egg-info/* *log.txt +examples/*/*/*/*/*/params.yaml diff --git a/deckard/base/data/data.py b/deckard/base/data/data.py index 9514a63a..0f1d7431 100644 --- a/deckard/base/data/data.py +++ b/deckard/base/data/data.py @@ -99,8 +99,7 @@ def initialize(self): result = self.load(self.name) if isinstance(result, list) and len(result) == 2: result = self.sample(*result) - else: - assert self.target is not None, "Target is not specified" + elif isinstance(result, DataFrame) and self.target is not None: if not isinstance(result, DataFrame): result = DataFrame(result) assert self.target in result, f"Target {self.target} not in data with columns {result.columns}" @@ -110,8 +109,8 @@ def initialize(self): else: X = result[~self.target] result = self.sample(X, y) - - assert len(result) == 4 + else: + assert len(result) == 4 result = self.sklearn_pipeline(*result) return result diff --git a/deckard/base/model/art_pipeline.py b/deckard/base/model/art_pipeline.py index b7182151..531c2cdb 100644 --- a/deckard/base/model/art_pipeline.py +++ b/deckard/base/model/art_pipeline.py @@ -249,6 +249,24 @@ def __call__(self, model: object, library: str = None, data=None) -> BaseEstimat config.update(**sub_kwargs) model = obj(model) if "trainer" in self.pipeline: - name, sub_kwargs = self.pipeline["trainer"]() + # name, sub_kwargs = self.pipeline["trainer"]() + # assert "attack" in sub_kwargs, "Attack must be specified if the adversarial training defence is chosen." + # attack = sub_kwargs.pop("attack") + # if isinstance(attack, DictConfig): + # attack = OmegaConf.to_container(attack, resolve=True) + # attack = instantiate(attack) + # elif is_dataclass(attack): + # attack = asdict(attack) + # attack['_target_'] = attack.pop('name') + # attack = instantiate(attack, model) + # elif isinstance(attack, dict): + # attack['_target_'] = attack.pop('name') + # attack = instantiate(attack, model) + # else: + # assert "art.attacks" in str(type(attack)), f"Attack must be an art attack. Got {type(attack)}" + # if name == "art.defences.trainer.AdversarialTrainer": + # from art.defences.trainer.adversarial_trainer import AdversarialTrainer + # model = AdversarialTrainer(classifier=model, attacks = attack, **sub_kwargs) + # else: raise NotImplementedError("Training Defense not implemented yet") return model diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 61b63ccd..18c80caf 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -185,8 +185,6 @@ def clean_data_for_plotting( def_gen_dict, atk_gen_dict, control_dict, - file, - folder, ): logger.info("Replacing attack and defence names with short names...") if hasattr(data, "def_gen"): @@ -199,15 +197,15 @@ def clean_data_for_plotting( logger.info("Dropping poorly merged columns...") data.dropna(axis=1, how="all", inplace=True) logger.info("Shortening model names...") - data["model_name"] = data["model.init.name"].str.split(".").str[-1] - data["model_layers"] = data["model_name"].str.split("Net").str[-1] + if "model.init.name" in data.columns: + data["model_name"] = data["model.init.name"].str.split(".").str[-1] + data["model_layers"] = data["model_name"].str.split("Net").str[-1] # Rename columns that end in '.1' by removing the '.1' data.columns.rename(lambda x: x[:-2] if x.endswith(".1") else x, inplace=True) logger.info("Replacing data.sample.random_state with random_state...") data["data.sample.random_state"].rename("random_state", inplace=True) data = format_control_parameter(data, control_dict) - logger.info(f"Saving data to {Path(folder) / file}") - data.to_csv(Path(folder) / "data.csv") + data.columns = data.columns.str.replace(" ", "") return data @@ -265,8 +263,6 @@ def save_results(results, results_file) -> str: def_gen_dict, atk_gen_dict, control_dict, - results_file, - report_folder, ) report_file = save_results(results, results_file) assert Path( diff --git a/deckard/layers/find_best.py b/deckard/layers/find_best.py index a3ed1406..71982efb 100644 --- a/deckard/layers/find_best.py +++ b/deckard/layers/find_best.py @@ -21,6 +21,8 @@ def find_optuna_best( config_subdir=None, direction=None, ): + logger.info(f"Study name: {study_name}") + logger.info(f"Storage name: {storage_name}") study = optuna.create_study( study_name=study_name, storage=storage_name, @@ -30,14 +32,17 @@ def find_optuna_best( df = study.trials_dataframe(attrs=("number", "value", "params", "state")) if study_csv is not None: df.to_csv(study_csv) - params = flatten_dict(study.best_params) - best_params = unflatten_dict(params) - best_params = flatten_dict(best_params) + best_params = flatten_dict(study.best_params) + more_params = flatten_dict(study.best_trial.user_attrs) + even_more_params = flatten_dict(study.best_trial.system_attrs) + logger.debug(f"Best params: {best_params}") + logger.debug(f"Best user params: {more_params}") + logger.debug(f"Best system params: {even_more_params}") + best_params = {**more_params, **best_params} overrides = [] for key, value in best_params.items(): logger.info(f"Overriding {key} with {value}") - if not key.startswith("+"): - overrides.append(f"++{key}={value}") + overrides.append(f"{key}={value}") with initialize_config_dir(config_dir=config_folder, version_base="1.3"): cfg = compose(config_name=default_config, overrides=overrides) cfg = OmegaConf.to_container(cfg, resolve=False) diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index 68964a6c..fbb80e25 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -84,12 +84,12 @@ def merge_params(default, params) -> dict: """ for key, value in params.items(): if key in default and isinstance(value, dict) and value is not None: - default[key] = merge_params(default[key], value) + default[key] = merge_params(default[key], value) elif ( isinstance(value, (list, tuple, int, float, str, bool, dict)) and value is not None ): - default[key] = value + default.update({key: value}) elif value is None: continue else: @@ -156,6 +156,9 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: if params is None: with open(Path(path, "params.yaml"), "r") as f: default_params = yaml.load(f, Loader=yaml.FullLoader) + default_params = OmegaConf.to_container( + OmegaConf.create(default_params), resolve=True, + ) key_list = [] for stage in stages: with open(Path(path, "dvc.yaml"), "r") as f: @@ -168,6 +171,9 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: elif isinstance(params, str) and Path(params).is_file() and Path(params).exists(): with open(Path(params), "r") as f: params = yaml.load(f, Loader=yaml.FullLoader) + params = OmegaConf.to_container( + OmegaConf.create(params), resolve=True, + ) assert isinstance( params, dict, @@ -238,9 +244,9 @@ def write_stage(params: dict, stage: str, path=None, working_dir=None) -> None: stage_params = {"stages": {stage: {}}} stage_params["stages"][stage] = dvc["stages"][stage] path.mkdir(exist_ok=True, parents=True) - with open(path / "dvc.yaml", "w") as f: - yaml.dump(stage_params, f, default_flow_style=False) - assert Path(path / "dvc.yaml").exists(), f"File {path/'dvc.yaml'} does not exist." + # with open(path / "dvc.yaml", "w") as f: + # yaml.dump(stage_params, f, default_flow_style=False) + # assert Path(path / "dvc.yaml").exists(), f"File {path/'dvc.yaml'} does not exist." with open(Path(path, "params.yaml"), "w") as f: yaml.dump(params, f, default_flow_style=False) assert Path( diff --git a/deckard/layers/utils.py b/deckard/layers/utils.py index fc208fdc..87057484 100644 --- a/deckard/layers/utils.py +++ b/deckard/layers/utils.py @@ -127,7 +127,7 @@ def save_params_file( config_dir = str(Path(Path(), config_dir).absolute().as_posix()) with initialize_config_dir(config_dir=config_dir, version_base="1.3"): cfg = compose(config_name=config_file, overrides=overrides) - params = OmegaConf.to_container(cfg, resolve=False) + params = OmegaConf.to_container(cfg, resolve=True) with open(params_file, "w") as f: yaml.dump(params, f) logger.info(f"Saved params file to {params_file}") diff --git a/examples/classification/.dvc/config b/examples/classification/.dvc/config new file mode 100644 index 00000000..e69de29b diff --git a/examples/classification/attacks.sh b/examples/classification/attacks.sh index ca773483..d9e3f1e0 100644 --- a/examples/classification/attacks.sh +++ b/examples/classification/attacks.sh @@ -1,17 +1,17 @@ #!/bin/bash - MODEL_CONFIGS=$(ls conf/model/best_*.yaml) CONFIG_NAMES=$(ls conf/model/best_*.yaml | cut -d'/' -f3 | cut -d'.' -f1) TOTAL=$(( ${#CONFIG_NAMES[@]} )) i=$(( 0 )) mkdir -p logs/attacks/ for model_config in $CONFIG_NAMES; do + kernel_name=$(echo $model_config | cut -d'_' -f2) i=$(( i + 1 )) if [ $model_config == "default" ]; then continue fi - echo "Running model $model_config. Number $i of $TOTAL" >> attack_log.txt HYDRA_FULL_ERROR=1 python -m deckard.layers.optimise \ + ++model.init.kernel=$kernel_name \ ++stage=attack \ ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent \ ++attack.init.norm=1,2,inf \ @@ -20,8 +20,9 @@ for model_config in $CONFIG_NAMES; do ++attack.init.eps=.001,.01,.1,.3,.5,1 \ ++attack.init.max_iter=1,10,100,1000 \ ++hydra.sweeper.study_name=$model_config \ + ++attack.attack_size=100 \ model=$model_config $@ --multirun >> logs/attacks/$model_config.log - echo "Successfully completed model $model_config. Number $i of $TOTAL" >> attack_log.txt + echo "Successfully completed model $model_config" >> attack_log.txt done # Other attacks listed below diff --git a/examples/classification/conf/attack.yaml b/examples/classification/conf/attack.yaml index c578880d..da237ab0 100644 --- a/examples/classification/conf/attack.yaml +++ b/examples/classification/conf/attack.yaml @@ -12,8 +12,6 @@ defaults: optimizers: adv_accuracy direction : minimize hydra: - run: - dir : "./${files.directory}" sweeper: sampler: _target_: optuna.samplers.GridSampler diff --git a/examples/classification/conf/default.yaml b/examples/classification/conf/default.yaml index b0340b69..271ce8c2 100644 --- a/examples/classification/conf/default.yaml +++ b/examples/classification/conf/default.yaml @@ -7,9 +7,9 @@ defaults: - scorers: default - override hydra/sweeper : optuna - override hydra/launcher : joblib +direction: maximize +optimizers : accuracy hydra: - run: - dir : "./${files.directory}" sweeper: sampler: _target_: optuna.samplers.TPESampler diff --git a/examples/classification/conf/model.yaml b/examples/classification/conf/model.yaml index 18ea5bc5..6380abc4 100644 --- a/examples/classification/conf/model.yaml +++ b/examples/classification/conf/model.yaml @@ -11,8 +11,6 @@ defaults: optimizers : accuracy direction : maximize hydra: - run: - dir : "./${files.directory}" sweeper: sampler: _target_: optuna.samplers.GridSampler diff --git a/examples/classification/dvc.lock b/examples/classification/dvc.lock index ea4d41ba..6ac79df2 100644 --- a/examples/classification/dvc.lock +++ b/examples/classification/dvc.lock @@ -49,7 +49,23 @@ stages: _target_: deckard.base.model.art_pipeline.ArtPipeline initialize: library: sklearn-svc - data: ${data} + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true init: C: 1.0 _target_: deckard.base.model.ModelInitializer @@ -84,8 +100,8 @@ stages: size: 42667 - path: output/reports/train/default/score_dict.json hash: md5 - md5: b0708f21e769603fd7bcda7f0ce9b9dd - size: 355 + md5: 195e2e518f502d3307629fb721f9583b + size: 357 attack: cmd: python -m deckard.layers.experiment attack deps: @@ -103,19 +119,97 @@ stages: size: 42667 - path: output/reports/train/default/score_dict.json hash: md5 - md5: b0708f21e769603fd7bcda7f0ce9b9dd - size: 355 + md5: 195e2e518f502d3307629fb721f9583b + size: 357 params: params.yaml: attack: _target_: deckard.base.attack.Attack attack_size: 10 - data: ${data} + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true init: - model: ${model} + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc name: art.attacks.evasion.ProjectedGradientDescent method: evasion - model: ${model} + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc data: _target_: deckard.base.data.Data generate: @@ -161,7 +255,23 @@ stages: _target_: deckard.base.model.art_pipeline.ArtPipeline initialize: library: sklearn-svc - data: ${data} + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true init: C: 1.0 _target_: deckard.base.model.ModelInitializer @@ -208,27 +318,27 @@ stages: size: 42667 - path: output/reports/attack/default/score_dict.json hash: md5 - md5: 53014285cc5719c85ae534242660b228 - size: 581 + md5: 41e353b3926d5482bea344665f6bf93f + size: 579 models: cmd: bash models.sh +stage=train --config-name=model.yaml deps: - path: conf/model.yaml hash: md5 - md5: daaa0663d05972a5b8645c35d364da88 - size: 990 + md5: d2fdcee453cdf1eb749eb28931e8ebbf + size: 950 - path: models.sh hash: md5 - md5: 62ba40f661a78745a3f6a6b2748ab5ea - size: 2967 + md5: 98694a486e52e950882ada2d2ce51377 + size: 2973 - path: output/reports/train/default/params.yaml hash: md5 md5: d4e0a34b2b15765ca71fa5ecaf7e3826 size: 2100 - path: params.yaml hash: md5 - md5: 96386b553b09cde769f5d7784db43b9a - size: 1804 + md5: 4b1c07b7f8c3a67f5c257b7c5fa72c85 + size: 4223 params: params.yaml: data: @@ -276,7 +386,23 @@ stages: _target_: deckard.base.model.art_pipeline.ArtPipeline initialize: library: sklearn-svc - data: ${data} + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true init: C: 1.0 _target_: deckard.base.model.ModelInitializer @@ -299,139 +425,139 @@ stages: outs: - path: logs/models/ hash: md5 - md5: b5204e3b5172d9080caf38c719c86536.dir - size: 4719104 - nfiles: 51 + md5: d9dd105b6ca60d5a6ff624e9a021d170.dir + size: 4938450 + nfiles: 60 - path: model.db hash: md5 - md5: 21aefda7997c5f31484f8d161ecb2138 - size: 352256 + md5: 08787c64b2b8e9d66873b6728c37632d + size: 1843200 compile_models: cmd: python -m deckard.layers.compile --report_folder output/reports/train/ --results_file output/train.csv deps: - path: logs/models/ hash: md5 - md5: b5204e3b5172d9080caf38c719c86536.dir - size: 4719104 - nfiles: 51 + md5: d9dd105b6ca60d5a6ff624e9a021d170.dir + size: 4938450 + nfiles: 60 - path: model.db hash: md5 - md5: 21aefda7997c5f31484f8d161ecb2138 - size: 352256 + md5: 08787c64b2b8e9d66873b6728c37632d + size: 1843200 - path: output/reports/train/ hash: md5 - md5: 7664c228456825f67dc1343d680dc23b.dir - size: 4275212383 - nfiles: 7517 + md5: 3cbcd641b8792e062a5e3f554b1ed2c8.dir + size: 4311984765 + nfiles: 11139 outs: - path: output/train.csv hash: md5 - md5: a0261aa2216ef56ff024dab0da7e9ff3 - size: 2720482 + md5: 64c56ee3b82fc42f885338d5a42bbd1c + size: 4323243 find_best_model@rbf: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model --params_file best_rbf --study_name=rbf_100_10000 --default_config model.yaml deps: - path: logs/models/ hash: md5 - md5: b5204e3b5172d9080caf38c719c86536.dir - size: 4719104 - nfiles: 51 + md5: d9dd105b6ca60d5a6ff624e9a021d170.dir + size: 4938450 + nfiles: 60 - path: model.db hash: md5 - md5: 21aefda7997c5f31484f8d161ecb2138 - size: 352256 + md5: 08787c64b2b8e9d66873b6728c37632d + size: 1843200 - path: output/train.csv hash: md5 - md5: a0261aa2216ef56ff024dab0da7e9ff3 - size: 2720482 + md5: 64c56ee3b82fc42f885338d5a42bbd1c + size: 4323243 outs: - path: conf/model/best_rbf.yaml hash: md5 - md5: f213864712f88d12667e195e9bee8310 - size: 328 + md5: 0a90767d020934a3cd6d0c42a6f21606 + size: 357 find_best_model@linear: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model --params_file best_linear --study_name=linear_100_10000 --default_config model.yaml deps: - path: logs/models/ hash: md5 - md5: b5204e3b5172d9080caf38c719c86536.dir - size: 4719104 - nfiles: 51 + md5: d9dd105b6ca60d5a6ff624e9a021d170.dir + size: 4938450 + nfiles: 60 - path: model.db hash: md5 - md5: 21aefda7997c5f31484f8d161ecb2138 - size: 352256 + md5: 08787c64b2b8e9d66873b6728c37632d + size: 1843200 - path: output/train.csv hash: md5 - md5: a0261aa2216ef56ff024dab0da7e9ff3 - size: 2720482 + md5: 64c56ee3b82fc42f885338d5a42bbd1c + size: 4323243 outs: - path: conf/model/best_linear.yaml hash: md5 - md5: 5f4c8becd800d6f6b9fe01c7c78ecac7 - size: 329 + md5: 23a7c49f5a8ddf63a7ac89fb61c0034d + size: 332 find_best_model@poly: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model --params_file best_poly --study_name=poly_100_10000 --default_config model.yaml deps: - path: logs/models/ hash: md5 - md5: b5204e3b5172d9080caf38c719c86536.dir - size: 4719104 - nfiles: 51 + md5: d9dd105b6ca60d5a6ff624e9a021d170.dir + size: 4938450 + nfiles: 60 - path: model.db hash: md5 - md5: 21aefda7997c5f31484f8d161ecb2138 - size: 352256 + md5: 08787c64b2b8e9d66873b6728c37632d + size: 1843200 - path: output/train.csv hash: md5 - md5: a0261aa2216ef56ff024dab0da7e9ff3 - size: 2720482 + md5: 64c56ee3b82fc42f885338d5a42bbd1c + size: 4323243 outs: - path: conf/model/best_poly.yaml hash: md5 - md5: 9ffbcdac2b9ca989ee6099a1c22946ba - size: 328 + md5: a9d600cc46e9f49c3a0cca90f7c7d876 + size: 370 attacks: cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml deps: - path: conf/model/best_linear.yaml hash: md5 - md5: 5f4c8becd800d6f6b9fe01c7c78ecac7 - size: 329 + md5: 23a7c49f5a8ddf63a7ac89fb61c0034d + size: 332 - path: conf/model/best_poly.yaml hash: md5 - md5: 9ffbcdac2b9ca989ee6099a1c22946ba - size: 328 + md5: a9d600cc46e9f49c3a0cca90f7c7d876 + size: 370 - path: conf/model/best_rbf.yaml hash: md5 - md5: f213864712f88d12667e195e9bee8310 - size: 328 + md5: 0a90767d020934a3cd6d0c42a6f21606 + size: 357 - path: logs/models/ hash: md5 - md5: b5204e3b5172d9080caf38c719c86536.dir - size: 4719104 - nfiles: 51 + md5: d9dd105b6ca60d5a6ff624e9a021d170.dir + size: 4938450 + nfiles: 60 - path: model.db hash: md5 - md5: 21aefda7997c5f31484f8d161ecb2138 - size: 352256 + md5: 08787c64b2b8e9d66873b6728c37632d + size: 1843200 - path: output/train.csv hash: md5 - md5: a0261aa2216ef56ff024dab0da7e9ff3 - size: 2720482 + md5: 64c56ee3b82fc42f885338d5a42bbd1c + size: 4323243 outs: - path: attack.db hash: md5 - md5: 8c98b86efe4e9aaf8a420edf03e32803 - size: 466944 + md5: df10d4e743f0a815bfd83744eb7ca4f3 + size: 2039808 - path: logs/attacks/ hash: md5 - md5: 8d7590472039d0efbea4210491ee5440.dir - size: 3625817 + md5: 79f819648d0ac189f0007dd29dc9ca35.dir + size: 17291838 nfiles: 3 compile_attacks: cmd: python -m deckard.layers.compile --report_folder output/reports/attack/ --results_file @@ -439,89 +565,89 @@ stages: deps: - path: attack.db hash: md5 - md5: 8c98b86efe4e9aaf8a420edf03e32803 - size: 466944 + md5: df10d4e743f0a815bfd83744eb7ca4f3 + size: 2039808 - path: logs/attacks/ hash: md5 - md5: 8d7590472039d0efbea4210491ee5440.dir - size: 3625817 + md5: 79f819648d0ac189f0007dd29dc9ca35.dir + size: 17291838 nfiles: 3 - path: output/reports/attack/ hash: md5 - md5: 413dec4ee12c0dbd64343bd0bc221b4b.dir - size: 30272123 - nfiles: 2415 + md5: 93e02cdd34be3720bba985edabd2fc6b.dir + size: 17374464 + nfiles: 1329 outs: - path: output/attack.csv hash: md5 - md5: 51c3f2ca216fe98169c192956c3d448a - size: 745826 + md5: a8b90230af3b4c71da865f2d4b0e689e + size: 398932 find_best_attack@linear: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack - --params_file best_linear --study_name=linear_100_10000 --default_config model.yaml + --params_file best_linear --study_name=best_linear --default_config attack.yaml deps: - path: logs/models/ hash: md5 - md5: b5204e3b5172d9080caf38c719c86536.dir - size: 4719104 - nfiles: 51 + md5: d9dd105b6ca60d5a6ff624e9a021d170.dir + size: 4938450 + nfiles: 60 - path: model.db hash: md5 - md5: 21aefda7997c5f31484f8d161ecb2138 - size: 352256 + md5: 08787c64b2b8e9d66873b6728c37632d + size: 1843200 - path: output/train.csv hash: md5 - md5: a0261aa2216ef56ff024dab0da7e9ff3 - size: 2720482 + md5: 64c56ee3b82fc42f885338d5a42bbd1c + size: 4323243 outs: - path: conf/attack/best_linear.yaml hash: md5 - md5: 7ff95c46b1c2d0dc489fae3f1db40005 - size: 9 + md5: 4bb6215963ae7f0025f72ec31e26f29d + size: 244 find_best_attack@rbf: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack - --params_file best_rbf --study_name=rbf_100_10000 --default_config model.yaml + --params_file best_rbf --study_name=best_rbf --default_config attack.yaml deps: - path: logs/models/ hash: md5 - md5: b5204e3b5172d9080caf38c719c86536.dir - size: 4719104 - nfiles: 51 + md5: d9dd105b6ca60d5a6ff624e9a021d170.dir + size: 4938450 + nfiles: 60 - path: model.db hash: md5 - md5: 21aefda7997c5f31484f8d161ecb2138 - size: 352256 + md5: 08787c64b2b8e9d66873b6728c37632d + size: 1843200 - path: output/train.csv hash: md5 - md5: a0261aa2216ef56ff024dab0da7e9ff3 - size: 2720482 + md5: 64c56ee3b82fc42f885338d5a42bbd1c + size: 4323243 outs: - path: conf/attack/best_rbf.yaml hash: md5 - md5: 7ff95c46b1c2d0dc489fae3f1db40005 - size: 9 + md5: eca3091f7c0eb0b8958bc6becf43191d + size: 244 find_best_attack@poly: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack - --params_file best_poly --study_name=poly_100_10000 --default_config model.yaml + --params_file best_poly --study_name=best_poly --default_config attack.yaml deps: - path: logs/models/ hash: md5 - md5: b5204e3b5172d9080caf38c719c86536.dir - size: 4719104 - nfiles: 51 + md5: d9dd105b6ca60d5a6ff624e9a021d170.dir + size: 4938450 + nfiles: 60 - path: model.db hash: md5 - md5: 21aefda7997c5f31484f8d161ecb2138 - size: 352256 + md5: 08787c64b2b8e9d66873b6728c37632d + size: 1843200 - path: output/train.csv hash: md5 - md5: a0261aa2216ef56ff024dab0da7e9ff3 - size: 2720482 + md5: 64c56ee3b82fc42f885338d5a42bbd1c + size: 4323243 outs: - path: conf/attack/best_poly.yaml hash: md5 - md5: 7ff95c46b1c2d0dc489fae3f1db40005 - size: 9 + md5: b5f8f874e44dbc8bdb0ababc67295174 + size: 246 other_data_train@kdd_nsl: cmd: DATASET_NAME=kdd_nsl bash other_data.sh data=kdd_nsl +stage=train --config-name=model.yaml deps: @@ -557,73 +683,109 @@ stages: deps: - path: conf/attack/best_linear.yaml hash: md5 - md5: 7ff95c46b1c2d0dc489fae3f1db40005 - size: 9 + md5: 4bb6215963ae7f0025f72ec31e26f29d + size: 244 - path: conf/attack/best_poly.yaml hash: md5 - md5: 7ff95c46b1c2d0dc489fae3f1db40005 - size: 9 + md5: b5f8f874e44dbc8bdb0ababc67295174 + size: 246 - path: conf/attack/best_rbf.yaml hash: md5 - md5: 7ff95c46b1c2d0dc489fae3f1db40005 - size: 9 + md5: eca3091f7c0eb0b8958bc6becf43191d + size: 244 - path: conf/model/best_linear.yaml hash: md5 - md5: cd69a0189843e6474613ea33e96bb29b - size: 327 + md5: 23a7c49f5a8ddf63a7ac89fb61c0034d + size: 332 - path: conf/model/best_poly.yaml hash: md5 - md5: cd69a0189843e6474613ea33e96bb29b - size: 327 + md5: a9d600cc46e9f49c3a0cca90f7c7d876 + size: 370 - path: conf/model/best_rbf.yaml hash: md5 - md5: cd69a0189843e6474613ea33e96bb29b - size: 327 + md5: 0a90767d020934a3cd6d0c42a6f21606 + size: 357 - path: output/attacks/ hash: md5 - md5: 00cf0085d338326da6d1738058368c49.dir - size: 1159530 - nfiles: 240 + md5: feacaf410b6d02d31060ca2eb7dda7e2.dir + size: 4819192 + nfiles: 121 - path: output/models/ hash: md5 - md5: 484d472b0b10beae09f796a1a0ceac57.dir - size: 2880142 - nfiles: 169 + md5: a1ae09e3b875da75aff46e8508e0e7d9.dir + size: 7290058 + nfiles: 613 outs: - path: plots/after_retrain_confidence.csv hash: md5 - md5: 4b2ab48e7315673d763502691ec4f7b2 - size: 539162 + md5: 439d2511d6be751a3b370a98792a8ba0 + size: 35819 - path: plots/before_retrain_confidence.csv hash: md5 - md5: 044f0f9851895662dfb01de5254c6b00 - size: 527699 + md5: d319aabcfcb3ad48ae8456cafd9db2c0 + size: 35802 - path: retrain/ hash: md5 - md5: 10e29a3b73fa5ac496881378f5b66463.dir - size: 193686 + md5: 5e416b4ed718b3a4404e48048930282c.dir + size: 176889 nfiles: 12 plots: cmd: python plots.py deps: + - path: output/attack.csv + hash: md5 + md5: a8b90230af3b4c71da865f2d4b0e689e + size: 398932 + - path: output/train.csv + hash: md5 + md5: 64c56ee3b82fc42f885338d5a42bbd1c + size: 4323243 - path: plots/after_retrain_confidence.csv hash: md5 - md5: 9d3b1fdeb33b7479703f4c47bb5a8687 - size: 365818 + md5: 439d2511d6be751a3b370a98792a8ba0 + size: 35819 - path: plots/before_retrain_confidence.csv hash: md5 - md5: 9d3b1fdeb33b7479703f4c47bb5a8687 - size: 365818 + md5: d319aabcfcb3ad48ae8456cafd9db2c0 + size: 35802 outs: - path: plots/accuracy_vs_attack_parameters.eps hash: md5 - md5: 38680ac0fd6d0983b46eec3d5adcae03 - size: 34747 + md5: ba869c3e82249de5f28ca823744a5405 + size: 41516 - path: plots/accuracy_vs_features.eps hash: md5 - md5: e641a4efda42a2d647ffe33d3a397ec2 - size: 24914 + md5: 3249e773665ec8fe050ab414a8d48d33 + size: 22707 - path: plots/accuracy_vs_samples.eps hash: md5 - md5: 7a967461c9b8159b7b5bdbb69f15f350 - size: 21284 + md5: be0c4724a0a10928ef696cb2a9c3661e + size: 24443 + - path: plots/confidence_vs_attack_parameters.eps + hash: md5 + md5: 625ddf8a03f34e361efe4b5ad0dbe727 + size: 42243 + - path: plots/retrain_accuracy.eps + hash: md5 + md5: 7fe23f4cc126f9135fa05941c9783eb9 + size: 23419 + - path: plots/retrain_confidence_vs_attack_parameters.eps + hash: md5 + md5: 50105ac743b6900c8f75bd08e4e935ad + size: 42192 + - path: plots/retrain_time.eps + hash: md5 + md5: d2cdf4c0f9266e87af87b427d835c5e9 + size: 20952 + - path: plots/train_time_vs_attack_parameters.eps + hash: md5 + md5: a04c540e19c01816f422d5ff65dc0885 + size: 38897 + - path: plots/train_time_vs_features.eps + hash: md5 + md5: 87cb1eafd89d657890de704d184b78e2 + size: 21125 + - path: plots/train_time_vs_samples.eps + hash: md5 + md5: 5ebae11c0fb43d301298274b657b8996 + size: 24477 diff --git a/examples/classification/dvc.yaml b/examples/classification/dvc.yaml index 58307f81..e44f6357 100644 --- a/examples/classification/dvc.yaml +++ b/examples/classification/dvc.yaml @@ -112,7 +112,7 @@ stages: - rbf - poly do: - cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack --params_file best_${item} --study_name=${item}_100_10000 --default_config model.yaml + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack --params_file best_${item} --study_name=best_${item} --default_config attack.yaml outs: - conf/attack/best_${item}.yaml deps: @@ -139,17 +139,17 @@ stages: cmd : python plots.py deps : - plots/after_retrain_confidence.csv - # - plots/attack_results.csv + - output/train.csv - plots/before_retrain_confidence.csv - # - plots/model_results.csv + - output/attack.csv plots : - plots/accuracy_vs_attack_parameters.eps - plots/accuracy_vs_features.eps - plots/accuracy_vs_samples.eps - # - plots/confidence_vs_attack_parameters.eps - # - plots/fit_time_vs_attack_parameters.eps - # - plots/fit_time_vs_features.eps - # - plots/fit_time_vs_samples.eps - # - plots/retrain_accuracy.eps - # - plots/retrain_confidence_vs_attack_parameters.eps - # - plots/retrain_time.eps + - plots/confidence_vs_attack_parameters.eps + - plots/train_time_vs_attack_parameters.eps + - plots/train_time_vs_features.eps + - plots/train_time_vs_samples.eps + - plots/retrain_accuracy.eps + - plots/retrain_confidence_vs_attack_parameters.eps + - plots/retrain_time.eps diff --git a/examples/classification/models.sh b/examples/classification/models.sh index 594bb31d..6934ee1a 100644 --- a/examples/classification/models.sh +++ b/examples/classification/models.sh @@ -1,6 +1,6 @@ # N_FEATURES=( 10 100 1000 10000 10000 100000 1000000) # TRAIN_SIZES=( 10 100 1000 ) -TRAIN_SIZES=( 10000 ) +TRAIN_SIZES=( 1000 ) N_FEATURES=( 100 ) N_SAMPLES=( 1010000 ) TOTAL=$(( ${#N_FEATURES[@]} * ${#N_SAMPLES[@]} * ${#TRAIN_SIZES[@]} )) @@ -19,7 +19,7 @@ for train_size in ${TRAIN_SIZES[@]}; do ++data.generate.n_features=$n_features \ ++data.generate.n_samples=$n_samples \ ++data.sample.train_size=$train_size \ - model.init.kernel=linear \ + ++model.init.kernel=linear \ model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ ++hydra.sweeper.study_name=linear_${n_features}_${train_size} "$@" --multirun \ # Keeps a log of the output for each experiment @@ -30,7 +30,7 @@ for train_size in ${TRAIN_SIZES[@]}; do ++data.generate.n_features=$n_features \ ++data.generate.n_samples=$n_samples \ ++data.sample.train_size=$train_size \ - model.init.kernel=rbf \ + ++model.init.kernel=rbf \ +model.init.gamma=scale \ model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ @@ -42,7 +42,7 @@ for train_size in ${TRAIN_SIZES[@]}; do ++data.generate.n_features=$n_features \ ++data.generate.n_samples=$n_samples \ ++data.sample.train_size=$train_size \ - model.init.kernel=poly \ + ++model.init.kernel=poly \ +model.init.degree=1,2,3,4,5 \ +model.init.gamma=scale \ +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ diff --git a/examples/classification/params.yaml b/examples/classification/params.yaml index b6dd6a11..82a3a418 100644 --- a/examples/classification/params.yaml +++ b/examples/classification/params.yaml @@ -46,12 +46,12 @@ attack: C: 1.0 _target_: deckard.base.model.ModelInitializer kernel: rbf - max_iter: 100 + max_iter: 10 name: sklearn.svm.SVC probability: true random_state: 0 library: sklearn-svc - name: art.attacks.evasion.HopSkipJump + name: art.attacks.evasion.ProjectedGradientDescent method: evasion model: _target_: deckard.base.model.Model @@ -80,7 +80,7 @@ attack: C: 1.0 _target_: deckard.base.model.ModelInitializer kernel: rbf - max_iter: 100 + max_iter: 10 name: sklearn.svm.SVC probability: true random_state: 0 @@ -102,6 +102,7 @@ data: name: sklearn.preprocessing.StandardScaler with_mean: true with_std: true +direction: maximize files: _target_: deckard.base.files.FileConfig adv_predictions_file: adv_predictions.json @@ -151,11 +152,12 @@ model: C: 1.0 _target_: deckard.base.model.ModelInitializer kernel: rbf - max_iter: 100 + max_iter: 10 name: sklearn.svm.SVC probability: true random_state: 0 library: sklearn-svc +optimizers: accuracy scorers: _target_: deckard.base.scorer.ScorerDict accuracy: diff --git a/examples/classification/plots.py b/examples/classification/plots.py index 1420e713..ba7aac20 100644 --- a/examples/classification/plots.py +++ b/examples/classification/plots.py @@ -3,7 +3,7 @@ from pathlib import Path import matplotlib.pyplot as plt -from deckard.layers.compile import parse_results +import matplotlib.pyplot as plt import logging sns.set_style("whitegrid") @@ -15,36 +15,27 @@ logger = logging.getLogger(__name__) -if Path("plots", "model_results.csv").exists(): - results = pd.read_csv("plots/model_results.csv") -else: - results = parse_results("reports/model_queue/") -input_size = results["data.generate.n_samples"] * results["data.generate.n_features"] -results["Kernel"] = results["model.init.kernel"].copy() -results["Features"] = results["data.generate.n_features"].copy() +# if Path("plots", "model_results.csv").exists(): +# results = pd.read_csv("plots/model_results.csv") +# else: +# results = parse_results("reports/model_queue/") +results = pd.read_csv("output/train.csv") +input_size = results["data.generate.kwargs.n_samples"] * results["data.generate.kwargs.n_features"] +results["Kernel"] = results["model.init.kwargs.kernel"].copy() +results["Features"] = results["data.generate.kwargs.n_features"].copy() results["Samples"] = results["data.sample.train_size"].copy() results["input_size"] = input_size -sample_list = results["data.generate.n_samples"].unique() -feature_list = results["data.generate.n_features"].unique() -kernel_list = results["model.init.kernel"].unique() if "Unnamed: 0" in results.columns: del results["Unnamed: 0"] for col in results.columns: if col == "data.name" and isinstance(results[col][0], list): results[col] = results[col].apply(lambda x: x[0]) -results = results[results["model.init.kernel"] != "sigmoid"] - +results = results[results["model.init.kwargs.kernel"] != "sigmoid"] -if Path("plots", "attack_results.csv").exists(): - attack_results = pd.read_csv("plots/attack_results.csv") -else: - attack_results = parse_results("reports/attack/") -attack_results["Kernel"] = attack_results["model.init.kernel"].copy() -attack_results["Features"] = attack_results["data.generate.n_features"].copy() +attack_results = pd.read_csv("output/attack.csv") +attack_results["Kernel"] = attack_results["model.init.kwargs.kernel"].copy() +attack_results["Features"] = attack_results["data.generate.kwargs.n_features"].copy() attack_results["Samples"] = attack_results["data.sample.train_size"].copy() -sample_list = attack_results["data.generate.n_samples"].unique() -feature_list = attack_results["data.generate.n_features"].unique() -kernel_list = attack_results["model.init.kernel"].unique() if "Unnamed: 0" in attack_results.columns: del attack_results["Unnamed: 0"] for col in attack_results.columns: @@ -65,11 +56,12 @@ graph1.set_ylabel("Accuracy") graph1.set_xscale("log") graph1.get_figure().tight_layout() +graph1.set(xlim=(10,1e6)) graph1.get_figure().savefig("plots/accuracy_vs_samples.pdf") - +plt.gcf().clear() graph2 = sns.lineplot( - x="data.generate.n_features", + x="data.generate.kwargs.n_features", y="accuracy", data=results, style="Kernel", @@ -81,16 +73,12 @@ graph2.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") graph2.get_figure().tight_layout() graph2.get_figure().savefig("plots/accuracy_vs_features.pdf") +plt.gcf().clear() -results["fit_time"] = ( - results["fit_time"] - * results["data.sample.train_size"] - * results["data.generate.n_samples"] -) graph3 = sns.lineplot( - x="data.generate.n_features", - y="fit_time", + x="data.generate.kwargs.n_features", + y="train_time", data=results, style="Kernel", style_order=["rbf", "poly", "linear"], @@ -100,27 +88,27 @@ graph3.set(yscale="log", xscale="log") graph3.legend(title="Kernel") graph3.get_figure().tight_layout() -graph3.get_figure().savefig("plots/fit_time_vs_features.pdf") - +graph3.get_figure().savefig("plots/train_time_vs_features.pdf") +plt.gcf().clear() graph4 = sns.lineplot( x="data.sample.train_size", - y="fit_time", + y="train_time", data=results, style="Kernel", style_order=["rbf", "poly", "linear"], ) graph4.set_xlabel("Number of Samples") graph4.set_ylabel("Training Time") -graph4.set(yscale="log", xscale="log") +graph4.set(yscale="log", xscale="log", xlim=(10,1e6)) graph4.legend(title="Kernel") graph4.get_figure().tight_layout() -graph4.get_figure().savefig("plots/fit_time_vs_samples.pdf") - +graph4.get_figure().savefig("plots/train_time_vs_samples.pdf") +plt.gcf().clear() fig, ax = plt.subplots(2, 2) graph5 = sns.lineplot( - x="attack.init.eps", + x="attack.init.kwargs.eps", y="accuracy", data=attack_results, style="Kernel", @@ -131,7 +119,7 @@ ) graph5.set(xscale="log", xlabel="Perturbation Distance", ylabel="Accuracy") graph6 = sns.lineplot( - x="attack.init.eps_step", + x="attack.init.kwargs.eps_step", y="accuracy", data=attack_results, style="Kernel", @@ -141,7 +129,7 @@ ) graph6.set(xscale="log", xlabel="Perturbation Step", ylabel="Accuracy") graph7 = sns.lineplot( - x="attack.init.max_iter", + x="attack.init.kwargs.max_iter", y="accuracy", data=attack_results, style="Kernel", @@ -152,7 +140,7 @@ ) graph7.set(xscale="log", xlabel="Maximum Iterations", ylabel="Accuracy") graph8 = sns.lineplot( - x="attack.init.batch_size", + x="attack.init.kwargs.batch_size", y="accuracy", data=attack_results, style="Kernel", @@ -165,12 +153,12 @@ graph6.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") fig.tight_layout() fig.savefig("plots/accuracy_vs_attack_parameters.pdf") - +plt.gcf().clear() fig, ax = plt.subplots(2, 2) graph9 = sns.lineplot( - x="attack.init.eps", - y="fit_time", + x="attack.init.kwargs.eps", + y="adv_fit_time", data=attack_results, style="Kernel", ax=ax[0, 0], @@ -180,8 +168,8 @@ ) graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="Attack Time") graph10 = sns.lineplot( - x="attack.init.eps_step", - y="fit_time", + x="attack.init.kwargs.eps_step", + y="adv_fit_time", data=attack_results, style="Kernel", ax=ax[0, 1], @@ -190,8 +178,8 @@ ) graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="Attack Time") graph11 = sns.lineplot( - x="attack.init.max_iter", - y="fit_time", + x="attack.init.kwargs.max_iter", + y="adv_fit_time", data=attack_results, style="Kernel", ax=ax[1, 0], @@ -201,8 +189,8 @@ ) graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="Attack Time") graph12 = sns.lineplot( - x="attack.init.batch_size", - y="fit_time", + x="attack.init.kwargs.batch_size", + y="adv_fit_time", data=attack_results, style="Kernel", ax=ax[1, 1], @@ -213,8 +201,8 @@ graph12.set(xscale="log", xlabel="Batch Size", ylabel="Attack Time") graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") fig.tight_layout(h_pad=0.5) -fig.savefig("plots/fit_time_vs_attack_parameters.pdf") - +fig.savefig("plots/train_time_vs_attack_parameters.pdf") +plt.gcf().clear() retrain_df = pd.DataFrame() for kernel in ["rbf", "linear", "poly"]: @@ -224,11 +212,10 @@ break tmp["Kernel"] = kernel tmp["Epochs"] = tmp.index - retrain_df = retrain_df.append(tmp) + retrain_df = pd.concat([retrain_df, tmp], axis=0) retrain_df = retrain_df.reset_index(drop=True) if "Unnamed: 0" in retrain_df.columns: del retrain_df["Unnamed: 0"] -# retrain_df['train_size'] = retrain_df['train_size'] * 100 retrain = sns.lineplot( @@ -252,7 +239,7 @@ retrain.set_ylabel("Accuracy") retrain.get_figure().tight_layout() retrain.get_figure().savefig("plots/retrain_accuracy.pdf") - +plt.gcf().clear() retrain_df["ben_time"] = retrain_df["ben_time"] * retrain_df["train_size"] * 10 retrain_df["adv_time"] = retrain_df["adv_time"] * retrain_df["attack_size"] @@ -278,7 +265,7 @@ retrain.set_yscale("log") retrain.get_figure().tight_layout() retrain.get_figure().savefig("plots/retrain_time.pdf") - +plt.gcf().clear() confidence_df = pd.read_csv("plots/before_retrain_confidence.csv") fig, ax = plt.subplots(2, 2) @@ -328,7 +315,7 @@ graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") fig.tight_layout(h_pad=0.5) fig.savefig("plots/confidence_vs_attack_parameters.pdf") - +plt.gcf().clear() confdence_df = pd.read_csv("plots/after_retrain_confidence.csv") confidence_df.columns @@ -379,3 +366,4 @@ graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") fig.tight_layout(h_pad=0.5) fig.savefig("plots/retrain_confidence_vs_attack_parameters.pdf") +plt.gcf().clear() \ No newline at end of file diff --git a/examples/classification/plots/.gitignore b/examples/classification/plots/.gitignore index adf6bc25..4c882c2e 100644 --- a/examples/classification/plots/.gitignore +++ b/examples/classification/plots/.gitignore @@ -1,3 +1,10 @@ /accuracy_vs_attack_parameters.eps /accuracy_vs_features.eps /accuracy_vs_samples.eps +/confidence_vs_attack_parameters.eps +/train_time_vs_attack_parameters.eps +/train_time_vs_features.eps +/train_time_vs_samples.eps +/retrain_accuracy.eps +/retrain_confidence_vs_attack_parameters.eps +/retrain_time.eps diff --git a/examples/classification/retrain.py b/examples/classification/retrain.py new file mode 100644 index 00000000..1fa27e49 --- /dev/null +++ b/examples/classification/retrain.py @@ -0,0 +1,411 @@ +import json +import logging +import os +import pickle +from pathlib import Path +from time import process_time +import json +from typing import List +import numpy as np +import pandas as pd +import yaml +from art.attacks.evasion import ProjectedGradientDescent +from art.estimators.classification.scikitlearn import ScikitlearnSVC +from art.utils import to_categorical +from pandas import DataFrame +from sklearn.svm import SVC +from tqdm import tqdm +from hydra.utils import instantiate + + +logger = logging.getLogger(__name__) + +logging.basicConfig(level=logging.INFO) +def parse_folder( + folder, + exclude=[ + "probabilities", + "predictions", + "plots", + "ground_truth", + "attack_predictions", + "attack_probabilities", + "samples", + ], +) -> pd.DataFrame: + """ + Parse a folder containing json files and return a dataframe with the results, excluding the files in the exclude list. + :param folder: Path to folder containing json files + :param exclude: List of files to exclude. Default: ['probabilities', 'predictions', 'plots', 'ground_truth']. + :return: Pandas dataframe with the results + """ + folder = Path(folder) + results = {} + results[folder] = {} + logger.debug(f"Parsing folder {folder}...") + for file in folder.glob("*.json"): + if Path(file).stem in exclude: + continue + else: + with open(file, "r") as f: + results[folder][Path(file).stem] = json.load(f) + return pd.DataFrame(results).T + + +def flatten_results(results): + """ + Flatten a dataframe containing json files. So that each json dict entry becomes a column with dot notation (e.g. "key1.subkey1") + :param results: Pandas dataframe containing json files + """ + new_results = pd.DataFrame() + logger.debug("Flattening results...") + for col in results.columns: + tmp = pd.json_normalize(results[col]) + new_results = pd.concat([new_results, tmp], axis=1) + return new_results + +def parse_results(result_dir, flatten=True): + """ + Recursively parse a directory containing json files and return a dataframe with the results. + :param result_dir: Path to directory containing json files + :param regex: Regex to match folders to parse. Default: "*/*" + :param flatten: Whether to flatten the results. Default: True + :return: Pandas dataframe with the results + """ + result_dir = Path(result_dir) + assert result_dir.is_dir(), f"Result directory {result_dir} does not exist." + results = pd.DataFrame() + logger.debug("Parsing results...") + total = len(list(Path(result_dir).iterdir())) + logger.info(f"Parsing {total} folders...") + for folder in Path(result_dir).iterdir(): + tmp = parse_folder(folder) + if flatten is True: + tmp = flatten_results(tmp) + tmp = tmp.loc[:, ~tmp.columns.duplicated()] + results = pd.concat([results, tmp]) + return results + + +def set_for_keys(my_dict, key_arr, val) -> dict: + """ + Set val at path in my_dict defined by the string (or serializable object) array key_arr. + :param my_dict: Dictionary to set value in + :param key_arr: Array of keys to set value at + :param val: Value to set + :return: Dictionary with value set + """ + current = my_dict + for i in range(len(key_arr)): + key = key_arr[i] + if key not in current: + if i == len(key_arr) - 1: + current[key] = val + else: + current[key] = {} + else: + if type(current[key]) is not dict: + logger.info( + "Given dictionary is not compatible with key structure requested", + ) + raise ValueError("Dictionary key already occupied") + current = current[key] + return my_dict + + +def unflatten_results(df, sep=".") -> List[dict]: + """ + Unflatten a dataframe with dot notation columns (e.g. "key1.subkey1") into a list of dictionaries. + :param df: Pandas dataframe with dot notation columns + :param sep: Separator to use. Default: "." + :return: List of dictionaries + """ + logger.debug("Unflattening results...") + result = [] + for _, row in df.iterrows(): + parsed_row = {} + for idx, val in row.iteritems(): + if val == val: + keys = idx.split(sep) + parsed_row = set_for_keys(parsed_row, keys, val) + result.append(parsed_row) + return result +def retrain_loop( + clf, + X_train, + y_train, + X_test, + y_test, + atk, + attack_size, + epochs, +) -> tuple: + i = 0 + results = [] + for epochs in tqdm(range(epochs), desc="Epochs"): + # Benign Experiment + logger.info("Epoch: {} - Benign Training".format(i)) + try: + y_train = to_categorical(y_train) + except: # noqa E722 + pass + try: + y_test = to_categorical(y_test) + except: # noqa E722 + pass + start = process_time() + clf.fit(X_train, y_train) + ben_time = (process_time() - start) / len(X_train) + ben_predictions = clf.predict(X_test) + ben_score = np.mean( + np.argmax(ben_predictions, axis=1) == np.argmax(y_test, axis=1), + ) + ben_loss = np.mean(ben_predictions[:, 0] - y_test[:, 0]) + # Adversarial Experiment + logger.info("Epoch: {} - Adversarial Training".format(i)) + start = process_time() + adv = atk.generate(X_test[:attack_size]) + adv_time = (process_time() - start) / attack_size + adv_predictions = clf.predict(adv) + adv_score = np.mean( + np.argmax(adv_predictions, axis=1) + == np.argmax(y_test[:attack_size], axis=1), + ) + adv_loss = np.mean(adv_predictions[:, 0] - y_test[:attack_size, 0]) + # Append Adversarial Examples to Training Set + X_train = np.concatenate((X_train, adv), axis=0) + adv_labels = to_categorical(y_test[:attack_size, 0]) + y_train = np.concatenate((y_train, adv_labels), axis=0) + i += 1 + # Save Results + results.append( + { + "ben_time": ben_time, + "ben_score": ben_score, + "adv_time": adv_time, + "adv_score": adv_score, + "ben_loss": ben_loss, + "adv_loss": adv_loss, + "attack_size": attack_size, + "train_size": len(X_train), + "test_size": attack_size, + }, + ) + outputs = { + "ben_predictions": DataFrame(ben_predictions), + "adv_predictions": DataFrame(adv_predictions), + } + # Some Logging + print( + "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( + i, + ben_time, + ben_score, + adv_time, + adv_score, + ), + ) + logger.info( + "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( + i, + ben_time, + ben_score, + adv_time, + adv_score, + ), + ) + results = pd.DataFrame(results) + return results, outputs + + +def save_results_and_outputs(results, outputs, path="retrain") -> list: + Path(path).mkdir(parents=True, exist_ok=True) + for output in outputs: + pd.DataFrame(outputs[output]).to_csv(f"{path}/{output}.csv") + assert Path(f"{path}/{output}.csv").exists(), f"Problem saving {path}/{output}" + pd.DataFrame(results).to_csv(f"{path}/results.csv") + assert Path( + f"{path}/results.csv", + ).exists(), f"Problem saving results to {path}/results.csv" + + +# Parse Model Results +results = pd.read_csv("output/train.csv") +# Some convenient variable names +input_size = results["data.generate.kwargs.n_samples"] * results["data.generate.kwargs.n_features"] +results["Kernel"] = results["model.init.kwargs.kernel"].copy() +results["Features"] = results["data.generate.kwargs.n_features"].copy() +results["Samples"] = results["data.sample.train_size"].copy() +results["input_size"] = input_size +# Clean up results +if "Unnamed: 0" in results.columns: + del results["Unnamed: 0"] +for col in results.columns: + if col == "data.name" and isinstance(results[col][0], list): + results[col] = results[col].apply(lambda x: x[0]) +# Subset results +subset = results[results["data.sample.train_size"] == 10000] +subset = subset[subset["data.generate.kwargs.n_features"] == 100] +with open("conf/model/best_rbf.yaml", "r") as f: + best_rbf = yaml.safe_load(f) +best_rbf['init'].pop("_target_", None) +best_rbf['init'].pop("name", None) +with open("conf/model/best_poly.yaml", "r") as f: + best_poly = yaml.safe_load(f) +best_poly['init'].pop("_target_", None) +best_poly['init'].pop("name", None) +with open("conf/model/best_linear.yaml", "r") as f: + best_lin = yaml.safe_load(f) +best_lin['init'].pop("_target_", None) +best_lin['init'].pop("name", None) +rbf_model = SVC(**best_rbf["init"]) +# Poly +poly_model = SVC(**best_poly["init"]) +# Linear +linear_model = SVC(**best_lin["init"]) +# Load Data +with open("conf/data/attack.yaml", "r") as f: + data = yaml.safe_load(f) +data = instantiate(data) +X_train, X_test, y_train, y_test = data() +# Fit Models +rbf_model.fit(X_train, y_train) +poly_model.fit(X_train, y_train) +linear_model.fit(X_train, y_train) +models = [rbf_model, poly_model, linear_model] +model_names = ["rbf", "poly", "linear"] +# ART Models +art_models = [ + ScikitlearnSVC(model=rbf_model), + ScikitlearnSVC(model=poly_model), + ScikitlearnSVC(model=linear_model), +] +i = 1 +epochs = 20 +df1 = pd.DataFrame() +df2 = pd.DataFrame() +for model in art_models: + # Define model name + name = model_names[i - 1] + # Define attack + atk = ProjectedGradientDescent( + estimator=model, + eps=1, + eps_step=0.1, + max_iter=10, + targeted=False, + num_random_init=0, + batch_size=10, + ) + confidence_df = pd.DataFrame() + for folder in tqdm(os.listdir("output/reports/attack")): + confidence_ser = pd.Series() + if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): + with open(Path("output/reports/attack", folder, "adv_probabilities.json"), "r") as f: + probs = json.load(f) + probs = np.array(probs) + false_confidence = y_test[: len(probs)] - probs[:, 1] + avg_prob = np.mean(false_confidence) + with open(Path("output/reports/attack", folder, "score_dict.json"), "r") as f: + try: + scores = json.load(f) + except: # noqa E722 + scores = {} + if "False Confidence" in scores: + del scores["False Confidence"] + scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob + with open(Path("output/reports/attack", folder, "score_dict.json"), "w") as f: + json.dump(scores, f) + yaml_file = Path("output/reports/attack", folder, "params.yaml") + json_file = Path("output/reports/attack", folder, "params.json") + if yaml_file.exists(): + params_file = yaml_file + with open(params_file, "r") as f: + params = yaml.safe_load(f) + elif json_file.exists(): + params_file = json_file + with open(params_file, "r") as f: + params = json.load(f) + else: + raise ValueError(f"No params file found for {folder}") + attack_params = params["attack"]["init"]['kwargs'] + attack_params.update({"name" : params["attack"]["init"]["name"]}) + confidence_ser["Kernel"] = name + confidence_ser["Average False Confidence"] = avg_prob + # print(f"Shape of confidence ser: {confidence_ser.shape}") + attack_ser = pd.Series(attack_params) + confidence_ser = confidence_ser._append(attack_ser) + # print(f"Shape of confidence ser: {confidence_ser.shape}") + if "Unnamed: 0" in confidence_df.columns: + del confidence_df["Unnamed: 0"] + confidence_df = confidence_df._append(confidence_ser, ignore_index=True) + # print(f"Shape of confidence df: {confidence_df.shape}") + # print(confidence_df.head()) + # input("Press Enter to continue...") + else: + pass + df1 = pd.concat([df1, confidence_df], ignore_index=True) + Path("plots").mkdir(parents=True, exist_ok=True) + df1.to_csv(Path("plots", "before_retrain_confidence.csv")) + # Train model on attack samples + print(f"Training Model {i} of {len(models)}") + if not Path("retrain", name, f"{epochs}.pkl").exists(): + results, outputs = retrain_loop( + model, + X_train, + y_train, + X_test, + y_test, + atk, + epochs=epochs, + attack_size=50, + ) + save_results_and_outputs(results, outputs, path=f"retrain/{name}/") + Path("retrain", name).mkdir(parents=True, exist_ok=True) + with Path("retrain", name, f"{epochs}.pkl").open("wb") as f: + pickle.dump(model, f) + else: + with open(Path("retrain", name, f"{epochs}.pkl"), "rb") as f: + model = pickle.load(f) + print(f"Evaluating Model {i} of {len(models)}") + # Get false confidence scores after retraining + confidence_df = pd.DataFrame() + for folder in tqdm(os.listdir("output/reports/attack")): + confidence_ser = pd.Series() + if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): + with open(Path("output/reports/attack", folder, "adv_probabilities.json"), "r") as f: + probs = json.load(f) + probs = np.array(probs) + false_confidence = y_test[: len(probs)] - probs[:, 1] + avg_prob = np.mean(false_confidence) + pd.DataFrame(probs).to_csv( + Path("output/reports/attack", folder, f"probs_after_retraining_{name}.csv"), + ) + with open(Path("output/reports/attack", folder, "score_dict.json"), "r") as f: + scores = json.load(f) + if "False Confidence" in scores: + del scores["False Confidence"] + scores[f"False Confidence {name.capitalize()}"] = avg_prob + with open(Path("output/reports/attack", folder, "score_dict.json"), "w") as f: + json.dump(scores, f) + if Path("output/reports/attack", folder, "params.yaml").exists(): + with open(Path("output/reports/attack", folder, "params.yaml"), "r") as f: + params = yaml.safe_load(f) + elif Path("output/reports/attack", folder, "params.json").exists(): + with open(Path("output/reports/attack", folder, "params.json"), "r") as f: + params = json.load(f) + else: + logger.warning(f"No params file found for {folder}") + continue + attack_params = params["attack"]["init"]["kwargs"] + attack_params.update({"name" : params["attack"]["init"]["name"]}) + confidence_ser["Kernel"] = name + confidence_ser["Average False Confidence After Retraining"] = avg_prob + attack_ser = pd.Series(attack_params) + confidence_ser = confidence_ser._append(attack_ser) + if "Unnamed: 0" in confidence_df.columns: + del confidence_df["Unnamed: 0"] + confidence_df = confidence_df._append(confidence_ser, ignore_index=True) + df2 = pd.concat([df2, confidence_df], ignore_index=True) + df2.to_csv(Path("plots", "after_retrain_confidence.csv")) + i += 1 diff --git a/examples/kdd-nsl/.gitignore b/examples/kdd-nsl/.gitignore index 8c8c8cf4..5adf8b0c 100644 --- a/examples/kdd-nsl/.gitignore +++ b/examples/kdd-nsl/.gitignore @@ -1 +1,2 @@ /kdd_nsl +/retrain diff --git a/examples/kdd-nsl/plots/.gitignore b/examples/kdd-nsl/plots/.gitignore new file mode 100644 index 00000000..d6e507d0 --- /dev/null +++ b/examples/kdd-nsl/plots/.gitignore @@ -0,0 +1,12 @@ +/accuracy_vs_attack_parameters.pdf +/confidence_vs_attack_parameters.pdf +/train_time_vs_attack_parameters.pdf +/retrain_accuracy.pdf +/retrain_confidence_vs_attack_parameters.pdf +/retrain_time.pdf +/accuracy_vs_attack_parameters.eps +/confidence_vs_attack_parameters.eps +/train_time_vs_attack_parameters.eps +/retrain_accuracy.eps +/retrain_confidence_vs_attack_parameters.eps +/retrain_time.eps diff --git a/examples/truthseeker/.dvc/.gitignore b/examples/truthseeker/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/truthseeker/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/truthseeker/.dvc/config b/examples/truthseeker/.dvc/config new file mode 100644 index 00000000..e69de29b diff --git a/examples/truthseeker/.dvcignore b/examples/truthseeker/.dvcignore new file mode 100644 index 00000000..51973055 --- /dev/null +++ b/examples/truthseeker/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/examples/truthseeker/.gitignore b/examples/truthseeker/.gitignore new file mode 100644 index 00000000..5adf8b0c --- /dev/null +++ b/examples/truthseeker/.gitignore @@ -0,0 +1,2 @@ +/kdd_nsl +/retrain diff --git a/examples/truthseeker/README.md b/examples/truthseeker/README.md new file mode 100644 index 00000000..c3c227e6 --- /dev/null +++ b/examples/truthseeker/README.md @@ -0,0 +1,32 @@ +# Classification Experiments + +## Directory Contents + +├── conf: contains the configuration files. +│ ├── attack +│ ├── config.yaml : contains the default configuration +│ ├── data +│ ├── files +│ ├── model +│ ├── plots +│ └── scorers +├── dag.md: contains the [dvc](https://dvc.org/doc/start/data-management/data-pipelines) pipeline, visualized as a graph +├── dvc.lock: contains the git trackable information for all the data and model binaries +├── dvc.yaml: specifies the pipeline visualized in `dag.md` +├── experiments.sh: specifies the grid search parameters and executes the pipeline for each set of parameters +├── multirun: contains the hydra configuration information +├── params.yaml: contains the parsed default configuration file +├── plots.ipynb: is a jupyter notebook for visualizing the data +├── queue: contains all of the configurations for all experiments +├── reports: contains the results of all experiments, with a folder for each layer (e.g. `reports/train/`), containing scores, plots, predictions, probabilities, and ground_truth json files. +├── train: contains the `data/` and `models/` for each model +│ ├── data +│ ├── models + +## Execution instructions + +To check basic code functionality, run +```dvc repro --force``` +which will execute the [dvc pipeline](https://dvc.org/doc/start/data-management/data-pipelines) that makes all of the inputs and outputs git trackable. This will execute all of the code on the default configurations first (stages `generate`, `train`, and `attack` on the `dvc.yaml`), followed by grid search on all of the models in stage `model-queue` and all of the attacks in stage `attack-queue`. After finding the best configuration for each kernel (stage `compile`) + +which overrides the default configurations, using [hydra](https://hydra.cc/docs/patterns/configuring_experiments/) and its [optuna plugin](https://hydra.cc/docs/plugins/optuna_sweeper/) to specify a search space for each set of parameters specified in the bash script. Sets of parameters that apply to all experiments are set in the `config.yaml`. Parameters particular to a experiment are specified using command line overrides within the bash script. diff --git a/examples/truthseeker/attacks.sh b/examples/truthseeker/attacks.sh new file mode 100644 index 00000000..76ed02bc --- /dev/null +++ b/examples/truthseeker/attacks.sh @@ -0,0 +1,54 @@ +#!/bin/bash +MODEL_CONFIGS=$(ls conf/model/best_*.yaml) +CONFIG_NAMES=$(ls conf/model/best_*.yaml | cut -d'/' -f3 | cut -d'.' -f1) +TOTAL=$(( ${#CONFIG_NAMES[@]} )) +i=$(( 0 )) +mkdir -p logs/attacks/ +for model_config in $CONFIG_NAMES; do + kernel_name=$(echo $model_config | cut -d'_' -f2) + i=$(( i + 1 )) + if [ $model_config == "default" ]; then + continue + fi + HYDRA_FULL_ERROR=1 python -m deckard.layers.optimise \ + ++model.init.kernel=kernel_name \ + ++stage=attack \ + ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent \ + ++attack.init.norm=1,2,inf \ + ++attack.init.eps_step=.001,.01,.1,.3,.5,1 \ + ++attack.init.batch_size=1,10,50,100 \ + ++attack.init.eps=.001,.01,.1,.3,.5,1 \ + ++attack.init.max_iter=1,10,100,1000 \ + ++hydra.sweeper.study_name=$model_config \ + ++attack.attack_size=100 \ + model=$model_config $@ --multirun >> logs/attacks/$model_config.log + echo "Successfully completed model $model_config" >> attack_log.txt +done + +# Other attacks listed below +# PGD +# bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.norm=1,2,inf ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=1,10,50,100 ++attack.init.eps=.001,.01,.1,.3,.5,1 $@ + +# # Carlini L0 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # Carlini L2 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # Carlini LInf +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # DeepFool +# bash models.sh ++attack.init.nb_grads=1,3,5,10 ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.batch_size=100 $@ + +# #Threshold Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ + +# #Pixel Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.PixelAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ + +# #Adversarial Patch +# bash models.sh ++attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.scale_max=.1,.2,.3,.5,.8,.9,.99 ++attack.init.batch_size=100 $@ + +# #Hop Skip Jump +# bash models.sh ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.batch_size=100 $@ diff --git a/examples/truthseeker/conf/attack.yaml b/examples/truthseeker/conf/attack.yaml new file mode 100644 index 00000000..4b7c0838 --- /dev/null +++ b/examples/truthseeker/conf/attack.yaml @@ -0,0 +1,36 @@ +defaults: + # - _target_ : deckard.base.experiment.Experiment + - _self_ + - data: kdd_nsl + - model: default + - attack : hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +optimizers: adv_accuracy +direction : minimize +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: ${direction} + study_name: attack + storage: sqlite:///attack.db + n_jobs: 1 + params: + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 32 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/truthseeker/conf/attack/.gitignore b/examples/truthseeker/conf/attack/.gitignore new file mode 100644 index 00000000..29ec330a --- /dev/null +++ b/examples/truthseeker/conf/attack/.gitignore @@ -0,0 +1,4 @@ +/best.yaml +/best_linear.yaml +/best_rbf.yaml +/best_poly.yaml diff --git a/examples/truthseeker/conf/attack/attack_grid.yaml b/examples/truthseeker/conf/attack/attack_grid.yaml new file mode 100644 index 00000000..d7dc3050 --- /dev/null +++ b/examples/truthseeker/conf/attack/attack_grid.yaml @@ -0,0 +1,49 @@ +- attack.init.name: [art.attacks.evasion.FastGradientMethod] + attack.init.eps : [.01, .03, .3, .1] + attack.init.norm : ['inf', 1, 2] + attack.init.eps_step : [.001, .003, .01] + attack.init.batch_size : [100] + +- attack.init.name: [art.attacks.evasion.ProjectedGradientDescent] + attack.init.eps : [.01, .03, .3, .1] + attack.init.norm : ['inf', 1, 2] + attack.init.eps_step : [.001, .003, .01] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.CarliniL0Method] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + attack.init.confidence : [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.CarliniL2Method] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + attack.init.confidence : [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.CarliniLInfMethod] + attack.init.max_iter : [10] + attack.init.confidence: [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.DeepFool] + attack.init.max_iter : [10] + attack.init.batch_size : [100] + attack.init.nb_grads : [10, 100, 1000] + +- attack.init.name: [art.attacks.evasion.HopSkipJump] + attack.init.max_iter : [10] + attack.init.max_eval : [10] + attack.init.init_eval : [10] + attack.init.norm : ['inf', 2] + +- attack.init.name: [art.attacks.evasion.PixelAttack] + attack.init.th : [.5, .9, .99] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.ThresholdAttack] + attack.init.th : [.5, .9, .99] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.AdversarialPatch] + attack.init.max_iter : [10] + attack.init.learning_rate : [.5, 5.0, 50.0] diff --git a/examples/truthseeker/conf/attack/default.yaml b/examples/truthseeker/conf/attack/default.yaml new file mode 100644 index 00000000..e0967ff7 --- /dev/null +++ b/examples/truthseeker/conf/attack/default.yaml @@ -0,0 +1,2 @@ +defaults: + - hsj diff --git a/examples/truthseeker/conf/attack/hsj.yaml b/examples/truthseeker/conf/attack/hsj.yaml new file mode 100644 index 00000000..cb2399a3 --- /dev/null +++ b/examples/truthseeker/conf/attack/hsj.yaml @@ -0,0 +1,8 @@ +data: ${data} +model: ${model} +_target_ : deckard.base.attack.Attack +init: + name: art.attacks.evasion.HopSkipJump + model: ${model} +attack_size : 10 +method : evasion diff --git a/examples/truthseeker/conf/compile.yaml b/examples/truthseeker/conf/compile.yaml new file mode 100644 index 00000000..109994af --- /dev/null +++ b/examples/truthseeker/conf/compile.yaml @@ -0,0 +1,8 @@ +attacks: + ProjectedGradientDescent: PGD + null : null +defences: + Control: Control +params: + Control: model.init.kwargs.kernel + PGD: attack.init.kwargs.eps \ No newline at end of file diff --git a/examples/truthseeker/conf/data/attack.yaml b/examples/truthseeker/conf/data/attack.yaml new file mode 100644 index 00000000..3d77ffb4 --- /dev/null +++ b/examples/truthseeker/conf/data/attack.yaml @@ -0,0 +1,23 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 11100 + n_features : 100 + n_informative : 99 + n_redundant : 0 + n_repeated : 0 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 10000 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/truthseeker/conf/data/default.yaml b/examples/truthseeker/conf/data/default.yaml new file mode 100644 index 00000000..b9487f7e --- /dev/null +++ b/examples/truthseeker/conf/data/default.yaml @@ -0,0 +1,2 @@ +defaults: + - kdd_nsl \ No newline at end of file diff --git a/examples/truthseeker/conf/data/kdd_nsl.yaml b/examples/truthseeker/conf/data/kdd_nsl.yaml new file mode 100644 index 00000000..e55be441 --- /dev/null +++ b/examples/truthseeker/conf/data/kdd_nsl.yaml @@ -0,0 +1,18 @@ +_target_: deckard.base.data.Data +name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv +target : label +sample: + _target_: deckard.base.data.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 5000 + test_size : 1000 +sklearn_pipeline: + encoder: + name : sklearn.preprocessing.OrdinalEncoder + handle_unknown : use_encoded_value + unknown_value : -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/truthseeker/conf/data/small.yaml b/examples/truthseeker/conf/data/small.yaml new file mode 100644 index 00000000..ff01d089 --- /dev/null +++ b/examples/truthseeker/conf/data/small.yaml @@ -0,0 +1,20 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 1100 + n_features : 20 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 100 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True \ No newline at end of file diff --git a/examples/truthseeker/conf/data/truthseeker.yaml b/examples/truthseeker/conf/data/truthseeker.yaml new file mode 100644 index 00000000..7fdb84a9 --- /dev/null +++ b/examples/truthseeker/conf/data/truthseeker.yaml @@ -0,0 +1,20 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/379b679bdea30724e9fa188931f0109ff422cce0/kdd_nsl.csv + target : -2 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 10000 + test_size : 1000 +ssklearn_pipeline: + encoder: + name : sklearn.preprocessing.OrdinalEncoder + handle_unknown : use_encoded_value + unknown_value : -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/truthseeker/conf/default.yaml b/examples/truthseeker/conf/default.yaml new file mode 100644 index 00000000..b0340b69 --- /dev/null +++ b/examples/truthseeker/conf/default.yaml @@ -0,0 +1,41 @@ +defaults: + - _self_ + - data: default + - model: default + - attack: hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + # seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 100 + n_jobs: 1 + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 6 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/truthseeker/conf/files/default.yaml b/examples/truthseeker/conf/files/default.yaml new file mode 100644 index 00000000..e2247d7e --- /dev/null +++ b/examples/truthseeker/conf/files/default.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: output +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pkl +params_file : params.yaml +train_labels_file : train_labels.json +test_labels_file : test_labels.json +probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +adv_probabilities_file: adv_probabilities.json +name : default diff --git a/examples/truthseeker/conf/model.yaml b/examples/truthseeker/conf/model.yaml new file mode 100644 index 00000000..d1c2344a --- /dev/null +++ b/examples/truthseeker/conf/model.yaml @@ -0,0 +1,36 @@ +defaults: + # - _target_ : deckard.base.experiment.Experiment + - _self_ + - data: kdd_nsl + - model: default + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +optimizers : accuracy +direction : maximize +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: ${direction} + study_name: model + storage: sqlite:///model.db + n_jobs: 1 + params: + model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 32 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/truthseeker/conf/model/.gitignore b/examples/truthseeker/conf/model/.gitignore new file mode 100644 index 00000000..9f645db6 --- /dev/null +++ b/examples/truthseeker/conf/model/.gitignore @@ -0,0 +1,4 @@ +/best.yaml +/best_rbf.yaml +/best_linear.yaml +/best_poly.yaml diff --git a/examples/truthseeker/conf/model/art/defense_grid.yaml b/examples/truthseeker/conf/model/art/defense_grid.yaml new file mode 100644 index 00000000..a3fe3719 --- /dev/null +++ b/examples/truthseeker/conf/model/art/defense_grid.yaml @@ -0,0 +1,44 @@ +- model.art.preprocessor.name: art.defences.preprocessor.FeatureSqueezing + model.art.preprocessor.params: + clip_values : + - [0,255] + bit_depth : [4, 8, 16, 32, 64] + +- model.art.preprocessor.name: art.defences.preprocessor.GaussianAugmentation + model.art.preprocessor.params: + clip_values : + - [0,255] + sigma : [.1, .3, 1] + ratio : [.1, .5, 1] + +- model.art.preprocessor.name: art.defences.preprocessor.SpatialSmoothing + model.art.preprocessor.params: + clip_values : + - [0,255] + window_size : [2,3,4] + +- model.art.preprocessor.name: art.defences.preprocessor.TotalVarMin + model.art.preprocessor.params: + clip_values : + - [0,255] + prob : [.001, .01, .1] + norm : [1, 2, 3] + lamb : [.05, .5, .95] + max_iter : [100] + +- model.art.postprocessor.name : art.defences.postprocessor.GaussianNoise + model.art.postprocessor.params: + clip_values : + - [0,255] + scale: [.1, .9, .999] + +- model.art.postprocessor.name : art.defences.postprocessor.HighConfidence + model.art.postprocessor.params: + cutoff : [.1, .5, .9, .99] + + +- model.art.postprocessor.name : art.defences.postprocessor.Rounded + model.art.preprocessor.params: + clip_values : + - [0,255] + decimals : [1, 2, 4, 8] diff --git a/examples/truthseeker/conf/model/art/postprocessor.yaml b/examples/truthseeker/conf/model/art/postprocessor.yaml new file mode 100644 index 00000000..91f3c00a --- /dev/null +++ b/examples/truthseeker/conf/model/art/postprocessor.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : sklearn-svc +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/truthseeker/conf/model/art/postprocessor/high_confidence.yaml b/examples/truthseeker/conf/model/art/postprocessor/high_confidence.yaml new file mode 100644 index 00000000..16ae0e50 --- /dev/null +++ b/examples/truthseeker/conf/model/art/postprocessor/high_confidence.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/truthseeker/conf/model/art/postprocessor/labels.yaml b/examples/truthseeker/conf/model/art/postprocessor/labels.yaml new file mode 100644 index 00000000..16ae0e50 --- /dev/null +++ b/examples/truthseeker/conf/model/art/postprocessor/labels.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/truthseeker/conf/model/art/preprocessor.yaml b/examples/truthseeker/conf/model/art/preprocessor.yaml new file mode 100644 index 00000000..ebf46909 --- /dev/null +++ b/examples/truthseeker/conf/model/art/preprocessor.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : sklearn-svc +preprocessor: + name: art.defences.preprocessor.FeatureSqueezing + bit_depth: 32 +initialize: diff --git a/examples/truthseeker/conf/model/art/preprocessor/feature_squeezing.yaml b/examples/truthseeker/conf/model/art/preprocessor/feature_squeezing.yaml new file mode 100644 index 00000000..1c48b3e7 --- /dev/null +++ b/examples/truthseeker/conf/model/art/preprocessor/feature_squeezing.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +preprocessor: + name: art.defences.preprocessor.FeatureSqueezing + bit_depth: 32 +initialize: diff --git a/examples/truthseeker/conf/model/default.yaml b/examples/truthseeker/conf/model/default.yaml new file mode 100644 index 00000000..5f8bd822 --- /dev/null +++ b/examples/truthseeker/conf/model/default.yaml @@ -0,0 +1,2 @@ +defaults: + - rbf diff --git a/examples/truthseeker/conf/model/linear.yaml b/examples/truthseeker/conf/model/linear.yaml new file mode 100644 index 00000000..337909e1 --- /dev/null +++ b/examples/truthseeker/conf/model/linear.yaml @@ -0,0 +1,15 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + kernel : linear + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/truthseeker/conf/model/poly.yaml b/examples/truthseeker/conf/model/poly.yaml new file mode 100644 index 00000000..8bdae408 --- /dev/null +++ b/examples/truthseeker/conf/model/poly.yaml @@ -0,0 +1,16 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + coef0 : 0.0 + kernel : poly + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/truthseeker/conf/model/rbf.yaml b/examples/truthseeker/conf/model/rbf.yaml new file mode 100644 index 00000000..036bc65f --- /dev/null +++ b/examples/truthseeker/conf/model/rbf.yaml @@ -0,0 +1,15 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + kernel : rbf + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: \ No newline at end of file diff --git a/examples/truthseeker/conf/scorers/default.yaml b/examples/truthseeker/conf/scorers/default.yaml new file mode 100644 index 00000000..108c1520 --- /dev/null +++ b/examples/truthseeker/conf/scorers/default.yaml @@ -0,0 +1,10 @@ +_target_: deckard.base.scorer.ScorerDict +accuracy: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.accuracy_score + direction: maximize + +log_loss: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.log_loss + direction: minimize diff --git a/examples/truthseeker/dvc.lock b/examples/truthseeker/dvc.lock new file mode 100644 index 00000000..09e87c52 --- /dev/null +++ b/examples/truthseeker/dvc.lock @@ -0,0 +1,798 @@ +schema: '2.0' +stages: + train: + cmd: python -m deckard.layers.experiment train + params: + params.yaml: + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/reports/train/default/params.yaml + hash: md5 + md5: 7234aab7d5edae504afa2090d96e4c3f + size: 2434 + - path: output/reports/train/default/predictions.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/probabilities.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/score_dict.json + hash: md5 + md5: 6d3cfc684f415652e388c55793d59191 + size: 358 + attack: + cmd: python -m deckard.layers.experiment attack + deps: + - path: output/reports/train/default/params.yaml + hash: md5 + md5: 7234aab7d5edae504afa2090d96e4c3f + size: 2434 + - path: output/reports/train/default/predictions.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/probabilities.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/score_dict.json + hash: md5 + md5: 6d3cfc684f415652e388c55793d59191 + size: 358 + params: + params.yaml: + attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/attacks/attack.pkl + hash: md5 + md5: 2e7cda9fbf135dd3204e7185e8b7bc3c + size: 1832 + - path: output/reports/attack/default/adv_predictions.json + hash: md5 + md5: d7dd22f45004b84bb9a3e27d3b28525e + size: 438 + - path: output/reports/attack/default/adv_probabilities.json + hash: md5 + md5: d7dd22f45004b84bb9a3e27d3b28525e + size: 438 + - path: output/reports/attack/default/params.yaml + hash: md5 + md5: b300c684dc58fc23684ccefbb9f83265 + size: 5832 + - path: output/reports/attack/default/predictions.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/attack/default/probabilities.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/attack/default/score_dict.json + hash: md5 + md5: 4777b08cf979362a082cd08a942de150 + size: 582 + models: + cmd: bash other_data.sh +stage=train --config-name=model.yaml + deps: + - path: conf/model.yaml + hash: md5 + md5: bfdd4743dda1272364c4bdf8c569972c + size: 990 + - path: models.sh + hash: md5 + md5: e622d712564afec3b5826e4e91044f90 + size: 2970 + - path: params.yaml + hash: md5 + md5: c7e85851f691450d5050508ebe39b823 + size: 5442 + params: + params.yaml: + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: model.db + hash: md5 + md5: dadeb40d9dd9eca4387a25275f38816e + size: 724992 + compile_models: + cmd: python -m deckard.layers.compile --report_folder output/reports/train/ --results_file + output/train.csv + deps: + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: model.db + hash: md5 + md5: dadeb40d9dd9eca4387a25275f38816e + size: 724992 + - path: output/reports/train/ + hash: md5 + md5: 1bb6c991ff80349437e71e756b14fced.dir + size: 39603815 + nfiles: 1541 + outs: + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + find_best_model@rbf: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_rbf --study_name=rbf --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: model.db + hash: md5 + md5: dadeb40d9dd9eca4387a25275f38816e + size: 724992 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: conf/model/best_rbf.yaml + hash: md5 + md5: 4932ceac75d6256ce2a7864aa4a5ea3c + size: 359 + find_best_model@linear: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_linear --study_name=linear --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: model.db + hash: md5 + md5: dadeb40d9dd9eca4387a25275f38816e + size: 724992 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: conf/model/best_linear.yaml + hash: md5 + md5: e4ae7059114d8724d4947e952145d4fe + size: 330 + find_best_model@poly: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_poly --study_name=poly --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: model.db + hash: md5 + md5: dadeb40d9dd9eca4387a25275f38816e + size: 724992 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: conf/model/best_poly.yaml + hash: md5 + md5: 12f892f3ba4ef8bab095b36bd7558d3e + size: 372 + attacks: + cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml + deps: + - path: conf/model/best_linear.yaml + hash: md5 + md5: e4ae7059114d8724d4947e952145d4fe + size: 330 + - path: conf/model/best_poly.yaml + hash: md5 + md5: 12f892f3ba4ef8bab095b36bd7558d3e + size: 372 + - path: conf/model/best_rbf.yaml + hash: md5 + md5: 4932ceac75d6256ce2a7864aa4a5ea3c + size: 359 + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: model.db + hash: md5 + md5: dadeb40d9dd9eca4387a25275f38816e + size: 724992 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: attack.db + hash: md5 + md5: bd26152de960368cdf78a53ca553e3cd + size: 389120 + - path: logs/attacks/ + hash: md5 + md5: 7c9359bc7817966313dbacae78a34ae8.dir + size: 3179344 + nfiles: 3 + compile_attacks: + cmd: python -m deckard.layers.compile --report_folder output/reports/attack/ --results_file + output/attack.csv + deps: + - path: attack.db + hash: md5 + md5: bd26152de960368cdf78a53ca553e3cd + size: 389120 + - path: logs/attacks/ + hash: md5 + md5: 7c9359bc7817966313dbacae78a34ae8.dir + size: 3179344 + nfiles: 3 + - path: output/reports/attack/ + hash: md5 + md5: 59384232efdbd374769f678c18db8999.dir + size: 29299858 + nfiles: 1968 + outs: + - path: output/attack.csv + hash: md5 + md5: efae37cc4b0186950ea8b638bed64e59 + size: 703047 + find_best_attack@linear: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_linear --study_name=best_linear --default_config attack.yaml + deps: + - path: attack.db + hash: md5 + md5: bd26152de960368cdf78a53ca553e3cd + size: 389120 + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: conf/attack/best_linear.yaml + hash: md5 + md5: df65ae18996a57abebd38df98db37edb + size: 245 + find_best_attack@rbf: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_rbf --study_name=best_rbf --default_config attack.yaml + deps: + - path: attack.db + hash: md5 + md5: bd26152de960368cdf78a53ca553e3cd + size: 389120 + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: conf/attack/best_rbf.yaml + hash: md5 + md5: 9871a9d8d50ef211c7f0ae884bb39fe4 + size: 247 + find_best_attack@poly: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_poly --study_name=best_poly --default_config attack.yaml + deps: + - path: attack.db + hash: md5 + md5: bd26152de960368cdf78a53ca553e3cd + size: 389120 + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: conf/attack/best_poly.yaml + hash: md5 + md5: d4c4945873617b0652018e6f27e52b89 + size: 247 + other_data_train@kdd_nsl: + cmd: DATASET_NAME=kdd_nsl bash other_data.sh data=kdd_nsl +stage=train --config-name=model.yaml + deps: + - path: conf/model.yaml + hash: md5 + md5: daaa0663d05972a5b8645c35d364da88 + size: 990 + - path: other_data.sh + hash: md5 + md5: 6ebecf100cc02847ad31901bebb2ee5a + size: 2759 + - path: output/reports/train/default/params.yaml + hash: md5 + md5: d4e0a34b2b15765ca71fa5ecaf7e3826 + size: 2100 + outs: + - path: kdd_nsl.db + hash: md5 + md5: 06933f8fc0a1feca0944c131b6a3854b + size: 348160 + - path: kdd_nsl/ + hash: md5 + md5: 9076c4e55fd1058e7446588d99930d58.dir + size: 39137423 + nfiles: 1072 + - path: logs/kdd_nsl/ + hash: md5 + md5: e7c227947468122b62f891c0d54e0c54.dir + size: 1314288 + nfiles: 12 + retrain: + cmd: python retrain.py + deps: + - path: conf/attack/best_linear.yaml + hash: md5 + md5: df65ae18996a57abebd38df98db37edb + size: 245 + - path: conf/attack/best_poly.yaml + hash: md5 + md5: d4c4945873617b0652018e6f27e52b89 + size: 247 + - path: conf/attack/best_rbf.yaml + hash: md5 + md5: 9871a9d8d50ef211c7f0ae884bb39fe4 + size: 247 + - path: conf/model/best_linear.yaml + hash: md5 + md5: e4ae7059114d8724d4947e952145d4fe + size: 330 + - path: conf/model/best_poly.yaml + hash: md5 + md5: 12f892f3ba4ef8bab095b36bd7558d3e + size: 372 + - path: conf/model/best_rbf.yaml + hash: md5 + md5: 4932ceac75d6256ce2a7864aa4a5ea3c + size: 359 + - path: output/attacks/ + hash: md5 + md5: 73abb55647e64c6606b4b85647cdec8f.dir + size: 327928 + nfiles: 179 + - path: output/models/ + hash: md5 + md5: eb0714fe755dd05dcb5825bd01a33bc2.dir + size: 2189662 + nfiles: 256 + outs: + - path: plots/after_retrain_confidence.csv + hash: md5 + md5: 3eb2c3fdaecb8dc4c392770f334dd053 + size: 52143 + - path: plots/before_retrain_confidence.csv + hash: md5 + md5: 47e8f88d3d987a1b830857c7ab11a3e6 + size: 52126 + - path: retrain/ + hash: md5 + md5: 5289901f2296645822cfcf4e05a5e7d8.dir + size: 174462 + nfiles: 12 + plots: + cmd: python plots.py + deps: + - path: output/attack.csv + hash: md5 + md5: efae37cc4b0186950ea8b638bed64e59 + size: 703047 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + - path: plots/after_retrain_confidence.csv + hash: md5 + md5: 3eb2c3fdaecb8dc4c392770f334dd053 + size: 52143 + - path: plots/before_retrain_confidence.csv + hash: md5 + md5: 47e8f88d3d987a1b830857c7ab11a3e6 + size: 52126 + outs: + - path: plots/accuracy_vs_attack_parameters.pdf + hash: md5 + md5: f0a257ff3ab0e6f2aec55d5ee5a2e2c3 + size: 15792 + - path: plots/confidence_vs_attack_parameters.pdf + hash: md5 + md5: d8cd02f26aa27ad611cf94ec2a47836d + size: 17496 + - path: plots/retrain_accuracy.pdf + hash: md5 + md5: ce6a6f8cdd5dc36f8f2050afe6ba7360 + size: 13913 + - path: plots/retrain_confidence_vs_attack_parameters.pdf + hash: md5 + md5: 005663dc89f0b9b3e7b409692307170c + size: 17506 + - path: plots/retrain_time.pdf + hash: md5 + md5: 8bd841840d88462fb0865fce1e8adf30 + size: 12907 + - path: plots/train_time_vs_attack_parameters.pdf + hash: md5 + md5: e91efb84487a1e9b54e0e057145a8d61 + size: 17150 diff --git a/examples/truthseeker/dvc.yaml b/examples/truthseeker/dvc.yaml new file mode 100644 index 00000000..6b6c8962 --- /dev/null +++ b/examples/truthseeker/dvc.yaml @@ -0,0 +1,154 @@ +stages: + attack: # Prototype stage + cmd: python -m deckard.layers.experiment attack + deps: + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} #optionally, store the data + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + metrics: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + outs: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.probabilities_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} + params: + - data + - model + - files + - scorers + - attack + train: # Prototype stage + cmd: python -m deckard.layers.experiment train + metrics: + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + outs: + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + params: + - data + - model + - files + - scorers + models: # Grid search stage + cmd: bash other_data.sh +stage=train --config-name=model.yaml # specifies the prototype stage, file to be used + outs: + - model.db: # This allows to run this section piecewise, adding features or samples inside models.sh which are then stored in the .db + cache: true + persist: true + - logs/models/: # Each feature/sample/train size combination log is stored in a separate file + cache: true + persist: true + # - ${files.directory}/${files.reports}/train/: # Each feature/sample/train size combination experiment is stored in a separate folder + # cache: true + # persist: true + params: # These are the parameters that are passed to the prototype stage + - data + - model + - files + - scorers + deps: + - params.yaml + - conf/model.yaml + - models.sh + # - conf/model/ + compile_models: + cmd: + python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/train/ --results_file ${files.directory}/train.csv + outs: + - ${files.directory}/train.csv + deps: + - ${files.directory}/${files.reports}/train/ # Each feature/sample/train size combination experiment is stored in a separate folder + - model.db + - logs/models/ + find_best_model: + foreach: + - linear + - rbf + - poly + do: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model --params_file best_${item} --study_name=${item} --default_config model.yaml + outs: + - conf/model/best_${item}.yaml + deps: + - ${files.directory}/train.csv + - model.db + - logs/models/ + attacks: + cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml + outs: + - logs/attacks/: + cache: true + persist: true + - attack.db: + cache: true + persist: true + deps: + - ${files.directory}/train.csv + - model.db + - logs/models/ + - conf/model/best_linear.yaml + - conf/model/best_rbf.yaml + - conf/model/best_poly.yaml + compile_attacks: + cmd: + python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/attack/ --results_file ${files.directory}/attack.csv + outs: + - ${files.directory}/attack.csv + deps: + - ${files.directory}/${files.reports}/attack/ # Each feature/sample/train size combination experiment is stored in a separate folder + - attack.db + - logs/attacks/ + find_best_attack: + foreach: + - linear + - rbf + - poly + do: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack --params_file best_${item} --study_name=best_${item} --default_config attack.yaml + outs: + - conf/attack/best_${item}.yaml + deps: + - ${files.directory}/train.csv + - attack.db + - logs/models/ + retrain: + cmd : python retrain.py + deps: + - ${files.directory}/models/ + - ${files.directory}/attacks/ + - conf/attack/best_linear.yaml + - conf/attack/best_rbf.yaml + - conf/attack/best_poly.yaml + - conf/model/best_linear.yaml + - conf/model/best_rbf.yaml + - conf/model/best_poly.yaml + outs: + - retrain/ + metrics: + - plots/before_retrain_confidence.csv + - plots/after_retrain_confidence.csv + plots: + cmd : python plots.py + deps : + - plots/after_retrain_confidence.csv + - output/attack.csv + - plots/before_retrain_confidence.csv + - output/train.csv + plots : + - plots/accuracy_vs_attack_parameters.pdf + # - plots/accuracy_vs_features.pdf + # - plots/accuracy_vs_samples.pdf + - plots/confidence_vs_attack_parameters.pdf + - plots/train_time_vs_attack_parameters.pdf + # - plots/train_time_vs_features.pdf + # - plots/train_time_vs_samples.pdf + - plots/retrain_accuracy.pdf + - plots/retrain_confidence_vs_attack_parameters.pdf + - plots/retrain_time.pdf diff --git a/examples/truthseeker/logs/.gitignore b/examples/truthseeker/logs/.gitignore new file mode 100644 index 00000000..99a64f76 --- /dev/null +++ b/examples/truthseeker/logs/.gitignore @@ -0,0 +1,2 @@ +/models +/attacks diff --git a/examples/truthseeker/models.sh b/examples/truthseeker/models.sh new file mode 100644 index 00000000..2cd3d313 --- /dev/null +++ b/examples/truthseeker/models.sh @@ -0,0 +1,56 @@ +# N_FEATURES=( 10 100 1000 10000 10000 100000 ) +# TRAIN_SIZES=( 10 100 1000 ) +TRAIN_SIZES=( 1000000 ) +N_FEATURES=( 10 100 1000 ) +N_SAMPLES=( 1010000 ) +TOTAL=$(( ${#N_FEATURES[@]} * ${#N_SAMPLES[@]} * ${#TRAIN_SIZES[@]} )) +i=$(( 0 )) +mkdir -p logs/models +for train_size in ${TRAIN_SIZES[@]}; do + for n_samples in ${N_SAMPLES[@]}; do + for n_features in ${N_FEATURES[@]}; do + i=$(( i + 1 )) + echo "Running experiment ${i} of ${TOTAL}" + # Keeps a meta log of the experiments + echo "Running experiment ${i} of ${TOTAL}" >> log.txt + echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" >> log.txt + # Runs the linear kernel, tries to find the best C value + HYDRA_FULL_ERROR=1; python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + model.init.kernel=linear \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=linear_${n_features}_${train_size} "$@" --multirun \ + # Keeps a log of the output for each experiment + >| logs/models/linear_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Linear Kernel Done" >> log.txt + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + model.init.kernel=rbf \ + +model.init.gamma=scale \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=rbf_${n_features}_${train_size} "$@" --multirun \ + >| logs/models/rbf_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "RBF Kernel Done" >> log.txt + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + model.init.kernel=poly \ + +model.init.degree=1,2,3,4,5 \ + +model.init.gamma=scale \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=poly_${n_features}_${train_size} "$@" --multirun \ + >| logs/models/poly_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Poly Kernel Done" >> log.txt + echo "Successfully completed experiment ${i} of ${TOTAL}" >> log.txt + done; + done; +done; \ No newline at end of file diff --git a/examples/truthseeker/other_data.sh b/examples/truthseeker/other_data.sh new file mode 100644 index 00000000..0d3ddcc3 --- /dev/null +++ b/examples/truthseeker/other_data.sh @@ -0,0 +1,35 @@ + + +mkdir -p logs/models +HYDRA_FULL_ERROR=1; python -m deckard.layers.optimise \ +++data.sample.test_size=1000 \ +model.init.kernel=linear \ +model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +++hydra.sweeper.storage=sqlite:///model.db \ +++hydra.sweeper.study_name=linear "$@" --multirun \ +>| logs/models/linear.log +echo "Linear Kernel Done" >> model_log.txt +# Runs the poly kernel +python -m deckard.layers.optimise \ +++data.sample.test_size=1000 \ +model.init.kernel=rbf \ ++model.init.gamma=scale \ +model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ ++model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +++hydra.sweeper.storage=sqlite:///model.db \ +++hydra.sweeper.study_name=rbf "$@" --multirun \ +>| logs/models/rbf.log +echo "RBF Kernel Done" >> model_log.txt +# Runs the poly kernel +python -m deckard.layers.optimise \ +++data.sample.test_size=1000 \ +model.init.kernel=poly \ ++model.init.degree=1,2,3,4,5 \ ++model.init.gamma=scale \ ++model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +++hydra.sweeper.storage=sqlite:///model.db \ +++hydra.sweeper.study_name=poly "$@" --multirun \ +>| logs/models/poly.log +echo "Poly Kernel Done" >> model_log.txt +echo "Successfully completed experiment ${i} of ${TOTAL}" >> model_log.txt \ No newline at end of file diff --git a/examples/truthseeker/params.yaml b/examples/truthseeker/params.yaml new file mode 100644 index 00000000..8aeb6d16 --- /dev/null +++ b/examples/truthseeker/params.yaml @@ -0,0 +1,178 @@ +attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: null + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: null + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc +data: + _target_: deckard.base.data.Data + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label +files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json +model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: null + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc +scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss diff --git a/examples/truthseeker/plots.py b/examples/truthseeker/plots.py new file mode 100644 index 00000000..30664f22 --- /dev/null +++ b/examples/truthseeker/plots.py @@ -0,0 +1,378 @@ +import pandas as pd +import seaborn as sns +from pathlib import Path +import matplotlib.pyplot as plt + +import matplotlib.pyplot as plt +import logging + +sns.set_style("whitegrid") +sns.set_context("paper", font_scale=1.5) +sns.set_style({"font.family": "serif", "font.serif": "Times New Roman"}) +Path("plots").mkdir(parents=True, exist_ok=True) + + +logger = logging.getLogger(__name__) + + +# if Path("plots", "model_results.csv").exists(): +# results = pd.read_csv("plots/model_results.csv") +# else: +# results = parse_results("reports/model_queue/") +results = pd.read_csv("output/train.csv") +# input_size = results["data.generate.kwargs.n_samples"] * results["data.generate.kwargs.n_features"] +results["Kernel"] = results["model.init.kwargs.kernel"].copy() +# results["Features"] = results["data.generate.kwargs.n_features"].copy() +results["Samples"] = results["data.sample.train_size"].copy() +# results["input_size"] = input_size +# sample_list = results["data.generate.kwargs.n_samples"].unique() +# feature_list = results["data.generate.kwargs.n_features"].unique() +kernel_list = results["model.init.kwargs.kernel"].unique() +if "Unnamed: 0" in results.columns: + del results["Unnamed: 0"] +for col in results.columns: + if col == "data.name" and isinstance(results[col][0], list): + results[col] = results[col].apply(lambda x: x[0]) +results = results[results["model.init.kwargs.kernel"] != "sigmoid"] + +attack_results = pd.read_csv("output/attack.csv") +attack_results["Kernel"] = attack_results["model.init.kwargs.kernel"].copy() +# attack_results["Features"] = attack_results["data.generate.kwargs.n_features"].copy() +# attack_results["Samples"] = attack_results["data.sample.train_size"].copy() +# sample_list = attack_results["data.generate.kwargs.n_samples"].unique() +# feature_list = attack_results["data.generate.kwargs.n_features"].unique() +kernel_list = attack_results["model.init.kwargs.kernel"].unique() +if "Unnamed: 0" in attack_results.columns: + del attack_results["Unnamed: 0"] +for col in attack_results.columns: + if col == "data.name" and isinstance(attack_results[col][0], list): + attack_results[col] = attack_results[col].apply(lambda x: x[0]) + + +# graph1 = sns.lineplot( +# x="data.sample.train_size", +# y="accuracy", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph1.legend(labels=["Linear", "RBF", "Poly"]) +# graph1.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +# graph1.set_xlabel("Number of Samples") +# graph1.set_ylabel("Accuracy") +# graph1.set_xscale("log") +# graph1.get_figure().tight_layout() +# graph1.get_figure().savefig("plots/accuracy_vs_samples.pdf") +# plt.gcf().clear() + +# graph2 = sns.lineplot( +# x="data.generate.kwargs.n_features", +# y="accuracy", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph2.set_xlabel("Number of Features") +# graph2.set_ylabel("Accuracy") +# graph2.set_xscale("log") +# graph2.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +# graph2.get_figure().tight_layout() +# graph2.get_figure().savefig("plots/accuracy_vs_features.pdf") +# plt.gcf().clear() + +# results["train_time"] = ( +# results["train_time"] +# * results["data.sample.train_size"] +# * results["data.generate.kwargs.n_samples"] +# ) +# graph3 = sns.lineplot( +# x="data.generate.kwargs.n_features", +# y="train_time", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph3.set_xlabel("Number of Features") +# graph3.set_ylabel("Training Time") +# graph3.set(yscale="log", xscale="log") +# graph3.legend(title="Kernel") +# graph3.get_figure().tight_layout() +# graph3.get_figure().savefig("plots/train_time_vs_features.pdf") +# plt.gcf().clear() + +# graph4 = sns.lineplot( +# x="data.sample.train_size", +# y="train_time", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph4.set_xlabel("Number of Samples") +# graph4.set_ylabel("Training Time") +# graph4.set(yscale="log", xscale="log") +# graph4.legend(title="Kernel") +# graph4.get_figure().tight_layout() +# graph4.get_figure().savefig("plots/train_time_vs_samples.eps") +# plt.gcf().clear() + +fig, ax = plt.subplots(2, 2) +graph5 = sns.lineplot( + x="attack.init.kwargs.eps", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph5.set(xscale="log", xlabel="Perturbation Distance", ylabel="Accuracy") +graph6 = sns.lineplot( + x="attack.init.kwargs.eps_step", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph6.set(xscale="log", xlabel="Perturbation Step", ylabel="Accuracy") +graph7 = sns.lineplot( + x="attack.init.kwargs.max_iter", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph7.set(xscale="log", xlabel="Maximum Iterations", ylabel="Accuracy") +graph8 = sns.lineplot( + x="attack.init.kwargs.batch_size", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph8.set(xscale="log", xlabel="Batch Size", ylabel="Accuracy") +graph6.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout() +fig.savefig("plots/accuracy_vs_attack_parameters.pdf") +plt.gcf().clear() + +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="attack.init.kwargs.eps", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="Attack Time") +graph10 = sns.lineplot( + x="attack.init.kwargs.eps_step", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="Attack Time") +graph11 = sns.lineplot( + x="attack.init.kwargs.max_iter", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="Attack Time") +graph12 = sns.lineplot( + x="attack.init.kwargs.batch_size", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="Attack Time") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/train_time_vs_attack_parameters.pdf") +plt.gcf().clear() + +retrain_df = pd.DataFrame() +for kernel in ["rbf", "linear", "poly"]: + try: + tmp = pd.read_csv(f"retrain/{kernel}/results.csv") + except FileNotFoundError: + break + tmp["Kernel"] = kernel + tmp["Epochs"] = tmp.index + retrain_df = pd.concat([retrain_df, tmp], axis=0) + retrain_df = retrain_df.reset_index(drop=True) + if "Unnamed: 0" in retrain_df.columns: + del retrain_df["Unnamed: 0"] + + +retrain = sns.lineplot( + x="Epochs", + y="ben_score", + data=retrain_df, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +retrain = sns.lineplot( + x="Epochs", + y="adv_score", + data=retrain_df, + style="Kernel", + color="darkred", + legend=False, + style_order=["rbf", "poly", "linear"], +) +retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +retrain.set_xlabel("Retraining Epochs") +retrain.set_ylabel("Accuracy") +retrain.get_figure().tight_layout() +retrain.get_figure().savefig("plots/retrain_accuracy.pdf") +plt.gcf().clear() + +retrain_df["ben_time"] = retrain_df["ben_time"] * retrain_df["train_size"] * 10 +retrain_df["adv_time"] = retrain_df["adv_time"] * retrain_df["attack_size"] +retrain = sns.lineplot( + x="Epochs", + y="ben_time", + data=retrain_df, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +retrain = sns.lineplot( + x="Epochs", + y="adv_time", + data=retrain_df, + style="Kernel", + color="darkred", + legend=False, + style_order=["rbf", "poly", "linear"], +) +retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +retrain.set_xlabel("Retraining Epochs") +retrain.set_ylabel("Time") +retrain.set_yscale("log") +retrain.get_figure().tight_layout() +retrain.get_figure().savefig("plots/retrain_time.pdf") +plt.gcf().clear() + +confidence_df = pd.read_csv("plots/before_retrain_confidence.csv") +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="eps", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") +graph10 = sns.lineplot( + x="eps_step", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") +graph11 = sns.lineplot( + x="max_iter", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") +graph12 = sns.lineplot( + x="batch_size", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/confidence_vs_attack_parameters.pdf") +plt.gcf().clear() + +confdence_df = pd.read_csv("plots/after_retrain_confidence.csv") +confidence_df.columns +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="eps", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") +graph10 = sns.lineplot( + x="eps_step", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") +graph11 = sns.lineplot( + x="max_iter", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") +graph12 = sns.lineplot( + x="batch_size", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/retrain_confidence_vs_attack_parameters.pdf") +plt.gcf().clear() \ No newline at end of file diff --git a/examples/truthseeker/plots/.gitignore b/examples/truthseeker/plots/.gitignore new file mode 100644 index 00000000..dd345776 --- /dev/null +++ b/examples/truthseeker/plots/.gitignore @@ -0,0 +1,6 @@ +/accuracy_vs_attack_parameters.pdf +/confidence_vs_attack_parameters.pdf +/train_time_vs_attack_parameters.pdf +/retrain_accuracy.pdf +/retrain_confidence_vs_attack_parameters.pdf +/retrain_time.pdf diff --git a/examples/truthseeker/retrain.py b/examples/truthseeker/retrain.py new file mode 100644 index 00000000..a359b01b --- /dev/null +++ b/examples/truthseeker/retrain.py @@ -0,0 +1,411 @@ +import json +import logging +import os +import pickle +from pathlib import Path +from time import process_time +import json +from typing import List +import numpy as np +import pandas as pd +import yaml +from art.attacks.evasion import ProjectedGradientDescent +from art.estimators.classification.scikitlearn import ScikitlearnSVC +from art.utils import to_categorical +from pandas import DataFrame +from sklearn.svm import SVC +from tqdm import tqdm +from hydra.utils import instantiate + + +logger = logging.getLogger(__name__) + +logging.basicConfig(level=logging.INFO) +def parse_folder( + folder, + exclude=[ + "probabilities", + "predictions", + "plots", + "ground_truth", + "attack_predictions", + "attack_probabilities", + "samples", + ], +) -> pd.DataFrame: + """ + Parse a folder containing json files and return a dataframe with the results, excluding the files in the exclude list. + :param folder: Path to folder containing json files + :param exclude: List of files to exclude. Default: ['probabilities', 'predictions', 'plots', 'ground_truth']. + :return: Pandas dataframe with the results + """ + folder = Path(folder) + results = {} + results[folder] = {} + logger.debug(f"Parsing folder {folder}...") + for file in folder.glob("*.json"): + if Path(file).stem in exclude: + continue + else: + with open(file, "r") as f: + results[folder][Path(file).stem] = json.load(f) + return pd.DataFrame(results).T + + +def flatten_results(results): + """ + Flatten a dataframe containing json files. So that each json dict entry becomes a column with dot notation (e.g. "key1.subkey1") + :param results: Pandas dataframe containing json files + """ + new_results = pd.DataFrame() + logger.debug("Flattening results...") + for col in results.columns: + tmp = pd.json_normalize(results[col]) + new_results = pd.concat([new_results, tmp], axis=1) + return new_results + +def parse_results(result_dir, flatten=True): + """ + Recursively parse a directory containing json files and return a dataframe with the results. + :param result_dir: Path to directory containing json files + :param regex: Regex to match folders to parse. Default: "*/*" + :param flatten: Whether to flatten the results. Default: True + :return: Pandas dataframe with the results + """ + result_dir = Path(result_dir) + assert result_dir.is_dir(), f"Result directory {result_dir} does not exist." + results = pd.DataFrame() + logger.debug("Parsing results...") + total = len(list(Path(result_dir).iterdir())) + logger.info(f"Parsing {total} folders...") + for folder in Path(result_dir).iterdir(): + tmp = parse_folder(folder) + if flatten is True: + tmp = flatten_results(tmp) + tmp = tmp.loc[:, ~tmp.columns.duplicated()] + results = pd.concat([results, tmp]) + return results + + +def set_for_keys(my_dict, key_arr, val) -> dict: + """ + Set val at path in my_dict defined by the string (or serializable object) array key_arr. + :param my_dict: Dictionary to set value in + :param key_arr: Array of keys to set value at + :param val: Value to set + :return: Dictionary with value set + """ + current = my_dict + for i in range(len(key_arr)): + key = key_arr[i] + if key not in current: + if i == len(key_arr) - 1: + current[key] = val + else: + current[key] = {} + else: + if type(current[key]) is not dict: + logger.info( + "Given dictionary is not compatible with key structure requested", + ) + raise ValueError("Dictionary key already occupied") + current = current[key] + return my_dict + + +def unflatten_results(df, sep=".") -> List[dict]: + """ + Unflatten a dataframe with dot notation columns (e.g. "key1.subkey1") into a list of dictionaries. + :param df: Pandas dataframe with dot notation columns + :param sep: Separator to use. Default: "." + :return: List of dictionaries + """ + logger.debug("Unflattening results...") + result = [] + for _, row in df.iterrows(): + parsed_row = {} + for idx, val in row.iteritems(): + if val == val: + keys = idx.split(sep) + parsed_row = set_for_keys(parsed_row, keys, val) + result.append(parsed_row) + return result +def retrain_loop( + clf, + X_train, + y_train, + X_test, + y_test, + atk, + attack_size, + epochs, +) -> tuple: + i = 0 + results = [] + for epochs in tqdm(range(epochs), desc="Epochs"): + # Benign Experiment + logger.info("Epoch: {} - Benign Training".format(i)) + try: + y_train = to_categorical(y_train) + except: # noqa E722 + pass + try: + y_test = to_categorical(y_test) + except: # noqa E722 + pass + start = process_time() + clf.fit(X_train, y_train) + ben_time = (process_time() - start) / len(X_train) + ben_predictions = clf.predict(X_test) + ben_score = np.mean( + np.argmax(ben_predictions, axis=1) == np.argmax(y_test, axis=1), + ) + ben_loss = np.mean(ben_predictions[:, 0] - y_test[:, 0]) + # Adversarial Experiment + logger.info("Epoch: {} - Adversarial Training".format(i)) + start = process_time() + adv = atk.generate(X_test[:attack_size]) + adv_time = (process_time() - start) / attack_size + adv_predictions = clf.predict(adv) + adv_score = np.mean( + np.argmax(adv_predictions, axis=1) + == np.argmax(y_test[:attack_size], axis=1), + ) + adv_loss = np.mean(adv_predictions[:, 0] - y_test[:attack_size, 0]) + # Append Adversarial Examples to Training Set + X_train = np.concatenate((X_train, adv), axis=0) + adv_labels = to_categorical(y_test[:attack_size, 0]) + y_train = np.concatenate((y_train, adv_labels), axis=0) + i += 1 + # Save Results + results.append( + { + "ben_time": ben_time, + "ben_score": ben_score, + "adv_time": adv_time, + "adv_score": adv_score, + "ben_loss": ben_loss, + "adv_loss": adv_loss, + "attack_size": attack_size, + "train_size": len(X_train), + "test_size": attack_size, + }, + ) + outputs = { + "ben_predictions": DataFrame(ben_predictions), + "adv_predictions": DataFrame(adv_predictions), + } + # Some Logging + print( + "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( + i, + ben_time, + ben_score, + adv_time, + adv_score, + ), + ) + logger.info( + "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( + i, + ben_time, + ben_score, + adv_time, + adv_score, + ), + ) + results = pd.DataFrame(results) + return results, outputs + + +def save_results_and_outputs(results, outputs, path="retrain") -> list: + Path(path).mkdir(parents=True, exist_ok=True) + for output in outputs: + pd.DataFrame(outputs[output]).to_csv(f"{path}/{output}.csv") + assert Path(f"{path}/{output}.csv").exists(), f"Problem saving {path}/{output}" + pd.DataFrame(results).to_csv(f"{path}/results.csv") + assert Path( + f"{path}/results.csv", + ).exists(), f"Problem saving results to {path}/results.csv" + + +# Parse Model Results +results = pd.read_csv("output/train.csv") +# Some convenient variable names +# input_size = results["data.generate.kwargs.n_samples"] * results["data.generate.kwargs.n_features"] +results["Kernel"] = results["model.init.kwargs.kernel"].copy() +# results["Features"] = results["data.generate.kwargs.n_features"].copy() +# results["Samples"] = results["data.sample.train_size"].copy() +# results["input_size"] = input_size +# Clean up results +if "Unnamed: 0" in results.columns: + del results["Unnamed: 0"] +for col in results.columns: + if col == "data.name" and isinstance(results[col][0], list): + results[col] = results[col].apply(lambda x: x[0]) +# Subset results +# subset = results[results["data.sample.train_size"] == 10000] +# subset = subset[subset["data.generate.kwargs.n_features"] == 100] +with open("conf/model/best_rbf.yaml", "r") as f: + best_rbf = yaml.safe_load(f) +best_rbf['init'].pop("_target_", None) +best_rbf['init'].pop("name", None) +with open("conf/model/best_poly.yaml", "r") as f: + best_poly = yaml.safe_load(f) +best_poly['init'].pop("_target_", None) +best_poly['init'].pop("name", None) +with open("conf/model/best_linear.yaml", "r") as f: + best_lin = yaml.safe_load(f) +best_lin['init'].pop("_target_", None) +best_lin['init'].pop("name", None) +rbf_model = SVC(**best_rbf["init"]) +# Poly +poly_model = SVC(**best_poly["init"]) +# Linear +linear_model = SVC(**best_lin["init"]) +# Load Data +with open("conf/data/attack.yaml", "r") as f: + data = yaml.safe_load(f) +data = instantiate(data) +X_train, X_test, y_train, y_test = data() +# Fit Models +rbf_model.fit(X_train, y_train) +poly_model.fit(X_train, y_train) +linear_model.fit(X_train, y_train) +models = [rbf_model, poly_model, linear_model] +model_names = ["rbf", "poly", "linear"] +# ART Models +art_models = [ + ScikitlearnSVC(model=rbf_model), + ScikitlearnSVC(model=poly_model), + ScikitlearnSVC(model=linear_model), +] +i = 1 +epochs = 20 +df1 = pd.DataFrame() +df2 = pd.DataFrame() +for model in art_models: + # Define model name + name = model_names[i - 1] + # Define attack + atk = ProjectedGradientDescent( + estimator=model, + eps=1, + eps_step=0.1, + max_iter=10, + targeted=False, + num_random_init=0, + batch_size=10, + ) + confidence_df = pd.DataFrame() + for folder in tqdm(os.listdir("output/reports/attack")): + confidence_ser = pd.Series() + if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): + with open(Path("output/reports/attack", folder, "adv_probabilities.json"), "r") as f: + probs = json.load(f) + probs = np.array(probs) + false_confidence = y_test[: len(probs)] - probs[:, 1] + avg_prob = np.mean(false_confidence) + with open(Path("output/reports/attack", folder, "score_dict.json"), "r") as f: + try: + scores = json.load(f) + except: # noqa E722 + scores = {} + if "False Confidence" in scores: + del scores["False Confidence"] + scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob + with open(Path("output/reports/attack", folder, "score_dict.json"), "w") as f: + json.dump(scores, f) + yaml_file = Path("output/reports/attack", folder, "params.yaml") + json_file = Path("output/reports/attack", folder, "params.json") + if yaml_file.exists(): + params_file = yaml_file + with open(params_file, "r") as f: + params = yaml.safe_load(f) + elif json_file.exists(): + params_file = json_file + with open(params_file, "r") as f: + params = json.load(f) + else: + raise ValueError(f"No params file found for {folder}") + attack_params = params["attack"]["init"]['kwargs'] + attack_params.update({"name" : params["attack"]["init"]["name"]}) + confidence_ser["Kernel"] = name + confidence_ser["Average False Confidence"] = avg_prob + # print(f"Shape of confidence ser: {confidence_ser.shape}") + attack_ser = pd.Series(attack_params) + confidence_ser = confidence_ser._append(attack_ser) + # print(f"Shape of confidence ser: {confidence_ser.shape}") + if "Unnamed: 0" in confidence_df.columns: + del confidence_df["Unnamed: 0"] + confidence_df = confidence_df._append(confidence_ser, ignore_index=True) + # print(f"Shape of confidence df: {confidence_df.shape}") + # print(confidence_df.head()) + # input("Press Enter to continue...") + else: + pass + df1 = pd.concat([df1, confidence_df], ignore_index=True) + Path("plots").mkdir(parents=True, exist_ok=True) + df1.to_csv(Path("plots", "before_retrain_confidence.csv")) + # Train model on attack samples + print(f"Training Model {i} of {len(models)}") + if not Path("retrain", name, f"{epochs}.pkl").exists(): + results, outputs = retrain_loop( + model, + X_train, + y_train, + X_test, + y_test, + atk, + epochs=epochs, + attack_size=50, + ) + save_results_and_outputs(results, outputs, path=f"retrain/{name}/") + Path("retrain", name).mkdir(parents=True, exist_ok=True) + with Path("retrain", name, f"{epochs}.pkl").open("wb") as f: + pickle.dump(model, f) + else: + with open(Path("retrain", name, f"{epochs}.pkl"), "rb") as f: + model = pickle.load(f) + print(f"Evaluating Model {i} of {len(models)}") + # Get false confidence scores after retraining + confidence_df = pd.DataFrame() + for folder in tqdm(os.listdir("output/reports/attack")): + confidence_ser = pd.Series() + if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): + with open(Path("output/reports/attack", folder, "adv_probabilities.json"), "r") as f: + probs = json.load(f) + probs = np.array(probs) + false_confidence = y_test[: len(probs)] - probs[:, 1] + avg_prob = np.mean(false_confidence) + pd.DataFrame(probs).to_csv( + Path("output/reports/attack", folder, f"probs_after_retraining_{name}.csv"), + ) + with open(Path("output/reports/attack", folder, "score_dict.json"), "r") as f: + scores = json.load(f) + if "False Confidence" in scores: + del scores["False Confidence"] + scores[f"False Confidence {name.capitalize()}"] = avg_prob + with open(Path("output/reports/attack", folder, "score_dict.json"), "w") as f: + json.dump(scores, f) + if Path("output/reports/attack", folder, "params.yaml").exists(): + with open(Path("output/reports/attack", folder, "params.yaml"), "r") as f: + params = yaml.safe_load(f) + elif Path("output/reports/attack", folder, "params.json").exists(): + with open(Path("output/reports/attack", folder, "params.json"), "r") as f: + params = json.load(f) + else: + logger.warning(f"No params file found for {folder}") + continue + attack_params = params["attack"]["init"]["kwargs"] + attack_params.update({"name" : params["attack"]["init"]["name"]}) + confidence_ser["Kernel"] = name + confidence_ser["Average False Confidence After Retraining"] = avg_prob + attack_ser = pd.Series(attack_params) + confidence_ser = confidence_ser._append(attack_ser) + if "Unnamed: 0" in confidence_df.columns: + del confidence_df["Unnamed: 0"] + confidence_df = confidence_df._append(confidence_ser, ignore_index=True) + df2 = pd.concat([df2, confidence_df], ignore_index=True) + df2.to_csv(Path("plots", "after_retrain_confidence.csv")) + i += 1 From 4d73ad4be82f05c96e9d633d1ea7f02cfc44a4eb Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Sun, 15 Oct 2023 00:56:45 +0200 Subject: [PATCH 104/148] add kdd-nsl data --- examples/classification/plots/.gitignore | 10 + examples/kdd-nsl/attacks.sh | 22 +- examples/kdd-nsl/conf/default.yaml | 2 - examples/kdd-nsl/conf/model.yaml | 2 - examples/kdd-nsl/dvc.lock | 453 +++++++++++++++++------ examples/kdd-nsl/dvc.yaml | 51 ++- examples/kdd-nsl/models.sh | 56 --- examples/kdd-nsl/other_data.sh | 35 ++ examples/kdd-nsl/params.yaml | 110 +++++- examples/kdd-nsl/plots.py | 378 +++++++++++++++++++ examples/kdd-nsl/plots/.gitignore | 12 +- examples/kdd-nsl/retrain.py | 447 ++++++++++++++++++++-- 12 files changed, 1317 insertions(+), 261 deletions(-) delete mode 100644 examples/kdd-nsl/models.sh create mode 100644 examples/kdd-nsl/other_data.sh create mode 100644 examples/kdd-nsl/plots.py diff --git a/examples/classification/plots/.gitignore b/examples/classification/plots/.gitignore index 4c882c2e..2617ec05 100644 --- a/examples/classification/plots/.gitignore +++ b/examples/classification/plots/.gitignore @@ -1,3 +1,13 @@ +/accuracy_vs_attack_parameters.pdf +/accuracy_vs_features.pdf +/accuracy_vs_samples.pdf +/confidence_vs_attack_parameters.pdf +/train_time_vs_attack_parameters.pdf +/train_time_vs_features.pdf +/train_time_vs_samples.pdf +/retrain_accuracy.pdf +/retrain_confidence_vs_attack_parameters.pdf +/retrain_time.pdf /accuracy_vs_attack_parameters.eps /accuracy_vs_features.eps /accuracy_vs_samples.eps diff --git a/examples/kdd-nsl/attacks.sh b/examples/kdd-nsl/attacks.sh index 70a9011b..76ed02bc 100644 --- a/examples/kdd-nsl/attacks.sh +++ b/examples/kdd-nsl/attacks.sh @@ -1,40 +1,28 @@ #!/bin/bash - - -# while getops m:d:n:s:b: flag -# do -# case "${flag}" in -# m) model=(-m ${OPTARG});; -# d) attack_distance=(-d ${OPTARG});; -# n) attack_norm=(-n ${OPTARG});; -# s) attack_step=(${OPTARG});; -# b) attack_batch=(${OPTARG});; -# esac -# done - -# model=$1 -# Pauses the script until the user presses enter MODEL_CONFIGS=$(ls conf/model/best_*.yaml) CONFIG_NAMES=$(ls conf/model/best_*.yaml | cut -d'/' -f3 | cut -d'.' -f1) TOTAL=$(( ${#CONFIG_NAMES[@]} )) i=$(( 0 )) mkdir -p logs/attacks/ for model_config in $CONFIG_NAMES; do + kernel_name=$(echo $model_config | cut -d'_' -f2) i=$(( i + 1 )) if [ $model_config == "default" ]; then continue fi - echo "Running model $model_config. Number $i of $TOTAL" >> attack_log.txt HYDRA_FULL_ERROR=1 python -m deckard.layers.optimise \ + ++model.init.kernel=kernel_name \ ++stage=attack \ ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent \ ++attack.init.norm=1,2,inf \ ++attack.init.eps_step=.001,.01,.1,.3,.5,1 \ ++attack.init.batch_size=1,10,50,100 \ ++attack.init.eps=.001,.01,.1,.3,.5,1 \ + ++attack.init.max_iter=1,10,100,1000 \ ++hydra.sweeper.study_name=$model_config \ + ++attack.attack_size=100 \ model=$model_config $@ --multirun >> logs/attacks/$model_config.log - echo "Successfully completed model $model_config. Number $i of $TOTAL" >> attack_log.txt + echo "Successfully completed model $model_config" >> attack_log.txt done # Other attacks listed below diff --git a/examples/kdd-nsl/conf/default.yaml b/examples/kdd-nsl/conf/default.yaml index b0340b69..e28d87bb 100644 --- a/examples/kdd-nsl/conf/default.yaml +++ b/examples/kdd-nsl/conf/default.yaml @@ -8,8 +8,6 @@ defaults: - override hydra/sweeper : optuna - override hydra/launcher : joblib hydra: - run: - dir : "./${files.directory}" sweeper: sampler: _target_: optuna.samplers.TPESampler diff --git a/examples/kdd-nsl/conf/model.yaml b/examples/kdd-nsl/conf/model.yaml index d1c2344a..7d52b51f 100644 --- a/examples/kdd-nsl/conf/model.yaml +++ b/examples/kdd-nsl/conf/model.yaml @@ -11,8 +11,6 @@ defaults: optimizers : accuracy direction : maximize hydra: - run: - dir : "./${files.directory}" sweeper: sampler: _target_: optuna.samplers.GridSampler diff --git a/examples/kdd-nsl/dvc.lock b/examples/kdd-nsl/dvc.lock index 5dbe8a15..287cb3d2 100644 --- a/examples/kdd-nsl/dvc.lock +++ b/examples/kdd-nsl/dvc.lock @@ -52,7 +52,26 @@ stages: _target_: deckard.base.model.art_pipeline.ArtPipeline initialize: library: sklearn-svc - data: ${data} + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label init: C: 1.0 _target_: deckard.base.model.ModelInitializer @@ -87,8 +106,8 @@ stages: size: 44061 - path: output/reports/train/default/score_dict.json hash: md5 - md5: 3180dfdc3315fec9863107ce5abbc63c - size: 360 + md5: e82308d212b314ee0e82e2d8e0bbdca0 + size: 358 attack: cmd: python -m deckard.layers.experiment attack deps: @@ -106,19 +125,106 @@ stages: size: 44061 - path: output/reports/train/default/score_dict.json hash: md5 - md5: 3180dfdc3315fec9863107ce5abbc63c - size: 360 + md5: e82308d212b314ee0e82e2d8e0bbdca0 + size: 358 params: params.yaml: attack: _target_: deckard.base.attack.Attack attack_size: 10 - data: ${data} + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label init: - model: ${model} + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc name: art.attacks.evasion.HopSkipJump method: evasion - model: ${model} + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc data: _target_: deckard.base.data.Data name: @@ -167,7 +273,26 @@ stages: _target_: deckard.base.model.art_pipeline.ArtPipeline initialize: library: sklearn-svc - data: ${data} + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label init: C: 1.0 _target_: deckard.base.model.ModelInitializer @@ -190,15 +315,15 @@ stages: outs: - path: output/attacks/attack.pkl hash: md5 - md5: 05f468294dfaef26c27370728c8cdc27 + md5: 13ab6d4b68ddf03df92475f5f6881590 size: 1832 - path: output/reports/attack/default/adv_predictions.json hash: md5 - md5: b72287defd6f04140c20387819262314 + md5: e8af38b6680c2109b29779ecc9aab259 size: 439 - path: output/reports/attack/default/adv_probabilities.json hash: md5 - md5: b72287defd6f04140c20387819262314 + md5: e8af38b6680c2109b29779ecc9aab259 size: 439 - path: output/reports/attack/default/params.yaml hash: md5 @@ -214,23 +339,23 @@ stages: size: 44061 - path: output/reports/attack/default/score_dict.json hash: md5 - md5: b248e6f36831463144658ef4db63d72a + md5: dc2399a3d8aa37830553f4a19b1ed07e size: 582 models: cmd: bash other_data.sh +stage=train --config-name=model.yaml deps: - path: conf/model.yaml hash: md5 - md5: bfdd4743dda1272364c4bdf8c569972c - size: 990 - - path: models.sh + md5: 7895842addd2cae0477e8bd3d1c3a075 + size: 950 + - path: other_data.sh hash: md5 - md5: e622d712564afec3b5826e4e91044f90 - size: 2970 + md5: 4360b9dfaf6373d673f2f940b7a645e8 + size: 1313 - path: params.yaml hash: md5 - md5: 6be251c3aee37ce999475434a0e94193 - size: 2040 + md5: c7e85851f691450d5050508ebe39b823 + size: 5442 params: params.yaml: data: @@ -281,7 +406,26 @@ stages: _target_: deckard.base.model.art_pipeline.ArtPipeline initialize: library: sklearn-svc - data: ${data} + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label init: C: 1.0 _target_: deckard.base.model.ModelInitializer @@ -304,139 +448,139 @@ stages: outs: - path: logs/models/ hash: md5 - md5: c50e45d9189db51c2bc7459750acc25f.dir - size: 383291 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 nfiles: 3 - path: model.db hash: md5 - md5: b26dbb51d030083d2bd6f03245c1b188 - size: 155648 + md5: 4afc0f97fb9b487396c761d8d4f43aa3 + size: 679936 compile_models: cmd: python -m deckard.layers.compile --report_folder output/reports/train/ --results_file output/train.csv deps: - path: logs/models/ hash: md5 - md5: c50e45d9189db51c2bc7459750acc25f.dir - size: 383291 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 nfiles: 3 - path: model.db hash: md5 - md5: b26dbb51d030083d2bd6f03245c1b188 - size: 155648 + md5: 4afc0f97fb9b487396c761d8d4f43aa3 + size: 679936 - path: output/reports/train/ hash: md5 - md5: 798ae5e5259be51e6e06cded878a2573.dir - size: 7440659 - nfiles: 293 + md5: 26644ddebe405806dafb68b1c541b7a9.dir + size: 39353128 + nfiles: 1552 outs: - path: output/train.csv hash: md5 - md5: e753967ae898a3f60a09b8c0acac625c - size: 108832 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 find_best_model@rbf: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model --params_file best_rbf --study_name=rbf --default_config model.yaml deps: - path: logs/models/ hash: md5 - md5: c50e45d9189db51c2bc7459750acc25f.dir - size: 383291 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 nfiles: 3 - path: model.db hash: md5 - md5: b26dbb51d030083d2bd6f03245c1b188 - size: 155648 + md5: 4afc0f97fb9b487396c761d8d4f43aa3 + size: 679936 - path: output/train.csv hash: md5 - md5: e753967ae898a3f60a09b8c0acac625c - size: 108832 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 outs: - path: conf/model/best_rbf.yaml hash: md5 - md5: cd69a0189843e6474613ea33e96bb29b - size: 327 + md5: 3092c0288833989d2e77d849993a2a40 + size: 360 find_best_model@linear: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model --params_file best_linear --study_name=linear --default_config model.yaml deps: - path: logs/models/ hash: md5 - md5: c50e45d9189db51c2bc7459750acc25f.dir - size: 383291 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 nfiles: 3 - path: model.db hash: md5 - md5: b26dbb51d030083d2bd6f03245c1b188 - size: 155648 + md5: 4afc0f97fb9b487396c761d8d4f43aa3 + size: 679936 - path: output/train.csv hash: md5 - md5: e753967ae898a3f60a09b8c0acac625c - size: 108832 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 outs: - path: conf/model/best_linear.yaml hash: md5 - md5: cd69a0189843e6474613ea33e96bb29b - size: 327 + md5: e4ae7059114d8724d4947e952145d4fe + size: 330 find_best_model@poly: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model --params_file best_poly --study_name=poly --default_config model.yaml deps: - path: logs/models/ hash: md5 - md5: c50e45d9189db51c2bc7459750acc25f.dir - size: 383291 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 nfiles: 3 - path: model.db hash: md5 - md5: b26dbb51d030083d2bd6f03245c1b188 - size: 155648 + md5: 4afc0f97fb9b487396c761d8d4f43aa3 + size: 679936 - path: output/train.csv hash: md5 - md5: e753967ae898a3f60a09b8c0acac625c - size: 108832 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 outs: - path: conf/model/best_poly.yaml hash: md5 - md5: cd69a0189843e6474613ea33e96bb29b - size: 327 + md5: 12f892f3ba4ef8bab095b36bd7558d3e + size: 372 attacks: cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml deps: - path: conf/model/best_linear.yaml hash: md5 - md5: cd69a0189843e6474613ea33e96bb29b - size: 327 + md5: e4ae7059114d8724d4947e952145d4fe + size: 330 - path: conf/model/best_poly.yaml hash: md5 - md5: cd69a0189843e6474613ea33e96bb29b - size: 327 + md5: 12f892f3ba4ef8bab095b36bd7558d3e + size: 372 - path: conf/model/best_rbf.yaml hash: md5 - md5: cd69a0189843e6474613ea33e96bb29b - size: 327 + md5: 3092c0288833989d2e77d849993a2a40 + size: 360 - path: logs/models/ hash: md5 - md5: c50e45d9189db51c2bc7459750acc25f.dir - size: 383291 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 nfiles: 3 - path: model.db hash: md5 - md5: b26dbb51d030083d2bd6f03245c1b188 - size: 155648 + md5: 4afc0f97fb9b487396c761d8d4f43aa3 + size: 679936 - path: output/train.csv hash: md5 - md5: e753967ae898a3f60a09b8c0acac625c - size: 108832 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 outs: - path: attack.db hash: md5 - md5: eb22685a9f6eafb2b953818cb912fcd1 - size: 184320 + md5: a234b8e39120ae2369ef7012ea8a1c2c + size: 700416 - path: logs/attacks/ hash: md5 - md5: 6fe487e166f75a031e2e75293b57641d.dir - size: 1038170 + md5: b703e5852611cc780892d5c508371061.dir + size: 7097696 nfiles: 3 compile_attacks: cmd: python -m deckard.layers.compile --report_folder output/reports/attack/ --results_file @@ -444,89 +588,89 @@ stages: deps: - path: attack.db hash: md5 - md5: eb22685a9f6eafb2b953818cb912fcd1 - size: 184320 + md5: a234b8e39120ae2369ef7012ea8a1c2c + size: 700416 - path: logs/attacks/ hash: md5 - md5: 6fe487e166f75a031e2e75293b57641d.dir - size: 1038170 + md5: b703e5852611cc780892d5c508371061.dir + size: 7097696 nfiles: 3 - path: output/reports/attack/ hash: md5 - md5: 11281d9592f318db700f772b287f4d46.dir - size: 9481051 - nfiles: 463 + md5: 28cff7ce7b900ec67d9a55be7a1b3665.dir + size: 64940924 + nfiles: 4355 outs: - path: output/attack.csv hash: md5 - md5: 37f5c7183361cdd6346fe17b4b119def - size: 214557 + md5: e346372d8713fe75bb16c02f12924027 + size: 1545938 find_best_attack@linear: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack --params_file best_linear --study_name=best_linear --default_config attack.yaml deps: - path: attack.db hash: md5 - md5: eb22685a9f6eafb2b953818cb912fcd1 - size: 184320 + md5: a234b8e39120ae2369ef7012ea8a1c2c + size: 700416 - path: logs/models/ hash: md5 - md5: c50e45d9189db51c2bc7459750acc25f.dir - size: 383291 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 nfiles: 3 - path: output/train.csv hash: md5 - md5: e753967ae898a3f60a09b8c0acac625c - size: 108832 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 outs: - path: conf/attack/best_linear.yaml hash: md5 - md5: e45d78ff616a657ab3f8ceee160d32e9 - size: 163 + md5: f048059aaa0e383f9c5ae9c085927588 + size: 231 find_best_attack@rbf: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack --params_file best_rbf --study_name=best_rbf --default_config attack.yaml deps: - path: attack.db hash: md5 - md5: eb22685a9f6eafb2b953818cb912fcd1 - size: 184320 + md5: a234b8e39120ae2369ef7012ea8a1c2c + size: 700416 - path: logs/models/ hash: md5 - md5: c50e45d9189db51c2bc7459750acc25f.dir - size: 383291 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 nfiles: 3 - path: output/train.csv hash: md5 - md5: e753967ae898a3f60a09b8c0acac625c - size: 108832 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 outs: - path: conf/attack/best_rbf.yaml hash: md5 - md5: e45d78ff616a657ab3f8ceee160d32e9 - size: 163 + md5: 936f60710cd2fba6d1b3584accc94943 + size: 246 find_best_attack@poly: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack --params_file best_poly --study_name=best_poly --default_config attack.yaml deps: - path: attack.db hash: md5 - md5: eb22685a9f6eafb2b953818cb912fcd1 - size: 184320 + md5: a234b8e39120ae2369ef7012ea8a1c2c + size: 700416 - path: logs/models/ hash: md5 - md5: c50e45d9189db51c2bc7459750acc25f.dir - size: 383291 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 nfiles: 3 - path: output/train.csv hash: md5 - md5: e753967ae898a3f60a09b8c0acac625c - size: 108832 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 outs: - path: conf/attack/best_poly.yaml hash: md5 - md5: e45d78ff616a657ab3f8ceee160d32e9 - size: 163 + md5: 26b55aad33b06e46b07904b00c5cb236 + size: 228 other_data_train@kdd_nsl: cmd: DATASET_NAME=kdd_nsl bash other_data.sh data=kdd_nsl +stage=train --config-name=model.yaml deps: @@ -557,3 +701,98 @@ stages: md5: e7c227947468122b62f891c0d54e0c54.dir size: 1314288 nfiles: 12 + retrain: + cmd: python retrain.py + deps: + - path: conf/attack/best_linear.yaml + hash: md5 + md5: f048059aaa0e383f9c5ae9c085927588 + size: 231 + - path: conf/attack/best_poly.yaml + hash: md5 + md5: 26b55aad33b06e46b07904b00c5cb236 + size: 228 + - path: conf/attack/best_rbf.yaml + hash: md5 + md5: 936f60710cd2fba6d1b3584accc94943 + size: 246 + - path: conf/model/best_linear.yaml + hash: md5 + md5: e4ae7059114d8724d4947e952145d4fe + size: 330 + - path: conf/model/best_poly.yaml + hash: md5 + md5: 12f892f3ba4ef8bab095b36bd7558d3e + size: 372 + - path: conf/model/best_rbf.yaml + hash: md5 + md5: 3092c0288833989d2e77d849993a2a40 + size: 360 + - path: output/attacks/ + hash: md5 + md5: 5dde4d7ab3e40708071184d4694123ee.dir + size: 725472 + nfiles: 396 + - path: output/models/ + hash: md5 + md5: d4a08c418e8ed1b0187f7f25a7299f0b.dir + size: 2228914 + nfiles: 260 + outs: + - path: plots/after_retrain_confidence.csv + hash: md5 + md5: cc14c3955a972163e623e0446766809a + size: 114012 + - path: plots/before_retrain_confidence.csv + hash: md5 + md5: 97809e651e09f8b326b8d30baa7ebeef + size: 113995 + - path: retrain/ + hash: md5 + md5: 2a50215eab128a532d22ed3c0d2cba50.dir + size: 174457 + nfiles: 12 + plots: + cmd: python plots.py + deps: + - path: output/attack.csv + hash: md5 + md5: e346372d8713fe75bb16c02f12924027 + size: 1545938 + - path: output/train.csv + hash: md5 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 + - path: plots/after_retrain_confidence.csv + hash: md5 + md5: cc14c3955a972163e623e0446766809a + size: 114012 + - path: plots/before_retrain_confidence.csv + hash: md5 + md5: 97809e651e09f8b326b8d30baa7ebeef + size: 113995 + outs: + - path: plots/accuracy_vs_attack_parameters.pdf + hash: md5 + md5: a82ad408178362b8f82239d18f90e5e0 + size: 16739 + - path: plots/confidence_vs_attack_parameters.pdf + hash: md5 + md5: d0632672e8312f99ee76c962f26144ae + size: 18215 + - path: plots/retrain_accuracy.pdf + hash: md5 + md5: 0027d350b615182d5b38cd86eb308dd5 + size: 13913 + - path: plots/retrain_confidence_vs_attack_parameters.pdf + hash: md5 + md5: 8c0e54e8402cc482ccd0dff257abd98c + size: 18186 + - path: plots/retrain_time.pdf + hash: md5 + md5: e03052e9715b9ed42016cc418ff9f7dc + size: 12900 + - path: plots/train_time_vs_attack_parameters.pdf + hash: md5 + md5: ef7acbcbb4ffa4eda0ff8e0107565eef + size: 17026 diff --git a/examples/kdd-nsl/dvc.yaml b/examples/kdd-nsl/dvc.yaml index 37eb4da5..04164939 100644 --- a/examples/kdd-nsl/dvc.yaml +++ b/examples/kdd-nsl/dvc.yaml @@ -45,9 +45,6 @@ stages: - logs/models/: # Each feature/sample/train size combination log is stored in a separate file cache: true persist: true - # - ${files.directory}/${files.reports}/train/: # Each feature/sample/train size combination experiment is stored in a separate folder - # cache: true - # persist: true params: # These are the parameters that are passed to the prototype stage - data - model @@ -56,7 +53,7 @@ stages: deps: - params.yaml - conf/model.yaml - - models.sh + - other_data.sh # - conf/model/ compile_models: cmd: @@ -118,37 +115,37 @@ stages: - ${files.directory}/train.csv - attack.db - logs/models/ - # retrain: - # cmd : python retrain.py - # deps: - # - ${files.directory}/models/ - # - ${files.directory}/attacks/ - # - conf/attack/best_linear.yaml - # - conf/attack/best_rbf.yaml - # - conf/attack/best_poly.yaml - # - conf/model/best_linear.yaml - # - conf/model/best_rbf.yaml - # - conf/model/best_poly.yaml - # outs: - # - retrain/ - # metrics: - # - plots/before_retrain_confidence.csv - # - plots/after_retrain_confidence.csv + retrain: + cmd : python retrain.py + deps: + - ${files.directory}/models/ + - ${files.directory}/attacks/ + - conf/attack/best_linear.yaml + - conf/attack/best_rbf.yaml + - conf/attack/best_poly.yaml + - conf/model/best_linear.yaml + - conf/model/best_rbf.yaml + - conf/model/best_poly.yaml + outs: + - retrain/ + metrics: + - plots/before_retrain_confidence.csv + - plots/after_retrain_confidence.csv plots: cmd : python plots.py deps : - plots/after_retrain_confidence.csv - - plots/attack_results.csv + - output/attack.csv - plots/before_retrain_confidence.csv - - plots/model_results.csv + - output/train.csv plots : - plots/accuracy_vs_attack_parameters.pdf - - plots/accuracy_vs_features.pdf - - plots/accuracy_vs_samples.pdf + # - plots/accuracy_vs_features.pdf + # - plots/accuracy_vs_samples.pdf - plots/confidence_vs_attack_parameters.pdf - - plots/fit_time_vs_attack_parameters.pdf - - plots/fit_time_vs_features.pdf - - plots/fit_time_vs_samples.pdf + - plots/train_time_vs_attack_parameters.pdf + # - plots/train_time_vs_features.pdf + # - plots/train_time_vs_samples.pdf - plots/retrain_accuracy.pdf - plots/retrain_confidence_vs_attack_parameters.pdf - plots/retrain_time.pdf diff --git a/examples/kdd-nsl/models.sh b/examples/kdd-nsl/models.sh deleted file mode 100644 index 2cd3d313..00000000 --- a/examples/kdd-nsl/models.sh +++ /dev/null @@ -1,56 +0,0 @@ -# N_FEATURES=( 10 100 1000 10000 10000 100000 ) -# TRAIN_SIZES=( 10 100 1000 ) -TRAIN_SIZES=( 1000000 ) -N_FEATURES=( 10 100 1000 ) -N_SAMPLES=( 1010000 ) -TOTAL=$(( ${#N_FEATURES[@]} * ${#N_SAMPLES[@]} * ${#TRAIN_SIZES[@]} )) -i=$(( 0 )) -mkdir -p logs/models -for train_size in ${TRAIN_SIZES[@]}; do - for n_samples in ${N_SAMPLES[@]}; do - for n_features in ${N_FEATURES[@]}; do - i=$(( i + 1 )) - echo "Running experiment ${i} of ${TOTAL}" - # Keeps a meta log of the experiments - echo "Running experiment ${i} of ${TOTAL}" >> log.txt - echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" >> log.txt - # Runs the linear kernel, tries to find the best C value - HYDRA_FULL_ERROR=1; python -m deckard.layers.optimise \ - ++data.generate.n_features=$n_features \ - ++data.generate.n_samples=$n_samples \ - ++data.sample.train_size=$train_size \ - model.init.kernel=linear \ - model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - ++hydra.sweeper.study_name=linear_${n_features}_${train_size} "$@" --multirun \ - # Keeps a log of the output for each experiment - >| logs/models/linear_features-${n_features}_samples-${n_samples}_train-${train_size}.log - echo "Linear Kernel Done" >> log.txt - # Runs the poly kernel - python -m deckard.layers.optimise \ - ++data.generate.n_features=$n_features \ - ++data.generate.n_samples=$n_samples \ - ++data.sample.train_size=$train_size \ - model.init.kernel=rbf \ - +model.init.gamma=scale \ - model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - ++hydra.sweeper.study_name=rbf_${n_features}_${train_size} "$@" --multirun \ - >| logs/models/rbf_features-${n_features}_samples-${n_samples}_train-${train_size}.log - echo "RBF Kernel Done" >> log.txt - # Runs the poly kernel - python -m deckard.layers.optimise \ - ++data.generate.n_features=$n_features \ - ++data.generate.n_samples=$n_samples \ - ++data.sample.train_size=$train_size \ - model.init.kernel=poly \ - +model.init.degree=1,2,3,4,5 \ - +model.init.gamma=scale \ - +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - ++hydra.sweeper.study_name=poly_${n_features}_${train_size} "$@" --multirun \ - >| logs/models/poly_features-${n_features}_samples-${n_samples}_train-${train_size}.log - echo "Poly Kernel Done" >> log.txt - echo "Successfully completed experiment ${i} of ${TOTAL}" >> log.txt - done; - done; -done; \ No newline at end of file diff --git a/examples/kdd-nsl/other_data.sh b/examples/kdd-nsl/other_data.sh new file mode 100644 index 00000000..0d3ddcc3 --- /dev/null +++ b/examples/kdd-nsl/other_data.sh @@ -0,0 +1,35 @@ + + +mkdir -p logs/models +HYDRA_FULL_ERROR=1; python -m deckard.layers.optimise \ +++data.sample.test_size=1000 \ +model.init.kernel=linear \ +model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +++hydra.sweeper.storage=sqlite:///model.db \ +++hydra.sweeper.study_name=linear "$@" --multirun \ +>| logs/models/linear.log +echo "Linear Kernel Done" >> model_log.txt +# Runs the poly kernel +python -m deckard.layers.optimise \ +++data.sample.test_size=1000 \ +model.init.kernel=rbf \ ++model.init.gamma=scale \ +model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ ++model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +++hydra.sweeper.storage=sqlite:///model.db \ +++hydra.sweeper.study_name=rbf "$@" --multirun \ +>| logs/models/rbf.log +echo "RBF Kernel Done" >> model_log.txt +# Runs the poly kernel +python -m deckard.layers.optimise \ +++data.sample.test_size=1000 \ +model.init.kernel=poly \ ++model.init.degree=1,2,3,4,5 \ ++model.init.gamma=scale \ ++model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +++hydra.sweeper.storage=sqlite:///model.db \ +++hydra.sweeper.study_name=poly "$@" --multirun \ +>| logs/models/poly.log +echo "Poly Kernel Done" >> model_log.txt +echo "Successfully completed experiment ${i} of ${TOTAL}" >> model_log.txt \ No newline at end of file diff --git a/examples/kdd-nsl/params.yaml b/examples/kdd-nsl/params.yaml index 67d79f9d..8aeb6d16 100644 --- a/examples/kdd-nsl/params.yaml +++ b/examples/kdd-nsl/params.yaml @@ -1,12 +1,96 @@ attack: _target_: deckard.base.attack.Attack attack_size: 10 - data: ${data} + data: + _target_: deckard.base.data.Data + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label init: - model: ${model} + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: null + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc name: art.attacks.evasion.HopSkipJump method: evasion - model: ${model} + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: null + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc data: _target_: deckard.base.data.Data name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv @@ -54,7 +138,25 @@ model: _target_: deckard.base.model.art_pipeline.ArtPipeline initialize: null library: sklearn-svc - data: ${data} + data: + _target_: deckard.base.data.Data + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label init: C: 1.0 _target_: deckard.base.model.ModelInitializer diff --git a/examples/kdd-nsl/plots.py b/examples/kdd-nsl/plots.py new file mode 100644 index 00000000..98e7bd21 --- /dev/null +++ b/examples/kdd-nsl/plots.py @@ -0,0 +1,378 @@ +import pandas as pd +import seaborn as sns +from pathlib import Path +import matplotlib.pyplot as plt + +import matplotlib.pyplot as plt +import logging + +sns.set_style("whitegrid") +sns.set_context("paper", font_scale=1.5) +sns.set_style({"font.family": "serif", "font.serif": "Times New Roman"}) +Path("plots").mkdir(parents=True, exist_ok=True) + + +logger = logging.getLogger(__name__) + + +# if Path("plots", "model_results.csv").exists(): +# results = pd.read_csv("plots/model_results.csv") +# else: +# results = parse_results("reports/model_queue/") +results = pd.read_csv("output/train.csv") +# input_size = results["data.generate.kwargs.n_samples"] * results["data.generate.kwargs.n_features"] +results["Kernel"] = results["model.init.kwargs.kernel"].copy() +# results["Features"] = results["data.generate.kwargs.n_features"].copy() +results["Samples"] = results["data.sample.train_size"].copy() +# results["input_size"] = input_size +# sample_list = results["data.generate.kwargs.n_samples"].unique() +# feature_list = results["data.generate.kwargs.n_features"].unique() +kernel_list = results["model.init.kwargs.kernel"].unique() +if "Unnamed: 0" in results.columns: + del results["Unnamed: 0"] +for col in results.columns: + if col == "data.name" and isinstance(results[col][0], list): + results[col] = results[col].apply(lambda x: x[0]) +results = results[results["model.init.kwargs.kernel"] != "sigmoid"] + +attack_results = pd.read_csv("output/attack.csv") +attack_results["Kernel"] = attack_results["model.init.kwargs.kernel"].copy() +# attack_results["Features"] = attack_results["data.generate.kwargs.n_features"].copy() +# attack_results["Samples"] = attack_results["data.sample.train_size"].copy() +# sample_list = attack_results["data.generate.kwargs.n_samples"].unique() +# feature_list = attack_results["data.generate.kwargs.n_features"].unique() +kernel_list = attack_results["model.init.kwargs.kernel"].unique() +if "Unnamed: 0" in attack_results.columns: + del attack_results["Unnamed: 0"] +for col in attack_results.columns: + if col == "data.name" and isinstance(attack_results[col][0], list): + attack_results[col] = attack_results[col].apply(lambda x: x[0]) + + +# graph1 = sns.lineplot( +# x="data.sample.train_size", +# y="accuracy", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph1.legend(labels=["Linear", "RBF", "Poly"]) +# graph1.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +# graph1.set_xlabel("Number of Samples") +# graph1.set_ylabel("Accuracy") +# graph1.set_xscale("log") +# graph1.get_figure().tight_layout() +# graph1.get_figure().savefig("plots/accuracy_vs_samples.pdf") +# plt.gcf().clear() + +# graph2 = sns.lineplot( +# x="data.generate.kwargs.n_features", +# y="accuracy", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph2.set_xlabel("Number of Features") +# graph2.set_ylabel("Accuracy") +# graph2.set_xscale("log") +# graph2.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +# graph2.get_figure().tight_layout() +# graph2.get_figure().savefig("plots/accuracy_vs_features.pdf") +# plt.gcf().clear() + +# results["train_time"] = ( +# results["train_time"] +# * results["data.sample.train_size"] +# * results["data.generate.kwargs.n_samples"] +# ) +# graph3 = sns.lineplot( +# x="data.generate.kwargs.n_features", +# y="train_time", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph3.set_xlabel("Number of Features") +# graph3.set_ylabel("Training Time") +# graph3.set(yscale="log", xscale="log") +# graph3.legend(title="Kernel") +# graph3.get_figure().tight_layout() +# graph3.get_figure().savefig("plots/train_time_vs_features.pdf") +# plt.gcf().clear() + +# graph4 = sns.lineplot( +# x="data.sample.train_size", +# y="train_time", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph4.set_xlabel("Number of Samples") +# graph4.set_ylabel("Training Time") +# graph4.set(yscale="log", xscale="log") +# graph4.legend(title="Kernel") +# graph4.get_figure().tight_layout() +# graph4.get_figure().savefig("plots/train_time_vs_samples.pdf") +# plt.gcf().clear() + +fig, ax = plt.subplots(2, 2) +graph5 = sns.lineplot( + x="attack.init.kwargs.eps", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph5.set(xscale="log", xlabel="Perturbation Distance", ylabel="Accuracy") +graph6 = sns.lineplot( + x="attack.init.kwargs.eps_step", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph6.set(xscale="log", xlabel="Perturbation Step", ylabel="Accuracy") +graph7 = sns.lineplot( + x="attack.init.kwargs.max_iter", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph7.set(xscale="log", xlabel="Maximum Iterations", ylabel="Accuracy") +graph8 = sns.lineplot( + x="attack.init.kwargs.batch_size", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph8.set(xscale="log", xlabel="Batch Size", ylabel="Accuracy") +graph6.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout() +fig.savefig("plots/accuracy_vs_attack_parameters.pdf") +plt.gcf().clear() + +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="attack.init.kwargs.eps", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="Attack Time") +graph10 = sns.lineplot( + x="attack.init.kwargs.eps_step", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="Attack Time") +graph11 = sns.lineplot( + x="attack.init.kwargs.max_iter", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="Attack Time") +graph12 = sns.lineplot( + x="attack.init.kwargs.batch_size", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="Attack Time") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/train_time_vs_attack_parameters.pdf") +plt.gcf().clear() + +retrain_df = pd.DataFrame() +for kernel in ["rbf", "linear", "poly"]: + try: + tmp = pd.read_csv(f"retrain/{kernel}/results.csv") + except FileNotFoundError: + break + tmp["Kernel"] = kernel + tmp["Epochs"] = tmp.index + retrain_df = pd.concat([retrain_df, tmp], axis=0) + retrain_df = retrain_df.reset_index(drop=True) + if "Unnamed: 0" in retrain_df.columns: + del retrain_df["Unnamed: 0"] + + +retrain = sns.lineplot( + x="Epochs", + y="ben_score", + data=retrain_df, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +retrain = sns.lineplot( + x="Epochs", + y="adv_score", + data=retrain_df, + style="Kernel", + color="darkred", + legend=False, + style_order=["rbf", "poly", "linear"], +) +retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +retrain.set_xlabel("Retraining Epochs") +retrain.set_ylabel("Accuracy") +retrain.get_figure().tight_layout() +retrain.get_figure().savefig("plots/retrain_accuracy.pdf") +plt.gcf().clear() + +retrain_df["ben_time"] = retrain_df["ben_time"] * retrain_df["train_size"] * 10 +retrain_df["adv_time"] = retrain_df["adv_time"] * retrain_df["attack_size"] +retrain = sns.lineplot( + x="Epochs", + y="ben_time", + data=retrain_df, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +retrain = sns.lineplot( + x="Epochs", + y="adv_time", + data=retrain_df, + style="Kernel", + color="darkred", + legend=False, + style_order=["rbf", "poly", "linear"], +) +retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +retrain.set_xlabel("Retraining Epochs") +retrain.set_ylabel("Time") +retrain.set_yscale("log") +retrain.get_figure().tight_layout() +retrain.get_figure().savefig("plots/retrain_time.pdf") +plt.gcf().clear() + +confidence_df = pd.read_csv("plots/before_retrain_confidence.csv") +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="eps", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") +graph10 = sns.lineplot( + x="eps_step", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") +graph11 = sns.lineplot( + x="max_iter", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") +graph12 = sns.lineplot( + x="batch_size", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/confidence_vs_attack_parameters.pdf") +plt.gcf().clear() + +confdence_df = pd.read_csv("plots/after_retrain_confidence.csv") +confidence_df.columns +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="eps", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") +graph10 = sns.lineplot( + x="eps_step", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") +graph11 = sns.lineplot( + x="max_iter", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") +graph12 = sns.lineplot( + x="batch_size", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/retrain_confidence_vs_attack_parameters.pdf") +plt.gcf().clear() \ No newline at end of file diff --git a/examples/kdd-nsl/plots/.gitignore b/examples/kdd-nsl/plots/.gitignore index d6e507d0..642f14d4 100644 --- a/examples/kdd-nsl/plots/.gitignore +++ b/examples/kdd-nsl/plots/.gitignore @@ -1,12 +1,12 @@ -/accuracy_vs_attack_parameters.pdf -/confidence_vs_attack_parameters.pdf -/train_time_vs_attack_parameters.pdf -/retrain_accuracy.pdf -/retrain_confidence_vs_attack_parameters.pdf -/retrain_time.pdf /accuracy_vs_attack_parameters.eps /confidence_vs_attack_parameters.eps /train_time_vs_attack_parameters.eps /retrain_accuracy.eps /retrain_confidence_vs_attack_parameters.eps /retrain_time.eps +/accuracy_vs_attack_parameters.pdf +/confidence_vs_attack_parameters.pdf +/train_time_vs_attack_parameters.pdf +/retrain_accuracy.pdf +/retrain_confidence_vs_attack_parameters.pdf +/retrain_time.pdf diff --git a/examples/kdd-nsl/retrain.py b/examples/kdd-nsl/retrain.py index 6d69faf7..a359b01b 100644 --- a/examples/kdd-nsl/retrain.py +++ b/examples/kdd-nsl/retrain.py @@ -1,44 +1,411 @@ -import optuna +import json import logging +import os +import pickle from pathlib import Path -from hydra import initialize_config_dir, compose -from hydra.utils import instantiate -from omegaconf import OmegaConf -import yaml +from time import process_time +import json +from typing import List +import numpy as np import pandas as pd -from sklearn.model_selection import train_test_split -from ..base.utils import flatten_dict, unflatten_dict - - - -def compose_experiment(config_dir, config_name, overrides): - with initialize_config_dir(config_dir=config_dir, version_base="1.3"): - cfg = compose(config_name=config_name, overrides=overrides) - cfg = OmegaConf.to_container(cfg, resolve=False) - return cfg - -def train_model(config_dir, config_name, overrides, data_file, model_file): - cfg = compose_experiment(config_dir, config_name, overrides) - cfg = OmegaConf.to_container(cfg, resolve=True) - exp = instantiate(cfg) - scores = exp(model_file=model_file, data_file=data_file) - return scores - -def create_new_dataset(attack_samples_file, data_file, ratio = .5): - attack_suffix = Path(attack_samples_file).suffix - if attack_suffix == ".csv": - attack_samples = pd.read_csv(attack_samples_file) - elif attack_suffix == ".pkl": - attack_samples = pd.read_pickle(attack_samples_file) - else: - raise ValueError(f"Unknown attack samples file format {attack_suffix}") - data_suffix = Path(data_file).suffix - if data_suffix == ".csv": - data = pd.read_csv(data_file) - elif data_suffix == ".pkl": - data = pd.read_pickle(data_file) +import yaml +from art.attacks.evasion import ProjectedGradientDescent +from art.estimators.classification.scikitlearn import ScikitlearnSVC +from art.utils import to_categorical +from pandas import DataFrame +from sklearn.svm import SVC +from tqdm import tqdm +from hydra.utils import instantiate + + +logger = logging.getLogger(__name__) + +logging.basicConfig(level=logging.INFO) +def parse_folder( + folder, + exclude=[ + "probabilities", + "predictions", + "plots", + "ground_truth", + "attack_predictions", + "attack_probabilities", + "samples", + ], +) -> pd.DataFrame: + """ + Parse a folder containing json files and return a dataframe with the results, excluding the files in the exclude list. + :param folder: Path to folder containing json files + :param exclude: List of files to exclude. Default: ['probabilities', 'predictions', 'plots', 'ground_truth']. + :return: Pandas dataframe with the results + """ + folder = Path(folder) + results = {} + results[folder] = {} + logger.debug(f"Parsing folder {folder}...") + for file in folder.glob("*.json"): + if Path(file).stem in exclude: + continue + else: + with open(file, "r") as f: + results[folder][Path(file).stem] = json.load(f) + return pd.DataFrame(results).T + + +def flatten_results(results): + """ + Flatten a dataframe containing json files. So that each json dict entry becomes a column with dot notation (e.g. "key1.subkey1") + :param results: Pandas dataframe containing json files + """ + new_results = pd.DataFrame() + logger.debug("Flattening results...") + for col in results.columns: + tmp = pd.json_normalize(results[col]) + new_results = pd.concat([new_results, tmp], axis=1) + return new_results + +def parse_results(result_dir, flatten=True): + """ + Recursively parse a directory containing json files and return a dataframe with the results. + :param result_dir: Path to directory containing json files + :param regex: Regex to match folders to parse. Default: "*/*" + :param flatten: Whether to flatten the results. Default: True + :return: Pandas dataframe with the results + """ + result_dir = Path(result_dir) + assert result_dir.is_dir(), f"Result directory {result_dir} does not exist." + results = pd.DataFrame() + logger.debug("Parsing results...") + total = len(list(Path(result_dir).iterdir())) + logger.info(f"Parsing {total} folders...") + for folder in Path(result_dir).iterdir(): + tmp = parse_folder(folder) + if flatten is True: + tmp = flatten_results(tmp) + tmp = tmp.loc[:, ~tmp.columns.duplicated()] + results = pd.concat([results, tmp]) + return results + + +def set_for_keys(my_dict, key_arr, val) -> dict: + """ + Set val at path in my_dict defined by the string (or serializable object) array key_arr. + :param my_dict: Dictionary to set value in + :param key_arr: Array of keys to set value at + :param val: Value to set + :return: Dictionary with value set + """ + current = my_dict + for i in range(len(key_arr)): + key = key_arr[i] + if key not in current: + if i == len(key_arr) - 1: + current[key] = val + else: + current[key] = {} + else: + if type(current[key]) is not dict: + logger.info( + "Given dictionary is not compatible with key structure requested", + ) + raise ValueError("Dictionary key already occupied") + current = current[key] + return my_dict + + +def unflatten_results(df, sep=".") -> List[dict]: + """ + Unflatten a dataframe with dot notation columns (e.g. "key1.subkey1") into a list of dictionaries. + :param df: Pandas dataframe with dot notation columns + :param sep: Separator to use. Default: "." + :return: List of dictionaries + """ + logger.debug("Unflattening results...") + result = [] + for _, row in df.iterrows(): + parsed_row = {} + for idx, val in row.iteritems(): + if val == val: + keys = idx.split(sep) + parsed_row = set_for_keys(parsed_row, keys, val) + result.append(parsed_row) + return result +def retrain_loop( + clf, + X_train, + y_train, + X_test, + y_test, + atk, + attack_size, + epochs, +) -> tuple: + i = 0 + results = [] + for epochs in tqdm(range(epochs), desc="Epochs"): + # Benign Experiment + logger.info("Epoch: {} - Benign Training".format(i)) + try: + y_train = to_categorical(y_train) + except: # noqa E722 + pass + try: + y_test = to_categorical(y_test) + except: # noqa E722 + pass + start = process_time() + clf.fit(X_train, y_train) + ben_time = (process_time() - start) / len(X_train) + ben_predictions = clf.predict(X_test) + ben_score = np.mean( + np.argmax(ben_predictions, axis=1) == np.argmax(y_test, axis=1), + ) + ben_loss = np.mean(ben_predictions[:, 0] - y_test[:, 0]) + # Adversarial Experiment + logger.info("Epoch: {} - Adversarial Training".format(i)) + start = process_time() + adv = atk.generate(X_test[:attack_size]) + adv_time = (process_time() - start) / attack_size + adv_predictions = clf.predict(adv) + adv_score = np.mean( + np.argmax(adv_predictions, axis=1) + == np.argmax(y_test[:attack_size], axis=1), + ) + adv_loss = np.mean(adv_predictions[:, 0] - y_test[:attack_size, 0]) + # Append Adversarial Examples to Training Set + X_train = np.concatenate((X_train, adv), axis=0) + adv_labels = to_categorical(y_test[:attack_size, 0]) + y_train = np.concatenate((y_train, adv_labels), axis=0) + i += 1 + # Save Results + results.append( + { + "ben_time": ben_time, + "ben_score": ben_score, + "adv_time": adv_time, + "adv_score": adv_score, + "ben_loss": ben_loss, + "adv_loss": adv_loss, + "attack_size": attack_size, + "train_size": len(X_train), + "test_size": attack_size, + }, + ) + outputs = { + "ben_predictions": DataFrame(ben_predictions), + "adv_predictions": DataFrame(adv_predictions), + } + # Some Logging + print( + "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( + i, + ben_time, + ben_score, + adv_time, + adv_score, + ), + ) + logger.info( + "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( + i, + ben_time, + ben_score, + adv_time, + adv_score, + ), + ) + results = pd.DataFrame(results) + return results, outputs + + +def save_results_and_outputs(results, outputs, path="retrain") -> list: + Path(path).mkdir(parents=True, exist_ok=True) + for output in outputs: + pd.DataFrame(outputs[output]).to_csv(f"{path}/{output}.csv") + assert Path(f"{path}/{output}.csv").exists(), f"Problem saving {path}/{output}" + pd.DataFrame(results).to_csv(f"{path}/results.csv") + assert Path( + f"{path}/results.csv", + ).exists(), f"Problem saving results to {path}/results.csv" + + +# Parse Model Results +results = pd.read_csv("output/train.csv") +# Some convenient variable names +# input_size = results["data.generate.kwargs.n_samples"] * results["data.generate.kwargs.n_features"] +results["Kernel"] = results["model.init.kwargs.kernel"].copy() +# results["Features"] = results["data.generate.kwargs.n_features"].copy() +# results["Samples"] = results["data.sample.train_size"].copy() +# results["input_size"] = input_size +# Clean up results +if "Unnamed: 0" in results.columns: + del results["Unnamed: 0"] +for col in results.columns: + if col == "data.name" and isinstance(results[col][0], list): + results[col] = results[col].apply(lambda x: x[0]) +# Subset results +# subset = results[results["data.sample.train_size"] == 10000] +# subset = subset[subset["data.generate.kwargs.n_features"] == 100] +with open("conf/model/best_rbf.yaml", "r") as f: + best_rbf = yaml.safe_load(f) +best_rbf['init'].pop("_target_", None) +best_rbf['init'].pop("name", None) +with open("conf/model/best_poly.yaml", "r") as f: + best_poly = yaml.safe_load(f) +best_poly['init'].pop("_target_", None) +best_poly['init'].pop("name", None) +with open("conf/model/best_linear.yaml", "r") as f: + best_lin = yaml.safe_load(f) +best_lin['init'].pop("_target_", None) +best_lin['init'].pop("name", None) +rbf_model = SVC(**best_rbf["init"]) +# Poly +poly_model = SVC(**best_poly["init"]) +# Linear +linear_model = SVC(**best_lin["init"]) +# Load Data +with open("conf/data/attack.yaml", "r") as f: + data = yaml.safe_load(f) +data = instantiate(data) +X_train, X_test, y_train, y_test = data() +# Fit Models +rbf_model.fit(X_train, y_train) +poly_model.fit(X_train, y_train) +linear_model.fit(X_train, y_train) +models = [rbf_model, poly_model, linear_model] +model_names = ["rbf", "poly", "linear"] +# ART Models +art_models = [ + ScikitlearnSVC(model=rbf_model), + ScikitlearnSVC(model=poly_model), + ScikitlearnSVC(model=linear_model), +] +i = 1 +epochs = 20 +df1 = pd.DataFrame() +df2 = pd.DataFrame() +for model in art_models: + # Define model name + name = model_names[i - 1] + # Define attack + atk = ProjectedGradientDescent( + estimator=model, + eps=1, + eps_step=0.1, + max_iter=10, + targeted=False, + num_random_init=0, + batch_size=10, + ) + confidence_df = pd.DataFrame() + for folder in tqdm(os.listdir("output/reports/attack")): + confidence_ser = pd.Series() + if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): + with open(Path("output/reports/attack", folder, "adv_probabilities.json"), "r") as f: + probs = json.load(f) + probs = np.array(probs) + false_confidence = y_test[: len(probs)] - probs[:, 1] + avg_prob = np.mean(false_confidence) + with open(Path("output/reports/attack", folder, "score_dict.json"), "r") as f: + try: + scores = json.load(f) + except: # noqa E722 + scores = {} + if "False Confidence" in scores: + del scores["False Confidence"] + scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob + with open(Path("output/reports/attack", folder, "score_dict.json"), "w") as f: + json.dump(scores, f) + yaml_file = Path("output/reports/attack", folder, "params.yaml") + json_file = Path("output/reports/attack", folder, "params.json") + if yaml_file.exists(): + params_file = yaml_file + with open(params_file, "r") as f: + params = yaml.safe_load(f) + elif json_file.exists(): + params_file = json_file + with open(params_file, "r") as f: + params = json.load(f) + else: + raise ValueError(f"No params file found for {folder}") + attack_params = params["attack"]["init"]['kwargs'] + attack_params.update({"name" : params["attack"]["init"]["name"]}) + confidence_ser["Kernel"] = name + confidence_ser["Average False Confidence"] = avg_prob + # print(f"Shape of confidence ser: {confidence_ser.shape}") + attack_ser = pd.Series(attack_params) + confidence_ser = confidence_ser._append(attack_ser) + # print(f"Shape of confidence ser: {confidence_ser.shape}") + if "Unnamed: 0" in confidence_df.columns: + del confidence_df["Unnamed: 0"] + confidence_df = confidence_df._append(confidence_ser, ignore_index=True) + # print(f"Shape of confidence df: {confidence_df.shape}") + # print(confidence_df.head()) + # input("Press Enter to continue...") + else: + pass + df1 = pd.concat([df1, confidence_df], ignore_index=True) + Path("plots").mkdir(parents=True, exist_ok=True) + df1.to_csv(Path("plots", "before_retrain_confidence.csv")) + # Train model on attack samples + print(f"Training Model {i} of {len(models)}") + if not Path("retrain", name, f"{epochs}.pkl").exists(): + results, outputs = retrain_loop( + model, + X_train, + y_train, + X_test, + y_test, + atk, + epochs=epochs, + attack_size=50, + ) + save_results_and_outputs(results, outputs, path=f"retrain/{name}/") + Path("retrain", name).mkdir(parents=True, exist_ok=True) + with Path("retrain", name, f"{epochs}.pkl").open("wb") as f: + pickle.dump(model, f) else: - raise ValueError(f"Unknown data file format {data_suffix}") - attack_len = len(attack_samples) - data_len = len(data) - new_len = int(attack_len * ratio) + with open(Path("retrain", name, f"{epochs}.pkl"), "rb") as f: + model = pickle.load(f) + print(f"Evaluating Model {i} of {len(models)}") + # Get false confidence scores after retraining + confidence_df = pd.DataFrame() + for folder in tqdm(os.listdir("output/reports/attack")): + confidence_ser = pd.Series() + if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): + with open(Path("output/reports/attack", folder, "adv_probabilities.json"), "r") as f: + probs = json.load(f) + probs = np.array(probs) + false_confidence = y_test[: len(probs)] - probs[:, 1] + avg_prob = np.mean(false_confidence) + pd.DataFrame(probs).to_csv( + Path("output/reports/attack", folder, f"probs_after_retraining_{name}.csv"), + ) + with open(Path("output/reports/attack", folder, "score_dict.json"), "r") as f: + scores = json.load(f) + if "False Confidence" in scores: + del scores["False Confidence"] + scores[f"False Confidence {name.capitalize()}"] = avg_prob + with open(Path("output/reports/attack", folder, "score_dict.json"), "w") as f: + json.dump(scores, f) + if Path("output/reports/attack", folder, "params.yaml").exists(): + with open(Path("output/reports/attack", folder, "params.yaml"), "r") as f: + params = yaml.safe_load(f) + elif Path("output/reports/attack", folder, "params.json").exists(): + with open(Path("output/reports/attack", folder, "params.json"), "r") as f: + params = json.load(f) + else: + logger.warning(f"No params file found for {folder}") + continue + attack_params = params["attack"]["init"]["kwargs"] + attack_params.update({"name" : params["attack"]["init"]["name"]}) + confidence_ser["Kernel"] = name + confidence_ser["Average False Confidence After Retraining"] = avg_prob + attack_ser = pd.Series(attack_params) + confidence_ser = confidence_ser._append(attack_ser) + if "Unnamed: 0" in confidence_df.columns: + del confidence_df["Unnamed: 0"] + confidence_df = confidence_df._append(confidence_ser, ignore_index=True) + df2 = pd.concat([df2, confidence_df], ignore_index=True) + df2.to_csv(Path("plots", "after_retrain_confidence.csv")) + i += 1 From 85cf4c6b1e12f49565fbe6d9fae6a5771cbb06f0 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Sun, 15 Oct 2023 00:58:53 +0200 Subject: [PATCH 105/148] moved security stuff to one folder --- .../classification/.dvc/.gitignore | 0 .../{ => security}/classification/.dvc/config | 0 .../{ => security}/classification/.dvcignore | 0 .../{ => security}/classification/.gitignore | 0 .../{ => security}/classification/README.md | 0 .../{ => security}/classification/attacks.sh | 0 .../{ => security}/classification/conf/RQ.md | 0 .../classification/conf/attack.yaml | 0 .../classification/conf/attack/.gitignore | 0 .../conf/attack/attack_grid.yaml | 0 .../classification/conf/attack/default.yaml | 0 .../classification/conf/attack/hsj.yaml | 0 .../classification/conf/compile.yaml | 0 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examples/classification/models.sh rename to examples/security/classification/models.sh diff --git a/examples/security/classification/multirun/2023-10-14/22-41-22/optimization_results.yaml b/examples/security/classification/multirun/2023-10-14/22-41-22/optimization_results.yaml new file mode 100644 index 00000000..33a976c7 --- /dev/null +++ b/examples/security/classification/multirun/2023-10-14/22-41-22/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.001 +best_value: 0.749 diff --git a/examples/security/classification/multirun/2023-10-14/22-41-48/optimization_results.yaml b/examples/security/classification/multirun/2023-10-14/22-41-48/optimization_results.yaml new file mode 100644 index 00000000..b87ff556 --- /dev/null +++ b/examples/security/classification/multirun/2023-10-14/22-41-48/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + model.init.C: 0.1 +best_value: 0.71 diff --git a/examples/security/classification/multirun/2023-10-14/22-42-29/optimization_results.yaml b/examples/security/classification/multirun/2023-10-14/22-42-29/optimization_results.yaml new file mode 100644 index 00000000..8fb552f1 --- /dev/null +++ b/examples/security/classification/multirun/2023-10-14/22-42-29/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 4 + model.init.C: 0.0001 +best_value: 0.727 diff --git a/examples/security/classification/multirun/2023-10-14/22-43-44/optimization_results.yaml b/examples/security/classification/multirun/2023-10-14/22-43-44/optimization_results.yaml new file mode 100644 index 00000000..8bd80667 --- /dev/null +++ b/examples/security/classification/multirun/2023-10-14/22-43-44/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 100 + ++attack.init.eps: 0.3 + ++attack.init.eps_step: 1 + ++attack.init.max_iter: 10 + ++attack.init.norm: 2 +best_value: 0.2 diff --git a/examples/security/classification/multirun/2023-10-14/22-46-19/optimization_results.yaml b/examples/security/classification/multirun/2023-10-14/22-46-19/optimization_results.yaml new file mode 100644 index 00000000..8467ece3 --- /dev/null +++ b/examples/security/classification/multirun/2023-10-14/22-46-19/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 10 + ++attack.init.eps: 0.1 + ++attack.init.eps_step: 0.5 + ++attack.init.max_iter: 100 + ++attack.init.norm: 2 +best_value: 0.2 diff --git a/examples/security/classification/multirun/2023-10-14/22-49-47/optimization_results.yaml b/examples/security/classification/multirun/2023-10-14/22-49-47/optimization_results.yaml new file mode 100644 index 00000000..5f816fa4 --- /dev/null +++ b/examples/security/classification/multirun/2023-10-14/22-49-47/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 1 + ++attack.init.eps: 0.5 + ++attack.init.eps_step: 1 + ++attack.init.max_iter: 1000 + ++attack.init.norm: 1 +best_value: 0.2 diff --git a/examples/security/classification/multirun/2023-10-14/22-57-57/optimization_results.yaml b/examples/security/classification/multirun/2023-10-14/22-57-57/optimization_results.yaml new file mode 100644 index 00000000..33a976c7 --- /dev/null +++ b/examples/security/classification/multirun/2023-10-14/22-57-57/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.001 +best_value: 0.749 diff --git a/examples/security/classification/multirun/2023-10-14/22-58-19/optimization_results.yaml b/examples/security/classification/multirun/2023-10-14/22-58-19/optimization_results.yaml new file mode 100644 index 00000000..b87ff556 --- /dev/null +++ b/examples/security/classification/multirun/2023-10-14/22-58-19/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + model.init.C: 0.1 +best_value: 0.71 diff --git a/examples/security/classification/multirun/2023-10-14/22-59-02/optimization_results.yaml b/examples/security/classification/multirun/2023-10-14/22-59-02/optimization_results.yaml new file mode 100644 index 00000000..8fb552f1 --- /dev/null +++ b/examples/security/classification/multirun/2023-10-14/22-59-02/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 4 + model.init.C: 0.0001 +best_value: 0.727 diff --git a/examples/security/classification/multirun/2023-10-14/23-00-17/optimization_results.yaml b/examples/security/classification/multirun/2023-10-14/23-00-17/optimization_results.yaml new file mode 100644 index 00000000..8bd80667 --- /dev/null +++ b/examples/security/classification/multirun/2023-10-14/23-00-17/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 100 + ++attack.init.eps: 0.3 + ++attack.init.eps_step: 1 + ++attack.init.max_iter: 10 + ++attack.init.norm: 2 +best_value: 0.2 diff --git a/examples/security/classification/multirun/2023-10-14/23-02-08/optimization_results.yaml b/examples/security/classification/multirun/2023-10-14/23-02-08/optimization_results.yaml new file mode 100644 index 00000000..8467ece3 --- /dev/null +++ b/examples/security/classification/multirun/2023-10-14/23-02-08/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 10 + ++attack.init.eps: 0.1 + ++attack.init.eps_step: 0.5 + ++attack.init.max_iter: 100 + ++attack.init.norm: 2 +best_value: 0.2 diff --git a/examples/security/classification/multirun/2023-10-14/23-06-52/optimization_results.yaml b/examples/security/classification/multirun/2023-10-14/23-06-52/optimization_results.yaml new file mode 100644 index 00000000..5f816fa4 --- /dev/null +++ b/examples/security/classification/multirun/2023-10-14/23-06-52/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 1 + ++attack.init.eps: 0.5 + ++attack.init.eps_step: 1 + ++attack.init.max_iter: 1000 + ++attack.init.norm: 1 +best_value: 0.2 diff --git a/examples/security/classification/output/plots/.gitignore b/examples/security/classification/output/plots/.gitignore new file mode 100644 index 00000000..e7f7378a --- /dev/null +++ b/examples/security/classification/output/plots/.gitignore @@ -0,0 +1,10 @@ +/accuracy_vs_attack_parameters.pdf +/accuracy_vs_features.pdf +/accuracy_vs_samples.pdf +/confidence_vs_attack_parameters.pdf +/train_time_vs_attack_parameters.pdf +/train_time_vs_features.pdf +/train_time_vs_samples.pdf +/retrain_accuracy.pdf +/retrain_confidence_vs_attack_parameters.pdf +/retrain_time.pdf diff --git a/examples/security/classification/output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/params.yaml b/examples/security/classification/output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/params.yaml new file mode 100644 index 00000000..a4b3e6c9 --- /dev/null +++ b/examples/security/classification/output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 1 + max_iter: 1000 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 3b5765f02de2c9ac351847de6f923a06 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/adv_predictions.json + adv_probabilities_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/adv_probabilities.json + attack_file: output/attacks/904bbdc1b81b6891328139bd6f03c17b.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/params.yaml + predictions_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/predictions.json + probabilities_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/probabilities.json + score_dict_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/score_dict.json + test_labels_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/test_labels.json + train_labels_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/train_labels.json + model_dir: models + name: 04ea26bc380fe6b10ad454dfec2e14ba + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 04ea26bc380fe6b10ad454dfec2e14ba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/params.yaml b/examples/security/classification/output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/params.yaml new file mode 100644 index 00000000..24bc36b2 --- /dev/null +++ b/examples/security/classification/output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.3 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: fbc9130bfa39d88145f62f64fe4b5f81 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/adv_predictions.json + adv_probabilities_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/adv_probabilities.json + attack_file: output/attacks/d3e607aa42c0a4222c8a20acc88c7f5f.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/params.yaml + predictions_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/predictions.json + probabilities_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/probabilities.json + score_dict_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/score_dict.json + test_labels_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/test_labels.json + train_labels_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/train_labels.json + model_dir: models + name: 05cd19bd97879eb733669370dcaa4bf9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 05cd19bd97879eb733669370dcaa4bf9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/params.yaml b/examples/security/classification/output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/params.yaml new file mode 100644 index 00000000..9f027c50 --- /dev/null +++ b/examples/security/classification/output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 42aa05d8e56e837209c3a5bb38f04283 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/adv_predictions.json + adv_probabilities_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/adv_probabilities.json + attack_file: output/attacks/9fc323308ffd4047482789e31e82b088.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/params.yaml + predictions_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/predictions.json + probabilities_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/probabilities.json + score_dict_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/score_dict.json + test_labels_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/test_labels.json + train_labels_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/train_labels.json + model_dir: models + name: 05e7e23ca25d2ca1042b8edef9ad1548 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 05e7e23ca25d2ca1042b8edef9ad1548 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/params.yaml b/examples/security/classification/output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/params.yaml new file mode 100644 index 00000000..ad2f5ba1 --- /dev/null +++ b/examples/security/classification/output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: f173cafe7a1c8d4d7eb14602d0c5752d +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/adv_predictions.json + adv_probabilities_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/adv_probabilities.json + attack_file: output/attacks/ce1e6a061ea5a4647ecce0c4d6570628.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/params.yaml + predictions_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/predictions.json + probabilities_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/probabilities.json + score_dict_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/score_dict.json + test_labels_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/test_labels.json + train_labels_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/train_labels.json + model_dir: models + name: 06b2f80df6dfa53eb83cde7cd02ea085 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 06b2f80df6dfa53eb83cde7cd02ea085 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/params.yaml b/examples/security/classification/output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/params.yaml new file mode 100644 index 00000000..b8c1621d --- /dev/null +++ b/examples/security/classification/output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.01 + max_iter: 1000 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 627235d7f8b29cf00b882303dcdf29be +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/adv_predictions.json + adv_probabilities_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/adv_probabilities.json + attack_file: output/attacks/abf34efaec9bef7d8fc932c42239de9b.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/params.yaml + predictions_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/predictions.json + probabilities_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/probabilities.json + score_dict_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/score_dict.json + test_labels_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/test_labels.json + train_labels_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/train_labels.json + model_dir: models + name: 07157c4727b477e9c69bb9885e27a2a0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 07157c4727b477e9c69bb9885e27a2a0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/0cdc224d197c450388cef6965763be7c/params.yaml b/examples/security/classification/output/reports/attack/0cdc224d197c450388cef6965763be7c/params.yaml new file mode 100644 index 00000000..2db5e17a --- /dev/null +++ b/examples/security/classification/output/reports/attack/0cdc224d197c450388cef6965763be7c/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.001 + max_iter: 1000 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 50e968a462215f622e220d58363a37f4 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/adv_predictions.json + adv_probabilities_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/adv_probabilities.json + attack_file: output/attacks/7a80d64427a17cd62f12faab5a5923b0.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/params.yaml + predictions_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/predictions.json + probabilities_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/probabilities.json + score_dict_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/score_dict.json + test_labels_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/test_labels.json + train_labels_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/train_labels.json + model_dir: models + name: 0cdc224d197c450388cef6965763be7c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 0cdc224d197c450388cef6965763be7c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/params.yaml b/examples/security/classification/output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/params.yaml new file mode 100644 index 00000000..100f811a --- /dev/null +++ b/examples/security/classification/output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.5 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: dcda6d29b01e01a5e5c7e75365a1a35f +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/adv_predictions.json + adv_probabilities_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/adv_probabilities.json + attack_file: output/attacks/890452ce08811807bcaffccb876ff2fc.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/params.yaml + predictions_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/predictions.json + probabilities_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/probabilities.json + score_dict_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/score_dict.json + test_labels_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/test_labels.json + train_labels_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/train_labels.json + model_dir: models + name: 0f1d476ab623be19fa4a7fcd01d17cf0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 0f1d476ab623be19fa4a7fcd01d17cf0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/params.yaml b/examples/security/classification/output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/params.yaml new file mode 100644 index 00000000..5532f5ea --- /dev/null +++ b/examples/security/classification/output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.3 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 8df9cc2e1f94da15e5a9771498972220 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/adv_predictions.json + adv_probabilities_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/adv_probabilities.json + attack_file: output/attacks/79adc798c19d54d7cb59dd6e38c5dc64.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/params.yaml + predictions_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/predictions.json + probabilities_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/probabilities.json + score_dict_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/score_dict.json + test_labels_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/test_labels.json + train_labels_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/train_labels.json + model_dir: models + name: 1222b5d1f13ffc0ecb68b6ddf1cfa544 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 1222b5d1f13ffc0ecb68b6ddf1cfa544 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/params.yaml b/examples/security/classification/output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/params.yaml new file mode 100644 index 00000000..bb0f6ffb --- /dev/null +++ b/examples/security/classification/output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.3 + max_iter: 1000 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 7bae9c379f60b2550294fd5f88e861a4 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/adv_predictions.json + adv_probabilities_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/adv_probabilities.json + attack_file: output/attacks/e079fcffb852f0e1aeaafa48d5018b29.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/params.yaml + predictions_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/predictions.json + probabilities_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/probabilities.json + score_dict_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/score_dict.json + test_labels_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/test_labels.json + train_labels_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/train_labels.json + model_dir: models + name: 12e0471fdb18fe6a2f348b2b4ea480fc + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 12e0471fdb18fe6a2f348b2b4ea480fc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/13f80fb91b7c61a65e59eb7106099446/params.yaml b/examples/security/classification/output/reports/attack/13f80fb91b7c61a65e59eb7106099446/params.yaml new file mode 100644 index 00000000..351341bb --- /dev/null +++ b/examples/security/classification/output/reports/attack/13f80fb91b7c61a65e59eb7106099446/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.01 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 24a90609f13365a76327e52aff4188a7 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/adv_predictions.json + adv_probabilities_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/adv_probabilities.json + attack_file: output/attacks/0b6b66ac476aa99b0de2b0af2f609ad0.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/params.yaml + predictions_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/predictions.json + probabilities_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/probabilities.json + score_dict_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/score_dict.json + test_labels_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/test_labels.json + train_labels_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/train_labels.json + model_dir: models + name: 13f80fb91b7c61a65e59eb7106099446 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 13f80fb91b7c61a65e59eb7106099446 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/14e23414b1089257270338b9ed3dd54a/params.yaml b/examples/security/classification/output/reports/attack/14e23414b1089257270338b9ed3dd54a/params.yaml new file mode 100644 index 00000000..4531cf68 --- /dev/null +++ b/examples/security/classification/output/reports/attack/14e23414b1089257270338b9ed3dd54a/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.001 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 6c95b6147748247b708706f373d59b11 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/adv_predictions.json + adv_probabilities_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/adv_probabilities.json + attack_file: output/attacks/e1fdc76d1cbdae1ada95c24bb1a58583.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/params.yaml + predictions_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/predictions.json + probabilities_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/probabilities.json + score_dict_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/score_dict.json + test_labels_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/test_labels.json + train_labels_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/train_labels.json + model_dir: models + name: 14e23414b1089257270338b9ed3dd54a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 14e23414b1089257270338b9ed3dd54a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/params.yaml b/examples/security/classification/output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/params.yaml new file mode 100644 index 00000000..d0b7df24 --- /dev/null +++ b/examples/security/classification/output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: a91b432012fe6732156ce758b7be91af +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/adv_predictions.json + adv_probabilities_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/adv_probabilities.json + attack_file: output/attacks/9bdecd56e8e471e223c965072717fddd.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/params.yaml + predictions_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/predictions.json + probabilities_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/probabilities.json + score_dict_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/score_dict.json + test_labels_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/test_labels.json + train_labels_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/train_labels.json + model_dir: models + name: 1545316a183b2a5c6ea3cbe01d777ee6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 1545316a183b2a5c6ea3cbe01d777ee6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/186e46c92ef66070a825683786db0871/params.yaml b/examples/security/classification/output/reports/attack/186e46c92ef66070a825683786db0871/params.yaml new file mode 100644 index 00000000..e2f47504 --- /dev/null +++ b/examples/security/classification/output/reports/attack/186e46c92ef66070a825683786db0871/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.001 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: d5beab1477c9e97f4b088e27bed90307 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/186e46c92ef66070a825683786db0871/adv_predictions.json + adv_probabilities_file: output/reports/attack/186e46c92ef66070a825683786db0871/adv_probabilities.json + attack_file: output/attacks/cd65a2449f25f362cb9a8651c006b6d8.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/186e46c92ef66070a825683786db0871/params.yaml + predictions_file: output/reports/attack/186e46c92ef66070a825683786db0871/predictions.json + probabilities_file: output/reports/attack/186e46c92ef66070a825683786db0871/probabilities.json + score_dict_file: output/reports/attack/186e46c92ef66070a825683786db0871/score_dict.json + test_labels_file: output/reports/attack/186e46c92ef66070a825683786db0871/test_labels.json + train_labels_file: output/reports/attack/186e46c92ef66070a825683786db0871/train_labels.json + model_dir: models + name: 186e46c92ef66070a825683786db0871 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 186e46c92ef66070a825683786db0871 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/params.yaml b/examples/security/classification/output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/params.yaml new file mode 100644 index 00000000..fa688e4f --- /dev/null +++ b/examples/security/classification/output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: c48392ef1dfabbe2569554f97e9c0c7b +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/adv_predictions.json + adv_probabilities_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/adv_probabilities.json + attack_file: output/attacks/b660ba4969aa2cb1f6c064cb0924de72.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/params.yaml + predictions_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/predictions.json + probabilities_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/probabilities.json + score_dict_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/score_dict.json + test_labels_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/test_labels.json + train_labels_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/train_labels.json + model_dir: models + name: 224ea87e6d3c0563c9859a6235fb6b3a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 224ea87e6d3c0563c9859a6235fb6b3a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/29d1387e691db094542db2b51279840b/params.yaml b/examples/security/classification/output/reports/attack/29d1387e691db094542db2b51279840b/params.yaml new file mode 100644 index 00000000..97d40f8a --- /dev/null +++ b/examples/security/classification/output/reports/attack/29d1387e691db094542db2b51279840b/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: baa9a7e500dee5cda429b8e49896a5a0 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/29d1387e691db094542db2b51279840b/adv_predictions.json + adv_probabilities_file: output/reports/attack/29d1387e691db094542db2b51279840b/adv_probabilities.json + attack_file: output/attacks/8045b98a0c76f7d3c3f365cd7c8fbf7b.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/29d1387e691db094542db2b51279840b/params.yaml + predictions_file: output/reports/attack/29d1387e691db094542db2b51279840b/predictions.json + probabilities_file: output/reports/attack/29d1387e691db094542db2b51279840b/probabilities.json + score_dict_file: output/reports/attack/29d1387e691db094542db2b51279840b/score_dict.json + test_labels_file: output/reports/attack/29d1387e691db094542db2b51279840b/test_labels.json + train_labels_file: output/reports/attack/29d1387e691db094542db2b51279840b/train_labels.json + model_dir: models + name: 29d1387e691db094542db2b51279840b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 29d1387e691db094542db2b51279840b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/params.yaml b/examples/security/classification/output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/params.yaml new file mode 100644 index 00000000..d489dd01 --- /dev/null +++ b/examples/security/classification/output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: c6e4741768d33da4569c0f4d55e0c95a +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/adv_predictions.json + adv_probabilities_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/adv_probabilities.json + attack_file: output/attacks/f0ba8b6a253cc4c60063b5aee9447bce.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/params.yaml + predictions_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/predictions.json + probabilities_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/probabilities.json + score_dict_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/score_dict.json + test_labels_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/test_labels.json + train_labels_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/train_labels.json + model_dir: models + name: 29e49c9f6dc5e062b1ff4fd1af5c7b6d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 29e49c9f6dc5e062b1ff4fd1af5c7b6d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/params.yaml b/examples/security/classification/output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/params.yaml new file mode 100644 index 00000000..67b78832 --- /dev/null +++ b/examples/security/classification/output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.001 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 54af8c7d59186e57be11b9d0d174e1e8 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/adv_predictions.json + adv_probabilities_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/adv_probabilities.json + attack_file: output/attacks/da756aa4105e9814b4c9da56c126b456.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/params.yaml + predictions_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/predictions.json + probabilities_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/probabilities.json + score_dict_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/score_dict.json + test_labels_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/test_labels.json + train_labels_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/train_labels.json + model_dir: models + name: 2b1db37c6d88356f910abbb892f78b8f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 2b1db37c6d88356f910abbb892f78b8f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/params.yaml b/examples/security/classification/output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/params.yaml new file mode 100644 index 00000000..6c483d29 --- /dev/null +++ b/examples/security/classification/output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: bd0553ddf61498da2dfa495e7fb514de +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/adv_predictions.json + adv_probabilities_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/adv_probabilities.json + attack_file: output/attacks/3042406749910eb46affce0b11a11cfe.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/params.yaml + predictions_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/predictions.json + probabilities_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/probabilities.json + score_dict_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/score_dict.json + test_labels_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/test_labels.json + train_labels_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/train_labels.json + model_dir: models + name: 2b5b145c8d1804ec7419123bbd4ef258 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 2b5b145c8d1804ec7419123bbd4ef258 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/params.yaml b/examples/security/classification/output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/params.yaml new file mode 100644 index 00000000..2274df05 --- /dev/null +++ b/examples/security/classification/output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.1 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 6e55d0685e917bedcdfc07c38dd39801 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/adv_predictions.json + adv_probabilities_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/adv_probabilities.json + attack_file: output/attacks/4f3ed4b4ed660cc92f42b4ff87e24063.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/params.yaml + predictions_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/predictions.json + probabilities_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/probabilities.json + score_dict_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/score_dict.json + test_labels_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/test_labels.json + train_labels_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/train_labels.json + model_dir: models + name: 2d60289ca42435ce4696ba71b9fb641f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 2d60289ca42435ce4696ba71b9fb641f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/params.yaml b/examples/security/classification/output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/params.yaml new file mode 100644 index 00000000..5dba2e80 --- /dev/null +++ b/examples/security/classification/output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 1 + max_iter: 1000 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 64cd1942d14dad4a3418d89fce4dd509 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/adv_predictions.json + adv_probabilities_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/adv_probabilities.json + attack_file: output/attacks/c2ec56d18b15b58662507efe7657c776.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/params.yaml + predictions_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/predictions.json + probabilities_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/probabilities.json + score_dict_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/score_dict.json + test_labels_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/test_labels.json + train_labels_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/train_labels.json + model_dir: models + name: 2d8533955630bf2c5f174b4a65abbac1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 2d8533955630bf2c5f174b4a65abbac1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/params.yaml b/examples/security/classification/output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/params.yaml new file mode 100644 index 00000000..efb534c7 --- /dev/null +++ b/examples/security/classification/output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.5 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 26fa2acf9368518c36dfe9c8ce782ddd +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/adv_predictions.json + adv_probabilities_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/adv_probabilities.json + attack_file: output/attacks/ab91fd1b63c5be4ae2ae0f542e50353e.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/params.yaml + predictions_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/predictions.json + probabilities_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/probabilities.json + score_dict_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/score_dict.json + test_labels_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/test_labels.json + train_labels_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/train_labels.json + model_dir: models + name: 348a212e2fce7fe3d8749d8b1a38efee + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 348a212e2fce7fe3d8749d8b1a38efee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/348b7a17642342880be2c8e4776e71ca/params.yaml b/examples/security/classification/output/reports/attack/348b7a17642342880be2c8e4776e71ca/params.yaml new file mode 100644 index 00000000..24c4e5d4 --- /dev/null +++ b/examples/security/classification/output/reports/attack/348b7a17642342880be2c8e4776e71ca/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 3131459e6596691b2eec9c9301ad41f1 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/adv_predictions.json + adv_probabilities_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/adv_probabilities.json + attack_file: output/attacks/acc17c51bbf330f7c191ee0d094c2fd9.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/params.yaml + predictions_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/predictions.json + probabilities_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/probabilities.json + score_dict_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/score_dict.json + test_labels_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/test_labels.json + train_labels_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/train_labels.json + model_dir: models + name: 348b7a17642342880be2c8e4776e71ca + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 348b7a17642342880be2c8e4776e71ca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/params.yaml b/examples/security/classification/output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/params.yaml new file mode 100644 index 00000000..9ae1fedc --- /dev/null +++ b/examples/security/classification/output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.001 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: c64e8eb8a9676a1addfd961047b57c45 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/adv_predictions.json + adv_probabilities_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/adv_probabilities.json + attack_file: output/attacks/84de5c16b1fbf141fcdfab370390d5a7.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/params.yaml + predictions_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/predictions.json + probabilities_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/probabilities.json + score_dict_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/score_dict.json + test_labels_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/test_labels.json + train_labels_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/train_labels.json + model_dir: models + name: 356c1286c4cf03fd40257074c3f47a9f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 356c1286c4cf03fd40257074c3f47a9f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/params.yaml b/examples/security/classification/output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/params.yaml new file mode 100644 index 00000000..2829ef43 --- /dev/null +++ b/examples/security/classification/output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: a072cc72b1cd1ba4e52fea3795bc4552 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/adv_predictions.json + adv_probabilities_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/adv_probabilities.json + attack_file: output/attacks/23a3d0771eed1a54f187fbe0357baf60.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/params.yaml + predictions_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/predictions.json + probabilities_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/probabilities.json + score_dict_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/score_dict.json + test_labels_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/test_labels.json + train_labels_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/train_labels.json + model_dir: models + name: 377569dcddfbe3c7ff187f38895ccaed + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 377569dcddfbe3c7ff187f38895ccaed +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/386a4dec58c199035136189958f04673/params.yaml b/examples/security/classification/output/reports/attack/386a4dec58c199035136189958f04673/params.yaml new file mode 100644 index 00000000..9151d0e7 --- /dev/null +++ b/examples/security/classification/output/reports/attack/386a4dec58c199035136189958f04673/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: df4ccdd86f3c6c74faa9b38c524660a3 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/386a4dec58c199035136189958f04673/adv_predictions.json + adv_probabilities_file: output/reports/attack/386a4dec58c199035136189958f04673/adv_probabilities.json + attack_file: output/attacks/71525f0cc9c947339ab34a43b4fed35e.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/386a4dec58c199035136189958f04673/params.yaml + predictions_file: output/reports/attack/386a4dec58c199035136189958f04673/predictions.json + probabilities_file: output/reports/attack/386a4dec58c199035136189958f04673/probabilities.json + score_dict_file: output/reports/attack/386a4dec58c199035136189958f04673/score_dict.json + test_labels_file: output/reports/attack/386a4dec58c199035136189958f04673/test_labels.json + train_labels_file: output/reports/attack/386a4dec58c199035136189958f04673/train_labels.json + model_dir: models + name: 386a4dec58c199035136189958f04673 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 386a4dec58c199035136189958f04673 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/params.yaml b/examples/security/classification/output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/params.yaml new file mode 100644 index 00000000..19d5e3cc --- /dev/null +++ b/examples/security/classification/output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.01 + max_iter: 1000 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 8b63374d147c6e0cb4aa4f0e281150f2 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/adv_predictions.json + adv_probabilities_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/adv_probabilities.json + attack_file: output/attacks/a8fb177802053dd54c1e2d81c72e3653.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/params.yaml + predictions_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/predictions.json + probabilities_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/probabilities.json + score_dict_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/score_dict.json + test_labels_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/test_labels.json + train_labels_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/train_labels.json + model_dir: models + name: 3a9de2601dec0634564f418e84cf7d2d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 3a9de2601dec0634564f418e84cf7d2d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/3bcf141cfab3423c15f99efa66385e18/params.yaml b/examples/security/classification/output/reports/attack/3bcf141cfab3423c15f99efa66385e18/params.yaml new file mode 100644 index 00000000..fe6c2a5e --- /dev/null +++ b/examples/security/classification/output/reports/attack/3bcf141cfab3423c15f99efa66385e18/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.001 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 171df0fd503cb89fb546449d9e4b6454 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/adv_predictions.json + adv_probabilities_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/adv_probabilities.json + attack_file: output/attacks/849c6e41c61ebfb7e25c7e29c4f4535b.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/params.yaml + predictions_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/predictions.json + probabilities_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/probabilities.json + score_dict_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/score_dict.json + test_labels_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/test_labels.json + train_labels_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/train_labels.json + model_dir: models + name: 3bcf141cfab3423c15f99efa66385e18 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 3bcf141cfab3423c15f99efa66385e18 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/3e972baeee5735882b06a7615a911841/params.yaml b/examples/security/classification/output/reports/attack/3e972baeee5735882b06a7615a911841/params.yaml new file mode 100644 index 00000000..3f11d377 --- /dev/null +++ b/examples/security/classification/output/reports/attack/3e972baeee5735882b06a7615a911841/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.1 + max_iter: 1000 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: f3fbf263e81bf94d4a39b565cfd7e847 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3e972baeee5735882b06a7615a911841/adv_predictions.json + adv_probabilities_file: output/reports/attack/3e972baeee5735882b06a7615a911841/adv_probabilities.json + attack_file: output/attacks/c14aee97098c5c7a68a694738ff9b52a.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/3e972baeee5735882b06a7615a911841/params.yaml + predictions_file: output/reports/attack/3e972baeee5735882b06a7615a911841/predictions.json + probabilities_file: output/reports/attack/3e972baeee5735882b06a7615a911841/probabilities.json + score_dict_file: output/reports/attack/3e972baeee5735882b06a7615a911841/score_dict.json + test_labels_file: output/reports/attack/3e972baeee5735882b06a7615a911841/test_labels.json + train_labels_file: output/reports/attack/3e972baeee5735882b06a7615a911841/train_labels.json + model_dir: models + name: 3e972baeee5735882b06a7615a911841 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 3e972baeee5735882b06a7615a911841 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/params.yaml b/examples/security/classification/output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/params.yaml new file mode 100644 index 00000000..50622d17 --- /dev/null +++ b/examples/security/classification/output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.001 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 503b4a409fec9023703815d74886b9c6 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/adv_predictions.json + adv_probabilities_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/adv_probabilities.json + attack_file: output/attacks/c4e896f3d4615f003f22070065bec4d3.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/params.yaml + predictions_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/predictions.json + probabilities_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/probabilities.json + score_dict_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/score_dict.json + test_labels_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/test_labels.json + train_labels_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/train_labels.json + model_dir: models + name: 40efb445118fe8ce98d27bfe156ac9ba + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 40efb445118fe8ce98d27bfe156ac9ba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/41a52a88333b166590d0783a69190134/params.yaml b/examples/security/classification/output/reports/attack/41a52a88333b166590d0783a69190134/params.yaml new file mode 100644 index 00000000..7844dca0 --- /dev/null +++ b/examples/security/classification/output/reports/attack/41a52a88333b166590d0783a69190134/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: f4fe8486040c4a065de70f6ad5334960 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/41a52a88333b166590d0783a69190134/adv_predictions.json + adv_probabilities_file: output/reports/attack/41a52a88333b166590d0783a69190134/adv_probabilities.json + attack_file: output/attacks/9223032732e02b80c604acc216940795.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/41a52a88333b166590d0783a69190134/params.yaml + predictions_file: output/reports/attack/41a52a88333b166590d0783a69190134/predictions.json + probabilities_file: output/reports/attack/41a52a88333b166590d0783a69190134/probabilities.json + score_dict_file: output/reports/attack/41a52a88333b166590d0783a69190134/score_dict.json + test_labels_file: output/reports/attack/41a52a88333b166590d0783a69190134/test_labels.json + train_labels_file: output/reports/attack/41a52a88333b166590d0783a69190134/train_labels.json + model_dir: models + name: 41a52a88333b166590d0783a69190134 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 41a52a88333b166590d0783a69190134 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/params.yaml b/examples/security/classification/output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/params.yaml new file mode 100644 index 00000000..72c5818e --- /dev/null +++ b/examples/security/classification/output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 53c6220162dd8fc7bc44e0958e5a6978 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/adv_predictions.json + adv_probabilities_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/adv_probabilities.json + attack_file: output/attacks/f18c097a1cfcffbfdeda36b4ba6da575.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/params.yaml + predictions_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/predictions.json + probabilities_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/probabilities.json + score_dict_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/score_dict.json + test_labels_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/test_labels.json + train_labels_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/train_labels.json + model_dir: models + name: 431a4054e5b497c68b9fc3ac480265e5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 431a4054e5b497c68b9fc3ac480265e5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/43609b18b6ecdcf78243575061377063/params.yaml b/examples/security/classification/output/reports/attack/43609b18b6ecdcf78243575061377063/params.yaml new file mode 100644 index 00000000..3655735e --- /dev/null +++ b/examples/security/classification/output/reports/attack/43609b18b6ecdcf78243575061377063/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.5 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 3766aaf766df04d12680c28dd2f2bca3 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/43609b18b6ecdcf78243575061377063/adv_predictions.json + adv_probabilities_file: output/reports/attack/43609b18b6ecdcf78243575061377063/adv_probabilities.json + attack_file: output/attacks/e25b35f91068de91354cc0c25417623a.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/43609b18b6ecdcf78243575061377063/params.yaml + predictions_file: output/reports/attack/43609b18b6ecdcf78243575061377063/predictions.json + probabilities_file: output/reports/attack/43609b18b6ecdcf78243575061377063/probabilities.json + score_dict_file: output/reports/attack/43609b18b6ecdcf78243575061377063/score_dict.json + test_labels_file: output/reports/attack/43609b18b6ecdcf78243575061377063/test_labels.json + train_labels_file: output/reports/attack/43609b18b6ecdcf78243575061377063/train_labels.json + model_dir: models + name: 43609b18b6ecdcf78243575061377063 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 43609b18b6ecdcf78243575061377063 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/params.yaml b/examples/security/classification/output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/params.yaml new file mode 100644 index 00000000..0d8e8a7e --- /dev/null +++ b/examples/security/classification/output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.01 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 413c592278b6f97c670c5b2272b07c6d +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/adv_predictions.json + adv_probabilities_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/adv_probabilities.json + attack_file: output/attacks/3fc761d3203b932a15586c45445c1607.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/params.yaml + predictions_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/predictions.json + probabilities_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/probabilities.json + score_dict_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/score_dict.json + test_labels_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/test_labels.json + train_labels_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/train_labels.json + model_dir: models + name: 45f15c74e96501d6c7e0b565b07acca6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 45f15c74e96501d6c7e0b565b07acca6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/490168005f33f8cf056a8f514bac8125/params.yaml b/examples/security/classification/output/reports/attack/490168005f33f8cf056a8f514bac8125/params.yaml new file mode 100644 index 00000000..6667babc --- /dev/null +++ b/examples/security/classification/output/reports/attack/490168005f33f8cf056a8f514bac8125/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: f59990c91c11f6837cd229afecbe9f0d +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/adv_predictions.json + adv_probabilities_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/adv_probabilities.json + attack_file: output/attacks/17c0cae5082a884f66fb5cde4751d143.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/params.yaml + predictions_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/predictions.json + probabilities_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/probabilities.json + score_dict_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/score_dict.json + test_labels_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/test_labels.json + train_labels_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/train_labels.json + model_dir: models + name: 490168005f33f8cf056a8f514bac8125 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 490168005f33f8cf056a8f514bac8125 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/49568c81416f48f42e29e47a3a4295ef/params.yaml b/examples/security/classification/output/reports/attack/49568c81416f48f42e29e47a3a4295ef/params.yaml new file mode 100644 index 00000000..82a7130d --- /dev/null +++ b/examples/security/classification/output/reports/attack/49568c81416f48f42e29e47a3a4295ef/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: ca8552b46511a42c6ea0063bd2cacc38 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/adv_predictions.json + adv_probabilities_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/adv_probabilities.json + attack_file: output/attacks/4dd66410e32eb3409ca8d429dc0c2ca2.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/params.yaml + predictions_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/predictions.json + probabilities_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/probabilities.json + score_dict_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/score_dict.json + test_labels_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/test_labels.json + train_labels_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/train_labels.json + model_dir: models + name: 49568c81416f48f42e29e47a3a4295ef + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 49568c81416f48f42e29e47a3a4295ef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/params.yaml b/examples/security/classification/output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/params.yaml new file mode 100644 index 00000000..5cf3c8f1 --- /dev/null +++ b/examples/security/classification/output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.5 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 926961aeecf1d3ea072210e900d62505 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/adv_predictions.json + adv_probabilities_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/adv_probabilities.json + attack_file: output/attacks/5977b859c4307dd02b904b87c2af2b43.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/params.yaml + predictions_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/predictions.json + probabilities_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/probabilities.json + score_dict_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/score_dict.json + test_labels_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/test_labels.json + train_labels_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/train_labels.json + model_dir: models + name: 4b727c670ac84e5e7e31fb4b88fc18a0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 4b727c670ac84e5e7e31fb4b88fc18a0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/params.yaml b/examples/security/classification/output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/params.yaml new file mode 100644 index 00000000..d976f27b --- /dev/null +++ b/examples/security/classification/output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 1b9ab5baae1529cdf8e6e5eb59b163c7 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/adv_predictions.json + adv_probabilities_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/adv_probabilities.json + attack_file: output/attacks/7f9e25a70d7c5e132f0f07705b507de3.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/params.yaml + predictions_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/predictions.json + probabilities_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/probabilities.json + score_dict_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/score_dict.json + test_labels_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/test_labels.json + train_labels_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/train_labels.json + model_dir: models + name: 4c5aabb5448357744bbed60c3ece4fd6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 4c5aabb5448357744bbed60c3ece4fd6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/params.yaml b/examples/security/classification/output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/params.yaml new file mode 100644 index 00000000..71bb6455 --- /dev/null +++ b/examples/security/classification/output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.1 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 08be4209293859526bf26bb631cd3052 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/adv_predictions.json + adv_probabilities_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/adv_probabilities.json + attack_file: output/attacks/519edd5db419e1756c387a967b9e01c6.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/params.yaml + predictions_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/predictions.json + probabilities_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/probabilities.json + score_dict_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/score_dict.json + test_labels_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/test_labels.json + train_labels_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/train_labels.json + model_dir: models + name: 4ce1e69ec2ec2df5f3d7458246c1e38e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 4ce1e69ec2ec2df5f3d7458246c1e38e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/params.yaml b/examples/security/classification/output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/params.yaml new file mode 100644 index 00000000..e0889827 --- /dev/null +++ b/examples/security/classification/output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.3 + max_iter: 1000 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 55545b33d6ac6e2994a43e8b5a9c5e48 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/adv_predictions.json + adv_probabilities_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/adv_probabilities.json + attack_file: output/attacks/6ed39cfdb81030ef37fc5049f7edbd9c.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/params.yaml + predictions_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/predictions.json + probabilities_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/probabilities.json + score_dict_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/score_dict.json + test_labels_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/test_labels.json + train_labels_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/train_labels.json + model_dir: models + name: 4fb90a14df596f581a170fa6fbe03f54 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 4fb90a14df596f581a170fa6fbe03f54 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/params.yaml b/examples/security/classification/output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/params.yaml new file mode 100644 index 00000000..31fc83e9 --- /dev/null +++ b/examples/security/classification/output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: a98a15974a5391e7aac1d1d7bb19d83a +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/adv_predictions.json + adv_probabilities_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/adv_probabilities.json + attack_file: output/attacks/7409e714128e1dc3111a076fe1226994.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/params.yaml + predictions_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/predictions.json + probabilities_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/probabilities.json + score_dict_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/score_dict.json + test_labels_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/test_labels.json + train_labels_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/train_labels.json + model_dir: models + name: 5125babc339b2b55a4a997a30aeb5f7d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 5125babc339b2b55a4a997a30aeb5f7d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/54373cbda6df703a7bbd92d496bad236/params.yaml b/examples/security/classification/output/reports/attack/54373cbda6df703a7bbd92d496bad236/params.yaml new file mode 100644 index 00000000..100dfd17 --- /dev/null +++ b/examples/security/classification/output/reports/attack/54373cbda6df703a7bbd92d496bad236/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.01 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 927b7d2025d4afd02f28ea528afdabe5 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/adv_predictions.json + adv_probabilities_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/adv_probabilities.json + attack_file: output/attacks/62545688420782143047b4ab7c8d7dee.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/params.yaml + predictions_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/predictions.json + probabilities_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/probabilities.json + score_dict_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/score_dict.json + test_labels_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/test_labels.json + train_labels_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/train_labels.json + model_dir: models + name: 54373cbda6df703a7bbd92d496bad236 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 54373cbda6df703a7bbd92d496bad236 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/58ae06ebc35f26c157bac01373d365ab/params.yaml b/examples/security/classification/output/reports/attack/58ae06ebc35f26c157bac01373d365ab/params.yaml new file mode 100644 index 00000000..1ddd4e8b --- /dev/null +++ b/examples/security/classification/output/reports/attack/58ae06ebc35f26c157bac01373d365ab/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.001 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 3ed8f172c1ffc23c7785e33d047c46f8 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/adv_predictions.json + adv_probabilities_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/adv_probabilities.json + attack_file: output/attacks/5cae022a5f76b0b6c78b71021215913d.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/params.yaml + predictions_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/predictions.json + probabilities_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/probabilities.json + score_dict_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/score_dict.json + test_labels_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/test_labels.json + train_labels_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/train_labels.json + model_dir: models + name: 58ae06ebc35f26c157bac01373d365ab + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 58ae06ebc35f26c157bac01373d365ab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/58f3502acbdf096a0606ac8e3d264122/params.yaml b/examples/security/classification/output/reports/attack/58f3502acbdf096a0606ac8e3d264122/params.yaml new file mode 100644 index 00000000..ec67d53b --- /dev/null +++ b/examples/security/classification/output/reports/attack/58f3502acbdf096a0606ac8e3d264122/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.5 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 9642bc90d44e9bef34cf2e236c74a39f +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/adv_predictions.json + adv_probabilities_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/adv_probabilities.json + attack_file: output/attacks/5df27b5dacdeb54d9beefcd23afb6819.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/params.yaml + predictions_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/predictions.json + probabilities_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/probabilities.json + score_dict_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/score_dict.json + test_labels_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/test_labels.json + train_labels_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/train_labels.json + model_dir: models + name: 58f3502acbdf096a0606ac8e3d264122 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 58f3502acbdf096a0606ac8e3d264122 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/params.yaml b/examples/security/classification/output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/params.yaml new file mode 100644 index 00000000..e97ea8aa --- /dev/null +++ b/examples/security/classification/output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: deedc83fd69c492eef98fa89f671d37d +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/adv_predictions.json + adv_probabilities_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/adv_probabilities.json + attack_file: output/attacks/1ae65e5906fd221835d94f864880d325.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/params.yaml + predictions_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/predictions.json + probabilities_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/probabilities.json + score_dict_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/score_dict.json + test_labels_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/test_labels.json + train_labels_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/train_labels.json + model_dir: models + name: 5a0fa0e337eba06ce1764286fcc12ef0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 5a0fa0e337eba06ce1764286fcc12ef0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/params.yaml b/examples/security/classification/output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/params.yaml new file mode 100644 index 00000000..fd6fa2a3 --- /dev/null +++ b/examples/security/classification/output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.001 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 210857021ebcc10f933284804aa1b44d +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/adv_predictions.json + adv_probabilities_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/adv_probabilities.json + attack_file: output/attacks/611b089dd9172792a94bef28e888f5a3.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/params.yaml + predictions_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/predictions.json + probabilities_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/probabilities.json + score_dict_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/score_dict.json + test_labels_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/test_labels.json + train_labels_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/train_labels.json + model_dir: models + name: 5d946b787415af772eaf5bd2c1a2269f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 5d946b787415af772eaf5bd2c1a2269f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/params.yaml b/examples/security/classification/output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/params.yaml new file mode 100644 index 00000000..f244734f --- /dev/null +++ b/examples/security/classification/output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.001 + max_iter: 1000 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 47b871c4ad24779b9f5c51b61a8bcace +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/adv_predictions.json + adv_probabilities_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/adv_probabilities.json + attack_file: output/attacks/fc3dc5f6546493f3721e09a9a141bdc3.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/params.yaml + predictions_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/predictions.json + probabilities_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/probabilities.json + score_dict_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/score_dict.json + test_labels_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/test_labels.json + train_labels_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/train_labels.json + model_dir: models + name: 60e86854e9773ca0ca4180010d4b1e5f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 60e86854e9773ca0ca4180010d4b1e5f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/params.yaml b/examples/security/classification/output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/params.yaml new file mode 100644 index 00000000..fb2cdc1b --- /dev/null +++ b/examples/security/classification/output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.1 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 96aaedc7c33cb5f04c85adfac613aa9c +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/adv_predictions.json + adv_probabilities_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/adv_probabilities.json + attack_file: output/attacks/0c0e190fbca487faae269ad51bd858fd.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/params.yaml + predictions_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/predictions.json + probabilities_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/probabilities.json + score_dict_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/score_dict.json + test_labels_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/test_labels.json + train_labels_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/train_labels.json + model_dir: models + name: 6200b3eb8c52dbd93b42de2c53d49d66 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 6200b3eb8c52dbd93b42de2c53d49d66 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/65789ef42fab964d5d1f4b522c842307/params.yaml b/examples/security/classification/output/reports/attack/65789ef42fab964d5d1f4b522c842307/params.yaml new file mode 100644 index 00000000..c406a7fe --- /dev/null +++ b/examples/security/classification/output/reports/attack/65789ef42fab964d5d1f4b522c842307/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.5 + max_iter: 1000 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 9ead075d9a95cc48fc37f085db586280 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/adv_predictions.json + adv_probabilities_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/adv_probabilities.json + attack_file: output/attacks/cdddc3440fd9dfd79fee720927274703.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/params.yaml + predictions_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/predictions.json + probabilities_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/probabilities.json + score_dict_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/score_dict.json + test_labels_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/test_labels.json + train_labels_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/train_labels.json + model_dir: models + name: 65789ef42fab964d5d1f4b522c842307 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 65789ef42fab964d5d1f4b522c842307 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/66778716c19365040a3c5661875c9384/params.yaml b/examples/security/classification/output/reports/attack/66778716c19365040a3c5661875c9384/params.yaml new file mode 100644 index 00000000..42992ff8 --- /dev/null +++ b/examples/security/classification/output/reports/attack/66778716c19365040a3c5661875c9384/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.001 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 8ee820e67bf0d80a5e556550bea25cfd +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/66778716c19365040a3c5661875c9384/adv_predictions.json + adv_probabilities_file: output/reports/attack/66778716c19365040a3c5661875c9384/adv_probabilities.json + attack_file: output/attacks/5088cd82720ef34e6c6e78041fa0d1e4.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/66778716c19365040a3c5661875c9384/params.yaml + predictions_file: output/reports/attack/66778716c19365040a3c5661875c9384/predictions.json + probabilities_file: output/reports/attack/66778716c19365040a3c5661875c9384/probabilities.json + score_dict_file: output/reports/attack/66778716c19365040a3c5661875c9384/score_dict.json + test_labels_file: output/reports/attack/66778716c19365040a3c5661875c9384/test_labels.json + train_labels_file: output/reports/attack/66778716c19365040a3c5661875c9384/train_labels.json + model_dir: models + name: 66778716c19365040a3c5661875c9384 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 66778716c19365040a3c5661875c9384 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/66ec144d4307a9a702145e83a16b92bd/params.yaml b/examples/security/classification/output/reports/attack/66ec144d4307a9a702145e83a16b92bd/params.yaml new file mode 100644 index 00000000..7ff6f306 --- /dev/null +++ b/examples/security/classification/output/reports/attack/66ec144d4307a9a702145e83a16b92bd/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.1 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: eab6f9ab7e94cb67a54dce6f22f529e9 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/adv_predictions.json + adv_probabilities_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/adv_probabilities.json + attack_file: output/attacks/95b7cff21924f1b9d4fccd6d9d608292.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/params.yaml + predictions_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/predictions.json + probabilities_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/probabilities.json + score_dict_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/score_dict.json + test_labels_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/test_labels.json + train_labels_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/train_labels.json + model_dir: models + name: 66ec144d4307a9a702145e83a16b92bd + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 66ec144d4307a9a702145e83a16b92bd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/68079f2892b8606cc3451033e13878aa/params.yaml b/examples/security/classification/output/reports/attack/68079f2892b8606cc3451033e13878aa/params.yaml new file mode 100644 index 00000000..9f38e840 --- /dev/null +++ b/examples/security/classification/output/reports/attack/68079f2892b8606cc3451033e13878aa/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.5 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: eed6d9b1ba45e38c749ac63408552e93 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/adv_predictions.json + adv_probabilities_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/adv_probabilities.json + attack_file: output/attacks/9476ffebd526259dccb15c7e7261550e.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/params.yaml + predictions_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/predictions.json + probabilities_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/probabilities.json + score_dict_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/score_dict.json + test_labels_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/test_labels.json + train_labels_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/train_labels.json + model_dir: models + name: 68079f2892b8606cc3451033e13878aa + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 68079f2892b8606cc3451033e13878aa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/68ac7e04e44143769c116c6989a829f6/params.yaml b/examples/security/classification/output/reports/attack/68ac7e04e44143769c116c6989a829f6/params.yaml new file mode 100644 index 00000000..82989790 --- /dev/null +++ b/examples/security/classification/output/reports/attack/68ac7e04e44143769c116c6989a829f6/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.001 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: a44ddb9d69de3905002c42b7b85285a3 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/adv_predictions.json + adv_probabilities_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/adv_probabilities.json + attack_file: output/attacks/78e52f4a6c167fdbef97cbf2640c848a.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/params.yaml + predictions_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/predictions.json + probabilities_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/probabilities.json + score_dict_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/score_dict.json + test_labels_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/test_labels.json + train_labels_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/train_labels.json + model_dir: models + name: 68ac7e04e44143769c116c6989a829f6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 68ac7e04e44143769c116c6989a829f6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/params.yaml b/examples/security/classification/output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/params.yaml new file mode 100644 index 00000000..8204fb49 --- /dev/null +++ b/examples/security/classification/output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.5 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: bfc645599bb1ff75d05caf2b8771762d +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/adv_predictions.json + adv_probabilities_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/adv_probabilities.json + attack_file: output/attacks/f5afb5bfffbaf3c232f3c69279b5d8bd.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/params.yaml + predictions_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/predictions.json + probabilities_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/probabilities.json + score_dict_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/score_dict.json + test_labels_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/test_labels.json + train_labels_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/train_labels.json + model_dir: models + name: 6de2ee4c7c4d542bd6e00156f49024a6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 6de2ee4c7c4d542bd6e00156f49024a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/7412d66f284793d574d73a7c93853353/params.yaml b/examples/security/classification/output/reports/attack/7412d66f284793d574d73a7c93853353/params.yaml new file mode 100644 index 00000000..5005720c --- /dev/null +++ b/examples/security/classification/output/reports/attack/7412d66f284793d574d73a7c93853353/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.001 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: bc50e98b2c96fd8a38e01bbefdd40ceb +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/7412d66f284793d574d73a7c93853353/adv_predictions.json + adv_probabilities_file: output/reports/attack/7412d66f284793d574d73a7c93853353/adv_probabilities.json + attack_file: output/attacks/293cea47f185f12bcd1c23f0eb3fdc7f.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/7412d66f284793d574d73a7c93853353/params.yaml + predictions_file: output/reports/attack/7412d66f284793d574d73a7c93853353/predictions.json + probabilities_file: output/reports/attack/7412d66f284793d574d73a7c93853353/probabilities.json + score_dict_file: output/reports/attack/7412d66f284793d574d73a7c93853353/score_dict.json + test_labels_file: output/reports/attack/7412d66f284793d574d73a7c93853353/test_labels.json + train_labels_file: output/reports/attack/7412d66f284793d574d73a7c93853353/train_labels.json + model_dir: models + name: 7412d66f284793d574d73a7c93853353 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: 7412d66f284793d574d73a7c93853353 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/8069811604c366444e374c8a5fea3227/params.yaml b/examples/security/classification/output/reports/attack/8069811604c366444e374c8a5fea3227/params.yaml new file mode 100644 index 00000000..ebcbbbd3 --- /dev/null +++ b/examples/security/classification/output/reports/attack/8069811604c366444e374c8a5fea3227/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.5 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: a24e4c3bf1b721aaa6573c0551f7a318 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8069811604c366444e374c8a5fea3227/adv_predictions.json + adv_probabilities_file: output/reports/attack/8069811604c366444e374c8a5fea3227/adv_probabilities.json + attack_file: output/attacks/22935140b76f01c109ba35cb4a1f4011.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/8069811604c366444e374c8a5fea3227/params.yaml + predictions_file: output/reports/attack/8069811604c366444e374c8a5fea3227/predictions.json + probabilities_file: output/reports/attack/8069811604c366444e374c8a5fea3227/probabilities.json + score_dict_file: output/reports/attack/8069811604c366444e374c8a5fea3227/score_dict.json + test_labels_file: output/reports/attack/8069811604c366444e374c8a5fea3227/test_labels.json + train_labels_file: output/reports/attack/8069811604c366444e374c8a5fea3227/train_labels.json + model_dir: models + name: 8069811604c366444e374c8a5fea3227 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 8069811604c366444e374c8a5fea3227 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/84a6c965b79c86855b822d7dddba2693/params.yaml b/examples/security/classification/output/reports/attack/84a6c965b79c86855b822d7dddba2693/params.yaml new file mode 100644 index 00000000..fdd5a97d --- /dev/null +++ b/examples/security/classification/output/reports/attack/84a6c965b79c86855b822d7dddba2693/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.01 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 2d235690b32db725ba0da28fff809038 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/adv_predictions.json + adv_probabilities_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/adv_probabilities.json + attack_file: output/attacks/5016185a9530e81c5b77406f17ec2fe7.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/params.yaml + predictions_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/predictions.json + probabilities_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/probabilities.json + score_dict_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/score_dict.json + test_labels_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/test_labels.json + train_labels_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/train_labels.json + model_dir: models + name: 84a6c965b79c86855b822d7dddba2693 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 84a6c965b79c86855b822d7dddba2693 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/889abbbd16cd836932303c02b0033666/params.yaml b/examples/security/classification/output/reports/attack/889abbbd16cd836932303c02b0033666/params.yaml new file mode 100644 index 00000000..5ad0a189 --- /dev/null +++ b/examples/security/classification/output/reports/attack/889abbbd16cd836932303c02b0033666/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 1 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 47f23b1abb623d944891ae7947623acc +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/889abbbd16cd836932303c02b0033666/adv_predictions.json + adv_probabilities_file: output/reports/attack/889abbbd16cd836932303c02b0033666/adv_probabilities.json + attack_file: output/attacks/509111db4c9b75851b65533265a9c8c3.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/889abbbd16cd836932303c02b0033666/params.yaml + predictions_file: output/reports/attack/889abbbd16cd836932303c02b0033666/predictions.json + probabilities_file: output/reports/attack/889abbbd16cd836932303c02b0033666/probabilities.json + score_dict_file: output/reports/attack/889abbbd16cd836932303c02b0033666/score_dict.json + test_labels_file: output/reports/attack/889abbbd16cd836932303c02b0033666/test_labels.json + train_labels_file: output/reports/attack/889abbbd16cd836932303c02b0033666/train_labels.json + model_dir: models + name: 889abbbd16cd836932303c02b0033666 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 889abbbd16cd836932303c02b0033666 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/params.yaml b/examples/security/classification/output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/params.yaml new file mode 100644 index 00000000..31970a50 --- /dev/null +++ b/examples/security/classification/output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.3 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 97e175a6a542c4f4258404e6898b0495 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/adv_predictions.json + adv_probabilities_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/adv_probabilities.json + attack_file: output/attacks/74f4763d9916645a1e7caff6cb8efa8b.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/params.yaml + predictions_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/predictions.json + probabilities_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/probabilities.json + score_dict_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/score_dict.json + test_labels_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/test_labels.json + train_labels_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/train_labels.json + model_dir: models + name: 8cc99206eaab3140d9f6e7e6cdd31859 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 8cc99206eaab3140d9f6e7e6cdd31859 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/params.yaml b/examples/security/classification/output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/params.yaml new file mode 100644 index 00000000..b39441cb --- /dev/null +++ b/examples/security/classification/output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.3 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: fa303fd1a3c4c0090c1aacda08f90ee3 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/adv_predictions.json + adv_probabilities_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/adv_probabilities.json + attack_file: output/attacks/281f4e847cc2f38a93067bb0b337f62b.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/params.yaml + predictions_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/predictions.json + probabilities_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/probabilities.json + score_dict_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/score_dict.json + test_labels_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/test_labels.json + train_labels_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/train_labels.json + model_dir: models + name: 92d6e5c964485a2cde78f204c83e7d31 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 92d6e5c964485a2cde78f204c83e7d31 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/949c89b39bb29cbbb87650da48c577c1/params.yaml b/examples/security/classification/output/reports/attack/949c89b39bb29cbbb87650da48c577c1/params.yaml new file mode 100644 index 00000000..aacab083 --- /dev/null +++ b/examples/security/classification/output/reports/attack/949c89b39bb29cbbb87650da48c577c1/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.1 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 6058fe3daee844c2e454cf75f17be062 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/adv_predictions.json + adv_probabilities_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/adv_probabilities.json + attack_file: output/attacks/5e2964cfbba51090971decbd93631c29.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/params.yaml + predictions_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/predictions.json + probabilities_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/probabilities.json + score_dict_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/score_dict.json + test_labels_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/test_labels.json + train_labels_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/train_labels.json + model_dir: models + name: 949c89b39bb29cbbb87650da48c577c1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: 949c89b39bb29cbbb87650da48c577c1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/params.yaml b/examples/security/classification/output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/params.yaml new file mode 100644 index 00000000..b3d19d07 --- /dev/null +++ b/examples/security/classification/output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.1 + max_iter: 1000 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 184c99a34218e996b8b9906cd44978d0 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/adv_predictions.json + adv_probabilities_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/adv_probabilities.json + attack_file: output/attacks/3fa9b85c35b5b110ce083d6cfd7ed83c.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/params.yaml + predictions_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/predictions.json + probabilities_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/probabilities.json + score_dict_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/score_dict.json + test_labels_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/test_labels.json + train_labels_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/train_labels.json + model_dir: models + name: 95ab58b1f03230de7b5978581e63c4b9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 95ab58b1f03230de7b5978581e63c4b9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/params.yaml b/examples/security/classification/output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/params.yaml new file mode 100644 index 00000000..f3dafe8d --- /dev/null +++ b/examples/security/classification/output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 703892d07fbbb0ace9a0951239a2bbdf +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/adv_predictions.json + adv_probabilities_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/adv_probabilities.json + attack_file: output/attacks/b83295cdd0fd9ae704a9500c9abdc831.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/params.yaml + predictions_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/predictions.json + probabilities_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/probabilities.json + score_dict_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/score_dict.json + test_labels_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/test_labels.json + train_labels_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/train_labels.json + model_dir: models + name: 9832b1f47d43d263ed23664ebf3ee5eb + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: 9832b1f47d43d263ed23664ebf3ee5eb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/a02603189d68a31a1e95098e4abd3cca/params.yaml b/examples/security/classification/output/reports/attack/a02603189d68a31a1e95098e4abd3cca/params.yaml new file mode 100644 index 00000000..1420f2e8 --- /dev/null +++ b/examples/security/classification/output/reports/attack/a02603189d68a31a1e95098e4abd3cca/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.001 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: b31f442a86aad0da8d29da829ab31098 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/adv_predictions.json + adv_probabilities_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/adv_probabilities.json + attack_file: output/attacks/d0b76872fbfeeab26b2e8c3a750c7287.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/params.yaml + predictions_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/predictions.json + probabilities_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/probabilities.json + score_dict_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/score_dict.json + test_labels_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/test_labels.json + train_labels_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/train_labels.json + model_dir: models + name: a02603189d68a31a1e95098e4abd3cca + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: a02603189d68a31a1e95098e4abd3cca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/params.yaml b/examples/security/classification/output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/params.yaml new file mode 100644 index 00000000..c137c2cf --- /dev/null +++ b/examples/security/classification/output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 7d2f1480dfb249baedfe4b7d6b435014 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/adv_predictions.json + adv_probabilities_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/adv_probabilities.json + attack_file: output/attacks/670c3ff351829ae34f21a64961161d3f.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/params.yaml + predictions_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/predictions.json + probabilities_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/probabilities.json + score_dict_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/score_dict.json + test_labels_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/test_labels.json + train_labels_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/train_labels.json + model_dir: models + name: a0e744ac00fed019ee24d862d1a8c629 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: a0e744ac00fed019ee24d862d1a8c629 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/params.yaml b/examples/security/classification/output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/params.yaml new file mode 100644 index 00000000..446b00ca --- /dev/null +++ b/examples/security/classification/output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: ffab9fc382d672759ee9451bb91a61a4 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/adv_predictions.json + adv_probabilities_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/adv_probabilities.json + attack_file: output/attacks/ea12fcc688eb964dda99d4a5e6caba81.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/params.yaml + predictions_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/predictions.json + probabilities_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/probabilities.json + score_dict_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/score_dict.json + test_labels_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/test_labels.json + train_labels_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/train_labels.json + model_dir: models + name: a2f91a6f21c4f4ccc56264a00f481d89 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: a2f91a6f21c4f4ccc56264a00f481d89 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/params.yaml b/examples/security/classification/output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/params.yaml new file mode 100644 index 00000000..134125d5 --- /dev/null +++ b/examples/security/classification/output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 6f05150cbc56fa307f098a5a67b9a71d +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/adv_predictions.json + adv_probabilities_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/adv_probabilities.json + attack_file: output/attacks/58af1859c5bef58532e70223314ab69c.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/params.yaml + predictions_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/predictions.json + probabilities_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/probabilities.json + score_dict_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/score_dict.json + test_labels_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/test_labels.json + train_labels_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/train_labels.json + model_dir: models + name: a49a6ffd24ce8950bdf7f1a9e940fb12 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: a49a6ffd24ce8950bdf7f1a9e940fb12 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/params.yaml b/examples/security/classification/output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/params.yaml new file mode 100644 index 00000000..6ccae740 --- /dev/null +++ b/examples/security/classification/output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.5 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 3fe34b691cb27d7840c530c9e6d54dd1 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/adv_predictions.json + adv_probabilities_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/adv_probabilities.json + attack_file: output/attacks/d8f8162fa2d39af956b0d939a71d7706.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/params.yaml + predictions_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/predictions.json + probabilities_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/probabilities.json + score_dict_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/score_dict.json + test_labels_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/test_labels.json + train_labels_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/train_labels.json + model_dir: models + name: a66b2ffb3d703b399cb1d012ab167d36 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: a66b2ffb3d703b399cb1d012ab167d36 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/a6f2e63544f4039fef7d33345209f171/params.yaml b/examples/security/classification/output/reports/attack/a6f2e63544f4039fef7d33345209f171/params.yaml new file mode 100644 index 00000000..6a8b65c2 --- /dev/null +++ b/examples/security/classification/output/reports/attack/a6f2e63544f4039fef7d33345209f171/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 8530fb8620d8c33d2da88c2b6f914191 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/adv_predictions.json + adv_probabilities_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/adv_probabilities.json + attack_file: output/attacks/7f1d1a0d6c0a3f99b9c1ecced72c1587.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/params.yaml + predictions_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/predictions.json + probabilities_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/probabilities.json + score_dict_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/score_dict.json + test_labels_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/test_labels.json + train_labels_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/train_labels.json + model_dir: models + name: a6f2e63544f4039fef7d33345209f171 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: a6f2e63544f4039fef7d33345209f171 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/params.yaml b/examples/security/classification/output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/params.yaml new file mode 100644 index 00000000..5d770e8a --- /dev/null +++ b/examples/security/classification/output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.01 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 7d96e6be3acec53dfef02cc8a2dd86e2 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/adv_predictions.json + adv_probabilities_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/adv_probabilities.json + attack_file: output/attacks/27727e6e87a2957b3c4c3fc3da85b480.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/params.yaml + predictions_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/predictions.json + probabilities_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/probabilities.json + score_dict_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/score_dict.json + test_labels_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/test_labels.json + train_labels_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/train_labels.json + model_dir: models + name: a98844e1bd450ddc9131e9c862242fb3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: a98844e1bd450ddc9131e9c862242fb3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/params.yaml b/examples/security/classification/output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/params.yaml new file mode 100644 index 00000000..56d07227 --- /dev/null +++ b/examples/security/classification/output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.01 + max_iter: 1000 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: b1bf52dbeee53a9f1421ff7c54d2b123 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/adv_predictions.json + adv_probabilities_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/adv_probabilities.json + attack_file: output/attacks/43ee52a9e4424269138d4ed7258de40e.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/params.yaml + predictions_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/predictions.json + probabilities_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/probabilities.json + score_dict_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/score_dict.json + test_labels_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/test_labels.json + train_labels_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/train_labels.json + model_dir: models + name: aa5063f3f3c57711a57af27c5a0b4c32 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: aa5063f3f3c57711a57af27c5a0b4c32 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/abc1a630c8d7055b732170695ef77168/params.yaml b/examples/security/classification/output/reports/attack/abc1a630c8d7055b732170695ef77168/params.yaml new file mode 100644 index 00000000..d3249fbb --- /dev/null +++ b/examples/security/classification/output/reports/attack/abc1a630c8d7055b732170695ef77168/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.001 + max_iter: 1000 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: bb2ed9272cfce72db44b199041537486 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/adv_predictions.json + adv_probabilities_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/adv_probabilities.json + attack_file: output/attacks/e5a42d82c7d5d10794fe2680538ae016.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/params.yaml + predictions_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/predictions.json + probabilities_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/probabilities.json + score_dict_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/score_dict.json + test_labels_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/test_labels.json + train_labels_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/train_labels.json + model_dir: models + name: abc1a630c8d7055b732170695ef77168 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: abc1a630c8d7055b732170695ef77168 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/params.yaml b/examples/security/classification/output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/params.yaml new file mode 100644 index 00000000..996f219b --- /dev/null +++ b/examples/security/classification/output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.01 + max_iter: 1000 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: bf2373223c67bf0db20206f61218457e +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/adv_predictions.json + adv_probabilities_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/adv_probabilities.json + attack_file: output/attacks/71677d4447e49ef57a4aaf8735f2b88a.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/params.yaml + predictions_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/predictions.json + probabilities_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/probabilities.json + score_dict_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/score_dict.json + test_labels_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/test_labels.json + train_labels_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/train_labels.json + model_dir: models + name: ac1f19994da7ed0f5580e1e2e49ef91a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: ac1f19994da7ed0f5580e1e2e49ef91a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/params.yaml b/examples/security/classification/output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/params.yaml new file mode 100644 index 00000000..706eb828 --- /dev/null +++ b/examples/security/classification/output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.001 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: f312716c1b96333aa9e82133d3c9dc3f +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/adv_predictions.json + adv_probabilities_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/adv_probabilities.json + attack_file: output/attacks/50e84aacf02abe678d990702a3b267d8.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/params.yaml + predictions_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/predictions.json + probabilities_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/probabilities.json + score_dict_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/score_dict.json + test_labels_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/test_labels.json + train_labels_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/train_labels.json + model_dir: models + name: b18b3b59888dd75c351d61436a55a6a5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: b18b3b59888dd75c351d61436a55a6a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/b52301d69894bf716f00e081a5a824db/params.yaml b/examples/security/classification/output/reports/attack/b52301d69894bf716f00e081a5a824db/params.yaml new file mode 100644 index 00000000..54faf1a4 --- /dev/null +++ b/examples/security/classification/output/reports/attack/b52301d69894bf716f00e081a5a824db/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.3 + max_iter: 1000 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: d9661a8e48a627de0627b0cefc662cfa +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/adv_predictions.json + adv_probabilities_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/adv_probabilities.json + attack_file: output/attacks/45177b30597b7f692d32dc71350c8572.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/params.yaml + predictions_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/predictions.json + probabilities_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/probabilities.json + score_dict_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/score_dict.json + test_labels_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/test_labels.json + train_labels_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/train_labels.json + model_dir: models + name: b52301d69894bf716f00e081a5a824db + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: b52301d69894bf716f00e081a5a824db +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/params.yaml b/examples/security/classification/output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/params.yaml new file mode 100644 index 00000000..a56939f9 --- /dev/null +++ b/examples/security/classification/output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: d4c3c2c7f6cfe40bf0d1545ce9a08f6b +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/adv_predictions.json + adv_probabilities_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/adv_probabilities.json + attack_file: output/attacks/c0474a20834597090a9dc368ea22db12.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/params.yaml + predictions_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/predictions.json + probabilities_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/probabilities.json + score_dict_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/score_dict.json + test_labels_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/test_labels.json + train_labels_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/train_labels.json + model_dir: models + name: b5b8f6ecc5195c2571295b2ddb5c88c1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: b5b8f6ecc5195c2571295b2ddb5c88c1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/params.yaml b/examples/security/classification/output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/params.yaml new file mode 100644 index 00000000..fa203edf --- /dev/null +++ b/examples/security/classification/output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: c32ce349c2746e0bc6e88e6eecbca055 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/adv_predictions.json + adv_probabilities_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/adv_probabilities.json + attack_file: output/attacks/2a75d11854438fbde78bf82dbe56c530.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/params.yaml + predictions_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/predictions.json + probabilities_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/probabilities.json + score_dict_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/score_dict.json + test_labels_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/test_labels.json + train_labels_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/train_labels.json + model_dir: models + name: b5cf3d0fef008d99f856ba99fb5c10b0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: b5cf3d0fef008d99f856ba99fb5c10b0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/b697176935d5caaecdc9123084f0efbd/params.yaml b/examples/security/classification/output/reports/attack/b697176935d5caaecdc9123084f0efbd/params.yaml new file mode 100644 index 00000000..a0c7ce8f --- /dev/null +++ b/examples/security/classification/output/reports/attack/b697176935d5caaecdc9123084f0efbd/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 9efa6eec98f841bee110e72d147e25a8 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/adv_predictions.json + adv_probabilities_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/adv_probabilities.json + attack_file: output/attacks/2fdbe53885ed551a348e2d707a699448.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/params.yaml + predictions_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/predictions.json + probabilities_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/probabilities.json + score_dict_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/score_dict.json + test_labels_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/test_labels.json + train_labels_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/train_labels.json + model_dir: models + name: b697176935d5caaecdc9123084f0efbd + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: b697176935d5caaecdc9123084f0efbd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/params.yaml b/examples/security/classification/output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/params.yaml new file mode 100644 index 00000000..5a5e3249 --- /dev/null +++ b/examples/security/classification/output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.5 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 95d97a653998836885d11ece958e24d1 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/adv_predictions.json + adv_probabilities_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/adv_probabilities.json + attack_file: output/attacks/178937285fd082a4766e2db123ad0adf.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/params.yaml + predictions_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/predictions.json + probabilities_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/probabilities.json + score_dict_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/score_dict.json + test_labels_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/test_labels.json + train_labels_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/train_labels.json + model_dir: models + name: b6d921b9f67933888fc05cfaeb74a091 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: b6d921b9f67933888fc05cfaeb74a091 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/params.yaml b/examples/security/classification/output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/params.yaml new file mode 100644 index 00000000..75b77c53 --- /dev/null +++ b/examples/security/classification/output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 432dac4801a936fc09026dbbb90b5df8 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/adv_predictions.json + adv_probabilities_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/adv_probabilities.json + attack_file: output/attacks/a09d1b64fcabeb450942064ed850155e.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/params.yaml + predictions_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/predictions.json + probabilities_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/probabilities.json + score_dict_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/score_dict.json + test_labels_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/test_labels.json + train_labels_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/train_labels.json + model_dir: models + name: ba2dd71a65dd0cb5b310a7852273f70a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: ba2dd71a65dd0cb5b310a7852273f70a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/bb959ad4d6313759872120214cbe7a6e/params.yaml b/examples/security/classification/output/reports/attack/bb959ad4d6313759872120214cbe7a6e/params.yaml new file mode 100644 index 00000000..6c8b2a25 --- /dev/null +++ b/examples/security/classification/output/reports/attack/bb959ad4d6313759872120214cbe7a6e/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.5 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: a2023bda340686331312ff0ae27dd4ea +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/adv_predictions.json + adv_probabilities_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/adv_probabilities.json + attack_file: output/attacks/4feef1230c39cda652f3617a9161b690.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/params.yaml + predictions_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/predictions.json + probabilities_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/probabilities.json + score_dict_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/score_dict.json + test_labels_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/test_labels.json + train_labels_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/train_labels.json + model_dir: models + name: bb959ad4d6313759872120214cbe7a6e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: bb959ad4d6313759872120214cbe7a6e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/bbb5777995a8d71f12831358700cba37/params.yaml b/examples/security/classification/output/reports/attack/bbb5777995a8d71f12831358700cba37/params.yaml new file mode 100644 index 00000000..c9d2a4f8 --- /dev/null +++ b/examples/security/classification/output/reports/attack/bbb5777995a8d71f12831358700cba37/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.5 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: cbda2c8f09efbb72d794d3cb03b1a287 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/adv_predictions.json + adv_probabilities_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/adv_probabilities.json + attack_file: output/attacks/b737f7a57f738b60df1f3ad3d763f323.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/params.yaml + predictions_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/predictions.json + probabilities_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/probabilities.json + score_dict_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/score_dict.json + test_labels_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/test_labels.json + train_labels_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/train_labels.json + model_dir: models + name: bbb5777995a8d71f12831358700cba37 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: bbb5777995a8d71f12831358700cba37 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/bbbb9f8821393a0a5915960139269aab/params.yaml b/examples/security/classification/output/reports/attack/bbbb9f8821393a0a5915960139269aab/params.yaml new file mode 100644 index 00000000..8017a1c7 --- /dev/null +++ b/examples/security/classification/output/reports/attack/bbbb9f8821393a0a5915960139269aab/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.3 + max_iter: 1000 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 95e661cde9dcc16835c35a40cd7442e6 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/adv_predictions.json + adv_probabilities_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/adv_probabilities.json + attack_file: output/attacks/fce346eaf2d6f78609034db7f61d7033.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/params.yaml + predictions_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/predictions.json + probabilities_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/probabilities.json + score_dict_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/score_dict.json + test_labels_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/test_labels.json + train_labels_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/train_labels.json + model_dir: models + name: bbbb9f8821393a0a5915960139269aab + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: bbbb9f8821393a0a5915960139269aab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/params.yaml b/examples/security/classification/output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/params.yaml new file mode 100644 index 00000000..4f1e5ddb --- /dev/null +++ b/examples/security/classification/output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.5 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 7680233d11aa441703740f57ee2b1966 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/adv_predictions.json + adv_probabilities_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/adv_probabilities.json + attack_file: output/attacks/8e4b673a505f4c1f531d3136ac309b34.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/params.yaml + predictions_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/predictions.json + probabilities_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/probabilities.json + score_dict_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/score_dict.json + test_labels_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/test_labels.json + train_labels_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/train_labels.json + model_dir: models + name: bc0efd09cb94d5a79d8f16c503147e4f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: bc0efd09cb94d5a79d8f16c503147e4f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/bc3d1ce32db16998772fe8079460a133/params.yaml b/examples/security/classification/output/reports/attack/bc3d1ce32db16998772fe8079460a133/params.yaml new file mode 100644 index 00000000..b1be400e --- /dev/null +++ b/examples/security/classification/output/reports/attack/bc3d1ce32db16998772fe8079460a133/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: aedabba049c64c1a2171000b226e931b +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/adv_predictions.json + adv_probabilities_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/adv_probabilities.json + attack_file: output/attacks/3a42325515cf726c492904fdd23117a0.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/params.yaml + predictions_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/predictions.json + probabilities_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/probabilities.json + score_dict_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/score_dict.json + test_labels_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/test_labels.json + train_labels_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/train_labels.json + model_dir: models + name: bc3d1ce32db16998772fe8079460a133 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: bc3d1ce32db16998772fe8079460a133 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/params.yaml b/examples/security/classification/output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/params.yaml new file mode 100644 index 00000000..1003207f --- /dev/null +++ b/examples/security/classification/output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: e0483c15c2d3d1f4b00ab464e1596e6a +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/adv_predictions.json + adv_probabilities_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/adv_probabilities.json + attack_file: output/attacks/2a092ec393809dc37ec1b2cac2c48219.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/params.yaml + predictions_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/predictions.json + probabilities_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/probabilities.json + score_dict_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/score_dict.json + test_labels_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/test_labels.json + train_labels_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/train_labels.json + model_dir: models + name: bce5a6b06c963e348aefdf0e910d5159 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: bce5a6b06c963e348aefdf0e910d5159 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/be923088aa789394a4d643fb4b67836b/params.yaml b/examples/security/classification/output/reports/attack/be923088aa789394a4d643fb4b67836b/params.yaml new file mode 100644 index 00000000..1cedc0d0 --- /dev/null +++ b/examples/security/classification/output/reports/attack/be923088aa789394a4d643fb4b67836b/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: c10e7f76f70d7d1aaeaf7ee1404d44f1 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/adv_predictions.json + adv_probabilities_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/adv_probabilities.json + attack_file: output/attacks/aa07c5f8ed3478b59ade749db50ff1c2.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/params.yaml + predictions_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/predictions.json + probabilities_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/probabilities.json + score_dict_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/score_dict.json + test_labels_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/test_labels.json + train_labels_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/train_labels.json + model_dir: models + name: be923088aa789394a4d643fb4b67836b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: be923088aa789394a4d643fb4b67836b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/c1102547c2f304a47e17dedf53dcad42/params.yaml b/examples/security/classification/output/reports/attack/c1102547c2f304a47e17dedf53dcad42/params.yaml new file mode 100644 index 00000000..e579e0fd --- /dev/null +++ b/examples/security/classification/output/reports/attack/c1102547c2f304a47e17dedf53dcad42/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.1 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 3f11ed77d945d8765963f78fe519a1bf +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/adv_predictions.json + adv_probabilities_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/adv_probabilities.json + attack_file: output/attacks/fa8e9590d68c07b0f3134a5080de8cec.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/params.yaml + predictions_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/predictions.json + probabilities_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/probabilities.json + score_dict_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/score_dict.json + test_labels_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/test_labels.json + train_labels_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/train_labels.json + model_dir: models + name: c1102547c2f304a47e17dedf53dcad42 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: c1102547c2f304a47e17dedf53dcad42 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/params.yaml b/examples/security/classification/output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/params.yaml new file mode 100644 index 00000000..9dc472e7 --- /dev/null +++ b/examples/security/classification/output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 4b590da1b52ac672c836580ba8b9a45d +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/adv_predictions.json + adv_probabilities_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/adv_probabilities.json + attack_file: output/attacks/08870d8783916c1fdb8bf31cade453e7.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/params.yaml + predictions_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/predictions.json + probabilities_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/probabilities.json + score_dict_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/score_dict.json + test_labels_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/test_labels.json + train_labels_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/train_labels.json + model_dir: models + name: c20bef1e31d6a4ac60aa724611735deb + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: c20bef1e31d6a4ac60aa724611735deb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/params.yaml b/examples/security/classification/output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/params.yaml new file mode 100644 index 00000000..c620f5b8 --- /dev/null +++ b/examples/security/classification/output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 92ab049ea9e6df935a4f919a7d7ec370 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/adv_predictions.json + adv_probabilities_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/adv_probabilities.json + attack_file: output/attacks/c063f351f831bdfb1f657164bd2f24eb.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/params.yaml + predictions_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/predictions.json + probabilities_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/probabilities.json + score_dict_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/score_dict.json + test_labels_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/test_labels.json + train_labels_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/train_labels.json + model_dir: models + name: c64d3383ed0989439cb0eeecdb47457b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: c64d3383ed0989439cb0eeecdb47457b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/params.yaml b/examples/security/classification/output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/params.yaml new file mode 100644 index 00000000..723ad2f9 --- /dev/null +++ b/examples/security/classification/output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 3f17b8511d8d47852053b2588e09c24e +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/adv_predictions.json + adv_probabilities_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/adv_probabilities.json + attack_file: output/attacks/c7123fa902f348f66852b56b928b070b.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/params.yaml + predictions_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/predictions.json + probabilities_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/probabilities.json + score_dict_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/score_dict.json + test_labels_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/test_labels.json + train_labels_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/train_labels.json + model_dir: models + name: c679004c91a6290c44902a9ddad1bb3a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: c679004c91a6290c44902a9ddad1bb3a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/params.yaml b/examples/security/classification/output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/params.yaml new file mode 100644 index 00000000..018137bc --- /dev/null +++ b/examples/security/classification/output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: dec9555092c668af6c0b61c24597317c +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/adv_predictions.json + adv_probabilities_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/adv_probabilities.json + attack_file: output/attacks/df76e5067db3a205e47852b5e970592a.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/params.yaml + predictions_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/predictions.json + probabilities_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/probabilities.json + score_dict_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/score_dict.json + test_labels_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/test_labels.json + train_labels_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/train_labels.json + model_dir: models + name: c6db7e0fbfd632f201ce1f826c8d1f29 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: c6db7e0fbfd632f201ce1f826c8d1f29 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/params.yaml b/examples/security/classification/output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/params.yaml new file mode 100644 index 00000000..720e5c36 --- /dev/null +++ b/examples/security/classification/output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: e3425ec1a558c80902d9df4710ac1f9a +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/adv_predictions.json + adv_probabilities_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/adv_probabilities.json + attack_file: output/attacks/bdf902a0ea756d6ec7710994c20fefc5.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/params.yaml + predictions_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/predictions.json + probabilities_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/probabilities.json + score_dict_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/score_dict.json + test_labels_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/test_labels.json + train_labels_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/train_labels.json + model_dir: models + name: c86efe6fff1474c1b735b66d36bc9d07 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: c86efe6fff1474c1b735b66d36bc9d07 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/params.yaml b/examples/security/classification/output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/params.yaml new file mode 100644 index 00000000..0ea84150 --- /dev/null +++ b/examples/security/classification/output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.01 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 2d54c9e7f035bf5c5529fa00c1d655c8 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/adv_predictions.json + adv_probabilities_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/adv_probabilities.json + attack_file: output/attacks/7adf2e7262ff9601792f0e0b1eca6415.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/params.yaml + predictions_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/predictions.json + probabilities_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/probabilities.json + score_dict_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/score_dict.json + test_labels_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/test_labels.json + train_labels_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/train_labels.json + model_dir: models + name: c8d769fbaf37149f8537a4e9ab28a166 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: c8d769fbaf37149f8537a4e9ab28a166 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/params.yaml b/examples/security/classification/output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/params.yaml new file mode 100644 index 00000000..5a7589af --- /dev/null +++ b/examples/security/classification/output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 1 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 04218b81316f60cb73615231ec778a53 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/adv_predictions.json + adv_probabilities_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/adv_probabilities.json + attack_file: output/attacks/03465bd1303e919e0e057925f35d11c7.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/params.yaml + predictions_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/predictions.json + probabilities_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/probabilities.json + score_dict_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/score_dict.json + test_labels_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/test_labels.json + train_labels_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/train_labels.json + model_dir: models + name: caec4fd4d685c0f099ca3b242eca1b27 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: caec4fd4d685c0f099ca3b242eca1b27 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/cb28258195e5d12352131d1fde7e255f/params.yaml b/examples/security/classification/output/reports/attack/cb28258195e5d12352131d1fde7e255f/params.yaml new file mode 100644 index 00000000..2d0d90c8 --- /dev/null +++ b/examples/security/classification/output/reports/attack/cb28258195e5d12352131d1fde7e255f/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 1 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: f2c7e58618863804e47458c1884d55e0 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/adv_predictions.json + adv_probabilities_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/adv_probabilities.json + attack_file: output/attacks/dde0c187c66a9589e585ce5806019e15.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/params.yaml + predictions_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/predictions.json + probabilities_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/probabilities.json + score_dict_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/score_dict.json + test_labels_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/test_labels.json + train_labels_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/train_labels.json + model_dir: models + name: cb28258195e5d12352131d1fde7e255f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: cb28258195e5d12352131d1fde7e255f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/cd3d3333255b48575620fdb8ddd38072/params.yaml b/examples/security/classification/output/reports/attack/cd3d3333255b48575620fdb8ddd38072/params.yaml new file mode 100644 index 00000000..011f188a --- /dev/null +++ b/examples/security/classification/output/reports/attack/cd3d3333255b48575620fdb8ddd38072/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.001 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 89a3e5039d98b956ec53ede8e8002dc5 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/adv_predictions.json + adv_probabilities_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/adv_probabilities.json + attack_file: output/attacks/b67690cb49f5283368efa4847ce6d29a.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/params.yaml + predictions_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/predictions.json + probabilities_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/probabilities.json + score_dict_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/score_dict.json + test_labels_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/test_labels.json + train_labels_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/train_labels.json + model_dir: models + name: cd3d3333255b48575620fdb8ddd38072 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: cd3d3333255b48575620fdb8ddd38072 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/params.yaml b/examples/security/classification/output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/params.yaml new file mode 100644 index 00000000..a77453d3 --- /dev/null +++ b/examples/security/classification/output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.01 + max_iter: 1000 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 2c19b04676e7b92b53810534fb630673 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/adv_predictions.json + adv_probabilities_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/adv_probabilities.json + attack_file: output/attacks/69412dfe021077f35475803b7dc18786.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/params.yaml + predictions_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/predictions.json + probabilities_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/probabilities.json + score_dict_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/score_dict.json + test_labels_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/test_labels.json + train_labels_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/train_labels.json + model_dir: models + name: cde8e7e4defa31c0c9313c7a17900d10 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: cde8e7e4defa31c0c9313c7a17900d10 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/params.yaml b/examples/security/classification/output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/params.yaml new file mode 100644 index 00000000..2184812a --- /dev/null +++ b/examples/security/classification/output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 28b4a67b9ba37fca532276214a393d1d +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/adv_predictions.json + adv_probabilities_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/adv_probabilities.json + attack_file: output/attacks/1aad1c5a5971e30a7d5ffd5ab747954d.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/params.yaml + predictions_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/predictions.json + probabilities_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/probabilities.json + score_dict_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/score_dict.json + test_labels_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/test_labels.json + train_labels_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/train_labels.json + model_dir: models + name: d23ef1fc5c7a2c220ac3da33a5f0f543 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: d23ef1fc5c7a2c220ac3da33a5f0f543 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/params.yaml b/examples/security/classification/output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/params.yaml new file mode 100644 index 00000000..831513f7 --- /dev/null +++ b/examples/security/classification/output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 6d569196024fed3733c7669f71b5a78c +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/adv_predictions.json + adv_probabilities_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/adv_probabilities.json + attack_file: output/attacks/aa20c87a460458baa500291f095562d1.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/params.yaml + predictions_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/predictions.json + probabilities_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/probabilities.json + score_dict_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/score_dict.json + test_labels_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/test_labels.json + train_labels_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/train_labels.json + model_dir: models + name: d83f9b2c942764715bffdfad6b2bdaf4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: d83f9b2c942764715bffdfad6b2bdaf4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/params.yaml b/examples/security/classification/output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/params.yaml new file mode 100644 index 00000000..d12cc2c3 --- /dev/null +++ b/examples/security/classification/output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.5 + max_iter: 1000 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 773292464bcbff341e81ee91c2148601 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/adv_predictions.json + adv_probabilities_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/adv_probabilities.json + attack_file: output/attacks/86b22f5841967b4ec54b97fc1f013d8d.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/params.yaml + predictions_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/predictions.json + probabilities_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/probabilities.json + score_dict_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/score_dict.json + test_labels_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/test_labels.json + train_labels_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/train_labels.json + model_dir: models + name: da4e0555455650a1ca606aa53c6cdfb8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: da4e0555455650a1ca606aa53c6cdfb8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/params.yaml b/examples/security/classification/output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/params.yaml new file mode 100644 index 00000000..e07b54a6 --- /dev/null +++ b/examples/security/classification/output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.001 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 1b011238e89838778980f452e536e8e7 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/adv_predictions.json + adv_probabilities_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/adv_probabilities.json + attack_file: output/attacks/850e948e6e2047c866757ff585487275.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/params.yaml + predictions_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/predictions.json + probabilities_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/probabilities.json + score_dict_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/score_dict.json + test_labels_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/test_labels.json + train_labels_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/train_labels.json + model_dir: models + name: de39c594b2bb0387b8ca45d41e951c96 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: de39c594b2bb0387b8ca45d41e951c96 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/default/params.yaml b/examples/security/classification/output/reports/attack/default/params.yaml new file mode 100644 index 00000000..36fda048 --- /dev/null +++ b/examples/security/classification/output/reports/attack/default/params.yaml @@ -0,0 +1,211 @@ +attack: + attack_size: 10 + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 433fc7aa2f77199ae99841829bb02ec9 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b1ae58b464df54ee8918089f870a3ad + trainer: + kwargs: {} + name: 31e0c762a66b2663b2fca3765877aab3 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/default/adv_predictions.json + adv_probabilities_file: output/reports/attack/default/adv_probabilities.json + attack_file: output/attacks/attack.pkl + params_file: output/reports/attack/default/params.yaml + predictions_file: output/reports/attack/default/predictions.json + probabilities_file: output/reports/attack/default/probabilities.json + score_dict_file: output/reports/attack/default/score_dict.json + model_dir: models + name: default + reports: reports + stage: attack +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b1ae58b464df54ee8918089f870a3ad + trainer: + kwargs: {} +name: default +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/df7b493149f4daf0d9543018014d39e4/params.yaml b/examples/security/classification/output/reports/attack/df7b493149f4daf0d9543018014d39e4/params.yaml new file mode 100644 index 00000000..4633f153 --- /dev/null +++ b/examples/security/classification/output/reports/attack/df7b493149f4daf0d9543018014d39e4/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.3 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 6cf63a8c333ea3596724465bbd2fa795 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/adv_predictions.json + adv_probabilities_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/adv_probabilities.json + attack_file: output/attacks/6a046ef983c586991c08309c9168c1bf.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/params.yaml + predictions_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/predictions.json + probabilities_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/probabilities.json + score_dict_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/score_dict.json + test_labels_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/test_labels.json + train_labels_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/train_labels.json + model_dir: models + name: df7b493149f4daf0d9543018014d39e4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: df7b493149f4daf0d9543018014d39e4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/params.yaml b/examples/security/classification/output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/params.yaml new file mode 100644 index 00000000..39ac519a --- /dev/null +++ b/examples/security/classification/output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.1 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 0d5e7ef801a17bed48e26b45bdfa1531 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/adv_predictions.json + adv_probabilities_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/adv_probabilities.json + attack_file: output/attacks/8bcbc68cdd71cd47763da0ad81e0ef5e.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/params.yaml + predictions_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/predictions.json + probabilities_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/probabilities.json + score_dict_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/score_dict.json + test_labels_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/test_labels.json + train_labels_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/train_labels.json + model_dir: models + name: dfbaf52d895803745c7a06e181e4f7e5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: dfbaf52d895803745c7a06e181e4f7e5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/params.yaml b/examples/security/classification/output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/params.yaml new file mode 100644 index 00000000..8f47dd19 --- /dev/null +++ b/examples/security/classification/output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 8f24e25c6a18645e0d6cc1543310c423 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/adv_predictions.json + adv_probabilities_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/adv_probabilities.json + attack_file: output/attacks/721876caa3295c865895dbe808a27e08.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/params.yaml + predictions_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/predictions.json + probabilities_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/probabilities.json + score_dict_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/score_dict.json + test_labels_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/test_labels.json + train_labels_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/train_labels.json + model_dir: models + name: e0fcc0b40a4b12272661a9b2612ff5e9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: e0fcc0b40a4b12272661a9b2612ff5e9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/e2b9bcec824489e09088cab69982352e/params.yaml b/examples/security/classification/output/reports/attack/e2b9bcec824489e09088cab69982352e/params.yaml new file mode 100644 index 00000000..37b45c16 --- /dev/null +++ b/examples/security/classification/output/reports/attack/e2b9bcec824489e09088cab69982352e/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: df20c3829ec256b768bbc6c846070284 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/adv_predictions.json + adv_probabilities_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/adv_probabilities.json + attack_file: output/attacks/f8be3ba4865a5b563ba586058200e643.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/params.yaml + predictions_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/predictions.json + probabilities_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/probabilities.json + score_dict_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/score_dict.json + test_labels_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/test_labels.json + train_labels_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/train_labels.json + model_dir: models + name: e2b9bcec824489e09088cab69982352e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: e2b9bcec824489e09088cab69982352e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/params.yaml b/examples/security/classification/output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/params.yaml new file mode 100644 index 00000000..6bdcc66c --- /dev/null +++ b/examples/security/classification/output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.001 + max_iter: 1000 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 9ece1d70c06add89cfc37bedb5ace006 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/adv_predictions.json + adv_probabilities_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/adv_probabilities.json + attack_file: output/attacks/70856d956f6156b06f73060da2886584.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/params.yaml + predictions_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/predictions.json + probabilities_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/probabilities.json + score_dict_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/score_dict.json + test_labels_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/test_labels.json + train_labels_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/train_labels.json + model_dir: models + name: e579a3109a16a2a33e979c697ab63a4a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: e579a3109a16a2a33e979c697ab63a4a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/params.yaml b/examples/security/classification/output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/params.yaml new file mode 100644 index 00000000..2a9d337d --- /dev/null +++ b/examples/security/classification/output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 1b8cd4f679b74cba64501ac11183678c +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/adv_predictions.json + adv_probabilities_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/adv_probabilities.json + attack_file: output/attacks/77208c589b0433f375db6785f40658d5.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/params.yaml + predictions_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/predictions.json + probabilities_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/probabilities.json + score_dict_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/score_dict.json + test_labels_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/test_labels.json + train_labels_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/train_labels.json + model_dir: models + name: e66b2f45d279f68b3cb2c3106d9f636c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: e66b2f45d279f68b3cb2c3106d9f636c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/params.yaml b/examples/security/classification/output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/params.yaml new file mode 100644 index 00000000..e431c059 --- /dev/null +++ b/examples/security/classification/output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.001 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 13d718c9fc44b2eed8829fad741ba879 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/adv_predictions.json + adv_probabilities_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/adv_probabilities.json + attack_file: output/attacks/c1bf6baf84573280df84497dd82a7d0c.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/params.yaml + predictions_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/predictions.json + probabilities_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/probabilities.json + score_dict_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/score_dict.json + test_labels_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/test_labels.json + train_labels_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/train_labels.json + model_dir: models + name: e72bd8fe158bacd0f30f7269cdeb1719 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: e72bd8fe158bacd0f30f7269cdeb1719 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/params.yaml b/examples/security/classification/output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/params.yaml new file mode 100644 index 00000000..767fd2a7 --- /dev/null +++ b/examples/security/classification/output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: eefd01477aad3c812ab28e0141636c01 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/adv_predictions.json + adv_probabilities_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/adv_probabilities.json + attack_file: output/attacks/40750f51ad6641c76bdc6a575bc8d985.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/params.yaml + predictions_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/predictions.json + probabilities_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/probabilities.json + score_dict_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/score_dict.json + test_labels_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/test_labels.json + train_labels_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/train_labels.json + model_dir: models + name: e7d69a830f033e4d16780cf4c88dab19 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: e7d69a830f033e4d16780cf4c88dab19 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/e89f1df76a9accbd7505f3771f97be27/params.yaml b/examples/security/classification/output/reports/attack/e89f1df76a9accbd7505f3771f97be27/params.yaml new file mode 100644 index 00000000..56060daf --- /dev/null +++ b/examples/security/classification/output/reports/attack/e89f1df76a9accbd7505f3771f97be27/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.3 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 66c90b82419d06ce73372f3f68a3a158 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/adv_predictions.json + adv_probabilities_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/adv_probabilities.json + attack_file: output/attacks/db9ffeb9f1e3c36c71fdeba20ac15379.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/params.yaml + predictions_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/predictions.json + probabilities_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/probabilities.json + score_dict_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/score_dict.json + test_labels_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/test_labels.json + train_labels_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/train_labels.json + model_dir: models + name: e89f1df76a9accbd7505f3771f97be27 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: e89f1df76a9accbd7505f3771f97be27 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/params.yaml b/examples/security/classification/output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/params.yaml new file mode 100644 index 00000000..3dd3306d --- /dev/null +++ b/examples/security/classification/output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: e1f9ba8cacc2754374e228d7e4a86632 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/adv_predictions.json + adv_probabilities_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/adv_probabilities.json + attack_file: output/attacks/19111b6c90a02d20638d7ec1054e486f.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/params.yaml + predictions_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/predictions.json + probabilities_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/probabilities.json + score_dict_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/score_dict.json + test_labels_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/test_labels.json + train_labels_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/train_labels.json + model_dir: models + name: e9b3408407a9c92df60dcdbcc5e175bb + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: e9b3408407a9c92df60dcdbcc5e175bb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/params.yaml b/examples/security/classification/output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/params.yaml new file mode 100644 index 00000000..af11eebb --- /dev/null +++ b/examples/security/classification/output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.1 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 2f7d4cae092d148b50f5ccbfe67c4107 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/adv_predictions.json + adv_probabilities_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/adv_probabilities.json + attack_file: output/attacks/e9cc974ee78fcbaf3b5181da7a1fc91e.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/params.yaml + predictions_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/predictions.json + probabilities_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/probabilities.json + score_dict_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/score_dict.json + test_labels_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/test_labels.json + train_labels_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/train_labels.json + model_dir: models + name: ea88aca97b2572dff5a51f0b5c9f3473 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: ea88aca97b2572dff5a51f0b5c9f3473 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/params.yaml b/examples/security/classification/output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/params.yaml new file mode 100644 index 00000000..61e512ad --- /dev/null +++ b/examples/security/classification/output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 5e984e132966df2e705f65fd75269e45 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/adv_predictions.json + adv_probabilities_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/adv_probabilities.json + attack_file: output/attacks/2eb89d26564fe4f822417313135409f6.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/params.yaml + predictions_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/predictions.json + probabilities_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/probabilities.json + score_dict_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/score_dict.json + test_labels_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/test_labels.json + train_labels_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/train_labels.json + model_dir: models + name: f10fb48933cb9fb310b30cfe6ef8972f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: f10fb48933cb9fb310b30cfe6ef8972f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/params.yaml b/examples/security/classification/output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/params.yaml new file mode 100644 index 00000000..a5370062 --- /dev/null +++ b/examples/security/classification/output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/params.yaml @@ -0,0 +1,245 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74376130120af776c27955c98e35448 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} + name: 8942e4b566dffd963d6c1feb135a31a7 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/adv_predictions.json + adv_probabilities_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/adv_probabilities.json + attack_file: output/attacks/740a50a7eea7ec81ab85dd3bca573b13.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl + params_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/params.yaml + predictions_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/predictions.json + probabilities_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/probabilities.json + score_dict_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/score_dict.json + test_labels_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/test_labels.json + train_labels_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/train_labels.json + model_dir: models + name: f41c4a587a1eefd82f6bffad8cf20ae2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f23663db59297764839458062c5f97f + trainer: + kwargs: {} +name: f41c4a587a1eefd82f6bffad8cf20ae2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/params.yaml b/examples/security/classification/output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/params.yaml new file mode 100644 index 00000000..44fe1322 --- /dev/null +++ b/examples/security/classification/output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 38579dfb39e79ad44de95e995613d1fe +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/adv_predictions.json + adv_probabilities_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/adv_probabilities.json + attack_file: output/attacks/8ff8664ee7ad2da87d55433db70f0b1b.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/params.yaml + predictions_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/predictions.json + probabilities_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/probabilities.json + score_dict_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/score_dict.json + test_labels_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/test_labels.json + train_labels_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/train_labels.json + model_dir: models + name: f5a7a716b340c343b84eeebda368fc5f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: f5a7a716b340c343b84eeebda368fc5f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/params.yaml b/examples/security/classification/output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/params.yaml new file mode 100644 index 00000000..694cb862 --- /dev/null +++ b/examples/security/classification/output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 1 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: fc0c615709f951ea2ec3ff3a6fad8297 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/adv_predictions.json + adv_probabilities_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/adv_probabilities.json + attack_file: output/attacks/615ba380c284d996a8400f27c9cef11d.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/params.yaml + predictions_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/predictions.json + probabilities_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/probabilities.json + score_dict_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/score_dict.json + test_labels_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/test_labels.json + train_labels_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/train_labels.json + model_dir: models + name: f5f45477cf75f8e5c7fa86de2da18bb6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: f5f45477cf75f8e5c7fa86de2da18bb6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/params.yaml b/examples/security/classification/output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/params.yaml new file mode 100644 index 00000000..c58248a5 --- /dev/null +++ b/examples/security/classification/output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/params.yaml @@ -0,0 +1,236 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.1 + max_iter: 1000 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66528d318bd7cfc7b1ab7e6f233b17e6 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} + name: 8234927e0b19ff2bb44319394b82d494 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/adv_predictions.json + adv_probabilities_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/adv_probabilities.json + attack_file: output/attacks/3b802398c0c9971a37ae11a29c920917.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl + params_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/params.yaml + predictions_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/predictions.json + probabilities_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/probabilities.json + score_dict_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/score_dict.json + test_labels_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/test_labels.json + train_labels_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/train_labels.json + model_dir: models + name: f6bc9387f2ee85f4db62ba3bde014fdc + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27857f11e234ec6587e9ab699619368f + trainer: + kwargs: {} +name: f6bc9387f2ee85f4db62ba3bde014fdc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/f9376ab290106335589016c44ad6bb9c/params.yaml b/examples/security/classification/output/reports/attack/f9376ab290106335589016c44ad6bb9c/params.yaml new file mode 100644 index 00000000..95042dfc --- /dev/null +++ b/examples/security/classification/output/reports/attack/f9376ab290106335589016c44ad6bb9c/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 003337a433b309b239ed4e268d08a894 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/adv_predictions.json + adv_probabilities_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/adv_probabilities.json + attack_file: output/attacks/4d119dfca0c668cb732afd4f7302fbf3.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/params.yaml + predictions_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/predictions.json + probabilities_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/probabilities.json + score_dict_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/score_dict.json + test_labels_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/test_labels.json + train_labels_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/train_labels.json + model_dir: models + name: f9376ab290106335589016c44ad6bb9c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: f9376ab290106335589016c44ad6bb9c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/f99979a98f927382eb45efb02e76703f/params.yaml b/examples/security/classification/output/reports/attack/f99979a98f927382eb45efb02e76703f/params.yaml new file mode 100644 index 00000000..88801e2c --- /dev/null +++ b/examples/security/classification/output/reports/attack/f99979a98f927382eb45efb02e76703f/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 1 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 7fd5cb6fd5ca4ceb499d4e0628330bd5 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/adv_predictions.json + adv_probabilities_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/adv_probabilities.json + attack_file: output/attacks/13e5d0da7d0ce62cf68adf7b16c0ef11.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/params.yaml + predictions_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/predictions.json + probabilities_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/probabilities.json + score_dict_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/score_dict.json + test_labels_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/test_labels.json + train_labels_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/train_labels.json + model_dir: models + name: f99979a98f927382eb45efb02e76703f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: f99979a98f927382eb45efb02e76703f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/attack/fed73ab02f758325b690937b5242841c/params.yaml b/examples/security/classification/output/reports/attack/fed73ab02f758325b690937b5242841c/params.yaml new file mode 100644 index 00000000..9d73a2db --- /dev/null +++ b/examples/security/classification/output/reports/attack/fed73ab02f758325b690937b5242841c/params.yaml @@ -0,0 +1,242 @@ +attack: + attack_size: 100 + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.5 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbc3781be6645662ab8fa6536aefcb46 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} + name: 35ad9ef0391113d6952b248b23b09641 +data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/fed73ab02f758325b690937b5242841c/adv_predictions.json + adv_probabilities_file: output/reports/attack/fed73ab02f758325b690937b5242841c/adv_probabilities.json + attack_file: output/attacks/60f3045b7faf9bdcb750ebbc8dba73d6.pkl + data_file: output/data/a858116080834fd47b74ae783316fef9.pkl + model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl + params_file: output/reports/attack/fed73ab02f758325b690937b5242841c/params.yaml + predictions_file: output/reports/attack/fed73ab02f758325b690937b5242841c/predictions.json + probabilities_file: output/reports/attack/fed73ab02f758325b690937b5242841c/probabilities.json + score_dict_file: output/reports/attack/fed73ab02f758325b690937b5242841c/score_dict.json + test_labels_file: output/reports/attack/fed73ab02f758325b690937b5242841c/test_labels.json + train_labels_file: output/reports/attack/fed73ab02f758325b690937b5242841c/train_labels.json + model_dir: models + name: fed73ab02f758325b690937b5242841c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_informative: 99 + n_redundant: 0 + n_repeated: 0 + n_samples: 11100 + random_state: 0 + name: classification + name: a64858fac5ed02f8fc79116d3cdf6c22 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edd1e5ee431b09141af50a5714b2a5f7 + trainer: + kwargs: {} +name: fed73ab02f758325b690937b5242841c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/000424de3cdde01eb26cd9e434dc736e/params.yaml b/examples/security/classification/output/reports/train/000424de3cdde01eb26cd9e434dc736e/params.yaml new file mode 100644 index 00000000..3b9077b4 --- /dev/null +++ b/examples/security/classification/output/reports/train/000424de3cdde01eb26cd9e434dc736e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/adv_predictions.json + adv_probabilities_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/177dac86eec16b1573c4556b0a10a669.pkl + params_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/params.yaml + predictions_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/predictions.json + probabilities_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/probabilities.json + score_dict_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/score_dict.json + test_labels_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/test_labels.json + train_labels_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/train_labels.json + model_dir: models + name: 000424de3cdde01eb26cd9e434dc736e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 875c7597e496443fb8702099c7cae816 + trainer: + kwargs: {} +name: 000424de3cdde01eb26cd9e434dc736e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/001ba8df2a743772c43daad225a5f7db/params.yaml b/examples/security/classification/output/reports/train/001ba8df2a743772c43daad225a5f7db/params.yaml new file mode 100644 index 00000000..e505eadd --- /dev/null +++ b/examples/security/classification/output/reports/train/001ba8df2a743772c43daad225a5f7db/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/adv_predictions.json + adv_probabilities_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/945fd15deb89aee1828b06f5942b3b55.pkl + params_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/params.yaml + predictions_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/predictions.json + probabilities_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/probabilities.json + score_dict_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/score_dict.json + test_labels_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/test_labels.json + train_labels_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/train_labels.json + model_dir: models + name: 001ba8df2a743772c43daad225a5f7db + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0bea9914d91fb38084cac1887ac4a091 + trainer: + kwargs: {} +name: 001ba8df2a743772c43daad225a5f7db +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/params.yaml b/examples/security/classification/output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/params.yaml new file mode 100644 index 00000000..acb6ce41 --- /dev/null +++ b/examples/security/classification/output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/adv_predictions.json + adv_probabilities_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/f05e1737bfbac75c769b7a8ced069c4c.pkl + params_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/params.yaml + predictions_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/predictions.json + probabilities_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/probabilities.json + score_dict_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/score_dict.json + test_labels_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/test_labels.json + train_labels_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/train_labels.json + model_dir: models + name: 001df026f0fc9e0e28c26e8b4a4cd29c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2940bd3c4ea11032878b290ba0fb581b + trainer: + kwargs: {} +name: 001df026f0fc9e0e28c26e8b4a4cd29c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/params.yaml b/examples/security/classification/output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/params.yaml new file mode 100644 index 00000000..1497ccb7 --- /dev/null +++ b/examples/security/classification/output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/adv_predictions.json + adv_probabilities_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/92fd54df0d4cd191dbb4697086028432.pkl + params_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/params.yaml + predictions_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/predictions.json + probabilities_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/probabilities.json + score_dict_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/score_dict.json + test_labels_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/test_labels.json + train_labels_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/train_labels.json + model_dir: models + name: 00261e7a9a5007a0513f43d613f7d7d2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b134377327f48f9d86f95850773d815b + trainer: + kwargs: {} +name: 00261e7a9a5007a0513f43d613f7d7d2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/002de43ae63fed842b522a51cdbaaef5/params.yaml b/examples/security/classification/output/reports/train/002de43ae63fed842b522a51cdbaaef5/params.yaml new file mode 100644 index 00000000..e2f212d7 --- /dev/null +++ b/examples/security/classification/output/reports/train/002de43ae63fed842b522a51cdbaaef5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/adv_predictions.json + adv_probabilities_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/bb5fc61cd21a967ab1856ff457f31fdb.pkl + params_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/params.yaml + predictions_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/predictions.json + probabilities_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/probabilities.json + score_dict_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/score_dict.json + test_labels_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/test_labels.json + train_labels_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/train_labels.json + model_dir: models + name: 002de43ae63fed842b522a51cdbaaef5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 94f3dcf4fafa34f2a4f6cbeae7e7537f + trainer: + kwargs: {} +name: 002de43ae63fed842b522a51cdbaaef5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/00471b964a87636526493b1efcd11d4d/params.yaml b/examples/security/classification/output/reports/train/00471b964a87636526493b1efcd11d4d/params.yaml new file mode 100644 index 00000000..c6e562a1 --- /dev/null +++ b/examples/security/classification/output/reports/train/00471b964a87636526493b1efcd11d4d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/00471b964a87636526493b1efcd11d4d/adv_predictions.json + adv_probabilities_file: output/reports/train/00471b964a87636526493b1efcd11d4d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/a8e7d6025e21d685bb61a2ca84fe034f.pkl + params_file: output/reports/train/00471b964a87636526493b1efcd11d4d/params.yaml + predictions_file: output/reports/train/00471b964a87636526493b1efcd11d4d/predictions.json + probabilities_file: output/reports/train/00471b964a87636526493b1efcd11d4d/probabilities.json + score_dict_file: output/reports/train/00471b964a87636526493b1efcd11d4d/score_dict.json + test_labels_file: output/reports/train/00471b964a87636526493b1efcd11d4d/test_labels.json + train_labels_file: output/reports/train/00471b964a87636526493b1efcd11d4d/train_labels.json + model_dir: models + name: 00471b964a87636526493b1efcd11d4d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cf1c13c3eb57fe9f01185f24ca6af67 + trainer: + kwargs: {} +name: 00471b964a87636526493b1efcd11d4d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/006eec1797100faea81a121088f5dfb4/params.yaml b/examples/security/classification/output/reports/train/006eec1797100faea81a121088f5dfb4/params.yaml new file mode 100644 index 00000000..35856261 --- /dev/null +++ b/examples/security/classification/output/reports/train/006eec1797100faea81a121088f5dfb4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/006eec1797100faea81a121088f5dfb4/adv_predictions.json + adv_probabilities_file: output/reports/train/006eec1797100faea81a121088f5dfb4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/a0e2067041c3e3f1507176a98b9b5dd4.pkl + params_file: output/reports/train/006eec1797100faea81a121088f5dfb4/params.yaml + predictions_file: output/reports/train/006eec1797100faea81a121088f5dfb4/predictions.json + probabilities_file: output/reports/train/006eec1797100faea81a121088f5dfb4/probabilities.json + score_dict_file: output/reports/train/006eec1797100faea81a121088f5dfb4/score_dict.json + test_labels_file: output/reports/train/006eec1797100faea81a121088f5dfb4/test_labels.json + train_labels_file: output/reports/train/006eec1797100faea81a121088f5dfb4/train_labels.json + model_dir: models + name: 006eec1797100faea81a121088f5dfb4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 58d5228ee47e78a802cecd6f2ee6a3a8 + trainer: + kwargs: {} +name: 006eec1797100faea81a121088f5dfb4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/params.yaml b/examples/security/classification/output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/params.yaml new file mode 100644 index 00000000..f542aa42 --- /dev/null +++ b/examples/security/classification/output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/adv_predictions.json + adv_probabilities_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/490683787733493416e24281a6c98272.pkl + params_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/params.yaml + predictions_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/predictions.json + probabilities_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/probabilities.json + score_dict_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/score_dict.json + test_labels_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/test_labels.json + train_labels_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/train_labels.json + model_dir: models + name: 0074f756b6a4f89a8571af22fedcbaa1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0c9de328e271cbbc0ee5158e30ace715 + trainer: + kwargs: {} +name: 0074f756b6a4f89a8571af22fedcbaa1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/008145b71e35b55b6ac5df92bcae3978/params.yaml b/examples/security/classification/output/reports/train/008145b71e35b55b6ac5df92bcae3978/params.yaml new file mode 100644 index 00000000..4fab4055 --- /dev/null +++ b/examples/security/classification/output/reports/train/008145b71e35b55b6ac5df92bcae3978/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/adv_predictions.json + adv_probabilities_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/25bf05a8920385b43c3b4526db047941.pkl + params_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/params.yaml + predictions_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/predictions.json + probabilities_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/probabilities.json + score_dict_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/score_dict.json + test_labels_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/test_labels.json + train_labels_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/train_labels.json + model_dir: models + name: 008145b71e35b55b6ac5df92bcae3978 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7983d126b4a1b0fb0adac43b8ee4d576 + trainer: + kwargs: {} +name: 008145b71e35b55b6ac5df92bcae3978 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/008b521057d46a0f84bf73a195f7623c/params.yaml b/examples/security/classification/output/reports/train/008b521057d46a0f84bf73a195f7623c/params.yaml new file mode 100644 index 00000000..b387456c --- /dev/null +++ b/examples/security/classification/output/reports/train/008b521057d46a0f84bf73a195f7623c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/adv_predictions.json + adv_probabilities_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/0b1efa3cb7a5404964f0c22429304bf7.pkl + params_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/params.yaml + predictions_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/predictions.json + probabilities_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/probabilities.json + score_dict_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/score_dict.json + test_labels_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/test_labels.json + train_labels_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/train_labels.json + model_dir: models + name: 008b521057d46a0f84bf73a195f7623c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a0492376b03bb08b86cf35f4b8d37ee3 + trainer: + kwargs: {} +name: 008b521057d46a0f84bf73a195f7623c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/008f1387800af7f30f2a036481cc5885/params.yaml b/examples/security/classification/output/reports/train/008f1387800af7f30f2a036481cc5885/params.yaml new file mode 100644 index 00000000..b91142f4 --- /dev/null +++ b/examples/security/classification/output/reports/train/008f1387800af7f30f2a036481cc5885/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/008f1387800af7f30f2a036481cc5885/adv_predictions.json + adv_probabilities_file: output/reports/train/008f1387800af7f30f2a036481cc5885/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/f7a9545548fed18bcae1d59b8c7e8c25.pkl + params_file: output/reports/train/008f1387800af7f30f2a036481cc5885/params.yaml + predictions_file: output/reports/train/008f1387800af7f30f2a036481cc5885/predictions.json + probabilities_file: output/reports/train/008f1387800af7f30f2a036481cc5885/probabilities.json + score_dict_file: output/reports/train/008f1387800af7f30f2a036481cc5885/score_dict.json + test_labels_file: output/reports/train/008f1387800af7f30f2a036481cc5885/test_labels.json + train_labels_file: output/reports/train/008f1387800af7f30f2a036481cc5885/train_labels.json + model_dir: models + name: 008f1387800af7f30f2a036481cc5885 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 04bef9cb8e2106bfbe64de598da03419 + trainer: + kwargs: {} +name: 008f1387800af7f30f2a036481cc5885 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/params.yaml b/examples/security/classification/output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/params.yaml new file mode 100644 index 00000000..02d9de88 --- /dev/null +++ b/examples/security/classification/output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/adv_predictions.json + adv_probabilities_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/938caa3b57ce89db67da1d9a53bb3038.pkl + params_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/params.yaml + predictions_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/predictions.json + probabilities_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/probabilities.json + score_dict_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/score_dict.json + test_labels_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/test_labels.json + train_labels_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/train_labels.json + model_dir: models + name: 00cbc264f1ebfdabe46e414e4a6e5386 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4609858b9c10e33d4085204e5a566247 + trainer: + kwargs: {} +name: 00cbc264f1ebfdabe46e414e4a6e5386 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/params.yaml b/examples/security/classification/output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/params.yaml new file mode 100644 index 00000000..a29ad21f --- /dev/null +++ b/examples/security/classification/output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/adv_predictions.json + adv_probabilities_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/48cbf9ee0ba36ec09dc6a2234e37aa2f.pkl + params_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/params.yaml + predictions_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/predictions.json + probabilities_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/probabilities.json + score_dict_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/score_dict.json + test_labels_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/test_labels.json + train_labels_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/train_labels.json + model_dir: models + name: 00e2a0bd64fd7fbef3976fb8e325524b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 650ec8be400df5380d5d9e303ff7554f + trainer: + kwargs: {} +name: 00e2a0bd64fd7fbef3976fb8e325524b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/00ed86574d5c0674be5c6d72009187a8/params.yaml b/examples/security/classification/output/reports/train/00ed86574d5c0674be5c6d72009187a8/params.yaml new file mode 100644 index 00000000..0dc1d693 --- /dev/null +++ b/examples/security/classification/output/reports/train/00ed86574d5c0674be5c6d72009187a8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/adv_predictions.json + adv_probabilities_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/e03e3030178ef221dd83d0647d20206e.pkl + params_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/params.yaml + predictions_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/predictions.json + probabilities_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/probabilities.json + score_dict_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/score_dict.json + test_labels_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/test_labels.json + train_labels_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/train_labels.json + model_dir: models + name: 00ed86574d5c0674be5c6d72009187a8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 41e60c8855ff4d4d19fce6d30109d958 + trainer: + kwargs: {} +name: 00ed86574d5c0674be5c6d72009187a8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/00fe05bd296c637755d060d6c1f728d3/params.yaml b/examples/security/classification/output/reports/train/00fe05bd296c637755d060d6c1f728d3/params.yaml new file mode 100644 index 00000000..4fa7bd8d --- /dev/null +++ b/examples/security/classification/output/reports/train/00fe05bd296c637755d060d6c1f728d3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/adv_predictions.json + adv_probabilities_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/72848ba8c94b0a2c3f3881e6ee773e07.pkl + params_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/params.yaml + predictions_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/predictions.json + probabilities_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/probabilities.json + score_dict_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/score_dict.json + test_labels_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/test_labels.json + train_labels_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/train_labels.json + model_dir: models + name: 00fe05bd296c637755d060d6c1f728d3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 60e768dacf6f146cfe083fbf3f753c83 + trainer: + kwargs: {} +name: 00fe05bd296c637755d060d6c1f728d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/01471934c0109c91667d61a37485e898/params.yaml b/examples/security/classification/output/reports/train/01471934c0109c91667d61a37485e898/params.yaml new file mode 100644 index 00000000..06142ea6 --- /dev/null +++ b/examples/security/classification/output/reports/train/01471934c0109c91667d61a37485e898/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/01471934c0109c91667d61a37485e898/adv_predictions.json + adv_probabilities_file: output/reports/train/01471934c0109c91667d61a37485e898/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/737717676d2e8a864103321840161e14.pkl + params_file: output/reports/train/01471934c0109c91667d61a37485e898/params.yaml + predictions_file: output/reports/train/01471934c0109c91667d61a37485e898/predictions.json + probabilities_file: output/reports/train/01471934c0109c91667d61a37485e898/probabilities.json + score_dict_file: output/reports/train/01471934c0109c91667d61a37485e898/score_dict.json + test_labels_file: output/reports/train/01471934c0109c91667d61a37485e898/test_labels.json + train_labels_file: output/reports/train/01471934c0109c91667d61a37485e898/train_labels.json + model_dir: models + name: 01471934c0109c91667d61a37485e898 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c8a2e002f2cbe6fc4b754e58981755b3 + trainer: + kwargs: {} +name: 01471934c0109c91667d61a37485e898 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/params.yaml b/examples/security/classification/output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/params.yaml new file mode 100644 index 00000000..9c26ca70 --- /dev/null +++ b/examples/security/classification/output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/adv_predictions.json + adv_probabilities_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/bc5b71f4c086473c00ef015a9ad097b8.pkl + params_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/params.yaml + predictions_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/predictions.json + probabilities_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/probabilities.json + score_dict_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/score_dict.json + test_labels_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/test_labels.json + train_labels_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/train_labels.json + model_dir: models + name: 0148a59aff967b5fc6421f3f7706fcb0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 212d1ced2bbf8de9a324b8d50dc753d5 + trainer: + kwargs: {} +name: 0148a59aff967b5fc6421f3f7706fcb0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0155216b09479199346ab27988db9489/params.yaml b/examples/security/classification/output/reports/train/0155216b09479199346ab27988db9489/params.yaml new file mode 100644 index 00000000..70b33543 --- /dev/null +++ b/examples/security/classification/output/reports/train/0155216b09479199346ab27988db9489/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0155216b09479199346ab27988db9489/adv_predictions.json + adv_probabilities_file: output/reports/train/0155216b09479199346ab27988db9489/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/0039d6c512d4cb62c7c94b7fedadc907.pkl + params_file: output/reports/train/0155216b09479199346ab27988db9489/params.yaml + predictions_file: output/reports/train/0155216b09479199346ab27988db9489/predictions.json + probabilities_file: output/reports/train/0155216b09479199346ab27988db9489/probabilities.json + score_dict_file: output/reports/train/0155216b09479199346ab27988db9489/score_dict.json + test_labels_file: output/reports/train/0155216b09479199346ab27988db9489/test_labels.json + train_labels_file: output/reports/train/0155216b09479199346ab27988db9489/train_labels.json + model_dir: models + name: 0155216b09479199346ab27988db9489 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dd59594e2b119b9e381687331cd8e6b7 + trainer: + kwargs: {} +name: 0155216b09479199346ab27988db9489 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/016861b2726fd64689bf970e77642548/params.yaml b/examples/security/classification/output/reports/train/016861b2726fd64689bf970e77642548/params.yaml new file mode 100644 index 00000000..ca421610 --- /dev/null +++ b/examples/security/classification/output/reports/train/016861b2726fd64689bf970e77642548/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/016861b2726fd64689bf970e77642548/adv_predictions.json + adv_probabilities_file: output/reports/train/016861b2726fd64689bf970e77642548/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/b7d38ef5dd72d10476995dd5fef247ec.pkl + params_file: output/reports/train/016861b2726fd64689bf970e77642548/params.yaml + predictions_file: output/reports/train/016861b2726fd64689bf970e77642548/predictions.json + probabilities_file: output/reports/train/016861b2726fd64689bf970e77642548/probabilities.json + score_dict_file: output/reports/train/016861b2726fd64689bf970e77642548/score_dict.json + test_labels_file: output/reports/train/016861b2726fd64689bf970e77642548/test_labels.json + train_labels_file: output/reports/train/016861b2726fd64689bf970e77642548/train_labels.json + model_dir: models + name: 016861b2726fd64689bf970e77642548 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d9dd354051953a3e2057028659b61689 + trainer: + kwargs: {} +name: 016861b2726fd64689bf970e77642548 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0181b2e981b7acacc65369ebb8f6e074/params.yaml b/examples/security/classification/output/reports/train/0181b2e981b7acacc65369ebb8f6e074/params.yaml new file mode 100644 index 00000000..f2db664b --- /dev/null +++ b/examples/security/classification/output/reports/train/0181b2e981b7acacc65369ebb8f6e074/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/adv_predictions.json + adv_probabilities_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/4a48eae37138aefbe83394c833ee1381.pkl + params_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/params.yaml + predictions_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/predictions.json + probabilities_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/probabilities.json + score_dict_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/score_dict.json + test_labels_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/test_labels.json + train_labels_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/train_labels.json + model_dir: models + name: 0181b2e981b7acacc65369ebb8f6e074 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f65ba873428a25dd0482fefb01c9ab37 + trainer: + kwargs: {} +name: 0181b2e981b7acacc65369ebb8f6e074 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/01a14cb96e201a11a44c6b38cc145175/params.yaml b/examples/security/classification/output/reports/train/01a14cb96e201a11a44c6b38cc145175/params.yaml new file mode 100644 index 00000000..f517746d --- /dev/null +++ b/examples/security/classification/output/reports/train/01a14cb96e201a11a44c6b38cc145175/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/adv_predictions.json + adv_probabilities_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/20eb94a05097e8dcfaac1da3806f30b6.pkl + params_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/params.yaml + predictions_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/predictions.json + probabilities_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/probabilities.json + score_dict_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/score_dict.json + test_labels_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/test_labels.json + train_labels_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/train_labels.json + model_dir: models + name: 01a14cb96e201a11a44c6b38cc145175 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eae8ea137c70fe37a4208ce09f82c94f + trainer: + kwargs: {} +name: 01a14cb96e201a11a44c6b38cc145175 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/01b37293d605577cb762a1a61096c10f/params.yaml b/examples/security/classification/output/reports/train/01b37293d605577cb762a1a61096c10f/params.yaml new file mode 100644 index 00000000..151f61ef --- /dev/null +++ b/examples/security/classification/output/reports/train/01b37293d605577cb762a1a61096c10f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/01b37293d605577cb762a1a61096c10f/adv_predictions.json + adv_probabilities_file: output/reports/train/01b37293d605577cb762a1a61096c10f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/df4c278f791033fb73e0f7fac93086b1.pkl + params_file: output/reports/train/01b37293d605577cb762a1a61096c10f/params.yaml + predictions_file: output/reports/train/01b37293d605577cb762a1a61096c10f/predictions.json + probabilities_file: output/reports/train/01b37293d605577cb762a1a61096c10f/probabilities.json + score_dict_file: output/reports/train/01b37293d605577cb762a1a61096c10f/score_dict.json + test_labels_file: output/reports/train/01b37293d605577cb762a1a61096c10f/test_labels.json + train_labels_file: output/reports/train/01b37293d605577cb762a1a61096c10f/train_labels.json + model_dir: models + name: 01b37293d605577cb762a1a61096c10f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d1a6db9abd7a1345633864eaa130555f + trainer: + kwargs: {} +name: 01b37293d605577cb762a1a61096c10f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/01bd4b56a941e92ea4628dee68e518e1/params.yaml b/examples/security/classification/output/reports/train/01bd4b56a941e92ea4628dee68e518e1/params.yaml new file mode 100644 index 00000000..19aeaddd --- /dev/null +++ b/examples/security/classification/output/reports/train/01bd4b56a941e92ea4628dee68e518e1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/adv_predictions.json + adv_probabilities_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/39df3c965f62c1a041bff9ebf7caf01d.pkl + params_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/params.yaml + predictions_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/predictions.json + probabilities_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/probabilities.json + score_dict_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/score_dict.json + test_labels_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/test_labels.json + train_labels_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/train_labels.json + model_dir: models + name: 01bd4b56a941e92ea4628dee68e518e1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b8f1962ccd367f146adaff4a1f0bc518 + trainer: + kwargs: {} +name: 01bd4b56a941e92ea4628dee68e518e1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/01be3ec9555691345657367f5d0c8989/params.yaml b/examples/security/classification/output/reports/train/01be3ec9555691345657367f5d0c8989/params.yaml new file mode 100644 index 00000000..b27def1d --- /dev/null +++ b/examples/security/classification/output/reports/train/01be3ec9555691345657367f5d0c8989/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/01be3ec9555691345657367f5d0c8989/adv_predictions.json + adv_probabilities_file: output/reports/train/01be3ec9555691345657367f5d0c8989/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/b8e3a99c73fae36d132d6e504e11aa98.pkl + params_file: output/reports/train/01be3ec9555691345657367f5d0c8989/params.yaml + predictions_file: output/reports/train/01be3ec9555691345657367f5d0c8989/predictions.json + probabilities_file: output/reports/train/01be3ec9555691345657367f5d0c8989/probabilities.json + score_dict_file: output/reports/train/01be3ec9555691345657367f5d0c8989/score_dict.json + test_labels_file: output/reports/train/01be3ec9555691345657367f5d0c8989/test_labels.json + train_labels_file: output/reports/train/01be3ec9555691345657367f5d0c8989/train_labels.json + model_dir: models + name: 01be3ec9555691345657367f5d0c8989 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2bcb9f370e42be373149d438cd6518d3 + trainer: + kwargs: {} +name: 01be3ec9555691345657367f5d0c8989 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/021112bafd0156a3bb03070cdb433ca0/params.yaml b/examples/security/classification/output/reports/train/021112bafd0156a3bb03070cdb433ca0/params.yaml new file mode 100644 index 00000000..e62930fc --- /dev/null +++ b/examples/security/classification/output/reports/train/021112bafd0156a3bb03070cdb433ca0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/adv_predictions.json + adv_probabilities_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/be4730abe350898f9a0c7831eb436ba2.pkl + params_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/params.yaml + predictions_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/predictions.json + probabilities_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/probabilities.json + score_dict_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/score_dict.json + test_labels_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/test_labels.json + train_labels_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/train_labels.json + model_dir: models + name: 021112bafd0156a3bb03070cdb433ca0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 75528a8bd08aef980e5324a2333badd2 + trainer: + kwargs: {} +name: 021112bafd0156a3bb03070cdb433ca0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/02201a32b9fe1dac953027105bdd01ef/params.yaml b/examples/security/classification/output/reports/train/02201a32b9fe1dac953027105bdd01ef/params.yaml new file mode 100644 index 00000000..f804261a --- /dev/null +++ b/examples/security/classification/output/reports/train/02201a32b9fe1dac953027105bdd01ef/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/adv_predictions.json + adv_probabilities_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/b1d0d763928294e1969cd163ae8bf17b.pkl + params_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/params.yaml + predictions_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/predictions.json + probabilities_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/probabilities.json + score_dict_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/score_dict.json + test_labels_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/test_labels.json + train_labels_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/train_labels.json + model_dir: models + name: 02201a32b9fe1dac953027105bdd01ef + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0feecf1215cd263350c5383e32d5e36b + trainer: + kwargs: {} +name: 02201a32b9fe1dac953027105bdd01ef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0240d3b9381b3703113c7645711007bf/params.yaml b/examples/security/classification/output/reports/train/0240d3b9381b3703113c7645711007bf/params.yaml new file mode 100644 index 00000000..9f8cb11a --- /dev/null +++ b/examples/security/classification/output/reports/train/0240d3b9381b3703113c7645711007bf/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0240d3b9381b3703113c7645711007bf/adv_predictions.json + adv_probabilities_file: output/reports/train/0240d3b9381b3703113c7645711007bf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/0ffe7b07456724bc1ae10c78fdb9efd3.pkl + params_file: output/reports/train/0240d3b9381b3703113c7645711007bf/params.yaml + predictions_file: output/reports/train/0240d3b9381b3703113c7645711007bf/predictions.json + probabilities_file: output/reports/train/0240d3b9381b3703113c7645711007bf/probabilities.json + score_dict_file: output/reports/train/0240d3b9381b3703113c7645711007bf/score_dict.json + test_labels_file: output/reports/train/0240d3b9381b3703113c7645711007bf/test_labels.json + train_labels_file: output/reports/train/0240d3b9381b3703113c7645711007bf/train_labels.json + model_dir: models + name: 0240d3b9381b3703113c7645711007bf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 53356695c6b385b1e26545bfe70cb50e + trainer: + kwargs: {} +name: 0240d3b9381b3703113c7645711007bf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/026c53739fb01e9460401f3b1946c0d6/params.yaml b/examples/security/classification/output/reports/train/026c53739fb01e9460401f3b1946c0d6/params.yaml new file mode 100644 index 00000000..ae0f6c60 --- /dev/null +++ b/examples/security/classification/output/reports/train/026c53739fb01e9460401f3b1946c0d6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/adv_predictions.json + adv_probabilities_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/f77211aad4b98d180f469fc485adf3a0.pkl + params_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/params.yaml + predictions_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/predictions.json + probabilities_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/probabilities.json + score_dict_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/score_dict.json + test_labels_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/test_labels.json + train_labels_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/train_labels.json + model_dir: models + name: 026c53739fb01e9460401f3b1946c0d6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 114c7a3ff26cf38834bb6b2f12565d9f + trainer: + kwargs: {} +name: 026c53739fb01e9460401f3b1946c0d6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/031df9824b8979901f18bdf9b8159d85/params.yaml b/examples/security/classification/output/reports/train/031df9824b8979901f18bdf9b8159d85/params.yaml new file mode 100644 index 00000000..6c00421b --- /dev/null +++ b/examples/security/classification/output/reports/train/031df9824b8979901f18bdf9b8159d85/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/adv_predictions.json + adv_probabilities_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/d893aff69b99d8a0656eabe4623d2a9b.pkl + params_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/params.yaml + predictions_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/predictions.json + probabilities_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/probabilities.json + score_dict_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/score_dict.json + test_labels_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/test_labels.json + train_labels_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/train_labels.json + model_dir: models + name: 031df9824b8979901f18bdf9b8159d85 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 276c24b9bb999be326e90b3e93f5ae27 + trainer: + kwargs: {} +name: 031df9824b8979901f18bdf9b8159d85 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0323097f0be1a89f479a736b437ecb1c/params.yaml b/examples/security/classification/output/reports/train/0323097f0be1a89f479a736b437ecb1c/params.yaml new file mode 100644 index 00000000..583c8cc7 --- /dev/null +++ b/examples/security/classification/output/reports/train/0323097f0be1a89f479a736b437ecb1c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/adv_predictions.json + adv_probabilities_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/af8bcfb8184c1a8b3c7d95fed35dd2da.pkl + params_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/params.yaml + predictions_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/predictions.json + probabilities_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/probabilities.json + score_dict_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/score_dict.json + test_labels_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/test_labels.json + train_labels_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/train_labels.json + model_dir: models + name: 0323097f0be1a89f479a736b437ecb1c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 29bf6b793eea7e667942609024e247c6 + trainer: + kwargs: {} +name: 0323097f0be1a89f479a736b437ecb1c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/params.yaml b/examples/security/classification/output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/params.yaml new file mode 100644 index 00000000..493ecd40 --- /dev/null +++ b/examples/security/classification/output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/adv_predictions.json + adv_probabilities_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/5a4c03bf9a9325b5ad1ac33b5e21af88.pkl + params_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/params.yaml + predictions_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/predictions.json + probabilities_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/probabilities.json + score_dict_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/score_dict.json + test_labels_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/test_labels.json + train_labels_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/train_labels.json + model_dir: models + name: 032f4f38d95aed3f5f46ea62dc66405a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9f040e30221f260cf9ecdbe6445a52bd + trainer: + kwargs: {} +name: 032f4f38d95aed3f5f46ea62dc66405a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0393497ffc75be7428b5645d371c7e27/params.yaml b/examples/security/classification/output/reports/train/0393497ffc75be7428b5645d371c7e27/params.yaml new file mode 100644 index 00000000..f5dae230 --- /dev/null +++ b/examples/security/classification/output/reports/train/0393497ffc75be7428b5645d371c7e27/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/adv_predictions.json + adv_probabilities_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/582b95d980d47f35c736914e9e758dfd.pkl + params_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/params.yaml + predictions_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/predictions.json + probabilities_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/probabilities.json + score_dict_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/score_dict.json + test_labels_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/test_labels.json + train_labels_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/train_labels.json + model_dir: models + name: 0393497ffc75be7428b5645d371c7e27 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3bd90c07370827fb9be49db2e973795e + trainer: + kwargs: {} +name: 0393497ffc75be7428b5645d371c7e27 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/params.yaml b/examples/security/classification/output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/params.yaml new file mode 100644 index 00000000..b39af030 --- /dev/null +++ b/examples/security/classification/output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/adv_predictions.json + adv_probabilities_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/b0a00fff0bc1c93db57025df150dca42.pkl + params_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/params.yaml + predictions_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/predictions.json + probabilities_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/probabilities.json + score_dict_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/score_dict.json + test_labels_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/test_labels.json + train_labels_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/train_labels.json + model_dir: models + name: 03c140faf2f2f80fad5f7e692c0a52cd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c27d632e7e519d0431a83585beadd47c + trainer: + kwargs: {} +name: 03c140faf2f2f80fad5f7e692c0a52cd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/params.yaml b/examples/security/classification/output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/params.yaml new file mode 100644 index 00000000..57f8b9ee --- /dev/null +++ b/examples/security/classification/output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/adv_predictions.json + adv_probabilities_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/b5c542a4bb2d00903552b010b86a69b3.pkl + params_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/params.yaml + predictions_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/predictions.json + probabilities_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/probabilities.json + score_dict_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/score_dict.json + test_labels_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/test_labels.json + train_labels_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/train_labels.json + model_dir: models + name: 03d0d6302f2054943d5e21ea3a43b8b4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3f890e8f0bf35832c6ac19c80207e138 + trainer: + kwargs: {} +name: 03d0d6302f2054943d5e21ea3a43b8b4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/params.yaml b/examples/security/classification/output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/params.yaml new file mode 100644 index 00000000..548d9694 --- /dev/null +++ b/examples/security/classification/output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/adv_predictions.json + adv_probabilities_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/ee5a712b7f6bb3c9b7bddbb3df1ab373.pkl + params_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/params.yaml + predictions_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/predictions.json + probabilities_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/probabilities.json + score_dict_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/score_dict.json + test_labels_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/test_labels.json + train_labels_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/train_labels.json + model_dir: models + name: 03e6a6818409ad68ab3f3ed95b956b3b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a8a4ff5cac4db8ae63df2cf3ec15a155 + trainer: + kwargs: {} +name: 03e6a6818409ad68ab3f3ed95b956b3b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/03f75cf135cf9823e46cd5f688577b1f/params.yaml b/examples/security/classification/output/reports/train/03f75cf135cf9823e46cd5f688577b1f/params.yaml new file mode 100644 index 00000000..4122d35a --- /dev/null +++ b/examples/security/classification/output/reports/train/03f75cf135cf9823e46cd5f688577b1f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/adv_predictions.json + adv_probabilities_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/3e1e1b2d6bff719d0fa851f3ffe6ad16.pkl + params_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/params.yaml + predictions_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/predictions.json + probabilities_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/probabilities.json + score_dict_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/score_dict.json + test_labels_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/test_labels.json + train_labels_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/train_labels.json + model_dir: models + name: 03f75cf135cf9823e46cd5f688577b1f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4608fba19d2a9477a76cf7df077fc6a5 + trainer: + kwargs: {} +name: 03f75cf135cf9823e46cd5f688577b1f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/params.yaml b/examples/security/classification/output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/params.yaml new file mode 100644 index 00000000..efa86159 --- /dev/null +++ b/examples/security/classification/output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/adv_predictions.json + adv_probabilities_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/5a616db39c5bd0fd2e3884d34717b39d.pkl + params_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/params.yaml + predictions_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/predictions.json + probabilities_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/probabilities.json + score_dict_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/score_dict.json + test_labels_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/test_labels.json + train_labels_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/train_labels.json + model_dir: models + name: 041f10bfcfc310c67dac0c6f7732bbbd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d67bffb97fea864a98bf0250f4f95127 + trainer: + kwargs: {} +name: 041f10bfcfc310c67dac0c6f7732bbbd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0460181507e6ceba946d67ce510dd727/params.yaml b/examples/security/classification/output/reports/train/0460181507e6ceba946d67ce510dd727/params.yaml new file mode 100644 index 00000000..b69ddece --- /dev/null +++ b/examples/security/classification/output/reports/train/0460181507e6ceba946d67ce510dd727/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0460181507e6ceba946d67ce510dd727/adv_predictions.json + adv_probabilities_file: output/reports/train/0460181507e6ceba946d67ce510dd727/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/89f716654010f7f9bfedf26981390785.pkl + params_file: output/reports/train/0460181507e6ceba946d67ce510dd727/params.yaml + predictions_file: output/reports/train/0460181507e6ceba946d67ce510dd727/predictions.json + probabilities_file: output/reports/train/0460181507e6ceba946d67ce510dd727/probabilities.json + score_dict_file: output/reports/train/0460181507e6ceba946d67ce510dd727/score_dict.json + test_labels_file: output/reports/train/0460181507e6ceba946d67ce510dd727/test_labels.json + train_labels_file: output/reports/train/0460181507e6ceba946d67ce510dd727/train_labels.json + model_dir: models + name: 0460181507e6ceba946d67ce510dd727 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 47ea74fea7d88f83d62f05686d98d6e1 + trainer: + kwargs: {} +name: 0460181507e6ceba946d67ce510dd727 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0477f59fcf1306e99f441bee8627816d/params.yaml b/examples/security/classification/output/reports/train/0477f59fcf1306e99f441bee8627816d/params.yaml new file mode 100644 index 00000000..02f539a7 --- /dev/null +++ b/examples/security/classification/output/reports/train/0477f59fcf1306e99f441bee8627816d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/adv_predictions.json + adv_probabilities_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/d4eedf98458a3e4ad6e7aff70f01f7ec.pkl + params_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/params.yaml + predictions_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/predictions.json + probabilities_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/probabilities.json + score_dict_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/score_dict.json + test_labels_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/test_labels.json + train_labels_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/train_labels.json + model_dir: models + name: 0477f59fcf1306e99f441bee8627816d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7cdfecef3bcef80b70cc074851fe6d1c + trainer: + kwargs: {} +name: 0477f59fcf1306e99f441bee8627816d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/047819a06e8118218f9ee6a8f7467c41/params.yaml b/examples/security/classification/output/reports/train/047819a06e8118218f9ee6a8f7467c41/params.yaml new file mode 100644 index 00000000..dfc15536 --- /dev/null +++ b/examples/security/classification/output/reports/train/047819a06e8118218f9ee6a8f7467c41/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/adv_predictions.json + adv_probabilities_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/3dabc3fc8e673125f7dd678ad38f7451.pkl + params_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/params.yaml + predictions_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/predictions.json + probabilities_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/probabilities.json + score_dict_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/score_dict.json + test_labels_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/test_labels.json + train_labels_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/train_labels.json + model_dir: models + name: 047819a06e8118218f9ee6a8f7467c41 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b4c865ac157b4b0711fa99a67272d382 + trainer: + kwargs: {} +name: 047819a06e8118218f9ee6a8f7467c41 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/047faf13591d5356bb0ef80306542a13/params.yaml b/examples/security/classification/output/reports/train/047faf13591d5356bb0ef80306542a13/params.yaml new file mode 100644 index 00000000..6459cf48 --- /dev/null +++ b/examples/security/classification/output/reports/train/047faf13591d5356bb0ef80306542a13/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/047faf13591d5356bb0ef80306542a13/adv_predictions.json + adv_probabilities_file: output/reports/train/047faf13591d5356bb0ef80306542a13/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/275b367c6bbc39d89bd36776a7fedcb5.pkl + params_file: output/reports/train/047faf13591d5356bb0ef80306542a13/params.yaml + predictions_file: output/reports/train/047faf13591d5356bb0ef80306542a13/predictions.json + probabilities_file: output/reports/train/047faf13591d5356bb0ef80306542a13/probabilities.json + score_dict_file: output/reports/train/047faf13591d5356bb0ef80306542a13/score_dict.json + test_labels_file: output/reports/train/047faf13591d5356bb0ef80306542a13/test_labels.json + train_labels_file: output/reports/train/047faf13591d5356bb0ef80306542a13/train_labels.json + model_dir: models + name: 047faf13591d5356bb0ef80306542a13 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fdd9959b9c68db7433ca614da7f3471e + trainer: + kwargs: {} +name: 047faf13591d5356bb0ef80306542a13 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/049958f7506b171feec2530a213ab1b3/params.yaml b/examples/security/classification/output/reports/train/049958f7506b171feec2530a213ab1b3/params.yaml new file mode 100644 index 00000000..f2395956 --- /dev/null +++ b/examples/security/classification/output/reports/train/049958f7506b171feec2530a213ab1b3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/049958f7506b171feec2530a213ab1b3/adv_predictions.json + adv_probabilities_file: output/reports/train/049958f7506b171feec2530a213ab1b3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/de4752eab610e9e19d24a069af024d27.pkl + params_file: output/reports/train/049958f7506b171feec2530a213ab1b3/params.yaml + predictions_file: output/reports/train/049958f7506b171feec2530a213ab1b3/predictions.json + probabilities_file: output/reports/train/049958f7506b171feec2530a213ab1b3/probabilities.json + score_dict_file: output/reports/train/049958f7506b171feec2530a213ab1b3/score_dict.json + test_labels_file: output/reports/train/049958f7506b171feec2530a213ab1b3/test_labels.json + train_labels_file: output/reports/train/049958f7506b171feec2530a213ab1b3/train_labels.json + model_dir: models + name: 049958f7506b171feec2530a213ab1b3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3bfffbca2965a0fc0e6a025a151dfe02 + trainer: + kwargs: {} +name: 049958f7506b171feec2530a213ab1b3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/04a1e4e7bbe21809db38d0e372623fce/params.yaml b/examples/security/classification/output/reports/train/04a1e4e7bbe21809db38d0e372623fce/params.yaml new file mode 100644 index 00000000..a96793e7 --- /dev/null +++ b/examples/security/classification/output/reports/train/04a1e4e7bbe21809db38d0e372623fce/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/adv_predictions.json + adv_probabilities_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/3ccb5368f36837270e365a3ebd2a1caa.pkl + params_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/params.yaml + predictions_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/predictions.json + probabilities_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/probabilities.json + score_dict_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/score_dict.json + test_labels_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/test_labels.json + train_labels_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/train_labels.json + model_dir: models + name: 04a1e4e7bbe21809db38d0e372623fce + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 093bef12529077f0ad020335c6f93d24 + trainer: + kwargs: {} +name: 04a1e4e7bbe21809db38d0e372623fce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/04a848664aa8493454d7ea565e1ad0cf/params.yaml b/examples/security/classification/output/reports/train/04a848664aa8493454d7ea565e1ad0cf/params.yaml new file mode 100644 index 00000000..c029115b --- /dev/null +++ b/examples/security/classification/output/reports/train/04a848664aa8493454d7ea565e1ad0cf/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/adv_predictions.json + adv_probabilities_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/533827f1e4903800b129f41fd9433a7d.pkl + params_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/params.yaml + predictions_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/predictions.json + probabilities_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/probabilities.json + score_dict_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/score_dict.json + test_labels_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/test_labels.json + train_labels_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/train_labels.json + model_dir: models + name: 04a848664aa8493454d7ea565e1ad0cf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4080d9008fc51b90a39d6df117e9eb7a + trainer: + kwargs: {} +name: 04a848664aa8493454d7ea565e1ad0cf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/params.yaml b/examples/security/classification/output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/params.yaml new file mode 100644 index 00000000..04822018 --- /dev/null +++ b/examples/security/classification/output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/adv_predictions.json + adv_probabilities_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/c2b3c7ef6c6a6ec5820aee6ce0bb3fb8.pkl + params_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/params.yaml + predictions_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/predictions.json + probabilities_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/probabilities.json + score_dict_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/score_dict.json + test_labels_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/test_labels.json + train_labels_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/train_labels.json + model_dir: models + name: 04e62a80b8f389ceff0d5f43ed9306ba + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e12915077f434f70a0c519f007a072cc + trainer: + kwargs: {} +name: 04e62a80b8f389ceff0d5f43ed9306ba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/params.yaml b/examples/security/classification/output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/params.yaml new file mode 100644 index 00000000..69ce352f --- /dev/null +++ b/examples/security/classification/output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/adv_predictions.json + adv_probabilities_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/f78ae74584efee26dbe71cee4daa7a07.pkl + params_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/params.yaml + predictions_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/predictions.json + probabilities_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/probabilities.json + score_dict_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/score_dict.json + test_labels_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/test_labels.json + train_labels_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/train_labels.json + model_dir: models + name: 04f3e3cadb943916fa155b42fa84c1ed + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b132baee274945bc59b4987713764bf6 + trainer: + kwargs: {} +name: 04f3e3cadb943916fa155b42fa84c1ed +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/051d7b953c51025aa631b63e71156053/params.yaml b/examples/security/classification/output/reports/train/051d7b953c51025aa631b63e71156053/params.yaml new file mode 100644 index 00000000..6c761020 --- /dev/null +++ b/examples/security/classification/output/reports/train/051d7b953c51025aa631b63e71156053/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/051d7b953c51025aa631b63e71156053/adv_predictions.json + adv_probabilities_file: output/reports/train/051d7b953c51025aa631b63e71156053/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/4e4d36dc0cdad9a420cf80973591fa93.pkl + params_file: output/reports/train/051d7b953c51025aa631b63e71156053/params.yaml + predictions_file: output/reports/train/051d7b953c51025aa631b63e71156053/predictions.json + probabilities_file: output/reports/train/051d7b953c51025aa631b63e71156053/probabilities.json + score_dict_file: output/reports/train/051d7b953c51025aa631b63e71156053/score_dict.json + test_labels_file: output/reports/train/051d7b953c51025aa631b63e71156053/test_labels.json + train_labels_file: output/reports/train/051d7b953c51025aa631b63e71156053/train_labels.json + model_dir: models + name: 051d7b953c51025aa631b63e71156053 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2c53805eb51960727861029aa8083579 + trainer: + kwargs: {} +name: 051d7b953c51025aa631b63e71156053 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/params.yaml b/examples/security/classification/output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/params.yaml new file mode 100644 index 00000000..ec82b65b --- /dev/null +++ b/examples/security/classification/output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/adv_predictions.json + adv_probabilities_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/9c7a8eb1af32a3884121db3ce5b1c948.pkl + params_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/params.yaml + predictions_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/predictions.json + probabilities_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/probabilities.json + score_dict_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/score_dict.json + test_labels_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/test_labels.json + train_labels_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/train_labels.json + model_dir: models + name: 052579c18fc160b2f989c8bc8bd4ce46 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: df27bf01a5ca7f99c32b4f2ce712bb9c + trainer: + kwargs: {} +name: 052579c18fc160b2f989c8bc8bd4ce46 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/params.yaml b/examples/security/classification/output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/params.yaml new file mode 100644 index 00000000..3d42cb9f --- /dev/null +++ b/examples/security/classification/output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/adv_predictions.json + adv_probabilities_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/88d5262c6d651b127e09451ccfb33170.pkl + params_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/params.yaml + predictions_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/predictions.json + probabilities_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/probabilities.json + score_dict_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/score_dict.json + test_labels_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/test_labels.json + train_labels_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/train_labels.json + model_dir: models + name: 052ceb5699bc60d366f5a2eaa2b318c2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eb2fef68b3e708caf268f139e938d8a4 + trainer: + kwargs: {} +name: 052ceb5699bc60d366f5a2eaa2b318c2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/05355e9deed6b39b89c091d2fe0ed808/params.yaml b/examples/security/classification/output/reports/train/05355e9deed6b39b89c091d2fe0ed808/params.yaml new file mode 100644 index 00000000..12d7a4e2 --- /dev/null +++ b/examples/security/classification/output/reports/train/05355e9deed6b39b89c091d2fe0ed808/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/adv_predictions.json + adv_probabilities_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/b59e273ab528742d946f2c067f821db7.pkl + params_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/params.yaml + predictions_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/predictions.json + probabilities_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/probabilities.json + score_dict_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/score_dict.json + test_labels_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/test_labels.json + train_labels_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/train_labels.json + model_dir: models + name: 05355e9deed6b39b89c091d2fe0ed808 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 60fa5c2e4c81db949e8d9087f7b211e6 + trainer: + kwargs: {} +name: 05355e9deed6b39b89c091d2fe0ed808 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/05aa1cb25083b4032299377ef9751dc0/params.yaml b/examples/security/classification/output/reports/train/05aa1cb25083b4032299377ef9751dc0/params.yaml new file mode 100644 index 00000000..fbd107ff --- /dev/null +++ b/examples/security/classification/output/reports/train/05aa1cb25083b4032299377ef9751dc0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/adv_predictions.json + adv_probabilities_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/0bb16481b7669ec43848e47e8994db84.pkl + params_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/params.yaml + predictions_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/predictions.json + probabilities_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/probabilities.json + score_dict_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/score_dict.json + test_labels_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/test_labels.json + train_labels_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/train_labels.json + model_dir: models + name: 05aa1cb25083b4032299377ef9751dc0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cfffaa48f425e132d70209806d32c14a + trainer: + kwargs: {} +name: 05aa1cb25083b4032299377ef9751dc0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/05b3ceb278df68fe087212ae5e90e807/params.yaml b/examples/security/classification/output/reports/train/05b3ceb278df68fe087212ae5e90e807/params.yaml new file mode 100644 index 00000000..e2aa1318 --- /dev/null +++ b/examples/security/classification/output/reports/train/05b3ceb278df68fe087212ae5e90e807/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/adv_predictions.json + adv_probabilities_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/8e90b59c78885a696c95578f777244e2.pkl + params_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/params.yaml + predictions_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/predictions.json + probabilities_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/probabilities.json + score_dict_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/score_dict.json + test_labels_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/test_labels.json + train_labels_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/train_labels.json + model_dir: models + name: 05b3ceb278df68fe087212ae5e90e807 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 85ebb22769419b40a336057a6e70c930 + trainer: + kwargs: {} +name: 05b3ceb278df68fe087212ae5e90e807 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/05b3eb565faba25505e5cfd3540af88f/params.yaml b/examples/security/classification/output/reports/train/05b3eb565faba25505e5cfd3540af88f/params.yaml new file mode 100644 index 00000000..8c628675 --- /dev/null +++ b/examples/security/classification/output/reports/train/05b3eb565faba25505e5cfd3540af88f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/adv_predictions.json + adv_probabilities_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/30029ffce8a66c184b09c99a30058a36.pkl + params_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/params.yaml + predictions_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/predictions.json + probabilities_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/probabilities.json + score_dict_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/score_dict.json + test_labels_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/test_labels.json + train_labels_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/train_labels.json + model_dir: models + name: 05b3eb565faba25505e5cfd3540af88f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0b737774f34d0bcca110348772af3e19 + trainer: + kwargs: {} +name: 05b3eb565faba25505e5cfd3540af88f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/05dba9648a6235a5636edf43ccaddd0e/params.yaml b/examples/security/classification/output/reports/train/05dba9648a6235a5636edf43ccaddd0e/params.yaml new file mode 100644 index 00000000..3764f46e --- /dev/null +++ b/examples/security/classification/output/reports/train/05dba9648a6235a5636edf43ccaddd0e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/adv_predictions.json + adv_probabilities_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/a388a867da56ec921ec9a073df18d536.pkl + params_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/params.yaml + predictions_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/predictions.json + probabilities_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/probabilities.json + score_dict_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/score_dict.json + test_labels_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/test_labels.json + train_labels_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/train_labels.json + model_dir: models + name: 05dba9648a6235a5636edf43ccaddd0e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 79bd8a2c365caa4a8e356ff6728ea129 + trainer: + kwargs: {} +name: 05dba9648a6235a5636edf43ccaddd0e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/05faf2d4786e2e295fb724a115914bd9/params.yaml b/examples/security/classification/output/reports/train/05faf2d4786e2e295fb724a115914bd9/params.yaml new file mode 100644 index 00000000..438645d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/05faf2d4786e2e295fb724a115914bd9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/adv_predictions.json + adv_probabilities_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/de3e65d0af964b21f335c7c2718852b0.pkl + params_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/params.yaml + predictions_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/predictions.json + probabilities_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/probabilities.json + score_dict_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/score_dict.json + test_labels_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/test_labels.json + train_labels_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/train_labels.json + model_dir: models + name: 05faf2d4786e2e295fb724a115914bd9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7556c916769c015350c5eec031de1651 + trainer: + kwargs: {} +name: 05faf2d4786e2e295fb724a115914bd9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0620ab72d6a4cf3938aa437549c9d596/params.yaml b/examples/security/classification/output/reports/train/0620ab72d6a4cf3938aa437549c9d596/params.yaml new file mode 100644 index 00000000..cb85a9f9 --- /dev/null +++ b/examples/security/classification/output/reports/train/0620ab72d6a4cf3938aa437549c9d596/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/adv_predictions.json + adv_probabilities_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/4239b06e9f2bc392f531e7f520022308.pkl + params_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/params.yaml + predictions_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/predictions.json + probabilities_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/probabilities.json + score_dict_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/score_dict.json + test_labels_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/test_labels.json + train_labels_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/train_labels.json + model_dir: models + name: 0620ab72d6a4cf3938aa437549c9d596 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4931e7f4350d552ed813565c4e9be2bd + trainer: + kwargs: {} +name: 0620ab72d6a4cf3938aa437549c9d596 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/params.yaml b/examples/security/classification/output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/params.yaml new file mode 100644 index 00000000..1dcaa777 --- /dev/null +++ b/examples/security/classification/output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/adv_predictions.json + adv_probabilities_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/482d5675da43062d0cb20a6b0ed59687.pkl + params_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/params.yaml + predictions_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/predictions.json + probabilities_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/probabilities.json + score_dict_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/score_dict.json + test_labels_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/test_labels.json + train_labels_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/train_labels.json + model_dir: models + name: 0621dfbdfbeb02be1dab12e4939ba248 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fe0724ebac4195f912541399c82f60aa + trainer: + kwargs: {} +name: 0621dfbdfbeb02be1dab12e4939ba248 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/063193340a3bc4b8869e007236e37e18/params.yaml b/examples/security/classification/output/reports/train/063193340a3bc4b8869e007236e37e18/params.yaml new file mode 100644 index 00000000..5c3feccc --- /dev/null +++ b/examples/security/classification/output/reports/train/063193340a3bc4b8869e007236e37e18/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/063193340a3bc4b8869e007236e37e18/adv_predictions.json + adv_probabilities_file: output/reports/train/063193340a3bc4b8869e007236e37e18/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/8d2dea3a0a2a6c28719f9ff6f36ea9a8.pkl + params_file: output/reports/train/063193340a3bc4b8869e007236e37e18/params.yaml + predictions_file: output/reports/train/063193340a3bc4b8869e007236e37e18/predictions.json + probabilities_file: output/reports/train/063193340a3bc4b8869e007236e37e18/probabilities.json + score_dict_file: output/reports/train/063193340a3bc4b8869e007236e37e18/score_dict.json + test_labels_file: output/reports/train/063193340a3bc4b8869e007236e37e18/test_labels.json + train_labels_file: output/reports/train/063193340a3bc4b8869e007236e37e18/train_labels.json + model_dir: models + name: 063193340a3bc4b8869e007236e37e18 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ed44d59edcc1893831ff15649df37874 + trainer: + kwargs: {} +name: 063193340a3bc4b8869e007236e37e18 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/06478db734b52b527c25a208d18e173f/params.yaml b/examples/security/classification/output/reports/train/06478db734b52b527c25a208d18e173f/params.yaml new file mode 100644 index 00000000..ab833ad5 --- /dev/null +++ b/examples/security/classification/output/reports/train/06478db734b52b527c25a208d18e173f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/06478db734b52b527c25a208d18e173f/adv_predictions.json + adv_probabilities_file: output/reports/train/06478db734b52b527c25a208d18e173f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/b3c23137953069dfb4662e8160278a4a.pkl + params_file: output/reports/train/06478db734b52b527c25a208d18e173f/params.yaml + predictions_file: output/reports/train/06478db734b52b527c25a208d18e173f/predictions.json + probabilities_file: output/reports/train/06478db734b52b527c25a208d18e173f/probabilities.json + score_dict_file: output/reports/train/06478db734b52b527c25a208d18e173f/score_dict.json + test_labels_file: output/reports/train/06478db734b52b527c25a208d18e173f/test_labels.json + train_labels_file: output/reports/train/06478db734b52b527c25a208d18e173f/train_labels.json + model_dir: models + name: 06478db734b52b527c25a208d18e173f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5c7769bb08e4fa023cf3b8ff91c7ba2f + trainer: + kwargs: {} +name: 06478db734b52b527c25a208d18e173f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/065511fdb5df3608c2c388875f1d7333/params.yaml b/examples/security/classification/output/reports/train/065511fdb5df3608c2c388875f1d7333/params.yaml new file mode 100644 index 00000000..dba1329a --- /dev/null +++ b/examples/security/classification/output/reports/train/065511fdb5df3608c2c388875f1d7333/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/adv_predictions.json + adv_probabilities_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/c54d59135336e913266800cced576432.pkl + params_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/params.yaml + predictions_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/predictions.json + probabilities_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/probabilities.json + score_dict_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/score_dict.json + test_labels_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/test_labels.json + train_labels_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/train_labels.json + model_dir: models + name: 065511fdb5df3608c2c388875f1d7333 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b2df69d984d1866fcca2e24ce8ec63b6 + trainer: + kwargs: {} +name: 065511fdb5df3608c2c388875f1d7333 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/params.yaml b/examples/security/classification/output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/params.yaml new file mode 100644 index 00000000..7f100c45 --- /dev/null +++ b/examples/security/classification/output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/adv_predictions.json + adv_probabilities_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/351ddf0395aafd332a2bee60f91d1213.pkl + params_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/params.yaml + predictions_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/predictions.json + probabilities_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/probabilities.json + score_dict_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/score_dict.json + test_labels_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/test_labels.json + train_labels_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/train_labels.json + model_dir: models + name: 065731fc30bfd6f2d894dd1ea00091c6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1f08735c7fd96973ae5fa878b2f98103 + trainer: + kwargs: {} +name: 065731fc30bfd6f2d894dd1ea00091c6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/065d4aeea58ff7f4425520158554c858/params.yaml b/examples/security/classification/output/reports/train/065d4aeea58ff7f4425520158554c858/params.yaml new file mode 100644 index 00000000..ab7adb9e --- /dev/null +++ b/examples/security/classification/output/reports/train/065d4aeea58ff7f4425520158554c858/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/065d4aeea58ff7f4425520158554c858/adv_predictions.json + adv_probabilities_file: output/reports/train/065d4aeea58ff7f4425520158554c858/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/6153611f9f92137883b7ab2f7540d122.pkl + params_file: output/reports/train/065d4aeea58ff7f4425520158554c858/params.yaml + predictions_file: output/reports/train/065d4aeea58ff7f4425520158554c858/predictions.json + probabilities_file: output/reports/train/065d4aeea58ff7f4425520158554c858/probabilities.json + score_dict_file: output/reports/train/065d4aeea58ff7f4425520158554c858/score_dict.json + test_labels_file: output/reports/train/065d4aeea58ff7f4425520158554c858/test_labels.json + train_labels_file: output/reports/train/065d4aeea58ff7f4425520158554c858/train_labels.json + model_dir: models + name: 065d4aeea58ff7f4425520158554c858 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3376ca5f768651409ac859517418faa9 + trainer: + kwargs: {} +name: 065d4aeea58ff7f4425520158554c858 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/params.yaml b/examples/security/classification/output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/params.yaml new file mode 100644 index 00000000..851ee070 --- /dev/null +++ b/examples/security/classification/output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/adv_predictions.json + adv_probabilities_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/c4870e2cb0427727316b623dc9b4e674.pkl + params_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/params.yaml + predictions_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/predictions.json + probabilities_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/probabilities.json + score_dict_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/score_dict.json + test_labels_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/test_labels.json + train_labels_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/train_labels.json + model_dir: models + name: 06f2ce56e8981de8ce6e3acbad656cc8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dcb5d447796b78fc3046a7df842caf97 + trainer: + kwargs: {} +name: 06f2ce56e8981de8ce6e3acbad656cc8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/params.yaml b/examples/security/classification/output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/params.yaml new file mode 100644 index 00000000..ef1235e9 --- /dev/null +++ b/examples/security/classification/output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/adv_predictions.json + adv_probabilities_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/bf35fb29c148f62f8c2f418f6f6c5175.pkl + params_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/params.yaml + predictions_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/predictions.json + probabilities_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/probabilities.json + score_dict_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/score_dict.json + test_labels_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/test_labels.json + train_labels_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/train_labels.json + model_dir: models + name: 06f8c1d52a1a5795e86e9186ebbacddc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e80f36224038b0b3ad6b2f0e1f2d7af8 + trainer: + kwargs: {} +name: 06f8c1d52a1a5795e86e9186ebbacddc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/070bda0a997206d128055ab43ad8c5f8/params.yaml b/examples/security/classification/output/reports/train/070bda0a997206d128055ab43ad8c5f8/params.yaml new file mode 100644 index 00000000..9de3a931 --- /dev/null +++ b/examples/security/classification/output/reports/train/070bda0a997206d128055ab43ad8c5f8/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/adv_predictions.json + adv_probabilities_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/f47de12bc3ec4e68e36d5e33828ff7cb.pkl + params_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/params.yaml + predictions_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/predictions.json + probabilities_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/probabilities.json + score_dict_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/score_dict.json + test_labels_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/test_labels.json + train_labels_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/train_labels.json + model_dir: models + name: 070bda0a997206d128055ab43ad8c5f8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 220ff2e81f8cb10b8b55b0df849a1dd3 + trainer: + kwargs: {} +name: 070bda0a997206d128055ab43ad8c5f8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/071dcf00b8185db311ba3e94652c9151/params.yaml b/examples/security/classification/output/reports/train/071dcf00b8185db311ba3e94652c9151/params.yaml new file mode 100644 index 00000000..11858e7a --- /dev/null +++ b/examples/security/classification/output/reports/train/071dcf00b8185db311ba3e94652c9151/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/adv_predictions.json + adv_probabilities_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/71e6ae42dd420b0a9e0188c181089bdf.pkl + params_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/params.yaml + predictions_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/predictions.json + probabilities_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/probabilities.json + score_dict_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/score_dict.json + test_labels_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/test_labels.json + train_labels_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/train_labels.json + model_dir: models + name: 071dcf00b8185db311ba3e94652c9151 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a2192aa99a4e0bb1e8afef86ab21d71d + trainer: + kwargs: {} +name: 071dcf00b8185db311ba3e94652c9151 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/072341877f0ab788171ed528b8bfda9d/params.yaml b/examples/security/classification/output/reports/train/072341877f0ab788171ed528b8bfda9d/params.yaml new file mode 100644 index 00000000..aa716b50 --- /dev/null +++ b/examples/security/classification/output/reports/train/072341877f0ab788171ed528b8bfda9d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/adv_predictions.json + adv_probabilities_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/b9638468b479cdd22b15d8227277f9aa.pkl + params_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/params.yaml + predictions_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/predictions.json + probabilities_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/probabilities.json + score_dict_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/score_dict.json + test_labels_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/test_labels.json + train_labels_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/train_labels.json + model_dir: models + name: 072341877f0ab788171ed528b8bfda9d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 417400b9095b735ef68098c3b8ab3fd8 + trainer: + kwargs: {} +name: 072341877f0ab788171ed528b8bfda9d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/07604af93db3690a7acfdbadb2fab8a3/params.yaml b/examples/security/classification/output/reports/train/07604af93db3690a7acfdbadb2fab8a3/params.yaml new file mode 100644 index 00000000..b2039fa2 --- /dev/null +++ b/examples/security/classification/output/reports/train/07604af93db3690a7acfdbadb2fab8a3/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/adv_predictions.json + adv_probabilities_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/18f2b75a3c495bf83909b9b29b28618d.pkl + params_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/params.yaml + predictions_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/predictions.json + probabilities_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/probabilities.json + score_dict_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/score_dict.json + test_labels_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/test_labels.json + train_labels_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/train_labels.json + model_dir: models + name: 07604af93db3690a7acfdbadb2fab8a3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 01bbac10afdf0b3c636a6602f485180c + trainer: + kwargs: {} +name: 07604af93db3690a7acfdbadb2fab8a3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/076440432993b653b79e17b5522c918c/params.yaml b/examples/security/classification/output/reports/train/076440432993b653b79e17b5522c918c/params.yaml new file mode 100644 index 00000000..e22fcebc --- /dev/null +++ b/examples/security/classification/output/reports/train/076440432993b653b79e17b5522c918c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/076440432993b653b79e17b5522c918c/adv_predictions.json + adv_probabilities_file: output/reports/train/076440432993b653b79e17b5522c918c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/bb71fb0c5a2676ceb43d4784694e4d69.pkl + params_file: output/reports/train/076440432993b653b79e17b5522c918c/params.yaml + predictions_file: output/reports/train/076440432993b653b79e17b5522c918c/predictions.json + probabilities_file: output/reports/train/076440432993b653b79e17b5522c918c/probabilities.json + score_dict_file: output/reports/train/076440432993b653b79e17b5522c918c/score_dict.json + test_labels_file: output/reports/train/076440432993b653b79e17b5522c918c/test_labels.json + train_labels_file: output/reports/train/076440432993b653b79e17b5522c918c/train_labels.json + model_dir: models + name: 076440432993b653b79e17b5522c918c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ca2abe1a4c549eee02c929357e38ab08 + trainer: + kwargs: {} +name: 076440432993b653b79e17b5522c918c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/077664147e5c73f126a8b889416552b1/params.yaml b/examples/security/classification/output/reports/train/077664147e5c73f126a8b889416552b1/params.yaml new file mode 100644 index 00000000..51887c89 --- /dev/null +++ b/examples/security/classification/output/reports/train/077664147e5c73f126a8b889416552b1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/077664147e5c73f126a8b889416552b1/adv_predictions.json + adv_probabilities_file: output/reports/train/077664147e5c73f126a8b889416552b1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/2dd546dcaad15f6c1163d1324c4dcb51.pkl + params_file: output/reports/train/077664147e5c73f126a8b889416552b1/params.yaml + predictions_file: output/reports/train/077664147e5c73f126a8b889416552b1/predictions.json + probabilities_file: output/reports/train/077664147e5c73f126a8b889416552b1/probabilities.json + score_dict_file: output/reports/train/077664147e5c73f126a8b889416552b1/score_dict.json + test_labels_file: output/reports/train/077664147e5c73f126a8b889416552b1/test_labels.json + train_labels_file: output/reports/train/077664147e5c73f126a8b889416552b1/train_labels.json + model_dir: models + name: 077664147e5c73f126a8b889416552b1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d0407bf0d5fa25d6bd8a4aa591294209 + trainer: + kwargs: {} +name: 077664147e5c73f126a8b889416552b1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/078779c88602dacbda4033ac87a09dc5/params.yaml b/examples/security/classification/output/reports/train/078779c88602dacbda4033ac87a09dc5/params.yaml new file mode 100644 index 00000000..2341ae4c --- /dev/null +++ b/examples/security/classification/output/reports/train/078779c88602dacbda4033ac87a09dc5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/adv_predictions.json + adv_probabilities_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/3f9a40d7ff21c1f10dd818fffef30148.pkl + params_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/params.yaml + predictions_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/predictions.json + probabilities_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/probabilities.json + score_dict_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/score_dict.json + test_labels_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/test_labels.json + train_labels_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/train_labels.json + model_dir: models + name: 078779c88602dacbda4033ac87a09dc5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ce5b3f517b53b8c7aa7d3571b4054c33 + trainer: + kwargs: {} +name: 078779c88602dacbda4033ac87a09dc5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/07a65af5708a2bc4620c687fe6a42755/params.yaml b/examples/security/classification/output/reports/train/07a65af5708a2bc4620c687fe6a42755/params.yaml new file mode 100644 index 00000000..c3821246 --- /dev/null +++ b/examples/security/classification/output/reports/train/07a65af5708a2bc4620c687fe6a42755/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/adv_predictions.json + adv_probabilities_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/d3a467d4520f6b7481449af0576ace88.pkl + params_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/params.yaml + predictions_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/predictions.json + probabilities_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/probabilities.json + score_dict_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/score_dict.json + test_labels_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/test_labels.json + train_labels_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/train_labels.json + model_dir: models + name: 07a65af5708a2bc4620c687fe6a42755 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 548cf1336f135ec87db2749f7f30fcdf + trainer: + kwargs: {} +name: 07a65af5708a2bc4620c687fe6a42755 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/07ace1ff12f9536010330bb193223333/params.yaml b/examples/security/classification/output/reports/train/07ace1ff12f9536010330bb193223333/params.yaml new file mode 100644 index 00000000..942d73f0 --- /dev/null +++ b/examples/security/classification/output/reports/train/07ace1ff12f9536010330bb193223333/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/07ace1ff12f9536010330bb193223333/adv_predictions.json + adv_probabilities_file: output/reports/train/07ace1ff12f9536010330bb193223333/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/7acbb4a037ce4690dae33acd3bbec713.pkl + params_file: output/reports/train/07ace1ff12f9536010330bb193223333/params.yaml + predictions_file: output/reports/train/07ace1ff12f9536010330bb193223333/predictions.json + probabilities_file: output/reports/train/07ace1ff12f9536010330bb193223333/probabilities.json + score_dict_file: output/reports/train/07ace1ff12f9536010330bb193223333/score_dict.json + test_labels_file: output/reports/train/07ace1ff12f9536010330bb193223333/test_labels.json + train_labels_file: output/reports/train/07ace1ff12f9536010330bb193223333/train_labels.json + model_dir: models + name: 07ace1ff12f9536010330bb193223333 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dfc2eb397e91c6e35969d2c7c34f66d5 + trainer: + kwargs: {} +name: 07ace1ff12f9536010330bb193223333 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/params.yaml b/examples/security/classification/output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/params.yaml new file mode 100644 index 00000000..7c8379ed --- /dev/null +++ b/examples/security/classification/output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/adv_predictions.json + adv_probabilities_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/6ad727474abd42317012a664da16957a.pkl + params_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/params.yaml + predictions_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/predictions.json + probabilities_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/probabilities.json + score_dict_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/score_dict.json + test_labels_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/test_labels.json + train_labels_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/train_labels.json + model_dir: models + name: 07b498f47eb70a50b6a1361ee5dd54ec + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d35661daae835192474c59a92045313a + trainer: + kwargs: {} +name: 07b498f47eb70a50b6a1361ee5dd54ec +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/07b61d280a3c423bc97b57f75e2570b2/params.yaml b/examples/security/classification/output/reports/train/07b61d280a3c423bc97b57f75e2570b2/params.yaml new file mode 100644 index 00000000..80d20ec9 --- /dev/null +++ b/examples/security/classification/output/reports/train/07b61d280a3c423bc97b57f75e2570b2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/adv_predictions.json + adv_probabilities_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/be05b31b3ae97d7211a92b8f72dc862b.pkl + params_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/params.yaml + predictions_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/predictions.json + probabilities_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/probabilities.json + score_dict_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/score_dict.json + test_labels_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/test_labels.json + train_labels_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/train_labels.json + model_dir: models + name: 07b61d280a3c423bc97b57f75e2570b2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f1ab9be6e0d7b69fc0a5b351c809edab + trainer: + kwargs: {} +name: 07b61d280a3c423bc97b57f75e2570b2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/params.yaml b/examples/security/classification/output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/params.yaml new file mode 100644 index 00000000..dc152a83 --- /dev/null +++ b/examples/security/classification/output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/adv_predictions.json + adv_probabilities_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/71fda9607654711ffdfad031e4289418.pkl + params_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/params.yaml + predictions_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/predictions.json + probabilities_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/probabilities.json + score_dict_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/score_dict.json + test_labels_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/test_labels.json + train_labels_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/train_labels.json + model_dir: models + name: 07ed8b6e219b1ae8ea98960e666531fa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c046962576e28ad021a32a342acb43c4 + trainer: + kwargs: {} +name: 07ed8b6e219b1ae8ea98960e666531fa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/07fa5ff14bc1f5725cdebda879544932/params.yaml b/examples/security/classification/output/reports/train/07fa5ff14bc1f5725cdebda879544932/params.yaml new file mode 100644 index 00000000..716da861 --- /dev/null +++ b/examples/security/classification/output/reports/train/07fa5ff14bc1f5725cdebda879544932/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/adv_predictions.json + adv_probabilities_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/e38f4e9500e94e036b2dc5e29f1ed172.pkl + params_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/params.yaml + predictions_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/predictions.json + probabilities_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/probabilities.json + score_dict_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/score_dict.json + test_labels_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/test_labels.json + train_labels_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/train_labels.json + model_dir: models + name: 07fa5ff14bc1f5725cdebda879544932 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a32676ba665b647d451ad593d4d3b096 + trainer: + kwargs: {} +name: 07fa5ff14bc1f5725cdebda879544932 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/07fea681075d007e481eaf6c3e9942f1/params.yaml b/examples/security/classification/output/reports/train/07fea681075d007e481eaf6c3e9942f1/params.yaml new file mode 100644 index 00000000..948d43fe --- /dev/null +++ b/examples/security/classification/output/reports/train/07fea681075d007e481eaf6c3e9942f1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/adv_predictions.json + adv_probabilities_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/a28f12e6e5f3f2f65b7d50859eeb0e37.pkl + params_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/params.yaml + predictions_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/predictions.json + probabilities_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/probabilities.json + score_dict_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/score_dict.json + test_labels_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/test_labels.json + train_labels_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/train_labels.json + model_dir: models + name: 07fea681075d007e481eaf6c3e9942f1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9d9c769e3b91fffbcff2572e00c71482 + trainer: + kwargs: {} +name: 07fea681075d007e481eaf6c3e9942f1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/08014a44ce06788947f6b5412256b2c6/params.yaml b/examples/security/classification/output/reports/train/08014a44ce06788947f6b5412256b2c6/params.yaml new file mode 100644 index 00000000..2cf870d4 --- /dev/null +++ b/examples/security/classification/output/reports/train/08014a44ce06788947f6b5412256b2c6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/adv_predictions.json + adv_probabilities_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/91bbb469aab625a51312329f5d8b4530.pkl + params_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/params.yaml + predictions_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/predictions.json + probabilities_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/probabilities.json + score_dict_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/score_dict.json + test_labels_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/test_labels.json + train_labels_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/train_labels.json + model_dir: models + name: 08014a44ce06788947f6b5412256b2c6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 37defb03f5c81e7c754c0690547b3e60 + trainer: + kwargs: {} +name: 08014a44ce06788947f6b5412256b2c6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/08110612fd27c41fb306e567774016a9/params.yaml b/examples/security/classification/output/reports/train/08110612fd27c41fb306e567774016a9/params.yaml new file mode 100644 index 00000000..fd882f3c --- /dev/null +++ b/examples/security/classification/output/reports/train/08110612fd27c41fb306e567774016a9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/08110612fd27c41fb306e567774016a9/adv_predictions.json + adv_probabilities_file: output/reports/train/08110612fd27c41fb306e567774016a9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/9620d00cd060c401500118f1a15a491b.pkl + params_file: output/reports/train/08110612fd27c41fb306e567774016a9/params.yaml + predictions_file: output/reports/train/08110612fd27c41fb306e567774016a9/predictions.json + probabilities_file: output/reports/train/08110612fd27c41fb306e567774016a9/probabilities.json + score_dict_file: output/reports/train/08110612fd27c41fb306e567774016a9/score_dict.json + test_labels_file: output/reports/train/08110612fd27c41fb306e567774016a9/test_labels.json + train_labels_file: output/reports/train/08110612fd27c41fb306e567774016a9/train_labels.json + model_dir: models + name: 08110612fd27c41fb306e567774016a9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 75549469b405784b79725701acce1a9e + trainer: + kwargs: {} +name: 08110612fd27c41fb306e567774016a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/083f639490166ed37e53c55111d80037/params.yaml b/examples/security/classification/output/reports/train/083f639490166ed37e53c55111d80037/params.yaml new file mode 100644 index 00000000..d7f8c0bd --- /dev/null +++ b/examples/security/classification/output/reports/train/083f639490166ed37e53c55111d80037/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/083f639490166ed37e53c55111d80037/adv_predictions.json + adv_probabilities_file: output/reports/train/083f639490166ed37e53c55111d80037/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/edd638b67bbbc98ac2ccd68c6f72f0a9.pkl + params_file: output/reports/train/083f639490166ed37e53c55111d80037/params.yaml + predictions_file: output/reports/train/083f639490166ed37e53c55111d80037/predictions.json + probabilities_file: output/reports/train/083f639490166ed37e53c55111d80037/probabilities.json + score_dict_file: output/reports/train/083f639490166ed37e53c55111d80037/score_dict.json + test_labels_file: output/reports/train/083f639490166ed37e53c55111d80037/test_labels.json + train_labels_file: output/reports/train/083f639490166ed37e53c55111d80037/train_labels.json + model_dir: models + name: 083f639490166ed37e53c55111d80037 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eb8cc8abe0b579589a9280ef084cb6b2 + trainer: + kwargs: {} +name: 083f639490166ed37e53c55111d80037 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/083fb0126b1f373ca7fdf49ed205c869/params.yaml b/examples/security/classification/output/reports/train/083fb0126b1f373ca7fdf49ed205c869/params.yaml new file mode 100644 index 00000000..b1dd80db --- /dev/null +++ b/examples/security/classification/output/reports/train/083fb0126b1f373ca7fdf49ed205c869/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/adv_predictions.json + adv_probabilities_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/879836a8bfd7504b9d0b75307a825b5e.pkl + params_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/params.yaml + predictions_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/predictions.json + probabilities_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/probabilities.json + score_dict_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/score_dict.json + test_labels_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/test_labels.json + train_labels_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/train_labels.json + model_dir: models + name: 083fb0126b1f373ca7fdf49ed205c869 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 399e641f05c0534e8135e90e8cbdb928 + trainer: + kwargs: {} +name: 083fb0126b1f373ca7fdf49ed205c869 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0842d77ea3dc797f305e84654dd2728e/params.yaml b/examples/security/classification/output/reports/train/0842d77ea3dc797f305e84654dd2728e/params.yaml new file mode 100644 index 00000000..89292277 --- /dev/null +++ b/examples/security/classification/output/reports/train/0842d77ea3dc797f305e84654dd2728e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/adv_predictions.json + adv_probabilities_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/e7d4cea33dcc0d8476d2d8faa1fd5198.pkl + params_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/params.yaml + predictions_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/predictions.json + probabilities_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/probabilities.json + score_dict_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/score_dict.json + test_labels_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/test_labels.json + train_labels_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/train_labels.json + model_dir: models + name: 0842d77ea3dc797f305e84654dd2728e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5c9c6e52d453ebb2e325389da4d40f8a + trainer: + kwargs: {} +name: 0842d77ea3dc797f305e84654dd2728e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/084d4beebdaf4302b14dbd94000189b5/params.yaml b/examples/security/classification/output/reports/train/084d4beebdaf4302b14dbd94000189b5/params.yaml new file mode 100644 index 00000000..e3e00b9c --- /dev/null +++ b/examples/security/classification/output/reports/train/084d4beebdaf4302b14dbd94000189b5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/adv_predictions.json + adv_probabilities_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/17b2a38926dd6cb57171f4d2fa598d50.pkl + params_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/params.yaml + predictions_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/predictions.json + probabilities_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/probabilities.json + score_dict_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/score_dict.json + test_labels_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/test_labels.json + train_labels_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/train_labels.json + model_dir: models + name: 084d4beebdaf4302b14dbd94000189b5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 69487662be8955559bcf70be16e5fdf0 + trainer: + kwargs: {} +name: 084d4beebdaf4302b14dbd94000189b5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0854ebb3208784b4dedf370b6041acc6/params.yaml b/examples/security/classification/output/reports/train/0854ebb3208784b4dedf370b6041acc6/params.yaml new file mode 100644 index 00000000..4b8f9ad2 --- /dev/null +++ b/examples/security/classification/output/reports/train/0854ebb3208784b4dedf370b6041acc6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/adv_predictions.json + adv_probabilities_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/8ecc0669ab0d167cce256a4cb1a91fde.pkl + params_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/params.yaml + predictions_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/predictions.json + probabilities_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/probabilities.json + score_dict_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/score_dict.json + test_labels_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/test_labels.json + train_labels_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/train_labels.json + model_dir: models + name: 0854ebb3208784b4dedf370b6041acc6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 171a69bf26cc399fd653b6900191464f + trainer: + kwargs: {} +name: 0854ebb3208784b4dedf370b6041acc6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/085854032908dc2eacffbdd920e7d0e0/params.yaml b/examples/security/classification/output/reports/train/085854032908dc2eacffbdd920e7d0e0/params.yaml new file mode 100644 index 00000000..fa3a5f22 --- /dev/null +++ b/examples/security/classification/output/reports/train/085854032908dc2eacffbdd920e7d0e0/params.yaml @@ -0,0 +1,212 @@ +attack: + attack_size: 10 + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 10.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6bd56a46ece28ceefd80f97967e041fd + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e82c16ae1bb5e334fc90c4434c4fb4f6 + trainer: + kwargs: {} + name: 170cbbf53b5289592070bb89ee6791d4 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/adv_predictions.json + adv_probabilities_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/adv_probabilities.json + attack_file: output/attacks/c85c6fc5ed8cde9f23606bfe9be8333b.pkl + data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl + model_file: output/models/63abcb7db4605ee026da8c8cbd8cec27.pkl + params_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/params.yaml + predictions_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/predictions.json + probabilities_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/probabilities.json + score_dict_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/score_dict.json + test_labels_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/test_labels.json + train_labels_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/train_labels.json + model_dir: models + name: 085854032908dc2eacffbdd920e7d0e0 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 84ea0798f74e7e66ca2f2140fe1653f6 + trainer: + kwargs: {} +name: 085854032908dc2eacffbdd920e7d0e0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/085ab58aee8cc449fc2371f050ce9d72/params.yaml b/examples/security/classification/output/reports/train/085ab58aee8cc449fc2371f050ce9d72/params.yaml new file mode 100644 index 00000000..1edc63c6 --- /dev/null +++ b/examples/security/classification/output/reports/train/085ab58aee8cc449fc2371f050ce9d72/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/adv_predictions.json + adv_probabilities_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/ee64a2c87683436f1ec711ed8dca9b9a.pkl + params_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/params.yaml + predictions_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/predictions.json + probabilities_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/probabilities.json + score_dict_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/score_dict.json + test_labels_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/test_labels.json + train_labels_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/train_labels.json + model_dir: models + name: 085ab58aee8cc449fc2371f050ce9d72 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 732cea3d76ccc44908543ca4c97e1435 + trainer: + kwargs: {} +name: 085ab58aee8cc449fc2371f050ce9d72 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/params.yaml b/examples/security/classification/output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/params.yaml new file mode 100644 index 00000000..61ee2c85 --- /dev/null +++ b/examples/security/classification/output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/adv_predictions.json + adv_probabilities_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/d7c04244bec2bfc716024eb8abba66a4.pkl + params_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/params.yaml + predictions_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/predictions.json + probabilities_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/probabilities.json + score_dict_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/score_dict.json + test_labels_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/test_labels.json + train_labels_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/train_labels.json + model_dir: models + name: 087ddedfb88f2b19d4f5afcb147ce9db + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 76caa6ec10d76007e7f5fdf4b596035e + trainer: + kwargs: {} +name: 087ddedfb88f2b19d4f5afcb147ce9db +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0886ff81f71f1abb574fa325b213d257/params.yaml b/examples/security/classification/output/reports/train/0886ff81f71f1abb574fa325b213d257/params.yaml new file mode 100644 index 00000000..c47f66d3 --- /dev/null +++ b/examples/security/classification/output/reports/train/0886ff81f71f1abb574fa325b213d257/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/adv_predictions.json + adv_probabilities_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/b616c9e5a9b6141a360082fbc6946dfc.pkl + params_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/params.yaml + predictions_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/predictions.json + probabilities_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/probabilities.json + score_dict_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/score_dict.json + test_labels_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/test_labels.json + train_labels_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/train_labels.json + model_dir: models + name: 0886ff81f71f1abb574fa325b213d257 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 54eda3d494c992528c52ac471b957113 + trainer: + kwargs: {} +name: 0886ff81f71f1abb574fa325b213d257 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/08b32344810163dd917651045576a6c8/params.yaml b/examples/security/classification/output/reports/train/08b32344810163dd917651045576a6c8/params.yaml new file mode 100644 index 00000000..8c021a14 --- /dev/null +++ b/examples/security/classification/output/reports/train/08b32344810163dd917651045576a6c8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/08b32344810163dd917651045576a6c8/adv_predictions.json + adv_probabilities_file: output/reports/train/08b32344810163dd917651045576a6c8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/93362ee1b9d55333a6871a2d597adc3a.pkl + params_file: output/reports/train/08b32344810163dd917651045576a6c8/params.yaml + predictions_file: output/reports/train/08b32344810163dd917651045576a6c8/predictions.json + probabilities_file: output/reports/train/08b32344810163dd917651045576a6c8/probabilities.json + score_dict_file: output/reports/train/08b32344810163dd917651045576a6c8/score_dict.json + test_labels_file: output/reports/train/08b32344810163dd917651045576a6c8/test_labels.json + train_labels_file: output/reports/train/08b32344810163dd917651045576a6c8/train_labels.json + model_dir: models + name: 08b32344810163dd917651045576a6c8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ee9aa74a503c8f1943e9d26dc8012f26 + trainer: + kwargs: {} +name: 08b32344810163dd917651045576a6c8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/params.yaml b/examples/security/classification/output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/params.yaml new file mode 100644 index 00000000..d79b8306 --- /dev/null +++ b/examples/security/classification/output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/adv_predictions.json + adv_probabilities_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/1d43cb97d58b9ecd5d9adc8913e36628.pkl + params_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/params.yaml + predictions_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/predictions.json + probabilities_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/probabilities.json + score_dict_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/score_dict.json + test_labels_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/test_labels.json + train_labels_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/train_labels.json + model_dir: models + name: 08ec835d594dcc0be01fd8f96ce20aec + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1b97ea29a6dfcac05781db345b23ebaa + trainer: + kwargs: {} +name: 08ec835d594dcc0be01fd8f96ce20aec +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/08f77eeb41da360916ecb8625c7e7895/params.yaml b/examples/security/classification/output/reports/train/08f77eeb41da360916ecb8625c7e7895/params.yaml new file mode 100644 index 00000000..cda77513 --- /dev/null +++ b/examples/security/classification/output/reports/train/08f77eeb41da360916ecb8625c7e7895/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/adv_predictions.json + adv_probabilities_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/1bd4e9497ecc38cd041860758478528d.pkl + params_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/params.yaml + predictions_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/predictions.json + probabilities_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/probabilities.json + score_dict_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/score_dict.json + test_labels_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/test_labels.json + train_labels_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/train_labels.json + model_dir: models + name: 08f77eeb41da360916ecb8625c7e7895 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ff93cfffe57fd98fb37cf07056347ea8 + trainer: + kwargs: {} +name: 08f77eeb41da360916ecb8625c7e7895 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0919742d7c92f35471b55f8911c0fc0e/params.yaml b/examples/security/classification/output/reports/train/0919742d7c92f35471b55f8911c0fc0e/params.yaml new file mode 100644 index 00000000..702fb19d --- /dev/null +++ b/examples/security/classification/output/reports/train/0919742d7c92f35471b55f8911c0fc0e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/adv_predictions.json + adv_probabilities_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/3f967968730a28826694c0bb6d329979.pkl + params_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/params.yaml + predictions_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/predictions.json + probabilities_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/probabilities.json + score_dict_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/score_dict.json + test_labels_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/test_labels.json + train_labels_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/train_labels.json + model_dir: models + name: 0919742d7c92f35471b55f8911c0fc0e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3161f5e54c3a52461d53235b3f14a4a4 + trainer: + kwargs: {} +name: 0919742d7c92f35471b55f8911c0fc0e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/091bb98634c741162fbafc0765d079f4/params.yaml b/examples/security/classification/output/reports/train/091bb98634c741162fbafc0765d079f4/params.yaml new file mode 100644 index 00000000..eaef3482 --- /dev/null +++ b/examples/security/classification/output/reports/train/091bb98634c741162fbafc0765d079f4/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/091bb98634c741162fbafc0765d079f4/adv_predictions.json + adv_probabilities_file: output/reports/train/091bb98634c741162fbafc0765d079f4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/dbb795859836bc04925cde441ec672da.pkl + params_file: output/reports/train/091bb98634c741162fbafc0765d079f4/params.yaml + predictions_file: output/reports/train/091bb98634c741162fbafc0765d079f4/predictions.json + probabilities_file: output/reports/train/091bb98634c741162fbafc0765d079f4/probabilities.json + score_dict_file: output/reports/train/091bb98634c741162fbafc0765d079f4/score_dict.json + test_labels_file: output/reports/train/091bb98634c741162fbafc0765d079f4/test_labels.json + train_labels_file: output/reports/train/091bb98634c741162fbafc0765d079f4/train_labels.json + model_dir: models + name: 091bb98634c741162fbafc0765d079f4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5691780d9c02f826c873b287ad485bd7 + trainer: + kwargs: {} +name: 091bb98634c741162fbafc0765d079f4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/params.yaml b/examples/security/classification/output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/params.yaml new file mode 100644 index 00000000..ab512115 --- /dev/null +++ b/examples/security/classification/output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/adv_predictions.json + adv_probabilities_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/5840a5b77474ddd5f8ef9a7b5be226a2.pkl + params_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/params.yaml + predictions_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/predictions.json + probabilities_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/probabilities.json + score_dict_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/score_dict.json + test_labels_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/test_labels.json + train_labels_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/train_labels.json + model_dir: models + name: 093a05bb4ce7518cf247d8e8c14619ce + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: efe9a5f8c205b993b1f3eb1868fe29f8 + trainer: + kwargs: {} +name: 093a05bb4ce7518cf247d8e8c14619ce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0947a7182a239fca0cd2c459fad9856f/params.yaml b/examples/security/classification/output/reports/train/0947a7182a239fca0cd2c459fad9856f/params.yaml new file mode 100644 index 00000000..754520d4 --- /dev/null +++ b/examples/security/classification/output/reports/train/0947a7182a239fca0cd2c459fad9856f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/adv_predictions.json + adv_probabilities_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/06adf4fc44408a0ab95b9d6ef71b8177.pkl + params_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/params.yaml + predictions_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/predictions.json + probabilities_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/probabilities.json + score_dict_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/score_dict.json + test_labels_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/test_labels.json + train_labels_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/train_labels.json + model_dir: models + name: 0947a7182a239fca0cd2c459fad9856f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b6f37722de458b364fd072f29e4b7c51 + trainer: + kwargs: {} +name: 0947a7182a239fca0cd2c459fad9856f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/params.yaml b/examples/security/classification/output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/params.yaml new file mode 100644 index 00000000..79f3fe82 --- /dev/null +++ b/examples/security/classification/output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/adv_predictions.json + adv_probabilities_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/e949874212e636fbad2e3a9de1375b9e.pkl + params_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/params.yaml + predictions_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/predictions.json + probabilities_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/probabilities.json + score_dict_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/score_dict.json + test_labels_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/test_labels.json + train_labels_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/train_labels.json + model_dir: models + name: 096b5e37cd5ff5d8b5027e23b00d9eec + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2604fb3cafdbc5317b77dea00f2d1c5c + trainer: + kwargs: {} +name: 096b5e37cd5ff5d8b5027e23b00d9eec +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/096e58ee22745e7daf06474d1d7cfae3/params.yaml b/examples/security/classification/output/reports/train/096e58ee22745e7daf06474d1d7cfae3/params.yaml new file mode 100644 index 00000000..e670e8cf --- /dev/null +++ b/examples/security/classification/output/reports/train/096e58ee22745e7daf06474d1d7cfae3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/adv_predictions.json + adv_probabilities_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/21e517ce30c65333da35f8173ef343f8.pkl + params_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/params.yaml + predictions_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/predictions.json + probabilities_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/probabilities.json + score_dict_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/score_dict.json + test_labels_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/test_labels.json + train_labels_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/train_labels.json + model_dir: models + name: 096e58ee22745e7daf06474d1d7cfae3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c7652445822689439e0f7a9946c13571 + trainer: + kwargs: {} +name: 096e58ee22745e7daf06474d1d7cfae3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/09cc402be03919bddc12fb77adad24f1/params.yaml b/examples/security/classification/output/reports/train/09cc402be03919bddc12fb77adad24f1/params.yaml new file mode 100644 index 00000000..1b8ffc92 --- /dev/null +++ b/examples/security/classification/output/reports/train/09cc402be03919bddc12fb77adad24f1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/adv_predictions.json + adv_probabilities_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/9062424fd8a207655eb060ac8bca50ef.pkl + params_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/params.yaml + predictions_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/predictions.json + probabilities_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/probabilities.json + score_dict_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/score_dict.json + test_labels_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/test_labels.json + train_labels_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/train_labels.json + model_dir: models + name: 09cc402be03919bddc12fb77adad24f1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fd6064511fa76af00bcf35fcd3df94f7 + trainer: + kwargs: {} +name: 09cc402be03919bddc12fb77adad24f1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/params.yaml b/examples/security/classification/output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/params.yaml new file mode 100644 index 00000000..914e7b19 --- /dev/null +++ b/examples/security/classification/output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/params.yaml @@ -0,0 +1,197 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0fb6062c51cb64aca2a63524ab4637cc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 905d11203fc8a3d26d729322bee9a795 + trainer: + kwargs: {} + name: 216db53eb0cad0928debfeef94e933bb +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/adv_predictions.json + adv_probabilities_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/adv_probabilities.json + attack_file: output/attacks/9f0dd8aaacf0385dfa29fa6e360ce27e.pkl + data_file: output/data/f34e0bf4a34ceef1b8d68462b86685e1.pkl + model_file: output/models/9c2940e6a514fb9de16018be07c76754.pkl + params_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/params.yaml + predictions_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/predictions.json + probabilities_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/probabilities.json + score_dict_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/score_dict.json + test_labels_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/test_labels.json + train_labels_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/train_labels.json + model_dir: models + name: 0a1b224f20a18b9e018aaa5eb61ab9a4 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 31307ed00c1b52640db29f72b1b97167 + trainer: + kwargs: {} +name: 0a1b224f20a18b9e018aaa5eb61ab9a4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/params.yaml b/examples/security/classification/output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/params.yaml new file mode 100644 index 00000000..0a3c20bc --- /dev/null +++ b/examples/security/classification/output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/adv_predictions.json + adv_probabilities_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/7aadc948fab68539409800f0b0257df2.pkl + params_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/params.yaml + predictions_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/predictions.json + probabilities_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/probabilities.json + score_dict_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/score_dict.json + test_labels_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/test_labels.json + train_labels_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/train_labels.json + model_dir: models + name: 0a44e058b2fabf5fad8b2636f1ccfcde + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a893072df276a0bfcbcdc1842f681487 + trainer: + kwargs: {} +name: 0a44e058b2fabf5fad8b2636f1ccfcde +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0a7131aaaa4a858247d64b830e288ddd/params.yaml b/examples/security/classification/output/reports/train/0a7131aaaa4a858247d64b830e288ddd/params.yaml new file mode 100644 index 00000000..0ee61843 --- /dev/null +++ b/examples/security/classification/output/reports/train/0a7131aaaa4a858247d64b830e288ddd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/adv_predictions.json + adv_probabilities_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/000764cb05ab06d75204a688e74793a4.pkl + params_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/params.yaml + predictions_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/predictions.json + probabilities_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/probabilities.json + score_dict_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/score_dict.json + test_labels_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/test_labels.json + train_labels_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/train_labels.json + model_dir: models + name: 0a7131aaaa4a858247d64b830e288ddd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e069cc23542169a068ddc9b8ea97fcb2 + trainer: + kwargs: {} +name: 0a7131aaaa4a858247d64b830e288ddd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/params.yaml b/examples/security/classification/output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/params.yaml new file mode 100644 index 00000000..d5ad70ea --- /dev/null +++ b/examples/security/classification/output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/adv_predictions.json + adv_probabilities_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/8553efcd357407f28d7af66cd328f762.pkl + params_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/params.yaml + predictions_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/predictions.json + probabilities_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/probabilities.json + score_dict_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/score_dict.json + test_labels_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/test_labels.json + train_labels_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/train_labels.json + model_dir: models + name: 0ac29c7012a3a5daa9b556e34773aef7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ed67a5c2b6b1fc2adcaf1077cc501845 + trainer: + kwargs: {} +name: 0ac29c7012a3a5daa9b556e34773aef7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0ae20830b42d77fbee347f78babb49aa/params.yaml b/examples/security/classification/output/reports/train/0ae20830b42d77fbee347f78babb49aa/params.yaml new file mode 100644 index 00000000..91fcf97b --- /dev/null +++ b/examples/security/classification/output/reports/train/0ae20830b42d77fbee347f78babb49aa/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/adv_predictions.json + adv_probabilities_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/f7dc2d22eb53248e18b6f704a887c3e9.pkl + params_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/params.yaml + predictions_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/predictions.json + probabilities_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/probabilities.json + score_dict_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/score_dict.json + test_labels_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/test_labels.json + train_labels_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/train_labels.json + model_dir: models + name: 0ae20830b42d77fbee347f78babb49aa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1c739c1b0f12d33b1973ff5c83e9b328 + trainer: + kwargs: {} +name: 0ae20830b42d77fbee347f78babb49aa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0aea350411f67f3a70931b1e640eeb9d/params.yaml b/examples/security/classification/output/reports/train/0aea350411f67f3a70931b1e640eeb9d/params.yaml new file mode 100644 index 00000000..ad818f0d --- /dev/null +++ b/examples/security/classification/output/reports/train/0aea350411f67f3a70931b1e640eeb9d/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/adv_predictions.json + adv_probabilities_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/391252c6ea47e9abe6d860cf248d6839.pkl + params_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/params.yaml + predictions_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/predictions.json + probabilities_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/probabilities.json + score_dict_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/score_dict.json + test_labels_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/test_labels.json + train_labels_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/train_labels.json + model_dir: models + name: 0aea350411f67f3a70931b1e640eeb9d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: acd0be5fe8036b86ff0eb338aa97b961 + trainer: + kwargs: {} +name: 0aea350411f67f3a70931b1e640eeb9d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0af2ec7717f053b3e38343d64f90d49d/params.yaml b/examples/security/classification/output/reports/train/0af2ec7717f053b3e38343d64f90d49d/params.yaml new file mode 100644 index 00000000..ced0b11b --- /dev/null +++ b/examples/security/classification/output/reports/train/0af2ec7717f053b3e38343d64f90d49d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/adv_predictions.json + adv_probabilities_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/36ac21e26acc298b952f7c83e67bff6a.pkl + params_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/params.yaml + predictions_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/predictions.json + probabilities_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/probabilities.json + score_dict_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/score_dict.json + test_labels_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/test_labels.json + train_labels_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/train_labels.json + model_dir: models + name: 0af2ec7717f053b3e38343d64f90d49d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aa782a11c13b3f125431703961864d8d + trainer: + kwargs: {} +name: 0af2ec7717f053b3e38343d64f90d49d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0b530a25e0e56937b372b2b98fff33fa/params.yaml b/examples/security/classification/output/reports/train/0b530a25e0e56937b372b2b98fff33fa/params.yaml new file mode 100644 index 00000000..75e6482d --- /dev/null +++ b/examples/security/classification/output/reports/train/0b530a25e0e56937b372b2b98fff33fa/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/adv_predictions.json + adv_probabilities_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/2ffe1488d8d3363c4a8b078b1669be3a.pkl + params_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/params.yaml + predictions_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/predictions.json + probabilities_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/probabilities.json + score_dict_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/score_dict.json + test_labels_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/test_labels.json + train_labels_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/train_labels.json + model_dir: models + name: 0b530a25e0e56937b372b2b98fff33fa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 755de1b257acd5b3ab16280283921762 + trainer: + kwargs: {} +name: 0b530a25e0e56937b372b2b98fff33fa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0b5758be778f032989bccadc9abac950/params.yaml b/examples/security/classification/output/reports/train/0b5758be778f032989bccadc9abac950/params.yaml new file mode 100644 index 00000000..6a00073e --- /dev/null +++ b/examples/security/classification/output/reports/train/0b5758be778f032989bccadc9abac950/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0b5758be778f032989bccadc9abac950/adv_predictions.json + adv_probabilities_file: output/reports/train/0b5758be778f032989bccadc9abac950/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/4d8f0a1cef2a094485bb3c88ebe79a68.pkl + params_file: output/reports/train/0b5758be778f032989bccadc9abac950/params.yaml + predictions_file: output/reports/train/0b5758be778f032989bccadc9abac950/predictions.json + probabilities_file: output/reports/train/0b5758be778f032989bccadc9abac950/probabilities.json + score_dict_file: output/reports/train/0b5758be778f032989bccadc9abac950/score_dict.json + test_labels_file: output/reports/train/0b5758be778f032989bccadc9abac950/test_labels.json + train_labels_file: output/reports/train/0b5758be778f032989bccadc9abac950/train_labels.json + model_dir: models + name: 0b5758be778f032989bccadc9abac950 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3caa82f8dbb76d570630325e0a3c1c51 + trainer: + kwargs: {} +name: 0b5758be778f032989bccadc9abac950 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0b6497f5dfed6229cddf97354471167c/params.yaml b/examples/security/classification/output/reports/train/0b6497f5dfed6229cddf97354471167c/params.yaml new file mode 100644 index 00000000..dd43ff8a --- /dev/null +++ b/examples/security/classification/output/reports/train/0b6497f5dfed6229cddf97354471167c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/adv_predictions.json + adv_probabilities_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/4786f65e345be645f6b8c04dcaa86257.pkl + params_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/params.yaml + predictions_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/predictions.json + probabilities_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/probabilities.json + score_dict_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/score_dict.json + test_labels_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/test_labels.json + train_labels_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/train_labels.json + model_dir: models + name: 0b6497f5dfed6229cddf97354471167c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 356e0759dfd492606a81a652adc9fddf + trainer: + kwargs: {} +name: 0b6497f5dfed6229cddf97354471167c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0b664b81a7779144482e86c041abab3e/params.yaml b/examples/security/classification/output/reports/train/0b664b81a7779144482e86c041abab3e/params.yaml new file mode 100644 index 00000000..569886c5 --- /dev/null +++ b/examples/security/classification/output/reports/train/0b664b81a7779144482e86c041abab3e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0b664b81a7779144482e86c041abab3e/adv_predictions.json + adv_probabilities_file: output/reports/train/0b664b81a7779144482e86c041abab3e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/8a5f5827eb1527b452a39ec66bd0f96e.pkl + params_file: output/reports/train/0b664b81a7779144482e86c041abab3e/params.yaml + predictions_file: output/reports/train/0b664b81a7779144482e86c041abab3e/predictions.json + probabilities_file: output/reports/train/0b664b81a7779144482e86c041abab3e/probabilities.json + score_dict_file: output/reports/train/0b664b81a7779144482e86c041abab3e/score_dict.json + test_labels_file: output/reports/train/0b664b81a7779144482e86c041abab3e/test_labels.json + train_labels_file: output/reports/train/0b664b81a7779144482e86c041abab3e/train_labels.json + model_dir: models + name: 0b664b81a7779144482e86c041abab3e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e560f64b9626b6ecbd08ae38b2871e88 + trainer: + kwargs: {} +name: 0b664b81a7779144482e86c041abab3e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0b7c164c5b4352f671ff99988820254e/params.yaml b/examples/security/classification/output/reports/train/0b7c164c5b4352f671ff99988820254e/params.yaml new file mode 100644 index 00000000..1f259a52 --- /dev/null +++ b/examples/security/classification/output/reports/train/0b7c164c5b4352f671ff99988820254e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/adv_predictions.json + adv_probabilities_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/e3bdbc5aa2e20006d13a92bf530e290d.pkl + params_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/params.yaml + predictions_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/predictions.json + probabilities_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/probabilities.json + score_dict_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/score_dict.json + test_labels_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/test_labels.json + train_labels_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/train_labels.json + model_dir: models + name: 0b7c164c5b4352f671ff99988820254e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2b4258e923666579595aa43a2e1e34f + trainer: + kwargs: {} +name: 0b7c164c5b4352f671ff99988820254e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/params.yaml b/examples/security/classification/output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/params.yaml new file mode 100644 index 00000000..f3d2a45e --- /dev/null +++ b/examples/security/classification/output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/adv_predictions.json + adv_probabilities_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/225e8d86e685e1291e01642174077ec7.pkl + params_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/params.yaml + predictions_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/predictions.json + probabilities_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/probabilities.json + score_dict_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/score_dict.json + test_labels_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/test_labels.json + train_labels_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/train_labels.json + model_dir: models + name: 0bc33dbd5c67dc25e800003f03eb4d26 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9f62bb96b3efaa775cf846642dc92d75 + trainer: + kwargs: {} +name: 0bc33dbd5c67dc25e800003f03eb4d26 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0bcf8fce10809531407948840bf8b95e/params.yaml b/examples/security/classification/output/reports/train/0bcf8fce10809531407948840bf8b95e/params.yaml new file mode 100644 index 00000000..4c682b9d --- /dev/null +++ b/examples/security/classification/output/reports/train/0bcf8fce10809531407948840bf8b95e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/adv_predictions.json + adv_probabilities_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/6ec66ad8d116c5659b6546e2cbb34f68.pkl + params_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/params.yaml + predictions_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/predictions.json + probabilities_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/probabilities.json + score_dict_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/score_dict.json + test_labels_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/test_labels.json + train_labels_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/train_labels.json + model_dir: models + name: 0bcf8fce10809531407948840bf8b95e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 507bd8de80936d5163133fbb7f9f73da + trainer: + kwargs: {} +name: 0bcf8fce10809531407948840bf8b95e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0bd09d7200925230d7db75c319b8c567/params.yaml b/examples/security/classification/output/reports/train/0bd09d7200925230d7db75c319b8c567/params.yaml new file mode 100644 index 00000000..93b4a024 --- /dev/null +++ b/examples/security/classification/output/reports/train/0bd09d7200925230d7db75c319b8c567/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/adv_predictions.json + adv_probabilities_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/15646283734df88918f39d49591fa1cf.pkl + params_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/params.yaml + predictions_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/predictions.json + probabilities_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/probabilities.json + score_dict_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/score_dict.json + test_labels_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/test_labels.json + train_labels_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/train_labels.json + model_dir: models + name: 0bd09d7200925230d7db75c319b8c567 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e5c12d68c9768a74749e793cf697eefb + trainer: + kwargs: {} +name: 0bd09d7200925230d7db75c319b8c567 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0be30eb5d95330f4ed9d28221825337d/params.yaml b/examples/security/classification/output/reports/train/0be30eb5d95330f4ed9d28221825337d/params.yaml new file mode 100644 index 00000000..1ace5425 --- /dev/null +++ b/examples/security/classification/output/reports/train/0be30eb5d95330f4ed9d28221825337d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/adv_predictions.json + adv_probabilities_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/9f8db3bf76806491ec75ac830b8bd3bb.pkl + params_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/params.yaml + predictions_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/predictions.json + probabilities_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/probabilities.json + score_dict_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/score_dict.json + test_labels_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/test_labels.json + train_labels_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/train_labels.json + model_dir: models + name: 0be30eb5d95330f4ed9d28221825337d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c04d8232dd73ee97b5b888f40dc9d7ad + trainer: + kwargs: {} +name: 0be30eb5d95330f4ed9d28221825337d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0be7948bbf44ee058d4de41456663815/params.yaml b/examples/security/classification/output/reports/train/0be7948bbf44ee058d4de41456663815/params.yaml new file mode 100644 index 00000000..8ed9b0e5 --- /dev/null +++ b/examples/security/classification/output/reports/train/0be7948bbf44ee058d4de41456663815/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0be7948bbf44ee058d4de41456663815/adv_predictions.json + adv_probabilities_file: output/reports/train/0be7948bbf44ee058d4de41456663815/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/48d627e6943770b5c66ca44fe44fb20d.pkl + params_file: output/reports/train/0be7948bbf44ee058d4de41456663815/params.yaml + predictions_file: output/reports/train/0be7948bbf44ee058d4de41456663815/predictions.json + probabilities_file: output/reports/train/0be7948bbf44ee058d4de41456663815/probabilities.json + score_dict_file: output/reports/train/0be7948bbf44ee058d4de41456663815/score_dict.json + test_labels_file: output/reports/train/0be7948bbf44ee058d4de41456663815/test_labels.json + train_labels_file: output/reports/train/0be7948bbf44ee058d4de41456663815/train_labels.json + model_dir: models + name: 0be7948bbf44ee058d4de41456663815 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fcf587e1a9d988bd3a7b021266451677 + trainer: + kwargs: {} +name: 0be7948bbf44ee058d4de41456663815 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/params.yaml b/examples/security/classification/output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/params.yaml new file mode 100644 index 00000000..0cc50a0f --- /dev/null +++ b/examples/security/classification/output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/adv_predictions.json + adv_probabilities_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/178d08a45d21c6ecd4e3a7dda8d2d7cf.pkl + params_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/params.yaml + predictions_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/predictions.json + probabilities_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/probabilities.json + score_dict_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/score_dict.json + test_labels_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/test_labels.json + train_labels_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/train_labels.json + model_dir: models + name: 0bee96996f3c26c261a4e91af93fa0c7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f97b0bac67ec2ed4cf0c393930315279 + trainer: + kwargs: {} +name: 0bee96996f3c26c261a4e91af93fa0c7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/params.yaml b/examples/security/classification/output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/params.yaml new file mode 100644 index 00000000..54b70ef5 --- /dev/null +++ b/examples/security/classification/output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/adv_predictions.json + adv_probabilities_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/7412524733f33755f6e6bf7fc9bfc12d.pkl + params_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/params.yaml + predictions_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/predictions.json + probabilities_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/probabilities.json + score_dict_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/score_dict.json + test_labels_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/test_labels.json + train_labels_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/train_labels.json + model_dir: models + name: 0bf1a99077756c90d5ee407ba1bd5bb2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d160a0311929c1de16d1551732b57f1f + trainer: + kwargs: {} +name: 0bf1a99077756c90d5ee407ba1bd5bb2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0c02ea4a215d61673a3b7a7e62751476/params.yaml b/examples/security/classification/output/reports/train/0c02ea4a215d61673a3b7a7e62751476/params.yaml new file mode 100644 index 00000000..f775f88e --- /dev/null +++ b/examples/security/classification/output/reports/train/0c02ea4a215d61673a3b7a7e62751476/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/adv_predictions.json + adv_probabilities_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/49e4bbe040cc8612f860a48e409b00e8.pkl + params_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/params.yaml + predictions_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/predictions.json + probabilities_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/probabilities.json + score_dict_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/score_dict.json + test_labels_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/test_labels.json + train_labels_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/train_labels.json + model_dir: models + name: 0c02ea4a215d61673a3b7a7e62751476 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1a24ceceee013fe17ad8a1d5dc49442b + trainer: + kwargs: {} +name: 0c02ea4a215d61673a3b7a7e62751476 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/params.yaml b/examples/security/classification/output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/params.yaml new file mode 100644 index 00000000..650507a9 --- /dev/null +++ b/examples/security/classification/output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/adv_predictions.json + adv_probabilities_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/b348b04c9d0f18cd6c8592a7c0851fa7.pkl + params_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/params.yaml + predictions_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/predictions.json + probabilities_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/probabilities.json + score_dict_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/score_dict.json + test_labels_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/test_labels.json + train_labels_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/train_labels.json + model_dir: models + name: 0c1a1a96c8a563b64aa21cc3e74b84f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2ef085a547de8700af3aa0ad8ac7a42d + trainer: + kwargs: {} +name: 0c1a1a96c8a563b64aa21cc3e74b84f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/params.yaml b/examples/security/classification/output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/params.yaml new file mode 100644 index 00000000..ee30bb72 --- /dev/null +++ b/examples/security/classification/output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/params.yaml @@ -0,0 +1,115 @@ +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/adv_predictions.json + adv_probabilities_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl + model_file: output/models/17dd2599534253c240a74baffdd03273.pkl + params_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/params.yaml + predictions_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/predictions.json + probabilities_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/probabilities.json + score_dict_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/score_dict.json + test_labels_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/test_labels.json + train_labels_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/train_labels.json + model_dir: models + name: 0c1fa0d07797ba21c91426b746d5ff9c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8f2ca3159ecefb60740f8fc10b8fb6a4 + trainer: + kwargs: {} +name: 0c1fa0d07797ba21c91426b746d5ff9c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/params.yaml b/examples/security/classification/output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/params.yaml new file mode 100644 index 00000000..6b1ecf8f --- /dev/null +++ b/examples/security/classification/output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/adv_predictions.json + adv_probabilities_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/a4d68dfc100dbe08f278f61a91edf587.pkl + params_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/params.yaml + predictions_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/predictions.json + probabilities_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/probabilities.json + score_dict_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/score_dict.json + test_labels_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/test_labels.json + train_labels_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/train_labels.json + model_dir: models + name: 0c27e60fa5e3ff10bc0a5994ddb248c5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6949463f4e71db738baf5787d82e361e + trainer: + kwargs: {} +name: 0c27e60fa5e3ff10bc0a5994ddb248c5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0c39316193b548fbe0bf524f4c8e7367/params.yaml b/examples/security/classification/output/reports/train/0c39316193b548fbe0bf524f4c8e7367/params.yaml new file mode 100644 index 00000000..41b76b5e --- /dev/null +++ b/examples/security/classification/output/reports/train/0c39316193b548fbe0bf524f4c8e7367/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/adv_predictions.json + adv_probabilities_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/923273f417aa098bbb964ac30ca2fa20.pkl + params_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/params.yaml + predictions_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/predictions.json + probabilities_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/probabilities.json + score_dict_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/score_dict.json + test_labels_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/test_labels.json + train_labels_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/train_labels.json + model_dir: models + name: 0c39316193b548fbe0bf524f4c8e7367 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7a05abad56e9b81cfc4b458fffb2d451 + trainer: + kwargs: {} +name: 0c39316193b548fbe0bf524f4c8e7367 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0c8061515992a72c3132639ae99fa34e/params.yaml b/examples/security/classification/output/reports/train/0c8061515992a72c3132639ae99fa34e/params.yaml new file mode 100644 index 00000000..a8e0faf1 --- /dev/null +++ b/examples/security/classification/output/reports/train/0c8061515992a72c3132639ae99fa34e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/adv_predictions.json + adv_probabilities_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/abc00bc8e3ac060c8395905fa17d3c4b.pkl + params_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/params.yaml + predictions_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/predictions.json + probabilities_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/probabilities.json + score_dict_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/score_dict.json + test_labels_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/test_labels.json + train_labels_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/train_labels.json + model_dir: models + name: 0c8061515992a72c3132639ae99fa34e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4b168ae1c3750de5d9da3ef11f89025b + trainer: + kwargs: {} +name: 0c8061515992a72c3132639ae99fa34e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/params.yaml b/examples/security/classification/output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/params.yaml new file mode 100644 index 00000000..5ac3fe81 --- /dev/null +++ b/examples/security/classification/output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/adv_predictions.json + adv_probabilities_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/c8f5e1ba7825a78f5dc1b20916d63014.pkl + params_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/params.yaml + predictions_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/predictions.json + probabilities_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/probabilities.json + score_dict_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/score_dict.json + test_labels_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/test_labels.json + train_labels_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/train_labels.json + model_dir: models + name: 0cadfbac2d03200b52fab0a4fc05d41f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d9ef872ad910f06aaadc01631d15c5f3 + trainer: + kwargs: {} +name: 0cadfbac2d03200b52fab0a4fc05d41f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/params.yaml b/examples/security/classification/output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/params.yaml new file mode 100644 index 00000000..3eedac60 --- /dev/null +++ b/examples/security/classification/output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/adv_predictions.json + adv_probabilities_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/72fe98c80b55c8d08c2e2fd9ef4f4044.pkl + params_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/params.yaml + predictions_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/predictions.json + probabilities_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/probabilities.json + score_dict_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/score_dict.json + test_labels_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/test_labels.json + train_labels_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/train_labels.json + model_dir: models + name: 0ce49a1bf2a29dea6d5b79c16fd33e3b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3689eae4c5799ed94f3624a9f44bd163 + trainer: + kwargs: {} +name: 0ce49a1bf2a29dea6d5b79c16fd33e3b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0d0549d4634421a342b5c5080adf670b/params.yaml b/examples/security/classification/output/reports/train/0d0549d4634421a342b5c5080adf670b/params.yaml new file mode 100644 index 00000000..a85e0f6a --- /dev/null +++ b/examples/security/classification/output/reports/train/0d0549d4634421a342b5c5080adf670b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/adv_predictions.json + adv_probabilities_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/5d67fd2d76b6bd0f83473d71ae62be32.pkl + params_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/params.yaml + predictions_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/predictions.json + probabilities_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/probabilities.json + score_dict_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/score_dict.json + test_labels_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/test_labels.json + train_labels_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/train_labels.json + model_dir: models + name: 0d0549d4634421a342b5c5080adf670b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2feca5ce7af775c35d427b18d5609fe + trainer: + kwargs: {} +name: 0d0549d4634421a342b5c5080adf670b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/params.yaml b/examples/security/classification/output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/params.yaml new file mode 100644 index 00000000..cbcab762 --- /dev/null +++ b/examples/security/classification/output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/adv_predictions.json + adv_probabilities_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/a63791701b434878552f6f61b12c7187.pkl + params_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/params.yaml + predictions_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/predictions.json + probabilities_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/probabilities.json + score_dict_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/score_dict.json + test_labels_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/test_labels.json + train_labels_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/train_labels.json + model_dir: models + name: 0d18dc180db870fa70a3b8c70f856ae9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c205269193ff8fd0292cfc0519bc39c7 + trainer: + kwargs: {} +name: 0d18dc180db870fa70a3b8c70f856ae9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0d18e2f53098fd32f854ddecce780de0/params.yaml b/examples/security/classification/output/reports/train/0d18e2f53098fd32f854ddecce780de0/params.yaml new file mode 100644 index 00000000..850cd0af --- /dev/null +++ b/examples/security/classification/output/reports/train/0d18e2f53098fd32f854ddecce780de0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/adv_predictions.json + adv_probabilities_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/8fa478857a3a0e15f186da97471660c5.pkl + params_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/params.yaml + predictions_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/predictions.json + probabilities_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/probabilities.json + score_dict_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/score_dict.json + test_labels_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/test_labels.json + train_labels_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/train_labels.json + model_dir: models + name: 0d18e2f53098fd32f854ddecce780de0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 12f4994f481446aaeb1b9a535eebb2fb + trainer: + kwargs: {} +name: 0d18e2f53098fd32f854ddecce780de0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/params.yaml b/examples/security/classification/output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/params.yaml new file mode 100644 index 00000000..1dd9a837 --- /dev/null +++ b/examples/security/classification/output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/adv_predictions.json + adv_probabilities_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/e7a0064fc4e65bedf92c4423990dddf6.pkl + params_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/params.yaml + predictions_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/predictions.json + probabilities_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/probabilities.json + score_dict_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/score_dict.json + test_labels_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/test_labels.json + train_labels_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/train_labels.json + model_dir: models + name: 0d30acae9f8cd9b29b0145ce697a0e45 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9313474e4bd081890219bbbc978f8d98 + trainer: + kwargs: {} +name: 0d30acae9f8cd9b29b0145ce697a0e45 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/params.yaml b/examples/security/classification/output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/params.yaml new file mode 100644 index 00000000..092b93a0 --- /dev/null +++ b/examples/security/classification/output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/adv_predictions.json + adv_probabilities_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/9ede3e3855c6487b6c8903f0113a35d7.pkl + params_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/params.yaml + predictions_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/predictions.json + probabilities_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/probabilities.json + score_dict_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/score_dict.json + test_labels_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/test_labels.json + train_labels_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/train_labels.json + model_dir: models + name: 0d4068b0d80543031ea46f95fdc2b48c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 53520543d262d56f44d864c06a97274d + trainer: + kwargs: {} +name: 0d4068b0d80543031ea46f95fdc2b48c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/params.yaml b/examples/security/classification/output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/params.yaml new file mode 100644 index 00000000..addd0dc5 --- /dev/null +++ b/examples/security/classification/output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/adv_predictions.json + adv_probabilities_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/f685424f7eb9581a1ce0176666b370a6.pkl + params_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/params.yaml + predictions_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/predictions.json + probabilities_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/probabilities.json + score_dict_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/score_dict.json + test_labels_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/test_labels.json + train_labels_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/train_labels.json + model_dir: models + name: 0d6fcce87274bddbef93dd553f90fa3f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cfaedb7eb32aec29a3d06e64d112d4dd + trainer: + kwargs: {} +name: 0d6fcce87274bddbef93dd553f90fa3f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/params.yaml b/examples/security/classification/output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/params.yaml new file mode 100644 index 00000000..ca430c38 --- /dev/null +++ b/examples/security/classification/output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/adv_predictions.json + adv_probabilities_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/1273a34205a1b632838dddac6f55c606.pkl + params_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/params.yaml + predictions_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/predictions.json + probabilities_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/probabilities.json + score_dict_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/score_dict.json + test_labels_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/test_labels.json + train_labels_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/train_labels.json + model_dir: models + name: 0d8d2aca7209fa1142cb57fc2ed23f46 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 58dfc74d7f06ef8431da989923b0fec9 + trainer: + kwargs: {} +name: 0d8d2aca7209fa1142cb57fc2ed23f46 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/params.yaml b/examples/security/classification/output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/params.yaml new file mode 100644 index 00000000..acb9fd11 --- /dev/null +++ b/examples/security/classification/output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/adv_predictions.json + adv_probabilities_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/5d7e4954b48aed1eeb2874922f290311.pkl + params_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/params.yaml + predictions_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/predictions.json + probabilities_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/probabilities.json + score_dict_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/score_dict.json + test_labels_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/test_labels.json + train_labels_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/train_labels.json + model_dir: models + name: 0dc5e4cb61a5f503c57dee87a7ca88c4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e5eb3c0e8b2e59312b28728492b39933 + trainer: + kwargs: {} +name: 0dc5e4cb61a5f503c57dee87a7ca88c4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0dd33efd478e4261942441610a42e04e/params.yaml b/examples/security/classification/output/reports/train/0dd33efd478e4261942441610a42e04e/params.yaml new file mode 100644 index 00000000..066772e0 --- /dev/null +++ b/examples/security/classification/output/reports/train/0dd33efd478e4261942441610a42e04e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0dd33efd478e4261942441610a42e04e/adv_predictions.json + adv_probabilities_file: output/reports/train/0dd33efd478e4261942441610a42e04e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/c276cc01da1158fb34e9a529301d75cb.pkl + params_file: output/reports/train/0dd33efd478e4261942441610a42e04e/params.yaml + predictions_file: output/reports/train/0dd33efd478e4261942441610a42e04e/predictions.json + probabilities_file: output/reports/train/0dd33efd478e4261942441610a42e04e/probabilities.json + score_dict_file: output/reports/train/0dd33efd478e4261942441610a42e04e/score_dict.json + test_labels_file: output/reports/train/0dd33efd478e4261942441610a42e04e/test_labels.json + train_labels_file: output/reports/train/0dd33efd478e4261942441610a42e04e/train_labels.json + model_dir: models + name: 0dd33efd478e4261942441610a42e04e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 849d6809c44d6d53ef634fc5784041f2 + trainer: + kwargs: {} +name: 0dd33efd478e4261942441610a42e04e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0def827fc0bb207b2693e3beb54e772c/params.yaml b/examples/security/classification/output/reports/train/0def827fc0bb207b2693e3beb54e772c/params.yaml new file mode 100644 index 00000000..7da0d978 --- /dev/null +++ b/examples/security/classification/output/reports/train/0def827fc0bb207b2693e3beb54e772c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/adv_predictions.json + adv_probabilities_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/1c57dc9f4efab09313669d888d6d0dab.pkl + params_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/params.yaml + predictions_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/predictions.json + probabilities_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/probabilities.json + score_dict_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/score_dict.json + test_labels_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/test_labels.json + train_labels_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/train_labels.json + model_dir: models + name: 0def827fc0bb207b2693e3beb54e772c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 164d07e1f3e8ffa2a7e5f4f7690592ec + trainer: + kwargs: {} +name: 0def827fc0bb207b2693e3beb54e772c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/params.yaml b/examples/security/classification/output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/params.yaml new file mode 100644 index 00000000..b9b15543 --- /dev/null +++ b/examples/security/classification/output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/adv_predictions.json + adv_probabilities_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/180988d29511a978881ddd64cc5453e8.pkl + params_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/params.yaml + predictions_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/predictions.json + probabilities_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/probabilities.json + score_dict_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/score_dict.json + test_labels_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/test_labels.json + train_labels_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/train_labels.json + model_dir: models + name: 0e0047344db5c6cb636c4ec18b14c7f9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 20c8c86d096aa4847b26a3a5a00b8a61 + trainer: + kwargs: {} +name: 0e0047344db5c6cb636c4ec18b14c7f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/params.yaml b/examples/security/classification/output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/params.yaml new file mode 100644 index 00000000..bccb2337 --- /dev/null +++ b/examples/security/classification/output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/adv_predictions.json + adv_probabilities_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/409e04438d927d2e3a37b8c6cc8a2103.pkl + params_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/params.yaml + predictions_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/predictions.json + probabilities_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/probabilities.json + score_dict_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/score_dict.json + test_labels_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/test_labels.json + train_labels_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/train_labels.json + model_dir: models + name: 0e0c3c8d02d83e3d7e1ea10ac24a7167 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 61c620a0c465d8d4e744508f1bc539d7 + trainer: + kwargs: {} +name: 0e0c3c8d02d83e3d7e1ea10ac24a7167 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0e206cd6447950cdede4c83ad03bec08/params.yaml b/examples/security/classification/output/reports/train/0e206cd6447950cdede4c83ad03bec08/params.yaml new file mode 100644 index 00000000..cb67ae82 --- /dev/null +++ b/examples/security/classification/output/reports/train/0e206cd6447950cdede4c83ad03bec08/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/adv_predictions.json + adv_probabilities_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/a833ec031491192a2fd536baf97e1409.pkl + params_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/params.yaml + predictions_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/predictions.json + probabilities_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/probabilities.json + score_dict_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/score_dict.json + test_labels_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/test_labels.json + train_labels_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/train_labels.json + model_dir: models + name: 0e206cd6447950cdede4c83ad03bec08 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e5e95fa45d6f3b3a28e94962367e9497 + trainer: + kwargs: {} +name: 0e206cd6447950cdede4c83ad03bec08 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0e228977c9e009dd73a125f58393de88/params.yaml b/examples/security/classification/output/reports/train/0e228977c9e009dd73a125f58393de88/params.yaml new file mode 100644 index 00000000..d85c8b94 --- /dev/null +++ b/examples/security/classification/output/reports/train/0e228977c9e009dd73a125f58393de88/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0e228977c9e009dd73a125f58393de88/adv_predictions.json + adv_probabilities_file: output/reports/train/0e228977c9e009dd73a125f58393de88/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/7f0263a3e317993633f64351f4166b04.pkl + params_file: output/reports/train/0e228977c9e009dd73a125f58393de88/params.yaml + predictions_file: output/reports/train/0e228977c9e009dd73a125f58393de88/predictions.json + probabilities_file: output/reports/train/0e228977c9e009dd73a125f58393de88/probabilities.json + score_dict_file: output/reports/train/0e228977c9e009dd73a125f58393de88/score_dict.json + test_labels_file: output/reports/train/0e228977c9e009dd73a125f58393de88/test_labels.json + train_labels_file: output/reports/train/0e228977c9e009dd73a125f58393de88/train_labels.json + model_dir: models + name: 0e228977c9e009dd73a125f58393de88 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d3728c2c59ebb23d0be1a5dfc2f48a98 + trainer: + kwargs: {} +name: 0e228977c9e009dd73a125f58393de88 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0e282965a48347c32a618209a80845ff/params.yaml b/examples/security/classification/output/reports/train/0e282965a48347c32a618209a80845ff/params.yaml new file mode 100644 index 00000000..880960b7 --- /dev/null +++ b/examples/security/classification/output/reports/train/0e282965a48347c32a618209a80845ff/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0e282965a48347c32a618209a80845ff/adv_predictions.json + adv_probabilities_file: output/reports/train/0e282965a48347c32a618209a80845ff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/d15a3410c58d1a48291caf8ef6bb3462.pkl + params_file: output/reports/train/0e282965a48347c32a618209a80845ff/params.yaml + predictions_file: output/reports/train/0e282965a48347c32a618209a80845ff/predictions.json + probabilities_file: output/reports/train/0e282965a48347c32a618209a80845ff/probabilities.json + score_dict_file: output/reports/train/0e282965a48347c32a618209a80845ff/score_dict.json + test_labels_file: output/reports/train/0e282965a48347c32a618209a80845ff/test_labels.json + train_labels_file: output/reports/train/0e282965a48347c32a618209a80845ff/train_labels.json + model_dir: models + name: 0e282965a48347c32a618209a80845ff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b2c14edfa9778152e04b73b9fbd24102 + trainer: + kwargs: {} +name: 0e282965a48347c32a618209a80845ff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/params.yaml b/examples/security/classification/output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/params.yaml new file mode 100644 index 00000000..95075a26 --- /dev/null +++ b/examples/security/classification/output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/adv_predictions.json + adv_probabilities_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/81ab94da7a46b0f47beedb1d40f52fc4.pkl + params_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/params.yaml + predictions_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/predictions.json + probabilities_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/probabilities.json + score_dict_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/score_dict.json + test_labels_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/test_labels.json + train_labels_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/train_labels.json + model_dir: models + name: 0e2cb367af60d92a7e73720bce5bd3f0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f1c6e4a6e4c50ac60eaa9a97292b68e + trainer: + kwargs: {} +name: 0e2cb367af60d92a7e73720bce5bd3f0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0e347f170a0697a5e87fdd068ca4a398/params.yaml b/examples/security/classification/output/reports/train/0e347f170a0697a5e87fdd068ca4a398/params.yaml new file mode 100644 index 00000000..6610a85b --- /dev/null +++ b/examples/security/classification/output/reports/train/0e347f170a0697a5e87fdd068ca4a398/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/adv_predictions.json + adv_probabilities_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/93845a378d2f3ceea660d8a4c92231f8.pkl + params_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/params.yaml + predictions_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/predictions.json + probabilities_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/probabilities.json + score_dict_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/score_dict.json + test_labels_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/test_labels.json + train_labels_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/train_labels.json + model_dir: models + name: 0e347f170a0697a5e87fdd068ca4a398 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3c01123ce1b1917d73d88771a83a52b9 + trainer: + kwargs: {} +name: 0e347f170a0697a5e87fdd068ca4a398 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/params.yaml b/examples/security/classification/output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/params.yaml new file mode 100644 index 00000000..6e996443 --- /dev/null +++ b/examples/security/classification/output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/adv_predictions.json + adv_probabilities_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/1189593d2dadad50863e99d2a764848f.pkl + params_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/params.yaml + predictions_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/predictions.json + probabilities_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/probabilities.json + score_dict_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/score_dict.json + test_labels_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/test_labels.json + train_labels_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/train_labels.json + model_dir: models + name: 0e38cab7e1d79fda0cad75bf1ea1607d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1d7b4b9af60a1db1212c0975fd974142 + trainer: + kwargs: {} +name: 0e38cab7e1d79fda0cad75bf1ea1607d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0f2b1c2c742e2503ceace020d6a16114/params.yaml b/examples/security/classification/output/reports/train/0f2b1c2c742e2503ceace020d6a16114/params.yaml new file mode 100644 index 00000000..2075a65a --- /dev/null +++ b/examples/security/classification/output/reports/train/0f2b1c2c742e2503ceace020d6a16114/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/adv_predictions.json + adv_probabilities_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/f873166f78313a898578258c372096a6.pkl + params_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/params.yaml + predictions_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/predictions.json + probabilities_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/probabilities.json + score_dict_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/score_dict.json + test_labels_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/test_labels.json + train_labels_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/train_labels.json + model_dir: models + name: 0f2b1c2c742e2503ceace020d6a16114 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0e97ea8d3862d96e40da5d0691d42ecc + trainer: + kwargs: {} +name: 0f2b1c2c742e2503ceace020d6a16114 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/params.yaml b/examples/security/classification/output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/params.yaml new file mode 100644 index 00000000..ee2792e2 --- /dev/null +++ b/examples/security/classification/output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/adv_predictions.json + adv_probabilities_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/c63f7af6eb028edc19c26784f86556dd.pkl + params_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/params.yaml + predictions_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/predictions.json + probabilities_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/probabilities.json + score_dict_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/score_dict.json + test_labels_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/test_labels.json + train_labels_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/train_labels.json + model_dir: models + name: 0f4451942d7932ac8de9d90738c2f2f7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9510dea77f928ddafa5d1d87bfc5d501 + trainer: + kwargs: {} +name: 0f4451942d7932ac8de9d90738c2f2f7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/params.yaml b/examples/security/classification/output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/params.yaml new file mode 100644 index 00000000..aa395cb0 --- /dev/null +++ b/examples/security/classification/output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/adv_predictions.json + adv_probabilities_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/744fde506838d018739ec503693098a3.pkl + params_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/params.yaml + predictions_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/predictions.json + probabilities_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/probabilities.json + score_dict_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/score_dict.json + test_labels_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/test_labels.json + train_labels_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/train_labels.json + model_dir: models + name: 0f5eb2d36927f272b7d1120cb7300fa7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f3e53cf4aa1489b1f524387b0ed54e14 + trainer: + kwargs: {} +name: 0f5eb2d36927f272b7d1120cb7300fa7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0f7a887deb670c4d82b67a7b0295b448/params.yaml b/examples/security/classification/output/reports/train/0f7a887deb670c4d82b67a7b0295b448/params.yaml new file mode 100644 index 00000000..888a70cb --- /dev/null +++ b/examples/security/classification/output/reports/train/0f7a887deb670c4d82b67a7b0295b448/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/adv_predictions.json + adv_probabilities_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/8de6098b6787faf64766b20e8363d617.pkl + params_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/params.yaml + predictions_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/predictions.json + probabilities_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/probabilities.json + score_dict_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/score_dict.json + test_labels_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/test_labels.json + train_labels_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/train_labels.json + model_dir: models + name: 0f7a887deb670c4d82b67a7b0295b448 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9e94d379ace1cabb4df3d526a609e0bd + trainer: + kwargs: {} +name: 0f7a887deb670c4d82b67a7b0295b448 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/params.yaml b/examples/security/classification/output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/params.yaml new file mode 100644 index 00000000..27f8ef6e --- /dev/null +++ b/examples/security/classification/output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/adv_predictions.json + adv_probabilities_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/1dd57086ca7cad679531f5341a4fd8c4.pkl + params_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/params.yaml + predictions_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/predictions.json + probabilities_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/probabilities.json + score_dict_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/score_dict.json + test_labels_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/test_labels.json + train_labels_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/train_labels.json + model_dir: models + name: 0f8ea54d75fa08c1bbb133a1f23815fb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7f467caeb2ae4fef0ca7fd7e81434097 + trainer: + kwargs: {} +name: 0f8ea54d75fa08c1bbb133a1f23815fb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0f9c218967a186ad7ce4f9af038ff173/params.yaml b/examples/security/classification/output/reports/train/0f9c218967a186ad7ce4f9af038ff173/params.yaml new file mode 100644 index 00000000..0e227fbc --- /dev/null +++ b/examples/security/classification/output/reports/train/0f9c218967a186ad7ce4f9af038ff173/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/adv_predictions.json + adv_probabilities_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/b3332ffb6a4bd25a27cc162c296102d0.pkl + params_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/params.yaml + predictions_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/predictions.json + probabilities_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/probabilities.json + score_dict_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/score_dict.json + test_labels_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/test_labels.json + train_labels_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/train_labels.json + model_dir: models + name: 0f9c218967a186ad7ce4f9af038ff173 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 17300090d914d3e80d8cc455d0a10b6f + trainer: + kwargs: {} +name: 0f9c218967a186ad7ce4f9af038ff173 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/params.yaml b/examples/security/classification/output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/params.yaml new file mode 100644 index 00000000..a60f4daf --- /dev/null +++ b/examples/security/classification/output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/adv_predictions.json + adv_probabilities_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/35b63cc28a9a9f8d031d43197433a360.pkl + params_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/params.yaml + predictions_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/predictions.json + probabilities_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/probabilities.json + score_dict_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/score_dict.json + test_labels_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/test_labels.json + train_labels_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/train_labels.json + model_dir: models + name: 0fad3c30765b8753b10bf5814fc6e8b9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 76f59f34d9b0984f771bb252b6925627 + trainer: + kwargs: {} +name: 0fad3c30765b8753b10bf5814fc6e8b9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0fb22992fa8091ea012f506c730e942b/params.yaml b/examples/security/classification/output/reports/train/0fb22992fa8091ea012f506c730e942b/params.yaml new file mode 100644 index 00000000..f25e5966 --- /dev/null +++ b/examples/security/classification/output/reports/train/0fb22992fa8091ea012f506c730e942b/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/adv_predictions.json + adv_probabilities_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/4c86a5fb2523fe885baa54416502eda0.pkl + params_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/params.yaml + predictions_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/predictions.json + probabilities_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/probabilities.json + score_dict_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/score_dict.json + test_labels_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/test_labels.json + train_labels_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/train_labels.json + model_dir: models + name: 0fb22992fa8091ea012f506c730e942b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e37a1fda7e5387485c9698f5ca8bf5e1 + trainer: + kwargs: {} +name: 0fb22992fa8091ea012f506c730e942b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/params.yaml b/examples/security/classification/output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/params.yaml new file mode 100644 index 00000000..b2b1cefc --- /dev/null +++ b/examples/security/classification/output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/adv_predictions.json + adv_probabilities_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/1b46c2f6d5ea07bc635893aea9d341a9.pkl + params_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/params.yaml + predictions_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/predictions.json + probabilities_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/probabilities.json + score_dict_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/score_dict.json + test_labels_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/test_labels.json + train_labels_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/train_labels.json + model_dir: models + name: 0fc23b8b42a7e9e6cfada58a9d0fc957 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f32b04d9dba7ad93727512acc93e1242 + trainer: + kwargs: {} +name: 0fc23b8b42a7e9e6cfada58a9d0fc957 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/0fe5811dffeb87eadc74552310808f68/params.yaml b/examples/security/classification/output/reports/train/0fe5811dffeb87eadc74552310808f68/params.yaml new file mode 100644 index 00000000..a27178d0 --- /dev/null +++ b/examples/security/classification/output/reports/train/0fe5811dffeb87eadc74552310808f68/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/adv_predictions.json + adv_probabilities_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/69466392620719c414c341eafcce2ddb.pkl + params_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/params.yaml + predictions_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/predictions.json + probabilities_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/probabilities.json + score_dict_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/score_dict.json + test_labels_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/test_labels.json + train_labels_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/train_labels.json + model_dir: models + name: 0fe5811dffeb87eadc74552310808f68 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4fc58c79b8e840e45a6eb0a43e2ccb75 + trainer: + kwargs: {} +name: 0fe5811dffeb87eadc74552310808f68 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/100318610dfae98f40dd89f78e5f8605/params.yaml b/examples/security/classification/output/reports/train/100318610dfae98f40dd89f78e5f8605/params.yaml new file mode 100644 index 00000000..43b8a4c7 --- /dev/null +++ b/examples/security/classification/output/reports/train/100318610dfae98f40dd89f78e5f8605/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/adv_predictions.json + adv_probabilities_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/8edb47491421578b3783002edb121041.pkl + params_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/params.yaml + predictions_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/predictions.json + probabilities_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/probabilities.json + score_dict_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/score_dict.json + test_labels_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/test_labels.json + train_labels_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/train_labels.json + model_dir: models + name: 100318610dfae98f40dd89f78e5f8605 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ac28c961f06663a8e08bab8b17f6861 + trainer: + kwargs: {} +name: 100318610dfae98f40dd89f78e5f8605 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/10358493e0bebb2e34330590fe5e69db/params.yaml b/examples/security/classification/output/reports/train/10358493e0bebb2e34330590fe5e69db/params.yaml new file mode 100644 index 00000000..3d4802f2 --- /dev/null +++ b/examples/security/classification/output/reports/train/10358493e0bebb2e34330590fe5e69db/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/adv_predictions.json + adv_probabilities_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/5cea2e292f1e10664890b39f5f26a09a.pkl + params_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/params.yaml + predictions_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/predictions.json + probabilities_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/probabilities.json + score_dict_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/score_dict.json + test_labels_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/test_labels.json + train_labels_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/train_labels.json + model_dir: models + name: 10358493e0bebb2e34330590fe5e69db + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e7b0a398149dc506173006db21952a39 + trainer: + kwargs: {} +name: 10358493e0bebb2e34330590fe5e69db +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/106341a777e70710628b5eed27ee7528/params.yaml b/examples/security/classification/output/reports/train/106341a777e70710628b5eed27ee7528/params.yaml new file mode 100644 index 00000000..e55c6bea --- /dev/null +++ b/examples/security/classification/output/reports/train/106341a777e70710628b5eed27ee7528/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/106341a777e70710628b5eed27ee7528/adv_predictions.json + adv_probabilities_file: output/reports/train/106341a777e70710628b5eed27ee7528/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/5f0326361c060dce17a8f0396b94a761.pkl + params_file: output/reports/train/106341a777e70710628b5eed27ee7528/params.yaml + predictions_file: output/reports/train/106341a777e70710628b5eed27ee7528/predictions.json + probabilities_file: output/reports/train/106341a777e70710628b5eed27ee7528/probabilities.json + score_dict_file: output/reports/train/106341a777e70710628b5eed27ee7528/score_dict.json + test_labels_file: output/reports/train/106341a777e70710628b5eed27ee7528/test_labels.json + train_labels_file: output/reports/train/106341a777e70710628b5eed27ee7528/train_labels.json + model_dir: models + name: 106341a777e70710628b5eed27ee7528 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 13bb9f56463fb8039c39b1a08ac759da + trainer: + kwargs: {} +name: 106341a777e70710628b5eed27ee7528 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/10826a8147a6ae548686397a04bd8bf6/params.yaml b/examples/security/classification/output/reports/train/10826a8147a6ae548686397a04bd8bf6/params.yaml new file mode 100644 index 00000000..4cdfa39c --- /dev/null +++ b/examples/security/classification/output/reports/train/10826a8147a6ae548686397a04bd8bf6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/adv_predictions.json + adv_probabilities_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/5674a69a6bdb6bdb998ba71926157d2e.pkl + params_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/params.yaml + predictions_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/predictions.json + probabilities_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/probabilities.json + score_dict_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/score_dict.json + test_labels_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/test_labels.json + train_labels_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/train_labels.json + model_dir: models + name: 10826a8147a6ae548686397a04bd8bf6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b47e5e24ca2df60dbb3997a713f172ef + trainer: + kwargs: {} +name: 10826a8147a6ae548686397a04bd8bf6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1090f25236da98c8be007ee98daadfcb/params.yaml b/examples/security/classification/output/reports/train/1090f25236da98c8be007ee98daadfcb/params.yaml new file mode 100644 index 00000000..f7628441 --- /dev/null +++ b/examples/security/classification/output/reports/train/1090f25236da98c8be007ee98daadfcb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/adv_predictions.json + adv_probabilities_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/3d26dbd2821b58098d4326a1518f6904.pkl + params_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/params.yaml + predictions_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/predictions.json + probabilities_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/probabilities.json + score_dict_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/score_dict.json + test_labels_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/test_labels.json + train_labels_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/train_labels.json + model_dir: models + name: 1090f25236da98c8be007ee98daadfcb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 99c94c94c4ce71bb574e3b9782973b82 + trainer: + kwargs: {} +name: 1090f25236da98c8be007ee98daadfcb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/params.yaml b/examples/security/classification/output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/params.yaml new file mode 100644 index 00000000..0539d445 --- /dev/null +++ b/examples/security/classification/output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/adv_predictions.json + adv_probabilities_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/d5c230e672f708b46a52a90e72e04b7a.pkl + params_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/params.yaml + predictions_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/predictions.json + probabilities_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/probabilities.json + score_dict_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/score_dict.json + test_labels_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/test_labels.json + train_labels_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/train_labels.json + model_dir: models + name: 111c6bab346fa1fa02d8d381dd5025e7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fd3db90368ff22ced0b65d15a5340645 + trainer: + kwargs: {} +name: 111c6bab346fa1fa02d8d381dd5025e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/118484e3b214b11f7f4967c1c20406f3/params.yaml b/examples/security/classification/output/reports/train/118484e3b214b11f7f4967c1c20406f3/params.yaml new file mode 100644 index 00000000..b0e4f2ec --- /dev/null +++ b/examples/security/classification/output/reports/train/118484e3b214b11f7f4967c1c20406f3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/adv_predictions.json + adv_probabilities_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/48096e1a553f449695d4e35a0fd4e437.pkl + params_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/params.yaml + predictions_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/predictions.json + probabilities_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/probabilities.json + score_dict_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/score_dict.json + test_labels_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/test_labels.json + train_labels_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/train_labels.json + model_dir: models + name: 118484e3b214b11f7f4967c1c20406f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dfdd288da267e5407432af125092414f + trainer: + kwargs: {} +name: 118484e3b214b11f7f4967c1c20406f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/params.yaml b/examples/security/classification/output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/params.yaml new file mode 100644 index 00000000..532591d6 --- /dev/null +++ b/examples/security/classification/output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/adv_predictions.json + adv_probabilities_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/f5fc29a44e0782cfa33444950119645e.pkl + params_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/params.yaml + predictions_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/predictions.json + probabilities_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/probabilities.json + score_dict_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/score_dict.json + test_labels_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/test_labels.json + train_labels_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/train_labels.json + model_dir: models + name: 11bc02c732f4c7e9aa085a7867293a3b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 97f1698801cf8443fd9f71e3714c1196 + trainer: + kwargs: {} +name: 11bc02c732f4c7e9aa085a7867293a3b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/11ea8d77c98f31eb034e9607965700c3/params.yaml b/examples/security/classification/output/reports/train/11ea8d77c98f31eb034e9607965700c3/params.yaml new file mode 100644 index 00000000..ac2db0e2 --- /dev/null +++ b/examples/security/classification/output/reports/train/11ea8d77c98f31eb034e9607965700c3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/adv_predictions.json + adv_probabilities_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/ea5a48359bfd57927cea43101a9187ac.pkl + params_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/params.yaml + predictions_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/predictions.json + probabilities_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/probabilities.json + score_dict_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/score_dict.json + test_labels_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/test_labels.json + train_labels_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/train_labels.json + model_dir: models + name: 11ea8d77c98f31eb034e9607965700c3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f91eab9df5889a56e1f6e16bfa2979b9 + trainer: + kwargs: {} +name: 11ea8d77c98f31eb034e9607965700c3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/params.yaml b/examples/security/classification/output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/params.yaml new file mode 100644 index 00000000..59686814 --- /dev/null +++ b/examples/security/classification/output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/adv_predictions.json + adv_probabilities_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/6f6f36f093175809b6b188e8da0483a8.pkl + params_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/params.yaml + predictions_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/predictions.json + probabilities_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/probabilities.json + score_dict_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/score_dict.json + test_labels_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/test_labels.json + train_labels_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/train_labels.json + model_dir: models + name: 11eb99ceec7cf8701bcbe395e7cf1dc3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 21be5c2198f8c3b44a9fb8ee490fd5cf + trainer: + kwargs: {} +name: 11eb99ceec7cf8701bcbe395e7cf1dc3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/params.yaml b/examples/security/classification/output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/params.yaml new file mode 100644 index 00000000..3a5cedfa --- /dev/null +++ b/examples/security/classification/output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/adv_predictions.json + adv_probabilities_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/7f7044ad2f5845c2d94334361dd565a5.pkl + params_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/params.yaml + predictions_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/predictions.json + probabilities_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/probabilities.json + score_dict_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/score_dict.json + test_labels_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/test_labels.json + train_labels_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/train_labels.json + model_dir: models + name: 11f81e53db4f1c1af2e39a804277a2ac + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 307ab2d765e6c68899cbc49a25c7d5f5 + trainer: + kwargs: {} +name: 11f81e53db4f1c1af2e39a804277a2ac +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/120f2e30cb41dce0befdd069db85fbe7/params.yaml b/examples/security/classification/output/reports/train/120f2e30cb41dce0befdd069db85fbe7/params.yaml new file mode 100644 index 00000000..59b074cf --- /dev/null +++ b/examples/security/classification/output/reports/train/120f2e30cb41dce0befdd069db85fbe7/params.yaml @@ -0,0 +1,104 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/adv_predictions.json + adv_probabilities_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/41dbc609bdf64827dc179690126cb28d.pkl + params_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/params.yaml + predictions_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/predictions.json + probabilities_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/probabilities.json + score_dict_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/score_dict.json + test_labels_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/test_labels.json + train_labels_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/train_labels.json + model_dir: models + name: 120f2e30cb41dce0befdd069db85fbe7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: + - linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea6d7a3e5079e5a6e5195670f738b448 + trainer: + kwargs: {} +name: 120f2e30cb41dce0befdd069db85fbe7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/121009943678cb5de20daa9dae3a7d5a/params.yaml b/examples/security/classification/output/reports/train/121009943678cb5de20daa9dae3a7d5a/params.yaml new file mode 100644 index 00000000..577466dd --- /dev/null +++ b/examples/security/classification/output/reports/train/121009943678cb5de20daa9dae3a7d5a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/adv_predictions.json + adv_probabilities_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/70374df26979aac80bd1761ed19fbef6.pkl + params_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/params.yaml + predictions_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/predictions.json + probabilities_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/probabilities.json + score_dict_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/score_dict.json + test_labels_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/test_labels.json + train_labels_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/train_labels.json + model_dir: models + name: 121009943678cb5de20daa9dae3a7d5a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 542fbef9e2bf4cf950c8fb135fa783a9 + trainer: + kwargs: {} +name: 121009943678cb5de20daa9dae3a7d5a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1275eb949779a1cb479617cb83181524/params.yaml b/examples/security/classification/output/reports/train/1275eb949779a1cb479617cb83181524/params.yaml new file mode 100644 index 00000000..8c80d1ed --- /dev/null +++ b/examples/security/classification/output/reports/train/1275eb949779a1cb479617cb83181524/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1275eb949779a1cb479617cb83181524/adv_predictions.json + adv_probabilities_file: output/reports/train/1275eb949779a1cb479617cb83181524/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/bba30c14494ac319508e55329f50a4ce.pkl + params_file: output/reports/train/1275eb949779a1cb479617cb83181524/params.yaml + predictions_file: output/reports/train/1275eb949779a1cb479617cb83181524/predictions.json + probabilities_file: output/reports/train/1275eb949779a1cb479617cb83181524/probabilities.json + score_dict_file: output/reports/train/1275eb949779a1cb479617cb83181524/score_dict.json + test_labels_file: output/reports/train/1275eb949779a1cb479617cb83181524/test_labels.json + train_labels_file: output/reports/train/1275eb949779a1cb479617cb83181524/train_labels.json + model_dir: models + name: 1275eb949779a1cb479617cb83181524 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9a8efb6959dc9426d83350d98cd95998 + trainer: + kwargs: {} +name: 1275eb949779a1cb479617cb83181524 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/params.yaml b/examples/security/classification/output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/params.yaml new file mode 100644 index 00000000..d0c090f6 --- /dev/null +++ b/examples/security/classification/output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/adv_predictions.json + adv_probabilities_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/7540c22fd98f6ebb5445a4075b208f41.pkl + params_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/params.yaml + predictions_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/predictions.json + probabilities_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/probabilities.json + score_dict_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/score_dict.json + test_labels_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/test_labels.json + train_labels_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/train_labels.json + model_dir: models + name: 1276bcf7673322a2ad6ec736fd0b9968 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + degree: 5 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1a19e02570637690f439cc8381bca86c + trainer: + kwargs: {} +name: 1276bcf7673322a2ad6ec736fd0b9968 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/12b6db77866de920c570921245988134/params.yaml b/examples/security/classification/output/reports/train/12b6db77866de920c570921245988134/params.yaml new file mode 100644 index 00000000..54380053 --- /dev/null +++ b/examples/security/classification/output/reports/train/12b6db77866de920c570921245988134/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/12b6db77866de920c570921245988134/adv_predictions.json + adv_probabilities_file: output/reports/train/12b6db77866de920c570921245988134/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/bdf5bda7db5bdd61bdb3b31fce8c22c3.pkl + params_file: output/reports/train/12b6db77866de920c570921245988134/params.yaml + predictions_file: output/reports/train/12b6db77866de920c570921245988134/predictions.json + probabilities_file: output/reports/train/12b6db77866de920c570921245988134/probabilities.json + score_dict_file: output/reports/train/12b6db77866de920c570921245988134/score_dict.json + test_labels_file: output/reports/train/12b6db77866de920c570921245988134/test_labels.json + train_labels_file: output/reports/train/12b6db77866de920c570921245988134/train_labels.json + model_dir: models + name: 12b6db77866de920c570921245988134 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 790f125b64769c85f526687bdbbfcce1 + trainer: + kwargs: {} +name: 12b6db77866de920c570921245988134 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/12d3e989d4e665423bf4fef63c096d12/params.yaml b/examples/security/classification/output/reports/train/12d3e989d4e665423bf4fef63c096d12/params.yaml new file mode 100644 index 00000000..859b1b7c --- /dev/null +++ b/examples/security/classification/output/reports/train/12d3e989d4e665423bf4fef63c096d12/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/adv_predictions.json + adv_probabilities_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/a9892ad628074772eca263cd6791e2e8.pkl + params_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/params.yaml + predictions_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/predictions.json + probabilities_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/probabilities.json + score_dict_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/score_dict.json + test_labels_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/test_labels.json + train_labels_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/train_labels.json + model_dir: models + name: 12d3e989d4e665423bf4fef63c096d12 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 718d2dd949f6242b1a1b72a2de696fbe + trainer: + kwargs: {} +name: 12d3e989d4e665423bf4fef63c096d12 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/params.yaml b/examples/security/classification/output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/params.yaml new file mode 100644 index 00000000..f724f5ec --- /dev/null +++ b/examples/security/classification/output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/adv_predictions.json + adv_probabilities_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/466ade6c1824e50cf5238401f4eb91ef.pkl + params_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/params.yaml + predictions_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/predictions.json + probabilities_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/probabilities.json + score_dict_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/score_dict.json + test_labels_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/test_labels.json + train_labels_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/train_labels.json + model_dir: models + name: 1332e4ea5ace8d9bec5a60237a51c4f7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b56cca625133a3f0cd392daffce0aa9c + trainer: + kwargs: {} +name: 1332e4ea5ace8d9bec5a60237a51c4f7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/134e17a661938c84607f12e0cc6e7d5f/params.yaml b/examples/security/classification/output/reports/train/134e17a661938c84607f12e0cc6e7d5f/params.yaml new file mode 100644 index 00000000..9e30e05a --- /dev/null +++ b/examples/security/classification/output/reports/train/134e17a661938c84607f12e0cc6e7d5f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/adv_predictions.json + adv_probabilities_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/15f325bf6cb87b78d099dfd09439d49c.pkl + params_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/params.yaml + predictions_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/predictions.json + probabilities_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/probabilities.json + score_dict_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/score_dict.json + test_labels_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/test_labels.json + train_labels_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/train_labels.json + model_dir: models + name: 134e17a661938c84607f12e0cc6e7d5f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 067a52cb119cffbada52271c39482a8e + trainer: + kwargs: {} +name: 134e17a661938c84607f12e0cc6e7d5f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/137f7bbd7a5dce39d486fba49f78d225/params.yaml b/examples/security/classification/output/reports/train/137f7bbd7a5dce39d486fba49f78d225/params.yaml new file mode 100644 index 00000000..d1b132b6 --- /dev/null +++ b/examples/security/classification/output/reports/train/137f7bbd7a5dce39d486fba49f78d225/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/adv_predictions.json + adv_probabilities_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/b910ff668784f88a0622fc06b0bd5718.pkl + params_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/params.yaml + predictions_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/predictions.json + probabilities_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/probabilities.json + score_dict_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/score_dict.json + test_labels_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/test_labels.json + train_labels_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/train_labels.json + model_dir: models + name: 137f7bbd7a5dce39d486fba49f78d225 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c47ebe2fefc687989fbe1fbaac8f4e27 + trainer: + kwargs: {} +name: 137f7bbd7a5dce39d486fba49f78d225 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/138b6f0393949a4fd99dc0f0a7237825/params.yaml b/examples/security/classification/output/reports/train/138b6f0393949a4fd99dc0f0a7237825/params.yaml new file mode 100644 index 00000000..e690e629 --- /dev/null +++ b/examples/security/classification/output/reports/train/138b6f0393949a4fd99dc0f0a7237825/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/adv_predictions.json + adv_probabilities_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/38e696a735037eb509b1cd77e487c8dc.pkl + params_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/params.yaml + predictions_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/predictions.json + probabilities_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/probabilities.json + score_dict_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/score_dict.json + test_labels_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/test_labels.json + train_labels_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/train_labels.json + model_dir: models + name: 138b6f0393949a4fd99dc0f0a7237825 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cb5be94ecfefebd9f2809e188b952066 + trainer: + kwargs: {} +name: 138b6f0393949a4fd99dc0f0a7237825 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/params.yaml b/examples/security/classification/output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/params.yaml new file mode 100644 index 00000000..6073eac7 --- /dev/null +++ b/examples/security/classification/output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/adv_predictions.json + adv_probabilities_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/707a384f777297e8e0f9ff69102f38e3.pkl + params_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/params.yaml + predictions_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/predictions.json + probabilities_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/probabilities.json + score_dict_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/score_dict.json + test_labels_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/test_labels.json + train_labels_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/train_labels.json + model_dir: models + name: 13a9afea89d9ba8ea9e1548f2ab56a92 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + degree: 3 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8443ae9f4793b0bf767d966206b90d1f + trainer: + kwargs: {} +name: 13a9afea89d9ba8ea9e1548f2ab56a92 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/13add0b2800a21855748642f0da14485/params.yaml b/examples/security/classification/output/reports/train/13add0b2800a21855748642f0da14485/params.yaml new file mode 100644 index 00000000..0f1c946c --- /dev/null +++ b/examples/security/classification/output/reports/train/13add0b2800a21855748642f0da14485/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/13add0b2800a21855748642f0da14485/adv_predictions.json + adv_probabilities_file: output/reports/train/13add0b2800a21855748642f0da14485/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/44d71f6f690ba77d76f8f6ece59456cc.pkl + params_file: output/reports/train/13add0b2800a21855748642f0da14485/params.yaml + predictions_file: output/reports/train/13add0b2800a21855748642f0da14485/predictions.json + probabilities_file: output/reports/train/13add0b2800a21855748642f0da14485/probabilities.json + score_dict_file: output/reports/train/13add0b2800a21855748642f0da14485/score_dict.json + test_labels_file: output/reports/train/13add0b2800a21855748642f0da14485/test_labels.json + train_labels_file: output/reports/train/13add0b2800a21855748642f0da14485/train_labels.json + model_dir: models + name: 13add0b2800a21855748642f0da14485 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 260d7e18a94d2d7b8652ef05c7c3e039 + trainer: + kwargs: {} +name: 13add0b2800a21855748642f0da14485 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/13da55851dd21ca361c6952d9e299ba4/params.yaml b/examples/security/classification/output/reports/train/13da55851dd21ca361c6952d9e299ba4/params.yaml new file mode 100644 index 00000000..e76a0336 --- /dev/null +++ b/examples/security/classification/output/reports/train/13da55851dd21ca361c6952d9e299ba4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/adv_predictions.json + adv_probabilities_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/b3c09a83b3bfaee19a350c03da2fc0a1.pkl + params_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/params.yaml + predictions_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/predictions.json + probabilities_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/probabilities.json + score_dict_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/score_dict.json + test_labels_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/test_labels.json + train_labels_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/train_labels.json + model_dir: models + name: 13da55851dd21ca361c6952d9e299ba4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7cd3ddd0dcd13a877686fe470f3b9f73 + trainer: + kwargs: {} +name: 13da55851dd21ca361c6952d9e299ba4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1403c0b709f069b5d268dc2180be68c7/params.yaml b/examples/security/classification/output/reports/train/1403c0b709f069b5d268dc2180be68c7/params.yaml new file mode 100644 index 00000000..104c5fca --- /dev/null +++ b/examples/security/classification/output/reports/train/1403c0b709f069b5d268dc2180be68c7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/adv_predictions.json + adv_probabilities_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/e36837df2432b15cdd7cfb9c8308ea7d.pkl + params_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/params.yaml + predictions_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/predictions.json + probabilities_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/probabilities.json + score_dict_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/score_dict.json + test_labels_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/test_labels.json + train_labels_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/train_labels.json + model_dir: models + name: 1403c0b709f069b5d268dc2180be68c7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2df8e6a6ec8a9179739a691f84766240 + trainer: + kwargs: {} +name: 1403c0b709f069b5d268dc2180be68c7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/141f9842c9bd134ff224303f8ffe4776/params.yaml b/examples/security/classification/output/reports/train/141f9842c9bd134ff224303f8ffe4776/params.yaml new file mode 100644 index 00000000..f6269207 --- /dev/null +++ b/examples/security/classification/output/reports/train/141f9842c9bd134ff224303f8ffe4776/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/adv_predictions.json + adv_probabilities_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/f9dcbe528a650ea0d2a0cda22d603e3e.pkl + params_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/params.yaml + predictions_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/predictions.json + probabilities_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/probabilities.json + score_dict_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/score_dict.json + test_labels_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/test_labels.json + train_labels_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/train_labels.json + model_dir: models + name: 141f9842c9bd134ff224303f8ffe4776 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 19c9423408b4f1f16543458c6279152f + trainer: + kwargs: {} +name: 141f9842c9bd134ff224303f8ffe4776 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1439debd522cec47886736238e68a397/params.yaml b/examples/security/classification/output/reports/train/1439debd522cec47886736238e68a397/params.yaml new file mode 100644 index 00000000..2bb13a7c --- /dev/null +++ b/examples/security/classification/output/reports/train/1439debd522cec47886736238e68a397/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1439debd522cec47886736238e68a397/adv_predictions.json + adv_probabilities_file: output/reports/train/1439debd522cec47886736238e68a397/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/e0602cd2730e0c364fe71c47363d992f.pkl + params_file: output/reports/train/1439debd522cec47886736238e68a397/params.yaml + predictions_file: output/reports/train/1439debd522cec47886736238e68a397/predictions.json + probabilities_file: output/reports/train/1439debd522cec47886736238e68a397/probabilities.json + score_dict_file: output/reports/train/1439debd522cec47886736238e68a397/score_dict.json + test_labels_file: output/reports/train/1439debd522cec47886736238e68a397/test_labels.json + train_labels_file: output/reports/train/1439debd522cec47886736238e68a397/train_labels.json + model_dir: models + name: 1439debd522cec47886736238e68a397 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 54172b2e3517e6413b20757de89ab848 + trainer: + kwargs: {} +name: 1439debd522cec47886736238e68a397 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/143df2901130c73517781cf8701305e1/params.yaml b/examples/security/classification/output/reports/train/143df2901130c73517781cf8701305e1/params.yaml new file mode 100644 index 00000000..bc387bae --- /dev/null +++ b/examples/security/classification/output/reports/train/143df2901130c73517781cf8701305e1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/143df2901130c73517781cf8701305e1/adv_predictions.json + adv_probabilities_file: output/reports/train/143df2901130c73517781cf8701305e1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/e1e7c85dd85f3f5809f907f163a81d62.pkl + params_file: output/reports/train/143df2901130c73517781cf8701305e1/params.yaml + predictions_file: output/reports/train/143df2901130c73517781cf8701305e1/predictions.json + probabilities_file: output/reports/train/143df2901130c73517781cf8701305e1/probabilities.json + score_dict_file: output/reports/train/143df2901130c73517781cf8701305e1/score_dict.json + test_labels_file: output/reports/train/143df2901130c73517781cf8701305e1/test_labels.json + train_labels_file: output/reports/train/143df2901130c73517781cf8701305e1/train_labels.json + model_dir: models + name: 143df2901130c73517781cf8701305e1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 59e65d050302e413f95c5902fedcef43 + trainer: + kwargs: {} +name: 143df2901130c73517781cf8701305e1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/147865c09e0df810777ef7b08cc1cce1/params.yaml b/examples/security/classification/output/reports/train/147865c09e0df810777ef7b08cc1cce1/params.yaml new file mode 100644 index 00000000..b3942573 --- /dev/null +++ b/examples/security/classification/output/reports/train/147865c09e0df810777ef7b08cc1cce1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/adv_predictions.json + adv_probabilities_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/5eb8346f718e3e349c4dc6a3c782b31b.pkl + params_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/params.yaml + predictions_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/predictions.json + probabilities_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/probabilities.json + score_dict_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/score_dict.json + test_labels_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/test_labels.json + train_labels_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/train_labels.json + model_dir: models + name: 147865c09e0df810777ef7b08cc1cce1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6b050b87eb9a1c9637dab12ce92ac266 + trainer: + kwargs: {} +name: 147865c09e0df810777ef7b08cc1cce1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/148d30dc884128bf35f91943a72b0055/params.yaml b/examples/security/classification/output/reports/train/148d30dc884128bf35f91943a72b0055/params.yaml new file mode 100644 index 00000000..a3e7e1e6 --- /dev/null +++ b/examples/security/classification/output/reports/train/148d30dc884128bf35f91943a72b0055/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/148d30dc884128bf35f91943a72b0055/adv_predictions.json + adv_probabilities_file: output/reports/train/148d30dc884128bf35f91943a72b0055/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/1b0d0a5a49e78c5e5f6004539de9ac7b.pkl + params_file: output/reports/train/148d30dc884128bf35f91943a72b0055/params.yaml + predictions_file: output/reports/train/148d30dc884128bf35f91943a72b0055/predictions.json + probabilities_file: output/reports/train/148d30dc884128bf35f91943a72b0055/probabilities.json + score_dict_file: output/reports/train/148d30dc884128bf35f91943a72b0055/score_dict.json + test_labels_file: output/reports/train/148d30dc884128bf35f91943a72b0055/test_labels.json + train_labels_file: output/reports/train/148d30dc884128bf35f91943a72b0055/train_labels.json + model_dir: models + name: 148d30dc884128bf35f91943a72b0055 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 63d7ae73f0fd299c7782cfcf4e8d90f3 + trainer: + kwargs: {} +name: 148d30dc884128bf35f91943a72b0055 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/params.yaml b/examples/security/classification/output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/params.yaml new file mode 100644 index 00000000..0293de23 --- /dev/null +++ b/examples/security/classification/output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/adv_predictions.json + adv_probabilities_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/d2d2feaf959775bddab0a59121a4da57.pkl + params_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/params.yaml + predictions_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/predictions.json + probabilities_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/probabilities.json + score_dict_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/score_dict.json + test_labels_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/test_labels.json + train_labels_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/train_labels.json + model_dir: models + name: 14a157c3cd09eaff8ca2d09cda767e92 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6095a76063b03f7fa108fc94a0963971 + trainer: + kwargs: {} +name: 14a157c3cd09eaff8ca2d09cda767e92 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/params.yaml b/examples/security/classification/output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/params.yaml new file mode 100644 index 00000000..fe342148 --- /dev/null +++ b/examples/security/classification/output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/adv_predictions.json + adv_probabilities_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/ff0923d0a09e2de20f5be4e451694461.pkl + params_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/params.yaml + predictions_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/predictions.json + probabilities_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/probabilities.json + score_dict_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/score_dict.json + test_labels_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/test_labels.json + train_labels_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/train_labels.json + model_dir: models + name: 14c19f51b442c6b1a12ebe5491c377c8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 37ddc1ab6995a6e7ba83478e41eaf5fb + trainer: + kwargs: {} +name: 14c19f51b442c6b1a12ebe5491c377c8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/15098fe67344bd8982332022be92b018/params.yaml b/examples/security/classification/output/reports/train/15098fe67344bd8982332022be92b018/params.yaml new file mode 100644 index 00000000..521011e9 --- /dev/null +++ b/examples/security/classification/output/reports/train/15098fe67344bd8982332022be92b018/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/15098fe67344bd8982332022be92b018/adv_predictions.json + adv_probabilities_file: output/reports/train/15098fe67344bd8982332022be92b018/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/2a665aed0ea985fd5b98fc14ebd7dc48.pkl + params_file: output/reports/train/15098fe67344bd8982332022be92b018/params.yaml + predictions_file: output/reports/train/15098fe67344bd8982332022be92b018/predictions.json + probabilities_file: output/reports/train/15098fe67344bd8982332022be92b018/probabilities.json + score_dict_file: output/reports/train/15098fe67344bd8982332022be92b018/score_dict.json + test_labels_file: output/reports/train/15098fe67344bd8982332022be92b018/test_labels.json + train_labels_file: output/reports/train/15098fe67344bd8982332022be92b018/train_labels.json + model_dir: models + name: 15098fe67344bd8982332022be92b018 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8f33f761e2d88992fb0154e6cf18ceee + trainer: + kwargs: {} +name: 15098fe67344bd8982332022be92b018 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/151e3deb462099cdf4496c4796b293c2/params.yaml b/examples/security/classification/output/reports/train/151e3deb462099cdf4496c4796b293c2/params.yaml new file mode 100644 index 00000000..e9618304 --- /dev/null +++ b/examples/security/classification/output/reports/train/151e3deb462099cdf4496c4796b293c2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/adv_predictions.json + adv_probabilities_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/5324c4c2c39310a7b3161a373a9f8313.pkl + params_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/params.yaml + predictions_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/predictions.json + probabilities_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/probabilities.json + score_dict_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/score_dict.json + test_labels_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/test_labels.json + train_labels_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/train_labels.json + model_dir: models + name: 151e3deb462099cdf4496c4796b293c2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cd25b2328adb62e604c082638a497800 + trainer: + kwargs: {} +name: 151e3deb462099cdf4496c4796b293c2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/15201afc0b8e43514ad879971941fa1e/params.yaml b/examples/security/classification/output/reports/train/15201afc0b8e43514ad879971941fa1e/params.yaml new file mode 100644 index 00000000..a8d00780 --- /dev/null +++ b/examples/security/classification/output/reports/train/15201afc0b8e43514ad879971941fa1e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/adv_predictions.json + adv_probabilities_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/bbc9bbb57da59d9742afd9e11ffbb81f.pkl + params_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/params.yaml + predictions_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/predictions.json + probabilities_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/probabilities.json + score_dict_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/score_dict.json + test_labels_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/test_labels.json + train_labels_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/train_labels.json + model_dir: models + name: 15201afc0b8e43514ad879971941fa1e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: da830d2f4a02a7d53d3b71ea968ab5a8 + trainer: + kwargs: {} +name: 15201afc0b8e43514ad879971941fa1e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/params.yaml b/examples/security/classification/output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/params.yaml new file mode 100644 index 00000000..93879ec1 --- /dev/null +++ b/examples/security/classification/output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/params.yaml @@ -0,0 +1,206 @@ +attack: + attack_size: 10 + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 10.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4a64a27887eddfba72101a2193a0427e + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a7503df302b92df27cf7049fdedde437 + trainer: + kwargs: {} + name: 6596818bd280cf9fb05bd0cbe16436aa +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/adv_predictions.json + adv_probabilities_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/adv_probabilities.json + attack_file: output/attacks/1d7a29e561ce6bc38bf891d1b54cd2f4.pkl + data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl + model_file: output/models/260dffdba880f065a90befb1e5c06a91.pkl + params_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/params.yaml + predictions_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/predictions.json + probabilities_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/probabilities.json + score_dict_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/score_dict.json + test_labels_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/test_labels.json + train_labels_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/train_labels.json + model_dir: models + name: 152178b0f0c8ce53a2d7d562004b1ca7 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8b2bbe7eb707d9f98887cfa8e8e8c70b + trainer: + kwargs: {} +name: 152178b0f0c8ce53a2d7d562004b1ca7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/params.yaml b/examples/security/classification/output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/params.yaml new file mode 100644 index 00000000..63b46ab4 --- /dev/null +++ b/examples/security/classification/output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/adv_predictions.json + adv_probabilities_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/efad4e9a55c7c4938aab5b756e373e78.pkl + params_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/params.yaml + predictions_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/predictions.json + probabilities_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/probabilities.json + score_dict_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/score_dict.json + test_labels_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/test_labels.json + train_labels_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/train_labels.json + model_dir: models + name: 1579105c4e9ce3f2aadbc7f43887906b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5548c9a85a09dcb8f060565244f4f75d + trainer: + kwargs: {} +name: 1579105c4e9ce3f2aadbc7f43887906b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/params.yaml b/examples/security/classification/output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/params.yaml new file mode 100644 index 00000000..0cb69955 --- /dev/null +++ b/examples/security/classification/output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/adv_predictions.json + adv_probabilities_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/a6629ff8c914806188cf99f326a0f20d.pkl + params_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/params.yaml + predictions_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/predictions.json + probabilities_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/probabilities.json + score_dict_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/score_dict.json + test_labels_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/test_labels.json + train_labels_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/train_labels.json + model_dir: models + name: 15b32c7b634e6ed64a1a6477cf926a1a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 71f54a45ae44efcc908900566b306085 + trainer: + kwargs: {} +name: 15b32c7b634e6ed64a1a6477cf926a1a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/15c048e332626e1e59aee6e65c2e98b5/params.yaml b/examples/security/classification/output/reports/train/15c048e332626e1e59aee6e65c2e98b5/params.yaml new file mode 100644 index 00000000..cbeae6cb --- /dev/null +++ b/examples/security/classification/output/reports/train/15c048e332626e1e59aee6e65c2e98b5/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/adv_predictions.json + adv_probabilities_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/10329f085c30b024374b9afc06a5bc17.pkl + params_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/params.yaml + predictions_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/predictions.json + probabilities_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/probabilities.json + score_dict_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/score_dict.json + test_labels_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/test_labels.json + train_labels_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/train_labels.json + model_dir: models + name: 15c048e332626e1e59aee6e65c2e98b5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5040e28503542a88f963e33e9bae8564 + trainer: + kwargs: {} +name: 15c048e332626e1e59aee6e65c2e98b5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/params.yaml b/examples/security/classification/output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/params.yaml new file mode 100644 index 00000000..26d920c4 --- /dev/null +++ b/examples/security/classification/output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/adv_predictions.json + adv_probabilities_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/9c3514d534f8b810c8ac7db47a8f75f0.pkl + params_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/params.yaml + predictions_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/predictions.json + probabilities_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/probabilities.json + score_dict_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/score_dict.json + test_labels_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/test_labels.json + train_labels_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/train_labels.json + model_dir: models + name: 15d2b3ea07a8a69be454b7c33d75a3d1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ff3242790ef596122728249f14822fd1 + trainer: + kwargs: {} +name: 15d2b3ea07a8a69be454b7c33d75a3d1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/params.yaml b/examples/security/classification/output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/params.yaml new file mode 100644 index 00000000..9e34173a --- /dev/null +++ b/examples/security/classification/output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/adv_predictions.json + adv_probabilities_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/8eb1e9d34f755dc4a5bb749ccd5d162a.pkl + params_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/params.yaml + predictions_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/predictions.json + probabilities_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/probabilities.json + score_dict_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/score_dict.json + test_labels_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/test_labels.json + train_labels_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/train_labels.json + model_dir: models + name: 15eb7e57e29435c26a27d6fb9cbdceab + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4b04510e6201ef58cf3f25d11a5c9dde + trainer: + kwargs: {} +name: 15eb7e57e29435c26a27d6fb9cbdceab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/params.yaml b/examples/security/classification/output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/params.yaml new file mode 100644 index 00000000..5cb75825 --- /dev/null +++ b/examples/security/classification/output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/adv_predictions.json + adv_probabilities_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/3738416970fdcb44c35242dd4a558655.pkl + params_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/params.yaml + predictions_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/predictions.json + probabilities_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/probabilities.json + score_dict_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/score_dict.json + test_labels_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/test_labels.json + train_labels_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/train_labels.json + model_dir: models + name: 1613a6c768c3023775d37f8c8cf4c09c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fdf913427db728fb0c90069a93f93f43 + trainer: + kwargs: {} +name: 1613a6c768c3023775d37f8c8cf4c09c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1614987e5279a2031253809bb2547a9c/params.yaml b/examples/security/classification/output/reports/train/1614987e5279a2031253809bb2547a9c/params.yaml new file mode 100644 index 00000000..f77270b4 --- /dev/null +++ b/examples/security/classification/output/reports/train/1614987e5279a2031253809bb2547a9c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1614987e5279a2031253809bb2547a9c/adv_predictions.json + adv_probabilities_file: output/reports/train/1614987e5279a2031253809bb2547a9c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/b09f451313031bea79441d0f77a7889e.pkl + params_file: output/reports/train/1614987e5279a2031253809bb2547a9c/params.yaml + predictions_file: output/reports/train/1614987e5279a2031253809bb2547a9c/predictions.json + probabilities_file: output/reports/train/1614987e5279a2031253809bb2547a9c/probabilities.json + score_dict_file: output/reports/train/1614987e5279a2031253809bb2547a9c/score_dict.json + test_labels_file: output/reports/train/1614987e5279a2031253809bb2547a9c/test_labels.json + train_labels_file: output/reports/train/1614987e5279a2031253809bb2547a9c/train_labels.json + model_dir: models + name: 1614987e5279a2031253809bb2547a9c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a2b46d609858a3e79dda3f29566f94c8 + trainer: + kwargs: {} +name: 1614987e5279a2031253809bb2547a9c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/16150a4a676ba686f1870b001a0f4fbd/params.yaml b/examples/security/classification/output/reports/train/16150a4a676ba686f1870b001a0f4fbd/params.yaml new file mode 100644 index 00000000..99729d80 --- /dev/null +++ b/examples/security/classification/output/reports/train/16150a4a676ba686f1870b001a0f4fbd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/adv_predictions.json + adv_probabilities_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/5897cb23f18dcc6760df7bf0be722a63.pkl + params_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/params.yaml + predictions_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/predictions.json + probabilities_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/probabilities.json + score_dict_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/score_dict.json + test_labels_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/test_labels.json + train_labels_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/train_labels.json + model_dir: models + name: 16150a4a676ba686f1870b001a0f4fbd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c0d10cc9291bed70387629aa6e2b7786 + trainer: + kwargs: {} +name: 16150a4a676ba686f1870b001a0f4fbd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/161b361d9f34e0133d4fc3856ece499c/params.yaml b/examples/security/classification/output/reports/train/161b361d9f34e0133d4fc3856ece499c/params.yaml new file mode 100644 index 00000000..dd086cf3 --- /dev/null +++ b/examples/security/classification/output/reports/train/161b361d9f34e0133d4fc3856ece499c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/adv_predictions.json + adv_probabilities_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/6dc7dfd8c04684d8705802e01eb6c239.pkl + params_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/params.yaml + predictions_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/predictions.json + probabilities_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/probabilities.json + score_dict_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/score_dict.json + test_labels_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/test_labels.json + train_labels_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/train_labels.json + model_dir: models + name: 161b361d9f34e0133d4fc3856ece499c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea43b510c676803030f23fc49c56d331 + trainer: + kwargs: {} +name: 161b361d9f34e0133d4fc3856ece499c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/163d2a9c46f06251509b446886270151/params.yaml b/examples/security/classification/output/reports/train/163d2a9c46f06251509b446886270151/params.yaml new file mode 100644 index 00000000..cf2524a4 --- /dev/null +++ b/examples/security/classification/output/reports/train/163d2a9c46f06251509b446886270151/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/163d2a9c46f06251509b446886270151/adv_predictions.json + adv_probabilities_file: output/reports/train/163d2a9c46f06251509b446886270151/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e2ac301c2212d0f75b7d935b728a6ba0.pkl + params_file: output/reports/train/163d2a9c46f06251509b446886270151/params.yaml + predictions_file: output/reports/train/163d2a9c46f06251509b446886270151/predictions.json + probabilities_file: output/reports/train/163d2a9c46f06251509b446886270151/probabilities.json + score_dict_file: output/reports/train/163d2a9c46f06251509b446886270151/score_dict.json + test_labels_file: output/reports/train/163d2a9c46f06251509b446886270151/test_labels.json + train_labels_file: output/reports/train/163d2a9c46f06251509b446886270151/train_labels.json + model_dir: models + name: 163d2a9c46f06251509b446886270151 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3b6e652200da4b8d9f409f3176286662 + trainer: + kwargs: {} +name: 163d2a9c46f06251509b446886270151 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/16442d640ef7b6020a37bbaf081f39db/params.yaml b/examples/security/classification/output/reports/train/16442d640ef7b6020a37bbaf081f39db/params.yaml new file mode 100644 index 00000000..1bf9e504 --- /dev/null +++ b/examples/security/classification/output/reports/train/16442d640ef7b6020a37bbaf081f39db/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/adv_predictions.json + adv_probabilities_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/3261493abbddc2629f6d06931e3a3bb5.pkl + params_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/params.yaml + predictions_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/predictions.json + probabilities_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/probabilities.json + score_dict_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/score_dict.json + test_labels_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/test_labels.json + train_labels_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/train_labels.json + model_dir: models + name: 16442d640ef7b6020a37bbaf081f39db + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 26e3fec9e84eac268d7450d6ffcbf55a + trainer: + kwargs: {} +name: 16442d640ef7b6020a37bbaf081f39db +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/16776c299566237d0345fd043fb687e5/params.yaml b/examples/security/classification/output/reports/train/16776c299566237d0345fd043fb687e5/params.yaml new file mode 100644 index 00000000..088f8b2b --- /dev/null +++ b/examples/security/classification/output/reports/train/16776c299566237d0345fd043fb687e5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/16776c299566237d0345fd043fb687e5/adv_predictions.json + adv_probabilities_file: output/reports/train/16776c299566237d0345fd043fb687e5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/723c3ed800566fdd5c53f3786ff8b79a.pkl + params_file: output/reports/train/16776c299566237d0345fd043fb687e5/params.yaml + predictions_file: output/reports/train/16776c299566237d0345fd043fb687e5/predictions.json + probabilities_file: output/reports/train/16776c299566237d0345fd043fb687e5/probabilities.json + score_dict_file: output/reports/train/16776c299566237d0345fd043fb687e5/score_dict.json + test_labels_file: output/reports/train/16776c299566237d0345fd043fb687e5/test_labels.json + train_labels_file: output/reports/train/16776c299566237d0345fd043fb687e5/train_labels.json + model_dir: models + name: 16776c299566237d0345fd043fb687e5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d06767b543e62bfcff9459bd760f46a + trainer: + kwargs: {} +name: 16776c299566237d0345fd043fb687e5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1679b897bcd7505034923d74482eb325/params.yaml b/examples/security/classification/output/reports/train/1679b897bcd7505034923d74482eb325/params.yaml new file mode 100644 index 00000000..ebd7cd2e --- /dev/null +++ b/examples/security/classification/output/reports/train/1679b897bcd7505034923d74482eb325/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1679b897bcd7505034923d74482eb325/adv_predictions.json + adv_probabilities_file: output/reports/train/1679b897bcd7505034923d74482eb325/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/a9d897cc49153e0f1baf87c585ea925a.pkl + params_file: output/reports/train/1679b897bcd7505034923d74482eb325/params.yaml + predictions_file: output/reports/train/1679b897bcd7505034923d74482eb325/predictions.json + probabilities_file: output/reports/train/1679b897bcd7505034923d74482eb325/probabilities.json + score_dict_file: output/reports/train/1679b897bcd7505034923d74482eb325/score_dict.json + test_labels_file: output/reports/train/1679b897bcd7505034923d74482eb325/test_labels.json + train_labels_file: output/reports/train/1679b897bcd7505034923d74482eb325/train_labels.json + model_dir: models + name: 1679b897bcd7505034923d74482eb325 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cfae4572b8d127750c0558df1c8ff309 + trainer: + kwargs: {} +name: 1679b897bcd7505034923d74482eb325 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1698f6d6dae06a20fac185ec06c77d04/params.yaml b/examples/security/classification/output/reports/train/1698f6d6dae06a20fac185ec06c77d04/params.yaml new file mode 100644 index 00000000..6881eb34 --- /dev/null +++ b/examples/security/classification/output/reports/train/1698f6d6dae06a20fac185ec06c77d04/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/adv_predictions.json + adv_probabilities_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/68bd15058b5ad135e31aed14acb881f6.pkl + params_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/params.yaml + predictions_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/predictions.json + probabilities_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/probabilities.json + score_dict_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/score_dict.json + test_labels_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/test_labels.json + train_labels_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/train_labels.json + model_dir: models + name: 1698f6d6dae06a20fac185ec06c77d04 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 23b86d93a2967e5160b4295cadd991fa + trainer: + kwargs: {} +name: 1698f6d6dae06a20fac185ec06c77d04 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/169d516458f33f7ff7a8666a242242f9/params.yaml b/examples/security/classification/output/reports/train/169d516458f33f7ff7a8666a242242f9/params.yaml new file mode 100644 index 00000000..c46e4711 --- /dev/null +++ b/examples/security/classification/output/reports/train/169d516458f33f7ff7a8666a242242f9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/adv_predictions.json + adv_probabilities_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/6cad2eed0e7a321b218a8eda391ca44b.pkl + params_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/params.yaml + predictions_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/predictions.json + probabilities_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/probabilities.json + score_dict_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/score_dict.json + test_labels_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/test_labels.json + train_labels_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/train_labels.json + model_dir: models + name: 169d516458f33f7ff7a8666a242242f9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46432c6097b6ee2568fc3a42d57769ab + trainer: + kwargs: {} +name: 169d516458f33f7ff7a8666a242242f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/params.yaml b/examples/security/classification/output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/params.yaml new file mode 100644 index 00000000..8f433bfa --- /dev/null +++ b/examples/security/classification/output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/adv_predictions.json + adv_probabilities_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/3bc47e7d1d1c9da6b769d81250f0ce48.pkl + params_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/params.yaml + predictions_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/predictions.json + probabilities_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/probabilities.json + score_dict_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/score_dict.json + test_labels_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/test_labels.json + train_labels_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/train_labels.json + model_dir: models + name: 16ae3eeb0124fb64e236b4890343f5a0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4afa4dd465ade2c222e34e67237e0d03 + trainer: + kwargs: {} +name: 16ae3eeb0124fb64e236b4890343f5a0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/16b14fbb166aab92432f45644a4be395/params.yaml b/examples/security/classification/output/reports/train/16b14fbb166aab92432f45644a4be395/params.yaml new file mode 100644 index 00000000..b455816a --- /dev/null +++ b/examples/security/classification/output/reports/train/16b14fbb166aab92432f45644a4be395/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/16b14fbb166aab92432f45644a4be395/adv_predictions.json + adv_probabilities_file: output/reports/train/16b14fbb166aab92432f45644a4be395/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/d6aa5210725a5ff17ded701863bc3a2d.pkl + params_file: output/reports/train/16b14fbb166aab92432f45644a4be395/params.yaml + predictions_file: output/reports/train/16b14fbb166aab92432f45644a4be395/predictions.json + probabilities_file: output/reports/train/16b14fbb166aab92432f45644a4be395/probabilities.json + score_dict_file: output/reports/train/16b14fbb166aab92432f45644a4be395/score_dict.json + test_labels_file: output/reports/train/16b14fbb166aab92432f45644a4be395/test_labels.json + train_labels_file: output/reports/train/16b14fbb166aab92432f45644a4be395/train_labels.json + model_dir: models + name: 16b14fbb166aab92432f45644a4be395 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 07e83b3df352bff110d9908acae6287f + trainer: + kwargs: {} +name: 16b14fbb166aab92432f45644a4be395 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/16babebec4f814699b12b1aa1d195ab2/params.yaml b/examples/security/classification/output/reports/train/16babebec4f814699b12b1aa1d195ab2/params.yaml new file mode 100644 index 00000000..33fa6ef7 --- /dev/null +++ b/examples/security/classification/output/reports/train/16babebec4f814699b12b1aa1d195ab2/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/adv_predictions.json + adv_probabilities_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/c61bd5e623a15071ca4a6ea9d9b6cb2a.pkl + params_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/params.yaml + predictions_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/predictions.json + probabilities_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/probabilities.json + score_dict_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/score_dict.json + test_labels_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/test_labels.json + train_labels_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/train_labels.json + model_dir: models + name: 16babebec4f814699b12b1aa1d195ab2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ddfa56158ec7763927b80c525a4470ff + trainer: + kwargs: {} +name: 16babebec4f814699b12b1aa1d195ab2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/params.yaml b/examples/security/classification/output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/params.yaml new file mode 100644 index 00000000..a6e77283 --- /dev/null +++ b/examples/security/classification/output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/adv_predictions.json + adv_probabilities_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/bfc3ca7b5e74ae55e63770b494e360cd.pkl + params_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/params.yaml + predictions_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/predictions.json + probabilities_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/probabilities.json + score_dict_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/score_dict.json + test_labels_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/test_labels.json + train_labels_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/train_labels.json + model_dir: models + name: 16c775c8ed7c91bf03cd7d01dd245ea4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea99efec069344d81bb4d4d16429f09f + trainer: + kwargs: {} +name: 16c775c8ed7c91bf03cd7d01dd245ea4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/16cd98524388a650a5b27a6d7c9b4968/params.yaml b/examples/security/classification/output/reports/train/16cd98524388a650a5b27a6d7c9b4968/params.yaml new file mode 100644 index 00000000..1c889bde --- /dev/null +++ b/examples/security/classification/output/reports/train/16cd98524388a650a5b27a6d7c9b4968/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/adv_predictions.json + adv_probabilities_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/4aaf589b7a309911b5dceb08b4a79525.pkl + params_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/params.yaml + predictions_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/predictions.json + probabilities_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/probabilities.json + score_dict_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/score_dict.json + test_labels_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/test_labels.json + train_labels_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/train_labels.json + model_dir: models + name: 16cd98524388a650a5b27a6d7c9b4968 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b41d57de4c248ba42ad50fc0ddb8079e + trainer: + kwargs: {} +name: 16cd98524388a650a5b27a6d7c9b4968 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/params.yaml b/examples/security/classification/output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/params.yaml new file mode 100644 index 00000000..c590330b --- /dev/null +++ b/examples/security/classification/output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/adv_predictions.json + adv_probabilities_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/57812ffff14e4c703113d2f95f331256.pkl + params_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/params.yaml + predictions_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/predictions.json + probabilities_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/probabilities.json + score_dict_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/score_dict.json + test_labels_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/test_labels.json + train_labels_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/train_labels.json + model_dir: models + name: 16e6867a809a9970cf13cea3e3ed3a8a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3ab78ed0da18c4a6729b9976c1a01678 + trainer: + kwargs: {} +name: 16e6867a809a9970cf13cea3e3ed3a8a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1731528739084d8d9391cb93695bfe0b/params.yaml b/examples/security/classification/output/reports/train/1731528739084d8d9391cb93695bfe0b/params.yaml new file mode 100644 index 00000000..41a3eac0 --- /dev/null +++ b/examples/security/classification/output/reports/train/1731528739084d8d9391cb93695bfe0b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/adv_predictions.json + adv_probabilities_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/9f44707b0cdc4e8b57dc5313c205d279.pkl + params_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/params.yaml + predictions_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/predictions.json + probabilities_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/probabilities.json + score_dict_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/score_dict.json + test_labels_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/test_labels.json + train_labels_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/train_labels.json + model_dir: models + name: 1731528739084d8d9391cb93695bfe0b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1421da396afef651905a7a8b695e5598 + trainer: + kwargs: {} +name: 1731528739084d8d9391cb93695bfe0b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/params.yaml b/examples/security/classification/output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/params.yaml new file mode 100644 index 00000000..53a61133 --- /dev/null +++ b/examples/security/classification/output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/adv_predictions.json + adv_probabilities_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/949971ff0fa9494df897ab5594e135dd.pkl + params_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/params.yaml + predictions_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/predictions.json + probabilities_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/probabilities.json + score_dict_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/score_dict.json + test_labels_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/test_labels.json + train_labels_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/train_labels.json + model_dir: models + name: 1746a5c21ecd31a2f9287a260d0b48c6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9d2c5e4bea8d92a4e3e10832ced42b2a + trainer: + kwargs: {} +name: 1746a5c21ecd31a2f9287a260d0b48c6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/175165f3c2a7c11edf1ea77931622459/params.yaml b/examples/security/classification/output/reports/train/175165f3c2a7c11edf1ea77931622459/params.yaml new file mode 100644 index 00000000..138cf921 --- /dev/null +++ b/examples/security/classification/output/reports/train/175165f3c2a7c11edf1ea77931622459/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/adv_predictions.json + adv_probabilities_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/4a7007a799d43d8625e0e52940c77ac0.pkl + params_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/params.yaml + predictions_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/predictions.json + probabilities_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/probabilities.json + score_dict_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/score_dict.json + test_labels_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/test_labels.json + train_labels_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/train_labels.json + model_dir: models + name: 175165f3c2a7c11edf1ea77931622459 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1ce1a8257d04e1b6f715f82e65524846 + trainer: + kwargs: {} +name: 175165f3c2a7c11edf1ea77931622459 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/176d6f297cef3b7d607e91463f840d39/params.yaml b/examples/security/classification/output/reports/train/176d6f297cef3b7d607e91463f840d39/params.yaml new file mode 100644 index 00000000..c9859254 --- /dev/null +++ b/examples/security/classification/output/reports/train/176d6f297cef3b7d607e91463f840d39/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/adv_predictions.json + adv_probabilities_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/b039c7dca78f9da2dc66a3267ab32901.pkl + params_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/params.yaml + predictions_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/predictions.json + probabilities_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/probabilities.json + score_dict_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/score_dict.json + test_labels_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/test_labels.json + train_labels_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/train_labels.json + model_dir: models + name: 176d6f297cef3b7d607e91463f840d39 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d2d1c3d8f2f90fca5869d5837068dc64 + trainer: + kwargs: {} +name: 176d6f297cef3b7d607e91463f840d39 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1777d439d081300005b926fbd130450e/params.yaml b/examples/security/classification/output/reports/train/1777d439d081300005b926fbd130450e/params.yaml new file mode 100644 index 00000000..404e0d56 --- /dev/null +++ b/examples/security/classification/output/reports/train/1777d439d081300005b926fbd130450e/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1777d439d081300005b926fbd130450e/adv_predictions.json + adv_probabilities_file: output/reports/train/1777d439d081300005b926fbd130450e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/95c40c84b8f65bb8fb949df3567a993d.pkl + params_file: output/reports/train/1777d439d081300005b926fbd130450e/params.yaml + predictions_file: output/reports/train/1777d439d081300005b926fbd130450e/predictions.json + probabilities_file: output/reports/train/1777d439d081300005b926fbd130450e/probabilities.json + score_dict_file: output/reports/train/1777d439d081300005b926fbd130450e/score_dict.json + test_labels_file: output/reports/train/1777d439d081300005b926fbd130450e/test_labels.json + train_labels_file: output/reports/train/1777d439d081300005b926fbd130450e/train_labels.json + model_dir: models + name: 1777d439d081300005b926fbd130450e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: db950bee681fb8ac4af10100791e258d + trainer: + kwargs: {} +name: 1777d439d081300005b926fbd130450e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/177faa4762399452a5521e988600082b/params.yaml b/examples/security/classification/output/reports/train/177faa4762399452a5521e988600082b/params.yaml new file mode 100644 index 00000000..8f940a01 --- /dev/null +++ b/examples/security/classification/output/reports/train/177faa4762399452a5521e988600082b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/177faa4762399452a5521e988600082b/adv_predictions.json + adv_probabilities_file: output/reports/train/177faa4762399452a5521e988600082b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/5fa2817e73c574a87f0c9cb2b55acf80.pkl + params_file: output/reports/train/177faa4762399452a5521e988600082b/params.yaml + predictions_file: output/reports/train/177faa4762399452a5521e988600082b/predictions.json + probabilities_file: output/reports/train/177faa4762399452a5521e988600082b/probabilities.json + score_dict_file: output/reports/train/177faa4762399452a5521e988600082b/score_dict.json + test_labels_file: output/reports/train/177faa4762399452a5521e988600082b/test_labels.json + train_labels_file: output/reports/train/177faa4762399452a5521e988600082b/train_labels.json + model_dir: models + name: 177faa4762399452a5521e988600082b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5f346f6765081e2d8d2d9de6e6c3a3d2 + trainer: + kwargs: {} +name: 177faa4762399452a5521e988600082b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/params.yaml b/examples/security/classification/output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/params.yaml new file mode 100644 index 00000000..a9866ab7 --- /dev/null +++ b/examples/security/classification/output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/adv_predictions.json + adv_probabilities_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/af9738fe361e2849c998bc142c84387d.pkl + params_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/params.yaml + predictions_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/predictions.json + probabilities_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/probabilities.json + score_dict_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/score_dict.json + test_labels_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/test_labels.json + train_labels_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/train_labels.json + model_dir: models + name: 178f38821f00bf6d57bb3dcf0766efbb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b75475adc212df574e0348b17bf85854 + trainer: + kwargs: {} +name: 178f38821f00bf6d57bb3dcf0766efbb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/17a83e782b44c0d733a65a60ee262fe1/params.yaml b/examples/security/classification/output/reports/train/17a83e782b44c0d733a65a60ee262fe1/params.yaml new file mode 100644 index 00000000..a21f8d1a --- /dev/null +++ b/examples/security/classification/output/reports/train/17a83e782b44c0d733a65a60ee262fe1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/adv_predictions.json + adv_probabilities_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/181a25515829fdfb04c0a6711e663c86.pkl + params_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/params.yaml + predictions_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/predictions.json + probabilities_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/probabilities.json + score_dict_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/score_dict.json + test_labels_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/test_labels.json + train_labels_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/train_labels.json + model_dir: models + name: 17a83e782b44c0d733a65a60ee262fe1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cb169d32132bd7b902e544fa30947fd + trainer: + kwargs: {} +name: 17a83e782b44c0d733a65a60ee262fe1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/17b18d9aa005b219865bef1bb524e022/params.yaml b/examples/security/classification/output/reports/train/17b18d9aa005b219865bef1bb524e022/params.yaml new file mode 100644 index 00000000..b00971a8 --- /dev/null +++ b/examples/security/classification/output/reports/train/17b18d9aa005b219865bef1bb524e022/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/adv_predictions.json + adv_probabilities_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/918a3b53a315b11017c84706333ed3e8.pkl + params_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/params.yaml + predictions_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/predictions.json + probabilities_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/probabilities.json + score_dict_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/score_dict.json + test_labels_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/test_labels.json + train_labels_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/train_labels.json + model_dir: models + name: 17b18d9aa005b219865bef1bb524e022 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 06f43f37e381f4bf8b21dd95490e0831 + trainer: + kwargs: {} +name: 17b18d9aa005b219865bef1bb524e022 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/17c7e130c453765d6b61756d176e4d1a/params.yaml b/examples/security/classification/output/reports/train/17c7e130c453765d6b61756d176e4d1a/params.yaml new file mode 100644 index 00000000..6f1c473f --- /dev/null +++ b/examples/security/classification/output/reports/train/17c7e130c453765d6b61756d176e4d1a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/adv_predictions.json + adv_probabilities_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/fa8b47e5d5edd775165df0992b22b2fd.pkl + params_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/params.yaml + predictions_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/predictions.json + probabilities_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/probabilities.json + score_dict_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/score_dict.json + test_labels_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/test_labels.json + train_labels_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/train_labels.json + model_dir: models + name: 17c7e130c453765d6b61756d176e4d1a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9a00b09d3f237376b0e8e81f821ab70f + trainer: + kwargs: {} +name: 17c7e130c453765d6b61756d176e4d1a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/180f842ba0fbcb28a0c331905f1669ef/params.yaml b/examples/security/classification/output/reports/train/180f842ba0fbcb28a0c331905f1669ef/params.yaml new file mode 100644 index 00000000..bcf5c351 --- /dev/null +++ b/examples/security/classification/output/reports/train/180f842ba0fbcb28a0c331905f1669ef/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/adv_predictions.json + adv_probabilities_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/44330192ef99a49d450da422f2905108.pkl + params_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/params.yaml + predictions_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/predictions.json + probabilities_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/probabilities.json + score_dict_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/score_dict.json + test_labels_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/test_labels.json + train_labels_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/train_labels.json + model_dir: models + name: 180f842ba0fbcb28a0c331905f1669ef + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3097146caf098c56315cc1772a595a73 + trainer: + kwargs: {} +name: 180f842ba0fbcb28a0c331905f1669ef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1812b08ac9553675a1f5f43791897e07/params.yaml b/examples/security/classification/output/reports/train/1812b08ac9553675a1f5f43791897e07/params.yaml new file mode 100644 index 00000000..040c5fa3 --- /dev/null +++ b/examples/security/classification/output/reports/train/1812b08ac9553675a1f5f43791897e07/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/adv_predictions.json + adv_probabilities_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/9e087900962ee4dcd653639f5d1e7cbf.pkl + params_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/params.yaml + predictions_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/predictions.json + probabilities_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/probabilities.json + score_dict_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/score_dict.json + test_labels_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/test_labels.json + train_labels_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/train_labels.json + model_dir: models + name: 1812b08ac9553675a1f5f43791897e07 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bc366863451f268c89cacbf04b31c347 + trainer: + kwargs: {} +name: 1812b08ac9553675a1f5f43791897e07 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/181d543700a7085e11643be4c67faea3/params.yaml b/examples/security/classification/output/reports/train/181d543700a7085e11643be4c67faea3/params.yaml new file mode 100644 index 00000000..4328e27c --- /dev/null +++ b/examples/security/classification/output/reports/train/181d543700a7085e11643be4c67faea3/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/181d543700a7085e11643be4c67faea3/adv_predictions.json + adv_probabilities_file: output/reports/train/181d543700a7085e11643be4c67faea3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/1de8603a2a5ad1cec3a2d209e41e8b7f.pkl + params_file: output/reports/train/181d543700a7085e11643be4c67faea3/params.yaml + predictions_file: output/reports/train/181d543700a7085e11643be4c67faea3/predictions.json + probabilities_file: output/reports/train/181d543700a7085e11643be4c67faea3/probabilities.json + score_dict_file: output/reports/train/181d543700a7085e11643be4c67faea3/score_dict.json + test_labels_file: output/reports/train/181d543700a7085e11643be4c67faea3/test_labels.json + train_labels_file: output/reports/train/181d543700a7085e11643be4c67faea3/train_labels.json + model_dir: models + name: 181d543700a7085e11643be4c67faea3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 14e4e058c5eda5d47ba6cdad701c6619 + trainer: + kwargs: {} +name: 181d543700a7085e11643be4c67faea3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1838b492b7cbe374b8d24336b97a5427/params.yaml b/examples/security/classification/output/reports/train/1838b492b7cbe374b8d24336b97a5427/params.yaml new file mode 100644 index 00000000..a68e378a --- /dev/null +++ b/examples/security/classification/output/reports/train/1838b492b7cbe374b8d24336b97a5427/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/adv_predictions.json + adv_probabilities_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/b5099248e5271e881663a9fbb85de6ef.pkl + params_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/params.yaml + predictions_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/predictions.json + probabilities_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/probabilities.json + score_dict_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/score_dict.json + test_labels_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/test_labels.json + train_labels_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/train_labels.json + model_dir: models + name: 1838b492b7cbe374b8d24336b97a5427 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 616c428a1b8aceccad66882047a0e729 + trainer: + kwargs: {} +name: 1838b492b7cbe374b8d24336b97a5427 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/params.yaml b/examples/security/classification/output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/params.yaml new file mode 100644 index 00000000..f4815e72 --- /dev/null +++ b/examples/security/classification/output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/adv_predictions.json + adv_probabilities_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/ec7b562faee79c0c1ecb6c4b2e8aa7a8.pkl + params_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/params.yaml + predictions_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/predictions.json + probabilities_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/probabilities.json + score_dict_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/score_dict.json + test_labels_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/test_labels.json + train_labels_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/train_labels.json + model_dir: models + name: 18734d419b6aed5650b7f469dfa0bc3d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ee69aa809a4f3c3129c4646b0a083e4 + trainer: + kwargs: {} +name: 18734d419b6aed5650b7f469dfa0bc3d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/params.yaml b/examples/security/classification/output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/params.yaml new file mode 100644 index 00000000..bac9c72c --- /dev/null +++ b/examples/security/classification/output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/adv_predictions.json + adv_probabilities_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/d15e252cc949ff3656999d7b6c3f5bce.pkl + params_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/params.yaml + predictions_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/predictions.json + probabilities_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/probabilities.json + score_dict_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/score_dict.json + test_labels_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/test_labels.json + train_labels_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/train_labels.json + model_dir: models + name: 18c3e40cfeb85daa878bee2117cf53ea + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4ded67cb5fa0606e3f78fb8a93633cf2 + trainer: + kwargs: {} +name: 18c3e40cfeb85daa878bee2117cf53ea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/18eab89ce02f5feac8951def13c627c7/params.yaml b/examples/security/classification/output/reports/train/18eab89ce02f5feac8951def13c627c7/params.yaml new file mode 100644 index 00000000..de75dc95 --- /dev/null +++ b/examples/security/classification/output/reports/train/18eab89ce02f5feac8951def13c627c7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/adv_predictions.json + adv_probabilities_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/79b82cf6d0ac773a62e6dc5343e0b8f6.pkl + params_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/params.yaml + predictions_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/predictions.json + probabilities_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/probabilities.json + score_dict_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/score_dict.json + test_labels_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/test_labels.json + train_labels_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/train_labels.json + model_dir: models + name: 18eab89ce02f5feac8951def13c627c7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 605935d00cded169a0cca1ba913dd123 + trainer: + kwargs: {} +name: 18eab89ce02f5feac8951def13c627c7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/params.yaml b/examples/security/classification/output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/params.yaml new file mode 100644 index 00000000..56328129 --- /dev/null +++ b/examples/security/classification/output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/adv_predictions.json + adv_probabilities_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/824222634f3f998709333d90e03ccd84.pkl + params_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/params.yaml + predictions_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/predictions.json + probabilities_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/probabilities.json + score_dict_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/score_dict.json + test_labels_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/test_labels.json + train_labels_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/train_labels.json + model_dir: models + name: 18f83b1131ac2c4e765a7b003ef4a406 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 61bea0ee57be328b5089ccf5f9317d0a + trainer: + kwargs: {} +name: 18f83b1131ac2c4e765a7b003ef4a406 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/194b684de23730ce579d0c776e3a6dc6/params.yaml b/examples/security/classification/output/reports/train/194b684de23730ce579d0c776e3a6dc6/params.yaml new file mode 100644 index 00000000..bb0c1087 --- /dev/null +++ b/examples/security/classification/output/reports/train/194b684de23730ce579d0c776e3a6dc6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/adv_predictions.json + adv_probabilities_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/8cd4c014a7fb5f8cb253dd8657cdd8ab.pkl + params_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/params.yaml + predictions_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/predictions.json + probabilities_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/probabilities.json + score_dict_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/score_dict.json + test_labels_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/test_labels.json + train_labels_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/train_labels.json + model_dir: models + name: 194b684de23730ce579d0c776e3a6dc6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7db9ce3de40ec336b2e2159dab8538c9 + trainer: + kwargs: {} +name: 194b684de23730ce579d0c776e3a6dc6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/params.yaml b/examples/security/classification/output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/params.yaml new file mode 100644 index 00000000..5076fec0 --- /dev/null +++ b/examples/security/classification/output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/adv_predictions.json + adv_probabilities_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/22e04e9e6359826aac485f0e9bb1e7ee.pkl + params_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/params.yaml + predictions_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/predictions.json + probabilities_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/probabilities.json + score_dict_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/score_dict.json + test_labels_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/test_labels.json + train_labels_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/train_labels.json + model_dir: models + name: 1953aa0cd09d78bbb09d91ddd3f1b522 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 58bd6e86953e8b9a33f7bb4efc6cd255 + trainer: + kwargs: {} +name: 1953aa0cd09d78bbb09d91ddd3f1b522 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/197d0205e1744e737f2f69618aa32668/params.yaml b/examples/security/classification/output/reports/train/197d0205e1744e737f2f69618aa32668/params.yaml new file mode 100644 index 00000000..417455d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/197d0205e1744e737f2f69618aa32668/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/197d0205e1744e737f2f69618aa32668/adv_predictions.json + adv_probabilities_file: output/reports/train/197d0205e1744e737f2f69618aa32668/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/1a15aff91c9a97fa7631111c0c34fd18.pkl + params_file: output/reports/train/197d0205e1744e737f2f69618aa32668/params.yaml + predictions_file: output/reports/train/197d0205e1744e737f2f69618aa32668/predictions.json + probabilities_file: output/reports/train/197d0205e1744e737f2f69618aa32668/probabilities.json + score_dict_file: output/reports/train/197d0205e1744e737f2f69618aa32668/score_dict.json + test_labels_file: output/reports/train/197d0205e1744e737f2f69618aa32668/test_labels.json + train_labels_file: output/reports/train/197d0205e1744e737f2f69618aa32668/train_labels.json + model_dir: models + name: 197d0205e1744e737f2f69618aa32668 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9f7469456f6e94fce8ac2e0655b3439c + trainer: + kwargs: {} +name: 197d0205e1744e737f2f69618aa32668 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/19a8d802acae97d43955369971a0b255/params.yaml b/examples/security/classification/output/reports/train/19a8d802acae97d43955369971a0b255/params.yaml new file mode 100644 index 00000000..9884cb18 --- /dev/null +++ b/examples/security/classification/output/reports/train/19a8d802acae97d43955369971a0b255/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/19a8d802acae97d43955369971a0b255/adv_predictions.json + adv_probabilities_file: output/reports/train/19a8d802acae97d43955369971a0b255/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/3cf258dff0c311070ad2b0f84f144c5f.pkl + params_file: output/reports/train/19a8d802acae97d43955369971a0b255/params.yaml + predictions_file: output/reports/train/19a8d802acae97d43955369971a0b255/predictions.json + probabilities_file: output/reports/train/19a8d802acae97d43955369971a0b255/probabilities.json + score_dict_file: output/reports/train/19a8d802acae97d43955369971a0b255/score_dict.json + test_labels_file: output/reports/train/19a8d802acae97d43955369971a0b255/test_labels.json + train_labels_file: output/reports/train/19a8d802acae97d43955369971a0b255/train_labels.json + model_dir: models + name: 19a8d802acae97d43955369971a0b255 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6a9c9ec1156b48d8138edea003a1df09 + trainer: + kwargs: {} +name: 19a8d802acae97d43955369971a0b255 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/params.yaml b/examples/security/classification/output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/params.yaml new file mode 100644 index 00000000..4f1c545b --- /dev/null +++ b/examples/security/classification/output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/adv_predictions.json + adv_probabilities_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/d20eb8cbd700e08f17b016a782e3ae35.pkl + params_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/params.yaml + predictions_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/predictions.json + probabilities_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/probabilities.json + score_dict_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/score_dict.json + test_labels_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/test_labels.json + train_labels_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/train_labels.json + model_dir: models + name: 19cefde76c33fa4a4d7a08919fde30f5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 84aaf0006dad72bd396519070077631c + trainer: + kwargs: {} +name: 19cefde76c33fa4a4d7a08919fde30f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/params.yaml b/examples/security/classification/output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/params.yaml new file mode 100644 index 00000000..76835a30 --- /dev/null +++ b/examples/security/classification/output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/adv_predictions.json + adv_probabilities_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/466bb3f38a908176ce42bb6cdc5666f2.pkl + params_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/params.yaml + predictions_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/predictions.json + probabilities_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/probabilities.json + score_dict_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/score_dict.json + test_labels_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/test_labels.json + train_labels_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/train_labels.json + model_dir: models + name: 19d115fa8e298f84c908ccc6fdc67b65 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 78c6b819b37e7f16b0ad5db0bf3b2c5f + trainer: + kwargs: {} +name: 19d115fa8e298f84c908ccc6fdc67b65 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/params.yaml b/examples/security/classification/output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/params.yaml new file mode 100644 index 00000000..d55b480f --- /dev/null +++ b/examples/security/classification/output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/params.yaml @@ -0,0 +1,206 @@ +attack: + attack_size: 10 + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 10.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6c89d09b982e41e1a128873c92bce386 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 63893db59d8e6a9fbf4c5947b48a7f8c + trainer: + kwargs: {} + name: 552429a58690906f2fcd046cfd184ed2 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/adv_predictions.json + adv_probabilities_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/adv_probabilities.json + attack_file: output/attacks/ab015de719d2b03fc6a81480559e384d.pkl + data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl + model_file: output/models/3cdbf74c840304670801560dc2ca5233.pkl + params_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/params.yaml + predictions_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/predictions.json + probabilities_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/probabilities.json + score_dict_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/score_dict.json + test_labels_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/test_labels.json + train_labels_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/train_labels.json + model_dir: models + name: 19e9066a52d41d2d8fb503400ae65ff5 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 605297d2df5f46e6932de76ce5e1b650 + trainer: + kwargs: {} +name: 19e9066a52d41d2d8fb503400ae65ff5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/params.yaml b/examples/security/classification/output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/params.yaml new file mode 100644 index 00000000..5f14bbf9 --- /dev/null +++ b/examples/security/classification/output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/adv_predictions.json + adv_probabilities_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/07ad5ac0e8b3fbe4e56d03c0edb0ef28.pkl + params_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/params.yaml + predictions_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/predictions.json + probabilities_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/probabilities.json + score_dict_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/score_dict.json + test_labels_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/test_labels.json + train_labels_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/train_labels.json + model_dir: models + name: 19fad42d2bbcd20b06bddd7477b0f7ca + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 965b7a605defa035f92dd8f3c484c5e3 + trainer: + kwargs: {} +name: 19fad42d2bbcd20b06bddd7477b0f7ca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1a09b6c7acaee30db106668759a2c3b0/params.yaml b/examples/security/classification/output/reports/train/1a09b6c7acaee30db106668759a2c3b0/params.yaml new file mode 100644 index 00000000..0832d062 --- /dev/null +++ b/examples/security/classification/output/reports/train/1a09b6c7acaee30db106668759a2c3b0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/adv_predictions.json + adv_probabilities_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/2cc588d48e50af4b7e68a901710ccf13.pkl + params_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/params.yaml + predictions_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/predictions.json + probabilities_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/probabilities.json + score_dict_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/score_dict.json + test_labels_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/test_labels.json + train_labels_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/train_labels.json + model_dir: models + name: 1a09b6c7acaee30db106668759a2c3b0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b6b7d0e3fc01eec81d845dd597d35811 + trainer: + kwargs: {} +name: 1a09b6c7acaee30db106668759a2c3b0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1a17b597e2fcaba117c5d2358c291688/params.yaml b/examples/security/classification/output/reports/train/1a17b597e2fcaba117c5d2358c291688/params.yaml new file mode 100644 index 00000000..7f78c26a --- /dev/null +++ b/examples/security/classification/output/reports/train/1a17b597e2fcaba117c5d2358c291688/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/adv_predictions.json + adv_probabilities_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/135d674502dc16db3258d6d81efc6253.pkl + params_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/params.yaml + predictions_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/predictions.json + probabilities_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/probabilities.json + score_dict_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/score_dict.json + test_labels_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/test_labels.json + train_labels_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/train_labels.json + model_dir: models + name: 1a17b597e2fcaba117c5d2358c291688 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ec82bb52c2c841e197f9bd2ebb5e5165 + trainer: + kwargs: {} +name: 1a17b597e2fcaba117c5d2358c291688 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/params.yaml b/examples/security/classification/output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/params.yaml new file mode 100644 index 00000000..a035582a --- /dev/null +++ b/examples/security/classification/output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/adv_predictions.json + adv_probabilities_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/2fd84220927379902f4f1f41eb0bdba4.pkl + params_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/params.yaml + predictions_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/predictions.json + probabilities_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/probabilities.json + score_dict_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/score_dict.json + test_labels_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/test_labels.json + train_labels_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/train_labels.json + model_dir: models + name: 1a1f01dc57c29e8cd1c1e178a3b89273 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 44adc7a7cf2094481acd6e82817e2b28 + trainer: + kwargs: {} +name: 1a1f01dc57c29e8cd1c1e178a3b89273 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1a255223760650fa354344bb111a47f5/params.yaml b/examples/security/classification/output/reports/train/1a255223760650fa354344bb111a47f5/params.yaml new file mode 100644 index 00000000..9dd7bd33 --- /dev/null +++ b/examples/security/classification/output/reports/train/1a255223760650fa354344bb111a47f5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1a255223760650fa354344bb111a47f5/adv_predictions.json + adv_probabilities_file: output/reports/train/1a255223760650fa354344bb111a47f5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/fc6dabefc9ca44266092c70d3aad313f.pkl + params_file: output/reports/train/1a255223760650fa354344bb111a47f5/params.yaml + predictions_file: output/reports/train/1a255223760650fa354344bb111a47f5/predictions.json + probabilities_file: output/reports/train/1a255223760650fa354344bb111a47f5/probabilities.json + score_dict_file: output/reports/train/1a255223760650fa354344bb111a47f5/score_dict.json + test_labels_file: output/reports/train/1a255223760650fa354344bb111a47f5/test_labels.json + train_labels_file: output/reports/train/1a255223760650fa354344bb111a47f5/train_labels.json + model_dir: models + name: 1a255223760650fa354344bb111a47f5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e251914fe67a33765ddbc593789f949f + trainer: + kwargs: {} +name: 1a255223760650fa354344bb111a47f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/params.yaml b/examples/security/classification/output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/params.yaml new file mode 100644 index 00000000..8570201f --- /dev/null +++ b/examples/security/classification/output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/adv_predictions.json + adv_probabilities_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/c78788600e2ca734aed1e3d5ffa5d592.pkl + params_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/params.yaml + predictions_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/predictions.json + probabilities_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/probabilities.json + score_dict_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/score_dict.json + test_labels_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/test_labels.json + train_labels_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/train_labels.json + model_dir: models + name: 1a2f5766f7d856b1e7c825ea10b518be + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dd0ee86f14693134639ec0179dbb60b3 + trainer: + kwargs: {} +name: 1a2f5766f7d856b1e7c825ea10b518be +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1a4dbcceba7b0811b9e181f642105415/params.yaml b/examples/security/classification/output/reports/train/1a4dbcceba7b0811b9e181f642105415/params.yaml new file mode 100644 index 00000000..79a85ff0 --- /dev/null +++ b/examples/security/classification/output/reports/train/1a4dbcceba7b0811b9e181f642105415/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/adv_predictions.json + adv_probabilities_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/32c04a863dc3ee9a863b6f00adcf9632.pkl + params_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/params.yaml + predictions_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/predictions.json + probabilities_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/probabilities.json + score_dict_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/score_dict.json + test_labels_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/test_labels.json + train_labels_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/train_labels.json + model_dir: models + name: 1a4dbcceba7b0811b9e181f642105415 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 887a0c2fb59ce612b6c4aa96743ae7da + trainer: + kwargs: {} +name: 1a4dbcceba7b0811b9e181f642105415 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/params.yaml b/examples/security/classification/output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/params.yaml new file mode 100644 index 00000000..46e95e08 --- /dev/null +++ b/examples/security/classification/output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/adv_predictions.json + adv_probabilities_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/6af9cce678867288841d9d64ee3e495d.pkl + params_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/params.yaml + predictions_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/predictions.json + probabilities_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/probabilities.json + score_dict_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/score_dict.json + test_labels_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/test_labels.json + train_labels_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/train_labels.json + model_dir: models + name: 1a4f7d44e8aa3bc55db4956f9992d678 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1132ae8706e071833a5077d97a3ac06f + trainer: + kwargs: {} +name: 1a4f7d44e8aa3bc55db4956f9992d678 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/params.yaml b/examples/security/classification/output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/params.yaml new file mode 100644 index 00000000..36add8f7 --- /dev/null +++ b/examples/security/classification/output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/adv_predictions.json + adv_probabilities_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/a3f58847265f143d463acbcfe6c36c41.pkl + params_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/params.yaml + predictions_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/predictions.json + probabilities_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/probabilities.json + score_dict_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/score_dict.json + test_labels_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/test_labels.json + train_labels_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/train_labels.json + model_dir: models + name: 1a6ecc5a5cc955c2ecb1421827497e8e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4b4ab785d4dc0f98be1e479221d6d4ea + trainer: + kwargs: {} +name: 1a6ecc5a5cc955c2ecb1421827497e8e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1a741b88bedb5bbb4034329f132ff6df/params.yaml b/examples/security/classification/output/reports/train/1a741b88bedb5bbb4034329f132ff6df/params.yaml new file mode 100644 index 00000000..eb653080 --- /dev/null +++ b/examples/security/classification/output/reports/train/1a741b88bedb5bbb4034329f132ff6df/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/adv_predictions.json + adv_probabilities_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/908646417c286c217f12dd1322dc9523.pkl + params_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/params.yaml + predictions_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/predictions.json + probabilities_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/probabilities.json + score_dict_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/score_dict.json + test_labels_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/test_labels.json + train_labels_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/train_labels.json + model_dir: models + name: 1a741b88bedb5bbb4034329f132ff6df + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4453b006a09492bc69d3c3d63ac84a16 + trainer: + kwargs: {} +name: 1a741b88bedb5bbb4034329f132ff6df +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/params.yaml b/examples/security/classification/output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/params.yaml new file mode 100644 index 00000000..5c4bb9b4 --- /dev/null +++ b/examples/security/classification/output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/adv_predictions.json + adv_probabilities_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/3cd529582cc11b527c68ed77920202bc.pkl + params_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/params.yaml + predictions_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/predictions.json + probabilities_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/probabilities.json + score_dict_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/score_dict.json + test_labels_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/test_labels.json + train_labels_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/train_labels.json + model_dir: models + name: 1a9897a1f6453cc473bd3165c74f2dd1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f98d5fb0a2673540ff51cf6d64115fd6 + trainer: + kwargs: {} +name: 1a9897a1f6453cc473bd3165c74f2dd1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/params.yaml b/examples/security/classification/output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/params.yaml new file mode 100644 index 00000000..df9b75af --- /dev/null +++ b/examples/security/classification/output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/adv_predictions.json + adv_probabilities_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/8cff1e7ca30b0f0124f772eebd7e2bcb.pkl + params_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/params.yaml + predictions_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/predictions.json + probabilities_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/probabilities.json + score_dict_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/score_dict.json + test_labels_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/test_labels.json + train_labels_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/train_labels.json + model_dir: models + name: 1abe763b2412d4e6335fc63097ea4ee9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6533cad81a73aa999e5a3423daddd2a0 + trainer: + kwargs: {} +name: 1abe763b2412d4e6335fc63097ea4ee9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/params.yaml b/examples/security/classification/output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/params.yaml new file mode 100644 index 00000000..6381cdb7 --- /dev/null +++ b/examples/security/classification/output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/adv_predictions.json + adv_probabilities_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/ef75076912895882cacd26103344c5b4.pkl + params_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/params.yaml + predictions_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/predictions.json + probabilities_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/probabilities.json + score_dict_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/score_dict.json + test_labels_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/test_labels.json + train_labels_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/train_labels.json + model_dir: models + name: 1aca8ec2bc805f2fbc5cbefd7235c353 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1f65f4b47a96859853c761f907b82f10 + trainer: + kwargs: {} +name: 1aca8ec2bc805f2fbc5cbefd7235c353 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/params.yaml b/examples/security/classification/output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/params.yaml new file mode 100644 index 00000000..47a0b24d --- /dev/null +++ b/examples/security/classification/output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/adv_predictions.json + adv_probabilities_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/f0518cb73de9b240066fbfcf0714db29.pkl + params_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/params.yaml + predictions_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/predictions.json + probabilities_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/probabilities.json + score_dict_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/score_dict.json + test_labels_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/test_labels.json + train_labels_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/train_labels.json + model_dir: models + name: 1ad213e466e7452b14cfb9be2bdcf9c6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 59be1057793f3537e578d614537349c7 + trainer: + kwargs: {} +name: 1ad213e466e7452b14cfb9be2bdcf9c6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1ad581cbe2cce02651a47ed0646485a1/params.yaml b/examples/security/classification/output/reports/train/1ad581cbe2cce02651a47ed0646485a1/params.yaml new file mode 100644 index 00000000..ab04d531 --- /dev/null +++ b/examples/security/classification/output/reports/train/1ad581cbe2cce02651a47ed0646485a1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/adv_predictions.json + adv_probabilities_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/df7977eff0949b0b5960d4b72c2a2c9e.pkl + params_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/params.yaml + predictions_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/predictions.json + probabilities_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/probabilities.json + score_dict_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/score_dict.json + test_labels_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/test_labels.json + train_labels_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/train_labels.json + model_dir: models + name: 1ad581cbe2cce02651a47ed0646485a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 876450348a867a59fd3a667869025442 + trainer: + kwargs: {} +name: 1ad581cbe2cce02651a47ed0646485a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/params.yaml b/examples/security/classification/output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/params.yaml new file mode 100644 index 00000000..45c9473b --- /dev/null +++ b/examples/security/classification/output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/adv_predictions.json + adv_probabilities_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/08f7472718aa8b78003253dbb9256868.pkl + params_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/params.yaml + predictions_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/predictions.json + probabilities_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/probabilities.json + score_dict_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/score_dict.json + test_labels_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/test_labels.json + train_labels_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/train_labels.json + model_dir: models + name: 1addf88bbd5e84ab5ec6c6251ed8b8e0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: afb1ddde23a2e15bff4399a5b46a8756 + trainer: + kwargs: {} +name: 1addf88bbd5e84ab5ec6c6251ed8b8e0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1af66f632700dd40699eb2f6230a0235/params.yaml b/examples/security/classification/output/reports/train/1af66f632700dd40699eb2f6230a0235/params.yaml new file mode 100644 index 00000000..16f6e161 --- /dev/null +++ b/examples/security/classification/output/reports/train/1af66f632700dd40699eb2f6230a0235/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/adv_predictions.json + adv_probabilities_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/001df357ed056c1a879f8621fbc26421.pkl + params_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/params.yaml + predictions_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/predictions.json + probabilities_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/probabilities.json + score_dict_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/score_dict.json + test_labels_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/test_labels.json + train_labels_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/train_labels.json + model_dir: models + name: 1af66f632700dd40699eb2f6230a0235 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2d15fbfdb5603a2437cdc5511063d123 + trainer: + kwargs: {} +name: 1af66f632700dd40699eb2f6230a0235 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/params.yaml b/examples/security/classification/output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/params.yaml new file mode 100644 index 00000000..2cc34f63 --- /dev/null +++ b/examples/security/classification/output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/adv_predictions.json + adv_probabilities_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/93fea5a76402043b56faf47efb1e2b1a.pkl + params_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/params.yaml + predictions_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/predictions.json + probabilities_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/probabilities.json + score_dict_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/score_dict.json + test_labels_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/test_labels.json + train_labels_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/train_labels.json + model_dir: models + name: 1b373cfd6d673b3a09a3a71661e7b4fe + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7b8e27408d6fe4e4875065dbfe77df16 + trainer: + kwargs: {} +name: 1b373cfd6d673b3a09a3a71661e7b4fe +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/params.yaml b/examples/security/classification/output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/params.yaml new file mode 100644 index 00000000..4a6e2d5b --- /dev/null +++ b/examples/security/classification/output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/adv_predictions.json + adv_probabilities_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/0efb8cd200ffdbd16919f37bf3dcce84.pkl + params_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/params.yaml + predictions_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/predictions.json + probabilities_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/probabilities.json + score_dict_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/score_dict.json + test_labels_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/test_labels.json + train_labels_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/train_labels.json + model_dir: models + name: 1b86bfe1a2beee971fb0dc206dd56d8f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6f88b9d84bd024ad73c3a0ed1153cb54 + trainer: + kwargs: {} +name: 1b86bfe1a2beee971fb0dc206dd56d8f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/params.yaml b/examples/security/classification/output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/params.yaml new file mode 100644 index 00000000..1a4c2cff --- /dev/null +++ b/examples/security/classification/output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/adv_predictions.json + adv_probabilities_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/554590ce9a7b644429ff853a664b40ac.pkl + params_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/params.yaml + predictions_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/predictions.json + probabilities_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/probabilities.json + score_dict_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/score_dict.json + test_labels_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/test_labels.json + train_labels_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/train_labels.json + model_dir: models + name: 1b96903f63f4419e05bd8e47ff85f4bc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27c9693ced23ec5318216fec31ea7291 + trainer: + kwargs: {} +name: 1b96903f63f4419e05bd8e47ff85f4bc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/params.yaml b/examples/security/classification/output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/params.yaml new file mode 100644 index 00000000..4d613be2 --- /dev/null +++ b/examples/security/classification/output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/adv_predictions.json + adv_probabilities_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/2c3ebbdfb6212851a5fb31be9c4fbb1e.pkl + params_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/params.yaml + predictions_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/predictions.json + probabilities_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/probabilities.json + score_dict_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/score_dict.json + test_labels_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/test_labels.json + train_labels_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/train_labels.json + model_dir: models + name: 1bbe4cd9430e4d44e270d499ef359a13 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c9a3e00263c41be1db00ced3b3ea71a5 + trainer: + kwargs: {} +name: 1bbe4cd9430e4d44e270d499ef359a13 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/params.yaml b/examples/security/classification/output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/params.yaml new file mode 100644 index 00000000..f7215dfe --- /dev/null +++ b/examples/security/classification/output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/adv_predictions.json + adv_probabilities_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/0cb4e05cbe83796b8930094bfb238ee6.pkl + params_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/params.yaml + predictions_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/predictions.json + probabilities_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/probabilities.json + score_dict_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/score_dict.json + test_labels_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/test_labels.json + train_labels_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/train_labels.json + model_dir: models + name: 1bd21df442e6e88124f96d5e734dcbf0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8931aeb31c207e2ed71cdc0464b62082 + trainer: + kwargs: {} +name: 1bd21df442e6e88124f96d5e734dcbf0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1bfe374b834b53991360cdafeb6ff76e/params.yaml b/examples/security/classification/output/reports/train/1bfe374b834b53991360cdafeb6ff76e/params.yaml new file mode 100644 index 00000000..740f62f3 --- /dev/null +++ b/examples/security/classification/output/reports/train/1bfe374b834b53991360cdafeb6ff76e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/adv_predictions.json + adv_probabilities_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/0a7ad961a5e89d673183c17d7f204f8b.pkl + params_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/params.yaml + predictions_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/predictions.json + probabilities_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/probabilities.json + score_dict_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/score_dict.json + test_labels_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/test_labels.json + train_labels_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/train_labels.json + model_dir: models + name: 1bfe374b834b53991360cdafeb6ff76e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7768c55257caea75bf406ebf1b92d60e + trainer: + kwargs: {} +name: 1bfe374b834b53991360cdafeb6ff76e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1c753e398eb607f7bb0639f03cc89d91/params.yaml b/examples/security/classification/output/reports/train/1c753e398eb607f7bb0639f03cc89d91/params.yaml new file mode 100644 index 00000000..a17981c4 --- /dev/null +++ b/examples/security/classification/output/reports/train/1c753e398eb607f7bb0639f03cc89d91/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/adv_predictions.json + adv_probabilities_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/b0a7e619451b44475300ee978855dee9.pkl + params_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/params.yaml + predictions_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/predictions.json + probabilities_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/probabilities.json + score_dict_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/score_dict.json + test_labels_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/test_labels.json + train_labels_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/train_labels.json + model_dir: models + name: 1c753e398eb607f7bb0639f03cc89d91 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b4884f2906be91a0e4edf0d31acaab4b + trainer: + kwargs: {} +name: 1c753e398eb607f7bb0639f03cc89d91 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/params.yaml b/examples/security/classification/output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/params.yaml new file mode 100644 index 00000000..18b2f66e --- /dev/null +++ b/examples/security/classification/output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/adv_predictions.json + adv_probabilities_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/94bbfd715a170fc7eb4acd8ba0522d42.pkl + params_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/params.yaml + predictions_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/predictions.json + probabilities_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/probabilities.json + score_dict_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/score_dict.json + test_labels_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/test_labels.json + train_labels_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/train_labels.json + model_dir: models + name: 1c875851dd4dec6c2aa014f854bb13e6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6c80a18654d6e31a80146434dc2ada86 + trainer: + kwargs: {} +name: 1c875851dd4dec6c2aa014f854bb13e6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/params.yaml b/examples/security/classification/output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/params.yaml new file mode 100644 index 00000000..ddf939bb --- /dev/null +++ b/examples/security/classification/output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/adv_predictions.json + adv_probabilities_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/0b11b9ccf5ae97f0e28e2b864424cba8.pkl + params_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/params.yaml + predictions_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/predictions.json + probabilities_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/probabilities.json + score_dict_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/score_dict.json + test_labels_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/test_labels.json + train_labels_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/train_labels.json + model_dir: models + name: 1c8f81bf2ab6b59051254e5d02ebd847 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 23057c816983d717986d69cdf72161bf + trainer: + kwargs: {} +name: 1c8f81bf2ab6b59051254e5d02ebd847 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1cb887b76ef0544accec908123aa13d9/params.yaml b/examples/security/classification/output/reports/train/1cb887b76ef0544accec908123aa13d9/params.yaml new file mode 100644 index 00000000..5852d149 --- /dev/null +++ b/examples/security/classification/output/reports/train/1cb887b76ef0544accec908123aa13d9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/adv_predictions.json + adv_probabilities_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/92228e6287172b35de4c82249c762483.pkl + params_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/params.yaml + predictions_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/predictions.json + probabilities_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/probabilities.json + score_dict_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/score_dict.json + test_labels_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/test_labels.json + train_labels_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/train_labels.json + model_dir: models + name: 1cb887b76ef0544accec908123aa13d9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ed18c09119a2f24f04ccb5406f80fc74 + trainer: + kwargs: {} +name: 1cb887b76ef0544accec908123aa13d9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1cf63da57a4b760baa61cb3259690799/params.yaml b/examples/security/classification/output/reports/train/1cf63da57a4b760baa61cb3259690799/params.yaml new file mode 100644 index 00000000..9461334e --- /dev/null +++ b/examples/security/classification/output/reports/train/1cf63da57a4b760baa61cb3259690799/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/adv_predictions.json + adv_probabilities_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/a9c7582e54ced8a79b4d7db5d65bee54.pkl + params_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/params.yaml + predictions_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/predictions.json + probabilities_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/probabilities.json + score_dict_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/score_dict.json + test_labels_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/test_labels.json + train_labels_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/train_labels.json + model_dir: models + name: 1cf63da57a4b760baa61cb3259690799 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a5d17741e3e213f27a1ed2ac0f0f8e06 + trainer: + kwargs: {} +name: 1cf63da57a4b760baa61cb3259690799 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1d1f8317a243341a1803e9ea2405db8f/params.yaml b/examples/security/classification/output/reports/train/1d1f8317a243341a1803e9ea2405db8f/params.yaml new file mode 100644 index 00000000..8a6e3159 --- /dev/null +++ b/examples/security/classification/output/reports/train/1d1f8317a243341a1803e9ea2405db8f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/adv_predictions.json + adv_probabilities_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/171ed42a3d35381bf7f01f8542da6961.pkl + params_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/params.yaml + predictions_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/predictions.json + probabilities_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/probabilities.json + score_dict_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/score_dict.json + test_labels_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/test_labels.json + train_labels_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/train_labels.json + model_dir: models + name: 1d1f8317a243341a1803e9ea2405db8f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bd538b9500e1224ba3c97d962db8d3b3 + trainer: + kwargs: {} +name: 1d1f8317a243341a1803e9ea2405db8f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1d1fc09a81c3f08febb6699908905fc7/params.yaml b/examples/security/classification/output/reports/train/1d1fc09a81c3f08febb6699908905fc7/params.yaml new file mode 100644 index 00000000..bb4df27e --- /dev/null +++ b/examples/security/classification/output/reports/train/1d1fc09a81c3f08febb6699908905fc7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/adv_predictions.json + adv_probabilities_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/c4ebeef92bc58427ac45e3e9330c8fce.pkl + params_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/params.yaml + predictions_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/predictions.json + probabilities_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/probabilities.json + score_dict_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/score_dict.json + test_labels_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/test_labels.json + train_labels_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/train_labels.json + model_dir: models + name: 1d1fc09a81c3f08febb6699908905fc7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 82b99f4eecb8809034de987f3212b6a8 + trainer: + kwargs: {} +name: 1d1fc09a81c3f08febb6699908905fc7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/params.yaml b/examples/security/classification/output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/params.yaml new file mode 100644 index 00000000..19654c6b --- /dev/null +++ b/examples/security/classification/output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/adv_predictions.json + adv_probabilities_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/2de3771e06ccdc0d9599e931959cd9e4.pkl + params_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/params.yaml + predictions_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/predictions.json + probabilities_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/probabilities.json + score_dict_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/score_dict.json + test_labels_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/test_labels.json + train_labels_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/train_labels.json + model_dir: models + name: 1d3544f6d3fc0e5d73b318df927b945c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9f8d7ac155d185bb3653be0c98b2e8b9 + trainer: + kwargs: {} +name: 1d3544f6d3fc0e5d73b318df927b945c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/params.yaml b/examples/security/classification/output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/params.yaml new file mode 100644 index 00000000..9bb37d62 --- /dev/null +++ b/examples/security/classification/output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/adv_predictions.json + adv_probabilities_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/c13e7022579d3a1009ace6d232600df3.pkl + params_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/params.yaml + predictions_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/predictions.json + probabilities_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/probabilities.json + score_dict_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/score_dict.json + test_labels_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/test_labels.json + train_labels_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/train_labels.json + model_dir: models + name: 1d51317e90442e3a6f4636e5d40f45c7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 449a4b0231d826d78177a35fdc08ad9a + trainer: + kwargs: {} +name: 1d51317e90442e3a6f4636e5d40f45c7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/params.yaml b/examples/security/classification/output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/params.yaml new file mode 100644 index 00000000..6b0f88fd --- /dev/null +++ b/examples/security/classification/output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/adv_predictions.json + adv_probabilities_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/dd82ee472748d3d2e991fd342640de81.pkl + params_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/params.yaml + predictions_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/predictions.json + probabilities_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/probabilities.json + score_dict_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/score_dict.json + test_labels_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/test_labels.json + train_labels_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/train_labels.json + model_dir: models + name: 1d634f3fea42c7769f7b115d504fb1fd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3ceb12fdf847ee7ec7704a5a246ab829 + trainer: + kwargs: {} +name: 1d634f3fea42c7769f7b115d504fb1fd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/params.yaml b/examples/security/classification/output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/params.yaml new file mode 100644 index 00000000..eff18934 --- /dev/null +++ b/examples/security/classification/output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/adv_predictions.json + adv_probabilities_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/0cff640d1d158f9e64921df61acc84e3.pkl + params_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/params.yaml + predictions_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/predictions.json + probabilities_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/probabilities.json + score_dict_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/score_dict.json + test_labels_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/test_labels.json + train_labels_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/train_labels.json + model_dir: models + name: 1d6eb1048a4ccf7c1e055acf882994a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbdfa127f99e9b9c66061ce73b7985b0 + trainer: + kwargs: {} +name: 1d6eb1048a4ccf7c1e055acf882994a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1d7b8e8ac3eff83935f702634d5447af/params.yaml b/examples/security/classification/output/reports/train/1d7b8e8ac3eff83935f702634d5447af/params.yaml new file mode 100644 index 00000000..2dad6efc --- /dev/null +++ b/examples/security/classification/output/reports/train/1d7b8e8ac3eff83935f702634d5447af/params.yaml @@ -0,0 +1,206 @@ +attack: + attack_size: 10 + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 10.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 99034aefb7fff584d81427ae2a1f5f27 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3d6cda61d3c13d4bb0cee27f0e2c8da5 + trainer: + kwargs: {} + name: 78aa87101cdd06b0cf83e07e3a949900 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/adv_predictions.json + adv_probabilities_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/adv_probabilities.json + attack_file: output/attacks/d87e96c976e0063eb50515b6d064f86f.pkl + data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl + model_file: output/models/c33f7a6117fc095b356f3a35dd8646a3.pkl + params_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/params.yaml + predictions_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/predictions.json + probabilities_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/probabilities.json + score_dict_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/score_dict.json + test_labels_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/test_labels.json + train_labels_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/train_labels.json + model_dir: models + name: 1d7b8e8ac3eff83935f702634d5447af + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6e6f54d79165d97f4d5a424cf6825820 + trainer: + kwargs: {} +name: 1d7b8e8ac3eff83935f702634d5447af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1dcce671dd15b880537dfcf7f5288804/params.yaml b/examples/security/classification/output/reports/train/1dcce671dd15b880537dfcf7f5288804/params.yaml new file mode 100644 index 00000000..c660b1ab --- /dev/null +++ b/examples/security/classification/output/reports/train/1dcce671dd15b880537dfcf7f5288804/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/adv_predictions.json + adv_probabilities_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/903a1cc8fce6c53e20096ed42f5c569e.pkl + params_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/params.yaml + predictions_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/predictions.json + probabilities_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/probabilities.json + score_dict_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/score_dict.json + test_labels_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/test_labels.json + train_labels_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/train_labels.json + model_dir: models + name: 1dcce671dd15b880537dfcf7f5288804 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: af0934f44c7bd3fa3e61fce9a2627b8c + trainer: + kwargs: {} +name: 1dcce671dd15b880537dfcf7f5288804 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1dd62be2af8f41619dece68599e591d6/params.yaml b/examples/security/classification/output/reports/train/1dd62be2af8f41619dece68599e591d6/params.yaml new file mode 100644 index 00000000..3c0371fd --- /dev/null +++ b/examples/security/classification/output/reports/train/1dd62be2af8f41619dece68599e591d6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/adv_predictions.json + adv_probabilities_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/dbc2c12eb930e911c389ba8a0fac55d9.pkl + params_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/params.yaml + predictions_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/predictions.json + probabilities_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/probabilities.json + score_dict_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/score_dict.json + test_labels_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/test_labels.json + train_labels_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/train_labels.json + model_dir: models + name: 1dd62be2af8f41619dece68599e591d6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6fdbdbcdcf55f119411fe85be434a65c + trainer: + kwargs: {} +name: 1dd62be2af8f41619dece68599e591d6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/params.yaml b/examples/security/classification/output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/params.yaml new file mode 100644 index 00000000..6810fe02 --- /dev/null +++ b/examples/security/classification/output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/adv_predictions.json + adv_probabilities_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/b520456a45e84f9d9a181bd8ee0b5bfc.pkl + params_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/params.yaml + predictions_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/predictions.json + probabilities_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/probabilities.json + score_dict_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/score_dict.json + test_labels_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/test_labels.json + train_labels_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/train_labels.json + model_dir: models + name: 1dee5bc9654534ded4352de2ad6d4ebe + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 56e992acfd4509fd9f2715decf95f3a3 + trainer: + kwargs: {} +name: 1dee5bc9654534ded4352de2ad6d4ebe +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/params.yaml b/examples/security/classification/output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/params.yaml new file mode 100644 index 00000000..117b68ad --- /dev/null +++ b/examples/security/classification/output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/adv_predictions.json + adv_probabilities_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/412ccd0ed53caf4029d6c648db5e5c2f.pkl + params_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/params.yaml + predictions_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/predictions.json + probabilities_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/probabilities.json + score_dict_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/score_dict.json + test_labels_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/test_labels.json + train_labels_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/train_labels.json + model_dir: models + name: 1df389a20c6e88d33e18d0be63bff0b6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0c34172e0cd57ff953b12b8fa65a6df6 + trainer: + kwargs: {} +name: 1df389a20c6e88d33e18d0be63bff0b6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/params.yaml b/examples/security/classification/output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/params.yaml new file mode 100644 index 00000000..f84c5615 --- /dev/null +++ b/examples/security/classification/output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/adv_predictions.json + adv_probabilities_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/f1899d4ca1cf54b4a93a29ba62bfb9f2.pkl + params_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/params.yaml + predictions_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/predictions.json + probabilities_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/probabilities.json + score_dict_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/score_dict.json + test_labels_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/test_labels.json + train_labels_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/train_labels.json + model_dir: models + name: 1dfbefe8210f30073f8c752a88d2dbff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 07692e306f6446f3f1611af9e2a6b376 + trainer: + kwargs: {} +name: 1dfbefe8210f30073f8c752a88d2dbff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1e0358564026367d3e932e39416ec8ef/params.yaml b/examples/security/classification/output/reports/train/1e0358564026367d3e932e39416ec8ef/params.yaml new file mode 100644 index 00000000..2ff1f1cc --- /dev/null +++ b/examples/security/classification/output/reports/train/1e0358564026367d3e932e39416ec8ef/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/adv_predictions.json + adv_probabilities_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/d897d45e22cd7caeee2beb44ac5ba598.pkl + params_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/params.yaml + predictions_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/predictions.json + probabilities_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/probabilities.json + score_dict_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/score_dict.json + test_labels_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/test_labels.json + train_labels_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/train_labels.json + model_dir: models + name: 1e0358564026367d3e932e39416ec8ef + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4aea27bd7471f098da3b85fa92e4c339 + trainer: + kwargs: {} +name: 1e0358564026367d3e932e39416ec8ef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1e0fa20fe1180c30965df2be58b71d33/params.yaml b/examples/security/classification/output/reports/train/1e0fa20fe1180c30965df2be58b71d33/params.yaml new file mode 100644 index 00000000..3f706176 --- /dev/null +++ b/examples/security/classification/output/reports/train/1e0fa20fe1180c30965df2be58b71d33/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/adv_predictions.json + adv_probabilities_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/d7dc9664b0d7577bdfa9a8964f47606e.pkl + params_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/params.yaml + predictions_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/predictions.json + probabilities_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/probabilities.json + score_dict_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/score_dict.json + test_labels_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/test_labels.json + train_labels_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/train_labels.json + model_dir: models + name: 1e0fa20fe1180c30965df2be58b71d33 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 505d2d1be225fe2f454bfe8b91576e20 + trainer: + kwargs: {} +name: 1e0fa20fe1180c30965df2be58b71d33 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/params.yaml b/examples/security/classification/output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/params.yaml new file mode 100644 index 00000000..d08dc235 --- /dev/null +++ b/examples/security/classification/output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/adv_predictions.json + adv_probabilities_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/9fc4cc62facdfaaa0447e770fa032eae.pkl + params_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/params.yaml + predictions_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/predictions.json + probabilities_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/probabilities.json + score_dict_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/score_dict.json + test_labels_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/test_labels.json + train_labels_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/train_labels.json + model_dir: models + name: 1e9c3da99a5d00fb15faf995f414d2b3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5c2d25dc6ca69c6ea2c4a6b0db953c42 + trainer: + kwargs: {} +name: 1e9c3da99a5d00fb15faf995f414d2b3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/params.yaml b/examples/security/classification/output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/params.yaml new file mode 100644 index 00000000..61434562 --- /dev/null +++ b/examples/security/classification/output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/adv_predictions.json + adv_probabilities_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/e33e0c60877d52b2ae4ae0cc980c57f4.pkl + params_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/params.yaml + predictions_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/predictions.json + probabilities_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/probabilities.json + score_dict_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/score_dict.json + test_labels_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/test_labels.json + train_labels_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/train_labels.json + model_dir: models + name: 1e9c3db1f7cfaeb0264fb2e2f9edd7a6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b27aae93d620f4a9b6617d385f847d4f + trainer: + kwargs: {} +name: 1e9c3db1f7cfaeb0264fb2e2f9edd7a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/params.yaml b/examples/security/classification/output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/params.yaml new file mode 100644 index 00000000..0fee5d24 --- /dev/null +++ b/examples/security/classification/output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/adv_predictions.json + adv_probabilities_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/5f2108b4407985fc0163aac802d0bae4.pkl + params_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/params.yaml + predictions_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/predictions.json + probabilities_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/probabilities.json + score_dict_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/score_dict.json + test_labels_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/test_labels.json + train_labels_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/train_labels.json + model_dir: models + name: 1e9fe8b1f3fd7fd72e16f1a6acb3af7b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b252615fcddffc8d63a49aac5da37660 + trainer: + kwargs: {} +name: 1e9fe8b1f3fd7fd72e16f1a6acb3af7b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1eda6ef43474ca10739b491f45cb81dd/params.yaml b/examples/security/classification/output/reports/train/1eda6ef43474ca10739b491f45cb81dd/params.yaml new file mode 100644 index 00000000..7979e612 --- /dev/null +++ b/examples/security/classification/output/reports/train/1eda6ef43474ca10739b491f45cb81dd/params.yaml @@ -0,0 +1,104 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/adv_predictions.json + adv_probabilities_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/94976d8632a33d866fec774289b78144.pkl + params_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/params.yaml + predictions_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/predictions.json + probabilities_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/probabilities.json + score_dict_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/score_dict.json + test_labels_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/test_labels.json + train_labels_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/train_labels.json + model_dir: models + name: 1eda6ef43474ca10739b491f45cb81dd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: + - linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2685c1f7b4fe814c0d7e3604b6b19e31 + trainer: + kwargs: {} +name: 1eda6ef43474ca10739b491f45cb81dd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/params.yaml b/examples/security/classification/output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/params.yaml new file mode 100644 index 00000000..7c5fd947 --- /dev/null +++ b/examples/security/classification/output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/adv_predictions.json + adv_probabilities_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/3edee2f340eb2a2468f4ecffeee68213.pkl + params_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/params.yaml + predictions_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/predictions.json + probabilities_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/probabilities.json + score_dict_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/score_dict.json + test_labels_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/test_labels.json + train_labels_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/train_labels.json + model_dir: models + name: 1ef453cfe87d6aa7e247c11b2b2a7516 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d820e9d9fac5f1c2438550f7431cadf5 + trainer: + kwargs: {} +name: 1ef453cfe87d6aa7e247c11b2b2a7516 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1f4726d533012f54440d6fa1b01c458a/params.yaml b/examples/security/classification/output/reports/train/1f4726d533012f54440d6fa1b01c458a/params.yaml new file mode 100644 index 00000000..4b065d98 --- /dev/null +++ b/examples/security/classification/output/reports/train/1f4726d533012f54440d6fa1b01c458a/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/adv_predictions.json + adv_probabilities_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/b226392f4a9ba54197f8a61deb5cbd3b.pkl + params_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/params.yaml + predictions_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/predictions.json + probabilities_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/probabilities.json + score_dict_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/score_dict.json + test_labels_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/test_labels.json + train_labels_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/train_labels.json + model_dir: models + name: 1f4726d533012f54440d6fa1b01c458a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 62ec7bc40ead19ecf3a4bde7f612ffd0 + trainer: + kwargs: {} +name: 1f4726d533012f54440d6fa1b01c458a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1f53b056514e00641604afb744be33ff/params.yaml b/examples/security/classification/output/reports/train/1f53b056514e00641604afb744be33ff/params.yaml new file mode 100644 index 00000000..6140c6a1 --- /dev/null +++ b/examples/security/classification/output/reports/train/1f53b056514e00641604afb744be33ff/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1f53b056514e00641604afb744be33ff/adv_predictions.json + adv_probabilities_file: output/reports/train/1f53b056514e00641604afb744be33ff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/66267d181e05ff8c83319674bab3b61a.pkl + params_file: output/reports/train/1f53b056514e00641604afb744be33ff/params.yaml + predictions_file: output/reports/train/1f53b056514e00641604afb744be33ff/predictions.json + probabilities_file: output/reports/train/1f53b056514e00641604afb744be33ff/probabilities.json + score_dict_file: output/reports/train/1f53b056514e00641604afb744be33ff/score_dict.json + test_labels_file: output/reports/train/1f53b056514e00641604afb744be33ff/test_labels.json + train_labels_file: output/reports/train/1f53b056514e00641604afb744be33ff/train_labels.json + model_dir: models + name: 1f53b056514e00641604afb744be33ff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f8b54c1bd2623c6b40196d4c9a9267ae + trainer: + kwargs: {} +name: 1f53b056514e00641604afb744be33ff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/params.yaml b/examples/security/classification/output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/params.yaml new file mode 100644 index 00000000..5c0a347b --- /dev/null +++ b/examples/security/classification/output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/adv_predictions.json + adv_probabilities_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/4b6289a822107c0bc4bcb7fdd1af0886.pkl + params_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/params.yaml + predictions_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/predictions.json + probabilities_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/probabilities.json + score_dict_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/score_dict.json + test_labels_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/test_labels.json + train_labels_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/train_labels.json + model_dir: models + name: 1f8ed4f0a1be217e03cfa8cbde7c2212 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b501eac51fed25d4f36ce28ccc101a7c + trainer: + kwargs: {} +name: 1f8ed4f0a1be217e03cfa8cbde7c2212 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/params.yaml b/examples/security/classification/output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/params.yaml new file mode 100644 index 00000000..7bd41555 --- /dev/null +++ b/examples/security/classification/output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/adv_predictions.json + adv_probabilities_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/b7af521d7ffc9136245394fa98ce49d0.pkl + params_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/params.yaml + predictions_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/predictions.json + probabilities_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/probabilities.json + score_dict_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/score_dict.json + test_labels_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/test_labels.json + train_labels_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/train_labels.json + model_dir: models + name: 1fcfee5f9a3b07f73d91dd0bb81c8589 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dfba054c00577fb0d661d239181b26ea + trainer: + kwargs: {} +name: 1fcfee5f9a3b07f73d91dd0bb81c8589 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1fdc26234b001a45b92f2301167ac2fd/params.yaml b/examples/security/classification/output/reports/train/1fdc26234b001a45b92f2301167ac2fd/params.yaml new file mode 100644 index 00000000..8e688103 --- /dev/null +++ b/examples/security/classification/output/reports/train/1fdc26234b001a45b92f2301167ac2fd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/adv_predictions.json + adv_probabilities_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/6871f63bfc591a4b6dd8de369593a7c9.pkl + params_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/params.yaml + predictions_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/predictions.json + probabilities_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/probabilities.json + score_dict_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/score_dict.json + test_labels_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/test_labels.json + train_labels_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/train_labels.json + model_dir: models + name: 1fdc26234b001a45b92f2301167ac2fd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a5d6ff64fccb4dc8a10a7c3f16c3416f + trainer: + kwargs: {} +name: 1fdc26234b001a45b92f2301167ac2fd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/params.yaml b/examples/security/classification/output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/params.yaml new file mode 100644 index 00000000..226ee0fe --- /dev/null +++ b/examples/security/classification/output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/adv_predictions.json + adv_probabilities_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/a75543c527e1f5edea75d33b09c54cb6.pkl + params_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/params.yaml + predictions_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/predictions.json + probabilities_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/probabilities.json + score_dict_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/score_dict.json + test_labels_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/test_labels.json + train_labels_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/train_labels.json + model_dir: models + name: 1fe641cdc1f0bad587cdf2fa10c63019 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2bb427dc56b6ce4ae99d8898f7acbc62 + trainer: + kwargs: {} +name: 1fe641cdc1f0bad587cdf2fa10c63019 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/201901a6f538910a91c05bf319e9e1d6/params.yaml b/examples/security/classification/output/reports/train/201901a6f538910a91c05bf319e9e1d6/params.yaml new file mode 100644 index 00000000..f260dbaf --- /dev/null +++ b/examples/security/classification/output/reports/train/201901a6f538910a91c05bf319e9e1d6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/adv_predictions.json + adv_probabilities_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/906671ca0ffa2c2140cfaf845d48445b.pkl + params_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/params.yaml + predictions_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/predictions.json + probabilities_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/probabilities.json + score_dict_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/score_dict.json + test_labels_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/test_labels.json + train_labels_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/train_labels.json + model_dir: models + name: 201901a6f538910a91c05bf319e9e1d6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cab64fa567e6b564c1b1563c6a64f67a + trainer: + kwargs: {} +name: 201901a6f538910a91c05bf319e9e1d6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2023196316784228f70b823bf00377ef/params.yaml b/examples/security/classification/output/reports/train/2023196316784228f70b823bf00377ef/params.yaml new file mode 100644 index 00000000..2fbdadfe --- /dev/null +++ b/examples/security/classification/output/reports/train/2023196316784228f70b823bf00377ef/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2023196316784228f70b823bf00377ef/adv_predictions.json + adv_probabilities_file: output/reports/train/2023196316784228f70b823bf00377ef/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/35f51ac0709d61df0f722097d7c90719.pkl + params_file: output/reports/train/2023196316784228f70b823bf00377ef/params.yaml + predictions_file: output/reports/train/2023196316784228f70b823bf00377ef/predictions.json + probabilities_file: output/reports/train/2023196316784228f70b823bf00377ef/probabilities.json + score_dict_file: output/reports/train/2023196316784228f70b823bf00377ef/score_dict.json + test_labels_file: output/reports/train/2023196316784228f70b823bf00377ef/test_labels.json + train_labels_file: output/reports/train/2023196316784228f70b823bf00377ef/train_labels.json + model_dir: models + name: 2023196316784228f70b823bf00377ef + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5f8e6e1c2ab2d91cda80c64c983b368c + trainer: + kwargs: {} +name: 2023196316784228f70b823bf00377ef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/params.yaml b/examples/security/classification/output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/params.yaml new file mode 100644 index 00000000..fc9224f6 --- /dev/null +++ b/examples/security/classification/output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/adv_predictions.json + adv_probabilities_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/58bc211add00b180bd583d86ec5cb5ab.pkl + params_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/params.yaml + predictions_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/predictions.json + probabilities_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/probabilities.json + score_dict_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/score_dict.json + test_labels_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/test_labels.json + train_labels_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/train_labels.json + model_dir: models + name: 2038f9f46e6e693ee453b30ca8644a1a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3129843977272e0190b237edfd37519e + trainer: + kwargs: {} +name: 2038f9f46e6e693ee453b30ca8644a1a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/params.yaml b/examples/security/classification/output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/params.yaml new file mode 100644 index 00000000..dda7c779 --- /dev/null +++ b/examples/security/classification/output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/adv_predictions.json + adv_probabilities_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/4d1a9156877d700701629e84f9ef1b28.pkl + params_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/params.yaml + predictions_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/predictions.json + probabilities_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/probabilities.json + score_dict_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/score_dict.json + test_labels_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/test_labels.json + train_labels_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/train_labels.json + model_dir: models + name: 203cfc3c53e14cfdc893f04ebbb78f56 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c85b64cd66739734131a9c25c45075fa + trainer: + kwargs: {} +name: 203cfc3c53e14cfdc893f04ebbb78f56 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/203dc1484db3435ac5991e6e785f0585/params.yaml b/examples/security/classification/output/reports/train/203dc1484db3435ac5991e6e785f0585/params.yaml new file mode 100644 index 00000000..833c6106 --- /dev/null +++ b/examples/security/classification/output/reports/train/203dc1484db3435ac5991e6e785f0585/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/adv_predictions.json + adv_probabilities_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/f156e61a175da8f5ed681393a0269263.pkl + params_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/params.yaml + predictions_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/predictions.json + probabilities_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/probabilities.json + score_dict_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/score_dict.json + test_labels_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/test_labels.json + train_labels_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/train_labels.json + model_dir: models + name: 203dc1484db3435ac5991e6e785f0585 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ba38bdf8f7c9f740ceab05354ce843d1 + trainer: + kwargs: {} +name: 203dc1484db3435ac5991e6e785f0585 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/204c686f63cdd905f681fe2131da0f5a/params.yaml b/examples/security/classification/output/reports/train/204c686f63cdd905f681fe2131da0f5a/params.yaml new file mode 100644 index 00000000..d127cf7b --- /dev/null +++ b/examples/security/classification/output/reports/train/204c686f63cdd905f681fe2131da0f5a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/adv_predictions.json + adv_probabilities_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/4f7f2f60623f1d793353bf2354c45f61.pkl + params_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/params.yaml + predictions_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/predictions.json + probabilities_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/probabilities.json + score_dict_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/score_dict.json + test_labels_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/test_labels.json + train_labels_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/train_labels.json + model_dir: models + name: 204c686f63cdd905f681fe2131da0f5a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 03aa057c897c8cdcf647f448feec892b + trainer: + kwargs: {} +name: 204c686f63cdd905f681fe2131da0f5a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/params.yaml b/examples/security/classification/output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/params.yaml new file mode 100644 index 00000000..023ea09c --- /dev/null +++ b/examples/security/classification/output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/adv_predictions.json + adv_probabilities_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/f8c39cb1e70a95fa580da4414f88d5ee.pkl + params_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/params.yaml + predictions_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/predictions.json + probabilities_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/probabilities.json + score_dict_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/score_dict.json + test_labels_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/test_labels.json + train_labels_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/train_labels.json + model_dir: models + name: 20585a62e351b7ab3f6c6e04ddb2a9d3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6de6eb233af0a114f8ccd525871073a9 + trainer: + kwargs: {} +name: 20585a62e351b7ab3f6c6e04ddb2a9d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/params.yaml b/examples/security/classification/output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/params.yaml new file mode 100644 index 00000000..5d8eaa80 --- /dev/null +++ b/examples/security/classification/output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/adv_predictions.json + adv_probabilities_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/06802fd7632df2d36155b9f22dca79ef.pkl + params_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/params.yaml + predictions_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/predictions.json + probabilities_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/probabilities.json + score_dict_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/score_dict.json + test_labels_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/test_labels.json + train_labels_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/train_labels.json + model_dir: models + name: 2058d12f3d246a16e816e3d43bd02ef5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a39f0d2f5a0f3844a65574fc53790148 + trainer: + kwargs: {} +name: 2058d12f3d246a16e816e3d43bd02ef5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/20848218acb6d9d8a97650479de68e3c/params.yaml b/examples/security/classification/output/reports/train/20848218acb6d9d8a97650479de68e3c/params.yaml new file mode 100644 index 00000000..f2901853 --- /dev/null +++ b/examples/security/classification/output/reports/train/20848218acb6d9d8a97650479de68e3c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/adv_predictions.json + adv_probabilities_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/63a15f4a7dc699f707a9145be96fa6ad.pkl + params_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/params.yaml + predictions_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/predictions.json + probabilities_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/probabilities.json + score_dict_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/score_dict.json + test_labels_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/test_labels.json + train_labels_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/train_labels.json + model_dir: models + name: 20848218acb6d9d8a97650479de68e3c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ec781c8bdc47953f0459b2461fcaf97d + trainer: + kwargs: {} +name: 20848218acb6d9d8a97650479de68e3c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/params.yaml b/examples/security/classification/output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/params.yaml new file mode 100644 index 00000000..7239ae69 --- /dev/null +++ b/examples/security/classification/output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/adv_predictions.json + adv_probabilities_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/59db51caec2d8c90fc5de6242e439f31.pkl + params_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/params.yaml + predictions_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/predictions.json + probabilities_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/probabilities.json + score_dict_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/score_dict.json + test_labels_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/test_labels.json + train_labels_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/train_labels.json + model_dir: models + name: 2097bbd38174b1c6ec1d3932fcec4d41 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d7f1694d3201fde9156d0c23bc9605e6 + trainer: + kwargs: {} +name: 2097bbd38174b1c6ec1d3932fcec4d41 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/20b4be760499a6df6f8c70f668d3197b/params.yaml b/examples/security/classification/output/reports/train/20b4be760499a6df6f8c70f668d3197b/params.yaml new file mode 100644 index 00000000..eae42a73 --- /dev/null +++ b/examples/security/classification/output/reports/train/20b4be760499a6df6f8c70f668d3197b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/adv_predictions.json + adv_probabilities_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/dd3ad907419cd11a5add56b1ef78917a.pkl + params_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/params.yaml + predictions_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/predictions.json + probabilities_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/probabilities.json + score_dict_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/score_dict.json + test_labels_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/test_labels.json + train_labels_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/train_labels.json + model_dir: models + name: 20b4be760499a6df6f8c70f668d3197b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2dc324a81beaeec38f5fd0e9e673e9cf + trainer: + kwargs: {} +name: 20b4be760499a6df6f8c70f668d3197b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/20ba096a9032e7810982d74015a1bcf2/params.yaml b/examples/security/classification/output/reports/train/20ba096a9032e7810982d74015a1bcf2/params.yaml new file mode 100644 index 00000000..da75fda1 --- /dev/null +++ b/examples/security/classification/output/reports/train/20ba096a9032e7810982d74015a1bcf2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/adv_predictions.json + adv_probabilities_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/918f92fb552fc82782fb703627305310.pkl + params_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/params.yaml + predictions_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/predictions.json + probabilities_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/probabilities.json + score_dict_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/score_dict.json + test_labels_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/test_labels.json + train_labels_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/train_labels.json + model_dir: models + name: 20ba096a9032e7810982d74015a1bcf2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 44f5f3f8e955e4c0cc6eee014f22d953 + trainer: + kwargs: {} +name: 20ba096a9032e7810982d74015a1bcf2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/params.yaml b/examples/security/classification/output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/params.yaml new file mode 100644 index 00000000..06fd83dd --- /dev/null +++ b/examples/security/classification/output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/adv_predictions.json + adv_probabilities_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/e3d6c67a3c0d48d863f4ff8114a3cdb8.pkl + params_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/params.yaml + predictions_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/predictions.json + probabilities_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/probabilities.json + score_dict_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/score_dict.json + test_labels_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/test_labels.json + train_labels_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/train_labels.json + model_dir: models + name: 2105f0388f4501b96f1c744fd7a46bc0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0038196db91603a1a5d038baf4d8c7b9 + trainer: + kwargs: {} +name: 2105f0388f4501b96f1c744fd7a46bc0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/210d1ed09e98b4deb406f28a19762b7b/params.yaml b/examples/security/classification/output/reports/train/210d1ed09e98b4deb406f28a19762b7b/params.yaml new file mode 100644 index 00000000..fb199be0 --- /dev/null +++ b/examples/security/classification/output/reports/train/210d1ed09e98b4deb406f28a19762b7b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/adv_predictions.json + adv_probabilities_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/337b28219161b6cb029b7854ad5a73be.pkl + params_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/params.yaml + predictions_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/predictions.json + probabilities_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/probabilities.json + score_dict_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/score_dict.json + test_labels_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/test_labels.json + train_labels_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/train_labels.json + model_dir: models + name: 210d1ed09e98b4deb406f28a19762b7b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6b2d410520aecba4b9ee8e44b952d994 + trainer: + kwargs: {} +name: 210d1ed09e98b4deb406f28a19762b7b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/params.yaml b/examples/security/classification/output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/params.yaml new file mode 100644 index 00000000..b2e5e3f6 --- /dev/null +++ b/examples/security/classification/output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/adv_predictions.json + adv_probabilities_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/5a37631368766fc1c7aae816bbb416b5.pkl + params_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/params.yaml + predictions_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/predictions.json + probabilities_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/probabilities.json + score_dict_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/score_dict.json + test_labels_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/test_labels.json + train_labels_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/train_labels.json + model_dir: models + name: 2178ee306ee4c35e03c7d25cefa72b96 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 02caf44f6b313ae4deebe9b02b700c13 + trainer: + kwargs: {} +name: 2178ee306ee4c35e03c7d25cefa72b96 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/21aa1a6474ef2302489522aa03bbfdec/params.yaml b/examples/security/classification/output/reports/train/21aa1a6474ef2302489522aa03bbfdec/params.yaml new file mode 100644 index 00000000..dd774787 --- /dev/null +++ b/examples/security/classification/output/reports/train/21aa1a6474ef2302489522aa03bbfdec/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/adv_predictions.json + adv_probabilities_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/f21a36afe02b703d15daab0c25521e4b.pkl + params_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/params.yaml + predictions_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/predictions.json + probabilities_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/probabilities.json + score_dict_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/score_dict.json + test_labels_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/test_labels.json + train_labels_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/train_labels.json + model_dir: models + name: 21aa1a6474ef2302489522aa03bbfdec + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c4a8831e9a44475446784875641b875c + trainer: + kwargs: {} +name: 21aa1a6474ef2302489522aa03bbfdec +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/21b691c0215f5e32c19674892a67ad03/params.yaml b/examples/security/classification/output/reports/train/21b691c0215f5e32c19674892a67ad03/params.yaml new file mode 100644 index 00000000..6f807c90 --- /dev/null +++ b/examples/security/classification/output/reports/train/21b691c0215f5e32c19674892a67ad03/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/adv_predictions.json + adv_probabilities_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/c9725d97c6fa22a8a7f5fb3fbb25cb51.pkl + params_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/params.yaml + predictions_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/predictions.json + probabilities_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/probabilities.json + score_dict_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/score_dict.json + test_labels_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/test_labels.json + train_labels_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/train_labels.json + model_dir: models + name: 21b691c0215f5e32c19674892a67ad03 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6e6709404ee69cc734a7f51972d89fa0 + trainer: + kwargs: {} +name: 21b691c0215f5e32c19674892a67ad03 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/params.yaml b/examples/security/classification/output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/params.yaml new file mode 100644 index 00000000..aa71e0ca --- /dev/null +++ b/examples/security/classification/output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/adv_predictions.json + adv_probabilities_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/95c3083585731a9e2300eb6351977c06.pkl + params_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/params.yaml + predictions_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/predictions.json + probabilities_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/probabilities.json + score_dict_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/score_dict.json + test_labels_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/test_labels.json + train_labels_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/train_labels.json + model_dir: models + name: 21bd68754a82f38b2cdd94b08c3b4228 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5ff585f82691974e8e0f3b8a708d3ea2 + trainer: + kwargs: {} +name: 21bd68754a82f38b2cdd94b08c3b4228 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/21beea562d9e7ba37e10b0967cb455d3/params.yaml b/examples/security/classification/output/reports/train/21beea562d9e7ba37e10b0967cb455d3/params.yaml new file mode 100644 index 00000000..9e39d708 --- /dev/null +++ b/examples/security/classification/output/reports/train/21beea562d9e7ba37e10b0967cb455d3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/adv_predictions.json + adv_probabilities_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/9e4d9a663301de71e21b861a8b207494.pkl + params_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/params.yaml + predictions_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/predictions.json + probabilities_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/probabilities.json + score_dict_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/score_dict.json + test_labels_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/test_labels.json + train_labels_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/train_labels.json + model_dir: models + name: 21beea562d9e7ba37e10b0967cb455d3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9c81aeba470c413a2c5696dc5ce46b1d + trainer: + kwargs: {} +name: 21beea562d9e7ba37e10b0967cb455d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/params.yaml b/examples/security/classification/output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/params.yaml new file mode 100644 index 00000000..e9721360 --- /dev/null +++ b/examples/security/classification/output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/adv_predictions.json + adv_probabilities_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/7ab5630c5a2c492039b4fad7dcc82290.pkl + params_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/params.yaml + predictions_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/predictions.json + probabilities_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/probabilities.json + score_dict_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/score_dict.json + test_labels_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/test_labels.json + train_labels_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/train_labels.json + model_dir: models + name: 21c933c4066bddc40f34e9ad7ce092a0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ec9971d0a95bb0501c1144b1f8cc608 + trainer: + kwargs: {} +name: 21c933c4066bddc40f34e9ad7ce092a0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/params.yaml b/examples/security/classification/output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/params.yaml new file mode 100644 index 00000000..8fc82dc7 --- /dev/null +++ b/examples/security/classification/output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/adv_predictions.json + adv_probabilities_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/7ad385d8d3b153b1aa3f4d4159bea64b.pkl + params_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/params.yaml + predictions_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/predictions.json + probabilities_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/probabilities.json + score_dict_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/score_dict.json + test_labels_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/test_labels.json + train_labels_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/train_labels.json + model_dir: models + name: 21d2d0b23198b7d6d52feada1d198cc8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7c7424280845c194479015160c411f76 + trainer: + kwargs: {} +name: 21d2d0b23198b7d6d52feada1d198cc8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/params.yaml b/examples/security/classification/output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/params.yaml new file mode 100644 index 00000000..abb24ca0 --- /dev/null +++ b/examples/security/classification/output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/adv_predictions.json + adv_probabilities_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/2400ebbf590a04d23c750fd2fb82b8b7.pkl + params_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/params.yaml + predictions_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/predictions.json + probabilities_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/probabilities.json + score_dict_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/score_dict.json + test_labels_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/test_labels.json + train_labels_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/train_labels.json + model_dir: models + name: 21e0a6c3704c832b5cd89cf85f29c37d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f051c99a958014c8c312cc971c6470fe + trainer: + kwargs: {} +name: 21e0a6c3704c832b5cd89cf85f29c37d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2204dad29eb62fed362787907bde90f7/params.yaml b/examples/security/classification/output/reports/train/2204dad29eb62fed362787907bde90f7/params.yaml new file mode 100644 index 00000000..35cce751 --- /dev/null +++ b/examples/security/classification/output/reports/train/2204dad29eb62fed362787907bde90f7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2204dad29eb62fed362787907bde90f7/adv_predictions.json + adv_probabilities_file: output/reports/train/2204dad29eb62fed362787907bde90f7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/2b23b154e2b16dc45106de8a3b78f62d.pkl + params_file: output/reports/train/2204dad29eb62fed362787907bde90f7/params.yaml + predictions_file: output/reports/train/2204dad29eb62fed362787907bde90f7/predictions.json + probabilities_file: output/reports/train/2204dad29eb62fed362787907bde90f7/probabilities.json + score_dict_file: output/reports/train/2204dad29eb62fed362787907bde90f7/score_dict.json + test_labels_file: output/reports/train/2204dad29eb62fed362787907bde90f7/test_labels.json + train_labels_file: output/reports/train/2204dad29eb62fed362787907bde90f7/train_labels.json + model_dir: models + name: 2204dad29eb62fed362787907bde90f7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1d79335216e7f359573eb9d6fbf7e060 + trainer: + kwargs: {} +name: 2204dad29eb62fed362787907bde90f7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/params.yaml b/examples/security/classification/output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/params.yaml new file mode 100644 index 00000000..ef419cd1 --- /dev/null +++ b/examples/security/classification/output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/adv_predictions.json + adv_probabilities_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/988f9b510798c97f27c920f22076c364.pkl + params_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/params.yaml + predictions_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/predictions.json + probabilities_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/probabilities.json + score_dict_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/score_dict.json + test_labels_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/test_labels.json + train_labels_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/train_labels.json + model_dir: models + name: 220b49b803cdab7bbc0d482d2ea943f2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fee03cfccffcf97b97758eaa04cb09bd + trainer: + kwargs: {} +name: 220b49b803cdab7bbc0d482d2ea943f2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/22267c7169a4eae20dbe53341a34ee32/params.yaml b/examples/security/classification/output/reports/train/22267c7169a4eae20dbe53341a34ee32/params.yaml new file mode 100644 index 00000000..7ad5e42a --- /dev/null +++ b/examples/security/classification/output/reports/train/22267c7169a4eae20dbe53341a34ee32/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/adv_predictions.json + adv_probabilities_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/36c2c6e1470fb8c079a77e7f001e3fe5.pkl + params_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/params.yaml + predictions_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/predictions.json + probabilities_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/probabilities.json + score_dict_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/score_dict.json + test_labels_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/test_labels.json + train_labels_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/train_labels.json + model_dir: models + name: 22267c7169a4eae20dbe53341a34ee32 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 351562a1c59304e012fb9d08371bc3f3 + trainer: + kwargs: {} +name: 22267c7169a4eae20dbe53341a34ee32 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2228151e789606916babaed9208c2eac/params.yaml b/examples/security/classification/output/reports/train/2228151e789606916babaed9208c2eac/params.yaml new file mode 100644 index 00000000..728c5a8e --- /dev/null +++ b/examples/security/classification/output/reports/train/2228151e789606916babaed9208c2eac/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2228151e789606916babaed9208c2eac/adv_predictions.json + adv_probabilities_file: output/reports/train/2228151e789606916babaed9208c2eac/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/54738d1214cbdaf5161a9e4ecf01af2b.pkl + params_file: output/reports/train/2228151e789606916babaed9208c2eac/params.yaml + predictions_file: output/reports/train/2228151e789606916babaed9208c2eac/predictions.json + probabilities_file: output/reports/train/2228151e789606916babaed9208c2eac/probabilities.json + score_dict_file: output/reports/train/2228151e789606916babaed9208c2eac/score_dict.json + test_labels_file: output/reports/train/2228151e789606916babaed9208c2eac/test_labels.json + train_labels_file: output/reports/train/2228151e789606916babaed9208c2eac/train_labels.json + model_dir: models + name: 2228151e789606916babaed9208c2eac + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c9a6af40cddc01da82cbba0191baac25 + trainer: + kwargs: {} +name: 2228151e789606916babaed9208c2eac +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/params.yaml b/examples/security/classification/output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/params.yaml new file mode 100644 index 00000000..aa489761 --- /dev/null +++ b/examples/security/classification/output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/adv_predictions.json + adv_probabilities_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/7fc914c0cdd9d25ea1b15da195a72abc.pkl + params_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/params.yaml + predictions_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/predictions.json + probabilities_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/probabilities.json + score_dict_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/score_dict.json + test_labels_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/test_labels.json + train_labels_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/train_labels.json + model_dir: models + name: 222d7b510ba8e8d081bbbe4aa4309e7d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c0cd3d2630bab4d133dac254f3bb7ecf + trainer: + kwargs: {} +name: 222d7b510ba8e8d081bbbe4aa4309e7d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/params.yaml b/examples/security/classification/output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/params.yaml new file mode 100644 index 00000000..1cd0989a --- /dev/null +++ b/examples/security/classification/output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/adv_predictions.json + adv_probabilities_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/f352653c999d32bfcdf092c1d5618b57.pkl + params_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/params.yaml + predictions_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/predictions.json + probabilities_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/probabilities.json + score_dict_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/score_dict.json + test_labels_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/test_labels.json + train_labels_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/train_labels.json + model_dir: models + name: 223a5012f76f9dea980b6e2d1d0f74cb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e059cfc2e0446d58abdeb86129a447b2 + trainer: + kwargs: {} +name: 223a5012f76f9dea980b6e2d1d0f74cb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/22bed80730d16c5890d549b09008f4d5/params.yaml b/examples/security/classification/output/reports/train/22bed80730d16c5890d549b09008f4d5/params.yaml new file mode 100644 index 00000000..c8bdca1b --- /dev/null +++ b/examples/security/classification/output/reports/train/22bed80730d16c5890d549b09008f4d5/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/adv_predictions.json + adv_probabilities_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/638eda91c54f332cc4b8f6648f4d6402.pkl + params_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/params.yaml + predictions_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/predictions.json + probabilities_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/probabilities.json + score_dict_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/score_dict.json + test_labels_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/test_labels.json + train_labels_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/train_labels.json + model_dir: models + name: 22bed80730d16c5890d549b09008f4d5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b1028c83ee50a57b9c4aa806da838e57 + trainer: + kwargs: {} +name: 22bed80730d16c5890d549b09008f4d5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/22c117f29070010d9c2988d3f4644323/params.yaml b/examples/security/classification/output/reports/train/22c117f29070010d9c2988d3f4644323/params.yaml new file mode 100644 index 00000000..019ad329 --- /dev/null +++ b/examples/security/classification/output/reports/train/22c117f29070010d9c2988d3f4644323/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/22c117f29070010d9c2988d3f4644323/adv_predictions.json + adv_probabilities_file: output/reports/train/22c117f29070010d9c2988d3f4644323/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/c90af7c05abf993ec9db88237f612412.pkl + params_file: output/reports/train/22c117f29070010d9c2988d3f4644323/params.yaml + predictions_file: output/reports/train/22c117f29070010d9c2988d3f4644323/predictions.json + probabilities_file: output/reports/train/22c117f29070010d9c2988d3f4644323/probabilities.json + score_dict_file: output/reports/train/22c117f29070010d9c2988d3f4644323/score_dict.json + test_labels_file: output/reports/train/22c117f29070010d9c2988d3f4644323/test_labels.json + train_labels_file: output/reports/train/22c117f29070010d9c2988d3f4644323/train_labels.json + model_dir: models + name: 22c117f29070010d9c2988d3f4644323 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bd5204d93f8adeb773755e087133f4c1 + trainer: + kwargs: {} +name: 22c117f29070010d9c2988d3f4644323 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/22d745717f86f83a940098580ea66f47/params.yaml b/examples/security/classification/output/reports/train/22d745717f86f83a940098580ea66f47/params.yaml new file mode 100644 index 00000000..a197988c --- /dev/null +++ b/examples/security/classification/output/reports/train/22d745717f86f83a940098580ea66f47/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/22d745717f86f83a940098580ea66f47/adv_predictions.json + adv_probabilities_file: output/reports/train/22d745717f86f83a940098580ea66f47/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/c0124dd8e10bb8e4a0f8719f3df438d7.pkl + params_file: output/reports/train/22d745717f86f83a940098580ea66f47/params.yaml + predictions_file: output/reports/train/22d745717f86f83a940098580ea66f47/predictions.json + probabilities_file: output/reports/train/22d745717f86f83a940098580ea66f47/probabilities.json + score_dict_file: output/reports/train/22d745717f86f83a940098580ea66f47/score_dict.json + test_labels_file: output/reports/train/22d745717f86f83a940098580ea66f47/test_labels.json + train_labels_file: output/reports/train/22d745717f86f83a940098580ea66f47/train_labels.json + model_dir: models + name: 22d745717f86f83a940098580ea66f47 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8f3970e2cf09d72bc4d2c576785f9145 + trainer: + kwargs: {} +name: 22d745717f86f83a940098580ea66f47 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/22e2541d8b26abcd71b11204329eb966/params.yaml b/examples/security/classification/output/reports/train/22e2541d8b26abcd71b11204329eb966/params.yaml new file mode 100644 index 00000000..6ab8bfb4 --- /dev/null +++ b/examples/security/classification/output/reports/train/22e2541d8b26abcd71b11204329eb966/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/adv_predictions.json + adv_probabilities_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/66b097704a410e1f0651800813cb86b3.pkl + params_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/params.yaml + predictions_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/predictions.json + probabilities_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/probabilities.json + score_dict_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/score_dict.json + test_labels_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/test_labels.json + train_labels_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/train_labels.json + model_dir: models + name: 22e2541d8b26abcd71b11204329eb966 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f94d26c35c6b4226766b890f9611c00b + trainer: + kwargs: {} +name: 22e2541d8b26abcd71b11204329eb966 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/22e557335f9d6517219356840b403d54/params.yaml b/examples/security/classification/output/reports/train/22e557335f9d6517219356840b403d54/params.yaml new file mode 100644 index 00000000..d059ddee --- /dev/null +++ b/examples/security/classification/output/reports/train/22e557335f9d6517219356840b403d54/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/22e557335f9d6517219356840b403d54/adv_predictions.json + adv_probabilities_file: output/reports/train/22e557335f9d6517219356840b403d54/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/a52364aa46be5601fbda07a7e7437aaa.pkl + params_file: output/reports/train/22e557335f9d6517219356840b403d54/params.yaml + predictions_file: output/reports/train/22e557335f9d6517219356840b403d54/predictions.json + probabilities_file: output/reports/train/22e557335f9d6517219356840b403d54/probabilities.json + score_dict_file: output/reports/train/22e557335f9d6517219356840b403d54/score_dict.json + test_labels_file: output/reports/train/22e557335f9d6517219356840b403d54/test_labels.json + train_labels_file: output/reports/train/22e557335f9d6517219356840b403d54/train_labels.json + model_dir: models + name: 22e557335f9d6517219356840b403d54 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8491f24f67d683a4a1cfbb562200ffe4 + trainer: + kwargs: {} +name: 22e557335f9d6517219356840b403d54 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/params.yaml b/examples/security/classification/output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/params.yaml new file mode 100644 index 00000000..13996407 --- /dev/null +++ b/examples/security/classification/output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/adv_predictions.json + adv_probabilities_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/22a74f78db76883c2b063f2654696acc.pkl + params_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/params.yaml + predictions_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/predictions.json + probabilities_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/probabilities.json + score_dict_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/score_dict.json + test_labels_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/test_labels.json + train_labels_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/train_labels.json + model_dir: models + name: 22f8a3ccaabe3fad4e6665a0d6039847 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7e848e301f3b39c49f7c26821f5c9100 + trainer: + kwargs: {} +name: 22f8a3ccaabe3fad4e6665a0d6039847 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/23014d7c01c296b3a315f2c330049d5f/params.yaml b/examples/security/classification/output/reports/train/23014d7c01c296b3a315f2c330049d5f/params.yaml new file mode 100644 index 00000000..3c7b09e5 --- /dev/null +++ b/examples/security/classification/output/reports/train/23014d7c01c296b3a315f2c330049d5f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/adv_predictions.json + adv_probabilities_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/5c84019b8f6e2335dce1cf4a3874bc99.pkl + params_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/params.yaml + predictions_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/predictions.json + probabilities_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/probabilities.json + score_dict_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/score_dict.json + test_labels_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/test_labels.json + train_labels_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/train_labels.json + model_dir: models + name: 23014d7c01c296b3a315f2c330049d5f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5d7c6f447ec5a286cddeb1d94c92b515 + trainer: + kwargs: {} +name: 23014d7c01c296b3a315f2c330049d5f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2302e1bf1d5b825f906618bff8d060cd/params.yaml b/examples/security/classification/output/reports/train/2302e1bf1d5b825f906618bff8d060cd/params.yaml new file mode 100644 index 00000000..5c63094f --- /dev/null +++ b/examples/security/classification/output/reports/train/2302e1bf1d5b825f906618bff8d060cd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/adv_predictions.json + adv_probabilities_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/30f3d7391f77591555686145ca17e58d.pkl + params_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/params.yaml + predictions_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/predictions.json + probabilities_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/probabilities.json + score_dict_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/score_dict.json + test_labels_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/test_labels.json + train_labels_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/train_labels.json + model_dir: models + name: 2302e1bf1d5b825f906618bff8d060cd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eb02acff58cb3df0d8f0e6c87bace2e2 + trainer: + kwargs: {} +name: 2302e1bf1d5b825f906618bff8d060cd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/237b9e7d8072653de4a04915f79ba88b/params.yaml b/examples/security/classification/output/reports/train/237b9e7d8072653de4a04915f79ba88b/params.yaml new file mode 100644 index 00000000..c1ee3bca --- /dev/null +++ b/examples/security/classification/output/reports/train/237b9e7d8072653de4a04915f79ba88b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/adv_predictions.json + adv_probabilities_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/74ba6bd9f20bd9b13094d7826927f932.pkl + params_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/params.yaml + predictions_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/predictions.json + probabilities_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/probabilities.json + score_dict_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/score_dict.json + test_labels_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/test_labels.json + train_labels_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/train_labels.json + model_dir: models + name: 237b9e7d8072653de4a04915f79ba88b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ab960b1d9c74bac37cfa1d0bfd3f8d59 + trainer: + kwargs: {} +name: 237b9e7d8072653de4a04915f79ba88b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/params.yaml b/examples/security/classification/output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/params.yaml new file mode 100644 index 00000000..3a6dfc73 --- /dev/null +++ b/examples/security/classification/output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/adv_predictions.json + adv_probabilities_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/28e183b5f5f797cb282bbf9a3d8cdf0a.pkl + params_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/params.yaml + predictions_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/predictions.json + probabilities_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/probabilities.json + score_dict_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/score_dict.json + test_labels_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/test_labels.json + train_labels_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/train_labels.json + model_dir: models + name: 2391d861fe9fc4eb73eb0138a7419ef6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2f5f3c056a4a9ae73bd734f074a98e96 + trainer: + kwargs: {} +name: 2391d861fe9fc4eb73eb0138a7419ef6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2393a3f569a3788985867ee25e26fc6b/params.yaml b/examples/security/classification/output/reports/train/2393a3f569a3788985867ee25e26fc6b/params.yaml new file mode 100644 index 00000000..f5315675 --- /dev/null +++ b/examples/security/classification/output/reports/train/2393a3f569a3788985867ee25e26fc6b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/adv_predictions.json + adv_probabilities_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/cee5e6334c37437f6b433781d47414ff.pkl + params_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/params.yaml + predictions_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/predictions.json + probabilities_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/probabilities.json + score_dict_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/score_dict.json + test_labels_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/test_labels.json + train_labels_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/train_labels.json + model_dir: models + name: 2393a3f569a3788985867ee25e26fc6b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 574a498855560c0d2fb38034b93680bc + trainer: + kwargs: {} +name: 2393a3f569a3788985867ee25e26fc6b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/23965b4e0e7e8bbafe925342c55bef46/params.yaml b/examples/security/classification/output/reports/train/23965b4e0e7e8bbafe925342c55bef46/params.yaml new file mode 100644 index 00000000..9361a082 --- /dev/null +++ b/examples/security/classification/output/reports/train/23965b4e0e7e8bbafe925342c55bef46/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/adv_predictions.json + adv_probabilities_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/0d049d97c0f6432315cf8a18b37e32b1.pkl + params_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/params.yaml + predictions_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/predictions.json + probabilities_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/probabilities.json + score_dict_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/score_dict.json + test_labels_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/test_labels.json + train_labels_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/train_labels.json + model_dir: models + name: 23965b4e0e7e8bbafe925342c55bef46 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8fb14482be4d7c812df7e71e906f5e1b + trainer: + kwargs: {} +name: 23965b4e0e7e8bbafe925342c55bef46 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/params.yaml b/examples/security/classification/output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/params.yaml new file mode 100644 index 00000000..5a235c4e --- /dev/null +++ b/examples/security/classification/output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/adv_predictions.json + adv_probabilities_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/80f9dcd562628e7a80fea933c3a03460.pkl + params_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/params.yaml + predictions_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/predictions.json + probabilities_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/probabilities.json + score_dict_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/score_dict.json + test_labels_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/test_labels.json + train_labels_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/train_labels.json + model_dir: models + name: 23d84444ddf67a2bddf1f48728eeccdd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5bc2a553793a961aa6e415239988deec + trainer: + kwargs: {} +name: 23d84444ddf67a2bddf1f48728eeccdd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2400f7ee5c89b7022a39d215a62496a8/params.yaml b/examples/security/classification/output/reports/train/2400f7ee5c89b7022a39d215a62496a8/params.yaml new file mode 100644 index 00000000..7f76635b --- /dev/null +++ b/examples/security/classification/output/reports/train/2400f7ee5c89b7022a39d215a62496a8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/adv_predictions.json + adv_probabilities_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/d9c173fd8931e4ed616e9b0976b9799a.pkl + params_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/params.yaml + predictions_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/predictions.json + probabilities_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/probabilities.json + score_dict_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/score_dict.json + test_labels_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/test_labels.json + train_labels_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/train_labels.json + model_dir: models + name: 2400f7ee5c89b7022a39d215a62496a8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2191b1fa9d7c0f2b827d67c03d93b334 + trainer: + kwargs: {} +name: 2400f7ee5c89b7022a39d215a62496a8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/params.yaml b/examples/security/classification/output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/params.yaml new file mode 100644 index 00000000..3c650ff1 --- /dev/null +++ b/examples/security/classification/output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/adv_predictions.json + adv_probabilities_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/294a559325d7e7cc19bc4f2f1f9e1f68.pkl + params_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/params.yaml + predictions_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/predictions.json + probabilities_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/probabilities.json + score_dict_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/score_dict.json + test_labels_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/test_labels.json + train_labels_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/train_labels.json + model_dir: models + name: 2440b0f6b2a7e2dead33a66d3081c6cf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 71a5d7a4fad07ab05d30c01cc804c21f + trainer: + kwargs: {} +name: 2440b0f6b2a7e2dead33a66d3081c6cf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/params.yaml b/examples/security/classification/output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/params.yaml new file mode 100644 index 00000000..4a151ad9 --- /dev/null +++ b/examples/security/classification/output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/adv_predictions.json + adv_probabilities_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/664d2e2814316f5d7972e33a977e62e4.pkl + params_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/params.yaml + predictions_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/predictions.json + probabilities_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/probabilities.json + score_dict_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/score_dict.json + test_labels_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/test_labels.json + train_labels_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/train_labels.json + model_dir: models + name: 244ed219d17d1d2a0c41ff8c11846c2d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a799ef8fd65eb83c1c24a44f00a8c35a + trainer: + kwargs: {} +name: 244ed219d17d1d2a0c41ff8c11846c2d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/245f396ab17242f597de35c3c5263296/params.yaml b/examples/security/classification/output/reports/train/245f396ab17242f597de35c3c5263296/params.yaml new file mode 100644 index 00000000..eddc5b5b --- /dev/null +++ b/examples/security/classification/output/reports/train/245f396ab17242f597de35c3c5263296/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/245f396ab17242f597de35c3c5263296/adv_predictions.json + adv_probabilities_file: output/reports/train/245f396ab17242f597de35c3c5263296/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/0f095ecd4fe07cd1bfae40e292ba4f46.pkl + params_file: output/reports/train/245f396ab17242f597de35c3c5263296/params.yaml + predictions_file: output/reports/train/245f396ab17242f597de35c3c5263296/predictions.json + probabilities_file: output/reports/train/245f396ab17242f597de35c3c5263296/probabilities.json + score_dict_file: output/reports/train/245f396ab17242f597de35c3c5263296/score_dict.json + test_labels_file: output/reports/train/245f396ab17242f597de35c3c5263296/test_labels.json + train_labels_file: output/reports/train/245f396ab17242f597de35c3c5263296/train_labels.json + model_dir: models + name: 245f396ab17242f597de35c3c5263296 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3f7b506e22baac3967574c0303f194bb + trainer: + kwargs: {} +name: 245f396ab17242f597de35c3c5263296 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/24628cb9061a750c7d27b9e041034a59/params.yaml b/examples/security/classification/output/reports/train/24628cb9061a750c7d27b9e041034a59/params.yaml new file mode 100644 index 00000000..2b45e090 --- /dev/null +++ b/examples/security/classification/output/reports/train/24628cb9061a750c7d27b9e041034a59/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/adv_predictions.json + adv_probabilities_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/8ea37d8e6305e5049b4e38b25daead49.pkl + params_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/params.yaml + predictions_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/predictions.json + probabilities_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/probabilities.json + score_dict_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/score_dict.json + test_labels_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/test_labels.json + train_labels_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/train_labels.json + model_dir: models + name: 24628cb9061a750c7d27b9e041034a59 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a2ae0db4711e2fc6e666adc1e3a77574 + trainer: + kwargs: {} +name: 24628cb9061a750c7d27b9e041034a59 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2491e00acdec08f14722ca1f5868dca0/params.yaml b/examples/security/classification/output/reports/train/2491e00acdec08f14722ca1f5868dca0/params.yaml new file mode 100644 index 00000000..8695cb02 --- /dev/null +++ b/examples/security/classification/output/reports/train/2491e00acdec08f14722ca1f5868dca0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/adv_predictions.json + adv_probabilities_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/de5cf2f37d3db9f69a30a8df1a2938c2.pkl + params_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/params.yaml + predictions_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/predictions.json + probabilities_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/probabilities.json + score_dict_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/score_dict.json + test_labels_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/test_labels.json + train_labels_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/train_labels.json + model_dir: models + name: 2491e00acdec08f14722ca1f5868dca0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 819af591f36e3fa0731a023863877cdb + trainer: + kwargs: {} +name: 2491e00acdec08f14722ca1f5868dca0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/24b242b3f30d6fce541b1a147de54b27/params.yaml b/examples/security/classification/output/reports/train/24b242b3f30d6fce541b1a147de54b27/params.yaml new file mode 100644 index 00000000..6a11c458 --- /dev/null +++ b/examples/security/classification/output/reports/train/24b242b3f30d6fce541b1a147de54b27/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/adv_predictions.json + adv_probabilities_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/1c578e7c99902a0ee757ec77a1749f81.pkl + params_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/params.yaml + predictions_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/predictions.json + probabilities_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/probabilities.json + score_dict_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/score_dict.json + test_labels_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/test_labels.json + train_labels_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/train_labels.json + model_dir: models + name: 24b242b3f30d6fce541b1a147de54b27 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8d7f3668045e6ddc1469ed3cb3c342c4 + trainer: + kwargs: {} +name: 24b242b3f30d6fce541b1a147de54b27 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/24b9e85744451176b67cf0f1190045e7/params.yaml b/examples/security/classification/output/reports/train/24b9e85744451176b67cf0f1190045e7/params.yaml new file mode 100644 index 00000000..e144a48a --- /dev/null +++ b/examples/security/classification/output/reports/train/24b9e85744451176b67cf0f1190045e7/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/adv_predictions.json + adv_probabilities_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/f46dce2e83951d8d225b6b77f87e6e14.pkl + params_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/params.yaml + predictions_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/predictions.json + probabilities_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/probabilities.json + score_dict_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/score_dict.json + test_labels_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/test_labels.json + train_labels_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/train_labels.json + model_dir: models + name: 24b9e85744451176b67cf0f1190045e7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 435bd9b90a487ccfc8021b51a6c2f433 + trainer: + kwargs: {} +name: 24b9e85744451176b67cf0f1190045e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/254bf08d27d75291ad6b93993fd66f40/params.yaml b/examples/security/classification/output/reports/train/254bf08d27d75291ad6b93993fd66f40/params.yaml new file mode 100644 index 00000000..7a940288 --- /dev/null +++ b/examples/security/classification/output/reports/train/254bf08d27d75291ad6b93993fd66f40/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/adv_predictions.json + adv_probabilities_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/1d512e07e9647269adb8b804acb1acc4.pkl + params_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/params.yaml + predictions_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/predictions.json + probabilities_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/probabilities.json + score_dict_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/score_dict.json + test_labels_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/test_labels.json + train_labels_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/train_labels.json + model_dir: models + name: 254bf08d27d75291ad6b93993fd66f40 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c651b51ad2b1fb86d3666aade08bd61f + trainer: + kwargs: {} +name: 254bf08d27d75291ad6b93993fd66f40 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2567d9833fde91492442a051998f7a31/params.yaml b/examples/security/classification/output/reports/train/2567d9833fde91492442a051998f7a31/params.yaml new file mode 100644 index 00000000..be0b7bf1 --- /dev/null +++ b/examples/security/classification/output/reports/train/2567d9833fde91492442a051998f7a31/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2567d9833fde91492442a051998f7a31/adv_predictions.json + adv_probabilities_file: output/reports/train/2567d9833fde91492442a051998f7a31/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/1be582c061242cf5e3648f004d3a7e65.pkl + params_file: output/reports/train/2567d9833fde91492442a051998f7a31/params.yaml + predictions_file: output/reports/train/2567d9833fde91492442a051998f7a31/predictions.json + probabilities_file: output/reports/train/2567d9833fde91492442a051998f7a31/probabilities.json + score_dict_file: output/reports/train/2567d9833fde91492442a051998f7a31/score_dict.json + test_labels_file: output/reports/train/2567d9833fde91492442a051998f7a31/test_labels.json + train_labels_file: output/reports/train/2567d9833fde91492442a051998f7a31/train_labels.json + model_dir: models + name: 2567d9833fde91492442a051998f7a31 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2aaa0b6ce06ff69bc0768975d4708668 + trainer: + kwargs: {} +name: 2567d9833fde91492442a051998f7a31 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/25c04dff949ea2e149d2342d7cd76674/params.yaml b/examples/security/classification/output/reports/train/25c04dff949ea2e149d2342d7cd76674/params.yaml new file mode 100644 index 00000000..d83eb27c --- /dev/null +++ b/examples/security/classification/output/reports/train/25c04dff949ea2e149d2342d7cd76674/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/adv_predictions.json + adv_probabilities_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/99206763e294ac79092035b8ce312dbf.pkl + params_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/params.yaml + predictions_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/predictions.json + probabilities_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/probabilities.json + score_dict_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/score_dict.json + test_labels_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/test_labels.json + train_labels_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/train_labels.json + model_dir: models + name: 25c04dff949ea2e149d2342d7cd76674 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0c3c56e32fed3f89434033e0c562dbcd + trainer: + kwargs: {} +name: 25c04dff949ea2e149d2342d7cd76674 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/25cc0c5ff3fa612310c071a127299653/params.yaml b/examples/security/classification/output/reports/train/25cc0c5ff3fa612310c071a127299653/params.yaml new file mode 100644 index 00000000..af111d44 --- /dev/null +++ b/examples/security/classification/output/reports/train/25cc0c5ff3fa612310c071a127299653/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/adv_predictions.json + adv_probabilities_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/0eb644a4e51cce0cf40d2e4f0bc2c67a.pkl + params_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/params.yaml + predictions_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/predictions.json + probabilities_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/probabilities.json + score_dict_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/score_dict.json + test_labels_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/test_labels.json + train_labels_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/train_labels.json + model_dir: models + name: 25cc0c5ff3fa612310c071a127299653 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a920bceacc5d95401e81b2e1c0e893e7 + trainer: + kwargs: {} +name: 25cc0c5ff3fa612310c071a127299653 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/25ef88397c717ba7adafb1dc5552a01c/params.yaml b/examples/security/classification/output/reports/train/25ef88397c717ba7adafb1dc5552a01c/params.yaml new file mode 100644 index 00000000..a708738e --- /dev/null +++ b/examples/security/classification/output/reports/train/25ef88397c717ba7adafb1dc5552a01c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/adv_predictions.json + adv_probabilities_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/01b68909648850d96221a6e9ff05a421.pkl + params_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/params.yaml + predictions_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/predictions.json + probabilities_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/probabilities.json + score_dict_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/score_dict.json + test_labels_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/test_labels.json + train_labels_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/train_labels.json + model_dir: models + name: 25ef88397c717ba7adafb1dc5552a01c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8de395b4817680b889d38a6726c5e7a5 + trainer: + kwargs: {} +name: 25ef88397c717ba7adafb1dc5552a01c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/params.yaml b/examples/security/classification/output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/params.yaml new file mode 100644 index 00000000..40eaa108 --- /dev/null +++ b/examples/security/classification/output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/adv_predictions.json + adv_probabilities_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/e5ea60ca1181c0cdb2e1ddb08b757471.pkl + params_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/params.yaml + predictions_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/predictions.json + probabilities_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/probabilities.json + score_dict_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/score_dict.json + test_labels_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/test_labels.json + train_labels_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/train_labels.json + model_dir: models + name: 25f5a04ae65b6284aea60e7b466c47f6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3a7de0cf1743a1cc0261d21eb7a2dba8 + trainer: + kwargs: {} +name: 25f5a04ae65b6284aea60e7b466c47f6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/26580811893cd34c38bce5fa1dad47c1/params.yaml b/examples/security/classification/output/reports/train/26580811893cd34c38bce5fa1dad47c1/params.yaml new file mode 100644 index 00000000..01f6e466 --- /dev/null +++ b/examples/security/classification/output/reports/train/26580811893cd34c38bce5fa1dad47c1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/adv_predictions.json + adv_probabilities_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/8d25369115ce7556a729f7550e8733fc.pkl + params_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/params.yaml + predictions_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/predictions.json + probabilities_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/probabilities.json + score_dict_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/score_dict.json + test_labels_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/test_labels.json + train_labels_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/train_labels.json + model_dir: models + name: 26580811893cd34c38bce5fa1dad47c1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fc0321c149fb1d4dad1db5b8f8710d0c + trainer: + kwargs: {} +name: 26580811893cd34c38bce5fa1dad47c1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/params.yaml b/examples/security/classification/output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/params.yaml new file mode 100644 index 00000000..8a7ea350 --- /dev/null +++ b/examples/security/classification/output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/adv_predictions.json + adv_probabilities_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/b608d8645d7361079d3361ea38534677.pkl + params_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/params.yaml + predictions_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/predictions.json + probabilities_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/probabilities.json + score_dict_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/score_dict.json + test_labels_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/test_labels.json + train_labels_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/train_labels.json + model_dir: models + name: 268c1c9e9fa7fcb8cea8f378660b36d4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d13b45f41a381c14787f592d62f94d0d + trainer: + kwargs: {} +name: 268c1c9e9fa7fcb8cea8f378660b36d4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/26c67d8513e5968566458339e9583a58/params.yaml b/examples/security/classification/output/reports/train/26c67d8513e5968566458339e9583a58/params.yaml new file mode 100644 index 00000000..51c49365 --- /dev/null +++ b/examples/security/classification/output/reports/train/26c67d8513e5968566458339e9583a58/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/26c67d8513e5968566458339e9583a58/adv_predictions.json + adv_probabilities_file: output/reports/train/26c67d8513e5968566458339e9583a58/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/33af021ad84faa1eaf2ea30053cc6cf5.pkl + params_file: output/reports/train/26c67d8513e5968566458339e9583a58/params.yaml + predictions_file: output/reports/train/26c67d8513e5968566458339e9583a58/predictions.json + probabilities_file: output/reports/train/26c67d8513e5968566458339e9583a58/probabilities.json + score_dict_file: output/reports/train/26c67d8513e5968566458339e9583a58/score_dict.json + test_labels_file: output/reports/train/26c67d8513e5968566458339e9583a58/test_labels.json + train_labels_file: output/reports/train/26c67d8513e5968566458339e9583a58/train_labels.json + model_dir: models + name: 26c67d8513e5968566458339e9583a58 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 924bf661398ccbbf2278b48976e4e795 + trainer: + kwargs: {} +name: 26c67d8513e5968566458339e9583a58 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/26f77f3c328baf8131a349e59ecf7786/params.yaml b/examples/security/classification/output/reports/train/26f77f3c328baf8131a349e59ecf7786/params.yaml new file mode 100644 index 00000000..65aae660 --- /dev/null +++ b/examples/security/classification/output/reports/train/26f77f3c328baf8131a349e59ecf7786/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/adv_predictions.json + adv_probabilities_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/0c261d5690be3ce9daef912cd89ba599.pkl + params_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/params.yaml + predictions_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/predictions.json + probabilities_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/probabilities.json + score_dict_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/score_dict.json + test_labels_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/test_labels.json + train_labels_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/train_labels.json + model_dir: models + name: 26f77f3c328baf8131a349e59ecf7786 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1cf3c52e341ab9c13b881535d3ea39f6 + trainer: + kwargs: {} +name: 26f77f3c328baf8131a349e59ecf7786 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/26fa1f634f55f3f026923a83f37dca63/params.yaml b/examples/security/classification/output/reports/train/26fa1f634f55f3f026923a83f37dca63/params.yaml new file mode 100644 index 00000000..00fe3e80 --- /dev/null +++ b/examples/security/classification/output/reports/train/26fa1f634f55f3f026923a83f37dca63/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/adv_predictions.json + adv_probabilities_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/231ee01f38b4708092d7a174f236a7f0.pkl + params_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/params.yaml + predictions_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/predictions.json + probabilities_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/probabilities.json + score_dict_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/score_dict.json + test_labels_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/test_labels.json + train_labels_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/train_labels.json + model_dir: models + name: 26fa1f634f55f3f026923a83f37dca63 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 691d572ab4fc9ee2565f91cae49bfc62 + trainer: + kwargs: {} +name: 26fa1f634f55f3f026923a83f37dca63 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2769e675d76d611d4d5fe41391debdea/params.yaml b/examples/security/classification/output/reports/train/2769e675d76d611d4d5fe41391debdea/params.yaml new file mode 100644 index 00000000..9042a055 --- /dev/null +++ b/examples/security/classification/output/reports/train/2769e675d76d611d4d5fe41391debdea/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/adv_predictions.json + adv_probabilities_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/9dae70e9aaee3ed2713aee4d10c083c1.pkl + params_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/params.yaml + predictions_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/predictions.json + probabilities_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/probabilities.json + score_dict_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/score_dict.json + test_labels_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/test_labels.json + train_labels_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/train_labels.json + model_dir: models + name: 2769e675d76d611d4d5fe41391debdea + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 50514f87f4e6a4f25419a3908ec948be + trainer: + kwargs: {} +name: 2769e675d76d611d4d5fe41391debdea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/277cd1437819fd36d5d2185efa6c0822/params.yaml b/examples/security/classification/output/reports/train/277cd1437819fd36d5d2185efa6c0822/params.yaml new file mode 100644 index 00000000..111e44e7 --- /dev/null +++ b/examples/security/classification/output/reports/train/277cd1437819fd36d5d2185efa6c0822/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/adv_predictions.json + adv_probabilities_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/5a521a8b49face7f585106d1498156be.pkl + params_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/params.yaml + predictions_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/predictions.json + probabilities_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/probabilities.json + score_dict_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/score_dict.json + test_labels_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/test_labels.json + train_labels_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/train_labels.json + model_dir: models + name: 277cd1437819fd36d5d2185efa6c0822 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 236e35d3781effc7351aee7e7cab275b + trainer: + kwargs: {} +name: 277cd1437819fd36d5d2185efa6c0822 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/params.yaml b/examples/security/classification/output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/params.yaml new file mode 100644 index 00000000..01318be0 --- /dev/null +++ b/examples/security/classification/output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/adv_predictions.json + adv_probabilities_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/c9dbf43974b8dc89757aa79eb8b6d5db.pkl + params_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/params.yaml + predictions_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/predictions.json + probabilities_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/probabilities.json + score_dict_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/score_dict.json + test_labels_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/test_labels.json + train_labels_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/train_labels.json + model_dir: models + name: 279d0dbecdc4d2201238fa7cb85bc4a5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fcc0190ec071feec2fe5c636f8f8fabe + trainer: + kwargs: {} +name: 279d0dbecdc4d2201238fa7cb85bc4a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/279fe902def2f3b5f027d0d1fd187671/params.yaml b/examples/security/classification/output/reports/train/279fe902def2f3b5f027d0d1fd187671/params.yaml new file mode 100644 index 00000000..b8cd11ea --- /dev/null +++ b/examples/security/classification/output/reports/train/279fe902def2f3b5f027d0d1fd187671/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/adv_predictions.json + adv_probabilities_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/1b008e8de2abc3aa5db2dba589681655.pkl + params_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/params.yaml + predictions_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/predictions.json + probabilities_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/probabilities.json + score_dict_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/score_dict.json + test_labels_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/test_labels.json + train_labels_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/train_labels.json + model_dir: models + name: 279fe902def2f3b5f027d0d1fd187671 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f28fc2227a3b6442e1d0cd3fade28584 + trainer: + kwargs: {} +name: 279fe902def2f3b5f027d0d1fd187671 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/27da50a91ddbab3f85806e78db8111f4/params.yaml b/examples/security/classification/output/reports/train/27da50a91ddbab3f85806e78db8111f4/params.yaml new file mode 100644 index 00000000..2555e1f4 --- /dev/null +++ b/examples/security/classification/output/reports/train/27da50a91ddbab3f85806e78db8111f4/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/adv_predictions.json + adv_probabilities_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/c34e9eeab7d515e918bdb6faf834aedd.pkl + params_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/params.yaml + predictions_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/predictions.json + probabilities_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/probabilities.json + score_dict_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/score_dict.json + test_labels_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/test_labels.json + train_labels_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/train_labels.json + model_dir: models + name: 27da50a91ddbab3f85806e78db8111f4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0957264b4d47595ede94e7adaecbb035 + trainer: + kwargs: {} +name: 27da50a91ddbab3f85806e78db8111f4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/params.yaml b/examples/security/classification/output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/params.yaml new file mode 100644 index 00000000..0071caeb --- /dev/null +++ b/examples/security/classification/output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/adv_predictions.json + adv_probabilities_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/14826f369662f52a9a74189260e6b7f5.pkl + params_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/params.yaml + predictions_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/predictions.json + probabilities_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/probabilities.json + score_dict_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/score_dict.json + test_labels_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/test_labels.json + train_labels_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/train_labels.json + model_dir: models + name: 27dd741cbb968b499eeb7bfebdc38b0b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4302c203d46a36740d29592038251134 + trainer: + kwargs: {} +name: 27dd741cbb968b499eeb7bfebdc38b0b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/27ebc09ea09efca57118309d42196b5c/params.yaml b/examples/security/classification/output/reports/train/27ebc09ea09efca57118309d42196b5c/params.yaml new file mode 100644 index 00000000..9f9ff082 --- /dev/null +++ b/examples/security/classification/output/reports/train/27ebc09ea09efca57118309d42196b5c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/adv_predictions.json + adv_probabilities_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/d50d5f30fe4666bb53bbf9dd25bbbcd6.pkl + params_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/params.yaml + predictions_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/predictions.json + probabilities_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/probabilities.json + score_dict_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/score_dict.json + test_labels_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/test_labels.json + train_labels_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/train_labels.json + model_dir: models + name: 27ebc09ea09efca57118309d42196b5c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 69669d697856397074f68a4d458449f7 + trainer: + kwargs: {} +name: 27ebc09ea09efca57118309d42196b5c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/params.yaml b/examples/security/classification/output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/params.yaml new file mode 100644 index 00000000..ef919476 --- /dev/null +++ b/examples/security/classification/output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/adv_predictions.json + adv_probabilities_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/ffc851be294d112120ecccef8c56bfb2.pkl + params_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/params.yaml + predictions_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/predictions.json + probabilities_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/probabilities.json + score_dict_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/score_dict.json + test_labels_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/test_labels.json + train_labels_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/train_labels.json + model_dir: models + name: 28178d3a16f1e7dd303c2c61c409bbe5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f34334fe0a323aefc94002df68478b9a + trainer: + kwargs: {} +name: 28178d3a16f1e7dd303c2c61c409bbe5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/289d9e160ccade9d48d1cab737e99116/params.yaml b/examples/security/classification/output/reports/train/289d9e160ccade9d48d1cab737e99116/params.yaml new file mode 100644 index 00000000..bd1b3af1 --- /dev/null +++ b/examples/security/classification/output/reports/train/289d9e160ccade9d48d1cab737e99116/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/adv_predictions.json + adv_probabilities_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/4d268492d3d5a7788487894c41782ff4.pkl + params_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/params.yaml + predictions_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/predictions.json + probabilities_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/probabilities.json + score_dict_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/score_dict.json + test_labels_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/test_labels.json + train_labels_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/train_labels.json + model_dir: models + name: 289d9e160ccade9d48d1cab737e99116 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f8c80e5431587761a69a5eef37ee002b + trainer: + kwargs: {} +name: 289d9e160ccade9d48d1cab737e99116 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/28b3bc4a40ff16f17151140f076c19ac/params.yaml b/examples/security/classification/output/reports/train/28b3bc4a40ff16f17151140f076c19ac/params.yaml new file mode 100644 index 00000000..b0bf85bc --- /dev/null +++ b/examples/security/classification/output/reports/train/28b3bc4a40ff16f17151140f076c19ac/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 93cd2ed2f65fde8ccf01e9ac6343c024 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/adv_predictions.json + adv_probabilities_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/04ed3d2b113dd268ca610c98b0b6ac69.pkl + model_file: output/models/7df2162c93c6e061f1869c5a1cd9f89d.pkl + params_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/params.yaml + predictions_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/predictions.json + probabilities_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/probabilities.json + score_dict_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/score_dict.json + test_labels_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/test_labels.json + train_labels_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/train_labels.json + model_dir: models + name: 28b3bc4a40ff16f17151140f076c19ac + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 93cd2ed2f65fde8ccf01e9ac6343c024 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f4298700d88fe2ae8cab0055630464c1 + trainer: + kwargs: {} +name: 28b3bc4a40ff16f17151140f076c19ac +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/28c44ee44232b2da59080438589a75a1/params.yaml b/examples/security/classification/output/reports/train/28c44ee44232b2da59080438589a75a1/params.yaml new file mode 100644 index 00000000..6ee951b7 --- /dev/null +++ b/examples/security/classification/output/reports/train/28c44ee44232b2da59080438589a75a1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/28c44ee44232b2da59080438589a75a1/adv_predictions.json + adv_probabilities_file: output/reports/train/28c44ee44232b2da59080438589a75a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/d4e848466bc3af643bc5c10f4c12b468.pkl + params_file: output/reports/train/28c44ee44232b2da59080438589a75a1/params.yaml + predictions_file: output/reports/train/28c44ee44232b2da59080438589a75a1/predictions.json + probabilities_file: output/reports/train/28c44ee44232b2da59080438589a75a1/probabilities.json + score_dict_file: output/reports/train/28c44ee44232b2da59080438589a75a1/score_dict.json + test_labels_file: output/reports/train/28c44ee44232b2da59080438589a75a1/test_labels.json + train_labels_file: output/reports/train/28c44ee44232b2da59080438589a75a1/train_labels.json + model_dir: models + name: 28c44ee44232b2da59080438589a75a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 453b5962240515fb5bb7f328bfea1e7a + trainer: + kwargs: {} +name: 28c44ee44232b2da59080438589a75a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/28c4d2041797b00de1e2e346173af6d0/params.yaml b/examples/security/classification/output/reports/train/28c4d2041797b00de1e2e346173af6d0/params.yaml new file mode 100644 index 00000000..29420048 --- /dev/null +++ b/examples/security/classification/output/reports/train/28c4d2041797b00de1e2e346173af6d0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/adv_predictions.json + adv_probabilities_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/9f037f95b9047d49cfd8b846782900f1.pkl + params_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/params.yaml + predictions_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/predictions.json + probabilities_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/probabilities.json + score_dict_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/score_dict.json + test_labels_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/test_labels.json + train_labels_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/train_labels.json + model_dir: models + name: 28c4d2041797b00de1e2e346173af6d0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cd94fcd7c033a3531bacbe2494d6c396 + trainer: + kwargs: {} +name: 28c4d2041797b00de1e2e346173af6d0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/28e9bf78985ed99b2a3f2794d049318a/params.yaml b/examples/security/classification/output/reports/train/28e9bf78985ed99b2a3f2794d049318a/params.yaml new file mode 100644 index 00000000..44ff70f7 --- /dev/null +++ b/examples/security/classification/output/reports/train/28e9bf78985ed99b2a3f2794d049318a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/adv_predictions.json + adv_probabilities_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/cf8f6b6b4d3039a1bc6b14f8a63a80e2.pkl + params_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/params.yaml + predictions_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/predictions.json + probabilities_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/probabilities.json + score_dict_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/score_dict.json + test_labels_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/test_labels.json + train_labels_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/train_labels.json + model_dir: models + name: 28e9bf78985ed99b2a3f2794d049318a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 74c7a1fbb8bc4125d3b73523ec327828 + trainer: + kwargs: {} +name: 28e9bf78985ed99b2a3f2794d049318a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/28f178446fb4d239c77246ee3096625c/params.yaml b/examples/security/classification/output/reports/train/28f178446fb4d239c77246ee3096625c/params.yaml new file mode 100644 index 00000000..0c35ef65 --- /dev/null +++ b/examples/security/classification/output/reports/train/28f178446fb4d239c77246ee3096625c/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/28f178446fb4d239c77246ee3096625c/adv_predictions.json + adv_probabilities_file: output/reports/train/28f178446fb4d239c77246ee3096625c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/8c76ef167b2ceb1c85d5c27814d954e5.pkl + params_file: output/reports/train/28f178446fb4d239c77246ee3096625c/params.yaml + predictions_file: output/reports/train/28f178446fb4d239c77246ee3096625c/predictions.json + probabilities_file: output/reports/train/28f178446fb4d239c77246ee3096625c/probabilities.json + score_dict_file: output/reports/train/28f178446fb4d239c77246ee3096625c/score_dict.json + test_labels_file: output/reports/train/28f178446fb4d239c77246ee3096625c/test_labels.json + train_labels_file: output/reports/train/28f178446fb4d239c77246ee3096625c/train_labels.json + model_dir: models + name: 28f178446fb4d239c77246ee3096625c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e84d39fb7cb06f45fbd3f2f824163534 + trainer: + kwargs: {} +name: 28f178446fb4d239c77246ee3096625c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/params.yaml b/examples/security/classification/output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/params.yaml new file mode 100644 index 00000000..73240b5b --- /dev/null +++ b/examples/security/classification/output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/adv_predictions.json + adv_probabilities_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/c6cc6bc6c157f43cfc6219c5b84d1249.pkl + params_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/params.yaml + predictions_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/predictions.json + probabilities_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/probabilities.json + score_dict_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/score_dict.json + test_labels_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/test_labels.json + train_labels_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/train_labels.json + model_dir: models + name: 2919e60a66c2d1c95ae3f81d9b757ef4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 455808040cbc3cd56c6b0f7cbb338aa9 + trainer: + kwargs: {} +name: 2919e60a66c2d1c95ae3f81d9b757ef4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2926639bdd57bec76e0117b4433ad6e1/params.yaml b/examples/security/classification/output/reports/train/2926639bdd57bec76e0117b4433ad6e1/params.yaml new file mode 100644 index 00000000..cebc60d9 --- /dev/null +++ b/examples/security/classification/output/reports/train/2926639bdd57bec76e0117b4433ad6e1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/adv_predictions.json + adv_probabilities_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/a3b5c8cf25cc87b5b35c88f50c9bd128.pkl + params_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/params.yaml + predictions_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/predictions.json + probabilities_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/probabilities.json + score_dict_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/score_dict.json + test_labels_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/test_labels.json + train_labels_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/train_labels.json + model_dir: models + name: 2926639bdd57bec76e0117b4433ad6e1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4ecb41b53d7dff6d105d8fa24b5367d3 + trainer: + kwargs: {} +name: 2926639bdd57bec76e0117b4433ad6e1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/292de071d7660631dfffa5fa9f6640e1/params.yaml b/examples/security/classification/output/reports/train/292de071d7660631dfffa5fa9f6640e1/params.yaml new file mode 100644 index 00000000..0bc7751f --- /dev/null +++ b/examples/security/classification/output/reports/train/292de071d7660631dfffa5fa9f6640e1/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/adv_predictions.json + adv_probabilities_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/c72170b70e8a7f35fbb53cc80057e795.pkl + params_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/params.yaml + predictions_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/predictions.json + probabilities_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/probabilities.json + score_dict_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/score_dict.json + test_labels_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/test_labels.json + train_labels_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/train_labels.json + model_dir: models + name: 292de071d7660631dfffa5fa9f6640e1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: be65a989c4372cb8d808c1f6d9f97e7f + trainer: + kwargs: {} +name: 292de071d7660631dfffa5fa9f6640e1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/params.yaml b/examples/security/classification/output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/params.yaml new file mode 100644 index 00000000..4c6b0b24 --- /dev/null +++ b/examples/security/classification/output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/adv_predictions.json + adv_probabilities_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/c3a377c40f17a4ab9505c75d6dcae60c.pkl + params_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/params.yaml + predictions_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/predictions.json + probabilities_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/probabilities.json + score_dict_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/score_dict.json + test_labels_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/test_labels.json + train_labels_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/train_labels.json + model_dir: models + name: 296aab2ea5fa8cd853b9dbb8e0c688f6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 126c95a4996cfad7a7d7948ecbf12f04 + trainer: + kwargs: {} +name: 296aab2ea5fa8cd853b9dbb8e0c688f6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/params.yaml b/examples/security/classification/output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/params.yaml new file mode 100644 index 00000000..99c6a164 --- /dev/null +++ b/examples/security/classification/output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/adv_predictions.json + adv_probabilities_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/e27b8b9b487e6c52952011482ecf8de8.pkl + params_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/params.yaml + predictions_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/predictions.json + probabilities_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/probabilities.json + score_dict_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/score_dict.json + test_labels_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/test_labels.json + train_labels_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/train_labels.json + model_dir: models + name: 297abfdc09a14ac480e864ccb67c7ba2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d958e80453df3305992ee261a94313e0 + trainer: + kwargs: {} +name: 297abfdc09a14ac480e864ccb67c7ba2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/params.yaml b/examples/security/classification/output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/params.yaml new file mode 100644 index 00000000..d385fa74 --- /dev/null +++ b/examples/security/classification/output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/adv_predictions.json + adv_probabilities_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/11687a40f8ea6c2f32d37fa67691fcac.pkl + params_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/params.yaml + predictions_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/predictions.json + probabilities_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/probabilities.json + score_dict_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/score_dict.json + test_labels_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/test_labels.json + train_labels_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/train_labels.json + model_dir: models + name: 29831e7f1cb93fecbc64228b1d2020f7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 61a3943d3ef375d80d42843f1ccfb431 + trainer: + kwargs: {} +name: 29831e7f1cb93fecbc64228b1d2020f7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/298ecd75ae285d9a504cec89c7ea1976/params.yaml b/examples/security/classification/output/reports/train/298ecd75ae285d9a504cec89c7ea1976/params.yaml new file mode 100644 index 00000000..a9363948 --- /dev/null +++ b/examples/security/classification/output/reports/train/298ecd75ae285d9a504cec89c7ea1976/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/adv_predictions.json + adv_probabilities_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/5b9d6ff0ed75136bfebc8d55dbe1ae1e.pkl + params_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/params.yaml + predictions_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/predictions.json + probabilities_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/probabilities.json + score_dict_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/score_dict.json + test_labels_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/test_labels.json + train_labels_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/train_labels.json + model_dir: models + name: 298ecd75ae285d9a504cec89c7ea1976 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f5fbeb8b4aa03ccee5d2471f6d269470 + trainer: + kwargs: {} +name: 298ecd75ae285d9a504cec89c7ea1976 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/29a98de777550258271d96313bc0756b/params.yaml b/examples/security/classification/output/reports/train/29a98de777550258271d96313bc0756b/params.yaml new file mode 100644 index 00000000..5c17d3c8 --- /dev/null +++ b/examples/security/classification/output/reports/train/29a98de777550258271d96313bc0756b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/29a98de777550258271d96313bc0756b/adv_predictions.json + adv_probabilities_file: output/reports/train/29a98de777550258271d96313bc0756b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/f9782076caca16f2266973375b405003.pkl + params_file: output/reports/train/29a98de777550258271d96313bc0756b/params.yaml + predictions_file: output/reports/train/29a98de777550258271d96313bc0756b/predictions.json + probabilities_file: output/reports/train/29a98de777550258271d96313bc0756b/probabilities.json + score_dict_file: output/reports/train/29a98de777550258271d96313bc0756b/score_dict.json + test_labels_file: output/reports/train/29a98de777550258271d96313bc0756b/test_labels.json + train_labels_file: output/reports/train/29a98de777550258271d96313bc0756b/train_labels.json + model_dir: models + name: 29a98de777550258271d96313bc0756b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0ab59824fe36ff2dd580085e6211af4d + trainer: + kwargs: {} +name: 29a98de777550258271d96313bc0756b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/29cc9099f81a02b8ac62dbbd73810273/params.yaml b/examples/security/classification/output/reports/train/29cc9099f81a02b8ac62dbbd73810273/params.yaml new file mode 100644 index 00000000..e1f517df --- /dev/null +++ b/examples/security/classification/output/reports/train/29cc9099f81a02b8ac62dbbd73810273/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/adv_predictions.json + adv_probabilities_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/5fde4b9e540ac578e226ca08ed2971f2.pkl + params_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/params.yaml + predictions_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/predictions.json + probabilities_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/probabilities.json + score_dict_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/score_dict.json + test_labels_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/test_labels.json + train_labels_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/train_labels.json + model_dir: models + name: 29cc9099f81a02b8ac62dbbd73810273 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 83f61a32e06c2af5fc69783cdabf1f58 + trainer: + kwargs: {} +name: 29cc9099f81a02b8ac62dbbd73810273 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/29db12ee4020e8a8c22b72d158eabc31/params.yaml b/examples/security/classification/output/reports/train/29db12ee4020e8a8c22b72d158eabc31/params.yaml new file mode 100644 index 00000000..d5cc5462 --- /dev/null +++ b/examples/security/classification/output/reports/train/29db12ee4020e8a8c22b72d158eabc31/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/adv_predictions.json + adv_probabilities_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/54a42451d549dc23448a00116871c68b.pkl + params_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/params.yaml + predictions_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/predictions.json + probabilities_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/probabilities.json + score_dict_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/score_dict.json + test_labels_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/test_labels.json + train_labels_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/train_labels.json + model_dir: models + name: 29db12ee4020e8a8c22b72d158eabc31 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 761dffd9a60fdc02c1691fbce0a35066 + trainer: + kwargs: {} +name: 29db12ee4020e8a8c22b72d158eabc31 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/params.yaml b/examples/security/classification/output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/params.yaml new file mode 100644 index 00000000..a8752741 --- /dev/null +++ b/examples/security/classification/output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/adv_predictions.json + adv_probabilities_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/34c6b2928c337122b6f1094c41a0ad18.pkl + params_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/params.yaml + predictions_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/predictions.json + probabilities_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/probabilities.json + score_dict_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/score_dict.json + test_labels_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/test_labels.json + train_labels_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/train_labels.json + model_dir: models + name: 29ec94e61f9372dded9f1b9673ec0da2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a4a76b32a69b5fdb43ac110575723fd8 + trainer: + kwargs: {} +name: 29ec94e61f9372dded9f1b9673ec0da2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2a1bac4e90303a9f77cdefb21815049e/params.yaml b/examples/security/classification/output/reports/train/2a1bac4e90303a9f77cdefb21815049e/params.yaml new file mode 100644 index 00000000..f47ef6e5 --- /dev/null +++ b/examples/security/classification/output/reports/train/2a1bac4e90303a9f77cdefb21815049e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/adv_predictions.json + adv_probabilities_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/2b0c66c50a8ef1a755fa4fc7f96801f0.pkl + params_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/params.yaml + predictions_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/predictions.json + probabilities_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/probabilities.json + score_dict_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/score_dict.json + test_labels_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/test_labels.json + train_labels_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/train_labels.json + model_dir: models + name: 2a1bac4e90303a9f77cdefb21815049e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 446ae6ca58c96e93d604886fefc758a4 + trainer: + kwargs: {} +name: 2a1bac4e90303a9f77cdefb21815049e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/params.yaml b/examples/security/classification/output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/params.yaml new file mode 100644 index 00000000..17f02fce --- /dev/null +++ b/examples/security/classification/output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/adv_predictions.json + adv_probabilities_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/4eda0697553cd9623da26ea025896c06.pkl + params_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/params.yaml + predictions_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/predictions.json + probabilities_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/probabilities.json + score_dict_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/score_dict.json + test_labels_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/test_labels.json + train_labels_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/train_labels.json + model_dir: models + name: 2a38f424d51d0d2baa108a5062ae85ea + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f0559f2676761fe2bd72c8f9790753d9 + trainer: + kwargs: {} +name: 2a38f424d51d0d2baa108a5062ae85ea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2a8b954f363c2dead575ed8e0953de84/params.yaml b/examples/security/classification/output/reports/train/2a8b954f363c2dead575ed8e0953de84/params.yaml new file mode 100644 index 00000000..f74738f1 --- /dev/null +++ b/examples/security/classification/output/reports/train/2a8b954f363c2dead575ed8e0953de84/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/adv_predictions.json + adv_probabilities_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/026a3c245f7e427df43c929e98a4db99.pkl + params_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/params.yaml + predictions_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/predictions.json + probabilities_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/probabilities.json + score_dict_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/score_dict.json + test_labels_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/test_labels.json + train_labels_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/train_labels.json + model_dir: models + name: 2a8b954f363c2dead575ed8e0953de84 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d0399e13f6ac55c86c2b57fc6face066 + trainer: + kwargs: {} +name: 2a8b954f363c2dead575ed8e0953de84 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/params.yaml b/examples/security/classification/output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/params.yaml new file mode 100644 index 00000000..cbe820d4 --- /dev/null +++ b/examples/security/classification/output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/adv_predictions.json + adv_probabilities_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/22bffe8cf630ab977b038005783fbd50.pkl + params_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/params.yaml + predictions_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/predictions.json + probabilities_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/probabilities.json + score_dict_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/score_dict.json + test_labels_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/test_labels.json + train_labels_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/train_labels.json + model_dir: models + name: 2a8c8a6bb587e40579fe1ae7b4746e36 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 88b21d614783a0e8ec2f7d5b78863121 + trainer: + kwargs: {} +name: 2a8c8a6bb587e40579fe1ae7b4746e36 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/params.yaml b/examples/security/classification/output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/params.yaml new file mode 100644 index 00000000..ff86381f --- /dev/null +++ b/examples/security/classification/output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/adv_predictions.json + adv_probabilities_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/f12f8884fd896247a3d5b21a1f9040df.pkl + params_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/params.yaml + predictions_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/predictions.json + probabilities_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/probabilities.json + score_dict_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/score_dict.json + test_labels_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/test_labels.json + train_labels_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/train_labels.json + model_dir: models + name: 2a8e0cf2eaef57b2fe8c5a04545fa0f8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c39da44a8f2e56106fbf94b0b075850a + trainer: + kwargs: {} +name: 2a8e0cf2eaef57b2fe8c5a04545fa0f8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/params.yaml b/examples/security/classification/output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/params.yaml new file mode 100644 index 00000000..7ba08186 --- /dev/null +++ b/examples/security/classification/output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/adv_predictions.json + adv_probabilities_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/b65325a00720b17ec8808fd6fb6250f8.pkl + params_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/params.yaml + predictions_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/predictions.json + probabilities_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/probabilities.json + score_dict_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/score_dict.json + test_labels_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/test_labels.json + train_labels_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/train_labels.json + model_dir: models + name: 2aa3f040bd4f3fce3990b9eeb841e4b8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8dc10f42858cdbec289d8e6a8d87fad9 + trainer: + kwargs: {} +name: 2aa3f040bd4f3fce3990b9eeb841e4b8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/params.yaml b/examples/security/classification/output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/params.yaml new file mode 100644 index 00000000..1c09db0d --- /dev/null +++ b/examples/security/classification/output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/adv_predictions.json + adv_probabilities_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/9964166fb090e47bd8a80b77be22bbea.pkl + params_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/params.yaml + predictions_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/predictions.json + probabilities_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/probabilities.json + score_dict_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/score_dict.json + test_labels_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/test_labels.json + train_labels_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/train_labels.json + model_dir: models + name: 2aa8c66f01be34d4c20f144ab5b30bb3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f0db94537112139b5ca53e87aaa4244b + trainer: + kwargs: {} +name: 2aa8c66f01be34d4c20f144ab5b30bb3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2abda47c8c0200484d3c4b1213377fe5/params.yaml b/examples/security/classification/output/reports/train/2abda47c8c0200484d3c4b1213377fe5/params.yaml new file mode 100644 index 00000000..4ccfa8b4 --- /dev/null +++ b/examples/security/classification/output/reports/train/2abda47c8c0200484d3c4b1213377fe5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/adv_predictions.json + adv_probabilities_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/3113a2485ce1d9e35c554ab81cd0f3fd.pkl + params_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/params.yaml + predictions_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/predictions.json + probabilities_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/probabilities.json + score_dict_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/score_dict.json + test_labels_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/test_labels.json + train_labels_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/train_labels.json + model_dir: models + name: 2abda47c8c0200484d3c4b1213377fe5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d72a9731a870b8d3f26e09c2149c4cfd + trainer: + kwargs: {} +name: 2abda47c8c0200484d3c4b1213377fe5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2acd707460101a05cedbfd71b2cfd738/params.yaml b/examples/security/classification/output/reports/train/2acd707460101a05cedbfd71b2cfd738/params.yaml new file mode 100644 index 00000000..18d843ba --- /dev/null +++ b/examples/security/classification/output/reports/train/2acd707460101a05cedbfd71b2cfd738/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/adv_predictions.json + adv_probabilities_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/955f87e691e69bb15425d26e5f986725.pkl + params_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/params.yaml + predictions_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/predictions.json + probabilities_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/probabilities.json + score_dict_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/score_dict.json + test_labels_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/test_labels.json + train_labels_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/train_labels.json + model_dir: models + name: 2acd707460101a05cedbfd71b2cfd738 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f343d092a6c9bbbd953a4ab5e7c85e28 + trainer: + kwargs: {} +name: 2acd707460101a05cedbfd71b2cfd738 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/params.yaml b/examples/security/classification/output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/params.yaml new file mode 100644 index 00000000..605eb034 --- /dev/null +++ b/examples/security/classification/output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/params.yaml @@ -0,0 +1,104 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/adv_predictions.json + adv_probabilities_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/648b5feba6d079f672b922a8416bf6e1.pkl + params_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/params.yaml + predictions_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/predictions.json + probabilities_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/probabilities.json + score_dict_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/score_dict.json + test_labels_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/test_labels.json + train_labels_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/train_labels.json + model_dir: models + name: 2b19558c12180d2a8ba8d1fa6468d6ff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: + - linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 92900985f53ec6777849af598192203c + trainer: + kwargs: {} +name: 2b19558c12180d2a8ba8d1fa6468d6ff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2b23c217e60bd5606583944cded1a342/params.yaml b/examples/security/classification/output/reports/train/2b23c217e60bd5606583944cded1a342/params.yaml new file mode 100644 index 00000000..a63b8df8 --- /dev/null +++ b/examples/security/classification/output/reports/train/2b23c217e60bd5606583944cded1a342/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2b23c217e60bd5606583944cded1a342/adv_predictions.json + adv_probabilities_file: output/reports/train/2b23c217e60bd5606583944cded1a342/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/ec7892e8e7818e430940e85db9341a23.pkl + params_file: output/reports/train/2b23c217e60bd5606583944cded1a342/params.yaml + predictions_file: output/reports/train/2b23c217e60bd5606583944cded1a342/predictions.json + probabilities_file: output/reports/train/2b23c217e60bd5606583944cded1a342/probabilities.json + score_dict_file: output/reports/train/2b23c217e60bd5606583944cded1a342/score_dict.json + test_labels_file: output/reports/train/2b23c217e60bd5606583944cded1a342/test_labels.json + train_labels_file: output/reports/train/2b23c217e60bd5606583944cded1a342/train_labels.json + model_dir: models + name: 2b23c217e60bd5606583944cded1a342 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e57eee8d7d7f6ebe66891d94323c6123 + trainer: + kwargs: {} +name: 2b23c217e60bd5606583944cded1a342 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/params.yaml b/examples/security/classification/output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/params.yaml new file mode 100644 index 00000000..6b1975a4 --- /dev/null +++ b/examples/security/classification/output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/adv_predictions.json + adv_probabilities_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/3c5a4392dfcb9c751853bf7dfeeaa7fb.pkl + params_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/params.yaml + predictions_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/predictions.json + probabilities_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/probabilities.json + score_dict_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/score_dict.json + test_labels_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/test_labels.json + train_labels_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/train_labels.json + model_dir: models + name: 2b2a88ebe2ca322635cfb75c144f6f02 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 07b46e11414b564a2097abb1f5462246 + trainer: + kwargs: {} +name: 2b2a88ebe2ca322635cfb75c144f6f02 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/params.yaml b/examples/security/classification/output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/params.yaml new file mode 100644 index 00000000..3340b8dc --- /dev/null +++ b/examples/security/classification/output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/adv_predictions.json + adv_probabilities_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/e2337ac2f6ed10b1380ef42ef613bad2.pkl + params_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/params.yaml + predictions_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/predictions.json + probabilities_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/probabilities.json + score_dict_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/score_dict.json + test_labels_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/test_labels.json + train_labels_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/train_labels.json + model_dir: models + name: 2b9141ff5c4affd8539ce41e92a53f35 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d7dbb226922b5296eeb7a017c1f235da + trainer: + kwargs: {} +name: 2b9141ff5c4affd8539ce41e92a53f35 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/params.yaml b/examples/security/classification/output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/params.yaml new file mode 100644 index 00000000..f47a6acf --- /dev/null +++ b/examples/security/classification/output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/adv_predictions.json + adv_probabilities_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/bc0fb80e459bd55ab3dc12c9197f5e4d.pkl + params_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/params.yaml + predictions_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/predictions.json + probabilities_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/probabilities.json + score_dict_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/score_dict.json + test_labels_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/test_labels.json + train_labels_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/train_labels.json + model_dir: models + name: 2bb4b5206bcbe5a7cdb0c6253edb962e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1e31a5666e63aee6af60b21888ae9496 + trainer: + kwargs: {} +name: 2bb4b5206bcbe5a7cdb0c6253edb962e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2bb80bdf250fa4cbe25b49b119571207/params.yaml b/examples/security/classification/output/reports/train/2bb80bdf250fa4cbe25b49b119571207/params.yaml new file mode 100644 index 00000000..9a0b09b5 --- /dev/null +++ b/examples/security/classification/output/reports/train/2bb80bdf250fa4cbe25b49b119571207/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/adv_predictions.json + adv_probabilities_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/fe4198447c0925f6c51c0e2bc2d3df57.pkl + params_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/params.yaml + predictions_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/predictions.json + probabilities_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/probabilities.json + score_dict_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/score_dict.json + test_labels_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/test_labels.json + train_labels_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/train_labels.json + model_dir: models + name: 2bb80bdf250fa4cbe25b49b119571207 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bae036ffb2115aaca50e151d819d9c26 + trainer: + kwargs: {} +name: 2bb80bdf250fa4cbe25b49b119571207 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/params.yaml b/examples/security/classification/output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/params.yaml new file mode 100644 index 00000000..399824fb --- /dev/null +++ b/examples/security/classification/output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/adv_predictions.json + adv_probabilities_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/fa8582eec429ce1a6b34be7abf4cf311.pkl + params_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/params.yaml + predictions_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/predictions.json + probabilities_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/probabilities.json + score_dict_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/score_dict.json + test_labels_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/test_labels.json + train_labels_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/train_labels.json + model_dir: models + name: 2bd96dbef483ba16a8b5f8c8ab6e3d87 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 997a9ee108e1e2c95e049ef254caf9cc + trainer: + kwargs: {} +name: 2bd96dbef483ba16a8b5f8c8ab6e3d87 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/params.yaml b/examples/security/classification/output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/params.yaml new file mode 100644 index 00000000..44a958d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/adv_predictions.json + adv_probabilities_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/e600ee5a29ca2089e37f3e25ed76a0ab.pkl + params_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/params.yaml + predictions_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/predictions.json + probabilities_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/probabilities.json + score_dict_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/score_dict.json + test_labels_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/test_labels.json + train_labels_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/train_labels.json + model_dir: models + name: 2bdf60e019d05068b74b8cbd83c0fea5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 056e430c05129605f09932ff302a5dd7 + trainer: + kwargs: {} +name: 2bdf60e019d05068b74b8cbd83c0fea5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2be2c1245575c399281364ea78574561/params.yaml b/examples/security/classification/output/reports/train/2be2c1245575c399281364ea78574561/params.yaml new file mode 100644 index 00000000..6eebf8ee --- /dev/null +++ b/examples/security/classification/output/reports/train/2be2c1245575c399281364ea78574561/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2be2c1245575c399281364ea78574561/adv_predictions.json + adv_probabilities_file: output/reports/train/2be2c1245575c399281364ea78574561/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/e3a8067d1279785e83ae489cde6619e5.pkl + params_file: output/reports/train/2be2c1245575c399281364ea78574561/params.yaml + predictions_file: output/reports/train/2be2c1245575c399281364ea78574561/predictions.json + probabilities_file: output/reports/train/2be2c1245575c399281364ea78574561/probabilities.json + score_dict_file: output/reports/train/2be2c1245575c399281364ea78574561/score_dict.json + test_labels_file: output/reports/train/2be2c1245575c399281364ea78574561/test_labels.json + train_labels_file: output/reports/train/2be2c1245575c399281364ea78574561/train_labels.json + model_dir: models + name: 2be2c1245575c399281364ea78574561 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f8211806c18aed8fe108655e885aee9a + trainer: + kwargs: {} +name: 2be2c1245575c399281364ea78574561 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2bfe7d4221e67015786634d92dabbc7b/params.yaml b/examples/security/classification/output/reports/train/2bfe7d4221e67015786634d92dabbc7b/params.yaml new file mode 100644 index 00000000..4e11a289 --- /dev/null +++ b/examples/security/classification/output/reports/train/2bfe7d4221e67015786634d92dabbc7b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/adv_predictions.json + adv_probabilities_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/bc67f6fb590ed50d343e01d2e8d5c3e9.pkl + params_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/params.yaml + predictions_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/predictions.json + probabilities_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/probabilities.json + score_dict_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/score_dict.json + test_labels_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/test_labels.json + train_labels_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/train_labels.json + model_dir: models + name: 2bfe7d4221e67015786634d92dabbc7b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7bb1ace5b31ffb3c008a385887ce3f67 + trainer: + kwargs: {} +name: 2bfe7d4221e67015786634d92dabbc7b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2c0f4b93d068963376753fb3c572ef15/params.yaml b/examples/security/classification/output/reports/train/2c0f4b93d068963376753fb3c572ef15/params.yaml new file mode 100644 index 00000000..99ca30aa --- /dev/null +++ b/examples/security/classification/output/reports/train/2c0f4b93d068963376753fb3c572ef15/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/adv_predictions.json + adv_probabilities_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/4d6a688cc2747af609661bee1c0c3dd4.pkl + params_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/params.yaml + predictions_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/predictions.json + probabilities_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/probabilities.json + score_dict_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/score_dict.json + test_labels_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/test_labels.json + train_labels_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/train_labels.json + model_dir: models + name: 2c0f4b93d068963376753fb3c572ef15 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 78a991c538df8e537727d6c17b5d212b + trainer: + kwargs: {} +name: 2c0f4b93d068963376753fb3c572ef15 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/params.yaml b/examples/security/classification/output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/params.yaml new file mode 100644 index 00000000..cfa0c2bd --- /dev/null +++ b/examples/security/classification/output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/adv_predictions.json + adv_probabilities_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/416bdda04a0dd4c3dd5e39946b7b0297.pkl + params_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/params.yaml + predictions_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/predictions.json + probabilities_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/probabilities.json + score_dict_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/score_dict.json + test_labels_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/test_labels.json + train_labels_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/train_labels.json + model_dir: models + name: 2c1bfe2bf07120188c6e7dae682f53ce + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 48443cf76a5ad05b3c4f7481b0b594cd + trainer: + kwargs: {} +name: 2c1bfe2bf07120188c6e7dae682f53ce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2c1da1457d61af82da49196442dbdf22/params.yaml b/examples/security/classification/output/reports/train/2c1da1457d61af82da49196442dbdf22/params.yaml new file mode 100644 index 00000000..fd4015fe --- /dev/null +++ b/examples/security/classification/output/reports/train/2c1da1457d61af82da49196442dbdf22/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/adv_predictions.json + adv_probabilities_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/bbc71f80a49e06cb3e9a60862fae8d42.pkl + params_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/params.yaml + predictions_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/predictions.json + probabilities_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/probabilities.json + score_dict_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/score_dict.json + test_labels_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/test_labels.json + train_labels_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/train_labels.json + model_dir: models + name: 2c1da1457d61af82da49196442dbdf22 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c3fc161e55153cc7a8a9c72a870d57a + trainer: + kwargs: {} +name: 2c1da1457d61af82da49196442dbdf22 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/params.yaml b/examples/security/classification/output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/params.yaml new file mode 100644 index 00000000..a697331e --- /dev/null +++ b/examples/security/classification/output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/adv_predictions.json + adv_probabilities_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/ebd581c41222fcefc2ad27d122ed0b24.pkl + params_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/params.yaml + predictions_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/predictions.json + probabilities_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/probabilities.json + score_dict_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/score_dict.json + test_labels_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/test_labels.json + train_labels_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/train_labels.json + model_dir: models + name: 2c52223a5e625229b07a4cdff7c52d6e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7517932302c428a2e68523deaa746de2 + trainer: + kwargs: {} +name: 2c52223a5e625229b07a4cdff7c52d6e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2c74137ba96612419f9a80927ffcdff4/params.yaml b/examples/security/classification/output/reports/train/2c74137ba96612419f9a80927ffcdff4/params.yaml new file mode 100644 index 00000000..b285fde8 --- /dev/null +++ b/examples/security/classification/output/reports/train/2c74137ba96612419f9a80927ffcdff4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/adv_predictions.json + adv_probabilities_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/7fe51a7625eca5f1a5423a8f198fe70c.pkl + params_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/params.yaml + predictions_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/predictions.json + probabilities_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/probabilities.json + score_dict_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/score_dict.json + test_labels_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/test_labels.json + train_labels_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/train_labels.json + model_dir: models + name: 2c74137ba96612419f9a80927ffcdff4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 00b72f5a36701cc6ecac2645b35c7f49 + trainer: + kwargs: {} +name: 2c74137ba96612419f9a80927ffcdff4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/params.yaml b/examples/security/classification/output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/params.yaml new file mode 100644 index 00000000..cec14777 --- /dev/null +++ b/examples/security/classification/output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/adv_predictions.json + adv_probabilities_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/d0d898b14d02f50435660ec10809b56f.pkl + params_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/params.yaml + predictions_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/predictions.json + probabilities_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/probabilities.json + score_dict_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/score_dict.json + test_labels_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/test_labels.json + train_labels_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/train_labels.json + model_dir: models + name: 2c788fb032659ddd77ee7af4ee7f7984 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8a625e8264ec76c686ae23f308d2cf64 + trainer: + kwargs: {} +name: 2c788fb032659ddd77ee7af4ee7f7984 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/params.yaml b/examples/security/classification/output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/params.yaml new file mode 100644 index 00000000..68a14648 --- /dev/null +++ b/examples/security/classification/output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/adv_predictions.json + adv_probabilities_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/d8e9ee421c462b120eebceb51780847b.pkl + params_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/params.yaml + predictions_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/predictions.json + probabilities_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/probabilities.json + score_dict_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/score_dict.json + test_labels_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/test_labels.json + train_labels_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/train_labels.json + model_dir: models + name: 2c86e2e530c17e1794e091d7003d4cb8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 99d1a7fc327a1605ed1d93551a84d361 + trainer: + kwargs: {} +name: 2c86e2e530c17e1794e091d7003d4cb8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2ca4538e355964ee71de087f88cadf1e/params.yaml b/examples/security/classification/output/reports/train/2ca4538e355964ee71de087f88cadf1e/params.yaml new file mode 100644 index 00000000..b3d918e7 --- /dev/null +++ b/examples/security/classification/output/reports/train/2ca4538e355964ee71de087f88cadf1e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/adv_predictions.json + adv_probabilities_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/3b20ffd6ef1d9b5f4ef00da05dda3138.pkl + params_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/params.yaml + predictions_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/predictions.json + probabilities_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/probabilities.json + score_dict_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/score_dict.json + test_labels_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/test_labels.json + train_labels_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/train_labels.json + model_dir: models + name: 2ca4538e355964ee71de087f88cadf1e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5353a2bfbd03fe2f11bc5e9596e08b6b + trainer: + kwargs: {} +name: 2ca4538e355964ee71de087f88cadf1e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/params.yaml b/examples/security/classification/output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/params.yaml new file mode 100644 index 00000000..746d7950 --- /dev/null +++ b/examples/security/classification/output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/adv_predictions.json + adv_probabilities_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/5b6e22ce8117caaea664616a157ff252.pkl + params_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/params.yaml + predictions_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/predictions.json + probabilities_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/probabilities.json + score_dict_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/score_dict.json + test_labels_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/test_labels.json + train_labels_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/train_labels.json + model_dir: models + name: 2cc6d95a79e2670b4eb45879ec8516b3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 54e94b7119bd0e4663dd9a7001f33122 + trainer: + kwargs: {} +name: 2cc6d95a79e2670b4eb45879ec8516b3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/params.yaml b/examples/security/classification/output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/params.yaml new file mode 100644 index 00000000..fd7c4ae2 --- /dev/null +++ b/examples/security/classification/output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/adv_predictions.json + adv_probabilities_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/b2c6ad559b3017ea716b3ab7da8cdb6f.pkl + params_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/params.yaml + predictions_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/predictions.json + probabilities_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/probabilities.json + score_dict_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/score_dict.json + test_labels_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/test_labels.json + train_labels_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/train_labels.json + model_dir: models + name: 2cf9dd3f68b9bae359ef96cc6ad395ca + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 83a5c46e5fb6d2c3db2bd4237b7a805b + trainer: + kwargs: {} +name: 2cf9dd3f68b9bae359ef96cc6ad395ca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2d1a5615b04fb97c029aa168d96b026c/params.yaml b/examples/security/classification/output/reports/train/2d1a5615b04fb97c029aa168d96b026c/params.yaml new file mode 100644 index 00000000..b7835313 --- /dev/null +++ b/examples/security/classification/output/reports/train/2d1a5615b04fb97c029aa168d96b026c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/adv_predictions.json + adv_probabilities_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/897b4387de6ad4835eeabef66d49d86a.pkl + params_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/params.yaml + predictions_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/predictions.json + probabilities_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/probabilities.json + score_dict_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/score_dict.json + test_labels_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/test_labels.json + train_labels_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/train_labels.json + model_dir: models + name: 2d1a5615b04fb97c029aa168d96b026c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9bb04938e5ef82295fa855129579004a + trainer: + kwargs: {} +name: 2d1a5615b04fb97c029aa168d96b026c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2d23cda7d9caa9810859f5e01af7688b/params.yaml b/examples/security/classification/output/reports/train/2d23cda7d9caa9810859f5e01af7688b/params.yaml new file mode 100644 index 00000000..8e54ca6a --- /dev/null +++ b/examples/security/classification/output/reports/train/2d23cda7d9caa9810859f5e01af7688b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/adv_predictions.json + adv_probabilities_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/12192e902e45a51a5f0f4bbbe5133e5d.pkl + params_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/params.yaml + predictions_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/predictions.json + probabilities_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/probabilities.json + score_dict_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/score_dict.json + test_labels_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/test_labels.json + train_labels_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/train_labels.json + model_dir: models + name: 2d23cda7d9caa9810859f5e01af7688b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 24f69c2650dcbd83a1313205c9a77ffb + trainer: + kwargs: {} +name: 2d23cda7d9caa9810859f5e01af7688b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2d3cad31399616f0e2121d765709e7d2/params.yaml b/examples/security/classification/output/reports/train/2d3cad31399616f0e2121d765709e7d2/params.yaml new file mode 100644 index 00000000..95e9a19d --- /dev/null +++ b/examples/security/classification/output/reports/train/2d3cad31399616f0e2121d765709e7d2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/adv_predictions.json + adv_probabilities_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/c20ec340606a0eae4a3927b7032a3322.pkl + params_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/params.yaml + predictions_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/predictions.json + probabilities_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/probabilities.json + score_dict_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/score_dict.json + test_labels_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/test_labels.json + train_labels_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/train_labels.json + model_dir: models + name: 2d3cad31399616f0e2121d765709e7d2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 283b034356b667510deae1386a114da9 + trainer: + kwargs: {} +name: 2d3cad31399616f0e2121d765709e7d2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/params.yaml b/examples/security/classification/output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/params.yaml new file mode 100644 index 00000000..ef6129f9 --- /dev/null +++ b/examples/security/classification/output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/adv_predictions.json + adv_probabilities_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/297bcd0c57be89308374e8dfde0b232f.pkl + params_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/params.yaml + predictions_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/predictions.json + probabilities_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/probabilities.json + score_dict_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/score_dict.json + test_labels_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/test_labels.json + train_labels_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/train_labels.json + model_dir: models + name: 2d9b9b8afe4ec3cc205f5d6da82f5197 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bb2fae02daa9c49d46d91533490d5704 + trainer: + kwargs: {} +name: 2d9b9b8afe4ec3cc205f5d6da82f5197 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/params.yaml b/examples/security/classification/output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/params.yaml new file mode 100644 index 00000000..7292e912 --- /dev/null +++ b/examples/security/classification/output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/adv_predictions.json + adv_probabilities_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/c7967e68b9d27eed548508dcf76b2128.pkl + params_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/params.yaml + predictions_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/predictions.json + probabilities_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/probabilities.json + score_dict_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/score_dict.json + test_labels_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/test_labels.json + train_labels_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/train_labels.json + model_dir: models + name: 2dd4d21b3b599023ecc83e579ab0b947 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3362097ad0843295d2bbad9147957dc3 + trainer: + kwargs: {} +name: 2dd4d21b3b599023ecc83e579ab0b947 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2de99cbec06dda15d2bd1236e76573ca/params.yaml b/examples/security/classification/output/reports/train/2de99cbec06dda15d2bd1236e76573ca/params.yaml new file mode 100644 index 00000000..53511d77 --- /dev/null +++ b/examples/security/classification/output/reports/train/2de99cbec06dda15d2bd1236e76573ca/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/adv_predictions.json + adv_probabilities_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/661fa373207a2b56f7d7b9942fe0fab3.pkl + params_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/params.yaml + predictions_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/predictions.json + probabilities_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/probabilities.json + score_dict_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/score_dict.json + test_labels_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/test_labels.json + train_labels_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/train_labels.json + model_dir: models + name: 2de99cbec06dda15d2bd1236e76573ca + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b5991b964e23e6cabcbb413f301622f7 + trainer: + kwargs: {} +name: 2de99cbec06dda15d2bd1236e76573ca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2dea64d650d37afaebe312aece96a8f5/params.yaml b/examples/security/classification/output/reports/train/2dea64d650d37afaebe312aece96a8f5/params.yaml new file mode 100644 index 00000000..704e8cf0 --- /dev/null +++ b/examples/security/classification/output/reports/train/2dea64d650d37afaebe312aece96a8f5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/adv_predictions.json + adv_probabilities_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/357eb9dc9ca3ea4741fec857b933a736.pkl + params_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/params.yaml + predictions_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/predictions.json + probabilities_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/probabilities.json + score_dict_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/score_dict.json + test_labels_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/test_labels.json + train_labels_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/train_labels.json + model_dir: models + name: 2dea64d650d37afaebe312aece96a8f5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6588b79d1da0e005ba1dbdad5669ecd9 + trainer: + kwargs: {} +name: 2dea64d650d37afaebe312aece96a8f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/params.yaml b/examples/security/classification/output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/params.yaml new file mode 100644 index 00000000..4a377aad --- /dev/null +++ b/examples/security/classification/output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/adv_predictions.json + adv_probabilities_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/0de28f76035d410b6b8657e91bd94d58.pkl + params_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/params.yaml + predictions_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/predictions.json + probabilities_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/probabilities.json + score_dict_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/score_dict.json + test_labels_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/test_labels.json + train_labels_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/train_labels.json + model_dir: models + name: 2e15cb4aba3e11a47abb1c96cedec857 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 476f2690c14d41490eb0c22cc968725f + trainer: + kwargs: {} +name: 2e15cb4aba3e11a47abb1c96cedec857 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2e2bdcf47903073081179bb6a806f697/params.yaml b/examples/security/classification/output/reports/train/2e2bdcf47903073081179bb6a806f697/params.yaml new file mode 100644 index 00000000..511ff744 --- /dev/null +++ b/examples/security/classification/output/reports/train/2e2bdcf47903073081179bb6a806f697/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/adv_predictions.json + adv_probabilities_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/6ef9beaaa8cfc751299137c0024ba122.pkl + params_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/params.yaml + predictions_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/predictions.json + probabilities_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/probabilities.json + score_dict_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/score_dict.json + test_labels_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/test_labels.json + train_labels_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/train_labels.json + model_dir: models + name: 2e2bdcf47903073081179bb6a806f697 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 777f17d5acaf89e62dcb4619977348e1 + trainer: + kwargs: {} +name: 2e2bdcf47903073081179bb6a806f697 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/params.yaml b/examples/security/classification/output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/params.yaml new file mode 100644 index 00000000..fcc696bb --- /dev/null +++ b/examples/security/classification/output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/adv_predictions.json + adv_probabilities_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/a18667b6a89d9b80825d18f6fd6b3fb0.pkl + params_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/params.yaml + predictions_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/predictions.json + probabilities_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/probabilities.json + score_dict_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/score_dict.json + test_labels_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/test_labels.json + train_labels_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/train_labels.json + model_dir: models + name: 2e69bc9e6a911131bf8e042ca88e1f9e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 883430d010e4148958bb1f51ae1fc5bf + trainer: + kwargs: {} +name: 2e69bc9e6a911131bf8e042ca88e1f9e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2e95144b56948be11857005b4776042d/params.yaml b/examples/security/classification/output/reports/train/2e95144b56948be11857005b4776042d/params.yaml new file mode 100644 index 00000000..ca1aa9d7 --- /dev/null +++ b/examples/security/classification/output/reports/train/2e95144b56948be11857005b4776042d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2e95144b56948be11857005b4776042d/adv_predictions.json + adv_probabilities_file: output/reports/train/2e95144b56948be11857005b4776042d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/dfa2e4580a9c5892020bba43126ccb32.pkl + params_file: output/reports/train/2e95144b56948be11857005b4776042d/params.yaml + predictions_file: output/reports/train/2e95144b56948be11857005b4776042d/predictions.json + probabilities_file: output/reports/train/2e95144b56948be11857005b4776042d/probabilities.json + score_dict_file: output/reports/train/2e95144b56948be11857005b4776042d/score_dict.json + test_labels_file: output/reports/train/2e95144b56948be11857005b4776042d/test_labels.json + train_labels_file: output/reports/train/2e95144b56948be11857005b4776042d/train_labels.json + model_dir: models + name: 2e95144b56948be11857005b4776042d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d5453b6a676478c7c84960e66d95329d + trainer: + kwargs: {} +name: 2e95144b56948be11857005b4776042d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2ef3014aa128150ce8976abe446d5c27/params.yaml b/examples/security/classification/output/reports/train/2ef3014aa128150ce8976abe446d5c27/params.yaml new file mode 100644 index 00000000..01beed0c --- /dev/null +++ b/examples/security/classification/output/reports/train/2ef3014aa128150ce8976abe446d5c27/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/adv_predictions.json + adv_probabilities_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/d4bf7c88af4493f192b8908a6c1d7a24.pkl + params_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/params.yaml + predictions_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/predictions.json + probabilities_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/probabilities.json + score_dict_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/score_dict.json + test_labels_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/test_labels.json + train_labels_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/train_labels.json + model_dir: models + name: 2ef3014aa128150ce8976abe446d5c27 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a9f0db7d04c445e7b9fde0d34fe0ab7f + trainer: + kwargs: {} +name: 2ef3014aa128150ce8976abe446d5c27 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2f01abc53302e53883bbe34ee09be1f3/params.yaml b/examples/security/classification/output/reports/train/2f01abc53302e53883bbe34ee09be1f3/params.yaml new file mode 100644 index 00000000..3d059a27 --- /dev/null +++ b/examples/security/classification/output/reports/train/2f01abc53302e53883bbe34ee09be1f3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/adv_predictions.json + adv_probabilities_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/1e78ef7fa3b1414ca3fb0f4b65173042.pkl + params_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/params.yaml + predictions_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/predictions.json + probabilities_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/probabilities.json + score_dict_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/score_dict.json + test_labels_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/test_labels.json + train_labels_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/train_labels.json + model_dir: models + name: 2f01abc53302e53883bbe34ee09be1f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0bf9350b0a8556b53d62c5e8ce1ce73e + trainer: + kwargs: {} +name: 2f01abc53302e53883bbe34ee09be1f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2f1d3426ad5e3453cbe068da99332726/params.yaml b/examples/security/classification/output/reports/train/2f1d3426ad5e3453cbe068da99332726/params.yaml new file mode 100644 index 00000000..353ee90d --- /dev/null +++ b/examples/security/classification/output/reports/train/2f1d3426ad5e3453cbe068da99332726/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/adv_predictions.json + adv_probabilities_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/3d169e4f9fac61f7e2b94fce98faefff.pkl + params_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/params.yaml + predictions_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/predictions.json + probabilities_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/probabilities.json + score_dict_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/score_dict.json + test_labels_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/test_labels.json + train_labels_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/train_labels.json + model_dir: models + name: 2f1d3426ad5e3453cbe068da99332726 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f4c2d004c1f709e136639ea6c9129b8c + trainer: + kwargs: {} +name: 2f1d3426ad5e3453cbe068da99332726 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2f368d21cae45f44bc83332d80ea6a97/params.yaml b/examples/security/classification/output/reports/train/2f368d21cae45f44bc83332d80ea6a97/params.yaml new file mode 100644 index 00000000..015153a8 --- /dev/null +++ b/examples/security/classification/output/reports/train/2f368d21cae45f44bc83332d80ea6a97/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/adv_predictions.json + adv_probabilities_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/4dd0792e703396eb8e774095b9ec9114.pkl + params_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/params.yaml + predictions_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/predictions.json + probabilities_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/probabilities.json + score_dict_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/score_dict.json + test_labels_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/test_labels.json + train_labels_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/train_labels.json + model_dir: models + name: 2f368d21cae45f44bc83332d80ea6a97 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e013c2b561460f35f9a4cb8db47bdd12 + trainer: + kwargs: {} +name: 2f368d21cae45f44bc83332d80ea6a97 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/params.yaml b/examples/security/classification/output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/params.yaml new file mode 100644 index 00000000..26904572 --- /dev/null +++ b/examples/security/classification/output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/adv_predictions.json + adv_probabilities_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/a13e941d41a69dde15d5bd65b687dc17.pkl + params_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/params.yaml + predictions_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/predictions.json + probabilities_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/probabilities.json + score_dict_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/score_dict.json + test_labels_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/test_labels.json + train_labels_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/train_labels.json + model_dir: models + name: 2f84b5e759023d6ea3cd6ebc53bcfd2c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ad4ff47ee79adcf86117b4d69177af6a + trainer: + kwargs: {} +name: 2f84b5e759023d6ea3cd6ebc53bcfd2c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/params.yaml b/examples/security/classification/output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/params.yaml new file mode 100644 index 00000000..206d9277 --- /dev/null +++ b/examples/security/classification/output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/adv_predictions.json + adv_probabilities_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/4574fe34ce0bae5b50c926b37eb588e0.pkl + params_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/params.yaml + predictions_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/predictions.json + probabilities_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/probabilities.json + score_dict_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/score_dict.json + test_labels_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/test_labels.json + train_labels_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/train_labels.json + model_dir: models + name: 2fa579aa4ca7712d33e2527c4d53a7b1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 24621bdd4a7a5d5c39664e5191298f3b + trainer: + kwargs: {} +name: 2fa579aa4ca7712d33e2527c4d53a7b1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/params.yaml b/examples/security/classification/output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/params.yaml new file mode 100644 index 00000000..51bbe66b --- /dev/null +++ b/examples/security/classification/output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/adv_predictions.json + adv_probabilities_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/b17967e3f9d5ffb2d4406121e64c46ea.pkl + params_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/params.yaml + predictions_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/predictions.json + probabilities_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/probabilities.json + score_dict_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/score_dict.json + test_labels_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/test_labels.json + train_labels_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/train_labels.json + model_dir: models + name: 2fd2ff90e1b1b703b7038349c78309a6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fcda614a10f2357b648e8b1a6ed7885b + trainer: + kwargs: {} +name: 2fd2ff90e1b1b703b7038349c78309a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3020459442d6be1c259fd23b6d10efa2/params.yaml b/examples/security/classification/output/reports/train/3020459442d6be1c259fd23b6d10efa2/params.yaml new file mode 100644 index 00000000..399c88b8 --- /dev/null +++ b/examples/security/classification/output/reports/train/3020459442d6be1c259fd23b6d10efa2/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/adv_predictions.json + adv_probabilities_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/1f0c3b53e22a0d231b1ba729dcece189.pkl + params_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/params.yaml + predictions_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/predictions.json + probabilities_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/probabilities.json + score_dict_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/score_dict.json + test_labels_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/test_labels.json + train_labels_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/train_labels.json + model_dir: models + name: 3020459442d6be1c259fd23b6d10efa2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2414b1ed9d00ae65dc60f952583551d3 + trainer: + kwargs: {} +name: 3020459442d6be1c259fd23b6d10efa2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/params.yaml b/examples/security/classification/output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/params.yaml new file mode 100644 index 00000000..e01a8ba7 --- /dev/null +++ b/examples/security/classification/output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/adv_predictions.json + adv_probabilities_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/7fb1a9b838c1c9656ac9f39267537017.pkl + params_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/params.yaml + predictions_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/predictions.json + probabilities_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/probabilities.json + score_dict_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/score_dict.json + test_labels_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/test_labels.json + train_labels_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/train_labels.json + model_dir: models + name: 304ebb1d266c37028e624c2a48c3c9a0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8f1c34ddc7fdf1aaa286e8e7dfd6cb3d + trainer: + kwargs: {} +name: 304ebb1d266c37028e624c2a48c3c9a0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/params.yaml b/examples/security/classification/output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/params.yaml new file mode 100644 index 00000000..71bc05d0 --- /dev/null +++ b/examples/security/classification/output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/params.yaml @@ -0,0 +1,104 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/adv_predictions.json + adv_probabilities_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/eac24411eb9fe7a4e67d5e85052443a6.pkl + params_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/params.yaml + predictions_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/predictions.json + probabilities_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/probabilities.json + score_dict_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/score_dict.json + test_labels_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/test_labels.json + train_labels_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/train_labels.json + model_dir: models + name: 306253fb0fa6c13ad69d36a3a3dbdb2b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: + - linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3dc5adbacb5bfd3906f898770bd5d386 + trainer: + kwargs: {} +name: 306253fb0fa6c13ad69d36a3a3dbdb2b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/307747c6684535da55fa8cb093951b14/params.yaml b/examples/security/classification/output/reports/train/307747c6684535da55fa8cb093951b14/params.yaml new file mode 100644 index 00000000..c9b012e8 --- /dev/null +++ b/examples/security/classification/output/reports/train/307747c6684535da55fa8cb093951b14/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/307747c6684535da55fa8cb093951b14/adv_predictions.json + adv_probabilities_file: output/reports/train/307747c6684535da55fa8cb093951b14/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/82f05b31c4767755b97c61ff4d719886.pkl + params_file: output/reports/train/307747c6684535da55fa8cb093951b14/params.yaml + predictions_file: output/reports/train/307747c6684535da55fa8cb093951b14/predictions.json + probabilities_file: output/reports/train/307747c6684535da55fa8cb093951b14/probabilities.json + score_dict_file: output/reports/train/307747c6684535da55fa8cb093951b14/score_dict.json + test_labels_file: output/reports/train/307747c6684535da55fa8cb093951b14/test_labels.json + train_labels_file: output/reports/train/307747c6684535da55fa8cb093951b14/train_labels.json + model_dir: models + name: 307747c6684535da55fa8cb093951b14 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5ccb916ce318ad32a54f2e3062332fb9 + trainer: + kwargs: {} +name: 307747c6684535da55fa8cb093951b14 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3088ccd20b5f1680b36983eeb26501d5/params.yaml b/examples/security/classification/output/reports/train/3088ccd20b5f1680b36983eeb26501d5/params.yaml new file mode 100644 index 00000000..2bf5788d --- /dev/null +++ b/examples/security/classification/output/reports/train/3088ccd20b5f1680b36983eeb26501d5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/adv_predictions.json + adv_probabilities_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/0b2fde7c5c0cd45ba813273415781afa.pkl + params_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/params.yaml + predictions_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/predictions.json + probabilities_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/probabilities.json + score_dict_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/score_dict.json + test_labels_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/test_labels.json + train_labels_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/train_labels.json + model_dir: models + name: 3088ccd20b5f1680b36983eeb26501d5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b517877ab4037b43d594c1e2569645ff + trainer: + kwargs: {} +name: 3088ccd20b5f1680b36983eeb26501d5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/params.yaml b/examples/security/classification/output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/params.yaml new file mode 100644 index 00000000..c94a9956 --- /dev/null +++ b/examples/security/classification/output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/adv_predictions.json + adv_probabilities_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/ce58b9779e467c500e3b3f4a46bcccd0.pkl + params_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/params.yaml + predictions_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/predictions.json + probabilities_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/probabilities.json + score_dict_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/score_dict.json + test_labels_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/test_labels.json + train_labels_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/train_labels.json + model_dir: models + name: 30e0c9ebd74a72376ad2f2819a3e9b52 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cea87939676f7393dfd85df2c4ab2247 + trainer: + kwargs: {} +name: 30e0c9ebd74a72376ad2f2819a3e9b52 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/30f33fd7b3d833eb4357f33b620688ca/params.yaml b/examples/security/classification/output/reports/train/30f33fd7b3d833eb4357f33b620688ca/params.yaml new file mode 100644 index 00000000..30bd3095 --- /dev/null +++ b/examples/security/classification/output/reports/train/30f33fd7b3d833eb4357f33b620688ca/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/adv_predictions.json + adv_probabilities_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/6bfb1290dfe912393ffe885971ce25b0.pkl + params_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/params.yaml + predictions_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/predictions.json + probabilities_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/probabilities.json + score_dict_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/score_dict.json + test_labels_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/test_labels.json + train_labels_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/train_labels.json + model_dir: models + name: 30f33fd7b3d833eb4357f33b620688ca + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b71e5e8e5635222dc773dcb297caf3b4 + trainer: + kwargs: {} +name: 30f33fd7b3d833eb4357f33b620688ca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/params.yaml b/examples/security/classification/output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/params.yaml new file mode 100644 index 00000000..222279da --- /dev/null +++ b/examples/security/classification/output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/adv_predictions.json + adv_probabilities_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/acfd5333c05168195e62f2b40152929f.pkl + params_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/params.yaml + predictions_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/predictions.json + probabilities_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/probabilities.json + score_dict_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/score_dict.json + test_labels_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/test_labels.json + train_labels_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/train_labels.json + model_dir: models + name: 315c8019ac2fcbb87f67bea61cfbd2a7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8a9fcb6fe1916c2a4eaba1225ba9a97c + trainer: + kwargs: {} +name: 315c8019ac2fcbb87f67bea61cfbd2a7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/315e6a9123260085b7f359ec265dde8d/params.yaml b/examples/security/classification/output/reports/train/315e6a9123260085b7f359ec265dde8d/params.yaml new file mode 100644 index 00000000..e887cf13 --- /dev/null +++ b/examples/security/classification/output/reports/train/315e6a9123260085b7f359ec265dde8d/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/adv_predictions.json + adv_probabilities_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/652089893b58c56df09ee4487d461265.pkl + params_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/params.yaml + predictions_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/predictions.json + probabilities_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/probabilities.json + score_dict_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/score_dict.json + test_labels_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/test_labels.json + train_labels_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/train_labels.json + model_dir: models + name: 315e6a9123260085b7f359ec265dde8d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fadcb463ab1e7d469b254615c05b9db8 + trainer: + kwargs: {} +name: 315e6a9123260085b7f359ec265dde8d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/params.yaml b/examples/security/classification/output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/params.yaml new file mode 100644 index 00000000..084cdd16 --- /dev/null +++ b/examples/security/classification/output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/adv_predictions.json + adv_probabilities_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/4c755aef3e9ec368cbda2431159ecb2b.pkl + params_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/params.yaml + predictions_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/predictions.json + probabilities_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/probabilities.json + score_dict_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/score_dict.json + test_labels_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/test_labels.json + train_labels_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/train_labels.json + model_dir: models + name: 31600d8f4ddc4b7460f2956ced05ddfd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d0f4dd962a239713b223e3c2d8ef304b + trainer: + kwargs: {} +name: 31600d8f4ddc4b7460f2956ced05ddfd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/params.yaml b/examples/security/classification/output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/params.yaml new file mode 100644 index 00000000..7c643947 --- /dev/null +++ b/examples/security/classification/output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/adv_predictions.json + adv_probabilities_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/3179998fc7ea554ef3136c9dee35eddf.pkl + params_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/params.yaml + predictions_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/predictions.json + probabilities_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/probabilities.json + score_dict_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/score_dict.json + test_labels_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/test_labels.json + train_labels_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/train_labels.json + model_dir: models + name: 317ea5d62b40c67fa32cadfa32577b1f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b1683c82540df3d144579221b4882499 + trainer: + kwargs: {} +name: 317ea5d62b40c67fa32cadfa32577b1f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/31ba5203e81f2206fcbc45695731a787/params.yaml b/examples/security/classification/output/reports/train/31ba5203e81f2206fcbc45695731a787/params.yaml new file mode 100644 index 00000000..829e1322 --- /dev/null +++ b/examples/security/classification/output/reports/train/31ba5203e81f2206fcbc45695731a787/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/adv_predictions.json + adv_probabilities_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/9e6ee169768ef23b51eed581cbe1f9fa.pkl + params_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/params.yaml + predictions_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/predictions.json + probabilities_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/probabilities.json + score_dict_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/score_dict.json + test_labels_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/test_labels.json + train_labels_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/train_labels.json + model_dir: models + name: 31ba5203e81f2206fcbc45695731a787 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 195eaabd6150bfc05120a49205e63612 + trainer: + kwargs: {} +name: 31ba5203e81f2206fcbc45695731a787 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3203e953de84465b703f23d47cf7e9eb/params.yaml b/examples/security/classification/output/reports/train/3203e953de84465b703f23d47cf7e9eb/params.yaml new file mode 100644 index 00000000..787c12c1 --- /dev/null +++ b/examples/security/classification/output/reports/train/3203e953de84465b703f23d47cf7e9eb/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/adv_predictions.json + adv_probabilities_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/968bceb816c562ec3f6d370f5ffe7d3d.pkl + params_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/params.yaml + predictions_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/predictions.json + probabilities_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/probabilities.json + score_dict_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/score_dict.json + test_labels_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/test_labels.json + train_labels_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/train_labels.json + model_dir: models + name: 3203e953de84465b703f23d47cf7e9eb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 3 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5fd621796b4fcc6091b7494db781c3d3 + trainer: + kwargs: {} +name: 3203e953de84465b703f23d47cf7e9eb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/321678724c0e7dcdb097146a482ba6ce/params.yaml b/examples/security/classification/output/reports/train/321678724c0e7dcdb097146a482ba6ce/params.yaml new file mode 100644 index 00000000..bb60a56b --- /dev/null +++ b/examples/security/classification/output/reports/train/321678724c0e7dcdb097146a482ba6ce/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/adv_predictions.json + adv_probabilities_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/3ea725cb3a7b56943d91c9e73e9b74cd.pkl + params_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/params.yaml + predictions_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/predictions.json + probabilities_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/probabilities.json + score_dict_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/score_dict.json + test_labels_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/test_labels.json + train_labels_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/train_labels.json + model_dir: models + name: 321678724c0e7dcdb097146a482ba6ce + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f5fda853f3fb3ab0b2e33d373232f020 + trainer: + kwargs: {} +name: 321678724c0e7dcdb097146a482ba6ce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/params.yaml b/examples/security/classification/output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/params.yaml new file mode 100644 index 00000000..e72f3cc6 --- /dev/null +++ b/examples/security/classification/output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/adv_predictions.json + adv_probabilities_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/a4cde4f2b84c7d280e0b18d35132f29f.pkl + params_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/params.yaml + predictions_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/predictions.json + probabilities_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/probabilities.json + score_dict_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/score_dict.json + test_labels_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/test_labels.json + train_labels_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/train_labels.json + model_dir: models + name: 321a3b66225abdcea2d7659e3e7fc81f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1a646011cc51ad081e0bb4eaec1c17b9 + trainer: + kwargs: {} +name: 321a3b66225abdcea2d7659e3e7fc81f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/321c5a63e7770fba48a455b181a556a1/params.yaml b/examples/security/classification/output/reports/train/321c5a63e7770fba48a455b181a556a1/params.yaml new file mode 100644 index 00000000..41b150a7 --- /dev/null +++ b/examples/security/classification/output/reports/train/321c5a63e7770fba48a455b181a556a1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/adv_predictions.json + adv_probabilities_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/348f85e12d7f2ed3812e2616bd5d9d62.pkl + params_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/params.yaml + predictions_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/predictions.json + probabilities_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/probabilities.json + score_dict_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/score_dict.json + test_labels_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/test_labels.json + train_labels_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/train_labels.json + model_dir: models + name: 321c5a63e7770fba48a455b181a556a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d167aff11b69ac076dae88217a0ffdf1 + trainer: + kwargs: {} +name: 321c5a63e7770fba48a455b181a556a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/params.yaml b/examples/security/classification/output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/params.yaml new file mode 100644 index 00000000..666128f4 --- /dev/null +++ b/examples/security/classification/output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/adv_predictions.json + adv_probabilities_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e334af916256e6cad46af35b3e3d5b52.pkl + params_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/params.yaml + predictions_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/predictions.json + probabilities_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/probabilities.json + score_dict_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/score_dict.json + test_labels_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/test_labels.json + train_labels_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/train_labels.json + model_dir: models + name: 32608cc6d6a3d7e8ee33e4c6e636e6b5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e0105e564a5f2e40d01add7a19968f77 + trainer: + kwargs: {} +name: 32608cc6d6a3d7e8ee33e4c6e636e6b5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/32757ca9337f3ea672ca91785e7bd75f/params.yaml b/examples/security/classification/output/reports/train/32757ca9337f3ea672ca91785e7bd75f/params.yaml new file mode 100644 index 00000000..dfea1234 --- /dev/null +++ b/examples/security/classification/output/reports/train/32757ca9337f3ea672ca91785e7bd75f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/adv_predictions.json + adv_probabilities_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/d71825da132d338c7fb603828e842ebd.pkl + params_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/params.yaml + predictions_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/predictions.json + probabilities_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/probabilities.json + score_dict_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/score_dict.json + test_labels_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/test_labels.json + train_labels_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/train_labels.json + model_dir: models + name: 32757ca9337f3ea672ca91785e7bd75f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7f991320a1cfffda2c822abbca9d8b3a + trainer: + kwargs: {} +name: 32757ca9337f3ea672ca91785e7bd75f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/328433f0f3127f2b99a1f1c9924545b3/params.yaml b/examples/security/classification/output/reports/train/328433f0f3127f2b99a1f1c9924545b3/params.yaml new file mode 100644 index 00000000..75573154 --- /dev/null +++ b/examples/security/classification/output/reports/train/328433f0f3127f2b99a1f1c9924545b3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/adv_predictions.json + adv_probabilities_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/5901b927f19fe4c86f279e5a8ecae56e.pkl + params_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/params.yaml + predictions_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/predictions.json + probabilities_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/probabilities.json + score_dict_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/score_dict.json + test_labels_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/test_labels.json + train_labels_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/train_labels.json + model_dir: models + name: 328433f0f3127f2b99a1f1c9924545b3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 63afcb96d22148ad9d20f6732e7fcbb6 + trainer: + kwargs: {} +name: 328433f0f3127f2b99a1f1c9924545b3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/329d127d698a9743ccb6b555b93304e4/params.yaml b/examples/security/classification/output/reports/train/329d127d698a9743ccb6b555b93304e4/params.yaml new file mode 100644 index 00000000..081a943e --- /dev/null +++ b/examples/security/classification/output/reports/train/329d127d698a9743ccb6b555b93304e4/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/adv_predictions.json + adv_probabilities_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/a9ec29625ea672e1ce2dce0ed2e157c0.pkl + params_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/params.yaml + predictions_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/predictions.json + probabilities_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/probabilities.json + score_dict_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/score_dict.json + test_labels_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/test_labels.json + train_labels_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/train_labels.json + model_dir: models + name: 329d127d698a9743ccb6b555b93304e4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fa32e2509ca18344f2471a0ee85676ca + trainer: + kwargs: {} +name: 329d127d698a9743ccb6b555b93304e4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/32a47571f3883f288c2f7689544c39d0/params.yaml b/examples/security/classification/output/reports/train/32a47571f3883f288c2f7689544c39d0/params.yaml new file mode 100644 index 00000000..551578dd --- /dev/null +++ b/examples/security/classification/output/reports/train/32a47571f3883f288c2f7689544c39d0/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/adv_predictions.json + adv_probabilities_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/3c67ec07db526873b1fa7a8824edc675.pkl + params_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/params.yaml + predictions_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/predictions.json + probabilities_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/probabilities.json + score_dict_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/score_dict.json + test_labels_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/test_labels.json + train_labels_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/train_labels.json + model_dir: models + name: 32a47571f3883f288c2f7689544c39d0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbcf1958a3c96e6fefa6341a2dc28741 + trainer: + kwargs: {} +name: 32a47571f3883f288c2f7689544c39d0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/params.yaml b/examples/security/classification/output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/params.yaml new file mode 100644 index 00000000..ef611198 --- /dev/null +++ b/examples/security/classification/output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/adv_predictions.json + adv_probabilities_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/e5d404cdda3c075627c5e0d9e400c958.pkl + params_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/params.yaml + predictions_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/predictions.json + probabilities_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/probabilities.json + score_dict_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/score_dict.json + test_labels_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/test_labels.json + train_labels_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/train_labels.json + model_dir: models + name: 32b17a903e18ce0bfda6ffe79dca0ce5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8eebfb1abe881743ec33ae21195cd3fe + trainer: + kwargs: {} +name: 32b17a903e18ce0bfda6ffe79dca0ce5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/32b2b493215258dddfc2c691160dafd8/params.yaml b/examples/security/classification/output/reports/train/32b2b493215258dddfc2c691160dafd8/params.yaml new file mode 100644 index 00000000..6c96d9c5 --- /dev/null +++ b/examples/security/classification/output/reports/train/32b2b493215258dddfc2c691160dafd8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/adv_predictions.json + adv_probabilities_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/546ed47d4e99e395e3f8310cb08135a6.pkl + params_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/params.yaml + predictions_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/predictions.json + probabilities_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/probabilities.json + score_dict_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/score_dict.json + test_labels_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/test_labels.json + train_labels_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/train_labels.json + model_dir: models + name: 32b2b493215258dddfc2c691160dafd8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8b1afdcbdc14d43f8222e2149fe884b0 + trainer: + kwargs: {} +name: 32b2b493215258dddfc2c691160dafd8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/params.yaml b/examples/security/classification/output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/params.yaml new file mode 100644 index 00000000..13284292 --- /dev/null +++ b/examples/security/classification/output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/adv_predictions.json + adv_probabilities_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/817a2d473b19dd7302d346d74577f1ee.pkl + params_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/params.yaml + predictions_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/predictions.json + probabilities_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/probabilities.json + score_dict_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/score_dict.json + test_labels_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/test_labels.json + train_labels_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/train_labels.json + model_dir: models + name: 32b8757d5f90c3fab71fcd2f3b23d7d0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3410091966e2b8f4d0fee3e790a8b700 + trainer: + kwargs: {} +name: 32b8757d5f90c3fab71fcd2f3b23d7d0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/params.yaml b/examples/security/classification/output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/params.yaml new file mode 100644 index 00000000..0344513f --- /dev/null +++ b/examples/security/classification/output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/adv_predictions.json + adv_probabilities_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/51b1929bbdf3d8b87d1b3e9685cde3e6.pkl + params_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/params.yaml + predictions_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/predictions.json + probabilities_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/probabilities.json + score_dict_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/score_dict.json + test_labels_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/test_labels.json + train_labels_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/train_labels.json + model_dir: models + name: 32be84f99d9e0bab5791d3b27ee994a7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5a1c5883c9895f19ad624ee61174c6d7 + trainer: + kwargs: {} +name: 32be84f99d9e0bab5791d3b27ee994a7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/330f5b93c916e35d13d3f22b40899acc/params.yaml b/examples/security/classification/output/reports/train/330f5b93c916e35d13d3f22b40899acc/params.yaml new file mode 100644 index 00000000..4296f42b --- /dev/null +++ b/examples/security/classification/output/reports/train/330f5b93c916e35d13d3f22b40899acc/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/adv_predictions.json + adv_probabilities_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/8e83ad92aa67144502187be39af5658f.pkl + params_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/params.yaml + predictions_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/predictions.json + probabilities_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/probabilities.json + score_dict_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/score_dict.json + test_labels_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/test_labels.json + train_labels_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/train_labels.json + model_dir: models + name: 330f5b93c916e35d13d3f22b40899acc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 11cf524f756a8f4b78d50790d3bd2a82 + trainer: + kwargs: {} +name: 330f5b93c916e35d13d3f22b40899acc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/332cba091c4ca5ed17a37d86c30e7217/params.yaml b/examples/security/classification/output/reports/train/332cba091c4ca5ed17a37d86c30e7217/params.yaml new file mode 100644 index 00000000..25dcab95 --- /dev/null +++ b/examples/security/classification/output/reports/train/332cba091c4ca5ed17a37d86c30e7217/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/adv_predictions.json + adv_probabilities_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/1a94a7b5cd02600b5a70e5224fae4a40.pkl + params_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/params.yaml + predictions_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/predictions.json + probabilities_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/probabilities.json + score_dict_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/score_dict.json + test_labels_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/test_labels.json + train_labels_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/train_labels.json + model_dir: models + name: 332cba091c4ca5ed17a37d86c30e7217 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8d824f048979e91f8d6e8c1bf42eedad + trainer: + kwargs: {} +name: 332cba091c4ca5ed17a37d86c30e7217 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/params.yaml b/examples/security/classification/output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/params.yaml new file mode 100644 index 00000000..6e46547c --- /dev/null +++ b/examples/security/classification/output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/adv_predictions.json + adv_probabilities_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/75471bc816a753916ff8ea2a0781d5e8.pkl + params_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/params.yaml + predictions_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/predictions.json + probabilities_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/probabilities.json + score_dict_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/score_dict.json + test_labels_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/test_labels.json + train_labels_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/train_labels.json + model_dir: models + name: 33716127245a5ffa9a2a6b76cdaac7f8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eb5731f85e28d958d6f2b73c1917a4fc + trainer: + kwargs: {} +name: 33716127245a5ffa9a2a6b76cdaac7f8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/339d05acb8eaa27f93098e3f7b036537/params.yaml b/examples/security/classification/output/reports/train/339d05acb8eaa27f93098e3f7b036537/params.yaml new file mode 100644 index 00000000..894da02d --- /dev/null +++ b/examples/security/classification/output/reports/train/339d05acb8eaa27f93098e3f7b036537/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/adv_predictions.json + adv_probabilities_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/c898dcd83bfa5b321dbf1e51a3c1c7f5.pkl + params_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/params.yaml + predictions_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/predictions.json + probabilities_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/probabilities.json + score_dict_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/score_dict.json + test_labels_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/test_labels.json + train_labels_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/train_labels.json + model_dir: models + name: 339d05acb8eaa27f93098e3f7b036537 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 75bc210717ec37301124f50f19eb20bc + trainer: + kwargs: {} +name: 339d05acb8eaa27f93098e3f7b036537 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/params.yaml b/examples/security/classification/output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/params.yaml new file mode 100644 index 00000000..71184084 --- /dev/null +++ b/examples/security/classification/output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/adv_predictions.json + adv_probabilities_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/05fa8f89d438c98ac91576761043f631.pkl + params_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/params.yaml + predictions_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/predictions.json + probabilities_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/probabilities.json + score_dict_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/score_dict.json + test_labels_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/test_labels.json + train_labels_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/train_labels.json + model_dir: models + name: 33cb1885a22b157bf766aaf34a7e1b92 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bfa326c8a06c5f3c892fc7d1277576b8 + trainer: + kwargs: {} +name: 33cb1885a22b157bf766aaf34a7e1b92 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/33fd828f7cb968c5863c0eb345ff1208/params.yaml b/examples/security/classification/output/reports/train/33fd828f7cb968c5863c0eb345ff1208/params.yaml new file mode 100644 index 00000000..3fcf27a2 --- /dev/null +++ b/examples/security/classification/output/reports/train/33fd828f7cb968c5863c0eb345ff1208/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/adv_predictions.json + adv_probabilities_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/abeb9bf3634c81abd2494a36c033eb02.pkl + params_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/params.yaml + predictions_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/predictions.json + probabilities_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/probabilities.json + score_dict_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/score_dict.json + test_labels_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/test_labels.json + train_labels_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/train_labels.json + model_dir: models + name: 33fd828f7cb968c5863c0eb345ff1208 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6ea0a10c8cbade874397668e0548b70c + trainer: + kwargs: {} +name: 33fd828f7cb968c5863c0eb345ff1208 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/34027642a91f58bce884f30fb4dc1ed5/params.yaml b/examples/security/classification/output/reports/train/34027642a91f58bce884f30fb4dc1ed5/params.yaml new file mode 100644 index 00000000..9efb57a6 --- /dev/null +++ b/examples/security/classification/output/reports/train/34027642a91f58bce884f30fb4dc1ed5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/adv_predictions.json + adv_probabilities_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/12f6dbab638de13fdf043b6f8739cec5.pkl + params_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/params.yaml + predictions_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/predictions.json + probabilities_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/probabilities.json + score_dict_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/score_dict.json + test_labels_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/test_labels.json + train_labels_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/train_labels.json + model_dir: models + name: 34027642a91f58bce884f30fb4dc1ed5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b0456e646452e31a19896c62b56dbe57 + trainer: + kwargs: {} +name: 34027642a91f58bce884f30fb4dc1ed5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/params.yaml b/examples/security/classification/output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/params.yaml new file mode 100644 index 00000000..40140212 --- /dev/null +++ b/examples/security/classification/output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/adv_predictions.json + adv_probabilities_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/c41b72f7205bb23cdc94b8ed08b0318e.pkl + params_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/params.yaml + predictions_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/predictions.json + probabilities_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/probabilities.json + score_dict_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/score_dict.json + test_labels_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/test_labels.json + train_labels_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/train_labels.json + model_dir: models + name: 3413df4bc2eaecea54077eba5a3bd4a2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d391d43c5de3dbc37e71fdcb60cffb81 + trainer: + kwargs: {} +name: 3413df4bc2eaecea54077eba5a3bd4a2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/341df9f6f04225cbb4939e0b947653f3/params.yaml b/examples/security/classification/output/reports/train/341df9f6f04225cbb4939e0b947653f3/params.yaml new file mode 100644 index 00000000..6d2edaf1 --- /dev/null +++ b/examples/security/classification/output/reports/train/341df9f6f04225cbb4939e0b947653f3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/adv_predictions.json + adv_probabilities_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/04e6cda0f7c3ed9b5199d56323be8e52.pkl + params_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/params.yaml + predictions_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/predictions.json + probabilities_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/probabilities.json + score_dict_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/score_dict.json + test_labels_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/test_labels.json + train_labels_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/train_labels.json + model_dir: models + name: 341df9f6f04225cbb4939e0b947653f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 53f6a2d0e8241d22e972ea2e3d6bf121 + trainer: + kwargs: {} +name: 341df9f6f04225cbb4939e0b947653f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/params.yaml b/examples/security/classification/output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/params.yaml new file mode 100644 index 00000000..c2e0afc1 --- /dev/null +++ b/examples/security/classification/output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/adv_predictions.json + adv_probabilities_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/e04ae05ae304fcfcd3f1e8d4e040cdc6.pkl + params_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/params.yaml + predictions_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/predictions.json + probabilities_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/probabilities.json + score_dict_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/score_dict.json + test_labels_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/test_labels.json + train_labels_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/train_labels.json + model_dir: models + name: 3456cfd57f7157cc275d73e0b4ff7804 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ce14d8de794b87924f0169e174dff045 + trainer: + kwargs: {} +name: 3456cfd57f7157cc275d73e0b4ff7804 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/34bae851ec52d1169a6efced36fb6d31/params.yaml b/examples/security/classification/output/reports/train/34bae851ec52d1169a6efced36fb6d31/params.yaml new file mode 100644 index 00000000..38ca8af9 --- /dev/null +++ b/examples/security/classification/output/reports/train/34bae851ec52d1169a6efced36fb6d31/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/adv_predictions.json + adv_probabilities_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/129249985d0ab6db4e6661af5385b171.pkl + params_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/params.yaml + predictions_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/predictions.json + probabilities_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/probabilities.json + score_dict_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/score_dict.json + test_labels_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/test_labels.json + train_labels_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/train_labels.json + model_dir: models + name: 34bae851ec52d1169a6efced36fb6d31 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0195e7d30029335ab59b3d7336e25fda + trainer: + kwargs: {} +name: 34bae851ec52d1169a6efced36fb6d31 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/34c36c3df17f351194635add21754c2e/params.yaml b/examples/security/classification/output/reports/train/34c36c3df17f351194635add21754c2e/params.yaml new file mode 100644 index 00000000..4c6a4656 --- /dev/null +++ b/examples/security/classification/output/reports/train/34c36c3df17f351194635add21754c2e/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/34c36c3df17f351194635add21754c2e/adv_predictions.json + adv_probabilities_file: output/reports/train/34c36c3df17f351194635add21754c2e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/4538f407b43f2a29826079d953608168.pkl + params_file: output/reports/train/34c36c3df17f351194635add21754c2e/params.yaml + predictions_file: output/reports/train/34c36c3df17f351194635add21754c2e/predictions.json + probabilities_file: output/reports/train/34c36c3df17f351194635add21754c2e/probabilities.json + score_dict_file: output/reports/train/34c36c3df17f351194635add21754c2e/score_dict.json + test_labels_file: output/reports/train/34c36c3df17f351194635add21754c2e/test_labels.json + train_labels_file: output/reports/train/34c36c3df17f351194635add21754c2e/train_labels.json + model_dir: models + name: 34c36c3df17f351194635add21754c2e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8070a75328dadff62aa3ed2c5258d91c + trainer: + kwargs: {} +name: 34c36c3df17f351194635add21754c2e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/34f5cfc9c57ffd6da4eb959122859851/params.yaml b/examples/security/classification/output/reports/train/34f5cfc9c57ffd6da4eb959122859851/params.yaml new file mode 100644 index 00000000..ff40d8f1 --- /dev/null +++ b/examples/security/classification/output/reports/train/34f5cfc9c57ffd6da4eb959122859851/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/adv_predictions.json + adv_probabilities_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/898878a6d2ed5379ccd8022b4ed3a9a5.pkl + params_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/params.yaml + predictions_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/predictions.json + probabilities_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/probabilities.json + score_dict_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/score_dict.json + test_labels_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/test_labels.json + train_labels_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/train_labels.json + model_dir: models + name: 34f5cfc9c57ffd6da4eb959122859851 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 79996fe89c4fa5e05f05957dfa5f9315 + trainer: + kwargs: {} +name: 34f5cfc9c57ffd6da4eb959122859851 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/350f844b1dbe2a52451767796ccec180/params.yaml b/examples/security/classification/output/reports/train/350f844b1dbe2a52451767796ccec180/params.yaml new file mode 100644 index 00000000..0811c74c --- /dev/null +++ b/examples/security/classification/output/reports/train/350f844b1dbe2a52451767796ccec180/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/350f844b1dbe2a52451767796ccec180/adv_predictions.json + adv_probabilities_file: output/reports/train/350f844b1dbe2a52451767796ccec180/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/7a1f4c7fc39913dc4d41fbf10710d782.pkl + params_file: output/reports/train/350f844b1dbe2a52451767796ccec180/params.yaml + predictions_file: output/reports/train/350f844b1dbe2a52451767796ccec180/predictions.json + probabilities_file: output/reports/train/350f844b1dbe2a52451767796ccec180/probabilities.json + score_dict_file: output/reports/train/350f844b1dbe2a52451767796ccec180/score_dict.json + test_labels_file: output/reports/train/350f844b1dbe2a52451767796ccec180/test_labels.json + train_labels_file: output/reports/train/350f844b1dbe2a52451767796ccec180/train_labels.json + model_dir: models + name: 350f844b1dbe2a52451767796ccec180 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 95f78d9fd8c5cf84729a1bdb77b962da + trainer: + kwargs: {} +name: 350f844b1dbe2a52451767796ccec180 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/352522088c73c8bce48a31b2d4317b94/params.yaml b/examples/security/classification/output/reports/train/352522088c73c8bce48a31b2d4317b94/params.yaml new file mode 100644 index 00000000..d6e9521c --- /dev/null +++ b/examples/security/classification/output/reports/train/352522088c73c8bce48a31b2d4317b94/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/adv_predictions.json + adv_probabilities_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/e12182af6bb0334868e45003a2f91f66.pkl + params_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/params.yaml + predictions_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/predictions.json + probabilities_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/probabilities.json + score_dict_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/score_dict.json + test_labels_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/test_labels.json + train_labels_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/train_labels.json + model_dir: models + name: 352522088c73c8bce48a31b2d4317b94 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f99da6148f11b8b9a2feabf5b473c2ec + trainer: + kwargs: {} +name: 352522088c73c8bce48a31b2d4317b94 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/35464b3444fcded662bb6474fc09a0b4/params.yaml b/examples/security/classification/output/reports/train/35464b3444fcded662bb6474fc09a0b4/params.yaml new file mode 100644 index 00000000..a3159242 --- /dev/null +++ b/examples/security/classification/output/reports/train/35464b3444fcded662bb6474fc09a0b4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/adv_predictions.json + adv_probabilities_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/09b07d2ffa4d6c71833928bdd99764df.pkl + params_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/params.yaml + predictions_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/predictions.json + probabilities_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/probabilities.json + score_dict_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/score_dict.json + test_labels_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/test_labels.json + train_labels_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/train_labels.json + model_dir: models + name: 35464b3444fcded662bb6474fc09a0b4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d36d9db2ca7a1ea9b8fcfaadded17656 + trainer: + kwargs: {} +name: 35464b3444fcded662bb6474fc09a0b4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/params.yaml b/examples/security/classification/output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/params.yaml new file mode 100644 index 00000000..82862717 --- /dev/null +++ b/examples/security/classification/output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/adv_predictions.json + adv_probabilities_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/ff0eb9b74a562964ff93a998aa2a9e2b.pkl + params_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/params.yaml + predictions_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/predictions.json + probabilities_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/probabilities.json + score_dict_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/score_dict.json + test_labels_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/test_labels.json + train_labels_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/train_labels.json + model_dir: models + name: 356533f7e13ae428ea1bcb9639d3bce8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0458efef597b9bc90e9a3c318566cfef + trainer: + kwargs: {} +name: 356533f7e13ae428ea1bcb9639d3bce8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3567913fcfd71d3c949f766e68810080/params.yaml b/examples/security/classification/output/reports/train/3567913fcfd71d3c949f766e68810080/params.yaml new file mode 100644 index 00000000..0244ba52 --- /dev/null +++ b/examples/security/classification/output/reports/train/3567913fcfd71d3c949f766e68810080/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3567913fcfd71d3c949f766e68810080/adv_predictions.json + adv_probabilities_file: output/reports/train/3567913fcfd71d3c949f766e68810080/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/737d6e8c36b65476c12b3bd386261903.pkl + params_file: output/reports/train/3567913fcfd71d3c949f766e68810080/params.yaml + predictions_file: output/reports/train/3567913fcfd71d3c949f766e68810080/predictions.json + probabilities_file: output/reports/train/3567913fcfd71d3c949f766e68810080/probabilities.json + score_dict_file: output/reports/train/3567913fcfd71d3c949f766e68810080/score_dict.json + test_labels_file: output/reports/train/3567913fcfd71d3c949f766e68810080/test_labels.json + train_labels_file: output/reports/train/3567913fcfd71d3c949f766e68810080/train_labels.json + model_dir: models + name: 3567913fcfd71d3c949f766e68810080 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0e48ee2ead9dc9ff7f17346df7e76af5 + trainer: + kwargs: {} +name: 3567913fcfd71d3c949f766e68810080 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/35c135be29b612b3845d2213116c1cda/params.yaml b/examples/security/classification/output/reports/train/35c135be29b612b3845d2213116c1cda/params.yaml new file mode 100644 index 00000000..864345ad --- /dev/null +++ b/examples/security/classification/output/reports/train/35c135be29b612b3845d2213116c1cda/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/35c135be29b612b3845d2213116c1cda/adv_predictions.json + adv_probabilities_file: output/reports/train/35c135be29b612b3845d2213116c1cda/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/f2de0a95f7980e866191e4b3fa02b9c1.pkl + params_file: output/reports/train/35c135be29b612b3845d2213116c1cda/params.yaml + predictions_file: output/reports/train/35c135be29b612b3845d2213116c1cda/predictions.json + probabilities_file: output/reports/train/35c135be29b612b3845d2213116c1cda/probabilities.json + score_dict_file: output/reports/train/35c135be29b612b3845d2213116c1cda/score_dict.json + test_labels_file: output/reports/train/35c135be29b612b3845d2213116c1cda/test_labels.json + train_labels_file: output/reports/train/35c135be29b612b3845d2213116c1cda/train_labels.json + model_dir: models + name: 35c135be29b612b3845d2213116c1cda + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5c0755e5552570a8d78f993b5d046751 + trainer: + kwargs: {} +name: 35c135be29b612b3845d2213116c1cda +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/params.yaml b/examples/security/classification/output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/params.yaml new file mode 100644 index 00000000..e1111e6f --- /dev/null +++ b/examples/security/classification/output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/adv_predictions.json + adv_probabilities_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/c9e1088d6b2b6d194e3fbc320876aa7f.pkl + params_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/params.yaml + predictions_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/predictions.json + probabilities_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/probabilities.json + score_dict_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/score_dict.json + test_labels_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/test_labels.json + train_labels_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/train_labels.json + model_dir: models + name: 35c19ec9fce2d71c43079c649d0d65d2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b3b8234b827fbe101418ffe2ec37cc97 + trainer: + kwargs: {} +name: 35c19ec9fce2d71c43079c649d0d65d2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3609049669911c261cb5afe0c624d331/params.yaml b/examples/security/classification/output/reports/train/3609049669911c261cb5afe0c624d331/params.yaml new file mode 100644 index 00000000..623d4965 --- /dev/null +++ b/examples/security/classification/output/reports/train/3609049669911c261cb5afe0c624d331/params.yaml @@ -0,0 +1,115 @@ +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3609049669911c261cb5afe0c624d331/adv_predictions.json + adv_probabilities_file: output/reports/train/3609049669911c261cb5afe0c624d331/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl + model_file: output/models/fecc8e06394fdec43bcedc921ee5198b.pkl + params_file: output/reports/train/3609049669911c261cb5afe0c624d331/params.yaml + predictions_file: output/reports/train/3609049669911c261cb5afe0c624d331/predictions.json + probabilities_file: output/reports/train/3609049669911c261cb5afe0c624d331/probabilities.json + score_dict_file: output/reports/train/3609049669911c261cb5afe0c624d331/score_dict.json + test_labels_file: output/reports/train/3609049669911c261cb5afe0c624d331/test_labels.json + train_labels_file: output/reports/train/3609049669911c261cb5afe0c624d331/train_labels.json + model_dir: models + name: 3609049669911c261cb5afe0c624d331 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b76e4586d91b64181c5c7e206a3fc040 + trainer: + kwargs: {} +name: 3609049669911c261cb5afe0c624d331 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/params.yaml b/examples/security/classification/output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/params.yaml new file mode 100644 index 00000000..ca145668 --- /dev/null +++ b/examples/security/classification/output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/adv_predictions.json + adv_probabilities_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/f2178e22647feab33e329e548b3a1b83.pkl + params_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/params.yaml + predictions_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/predictions.json + probabilities_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/probabilities.json + score_dict_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/score_dict.json + test_labels_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/test_labels.json + train_labels_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/train_labels.json + model_dir: models + name: 360dc7d2bbf4f17faa370ac38e6808f8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1d130719b7019d78d4c2310c8ba886e0 + trainer: + kwargs: {} +name: 360dc7d2bbf4f17faa370ac38e6808f8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/362abf2367321024876f2ef624268d94/params.yaml b/examples/security/classification/output/reports/train/362abf2367321024876f2ef624268d94/params.yaml new file mode 100644 index 00000000..a20d4c47 --- /dev/null +++ b/examples/security/classification/output/reports/train/362abf2367321024876f2ef624268d94/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/362abf2367321024876f2ef624268d94/adv_predictions.json + adv_probabilities_file: output/reports/train/362abf2367321024876f2ef624268d94/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/bfd58136929a630f09c0a70e0062215b.pkl + params_file: output/reports/train/362abf2367321024876f2ef624268d94/params.yaml + predictions_file: output/reports/train/362abf2367321024876f2ef624268d94/predictions.json + probabilities_file: output/reports/train/362abf2367321024876f2ef624268d94/probabilities.json + score_dict_file: output/reports/train/362abf2367321024876f2ef624268d94/score_dict.json + test_labels_file: output/reports/train/362abf2367321024876f2ef624268d94/test_labels.json + train_labels_file: output/reports/train/362abf2367321024876f2ef624268d94/train_labels.json + model_dir: models + name: 362abf2367321024876f2ef624268d94 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 78b4b1ce568d2a3871d07e28c786e30c + trainer: + kwargs: {} +name: 362abf2367321024876f2ef624268d94 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/363f20c853ef9ec45f81d108ca8665c3/params.yaml b/examples/security/classification/output/reports/train/363f20c853ef9ec45f81d108ca8665c3/params.yaml new file mode 100644 index 00000000..3a57478d --- /dev/null +++ b/examples/security/classification/output/reports/train/363f20c853ef9ec45f81d108ca8665c3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/adv_predictions.json + adv_probabilities_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/f01287f63b9720126ed847fe99b4f2f8.pkl + params_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/params.yaml + predictions_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/predictions.json + probabilities_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/probabilities.json + score_dict_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/score_dict.json + test_labels_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/test_labels.json + train_labels_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/train_labels.json + model_dir: models + name: 363f20c853ef9ec45f81d108ca8665c3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 36dd16ed4498705d85911855ddac78fe + trainer: + kwargs: {} +name: 363f20c853ef9ec45f81d108ca8665c3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3659b64f748408e2494d084cf5dc512e/params.yaml b/examples/security/classification/output/reports/train/3659b64f748408e2494d084cf5dc512e/params.yaml new file mode 100644 index 00000000..8fa69490 --- /dev/null +++ b/examples/security/classification/output/reports/train/3659b64f748408e2494d084cf5dc512e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/adv_predictions.json + adv_probabilities_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/7e94fa9086f444d7cba8d62a9ae3321b.pkl + params_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/params.yaml + predictions_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/predictions.json + probabilities_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/probabilities.json + score_dict_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/score_dict.json + test_labels_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/test_labels.json + train_labels_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/train_labels.json + model_dir: models + name: 3659b64f748408e2494d084cf5dc512e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d6f37e652b3e62dd33892e04f489fa82 + trainer: + kwargs: {} +name: 3659b64f748408e2494d084cf5dc512e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/params.yaml b/examples/security/classification/output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/params.yaml new file mode 100644 index 00000000..548ed1ac --- /dev/null +++ b/examples/security/classification/output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/adv_predictions.json + adv_probabilities_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/0377d14c8a80d22ddf4d906573624059.pkl + params_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/params.yaml + predictions_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/predictions.json + probabilities_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/probabilities.json + score_dict_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/score_dict.json + test_labels_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/test_labels.json + train_labels_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/train_labels.json + model_dir: models + name: 36858219ae4e4cb3bf21e75a4d8a55fb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 65d4983f276069db3d9e773a4499664a + trainer: + kwargs: {} +name: 36858219ae4e4cb3bf21e75a4d8a55fb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/36a6cb189a7014b30ff961bce09e1244/params.yaml b/examples/security/classification/output/reports/train/36a6cb189a7014b30ff961bce09e1244/params.yaml new file mode 100644 index 00000000..7551ec6b --- /dev/null +++ b/examples/security/classification/output/reports/train/36a6cb189a7014b30ff961bce09e1244/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/adv_predictions.json + adv_probabilities_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/fb384ae8bc54af51ab99d68bd9482343.pkl + params_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/params.yaml + predictions_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/predictions.json + probabilities_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/probabilities.json + score_dict_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/score_dict.json + test_labels_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/test_labels.json + train_labels_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/train_labels.json + model_dir: models + name: 36a6cb189a7014b30ff961bce09e1244 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bf1c1e6b3e1213c9da9f2b2ff5a1e272 + trainer: + kwargs: {} +name: 36a6cb189a7014b30ff961bce09e1244 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/36b2136d76b62e2cd833fc13a592df26/params.yaml b/examples/security/classification/output/reports/train/36b2136d76b62e2cd833fc13a592df26/params.yaml new file mode 100644 index 00000000..36b253ab --- /dev/null +++ b/examples/security/classification/output/reports/train/36b2136d76b62e2cd833fc13a592df26/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/adv_predictions.json + adv_probabilities_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/08a20bc06f34c87f5be8ef60e9c1ef10.pkl + params_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/params.yaml + predictions_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/predictions.json + probabilities_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/probabilities.json + score_dict_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/score_dict.json + test_labels_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/test_labels.json + train_labels_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/train_labels.json + model_dir: models + name: 36b2136d76b62e2cd833fc13a592df26 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 952dab583acc0ef81666ecf1209260e7 + trainer: + kwargs: {} +name: 36b2136d76b62e2cd833fc13a592df26 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/params.yaml b/examples/security/classification/output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/params.yaml new file mode 100644 index 00000000..c0d8a194 --- /dev/null +++ b/examples/security/classification/output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/adv_predictions.json + adv_probabilities_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/3cf187e8f4b1642510ed739a24614a36.pkl + params_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/params.yaml + predictions_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/predictions.json + probabilities_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/probabilities.json + score_dict_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/score_dict.json + test_labels_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/test_labels.json + train_labels_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/train_labels.json + model_dir: models + name: 36b265eaaf1e3520caf424133fc1f5ae + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ae604d236b9ff2d73f5cc21f30f1b3e6 + trainer: + kwargs: {} +name: 36b265eaaf1e3520caf424133fc1f5ae +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/36b33b3c37839e72cda79d84a45976c3/params.yaml b/examples/security/classification/output/reports/train/36b33b3c37839e72cda79d84a45976c3/params.yaml new file mode 100644 index 00000000..0a8766ad --- /dev/null +++ b/examples/security/classification/output/reports/train/36b33b3c37839e72cda79d84a45976c3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/adv_predictions.json + adv_probabilities_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/37216796bd69dd76fb3116da8fee0d7c.pkl + params_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/params.yaml + predictions_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/predictions.json + probabilities_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/probabilities.json + score_dict_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/score_dict.json + test_labels_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/test_labels.json + train_labels_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/train_labels.json + model_dir: models + name: 36b33b3c37839e72cda79d84a45976c3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ab2d526493b2fb4f4ca112f97ee90143 + trainer: + kwargs: {} +name: 36b33b3c37839e72cda79d84a45976c3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/36c90131fe1155a959004e5f29be11c9/params.yaml b/examples/security/classification/output/reports/train/36c90131fe1155a959004e5f29be11c9/params.yaml new file mode 100644 index 00000000..a4367cf7 --- /dev/null +++ b/examples/security/classification/output/reports/train/36c90131fe1155a959004e5f29be11c9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/adv_predictions.json + adv_probabilities_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/59220608526086bc48ff76f059f88aa1.pkl + params_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/params.yaml + predictions_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/predictions.json + probabilities_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/probabilities.json + score_dict_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/score_dict.json + test_labels_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/test_labels.json + train_labels_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/train_labels.json + model_dir: models + name: 36c90131fe1155a959004e5f29be11c9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e0aa88cc5d71776b72961bcf10961e2d + trainer: + kwargs: {} +name: 36c90131fe1155a959004e5f29be11c9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3716c7a07f7a7db09d32221454640a38/params.yaml b/examples/security/classification/output/reports/train/3716c7a07f7a7db09d32221454640a38/params.yaml new file mode 100644 index 00000000..6d58a07a --- /dev/null +++ b/examples/security/classification/output/reports/train/3716c7a07f7a7db09d32221454640a38/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/adv_predictions.json + adv_probabilities_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/31dabecfb66ea8ec42dbad054acb0dc7.pkl + params_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/params.yaml + predictions_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/predictions.json + probabilities_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/probabilities.json + score_dict_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/score_dict.json + test_labels_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/test_labels.json + train_labels_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/train_labels.json + model_dir: models + name: 3716c7a07f7a7db09d32221454640a38 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 60ad47e13a95f20ba6cb53f994b8faa1 + trainer: + kwargs: {} +name: 3716c7a07f7a7db09d32221454640a38 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/374880df812ca475b1883f868f2ed07d/params.yaml b/examples/security/classification/output/reports/train/374880df812ca475b1883f868f2ed07d/params.yaml new file mode 100644 index 00000000..6fadccca --- /dev/null +++ b/examples/security/classification/output/reports/train/374880df812ca475b1883f868f2ed07d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/374880df812ca475b1883f868f2ed07d/adv_predictions.json + adv_probabilities_file: output/reports/train/374880df812ca475b1883f868f2ed07d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/d7d1e8800d934967aa08b7aec165e641.pkl + params_file: output/reports/train/374880df812ca475b1883f868f2ed07d/params.yaml + predictions_file: output/reports/train/374880df812ca475b1883f868f2ed07d/predictions.json + probabilities_file: output/reports/train/374880df812ca475b1883f868f2ed07d/probabilities.json + score_dict_file: output/reports/train/374880df812ca475b1883f868f2ed07d/score_dict.json + test_labels_file: output/reports/train/374880df812ca475b1883f868f2ed07d/test_labels.json + train_labels_file: output/reports/train/374880df812ca475b1883f868f2ed07d/train_labels.json + model_dir: models + name: 374880df812ca475b1883f868f2ed07d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4a06f5e44c354bdf20e6ac9d6f0c6f53 + trainer: + kwargs: {} +name: 374880df812ca475b1883f868f2ed07d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/params.yaml b/examples/security/classification/output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/params.yaml new file mode 100644 index 00000000..d4fff480 --- /dev/null +++ b/examples/security/classification/output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/adv_predictions.json + adv_probabilities_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/304c9fa1c9e06de865874ca1b21df377.pkl + params_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/params.yaml + predictions_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/predictions.json + probabilities_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/probabilities.json + score_dict_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/score_dict.json + test_labels_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/test_labels.json + train_labels_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/train_labels.json + model_dir: models + name: 3774dd8cb4c0a59f44a1e6d4719d0775 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 21b33b4495735d11384003659737ce10 + trainer: + kwargs: {} +name: 3774dd8cb4c0a59f44a1e6d4719d0775 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/37850cdf8f35db2213565fa9519e21d4/params.yaml b/examples/security/classification/output/reports/train/37850cdf8f35db2213565fa9519e21d4/params.yaml new file mode 100644 index 00000000..7e42bf93 --- /dev/null +++ b/examples/security/classification/output/reports/train/37850cdf8f35db2213565fa9519e21d4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/adv_predictions.json + adv_probabilities_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/9fe312d6c650c5c6892fb1911a84643b.pkl + params_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/params.yaml + predictions_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/predictions.json + probabilities_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/probabilities.json + score_dict_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/score_dict.json + test_labels_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/test_labels.json + train_labels_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/train_labels.json + model_dir: models + name: 37850cdf8f35db2213565fa9519e21d4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 47293dfb9ccf9b23afd8d9c6e32d45fe + trainer: + kwargs: {} +name: 37850cdf8f35db2213565fa9519e21d4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/37d6322e2a28e3132134bcb8739c821d/params.yaml b/examples/security/classification/output/reports/train/37d6322e2a28e3132134bcb8739c821d/params.yaml new file mode 100644 index 00000000..399ffc72 --- /dev/null +++ b/examples/security/classification/output/reports/train/37d6322e2a28e3132134bcb8739c821d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/adv_predictions.json + adv_probabilities_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/a2a13a8d41cdbaa2a5e628a8053ec1d8.pkl + params_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/params.yaml + predictions_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/predictions.json + probabilities_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/probabilities.json + score_dict_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/score_dict.json + test_labels_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/test_labels.json + train_labels_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/train_labels.json + model_dir: models + name: 37d6322e2a28e3132134bcb8739c821d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1900ac8c1a8d6494a58e32aa6cc22f8a + trainer: + kwargs: {} +name: 37d6322e2a28e3132134bcb8739c821d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/37d986c18aa82788e4bec09689f873da/params.yaml b/examples/security/classification/output/reports/train/37d986c18aa82788e4bec09689f873da/params.yaml new file mode 100644 index 00000000..eef4bbca --- /dev/null +++ b/examples/security/classification/output/reports/train/37d986c18aa82788e4bec09689f873da/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/37d986c18aa82788e4bec09689f873da/adv_predictions.json + adv_probabilities_file: output/reports/train/37d986c18aa82788e4bec09689f873da/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/cd24671c5bc8d49b5910e482bf36e989.pkl + params_file: output/reports/train/37d986c18aa82788e4bec09689f873da/params.yaml + predictions_file: output/reports/train/37d986c18aa82788e4bec09689f873da/predictions.json + probabilities_file: output/reports/train/37d986c18aa82788e4bec09689f873da/probabilities.json + score_dict_file: output/reports/train/37d986c18aa82788e4bec09689f873da/score_dict.json + test_labels_file: output/reports/train/37d986c18aa82788e4bec09689f873da/test_labels.json + train_labels_file: output/reports/train/37d986c18aa82788e4bec09689f873da/train_labels.json + model_dir: models + name: 37d986c18aa82788e4bec09689f873da + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9f42ac16522de476dcc61e0b52c7a33f + trainer: + kwargs: {} +name: 37d986c18aa82788e4bec09689f873da +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/37e34d86125aa84f8f2a1df8782912f4/params.yaml b/examples/security/classification/output/reports/train/37e34d86125aa84f8f2a1df8782912f4/params.yaml new file mode 100644 index 00000000..0d4e941c --- /dev/null +++ b/examples/security/classification/output/reports/train/37e34d86125aa84f8f2a1df8782912f4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/adv_predictions.json + adv_probabilities_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/7fbc197c2c4da06907c83d0421662132.pkl + params_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/params.yaml + predictions_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/predictions.json + probabilities_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/probabilities.json + score_dict_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/score_dict.json + test_labels_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/test_labels.json + train_labels_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/train_labels.json + model_dir: models + name: 37e34d86125aa84f8f2a1df8782912f4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ad2fbe238fea0ccd7d8cdec6f701da9f + trainer: + kwargs: {} +name: 37e34d86125aa84f8f2a1df8782912f4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/37f515200391547010a8b410e5013924/params.yaml b/examples/security/classification/output/reports/train/37f515200391547010a8b410e5013924/params.yaml new file mode 100644 index 00000000..766db738 --- /dev/null +++ b/examples/security/classification/output/reports/train/37f515200391547010a8b410e5013924/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/37f515200391547010a8b410e5013924/adv_predictions.json + adv_probabilities_file: output/reports/train/37f515200391547010a8b410e5013924/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/431722f0bb483d61f6fb9d0e5ac5d7d0.pkl + params_file: output/reports/train/37f515200391547010a8b410e5013924/params.yaml + predictions_file: output/reports/train/37f515200391547010a8b410e5013924/predictions.json + probabilities_file: output/reports/train/37f515200391547010a8b410e5013924/probabilities.json + score_dict_file: output/reports/train/37f515200391547010a8b410e5013924/score_dict.json + test_labels_file: output/reports/train/37f515200391547010a8b410e5013924/test_labels.json + train_labels_file: output/reports/train/37f515200391547010a8b410e5013924/train_labels.json + model_dir: models + name: 37f515200391547010a8b410e5013924 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b3b9250d269aaedf1e6c59794e122509 + trainer: + kwargs: {} +name: 37f515200391547010a8b410e5013924 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/381b7eeeb9bee4bed578e059617c1da3/params.yaml b/examples/security/classification/output/reports/train/381b7eeeb9bee4bed578e059617c1da3/params.yaml new file mode 100644 index 00000000..e3c9cceb --- /dev/null +++ b/examples/security/classification/output/reports/train/381b7eeeb9bee4bed578e059617c1da3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/adv_predictions.json + adv_probabilities_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/1ed162b62c29ff7a7f913c87433121ed.pkl + params_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/params.yaml + predictions_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/predictions.json + probabilities_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/probabilities.json + score_dict_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/score_dict.json + test_labels_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/test_labels.json + train_labels_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/train_labels.json + model_dir: models + name: 381b7eeeb9bee4bed578e059617c1da3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 196069c24fa732264cadd297df2e1eb0 + trainer: + kwargs: {} +name: 381b7eeeb9bee4bed578e059617c1da3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/382badbb4db3f2efd20d3042272a7405/params.yaml b/examples/security/classification/output/reports/train/382badbb4db3f2efd20d3042272a7405/params.yaml new file mode 100644 index 00000000..6d4d8f46 --- /dev/null +++ b/examples/security/classification/output/reports/train/382badbb4db3f2efd20d3042272a7405/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/adv_predictions.json + adv_probabilities_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/8361a3af07c5916dfb645af670a62986.pkl + params_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/params.yaml + predictions_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/predictions.json + probabilities_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/probabilities.json + score_dict_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/score_dict.json + test_labels_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/test_labels.json + train_labels_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/train_labels.json + model_dir: models + name: 382badbb4db3f2efd20d3042272a7405 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6aade8363bb5ebef234a42bee13435cb + trainer: + kwargs: {} +name: 382badbb4db3f2efd20d3042272a7405 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/384e745ec2d19b78bdf13a6c8a590006/params.yaml b/examples/security/classification/output/reports/train/384e745ec2d19b78bdf13a6c8a590006/params.yaml new file mode 100644 index 00000000..95748ae7 --- /dev/null +++ b/examples/security/classification/output/reports/train/384e745ec2d19b78bdf13a6c8a590006/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/adv_predictions.json + adv_probabilities_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/16d917b9942a8a92a8f820514e5e399b.pkl + params_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/params.yaml + predictions_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/predictions.json + probabilities_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/probabilities.json + score_dict_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/score_dict.json + test_labels_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/test_labels.json + train_labels_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/train_labels.json + model_dir: models + name: 384e745ec2d19b78bdf13a6c8a590006 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 68e70a4e181a40c6fd31ae8c81a9db68 + trainer: + kwargs: {} +name: 384e745ec2d19b78bdf13a6c8a590006 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/params.yaml b/examples/security/classification/output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/params.yaml new file mode 100644 index 00000000..1e5dea14 --- /dev/null +++ b/examples/security/classification/output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/adv_predictions.json + adv_probabilities_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/553e3fad7d217bb938f266f05edac390.pkl + params_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/params.yaml + predictions_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/predictions.json + probabilities_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/probabilities.json + score_dict_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/score_dict.json + test_labels_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/test_labels.json + train_labels_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/train_labels.json + model_dir: models + name: 38500784bea2a23fa4bc6f7df860d7d3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3030b87d2c62e0a7685eed730fff9197 + trainer: + kwargs: {} +name: 38500784bea2a23fa4bc6f7df860d7d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3855f158433350f0cbf4c155b4e92a4d/params.yaml b/examples/security/classification/output/reports/train/3855f158433350f0cbf4c155b4e92a4d/params.yaml new file mode 100644 index 00000000..2b8fd8f8 --- /dev/null +++ b/examples/security/classification/output/reports/train/3855f158433350f0cbf4c155b4e92a4d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/adv_predictions.json + adv_probabilities_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/62514d4a9a5cd6b9e20f2596aa503e48.pkl + params_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/params.yaml + predictions_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/predictions.json + probabilities_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/probabilities.json + score_dict_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/score_dict.json + test_labels_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/test_labels.json + train_labels_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/train_labels.json + model_dir: models + name: 3855f158433350f0cbf4c155b4e92a4d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 68114c6603d539867177d70341dfdf57 + trainer: + kwargs: {} +name: 3855f158433350f0cbf4c155b4e92a4d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/38615915ae1660ceb384f35b8aef05ce/params.yaml b/examples/security/classification/output/reports/train/38615915ae1660ceb384f35b8aef05ce/params.yaml new file mode 100644 index 00000000..212d6c98 --- /dev/null +++ b/examples/security/classification/output/reports/train/38615915ae1660ceb384f35b8aef05ce/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/adv_predictions.json + adv_probabilities_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/8abde679f673e00c8ec6ed5b4c0f71d1.pkl + params_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/params.yaml + predictions_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/predictions.json + probabilities_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/probabilities.json + score_dict_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/score_dict.json + test_labels_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/test_labels.json + train_labels_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/train_labels.json + model_dir: models + name: 38615915ae1660ceb384f35b8aef05ce + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a94ee6a3d228f78c6204db35108caf53 + trainer: + kwargs: {} +name: 38615915ae1660ceb384f35b8aef05ce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/params.yaml b/examples/security/classification/output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/params.yaml new file mode 100644 index 00000000..6af1ea36 --- /dev/null +++ b/examples/security/classification/output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/adv_predictions.json + adv_probabilities_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/3e10fdfe8b848c74802f13a703f803f1.pkl + params_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/params.yaml + predictions_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/predictions.json + probabilities_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/probabilities.json + score_dict_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/score_dict.json + test_labels_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/test_labels.json + train_labels_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/train_labels.json + model_dir: models + name: 3870ebfbfe65fe1034a9d7ee0b8b77d3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 18d64393a844b9cb238777f4b4bebe2b + trainer: + kwargs: {} +name: 3870ebfbfe65fe1034a9d7ee0b8b77d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/params.yaml b/examples/security/classification/output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/params.yaml new file mode 100644 index 00000000..e887ca99 --- /dev/null +++ b/examples/security/classification/output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/adv_predictions.json + adv_probabilities_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/47c4e6167e56fb1b8d4314ef6d3d780d.pkl + params_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/params.yaml + predictions_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/predictions.json + probabilities_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/probabilities.json + score_dict_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/score_dict.json + test_labels_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/test_labels.json + train_labels_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/train_labels.json + model_dir: models + name: 38aaebee930ce21eb3e72266b6bdf1b2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 764e079e2e85f123d14b2d83effb9eda + trainer: + kwargs: {} +name: 38aaebee930ce21eb3e72266b6bdf1b2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/38b06c39262826eed42d62e30b5cac39/params.yaml b/examples/security/classification/output/reports/train/38b06c39262826eed42d62e30b5cac39/params.yaml new file mode 100644 index 00000000..58494651 --- /dev/null +++ b/examples/security/classification/output/reports/train/38b06c39262826eed42d62e30b5cac39/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/adv_predictions.json + adv_probabilities_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e3adb677335aa1852966f0934d516bce.pkl + params_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/params.yaml + predictions_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/predictions.json + probabilities_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/probabilities.json + score_dict_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/score_dict.json + test_labels_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/test_labels.json + train_labels_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/train_labels.json + model_dir: models + name: 38b06c39262826eed42d62e30b5cac39 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 460a00690f992003a9b01b0ad7723b08 + trainer: + kwargs: {} +name: 38b06c39262826eed42d62e30b5cac39 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/38d60af3b4864dcf49496951ab96a6e8/params.yaml b/examples/security/classification/output/reports/train/38d60af3b4864dcf49496951ab96a6e8/params.yaml new file mode 100644 index 00000000..c76806df --- /dev/null +++ b/examples/security/classification/output/reports/train/38d60af3b4864dcf49496951ab96a6e8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/adv_predictions.json + adv_probabilities_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/8d1cdd2901f26b196f059b43bc54893a.pkl + params_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/params.yaml + predictions_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/predictions.json + probabilities_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/probabilities.json + score_dict_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/score_dict.json + test_labels_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/test_labels.json + train_labels_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/train_labels.json + model_dir: models + name: 38d60af3b4864dcf49496951ab96a6e8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c5f2a4f69eddf5d0a84e691d3097a55c + trainer: + kwargs: {} +name: 38d60af3b4864dcf49496951ab96a6e8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/39029b80bfe201b59fc657c29627a775/params.yaml b/examples/security/classification/output/reports/train/39029b80bfe201b59fc657c29627a775/params.yaml new file mode 100644 index 00000000..bfd26b6f --- /dev/null +++ b/examples/security/classification/output/reports/train/39029b80bfe201b59fc657c29627a775/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/39029b80bfe201b59fc657c29627a775/adv_predictions.json + adv_probabilities_file: output/reports/train/39029b80bfe201b59fc657c29627a775/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/c12dd3da448ec5c32fc08bf022836446.pkl + params_file: output/reports/train/39029b80bfe201b59fc657c29627a775/params.yaml + predictions_file: output/reports/train/39029b80bfe201b59fc657c29627a775/predictions.json + probabilities_file: output/reports/train/39029b80bfe201b59fc657c29627a775/probabilities.json + score_dict_file: output/reports/train/39029b80bfe201b59fc657c29627a775/score_dict.json + test_labels_file: output/reports/train/39029b80bfe201b59fc657c29627a775/test_labels.json + train_labels_file: output/reports/train/39029b80bfe201b59fc657c29627a775/train_labels.json + model_dir: models + name: 39029b80bfe201b59fc657c29627a775 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c6f7481d899514edf6e58a835a44fe2 + trainer: + kwargs: {} +name: 39029b80bfe201b59fc657c29627a775 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/params.yaml b/examples/security/classification/output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/params.yaml new file mode 100644 index 00000000..37720f5d --- /dev/null +++ b/examples/security/classification/output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/adv_predictions.json + adv_probabilities_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/d74a86267ad0ba567cda3a27ad743667.pkl + params_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/params.yaml + predictions_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/predictions.json + probabilities_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/probabilities.json + score_dict_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/score_dict.json + test_labels_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/test_labels.json + train_labels_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/train_labels.json + model_dir: models + name: 392edce3f4ad1f2c98e2c8e35323b4d1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3503d27e3f9cdf027ff709c5be6713d3 + trainer: + kwargs: {} +name: 392edce3f4ad1f2c98e2c8e35323b4d1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3933221e8fafe07633adf25b26281c6e/params.yaml b/examples/security/classification/output/reports/train/3933221e8fafe07633adf25b26281c6e/params.yaml new file mode 100644 index 00000000..2a2949c7 --- /dev/null +++ b/examples/security/classification/output/reports/train/3933221e8fafe07633adf25b26281c6e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/adv_predictions.json + adv_probabilities_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/70dbcb3a114750cc5c26c0248f5163c2.pkl + params_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/params.yaml + predictions_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/predictions.json + probabilities_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/probabilities.json + score_dict_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/score_dict.json + test_labels_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/test_labels.json + train_labels_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/train_labels.json + model_dir: models + name: 3933221e8fafe07633adf25b26281c6e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eac366c6f5f45e44766b2769fd96b9a5 + trainer: + kwargs: {} +name: 3933221e8fafe07633adf25b26281c6e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/394af7a247cba6260f1c4e6b41504970/params.yaml b/examples/security/classification/output/reports/train/394af7a247cba6260f1c4e6b41504970/params.yaml new file mode 100644 index 00000000..40cf5a79 --- /dev/null +++ b/examples/security/classification/output/reports/train/394af7a247cba6260f1c4e6b41504970/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/adv_predictions.json + adv_probabilities_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/d0c013e38f8d9d032327e9b63bf4c235.pkl + params_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/params.yaml + predictions_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/predictions.json + probabilities_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/probabilities.json + score_dict_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/score_dict.json + test_labels_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/test_labels.json + train_labels_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/train_labels.json + model_dir: models + name: 394af7a247cba6260f1c4e6b41504970 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2ba8cf6cbbab37c81a5b95d73132d263 + trainer: + kwargs: {} +name: 394af7a247cba6260f1c4e6b41504970 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/params.yaml b/examples/security/classification/output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/params.yaml new file mode 100644 index 00000000..d353e324 --- /dev/null +++ b/examples/security/classification/output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/adv_predictions.json + adv_probabilities_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/6240e3886c9066247918afa3423469eb.pkl + params_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/params.yaml + predictions_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/predictions.json + probabilities_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/probabilities.json + score_dict_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/score_dict.json + test_labels_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/test_labels.json + train_labels_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/train_labels.json + model_dir: models + name: 395a2050f923b6ec80cb3fda4aec56bc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a2b788ae2f0a5c292915f34d7ed3eb32 + trainer: + kwargs: {} +name: 395a2050f923b6ec80cb3fda4aec56bc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/396a7d7c21d8069aab788aecbce79856/params.yaml b/examples/security/classification/output/reports/train/396a7d7c21d8069aab788aecbce79856/params.yaml new file mode 100644 index 00000000..15d3a43f --- /dev/null +++ b/examples/security/classification/output/reports/train/396a7d7c21d8069aab788aecbce79856/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/adv_predictions.json + adv_probabilities_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/665cebf49dead51bbe26a21022e5b2d2.pkl + params_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/params.yaml + predictions_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/predictions.json + probabilities_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/probabilities.json + score_dict_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/score_dict.json + test_labels_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/test_labels.json + train_labels_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/train_labels.json + model_dir: models + name: 396a7d7c21d8069aab788aecbce79856 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 17a32aefc767c5adf843b5390b6f46de + trainer: + kwargs: {} +name: 396a7d7c21d8069aab788aecbce79856 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/39754482e5242e72aa325834ac26f3df/params.yaml b/examples/security/classification/output/reports/train/39754482e5242e72aa325834ac26f3df/params.yaml new file mode 100644 index 00000000..ed2c8cef --- /dev/null +++ b/examples/security/classification/output/reports/train/39754482e5242e72aa325834ac26f3df/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/39754482e5242e72aa325834ac26f3df/adv_predictions.json + adv_probabilities_file: output/reports/train/39754482e5242e72aa325834ac26f3df/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/3d0a5a347e3dbd4241d7dddb99c1b54e.pkl + params_file: output/reports/train/39754482e5242e72aa325834ac26f3df/params.yaml + predictions_file: output/reports/train/39754482e5242e72aa325834ac26f3df/predictions.json + probabilities_file: output/reports/train/39754482e5242e72aa325834ac26f3df/probabilities.json + score_dict_file: output/reports/train/39754482e5242e72aa325834ac26f3df/score_dict.json + test_labels_file: output/reports/train/39754482e5242e72aa325834ac26f3df/test_labels.json + train_labels_file: output/reports/train/39754482e5242e72aa325834ac26f3df/train_labels.json + model_dir: models + name: 39754482e5242e72aa325834ac26f3df + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ead9452cb872d5904673199345aae4bf + trainer: + kwargs: {} +name: 39754482e5242e72aa325834ac26f3df +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/params.yaml b/examples/security/classification/output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/params.yaml new file mode 100644 index 00000000..1f60e117 --- /dev/null +++ b/examples/security/classification/output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/adv_predictions.json + adv_probabilities_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/12b0ed387c6424c46374a2306bb15cfb.pkl + params_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/params.yaml + predictions_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/predictions.json + probabilities_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/probabilities.json + score_dict_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/score_dict.json + test_labels_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/test_labels.json + train_labels_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/train_labels.json + model_dir: models + name: 3983a67b8a4057ead42dfdb69ededaeb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bc4f514b67c3833261bbd3679592c64d + trainer: + kwargs: {} +name: 3983a67b8a4057ead42dfdb69ededaeb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/params.yaml b/examples/security/classification/output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/params.yaml new file mode 100644 index 00000000..38757300 --- /dev/null +++ b/examples/security/classification/output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/adv_predictions.json + adv_probabilities_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/fd6c84444cbdbf1106f8f2f770d2ba6d.pkl + params_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/params.yaml + predictions_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/predictions.json + probabilities_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/probabilities.json + score_dict_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/score_dict.json + test_labels_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/test_labels.json + train_labels_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/train_labels.json + model_dir: models + name: 398bd4cbc0112d8584b1ec453f9f2a01 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ef956373218b58daa6dba737f4b429f + trainer: + kwargs: {} +name: 398bd4cbc0112d8584b1ec453f9f2a01 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/39a8e006e9521a8a3ac8aedcad223869/params.yaml b/examples/security/classification/output/reports/train/39a8e006e9521a8a3ac8aedcad223869/params.yaml new file mode 100644 index 00000000..dcc15e67 --- /dev/null +++ b/examples/security/classification/output/reports/train/39a8e006e9521a8a3ac8aedcad223869/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/adv_predictions.json + adv_probabilities_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/e5cfba1dbb9849cc90de4879402d7acb.pkl + params_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/params.yaml + predictions_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/predictions.json + probabilities_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/probabilities.json + score_dict_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/score_dict.json + test_labels_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/test_labels.json + train_labels_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/train_labels.json + model_dir: models + name: 39a8e006e9521a8a3ac8aedcad223869 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: da6dc0bdcbea2bb90f45eedc7c925e9e + trainer: + kwargs: {} +name: 39a8e006e9521a8a3ac8aedcad223869 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/39aeae586c478d7560fd778adce6b062/params.yaml b/examples/security/classification/output/reports/train/39aeae586c478d7560fd778adce6b062/params.yaml new file mode 100644 index 00000000..5b0d9c66 --- /dev/null +++ b/examples/security/classification/output/reports/train/39aeae586c478d7560fd778adce6b062/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/39aeae586c478d7560fd778adce6b062/adv_predictions.json + adv_probabilities_file: output/reports/train/39aeae586c478d7560fd778adce6b062/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/ec648fa4e65374e220e58c296531a3f0.pkl + params_file: output/reports/train/39aeae586c478d7560fd778adce6b062/params.yaml + predictions_file: output/reports/train/39aeae586c478d7560fd778adce6b062/predictions.json + probabilities_file: output/reports/train/39aeae586c478d7560fd778adce6b062/probabilities.json + score_dict_file: output/reports/train/39aeae586c478d7560fd778adce6b062/score_dict.json + test_labels_file: output/reports/train/39aeae586c478d7560fd778adce6b062/test_labels.json + train_labels_file: output/reports/train/39aeae586c478d7560fd778adce6b062/train_labels.json + model_dir: models + name: 39aeae586c478d7560fd778adce6b062 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 381edc7996245e261dfeb940095c554f + trainer: + kwargs: {} +name: 39aeae586c478d7560fd778adce6b062 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/39f7317b84740b245937f9e6a6d5975c/params.yaml b/examples/security/classification/output/reports/train/39f7317b84740b245937f9e6a6d5975c/params.yaml new file mode 100644 index 00000000..2c7d2cae --- /dev/null +++ b/examples/security/classification/output/reports/train/39f7317b84740b245937f9e6a6d5975c/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/adv_predictions.json + adv_probabilities_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/a2f2854cbcf6ca2bddd52e86ff2f4508.pkl + params_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/params.yaml + predictions_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/predictions.json + probabilities_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/probabilities.json + score_dict_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/score_dict.json + test_labels_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/test_labels.json + train_labels_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/train_labels.json + model_dir: models + name: 39f7317b84740b245937f9e6a6d5975c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 64b8b531961e682308ed04348c6466ae + trainer: + kwargs: {} +name: 39f7317b84740b245937f9e6a6d5975c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3a087746b68cdb9c323f1389fe04d063/params.yaml b/examples/security/classification/output/reports/train/3a087746b68cdb9c323f1389fe04d063/params.yaml new file mode 100644 index 00000000..78469b92 --- /dev/null +++ b/examples/security/classification/output/reports/train/3a087746b68cdb9c323f1389fe04d063/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/adv_predictions.json + adv_probabilities_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/393a03a91f1ec0dfe60c9a1aa2f5e4ae.pkl + params_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/params.yaml + predictions_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/predictions.json + probabilities_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/probabilities.json + score_dict_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/score_dict.json + test_labels_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/test_labels.json + train_labels_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/train_labels.json + model_dir: models + name: 3a087746b68cdb9c323f1389fe04d063 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ee35ac27960dd5f604aad6e4b02377a1 + trainer: + kwargs: {} +name: 3a087746b68cdb9c323f1389fe04d063 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3a44dae705aeb56908b85440cf50cf50/params.yaml b/examples/security/classification/output/reports/train/3a44dae705aeb56908b85440cf50cf50/params.yaml new file mode 100644 index 00000000..47d0b119 --- /dev/null +++ b/examples/security/classification/output/reports/train/3a44dae705aeb56908b85440cf50cf50/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/adv_predictions.json + adv_probabilities_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/1c2d5eb5c19992c826514236fbf68909.pkl + params_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/params.yaml + predictions_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/predictions.json + probabilities_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/probabilities.json + score_dict_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/score_dict.json + test_labels_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/test_labels.json + train_labels_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/train_labels.json + model_dir: models + name: 3a44dae705aeb56908b85440cf50cf50 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ee91604df36dc1e3d1659c361b93dfb8 + trainer: + kwargs: {} +name: 3a44dae705aeb56908b85440cf50cf50 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/params.yaml b/examples/security/classification/output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/params.yaml new file mode 100644 index 00000000..c9e6d8cf --- /dev/null +++ b/examples/security/classification/output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/adv_predictions.json + adv_probabilities_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/925e2baf0531247779d638f85a11f038.pkl + params_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/params.yaml + predictions_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/predictions.json + probabilities_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/probabilities.json + score_dict_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/score_dict.json + test_labels_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/test_labels.json + train_labels_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/train_labels.json + model_dir: models + name: 3a4760299155c3b3caf615a7aa97eb0b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 97d6b5c11af5d984c2379d04d45959a3 + trainer: + kwargs: {} +name: 3a4760299155c3b3caf615a7aa97eb0b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3a4fed2414f2e89395b1169db67b06f1/params.yaml b/examples/security/classification/output/reports/train/3a4fed2414f2e89395b1169db67b06f1/params.yaml new file mode 100644 index 00000000..6893df89 --- /dev/null +++ b/examples/security/classification/output/reports/train/3a4fed2414f2e89395b1169db67b06f1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/adv_predictions.json + adv_probabilities_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/69e6d0deb97aac908b7224fc90b658dc.pkl + params_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/params.yaml + predictions_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/predictions.json + probabilities_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/probabilities.json + score_dict_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/score_dict.json + test_labels_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/test_labels.json + train_labels_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/train_labels.json + model_dir: models + name: 3a4fed2414f2e89395b1169db67b06f1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b0115ca567bc4af716207c89591da7e0 + trainer: + kwargs: {} +name: 3a4fed2414f2e89395b1169db67b06f1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3a7426b1d77943ea16b0532f56666d10/params.yaml b/examples/security/classification/output/reports/train/3a7426b1d77943ea16b0532f56666d10/params.yaml new file mode 100644 index 00000000..917976ef --- /dev/null +++ b/examples/security/classification/output/reports/train/3a7426b1d77943ea16b0532f56666d10/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/adv_predictions.json + adv_probabilities_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/03b62f20c6616d60c9a97812c87e8b5a.pkl + params_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/params.yaml + predictions_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/predictions.json + probabilities_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/probabilities.json + score_dict_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/score_dict.json + test_labels_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/test_labels.json + train_labels_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/train_labels.json + model_dir: models + name: 3a7426b1d77943ea16b0532f56666d10 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7fd0d60b1a6e1a67d39203a89146a0c6 + trainer: + kwargs: {} +name: 3a7426b1d77943ea16b0532f56666d10 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/params.yaml b/examples/security/classification/output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/params.yaml new file mode 100644 index 00000000..b148c7a9 --- /dev/null +++ b/examples/security/classification/output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/adv_predictions.json + adv_probabilities_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/cbb175a98a9488c1b93a0429f1fbff48.pkl + params_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/params.yaml + predictions_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/predictions.json + probabilities_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/probabilities.json + score_dict_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/score_dict.json + test_labels_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/test_labels.json + train_labels_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/train_labels.json + model_dir: models + name: 3a832dfaaced25bcde1bbe57baa0b2ac + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8f40344262bbabf6471e62064ba4256d + trainer: + kwargs: {} +name: 3a832dfaaced25bcde1bbe57baa0b2ac +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/params.yaml b/examples/security/classification/output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/params.yaml new file mode 100644 index 00000000..35c9ee02 --- /dev/null +++ b/examples/security/classification/output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/adv_predictions.json + adv_probabilities_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/d3da0e36374c561648c85b2eaf3a63ae.pkl + params_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/params.yaml + predictions_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/predictions.json + probabilities_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/probabilities.json + score_dict_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/score_dict.json + test_labels_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/test_labels.json + train_labels_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/train_labels.json + model_dir: models + name: 3a90a0d2d8f67cc9ec7c945a798428c2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b23e6b7df8109b1eb3b46f11e9f30a48 + trainer: + kwargs: {} +name: 3a90a0d2d8f67cc9ec7c945a798428c2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3ab98552789ce09182cfa2f13ecf763e/params.yaml b/examples/security/classification/output/reports/train/3ab98552789ce09182cfa2f13ecf763e/params.yaml new file mode 100644 index 00000000..e3ed47ce --- /dev/null +++ b/examples/security/classification/output/reports/train/3ab98552789ce09182cfa2f13ecf763e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/adv_predictions.json + adv_probabilities_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/ca6e418b702e6b3c57e4b31a1e4e1d10.pkl + params_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/params.yaml + predictions_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/predictions.json + probabilities_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/probabilities.json + score_dict_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/score_dict.json + test_labels_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/test_labels.json + train_labels_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/train_labels.json + model_dir: models + name: 3ab98552789ce09182cfa2f13ecf763e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 54fb62afb21412efc5a581b56ed84e58 + trainer: + kwargs: {} +name: 3ab98552789ce09182cfa2f13ecf763e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3afb64b04178a88f25d5922ad930b19c/params.yaml b/examples/security/classification/output/reports/train/3afb64b04178a88f25d5922ad930b19c/params.yaml new file mode 100644 index 00000000..bb9b420d --- /dev/null +++ b/examples/security/classification/output/reports/train/3afb64b04178a88f25d5922ad930b19c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/adv_predictions.json + adv_probabilities_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/f916d2385b9c4eb4584b3a19d94a97e4.pkl + params_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/params.yaml + predictions_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/predictions.json + probabilities_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/probabilities.json + score_dict_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/score_dict.json + test_labels_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/test_labels.json + train_labels_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/train_labels.json + model_dir: models + name: 3afb64b04178a88f25d5922ad930b19c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5db70d81d5a8b13688d3a405f97a73c7 + trainer: + kwargs: {} +name: 3afb64b04178a88f25d5922ad930b19c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/params.yaml b/examples/security/classification/output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/params.yaml new file mode 100644 index 00000000..55a5f1fa --- /dev/null +++ b/examples/security/classification/output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/adv_predictions.json + adv_probabilities_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/f48083be8b0c06dbac9a897526bcebdf.pkl + params_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/params.yaml + predictions_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/predictions.json + probabilities_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/probabilities.json + score_dict_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/score_dict.json + test_labels_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/test_labels.json + train_labels_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/train_labels.json + model_dir: models + name: 3b0f971da7bf95ffa96f88c2fb3124c3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7abc26bb2bc35b8608d92327f014fdc0 + trainer: + kwargs: {} +name: 3b0f971da7bf95ffa96f88c2fb3124c3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/params.yaml b/examples/security/classification/output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/params.yaml new file mode 100644 index 00000000..28c0a8d8 --- /dev/null +++ b/examples/security/classification/output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/adv_predictions.json + adv_probabilities_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/cf6c0909071aa18655e3d32145963b39.pkl + params_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/params.yaml + predictions_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/predictions.json + probabilities_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/probabilities.json + score_dict_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/score_dict.json + test_labels_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/test_labels.json + train_labels_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/train_labels.json + model_dir: models + name: 3b5be3a5d99d3f8266edddc5062dea3c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 487dca9c32b7d27d6c57556515cabf30 + trainer: + kwargs: {} +name: 3b5be3a5d99d3f8266edddc5062dea3c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3baf817ca53374775736e3770c5ac72d/params.yaml b/examples/security/classification/output/reports/train/3baf817ca53374775736e3770c5ac72d/params.yaml new file mode 100644 index 00000000..74b3f0cf --- /dev/null +++ b/examples/security/classification/output/reports/train/3baf817ca53374775736e3770c5ac72d/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/adv_predictions.json + adv_probabilities_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/5765ce95d1abe7251689c33d30d08aca.pkl + params_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/params.yaml + predictions_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/predictions.json + probabilities_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/probabilities.json + score_dict_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/score_dict.json + test_labels_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/test_labels.json + train_labels_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/train_labels.json + model_dir: models + name: 3baf817ca53374775736e3770c5ac72d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0c43afb6dd4853ea7248386f9ae4c6d8 + trainer: + kwargs: {} +name: 3baf817ca53374775736e3770c5ac72d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3bb1154f2c86770440b01814ec8a97c1/params.yaml b/examples/security/classification/output/reports/train/3bb1154f2c86770440b01814ec8a97c1/params.yaml new file mode 100644 index 00000000..7205bf11 --- /dev/null +++ b/examples/security/classification/output/reports/train/3bb1154f2c86770440b01814ec8a97c1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/adv_predictions.json + adv_probabilities_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/4dff07915f66876bc115b5b6f5607969.pkl + params_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/params.yaml + predictions_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/predictions.json + probabilities_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/probabilities.json + score_dict_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/score_dict.json + test_labels_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/test_labels.json + train_labels_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/train_labels.json + model_dir: models + name: 3bb1154f2c86770440b01814ec8a97c1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b8b9b31a045fdb1c50c345fe08a9e61e + trainer: + kwargs: {} +name: 3bb1154f2c86770440b01814ec8a97c1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3bbc457d3368df994b8adc841370677c/params.yaml b/examples/security/classification/output/reports/train/3bbc457d3368df994b8adc841370677c/params.yaml new file mode 100644 index 00000000..5e399480 --- /dev/null +++ b/examples/security/classification/output/reports/train/3bbc457d3368df994b8adc841370677c/params.yaml @@ -0,0 +1,104 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3bbc457d3368df994b8adc841370677c/adv_predictions.json + adv_probabilities_file: output/reports/train/3bbc457d3368df994b8adc841370677c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/4715bf9063d084b48e5ac97a1023585b.pkl + params_file: output/reports/train/3bbc457d3368df994b8adc841370677c/params.yaml + predictions_file: output/reports/train/3bbc457d3368df994b8adc841370677c/predictions.json + probabilities_file: output/reports/train/3bbc457d3368df994b8adc841370677c/probabilities.json + score_dict_file: output/reports/train/3bbc457d3368df994b8adc841370677c/score_dict.json + test_labels_file: output/reports/train/3bbc457d3368df994b8adc841370677c/test_labels.json + train_labels_file: output/reports/train/3bbc457d3368df994b8adc841370677c/train_labels.json + model_dir: models + name: 3bbc457d3368df994b8adc841370677c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: + - linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e27ce573b383c335e3d87918133b0936 + trainer: + kwargs: {} +name: 3bbc457d3368df994b8adc841370677c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/params.yaml b/examples/security/classification/output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/params.yaml new file mode 100644 index 00000000..9868f9e2 --- /dev/null +++ b/examples/security/classification/output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/adv_predictions.json + adv_probabilities_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/baad3a54c719895e0061af9252ad46d1.pkl + params_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/params.yaml + predictions_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/predictions.json + probabilities_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/probabilities.json + score_dict_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/score_dict.json + test_labels_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/test_labels.json + train_labels_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/train_labels.json + model_dir: models + name: 3bc2c5a6b8f5078e80371570e0d32ebb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: df90dd1b96e8188a1c102e2e65ccac14 + trainer: + kwargs: {} +name: 3bc2c5a6b8f5078e80371570e0d32ebb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3bc2f581bb163fbad21055479cd7e345/params.yaml b/examples/security/classification/output/reports/train/3bc2f581bb163fbad21055479cd7e345/params.yaml new file mode 100644 index 00000000..676a09cb --- /dev/null +++ b/examples/security/classification/output/reports/train/3bc2f581bb163fbad21055479cd7e345/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/adv_predictions.json + adv_probabilities_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/31e504167c1f673368672ebdfcd0a389.pkl + params_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/params.yaml + predictions_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/predictions.json + probabilities_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/probabilities.json + score_dict_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/score_dict.json + test_labels_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/test_labels.json + train_labels_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/train_labels.json + model_dir: models + name: 3bc2f581bb163fbad21055479cd7e345 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9a51bd02c684c6f9163d1406253ff7a2 + trainer: + kwargs: {} +name: 3bc2f581bb163fbad21055479cd7e345 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3be959c29466ab668a29991179516fa6/params.yaml b/examples/security/classification/output/reports/train/3be959c29466ab668a29991179516fa6/params.yaml new file mode 100644 index 00000000..476fb77a --- /dev/null +++ b/examples/security/classification/output/reports/train/3be959c29466ab668a29991179516fa6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3be959c29466ab668a29991179516fa6/adv_predictions.json + adv_probabilities_file: output/reports/train/3be959c29466ab668a29991179516fa6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/70baa7fa5b76590041a4b5a35505a8cf.pkl + params_file: output/reports/train/3be959c29466ab668a29991179516fa6/params.yaml + predictions_file: output/reports/train/3be959c29466ab668a29991179516fa6/predictions.json + probabilities_file: output/reports/train/3be959c29466ab668a29991179516fa6/probabilities.json + score_dict_file: output/reports/train/3be959c29466ab668a29991179516fa6/score_dict.json + test_labels_file: output/reports/train/3be959c29466ab668a29991179516fa6/test_labels.json + train_labels_file: output/reports/train/3be959c29466ab668a29991179516fa6/train_labels.json + model_dir: models + name: 3be959c29466ab668a29991179516fa6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0d0e10a5c338be73f10571b69e4d07bf + trainer: + kwargs: {} +name: 3be959c29466ab668a29991179516fa6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/params.yaml b/examples/security/classification/output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/params.yaml new file mode 100644 index 00000000..f4522964 --- /dev/null +++ b/examples/security/classification/output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/adv_predictions.json + adv_probabilities_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/5ef6bec184bc7c3d2a2da382d09df218.pkl + params_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/params.yaml + predictions_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/predictions.json + probabilities_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/probabilities.json + score_dict_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/score_dict.json + test_labels_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/test_labels.json + train_labels_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/train_labels.json + model_dir: models + name: 3c4a98cda5429230b0a7c9f54ec2a184 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 150e114477c9b8dd8f3bfd1ae4526420 + trainer: + kwargs: {} +name: 3c4a98cda5429230b0a7c9f54ec2a184 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/params.yaml b/examples/security/classification/output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/params.yaml new file mode 100644 index 00000000..4b49af79 --- /dev/null +++ b/examples/security/classification/output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/adv_predictions.json + adv_probabilities_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/03fb2824516e4bb9ab027882b08972a2.pkl + params_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/params.yaml + predictions_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/predictions.json + probabilities_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/probabilities.json + score_dict_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/score_dict.json + test_labels_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/test_labels.json + train_labels_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/train_labels.json + model_dir: models + name: 3cc1f549a964f618fcc84cf381cdafbc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2501dd3abb6312086ef451689d064d52 + trainer: + kwargs: {} +name: 3cc1f549a964f618fcc84cf381cdafbc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3cc284269a48628f343f463d712e0b45/params.yaml b/examples/security/classification/output/reports/train/3cc284269a48628f343f463d712e0b45/params.yaml new file mode 100644 index 00000000..2409b233 --- /dev/null +++ b/examples/security/classification/output/reports/train/3cc284269a48628f343f463d712e0b45/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3cc284269a48628f343f463d712e0b45/adv_predictions.json + adv_probabilities_file: output/reports/train/3cc284269a48628f343f463d712e0b45/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/a334e53fc4c95ce44160e2b0550a9f4b.pkl + params_file: output/reports/train/3cc284269a48628f343f463d712e0b45/params.yaml + predictions_file: output/reports/train/3cc284269a48628f343f463d712e0b45/predictions.json + probabilities_file: output/reports/train/3cc284269a48628f343f463d712e0b45/probabilities.json + score_dict_file: output/reports/train/3cc284269a48628f343f463d712e0b45/score_dict.json + test_labels_file: output/reports/train/3cc284269a48628f343f463d712e0b45/test_labels.json + train_labels_file: output/reports/train/3cc284269a48628f343f463d712e0b45/train_labels.json + model_dir: models + name: 3cc284269a48628f343f463d712e0b45 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4a4a88c6d5a796252ce63e00443dbdea + trainer: + kwargs: {} +name: 3cc284269a48628f343f463d712e0b45 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3ceedf9a8259cd4f916c248073618c74/params.yaml b/examples/security/classification/output/reports/train/3ceedf9a8259cd4f916c248073618c74/params.yaml new file mode 100644 index 00000000..74a101be --- /dev/null +++ b/examples/security/classification/output/reports/train/3ceedf9a8259cd4f916c248073618c74/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/adv_predictions.json + adv_probabilities_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/d268811b068b0a8a493d91c15d69f027.pkl + params_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/params.yaml + predictions_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/predictions.json + probabilities_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/probabilities.json + score_dict_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/score_dict.json + test_labels_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/test_labels.json + train_labels_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/train_labels.json + model_dir: models + name: 3ceedf9a8259cd4f916c248073618c74 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c7d7adb4d30e546ec5547f4ea88872a3 + trainer: + kwargs: {} +name: 3ceedf9a8259cd4f916c248073618c74 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3d3fead877f029a67100a0c159461ede/params.yaml b/examples/security/classification/output/reports/train/3d3fead877f029a67100a0c159461ede/params.yaml new file mode 100644 index 00000000..9603dbb3 --- /dev/null +++ b/examples/security/classification/output/reports/train/3d3fead877f029a67100a0c159461ede/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3d3fead877f029a67100a0c159461ede/adv_predictions.json + adv_probabilities_file: output/reports/train/3d3fead877f029a67100a0c159461ede/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/03d3eb9ed0f6e9fe94348ce968ce80b0.pkl + params_file: output/reports/train/3d3fead877f029a67100a0c159461ede/params.yaml + predictions_file: output/reports/train/3d3fead877f029a67100a0c159461ede/predictions.json + probabilities_file: output/reports/train/3d3fead877f029a67100a0c159461ede/probabilities.json + score_dict_file: output/reports/train/3d3fead877f029a67100a0c159461ede/score_dict.json + test_labels_file: output/reports/train/3d3fead877f029a67100a0c159461ede/test_labels.json + train_labels_file: output/reports/train/3d3fead877f029a67100a0c159461ede/train_labels.json + model_dir: models + name: 3d3fead877f029a67100a0c159461ede + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7fcc3613f38fd1abdf64cde82e6763eb + trainer: + kwargs: {} +name: 3d3fead877f029a67100a0c159461ede +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/params.yaml b/examples/security/classification/output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/params.yaml new file mode 100644 index 00000000..c8b3ea05 --- /dev/null +++ b/examples/security/classification/output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/adv_predictions.json + adv_probabilities_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/ac766c1c3c24180ad584fa1cd77f3d41.pkl + params_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/params.yaml + predictions_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/predictions.json + probabilities_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/probabilities.json + score_dict_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/score_dict.json + test_labels_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/test_labels.json + train_labels_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/train_labels.json + model_dir: models + name: 3da8efbcbcf79f16f84d53631a8a0682 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ed287e0c47b0dfc17b4c8bf69ddbd0c5 + trainer: + kwargs: {} +name: 3da8efbcbcf79f16f84d53631a8a0682 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3dd352d032922a57816d4f639b582aa8/params.yaml b/examples/security/classification/output/reports/train/3dd352d032922a57816d4f639b582aa8/params.yaml new file mode 100644 index 00000000..7e815298 --- /dev/null +++ b/examples/security/classification/output/reports/train/3dd352d032922a57816d4f639b582aa8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/adv_predictions.json + adv_probabilities_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/415f63c6e42d7bda27e5162a4685a667.pkl + params_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/params.yaml + predictions_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/predictions.json + probabilities_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/probabilities.json + score_dict_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/score_dict.json + test_labels_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/test_labels.json + train_labels_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/train_labels.json + model_dir: models + name: 3dd352d032922a57816d4f639b582aa8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 196fc62543dad198c810b791e9980641 + trainer: + kwargs: {} +name: 3dd352d032922a57816d4f639b582aa8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3e156ec6d9cf161bf16d231966609c0e/params.yaml b/examples/security/classification/output/reports/train/3e156ec6d9cf161bf16d231966609c0e/params.yaml new file mode 100644 index 00000000..ca281de2 --- /dev/null +++ b/examples/security/classification/output/reports/train/3e156ec6d9cf161bf16d231966609c0e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/adv_predictions.json + adv_probabilities_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/dd876dff7890b7c94fc97431cb06f515.pkl + params_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/params.yaml + predictions_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/predictions.json + probabilities_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/probabilities.json + score_dict_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/score_dict.json + test_labels_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/test_labels.json + train_labels_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/train_labels.json + model_dir: models + name: 3e156ec6d9cf161bf16d231966609c0e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8432e8d39a726d14dd6d2e3f88f0fb75 + trainer: + kwargs: {} +name: 3e156ec6d9cf161bf16d231966609c0e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/params.yaml b/examples/security/classification/output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/params.yaml new file mode 100644 index 00000000..a2366ba3 --- /dev/null +++ b/examples/security/classification/output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/adv_predictions.json + adv_probabilities_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/a0d66cc783a953380be2ef8c17faa18f.pkl + params_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/params.yaml + predictions_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/predictions.json + probabilities_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/probabilities.json + score_dict_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/score_dict.json + test_labels_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/test_labels.json + train_labels_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/train_labels.json + model_dir: models + name: 3e2af0c3fea57bb2069e436ef8a5b7f9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4cfea40759ec87d53be5e91683cd12c4 + trainer: + kwargs: {} +name: 3e2af0c3fea57bb2069e436ef8a5b7f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3e3b77428f9ba3246184c368ca84703a/params.yaml b/examples/security/classification/output/reports/train/3e3b77428f9ba3246184c368ca84703a/params.yaml new file mode 100644 index 00000000..f448044d --- /dev/null +++ b/examples/security/classification/output/reports/train/3e3b77428f9ba3246184c368ca84703a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/adv_predictions.json + adv_probabilities_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/50bfae8bcfcbd9383d0d35165214c06f.pkl + params_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/params.yaml + predictions_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/predictions.json + probabilities_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/probabilities.json + score_dict_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/score_dict.json + test_labels_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/test_labels.json + train_labels_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/train_labels.json + model_dir: models + name: 3e3b77428f9ba3246184c368ca84703a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 412ceb96c9463264950ff219084f28da + trainer: + kwargs: {} +name: 3e3b77428f9ba3246184c368ca84703a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/params.yaml b/examples/security/classification/output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/params.yaml new file mode 100644 index 00000000..778c5433 --- /dev/null +++ b/examples/security/classification/output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/adv_predictions.json + adv_probabilities_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/f04487af3de4c1514059cad89b689967.pkl + params_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/params.yaml + predictions_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/predictions.json + probabilities_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/probabilities.json + score_dict_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/score_dict.json + test_labels_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/test_labels.json + train_labels_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/train_labels.json + model_dir: models + name: 3e4b01d604e413a6a27d1f4c8b3efc6b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5357fcb7a4ff8ffe09090402302c0d87 + trainer: + kwargs: {} +name: 3e4b01d604e413a6a27d1f4c8b3efc6b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/params.yaml b/examples/security/classification/output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/params.yaml new file mode 100644 index 00000000..d255a95b --- /dev/null +++ b/examples/security/classification/output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/adv_predictions.json + adv_probabilities_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/4555c7e68cef92d75a13d6d07a714015.pkl + params_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/params.yaml + predictions_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/predictions.json + probabilities_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/probabilities.json + score_dict_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/score_dict.json + test_labels_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/test_labels.json + train_labels_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/train_labels.json + model_dir: models + name: 3ea061edcdc266e1857ee59a1b5a1136 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f6173122ee2eba0e135f9365ad5b4720 + trainer: + kwargs: {} +name: 3ea061edcdc266e1857ee59a1b5a1136 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3eb24df17a345052a55bde11ae217078/params.yaml b/examples/security/classification/output/reports/train/3eb24df17a345052a55bde11ae217078/params.yaml new file mode 100644 index 00000000..c372bde8 --- /dev/null +++ b/examples/security/classification/output/reports/train/3eb24df17a345052a55bde11ae217078/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3eb24df17a345052a55bde11ae217078/adv_predictions.json + adv_probabilities_file: output/reports/train/3eb24df17a345052a55bde11ae217078/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/8010e355c988185b13368fa92a703e83.pkl + params_file: output/reports/train/3eb24df17a345052a55bde11ae217078/params.yaml + predictions_file: output/reports/train/3eb24df17a345052a55bde11ae217078/predictions.json + probabilities_file: output/reports/train/3eb24df17a345052a55bde11ae217078/probabilities.json + score_dict_file: output/reports/train/3eb24df17a345052a55bde11ae217078/score_dict.json + test_labels_file: output/reports/train/3eb24df17a345052a55bde11ae217078/test_labels.json + train_labels_file: output/reports/train/3eb24df17a345052a55bde11ae217078/train_labels.json + model_dir: models + name: 3eb24df17a345052a55bde11ae217078 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e12d4d46de451706700670fd21622471 + trainer: + kwargs: {} +name: 3eb24df17a345052a55bde11ae217078 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/params.yaml b/examples/security/classification/output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/params.yaml new file mode 100644 index 00000000..299b330e --- /dev/null +++ b/examples/security/classification/output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/adv_predictions.json + adv_probabilities_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/0a358fe37bf78b37e8a037431fc03796.pkl + params_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/params.yaml + predictions_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/predictions.json + probabilities_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/probabilities.json + score_dict_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/score_dict.json + test_labels_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/test_labels.json + train_labels_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/train_labels.json + model_dir: models + name: 3ecca09e4bd0bfb13014a44fd81caf53 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 396372720711e10d71e9e30009eea90f + trainer: + kwargs: {} +name: 3ecca09e4bd0bfb13014a44fd81caf53 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3ecff33ec1285ab4414c5211101281f9/params.yaml b/examples/security/classification/output/reports/train/3ecff33ec1285ab4414c5211101281f9/params.yaml new file mode 100644 index 00000000..2802d247 --- /dev/null +++ b/examples/security/classification/output/reports/train/3ecff33ec1285ab4414c5211101281f9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/adv_predictions.json + adv_probabilities_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/d1f1644991d7653f5f8e0d5c8509634a.pkl + params_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/params.yaml + predictions_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/predictions.json + probabilities_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/probabilities.json + score_dict_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/score_dict.json + test_labels_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/test_labels.json + train_labels_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/train_labels.json + model_dir: models + name: 3ecff33ec1285ab4414c5211101281f9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3b545b941f219278e9e23603610c2b1a + trainer: + kwargs: {} +name: 3ecff33ec1285ab4414c5211101281f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3eddf0e349f03334974b18f5e609afa6/params.yaml b/examples/security/classification/output/reports/train/3eddf0e349f03334974b18f5e609afa6/params.yaml new file mode 100644 index 00000000..fac2dfc0 --- /dev/null +++ b/examples/security/classification/output/reports/train/3eddf0e349f03334974b18f5e609afa6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/adv_predictions.json + adv_probabilities_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/2d0f8ae31410468f4925ec7eba463f77.pkl + params_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/params.yaml + predictions_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/predictions.json + probabilities_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/probabilities.json + score_dict_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/score_dict.json + test_labels_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/test_labels.json + train_labels_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/train_labels.json + model_dir: models + name: 3eddf0e349f03334974b18f5e609afa6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fcc06390ce9d509f1b5cd1d93851c84d + trainer: + kwargs: {} +name: 3eddf0e349f03334974b18f5e609afa6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/params.yaml b/examples/security/classification/output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/params.yaml new file mode 100644 index 00000000..3a33abab --- /dev/null +++ b/examples/security/classification/output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/adv_predictions.json + adv_probabilities_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/eabcb156e5a779c46fbca7898d2db5f4.pkl + params_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/params.yaml + predictions_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/predictions.json + probabilities_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/probabilities.json + score_dict_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/score_dict.json + test_labels_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/test_labels.json + train_labels_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/train_labels.json + model_dir: models + name: 3ef48cfd3aca6f2ec70da1afeea96998 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f9df8704aaef136cc187df9d8909be2f + trainer: + kwargs: {} +name: 3ef48cfd3aca6f2ec70da1afeea96998 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/params.yaml b/examples/security/classification/output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/params.yaml new file mode 100644 index 00000000..c83e56c4 --- /dev/null +++ b/examples/security/classification/output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/adv_predictions.json + adv_probabilities_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/26f813e552d18eee58129ec8373ce0a3.pkl + params_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/params.yaml + predictions_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/predictions.json + probabilities_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/probabilities.json + score_dict_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/score_dict.json + test_labels_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/test_labels.json + train_labels_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/train_labels.json + model_dir: models + name: 3f1b1dd81e14b86fcef44aa611846e89 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6156791587e731402cc6635c5538947c + trainer: + kwargs: {} +name: 3f1b1dd81e14b86fcef44aa611846e89 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/params.yaml b/examples/security/classification/output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/params.yaml new file mode 100644 index 00000000..86539af5 --- /dev/null +++ b/examples/security/classification/output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/adv_predictions.json + adv_probabilities_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/25e37d84b96855a3e5065cba79a14e5d.pkl + params_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/params.yaml + predictions_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/predictions.json + probabilities_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/probabilities.json + score_dict_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/score_dict.json + test_labels_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/test_labels.json + train_labels_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/train_labels.json + model_dir: models + name: 3f3d8335016ac87d9454fe25ed28b3e8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e9e1138302f58e0f257552487515107c + trainer: + kwargs: {} +name: 3f3d8335016ac87d9454fe25ed28b3e8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3f465aa09fb7b7450034fce3c46941e2/params.yaml b/examples/security/classification/output/reports/train/3f465aa09fb7b7450034fce3c46941e2/params.yaml new file mode 100644 index 00000000..224eb2f6 --- /dev/null +++ b/examples/security/classification/output/reports/train/3f465aa09fb7b7450034fce3c46941e2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/adv_predictions.json + adv_probabilities_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/e05c1f754d86f6c251d7bbae6f12269e.pkl + params_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/params.yaml + predictions_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/predictions.json + probabilities_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/probabilities.json + score_dict_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/score_dict.json + test_labels_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/test_labels.json + train_labels_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/train_labels.json + model_dir: models + name: 3f465aa09fb7b7450034fce3c46941e2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 17368a86eda2d1933a23668e5a423e04 + trainer: + kwargs: {} +name: 3f465aa09fb7b7450034fce3c46941e2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/params.yaml b/examples/security/classification/output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/params.yaml new file mode 100644 index 00000000..5f8e6827 --- /dev/null +++ b/examples/security/classification/output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/adv_predictions.json + adv_probabilities_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/9cf4726086e0159eb8eeeb129651d4b9.pkl + params_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/params.yaml + predictions_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/predictions.json + probabilities_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/probabilities.json + score_dict_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/score_dict.json + test_labels_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/test_labels.json + train_labels_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/train_labels.json + model_dir: models + name: 3f652c9408c3f6c726d17c1eb54b2dc5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 49607d4de06f05ea0f03759f93e24538 + trainer: + kwargs: {} +name: 3f652c9408c3f6c726d17c1eb54b2dc5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/params.yaml b/examples/security/classification/output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/params.yaml new file mode 100644 index 00000000..9096f314 --- /dev/null +++ b/examples/security/classification/output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/adv_predictions.json + adv_probabilities_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/a1ba0690b7e301d3b837137d83802e20.pkl + params_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/params.yaml + predictions_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/predictions.json + probabilities_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/probabilities.json + score_dict_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/score_dict.json + test_labels_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/test_labels.json + train_labels_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/train_labels.json + model_dir: models + name: 3f9ce81fd04e84d3f41bc2508a21d05a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8beec78980903429edee17bf860659e4 + trainer: + kwargs: {} +name: 3f9ce81fd04e84d3f41bc2508a21d05a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3f9e13c157c200fdce8eeec0f513105d/params.yaml b/examples/security/classification/output/reports/train/3f9e13c157c200fdce8eeec0f513105d/params.yaml new file mode 100644 index 00000000..de51ec0d --- /dev/null +++ b/examples/security/classification/output/reports/train/3f9e13c157c200fdce8eeec0f513105d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/adv_predictions.json + adv_probabilities_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/9c54d62914f9486151bbcf17651bc2c2.pkl + params_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/params.yaml + predictions_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/predictions.json + probabilities_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/probabilities.json + score_dict_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/score_dict.json + test_labels_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/test_labels.json + train_labels_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/train_labels.json + model_dir: models + name: 3f9e13c157c200fdce8eeec0f513105d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 26b6f45cc053ad4ee15c418a19129221 + trainer: + kwargs: {} +name: 3f9e13c157c200fdce8eeec0f513105d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3fafa88075220a240d35b37300145252/params.yaml b/examples/security/classification/output/reports/train/3fafa88075220a240d35b37300145252/params.yaml new file mode 100644 index 00000000..dd396dea --- /dev/null +++ b/examples/security/classification/output/reports/train/3fafa88075220a240d35b37300145252/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3fafa88075220a240d35b37300145252/adv_predictions.json + adv_probabilities_file: output/reports/train/3fafa88075220a240d35b37300145252/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/66ccc3b5d3f4c7053435a39bff7169d1.pkl + params_file: output/reports/train/3fafa88075220a240d35b37300145252/params.yaml + predictions_file: output/reports/train/3fafa88075220a240d35b37300145252/predictions.json + probabilities_file: output/reports/train/3fafa88075220a240d35b37300145252/probabilities.json + score_dict_file: output/reports/train/3fafa88075220a240d35b37300145252/score_dict.json + test_labels_file: output/reports/train/3fafa88075220a240d35b37300145252/test_labels.json + train_labels_file: output/reports/train/3fafa88075220a240d35b37300145252/train_labels.json + model_dir: models + name: 3fafa88075220a240d35b37300145252 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: df6a8f789dbcb8e62dd976b755e179b8 + trainer: + kwargs: {} +name: 3fafa88075220a240d35b37300145252 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/params.yaml b/examples/security/classification/output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/params.yaml new file mode 100644 index 00000000..6e57b249 --- /dev/null +++ b/examples/security/classification/output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/adv_predictions.json + adv_probabilities_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/d084527dbdeba1d0be716245ebe4c8df.pkl + params_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/params.yaml + predictions_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/predictions.json + probabilities_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/probabilities.json + score_dict_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/score_dict.json + test_labels_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/test_labels.json + train_labels_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/train_labels.json + model_dir: models + name: 3fb3bcda0a9ee278fa2bebaff94857d4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6238300f97dde8c2027321ff25052bbe + trainer: + kwargs: {} +name: 3fb3bcda0a9ee278fa2bebaff94857d4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/3fcad77159a037762651201029459a10/params.yaml b/examples/security/classification/output/reports/train/3fcad77159a037762651201029459a10/params.yaml new file mode 100644 index 00000000..aad60265 --- /dev/null +++ b/examples/security/classification/output/reports/train/3fcad77159a037762651201029459a10/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3fcad77159a037762651201029459a10/adv_predictions.json + adv_probabilities_file: output/reports/train/3fcad77159a037762651201029459a10/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/9ba0e261ca67239f2eb030eed036db8f.pkl + params_file: output/reports/train/3fcad77159a037762651201029459a10/params.yaml + predictions_file: output/reports/train/3fcad77159a037762651201029459a10/predictions.json + probabilities_file: output/reports/train/3fcad77159a037762651201029459a10/probabilities.json + score_dict_file: output/reports/train/3fcad77159a037762651201029459a10/score_dict.json + test_labels_file: output/reports/train/3fcad77159a037762651201029459a10/test_labels.json + train_labels_file: output/reports/train/3fcad77159a037762651201029459a10/train_labels.json + model_dir: models + name: 3fcad77159a037762651201029459a10 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ac8af7d0b42136fd3025e25bf035f1ca + trainer: + kwargs: {} +name: 3fcad77159a037762651201029459a10 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4000882a9b95df6f032f2cc816bd52fc/params.yaml b/examples/security/classification/output/reports/train/4000882a9b95df6f032f2cc816bd52fc/params.yaml new file mode 100644 index 00000000..c4fe1da7 --- /dev/null +++ b/examples/security/classification/output/reports/train/4000882a9b95df6f032f2cc816bd52fc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/adv_predictions.json + adv_probabilities_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e66b8296cc0f1f0cfd7a2760172e2ade.pkl + params_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/params.yaml + predictions_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/predictions.json + probabilities_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/probabilities.json + score_dict_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/score_dict.json + test_labels_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/test_labels.json + train_labels_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/train_labels.json + model_dir: models + name: 4000882a9b95df6f032f2cc816bd52fc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7de0532ed39cbe2b69a6e9cdf4f909ca + trainer: + kwargs: {} +name: 4000882a9b95df6f032f2cc816bd52fc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4018bf629f1204680c23d1a912d8497e/params.yaml b/examples/security/classification/output/reports/train/4018bf629f1204680c23d1a912d8497e/params.yaml new file mode 100644 index 00000000..69f2f526 --- /dev/null +++ b/examples/security/classification/output/reports/train/4018bf629f1204680c23d1a912d8497e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/adv_predictions.json + adv_probabilities_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/7acd15690cccdaeb361add0f209f4f16.pkl + params_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/params.yaml + predictions_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/predictions.json + probabilities_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/probabilities.json + score_dict_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/score_dict.json + test_labels_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/test_labels.json + train_labels_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/train_labels.json + model_dir: models + name: 4018bf629f1204680c23d1a912d8497e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 72421d47118dcfc94c0ce6fd890d9a63 + trainer: + kwargs: {} +name: 4018bf629f1204680c23d1a912d8497e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/403576dbc35f575f739cb11a571066c7/params.yaml b/examples/security/classification/output/reports/train/403576dbc35f575f739cb11a571066c7/params.yaml new file mode 100644 index 00000000..32a1cffd --- /dev/null +++ b/examples/security/classification/output/reports/train/403576dbc35f575f739cb11a571066c7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/403576dbc35f575f739cb11a571066c7/adv_predictions.json + adv_probabilities_file: output/reports/train/403576dbc35f575f739cb11a571066c7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/cf19be4be54b659d38575392ff7f5330.pkl + params_file: output/reports/train/403576dbc35f575f739cb11a571066c7/params.yaml + predictions_file: output/reports/train/403576dbc35f575f739cb11a571066c7/predictions.json + probabilities_file: output/reports/train/403576dbc35f575f739cb11a571066c7/probabilities.json + score_dict_file: output/reports/train/403576dbc35f575f739cb11a571066c7/score_dict.json + test_labels_file: output/reports/train/403576dbc35f575f739cb11a571066c7/test_labels.json + train_labels_file: output/reports/train/403576dbc35f575f739cb11a571066c7/train_labels.json + model_dir: models + name: 403576dbc35f575f739cb11a571066c7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5e7f7e836cceca63868378b7a6ab0989 + trainer: + kwargs: {} +name: 403576dbc35f575f739cb11a571066c7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4046b0c93b02898761533cdd1a198086/params.yaml b/examples/security/classification/output/reports/train/4046b0c93b02898761533cdd1a198086/params.yaml new file mode 100644 index 00000000..c53096c4 --- /dev/null +++ b/examples/security/classification/output/reports/train/4046b0c93b02898761533cdd1a198086/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4046b0c93b02898761533cdd1a198086/adv_predictions.json + adv_probabilities_file: output/reports/train/4046b0c93b02898761533cdd1a198086/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/d6194862ff4cd0cda5755dc69f0a9095.pkl + params_file: output/reports/train/4046b0c93b02898761533cdd1a198086/params.yaml + predictions_file: output/reports/train/4046b0c93b02898761533cdd1a198086/predictions.json + probabilities_file: output/reports/train/4046b0c93b02898761533cdd1a198086/probabilities.json + score_dict_file: output/reports/train/4046b0c93b02898761533cdd1a198086/score_dict.json + test_labels_file: output/reports/train/4046b0c93b02898761533cdd1a198086/test_labels.json + train_labels_file: output/reports/train/4046b0c93b02898761533cdd1a198086/train_labels.json + model_dir: models + name: 4046b0c93b02898761533cdd1a198086 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ad73d769bb663ce706a1543743ab6de8 + trainer: + kwargs: {} +name: 4046b0c93b02898761533cdd1a198086 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/params.yaml b/examples/security/classification/output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/params.yaml new file mode 100644 index 00000000..ec9d2166 --- /dev/null +++ b/examples/security/classification/output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/adv_predictions.json + adv_probabilities_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/295009ae54afa9237f87cffe94041a6a.pkl + params_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/params.yaml + predictions_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/predictions.json + probabilities_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/probabilities.json + score_dict_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/score_dict.json + test_labels_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/test_labels.json + train_labels_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/train_labels.json + model_dir: models + name: 40556ac210d2c1a7c2d9e5f97d4bedf1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e5cea785d18e9b9d6b20415d5340ddf2 + trainer: + kwargs: {} +name: 40556ac210d2c1a7c2d9e5f97d4bedf1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/405a2425f5df778b62a938602eeb1828/params.yaml b/examples/security/classification/output/reports/train/405a2425f5df778b62a938602eeb1828/params.yaml new file mode 100644 index 00000000..e48b0996 --- /dev/null +++ b/examples/security/classification/output/reports/train/405a2425f5df778b62a938602eeb1828/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/405a2425f5df778b62a938602eeb1828/adv_predictions.json + adv_probabilities_file: output/reports/train/405a2425f5df778b62a938602eeb1828/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/ea8de26535cf8f5a6ad1dd4e50298a0e.pkl + params_file: output/reports/train/405a2425f5df778b62a938602eeb1828/params.yaml + predictions_file: output/reports/train/405a2425f5df778b62a938602eeb1828/predictions.json + probabilities_file: output/reports/train/405a2425f5df778b62a938602eeb1828/probabilities.json + score_dict_file: output/reports/train/405a2425f5df778b62a938602eeb1828/score_dict.json + test_labels_file: output/reports/train/405a2425f5df778b62a938602eeb1828/test_labels.json + train_labels_file: output/reports/train/405a2425f5df778b62a938602eeb1828/train_labels.json + model_dir: models + name: 405a2425f5df778b62a938602eeb1828 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bd2ba3cfc35d201ad65ef81a8d59b1e3 + trainer: + kwargs: {} +name: 405a2425f5df778b62a938602eeb1828 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4070982537f3337f45d894ab43747302/params.yaml b/examples/security/classification/output/reports/train/4070982537f3337f45d894ab43747302/params.yaml new file mode 100644 index 00000000..981046b0 --- /dev/null +++ b/examples/security/classification/output/reports/train/4070982537f3337f45d894ab43747302/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4070982537f3337f45d894ab43747302/adv_predictions.json + adv_probabilities_file: output/reports/train/4070982537f3337f45d894ab43747302/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/1639140e0558ed1564642a6c424aee96.pkl + params_file: output/reports/train/4070982537f3337f45d894ab43747302/params.yaml + predictions_file: output/reports/train/4070982537f3337f45d894ab43747302/predictions.json + probabilities_file: output/reports/train/4070982537f3337f45d894ab43747302/probabilities.json + score_dict_file: output/reports/train/4070982537f3337f45d894ab43747302/score_dict.json + test_labels_file: output/reports/train/4070982537f3337f45d894ab43747302/test_labels.json + train_labels_file: output/reports/train/4070982537f3337f45d894ab43747302/train_labels.json + model_dir: models + name: 4070982537f3337f45d894ab43747302 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 516eeb5d8746a41f64cde47ec827870c + trainer: + kwargs: {} +name: 4070982537f3337f45d894ab43747302 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/408485404f987fdec98cbc4d440efae0/params.yaml b/examples/security/classification/output/reports/train/408485404f987fdec98cbc4d440efae0/params.yaml new file mode 100644 index 00000000..a9ca35a3 --- /dev/null +++ b/examples/security/classification/output/reports/train/408485404f987fdec98cbc4d440efae0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/408485404f987fdec98cbc4d440efae0/adv_predictions.json + adv_probabilities_file: output/reports/train/408485404f987fdec98cbc4d440efae0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/72504deb5d00c64d2fc95d2849f70080.pkl + params_file: output/reports/train/408485404f987fdec98cbc4d440efae0/params.yaml + predictions_file: output/reports/train/408485404f987fdec98cbc4d440efae0/predictions.json + probabilities_file: output/reports/train/408485404f987fdec98cbc4d440efae0/probabilities.json + score_dict_file: output/reports/train/408485404f987fdec98cbc4d440efae0/score_dict.json + test_labels_file: output/reports/train/408485404f987fdec98cbc4d440efae0/test_labels.json + train_labels_file: output/reports/train/408485404f987fdec98cbc4d440efae0/train_labels.json + model_dir: models + name: 408485404f987fdec98cbc4d440efae0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 435fbb819cf8ae63ee814b705b9392d3 + trainer: + kwargs: {} +name: 408485404f987fdec98cbc4d440efae0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/408c7f605ce24ebf9a1f82c955316632/params.yaml b/examples/security/classification/output/reports/train/408c7f605ce24ebf9a1f82c955316632/params.yaml new file mode 100644 index 00000000..8c5dfa73 --- /dev/null +++ b/examples/security/classification/output/reports/train/408c7f605ce24ebf9a1f82c955316632/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/adv_predictions.json + adv_probabilities_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/9f2527914e86b057e2d8bf2a54b03baf.pkl + params_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/params.yaml + predictions_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/predictions.json + probabilities_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/probabilities.json + score_dict_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/score_dict.json + test_labels_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/test_labels.json + train_labels_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/train_labels.json + model_dir: models + name: 408c7f605ce24ebf9a1f82c955316632 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7094b11d04f0b7f1a4ebdf4bf25e883e + trainer: + kwargs: {} +name: 408c7f605ce24ebf9a1f82c955316632 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4099fb6e786d86420030035f1ae61f12/params.yaml b/examples/security/classification/output/reports/train/4099fb6e786d86420030035f1ae61f12/params.yaml new file mode 100644 index 00000000..85ee19ae --- /dev/null +++ b/examples/security/classification/output/reports/train/4099fb6e786d86420030035f1ae61f12/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/adv_predictions.json + adv_probabilities_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/166e3b5e1a67c66e88a5deb48bc7de09.pkl + params_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/params.yaml + predictions_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/predictions.json + probabilities_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/probabilities.json + score_dict_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/score_dict.json + test_labels_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/test_labels.json + train_labels_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/train_labels.json + model_dir: models + name: 4099fb6e786d86420030035f1ae61f12 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 450b13aa903401a6f3cc8b79cdce01c1 + trainer: + kwargs: {} +name: 4099fb6e786d86420030035f1ae61f12 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/params.yaml b/examples/security/classification/output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/params.yaml new file mode 100644 index 00000000..e781fc6d --- /dev/null +++ b/examples/security/classification/output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/adv_predictions.json + adv_probabilities_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/f6cd5301abd18e1256e0ea6a8e425ac3.pkl + params_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/params.yaml + predictions_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/predictions.json + probabilities_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/probabilities.json + score_dict_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/score_dict.json + test_labels_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/test_labels.json + train_labels_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/train_labels.json + model_dir: models + name: 40a838a1a63c38151dfbf8e5f71c6ccb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3c691fccb245073eb5010b03f71ec067 + trainer: + kwargs: {} +name: 40a838a1a63c38151dfbf8e5f71c6ccb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/params.yaml b/examples/security/classification/output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/params.yaml new file mode 100644 index 00000000..147a3290 --- /dev/null +++ b/examples/security/classification/output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/adv_predictions.json + adv_probabilities_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/35f4778e4ea0bf93490ba9b3b699ede2.pkl + params_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/params.yaml + predictions_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/predictions.json + probabilities_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/probabilities.json + score_dict_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/score_dict.json + test_labels_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/test_labels.json + train_labels_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/train_labels.json + model_dir: models + name: 40ab3d23ed2a73180f718ec40d1d3748 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bab47fcc5abe8b9ee3c0922cd936bbfa + trainer: + kwargs: {} +name: 40ab3d23ed2a73180f718ec40d1d3748 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/params.yaml b/examples/security/classification/output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/params.yaml new file mode 100644 index 00000000..e2128d5d --- /dev/null +++ b/examples/security/classification/output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/adv_predictions.json + adv_probabilities_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/12d3bd3f647ab5c4a65951441a8fb843.pkl + params_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/params.yaml + predictions_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/predictions.json + probabilities_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/probabilities.json + score_dict_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/score_dict.json + test_labels_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/test_labels.json + train_labels_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/train_labels.json + model_dir: models + name: 40b0e13eb5b9ec64a869d86f13643b5f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 990f26f6eb24d056a36e38ca335eb44f + trainer: + kwargs: {} +name: 40b0e13eb5b9ec64a869d86f13643b5f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/params.yaml b/examples/security/classification/output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/params.yaml new file mode 100644 index 00000000..41e2b7a8 --- /dev/null +++ b/examples/security/classification/output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/adv_predictions.json + adv_probabilities_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/49986782145ea06d0d64a773203447b9.pkl + params_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/params.yaml + predictions_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/predictions.json + probabilities_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/probabilities.json + score_dict_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/score_dict.json + test_labels_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/test_labels.json + train_labels_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/train_labels.json + model_dir: models + name: 40bcd2b44f372726d82df8e999b0ca9e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d7e549771940cba2dd7e7f65137552a4 + trainer: + kwargs: {} +name: 40bcd2b44f372726d82df8e999b0ca9e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/40cf75c347eb1f60315a0d9571697aae/params.yaml b/examples/security/classification/output/reports/train/40cf75c347eb1f60315a0d9571697aae/params.yaml new file mode 100644 index 00000000..12255d09 --- /dev/null +++ b/examples/security/classification/output/reports/train/40cf75c347eb1f60315a0d9571697aae/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/adv_predictions.json + adv_probabilities_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/7de9df1f5ba0bbc0de9968d6819c0805.pkl + params_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/params.yaml + predictions_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/predictions.json + probabilities_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/probabilities.json + score_dict_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/score_dict.json + test_labels_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/test_labels.json + train_labels_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/train_labels.json + model_dir: models + name: 40cf75c347eb1f60315a0d9571697aae + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6d22f71c4faa4c466e84bc7bfebc531c + trainer: + kwargs: {} +name: 40cf75c347eb1f60315a0d9571697aae +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/params.yaml b/examples/security/classification/output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/params.yaml new file mode 100644 index 00000000..e754b2f0 --- /dev/null +++ b/examples/security/classification/output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/adv_predictions.json + adv_probabilities_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/3dc14ee9dbd3c61da7bb840fd430cba9.pkl + params_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/params.yaml + predictions_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/predictions.json + probabilities_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/probabilities.json + score_dict_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/score_dict.json + test_labels_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/test_labels.json + train_labels_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/train_labels.json + model_dir: models + name: 40e0c6f8b058393a2c6342ff7bfe8d5c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6f9f47bf5009f2d8ff5b47f9d13eabc7 + trainer: + kwargs: {} +name: 40e0c6f8b058393a2c6342ff7bfe8d5c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/40ff562d939537dab5edb864c80551cb/params.yaml b/examples/security/classification/output/reports/train/40ff562d939537dab5edb864c80551cb/params.yaml new file mode 100644 index 00000000..cd7d8816 --- /dev/null +++ b/examples/security/classification/output/reports/train/40ff562d939537dab5edb864c80551cb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/40ff562d939537dab5edb864c80551cb/adv_predictions.json + adv_probabilities_file: output/reports/train/40ff562d939537dab5edb864c80551cb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/b7a794cef75e95d66b71436499396783.pkl + params_file: output/reports/train/40ff562d939537dab5edb864c80551cb/params.yaml + predictions_file: output/reports/train/40ff562d939537dab5edb864c80551cb/predictions.json + probabilities_file: output/reports/train/40ff562d939537dab5edb864c80551cb/probabilities.json + score_dict_file: output/reports/train/40ff562d939537dab5edb864c80551cb/score_dict.json + test_labels_file: output/reports/train/40ff562d939537dab5edb864c80551cb/test_labels.json + train_labels_file: output/reports/train/40ff562d939537dab5edb864c80551cb/train_labels.json + model_dir: models + name: 40ff562d939537dab5edb864c80551cb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9944aef248b0566adf354a4f60301ecc + trainer: + kwargs: {} +name: 40ff562d939537dab5edb864c80551cb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/410627d17d0eaa117243d2e7596d5460/params.yaml b/examples/security/classification/output/reports/train/410627d17d0eaa117243d2e7596d5460/params.yaml new file mode 100644 index 00000000..a3b5dc54 --- /dev/null +++ b/examples/security/classification/output/reports/train/410627d17d0eaa117243d2e7596d5460/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/adv_predictions.json + adv_probabilities_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/41a1be4f9881335cd712c4b45a6e568f.pkl + params_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/params.yaml + predictions_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/predictions.json + probabilities_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/probabilities.json + score_dict_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/score_dict.json + test_labels_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/test_labels.json + train_labels_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/train_labels.json + model_dir: models + name: 410627d17d0eaa117243d2e7596d5460 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 280cae68f27bcd07d364064fe9ceed8e + trainer: + kwargs: {} +name: 410627d17d0eaa117243d2e7596d5460 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/41142fd5b8692344f65219e8ee9a8346/params.yaml b/examples/security/classification/output/reports/train/41142fd5b8692344f65219e8ee9a8346/params.yaml new file mode 100644 index 00000000..35d462e1 --- /dev/null +++ b/examples/security/classification/output/reports/train/41142fd5b8692344f65219e8ee9a8346/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/adv_predictions.json + adv_probabilities_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/96e9fe53055b0d4d2cc7c81c96e9a527.pkl + params_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/params.yaml + predictions_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/predictions.json + probabilities_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/probabilities.json + score_dict_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/score_dict.json + test_labels_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/test_labels.json + train_labels_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/train_labels.json + model_dir: models + name: 41142fd5b8692344f65219e8ee9a8346 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 801fb39780c97f3bbddb1b91c4c8183f + trainer: + kwargs: {} +name: 41142fd5b8692344f65219e8ee9a8346 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/params.yaml b/examples/security/classification/output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/params.yaml new file mode 100644 index 00000000..2838bb2d --- /dev/null +++ b/examples/security/classification/output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/adv_predictions.json + adv_probabilities_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/43d5b6dce72e6f574bb1def0673ce1cd.pkl + params_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/params.yaml + predictions_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/predictions.json + probabilities_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/probabilities.json + score_dict_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/score_dict.json + test_labels_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/test_labels.json + train_labels_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/train_labels.json + model_dir: models + name: 411a80c29685e9b6417ae1d99ad25ddd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 706d7c0eb90208ac90374230344b2130 + trainer: + kwargs: {} +name: 411a80c29685e9b6417ae1d99ad25ddd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/411cea795a6cfc4b650f66573481dfae/params.yaml b/examples/security/classification/output/reports/train/411cea795a6cfc4b650f66573481dfae/params.yaml new file mode 100644 index 00000000..a0c44d49 --- /dev/null +++ b/examples/security/classification/output/reports/train/411cea795a6cfc4b650f66573481dfae/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/adv_predictions.json + adv_probabilities_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/1da12fcb827626f290a5a20b721db355.pkl + params_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/params.yaml + predictions_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/predictions.json + probabilities_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/probabilities.json + score_dict_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/score_dict.json + test_labels_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/test_labels.json + train_labels_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/train_labels.json + model_dir: models + name: 411cea795a6cfc4b650f66573481dfae + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 74ba3906dd281f9bed97ab0bffe1e76f + trainer: + kwargs: {} +name: 411cea795a6cfc4b650f66573481dfae +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/params.yaml b/examples/security/classification/output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/params.yaml new file mode 100644 index 00000000..c4aea24d --- /dev/null +++ b/examples/security/classification/output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/adv_predictions.json + adv_probabilities_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/010abfc6521851d94d05ef59d0455e3d.pkl + params_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/params.yaml + predictions_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/predictions.json + probabilities_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/probabilities.json + score_dict_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/score_dict.json + test_labels_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/test_labels.json + train_labels_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/train_labels.json + model_dir: models + name: 4154738737ca7fbbc7da27ef1f0ddea6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8c59fd4d96ab20360bf4105471a26b6b + trainer: + kwargs: {} +name: 4154738737ca7fbbc7da27ef1f0ddea6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4162ded92efae510b9d94490b49eeb03/params.yaml b/examples/security/classification/output/reports/train/4162ded92efae510b9d94490b49eeb03/params.yaml new file mode 100644 index 00000000..2348b81c --- /dev/null +++ b/examples/security/classification/output/reports/train/4162ded92efae510b9d94490b49eeb03/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/adv_predictions.json + adv_probabilities_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/3f170bf856ae6d3e39b0ad7e91a07f37.pkl + params_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/params.yaml + predictions_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/predictions.json + probabilities_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/probabilities.json + score_dict_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/score_dict.json + test_labels_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/test_labels.json + train_labels_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/train_labels.json + model_dir: models + name: 4162ded92efae510b9d94490b49eeb03 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 71431171bfd75e49473794fc10ebfba3 + trainer: + kwargs: {} +name: 4162ded92efae510b9d94490b49eeb03 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/41a797a45ec373b9f2aecd9363ad356c/params.yaml b/examples/security/classification/output/reports/train/41a797a45ec373b9f2aecd9363ad356c/params.yaml new file mode 100644 index 00000000..ea3c8f44 --- /dev/null +++ b/examples/security/classification/output/reports/train/41a797a45ec373b9f2aecd9363ad356c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/adv_predictions.json + adv_probabilities_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/330d2c529888cac811ee4ddd1443387c.pkl + params_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/params.yaml + predictions_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/predictions.json + probabilities_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/probabilities.json + score_dict_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/score_dict.json + test_labels_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/test_labels.json + train_labels_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/train_labels.json + model_dir: models + name: 41a797a45ec373b9f2aecd9363ad356c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eaa0550daee009d8d3b26303f67dac99 + trainer: + kwargs: {} +name: 41a797a45ec373b9f2aecd9363ad356c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/41b2c60f95fe6635566543b753934c26/params.yaml b/examples/security/classification/output/reports/train/41b2c60f95fe6635566543b753934c26/params.yaml new file mode 100644 index 00000000..081c3ee3 --- /dev/null +++ b/examples/security/classification/output/reports/train/41b2c60f95fe6635566543b753934c26/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/41b2c60f95fe6635566543b753934c26/adv_predictions.json + adv_probabilities_file: output/reports/train/41b2c60f95fe6635566543b753934c26/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/a20af723ea23cebd6197fb002009303c.pkl + params_file: output/reports/train/41b2c60f95fe6635566543b753934c26/params.yaml + predictions_file: output/reports/train/41b2c60f95fe6635566543b753934c26/predictions.json + probabilities_file: output/reports/train/41b2c60f95fe6635566543b753934c26/probabilities.json + score_dict_file: output/reports/train/41b2c60f95fe6635566543b753934c26/score_dict.json + test_labels_file: output/reports/train/41b2c60f95fe6635566543b753934c26/test_labels.json + train_labels_file: output/reports/train/41b2c60f95fe6635566543b753934c26/train_labels.json + model_dir: models + name: 41b2c60f95fe6635566543b753934c26 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ba04619b6786f270294ca6a5c05db222 + trainer: + kwargs: {} +name: 41b2c60f95fe6635566543b753934c26 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/41bba8e14155b4a6895a782912617774/params.yaml b/examples/security/classification/output/reports/train/41bba8e14155b4a6895a782912617774/params.yaml new file mode 100644 index 00000000..fcfd3210 --- /dev/null +++ b/examples/security/classification/output/reports/train/41bba8e14155b4a6895a782912617774/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/41bba8e14155b4a6895a782912617774/adv_predictions.json + adv_probabilities_file: output/reports/train/41bba8e14155b4a6895a782912617774/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/947432a0273c3fa04a5decd549248c3e.pkl + params_file: output/reports/train/41bba8e14155b4a6895a782912617774/params.yaml + predictions_file: output/reports/train/41bba8e14155b4a6895a782912617774/predictions.json + probabilities_file: output/reports/train/41bba8e14155b4a6895a782912617774/probabilities.json + score_dict_file: output/reports/train/41bba8e14155b4a6895a782912617774/score_dict.json + test_labels_file: output/reports/train/41bba8e14155b4a6895a782912617774/test_labels.json + train_labels_file: output/reports/train/41bba8e14155b4a6895a782912617774/train_labels.json + model_dir: models + name: 41bba8e14155b4a6895a782912617774 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dd3caa07eeda3ea746bd26ab7c87aba5 + trainer: + kwargs: {} +name: 41bba8e14155b4a6895a782912617774 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/41ea5091d916842904adfedcd57c0998/params.yaml b/examples/security/classification/output/reports/train/41ea5091d916842904adfedcd57c0998/params.yaml new file mode 100644 index 00000000..2c518a55 --- /dev/null +++ b/examples/security/classification/output/reports/train/41ea5091d916842904adfedcd57c0998/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/41ea5091d916842904adfedcd57c0998/adv_predictions.json + adv_probabilities_file: output/reports/train/41ea5091d916842904adfedcd57c0998/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/43cb1d2bb2b822b4f6551462b99cb2cf.pkl + params_file: output/reports/train/41ea5091d916842904adfedcd57c0998/params.yaml + predictions_file: output/reports/train/41ea5091d916842904adfedcd57c0998/predictions.json + probabilities_file: output/reports/train/41ea5091d916842904adfedcd57c0998/probabilities.json + score_dict_file: output/reports/train/41ea5091d916842904adfedcd57c0998/score_dict.json + test_labels_file: output/reports/train/41ea5091d916842904adfedcd57c0998/test_labels.json + train_labels_file: output/reports/train/41ea5091d916842904adfedcd57c0998/train_labels.json + model_dir: models + name: 41ea5091d916842904adfedcd57c0998 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b78d71e58cf95b9281e9ef0ecd5729c5 + trainer: + kwargs: {} +name: 41ea5091d916842904adfedcd57c0998 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/41f60de5b4a3b89a7cd79204ed374984/params.yaml b/examples/security/classification/output/reports/train/41f60de5b4a3b89a7cd79204ed374984/params.yaml new file mode 100644 index 00000000..b58ea017 --- /dev/null +++ b/examples/security/classification/output/reports/train/41f60de5b4a3b89a7cd79204ed374984/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/adv_predictions.json + adv_probabilities_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/5cd7aa456fdfd052473396b08b0b4802.pkl + params_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/params.yaml + predictions_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/predictions.json + probabilities_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/probabilities.json + score_dict_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/score_dict.json + test_labels_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/test_labels.json + train_labels_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/train_labels.json + model_dir: models + name: 41f60de5b4a3b89a7cd79204ed374984 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f204b3d1e7c20a7da0c0e27fba47437f + trainer: + kwargs: {} +name: 41f60de5b4a3b89a7cd79204ed374984 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/params.yaml b/examples/security/classification/output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/params.yaml new file mode 100644 index 00000000..d068f1a7 --- /dev/null +++ b/examples/security/classification/output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/adv_predictions.json + adv_probabilities_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/83a3cab7666747da6c94b9919851207c.pkl + params_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/params.yaml + predictions_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/predictions.json + probabilities_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/probabilities.json + score_dict_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/score_dict.json + test_labels_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/test_labels.json + train_labels_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/train_labels.json + model_dir: models + name: 41fa7e6f5a57fd2efe95ff77601a87ee + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5c9bf7c1fb4b94f7b3ff34654858fc5d + trainer: + kwargs: {} +name: 41fa7e6f5a57fd2efe95ff77601a87ee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/42266d5a97a33692d4e16226b64f23fa/params.yaml b/examples/security/classification/output/reports/train/42266d5a97a33692d4e16226b64f23fa/params.yaml new file mode 100644 index 00000000..e02a011d --- /dev/null +++ b/examples/security/classification/output/reports/train/42266d5a97a33692d4e16226b64f23fa/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/adv_predictions.json + adv_probabilities_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/fb7baaebf9b7d1c43238947813c03a54.pkl + params_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/params.yaml + predictions_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/predictions.json + probabilities_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/probabilities.json + score_dict_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/score_dict.json + test_labels_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/test_labels.json + train_labels_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/train_labels.json + model_dir: models + name: 42266d5a97a33692d4e16226b64f23fa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ab62a7eb2a76d892fc2b3fc2fd4dedcb + trainer: + kwargs: {} +name: 42266d5a97a33692d4e16226b64f23fa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4234a74902abc64414c468a7a9022253/params.yaml b/examples/security/classification/output/reports/train/4234a74902abc64414c468a7a9022253/params.yaml new file mode 100644 index 00000000..2fdc529d --- /dev/null +++ b/examples/security/classification/output/reports/train/4234a74902abc64414c468a7a9022253/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4234a74902abc64414c468a7a9022253/adv_predictions.json + adv_probabilities_file: output/reports/train/4234a74902abc64414c468a7a9022253/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/12a245af57522f24675cfeee1d669282.pkl + params_file: output/reports/train/4234a74902abc64414c468a7a9022253/params.yaml + predictions_file: output/reports/train/4234a74902abc64414c468a7a9022253/predictions.json + probabilities_file: output/reports/train/4234a74902abc64414c468a7a9022253/probabilities.json + score_dict_file: output/reports/train/4234a74902abc64414c468a7a9022253/score_dict.json + test_labels_file: output/reports/train/4234a74902abc64414c468a7a9022253/test_labels.json + train_labels_file: output/reports/train/4234a74902abc64414c468a7a9022253/train_labels.json + model_dir: models + name: 4234a74902abc64414c468a7a9022253 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 43d6610c2a7b9bf65b43153f5f8c1447 + trainer: + kwargs: {} +name: 4234a74902abc64414c468a7a9022253 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/42b7b63522472c072078968261c157f8/params.yaml b/examples/security/classification/output/reports/train/42b7b63522472c072078968261c157f8/params.yaml new file mode 100644 index 00000000..427fe5fd --- /dev/null +++ b/examples/security/classification/output/reports/train/42b7b63522472c072078968261c157f8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/42b7b63522472c072078968261c157f8/adv_predictions.json + adv_probabilities_file: output/reports/train/42b7b63522472c072078968261c157f8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/74d2b31201042c35b610d59603d51ef1.pkl + params_file: output/reports/train/42b7b63522472c072078968261c157f8/params.yaml + predictions_file: output/reports/train/42b7b63522472c072078968261c157f8/predictions.json + probabilities_file: output/reports/train/42b7b63522472c072078968261c157f8/probabilities.json + score_dict_file: output/reports/train/42b7b63522472c072078968261c157f8/score_dict.json + test_labels_file: output/reports/train/42b7b63522472c072078968261c157f8/test_labels.json + train_labels_file: output/reports/train/42b7b63522472c072078968261c157f8/train_labels.json + model_dir: models + name: 42b7b63522472c072078968261c157f8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b986ead8a8b8c4596f270d3755b5962d + trainer: + kwargs: {} +name: 42b7b63522472c072078968261c157f8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4302ba616617db9b9742f199638dd708/params.yaml b/examples/security/classification/output/reports/train/4302ba616617db9b9742f199638dd708/params.yaml new file mode 100644 index 00000000..9ed4fd7f --- /dev/null +++ b/examples/security/classification/output/reports/train/4302ba616617db9b9742f199638dd708/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4302ba616617db9b9742f199638dd708/adv_predictions.json + adv_probabilities_file: output/reports/train/4302ba616617db9b9742f199638dd708/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/0a19443c78920504262b2996d90e50f7.pkl + params_file: output/reports/train/4302ba616617db9b9742f199638dd708/params.yaml + predictions_file: output/reports/train/4302ba616617db9b9742f199638dd708/predictions.json + probabilities_file: output/reports/train/4302ba616617db9b9742f199638dd708/probabilities.json + score_dict_file: output/reports/train/4302ba616617db9b9742f199638dd708/score_dict.json + test_labels_file: output/reports/train/4302ba616617db9b9742f199638dd708/test_labels.json + train_labels_file: output/reports/train/4302ba616617db9b9742f199638dd708/train_labels.json + model_dir: models + name: 4302ba616617db9b9742f199638dd708 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2b791b48ce956a63bd9014d45e8d5340 + trainer: + kwargs: {} +name: 4302ba616617db9b9742f199638dd708 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/437382e1b3a3b038239723041109900f/params.yaml b/examples/security/classification/output/reports/train/437382e1b3a3b038239723041109900f/params.yaml new file mode 100644 index 00000000..ddcef22c --- /dev/null +++ b/examples/security/classification/output/reports/train/437382e1b3a3b038239723041109900f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/437382e1b3a3b038239723041109900f/adv_predictions.json + adv_probabilities_file: output/reports/train/437382e1b3a3b038239723041109900f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/a5567560fa586194be16017d8e02b0c5.pkl + params_file: output/reports/train/437382e1b3a3b038239723041109900f/params.yaml + predictions_file: output/reports/train/437382e1b3a3b038239723041109900f/predictions.json + probabilities_file: output/reports/train/437382e1b3a3b038239723041109900f/probabilities.json + score_dict_file: output/reports/train/437382e1b3a3b038239723041109900f/score_dict.json + test_labels_file: output/reports/train/437382e1b3a3b038239723041109900f/test_labels.json + train_labels_file: output/reports/train/437382e1b3a3b038239723041109900f/train_labels.json + model_dir: models + name: 437382e1b3a3b038239723041109900f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6633ffaaf8210b2937a38fd140af3ff5 + trainer: + kwargs: {} +name: 437382e1b3a3b038239723041109900f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/438228aad785e28784a085dc4507ba85/params.yaml b/examples/security/classification/output/reports/train/438228aad785e28784a085dc4507ba85/params.yaml new file mode 100644 index 00000000..b0d83dae --- /dev/null +++ b/examples/security/classification/output/reports/train/438228aad785e28784a085dc4507ba85/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/438228aad785e28784a085dc4507ba85/adv_predictions.json + adv_probabilities_file: output/reports/train/438228aad785e28784a085dc4507ba85/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/c45fceeb4a140fa935dff14d50c68c0c.pkl + params_file: output/reports/train/438228aad785e28784a085dc4507ba85/params.yaml + predictions_file: output/reports/train/438228aad785e28784a085dc4507ba85/predictions.json + probabilities_file: output/reports/train/438228aad785e28784a085dc4507ba85/probabilities.json + score_dict_file: output/reports/train/438228aad785e28784a085dc4507ba85/score_dict.json + test_labels_file: output/reports/train/438228aad785e28784a085dc4507ba85/test_labels.json + train_labels_file: output/reports/train/438228aad785e28784a085dc4507ba85/train_labels.json + model_dir: models + name: 438228aad785e28784a085dc4507ba85 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0da48bf1d74c9b6f8ec64c47c4247282 + trainer: + kwargs: {} +name: 438228aad785e28784a085dc4507ba85 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4384e894ef431c73873837bb4604f12c/params.yaml b/examples/security/classification/output/reports/train/4384e894ef431c73873837bb4604f12c/params.yaml new file mode 100644 index 00000000..b19e5607 --- /dev/null +++ b/examples/security/classification/output/reports/train/4384e894ef431c73873837bb4604f12c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4384e894ef431c73873837bb4604f12c/adv_predictions.json + adv_probabilities_file: output/reports/train/4384e894ef431c73873837bb4604f12c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/0c988975597954c22658b21b3743b74f.pkl + params_file: output/reports/train/4384e894ef431c73873837bb4604f12c/params.yaml + predictions_file: output/reports/train/4384e894ef431c73873837bb4604f12c/predictions.json + probabilities_file: output/reports/train/4384e894ef431c73873837bb4604f12c/probabilities.json + score_dict_file: output/reports/train/4384e894ef431c73873837bb4604f12c/score_dict.json + test_labels_file: output/reports/train/4384e894ef431c73873837bb4604f12c/test_labels.json + train_labels_file: output/reports/train/4384e894ef431c73873837bb4604f12c/train_labels.json + model_dir: models + name: 4384e894ef431c73873837bb4604f12c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2a1cc90c762b83f79a0a1fe052a16a5c + trainer: + kwargs: {} +name: 4384e894ef431c73873837bb4604f12c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/438ad8eaac22fc24f522621f66afb307/params.yaml b/examples/security/classification/output/reports/train/438ad8eaac22fc24f522621f66afb307/params.yaml new file mode 100644 index 00000000..71e6c6d8 --- /dev/null +++ b/examples/security/classification/output/reports/train/438ad8eaac22fc24f522621f66afb307/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/adv_predictions.json + adv_probabilities_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/40b1b6785306a5e999f6947ab9f75b9e.pkl + params_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/params.yaml + predictions_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/predictions.json + probabilities_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/probabilities.json + score_dict_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/score_dict.json + test_labels_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/test_labels.json + train_labels_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/train_labels.json + model_dir: models + name: 438ad8eaac22fc24f522621f66afb307 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1fd6e7666ac21155b0a27d0b5a53f5d6 + trainer: + kwargs: {} +name: 438ad8eaac22fc24f522621f66afb307 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/438e9ce8557724ce2ec010f664bea12e/params.yaml b/examples/security/classification/output/reports/train/438e9ce8557724ce2ec010f664bea12e/params.yaml new file mode 100644 index 00000000..30fe0b34 --- /dev/null +++ b/examples/security/classification/output/reports/train/438e9ce8557724ce2ec010f664bea12e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/adv_predictions.json + adv_probabilities_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/26bdb7598ee3755701118309378fa801.pkl + params_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/params.yaml + predictions_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/predictions.json + probabilities_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/probabilities.json + score_dict_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/score_dict.json + test_labels_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/test_labels.json + train_labels_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/train_labels.json + model_dir: models + name: 438e9ce8557724ce2ec010f664bea12e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 41a94b5b1346656623c2b9e5a77d0d24 + trainer: + kwargs: {} +name: 438e9ce8557724ce2ec010f664bea12e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/439cb6115a2ed3387acccbb27382aa78/params.yaml b/examples/security/classification/output/reports/train/439cb6115a2ed3387acccbb27382aa78/params.yaml new file mode 100644 index 00000000..d81f54e0 --- /dev/null +++ b/examples/security/classification/output/reports/train/439cb6115a2ed3387acccbb27382aa78/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/adv_predictions.json + adv_probabilities_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/e6ab19df51b69bbbb9c0d0cd42b54b98.pkl + params_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/params.yaml + predictions_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/predictions.json + probabilities_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/probabilities.json + score_dict_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/score_dict.json + test_labels_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/test_labels.json + train_labels_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/train_labels.json + model_dir: models + name: 439cb6115a2ed3387acccbb27382aa78 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9a6c594303863fc9fdcaafc5035652cf + trainer: + kwargs: {} +name: 439cb6115a2ed3387acccbb27382aa78 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/params.yaml b/examples/security/classification/output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/params.yaml new file mode 100644 index 00000000..68b7ea0b --- /dev/null +++ b/examples/security/classification/output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/adv_predictions.json + adv_probabilities_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/6bd2dea59612511625685d89e1bfa6ec.pkl + params_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/params.yaml + predictions_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/predictions.json + probabilities_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/probabilities.json + score_dict_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/score_dict.json + test_labels_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/test_labels.json + train_labels_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/train_labels.json + model_dir: models + name: 43bcba5db33490fd14e8cdd251b72d5f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 10bb9e5164dd50d4eb0f3ffa015e2237 + trainer: + kwargs: {} +name: 43bcba5db33490fd14e8cdd251b72d5f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/params.yaml b/examples/security/classification/output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/params.yaml new file mode 100644 index 00000000..76addd49 --- /dev/null +++ b/examples/security/classification/output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/adv_predictions.json + adv_probabilities_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/fa220dbdd1c286ea07301a18ba2f5e5e.pkl + params_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/params.yaml + predictions_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/predictions.json + probabilities_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/probabilities.json + score_dict_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/score_dict.json + test_labels_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/test_labels.json + train_labels_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/train_labels.json + model_dir: models + name: 43c3d8ce0e7bf69684f3d4fd92fe2b41 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 853b2ad05c6957fcb40224c40d2dfd58 + trainer: + kwargs: {} +name: 43c3d8ce0e7bf69684f3d4fd92fe2b41 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/params.yaml b/examples/security/classification/output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/params.yaml new file mode 100644 index 00000000..a5ad616d --- /dev/null +++ b/examples/security/classification/output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/adv_predictions.json + adv_probabilities_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/a663194e85e7b2de0bb47dddd60588c3.pkl + params_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/params.yaml + predictions_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/predictions.json + probabilities_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/probabilities.json + score_dict_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/score_dict.json + test_labels_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/test_labels.json + train_labels_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/train_labels.json + model_dir: models + name: 43c75b43c5da43e3d3f9a9ea5ee9468d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0948bb3c542491a4aa0f64214d212baa + trainer: + kwargs: {} +name: 43c75b43c5da43e3d3f9a9ea5ee9468d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/43e420d203554c01f49af05ef285d123/params.yaml b/examples/security/classification/output/reports/train/43e420d203554c01f49af05ef285d123/params.yaml new file mode 100644 index 00000000..6c03a5be --- /dev/null +++ b/examples/security/classification/output/reports/train/43e420d203554c01f49af05ef285d123/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/43e420d203554c01f49af05ef285d123/adv_predictions.json + adv_probabilities_file: output/reports/train/43e420d203554c01f49af05ef285d123/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/58f5e53516442571bd1a92beaf65d225.pkl + params_file: output/reports/train/43e420d203554c01f49af05ef285d123/params.yaml + predictions_file: output/reports/train/43e420d203554c01f49af05ef285d123/predictions.json + probabilities_file: output/reports/train/43e420d203554c01f49af05ef285d123/probabilities.json + score_dict_file: output/reports/train/43e420d203554c01f49af05ef285d123/score_dict.json + test_labels_file: output/reports/train/43e420d203554c01f49af05ef285d123/test_labels.json + train_labels_file: output/reports/train/43e420d203554c01f49af05ef285d123/train_labels.json + model_dir: models + name: 43e420d203554c01f49af05ef285d123 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0f0e10d4411ff76ecd6a9eb640da2f36 + trainer: + kwargs: {} +name: 43e420d203554c01f49af05ef285d123 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/params.yaml b/examples/security/classification/output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/params.yaml new file mode 100644 index 00000000..9750607d --- /dev/null +++ b/examples/security/classification/output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/adv_predictions.json + adv_probabilities_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/97267e27baded7f5f3fcf17b3f95fb6b.pkl + params_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/params.yaml + predictions_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/predictions.json + probabilities_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/probabilities.json + score_dict_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/score_dict.json + test_labels_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/test_labels.json + train_labels_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/train_labels.json + model_dir: models + name: 4408fb8be4c8201adb9a38bcf2979f30 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 56d695f59553c606c211d3bef3fdd7c1 + trainer: + kwargs: {} +name: 4408fb8be4c8201adb9a38bcf2979f30 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/params.yaml b/examples/security/classification/output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/params.yaml new file mode 100644 index 00000000..b1d74348 --- /dev/null +++ b/examples/security/classification/output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/adv_predictions.json + adv_probabilities_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/73fa35ef00c16c29561c92bae4a05a26.pkl + params_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/params.yaml + predictions_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/predictions.json + probabilities_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/probabilities.json + score_dict_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/score_dict.json + test_labels_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/test_labels.json + train_labels_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/train_labels.json + model_dir: models + name: 440f0bf2ae2b5d4266c70ae7cffaf649 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 78c64b8e677462a2d667f1b5c6df06ee + trainer: + kwargs: {} +name: 440f0bf2ae2b5d4266c70ae7cffaf649 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/442b739179fc2666864fff8fb101f2f9/params.yaml b/examples/security/classification/output/reports/train/442b739179fc2666864fff8fb101f2f9/params.yaml new file mode 100644 index 00000000..1471bdae --- /dev/null +++ b/examples/security/classification/output/reports/train/442b739179fc2666864fff8fb101f2f9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/adv_predictions.json + adv_probabilities_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/c9c690c9799855c8b984741872d3c783.pkl + params_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/params.yaml + predictions_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/predictions.json + probabilities_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/probabilities.json + score_dict_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/score_dict.json + test_labels_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/test_labels.json + train_labels_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/train_labels.json + model_dir: models + name: 442b739179fc2666864fff8fb101f2f9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 313fd574892eaf1c2b7df29898200039 + trainer: + kwargs: {} +name: 442b739179fc2666864fff8fb101f2f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4437f589d0eb882916c56b785bf305fc/params.yaml b/examples/security/classification/output/reports/train/4437f589d0eb882916c56b785bf305fc/params.yaml new file mode 100644 index 00000000..a319b0fb --- /dev/null +++ b/examples/security/classification/output/reports/train/4437f589d0eb882916c56b785bf305fc/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/adv_predictions.json + adv_probabilities_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/5ce996f249935bec5fa8778420871b6a.pkl + params_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/params.yaml + predictions_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/predictions.json + probabilities_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/probabilities.json + score_dict_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/score_dict.json + test_labels_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/test_labels.json + train_labels_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/train_labels.json + model_dir: models + name: 4437f589d0eb882916c56b785bf305fc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bca1a03392f805f1114fc0db84d536aa + trainer: + kwargs: {} +name: 4437f589d0eb882916c56b785bf305fc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4496eb6a7558cb466b5704171731d183/params.yaml b/examples/security/classification/output/reports/train/4496eb6a7558cb466b5704171731d183/params.yaml new file mode 100644 index 00000000..974ea906 --- /dev/null +++ b/examples/security/classification/output/reports/train/4496eb6a7558cb466b5704171731d183/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4496eb6a7558cb466b5704171731d183/adv_predictions.json + adv_probabilities_file: output/reports/train/4496eb6a7558cb466b5704171731d183/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/7eb6afc3868559db205a84fd69952984.pkl + params_file: output/reports/train/4496eb6a7558cb466b5704171731d183/params.yaml + predictions_file: output/reports/train/4496eb6a7558cb466b5704171731d183/predictions.json + probabilities_file: output/reports/train/4496eb6a7558cb466b5704171731d183/probabilities.json + score_dict_file: output/reports/train/4496eb6a7558cb466b5704171731d183/score_dict.json + test_labels_file: output/reports/train/4496eb6a7558cb466b5704171731d183/test_labels.json + train_labels_file: output/reports/train/4496eb6a7558cb466b5704171731d183/train_labels.json + model_dir: models + name: 4496eb6a7558cb466b5704171731d183 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cc5f4b2ec1e4f54480b6aa18e9c9af26 + trainer: + kwargs: {} +name: 4496eb6a7558cb466b5704171731d183 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/44bfaa22b4aa46a46883e2f05d042405/params.yaml b/examples/security/classification/output/reports/train/44bfaa22b4aa46a46883e2f05d042405/params.yaml new file mode 100644 index 00000000..6784bed6 --- /dev/null +++ b/examples/security/classification/output/reports/train/44bfaa22b4aa46a46883e2f05d042405/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/adv_predictions.json + adv_probabilities_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/5987986ed2c279a850f459dcbb943d7c.pkl + params_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/params.yaml + predictions_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/predictions.json + probabilities_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/probabilities.json + score_dict_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/score_dict.json + test_labels_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/test_labels.json + train_labels_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/train_labels.json + model_dir: models + name: 44bfaa22b4aa46a46883e2f05d042405 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f7f55648a48ead0ab9da47eb1ffaa8b6 + trainer: + kwargs: {} +name: 44bfaa22b4aa46a46883e2f05d042405 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/44c2efed1499b01693aefc27375cb521/params.yaml b/examples/security/classification/output/reports/train/44c2efed1499b01693aefc27375cb521/params.yaml new file mode 100644 index 00000000..a00c0166 --- /dev/null +++ b/examples/security/classification/output/reports/train/44c2efed1499b01693aefc27375cb521/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/44c2efed1499b01693aefc27375cb521/adv_predictions.json + adv_probabilities_file: output/reports/train/44c2efed1499b01693aefc27375cb521/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/8a778862dd65e9a4ede7fd57711ca6ae.pkl + params_file: output/reports/train/44c2efed1499b01693aefc27375cb521/params.yaml + predictions_file: output/reports/train/44c2efed1499b01693aefc27375cb521/predictions.json + probabilities_file: output/reports/train/44c2efed1499b01693aefc27375cb521/probabilities.json + score_dict_file: output/reports/train/44c2efed1499b01693aefc27375cb521/score_dict.json + test_labels_file: output/reports/train/44c2efed1499b01693aefc27375cb521/test_labels.json + train_labels_file: output/reports/train/44c2efed1499b01693aefc27375cb521/train_labels.json + model_dir: models + name: 44c2efed1499b01693aefc27375cb521 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9968c281daffc94a6499d5041031cb66 + trainer: + kwargs: {} +name: 44c2efed1499b01693aefc27375cb521 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/453f04bfed02a38cdccb0191772bfcd2/params.yaml b/examples/security/classification/output/reports/train/453f04bfed02a38cdccb0191772bfcd2/params.yaml new file mode 100644 index 00000000..5064af0e --- /dev/null +++ b/examples/security/classification/output/reports/train/453f04bfed02a38cdccb0191772bfcd2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/adv_predictions.json + adv_probabilities_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/15581c0219df49b4312dbfaa74308270.pkl + params_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/params.yaml + predictions_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/predictions.json + probabilities_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/probabilities.json + score_dict_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/score_dict.json + test_labels_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/test_labels.json + train_labels_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/train_labels.json + model_dir: models + name: 453f04bfed02a38cdccb0191772bfcd2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1ee76a4795f2a76cc7800ff849c02f13 + trainer: + kwargs: {} +name: 453f04bfed02a38cdccb0191772bfcd2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4558027c552ec5d355df56f6d52a39e2/params.yaml b/examples/security/classification/output/reports/train/4558027c552ec5d355df56f6d52a39e2/params.yaml new file mode 100644 index 00000000..ee977d4e --- /dev/null +++ b/examples/security/classification/output/reports/train/4558027c552ec5d355df56f6d52a39e2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/adv_predictions.json + adv_probabilities_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/a253b94f03a4e0cff5bf65b74b1e0015.pkl + params_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/params.yaml + predictions_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/predictions.json + probabilities_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/probabilities.json + score_dict_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/score_dict.json + test_labels_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/test_labels.json + train_labels_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/train_labels.json + model_dir: models + name: 4558027c552ec5d355df56f6d52a39e2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea95bbcd13bce6d202717a0265953913 + trainer: + kwargs: {} +name: 4558027c552ec5d355df56f6d52a39e2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/params.yaml b/examples/security/classification/output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/params.yaml new file mode 100644 index 00000000..190f9a3e --- /dev/null +++ b/examples/security/classification/output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/adv_predictions.json + adv_probabilities_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/9def92d47515befe20db14d8f9d6699d.pkl + params_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/params.yaml + predictions_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/predictions.json + probabilities_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/probabilities.json + score_dict_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/score_dict.json + test_labels_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/test_labels.json + train_labels_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/train_labels.json + model_dir: models + name: 45777ba1f9dfe0c3216bccda7593bb0e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 49ccf0e6b27b463bf57829fc3a270df5 + trainer: + kwargs: {} +name: 45777ba1f9dfe0c3216bccda7593bb0e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/45882e8c3acad894666fe57d8f2f73f5/params.yaml b/examples/security/classification/output/reports/train/45882e8c3acad894666fe57d8f2f73f5/params.yaml new file mode 100644 index 00000000..1b8b04ed --- /dev/null +++ b/examples/security/classification/output/reports/train/45882e8c3acad894666fe57d8f2f73f5/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/adv_predictions.json + adv_probabilities_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/b76cc2775ac05de2063f050708b51048.pkl + params_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/params.yaml + predictions_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/predictions.json + probabilities_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/probabilities.json + score_dict_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/score_dict.json + test_labels_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/test_labels.json + train_labels_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/train_labels.json + model_dir: models + name: 45882e8c3acad894666fe57d8f2f73f5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fe753c92dc18f012d313e7d917ea4482 + trainer: + kwargs: {} +name: 45882e8c3acad894666fe57d8f2f73f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/45a686218387f42cbf755abbc309fdf8/params.yaml b/examples/security/classification/output/reports/train/45a686218387f42cbf755abbc309fdf8/params.yaml new file mode 100644 index 00000000..c6e6905c --- /dev/null +++ b/examples/security/classification/output/reports/train/45a686218387f42cbf755abbc309fdf8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/adv_predictions.json + adv_probabilities_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/51631478706c85b1bd76609307f83ab3.pkl + params_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/params.yaml + predictions_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/predictions.json + probabilities_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/probabilities.json + score_dict_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/score_dict.json + test_labels_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/test_labels.json + train_labels_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/train_labels.json + model_dir: models + name: 45a686218387f42cbf755abbc309fdf8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c14c38629b7b1250d86f183725b1fd7e + trainer: + kwargs: {} +name: 45a686218387f42cbf755abbc309fdf8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/45c2df00872d50f0df473b9ffbff5207/params.yaml b/examples/security/classification/output/reports/train/45c2df00872d50f0df473b9ffbff5207/params.yaml new file mode 100644 index 00000000..66b3c8a9 --- /dev/null +++ b/examples/security/classification/output/reports/train/45c2df00872d50f0df473b9ffbff5207/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/adv_predictions.json + adv_probabilities_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/318928c7aea32f7d987b0b14550fea5e.pkl + params_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/params.yaml + predictions_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/predictions.json + probabilities_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/probabilities.json + score_dict_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/score_dict.json + test_labels_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/test_labels.json + train_labels_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/train_labels.json + model_dir: models + name: 45c2df00872d50f0df473b9ffbff5207 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1f7b8d623889a8ffa878f9625985ec69 + trainer: + kwargs: {} +name: 45c2df00872d50f0df473b9ffbff5207 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/params.yaml b/examples/security/classification/output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/params.yaml new file mode 100644 index 00000000..ea25803f --- /dev/null +++ b/examples/security/classification/output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/adv_predictions.json + adv_probabilities_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/4b000bceb6776a613d60e8e70680f2ce.pkl + params_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/params.yaml + predictions_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/predictions.json + probabilities_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/probabilities.json + score_dict_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/score_dict.json + test_labels_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/test_labels.json + train_labels_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/train_labels.json + model_dir: models + name: 45c4cc6d21760a24f684a5de5b68fb94 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6ecb462a9e04ce45a5836a907b5f6129 + trainer: + kwargs: {} +name: 45c4cc6d21760a24f684a5de5b68fb94 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/params.yaml b/examples/security/classification/output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/params.yaml new file mode 100644 index 00000000..e05b5a0d --- /dev/null +++ b/examples/security/classification/output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/adv_predictions.json + adv_probabilities_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/27e93fe0b69318baa4a504296a90c074.pkl + params_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/params.yaml + predictions_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/predictions.json + probabilities_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/probabilities.json + score_dict_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/score_dict.json + test_labels_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/test_labels.json + train_labels_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/train_labels.json + model_dir: models + name: 45ca4342031d7a6ca4a9fe4b9b8b4a03 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 719563ccfc4b69e0ea0ca1ba53ed5902 + trainer: + kwargs: {} +name: 45ca4342031d7a6ca4a9fe4b9b8b4a03 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/params.yaml b/examples/security/classification/output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/params.yaml new file mode 100644 index 00000000..2ba9d53e --- /dev/null +++ b/examples/security/classification/output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/adv_predictions.json + adv_probabilities_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/72948e4247959357104a0d346355ad9b.pkl + params_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/params.yaml + predictions_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/predictions.json + probabilities_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/probabilities.json + score_dict_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/score_dict.json + test_labels_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/test_labels.json + train_labels_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/train_labels.json + model_dir: models + name: 45f069d3ec3a7b74d81212c294df5bcc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 45c5a2c5229d084046b1d40c7cce444c + trainer: + kwargs: {} +name: 45f069d3ec3a7b74d81212c294df5bcc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4600ee75f171e5595764bf91c4d113e6/params.yaml b/examples/security/classification/output/reports/train/4600ee75f171e5595764bf91c4d113e6/params.yaml new file mode 100644 index 00000000..1edb45b5 --- /dev/null +++ b/examples/security/classification/output/reports/train/4600ee75f171e5595764bf91c4d113e6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/adv_predictions.json + adv_probabilities_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/1a144bab686653a0824082f179fe3edc.pkl + params_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/params.yaml + predictions_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/predictions.json + probabilities_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/probabilities.json + score_dict_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/score_dict.json + test_labels_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/test_labels.json + train_labels_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/train_labels.json + model_dir: models + name: 4600ee75f171e5595764bf91c4d113e6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1acb4605ba214b18ccca948afc3493c3 + trainer: + kwargs: {} +name: 4600ee75f171e5595764bf91c4d113e6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/464324bc0527840737f4e0f18210ab5f/params.yaml b/examples/security/classification/output/reports/train/464324bc0527840737f4e0f18210ab5f/params.yaml new file mode 100644 index 00000000..4bec0166 --- /dev/null +++ b/examples/security/classification/output/reports/train/464324bc0527840737f4e0f18210ab5f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/adv_predictions.json + adv_probabilities_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/f71af7336b5ecddba9c933c8cfc85ede.pkl + params_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/params.yaml + predictions_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/predictions.json + probabilities_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/probabilities.json + score_dict_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/score_dict.json + test_labels_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/test_labels.json + train_labels_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/train_labels.json + model_dir: models + name: 464324bc0527840737f4e0f18210ab5f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 37f69ea72800ae8332ad59ceae3edaa1 + trainer: + kwargs: {} +name: 464324bc0527840737f4e0f18210ab5f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/465257a5f6f0d49830c194a2ff8952a1/params.yaml b/examples/security/classification/output/reports/train/465257a5f6f0d49830c194a2ff8952a1/params.yaml new file mode 100644 index 00000000..878272e1 --- /dev/null +++ b/examples/security/classification/output/reports/train/465257a5f6f0d49830c194a2ff8952a1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/adv_predictions.json + adv_probabilities_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/9506109e157eeb36fd293ac5b5660775.pkl + params_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/params.yaml + predictions_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/predictions.json + probabilities_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/probabilities.json + score_dict_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/score_dict.json + test_labels_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/test_labels.json + train_labels_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/train_labels.json + model_dir: models + name: 465257a5f6f0d49830c194a2ff8952a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c6d8fea82e3ad6a57939c36db2d6616d + trainer: + kwargs: {} +name: 465257a5f6f0d49830c194a2ff8952a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/465d01e6814ac90042fb095146b24937/params.yaml b/examples/security/classification/output/reports/train/465d01e6814ac90042fb095146b24937/params.yaml new file mode 100644 index 00000000..625cf6f0 --- /dev/null +++ b/examples/security/classification/output/reports/train/465d01e6814ac90042fb095146b24937/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/465d01e6814ac90042fb095146b24937/adv_predictions.json + adv_probabilities_file: output/reports/train/465d01e6814ac90042fb095146b24937/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/e36bf197de936d2d10404517a7f3550c.pkl + params_file: output/reports/train/465d01e6814ac90042fb095146b24937/params.yaml + predictions_file: output/reports/train/465d01e6814ac90042fb095146b24937/predictions.json + probabilities_file: output/reports/train/465d01e6814ac90042fb095146b24937/probabilities.json + score_dict_file: output/reports/train/465d01e6814ac90042fb095146b24937/score_dict.json + test_labels_file: output/reports/train/465d01e6814ac90042fb095146b24937/test_labels.json + train_labels_file: output/reports/train/465d01e6814ac90042fb095146b24937/train_labels.json + model_dir: models + name: 465d01e6814ac90042fb095146b24937 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7af8e3da9edcb299b61eea9349c2568e + trainer: + kwargs: {} +name: 465d01e6814ac90042fb095146b24937 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/466dbfd77747a48e681757500b40d2cc/params.yaml b/examples/security/classification/output/reports/train/466dbfd77747a48e681757500b40d2cc/params.yaml new file mode 100644 index 00000000..5a78ce68 --- /dev/null +++ b/examples/security/classification/output/reports/train/466dbfd77747a48e681757500b40d2cc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/adv_predictions.json + adv_probabilities_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/7b09577b589ef13bde7e2a1f7bb2716e.pkl + params_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/params.yaml + predictions_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/predictions.json + probabilities_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/probabilities.json + score_dict_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/score_dict.json + test_labels_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/test_labels.json + train_labels_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/train_labels.json + model_dir: models + name: 466dbfd77747a48e681757500b40d2cc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a421893b18786a88118373f9152e80da + trainer: + kwargs: {} +name: 466dbfd77747a48e681757500b40d2cc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/467a01440d7ebae95a7bda2e79514fd1/params.yaml b/examples/security/classification/output/reports/train/467a01440d7ebae95a7bda2e79514fd1/params.yaml new file mode 100644 index 00000000..d5ccb8f6 --- /dev/null +++ b/examples/security/classification/output/reports/train/467a01440d7ebae95a7bda2e79514fd1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/adv_predictions.json + adv_probabilities_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/7f8c2dc24e9d6ee48eb1daa71a25c997.pkl + params_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/params.yaml + predictions_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/predictions.json + probabilities_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/probabilities.json + score_dict_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/score_dict.json + test_labels_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/test_labels.json + train_labels_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/train_labels.json + model_dir: models + name: 467a01440d7ebae95a7bda2e79514fd1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e73c01bd707ee72f211e7835000d402d + trainer: + kwargs: {} +name: 467a01440d7ebae95a7bda2e79514fd1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/params.yaml b/examples/security/classification/output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/params.yaml new file mode 100644 index 00000000..af85a7ca --- /dev/null +++ b/examples/security/classification/output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/adv_predictions.json + adv_probabilities_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/51ca3fd480e63309a4a3eee8d4045681.pkl + params_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/params.yaml + predictions_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/predictions.json + probabilities_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/probabilities.json + score_dict_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/score_dict.json + test_labels_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/test_labels.json + train_labels_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/train_labels.json + model_dir: models + name: 468f6ae008906ab73d56c6bb172dc7c2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e96c79c5f3cde1b795188ad46e65a00b + trainer: + kwargs: {} +name: 468f6ae008906ab73d56c6bb172dc7c2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/params.yaml b/examples/security/classification/output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/params.yaml new file mode 100644 index 00000000..855d485d --- /dev/null +++ b/examples/security/classification/output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/adv_predictions.json + adv_probabilities_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/2bef32d46acfd65addf4196915407249.pkl + params_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/params.yaml + predictions_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/predictions.json + probabilities_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/probabilities.json + score_dict_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/score_dict.json + test_labels_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/test_labels.json + train_labels_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/train_labels.json + model_dir: models + name: 46977a64fe4466f14e4c7629b83a1e5c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d65a1e9a53e71f871ccb51344c7db029 + trainer: + kwargs: {} +name: 46977a64fe4466f14e4c7629b83a1e5c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/46b0be9993b62cd919482c0dca3a80cb/params.yaml b/examples/security/classification/output/reports/train/46b0be9993b62cd919482c0dca3a80cb/params.yaml new file mode 100644 index 00000000..daef0d8d --- /dev/null +++ b/examples/security/classification/output/reports/train/46b0be9993b62cd919482c0dca3a80cb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/adv_predictions.json + adv_probabilities_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e60785c743d5a9c63b4c5a37daacdab7.pkl + params_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/params.yaml + predictions_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/predictions.json + probabilities_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/probabilities.json + score_dict_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/score_dict.json + test_labels_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/test_labels.json + train_labels_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/train_labels.json + model_dir: models + name: 46b0be9993b62cd919482c0dca3a80cb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 97ff54b48d485c54569d4ff8e448bca8 + trainer: + kwargs: {} +name: 46b0be9993b62cd919482c0dca3a80cb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/params.yaml b/examples/security/classification/output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/params.yaml new file mode 100644 index 00000000..e8e07704 --- /dev/null +++ b/examples/security/classification/output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/adv_predictions.json + adv_probabilities_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/0108cb346fc463290944c3413212a958.pkl + params_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/params.yaml + predictions_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/predictions.json + probabilities_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/probabilities.json + score_dict_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/score_dict.json + test_labels_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/test_labels.json + train_labels_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/train_labels.json + model_dir: models + name: 46b1b795c4b7fdb5395d2f7359d2adf9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2bbc5753c91f1ff3cde034c2c9ee83f2 + trainer: + kwargs: {} +name: 46b1b795c4b7fdb5395d2f7359d2adf9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/params.yaml b/examples/security/classification/output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/params.yaml new file mode 100644 index 00000000..7ca4f19f --- /dev/null +++ b/examples/security/classification/output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/adv_predictions.json + adv_probabilities_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/2a06273cd7a33f7e5182a0e45b6619a7.pkl + params_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/params.yaml + predictions_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/predictions.json + probabilities_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/probabilities.json + score_dict_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/score_dict.json + test_labels_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/test_labels.json + train_labels_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/train_labels.json + model_dir: models + name: 46bcdbf1782f8950660c1c4d9f8f7148 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fd4662af1f507c7ba7802a873842e527 + trainer: + kwargs: {} +name: 46bcdbf1782f8950660c1c4d9f8f7148 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/params.yaml b/examples/security/classification/output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/params.yaml new file mode 100644 index 00000000..c68058e3 --- /dev/null +++ b/examples/security/classification/output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/adv_predictions.json + adv_probabilities_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/b91658c5aaeac0b9a36968441979149f.pkl + params_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/params.yaml + predictions_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/predictions.json + probabilities_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/probabilities.json + score_dict_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/score_dict.json + test_labels_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/test_labels.json + train_labels_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/train_labels.json + model_dir: models + name: 46d08e1a731fd799d4fa91e376a4f1b7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 93d1a28dc6131296be4a88b62e9bb1bf + trainer: + kwargs: {} +name: 46d08e1a731fd799d4fa91e376a4f1b7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/46f70529a9f0f544ce24f73c678a336e/params.yaml b/examples/security/classification/output/reports/train/46f70529a9f0f544ce24f73c678a336e/params.yaml new file mode 100644 index 00000000..b8de4e04 --- /dev/null +++ b/examples/security/classification/output/reports/train/46f70529a9f0f544ce24f73c678a336e/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/adv_predictions.json + adv_probabilities_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/41327749dfd591b3f2e01ea6fc060cd9.pkl + params_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/params.yaml + predictions_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/predictions.json + probabilities_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/probabilities.json + score_dict_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/score_dict.json + test_labels_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/test_labels.json + train_labels_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/train_labels.json + model_dir: models + name: 46f70529a9f0f544ce24f73c678a336e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5f2699eb9ff974089a7371bc4cb7b084 + trainer: + kwargs: {} +name: 46f70529a9f0f544ce24f73c678a336e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/46f9420cf2030f179a0765b6664f9fe1/params.yaml b/examples/security/classification/output/reports/train/46f9420cf2030f179a0765b6664f9fe1/params.yaml new file mode 100644 index 00000000..93cd1c5e --- /dev/null +++ b/examples/security/classification/output/reports/train/46f9420cf2030f179a0765b6664f9fe1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/adv_predictions.json + adv_probabilities_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/bfbf774824f20c607865dfea5dd2d950.pkl + params_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/params.yaml + predictions_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/predictions.json + probabilities_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/probabilities.json + score_dict_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/score_dict.json + test_labels_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/test_labels.json + train_labels_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/train_labels.json + model_dir: models + name: 46f9420cf2030f179a0765b6664f9fe1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 50cb85544282cbb66ba27a58e7e225b0 + trainer: + kwargs: {} +name: 46f9420cf2030f179a0765b6664f9fe1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4710dcde54e43b16d21cb6e262167b8d/params.yaml b/examples/security/classification/output/reports/train/4710dcde54e43b16d21cb6e262167b8d/params.yaml new file mode 100644 index 00000000..29f5a897 --- /dev/null +++ b/examples/security/classification/output/reports/train/4710dcde54e43b16d21cb6e262167b8d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/adv_predictions.json + adv_probabilities_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/b8143720098411a8533486ad0cdd75f5.pkl + params_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/params.yaml + predictions_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/predictions.json + probabilities_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/probabilities.json + score_dict_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/score_dict.json + test_labels_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/test_labels.json + train_labels_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/train_labels.json + model_dir: models + name: 4710dcde54e43b16d21cb6e262167b8d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 26741c72dee967b6e3cfb3358822813f + trainer: + kwargs: {} +name: 4710dcde54e43b16d21cb6e262167b8d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/params.yaml b/examples/security/classification/output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/params.yaml new file mode 100644 index 00000000..d36b46bd --- /dev/null +++ b/examples/security/classification/output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/adv_predictions.json + adv_probabilities_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/3a54681ed6b01354230efd7a500b5f4a.pkl + params_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/params.yaml + predictions_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/predictions.json + probabilities_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/probabilities.json + score_dict_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/score_dict.json + test_labels_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/test_labels.json + train_labels_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/train_labels.json + model_dir: models + name: 471ceedcc8f090aaf9f689f557dd2af2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1f42352fadd4e5f61e6411f502c0492b + trainer: + kwargs: {} +name: 471ceedcc8f090aaf9f689f557dd2af2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/471e042f03cd18a02f726f81b210b868/params.yaml b/examples/security/classification/output/reports/train/471e042f03cd18a02f726f81b210b868/params.yaml new file mode 100644 index 00000000..2f9e880c --- /dev/null +++ b/examples/security/classification/output/reports/train/471e042f03cd18a02f726f81b210b868/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/471e042f03cd18a02f726f81b210b868/adv_predictions.json + adv_probabilities_file: output/reports/train/471e042f03cd18a02f726f81b210b868/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/1b0cbc6b1c0cc7359ba0bd6f7354d64e.pkl + params_file: output/reports/train/471e042f03cd18a02f726f81b210b868/params.yaml + predictions_file: output/reports/train/471e042f03cd18a02f726f81b210b868/predictions.json + probabilities_file: output/reports/train/471e042f03cd18a02f726f81b210b868/probabilities.json + score_dict_file: output/reports/train/471e042f03cd18a02f726f81b210b868/score_dict.json + test_labels_file: output/reports/train/471e042f03cd18a02f726f81b210b868/test_labels.json + train_labels_file: output/reports/train/471e042f03cd18a02f726f81b210b868/train_labels.json + model_dir: models + name: 471e042f03cd18a02f726f81b210b868 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 45ce3d753eaec683cd92cee1f4f1caa8 + trainer: + kwargs: {} +name: 471e042f03cd18a02f726f81b210b868 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/475ae02b72cb52d9e668b87fdbe17881/params.yaml b/examples/security/classification/output/reports/train/475ae02b72cb52d9e668b87fdbe17881/params.yaml new file mode 100644 index 00000000..28ef81f4 --- /dev/null +++ b/examples/security/classification/output/reports/train/475ae02b72cb52d9e668b87fdbe17881/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/adv_predictions.json + adv_probabilities_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/f02b4634d34477f1afa788b6f9697581.pkl + params_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/params.yaml + predictions_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/predictions.json + probabilities_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/probabilities.json + score_dict_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/score_dict.json + test_labels_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/test_labels.json + train_labels_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/train_labels.json + model_dir: models + name: 475ae02b72cb52d9e668b87fdbe17881 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fac62bd132c867ea7d931fdfe3517a42 + trainer: + kwargs: {} +name: 475ae02b72cb52d9e668b87fdbe17881 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/params.yaml b/examples/security/classification/output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/params.yaml new file mode 100644 index 00000000..036cda63 --- /dev/null +++ b/examples/security/classification/output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/adv_predictions.json + adv_probabilities_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/dfae5e590f0886fbd3d4b3814e8c086c.pkl + params_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/params.yaml + predictions_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/predictions.json + probabilities_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/probabilities.json + score_dict_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/score_dict.json + test_labels_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/test_labels.json + train_labels_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/train_labels.json + model_dir: models + name: 478243d5b163ae6c8cfee47f3b4b068c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 455cd059dd96765a99bce1e1d164ad32 + trainer: + kwargs: {} +name: 478243d5b163ae6c8cfee47f3b4b068c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/params.yaml b/examples/security/classification/output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/params.yaml new file mode 100644 index 00000000..273c995d --- /dev/null +++ b/examples/security/classification/output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/adv_predictions.json + adv_probabilities_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/eac370f71c6bfae3bf2e0f80b575a971.pkl + params_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/params.yaml + predictions_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/predictions.json + probabilities_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/probabilities.json + score_dict_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/score_dict.json + test_labels_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/test_labels.json + train_labels_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/train_labels.json + model_dir: models + name: 4795d4b29df05009c3ba0cd454d2e4e3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8912e931bd6bb82e13288588fdc9855e + trainer: + kwargs: {} +name: 4795d4b29df05009c3ba0cd454d2e4e3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/479d54ea9ea8dffeb01102e950d90145/params.yaml b/examples/security/classification/output/reports/train/479d54ea9ea8dffeb01102e950d90145/params.yaml new file mode 100644 index 00000000..33700542 --- /dev/null +++ b/examples/security/classification/output/reports/train/479d54ea9ea8dffeb01102e950d90145/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/adv_predictions.json + adv_probabilities_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/9020f07193cf3a3b70b6038bd5f7d496.pkl + params_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/params.yaml + predictions_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/predictions.json + probabilities_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/probabilities.json + score_dict_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/score_dict.json + test_labels_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/test_labels.json + train_labels_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/train_labels.json + model_dir: models + name: 479d54ea9ea8dffeb01102e950d90145 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a9567a39a5d93cc09973e0e6748fbf46 + trainer: + kwargs: {} +name: 479d54ea9ea8dffeb01102e950d90145 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/params.yaml b/examples/security/classification/output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/params.yaml new file mode 100644 index 00000000..d109711d --- /dev/null +++ b/examples/security/classification/output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/adv_predictions.json + adv_probabilities_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/73e1d59253d3e593c062c586196bf3a7.pkl + params_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/params.yaml + predictions_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/predictions.json + probabilities_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/probabilities.json + score_dict_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/score_dict.json + test_labels_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/test_labels.json + train_labels_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/train_labels.json + model_dir: models + name: 47a20378a3170e54c739dfc7f9dc89e2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b88b7c2bf990f8888b0fff8f05e10bc9 + trainer: + kwargs: {} +name: 47a20378a3170e54c739dfc7f9dc89e2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/params.yaml b/examples/security/classification/output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/params.yaml new file mode 100644 index 00000000..477ae6cf --- /dev/null +++ b/examples/security/classification/output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/adv_predictions.json + adv_probabilities_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/d5cec6027aadae31490771c8d61e1d05.pkl + params_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/params.yaml + predictions_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/predictions.json + probabilities_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/probabilities.json + score_dict_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/score_dict.json + test_labels_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/test_labels.json + train_labels_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/train_labels.json + model_dir: models + name: 480d8c3c7f2caefcde15b4e7d2830aec + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 769cbbeaaaabf9fd85a1f1c91f565ba1 + trainer: + kwargs: {} +name: 480d8c3c7f2caefcde15b4e7d2830aec +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/481440de2cea4312f503f2e2337c54de/params.yaml b/examples/security/classification/output/reports/train/481440de2cea4312f503f2e2337c54de/params.yaml new file mode 100644 index 00000000..ba12d587 --- /dev/null +++ b/examples/security/classification/output/reports/train/481440de2cea4312f503f2e2337c54de/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/481440de2cea4312f503f2e2337c54de/adv_predictions.json + adv_probabilities_file: output/reports/train/481440de2cea4312f503f2e2337c54de/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/c535e6f44e4276e7e60d3fcd09863676.pkl + params_file: output/reports/train/481440de2cea4312f503f2e2337c54de/params.yaml + predictions_file: output/reports/train/481440de2cea4312f503f2e2337c54de/predictions.json + probabilities_file: output/reports/train/481440de2cea4312f503f2e2337c54de/probabilities.json + score_dict_file: output/reports/train/481440de2cea4312f503f2e2337c54de/score_dict.json + test_labels_file: output/reports/train/481440de2cea4312f503f2e2337c54de/test_labels.json + train_labels_file: output/reports/train/481440de2cea4312f503f2e2337c54de/train_labels.json + model_dir: models + name: 481440de2cea4312f503f2e2337c54de + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4384760d135b4adc9278c65f2b712d9e + trainer: + kwargs: {} +name: 481440de2cea4312f503f2e2337c54de +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/params.yaml b/examples/security/classification/output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/params.yaml new file mode 100644 index 00000000..624faf85 --- /dev/null +++ b/examples/security/classification/output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/adv_predictions.json + adv_probabilities_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/811c05123651f5f69a26e753edd964f4.pkl + params_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/params.yaml + predictions_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/predictions.json + probabilities_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/probabilities.json + score_dict_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/score_dict.json + test_labels_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/test_labels.json + train_labels_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/train_labels.json + model_dir: models + name: 48a10e94f91fd7dad7084096dbfcc8d4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b7f06aa015631270839f8d55d98a627a + trainer: + kwargs: {} +name: 48a10e94f91fd7dad7084096dbfcc8d4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/48d719aebe8462cb3905089498acdb35/params.yaml b/examples/security/classification/output/reports/train/48d719aebe8462cb3905089498acdb35/params.yaml new file mode 100644 index 00000000..93a8fd5b --- /dev/null +++ b/examples/security/classification/output/reports/train/48d719aebe8462cb3905089498acdb35/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/48d719aebe8462cb3905089498acdb35/adv_predictions.json + adv_probabilities_file: output/reports/train/48d719aebe8462cb3905089498acdb35/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/a4cea0e6f6d64308919e24254128a782.pkl + params_file: output/reports/train/48d719aebe8462cb3905089498acdb35/params.yaml + predictions_file: output/reports/train/48d719aebe8462cb3905089498acdb35/predictions.json + probabilities_file: output/reports/train/48d719aebe8462cb3905089498acdb35/probabilities.json + score_dict_file: output/reports/train/48d719aebe8462cb3905089498acdb35/score_dict.json + test_labels_file: output/reports/train/48d719aebe8462cb3905089498acdb35/test_labels.json + train_labels_file: output/reports/train/48d719aebe8462cb3905089498acdb35/train_labels.json + model_dir: models + name: 48d719aebe8462cb3905089498acdb35 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 23d22e793face3f263a9ea2400489d1a + trainer: + kwargs: {} +name: 48d719aebe8462cb3905089498acdb35 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/params.yaml b/examples/security/classification/output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/params.yaml new file mode 100644 index 00000000..ed4397e2 --- /dev/null +++ b/examples/security/classification/output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/adv_predictions.json + adv_probabilities_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/2192a6c204731bb37c2c4e00a96a525e.pkl + params_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/params.yaml + predictions_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/predictions.json + probabilities_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/probabilities.json + score_dict_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/score_dict.json + test_labels_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/test_labels.json + train_labels_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/train_labels.json + model_dir: models + name: 48e59e1bb1f93b4e3cea60447659c38e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 09c56571729267efeab9f25a57111f94 + trainer: + kwargs: {} +name: 48e59e1bb1f93b4e3cea60447659c38e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/48f8c553f2ef76ba24323a21e6956e70/params.yaml b/examples/security/classification/output/reports/train/48f8c553f2ef76ba24323a21e6956e70/params.yaml new file mode 100644 index 00000000..24797965 --- /dev/null +++ b/examples/security/classification/output/reports/train/48f8c553f2ef76ba24323a21e6956e70/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/adv_predictions.json + adv_probabilities_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/af0d9fe014c646ece2830c99ad46a7bd.pkl + params_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/params.yaml + predictions_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/predictions.json + probabilities_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/probabilities.json + score_dict_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/score_dict.json + test_labels_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/test_labels.json + train_labels_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/train_labels.json + model_dir: models + name: 48f8c553f2ef76ba24323a21e6956e70 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4af1dbf6fd855b7f1506c5c85102b227 + trainer: + kwargs: {} +name: 48f8c553f2ef76ba24323a21e6956e70 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/490627ffb7697dcc195c78612da1572a/params.yaml b/examples/security/classification/output/reports/train/490627ffb7697dcc195c78612da1572a/params.yaml new file mode 100644 index 00000000..48e5135e --- /dev/null +++ b/examples/security/classification/output/reports/train/490627ffb7697dcc195c78612da1572a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/490627ffb7697dcc195c78612da1572a/adv_predictions.json + adv_probabilities_file: output/reports/train/490627ffb7697dcc195c78612da1572a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/0085b5acbf8377fda3550637101cbe79.pkl + params_file: output/reports/train/490627ffb7697dcc195c78612da1572a/params.yaml + predictions_file: output/reports/train/490627ffb7697dcc195c78612da1572a/predictions.json + probabilities_file: output/reports/train/490627ffb7697dcc195c78612da1572a/probabilities.json + score_dict_file: output/reports/train/490627ffb7697dcc195c78612da1572a/score_dict.json + test_labels_file: output/reports/train/490627ffb7697dcc195c78612da1572a/test_labels.json + train_labels_file: output/reports/train/490627ffb7697dcc195c78612da1572a/train_labels.json + model_dir: models + name: 490627ffb7697dcc195c78612da1572a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0a2df038054c0bcd3cf651787001ca58 + trainer: + kwargs: {} +name: 490627ffb7697dcc195c78612da1572a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/49533869f7a66c04df94ff5e9244a8df/params.yaml b/examples/security/classification/output/reports/train/49533869f7a66c04df94ff5e9244a8df/params.yaml new file mode 100644 index 00000000..3b334526 --- /dev/null +++ b/examples/security/classification/output/reports/train/49533869f7a66c04df94ff5e9244a8df/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/adv_predictions.json + adv_probabilities_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/a9521020014633daa12533900a52f30e.pkl + params_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/params.yaml + predictions_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/predictions.json + probabilities_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/probabilities.json + score_dict_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/score_dict.json + test_labels_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/test_labels.json + train_labels_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/train_labels.json + model_dir: models + name: 49533869f7a66c04df94ff5e9244a8df + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 97b58c43f21605a4b1d11b23554018c0 + trainer: + kwargs: {} +name: 49533869f7a66c04df94ff5e9244a8df +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/params.yaml b/examples/security/classification/output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/params.yaml new file mode 100644 index 00000000..4e50152a --- /dev/null +++ b/examples/security/classification/output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/adv_predictions.json + adv_probabilities_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/0117c744a008e1161328302cd158f07d.pkl + params_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/params.yaml + predictions_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/predictions.json + probabilities_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/probabilities.json + score_dict_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/score_dict.json + test_labels_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/test_labels.json + train_labels_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/train_labels.json + model_dir: models + name: 49724daa63c7e5059d18ecdb050f0bfb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e1181bf04f8d5d8ea050d983905418d1 + trainer: + kwargs: {} +name: 49724daa63c7e5059d18ecdb050f0bfb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/499dfc79cd805a6f91258d189ee32680/params.yaml b/examples/security/classification/output/reports/train/499dfc79cd805a6f91258d189ee32680/params.yaml new file mode 100644 index 00000000..736796ff --- /dev/null +++ b/examples/security/classification/output/reports/train/499dfc79cd805a6f91258d189ee32680/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/adv_predictions.json + adv_probabilities_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/f60d591c8bad442f025ba7a0e56d950b.pkl + params_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/params.yaml + predictions_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/predictions.json + probabilities_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/probabilities.json + score_dict_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/score_dict.json + test_labels_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/test_labels.json + train_labels_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/train_labels.json + model_dir: models + name: 499dfc79cd805a6f91258d189ee32680 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9d2d452126302a91cd4bd42a4f094ae5 + trainer: + kwargs: {} +name: 499dfc79cd805a6f91258d189ee32680 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/params.yaml b/examples/security/classification/output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/params.yaml new file mode 100644 index 00000000..13b09369 --- /dev/null +++ b/examples/security/classification/output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/adv_predictions.json + adv_probabilities_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/9e985dcba2681b28c95f479c7ec728eb.pkl + params_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/params.yaml + predictions_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/predictions.json + probabilities_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/probabilities.json + score_dict_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/score_dict.json + test_labels_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/test_labels.json + train_labels_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/train_labels.json + model_dir: models + name: 49a0a9c37a43a5d1422d78cc97814e87 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bba7eaee925931e267c04a018df0a204 + trainer: + kwargs: {} +name: 49a0a9c37a43a5d1422d78cc97814e87 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/49b8b52fb40d7f4f575096353dcf2961/params.yaml b/examples/security/classification/output/reports/train/49b8b52fb40d7f4f575096353dcf2961/params.yaml new file mode 100644 index 00000000..196728b3 --- /dev/null +++ b/examples/security/classification/output/reports/train/49b8b52fb40d7f4f575096353dcf2961/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/adv_predictions.json + adv_probabilities_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/dc248f770b14d61ed4eac281cbe9a11a.pkl + params_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/params.yaml + predictions_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/predictions.json + probabilities_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/probabilities.json + score_dict_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/score_dict.json + test_labels_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/test_labels.json + train_labels_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/train_labels.json + model_dir: models + name: 49b8b52fb40d7f4f575096353dcf2961 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bc80f254c6e729ca53cdde39c3d2a5ad + trainer: + kwargs: {} +name: 49b8b52fb40d7f4f575096353dcf2961 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/49c447fd2490e5db9a11b318722981b2/params.yaml b/examples/security/classification/output/reports/train/49c447fd2490e5db9a11b318722981b2/params.yaml new file mode 100644 index 00000000..c022d50a --- /dev/null +++ b/examples/security/classification/output/reports/train/49c447fd2490e5db9a11b318722981b2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/adv_predictions.json + adv_probabilities_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/9695b095f781a7e25a258fc80fcf773e.pkl + params_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/params.yaml + predictions_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/predictions.json + probabilities_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/probabilities.json + score_dict_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/score_dict.json + test_labels_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/test_labels.json + train_labels_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/train_labels.json + model_dir: models + name: 49c447fd2490e5db9a11b318722981b2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 69e2378adf8d7b561e593d9fb371e905 + trainer: + kwargs: {} +name: 49c447fd2490e5db9a11b318722981b2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/49ce7b6409043c14babce3d1ae6b8085/params.yaml b/examples/security/classification/output/reports/train/49ce7b6409043c14babce3d1ae6b8085/params.yaml new file mode 100644 index 00000000..7e35330c --- /dev/null +++ b/examples/security/classification/output/reports/train/49ce7b6409043c14babce3d1ae6b8085/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/adv_predictions.json + adv_probabilities_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/edc1b2e753a132a3c7a05949b1fd307f.pkl + params_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/params.yaml + predictions_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/predictions.json + probabilities_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/probabilities.json + score_dict_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/score_dict.json + test_labels_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/test_labels.json + train_labels_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/train_labels.json + model_dir: models + name: 49ce7b6409043c14babce3d1ae6b8085 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c2dd3ca08c4cfb70b316df15ba963d65 + trainer: + kwargs: {} +name: 49ce7b6409043c14babce3d1ae6b8085 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/49f91bad1c44419aab51ce19c297e070/params.yaml b/examples/security/classification/output/reports/train/49f91bad1c44419aab51ce19c297e070/params.yaml new file mode 100644 index 00000000..429ab776 --- /dev/null +++ b/examples/security/classification/output/reports/train/49f91bad1c44419aab51ce19c297e070/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/adv_predictions.json + adv_probabilities_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/95d4990f4aa5539e982ff1f20af86196.pkl + params_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/params.yaml + predictions_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/predictions.json + probabilities_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/probabilities.json + score_dict_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/score_dict.json + test_labels_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/test_labels.json + train_labels_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/train_labels.json + model_dir: models + name: 49f91bad1c44419aab51ce19c297e070 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 095692ec7248ac12f5f1efb8bde130c8 + trainer: + kwargs: {} +name: 49f91bad1c44419aab51ce19c297e070 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/params.yaml b/examples/security/classification/output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/params.yaml new file mode 100644 index 00000000..7abb3f14 --- /dev/null +++ b/examples/security/classification/output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 253171c8fbfdc2625fe7ee98386b57db + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 29970b61a2e8f9c08d02ce5c21fcb20b + trainer: + kwargs: {} + name: c48f3b8e63385e866731aaa803c06776 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/adv_predictions.json + adv_probabilities_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/adv_probabilities.json + attack_file: output/attacks/b0dbd6b9ffce3957ff44d85bc4bf620a.pkl + data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl + model_file: output/models/17dd2599534253c240a74baffdd03273.pkl + params_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/params.yaml + predictions_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/predictions.json + probabilities_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/probabilities.json + score_dict_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/score_dict.json + test_labels_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/test_labels.json + train_labels_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/train_labels.json + model_dir: models + name: 4a0ffcecf5d72c142085d3ee450af9b6 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8f2ca3159ecefb60740f8fc10b8fb6a4 + trainer: + kwargs: {} +name: 4a0ffcecf5d72c142085d3ee450af9b6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/params.yaml b/examples/security/classification/output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/params.yaml new file mode 100644 index 00000000..5ced7b44 --- /dev/null +++ b/examples/security/classification/output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/adv_predictions.json + adv_probabilities_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/cc8ed8b3b067f385d7f540c3292a6328.pkl + params_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/params.yaml + predictions_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/predictions.json + probabilities_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/probabilities.json + score_dict_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/score_dict.json + test_labels_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/test_labels.json + train_labels_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/train_labels.json + model_dir: models + name: 4a14ffca850a0111d8325a99f9eeaff3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3f85e1d1ab7ceb0e597b0d00c0b5fdb3 + trainer: + kwargs: {} +name: 4a14ffca850a0111d8325a99f9eeaff3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4a184ef8a58879acbc0885d28fc521d4/params.yaml b/examples/security/classification/output/reports/train/4a184ef8a58879acbc0885d28fc521d4/params.yaml new file mode 100644 index 00000000..e71a71dd --- /dev/null +++ b/examples/security/classification/output/reports/train/4a184ef8a58879acbc0885d28fc521d4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/adv_predictions.json + adv_probabilities_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/a277fdee93338d631a8e08cdd30f8ddd.pkl + params_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/params.yaml + predictions_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/predictions.json + probabilities_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/probabilities.json + score_dict_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/score_dict.json + test_labels_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/test_labels.json + train_labels_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/train_labels.json + model_dir: models + name: 4a184ef8a58879acbc0885d28fc521d4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 160ada13ef944fc23ecdcbb4fd65b116 + trainer: + kwargs: {} +name: 4a184ef8a58879acbc0885d28fc521d4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4a3566901b3aa4609faf9b713d0eebea/params.yaml b/examples/security/classification/output/reports/train/4a3566901b3aa4609faf9b713d0eebea/params.yaml new file mode 100644 index 00000000..17f585b0 --- /dev/null +++ b/examples/security/classification/output/reports/train/4a3566901b3aa4609faf9b713d0eebea/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/adv_predictions.json + adv_probabilities_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/bb0cc30ddcc4f77212b75c2f73d7381d.pkl + params_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/params.yaml + predictions_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/predictions.json + probabilities_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/probabilities.json + score_dict_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/score_dict.json + test_labels_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/test_labels.json + train_labels_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/train_labels.json + model_dir: models + name: 4a3566901b3aa4609faf9b713d0eebea + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f22824c61657bf8883cade917a2173c4 + trainer: + kwargs: {} +name: 4a3566901b3aa4609faf9b713d0eebea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4a3cbd42be8bd19341c92beed59de6be/params.yaml b/examples/security/classification/output/reports/train/4a3cbd42be8bd19341c92beed59de6be/params.yaml new file mode 100644 index 00000000..589a92e1 --- /dev/null +++ b/examples/security/classification/output/reports/train/4a3cbd42be8bd19341c92beed59de6be/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/adv_predictions.json + adv_probabilities_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/a5aa6d9b1944e8a2a298cb7797f78a03.pkl + params_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/params.yaml + predictions_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/predictions.json + probabilities_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/probabilities.json + score_dict_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/score_dict.json + test_labels_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/test_labels.json + train_labels_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/train_labels.json + model_dir: models + name: 4a3cbd42be8bd19341c92beed59de6be + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 94bc5724d180932bc0a56e0e5c4703c8 + trainer: + kwargs: {} +name: 4a3cbd42be8bd19341c92beed59de6be +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4a50097467dc60eb39758204db4a87a8/params.yaml b/examples/security/classification/output/reports/train/4a50097467dc60eb39758204db4a87a8/params.yaml new file mode 100644 index 00000000..dfdfcac8 --- /dev/null +++ b/examples/security/classification/output/reports/train/4a50097467dc60eb39758204db4a87a8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/adv_predictions.json + adv_probabilities_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/0a2bfe759d65defc3ee38716a6fe3e8d.pkl + params_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/params.yaml + predictions_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/predictions.json + probabilities_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/probabilities.json + score_dict_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/score_dict.json + test_labels_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/test_labels.json + train_labels_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/train_labels.json + model_dir: models + name: 4a50097467dc60eb39758204db4a87a8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0478de635d75660f96aca91f440f8e5f + trainer: + kwargs: {} +name: 4a50097467dc60eb39758204db4a87a8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/params.yaml b/examples/security/classification/output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/params.yaml new file mode 100644 index 00000000..40e66948 --- /dev/null +++ b/examples/security/classification/output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/adv_predictions.json + adv_probabilities_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/ec932da709a051f7e990a7d60d200677.pkl + params_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/params.yaml + predictions_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/predictions.json + probabilities_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/probabilities.json + score_dict_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/score_dict.json + test_labels_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/test_labels.json + train_labels_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/train_labels.json + model_dir: models + name: 4a65fa18361d694c72b9b51c757a6cc0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 819a2c747383b35c79a07aa66a55dfb4 + trainer: + kwargs: {} +name: 4a65fa18361d694c72b9b51c757a6cc0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4a9bd9e3571884361e5494d12cf73f52/params.yaml b/examples/security/classification/output/reports/train/4a9bd9e3571884361e5494d12cf73f52/params.yaml new file mode 100644 index 00000000..e73ab107 --- /dev/null +++ b/examples/security/classification/output/reports/train/4a9bd9e3571884361e5494d12cf73f52/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/adv_predictions.json + adv_probabilities_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/53e4cc9f95747c561cc2c841d0706c05.pkl + params_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/params.yaml + predictions_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/predictions.json + probabilities_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/probabilities.json + score_dict_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/score_dict.json + test_labels_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/test_labels.json + train_labels_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/train_labels.json + model_dir: models + name: 4a9bd9e3571884361e5494d12cf73f52 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 90d99eb0dcc583b82cd3c01124077275 + trainer: + kwargs: {} +name: 4a9bd9e3571884361e5494d12cf73f52 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4ab0f95a641a6c322711711f819494f1/params.yaml b/examples/security/classification/output/reports/train/4ab0f95a641a6c322711711f819494f1/params.yaml new file mode 100644 index 00000000..baaf8299 --- /dev/null +++ b/examples/security/classification/output/reports/train/4ab0f95a641a6c322711711f819494f1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/adv_predictions.json + adv_probabilities_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/530ffd4e33869d3bebe9905aec07338d.pkl + params_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/params.yaml + predictions_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/predictions.json + probabilities_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/probabilities.json + score_dict_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/score_dict.json + test_labels_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/test_labels.json + train_labels_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/train_labels.json + model_dir: models + name: 4ab0f95a641a6c322711711f819494f1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7c9a85f1e28ba5531b88a5b7e457578d + trainer: + kwargs: {} +name: 4ab0f95a641a6c322711711f819494f1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4ad45e711e936495f1f17b4796821856/params.yaml b/examples/security/classification/output/reports/train/4ad45e711e936495f1f17b4796821856/params.yaml new file mode 100644 index 00000000..dbb45679 --- /dev/null +++ b/examples/security/classification/output/reports/train/4ad45e711e936495f1f17b4796821856/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4ad45e711e936495f1f17b4796821856/adv_predictions.json + adv_probabilities_file: output/reports/train/4ad45e711e936495f1f17b4796821856/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/0e93385aba61430548ce1a63f7ac0e44.pkl + params_file: output/reports/train/4ad45e711e936495f1f17b4796821856/params.yaml + predictions_file: output/reports/train/4ad45e711e936495f1f17b4796821856/predictions.json + probabilities_file: output/reports/train/4ad45e711e936495f1f17b4796821856/probabilities.json + score_dict_file: output/reports/train/4ad45e711e936495f1f17b4796821856/score_dict.json + test_labels_file: output/reports/train/4ad45e711e936495f1f17b4796821856/test_labels.json + train_labels_file: output/reports/train/4ad45e711e936495f1f17b4796821856/train_labels.json + model_dir: models + name: 4ad45e711e936495f1f17b4796821856 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 36d84b04df112cc48e7aab8b0eba1574 + trainer: + kwargs: {} +name: 4ad45e711e936495f1f17b4796821856 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4b28e94538699157eb430f7a89a80e73/params.yaml b/examples/security/classification/output/reports/train/4b28e94538699157eb430f7a89a80e73/params.yaml new file mode 100644 index 00000000..1b7defd7 --- /dev/null +++ b/examples/security/classification/output/reports/train/4b28e94538699157eb430f7a89a80e73/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/adv_predictions.json + adv_probabilities_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/7165635722a5741e0c5d8e83184036ab.pkl + params_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/params.yaml + predictions_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/predictions.json + probabilities_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/probabilities.json + score_dict_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/score_dict.json + test_labels_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/test_labels.json + train_labels_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/train_labels.json + model_dir: models + name: 4b28e94538699157eb430f7a89a80e73 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e03a32042d4d8fa1e221e80320408708 + trainer: + kwargs: {} +name: 4b28e94538699157eb430f7a89a80e73 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4b63ac2e506474fae341d0e75ad69b36/params.yaml b/examples/security/classification/output/reports/train/4b63ac2e506474fae341d0e75ad69b36/params.yaml new file mode 100644 index 00000000..65771825 --- /dev/null +++ b/examples/security/classification/output/reports/train/4b63ac2e506474fae341d0e75ad69b36/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/adv_predictions.json + adv_probabilities_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/2d048baf82b5939928c653683a44aba3.pkl + params_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/params.yaml + predictions_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/predictions.json + probabilities_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/probabilities.json + score_dict_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/score_dict.json + test_labels_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/test_labels.json + train_labels_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/train_labels.json + model_dir: models + name: 4b63ac2e506474fae341d0e75ad69b36 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 85ede6dce7618835df4515bdde44fb67 + trainer: + kwargs: {} +name: 4b63ac2e506474fae341d0e75ad69b36 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4bb675259c051574dab809a7dcec7555/params.yaml b/examples/security/classification/output/reports/train/4bb675259c051574dab809a7dcec7555/params.yaml new file mode 100644 index 00000000..113dc3af --- /dev/null +++ b/examples/security/classification/output/reports/train/4bb675259c051574dab809a7dcec7555/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4bb675259c051574dab809a7dcec7555/adv_predictions.json + adv_probabilities_file: output/reports/train/4bb675259c051574dab809a7dcec7555/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/6ebc435b2d49ae94e7db8fb10e742603.pkl + params_file: output/reports/train/4bb675259c051574dab809a7dcec7555/params.yaml + predictions_file: output/reports/train/4bb675259c051574dab809a7dcec7555/predictions.json + probabilities_file: output/reports/train/4bb675259c051574dab809a7dcec7555/probabilities.json + score_dict_file: output/reports/train/4bb675259c051574dab809a7dcec7555/score_dict.json + test_labels_file: output/reports/train/4bb675259c051574dab809a7dcec7555/test_labels.json + train_labels_file: output/reports/train/4bb675259c051574dab809a7dcec7555/train_labels.json + model_dir: models + name: 4bb675259c051574dab809a7dcec7555 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f5f0640390fb2a4dbda150c4eaa63ece + trainer: + kwargs: {} +name: 4bb675259c051574dab809a7dcec7555 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/params.yaml b/examples/security/classification/output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/params.yaml new file mode 100644 index 00000000..af2b62eb --- /dev/null +++ b/examples/security/classification/output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/adv_predictions.json + adv_probabilities_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/e623aff809ac4ee6fac43903ca8391a7.pkl + params_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/params.yaml + predictions_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/predictions.json + probabilities_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/probabilities.json + score_dict_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/score_dict.json + test_labels_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/test_labels.json + train_labels_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/train_labels.json + model_dir: models + name: 4bc9608568a694b0ea9f9075ad824f4c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a27b94b8634b0f16b0ce96b25358c128 + trainer: + kwargs: {} +name: 4bc9608568a694b0ea9f9075ad824f4c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4bd55917487242c160c07f478bccaef2/params.yaml b/examples/security/classification/output/reports/train/4bd55917487242c160c07f478bccaef2/params.yaml new file mode 100644 index 00000000..67d0fa58 --- /dev/null +++ b/examples/security/classification/output/reports/train/4bd55917487242c160c07f478bccaef2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4bd55917487242c160c07f478bccaef2/adv_predictions.json + adv_probabilities_file: output/reports/train/4bd55917487242c160c07f478bccaef2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/342060e97c2c293ded1f31520eb5ae03.pkl + params_file: output/reports/train/4bd55917487242c160c07f478bccaef2/params.yaml + predictions_file: output/reports/train/4bd55917487242c160c07f478bccaef2/predictions.json + probabilities_file: output/reports/train/4bd55917487242c160c07f478bccaef2/probabilities.json + score_dict_file: output/reports/train/4bd55917487242c160c07f478bccaef2/score_dict.json + test_labels_file: output/reports/train/4bd55917487242c160c07f478bccaef2/test_labels.json + train_labels_file: output/reports/train/4bd55917487242c160c07f478bccaef2/train_labels.json + model_dir: models + name: 4bd55917487242c160c07f478bccaef2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 64c0d8899ebf648e252a68c8c86141a9 + trainer: + kwargs: {} +name: 4bd55917487242c160c07f478bccaef2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4bd6646f03c6f58060ea195a46ebac23/params.yaml b/examples/security/classification/output/reports/train/4bd6646f03c6f58060ea195a46ebac23/params.yaml new file mode 100644 index 00000000..4b40eed0 --- /dev/null +++ b/examples/security/classification/output/reports/train/4bd6646f03c6f58060ea195a46ebac23/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/adv_predictions.json + adv_probabilities_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/9efeca7d3aea0b6db3032f1c8661d9d0.pkl + params_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/params.yaml + predictions_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/predictions.json + probabilities_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/probabilities.json + score_dict_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/score_dict.json + test_labels_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/test_labels.json + train_labels_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/train_labels.json + model_dir: models + name: 4bd6646f03c6f58060ea195a46ebac23 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9d2a56482c0575227301151a43ad4270 + trainer: + kwargs: {} +name: 4bd6646f03c6f58060ea195a46ebac23 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/params.yaml b/examples/security/classification/output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/params.yaml new file mode 100644 index 00000000..712aa827 --- /dev/null +++ b/examples/security/classification/output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/adv_predictions.json + adv_probabilities_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/b66011dc4f5021746f924ebd7466d8e9.pkl + params_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/params.yaml + predictions_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/predictions.json + probabilities_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/probabilities.json + score_dict_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/score_dict.json + test_labels_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/test_labels.json + train_labels_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/train_labels.json + model_dir: models + name: 4bdd1f9dbc8266ebc86ed6eb4f451578 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dd8cf3a112c84647c788490eccd552ed + trainer: + kwargs: {} +name: 4bdd1f9dbc8266ebc86ed6eb4f451578 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/params.yaml b/examples/security/classification/output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/params.yaml new file mode 100644 index 00000000..98128884 --- /dev/null +++ b/examples/security/classification/output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/adv_predictions.json + adv_probabilities_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/eb87d2119a0ac9159268dd00db5663ea.pkl + params_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/params.yaml + predictions_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/predictions.json + probabilities_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/probabilities.json + score_dict_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/score_dict.json + test_labels_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/test_labels.json + train_labels_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/train_labels.json + model_dir: models + name: 4be2832f85c1d507df9d0f6cccc174f6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1ae99243fe9ebe0582bb5e56b35eee78 + trainer: + kwargs: {} +name: 4be2832f85c1d507df9d0f6cccc174f6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4bf49eb696a915f2ef7750f6669bd774/params.yaml b/examples/security/classification/output/reports/train/4bf49eb696a915f2ef7750f6669bd774/params.yaml new file mode 100644 index 00000000..8e88fd76 --- /dev/null +++ b/examples/security/classification/output/reports/train/4bf49eb696a915f2ef7750f6669bd774/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/adv_predictions.json + adv_probabilities_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/1613d2ae2fb8e0120c7fae1983863ab2.pkl + params_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/params.yaml + predictions_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/predictions.json + probabilities_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/probabilities.json + score_dict_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/score_dict.json + test_labels_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/test_labels.json + train_labels_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/train_labels.json + model_dir: models + name: 4bf49eb696a915f2ef7750f6669bd774 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9268e481a6e31db6c18a15b7b221e071 + trainer: + kwargs: {} +name: 4bf49eb696a915f2ef7750f6669bd774 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4bf8cbc4120f965398b1816546f12988/params.yaml b/examples/security/classification/output/reports/train/4bf8cbc4120f965398b1816546f12988/params.yaml new file mode 100644 index 00000000..24fdae24 --- /dev/null +++ b/examples/security/classification/output/reports/train/4bf8cbc4120f965398b1816546f12988/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/adv_predictions.json + adv_probabilities_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/67a643efd2609b313541b5d76d1d5f66.pkl + params_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/params.yaml + predictions_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/predictions.json + probabilities_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/probabilities.json + score_dict_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/score_dict.json + test_labels_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/test_labels.json + train_labels_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/train_labels.json + model_dir: models + name: 4bf8cbc4120f965398b1816546f12988 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1f0b2917b0a1f4b1500ae7bfe8cd6bee + trainer: + kwargs: {} +name: 4bf8cbc4120f965398b1816546f12988 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4c43cad4a0eec4727551367449b62da1/params.yaml b/examples/security/classification/output/reports/train/4c43cad4a0eec4727551367449b62da1/params.yaml new file mode 100644 index 00000000..08516807 --- /dev/null +++ b/examples/security/classification/output/reports/train/4c43cad4a0eec4727551367449b62da1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/adv_predictions.json + adv_probabilities_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/2b6aad3dd4cca985f51c484a39f94ea0.pkl + params_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/params.yaml + predictions_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/predictions.json + probabilities_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/probabilities.json + score_dict_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/score_dict.json + test_labels_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/test_labels.json + train_labels_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/train_labels.json + model_dir: models + name: 4c43cad4a0eec4727551367449b62da1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c1f5eea2b5f1121bd01e876b5abbddbf + trainer: + kwargs: {} +name: 4c43cad4a0eec4727551367449b62da1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4c5571df958e0111899315f7334f5eef/params.yaml b/examples/security/classification/output/reports/train/4c5571df958e0111899315f7334f5eef/params.yaml new file mode 100644 index 00000000..4b0a56fa --- /dev/null +++ b/examples/security/classification/output/reports/train/4c5571df958e0111899315f7334f5eef/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4c5571df958e0111899315f7334f5eef/adv_predictions.json + adv_probabilities_file: output/reports/train/4c5571df958e0111899315f7334f5eef/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/39f8403f780f1de47732d1453b458936.pkl + params_file: output/reports/train/4c5571df958e0111899315f7334f5eef/params.yaml + predictions_file: output/reports/train/4c5571df958e0111899315f7334f5eef/predictions.json + probabilities_file: output/reports/train/4c5571df958e0111899315f7334f5eef/probabilities.json + score_dict_file: output/reports/train/4c5571df958e0111899315f7334f5eef/score_dict.json + test_labels_file: output/reports/train/4c5571df958e0111899315f7334f5eef/test_labels.json + train_labels_file: output/reports/train/4c5571df958e0111899315f7334f5eef/train_labels.json + model_dir: models + name: 4c5571df958e0111899315f7334f5eef + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ddf86ebbfcc664f00f510326103ebf2f + trainer: + kwargs: {} +name: 4c5571df958e0111899315f7334f5eef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/params.yaml b/examples/security/classification/output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/params.yaml new file mode 100644 index 00000000..5687174e --- /dev/null +++ b/examples/security/classification/output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/adv_predictions.json + adv_probabilities_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/d3bbfdb5f521b3170d9023bbc262e92a.pkl + params_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/params.yaml + predictions_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/predictions.json + probabilities_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/probabilities.json + score_dict_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/score_dict.json + test_labels_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/test_labels.json + train_labels_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/train_labels.json + model_dir: models + name: 4c6a110a656aa7361b2b5155a80b22ff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c029f113fc1f27fe46c49fb994277aa5 + trainer: + kwargs: {} +name: 4c6a110a656aa7361b2b5155a80b22ff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4c89819e206a145deddedf45c09e7d4d/params.yaml b/examples/security/classification/output/reports/train/4c89819e206a145deddedf45c09e7d4d/params.yaml new file mode 100644 index 00000000..2c89f6f9 --- /dev/null +++ b/examples/security/classification/output/reports/train/4c89819e206a145deddedf45c09e7d4d/params.yaml @@ -0,0 +1,115 @@ +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/adv_predictions.json + adv_probabilities_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl + model_file: output/models/77dc9e5c469b115080f769aef30a9bab.pkl + params_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/params.yaml + predictions_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/predictions.json + probabilities_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/probabilities.json + score_dict_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/score_dict.json + test_labels_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/test_labels.json + train_labels_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/train_labels.json + model_dir: models + name: 4c89819e206a145deddedf45c09e7d4d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2f0f4e7c0aa94810059ba180d68b630 + trainer: + kwargs: {} +name: 4c89819e206a145deddedf45c09e7d4d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/params.yaml b/examples/security/classification/output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/params.yaml new file mode 100644 index 00000000..a01940f0 --- /dev/null +++ b/examples/security/classification/output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/adv_predictions.json + adv_probabilities_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/bf75e535a2c26f3535acdb29473d0231.pkl + params_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/params.yaml + predictions_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/predictions.json + probabilities_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/probabilities.json + score_dict_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/score_dict.json + test_labels_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/test_labels.json + train_labels_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/train_labels.json + model_dir: models + name: 4c8c6f36f7f931fb9163354ff8e72ac6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d930c78d094aefaedb7c78f41fe2b71a + trainer: + kwargs: {} +name: 4c8c6f36f7f931fb9163354ff8e72ac6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4ca1d3df012951326e2562828e13acf9/params.yaml b/examples/security/classification/output/reports/train/4ca1d3df012951326e2562828e13acf9/params.yaml new file mode 100644 index 00000000..b3997701 --- /dev/null +++ b/examples/security/classification/output/reports/train/4ca1d3df012951326e2562828e13acf9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/adv_predictions.json + adv_probabilities_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/c3e7d5021e8d00760ccb7bba13ee2bdc.pkl + params_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/params.yaml + predictions_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/predictions.json + probabilities_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/probabilities.json + score_dict_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/score_dict.json + test_labels_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/test_labels.json + train_labels_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/train_labels.json + model_dir: models + name: 4ca1d3df012951326e2562828e13acf9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 056b73cbd56989bd4772eb44b6ede007 + trainer: + kwargs: {} +name: 4ca1d3df012951326e2562828e13acf9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/params.yaml b/examples/security/classification/output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/params.yaml new file mode 100644 index 00000000..de90c2cf --- /dev/null +++ b/examples/security/classification/output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/adv_predictions.json + adv_probabilities_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/73fd44df0ab5c0fb148a84a629c057b5.pkl + params_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/params.yaml + predictions_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/predictions.json + probabilities_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/probabilities.json + score_dict_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/score_dict.json + test_labels_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/test_labels.json + train_labels_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/train_labels.json + model_dir: models + name: 4cbdfbf71b75b299b4d02397e74da2d6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 643ef1d4111596e5553b06e3b34d2ce1 + trainer: + kwargs: {} +name: 4cbdfbf71b75b299b4d02397e74da2d6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4cd2f0aa194ad793b830df9027887840/params.yaml b/examples/security/classification/output/reports/train/4cd2f0aa194ad793b830df9027887840/params.yaml new file mode 100644 index 00000000..8329f101 --- /dev/null +++ b/examples/security/classification/output/reports/train/4cd2f0aa194ad793b830df9027887840/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/adv_predictions.json + adv_probabilities_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/7b7a952e18a01d889c3ba21fc3c3be3e.pkl + params_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/params.yaml + predictions_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/predictions.json + probabilities_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/probabilities.json + score_dict_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/score_dict.json + test_labels_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/test_labels.json + train_labels_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/train_labels.json + model_dir: models + name: 4cd2f0aa194ad793b830df9027887840 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 591f4ba263a02435db44a60eff440cc4 + trainer: + kwargs: {} +name: 4cd2f0aa194ad793b830df9027887840 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4cefa6721039bc48f820386c707ebe80/params.yaml b/examples/security/classification/output/reports/train/4cefa6721039bc48f820386c707ebe80/params.yaml new file mode 100644 index 00000000..67a4d687 --- /dev/null +++ b/examples/security/classification/output/reports/train/4cefa6721039bc48f820386c707ebe80/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/adv_predictions.json + adv_probabilities_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/0d44265e3fd476d634e9758c74deb15f.pkl + params_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/params.yaml + predictions_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/predictions.json + probabilities_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/probabilities.json + score_dict_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/score_dict.json + test_labels_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/test_labels.json + train_labels_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/train_labels.json + model_dir: models + name: 4cefa6721039bc48f820386c707ebe80 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cba6dfbc8a87a4cf483abba6c2ce7267 + trainer: + kwargs: {} +name: 4cefa6721039bc48f820386c707ebe80 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/params.yaml b/examples/security/classification/output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/params.yaml new file mode 100644 index 00000000..a527970d --- /dev/null +++ b/examples/security/classification/output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/adv_predictions.json + adv_probabilities_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/988ed0f02812824bad6112e26a7b3c0b.pkl + params_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/params.yaml + predictions_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/predictions.json + probabilities_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/probabilities.json + score_dict_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/score_dict.json + test_labels_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/test_labels.json + train_labels_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/train_labels.json + model_dir: models + name: 4d05d57cb58f07efe77044d4b0fb4691 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2d4e5e40c641e9fbf0e5e31daaa62826 + trainer: + kwargs: {} +name: 4d05d57cb58f07efe77044d4b0fb4691 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4d160b72628465117cb363a4873cab65/params.yaml b/examples/security/classification/output/reports/train/4d160b72628465117cb363a4873cab65/params.yaml new file mode 100644 index 00000000..96019df1 --- /dev/null +++ b/examples/security/classification/output/reports/train/4d160b72628465117cb363a4873cab65/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4d160b72628465117cb363a4873cab65/adv_predictions.json + adv_probabilities_file: output/reports/train/4d160b72628465117cb363a4873cab65/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/f44b17b7df23486e6a92bc8bb3d9e986.pkl + params_file: output/reports/train/4d160b72628465117cb363a4873cab65/params.yaml + predictions_file: output/reports/train/4d160b72628465117cb363a4873cab65/predictions.json + probabilities_file: output/reports/train/4d160b72628465117cb363a4873cab65/probabilities.json + score_dict_file: output/reports/train/4d160b72628465117cb363a4873cab65/score_dict.json + test_labels_file: output/reports/train/4d160b72628465117cb363a4873cab65/test_labels.json + train_labels_file: output/reports/train/4d160b72628465117cb363a4873cab65/train_labels.json + model_dir: models + name: 4d160b72628465117cb363a4873cab65 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 681841b07170a27581b80fdb19df2fe8 + trainer: + kwargs: {} +name: 4d160b72628465117cb363a4873cab65 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/params.yaml b/examples/security/classification/output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/params.yaml new file mode 100644 index 00000000..fd6a74e8 --- /dev/null +++ b/examples/security/classification/output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/adv_predictions.json + adv_probabilities_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/84d5d0b6479867b95826073cc703b2d3.pkl + params_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/params.yaml + predictions_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/predictions.json + probabilities_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/probabilities.json + score_dict_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/score_dict.json + test_labels_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/test_labels.json + train_labels_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/train_labels.json + model_dir: models + name: 4d30d8dc66a47f463d7b9cb36d1000a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 683664c69d6cb42ad75a1198bd3c863e + trainer: + kwargs: {} +name: 4d30d8dc66a47f463d7b9cb36d1000a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4d3b5374c31d6b43e807630e08d990fc/params.yaml b/examples/security/classification/output/reports/train/4d3b5374c31d6b43e807630e08d990fc/params.yaml new file mode 100644 index 00000000..d2f7b867 --- /dev/null +++ b/examples/security/classification/output/reports/train/4d3b5374c31d6b43e807630e08d990fc/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/adv_predictions.json + adv_probabilities_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/bd7a94a66e57e07c01324faba7f7728f.pkl + params_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/params.yaml + predictions_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/predictions.json + probabilities_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/probabilities.json + score_dict_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/score_dict.json + test_labels_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/test_labels.json + train_labels_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/train_labels.json + model_dir: models + name: 4d3b5374c31d6b43e807630e08d990fc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 140fc861ebefbffbfa137b0ec828adeb + trainer: + kwargs: {} +name: 4d3b5374c31d6b43e807630e08d990fc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/params.yaml b/examples/security/classification/output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/params.yaml new file mode 100644 index 00000000..86868486 --- /dev/null +++ b/examples/security/classification/output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/adv_predictions.json + adv_probabilities_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/96cc05350fc39dae77c385142f7ece58.pkl + params_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/params.yaml + predictions_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/predictions.json + probabilities_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/probabilities.json + score_dict_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/score_dict.json + test_labels_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/test_labels.json + train_labels_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/train_labels.json + model_dir: models + name: 4d6228938ae4018bf70cc9aafe0b280c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 22cc8abcf8d43ed992f149b679457c53 + trainer: + kwargs: {} +name: 4d6228938ae4018bf70cc9aafe0b280c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/params.yaml b/examples/security/classification/output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/params.yaml new file mode 100644 index 00000000..82c5a13c --- /dev/null +++ b/examples/security/classification/output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/adv_predictions.json + adv_probabilities_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/1410e177bc8f7a943ffcedf148720ab5.pkl + params_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/params.yaml + predictions_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/predictions.json + probabilities_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/probabilities.json + score_dict_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/score_dict.json + test_labels_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/test_labels.json + train_labels_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/train_labels.json + model_dir: models + name: 4d6a192fce463121e20d9fd6eee99ce8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 237456c5dcf6a23c9c5846f672ef1298 + trainer: + kwargs: {} +name: 4d6a192fce463121e20d9fd6eee99ce8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4db579196909d67ccb0df50b461e2660/params.yaml b/examples/security/classification/output/reports/train/4db579196909d67ccb0df50b461e2660/params.yaml new file mode 100644 index 00000000..a651c50f --- /dev/null +++ b/examples/security/classification/output/reports/train/4db579196909d67ccb0df50b461e2660/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4db579196909d67ccb0df50b461e2660/adv_predictions.json + adv_probabilities_file: output/reports/train/4db579196909d67ccb0df50b461e2660/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/01f960a226a44356397b49701cdec2b2.pkl + params_file: output/reports/train/4db579196909d67ccb0df50b461e2660/params.yaml + predictions_file: output/reports/train/4db579196909d67ccb0df50b461e2660/predictions.json + probabilities_file: output/reports/train/4db579196909d67ccb0df50b461e2660/probabilities.json + score_dict_file: output/reports/train/4db579196909d67ccb0df50b461e2660/score_dict.json + test_labels_file: output/reports/train/4db579196909d67ccb0df50b461e2660/test_labels.json + train_labels_file: output/reports/train/4db579196909d67ccb0df50b461e2660/train_labels.json + model_dir: models + name: 4db579196909d67ccb0df50b461e2660 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7c38dc4dd0d183d25e5896c830575157 + trainer: + kwargs: {} +name: 4db579196909d67ccb0df50b461e2660 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/params.yaml b/examples/security/classification/output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/params.yaml new file mode 100644 index 00000000..312ae0fa --- /dev/null +++ b/examples/security/classification/output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/adv_predictions.json + adv_probabilities_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e0d8a281b1cdd731ea2b89c346c869ca.pkl + params_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/params.yaml + predictions_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/predictions.json + probabilities_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/probabilities.json + score_dict_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/score_dict.json + test_labels_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/test_labels.json + train_labels_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/train_labels.json + model_dir: models + name: 4db9de4939a2750d5413ad1a4c8c1471 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3401a06e9656d7cf5f152a63a590c65 + trainer: + kwargs: {} +name: 4db9de4939a2750d5413ad1a4c8c1471 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/params.yaml b/examples/security/classification/output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/params.yaml new file mode 100644 index 00000000..9cc1f9e1 --- /dev/null +++ b/examples/security/classification/output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/adv_predictions.json + adv_probabilities_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/37b18daf9db03e42cc2072242dcee3d4.pkl + params_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/params.yaml + predictions_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/predictions.json + probabilities_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/probabilities.json + score_dict_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/score_dict.json + test_labels_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/test_labels.json + train_labels_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/train_labels.json + model_dir: models + name: 4deccaefd1ef20f2e9341c080527d9a7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ed56935caf239cec50696ce3b3a486a8 + trainer: + kwargs: {} +name: 4deccaefd1ef20f2e9341c080527d9a7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4e1f26073cf636e4c870909008f8c194/params.yaml b/examples/security/classification/output/reports/train/4e1f26073cf636e4c870909008f8c194/params.yaml new file mode 100644 index 00000000..dc43a8a4 --- /dev/null +++ b/examples/security/classification/output/reports/train/4e1f26073cf636e4c870909008f8c194/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/adv_predictions.json + adv_probabilities_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/1b197a8fa118085952a288d7f5c9be72.pkl + params_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/params.yaml + predictions_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/predictions.json + probabilities_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/probabilities.json + score_dict_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/score_dict.json + test_labels_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/test_labels.json + train_labels_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/train_labels.json + model_dir: models + name: 4e1f26073cf636e4c870909008f8c194 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 890a86b3ee04ca5c7375ef4032259e72 + trainer: + kwargs: {} +name: 4e1f26073cf636e4c870909008f8c194 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/params.yaml b/examples/security/classification/output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/params.yaml new file mode 100644 index 00000000..2dca7924 --- /dev/null +++ b/examples/security/classification/output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/adv_predictions.json + adv_probabilities_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/50bda82bcb3ece227bb2132d7e22bab1.pkl + params_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/params.yaml + predictions_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/predictions.json + probabilities_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/probabilities.json + score_dict_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/score_dict.json + test_labels_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/test_labels.json + train_labels_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/train_labels.json + model_dir: models + name: 4e22743d35c879b6ba1ed74d56b218e3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edad89f67e5c3b616da36aedd92d2bbf + trainer: + kwargs: {} +name: 4e22743d35c879b6ba1ed74d56b218e3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/params.yaml b/examples/security/classification/output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/params.yaml new file mode 100644 index 00000000..7b68ca96 --- /dev/null +++ b/examples/security/classification/output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/adv_predictions.json + adv_probabilities_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/ada67058f947d28fc8caa9967b3d2a87.pkl + params_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/params.yaml + predictions_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/predictions.json + probabilities_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/probabilities.json + score_dict_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/score_dict.json + test_labels_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/test_labels.json + train_labels_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/train_labels.json + model_dir: models + name: 4e29b7a31cc375a26d053512e6b58b4b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 932e8ac92bdc655c47162a516f75db68 + trainer: + kwargs: {} +name: 4e29b7a31cc375a26d053512e6b58b4b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4e3083c4dbb4889a70011b341072ae1f/params.yaml b/examples/security/classification/output/reports/train/4e3083c4dbb4889a70011b341072ae1f/params.yaml new file mode 100644 index 00000000..5f4ed328 --- /dev/null +++ b/examples/security/classification/output/reports/train/4e3083c4dbb4889a70011b341072ae1f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/adv_predictions.json + adv_probabilities_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/d5568192ddf3c805cee66e394b5dde43.pkl + params_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/params.yaml + predictions_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/predictions.json + probabilities_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/probabilities.json + score_dict_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/score_dict.json + test_labels_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/test_labels.json + train_labels_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/train_labels.json + model_dir: models + name: 4e3083c4dbb4889a70011b341072ae1f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e7c5dac7bd6b4bdc852d9771063ef9d7 + trainer: + kwargs: {} +name: 4e3083c4dbb4889a70011b341072ae1f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4e57e62f413620cbc8a5492a6b729d32/params.yaml b/examples/security/classification/output/reports/train/4e57e62f413620cbc8a5492a6b729d32/params.yaml new file mode 100644 index 00000000..982449ca --- /dev/null +++ b/examples/security/classification/output/reports/train/4e57e62f413620cbc8a5492a6b729d32/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/adv_predictions.json + adv_probabilities_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/12d30dac7ceca154db885eff8483b961.pkl + params_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/params.yaml + predictions_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/predictions.json + probabilities_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/probabilities.json + score_dict_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/score_dict.json + test_labels_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/test_labels.json + train_labels_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/train_labels.json + model_dir: models + name: 4e57e62f413620cbc8a5492a6b729d32 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fbb142f83f5b415ed64a8e0757663d95 + trainer: + kwargs: {} +name: 4e57e62f413620cbc8a5492a6b729d32 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4e6624d695112a44c9134fe2d6537069/params.yaml b/examples/security/classification/output/reports/train/4e6624d695112a44c9134fe2d6537069/params.yaml new file mode 100644 index 00000000..9da5410a --- /dev/null +++ b/examples/security/classification/output/reports/train/4e6624d695112a44c9134fe2d6537069/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/adv_predictions.json + adv_probabilities_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/bc316f2920a2f5b89223ee5823d81f5e.pkl + params_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/params.yaml + predictions_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/predictions.json + probabilities_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/probabilities.json + score_dict_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/score_dict.json + test_labels_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/test_labels.json + train_labels_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/train_labels.json + model_dir: models + name: 4e6624d695112a44c9134fe2d6537069 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3ae3abb620bf3b5f981f7510e4d4aa39 + trainer: + kwargs: {} +name: 4e6624d695112a44c9134fe2d6537069 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/params.yaml b/examples/security/classification/output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/params.yaml new file mode 100644 index 00000000..67747872 --- /dev/null +++ b/examples/security/classification/output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/adv_predictions.json + adv_probabilities_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/008504ba1fbcc5d1c6f6481d476b144d.pkl + params_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/params.yaml + predictions_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/predictions.json + probabilities_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/probabilities.json + score_dict_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/score_dict.json + test_labels_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/test_labels.json + train_labels_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/train_labels.json + model_dir: models + name: 4e8acfbced6c7f5db4daf8817d05de9d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8119c10261edf4b8493238f49aec087e + trainer: + kwargs: {} +name: 4e8acfbced6c7f5db4daf8817d05de9d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/params.yaml b/examples/security/classification/output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/params.yaml new file mode 100644 index 00000000..20c3b7ce --- /dev/null +++ b/examples/security/classification/output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/adv_predictions.json + adv_probabilities_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/cdfcfd004745865e944e62e1fcd73bf8.pkl + params_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/params.yaml + predictions_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/predictions.json + probabilities_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/probabilities.json + score_dict_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/score_dict.json + test_labels_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/test_labels.json + train_labels_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/train_labels.json + model_dir: models + name: 4ea9ebf2cf74bd2da81f1465c1471352 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 325ebb3fbcdc7c3e4ba741af5527b253 + trainer: + kwargs: {} +name: 4ea9ebf2cf74bd2da81f1465c1471352 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4ed057afb0e707bdc57a345848d2731e/params.yaml b/examples/security/classification/output/reports/train/4ed057afb0e707bdc57a345848d2731e/params.yaml new file mode 100644 index 00000000..7623c899 --- /dev/null +++ b/examples/security/classification/output/reports/train/4ed057afb0e707bdc57a345848d2731e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/adv_predictions.json + adv_probabilities_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/0c5f58f8da9828e48e022ae15f7f575a.pkl + params_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/params.yaml + predictions_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/predictions.json + probabilities_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/probabilities.json + score_dict_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/score_dict.json + test_labels_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/test_labels.json + train_labels_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/train_labels.json + model_dir: models + name: 4ed057afb0e707bdc57a345848d2731e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2acffbc41974dd82ca307feb1051a01 + trainer: + kwargs: {} +name: 4ed057afb0e707bdc57a345848d2731e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/params.yaml b/examples/security/classification/output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/params.yaml new file mode 100644 index 00000000..392b4bac --- /dev/null +++ b/examples/security/classification/output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/adv_predictions.json + adv_probabilities_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/5058f8390f2a820c99d0dc078d717b04.pkl + params_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/params.yaml + predictions_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/predictions.json + probabilities_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/probabilities.json + score_dict_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/score_dict.json + test_labels_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/test_labels.json + train_labels_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/train_labels.json + model_dir: models + name: 4ed7359106236f89d4a1c3bea6a1ac5b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8bf6aa279ded50348713bd58384564a1 + trainer: + kwargs: {} +name: 4ed7359106236f89d4a1c3bea6a1ac5b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/params.yaml b/examples/security/classification/output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/params.yaml new file mode 100644 index 00000000..35967e23 --- /dev/null +++ b/examples/security/classification/output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/adv_predictions.json + adv_probabilities_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/2cd056af4e68a3f993fe9fde6ea858ba.pkl + params_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/params.yaml + predictions_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/predictions.json + probabilities_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/probabilities.json + score_dict_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/score_dict.json + test_labels_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/test_labels.json + train_labels_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/train_labels.json + model_dir: models + name: 4ef3363f80f316ce7e14ac752210c4d6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: af969c080c765821f72c0323f477351d + trainer: + kwargs: {} +name: 4ef3363f80f316ce7e14ac752210c4d6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4efc464c07bb46317e18d14a4567c758/params.yaml b/examples/security/classification/output/reports/train/4efc464c07bb46317e18d14a4567c758/params.yaml new file mode 100644 index 00000000..a1f6032d --- /dev/null +++ b/examples/security/classification/output/reports/train/4efc464c07bb46317e18d14a4567c758/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/adv_predictions.json + adv_probabilities_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/d2e38e7c1692ff2b5b930b148ee0b357.pkl + params_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/params.yaml + predictions_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/predictions.json + probabilities_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/probabilities.json + score_dict_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/score_dict.json + test_labels_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/test_labels.json + train_labels_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/train_labels.json + model_dir: models + name: 4efc464c07bb46317e18d14a4567c758 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 303f59ee6c50b571b51e8035d15c6360 + trainer: + kwargs: {} +name: 4efc464c07bb46317e18d14a4567c758 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4efe796bd7d31a9229a576d72e32b554/params.yaml b/examples/security/classification/output/reports/train/4efe796bd7d31a9229a576d72e32b554/params.yaml new file mode 100644 index 00000000..a1cbdb28 --- /dev/null +++ b/examples/security/classification/output/reports/train/4efe796bd7d31a9229a576d72e32b554/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/adv_predictions.json + adv_probabilities_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/588d8a53b628534ee8b918b884c550a4.pkl + params_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/params.yaml + predictions_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/predictions.json + probabilities_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/probabilities.json + score_dict_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/score_dict.json + test_labels_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/test_labels.json + train_labels_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/train_labels.json + model_dir: models + name: 4efe796bd7d31a9229a576d72e32b554 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 44331a725f2711f0ed803e41aaa81f57 + trainer: + kwargs: {} +name: 4efe796bd7d31a9229a576d72e32b554 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/params.yaml b/examples/security/classification/output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/params.yaml new file mode 100644 index 00000000..e3d1ac3e --- /dev/null +++ b/examples/security/classification/output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/adv_predictions.json + adv_probabilities_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/1d4b15aae20ba48780ade0ab37a9a4ac.pkl + params_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/params.yaml + predictions_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/predictions.json + probabilities_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/probabilities.json + score_dict_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/score_dict.json + test_labels_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/test_labels.json + train_labels_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/train_labels.json + model_dir: models + name: 4f08a3c0e8af2116d4e89473fcc67728 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 772882005d13bee33e171b00c38b0099 + trainer: + kwargs: {} +name: 4f08a3c0e8af2116d4e89473fcc67728 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/params.yaml b/examples/security/classification/output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/params.yaml new file mode 100644 index 00000000..41b11ddd --- /dev/null +++ b/examples/security/classification/output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/adv_predictions.json + adv_probabilities_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/036ae6433b8a31caad59bcc2e9652d45.pkl + params_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/params.yaml + predictions_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/predictions.json + probabilities_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/probabilities.json + score_dict_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/score_dict.json + test_labels_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/test_labels.json + train_labels_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/train_labels.json + model_dir: models + name: 4f08ea1caf4e3b6f966cfc4915fb7729 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3e729fb747a015cfb4d147fc972220ce + trainer: + kwargs: {} +name: 4f08ea1caf4e3b6f966cfc4915fb7729 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/params.yaml b/examples/security/classification/output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/params.yaml new file mode 100644 index 00000000..9d5f0456 --- /dev/null +++ b/examples/security/classification/output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/adv_predictions.json + adv_probabilities_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/09a7f200a8d3c809fac1c35e8c57ef10.pkl + params_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/params.yaml + predictions_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/predictions.json + probabilities_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/probabilities.json + score_dict_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/score_dict.json + test_labels_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/test_labels.json + train_labels_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/train_labels.json + model_dir: models + name: 4f0abb44b6d14787b7c0e14e3c62c325 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7625340c6b88a9c02f5f4e6a957e0a58 + trainer: + kwargs: {} +name: 4f0abb44b6d14787b7c0e14e3c62c325 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/params.yaml b/examples/security/classification/output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/params.yaml new file mode 100644 index 00000000..9a8623ff --- /dev/null +++ b/examples/security/classification/output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/adv_predictions.json + adv_probabilities_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/fa8dce5a08080c69cd91acae9fa22c24.pkl + params_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/params.yaml + predictions_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/predictions.json + probabilities_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/probabilities.json + score_dict_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/score_dict.json + test_labels_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/test_labels.json + train_labels_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/train_labels.json + model_dir: models + name: 4f26c8b1d60fca5ae0e35553479b4359 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 04ea1887ad1ce85040c97225d613ebfb + trainer: + kwargs: {} +name: 4f26c8b1d60fca5ae0e35553479b4359 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/params.yaml b/examples/security/classification/output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/params.yaml new file mode 100644 index 00000000..e62364fb --- /dev/null +++ b/examples/security/classification/output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/adv_predictions.json + adv_probabilities_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/90c0316667c9fee400b2faa50e8f313b.pkl + params_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/params.yaml + predictions_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/predictions.json + probabilities_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/probabilities.json + score_dict_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/score_dict.json + test_labels_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/test_labels.json + train_labels_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/train_labels.json + model_dir: models + name: 4f3871fe5b7b5b80cfd79d1c11e27d6f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 17b7b9bf1c7508f70734238ab59b633e + trainer: + kwargs: {} +name: 4f3871fe5b7b5b80cfd79d1c11e27d6f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4f3e64c1f768341f70c48568c10f8df6/params.yaml b/examples/security/classification/output/reports/train/4f3e64c1f768341f70c48568c10f8df6/params.yaml new file mode 100644 index 00000000..3a11e4ca --- /dev/null +++ b/examples/security/classification/output/reports/train/4f3e64c1f768341f70c48568c10f8df6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/adv_predictions.json + adv_probabilities_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/5b97b43d3365ae41d3cedd58d974dc0a.pkl + params_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/params.yaml + predictions_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/predictions.json + probabilities_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/probabilities.json + score_dict_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/score_dict.json + test_labels_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/test_labels.json + train_labels_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/train_labels.json + model_dir: models + name: 4f3e64c1f768341f70c48568c10f8df6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 63deddcf992fe530f27e4783e7a969ec + trainer: + kwargs: {} +name: 4f3e64c1f768341f70c48568c10f8df6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4f66bf1ac70407f6b675cffee2710c33/params.yaml b/examples/security/classification/output/reports/train/4f66bf1ac70407f6b675cffee2710c33/params.yaml new file mode 100644 index 00000000..49289a2c --- /dev/null +++ b/examples/security/classification/output/reports/train/4f66bf1ac70407f6b675cffee2710c33/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/adv_predictions.json + adv_probabilities_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/b7f4d74ca559632b2ac061089c4d475b.pkl + params_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/params.yaml + predictions_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/predictions.json + probabilities_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/probabilities.json + score_dict_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/score_dict.json + test_labels_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/test_labels.json + train_labels_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/train_labels.json + model_dir: models + name: 4f66bf1ac70407f6b675cffee2710c33 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2fb640502965c573f5b89af9251f7359 + trainer: + kwargs: {} +name: 4f66bf1ac70407f6b675cffee2710c33 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/params.yaml b/examples/security/classification/output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/params.yaml new file mode 100644 index 00000000..839de9a6 --- /dev/null +++ b/examples/security/classification/output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/adv_predictions.json + adv_probabilities_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/262de918a03ea293a99280bd04790e88.pkl + params_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/params.yaml + predictions_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/predictions.json + probabilities_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/probabilities.json + score_dict_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/score_dict.json + test_labels_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/test_labels.json + train_labels_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/train_labels.json + model_dir: models + name: 4fb0a5670414ebeda04a5e5ea3b7bcba + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b8d6305d061d3c02db9e68da1f6491c + trainer: + kwargs: {} +name: 4fb0a5670414ebeda04a5e5ea3b7bcba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/params.yaml b/examples/security/classification/output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/params.yaml new file mode 100644 index 00000000..e6cb0e7f --- /dev/null +++ b/examples/security/classification/output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/adv_predictions.json + adv_probabilities_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/c0f0e38a724ece6a6f45466888903265.pkl + params_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/params.yaml + predictions_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/predictions.json + probabilities_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/probabilities.json + score_dict_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/score_dict.json + test_labels_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/test_labels.json + train_labels_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/train_labels.json + model_dir: models + name: 4fbe4ad9b9ad9c174e81c2021df6f523 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 852fa618c649f5ec7028aace31a1316e + trainer: + kwargs: {} +name: 4fbe4ad9b9ad9c174e81c2021df6f523 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4fdc1308c5041b700640e1aec5a38de0/params.yaml b/examples/security/classification/output/reports/train/4fdc1308c5041b700640e1aec5a38de0/params.yaml new file mode 100644 index 00000000..0815e07a --- /dev/null +++ b/examples/security/classification/output/reports/train/4fdc1308c5041b700640e1aec5a38de0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/adv_predictions.json + adv_probabilities_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/50c6233f809609251e61cfd582decd2c.pkl + params_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/params.yaml + predictions_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/predictions.json + probabilities_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/probabilities.json + score_dict_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/score_dict.json + test_labels_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/test_labels.json + train_labels_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/train_labels.json + model_dir: models + name: 4fdc1308c5041b700640e1aec5a38de0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a62ef65179613a98666ca678295fcf96 + trainer: + kwargs: {} +name: 4fdc1308c5041b700640e1aec5a38de0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4fe12f069d4d34ad852561df03e8c50a/params.yaml b/examples/security/classification/output/reports/train/4fe12f069d4d34ad852561df03e8c50a/params.yaml new file mode 100644 index 00000000..1b179d50 --- /dev/null +++ b/examples/security/classification/output/reports/train/4fe12f069d4d34ad852561df03e8c50a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/adv_predictions.json + adv_probabilities_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/62ee45bf69e18c8867bda719f8541ed3.pkl + params_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/params.yaml + predictions_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/predictions.json + probabilities_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/probabilities.json + score_dict_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/score_dict.json + test_labels_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/test_labels.json + train_labels_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/train_labels.json + model_dir: models + name: 4fe12f069d4d34ad852561df03e8c50a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f1d9f3cedfe8488de313fa434c5fa8ab + trainer: + kwargs: {} +name: 4fe12f069d4d34ad852561df03e8c50a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/4fec0ceb8e977702930bca93e62e91b3/params.yaml b/examples/security/classification/output/reports/train/4fec0ceb8e977702930bca93e62e91b3/params.yaml new file mode 100644 index 00000000..f194d8d3 --- /dev/null +++ b/examples/security/classification/output/reports/train/4fec0ceb8e977702930bca93e62e91b3/params.yaml @@ -0,0 +1,206 @@ +attack: + attack_size: 10 + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 10.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cb07a4eeb8b1b7d80ac33d2d4ee67b92 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d1eb6b6992361f23a0b90e848b0f6165 + trainer: + kwargs: {} + name: 25e875ffa58457f925d14640bfe10268 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/adv_predictions.json + adv_probabilities_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/adv_probabilities.json + attack_file: output/attacks/1eba92e25c9b4202e89ec2958fb6ff56.pkl + data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl + model_file: output/models/1697e64c20873080433ffaf85cc99b44.pkl + params_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/params.yaml + predictions_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/predictions.json + probabilities_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/probabilities.json + score_dict_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/score_dict.json + test_labels_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/test_labels.json + train_labels_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/train_labels.json + model_dir: models + name: 4fec0ceb8e977702930bca93e62e91b3 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aafb5936b55f8df520328a275ef4f4bb + trainer: + kwargs: {} +name: 4fec0ceb8e977702930bca93e62e91b3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/500feef99ec799208d2da5272207dceb/params.yaml b/examples/security/classification/output/reports/train/500feef99ec799208d2da5272207dceb/params.yaml new file mode 100644 index 00000000..1347a01c --- /dev/null +++ b/examples/security/classification/output/reports/train/500feef99ec799208d2da5272207dceb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/500feef99ec799208d2da5272207dceb/adv_predictions.json + adv_probabilities_file: output/reports/train/500feef99ec799208d2da5272207dceb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/d1c9521dd4dae638f55b4a0a6606f595.pkl + params_file: output/reports/train/500feef99ec799208d2da5272207dceb/params.yaml + predictions_file: output/reports/train/500feef99ec799208d2da5272207dceb/predictions.json + probabilities_file: output/reports/train/500feef99ec799208d2da5272207dceb/probabilities.json + score_dict_file: output/reports/train/500feef99ec799208d2da5272207dceb/score_dict.json + test_labels_file: output/reports/train/500feef99ec799208d2da5272207dceb/test_labels.json + train_labels_file: output/reports/train/500feef99ec799208d2da5272207dceb/train_labels.json + model_dir: models + name: 500feef99ec799208d2da5272207dceb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 833eac1f35a64864eda71180648d01d4 + trainer: + kwargs: {} +name: 500feef99ec799208d2da5272207dceb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/505b2e3d036b398852e4940b845db76b/params.yaml b/examples/security/classification/output/reports/train/505b2e3d036b398852e4940b845db76b/params.yaml new file mode 100644 index 00000000..96c29fdf --- /dev/null +++ b/examples/security/classification/output/reports/train/505b2e3d036b398852e4940b845db76b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/505b2e3d036b398852e4940b845db76b/adv_predictions.json + adv_probabilities_file: output/reports/train/505b2e3d036b398852e4940b845db76b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/e10cc234486ee4a017f612ea128ced6b.pkl + params_file: output/reports/train/505b2e3d036b398852e4940b845db76b/params.yaml + predictions_file: output/reports/train/505b2e3d036b398852e4940b845db76b/predictions.json + probabilities_file: output/reports/train/505b2e3d036b398852e4940b845db76b/probabilities.json + score_dict_file: output/reports/train/505b2e3d036b398852e4940b845db76b/score_dict.json + test_labels_file: output/reports/train/505b2e3d036b398852e4940b845db76b/test_labels.json + train_labels_file: output/reports/train/505b2e3d036b398852e4940b845db76b/train_labels.json + model_dir: models + name: 505b2e3d036b398852e4940b845db76b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d51a58b4f648cbe58ea464445f9e71b7 + trainer: + kwargs: {} +name: 505b2e3d036b398852e4940b845db76b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/params.yaml b/examples/security/classification/output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/params.yaml new file mode 100644 index 00000000..829df363 --- /dev/null +++ b/examples/security/classification/output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/adv_predictions.json + adv_probabilities_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/1c462aa56219a93242f4f8cb8612eadc.pkl + params_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/params.yaml + predictions_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/predictions.json + probabilities_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/probabilities.json + score_dict_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/score_dict.json + test_labels_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/test_labels.json + train_labels_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/train_labels.json + model_dir: models + name: 505caf9a0de97bd7b4080a0b5fcf299d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e77256e323c3ce3080e40c2540275e47 + trainer: + kwargs: {} +name: 505caf9a0de97bd7b4080a0b5fcf299d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/50bfc88fcc83250789358a47b3419065/params.yaml b/examples/security/classification/output/reports/train/50bfc88fcc83250789358a47b3419065/params.yaml new file mode 100644 index 00000000..8dd195d3 --- /dev/null +++ b/examples/security/classification/output/reports/train/50bfc88fcc83250789358a47b3419065/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/50bfc88fcc83250789358a47b3419065/adv_predictions.json + adv_probabilities_file: output/reports/train/50bfc88fcc83250789358a47b3419065/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/2cf3acf742d748aa12a28e6359c18f7b.pkl + params_file: output/reports/train/50bfc88fcc83250789358a47b3419065/params.yaml + predictions_file: output/reports/train/50bfc88fcc83250789358a47b3419065/predictions.json + probabilities_file: output/reports/train/50bfc88fcc83250789358a47b3419065/probabilities.json + score_dict_file: output/reports/train/50bfc88fcc83250789358a47b3419065/score_dict.json + test_labels_file: output/reports/train/50bfc88fcc83250789358a47b3419065/test_labels.json + train_labels_file: output/reports/train/50bfc88fcc83250789358a47b3419065/train_labels.json + model_dir: models + name: 50bfc88fcc83250789358a47b3419065 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e438e27f9dc80832a945ef360fada0dc + trainer: + kwargs: {} +name: 50bfc88fcc83250789358a47b3419065 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/50f31bda43c11dce50597912f9efe1c7/params.yaml b/examples/security/classification/output/reports/train/50f31bda43c11dce50597912f9efe1c7/params.yaml new file mode 100644 index 00000000..d5903efb --- /dev/null +++ b/examples/security/classification/output/reports/train/50f31bda43c11dce50597912f9efe1c7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/adv_predictions.json + adv_probabilities_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/13a78e4a031a90ff5dafbce93e30d1d3.pkl + params_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/params.yaml + predictions_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/predictions.json + probabilities_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/probabilities.json + score_dict_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/score_dict.json + test_labels_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/test_labels.json + train_labels_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/train_labels.json + model_dir: models + name: 50f31bda43c11dce50597912f9efe1c7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2550926a35e93c272b7fb22eec89fc1f + trainer: + kwargs: {} +name: 50f31bda43c11dce50597912f9efe1c7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/512470107636657930baca09f1c42dba/params.yaml b/examples/security/classification/output/reports/train/512470107636657930baca09f1c42dba/params.yaml new file mode 100644 index 00000000..47f096f7 --- /dev/null +++ b/examples/security/classification/output/reports/train/512470107636657930baca09f1c42dba/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/512470107636657930baca09f1c42dba/adv_predictions.json + adv_probabilities_file: output/reports/train/512470107636657930baca09f1c42dba/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/6b34aa33cc377762ec2637e28764b351.pkl + params_file: output/reports/train/512470107636657930baca09f1c42dba/params.yaml + predictions_file: output/reports/train/512470107636657930baca09f1c42dba/predictions.json + probabilities_file: output/reports/train/512470107636657930baca09f1c42dba/probabilities.json + score_dict_file: output/reports/train/512470107636657930baca09f1c42dba/score_dict.json + test_labels_file: output/reports/train/512470107636657930baca09f1c42dba/test_labels.json + train_labels_file: output/reports/train/512470107636657930baca09f1c42dba/train_labels.json + model_dir: models + name: 512470107636657930baca09f1c42dba + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a59409a9e4765c5a5db884efe4bb5ef3 + trainer: + kwargs: {} +name: 512470107636657930baca09f1c42dba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/51563400f5265942ec72e69b4c865349/params.yaml b/examples/security/classification/output/reports/train/51563400f5265942ec72e69b4c865349/params.yaml new file mode 100644 index 00000000..d4f93dab --- /dev/null +++ b/examples/security/classification/output/reports/train/51563400f5265942ec72e69b4c865349/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/51563400f5265942ec72e69b4c865349/adv_predictions.json + adv_probabilities_file: output/reports/train/51563400f5265942ec72e69b4c865349/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/ae64b23e157846f7dc9f972f99246877.pkl + params_file: output/reports/train/51563400f5265942ec72e69b4c865349/params.yaml + predictions_file: output/reports/train/51563400f5265942ec72e69b4c865349/predictions.json + probabilities_file: output/reports/train/51563400f5265942ec72e69b4c865349/probabilities.json + score_dict_file: output/reports/train/51563400f5265942ec72e69b4c865349/score_dict.json + test_labels_file: output/reports/train/51563400f5265942ec72e69b4c865349/test_labels.json + train_labels_file: output/reports/train/51563400f5265942ec72e69b4c865349/train_labels.json + model_dir: models + name: 51563400f5265942ec72e69b4c865349 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f3407df1b980e72510e86747c8d4c78e + trainer: + kwargs: {} +name: 51563400f5265942ec72e69b4c865349 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5162008249ff15d48bc4a286e67aceed/params.yaml b/examples/security/classification/output/reports/train/5162008249ff15d48bc4a286e67aceed/params.yaml new file mode 100644 index 00000000..7f6f0219 --- /dev/null +++ b/examples/security/classification/output/reports/train/5162008249ff15d48bc4a286e67aceed/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/adv_predictions.json + adv_probabilities_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/7d27d0fdea10678ef7cfb9ecf9257dc6.pkl + params_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/params.yaml + predictions_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/predictions.json + probabilities_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/probabilities.json + score_dict_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/score_dict.json + test_labels_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/test_labels.json + train_labels_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/train_labels.json + model_dir: models + name: 5162008249ff15d48bc4a286e67aceed + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66cd21b8241c9a8ae3027c1edd8044dd + trainer: + kwargs: {} +name: 5162008249ff15d48bc4a286e67aceed +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/51870231da6307cfe310b432aa7e8be7/params.yaml b/examples/security/classification/output/reports/train/51870231da6307cfe310b432aa7e8be7/params.yaml new file mode 100644 index 00000000..ffe88b74 --- /dev/null +++ b/examples/security/classification/output/reports/train/51870231da6307cfe310b432aa7e8be7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/adv_predictions.json + adv_probabilities_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/a6b5309e713d7b0dc90babb11e4e14c5.pkl + params_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/params.yaml + predictions_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/predictions.json + probabilities_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/probabilities.json + score_dict_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/score_dict.json + test_labels_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/test_labels.json + train_labels_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/train_labels.json + model_dir: models + name: 51870231da6307cfe310b432aa7e8be7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ac28bdb2df50598e355911dab1494ecf + trainer: + kwargs: {} +name: 51870231da6307cfe310b432aa7e8be7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/519717be015b1b1306fd5cbeda4b180c/params.yaml b/examples/security/classification/output/reports/train/519717be015b1b1306fd5cbeda4b180c/params.yaml new file mode 100644 index 00000000..bee92fcd --- /dev/null +++ b/examples/security/classification/output/reports/train/519717be015b1b1306fd5cbeda4b180c/params.yaml @@ -0,0 +1,104 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/adv_predictions.json + adv_probabilities_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/5c0a8f32fd9b960dd388cba791e507bb.pkl + params_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/params.yaml + predictions_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/predictions.json + probabilities_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/probabilities.json + score_dict_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/score_dict.json + test_labels_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/test_labels.json + train_labels_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/train_labels.json + model_dir: models + name: 519717be015b1b1306fd5cbeda4b180c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: + - linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 09472754bc810e84bba25f75f677ed1c + trainer: + kwargs: {} +name: 519717be015b1b1306fd5cbeda4b180c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/51975394c79a93069f090eea6ddaefff/params.yaml b/examples/security/classification/output/reports/train/51975394c79a93069f090eea6ddaefff/params.yaml new file mode 100644 index 00000000..479ce6a0 --- /dev/null +++ b/examples/security/classification/output/reports/train/51975394c79a93069f090eea6ddaefff/params.yaml @@ -0,0 +1,202 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cb7496c7ad411f4919f150fb4033c5f8 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: adf238bc307f919f46f2ba43c35ab80d + trainer: + kwargs: {} + name: f968f36593e83a37633f1ea3beb48d11 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/51975394c79a93069f090eea6ddaefff/adv_predictions.json + adv_probabilities_file: output/reports/train/51975394c79a93069f090eea6ddaefff/adv_probabilities.json + attack_file: output/attacks/79aaf01298f1b324fe5a76f24f62d5ca.pkl + data_file: output/data/051f101486d067e3f403ac87eba325c2.pkl + model_file: output/models/c29bd639be38539e177c574845e347ec.pkl + params_file: output/reports/train/51975394c79a93069f090eea6ddaefff/params.yaml + predictions_file: output/reports/train/51975394c79a93069f090eea6ddaefff/predictions.json + probabilities_file: output/reports/train/51975394c79a93069f090eea6ddaefff/probabilities.json + score_dict_file: output/reports/train/51975394c79a93069f090eea6ddaefff/score_dict.json + test_labels_file: output/reports/train/51975394c79a93069f090eea6ddaefff/test_labels.json + train_labels_file: output/reports/train/51975394c79a93069f090eea6ddaefff/train_labels.json + model_dir: models + name: 51975394c79a93069f090eea6ddaefff + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1852678c950600b1beb8613d89bf2950 + trainer: + kwargs: {} +name: 51975394c79a93069f090eea6ddaefff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/params.yaml b/examples/security/classification/output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/params.yaml new file mode 100644 index 00000000..8edd62e4 --- /dev/null +++ b/examples/security/classification/output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/params.yaml @@ -0,0 +1,202 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 10000.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 752225dad0d39491658535ea793d9b86 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0b0c4a6366d9c9ed73841d305d1deaec + trainer: + kwargs: {} + name: e9683fce3c41b02dca23381997568dee +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/adv_predictions.json + adv_probabilities_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/adv_probabilities.json + attack_file: output/attacks/f141c591385aab10903b4bb653346673.pkl + data_file: output/data/579db4bb6f9d6b612a1cf6bf1f51ea05.pkl + model_file: output/models/66b5d604d3fd6b8f31ba0326c10583ad.pkl + params_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/params.yaml + predictions_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/predictions.json + probabilities_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/probabilities.json + score_dict_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/score_dict.json + test_labels_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/test_labels.json + train_labels_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/train_labels.json + model_dir: models + name: 51b8e289b8f3a49d0b88468c8049f8d1 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aae9e0d6c72fb30e904ad431759aa466 + trainer: + kwargs: {} +name: 51b8e289b8f3a49d0b88468c8049f8d1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/params.yaml b/examples/security/classification/output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/params.yaml new file mode 100644 index 00000000..4b43845a --- /dev/null +++ b/examples/security/classification/output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/adv_predictions.json + adv_probabilities_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/06a1d1cc348b09f925cd55d69747e8cd.pkl + params_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/params.yaml + predictions_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/predictions.json + probabilities_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/probabilities.json + score_dict_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/score_dict.json + test_labels_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/test_labels.json + train_labels_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/train_labels.json + model_dir: models + name: 51e1d8aa6dce349741a6ccf84e4c0af8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 83bcc411c7ec34a7ec7f40eba46e3a52 + trainer: + kwargs: {} +name: 51e1d8aa6dce349741a6ccf84e4c0af8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/51e8593194ae68c4b62208f654d6c811/params.yaml b/examples/security/classification/output/reports/train/51e8593194ae68c4b62208f654d6c811/params.yaml new file mode 100644 index 00000000..88473b05 --- /dev/null +++ b/examples/security/classification/output/reports/train/51e8593194ae68c4b62208f654d6c811/params.yaml @@ -0,0 +1,202 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 2000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 2000.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 858b009d4cbb08fd74fc49e78b15b430 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 2000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7192300522bbc22ed827a454cd9dd5c3 + trainer: + kwargs: {} + name: 73a00c1ddb74dd9f132a16dd533e8034 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 2000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/adv_predictions.json + adv_probabilities_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/adv_probabilities.json + attack_file: output/attacks/5aa4baffe2159155393b7e70433cc149.pkl + data_file: output/data/66c37c36c533a0007a8deb95b9d0974c.pkl + model_file: output/models/1a15fc5c0a5fc62b2d80a0245a4667e1.pkl + params_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/params.yaml + predictions_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/predictions.json + probabilities_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/probabilities.json + score_dict_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/score_dict.json + test_labels_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/test_labels.json + train_labels_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/train_labels.json + model_dir: models + name: 51e8593194ae68c4b62208f654d6c811 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 2000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2d77f2a4494339d76dda10ca26c6046b + trainer: + kwargs: {} +name: 51e8593194ae68c4b62208f654d6c811 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/params.yaml b/examples/security/classification/output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/params.yaml new file mode 100644 index 00000000..c0b7f7bd --- /dev/null +++ b/examples/security/classification/output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/adv_predictions.json + adv_probabilities_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/d5f1f21907ff22d356090c497e141f97.pkl + params_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/params.yaml + predictions_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/predictions.json + probabilities_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/probabilities.json + score_dict_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/score_dict.json + test_labels_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/test_labels.json + train_labels_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/train_labels.json + model_dir: models + name: 521dffa0b87f393212ddd76d6ab4a5d0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 97e45dfd831024503e8a4e1e6d7dae51 + trainer: + kwargs: {} +name: 521dffa0b87f393212ddd76d6ab4a5d0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/52496aca8af2cf71adedf23baca703f2/params.yaml b/examples/security/classification/output/reports/train/52496aca8af2cf71adedf23baca703f2/params.yaml new file mode 100644 index 00000000..64d104da --- /dev/null +++ b/examples/security/classification/output/reports/train/52496aca8af2cf71adedf23baca703f2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/adv_predictions.json + adv_probabilities_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/d988ff3f9c844f3e12e77b93b026ebc4.pkl + params_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/params.yaml + predictions_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/predictions.json + probabilities_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/probabilities.json + score_dict_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/score_dict.json + test_labels_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/test_labels.json + train_labels_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/train_labels.json + model_dir: models + name: 52496aca8af2cf71adedf23baca703f2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 48b2bfe1f05dc5fbc5257006ed6bf2c4 + trainer: + kwargs: {} +name: 52496aca8af2cf71adedf23baca703f2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/52a3bd3871354f37d02314a0be918e5e/params.yaml b/examples/security/classification/output/reports/train/52a3bd3871354f37d02314a0be918e5e/params.yaml new file mode 100644 index 00000000..4749e931 --- /dev/null +++ b/examples/security/classification/output/reports/train/52a3bd3871354f37d02314a0be918e5e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/adv_predictions.json + adv_probabilities_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/0dbf24656c9d3a506ee0f5f06e246f45.pkl + params_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/params.yaml + predictions_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/predictions.json + probabilities_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/probabilities.json + score_dict_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/score_dict.json + test_labels_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/test_labels.json + train_labels_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/train_labels.json + model_dir: models + name: 52a3bd3871354f37d02314a0be918e5e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: de63cf365af34fa42101b349b9635f27 + trainer: + kwargs: {} +name: 52a3bd3871354f37d02314a0be918e5e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/530ba5a34f581cbaa41454a505b5a691/params.yaml b/examples/security/classification/output/reports/train/530ba5a34f581cbaa41454a505b5a691/params.yaml new file mode 100644 index 00000000..3d510acc --- /dev/null +++ b/examples/security/classification/output/reports/train/530ba5a34f581cbaa41454a505b5a691/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/adv_predictions.json + adv_probabilities_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/dda6105923860b29d3fd3821ab53b43d.pkl + params_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/params.yaml + predictions_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/predictions.json + probabilities_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/probabilities.json + score_dict_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/score_dict.json + test_labels_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/test_labels.json + train_labels_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/train_labels.json + model_dir: models + name: 530ba5a34f581cbaa41454a505b5a691 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bd5be377fa53ae26e10ebdeabd32110e + trainer: + kwargs: {} +name: 530ba5a34f581cbaa41454a505b5a691 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/536fe070398da6c64253898981cf8b12/params.yaml b/examples/security/classification/output/reports/train/536fe070398da6c64253898981cf8b12/params.yaml new file mode 100644 index 00000000..64070a0a --- /dev/null +++ b/examples/security/classification/output/reports/train/536fe070398da6c64253898981cf8b12/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/536fe070398da6c64253898981cf8b12/adv_predictions.json + adv_probabilities_file: output/reports/train/536fe070398da6c64253898981cf8b12/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/c75b8859d1efd2e02fa79f751e2ba4e5.pkl + params_file: output/reports/train/536fe070398da6c64253898981cf8b12/params.yaml + predictions_file: output/reports/train/536fe070398da6c64253898981cf8b12/predictions.json + probabilities_file: output/reports/train/536fe070398da6c64253898981cf8b12/probabilities.json + score_dict_file: output/reports/train/536fe070398da6c64253898981cf8b12/score_dict.json + test_labels_file: output/reports/train/536fe070398da6c64253898981cf8b12/test_labels.json + train_labels_file: output/reports/train/536fe070398da6c64253898981cf8b12/train_labels.json + model_dir: models + name: 536fe070398da6c64253898981cf8b12 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7b873eb5e0ca8608f7f6c2dd515a2449 + trainer: + kwargs: {} +name: 536fe070398da6c64253898981cf8b12 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/params.yaml b/examples/security/classification/output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/params.yaml new file mode 100644 index 00000000..fb1bdf3c --- /dev/null +++ b/examples/security/classification/output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/adv_predictions.json + adv_probabilities_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/f64bf59e951afc3cda3dcd10c5092188.pkl + params_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/params.yaml + predictions_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/predictions.json + probabilities_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/probabilities.json + score_dict_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/score_dict.json + test_labels_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/test_labels.json + train_labels_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/train_labels.json + model_dir: models + name: 53a41bb216684a29d3a0ed9c9a6c3a8a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 982a17a1342ebeffcdefff43b8dd322c + trainer: + kwargs: {} +name: 53a41bb216684a29d3a0ed9c9a6c3a8a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/541162c11026a972e6e13ccb030eb0c4/params.yaml b/examples/security/classification/output/reports/train/541162c11026a972e6e13ccb030eb0c4/params.yaml new file mode 100644 index 00000000..f18700bd --- /dev/null +++ b/examples/security/classification/output/reports/train/541162c11026a972e6e13ccb030eb0c4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/adv_predictions.json + adv_probabilities_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/bd80a7ecf2ca32bbd543335d1ab764df.pkl + params_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/params.yaml + predictions_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/predictions.json + probabilities_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/probabilities.json + score_dict_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/score_dict.json + test_labels_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/test_labels.json + train_labels_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/train_labels.json + model_dir: models + name: 541162c11026a972e6e13ccb030eb0c4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2250f02c23b9046fce268cb5b703406f + trainer: + kwargs: {} +name: 541162c11026a972e6e13ccb030eb0c4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/541b02f8f11691bae75333d27e5bdf7f/params.yaml b/examples/security/classification/output/reports/train/541b02f8f11691bae75333d27e5bdf7f/params.yaml new file mode 100644 index 00000000..5b4e740e --- /dev/null +++ b/examples/security/classification/output/reports/train/541b02f8f11691bae75333d27e5bdf7f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/adv_predictions.json + adv_probabilities_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/a190affa2966eff55af1627e05d3fbf9.pkl + params_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/params.yaml + predictions_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/predictions.json + probabilities_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/probabilities.json + score_dict_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/score_dict.json + test_labels_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/test_labels.json + train_labels_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/train_labels.json + model_dir: models + name: 541b02f8f11691bae75333d27e5bdf7f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d3ba31fa78c1098bc9780253712102bc + trainer: + kwargs: {} +name: 541b02f8f11691bae75333d27e5bdf7f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/542a18b06a05de137140fdda913ea368/params.yaml b/examples/security/classification/output/reports/train/542a18b06a05de137140fdda913ea368/params.yaml new file mode 100644 index 00000000..3f3cb317 --- /dev/null +++ b/examples/security/classification/output/reports/train/542a18b06a05de137140fdda913ea368/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/542a18b06a05de137140fdda913ea368/adv_predictions.json + adv_probabilities_file: output/reports/train/542a18b06a05de137140fdda913ea368/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/0052686f802e285eaa90abecea5b9be9.pkl + params_file: output/reports/train/542a18b06a05de137140fdda913ea368/params.yaml + predictions_file: output/reports/train/542a18b06a05de137140fdda913ea368/predictions.json + probabilities_file: output/reports/train/542a18b06a05de137140fdda913ea368/probabilities.json + score_dict_file: output/reports/train/542a18b06a05de137140fdda913ea368/score_dict.json + test_labels_file: output/reports/train/542a18b06a05de137140fdda913ea368/test_labels.json + train_labels_file: output/reports/train/542a18b06a05de137140fdda913ea368/train_labels.json + model_dir: models + name: 542a18b06a05de137140fdda913ea368 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 32dd2c04d8d4e2669bb159f87742920d + trainer: + kwargs: {} +name: 542a18b06a05de137140fdda913ea368 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5444a560e035b3366a3a444f932069bb/params.yaml b/examples/security/classification/output/reports/train/5444a560e035b3366a3a444f932069bb/params.yaml new file mode 100644 index 00000000..cb951860 --- /dev/null +++ b/examples/security/classification/output/reports/train/5444a560e035b3366a3a444f932069bb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5444a560e035b3366a3a444f932069bb/adv_predictions.json + adv_probabilities_file: output/reports/train/5444a560e035b3366a3a444f932069bb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/24b86cf67064c94f1bb610392302af1a.pkl + params_file: output/reports/train/5444a560e035b3366a3a444f932069bb/params.yaml + predictions_file: output/reports/train/5444a560e035b3366a3a444f932069bb/predictions.json + probabilities_file: output/reports/train/5444a560e035b3366a3a444f932069bb/probabilities.json + score_dict_file: output/reports/train/5444a560e035b3366a3a444f932069bb/score_dict.json + test_labels_file: output/reports/train/5444a560e035b3366a3a444f932069bb/test_labels.json + train_labels_file: output/reports/train/5444a560e035b3366a3a444f932069bb/train_labels.json + model_dir: models + name: 5444a560e035b3366a3a444f932069bb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e15c6c41595b6897779cd221cec92b37 + trainer: + kwargs: {} +name: 5444a560e035b3366a3a444f932069bb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/544ce392628e03c9bf5076a0622e3eba/params.yaml b/examples/security/classification/output/reports/train/544ce392628e03c9bf5076a0622e3eba/params.yaml new file mode 100644 index 00000000..1c5b93d0 --- /dev/null +++ b/examples/security/classification/output/reports/train/544ce392628e03c9bf5076a0622e3eba/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/adv_predictions.json + adv_probabilities_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/3a790b8f5cf957cd279076897d709352.pkl + params_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/params.yaml + predictions_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/predictions.json + probabilities_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/probabilities.json + score_dict_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/score_dict.json + test_labels_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/test_labels.json + train_labels_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/train_labels.json + model_dir: models + name: 544ce392628e03c9bf5076a0622e3eba + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fb1284e1002014e858fd3b57eb8944fa + trainer: + kwargs: {} +name: 544ce392628e03c9bf5076a0622e3eba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/54563396dec51efe6c557ee8abb32a5b/params.yaml b/examples/security/classification/output/reports/train/54563396dec51efe6c557ee8abb32a5b/params.yaml new file mode 100644 index 00000000..7c6ef115 --- /dev/null +++ b/examples/security/classification/output/reports/train/54563396dec51efe6c557ee8abb32a5b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/adv_predictions.json + adv_probabilities_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/b26bf27122c304743429e4c36bc339a7.pkl + params_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/params.yaml + predictions_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/predictions.json + probabilities_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/probabilities.json + score_dict_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/score_dict.json + test_labels_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/test_labels.json + train_labels_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/train_labels.json + model_dir: models + name: 54563396dec51efe6c557ee8abb32a5b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 68533208e4d4bd8c5d95c6ca83cb5e96 + trainer: + kwargs: {} +name: 54563396dec51efe6c557ee8abb32a5b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/54584c096f30ce10bfe766a6300a8999/params.yaml b/examples/security/classification/output/reports/train/54584c096f30ce10bfe766a6300a8999/params.yaml new file mode 100644 index 00000000..bb8710ef --- /dev/null +++ b/examples/security/classification/output/reports/train/54584c096f30ce10bfe766a6300a8999/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/adv_predictions.json + adv_probabilities_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/1a99a7951baa052999be54b6501f7c46.pkl + params_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/params.yaml + predictions_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/predictions.json + probabilities_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/probabilities.json + score_dict_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/score_dict.json + test_labels_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/test_labels.json + train_labels_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/train_labels.json + model_dir: models + name: 54584c096f30ce10bfe766a6300a8999 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 76d852d2eef62aa4d5946c3fc3fa396f + trainer: + kwargs: {} +name: 54584c096f30ce10bfe766a6300a8999 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5466cbbf5f7df59abda71db227dce684/params.yaml b/examples/security/classification/output/reports/train/5466cbbf5f7df59abda71db227dce684/params.yaml new file mode 100644 index 00000000..5e859700 --- /dev/null +++ b/examples/security/classification/output/reports/train/5466cbbf5f7df59abda71db227dce684/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/adv_predictions.json + adv_probabilities_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/4b791db8c2f242d44793293cc3e579a0.pkl + params_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/params.yaml + predictions_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/predictions.json + probabilities_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/probabilities.json + score_dict_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/score_dict.json + test_labels_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/test_labels.json + train_labels_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/train_labels.json + model_dir: models + name: 5466cbbf5f7df59abda71db227dce684 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9dcd786f16121bc0ed939453d02618f7 + trainer: + kwargs: {} +name: 5466cbbf5f7df59abda71db227dce684 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5466fffc76af713231184777c391496c/params.yaml b/examples/security/classification/output/reports/train/5466fffc76af713231184777c391496c/params.yaml new file mode 100644 index 00000000..9fd22833 --- /dev/null +++ b/examples/security/classification/output/reports/train/5466fffc76af713231184777c391496c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5466fffc76af713231184777c391496c/adv_predictions.json + adv_probabilities_file: output/reports/train/5466fffc76af713231184777c391496c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/d7435d809bf61e0e34e4eccd4d340867.pkl + params_file: output/reports/train/5466fffc76af713231184777c391496c/params.yaml + predictions_file: output/reports/train/5466fffc76af713231184777c391496c/predictions.json + probabilities_file: output/reports/train/5466fffc76af713231184777c391496c/probabilities.json + score_dict_file: output/reports/train/5466fffc76af713231184777c391496c/score_dict.json + test_labels_file: output/reports/train/5466fffc76af713231184777c391496c/test_labels.json + train_labels_file: output/reports/train/5466fffc76af713231184777c391496c/train_labels.json + model_dir: models + name: 5466fffc76af713231184777c391496c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c249dc5b94c95668891ae8cd355141fb + trainer: + kwargs: {} +name: 5466fffc76af713231184777c391496c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/params.yaml b/examples/security/classification/output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/params.yaml new file mode 100644 index 00000000..479f1764 --- /dev/null +++ b/examples/security/classification/output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/adv_predictions.json + adv_probabilities_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/c028a4e31641bafb6bb46bae9999c44e.pkl + params_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/params.yaml + predictions_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/predictions.json + probabilities_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/probabilities.json + score_dict_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/score_dict.json + test_labels_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/test_labels.json + train_labels_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/train_labels.json + model_dir: models + name: 5469d9e9fac6347eb1f80c7070433bf9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f270c043039adb0ec6207b9f068583b5 + trainer: + kwargs: {} +name: 5469d9e9fac6347eb1f80c7070433bf9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/54a5bc198907525664154e5745898160/params.yaml b/examples/security/classification/output/reports/train/54a5bc198907525664154e5745898160/params.yaml new file mode 100644 index 00000000..a455c8d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/54a5bc198907525664154e5745898160/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/54a5bc198907525664154e5745898160/adv_predictions.json + adv_probabilities_file: output/reports/train/54a5bc198907525664154e5745898160/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/f652e0662b583e8ab96f076439a664a7.pkl + params_file: output/reports/train/54a5bc198907525664154e5745898160/params.yaml + predictions_file: output/reports/train/54a5bc198907525664154e5745898160/predictions.json + probabilities_file: output/reports/train/54a5bc198907525664154e5745898160/probabilities.json + score_dict_file: output/reports/train/54a5bc198907525664154e5745898160/score_dict.json + test_labels_file: output/reports/train/54a5bc198907525664154e5745898160/test_labels.json + train_labels_file: output/reports/train/54a5bc198907525664154e5745898160/train_labels.json + model_dir: models + name: 54a5bc198907525664154e5745898160 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0b5f5fc1bd73a5783f35c7923e3e49df + trainer: + kwargs: {} +name: 54a5bc198907525664154e5745898160 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/54b542cb61014be495eae3660cdcc097/params.yaml b/examples/security/classification/output/reports/train/54b542cb61014be495eae3660cdcc097/params.yaml new file mode 100644 index 00000000..1a9705cc --- /dev/null +++ b/examples/security/classification/output/reports/train/54b542cb61014be495eae3660cdcc097/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/54b542cb61014be495eae3660cdcc097/adv_predictions.json + adv_probabilities_file: output/reports/train/54b542cb61014be495eae3660cdcc097/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/69af359a860b689154ed1c715c3e3a1d.pkl + params_file: output/reports/train/54b542cb61014be495eae3660cdcc097/params.yaml + predictions_file: output/reports/train/54b542cb61014be495eae3660cdcc097/predictions.json + probabilities_file: output/reports/train/54b542cb61014be495eae3660cdcc097/probabilities.json + score_dict_file: output/reports/train/54b542cb61014be495eae3660cdcc097/score_dict.json + test_labels_file: output/reports/train/54b542cb61014be495eae3660cdcc097/test_labels.json + train_labels_file: output/reports/train/54b542cb61014be495eae3660cdcc097/train_labels.json + model_dir: models + name: 54b542cb61014be495eae3660cdcc097 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 386f7dfd47d6b4ac005173ec68b094de + trainer: + kwargs: {} +name: 54b542cb61014be495eae3660cdcc097 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/params.yaml b/examples/security/classification/output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/params.yaml new file mode 100644 index 00000000..0cc47dc3 --- /dev/null +++ b/examples/security/classification/output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/adv_predictions.json + adv_probabilities_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/571c1a856ef36b4f15cf0e7f3c16ab94.pkl + params_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/params.yaml + predictions_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/predictions.json + probabilities_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/probabilities.json + score_dict_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/score_dict.json + test_labels_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/test_labels.json + train_labels_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/train_labels.json + model_dir: models + name: 54d90ec4ff1d45f363a14f866c05ed85 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3eb653572dd7f8f9c49c165c049324a2 + trainer: + kwargs: {} +name: 54d90ec4ff1d45f363a14f866c05ed85 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/params.yaml b/examples/security/classification/output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/params.yaml new file mode 100644 index 00000000..0139eb70 --- /dev/null +++ b/examples/security/classification/output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/adv_predictions.json + adv_probabilities_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/a4f5425260fb74a61eb0c4096459b1b6.pkl + params_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/params.yaml + predictions_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/predictions.json + probabilities_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/probabilities.json + score_dict_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/score_dict.json + test_labels_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/test_labels.json + train_labels_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/train_labels.json + model_dir: models + name: 54de4d912b625b9a9ea9ddea9c7f7872 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5e577bd3c9de09b3238c459e2215bd27 + trainer: + kwargs: {} +name: 54de4d912b625b9a9ea9ddea9c7f7872 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/550369fba54fa78c9b49848e05a568ed/params.yaml b/examples/security/classification/output/reports/train/550369fba54fa78c9b49848e05a568ed/params.yaml new file mode 100644 index 00000000..3f9adc87 --- /dev/null +++ b/examples/security/classification/output/reports/train/550369fba54fa78c9b49848e05a568ed/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/adv_predictions.json + adv_probabilities_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/67babded34fe6d7d6f47509a4465750c.pkl + params_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/params.yaml + predictions_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/predictions.json + probabilities_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/probabilities.json + score_dict_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/score_dict.json + test_labels_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/test_labels.json + train_labels_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/train_labels.json + model_dir: models + name: 550369fba54fa78c9b49848e05a568ed + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 95f925cfb9ed291467f5457bd0c93328 + trainer: + kwargs: {} +name: 550369fba54fa78c9b49848e05a568ed +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5506730a7b97e5c200cfcd45ff721eac/params.yaml b/examples/security/classification/output/reports/train/5506730a7b97e5c200cfcd45ff721eac/params.yaml new file mode 100644 index 00000000..8f1ad482 --- /dev/null +++ b/examples/security/classification/output/reports/train/5506730a7b97e5c200cfcd45ff721eac/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/adv_predictions.json + adv_probabilities_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/fb2e205cce24dd210e39c130cab310e2.pkl + params_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/params.yaml + predictions_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/predictions.json + probabilities_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/probabilities.json + score_dict_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/score_dict.json + test_labels_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/test_labels.json + train_labels_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/train_labels.json + model_dir: models + name: 5506730a7b97e5c200cfcd45ff721eac + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 460de0b53bcb698e012d935e7e9d7744 + trainer: + kwargs: {} +name: 5506730a7b97e5c200cfcd45ff721eac +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/params.yaml b/examples/security/classification/output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/params.yaml new file mode 100644 index 00000000..0ad6d5c1 --- /dev/null +++ b/examples/security/classification/output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/adv_predictions.json + adv_probabilities_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/27b8f44f0ba7517a0a4c2ef1af5e827c.pkl + params_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/params.yaml + predictions_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/predictions.json + probabilities_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/probabilities.json + score_dict_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/score_dict.json + test_labels_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/test_labels.json + train_labels_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/train_labels.json + model_dir: models + name: 551cfb2c0c7f1c847dfce30baecc6e94 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e777f9eb42fb6c1a1edde4f8c91a295a + trainer: + kwargs: {} +name: 551cfb2c0c7f1c847dfce30baecc6e94 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5524b354a74912549a2b2c6f248666df/params.yaml b/examples/security/classification/output/reports/train/5524b354a74912549a2b2c6f248666df/params.yaml new file mode 100644 index 00000000..b1869bef --- /dev/null +++ b/examples/security/classification/output/reports/train/5524b354a74912549a2b2c6f248666df/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5524b354a74912549a2b2c6f248666df/adv_predictions.json + adv_probabilities_file: output/reports/train/5524b354a74912549a2b2c6f248666df/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/c78c7807c4beb689054404868db4593e.pkl + params_file: output/reports/train/5524b354a74912549a2b2c6f248666df/params.yaml + predictions_file: output/reports/train/5524b354a74912549a2b2c6f248666df/predictions.json + probabilities_file: output/reports/train/5524b354a74912549a2b2c6f248666df/probabilities.json + score_dict_file: output/reports/train/5524b354a74912549a2b2c6f248666df/score_dict.json + test_labels_file: output/reports/train/5524b354a74912549a2b2c6f248666df/test_labels.json + train_labels_file: output/reports/train/5524b354a74912549a2b2c6f248666df/train_labels.json + model_dir: models + name: 5524b354a74912549a2b2c6f248666df + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4e5b3733d7c0a33479a1dd9f2f0af8a6 + trainer: + kwargs: {} +name: 5524b354a74912549a2b2c6f248666df +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/552bcfbc5e8669a23d481b26819924f3/params.yaml b/examples/security/classification/output/reports/train/552bcfbc5e8669a23d481b26819924f3/params.yaml new file mode 100644 index 00000000..baeebfa4 --- /dev/null +++ b/examples/security/classification/output/reports/train/552bcfbc5e8669a23d481b26819924f3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/adv_predictions.json + adv_probabilities_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/519878df3a7614b562663704ef0c8391.pkl + params_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/params.yaml + predictions_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/predictions.json + probabilities_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/probabilities.json + score_dict_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/score_dict.json + test_labels_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/test_labels.json + train_labels_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/train_labels.json + model_dir: models + name: 552bcfbc5e8669a23d481b26819924f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 76226f7f6719ae19fd603411cb94aef7 + trainer: + kwargs: {} +name: 552bcfbc5e8669a23d481b26819924f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/params.yaml b/examples/security/classification/output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/params.yaml new file mode 100644 index 00000000..c534dee4 --- /dev/null +++ b/examples/security/classification/output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/adv_predictions.json + adv_probabilities_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/757496301a02714a003b0a8dc40af92b.pkl + params_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/params.yaml + predictions_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/predictions.json + probabilities_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/probabilities.json + score_dict_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/score_dict.json + test_labels_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/test_labels.json + train_labels_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/train_labels.json + model_dir: models + name: 553b3a11c7ffb91f5d0a6f4674e82b0f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fff508c20f214f4d470278d7938117cf + trainer: + kwargs: {} +name: 553b3a11c7ffb91f5d0a6f4674e82b0f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/55572fd810232ce801aff52f0fd94377/params.yaml b/examples/security/classification/output/reports/train/55572fd810232ce801aff52f0fd94377/params.yaml new file mode 100644 index 00000000..f6749533 --- /dev/null +++ b/examples/security/classification/output/reports/train/55572fd810232ce801aff52f0fd94377/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/55572fd810232ce801aff52f0fd94377/adv_predictions.json + adv_probabilities_file: output/reports/train/55572fd810232ce801aff52f0fd94377/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/ee13061f1cf7ae0be3d7cc13c46e7459.pkl + params_file: output/reports/train/55572fd810232ce801aff52f0fd94377/params.yaml + predictions_file: output/reports/train/55572fd810232ce801aff52f0fd94377/predictions.json + probabilities_file: output/reports/train/55572fd810232ce801aff52f0fd94377/probabilities.json + score_dict_file: output/reports/train/55572fd810232ce801aff52f0fd94377/score_dict.json + test_labels_file: output/reports/train/55572fd810232ce801aff52f0fd94377/test_labels.json + train_labels_file: output/reports/train/55572fd810232ce801aff52f0fd94377/train_labels.json + model_dir: models + name: 55572fd810232ce801aff52f0fd94377 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ecd586abd5b7fee4a2a3abde2bba8ae + trainer: + kwargs: {} +name: 55572fd810232ce801aff52f0fd94377 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/557af1520f334a69d65b1aecd340fa2d/params.yaml b/examples/security/classification/output/reports/train/557af1520f334a69d65b1aecd340fa2d/params.yaml new file mode 100644 index 00000000..bbb5f8d1 --- /dev/null +++ b/examples/security/classification/output/reports/train/557af1520f334a69d65b1aecd340fa2d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/adv_predictions.json + adv_probabilities_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/a7f9ebb5662d07b8ef59aa8fd9c6fb88.pkl + params_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/params.yaml + predictions_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/predictions.json + probabilities_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/probabilities.json + score_dict_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/score_dict.json + test_labels_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/test_labels.json + train_labels_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/train_labels.json + model_dir: models + name: 557af1520f334a69d65b1aecd340fa2d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5a49e798eaab0d544a588096b9bbf9c0 + trainer: + kwargs: {} +name: 557af1520f334a69d65b1aecd340fa2d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/557b033314915cc8329e071797a03250/params.yaml b/examples/security/classification/output/reports/train/557b033314915cc8329e071797a03250/params.yaml new file mode 100644 index 00000000..bb6d903c --- /dev/null +++ b/examples/security/classification/output/reports/train/557b033314915cc8329e071797a03250/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/557b033314915cc8329e071797a03250/adv_predictions.json + adv_probabilities_file: output/reports/train/557b033314915cc8329e071797a03250/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/ed964cc5033a2380ddabe562f3b9e460.pkl + params_file: output/reports/train/557b033314915cc8329e071797a03250/params.yaml + predictions_file: output/reports/train/557b033314915cc8329e071797a03250/predictions.json + probabilities_file: output/reports/train/557b033314915cc8329e071797a03250/probabilities.json + score_dict_file: output/reports/train/557b033314915cc8329e071797a03250/score_dict.json + test_labels_file: output/reports/train/557b033314915cc8329e071797a03250/test_labels.json + train_labels_file: output/reports/train/557b033314915cc8329e071797a03250/train_labels.json + model_dir: models + name: 557b033314915cc8329e071797a03250 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: efd1813dd903aa109896662440f276d8 + trainer: + kwargs: {} +name: 557b033314915cc8329e071797a03250 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/558003aec4c2abe27cea646204db0adc/params.yaml b/examples/security/classification/output/reports/train/558003aec4c2abe27cea646204db0adc/params.yaml new file mode 100644 index 00000000..7ebba8cb --- /dev/null +++ b/examples/security/classification/output/reports/train/558003aec4c2abe27cea646204db0adc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/558003aec4c2abe27cea646204db0adc/adv_predictions.json + adv_probabilities_file: output/reports/train/558003aec4c2abe27cea646204db0adc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/21d7b299c94b45b746d23305a33715e1.pkl + params_file: output/reports/train/558003aec4c2abe27cea646204db0adc/params.yaml + predictions_file: output/reports/train/558003aec4c2abe27cea646204db0adc/predictions.json + probabilities_file: output/reports/train/558003aec4c2abe27cea646204db0adc/probabilities.json + score_dict_file: output/reports/train/558003aec4c2abe27cea646204db0adc/score_dict.json + test_labels_file: output/reports/train/558003aec4c2abe27cea646204db0adc/test_labels.json + train_labels_file: output/reports/train/558003aec4c2abe27cea646204db0adc/train_labels.json + model_dir: models + name: 558003aec4c2abe27cea646204db0adc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 87bade3b71a6117ef3010b2dd409178d + trainer: + kwargs: {} +name: 558003aec4c2abe27cea646204db0adc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/params.yaml b/examples/security/classification/output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/params.yaml new file mode 100644 index 00000000..1c95acd9 --- /dev/null +++ b/examples/security/classification/output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/adv_predictions.json + adv_probabilities_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/9dc0681f225ba6fe140538b297749b4f.pkl + params_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/params.yaml + predictions_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/predictions.json + probabilities_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/probabilities.json + score_dict_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/score_dict.json + test_labels_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/test_labels.json + train_labels_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/train_labels.json + model_dir: models + name: 55986d2db564aef8d02ce90ceb39a1a0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 02d8e826addb70520e536ca713ae3992 + trainer: + kwargs: {} +name: 55986d2db564aef8d02ce90ceb39a1a0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/params.yaml b/examples/security/classification/output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/params.yaml new file mode 100644 index 00000000..c0a2b28b --- /dev/null +++ b/examples/security/classification/output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/adv_predictions.json + adv_probabilities_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/58abdfef416473fe39308a0def75c3cd.pkl + params_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/params.yaml + predictions_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/predictions.json + probabilities_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/probabilities.json + score_dict_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/score_dict.json + test_labels_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/test_labels.json + train_labels_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/train_labels.json + model_dir: models + name: 55bfae96534bccc2b9fdf8e6b3bf5467 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c25dc6902c5b756fc60e27bfee597178 + trainer: + kwargs: {} +name: 55bfae96534bccc2b9fdf8e6b3bf5467 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/params.yaml b/examples/security/classification/output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/params.yaml new file mode 100644 index 00000000..cb7b6b82 --- /dev/null +++ b/examples/security/classification/output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/adv_predictions.json + adv_probabilities_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/edfcd1bc4121d2543e682455ee2c82f0.pkl + params_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/params.yaml + predictions_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/predictions.json + probabilities_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/probabilities.json + score_dict_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/score_dict.json + test_labels_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/test_labels.json + train_labels_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/train_labels.json + model_dir: models + name: 55cba176b52b8ce0ab7b8df37107e63d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ec0d661ca3897485fd5aace0e465597d + trainer: + kwargs: {} +name: 55cba176b52b8ce0ab7b8df37107e63d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/55d24251b3799994488d2090a80973d3/params.yaml b/examples/security/classification/output/reports/train/55d24251b3799994488d2090a80973d3/params.yaml new file mode 100644 index 00000000..bfeaedda --- /dev/null +++ b/examples/security/classification/output/reports/train/55d24251b3799994488d2090a80973d3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/55d24251b3799994488d2090a80973d3/adv_predictions.json + adv_probabilities_file: output/reports/train/55d24251b3799994488d2090a80973d3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/7b50b147c9ded2eb22318ad6e15c3b60.pkl + params_file: output/reports/train/55d24251b3799994488d2090a80973d3/params.yaml + predictions_file: output/reports/train/55d24251b3799994488d2090a80973d3/predictions.json + probabilities_file: output/reports/train/55d24251b3799994488d2090a80973d3/probabilities.json + score_dict_file: output/reports/train/55d24251b3799994488d2090a80973d3/score_dict.json + test_labels_file: output/reports/train/55d24251b3799994488d2090a80973d3/test_labels.json + train_labels_file: output/reports/train/55d24251b3799994488d2090a80973d3/train_labels.json + model_dir: models + name: 55d24251b3799994488d2090a80973d3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e4464064146066f71d18c5e18cc995fc + trainer: + kwargs: {} +name: 55d24251b3799994488d2090a80973d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/params.yaml b/examples/security/classification/output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/params.yaml new file mode 100644 index 00000000..03441b7f --- /dev/null +++ b/examples/security/classification/output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/adv_predictions.json + adv_probabilities_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/49abe4dc6f154bd59830e92121d6d84e.pkl + params_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/params.yaml + predictions_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/predictions.json + probabilities_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/probabilities.json + score_dict_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/score_dict.json + test_labels_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/test_labels.json + train_labels_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/train_labels.json + model_dir: models + name: 55df3aa6582b2dce6cb6608c0e7d8dce + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 29e85514f11575806204275ea33be48b + trainer: + kwargs: {} +name: 55df3aa6582b2dce6cb6608c0e7d8dce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/params.yaml b/examples/security/classification/output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/params.yaml new file mode 100644 index 00000000..faf57262 --- /dev/null +++ b/examples/security/classification/output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/adv_predictions.json + adv_probabilities_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/9a8f3655adabfdd5be99c19c0f4ec743.pkl + params_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/params.yaml + predictions_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/predictions.json + probabilities_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/probabilities.json + score_dict_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/score_dict.json + test_labels_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/test_labels.json + train_labels_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/train_labels.json + model_dir: models + name: 55f99863f858e2989b4a2ac5c8d5e16f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 91d345a42b81a151f384f67757d40d0a + trainer: + kwargs: {} +name: 55f99863f858e2989b4a2ac5c8d5e16f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/params.yaml b/examples/security/classification/output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/params.yaml new file mode 100644 index 00000000..2431e133 --- /dev/null +++ b/examples/security/classification/output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/adv_predictions.json + adv_probabilities_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/f952beb729317a3cfca2aace0062dd9f.pkl + params_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/params.yaml + predictions_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/predictions.json + probabilities_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/probabilities.json + score_dict_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/score_dict.json + test_labels_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/test_labels.json + train_labels_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/train_labels.json + model_dir: models + name: 561fa37abe2dab0a256861e0fb71d9ac + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1ac68b81ace77f12d35053d162117de7 + trainer: + kwargs: {} +name: 561fa37abe2dab0a256861e0fb71d9ac +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/56484c2a2da13b617eb5ec199eee371b/params.yaml b/examples/security/classification/output/reports/train/56484c2a2da13b617eb5ec199eee371b/params.yaml new file mode 100644 index 00000000..e592e6df --- /dev/null +++ b/examples/security/classification/output/reports/train/56484c2a2da13b617eb5ec199eee371b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/adv_predictions.json + adv_probabilities_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/3cd54e4f63b0762da9b25ee04f6f2e95.pkl + params_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/params.yaml + predictions_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/predictions.json + probabilities_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/probabilities.json + score_dict_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/score_dict.json + test_labels_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/test_labels.json + train_labels_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/train_labels.json + model_dir: models + name: 56484c2a2da13b617eb5ec199eee371b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d3f3f21ba4df836693b7d5b307d4d96 + trainer: + kwargs: {} +name: 56484c2a2da13b617eb5ec199eee371b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5660948910fe72e243b0123b0add14af/params.yaml b/examples/security/classification/output/reports/train/5660948910fe72e243b0123b0add14af/params.yaml new file mode 100644 index 00000000..c90912ff --- /dev/null +++ b/examples/security/classification/output/reports/train/5660948910fe72e243b0123b0add14af/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5660948910fe72e243b0123b0add14af/adv_predictions.json + adv_probabilities_file: output/reports/train/5660948910fe72e243b0123b0add14af/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/0f4fbac76f8c2f3c32d4a110c2d8cea0.pkl + params_file: output/reports/train/5660948910fe72e243b0123b0add14af/params.yaml + predictions_file: output/reports/train/5660948910fe72e243b0123b0add14af/predictions.json + probabilities_file: output/reports/train/5660948910fe72e243b0123b0add14af/probabilities.json + score_dict_file: output/reports/train/5660948910fe72e243b0123b0add14af/score_dict.json + test_labels_file: output/reports/train/5660948910fe72e243b0123b0add14af/test_labels.json + train_labels_file: output/reports/train/5660948910fe72e243b0123b0add14af/train_labels.json + model_dir: models + name: 5660948910fe72e243b0123b0add14af + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3e6426437bf7409206bb397823350e5f + trainer: + kwargs: {} +name: 5660948910fe72e243b0123b0add14af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/56af31d4fd9e4d1d947f621613070020/params.yaml b/examples/security/classification/output/reports/train/56af31d4fd9e4d1d947f621613070020/params.yaml new file mode 100644 index 00000000..1143f48b --- /dev/null +++ b/examples/security/classification/output/reports/train/56af31d4fd9e4d1d947f621613070020/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/adv_predictions.json + adv_probabilities_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/2dbea57763c74e74706be166c3442b33.pkl + params_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/params.yaml + predictions_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/predictions.json + probabilities_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/probabilities.json + score_dict_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/score_dict.json + test_labels_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/test_labels.json + train_labels_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/train_labels.json + model_dir: models + name: 56af31d4fd9e4d1d947f621613070020 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5dced44973f4c7a79bfebc18f151b5e5 + trainer: + kwargs: {} +name: 56af31d4fd9e4d1d947f621613070020 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/56afef145a18f473e6d8be24fb11bbfd/params.yaml b/examples/security/classification/output/reports/train/56afef145a18f473e6d8be24fb11bbfd/params.yaml new file mode 100644 index 00000000..0adff983 --- /dev/null +++ b/examples/security/classification/output/reports/train/56afef145a18f473e6d8be24fb11bbfd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/adv_predictions.json + adv_probabilities_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/282f9964b55565161551d844d1cda033.pkl + params_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/params.yaml + predictions_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/predictions.json + probabilities_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/probabilities.json + score_dict_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/score_dict.json + test_labels_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/test_labels.json + train_labels_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/train_labels.json + model_dir: models + name: 56afef145a18f473e6d8be24fb11bbfd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ee49abd17d900939ef1c8369c28fdd6 + trainer: + kwargs: {} +name: 56afef145a18f473e6d8be24fb11bbfd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/params.yaml b/examples/security/classification/output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/params.yaml new file mode 100644 index 00000000..f4da2bad --- /dev/null +++ b/examples/security/classification/output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/adv_predictions.json + adv_probabilities_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/49a2df4832437fdb7157ce9ab742e10e.pkl + params_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/params.yaml + predictions_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/predictions.json + probabilities_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/probabilities.json + score_dict_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/score_dict.json + test_labels_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/test_labels.json + train_labels_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/train_labels.json + model_dir: models + name: 56cd9d4444dc965474c1b2ab4bd3b617 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 52de23d438d1b9bcca72671823ca43bd + trainer: + kwargs: {} +name: 56cd9d4444dc965474c1b2ab4bd3b617 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/56e7f08fc318f05d1c248326d5dff55c/params.yaml b/examples/security/classification/output/reports/train/56e7f08fc318f05d1c248326d5dff55c/params.yaml new file mode 100644 index 00000000..79d2500b --- /dev/null +++ b/examples/security/classification/output/reports/train/56e7f08fc318f05d1c248326d5dff55c/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/adv_predictions.json + adv_probabilities_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/11e9d205776f6d181cc9c5f542094f21.pkl + params_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/params.yaml + predictions_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/predictions.json + probabilities_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/probabilities.json + score_dict_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/score_dict.json + test_labels_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/test_labels.json + train_labels_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/train_labels.json + model_dir: models + name: 56e7f08fc318f05d1c248326d5dff55c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4dcf06f63fc90d0bfdced4ed9e883ac5 + trainer: + kwargs: {} +name: 56e7f08fc318f05d1c248326d5dff55c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/params.yaml b/examples/security/classification/output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/params.yaml new file mode 100644 index 00000000..9437b4ab --- /dev/null +++ b/examples/security/classification/output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/adv_predictions.json + adv_probabilities_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/5556b002ac4ce1e149d88f7cba4a9697.pkl + params_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/params.yaml + predictions_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/predictions.json + probabilities_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/probabilities.json + score_dict_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/score_dict.json + test_labels_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/test_labels.json + train_labels_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/train_labels.json + model_dir: models + name: 5704a91f1db5161e5e2b2bba6ef0dbc4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4b963967744c09f9cd14e9590572430a + trainer: + kwargs: {} +name: 5704a91f1db5161e5e2b2bba6ef0dbc4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/params.yaml b/examples/security/classification/output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/params.yaml new file mode 100644 index 00000000..f32008a6 --- /dev/null +++ b/examples/security/classification/output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/adv_predictions.json + adv_probabilities_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/f7c98d7e4586679d591f0e254be758ed.pkl + params_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/params.yaml + predictions_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/predictions.json + probabilities_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/probabilities.json + score_dict_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/score_dict.json + test_labels_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/test_labels.json + train_labels_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/train_labels.json + model_dir: models + name: 570cfe7df0b46c03494b539cf0b17dc3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2e2f830839349004e95c2e03f7bfa662 + trainer: + kwargs: {} +name: 570cfe7df0b46c03494b539cf0b17dc3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/params.yaml b/examples/security/classification/output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/params.yaml new file mode 100644 index 00000000..4254b55e --- /dev/null +++ b/examples/security/classification/output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/adv_predictions.json + adv_probabilities_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/1c85fcf4fa12a46e4c48310a761e6213.pkl + params_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/params.yaml + predictions_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/predictions.json + probabilities_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/probabilities.json + score_dict_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/score_dict.json + test_labels_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/test_labels.json + train_labels_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/train_labels.json + model_dir: models + name: 572c9ad35fe58ed4efec0c95ccff76f1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fcb1a41ff02f3bf6a0ffe4d8ef0582e4 + trainer: + kwargs: {} +name: 572c9ad35fe58ed4efec0c95ccff76f1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/params.yaml b/examples/security/classification/output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/params.yaml new file mode 100644 index 00000000..45669834 --- /dev/null +++ b/examples/security/classification/output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/adv_predictions.json + adv_probabilities_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/d4f50f9989ea52364f8863c38296825b.pkl + params_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/params.yaml + predictions_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/predictions.json + probabilities_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/probabilities.json + score_dict_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/score_dict.json + test_labels_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/test_labels.json + train_labels_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/train_labels.json + model_dir: models + name: 572e8ea7b0d31d54e7801d2ea8d9c539 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7131e14f30da55323b37a9f8326d7a1c + trainer: + kwargs: {} +name: 572e8ea7b0d31d54e7801d2ea8d9c539 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/57452003995f5259e4fa8395fdef4ec2/params.yaml b/examples/security/classification/output/reports/train/57452003995f5259e4fa8395fdef4ec2/params.yaml new file mode 100644 index 00000000..2084c0f5 --- /dev/null +++ b/examples/security/classification/output/reports/train/57452003995f5259e4fa8395fdef4ec2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/adv_predictions.json + adv_probabilities_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/07ac1128cdcf7dd4e16e8c8fc94dfdeb.pkl + params_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/params.yaml + predictions_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/predictions.json + probabilities_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/probabilities.json + score_dict_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/score_dict.json + test_labels_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/test_labels.json + train_labels_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/train_labels.json + model_dir: models + name: 57452003995f5259e4fa8395fdef4ec2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3110caae82aedc90f09bf2cc7253a541 + trainer: + kwargs: {} +name: 57452003995f5259e4fa8395fdef4ec2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/575ea89433a37bea6083ac19ea04051d/params.yaml b/examples/security/classification/output/reports/train/575ea89433a37bea6083ac19ea04051d/params.yaml new file mode 100644 index 00000000..a6936c65 --- /dev/null +++ b/examples/security/classification/output/reports/train/575ea89433a37bea6083ac19ea04051d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/adv_predictions.json + adv_probabilities_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/6c7edfc9d384b05331035c92236fa7b4.pkl + params_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/params.yaml + predictions_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/predictions.json + probabilities_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/probabilities.json + score_dict_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/score_dict.json + test_labels_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/test_labels.json + train_labels_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/train_labels.json + model_dir: models + name: 575ea89433a37bea6083ac19ea04051d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 90cc5cfa125c8bb48e525d8f84fd869c + trainer: + kwargs: {} +name: 575ea89433a37bea6083ac19ea04051d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5780927248d62f25dfac023435bfa3b0/params.yaml b/examples/security/classification/output/reports/train/5780927248d62f25dfac023435bfa3b0/params.yaml new file mode 100644 index 00000000..8e77d30d --- /dev/null +++ b/examples/security/classification/output/reports/train/5780927248d62f25dfac023435bfa3b0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/adv_predictions.json + adv_probabilities_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/fd97afb480628d0eef97b4b73da9cf2d.pkl + params_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/params.yaml + predictions_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/predictions.json + probabilities_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/probabilities.json + score_dict_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/score_dict.json + test_labels_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/test_labels.json + train_labels_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/train_labels.json + model_dir: models + name: 5780927248d62f25dfac023435bfa3b0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a884a323a1720c87dc81e7beaf0fa2b5 + trainer: + kwargs: {} +name: 5780927248d62f25dfac023435bfa3b0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/57a991c65041f180d0984a2d0168ce40/params.yaml b/examples/security/classification/output/reports/train/57a991c65041f180d0984a2d0168ce40/params.yaml new file mode 100644 index 00000000..ef7de1f8 --- /dev/null +++ b/examples/security/classification/output/reports/train/57a991c65041f180d0984a2d0168ce40/params.yaml @@ -0,0 +1,197 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d5294e8ab1041edae33534ea52ca8ac1 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5807854823e97434b6fcd7b2a82346e8 + trainer: + kwargs: {} + name: 4b63f8fa43b854ff1187842f7138f4ba +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/adv_predictions.json + adv_probabilities_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/adv_probabilities.json + attack_file: output/attacks/5cfe3fccdcdb0024b76e93cf446f66cd.pkl + data_file: output/data/1d9e337636137b595d73f82016ba0ef9.pkl + model_file: output/models/19e91e79d76ceed771ccd133e61b542b.pkl + params_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/params.yaml + predictions_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/predictions.json + probabilities_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/probabilities.json + score_dict_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/score_dict.json + test_labels_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/test_labels.json + train_labels_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/train_labels.json + model_dir: models + name: 57a991c65041f180d0984a2d0168ce40 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 85285c84d7f24301c3d23f9a50635d2e + trainer: + kwargs: {} +name: 57a991c65041f180d0984a2d0168ce40 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/57ae32094abbffc372f3b575d2851070/params.yaml b/examples/security/classification/output/reports/train/57ae32094abbffc372f3b575d2851070/params.yaml new file mode 100644 index 00000000..4197b332 --- /dev/null +++ b/examples/security/classification/output/reports/train/57ae32094abbffc372f3b575d2851070/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/57ae32094abbffc372f3b575d2851070/adv_predictions.json + adv_probabilities_file: output/reports/train/57ae32094abbffc372f3b575d2851070/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/d17445375deb83426a8dad0dae9fd059.pkl + params_file: output/reports/train/57ae32094abbffc372f3b575d2851070/params.yaml + predictions_file: output/reports/train/57ae32094abbffc372f3b575d2851070/predictions.json + probabilities_file: output/reports/train/57ae32094abbffc372f3b575d2851070/probabilities.json + score_dict_file: output/reports/train/57ae32094abbffc372f3b575d2851070/score_dict.json + test_labels_file: output/reports/train/57ae32094abbffc372f3b575d2851070/test_labels.json + train_labels_file: output/reports/train/57ae32094abbffc372f3b575d2851070/train_labels.json + model_dir: models + name: 57ae32094abbffc372f3b575d2851070 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 175a21466a0c9c753784c8be66c558d9 + trainer: + kwargs: {} +name: 57ae32094abbffc372f3b575d2851070 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/57c66da7ec54232190a9f7a2f75884a5/params.yaml b/examples/security/classification/output/reports/train/57c66da7ec54232190a9f7a2f75884a5/params.yaml new file mode 100644 index 00000000..cab6e07e --- /dev/null +++ b/examples/security/classification/output/reports/train/57c66da7ec54232190a9f7a2f75884a5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/adv_predictions.json + adv_probabilities_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/7558a4ebc7f5e58268ee2b5868244650.pkl + params_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/params.yaml + predictions_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/predictions.json + probabilities_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/probabilities.json + score_dict_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/score_dict.json + test_labels_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/test_labels.json + train_labels_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/train_labels.json + model_dir: models + name: 57c66da7ec54232190a9f7a2f75884a5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: db052b22178e8f939bf743a619afad14 + trainer: + kwargs: {} +name: 57c66da7ec54232190a9f7a2f75884a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/params.yaml b/examples/security/classification/output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/params.yaml new file mode 100644 index 00000000..292605ef --- /dev/null +++ b/examples/security/classification/output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/adv_predictions.json + adv_probabilities_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e5922e8e921f0a5cba98516d2e0a4bcf.pkl + params_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/params.yaml + predictions_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/predictions.json + probabilities_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/probabilities.json + score_dict_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/score_dict.json + test_labels_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/test_labels.json + train_labels_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/train_labels.json + model_dir: models + name: 57eb253e0ce8cdba3c8df8261abb2cd9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a7867024b4f3f7361dcf921009a670ee + trainer: + kwargs: {} +name: 57eb253e0ce8cdba3c8df8261abb2cd9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/params.yaml b/examples/security/classification/output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/params.yaml new file mode 100644 index 00000000..0fab8b61 --- /dev/null +++ b/examples/security/classification/output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/adv_predictions.json + adv_probabilities_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/a6d5cb75097cbac7c170f2ebc2d855cd.pkl + params_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/params.yaml + predictions_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/predictions.json + probabilities_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/probabilities.json + score_dict_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/score_dict.json + test_labels_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/test_labels.json + train_labels_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/train_labels.json + model_dir: models + name: 580c758bb53f1e06dfdf5e42b4ef3b43 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bf6b076570b4528d8a85ac3dbf37c10c + trainer: + kwargs: {} +name: 580c758bb53f1e06dfdf5e42b4ef3b43 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5835a6450617b3c7e24da11c7213a993/params.yaml b/examples/security/classification/output/reports/train/5835a6450617b3c7e24da11c7213a993/params.yaml new file mode 100644 index 00000000..c21c5e86 --- /dev/null +++ b/examples/security/classification/output/reports/train/5835a6450617b3c7e24da11c7213a993/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/adv_predictions.json + adv_probabilities_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/71d826871fae11236bb3e178be73f591.pkl + params_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/params.yaml + predictions_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/predictions.json + probabilities_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/probabilities.json + score_dict_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/score_dict.json + test_labels_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/test_labels.json + train_labels_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/train_labels.json + model_dir: models + name: 5835a6450617b3c7e24da11c7213a993 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b6c840376aa619e1363c2487029cd7c7 + trainer: + kwargs: {} +name: 5835a6450617b3c7e24da11c7213a993 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/583768f9400fb4fc5a5caa2819c21671/params.yaml b/examples/security/classification/output/reports/train/583768f9400fb4fc5a5caa2819c21671/params.yaml new file mode 100644 index 00000000..b21066c8 --- /dev/null +++ b/examples/security/classification/output/reports/train/583768f9400fb4fc5a5caa2819c21671/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/adv_predictions.json + adv_probabilities_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/a557fa366512755791d00d7ea8a2a35f.pkl + params_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/params.yaml + predictions_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/predictions.json + probabilities_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/probabilities.json + score_dict_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/score_dict.json + test_labels_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/test_labels.json + train_labels_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/train_labels.json + model_dir: models + name: 583768f9400fb4fc5a5caa2819c21671 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b0032ecbf264b5943796e0eea2edae3c + trainer: + kwargs: {} +name: 583768f9400fb4fc5a5caa2819c21671 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/params.yaml b/examples/security/classification/output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/params.yaml new file mode 100644 index 00000000..509b0fc2 --- /dev/null +++ b/examples/security/classification/output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/adv_predictions.json + adv_probabilities_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/28c1ba7b220dae3cf28e0656292ac096.pkl + params_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/params.yaml + predictions_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/predictions.json + probabilities_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/probabilities.json + score_dict_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/score_dict.json + test_labels_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/test_labels.json + train_labels_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/train_labels.json + model_dir: models + name: 584cd0cba01d7c5be0fa7ce246b73f6a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3c47488388eb9715e7ffd8116d9d0b55 + trainer: + kwargs: {} +name: 584cd0cba01d7c5be0fa7ce246b73f6a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5872219c935df616bcd1b8fcc784327e/params.yaml b/examples/security/classification/output/reports/train/5872219c935df616bcd1b8fcc784327e/params.yaml new file mode 100644 index 00000000..519c50c9 --- /dev/null +++ b/examples/security/classification/output/reports/train/5872219c935df616bcd1b8fcc784327e/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/adv_predictions.json + adv_probabilities_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/63887f6e311a03d562ae798714ec6131.pkl + params_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/params.yaml + predictions_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/predictions.json + probabilities_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/probabilities.json + score_dict_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/score_dict.json + test_labels_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/test_labels.json + train_labels_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/train_labels.json + model_dir: models + name: 5872219c935df616bcd1b8fcc784327e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: be6a34ec1655e74741233b51bc1de590 + trainer: + kwargs: {} +name: 5872219c935df616bcd1b8fcc784327e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/params.yaml b/examples/security/classification/output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/params.yaml new file mode 100644 index 00000000..33749ee7 --- /dev/null +++ b/examples/security/classification/output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/adv_predictions.json + adv_probabilities_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e7981dc921475926a9ef59fc936585a7.pkl + params_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/params.yaml + predictions_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/predictions.json + probabilities_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/probabilities.json + score_dict_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/score_dict.json + test_labels_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/test_labels.json + train_labels_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/train_labels.json + model_dir: models + name: 588ecc2ffd6817942d1b286b15c2bc80 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 743dfa7ccef3f360bd7cab6ba01758e5 + trainer: + kwargs: {} +name: 588ecc2ffd6817942d1b286b15c2bc80 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/params.yaml b/examples/security/classification/output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/params.yaml new file mode 100644 index 00000000..41298655 --- /dev/null +++ b/examples/security/classification/output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/adv_predictions.json + adv_probabilities_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/30f8b68e2259a0b36aecc7a160739ddd.pkl + params_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/params.yaml + predictions_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/predictions.json + probabilities_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/probabilities.json + score_dict_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/score_dict.json + test_labels_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/test_labels.json + train_labels_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/train_labels.json + model_dir: models + name: 58a308c6b4f8d86cce582b8f96b6c968 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 2 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aca1cea26720fe3c197964bb8df66a82 + trainer: + kwargs: {} +name: 58a308c6b4f8d86cce582b8f96b6c968 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/58af91b8cbc10584b119000cb3504f89/params.yaml b/examples/security/classification/output/reports/train/58af91b8cbc10584b119000cb3504f89/params.yaml new file mode 100644 index 00000000..85159395 --- /dev/null +++ b/examples/security/classification/output/reports/train/58af91b8cbc10584b119000cb3504f89/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/adv_predictions.json + adv_probabilities_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/9104b28ec4af8f39bb09389c438e1a31.pkl + params_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/params.yaml + predictions_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/predictions.json + probabilities_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/probabilities.json + score_dict_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/score_dict.json + test_labels_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/test_labels.json + train_labels_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/train_labels.json + model_dir: models + name: 58af91b8cbc10584b119000cb3504f89 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ebc45224568fc3bc22f41ca2e0dd620d + trainer: + kwargs: {} +name: 58af91b8cbc10584b119000cb3504f89 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/params.yaml b/examples/security/classification/output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/params.yaml new file mode 100644 index 00000000..ba8837e0 --- /dev/null +++ b/examples/security/classification/output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/adv_predictions.json + adv_probabilities_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/e7560f6d1375c96a703dba9e5ec7eff2.pkl + params_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/params.yaml + predictions_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/predictions.json + probabilities_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/probabilities.json + score_dict_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/score_dict.json + test_labels_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/test_labels.json + train_labels_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/train_labels.json + model_dir: models + name: 58dd1e85c6e0d4ccafe772d90402135b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1cafc4043d22ded1c0297e93a79dfbba + trainer: + kwargs: {} +name: 58dd1e85c6e0d4ccafe772d90402135b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5926d09c350b655e12a954d4bbbde90f/params.yaml b/examples/security/classification/output/reports/train/5926d09c350b655e12a954d4bbbde90f/params.yaml new file mode 100644 index 00000000..13a5722e --- /dev/null +++ b/examples/security/classification/output/reports/train/5926d09c350b655e12a954d4bbbde90f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/adv_predictions.json + adv_probabilities_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/49f1b0c8ea99b7e5d8ece22adf692668.pkl + params_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/params.yaml + predictions_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/predictions.json + probabilities_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/probabilities.json + score_dict_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/score_dict.json + test_labels_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/test_labels.json + train_labels_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/train_labels.json + model_dir: models + name: 5926d09c350b655e12a954d4bbbde90f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4fce58fcc41960a1f64db10ad56c02aa + trainer: + kwargs: {} +name: 5926d09c350b655e12a954d4bbbde90f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/params.yaml b/examples/security/classification/output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/params.yaml new file mode 100644 index 00000000..a844bb3b --- /dev/null +++ b/examples/security/classification/output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/adv_predictions.json + adv_probabilities_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/40b624523fa394d7c82f3fdb2bef3b1b.pkl + params_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/params.yaml + predictions_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/predictions.json + probabilities_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/probabilities.json + score_dict_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/score_dict.json + test_labels_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/test_labels.json + train_labels_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/train_labels.json + model_dir: models + name: 593bd5d3ab8ddb25fd7eece1dfcb959c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 49b47e193965c7edb0981eb1798a2581 + trainer: + kwargs: {} +name: 593bd5d3ab8ddb25fd7eece1dfcb959c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/59a342d161702ff023e091b0127435f5/params.yaml b/examples/security/classification/output/reports/train/59a342d161702ff023e091b0127435f5/params.yaml new file mode 100644 index 00000000..c922d215 --- /dev/null +++ b/examples/security/classification/output/reports/train/59a342d161702ff023e091b0127435f5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/59a342d161702ff023e091b0127435f5/adv_predictions.json + adv_probabilities_file: output/reports/train/59a342d161702ff023e091b0127435f5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/55e59759d2a44038e3b76355a1957f35.pkl + params_file: output/reports/train/59a342d161702ff023e091b0127435f5/params.yaml + predictions_file: output/reports/train/59a342d161702ff023e091b0127435f5/predictions.json + probabilities_file: output/reports/train/59a342d161702ff023e091b0127435f5/probabilities.json + score_dict_file: output/reports/train/59a342d161702ff023e091b0127435f5/score_dict.json + test_labels_file: output/reports/train/59a342d161702ff023e091b0127435f5/test_labels.json + train_labels_file: output/reports/train/59a342d161702ff023e091b0127435f5/train_labels.json + model_dir: models + name: 59a342d161702ff023e091b0127435f5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e15416d24902c668169a44f797b2c3eb + trainer: + kwargs: {} +name: 59a342d161702ff023e091b0127435f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/params.yaml b/examples/security/classification/output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/params.yaml new file mode 100644 index 00000000..31231a0b --- /dev/null +++ b/examples/security/classification/output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/adv_predictions.json + adv_probabilities_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/98d86a4996e28469dcd8aa1618d36653.pkl + params_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/params.yaml + predictions_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/predictions.json + probabilities_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/probabilities.json + score_dict_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/score_dict.json + test_labels_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/test_labels.json + train_labels_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/train_labels.json + model_dir: models + name: 59cb5011c5c2e9fcb0b289af27ef0ca6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fe896a29d41011c9f3216829a5e081d3 + trainer: + kwargs: {} +name: 59cb5011c5c2e9fcb0b289af27ef0ca6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/59d05917adffc4028f2edfe08f12ca58/params.yaml b/examples/security/classification/output/reports/train/59d05917adffc4028f2edfe08f12ca58/params.yaml new file mode 100644 index 00000000..2a642f9e --- /dev/null +++ b/examples/security/classification/output/reports/train/59d05917adffc4028f2edfe08f12ca58/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/adv_predictions.json + adv_probabilities_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/6b2ad0ce8d01f10c1c387039dff2fa2c.pkl + params_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/params.yaml + predictions_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/predictions.json + probabilities_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/probabilities.json + score_dict_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/score_dict.json + test_labels_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/test_labels.json + train_labels_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/train_labels.json + model_dir: models + name: 59d05917adffc4028f2edfe08f12ca58 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7e00af5c8d8f132fff2ed7bc20881543 + trainer: + kwargs: {} +name: 59d05917adffc4028f2edfe08f12ca58 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/59e076256bb93556b5556dea0921ced8/params.yaml b/examples/security/classification/output/reports/train/59e076256bb93556b5556dea0921ced8/params.yaml new file mode 100644 index 00000000..b7f2986b --- /dev/null +++ b/examples/security/classification/output/reports/train/59e076256bb93556b5556dea0921ced8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/59e076256bb93556b5556dea0921ced8/adv_predictions.json + adv_probabilities_file: output/reports/train/59e076256bb93556b5556dea0921ced8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/c42dca68dac6c33dd457d375ae52596b.pkl + params_file: output/reports/train/59e076256bb93556b5556dea0921ced8/params.yaml + predictions_file: output/reports/train/59e076256bb93556b5556dea0921ced8/predictions.json + probabilities_file: output/reports/train/59e076256bb93556b5556dea0921ced8/probabilities.json + score_dict_file: output/reports/train/59e076256bb93556b5556dea0921ced8/score_dict.json + test_labels_file: output/reports/train/59e076256bb93556b5556dea0921ced8/test_labels.json + train_labels_file: output/reports/train/59e076256bb93556b5556dea0921ced8/train_labels.json + model_dir: models + name: 59e076256bb93556b5556dea0921ced8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1fe38f9edc9ac24cbc3a2489696e56d3 + trainer: + kwargs: {} +name: 59e076256bb93556b5556dea0921ced8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5a1265a80aad627121ba59274d74f4e2/params.yaml b/examples/security/classification/output/reports/train/5a1265a80aad627121ba59274d74f4e2/params.yaml new file mode 100644 index 00000000..92881290 --- /dev/null +++ b/examples/security/classification/output/reports/train/5a1265a80aad627121ba59274d74f4e2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/adv_predictions.json + adv_probabilities_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/deb03b3606e16b7d8367abd9e4ef8d7a.pkl + params_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/params.yaml + predictions_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/predictions.json + probabilities_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/probabilities.json + score_dict_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/score_dict.json + test_labels_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/test_labels.json + train_labels_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/train_labels.json + model_dir: models + name: 5a1265a80aad627121ba59274d74f4e2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6f56587fcff38cd93bf1e954ece15512 + trainer: + kwargs: {} +name: 5a1265a80aad627121ba59274d74f4e2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5a248cd148bb2159127facb48a775f20/params.yaml b/examples/security/classification/output/reports/train/5a248cd148bb2159127facb48a775f20/params.yaml new file mode 100644 index 00000000..fc1528da --- /dev/null +++ b/examples/security/classification/output/reports/train/5a248cd148bb2159127facb48a775f20/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5a248cd148bb2159127facb48a775f20/adv_predictions.json + adv_probabilities_file: output/reports/train/5a248cd148bb2159127facb48a775f20/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/05c07f97049a2f09e0d0aac2d7e3fa42.pkl + params_file: output/reports/train/5a248cd148bb2159127facb48a775f20/params.yaml + predictions_file: output/reports/train/5a248cd148bb2159127facb48a775f20/predictions.json + probabilities_file: output/reports/train/5a248cd148bb2159127facb48a775f20/probabilities.json + score_dict_file: output/reports/train/5a248cd148bb2159127facb48a775f20/score_dict.json + test_labels_file: output/reports/train/5a248cd148bb2159127facb48a775f20/test_labels.json + train_labels_file: output/reports/train/5a248cd148bb2159127facb48a775f20/train_labels.json + model_dir: models + name: 5a248cd148bb2159127facb48a775f20 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a65903b246b36217851027ebacaf672d + trainer: + kwargs: {} +name: 5a248cd148bb2159127facb48a775f20 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/params.yaml b/examples/security/classification/output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/params.yaml new file mode 100644 index 00000000..3f36b549 --- /dev/null +++ b/examples/security/classification/output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/adv_predictions.json + adv_probabilities_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/920b828921304454150b08f635fcc91f.pkl + params_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/params.yaml + predictions_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/predictions.json + probabilities_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/probabilities.json + score_dict_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/score_dict.json + test_labels_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/test_labels.json + train_labels_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/train_labels.json + model_dir: models + name: 5a491de32a531a80cb9fbc3c47693bf0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ce635c7c2d8beceed4ae53764c96b146 + trainer: + kwargs: {} +name: 5a491de32a531a80cb9fbc3c47693bf0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5aa510975bb5dede04c709d24e5d8d67/params.yaml b/examples/security/classification/output/reports/train/5aa510975bb5dede04c709d24e5d8d67/params.yaml new file mode 100644 index 00000000..bdaa912d --- /dev/null +++ b/examples/security/classification/output/reports/train/5aa510975bb5dede04c709d24e5d8d67/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/adv_predictions.json + adv_probabilities_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/46b0a438f17991cd112e9650b44e73ae.pkl + params_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/params.yaml + predictions_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/predictions.json + probabilities_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/probabilities.json + score_dict_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/score_dict.json + test_labels_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/test_labels.json + train_labels_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/train_labels.json + model_dir: models + name: 5aa510975bb5dede04c709d24e5d8d67 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a1bc05ce82af211b2791a34f193f9d8d + trainer: + kwargs: {} +name: 5aa510975bb5dede04c709d24e5d8d67 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/params.yaml b/examples/security/classification/output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/params.yaml new file mode 100644 index 00000000..37f76bb3 --- /dev/null +++ b/examples/security/classification/output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/adv_predictions.json + adv_probabilities_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/0c66fdfed260a80e281036e99af68c28.pkl + params_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/params.yaml + predictions_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/predictions.json + probabilities_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/probabilities.json + score_dict_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/score_dict.json + test_labels_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/test_labels.json + train_labels_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/train_labels.json + model_dir: models + name: 5aed33fc23dcf9b1ef3eff84c5f871e6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 16b9f39457ad69e1220510b8ba53f066 + trainer: + kwargs: {} +name: 5aed33fc23dcf9b1ef3eff84c5f871e6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/params.yaml b/examples/security/classification/output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/params.yaml new file mode 100644 index 00000000..0d0f61b1 --- /dev/null +++ b/examples/security/classification/output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/adv_predictions.json + adv_probabilities_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/42f9ca78397ae1650e5335e7090e598b.pkl + params_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/params.yaml + predictions_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/predictions.json + probabilities_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/probabilities.json + score_dict_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/score_dict.json + test_labels_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/test_labels.json + train_labels_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/train_labels.json + model_dir: models + name: 5af01270f7ad34b1d96420bdc6dcbd2c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e8a3b70ffabcca3b3358d472e603c81f + trainer: + kwargs: {} +name: 5af01270f7ad34b1d96420bdc6dcbd2c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/params.yaml b/examples/security/classification/output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/params.yaml new file mode 100644 index 00000000..35e67da0 --- /dev/null +++ b/examples/security/classification/output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/adv_predictions.json + adv_probabilities_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/9be1c5d8fb4bb6fec67840a525476dcb.pkl + params_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/params.yaml + predictions_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/predictions.json + probabilities_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/probabilities.json + score_dict_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/score_dict.json + test_labels_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/test_labels.json + train_labels_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/train_labels.json + model_dir: models + name: 5b11c0dad77ee6ca9690f0e82d4ac899 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5e5687ebe37b126f6f52625845c44c16 + trainer: + kwargs: {} +name: 5b11c0dad77ee6ca9690f0e82d4ac899 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/params.yaml b/examples/security/classification/output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/params.yaml new file mode 100644 index 00000000..25650854 --- /dev/null +++ b/examples/security/classification/output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/adv_predictions.json + adv_probabilities_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/2b99e6e49aeaadddbd4d747f0d61b761.pkl + params_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/params.yaml + predictions_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/predictions.json + probabilities_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/probabilities.json + score_dict_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/score_dict.json + test_labels_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/test_labels.json + train_labels_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/train_labels.json + model_dir: models + name: 5b2ae5bb19da8592e8cbda34e0a6c729 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 73d0e5b328aff62c9947579fbfeca1f1 + trainer: + kwargs: {} +name: 5b2ae5bb19da8592e8cbda34e0a6c729 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5b2b76d281531e3a12936d640e47a35b/params.yaml b/examples/security/classification/output/reports/train/5b2b76d281531e3a12936d640e47a35b/params.yaml new file mode 100644 index 00000000..95de609e --- /dev/null +++ b/examples/security/classification/output/reports/train/5b2b76d281531e3a12936d640e47a35b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/adv_predictions.json + adv_probabilities_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/df2368a325c0fdecb4879132f8a00550.pkl + params_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/params.yaml + predictions_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/predictions.json + probabilities_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/probabilities.json + score_dict_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/score_dict.json + test_labels_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/test_labels.json + train_labels_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/train_labels.json + model_dir: models + name: 5b2b76d281531e3a12936d640e47a35b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d58c0e60139488b426fac924110f5ffb + trainer: + kwargs: {} +name: 5b2b76d281531e3a12936d640e47a35b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/params.yaml b/examples/security/classification/output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/params.yaml new file mode 100644 index 00000000..59885c28 --- /dev/null +++ b/examples/security/classification/output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/adv_predictions.json + adv_probabilities_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/40c25263acd2154f7d926e34f55e5a49.pkl + params_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/params.yaml + predictions_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/predictions.json + probabilities_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/probabilities.json + score_dict_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/score_dict.json + test_labels_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/test_labels.json + train_labels_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/train_labels.json + model_dir: models + name: 5b2eb9093b173d4a366fc33bb429b4f1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: db3f843cbcd9c69ed56643aaa22cedf4 + trainer: + kwargs: {} +name: 5b2eb9093b173d4a366fc33bb429b4f1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5b605083b28c329736a3bfd2117539fd/params.yaml b/examples/security/classification/output/reports/train/5b605083b28c329736a3bfd2117539fd/params.yaml new file mode 100644 index 00000000..d1b56480 --- /dev/null +++ b/examples/security/classification/output/reports/train/5b605083b28c329736a3bfd2117539fd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/adv_predictions.json + adv_probabilities_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/d0263cdd60c085e05c071e7221b84c4a.pkl + params_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/params.yaml + predictions_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/predictions.json + probabilities_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/probabilities.json + score_dict_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/score_dict.json + test_labels_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/test_labels.json + train_labels_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/train_labels.json + model_dir: models + name: 5b605083b28c329736a3bfd2117539fd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 759caa752ecb0ddae8347687c5ac4992 + trainer: + kwargs: {} +name: 5b605083b28c329736a3bfd2117539fd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5b67bab7191c57c5e394f4a5147a9932/params.yaml b/examples/security/classification/output/reports/train/5b67bab7191c57c5e394f4a5147a9932/params.yaml new file mode 100644 index 00000000..4baabf57 --- /dev/null +++ b/examples/security/classification/output/reports/train/5b67bab7191c57c5e394f4a5147a9932/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/adv_predictions.json + adv_probabilities_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/b600126b63eefb6ed2030a691febe52e.pkl + params_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/params.yaml + predictions_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/predictions.json + probabilities_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/probabilities.json + score_dict_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/score_dict.json + test_labels_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/test_labels.json + train_labels_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/train_labels.json + model_dir: models + name: 5b67bab7191c57c5e394f4a5147a9932 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1200c32804f6d9be0eb2f2382dc7d59e + trainer: + kwargs: {} +name: 5b67bab7191c57c5e394f4a5147a9932 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5b828111dce5318f832aecdd2f42aad3/params.yaml b/examples/security/classification/output/reports/train/5b828111dce5318f832aecdd2f42aad3/params.yaml new file mode 100644 index 00000000..f9939261 --- /dev/null +++ b/examples/security/classification/output/reports/train/5b828111dce5318f832aecdd2f42aad3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/adv_predictions.json + adv_probabilities_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/a4a8c3f2acb36f703615e4df414207c7.pkl + params_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/params.yaml + predictions_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/predictions.json + probabilities_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/probabilities.json + score_dict_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/score_dict.json + test_labels_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/test_labels.json + train_labels_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/train_labels.json + model_dir: models + name: 5b828111dce5318f832aecdd2f42aad3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a8ee2a26128ec7fcd99f17741ce4b8af + trainer: + kwargs: {} +name: 5b828111dce5318f832aecdd2f42aad3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/params.yaml b/examples/security/classification/output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/params.yaml new file mode 100644 index 00000000..95195f58 --- /dev/null +++ b/examples/security/classification/output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/adv_predictions.json + adv_probabilities_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/7ada70915c09e942addca3f544cc289b.pkl + params_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/params.yaml + predictions_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/predictions.json + probabilities_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/probabilities.json + score_dict_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/score_dict.json + test_labels_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/test_labels.json + train_labels_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/train_labels.json + model_dir: models + name: 5b88c78462c6b872c4bd02cb7738fe4e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 49ad25fbcb3b50b0ac663ac8dd1ee85e + trainer: + kwargs: {} +name: 5b88c78462c6b872c4bd02cb7738fe4e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/params.yaml b/examples/security/classification/output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/params.yaml new file mode 100644 index 00000000..e1be215b --- /dev/null +++ b/examples/security/classification/output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/adv_predictions.json + adv_probabilities_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/a2202a8b4047ae1852372e8b2fbb51f3.pkl + params_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/params.yaml + predictions_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/predictions.json + probabilities_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/probabilities.json + score_dict_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/score_dict.json + test_labels_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/test_labels.json + train_labels_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/train_labels.json + model_dir: models + name: 5b8b1d3c7a22d6fefd196842edd8503a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d56098f70c72df6897d86d3985e53e27 + trainer: + kwargs: {} +name: 5b8b1d3c7a22d6fefd196842edd8503a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/params.yaml b/examples/security/classification/output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/params.yaml new file mode 100644 index 00000000..616dfe87 --- /dev/null +++ b/examples/security/classification/output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/adv_predictions.json + adv_probabilities_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/c58bd7cdf955f55f29e5ccfde09eac3c.pkl + params_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/params.yaml + predictions_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/predictions.json + probabilities_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/probabilities.json + score_dict_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/score_dict.json + test_labels_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/test_labels.json + train_labels_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/train_labels.json + model_dir: models + name: 5b8c67e63f3e7db96b4e853ee4da4841 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 57d680eb972e4126493b2956b083b2e6 + trainer: + kwargs: {} +name: 5b8c67e63f3e7db96b4e853ee4da4841 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/params.yaml b/examples/security/classification/output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/params.yaml new file mode 100644 index 00000000..0510f0ea --- /dev/null +++ b/examples/security/classification/output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/adv_predictions.json + adv_probabilities_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/568fd9eb22d2df4069b1a867ab1fe9c0.pkl + params_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/params.yaml + predictions_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/predictions.json + probabilities_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/probabilities.json + score_dict_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/score_dict.json + test_labels_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/test_labels.json + train_labels_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/train_labels.json + model_dir: models + name: 5b8ee8d1519e149f06b2dc58473d01d3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: db9a87d063df5ee4ff79184d18f4eff7 + trainer: + kwargs: {} +name: 5b8ee8d1519e149f06b2dc58473d01d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/params.yaml b/examples/security/classification/output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/params.yaml new file mode 100644 index 00000000..a899c943 --- /dev/null +++ b/examples/security/classification/output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/adv_predictions.json + adv_probabilities_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/6043bd94b0ab5a431cb6cb6037cd090b.pkl + params_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/params.yaml + predictions_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/predictions.json + probabilities_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/probabilities.json + score_dict_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/score_dict.json + test_labels_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/test_labels.json + train_labels_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/train_labels.json + model_dir: models + name: 5c2bf92f22a09f2b6189003a7f53ba97 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ad11e1fb6fb33eb9501f57afa22021c2 + trainer: + kwargs: {} +name: 5c2bf92f22a09f2b6189003a7f53ba97 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/params.yaml b/examples/security/classification/output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/params.yaml new file mode 100644 index 00000000..2cd2bfd5 --- /dev/null +++ b/examples/security/classification/output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/adv_predictions.json + adv_probabilities_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/236f63eb872672956270e6be9da91a92.pkl + params_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/params.yaml + predictions_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/predictions.json + probabilities_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/probabilities.json + score_dict_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/score_dict.json + test_labels_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/test_labels.json + train_labels_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/train_labels.json + model_dir: models + name: 5c640d2ab1bee9e55681ef5641a8e7fe + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cff99e1e3e85f71dd1f142f5b43dd9e5 + trainer: + kwargs: {} +name: 5c640d2ab1bee9e55681ef5641a8e7fe +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/params.yaml b/examples/security/classification/output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/params.yaml new file mode 100644 index 00000000..f07775d3 --- /dev/null +++ b/examples/security/classification/output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/adv_predictions.json + adv_probabilities_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/71d506dd8cf2b110d3ab4376a39462fb.pkl + params_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/params.yaml + predictions_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/predictions.json + probabilities_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/probabilities.json + score_dict_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/score_dict.json + test_labels_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/test_labels.json + train_labels_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/train_labels.json + model_dir: models + name: 5c99d60b8fe3ee587bbb716c286ede4b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d9da5263a8f28b34a2dbae53acc184e + trainer: + kwargs: {} +name: 5c99d60b8fe3ee587bbb716c286ede4b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/params.yaml b/examples/security/classification/output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/params.yaml new file mode 100644 index 00000000..cb212c44 --- /dev/null +++ b/examples/security/classification/output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/adv_predictions.json + adv_probabilities_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/484a31bff7a247c77b7ca2af9c01cf56.pkl + params_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/params.yaml + predictions_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/predictions.json + probabilities_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/probabilities.json + score_dict_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/score_dict.json + test_labels_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/test_labels.json + train_labels_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/train_labels.json + model_dir: models + name: 5cb66bac32991399d0dd0a2a438dba6f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d3a14b88fb753d113409c10a5ece8059 + trainer: + kwargs: {} +name: 5cb66bac32991399d0dd0a2a438dba6f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5cc77cd0464054cf2666377741f05f88/params.yaml b/examples/security/classification/output/reports/train/5cc77cd0464054cf2666377741f05f88/params.yaml new file mode 100644 index 00000000..5bf6fa1e --- /dev/null +++ b/examples/security/classification/output/reports/train/5cc77cd0464054cf2666377741f05f88/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/adv_predictions.json + adv_probabilities_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/3a5ed244e667b09c135bf3b57675a322.pkl + params_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/params.yaml + predictions_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/predictions.json + probabilities_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/probabilities.json + score_dict_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/score_dict.json + test_labels_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/test_labels.json + train_labels_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/train_labels.json + model_dir: models + name: 5cc77cd0464054cf2666377741f05f88 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 98ac1f6581d235d6f7e76da001d8340c + trainer: + kwargs: {} +name: 5cc77cd0464054cf2666377741f05f88 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5cd488954437aa0400e9b8602007b50c/params.yaml b/examples/security/classification/output/reports/train/5cd488954437aa0400e9b8602007b50c/params.yaml new file mode 100644 index 00000000..6879d607 --- /dev/null +++ b/examples/security/classification/output/reports/train/5cd488954437aa0400e9b8602007b50c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/adv_predictions.json + adv_probabilities_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/3d51a05f55561e96e9edfdf86e678ec8.pkl + params_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/params.yaml + predictions_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/predictions.json + probabilities_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/probabilities.json + score_dict_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/score_dict.json + test_labels_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/test_labels.json + train_labels_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/train_labels.json + model_dir: models + name: 5cd488954437aa0400e9b8602007b50c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dadfc1dae5746ba8bfcb3609f603232f + trainer: + kwargs: {} +name: 5cd488954437aa0400e9b8602007b50c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/params.yaml b/examples/security/classification/output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/params.yaml new file mode 100644 index 00000000..f99c57a1 --- /dev/null +++ b/examples/security/classification/output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/adv_predictions.json + adv_probabilities_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/4bdb2268d66046d72d460fc2f89268e1.pkl + params_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/params.yaml + predictions_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/predictions.json + probabilities_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/probabilities.json + score_dict_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/score_dict.json + test_labels_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/test_labels.json + train_labels_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/train_labels.json + model_dir: models + name: 5cdf31e755d5b774fd498b40ca1dc4f7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0d28ac388e34c6968f6a960cc43a6f4b + trainer: + kwargs: {} +name: 5cdf31e755d5b774fd498b40ca1dc4f7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/params.yaml b/examples/security/classification/output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/params.yaml new file mode 100644 index 00000000..cdbde087 --- /dev/null +++ b/examples/security/classification/output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/adv_predictions.json + adv_probabilities_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/f8c435fd023a490f0125e49ea001e0ba.pkl + params_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/params.yaml + predictions_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/predictions.json + probabilities_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/probabilities.json + score_dict_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/score_dict.json + test_labels_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/test_labels.json + train_labels_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/train_labels.json + model_dir: models + name: 5cf660ef3af069c1af26c29e8d7188d5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 679b3f68b2307f6d0cb65a835b7345ee + trainer: + kwargs: {} +name: 5cf660ef3af069c1af26c29e8d7188d5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5d25b172041462b0bd0b5251f0fb1356/params.yaml b/examples/security/classification/output/reports/train/5d25b172041462b0bd0b5251f0fb1356/params.yaml new file mode 100644 index 00000000..838c7cea --- /dev/null +++ b/examples/security/classification/output/reports/train/5d25b172041462b0bd0b5251f0fb1356/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/adv_predictions.json + adv_probabilities_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/57977b61336f11ba026337a4b34a465e.pkl + params_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/params.yaml + predictions_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/predictions.json + probabilities_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/probabilities.json + score_dict_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/score_dict.json + test_labels_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/test_labels.json + train_labels_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/train_labels.json + model_dir: models + name: 5d25b172041462b0bd0b5251f0fb1356 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e58657180e3c261883b7f7734819b2b1 + trainer: + kwargs: {} +name: 5d25b172041462b0bd0b5251f0fb1356 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5d54e901b94761d8676b74e82f790ce5/params.yaml b/examples/security/classification/output/reports/train/5d54e901b94761d8676b74e82f790ce5/params.yaml new file mode 100644 index 00000000..c27dfa15 --- /dev/null +++ b/examples/security/classification/output/reports/train/5d54e901b94761d8676b74e82f790ce5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/adv_predictions.json + adv_probabilities_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/74843a40073dd1b5934801085e12fd6e.pkl + params_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/params.yaml + predictions_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/predictions.json + probabilities_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/probabilities.json + score_dict_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/score_dict.json + test_labels_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/test_labels.json + train_labels_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/train_labels.json + model_dir: models + name: 5d54e901b94761d8676b74e82f790ce5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eccf126d130cfea0fd70025fe0983025 + trainer: + kwargs: {} +name: 5d54e901b94761d8676b74e82f790ce5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5d717740c85f5ea364a99f1aedb8f098/params.yaml b/examples/security/classification/output/reports/train/5d717740c85f5ea364a99f1aedb8f098/params.yaml new file mode 100644 index 00000000..21fe4114 --- /dev/null +++ b/examples/security/classification/output/reports/train/5d717740c85f5ea364a99f1aedb8f098/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/adv_predictions.json + adv_probabilities_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/d6438daefb5b15f26c6725c3732c871d.pkl + params_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/params.yaml + predictions_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/predictions.json + probabilities_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/probabilities.json + score_dict_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/score_dict.json + test_labels_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/test_labels.json + train_labels_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/train_labels.json + model_dir: models + name: 5d717740c85f5ea364a99f1aedb8f098 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a7dfa7ef1e02cdead37b808e71ebabac + trainer: + kwargs: {} +name: 5d717740c85f5ea364a99f1aedb8f098 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5d76f06cf217bc627f379fad3cf2782a/params.yaml b/examples/security/classification/output/reports/train/5d76f06cf217bc627f379fad3cf2782a/params.yaml new file mode 100644 index 00000000..88daa9c7 --- /dev/null +++ b/examples/security/classification/output/reports/train/5d76f06cf217bc627f379fad3cf2782a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/adv_predictions.json + adv_probabilities_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/b62514ba6ba1b22c55be2a6ef58b8370.pkl + params_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/params.yaml + predictions_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/predictions.json + probabilities_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/probabilities.json + score_dict_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/score_dict.json + test_labels_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/test_labels.json + train_labels_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/train_labels.json + model_dir: models + name: 5d76f06cf217bc627f379fad3cf2782a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9403233a23dea1058b32a50aa751299d + trainer: + kwargs: {} +name: 5d76f06cf217bc627f379fad3cf2782a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/params.yaml b/examples/security/classification/output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/params.yaml new file mode 100644 index 00000000..cb4b8d17 --- /dev/null +++ b/examples/security/classification/output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/adv_predictions.json + adv_probabilities_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/29f323c68f0d35f8b3ffa94e552c1656.pkl + params_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/params.yaml + predictions_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/predictions.json + probabilities_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/probabilities.json + score_dict_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/score_dict.json + test_labels_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/test_labels.json + train_labels_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/train_labels.json + model_dir: models + name: 5d770808449c7ad5d13ac2badbbffdb5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b8d4794872c8628b7ccd468671a30f5f + trainer: + kwargs: {} +name: 5d770808449c7ad5d13ac2badbbffdb5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/params.yaml b/examples/security/classification/output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/params.yaml new file mode 100644 index 00000000..81444a62 --- /dev/null +++ b/examples/security/classification/output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/adv_predictions.json + adv_probabilities_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/b9a42431e0f476db0708c4000695b923.pkl + params_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/params.yaml + predictions_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/predictions.json + probabilities_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/probabilities.json + score_dict_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/score_dict.json + test_labels_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/test_labels.json + train_labels_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/train_labels.json + model_dir: models + name: 5d7d0dd6803049a2de8271aea0e0e1a5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ba25cf0266b7c2363b1cbdb94ad50291 + trainer: + kwargs: {} +name: 5d7d0dd6803049a2de8271aea0e0e1a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/params.yaml b/examples/security/classification/output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/params.yaml new file mode 100644 index 00000000..5276e983 --- /dev/null +++ b/examples/security/classification/output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/adv_predictions.json + adv_probabilities_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/4b79e86ed87943dacba19f94a55e8aa1.pkl + params_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/params.yaml + predictions_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/predictions.json + probabilities_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/probabilities.json + score_dict_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/score_dict.json + test_labels_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/test_labels.json + train_labels_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/train_labels.json + model_dir: models + name: 5d98e0ff9fda42c6c7f14a0f5f09cdd7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f11c65bab235b59c7f10577a54142fc9 + trainer: + kwargs: {} +name: 5d98e0ff9fda42c6c7f14a0f5f09cdd7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/params.yaml b/examples/security/classification/output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/params.yaml new file mode 100644 index 00000000..97e8c145 --- /dev/null +++ b/examples/security/classification/output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/adv_predictions.json + adv_probabilities_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/7e3b7140dc31fee2a9aa532682d513c2.pkl + params_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/params.yaml + predictions_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/predictions.json + probabilities_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/probabilities.json + score_dict_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/score_dict.json + test_labels_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/test_labels.json + train_labels_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/train_labels.json + model_dir: models + name: 5da1e61d9b31bb3fa8f57a2ed62fe55f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b27a8f166909e2b83a794953fe61608 + trainer: + kwargs: {} +name: 5da1e61d9b31bb3fa8f57a2ed62fe55f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5da22cb7c862afbf92015179328a80dd/params.yaml b/examples/security/classification/output/reports/train/5da22cb7c862afbf92015179328a80dd/params.yaml new file mode 100644 index 00000000..d4f44c82 --- /dev/null +++ b/examples/security/classification/output/reports/train/5da22cb7c862afbf92015179328a80dd/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/adv_predictions.json + adv_probabilities_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/be3b0245e5a0df7558b30e32a4945aba.pkl + params_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/params.yaml + predictions_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/predictions.json + probabilities_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/probabilities.json + score_dict_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/score_dict.json + test_labels_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/test_labels.json + train_labels_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/train_labels.json + model_dir: models + name: 5da22cb7c862afbf92015179328a80dd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 92c0b687769e86f207e80899050daded + trainer: + kwargs: {} +name: 5da22cb7c862afbf92015179328a80dd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5db7b823a7455b921b2aff827f94f0de/params.yaml b/examples/security/classification/output/reports/train/5db7b823a7455b921b2aff827f94f0de/params.yaml new file mode 100644 index 00000000..4687bbde --- /dev/null +++ b/examples/security/classification/output/reports/train/5db7b823a7455b921b2aff827f94f0de/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/adv_predictions.json + adv_probabilities_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/a3e705af09fe43a5b34ef3c908d1f34a.pkl + params_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/params.yaml + predictions_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/predictions.json + probabilities_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/probabilities.json + score_dict_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/score_dict.json + test_labels_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/test_labels.json + train_labels_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/train_labels.json + model_dir: models + name: 5db7b823a7455b921b2aff827f94f0de + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f1a462df8875f64f3904ba3b3706cfa1 + trainer: + kwargs: {} +name: 5db7b823a7455b921b2aff827f94f0de +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5dec8633d23ee575a63d31ad2b45a383/params.yaml b/examples/security/classification/output/reports/train/5dec8633d23ee575a63d31ad2b45a383/params.yaml new file mode 100644 index 00000000..defa578a --- /dev/null +++ b/examples/security/classification/output/reports/train/5dec8633d23ee575a63d31ad2b45a383/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/adv_predictions.json + adv_probabilities_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/73a4b73bc8b9ae29ae983f9c08261843.pkl + params_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/params.yaml + predictions_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/predictions.json + probabilities_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/probabilities.json + score_dict_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/score_dict.json + test_labels_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/test_labels.json + train_labels_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/train_labels.json + model_dir: models + name: 5dec8633d23ee575a63d31ad2b45a383 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c8b5ebd90549148e17e72bda04c64e87 + trainer: + kwargs: {} +name: 5dec8633d23ee575a63d31ad2b45a383 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/params.yaml b/examples/security/classification/output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/params.yaml new file mode 100644 index 00000000..cba42fba --- /dev/null +++ b/examples/security/classification/output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/adv_predictions.json + adv_probabilities_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/988d35deaffe9a54275aaea279234b10.pkl + params_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/params.yaml + predictions_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/predictions.json + probabilities_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/probabilities.json + score_dict_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/score_dict.json + test_labels_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/test_labels.json + train_labels_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/train_labels.json + model_dir: models + name: 5dfab1a52dcfd17f51d7938ef131674c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3f5cf8f29b51a62607ec547a3d2f4fb8 + trainer: + kwargs: {} +name: 5dfab1a52dcfd17f51d7938ef131674c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/params.yaml b/examples/security/classification/output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/params.yaml new file mode 100644 index 00000000..f53f532c --- /dev/null +++ b/examples/security/classification/output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/adv_predictions.json + adv_probabilities_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/d77c66006c1b01d86fdb97b82f742008.pkl + params_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/params.yaml + predictions_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/predictions.json + probabilities_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/probabilities.json + score_dict_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/score_dict.json + test_labels_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/test_labels.json + train_labels_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/train_labels.json + model_dir: models + name: 5e00c8a58ba6c13b75f2690ddfe81191 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 706ba90254a9dcf4c5998d8eb2c86195 + trainer: + kwargs: {} +name: 5e00c8a58ba6c13b75f2690ddfe81191 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5e0b1396f8eebed981104f9085baa8fc/params.yaml b/examples/security/classification/output/reports/train/5e0b1396f8eebed981104f9085baa8fc/params.yaml new file mode 100644 index 00000000..4469309c --- /dev/null +++ b/examples/security/classification/output/reports/train/5e0b1396f8eebed981104f9085baa8fc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/adv_predictions.json + adv_probabilities_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/7ce075cfe3884e4228b6e8dea5c237e4.pkl + params_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/params.yaml + predictions_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/predictions.json + probabilities_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/probabilities.json + score_dict_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/score_dict.json + test_labels_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/test_labels.json + train_labels_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/train_labels.json + model_dir: models + name: 5e0b1396f8eebed981104f9085baa8fc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fdc0adc38833d6e1b3aa06fe665bc187 + trainer: + kwargs: {} +name: 5e0b1396f8eebed981104f9085baa8fc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5e198e68433d01a6e6b8b4e808215a91/params.yaml b/examples/security/classification/output/reports/train/5e198e68433d01a6e6b8b4e808215a91/params.yaml new file mode 100644 index 00000000..4a3c86cc --- /dev/null +++ b/examples/security/classification/output/reports/train/5e198e68433d01a6e6b8b4e808215a91/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/adv_predictions.json + adv_probabilities_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/4db06fd15af4902b4db8444b652f196c.pkl + params_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/params.yaml + predictions_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/predictions.json + probabilities_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/probabilities.json + score_dict_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/score_dict.json + test_labels_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/test_labels.json + train_labels_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/train_labels.json + model_dir: models + name: 5e198e68433d01a6e6b8b4e808215a91 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4b27b251eb51a551140866ea87c32d70 + trainer: + kwargs: {} +name: 5e198e68433d01a6e6b8b4e808215a91 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5e1c99729aff25ae394714609531adeb/params.yaml b/examples/security/classification/output/reports/train/5e1c99729aff25ae394714609531adeb/params.yaml new file mode 100644 index 00000000..7c472d5d --- /dev/null +++ b/examples/security/classification/output/reports/train/5e1c99729aff25ae394714609531adeb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5e1c99729aff25ae394714609531adeb/adv_predictions.json + adv_probabilities_file: output/reports/train/5e1c99729aff25ae394714609531adeb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/192dc6940fe9eefdf0a6096abc53253a.pkl + params_file: output/reports/train/5e1c99729aff25ae394714609531adeb/params.yaml + predictions_file: output/reports/train/5e1c99729aff25ae394714609531adeb/predictions.json + probabilities_file: output/reports/train/5e1c99729aff25ae394714609531adeb/probabilities.json + score_dict_file: output/reports/train/5e1c99729aff25ae394714609531adeb/score_dict.json + test_labels_file: output/reports/train/5e1c99729aff25ae394714609531adeb/test_labels.json + train_labels_file: output/reports/train/5e1c99729aff25ae394714609531adeb/train_labels.json + model_dir: models + name: 5e1c99729aff25ae394714609531adeb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1db7a1f3e919f3ebaf611100901e40a3 + trainer: + kwargs: {} +name: 5e1c99729aff25ae394714609531adeb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/params.yaml b/examples/security/classification/output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/params.yaml new file mode 100644 index 00000000..82f45dc5 --- /dev/null +++ b/examples/security/classification/output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/params.yaml @@ -0,0 +1,115 @@ +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/adv_predictions.json + adv_probabilities_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl + model_file: output/models/67b83ea4c8aab1c331a042d155307cbe.pkl + params_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/params.yaml + predictions_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/predictions.json + probabilities_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/probabilities.json + score_dict_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/score_dict.json + test_labels_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/test_labels.json + train_labels_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/train_labels.json + model_dir: models + name: 5e1eeefe8424bc2a666dc324b03a1132 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 300ba1d558197ae7fbefd3d5dc4b1276 + trainer: + kwargs: {} +name: 5e1eeefe8424bc2a666dc324b03a1132 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/params.yaml b/examples/security/classification/output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/params.yaml new file mode 100644 index 00000000..bdf888e8 --- /dev/null +++ b/examples/security/classification/output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/adv_predictions.json + adv_probabilities_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/46a75840227d3b2a60ff7721e2ed448d.pkl + params_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/params.yaml + predictions_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/predictions.json + probabilities_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/probabilities.json + score_dict_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/score_dict.json + test_labels_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/test_labels.json + train_labels_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/train_labels.json + model_dir: models + name: 5e2759c9050bfffcd0f0a4eddcec3273 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aab43c1e1ce00337fe7145cdc0c3bfbe + trainer: + kwargs: {} +name: 5e2759c9050bfffcd0f0a4eddcec3273 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5e400872ab68a5605560126944d063d4/params.yaml b/examples/security/classification/output/reports/train/5e400872ab68a5605560126944d063d4/params.yaml new file mode 100644 index 00000000..edc6a610 --- /dev/null +++ b/examples/security/classification/output/reports/train/5e400872ab68a5605560126944d063d4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5e400872ab68a5605560126944d063d4/adv_predictions.json + adv_probabilities_file: output/reports/train/5e400872ab68a5605560126944d063d4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/bd7cf51b2af1bc95400df1302a76f813.pkl + params_file: output/reports/train/5e400872ab68a5605560126944d063d4/params.yaml + predictions_file: output/reports/train/5e400872ab68a5605560126944d063d4/predictions.json + probabilities_file: output/reports/train/5e400872ab68a5605560126944d063d4/probabilities.json + score_dict_file: output/reports/train/5e400872ab68a5605560126944d063d4/score_dict.json + test_labels_file: output/reports/train/5e400872ab68a5605560126944d063d4/test_labels.json + train_labels_file: output/reports/train/5e400872ab68a5605560126944d063d4/train_labels.json + model_dir: models + name: 5e400872ab68a5605560126944d063d4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6926894ff468b8c4a27ce7fc8a831ff8 + trainer: + kwargs: {} +name: 5e400872ab68a5605560126944d063d4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/params.yaml b/examples/security/classification/output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/params.yaml new file mode 100644 index 00000000..736a27fa --- /dev/null +++ b/examples/security/classification/output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/adv_predictions.json + adv_probabilities_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/52c24d1356fcab4aa35d2572f19df910.pkl + params_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/params.yaml + predictions_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/predictions.json + probabilities_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/probabilities.json + score_dict_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/score_dict.json + test_labels_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/test_labels.json + train_labels_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/train_labels.json + model_dir: models + name: 5eb0a2d0436af2ecced061e724db3dc9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 77583d0a9f988dba343eaec468a9684b + trainer: + kwargs: {} +name: 5eb0a2d0436af2ecced061e724db3dc9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5edccfe72d7c6531749aa56f561203c8/params.yaml b/examples/security/classification/output/reports/train/5edccfe72d7c6531749aa56f561203c8/params.yaml new file mode 100644 index 00000000..f199b279 --- /dev/null +++ b/examples/security/classification/output/reports/train/5edccfe72d7c6531749aa56f561203c8/params.yaml @@ -0,0 +1,115 @@ +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/adv_predictions.json + adv_probabilities_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl + model_file: output/models/6433f0d8d89ca1b86c01b23723145f0e.pkl + params_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/params.yaml + predictions_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/predictions.json + probabilities_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/probabilities.json + score_dict_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/score_dict.json + test_labels_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/test_labels.json + train_labels_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/train_labels.json + model_dir: models + name: 5edccfe72d7c6531749aa56f561203c8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: adbfdc19580f6fb87929a6fb20dfa2ab + trainer: + kwargs: {} +name: 5edccfe72d7c6531749aa56f561203c8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5f0a5925daa47e3754249380223f92e7/params.yaml b/examples/security/classification/output/reports/train/5f0a5925daa47e3754249380223f92e7/params.yaml new file mode 100644 index 00000000..93de0711 --- /dev/null +++ b/examples/security/classification/output/reports/train/5f0a5925daa47e3754249380223f92e7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/adv_predictions.json + adv_probabilities_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/d6672a6902778bf5d075dcd27415d119.pkl + params_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/params.yaml + predictions_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/predictions.json + probabilities_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/probabilities.json + score_dict_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/score_dict.json + test_labels_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/test_labels.json + train_labels_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/train_labels.json + model_dir: models + name: 5f0a5925daa47e3754249380223f92e7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 37195eb9f417ec03f1acaee2b3ca234a + trainer: + kwargs: {} +name: 5f0a5925daa47e3754249380223f92e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5f1a06dc64155a526d615a0f1c52805a/params.yaml b/examples/security/classification/output/reports/train/5f1a06dc64155a526d615a0f1c52805a/params.yaml new file mode 100644 index 00000000..6bdb0921 --- /dev/null +++ b/examples/security/classification/output/reports/train/5f1a06dc64155a526d615a0f1c52805a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/adv_predictions.json + adv_probabilities_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/6091c2e88ae75f8da01d4343d71e933b.pkl + params_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/params.yaml + predictions_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/predictions.json + probabilities_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/probabilities.json + score_dict_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/score_dict.json + test_labels_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/test_labels.json + train_labels_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/train_labels.json + model_dir: models + name: 5f1a06dc64155a526d615a0f1c52805a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8bfaa512cfafeeafbfafaff70436e406 + trainer: + kwargs: {} +name: 5f1a06dc64155a526d615a0f1c52805a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5f23f8fbbb12da531af951221649e1ee/params.yaml b/examples/security/classification/output/reports/train/5f23f8fbbb12da531af951221649e1ee/params.yaml new file mode 100644 index 00000000..471afaf0 --- /dev/null +++ b/examples/security/classification/output/reports/train/5f23f8fbbb12da531af951221649e1ee/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/adv_predictions.json + adv_probabilities_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/e830ccab712c566cfc7d847915dda5a5.pkl + params_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/params.yaml + predictions_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/predictions.json + probabilities_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/probabilities.json + score_dict_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/score_dict.json + test_labels_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/test_labels.json + train_labels_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/train_labels.json + model_dir: models + name: 5f23f8fbbb12da531af951221649e1ee + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4dfc53485313915966686ffa77100413 + trainer: + kwargs: {} +name: 5f23f8fbbb12da531af951221649e1ee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/params.yaml b/examples/security/classification/output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/params.yaml new file mode 100644 index 00000000..ad7d3480 --- /dev/null +++ b/examples/security/classification/output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/adv_predictions.json + adv_probabilities_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/2b8957412e3e5b673278f047b07feb64.pkl + params_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/params.yaml + predictions_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/predictions.json + probabilities_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/probabilities.json + score_dict_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/score_dict.json + test_labels_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/test_labels.json + train_labels_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/train_labels.json + model_dir: models + name: 5f27b5f6c9fa8e19ef48f43889078576 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 723b44b4666cc3e9c9b809dbae57c8e5 + trainer: + kwargs: {} +name: 5f27b5f6c9fa8e19ef48f43889078576 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5f45f4c19bebedb91d14d30c702860f9/params.yaml b/examples/security/classification/output/reports/train/5f45f4c19bebedb91d14d30c702860f9/params.yaml new file mode 100644 index 00000000..b10064f3 --- /dev/null +++ b/examples/security/classification/output/reports/train/5f45f4c19bebedb91d14d30c702860f9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/adv_predictions.json + adv_probabilities_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/d545900108a0a84c304a3ddc287ceca1.pkl + params_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/params.yaml + predictions_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/predictions.json + probabilities_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/probabilities.json + score_dict_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/score_dict.json + test_labels_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/test_labels.json + train_labels_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/train_labels.json + model_dir: models + name: 5f45f4c19bebedb91d14d30c702860f9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 86c934eb9767ae6f0839e128460f0f2d + trainer: + kwargs: {} +name: 5f45f4c19bebedb91d14d30c702860f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5f6ce96752f483675e53f5ac7923fb17/params.yaml b/examples/security/classification/output/reports/train/5f6ce96752f483675e53f5ac7923fb17/params.yaml new file mode 100644 index 00000000..b54b1986 --- /dev/null +++ b/examples/security/classification/output/reports/train/5f6ce96752f483675e53f5ac7923fb17/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/adv_predictions.json + adv_probabilities_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/957ff0f4a34400638451bcef2a6f2aa4.pkl + params_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/params.yaml + predictions_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/predictions.json + probabilities_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/probabilities.json + score_dict_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/score_dict.json + test_labels_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/test_labels.json + train_labels_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/train_labels.json + model_dir: models + name: 5f6ce96752f483675e53f5ac7923fb17 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2c5eb1e8961bd51979c6feca868ac287 + trainer: + kwargs: {} +name: 5f6ce96752f483675e53f5ac7923fb17 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5f749bcf5a2db94b85403558cdba1944/params.yaml b/examples/security/classification/output/reports/train/5f749bcf5a2db94b85403558cdba1944/params.yaml new file mode 100644 index 00000000..02c41a0a --- /dev/null +++ b/examples/security/classification/output/reports/train/5f749bcf5a2db94b85403558cdba1944/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/adv_predictions.json + adv_probabilities_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/b77b0c383dac6b5d8c21830a43e5b275.pkl + params_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/params.yaml + predictions_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/predictions.json + probabilities_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/probabilities.json + score_dict_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/score_dict.json + test_labels_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/test_labels.json + train_labels_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/train_labels.json + model_dir: models + name: 5f749bcf5a2db94b85403558cdba1944 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6059d354b02ab28cbc48f5fe7f8124d4 + trainer: + kwargs: {} +name: 5f749bcf5a2db94b85403558cdba1944 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/params.yaml b/examples/security/classification/output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/params.yaml new file mode 100644 index 00000000..5801890c --- /dev/null +++ b/examples/security/classification/output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/adv_predictions.json + adv_probabilities_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/69e8dcfdb6e3ee44c90c2d8174df7afb.pkl + params_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/params.yaml + predictions_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/predictions.json + probabilities_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/probabilities.json + score_dict_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/score_dict.json + test_labels_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/test_labels.json + train_labels_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/train_labels.json + model_dir: models + name: 5f9447342e6eee9aaa4b503e9f9d6c9e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8c2e6fdba1ca69194ce4debc51863af6 + trainer: + kwargs: {} +name: 5f9447342e6eee9aaa4b503e9f9d6c9e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/params.yaml b/examples/security/classification/output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/params.yaml new file mode 100644 index 00000000..35da6005 --- /dev/null +++ b/examples/security/classification/output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/adv_predictions.json + adv_probabilities_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/4ee652ecd3d27847164455fae99ef356.pkl + params_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/params.yaml + predictions_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/predictions.json + probabilities_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/probabilities.json + score_dict_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/score_dict.json + test_labels_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/test_labels.json + train_labels_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/train_labels.json + model_dir: models + name: 5f9b17742b26c6cc62d2b50c532c2283 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5c235ba96542f438d69190b21af34116 + trainer: + kwargs: {} +name: 5f9b17742b26c6cc62d2b50c532c2283 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5fa4bb01d405687e810727f33d5d8631/params.yaml b/examples/security/classification/output/reports/train/5fa4bb01d405687e810727f33d5d8631/params.yaml new file mode 100644 index 00000000..f2340abd --- /dev/null +++ b/examples/security/classification/output/reports/train/5fa4bb01d405687e810727f33d5d8631/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/adv_predictions.json + adv_probabilities_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/346aa775a703d0d92ebcc8b6cacdecf7.pkl + params_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/params.yaml + predictions_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/predictions.json + probabilities_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/probabilities.json + score_dict_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/score_dict.json + test_labels_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/test_labels.json + train_labels_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/train_labels.json + model_dir: models + name: 5fa4bb01d405687e810727f33d5d8631 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4b8879f947f0321d30409dec58519bcd + trainer: + kwargs: {} +name: 5fa4bb01d405687e810727f33d5d8631 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/params.yaml b/examples/security/classification/output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/params.yaml new file mode 100644 index 00000000..ae7272a6 --- /dev/null +++ b/examples/security/classification/output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/adv_predictions.json + adv_probabilities_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/5265a581a909fde2ff025bd9e8f51767.pkl + params_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/params.yaml + predictions_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/predictions.json + probabilities_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/probabilities.json + score_dict_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/score_dict.json + test_labels_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/test_labels.json + train_labels_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/train_labels.json + model_dir: models + name: 5ffa7c6202feaf0e3bf6fa838006fcf7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c3d7b25aa96348841ce07ffb8d5caf92 + trainer: + kwargs: {} +name: 5ffa7c6202feaf0e3bf6fa838006fcf7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/params.yaml b/examples/security/classification/output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/params.yaml new file mode 100644 index 00000000..95341b3d --- /dev/null +++ b/examples/security/classification/output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/adv_predictions.json + adv_probabilities_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/86f7f9779a32e3921683cbc0c054e6f0.pkl + params_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/params.yaml + predictions_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/predictions.json + probabilities_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/probabilities.json + score_dict_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/score_dict.json + test_labels_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/test_labels.json + train_labels_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/train_labels.json + model_dir: models + name: 5ffd5e6a599f89e5df349d92cdb5a52d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8b81882eacbe584cfae30af24d5168f2 + trainer: + kwargs: {} +name: 5ffd5e6a599f89e5df349d92cdb5a52d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/params.yaml b/examples/security/classification/output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/params.yaml new file mode 100644 index 00000000..eb728327 --- /dev/null +++ b/examples/security/classification/output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/adv_predictions.json + adv_probabilities_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/8a6824d8d42ca1c739aed57ade9d9d00.pkl + params_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/params.yaml + predictions_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/predictions.json + probabilities_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/probabilities.json + score_dict_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/score_dict.json + test_labels_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/test_labels.json + train_labels_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/train_labels.json + model_dir: models + name: 6006925ee4ff5afbfb74e9a34e585f52 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f174a795fc383fad168e634c4edda51b + trainer: + kwargs: {} +name: 6006925ee4ff5afbfb74e9a34e585f52 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6064f20d09e29a664a00b8862a1ffa97/params.yaml b/examples/security/classification/output/reports/train/6064f20d09e29a664a00b8862a1ffa97/params.yaml new file mode 100644 index 00000000..eb02c51a --- /dev/null +++ b/examples/security/classification/output/reports/train/6064f20d09e29a664a00b8862a1ffa97/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/adv_predictions.json + adv_probabilities_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/d6a88f2bd50c18443457e975867f1233.pkl + params_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/params.yaml + predictions_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/predictions.json + probabilities_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/probabilities.json + score_dict_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/score_dict.json + test_labels_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/test_labels.json + train_labels_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/train_labels.json + model_dir: models + name: 6064f20d09e29a664a00b8862a1ffa97 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fae8ad838f4d77a42862930011d06115 + trainer: + kwargs: {} +name: 6064f20d09e29a664a00b8862a1ffa97 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6069225b36f1fc2d7434d27643c4a073/params.yaml b/examples/security/classification/output/reports/train/6069225b36f1fc2d7434d27643c4a073/params.yaml new file mode 100644 index 00000000..1fbd4be8 --- /dev/null +++ b/examples/security/classification/output/reports/train/6069225b36f1fc2d7434d27643c4a073/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/adv_predictions.json + adv_probabilities_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/47d92edaea5bb9e5f743a88476078129.pkl + params_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/params.yaml + predictions_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/predictions.json + probabilities_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/probabilities.json + score_dict_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/score_dict.json + test_labels_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/test_labels.json + train_labels_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/train_labels.json + model_dir: models + name: 6069225b36f1fc2d7434d27643c4a073 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 531f005e3fa06e14e71807b059f89fd6 + trainer: + kwargs: {} +name: 6069225b36f1fc2d7434d27643c4a073 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/607442709380a81928ae56a35d23b9c9/params.yaml b/examples/security/classification/output/reports/train/607442709380a81928ae56a35d23b9c9/params.yaml new file mode 100644 index 00000000..51d0d127 --- /dev/null +++ b/examples/security/classification/output/reports/train/607442709380a81928ae56a35d23b9c9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/607442709380a81928ae56a35d23b9c9/adv_predictions.json + adv_probabilities_file: output/reports/train/607442709380a81928ae56a35d23b9c9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/47f95db3633aeb6dda4da66ac63901a7.pkl + params_file: output/reports/train/607442709380a81928ae56a35d23b9c9/params.yaml + predictions_file: output/reports/train/607442709380a81928ae56a35d23b9c9/predictions.json + probabilities_file: output/reports/train/607442709380a81928ae56a35d23b9c9/probabilities.json + score_dict_file: output/reports/train/607442709380a81928ae56a35d23b9c9/score_dict.json + test_labels_file: output/reports/train/607442709380a81928ae56a35d23b9c9/test_labels.json + train_labels_file: output/reports/train/607442709380a81928ae56a35d23b9c9/train_labels.json + model_dir: models + name: 607442709380a81928ae56a35d23b9c9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0508e88b02572d3d0e4168943e28ab36 + trainer: + kwargs: {} +name: 607442709380a81928ae56a35d23b9c9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/params.yaml b/examples/security/classification/output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/params.yaml new file mode 100644 index 00000000..98f8d8dd --- /dev/null +++ b/examples/security/classification/output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/adv_predictions.json + adv_probabilities_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/825f6e2eb47382471953c1c473bc3ac2.pkl + params_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/params.yaml + predictions_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/predictions.json + probabilities_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/probabilities.json + score_dict_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/score_dict.json + test_labels_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/test_labels.json + train_labels_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/train_labels.json + model_dir: models + name: 607b7becfc4ceebca60a0cad5a17e62a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 2 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 562c64bdbcae37d4784ec50a60c81c21 + trainer: + kwargs: {} +name: 607b7becfc4ceebca60a0cad5a17e62a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/608ba0fd0599b25289a5089964fb586e/params.yaml b/examples/security/classification/output/reports/train/608ba0fd0599b25289a5089964fb586e/params.yaml new file mode 100644 index 00000000..96a968c2 --- /dev/null +++ b/examples/security/classification/output/reports/train/608ba0fd0599b25289a5089964fb586e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/adv_predictions.json + adv_probabilities_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/af650905465f60d95bcbb973f9865157.pkl + params_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/params.yaml + predictions_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/predictions.json + probabilities_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/probabilities.json + score_dict_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/score_dict.json + test_labels_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/test_labels.json + train_labels_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/train_labels.json + model_dir: models + name: 608ba0fd0599b25289a5089964fb586e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 830ca75baa5170a2040dc11811d76171 + trainer: + kwargs: {} +name: 608ba0fd0599b25289a5089964fb586e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/608f097ce9fd306252d87e85564e7e6c/params.yaml b/examples/security/classification/output/reports/train/608f097ce9fd306252d87e85564e7e6c/params.yaml new file mode 100644 index 00000000..f0857037 --- /dev/null +++ b/examples/security/classification/output/reports/train/608f097ce9fd306252d87e85564e7e6c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/adv_predictions.json + adv_probabilities_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/9a3108f20c6e06554ab4b08cf3eb3909.pkl + params_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/params.yaml + predictions_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/predictions.json + probabilities_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/probabilities.json + score_dict_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/score_dict.json + test_labels_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/test_labels.json + train_labels_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/train_labels.json + model_dir: models + name: 608f097ce9fd306252d87e85564e7e6c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 409a3324c476eebcf2727e7d9b708933 + trainer: + kwargs: {} +name: 608f097ce9fd306252d87e85564e7e6c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/60a092d859a107021e19a07fa5e26c04/params.yaml b/examples/security/classification/output/reports/train/60a092d859a107021e19a07fa5e26c04/params.yaml new file mode 100644 index 00000000..34484944 --- /dev/null +++ b/examples/security/classification/output/reports/train/60a092d859a107021e19a07fa5e26c04/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/adv_predictions.json + adv_probabilities_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/a4d98f20e3c50c6f7ece9b4b1aee1897.pkl + params_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/params.yaml + predictions_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/predictions.json + probabilities_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/probabilities.json + score_dict_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/score_dict.json + test_labels_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/test_labels.json + train_labels_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/train_labels.json + model_dir: models + name: 60a092d859a107021e19a07fa5e26c04 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2200bcab90b3a751a9bbb2e0087e6a9b + trainer: + kwargs: {} +name: 60a092d859a107021e19a07fa5e26c04 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/60cb32222c720368439fcfed0556666a/params.yaml b/examples/security/classification/output/reports/train/60cb32222c720368439fcfed0556666a/params.yaml new file mode 100644 index 00000000..3c9e37f7 --- /dev/null +++ b/examples/security/classification/output/reports/train/60cb32222c720368439fcfed0556666a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/60cb32222c720368439fcfed0556666a/adv_predictions.json + adv_probabilities_file: output/reports/train/60cb32222c720368439fcfed0556666a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/bf8706cf60ffb2334f7374ec626b334b.pkl + params_file: output/reports/train/60cb32222c720368439fcfed0556666a/params.yaml + predictions_file: output/reports/train/60cb32222c720368439fcfed0556666a/predictions.json + probabilities_file: output/reports/train/60cb32222c720368439fcfed0556666a/probabilities.json + score_dict_file: output/reports/train/60cb32222c720368439fcfed0556666a/score_dict.json + test_labels_file: output/reports/train/60cb32222c720368439fcfed0556666a/test_labels.json + train_labels_file: output/reports/train/60cb32222c720368439fcfed0556666a/train_labels.json + model_dir: models + name: 60cb32222c720368439fcfed0556666a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fc2bf33b74df4e8c7e59750052c81838 + trainer: + kwargs: {} +name: 60cb32222c720368439fcfed0556666a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/60d173003e57e486ad364991f65928cf/params.yaml b/examples/security/classification/output/reports/train/60d173003e57e486ad364991f65928cf/params.yaml new file mode 100644 index 00000000..7dffac70 --- /dev/null +++ b/examples/security/classification/output/reports/train/60d173003e57e486ad364991f65928cf/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/60d173003e57e486ad364991f65928cf/adv_predictions.json + adv_probabilities_file: output/reports/train/60d173003e57e486ad364991f65928cf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/37d5d18a7a51c18536043461fded5d9c.pkl + params_file: output/reports/train/60d173003e57e486ad364991f65928cf/params.yaml + predictions_file: output/reports/train/60d173003e57e486ad364991f65928cf/predictions.json + probabilities_file: output/reports/train/60d173003e57e486ad364991f65928cf/probabilities.json + score_dict_file: output/reports/train/60d173003e57e486ad364991f65928cf/score_dict.json + test_labels_file: output/reports/train/60d173003e57e486ad364991f65928cf/test_labels.json + train_labels_file: output/reports/train/60d173003e57e486ad364991f65928cf/train_labels.json + model_dir: models + name: 60d173003e57e486ad364991f65928cf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 435e6aa6c223e523f537aa336132ccbe + trainer: + kwargs: {} +name: 60d173003e57e486ad364991f65928cf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/params.yaml b/examples/security/classification/output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/params.yaml new file mode 100644 index 00000000..8cf6ef00 --- /dev/null +++ b/examples/security/classification/output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/adv_predictions.json + adv_probabilities_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/86f61368ac2a62ae4e2d9c8831dfa186.pkl + params_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/params.yaml + predictions_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/predictions.json + probabilities_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/probabilities.json + score_dict_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/score_dict.json + test_labels_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/test_labels.json + train_labels_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/train_labels.json + model_dir: models + name: 60db2b7f05fbc15b6e4e8a389bfc2e24 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 362b9c82bef0e86e8654d63581a6c01e + trainer: + kwargs: {} +name: 60db2b7f05fbc15b6e4e8a389bfc2e24 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/60ecb155830fbe33531beea9e0923f34/params.yaml b/examples/security/classification/output/reports/train/60ecb155830fbe33531beea9e0923f34/params.yaml new file mode 100644 index 00000000..072daf6b --- /dev/null +++ b/examples/security/classification/output/reports/train/60ecb155830fbe33531beea9e0923f34/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/adv_predictions.json + adv_probabilities_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/662449142d0b2f7896828722c6cf0587.pkl + params_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/params.yaml + predictions_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/predictions.json + probabilities_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/probabilities.json + score_dict_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/score_dict.json + test_labels_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/test_labels.json + train_labels_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/train_labels.json + model_dir: models + name: 60ecb155830fbe33531beea9e0923f34 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c5ae8f4898118198816695ca222f95b7 + trainer: + kwargs: {} +name: 60ecb155830fbe33531beea9e0923f34 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/60f7927d0412e3b8dace04d766aec9fa/params.yaml b/examples/security/classification/output/reports/train/60f7927d0412e3b8dace04d766aec9fa/params.yaml new file mode 100644 index 00000000..9f82df5e --- /dev/null +++ b/examples/security/classification/output/reports/train/60f7927d0412e3b8dace04d766aec9fa/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/adv_predictions.json + adv_probabilities_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/8fe310a63727033e5870e8478cbda67d.pkl + params_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/params.yaml + predictions_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/predictions.json + probabilities_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/probabilities.json + score_dict_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/score_dict.json + test_labels_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/test_labels.json + train_labels_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/train_labels.json + model_dir: models + name: 60f7927d0412e3b8dace04d766aec9fa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f3be266621758b31eae737638988a7be + trainer: + kwargs: {} +name: 60f7927d0412e3b8dace04d766aec9fa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/61254b1beac95cb05ad11bff429cc1c9/params.yaml b/examples/security/classification/output/reports/train/61254b1beac95cb05ad11bff429cc1c9/params.yaml new file mode 100644 index 00000000..d48d5512 --- /dev/null +++ b/examples/security/classification/output/reports/train/61254b1beac95cb05ad11bff429cc1c9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/adv_predictions.json + adv_probabilities_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/e4eb7fefae1d5c3782e19c0841574149.pkl + params_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/params.yaml + predictions_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/predictions.json + probabilities_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/probabilities.json + score_dict_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/score_dict.json + test_labels_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/test_labels.json + train_labels_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/train_labels.json + model_dir: models + name: 61254b1beac95cb05ad11bff429cc1c9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3e128f862e741d92525ad0f95e48ec3d + trainer: + kwargs: {} +name: 61254b1beac95cb05ad11bff429cc1c9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/params.yaml b/examples/security/classification/output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/params.yaml new file mode 100644 index 00000000..deac5274 --- /dev/null +++ b/examples/security/classification/output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/adv_predictions.json + adv_probabilities_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/5432227db192f36cd77c927dadd64a00.pkl + params_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/params.yaml + predictions_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/predictions.json + probabilities_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/probabilities.json + score_dict_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/score_dict.json + test_labels_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/test_labels.json + train_labels_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/train_labels.json + model_dir: models + name: 615a6dc4390247768e2d0d43a1b7fe14 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: da26e75c86b75ab7345500e0f0e75388 + trainer: + kwargs: {} +name: 615a6dc4390247768e2d0d43a1b7fe14 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/params.yaml b/examples/security/classification/output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/params.yaml new file mode 100644 index 00000000..234a7f89 --- /dev/null +++ b/examples/security/classification/output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/adv_predictions.json + adv_probabilities_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/4b21d041fdba68d6677b3a6de4b467d6.pkl + params_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/params.yaml + predictions_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/predictions.json + probabilities_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/probabilities.json + score_dict_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/score_dict.json + test_labels_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/test_labels.json + train_labels_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/train_labels.json + model_dir: models + name: 615d4c8a091ae4a84c3c5e5a29312d89 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 576200abd5b89fd4b4db59fba9afc66c + trainer: + kwargs: {} +name: 615d4c8a091ae4a84c3c5e5a29312d89 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/617ab7417ec483782157f7583e879960/params.yaml b/examples/security/classification/output/reports/train/617ab7417ec483782157f7583e879960/params.yaml new file mode 100644 index 00000000..db3e9476 --- /dev/null +++ b/examples/security/classification/output/reports/train/617ab7417ec483782157f7583e879960/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/617ab7417ec483782157f7583e879960/adv_predictions.json + adv_probabilities_file: output/reports/train/617ab7417ec483782157f7583e879960/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/9eb1d2c49a1cde0411d6de5f0a9ef8cc.pkl + params_file: output/reports/train/617ab7417ec483782157f7583e879960/params.yaml + predictions_file: output/reports/train/617ab7417ec483782157f7583e879960/predictions.json + probabilities_file: output/reports/train/617ab7417ec483782157f7583e879960/probabilities.json + score_dict_file: output/reports/train/617ab7417ec483782157f7583e879960/score_dict.json + test_labels_file: output/reports/train/617ab7417ec483782157f7583e879960/test_labels.json + train_labels_file: output/reports/train/617ab7417ec483782157f7583e879960/train_labels.json + model_dir: models + name: 617ab7417ec483782157f7583e879960 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5937c88b70053a337f4ca8e0fa08cc64 + trainer: + kwargs: {} +name: 617ab7417ec483782157f7583e879960 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/617bc9f664c6c64a0357b9bf6339e144/params.yaml b/examples/security/classification/output/reports/train/617bc9f664c6c64a0357b9bf6339e144/params.yaml new file mode 100644 index 00000000..e1c02283 --- /dev/null +++ b/examples/security/classification/output/reports/train/617bc9f664c6c64a0357b9bf6339e144/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/adv_predictions.json + adv_probabilities_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/dc837b09cf7be0d77b14b76681207d6f.pkl + params_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/params.yaml + predictions_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/predictions.json + probabilities_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/probabilities.json + score_dict_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/score_dict.json + test_labels_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/test_labels.json + train_labels_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/train_labels.json + model_dir: models + name: 617bc9f664c6c64a0357b9bf6339e144 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c5994b51075e62317e6633a32b697aa6 + trainer: + kwargs: {} +name: 617bc9f664c6c64a0357b9bf6339e144 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/params.yaml b/examples/security/classification/output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/params.yaml new file mode 100644 index 00000000..d4afc065 --- /dev/null +++ b/examples/security/classification/output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/adv_predictions.json + adv_probabilities_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/7482e32a6f3dca2cf931d2502eced844.pkl + params_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/params.yaml + predictions_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/predictions.json + probabilities_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/probabilities.json + score_dict_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/score_dict.json + test_labels_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/test_labels.json + train_labels_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/train_labels.json + model_dir: models + name: 6182f973ee0934b9e569845f4d7d2cb4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3641764628f3acb1da8d58b4b050e55d + trainer: + kwargs: {} +name: 6182f973ee0934b9e569845f4d7d2cb4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/61ac2e73f93e0a14ba34c214864e6827/params.yaml b/examples/security/classification/output/reports/train/61ac2e73f93e0a14ba34c214864e6827/params.yaml new file mode 100644 index 00000000..41c02470 --- /dev/null +++ b/examples/security/classification/output/reports/train/61ac2e73f93e0a14ba34c214864e6827/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/adv_predictions.json + adv_probabilities_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/64705b8c3c6c6e73129daa66cca6e529.pkl + params_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/params.yaml + predictions_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/predictions.json + probabilities_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/probabilities.json + score_dict_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/score_dict.json + test_labels_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/test_labels.json + train_labels_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/train_labels.json + model_dir: models + name: 61ac2e73f93e0a14ba34c214864e6827 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 50dfb42923d9d8219b7ecf8f541c7d2c + trainer: + kwargs: {} +name: 61ac2e73f93e0a14ba34c214864e6827 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/61e6334e12152199dd439ed4432e987e/params.yaml b/examples/security/classification/output/reports/train/61e6334e12152199dd439ed4432e987e/params.yaml new file mode 100644 index 00000000..89858af0 --- /dev/null +++ b/examples/security/classification/output/reports/train/61e6334e12152199dd439ed4432e987e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/61e6334e12152199dd439ed4432e987e/adv_predictions.json + adv_probabilities_file: output/reports/train/61e6334e12152199dd439ed4432e987e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/53d1da128b9d507fa2c19b84f5f3549b.pkl + params_file: output/reports/train/61e6334e12152199dd439ed4432e987e/params.yaml + predictions_file: output/reports/train/61e6334e12152199dd439ed4432e987e/predictions.json + probabilities_file: output/reports/train/61e6334e12152199dd439ed4432e987e/probabilities.json + score_dict_file: output/reports/train/61e6334e12152199dd439ed4432e987e/score_dict.json + test_labels_file: output/reports/train/61e6334e12152199dd439ed4432e987e/test_labels.json + train_labels_file: output/reports/train/61e6334e12152199dd439ed4432e987e/train_labels.json + model_dir: models + name: 61e6334e12152199dd439ed4432e987e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e48de9a11f2f523f27a3bc4c2b8a988a + trainer: + kwargs: {} +name: 61e6334e12152199dd439ed4432e987e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6228187ca82dd5394ac44bd4f7414080/params.yaml b/examples/security/classification/output/reports/train/6228187ca82dd5394ac44bd4f7414080/params.yaml new file mode 100644 index 00000000..18b6a828 --- /dev/null +++ b/examples/security/classification/output/reports/train/6228187ca82dd5394ac44bd4f7414080/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/adv_predictions.json + adv_probabilities_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/17954edde5adced4259b0d257394973c.pkl + params_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/params.yaml + predictions_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/predictions.json + probabilities_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/probabilities.json + score_dict_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/score_dict.json + test_labels_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/test_labels.json + train_labels_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/train_labels.json + model_dir: models + name: 6228187ca82dd5394ac44bd4f7414080 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ce185c6cda7a7263d7f1fe36e912c7c + trainer: + kwargs: {} +name: 6228187ca82dd5394ac44bd4f7414080 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/params.yaml b/examples/security/classification/output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/params.yaml new file mode 100644 index 00000000..739eaf79 --- /dev/null +++ b/examples/security/classification/output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/adv_predictions.json + adv_probabilities_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/a2ddf64c0a24cb16a9cd8b094990335f.pkl + params_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/params.yaml + predictions_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/predictions.json + probabilities_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/probabilities.json + score_dict_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/score_dict.json + test_labels_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/test_labels.json + train_labels_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/train_labels.json + model_dir: models + name: 6230e241d5e0c7929dab34e4cb0efcb5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bf0ea9df137cfc6a8e5be857c485b113 + trainer: + kwargs: {} +name: 6230e241d5e0c7929dab34e4cb0efcb5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/625577e9c13f002b0a37a28272b24576/params.yaml b/examples/security/classification/output/reports/train/625577e9c13f002b0a37a28272b24576/params.yaml new file mode 100644 index 00000000..c0fe6df0 --- /dev/null +++ b/examples/security/classification/output/reports/train/625577e9c13f002b0a37a28272b24576/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/625577e9c13f002b0a37a28272b24576/adv_predictions.json + adv_probabilities_file: output/reports/train/625577e9c13f002b0a37a28272b24576/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/3434bd55f04bbdfa75f89fe3194cd63f.pkl + params_file: output/reports/train/625577e9c13f002b0a37a28272b24576/params.yaml + predictions_file: output/reports/train/625577e9c13f002b0a37a28272b24576/predictions.json + probabilities_file: output/reports/train/625577e9c13f002b0a37a28272b24576/probabilities.json + score_dict_file: output/reports/train/625577e9c13f002b0a37a28272b24576/score_dict.json + test_labels_file: output/reports/train/625577e9c13f002b0a37a28272b24576/test_labels.json + train_labels_file: output/reports/train/625577e9c13f002b0a37a28272b24576/train_labels.json + model_dir: models + name: 625577e9c13f002b0a37a28272b24576 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 16008022c917c85db08813ffbd92f2cf + trainer: + kwargs: {} +name: 625577e9c13f002b0a37a28272b24576 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/params.yaml b/examples/security/classification/output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/params.yaml new file mode 100644 index 00000000..a682c24c --- /dev/null +++ b/examples/security/classification/output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/adv_predictions.json + adv_probabilities_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/4afbfea8d41eeaa6c31baeed69dabd36.pkl + params_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/params.yaml + predictions_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/predictions.json + probabilities_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/probabilities.json + score_dict_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/score_dict.json + test_labels_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/test_labels.json + train_labels_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/train_labels.json + model_dir: models + name: 62c7eb5e23d33b38f0d84ee02541280e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3efd5c69e1d430aa0a662aed94019e24 + trainer: + kwargs: {} +name: 62c7eb5e23d33b38f0d84ee02541280e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/params.yaml b/examples/security/classification/output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/params.yaml new file mode 100644 index 00000000..dd52d327 --- /dev/null +++ b/examples/security/classification/output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/adv_predictions.json + adv_probabilities_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/7e92dbf8e2bd28f681d3634297527d76.pkl + params_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/params.yaml + predictions_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/predictions.json + probabilities_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/probabilities.json + score_dict_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/score_dict.json + test_labels_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/test_labels.json + train_labels_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/train_labels.json + model_dir: models + name: 62c9f4e051c8652d0ab9af36b530bfef + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f04d2c7e34be1c42f88a5c40e9b4060a + trainer: + kwargs: {} +name: 62c9f4e051c8652d0ab9af36b530bfef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/62d3569241d28a4211c6caed0a0f77ee/params.yaml b/examples/security/classification/output/reports/train/62d3569241d28a4211c6caed0a0f77ee/params.yaml new file mode 100644 index 00000000..71e30793 --- /dev/null +++ b/examples/security/classification/output/reports/train/62d3569241d28a4211c6caed0a0f77ee/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/adv_predictions.json + adv_probabilities_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/8cbb1231b881d325d61598967c392590.pkl + params_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/params.yaml + predictions_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/predictions.json + probabilities_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/probabilities.json + score_dict_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/score_dict.json + test_labels_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/test_labels.json + train_labels_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/train_labels.json + model_dir: models + name: 62d3569241d28a4211c6caed0a0f77ee + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4558bb6b5255d259ab2df3d05b7fa818 + trainer: + kwargs: {} +name: 62d3569241d28a4211c6caed0a0f77ee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/62ef184c456bdd6f910eaa180442a5c6/params.yaml b/examples/security/classification/output/reports/train/62ef184c456bdd6f910eaa180442a5c6/params.yaml new file mode 100644 index 00000000..f67a0bff --- /dev/null +++ b/examples/security/classification/output/reports/train/62ef184c456bdd6f910eaa180442a5c6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/adv_predictions.json + adv_probabilities_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/fb249987afdec6f4677539a0e5e29588.pkl + params_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/params.yaml + predictions_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/predictions.json + probabilities_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/probabilities.json + score_dict_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/score_dict.json + test_labels_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/test_labels.json + train_labels_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/train_labels.json + model_dir: models + name: 62ef184c456bdd6f910eaa180442a5c6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b61b45fb388195c560ea03893ce69e22 + trainer: + kwargs: {} +name: 62ef184c456bdd6f910eaa180442a5c6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/62fcf51b134d1065176ed0538cf325a9/params.yaml b/examples/security/classification/output/reports/train/62fcf51b134d1065176ed0538cf325a9/params.yaml new file mode 100644 index 00000000..2ee5cf75 --- /dev/null +++ b/examples/security/classification/output/reports/train/62fcf51b134d1065176ed0538cf325a9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/adv_predictions.json + adv_probabilities_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/1898cf251ed92497f56c2eeb5dccbb07.pkl + params_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/params.yaml + predictions_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/predictions.json + probabilities_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/probabilities.json + score_dict_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/score_dict.json + test_labels_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/test_labels.json + train_labels_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/train_labels.json + model_dir: models + name: 62fcf51b134d1065176ed0538cf325a9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a211b43101994f8f1d97819111ab5774 + trainer: + kwargs: {} +name: 62fcf51b134d1065176ed0538cf325a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/params.yaml b/examples/security/classification/output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/params.yaml new file mode 100644 index 00000000..4fe25203 --- /dev/null +++ b/examples/security/classification/output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/adv_predictions.json + adv_probabilities_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/7aed35ee29e98d7fe6a9d89e4156ee83.pkl + params_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/params.yaml + predictions_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/predictions.json + probabilities_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/probabilities.json + score_dict_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/score_dict.json + test_labels_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/test_labels.json + train_labels_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/train_labels.json + model_dir: models + name: 631fdead3acc05ea41c89c5a187c3bd6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 79ed7b40f257c606e1d92a0f9ea6f989 + trainer: + kwargs: {} +name: 631fdead3acc05ea41c89c5a187c3bd6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/params.yaml b/examples/security/classification/output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/params.yaml new file mode 100644 index 00000000..09c67df0 --- /dev/null +++ b/examples/security/classification/output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/adv_predictions.json + adv_probabilities_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/157ca98ab902d50a1ee7ba5d0e270c42.pkl + params_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/params.yaml + predictions_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/predictions.json + probabilities_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/probabilities.json + score_dict_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/score_dict.json + test_labels_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/test_labels.json + train_labels_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/train_labels.json + model_dir: models + name: 632e9e5c7dc748fc1a8072ad2248ca1d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6b0f09c3f7602e025d9ffddc3b9618b9 + trainer: + kwargs: {} +name: 632e9e5c7dc748fc1a8072ad2248ca1d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/63447d821e9d864dd2da27ca96cd3f23/params.yaml b/examples/security/classification/output/reports/train/63447d821e9d864dd2da27ca96cd3f23/params.yaml new file mode 100644 index 00000000..89417951 --- /dev/null +++ b/examples/security/classification/output/reports/train/63447d821e9d864dd2da27ca96cd3f23/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/adv_predictions.json + adv_probabilities_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/885b32405446c22bc1991e2a7480f881.pkl + params_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/params.yaml + predictions_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/predictions.json + probabilities_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/probabilities.json + score_dict_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/score_dict.json + test_labels_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/test_labels.json + train_labels_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/train_labels.json + model_dir: models + name: 63447d821e9d864dd2da27ca96cd3f23 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2ca979d355d77f0e9e90dc03dfebce98 + trainer: + kwargs: {} +name: 63447d821e9d864dd2da27ca96cd3f23 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/637a69d7faab053f4bfb56725c99f149/params.yaml b/examples/security/classification/output/reports/train/637a69d7faab053f4bfb56725c99f149/params.yaml new file mode 100644 index 00000000..097200b9 --- /dev/null +++ b/examples/security/classification/output/reports/train/637a69d7faab053f4bfb56725c99f149/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/adv_predictions.json + adv_probabilities_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/019892bfa5cab7350db559558952b371.pkl + params_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/params.yaml + predictions_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/predictions.json + probabilities_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/probabilities.json + score_dict_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/score_dict.json + test_labels_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/test_labels.json + train_labels_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/train_labels.json + model_dir: models + name: 637a69d7faab053f4bfb56725c99f149 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0510b82a73961c39c45ea749da2e12c3 + trainer: + kwargs: {} +name: 637a69d7faab053f4bfb56725c99f149 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/637ce5ed1e5cb4a91c009609c206a078/params.yaml b/examples/security/classification/output/reports/train/637ce5ed1e5cb4a91c009609c206a078/params.yaml new file mode 100644 index 00000000..5efd4d3b --- /dev/null +++ b/examples/security/classification/output/reports/train/637ce5ed1e5cb4a91c009609c206a078/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/adv_predictions.json + adv_probabilities_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/f2ebc207a8e9fa3872583b77c4391bed.pkl + params_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/params.yaml + predictions_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/predictions.json + probabilities_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/probabilities.json + score_dict_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/score_dict.json + test_labels_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/test_labels.json + train_labels_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/train_labels.json + model_dir: models + name: 637ce5ed1e5cb4a91c009609c206a078 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 91a2f7979f8f8bc8d4f538c1bef3bd49 + trainer: + kwargs: {} +name: 637ce5ed1e5cb4a91c009609c206a078 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6380d387aca64368fa85380e502c6d22/params.yaml b/examples/security/classification/output/reports/train/6380d387aca64368fa85380e502c6d22/params.yaml new file mode 100644 index 00000000..163c373b --- /dev/null +++ b/examples/security/classification/output/reports/train/6380d387aca64368fa85380e502c6d22/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6380d387aca64368fa85380e502c6d22/adv_predictions.json + adv_probabilities_file: output/reports/train/6380d387aca64368fa85380e502c6d22/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/71782928f48f6eb5523712befce353d0.pkl + params_file: output/reports/train/6380d387aca64368fa85380e502c6d22/params.yaml + predictions_file: output/reports/train/6380d387aca64368fa85380e502c6d22/predictions.json + probabilities_file: output/reports/train/6380d387aca64368fa85380e502c6d22/probabilities.json + score_dict_file: output/reports/train/6380d387aca64368fa85380e502c6d22/score_dict.json + test_labels_file: output/reports/train/6380d387aca64368fa85380e502c6d22/test_labels.json + train_labels_file: output/reports/train/6380d387aca64368fa85380e502c6d22/train_labels.json + model_dir: models + name: 6380d387aca64368fa85380e502c6d22 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3cd22f1f775d0e02d7e990dd4b768473 + trainer: + kwargs: {} +name: 6380d387aca64368fa85380e502c6d22 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/params.yaml b/examples/security/classification/output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/params.yaml new file mode 100644 index 00000000..cdab6eef --- /dev/null +++ b/examples/security/classification/output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/adv_predictions.json + adv_probabilities_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/5c1b945cab49bf220a1532737cd5c813.pkl + params_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/params.yaml + predictions_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/predictions.json + probabilities_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/probabilities.json + score_dict_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/score_dict.json + test_labels_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/test_labels.json + train_labels_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/train_labels.json + model_dir: models + name: 638ed39a63fa3bc5b8e771caf5f39eb7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7861095494d79dbfe63a65d8b5551e24 + trainer: + kwargs: {} +name: 638ed39a63fa3bc5b8e771caf5f39eb7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/params.yaml b/examples/security/classification/output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/params.yaml new file mode 100644 index 00000000..120fb680 --- /dev/null +++ b/examples/security/classification/output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/adv_predictions.json + adv_probabilities_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/bcee52397d6b8d4e81c2b325c9624c7e.pkl + params_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/params.yaml + predictions_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/predictions.json + probabilities_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/probabilities.json + score_dict_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/score_dict.json + test_labels_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/test_labels.json + train_labels_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/train_labels.json + model_dir: models + name: 63b5dc650d4643fa8f9a4ed87eeab9e4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a64917922f075cc5f047e2dc781ca33e + trainer: + kwargs: {} +name: 63b5dc650d4643fa8f9a4ed87eeab9e4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6406651174b056d17c7a2aa6ab176e9e/params.yaml b/examples/security/classification/output/reports/train/6406651174b056d17c7a2aa6ab176e9e/params.yaml new file mode 100644 index 00000000..d68e93ff --- /dev/null +++ b/examples/security/classification/output/reports/train/6406651174b056d17c7a2aa6ab176e9e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/adv_predictions.json + adv_probabilities_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/8715ae92df4f535d816065853b9b6ce7.pkl + params_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/params.yaml + predictions_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/predictions.json + probabilities_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/probabilities.json + score_dict_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/score_dict.json + test_labels_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/test_labels.json + train_labels_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/train_labels.json + model_dir: models + name: 6406651174b056d17c7a2aa6ab176e9e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ba1215bb493535f133672ce9793f4c75 + trainer: + kwargs: {} +name: 6406651174b056d17c7a2aa6ab176e9e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/params.yaml b/examples/security/classification/output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/params.yaml new file mode 100644 index 00000000..24212033 --- /dev/null +++ b/examples/security/classification/output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/adv_predictions.json + adv_probabilities_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/9bcfd8052c610101f455d9edd80c2735.pkl + params_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/params.yaml + predictions_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/predictions.json + probabilities_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/probabilities.json + score_dict_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/score_dict.json + test_labels_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/test_labels.json + train_labels_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/train_labels.json + model_dir: models + name: 642679f669fb6d4dd25c78eb2d60ff04 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: baba484ec59c034366dc507e3d53d46e + trainer: + kwargs: {} +name: 642679f669fb6d4dd25c78eb2d60ff04 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/642cb8b2f97061cae17f98f65cab2de6/params.yaml b/examples/security/classification/output/reports/train/642cb8b2f97061cae17f98f65cab2de6/params.yaml new file mode 100644 index 00000000..7415b17d --- /dev/null +++ b/examples/security/classification/output/reports/train/642cb8b2f97061cae17f98f65cab2de6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/adv_predictions.json + adv_probabilities_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/2570a8017fad689c60e3d52a16fcdeaa.pkl + params_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/params.yaml + predictions_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/predictions.json + probabilities_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/probabilities.json + score_dict_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/score_dict.json + test_labels_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/test_labels.json + train_labels_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/train_labels.json + model_dir: models + name: 642cb8b2f97061cae17f98f65cab2de6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 56f3f7c4f3682452b8ff717def55996b + trainer: + kwargs: {} +name: 642cb8b2f97061cae17f98f65cab2de6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/params.yaml b/examples/security/classification/output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/params.yaml new file mode 100644 index 00000000..933c7536 --- /dev/null +++ b/examples/security/classification/output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/adv_predictions.json + adv_probabilities_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/8309c527e1b22db4b24c658dbfb72ff7.pkl + params_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/params.yaml + predictions_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/predictions.json + probabilities_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/probabilities.json + score_dict_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/score_dict.json + test_labels_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/test_labels.json + train_labels_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/train_labels.json + model_dir: models + name: 643dfbdcc1bb5331ded3c5dbf63f6902 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: be6bbbe46ea957959dd9038f00763ca9 + trainer: + kwargs: {} +name: 643dfbdcc1bb5331ded3c5dbf63f6902 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6459a4450c1911d5f06cea16d48c891f/params.yaml b/examples/security/classification/output/reports/train/6459a4450c1911d5f06cea16d48c891f/params.yaml new file mode 100644 index 00000000..a3dc9045 --- /dev/null +++ b/examples/security/classification/output/reports/train/6459a4450c1911d5f06cea16d48c891f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/adv_predictions.json + adv_probabilities_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/013f4dd415b76f8f665696140ee2296b.pkl + params_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/params.yaml + predictions_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/predictions.json + probabilities_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/probabilities.json + score_dict_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/score_dict.json + test_labels_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/test_labels.json + train_labels_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/train_labels.json + model_dir: models + name: 6459a4450c1911d5f06cea16d48c891f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5c690a481123888543a8e10a70d1d0ed + trainer: + kwargs: {} +name: 6459a4450c1911d5f06cea16d48c891f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/params.yaml b/examples/security/classification/output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/params.yaml new file mode 100644 index 00000000..b0fa74e1 --- /dev/null +++ b/examples/security/classification/output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/adv_predictions.json + adv_probabilities_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/983a4a72680889c3ce07862426625af9.pkl + params_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/params.yaml + predictions_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/predictions.json + probabilities_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/probabilities.json + score_dict_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/score_dict.json + test_labels_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/test_labels.json + train_labels_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/train_labels.json + model_dir: models + name: 648742c5a5dbb69ae76d7fc2976f0176 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbd4c43d7e93d9db38379b169663ac4c + trainer: + kwargs: {} +name: 648742c5a5dbb69ae76d7fc2976f0176 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/params.yaml b/examples/security/classification/output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/params.yaml new file mode 100644 index 00000000..ca7367c5 --- /dev/null +++ b/examples/security/classification/output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/adv_predictions.json + adv_probabilities_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/29d5a14b61e41b86aff7ca71de355686.pkl + params_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/params.yaml + predictions_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/predictions.json + probabilities_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/probabilities.json + score_dict_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/score_dict.json + test_labels_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/test_labels.json + train_labels_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/train_labels.json + model_dir: models + name: 648ec8ed6be2cc0cdf2eaf55121e8cc7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0898132239fe41c91504e317fcebeb80 + trainer: + kwargs: {} +name: 648ec8ed6be2cc0cdf2eaf55121e8cc7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/64947d256a325a372489f75d1ba7db32/params.yaml b/examples/security/classification/output/reports/train/64947d256a325a372489f75d1ba7db32/params.yaml new file mode 100644 index 00000000..f2510024 --- /dev/null +++ b/examples/security/classification/output/reports/train/64947d256a325a372489f75d1ba7db32/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/64947d256a325a372489f75d1ba7db32/adv_predictions.json + adv_probabilities_file: output/reports/train/64947d256a325a372489f75d1ba7db32/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/f18b20b6229a9613f4a30cd52d1bf6ee.pkl + params_file: output/reports/train/64947d256a325a372489f75d1ba7db32/params.yaml + predictions_file: output/reports/train/64947d256a325a372489f75d1ba7db32/predictions.json + probabilities_file: output/reports/train/64947d256a325a372489f75d1ba7db32/probabilities.json + score_dict_file: output/reports/train/64947d256a325a372489f75d1ba7db32/score_dict.json + test_labels_file: output/reports/train/64947d256a325a372489f75d1ba7db32/test_labels.json + train_labels_file: output/reports/train/64947d256a325a372489f75d1ba7db32/train_labels.json + model_dir: models + name: 64947d256a325a372489f75d1ba7db32 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 16cdd7d57cfcb3991c738654d3f605fb + trainer: + kwargs: {} +name: 64947d256a325a372489f75d1ba7db32 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/64977073273b87a949bb4d2c7ba4a158/params.yaml b/examples/security/classification/output/reports/train/64977073273b87a949bb4d2c7ba4a158/params.yaml new file mode 100644 index 00000000..a37de1dc --- /dev/null +++ b/examples/security/classification/output/reports/train/64977073273b87a949bb4d2c7ba4a158/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/adv_predictions.json + adv_probabilities_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/50aeecabfa1b699fb8f506a6b39e9e58.pkl + params_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/params.yaml + predictions_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/predictions.json + probabilities_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/probabilities.json + score_dict_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/score_dict.json + test_labels_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/test_labels.json + train_labels_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/train_labels.json + model_dir: models + name: 64977073273b87a949bb4d2c7ba4a158 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d6ee2c2c59d9500601d74e1caf1f854b + trainer: + kwargs: {} +name: 64977073273b87a949bb4d2c7ba4a158 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/64b064890ffa101114af628fff6598f6/params.yaml b/examples/security/classification/output/reports/train/64b064890ffa101114af628fff6598f6/params.yaml new file mode 100644 index 00000000..ed5fc83e --- /dev/null +++ b/examples/security/classification/output/reports/train/64b064890ffa101114af628fff6598f6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/64b064890ffa101114af628fff6598f6/adv_predictions.json + adv_probabilities_file: output/reports/train/64b064890ffa101114af628fff6598f6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/41bc27b99402c76c0c27a333af17039b.pkl + params_file: output/reports/train/64b064890ffa101114af628fff6598f6/params.yaml + predictions_file: output/reports/train/64b064890ffa101114af628fff6598f6/predictions.json + probabilities_file: output/reports/train/64b064890ffa101114af628fff6598f6/probabilities.json + score_dict_file: output/reports/train/64b064890ffa101114af628fff6598f6/score_dict.json + test_labels_file: output/reports/train/64b064890ffa101114af628fff6598f6/test_labels.json + train_labels_file: output/reports/train/64b064890ffa101114af628fff6598f6/train_labels.json + model_dir: models + name: 64b064890ffa101114af628fff6598f6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d431cb53ea5668b20c736e6a7ecd8a52 + trainer: + kwargs: {} +name: 64b064890ffa101114af628fff6598f6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/653601fda34c27f463473547b0b1211d/params.yaml b/examples/security/classification/output/reports/train/653601fda34c27f463473547b0b1211d/params.yaml new file mode 100644 index 00000000..8a146086 --- /dev/null +++ b/examples/security/classification/output/reports/train/653601fda34c27f463473547b0b1211d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/653601fda34c27f463473547b0b1211d/adv_predictions.json + adv_probabilities_file: output/reports/train/653601fda34c27f463473547b0b1211d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/2196dfc3ec98c3be5976f47f0ba48e65.pkl + params_file: output/reports/train/653601fda34c27f463473547b0b1211d/params.yaml + predictions_file: output/reports/train/653601fda34c27f463473547b0b1211d/predictions.json + probabilities_file: output/reports/train/653601fda34c27f463473547b0b1211d/probabilities.json + score_dict_file: output/reports/train/653601fda34c27f463473547b0b1211d/score_dict.json + test_labels_file: output/reports/train/653601fda34c27f463473547b0b1211d/test_labels.json + train_labels_file: output/reports/train/653601fda34c27f463473547b0b1211d/train_labels.json + model_dir: models + name: 653601fda34c27f463473547b0b1211d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3e34bdf44bfc359adeb963dbb8682e62 + trainer: + kwargs: {} +name: 653601fda34c27f463473547b0b1211d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/65376b31f93dd6eee6dca534fca03f7d/params.yaml b/examples/security/classification/output/reports/train/65376b31f93dd6eee6dca534fca03f7d/params.yaml new file mode 100644 index 00000000..f88c3514 --- /dev/null +++ b/examples/security/classification/output/reports/train/65376b31f93dd6eee6dca534fca03f7d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/adv_predictions.json + adv_probabilities_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/4316db0ad1269550b0abf559d18c042c.pkl + params_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/params.yaml + predictions_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/predictions.json + probabilities_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/probabilities.json + score_dict_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/score_dict.json + test_labels_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/test_labels.json + train_labels_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/train_labels.json + model_dir: models + name: 65376b31f93dd6eee6dca534fca03f7d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 644fae3ad242b61773896edf874c4545 + trainer: + kwargs: {} +name: 65376b31f93dd6eee6dca534fca03f7d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/params.yaml b/examples/security/classification/output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/params.yaml new file mode 100644 index 00000000..4f827885 --- /dev/null +++ b/examples/security/classification/output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/adv_predictions.json + adv_probabilities_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/2fd604e30cced4b5d15b916c89d3bae5.pkl + params_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/params.yaml + predictions_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/predictions.json + probabilities_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/probabilities.json + score_dict_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/score_dict.json + test_labels_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/test_labels.json + train_labels_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/train_labels.json + model_dir: models + name: 654cb6f98cffbfdf8e8522d43bfdf69b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4ad5af367140055ba35b87af85708bc0 + trainer: + kwargs: {} +name: 654cb6f98cffbfdf8e8522d43bfdf69b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6562352b7c068f984b672c25c480d7c6/params.yaml b/examples/security/classification/output/reports/train/6562352b7c068f984b672c25c480d7c6/params.yaml new file mode 100644 index 00000000..d8471bb0 --- /dev/null +++ b/examples/security/classification/output/reports/train/6562352b7c068f984b672c25c480d7c6/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/adv_predictions.json + adv_probabilities_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/7d1a0e42090b33b15a1983af61e4e04d.pkl + params_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/params.yaml + predictions_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/predictions.json + probabilities_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/probabilities.json + score_dict_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/score_dict.json + test_labels_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/test_labels.json + train_labels_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/train_labels.json + model_dir: models + name: 6562352b7c068f984b672c25c480d7c6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cdd31b998738835d623079305381b278 + trainer: + kwargs: {} +name: 6562352b7c068f984b672c25c480d7c6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/65bf03ae120e6ef11d759374bb313769/params.yaml b/examples/security/classification/output/reports/train/65bf03ae120e6ef11d759374bb313769/params.yaml new file mode 100644 index 00000000..fa17ec5a --- /dev/null +++ b/examples/security/classification/output/reports/train/65bf03ae120e6ef11d759374bb313769/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/adv_predictions.json + adv_probabilities_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/7b7ff0650294dcd8dbd9466e4ba896b9.pkl + params_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/params.yaml + predictions_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/predictions.json + probabilities_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/probabilities.json + score_dict_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/score_dict.json + test_labels_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/test_labels.json + train_labels_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/train_labels.json + model_dir: models + name: 65bf03ae120e6ef11d759374bb313769 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 59ceeb8f75c859410f3b2a85b38a8eb8 + trainer: + kwargs: {} +name: 65bf03ae120e6ef11d759374bb313769 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/params.yaml b/examples/security/classification/output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/params.yaml new file mode 100644 index 00000000..318e22aa --- /dev/null +++ b/examples/security/classification/output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/adv_predictions.json + adv_probabilities_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/1a3617eb065587cd4f64672bff18a738.pkl + params_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/params.yaml + predictions_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/predictions.json + probabilities_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/probabilities.json + score_dict_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/score_dict.json + test_labels_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/test_labels.json + train_labels_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/train_labels.json + model_dir: models + name: 65ee1016f26bc759f08d7c9d20c5c11c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 512cfb3818ed4edea11386a7f76ca3ea + trainer: + kwargs: {} +name: 65ee1016f26bc759f08d7c9d20c5c11c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/660be89fefe6a78eddc794df695d6053/params.yaml b/examples/security/classification/output/reports/train/660be89fefe6a78eddc794df695d6053/params.yaml new file mode 100644 index 00000000..b93b3f6a --- /dev/null +++ b/examples/security/classification/output/reports/train/660be89fefe6a78eddc794df695d6053/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/660be89fefe6a78eddc794df695d6053/adv_predictions.json + adv_probabilities_file: output/reports/train/660be89fefe6a78eddc794df695d6053/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/3904fce38b437fa53cf99975c52f1504.pkl + params_file: output/reports/train/660be89fefe6a78eddc794df695d6053/params.yaml + predictions_file: output/reports/train/660be89fefe6a78eddc794df695d6053/predictions.json + probabilities_file: output/reports/train/660be89fefe6a78eddc794df695d6053/probabilities.json + score_dict_file: output/reports/train/660be89fefe6a78eddc794df695d6053/score_dict.json + test_labels_file: output/reports/train/660be89fefe6a78eddc794df695d6053/test_labels.json + train_labels_file: output/reports/train/660be89fefe6a78eddc794df695d6053/train_labels.json + model_dir: models + name: 660be89fefe6a78eddc794df695d6053 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8e13cc024636500f816529140e666180 + trainer: + kwargs: {} +name: 660be89fefe6a78eddc794df695d6053 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/660f7e30fba92967335df80ca8e4ae43/params.yaml b/examples/security/classification/output/reports/train/660f7e30fba92967335df80ca8e4ae43/params.yaml new file mode 100644 index 00000000..70d75895 --- /dev/null +++ b/examples/security/classification/output/reports/train/660f7e30fba92967335df80ca8e4ae43/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/adv_predictions.json + adv_probabilities_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/64ea8fe310e8a1def2a261b1cfaf0aa0.pkl + params_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/params.yaml + predictions_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/predictions.json + probabilities_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/probabilities.json + score_dict_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/score_dict.json + test_labels_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/test_labels.json + train_labels_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/train_labels.json + model_dir: models + name: 660f7e30fba92967335df80ca8e4ae43 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 81f81d6bb26da9afc0af8e9ceab8e926 + trainer: + kwargs: {} +name: 660f7e30fba92967335df80ca8e4ae43 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/params.yaml b/examples/security/classification/output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/params.yaml new file mode 100644 index 00000000..a330f3de --- /dev/null +++ b/examples/security/classification/output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/adv_predictions.json + adv_probabilities_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e031f496feae9877636560ac22a3cd99.pkl + params_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/params.yaml + predictions_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/predictions.json + probabilities_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/probabilities.json + score_dict_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/score_dict.json + test_labels_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/test_labels.json + train_labels_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/train_labels.json + model_dir: models + name: 66297a2107356eeb9c6e71d3aa3e4271 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ddde247bccb71c28bc33e549f2b4a249 + trainer: + kwargs: {} +name: 66297a2107356eeb9c6e71d3aa3e4271 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/662a15e30f4dbe268b72f21debaee4bd/params.yaml b/examples/security/classification/output/reports/train/662a15e30f4dbe268b72f21debaee4bd/params.yaml new file mode 100644 index 00000000..cf0f0fd7 --- /dev/null +++ b/examples/security/classification/output/reports/train/662a15e30f4dbe268b72f21debaee4bd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/adv_predictions.json + adv_probabilities_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/3509db9f58b675f2c0bb91ec20071561.pkl + params_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/params.yaml + predictions_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/predictions.json + probabilities_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/probabilities.json + score_dict_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/score_dict.json + test_labels_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/test_labels.json + train_labels_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/train_labels.json + model_dir: models + name: 662a15e30f4dbe268b72f21debaee4bd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d2766fe39752164440e3df982d8260c + trainer: + kwargs: {} +name: 662a15e30f4dbe268b72f21debaee4bd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/662d499ff1527b584384dd4475824aaf/params.yaml b/examples/security/classification/output/reports/train/662d499ff1527b584384dd4475824aaf/params.yaml new file mode 100644 index 00000000..2db67612 --- /dev/null +++ b/examples/security/classification/output/reports/train/662d499ff1527b584384dd4475824aaf/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/662d499ff1527b584384dd4475824aaf/adv_predictions.json + adv_probabilities_file: output/reports/train/662d499ff1527b584384dd4475824aaf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/552833eabe7e078a02433d36ac270113.pkl + params_file: output/reports/train/662d499ff1527b584384dd4475824aaf/params.yaml + predictions_file: output/reports/train/662d499ff1527b584384dd4475824aaf/predictions.json + probabilities_file: output/reports/train/662d499ff1527b584384dd4475824aaf/probabilities.json + score_dict_file: output/reports/train/662d499ff1527b584384dd4475824aaf/score_dict.json + test_labels_file: output/reports/train/662d499ff1527b584384dd4475824aaf/test_labels.json + train_labels_file: output/reports/train/662d499ff1527b584384dd4475824aaf/train_labels.json + model_dir: models + name: 662d499ff1527b584384dd4475824aaf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f3ef190a7bfbeb813d58c30e5cdae62c + trainer: + kwargs: {} +name: 662d499ff1527b584384dd4475824aaf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/params.yaml b/examples/security/classification/output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/params.yaml new file mode 100644 index 00000000..f9b0fe66 --- /dev/null +++ b/examples/security/classification/output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/adv_predictions.json + adv_probabilities_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/2b20e147c723fafce058c3cd6ac9dbd9.pkl + params_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/params.yaml + predictions_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/predictions.json + probabilities_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/probabilities.json + score_dict_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/score_dict.json + test_labels_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/test_labels.json + train_labels_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/train_labels.json + model_dir: models + name: 663a1aaa4cfda00b26468cbc425ee13c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2665d67746d49341961b5c7be831ae6 + trainer: + kwargs: {} +name: 663a1aaa4cfda00b26468cbc425ee13c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/params.yaml b/examples/security/classification/output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/params.yaml new file mode 100644 index 00000000..3b788d97 --- /dev/null +++ b/examples/security/classification/output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/adv_predictions.json + adv_probabilities_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/6a54a8f3e306831e760c83605ef4b251.pkl + params_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/params.yaml + predictions_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/predictions.json + probabilities_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/probabilities.json + score_dict_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/score_dict.json + test_labels_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/test_labels.json + train_labels_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/train_labels.json + model_dir: models + name: 66562f10769e30aa6436a8a5ed9ac7d0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 33e7d835aec9f50e6de1bbf3cd57f584 + trainer: + kwargs: {} +name: 66562f10769e30aa6436a8a5ed9ac7d0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/667c4efaecfad617b1475850d323af69/params.yaml b/examples/security/classification/output/reports/train/667c4efaecfad617b1475850d323af69/params.yaml new file mode 100644 index 00000000..0ca19fbf --- /dev/null +++ b/examples/security/classification/output/reports/train/667c4efaecfad617b1475850d323af69/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/667c4efaecfad617b1475850d323af69/adv_predictions.json + adv_probabilities_file: output/reports/train/667c4efaecfad617b1475850d323af69/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/650c9d9ae51513d68891e768a94f4895.pkl + params_file: output/reports/train/667c4efaecfad617b1475850d323af69/params.yaml + predictions_file: output/reports/train/667c4efaecfad617b1475850d323af69/predictions.json + probabilities_file: output/reports/train/667c4efaecfad617b1475850d323af69/probabilities.json + score_dict_file: output/reports/train/667c4efaecfad617b1475850d323af69/score_dict.json + test_labels_file: output/reports/train/667c4efaecfad617b1475850d323af69/test_labels.json + train_labels_file: output/reports/train/667c4efaecfad617b1475850d323af69/train_labels.json + model_dir: models + name: 667c4efaecfad617b1475850d323af69 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edce649b1298417e6724a171b1ea129a + trainer: + kwargs: {} +name: 667c4efaecfad617b1475850d323af69 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6689011d3eb3a647e98295f7bf339393/params.yaml b/examples/security/classification/output/reports/train/6689011d3eb3a647e98295f7bf339393/params.yaml new file mode 100644 index 00000000..98defda1 --- /dev/null +++ b/examples/security/classification/output/reports/train/6689011d3eb3a647e98295f7bf339393/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/adv_predictions.json + adv_probabilities_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/1d395c77a051b8621053956dee40cfa1.pkl + params_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/params.yaml + predictions_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/predictions.json + probabilities_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/probabilities.json + score_dict_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/score_dict.json + test_labels_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/test_labels.json + train_labels_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/train_labels.json + model_dir: models + name: 6689011d3eb3a647e98295f7bf339393 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fc4dd62cbf0c1c567de06e6312f9ca8e + trainer: + kwargs: {} +name: 6689011d3eb3a647e98295f7bf339393 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/66a58622dccd6846f2340e9c2f084b49/params.yaml b/examples/security/classification/output/reports/train/66a58622dccd6846f2340e9c2f084b49/params.yaml new file mode 100644 index 00000000..8c10b9b5 --- /dev/null +++ b/examples/security/classification/output/reports/train/66a58622dccd6846f2340e9c2f084b49/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/adv_predictions.json + adv_probabilities_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/973182f6bfc36c458baf6a0cf5d95c49.pkl + params_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/params.yaml + predictions_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/predictions.json + probabilities_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/probabilities.json + score_dict_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/score_dict.json + test_labels_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/test_labels.json + train_labels_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/train_labels.json + model_dir: models + name: 66a58622dccd6846f2340e9c2f084b49 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 19060661415a3836f6addcd862908f88 + trainer: + kwargs: {} +name: 66a58622dccd6846f2340e9c2f084b49 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/66f9160834be921cca8c3b218d46f033/params.yaml b/examples/security/classification/output/reports/train/66f9160834be921cca8c3b218d46f033/params.yaml new file mode 100644 index 00000000..1cb0c1cf --- /dev/null +++ b/examples/security/classification/output/reports/train/66f9160834be921cca8c3b218d46f033/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/66f9160834be921cca8c3b218d46f033/adv_predictions.json + adv_probabilities_file: output/reports/train/66f9160834be921cca8c3b218d46f033/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/aea56dd96a9fea1acdba4e184f78433d.pkl + params_file: output/reports/train/66f9160834be921cca8c3b218d46f033/params.yaml + predictions_file: output/reports/train/66f9160834be921cca8c3b218d46f033/predictions.json + probabilities_file: output/reports/train/66f9160834be921cca8c3b218d46f033/probabilities.json + score_dict_file: output/reports/train/66f9160834be921cca8c3b218d46f033/score_dict.json + test_labels_file: output/reports/train/66f9160834be921cca8c3b218d46f033/test_labels.json + train_labels_file: output/reports/train/66f9160834be921cca8c3b218d46f033/train_labels.json + model_dir: models + name: 66f9160834be921cca8c3b218d46f033 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 427b93e3616b74d9d4ad5354a0c30cf6 + trainer: + kwargs: {} +name: 66f9160834be921cca8c3b218d46f033 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/66fd64466e40586700b67bfdb471d14c/params.yaml b/examples/security/classification/output/reports/train/66fd64466e40586700b67bfdb471d14c/params.yaml new file mode 100644 index 00000000..a3fdd962 --- /dev/null +++ b/examples/security/classification/output/reports/train/66fd64466e40586700b67bfdb471d14c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/adv_predictions.json + adv_probabilities_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/2ffba9babe5747d7abdf2a051f7252bf.pkl + params_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/params.yaml + predictions_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/predictions.json + probabilities_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/probabilities.json + score_dict_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/score_dict.json + test_labels_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/test_labels.json + train_labels_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/train_labels.json + model_dir: models + name: 66fd64466e40586700b67bfdb471d14c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 51ce2e4b21c5a65f8bb774e83b3addfa + trainer: + kwargs: {} +name: 66fd64466e40586700b67bfdb471d14c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/673c1fe5864b54eaca3097558d8e9b58/params.yaml b/examples/security/classification/output/reports/train/673c1fe5864b54eaca3097558d8e9b58/params.yaml new file mode 100644 index 00000000..3cd846ac --- /dev/null +++ b/examples/security/classification/output/reports/train/673c1fe5864b54eaca3097558d8e9b58/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/adv_predictions.json + adv_probabilities_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/2c886e519d071a3e56dc69ad78e94ed0.pkl + params_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/params.yaml + predictions_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/predictions.json + probabilities_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/probabilities.json + score_dict_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/score_dict.json + test_labels_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/test_labels.json + train_labels_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/train_labels.json + model_dir: models + name: 673c1fe5864b54eaca3097558d8e9b58 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 956d409bbdf235414103d0c9a1ca2a1a + trainer: + kwargs: {} +name: 673c1fe5864b54eaca3097558d8e9b58 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/67474eedc61be7ccd7169bdfc446016e/params.yaml b/examples/security/classification/output/reports/train/67474eedc61be7ccd7169bdfc446016e/params.yaml new file mode 100644 index 00000000..fc46495a --- /dev/null +++ b/examples/security/classification/output/reports/train/67474eedc61be7ccd7169bdfc446016e/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/adv_predictions.json + adv_probabilities_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/415626977807315bb082e942b950bdc8.pkl + params_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/params.yaml + predictions_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/predictions.json + probabilities_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/probabilities.json + score_dict_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/score_dict.json + test_labels_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/test_labels.json + train_labels_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/train_labels.json + model_dir: models + name: 67474eedc61be7ccd7169bdfc446016e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1ba44010b4472761dba9fa87f6ba3f6f + trainer: + kwargs: {} +name: 67474eedc61be7ccd7169bdfc446016e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6780043369fd79f77c607db5d7c3d364/params.yaml b/examples/security/classification/output/reports/train/6780043369fd79f77c607db5d7c3d364/params.yaml new file mode 100644 index 00000000..39ce77cf --- /dev/null +++ b/examples/security/classification/output/reports/train/6780043369fd79f77c607db5d7c3d364/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/adv_predictions.json + adv_probabilities_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/d222453183ab615a612c1751093c3bd3.pkl + params_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/params.yaml + predictions_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/predictions.json + probabilities_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/probabilities.json + score_dict_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/score_dict.json + test_labels_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/test_labels.json + train_labels_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/train_labels.json + model_dir: models + name: 6780043369fd79f77c607db5d7c3d364 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fc07a7198b81117b61254de7b99dd082 + trainer: + kwargs: {} +name: 6780043369fd79f77c607db5d7c3d364 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/679ec9b960183913a7f0f76709427729/params.yaml b/examples/security/classification/output/reports/train/679ec9b960183913a7f0f76709427729/params.yaml new file mode 100644 index 00000000..282f3056 --- /dev/null +++ b/examples/security/classification/output/reports/train/679ec9b960183913a7f0f76709427729/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/679ec9b960183913a7f0f76709427729/adv_predictions.json + adv_probabilities_file: output/reports/train/679ec9b960183913a7f0f76709427729/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/24be310b727403bd49f9d0abc44083a3.pkl + params_file: output/reports/train/679ec9b960183913a7f0f76709427729/params.yaml + predictions_file: output/reports/train/679ec9b960183913a7f0f76709427729/predictions.json + probabilities_file: output/reports/train/679ec9b960183913a7f0f76709427729/probabilities.json + score_dict_file: output/reports/train/679ec9b960183913a7f0f76709427729/score_dict.json + test_labels_file: output/reports/train/679ec9b960183913a7f0f76709427729/test_labels.json + train_labels_file: output/reports/train/679ec9b960183913a7f0f76709427729/train_labels.json + model_dir: models + name: 679ec9b960183913a7f0f76709427729 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 86c59abb1313574781433287ca215ee0 + trainer: + kwargs: {} +name: 679ec9b960183913a7f0f76709427729 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/67ab356365411d98ad7a41d3001d459f/params.yaml b/examples/security/classification/output/reports/train/67ab356365411d98ad7a41d3001d459f/params.yaml new file mode 100644 index 00000000..cbfe2e43 --- /dev/null +++ b/examples/security/classification/output/reports/train/67ab356365411d98ad7a41d3001d459f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/adv_predictions.json + adv_probabilities_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/9bd75937f6504438710d50cc971b8b52.pkl + params_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/params.yaml + predictions_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/predictions.json + probabilities_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/probabilities.json + score_dict_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/score_dict.json + test_labels_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/test_labels.json + train_labels_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/train_labels.json + model_dir: models + name: 67ab356365411d98ad7a41d3001d459f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 47a2ca1c4f0159971938213413869f3c + trainer: + kwargs: {} +name: 67ab356365411d98ad7a41d3001d459f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/params.yaml b/examples/security/classification/output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/params.yaml new file mode 100644 index 00000000..05e3655c --- /dev/null +++ b/examples/security/classification/output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/adv_predictions.json + adv_probabilities_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/49cb831b3fdc7b6f26b02ba3528aa4e9.pkl + params_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/params.yaml + predictions_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/predictions.json + probabilities_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/probabilities.json + score_dict_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/score_dict.json + test_labels_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/test_labels.json + train_labels_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/train_labels.json + model_dir: models + name: 681700b94ff8fcdc3b065f256c04d5a4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 87a6cd65a4fa88c93a4f783c544849ae + trainer: + kwargs: {} +name: 681700b94ff8fcdc3b065f256c04d5a4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/params.yaml b/examples/security/classification/output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/params.yaml new file mode 100644 index 00000000..bf856633 --- /dev/null +++ b/examples/security/classification/output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/adv_predictions.json + adv_probabilities_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/c2bb2b8ab3497b652a9991d2ce71706d.pkl + params_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/params.yaml + predictions_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/predictions.json + probabilities_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/probabilities.json + score_dict_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/score_dict.json + test_labels_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/test_labels.json + train_labels_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/train_labels.json + model_dir: models + name: 6831f8bc1abf072bab6b5e9d871e7667 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a567fef92fd5727a463712eedf7d4158 + trainer: + kwargs: {} +name: 6831f8bc1abf072bab6b5e9d871e7667 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/685bc14371c8978a854f2bc1e7aae457/params.yaml b/examples/security/classification/output/reports/train/685bc14371c8978a854f2bc1e7aae457/params.yaml new file mode 100644 index 00000000..f795ac75 --- /dev/null +++ b/examples/security/classification/output/reports/train/685bc14371c8978a854f2bc1e7aae457/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/adv_predictions.json + adv_probabilities_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/d804c92c7b793b8e83dbca609a829551.pkl + params_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/params.yaml + predictions_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/predictions.json + probabilities_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/probabilities.json + score_dict_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/score_dict.json + test_labels_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/test_labels.json + train_labels_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/train_labels.json + model_dir: models + name: 685bc14371c8978a854f2bc1e7aae457 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 56d7631bed4ce869e8fd1f23fb588e2c + trainer: + kwargs: {} +name: 685bc14371c8978a854f2bc1e7aae457 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/687c18366d6fabc3befca22fef4e136a/params.yaml b/examples/security/classification/output/reports/train/687c18366d6fabc3befca22fef4e136a/params.yaml new file mode 100644 index 00000000..b92f6759 --- /dev/null +++ b/examples/security/classification/output/reports/train/687c18366d6fabc3befca22fef4e136a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/adv_predictions.json + adv_probabilities_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/4074cdbf84e17b094e63a521dfbc2bd5.pkl + params_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/params.yaml + predictions_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/predictions.json + probabilities_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/probabilities.json + score_dict_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/score_dict.json + test_labels_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/test_labels.json + train_labels_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/train_labels.json + model_dir: models + name: 687c18366d6fabc3befca22fef4e136a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e386f417828da37f9fb3421ffd92cbec + trainer: + kwargs: {} +name: 687c18366d6fabc3befca22fef4e136a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/68bd05a8f808cd8e6f664a188ac64801/params.yaml b/examples/security/classification/output/reports/train/68bd05a8f808cd8e6f664a188ac64801/params.yaml new file mode 100644 index 00000000..7bf97398 --- /dev/null +++ b/examples/security/classification/output/reports/train/68bd05a8f808cd8e6f664a188ac64801/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/adv_predictions.json + adv_probabilities_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/83ff7095a6db197c33af2eb4b2480b72.pkl + params_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/params.yaml + predictions_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/predictions.json + probabilities_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/probabilities.json + score_dict_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/score_dict.json + test_labels_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/test_labels.json + train_labels_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/train_labels.json + model_dir: models + name: 68bd05a8f808cd8e6f664a188ac64801 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a4dbc435eb5c304dac481a0801f1533c + trainer: + kwargs: {} +name: 68bd05a8f808cd8e6f664a188ac64801 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/params.yaml b/examples/security/classification/output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/params.yaml new file mode 100644 index 00000000..15a464ed --- /dev/null +++ b/examples/security/classification/output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/adv_predictions.json + adv_probabilities_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/8184620e9626b46f8c3ca105dd66efe4.pkl + params_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/params.yaml + predictions_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/predictions.json + probabilities_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/probabilities.json + score_dict_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/score_dict.json + test_labels_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/test_labels.json + train_labels_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/train_labels.json + model_dir: models + name: 68c2bf9adc79dc40beb5034fa5a6ad23 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d5f9eee3cf6b56319c8f9153498f900f + trainer: + kwargs: {} +name: 68c2bf9adc79dc40beb5034fa5a6ad23 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/params.yaml b/examples/security/classification/output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/params.yaml new file mode 100644 index 00000000..9f3644c6 --- /dev/null +++ b/examples/security/classification/output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/adv_predictions.json + adv_probabilities_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/5fdc57cf386b8f0132fd2bfe01b43694.pkl + params_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/params.yaml + predictions_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/predictions.json + probabilities_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/probabilities.json + score_dict_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/score_dict.json + test_labels_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/test_labels.json + train_labels_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/train_labels.json + model_dir: models + name: 6927a37b10e9e38b1e9201f3ebf18c2e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e89261075b6e093b5030efae3a1c7ef1 + trainer: + kwargs: {} +name: 6927a37b10e9e38b1e9201f3ebf18c2e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/params.yaml b/examples/security/classification/output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/params.yaml new file mode 100644 index 00000000..0291a005 --- /dev/null +++ b/examples/security/classification/output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/adv_predictions.json + adv_probabilities_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/b3c7cf7e91d56af5bb2bd6d5d3853eca.pkl + params_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/params.yaml + predictions_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/predictions.json + probabilities_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/probabilities.json + score_dict_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/score_dict.json + test_labels_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/test_labels.json + train_labels_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/train_labels.json + model_dir: models + name: 69330a9d873dc65a8fb9ffffe53d5694 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a1acebce094fe724c53aa27205290645 + trainer: + kwargs: {} +name: 69330a9d873dc65a8fb9ffffe53d5694 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/693a8509a94b2977402ee6d52af9ec07/params.yaml b/examples/security/classification/output/reports/train/693a8509a94b2977402ee6d52af9ec07/params.yaml new file mode 100644 index 00000000..7fbc4fd1 --- /dev/null +++ b/examples/security/classification/output/reports/train/693a8509a94b2977402ee6d52af9ec07/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/adv_predictions.json + adv_probabilities_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/a1782b475556bc4740563ba9074f666b.pkl + params_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/params.yaml + predictions_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/predictions.json + probabilities_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/probabilities.json + score_dict_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/score_dict.json + test_labels_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/test_labels.json + train_labels_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/train_labels.json + model_dir: models + name: 693a8509a94b2977402ee6d52af9ec07 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e59bf1428575073832d8dc2fd23da12e + trainer: + kwargs: {} +name: 693a8509a94b2977402ee6d52af9ec07 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6953a01b774a592e46d917a2134ac753/params.yaml b/examples/security/classification/output/reports/train/6953a01b774a592e46d917a2134ac753/params.yaml new file mode 100644 index 00000000..db7cb6fd --- /dev/null +++ b/examples/security/classification/output/reports/train/6953a01b774a592e46d917a2134ac753/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6953a01b774a592e46d917a2134ac753/adv_predictions.json + adv_probabilities_file: output/reports/train/6953a01b774a592e46d917a2134ac753/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/90b03c06e4cf060916a5b9a54e6e7a28.pkl + params_file: output/reports/train/6953a01b774a592e46d917a2134ac753/params.yaml + predictions_file: output/reports/train/6953a01b774a592e46d917a2134ac753/predictions.json + probabilities_file: output/reports/train/6953a01b774a592e46d917a2134ac753/probabilities.json + score_dict_file: output/reports/train/6953a01b774a592e46d917a2134ac753/score_dict.json + test_labels_file: output/reports/train/6953a01b774a592e46d917a2134ac753/test_labels.json + train_labels_file: output/reports/train/6953a01b774a592e46d917a2134ac753/train_labels.json + model_dir: models + name: 6953a01b774a592e46d917a2134ac753 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dae3e299a893a501d9d9c2a4e1499dff + trainer: + kwargs: {} +name: 6953a01b774a592e46d917a2134ac753 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/69642cb7febdb4e005a2aa592051c1b6/params.yaml b/examples/security/classification/output/reports/train/69642cb7febdb4e005a2aa592051c1b6/params.yaml new file mode 100644 index 00000000..39013e9f --- /dev/null +++ b/examples/security/classification/output/reports/train/69642cb7febdb4e005a2aa592051c1b6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/adv_predictions.json + adv_probabilities_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/066d838bc76651adf450925b500c3afb.pkl + params_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/params.yaml + predictions_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/predictions.json + probabilities_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/probabilities.json + score_dict_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/score_dict.json + test_labels_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/test_labels.json + train_labels_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/train_labels.json + model_dir: models + name: 69642cb7febdb4e005a2aa592051c1b6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b5d846e4050ed7d3011897e032210d65 + trainer: + kwargs: {} +name: 69642cb7febdb4e005a2aa592051c1b6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/params.yaml b/examples/security/classification/output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/params.yaml new file mode 100644 index 00000000..5bc0f453 --- /dev/null +++ b/examples/security/classification/output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/adv_predictions.json + adv_probabilities_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/1e36b8c61e8bc91558487a8935d71009.pkl + params_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/params.yaml + predictions_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/predictions.json + probabilities_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/probabilities.json + score_dict_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/score_dict.json + test_labels_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/test_labels.json + train_labels_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/train_labels.json + model_dir: models + name: 69746a4d0db0a4093dd48730e5ce8fcd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 75a8a36509357c5f531a7add9722c52d + trainer: + kwargs: {} +name: 69746a4d0db0a4093dd48730e5ce8fcd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/params.yaml b/examples/security/classification/output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/params.yaml new file mode 100644 index 00000000..005d3bfe --- /dev/null +++ b/examples/security/classification/output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/adv_predictions.json + adv_probabilities_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/72bd4bb17dad23fae5c8515c80f47ab7.pkl + params_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/params.yaml + predictions_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/predictions.json + probabilities_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/probabilities.json + score_dict_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/score_dict.json + test_labels_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/test_labels.json + train_labels_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/train_labels.json + model_dir: models + name: 697d28d4a7fe581cb1343dbf6fd321a5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d7a0b33bae5825f037783cc2e6b9c013 + trainer: + kwargs: {} +name: 697d28d4a7fe581cb1343dbf6fd321a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6981edfe00a28176c32ac1885c279321/params.yaml b/examples/security/classification/output/reports/train/6981edfe00a28176c32ac1885c279321/params.yaml new file mode 100644 index 00000000..dc6551fb --- /dev/null +++ b/examples/security/classification/output/reports/train/6981edfe00a28176c32ac1885c279321/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6981edfe00a28176c32ac1885c279321/adv_predictions.json + adv_probabilities_file: output/reports/train/6981edfe00a28176c32ac1885c279321/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/83d851bbde2edac9bcd9cd204c3b5f2f.pkl + params_file: output/reports/train/6981edfe00a28176c32ac1885c279321/params.yaml + predictions_file: output/reports/train/6981edfe00a28176c32ac1885c279321/predictions.json + probabilities_file: output/reports/train/6981edfe00a28176c32ac1885c279321/probabilities.json + score_dict_file: output/reports/train/6981edfe00a28176c32ac1885c279321/score_dict.json + test_labels_file: output/reports/train/6981edfe00a28176c32ac1885c279321/test_labels.json + train_labels_file: output/reports/train/6981edfe00a28176c32ac1885c279321/train_labels.json + model_dir: models + name: 6981edfe00a28176c32ac1885c279321 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d3e7f863c189a14ee0ed6c498bb8c1db + trainer: + kwargs: {} +name: 6981edfe00a28176c32ac1885c279321 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/69898ef2f92571389a14ccaa6ba341dc/params.yaml b/examples/security/classification/output/reports/train/69898ef2f92571389a14ccaa6ba341dc/params.yaml new file mode 100644 index 00000000..23456463 --- /dev/null +++ b/examples/security/classification/output/reports/train/69898ef2f92571389a14ccaa6ba341dc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/adv_predictions.json + adv_probabilities_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/9d67630db6792c789137cb20476fb09c.pkl + params_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/params.yaml + predictions_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/predictions.json + probabilities_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/probabilities.json + score_dict_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/score_dict.json + test_labels_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/test_labels.json + train_labels_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/train_labels.json + model_dir: models + name: 69898ef2f92571389a14ccaa6ba341dc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3cfc6482a4808b6e41329280e1e3c10 + trainer: + kwargs: {} +name: 69898ef2f92571389a14ccaa6ba341dc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/69a1c15b525bb1b8845b42b9b571365a/params.yaml b/examples/security/classification/output/reports/train/69a1c15b525bb1b8845b42b9b571365a/params.yaml new file mode 100644 index 00000000..458e8eed --- /dev/null +++ b/examples/security/classification/output/reports/train/69a1c15b525bb1b8845b42b9b571365a/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/adv_predictions.json + adv_probabilities_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/5c90b05b97266342461d4dcc71fe7921.pkl + params_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/params.yaml + predictions_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/predictions.json + probabilities_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/probabilities.json + score_dict_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/score_dict.json + test_labels_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/test_labels.json + train_labels_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/train_labels.json + model_dir: models + name: 69a1c15b525bb1b8845b42b9b571365a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ffc02fd19e24e4fc43c3225e1a33b319 + trainer: + kwargs: {} +name: 69a1c15b525bb1b8845b42b9b571365a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/69c279618f13faed659f00c17dfb142a/params.yaml b/examples/security/classification/output/reports/train/69c279618f13faed659f00c17dfb142a/params.yaml new file mode 100644 index 00000000..b4a8f387 --- /dev/null +++ b/examples/security/classification/output/reports/train/69c279618f13faed659f00c17dfb142a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/69c279618f13faed659f00c17dfb142a/adv_predictions.json + adv_probabilities_file: output/reports/train/69c279618f13faed659f00c17dfb142a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/cc83c90e27dc89a84ea78ea1df1ed334.pkl + params_file: output/reports/train/69c279618f13faed659f00c17dfb142a/params.yaml + predictions_file: output/reports/train/69c279618f13faed659f00c17dfb142a/predictions.json + probabilities_file: output/reports/train/69c279618f13faed659f00c17dfb142a/probabilities.json + score_dict_file: output/reports/train/69c279618f13faed659f00c17dfb142a/score_dict.json + test_labels_file: output/reports/train/69c279618f13faed659f00c17dfb142a/test_labels.json + train_labels_file: output/reports/train/69c279618f13faed659f00c17dfb142a/train_labels.json + model_dir: models + name: 69c279618f13faed659f00c17dfb142a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4a0b6d0858abf88c2db2f14138bbbac0 + trainer: + kwargs: {} +name: 69c279618f13faed659f00c17dfb142a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/69d2f0003a82cb91c077c0ccf763a853/params.yaml b/examples/security/classification/output/reports/train/69d2f0003a82cb91c077c0ccf763a853/params.yaml new file mode 100644 index 00000000..9c45f79a --- /dev/null +++ b/examples/security/classification/output/reports/train/69d2f0003a82cb91c077c0ccf763a853/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/adv_predictions.json + adv_probabilities_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/3368e5cb7a59dcb39f045527dbb4f558.pkl + params_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/params.yaml + predictions_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/predictions.json + probabilities_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/probabilities.json + score_dict_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/score_dict.json + test_labels_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/test_labels.json + train_labels_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/train_labels.json + model_dir: models + name: 69d2f0003a82cb91c077c0ccf763a853 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 61fb398d0639262b8ede1c200916c013 + trainer: + kwargs: {} +name: 69d2f0003a82cb91c077c0ccf763a853 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/69ecb7186f579836de7726033bf5e830/params.yaml b/examples/security/classification/output/reports/train/69ecb7186f579836de7726033bf5e830/params.yaml new file mode 100644 index 00000000..bd653a1b --- /dev/null +++ b/examples/security/classification/output/reports/train/69ecb7186f579836de7726033bf5e830/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/69ecb7186f579836de7726033bf5e830/adv_predictions.json + adv_probabilities_file: output/reports/train/69ecb7186f579836de7726033bf5e830/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/86c7726adf3cd974fa584a617f92fadd.pkl + params_file: output/reports/train/69ecb7186f579836de7726033bf5e830/params.yaml + predictions_file: output/reports/train/69ecb7186f579836de7726033bf5e830/predictions.json + probabilities_file: output/reports/train/69ecb7186f579836de7726033bf5e830/probabilities.json + score_dict_file: output/reports/train/69ecb7186f579836de7726033bf5e830/score_dict.json + test_labels_file: output/reports/train/69ecb7186f579836de7726033bf5e830/test_labels.json + train_labels_file: output/reports/train/69ecb7186f579836de7726033bf5e830/train_labels.json + model_dir: models + name: 69ecb7186f579836de7726033bf5e830 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e8e6c784305f84cfe743089eb1f520c5 + trainer: + kwargs: {} +name: 69ecb7186f579836de7726033bf5e830 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/params.yaml b/examples/security/classification/output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/params.yaml new file mode 100644 index 00000000..e9cf757a --- /dev/null +++ b/examples/security/classification/output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/adv_predictions.json + adv_probabilities_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/e9fb5a698d257a2b39ffd3df16cfb5ed.pkl + params_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/params.yaml + predictions_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/predictions.json + probabilities_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/probabilities.json + score_dict_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/score_dict.json + test_labels_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/test_labels.json + train_labels_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/train_labels.json + model_dir: models + name: 69f4cc2cb0c972db22c296613cc9d09f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 805f03eab8997e319e1814db73bc3e9d + trainer: + kwargs: {} +name: 69f4cc2cb0c972db22c296613cc9d09f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/params.yaml b/examples/security/classification/output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/params.yaml new file mode 100644 index 00000000..859eb7db --- /dev/null +++ b/examples/security/classification/output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/adv_predictions.json + adv_probabilities_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/d3b5c5d76ecfe07665eda0efcce65555.pkl + params_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/params.yaml + predictions_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/predictions.json + probabilities_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/probabilities.json + score_dict_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/score_dict.json + test_labels_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/test_labels.json + train_labels_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/train_labels.json + model_dir: models + name: 69fbd82eebdc0f3a7de6ce115b36a69e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 48a3f51c2f1350bf1356b90192e29dd4 + trainer: + kwargs: {} +name: 69fbd82eebdc0f3a7de6ce115b36a69e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/params.yaml b/examples/security/classification/output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/params.yaml new file mode 100644 index 00000000..c577b8b1 --- /dev/null +++ b/examples/security/classification/output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/adv_predictions.json + adv_probabilities_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/3920c9b0c22c48fd6a178b5db392f442.pkl + params_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/params.yaml + predictions_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/predictions.json + probabilities_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/probabilities.json + score_dict_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/score_dict.json + test_labels_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/test_labels.json + train_labels_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/train_labels.json + model_dir: models + name: 6a3cb9f6081ec877879124d1ccc86f36 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e5c344b529ee006681b6993988576134 + trainer: + kwargs: {} +name: 6a3cb9f6081ec877879124d1ccc86f36 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6a45d861b6bed7a079ff804f005f09a4/params.yaml b/examples/security/classification/output/reports/train/6a45d861b6bed7a079ff804f005f09a4/params.yaml new file mode 100644 index 00000000..7d6133b5 --- /dev/null +++ b/examples/security/classification/output/reports/train/6a45d861b6bed7a079ff804f005f09a4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/adv_predictions.json + adv_probabilities_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/0fbdcd4f1daf8f6ba2086aad795d0820.pkl + params_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/params.yaml + predictions_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/predictions.json + probabilities_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/probabilities.json + score_dict_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/score_dict.json + test_labels_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/test_labels.json + train_labels_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/train_labels.json + model_dir: models + name: 6a45d861b6bed7a079ff804f005f09a4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6a667ff4960adc5dbe602047fd23cc76 + trainer: + kwargs: {} +name: 6a45d861b6bed7a079ff804f005f09a4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/params.yaml b/examples/security/classification/output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/params.yaml new file mode 100644 index 00000000..329b0352 --- /dev/null +++ b/examples/security/classification/output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/adv_predictions.json + adv_probabilities_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/6d4619c67d92af4accc88a39d23f7030.pkl + params_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/params.yaml + predictions_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/predictions.json + probabilities_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/probabilities.json + score_dict_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/score_dict.json + test_labels_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/test_labels.json + train_labels_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/train_labels.json + model_dir: models + name: 6a5ae1951cdd50482c803e39c2ba3a67 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3030dc0bc6711bc001b20aef5046feab + trainer: + kwargs: {} +name: 6a5ae1951cdd50482c803e39c2ba3a67 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/params.yaml b/examples/security/classification/output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/params.yaml new file mode 100644 index 00000000..39c025d3 --- /dev/null +++ b/examples/security/classification/output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/adv_predictions.json + adv_probabilities_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/ba8786b6048b1838587811d805d70df4.pkl + params_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/params.yaml + predictions_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/predictions.json + probabilities_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/probabilities.json + score_dict_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/score_dict.json + test_labels_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/test_labels.json + train_labels_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/train_labels.json + model_dir: models + name: 6a5b746f4b5b29ed7c875b6c964c0094 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 60a6292b7e75d94d56c976f34625c8dd + trainer: + kwargs: {} +name: 6a5b746f4b5b29ed7c875b6c964c0094 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6a61498ef02bdcf7119d61301979d33b/params.yaml b/examples/security/classification/output/reports/train/6a61498ef02bdcf7119d61301979d33b/params.yaml new file mode 100644 index 00000000..60ec05fb --- /dev/null +++ b/examples/security/classification/output/reports/train/6a61498ef02bdcf7119d61301979d33b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/adv_predictions.json + adv_probabilities_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/61255bc2ebcd29e0919acbe438e1c86a.pkl + params_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/params.yaml + predictions_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/predictions.json + probabilities_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/probabilities.json + score_dict_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/score_dict.json + test_labels_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/test_labels.json + train_labels_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/train_labels.json + model_dir: models + name: 6a61498ef02bdcf7119d61301979d33b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4bfa0d83bedca03492b5f7385fc74aea + trainer: + kwargs: {} +name: 6a61498ef02bdcf7119d61301979d33b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6a7d572d377dcd451790887ebf72753b/params.yaml b/examples/security/classification/output/reports/train/6a7d572d377dcd451790887ebf72753b/params.yaml new file mode 100644 index 00000000..23032aac --- /dev/null +++ b/examples/security/classification/output/reports/train/6a7d572d377dcd451790887ebf72753b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/adv_predictions.json + adv_probabilities_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/eabeef6a1c39f8220b648bb666595a50.pkl + params_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/params.yaml + predictions_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/predictions.json + probabilities_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/probabilities.json + score_dict_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/score_dict.json + test_labels_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/test_labels.json + train_labels_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/train_labels.json + model_dir: models + name: 6a7d572d377dcd451790887ebf72753b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 00e3cd0f7d1277abf1ed25e9d5b9efe6 + trainer: + kwargs: {} +name: 6a7d572d377dcd451790887ebf72753b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/params.yaml b/examples/security/classification/output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/params.yaml new file mode 100644 index 00000000..76b06bcb --- /dev/null +++ b/examples/security/classification/output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/adv_predictions.json + adv_probabilities_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/656435913548910947013f89710cfd55.pkl + params_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/params.yaml + predictions_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/predictions.json + probabilities_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/probabilities.json + score_dict_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/score_dict.json + test_labels_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/test_labels.json + train_labels_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/train_labels.json + model_dir: models + name: 6aa9a4bbac1680f66407bf6bce43342f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cf9a47f88f8b3b4b2d5b07eeb353ef4 + trainer: + kwargs: {} +name: 6aa9a4bbac1680f66407bf6bce43342f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6ab4308823cd60c03937aea7d18310a5/params.yaml b/examples/security/classification/output/reports/train/6ab4308823cd60c03937aea7d18310a5/params.yaml new file mode 100644 index 00000000..f55bee16 --- /dev/null +++ b/examples/security/classification/output/reports/train/6ab4308823cd60c03937aea7d18310a5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/adv_predictions.json + adv_probabilities_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/193383eba4cbfa6a7840a8822e3fff74.pkl + params_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/params.yaml + predictions_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/predictions.json + probabilities_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/probabilities.json + score_dict_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/score_dict.json + test_labels_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/test_labels.json + train_labels_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/train_labels.json + model_dir: models + name: 6ab4308823cd60c03937aea7d18310a5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f14a6f84845a6a5234f1ae65eaefdba7 + trainer: + kwargs: {} +name: 6ab4308823cd60c03937aea7d18310a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6afa543b7fc6d9605ada46847e14736f/params.yaml b/examples/security/classification/output/reports/train/6afa543b7fc6d9605ada46847e14736f/params.yaml new file mode 100644 index 00000000..27315ec2 --- /dev/null +++ b/examples/security/classification/output/reports/train/6afa543b7fc6d9605ada46847e14736f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/adv_predictions.json + adv_probabilities_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/68b7a4e5a006c029185a42d41fa8ad5f.pkl + params_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/params.yaml + predictions_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/predictions.json + probabilities_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/probabilities.json + score_dict_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/score_dict.json + test_labels_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/test_labels.json + train_labels_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/train_labels.json + model_dir: models + name: 6afa543b7fc6d9605ada46847e14736f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0ee15abab5e2befb17a6281ec0f1539c + trainer: + kwargs: {} +name: 6afa543b7fc6d9605ada46847e14736f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/params.yaml b/examples/security/classification/output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/params.yaml new file mode 100644 index 00000000..c755a342 --- /dev/null +++ b/examples/security/classification/output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/adv_predictions.json + adv_probabilities_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/fbd557d366cc76da8557fb200535e225.pkl + params_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/params.yaml + predictions_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/predictions.json + probabilities_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/probabilities.json + score_dict_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/score_dict.json + test_labels_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/test_labels.json + train_labels_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/train_labels.json + model_dir: models + name: 6b951a762fa679ab1d80d0bd9c94e00c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 45258da373506f7c491abc6f487213fd + trainer: + kwargs: {} +name: 6b951a762fa679ab1d80d0bd9c94e00c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/params.yaml b/examples/security/classification/output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/params.yaml new file mode 100644 index 00000000..1a37a7b6 --- /dev/null +++ b/examples/security/classification/output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/adv_predictions.json + adv_probabilities_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/872711afe7688c4b9f0a2513d42a3dd7.pkl + params_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/params.yaml + predictions_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/predictions.json + probabilities_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/probabilities.json + score_dict_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/score_dict.json + test_labels_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/test_labels.json + train_labels_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/train_labels.json + model_dir: models + name: 6ba7fb2da7818bf7569680c3694eb34d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5e7e77679258530ee2d93480400c5582 + trainer: + kwargs: {} +name: 6ba7fb2da7818bf7569680c3694eb34d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6baed18071eec33f49c7025a7b611571/params.yaml b/examples/security/classification/output/reports/train/6baed18071eec33f49c7025a7b611571/params.yaml new file mode 100644 index 00000000..b3b85469 --- /dev/null +++ b/examples/security/classification/output/reports/train/6baed18071eec33f49c7025a7b611571/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6baed18071eec33f49c7025a7b611571/adv_predictions.json + adv_probabilities_file: output/reports/train/6baed18071eec33f49c7025a7b611571/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/992648b3721fe5cfdef2c1113727905f.pkl + params_file: output/reports/train/6baed18071eec33f49c7025a7b611571/params.yaml + predictions_file: output/reports/train/6baed18071eec33f49c7025a7b611571/predictions.json + probabilities_file: output/reports/train/6baed18071eec33f49c7025a7b611571/probabilities.json + score_dict_file: output/reports/train/6baed18071eec33f49c7025a7b611571/score_dict.json + test_labels_file: output/reports/train/6baed18071eec33f49c7025a7b611571/test_labels.json + train_labels_file: output/reports/train/6baed18071eec33f49c7025a7b611571/train_labels.json + model_dir: models + name: 6baed18071eec33f49c7025a7b611571 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7c0f46f5f9363968471810649d52af90 + trainer: + kwargs: {} +name: 6baed18071eec33f49c7025a7b611571 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6bb908b20365d7623e23fe29326834ee/params.yaml b/examples/security/classification/output/reports/train/6bb908b20365d7623e23fe29326834ee/params.yaml new file mode 100644 index 00000000..aa188cc4 --- /dev/null +++ b/examples/security/classification/output/reports/train/6bb908b20365d7623e23fe29326834ee/params.yaml @@ -0,0 +1,197 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 2000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 2000.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 70f4fa555cd8abb19e95d7b947f3bd17 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 2000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 13f19bb1e523e45d4339097324c905ea + trainer: + kwargs: {} + name: 4f4137502a34816ab46e7f436400aec9 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 2000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/adv_predictions.json + adv_probabilities_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/adv_probabilities.json + attack_file: output/attacks/5ea6e3bb4ca138d6670200aa2bd0d46c.pkl + data_file: output/data/d48637d71e55ddbb9a1fd3101e086a47.pkl + model_file: output/models/44c599c2f6f5a1e8bcd9a14a2807724d.pkl + params_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/params.yaml + predictions_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/predictions.json + probabilities_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/probabilities.json + score_dict_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/score_dict.json + test_labels_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/test_labels.json + train_labels_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/train_labels.json + model_dir: models + name: 6bb908b20365d7623e23fe29326834ee + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 2000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 950578ad7710c6828f11b3c267acdfb9 + trainer: + kwargs: {} +name: 6bb908b20365d7623e23fe29326834ee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/params.yaml b/examples/security/classification/output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/params.yaml new file mode 100644 index 00000000..efa404bf --- /dev/null +++ b/examples/security/classification/output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/adv_predictions.json + adv_probabilities_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/ebbeede5946fb51f71e1288e14f67c9c.pkl + params_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/params.yaml + predictions_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/predictions.json + probabilities_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/probabilities.json + score_dict_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/score_dict.json + test_labels_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/test_labels.json + train_labels_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/train_labels.json + model_dir: models + name: 6bbf4da79de16ae44ee6d9a1b84ae410 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8f1410a68269749d60703830774c81aa + trainer: + kwargs: {} +name: 6bbf4da79de16ae44ee6d9a1b84ae410 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/params.yaml b/examples/security/classification/output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/params.yaml new file mode 100644 index 00000000..aeb7c714 --- /dev/null +++ b/examples/security/classification/output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/adv_predictions.json + adv_probabilities_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/b2439d998a2aa745f8ed7d45edc330c0.pkl + params_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/params.yaml + predictions_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/predictions.json + probabilities_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/probabilities.json + score_dict_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/score_dict.json + test_labels_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/test_labels.json + train_labels_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/train_labels.json + model_dir: models + name: 6bf4bcd452a168cbdb4a9c7f838f5367 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1a1d8038093d97f5425f3c2d6a6df1b4 + trainer: + kwargs: {} +name: 6bf4bcd452a168cbdb4a9c7f838f5367 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6c6b954587dc38d6d4364780d613ee3f/params.yaml b/examples/security/classification/output/reports/train/6c6b954587dc38d6d4364780d613ee3f/params.yaml new file mode 100644 index 00000000..7c00f731 --- /dev/null +++ b/examples/security/classification/output/reports/train/6c6b954587dc38d6d4364780d613ee3f/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/adv_predictions.json + adv_probabilities_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/33640f36d962099f93330b2a84ce76d5.pkl + params_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/params.yaml + predictions_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/predictions.json + probabilities_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/probabilities.json + score_dict_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/score_dict.json + test_labels_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/test_labels.json + train_labels_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/train_labels.json + model_dir: models + name: 6c6b954587dc38d6d4364780d613ee3f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d694015e780dca53701b62731a815cc6 + trainer: + kwargs: {} +name: 6c6b954587dc38d6d4364780d613ee3f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/params.yaml b/examples/security/classification/output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/params.yaml new file mode 100644 index 00000000..310423fb --- /dev/null +++ b/examples/security/classification/output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/adv_predictions.json + adv_probabilities_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/739075319b7141b65149a021a5dfdc72.pkl + params_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/params.yaml + predictions_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/predictions.json + probabilities_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/probabilities.json + score_dict_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/score_dict.json + test_labels_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/test_labels.json + train_labels_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/train_labels.json + model_dir: models + name: 6c6e5a2b263601c9b95ded6bf13e6938 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fb14bcf642b06e0d7d217e993a064c94 + trainer: + kwargs: {} +name: 6c6e5a2b263601c9b95ded6bf13e6938 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6c761ceeefd59238b08976262bd1b8a7/params.yaml b/examples/security/classification/output/reports/train/6c761ceeefd59238b08976262bd1b8a7/params.yaml new file mode 100644 index 00000000..da8dd1a8 --- /dev/null +++ b/examples/security/classification/output/reports/train/6c761ceeefd59238b08976262bd1b8a7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/adv_predictions.json + adv_probabilities_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/df04755723f24e459b08aeda602d4c12.pkl + params_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/params.yaml + predictions_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/predictions.json + probabilities_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/probabilities.json + score_dict_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/score_dict.json + test_labels_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/test_labels.json + train_labels_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/train_labels.json + model_dir: models + name: 6c761ceeefd59238b08976262bd1b8a7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bfa74bb272fafd34697364fc9dc91a23 + trainer: + kwargs: {} +name: 6c761ceeefd59238b08976262bd1b8a7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6cbc82336640e789764d20ff13618c21/params.yaml b/examples/security/classification/output/reports/train/6cbc82336640e789764d20ff13618c21/params.yaml new file mode 100644 index 00000000..e26e7746 --- /dev/null +++ b/examples/security/classification/output/reports/train/6cbc82336640e789764d20ff13618c21/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6cbc82336640e789764d20ff13618c21/adv_predictions.json + adv_probabilities_file: output/reports/train/6cbc82336640e789764d20ff13618c21/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/8d4cc85acfee286880f3788d17e9c0b4.pkl + params_file: output/reports/train/6cbc82336640e789764d20ff13618c21/params.yaml + predictions_file: output/reports/train/6cbc82336640e789764d20ff13618c21/predictions.json + probabilities_file: output/reports/train/6cbc82336640e789764d20ff13618c21/probabilities.json + score_dict_file: output/reports/train/6cbc82336640e789764d20ff13618c21/score_dict.json + test_labels_file: output/reports/train/6cbc82336640e789764d20ff13618c21/test_labels.json + train_labels_file: output/reports/train/6cbc82336640e789764d20ff13618c21/train_labels.json + model_dir: models + name: 6cbc82336640e789764d20ff13618c21 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e761d767e5ce81d0d1ce9d7f7985808d + trainer: + kwargs: {} +name: 6cbc82336640e789764d20ff13618c21 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/params.yaml b/examples/security/classification/output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/params.yaml new file mode 100644 index 00000000..88786979 --- /dev/null +++ b/examples/security/classification/output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/adv_predictions.json + adv_probabilities_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/b95efb8d770e4c9ff1b41a2d1a5e29b5.pkl + params_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/params.yaml + predictions_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/predictions.json + probabilities_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/probabilities.json + score_dict_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/score_dict.json + test_labels_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/test_labels.json + train_labels_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/train_labels.json + model_dir: models + name: 6cc9fcef1537ea6b227822bcbbd601e3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 160a4d2d4d1048680eec953027f3a40b + trainer: + kwargs: {} +name: 6cc9fcef1537ea6b227822bcbbd601e3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/params.yaml b/examples/security/classification/output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/params.yaml new file mode 100644 index 00000000..89d0e38a --- /dev/null +++ b/examples/security/classification/output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/adv_predictions.json + adv_probabilities_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/decb2daab882916de6b04d119faf6743.pkl + params_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/params.yaml + predictions_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/predictions.json + probabilities_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/probabilities.json + score_dict_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/score_dict.json + test_labels_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/test_labels.json + train_labels_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/train_labels.json + model_dir: models + name: 6cd9b29cfaf9d37a99e653b9a3d1b6e7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fb661ce8f2ed6d0e87c829746d6572cf + trainer: + kwargs: {} +name: 6cd9b29cfaf9d37a99e653b9a3d1b6e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/params.yaml b/examples/security/classification/output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/params.yaml new file mode 100644 index 00000000..45e94910 --- /dev/null +++ b/examples/security/classification/output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/adv_predictions.json + adv_probabilities_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/90c78d4254d6d25260c21c413d6141c1.pkl + params_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/params.yaml + predictions_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/predictions.json + probabilities_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/probabilities.json + score_dict_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/score_dict.json + test_labels_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/test_labels.json + train_labels_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/train_labels.json + model_dir: models + name: 6cdcc24c1162c714d8a2054aa2da8f7d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 78884464eb571f3dc32b01d6c3de5893 + trainer: + kwargs: {} +name: 6cdcc24c1162c714d8a2054aa2da8f7d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/params.yaml b/examples/security/classification/output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/params.yaml new file mode 100644 index 00000000..3dba9e82 --- /dev/null +++ b/examples/security/classification/output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/adv_predictions.json + adv_probabilities_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/64d4cfa942f989139ac14e4d0c77a499.pkl + params_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/params.yaml + predictions_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/predictions.json + probabilities_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/probabilities.json + score_dict_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/score_dict.json + test_labels_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/test_labels.json + train_labels_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/train_labels.json + model_dir: models + name: 6ce2bea1ed0f929c9e58c05642987c67 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4e0f2922a84a8c46074f8477a66682ea + trainer: + kwargs: {} +name: 6ce2bea1ed0f929c9e58c05642987c67 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/params.yaml b/examples/security/classification/output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/params.yaml new file mode 100644 index 00000000..431b08b5 --- /dev/null +++ b/examples/security/classification/output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/adv_predictions.json + adv_probabilities_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/811bc450dfa513773af496a77d973f7c.pkl + params_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/params.yaml + predictions_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/predictions.json + probabilities_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/probabilities.json + score_dict_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/score_dict.json + test_labels_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/test_labels.json + train_labels_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/train_labels.json + model_dir: models + name: 6d0fb0e50ef0839e80ecb948e29a343d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 81ab089556507e46a248be6096a4ee58 + trainer: + kwargs: {} +name: 6d0fb0e50ef0839e80ecb948e29a343d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/params.yaml b/examples/security/classification/output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/params.yaml new file mode 100644 index 00000000..384660eb --- /dev/null +++ b/examples/security/classification/output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/adv_predictions.json + adv_probabilities_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/c6fb8f2c5b46f1ca2d4d1a8248261c88.pkl + params_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/params.yaml + predictions_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/predictions.json + probabilities_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/probabilities.json + score_dict_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/score_dict.json + test_labels_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/test_labels.json + train_labels_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/train_labels.json + model_dir: models + name: 6d2783ea121f59db31181f98c1e9f4a2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 222bda93b2cb8684c1f54fcfeb3f2a2c + trainer: + kwargs: {} +name: 6d2783ea121f59db31181f98c1e9f4a2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/params.yaml b/examples/security/classification/output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/params.yaml new file mode 100644 index 00000000..6c9b644a --- /dev/null +++ b/examples/security/classification/output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/adv_predictions.json + adv_probabilities_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/850d946ccf84d45e598961c552e578c4.pkl + params_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/params.yaml + predictions_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/predictions.json + probabilities_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/probabilities.json + score_dict_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/score_dict.json + test_labels_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/test_labels.json + train_labels_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/train_labels.json + model_dir: models + name: 6d56e234ff345aea0f2eb42f7078f42f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c75a02295d2ed61e754e6936ca7e79f9 + trainer: + kwargs: {} +name: 6d56e234ff345aea0f2eb42f7078f42f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/params.yaml b/examples/security/classification/output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/params.yaml new file mode 100644 index 00000000..1807302e --- /dev/null +++ b/examples/security/classification/output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/adv_predictions.json + adv_probabilities_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/58d20bde07f65b16b8d9ef0e0211f31b.pkl + params_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/params.yaml + predictions_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/predictions.json + probabilities_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/probabilities.json + score_dict_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/score_dict.json + test_labels_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/test_labels.json + train_labels_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/train_labels.json + model_dir: models + name: 6d774dcc92b649fe1ed5b933c6631d7f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 268a605ec3dea619bc4c9e13f25d3b05 + trainer: + kwargs: {} +name: 6d774dcc92b649fe1ed5b933c6631d7f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6d7843bb99a9a02affb9b28740f55638/params.yaml b/examples/security/classification/output/reports/train/6d7843bb99a9a02affb9b28740f55638/params.yaml new file mode 100644 index 00000000..4b10d86b --- /dev/null +++ b/examples/security/classification/output/reports/train/6d7843bb99a9a02affb9b28740f55638/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/adv_predictions.json + adv_probabilities_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/7bb6071198cde2d9e33da36239921f43.pkl + params_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/params.yaml + predictions_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/predictions.json + probabilities_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/probabilities.json + score_dict_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/score_dict.json + test_labels_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/test_labels.json + train_labels_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/train_labels.json + model_dir: models + name: 6d7843bb99a9a02affb9b28740f55638 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ca61ca4fad257e2f9d2b775b79756ab9 + trainer: + kwargs: {} +name: 6d7843bb99a9a02affb9b28740f55638 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/params.yaml b/examples/security/classification/output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/params.yaml new file mode 100644 index 00000000..2d44683f --- /dev/null +++ b/examples/security/classification/output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/adv_predictions.json + adv_probabilities_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/82d6bfda5bc9c3a6318d24a3d026b0aa.pkl + params_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/params.yaml + predictions_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/predictions.json + probabilities_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/probabilities.json + score_dict_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/score_dict.json + test_labels_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/test_labels.json + train_labels_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/train_labels.json + model_dir: models + name: 6d9218c0f1dd611cb029c5409fd1d7bc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f89dc2bf7c79b607780ea0ee08534bb9 + trainer: + kwargs: {} +name: 6d9218c0f1dd611cb029c5409fd1d7bc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/params.yaml b/examples/security/classification/output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/params.yaml new file mode 100644 index 00000000..d4d5d8e4 --- /dev/null +++ b/examples/security/classification/output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/adv_predictions.json + adv_probabilities_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/17851692d3bc90e88b2e089972a532ad.pkl + params_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/params.yaml + predictions_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/predictions.json + probabilities_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/probabilities.json + score_dict_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/score_dict.json + test_labels_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/test_labels.json + train_labels_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/train_labels.json + model_dir: models + name: 6d94a0bb4c29d8107af6f5083bdcf49f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b51ac2939ae2101fb054c54e4ca872b9 + trainer: + kwargs: {} +name: 6d94a0bb4c29d8107af6f5083bdcf49f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/params.yaml b/examples/security/classification/output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/params.yaml new file mode 100644 index 00000000..76f2dd1d --- /dev/null +++ b/examples/security/classification/output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/adv_predictions.json + adv_probabilities_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/bf08179ef455f52149df85d2da803077.pkl + params_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/params.yaml + predictions_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/predictions.json + probabilities_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/probabilities.json + score_dict_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/score_dict.json + test_labels_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/test_labels.json + train_labels_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/train_labels.json + model_dir: models + name: 6dae64ca9889f2a09a12d1b12d04809c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ca0696260a2b69a1ead20c832a1dd399 + trainer: + kwargs: {} +name: 6dae64ca9889f2a09a12d1b12d04809c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/params.yaml b/examples/security/classification/output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/params.yaml new file mode 100644 index 00000000..cf5e3d75 --- /dev/null +++ b/examples/security/classification/output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/adv_predictions.json + adv_probabilities_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/6ebddc11bd903ffd6654a41152117a4c.pkl + params_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/params.yaml + predictions_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/predictions.json + probabilities_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/probabilities.json + score_dict_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/score_dict.json + test_labels_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/test_labels.json + train_labels_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/train_labels.json + model_dir: models + name: 6db206f304e1e16cc3bc7c319c8b73e5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 99a055cc0054a3ad7c8706d4bda61832 + trainer: + kwargs: {} +name: 6db206f304e1e16cc3bc7c319c8b73e5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/params.yaml b/examples/security/classification/output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/params.yaml new file mode 100644 index 00000000..a9d68d7a --- /dev/null +++ b/examples/security/classification/output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/adv_predictions.json + adv_probabilities_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/e0b436adf3746a9262921fbb5d6654e1.pkl + params_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/params.yaml + predictions_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/predictions.json + probabilities_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/probabilities.json + score_dict_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/score_dict.json + test_labels_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/test_labels.json + train_labels_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/train_labels.json + model_dir: models + name: 6dbb4f43377fcc9f79b04caf7d81f01d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8c10ceb650ffc6945b4ccf1d3d917f55 + trainer: + kwargs: {} +name: 6dbb4f43377fcc9f79b04caf7d81f01d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/params.yaml b/examples/security/classification/output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/params.yaml new file mode 100644 index 00000000..703c88be --- /dev/null +++ b/examples/security/classification/output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/adv_predictions.json + adv_probabilities_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/52cddb53287f0e2689f81e79c7d604de.pkl + params_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/params.yaml + predictions_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/predictions.json + probabilities_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/probabilities.json + score_dict_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/score_dict.json + test_labels_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/test_labels.json + train_labels_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/train_labels.json + model_dir: models + name: 6dbeeca3e9a8db1c520b38395abc6905 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0f46e4b58cbc110c5773cde2b18fd000 + trainer: + kwargs: {} +name: 6dbeeca3e9a8db1c520b38395abc6905 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6dc109a8b3f22b68ccd83108307cb378/params.yaml b/examples/security/classification/output/reports/train/6dc109a8b3f22b68ccd83108307cb378/params.yaml new file mode 100644 index 00000000..7d3b0f19 --- /dev/null +++ b/examples/security/classification/output/reports/train/6dc109a8b3f22b68ccd83108307cb378/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/adv_predictions.json + adv_probabilities_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/d25531adc2eeb38a08178263eb65c90a.pkl + params_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/params.yaml + predictions_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/predictions.json + probabilities_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/probabilities.json + score_dict_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/score_dict.json + test_labels_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/test_labels.json + train_labels_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/train_labels.json + model_dir: models + name: 6dc109a8b3f22b68ccd83108307cb378 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: de84deaf9ff302f1dca4d4135030e560 + trainer: + kwargs: {} +name: 6dc109a8b3f22b68ccd83108307cb378 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6dc6241f817594e36f4f08b5a133afa3/params.yaml b/examples/security/classification/output/reports/train/6dc6241f817594e36f4f08b5a133afa3/params.yaml new file mode 100644 index 00000000..f7c48645 --- /dev/null +++ b/examples/security/classification/output/reports/train/6dc6241f817594e36f4f08b5a133afa3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/adv_predictions.json + adv_probabilities_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/d14ffc4ba3506be47ae6fc7c06ad5f83.pkl + params_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/params.yaml + predictions_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/predictions.json + probabilities_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/probabilities.json + score_dict_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/score_dict.json + test_labels_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/test_labels.json + train_labels_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/train_labels.json + model_dir: models + name: 6dc6241f817594e36f4f08b5a133afa3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4b401d8179fd1af53eaae6a5ecd0c174 + trainer: + kwargs: {} +name: 6dc6241f817594e36f4f08b5a133afa3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/params.yaml b/examples/security/classification/output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/params.yaml new file mode 100644 index 00000000..b8ae3c40 --- /dev/null +++ b/examples/security/classification/output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/adv_predictions.json + adv_probabilities_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/2e10d51bc92e77a2a9ab41c2aeafcf11.pkl + params_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/params.yaml + predictions_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/predictions.json + probabilities_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/probabilities.json + score_dict_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/score_dict.json + test_labels_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/test_labels.json + train_labels_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/train_labels.json + model_dir: models + name: 6df9c611c1cd11a76be7fa0c6b2c30d3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d7ada450afc2ca5dc9cbe8f2c6494097 + trainer: + kwargs: {} +name: 6df9c611c1cd11a76be7fa0c6b2c30d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/params.yaml b/examples/security/classification/output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/params.yaml new file mode 100644 index 00000000..a8dee60d --- /dev/null +++ b/examples/security/classification/output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/adv_predictions.json + adv_probabilities_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/36b86a8cb7b5dd993c8a19688eda52eb.pkl + params_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/params.yaml + predictions_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/predictions.json + probabilities_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/probabilities.json + score_dict_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/score_dict.json + test_labels_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/test_labels.json + train_labels_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/train_labels.json + model_dir: models + name: 6e4f4d4ce68e9d5bf58f94708c63c249 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ed9ab28c100046d8892bfb563798658 + trainer: + kwargs: {} +name: 6e4f4d4ce68e9d5bf58f94708c63c249 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6e73ce03f629192d785a94172a8016c0/params.yaml b/examples/security/classification/output/reports/train/6e73ce03f629192d785a94172a8016c0/params.yaml new file mode 100644 index 00000000..6a4569b0 --- /dev/null +++ b/examples/security/classification/output/reports/train/6e73ce03f629192d785a94172a8016c0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/adv_predictions.json + adv_probabilities_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/35092eb1cac5735460db8c436e915308.pkl + params_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/params.yaml + predictions_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/predictions.json + probabilities_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/probabilities.json + score_dict_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/score_dict.json + test_labels_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/test_labels.json + train_labels_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/train_labels.json + model_dir: models + name: 6e73ce03f629192d785a94172a8016c0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f830281b9f67e554b1b186f2c718359c + trainer: + kwargs: {} +name: 6e73ce03f629192d785a94172a8016c0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/params.yaml b/examples/security/classification/output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/params.yaml new file mode 100644 index 00000000..1d43ebff --- /dev/null +++ b/examples/security/classification/output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/adv_predictions.json + adv_probabilities_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/b0388ddd87fc8ff9f745c92db639e040.pkl + params_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/params.yaml + predictions_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/predictions.json + probabilities_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/probabilities.json + score_dict_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/score_dict.json + test_labels_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/test_labels.json + train_labels_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/train_labels.json + model_dir: models + name: 6e943ee8f5701a90a8850e3ef7f1ac1e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ee71638bca125acd6592618a6aaa1fdf + trainer: + kwargs: {} +name: 6e943ee8f5701a90a8850e3ef7f1ac1e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/params.yaml b/examples/security/classification/output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/params.yaml new file mode 100644 index 00000000..ca7abc98 --- /dev/null +++ b/examples/security/classification/output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/adv_predictions.json + adv_probabilities_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/2fac07deb9c0f8a3cb9149a532cc56f0.pkl + params_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/params.yaml + predictions_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/predictions.json + probabilities_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/probabilities.json + score_dict_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/score_dict.json + test_labels_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/test_labels.json + train_labels_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/train_labels.json + model_dir: models + name: 6eb5bd43b8af53f849bc1b06573ad34b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbac90fd6c7f091023cf203809e27c90 + trainer: + kwargs: {} +name: 6eb5bd43b8af53f849bc1b06573ad34b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/params.yaml b/examples/security/classification/output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/params.yaml new file mode 100644 index 00000000..7341df55 --- /dev/null +++ b/examples/security/classification/output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/adv_predictions.json + adv_probabilities_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/0f43986be594ec409677271894882846.pkl + params_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/params.yaml + predictions_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/predictions.json + probabilities_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/probabilities.json + score_dict_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/score_dict.json + test_labels_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/test_labels.json + train_labels_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/train_labels.json + model_dir: models + name: 6f27a59dde5f5e2b15dc80f1daeb8705 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ceabdcbe106d946abc7dd2fb6de6a116 + trainer: + kwargs: {} +name: 6f27a59dde5f5e2b15dc80f1daeb8705 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/params.yaml b/examples/security/classification/output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/params.yaml new file mode 100644 index 00000000..0cf540de --- /dev/null +++ b/examples/security/classification/output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/adv_predictions.json + adv_probabilities_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/aa9ef4b1a1b4d00b2f84a2e71d6f2d1f.pkl + params_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/params.yaml + predictions_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/predictions.json + probabilities_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/probabilities.json + score_dict_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/score_dict.json + test_labels_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/test_labels.json + train_labels_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/train_labels.json + model_dir: models + name: 6f3720cc11c82b58af37ddc54c2499ff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c1f579d913b1cbaf2cbe74db664f0a1 + trainer: + kwargs: {} +name: 6f3720cc11c82b58af37ddc54c2499ff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/params.yaml b/examples/security/classification/output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/params.yaml new file mode 100644 index 00000000..c5f50447 --- /dev/null +++ b/examples/security/classification/output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/adv_predictions.json + adv_probabilities_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/2b1e51b1a88f2b4b90cc759bb4ac64b0.pkl + params_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/params.yaml + predictions_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/predictions.json + probabilities_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/probabilities.json + score_dict_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/score_dict.json + test_labels_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/test_labels.json + train_labels_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/train_labels.json + model_dir: models + name: 6f3ccdfde1d26dae907d99962a93c5ab + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a7dce3d36a3b43f035d153d5ec91c628 + trainer: + kwargs: {} +name: 6f3ccdfde1d26dae907d99962a93c5ab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/params.yaml b/examples/security/classification/output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/params.yaml new file mode 100644 index 00000000..dc2b4c9e --- /dev/null +++ b/examples/security/classification/output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/adv_predictions.json + adv_probabilities_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/bb7640ac7a587eb2960a1b6282d8406c.pkl + params_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/params.yaml + predictions_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/predictions.json + probabilities_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/probabilities.json + score_dict_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/score_dict.json + test_labels_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/test_labels.json + train_labels_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/train_labels.json + model_dir: models + name: 6f7299a2f7a89059d14ce1f1ce2fb840 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9988f0ef3b0be88f09fba53eb7ba92db + trainer: + kwargs: {} +name: 6f7299a2f7a89059d14ce1f1ce2fb840 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/params.yaml b/examples/security/classification/output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/params.yaml new file mode 100644 index 00000000..a2e51629 --- /dev/null +++ b/examples/security/classification/output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/adv_predictions.json + adv_probabilities_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/3fa846dcf62bb72cf86ba9e71fcf38d2.pkl + params_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/params.yaml + predictions_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/predictions.json + probabilities_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/probabilities.json + score_dict_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/score_dict.json + test_labels_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/test_labels.json + train_labels_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/train_labels.json + model_dir: models + name: 6fa4a47e0fefb4c7211e344ea044b642 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7dc39211017fb61a18a8b93ee4cb6798 + trainer: + kwargs: {} +name: 6fa4a47e0fefb4c7211e344ea044b642 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/params.yaml b/examples/security/classification/output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/params.yaml new file mode 100644 index 00000000..e47c92fd --- /dev/null +++ b/examples/security/classification/output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/adv_predictions.json + adv_probabilities_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/77745dae9fde26eda07bd9dbd54fb748.pkl + params_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/params.yaml + predictions_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/predictions.json + probabilities_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/probabilities.json + score_dict_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/score_dict.json + test_labels_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/test_labels.json + train_labels_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/train_labels.json + model_dir: models + name: 6fc8506ca4dde6a9c791324c311d92d2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1e32ddba5627d14f06618f8195d6c3f1 + trainer: + kwargs: {} +name: 6fc8506ca4dde6a9c791324c311d92d2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6fd62a74ae36b92122894def5cae7c76/params.yaml b/examples/security/classification/output/reports/train/6fd62a74ae36b92122894def5cae7c76/params.yaml new file mode 100644 index 00000000..64b5ea94 --- /dev/null +++ b/examples/security/classification/output/reports/train/6fd62a74ae36b92122894def5cae7c76/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/adv_predictions.json + adv_probabilities_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/528b960a0f2769b83407ddc17a4efcbd.pkl + params_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/params.yaml + predictions_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/predictions.json + probabilities_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/probabilities.json + score_dict_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/score_dict.json + test_labels_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/test_labels.json + train_labels_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/train_labels.json + model_dir: models + name: 6fd62a74ae36b92122894def5cae7c76 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e063dc83fbf6c23ba102d2634c890b49 + trainer: + kwargs: {} +name: 6fd62a74ae36b92122894def5cae7c76 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6fd64214566b1ce33f475bf133cb18e4/params.yaml b/examples/security/classification/output/reports/train/6fd64214566b1ce33f475bf133cb18e4/params.yaml new file mode 100644 index 00000000..9daac7c4 --- /dev/null +++ b/examples/security/classification/output/reports/train/6fd64214566b1ce33f475bf133cb18e4/params.yaml @@ -0,0 +1,104 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/adv_predictions.json + adv_probabilities_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/656765a1a1a57a1a6fcaed8a6dfce17e.pkl + params_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/params.yaml + predictions_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/predictions.json + probabilities_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/probabilities.json + score_dict_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/score_dict.json + test_labels_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/test_labels.json + train_labels_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/train_labels.json + model_dir: models + name: 6fd64214566b1ce33f475bf133cb18e4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: + - linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 93acd32c2f54af6150b1e310609dcdd0 + trainer: + kwargs: {} +name: 6fd64214566b1ce33f475bf133cb18e4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6fe46144087100252df62153fc322ddb/params.yaml b/examples/security/classification/output/reports/train/6fe46144087100252df62153fc322ddb/params.yaml new file mode 100644 index 00000000..d01aab2d --- /dev/null +++ b/examples/security/classification/output/reports/train/6fe46144087100252df62153fc322ddb/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6fe46144087100252df62153fc322ddb/adv_predictions.json + adv_probabilities_file: output/reports/train/6fe46144087100252df62153fc322ddb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/a0462197adeaebda45016935b45e7f36.pkl + params_file: output/reports/train/6fe46144087100252df62153fc322ddb/params.yaml + predictions_file: output/reports/train/6fe46144087100252df62153fc322ddb/predictions.json + probabilities_file: output/reports/train/6fe46144087100252df62153fc322ddb/probabilities.json + score_dict_file: output/reports/train/6fe46144087100252df62153fc322ddb/score_dict.json + test_labels_file: output/reports/train/6fe46144087100252df62153fc322ddb/test_labels.json + train_labels_file: output/reports/train/6fe46144087100252df62153fc322ddb/train_labels.json + model_dir: models + name: 6fe46144087100252df62153fc322ddb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: babd9913ed9dba19687d2ddfd57021fb + trainer: + kwargs: {} +name: 6fe46144087100252df62153fc322ddb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/params.yaml b/examples/security/classification/output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/params.yaml new file mode 100644 index 00000000..05587faf --- /dev/null +++ b/examples/security/classification/output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/adv_predictions.json + adv_probabilities_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/30888f6a728bc1287c783dc8ed9eb0fa.pkl + params_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/params.yaml + predictions_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/predictions.json + probabilities_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/probabilities.json + score_dict_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/score_dict.json + test_labels_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/test_labels.json + train_labels_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/train_labels.json + model_dir: models + name: 6ffec5c2a73866b61a0747b6114d05f2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a1ccea44241d84b8067c65dbe4eae1e8 + trainer: + kwargs: {} +name: 6ffec5c2a73866b61a0747b6114d05f2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/params.yaml b/examples/security/classification/output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/params.yaml new file mode 100644 index 00000000..ca0b1f00 --- /dev/null +++ b/examples/security/classification/output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/adv_predictions.json + adv_probabilities_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/d371c8c623f43f49b44529f54d7cebee.pkl + params_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/params.yaml + predictions_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/predictions.json + probabilities_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/probabilities.json + score_dict_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/score_dict.json + test_labels_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/test_labels.json + train_labels_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/train_labels.json + model_dir: models + name: 70035a1e0b7f1d4b6ef0d20278e8a45f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ccbabe1b7bb01f77df8011622d385aca + trainer: + kwargs: {} +name: 70035a1e0b7f1d4b6ef0d20278e8a45f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7007d6fa536ce83029b9852c81fa24cd/params.yaml b/examples/security/classification/output/reports/train/7007d6fa536ce83029b9852c81fa24cd/params.yaml new file mode 100644 index 00000000..f61a408f --- /dev/null +++ b/examples/security/classification/output/reports/train/7007d6fa536ce83029b9852c81fa24cd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/adv_predictions.json + adv_probabilities_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/21cef85e5ea09b1b86aef977a7326be2.pkl + params_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/params.yaml + predictions_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/predictions.json + probabilities_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/probabilities.json + score_dict_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/score_dict.json + test_labels_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/test_labels.json + train_labels_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/train_labels.json + model_dir: models + name: 7007d6fa536ce83029b9852c81fa24cd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c6a5e5bdaaf847ce5a3f22146a58c02e + trainer: + kwargs: {} +name: 7007d6fa536ce83029b9852c81fa24cd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/params.yaml b/examples/security/classification/output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/params.yaml new file mode 100644 index 00000000..dec90cac --- /dev/null +++ b/examples/security/classification/output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/adv_predictions.json + adv_probabilities_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/738b115176a7f941683ea375a1b569b7.pkl + params_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/params.yaml + predictions_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/predictions.json + probabilities_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/probabilities.json + score_dict_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/score_dict.json + test_labels_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/test_labels.json + train_labels_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/train_labels.json + model_dir: models + name: 70257a6ae3d26fe45efaa549d9c03f91 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d2b4770247be95eff64fda3c90f91d60 + trainer: + kwargs: {} +name: 70257a6ae3d26fe45efaa549d9c03f91 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/703023ea0762d60943628bc17e587d25/params.yaml b/examples/security/classification/output/reports/train/703023ea0762d60943628bc17e587d25/params.yaml new file mode 100644 index 00000000..0c83d8ca --- /dev/null +++ b/examples/security/classification/output/reports/train/703023ea0762d60943628bc17e587d25/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/703023ea0762d60943628bc17e587d25/adv_predictions.json + adv_probabilities_file: output/reports/train/703023ea0762d60943628bc17e587d25/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/bf743c8d2c3b937442d28317a987f2e5.pkl + params_file: output/reports/train/703023ea0762d60943628bc17e587d25/params.yaml + predictions_file: output/reports/train/703023ea0762d60943628bc17e587d25/predictions.json + probabilities_file: output/reports/train/703023ea0762d60943628bc17e587d25/probabilities.json + score_dict_file: output/reports/train/703023ea0762d60943628bc17e587d25/score_dict.json + test_labels_file: output/reports/train/703023ea0762d60943628bc17e587d25/test_labels.json + train_labels_file: output/reports/train/703023ea0762d60943628bc17e587d25/train_labels.json + model_dir: models + name: 703023ea0762d60943628bc17e587d25 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 52041ea50facdc16f4223d8f6dedb73a + trainer: + kwargs: {} +name: 703023ea0762d60943628bc17e587d25 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/params.yaml b/examples/security/classification/output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/params.yaml new file mode 100644 index 00000000..ac91d2d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/adv_predictions.json + adv_probabilities_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/3bee6a7b567140dcd459ae906ef0e438.pkl + params_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/params.yaml + predictions_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/predictions.json + probabilities_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/probabilities.json + score_dict_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/score_dict.json + test_labels_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/test_labels.json + train_labels_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/train_labels.json + model_dir: models + name: 703de213b0337f3796c0d2ecebc3f2c4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7fe640d4aa8f58a5d43770c58552f042 + trainer: + kwargs: {} +name: 703de213b0337f3796c0d2ecebc3f2c4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/70642247962dddcfcb2cb118ce68d621/params.yaml b/examples/security/classification/output/reports/train/70642247962dddcfcb2cb118ce68d621/params.yaml new file mode 100644 index 00000000..06689dda --- /dev/null +++ b/examples/security/classification/output/reports/train/70642247962dddcfcb2cb118ce68d621/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/adv_predictions.json + adv_probabilities_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/19ba4432773060ae6220d13bb52959e7.pkl + params_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/params.yaml + predictions_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/predictions.json + probabilities_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/probabilities.json + score_dict_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/score_dict.json + test_labels_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/test_labels.json + train_labels_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/train_labels.json + model_dir: models + name: 70642247962dddcfcb2cb118ce68d621 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3ef400280926feb7696b036843face12 + trainer: + kwargs: {} +name: 70642247962dddcfcb2cb118ce68d621 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/params.yaml b/examples/security/classification/output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/params.yaml new file mode 100644 index 00000000..a6ec205b --- /dev/null +++ b/examples/security/classification/output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/adv_predictions.json + adv_probabilities_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/2db1e3d9945937a5215c63c95bdb6ddf.pkl + params_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/params.yaml + predictions_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/predictions.json + probabilities_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/probabilities.json + score_dict_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/score_dict.json + test_labels_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/test_labels.json + train_labels_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/train_labels.json + model_dir: models + name: 70aa79b126ff0acbd4d96eaa777767e3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7434efcba24c9d9a3e2c1c1ca753c758 + trainer: + kwargs: {} +name: 70aa79b126ff0acbd4d96eaa777767e3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/params.yaml b/examples/security/classification/output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/params.yaml new file mode 100644 index 00000000..d3c99de4 --- /dev/null +++ b/examples/security/classification/output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/adv_predictions.json + adv_probabilities_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/93fb9cfed30de5c061c399973f5c3cfe.pkl + params_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/params.yaml + predictions_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/predictions.json + probabilities_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/probabilities.json + score_dict_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/score_dict.json + test_labels_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/test_labels.json + train_labels_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/train_labels.json + model_dir: models + name: 70b30fff7d37bc3528efaa2d97a5c2ea + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 75c77cc069f2e60cc52c723b98928dc3 + trainer: + kwargs: {} +name: 70b30fff7d37bc3528efaa2d97a5c2ea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/70bb9717adb6c1333a84cb02dff6f219/params.yaml b/examples/security/classification/output/reports/train/70bb9717adb6c1333a84cb02dff6f219/params.yaml new file mode 100644 index 00000000..15ef4e32 --- /dev/null +++ b/examples/security/classification/output/reports/train/70bb9717adb6c1333a84cb02dff6f219/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/adv_predictions.json + adv_probabilities_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/5f2adaa5b38b80b82ad03ee18ddd6bc9.pkl + params_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/params.yaml + predictions_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/predictions.json + probabilities_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/probabilities.json + score_dict_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/score_dict.json + test_labels_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/test_labels.json + train_labels_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/train_labels.json + model_dir: models + name: 70bb9717adb6c1333a84cb02dff6f219 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 25e9e6ab2e9d76795746cf09629b5e56 + trainer: + kwargs: {} +name: 70bb9717adb6c1333a84cb02dff6f219 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/70bfde1b161030506ea7452db6110d0d/params.yaml b/examples/security/classification/output/reports/train/70bfde1b161030506ea7452db6110d0d/params.yaml new file mode 100644 index 00000000..b7a4261f --- /dev/null +++ b/examples/security/classification/output/reports/train/70bfde1b161030506ea7452db6110d0d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/adv_predictions.json + adv_probabilities_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/4dbe5962c554342b5efb25c8d6852ccb.pkl + params_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/params.yaml + predictions_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/predictions.json + probabilities_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/probabilities.json + score_dict_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/score_dict.json + test_labels_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/test_labels.json + train_labels_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/train_labels.json + model_dir: models + name: 70bfde1b161030506ea7452db6110d0d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 484a7ba82bd6b5f3d5cfadb26301da9e + trainer: + kwargs: {} +name: 70bfde1b161030506ea7452db6110d0d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/params.yaml b/examples/security/classification/output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/params.yaml new file mode 100644 index 00000000..86c77f6c --- /dev/null +++ b/examples/security/classification/output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/adv_predictions.json + adv_probabilities_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/a746d0ad04240597ff690440ed3fa886.pkl + params_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/params.yaml + predictions_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/predictions.json + probabilities_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/probabilities.json + score_dict_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/score_dict.json + test_labels_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/test_labels.json + train_labels_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/train_labels.json + model_dir: models + name: 70c8e01b4b3f3f320242220e2ba85cce + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cf7c092cfc69acb3a153be504d48ae23 + trainer: + kwargs: {} +name: 70c8e01b4b3f3f320242220e2ba85cce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/params.yaml b/examples/security/classification/output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/params.yaml new file mode 100644 index 00000000..0698b96a --- /dev/null +++ b/examples/security/classification/output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/adv_predictions.json + adv_probabilities_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/7e5d641531a8cfd0149969f1ceb7305d.pkl + params_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/params.yaml + predictions_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/predictions.json + probabilities_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/probabilities.json + score_dict_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/score_dict.json + test_labels_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/test_labels.json + train_labels_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/train_labels.json + model_dir: models + name: 70f70056796bf9e26ffdde0a8f04dc86 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 92a8ca46be8527d3814d6ab14579bb36 + trainer: + kwargs: {} +name: 70f70056796bf9e26ffdde0a8f04dc86 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/710b44e5f413e77a1809c907c3bea871/params.yaml b/examples/security/classification/output/reports/train/710b44e5f413e77a1809c907c3bea871/params.yaml new file mode 100644 index 00000000..528ae032 --- /dev/null +++ b/examples/security/classification/output/reports/train/710b44e5f413e77a1809c907c3bea871/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/adv_predictions.json + adv_probabilities_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/19df21f9e539800098b57a0c11c79d0e.pkl + params_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/params.yaml + predictions_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/predictions.json + probabilities_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/probabilities.json + score_dict_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/score_dict.json + test_labels_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/test_labels.json + train_labels_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/train_labels.json + model_dir: models + name: 710b44e5f413e77a1809c907c3bea871 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea9ed192784d83669ec6f3a84edf6148 + trainer: + kwargs: {} +name: 710b44e5f413e77a1809c907c3bea871 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/711c7611c6bd4974065d224a60effb00/params.yaml b/examples/security/classification/output/reports/train/711c7611c6bd4974065d224a60effb00/params.yaml new file mode 100644 index 00000000..6e0ca23b --- /dev/null +++ b/examples/security/classification/output/reports/train/711c7611c6bd4974065d224a60effb00/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/711c7611c6bd4974065d224a60effb00/adv_predictions.json + adv_probabilities_file: output/reports/train/711c7611c6bd4974065d224a60effb00/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/307ad291801e36581c05128ba5f52fe1.pkl + params_file: output/reports/train/711c7611c6bd4974065d224a60effb00/params.yaml + predictions_file: output/reports/train/711c7611c6bd4974065d224a60effb00/predictions.json + probabilities_file: output/reports/train/711c7611c6bd4974065d224a60effb00/probabilities.json + score_dict_file: output/reports/train/711c7611c6bd4974065d224a60effb00/score_dict.json + test_labels_file: output/reports/train/711c7611c6bd4974065d224a60effb00/test_labels.json + train_labels_file: output/reports/train/711c7611c6bd4974065d224a60effb00/train_labels.json + model_dir: models + name: 711c7611c6bd4974065d224a60effb00 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 53cacc2e4ed7f1ea816fb0b907b54136 + trainer: + kwargs: {} +name: 711c7611c6bd4974065d224a60effb00 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/711d9be1aa806223099d34a250671f0f/params.yaml b/examples/security/classification/output/reports/train/711d9be1aa806223099d34a250671f0f/params.yaml new file mode 100644 index 00000000..51baf92d --- /dev/null +++ b/examples/security/classification/output/reports/train/711d9be1aa806223099d34a250671f0f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/711d9be1aa806223099d34a250671f0f/adv_predictions.json + adv_probabilities_file: output/reports/train/711d9be1aa806223099d34a250671f0f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/b6204df3c37834726603ce02ec72fc28.pkl + params_file: output/reports/train/711d9be1aa806223099d34a250671f0f/params.yaml + predictions_file: output/reports/train/711d9be1aa806223099d34a250671f0f/predictions.json + probabilities_file: output/reports/train/711d9be1aa806223099d34a250671f0f/probabilities.json + score_dict_file: output/reports/train/711d9be1aa806223099d34a250671f0f/score_dict.json + test_labels_file: output/reports/train/711d9be1aa806223099d34a250671f0f/test_labels.json + train_labels_file: output/reports/train/711d9be1aa806223099d34a250671f0f/train_labels.json + model_dir: models + name: 711d9be1aa806223099d34a250671f0f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4dd08f123a388645fa6db4995397cd8d + trainer: + kwargs: {} +name: 711d9be1aa806223099d34a250671f0f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/71374f5a0451241cd0932959be89d48e/params.yaml b/examples/security/classification/output/reports/train/71374f5a0451241cd0932959be89d48e/params.yaml new file mode 100644 index 00000000..4c6bb7a9 --- /dev/null +++ b/examples/security/classification/output/reports/train/71374f5a0451241cd0932959be89d48e/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/71374f5a0451241cd0932959be89d48e/adv_predictions.json + adv_probabilities_file: output/reports/train/71374f5a0451241cd0932959be89d48e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/41cb502237b975f3a4ad30528ec054a2.pkl + params_file: output/reports/train/71374f5a0451241cd0932959be89d48e/params.yaml + predictions_file: output/reports/train/71374f5a0451241cd0932959be89d48e/predictions.json + probabilities_file: output/reports/train/71374f5a0451241cd0932959be89d48e/probabilities.json + score_dict_file: output/reports/train/71374f5a0451241cd0932959be89d48e/score_dict.json + test_labels_file: output/reports/train/71374f5a0451241cd0932959be89d48e/test_labels.json + train_labels_file: output/reports/train/71374f5a0451241cd0932959be89d48e/train_labels.json + model_dir: models + name: 71374f5a0451241cd0932959be89d48e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 68feb414235f6cfd4ab7de844241ca5c + trainer: + kwargs: {} +name: 71374f5a0451241cd0932959be89d48e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/716d1517cd69c849833847e81570d2f6/params.yaml b/examples/security/classification/output/reports/train/716d1517cd69c849833847e81570d2f6/params.yaml new file mode 100644 index 00000000..d248ebc6 --- /dev/null +++ b/examples/security/classification/output/reports/train/716d1517cd69c849833847e81570d2f6/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/716d1517cd69c849833847e81570d2f6/adv_predictions.json + adv_probabilities_file: output/reports/train/716d1517cd69c849833847e81570d2f6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/03d1766ae671ff3dd5f6024da6ea9bf7.pkl + params_file: output/reports/train/716d1517cd69c849833847e81570d2f6/params.yaml + predictions_file: output/reports/train/716d1517cd69c849833847e81570d2f6/predictions.json + probabilities_file: output/reports/train/716d1517cd69c849833847e81570d2f6/probabilities.json + score_dict_file: output/reports/train/716d1517cd69c849833847e81570d2f6/score_dict.json + test_labels_file: output/reports/train/716d1517cd69c849833847e81570d2f6/test_labels.json + train_labels_file: output/reports/train/716d1517cd69c849833847e81570d2f6/train_labels.json + model_dir: models + name: 716d1517cd69c849833847e81570d2f6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e6a9bc10f9699e84bb8a6889fae65277 + trainer: + kwargs: {} +name: 716d1517cd69c849833847e81570d2f6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7187d523031594631738ef1e2a75d35a/params.yaml b/examples/security/classification/output/reports/train/7187d523031594631738ef1e2a75d35a/params.yaml new file mode 100644 index 00000000..144654eb --- /dev/null +++ b/examples/security/classification/output/reports/train/7187d523031594631738ef1e2a75d35a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7187d523031594631738ef1e2a75d35a/adv_predictions.json + adv_probabilities_file: output/reports/train/7187d523031594631738ef1e2a75d35a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/d47944bcd357716add06a6d30a4c6a94.pkl + params_file: output/reports/train/7187d523031594631738ef1e2a75d35a/params.yaml + predictions_file: output/reports/train/7187d523031594631738ef1e2a75d35a/predictions.json + probabilities_file: output/reports/train/7187d523031594631738ef1e2a75d35a/probabilities.json + score_dict_file: output/reports/train/7187d523031594631738ef1e2a75d35a/score_dict.json + test_labels_file: output/reports/train/7187d523031594631738ef1e2a75d35a/test_labels.json + train_labels_file: output/reports/train/7187d523031594631738ef1e2a75d35a/train_labels.json + model_dir: models + name: 7187d523031594631738ef1e2a75d35a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6c8d24fc1565f0b345911377b99a730e + trainer: + kwargs: {} +name: 7187d523031594631738ef1e2a75d35a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/params.yaml b/examples/security/classification/output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/params.yaml new file mode 100644 index 00000000..0a254a29 --- /dev/null +++ b/examples/security/classification/output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/adv_predictions.json + adv_probabilities_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/c078852816b20c8acec5b1f03ab78acb.pkl + params_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/params.yaml + predictions_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/predictions.json + probabilities_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/probabilities.json + score_dict_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/score_dict.json + test_labels_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/test_labels.json + train_labels_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/train_labels.json + model_dir: models + name: 71ae134d171a52bb1c52fe9be1ae799c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ebffc85f13f38827bf7dba37fba0e52 + trainer: + kwargs: {} +name: 71ae134d171a52bb1c52fe9be1ae799c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/71dce034a9e750311561b0024eeb977c/params.yaml b/examples/security/classification/output/reports/train/71dce034a9e750311561b0024eeb977c/params.yaml new file mode 100644 index 00000000..4466531c --- /dev/null +++ b/examples/security/classification/output/reports/train/71dce034a9e750311561b0024eeb977c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/71dce034a9e750311561b0024eeb977c/adv_predictions.json + adv_probabilities_file: output/reports/train/71dce034a9e750311561b0024eeb977c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/936c0275d23e5a95d14c64f6f63006a2.pkl + params_file: output/reports/train/71dce034a9e750311561b0024eeb977c/params.yaml + predictions_file: output/reports/train/71dce034a9e750311561b0024eeb977c/predictions.json + probabilities_file: output/reports/train/71dce034a9e750311561b0024eeb977c/probabilities.json + score_dict_file: output/reports/train/71dce034a9e750311561b0024eeb977c/score_dict.json + test_labels_file: output/reports/train/71dce034a9e750311561b0024eeb977c/test_labels.json + train_labels_file: output/reports/train/71dce034a9e750311561b0024eeb977c/train_labels.json + model_dir: models + name: 71dce034a9e750311561b0024eeb977c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9690cb76ceaf48d0fa79c30a16d300a7 + trainer: + kwargs: {} +name: 71dce034a9e750311561b0024eeb977c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/71f1bca7ba0edb940d24066115a7733f/params.yaml b/examples/security/classification/output/reports/train/71f1bca7ba0edb940d24066115a7733f/params.yaml new file mode 100644 index 00000000..4313a7ca --- /dev/null +++ b/examples/security/classification/output/reports/train/71f1bca7ba0edb940d24066115a7733f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/adv_predictions.json + adv_probabilities_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/58e601a76ae0fc3fcf72ce471914b744.pkl + params_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/params.yaml + predictions_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/predictions.json + probabilities_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/probabilities.json + score_dict_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/score_dict.json + test_labels_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/test_labels.json + train_labels_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/train_labels.json + model_dir: models + name: 71f1bca7ba0edb940d24066115a7733f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 626b9482cb430f4d01ac7902608fc86d + trainer: + kwargs: {} +name: 71f1bca7ba0edb940d24066115a7733f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/71fc7919e08a23ea190bbcc563053a6d/params.yaml b/examples/security/classification/output/reports/train/71fc7919e08a23ea190bbcc563053a6d/params.yaml new file mode 100644 index 00000000..b4b08c1e --- /dev/null +++ b/examples/security/classification/output/reports/train/71fc7919e08a23ea190bbcc563053a6d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/adv_predictions.json + adv_probabilities_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/43eb13676ca2b3f8ef2c2a79cc6945e0.pkl + params_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/params.yaml + predictions_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/predictions.json + probabilities_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/probabilities.json + score_dict_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/score_dict.json + test_labels_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/test_labels.json + train_labels_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/train_labels.json + model_dir: models + name: 71fc7919e08a23ea190bbcc563053a6d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bd8a717d0ae7c82095b8f731567cee59 + trainer: + kwargs: {} +name: 71fc7919e08a23ea190bbcc563053a6d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7208366df358307a415e62a26c0112cb/params.yaml b/examples/security/classification/output/reports/train/7208366df358307a415e62a26c0112cb/params.yaml new file mode 100644 index 00000000..e27e49b8 --- /dev/null +++ b/examples/security/classification/output/reports/train/7208366df358307a415e62a26c0112cb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7208366df358307a415e62a26c0112cb/adv_predictions.json + adv_probabilities_file: output/reports/train/7208366df358307a415e62a26c0112cb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/eb5b23d5499ec28a9ac4bb390db6f7b7.pkl + params_file: output/reports/train/7208366df358307a415e62a26c0112cb/params.yaml + predictions_file: output/reports/train/7208366df358307a415e62a26c0112cb/predictions.json + probabilities_file: output/reports/train/7208366df358307a415e62a26c0112cb/probabilities.json + score_dict_file: output/reports/train/7208366df358307a415e62a26c0112cb/score_dict.json + test_labels_file: output/reports/train/7208366df358307a415e62a26c0112cb/test_labels.json + train_labels_file: output/reports/train/7208366df358307a415e62a26c0112cb/train_labels.json + model_dir: models + name: 7208366df358307a415e62a26c0112cb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 43364d6c62a94ad9c9f52795d4a44280 + trainer: + kwargs: {} +name: 7208366df358307a415e62a26c0112cb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/72140775c00a51cba3238e2a0f77ec6a/params.yaml b/examples/security/classification/output/reports/train/72140775c00a51cba3238e2a0f77ec6a/params.yaml new file mode 100644 index 00000000..962fb4ed --- /dev/null +++ b/examples/security/classification/output/reports/train/72140775c00a51cba3238e2a0f77ec6a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/adv_predictions.json + adv_probabilities_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/b37a451cf661aeb8a76fb27d93242553.pkl + params_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/params.yaml + predictions_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/predictions.json + probabilities_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/probabilities.json + score_dict_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/score_dict.json + test_labels_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/test_labels.json + train_labels_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/train_labels.json + model_dir: models + name: 72140775c00a51cba3238e2a0f77ec6a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1a1af3125e43b1b320e964b8d33c6bf1 + trainer: + kwargs: {} +name: 72140775c00a51cba3238e2a0f77ec6a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/params.yaml b/examples/security/classification/output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/params.yaml new file mode 100644 index 00000000..fd7afe3f --- /dev/null +++ b/examples/security/classification/output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/adv_predictions.json + adv_probabilities_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/513de5dc06ac54e8769a54ccf9ede64a.pkl + params_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/params.yaml + predictions_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/predictions.json + probabilities_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/probabilities.json + score_dict_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/score_dict.json + test_labels_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/test_labels.json + train_labels_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/train_labels.json + model_dir: models + name: 7222d85a71ea4756ae7e3c1c56db42b9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d0c1e136ec590e8c51d910b29588728f + trainer: + kwargs: {} +name: 7222d85a71ea4756ae7e3c1c56db42b9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/params.yaml b/examples/security/classification/output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/params.yaml new file mode 100644 index 00000000..c8faf3d8 --- /dev/null +++ b/examples/security/classification/output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/adv_predictions.json + adv_probabilities_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/d5bdbb1895998f26c20585cb85e5cfd7.pkl + params_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/params.yaml + predictions_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/predictions.json + probabilities_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/probabilities.json + score_dict_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/score_dict.json + test_labels_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/test_labels.json + train_labels_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/train_labels.json + model_dir: models + name: 723f5015060c0a90ee5c7b9e8d3900b4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4455208a6c103650a6318bfd4f4c2602 + trainer: + kwargs: {} +name: 723f5015060c0a90ee5c7b9e8d3900b4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/724153c7ab286ef87ef34721feb154e7/params.yaml b/examples/security/classification/output/reports/train/724153c7ab286ef87ef34721feb154e7/params.yaml new file mode 100644 index 00000000..a9a8b04d --- /dev/null +++ b/examples/security/classification/output/reports/train/724153c7ab286ef87ef34721feb154e7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/adv_predictions.json + adv_probabilities_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/de4fd574ea4692c745b2972b37f65262.pkl + params_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/params.yaml + predictions_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/predictions.json + probabilities_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/probabilities.json + score_dict_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/score_dict.json + test_labels_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/test_labels.json + train_labels_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/train_labels.json + model_dir: models + name: 724153c7ab286ef87ef34721feb154e7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 699e7a48c29c00c43f68a4fa9ffd7dae + trainer: + kwargs: {} +name: 724153c7ab286ef87ef34721feb154e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/724d0841112116dd3e261064c8d2e72d/params.yaml b/examples/security/classification/output/reports/train/724d0841112116dd3e261064c8d2e72d/params.yaml new file mode 100644 index 00000000..fcc46df1 --- /dev/null +++ b/examples/security/classification/output/reports/train/724d0841112116dd3e261064c8d2e72d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/adv_predictions.json + adv_probabilities_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/b4f212d5e1f9a2be12dcb6f543453d76.pkl + params_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/params.yaml + predictions_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/predictions.json + probabilities_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/probabilities.json + score_dict_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/score_dict.json + test_labels_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/test_labels.json + train_labels_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/train_labels.json + model_dir: models + name: 724d0841112116dd3e261064c8d2e72d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f9a96fb5a4cd17992e5593ad414766c5 + trainer: + kwargs: {} +name: 724d0841112116dd3e261064c8d2e72d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/params.yaml b/examples/security/classification/output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/params.yaml new file mode 100644 index 00000000..dcacf36e --- /dev/null +++ b/examples/security/classification/output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/adv_predictions.json + adv_probabilities_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/178898dec863e96b0043b4ff1ea162ce.pkl + params_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/params.yaml + predictions_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/predictions.json + probabilities_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/probabilities.json + score_dict_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/score_dict.json + test_labels_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/test_labels.json + train_labels_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/train_labels.json + model_dir: models + name: 7295f28f097a7c39335f6a2a4f15a86b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1aa52d5c6b23f14bbf27ece6cbd48849 + trainer: + kwargs: {} +name: 7295f28f097a7c39335f6a2a4f15a86b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/72ebaff61ba085cb4f2a816e48734370/params.yaml b/examples/security/classification/output/reports/train/72ebaff61ba085cb4f2a816e48734370/params.yaml new file mode 100644 index 00000000..07a72563 --- /dev/null +++ b/examples/security/classification/output/reports/train/72ebaff61ba085cb4f2a816e48734370/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/adv_predictions.json + adv_probabilities_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/762efe38c0e716ed7dbd42b32b07baea.pkl + params_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/params.yaml + predictions_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/predictions.json + probabilities_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/probabilities.json + score_dict_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/score_dict.json + test_labels_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/test_labels.json + train_labels_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/train_labels.json + model_dir: models + name: 72ebaff61ba085cb4f2a816e48734370 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 20edc57b965f625a27a9070e0face4dd + trainer: + kwargs: {} +name: 72ebaff61ba085cb4f2a816e48734370 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/72f920ea8182b65ee301ff5a68a28327/params.yaml b/examples/security/classification/output/reports/train/72f920ea8182b65ee301ff5a68a28327/params.yaml new file mode 100644 index 00000000..cf116142 --- /dev/null +++ b/examples/security/classification/output/reports/train/72f920ea8182b65ee301ff5a68a28327/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/adv_predictions.json + adv_probabilities_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/4902a9bfcc27868bb81a05bf282f9899.pkl + params_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/params.yaml + predictions_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/predictions.json + probabilities_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/probabilities.json + score_dict_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/score_dict.json + test_labels_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/test_labels.json + train_labels_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/train_labels.json + model_dir: models + name: 72f920ea8182b65ee301ff5a68a28327 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 61ce5fc69bf98a5e353a163fc285e34c + trainer: + kwargs: {} +name: 72f920ea8182b65ee301ff5a68a28327 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/72fddc997412eecd0a2e67f347d3fe67/params.yaml b/examples/security/classification/output/reports/train/72fddc997412eecd0a2e67f347d3fe67/params.yaml new file mode 100644 index 00000000..1aea8549 --- /dev/null +++ b/examples/security/classification/output/reports/train/72fddc997412eecd0a2e67f347d3fe67/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/adv_predictions.json + adv_probabilities_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/5bc8ed02ffe4e8ff9d331220421a2414.pkl + params_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/params.yaml + predictions_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/predictions.json + probabilities_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/probabilities.json + score_dict_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/score_dict.json + test_labels_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/test_labels.json + train_labels_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/train_labels.json + model_dir: models + name: 72fddc997412eecd0a2e67f347d3fe67 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bc2da2e36e8aeb68a39c38c8e70d300e + trainer: + kwargs: {} +name: 72fddc997412eecd0a2e67f347d3fe67 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7329fe57d5a328d98931c4aab388aab1/params.yaml b/examples/security/classification/output/reports/train/7329fe57d5a328d98931c4aab388aab1/params.yaml new file mode 100644 index 00000000..920e0ead --- /dev/null +++ b/examples/security/classification/output/reports/train/7329fe57d5a328d98931c4aab388aab1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/adv_predictions.json + adv_probabilities_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/90033f07b64bce72bc37c8326c339006.pkl + params_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/params.yaml + predictions_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/predictions.json + probabilities_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/probabilities.json + score_dict_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/score_dict.json + test_labels_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/test_labels.json + train_labels_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/train_labels.json + model_dir: models + name: 7329fe57d5a328d98931c4aab388aab1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1ed07c40e54412d0694152aac2da9cac + trainer: + kwargs: {} +name: 7329fe57d5a328d98931c4aab388aab1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7345cb2266426f89686081d32b359da1/params.yaml b/examples/security/classification/output/reports/train/7345cb2266426f89686081d32b359da1/params.yaml new file mode 100644 index 00000000..d5be402b --- /dev/null +++ b/examples/security/classification/output/reports/train/7345cb2266426f89686081d32b359da1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7345cb2266426f89686081d32b359da1/adv_predictions.json + adv_probabilities_file: output/reports/train/7345cb2266426f89686081d32b359da1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/843ae707782fd79fbe5f7d1cc058809b.pkl + params_file: output/reports/train/7345cb2266426f89686081d32b359da1/params.yaml + predictions_file: output/reports/train/7345cb2266426f89686081d32b359da1/predictions.json + probabilities_file: output/reports/train/7345cb2266426f89686081d32b359da1/probabilities.json + score_dict_file: output/reports/train/7345cb2266426f89686081d32b359da1/score_dict.json + test_labels_file: output/reports/train/7345cb2266426f89686081d32b359da1/test_labels.json + train_labels_file: output/reports/train/7345cb2266426f89686081d32b359da1/train_labels.json + model_dir: models + name: 7345cb2266426f89686081d32b359da1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6775af5ca2db2ef5e607f53d791f5260 + trainer: + kwargs: {} +name: 7345cb2266426f89686081d32b359da1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/737e088b5c25453cfed5c28b48b03b2a/params.yaml b/examples/security/classification/output/reports/train/737e088b5c25453cfed5c28b48b03b2a/params.yaml new file mode 100644 index 00000000..5ef26ae8 --- /dev/null +++ b/examples/security/classification/output/reports/train/737e088b5c25453cfed5c28b48b03b2a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/adv_predictions.json + adv_probabilities_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/8134b6ca80df59b57eb4593a1ffdbccc.pkl + params_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/params.yaml + predictions_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/predictions.json + probabilities_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/probabilities.json + score_dict_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/score_dict.json + test_labels_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/test_labels.json + train_labels_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/train_labels.json + model_dir: models + name: 737e088b5c25453cfed5c28b48b03b2a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9d9b3145163949bc108b8645841a19ce + trainer: + kwargs: {} +name: 737e088b5c25453cfed5c28b48b03b2a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/739ab8e381252234ae338e6c86f9eec5/params.yaml b/examples/security/classification/output/reports/train/739ab8e381252234ae338e6c86f9eec5/params.yaml new file mode 100644 index 00000000..331f5d3f --- /dev/null +++ b/examples/security/classification/output/reports/train/739ab8e381252234ae338e6c86f9eec5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/adv_predictions.json + adv_probabilities_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/a85c314ae8c82bdc82d12ab871cd0aee.pkl + params_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/params.yaml + predictions_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/predictions.json + probabilities_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/probabilities.json + score_dict_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/score_dict.json + test_labels_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/test_labels.json + train_labels_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/train_labels.json + model_dir: models + name: 739ab8e381252234ae338e6c86f9eec5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9fdfaada8e130932c6f8bc701390ea52 + trainer: + kwargs: {} +name: 739ab8e381252234ae338e6c86f9eec5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/73acc7128a9a36598c4eca5abaa04754/params.yaml b/examples/security/classification/output/reports/train/73acc7128a9a36598c4eca5abaa04754/params.yaml new file mode 100644 index 00000000..9372bcc3 --- /dev/null +++ b/examples/security/classification/output/reports/train/73acc7128a9a36598c4eca5abaa04754/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/adv_predictions.json + adv_probabilities_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/9ae572a6cebfdaaa267c82a6653dd2a0.pkl + params_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/params.yaml + predictions_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/predictions.json + probabilities_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/probabilities.json + score_dict_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/score_dict.json + test_labels_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/test_labels.json + train_labels_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/train_labels.json + model_dir: models + name: 73acc7128a9a36598c4eca5abaa04754 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 94e17e49226423f394cd1f0e0f3f6e53 + trainer: + kwargs: {} +name: 73acc7128a9a36598c4eca5abaa04754 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/73c90714880dea7d0052fa48b3619c0b/params.yaml b/examples/security/classification/output/reports/train/73c90714880dea7d0052fa48b3619c0b/params.yaml new file mode 100644 index 00000000..485347be --- /dev/null +++ b/examples/security/classification/output/reports/train/73c90714880dea7d0052fa48b3619c0b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/adv_predictions.json + adv_probabilities_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/96fd69a8bd3e57ad68763dd3d053c16d.pkl + params_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/params.yaml + predictions_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/predictions.json + probabilities_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/probabilities.json + score_dict_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/score_dict.json + test_labels_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/test_labels.json + train_labels_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/train_labels.json + model_dir: models + name: 73c90714880dea7d0052fa48b3619c0b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7d249247ae22571cfc52e13978c78b8d + trainer: + kwargs: {} +name: 73c90714880dea7d0052fa48b3619c0b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/73e7d220c3131e6bae03132759fa9cce/params.yaml b/examples/security/classification/output/reports/train/73e7d220c3131e6bae03132759fa9cce/params.yaml new file mode 100644 index 00000000..bae1b8ef --- /dev/null +++ b/examples/security/classification/output/reports/train/73e7d220c3131e6bae03132759fa9cce/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/adv_predictions.json + adv_probabilities_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/ca60960a37cfd3e20623a8acec5ed3d1.pkl + params_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/params.yaml + predictions_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/predictions.json + probabilities_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/probabilities.json + score_dict_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/score_dict.json + test_labels_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/test_labels.json + train_labels_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/train_labels.json + model_dir: models + name: 73e7d220c3131e6bae03132759fa9cce + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e445f48196e724332f5f448e9fd6e7ef + trainer: + kwargs: {} +name: 73e7d220c3131e6bae03132759fa9cce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/73f3fb4c880ab72e740e39904ed1454d/params.yaml b/examples/security/classification/output/reports/train/73f3fb4c880ab72e740e39904ed1454d/params.yaml new file mode 100644 index 00000000..c7f92d38 --- /dev/null +++ b/examples/security/classification/output/reports/train/73f3fb4c880ab72e740e39904ed1454d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/adv_predictions.json + adv_probabilities_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/ffed06a756cb841fe09de3d34eedee34.pkl + params_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/params.yaml + predictions_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/predictions.json + probabilities_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/probabilities.json + score_dict_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/score_dict.json + test_labels_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/test_labels.json + train_labels_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/train_labels.json + model_dir: models + name: 73f3fb4c880ab72e740e39904ed1454d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a330afc35242e05dff2ecdc6f6bc1cce + trainer: + kwargs: {} +name: 73f3fb4c880ab72e740e39904ed1454d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/73f7792d55663207d9af8dff9acaaf88/params.yaml b/examples/security/classification/output/reports/train/73f7792d55663207d9af8dff9acaaf88/params.yaml new file mode 100644 index 00000000..24949fa2 --- /dev/null +++ b/examples/security/classification/output/reports/train/73f7792d55663207d9af8dff9acaaf88/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/adv_predictions.json + adv_probabilities_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/db1ae00f7fc9326f38988e0d96a29392.pkl + params_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/params.yaml + predictions_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/predictions.json + probabilities_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/probabilities.json + score_dict_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/score_dict.json + test_labels_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/test_labels.json + train_labels_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/train_labels.json + model_dir: models + name: 73f7792d55663207d9af8dff9acaaf88 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ad6785ceb328843b1fa7c973b4ae9059 + trainer: + kwargs: {} +name: 73f7792d55663207d9af8dff9acaaf88 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/params.yaml b/examples/security/classification/output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/params.yaml new file mode 100644 index 00000000..2b9f6afb --- /dev/null +++ b/examples/security/classification/output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/adv_predictions.json + adv_probabilities_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/3d5ee4641ef0821ffc77ef8704c91ea1.pkl + params_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/params.yaml + predictions_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/predictions.json + probabilities_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/probabilities.json + score_dict_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/score_dict.json + test_labels_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/test_labels.json + train_labels_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/train_labels.json + model_dir: models + name: 741f2b909a6f148c21dcd51ca6879ec1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c2fc0ef2e8c62bfdf8a98ef78c38cdc6 + trainer: + kwargs: {} +name: 741f2b909a6f148c21dcd51ca6879ec1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7434b97b339f912c26d9887b1bb45f92/params.yaml b/examples/security/classification/output/reports/train/7434b97b339f912c26d9887b1bb45f92/params.yaml new file mode 100644 index 00000000..c41b58d7 --- /dev/null +++ b/examples/security/classification/output/reports/train/7434b97b339f912c26d9887b1bb45f92/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/adv_predictions.json + adv_probabilities_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/ed556fc48c87351d1240d5f68cb2781c.pkl + params_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/params.yaml + predictions_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/predictions.json + probabilities_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/probabilities.json + score_dict_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/score_dict.json + test_labels_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/test_labels.json + train_labels_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/train_labels.json + model_dir: models + name: 7434b97b339f912c26d9887b1bb45f92 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a1dd1b9c196a57e61ddc8a7689e0ee20 + trainer: + kwargs: {} +name: 7434b97b339f912c26d9887b1bb45f92 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7443674207c288482312dadb6724ec94/params.yaml b/examples/security/classification/output/reports/train/7443674207c288482312dadb6724ec94/params.yaml new file mode 100644 index 00000000..d859e61e --- /dev/null +++ b/examples/security/classification/output/reports/train/7443674207c288482312dadb6724ec94/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7443674207c288482312dadb6724ec94/adv_predictions.json + adv_probabilities_file: output/reports/train/7443674207c288482312dadb6724ec94/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/07504f693ee961aa145b6e6302e54f6e.pkl + params_file: output/reports/train/7443674207c288482312dadb6724ec94/params.yaml + predictions_file: output/reports/train/7443674207c288482312dadb6724ec94/predictions.json + probabilities_file: output/reports/train/7443674207c288482312dadb6724ec94/probabilities.json + score_dict_file: output/reports/train/7443674207c288482312dadb6724ec94/score_dict.json + test_labels_file: output/reports/train/7443674207c288482312dadb6724ec94/test_labels.json + train_labels_file: output/reports/train/7443674207c288482312dadb6724ec94/train_labels.json + model_dir: models + name: 7443674207c288482312dadb6724ec94 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4aa5140409c8724784017b502a0ee473 + trainer: + kwargs: {} +name: 7443674207c288482312dadb6724ec94 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/params.yaml b/examples/security/classification/output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/params.yaml new file mode 100644 index 00000000..b42bdfce --- /dev/null +++ b/examples/security/classification/output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/adv_predictions.json + adv_probabilities_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/4d35048a5954b4c0e7e8e74b7bd08de7.pkl + params_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/params.yaml + predictions_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/predictions.json + probabilities_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/probabilities.json + score_dict_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/score_dict.json + test_labels_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/test_labels.json + train_labels_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/train_labels.json + model_dir: models + name: 7455a0a8ff17b72b55117216d9f9bf1e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c8fd5700ff59b70c9f168d02bb2039fb + trainer: + kwargs: {} +name: 7455a0a8ff17b72b55117216d9f9bf1e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/params.yaml b/examples/security/classification/output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/params.yaml new file mode 100644 index 00000000..a9cba725 --- /dev/null +++ b/examples/security/classification/output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/adv_predictions.json + adv_probabilities_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/148736f24e042b932404186e1b40230c.pkl + params_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/params.yaml + predictions_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/predictions.json + probabilities_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/probabilities.json + score_dict_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/score_dict.json + test_labels_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/test_labels.json + train_labels_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/train_labels.json + model_dir: models + name: 7499d7e10aaca7aad452304b43d6ebfd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ce823bce87fa90c811f62c989ffc5f27 + trainer: + kwargs: {} +name: 7499d7e10aaca7aad452304b43d6ebfd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/params.yaml b/examples/security/classification/output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/params.yaml new file mode 100644 index 00000000..862be2f8 --- /dev/null +++ b/examples/security/classification/output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/adv_predictions.json + adv_probabilities_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/6bf45389c6c0ee1c64c1d9742a0e7df8.pkl + params_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/params.yaml + predictions_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/predictions.json + probabilities_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/probabilities.json + score_dict_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/score_dict.json + test_labels_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/test_labels.json + train_labels_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/train_labels.json + model_dir: models + name: 749cb5ae985f7ed5670d53e0cdde2a6e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 83073b85840145a6d30ed51b7c456811 + trainer: + kwargs: {} +name: 749cb5ae985f7ed5670d53e0cdde2a6e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/74a0443a293985b6f13316d2237c93af/params.yaml b/examples/security/classification/output/reports/train/74a0443a293985b6f13316d2237c93af/params.yaml new file mode 100644 index 00000000..e76985e5 --- /dev/null +++ b/examples/security/classification/output/reports/train/74a0443a293985b6f13316d2237c93af/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/74a0443a293985b6f13316d2237c93af/adv_predictions.json + adv_probabilities_file: output/reports/train/74a0443a293985b6f13316d2237c93af/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/4f12e858060f149b9664670b1d0cf594.pkl + params_file: output/reports/train/74a0443a293985b6f13316d2237c93af/params.yaml + predictions_file: output/reports/train/74a0443a293985b6f13316d2237c93af/predictions.json + probabilities_file: output/reports/train/74a0443a293985b6f13316d2237c93af/probabilities.json + score_dict_file: output/reports/train/74a0443a293985b6f13316d2237c93af/score_dict.json + test_labels_file: output/reports/train/74a0443a293985b6f13316d2237c93af/test_labels.json + train_labels_file: output/reports/train/74a0443a293985b6f13316d2237c93af/train_labels.json + model_dir: models + name: 74a0443a293985b6f13316d2237c93af + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8941771a003e3ce2aa27cad6aa959fe2 + trainer: + kwargs: {} +name: 74a0443a293985b6f13316d2237c93af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/params.yaml b/examples/security/classification/output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/params.yaml new file mode 100644 index 00000000..595a764b --- /dev/null +++ b/examples/security/classification/output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/adv_predictions.json + adv_probabilities_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/1fb5bf53edea8157c1a041e79340aa70.pkl + params_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/params.yaml + predictions_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/predictions.json + probabilities_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/probabilities.json + score_dict_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/score_dict.json + test_labels_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/test_labels.json + train_labels_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/train_labels.json + model_dir: models + name: 74bcd789523b22553dba7d29cbe6b4c0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fff76779eacb404730cae97e230e360a + trainer: + kwargs: {} +name: 74bcd789523b22553dba7d29cbe6b4c0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/750872c8c5fb8dcebef92d49172f6408/params.yaml b/examples/security/classification/output/reports/train/750872c8c5fb8dcebef92d49172f6408/params.yaml new file mode 100644 index 00000000..c55114e5 --- /dev/null +++ b/examples/security/classification/output/reports/train/750872c8c5fb8dcebef92d49172f6408/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/adv_predictions.json + adv_probabilities_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/ab6cb94596dfda8e66f254f2f36defd2.pkl + params_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/params.yaml + predictions_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/predictions.json + probabilities_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/probabilities.json + score_dict_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/score_dict.json + test_labels_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/test_labels.json + train_labels_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/train_labels.json + model_dir: models + name: 750872c8c5fb8dcebef92d49172f6408 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 090673c969b3a29ef273fdddef147957 + trainer: + kwargs: {} +name: 750872c8c5fb8dcebef92d49172f6408 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/751198a067bdf99bfb13fde03c2c23e9/params.yaml b/examples/security/classification/output/reports/train/751198a067bdf99bfb13fde03c2c23e9/params.yaml new file mode 100644 index 00000000..da199145 --- /dev/null +++ b/examples/security/classification/output/reports/train/751198a067bdf99bfb13fde03c2c23e9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/adv_predictions.json + adv_probabilities_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/647deebd6b4340dd1427d1a2ac3c082f.pkl + params_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/params.yaml + predictions_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/predictions.json + probabilities_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/probabilities.json + score_dict_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/score_dict.json + test_labels_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/test_labels.json + train_labels_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/train_labels.json + model_dir: models + name: 751198a067bdf99bfb13fde03c2c23e9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2e3b014a9279803632122cd90d92682f + trainer: + kwargs: {} +name: 751198a067bdf99bfb13fde03c2c23e9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/756812dca04cfd760c7d59b64e0fdb25/params.yaml b/examples/security/classification/output/reports/train/756812dca04cfd760c7d59b64e0fdb25/params.yaml new file mode 100644 index 00000000..df14f529 --- /dev/null +++ b/examples/security/classification/output/reports/train/756812dca04cfd760c7d59b64e0fdb25/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/adv_predictions.json + adv_probabilities_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/a1e872bbc85e57a3abbc5ad7c62f775d.pkl + params_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/params.yaml + predictions_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/predictions.json + probabilities_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/probabilities.json + score_dict_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/score_dict.json + test_labels_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/test_labels.json + train_labels_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/train_labels.json + model_dir: models + name: 756812dca04cfd760c7d59b64e0fdb25 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 60804dfb0c4d906164ef7912523848ce + trainer: + kwargs: {} +name: 756812dca04cfd760c7d59b64e0fdb25 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7574aef98a3c70629678194d3d656a9c/params.yaml b/examples/security/classification/output/reports/train/7574aef98a3c70629678194d3d656a9c/params.yaml new file mode 100644 index 00000000..7643db1f --- /dev/null +++ b/examples/security/classification/output/reports/train/7574aef98a3c70629678194d3d656a9c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/adv_predictions.json + adv_probabilities_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/973076ea86d779cf2ad4ab369ded2459.pkl + params_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/params.yaml + predictions_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/predictions.json + probabilities_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/probabilities.json + score_dict_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/score_dict.json + test_labels_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/test_labels.json + train_labels_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/train_labels.json + model_dir: models + name: 7574aef98a3c70629678194d3d656a9c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 88d88456158d51e276708a7fc35de46b + trainer: + kwargs: {} +name: 7574aef98a3c70629678194d3d656a9c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/759827f61234097b09f7bf3673cac3f1/params.yaml b/examples/security/classification/output/reports/train/759827f61234097b09f7bf3673cac3f1/params.yaml new file mode 100644 index 00000000..3af8abc5 --- /dev/null +++ b/examples/security/classification/output/reports/train/759827f61234097b09f7bf3673cac3f1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/adv_predictions.json + adv_probabilities_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/b686c27d203097c892d9e0b055b1e357.pkl + params_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/params.yaml + predictions_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/predictions.json + probabilities_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/probabilities.json + score_dict_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/score_dict.json + test_labels_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/test_labels.json + train_labels_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/train_labels.json + model_dir: models + name: 759827f61234097b09f7bf3673cac3f1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 214193a840aeeb0adc7f4744566a14f1 + trainer: + kwargs: {} +name: 759827f61234097b09f7bf3673cac3f1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/75beeac725705420a59c0be987812d02/params.yaml b/examples/security/classification/output/reports/train/75beeac725705420a59c0be987812d02/params.yaml new file mode 100644 index 00000000..0de33533 --- /dev/null +++ b/examples/security/classification/output/reports/train/75beeac725705420a59c0be987812d02/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/75beeac725705420a59c0be987812d02/adv_predictions.json + adv_probabilities_file: output/reports/train/75beeac725705420a59c0be987812d02/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/c6f0c788e91d0e5fc529ccab72e6b361.pkl + params_file: output/reports/train/75beeac725705420a59c0be987812d02/params.yaml + predictions_file: output/reports/train/75beeac725705420a59c0be987812d02/predictions.json + probabilities_file: output/reports/train/75beeac725705420a59c0be987812d02/probabilities.json + score_dict_file: output/reports/train/75beeac725705420a59c0be987812d02/score_dict.json + test_labels_file: output/reports/train/75beeac725705420a59c0be987812d02/test_labels.json + train_labels_file: output/reports/train/75beeac725705420a59c0be987812d02/train_labels.json + model_dir: models + name: 75beeac725705420a59c0be987812d02 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2ff843c4d128b45b50129cb98171fb6a + trainer: + kwargs: {} +name: 75beeac725705420a59c0be987812d02 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/75c14cdae535aeb6c246db59faeea8da/params.yaml b/examples/security/classification/output/reports/train/75c14cdae535aeb6c246db59faeea8da/params.yaml new file mode 100644 index 00000000..e8ff762c --- /dev/null +++ b/examples/security/classification/output/reports/train/75c14cdae535aeb6c246db59faeea8da/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/adv_predictions.json + adv_probabilities_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/dacb7c03825889c9fb24dfd8f7db7c52.pkl + params_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/params.yaml + predictions_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/predictions.json + probabilities_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/probabilities.json + score_dict_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/score_dict.json + test_labels_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/test_labels.json + train_labels_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/train_labels.json + model_dir: models + name: 75c14cdae535aeb6c246db59faeea8da + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3b0953f98286e7645030ad19c576366e + trainer: + kwargs: {} +name: 75c14cdae535aeb6c246db59faeea8da +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/75c86e7df52a72bd549ba759c0db03b8/params.yaml b/examples/security/classification/output/reports/train/75c86e7df52a72bd549ba759c0db03b8/params.yaml new file mode 100644 index 00000000..02d62006 --- /dev/null +++ b/examples/security/classification/output/reports/train/75c86e7df52a72bd549ba759c0db03b8/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/adv_predictions.json + adv_probabilities_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/e333cdb3816aa06baacfafda8eee7964.pkl + params_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/params.yaml + predictions_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/predictions.json + probabilities_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/probabilities.json + score_dict_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/score_dict.json + test_labels_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/test_labels.json + train_labels_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/train_labels.json + model_dir: models + name: 75c86e7df52a72bd549ba759c0db03b8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d1188a216eaddca2d57d4bb6793e9450 + trainer: + kwargs: {} +name: 75c86e7df52a72bd549ba759c0db03b8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/params.yaml b/examples/security/classification/output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/params.yaml new file mode 100644 index 00000000..d190acf2 --- /dev/null +++ b/examples/security/classification/output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/adv_predictions.json + adv_probabilities_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/b173dfeaf20bbed8b06ecfcac31aa2fe.pkl + params_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/params.yaml + predictions_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/predictions.json + probabilities_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/probabilities.json + score_dict_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/score_dict.json + test_labels_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/test_labels.json + train_labels_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/train_labels.json + model_dir: models + name: 75fd25dc7b13a1670a852e02448bf9ee + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4faaade7b37e946f9370c81cd93e8575 + trainer: + kwargs: {} +name: 75fd25dc7b13a1670a852e02448bf9ee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/75fd5f39eff9874699c642b0ac1f607c/params.yaml b/examples/security/classification/output/reports/train/75fd5f39eff9874699c642b0ac1f607c/params.yaml new file mode 100644 index 00000000..6ef1ea29 --- /dev/null +++ b/examples/security/classification/output/reports/train/75fd5f39eff9874699c642b0ac1f607c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/adv_predictions.json + adv_probabilities_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/dcdb16f471e84dd573ae27bace177497.pkl + params_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/params.yaml + predictions_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/predictions.json + probabilities_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/probabilities.json + score_dict_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/score_dict.json + test_labels_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/test_labels.json + train_labels_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/train_labels.json + model_dir: models + name: 75fd5f39eff9874699c642b0ac1f607c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cd78518717bbaa1d3c65d99d7224ed3e + trainer: + kwargs: {} +name: 75fd5f39eff9874699c642b0ac1f607c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7629738353463cb686fa87f794e3e17c/params.yaml b/examples/security/classification/output/reports/train/7629738353463cb686fa87f794e3e17c/params.yaml new file mode 100644 index 00000000..6f426239 --- /dev/null +++ b/examples/security/classification/output/reports/train/7629738353463cb686fa87f794e3e17c/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7629738353463cb686fa87f794e3e17c/adv_predictions.json + adv_probabilities_file: output/reports/train/7629738353463cb686fa87f794e3e17c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/5b8baa19d97d4ee568fb5945ab6da400.pkl + params_file: output/reports/train/7629738353463cb686fa87f794e3e17c/params.yaml + predictions_file: output/reports/train/7629738353463cb686fa87f794e3e17c/predictions.json + probabilities_file: output/reports/train/7629738353463cb686fa87f794e3e17c/probabilities.json + score_dict_file: output/reports/train/7629738353463cb686fa87f794e3e17c/score_dict.json + test_labels_file: output/reports/train/7629738353463cb686fa87f794e3e17c/test_labels.json + train_labels_file: output/reports/train/7629738353463cb686fa87f794e3e17c/train_labels.json + model_dir: models + name: 7629738353463cb686fa87f794e3e17c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3e1ef325b00a683810a37d9252173af + trainer: + kwargs: {} +name: 7629738353463cb686fa87f794e3e17c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/763356da55ee97986ab5eb652f4ae737/params.yaml b/examples/security/classification/output/reports/train/763356da55ee97986ab5eb652f4ae737/params.yaml new file mode 100644 index 00000000..d133483f --- /dev/null +++ b/examples/security/classification/output/reports/train/763356da55ee97986ab5eb652f4ae737/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/adv_predictions.json + adv_probabilities_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/60cac9da028cd0af7b2446496ddc9c4b.pkl + params_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/params.yaml + predictions_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/predictions.json + probabilities_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/probabilities.json + score_dict_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/score_dict.json + test_labels_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/test_labels.json + train_labels_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/train_labels.json + model_dir: models + name: 763356da55ee97986ab5eb652f4ae737 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b6175765acbbdac2d8fe5ad9d5bbb6de + trainer: + kwargs: {} +name: 763356da55ee97986ab5eb652f4ae737 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/params.yaml b/examples/security/classification/output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/params.yaml new file mode 100644 index 00000000..336756cc --- /dev/null +++ b/examples/security/classification/output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/adv_predictions.json + adv_probabilities_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/a476fbccb4f706c72e1c4b0506b2ee15.pkl + params_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/params.yaml + predictions_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/predictions.json + probabilities_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/probabilities.json + score_dict_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/score_dict.json + test_labels_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/test_labels.json + train_labels_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/train_labels.json + model_dir: models + name: 764efbf3e462d4aa8c17ce21ffc0ca77 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 71d626d4b29ad7724cd9af2a95e29ef3 + trainer: + kwargs: {} +name: 764efbf3e462d4aa8c17ce21ffc0ca77 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/params.yaml b/examples/security/classification/output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/params.yaml new file mode 100644 index 00000000..83ff6cb9 --- /dev/null +++ b/examples/security/classification/output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/adv_predictions.json + adv_probabilities_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/227d7091e3c68752ec30711a93962b3d.pkl + params_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/params.yaml + predictions_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/predictions.json + probabilities_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/probabilities.json + score_dict_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/score_dict.json + test_labels_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/test_labels.json + train_labels_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/train_labels.json + model_dir: models + name: 76563d36e9ae26ab86ccf2dacebd4bb0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7282f28c212bda4bf205c480e82ee7d1 + trainer: + kwargs: {} +name: 76563d36e9ae26ab86ccf2dacebd4bb0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7656b2c596027084ab4b649303d26417/params.yaml b/examples/security/classification/output/reports/train/7656b2c596027084ab4b649303d26417/params.yaml new file mode 100644 index 00000000..87d84fde --- /dev/null +++ b/examples/security/classification/output/reports/train/7656b2c596027084ab4b649303d26417/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7656b2c596027084ab4b649303d26417/adv_predictions.json + adv_probabilities_file: output/reports/train/7656b2c596027084ab4b649303d26417/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/a97aa5d4e40b091c41af0d57e813cddc.pkl + params_file: output/reports/train/7656b2c596027084ab4b649303d26417/params.yaml + predictions_file: output/reports/train/7656b2c596027084ab4b649303d26417/predictions.json + probabilities_file: output/reports/train/7656b2c596027084ab4b649303d26417/probabilities.json + score_dict_file: output/reports/train/7656b2c596027084ab4b649303d26417/score_dict.json + test_labels_file: output/reports/train/7656b2c596027084ab4b649303d26417/test_labels.json + train_labels_file: output/reports/train/7656b2c596027084ab4b649303d26417/train_labels.json + model_dir: models + name: 7656b2c596027084ab4b649303d26417 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 625fb6aa8710478e5d85bf7fddc3c452 + trainer: + kwargs: {} +name: 7656b2c596027084ab4b649303d26417 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7663d4b30d84bb89a40faf0d53accf97/params.yaml b/examples/security/classification/output/reports/train/7663d4b30d84bb89a40faf0d53accf97/params.yaml new file mode 100644 index 00000000..79259df4 --- /dev/null +++ b/examples/security/classification/output/reports/train/7663d4b30d84bb89a40faf0d53accf97/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/adv_predictions.json + adv_probabilities_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/d5ef01dbeeb9fd574be1629e29bc4fd0.pkl + params_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/params.yaml + predictions_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/predictions.json + probabilities_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/probabilities.json + score_dict_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/score_dict.json + test_labels_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/test_labels.json + train_labels_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/train_labels.json + model_dir: models + name: 7663d4b30d84bb89a40faf0d53accf97 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 68dbea5cabdc51f1f640176cb0d93c15 + trainer: + kwargs: {} +name: 7663d4b30d84bb89a40faf0d53accf97 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/76673ad0bfd7e6751106c8bd8993c801/params.yaml b/examples/security/classification/output/reports/train/76673ad0bfd7e6751106c8bd8993c801/params.yaml new file mode 100644 index 00000000..ee8db7a6 --- /dev/null +++ b/examples/security/classification/output/reports/train/76673ad0bfd7e6751106c8bd8993c801/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/adv_predictions.json + adv_probabilities_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/8e1909aa1d7eba1fdb9391230c8aba3b.pkl + params_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/params.yaml + predictions_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/predictions.json + probabilities_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/probabilities.json + score_dict_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/score_dict.json + test_labels_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/test_labels.json + train_labels_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/train_labels.json + model_dir: models + name: 76673ad0bfd7e6751106c8bd8993c801 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: af8d883dc18a5627b45f8ab2839e743d + trainer: + kwargs: {} +name: 76673ad0bfd7e6751106c8bd8993c801 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/params.yaml b/examples/security/classification/output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/params.yaml new file mode 100644 index 00000000..e551358b --- /dev/null +++ b/examples/security/classification/output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/adv_predictions.json + adv_probabilities_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/8f957595a30fa9fdfdb7ddd8eac61879.pkl + params_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/params.yaml + predictions_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/predictions.json + probabilities_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/probabilities.json + score_dict_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/score_dict.json + test_labels_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/test_labels.json + train_labels_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/train_labels.json + model_dir: models + name: 7683a7ff95655d4269a6ded582f3fc2d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6dc80ad0bb5600f1ad367844d3c21f41 + trainer: + kwargs: {} +name: 7683a7ff95655d4269a6ded582f3fc2d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/params.yaml b/examples/security/classification/output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/params.yaml new file mode 100644 index 00000000..735ba2d5 --- /dev/null +++ b/examples/security/classification/output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/adv_predictions.json + adv_probabilities_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/19944b6e7d8a0ff50eed59fbb9f45784.pkl + params_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/params.yaml + predictions_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/predictions.json + probabilities_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/probabilities.json + score_dict_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/score_dict.json + test_labels_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/test_labels.json + train_labels_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/train_labels.json + model_dir: models + name: 768bd3edd67c73e359d8e9bd9e4a94fd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f1c1b7237b5c7104459a35288bbe57ab + trainer: + kwargs: {} +name: 768bd3edd67c73e359d8e9bd9e4a94fd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/769044657f47e894b39e955f1d3d5a17/params.yaml b/examples/security/classification/output/reports/train/769044657f47e894b39e955f1d3d5a17/params.yaml new file mode 100644 index 00000000..da34fc2b --- /dev/null +++ b/examples/security/classification/output/reports/train/769044657f47e894b39e955f1d3d5a17/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/adv_predictions.json + adv_probabilities_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/ef98cc6b285af785d582d5cc4fe63793.pkl + params_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/params.yaml + predictions_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/predictions.json + probabilities_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/probabilities.json + score_dict_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/score_dict.json + test_labels_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/test_labels.json + train_labels_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/train_labels.json + model_dir: models + name: 769044657f47e894b39e955f1d3d5a17 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4b7379b4b8906fabd6cc73a6079f6322 + trainer: + kwargs: {} +name: 769044657f47e894b39e955f1d3d5a17 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/params.yaml b/examples/security/classification/output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/params.yaml new file mode 100644 index 00000000..b0fde7a0 --- /dev/null +++ b/examples/security/classification/output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/adv_predictions.json + adv_probabilities_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/2fa5efcccb2423ded1375d166a3f3251.pkl + params_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/params.yaml + predictions_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/predictions.json + probabilities_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/probabilities.json + score_dict_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/score_dict.json + test_labels_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/test_labels.json + train_labels_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/train_labels.json + model_dir: models + name: 7693b7797936bc5b0e7a003cb5dcd7c2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 741dae64203557250521bd0af43fdf2f + trainer: + kwargs: {} +name: 7693b7797936bc5b0e7a003cb5dcd7c2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/params.yaml b/examples/security/classification/output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/params.yaml new file mode 100644 index 00000000..af604946 --- /dev/null +++ b/examples/security/classification/output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/adv_predictions.json + adv_probabilities_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/a74eb260bfbd23fe803c7d7feda1f38d.pkl + params_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/params.yaml + predictions_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/predictions.json + probabilities_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/probabilities.json + score_dict_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/score_dict.json + test_labels_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/test_labels.json + train_labels_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/train_labels.json + model_dir: models + name: 7696371b977c1ef1e1c9ea19a21768a5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 53fac683ca748693a37b0908822250fb + trainer: + kwargs: {} +name: 7696371b977c1ef1e1c9ea19a21768a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/769a6e379d7d19d530d2e53ba19ea229/params.yaml b/examples/security/classification/output/reports/train/769a6e379d7d19d530d2e53ba19ea229/params.yaml new file mode 100644 index 00000000..59a737fd --- /dev/null +++ b/examples/security/classification/output/reports/train/769a6e379d7d19d530d2e53ba19ea229/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/adv_predictions.json + adv_probabilities_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/f6e4979fade1f0d510a12232963cec57.pkl + params_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/params.yaml + predictions_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/predictions.json + probabilities_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/probabilities.json + score_dict_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/score_dict.json + test_labels_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/test_labels.json + train_labels_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/train_labels.json + model_dir: models + name: 769a6e379d7d19d530d2e53ba19ea229 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 573ef69775932b6c25a9c79fc0fbf43d + trainer: + kwargs: {} +name: 769a6e379d7d19d530d2e53ba19ea229 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/76acafa3b1642199303c83f2c8963a59/params.yaml b/examples/security/classification/output/reports/train/76acafa3b1642199303c83f2c8963a59/params.yaml new file mode 100644 index 00000000..7b9d77b4 --- /dev/null +++ b/examples/security/classification/output/reports/train/76acafa3b1642199303c83f2c8963a59/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/adv_predictions.json + adv_probabilities_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/940a1fff91d167c4c6543e378aceb629.pkl + params_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/params.yaml + predictions_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/predictions.json + probabilities_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/probabilities.json + score_dict_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/score_dict.json + test_labels_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/test_labels.json + train_labels_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/train_labels.json + model_dir: models + name: 76acafa3b1642199303c83f2c8963a59 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7235968dd92dd48a0a18ad6523a44c7c + trainer: + kwargs: {} +name: 76acafa3b1642199303c83f2c8963a59 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/params.yaml b/examples/security/classification/output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/params.yaml new file mode 100644 index 00000000..0bedc922 --- /dev/null +++ b/examples/security/classification/output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/adv_predictions.json + adv_probabilities_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/d8005ce9cc53ed5c9659e46e704db9d8.pkl + params_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/params.yaml + predictions_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/predictions.json + probabilities_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/probabilities.json + score_dict_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/score_dict.json + test_labels_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/test_labels.json + train_labels_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/train_labels.json + model_dir: models + name: 76b85a4ad1229bc26e5acc3eebc7d879 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7c5facc7ad2d187c08723f9055afa86b + trainer: + kwargs: {} +name: 76b85a4ad1229bc26e5acc3eebc7d879 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/76bfd82137c976850ea4a264b329ab49/params.yaml b/examples/security/classification/output/reports/train/76bfd82137c976850ea4a264b329ab49/params.yaml new file mode 100644 index 00000000..e0307295 --- /dev/null +++ b/examples/security/classification/output/reports/train/76bfd82137c976850ea4a264b329ab49/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/adv_predictions.json + adv_probabilities_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/a2b68db9ce77deeddbb6c66d3eca50c4.pkl + params_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/params.yaml + predictions_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/predictions.json + probabilities_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/probabilities.json + score_dict_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/score_dict.json + test_labels_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/test_labels.json + train_labels_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/train_labels.json + model_dir: models + name: 76bfd82137c976850ea4a264b329ab49 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 01ee17cb3122042c90c5d8610c665ffa + trainer: + kwargs: {} +name: 76bfd82137c976850ea4a264b329ab49 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/params.yaml b/examples/security/classification/output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/params.yaml new file mode 100644 index 00000000..c38b002b --- /dev/null +++ b/examples/security/classification/output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/adv_predictions.json + adv_probabilities_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/42881817a38e0217b6921e029f7cc7f3.pkl + params_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/params.yaml + predictions_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/predictions.json + probabilities_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/probabilities.json + score_dict_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/score_dict.json + test_labels_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/test_labels.json + train_labels_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/train_labels.json + model_dir: models + name: 76d2805b1e63e51860b5a79b6e4fb4bd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cf8963ee9afb9ad68d61cb8e87e72e05 + trainer: + kwargs: {} +name: 76d2805b1e63e51860b5a79b6e4fb4bd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/params.yaml b/examples/security/classification/output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/params.yaml new file mode 100644 index 00000000..c972c065 --- /dev/null +++ b/examples/security/classification/output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/params.yaml @@ -0,0 +1,206 @@ +attack: + attack_size: 10 + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 10.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f630ddce89f7884608f3844d9f9b2d06 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7caa19a44958b110a96fead78147243c + trainer: + kwargs: {} + name: 7c393bc52e8492a69de323b1005be838 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/adv_predictions.json + adv_probabilities_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/adv_probabilities.json + attack_file: output/attacks/d0ff171710284fafe13204a5a20a5c9f.pkl + data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl + model_file: output/models/e438a4b6a2cb134cb3278a89147f66c8.pkl + params_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/params.yaml + predictions_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/predictions.json + probabilities_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/probabilities.json + score_dict_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/score_dict.json + test_labels_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/test_labels.json + train_labels_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/train_labels.json + model_dir: models + name: 76e8cdd2a6b20ba65a2dd48f254c2ca3 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 59eb21345a29edb531b22f94b764fcd7 + trainer: + kwargs: {} +name: 76e8cdd2a6b20ba65a2dd48f254c2ca3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/76faecca5dcb787b722ca53eabfae08a/params.yaml b/examples/security/classification/output/reports/train/76faecca5dcb787b722ca53eabfae08a/params.yaml new file mode 100644 index 00000000..3c084353 --- /dev/null +++ b/examples/security/classification/output/reports/train/76faecca5dcb787b722ca53eabfae08a/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/adv_predictions.json + adv_probabilities_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/366e414a7cede5e47b129841cc8ac05b.pkl + params_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/params.yaml + predictions_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/predictions.json + probabilities_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/probabilities.json + score_dict_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/score_dict.json + test_labels_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/test_labels.json + train_labels_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/train_labels.json + model_dir: models + name: 76faecca5dcb787b722ca53eabfae08a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fc6498baf6765a4da53f209515b5c21d + trainer: + kwargs: {} +name: 76faecca5dcb787b722ca53eabfae08a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7708413db3c8e4f168d1807d8426f962/params.yaml b/examples/security/classification/output/reports/train/7708413db3c8e4f168d1807d8426f962/params.yaml new file mode 100644 index 00000000..c5e1a2e3 --- /dev/null +++ b/examples/security/classification/output/reports/train/7708413db3c8e4f168d1807d8426f962/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/adv_predictions.json + adv_probabilities_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/a8c52948852476c751f06b3409f3bd0c.pkl + params_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/params.yaml + predictions_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/predictions.json + probabilities_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/probabilities.json + score_dict_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/score_dict.json + test_labels_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/test_labels.json + train_labels_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/train_labels.json + model_dir: models + name: 7708413db3c8e4f168d1807d8426f962 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e9f863bb70dd534cb7372b04dac857ce + trainer: + kwargs: {} +name: 7708413db3c8e4f168d1807d8426f962 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/params.yaml b/examples/security/classification/output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/params.yaml new file mode 100644 index 00000000..8deb59be --- /dev/null +++ b/examples/security/classification/output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/adv_predictions.json + adv_probabilities_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/b2e9dd6c4bcdf7c759c99c11f39d1da5.pkl + params_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/params.yaml + predictions_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/predictions.json + probabilities_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/probabilities.json + score_dict_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/score_dict.json + test_labels_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/test_labels.json + train_labels_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/train_labels.json + model_dir: models + name: 7749f7c5a5dd14f4b17240734e4af6ac + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ef915bedccf61929fe41a7f729478a54 + trainer: + kwargs: {} +name: 7749f7c5a5dd14f4b17240734e4af6ac +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/774b075fe5ad36a85b1f74dcacac6818/params.yaml b/examples/security/classification/output/reports/train/774b075fe5ad36a85b1f74dcacac6818/params.yaml new file mode 100644 index 00000000..2c5fedd7 --- /dev/null +++ b/examples/security/classification/output/reports/train/774b075fe5ad36a85b1f74dcacac6818/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/adv_predictions.json + adv_probabilities_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/07121884f326774e89e55413f98ebeec.pkl + params_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/params.yaml + predictions_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/predictions.json + probabilities_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/probabilities.json + score_dict_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/score_dict.json + test_labels_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/test_labels.json + train_labels_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/train_labels.json + model_dir: models + name: 774b075fe5ad36a85b1f74dcacac6818 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbef541ae19f0239e3d4341ffc2f5a72 + trainer: + kwargs: {} +name: 774b075fe5ad36a85b1f74dcacac6818 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/774e3dc54cd53a1979f6678defad8225/params.yaml b/examples/security/classification/output/reports/train/774e3dc54cd53a1979f6678defad8225/params.yaml new file mode 100644 index 00000000..069199d5 --- /dev/null +++ b/examples/security/classification/output/reports/train/774e3dc54cd53a1979f6678defad8225/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/adv_predictions.json + adv_probabilities_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/414b17879eb10674d36dc9fbf98df84a.pkl + params_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/params.yaml + predictions_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/predictions.json + probabilities_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/probabilities.json + score_dict_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/score_dict.json + test_labels_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/test_labels.json + train_labels_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/train_labels.json + model_dir: models + name: 774e3dc54cd53a1979f6678defad8225 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4caf64b63270b32a518fd3ef0081908e + trainer: + kwargs: {} +name: 774e3dc54cd53a1979f6678defad8225 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7760a164ebcaeca288e6c986c81942e7/params.yaml b/examples/security/classification/output/reports/train/7760a164ebcaeca288e6c986c81942e7/params.yaml new file mode 100644 index 00000000..7b251d78 --- /dev/null +++ b/examples/security/classification/output/reports/train/7760a164ebcaeca288e6c986c81942e7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/adv_predictions.json + adv_probabilities_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/51d0257495c453def02a6d27fb55cd9f.pkl + params_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/params.yaml + predictions_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/predictions.json + probabilities_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/probabilities.json + score_dict_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/score_dict.json + test_labels_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/test_labels.json + train_labels_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/train_labels.json + model_dir: models + name: 7760a164ebcaeca288e6c986c81942e7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46d8b44354ec0174c597f239905f7144 + trainer: + kwargs: {} +name: 7760a164ebcaeca288e6c986c81942e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/777111e8a77eed89facf2744c32dab34/params.yaml b/examples/security/classification/output/reports/train/777111e8a77eed89facf2744c32dab34/params.yaml new file mode 100644 index 00000000..77e761d1 --- /dev/null +++ b/examples/security/classification/output/reports/train/777111e8a77eed89facf2744c32dab34/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/777111e8a77eed89facf2744c32dab34/adv_predictions.json + adv_probabilities_file: output/reports/train/777111e8a77eed89facf2744c32dab34/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/dbc948692a516c06ce5004d2bd4800cd.pkl + params_file: output/reports/train/777111e8a77eed89facf2744c32dab34/params.yaml + predictions_file: output/reports/train/777111e8a77eed89facf2744c32dab34/predictions.json + probabilities_file: output/reports/train/777111e8a77eed89facf2744c32dab34/probabilities.json + score_dict_file: output/reports/train/777111e8a77eed89facf2744c32dab34/score_dict.json + test_labels_file: output/reports/train/777111e8a77eed89facf2744c32dab34/test_labels.json + train_labels_file: output/reports/train/777111e8a77eed89facf2744c32dab34/train_labels.json + model_dir: models + name: 777111e8a77eed89facf2744c32dab34 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3d89dc73bbf593b0c333ae25675b7f68 + trainer: + kwargs: {} +name: 777111e8a77eed89facf2744c32dab34 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/778eebbb853d8bbac0e515b043984c0e/params.yaml b/examples/security/classification/output/reports/train/778eebbb853d8bbac0e515b043984c0e/params.yaml new file mode 100644 index 00000000..30dc2a07 --- /dev/null +++ b/examples/security/classification/output/reports/train/778eebbb853d8bbac0e515b043984c0e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/adv_predictions.json + adv_probabilities_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/32c7ce10eba1c580ede59421d9f79734.pkl + params_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/params.yaml + predictions_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/predictions.json + probabilities_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/probabilities.json + score_dict_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/score_dict.json + test_labels_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/test_labels.json + train_labels_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/train_labels.json + model_dir: models + name: 778eebbb853d8bbac0e515b043984c0e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f70352e0e2a8f7b9e6e95ab12beadfb8 + trainer: + kwargs: {} +name: 778eebbb853d8bbac0e515b043984c0e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/779758228b9d3518db63c2ec78122479/params.yaml b/examples/security/classification/output/reports/train/779758228b9d3518db63c2ec78122479/params.yaml new file mode 100644 index 00000000..43f65bfc --- /dev/null +++ b/examples/security/classification/output/reports/train/779758228b9d3518db63c2ec78122479/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/779758228b9d3518db63c2ec78122479/adv_predictions.json + adv_probabilities_file: output/reports/train/779758228b9d3518db63c2ec78122479/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/291c4908e1dd9ce4e48435df4c6e2571.pkl + params_file: output/reports/train/779758228b9d3518db63c2ec78122479/params.yaml + predictions_file: output/reports/train/779758228b9d3518db63c2ec78122479/predictions.json + probabilities_file: output/reports/train/779758228b9d3518db63c2ec78122479/probabilities.json + score_dict_file: output/reports/train/779758228b9d3518db63c2ec78122479/score_dict.json + test_labels_file: output/reports/train/779758228b9d3518db63c2ec78122479/test_labels.json + train_labels_file: output/reports/train/779758228b9d3518db63c2ec78122479/train_labels.json + model_dir: models + name: 779758228b9d3518db63c2ec78122479 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 925dc6d3d3d7a8f64a6553b1a34d10a2 + trainer: + kwargs: {} +name: 779758228b9d3518db63c2ec78122479 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/params.yaml b/examples/security/classification/output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/params.yaml new file mode 100644 index 00000000..36e72c19 --- /dev/null +++ b/examples/security/classification/output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/adv_predictions.json + adv_probabilities_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/15df9a3c9072093ed91b227f1962a56a.pkl + params_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/params.yaml + predictions_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/predictions.json + probabilities_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/probabilities.json + score_dict_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/score_dict.json + test_labels_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/test_labels.json + train_labels_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/train_labels.json + model_dir: models + name: 779c08e3bf814cc39c8abb0d87f42f48 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c1283c0cd7fb3ae78e6664b24e0cd2b3 + trainer: + kwargs: {} +name: 779c08e3bf814cc39c8abb0d87f42f48 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/77d61212f874fda5bd98609df10720b2/params.yaml b/examples/security/classification/output/reports/train/77d61212f874fda5bd98609df10720b2/params.yaml new file mode 100644 index 00000000..0b8d8dc9 --- /dev/null +++ b/examples/security/classification/output/reports/train/77d61212f874fda5bd98609df10720b2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/77d61212f874fda5bd98609df10720b2/adv_predictions.json + adv_probabilities_file: output/reports/train/77d61212f874fda5bd98609df10720b2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/1969f358d54678ec1b50135100764397.pkl + params_file: output/reports/train/77d61212f874fda5bd98609df10720b2/params.yaml + predictions_file: output/reports/train/77d61212f874fda5bd98609df10720b2/predictions.json + probabilities_file: output/reports/train/77d61212f874fda5bd98609df10720b2/probabilities.json + score_dict_file: output/reports/train/77d61212f874fda5bd98609df10720b2/score_dict.json + test_labels_file: output/reports/train/77d61212f874fda5bd98609df10720b2/test_labels.json + train_labels_file: output/reports/train/77d61212f874fda5bd98609df10720b2/train_labels.json + model_dir: models + name: 77d61212f874fda5bd98609df10720b2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: df69c5ed82b841825133c936f1a5c50a + trainer: + kwargs: {} +name: 77d61212f874fda5bd98609df10720b2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/params.yaml b/examples/security/classification/output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/params.yaml new file mode 100644 index 00000000..37e804dc --- /dev/null +++ b/examples/security/classification/output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/adv_predictions.json + adv_probabilities_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/ad0b1a6b94f9ff5ac35b2c1e1f0a0b42.pkl + params_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/params.yaml + predictions_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/predictions.json + probabilities_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/probabilities.json + score_dict_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/score_dict.json + test_labels_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/test_labels.json + train_labels_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/train_labels.json + model_dir: models + name: 77dd0ca460430ca027bc912f2c95c3e4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6459aee1317df38f95b2876ecabf725e + trainer: + kwargs: {} +name: 77dd0ca460430ca027bc912f2c95c3e4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/77ee84a703db9a0edefcf4b35d81928d/params.yaml b/examples/security/classification/output/reports/train/77ee84a703db9a0edefcf4b35d81928d/params.yaml new file mode 100644 index 00000000..448a920c --- /dev/null +++ b/examples/security/classification/output/reports/train/77ee84a703db9a0edefcf4b35d81928d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/adv_predictions.json + adv_probabilities_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/189d6756b19d1980710c0718d7e9ad65.pkl + params_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/params.yaml + predictions_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/predictions.json + probabilities_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/probabilities.json + score_dict_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/score_dict.json + test_labels_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/test_labels.json + train_labels_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/train_labels.json + model_dir: models + name: 77ee84a703db9a0edefcf4b35d81928d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: de7b0298e5ecc623a1a5f9885cae05c3 + trainer: + kwargs: {} +name: 77ee84a703db9a0edefcf4b35d81928d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/params.yaml b/examples/security/classification/output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/params.yaml new file mode 100644 index 00000000..1047c319 --- /dev/null +++ b/examples/security/classification/output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/adv_predictions.json + adv_probabilities_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/211793c986f3d2c79fd61068c6a542ee.pkl + params_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/params.yaml + predictions_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/predictions.json + probabilities_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/probabilities.json + score_dict_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/score_dict.json + test_labels_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/test_labels.json + train_labels_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/train_labels.json + model_dir: models + name: 7803d6407ba3d9a9be46a4582cc6b8d5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7c5b93eb80eb7432d1e4336e50e1c53f + trainer: + kwargs: {} +name: 7803d6407ba3d9a9be46a4582cc6b8d5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7833f12805f90e2a178c1c152f14a627/params.yaml b/examples/security/classification/output/reports/train/7833f12805f90e2a178c1c152f14a627/params.yaml new file mode 100644 index 00000000..c18d33f9 --- /dev/null +++ b/examples/security/classification/output/reports/train/7833f12805f90e2a178c1c152f14a627/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/adv_predictions.json + adv_probabilities_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/7847c5d43b8be94af995819acb79b064.pkl + params_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/params.yaml + predictions_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/predictions.json + probabilities_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/probabilities.json + score_dict_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/score_dict.json + test_labels_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/test_labels.json + train_labels_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/train_labels.json + model_dir: models + name: 7833f12805f90e2a178c1c152f14a627 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 411adb037574961369d899a8d573ffd9 + trainer: + kwargs: {} +name: 7833f12805f90e2a178c1c152f14a627 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/784c603d7b3170ac97e017ee241004ad/params.yaml b/examples/security/classification/output/reports/train/784c603d7b3170ac97e017ee241004ad/params.yaml new file mode 100644 index 00000000..458775c9 --- /dev/null +++ b/examples/security/classification/output/reports/train/784c603d7b3170ac97e017ee241004ad/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/adv_predictions.json + adv_probabilities_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/4a89c888e453b8ab6b9b725197e5518a.pkl + params_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/params.yaml + predictions_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/predictions.json + probabilities_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/probabilities.json + score_dict_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/score_dict.json + test_labels_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/test_labels.json + train_labels_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/train_labels.json + model_dir: models + name: 784c603d7b3170ac97e017ee241004ad + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f7b0451ca8ff36551409ca3d147f220 + trainer: + kwargs: {} +name: 784c603d7b3170ac97e017ee241004ad +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/785dd8f22b06c9cc798c37664c62b722/params.yaml b/examples/security/classification/output/reports/train/785dd8f22b06c9cc798c37664c62b722/params.yaml new file mode 100644 index 00000000..d9649edb --- /dev/null +++ b/examples/security/classification/output/reports/train/785dd8f22b06c9cc798c37664c62b722/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/adv_predictions.json + adv_probabilities_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/6f399d8fbdb443e3cba133bfc1448073.pkl + params_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/params.yaml + predictions_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/predictions.json + probabilities_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/probabilities.json + score_dict_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/score_dict.json + test_labels_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/test_labels.json + train_labels_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/train_labels.json + model_dir: models + name: 785dd8f22b06c9cc798c37664c62b722 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 505d6f86ae99b45b01960bff18b89296 + trainer: + kwargs: {} +name: 785dd8f22b06c9cc798c37664c62b722 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/78996e2f500db1fb7b6ba8274d11678f/params.yaml b/examples/security/classification/output/reports/train/78996e2f500db1fb7b6ba8274d11678f/params.yaml new file mode 100644 index 00000000..8e35bf68 --- /dev/null +++ b/examples/security/classification/output/reports/train/78996e2f500db1fb7b6ba8274d11678f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/adv_predictions.json + adv_probabilities_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/e8a82dbe7e389c58336fdc92a993a7d8.pkl + params_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/params.yaml + predictions_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/predictions.json + probabilities_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/probabilities.json + score_dict_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/score_dict.json + test_labels_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/test_labels.json + train_labels_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/train_labels.json + model_dir: models + name: 78996e2f500db1fb7b6ba8274d11678f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 19e0cbe36a99755829fb24583a16b4a6 + trainer: + kwargs: {} +name: 78996e2f500db1fb7b6ba8274d11678f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/789dde02275a7ae1b006ebe6cda7b698/params.yaml b/examples/security/classification/output/reports/train/789dde02275a7ae1b006ebe6cda7b698/params.yaml new file mode 100644 index 00000000..53d888ac --- /dev/null +++ b/examples/security/classification/output/reports/train/789dde02275a7ae1b006ebe6cda7b698/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/adv_predictions.json + adv_probabilities_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/7fc62b8b35a63e91d236341bdf362ff3.pkl + params_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/params.yaml + predictions_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/predictions.json + probabilities_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/probabilities.json + score_dict_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/score_dict.json + test_labels_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/test_labels.json + train_labels_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/train_labels.json + model_dir: models + name: 789dde02275a7ae1b006ebe6cda7b698 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cef9441ab62d2dd9be6b0e3fe04229d2 + trainer: + kwargs: {} +name: 789dde02275a7ae1b006ebe6cda7b698 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/params.yaml b/examples/security/classification/output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/params.yaml new file mode 100644 index 00000000..6f5e3395 --- /dev/null +++ b/examples/security/classification/output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/adv_predictions.json + adv_probabilities_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/dfae723e0c7040d28d0981439c28b2cc.pkl + params_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/params.yaml + predictions_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/predictions.json + probabilities_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/probabilities.json + score_dict_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/score_dict.json + test_labels_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/test_labels.json + train_labels_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/train_labels.json + model_dir: models + name: 78b2eea060e5613c0fa1c39420ad5c2b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7a640964a9e2840fef1fa8e73b823b4a + trainer: + kwargs: {} +name: 78b2eea060e5613c0fa1c39420ad5c2b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/78b623c22869b757bd1145496daa5a1c/params.yaml b/examples/security/classification/output/reports/train/78b623c22869b757bd1145496daa5a1c/params.yaml new file mode 100644 index 00000000..5b0521e3 --- /dev/null +++ b/examples/security/classification/output/reports/train/78b623c22869b757bd1145496daa5a1c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/adv_predictions.json + adv_probabilities_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/36123b1915ae6053b0707c0fe112e5d3.pkl + params_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/params.yaml + predictions_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/predictions.json + probabilities_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/probabilities.json + score_dict_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/score_dict.json + test_labels_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/test_labels.json + train_labels_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/train_labels.json + model_dir: models + name: 78b623c22869b757bd1145496daa5a1c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f6120598d57bde5ac77983d08990c40a + trainer: + kwargs: {} +name: 78b623c22869b757bd1145496daa5a1c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/params.yaml b/examples/security/classification/output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/params.yaml new file mode 100644 index 00000000..f7875b59 --- /dev/null +++ b/examples/security/classification/output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/adv_predictions.json + adv_probabilities_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/20b76a7b03605c6a52fad102bcabe1a9.pkl + params_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/params.yaml + predictions_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/predictions.json + probabilities_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/probabilities.json + score_dict_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/score_dict.json + test_labels_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/test_labels.json + train_labels_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/train_labels.json + model_dir: models + name: 78bf21e2d7558f0c4854dd2d1d2c6b84 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a29b5ee001811192052a3ede43c927e4 + trainer: + kwargs: {} +name: 78bf21e2d7558f0c4854dd2d1d2c6b84 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/78c690412f619ecbb7e534ab284ccd15/params.yaml b/examples/security/classification/output/reports/train/78c690412f619ecbb7e534ab284ccd15/params.yaml new file mode 100644 index 00000000..ff5c53c4 --- /dev/null +++ b/examples/security/classification/output/reports/train/78c690412f619ecbb7e534ab284ccd15/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/adv_predictions.json + adv_probabilities_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/ae86a055dfde16069c5feb4d2643809e.pkl + params_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/params.yaml + predictions_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/predictions.json + probabilities_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/probabilities.json + score_dict_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/score_dict.json + test_labels_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/test_labels.json + train_labels_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/train_labels.json + model_dir: models + name: 78c690412f619ecbb7e534ab284ccd15 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 667b9f4a2ad9ecac3ab01b60d77ba3e9 + trainer: + kwargs: {} +name: 78c690412f619ecbb7e534ab284ccd15 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/78d5f2c2301b1b344accce5bf493efd6/params.yaml b/examples/security/classification/output/reports/train/78d5f2c2301b1b344accce5bf493efd6/params.yaml new file mode 100644 index 00000000..cc4c4920 --- /dev/null +++ b/examples/security/classification/output/reports/train/78d5f2c2301b1b344accce5bf493efd6/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/adv_predictions.json + adv_probabilities_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/aad7e13ab66ac61a8e46389b9a724aa5.pkl + params_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/params.yaml + predictions_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/predictions.json + probabilities_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/probabilities.json + score_dict_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/score_dict.json + test_labels_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/test_labels.json + train_labels_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/train_labels.json + model_dir: models + name: 78d5f2c2301b1b344accce5bf493efd6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6e18b5d06124a5f51823f5ce6a466f42 + trainer: + kwargs: {} +name: 78d5f2c2301b1b344accce5bf493efd6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/params.yaml b/examples/security/classification/output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/params.yaml new file mode 100644 index 00000000..30a64a5f --- /dev/null +++ b/examples/security/classification/output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/adv_predictions.json + adv_probabilities_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/a86919ba5e8404499a0debff407fc534.pkl + params_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/params.yaml + predictions_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/predictions.json + probabilities_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/probabilities.json + score_dict_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/score_dict.json + test_labels_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/test_labels.json + train_labels_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/train_labels.json + model_dir: models + name: 7919ec08aa045c0959ea7aa4b8c1f1a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d46a7b5ceeaba536ecb439e075b6b676 + trainer: + kwargs: {} +name: 7919ec08aa045c0959ea7aa4b8c1f1a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7920fca06ffeb482b0786132659e8415/params.yaml b/examples/security/classification/output/reports/train/7920fca06ffeb482b0786132659e8415/params.yaml new file mode 100644 index 00000000..03d1e729 --- /dev/null +++ b/examples/security/classification/output/reports/train/7920fca06ffeb482b0786132659e8415/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7920fca06ffeb482b0786132659e8415/adv_predictions.json + adv_probabilities_file: output/reports/train/7920fca06ffeb482b0786132659e8415/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/ba801da8907f81b7a08c3ee4d15c410f.pkl + params_file: output/reports/train/7920fca06ffeb482b0786132659e8415/params.yaml + predictions_file: output/reports/train/7920fca06ffeb482b0786132659e8415/predictions.json + probabilities_file: output/reports/train/7920fca06ffeb482b0786132659e8415/probabilities.json + score_dict_file: output/reports/train/7920fca06ffeb482b0786132659e8415/score_dict.json + test_labels_file: output/reports/train/7920fca06ffeb482b0786132659e8415/test_labels.json + train_labels_file: output/reports/train/7920fca06ffeb482b0786132659e8415/train_labels.json + model_dir: models + name: 7920fca06ffeb482b0786132659e8415 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3ca50155fd9cfecb2b87b6fc911cde92 + trainer: + kwargs: {} +name: 7920fca06ffeb482b0786132659e8415 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7922248f1319b69207980ab363a81153/params.yaml b/examples/security/classification/output/reports/train/7922248f1319b69207980ab363a81153/params.yaml new file mode 100644 index 00000000..83b18960 --- /dev/null +++ b/examples/security/classification/output/reports/train/7922248f1319b69207980ab363a81153/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7922248f1319b69207980ab363a81153/adv_predictions.json + adv_probabilities_file: output/reports/train/7922248f1319b69207980ab363a81153/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/08f7a63c3817ab14b0998d047a0d465a.pkl + params_file: output/reports/train/7922248f1319b69207980ab363a81153/params.yaml + predictions_file: output/reports/train/7922248f1319b69207980ab363a81153/predictions.json + probabilities_file: output/reports/train/7922248f1319b69207980ab363a81153/probabilities.json + score_dict_file: output/reports/train/7922248f1319b69207980ab363a81153/score_dict.json + test_labels_file: output/reports/train/7922248f1319b69207980ab363a81153/test_labels.json + train_labels_file: output/reports/train/7922248f1319b69207980ab363a81153/train_labels.json + model_dir: models + name: 7922248f1319b69207980ab363a81153 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 18e716781ad6f3527bdf37726da27250 + trainer: + kwargs: {} +name: 7922248f1319b69207980ab363a81153 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/79300bfc3bad0baa8d53159f61c76dde/params.yaml b/examples/security/classification/output/reports/train/79300bfc3bad0baa8d53159f61c76dde/params.yaml new file mode 100644 index 00000000..1baebcc9 --- /dev/null +++ b/examples/security/classification/output/reports/train/79300bfc3bad0baa8d53159f61c76dde/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/adv_predictions.json + adv_probabilities_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/284d8f7aefdd460c9750ee784f83208c.pkl + params_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/params.yaml + predictions_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/predictions.json + probabilities_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/probabilities.json + score_dict_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/score_dict.json + test_labels_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/test_labels.json + train_labels_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/train_labels.json + model_dir: models + name: 79300bfc3bad0baa8d53159f61c76dde + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8c251eccf13f9f7fc2c14239946dd35e + trainer: + kwargs: {} +name: 79300bfc3bad0baa8d53159f61c76dde +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/793b06ba9e3221f2940ec698f58c21ff/params.yaml b/examples/security/classification/output/reports/train/793b06ba9e3221f2940ec698f58c21ff/params.yaml new file mode 100644 index 00000000..0e79f9d7 --- /dev/null +++ b/examples/security/classification/output/reports/train/793b06ba9e3221f2940ec698f58c21ff/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/adv_predictions.json + adv_probabilities_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/ce35f2f577cf65a3891feea16845007e.pkl + params_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/params.yaml + predictions_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/predictions.json + probabilities_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/probabilities.json + score_dict_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/score_dict.json + test_labels_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/test_labels.json + train_labels_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/train_labels.json + model_dir: models + name: 793b06ba9e3221f2940ec698f58c21ff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 804e35c8472972ea36092b4922461401 + trainer: + kwargs: {} +name: 793b06ba9e3221f2940ec698f58c21ff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/params.yaml b/examples/security/classification/output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/params.yaml new file mode 100644 index 00000000..09a7c23d --- /dev/null +++ b/examples/security/classification/output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/adv_predictions.json + adv_probabilities_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/c576225555b9e72c42bd61e0889d1000.pkl + params_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/params.yaml + predictions_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/predictions.json + probabilities_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/probabilities.json + score_dict_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/score_dict.json + test_labels_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/test_labels.json + train_labels_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/train_labels.json + model_dir: models + name: 796b3f8d3ebd7175bf587bfb13aa54e2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 662b1ba404cad48de6607d9d2f576374 + trainer: + kwargs: {} +name: 796b3f8d3ebd7175bf587bfb13aa54e2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/799012eb13dd8b49d369757292c35376/params.yaml b/examples/security/classification/output/reports/train/799012eb13dd8b49d369757292c35376/params.yaml new file mode 100644 index 00000000..438867cc --- /dev/null +++ b/examples/security/classification/output/reports/train/799012eb13dd8b49d369757292c35376/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/799012eb13dd8b49d369757292c35376/adv_predictions.json + adv_probabilities_file: output/reports/train/799012eb13dd8b49d369757292c35376/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/b5539bbc827ceef6fb88f133caa0f22d.pkl + params_file: output/reports/train/799012eb13dd8b49d369757292c35376/params.yaml + predictions_file: output/reports/train/799012eb13dd8b49d369757292c35376/predictions.json + probabilities_file: output/reports/train/799012eb13dd8b49d369757292c35376/probabilities.json + score_dict_file: output/reports/train/799012eb13dd8b49d369757292c35376/score_dict.json + test_labels_file: output/reports/train/799012eb13dd8b49d369757292c35376/test_labels.json + train_labels_file: output/reports/train/799012eb13dd8b49d369757292c35376/train_labels.json + model_dir: models + name: 799012eb13dd8b49d369757292c35376 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f6245a572b4ae26d5ac678daff1367b2 + trainer: + kwargs: {} +name: 799012eb13dd8b49d369757292c35376 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/79eff8b7fa97f52afc69913f4581611e/params.yaml b/examples/security/classification/output/reports/train/79eff8b7fa97f52afc69913f4581611e/params.yaml new file mode 100644 index 00000000..f44bd181 --- /dev/null +++ b/examples/security/classification/output/reports/train/79eff8b7fa97f52afc69913f4581611e/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/adv_predictions.json + adv_probabilities_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/b877a6e40702b2ccd2c26b58c61a8209.pkl + params_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/params.yaml + predictions_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/predictions.json + probabilities_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/probabilities.json + score_dict_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/score_dict.json + test_labels_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/test_labels.json + train_labels_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/train_labels.json + model_dir: models + name: 79eff8b7fa97f52afc69913f4581611e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f2c47171748cb6a7427ea3014979b556 + trainer: + kwargs: {} +name: 79eff8b7fa97f52afc69913f4581611e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/params.yaml b/examples/security/classification/output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/params.yaml new file mode 100644 index 00000000..69ab3e71 --- /dev/null +++ b/examples/security/classification/output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/adv_predictions.json + adv_probabilities_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/d58ff03519ab47342f82c4b2e3832545.pkl + params_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/params.yaml + predictions_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/predictions.json + probabilities_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/probabilities.json + score_dict_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/score_dict.json + test_labels_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/test_labels.json + train_labels_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/train_labels.json + model_dir: models + name: 7a06a82e1a2e328955353d51ef6c4f75 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2a1377e480b5d372c246c4223abd02ff + trainer: + kwargs: {} +name: 7a06a82e1a2e328955353d51ef6c4f75 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/params.yaml b/examples/security/classification/output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/params.yaml new file mode 100644 index 00000000..ee65f6e5 --- /dev/null +++ b/examples/security/classification/output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/adv_predictions.json + adv_probabilities_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/a8b2d075fe5a172cc11576a3cfacbd4c.pkl + params_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/params.yaml + predictions_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/predictions.json + probabilities_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/probabilities.json + score_dict_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/score_dict.json + test_labels_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/test_labels.json + train_labels_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/train_labels.json + model_dir: models + name: 7a19ae69bad53407afbb0cfa83dcca59 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 970c7364cb5f279d7adc717b3a094536 + trainer: + kwargs: {} +name: 7a19ae69bad53407afbb0cfa83dcca59 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/params.yaml b/examples/security/classification/output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/params.yaml new file mode 100644 index 00000000..512f243b --- /dev/null +++ b/examples/security/classification/output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/adv_predictions.json + adv_probabilities_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/8604e2c738c6f43ca8baa793b7cc255d.pkl + params_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/params.yaml + predictions_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/predictions.json + probabilities_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/probabilities.json + score_dict_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/score_dict.json + test_labels_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/test_labels.json + train_labels_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/train_labels.json + model_dir: models + name: 7a2aec9e13b69f66eefa972b73d42e41 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 94234c6bcf37ca5b6a1993acd5cd94cb + trainer: + kwargs: {} +name: 7a2aec9e13b69f66eefa972b73d42e41 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7a371010714827d8ca15a4bd91204d61/params.yaml b/examples/security/classification/output/reports/train/7a371010714827d8ca15a4bd91204d61/params.yaml new file mode 100644 index 00000000..4f9b9cfe --- /dev/null +++ b/examples/security/classification/output/reports/train/7a371010714827d8ca15a4bd91204d61/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/adv_predictions.json + adv_probabilities_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/b414997b2e7c9ac98b8851564acfe5b2.pkl + params_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/params.yaml + predictions_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/predictions.json + probabilities_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/probabilities.json + score_dict_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/score_dict.json + test_labels_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/test_labels.json + train_labels_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/train_labels.json + model_dir: models + name: 7a371010714827d8ca15a4bd91204d61 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e66e54d8174cad8c31c1d161da471ff2 + trainer: + kwargs: {} +name: 7a371010714827d8ca15a4bd91204d61 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/params.yaml b/examples/security/classification/output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/params.yaml new file mode 100644 index 00000000..1747b1d0 --- /dev/null +++ b/examples/security/classification/output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/adv_predictions.json + adv_probabilities_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/1c76ff175608a423449d51cc6af22f23.pkl + params_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/params.yaml + predictions_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/predictions.json + probabilities_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/probabilities.json + score_dict_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/score_dict.json + test_labels_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/test_labels.json + train_labels_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/train_labels.json + model_dir: models + name: 7a4a9fd3951355e1cde31393fe772e6e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f6e734a68afdad0fea66d4720d504c05 + trainer: + kwargs: {} +name: 7a4a9fd3951355e1cde31393fe772e6e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7a4e20b4aca9092cc993abd964f48a11/params.yaml b/examples/security/classification/output/reports/train/7a4e20b4aca9092cc993abd964f48a11/params.yaml new file mode 100644 index 00000000..f0891f3c --- /dev/null +++ b/examples/security/classification/output/reports/train/7a4e20b4aca9092cc993abd964f48a11/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/adv_predictions.json + adv_probabilities_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/eea8f14762f8800b4b9babfc75a17b0b.pkl + params_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/params.yaml + predictions_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/predictions.json + probabilities_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/probabilities.json + score_dict_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/score_dict.json + test_labels_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/test_labels.json + train_labels_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/train_labels.json + model_dir: models + name: 7a4e20b4aca9092cc993abd964f48a11 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 35140c724bfee3f71389f0c332ef4e3c + trainer: + kwargs: {} +name: 7a4e20b4aca9092cc993abd964f48a11 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7a5a5a519779312f31d325e3efa12a69/params.yaml b/examples/security/classification/output/reports/train/7a5a5a519779312f31d325e3efa12a69/params.yaml new file mode 100644 index 00000000..443a902b --- /dev/null +++ b/examples/security/classification/output/reports/train/7a5a5a519779312f31d325e3efa12a69/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/adv_predictions.json + adv_probabilities_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/a0390e617862f7f319547596ce256117.pkl + params_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/params.yaml + predictions_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/predictions.json + probabilities_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/probabilities.json + score_dict_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/score_dict.json + test_labels_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/test_labels.json + train_labels_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/train_labels.json + model_dir: models + name: 7a5a5a519779312f31d325e3efa12a69 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2b2705df651b517cc9f77f0d189a3027 + trainer: + kwargs: {} +name: 7a5a5a519779312f31d325e3efa12a69 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/params.yaml b/examples/security/classification/output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/params.yaml new file mode 100644 index 00000000..b3dc7165 --- /dev/null +++ b/examples/security/classification/output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/adv_predictions.json + adv_probabilities_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/629c546c9a29b18814b5afcd539367ef.pkl + params_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/params.yaml + predictions_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/predictions.json + probabilities_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/probabilities.json + score_dict_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/score_dict.json + test_labels_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/test_labels.json + train_labels_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/train_labels.json + model_dir: models + name: 7a6d22aac3bc31a091ca7b7da8f6bb35 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8360663ed9d55690d8b77579d76e0589 + trainer: + kwargs: {} +name: 7a6d22aac3bc31a091ca7b7da8f6bb35 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7a7130bdedfc550af08d9630125939b3/params.yaml b/examples/security/classification/output/reports/train/7a7130bdedfc550af08d9630125939b3/params.yaml new file mode 100644 index 00000000..7c4b50fc --- /dev/null +++ b/examples/security/classification/output/reports/train/7a7130bdedfc550af08d9630125939b3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/adv_predictions.json + adv_probabilities_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/148735e8e52c05b8d00862b37fdc2702.pkl + params_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/params.yaml + predictions_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/predictions.json + probabilities_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/probabilities.json + score_dict_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/score_dict.json + test_labels_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/test_labels.json + train_labels_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/train_labels.json + model_dir: models + name: 7a7130bdedfc550af08d9630125939b3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6a6e2bb8b0a51a74dc88b08f3764fdd4 + trainer: + kwargs: {} +name: 7a7130bdedfc550af08d9630125939b3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7aa078a1af3025b03c9c543d53c50938/params.yaml b/examples/security/classification/output/reports/train/7aa078a1af3025b03c9c543d53c50938/params.yaml new file mode 100644 index 00000000..47a680f7 --- /dev/null +++ b/examples/security/classification/output/reports/train/7aa078a1af3025b03c9c543d53c50938/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/adv_predictions.json + adv_probabilities_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/2e929489e3e60e9799d4f0cb535e0351.pkl + params_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/params.yaml + predictions_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/predictions.json + probabilities_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/probabilities.json + score_dict_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/score_dict.json + test_labels_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/test_labels.json + train_labels_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/train_labels.json + model_dir: models + name: 7aa078a1af3025b03c9c543d53c50938 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 28562e48e4f301d02254a8e5e9aff3b6 + trainer: + kwargs: {} +name: 7aa078a1af3025b03c9c543d53c50938 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/params.yaml b/examples/security/classification/output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/params.yaml new file mode 100644 index 00000000..c0f671dd --- /dev/null +++ b/examples/security/classification/output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/adv_predictions.json + adv_probabilities_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/4823e096e5496cda832407a95598f3ba.pkl + params_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/params.yaml + predictions_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/predictions.json + probabilities_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/probabilities.json + score_dict_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/score_dict.json + test_labels_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/test_labels.json + train_labels_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/train_labels.json + model_dir: models + name: 7ae7a5dceba8a3f7970644fd1650f26f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 334b2eebf29985c96436ace16bba385c + trainer: + kwargs: {} +name: 7ae7a5dceba8a3f7970644fd1650f26f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/params.yaml b/examples/security/classification/output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/params.yaml new file mode 100644 index 00000000..1250be5c --- /dev/null +++ b/examples/security/classification/output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/adv_predictions.json + adv_probabilities_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/04badb9749cb9a168662f4bc24c50dbd.pkl + params_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/params.yaml + predictions_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/predictions.json + probabilities_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/probabilities.json + score_dict_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/score_dict.json + test_labels_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/test_labels.json + train_labels_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/train_labels.json + model_dir: models + name: 7b1ccd68a1373d215e7d8a08424bd275 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c5b2fb893c1d95b3b05fe1916f57b9aa + trainer: + kwargs: {} +name: 7b1ccd68a1373d215e7d8a08424bd275 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7b47104a0cfe51901771971493f05e52/params.yaml b/examples/security/classification/output/reports/train/7b47104a0cfe51901771971493f05e52/params.yaml new file mode 100644 index 00000000..9c24ef4c --- /dev/null +++ b/examples/security/classification/output/reports/train/7b47104a0cfe51901771971493f05e52/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7b47104a0cfe51901771971493f05e52/adv_predictions.json + adv_probabilities_file: output/reports/train/7b47104a0cfe51901771971493f05e52/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/07858d1ccb89ba82804ee125b7f2bf16.pkl + params_file: output/reports/train/7b47104a0cfe51901771971493f05e52/params.yaml + predictions_file: output/reports/train/7b47104a0cfe51901771971493f05e52/predictions.json + probabilities_file: output/reports/train/7b47104a0cfe51901771971493f05e52/probabilities.json + score_dict_file: output/reports/train/7b47104a0cfe51901771971493f05e52/score_dict.json + test_labels_file: output/reports/train/7b47104a0cfe51901771971493f05e52/test_labels.json + train_labels_file: output/reports/train/7b47104a0cfe51901771971493f05e52/train_labels.json + model_dir: models + name: 7b47104a0cfe51901771971493f05e52 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 15e7a226950b8544c40d8216dc6922d8 + trainer: + kwargs: {} +name: 7b47104a0cfe51901771971493f05e52 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7bab3bd1642fb706891efc6603988cd3/params.yaml b/examples/security/classification/output/reports/train/7bab3bd1642fb706891efc6603988cd3/params.yaml new file mode 100644 index 00000000..c77fd657 --- /dev/null +++ b/examples/security/classification/output/reports/train/7bab3bd1642fb706891efc6603988cd3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/adv_predictions.json + adv_probabilities_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/5185016dfdc83e345234881a30b3f6e1.pkl + params_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/params.yaml + predictions_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/predictions.json + probabilities_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/probabilities.json + score_dict_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/score_dict.json + test_labels_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/test_labels.json + train_labels_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/train_labels.json + model_dir: models + name: 7bab3bd1642fb706891efc6603988cd3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5623721dceba36b96197428cfeeb22a7 + trainer: + kwargs: {} +name: 7bab3bd1642fb706891efc6603988cd3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7be24ce873cf11cc518525f72466bb67/params.yaml b/examples/security/classification/output/reports/train/7be24ce873cf11cc518525f72466bb67/params.yaml new file mode 100644 index 00000000..26587771 --- /dev/null +++ b/examples/security/classification/output/reports/train/7be24ce873cf11cc518525f72466bb67/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/adv_predictions.json + adv_probabilities_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/f59110595978a9649e8f52a26a7f852b.pkl + params_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/params.yaml + predictions_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/predictions.json + probabilities_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/probabilities.json + score_dict_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/score_dict.json + test_labels_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/test_labels.json + train_labels_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/train_labels.json + model_dir: models + name: 7be24ce873cf11cc518525f72466bb67 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2f596f93e3ce0f0e86743ae437752dfb + trainer: + kwargs: {} +name: 7be24ce873cf11cc518525f72466bb67 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/params.yaml b/examples/security/classification/output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/params.yaml new file mode 100644 index 00000000..d7e4c5ad --- /dev/null +++ b/examples/security/classification/output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/adv_predictions.json + adv_probabilities_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/cca55dfc14e5c0f91865bf6545d01620.pkl + params_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/params.yaml + predictions_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/predictions.json + probabilities_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/probabilities.json + score_dict_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/score_dict.json + test_labels_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/test_labels.json + train_labels_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/train_labels.json + model_dir: models + name: 7bf466bc7db1c6f7b6d316bf90573a41 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 69321176e61dc5ce65ffbff314aa0117 + trainer: + kwargs: {} +name: 7bf466bc7db1c6f7b6d316bf90573a41 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7c04bab5c761a44b42ded839be5369a5/params.yaml b/examples/security/classification/output/reports/train/7c04bab5c761a44b42ded839be5369a5/params.yaml new file mode 100644 index 00000000..a534b36e --- /dev/null +++ b/examples/security/classification/output/reports/train/7c04bab5c761a44b42ded839be5369a5/params.yaml @@ -0,0 +1,206 @@ +attack: + attack_size: 10 + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 10.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 388924becad0b51fffc74334a2566529 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bf574b9f84947d7fcceb05ce699fc30b + trainer: + kwargs: {} + name: 8f7f2ed1518e547f80392f673bf717df +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/adv_predictions.json + adv_probabilities_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/adv_probabilities.json + attack_file: output/attacks/96cc7582aa77498b90419ee40b8aaf7a.pkl + data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl + model_file: output/models/5d5f89bdef3cc6202af58867139353d4.pkl + params_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/params.yaml + predictions_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/predictions.json + probabilities_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/probabilities.json + score_dict_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/score_dict.json + test_labels_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/test_labels.json + train_labels_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/train_labels.json + model_dir: models + name: 7c04bab5c761a44b42ded839be5369a5 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7014473b2a2deb1fb79e8381cb573c9c + trainer: + kwargs: {} +name: 7c04bab5c761a44b42ded839be5369a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/params.yaml b/examples/security/classification/output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/params.yaml new file mode 100644 index 00000000..df7aac93 --- /dev/null +++ b/examples/security/classification/output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/adv_predictions.json + adv_probabilities_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/27f911590d1fda62c4fc5afdab11e71c.pkl + params_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/params.yaml + predictions_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/predictions.json + probabilities_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/probabilities.json + score_dict_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/score_dict.json + test_labels_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/test_labels.json + train_labels_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/train_labels.json + model_dir: models + name: 7c38e5f7e0aea8bbf7d47d8636f6128d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fc7e19738ad46d3d036ef0fb3f93ae6e + trainer: + kwargs: {} +name: 7c38e5f7e0aea8bbf7d47d8636f6128d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/params.yaml b/examples/security/classification/output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/params.yaml new file mode 100644 index 00000000..8cf4694f --- /dev/null +++ b/examples/security/classification/output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/adv_predictions.json + adv_probabilities_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/e27fa8c864bf3a72d9a6d57146c52865.pkl + params_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/params.yaml + predictions_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/predictions.json + probabilities_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/probabilities.json + score_dict_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/score_dict.json + test_labels_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/test_labels.json + train_labels_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/train_labels.json + model_dir: models + name: 7c60a8bf486ff9846cd6d46116b21dbe + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3605ff060bd79757ea57852f771f14f7 + trainer: + kwargs: {} +name: 7c60a8bf486ff9846cd6d46116b21dbe +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/params.yaml b/examples/security/classification/output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/params.yaml new file mode 100644 index 00000000..9b3cea81 --- /dev/null +++ b/examples/security/classification/output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/adv_predictions.json + adv_probabilities_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/3303431a107e9f4dc3679e27857ae8bb.pkl + params_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/params.yaml + predictions_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/predictions.json + probabilities_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/probabilities.json + score_dict_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/score_dict.json + test_labels_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/test_labels.json + train_labels_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/train_labels.json + model_dir: models + name: 7cc31a0cc25b3a9044b4af06797a9857 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 18442a3636578b31ca2547c83e63f351 + trainer: + kwargs: {} +name: 7cc31a0cc25b3a9044b4af06797a9857 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/params.yaml b/examples/security/classification/output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/params.yaml new file mode 100644 index 00000000..f6bf141f --- /dev/null +++ b/examples/security/classification/output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/adv_predictions.json + adv_probabilities_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/dbdaa87fbfbc0dbf810e9a916a326283.pkl + params_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/params.yaml + predictions_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/predictions.json + probabilities_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/probabilities.json + score_dict_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/score_dict.json + test_labels_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/test_labels.json + train_labels_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/train_labels.json + model_dir: models + name: 7d1563fcd0611d3ca2bd018ba8fb67ee + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1e2e394c1fc2a0b23a3e59f18505d83f + trainer: + kwargs: {} +name: 7d1563fcd0611d3ca2bd018ba8fb67ee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/params.yaml b/examples/security/classification/output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/params.yaml new file mode 100644 index 00000000..45c67074 --- /dev/null +++ b/examples/security/classification/output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/adv_predictions.json + adv_probabilities_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/1e85017a140efeb5fe3fc881bf7675f5.pkl + params_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/params.yaml + predictions_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/predictions.json + probabilities_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/probabilities.json + score_dict_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/score_dict.json + test_labels_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/test_labels.json + train_labels_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/train_labels.json + model_dir: models + name: 7d2bdddd12d4714226e2b57a456a4e50 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b6c93f6d3d88983570f6249b4385c544 + trainer: + kwargs: {} +name: 7d2bdddd12d4714226e2b57a456a4e50 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7d2cff9eb1840bff9f3411a953131483/params.yaml b/examples/security/classification/output/reports/train/7d2cff9eb1840bff9f3411a953131483/params.yaml new file mode 100644 index 00000000..15323770 --- /dev/null +++ b/examples/security/classification/output/reports/train/7d2cff9eb1840bff9f3411a953131483/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/adv_predictions.json + adv_probabilities_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/abd3556c13f0fb08ce6645d8fda8aeed.pkl + params_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/params.yaml + predictions_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/predictions.json + probabilities_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/probabilities.json + score_dict_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/score_dict.json + test_labels_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/test_labels.json + train_labels_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/train_labels.json + model_dir: models + name: 7d2cff9eb1840bff9f3411a953131483 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5c534174828106203017dc2338d0e416 + trainer: + kwargs: {} +name: 7d2cff9eb1840bff9f3411a953131483 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/params.yaml b/examples/security/classification/output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/params.yaml new file mode 100644 index 00000000..73bf016c --- /dev/null +++ b/examples/security/classification/output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/adv_predictions.json + adv_probabilities_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/7f785b132c8d0b148ed431a6684df151.pkl + params_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/params.yaml + predictions_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/predictions.json + probabilities_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/probabilities.json + score_dict_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/score_dict.json + test_labels_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/test_labels.json + train_labels_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/train_labels.json + model_dir: models + name: 7d40cc9308263ff7cb2802a69bf5aa01 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d525c9db56dfb878adc732478c6d840c + trainer: + kwargs: {} +name: 7d40cc9308263ff7cb2802a69bf5aa01 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/params.yaml b/examples/security/classification/output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/params.yaml new file mode 100644 index 00000000..ef81adda --- /dev/null +++ b/examples/security/classification/output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/adv_predictions.json + adv_probabilities_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/2d557f5dcd248f83001489566b329640.pkl + params_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/params.yaml + predictions_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/predictions.json + probabilities_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/probabilities.json + score_dict_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/score_dict.json + test_labels_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/test_labels.json + train_labels_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/train_labels.json + model_dir: models + name: 7d61ef9c05b2afc0683c248dbad638d9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b79cdf14725253e7d5a7a556a4523a6a + trainer: + kwargs: {} +name: 7d61ef9c05b2afc0683c248dbad638d9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/params.yaml b/examples/security/classification/output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/params.yaml new file mode 100644 index 00000000..ebb5d1a6 --- /dev/null +++ b/examples/security/classification/output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/params.yaml @@ -0,0 +1,115 @@ +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/adv_predictions.json + adv_probabilities_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl + model_file: output/models/d5f0276d20b7198eccf2a7d6fd9b4fb3.pkl + params_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/params.yaml + predictions_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/predictions.json + probabilities_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/probabilities.json + score_dict_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/score_dict.json + test_labels_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/test_labels.json + train_labels_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/train_labels.json + model_dir: models + name: 7d64a57e89ac17c6592fe94d6f9c61ef + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0e-05 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1977e506992eacbfd95b3e224d590292 + trainer: + kwargs: {} +name: 7d64a57e89ac17c6592fe94d6f9c61ef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/params.yaml b/examples/security/classification/output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/params.yaml new file mode 100644 index 00000000..ad8ceded --- /dev/null +++ b/examples/security/classification/output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/adv_predictions.json + adv_probabilities_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/95f6548299a2ba427e44c908c3edffd6.pkl + params_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/params.yaml + predictions_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/predictions.json + probabilities_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/probabilities.json + score_dict_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/score_dict.json + test_labels_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/test_labels.json + train_labels_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/train_labels.json + model_dir: models + name: 7d84ffb388f90d465c9e7ab88e3311c0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 693c9bf5aa6ef7aa568c4f8977471ced + trainer: + kwargs: {} +name: 7d84ffb388f90d465c9e7ab88e3311c0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/params.yaml b/examples/security/classification/output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/params.yaml new file mode 100644 index 00000000..e536a2a4 --- /dev/null +++ b/examples/security/classification/output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/adv_predictions.json + adv_probabilities_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/bc881cc8d39ccb553269bf98ad5332a7.pkl + params_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/params.yaml + predictions_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/predictions.json + probabilities_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/probabilities.json + score_dict_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/score_dict.json + test_labels_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/test_labels.json + train_labels_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/train_labels.json + model_dir: models + name: 7d87ea29263f3afbac3e2e1adbc8cb6a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 57cd0fea10c6783e9d70b7a375443fd8 + trainer: + kwargs: {} +name: 7d87ea29263f3afbac3e2e1adbc8cb6a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7db8297af502cd031abe3d1d4d8ed025/params.yaml b/examples/security/classification/output/reports/train/7db8297af502cd031abe3d1d4d8ed025/params.yaml new file mode 100644 index 00000000..ee035d91 --- /dev/null +++ b/examples/security/classification/output/reports/train/7db8297af502cd031abe3d1d4d8ed025/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/adv_predictions.json + adv_probabilities_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/1aee8c9620a5a88046818b12847c3edb.pkl + params_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/params.yaml + predictions_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/predictions.json + probabilities_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/probabilities.json + score_dict_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/score_dict.json + test_labels_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/test_labels.json + train_labels_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/train_labels.json + model_dir: models + name: 7db8297af502cd031abe3d1d4d8ed025 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 29dba220dda29b160560177fa6ba192d + trainer: + kwargs: {} +name: 7db8297af502cd031abe3d1d4d8ed025 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/params.yaml b/examples/security/classification/output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/params.yaml new file mode 100644 index 00000000..39d8327b --- /dev/null +++ b/examples/security/classification/output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/adv_predictions.json + adv_probabilities_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/c0970ceeba1ce624081de0bbc6867411.pkl + params_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/params.yaml + predictions_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/predictions.json + probabilities_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/probabilities.json + score_dict_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/score_dict.json + test_labels_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/test_labels.json + train_labels_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/train_labels.json + model_dir: models + name: 7dce1cb98fee6f4379e2fd5aa948a736 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a2b5974b7831fb1a4a254214b0ee65c2 + trainer: + kwargs: {} +name: 7dce1cb98fee6f4379e2fd5aa948a736 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/params.yaml b/examples/security/classification/output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/params.yaml new file mode 100644 index 00000000..430b5518 --- /dev/null +++ b/examples/security/classification/output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/adv_predictions.json + adv_probabilities_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/2bebf82f76a195df47f01be05d8d43f5.pkl + params_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/params.yaml + predictions_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/predictions.json + probabilities_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/probabilities.json + score_dict_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/score_dict.json + test_labels_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/test_labels.json + train_labels_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/train_labels.json + model_dir: models + name: 7dce78c8dd1ecc705f985d40a433f58e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d23a0ae721555c0c3c3a4c60ff15d504 + trainer: + kwargs: {} +name: 7dce78c8dd1ecc705f985d40a433f58e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7de39089880ee6867517c24f9030a258/params.yaml b/examples/security/classification/output/reports/train/7de39089880ee6867517c24f9030a258/params.yaml new file mode 100644 index 00000000..9c91a27e --- /dev/null +++ b/examples/security/classification/output/reports/train/7de39089880ee6867517c24f9030a258/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7de39089880ee6867517c24f9030a258/adv_predictions.json + adv_probabilities_file: output/reports/train/7de39089880ee6867517c24f9030a258/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/1a68543bf3606e1dae18197617aa0e0c.pkl + params_file: output/reports/train/7de39089880ee6867517c24f9030a258/params.yaml + predictions_file: output/reports/train/7de39089880ee6867517c24f9030a258/predictions.json + probabilities_file: output/reports/train/7de39089880ee6867517c24f9030a258/probabilities.json + score_dict_file: output/reports/train/7de39089880ee6867517c24f9030a258/score_dict.json + test_labels_file: output/reports/train/7de39089880ee6867517c24f9030a258/test_labels.json + train_labels_file: output/reports/train/7de39089880ee6867517c24f9030a258/train_labels.json + model_dir: models + name: 7de39089880ee6867517c24f9030a258 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 37dcfc79b50ee0decfe1b0f3b2c33740 + trainer: + kwargs: {} +name: 7de39089880ee6867517c24f9030a258 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/params.yaml b/examples/security/classification/output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/params.yaml new file mode 100644 index 00000000..e85c563b --- /dev/null +++ b/examples/security/classification/output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/adv_predictions.json + adv_probabilities_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/c41556cff8f7cb535a6be17b7b6dd835.pkl + params_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/params.yaml + predictions_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/predictions.json + probabilities_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/probabilities.json + score_dict_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/score_dict.json + test_labels_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/test_labels.json + train_labels_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/train_labels.json + model_dir: models + name: 7ded7bba5a4cb4eb70af000e45af2e16 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 856d8b90c2435b9face4cb7a151eb291 + trainer: + kwargs: {} +name: 7ded7bba5a4cb4eb70af000e45af2e16 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/params.yaml b/examples/security/classification/output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/params.yaml new file mode 100644 index 00000000..09da6eaf --- /dev/null +++ b/examples/security/classification/output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/params.yaml @@ -0,0 +1,115 @@ +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/adv_predictions.json + adv_probabilities_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl + model_file: output/models/679755da19d5691b7f0ca7582166f7e8.pkl + params_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/params.yaml + predictions_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/predictions.json + probabilities_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/probabilities.json + score_dict_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/score_dict.json + test_labels_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/test_labels.json + train_labels_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/train_labels.json + model_dir: models + name: 7dfb31f8759dfa2f30f55166b074c67e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 80522c9ba43b39cb53be5b293cef121d + trainer: + kwargs: {} +name: 7dfb31f8759dfa2f30f55166b074c67e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7dfd50006717472825478c09b5c42970/params.yaml b/examples/security/classification/output/reports/train/7dfd50006717472825478c09b5c42970/params.yaml new file mode 100644 index 00000000..945e7c2b --- /dev/null +++ b/examples/security/classification/output/reports/train/7dfd50006717472825478c09b5c42970/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7dfd50006717472825478c09b5c42970/adv_predictions.json + adv_probabilities_file: output/reports/train/7dfd50006717472825478c09b5c42970/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/4a1cefb2bb7b696f9cbfce5e20e92ef3.pkl + params_file: output/reports/train/7dfd50006717472825478c09b5c42970/params.yaml + predictions_file: output/reports/train/7dfd50006717472825478c09b5c42970/predictions.json + probabilities_file: output/reports/train/7dfd50006717472825478c09b5c42970/probabilities.json + score_dict_file: output/reports/train/7dfd50006717472825478c09b5c42970/score_dict.json + test_labels_file: output/reports/train/7dfd50006717472825478c09b5c42970/test_labels.json + train_labels_file: output/reports/train/7dfd50006717472825478c09b5c42970/train_labels.json + model_dir: models + name: 7dfd50006717472825478c09b5c42970 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0a47ee3b5f4697545454c13197f201bd + trainer: + kwargs: {} +name: 7dfd50006717472825478c09b5c42970 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7e11aff007778f24d3437491087b4af8/params.yaml b/examples/security/classification/output/reports/train/7e11aff007778f24d3437491087b4af8/params.yaml new file mode 100644 index 00000000..6015ad5a --- /dev/null +++ b/examples/security/classification/output/reports/train/7e11aff007778f24d3437491087b4af8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7e11aff007778f24d3437491087b4af8/adv_predictions.json + adv_probabilities_file: output/reports/train/7e11aff007778f24d3437491087b4af8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/f8dee826f98dfd0a197196dcafeaa576.pkl + params_file: output/reports/train/7e11aff007778f24d3437491087b4af8/params.yaml + predictions_file: output/reports/train/7e11aff007778f24d3437491087b4af8/predictions.json + probabilities_file: output/reports/train/7e11aff007778f24d3437491087b4af8/probabilities.json + score_dict_file: output/reports/train/7e11aff007778f24d3437491087b4af8/score_dict.json + test_labels_file: output/reports/train/7e11aff007778f24d3437491087b4af8/test_labels.json + train_labels_file: output/reports/train/7e11aff007778f24d3437491087b4af8/train_labels.json + model_dir: models + name: 7e11aff007778f24d3437491087b4af8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f12fe051be42369fcf4cd7a80291578 + trainer: + kwargs: {} +name: 7e11aff007778f24d3437491087b4af8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/params.yaml b/examples/security/classification/output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/params.yaml new file mode 100644 index 00000000..d2f8d2ba --- /dev/null +++ b/examples/security/classification/output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/adv_predictions.json + adv_probabilities_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/ce219ad49a4ea5b6e179a3f9365945ad.pkl + params_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/params.yaml + predictions_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/predictions.json + probabilities_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/probabilities.json + score_dict_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/score_dict.json + test_labels_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/test_labels.json + train_labels_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/train_labels.json + model_dir: models + name: 7e3e1e4599f1ccc4452d145a502650d4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ffa30f88de3480aeb1cacc2b486ee76c + trainer: + kwargs: {} +name: 7e3e1e4599f1ccc4452d145a502650d4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7e470e9710a948c8aca6019e73aacb3c/params.yaml b/examples/security/classification/output/reports/train/7e470e9710a948c8aca6019e73aacb3c/params.yaml new file mode 100644 index 00000000..44d180ab --- /dev/null +++ b/examples/security/classification/output/reports/train/7e470e9710a948c8aca6019e73aacb3c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/adv_predictions.json + adv_probabilities_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/f59fa4faa6fae488102f75644650eae7.pkl + params_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/params.yaml + predictions_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/predictions.json + probabilities_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/probabilities.json + score_dict_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/score_dict.json + test_labels_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/test_labels.json + train_labels_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/train_labels.json + model_dir: models + name: 7e470e9710a948c8aca6019e73aacb3c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9776f2277ceaea5346599337f1f3bfe6 + trainer: + kwargs: {} +name: 7e470e9710a948c8aca6019e73aacb3c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/params.yaml b/examples/security/classification/output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/params.yaml new file mode 100644 index 00000000..4b803062 --- /dev/null +++ b/examples/security/classification/output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/adv_predictions.json + adv_probabilities_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/5bb363e0e980bef71179d31310cb34b2.pkl + params_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/params.yaml + predictions_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/predictions.json + probabilities_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/probabilities.json + score_dict_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/score_dict.json + test_labels_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/test_labels.json + train_labels_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/train_labels.json + model_dir: models + name: 7e6b11e43b10cfda0a606ec60bc9f539 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b15bd97d97dc2753848e699eb1750167 + trainer: + kwargs: {} +name: 7e6b11e43b10cfda0a606ec60bc9f539 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/params.yaml b/examples/security/classification/output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/params.yaml new file mode 100644 index 00000000..d74f8b71 --- /dev/null +++ b/examples/security/classification/output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/adv_predictions.json + adv_probabilities_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/d09d29578dfe3157a6ba247d538bbe7c.pkl + params_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/params.yaml + predictions_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/predictions.json + probabilities_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/probabilities.json + score_dict_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/score_dict.json + test_labels_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/test_labels.json + train_labels_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/train_labels.json + model_dir: models + name: 7e7a0e7fb57f14424d5d69b94a1fff8c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 58f300179958776f7d79ead729e5760d + trainer: + kwargs: {} +name: 7e7a0e7fb57f14424d5d69b94a1fff8c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/params.yaml b/examples/security/classification/output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/params.yaml new file mode 100644 index 00000000..075c64ea --- /dev/null +++ b/examples/security/classification/output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/adv_predictions.json + adv_probabilities_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/6ced7f6ac3d38a0185f14ae94ee98158.pkl + params_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/params.yaml + predictions_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/predictions.json + probabilities_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/probabilities.json + score_dict_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/score_dict.json + test_labels_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/test_labels.json + train_labels_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/train_labels.json + model_dir: models + name: 7ec344c1cd366e9b916f7b60cf61849f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 526a2d3d10d4c22ea66a9c8fb5c4dc29 + trainer: + kwargs: {} +name: 7ec344c1cd366e9b916f7b60cf61849f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/params.yaml b/examples/security/classification/output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/params.yaml new file mode 100644 index 00000000..bb9e62fd --- /dev/null +++ b/examples/security/classification/output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/adv_predictions.json + adv_probabilities_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/0d01f2e42cf3323a065223d3cbb29f64.pkl + params_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/params.yaml + predictions_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/predictions.json + probabilities_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/probabilities.json + score_dict_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/score_dict.json + test_labels_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/test_labels.json + train_labels_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/train_labels.json + model_dir: models + name: 7ee7eb8b80f270ef4828d04feb2ce6c4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 31b71330b1ff41dc9b7eab0381fcf07f + trainer: + kwargs: {} +name: 7ee7eb8b80f270ef4828d04feb2ce6c4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/params.yaml b/examples/security/classification/output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/params.yaml new file mode 100644 index 00000000..6ee0c179 --- /dev/null +++ b/examples/security/classification/output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/adv_predictions.json + adv_probabilities_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/e2afb65f8779e535e60d2f024ec3ea52.pkl + params_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/params.yaml + predictions_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/predictions.json + probabilities_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/probabilities.json + score_dict_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/score_dict.json + test_labels_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/test_labels.json + train_labels_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/train_labels.json + model_dir: models + name: 7ef0d54c0484264eecdf2bacf9f44b10 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 438cf02c2d90d304f6333d9d3192da80 + trainer: + kwargs: {} +name: 7ef0d54c0484264eecdf2bacf9f44b10 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/params.yaml b/examples/security/classification/output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/params.yaml new file mode 100644 index 00000000..2fa91106 --- /dev/null +++ b/examples/security/classification/output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/adv_predictions.json + adv_probabilities_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/382ef1685e502f3998c99074f7ccf479.pkl + params_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/params.yaml + predictions_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/predictions.json + probabilities_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/probabilities.json + score_dict_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/score_dict.json + test_labels_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/test_labels.json + train_labels_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/train_labels.json + model_dir: models + name: 7f2ddf3d3283f75e5eb11b388670169e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 61b5618cb786dca09f85e52e31412f15 + trainer: + kwargs: {} +name: 7f2ddf3d3283f75e5eb11b388670169e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/params.yaml b/examples/security/classification/output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/params.yaml new file mode 100644 index 00000000..ee69ae2a --- /dev/null +++ b/examples/security/classification/output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/adv_predictions.json + adv_probabilities_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/8108d0f5c9a0568c4541d0c1a961a488.pkl + params_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/params.yaml + predictions_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/predictions.json + probabilities_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/probabilities.json + score_dict_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/score_dict.json + test_labels_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/test_labels.json + train_labels_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/train_labels.json + model_dir: models + name: 7f4b80774ed2290f38c422d0b12acf1f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 93a477ebafda62533b3b905dc08cc940 + trainer: + kwargs: {} +name: 7f4b80774ed2290f38c422d0b12acf1f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7f6768417633a0436cea9a0e98fa744b/params.yaml b/examples/security/classification/output/reports/train/7f6768417633a0436cea9a0e98fa744b/params.yaml new file mode 100644 index 00000000..27f46804 --- /dev/null +++ b/examples/security/classification/output/reports/train/7f6768417633a0436cea9a0e98fa744b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/adv_predictions.json + adv_probabilities_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/dccf2757a517f0f40d271b898b36513d.pkl + params_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/params.yaml + predictions_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/predictions.json + probabilities_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/probabilities.json + score_dict_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/score_dict.json + test_labels_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/test_labels.json + train_labels_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/train_labels.json + model_dir: models + name: 7f6768417633a0436cea9a0e98fa744b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 16f5587263a86434dedea181c31cbe6d + trainer: + kwargs: {} +name: 7f6768417633a0436cea9a0e98fa744b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7faa91537a1f200580ae4d1798562a47/params.yaml b/examples/security/classification/output/reports/train/7faa91537a1f200580ae4d1798562a47/params.yaml new file mode 100644 index 00000000..d9bc0132 --- /dev/null +++ b/examples/security/classification/output/reports/train/7faa91537a1f200580ae4d1798562a47/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/adv_predictions.json + adv_probabilities_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/f6ed1a9670445ef3e0bbb09bd0edc6c0.pkl + params_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/params.yaml + predictions_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/predictions.json + probabilities_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/probabilities.json + score_dict_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/score_dict.json + test_labels_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/test_labels.json + train_labels_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/train_labels.json + model_dir: models + name: 7faa91537a1f200580ae4d1798562a47 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ec3f2d62cc7ff0b903923a04baee4aac + trainer: + kwargs: {} +name: 7faa91537a1f200580ae4d1798562a47 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/params.yaml b/examples/security/classification/output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/params.yaml new file mode 100644 index 00000000..84e388be --- /dev/null +++ b/examples/security/classification/output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/adv_predictions.json + adv_probabilities_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/f954273c52dadb1a11e5970b381ab2ad.pkl + params_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/params.yaml + predictions_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/predictions.json + probabilities_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/probabilities.json + score_dict_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/score_dict.json + test_labels_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/test_labels.json + train_labels_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/train_labels.json + model_dir: models + name: 7fb6dfd4c528b9679362735e9dc2a2dc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6e698a32bd992a2cc08e6bd3a9478d0e + trainer: + kwargs: {} +name: 7fb6dfd4c528b9679362735e9dc2a2dc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/params.yaml b/examples/security/classification/output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/params.yaml new file mode 100644 index 00000000..4fc7566a --- /dev/null +++ b/examples/security/classification/output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/adv_predictions.json + adv_probabilities_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/d9eee7c12f406ebf2e091e2f50f67aab.pkl + params_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/params.yaml + predictions_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/predictions.json + probabilities_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/probabilities.json + score_dict_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/score_dict.json + test_labels_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/test_labels.json + train_labels_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/train_labels.json + model_dir: models + name: 7fd5324183b1f76108d9c84f6d1cf6bd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 01b835fe4edcbc5bae415c61c35e2686 + trainer: + kwargs: {} +name: 7fd5324183b1f76108d9c84f6d1cf6bd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/800a4eca7fb319b6cae02805309f2345/params.yaml b/examples/security/classification/output/reports/train/800a4eca7fb319b6cae02805309f2345/params.yaml new file mode 100644 index 00000000..6a124bd4 --- /dev/null +++ b/examples/security/classification/output/reports/train/800a4eca7fb319b6cae02805309f2345/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/adv_predictions.json + adv_probabilities_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/91e144aec3c8018fe46d0a6d39b250f4.pkl + params_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/params.yaml + predictions_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/predictions.json + probabilities_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/probabilities.json + score_dict_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/score_dict.json + test_labels_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/test_labels.json + train_labels_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/train_labels.json + model_dir: models + name: 800a4eca7fb319b6cae02805309f2345 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 57dcabc5cb64b7b3e9595d01fdefb58d + trainer: + kwargs: {} +name: 800a4eca7fb319b6cae02805309f2345 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/80201e16867e9c2061beab76aa02c56e/params.yaml b/examples/security/classification/output/reports/train/80201e16867e9c2061beab76aa02c56e/params.yaml new file mode 100644 index 00000000..f900bc97 --- /dev/null +++ b/examples/security/classification/output/reports/train/80201e16867e9c2061beab76aa02c56e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/adv_predictions.json + adv_probabilities_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/4a2f63eb9b79dab18ad069b0f25a064d.pkl + params_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/params.yaml + predictions_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/predictions.json + probabilities_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/probabilities.json + score_dict_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/score_dict.json + test_labels_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/test_labels.json + train_labels_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/train_labels.json + model_dir: models + name: 80201e16867e9c2061beab76aa02c56e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c72cf71beaf993df452964c8f49693e1 + trainer: + kwargs: {} +name: 80201e16867e9c2061beab76aa02c56e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/80474afa5f435f7e922fece05f649a43/params.yaml b/examples/security/classification/output/reports/train/80474afa5f435f7e922fece05f649a43/params.yaml new file mode 100644 index 00000000..995e0f77 --- /dev/null +++ b/examples/security/classification/output/reports/train/80474afa5f435f7e922fece05f649a43/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/80474afa5f435f7e922fece05f649a43/adv_predictions.json + adv_probabilities_file: output/reports/train/80474afa5f435f7e922fece05f649a43/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/e8ff50621d9a5260b11a7110403428bc.pkl + params_file: output/reports/train/80474afa5f435f7e922fece05f649a43/params.yaml + predictions_file: output/reports/train/80474afa5f435f7e922fece05f649a43/predictions.json + probabilities_file: output/reports/train/80474afa5f435f7e922fece05f649a43/probabilities.json + score_dict_file: output/reports/train/80474afa5f435f7e922fece05f649a43/score_dict.json + test_labels_file: output/reports/train/80474afa5f435f7e922fece05f649a43/test_labels.json + train_labels_file: output/reports/train/80474afa5f435f7e922fece05f649a43/train_labels.json + model_dir: models + name: 80474afa5f435f7e922fece05f649a43 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9c79e6d740542f1b284eb93039825d96 + trainer: + kwargs: {} +name: 80474afa5f435f7e922fece05f649a43 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/params.yaml b/examples/security/classification/output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/params.yaml new file mode 100644 index 00000000..0801f2ac --- /dev/null +++ b/examples/security/classification/output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/adv_predictions.json + adv_probabilities_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/b0bec543e4b5a6c78dc361fb1b04b2d9.pkl + params_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/params.yaml + predictions_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/predictions.json + probabilities_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/probabilities.json + score_dict_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/score_dict.json + test_labels_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/test_labels.json + train_labels_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/train_labels.json + model_dir: models + name: 80729b5d6baed5b6c40402de9b7eb7f9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 452d5ac3244ef488f9ca9381493e56b4 + trainer: + kwargs: {} +name: 80729b5d6baed5b6c40402de9b7eb7f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/params.yaml b/examples/security/classification/output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/params.yaml new file mode 100644 index 00000000..9d3c7845 --- /dev/null +++ b/examples/security/classification/output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/adv_predictions.json + adv_probabilities_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/3c7ca6f814826b46d20ea5b07137e954.pkl + params_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/params.yaml + predictions_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/predictions.json + probabilities_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/probabilities.json + score_dict_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/score_dict.json + test_labels_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/test_labels.json + train_labels_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/train_labels.json + model_dir: models + name: 80756d09ef3519bc71aaf5b8251ce5e4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5d04c787dbb7dc2c6d3bf7e90b42ae3b + trainer: + kwargs: {} +name: 80756d09ef3519bc71aaf5b8251ce5e4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/80aaa16ec930a784e4640eb553268591/params.yaml b/examples/security/classification/output/reports/train/80aaa16ec930a784e4640eb553268591/params.yaml new file mode 100644 index 00000000..1910b2be --- /dev/null +++ b/examples/security/classification/output/reports/train/80aaa16ec930a784e4640eb553268591/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/80aaa16ec930a784e4640eb553268591/adv_predictions.json + adv_probabilities_file: output/reports/train/80aaa16ec930a784e4640eb553268591/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/33763598c6254935a823b1067c1245ed.pkl + params_file: output/reports/train/80aaa16ec930a784e4640eb553268591/params.yaml + predictions_file: output/reports/train/80aaa16ec930a784e4640eb553268591/predictions.json + probabilities_file: output/reports/train/80aaa16ec930a784e4640eb553268591/probabilities.json + score_dict_file: output/reports/train/80aaa16ec930a784e4640eb553268591/score_dict.json + test_labels_file: output/reports/train/80aaa16ec930a784e4640eb553268591/test_labels.json + train_labels_file: output/reports/train/80aaa16ec930a784e4640eb553268591/train_labels.json + model_dir: models + name: 80aaa16ec930a784e4640eb553268591 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d45a40c754bd2ee8645e92a81a1da71e + trainer: + kwargs: {} +name: 80aaa16ec930a784e4640eb553268591 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/params.yaml b/examples/security/classification/output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/params.yaml new file mode 100644 index 00000000..76d1a503 --- /dev/null +++ b/examples/security/classification/output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/adv_predictions.json + adv_probabilities_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/8e55f473630362bb4fa781502022bbae.pkl + params_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/params.yaml + predictions_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/predictions.json + probabilities_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/probabilities.json + score_dict_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/score_dict.json + test_labels_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/test_labels.json + train_labels_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/train_labels.json + model_dir: models + name: 80b85ee81048241d2194b9f0ff8e15dd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 83af83f63cd019d6e9554c23a3ce4a83 + trainer: + kwargs: {} +name: 80b85ee81048241d2194b9f0ff8e15dd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/params.yaml b/examples/security/classification/output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/params.yaml new file mode 100644 index 00000000..e6102147 --- /dev/null +++ b/examples/security/classification/output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/adv_predictions.json + adv_probabilities_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/d18df932c9a3ee81356d962aa00e00b3.pkl + params_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/params.yaml + predictions_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/predictions.json + probabilities_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/probabilities.json + score_dict_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/score_dict.json + test_labels_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/test_labels.json + train_labels_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/train_labels.json + model_dir: models + name: 80bdc3da1507e8ca6f8d7d5b2641f094 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 589834c586b5903c89686563229a7714 + trainer: + kwargs: {} +name: 80bdc3da1507e8ca6f8d7d5b2641f094 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/80e01be0f2592d17eeb8e3754354a57d/params.yaml b/examples/security/classification/output/reports/train/80e01be0f2592d17eeb8e3754354a57d/params.yaml new file mode 100644 index 00000000..398e19aa --- /dev/null +++ b/examples/security/classification/output/reports/train/80e01be0f2592d17eeb8e3754354a57d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/adv_predictions.json + adv_probabilities_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/e02e0388d4a14091f1ad6fb92eb02f1d.pkl + params_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/params.yaml + predictions_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/predictions.json + probabilities_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/probabilities.json + score_dict_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/score_dict.json + test_labels_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/test_labels.json + train_labels_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/train_labels.json + model_dir: models + name: 80e01be0f2592d17eeb8e3754354a57d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c0ba7ff5a8cdd5c7dfe5be13b8363c7f + trainer: + kwargs: {} +name: 80e01be0f2592d17eeb8e3754354a57d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/params.yaml b/examples/security/classification/output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/params.yaml new file mode 100644 index 00000000..97d0acff --- /dev/null +++ b/examples/security/classification/output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/adv_predictions.json + adv_probabilities_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/f52fd93b0344c0fbdaf3deb43cec12fe.pkl + params_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/params.yaml + predictions_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/predictions.json + probabilities_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/probabilities.json + score_dict_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/score_dict.json + test_labels_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/test_labels.json + train_labels_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/train_labels.json + model_dir: models + name: 80f7af5f20cd8185b1299ed3d98689cb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d88e4b7cadbec656b33a5d915e50203 + trainer: + kwargs: {} +name: 80f7af5f20cd8185b1299ed3d98689cb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/810e6946c484a430a593fd1b562479af/params.yaml b/examples/security/classification/output/reports/train/810e6946c484a430a593fd1b562479af/params.yaml new file mode 100644 index 00000000..aef1a96c --- /dev/null +++ b/examples/security/classification/output/reports/train/810e6946c484a430a593fd1b562479af/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/810e6946c484a430a593fd1b562479af/adv_predictions.json + adv_probabilities_file: output/reports/train/810e6946c484a430a593fd1b562479af/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/bad907f3a3f5125177e2feb3d1bc9dad.pkl + params_file: output/reports/train/810e6946c484a430a593fd1b562479af/params.yaml + predictions_file: output/reports/train/810e6946c484a430a593fd1b562479af/predictions.json + probabilities_file: output/reports/train/810e6946c484a430a593fd1b562479af/probabilities.json + score_dict_file: output/reports/train/810e6946c484a430a593fd1b562479af/score_dict.json + test_labels_file: output/reports/train/810e6946c484a430a593fd1b562479af/test_labels.json + train_labels_file: output/reports/train/810e6946c484a430a593fd1b562479af/train_labels.json + model_dir: models + name: 810e6946c484a430a593fd1b562479af + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0168c3e43426fe1ba8fa3efe85c65245 + trainer: + kwargs: {} +name: 810e6946c484a430a593fd1b562479af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/81190866ea7daee492b6d8035bf4e3c8/params.yaml b/examples/security/classification/output/reports/train/81190866ea7daee492b6d8035bf4e3c8/params.yaml new file mode 100644 index 00000000..f8d82829 --- /dev/null +++ b/examples/security/classification/output/reports/train/81190866ea7daee492b6d8035bf4e3c8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/adv_predictions.json + adv_probabilities_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/484e66053e66095e11792d91fdc089db.pkl + params_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/params.yaml + predictions_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/predictions.json + probabilities_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/probabilities.json + score_dict_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/score_dict.json + test_labels_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/test_labels.json + train_labels_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/train_labels.json + model_dir: models + name: 81190866ea7daee492b6d8035bf4e3c8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b6ef5f5bebfbc040ebca9c00b3453cb3 + trainer: + kwargs: {} +name: 81190866ea7daee492b6d8035bf4e3c8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/812aa38a4907f33dae8f2a81c24628f3/params.yaml b/examples/security/classification/output/reports/train/812aa38a4907f33dae8f2a81c24628f3/params.yaml new file mode 100644 index 00000000..f4dbe1a8 --- /dev/null +++ b/examples/security/classification/output/reports/train/812aa38a4907f33dae8f2a81c24628f3/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/adv_predictions.json + adv_probabilities_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/dfebf35bb63fffe5efc82504fa582417.pkl + params_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/params.yaml + predictions_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/predictions.json + probabilities_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/probabilities.json + score_dict_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/score_dict.json + test_labels_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/test_labels.json + train_labels_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/train_labels.json + model_dir: models + name: 812aa38a4907f33dae8f2a81c24628f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 78c8c0b7d33166bf14a04492848c8382 + trainer: + kwargs: {} +name: 812aa38a4907f33dae8f2a81c24628f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/81782cf2ac3d394adebb9385360416d8/params.yaml b/examples/security/classification/output/reports/train/81782cf2ac3d394adebb9385360416d8/params.yaml new file mode 100644 index 00000000..a7698845 --- /dev/null +++ b/examples/security/classification/output/reports/train/81782cf2ac3d394adebb9385360416d8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/adv_predictions.json + adv_probabilities_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/e938389d2c7d2528f59d953d5b44ac4c.pkl + params_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/params.yaml + predictions_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/predictions.json + probabilities_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/probabilities.json + score_dict_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/score_dict.json + test_labels_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/test_labels.json + train_labels_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/train_labels.json + model_dir: models + name: 81782cf2ac3d394adebb9385360416d8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cdf9240180eb9031f8bb89fbb6ea73bc + trainer: + kwargs: {} +name: 81782cf2ac3d394adebb9385360416d8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/819f3e2f9f190233a32b99b354295053/params.yaml b/examples/security/classification/output/reports/train/819f3e2f9f190233a32b99b354295053/params.yaml new file mode 100644 index 00000000..ab6595cd --- /dev/null +++ b/examples/security/classification/output/reports/train/819f3e2f9f190233a32b99b354295053/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/819f3e2f9f190233a32b99b354295053/adv_predictions.json + adv_probabilities_file: output/reports/train/819f3e2f9f190233a32b99b354295053/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/ee105039f299bbeda68806b7d06f00e6.pkl + params_file: output/reports/train/819f3e2f9f190233a32b99b354295053/params.yaml + predictions_file: output/reports/train/819f3e2f9f190233a32b99b354295053/predictions.json + probabilities_file: output/reports/train/819f3e2f9f190233a32b99b354295053/probabilities.json + score_dict_file: output/reports/train/819f3e2f9f190233a32b99b354295053/score_dict.json + test_labels_file: output/reports/train/819f3e2f9f190233a32b99b354295053/test_labels.json + train_labels_file: output/reports/train/819f3e2f9f190233a32b99b354295053/train_labels.json + model_dir: models + name: 819f3e2f9f190233a32b99b354295053 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 87d57e3f5860de93234cd7f3175d0152 + trainer: + kwargs: {} +name: 819f3e2f9f190233a32b99b354295053 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/params.yaml b/examples/security/classification/output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/params.yaml new file mode 100644 index 00000000..57c38214 --- /dev/null +++ b/examples/security/classification/output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/adv_predictions.json + adv_probabilities_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/529404233f86ca122cff94f1762fc28e.pkl + params_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/params.yaml + predictions_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/predictions.json + probabilities_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/probabilities.json + score_dict_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/score_dict.json + test_labels_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/test_labels.json + train_labels_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/train_labels.json + model_dir: models + name: 81e19a6bb8593f9a4b71f5669722cf54 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: addeb9129f601069a39556a0baf9fc3e + trainer: + kwargs: {} +name: 81e19a6bb8593f9a4b71f5669722cf54 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/82008bc15714db1e622a8459b79bddef/params.yaml b/examples/security/classification/output/reports/train/82008bc15714db1e622a8459b79bddef/params.yaml new file mode 100644 index 00000000..0e007d6f --- /dev/null +++ b/examples/security/classification/output/reports/train/82008bc15714db1e622a8459b79bddef/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/82008bc15714db1e622a8459b79bddef/adv_predictions.json + adv_probabilities_file: output/reports/train/82008bc15714db1e622a8459b79bddef/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/1ad1b35261c0da50306ea96c134a4340.pkl + params_file: output/reports/train/82008bc15714db1e622a8459b79bddef/params.yaml + predictions_file: output/reports/train/82008bc15714db1e622a8459b79bddef/predictions.json + probabilities_file: output/reports/train/82008bc15714db1e622a8459b79bddef/probabilities.json + score_dict_file: output/reports/train/82008bc15714db1e622a8459b79bddef/score_dict.json + test_labels_file: output/reports/train/82008bc15714db1e622a8459b79bddef/test_labels.json + train_labels_file: output/reports/train/82008bc15714db1e622a8459b79bddef/train_labels.json + model_dir: models + name: 82008bc15714db1e622a8459b79bddef + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 31a95ec6c1100352271fce002321d6cb + trainer: + kwargs: {} +name: 82008bc15714db1e622a8459b79bddef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8221641e09a02edb00bd3172b78b3fbe/params.yaml b/examples/security/classification/output/reports/train/8221641e09a02edb00bd3172b78b3fbe/params.yaml new file mode 100644 index 00000000..12325204 --- /dev/null +++ b/examples/security/classification/output/reports/train/8221641e09a02edb00bd3172b78b3fbe/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/adv_predictions.json + adv_probabilities_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/6732bd59bbf8a838c280f70ba80d9dd7.pkl + params_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/params.yaml + predictions_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/predictions.json + probabilities_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/probabilities.json + score_dict_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/score_dict.json + test_labels_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/test_labels.json + train_labels_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/train_labels.json + model_dir: models + name: 8221641e09a02edb00bd3172b78b3fbe + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 14e75645bbda09ade207e6a407d1fd6b + trainer: + kwargs: {} +name: 8221641e09a02edb00bd3172b78b3fbe +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/params.yaml b/examples/security/classification/output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/params.yaml new file mode 100644 index 00000000..f4920fd3 --- /dev/null +++ b/examples/security/classification/output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/adv_predictions.json + adv_probabilities_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/169e06ea9dd5a1322d7cb8588df08535.pkl + params_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/params.yaml + predictions_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/predictions.json + probabilities_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/probabilities.json + score_dict_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/score_dict.json + test_labels_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/test_labels.json + train_labels_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/train_labels.json + model_dir: models + name: 8223c79d2370ba2612bfd88d2e1eadc2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3681912bc7ed2431c407f2635cc80df9 + trainer: + kwargs: {} +name: 8223c79d2370ba2612bfd88d2e1eadc2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/params.yaml b/examples/security/classification/output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/params.yaml new file mode 100644 index 00000000..f73f3edc --- /dev/null +++ b/examples/security/classification/output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/adv_predictions.json + adv_probabilities_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/f4f3016e8d1467382d7b8e96017c050d.pkl + params_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/params.yaml + predictions_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/predictions.json + probabilities_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/probabilities.json + score_dict_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/score_dict.json + test_labels_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/test_labels.json + train_labels_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/train_labels.json + model_dir: models + name: 822f1af8fdfc45ee55db6dc2dc78f990 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.001 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 65b65b07172c8e6b0543c3bab49fc3f7 + trainer: + kwargs: {} +name: 822f1af8fdfc45ee55db6dc2dc78f990 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/82742e42c9b02ec181be1e8481d185f8/params.yaml b/examples/security/classification/output/reports/train/82742e42c9b02ec181be1e8481d185f8/params.yaml new file mode 100644 index 00000000..fb819c55 --- /dev/null +++ b/examples/security/classification/output/reports/train/82742e42c9b02ec181be1e8481d185f8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/adv_predictions.json + adv_probabilities_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/461f5859e93ab37766f0cd935dc51939.pkl + params_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/params.yaml + predictions_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/predictions.json + probabilities_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/probabilities.json + score_dict_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/score_dict.json + test_labels_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/test_labels.json + train_labels_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/train_labels.json + model_dir: models + name: 82742e42c9b02ec181be1e8481d185f8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 42679a70b6c757c84492cd023d7eb23e + trainer: + kwargs: {} +name: 82742e42c9b02ec181be1e8481d185f8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/82787ce49222cfcc416d98979a1aab51/params.yaml b/examples/security/classification/output/reports/train/82787ce49222cfcc416d98979a1aab51/params.yaml new file mode 100644 index 00000000..d10dbad4 --- /dev/null +++ b/examples/security/classification/output/reports/train/82787ce49222cfcc416d98979a1aab51/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/adv_predictions.json + adv_probabilities_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/14a0b0991566981cb3f29241d1dc8fe0.pkl + params_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/params.yaml + predictions_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/predictions.json + probabilities_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/probabilities.json + score_dict_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/score_dict.json + test_labels_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/test_labels.json + train_labels_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/train_labels.json + model_dir: models + name: 82787ce49222cfcc416d98979a1aab51 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cabb2d8bd13ca2811abab0af9ae70a48 + trainer: + kwargs: {} +name: 82787ce49222cfcc416d98979a1aab51 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/828360be6317b9f0965ed65cb969ed34/params.yaml b/examples/security/classification/output/reports/train/828360be6317b9f0965ed65cb969ed34/params.yaml new file mode 100644 index 00000000..7bb0d847 --- /dev/null +++ b/examples/security/classification/output/reports/train/828360be6317b9f0965ed65cb969ed34/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/adv_predictions.json + adv_probabilities_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/376a3b4ee6f1976e56fde29e1df337d9.pkl + params_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/params.yaml + predictions_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/predictions.json + probabilities_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/probabilities.json + score_dict_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/score_dict.json + test_labels_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/test_labels.json + train_labels_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/train_labels.json + model_dir: models + name: 828360be6317b9f0965ed65cb969ed34 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 90dd4041c07e66272fd16f1718d574ec + trainer: + kwargs: {} +name: 828360be6317b9f0965ed65cb969ed34 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8294f89363afa252f1e5a597a175f877/params.yaml b/examples/security/classification/output/reports/train/8294f89363afa252f1e5a597a175f877/params.yaml new file mode 100644 index 00000000..f8c80368 --- /dev/null +++ b/examples/security/classification/output/reports/train/8294f89363afa252f1e5a597a175f877/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8294f89363afa252f1e5a597a175f877/adv_predictions.json + adv_probabilities_file: output/reports/train/8294f89363afa252f1e5a597a175f877/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/a3c7ec497d9898094421b79bb38545e4.pkl + params_file: output/reports/train/8294f89363afa252f1e5a597a175f877/params.yaml + predictions_file: output/reports/train/8294f89363afa252f1e5a597a175f877/predictions.json + probabilities_file: output/reports/train/8294f89363afa252f1e5a597a175f877/probabilities.json + score_dict_file: output/reports/train/8294f89363afa252f1e5a597a175f877/score_dict.json + test_labels_file: output/reports/train/8294f89363afa252f1e5a597a175f877/test_labels.json + train_labels_file: output/reports/train/8294f89363afa252f1e5a597a175f877/train_labels.json + model_dir: models + name: 8294f89363afa252f1e5a597a175f877 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d579306e508ebf11cdee33c23085d5ba + trainer: + kwargs: {} +name: 8294f89363afa252f1e5a597a175f877 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/829c6d97bc7dc632864c1c979e2bedae/params.yaml b/examples/security/classification/output/reports/train/829c6d97bc7dc632864c1c979e2bedae/params.yaml new file mode 100644 index 00000000..61218223 --- /dev/null +++ b/examples/security/classification/output/reports/train/829c6d97bc7dc632864c1c979e2bedae/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/adv_predictions.json + adv_probabilities_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/795d2cd38ae2a252a0ee05afbaf318a8.pkl + params_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/params.yaml + predictions_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/predictions.json + probabilities_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/probabilities.json + score_dict_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/score_dict.json + test_labels_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/test_labels.json + train_labels_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/train_labels.json + model_dir: models + name: 829c6d97bc7dc632864c1c979e2bedae + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8588cb415642ba871c453de7757834e5 + trainer: + kwargs: {} +name: 829c6d97bc7dc632864c1c979e2bedae +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/params.yaml b/examples/security/classification/output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/params.yaml new file mode 100644 index 00000000..ad1f51b1 --- /dev/null +++ b/examples/security/classification/output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/adv_predictions.json + adv_probabilities_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/cb7869432921616ce841a09e1ca77110.pkl + params_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/params.yaml + predictions_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/predictions.json + probabilities_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/probabilities.json + score_dict_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/score_dict.json + test_labels_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/test_labels.json + train_labels_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/train_labels.json + model_dir: models + name: 829e8042ca0b53ee1bd1e8c08c4b139e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d8d877de3b1e07e5d4220d0fd6c1e1b0 + trainer: + kwargs: {} +name: 829e8042ca0b53ee1bd1e8c08c4b139e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/params.yaml b/examples/security/classification/output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/params.yaml new file mode 100644 index 00000000..9b8f176e --- /dev/null +++ b/examples/security/classification/output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/adv_predictions.json + adv_probabilities_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/f4661589799be5977b6c02f1716ac122.pkl + params_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/params.yaml + predictions_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/predictions.json + probabilities_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/probabilities.json + score_dict_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/score_dict.json + test_labels_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/test_labels.json + train_labels_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/train_labels.json + model_dir: models + name: 82aaed7adc5f7a3ec5badd12547b9550 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a87cd28a2d58abda9418a58b53011257 + trainer: + kwargs: {} +name: 82aaed7adc5f7a3ec5badd12547b9550 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/82c00e921dabed33298b27a571a9e718/params.yaml b/examples/security/classification/output/reports/train/82c00e921dabed33298b27a571a9e718/params.yaml new file mode 100644 index 00000000..ef5d34b4 --- /dev/null +++ b/examples/security/classification/output/reports/train/82c00e921dabed33298b27a571a9e718/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/82c00e921dabed33298b27a571a9e718/adv_predictions.json + adv_probabilities_file: output/reports/train/82c00e921dabed33298b27a571a9e718/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/8bbee02f651a75581c324be8f0ff8c81.pkl + params_file: output/reports/train/82c00e921dabed33298b27a571a9e718/params.yaml + predictions_file: output/reports/train/82c00e921dabed33298b27a571a9e718/predictions.json + probabilities_file: output/reports/train/82c00e921dabed33298b27a571a9e718/probabilities.json + score_dict_file: output/reports/train/82c00e921dabed33298b27a571a9e718/score_dict.json + test_labels_file: output/reports/train/82c00e921dabed33298b27a571a9e718/test_labels.json + train_labels_file: output/reports/train/82c00e921dabed33298b27a571a9e718/train_labels.json + model_dir: models + name: 82c00e921dabed33298b27a571a9e718 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 67e03e0c440cd941756e52dea87ac32b + trainer: + kwargs: {} +name: 82c00e921dabed33298b27a571a9e718 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/834ec47b0659293267a615f62c5735a6/params.yaml b/examples/security/classification/output/reports/train/834ec47b0659293267a615f62c5735a6/params.yaml new file mode 100644 index 00000000..22515cbe --- /dev/null +++ b/examples/security/classification/output/reports/train/834ec47b0659293267a615f62c5735a6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/834ec47b0659293267a615f62c5735a6/adv_predictions.json + adv_probabilities_file: output/reports/train/834ec47b0659293267a615f62c5735a6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/22e93dc4f7db522126af5f94f693939c.pkl + params_file: output/reports/train/834ec47b0659293267a615f62c5735a6/params.yaml + predictions_file: output/reports/train/834ec47b0659293267a615f62c5735a6/predictions.json + probabilities_file: output/reports/train/834ec47b0659293267a615f62c5735a6/probabilities.json + score_dict_file: output/reports/train/834ec47b0659293267a615f62c5735a6/score_dict.json + test_labels_file: output/reports/train/834ec47b0659293267a615f62c5735a6/test_labels.json + train_labels_file: output/reports/train/834ec47b0659293267a615f62c5735a6/train_labels.json + model_dir: models + name: 834ec47b0659293267a615f62c5735a6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e03f70a73495979ac12c511488ca9191 + trainer: + kwargs: {} +name: 834ec47b0659293267a615f62c5735a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/836e158923a7ba76e05f5ebefca67ed3/params.yaml b/examples/security/classification/output/reports/train/836e158923a7ba76e05f5ebefca67ed3/params.yaml new file mode 100644 index 00000000..df9c5234 --- /dev/null +++ b/examples/security/classification/output/reports/train/836e158923a7ba76e05f5ebefca67ed3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/adv_predictions.json + adv_probabilities_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/18412478b9f2c6297e3ab0b2878d341e.pkl + params_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/params.yaml + predictions_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/predictions.json + probabilities_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/probabilities.json + score_dict_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/score_dict.json + test_labels_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/test_labels.json + train_labels_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/train_labels.json + model_dir: models + name: 836e158923a7ba76e05f5ebefca67ed3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b2d044384bde8b176d31e58229910401 + trainer: + kwargs: {} +name: 836e158923a7ba76e05f5ebefca67ed3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/params.yaml b/examples/security/classification/output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/params.yaml new file mode 100644 index 00000000..f6b85156 --- /dev/null +++ b/examples/security/classification/output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/adv_predictions.json + adv_probabilities_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/735ad1e47f32849185fb92d7fefa5a56.pkl + params_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/params.yaml + predictions_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/predictions.json + probabilities_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/probabilities.json + score_dict_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/score_dict.json + test_labels_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/test_labels.json + train_labels_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/train_labels.json + model_dir: models + name: 83a067a6dbe1f4b4f2f8b893c95dd46d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cbc0925401f4fed3bb6290f1d0eb7d00 + trainer: + kwargs: {} +name: 83a067a6dbe1f4b4f2f8b893c95dd46d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/83e2dbad6271d074374836bbec4abea3/params.yaml b/examples/security/classification/output/reports/train/83e2dbad6271d074374836bbec4abea3/params.yaml new file mode 100644 index 00000000..ab1969f9 --- /dev/null +++ b/examples/security/classification/output/reports/train/83e2dbad6271d074374836bbec4abea3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/adv_predictions.json + adv_probabilities_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/740ed5426b4e4629f4112363514d61e4.pkl + params_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/params.yaml + predictions_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/predictions.json + probabilities_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/probabilities.json + score_dict_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/score_dict.json + test_labels_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/test_labels.json + train_labels_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/train_labels.json + model_dir: models + name: 83e2dbad6271d074374836bbec4abea3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 50df3d41dd86ad6dcfbbd1e48e59c95b + trainer: + kwargs: {} +name: 83e2dbad6271d074374836bbec4abea3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/83faa2a56109e0ba252b7cda3ab37703/params.yaml b/examples/security/classification/output/reports/train/83faa2a56109e0ba252b7cda3ab37703/params.yaml new file mode 100644 index 00000000..a87d4779 --- /dev/null +++ b/examples/security/classification/output/reports/train/83faa2a56109e0ba252b7cda3ab37703/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/adv_predictions.json + adv_probabilities_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/9cfc54677c627650b47e67042eec7c74.pkl + params_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/params.yaml + predictions_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/predictions.json + probabilities_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/probabilities.json + score_dict_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/score_dict.json + test_labels_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/test_labels.json + train_labels_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/train_labels.json + model_dir: models + name: 83faa2a56109e0ba252b7cda3ab37703 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 313ae4de650f32beee4f1d24feff98d3 + trainer: + kwargs: {} +name: 83faa2a56109e0ba252b7cda3ab37703 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/params.yaml b/examples/security/classification/output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/params.yaml new file mode 100644 index 00000000..d547df13 --- /dev/null +++ b/examples/security/classification/output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/adv_predictions.json + adv_probabilities_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/02a89a41d4bf1d02fcde6cc214147391.pkl + params_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/params.yaml + predictions_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/predictions.json + probabilities_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/probabilities.json + score_dict_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/score_dict.json + test_labels_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/test_labels.json + train_labels_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/train_labels.json + model_dir: models + name: 83fb5e6510768e0a4f5ff9a0e3a0b622 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2a9aa36408f6c92373f528a39ff69816 + trainer: + kwargs: {} +name: 83fb5e6510768e0a4f5ff9a0e3a0b622 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/840622f460065f0af78fbc39bc52345d/params.yaml b/examples/security/classification/output/reports/train/840622f460065f0af78fbc39bc52345d/params.yaml new file mode 100644 index 00000000..2e3a4c21 --- /dev/null +++ b/examples/security/classification/output/reports/train/840622f460065f0af78fbc39bc52345d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/840622f460065f0af78fbc39bc52345d/adv_predictions.json + adv_probabilities_file: output/reports/train/840622f460065f0af78fbc39bc52345d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/fb1970d07d33f925407faa9555409abc.pkl + params_file: output/reports/train/840622f460065f0af78fbc39bc52345d/params.yaml + predictions_file: output/reports/train/840622f460065f0af78fbc39bc52345d/predictions.json + probabilities_file: output/reports/train/840622f460065f0af78fbc39bc52345d/probabilities.json + score_dict_file: output/reports/train/840622f460065f0af78fbc39bc52345d/score_dict.json + test_labels_file: output/reports/train/840622f460065f0af78fbc39bc52345d/test_labels.json + train_labels_file: output/reports/train/840622f460065f0af78fbc39bc52345d/train_labels.json + model_dir: models + name: 840622f460065f0af78fbc39bc52345d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b3565f785968706dfb013f35308495ee + trainer: + kwargs: {} +name: 840622f460065f0af78fbc39bc52345d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/84384eed5b73153c580b8d5ca535d0a1/params.yaml b/examples/security/classification/output/reports/train/84384eed5b73153c580b8d5ca535d0a1/params.yaml new file mode 100644 index 00000000..42aa2084 --- /dev/null +++ b/examples/security/classification/output/reports/train/84384eed5b73153c580b8d5ca535d0a1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/adv_predictions.json + adv_probabilities_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/a960d1c170c5f26706d612b75186aef5.pkl + params_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/params.yaml + predictions_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/predictions.json + probabilities_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/probabilities.json + score_dict_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/score_dict.json + test_labels_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/test_labels.json + train_labels_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/train_labels.json + model_dir: models + name: 84384eed5b73153c580b8d5ca535d0a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1001bd20895a7a9f906370e2938e145b + trainer: + kwargs: {} +name: 84384eed5b73153c580b8d5ca535d0a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/params.yaml b/examples/security/classification/output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/params.yaml new file mode 100644 index 00000000..44374fb0 --- /dev/null +++ b/examples/security/classification/output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/adv_predictions.json + adv_probabilities_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/95b96e01c5edf9378a85492594690e32.pkl + params_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/params.yaml + predictions_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/predictions.json + probabilities_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/probabilities.json + score_dict_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/score_dict.json + test_labels_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/test_labels.json + train_labels_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/train_labels.json + model_dir: models + name: 845ad3fba9192e5943bca27b3c6bfc50 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2974ce9860cacd1a9ee7c916b8da65a + trainer: + kwargs: {} +name: 845ad3fba9192e5943bca27b3c6bfc50 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/params.yaml b/examples/security/classification/output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/params.yaml new file mode 100644 index 00000000..d3f09cd3 --- /dev/null +++ b/examples/security/classification/output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/params.yaml @@ -0,0 +1,115 @@ +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/adv_predictions.json + adv_probabilities_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl + model_file: output/models/a952e8bc9502a06c097e5c453a2c77bd.pkl + params_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/params.yaml + predictions_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/predictions.json + probabilities_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/probabilities.json + score_dict_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/score_dict.json + test_labels_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/test_labels.json + train_labels_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/train_labels.json + model_dir: models + name: 8462f601caf74e4fa3f819a5c0ee5db8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cb5ba0e2593feb3526e3b3f00318eabb + trainer: + kwargs: {} +name: 8462f601caf74e4fa3f819a5c0ee5db8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8477725447642bc3075860a072ecdb8c/params.yaml b/examples/security/classification/output/reports/train/8477725447642bc3075860a072ecdb8c/params.yaml new file mode 100644 index 00000000..f1e8c42d --- /dev/null +++ b/examples/security/classification/output/reports/train/8477725447642bc3075860a072ecdb8c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8477725447642bc3075860a072ecdb8c/adv_predictions.json + adv_probabilities_file: output/reports/train/8477725447642bc3075860a072ecdb8c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/584b1ba70f1ffc6e78c5158d937d7a46.pkl + params_file: output/reports/train/8477725447642bc3075860a072ecdb8c/params.yaml + predictions_file: output/reports/train/8477725447642bc3075860a072ecdb8c/predictions.json + probabilities_file: output/reports/train/8477725447642bc3075860a072ecdb8c/probabilities.json + score_dict_file: output/reports/train/8477725447642bc3075860a072ecdb8c/score_dict.json + test_labels_file: output/reports/train/8477725447642bc3075860a072ecdb8c/test_labels.json + train_labels_file: output/reports/train/8477725447642bc3075860a072ecdb8c/train_labels.json + model_dir: models + name: 8477725447642bc3075860a072ecdb8c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3f34059a170d1ece3b54bae1a294a1e + trainer: + kwargs: {} +name: 8477725447642bc3075860a072ecdb8c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/848501996217976afa734e788c347dd7/params.yaml b/examples/security/classification/output/reports/train/848501996217976afa734e788c347dd7/params.yaml new file mode 100644 index 00000000..0b598851 --- /dev/null +++ b/examples/security/classification/output/reports/train/848501996217976afa734e788c347dd7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/848501996217976afa734e788c347dd7/adv_predictions.json + adv_probabilities_file: output/reports/train/848501996217976afa734e788c347dd7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/5570eed979ad6b3dbeae0527a6b5a710.pkl + params_file: output/reports/train/848501996217976afa734e788c347dd7/params.yaml + predictions_file: output/reports/train/848501996217976afa734e788c347dd7/predictions.json + probabilities_file: output/reports/train/848501996217976afa734e788c347dd7/probabilities.json + score_dict_file: output/reports/train/848501996217976afa734e788c347dd7/score_dict.json + test_labels_file: output/reports/train/848501996217976afa734e788c347dd7/test_labels.json + train_labels_file: output/reports/train/848501996217976afa734e788c347dd7/train_labels.json + model_dir: models + name: 848501996217976afa734e788c347dd7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 32cb314672d45efd24459e724df21791 + trainer: + kwargs: {} +name: 848501996217976afa734e788c347dd7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/84866bce100fd5e96c84256ead3e7749/params.yaml b/examples/security/classification/output/reports/train/84866bce100fd5e96c84256ead3e7749/params.yaml new file mode 100644 index 00000000..419e5fa7 --- /dev/null +++ b/examples/security/classification/output/reports/train/84866bce100fd5e96c84256ead3e7749/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/adv_predictions.json + adv_probabilities_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/a2c399fe1604908247af0733decc740f.pkl + params_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/params.yaml + predictions_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/predictions.json + probabilities_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/probabilities.json + score_dict_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/score_dict.json + test_labels_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/test_labels.json + train_labels_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/train_labels.json + model_dir: models + name: 84866bce100fd5e96c84256ead3e7749 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7179ef7084413e0127a34db49e1709dc + trainer: + kwargs: {} +name: 84866bce100fd5e96c84256ead3e7749 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/params.yaml b/examples/security/classification/output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/params.yaml new file mode 100644 index 00000000..af27c0c4 --- /dev/null +++ b/examples/security/classification/output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/params.yaml @@ -0,0 +1,206 @@ +attack: + attack_size: 10 + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 10.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0c012452aa7fe44fd025bb038da6b505 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 62e9935b8f9919fa5c8899f0c05f3f2a + trainer: + kwargs: {} + name: ed0c95ed9402534ff4c99a3247dd4302 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/adv_predictions.json + adv_probabilities_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/adv_probabilities.json + attack_file: output/attacks/0a0ab0f67a44e8b63fca04701af4011f.pkl + data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl + model_file: output/models/dc6e3c15bb6116053e786b748bac8835.pkl + params_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/params.yaml + predictions_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/predictions.json + probabilities_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/probabilities.json + score_dict_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/score_dict.json + test_labels_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/test_labels.json + train_labels_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/train_labels.json + model_dir: models + name: 84a4fb778a1cfb2c300ca4f10631cc1b + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 333762617eb08ba38207efe77f644c01 + trainer: + kwargs: {} +name: 84a4fb778a1cfb2c300ca4f10631cc1b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8514049ea118230ce7f1f99cdca7b126/params.yaml b/examples/security/classification/output/reports/train/8514049ea118230ce7f1f99cdca7b126/params.yaml new file mode 100644 index 00000000..f70eaa2c --- /dev/null +++ b/examples/security/classification/output/reports/train/8514049ea118230ce7f1f99cdca7b126/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/adv_predictions.json + adv_probabilities_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/eaef5c8594766723a49cac08315ae76a.pkl + params_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/params.yaml + predictions_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/predictions.json + probabilities_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/probabilities.json + score_dict_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/score_dict.json + test_labels_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/test_labels.json + train_labels_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/train_labels.json + model_dir: models + name: 8514049ea118230ce7f1f99cdca7b126 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2b878c7cf4af359208381a296bebe2fa + trainer: + kwargs: {} +name: 8514049ea118230ce7f1f99cdca7b126 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/params.yaml b/examples/security/classification/output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/params.yaml new file mode 100644 index 00000000..00f50c96 --- /dev/null +++ b/examples/security/classification/output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/adv_predictions.json + adv_probabilities_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/d15025c9aac7d8bf72fb3b26411d4355.pkl + params_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/params.yaml + predictions_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/predictions.json + probabilities_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/probabilities.json + score_dict_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/score_dict.json + test_labels_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/test_labels.json + train_labels_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/train_labels.json + model_dir: models + name: 8519d6c43dca33a8de9a16fbf01ef6fa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bae409a1b36c23573d78505b92a2a159 + trainer: + kwargs: {} +name: 8519d6c43dca33a8de9a16fbf01ef6fa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/params.yaml b/examples/security/classification/output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/params.yaml new file mode 100644 index 00000000..047b9bb0 --- /dev/null +++ b/examples/security/classification/output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/adv_predictions.json + adv_probabilities_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/dd464dffe1d8ca7d2fe27f8366183662.pkl + params_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/params.yaml + predictions_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/predictions.json + probabilities_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/probabilities.json + score_dict_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/score_dict.json + test_labels_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/test_labels.json + train_labels_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/train_labels.json + model_dir: models + name: 85439d044fd4224c456c7d1dc8cb9d4a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1a73d5f51d89689dec4308aabb5f11bf + trainer: + kwargs: {} +name: 85439d044fd4224c456c7d1dc8cb9d4a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/857249d776ec69932704ee22616c6adc/params.yaml b/examples/security/classification/output/reports/train/857249d776ec69932704ee22616c6adc/params.yaml new file mode 100644 index 00000000..ac880d90 --- /dev/null +++ b/examples/security/classification/output/reports/train/857249d776ec69932704ee22616c6adc/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/857249d776ec69932704ee22616c6adc/adv_predictions.json + adv_probabilities_file: output/reports/train/857249d776ec69932704ee22616c6adc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/10c2ac1f7e254e05ebcc2bf23852056b.pkl + params_file: output/reports/train/857249d776ec69932704ee22616c6adc/params.yaml + predictions_file: output/reports/train/857249d776ec69932704ee22616c6adc/predictions.json + probabilities_file: output/reports/train/857249d776ec69932704ee22616c6adc/probabilities.json + score_dict_file: output/reports/train/857249d776ec69932704ee22616c6adc/score_dict.json + test_labels_file: output/reports/train/857249d776ec69932704ee22616c6adc/test_labels.json + train_labels_file: output/reports/train/857249d776ec69932704ee22616c6adc/train_labels.json + model_dir: models + name: 857249d776ec69932704ee22616c6adc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3551abc243224d844ff22df51428e954 + trainer: + kwargs: {} +name: 857249d776ec69932704ee22616c6adc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/params.yaml b/examples/security/classification/output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/params.yaml new file mode 100644 index 00000000..65ac65bf --- /dev/null +++ b/examples/security/classification/output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/adv_predictions.json + adv_probabilities_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/2735b6f6dab6cb94a0c0749f352c6112.pkl + params_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/params.yaml + predictions_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/predictions.json + probabilities_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/probabilities.json + score_dict_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/score_dict.json + test_labels_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/test_labels.json + train_labels_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/train_labels.json + model_dir: models + name: 85c85b116574dd5bc640ef6e9a21f9f5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4ecc0aefce0f14385599868cd2a48f4c + trainer: + kwargs: {} +name: 85c85b116574dd5bc640ef6e9a21f9f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/85d18c8130c621eaac00f1d1013296b8/dvc.yaml b/examples/security/classification/output/reports/train/85d18c8130c621eaac00f1d1013296b8/dvc.yaml new file mode 100644 index 00000000..f914f5af --- /dev/null +++ b/examples/security/classification/output/reports/train/85d18c8130c621eaac00f1d1013296b8/dvc.yaml @@ -0,0 +1,14 @@ +stages: + train: + cmd: python -m deckard.layers.experiment train + metrics: + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + outs: + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + params: + - data + - model + - files + - scorers diff --git a/examples/security/classification/output/reports/train/85d18c8130c621eaac00f1d1013296b8/params.yaml b/examples/security/classification/output/reports/train/85d18c8130c621eaac00f1d1013296b8/params.yaml new file mode 100644 index 00000000..81715afd --- /dev/null +++ b/examples/security/classification/output/reports/train/85d18c8130c621eaac00f1d1013296b8/params.yaml @@ -0,0 +1,215 @@ +attack: + attack_size: 10 + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 100.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cae49bbcb6dff3c0e2f0b3a53d5f104 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b1ae58b464df54ee8918089f870a3ad + trainer: + kwargs: {} + name: 4aece7a6fa679e2415af83d26b3d61bd +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/adv_predictions.json + adv_probabilities_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/adv_probabilities.json + attack_file: output/attacks/ce690e1d2af793d75449f34a1c13a601.pkl + data_file: output/data/2db9ede9d723385cf4052c8e5300be74.pkl + model_file: output/models/e187c2eacc99734fa834a681c34ec11f.pkl + params_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/params.yaml + predictions_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/predictions.json + probabilities_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/probabilities.json + score_dict_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/score_dict.json + test_labels_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/test_labels.json + train_labels_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/train_labels.json + model_dir: models + name: 85d18c8130c621eaac00f1d1013296b8 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b1ae58b464df54ee8918089f870a3ad + trainer: + kwargs: {} +name: 85d18c8130c621eaac00f1d1013296b8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/params.yaml b/examples/security/classification/output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/params.yaml new file mode 100644 index 00000000..a55c35db --- /dev/null +++ b/examples/security/classification/output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/adv_predictions.json + adv_probabilities_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/ac492a48780d83f42de46656c93c9683.pkl + params_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/params.yaml + predictions_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/predictions.json + probabilities_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/probabilities.json + score_dict_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/score_dict.json + test_labels_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/test_labels.json + train_labels_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/train_labels.json + model_dir: models + name: 85ead37a5255ce5e3c3f0d2cb1568afb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9c39c032a09e5231a22df6bd38e4dc40 + trainer: + kwargs: {} +name: 85ead37a5255ce5e3c3f0d2cb1568afb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/params.yaml b/examples/security/classification/output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/params.yaml new file mode 100644 index 00000000..845fe62d --- /dev/null +++ b/examples/security/classification/output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/adv_predictions.json + adv_probabilities_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/25e3d1db602c304f76472fdbfb8ca060.pkl + params_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/params.yaml + predictions_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/predictions.json + probabilities_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/probabilities.json + score_dict_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/score_dict.json + test_labels_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/test_labels.json + train_labels_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/train_labels.json + model_dir: models + name: 85efdb113a16b0a70fc484f9b4f67bdd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 192a5875bbc8c4824a5b5729ead37583 + trainer: + kwargs: {} +name: 85efdb113a16b0a70fc484f9b4f67bdd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/params.yaml b/examples/security/classification/output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/params.yaml new file mode 100644 index 00000000..7881a58f --- /dev/null +++ b/examples/security/classification/output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/adv_predictions.json + adv_probabilities_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/fd12347bf6dfc44fe645a4a67109e64d.pkl + params_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/params.yaml + predictions_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/predictions.json + probabilities_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/probabilities.json + score_dict_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/score_dict.json + test_labels_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/test_labels.json + train_labels_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/train_labels.json + model_dir: models + name: 85f6a4d39b1ec69825c02ecda2744d54 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 057225ea83d3f424c3b887d55fdb97bf + trainer: + kwargs: {} +name: 85f6a4d39b1ec69825c02ecda2744d54 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/86074efaab70b55093c901add38d1c07/params.yaml b/examples/security/classification/output/reports/train/86074efaab70b55093c901add38d1c07/params.yaml new file mode 100644 index 00000000..85936550 --- /dev/null +++ b/examples/security/classification/output/reports/train/86074efaab70b55093c901add38d1c07/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/86074efaab70b55093c901add38d1c07/adv_predictions.json + adv_probabilities_file: output/reports/train/86074efaab70b55093c901add38d1c07/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/86ed43d665a550454f6b1d4d657b407c.pkl + params_file: output/reports/train/86074efaab70b55093c901add38d1c07/params.yaml + predictions_file: output/reports/train/86074efaab70b55093c901add38d1c07/predictions.json + probabilities_file: output/reports/train/86074efaab70b55093c901add38d1c07/probabilities.json + score_dict_file: output/reports/train/86074efaab70b55093c901add38d1c07/score_dict.json + test_labels_file: output/reports/train/86074efaab70b55093c901add38d1c07/test_labels.json + train_labels_file: output/reports/train/86074efaab70b55093c901add38d1c07/train_labels.json + model_dir: models + name: 86074efaab70b55093c901add38d1c07 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d3063adaf311bf1069bbbf66e388a6a4 + trainer: + kwargs: {} +name: 86074efaab70b55093c901add38d1c07 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/86181fba20afc49ee51965329c55f882/params.yaml b/examples/security/classification/output/reports/train/86181fba20afc49ee51965329c55f882/params.yaml new file mode 100644 index 00000000..ca078551 --- /dev/null +++ b/examples/security/classification/output/reports/train/86181fba20afc49ee51965329c55f882/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/86181fba20afc49ee51965329c55f882/adv_predictions.json + adv_probabilities_file: output/reports/train/86181fba20afc49ee51965329c55f882/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/692bc71168bd2ab6c83f544bb8d6ba78.pkl + params_file: output/reports/train/86181fba20afc49ee51965329c55f882/params.yaml + predictions_file: output/reports/train/86181fba20afc49ee51965329c55f882/predictions.json + probabilities_file: output/reports/train/86181fba20afc49ee51965329c55f882/probabilities.json + score_dict_file: output/reports/train/86181fba20afc49ee51965329c55f882/score_dict.json + test_labels_file: output/reports/train/86181fba20afc49ee51965329c55f882/test_labels.json + train_labels_file: output/reports/train/86181fba20afc49ee51965329c55f882/train_labels.json + model_dir: models + name: 86181fba20afc49ee51965329c55f882 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 52ee2035127fb152e89eacd92d75c36c + trainer: + kwargs: {} +name: 86181fba20afc49ee51965329c55f882 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/params.yaml b/examples/security/classification/output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/params.yaml new file mode 100644 index 00000000..ffe82fec --- /dev/null +++ b/examples/security/classification/output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/adv_predictions.json + adv_probabilities_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/812f84d37a78fc1801f9ac2f5d597fce.pkl + params_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/params.yaml + predictions_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/predictions.json + probabilities_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/probabilities.json + score_dict_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/score_dict.json + test_labels_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/test_labels.json + train_labels_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/train_labels.json + model_dir: models + name: 86472a76b5a034ed8da7f0d4c5255b28 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a94ea942e2ee81bf1a2eeac73f2228e3 + trainer: + kwargs: {} +name: 86472a76b5a034ed8da7f0d4c5255b28 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/86510ba48c0b27fcd41abf596033aa1d/params.yaml b/examples/security/classification/output/reports/train/86510ba48c0b27fcd41abf596033aa1d/params.yaml new file mode 100644 index 00000000..75e00075 --- /dev/null +++ b/examples/security/classification/output/reports/train/86510ba48c0b27fcd41abf596033aa1d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/adv_predictions.json + adv_probabilities_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/e9de405677ed41f7cf7cdad0f5be1519.pkl + params_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/params.yaml + predictions_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/predictions.json + probabilities_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/probabilities.json + score_dict_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/score_dict.json + test_labels_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/test_labels.json + train_labels_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/train_labels.json + model_dir: models + name: 86510ba48c0b27fcd41abf596033aa1d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 568382fa0cc71e6ce822372cb9f7cff6 + trainer: + kwargs: {} +name: 86510ba48c0b27fcd41abf596033aa1d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/params.yaml b/examples/security/classification/output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/params.yaml new file mode 100644 index 00000000..d5414bd8 --- /dev/null +++ b/examples/security/classification/output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/adv_predictions.json + adv_probabilities_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/04edbf8c1876e7cb3ebcb90212a2506d.pkl + params_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/params.yaml + predictions_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/predictions.json + probabilities_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/probabilities.json + score_dict_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/score_dict.json + test_labels_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/test_labels.json + train_labels_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/train_labels.json + model_dir: models + name: 866e524b6b4ded3f8e1a028f88002ef7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: de35725fa80e326b24d14684098dd785 + trainer: + kwargs: {} +name: 866e524b6b4ded3f8e1a028f88002ef7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/86724816bcab6ebece3b259ebaaa69e2/params.yaml b/examples/security/classification/output/reports/train/86724816bcab6ebece3b259ebaaa69e2/params.yaml new file mode 100644 index 00000000..69ad6462 --- /dev/null +++ b/examples/security/classification/output/reports/train/86724816bcab6ebece3b259ebaaa69e2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/adv_predictions.json + adv_probabilities_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/440ca43365a54269eea687fc0e80501f.pkl + params_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/params.yaml + predictions_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/predictions.json + probabilities_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/probabilities.json + score_dict_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/score_dict.json + test_labels_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/test_labels.json + train_labels_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/train_labels.json + model_dir: models + name: 86724816bcab6ebece3b259ebaaa69e2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2b5dae09623fbc7fb626aed79677f4d0 + trainer: + kwargs: {} +name: 86724816bcab6ebece3b259ebaaa69e2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/86cd1c783e061937029ef34091416e2b/params.yaml b/examples/security/classification/output/reports/train/86cd1c783e061937029ef34091416e2b/params.yaml new file mode 100644 index 00000000..f37b1d66 --- /dev/null +++ b/examples/security/classification/output/reports/train/86cd1c783e061937029ef34091416e2b/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/86cd1c783e061937029ef34091416e2b/adv_predictions.json + adv_probabilities_file: output/reports/train/86cd1c783e061937029ef34091416e2b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/e52b21bae47d41b5127c652b3b36045a.pkl + params_file: output/reports/train/86cd1c783e061937029ef34091416e2b/params.yaml + predictions_file: output/reports/train/86cd1c783e061937029ef34091416e2b/predictions.json + probabilities_file: output/reports/train/86cd1c783e061937029ef34091416e2b/probabilities.json + score_dict_file: output/reports/train/86cd1c783e061937029ef34091416e2b/score_dict.json + test_labels_file: output/reports/train/86cd1c783e061937029ef34091416e2b/test_labels.json + train_labels_file: output/reports/train/86cd1c783e061937029ef34091416e2b/train_labels.json + model_dir: models + name: 86cd1c783e061937029ef34091416e2b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7b045b79cab849e16e52e7a084255e90 + trainer: + kwargs: {} +name: 86cd1c783e061937029ef34091416e2b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/params.yaml b/examples/security/classification/output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/params.yaml new file mode 100644 index 00000000..25331127 --- /dev/null +++ b/examples/security/classification/output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/adv_predictions.json + adv_probabilities_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/f4f6f94bce97734852576855cb6bfa74.pkl + params_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/params.yaml + predictions_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/predictions.json + probabilities_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/probabilities.json + score_dict_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/score_dict.json + test_labels_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/test_labels.json + train_labels_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/train_labels.json + model_dir: models + name: 86cf6eea4a59c25e9c8ffa8c4a595c4b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 16084e1b89880e2bb5d869a38a3e947f + trainer: + kwargs: {} +name: 86cf6eea4a59c25e9c8ffa8c4a595c4b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/87126b5b9d02b829fe323ff0fe39af68/params.yaml b/examples/security/classification/output/reports/train/87126b5b9d02b829fe323ff0fe39af68/params.yaml new file mode 100644 index 00000000..f8fa535c --- /dev/null +++ b/examples/security/classification/output/reports/train/87126b5b9d02b829fe323ff0fe39af68/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/adv_predictions.json + adv_probabilities_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/6be53aa8c0a7c57ccb7f94056d9a53af.pkl + params_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/params.yaml + predictions_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/predictions.json + probabilities_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/probabilities.json + score_dict_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/score_dict.json + test_labels_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/test_labels.json + train_labels_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/train_labels.json + model_dir: models + name: 87126b5b9d02b829fe323ff0fe39af68 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a4b64139966c42e29c6a5c0f10576025 + trainer: + kwargs: {} +name: 87126b5b9d02b829fe323ff0fe39af68 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/871975f05f5771c104b0485db958a2f1/params.yaml b/examples/security/classification/output/reports/train/871975f05f5771c104b0485db958a2f1/params.yaml new file mode 100644 index 00000000..04bcb8cc --- /dev/null +++ b/examples/security/classification/output/reports/train/871975f05f5771c104b0485db958a2f1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/871975f05f5771c104b0485db958a2f1/adv_predictions.json + adv_probabilities_file: output/reports/train/871975f05f5771c104b0485db958a2f1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/219ee8034f8d546458523b769b8d67f2.pkl + params_file: output/reports/train/871975f05f5771c104b0485db958a2f1/params.yaml + predictions_file: output/reports/train/871975f05f5771c104b0485db958a2f1/predictions.json + probabilities_file: output/reports/train/871975f05f5771c104b0485db958a2f1/probabilities.json + score_dict_file: output/reports/train/871975f05f5771c104b0485db958a2f1/score_dict.json + test_labels_file: output/reports/train/871975f05f5771c104b0485db958a2f1/test_labels.json + train_labels_file: output/reports/train/871975f05f5771c104b0485db958a2f1/train_labels.json + model_dir: models + name: 871975f05f5771c104b0485db958a2f1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fbc15a1e4d678eb2402d76b15f1428a6 + trainer: + kwargs: {} +name: 871975f05f5771c104b0485db958a2f1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/87214e96634ed36203ba574e3d65c1bf/params.yaml b/examples/security/classification/output/reports/train/87214e96634ed36203ba574e3d65c1bf/params.yaml new file mode 100644 index 00000000..5e434af9 --- /dev/null +++ b/examples/security/classification/output/reports/train/87214e96634ed36203ba574e3d65c1bf/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/adv_predictions.json + adv_probabilities_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/d5da6121aad1d325cedb38b788ee649c.pkl + params_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/params.yaml + predictions_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/predictions.json + probabilities_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/probabilities.json + score_dict_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/score_dict.json + test_labels_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/test_labels.json + train_labels_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/train_labels.json + model_dir: models + name: 87214e96634ed36203ba574e3d65c1bf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b2b8352333f373c763505b1576ce85c5 + trainer: + kwargs: {} +name: 87214e96634ed36203ba574e3d65c1bf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8764a82b0016391e7272f8b04fd69465/params.yaml b/examples/security/classification/output/reports/train/8764a82b0016391e7272f8b04fd69465/params.yaml new file mode 100644 index 00000000..2602fc0d --- /dev/null +++ b/examples/security/classification/output/reports/train/8764a82b0016391e7272f8b04fd69465/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/adv_predictions.json + adv_probabilities_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/2cd1e67e5acbc727bd6d47adceb72162.pkl + params_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/params.yaml + predictions_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/predictions.json + probabilities_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/probabilities.json + score_dict_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/score_dict.json + test_labels_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/test_labels.json + train_labels_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/train_labels.json + model_dir: models + name: 8764a82b0016391e7272f8b04fd69465 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8fbad9d7b1b5d41126d1e90bbd735f49 + trainer: + kwargs: {} +name: 8764a82b0016391e7272f8b04fd69465 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8773385ce29212115799528db12f2b6e/params.yaml b/examples/security/classification/output/reports/train/8773385ce29212115799528db12f2b6e/params.yaml new file mode 100644 index 00000000..41ea4b80 --- /dev/null +++ b/examples/security/classification/output/reports/train/8773385ce29212115799528db12f2b6e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8773385ce29212115799528db12f2b6e/adv_predictions.json + adv_probabilities_file: output/reports/train/8773385ce29212115799528db12f2b6e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/a5a1c76bd2642769ab11def5f46b862c.pkl + params_file: output/reports/train/8773385ce29212115799528db12f2b6e/params.yaml + predictions_file: output/reports/train/8773385ce29212115799528db12f2b6e/predictions.json + probabilities_file: output/reports/train/8773385ce29212115799528db12f2b6e/probabilities.json + score_dict_file: output/reports/train/8773385ce29212115799528db12f2b6e/score_dict.json + test_labels_file: output/reports/train/8773385ce29212115799528db12f2b6e/test_labels.json + train_labels_file: output/reports/train/8773385ce29212115799528db12f2b6e/train_labels.json + model_dir: models + name: 8773385ce29212115799528db12f2b6e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 33c1595063a82496ff3410651bca8530 + trainer: + kwargs: {} +name: 8773385ce29212115799528db12f2b6e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8776805c36455b1db7a793323296cd85/params.yaml b/examples/security/classification/output/reports/train/8776805c36455b1db7a793323296cd85/params.yaml new file mode 100644 index 00000000..58a4ea69 --- /dev/null +++ b/examples/security/classification/output/reports/train/8776805c36455b1db7a793323296cd85/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8776805c36455b1db7a793323296cd85/adv_predictions.json + adv_probabilities_file: output/reports/train/8776805c36455b1db7a793323296cd85/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/208bfd3621c4684d27d80e1b291aefb8.pkl + params_file: output/reports/train/8776805c36455b1db7a793323296cd85/params.yaml + predictions_file: output/reports/train/8776805c36455b1db7a793323296cd85/predictions.json + probabilities_file: output/reports/train/8776805c36455b1db7a793323296cd85/probabilities.json + score_dict_file: output/reports/train/8776805c36455b1db7a793323296cd85/score_dict.json + test_labels_file: output/reports/train/8776805c36455b1db7a793323296cd85/test_labels.json + train_labels_file: output/reports/train/8776805c36455b1db7a793323296cd85/train_labels.json + model_dir: models + name: 8776805c36455b1db7a793323296cd85 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 84484981489db2b895f6ba4712e00e74 + trainer: + kwargs: {} +name: 8776805c36455b1db7a793323296cd85 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/params.yaml b/examples/security/classification/output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/params.yaml new file mode 100644 index 00000000..c482cb4c --- /dev/null +++ b/examples/security/classification/output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/adv_predictions.json + adv_probabilities_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/653029789218a2ea424e5812c5c2ac19.pkl + params_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/params.yaml + predictions_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/predictions.json + probabilities_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/probabilities.json + score_dict_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/score_dict.json + test_labels_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/test_labels.json + train_labels_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/train_labels.json + model_dir: models + name: 8787d1a18b053ec5b6b05a63c9c4b8c8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9249db76ad11b892854736c8a2fddd30 + trainer: + kwargs: {} +name: 8787d1a18b053ec5b6b05a63c9c4b8c8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/87cefca48a809d8e79d1571608bc788f/params.yaml b/examples/security/classification/output/reports/train/87cefca48a809d8e79d1571608bc788f/params.yaml new file mode 100644 index 00000000..274ccae3 --- /dev/null +++ b/examples/security/classification/output/reports/train/87cefca48a809d8e79d1571608bc788f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/adv_predictions.json + adv_probabilities_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/664c9d48433afadf1b2e795f7ee50843.pkl + params_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/params.yaml + predictions_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/predictions.json + probabilities_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/probabilities.json + score_dict_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/score_dict.json + test_labels_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/test_labels.json + train_labels_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/train_labels.json + model_dir: models + name: 87cefca48a809d8e79d1571608bc788f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fbe51bfb5ada085f8cb0f8e1d9bc0478 + trainer: + kwargs: {} +name: 87cefca48a809d8e79d1571608bc788f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/87f6068448648d1beabf5486e7f7c3c9/params.yaml b/examples/security/classification/output/reports/train/87f6068448648d1beabf5486e7f7c3c9/params.yaml new file mode 100644 index 00000000..d2654767 --- /dev/null +++ b/examples/security/classification/output/reports/train/87f6068448648d1beabf5486e7f7c3c9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/adv_predictions.json + adv_probabilities_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/c6685ee96739de0b3a439ab61a199d35.pkl + params_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/params.yaml + predictions_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/predictions.json + probabilities_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/probabilities.json + score_dict_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/score_dict.json + test_labels_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/test_labels.json + train_labels_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/train_labels.json + model_dir: models + name: 87f6068448648d1beabf5486e7f7c3c9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8dd16e0b9eb30276f89fe6930d6772ab + trainer: + kwargs: {} +name: 87f6068448648d1beabf5486e7f7c3c9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/params.yaml b/examples/security/classification/output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/params.yaml new file mode 100644 index 00000000..30bf0ee5 --- /dev/null +++ b/examples/security/classification/output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/adv_predictions.json + adv_probabilities_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/fa6b52922baedca82c7fe9ffd685832c.pkl + params_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/params.yaml + predictions_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/predictions.json + probabilities_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/probabilities.json + score_dict_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/score_dict.json + test_labels_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/test_labels.json + train_labels_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/train_labels.json + model_dir: models + name: 88188c5c91b2ccbe1b6d9e3a3244823f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c4099549e655e1e833b221f689502c90 + trainer: + kwargs: {} +name: 88188c5c91b2ccbe1b6d9e3a3244823f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/params.yaml b/examples/security/classification/output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/params.yaml new file mode 100644 index 00000000..7c05cd2f --- /dev/null +++ b/examples/security/classification/output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/adv_predictions.json + adv_probabilities_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/d690a16bd06a9a36c6b0a52dad1d3bf1.pkl + params_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/params.yaml + predictions_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/predictions.json + probabilities_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/probabilities.json + score_dict_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/score_dict.json + test_labels_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/test_labels.json + train_labels_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/train_labels.json + model_dir: models + name: 883e1c8e8b277dd2c3d35cbff3d5c729 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b5930e29ed19b29a5e027fdebee704bf + trainer: + kwargs: {} +name: 883e1c8e8b277dd2c3d35cbff3d5c729 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/params.yaml b/examples/security/classification/output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/params.yaml new file mode 100644 index 00000000..00e96ff5 --- /dev/null +++ b/examples/security/classification/output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/adv_predictions.json + adv_probabilities_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/20127c08294dca7e4b51a386856a0505.pkl + params_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/params.yaml + predictions_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/predictions.json + probabilities_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/probabilities.json + score_dict_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/score_dict.json + test_labels_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/test_labels.json + train_labels_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/train_labels.json + model_dir: models + name: 884f7e8c69b90ccecf2d31b5bd77ee83 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: da5fb2974554249c40448054a38710f8 + trainer: + kwargs: {} +name: 884f7e8c69b90ccecf2d31b5bd77ee83 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/886c32944577836facbeadeb88c0ea0e/params.yaml b/examples/security/classification/output/reports/train/886c32944577836facbeadeb88c0ea0e/params.yaml new file mode 100644 index 00000000..5556de4c --- /dev/null +++ b/examples/security/classification/output/reports/train/886c32944577836facbeadeb88c0ea0e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/adv_predictions.json + adv_probabilities_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/1e7a1922697eba491e1d7bda703fa994.pkl + params_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/params.yaml + predictions_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/predictions.json + probabilities_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/probabilities.json + score_dict_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/score_dict.json + test_labels_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/test_labels.json + train_labels_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/train_labels.json + model_dir: models + name: 886c32944577836facbeadeb88c0ea0e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0182782508fd381c3463ef6fca44b9d1 + trainer: + kwargs: {} +name: 886c32944577836facbeadeb88c0ea0e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/params.yaml b/examples/security/classification/output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/params.yaml new file mode 100644 index 00000000..3db153b9 --- /dev/null +++ b/examples/security/classification/output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/adv_predictions.json + adv_probabilities_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/5edd5b46912be67daa2146b33dfba7a9.pkl + params_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/params.yaml + predictions_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/predictions.json + probabilities_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/probabilities.json + score_dict_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/score_dict.json + test_labels_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/test_labels.json + train_labels_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/train_labels.json + model_dir: models + name: 888f39cf347c8d31ecf13f965a0a5a97 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8b9fbe1d0939a3e0f5262846d4dd15cb + trainer: + kwargs: {} +name: 888f39cf347c8d31ecf13f965a0a5a97 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8945e12c2f1d7757f666aff48468d735/params.yaml b/examples/security/classification/output/reports/train/8945e12c2f1d7757f666aff48468d735/params.yaml new file mode 100644 index 00000000..23e7353b --- /dev/null +++ b/examples/security/classification/output/reports/train/8945e12c2f1d7757f666aff48468d735/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/adv_predictions.json + adv_probabilities_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/270bc03c895960f8acf40b15ad0c7bbb.pkl + params_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/params.yaml + predictions_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/predictions.json + probabilities_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/probabilities.json + score_dict_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/score_dict.json + test_labels_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/test_labels.json + train_labels_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/train_labels.json + model_dir: models + name: 8945e12c2f1d7757f666aff48468d735 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dc248f2ffd355b3f4ca5e32c6ddf9a8e + trainer: + kwargs: {} +name: 8945e12c2f1d7757f666aff48468d735 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/89c71f60bf27969bcae44b4bcab5a945/params.yaml b/examples/security/classification/output/reports/train/89c71f60bf27969bcae44b4bcab5a945/params.yaml new file mode 100644 index 00000000..28bb583d --- /dev/null +++ b/examples/security/classification/output/reports/train/89c71f60bf27969bcae44b4bcab5a945/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/adv_predictions.json + adv_probabilities_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/6757be524f626ab6f25fce892a961f24.pkl + params_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/params.yaml + predictions_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/predictions.json + probabilities_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/probabilities.json + score_dict_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/score_dict.json + test_labels_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/test_labels.json + train_labels_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/train_labels.json + model_dir: models + name: 89c71f60bf27969bcae44b4bcab5a945 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ec0a9d26ac61c4cd462b10049092c865 + trainer: + kwargs: {} +name: 89c71f60bf27969bcae44b4bcab5a945 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/89cc805cfe06028825fbd0e6172249a3/params.yaml b/examples/security/classification/output/reports/train/89cc805cfe06028825fbd0e6172249a3/params.yaml new file mode 100644 index 00000000..a13a3610 --- /dev/null +++ b/examples/security/classification/output/reports/train/89cc805cfe06028825fbd0e6172249a3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/adv_predictions.json + adv_probabilities_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/7d39744d4599864d42712486edaf58d7.pkl + params_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/params.yaml + predictions_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/predictions.json + probabilities_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/probabilities.json + score_dict_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/score_dict.json + test_labels_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/test_labels.json + train_labels_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/train_labels.json + model_dir: models + name: 89cc805cfe06028825fbd0e6172249a3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e96f10d7ec9211b6b834f30e3a5201cf + trainer: + kwargs: {} +name: 89cc805cfe06028825fbd0e6172249a3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/89f3e9b25dee47906b196e73947c3bae/params.yaml b/examples/security/classification/output/reports/train/89f3e9b25dee47906b196e73947c3bae/params.yaml new file mode 100644 index 00000000..8ce781d4 --- /dev/null +++ b/examples/security/classification/output/reports/train/89f3e9b25dee47906b196e73947c3bae/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/adv_predictions.json + adv_probabilities_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/4a8c3712492c3b3bef1a3aa622692dee.pkl + params_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/params.yaml + predictions_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/predictions.json + probabilities_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/probabilities.json + score_dict_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/score_dict.json + test_labels_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/test_labels.json + train_labels_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/train_labels.json + model_dir: models + name: 89f3e9b25dee47906b196e73947c3bae + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 57e300ed0d3f2be550ecfedb128f8840 + trainer: + kwargs: {} +name: 89f3e9b25dee47906b196e73947c3bae +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/params.yaml b/examples/security/classification/output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/params.yaml new file mode 100644 index 00000000..f93526de --- /dev/null +++ b/examples/security/classification/output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/adv_predictions.json + adv_probabilities_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/5a54029f115ecb7db21b103a2372964a.pkl + params_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/params.yaml + predictions_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/predictions.json + probabilities_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/probabilities.json + score_dict_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/score_dict.json + test_labels_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/test_labels.json + train_labels_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/train_labels.json + model_dir: models + name: 8a0d73dd7b208e98a6618d8734754f4e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fc3e7d08e15723dea51ed82ef3174a47 + trainer: + kwargs: {} +name: 8a0d73dd7b208e98a6618d8734754f4e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/params.yaml b/examples/security/classification/output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/params.yaml new file mode 100644 index 00000000..7729253a --- /dev/null +++ b/examples/security/classification/output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/adv_predictions.json + adv_probabilities_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/38bcd9c1d5f6f44218b4cf2719a1495e.pkl + params_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/params.yaml + predictions_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/predictions.json + probabilities_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/probabilities.json + score_dict_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/score_dict.json + test_labels_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/test_labels.json + train_labels_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/train_labels.json + model_dir: models + name: 8a3e0658f4676c051eb9f19396f1ef06 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6f4579b3a9f3241f324f78e6c3c8fa93 + trainer: + kwargs: {} +name: 8a3e0658f4676c051eb9f19396f1ef06 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8a5f7f562e6fec536bcc9633b7944097/params.yaml b/examples/security/classification/output/reports/train/8a5f7f562e6fec536bcc9633b7944097/params.yaml new file mode 100644 index 00000000..dc8c0c8a --- /dev/null +++ b/examples/security/classification/output/reports/train/8a5f7f562e6fec536bcc9633b7944097/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/adv_predictions.json + adv_probabilities_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/b452524fed285bf12d12619a142e1e16.pkl + params_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/params.yaml + predictions_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/predictions.json + probabilities_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/probabilities.json + score_dict_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/score_dict.json + test_labels_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/test_labels.json + train_labels_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/train_labels.json + model_dir: models + name: 8a5f7f562e6fec536bcc9633b7944097 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dfb865e576deea8231f6917d0b6b7af8 + trainer: + kwargs: {} +name: 8a5f7f562e6fec536bcc9633b7944097 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/params.yaml b/examples/security/classification/output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/params.yaml new file mode 100644 index 00000000..9eb1ca76 --- /dev/null +++ b/examples/security/classification/output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/adv_predictions.json + adv_probabilities_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/c4d1f30c094e26388799cacd671b4c8a.pkl + params_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/params.yaml + predictions_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/predictions.json + probabilities_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/probabilities.json + score_dict_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/score_dict.json + test_labels_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/test_labels.json + train_labels_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/train_labels.json + model_dir: models + name: 8a64f63313dde08bf8af1f5eaf4ee993 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 91f67a03dc443e813d00bdd76427cf09 + trainer: + kwargs: {} +name: 8a64f63313dde08bf8af1f5eaf4ee993 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8a7d3e68628fa474e8a52f8280401494/params.yaml b/examples/security/classification/output/reports/train/8a7d3e68628fa474e8a52f8280401494/params.yaml new file mode 100644 index 00000000..e827d78d --- /dev/null +++ b/examples/security/classification/output/reports/train/8a7d3e68628fa474e8a52f8280401494/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/adv_predictions.json + adv_probabilities_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/28db062bc8721d298fa89a5946f58d18.pkl + params_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/params.yaml + predictions_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/predictions.json + probabilities_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/probabilities.json + score_dict_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/score_dict.json + test_labels_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/test_labels.json + train_labels_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/train_labels.json + model_dir: models + name: 8a7d3e68628fa474e8a52f8280401494 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0cdece709c75ec0a27c4ffa3ddf1ff46 + trainer: + kwargs: {} +name: 8a7d3e68628fa474e8a52f8280401494 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/params.yaml b/examples/security/classification/output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/params.yaml new file mode 100644 index 00000000..3d83cf10 --- /dev/null +++ b/examples/security/classification/output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/adv_predictions.json + adv_probabilities_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/53df7cc7ede3ce8bdf75b447ff44c3ef.pkl + params_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/params.yaml + predictions_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/predictions.json + probabilities_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/probabilities.json + score_dict_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/score_dict.json + test_labels_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/test_labels.json + train_labels_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/train_labels.json + model_dir: models + name: 8a988aca328b57ca8ebfa8de4e7583ba + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 725da9a0a22d6f3ab35bc1c5b8bc41ec + trainer: + kwargs: {} +name: 8a988aca328b57ca8ebfa8de4e7583ba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/params.yaml b/examples/security/classification/output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/params.yaml new file mode 100644 index 00000000..b6652e41 --- /dev/null +++ b/examples/security/classification/output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/adv_predictions.json + adv_probabilities_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/155f4aa444d28e966cc23b7e7dca3187.pkl + params_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/params.yaml + predictions_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/predictions.json + probabilities_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/probabilities.json + score_dict_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/score_dict.json + test_labels_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/test_labels.json + train_labels_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/train_labels.json + model_dir: models + name: 8ac7bca10ff0af2c4bd55a51c25913d3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 25565b58c2f7f3f0e2c0eb46e437662b + trainer: + kwargs: {} +name: 8ac7bca10ff0af2c4bd55a51c25913d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/params.yaml b/examples/security/classification/output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/params.yaml new file mode 100644 index 00000000..082403f9 --- /dev/null +++ b/examples/security/classification/output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/adv_predictions.json + adv_probabilities_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/ead9321d355a0800173393f29abfce31.pkl + params_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/params.yaml + predictions_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/predictions.json + probabilities_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/probabilities.json + score_dict_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/score_dict.json + test_labels_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/test_labels.json + train_labels_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/train_labels.json + model_dir: models + name: 8ae6cf3da1317cf6e4c6dbfcb62a553c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 78ef26c25e4812540dcf54d5ba431267 + trainer: + kwargs: {} +name: 8ae6cf3da1317cf6e4c6dbfcb62a553c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8b22c2875553fe165c6a3d6958e68f48/params.yaml b/examples/security/classification/output/reports/train/8b22c2875553fe165c6a3d6958e68f48/params.yaml new file mode 100644 index 00000000..686307d5 --- /dev/null +++ b/examples/security/classification/output/reports/train/8b22c2875553fe165c6a3d6958e68f48/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/adv_predictions.json + adv_probabilities_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/d47f51101a47bd8ebff62cbe53256d5f.pkl + params_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/params.yaml + predictions_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/predictions.json + probabilities_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/probabilities.json + score_dict_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/score_dict.json + test_labels_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/test_labels.json + train_labels_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/train_labels.json + model_dir: models + name: 8b22c2875553fe165c6a3d6958e68f48 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 36f97f6a081ce2e66ba1fde97c768157 + trainer: + kwargs: {} +name: 8b22c2875553fe165c6a3d6958e68f48 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/params.yaml b/examples/security/classification/output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/params.yaml new file mode 100644 index 00000000..de23e47d --- /dev/null +++ b/examples/security/classification/output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/adv_predictions.json + adv_probabilities_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/df31d03f1c87c1e36aacbd89a45d0ece.pkl + params_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/params.yaml + predictions_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/predictions.json + probabilities_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/probabilities.json + score_dict_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/score_dict.json + test_labels_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/test_labels.json + train_labels_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/train_labels.json + model_dir: models + name: 8b4487c5fee63748c4591eb71c1afaa3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d833222f5c1f5ad8e83d005a98b7fb84 + trainer: + kwargs: {} +name: 8b4487c5fee63748c4591eb71c1afaa3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/params.yaml b/examples/security/classification/output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/params.yaml new file mode 100644 index 00000000..add8584e --- /dev/null +++ b/examples/security/classification/output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/adv_predictions.json + adv_probabilities_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/74c9ff49debc8bd1d2940194b937c2f3.pkl + params_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/params.yaml + predictions_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/predictions.json + probabilities_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/probabilities.json + score_dict_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/score_dict.json + test_labels_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/test_labels.json + train_labels_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/train_labels.json + model_dir: models + name: 8b81129e4fd27e62d97b0c7cc6972899 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 243e650e91e3b545edd8d8c73b3c855b + trainer: + kwargs: {} +name: 8b81129e4fd27e62d97b0c7cc6972899 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8b96973bdabff522e2a58cff36ad1f53/params.yaml b/examples/security/classification/output/reports/train/8b96973bdabff522e2a58cff36ad1f53/params.yaml new file mode 100644 index 00000000..4d74fb20 --- /dev/null +++ b/examples/security/classification/output/reports/train/8b96973bdabff522e2a58cff36ad1f53/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/adv_predictions.json + adv_probabilities_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/8b20e21837d54ac94c1f40064d95b326.pkl + params_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/params.yaml + predictions_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/predictions.json + probabilities_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/probabilities.json + score_dict_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/score_dict.json + test_labels_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/test_labels.json + train_labels_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/train_labels.json + model_dir: models + name: 8b96973bdabff522e2a58cff36ad1f53 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2102179994717547217e8e9f92899bc5 + trainer: + kwargs: {} +name: 8b96973bdabff522e2a58cff36ad1f53 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8ba90caf325df6ea1a4661594ca0b093/params.yaml b/examples/security/classification/output/reports/train/8ba90caf325df6ea1a4661594ca0b093/params.yaml new file mode 100644 index 00000000..39c0245f --- /dev/null +++ b/examples/security/classification/output/reports/train/8ba90caf325df6ea1a4661594ca0b093/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/adv_predictions.json + adv_probabilities_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/ed63b540868110117313132d44ab566a.pkl + params_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/params.yaml + predictions_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/predictions.json + probabilities_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/probabilities.json + score_dict_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/score_dict.json + test_labels_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/test_labels.json + train_labels_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/train_labels.json + model_dir: models + name: 8ba90caf325df6ea1a4661594ca0b093 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 76eee1be6faae12d7c7fa21d6455c418 + trainer: + kwargs: {} +name: 8ba90caf325df6ea1a4661594ca0b093 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/params.yaml b/examples/security/classification/output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/params.yaml new file mode 100644 index 00000000..a9b665c0 --- /dev/null +++ b/examples/security/classification/output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/adv_predictions.json + adv_probabilities_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/1c705362aea77395a79d1e01a8eac113.pkl + params_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/params.yaml + predictions_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/predictions.json + probabilities_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/probabilities.json + score_dict_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/score_dict.json + test_labels_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/test_labels.json + train_labels_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/train_labels.json + model_dir: models + name: 8bdda91ace925b08248a0d2c0181d3c0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5bda220d43f7919fd44e3f677c13afdc + trainer: + kwargs: {} +name: 8bdda91ace925b08248a0d2c0181d3c0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8bde41971ba2d57dbd95d33e2178e038/params.yaml b/examples/security/classification/output/reports/train/8bde41971ba2d57dbd95d33e2178e038/params.yaml new file mode 100644 index 00000000..c47efcef --- /dev/null +++ b/examples/security/classification/output/reports/train/8bde41971ba2d57dbd95d33e2178e038/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/adv_predictions.json + adv_probabilities_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/11a8c962bee7e19bb0d2d942499e91a5.pkl + params_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/params.yaml + predictions_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/predictions.json + probabilities_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/probabilities.json + score_dict_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/score_dict.json + test_labels_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/test_labels.json + train_labels_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/train_labels.json + model_dir: models + name: 8bde41971ba2d57dbd95d33e2178e038 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9500b827501fd5b894dc7efb3b55b49c + trainer: + kwargs: {} +name: 8bde41971ba2d57dbd95d33e2178e038 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/params.yaml b/examples/security/classification/output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/params.yaml new file mode 100644 index 00000000..2f465978 --- /dev/null +++ b/examples/security/classification/output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/adv_predictions.json + adv_probabilities_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/1fd8359b8de467f1ad71fa20beb4b168.pkl + params_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/params.yaml + predictions_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/predictions.json + probabilities_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/probabilities.json + score_dict_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/score_dict.json + test_labels_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/test_labels.json + train_labels_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/train_labels.json + model_dir: models + name: 8befd2d9092e8ad33eead25c6ead38fe + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: afb5295fdf33e8c052c259ac2cb069be + trainer: + kwargs: {} +name: 8befd2d9092e8ad33eead25c6ead38fe +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8c185b51a35cdda7afd4f366c50d8494/params.yaml b/examples/security/classification/output/reports/train/8c185b51a35cdda7afd4f366c50d8494/params.yaml new file mode 100644 index 00000000..bef1b404 --- /dev/null +++ b/examples/security/classification/output/reports/train/8c185b51a35cdda7afd4f366c50d8494/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/adv_predictions.json + adv_probabilities_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/0ff90ee60420426f465db45b4caf22f3.pkl + params_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/params.yaml + predictions_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/predictions.json + probabilities_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/probabilities.json + score_dict_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/score_dict.json + test_labels_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/test_labels.json + train_labels_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/train_labels.json + model_dir: models + name: 8c185b51a35cdda7afd4f366c50d8494 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a3b877a249de326555480675c6f15451 + trainer: + kwargs: {} +name: 8c185b51a35cdda7afd4f366c50d8494 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8c2085700da92c43bbb7594d995bcba5/params.yaml b/examples/security/classification/output/reports/train/8c2085700da92c43bbb7594d995bcba5/params.yaml new file mode 100644 index 00000000..33c9691e --- /dev/null +++ b/examples/security/classification/output/reports/train/8c2085700da92c43bbb7594d995bcba5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/adv_predictions.json + adv_probabilities_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/2aa2021b4bfc4faaaf732408975d9e54.pkl + params_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/params.yaml + predictions_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/predictions.json + probabilities_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/probabilities.json + score_dict_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/score_dict.json + test_labels_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/test_labels.json + train_labels_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/train_labels.json + model_dir: models + name: 8c2085700da92c43bbb7594d995bcba5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d194f97ec72fee3c1e75e65c3c04422c + trainer: + kwargs: {} +name: 8c2085700da92c43bbb7594d995bcba5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8c24bee058ef2d610e27488e4e312dff/params.yaml b/examples/security/classification/output/reports/train/8c24bee058ef2d610e27488e4e312dff/params.yaml new file mode 100644 index 00000000..7ff1a648 --- /dev/null +++ b/examples/security/classification/output/reports/train/8c24bee058ef2d610e27488e4e312dff/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/adv_predictions.json + adv_probabilities_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/552c5d8c4e8f38695428030204c82ed6.pkl + params_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/params.yaml + predictions_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/predictions.json + probabilities_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/probabilities.json + score_dict_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/score_dict.json + test_labels_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/test_labels.json + train_labels_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/train_labels.json + model_dir: models + name: 8c24bee058ef2d610e27488e4e312dff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c34fb9779e849848746f79ce730e0213 + trainer: + kwargs: {} +name: 8c24bee058ef2d610e27488e4e312dff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8c37023f3177aef03a4aef1913c81344/params.yaml b/examples/security/classification/output/reports/train/8c37023f3177aef03a4aef1913c81344/params.yaml new file mode 100644 index 00000000..e4e51295 --- /dev/null +++ b/examples/security/classification/output/reports/train/8c37023f3177aef03a4aef1913c81344/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/adv_predictions.json + adv_probabilities_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/e9b365afe3c4bf47de26cba54708d200.pkl + params_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/params.yaml + predictions_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/predictions.json + probabilities_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/probabilities.json + score_dict_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/score_dict.json + test_labels_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/test_labels.json + train_labels_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/train_labels.json + model_dir: models + name: 8c37023f3177aef03a4aef1913c81344 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cd72189069bc90567bd4aaf352888ae9 + trainer: + kwargs: {} +name: 8c37023f3177aef03a4aef1913c81344 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8c5b7638ca557ca16431d12b87991686/params.yaml b/examples/security/classification/output/reports/train/8c5b7638ca557ca16431d12b87991686/params.yaml new file mode 100644 index 00000000..c1761ae3 --- /dev/null +++ b/examples/security/classification/output/reports/train/8c5b7638ca557ca16431d12b87991686/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/adv_predictions.json + adv_probabilities_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/1bf6061f11025d3bad6ca164816913a3.pkl + params_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/params.yaml + predictions_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/predictions.json + probabilities_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/probabilities.json + score_dict_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/score_dict.json + test_labels_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/test_labels.json + train_labels_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/train_labels.json + model_dir: models + name: 8c5b7638ca557ca16431d12b87991686 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 127a16b8ea95480242262fcf9d6a12fc + trainer: + kwargs: {} +name: 8c5b7638ca557ca16431d12b87991686 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8c855a0903e679dc936ea07b3087e9c2/params.yaml b/examples/security/classification/output/reports/train/8c855a0903e679dc936ea07b3087e9c2/params.yaml new file mode 100644 index 00000000..683ad2ab --- /dev/null +++ b/examples/security/classification/output/reports/train/8c855a0903e679dc936ea07b3087e9c2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/adv_predictions.json + adv_probabilities_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/7874ef4ce7b87bdbfd416a5e6b297e37.pkl + params_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/params.yaml + predictions_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/predictions.json + probabilities_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/probabilities.json + score_dict_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/score_dict.json + test_labels_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/test_labels.json + train_labels_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/train_labels.json + model_dir: models + name: 8c855a0903e679dc936ea07b3087e9c2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b6f162a52722b4a7b23fef3a8d565a37 + trainer: + kwargs: {} +name: 8c855a0903e679dc936ea07b3087e9c2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8c933f637883c2f82549d1ce6b9f7301/params.yaml b/examples/security/classification/output/reports/train/8c933f637883c2f82549d1ce6b9f7301/params.yaml new file mode 100644 index 00000000..15033f0f --- /dev/null +++ b/examples/security/classification/output/reports/train/8c933f637883c2f82549d1ce6b9f7301/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/adv_predictions.json + adv_probabilities_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/aac97a6efeaeddf548c3473fb103f892.pkl + params_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/params.yaml + predictions_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/predictions.json + probabilities_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/probabilities.json + score_dict_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/score_dict.json + test_labels_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/test_labels.json + train_labels_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/train_labels.json + model_dir: models + name: 8c933f637883c2f82549d1ce6b9f7301 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ec04c4a81e69d3c54c6bc0b37758edba + trainer: + kwargs: {} +name: 8c933f637883c2f82549d1ce6b9f7301 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/params.yaml b/examples/security/classification/output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/params.yaml new file mode 100644 index 00000000..d3d7a8e9 --- /dev/null +++ b/examples/security/classification/output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/adv_predictions.json + adv_probabilities_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/521d15ad9aa534e20ec8115627705d53.pkl + params_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/params.yaml + predictions_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/predictions.json + probabilities_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/probabilities.json + score_dict_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/score_dict.json + test_labels_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/test_labels.json + train_labels_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/train_labels.json + model_dir: models + name: 8c94eb8d0f28fd44340ab2d9ed522eb0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a22b65a8a9a0c4393a10a9c6802b37b1 + trainer: + kwargs: {} +name: 8c94eb8d0f28fd44340ab2d9ed522eb0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/params.yaml b/examples/security/classification/output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/params.yaml new file mode 100644 index 00000000..ed8f59b5 --- /dev/null +++ b/examples/security/classification/output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/adv_predictions.json + adv_probabilities_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/0b9a67406dd30f2bdc574fb20a709740.pkl + params_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/params.yaml + predictions_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/predictions.json + probabilities_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/probabilities.json + score_dict_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/score_dict.json + test_labels_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/test_labels.json + train_labels_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/train_labels.json + model_dir: models + name: 8caafd9d8ae4175a6400d69d8d038ec3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0765535c43014ec6182d85e806efaf1b + trainer: + kwargs: {} +name: 8caafd9d8ae4175a6400d69d8d038ec3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/params.yaml b/examples/security/classification/output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/params.yaml new file mode 100644 index 00000000..1da508ab --- /dev/null +++ b/examples/security/classification/output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/adv_predictions.json + adv_probabilities_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/37fac2c70a5946329e76212c19c0a724.pkl + params_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/params.yaml + predictions_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/predictions.json + probabilities_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/probabilities.json + score_dict_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/score_dict.json + test_labels_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/test_labels.json + train_labels_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/train_labels.json + model_dir: models + name: 8caffe8d3c901510c9bb1382796c4bd4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 57cac9a21f78293c9cc7fc3259581895 + trainer: + kwargs: {} +name: 8caffe8d3c901510c9bb1382796c4bd4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/params.yaml b/examples/security/classification/output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/params.yaml new file mode 100644 index 00000000..e9a04ebf --- /dev/null +++ b/examples/security/classification/output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/adv_predictions.json + adv_probabilities_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/d5801f226deacf42c0f6de49c940bf82.pkl + params_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/params.yaml + predictions_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/predictions.json + probabilities_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/probabilities.json + score_dict_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/score_dict.json + test_labels_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/test_labels.json + train_labels_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/train_labels.json + model_dir: models + name: 8cca7e5e85abc881070ed5f37a7f332b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d869f4a84824864c3a7be0b3aeb7d232 + trainer: + kwargs: {} +name: 8cca7e5e85abc881070ed5f37a7f332b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8d31bb8a4a09884d889b85f36faf8375/params.yaml b/examples/security/classification/output/reports/train/8d31bb8a4a09884d889b85f36faf8375/params.yaml new file mode 100644 index 00000000..4ce8cb02 --- /dev/null +++ b/examples/security/classification/output/reports/train/8d31bb8a4a09884d889b85f36faf8375/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/adv_predictions.json + adv_probabilities_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/051eaf0dad8df18ceacd5ad2b9765220.pkl + params_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/params.yaml + predictions_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/predictions.json + probabilities_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/probabilities.json + score_dict_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/score_dict.json + test_labels_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/test_labels.json + train_labels_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/train_labels.json + model_dir: models + name: 8d31bb8a4a09884d889b85f36faf8375 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3bf9ae1c7fc927e606b328aee8ff76b0 + trainer: + kwargs: {} +name: 8d31bb8a4a09884d889b85f36faf8375 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8d5199b5c97c961931385a6fed44effa/params.yaml b/examples/security/classification/output/reports/train/8d5199b5c97c961931385a6fed44effa/params.yaml new file mode 100644 index 00000000..c256401c --- /dev/null +++ b/examples/security/classification/output/reports/train/8d5199b5c97c961931385a6fed44effa/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/adv_predictions.json + adv_probabilities_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/cbed577a105bc44fcb45ca628e3d435a.pkl + params_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/params.yaml + predictions_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/predictions.json + probabilities_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/probabilities.json + score_dict_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/score_dict.json + test_labels_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/test_labels.json + train_labels_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/train_labels.json + model_dir: models + name: 8d5199b5c97c961931385a6fed44effa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c62615ac59167887be56c65c9bf608b7 + trainer: + kwargs: {} +name: 8d5199b5c97c961931385a6fed44effa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/params.yaml b/examples/security/classification/output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/params.yaml new file mode 100644 index 00000000..06ba483c --- /dev/null +++ b/examples/security/classification/output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/adv_predictions.json + adv_probabilities_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/f8f9224ac7c10f398ce5693499a6da1b.pkl + params_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/params.yaml + predictions_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/predictions.json + probabilities_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/probabilities.json + score_dict_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/score_dict.json + test_labels_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/test_labels.json + train_labels_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/train_labels.json + model_dir: models + name: 8d87db1aeaebd77aaee7a22d59fae315 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0b94b0c815d331ca2d25954ced67be1c + trainer: + kwargs: {} +name: 8d87db1aeaebd77aaee7a22d59fae315 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8db03852ca019f67daad11bd13741630/params.yaml b/examples/security/classification/output/reports/train/8db03852ca019f67daad11bd13741630/params.yaml new file mode 100644 index 00000000..b83c6837 --- /dev/null +++ b/examples/security/classification/output/reports/train/8db03852ca019f67daad11bd13741630/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8db03852ca019f67daad11bd13741630/adv_predictions.json + adv_probabilities_file: output/reports/train/8db03852ca019f67daad11bd13741630/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/b32603d420a657ee07e18083c140d7d8.pkl + params_file: output/reports/train/8db03852ca019f67daad11bd13741630/params.yaml + predictions_file: output/reports/train/8db03852ca019f67daad11bd13741630/predictions.json + probabilities_file: output/reports/train/8db03852ca019f67daad11bd13741630/probabilities.json + score_dict_file: output/reports/train/8db03852ca019f67daad11bd13741630/score_dict.json + test_labels_file: output/reports/train/8db03852ca019f67daad11bd13741630/test_labels.json + train_labels_file: output/reports/train/8db03852ca019f67daad11bd13741630/train_labels.json + model_dir: models + name: 8db03852ca019f67daad11bd13741630 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eff55ee2fdad0ab1f654a8a0e2f1f697 + trainer: + kwargs: {} +name: 8db03852ca019f67daad11bd13741630 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/params.yaml b/examples/security/classification/output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/params.yaml new file mode 100644 index 00000000..47fda7fe --- /dev/null +++ b/examples/security/classification/output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/adv_predictions.json + adv_probabilities_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/f88f57f574cb4b38f865730e5dcce99c.pkl + params_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/params.yaml + predictions_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/predictions.json + probabilities_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/probabilities.json + score_dict_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/score_dict.json + test_labels_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/test_labels.json + train_labels_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/train_labels.json + model_dir: models + name: 8db67c05afcac2d515c9b3c6b6938e20 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f9357dabe6962eb74829d24f5b765627 + trainer: + kwargs: {} +name: 8db67c05afcac2d515c9b3c6b6938e20 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8e0e2842d78e20788dfe619498079235/params.yaml b/examples/security/classification/output/reports/train/8e0e2842d78e20788dfe619498079235/params.yaml new file mode 100644 index 00000000..7fb69069 --- /dev/null +++ b/examples/security/classification/output/reports/train/8e0e2842d78e20788dfe619498079235/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8e0e2842d78e20788dfe619498079235/adv_predictions.json + adv_probabilities_file: output/reports/train/8e0e2842d78e20788dfe619498079235/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/dad278776bff4e61508046b8e596c09b.pkl + params_file: output/reports/train/8e0e2842d78e20788dfe619498079235/params.yaml + predictions_file: output/reports/train/8e0e2842d78e20788dfe619498079235/predictions.json + probabilities_file: output/reports/train/8e0e2842d78e20788dfe619498079235/probabilities.json + score_dict_file: output/reports/train/8e0e2842d78e20788dfe619498079235/score_dict.json + test_labels_file: output/reports/train/8e0e2842d78e20788dfe619498079235/test_labels.json + train_labels_file: output/reports/train/8e0e2842d78e20788dfe619498079235/train_labels.json + model_dir: models + name: 8e0e2842d78e20788dfe619498079235 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 72b9fe7708fe8fa54cae458fc89fca21 + trainer: + kwargs: {} +name: 8e0e2842d78e20788dfe619498079235 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8e312be2b3bcc9acd94e478c21890a64/params.yaml b/examples/security/classification/output/reports/train/8e312be2b3bcc9acd94e478c21890a64/params.yaml new file mode 100644 index 00000000..bf78e560 --- /dev/null +++ b/examples/security/classification/output/reports/train/8e312be2b3bcc9acd94e478c21890a64/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/adv_predictions.json + adv_probabilities_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/ee62c372070b74f0b3b2b98430855334.pkl + params_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/params.yaml + predictions_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/predictions.json + probabilities_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/probabilities.json + score_dict_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/score_dict.json + test_labels_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/test_labels.json + train_labels_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/train_labels.json + model_dir: models + name: 8e312be2b3bcc9acd94e478c21890a64 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e190e57b585004b2a2ef9a55e1bceef5 + trainer: + kwargs: {} +name: 8e312be2b3bcc9acd94e478c21890a64 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/params.yaml b/examples/security/classification/output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/params.yaml new file mode 100644 index 00000000..5ecf424f --- /dev/null +++ b/examples/security/classification/output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/adv_predictions.json + adv_probabilities_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/e15978b1ee3db1836a2a6fb4896591db.pkl + params_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/params.yaml + predictions_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/predictions.json + probabilities_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/probabilities.json + score_dict_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/score_dict.json + test_labels_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/test_labels.json + train_labels_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/train_labels.json + model_dir: models + name: 8e42dc1f350b6dde70235b673ebf7b10 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4e2c42bc70612c02a32cf9e449d31a3e + trainer: + kwargs: {} +name: 8e42dc1f350b6dde70235b673ebf7b10 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/params.yaml b/examples/security/classification/output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/params.yaml new file mode 100644 index 00000000..8ec72293 --- /dev/null +++ b/examples/security/classification/output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/adv_predictions.json + adv_probabilities_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/129431cabb119a284c8df078716fed74.pkl + params_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/params.yaml + predictions_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/predictions.json + probabilities_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/probabilities.json + score_dict_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/score_dict.json + test_labels_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/test_labels.json + train_labels_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/train_labels.json + model_dir: models + name: 8e5db43ee09c3757fb9bb36884e97ad8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9dae46f99a50a005a71db1de2ff480f8 + trainer: + kwargs: {} +name: 8e5db43ee09c3757fb9bb36884e97ad8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8e64ccf01262acf65b80fe81b813276b/params.yaml b/examples/security/classification/output/reports/train/8e64ccf01262acf65b80fe81b813276b/params.yaml new file mode 100644 index 00000000..65409cd4 --- /dev/null +++ b/examples/security/classification/output/reports/train/8e64ccf01262acf65b80fe81b813276b/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/adv_predictions.json + adv_probabilities_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/de52313c5a424b51087d762ce462582b.pkl + params_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/params.yaml + predictions_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/predictions.json + probabilities_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/probabilities.json + score_dict_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/score_dict.json + test_labels_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/test_labels.json + train_labels_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/train_labels.json + model_dir: models + name: 8e64ccf01262acf65b80fe81b813276b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 13153fd13c95ca4a4070a90e03722688 + trainer: + kwargs: {} +name: 8e64ccf01262acf65b80fe81b813276b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/params.yaml b/examples/security/classification/output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/params.yaml new file mode 100644 index 00000000..fd95d491 --- /dev/null +++ b/examples/security/classification/output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/adv_predictions.json + adv_probabilities_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/bbb1984239839c76e9eca1ab1661b32c.pkl + params_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/params.yaml + predictions_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/predictions.json + probabilities_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/probabilities.json + score_dict_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/score_dict.json + test_labels_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/test_labels.json + train_labels_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/train_labels.json + model_dir: models + name: 8e7dacaf1228cb5fdd1ca646472686f0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bc27a137d6f3dca8b89109886042e692 + trainer: + kwargs: {} +name: 8e7dacaf1228cb5fdd1ca646472686f0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/params.yaml b/examples/security/classification/output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/params.yaml new file mode 100644 index 00000000..f7be8bbe --- /dev/null +++ b/examples/security/classification/output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/adv_predictions.json + adv_probabilities_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/caf6e368219452e8d9df16743ae7f160.pkl + params_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/params.yaml + predictions_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/predictions.json + probabilities_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/probabilities.json + score_dict_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/score_dict.json + test_labels_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/test_labels.json + train_labels_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/train_labels.json + model_dir: models + name: 8e9be1f3dcf2443bfe78e59f13352268 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 56f6f2f3eef8110b32d639a1e9a8dc63 + trainer: + kwargs: {} +name: 8e9be1f3dcf2443bfe78e59f13352268 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/params.yaml b/examples/security/classification/output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/params.yaml new file mode 100644 index 00000000..6f0dd81f --- /dev/null +++ b/examples/security/classification/output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/adv_predictions.json + adv_probabilities_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/c19e932b9d81e594f06ffdb0f1fca641.pkl + params_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/params.yaml + predictions_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/predictions.json + probabilities_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/probabilities.json + score_dict_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/score_dict.json + test_labels_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/test_labels.json + train_labels_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/train_labels.json + model_dir: models + name: 8e9d7fe474fb2748815078dcb9403b7f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4e84e2a02c99980dbcc97e83807fab80 + trainer: + kwargs: {} +name: 8e9d7fe474fb2748815078dcb9403b7f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/params.yaml b/examples/security/classification/output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/params.yaml new file mode 100644 index 00000000..7a1ce7b1 --- /dev/null +++ b/examples/security/classification/output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/adv_predictions.json + adv_probabilities_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/f64c0a07a2227c8c06ec9e3056eb74c2.pkl + params_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/params.yaml + predictions_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/predictions.json + probabilities_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/probabilities.json + score_dict_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/score_dict.json + test_labels_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/test_labels.json + train_labels_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/train_labels.json + model_dir: models + name: 8edc2cf1eb82ee94c79a36d7a47928dc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4b9e0358d96fc725629a41ed4bbe1c65 + trainer: + kwargs: {} +name: 8edc2cf1eb82ee94c79a36d7a47928dc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/params.yaml b/examples/security/classification/output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/params.yaml new file mode 100644 index 00000000..3f967d83 --- /dev/null +++ b/examples/security/classification/output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/adv_predictions.json + adv_probabilities_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/265e29395c62f3ab03183e29648b6a28.pkl + params_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/params.yaml + predictions_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/predictions.json + probabilities_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/probabilities.json + score_dict_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/score_dict.json + test_labels_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/test_labels.json + train_labels_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/train_labels.json + model_dir: models + name: 8efd99aaab1921cdc9bef9d80e0ac9b6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3484a2f4c1ebba05b3fb15a5461410f2 + trainer: + kwargs: {} +name: 8efd99aaab1921cdc9bef9d80e0ac9b6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8f024e116370bbed47afee11f6ab3386/params.yaml b/examples/security/classification/output/reports/train/8f024e116370bbed47afee11f6ab3386/params.yaml new file mode 100644 index 00000000..6648101a --- /dev/null +++ b/examples/security/classification/output/reports/train/8f024e116370bbed47afee11f6ab3386/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/adv_predictions.json + adv_probabilities_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/a1fc3e1fa80481b5e2ae27baa89e2715.pkl + params_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/params.yaml + predictions_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/predictions.json + probabilities_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/probabilities.json + score_dict_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/score_dict.json + test_labels_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/test_labels.json + train_labels_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/train_labels.json + model_dir: models + name: 8f024e116370bbed47afee11f6ab3386 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 13e42a680655f2a54f563ce7f1005b13 + trainer: + kwargs: {} +name: 8f024e116370bbed47afee11f6ab3386 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/params.yaml b/examples/security/classification/output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/params.yaml new file mode 100644 index 00000000..dd8b4b26 --- /dev/null +++ b/examples/security/classification/output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/adv_predictions.json + adv_probabilities_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/bef0f15b3e001f0aa75a7fc1bf5adb32.pkl + params_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/params.yaml + predictions_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/predictions.json + probabilities_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/probabilities.json + score_dict_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/score_dict.json + test_labels_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/test_labels.json + train_labels_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/train_labels.json + model_dir: models + name: 8f2e4f6ca2c1c526d2b121cbbb1b13a8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0e9a7ffd5f6a68dc233835201ff1259a + trainer: + kwargs: {} +name: 8f2e4f6ca2c1c526d2b121cbbb1b13a8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/params.yaml b/examples/security/classification/output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/params.yaml new file mode 100644 index 00000000..d7d93a6e --- /dev/null +++ b/examples/security/classification/output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/adv_predictions.json + adv_probabilities_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/ca72baa7b57efe7694a03745baa6d661.pkl + params_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/params.yaml + predictions_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/predictions.json + probabilities_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/probabilities.json + score_dict_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/score_dict.json + test_labels_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/test_labels.json + train_labels_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/train_labels.json + model_dir: models + name: 8f4ad92f4c39620b3c97f90b4410856c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 699cbc0782ce944c16414d4e69da035d + trainer: + kwargs: {} +name: 8f4ad92f4c39620b3c97f90b4410856c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/params.yaml b/examples/security/classification/output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/params.yaml new file mode 100644 index 00000000..37c33260 --- /dev/null +++ b/examples/security/classification/output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/adv_predictions.json + adv_probabilities_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/06513a2c00a6fa586fb3897cd39d9fd2.pkl + params_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/params.yaml + predictions_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/predictions.json + probabilities_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/probabilities.json + score_dict_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/score_dict.json + test_labels_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/test_labels.json + train_labels_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/train_labels.json + model_dir: models + name: 8f637709ff4f325bb2c871eefc6baf5b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0993b371594437885bee372329c5fe02 + trainer: + kwargs: {} +name: 8f637709ff4f325bb2c871eefc6baf5b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8f99af0e2373b660b7ffecdff13059cd/params.yaml b/examples/security/classification/output/reports/train/8f99af0e2373b660b7ffecdff13059cd/params.yaml new file mode 100644 index 00000000..8ab10da6 --- /dev/null +++ b/examples/security/classification/output/reports/train/8f99af0e2373b660b7ffecdff13059cd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/adv_predictions.json + adv_probabilities_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/ff1692a6b58b671ba3872f9a8fe1c51e.pkl + params_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/params.yaml + predictions_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/predictions.json + probabilities_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/probabilities.json + score_dict_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/score_dict.json + test_labels_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/test_labels.json + train_labels_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/train_labels.json + model_dir: models + name: 8f99af0e2373b660b7ffecdff13059cd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6d3201a82fe2c4772af3b3716d4b81d7 + trainer: + kwargs: {} +name: 8f99af0e2373b660b7ffecdff13059cd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8fa7667d59626f1422aeb46f5b93c083/params.yaml b/examples/security/classification/output/reports/train/8fa7667d59626f1422aeb46f5b93c083/params.yaml new file mode 100644 index 00000000..afab5055 --- /dev/null +++ b/examples/security/classification/output/reports/train/8fa7667d59626f1422aeb46f5b93c083/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/adv_predictions.json + adv_probabilities_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/4da0cab01d2c2f16504fa4bc568a33d9.pkl + params_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/params.yaml + predictions_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/predictions.json + probabilities_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/probabilities.json + score_dict_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/score_dict.json + test_labels_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/test_labels.json + train_labels_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/train_labels.json + model_dir: models + name: 8fa7667d59626f1422aeb46f5b93c083 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f59217cf6f66149214dbae7116c67bf7 + trainer: + kwargs: {} +name: 8fa7667d59626f1422aeb46f5b93c083 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/params.yaml b/examples/security/classification/output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/params.yaml new file mode 100644 index 00000000..0fb43781 --- /dev/null +++ b/examples/security/classification/output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/adv_predictions.json + adv_probabilities_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/96f88c44883a09419334572270eb3291.pkl + params_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/params.yaml + predictions_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/predictions.json + probabilities_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/probabilities.json + score_dict_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/score_dict.json + test_labels_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/test_labels.json + train_labels_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/train_labels.json + model_dir: models + name: 8fcb3d1c16524ecd8a50290aad5b333f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 387684d269c3fa23bdf8205a13c43b43 + trainer: + kwargs: {} +name: 8fcb3d1c16524ecd8a50290aad5b333f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/params.yaml b/examples/security/classification/output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/params.yaml new file mode 100644 index 00000000..151deb24 --- /dev/null +++ b/examples/security/classification/output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/adv_predictions.json + adv_probabilities_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/ac69d5d0bef3e2f6607a84e77cebcf18.pkl + params_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/params.yaml + predictions_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/predictions.json + probabilities_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/probabilities.json + score_dict_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/score_dict.json + test_labels_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/test_labels.json + train_labels_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/train_labels.json + model_dir: models + name: 8fd1ee98a7ffa0ee9b379d67989c7bb9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 08c1aa4d39ecfb42e94065018ff33f42 + trainer: + kwargs: {} +name: 8fd1ee98a7ffa0ee9b379d67989c7bb9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/900a983b6857c519df3dd1bc302fcff3/params.yaml b/examples/security/classification/output/reports/train/900a983b6857c519df3dd1bc302fcff3/params.yaml new file mode 100644 index 00000000..3b2a9769 --- /dev/null +++ b/examples/security/classification/output/reports/train/900a983b6857c519df3dd1bc302fcff3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/adv_predictions.json + adv_probabilities_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/2ab0ce6c93a6eac2a4db8dff6e32a855.pkl + params_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/params.yaml + predictions_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/predictions.json + probabilities_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/probabilities.json + score_dict_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/score_dict.json + test_labels_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/test_labels.json + train_labels_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/train_labels.json + model_dir: models + name: 900a983b6857c519df3dd1bc302fcff3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5dd3d7bbe224f239d32784e95db04406 + trainer: + kwargs: {} +name: 900a983b6857c519df3dd1bc302fcff3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/90178dd718a39d852e723419279dabb1/params.yaml b/examples/security/classification/output/reports/train/90178dd718a39d852e723419279dabb1/params.yaml new file mode 100644 index 00000000..ac4c3630 --- /dev/null +++ b/examples/security/classification/output/reports/train/90178dd718a39d852e723419279dabb1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/90178dd718a39d852e723419279dabb1/adv_predictions.json + adv_probabilities_file: output/reports/train/90178dd718a39d852e723419279dabb1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/5ca185b1f2dd6ea26f406686e6ef3dba.pkl + params_file: output/reports/train/90178dd718a39d852e723419279dabb1/params.yaml + predictions_file: output/reports/train/90178dd718a39d852e723419279dabb1/predictions.json + probabilities_file: output/reports/train/90178dd718a39d852e723419279dabb1/probabilities.json + score_dict_file: output/reports/train/90178dd718a39d852e723419279dabb1/score_dict.json + test_labels_file: output/reports/train/90178dd718a39d852e723419279dabb1/test_labels.json + train_labels_file: output/reports/train/90178dd718a39d852e723419279dabb1/train_labels.json + model_dir: models + name: 90178dd718a39d852e723419279dabb1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 33bdcd58167c726358320c1c0ecf79d5 + trainer: + kwargs: {} +name: 90178dd718a39d852e723419279dabb1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/90251253925daddfc1b6e740f7ff7ccc/params.yaml b/examples/security/classification/output/reports/train/90251253925daddfc1b6e740f7ff7ccc/params.yaml new file mode 100644 index 00000000..352bdd95 --- /dev/null +++ b/examples/security/classification/output/reports/train/90251253925daddfc1b6e740f7ff7ccc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/adv_predictions.json + adv_probabilities_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/170aaad169118a88d926fde7a55d79d5.pkl + params_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/params.yaml + predictions_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/predictions.json + probabilities_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/probabilities.json + score_dict_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/score_dict.json + test_labels_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/test_labels.json + train_labels_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/train_labels.json + model_dir: models + name: 90251253925daddfc1b6e740f7ff7ccc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b2a08c8077b23d0743ec3f2ac073d3df + trainer: + kwargs: {} +name: 90251253925daddfc1b6e740f7ff7ccc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/90338c9b8ddf30392668ca086f9d77f3/params.yaml b/examples/security/classification/output/reports/train/90338c9b8ddf30392668ca086f9d77f3/params.yaml new file mode 100644 index 00000000..d356e75c --- /dev/null +++ b/examples/security/classification/output/reports/train/90338c9b8ddf30392668ca086f9d77f3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/adv_predictions.json + adv_probabilities_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/8048482d83c0edb9431d13c4f26200c2.pkl + params_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/params.yaml + predictions_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/predictions.json + probabilities_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/probabilities.json + score_dict_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/score_dict.json + test_labels_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/test_labels.json + train_labels_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/train_labels.json + model_dir: models + name: 90338c9b8ddf30392668ca086f9d77f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c687f793c13533cbef4df3e37193235f + trainer: + kwargs: {} +name: 90338c9b8ddf30392668ca086f9d77f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/params.yaml b/examples/security/classification/output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/params.yaml new file mode 100644 index 00000000..a23c863c --- /dev/null +++ b/examples/security/classification/output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/adv_predictions.json + adv_probabilities_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/f480e3c6a9d31817ce1cbb228adce231.pkl + params_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/params.yaml + predictions_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/predictions.json + probabilities_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/probabilities.json + score_dict_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/score_dict.json + test_labels_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/test_labels.json + train_labels_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/train_labels.json + model_dir: models + name: 909140e71fce1b3b6ae6d9ff17d8cabb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7c08677d8b621fc3a0272cadd96677ea + trainer: + kwargs: {} +name: 909140e71fce1b3b6ae6d9ff17d8cabb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/params.yaml b/examples/security/classification/output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/params.yaml new file mode 100644 index 00000000..a9c929c9 --- /dev/null +++ b/examples/security/classification/output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/adv_predictions.json + adv_probabilities_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/9faafc1dcd858b02433f3596a77ae239.pkl + params_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/params.yaml + predictions_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/predictions.json + probabilities_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/probabilities.json + score_dict_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/score_dict.json + test_labels_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/test_labels.json + train_labels_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/train_labels.json + model_dir: models + name: 90bbfd1883d4a9872294ce3216ca8ba5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 38b2e5e0aec2a27f5c4e7adf1d689cf4 + trainer: + kwargs: {} +name: 90bbfd1883d4a9872294ce3216ca8ba5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/90cc624073daad38c0f2ee45443e399f/params.yaml b/examples/security/classification/output/reports/train/90cc624073daad38c0f2ee45443e399f/params.yaml new file mode 100644 index 00000000..6b0164a4 --- /dev/null +++ b/examples/security/classification/output/reports/train/90cc624073daad38c0f2ee45443e399f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/adv_predictions.json + adv_probabilities_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/c9cbfd8ab0bccff4c0e2da20facd3720.pkl + params_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/params.yaml + predictions_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/predictions.json + probabilities_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/probabilities.json + score_dict_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/score_dict.json + test_labels_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/test_labels.json + train_labels_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/train_labels.json + model_dir: models + name: 90cc624073daad38c0f2ee45443e399f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2af4d8410274dca2a6c9fc12bb1d278e + trainer: + kwargs: {} +name: 90cc624073daad38c0f2ee45443e399f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/params.yaml b/examples/security/classification/output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/params.yaml new file mode 100644 index 00000000..6a37036b --- /dev/null +++ b/examples/security/classification/output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/adv_predictions.json + adv_probabilities_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/313ecf4a9f31aa526cc9e914efd55363.pkl + params_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/params.yaml + predictions_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/predictions.json + probabilities_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/probabilities.json + score_dict_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/score_dict.json + test_labels_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/test_labels.json + train_labels_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/train_labels.json + model_dir: models + name: 90e090e7cc51dd81a5204f805cd8c8f8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fed52635a6ba2d58634b77e11036593c + trainer: + kwargs: {} +name: 90e090e7cc51dd81a5204f805cd8c8f8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/90e23cef329532186544dcd004700152/params.yaml b/examples/security/classification/output/reports/train/90e23cef329532186544dcd004700152/params.yaml new file mode 100644 index 00000000..1e560dd9 --- /dev/null +++ b/examples/security/classification/output/reports/train/90e23cef329532186544dcd004700152/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/90e23cef329532186544dcd004700152/adv_predictions.json + adv_probabilities_file: output/reports/train/90e23cef329532186544dcd004700152/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/6b381c898de23cf5afa03190280c1778.pkl + params_file: output/reports/train/90e23cef329532186544dcd004700152/params.yaml + predictions_file: output/reports/train/90e23cef329532186544dcd004700152/predictions.json + probabilities_file: output/reports/train/90e23cef329532186544dcd004700152/probabilities.json + score_dict_file: output/reports/train/90e23cef329532186544dcd004700152/score_dict.json + test_labels_file: output/reports/train/90e23cef329532186544dcd004700152/test_labels.json + train_labels_file: output/reports/train/90e23cef329532186544dcd004700152/train_labels.json + model_dir: models + name: 90e23cef329532186544dcd004700152 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 067cec326fe685c11e0e5e686caf3685 + trainer: + kwargs: {} +name: 90e23cef329532186544dcd004700152 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9101ff79c72f4a697800a5a63280cda0/params.yaml b/examples/security/classification/output/reports/train/9101ff79c72f4a697800a5a63280cda0/params.yaml new file mode 100644 index 00000000..c7ad1fa0 --- /dev/null +++ b/examples/security/classification/output/reports/train/9101ff79c72f4a697800a5a63280cda0/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/adv_predictions.json + adv_probabilities_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/3fff19b7d7f11edb2ea60ee03af5379e.pkl + params_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/params.yaml + predictions_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/predictions.json + probabilities_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/probabilities.json + score_dict_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/score_dict.json + test_labels_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/test_labels.json + train_labels_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/train_labels.json + model_dir: models + name: 9101ff79c72f4a697800a5a63280cda0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 94b9010d803c55069a70d9b7ab7ff449 + trainer: + kwargs: {} +name: 9101ff79c72f4a697800a5a63280cda0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/911c83e80158f500ec35df3c8aa8813e/params.yaml b/examples/security/classification/output/reports/train/911c83e80158f500ec35df3c8aa8813e/params.yaml new file mode 100644 index 00000000..b67e30f8 --- /dev/null +++ b/examples/security/classification/output/reports/train/911c83e80158f500ec35df3c8aa8813e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/adv_predictions.json + adv_probabilities_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/c4f0c9940866c99542c10225ff3c32fc.pkl + params_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/params.yaml + predictions_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/predictions.json + probabilities_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/probabilities.json + score_dict_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/score_dict.json + test_labels_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/test_labels.json + train_labels_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/train_labels.json + model_dir: models + name: 911c83e80158f500ec35df3c8aa8813e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8516bba5fb0c2b6628e6ac44d60c6dcb + trainer: + kwargs: {} +name: 911c83e80158f500ec35df3c8aa8813e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9132afba265736635b94846de8012165/params.yaml b/examples/security/classification/output/reports/train/9132afba265736635b94846de8012165/params.yaml new file mode 100644 index 00000000..ced21055 --- /dev/null +++ b/examples/security/classification/output/reports/train/9132afba265736635b94846de8012165/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9132afba265736635b94846de8012165/adv_predictions.json + adv_probabilities_file: output/reports/train/9132afba265736635b94846de8012165/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/ca9501c2deb65d8f685f950d4a9c1d45.pkl + params_file: output/reports/train/9132afba265736635b94846de8012165/params.yaml + predictions_file: output/reports/train/9132afba265736635b94846de8012165/predictions.json + probabilities_file: output/reports/train/9132afba265736635b94846de8012165/probabilities.json + score_dict_file: output/reports/train/9132afba265736635b94846de8012165/score_dict.json + test_labels_file: output/reports/train/9132afba265736635b94846de8012165/test_labels.json + train_labels_file: output/reports/train/9132afba265736635b94846de8012165/train_labels.json + model_dir: models + name: 9132afba265736635b94846de8012165 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f39d067994799861b45bec4c502d56ea + trainer: + kwargs: {} +name: 9132afba265736635b94846de8012165 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9153f097023b55f880804709379c0ece/params.yaml b/examples/security/classification/output/reports/train/9153f097023b55f880804709379c0ece/params.yaml new file mode 100644 index 00000000..d85118b3 --- /dev/null +++ b/examples/security/classification/output/reports/train/9153f097023b55f880804709379c0ece/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9153f097023b55f880804709379c0ece/adv_predictions.json + adv_probabilities_file: output/reports/train/9153f097023b55f880804709379c0ece/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/aa9661945898a2d621d675a52ce41328.pkl + params_file: output/reports/train/9153f097023b55f880804709379c0ece/params.yaml + predictions_file: output/reports/train/9153f097023b55f880804709379c0ece/predictions.json + probabilities_file: output/reports/train/9153f097023b55f880804709379c0ece/probabilities.json + score_dict_file: output/reports/train/9153f097023b55f880804709379c0ece/score_dict.json + test_labels_file: output/reports/train/9153f097023b55f880804709379c0ece/test_labels.json + train_labels_file: output/reports/train/9153f097023b55f880804709379c0ece/train_labels.json + model_dir: models + name: 9153f097023b55f880804709379c0ece + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2a77dc5903589eddec6176881f7451c1 + trainer: + kwargs: {} +name: 9153f097023b55f880804709379c0ece +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/916c2ff29bd5d47eeb773124be6c814f/params.yaml b/examples/security/classification/output/reports/train/916c2ff29bd5d47eeb773124be6c814f/params.yaml new file mode 100644 index 00000000..72746bc8 --- /dev/null +++ b/examples/security/classification/output/reports/train/916c2ff29bd5d47eeb773124be6c814f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/adv_predictions.json + adv_probabilities_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/51b1d81ca5beb3af7a48db89f9dcafd2.pkl + params_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/params.yaml + predictions_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/predictions.json + probabilities_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/probabilities.json + score_dict_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/score_dict.json + test_labels_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/test_labels.json + train_labels_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/train_labels.json + model_dir: models + name: 916c2ff29bd5d47eeb773124be6c814f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 250a81021c1d69e6eccfd2288b2ea377 + trainer: + kwargs: {} +name: 916c2ff29bd5d47eeb773124be6c814f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/91732fd30a30873dea38a72e305f11dc/params.yaml b/examples/security/classification/output/reports/train/91732fd30a30873dea38a72e305f11dc/params.yaml new file mode 100644 index 00000000..e340ceae --- /dev/null +++ b/examples/security/classification/output/reports/train/91732fd30a30873dea38a72e305f11dc/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/adv_predictions.json + adv_probabilities_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/dbfe710a8a09cf1bdc1c95424b63b7c6.pkl + params_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/params.yaml + predictions_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/predictions.json + probabilities_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/probabilities.json + score_dict_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/score_dict.json + test_labels_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/test_labels.json + train_labels_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/train_labels.json + model_dir: models + name: 91732fd30a30873dea38a72e305f11dc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bd97cefdc147c0db630ddb4c6c682879 + trainer: + kwargs: {} +name: 91732fd30a30873dea38a72e305f11dc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/params.yaml b/examples/security/classification/output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/params.yaml new file mode 100644 index 00000000..6d2f7c71 --- /dev/null +++ b/examples/security/classification/output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/adv_predictions.json + adv_probabilities_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/fdef4ae4e18b0f9d4dc30987129c9c1d.pkl + params_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/params.yaml + predictions_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/predictions.json + probabilities_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/probabilities.json + score_dict_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/score_dict.json + test_labels_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/test_labels.json + train_labels_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/train_labels.json + model_dir: models + name: 91834b22b7e26bc67bc9928ca1e8936e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 410b6e7b7895767b4b6fd4dd9d01df43 + trainer: + kwargs: {} +name: 91834b22b7e26bc67bc9928ca1e8936e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/91984a9b30818ba47d0523418f83dd59/params.yaml b/examples/security/classification/output/reports/train/91984a9b30818ba47d0523418f83dd59/params.yaml new file mode 100644 index 00000000..8338370b --- /dev/null +++ b/examples/security/classification/output/reports/train/91984a9b30818ba47d0523418f83dd59/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/adv_predictions.json + adv_probabilities_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/f5483170a52beaade69fa42463dd10db.pkl + params_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/params.yaml + predictions_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/predictions.json + probabilities_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/probabilities.json + score_dict_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/score_dict.json + test_labels_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/test_labels.json + train_labels_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/train_labels.json + model_dir: models + name: 91984a9b30818ba47d0523418f83dd59 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f8714f357a3c306b519b52d42dc68b12 + trainer: + kwargs: {} +name: 91984a9b30818ba47d0523418f83dd59 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/91a77a2474baa9eba2e727a379e79e11/params.yaml b/examples/security/classification/output/reports/train/91a77a2474baa9eba2e727a379e79e11/params.yaml new file mode 100644 index 00000000..6fd40754 --- /dev/null +++ b/examples/security/classification/output/reports/train/91a77a2474baa9eba2e727a379e79e11/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/adv_predictions.json + adv_probabilities_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/9b2f48aa61d53a6ca38a70802845bed3.pkl + params_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/params.yaml + predictions_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/predictions.json + probabilities_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/probabilities.json + score_dict_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/score_dict.json + test_labels_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/test_labels.json + train_labels_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/train_labels.json + model_dir: models + name: 91a77a2474baa9eba2e727a379e79e11 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d0f46bf839f2a750b20715d4b83dbbac + trainer: + kwargs: {} +name: 91a77a2474baa9eba2e727a379e79e11 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/91d483132757e15a5ed166872535c237/params.yaml b/examples/security/classification/output/reports/train/91d483132757e15a5ed166872535c237/params.yaml new file mode 100644 index 00000000..4c6614ba --- /dev/null +++ b/examples/security/classification/output/reports/train/91d483132757e15a5ed166872535c237/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/91d483132757e15a5ed166872535c237/adv_predictions.json + adv_probabilities_file: output/reports/train/91d483132757e15a5ed166872535c237/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/da5b82af660970ad747b64a9e4d10efa.pkl + params_file: output/reports/train/91d483132757e15a5ed166872535c237/params.yaml + predictions_file: output/reports/train/91d483132757e15a5ed166872535c237/predictions.json + probabilities_file: output/reports/train/91d483132757e15a5ed166872535c237/probabilities.json + score_dict_file: output/reports/train/91d483132757e15a5ed166872535c237/score_dict.json + test_labels_file: output/reports/train/91d483132757e15a5ed166872535c237/test_labels.json + train_labels_file: output/reports/train/91d483132757e15a5ed166872535c237/train_labels.json + model_dir: models + name: 91d483132757e15a5ed166872535c237 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a0e3bab3f4f11932d131ab1c02e0026b + trainer: + kwargs: {} +name: 91d483132757e15a5ed166872535c237 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/params.yaml b/examples/security/classification/output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/params.yaml new file mode 100644 index 00000000..93894917 --- /dev/null +++ b/examples/security/classification/output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/adv_predictions.json + adv_probabilities_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/de6dff8b4bcb3104b102c3525770faef.pkl + params_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/params.yaml + predictions_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/predictions.json + probabilities_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/probabilities.json + score_dict_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/score_dict.json + test_labels_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/test_labels.json + train_labels_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/train_labels.json + model_dir: models + name: 91f9e04fa18e2e9bd52aac7ad22c345c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0d69bd4f1c57de80ff3b5363854a3bc0 + trainer: + kwargs: {} +name: 91f9e04fa18e2e9bd52aac7ad22c345c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/923f5c519cfa1431603a73d3fe3451ab/params.yaml b/examples/security/classification/output/reports/train/923f5c519cfa1431603a73d3fe3451ab/params.yaml new file mode 100644 index 00000000..7ea3c8cd --- /dev/null +++ b/examples/security/classification/output/reports/train/923f5c519cfa1431603a73d3fe3451ab/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/adv_predictions.json + adv_probabilities_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/bf83ab69d36317ff678edcfe0773867a.pkl + params_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/params.yaml + predictions_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/predictions.json + probabilities_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/probabilities.json + score_dict_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/score_dict.json + test_labels_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/test_labels.json + train_labels_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/train_labels.json + model_dir: models + name: 923f5c519cfa1431603a73d3fe3451ab + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c5cac6677ebd338c7419232b92bc21ea + trainer: + kwargs: {} +name: 923f5c519cfa1431603a73d3fe3451ab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/params.yaml b/examples/security/classification/output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/params.yaml new file mode 100644 index 00000000..8f373a15 --- /dev/null +++ b/examples/security/classification/output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/adv_predictions.json + adv_probabilities_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/a481692f6d066d75c300aedb1edeac73.pkl + params_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/params.yaml + predictions_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/predictions.json + probabilities_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/probabilities.json + score_dict_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/score_dict.json + test_labels_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/test_labels.json + train_labels_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/train_labels.json + model_dir: models + name: 926c428cdcd6bca8c31bca51b4a32b28 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 827700bdef4d4c0e9c40ee5f648c9a76 + trainer: + kwargs: {} +name: 926c428cdcd6bca8c31bca51b4a32b28 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/params.yaml b/examples/security/classification/output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/params.yaml new file mode 100644 index 00000000..8390a4f7 --- /dev/null +++ b/examples/security/classification/output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/adv_predictions.json + adv_probabilities_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/680897e0eaed49f6c1fcca20523ea9cf.pkl + params_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/params.yaml + predictions_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/predictions.json + probabilities_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/probabilities.json + score_dict_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/score_dict.json + test_labels_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/test_labels.json + train_labels_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/train_labels.json + model_dir: models + name: 9274492a2fa76aceae9dc57aeb8c0e6d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 73b67d1fa6e5e535dc99756b874d46ea + trainer: + kwargs: {} +name: 9274492a2fa76aceae9dc57aeb8c0e6d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/params.yaml b/examples/security/classification/output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/params.yaml new file mode 100644 index 00000000..b109e051 --- /dev/null +++ b/examples/security/classification/output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/adv_predictions.json + adv_probabilities_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/38c1cd654865dd76aead2e931e6542b1.pkl + params_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/params.yaml + predictions_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/predictions.json + probabilities_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/probabilities.json + score_dict_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/score_dict.json + test_labels_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/test_labels.json + train_labels_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/train_labels.json + model_dir: models + name: 92b999fbda0d5630738108ea9b3d6d3f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1320e341004e74107c128cab6729037a + trainer: + kwargs: {} +name: 92b999fbda0d5630738108ea9b3d6d3f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/params.yaml b/examples/security/classification/output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/params.yaml new file mode 100644 index 00000000..b626b500 --- /dev/null +++ b/examples/security/classification/output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/adv_predictions.json + adv_probabilities_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/3ea9841ca12090a0c18c242f153ef992.pkl + params_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/params.yaml + predictions_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/predictions.json + probabilities_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/probabilities.json + score_dict_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/score_dict.json + test_labels_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/test_labels.json + train_labels_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/train_labels.json + model_dir: models + name: 92d2f79adbfe33d37c73be647b28e7c1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 13ed0deb36246601fc4d54af6dc0f6d7 + trainer: + kwargs: {} +name: 92d2f79adbfe33d37c73be647b28e7c1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/931f9ba3a65708a2cb63563c611d42f8/params.yaml b/examples/security/classification/output/reports/train/931f9ba3a65708a2cb63563c611d42f8/params.yaml new file mode 100644 index 00000000..faaec31c --- /dev/null +++ b/examples/security/classification/output/reports/train/931f9ba3a65708a2cb63563c611d42f8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/adv_predictions.json + adv_probabilities_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/130630ca31401dc7dcf36cff977aa7fd.pkl + params_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/params.yaml + predictions_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/predictions.json + probabilities_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/probabilities.json + score_dict_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/score_dict.json + test_labels_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/test_labels.json + train_labels_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/train_labels.json + model_dir: models + name: 931f9ba3a65708a2cb63563c611d42f8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1ae89ccfba152a3930717bdbee4e2d1a + trainer: + kwargs: {} +name: 931f9ba3a65708a2cb63563c611d42f8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9343beedde54458e718e875d764af2de/params.yaml b/examples/security/classification/output/reports/train/9343beedde54458e718e875d764af2de/params.yaml new file mode 100644 index 00000000..68669bf1 --- /dev/null +++ b/examples/security/classification/output/reports/train/9343beedde54458e718e875d764af2de/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9343beedde54458e718e875d764af2de/adv_predictions.json + adv_probabilities_file: output/reports/train/9343beedde54458e718e875d764af2de/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/5ec09d5a82fbbb08dc0e2c75b70900b0.pkl + params_file: output/reports/train/9343beedde54458e718e875d764af2de/params.yaml + predictions_file: output/reports/train/9343beedde54458e718e875d764af2de/predictions.json + probabilities_file: output/reports/train/9343beedde54458e718e875d764af2de/probabilities.json + score_dict_file: output/reports/train/9343beedde54458e718e875d764af2de/score_dict.json + test_labels_file: output/reports/train/9343beedde54458e718e875d764af2de/test_labels.json + train_labels_file: output/reports/train/9343beedde54458e718e875d764af2de/train_labels.json + model_dir: models + name: 9343beedde54458e718e875d764af2de + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dcd6a26dc4866f78125be5a356aa23f6 + trainer: + kwargs: {} +name: 9343beedde54458e718e875d764af2de +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/934abf0d92bd01e993315de6cfb0d78f/params.yaml b/examples/security/classification/output/reports/train/934abf0d92bd01e993315de6cfb0d78f/params.yaml new file mode 100644 index 00000000..e06f7e6a --- /dev/null +++ b/examples/security/classification/output/reports/train/934abf0d92bd01e993315de6cfb0d78f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/adv_predictions.json + adv_probabilities_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/5534c3fa00e05ebc189ce85b9bc87842.pkl + params_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/params.yaml + predictions_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/predictions.json + probabilities_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/probabilities.json + score_dict_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/score_dict.json + test_labels_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/test_labels.json + train_labels_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/train_labels.json + model_dir: models + name: 934abf0d92bd01e993315de6cfb0d78f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6a20b63e45c292487acd6e66d98d63ea + trainer: + kwargs: {} +name: 934abf0d92bd01e993315de6cfb0d78f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9351dc19a7247fa4e29ba765e189ae04/params.yaml b/examples/security/classification/output/reports/train/9351dc19a7247fa4e29ba765e189ae04/params.yaml new file mode 100644 index 00000000..d4c73087 --- /dev/null +++ b/examples/security/classification/output/reports/train/9351dc19a7247fa4e29ba765e189ae04/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/adv_predictions.json + adv_probabilities_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/f31272c34bfe775d0c9d3b272b17091d.pkl + params_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/params.yaml + predictions_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/predictions.json + probabilities_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/probabilities.json + score_dict_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/score_dict.json + test_labels_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/test_labels.json + train_labels_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/train_labels.json + model_dir: models + name: 9351dc19a7247fa4e29ba765e189ae04 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 746848bdd2189e586aa885e1a82aa87e + trainer: + kwargs: {} +name: 9351dc19a7247fa4e29ba765e189ae04 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/params.yaml b/examples/security/classification/output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/params.yaml new file mode 100644 index 00000000..a8d28fc2 --- /dev/null +++ b/examples/security/classification/output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/adv_predictions.json + adv_probabilities_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/4d78cd7b615efdba369bd1cec9216d7e.pkl + params_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/params.yaml + predictions_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/predictions.json + probabilities_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/probabilities.json + score_dict_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/score_dict.json + test_labels_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/test_labels.json + train_labels_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/train_labels.json + model_dir: models + name: 9376989c55002fbd3bb4ad2cdbcbf8c3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6104d74ff9a78e6f80dc2a9b4f7bdee1 + trainer: + kwargs: {} +name: 9376989c55002fbd3bb4ad2cdbcbf8c3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/93b550b76eb9367b9a9788ac35f5714a/params.yaml b/examples/security/classification/output/reports/train/93b550b76eb9367b9a9788ac35f5714a/params.yaml new file mode 100644 index 00000000..9504aa9a --- /dev/null +++ b/examples/security/classification/output/reports/train/93b550b76eb9367b9a9788ac35f5714a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/adv_predictions.json + adv_probabilities_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/5edfa2412f5dd196be89ab7d6215a280.pkl + params_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/params.yaml + predictions_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/predictions.json + probabilities_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/probabilities.json + score_dict_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/score_dict.json + test_labels_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/test_labels.json + train_labels_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/train_labels.json + model_dir: models + name: 93b550b76eb9367b9a9788ac35f5714a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8714d972fc540483cb4483cb4231ccad + trainer: + kwargs: {} +name: 93b550b76eb9367b9a9788ac35f5714a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/params.yaml b/examples/security/classification/output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/params.yaml new file mode 100644 index 00000000..0806272d --- /dev/null +++ b/examples/security/classification/output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/adv_predictions.json + adv_probabilities_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/0ab516daea81c68ba4b767744d924ce2.pkl + params_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/params.yaml + predictions_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/predictions.json + probabilities_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/probabilities.json + score_dict_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/score_dict.json + test_labels_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/test_labels.json + train_labels_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/train_labels.json + model_dir: models + name: 93b806a0badbb8ffa2fc988ecf054d2b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b8f1752c536ca85a224dc2f32c6799f3 + trainer: + kwargs: {} +name: 93b806a0badbb8ffa2fc988ecf054d2b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/params.yaml b/examples/security/classification/output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/params.yaml new file mode 100644 index 00000000..e981112d --- /dev/null +++ b/examples/security/classification/output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/adv_predictions.json + adv_probabilities_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/6ed6c0842351c635d70811d876063ab3.pkl + params_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/params.yaml + predictions_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/predictions.json + probabilities_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/probabilities.json + score_dict_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/score_dict.json + test_labels_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/test_labels.json + train_labels_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/train_labels.json + model_dir: models + name: 93bd69da59aebbbb8a9c9a0a12c6e78c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e5c0d8a71019e57e70ab047418ce9497 + trainer: + kwargs: {} +name: 93bd69da59aebbbb8a9c9a0a12c6e78c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/params.yaml b/examples/security/classification/output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/params.yaml new file mode 100644 index 00000000..806ec02f --- /dev/null +++ b/examples/security/classification/output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/adv_predictions.json + adv_probabilities_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/790b6acebf2d621d013076cf16412320.pkl + params_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/params.yaml + predictions_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/predictions.json + probabilities_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/probabilities.json + score_dict_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/score_dict.json + test_labels_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/test_labels.json + train_labels_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/train_labels.json + model_dir: models + name: 93c51e724f7eba15cb63a8a9d2c85b19 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ae5e2a56747248a450c44f767128a171 + trainer: + kwargs: {} +name: 93c51e724f7eba15cb63a8a9d2c85b19 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/params.yaml b/examples/security/classification/output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/params.yaml new file mode 100644 index 00000000..352231b5 --- /dev/null +++ b/examples/security/classification/output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/adv_predictions.json + adv_probabilities_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/44d03061e46ace847faa096895037359.pkl + params_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/params.yaml + predictions_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/predictions.json + probabilities_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/probabilities.json + score_dict_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/score_dict.json + test_labels_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/test_labels.json + train_labels_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/train_labels.json + model_dir: models + name: 93cc459b4efa826b54ac2bcd956d7c82 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: de6979c105993dfc02e5de778213b683 + trainer: + kwargs: {} +name: 93cc459b4efa826b54ac2bcd956d7c82 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/941ebbf64dd77173d7a147f695289541/params.yaml b/examples/security/classification/output/reports/train/941ebbf64dd77173d7a147f695289541/params.yaml new file mode 100644 index 00000000..6c0b637f --- /dev/null +++ b/examples/security/classification/output/reports/train/941ebbf64dd77173d7a147f695289541/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/941ebbf64dd77173d7a147f695289541/adv_predictions.json + adv_probabilities_file: output/reports/train/941ebbf64dd77173d7a147f695289541/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/dba1034b36fb84bb7524247246d97937.pkl + params_file: output/reports/train/941ebbf64dd77173d7a147f695289541/params.yaml + predictions_file: output/reports/train/941ebbf64dd77173d7a147f695289541/predictions.json + probabilities_file: output/reports/train/941ebbf64dd77173d7a147f695289541/probabilities.json + score_dict_file: output/reports/train/941ebbf64dd77173d7a147f695289541/score_dict.json + test_labels_file: output/reports/train/941ebbf64dd77173d7a147f695289541/test_labels.json + train_labels_file: output/reports/train/941ebbf64dd77173d7a147f695289541/train_labels.json + model_dir: models + name: 941ebbf64dd77173d7a147f695289541 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 055c19bc153e3a0fec209e58c24f62b6 + trainer: + kwargs: {} +name: 941ebbf64dd77173d7a147f695289541 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/params.yaml b/examples/security/classification/output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/params.yaml new file mode 100644 index 00000000..47b710f1 --- /dev/null +++ b/examples/security/classification/output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/adv_predictions.json + adv_probabilities_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/7c4c67bb3b48394b8e84da74577782e3.pkl + params_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/params.yaml + predictions_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/predictions.json + probabilities_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/probabilities.json + score_dict_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/score_dict.json + test_labels_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/test_labels.json + train_labels_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/train_labels.json + model_dir: models + name: 9428f73c64ca35b3acb71b5e289a1d18 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1b0971c19bab22ec571dbbd2c5c74d32 + trainer: + kwargs: {} +name: 9428f73c64ca35b3acb71b5e289a1d18 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/94643560d092400898b154226fbe2b25/params.yaml b/examples/security/classification/output/reports/train/94643560d092400898b154226fbe2b25/params.yaml new file mode 100644 index 00000000..4b35b02f --- /dev/null +++ b/examples/security/classification/output/reports/train/94643560d092400898b154226fbe2b25/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/94643560d092400898b154226fbe2b25/adv_predictions.json + adv_probabilities_file: output/reports/train/94643560d092400898b154226fbe2b25/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/cd190ff1ffda1bc6ce19c092bffa23f3.pkl + params_file: output/reports/train/94643560d092400898b154226fbe2b25/params.yaml + predictions_file: output/reports/train/94643560d092400898b154226fbe2b25/predictions.json + probabilities_file: output/reports/train/94643560d092400898b154226fbe2b25/probabilities.json + score_dict_file: output/reports/train/94643560d092400898b154226fbe2b25/score_dict.json + test_labels_file: output/reports/train/94643560d092400898b154226fbe2b25/test_labels.json + train_labels_file: output/reports/train/94643560d092400898b154226fbe2b25/train_labels.json + model_dir: models + name: 94643560d092400898b154226fbe2b25 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ad27c578da5942130f204309e119e816 + trainer: + kwargs: {} +name: 94643560d092400898b154226fbe2b25 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/949936167a624c64aa13df8777920dce/params.yaml b/examples/security/classification/output/reports/train/949936167a624c64aa13df8777920dce/params.yaml new file mode 100644 index 00000000..5268418f --- /dev/null +++ b/examples/security/classification/output/reports/train/949936167a624c64aa13df8777920dce/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/949936167a624c64aa13df8777920dce/adv_predictions.json + adv_probabilities_file: output/reports/train/949936167a624c64aa13df8777920dce/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/ddade0b7a735bab5dbe527a0f7cfb43e.pkl + params_file: output/reports/train/949936167a624c64aa13df8777920dce/params.yaml + predictions_file: output/reports/train/949936167a624c64aa13df8777920dce/predictions.json + probabilities_file: output/reports/train/949936167a624c64aa13df8777920dce/probabilities.json + score_dict_file: output/reports/train/949936167a624c64aa13df8777920dce/score_dict.json + test_labels_file: output/reports/train/949936167a624c64aa13df8777920dce/test_labels.json + train_labels_file: output/reports/train/949936167a624c64aa13df8777920dce/train_labels.json + model_dir: models + name: 949936167a624c64aa13df8777920dce + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b8016631850a4866e8163b3c37b0e493 + trainer: + kwargs: {} +name: 949936167a624c64aa13df8777920dce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/94aa230c2531c5455bc8c1464bb49915/params.yaml b/examples/security/classification/output/reports/train/94aa230c2531c5455bc8c1464bb49915/params.yaml new file mode 100644 index 00000000..794fffe3 --- /dev/null +++ b/examples/security/classification/output/reports/train/94aa230c2531c5455bc8c1464bb49915/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/adv_predictions.json + adv_probabilities_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/11377c386a39c27457843519ef38a4c7.pkl + params_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/params.yaml + predictions_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/predictions.json + probabilities_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/probabilities.json + score_dict_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/score_dict.json + test_labels_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/test_labels.json + train_labels_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/train_labels.json + model_dir: models + name: 94aa230c2531c5455bc8c1464bb49915 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 67ac052e2d5bc9531599774e9e970d74 + trainer: + kwargs: {} +name: 94aa230c2531c5455bc8c1464bb49915 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/94b39db6f255f1e311f14c0f8a775a80/params.yaml b/examples/security/classification/output/reports/train/94b39db6f255f1e311f14c0f8a775a80/params.yaml new file mode 100644 index 00000000..67252191 --- /dev/null +++ b/examples/security/classification/output/reports/train/94b39db6f255f1e311f14c0f8a775a80/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/adv_predictions.json + adv_probabilities_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/5bff4d0205bccb3c0c9ec110b4ecde4d.pkl + params_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/params.yaml + predictions_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/predictions.json + probabilities_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/probabilities.json + score_dict_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/score_dict.json + test_labels_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/test_labels.json + train_labels_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/train_labels.json + model_dir: models + name: 94b39db6f255f1e311f14c0f8a775a80 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a79ae8e51a799d4a68fe9a97698fd024 + trainer: + kwargs: {} +name: 94b39db6f255f1e311f14c0f8a775a80 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/94bee7c3df202d2aa865858727ff116d/params.yaml b/examples/security/classification/output/reports/train/94bee7c3df202d2aa865858727ff116d/params.yaml new file mode 100644 index 00000000..d7acf59b --- /dev/null +++ b/examples/security/classification/output/reports/train/94bee7c3df202d2aa865858727ff116d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/adv_predictions.json + adv_probabilities_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/3b3bcf4ffc8f4806de1ba23522f5f589.pkl + params_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/params.yaml + predictions_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/predictions.json + probabilities_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/probabilities.json + score_dict_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/score_dict.json + test_labels_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/test_labels.json + train_labels_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/train_labels.json + model_dir: models + name: 94bee7c3df202d2aa865858727ff116d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 41fa9faa6290b097077571770f27b74c + trainer: + kwargs: {} +name: 94bee7c3df202d2aa865858727ff116d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/params.yaml b/examples/security/classification/output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/params.yaml new file mode 100644 index 00000000..d74a58cb --- /dev/null +++ b/examples/security/classification/output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/adv_predictions.json + adv_probabilities_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/b63e74103dc67a4a8e31354bce1663e8.pkl + params_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/params.yaml + predictions_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/predictions.json + probabilities_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/probabilities.json + score_dict_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/score_dict.json + test_labels_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/test_labels.json + train_labels_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/train_labels.json + model_dir: models + name: 94e4cb28f06d3ed47d6ab401683699f0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cb6567095bcae656e6ad94060a31c854 + trainer: + kwargs: {} +name: 94e4cb28f06d3ed47d6ab401683699f0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/params.yaml b/examples/security/classification/output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/params.yaml new file mode 100644 index 00000000..1da41547 --- /dev/null +++ b/examples/security/classification/output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/adv_predictions.json + adv_probabilities_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/72af0340a1c1d21e5b17aec0162fb6f3.pkl + params_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/params.yaml + predictions_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/predictions.json + probabilities_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/probabilities.json + score_dict_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/score_dict.json + test_labels_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/test_labels.json + train_labels_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/train_labels.json + model_dir: models + name: 94ef5194dbb5bfd35e7aceb61fadb0bb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2bb1b45d3f4fc960d57abc7dcdfe1194 + trainer: + kwargs: {} +name: 94ef5194dbb5bfd35e7aceb61fadb0bb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/params.yaml b/examples/security/classification/output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/params.yaml new file mode 100644 index 00000000..d1ebc08a --- /dev/null +++ b/examples/security/classification/output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/adv_predictions.json + adv_probabilities_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/222a13831826917b64a90afb7e578c31.pkl + params_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/params.yaml + predictions_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/predictions.json + probabilities_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/probabilities.json + score_dict_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/score_dict.json + test_labels_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/test_labels.json + train_labels_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/train_labels.json + model_dir: models + name: 9534c6ce4eebd7d6e9f6b29853953957 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 923e0f8c7a0b67007daaac9001eac16a + trainer: + kwargs: {} +name: 9534c6ce4eebd7d6e9f6b29853953957 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/953eb8e6a313d912f3fffc5d374919ee/params.yaml b/examples/security/classification/output/reports/train/953eb8e6a313d912f3fffc5d374919ee/params.yaml new file mode 100644 index 00000000..60aa34f0 --- /dev/null +++ b/examples/security/classification/output/reports/train/953eb8e6a313d912f3fffc5d374919ee/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/adv_predictions.json + adv_probabilities_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/3dd8796080b66d5586c8806c43d48e14.pkl + params_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/params.yaml + predictions_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/predictions.json + probabilities_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/probabilities.json + score_dict_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/score_dict.json + test_labels_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/test_labels.json + train_labels_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/train_labels.json + model_dir: models + name: 953eb8e6a313d912f3fffc5d374919ee + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 99187509dd1d1d057a9171e229805040 + trainer: + kwargs: {} +name: 953eb8e6a313d912f3fffc5d374919ee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/955bc923dd984559b937b6de28a27179/params.yaml b/examples/security/classification/output/reports/train/955bc923dd984559b937b6de28a27179/params.yaml new file mode 100644 index 00000000..357fd97c --- /dev/null +++ b/examples/security/classification/output/reports/train/955bc923dd984559b937b6de28a27179/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/955bc923dd984559b937b6de28a27179/adv_predictions.json + adv_probabilities_file: output/reports/train/955bc923dd984559b937b6de28a27179/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/79b72b0dd6f8930566dc086d401cf096.pkl + params_file: output/reports/train/955bc923dd984559b937b6de28a27179/params.yaml + predictions_file: output/reports/train/955bc923dd984559b937b6de28a27179/predictions.json + probabilities_file: output/reports/train/955bc923dd984559b937b6de28a27179/probabilities.json + score_dict_file: output/reports/train/955bc923dd984559b937b6de28a27179/score_dict.json + test_labels_file: output/reports/train/955bc923dd984559b937b6de28a27179/test_labels.json + train_labels_file: output/reports/train/955bc923dd984559b937b6de28a27179/train_labels.json + model_dir: models + name: 955bc923dd984559b937b6de28a27179 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e4e75e34e1da9acdff876204557bc69e + trainer: + kwargs: {} +name: 955bc923dd984559b937b6de28a27179 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/955e2ab947fde293d8d991a71d786c0a/params.yaml b/examples/security/classification/output/reports/train/955e2ab947fde293d8d991a71d786c0a/params.yaml new file mode 100644 index 00000000..b91bf334 --- /dev/null +++ b/examples/security/classification/output/reports/train/955e2ab947fde293d8d991a71d786c0a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/adv_predictions.json + adv_probabilities_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/7a125b96cb67af89b7511b5d34cc0688.pkl + params_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/params.yaml + predictions_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/predictions.json + probabilities_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/probabilities.json + score_dict_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/score_dict.json + test_labels_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/test_labels.json + train_labels_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/train_labels.json + model_dir: models + name: 955e2ab947fde293d8d991a71d786c0a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b52731b4974c7c19cc9b487d83931b7a + trainer: + kwargs: {} +name: 955e2ab947fde293d8d991a71d786c0a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/957c10bc3a082318126caa107e75f0a0/params.yaml b/examples/security/classification/output/reports/train/957c10bc3a082318126caa107e75f0a0/params.yaml new file mode 100644 index 00000000..ca93aae2 --- /dev/null +++ b/examples/security/classification/output/reports/train/957c10bc3a082318126caa107e75f0a0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/adv_predictions.json + adv_probabilities_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/c93476b1d15c12b36535ab07b0ee1f1a.pkl + params_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/params.yaml + predictions_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/predictions.json + probabilities_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/probabilities.json + score_dict_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/score_dict.json + test_labels_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/test_labels.json + train_labels_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/train_labels.json + model_dir: models + name: 957c10bc3a082318126caa107e75f0a0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4453d2e304102c37d1bce38f2e26c9e8 + trainer: + kwargs: {} +name: 957c10bc3a082318126caa107e75f0a0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/959c41710839016512e17122b9b1a917/params.yaml b/examples/security/classification/output/reports/train/959c41710839016512e17122b9b1a917/params.yaml new file mode 100644 index 00000000..650b606c --- /dev/null +++ b/examples/security/classification/output/reports/train/959c41710839016512e17122b9b1a917/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/959c41710839016512e17122b9b1a917/adv_predictions.json + adv_probabilities_file: output/reports/train/959c41710839016512e17122b9b1a917/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/47d0641cb382e3f24b376b5dcbf9ee5e.pkl + params_file: output/reports/train/959c41710839016512e17122b9b1a917/params.yaml + predictions_file: output/reports/train/959c41710839016512e17122b9b1a917/predictions.json + probabilities_file: output/reports/train/959c41710839016512e17122b9b1a917/probabilities.json + score_dict_file: output/reports/train/959c41710839016512e17122b9b1a917/score_dict.json + test_labels_file: output/reports/train/959c41710839016512e17122b9b1a917/test_labels.json + train_labels_file: output/reports/train/959c41710839016512e17122b9b1a917/train_labels.json + model_dir: models + name: 959c41710839016512e17122b9b1a917 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: add83a53520b01202c3a86b958fab818 + trainer: + kwargs: {} +name: 959c41710839016512e17122b9b1a917 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/95f874a1e6f7398a014d892bbf94153a/params.yaml b/examples/security/classification/output/reports/train/95f874a1e6f7398a014d892bbf94153a/params.yaml new file mode 100644 index 00000000..9d9cfaf5 --- /dev/null +++ b/examples/security/classification/output/reports/train/95f874a1e6f7398a014d892bbf94153a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/adv_predictions.json + adv_probabilities_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/c137f77e4c771c5321ba61fa09a76e85.pkl + params_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/params.yaml + predictions_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/predictions.json + probabilities_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/probabilities.json + score_dict_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/score_dict.json + test_labels_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/test_labels.json + train_labels_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/train_labels.json + model_dir: models + name: 95f874a1e6f7398a014d892bbf94153a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 507515cae8714af773c90c987e82da6f + trainer: + kwargs: {} +name: 95f874a1e6f7398a014d892bbf94153a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/96345dc957184419cb60e8c3c9d64898/params.yaml b/examples/security/classification/output/reports/train/96345dc957184419cb60e8c3c9d64898/params.yaml new file mode 100644 index 00000000..ad2c4ab9 --- /dev/null +++ b/examples/security/classification/output/reports/train/96345dc957184419cb60e8c3c9d64898/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/adv_predictions.json + adv_probabilities_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/a55927e8c68f7a24f5a2f8716ed61a07.pkl + params_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/params.yaml + predictions_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/predictions.json + probabilities_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/probabilities.json + score_dict_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/score_dict.json + test_labels_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/test_labels.json + train_labels_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/train_labels.json + model_dir: models + name: 96345dc957184419cb60e8c3c9d64898 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9c10220a06a7b60ac3c8a2f0214deec1 + trainer: + kwargs: {} +name: 96345dc957184419cb60e8c3c9d64898 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/965908f2652389f26014fc7c84f25e78/params.yaml b/examples/security/classification/output/reports/train/965908f2652389f26014fc7c84f25e78/params.yaml new file mode 100644 index 00000000..60f8007a --- /dev/null +++ b/examples/security/classification/output/reports/train/965908f2652389f26014fc7c84f25e78/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/965908f2652389f26014fc7c84f25e78/adv_predictions.json + adv_probabilities_file: output/reports/train/965908f2652389f26014fc7c84f25e78/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/98621ed0aa67a762a95c58361b0334d5.pkl + params_file: output/reports/train/965908f2652389f26014fc7c84f25e78/params.yaml + predictions_file: output/reports/train/965908f2652389f26014fc7c84f25e78/predictions.json + probabilities_file: output/reports/train/965908f2652389f26014fc7c84f25e78/probabilities.json + score_dict_file: output/reports/train/965908f2652389f26014fc7c84f25e78/score_dict.json + test_labels_file: output/reports/train/965908f2652389f26014fc7c84f25e78/test_labels.json + train_labels_file: output/reports/train/965908f2652389f26014fc7c84f25e78/train_labels.json + model_dir: models + name: 965908f2652389f26014fc7c84f25e78 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8619cac6c5cdc6dc587d21a1d6adfc63 + trainer: + kwargs: {} +name: 965908f2652389f26014fc7c84f25e78 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/96a486b63a11301345c6fe4339dacc63/params.yaml b/examples/security/classification/output/reports/train/96a486b63a11301345c6fe4339dacc63/params.yaml new file mode 100644 index 00000000..c99982e5 --- /dev/null +++ b/examples/security/classification/output/reports/train/96a486b63a11301345c6fe4339dacc63/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/adv_predictions.json + adv_probabilities_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/164ede0313c9472ad160df1e7e0f3fab.pkl + params_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/params.yaml + predictions_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/predictions.json + probabilities_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/probabilities.json + score_dict_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/score_dict.json + test_labels_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/test_labels.json + train_labels_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/train_labels.json + model_dir: models + name: 96a486b63a11301345c6fe4339dacc63 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f27f5847bc4842f27f14c47b70c768cb + trainer: + kwargs: {} +name: 96a486b63a11301345c6fe4339dacc63 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/96b4331edcb507e783b12e801aa88c50/params.yaml b/examples/security/classification/output/reports/train/96b4331edcb507e783b12e801aa88c50/params.yaml new file mode 100644 index 00000000..60fa0c50 --- /dev/null +++ b/examples/security/classification/output/reports/train/96b4331edcb507e783b12e801aa88c50/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/adv_predictions.json + adv_probabilities_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/acf98d0d626cb51585966fc7b921478a.pkl + params_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/params.yaml + predictions_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/predictions.json + probabilities_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/probabilities.json + score_dict_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/score_dict.json + test_labels_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/test_labels.json + train_labels_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/train_labels.json + model_dir: models + name: 96b4331edcb507e783b12e801aa88c50 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 99409fef7f6d19426654bc805fd9bcf8 + trainer: + kwargs: {} +name: 96b4331edcb507e783b12e801aa88c50 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/96b572df833549dad96780ce04fef589/params.yaml b/examples/security/classification/output/reports/train/96b572df833549dad96780ce04fef589/params.yaml new file mode 100644 index 00000000..42fe65da --- /dev/null +++ b/examples/security/classification/output/reports/train/96b572df833549dad96780ce04fef589/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/96b572df833549dad96780ce04fef589/adv_predictions.json + adv_probabilities_file: output/reports/train/96b572df833549dad96780ce04fef589/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/f636ff7cfaa68f6043ebbf212a0a12e2.pkl + params_file: output/reports/train/96b572df833549dad96780ce04fef589/params.yaml + predictions_file: output/reports/train/96b572df833549dad96780ce04fef589/predictions.json + probabilities_file: output/reports/train/96b572df833549dad96780ce04fef589/probabilities.json + score_dict_file: output/reports/train/96b572df833549dad96780ce04fef589/score_dict.json + test_labels_file: output/reports/train/96b572df833549dad96780ce04fef589/test_labels.json + train_labels_file: output/reports/train/96b572df833549dad96780ce04fef589/train_labels.json + model_dir: models + name: 96b572df833549dad96780ce04fef589 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 338799ee8599faed4e8493e13f8d0937 + trainer: + kwargs: {} +name: 96b572df833549dad96780ce04fef589 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/params.yaml b/examples/security/classification/output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/params.yaml new file mode 100644 index 00000000..3772319e --- /dev/null +++ b/examples/security/classification/output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/adv_predictions.json + adv_probabilities_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/d0ba3539f6bd369a3a2c6c7ee048cbd8.pkl + params_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/params.yaml + predictions_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/predictions.json + probabilities_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/probabilities.json + score_dict_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/score_dict.json + test_labels_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/test_labels.json + train_labels_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/train_labels.json + model_dir: models + name: 96bdeacfcabdcd894fec214c7aab2dd7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1b95288ddb9525bfc50189bec88ff212 + trainer: + kwargs: {} +name: 96bdeacfcabdcd894fec214c7aab2dd7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/params.yaml b/examples/security/classification/output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/params.yaml new file mode 100644 index 00000000..902c3abd --- /dev/null +++ b/examples/security/classification/output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/adv_predictions.json + adv_probabilities_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/1ab4a63db4f82475412898fab4070394.pkl + params_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/params.yaml + predictions_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/predictions.json + probabilities_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/probabilities.json + score_dict_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/score_dict.json + test_labels_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/test_labels.json + train_labels_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/train_labels.json + model_dir: models + name: 96e2cedb80a2bc4614495bd06d3e41e7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 320502698241383a5549ac7f5652c13f + trainer: + kwargs: {} +name: 96e2cedb80a2bc4614495bd06d3e41e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/96eebd73552f97c4d920153fb747ea06/params.yaml b/examples/security/classification/output/reports/train/96eebd73552f97c4d920153fb747ea06/params.yaml new file mode 100644 index 00000000..801d79e6 --- /dev/null +++ b/examples/security/classification/output/reports/train/96eebd73552f97c4d920153fb747ea06/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/adv_predictions.json + adv_probabilities_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/d68b96fdcabeb39d6529894f48a18862.pkl + params_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/params.yaml + predictions_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/predictions.json + probabilities_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/probabilities.json + score_dict_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/score_dict.json + test_labels_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/test_labels.json + train_labels_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/train_labels.json + model_dir: models + name: 96eebd73552f97c4d920153fb747ea06 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c7cd3faca43db3dd2ccbcabe0653afe6 + trainer: + kwargs: {} +name: 96eebd73552f97c4d920153fb747ea06 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/96fa8f276ce55d865193469b63e71c25/params.yaml b/examples/security/classification/output/reports/train/96fa8f276ce55d865193469b63e71c25/params.yaml new file mode 100644 index 00000000..9a909cc9 --- /dev/null +++ b/examples/security/classification/output/reports/train/96fa8f276ce55d865193469b63e71c25/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/adv_predictions.json + adv_probabilities_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/c7243766f1d1ee8c011065b9183e1a42.pkl + params_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/params.yaml + predictions_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/predictions.json + probabilities_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/probabilities.json + score_dict_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/score_dict.json + test_labels_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/test_labels.json + train_labels_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/train_labels.json + model_dir: models + name: 96fa8f276ce55d865193469b63e71c25 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b00c9355e6c4d8a7ea7ed64e3b3f3a13 + trainer: + kwargs: {} +name: 96fa8f276ce55d865193469b63e71c25 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9707a6d0e9d7911f7d5404b76229717e/params.yaml b/examples/security/classification/output/reports/train/9707a6d0e9d7911f7d5404b76229717e/params.yaml new file mode 100644 index 00000000..55aad2e8 --- /dev/null +++ b/examples/security/classification/output/reports/train/9707a6d0e9d7911f7d5404b76229717e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/adv_predictions.json + adv_probabilities_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/37a6c1a477202dc8693291648319adcf.pkl + params_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/params.yaml + predictions_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/predictions.json + probabilities_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/probabilities.json + score_dict_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/score_dict.json + test_labels_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/test_labels.json + train_labels_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/train_labels.json + model_dir: models + name: 9707a6d0e9d7911f7d5404b76229717e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0947520ea7338aa5279692e3b059dc54 + trainer: + kwargs: {} +name: 9707a6d0e9d7911f7d5404b76229717e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/params.yaml b/examples/security/classification/output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/params.yaml new file mode 100644 index 00000000..d5f147cf --- /dev/null +++ b/examples/security/classification/output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/adv_predictions.json + adv_probabilities_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/e163c13078176ad887b3dc6eb689e2c5.pkl + params_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/params.yaml + predictions_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/predictions.json + probabilities_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/probabilities.json + score_dict_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/score_dict.json + test_labels_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/test_labels.json + train_labels_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/train_labels.json + model_dir: models + name: 97821d1fd3ca494db39aca78fc0ddefd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4e11bd59c8437e4b91f69d919aa6100d + trainer: + kwargs: {} +name: 97821d1fd3ca494db39aca78fc0ddefd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/params.yaml b/examples/security/classification/output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/params.yaml new file mode 100644 index 00000000..ff4be689 --- /dev/null +++ b/examples/security/classification/output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/adv_predictions.json + adv_probabilities_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/e47d66a49988946cb21d63767d0594f0.pkl + params_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/params.yaml + predictions_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/predictions.json + probabilities_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/probabilities.json + score_dict_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/score_dict.json + test_labels_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/test_labels.json + train_labels_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/train_labels.json + model_dir: models + name: 97a84fe8529e6e8130ec441ce3b4f5e1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 44c4d1b60fda07f99adb75130aaee593 + trainer: + kwargs: {} +name: 97a84fe8529e6e8130ec441ce3b4f5e1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/97b470c188fe9048fa883ba02f1662fa/params.yaml b/examples/security/classification/output/reports/train/97b470c188fe9048fa883ba02f1662fa/params.yaml new file mode 100644 index 00000000..af62d75c --- /dev/null +++ b/examples/security/classification/output/reports/train/97b470c188fe9048fa883ba02f1662fa/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/adv_predictions.json + adv_probabilities_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/787421d4c0374649aee132438852a6d7.pkl + params_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/params.yaml + predictions_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/predictions.json + probabilities_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/probabilities.json + score_dict_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/score_dict.json + test_labels_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/test_labels.json + train_labels_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/train_labels.json + model_dir: models + name: 97b470c188fe9048fa883ba02f1662fa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 288589c63753df094fe9f8899cfc1f6b + trainer: + kwargs: {} +name: 97b470c188fe9048fa883ba02f1662fa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/97ba47162a3bef38fdbebf02cead31cd/params.yaml b/examples/security/classification/output/reports/train/97ba47162a3bef38fdbebf02cead31cd/params.yaml new file mode 100644 index 00000000..34ca96d1 --- /dev/null +++ b/examples/security/classification/output/reports/train/97ba47162a3bef38fdbebf02cead31cd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/adv_predictions.json + adv_probabilities_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/c8362e4385c7e74b9873f1125879c7fc.pkl + params_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/params.yaml + predictions_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/predictions.json + probabilities_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/probabilities.json + score_dict_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/score_dict.json + test_labels_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/test_labels.json + train_labels_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/train_labels.json + model_dir: models + name: 97ba47162a3bef38fdbebf02cead31cd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5d7302b02eaca8bae4d51ba04c977005 + trainer: + kwargs: {} +name: 97ba47162a3bef38fdbebf02cead31cd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/params.yaml b/examples/security/classification/output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/params.yaml new file mode 100644 index 00000000..ffe625fc --- /dev/null +++ b/examples/security/classification/output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/adv_predictions.json + adv_probabilities_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/8216e0d8e5585972e636a00aee14f8bb.pkl + params_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/params.yaml + predictions_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/predictions.json + probabilities_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/probabilities.json + score_dict_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/score_dict.json + test_labels_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/test_labels.json + train_labels_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/train_labels.json + model_dir: models + name: 97d8471de1ce0cb9dd9fa5e09a98a16b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d1c51986d424aeafac1fa34ea51415bd + trainer: + kwargs: {} +name: 97d8471de1ce0cb9dd9fa5e09a98a16b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/97da87a7e603985bb99537e4a018fbec/params.yaml b/examples/security/classification/output/reports/train/97da87a7e603985bb99537e4a018fbec/params.yaml new file mode 100644 index 00000000..99caeeff --- /dev/null +++ b/examples/security/classification/output/reports/train/97da87a7e603985bb99537e4a018fbec/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/adv_predictions.json + adv_probabilities_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/d07336a75f7597f0485320d37e56f8c9.pkl + params_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/params.yaml + predictions_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/predictions.json + probabilities_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/probabilities.json + score_dict_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/score_dict.json + test_labels_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/test_labels.json + train_labels_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/train_labels.json + model_dir: models + name: 97da87a7e603985bb99537e4a018fbec + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7a73d79c2b7a276cf47880b3869ed2ae + trainer: + kwargs: {} +name: 97da87a7e603985bb99537e4a018fbec +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/97f70738e3506072d35b557daaab6dd3/params.yaml b/examples/security/classification/output/reports/train/97f70738e3506072d35b557daaab6dd3/params.yaml new file mode 100644 index 00000000..d4f874bc --- /dev/null +++ b/examples/security/classification/output/reports/train/97f70738e3506072d35b557daaab6dd3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/adv_predictions.json + adv_probabilities_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/1def6ef35760885c2dc6c9369ebc936d.pkl + params_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/params.yaml + predictions_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/predictions.json + probabilities_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/probabilities.json + score_dict_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/score_dict.json + test_labels_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/test_labels.json + train_labels_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/train_labels.json + model_dir: models + name: 97f70738e3506072d35b557daaab6dd3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 170289bf874d2b1541e76380cc15e37b + trainer: + kwargs: {} +name: 97f70738e3506072d35b557daaab6dd3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/params.yaml b/examples/security/classification/output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/params.yaml new file mode 100644 index 00000000..02effbc9 --- /dev/null +++ b/examples/security/classification/output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/adv_predictions.json + adv_probabilities_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/6a403281598e3bafc0f8016eaa66777e.pkl + params_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/params.yaml + predictions_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/predictions.json + probabilities_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/probabilities.json + score_dict_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/score_dict.json + test_labels_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/test_labels.json + train_labels_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/train_labels.json + model_dir: models + name: 987ddbb8664d3d1a33cb143b9ab0194d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5d3fcde783e90ddacd45a4b2320673bc + trainer: + kwargs: {} +name: 987ddbb8664d3d1a33cb143b9ab0194d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/988758a7fcb530b1aee478c5cd622cf0/params.yaml b/examples/security/classification/output/reports/train/988758a7fcb530b1aee478c5cd622cf0/params.yaml new file mode 100644 index 00000000..b1f90be4 --- /dev/null +++ b/examples/security/classification/output/reports/train/988758a7fcb530b1aee478c5cd622cf0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/adv_predictions.json + adv_probabilities_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/7b0958d0cfc8e52364ce14ba30736c61.pkl + params_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/params.yaml + predictions_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/predictions.json + probabilities_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/probabilities.json + score_dict_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/score_dict.json + test_labels_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/test_labels.json + train_labels_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/train_labels.json + model_dir: models + name: 988758a7fcb530b1aee478c5cd622cf0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7a25480b094e7a47f41410be541bae97 + trainer: + kwargs: {} +name: 988758a7fcb530b1aee478c5cd622cf0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/98b14467c2442f11634390bb39c493ad/params.yaml b/examples/security/classification/output/reports/train/98b14467c2442f11634390bb39c493ad/params.yaml new file mode 100644 index 00000000..b87db22f --- /dev/null +++ b/examples/security/classification/output/reports/train/98b14467c2442f11634390bb39c493ad/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/98b14467c2442f11634390bb39c493ad/adv_predictions.json + adv_probabilities_file: output/reports/train/98b14467c2442f11634390bb39c493ad/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/14d5799e3130d455574d877dd0e0b40d.pkl + params_file: output/reports/train/98b14467c2442f11634390bb39c493ad/params.yaml + predictions_file: output/reports/train/98b14467c2442f11634390bb39c493ad/predictions.json + probabilities_file: output/reports/train/98b14467c2442f11634390bb39c493ad/probabilities.json + score_dict_file: output/reports/train/98b14467c2442f11634390bb39c493ad/score_dict.json + test_labels_file: output/reports/train/98b14467c2442f11634390bb39c493ad/test_labels.json + train_labels_file: output/reports/train/98b14467c2442f11634390bb39c493ad/train_labels.json + model_dir: models + name: 98b14467c2442f11634390bb39c493ad + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1ff429983bc93f34cf04858bcd712650 + trainer: + kwargs: {} +name: 98b14467c2442f11634390bb39c493ad +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/params.yaml b/examples/security/classification/output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/params.yaml new file mode 100644 index 00000000..27194568 --- /dev/null +++ b/examples/security/classification/output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/adv_predictions.json + adv_probabilities_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e9189e1c48ae5c282a956478d85dcb82.pkl + params_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/params.yaml + predictions_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/predictions.json + probabilities_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/probabilities.json + score_dict_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/score_dict.json + test_labels_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/test_labels.json + train_labels_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/train_labels.json + model_dir: models + name: 98d53f8d8837eae269e4cdd3df09ce77 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2fe49ca3fbaefbe63ee989d3067642ea + trainer: + kwargs: {} +name: 98d53f8d8837eae269e4cdd3df09ce77 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/98dca71229abf6d9914c405064085a2c/params.yaml b/examples/security/classification/output/reports/train/98dca71229abf6d9914c405064085a2c/params.yaml new file mode 100644 index 00000000..de7a0612 --- /dev/null +++ b/examples/security/classification/output/reports/train/98dca71229abf6d9914c405064085a2c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/98dca71229abf6d9914c405064085a2c/adv_predictions.json + adv_probabilities_file: output/reports/train/98dca71229abf6d9914c405064085a2c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/7c091d4d0db1d500193db54fdbb8d2d5.pkl + params_file: output/reports/train/98dca71229abf6d9914c405064085a2c/params.yaml + predictions_file: output/reports/train/98dca71229abf6d9914c405064085a2c/predictions.json + probabilities_file: output/reports/train/98dca71229abf6d9914c405064085a2c/probabilities.json + score_dict_file: output/reports/train/98dca71229abf6d9914c405064085a2c/score_dict.json + test_labels_file: output/reports/train/98dca71229abf6d9914c405064085a2c/test_labels.json + train_labels_file: output/reports/train/98dca71229abf6d9914c405064085a2c/train_labels.json + model_dir: models + name: 98dca71229abf6d9914c405064085a2c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dc0f742bc8822b0a61ff7216fc540163 + trainer: + kwargs: {} +name: 98dca71229abf6d9914c405064085a2c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/98f7a4da41c51e63aaeea15480a69f91/params.yaml b/examples/security/classification/output/reports/train/98f7a4da41c51e63aaeea15480a69f91/params.yaml new file mode 100644 index 00000000..38f88e7b --- /dev/null +++ b/examples/security/classification/output/reports/train/98f7a4da41c51e63aaeea15480a69f91/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/adv_predictions.json + adv_probabilities_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/40cc391464ce9bc3d04be0e87982be51.pkl + params_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/params.yaml + predictions_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/predictions.json + probabilities_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/probabilities.json + score_dict_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/score_dict.json + test_labels_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/test_labels.json + train_labels_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/train_labels.json + model_dir: models + name: 98f7a4da41c51e63aaeea15480a69f91 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 567a12c1c9efd27c8a92abc770bc3cc8 + trainer: + kwargs: {} +name: 98f7a4da41c51e63aaeea15480a69f91 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/params.yaml b/examples/security/classification/output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/params.yaml new file mode 100644 index 00000000..e6df8eb4 --- /dev/null +++ b/examples/security/classification/output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/adv_predictions.json + adv_probabilities_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/51f7249d0fac2e483f1df2ef64cddb6d.pkl + params_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/params.yaml + predictions_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/predictions.json + probabilities_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/probabilities.json + score_dict_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/score_dict.json + test_labels_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/test_labels.json + train_labels_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/train_labels.json + model_dir: models + name: 98ffa61d8c97dece8a221c3e387e04c8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 004fd22a6886c642872e258c948f67af + trainer: + kwargs: {} +name: 98ffa61d8c97dece8a221c3e387e04c8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/991138fb41884473a9925b2d6eb3c7fc/params.yaml b/examples/security/classification/output/reports/train/991138fb41884473a9925b2d6eb3c7fc/params.yaml new file mode 100644 index 00000000..bbadec23 --- /dev/null +++ b/examples/security/classification/output/reports/train/991138fb41884473a9925b2d6eb3c7fc/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/adv_predictions.json + adv_probabilities_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/420192ee99b19fcf973747f969b7baa0.pkl + params_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/params.yaml + predictions_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/predictions.json + probabilities_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/probabilities.json + score_dict_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/score_dict.json + test_labels_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/test_labels.json + train_labels_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/train_labels.json + model_dir: models + name: 991138fb41884473a9925b2d6eb3c7fc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 37e49563c31adc0eecce712131c06049 + trainer: + kwargs: {} +name: 991138fb41884473a9925b2d6eb3c7fc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9911d26b0a446a35e64d097bd2ae455a/params.yaml b/examples/security/classification/output/reports/train/9911d26b0a446a35e64d097bd2ae455a/params.yaml new file mode 100644 index 00000000..95047f17 --- /dev/null +++ b/examples/security/classification/output/reports/train/9911d26b0a446a35e64d097bd2ae455a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/adv_predictions.json + adv_probabilities_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/3e2d90c9e95411617294c7ab167d5193.pkl + params_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/params.yaml + predictions_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/predictions.json + probabilities_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/probabilities.json + score_dict_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/score_dict.json + test_labels_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/test_labels.json + train_labels_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/train_labels.json + model_dir: models + name: 9911d26b0a446a35e64d097bd2ae455a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 88d761d2f32082d6807323a4c18cd2ee + trainer: + kwargs: {} +name: 9911d26b0a446a35e64d097bd2ae455a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9929d1ee5a304f555d5a5d877df72724/params.yaml b/examples/security/classification/output/reports/train/9929d1ee5a304f555d5a5d877df72724/params.yaml new file mode 100644 index 00000000..5e278dc9 --- /dev/null +++ b/examples/security/classification/output/reports/train/9929d1ee5a304f555d5a5d877df72724/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/adv_predictions.json + adv_probabilities_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/0c90694cf5338093ff976550b405fb1f.pkl + params_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/params.yaml + predictions_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/predictions.json + probabilities_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/probabilities.json + score_dict_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/score_dict.json + test_labels_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/test_labels.json + train_labels_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/train_labels.json + model_dir: models + name: 9929d1ee5a304f555d5a5d877df72724 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f819ae0d1e6bd2c36c9f18c2d7e54564 + trainer: + kwargs: {} +name: 9929d1ee5a304f555d5a5d877df72724 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/994cbc972114e7041e27a6377b2a293a/params.yaml b/examples/security/classification/output/reports/train/994cbc972114e7041e27a6377b2a293a/params.yaml new file mode 100644 index 00000000..54e204b1 --- /dev/null +++ b/examples/security/classification/output/reports/train/994cbc972114e7041e27a6377b2a293a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/adv_predictions.json + adv_probabilities_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/25ed70f34dae92bc2a3c8c35e87dcf17.pkl + params_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/params.yaml + predictions_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/predictions.json + probabilities_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/probabilities.json + score_dict_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/score_dict.json + test_labels_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/test_labels.json + train_labels_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/train_labels.json + model_dir: models + name: 994cbc972114e7041e27a6377b2a293a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 21ad0c80b8bd1ea1e9154346ee1a6634 + trainer: + kwargs: {} +name: 994cbc972114e7041e27a6377b2a293a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/995d4945cd20e40ab7c14d05439d0597/params.yaml b/examples/security/classification/output/reports/train/995d4945cd20e40ab7c14d05439d0597/params.yaml new file mode 100644 index 00000000..db4d3488 --- /dev/null +++ b/examples/security/classification/output/reports/train/995d4945cd20e40ab7c14d05439d0597/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/adv_predictions.json + adv_probabilities_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/06a055e17840dc970d464fe22dc2e4ad.pkl + params_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/params.yaml + predictions_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/predictions.json + probabilities_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/probabilities.json + score_dict_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/score_dict.json + test_labels_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/test_labels.json + train_labels_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/train_labels.json + model_dir: models + name: 995d4945cd20e40ab7c14d05439d0597 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ac0df191c7b3f6c2ce28e28ccadd70fe + trainer: + kwargs: {} +name: 995d4945cd20e40ab7c14d05439d0597 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/params.yaml b/examples/security/classification/output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/params.yaml new file mode 100644 index 00000000..ae9678bc --- /dev/null +++ b/examples/security/classification/output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/adv_predictions.json + adv_probabilities_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/53c565df3037862325413431bf529580.pkl + params_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/params.yaml + predictions_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/predictions.json + probabilities_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/probabilities.json + score_dict_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/score_dict.json + test_labels_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/test_labels.json + train_labels_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/train_labels.json + model_dir: models + name: 9975dd952460c9e6ed213e16fbfa0bf8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c9052894a3ffc213b3a852342981e16 + trainer: + kwargs: {} +name: 9975dd952460c9e6ed213e16fbfa0bf8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/99859d802f75f6e5ed27b172287e030d/params.yaml b/examples/security/classification/output/reports/train/99859d802f75f6e5ed27b172287e030d/params.yaml new file mode 100644 index 00000000..ce309e31 --- /dev/null +++ b/examples/security/classification/output/reports/train/99859d802f75f6e5ed27b172287e030d/params.yaml @@ -0,0 +1,211 @@ +attack: + attack_size: 10 + data: + generate: + kwargs: + target: label + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: ad395d4f67bd301a0eaa8ea0e3d6240d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: + target: label + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: ad395d4f67bd301a0eaa8ea0e3d6240d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 10000.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d4aa8ac0a3a533383a96f260f3fe8351 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + target: label + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: ad395d4f67bd301a0eaa8ea0e3d6240d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5a02bbcdb25527687d0cb6ab1895391e + trainer: + kwargs: {} + name: 17481bcdfcbe588173d11745dd7a8457 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + target: label + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: fc4f9d93882b1b65c2cfc17d3d14b2e6 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/adv_predictions.json + adv_probabilities_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/adv_probabilities.json + attack_file: output/attacks/88a2d5998fb35f0f56b42c0bf39f9a4a.pkl + data_file: output/data/f6b10a7e3f0a1b0db33d73e886f4297c.pkl + model_file: output/models/9564a9eb9babd5fd1c93f1dc2fa40183.pkl + params_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/params.yaml + predictions_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/predictions.json + probabilities_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/probabilities.json + score_dict_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/score_dict.json + test_labels_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/test_labels.json + train_labels_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/train_labels.json + model_dir: models + name: 99859d802f75f6e5ed27b172287e030d + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + target: label + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: fc4f9d93882b1b65c2cfc17d3d14b2e6 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1839e3f58ae8e974ba78387cb3aa4d45 + trainer: + kwargs: {} +name: 99859d802f75f6e5ed27b172287e030d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/99b147d7df6a5fbf6908893231042623/params.yaml b/examples/security/classification/output/reports/train/99b147d7df6a5fbf6908893231042623/params.yaml new file mode 100644 index 00000000..7b2d930b --- /dev/null +++ b/examples/security/classification/output/reports/train/99b147d7df6a5fbf6908893231042623/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/99b147d7df6a5fbf6908893231042623/adv_predictions.json + adv_probabilities_file: output/reports/train/99b147d7df6a5fbf6908893231042623/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/4afadb0088a86587be2a729200d4ccf1.pkl + params_file: output/reports/train/99b147d7df6a5fbf6908893231042623/params.yaml + predictions_file: output/reports/train/99b147d7df6a5fbf6908893231042623/predictions.json + probabilities_file: output/reports/train/99b147d7df6a5fbf6908893231042623/probabilities.json + score_dict_file: output/reports/train/99b147d7df6a5fbf6908893231042623/score_dict.json + test_labels_file: output/reports/train/99b147d7df6a5fbf6908893231042623/test_labels.json + train_labels_file: output/reports/train/99b147d7df6a5fbf6908893231042623/train_labels.json + model_dir: models + name: 99b147d7df6a5fbf6908893231042623 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d0f7dc8c68e60e241486f291a2b16d34 + trainer: + kwargs: {} +name: 99b147d7df6a5fbf6908893231042623 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/params.yaml b/examples/security/classification/output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/params.yaml new file mode 100644 index 00000000..9439fa22 --- /dev/null +++ b/examples/security/classification/output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/adv_predictions.json + adv_probabilities_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/eccfe44331efa3bef90c3e92cb313390.pkl + params_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/params.yaml + predictions_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/predictions.json + probabilities_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/probabilities.json + score_dict_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/score_dict.json + test_labels_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/test_labels.json + train_labels_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/train_labels.json + model_dir: models + name: 99f4cacc101b36f602914c51bf2fcfe3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46fb60194154d635c8e265c05b2268e2 + trainer: + kwargs: {} +name: 99f4cacc101b36f602914c51bf2fcfe3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/params.yaml b/examples/security/classification/output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/params.yaml new file mode 100644 index 00000000..d4dd6506 --- /dev/null +++ b/examples/security/classification/output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/adv_predictions.json + adv_probabilities_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/ed155707b49a8d7aacc97c97512b2f5e.pkl + params_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/params.yaml + predictions_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/predictions.json + probabilities_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/probabilities.json + score_dict_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/score_dict.json + test_labels_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/test_labels.json + train_labels_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/train_labels.json + model_dir: models + name: 9a1e0a32ee438b3000e4a2b88b71629c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a7635f226cdf455b0d55460293ba1c44 + trainer: + kwargs: {} +name: 9a1e0a32ee438b3000e4a2b88b71629c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/params.yaml b/examples/security/classification/output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/params.yaml new file mode 100644 index 00000000..4049103c --- /dev/null +++ b/examples/security/classification/output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/adv_predictions.json + adv_probabilities_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/83357bd38f357cca6adaab4e1ce1f112.pkl + params_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/params.yaml + predictions_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/predictions.json + probabilities_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/probabilities.json + score_dict_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/score_dict.json + test_labels_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/test_labels.json + train_labels_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/train_labels.json + model_dir: models + name: 9a2a97f40f0ffb84adae7072337eb09d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0c76e203007c8af55d22a5c009ef407b + trainer: + kwargs: {} +name: 9a2a97f40f0ffb84adae7072337eb09d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/params.yaml b/examples/security/classification/output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/params.yaml new file mode 100644 index 00000000..c4dca2e9 --- /dev/null +++ b/examples/security/classification/output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/adv_predictions.json + adv_probabilities_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/3dc1f0c7487bf86bec2766ad74014684.pkl + params_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/params.yaml + predictions_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/predictions.json + probabilities_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/probabilities.json + score_dict_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/score_dict.json + test_labels_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/test_labels.json + train_labels_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/train_labels.json + model_dir: models + name: 9a2ddf3a5ddd7e37b42f5d6ba58b11a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9d9f2d280a09205de9a5cc671cc4386b + trainer: + kwargs: {} +name: 9a2ddf3a5ddd7e37b42f5d6ba58b11a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9a32614a4863bba51feb12173341dcdb/params.yaml b/examples/security/classification/output/reports/train/9a32614a4863bba51feb12173341dcdb/params.yaml new file mode 100644 index 00000000..6a70f213 --- /dev/null +++ b/examples/security/classification/output/reports/train/9a32614a4863bba51feb12173341dcdb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/adv_predictions.json + adv_probabilities_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/1ff150ccc43429be8dd17ac9b790ab93.pkl + params_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/params.yaml + predictions_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/predictions.json + probabilities_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/probabilities.json + score_dict_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/score_dict.json + test_labels_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/test_labels.json + train_labels_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/train_labels.json + model_dir: models + name: 9a32614a4863bba51feb12173341dcdb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a5ec47f52e60bec7f725ec8dcd7f4f67 + trainer: + kwargs: {} +name: 9a32614a4863bba51feb12173341dcdb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9a326369abceb3c9649aeab1c2108d26/params.yaml b/examples/security/classification/output/reports/train/9a326369abceb3c9649aeab1c2108d26/params.yaml new file mode 100644 index 00000000..f9b19309 --- /dev/null +++ b/examples/security/classification/output/reports/train/9a326369abceb3c9649aeab1c2108d26/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/adv_predictions.json + adv_probabilities_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/085fb9a2cffdfe461fc0d7cd3eeb7f61.pkl + params_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/params.yaml + predictions_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/predictions.json + probabilities_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/probabilities.json + score_dict_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/score_dict.json + test_labels_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/test_labels.json + train_labels_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/train_labels.json + model_dir: models + name: 9a326369abceb3c9649aeab1c2108d26 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 844b2efbbb268c5c27c700136108feed + trainer: + kwargs: {} +name: 9a326369abceb3c9649aeab1c2108d26 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/params.yaml b/examples/security/classification/output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/params.yaml new file mode 100644 index 00000000..3eb5cd0c --- /dev/null +++ b/examples/security/classification/output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/adv_predictions.json + adv_probabilities_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/11403891de7d10821e356d2117729913.pkl + params_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/params.yaml + predictions_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/predictions.json + probabilities_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/probabilities.json + score_dict_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/score_dict.json + test_labels_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/test_labels.json + train_labels_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/train_labels.json + model_dir: models + name: 9a367b6ab5526a5c5c39a1c552386bc3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9de70e37f396cac9918abce379e1e7ab + trainer: + kwargs: {} +name: 9a367b6ab5526a5c5c39a1c552386bc3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/params.yaml b/examples/security/classification/output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/params.yaml new file mode 100644 index 00000000..741438b8 --- /dev/null +++ b/examples/security/classification/output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/adv_predictions.json + adv_probabilities_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/dcf11ed41c0c782ff4081d05ae50613e.pkl + params_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/params.yaml + predictions_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/predictions.json + probabilities_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/probabilities.json + score_dict_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/score_dict.json + test_labels_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/test_labels.json + train_labels_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/train_labels.json + model_dir: models + name: 9a84b3c7a1f01b393fec7bceffe25a1b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4e342be0b89fc5875f4e18590536877e + trainer: + kwargs: {} +name: 9a84b3c7a1f01b393fec7bceffe25a1b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/params.yaml b/examples/security/classification/output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/params.yaml new file mode 100644 index 00000000..7007e140 --- /dev/null +++ b/examples/security/classification/output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/adv_predictions.json + adv_probabilities_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/639a46dd3e5f4ea564bf715f2d59522e.pkl + params_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/params.yaml + predictions_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/predictions.json + probabilities_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/probabilities.json + score_dict_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/score_dict.json + test_labels_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/test_labels.json + train_labels_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/train_labels.json + model_dir: models + name: 9aa48053ea6a4c393bbef81fdbe0dc7b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8e1d8c536f528ad03c794e2672e750c9 + trainer: + kwargs: {} +name: 9aa48053ea6a4c393bbef81fdbe0dc7b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9ac67edf8850addd3cc50b645eb4a027/params.yaml b/examples/security/classification/output/reports/train/9ac67edf8850addd3cc50b645eb4a027/params.yaml new file mode 100644 index 00000000..236a7b7d --- /dev/null +++ b/examples/security/classification/output/reports/train/9ac67edf8850addd3cc50b645eb4a027/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/adv_predictions.json + adv_probabilities_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/e2506240e8a1449a9518c72bce239313.pkl + params_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/params.yaml + predictions_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/predictions.json + probabilities_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/probabilities.json + score_dict_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/score_dict.json + test_labels_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/test_labels.json + train_labels_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/train_labels.json + model_dir: models + name: 9ac67edf8850addd3cc50b645eb4a027 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6e749ea0d2da6dee4a2c217acc533891 + trainer: + kwargs: {} +name: 9ac67edf8850addd3cc50b645eb4a027 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/params.yaml b/examples/security/classification/output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/params.yaml new file mode 100644 index 00000000..6fab22e9 --- /dev/null +++ b/examples/security/classification/output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/adv_predictions.json + adv_probabilities_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/6142a5364938d0bc5c4d011048e325d7.pkl + params_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/params.yaml + predictions_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/predictions.json + probabilities_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/probabilities.json + score_dict_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/score_dict.json + test_labels_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/test_labels.json + train_labels_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/train_labels.json + model_dir: models + name: 9aced515cf80c6b9f3b075dc0e49a2a6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 90459d17e836ce3e349ba307c466b355 + trainer: + kwargs: {} +name: 9aced515cf80c6b9f3b075dc0e49a2a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9b43e561d8a06a41880caac37b238cc7/params.yaml b/examples/security/classification/output/reports/train/9b43e561d8a06a41880caac37b238cc7/params.yaml new file mode 100644 index 00000000..8a030b06 --- /dev/null +++ b/examples/security/classification/output/reports/train/9b43e561d8a06a41880caac37b238cc7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/adv_predictions.json + adv_probabilities_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/a527fab58ad1a573434d2a512342987c.pkl + params_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/params.yaml + predictions_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/predictions.json + probabilities_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/probabilities.json + score_dict_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/score_dict.json + test_labels_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/test_labels.json + train_labels_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/train_labels.json + model_dir: models + name: 9b43e561d8a06a41880caac37b238cc7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a21c7010abf6e59068fec27a1e9621fc + trainer: + kwargs: {} +name: 9b43e561d8a06a41880caac37b238cc7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9b6194044f68e270ffe5eedab9c16e01/params.yaml b/examples/security/classification/output/reports/train/9b6194044f68e270ffe5eedab9c16e01/params.yaml new file mode 100644 index 00000000..13af756b --- /dev/null +++ b/examples/security/classification/output/reports/train/9b6194044f68e270ffe5eedab9c16e01/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/adv_predictions.json + adv_probabilities_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/da05c4d2d72502304a51c1a5364b64b6.pkl + params_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/params.yaml + predictions_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/predictions.json + probabilities_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/probabilities.json + score_dict_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/score_dict.json + test_labels_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/test_labels.json + train_labels_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/train_labels.json + model_dir: models + name: 9b6194044f68e270ffe5eedab9c16e01 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7c5b6b7f84b14e931d37bb39a1b94278 + trainer: + kwargs: {} +name: 9b6194044f68e270ffe5eedab9c16e01 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9b975b9de8160b665b659ec38c6f0323/params.yaml b/examples/security/classification/output/reports/train/9b975b9de8160b665b659ec38c6f0323/params.yaml new file mode 100644 index 00000000..076db4cb --- /dev/null +++ b/examples/security/classification/output/reports/train/9b975b9de8160b665b659ec38c6f0323/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/adv_predictions.json + adv_probabilities_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/01c81d8c55822a07dbd6eff24f31fbf9.pkl + params_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/params.yaml + predictions_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/predictions.json + probabilities_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/probabilities.json + score_dict_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/score_dict.json + test_labels_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/test_labels.json + train_labels_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/train_labels.json + model_dir: models + name: 9b975b9de8160b665b659ec38c6f0323 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 77f1efab7230fc9bc59ffc2a58008e46 + trainer: + kwargs: {} +name: 9b975b9de8160b665b659ec38c6f0323 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/params.yaml b/examples/security/classification/output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/params.yaml new file mode 100644 index 00000000..736a9003 --- /dev/null +++ b/examples/security/classification/output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/adv_predictions.json + adv_probabilities_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/f819a48a741e1c87b52a1c82d1182371.pkl + params_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/params.yaml + predictions_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/predictions.json + probabilities_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/probabilities.json + score_dict_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/score_dict.json + test_labels_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/test_labels.json + train_labels_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/train_labels.json + model_dir: models + name: 9baf0c43d93fc60bc9433b7b6ca119af + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ca0a1f53426e616a3eb27717c417f23c + trainer: + kwargs: {} +name: 9baf0c43d93fc60bc9433b7b6ca119af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9c1c9021c244634c95149349b13e9dd7/params.yaml b/examples/security/classification/output/reports/train/9c1c9021c244634c95149349b13e9dd7/params.yaml new file mode 100644 index 00000000..ac2300be --- /dev/null +++ b/examples/security/classification/output/reports/train/9c1c9021c244634c95149349b13e9dd7/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/adv_predictions.json + adv_probabilities_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/d32e36039e3eaddb49ed5a6bac26e7bf.pkl + params_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/params.yaml + predictions_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/predictions.json + probabilities_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/probabilities.json + score_dict_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/score_dict.json + test_labels_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/test_labels.json + train_labels_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/train_labels.json + model_dir: models + name: 9c1c9021c244634c95149349b13e9dd7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d899a138013fad7c3707e9a73fcef440 + trainer: + kwargs: {} +name: 9c1c9021c244634c95149349b13e9dd7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/params.yaml b/examples/security/classification/output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/params.yaml new file mode 100644 index 00000000..c74f75dd --- /dev/null +++ b/examples/security/classification/output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/adv_predictions.json + adv_probabilities_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/b3f0e4989392d3aed252983fbe052919.pkl + params_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/params.yaml + predictions_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/predictions.json + probabilities_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/probabilities.json + score_dict_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/score_dict.json + test_labels_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/test_labels.json + train_labels_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/train_labels.json + model_dir: models + name: 9c23df3ae9b5c9684dc83dc41e5849c9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dcc3acfef81ca497977470a3abd2644e + trainer: + kwargs: {} +name: 9c23df3ae9b5c9684dc83dc41e5849c9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/params.yaml b/examples/security/classification/output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/params.yaml new file mode 100644 index 00000000..8f3c40d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/adv_predictions.json + adv_probabilities_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/de47b3cdf955b3e15c5810cdc53a6d18.pkl + params_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/params.yaml + predictions_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/predictions.json + probabilities_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/probabilities.json + score_dict_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/score_dict.json + test_labels_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/test_labels.json + train_labels_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/train_labels.json + model_dir: models + name: 9c37ae4dffd6e2cbe6d193243630ccb7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8edfccb4772d15cf4a025d657cba6a6a + trainer: + kwargs: {} +name: 9c37ae4dffd6e2cbe6d193243630ccb7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/params.yaml b/examples/security/classification/output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/params.yaml new file mode 100644 index 00000000..4bfd89b5 --- /dev/null +++ b/examples/security/classification/output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/adv_predictions.json + adv_probabilities_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/2073fb1e629b80e9ed0f6c21a15a8d99.pkl + params_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/params.yaml + predictions_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/predictions.json + probabilities_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/probabilities.json + score_dict_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/score_dict.json + test_labels_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/test_labels.json + train_labels_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/train_labels.json + model_dir: models + name: 9c3cfa7dedf51cdc8a14ad2e921795ff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0843735fc47f92c441eba2d951b405bc + trainer: + kwargs: {} +name: 9c3cfa7dedf51cdc8a14ad2e921795ff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/params.yaml b/examples/security/classification/output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/params.yaml new file mode 100644 index 00000000..f2385097 --- /dev/null +++ b/examples/security/classification/output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/adv_predictions.json + adv_probabilities_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/07e9e93aed04067c046a75861ce74192.pkl + params_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/params.yaml + predictions_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/predictions.json + probabilities_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/probabilities.json + score_dict_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/score_dict.json + test_labels_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/test_labels.json + train_labels_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/train_labels.json + model_dir: models + name: 9c3f94ba138b0d4ac17e87fa223225ec + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c6e3b9b9602d6240fc7d4f98eb875280 + trainer: + kwargs: {} +name: 9c3f94ba138b0d4ac17e87fa223225ec +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9c49cb6a617bc9300ec9c0156e221867/params.yaml b/examples/security/classification/output/reports/train/9c49cb6a617bc9300ec9c0156e221867/params.yaml new file mode 100644 index 00000000..adf63a9e --- /dev/null +++ b/examples/security/classification/output/reports/train/9c49cb6a617bc9300ec9c0156e221867/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/adv_predictions.json + adv_probabilities_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/3e1a3a918cfa6caabe99e2c71f602ecc.pkl + params_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/params.yaml + predictions_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/predictions.json + probabilities_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/probabilities.json + score_dict_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/score_dict.json + test_labels_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/test_labels.json + train_labels_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/train_labels.json + model_dir: models + name: 9c49cb6a617bc9300ec9c0156e221867 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 403d26680782e177402093576cd710d2 + trainer: + kwargs: {} +name: 9c49cb6a617bc9300ec9c0156e221867 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9c63778222cb2a86a11b1f7653bc4664/params.yaml b/examples/security/classification/output/reports/train/9c63778222cb2a86a11b1f7653bc4664/params.yaml new file mode 100644 index 00000000..d120b2cc --- /dev/null +++ b/examples/security/classification/output/reports/train/9c63778222cb2a86a11b1f7653bc4664/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/adv_predictions.json + adv_probabilities_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/3fa97e1023dd902e5888a03bdb086067.pkl + params_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/params.yaml + predictions_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/predictions.json + probabilities_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/probabilities.json + score_dict_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/score_dict.json + test_labels_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/test_labels.json + train_labels_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/train_labels.json + model_dir: models + name: 9c63778222cb2a86a11b1f7653bc4664 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c5cd05ba41f8c4fb0c18f4b002628098 + trainer: + kwargs: {} +name: 9c63778222cb2a86a11b1f7653bc4664 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/params.yaml b/examples/security/classification/output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/params.yaml new file mode 100644 index 00000000..73cca965 --- /dev/null +++ b/examples/security/classification/output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/adv_predictions.json + adv_probabilities_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/d3bf9b9775197682f0a5b1e124f35819.pkl + params_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/params.yaml + predictions_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/predictions.json + probabilities_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/probabilities.json + score_dict_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/score_dict.json + test_labels_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/test_labels.json + train_labels_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/train_labels.json + model_dir: models + name: 9caa9ceba878f9a0ee762ea6ed4b72e4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4275e5757a4be32149b6986aeac2e2aa + trainer: + kwargs: {} +name: 9caa9ceba878f9a0ee762ea6ed4b72e4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/params.yaml b/examples/security/classification/output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/params.yaml new file mode 100644 index 00000000..569c43ee --- /dev/null +++ b/examples/security/classification/output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/adv_predictions.json + adv_probabilities_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/b52fb049f5d0e8fa8780dc6c3efd31cb.pkl + params_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/params.yaml + predictions_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/predictions.json + probabilities_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/probabilities.json + score_dict_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/score_dict.json + test_labels_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/test_labels.json + train_labels_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/train_labels.json + model_dir: models + name: 9cbd9bd78cb28ce186fc50bfb1c22bae + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b50327731e54fcbe9f6ea473b6fbd3c8 + trainer: + kwargs: {} +name: 9cbd9bd78cb28ce186fc50bfb1c22bae +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9ccae745b589b789294b1ca060063878/params.yaml b/examples/security/classification/output/reports/train/9ccae745b589b789294b1ca060063878/params.yaml new file mode 100644 index 00000000..834e1ba8 --- /dev/null +++ b/examples/security/classification/output/reports/train/9ccae745b589b789294b1ca060063878/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9ccae745b589b789294b1ca060063878/adv_predictions.json + adv_probabilities_file: output/reports/train/9ccae745b589b789294b1ca060063878/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/238351ee4f115925b030d293b3632445.pkl + params_file: output/reports/train/9ccae745b589b789294b1ca060063878/params.yaml + predictions_file: output/reports/train/9ccae745b589b789294b1ca060063878/predictions.json + probabilities_file: output/reports/train/9ccae745b589b789294b1ca060063878/probabilities.json + score_dict_file: output/reports/train/9ccae745b589b789294b1ca060063878/score_dict.json + test_labels_file: output/reports/train/9ccae745b589b789294b1ca060063878/test_labels.json + train_labels_file: output/reports/train/9ccae745b589b789294b1ca060063878/train_labels.json + model_dir: models + name: 9ccae745b589b789294b1ca060063878 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 443dfdb0b7964ccf02f391fb51f98b1f + trainer: + kwargs: {} +name: 9ccae745b589b789294b1ca060063878 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/params.yaml b/examples/security/classification/output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/params.yaml new file mode 100644 index 00000000..2504a058 --- /dev/null +++ b/examples/security/classification/output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/adv_predictions.json + adv_probabilities_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/19f2f9ba9d762dc256ab40380609e39e.pkl + params_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/params.yaml + predictions_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/predictions.json + probabilities_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/probabilities.json + score_dict_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/score_dict.json + test_labels_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/test_labels.json + train_labels_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/train_labels.json + model_dir: models + name: 9ccfc5e835af2f6390bced4701b9d42e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4df01e903c6593c43c9f34f7339fe555 + trainer: + kwargs: {} +name: 9ccfc5e835af2f6390bced4701b9d42e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9ce7ab7e4118179be709e22579ba0607/params.yaml b/examples/security/classification/output/reports/train/9ce7ab7e4118179be709e22579ba0607/params.yaml new file mode 100644 index 00000000..f53aa837 --- /dev/null +++ b/examples/security/classification/output/reports/train/9ce7ab7e4118179be709e22579ba0607/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/adv_predictions.json + adv_probabilities_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/4df77092eb1f501233e9a6dd3a2a24c1.pkl + params_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/params.yaml + predictions_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/predictions.json + probabilities_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/probabilities.json + score_dict_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/score_dict.json + test_labels_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/test_labels.json + train_labels_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/train_labels.json + model_dir: models + name: 9ce7ab7e4118179be709e22579ba0607 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 71ba1c0d64fac6b055a8865036b5ee14 + trainer: + kwargs: {} +name: 9ce7ab7e4118179be709e22579ba0607 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9cf61300da9878858004be857e1c11e3/params.yaml b/examples/security/classification/output/reports/train/9cf61300da9878858004be857e1c11e3/params.yaml new file mode 100644 index 00000000..821f528f --- /dev/null +++ b/examples/security/classification/output/reports/train/9cf61300da9878858004be857e1c11e3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9cf61300da9878858004be857e1c11e3/adv_predictions.json + adv_probabilities_file: output/reports/train/9cf61300da9878858004be857e1c11e3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/d0810912df3a9bb086366d4034c75f27.pkl + params_file: output/reports/train/9cf61300da9878858004be857e1c11e3/params.yaml + predictions_file: output/reports/train/9cf61300da9878858004be857e1c11e3/predictions.json + probabilities_file: output/reports/train/9cf61300da9878858004be857e1c11e3/probabilities.json + score_dict_file: output/reports/train/9cf61300da9878858004be857e1c11e3/score_dict.json + test_labels_file: output/reports/train/9cf61300da9878858004be857e1c11e3/test_labels.json + train_labels_file: output/reports/train/9cf61300da9878858004be857e1c11e3/train_labels.json + model_dir: models + name: 9cf61300da9878858004be857e1c11e3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9eee77696a67b12ff0232550c5077009 + trainer: + kwargs: {} +name: 9cf61300da9878858004be857e1c11e3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/params.yaml b/examples/security/classification/output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/params.yaml new file mode 100644 index 00000000..17dd217f --- /dev/null +++ b/examples/security/classification/output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/adv_predictions.json + adv_probabilities_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/64765dc9072b1da4ec6cab4fc723ae6c.pkl + params_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/params.yaml + predictions_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/predictions.json + probabilities_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/probabilities.json + score_dict_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/score_dict.json + test_labels_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/test_labels.json + train_labels_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/train_labels.json + model_dir: models + name: 9d1d23c6a94a8108d0133e7aac8b3c5d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b519b7cc7c1dfaabc4452fc06aa3c5a6 + trainer: + kwargs: {} +name: 9d1d23c6a94a8108d0133e7aac8b3c5d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9d244f5d37e5234bde6012dfd910c507/params.yaml b/examples/security/classification/output/reports/train/9d244f5d37e5234bde6012dfd910c507/params.yaml new file mode 100644 index 00000000..c714a8fd --- /dev/null +++ b/examples/security/classification/output/reports/train/9d244f5d37e5234bde6012dfd910c507/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/adv_predictions.json + adv_probabilities_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/68340ee79786c9627dc8d5c6627f4185.pkl + params_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/params.yaml + predictions_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/predictions.json + probabilities_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/probabilities.json + score_dict_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/score_dict.json + test_labels_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/test_labels.json + train_labels_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/train_labels.json + model_dir: models + name: 9d244f5d37e5234bde6012dfd910c507 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 10ebdafaf0ce659ff408132f301f32c0 + trainer: + kwargs: {} +name: 9d244f5d37e5234bde6012dfd910c507 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/params.yaml b/examples/security/classification/output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/params.yaml new file mode 100644 index 00000000..cc119920 --- /dev/null +++ b/examples/security/classification/output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/adv_predictions.json + adv_probabilities_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/755cc7f452e2a081a139a291be01f19e.pkl + params_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/params.yaml + predictions_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/predictions.json + probabilities_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/probabilities.json + score_dict_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/score_dict.json + test_labels_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/test_labels.json + train_labels_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/train_labels.json + model_dir: models + name: 9d4d1cd55dff13de76cd8aad7654326a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 43af58a792e88fed64b7141d0ff2613b + trainer: + kwargs: {} +name: 9d4d1cd55dff13de76cd8aad7654326a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9d747b760f721835fb44ad87e852bb90/params.yaml b/examples/security/classification/output/reports/train/9d747b760f721835fb44ad87e852bb90/params.yaml new file mode 100644 index 00000000..0368ba1d --- /dev/null +++ b/examples/security/classification/output/reports/train/9d747b760f721835fb44ad87e852bb90/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/adv_predictions.json + adv_probabilities_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/88863e3541df1e8926accf8adfea98bb.pkl + params_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/params.yaml + predictions_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/predictions.json + probabilities_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/probabilities.json + score_dict_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/score_dict.json + test_labels_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/test_labels.json + train_labels_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/train_labels.json + model_dir: models + name: 9d747b760f721835fb44ad87e852bb90 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 160d633b83cf68389d0cb0431f39a796 + trainer: + kwargs: {} +name: 9d747b760f721835fb44ad87e852bb90 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9d82827878a4e0d23d27fd522251aa4b/params.yaml b/examples/security/classification/output/reports/train/9d82827878a4e0d23d27fd522251aa4b/params.yaml new file mode 100644 index 00000000..3f366f2e --- /dev/null +++ b/examples/security/classification/output/reports/train/9d82827878a4e0d23d27fd522251aa4b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/adv_predictions.json + adv_probabilities_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/bbf1e66ce1dfb02b69977386af102175.pkl + params_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/params.yaml + predictions_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/predictions.json + probabilities_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/probabilities.json + score_dict_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/score_dict.json + test_labels_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/test_labels.json + train_labels_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/train_labels.json + model_dir: models + name: 9d82827878a4e0d23d27fd522251aa4b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4b8cd16e450707a72c4f83a476c85ca4 + trainer: + kwargs: {} +name: 9d82827878a4e0d23d27fd522251aa4b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/params.yaml b/examples/security/classification/output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/params.yaml new file mode 100644 index 00000000..e0442471 --- /dev/null +++ b/examples/security/classification/output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/adv_predictions.json + adv_probabilities_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/7377c5cf90796f03def78e8c3d5fad18.pkl + params_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/params.yaml + predictions_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/predictions.json + probabilities_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/probabilities.json + score_dict_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/score_dict.json + test_labels_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/test_labels.json + train_labels_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/train_labels.json + model_dir: models + name: 9d9a5d6717de24d02b14ae9da9b4f1c4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 185a9eab89bfde7ab0846dd1020fccb3 + trainer: + kwargs: {} +name: 9d9a5d6717de24d02b14ae9da9b4f1c4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/params.yaml b/examples/security/classification/output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/params.yaml new file mode 100644 index 00000000..b54843b2 --- /dev/null +++ b/examples/security/classification/output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/adv_predictions.json + adv_probabilities_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/d9df668b5bba107e43fee2eee1866e54.pkl + params_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/params.yaml + predictions_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/predictions.json + probabilities_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/probabilities.json + score_dict_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/score_dict.json + test_labels_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/test_labels.json + train_labels_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/train_labels.json + model_dir: models + name: 9dbd90791e66d3645b4ba5b3525ed072 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ef047c8838f1cd94a32cd6341e9827ed + trainer: + kwargs: {} +name: 9dbd90791e66d3645b4ba5b3525ed072 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9decd6e2e664340695681c856074af17/params.yaml b/examples/security/classification/output/reports/train/9decd6e2e664340695681c856074af17/params.yaml new file mode 100644 index 00000000..528fe2d7 --- /dev/null +++ b/examples/security/classification/output/reports/train/9decd6e2e664340695681c856074af17/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9decd6e2e664340695681c856074af17/adv_predictions.json + adv_probabilities_file: output/reports/train/9decd6e2e664340695681c856074af17/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/7d5fd50667334996fa6a9adf2496d792.pkl + params_file: output/reports/train/9decd6e2e664340695681c856074af17/params.yaml + predictions_file: output/reports/train/9decd6e2e664340695681c856074af17/predictions.json + probabilities_file: output/reports/train/9decd6e2e664340695681c856074af17/probabilities.json + score_dict_file: output/reports/train/9decd6e2e664340695681c856074af17/score_dict.json + test_labels_file: output/reports/train/9decd6e2e664340695681c856074af17/test_labels.json + train_labels_file: output/reports/train/9decd6e2e664340695681c856074af17/train_labels.json + model_dir: models + name: 9decd6e2e664340695681c856074af17 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 90e9a746cdb38ed97b8637cef338aecf + trainer: + kwargs: {} +name: 9decd6e2e664340695681c856074af17 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9e4122225b62ebbdf06d58d603a786a1/params.yaml b/examples/security/classification/output/reports/train/9e4122225b62ebbdf06d58d603a786a1/params.yaml new file mode 100644 index 00000000..4dd37b5d --- /dev/null +++ b/examples/security/classification/output/reports/train/9e4122225b62ebbdf06d58d603a786a1/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/adv_predictions.json + adv_probabilities_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/08eec954a7a0ad32a7b5a2ebb41d99c4.pkl + params_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/params.yaml + predictions_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/predictions.json + probabilities_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/probabilities.json + score_dict_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/score_dict.json + test_labels_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/test_labels.json + train_labels_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/train_labels.json + model_dir: models + name: 9e4122225b62ebbdf06d58d603a786a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0a05d12e73962cfffaf9ec1974f51eba + trainer: + kwargs: {} +name: 9e4122225b62ebbdf06d58d603a786a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9e741fe262a805b95fd4aa180cb68861/params.yaml b/examples/security/classification/output/reports/train/9e741fe262a805b95fd4aa180cb68861/params.yaml new file mode 100644 index 00000000..bf163591 --- /dev/null +++ b/examples/security/classification/output/reports/train/9e741fe262a805b95fd4aa180cb68861/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/adv_predictions.json + adv_probabilities_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/400e5b8af83c070bc2a8b56a5f049b94.pkl + params_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/params.yaml + predictions_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/predictions.json + probabilities_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/probabilities.json + score_dict_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/score_dict.json + test_labels_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/test_labels.json + train_labels_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/train_labels.json + model_dir: models + name: 9e741fe262a805b95fd4aa180cb68861 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e13f0d132c392e115c25110a577b4349 + trainer: + kwargs: {} +name: 9e741fe262a805b95fd4aa180cb68861 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9e8987520dd27eb8b2187c47dc678027/params.yaml b/examples/security/classification/output/reports/train/9e8987520dd27eb8b2187c47dc678027/params.yaml new file mode 100644 index 00000000..e1c67acf --- /dev/null +++ b/examples/security/classification/output/reports/train/9e8987520dd27eb8b2187c47dc678027/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/adv_predictions.json + adv_probabilities_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/2775fe9c21780d2ba8f9b3121d054d35.pkl + params_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/params.yaml + predictions_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/predictions.json + probabilities_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/probabilities.json + score_dict_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/score_dict.json + test_labels_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/test_labels.json + train_labels_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/train_labels.json + model_dir: models + name: 9e8987520dd27eb8b2187c47dc678027 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 11770d4d7cc3bbbd2a47dc30ce6d5163 + trainer: + kwargs: {} +name: 9e8987520dd27eb8b2187c47dc678027 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9f5be87069fb07821d847d822d3edd8e/params.yaml b/examples/security/classification/output/reports/train/9f5be87069fb07821d847d822d3edd8e/params.yaml new file mode 100644 index 00000000..05a383eb --- /dev/null +++ b/examples/security/classification/output/reports/train/9f5be87069fb07821d847d822d3edd8e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/adv_predictions.json + adv_probabilities_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/fc63522fb9b119dc216474cd1dcd2aff.pkl + params_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/params.yaml + predictions_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/predictions.json + probabilities_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/probabilities.json + score_dict_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/score_dict.json + test_labels_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/test_labels.json + train_labels_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/train_labels.json + model_dir: models + name: 9f5be87069fb07821d847d822d3edd8e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a2fd552adf69afc2b573ae3275684eba + trainer: + kwargs: {} +name: 9f5be87069fb07821d847d822d3edd8e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/params.yaml b/examples/security/classification/output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/params.yaml new file mode 100644 index 00000000..cb907a15 --- /dev/null +++ b/examples/security/classification/output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/adv_predictions.json + adv_probabilities_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/6323394ec28dcec86d9c1c1a9c675d8b.pkl + params_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/params.yaml + predictions_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/predictions.json + probabilities_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/probabilities.json + score_dict_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/score_dict.json + test_labels_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/test_labels.json + train_labels_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/train_labels.json + model_dir: models + name: 9f8b2f3876cbfd24936071c55c3b1228 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c86756555a535032166f9abe344ed699 + trainer: + kwargs: {} +name: 9f8b2f3876cbfd24936071c55c3b1228 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/params.yaml b/examples/security/classification/output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/params.yaml new file mode 100644 index 00000000..8aa08f95 --- /dev/null +++ b/examples/security/classification/output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/adv_predictions.json + adv_probabilities_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/5deb89883fa6c7edac14ff565b3db60a.pkl + params_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/params.yaml + predictions_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/predictions.json + probabilities_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/probabilities.json + score_dict_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/score_dict.json + test_labels_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/test_labels.json + train_labels_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/train_labels.json + model_dir: models + name: 9fc08ddc5331daec329e55e0209d7c1d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b2a6cc4f888e5cef6950e1ecfef59ef1 + trainer: + kwargs: {} +name: 9fc08ddc5331daec329e55e0209d7c1d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/9fdf681154346b46bef7c85d8e32c714/params.yaml b/examples/security/classification/output/reports/train/9fdf681154346b46bef7c85d8e32c714/params.yaml new file mode 100644 index 00000000..414780d1 --- /dev/null +++ b/examples/security/classification/output/reports/train/9fdf681154346b46bef7c85d8e32c714/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/adv_predictions.json + adv_probabilities_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/220358e05a9848cb54758f1cc85a20dc.pkl + params_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/params.yaml + predictions_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/predictions.json + probabilities_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/probabilities.json + score_dict_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/score_dict.json + test_labels_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/test_labels.json + train_labels_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/train_labels.json + model_dir: models + name: 9fdf681154346b46bef7c85d8e32c714 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0f05db395f94a2570daadb7fcb036e5f + trainer: + kwargs: {} +name: 9fdf681154346b46bef7c85d8e32c714 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a00025dafd87d76e60595b77ccb21bd4/params.yaml b/examples/security/classification/output/reports/train/a00025dafd87d76e60595b77ccb21bd4/params.yaml new file mode 100644 index 00000000..df7459ba --- /dev/null +++ b/examples/security/classification/output/reports/train/a00025dafd87d76e60595b77ccb21bd4/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/adv_predictions.json + adv_probabilities_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/c65a2b597f50cffc4fa37bbd2f9201fb.pkl + params_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/params.yaml + predictions_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/predictions.json + probabilities_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/probabilities.json + score_dict_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/score_dict.json + test_labels_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/test_labels.json + train_labels_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/train_labels.json + model_dir: models + name: a00025dafd87d76e60595b77ccb21bd4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6cb58ebb5a7537683299b14882fed2f5 + trainer: + kwargs: {} +name: a00025dafd87d76e60595b77ccb21bd4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/params.yaml b/examples/security/classification/output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/params.yaml new file mode 100644 index 00000000..bd3347a1 --- /dev/null +++ b/examples/security/classification/output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/adv_predictions.json + adv_probabilities_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/09de99034b4c834b7e8ea166514d678a.pkl + params_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/params.yaml + predictions_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/predictions.json + probabilities_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/probabilities.json + score_dict_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/score_dict.json + test_labels_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/test_labels.json + train_labels_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/train_labels.json + model_dir: models + name: a059e8ea1cbbaf8f26caa8d9baac9e2a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9019e4ebbf98cda48bf85dfa25fa9cc5 + trainer: + kwargs: {} +name: a059e8ea1cbbaf8f26caa8d9baac9e2a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/params.yaml b/examples/security/classification/output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/params.yaml new file mode 100644 index 00000000..8fc636a3 --- /dev/null +++ b/examples/security/classification/output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/adv_predictions.json + adv_probabilities_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/2b31d9d8ba0eb6fb09a4f7f7cf8906a8.pkl + params_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/params.yaml + predictions_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/predictions.json + probabilities_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/probabilities.json + score_dict_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/score_dict.json + test_labels_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/test_labels.json + train_labels_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/train_labels.json + model_dir: models + name: a0674b16939b9be7c3a082156d2ddbd3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 707d4c626306327b001974454e34f13c + trainer: + kwargs: {} +name: a0674b16939b9be7c3a082156d2ddbd3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a115c05d28e80a9c81a93792426ddf4e/params.yaml b/examples/security/classification/output/reports/train/a115c05d28e80a9c81a93792426ddf4e/params.yaml new file mode 100644 index 00000000..6d515eac --- /dev/null +++ b/examples/security/classification/output/reports/train/a115c05d28e80a9c81a93792426ddf4e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/adv_predictions.json + adv_probabilities_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/5ac698adb1bafbd7120361e8b0fef2d1.pkl + params_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/params.yaml + predictions_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/predictions.json + probabilities_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/probabilities.json + score_dict_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/score_dict.json + test_labels_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/test_labels.json + train_labels_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/train_labels.json + model_dir: models + name: a115c05d28e80a9c81a93792426ddf4e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f750b500a21b1f042fccc3bfb74cc926 + trainer: + kwargs: {} +name: a115c05d28e80a9c81a93792426ddf4e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a11da6a15b260a11982f474d0c4540ac/params.yaml b/examples/security/classification/output/reports/train/a11da6a15b260a11982f474d0c4540ac/params.yaml new file mode 100644 index 00000000..1699009f --- /dev/null +++ b/examples/security/classification/output/reports/train/a11da6a15b260a11982f474d0c4540ac/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/adv_predictions.json + adv_probabilities_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/b5efdd8242c114950fc315711caf6675.pkl + params_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/params.yaml + predictions_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/predictions.json + probabilities_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/probabilities.json + score_dict_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/score_dict.json + test_labels_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/test_labels.json + train_labels_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/train_labels.json + model_dir: models + name: a11da6a15b260a11982f474d0c4540ac + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e7fc975600cd24dd17ae7bd92228b127 + trainer: + kwargs: {} +name: a11da6a15b260a11982f474d0c4540ac +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/params.yaml b/examples/security/classification/output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/params.yaml new file mode 100644 index 00000000..8ceb719b --- /dev/null +++ b/examples/security/classification/output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/adv_predictions.json + adv_probabilities_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/d44fc83a3e815a3759504522c0078779.pkl + params_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/params.yaml + predictions_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/predictions.json + probabilities_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/probabilities.json + score_dict_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/score_dict.json + test_labels_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/test_labels.json + train_labels_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/train_labels.json + model_dir: models + name: a12b0adb069ff9c8ac5cacc6fbff324d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 413ca859f76d56b78663ea9aa9a2b120 + trainer: + kwargs: {} +name: a12b0adb069ff9c8ac5cacc6fbff324d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a131fb219be89309abb9b75b730a6175/params.yaml b/examples/security/classification/output/reports/train/a131fb219be89309abb9b75b730a6175/params.yaml new file mode 100644 index 00000000..67aefd4f --- /dev/null +++ b/examples/security/classification/output/reports/train/a131fb219be89309abb9b75b730a6175/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a131fb219be89309abb9b75b730a6175/adv_predictions.json + adv_probabilities_file: output/reports/train/a131fb219be89309abb9b75b730a6175/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/ea05caa4867632c0f1d63756141da148.pkl + params_file: output/reports/train/a131fb219be89309abb9b75b730a6175/params.yaml + predictions_file: output/reports/train/a131fb219be89309abb9b75b730a6175/predictions.json + probabilities_file: output/reports/train/a131fb219be89309abb9b75b730a6175/probabilities.json + score_dict_file: output/reports/train/a131fb219be89309abb9b75b730a6175/score_dict.json + test_labels_file: output/reports/train/a131fb219be89309abb9b75b730a6175/test_labels.json + train_labels_file: output/reports/train/a131fb219be89309abb9b75b730a6175/train_labels.json + model_dir: models + name: a131fb219be89309abb9b75b730a6175 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 443fc7edda079bc3bdd67b8629de0e13 + trainer: + kwargs: {} +name: a131fb219be89309abb9b75b730a6175 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a13c6be370cec34097177c48b65b4a1d/params.yaml b/examples/security/classification/output/reports/train/a13c6be370cec34097177c48b65b4a1d/params.yaml new file mode 100644 index 00000000..c62a1c18 --- /dev/null +++ b/examples/security/classification/output/reports/train/a13c6be370cec34097177c48b65b4a1d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/adv_predictions.json + adv_probabilities_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/9b0cd14ccf4a5d587fceea78cb6a0aa1.pkl + params_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/params.yaml + predictions_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/predictions.json + probabilities_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/probabilities.json + score_dict_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/score_dict.json + test_labels_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/test_labels.json + train_labels_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/train_labels.json + model_dir: models + name: a13c6be370cec34097177c48b65b4a1d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ce6fc413d7bce17d7774ed9f3e32541e + trainer: + kwargs: {} +name: a13c6be370cec34097177c48b65b4a1d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a1523110e8031fa5720540b15da9e41a/params.yaml b/examples/security/classification/output/reports/train/a1523110e8031fa5720540b15da9e41a/params.yaml new file mode 100644 index 00000000..b3dc9a25 --- /dev/null +++ b/examples/security/classification/output/reports/train/a1523110e8031fa5720540b15da9e41a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/adv_predictions.json + adv_probabilities_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/f9805f4681f332b23e3590b6e721eea7.pkl + params_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/params.yaml + predictions_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/predictions.json + probabilities_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/probabilities.json + score_dict_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/score_dict.json + test_labels_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/test_labels.json + train_labels_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/train_labels.json + model_dir: models + name: a1523110e8031fa5720540b15da9e41a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ee8d17b3edd77e8cda93a2accaa4f1f8 + trainer: + kwargs: {} +name: a1523110e8031fa5720540b15da9e41a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a153058599e383406804c081a093c24b/params.yaml b/examples/security/classification/output/reports/train/a153058599e383406804c081a093c24b/params.yaml new file mode 100644 index 00000000..437c5ddc --- /dev/null +++ b/examples/security/classification/output/reports/train/a153058599e383406804c081a093c24b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a153058599e383406804c081a093c24b/adv_predictions.json + adv_probabilities_file: output/reports/train/a153058599e383406804c081a093c24b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/1df53a4bfdc9eda6e4ad7b3c3805cc92.pkl + params_file: output/reports/train/a153058599e383406804c081a093c24b/params.yaml + predictions_file: output/reports/train/a153058599e383406804c081a093c24b/predictions.json + probabilities_file: output/reports/train/a153058599e383406804c081a093c24b/probabilities.json + score_dict_file: output/reports/train/a153058599e383406804c081a093c24b/score_dict.json + test_labels_file: output/reports/train/a153058599e383406804c081a093c24b/test_labels.json + train_labels_file: output/reports/train/a153058599e383406804c081a093c24b/train_labels.json + model_dir: models + name: a153058599e383406804c081a093c24b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 85eb0a4eb74c18decbb95677314d5c04 + trainer: + kwargs: {} +name: a153058599e383406804c081a093c24b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a18076accc756f0c0afa648c2245b4d3/params.yaml b/examples/security/classification/output/reports/train/a18076accc756f0c0afa648c2245b4d3/params.yaml new file mode 100644 index 00000000..00f6f80d --- /dev/null +++ b/examples/security/classification/output/reports/train/a18076accc756f0c0afa648c2245b4d3/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/adv_predictions.json + adv_probabilities_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/70bda7e5149c819bfd28fe9807013f57.pkl + params_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/params.yaml + predictions_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/predictions.json + probabilities_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/probabilities.json + score_dict_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/score_dict.json + test_labels_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/test_labels.json + train_labels_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/train_labels.json + model_dir: models + name: a18076accc756f0c0afa648c2245b4d3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 102948e067e01df7395a3b3fe548ea6b + trainer: + kwargs: {} +name: a18076accc756f0c0afa648c2245b4d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/params.yaml b/examples/security/classification/output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/params.yaml new file mode 100644 index 00000000..364eb635 --- /dev/null +++ b/examples/security/classification/output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/adv_predictions.json + adv_probabilities_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/256fe7d91d6b73b82238a062a547f56e.pkl + params_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/params.yaml + predictions_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/predictions.json + probabilities_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/probabilities.json + score_dict_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/score_dict.json + test_labels_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/test_labels.json + train_labels_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/train_labels.json + model_dir: models + name: a19d396d14f45e75ed90c21a4f83d35c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f81d138ca3097b94b87a2d753f3887f5 + trainer: + kwargs: {} +name: a19d396d14f45e75ed90c21a4f83d35c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a19ddbf9b31e353d3313932893c235ce/params.yaml b/examples/security/classification/output/reports/train/a19ddbf9b31e353d3313932893c235ce/params.yaml new file mode 100644 index 00000000..3de668cb --- /dev/null +++ b/examples/security/classification/output/reports/train/a19ddbf9b31e353d3313932893c235ce/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/adv_predictions.json + adv_probabilities_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/a63046270844a1621f5abea0856d556a.pkl + params_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/params.yaml + predictions_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/predictions.json + probabilities_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/probabilities.json + score_dict_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/score_dict.json + test_labels_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/test_labels.json + train_labels_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/train_labels.json + model_dir: models + name: a19ddbf9b31e353d3313932893c235ce + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1067029f87720e28d6e46e5691a175f1 + trainer: + kwargs: {} +name: a19ddbf9b31e353d3313932893c235ce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/params.yaml b/examples/security/classification/output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/params.yaml new file mode 100644 index 00000000..fd122f10 --- /dev/null +++ b/examples/security/classification/output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/adv_predictions.json + adv_probabilities_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/fdd2c76d629706c7b3d50402b9a96c65.pkl + params_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/params.yaml + predictions_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/predictions.json + probabilities_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/probabilities.json + score_dict_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/score_dict.json + test_labels_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/test_labels.json + train_labels_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/train_labels.json + model_dir: models + name: a1cd9d6ca714960ad24f809e14d323bf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3445a35fef609d07ba1aa89df1c7a0a7 + trainer: + kwargs: {} +name: a1cd9d6ca714960ad24f809e14d323bf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a201ffa05f1237d86b649db5821d7100/params.yaml b/examples/security/classification/output/reports/train/a201ffa05f1237d86b649db5821d7100/params.yaml new file mode 100644 index 00000000..7a7769b6 --- /dev/null +++ b/examples/security/classification/output/reports/train/a201ffa05f1237d86b649db5821d7100/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/adv_predictions.json + adv_probabilities_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/acb0aebaeb1ebe924f7c59c07db679c8.pkl + params_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/params.yaml + predictions_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/predictions.json + probabilities_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/probabilities.json + score_dict_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/score_dict.json + test_labels_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/test_labels.json + train_labels_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/train_labels.json + model_dir: models + name: a201ffa05f1237d86b649db5821d7100 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d78b16b145c5d17856a183f52dcc28e3 + trainer: + kwargs: {} +name: a201ffa05f1237d86b649db5821d7100 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a2086ba52f947f963a39b3b384da85c8/params.yaml b/examples/security/classification/output/reports/train/a2086ba52f947f963a39b3b384da85c8/params.yaml new file mode 100644 index 00000000..b510ab87 --- /dev/null +++ b/examples/security/classification/output/reports/train/a2086ba52f947f963a39b3b384da85c8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/adv_predictions.json + adv_probabilities_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/c877e7b719fef1324a692d47d9e32b36.pkl + params_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/params.yaml + predictions_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/predictions.json + probabilities_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/probabilities.json + score_dict_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/score_dict.json + test_labels_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/test_labels.json + train_labels_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/train_labels.json + model_dir: models + name: a2086ba52f947f963a39b3b384da85c8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0b5f26fb58dcd33981909a95293b402b + trainer: + kwargs: {} +name: a2086ba52f947f963a39b3b384da85c8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a222794142146d1c86ef64b4540d1112/params.yaml b/examples/security/classification/output/reports/train/a222794142146d1c86ef64b4540d1112/params.yaml new file mode 100644 index 00000000..65080bfd --- /dev/null +++ b/examples/security/classification/output/reports/train/a222794142146d1c86ef64b4540d1112/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a222794142146d1c86ef64b4540d1112/adv_predictions.json + adv_probabilities_file: output/reports/train/a222794142146d1c86ef64b4540d1112/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/73a0baba27735a6bc48446dd7946b99e.pkl + params_file: output/reports/train/a222794142146d1c86ef64b4540d1112/params.yaml + predictions_file: output/reports/train/a222794142146d1c86ef64b4540d1112/predictions.json + probabilities_file: output/reports/train/a222794142146d1c86ef64b4540d1112/probabilities.json + score_dict_file: output/reports/train/a222794142146d1c86ef64b4540d1112/score_dict.json + test_labels_file: output/reports/train/a222794142146d1c86ef64b4540d1112/test_labels.json + train_labels_file: output/reports/train/a222794142146d1c86ef64b4540d1112/train_labels.json + model_dir: models + name: a222794142146d1c86ef64b4540d1112 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5f16e54be7ccd2d1fd4b971858c83a27 + trainer: + kwargs: {} +name: a222794142146d1c86ef64b4540d1112 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/params.yaml b/examples/security/classification/output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/params.yaml new file mode 100644 index 00000000..9ef32186 --- /dev/null +++ b/examples/security/classification/output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/adv_predictions.json + adv_probabilities_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/f592d0999dbb3d47bf0023fa14c883d9.pkl + params_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/params.yaml + predictions_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/predictions.json + probabilities_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/probabilities.json + score_dict_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/score_dict.json + test_labels_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/test_labels.json + train_labels_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/train_labels.json + model_dir: models + name: a24d3400e42f6d80682bf96ce6fcd2aa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ecd818faeeab4777bb6e994cf81bb3ec + trainer: + kwargs: {} +name: a24d3400e42f6d80682bf96ce6fcd2aa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a256db550d24ea8edb0c651433eac3be/params.yaml b/examples/security/classification/output/reports/train/a256db550d24ea8edb0c651433eac3be/params.yaml new file mode 100644 index 00000000..6d021c55 --- /dev/null +++ b/examples/security/classification/output/reports/train/a256db550d24ea8edb0c651433eac3be/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/adv_predictions.json + adv_probabilities_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/ed433a148ad42b0da11ad6b5d550a098.pkl + params_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/params.yaml + predictions_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/predictions.json + probabilities_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/probabilities.json + score_dict_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/score_dict.json + test_labels_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/test_labels.json + train_labels_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/train_labels.json + model_dir: models + name: a256db550d24ea8edb0c651433eac3be + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a1177b363abbdb9b4df40674d53fadf0 + trainer: + kwargs: {} +name: a256db550d24ea8edb0c651433eac3be +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a25a54c1c0b9a3258e68e37a22998525/params.yaml b/examples/security/classification/output/reports/train/a25a54c1c0b9a3258e68e37a22998525/params.yaml new file mode 100644 index 00000000..6c018394 --- /dev/null +++ b/examples/security/classification/output/reports/train/a25a54c1c0b9a3258e68e37a22998525/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/adv_predictions.json + adv_probabilities_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/2893f52305ec2f6db447faa511dde915.pkl + params_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/params.yaml + predictions_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/predictions.json + probabilities_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/probabilities.json + score_dict_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/score_dict.json + test_labels_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/test_labels.json + train_labels_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/train_labels.json + model_dir: models + name: a25a54c1c0b9a3258e68e37a22998525 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4cf0ab8f6ca95a5056cf624f5bf29003 + trainer: + kwargs: {} +name: a25a54c1c0b9a3258e68e37a22998525 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/params.yaml b/examples/security/classification/output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/params.yaml new file mode 100644 index 00000000..3fa4ce94 --- /dev/null +++ b/examples/security/classification/output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/adv_predictions.json + adv_probabilities_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/4ccd977096f72731781e01ac3c9db514.pkl + params_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/params.yaml + predictions_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/predictions.json + probabilities_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/probabilities.json + score_dict_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/score_dict.json + test_labels_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/test_labels.json + train_labels_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/train_labels.json + model_dir: models + name: a26f27bdbe4106e0399f3ec015c7672b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d67cf99c52fb78abf0de8a3b4597a252 + trainer: + kwargs: {} +name: a26f27bdbe4106e0399f3ec015c7672b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a2b8aa20917bd043a089865ee4d809fc/params.yaml b/examples/security/classification/output/reports/train/a2b8aa20917bd043a089865ee4d809fc/params.yaml new file mode 100644 index 00000000..8e1c892f --- /dev/null +++ b/examples/security/classification/output/reports/train/a2b8aa20917bd043a089865ee4d809fc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/adv_predictions.json + adv_probabilities_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/4e8c838db680d8f5c1456f97cf1ccaea.pkl + params_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/params.yaml + predictions_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/predictions.json + probabilities_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/probabilities.json + score_dict_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/score_dict.json + test_labels_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/test_labels.json + train_labels_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/train_labels.json + model_dir: models + name: a2b8aa20917bd043a089865ee4d809fc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3eb5d065013ed9b93f9cce9b5f96d9cc + trainer: + kwargs: {} +name: a2b8aa20917bd043a089865ee4d809fc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/params.yaml b/examples/security/classification/output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/params.yaml new file mode 100644 index 00000000..e0f74387 --- /dev/null +++ b/examples/security/classification/output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/adv_predictions.json + adv_probabilities_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/da9523d62fdfe47040de5a5852555681.pkl + params_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/params.yaml + predictions_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/predictions.json + probabilities_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/probabilities.json + score_dict_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/score_dict.json + test_labels_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/test_labels.json + train_labels_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/train_labels.json + model_dir: models + name: a2c0e27c6d1f98b109018d7cc178b806 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ff6372096509e2a0e45c49e0ced61d8b + trainer: + kwargs: {} +name: a2c0e27c6d1f98b109018d7cc178b806 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/params.yaml b/examples/security/classification/output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/params.yaml new file mode 100644 index 00000000..55849e69 --- /dev/null +++ b/examples/security/classification/output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/adv_predictions.json + adv_probabilities_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/7391b6f6969596b8bc1b5c911f42ef3d.pkl + params_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/params.yaml + predictions_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/predictions.json + probabilities_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/probabilities.json + score_dict_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/score_dict.json + test_labels_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/test_labels.json + train_labels_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/train_labels.json + model_dir: models + name: a2f6bfb1b851055e1d9f27e3e72d38c3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fabdcfde252523b0fc0e03226977a3ac + trainer: + kwargs: {} +name: a2f6bfb1b851055e1d9f27e3e72d38c3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/params.yaml b/examples/security/classification/output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/params.yaml new file mode 100644 index 00000000..c2961e5e --- /dev/null +++ b/examples/security/classification/output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/adv_predictions.json + adv_probabilities_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/925348a69c80547ed58ae97323083a82.pkl + params_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/params.yaml + predictions_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/predictions.json + probabilities_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/probabilities.json + score_dict_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/score_dict.json + test_labels_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/test_labels.json + train_labels_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/train_labels.json + model_dir: models + name: a302d253f1e2b7e06db79149ce65ad7f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 50da4439d4ce9ff4cbb92ded2cc05b97 + trainer: + kwargs: {} +name: a302d253f1e2b7e06db79149ce65ad7f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a327f6e43c0f4ccf31e861155496307b/params.yaml b/examples/security/classification/output/reports/train/a327f6e43c0f4ccf31e861155496307b/params.yaml new file mode 100644 index 00000000..96eef54f --- /dev/null +++ b/examples/security/classification/output/reports/train/a327f6e43c0f4ccf31e861155496307b/params.yaml @@ -0,0 +1,104 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/adv_predictions.json + adv_probabilities_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/ed05a3455871d532f0918e6f2f15ec96.pkl + params_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/params.yaml + predictions_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/predictions.json + probabilities_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/probabilities.json + score_dict_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/score_dict.json + test_labels_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/test_labels.json + train_labels_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/train_labels.json + model_dir: models + name: a327f6e43c0f4ccf31e861155496307b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: + - linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 951cedde8157a1d38083d3f155303db4 + trainer: + kwargs: {} +name: a327f6e43c0f4ccf31e861155496307b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/params.yaml b/examples/security/classification/output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/params.yaml new file mode 100644 index 00000000..889d3671 --- /dev/null +++ b/examples/security/classification/output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/adv_predictions.json + adv_probabilities_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/a5779a5604e93aca48d56e8ec586eb15.pkl + params_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/params.yaml + predictions_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/predictions.json + probabilities_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/probabilities.json + score_dict_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/score_dict.json + test_labels_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/test_labels.json + train_labels_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/train_labels.json + model_dir: models + name: a34095aa51e2ecff74cf0b9e4c5f6949 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1d9f0041fce6e0a5bfa14376b0b518e0 + trainer: + kwargs: {} +name: a34095aa51e2ecff74cf0b9e4c5f6949 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a340d3528f81cada226f7a6b7941509e/params.yaml b/examples/security/classification/output/reports/train/a340d3528f81cada226f7a6b7941509e/params.yaml new file mode 100644 index 00000000..55342ad2 --- /dev/null +++ b/examples/security/classification/output/reports/train/a340d3528f81cada226f7a6b7941509e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/adv_predictions.json + adv_probabilities_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/0ead9614d60a01face1c47904a909633.pkl + params_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/params.yaml + predictions_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/predictions.json + probabilities_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/probabilities.json + score_dict_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/score_dict.json + test_labels_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/test_labels.json + train_labels_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/train_labels.json + model_dir: models + name: a340d3528f81cada226f7a6b7941509e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ed5aaccf2f20fa51a641e60ed6ebeb7 + trainer: + kwargs: {} +name: a340d3528f81cada226f7a6b7941509e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a3553091d0fe90edf17ff73110d96eff/params.yaml b/examples/security/classification/output/reports/train/a3553091d0fe90edf17ff73110d96eff/params.yaml new file mode 100644 index 00000000..9c661f9c --- /dev/null +++ b/examples/security/classification/output/reports/train/a3553091d0fe90edf17ff73110d96eff/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/adv_predictions.json + adv_probabilities_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/ffe183ee9b4da3bd6c216ae837e38a85.pkl + params_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/params.yaml + predictions_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/predictions.json + probabilities_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/probabilities.json + score_dict_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/score_dict.json + test_labels_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/test_labels.json + train_labels_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/train_labels.json + model_dir: models + name: a3553091d0fe90edf17ff73110d96eff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 08acb41df95fa7bb575f1182b7fe26d4 + trainer: + kwargs: {} +name: a3553091d0fe90edf17ff73110d96eff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/params.yaml b/examples/security/classification/output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/params.yaml new file mode 100644 index 00000000..70a0fe2b --- /dev/null +++ b/examples/security/classification/output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/adv_predictions.json + adv_probabilities_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/94cc6d62be8453e29bf70c4ef896e739.pkl + params_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/params.yaml + predictions_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/predictions.json + probabilities_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/probabilities.json + score_dict_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/score_dict.json + test_labels_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/test_labels.json + train_labels_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/train_labels.json + model_dir: models + name: a35bd0d7fa78b1b8b89458ae45c9977d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f1022172fe3d7fd3a0dfe479d98248b4 + trainer: + kwargs: {} +name: a35bd0d7fa78b1b8b89458ae45c9977d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a367434727f2771d22f737f5fecd1af5/params.yaml b/examples/security/classification/output/reports/train/a367434727f2771d22f737f5fecd1af5/params.yaml new file mode 100644 index 00000000..c866fcdb --- /dev/null +++ b/examples/security/classification/output/reports/train/a367434727f2771d22f737f5fecd1af5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/adv_predictions.json + adv_probabilities_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/eab360aa3177b15f60e15a7343771648.pkl + params_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/params.yaml + predictions_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/predictions.json + probabilities_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/probabilities.json + score_dict_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/score_dict.json + test_labels_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/test_labels.json + train_labels_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/train_labels.json + model_dir: models + name: a367434727f2771d22f737f5fecd1af5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 092123bae25316579bcdd50fcb4be105 + trainer: + kwargs: {} +name: a367434727f2771d22f737f5fecd1af5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/params.yaml b/examples/security/classification/output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/params.yaml new file mode 100644 index 00000000..53ac1b06 --- /dev/null +++ b/examples/security/classification/output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3eeeae4f5fc7322ec26e3067eea6ac63 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a69fe21d7cc8b85a2791967c48b96352 + trainer: + kwargs: {} + name: ace9393f8458a8f5c55ab4c835676ac0 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/adv_predictions.json + adv_probabilities_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/adv_probabilities.json + attack_file: output/attacks/8430e3b494eb9233138cd46931395ede.pkl + data_file: output/data/4281f4ac2b8c4bfb618284c4afa4857c.pkl + model_file: output/models/1c62baf594b27c80b7716bea3348f279.pkl + params_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/params.yaml + predictions_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/predictions.json + probabilities_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/probabilities.json + score_dict_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/score_dict.json + test_labels_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/test_labels.json + train_labels_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/train_labels.json + model_dir: models + name: a39faaa1e6cdc3e9d0e24cfba24d9a06 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8798e0a8d1c5c1910f93a39d684af46f + trainer: + kwargs: {} +name: a39faaa1e6cdc3e9d0e24cfba24d9a06 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/params.yaml b/examples/security/classification/output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/params.yaml new file mode 100644 index 00000000..97ada161 --- /dev/null +++ b/examples/security/classification/output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/adv_predictions.json + adv_probabilities_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/cbc5819cef35e9f592fcdef5447930ae.pkl + params_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/params.yaml + predictions_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/predictions.json + probabilities_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/probabilities.json + score_dict_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/score_dict.json + test_labels_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/test_labels.json + train_labels_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/train_labels.json + model_dir: models + name: a3a3cfaf325c806ae0ef0f062ecb732f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f5e5ef4a3bc94b313378e21abe04f509 + trainer: + kwargs: {} +name: a3a3cfaf325c806ae0ef0f062ecb732f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/params.yaml b/examples/security/classification/output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/params.yaml new file mode 100644 index 00000000..e07d7b3b --- /dev/null +++ b/examples/security/classification/output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/adv_predictions.json + adv_probabilities_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/3432c8a4516eac96385d732649fbf6d5.pkl + params_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/params.yaml + predictions_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/predictions.json + probabilities_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/probabilities.json + score_dict_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/score_dict.json + test_labels_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/test_labels.json + train_labels_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/train_labels.json + model_dir: models + name: a3b45a725e6605c4acb91d4c4d8073db + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4acaef160fbd047e48a994b3d453b635 + trainer: + kwargs: {} +name: a3b45a725e6605c4acb91d4c4d8073db +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a3c05ef1bb8242338a28165480ec06bd/params.yaml b/examples/security/classification/output/reports/train/a3c05ef1bb8242338a28165480ec06bd/params.yaml new file mode 100644 index 00000000..aded74d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/a3c05ef1bb8242338a28165480ec06bd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/adv_predictions.json + adv_probabilities_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/0e4f0b19e664ab6bf5d18e93278fa649.pkl + params_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/params.yaml + predictions_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/predictions.json + probabilities_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/probabilities.json + score_dict_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/score_dict.json + test_labels_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/test_labels.json + train_labels_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/train_labels.json + model_dir: models + name: a3c05ef1bb8242338a28165480ec06bd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 74fc16aafe77610355cefd5db5abbbe4 + trainer: + kwargs: {} +name: a3c05ef1bb8242338a28165480ec06bd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/params.yaml b/examples/security/classification/output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/params.yaml new file mode 100644 index 00000000..a192fb7f --- /dev/null +++ b/examples/security/classification/output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/adv_predictions.json + adv_probabilities_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/136993d858875e71d6b8317fafb05357.pkl + params_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/params.yaml + predictions_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/predictions.json + probabilities_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/probabilities.json + score_dict_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/score_dict.json + test_labels_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/test_labels.json + train_labels_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/train_labels.json + model_dir: models + name: a3d0da6b0acd8f1c2b8da89d1d4eaf26 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5a43c0e47baa49eaa7dae413fa9049fd + trainer: + kwargs: {} +name: a3d0da6b0acd8f1c2b8da89d1d4eaf26 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/params.yaml b/examples/security/classification/output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/params.yaml new file mode 100644 index 00000000..ac65a3b5 --- /dev/null +++ b/examples/security/classification/output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/adv_predictions.json + adv_probabilities_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/7d06fb50ee4d5d0c06f34c873a7c5b63.pkl + params_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/params.yaml + predictions_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/predictions.json + probabilities_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/probabilities.json + score_dict_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/score_dict.json + test_labels_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/test_labels.json + train_labels_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/train_labels.json + model_dir: models + name: a3f701da8abc78ebda57788ba4cd3ce8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cbdc91e8a5ee3e35172323fe01a34e84 + trainer: + kwargs: {} +name: a3f701da8abc78ebda57788ba4cd3ce8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a40209a639c8f1b9802b339bd4aee976/params.yaml b/examples/security/classification/output/reports/train/a40209a639c8f1b9802b339bd4aee976/params.yaml new file mode 100644 index 00000000..ed719248 --- /dev/null +++ b/examples/security/classification/output/reports/train/a40209a639c8f1b9802b339bd4aee976/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/adv_predictions.json + adv_probabilities_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/151dec8b8e6dfeaa970d593719a61d62.pkl + params_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/params.yaml + predictions_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/predictions.json + probabilities_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/probabilities.json + score_dict_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/score_dict.json + test_labels_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/test_labels.json + train_labels_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/train_labels.json + model_dir: models + name: a40209a639c8f1b9802b339bd4aee976 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b043ae5b7a3bcc9d6f9373840c217df + trainer: + kwargs: {} +name: a40209a639c8f1b9802b339bd4aee976 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a4399e9abf80267200a6cb4c70b9015e/params.yaml b/examples/security/classification/output/reports/train/a4399e9abf80267200a6cb4c70b9015e/params.yaml new file mode 100644 index 00000000..5cd565a5 --- /dev/null +++ b/examples/security/classification/output/reports/train/a4399e9abf80267200a6cb4c70b9015e/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/adv_predictions.json + adv_probabilities_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/ca71410d6032e1a943220e389c88f3cc.pkl + params_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/params.yaml + predictions_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/predictions.json + probabilities_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/probabilities.json + score_dict_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/score_dict.json + test_labels_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/test_labels.json + train_labels_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/train_labels.json + model_dir: models + name: a4399e9abf80267200a6cb4c70b9015e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8b916f385679c37eaba4eab2f7b1a15e + trainer: + kwargs: {} +name: a4399e9abf80267200a6cb4c70b9015e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a43a3246f7e2292ff2d7ee530312e507/params.yaml b/examples/security/classification/output/reports/train/a43a3246f7e2292ff2d7ee530312e507/params.yaml new file mode 100644 index 00000000..644f9409 --- /dev/null +++ b/examples/security/classification/output/reports/train/a43a3246f7e2292ff2d7ee530312e507/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/adv_predictions.json + adv_probabilities_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/524e7ba04781956148b3de1a1180952e.pkl + params_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/params.yaml + predictions_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/predictions.json + probabilities_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/probabilities.json + score_dict_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/score_dict.json + test_labels_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/test_labels.json + train_labels_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/train_labels.json + model_dir: models + name: a43a3246f7e2292ff2d7ee530312e507 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 257f89c70c95f9d619297ddb7c6fbd6c + trainer: + kwargs: {} +name: a43a3246f7e2292ff2d7ee530312e507 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a446a0f192a2f8468721e718b1dad0a9/params.yaml b/examples/security/classification/output/reports/train/a446a0f192a2f8468721e718b1dad0a9/params.yaml new file mode 100644 index 00000000..ed359ee4 --- /dev/null +++ b/examples/security/classification/output/reports/train/a446a0f192a2f8468721e718b1dad0a9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/adv_predictions.json + adv_probabilities_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/6a92f6ddc766c392d5006fba35129f66.pkl + params_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/params.yaml + predictions_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/predictions.json + probabilities_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/probabilities.json + score_dict_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/score_dict.json + test_labels_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/test_labels.json + train_labels_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/train_labels.json + model_dir: models + name: a446a0f192a2f8468721e718b1dad0a9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 36a1e48deb3a85b7bcd795d629364807 + trainer: + kwargs: {} +name: a446a0f192a2f8468721e718b1dad0a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/params.yaml b/examples/security/classification/output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/params.yaml new file mode 100644 index 00000000..6a01fa2f --- /dev/null +++ b/examples/security/classification/output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/adv_predictions.json + adv_probabilities_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/9cb3f7464c73407390ecb644771a80f1.pkl + params_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/params.yaml + predictions_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/predictions.json + probabilities_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/probabilities.json + score_dict_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/score_dict.json + test_labels_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/test_labels.json + train_labels_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/train_labels.json + model_dir: models + name: a464c0c3cfb5fb9e3f0a0e65b516409c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3cd86c2ddeb0d6aa51d0691567f3fa2 + trainer: + kwargs: {} +name: a464c0c3cfb5fb9e3f0a0e65b516409c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a47b148dfdfa93407771b5665e46c361/params.yaml b/examples/security/classification/output/reports/train/a47b148dfdfa93407771b5665e46c361/params.yaml new file mode 100644 index 00000000..eea566b0 --- /dev/null +++ b/examples/security/classification/output/reports/train/a47b148dfdfa93407771b5665e46c361/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/adv_predictions.json + adv_probabilities_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/0141751b4587a9b64962b4a12c5ff7de.pkl + params_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/params.yaml + predictions_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/predictions.json + probabilities_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/probabilities.json + score_dict_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/score_dict.json + test_labels_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/test_labels.json + train_labels_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/train_labels.json + model_dir: models + name: a47b148dfdfa93407771b5665e46c361 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ccf3eb197dff2bc9ed4f3850c4b69546 + trainer: + kwargs: {} +name: a47b148dfdfa93407771b5665e46c361 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a48be9862a024a19e56c397ed67123a6/params.yaml b/examples/security/classification/output/reports/train/a48be9862a024a19e56c397ed67123a6/params.yaml new file mode 100644 index 00000000..9e6a3d4e --- /dev/null +++ b/examples/security/classification/output/reports/train/a48be9862a024a19e56c397ed67123a6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/adv_predictions.json + adv_probabilities_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/af4ae425d89ec508b08e3605e07c0a79.pkl + params_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/params.yaml + predictions_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/predictions.json + probabilities_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/probabilities.json + score_dict_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/score_dict.json + test_labels_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/test_labels.json + train_labels_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/train_labels.json + model_dir: models + name: a48be9862a024a19e56c397ed67123a6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b115083e0c5a9f7466e4d2bce9f12a8b + trainer: + kwargs: {} +name: a48be9862a024a19e56c397ed67123a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/params.yaml b/examples/security/classification/output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/params.yaml new file mode 100644 index 00000000..670d42c2 --- /dev/null +++ b/examples/security/classification/output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/adv_predictions.json + adv_probabilities_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/aa3cf523688cc49aadf9cd0c6e198a96.pkl + params_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/params.yaml + predictions_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/predictions.json + probabilities_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/probabilities.json + score_dict_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/score_dict.json + test_labels_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/test_labels.json + train_labels_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/train_labels.json + model_dir: models + name: a4a3679e567a52a60eacf24e06ca11f9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cfca503d60295e6028fdcfd164fc236d + trainer: + kwargs: {} +name: a4a3679e567a52a60eacf24e06ca11f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a4af362d9370232375e913bbc33eaff7/params.yaml b/examples/security/classification/output/reports/train/a4af362d9370232375e913bbc33eaff7/params.yaml new file mode 100644 index 00000000..9452be51 --- /dev/null +++ b/examples/security/classification/output/reports/train/a4af362d9370232375e913bbc33eaff7/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/adv_predictions.json + adv_probabilities_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/759c62a9b3e11b483d575b8286364ac9.pkl + params_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/params.yaml + predictions_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/predictions.json + probabilities_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/probabilities.json + score_dict_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/score_dict.json + test_labels_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/test_labels.json + train_labels_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/train_labels.json + model_dir: models + name: a4af362d9370232375e913bbc33eaff7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6cc68e50193c3bac08d756a94ba6eafb + trainer: + kwargs: {} +name: a4af362d9370232375e913bbc33eaff7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a50603e45461cb16ffea69e5997cef65/params.yaml b/examples/security/classification/output/reports/train/a50603e45461cb16ffea69e5997cef65/params.yaml new file mode 100644 index 00000000..79db3fc7 --- /dev/null +++ b/examples/security/classification/output/reports/train/a50603e45461cb16ffea69e5997cef65/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/adv_predictions.json + adv_probabilities_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/5ca040b6cd0e67143a4c14ba70eb0162.pkl + params_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/params.yaml + predictions_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/predictions.json + probabilities_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/probabilities.json + score_dict_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/score_dict.json + test_labels_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/test_labels.json + train_labels_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/train_labels.json + model_dir: models + name: a50603e45461cb16ffea69e5997cef65 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4339bbbe124cdd7d798dafa9ac3fdf85 + trainer: + kwargs: {} +name: a50603e45461cb16ffea69e5997cef65 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/params.yaml b/examples/security/classification/output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/params.yaml new file mode 100644 index 00000000..9e3dce4d --- /dev/null +++ b/examples/security/classification/output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/adv_predictions.json + adv_probabilities_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/092d3da2d867f3fdba6d8bec0c0af86f.pkl + params_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/params.yaml + predictions_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/predictions.json + probabilities_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/probabilities.json + score_dict_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/score_dict.json + test_labels_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/test_labels.json + train_labels_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/train_labels.json + model_dir: models + name: a50b41cda1c78b76114ffbc1458e9cc2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 84126a9bb30d27f3d6048fd0ea9b744c + trainer: + kwargs: {} +name: a50b41cda1c78b76114ffbc1458e9cc2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/params.yaml b/examples/security/classification/output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/params.yaml new file mode 100644 index 00000000..b90caf06 --- /dev/null +++ b/examples/security/classification/output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/adv_predictions.json + adv_probabilities_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/720add5effc700cd5c5a9a60df0916fa.pkl + params_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/params.yaml + predictions_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/predictions.json + probabilities_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/probabilities.json + score_dict_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/score_dict.json + test_labels_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/test_labels.json + train_labels_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/train_labels.json + model_dir: models + name: a5454f961ccda6dad2f1006d9fa7dfca + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d21f6ffa4830dfe52728b88e653e74c8 + trainer: + kwargs: {} +name: a5454f961ccda6dad2f1006d9fa7dfca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/params.yaml b/examples/security/classification/output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/params.yaml new file mode 100644 index 00000000..efc1f9ed --- /dev/null +++ b/examples/security/classification/output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/adv_predictions.json + adv_probabilities_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/d9daf8569341dd3a8bb21f3ec744ee92.pkl + params_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/params.yaml + predictions_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/predictions.json + probabilities_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/probabilities.json + score_dict_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/score_dict.json + test_labels_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/test_labels.json + train_labels_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/train_labels.json + model_dir: models + name: a57cc3f1d7d1b449e7085f11ad758388 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 985b12fd395b9ecdf1f1c2136a124b01 + trainer: + kwargs: {} +name: a57cc3f1d7d1b449e7085f11ad758388 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a58d7415045358e7190c0cdebd9a7a03/params.yaml b/examples/security/classification/output/reports/train/a58d7415045358e7190c0cdebd9a7a03/params.yaml new file mode 100644 index 00000000..cefbe162 --- /dev/null +++ b/examples/security/classification/output/reports/train/a58d7415045358e7190c0cdebd9a7a03/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/adv_predictions.json + adv_probabilities_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/cee0227242b313ecfe614991eeda705d.pkl + params_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/params.yaml + predictions_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/predictions.json + probabilities_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/probabilities.json + score_dict_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/score_dict.json + test_labels_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/test_labels.json + train_labels_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/train_labels.json + model_dir: models + name: a58d7415045358e7190c0cdebd9a7a03 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dca1c7aa71af2599178915fba22bf2ec + trainer: + kwargs: {} +name: a58d7415045358e7190c0cdebd9a7a03 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a590a282d42d532d14187fc29996b60e/params.yaml b/examples/security/classification/output/reports/train/a590a282d42d532d14187fc29996b60e/params.yaml new file mode 100644 index 00000000..26187ad5 --- /dev/null +++ b/examples/security/classification/output/reports/train/a590a282d42d532d14187fc29996b60e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a590a282d42d532d14187fc29996b60e/adv_predictions.json + adv_probabilities_file: output/reports/train/a590a282d42d532d14187fc29996b60e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/ee362c02a35dcf5c8401a8f0d29fcf3e.pkl + params_file: output/reports/train/a590a282d42d532d14187fc29996b60e/params.yaml + predictions_file: output/reports/train/a590a282d42d532d14187fc29996b60e/predictions.json + probabilities_file: output/reports/train/a590a282d42d532d14187fc29996b60e/probabilities.json + score_dict_file: output/reports/train/a590a282d42d532d14187fc29996b60e/score_dict.json + test_labels_file: output/reports/train/a590a282d42d532d14187fc29996b60e/test_labels.json + train_labels_file: output/reports/train/a590a282d42d532d14187fc29996b60e/train_labels.json + model_dir: models + name: a590a282d42d532d14187fc29996b60e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 84155e03ece1858457c60b1ab5eb5147 + trainer: + kwargs: {} +name: a590a282d42d532d14187fc29996b60e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a59a0b126477bb9dc19a74eac3c17770/params.yaml b/examples/security/classification/output/reports/train/a59a0b126477bb9dc19a74eac3c17770/params.yaml new file mode 100644 index 00000000..ed2df8bc --- /dev/null +++ b/examples/security/classification/output/reports/train/a59a0b126477bb9dc19a74eac3c17770/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/adv_predictions.json + adv_probabilities_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/a1350091b7cce3e0165c89d840b97d42.pkl + params_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/params.yaml + predictions_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/predictions.json + probabilities_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/probabilities.json + score_dict_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/score_dict.json + test_labels_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/test_labels.json + train_labels_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/train_labels.json + model_dir: models + name: a59a0b126477bb9dc19a74eac3c17770 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: df06b1354b2ab4dfddf020ccf524e346 + trainer: + kwargs: {} +name: a59a0b126477bb9dc19a74eac3c17770 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/params.yaml b/examples/security/classification/output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/params.yaml new file mode 100644 index 00000000..22d34c09 --- /dev/null +++ b/examples/security/classification/output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/adv_predictions.json + adv_probabilities_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/35613382351401f2c26144d7853ae450.pkl + params_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/params.yaml + predictions_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/predictions.json + probabilities_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/probabilities.json + score_dict_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/score_dict.json + test_labels_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/test_labels.json + train_labels_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/train_labels.json + model_dir: models + name: a5a43ac8a2e3829fc4a1c9b2f66b803e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 63a7391f584952bad4ad45a4db6ab7ac + trainer: + kwargs: {} +name: a5a43ac8a2e3829fc4a1c9b2f66b803e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a5a559791364c3698f084d345e7553d1/params.yaml b/examples/security/classification/output/reports/train/a5a559791364c3698f084d345e7553d1/params.yaml new file mode 100644 index 00000000..8abcb8b6 --- /dev/null +++ b/examples/security/classification/output/reports/train/a5a559791364c3698f084d345e7553d1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a5a559791364c3698f084d345e7553d1/adv_predictions.json + adv_probabilities_file: output/reports/train/a5a559791364c3698f084d345e7553d1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/110f67521597f34bc230fdb2908ef26b.pkl + params_file: output/reports/train/a5a559791364c3698f084d345e7553d1/params.yaml + predictions_file: output/reports/train/a5a559791364c3698f084d345e7553d1/predictions.json + probabilities_file: output/reports/train/a5a559791364c3698f084d345e7553d1/probabilities.json + score_dict_file: output/reports/train/a5a559791364c3698f084d345e7553d1/score_dict.json + test_labels_file: output/reports/train/a5a559791364c3698f084d345e7553d1/test_labels.json + train_labels_file: output/reports/train/a5a559791364c3698f084d345e7553d1/train_labels.json + model_dir: models + name: a5a559791364c3698f084d345e7553d1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0cbda44821e2cc029426f325fba74c3a + trainer: + kwargs: {} +name: a5a559791364c3698f084d345e7553d1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/params.yaml b/examples/security/classification/output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/params.yaml new file mode 100644 index 00000000..d30a35ad --- /dev/null +++ b/examples/security/classification/output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/adv_predictions.json + adv_probabilities_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/67e2bd0454ca0ec954d0cc412cbddf49.pkl + params_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/params.yaml + predictions_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/predictions.json + probabilities_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/probabilities.json + score_dict_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/score_dict.json + test_labels_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/test_labels.json + train_labels_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/train_labels.json + model_dir: models + name: a5b2b557d0ecc7f7d43d87a036743989 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 67f7552d549ee2f7224e24895a3a4d76 + trainer: + kwargs: {} +name: a5b2b557d0ecc7f7d43d87a036743989 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/params.yaml b/examples/security/classification/output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/params.yaml new file mode 100644 index 00000000..a7034d62 --- /dev/null +++ b/examples/security/classification/output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/adv_predictions.json + adv_probabilities_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/48e2b378c50b4adc38fe0c032c086790.pkl + params_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/params.yaml + predictions_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/predictions.json + probabilities_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/probabilities.json + score_dict_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/score_dict.json + test_labels_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/test_labels.json + train_labels_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/train_labels.json + model_dir: models + name: a5ce95b9c844c40cd05b94af46fd4a45 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8c38771c2672bc8e0a23c64ed455f1bf + trainer: + kwargs: {} +name: a5ce95b9c844c40cd05b94af46fd4a45 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a61870c04d57efcdbc23558d37c319c9/params.yaml b/examples/security/classification/output/reports/train/a61870c04d57efcdbc23558d37c319c9/params.yaml new file mode 100644 index 00000000..95d53af8 --- /dev/null +++ b/examples/security/classification/output/reports/train/a61870c04d57efcdbc23558d37c319c9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/adv_predictions.json + adv_probabilities_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/2205789959f1f7cc3130ac6413ddba1b.pkl + params_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/params.yaml + predictions_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/predictions.json + probabilities_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/probabilities.json + score_dict_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/score_dict.json + test_labels_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/test_labels.json + train_labels_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/train_labels.json + model_dir: models + name: a61870c04d57efcdbc23558d37c319c9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2a57c3767cd8b6f208318b9ee63a053f + trainer: + kwargs: {} +name: a61870c04d57efcdbc23558d37c319c9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a66f073278b10e184d5e492effb45266/params.yaml b/examples/security/classification/output/reports/train/a66f073278b10e184d5e492effb45266/params.yaml new file mode 100644 index 00000000..fe2e1858 --- /dev/null +++ b/examples/security/classification/output/reports/train/a66f073278b10e184d5e492effb45266/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a66f073278b10e184d5e492effb45266/adv_predictions.json + adv_probabilities_file: output/reports/train/a66f073278b10e184d5e492effb45266/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/f48602affc7f92289a0a0081da354481.pkl + params_file: output/reports/train/a66f073278b10e184d5e492effb45266/params.yaml + predictions_file: output/reports/train/a66f073278b10e184d5e492effb45266/predictions.json + probabilities_file: output/reports/train/a66f073278b10e184d5e492effb45266/probabilities.json + score_dict_file: output/reports/train/a66f073278b10e184d5e492effb45266/score_dict.json + test_labels_file: output/reports/train/a66f073278b10e184d5e492effb45266/test_labels.json + train_labels_file: output/reports/train/a66f073278b10e184d5e492effb45266/train_labels.json + model_dir: models + name: a66f073278b10e184d5e492effb45266 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f2e1349ef54b2f4dad4d080bc5ac406 + trainer: + kwargs: {} +name: a66f073278b10e184d5e492effb45266 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/params.yaml b/examples/security/classification/output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/params.yaml new file mode 100644 index 00000000..cd5a98eb --- /dev/null +++ b/examples/security/classification/output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/adv_predictions.json + adv_probabilities_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/3309adebcce2315332f7eb777d472cdd.pkl + params_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/params.yaml + predictions_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/predictions.json + probabilities_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/probabilities.json + score_dict_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/score_dict.json + test_labels_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/test_labels.json + train_labels_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/train_labels.json + model_dir: models + name: a67b9eeccf5f7b56ffb454a8c2fbb264 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b99ebb38bafc697d665b010af1bf3dc2 + trainer: + kwargs: {} +name: a67b9eeccf5f7b56ffb454a8c2fbb264 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a687227e8c474a8f0a26be341c559140/params.yaml b/examples/security/classification/output/reports/train/a687227e8c474a8f0a26be341c559140/params.yaml new file mode 100644 index 00000000..6d8fcb96 --- /dev/null +++ b/examples/security/classification/output/reports/train/a687227e8c474a8f0a26be341c559140/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a687227e8c474a8f0a26be341c559140/adv_predictions.json + adv_probabilities_file: output/reports/train/a687227e8c474a8f0a26be341c559140/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/c6063ee0f3d4059e0026543c92fc88f8.pkl + params_file: output/reports/train/a687227e8c474a8f0a26be341c559140/params.yaml + predictions_file: output/reports/train/a687227e8c474a8f0a26be341c559140/predictions.json + probabilities_file: output/reports/train/a687227e8c474a8f0a26be341c559140/probabilities.json + score_dict_file: output/reports/train/a687227e8c474a8f0a26be341c559140/score_dict.json + test_labels_file: output/reports/train/a687227e8c474a8f0a26be341c559140/test_labels.json + train_labels_file: output/reports/train/a687227e8c474a8f0a26be341c559140/train_labels.json + model_dir: models + name: a687227e8c474a8f0a26be341c559140 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 09c19827dbd5fab2781ee9be302987d4 + trainer: + kwargs: {} +name: a687227e8c474a8f0a26be341c559140 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a6b406419d3aad5f462c0af9e4faf132/params.yaml b/examples/security/classification/output/reports/train/a6b406419d3aad5f462c0af9e4faf132/params.yaml new file mode 100644 index 00000000..9a93453f --- /dev/null +++ b/examples/security/classification/output/reports/train/a6b406419d3aad5f462c0af9e4faf132/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/adv_predictions.json + adv_probabilities_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/c5516a3931a4a555a8399684a5c9f2c5.pkl + params_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/params.yaml + predictions_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/predictions.json + probabilities_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/probabilities.json + score_dict_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/score_dict.json + test_labels_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/test_labels.json + train_labels_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/train_labels.json + model_dir: models + name: a6b406419d3aad5f462c0af9e4faf132 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 21c15b6e58f66f81e27e8d0a8bfb7ec8 + trainer: + kwargs: {} +name: a6b406419d3aad5f462c0af9e4faf132 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a6c99e07d24a1762aada188e838734e9/params.yaml b/examples/security/classification/output/reports/train/a6c99e07d24a1762aada188e838734e9/params.yaml new file mode 100644 index 00000000..1c7744f1 --- /dev/null +++ b/examples/security/classification/output/reports/train/a6c99e07d24a1762aada188e838734e9/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/adv_predictions.json + adv_probabilities_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/9287295517711c9a033c2c1723f23b2b.pkl + params_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/params.yaml + predictions_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/predictions.json + probabilities_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/probabilities.json + score_dict_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/score_dict.json + test_labels_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/test_labels.json + train_labels_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/train_labels.json + model_dir: models + name: a6c99e07d24a1762aada188e838734e9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8fb98cacbc283dedec469261149c319f + trainer: + kwargs: {} +name: a6c99e07d24a1762aada188e838734e9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/params.yaml b/examples/security/classification/output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/params.yaml new file mode 100644 index 00000000..d48ce5cb --- /dev/null +++ b/examples/security/classification/output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/adv_predictions.json + adv_probabilities_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e990daeb2ca22f098f9df7c420790721.pkl + params_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/params.yaml + predictions_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/predictions.json + probabilities_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/probabilities.json + score_dict_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/score_dict.json + test_labels_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/test_labels.json + train_labels_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/train_labels.json + model_dir: models + name: a709b5b3e2ca20ac12a6bc30d12fbf7a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 1 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: def533b60f4a54e38b79728d65e2af84 + trainer: + kwargs: {} +name: a709b5b3e2ca20ac12a6bc30d12fbf7a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a71451127e480314bcfc1e31b40c7985/params.yaml b/examples/security/classification/output/reports/train/a71451127e480314bcfc1e31b40c7985/params.yaml new file mode 100644 index 00000000..579a270f --- /dev/null +++ b/examples/security/classification/output/reports/train/a71451127e480314bcfc1e31b40c7985/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/adv_predictions.json + adv_probabilities_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/19498bfed45301d54bb07f252235e2c2.pkl + params_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/params.yaml + predictions_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/predictions.json + probabilities_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/probabilities.json + score_dict_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/score_dict.json + test_labels_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/test_labels.json + train_labels_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/train_labels.json + model_dir: models + name: a71451127e480314bcfc1e31b40c7985 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c4007bb6b0d6d16b3e8a1a6d02603421 + trainer: + kwargs: {} +name: a71451127e480314bcfc1e31b40c7985 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/params.yaml b/examples/security/classification/output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/params.yaml new file mode 100644 index 00000000..87542f7b --- /dev/null +++ b/examples/security/classification/output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/adv_predictions.json + adv_probabilities_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/40bf6d973462a9f1579b97074a209afa.pkl + params_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/params.yaml + predictions_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/predictions.json + probabilities_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/probabilities.json + score_dict_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/score_dict.json + test_labels_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/test_labels.json + train_labels_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/train_labels.json + model_dir: models + name: a754c9b68bce4b7ab00ec8ba4f1d3f88 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0ba4b9217dc923d089011b8bc1651608 + trainer: + kwargs: {} +name: a754c9b68bce4b7ab00ec8ba4f1d3f88 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a76addfb000e1970b56aaeb6532e1932/params.yaml b/examples/security/classification/output/reports/train/a76addfb000e1970b56aaeb6532e1932/params.yaml new file mode 100644 index 00000000..c038c395 --- /dev/null +++ b/examples/security/classification/output/reports/train/a76addfb000e1970b56aaeb6532e1932/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/adv_predictions.json + adv_probabilities_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/205c188ace7fe6514d92b37a2a9e0387.pkl + params_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/params.yaml + predictions_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/predictions.json + probabilities_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/probabilities.json + score_dict_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/score_dict.json + test_labels_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/test_labels.json + train_labels_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/train_labels.json + model_dir: models + name: a76addfb000e1970b56aaeb6532e1932 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bd17164bfedbba2ad4289ced8b516d4a + trainer: + kwargs: {} +name: a76addfb000e1970b56aaeb6532e1932 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/params.yaml b/examples/security/classification/output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/params.yaml new file mode 100644 index 00000000..80b45c04 --- /dev/null +++ b/examples/security/classification/output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/adv_predictions.json + adv_probabilities_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/f639b089f87671a118eb7dca2742b6d2.pkl + params_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/params.yaml + predictions_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/predictions.json + probabilities_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/probabilities.json + score_dict_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/score_dict.json + test_labels_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/test_labels.json + train_labels_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/train_labels.json + model_dir: models + name: a78f2cdbef28bc4a4d9b4999ab6c57b7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 237526123672c99c4c3c6a522ad18400 + trainer: + kwargs: {} +name: a78f2cdbef28bc4a4d9b4999ab6c57b7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/params.yaml b/examples/security/classification/output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/params.yaml new file mode 100644 index 00000000..6b4dd69b --- /dev/null +++ b/examples/security/classification/output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/adv_predictions.json + adv_probabilities_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/5c6e1ad97e9d3cd04e00fd54c572cbe6.pkl + params_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/params.yaml + predictions_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/predictions.json + probabilities_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/probabilities.json + score_dict_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/score_dict.json + test_labels_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/test_labels.json + train_labels_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/train_labels.json + model_dir: models + name: a79cf7f082b3175dcd3e6dcd266e035e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c641d78c08711e6bb2de7bd459736ed1 + trainer: + kwargs: {} +name: a79cf7f082b3175dcd3e6dcd266e035e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/params.yaml b/examples/security/classification/output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/params.yaml new file mode 100644 index 00000000..e38ace92 --- /dev/null +++ b/examples/security/classification/output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/adv_predictions.json + adv_probabilities_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/977ca69c164a0c9262882270e9a6436e.pkl + params_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/params.yaml + predictions_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/predictions.json + probabilities_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/probabilities.json + score_dict_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/score_dict.json + test_labels_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/test_labels.json + train_labels_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/train_labels.json + model_dir: models + name: a7b0d5b9853ae2bd067f63a373b24307 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ed9b2e0a03cb3c6574df42e1ba97e18f + trainer: + kwargs: {} +name: a7b0d5b9853ae2bd067f63a373b24307 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/params.yaml b/examples/security/classification/output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/params.yaml new file mode 100644 index 00000000..1b5a265d --- /dev/null +++ b/examples/security/classification/output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/adv_predictions.json + adv_probabilities_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/081b363802691c825bc6152d1ab1193f.pkl + params_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/params.yaml + predictions_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/predictions.json + probabilities_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/probabilities.json + score_dict_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/score_dict.json + test_labels_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/test_labels.json + train_labels_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/train_labels.json + model_dir: models + name: a7e1e32b8f77f6c7a6c0dee7d7ea9db0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 30b62abf7513b7dce42ce5fdcd85b9a5 + trainer: + kwargs: {} +name: a7e1e32b8f77f6c7a6c0dee7d7ea9db0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a7e7245d271d19722067ee3fb4d8f706/params.yaml b/examples/security/classification/output/reports/train/a7e7245d271d19722067ee3fb4d8f706/params.yaml new file mode 100644 index 00000000..a48cd58c --- /dev/null +++ b/examples/security/classification/output/reports/train/a7e7245d271d19722067ee3fb4d8f706/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/adv_predictions.json + adv_probabilities_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/28b510e46ec28f052d6559f9cb554979.pkl + params_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/params.yaml + predictions_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/predictions.json + probabilities_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/probabilities.json + score_dict_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/score_dict.json + test_labels_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/test_labels.json + train_labels_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/train_labels.json + model_dir: models + name: a7e7245d271d19722067ee3fb4d8f706 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1cae82d95e29b3b8bcc8a3df201d149d + trainer: + kwargs: {} +name: a7e7245d271d19722067ee3fb4d8f706 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/params.yaml b/examples/security/classification/output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/params.yaml new file mode 100644 index 00000000..ae89799a --- /dev/null +++ b/examples/security/classification/output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/adv_predictions.json + adv_probabilities_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/127c3f328f2637dc4c38e9377d38dc3e.pkl + params_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/params.yaml + predictions_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/predictions.json + probabilities_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/probabilities.json + score_dict_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/score_dict.json + test_labels_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/test_labels.json + train_labels_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/train_labels.json + model_dir: models + name: a7f3fde7da75225e24e3c66b87cdfd82 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 374e578e7556b6429dc80cf8ea26564c + trainer: + kwargs: {} +name: a7f3fde7da75225e24e3c66b87cdfd82 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a7f864155a304f14b3a2c62520ad8887/params.yaml b/examples/security/classification/output/reports/train/a7f864155a304f14b3a2c62520ad8887/params.yaml new file mode 100644 index 00000000..383c8ad7 --- /dev/null +++ b/examples/security/classification/output/reports/train/a7f864155a304f14b3a2c62520ad8887/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/adv_predictions.json + adv_probabilities_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/7d05a1687fe0392d6f468a26b8b6ae22.pkl + params_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/params.yaml + predictions_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/predictions.json + probabilities_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/probabilities.json + score_dict_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/score_dict.json + test_labels_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/test_labels.json + train_labels_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/train_labels.json + model_dir: models + name: a7f864155a304f14b3a2c62520ad8887 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bb009f71fae8a2c82e3bb8eb8a858940 + trainer: + kwargs: {} +name: a7f864155a304f14b3a2c62520ad8887 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a8135ac60cdee920ced5264287d35249/params.yaml b/examples/security/classification/output/reports/train/a8135ac60cdee920ced5264287d35249/params.yaml new file mode 100644 index 00000000..8e1a274c --- /dev/null +++ b/examples/security/classification/output/reports/train/a8135ac60cdee920ced5264287d35249/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a8135ac60cdee920ced5264287d35249/adv_predictions.json + adv_probabilities_file: output/reports/train/a8135ac60cdee920ced5264287d35249/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/897326bc85a00a55220da92fe57a4882.pkl + params_file: output/reports/train/a8135ac60cdee920ced5264287d35249/params.yaml + predictions_file: output/reports/train/a8135ac60cdee920ced5264287d35249/predictions.json + probabilities_file: output/reports/train/a8135ac60cdee920ced5264287d35249/probabilities.json + score_dict_file: output/reports/train/a8135ac60cdee920ced5264287d35249/score_dict.json + test_labels_file: output/reports/train/a8135ac60cdee920ced5264287d35249/test_labels.json + train_labels_file: output/reports/train/a8135ac60cdee920ced5264287d35249/train_labels.json + model_dir: models + name: a8135ac60cdee920ced5264287d35249 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a2e1784426d6358e1c58b7529f01cdc9 + trainer: + kwargs: {} +name: a8135ac60cdee920ced5264287d35249 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a82848ffc54e0712b820e7101bed15ab/params.yaml b/examples/security/classification/output/reports/train/a82848ffc54e0712b820e7101bed15ab/params.yaml new file mode 100644 index 00000000..7790f360 --- /dev/null +++ b/examples/security/classification/output/reports/train/a82848ffc54e0712b820e7101bed15ab/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/adv_predictions.json + adv_probabilities_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/49fe9399ba265913c97271ad4d615033.pkl + params_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/params.yaml + predictions_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/predictions.json + probabilities_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/probabilities.json + score_dict_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/score_dict.json + test_labels_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/test_labels.json + train_labels_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/train_labels.json + model_dir: models + name: a82848ffc54e0712b820e7101bed15ab + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27695bd284d2872b4de9df7a2aed6655 + trainer: + kwargs: {} +name: a82848ffc54e0712b820e7101bed15ab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a8340246d95d8c455359613540d7cbe1/params.yaml b/examples/security/classification/output/reports/train/a8340246d95d8c455359613540d7cbe1/params.yaml new file mode 100644 index 00000000..7fc2c156 --- /dev/null +++ b/examples/security/classification/output/reports/train/a8340246d95d8c455359613540d7cbe1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/adv_predictions.json + adv_probabilities_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/60d0595a497c4a4784d1b6fa8b47d087.pkl + params_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/params.yaml + predictions_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/predictions.json + probabilities_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/probabilities.json + score_dict_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/score_dict.json + test_labels_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/test_labels.json + train_labels_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/train_labels.json + model_dir: models + name: a8340246d95d8c455359613540d7cbe1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f1bf804fda6696f058690047a8be18e7 + trainer: + kwargs: {} +name: a8340246d95d8c455359613540d7cbe1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/params.yaml b/examples/security/classification/output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/params.yaml new file mode 100644 index 00000000..cca09d60 --- /dev/null +++ b/examples/security/classification/output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/adv_predictions.json + adv_probabilities_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/36ffea9eff1d9fe19d437365b868fb45.pkl + params_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/params.yaml + predictions_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/predictions.json + probabilities_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/probabilities.json + score_dict_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/score_dict.json + test_labels_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/test_labels.json + train_labels_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/train_labels.json + model_dir: models + name: a83432dafd16c5d5748a9ba6e430ef97 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 35d701b92fa9622cb2061b9a30eda505 + trainer: + kwargs: {} +name: a83432dafd16c5d5748a9ba6e430ef97 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/params.yaml b/examples/security/classification/output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/params.yaml new file mode 100644 index 00000000..4b5d3926 --- /dev/null +++ b/examples/security/classification/output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/adv_predictions.json + adv_probabilities_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/726afa7369f2e1965393be774c1c3e71.pkl + params_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/params.yaml + predictions_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/predictions.json + probabilities_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/probabilities.json + score_dict_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/score_dict.json + test_labels_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/test_labels.json + train_labels_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/train_labels.json + model_dir: models + name: a83e4aeb62e9a279fb17c97b5f45a729 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 89545b55803624ac03482af0a3f879ac + trainer: + kwargs: {} +name: a83e4aeb62e9a279fb17c97b5f45a729 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/params.yaml b/examples/security/classification/output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/params.yaml new file mode 100644 index 00000000..176df04b --- /dev/null +++ b/examples/security/classification/output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/adv_predictions.json + adv_probabilities_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/8ca4a636aaed54aa7cd8511754aa782c.pkl + params_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/params.yaml + predictions_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/predictions.json + probabilities_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/probabilities.json + score_dict_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/score_dict.json + test_labels_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/test_labels.json + train_labels_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/train_labels.json + model_dir: models + name: a8415e20f3e08faebc112ea2eb8c4eb7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7e317557d0d8c48aba60e71a0065f268 + trainer: + kwargs: {} +name: a8415e20f3e08faebc112ea2eb8c4eb7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a87578840849005d5ab95107f28e06ca/params.yaml b/examples/security/classification/output/reports/train/a87578840849005d5ab95107f28e06ca/params.yaml new file mode 100644 index 00000000..28ac9f21 --- /dev/null +++ b/examples/security/classification/output/reports/train/a87578840849005d5ab95107f28e06ca/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a87578840849005d5ab95107f28e06ca/adv_predictions.json + adv_probabilities_file: output/reports/train/a87578840849005d5ab95107f28e06ca/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/5b14ca21470b5616d3edb67714bd9bf1.pkl + params_file: output/reports/train/a87578840849005d5ab95107f28e06ca/params.yaml + predictions_file: output/reports/train/a87578840849005d5ab95107f28e06ca/predictions.json + probabilities_file: output/reports/train/a87578840849005d5ab95107f28e06ca/probabilities.json + score_dict_file: output/reports/train/a87578840849005d5ab95107f28e06ca/score_dict.json + test_labels_file: output/reports/train/a87578840849005d5ab95107f28e06ca/test_labels.json + train_labels_file: output/reports/train/a87578840849005d5ab95107f28e06ca/train_labels.json + model_dir: models + name: a87578840849005d5ab95107f28e06ca + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5b8daac61c5a8f2828435656c8815256 + trainer: + kwargs: {} +name: a87578840849005d5ab95107f28e06ca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a87febee068e946a95a5d7369cc3f567/params.yaml b/examples/security/classification/output/reports/train/a87febee068e946a95a5d7369cc3f567/params.yaml new file mode 100644 index 00000000..d30565ae --- /dev/null +++ b/examples/security/classification/output/reports/train/a87febee068e946a95a5d7369cc3f567/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/adv_predictions.json + adv_probabilities_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/2838fece93db69d57963329595856633.pkl + params_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/params.yaml + predictions_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/predictions.json + probabilities_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/probabilities.json + score_dict_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/score_dict.json + test_labels_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/test_labels.json + train_labels_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/train_labels.json + model_dir: models + name: a87febee068e946a95a5d7369cc3f567 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7f7bb8b87a903284282c8a5266d9b3b6 + trainer: + kwargs: {} +name: a87febee068e946a95a5d7369cc3f567 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/params.yaml b/examples/security/classification/output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/params.yaml new file mode 100644 index 00000000..4cd221d3 --- /dev/null +++ b/examples/security/classification/output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/adv_predictions.json + adv_probabilities_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/1cbb7d102aa8ecfe8e87eaa0b476e66d.pkl + params_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/params.yaml + predictions_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/predictions.json + probabilities_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/probabilities.json + score_dict_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/score_dict.json + test_labels_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/test_labels.json + train_labels_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/train_labels.json + model_dir: models + name: a8853ec30b06b00961832ad6e72d1f0b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b1835f90f4b5c1fcb020a6af4700c5e7 + trainer: + kwargs: {} +name: a8853ec30b06b00961832ad6e72d1f0b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/params.yaml b/examples/security/classification/output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/params.yaml new file mode 100644 index 00000000..37f1817c --- /dev/null +++ b/examples/security/classification/output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/adv_predictions.json + adv_probabilities_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/0856c9a8e21e8594bcb4d0339790a1d7.pkl + params_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/params.yaml + predictions_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/predictions.json + probabilities_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/probabilities.json + score_dict_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/score_dict.json + test_labels_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/test_labels.json + train_labels_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/train_labels.json + model_dir: models + name: a885d0ba60c14271e0ed7fd38c2c2745 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bd29212c04c661dbff36b2ae0fd2308f + trainer: + kwargs: {} +name: a885d0ba60c14271e0ed7fd38c2c2745 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a8d046b3571456df5e345180ca4990bf/params.yaml b/examples/security/classification/output/reports/train/a8d046b3571456df5e345180ca4990bf/params.yaml new file mode 100644 index 00000000..79af84a6 --- /dev/null +++ b/examples/security/classification/output/reports/train/a8d046b3571456df5e345180ca4990bf/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/adv_predictions.json + adv_probabilities_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/f0a480633aeec853fccd58e45c36f4bd.pkl + params_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/params.yaml + predictions_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/predictions.json + probabilities_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/probabilities.json + score_dict_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/score_dict.json + test_labels_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/test_labels.json + train_labels_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/train_labels.json + model_dir: models + name: a8d046b3571456df5e345180ca4990bf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2290a461c0d9572238030312bf86a983 + trainer: + kwargs: {} +name: a8d046b3571456df5e345180ca4990bf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/params.yaml b/examples/security/classification/output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/params.yaml new file mode 100644 index 00000000..a0a23e8c --- /dev/null +++ b/examples/security/classification/output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/adv_predictions.json + adv_probabilities_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/632ae35801b4cc51ae09088e499760aa.pkl + params_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/params.yaml + predictions_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/predictions.json + probabilities_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/probabilities.json + score_dict_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/score_dict.json + test_labels_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/test_labels.json + train_labels_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/train_labels.json + model_dir: models + name: a8f654a9b24219bdaafa96bb53ab82da + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 60414b14a7d4049f8a0b18d95ee99639 + trainer: + kwargs: {} +name: a8f654a9b24219bdaafa96bb53ab82da +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/params.yaml b/examples/security/classification/output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/params.yaml new file mode 100644 index 00000000..d75e2f4d --- /dev/null +++ b/examples/security/classification/output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/adv_predictions.json + adv_probabilities_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/adb0348b424fb1e27424a7d25c8fe225.pkl + params_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/params.yaml + predictions_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/predictions.json + probabilities_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/probabilities.json + score_dict_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/score_dict.json + test_labels_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/test_labels.json + train_labels_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/train_labels.json + model_dir: models + name: a8fb7f6707ce69fa331b0c01703349f9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d2ec846a3bdca669a8f4e770c006e9b + trainer: + kwargs: {} +name: a8fb7f6707ce69fa331b0c01703349f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/params.yaml b/examples/security/classification/output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/params.yaml new file mode 100644 index 00000000..b3c8cb92 --- /dev/null +++ b/examples/security/classification/output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/adv_predictions.json + adv_probabilities_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/cfabd68eaba3e90dd51e870ceeafedb1.pkl + params_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/params.yaml + predictions_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/predictions.json + probabilities_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/probabilities.json + score_dict_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/score_dict.json + test_labels_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/test_labels.json + train_labels_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/train_labels.json + model_dir: models + name: a8fe4625a81e981f1f4fadd0339eeb59 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ad2dc5fb862dc591e088e14bea8df925 + trainer: + kwargs: {} +name: a8fe4625a81e981f1f4fadd0339eeb59 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/params.yaml b/examples/security/classification/output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/params.yaml new file mode 100644 index 00000000..e3c11de7 --- /dev/null +++ b/examples/security/classification/output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/adv_predictions.json + adv_probabilities_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/4e7f487f221c91bb31b011b02606faf6.pkl + params_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/params.yaml + predictions_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/predictions.json + probabilities_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/probabilities.json + score_dict_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/score_dict.json + test_labels_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/test_labels.json + train_labels_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/train_labels.json + model_dir: models + name: a92c60c57c88f7512065d16b3d0a0ef1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3b935c1fdaf28f4645d9c98c458e1bfc + trainer: + kwargs: {} +name: a92c60c57c88f7512065d16b3d0a0ef1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a9886a74a6030b31a40715b7715df0aa/params.yaml b/examples/security/classification/output/reports/train/a9886a74a6030b31a40715b7715df0aa/params.yaml new file mode 100644 index 00000000..03214ee2 --- /dev/null +++ b/examples/security/classification/output/reports/train/a9886a74a6030b31a40715b7715df0aa/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/adv_predictions.json + adv_probabilities_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/dd2c0902510caba68a8a6b1f8d38840d.pkl + params_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/params.yaml + predictions_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/predictions.json + probabilities_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/probabilities.json + score_dict_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/score_dict.json + test_labels_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/test_labels.json + train_labels_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/train_labels.json + model_dir: models + name: a9886a74a6030b31a40715b7715df0aa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e13e5d89365bf1f42aa022049d9c1752 + trainer: + kwargs: {} +name: a9886a74a6030b31a40715b7715df0aa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/params.yaml b/examples/security/classification/output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/params.yaml new file mode 100644 index 00000000..893628f8 --- /dev/null +++ b/examples/security/classification/output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/adv_predictions.json + adv_probabilities_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/5455b28ad32a818e2f662c6c293014e5.pkl + params_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/params.yaml + predictions_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/predictions.json + probabilities_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/probabilities.json + score_dict_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/score_dict.json + test_labels_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/test_labels.json + train_labels_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/train_labels.json + model_dir: models + name: a98babb8e52c5fca8a9a56d45d42aeb4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 428eeb07069b984257a60cb3086674b9 + trainer: + kwargs: {} +name: a98babb8e52c5fca8a9a56d45d42aeb4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a9be778efdd15774415920e4168b241e/params.yaml b/examples/security/classification/output/reports/train/a9be778efdd15774415920e4168b241e/params.yaml new file mode 100644 index 00000000..b53e989f --- /dev/null +++ b/examples/security/classification/output/reports/train/a9be778efdd15774415920e4168b241e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a9be778efdd15774415920e4168b241e/adv_predictions.json + adv_probabilities_file: output/reports/train/a9be778efdd15774415920e4168b241e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/5d1e75a00af023fefe3f945427cf36a5.pkl + params_file: output/reports/train/a9be778efdd15774415920e4168b241e/params.yaml + predictions_file: output/reports/train/a9be778efdd15774415920e4168b241e/predictions.json + probabilities_file: output/reports/train/a9be778efdd15774415920e4168b241e/probabilities.json + score_dict_file: output/reports/train/a9be778efdd15774415920e4168b241e/score_dict.json + test_labels_file: output/reports/train/a9be778efdd15774415920e4168b241e/test_labels.json + train_labels_file: output/reports/train/a9be778efdd15774415920e4168b241e/train_labels.json + model_dir: models + name: a9be778efdd15774415920e4168b241e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 29a26838f325f4cdddbb23d7c4a34310 + trainer: + kwargs: {} +name: a9be778efdd15774415920e4168b241e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a9cc2132d446a4121484c89e3a96f163/params.yaml b/examples/security/classification/output/reports/train/a9cc2132d446a4121484c89e3a96f163/params.yaml new file mode 100644 index 00000000..d03ad2d7 --- /dev/null +++ b/examples/security/classification/output/reports/train/a9cc2132d446a4121484c89e3a96f163/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/adv_predictions.json + adv_probabilities_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/cf8d77f1fb901d51ee193d6c65993a77.pkl + params_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/params.yaml + predictions_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/predictions.json + probabilities_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/probabilities.json + score_dict_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/score_dict.json + test_labels_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/test_labels.json + train_labels_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/train_labels.json + model_dir: models + name: a9cc2132d446a4121484c89e3a96f163 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a46f02b4a26832382714a4ccbde89436 + trainer: + kwargs: {} +name: a9cc2132d446a4121484c89e3a96f163 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a9dc535921401e87dee2be776ec5454e/params.yaml b/examples/security/classification/output/reports/train/a9dc535921401e87dee2be776ec5454e/params.yaml new file mode 100644 index 00000000..a68e628a --- /dev/null +++ b/examples/security/classification/output/reports/train/a9dc535921401e87dee2be776ec5454e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/adv_predictions.json + adv_probabilities_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/cc6801a594ab3362a30bc9401af04968.pkl + params_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/params.yaml + predictions_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/predictions.json + probabilities_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/probabilities.json + score_dict_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/score_dict.json + test_labels_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/test_labels.json + train_labels_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/train_labels.json + model_dir: models + name: a9dc535921401e87dee2be776ec5454e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 35ea584f91f36cbf826a21fbaae5f5dc + trainer: + kwargs: {} +name: a9dc535921401e87dee2be776ec5454e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/a9df98da4626f217911b54e16423e132/params.yaml b/examples/security/classification/output/reports/train/a9df98da4626f217911b54e16423e132/params.yaml new file mode 100644 index 00000000..760e50a2 --- /dev/null +++ b/examples/security/classification/output/reports/train/a9df98da4626f217911b54e16423e132/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a9df98da4626f217911b54e16423e132/adv_predictions.json + adv_probabilities_file: output/reports/train/a9df98da4626f217911b54e16423e132/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/af6247d3c7beab493dd197140a5a547c.pkl + params_file: output/reports/train/a9df98da4626f217911b54e16423e132/params.yaml + predictions_file: output/reports/train/a9df98da4626f217911b54e16423e132/predictions.json + probabilities_file: output/reports/train/a9df98da4626f217911b54e16423e132/probabilities.json + score_dict_file: output/reports/train/a9df98da4626f217911b54e16423e132/score_dict.json + test_labels_file: output/reports/train/a9df98da4626f217911b54e16423e132/test_labels.json + train_labels_file: output/reports/train/a9df98da4626f217911b54e16423e132/train_labels.json + model_dir: models + name: a9df98da4626f217911b54e16423e132 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e6b36042d6cc65599e47c1860c7d44c5 + trainer: + kwargs: {} +name: a9df98da4626f217911b54e16423e132 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aa130eb20801e3a88b60800ba4660356/params.yaml b/examples/security/classification/output/reports/train/aa130eb20801e3a88b60800ba4660356/params.yaml new file mode 100644 index 00000000..5f4507e0 --- /dev/null +++ b/examples/security/classification/output/reports/train/aa130eb20801e3a88b60800ba4660356/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/adv_predictions.json + adv_probabilities_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/eea5a78cb8ca4bd72203cd0414f4f627.pkl + params_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/params.yaml + predictions_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/predictions.json + probabilities_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/probabilities.json + score_dict_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/score_dict.json + test_labels_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/test_labels.json + train_labels_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/train_labels.json + model_dir: models + name: aa130eb20801e3a88b60800ba4660356 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fa3f159301843d361231fd9b5bf0015d + trainer: + kwargs: {} +name: aa130eb20801e3a88b60800ba4660356 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aa2173618540d5b98dd8d276219ecb59/params.yaml b/examples/security/classification/output/reports/train/aa2173618540d5b98dd8d276219ecb59/params.yaml new file mode 100644 index 00000000..4ad65588 --- /dev/null +++ b/examples/security/classification/output/reports/train/aa2173618540d5b98dd8d276219ecb59/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/adv_predictions.json + adv_probabilities_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/2f61e2f7f05e04dde8ff3f794106386c.pkl + params_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/params.yaml + predictions_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/predictions.json + probabilities_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/probabilities.json + score_dict_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/score_dict.json + test_labels_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/test_labels.json + train_labels_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/train_labels.json + model_dir: models + name: aa2173618540d5b98dd8d276219ecb59 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ffeed597cd6f3b64212ec9bb9059d669 + trainer: + kwargs: {} +name: aa2173618540d5b98dd8d276219ecb59 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aa280882f1b730f453fd924a3cf2270f/params.yaml b/examples/security/classification/output/reports/train/aa280882f1b730f453fd924a3cf2270f/params.yaml new file mode 100644 index 00000000..ad485097 --- /dev/null +++ b/examples/security/classification/output/reports/train/aa280882f1b730f453fd924a3cf2270f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/adv_predictions.json + adv_probabilities_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/600d75cb5c82e00e53f1cf4f37c57cfe.pkl + params_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/params.yaml + predictions_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/predictions.json + probabilities_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/probabilities.json + score_dict_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/score_dict.json + test_labels_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/test_labels.json + train_labels_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/train_labels.json + model_dir: models + name: aa280882f1b730f453fd924a3cf2270f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 774b0ba78c1475043e8d0467a2c11ad4 + trainer: + kwargs: {} +name: aa280882f1b730f453fd924a3cf2270f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aa466f7ee59221aa63f4f85898760d59/params.yaml b/examples/security/classification/output/reports/train/aa466f7ee59221aa63f4f85898760d59/params.yaml new file mode 100644 index 00000000..3e3de1e0 --- /dev/null +++ b/examples/security/classification/output/reports/train/aa466f7ee59221aa63f4f85898760d59/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/adv_predictions.json + adv_probabilities_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/1e35c51a511c3b8d51153f491af33fe8.pkl + params_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/params.yaml + predictions_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/predictions.json + probabilities_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/probabilities.json + score_dict_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/score_dict.json + test_labels_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/test_labels.json + train_labels_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/train_labels.json + model_dir: models + name: aa466f7ee59221aa63f4f85898760d59 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5d4338003d3b2ce66af4f49339b472e9 + trainer: + kwargs: {} +name: aa466f7ee59221aa63f4f85898760d59 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aa4fcf3ef6083366358708be012530a9/params.yaml b/examples/security/classification/output/reports/train/aa4fcf3ef6083366358708be012530a9/params.yaml new file mode 100644 index 00000000..f5032369 --- /dev/null +++ b/examples/security/classification/output/reports/train/aa4fcf3ef6083366358708be012530a9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/adv_predictions.json + adv_probabilities_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/0d7fa8a191286da7b612406fb3b6a479.pkl + params_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/params.yaml + predictions_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/predictions.json + probabilities_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/probabilities.json + score_dict_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/score_dict.json + test_labels_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/test_labels.json + train_labels_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/train_labels.json + model_dir: models + name: aa4fcf3ef6083366358708be012530a9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f6ad56bf404b62db02d476e2dc3447a0 + trainer: + kwargs: {} +name: aa4fcf3ef6083366358708be012530a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aa575571d93d998807f15b752cf96081/params.yaml b/examples/security/classification/output/reports/train/aa575571d93d998807f15b752cf96081/params.yaml new file mode 100644 index 00000000..1aee6387 --- /dev/null +++ b/examples/security/classification/output/reports/train/aa575571d93d998807f15b752cf96081/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aa575571d93d998807f15b752cf96081/adv_predictions.json + adv_probabilities_file: output/reports/train/aa575571d93d998807f15b752cf96081/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/5e1b2f5a8f5a1c429f6632fde220c0e0.pkl + params_file: output/reports/train/aa575571d93d998807f15b752cf96081/params.yaml + predictions_file: output/reports/train/aa575571d93d998807f15b752cf96081/predictions.json + probabilities_file: output/reports/train/aa575571d93d998807f15b752cf96081/probabilities.json + score_dict_file: output/reports/train/aa575571d93d998807f15b752cf96081/score_dict.json + test_labels_file: output/reports/train/aa575571d93d998807f15b752cf96081/test_labels.json + train_labels_file: output/reports/train/aa575571d93d998807f15b752cf96081/train_labels.json + model_dir: models + name: aa575571d93d998807f15b752cf96081 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: abdd3e9dd6b412b59dacb96df3f92be5 + trainer: + kwargs: {} +name: aa575571d93d998807f15b752cf96081 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/params.yaml b/examples/security/classification/output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/params.yaml new file mode 100644 index 00000000..f331333a --- /dev/null +++ b/examples/security/classification/output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/adv_predictions.json + adv_probabilities_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/48230b12f3ed967409ea041e1f92929e.pkl + params_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/params.yaml + predictions_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/predictions.json + probabilities_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/probabilities.json + score_dict_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/score_dict.json + test_labels_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/test_labels.json + train_labels_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/train_labels.json + model_dir: models + name: aa5d3521a3ba8eddf220ec835ccd030d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8442938d2778bd4a71fa2ae437114ea2 + trainer: + kwargs: {} +name: aa5d3521a3ba8eddf220ec835ccd030d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/params.yaml b/examples/security/classification/output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/params.yaml new file mode 100644 index 00000000..8698d8e5 --- /dev/null +++ b/examples/security/classification/output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/adv_predictions.json + adv_probabilities_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/9db7bef06f7f667eeae472787c4f66ec.pkl + params_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/params.yaml + predictions_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/predictions.json + probabilities_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/probabilities.json + score_dict_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/score_dict.json + test_labels_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/test_labels.json + train_labels_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/train_labels.json + model_dir: models + name: aa6ab6f65068f8b3e01898e47a87cbbd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 63c64b76905cb65790be376d70bc9295 + trainer: + kwargs: {} +name: aa6ab6f65068f8b3e01898e47a87cbbd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/params.yaml b/examples/security/classification/output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/params.yaml new file mode 100644 index 00000000..c363f76f --- /dev/null +++ b/examples/security/classification/output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/adv_predictions.json + adv_probabilities_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/d1691433a9c6a1b12c6d8a10a58f2c2c.pkl + params_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/params.yaml + predictions_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/predictions.json + probabilities_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/probabilities.json + score_dict_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/score_dict.json + test_labels_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/test_labels.json + train_labels_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/train_labels.json + model_dir: models + name: aa749f1b8b5f235b6dbbe172b58768eb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 69f3aa7a0f86adcd50ce6d972ca1f535 + trainer: + kwargs: {} +name: aa749f1b8b5f235b6dbbe172b58768eb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/params.yaml b/examples/security/classification/output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/params.yaml new file mode 100644 index 00000000..b64c68e8 --- /dev/null +++ b/examples/security/classification/output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/adv_predictions.json + adv_probabilities_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/73a03929e768e1eaed59bda15cc73c3a.pkl + params_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/params.yaml + predictions_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/predictions.json + probabilities_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/probabilities.json + score_dict_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/score_dict.json + test_labels_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/test_labels.json + train_labels_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/train_labels.json + model_dir: models + name: aa987ac3366c1c5f0b929717e7f47b0e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 929c3b265e8a5a82066c0ecebe5ca470 + trainer: + kwargs: {} +name: aa987ac3366c1c5f0b929717e7f47b0e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/params.yaml b/examples/security/classification/output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/params.yaml new file mode 100644 index 00000000..075be943 --- /dev/null +++ b/examples/security/classification/output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/adv_predictions.json + adv_probabilities_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/6c1eb98dc0ba3126b44854bd852f5e45.pkl + params_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/params.yaml + predictions_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/predictions.json + probabilities_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/probabilities.json + score_dict_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/score_dict.json + test_labels_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/test_labels.json + train_labels_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/train_labels.json + model_dir: models + name: aab377c977c33ff8cd18fa31d1cfb6e3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e319c20fa881d4578cf172116c58b887 + trainer: + kwargs: {} +name: aab377c977c33ff8cd18fa31d1cfb6e3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/params.yaml b/examples/security/classification/output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/params.yaml new file mode 100644 index 00000000..ef4790af --- /dev/null +++ b/examples/security/classification/output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/adv_predictions.json + adv_probabilities_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/2e98ff30074152ece783283ad9f2c7ee.pkl + params_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/params.yaml + predictions_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/predictions.json + probabilities_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/probabilities.json + score_dict_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/score_dict.json + test_labels_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/test_labels.json + train_labels_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/train_labels.json + model_dir: models + name: aacf4fedf0fc579a3507a5ca7bc1fe66 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c157822bade1de528e2c06d4bdd7adfc + trainer: + kwargs: {} +name: aacf4fedf0fc579a3507a5ca7bc1fe66 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aad82667408d50fcf907185a5925226e/params.yaml b/examples/security/classification/output/reports/train/aad82667408d50fcf907185a5925226e/params.yaml new file mode 100644 index 00000000..87220b82 --- /dev/null +++ b/examples/security/classification/output/reports/train/aad82667408d50fcf907185a5925226e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aad82667408d50fcf907185a5925226e/adv_predictions.json + adv_probabilities_file: output/reports/train/aad82667408d50fcf907185a5925226e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/8abcfe4541c78656edc4ee28fe19df68.pkl + params_file: output/reports/train/aad82667408d50fcf907185a5925226e/params.yaml + predictions_file: output/reports/train/aad82667408d50fcf907185a5925226e/predictions.json + probabilities_file: output/reports/train/aad82667408d50fcf907185a5925226e/probabilities.json + score_dict_file: output/reports/train/aad82667408d50fcf907185a5925226e/score_dict.json + test_labels_file: output/reports/train/aad82667408d50fcf907185a5925226e/test_labels.json + train_labels_file: output/reports/train/aad82667408d50fcf907185a5925226e/train_labels.json + model_dir: models + name: aad82667408d50fcf907185a5925226e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e58fa80c0d99bd1c79947322539445b4 + trainer: + kwargs: {} +name: aad82667408d50fcf907185a5925226e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aafba71548e417f607383515ad034942/params.yaml b/examples/security/classification/output/reports/train/aafba71548e417f607383515ad034942/params.yaml new file mode 100644 index 00000000..f2d6429c --- /dev/null +++ b/examples/security/classification/output/reports/train/aafba71548e417f607383515ad034942/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aafba71548e417f607383515ad034942/adv_predictions.json + adv_probabilities_file: output/reports/train/aafba71548e417f607383515ad034942/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/dd51ceadfe260b770ca5052c72e720ee.pkl + params_file: output/reports/train/aafba71548e417f607383515ad034942/params.yaml + predictions_file: output/reports/train/aafba71548e417f607383515ad034942/predictions.json + probabilities_file: output/reports/train/aafba71548e417f607383515ad034942/probabilities.json + score_dict_file: output/reports/train/aafba71548e417f607383515ad034942/score_dict.json + test_labels_file: output/reports/train/aafba71548e417f607383515ad034942/test_labels.json + train_labels_file: output/reports/train/aafba71548e417f607383515ad034942/train_labels.json + model_dir: models + name: aafba71548e417f607383515ad034942 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 401859c7947fdd41083a932a5331b3df + trainer: + kwargs: {} +name: aafba71548e417f607383515ad034942 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/params.yaml b/examples/security/classification/output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/params.yaml new file mode 100644 index 00000000..2178e6ea --- /dev/null +++ b/examples/security/classification/output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/adv_predictions.json + adv_probabilities_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/673890981671c43e203574954eb03637.pkl + params_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/params.yaml + predictions_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/predictions.json + probabilities_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/probabilities.json + score_dict_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/score_dict.json + test_labels_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/test_labels.json + train_labels_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/train_labels.json + model_dir: models + name: ab134dda8ae47bfaf4cd4ca1cd19068c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 554cd39bf200be827b531f342ed6a565 + trainer: + kwargs: {} +name: ab134dda8ae47bfaf4cd4ca1cd19068c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/params.yaml b/examples/security/classification/output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/params.yaml new file mode 100644 index 00000000..eadeeeb2 --- /dev/null +++ b/examples/security/classification/output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/adv_predictions.json + adv_probabilities_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/2ff323a299d39647e93ced563da0adf3.pkl + params_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/params.yaml + predictions_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/predictions.json + probabilities_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/probabilities.json + score_dict_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/score_dict.json + test_labels_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/test_labels.json + train_labels_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/train_labels.json + model_dir: models + name: ab14bf9f41e3d3af8fe9b2bc708e32d8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c9cc3900a49b3cc9da6f3c547426cc98 + trainer: + kwargs: {} +name: ab14bf9f41e3d3af8fe9b2bc708e32d8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ab4cd05856a2cb8f38a82073842182d7/params.yaml b/examples/security/classification/output/reports/train/ab4cd05856a2cb8f38a82073842182d7/params.yaml new file mode 100644 index 00000000..cd067ba9 --- /dev/null +++ b/examples/security/classification/output/reports/train/ab4cd05856a2cb8f38a82073842182d7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/adv_predictions.json + adv_probabilities_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/123f11c149cf3beec9489247d79bd677.pkl + params_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/params.yaml + predictions_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/predictions.json + probabilities_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/probabilities.json + score_dict_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/score_dict.json + test_labels_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/test_labels.json + train_labels_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/train_labels.json + model_dir: models + name: ab4cd05856a2cb8f38a82073842182d7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a9de782559bd9adf7d5a324e11722c53 + trainer: + kwargs: {} +name: ab4cd05856a2cb8f38a82073842182d7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ab62f505590371b23a0ebcc6a3878f24/params.yaml b/examples/security/classification/output/reports/train/ab62f505590371b23a0ebcc6a3878f24/params.yaml new file mode 100644 index 00000000..92caf260 --- /dev/null +++ b/examples/security/classification/output/reports/train/ab62f505590371b23a0ebcc6a3878f24/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/adv_predictions.json + adv_probabilities_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/6d9508f49d5f764a111d962d6c2091ff.pkl + params_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/params.yaml + predictions_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/predictions.json + probabilities_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/probabilities.json + score_dict_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/score_dict.json + test_labels_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/test_labels.json + train_labels_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/train_labels.json + model_dir: models + name: ab62f505590371b23a0ebcc6a3878f24 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e12dca8a8d994da3d24952aafed52c94 + trainer: + kwargs: {} +name: ab62f505590371b23a0ebcc6a3878f24 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/params.yaml b/examples/security/classification/output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/params.yaml new file mode 100644 index 00000000..f1a86375 --- /dev/null +++ b/examples/security/classification/output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/adv_predictions.json + adv_probabilities_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/f30384affe19bf306a52770be6beb1e2.pkl + params_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/params.yaml + predictions_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/predictions.json + probabilities_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/probabilities.json + score_dict_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/score_dict.json + test_labels_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/test_labels.json + train_labels_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/train_labels.json + model_dir: models + name: ab8e10347e4d73b1ff6d316dbf41dac1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ad97b92634f6504548b47a8851014ede + trainer: + kwargs: {} +name: ab8e10347e4d73b1ff6d316dbf41dac1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ab9434881d7c83ff823656c11ee3795e/params.yaml b/examples/security/classification/output/reports/train/ab9434881d7c83ff823656c11ee3795e/params.yaml new file mode 100644 index 00000000..f656ca2b --- /dev/null +++ b/examples/security/classification/output/reports/train/ab9434881d7c83ff823656c11ee3795e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/adv_predictions.json + adv_probabilities_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/93a19674a9bf1d13e31b2813e0dea186.pkl + params_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/params.yaml + predictions_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/predictions.json + probabilities_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/probabilities.json + score_dict_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/score_dict.json + test_labels_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/test_labels.json + train_labels_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/train_labels.json + model_dir: models + name: ab9434881d7c83ff823656c11ee3795e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9307148306cb8ece61c5269439332d75 + trainer: + kwargs: {} +name: ab9434881d7c83ff823656c11ee3795e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/abb35929794bb5c403acb7448eb6b2bc/params.yaml b/examples/security/classification/output/reports/train/abb35929794bb5c403acb7448eb6b2bc/params.yaml new file mode 100644 index 00000000..ba738b87 --- /dev/null +++ b/examples/security/classification/output/reports/train/abb35929794bb5c403acb7448eb6b2bc/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/adv_predictions.json + adv_probabilities_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/f8ec654aef2ae419f4e561e454f5ca82.pkl + params_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/params.yaml + predictions_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/predictions.json + probabilities_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/probabilities.json + score_dict_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/score_dict.json + test_labels_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/test_labels.json + train_labels_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/train_labels.json + model_dir: models + name: abb35929794bb5c403acb7448eb6b2bc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1fd8583de9c6219a7765a69b7726c4d3 + trainer: + kwargs: {} +name: abb35929794bb5c403acb7448eb6b2bc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/abb6b5a56053a57979f58acfad688cc1/params.yaml b/examples/security/classification/output/reports/train/abb6b5a56053a57979f58acfad688cc1/params.yaml new file mode 100644 index 00000000..d7df03ae --- /dev/null +++ b/examples/security/classification/output/reports/train/abb6b5a56053a57979f58acfad688cc1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/adv_predictions.json + adv_probabilities_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/e58fd1564f32934e31b0b01b49eb9538.pkl + params_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/params.yaml + predictions_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/predictions.json + probabilities_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/probabilities.json + score_dict_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/score_dict.json + test_labels_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/test_labels.json + train_labels_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/train_labels.json + model_dir: models + name: abb6b5a56053a57979f58acfad688cc1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46d46794b95acad6f667da72c980fc59 + trainer: + kwargs: {} +name: abb6b5a56053a57979f58acfad688cc1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/abca74170f7be90488c55722c0ba00a4/params.yaml b/examples/security/classification/output/reports/train/abca74170f7be90488c55722c0ba00a4/params.yaml new file mode 100644 index 00000000..873959e0 --- /dev/null +++ b/examples/security/classification/output/reports/train/abca74170f7be90488c55722c0ba00a4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/adv_predictions.json + adv_probabilities_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/0389404b8ff5834a2387c965f89143fc.pkl + params_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/params.yaml + predictions_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/predictions.json + probabilities_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/probabilities.json + score_dict_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/score_dict.json + test_labels_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/test_labels.json + train_labels_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/train_labels.json + model_dir: models + name: abca74170f7be90488c55722c0ba00a4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f3aedae8a4d77bbb33360e50ed74e7ec + trainer: + kwargs: {} +name: abca74170f7be90488c55722c0ba00a4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/params.yaml b/examples/security/classification/output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/params.yaml new file mode 100644 index 00000000..fc743f09 --- /dev/null +++ b/examples/security/classification/output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/adv_predictions.json + adv_probabilities_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/e28a8dfb37e9e1486c5029e68e311892.pkl + params_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/params.yaml + predictions_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/predictions.json + probabilities_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/probabilities.json + score_dict_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/score_dict.json + test_labels_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/test_labels.json + train_labels_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/train_labels.json + model_dir: models + name: abee2a355ab8ea1bceb5c06cc6c8ab7f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0c28b93e1012ade88c6e34ef302a6986 + trainer: + kwargs: {} +name: abee2a355ab8ea1bceb5c06cc6c8ab7f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/params.yaml b/examples/security/classification/output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/params.yaml new file mode 100644 index 00000000..73d1fcbe --- /dev/null +++ b/examples/security/classification/output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/adv_predictions.json + adv_probabilities_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/f2201365034d4da1ea7c79124a88c75c.pkl + params_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/params.yaml + predictions_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/predictions.json + probabilities_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/probabilities.json + score_dict_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/score_dict.json + test_labels_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/test_labels.json + train_labels_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/train_labels.json + model_dir: models + name: ac612b9eddc48d5cfee08a86c1e1b5c9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 468fef5afd65c8861872616c02a3fdc5 + trainer: + kwargs: {} +name: ac612b9eddc48d5cfee08a86c1e1b5c9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ac8f813ea117c627bcfa93189ec5959d/params.yaml b/examples/security/classification/output/reports/train/ac8f813ea117c627bcfa93189ec5959d/params.yaml new file mode 100644 index 00000000..d7aa55e1 --- /dev/null +++ b/examples/security/classification/output/reports/train/ac8f813ea117c627bcfa93189ec5959d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/adv_predictions.json + adv_probabilities_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/c4f2a7509551d8619ea27e8ae1d09237.pkl + params_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/params.yaml + predictions_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/predictions.json + probabilities_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/probabilities.json + score_dict_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/score_dict.json + test_labels_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/test_labels.json + train_labels_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/train_labels.json + model_dir: models + name: ac8f813ea117c627bcfa93189ec5959d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6d7d9d2b5f8086389b19ebbc1b87541e + trainer: + kwargs: {} +name: ac8f813ea117c627bcfa93189ec5959d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ac96494b0461749b2241d24c962f9893/params.yaml b/examples/security/classification/output/reports/train/ac96494b0461749b2241d24c962f9893/params.yaml new file mode 100644 index 00000000..66c423f6 --- /dev/null +++ b/examples/security/classification/output/reports/train/ac96494b0461749b2241d24c962f9893/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ac96494b0461749b2241d24c962f9893/adv_predictions.json + adv_probabilities_file: output/reports/train/ac96494b0461749b2241d24c962f9893/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/7c889216c08000188f800b760d9814f1.pkl + params_file: output/reports/train/ac96494b0461749b2241d24c962f9893/params.yaml + predictions_file: output/reports/train/ac96494b0461749b2241d24c962f9893/predictions.json + probabilities_file: output/reports/train/ac96494b0461749b2241d24c962f9893/probabilities.json + score_dict_file: output/reports/train/ac96494b0461749b2241d24c962f9893/score_dict.json + test_labels_file: output/reports/train/ac96494b0461749b2241d24c962f9893/test_labels.json + train_labels_file: output/reports/train/ac96494b0461749b2241d24c962f9893/train_labels.json + model_dir: models + name: ac96494b0461749b2241d24c962f9893 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f6c01c63b88e061a1f550867a6e3b0c5 + trainer: + kwargs: {} +name: ac96494b0461749b2241d24c962f9893 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/params.yaml b/examples/security/classification/output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/params.yaml new file mode 100644 index 00000000..e5a54193 --- /dev/null +++ b/examples/security/classification/output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/adv_predictions.json + adv_probabilities_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/c18b935fa9bc6a5a8a80dd43dcda3bec.pkl + params_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/params.yaml + predictions_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/predictions.json + probabilities_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/probabilities.json + score_dict_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/score_dict.json + test_labels_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/test_labels.json + train_labels_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/train_labels.json + model_dir: models + name: acaf47f171b713ec54e5582ff7fa5ad7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a2f6df54d0d85116f83d9194c9d925ca + trainer: + kwargs: {} +name: acaf47f171b713ec54e5582ff7fa5ad7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/acd47903f172fe9830fbc88589d11e11/params.yaml b/examples/security/classification/output/reports/train/acd47903f172fe9830fbc88589d11e11/params.yaml new file mode 100644 index 00000000..3bf2eade --- /dev/null +++ b/examples/security/classification/output/reports/train/acd47903f172fe9830fbc88589d11e11/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/adv_predictions.json + adv_probabilities_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/8a7a4b808102a0a6bae5cd93df8bda48.pkl + params_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/params.yaml + predictions_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/predictions.json + probabilities_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/probabilities.json + score_dict_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/score_dict.json + test_labels_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/test_labels.json + train_labels_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/train_labels.json + model_dir: models + name: acd47903f172fe9830fbc88589d11e11 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8a81424fd850589c96b3c08e10eb6af5 + trainer: + kwargs: {} +name: acd47903f172fe9830fbc88589d11e11 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/params.yaml b/examples/security/classification/output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/params.yaml new file mode 100644 index 00000000..9bfaa6e8 --- /dev/null +++ b/examples/security/classification/output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/adv_predictions.json + adv_probabilities_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/6376dca2a4f28e397ef1cb74f898fd8b.pkl + params_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/params.yaml + predictions_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/predictions.json + probabilities_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/probabilities.json + score_dict_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/score_dict.json + test_labels_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/test_labels.json + train_labels_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/train_labels.json + model_dir: models + name: acff3dc338f14b3a9d4d94949b64aa64 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 061ad697b2a6440ef726d712fef704e7 + trainer: + kwargs: {} +name: acff3dc338f14b3a9d4d94949b64aa64 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/params.yaml b/examples/security/classification/output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/params.yaml new file mode 100644 index 00000000..c1440adb --- /dev/null +++ b/examples/security/classification/output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/adv_predictions.json + adv_probabilities_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/3875f4666585c2a7e73cf19a41c02f6a.pkl + params_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/params.yaml + predictions_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/predictions.json + probabilities_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/probabilities.json + score_dict_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/score_dict.json + test_labels_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/test_labels.json + train_labels_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/train_labels.json + model_dir: models + name: ad1f3f40fde39f8c8ccf546c4738b33a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 878f7d1060054df2fb0d0c4e49f6abb6 + trainer: + kwargs: {} +name: ad1f3f40fde39f8c8ccf546c4738b33a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ad3c993644f25e35f252cb79c7425e42/params.yaml b/examples/security/classification/output/reports/train/ad3c993644f25e35f252cb79c7425e42/params.yaml new file mode 100644 index 00000000..869284d3 --- /dev/null +++ b/examples/security/classification/output/reports/train/ad3c993644f25e35f252cb79c7425e42/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/adv_predictions.json + adv_probabilities_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/4963520e300bb124882ce186dd8315ae.pkl + params_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/params.yaml + predictions_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/predictions.json + probabilities_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/probabilities.json + score_dict_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/score_dict.json + test_labels_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/test_labels.json + train_labels_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/train_labels.json + model_dir: models + name: ad3c993644f25e35f252cb79c7425e42 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0d3c2f4d07681963c4892cd1e0e716fe + trainer: + kwargs: {} +name: ad3c993644f25e35f252cb79c7425e42 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/params.yaml b/examples/security/classification/output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/params.yaml new file mode 100644 index 00000000..cc4677b5 --- /dev/null +++ b/examples/security/classification/output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/adv_predictions.json + adv_probabilities_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/c8c365e8e5db52db26886a35931eeaa1.pkl + params_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/params.yaml + predictions_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/predictions.json + probabilities_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/probabilities.json + score_dict_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/score_dict.json + test_labels_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/test_labels.json + train_labels_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/train_labels.json + model_dir: models + name: ad44ba010f9faee4e38cc9d167412c5c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e7cd9e7e4ee0d4f494f766ff5c40587c + trainer: + kwargs: {} +name: ad44ba010f9faee4e38cc9d167412c5c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ad5889ec619085a52cb2d5f545702c80/params.yaml b/examples/security/classification/output/reports/train/ad5889ec619085a52cb2d5f545702c80/params.yaml new file mode 100644 index 00000000..ae635330 --- /dev/null +++ b/examples/security/classification/output/reports/train/ad5889ec619085a52cb2d5f545702c80/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/adv_predictions.json + adv_probabilities_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/dd4be87438bc7b9fef2bc3de59f58c48.pkl + params_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/params.yaml + predictions_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/predictions.json + probabilities_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/probabilities.json + score_dict_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/score_dict.json + test_labels_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/test_labels.json + train_labels_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/train_labels.json + model_dir: models + name: ad5889ec619085a52cb2d5f545702c80 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c81fe956b7a0fecec43c4a8294a2ef44 + trainer: + kwargs: {} +name: ad5889ec619085a52cb2d5f545702c80 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ad8735a67a823a4015bf907ba89e6e11/params.yaml b/examples/security/classification/output/reports/train/ad8735a67a823a4015bf907ba89e6e11/params.yaml new file mode 100644 index 00000000..9daabe90 --- /dev/null +++ b/examples/security/classification/output/reports/train/ad8735a67a823a4015bf907ba89e6e11/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/adv_predictions.json + adv_probabilities_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/65278cb975224d562bf9ef182db40dd7.pkl + params_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/params.yaml + predictions_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/predictions.json + probabilities_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/probabilities.json + score_dict_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/score_dict.json + test_labels_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/test_labels.json + train_labels_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/train_labels.json + model_dir: models + name: ad8735a67a823a4015bf907ba89e6e11 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6fddf8f0b8c92f3ffb986570b335f3ff + trainer: + kwargs: {} +name: ad8735a67a823a4015bf907ba89e6e11 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/params.yaml b/examples/security/classification/output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/params.yaml new file mode 100644 index 00000000..dcc1c9d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/adv_predictions.json + adv_probabilities_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/c23b54c7f16d354265a1bee7368dbfa4.pkl + params_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/params.yaml + predictions_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/predictions.json + probabilities_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/probabilities.json + score_dict_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/score_dict.json + test_labels_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/test_labels.json + train_labels_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/train_labels.json + model_dir: models + name: ad9c0c04f2e99d101a2b47611e5af9b7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aa84843f7899b9f28bcc31c877c64a35 + trainer: + kwargs: {} +name: ad9c0c04f2e99d101a2b47611e5af9b7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ada667b0682fe3f962e7eac4728d839c/params.yaml b/examples/security/classification/output/reports/train/ada667b0682fe3f962e7eac4728d839c/params.yaml new file mode 100644 index 00000000..f548b57c --- /dev/null +++ b/examples/security/classification/output/reports/train/ada667b0682fe3f962e7eac4728d839c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/adv_predictions.json + adv_probabilities_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/5b11b198bc0d70d3d6db2c4285832d63.pkl + params_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/params.yaml + predictions_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/predictions.json + probabilities_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/probabilities.json + score_dict_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/score_dict.json + test_labels_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/test_labels.json + train_labels_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/train_labels.json + model_dir: models + name: ada667b0682fe3f962e7eac4728d839c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 762a24119d83327bc235473f4a73bb57 + trainer: + kwargs: {} +name: ada667b0682fe3f962e7eac4728d839c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/params.yaml b/examples/security/classification/output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/params.yaml new file mode 100644 index 00000000..aaa93a48 --- /dev/null +++ b/examples/security/classification/output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/adv_predictions.json + adv_probabilities_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/ade32c1df57c36e2a33415652440ee48.pkl + params_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/params.yaml + predictions_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/predictions.json + probabilities_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/probabilities.json + score_dict_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/score_dict.json + test_labels_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/test_labels.json + train_labels_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/train_labels.json + model_dir: models + name: ada9150d53d6388e0d1c926dbe6ef747 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6b555705bc328b22769d29bfe61aeaff + trainer: + kwargs: {} +name: ada9150d53d6388e0d1c926dbe6ef747 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/addfe639db7a07c0cb96f5772c6aa867/params.yaml b/examples/security/classification/output/reports/train/addfe639db7a07c0cb96f5772c6aa867/params.yaml new file mode 100644 index 00000000..16542c14 --- /dev/null +++ b/examples/security/classification/output/reports/train/addfe639db7a07c0cb96f5772c6aa867/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/adv_predictions.json + adv_probabilities_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/c4ef3e1bbf9245ff3abea6adb26428ff.pkl + params_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/params.yaml + predictions_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/predictions.json + probabilities_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/probabilities.json + score_dict_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/score_dict.json + test_labels_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/test_labels.json + train_labels_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/train_labels.json + model_dir: models + name: addfe639db7a07c0cb96f5772c6aa867 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 23a6357c324c89eae6d070f6837db13c + trainer: + kwargs: {} +name: addfe639db7a07c0cb96f5772c6aa867 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/adff01e0049fa7109f80d43a36a01612/params.yaml b/examples/security/classification/output/reports/train/adff01e0049fa7109f80d43a36a01612/params.yaml new file mode 100644 index 00000000..f7aa4f7f --- /dev/null +++ b/examples/security/classification/output/reports/train/adff01e0049fa7109f80d43a36a01612/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/adv_predictions.json + adv_probabilities_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/d85757435ea684df4c61a63bbac3b21c.pkl + params_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/params.yaml + predictions_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/predictions.json + probabilities_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/probabilities.json + score_dict_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/score_dict.json + test_labels_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/test_labels.json + train_labels_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/train_labels.json + model_dir: models + name: adff01e0049fa7109f80d43a36a01612 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b33d1efb56db392a968a0e4dc5393240 + trainer: + kwargs: {} +name: adff01e0049fa7109f80d43a36a01612 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ae2087d8d98dd1805586e3f90b75b483/params.yaml b/examples/security/classification/output/reports/train/ae2087d8d98dd1805586e3f90b75b483/params.yaml new file mode 100644 index 00000000..628fb624 --- /dev/null +++ b/examples/security/classification/output/reports/train/ae2087d8d98dd1805586e3f90b75b483/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/adv_predictions.json + adv_probabilities_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/f91a93437154f47cc018f6b805a5900e.pkl + params_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/params.yaml + predictions_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/predictions.json + probabilities_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/probabilities.json + score_dict_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/score_dict.json + test_labels_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/test_labels.json + train_labels_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/train_labels.json + model_dir: models + name: ae2087d8d98dd1805586e3f90b75b483 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c3033fb023c33cc41fe3fc757098c3e9 + trainer: + kwargs: {} +name: ae2087d8d98dd1805586e3f90b75b483 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ae31d530dabcb6f6125622d5be610074/params.yaml b/examples/security/classification/output/reports/train/ae31d530dabcb6f6125622d5be610074/params.yaml new file mode 100644 index 00000000..77b4673d --- /dev/null +++ b/examples/security/classification/output/reports/train/ae31d530dabcb6f6125622d5be610074/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/adv_predictions.json + adv_probabilities_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/2bec783c1cb770dcde0c788f97ca6a2d.pkl + params_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/params.yaml + predictions_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/predictions.json + probabilities_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/probabilities.json + score_dict_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/score_dict.json + test_labels_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/test_labels.json + train_labels_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/train_labels.json + model_dir: models + name: ae31d530dabcb6f6125622d5be610074 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f1692b82a78b6b049f1f3d4f6b63990c + trainer: + kwargs: {} +name: ae31d530dabcb6f6125622d5be610074 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ae4434cb92786bde76f2a68017616e4d/params.yaml b/examples/security/classification/output/reports/train/ae4434cb92786bde76f2a68017616e4d/params.yaml new file mode 100644 index 00000000..ad7df2fe --- /dev/null +++ b/examples/security/classification/output/reports/train/ae4434cb92786bde76f2a68017616e4d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/adv_predictions.json + adv_probabilities_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/896443a56c72471a5753a25c1a0aedcf.pkl + params_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/params.yaml + predictions_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/predictions.json + probabilities_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/probabilities.json + score_dict_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/score_dict.json + test_labels_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/test_labels.json + train_labels_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/train_labels.json + model_dir: models + name: ae4434cb92786bde76f2a68017616e4d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 07f703c9ae6eef6288829293bef82d77 + trainer: + kwargs: {} +name: ae4434cb92786bde76f2a68017616e4d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ae642c4e75604fee38a7c7a975e90617/params.yaml b/examples/security/classification/output/reports/train/ae642c4e75604fee38a7c7a975e90617/params.yaml new file mode 100644 index 00000000..d091d66b --- /dev/null +++ b/examples/security/classification/output/reports/train/ae642c4e75604fee38a7c7a975e90617/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/adv_predictions.json + adv_probabilities_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/a333f1ac2eb827b6bd52d918cf9e1340.pkl + params_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/params.yaml + predictions_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/predictions.json + probabilities_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/probabilities.json + score_dict_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/score_dict.json + test_labels_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/test_labels.json + train_labels_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/train_labels.json + model_dir: models + name: ae642c4e75604fee38a7c7a975e90617 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ac5570ea97a9b566bd66eb0c691e1f78 + trainer: + kwargs: {} +name: ae642c4e75604fee38a7c7a975e90617 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aec637019225e04a01ba708a69f20138/params.yaml b/examples/security/classification/output/reports/train/aec637019225e04a01ba708a69f20138/params.yaml new file mode 100644 index 00000000..3055e137 --- /dev/null +++ b/examples/security/classification/output/reports/train/aec637019225e04a01ba708a69f20138/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aec637019225e04a01ba708a69f20138/adv_predictions.json + adv_probabilities_file: output/reports/train/aec637019225e04a01ba708a69f20138/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/8e77fc78fcc653b01bdb114c45c7ad4f.pkl + params_file: output/reports/train/aec637019225e04a01ba708a69f20138/params.yaml + predictions_file: output/reports/train/aec637019225e04a01ba708a69f20138/predictions.json + probabilities_file: output/reports/train/aec637019225e04a01ba708a69f20138/probabilities.json + score_dict_file: output/reports/train/aec637019225e04a01ba708a69f20138/score_dict.json + test_labels_file: output/reports/train/aec637019225e04a01ba708a69f20138/test_labels.json + train_labels_file: output/reports/train/aec637019225e04a01ba708a69f20138/train_labels.json + model_dir: models + name: aec637019225e04a01ba708a69f20138 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7c6a2278d378ae458a7e349116c9bead + trainer: + kwargs: {} +name: aec637019225e04a01ba708a69f20138 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aec89520903fd09599c9545e6ddc4d82/params.yaml b/examples/security/classification/output/reports/train/aec89520903fd09599c9545e6ddc4d82/params.yaml new file mode 100644 index 00000000..b7182dfe --- /dev/null +++ b/examples/security/classification/output/reports/train/aec89520903fd09599c9545e6ddc4d82/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/adv_predictions.json + adv_probabilities_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/d59e9f7f5282de64a02bb7dadfc4c680.pkl + params_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/params.yaml + predictions_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/predictions.json + probabilities_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/probabilities.json + score_dict_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/score_dict.json + test_labels_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/test_labels.json + train_labels_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/train_labels.json + model_dir: models + name: aec89520903fd09599c9545e6ddc4d82 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c631922bf1119e97bf6ecb4c067f1a35 + trainer: + kwargs: {} +name: aec89520903fd09599c9545e6ddc4d82 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/aed59d4c35c0be56408eb0ef8e455655/params.yaml b/examples/security/classification/output/reports/train/aed59d4c35c0be56408eb0ef8e455655/params.yaml new file mode 100644 index 00000000..28e5e351 --- /dev/null +++ b/examples/security/classification/output/reports/train/aed59d4c35c0be56408eb0ef8e455655/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/adv_predictions.json + adv_probabilities_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/35a6a20376e0f2df7c463ca197b1a05f.pkl + params_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/params.yaml + predictions_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/predictions.json + probabilities_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/probabilities.json + score_dict_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/score_dict.json + test_labels_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/test_labels.json + train_labels_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/train_labels.json + model_dir: models + name: aed59d4c35c0be56408eb0ef8e455655 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0e55cf911237a85dc6754fcc9c4f53c0 + trainer: + kwargs: {} +name: aed59d4c35c0be56408eb0ef8e455655 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/af0f13738fc124aa24175581d9ec315b/params.yaml b/examples/security/classification/output/reports/train/af0f13738fc124aa24175581d9ec315b/params.yaml new file mode 100644 index 00000000..4b8b524a --- /dev/null +++ b/examples/security/classification/output/reports/train/af0f13738fc124aa24175581d9ec315b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/adv_predictions.json + adv_probabilities_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/fa8db612f350da087535b418e0b5a9d4.pkl + params_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/params.yaml + predictions_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/predictions.json + probabilities_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/probabilities.json + score_dict_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/score_dict.json + test_labels_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/test_labels.json + train_labels_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/train_labels.json + model_dir: models + name: af0f13738fc124aa24175581d9ec315b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8772faddca9cf3b3f20cf34768521acf + trainer: + kwargs: {} +name: af0f13738fc124aa24175581d9ec315b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/af253b097e87a1be907b9577817a2e4b/params.yaml b/examples/security/classification/output/reports/train/af253b097e87a1be907b9577817a2e4b/params.yaml new file mode 100644 index 00000000..7b30d7d1 --- /dev/null +++ b/examples/security/classification/output/reports/train/af253b097e87a1be907b9577817a2e4b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/adv_predictions.json + adv_probabilities_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/6c11b6fd8fcb732bbd4f7d0885eccbf7.pkl + params_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/params.yaml + predictions_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/predictions.json + probabilities_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/probabilities.json + score_dict_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/score_dict.json + test_labels_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/test_labels.json + train_labels_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/train_labels.json + model_dir: models + name: af253b097e87a1be907b9577817a2e4b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4949c855e2eee9b24acd6651fa5f23f4 + trainer: + kwargs: {} +name: af253b097e87a1be907b9577817a2e4b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/params.yaml b/examples/security/classification/output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/params.yaml new file mode 100644 index 00000000..37bc5854 --- /dev/null +++ b/examples/security/classification/output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/adv_predictions.json + adv_probabilities_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/2bbddea058ddcafa9e0c5e386cd2fcf6.pkl + params_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/params.yaml + predictions_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/predictions.json + probabilities_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/probabilities.json + score_dict_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/score_dict.json + test_labels_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/test_labels.json + train_labels_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/train_labels.json + model_dir: models + name: af4a35ea6f52f4cb96f4b55204404c91 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6d6388462ca3f0b158a09674fcb3eef8 + trainer: + kwargs: {} +name: af4a35ea6f52f4cb96f4b55204404c91 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/params.yaml b/examples/security/classification/output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/params.yaml new file mode 100644 index 00000000..2396d8e0 --- /dev/null +++ b/examples/security/classification/output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/adv_predictions.json + adv_probabilities_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/95a61b99cdbf0611c223b7943d2e4521.pkl + params_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/params.yaml + predictions_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/predictions.json + probabilities_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/probabilities.json + score_dict_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/score_dict.json + test_labels_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/test_labels.json + train_labels_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/train_labels.json + model_dir: models + name: af4e544fdbc53ac677c67493a3afcbc8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cfdc9c2c7e5fb2e7ce3eaeb55c16af3d + trainer: + kwargs: {} +name: af4e544fdbc53ac677c67493a3afcbc8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/af62c23ced4ff1e076e55bfb856413c7/params.yaml b/examples/security/classification/output/reports/train/af62c23ced4ff1e076e55bfb856413c7/params.yaml new file mode 100644 index 00000000..dae3e9af --- /dev/null +++ b/examples/security/classification/output/reports/train/af62c23ced4ff1e076e55bfb856413c7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/adv_predictions.json + adv_probabilities_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/8df751f4ab8b80799e2fc9393db47f73.pkl + params_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/params.yaml + predictions_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/predictions.json + probabilities_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/probabilities.json + score_dict_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/score_dict.json + test_labels_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/test_labels.json + train_labels_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/train_labels.json + model_dir: models + name: af62c23ced4ff1e076e55bfb856413c7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 894d19689148ac429adb72f47cc091cc + trainer: + kwargs: {} +name: af62c23ced4ff1e076e55bfb856413c7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/af68184484e8e9169640b6b207abee2f/params.yaml b/examples/security/classification/output/reports/train/af68184484e8e9169640b6b207abee2f/params.yaml new file mode 100644 index 00000000..084c4a8b --- /dev/null +++ b/examples/security/classification/output/reports/train/af68184484e8e9169640b6b207abee2f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/af68184484e8e9169640b6b207abee2f/adv_predictions.json + adv_probabilities_file: output/reports/train/af68184484e8e9169640b6b207abee2f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/eb85a10ab84eb2efb2fe0fff537039eb.pkl + params_file: output/reports/train/af68184484e8e9169640b6b207abee2f/params.yaml + predictions_file: output/reports/train/af68184484e8e9169640b6b207abee2f/predictions.json + probabilities_file: output/reports/train/af68184484e8e9169640b6b207abee2f/probabilities.json + score_dict_file: output/reports/train/af68184484e8e9169640b6b207abee2f/score_dict.json + test_labels_file: output/reports/train/af68184484e8e9169640b6b207abee2f/test_labels.json + train_labels_file: output/reports/train/af68184484e8e9169640b6b207abee2f/train_labels.json + model_dir: models + name: af68184484e8e9169640b6b207abee2f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 73f10e4f09af6a93a2a5618176164759 + trainer: + kwargs: {} +name: af68184484e8e9169640b6b207abee2f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/params.yaml b/examples/security/classification/output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/params.yaml new file mode 100644 index 00000000..55d48961 --- /dev/null +++ b/examples/security/classification/output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/adv_predictions.json + adv_probabilities_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/ad40ff65648c7a1c19a62740347ddb05.pkl + params_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/params.yaml + predictions_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/predictions.json + probabilities_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/probabilities.json + score_dict_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/score_dict.json + test_labels_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/test_labels.json + train_labels_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/train_labels.json + model_dir: models + name: af70b3d25ab02fe8b90da3a3b2a641c7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c5ebb719f318f14b950ea0b29bb69378 + trainer: + kwargs: {} +name: af70b3d25ab02fe8b90da3a3b2a641c7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/params.yaml b/examples/security/classification/output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/params.yaml new file mode 100644 index 00000000..7ec4e1a4 --- /dev/null +++ b/examples/security/classification/output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/adv_predictions.json + adv_probabilities_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/133cabe67294b6e94d8ccb1e79d3b576.pkl + params_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/params.yaml + predictions_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/predictions.json + probabilities_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/probabilities.json + score_dict_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/score_dict.json + test_labels_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/test_labels.json + train_labels_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/train_labels.json + model_dir: models + name: af82aae4bba3bb6ce5ee88e3e2845731 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 43ebb2ce5f71f3c066d917aaded080ec + trainer: + kwargs: {} +name: af82aae4bba3bb6ce5ee88e3e2845731 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/params.yaml b/examples/security/classification/output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/params.yaml new file mode 100644 index 00000000..adeb5af4 --- /dev/null +++ b/examples/security/classification/output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/adv_predictions.json + adv_probabilities_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/af810d9c15ee3a32c225a4d640a5a40a.pkl + params_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/params.yaml + predictions_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/predictions.json + probabilities_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/probabilities.json + score_dict_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/score_dict.json + test_labels_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/test_labels.json + train_labels_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/train_labels.json + model_dir: models + name: af85d1d5c07f3a9d614bc61579e2ce00 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3a48201b98fc70b27eab3d672d060ab1 + trainer: + kwargs: {} +name: af85d1d5c07f3a9d614bc61579e2ce00 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/afd0f0c00117d1871b49caee8a0dd177/params.yaml b/examples/security/classification/output/reports/train/afd0f0c00117d1871b49caee8a0dd177/params.yaml new file mode 100644 index 00000000..a5cd0548 --- /dev/null +++ b/examples/security/classification/output/reports/train/afd0f0c00117d1871b49caee8a0dd177/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/adv_predictions.json + adv_probabilities_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/a6d6e6a3079ada8f2c86204e28448e86.pkl + params_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/params.yaml + predictions_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/predictions.json + probabilities_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/probabilities.json + score_dict_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/score_dict.json + test_labels_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/test_labels.json + train_labels_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/train_labels.json + model_dir: models + name: afd0f0c00117d1871b49caee8a0dd177 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 88b2e6933cc0ec59a4d24f0210a33c62 + trainer: + kwargs: {} +name: afd0f0c00117d1871b49caee8a0dd177 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/afe9bf594b563997e4db920cef3664b6/params.yaml b/examples/security/classification/output/reports/train/afe9bf594b563997e4db920cef3664b6/params.yaml new file mode 100644 index 00000000..0ae37277 --- /dev/null +++ b/examples/security/classification/output/reports/train/afe9bf594b563997e4db920cef3664b6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/adv_predictions.json + adv_probabilities_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/f99dc1b6e1cc22ed0a68e5142a8200b8.pkl + params_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/params.yaml + predictions_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/predictions.json + probabilities_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/probabilities.json + score_dict_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/score_dict.json + test_labels_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/test_labels.json + train_labels_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/train_labels.json + model_dir: models + name: afe9bf594b563997e4db920cef3664b6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 12d8f380bdc7b4aa1eaa0d784393215b + trainer: + kwargs: {} +name: afe9bf594b563997e4db920cef3664b6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/params.yaml b/examples/security/classification/output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/params.yaml new file mode 100644 index 00000000..bc4a5245 --- /dev/null +++ b/examples/security/classification/output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/adv_predictions.json + adv_probabilities_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/ac045f6bd3c27ac90498422e2e234379.pkl + params_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/params.yaml + predictions_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/predictions.json + probabilities_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/probabilities.json + score_dict_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/score_dict.json + test_labels_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/test_labels.json + train_labels_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/train_labels.json + model_dir: models + name: b012880bcb540d8ce4a0e191ef742b0e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a6eb1d90acef172238b9ef76690c8d18 + trainer: + kwargs: {} +name: b012880bcb540d8ce4a0e191ef742b0e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b01653933b09c45899349536ca39922a/params.yaml b/examples/security/classification/output/reports/train/b01653933b09c45899349536ca39922a/params.yaml new file mode 100644 index 00000000..b105c587 --- /dev/null +++ b/examples/security/classification/output/reports/train/b01653933b09c45899349536ca39922a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b01653933b09c45899349536ca39922a/adv_predictions.json + adv_probabilities_file: output/reports/train/b01653933b09c45899349536ca39922a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/08931484c492e5e99f35a594d5df25d0.pkl + params_file: output/reports/train/b01653933b09c45899349536ca39922a/params.yaml + predictions_file: output/reports/train/b01653933b09c45899349536ca39922a/predictions.json + probabilities_file: output/reports/train/b01653933b09c45899349536ca39922a/probabilities.json + score_dict_file: output/reports/train/b01653933b09c45899349536ca39922a/score_dict.json + test_labels_file: output/reports/train/b01653933b09c45899349536ca39922a/test_labels.json + train_labels_file: output/reports/train/b01653933b09c45899349536ca39922a/train_labels.json + model_dir: models + name: b01653933b09c45899349536ca39922a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bd7a6ea450361e1c0ad26d4964c12016 + trainer: + kwargs: {} +name: b01653933b09c45899349536ca39922a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b055507b9ccdd0127e578dca170e7550/params.yaml b/examples/security/classification/output/reports/train/b055507b9ccdd0127e578dca170e7550/params.yaml new file mode 100644 index 00000000..2db78917 --- /dev/null +++ b/examples/security/classification/output/reports/train/b055507b9ccdd0127e578dca170e7550/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/adv_predictions.json + adv_probabilities_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/74959b6634576e12bd4455ec83a4e3cb.pkl + params_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/params.yaml + predictions_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/predictions.json + probabilities_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/probabilities.json + score_dict_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/score_dict.json + test_labels_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/test_labels.json + train_labels_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/train_labels.json + model_dir: models + name: b055507b9ccdd0127e578dca170e7550 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 97ca2227b89c496380d4ba72a329c561 + trainer: + kwargs: {} +name: b055507b9ccdd0127e578dca170e7550 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/params.yaml b/examples/security/classification/output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/params.yaml new file mode 100644 index 00000000..5d4dab98 --- /dev/null +++ b/examples/security/classification/output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/adv_predictions.json + adv_probabilities_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/cb0dcc18f715241285ffdebf98929ec2.pkl + params_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/params.yaml + predictions_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/predictions.json + probabilities_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/probabilities.json + score_dict_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/score_dict.json + test_labels_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/test_labels.json + train_labels_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/train_labels.json + model_dir: models + name: b05bdc3d7501c589a622ff00f5dd3567 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fe4aceb373cb9ab8d48b67b3369622e6 + trainer: + kwargs: {} +name: b05bdc3d7501c589a622ff00f5dd3567 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b073bf9c0864dbe478be45834fba4bfd/params.yaml b/examples/security/classification/output/reports/train/b073bf9c0864dbe478be45834fba4bfd/params.yaml new file mode 100644 index 00000000..0de5e0d9 --- /dev/null +++ b/examples/security/classification/output/reports/train/b073bf9c0864dbe478be45834fba4bfd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/adv_predictions.json + adv_probabilities_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/777d313f8a44e94391f9626a2a8e7af2.pkl + params_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/params.yaml + predictions_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/predictions.json + probabilities_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/probabilities.json + score_dict_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/score_dict.json + test_labels_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/test_labels.json + train_labels_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/train_labels.json + model_dir: models + name: b073bf9c0864dbe478be45834fba4bfd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a7ebf0e2abe70c0f965bb8a633e7112b + trainer: + kwargs: {} +name: b073bf9c0864dbe478be45834fba4bfd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/params.yaml b/examples/security/classification/output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/params.yaml new file mode 100644 index 00000000..fcce30bc --- /dev/null +++ b/examples/security/classification/output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/adv_predictions.json + adv_probabilities_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/ac48774d8f6903ffb615ead5280f6df1.pkl + params_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/params.yaml + predictions_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/predictions.json + probabilities_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/probabilities.json + score_dict_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/score_dict.json + test_labels_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/test_labels.json + train_labels_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/train_labels.json + model_dir: models + name: b08d67f463bc07fbcfca0800f88a21fd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 86cbf7c1e4a09aacde3b4c36d135a369 + trainer: + kwargs: {} +name: b08d67f463bc07fbcfca0800f88a21fd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/params.yaml b/examples/security/classification/output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/params.yaml new file mode 100644 index 00000000..3d761b7e --- /dev/null +++ b/examples/security/classification/output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/adv_predictions.json + adv_probabilities_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/ced3974dabdc9c9c5baea73e49f90827.pkl + params_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/params.yaml + predictions_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/predictions.json + probabilities_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/probabilities.json + score_dict_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/score_dict.json + test_labels_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/test_labels.json + train_labels_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/train_labels.json + model_dir: models + name: b09715b1878c5e9d7a9c0e110be095d1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 689fa324962cab081d8ca2d1f4bfd079 + trainer: + kwargs: {} +name: b09715b1878c5e9d7a9c0e110be095d1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/params.yaml b/examples/security/classification/output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/params.yaml new file mode 100644 index 00000000..0571ec76 --- /dev/null +++ b/examples/security/classification/output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/adv_predictions.json + adv_probabilities_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/f542cadbfd376493fb073801d0d5772a.pkl + params_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/params.yaml + predictions_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/predictions.json + probabilities_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/probabilities.json + score_dict_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/score_dict.json + test_labels_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/test_labels.json + train_labels_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/train_labels.json + model_dir: models + name: b09b4524732cec7c943f61d7ddbac3a0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b2e067155093df9a02364b0abdda1bd1 + trainer: + kwargs: {} +name: b09b4524732cec7c943f61d7ddbac3a0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/params.yaml b/examples/security/classification/output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/params.yaml new file mode 100644 index 00000000..75cbf0a0 --- /dev/null +++ b/examples/security/classification/output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/adv_predictions.json + adv_probabilities_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/fa830f178d58c7def8678fce834e77fa.pkl + params_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/params.yaml + predictions_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/predictions.json + probabilities_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/probabilities.json + score_dict_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/score_dict.json + test_labels_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/test_labels.json + train_labels_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/train_labels.json + model_dir: models + name: b0ac28588ddf68cf92ad4469b09a81da + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d25fbed063bc68511a9b0c01e19ef6c2 + trainer: + kwargs: {} +name: b0ac28588ddf68cf92ad4469b09a81da +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/params.yaml b/examples/security/classification/output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/params.yaml new file mode 100644 index 00000000..0c7d7aff --- /dev/null +++ b/examples/security/classification/output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/adv_predictions.json + adv_probabilities_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/4a7b68e0326bf9fde7b0217a900cd3b3.pkl + params_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/params.yaml + predictions_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/predictions.json + probabilities_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/probabilities.json + score_dict_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/score_dict.json + test_labels_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/test_labels.json + train_labels_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/train_labels.json + model_dir: models + name: b0b1fc0503a7aada52c030973c05f3d9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 747af60beddb02af93dba6452928c02a + trainer: + kwargs: {} +name: b0b1fc0503a7aada52c030973c05f3d9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/params.yaml b/examples/security/classification/output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/params.yaml new file mode 100644 index 00000000..e1b38216 --- /dev/null +++ b/examples/security/classification/output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/adv_predictions.json + adv_probabilities_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/c38b59fffec724e8c1304d4e9dc16def.pkl + params_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/params.yaml + predictions_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/predictions.json + probabilities_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/probabilities.json + score_dict_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/score_dict.json + test_labels_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/test_labels.json + train_labels_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/train_labels.json + model_dir: models + name: b0ca43c78c36d5ab6d9480764ecc3f41 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3405c92a11bfb2342ebea7b064c145e0 + trainer: + kwargs: {} +name: b0ca43c78c36d5ab6d9480764ecc3f41 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b0e618d6948e659d5b10de208c5e28d2/params.yaml b/examples/security/classification/output/reports/train/b0e618d6948e659d5b10de208c5e28d2/params.yaml new file mode 100644 index 00000000..be3fe09f --- /dev/null +++ b/examples/security/classification/output/reports/train/b0e618d6948e659d5b10de208c5e28d2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/adv_predictions.json + adv_probabilities_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/45b9ec01ca85badd1efcc4aeaae9f987.pkl + params_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/params.yaml + predictions_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/predictions.json + probabilities_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/probabilities.json + score_dict_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/score_dict.json + test_labels_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/test_labels.json + train_labels_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/train_labels.json + model_dir: models + name: b0e618d6948e659d5b10de208c5e28d2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e989eb6de85667fba02ed752f87a3f47 + trainer: + kwargs: {} +name: b0e618d6948e659d5b10de208c5e28d2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b101166f2f63d4c89032296e7aba1d14/params.yaml b/examples/security/classification/output/reports/train/b101166f2f63d4c89032296e7aba1d14/params.yaml new file mode 100644 index 00000000..c57f6ca9 --- /dev/null +++ b/examples/security/classification/output/reports/train/b101166f2f63d4c89032296e7aba1d14/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/adv_predictions.json + adv_probabilities_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/f81d6c0f9e739d1ada849b37b4322cfb.pkl + params_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/params.yaml + predictions_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/predictions.json + probabilities_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/probabilities.json + score_dict_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/score_dict.json + test_labels_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/test_labels.json + train_labels_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/train_labels.json + model_dir: models + name: b101166f2f63d4c89032296e7aba1d14 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fa101929735b869cd658de351f437508 + trainer: + kwargs: {} +name: b101166f2f63d4c89032296e7aba1d14 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b115bb5836f022a6622b76f60de5c187/params.yaml b/examples/security/classification/output/reports/train/b115bb5836f022a6622b76f60de5c187/params.yaml new file mode 100644 index 00000000..b0752e68 --- /dev/null +++ b/examples/security/classification/output/reports/train/b115bb5836f022a6622b76f60de5c187/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/adv_predictions.json + adv_probabilities_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/579a3da495777545951539e21b6815ed.pkl + params_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/params.yaml + predictions_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/predictions.json + probabilities_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/probabilities.json + score_dict_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/score_dict.json + test_labels_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/test_labels.json + train_labels_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/train_labels.json + model_dir: models + name: b115bb5836f022a6622b76f60de5c187 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 171e786f958decc759f5492a983bd739 + trainer: + kwargs: {} +name: b115bb5836f022a6622b76f60de5c187 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b144ce87e635b5009f96954d62b457a9/params.yaml b/examples/security/classification/output/reports/train/b144ce87e635b5009f96954d62b457a9/params.yaml new file mode 100644 index 00000000..f5cb98c9 --- /dev/null +++ b/examples/security/classification/output/reports/train/b144ce87e635b5009f96954d62b457a9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/adv_predictions.json + adv_probabilities_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/c616caf3545995c371048faf19eefc5f.pkl + params_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/params.yaml + predictions_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/predictions.json + probabilities_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/probabilities.json + score_dict_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/score_dict.json + test_labels_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/test_labels.json + train_labels_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/train_labels.json + model_dir: models + name: b144ce87e635b5009f96954d62b457a9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9985f1b0132b35000387e0123ee02662 + trainer: + kwargs: {} +name: b144ce87e635b5009f96954d62b457a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b16411c8f872185d6a5955debbac01af/params.yaml b/examples/security/classification/output/reports/train/b16411c8f872185d6a5955debbac01af/params.yaml new file mode 100644 index 00000000..67cb0820 --- /dev/null +++ b/examples/security/classification/output/reports/train/b16411c8f872185d6a5955debbac01af/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b16411c8f872185d6a5955debbac01af/adv_predictions.json + adv_probabilities_file: output/reports/train/b16411c8f872185d6a5955debbac01af/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/5b44463d43a412981bacd6a4609540a2.pkl + params_file: output/reports/train/b16411c8f872185d6a5955debbac01af/params.yaml + predictions_file: output/reports/train/b16411c8f872185d6a5955debbac01af/predictions.json + probabilities_file: output/reports/train/b16411c8f872185d6a5955debbac01af/probabilities.json + score_dict_file: output/reports/train/b16411c8f872185d6a5955debbac01af/score_dict.json + test_labels_file: output/reports/train/b16411c8f872185d6a5955debbac01af/test_labels.json + train_labels_file: output/reports/train/b16411c8f872185d6a5955debbac01af/train_labels.json + model_dir: models + name: b16411c8f872185d6a5955debbac01af + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ef56d8cb480d48bd4a1dea4f75919232 + trainer: + kwargs: {} +name: b16411c8f872185d6a5955debbac01af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b16ee196d18e3476d7524c03a462cb9c/params.yaml b/examples/security/classification/output/reports/train/b16ee196d18e3476d7524c03a462cb9c/params.yaml new file mode 100644 index 00000000..e372d673 --- /dev/null +++ b/examples/security/classification/output/reports/train/b16ee196d18e3476d7524c03a462cb9c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/adv_predictions.json + adv_probabilities_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/76a6adaead1a24255f3884113a825421.pkl + params_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/params.yaml + predictions_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/predictions.json + probabilities_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/probabilities.json + score_dict_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/score_dict.json + test_labels_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/test_labels.json + train_labels_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/train_labels.json + model_dir: models + name: b16ee196d18e3476d7524c03a462cb9c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8e99047255178d8e2a0b0f00a2b1ff04 + trainer: + kwargs: {} +name: b16ee196d18e3476d7524c03a462cb9c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b186a561bfb6d991711ea844aa49c184/params.yaml b/examples/security/classification/output/reports/train/b186a561bfb6d991711ea844aa49c184/params.yaml new file mode 100644 index 00000000..29640511 --- /dev/null +++ b/examples/security/classification/output/reports/train/b186a561bfb6d991711ea844aa49c184/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/adv_predictions.json + adv_probabilities_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/930ea8546e1e3526ddf742770c1804e6.pkl + params_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/params.yaml + predictions_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/predictions.json + probabilities_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/probabilities.json + score_dict_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/score_dict.json + test_labels_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/test_labels.json + train_labels_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/train_labels.json + model_dir: models + name: b186a561bfb6d991711ea844aa49c184 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7a30672c4cd58c8aaa150c4008cd2a3b + trainer: + kwargs: {} +name: b186a561bfb6d991711ea844aa49c184 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b18e1705cd48728742ac0b744699ca62/params.yaml b/examples/security/classification/output/reports/train/b18e1705cd48728742ac0b744699ca62/params.yaml new file mode 100644 index 00000000..eebf1d87 --- /dev/null +++ b/examples/security/classification/output/reports/train/b18e1705cd48728742ac0b744699ca62/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/adv_predictions.json + adv_probabilities_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/f028cdff38d532c53568acb1276a70bd.pkl + params_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/params.yaml + predictions_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/predictions.json + probabilities_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/probabilities.json + score_dict_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/score_dict.json + test_labels_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/test_labels.json + train_labels_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/train_labels.json + model_dir: models + name: b18e1705cd48728742ac0b744699ca62 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 65afaf613d4d085757f8ac5ae4e66386 + trainer: + kwargs: {} +name: b18e1705cd48728742ac0b744699ca62 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/params.yaml b/examples/security/classification/output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/params.yaml new file mode 100644 index 00000000..8fc2125a --- /dev/null +++ b/examples/security/classification/output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/adv_predictions.json + adv_probabilities_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/eef12e64836a22f728a4fd66c4ef2f77.pkl + params_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/params.yaml + predictions_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/predictions.json + probabilities_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/probabilities.json + score_dict_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/score_dict.json + test_labels_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/test_labels.json + train_labels_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/train_labels.json + model_dir: models + name: b19e9c4cf442acef23a5ffc29400ee16 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d9fe3b1790831349872dafb1bea10011 + trainer: + kwargs: {} +name: b19e9c4cf442acef23a5ffc29400ee16 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/params.yaml b/examples/security/classification/output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/params.yaml new file mode 100644 index 00000000..20cb5e37 --- /dev/null +++ b/examples/security/classification/output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/adv_predictions.json + adv_probabilities_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/3f7400b13338204448e8bbf1fb57bf62.pkl + params_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/params.yaml + predictions_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/predictions.json + probabilities_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/probabilities.json + score_dict_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/score_dict.json + test_labels_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/test_labels.json + train_labels_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/train_labels.json + model_dir: models + name: b1bdc9c2e9a23eecd373a174cee69d9c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3a7fd503710857efdb7df6249b193ad9 + trainer: + kwargs: {} +name: b1bdc9c2e9a23eecd373a174cee69d9c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/params.yaml b/examples/security/classification/output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/params.yaml new file mode 100644 index 00000000..3e83f82f --- /dev/null +++ b/examples/security/classification/output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/adv_predictions.json + adv_probabilities_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/f5a0fec54c3becb6156cef8f2c680257.pkl + params_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/params.yaml + predictions_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/predictions.json + probabilities_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/probabilities.json + score_dict_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/score_dict.json + test_labels_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/test_labels.json + train_labels_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/train_labels.json + model_dir: models + name: b1c79fd6256a6c94ab9e424b18f97e06 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ccdbcfed7ddbe974f16200612fa4750 + trainer: + kwargs: {} +name: b1c79fd6256a6c94ab9e424b18f97e06 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/params.yaml b/examples/security/classification/output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/params.yaml new file mode 100644 index 00000000..38d98fb5 --- /dev/null +++ b/examples/security/classification/output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/adv_predictions.json + adv_probabilities_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/7bb8ef06e4d54b0a46e5d8ac498b717e.pkl + params_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/params.yaml + predictions_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/predictions.json + probabilities_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/probabilities.json + score_dict_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/score_dict.json + test_labels_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/test_labels.json + train_labels_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/train_labels.json + model_dir: models + name: b1cb6017da805a6e56df3ef6de028ecb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 212371939eced90881e29348c637e712 + trainer: + kwargs: {} +name: b1cb6017da805a6e56df3ef6de028ecb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b1d354ebffed26f6b367770a746d44c2/params.yaml b/examples/security/classification/output/reports/train/b1d354ebffed26f6b367770a746d44c2/params.yaml new file mode 100644 index 00000000..f2c1d28b --- /dev/null +++ b/examples/security/classification/output/reports/train/b1d354ebffed26f6b367770a746d44c2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/adv_predictions.json + adv_probabilities_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/62c240c5b8a978c04835baa76580112c.pkl + params_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/params.yaml + predictions_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/predictions.json + probabilities_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/probabilities.json + score_dict_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/score_dict.json + test_labels_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/test_labels.json + train_labels_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/train_labels.json + model_dir: models + name: b1d354ebffed26f6b367770a746d44c2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b16a67f5b43fc267bdf68636054caef6 + trainer: + kwargs: {} +name: b1d354ebffed26f6b367770a746d44c2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/params.yaml b/examples/security/classification/output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/params.yaml new file mode 100644 index 00000000..eefcf414 --- /dev/null +++ b/examples/security/classification/output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/adv_predictions.json + adv_probabilities_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/9a2abb54c915d0cf947e9a4d5ee27448.pkl + params_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/params.yaml + predictions_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/predictions.json + probabilities_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/probabilities.json + score_dict_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/score_dict.json + test_labels_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/test_labels.json + train_labels_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/train_labels.json + model_dir: models + name: b2374371b21a0cf5b9106cedb3e8d277 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5b081fe0c3f1cd26bfc2ee001e84057f + trainer: + kwargs: {} +name: b2374371b21a0cf5b9106cedb3e8d277 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b23e641fa13500b02712fc234f9b5ec0/params.yaml b/examples/security/classification/output/reports/train/b23e641fa13500b02712fc234f9b5ec0/params.yaml new file mode 100644 index 00000000..1a4d2c82 --- /dev/null +++ b/examples/security/classification/output/reports/train/b23e641fa13500b02712fc234f9b5ec0/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/adv_predictions.json + adv_probabilities_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/a55f8e267f8ece1a566c89e1759d38ba.pkl + params_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/params.yaml + predictions_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/predictions.json + probabilities_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/probabilities.json + score_dict_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/score_dict.json + test_labels_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/test_labels.json + train_labels_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/train_labels.json + model_dir: models + name: b23e641fa13500b02712fc234f9b5ec0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e89cc737f5ae323f26f0f63f5af6d7d9 + trainer: + kwargs: {} +name: b23e641fa13500b02712fc234f9b5ec0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b25b350d11ba86480ba563458a999744/params.yaml b/examples/security/classification/output/reports/train/b25b350d11ba86480ba563458a999744/params.yaml new file mode 100644 index 00000000..27dff65c --- /dev/null +++ b/examples/security/classification/output/reports/train/b25b350d11ba86480ba563458a999744/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b25b350d11ba86480ba563458a999744/adv_predictions.json + adv_probabilities_file: output/reports/train/b25b350d11ba86480ba563458a999744/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/e79b283d6662cbe71bd905d42bde0050.pkl + params_file: output/reports/train/b25b350d11ba86480ba563458a999744/params.yaml + predictions_file: output/reports/train/b25b350d11ba86480ba563458a999744/predictions.json + probabilities_file: output/reports/train/b25b350d11ba86480ba563458a999744/probabilities.json + score_dict_file: output/reports/train/b25b350d11ba86480ba563458a999744/score_dict.json + test_labels_file: output/reports/train/b25b350d11ba86480ba563458a999744/test_labels.json + train_labels_file: output/reports/train/b25b350d11ba86480ba563458a999744/train_labels.json + model_dir: models + name: b25b350d11ba86480ba563458a999744 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6c902c54d032e1d9826eca81620fb2bc + trainer: + kwargs: {} +name: b25b350d11ba86480ba563458a999744 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/params.yaml b/examples/security/classification/output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/params.yaml new file mode 100644 index 00000000..4dfd455d --- /dev/null +++ b/examples/security/classification/output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/adv_predictions.json + adv_probabilities_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/567cf7514cfdfb7cb759729dc5ff89b1.pkl + params_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/params.yaml + predictions_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/predictions.json + probabilities_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/probabilities.json + score_dict_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/score_dict.json + test_labels_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/test_labels.json + train_labels_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/train_labels.json + model_dir: models + name: b266005d2f02fb7cc293c8c67a357fb0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d6d8ace2daaf241fbd0e7637c0f60b2c + trainer: + kwargs: {} +name: b266005d2f02fb7cc293c8c67a357fb0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b28303e8ba27bfbbb77ae721e264104f/params.yaml b/examples/security/classification/output/reports/train/b28303e8ba27bfbbb77ae721e264104f/params.yaml new file mode 100644 index 00000000..54fccdc6 --- /dev/null +++ b/examples/security/classification/output/reports/train/b28303e8ba27bfbbb77ae721e264104f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/adv_predictions.json + adv_probabilities_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/56f7ff378a9be56451d0fd46e34296f2.pkl + params_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/params.yaml + predictions_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/predictions.json + probabilities_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/probabilities.json + score_dict_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/score_dict.json + test_labels_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/test_labels.json + train_labels_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/train_labels.json + model_dir: models + name: b28303e8ba27bfbbb77ae721e264104f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b1b5612679a08224ef94b8c0e8914426 + trainer: + kwargs: {} +name: b28303e8ba27bfbbb77ae721e264104f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b2a349c668a2fe304d32917d7334e428/params.yaml b/examples/security/classification/output/reports/train/b2a349c668a2fe304d32917d7334e428/params.yaml new file mode 100644 index 00000000..e6bbc1a2 --- /dev/null +++ b/examples/security/classification/output/reports/train/b2a349c668a2fe304d32917d7334e428/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/adv_predictions.json + adv_probabilities_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/0c25133068ff19e36fcc2b8f00b1e89f.pkl + params_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/params.yaml + predictions_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/predictions.json + probabilities_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/probabilities.json + score_dict_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/score_dict.json + test_labels_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/test_labels.json + train_labels_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/train_labels.json + model_dir: models + name: b2a349c668a2fe304d32917d7334e428 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7ccc0c563096a679e7ce970a33256c27 + trainer: + kwargs: {} +name: b2a349c668a2fe304d32917d7334e428 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b2e4880b7012abdccf7d65bcd027afde/params.yaml b/examples/security/classification/output/reports/train/b2e4880b7012abdccf7d65bcd027afde/params.yaml new file mode 100644 index 00000000..96db6a59 --- /dev/null +++ b/examples/security/classification/output/reports/train/b2e4880b7012abdccf7d65bcd027afde/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/adv_predictions.json + adv_probabilities_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/9dd1c6554bab881d09c5ee9385a6f146.pkl + params_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/params.yaml + predictions_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/predictions.json + probabilities_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/probabilities.json + score_dict_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/score_dict.json + test_labels_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/test_labels.json + train_labels_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/train_labels.json + model_dir: models + name: b2e4880b7012abdccf7d65bcd027afde + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 336dbedf8d94377ef7723ddb83954cb3 + trainer: + kwargs: {} +name: b2e4880b7012abdccf7d65bcd027afde +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b3119f0403cdf4582a1626bad0d3e288/params.yaml b/examples/security/classification/output/reports/train/b3119f0403cdf4582a1626bad0d3e288/params.yaml new file mode 100644 index 00000000..dd1a5939 --- /dev/null +++ b/examples/security/classification/output/reports/train/b3119f0403cdf4582a1626bad0d3e288/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/adv_predictions.json + adv_probabilities_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/f6b9e651e088ebcf8422012cd7ab3acc.pkl + params_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/params.yaml + predictions_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/predictions.json + probabilities_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/probabilities.json + score_dict_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/score_dict.json + test_labels_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/test_labels.json + train_labels_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/train_labels.json + model_dir: models + name: b3119f0403cdf4582a1626bad0d3e288 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d9847db4ee215b0e92c10a8db973712a + trainer: + kwargs: {} +name: b3119f0403cdf4582a1626bad0d3e288 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b31d529f0333ee008669c642de75d1c0/params.yaml b/examples/security/classification/output/reports/train/b31d529f0333ee008669c642de75d1c0/params.yaml new file mode 100644 index 00000000..6d4d620a --- /dev/null +++ b/examples/security/classification/output/reports/train/b31d529f0333ee008669c642de75d1c0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/adv_predictions.json + adv_probabilities_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/c7920214a7e7c1c5df964ee62b5e8ff1.pkl + params_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/params.yaml + predictions_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/predictions.json + probabilities_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/probabilities.json + score_dict_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/score_dict.json + test_labels_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/test_labels.json + train_labels_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/train_labels.json + model_dir: models + name: b31d529f0333ee008669c642de75d1c0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ee777e014e7846da3c20f262c342647d + trainer: + kwargs: {} +name: b31d529f0333ee008669c642de75d1c0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b331b411b3fb0bd9082b366763587fe1/params.yaml b/examples/security/classification/output/reports/train/b331b411b3fb0bd9082b366763587fe1/params.yaml new file mode 100644 index 00000000..845f89ad --- /dev/null +++ b/examples/security/classification/output/reports/train/b331b411b3fb0bd9082b366763587fe1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/adv_predictions.json + adv_probabilities_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/d5e3b77f42401e60180ee8df64dea98a.pkl + params_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/params.yaml + predictions_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/predictions.json + probabilities_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/probabilities.json + score_dict_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/score_dict.json + test_labels_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/test_labels.json + train_labels_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/train_labels.json + model_dir: models + name: b331b411b3fb0bd9082b366763587fe1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 59acd8432a806802cd41deb69bc79999 + trainer: + kwargs: {} +name: b331b411b3fb0bd9082b366763587fe1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/params.yaml b/examples/security/classification/output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/params.yaml new file mode 100644 index 00000000..c25d7a43 --- /dev/null +++ b/examples/security/classification/output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/adv_predictions.json + adv_probabilities_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/79207429287df6312476579489f6e840.pkl + params_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/params.yaml + predictions_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/predictions.json + probabilities_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/probabilities.json + score_dict_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/score_dict.json + test_labels_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/test_labels.json + train_labels_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/train_labels.json + model_dir: models + name: b3442be7ae85249e5be15dc5912c5d6d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 310b45acd29dc0a5be693e1b661369b8 + trainer: + kwargs: {} +name: b3442be7ae85249e5be15dc5912c5d6d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b3450c14390ce6087e1a0bb29d887909/params.yaml b/examples/security/classification/output/reports/train/b3450c14390ce6087e1a0bb29d887909/params.yaml new file mode 100644 index 00000000..9c05763b --- /dev/null +++ b/examples/security/classification/output/reports/train/b3450c14390ce6087e1a0bb29d887909/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/adv_predictions.json + adv_probabilities_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/ff944458294e23bbd01c4c675f86f3c7.pkl + params_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/params.yaml + predictions_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/predictions.json + probabilities_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/probabilities.json + score_dict_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/score_dict.json + test_labels_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/test_labels.json + train_labels_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/train_labels.json + model_dir: models + name: b3450c14390ce6087e1a0bb29d887909 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9433972d7d3d88fb71285376e0a11981 + trainer: + kwargs: {} +name: b3450c14390ce6087e1a0bb29d887909 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b35a6ca7a6955989b0f18ade83048016/params.yaml b/examples/security/classification/output/reports/train/b35a6ca7a6955989b0f18ade83048016/params.yaml new file mode 100644 index 00000000..2533e350 --- /dev/null +++ b/examples/security/classification/output/reports/train/b35a6ca7a6955989b0f18ade83048016/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/adv_predictions.json + adv_probabilities_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/ccb9f1f844a43726b7608df1010cb39c.pkl + params_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/params.yaml + predictions_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/predictions.json + probabilities_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/probabilities.json + score_dict_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/score_dict.json + test_labels_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/test_labels.json + train_labels_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/train_labels.json + model_dir: models + name: b35a6ca7a6955989b0f18ade83048016 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a84f47c604fc9574dea66c21862b8173 + trainer: + kwargs: {} +name: b35a6ca7a6955989b0f18ade83048016 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b36bd99af799282c0b3d31e75527d319/params.yaml b/examples/security/classification/output/reports/train/b36bd99af799282c0b3d31e75527d319/params.yaml new file mode 100644 index 00000000..70933af6 --- /dev/null +++ b/examples/security/classification/output/reports/train/b36bd99af799282c0b3d31e75527d319/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/adv_predictions.json + adv_probabilities_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/04199156f6584f50eb04a78d18418c05.pkl + params_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/params.yaml + predictions_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/predictions.json + probabilities_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/probabilities.json + score_dict_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/score_dict.json + test_labels_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/test_labels.json + train_labels_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/train_labels.json + model_dir: models + name: b36bd99af799282c0b3d31e75527d319 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 409621bf18548125a05201cb4e17575d + trainer: + kwargs: {} +name: b36bd99af799282c0b3d31e75527d319 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b36e66880452690a16d0fc1c39d7801c/params.yaml b/examples/security/classification/output/reports/train/b36e66880452690a16d0fc1c39d7801c/params.yaml new file mode 100644 index 00000000..979d54b5 --- /dev/null +++ b/examples/security/classification/output/reports/train/b36e66880452690a16d0fc1c39d7801c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/adv_predictions.json + adv_probabilities_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/47fd50a26513ef12c1acd59da30c2eb1.pkl + params_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/params.yaml + predictions_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/predictions.json + probabilities_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/probabilities.json + score_dict_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/score_dict.json + test_labels_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/test_labels.json + train_labels_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/train_labels.json + model_dir: models + name: b36e66880452690a16d0fc1c39d7801c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c1a0c66c34e26375605bea55b2dec246 + trainer: + kwargs: {} +name: b36e66880452690a16d0fc1c39d7801c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/params.yaml b/examples/security/classification/output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/params.yaml new file mode 100644 index 00000000..4d8b1668 --- /dev/null +++ b/examples/security/classification/output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/adv_predictions.json + adv_probabilities_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/cfc034174f2315cd2bcdbd7313714bfd.pkl + params_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/params.yaml + predictions_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/predictions.json + probabilities_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/probabilities.json + score_dict_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/score_dict.json + test_labels_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/test_labels.json + train_labels_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/train_labels.json + model_dir: models + name: b3ef240e0f55a5c309b43d4bc35ea062 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e44cd7b38e4965055ed9acc22a40c700 + trainer: + kwargs: {} +name: b3ef240e0f55a5c309b43d4bc35ea062 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/params.yaml b/examples/security/classification/output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/params.yaml new file mode 100644 index 00000000..60fe65fa --- /dev/null +++ b/examples/security/classification/output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/adv_predictions.json + adv_probabilities_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/e674e63afdbcbf7639968233c9884e8a.pkl + params_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/params.yaml + predictions_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/predictions.json + probabilities_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/probabilities.json + score_dict_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/score_dict.json + test_labels_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/test_labels.json + train_labels_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/train_labels.json + model_dir: models + name: b3f7ae5c0c4e50adc6f90612151ba2aa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 50b81f12ab9c457d1f157048a83671d8 + trainer: + kwargs: {} +name: b3f7ae5c0c4e50adc6f90612151ba2aa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/params.yaml b/examples/security/classification/output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/params.yaml new file mode 100644 index 00000000..e2fea91d --- /dev/null +++ b/examples/security/classification/output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/adv_predictions.json + adv_probabilities_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/d9d5d8c027c19c97ae521d36137f8867.pkl + params_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/params.yaml + predictions_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/predictions.json + probabilities_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/probabilities.json + score_dict_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/score_dict.json + test_labels_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/test_labels.json + train_labels_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/train_labels.json + model_dir: models + name: b3fd461471eb35c2aeef84d33f51e57f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d98fa551231cd2ffeb0aff82c46b74eb + trainer: + kwargs: {} +name: b3fd461471eb35c2aeef84d33f51e57f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b4347aba65f776f8be3ec133765739c6/params.yaml b/examples/security/classification/output/reports/train/b4347aba65f776f8be3ec133765739c6/params.yaml new file mode 100644 index 00000000..a9882dc7 --- /dev/null +++ b/examples/security/classification/output/reports/train/b4347aba65f776f8be3ec133765739c6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/adv_predictions.json + adv_probabilities_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/7963eab8a274989cb2933b065f2611dd.pkl + params_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/params.yaml + predictions_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/predictions.json + probabilities_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/probabilities.json + score_dict_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/score_dict.json + test_labels_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/test_labels.json + train_labels_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/train_labels.json + model_dir: models + name: b4347aba65f776f8be3ec133765739c6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3e6a86fc428d6d2fa1a6e9b307e64b66 + trainer: + kwargs: {} +name: b4347aba65f776f8be3ec133765739c6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/params.yaml b/examples/security/classification/output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/params.yaml new file mode 100644 index 00000000..cc45a55f --- /dev/null +++ b/examples/security/classification/output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/adv_predictions.json + adv_probabilities_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/1d7c8dfdfbd5e108cca52ecfb5548753.pkl + params_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/params.yaml + predictions_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/predictions.json + probabilities_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/probabilities.json + score_dict_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/score_dict.json + test_labels_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/test_labels.json + train_labels_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/train_labels.json + model_dir: models + name: b439a0efccf158906cd2c5f4f8c3bbff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 776fae8f95eb8bf900a7ccb02e3957f6 + trainer: + kwargs: {} +name: b439a0efccf158906cd2c5f4f8c3bbff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b448cd347129df31e3592b793da3c022/params.yaml b/examples/security/classification/output/reports/train/b448cd347129df31e3592b793da3c022/params.yaml new file mode 100644 index 00000000..019c1b2f --- /dev/null +++ b/examples/security/classification/output/reports/train/b448cd347129df31e3592b793da3c022/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b448cd347129df31e3592b793da3c022/adv_predictions.json + adv_probabilities_file: output/reports/train/b448cd347129df31e3592b793da3c022/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/d2cc9ca096a3de0fd30ad6255fc62367.pkl + params_file: output/reports/train/b448cd347129df31e3592b793da3c022/params.yaml + predictions_file: output/reports/train/b448cd347129df31e3592b793da3c022/predictions.json + probabilities_file: output/reports/train/b448cd347129df31e3592b793da3c022/probabilities.json + score_dict_file: output/reports/train/b448cd347129df31e3592b793da3c022/score_dict.json + test_labels_file: output/reports/train/b448cd347129df31e3592b793da3c022/test_labels.json + train_labels_file: output/reports/train/b448cd347129df31e3592b793da3c022/train_labels.json + model_dir: models + name: b448cd347129df31e3592b793da3c022 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 012c2279d3343472102505dc6cc46ced + trainer: + kwargs: {} +name: b448cd347129df31e3592b793da3c022 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/params.yaml b/examples/security/classification/output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/params.yaml new file mode 100644 index 00000000..e848f972 --- /dev/null +++ b/examples/security/classification/output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/adv_predictions.json + adv_probabilities_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/facc60c8ce987094127df32f360309ea.pkl + params_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/params.yaml + predictions_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/predictions.json + probabilities_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/probabilities.json + score_dict_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/score_dict.json + test_labels_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/test_labels.json + train_labels_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/train_labels.json + model_dir: models + name: b4826a6dc881263fa5ae966e3df6bb07 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 919e15e89f29d2f1a322acbb5038598c + trainer: + kwargs: {} +name: b4826a6dc881263fa5ae966e3df6bb07 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/params.yaml b/examples/security/classification/output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/params.yaml new file mode 100644 index 00000000..9b584e3c --- /dev/null +++ b/examples/security/classification/output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/adv_predictions.json + adv_probabilities_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/cca6fceaec58a47e11385bea56925b77.pkl + params_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/params.yaml + predictions_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/predictions.json + probabilities_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/probabilities.json + score_dict_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/score_dict.json + test_labels_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/test_labels.json + train_labels_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/train_labels.json + model_dir: models + name: b48bfeb80d5c92230840aa55ba2b95b4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 017e0cf88e8e833d55872d706262aca6 + trainer: + kwargs: {} +name: b48bfeb80d5c92230840aa55ba2b95b4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b53df724cd32d17761578a10935d939b/params.yaml b/examples/security/classification/output/reports/train/b53df724cd32d17761578a10935d939b/params.yaml new file mode 100644 index 00000000..d12da70d --- /dev/null +++ b/examples/security/classification/output/reports/train/b53df724cd32d17761578a10935d939b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b53df724cd32d17761578a10935d939b/adv_predictions.json + adv_probabilities_file: output/reports/train/b53df724cd32d17761578a10935d939b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/04d29a7bb3550e5f905c54f957564063.pkl + params_file: output/reports/train/b53df724cd32d17761578a10935d939b/params.yaml + predictions_file: output/reports/train/b53df724cd32d17761578a10935d939b/predictions.json + probabilities_file: output/reports/train/b53df724cd32d17761578a10935d939b/probabilities.json + score_dict_file: output/reports/train/b53df724cd32d17761578a10935d939b/score_dict.json + test_labels_file: output/reports/train/b53df724cd32d17761578a10935d939b/test_labels.json + train_labels_file: output/reports/train/b53df724cd32d17761578a10935d939b/train_labels.json + model_dir: models + name: b53df724cd32d17761578a10935d939b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1aaf9aa63405270ec321f119fc4901bb + trainer: + kwargs: {} +name: b53df724cd32d17761578a10935d939b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/params.yaml b/examples/security/classification/output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/params.yaml new file mode 100644 index 00000000..1d7e722d --- /dev/null +++ b/examples/security/classification/output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/adv_predictions.json + adv_probabilities_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/fc7d95da492211c442e4b1b3d7d70884.pkl + params_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/params.yaml + predictions_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/predictions.json + probabilities_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/probabilities.json + score_dict_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/score_dict.json + test_labels_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/test_labels.json + train_labels_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/train_labels.json + model_dir: models + name: b54424cf5b86b9ff8d67bbdefea78412 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3155eef2acd41775cce077e2e2816915 + trainer: + kwargs: {} +name: b54424cf5b86b9ff8d67bbdefea78412 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b548ae1bd6c36238751519fcff86c2c6/params.yaml b/examples/security/classification/output/reports/train/b548ae1bd6c36238751519fcff86c2c6/params.yaml new file mode 100644 index 00000000..73c40472 --- /dev/null +++ b/examples/security/classification/output/reports/train/b548ae1bd6c36238751519fcff86c2c6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/adv_predictions.json + adv_probabilities_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/799d12ddd24c2ead1bc98a5c7e4d9ef0.pkl + params_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/params.yaml + predictions_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/predictions.json + probabilities_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/probabilities.json + score_dict_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/score_dict.json + test_labels_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/test_labels.json + train_labels_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/train_labels.json + model_dir: models + name: b548ae1bd6c36238751519fcff86c2c6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2fe61643c9618450d5b2310ad4042edf + trainer: + kwargs: {} +name: b548ae1bd6c36238751519fcff86c2c6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/params.yaml b/examples/security/classification/output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/params.yaml new file mode 100644 index 00000000..7f550285 --- /dev/null +++ b/examples/security/classification/output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/adv_predictions.json + adv_probabilities_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/054679bda69ac91cc265ce84a038c0bb.pkl + params_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/params.yaml + predictions_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/predictions.json + probabilities_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/probabilities.json + score_dict_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/score_dict.json + test_labels_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/test_labels.json + train_labels_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/train_labels.json + model_dir: models + name: b55a22b0c88b0c6613fa1a4ae68b8bbc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1dd760efc13540bed9a0e8393add768c + trainer: + kwargs: {} +name: b55a22b0c88b0c6613fa1a4ae68b8bbc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b55b5efe408ba9665223dcefa2706bd4/params.yaml b/examples/security/classification/output/reports/train/b55b5efe408ba9665223dcefa2706bd4/params.yaml new file mode 100644 index 00000000..4d58d5d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/b55b5efe408ba9665223dcefa2706bd4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/adv_predictions.json + adv_probabilities_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e8948f3857b7a66a7dcd16f763e8ad4d.pkl + params_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/params.yaml + predictions_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/predictions.json + probabilities_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/probabilities.json + score_dict_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/score_dict.json + test_labels_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/test_labels.json + train_labels_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/train_labels.json + model_dir: models + name: b55b5efe408ba9665223dcefa2706bd4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bc4e3aee2468b3aaa5621d97f4bce51b + trainer: + kwargs: {} +name: b55b5efe408ba9665223dcefa2706bd4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/params.yaml b/examples/security/classification/output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/params.yaml new file mode 100644 index 00000000..6d9824af --- /dev/null +++ b/examples/security/classification/output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/adv_predictions.json + adv_probabilities_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/02a1c7bceefd3497782f329e5924148f.pkl + params_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/params.yaml + predictions_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/predictions.json + probabilities_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/probabilities.json + score_dict_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/score_dict.json + test_labels_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/test_labels.json + train_labels_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/train_labels.json + model_dir: models + name: b5bf5356d2fc64acd27d65d89e2f0d84 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e9063a27c282c6497072f9e7f27c2e31 + trainer: + kwargs: {} +name: b5bf5356d2fc64acd27d65d89e2f0d84 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b60204be05c88754b96dcba7ffcc6b79/params.yaml b/examples/security/classification/output/reports/train/b60204be05c88754b96dcba7ffcc6b79/params.yaml new file mode 100644 index 00000000..c48a2ce6 --- /dev/null +++ b/examples/security/classification/output/reports/train/b60204be05c88754b96dcba7ffcc6b79/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/adv_predictions.json + adv_probabilities_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/bafc9e81137a6cd0bbeb5d948f7b3c79.pkl + params_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/params.yaml + predictions_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/predictions.json + probabilities_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/probabilities.json + score_dict_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/score_dict.json + test_labels_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/test_labels.json + train_labels_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/train_labels.json + model_dir: models + name: b60204be05c88754b96dcba7ffcc6b79 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 38ab3ca758ae98b928efa6c70e43077b + trainer: + kwargs: {} +name: b60204be05c88754b96dcba7ffcc6b79 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b60a62c0592f813af995d3401e7caa1a/params.yaml b/examples/security/classification/output/reports/train/b60a62c0592f813af995d3401e7caa1a/params.yaml new file mode 100644 index 00000000..517e5724 --- /dev/null +++ b/examples/security/classification/output/reports/train/b60a62c0592f813af995d3401e7caa1a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/adv_predictions.json + adv_probabilities_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/d05d96bdc1faaae35570ec8677308216.pkl + params_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/params.yaml + predictions_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/predictions.json + probabilities_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/probabilities.json + score_dict_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/score_dict.json + test_labels_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/test_labels.json + train_labels_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/train_labels.json + model_dir: models + name: b60a62c0592f813af995d3401e7caa1a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e6eb147dbd55dfb3cdeb695a95a02fa5 + trainer: + kwargs: {} +name: b60a62c0592f813af995d3401e7caa1a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b63762c712d097e444a81bc97bfe889a/params.yaml b/examples/security/classification/output/reports/train/b63762c712d097e444a81bc97bfe889a/params.yaml new file mode 100644 index 00000000..bf16090e --- /dev/null +++ b/examples/security/classification/output/reports/train/b63762c712d097e444a81bc97bfe889a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/adv_predictions.json + adv_probabilities_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/d84b9b980e577bf1e28edf162c1e06f1.pkl + params_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/params.yaml + predictions_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/predictions.json + probabilities_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/probabilities.json + score_dict_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/score_dict.json + test_labels_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/test_labels.json + train_labels_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/train_labels.json + model_dir: models + name: b63762c712d097e444a81bc97bfe889a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 02411dec9e111e10f905a54cc827ece5 + trainer: + kwargs: {} +name: b63762c712d097e444a81bc97bfe889a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b6613c3c565c506c80c939afbb2135b9/params.yaml b/examples/security/classification/output/reports/train/b6613c3c565c506c80c939afbb2135b9/params.yaml new file mode 100644 index 00000000..a3682e16 --- /dev/null +++ b/examples/security/classification/output/reports/train/b6613c3c565c506c80c939afbb2135b9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/adv_predictions.json + adv_probabilities_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/a66e326f26e363f76fdaeb14f278c9db.pkl + params_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/params.yaml + predictions_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/predictions.json + probabilities_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/probabilities.json + score_dict_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/score_dict.json + test_labels_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/test_labels.json + train_labels_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/train_labels.json + model_dir: models + name: b6613c3c565c506c80c939afbb2135b9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 45490b4a66c5978ce212a81914aef517 + trainer: + kwargs: {} +name: b6613c3c565c506c80c939afbb2135b9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b668f51d862309cf70b8a342024b42d2/params.yaml b/examples/security/classification/output/reports/train/b668f51d862309cf70b8a342024b42d2/params.yaml new file mode 100644 index 00000000..3c61978a --- /dev/null +++ b/examples/security/classification/output/reports/train/b668f51d862309cf70b8a342024b42d2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/adv_predictions.json + adv_probabilities_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/06369cf98874d64b42014fe922fd69bc.pkl + params_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/params.yaml + predictions_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/predictions.json + probabilities_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/probabilities.json + score_dict_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/score_dict.json + test_labels_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/test_labels.json + train_labels_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/train_labels.json + model_dir: models + name: b668f51d862309cf70b8a342024b42d2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1ab65fc24f7413374b87f6905a6bc602 + trainer: + kwargs: {} +name: b668f51d862309cf70b8a342024b42d2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/params.yaml b/examples/security/classification/output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/params.yaml new file mode 100644 index 00000000..9bec8241 --- /dev/null +++ b/examples/security/classification/output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/adv_predictions.json + adv_probabilities_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/08c73f998ed8427041c2a3e51d49479f.pkl + params_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/params.yaml + predictions_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/predictions.json + probabilities_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/probabilities.json + score_dict_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/score_dict.json + test_labels_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/test_labels.json + train_labels_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/train_labels.json + model_dir: models + name: b66ac776b9e72e8dc83a0526e77afb5d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c3f4c04cc8017b44d19b872e74074b8d + trainer: + kwargs: {} +name: b66ac776b9e72e8dc83a0526e77afb5d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/params.yaml b/examples/security/classification/output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/params.yaml new file mode 100644 index 00000000..b803949f --- /dev/null +++ b/examples/security/classification/output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/adv_predictions.json + adv_probabilities_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/7ff492bd94cd1181d84401e93d3738e5.pkl + params_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/params.yaml + predictions_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/predictions.json + probabilities_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/probabilities.json + score_dict_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/score_dict.json + test_labels_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/test_labels.json + train_labels_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/train_labels.json + model_dir: models + name: b673f6c4d60fe62c6bd3df79fa3adc45 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e812b0dd4e6345081856ce109b0bd335 + trainer: + kwargs: {} +name: b673f6c4d60fe62c6bd3df79fa3adc45 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/params.yaml b/examples/security/classification/output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/params.yaml new file mode 100644 index 00000000..a33a54c2 --- /dev/null +++ b/examples/security/classification/output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/adv_predictions.json + adv_probabilities_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/bdf6a2dc627b642d3786f23dd4502270.pkl + params_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/params.yaml + predictions_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/predictions.json + probabilities_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/probabilities.json + score_dict_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/score_dict.json + test_labels_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/test_labels.json + train_labels_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/train_labels.json + model_dir: models + name: b68cbb13eb5e030b7c7f0b7cf74c1cbf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f22256fba1a9f840cd089cbeeeff6be4 + trainer: + kwargs: {} +name: b68cbb13eb5e030b7c7f0b7cf74c1cbf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b69fefe12e1a9b3660490e9b8691a493/params.yaml b/examples/security/classification/output/reports/train/b69fefe12e1a9b3660490e9b8691a493/params.yaml new file mode 100644 index 00000000..92f3278a --- /dev/null +++ b/examples/security/classification/output/reports/train/b69fefe12e1a9b3660490e9b8691a493/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/adv_predictions.json + adv_probabilities_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/30d4990134ba39931d81f08c107d225c.pkl + params_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/params.yaml + predictions_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/predictions.json + probabilities_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/probabilities.json + score_dict_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/score_dict.json + test_labels_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/test_labels.json + train_labels_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/train_labels.json + model_dir: models + name: b69fefe12e1a9b3660490e9b8691a493 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5c49313d65bbec400fb73784160aacb9 + trainer: + kwargs: {} +name: b69fefe12e1a9b3660490e9b8691a493 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/params.yaml b/examples/security/classification/output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/params.yaml new file mode 100644 index 00000000..5ee6f06a --- /dev/null +++ b/examples/security/classification/output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/adv_predictions.json + adv_probabilities_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/e8f6b6025a34a602146bcae789d8be0c.pkl + params_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/params.yaml + predictions_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/predictions.json + probabilities_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/probabilities.json + score_dict_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/score_dict.json + test_labels_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/test_labels.json + train_labels_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/train_labels.json + model_dir: models + name: b6aa456baf8c7416560fbcec8ca91d19 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 88e2b00b889f1270dabfbbc4c89a256b + trainer: + kwargs: {} +name: b6aa456baf8c7416560fbcec8ca91d19 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/params.yaml b/examples/security/classification/output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/params.yaml new file mode 100644 index 00000000..79f6df49 --- /dev/null +++ b/examples/security/classification/output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/adv_predictions.json + adv_probabilities_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/d0054cbb9861b08dfedb06deaf52928e.pkl + params_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/params.yaml + predictions_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/predictions.json + probabilities_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/probabilities.json + score_dict_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/score_dict.json + test_labels_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/test_labels.json + train_labels_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/train_labels.json + model_dir: models + name: b6bd47bfef20aa2e7af28184fd29e3d6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dd9322cf2ef93990ab69684fd34b6901 + trainer: + kwargs: {} +name: b6bd47bfef20aa2e7af28184fd29e3d6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b6c4bdda2d755c51203f0901f460de3a/params.yaml b/examples/security/classification/output/reports/train/b6c4bdda2d755c51203f0901f460de3a/params.yaml new file mode 100644 index 00000000..9de21ea0 --- /dev/null +++ b/examples/security/classification/output/reports/train/b6c4bdda2d755c51203f0901f460de3a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/adv_predictions.json + adv_probabilities_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/dbc7506699a570c20489b2b7c91f7d6c.pkl + params_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/params.yaml + predictions_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/predictions.json + probabilities_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/probabilities.json + score_dict_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/score_dict.json + test_labels_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/test_labels.json + train_labels_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/train_labels.json + model_dir: models + name: b6c4bdda2d755c51203f0901f460de3a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b6524aa2d6e78411207acd1281f2be28 + trainer: + kwargs: {} +name: b6c4bdda2d755c51203f0901f460de3a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/params.yaml b/examples/security/classification/output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/params.yaml new file mode 100644 index 00000000..f2310975 --- /dev/null +++ b/examples/security/classification/output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/adv_predictions.json + adv_probabilities_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/64a682e96d6e1fbe26f7b9adb68143b0.pkl + params_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/params.yaml + predictions_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/predictions.json + probabilities_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/probabilities.json + score_dict_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/score_dict.json + test_labels_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/test_labels.json + train_labels_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/train_labels.json + model_dir: models + name: b6d0c987a1a14591d7865f5b4ddce6b2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8acd494d6c32381606d5dc33317e464d + trainer: + kwargs: {} +name: b6d0c987a1a14591d7865f5b4ddce6b2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b6edc9d763e5d16e7131cb556a699408/params.yaml b/examples/security/classification/output/reports/train/b6edc9d763e5d16e7131cb556a699408/params.yaml new file mode 100644 index 00000000..589b702f --- /dev/null +++ b/examples/security/classification/output/reports/train/b6edc9d763e5d16e7131cb556a699408/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/adv_predictions.json + adv_probabilities_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/a50d8bf7e008d896cf4c36ef25a83ec6.pkl + params_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/params.yaml + predictions_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/predictions.json + probabilities_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/probabilities.json + score_dict_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/score_dict.json + test_labels_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/test_labels.json + train_labels_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/train_labels.json + model_dir: models + name: b6edc9d763e5d16e7131cb556a699408 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f6543bcbda12c802eee109bbd764cd16 + trainer: + kwargs: {} +name: b6edc9d763e5d16e7131cb556a699408 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/params.yaml b/examples/security/classification/output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/params.yaml new file mode 100644 index 00000000..e332b20c --- /dev/null +++ b/examples/security/classification/output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/adv_predictions.json + adv_probabilities_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/7dd5aa4a423abc77218b920c215a203f.pkl + params_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/params.yaml + predictions_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/predictions.json + probabilities_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/probabilities.json + score_dict_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/score_dict.json + test_labels_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/test_labels.json + train_labels_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/train_labels.json + model_dir: models + name: b6f1e3373b763f0d52ecd631d78624ab + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c14e8914a01964e262bd0904a698d892 + trainer: + kwargs: {} +name: b6f1e3373b763f0d52ecd631d78624ab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/params.yaml b/examples/security/classification/output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/params.yaml new file mode 100644 index 00000000..e38a001e --- /dev/null +++ b/examples/security/classification/output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/adv_predictions.json + adv_probabilities_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/9b813e6ae0876e440c55501ef28b2433.pkl + params_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/params.yaml + predictions_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/predictions.json + probabilities_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/probabilities.json + score_dict_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/score_dict.json + test_labels_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/test_labels.json + train_labels_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/train_labels.json + model_dir: models + name: b6f2f1c932258f4c2487648e0c9c620f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1fa0537985acd167e03115c8305bd15a + trainer: + kwargs: {} +name: b6f2f1c932258f4c2487648e0c9c620f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b72727ff278a85a00e80535dbdfc921e/params.yaml b/examples/security/classification/output/reports/train/b72727ff278a85a00e80535dbdfc921e/params.yaml new file mode 100644 index 00000000..0d3b30b1 --- /dev/null +++ b/examples/security/classification/output/reports/train/b72727ff278a85a00e80535dbdfc921e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/adv_predictions.json + adv_probabilities_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/4d22807ef8fea96b41c9de12270fb797.pkl + params_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/params.yaml + predictions_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/predictions.json + probabilities_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/probabilities.json + score_dict_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/score_dict.json + test_labels_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/test_labels.json + train_labels_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/train_labels.json + model_dir: models + name: b72727ff278a85a00e80535dbdfc921e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 30efd902b0023b614d64fc3c9be780e4 + trainer: + kwargs: {} +name: b72727ff278a85a00e80535dbdfc921e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/params.yaml b/examples/security/classification/output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/params.yaml new file mode 100644 index 00000000..d1484016 --- /dev/null +++ b/examples/security/classification/output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/adv_predictions.json + adv_probabilities_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/3eddc4d781985a51ec6b414ed39f7e84.pkl + params_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/params.yaml + predictions_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/predictions.json + probabilities_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/probabilities.json + score_dict_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/score_dict.json + test_labels_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/test_labels.json + train_labels_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/train_labels.json + model_dir: models + name: b72edca2a6cfcf1d1c2d9d5dc292ce39 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8bfbe7c626ad91830d6bc94f42ce9c8f + trainer: + kwargs: {} +name: b72edca2a6cfcf1d1c2d9d5dc292ce39 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b77425388d3d58f6e3a0a148ca15455b/params.yaml b/examples/security/classification/output/reports/train/b77425388d3d58f6e3a0a148ca15455b/params.yaml new file mode 100644 index 00000000..75e3f667 --- /dev/null +++ b/examples/security/classification/output/reports/train/b77425388d3d58f6e3a0a148ca15455b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/adv_predictions.json + adv_probabilities_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/c9cdbb6db0c1a61140343d2eadb92488.pkl + params_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/params.yaml + predictions_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/predictions.json + probabilities_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/probabilities.json + score_dict_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/score_dict.json + test_labels_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/test_labels.json + train_labels_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/train_labels.json + model_dir: models + name: b77425388d3d58f6e3a0a148ca15455b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 84d1bfc33d553749180fea2a1c6abc0e + trainer: + kwargs: {} +name: b77425388d3d58f6e3a0a148ca15455b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/params.yaml b/examples/security/classification/output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/params.yaml new file mode 100644 index 00000000..892373d5 --- /dev/null +++ b/examples/security/classification/output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/adv_predictions.json + adv_probabilities_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/3edfb90a132d1f5ed26bd971a31bc485.pkl + params_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/params.yaml + predictions_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/predictions.json + probabilities_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/probabilities.json + score_dict_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/score_dict.json + test_labels_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/test_labels.json + train_labels_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/train_labels.json + model_dir: models + name: b79f1dd92c7e2c7375a2ddca446ae993 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8b316de0adc33804432b5170d84dc87c + trainer: + kwargs: {} +name: b79f1dd92c7e2c7375a2ddca446ae993 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/params.yaml b/examples/security/classification/output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/params.yaml new file mode 100644 index 00000000..8301035b --- /dev/null +++ b/examples/security/classification/output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/adv_predictions.json + adv_probabilities_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/ac94b186131b44b3d38faccdd547ab1b.pkl + params_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/params.yaml + predictions_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/predictions.json + probabilities_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/probabilities.json + score_dict_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/score_dict.json + test_labels_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/test_labels.json + train_labels_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/train_labels.json + model_dir: models + name: b7a0d5a9655477b8989ce8256959bd5f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 20312982c46c86615a30a20f68e08fee + trainer: + kwargs: {} +name: b7a0d5a9655477b8989ce8256959bd5f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b7a2cc0496252e67314a67831a48348a/params.yaml b/examples/security/classification/output/reports/train/b7a2cc0496252e67314a67831a48348a/params.yaml new file mode 100644 index 00000000..76640f16 --- /dev/null +++ b/examples/security/classification/output/reports/train/b7a2cc0496252e67314a67831a48348a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/adv_predictions.json + adv_probabilities_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/8cb3394ec6a45639fde4513b1f6ea86a.pkl + params_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/params.yaml + predictions_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/predictions.json + probabilities_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/probabilities.json + score_dict_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/score_dict.json + test_labels_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/test_labels.json + train_labels_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/train_labels.json + model_dir: models + name: b7a2cc0496252e67314a67831a48348a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3b077aa4b381bc49b53475ad26ae59ea + trainer: + kwargs: {} +name: b7a2cc0496252e67314a67831a48348a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/params.yaml b/examples/security/classification/output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/params.yaml new file mode 100644 index 00000000..ace27c29 --- /dev/null +++ b/examples/security/classification/output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/adv_predictions.json + adv_probabilities_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/ce750dc4f2c6699e2d58946f042eb413.pkl + params_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/params.yaml + predictions_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/predictions.json + probabilities_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/probabilities.json + score_dict_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/score_dict.json + test_labels_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/test_labels.json + train_labels_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/train_labels.json + model_dir: models + name: b7bd890755f7f2055bdf550fff85c7f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 389ce40b4db4fda1ec66e54101b3f666 + trainer: + kwargs: {} +name: b7bd890755f7f2055bdf550fff85c7f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b7e45486b414833805ee708a5833d9a7/params.yaml b/examples/security/classification/output/reports/train/b7e45486b414833805ee708a5833d9a7/params.yaml new file mode 100644 index 00000000..722501d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/b7e45486b414833805ee708a5833d9a7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/adv_predictions.json + adv_probabilities_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/941de10cd2828f717d7be4ebbafe0d99.pkl + params_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/params.yaml + predictions_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/predictions.json + probabilities_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/probabilities.json + score_dict_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/score_dict.json + test_labels_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/test_labels.json + train_labels_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/train_labels.json + model_dir: models + name: b7e45486b414833805ee708a5833d9a7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 385aac28511cc6a91201ddd90107eee8 + trainer: + kwargs: {} +name: b7e45486b414833805ee708a5833d9a7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/params.yaml b/examples/security/classification/output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/params.yaml new file mode 100644 index 00000000..fdcc9511 --- /dev/null +++ b/examples/security/classification/output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/adv_predictions.json + adv_probabilities_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/f1269dbcc187d32da86a3eebcb2b53b5.pkl + params_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/params.yaml + predictions_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/predictions.json + probabilities_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/probabilities.json + score_dict_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/score_dict.json + test_labels_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/test_labels.json + train_labels_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/train_labels.json + model_dir: models + name: b7e8cdbbd45dc1da7fc48597d71d39c4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 004465f2716e12fba6c8783d8d7f7c5f + trainer: + kwargs: {} +name: b7e8cdbbd45dc1da7fc48597d71d39c4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b7ef33477f98939d7388d7e91c5963cd/params.yaml b/examples/security/classification/output/reports/train/b7ef33477f98939d7388d7e91c5963cd/params.yaml new file mode 100644 index 00000000..01ea6e79 --- /dev/null +++ b/examples/security/classification/output/reports/train/b7ef33477f98939d7388d7e91c5963cd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/adv_predictions.json + adv_probabilities_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/06d4dee21e8a465b0db2f62f9a2aedbc.pkl + params_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/params.yaml + predictions_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/predictions.json + probabilities_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/probabilities.json + score_dict_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/score_dict.json + test_labels_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/test_labels.json + train_labels_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/train_labels.json + model_dir: models + name: b7ef33477f98939d7388d7e91c5963cd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ebd46a65621492908e632cf1e69eec8f + trainer: + kwargs: {} +name: b7ef33477f98939d7388d7e91c5963cd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b7f496e4c721159abb82a217a048dd4e/params.yaml b/examples/security/classification/output/reports/train/b7f496e4c721159abb82a217a048dd4e/params.yaml new file mode 100644 index 00000000..03f125ac --- /dev/null +++ b/examples/security/classification/output/reports/train/b7f496e4c721159abb82a217a048dd4e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/adv_predictions.json + adv_probabilities_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/fb53bc23f20bf903de5d8ac32a9f98b4.pkl + params_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/params.yaml + predictions_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/predictions.json + probabilities_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/probabilities.json + score_dict_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/score_dict.json + test_labels_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/test_labels.json + train_labels_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/train_labels.json + model_dir: models + name: b7f496e4c721159abb82a217a048dd4e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4ab5e0636374119e2d2c51fb92e422d3 + trainer: + kwargs: {} +name: b7f496e4c721159abb82a217a048dd4e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/params.yaml b/examples/security/classification/output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/params.yaml new file mode 100644 index 00000000..c73ef7c1 --- /dev/null +++ b/examples/security/classification/output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/adv_predictions.json + adv_probabilities_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/2f73357245d13a550d3eed2f2b10ac67.pkl + params_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/params.yaml + predictions_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/predictions.json + probabilities_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/probabilities.json + score_dict_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/score_dict.json + test_labels_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/test_labels.json + train_labels_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/train_labels.json + model_dir: models + name: b7f506104ccf6071fa8ebc1a6ff058c2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 523ccd2145e027c1c9074f9177b5524d + trainer: + kwargs: {} +name: b7f506104ccf6071fa8ebc1a6ff058c2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/params.yaml b/examples/security/classification/output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/params.yaml new file mode 100644 index 00000000..645db07f --- /dev/null +++ b/examples/security/classification/output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/params.yaml @@ -0,0 +1,115 @@ +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/adv_predictions.json + adv_probabilities_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl + model_file: output/models/22c84401c75484ca223cbcbabc8772dd.pkl + params_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/params.yaml + predictions_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/predictions.json + probabilities_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/probabilities.json + score_dict_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/score_dict.json + test_labels_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/test_labels.json + train_labels_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/train_labels.json + model_dir: models + name: b7fb9e3a5ca1d84093a6fec5940a0480 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e4bf40bf5f9a6bdf1f2ec21f474afcfd + trainer: + kwargs: {} +name: b7fb9e3a5ca1d84093a6fec5940a0480 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b7ffdd786addd5c6da587744db58f57c/params.yaml b/examples/security/classification/output/reports/train/b7ffdd786addd5c6da587744db58f57c/params.yaml new file mode 100644 index 00000000..0578d6c6 --- /dev/null +++ b/examples/security/classification/output/reports/train/b7ffdd786addd5c6da587744db58f57c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/adv_predictions.json + adv_probabilities_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/b119ddd1de872a17bdcaf3310e8189b1.pkl + params_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/params.yaml + predictions_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/predictions.json + probabilities_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/probabilities.json + score_dict_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/score_dict.json + test_labels_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/test_labels.json + train_labels_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/train_labels.json + model_dir: models + name: b7ffdd786addd5c6da587744db58f57c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9754a01500d1d143d56bc485054dcbf2 + trainer: + kwargs: {} +name: b7ffdd786addd5c6da587744db58f57c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b839a0dc0ec4f634732086e79b47f90b/params.yaml b/examples/security/classification/output/reports/train/b839a0dc0ec4f634732086e79b47f90b/params.yaml new file mode 100644 index 00000000..d46a1ec6 --- /dev/null +++ b/examples/security/classification/output/reports/train/b839a0dc0ec4f634732086e79b47f90b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/adv_predictions.json + adv_probabilities_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/65920f962bcb3149e90efdd29ddebc7f.pkl + params_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/params.yaml + predictions_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/predictions.json + probabilities_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/probabilities.json + score_dict_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/score_dict.json + test_labels_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/test_labels.json + train_labels_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/train_labels.json + model_dir: models + name: b839a0dc0ec4f634732086e79b47f90b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 419e0905ec7f126d1b1aead7b16adbb6 + trainer: + kwargs: {} +name: b839a0dc0ec4f634732086e79b47f90b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/params.yaml b/examples/security/classification/output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/params.yaml new file mode 100644 index 00000000..c3704dba --- /dev/null +++ b/examples/security/classification/output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/adv_predictions.json + adv_probabilities_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/4d67f432bfaa214cc63f41b6e2e6b56b.pkl + params_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/params.yaml + predictions_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/predictions.json + probabilities_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/probabilities.json + score_dict_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/score_dict.json + test_labels_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/test_labels.json + train_labels_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/train_labels.json + model_dir: models + name: b8537e6d4abc1261acfc16121f0ac2d5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 87979735992d9254c11a020fc7c23a23 + trainer: + kwargs: {} +name: b8537e6d4abc1261acfc16121f0ac2d5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/params.yaml b/examples/security/classification/output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/params.yaml new file mode 100644 index 00000000..f895359e --- /dev/null +++ b/examples/security/classification/output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/adv_predictions.json + adv_probabilities_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/125e519f4cd7dc31e024673acfece592.pkl + params_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/params.yaml + predictions_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/predictions.json + probabilities_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/probabilities.json + score_dict_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/score_dict.json + test_labels_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/test_labels.json + train_labels_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/train_labels.json + model_dir: models + name: b85d5757b0b2b87316daeaefb6a607ee + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 96593ded75bfa60df0fa97afd4736ad6 + trainer: + kwargs: {} +name: b85d5757b0b2b87316daeaefb6a607ee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b895db61daa9dec530d10aafc79da873/params.yaml b/examples/security/classification/output/reports/train/b895db61daa9dec530d10aafc79da873/params.yaml new file mode 100644 index 00000000..14843b75 --- /dev/null +++ b/examples/security/classification/output/reports/train/b895db61daa9dec530d10aafc79da873/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b895db61daa9dec530d10aafc79da873/adv_predictions.json + adv_probabilities_file: output/reports/train/b895db61daa9dec530d10aafc79da873/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/5f4a05647e2353edc70442f752cb60cd.pkl + params_file: output/reports/train/b895db61daa9dec530d10aafc79da873/params.yaml + predictions_file: output/reports/train/b895db61daa9dec530d10aafc79da873/predictions.json + probabilities_file: output/reports/train/b895db61daa9dec530d10aafc79da873/probabilities.json + score_dict_file: output/reports/train/b895db61daa9dec530d10aafc79da873/score_dict.json + test_labels_file: output/reports/train/b895db61daa9dec530d10aafc79da873/test_labels.json + train_labels_file: output/reports/train/b895db61daa9dec530d10aafc79da873/train_labels.json + model_dir: models + name: b895db61daa9dec530d10aafc79da873 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 5 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c87fa47da866dfc907b939f03216b4f4 + trainer: + kwargs: {} +name: b895db61daa9dec530d10aafc79da873 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b8fa2b00b52830b919a8ba8733434d81/params.yaml b/examples/security/classification/output/reports/train/b8fa2b00b52830b919a8ba8733434d81/params.yaml new file mode 100644 index 00000000..884b7c37 --- /dev/null +++ b/examples/security/classification/output/reports/train/b8fa2b00b52830b919a8ba8733434d81/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/adv_predictions.json + adv_probabilities_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/cc84d37f984f1f585db7a750ac228c25.pkl + params_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/params.yaml + predictions_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/predictions.json + probabilities_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/probabilities.json + score_dict_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/score_dict.json + test_labels_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/test_labels.json + train_labels_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/train_labels.json + model_dir: models + name: b8fa2b00b52830b919a8ba8733434d81 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ab9c58dd4030fa07b62f62e9acbf3011 + trainer: + kwargs: {} +name: b8fa2b00b52830b919a8ba8733434d81 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/params.yaml b/examples/security/classification/output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/params.yaml new file mode 100644 index 00000000..94b94e71 --- /dev/null +++ b/examples/security/classification/output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/adv_predictions.json + adv_probabilities_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/ee46323197f594de7565075712a8fc49.pkl + params_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/params.yaml + predictions_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/predictions.json + probabilities_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/probabilities.json + score_dict_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/score_dict.json + test_labels_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/test_labels.json + train_labels_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/train_labels.json + model_dir: models + name: b921c13bdcc42f9cdbb3fd8657142ac6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0fd78cbc2ce93343862d8a72ae3c9a75 + trainer: + kwargs: {} +name: b921c13bdcc42f9cdbb3fd8657142ac6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b93e85675f1cfffaa262828a20778065/params.yaml b/examples/security/classification/output/reports/train/b93e85675f1cfffaa262828a20778065/params.yaml new file mode 100644 index 00000000..402fc98d --- /dev/null +++ b/examples/security/classification/output/reports/train/b93e85675f1cfffaa262828a20778065/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b93e85675f1cfffaa262828a20778065/adv_predictions.json + adv_probabilities_file: output/reports/train/b93e85675f1cfffaa262828a20778065/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/98b99d884520e46a2b5ad84fb40b27bd.pkl + params_file: output/reports/train/b93e85675f1cfffaa262828a20778065/params.yaml + predictions_file: output/reports/train/b93e85675f1cfffaa262828a20778065/predictions.json + probabilities_file: output/reports/train/b93e85675f1cfffaa262828a20778065/probabilities.json + score_dict_file: output/reports/train/b93e85675f1cfffaa262828a20778065/score_dict.json + test_labels_file: output/reports/train/b93e85675f1cfffaa262828a20778065/test_labels.json + train_labels_file: output/reports/train/b93e85675f1cfffaa262828a20778065/train_labels.json + model_dir: models + name: b93e85675f1cfffaa262828a20778065 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 729127645e1552dd0dffecc146cba14f + trainer: + kwargs: {} +name: b93e85675f1cfffaa262828a20778065 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/params.yaml b/examples/security/classification/output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/params.yaml new file mode 100644 index 00000000..6c785c1f --- /dev/null +++ b/examples/security/classification/output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/adv_predictions.json + adv_probabilities_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/bd133b7b1487e95eab0e08b5b1c25fb5.pkl + params_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/params.yaml + predictions_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/predictions.json + probabilities_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/probabilities.json + score_dict_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/score_dict.json + test_labels_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/test_labels.json + train_labels_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/train_labels.json + model_dir: models + name: b958a8a870f0c51e4cbfd585f0f4d4ef + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b52e3f4869f67e1c1f06760a1370a8f5 + trainer: + kwargs: {} +name: b958a8a870f0c51e4cbfd585f0f4d4ef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b96e6a9d841a682d65b690cd35842cb8/params.yaml b/examples/security/classification/output/reports/train/b96e6a9d841a682d65b690cd35842cb8/params.yaml new file mode 100644 index 00000000..7daf4174 --- /dev/null +++ b/examples/security/classification/output/reports/train/b96e6a9d841a682d65b690cd35842cb8/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/adv_predictions.json + adv_probabilities_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/877d722e658459d6fa0b012f2fd0c196.pkl + params_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/params.yaml + predictions_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/predictions.json + probabilities_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/probabilities.json + score_dict_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/score_dict.json + test_labels_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/test_labels.json + train_labels_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/train_labels.json + model_dir: models + name: b96e6a9d841a682d65b690cd35842cb8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f1db7a51590d45aa303f982b366d75a1 + trainer: + kwargs: {} +name: b96e6a9d841a682d65b690cd35842cb8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b97ce643de0c881f07936bb55ecbcf96/params.yaml b/examples/security/classification/output/reports/train/b97ce643de0c881f07936bb55ecbcf96/params.yaml new file mode 100644 index 00000000..7d3025ea --- /dev/null +++ b/examples/security/classification/output/reports/train/b97ce643de0c881f07936bb55ecbcf96/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/adv_predictions.json + adv_probabilities_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/8550f76c8c11932d43568e124430a8e2.pkl + params_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/params.yaml + predictions_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/predictions.json + probabilities_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/probabilities.json + score_dict_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/score_dict.json + test_labels_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/test_labels.json + train_labels_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/train_labels.json + model_dir: models + name: b97ce643de0c881f07936bb55ecbcf96 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8194557cc872f0ce81529aca85187ec6 + trainer: + kwargs: {} +name: b97ce643de0c881f07936bb55ecbcf96 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/params.yaml b/examples/security/classification/output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/params.yaml new file mode 100644 index 00000000..3401d31a --- /dev/null +++ b/examples/security/classification/output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/adv_predictions.json + adv_probabilities_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/5a3f273a3039ff7a902bac18862795ac.pkl + params_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/params.yaml + predictions_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/predictions.json + probabilities_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/probabilities.json + score_dict_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/score_dict.json + test_labels_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/test_labels.json + train_labels_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/train_labels.json + model_dir: models + name: b98a33f558befb4fd90d9bdb1b7f9aca + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2c2e8dfe41e60e06fd632465359bb1f + trainer: + kwargs: {} +name: b98a33f558befb4fd90d9bdb1b7f9aca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/params.yaml b/examples/security/classification/output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/params.yaml new file mode 100644 index 00000000..e270ae71 --- /dev/null +++ b/examples/security/classification/output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/adv_predictions.json + adv_probabilities_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/4fc61eae59bc68d9ec210163ad20fa91.pkl + params_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/params.yaml + predictions_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/predictions.json + probabilities_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/probabilities.json + score_dict_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/score_dict.json + test_labels_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/test_labels.json + train_labels_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/train_labels.json + model_dir: models + name: b9b0f943a7c110b1839a0e58da3a508e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: efe5ea171fc1f9d5af3db3b548a18716 + trainer: + kwargs: {} +name: b9b0f943a7c110b1839a0e58da3a508e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/params.yaml b/examples/security/classification/output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/params.yaml new file mode 100644 index 00000000..526db5dd --- /dev/null +++ b/examples/security/classification/output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/adv_predictions.json + adv_probabilities_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/76ff275e2819bd23c2ac1e038d95fd60.pkl + params_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/params.yaml + predictions_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/predictions.json + probabilities_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/probabilities.json + score_dict_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/score_dict.json + test_labels_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/test_labels.json + train_labels_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/train_labels.json + model_dir: models + name: b9c07bbb5d6eaf0e8ef52e036d770731 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c6103f9a27a543cd269af2626e7859f9 + trainer: + kwargs: {} +name: b9c07bbb5d6eaf0e8ef52e036d770731 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/params.yaml b/examples/security/classification/output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/params.yaml new file mode 100644 index 00000000..e5a8d221 --- /dev/null +++ b/examples/security/classification/output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/adv_predictions.json + adv_probabilities_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/ecc8b130f49e96bbcaaca2f3ca70d471.pkl + params_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/params.yaml + predictions_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/predictions.json + probabilities_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/probabilities.json + score_dict_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/score_dict.json + test_labels_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/test_labels.json + train_labels_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/train_labels.json + model_dir: models + name: b9eea72f450a4feddb1b95bf7b6df9a9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7cafec5922da90d24ad3e5a865653ec4 + trainer: + kwargs: {} +name: b9eea72f450a4feddb1b95bf7b6df9a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/params.yaml b/examples/security/classification/output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/params.yaml new file mode 100644 index 00000000..2c0845ab --- /dev/null +++ b/examples/security/classification/output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/adv_predictions.json + adv_probabilities_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/996a266dacf71b1400b3827fa84fc656.pkl + params_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/params.yaml + predictions_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/predictions.json + probabilities_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/probabilities.json + score_dict_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/score_dict.json + test_labels_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/test_labels.json + train_labels_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/train_labels.json + model_dir: models + name: ba21c03ad3a72d8e8feeec7c0c04a74a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7f51b54d02e236c1084bde16e7b97f14 + trainer: + kwargs: {} +name: ba21c03ad3a72d8e8feeec7c0c04a74a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/params.yaml b/examples/security/classification/output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/params.yaml new file mode 100644 index 00000000..06d606ac --- /dev/null +++ b/examples/security/classification/output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/adv_predictions.json + adv_probabilities_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/577667a8d39b46f682c4919745637c13.pkl + params_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/params.yaml + predictions_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/predictions.json + probabilities_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/probabilities.json + score_dict_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/score_dict.json + test_labels_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/test_labels.json + train_labels_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/train_labels.json + model_dir: models + name: ba2cecf45154cf30e6ef02a27ec76b6e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 52d47b8a28d1eebb5917d4d143f2d72b + trainer: + kwargs: {} +name: ba2cecf45154cf30e6ef02a27ec76b6e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ba6073e354ed2f48ed572869ac753ed5/params.yaml b/examples/security/classification/output/reports/train/ba6073e354ed2f48ed572869ac753ed5/params.yaml new file mode 100644 index 00000000..6266c1a1 --- /dev/null +++ b/examples/security/classification/output/reports/train/ba6073e354ed2f48ed572869ac753ed5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/adv_predictions.json + adv_probabilities_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/2b82380abeafb95939a9bfd67ef1b410.pkl + params_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/params.yaml + predictions_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/predictions.json + probabilities_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/probabilities.json + score_dict_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/score_dict.json + test_labels_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/test_labels.json + train_labels_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/train_labels.json + model_dir: models + name: ba6073e354ed2f48ed572869ac753ed5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 29c5ef33cc497502f0d3c1fcba4042b4 + trainer: + kwargs: {} +name: ba6073e354ed2f48ed572869ac753ed5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ba78f110a71b8670193d39d64510b377/params.yaml b/examples/security/classification/output/reports/train/ba78f110a71b8670193d39d64510b377/params.yaml new file mode 100644 index 00000000..26a6e8c7 --- /dev/null +++ b/examples/security/classification/output/reports/train/ba78f110a71b8670193d39d64510b377/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ba78f110a71b8670193d39d64510b377/adv_predictions.json + adv_probabilities_file: output/reports/train/ba78f110a71b8670193d39d64510b377/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/842266516d1e8af5f026a10f77c4024d.pkl + params_file: output/reports/train/ba78f110a71b8670193d39d64510b377/params.yaml + predictions_file: output/reports/train/ba78f110a71b8670193d39d64510b377/predictions.json + probabilities_file: output/reports/train/ba78f110a71b8670193d39d64510b377/probabilities.json + score_dict_file: output/reports/train/ba78f110a71b8670193d39d64510b377/score_dict.json + test_labels_file: output/reports/train/ba78f110a71b8670193d39d64510b377/test_labels.json + train_labels_file: output/reports/train/ba78f110a71b8670193d39d64510b377/train_labels.json + model_dir: models + name: ba78f110a71b8670193d39d64510b377 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: de8382222f196f0491bbc4401626236f + trainer: + kwargs: {} +name: ba78f110a71b8670193d39d64510b377 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/params.yaml b/examples/security/classification/output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/params.yaml new file mode 100644 index 00000000..72f6b967 --- /dev/null +++ b/examples/security/classification/output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/adv_predictions.json + adv_probabilities_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/4c1ab493f796eeaa78ad2855cba5ecee.pkl + params_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/params.yaml + predictions_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/predictions.json + probabilities_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/probabilities.json + score_dict_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/score_dict.json + test_labels_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/test_labels.json + train_labels_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/train_labels.json + model_dir: models + name: ba8c9e2ab93345ecccb1cfa6746392aa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bad4ab78e89b6746d64f06ee8ae32378 + trainer: + kwargs: {} +name: ba8c9e2ab93345ecccb1cfa6746392aa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/baa0b6682949e7b6970c9548d0463501/params.yaml b/examples/security/classification/output/reports/train/baa0b6682949e7b6970c9548d0463501/params.yaml new file mode 100644 index 00000000..e3808083 --- /dev/null +++ b/examples/security/classification/output/reports/train/baa0b6682949e7b6970c9548d0463501/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/adv_predictions.json + adv_probabilities_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/984b0bf62b49e55b6ab0f7e4783991af.pkl + params_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/params.yaml + predictions_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/predictions.json + probabilities_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/probabilities.json + score_dict_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/score_dict.json + test_labels_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/test_labels.json + train_labels_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/train_labels.json + model_dir: models + name: baa0b6682949e7b6970c9548d0463501 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 29e302354d5a9a0714b3598a391dc705 + trainer: + kwargs: {} +name: baa0b6682949e7b6970c9548d0463501 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/baaa89eddfb69f4b3f313d1090835968/params.yaml b/examples/security/classification/output/reports/train/baaa89eddfb69f4b3f313d1090835968/params.yaml new file mode 100644 index 00000000..41aaee88 --- /dev/null +++ b/examples/security/classification/output/reports/train/baaa89eddfb69f4b3f313d1090835968/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/adv_predictions.json + adv_probabilities_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/c1b412f4c93bc7e9681c4603a931aba8.pkl + params_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/params.yaml + predictions_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/predictions.json + probabilities_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/probabilities.json + score_dict_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/score_dict.json + test_labels_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/test_labels.json + train_labels_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/train_labels.json + model_dir: models + name: baaa89eddfb69f4b3f313d1090835968 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 78e7c925a701fed87c695cca6b9d8b90 + trainer: + kwargs: {} +name: baaa89eddfb69f4b3f313d1090835968 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/params.yaml b/examples/security/classification/output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/params.yaml new file mode 100644 index 00000000..c4806442 --- /dev/null +++ b/examples/security/classification/output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/adv_predictions.json + adv_probabilities_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/d95cd17f40d44b953c96cbc9eae1bdd1.pkl + params_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/params.yaml + predictions_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/predictions.json + probabilities_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/probabilities.json + score_dict_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/score_dict.json + test_labels_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/test_labels.json + train_labels_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/train_labels.json + model_dir: models + name: bace2da3bd6de72753a6cc7760eb7a02 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c34800e371c9714bfe385727a045ab85 + trainer: + kwargs: {} +name: bace2da3bd6de72753a6cc7760eb7a02 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bad689b33de238b7022e3f678397b0c0/params.yaml b/examples/security/classification/output/reports/train/bad689b33de238b7022e3f678397b0c0/params.yaml new file mode 100644 index 00000000..87cb0371 --- /dev/null +++ b/examples/security/classification/output/reports/train/bad689b33de238b7022e3f678397b0c0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/adv_predictions.json + adv_probabilities_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/dfa5220d1129d3ce990c27c0c707dd0f.pkl + params_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/params.yaml + predictions_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/predictions.json + probabilities_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/probabilities.json + score_dict_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/score_dict.json + test_labels_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/test_labels.json + train_labels_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/train_labels.json + model_dir: models + name: bad689b33de238b7022e3f678397b0c0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1e3467c5b200c4fee28633500a66ed6c + trainer: + kwargs: {} +name: bad689b33de238b7022e3f678397b0c0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bb2c522f81b45527334f57c19ca86172/params.yaml b/examples/security/classification/output/reports/train/bb2c522f81b45527334f57c19ca86172/params.yaml new file mode 100644 index 00000000..f380ccfa --- /dev/null +++ b/examples/security/classification/output/reports/train/bb2c522f81b45527334f57c19ca86172/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/adv_predictions.json + adv_probabilities_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/c91f582f4010271f4f4ea89710d763da.pkl + params_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/params.yaml + predictions_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/predictions.json + probabilities_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/probabilities.json + score_dict_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/score_dict.json + test_labels_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/test_labels.json + train_labels_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/train_labels.json + model_dir: models + name: bb2c522f81b45527334f57c19ca86172 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4904c5c5db2c4618292bb6d92a2f0a27 + trainer: + kwargs: {} +name: bb2c522f81b45527334f57c19ca86172 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/params.yaml b/examples/security/classification/output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/params.yaml new file mode 100644 index 00000000..60492fb7 --- /dev/null +++ b/examples/security/classification/output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/adv_predictions.json + adv_probabilities_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/6588bf9c77fa870efdb66c940b805a47.pkl + params_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/params.yaml + predictions_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/predictions.json + probabilities_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/probabilities.json + score_dict_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/score_dict.json + test_labels_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/test_labels.json + train_labels_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/train_labels.json + model_dir: models + name: bb2e0d02101e90eeb01618be7e9bd817 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fe72b1755249e905fbc7f6eb467d10a2 + trainer: + kwargs: {} +name: bb2e0d02101e90eeb01618be7e9bd817 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bb3a72b508620d6e2082f0afffd01252/params.yaml b/examples/security/classification/output/reports/train/bb3a72b508620d6e2082f0afffd01252/params.yaml new file mode 100644 index 00000000..9a76bcec --- /dev/null +++ b/examples/security/classification/output/reports/train/bb3a72b508620d6e2082f0afffd01252/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/adv_predictions.json + adv_probabilities_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/cc88283a98ec88894fd7cc0b2f8d7eaf.pkl + params_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/params.yaml + predictions_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/predictions.json + probabilities_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/probabilities.json + score_dict_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/score_dict.json + test_labels_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/test_labels.json + train_labels_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/train_labels.json + model_dir: models + name: bb3a72b508620d6e2082f0afffd01252 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 079856428833d0ead8902ad7c2d01cdc + trainer: + kwargs: {} +name: bb3a72b508620d6e2082f0afffd01252 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bb43f2459493583ae8dd03d307d37e7b/params.yaml b/examples/security/classification/output/reports/train/bb43f2459493583ae8dd03d307d37e7b/params.yaml new file mode 100644 index 00000000..5a8276ec --- /dev/null +++ b/examples/security/classification/output/reports/train/bb43f2459493583ae8dd03d307d37e7b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/adv_predictions.json + adv_probabilities_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/687f219e74fef8762f1d82871e3e4154.pkl + params_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/params.yaml + predictions_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/predictions.json + probabilities_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/probabilities.json + score_dict_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/score_dict.json + test_labels_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/test_labels.json + train_labels_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/train_labels.json + model_dir: models + name: bb43f2459493583ae8dd03d307d37e7b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0c50558aad5f59e928599fa4226cd400 + trainer: + kwargs: {} +name: bb43f2459493583ae8dd03d307d37e7b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bb56d13631809f9e120f45154373b045/params.yaml b/examples/security/classification/output/reports/train/bb56d13631809f9e120f45154373b045/params.yaml new file mode 100644 index 00000000..4aa42292 --- /dev/null +++ b/examples/security/classification/output/reports/train/bb56d13631809f9e120f45154373b045/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bb56d13631809f9e120f45154373b045/adv_predictions.json + adv_probabilities_file: output/reports/train/bb56d13631809f9e120f45154373b045/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/f329056c25085f2825f0bbbc9fdd3498.pkl + params_file: output/reports/train/bb56d13631809f9e120f45154373b045/params.yaml + predictions_file: output/reports/train/bb56d13631809f9e120f45154373b045/predictions.json + probabilities_file: output/reports/train/bb56d13631809f9e120f45154373b045/probabilities.json + score_dict_file: output/reports/train/bb56d13631809f9e120f45154373b045/score_dict.json + test_labels_file: output/reports/train/bb56d13631809f9e120f45154373b045/test_labels.json + train_labels_file: output/reports/train/bb56d13631809f9e120f45154373b045/train_labels.json + model_dir: models + name: bb56d13631809f9e120f45154373b045 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b9a7419d77756fb317834f42af7caab2 + trainer: + kwargs: {} +name: bb56d13631809f9e120f45154373b045 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bb79ea4778714a472a52c5892287a62f/params.yaml b/examples/security/classification/output/reports/train/bb79ea4778714a472a52c5892287a62f/params.yaml new file mode 100644 index 00000000..84558981 --- /dev/null +++ b/examples/security/classification/output/reports/train/bb79ea4778714a472a52c5892287a62f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/adv_predictions.json + adv_probabilities_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/a632ba64ce28b0212aecc321a84fd177.pkl + params_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/params.yaml + predictions_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/predictions.json + probabilities_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/probabilities.json + score_dict_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/score_dict.json + test_labels_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/test_labels.json + train_labels_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/train_labels.json + model_dir: models + name: bb79ea4778714a472a52c5892287a62f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9d784eb3e919c8e1491e642ae0c0945a + trainer: + kwargs: {} +name: bb79ea4778714a472a52c5892287a62f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bb7b780eb55b79747a369868951088bd/params.yaml b/examples/security/classification/output/reports/train/bb7b780eb55b79747a369868951088bd/params.yaml new file mode 100644 index 00000000..7aaf49d6 --- /dev/null +++ b/examples/security/classification/output/reports/train/bb7b780eb55b79747a369868951088bd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bb7b780eb55b79747a369868951088bd/adv_predictions.json + adv_probabilities_file: output/reports/train/bb7b780eb55b79747a369868951088bd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/8dca07814d12e18226a279902a9262a6.pkl + params_file: output/reports/train/bb7b780eb55b79747a369868951088bd/params.yaml + predictions_file: output/reports/train/bb7b780eb55b79747a369868951088bd/predictions.json + probabilities_file: output/reports/train/bb7b780eb55b79747a369868951088bd/probabilities.json + score_dict_file: output/reports/train/bb7b780eb55b79747a369868951088bd/score_dict.json + test_labels_file: output/reports/train/bb7b780eb55b79747a369868951088bd/test_labels.json + train_labels_file: output/reports/train/bb7b780eb55b79747a369868951088bd/train_labels.json + model_dir: models + name: bb7b780eb55b79747a369868951088bd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 932d0bd0c6b3cc188b9353b1b9993c86 + trainer: + kwargs: {} +name: bb7b780eb55b79747a369868951088bd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/params.yaml b/examples/security/classification/output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/params.yaml new file mode 100644 index 00000000..0b8ccf64 --- /dev/null +++ b/examples/security/classification/output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/adv_predictions.json + adv_probabilities_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/719afc57747a3d0e2070add47c5aad9d.pkl + params_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/params.yaml + predictions_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/predictions.json + probabilities_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/probabilities.json + score_dict_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/score_dict.json + test_labels_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/test_labels.json + train_labels_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/train_labels.json + model_dir: models + name: bb8cf5000f713b5c51df0505850dd5bd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e9110baf32f53c92204ff8967c01ac29 + trainer: + kwargs: {} +name: bb8cf5000f713b5c51df0505850dd5bd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bbbcc6abc200a898c6504add3aa79d52/params.yaml b/examples/security/classification/output/reports/train/bbbcc6abc200a898c6504add3aa79d52/params.yaml new file mode 100644 index 00000000..d9f54e7a --- /dev/null +++ b/examples/security/classification/output/reports/train/bbbcc6abc200a898c6504add3aa79d52/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/adv_predictions.json + adv_probabilities_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/bce1a4934e5c587a49cd1beef50d2281.pkl + params_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/params.yaml + predictions_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/predictions.json + probabilities_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/probabilities.json + score_dict_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/score_dict.json + test_labels_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/test_labels.json + train_labels_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/train_labels.json + model_dir: models + name: bbbcc6abc200a898c6504add3aa79d52 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b241b1d32bbd3c34e297d9ccf8b495da + trainer: + kwargs: {} +name: bbbcc6abc200a898c6504add3aa79d52 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/params.yaml b/examples/security/classification/output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/params.yaml new file mode 100644 index 00000000..1c1c31ff --- /dev/null +++ b/examples/security/classification/output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/adv_predictions.json + adv_probabilities_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/f59632aebd32d9cf81f74016ada1b7a9.pkl + params_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/params.yaml + predictions_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/predictions.json + probabilities_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/probabilities.json + score_dict_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/score_dict.json + test_labels_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/test_labels.json + train_labels_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/train_labels.json + model_dir: models + name: bbc81384d3f2ca32e25b844e290a68f2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5d6018cbf06a77a9b8aa5b10a06f537e + trainer: + kwargs: {} +name: bbc81384d3f2ca32e25b844e290a68f2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bbddea1525acc855a2c83670090f8dc7/params.yaml b/examples/security/classification/output/reports/train/bbddea1525acc855a2c83670090f8dc7/params.yaml new file mode 100644 index 00000000..5df3b179 --- /dev/null +++ b/examples/security/classification/output/reports/train/bbddea1525acc855a2c83670090f8dc7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/adv_predictions.json + adv_probabilities_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/bce67ddd94c42073b8329276c01b3eb2.pkl + params_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/params.yaml + predictions_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/predictions.json + probabilities_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/probabilities.json + score_dict_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/score_dict.json + test_labels_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/test_labels.json + train_labels_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/train_labels.json + model_dir: models + name: bbddea1525acc855a2c83670090f8dc7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6280e8d82a6ef6acd7bfcc266513bd1b + trainer: + kwargs: {} +name: bbddea1525acc855a2c83670090f8dc7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bbe6c7acb81c2a624b875baa044198a4/params.yaml b/examples/security/classification/output/reports/train/bbe6c7acb81c2a624b875baa044198a4/params.yaml new file mode 100644 index 00000000..8b8e14fa --- /dev/null +++ b/examples/security/classification/output/reports/train/bbe6c7acb81c2a624b875baa044198a4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/adv_predictions.json + adv_probabilities_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/634d7eb9fc728b31fcfe9ac583086ede.pkl + params_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/params.yaml + predictions_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/predictions.json + probabilities_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/probabilities.json + score_dict_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/score_dict.json + test_labels_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/test_labels.json + train_labels_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/train_labels.json + model_dir: models + name: bbe6c7acb81c2a624b875baa044198a4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 851acbcef81da278a10d7d23c989d0a7 + trainer: + kwargs: {} +name: bbe6c7acb81c2a624b875baa044198a4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bbf96174af361a073ad5676da30fba41/params.yaml b/examples/security/classification/output/reports/train/bbf96174af361a073ad5676da30fba41/params.yaml new file mode 100644 index 00000000..4c65c2cd --- /dev/null +++ b/examples/security/classification/output/reports/train/bbf96174af361a073ad5676da30fba41/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bbf96174af361a073ad5676da30fba41/adv_predictions.json + adv_probabilities_file: output/reports/train/bbf96174af361a073ad5676da30fba41/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/d2c2ba3c50c83d547c559ea1724b6974.pkl + params_file: output/reports/train/bbf96174af361a073ad5676da30fba41/params.yaml + predictions_file: output/reports/train/bbf96174af361a073ad5676da30fba41/predictions.json + probabilities_file: output/reports/train/bbf96174af361a073ad5676da30fba41/probabilities.json + score_dict_file: output/reports/train/bbf96174af361a073ad5676da30fba41/score_dict.json + test_labels_file: output/reports/train/bbf96174af361a073ad5676da30fba41/test_labels.json + train_labels_file: output/reports/train/bbf96174af361a073ad5676da30fba41/train_labels.json + model_dir: models + name: bbf96174af361a073ad5676da30fba41 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c105e358864001c53ced91ff670ceee3 + trainer: + kwargs: {} +name: bbf96174af361a073ad5676da30fba41 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/params.yaml b/examples/security/classification/output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/params.yaml new file mode 100644 index 00000000..d9f30435 --- /dev/null +++ b/examples/security/classification/output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/adv_predictions.json + adv_probabilities_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/87f806dfc24b33ebfc25b089eb1317c6.pkl + params_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/params.yaml + predictions_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/predictions.json + probabilities_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/probabilities.json + score_dict_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/score_dict.json + test_labels_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/test_labels.json + train_labels_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/train_labels.json + model_dir: models + name: bc09d4c37d7427f960bdf4fe7e4eccdf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fc3b4da535448930ec17f4792e4b7546 + trainer: + kwargs: {} +name: bc09d4c37d7427f960bdf4fe7e4eccdf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bc0f936807b548353a0b789cfed9c6a2/params.yaml b/examples/security/classification/output/reports/train/bc0f936807b548353a0b789cfed9c6a2/params.yaml new file mode 100644 index 00000000..22470834 --- /dev/null +++ b/examples/security/classification/output/reports/train/bc0f936807b548353a0b789cfed9c6a2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/adv_predictions.json + adv_probabilities_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/e67544c2ac948d2a30bf2b00ffac1623.pkl + params_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/params.yaml + predictions_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/predictions.json + probabilities_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/probabilities.json + score_dict_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/score_dict.json + test_labels_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/test_labels.json + train_labels_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/train_labels.json + model_dir: models + name: bc0f936807b548353a0b789cfed9c6a2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fdca564b21cb003aa07b267bcc589697 + trainer: + kwargs: {} +name: bc0f936807b548353a0b789cfed9c6a2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/params.yaml b/examples/security/classification/output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/params.yaml new file mode 100644 index 00000000..3d1c7172 --- /dev/null +++ b/examples/security/classification/output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/adv_predictions.json + adv_probabilities_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/41d70375ed4c9a9822339e583a7d046c.pkl + params_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/params.yaml + predictions_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/predictions.json + probabilities_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/probabilities.json + score_dict_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/score_dict.json + test_labels_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/test_labels.json + train_labels_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/train_labels.json + model_dir: models + name: bc16dab9ebe43214670a56fe28a89d2b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9aca1db0b4dfcea6c010ab5b80ec7d7e + trainer: + kwargs: {} +name: bc16dab9ebe43214670a56fe28a89d2b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/params.yaml b/examples/security/classification/output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/params.yaml new file mode 100644 index 00000000..69944f1d --- /dev/null +++ b/examples/security/classification/output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/adv_predictions.json + adv_probabilities_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/65b3237c4f0706bd629c41472715a3e8.pkl + params_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/params.yaml + predictions_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/predictions.json + probabilities_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/probabilities.json + score_dict_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/score_dict.json + test_labels_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/test_labels.json + train_labels_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/train_labels.json + model_dir: models + name: bc4da34bb10e20bf565ebe9cc782a895 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 64fd2baca596376c4d6ca65bcba58214 + trainer: + kwargs: {} +name: bc4da34bb10e20bf565ebe9cc782a895 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bc56356cec4a9e45aea76d99587c6f47/params.yaml b/examples/security/classification/output/reports/train/bc56356cec4a9e45aea76d99587c6f47/params.yaml new file mode 100644 index 00000000..bfe1115a --- /dev/null +++ b/examples/security/classification/output/reports/train/bc56356cec4a9e45aea76d99587c6f47/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/adv_predictions.json + adv_probabilities_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/9db49605c25fc5399421213bdfe42995.pkl + params_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/params.yaml + predictions_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/predictions.json + probabilities_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/probabilities.json + score_dict_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/score_dict.json + test_labels_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/test_labels.json + train_labels_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/train_labels.json + model_dir: models + name: bc56356cec4a9e45aea76d99587c6f47 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27e33fb7ed33522e0d1dfee8da641c65 + trainer: + kwargs: {} +name: bc56356cec4a9e45aea76d99587c6f47 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bc8f586ec3f08ca3269b650699e0719b/params.yaml b/examples/security/classification/output/reports/train/bc8f586ec3f08ca3269b650699e0719b/params.yaml new file mode 100644 index 00000000..262146fe --- /dev/null +++ b/examples/security/classification/output/reports/train/bc8f586ec3f08ca3269b650699e0719b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/adv_predictions.json + adv_probabilities_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/30d74d9616ed0e40c1e1a5b13a4cd95b.pkl + params_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/params.yaml + predictions_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/predictions.json + probabilities_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/probabilities.json + score_dict_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/score_dict.json + test_labels_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/test_labels.json + train_labels_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/train_labels.json + model_dir: models + name: bc8f586ec3f08ca3269b650699e0719b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3820a4b251dc00266a3d569fd174f165 + trainer: + kwargs: {} +name: bc8f586ec3f08ca3269b650699e0719b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/params.yaml b/examples/security/classification/output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/params.yaml new file mode 100644 index 00000000..c600c298 --- /dev/null +++ b/examples/security/classification/output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/adv_predictions.json + adv_probabilities_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/0a27bda0cc746a535a9b7743573095ba.pkl + params_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/params.yaml + predictions_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/predictions.json + probabilities_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/probabilities.json + score_dict_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/score_dict.json + test_labels_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/test_labels.json + train_labels_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/train_labels.json + model_dir: models + name: bc8fff020b44aa7fa1346a0b9f9e468b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 727bef4145283447969be6df2a01b179 + trainer: + kwargs: {} +name: bc8fff020b44aa7fa1346a0b9f9e468b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/params.yaml b/examples/security/classification/output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/params.yaml new file mode 100644 index 00000000..109745d9 --- /dev/null +++ b/examples/security/classification/output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/adv_predictions.json + adv_probabilities_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/367e550b04cd72d9aabe6c0bb4c2150a.pkl + params_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/params.yaml + predictions_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/predictions.json + probabilities_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/probabilities.json + score_dict_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/score_dict.json + test_labels_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/test_labels.json + train_labels_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/train_labels.json + model_dir: models + name: bca1fca06cfdfc46953adc729fdca8ab + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5c039446d6dbd8a565bb850ae9456767 + trainer: + kwargs: {} +name: bca1fca06cfdfc46953adc729fdca8ab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/params.yaml b/examples/security/classification/output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/params.yaml new file mode 100644 index 00000000..016ec12c --- /dev/null +++ b/examples/security/classification/output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/adv_predictions.json + adv_probabilities_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/3366d6b02f9cb6efb5f3059e2ae40698.pkl + params_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/params.yaml + predictions_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/predictions.json + probabilities_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/probabilities.json + score_dict_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/score_dict.json + test_labels_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/test_labels.json + train_labels_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/train_labels.json + model_dir: models + name: bcd7ec9da436ca1eb750f1895197bf63 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3221c10069dd49e20c113431365ffefe + trainer: + kwargs: {} +name: bcd7ec9da436ca1eb750f1895197bf63 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bcdaea33e4844c0396eae9474f14ea73/params.yaml b/examples/security/classification/output/reports/train/bcdaea33e4844c0396eae9474f14ea73/params.yaml new file mode 100644 index 00000000..567b3654 --- /dev/null +++ b/examples/security/classification/output/reports/train/bcdaea33e4844c0396eae9474f14ea73/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/adv_predictions.json + adv_probabilities_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/3b044c2f0c354f34af7f8b9d53d3e6f7.pkl + params_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/params.yaml + predictions_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/predictions.json + probabilities_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/probabilities.json + score_dict_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/score_dict.json + test_labels_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/test_labels.json + train_labels_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/train_labels.json + model_dir: models + name: bcdaea33e4844c0396eae9474f14ea73 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ded4a94ddf112effb47e2af242a142f7 + trainer: + kwargs: {} +name: bcdaea33e4844c0396eae9474f14ea73 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/params.yaml b/examples/security/classification/output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/params.yaml new file mode 100644 index 00000000..368d3931 --- /dev/null +++ b/examples/security/classification/output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/adv_predictions.json + adv_probabilities_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/734835d117e6111b2e35625e82ca3753.pkl + params_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/params.yaml + predictions_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/predictions.json + probabilities_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/probabilities.json + score_dict_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/score_dict.json + test_labels_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/test_labels.json + train_labels_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/train_labels.json + model_dir: models + name: bcfcabe23df2e4e1d7d54c15f5bd7911 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cbfbadf63d00a0a59a59d39e79242520 + trainer: + kwargs: {} +name: bcfcabe23df2e4e1d7d54c15f5bd7911 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/params.yaml b/examples/security/classification/output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/params.yaml new file mode 100644 index 00000000..58d6f03a --- /dev/null +++ b/examples/security/classification/output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/adv_predictions.json + adv_probabilities_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/6a9fa6b7cec0613fb43b47e152d53626.pkl + params_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/params.yaml + predictions_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/predictions.json + probabilities_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/probabilities.json + score_dict_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/score_dict.json + test_labels_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/test_labels.json + train_labels_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/train_labels.json + model_dir: models + name: bd27351c6c3470ba7d52ae0303fbffce + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cd63ed3a9bc27696e7d92a4347a9ba17 + trainer: + kwargs: {} +name: bd27351c6c3470ba7d52ae0303fbffce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/params.yaml b/examples/security/classification/output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/params.yaml new file mode 100644 index 00000000..9093144e --- /dev/null +++ b/examples/security/classification/output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/adv_predictions.json + adv_probabilities_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/06a4af1179393e3a84307b43d6aefb4a.pkl + params_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/params.yaml + predictions_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/predictions.json + probabilities_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/probabilities.json + score_dict_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/score_dict.json + test_labels_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/test_labels.json + train_labels_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/train_labels.json + model_dir: models + name: bd3f9dbea7550b0de658f0cd36e27786 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 173db435713546e585db91f58e674fe2 + trainer: + kwargs: {} +name: bd3f9dbea7550b0de658f0cd36e27786 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bd42e549da46e1b115e910b922808e48/params.yaml b/examples/security/classification/output/reports/train/bd42e549da46e1b115e910b922808e48/params.yaml new file mode 100644 index 00000000..e6fae29b --- /dev/null +++ b/examples/security/classification/output/reports/train/bd42e549da46e1b115e910b922808e48/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bd42e549da46e1b115e910b922808e48/adv_predictions.json + adv_probabilities_file: output/reports/train/bd42e549da46e1b115e910b922808e48/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/f87235a52b363dbc70ddab990042d777.pkl + params_file: output/reports/train/bd42e549da46e1b115e910b922808e48/params.yaml + predictions_file: output/reports/train/bd42e549da46e1b115e910b922808e48/predictions.json + probabilities_file: output/reports/train/bd42e549da46e1b115e910b922808e48/probabilities.json + score_dict_file: output/reports/train/bd42e549da46e1b115e910b922808e48/score_dict.json + test_labels_file: output/reports/train/bd42e549da46e1b115e910b922808e48/test_labels.json + train_labels_file: output/reports/train/bd42e549da46e1b115e910b922808e48/train_labels.json + model_dir: models + name: bd42e549da46e1b115e910b922808e48 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 20885c90506f1e129cc614c3f592b178 + trainer: + kwargs: {} +name: bd42e549da46e1b115e910b922808e48 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bd75d6e06b94e3011b70637056647dac/params.yaml b/examples/security/classification/output/reports/train/bd75d6e06b94e3011b70637056647dac/params.yaml new file mode 100644 index 00000000..cde1e379 --- /dev/null +++ b/examples/security/classification/output/reports/train/bd75d6e06b94e3011b70637056647dac/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/adv_predictions.json + adv_probabilities_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/70cb371f3c03d790d91eaccec198cb9e.pkl + params_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/params.yaml + predictions_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/predictions.json + probabilities_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/probabilities.json + score_dict_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/score_dict.json + test_labels_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/test_labels.json + train_labels_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/train_labels.json + model_dir: models + name: bd75d6e06b94e3011b70637056647dac + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 52d6f23ec25e5a472efce1ef27d83c43 + trainer: + kwargs: {} +name: bd75d6e06b94e3011b70637056647dac +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bd7708a1e74405b4f51332d8f29122a1/params.yaml b/examples/security/classification/output/reports/train/bd7708a1e74405b4f51332d8f29122a1/params.yaml new file mode 100644 index 00000000..b721a6bb --- /dev/null +++ b/examples/security/classification/output/reports/train/bd7708a1e74405b4f51332d8f29122a1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/adv_predictions.json + adv_probabilities_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/0a11b1a69ec66affda1f7f3be0b7b2de.pkl + params_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/params.yaml + predictions_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/predictions.json + probabilities_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/probabilities.json + score_dict_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/score_dict.json + test_labels_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/test_labels.json + train_labels_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/train_labels.json + model_dir: models + name: bd7708a1e74405b4f51332d8f29122a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2534c77838d0cc538b6b2b591e71f863 + trainer: + kwargs: {} +name: bd7708a1e74405b4f51332d8f29122a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/params.yaml b/examples/security/classification/output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/params.yaml new file mode 100644 index 00000000..9f210eba --- /dev/null +++ b/examples/security/classification/output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/adv_predictions.json + adv_probabilities_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/d919253ce869404989a1c0c040e7cdee.pkl + params_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/params.yaml + predictions_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/predictions.json + probabilities_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/probabilities.json + score_dict_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/score_dict.json + test_labels_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/test_labels.json + train_labels_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/train_labels.json + model_dir: models + name: bd8d446a22bb0b79122c37f5f65e3813 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6659442b997079bc4278d2da6a9df57c + trainer: + kwargs: {} +name: bd8d446a22bb0b79122c37f5f65e3813 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bda59a87b059030c2971ceb484d164ae/params.yaml b/examples/security/classification/output/reports/train/bda59a87b059030c2971ceb484d164ae/params.yaml new file mode 100644 index 00000000..b5a90d7c --- /dev/null +++ b/examples/security/classification/output/reports/train/bda59a87b059030c2971ceb484d164ae/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/adv_predictions.json + adv_probabilities_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/829b1f6729da6ef7bae719a04e8d6c11.pkl + params_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/params.yaml + predictions_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/predictions.json + probabilities_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/probabilities.json + score_dict_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/score_dict.json + test_labels_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/test_labels.json + train_labels_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/train_labels.json + model_dir: models + name: bda59a87b059030c2971ceb484d164ae + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3b30cd3628c7573a9deb0c2ad71c4d0 + trainer: + kwargs: {} +name: bda59a87b059030c2971ceb484d164ae +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/params.yaml b/examples/security/classification/output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/params.yaml new file mode 100644 index 00000000..91feecd9 --- /dev/null +++ b/examples/security/classification/output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/adv_predictions.json + adv_probabilities_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/173c60c1cb97e774d15efc82cc37ba3d.pkl + params_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/params.yaml + predictions_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/predictions.json + probabilities_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/probabilities.json + score_dict_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/score_dict.json + test_labels_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/test_labels.json + train_labels_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/train_labels.json + model_dir: models + name: bdc55e96315eae96bcca9bb6ab5863ad + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f2d5f09bf8af30ed21f859ca1e6ad4cd + trainer: + kwargs: {} +name: bdc55e96315eae96bcca9bb6ab5863ad +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bdcefcec280844587000667c4fbc07f3/params.yaml b/examples/security/classification/output/reports/train/bdcefcec280844587000667c4fbc07f3/params.yaml new file mode 100644 index 00000000..6791ea57 --- /dev/null +++ b/examples/security/classification/output/reports/train/bdcefcec280844587000667c4fbc07f3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/adv_predictions.json + adv_probabilities_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/09d849283da4740e17e7d6e81bb2d6af.pkl + params_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/params.yaml + predictions_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/predictions.json + probabilities_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/probabilities.json + score_dict_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/score_dict.json + test_labels_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/test_labels.json + train_labels_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/train_labels.json + model_dir: models + name: bdcefcec280844587000667c4fbc07f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 650c50bc67d82991eac08ed73dee3a99 + trainer: + kwargs: {} +name: bdcefcec280844587000667c4fbc07f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bdd09d520e9760fc41806b28065e831c/params.yaml b/examples/security/classification/output/reports/train/bdd09d520e9760fc41806b28065e831c/params.yaml new file mode 100644 index 00000000..c177948c --- /dev/null +++ b/examples/security/classification/output/reports/train/bdd09d520e9760fc41806b28065e831c/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/adv_predictions.json + adv_probabilities_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/3d76ca554ffb8b15048807ced168440b.pkl + params_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/params.yaml + predictions_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/predictions.json + probabilities_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/probabilities.json + score_dict_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/score_dict.json + test_labels_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/test_labels.json + train_labels_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/train_labels.json + model_dir: models + name: bdd09d520e9760fc41806b28065e831c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 81d9b68353daa78ec51f570cc14c38f8 + trainer: + kwargs: {} +name: bdd09d520e9760fc41806b28065e831c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bde7294ddca01175962e97ceeff2d420/params.yaml b/examples/security/classification/output/reports/train/bde7294ddca01175962e97ceeff2d420/params.yaml new file mode 100644 index 00000000..d7657774 --- /dev/null +++ b/examples/security/classification/output/reports/train/bde7294ddca01175962e97ceeff2d420/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/adv_predictions.json + adv_probabilities_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/6868ab7c7a76926899bfd2484320b1ac.pkl + params_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/params.yaml + predictions_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/predictions.json + probabilities_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/probabilities.json + score_dict_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/score_dict.json + test_labels_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/test_labels.json + train_labels_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/train_labels.json + model_dir: models + name: bde7294ddca01175962e97ceeff2d420 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8d39342fb0f4b9221dac714ea6598bbc + trainer: + kwargs: {} +name: bde7294ddca01175962e97ceeff2d420 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bdfc768b20609d71981f7622fa66bad2/params.yaml b/examples/security/classification/output/reports/train/bdfc768b20609d71981f7622fa66bad2/params.yaml new file mode 100644 index 00000000..51ffd7a3 --- /dev/null +++ b/examples/security/classification/output/reports/train/bdfc768b20609d71981f7622fa66bad2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/adv_predictions.json + adv_probabilities_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/ae31e34ec69bc499dd11811eec9cf424.pkl + params_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/params.yaml + predictions_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/predictions.json + probabilities_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/probabilities.json + score_dict_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/score_dict.json + test_labels_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/test_labels.json + train_labels_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/train_labels.json + model_dir: models + name: bdfc768b20609d71981f7622fa66bad2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f99cd785e9402ade5fcf16d091c95eff + trainer: + kwargs: {} +name: bdfc768b20609d71981f7622fa66bad2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/be1d77333efb16467e2dbd028d89da75/params.yaml b/examples/security/classification/output/reports/train/be1d77333efb16467e2dbd028d89da75/params.yaml new file mode 100644 index 00000000..b93faa7f --- /dev/null +++ b/examples/security/classification/output/reports/train/be1d77333efb16467e2dbd028d89da75/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/adv_predictions.json + adv_probabilities_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/5dc2228d6729b4720aa87a7f5e6d96cd.pkl + params_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/params.yaml + predictions_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/predictions.json + probabilities_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/probabilities.json + score_dict_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/score_dict.json + test_labels_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/test_labels.json + train_labels_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/train_labels.json + model_dir: models + name: be1d77333efb16467e2dbd028d89da75 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7ea9d6c30d8f50701d87cde7a4bff02d + trainer: + kwargs: {} +name: be1d77333efb16467e2dbd028d89da75 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/be2008863ef4822daf8362a3c773386e/params.yaml b/examples/security/classification/output/reports/train/be2008863ef4822daf8362a3c773386e/params.yaml new file mode 100644 index 00000000..589ed82a --- /dev/null +++ b/examples/security/classification/output/reports/train/be2008863ef4822daf8362a3c773386e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/be2008863ef4822daf8362a3c773386e/adv_predictions.json + adv_probabilities_file: output/reports/train/be2008863ef4822daf8362a3c773386e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/500e396a4239ebb3deb074f70dd2fe78.pkl + params_file: output/reports/train/be2008863ef4822daf8362a3c773386e/params.yaml + predictions_file: output/reports/train/be2008863ef4822daf8362a3c773386e/predictions.json + probabilities_file: output/reports/train/be2008863ef4822daf8362a3c773386e/probabilities.json + score_dict_file: output/reports/train/be2008863ef4822daf8362a3c773386e/score_dict.json + test_labels_file: output/reports/train/be2008863ef4822daf8362a3c773386e/test_labels.json + train_labels_file: output/reports/train/be2008863ef4822daf8362a3c773386e/train_labels.json + model_dir: models + name: be2008863ef4822daf8362a3c773386e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2d6cd00a2262f05577230a96c0b03038 + trainer: + kwargs: {} +name: be2008863ef4822daf8362a3c773386e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/be38edb90aaff5020c085a7dc1f7f315/params.yaml b/examples/security/classification/output/reports/train/be38edb90aaff5020c085a7dc1f7f315/params.yaml new file mode 100644 index 00000000..62a3ab3d --- /dev/null +++ b/examples/security/classification/output/reports/train/be38edb90aaff5020c085a7dc1f7f315/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/adv_predictions.json + adv_probabilities_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/3509d9cde2567831c57638d9996f2936.pkl + params_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/params.yaml + predictions_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/predictions.json + probabilities_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/probabilities.json + score_dict_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/score_dict.json + test_labels_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/test_labels.json + train_labels_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/train_labels.json + model_dir: models + name: be38edb90aaff5020c085a7dc1f7f315 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d2bb130c4857f65d950a9e8e3ad30065 + trainer: + kwargs: {} +name: be38edb90aaff5020c085a7dc1f7f315 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/be5982f17348d5706916166095db1676/params.yaml b/examples/security/classification/output/reports/train/be5982f17348d5706916166095db1676/params.yaml new file mode 100644 index 00000000..db43f489 --- /dev/null +++ b/examples/security/classification/output/reports/train/be5982f17348d5706916166095db1676/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/be5982f17348d5706916166095db1676/adv_predictions.json + adv_probabilities_file: output/reports/train/be5982f17348d5706916166095db1676/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/286140ccc810715d6245dd3c53ac38fb.pkl + params_file: output/reports/train/be5982f17348d5706916166095db1676/params.yaml + predictions_file: output/reports/train/be5982f17348d5706916166095db1676/predictions.json + probabilities_file: output/reports/train/be5982f17348d5706916166095db1676/probabilities.json + score_dict_file: output/reports/train/be5982f17348d5706916166095db1676/score_dict.json + test_labels_file: output/reports/train/be5982f17348d5706916166095db1676/test_labels.json + train_labels_file: output/reports/train/be5982f17348d5706916166095db1676/train_labels.json + model_dir: models + name: be5982f17348d5706916166095db1676 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4ff66d01eb44cfafa0ab143a3885ac6a + trainer: + kwargs: {} +name: be5982f17348d5706916166095db1676 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/be5d76b5f971a05637ff5372b0d289af/params.yaml b/examples/security/classification/output/reports/train/be5d76b5f971a05637ff5372b0d289af/params.yaml new file mode 100644 index 00000000..51391cd1 --- /dev/null +++ b/examples/security/classification/output/reports/train/be5d76b5f971a05637ff5372b0d289af/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/adv_predictions.json + adv_probabilities_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/038cd4ef2d7ae25698832d2413e96245.pkl + params_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/params.yaml + predictions_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/predictions.json + probabilities_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/probabilities.json + score_dict_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/score_dict.json + test_labels_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/test_labels.json + train_labels_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/train_labels.json + model_dir: models + name: be5d76b5f971a05637ff5372b0d289af + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a2cc2ff3e200e07ef65581d26986da93 + trainer: + kwargs: {} +name: be5d76b5f971a05637ff5372b0d289af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/be6a61a43da3a3d25648e8683c037e9f/params.yaml b/examples/security/classification/output/reports/train/be6a61a43da3a3d25648e8683c037e9f/params.yaml new file mode 100644 index 00000000..4e851119 --- /dev/null +++ b/examples/security/classification/output/reports/train/be6a61a43da3a3d25648e8683c037e9f/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/adv_predictions.json + adv_probabilities_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/a383f27acc6ddb9b36e0b1b1e5ddbcf3.pkl + params_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/params.yaml + predictions_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/predictions.json + probabilities_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/probabilities.json + score_dict_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/score_dict.json + test_labels_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/test_labels.json + train_labels_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/train_labels.json + model_dir: models + name: be6a61a43da3a3d25648e8683c037e9f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 42dd40e9c1d183e01697ef5198bd68aa + trainer: + kwargs: {} +name: be6a61a43da3a3d25648e8683c037e9f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/params.yaml b/examples/security/classification/output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/params.yaml new file mode 100644 index 00000000..aae000d1 --- /dev/null +++ b/examples/security/classification/output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/adv_predictions.json + adv_probabilities_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/f72ccd22ffc242757a1cd6046829cd14.pkl + params_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/params.yaml + predictions_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/predictions.json + probabilities_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/probabilities.json + score_dict_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/score_dict.json + test_labels_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/test_labels.json + train_labels_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/train_labels.json + model_dir: models + name: be809044142ea80b2ba2fb5fc2a3edba + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 098fd811423c5ef90c5a10abd5f072df + trainer: + kwargs: {} +name: be809044142ea80b2ba2fb5fc2a3edba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/params.yaml b/examples/security/classification/output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/params.yaml new file mode 100644 index 00000000..8509d60c --- /dev/null +++ b/examples/security/classification/output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/adv_predictions.json + adv_probabilities_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/ae77ccc77a0d9d867853bb1500a8175c.pkl + params_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/params.yaml + predictions_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/predictions.json + probabilities_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/probabilities.json + score_dict_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/score_dict.json + test_labels_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/test_labels.json + train_labels_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/train_labels.json + model_dir: models + name: be923c6ddcc8cc5f8954e4ba53b5ab72 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0dbe6845697538e47bbd165f28dd52da + trainer: + kwargs: {} +name: be923c6ddcc8cc5f8954e4ba53b5ab72 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/be92986a16358d3380f835400e95068c/params.yaml b/examples/security/classification/output/reports/train/be92986a16358d3380f835400e95068c/params.yaml new file mode 100644 index 00000000..5b1dd220 --- /dev/null +++ b/examples/security/classification/output/reports/train/be92986a16358d3380f835400e95068c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/be92986a16358d3380f835400e95068c/adv_predictions.json + adv_probabilities_file: output/reports/train/be92986a16358d3380f835400e95068c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/cd7d2f5ca6dd38c4656c98d4645099cd.pkl + params_file: output/reports/train/be92986a16358d3380f835400e95068c/params.yaml + predictions_file: output/reports/train/be92986a16358d3380f835400e95068c/predictions.json + probabilities_file: output/reports/train/be92986a16358d3380f835400e95068c/probabilities.json + score_dict_file: output/reports/train/be92986a16358d3380f835400e95068c/score_dict.json + test_labels_file: output/reports/train/be92986a16358d3380f835400e95068c/test_labels.json + train_labels_file: output/reports/train/be92986a16358d3380f835400e95068c/train_labels.json + model_dir: models + name: be92986a16358d3380f835400e95068c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d996353069c18e674d6fd803e4f560b0 + trainer: + kwargs: {} +name: be92986a16358d3380f835400e95068c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/be94fe0e390955218284455e5970f181/params.yaml b/examples/security/classification/output/reports/train/be94fe0e390955218284455e5970f181/params.yaml new file mode 100644 index 00000000..049ccdb9 --- /dev/null +++ b/examples/security/classification/output/reports/train/be94fe0e390955218284455e5970f181/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/be94fe0e390955218284455e5970f181/adv_predictions.json + adv_probabilities_file: output/reports/train/be94fe0e390955218284455e5970f181/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/ad3428c38c42be2447f17ddd3edf521e.pkl + params_file: output/reports/train/be94fe0e390955218284455e5970f181/params.yaml + predictions_file: output/reports/train/be94fe0e390955218284455e5970f181/predictions.json + probabilities_file: output/reports/train/be94fe0e390955218284455e5970f181/probabilities.json + score_dict_file: output/reports/train/be94fe0e390955218284455e5970f181/score_dict.json + test_labels_file: output/reports/train/be94fe0e390955218284455e5970f181/test_labels.json + train_labels_file: output/reports/train/be94fe0e390955218284455e5970f181/train_labels.json + model_dir: models + name: be94fe0e390955218284455e5970f181 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a86b86fd8ba677dacd6885097f6b2595 + trainer: + kwargs: {} +name: be94fe0e390955218284455e5970f181 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/params.yaml b/examples/security/classification/output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/params.yaml new file mode 100644 index 00000000..9106045f --- /dev/null +++ b/examples/security/classification/output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/adv_predictions.json + adv_probabilities_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/7c8e1ef7552a6106832e8b75ac62ca3e.pkl + params_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/params.yaml + predictions_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/predictions.json + probabilities_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/probabilities.json + score_dict_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/score_dict.json + test_labels_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/test_labels.json + train_labels_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/train_labels.json + model_dir: models + name: beb5c6fa43c5ccf2145e6eeb0d3e5680 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dd6b8e03743122afa3017784b2190c28 + trainer: + kwargs: {} +name: beb5c6fa43c5ccf2145e6eeb0d3e5680 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/params.yaml b/examples/security/classification/output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/params.yaml new file mode 100644 index 00000000..b5f72248 --- /dev/null +++ b/examples/security/classification/output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/adv_predictions.json + adv_probabilities_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/59424b3adf2400fb531c32905597b202.pkl + params_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/params.yaml + predictions_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/predictions.json + probabilities_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/probabilities.json + score_dict_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/score_dict.json + test_labels_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/test_labels.json + train_labels_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/train_labels.json + model_dir: models + name: bebc916cdfa9996dcdd94a75c376bc9a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e432578d3bb1100fda61159e6e9bcbae + trainer: + kwargs: {} +name: bebc916cdfa9996dcdd94a75c376bc9a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bebe212558bc34a45a31eb8087af3b84/params.yaml b/examples/security/classification/output/reports/train/bebe212558bc34a45a31eb8087af3b84/params.yaml new file mode 100644 index 00000000..b3b0624d --- /dev/null +++ b/examples/security/classification/output/reports/train/bebe212558bc34a45a31eb8087af3b84/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/adv_predictions.json + adv_probabilities_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/d9cdcd9abe36ad017a7f6d3cd30d6539.pkl + params_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/params.yaml + predictions_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/predictions.json + probabilities_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/probabilities.json + score_dict_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/score_dict.json + test_labels_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/test_labels.json + train_labels_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/train_labels.json + model_dir: models + name: bebe212558bc34a45a31eb8087af3b84 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9a43d0e937f6cc6fceb26d42dd5cbe4d + trainer: + kwargs: {} +name: bebe212558bc34a45a31eb8087af3b84 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bee32160854e5414f0c2f074fd388c56/params.yaml b/examples/security/classification/output/reports/train/bee32160854e5414f0c2f074fd388c56/params.yaml new file mode 100644 index 00000000..d81d5980 --- /dev/null +++ b/examples/security/classification/output/reports/train/bee32160854e5414f0c2f074fd388c56/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/adv_predictions.json + adv_probabilities_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/d3b3357867e72786780422ecaa9339c8.pkl + params_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/params.yaml + predictions_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/predictions.json + probabilities_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/probabilities.json + score_dict_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/score_dict.json + test_labels_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/test_labels.json + train_labels_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/train_labels.json + model_dir: models + name: bee32160854e5414f0c2f074fd388c56 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ffda291eab9cd2fbbf83b4dd3c1751ad + trainer: + kwargs: {} +name: bee32160854e5414f0c2f074fd388c56 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/beeafa37b43439b81f70afc9df29fc6d/params.yaml b/examples/security/classification/output/reports/train/beeafa37b43439b81f70afc9df29fc6d/params.yaml new file mode 100644 index 00000000..4ab9dd95 --- /dev/null +++ b/examples/security/classification/output/reports/train/beeafa37b43439b81f70afc9df29fc6d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/adv_predictions.json + adv_probabilities_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/e1ee2e819e4f2af76db10ef608a2d248.pkl + params_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/params.yaml + predictions_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/predictions.json + probabilities_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/probabilities.json + score_dict_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/score_dict.json + test_labels_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/test_labels.json + train_labels_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/train_labels.json + model_dir: models + name: beeafa37b43439b81f70afc9df29fc6d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2a4ec4b55c8c3a83b6a7c1d85b5242ca + trainer: + kwargs: {} +name: beeafa37b43439b81f70afc9df29fc6d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/params.yaml b/examples/security/classification/output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/params.yaml new file mode 100644 index 00000000..3e924136 --- /dev/null +++ b/examples/security/classification/output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/adv_predictions.json + adv_probabilities_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/1d2275b82100da2ee32b57c9d2159268.pkl + params_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/params.yaml + predictions_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/predictions.json + probabilities_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/probabilities.json + score_dict_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/score_dict.json + test_labels_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/test_labels.json + train_labels_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/train_labels.json + model_dir: models + name: bf284b12ee0213c1d53ce0fa751039fd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dd495c6e19c66d4a6e3de4875b18c137 + trainer: + kwargs: {} +name: bf284b12ee0213c1d53ce0fa751039fd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bf66e0f4f437d25d612573c8d1737226/params.yaml b/examples/security/classification/output/reports/train/bf66e0f4f437d25d612573c8d1737226/params.yaml new file mode 100644 index 00000000..bfd60b6d --- /dev/null +++ b/examples/security/classification/output/reports/train/bf66e0f4f437d25d612573c8d1737226/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/adv_predictions.json + adv_probabilities_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/959d68b2f523c7f231772e2c653cc1ae.pkl + params_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/params.yaml + predictions_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/predictions.json + probabilities_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/probabilities.json + score_dict_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/score_dict.json + test_labels_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/test_labels.json + train_labels_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/train_labels.json + model_dir: models + name: bf66e0f4f437d25d612573c8d1737226 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3c4570f51612018f11ca4e97d369bb0a + trainer: + kwargs: {} +name: bf66e0f4f437d25d612573c8d1737226 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bf71718b6b6867da41cb4d606c651180/params.yaml b/examples/security/classification/output/reports/train/bf71718b6b6867da41cb4d606c651180/params.yaml new file mode 100644 index 00000000..22b7bb3b --- /dev/null +++ b/examples/security/classification/output/reports/train/bf71718b6b6867da41cb4d606c651180/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/adv_predictions.json + adv_probabilities_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/bbc337887c63f19a716c7adeda1f7918.pkl + params_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/params.yaml + predictions_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/predictions.json + probabilities_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/probabilities.json + score_dict_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/score_dict.json + test_labels_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/test_labels.json + train_labels_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/train_labels.json + model_dir: models + name: bf71718b6b6867da41cb4d606c651180 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 79b056853dc06aa353b9d0936263c320 + trainer: + kwargs: {} +name: bf71718b6b6867da41cb4d606c651180 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bf7984d7ef1761dec055e9aab5465247/params.yaml b/examples/security/classification/output/reports/train/bf7984d7ef1761dec055e9aab5465247/params.yaml new file mode 100644 index 00000000..f42d6004 --- /dev/null +++ b/examples/security/classification/output/reports/train/bf7984d7ef1761dec055e9aab5465247/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/adv_predictions.json + adv_probabilities_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/0d0de4ac26d9249533c5a1eb0cd2938e.pkl + params_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/params.yaml + predictions_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/predictions.json + probabilities_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/probabilities.json + score_dict_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/score_dict.json + test_labels_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/test_labels.json + train_labels_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/train_labels.json + model_dir: models + name: bf7984d7ef1761dec055e9aab5465247 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3d95912de644fd9b20caaa226dc9a0f2 + trainer: + kwargs: {} +name: bf7984d7ef1761dec055e9aab5465247 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bf901849185724d14b61afd0a0d702e7/params.yaml b/examples/security/classification/output/reports/train/bf901849185724d14b61afd0a0d702e7/params.yaml new file mode 100644 index 00000000..189970d9 --- /dev/null +++ b/examples/security/classification/output/reports/train/bf901849185724d14b61afd0a0d702e7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/adv_predictions.json + adv_probabilities_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/e91f1ba238731e1596389e112c65b667.pkl + params_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/params.yaml + predictions_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/predictions.json + probabilities_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/probabilities.json + score_dict_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/score_dict.json + test_labels_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/test_labels.json + train_labels_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/train_labels.json + model_dir: models + name: bf901849185724d14b61afd0a0d702e7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e5ded2ccec88fe51a41894f8cb6cf36a + trainer: + kwargs: {} +name: bf901849185724d14b61afd0a0d702e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bf949a0986ad1c193b66115774a6125c/params.yaml b/examples/security/classification/output/reports/train/bf949a0986ad1c193b66115774a6125c/params.yaml new file mode 100644 index 00000000..e41769bc --- /dev/null +++ b/examples/security/classification/output/reports/train/bf949a0986ad1c193b66115774a6125c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/adv_predictions.json + adv_probabilities_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/42bd2c6ecaba92322f7636b097383107.pkl + params_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/params.yaml + predictions_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/predictions.json + probabilities_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/probabilities.json + score_dict_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/score_dict.json + test_labels_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/test_labels.json + train_labels_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/train_labels.json + model_dir: models + name: bf949a0986ad1c193b66115774a6125c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 60d940b61070097aee9d40e175e45a2d + trainer: + kwargs: {} +name: bf949a0986ad1c193b66115774a6125c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/params.yaml b/examples/security/classification/output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/params.yaml new file mode 100644 index 00000000..a7487f76 --- /dev/null +++ b/examples/security/classification/output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/adv_predictions.json + adv_probabilities_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/19e179b7fe6d3e12a239178c20e5924f.pkl + params_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/params.yaml + predictions_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/predictions.json + probabilities_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/probabilities.json + score_dict_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/score_dict.json + test_labels_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/test_labels.json + train_labels_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/train_labels.json + model_dir: models + name: bf98bc28d7040201ab7a1914f86f0f1d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 40a62d82e51bbfc64c597992fd856e1d + trainer: + kwargs: {} +name: bf98bc28d7040201ab7a1914f86f0f1d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/params.yaml b/examples/security/classification/output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/params.yaml new file mode 100644 index 00000000..d5ffcebe --- /dev/null +++ b/examples/security/classification/output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/adv_predictions.json + adv_probabilities_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/bf87c13441dd7df782ac648d343ac26b.pkl + params_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/params.yaml + predictions_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/predictions.json + probabilities_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/probabilities.json + score_dict_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/score_dict.json + test_labels_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/test_labels.json + train_labels_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/train_labels.json + model_dir: models + name: bfcf7d3a6edb3f1f277982d6b646b248 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a95267c0f4b33802a8fb35a6cf2602b1 + trainer: + kwargs: {} +name: bfcf7d3a6edb3f1f277982d6b646b248 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/params.yaml b/examples/security/classification/output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/params.yaml new file mode 100644 index 00000000..48dcf5ce --- /dev/null +++ b/examples/security/classification/output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/adv_predictions.json + adv_probabilities_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/6cb9fd008348860121e52abc2e31038f.pkl + params_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/params.yaml + predictions_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/predictions.json + probabilities_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/probabilities.json + score_dict_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/score_dict.json + test_labels_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/test_labels.json + train_labels_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/train_labels.json + model_dir: models + name: bfe6f88bebec7e22c6bd2113662e57b1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cb29e7011eb1ce44d3d512ae0366b0b7 + trainer: + kwargs: {} +name: bfe6f88bebec7e22c6bd2113662e57b1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/params.yaml b/examples/security/classification/output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/params.yaml new file mode 100644 index 00000000..3bada70e --- /dev/null +++ b/examples/security/classification/output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/adv_predictions.json + adv_probabilities_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/68c790c69c67ae0f739969a1cb9b7855.pkl + params_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/params.yaml + predictions_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/predictions.json + probabilities_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/probabilities.json + score_dict_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/score_dict.json + test_labels_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/test_labels.json + train_labels_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/train_labels.json + model_dir: models + name: c014e22dbb8c08fd668925ea068c0a9b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 615032e76e4e4fea6c60e3b8358b5fdc + trainer: + kwargs: {} +name: c014e22dbb8c08fd668925ea068c0a9b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/params.yaml b/examples/security/classification/output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/params.yaml new file mode 100644 index 00000000..a43dfb0d --- /dev/null +++ b/examples/security/classification/output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/adv_predictions.json + adv_probabilities_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/6931453db5c2bba442635a1c31fcacc9.pkl + params_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/params.yaml + predictions_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/predictions.json + probabilities_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/probabilities.json + score_dict_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/score_dict.json + test_labels_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/test_labels.json + train_labels_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/train_labels.json + model_dir: models + name: c0b7cd22f88dfada5b40871f52ba99ad + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 92e4c40fff13582dea45383596a72317 + trainer: + kwargs: {} +name: c0b7cd22f88dfada5b40871f52ba99ad +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c108ca96df8bf0e8e06074e22914beed/params.yaml b/examples/security/classification/output/reports/train/c108ca96df8bf0e8e06074e22914beed/params.yaml new file mode 100644 index 00000000..7245d5ea --- /dev/null +++ b/examples/security/classification/output/reports/train/c108ca96df8bf0e8e06074e22914beed/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/adv_predictions.json + adv_probabilities_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/29f92b839f64c5f8c7d34d340b7e8586.pkl + params_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/params.yaml + predictions_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/predictions.json + probabilities_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/probabilities.json + score_dict_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/score_dict.json + test_labels_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/test_labels.json + train_labels_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/train_labels.json + model_dir: models + name: c108ca96df8bf0e8e06074e22914beed + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2d418e865469630af2c4f0ec6a40e3ad + trainer: + kwargs: {} +name: c108ca96df8bf0e8e06074e22914beed +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/params.yaml b/examples/security/classification/output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/params.yaml new file mode 100644 index 00000000..93400be0 --- /dev/null +++ b/examples/security/classification/output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/adv_predictions.json + adv_probabilities_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/e76daf18e0394e14fe4dcb322ae073e2.pkl + params_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/params.yaml + predictions_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/predictions.json + probabilities_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/probabilities.json + score_dict_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/score_dict.json + test_labels_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/test_labels.json + train_labels_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/train_labels.json + model_dir: models + name: c11545eeba33d984fd40d6e03f0dac0e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0f7ee024531cbbb1594c7128fe032f21 + trainer: + kwargs: {} +name: c11545eeba33d984fd40d6e03f0dac0e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c11cec1fa09084e0a182db0f6671a24f/params.yaml b/examples/security/classification/output/reports/train/c11cec1fa09084e0a182db0f6671a24f/params.yaml new file mode 100644 index 00000000..0d5aa801 --- /dev/null +++ b/examples/security/classification/output/reports/train/c11cec1fa09084e0a182db0f6671a24f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/adv_predictions.json + adv_probabilities_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/db79aa93f63026fbd94d9dc3399cd0d3.pkl + params_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/params.yaml + predictions_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/predictions.json + probabilities_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/probabilities.json + score_dict_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/score_dict.json + test_labels_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/test_labels.json + train_labels_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/train_labels.json + model_dir: models + name: c11cec1fa09084e0a182db0f6671a24f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bf6d36e9a4da7cf90451bd67d7723348 + trainer: + kwargs: {} +name: c11cec1fa09084e0a182db0f6671a24f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/params.yaml b/examples/security/classification/output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/params.yaml new file mode 100644 index 00000000..e73bc1cd --- /dev/null +++ b/examples/security/classification/output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/adv_predictions.json + adv_probabilities_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/58e03f0fd960c96e58a140933e416a2d.pkl + params_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/params.yaml + predictions_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/predictions.json + probabilities_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/probabilities.json + score_dict_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/score_dict.json + test_labels_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/test_labels.json + train_labels_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/train_labels.json + model_dir: models + name: c12fe34ba8589bd9f98b0a07ab694f47 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4871632995e732cd8b38c2defbf877b3 + trainer: + kwargs: {} +name: c12fe34ba8589bd9f98b0a07ab694f47 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/params.yaml b/examples/security/classification/output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/params.yaml new file mode 100644 index 00000000..4d194ea5 --- /dev/null +++ b/examples/security/classification/output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/adv_predictions.json + adv_probabilities_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/69cedd8b61ea140052e859cad128d338.pkl + params_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/params.yaml + predictions_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/predictions.json + probabilities_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/probabilities.json + score_dict_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/score_dict.json + test_labels_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/test_labels.json + train_labels_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/train_labels.json + model_dir: models + name: c16094c874c4bebdaf00a4c9aa228ab0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9a76065fb6857c534f3a7cd32be7ae4d + trainer: + kwargs: {} +name: c16094c874c4bebdaf00a4c9aa228ab0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c16e951595963a3646a99a33712726b6/params.yaml b/examples/security/classification/output/reports/train/c16e951595963a3646a99a33712726b6/params.yaml new file mode 100644 index 00000000..d109bed7 --- /dev/null +++ b/examples/security/classification/output/reports/train/c16e951595963a3646a99a33712726b6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c16e951595963a3646a99a33712726b6/adv_predictions.json + adv_probabilities_file: output/reports/train/c16e951595963a3646a99a33712726b6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/6cc0e474a471f2eb1c1beaa705010589.pkl + params_file: output/reports/train/c16e951595963a3646a99a33712726b6/params.yaml + predictions_file: output/reports/train/c16e951595963a3646a99a33712726b6/predictions.json + probabilities_file: output/reports/train/c16e951595963a3646a99a33712726b6/probabilities.json + score_dict_file: output/reports/train/c16e951595963a3646a99a33712726b6/score_dict.json + test_labels_file: output/reports/train/c16e951595963a3646a99a33712726b6/test_labels.json + train_labels_file: output/reports/train/c16e951595963a3646a99a33712726b6/train_labels.json + model_dir: models + name: c16e951595963a3646a99a33712726b6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5dbbbbfb6e23e1773ac874eb40c2b68a + trainer: + kwargs: {} +name: c16e951595963a3646a99a33712726b6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/params.yaml b/examples/security/classification/output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/params.yaml new file mode 100644 index 00000000..69527df3 --- /dev/null +++ b/examples/security/classification/output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/adv_predictions.json + adv_probabilities_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/6c4cab2cf05cfa04619149d7c22d9a13.pkl + params_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/params.yaml + predictions_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/predictions.json + probabilities_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/probabilities.json + score_dict_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/score_dict.json + test_labels_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/test_labels.json + train_labels_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/train_labels.json + model_dir: models + name: c1add3431b048ccd1483cbcfb6b4d453 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4bda9a40cc92dd1b7e981f8d2920d70d + trainer: + kwargs: {} +name: c1add3431b048ccd1483cbcfb6b4d453 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c1bea749d2575de05360adb4a72e778d/params.yaml b/examples/security/classification/output/reports/train/c1bea749d2575de05360adb4a72e778d/params.yaml new file mode 100644 index 00000000..64c7d295 --- /dev/null +++ b/examples/security/classification/output/reports/train/c1bea749d2575de05360adb4a72e778d/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/adv_predictions.json + adv_probabilities_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/13d3f33a12c808bae18199e7145be4d8.pkl + params_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/params.yaml + predictions_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/predictions.json + probabilities_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/probabilities.json + score_dict_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/score_dict.json + test_labels_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/test_labels.json + train_labels_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/train_labels.json + model_dir: models + name: c1bea749d2575de05360adb4a72e778d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 632c93da21fe6abd127a7e7f80c25ce5 + trainer: + kwargs: {} +name: c1bea749d2575de05360adb4a72e778d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/params.yaml b/examples/security/classification/output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/params.yaml new file mode 100644 index 00000000..13ec0f20 --- /dev/null +++ b/examples/security/classification/output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/adv_predictions.json + adv_probabilities_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/dbc8f37807c2fae1bf3baca3e87d2ae7.pkl + params_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/params.yaml + predictions_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/predictions.json + probabilities_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/probabilities.json + score_dict_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/score_dict.json + test_labels_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/test_labels.json + train_labels_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/train_labels.json + model_dir: models + name: c20abc3f690bd3772f20faafa14cd7b5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eefdb05f76eccb962630d6fd381f7b27 + trainer: + kwargs: {} +name: c20abc3f690bd3772f20faafa14cd7b5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c22d1e859e6c559be0721de999ed2c7e/params.yaml b/examples/security/classification/output/reports/train/c22d1e859e6c559be0721de999ed2c7e/params.yaml new file mode 100644 index 00000000..ca61ea60 --- /dev/null +++ b/examples/security/classification/output/reports/train/c22d1e859e6c559be0721de999ed2c7e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/adv_predictions.json + adv_probabilities_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/6df9527527f46524144fdc53ae13e41d.pkl + params_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/params.yaml + predictions_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/predictions.json + probabilities_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/probabilities.json + score_dict_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/score_dict.json + test_labels_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/test_labels.json + train_labels_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/train_labels.json + model_dir: models + name: c22d1e859e6c559be0721de999ed2c7e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 661245fe5d603057b78ac72d41d338f5 + trainer: + kwargs: {} +name: c22d1e859e6c559be0721de999ed2c7e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/params.yaml b/examples/security/classification/output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/params.yaml new file mode 100644 index 00000000..3e3d051c --- /dev/null +++ b/examples/security/classification/output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/adv_predictions.json + adv_probabilities_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/6ce82b49bcd66b2f6ee99044db6c38ac.pkl + params_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/params.yaml + predictions_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/predictions.json + probabilities_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/probabilities.json + score_dict_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/score_dict.json + test_labels_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/test_labels.json + train_labels_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/train_labels.json + model_dir: models + name: c22f22d34c1a32e2f634b15f8f7e9b5c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e855a87927a9f7500a550a93da4d5291 + trainer: + kwargs: {} +name: c22f22d34c1a32e2f634b15f8f7e9b5c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c23fa55c7757f3ed87fabae473bfd197/params.yaml b/examples/security/classification/output/reports/train/c23fa55c7757f3ed87fabae473bfd197/params.yaml new file mode 100644 index 00000000..a8adf634 --- /dev/null +++ b/examples/security/classification/output/reports/train/c23fa55c7757f3ed87fabae473bfd197/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/adv_predictions.json + adv_probabilities_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/a0fe58404124751e88a81edb765abdda.pkl + params_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/params.yaml + predictions_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/predictions.json + probabilities_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/probabilities.json + score_dict_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/score_dict.json + test_labels_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/test_labels.json + train_labels_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/train_labels.json + model_dir: models + name: c23fa55c7757f3ed87fabae473bfd197 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cb6f31ac89e389455beb4134c76a479c + trainer: + kwargs: {} +name: c23fa55c7757f3ed87fabae473bfd197 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/params.yaml b/examples/security/classification/output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/params.yaml new file mode 100644 index 00000000..f9698f88 --- /dev/null +++ b/examples/security/classification/output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/adv_predictions.json + adv_probabilities_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/ec638b6c810a3f4aebbc8367d8cca7df.pkl + params_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/params.yaml + predictions_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/predictions.json + probabilities_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/probabilities.json + score_dict_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/score_dict.json + test_labels_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/test_labels.json + train_labels_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/train_labels.json + model_dir: models + name: c24a3455cb4412dc688eab1f6fae13d8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3421f620dbc31cf16de2d68700a3282 + trainer: + kwargs: {} +name: c24a3455cb4412dc688eab1f6fae13d8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c261da321db0a069a2b4556b76b38593/params.yaml b/examples/security/classification/output/reports/train/c261da321db0a069a2b4556b76b38593/params.yaml new file mode 100644 index 00000000..0953bea4 --- /dev/null +++ b/examples/security/classification/output/reports/train/c261da321db0a069a2b4556b76b38593/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c261da321db0a069a2b4556b76b38593/adv_predictions.json + adv_probabilities_file: output/reports/train/c261da321db0a069a2b4556b76b38593/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/e4cf34ef5b2a28c7ac2b1c11322fca10.pkl + params_file: output/reports/train/c261da321db0a069a2b4556b76b38593/params.yaml + predictions_file: output/reports/train/c261da321db0a069a2b4556b76b38593/predictions.json + probabilities_file: output/reports/train/c261da321db0a069a2b4556b76b38593/probabilities.json + score_dict_file: output/reports/train/c261da321db0a069a2b4556b76b38593/score_dict.json + test_labels_file: output/reports/train/c261da321db0a069a2b4556b76b38593/test_labels.json + train_labels_file: output/reports/train/c261da321db0a069a2b4556b76b38593/train_labels.json + model_dir: models + name: c261da321db0a069a2b4556b76b38593 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d0e08cd00819310e162aad1e59c01b7a + trainer: + kwargs: {} +name: c261da321db0a069a2b4556b76b38593 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c2716b625367d8781cb6de061e6302ff/params.yaml b/examples/security/classification/output/reports/train/c2716b625367d8781cb6de061e6302ff/params.yaml new file mode 100644 index 00000000..8a29ed17 --- /dev/null +++ b/examples/security/classification/output/reports/train/c2716b625367d8781cb6de061e6302ff/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/adv_predictions.json + adv_probabilities_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/11595f060afae4cc9a311a2e15907ab3.pkl + params_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/params.yaml + predictions_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/predictions.json + probabilities_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/probabilities.json + score_dict_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/score_dict.json + test_labels_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/test_labels.json + train_labels_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/train_labels.json + model_dir: models + name: c2716b625367d8781cb6de061e6302ff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f175740e1df6012b83c23210a4b3c559 + trainer: + kwargs: {} +name: c2716b625367d8781cb6de061e6302ff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c2718a003fc5a5908d54890e1213567a/params.yaml b/examples/security/classification/output/reports/train/c2718a003fc5a5908d54890e1213567a/params.yaml new file mode 100644 index 00000000..eb432c92 --- /dev/null +++ b/examples/security/classification/output/reports/train/c2718a003fc5a5908d54890e1213567a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/adv_predictions.json + adv_probabilities_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/82e206b999d973c4a027697a432cad67.pkl + params_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/params.yaml + predictions_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/predictions.json + probabilities_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/probabilities.json + score_dict_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/score_dict.json + test_labels_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/test_labels.json + train_labels_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/train_labels.json + model_dir: models + name: c2718a003fc5a5908d54890e1213567a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 81814deb4cfda1084f00c8861e1bf6ff + trainer: + kwargs: {} +name: c2718a003fc5a5908d54890e1213567a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c29e9baadd22fb113afb364e6d3f4815/params.yaml b/examples/security/classification/output/reports/train/c29e9baadd22fb113afb364e6d3f4815/params.yaml new file mode 100644 index 00000000..60b13d66 --- /dev/null +++ b/examples/security/classification/output/reports/train/c29e9baadd22fb113afb364e6d3f4815/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/adv_predictions.json + adv_probabilities_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/2a14f3f6ef3241b52dd66f35d5e15d20.pkl + params_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/params.yaml + predictions_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/predictions.json + probabilities_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/probabilities.json + score_dict_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/score_dict.json + test_labels_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/test_labels.json + train_labels_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/train_labels.json + model_dir: models + name: c29e9baadd22fb113afb364e6d3f4815 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6e47d249d154003a034afb1e40b61d67 + trainer: + kwargs: {} +name: c29e9baadd22fb113afb364e6d3f4815 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/params.yaml b/examples/security/classification/output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/params.yaml new file mode 100644 index 00000000..045ceb47 --- /dev/null +++ b/examples/security/classification/output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/adv_predictions.json + adv_probabilities_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/6c8e05be41ece7c09ba199e0a5c9c74a.pkl + params_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/params.yaml + predictions_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/predictions.json + probabilities_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/probabilities.json + score_dict_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/score_dict.json + test_labels_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/test_labels.json + train_labels_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/train_labels.json + model_dir: models + name: c2b9e268390c7a272d200a7e5bb145dc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1c56e5a9dd178d1e1168dc0d3001c14d + trainer: + kwargs: {} +name: c2b9e268390c7a272d200a7e5bb145dc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c2dfb170b77d277a6703017db23932a6/params.yaml b/examples/security/classification/output/reports/train/c2dfb170b77d277a6703017db23932a6/params.yaml new file mode 100644 index 00000000..5efb35d9 --- /dev/null +++ b/examples/security/classification/output/reports/train/c2dfb170b77d277a6703017db23932a6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/adv_predictions.json + adv_probabilities_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/6d8d4edd6517386c74f29a737a0b9801.pkl + params_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/params.yaml + predictions_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/predictions.json + probabilities_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/probabilities.json + score_dict_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/score_dict.json + test_labels_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/test_labels.json + train_labels_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/train_labels.json + model_dir: models + name: c2dfb170b77d277a6703017db23932a6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 443b71764ca8a74e4b45b0f80e65391e + trainer: + kwargs: {} +name: c2dfb170b77d277a6703017db23932a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/params.yaml b/examples/security/classification/output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/params.yaml new file mode 100644 index 00000000..97ad2d8f --- /dev/null +++ b/examples/security/classification/output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/adv_predictions.json + adv_probabilities_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/277a3bf6c128e47a3bcb1f91a9ad9952.pkl + params_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/params.yaml + predictions_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/predictions.json + probabilities_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/probabilities.json + score_dict_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/score_dict.json + test_labels_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/test_labels.json + train_labels_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/train_labels.json + model_dir: models + name: c2dfe8265a4637ba7ea38b7d8e9e4e06 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d163acc7cb2be488d4f89211f6a4560 + trainer: + kwargs: {} +name: c2dfe8265a4637ba7ea38b7d8e9e4e06 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/params.yaml b/examples/security/classification/output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/params.yaml new file mode 100644 index 00000000..88191d10 --- /dev/null +++ b/examples/security/classification/output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/adv_predictions.json + adv_probabilities_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/e10cee68e694554f49b09b00caa9ec8a.pkl + params_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/params.yaml + predictions_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/predictions.json + probabilities_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/probabilities.json + score_dict_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/score_dict.json + test_labels_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/test_labels.json + train_labels_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/train_labels.json + model_dir: models + name: c30c0242e1bc367dd8acbfbf27456f41 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 87bc4ef74d0aba086c52f3c2752bc856 + trainer: + kwargs: {} +name: c30c0242e1bc367dd8acbfbf27456f41 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c3179ff58af733b4e2ab50d349e94110/params.yaml b/examples/security/classification/output/reports/train/c3179ff58af733b4e2ab50d349e94110/params.yaml new file mode 100644 index 00000000..664836a2 --- /dev/null +++ b/examples/security/classification/output/reports/train/c3179ff58af733b4e2ab50d349e94110/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/adv_predictions.json + adv_probabilities_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/622d45879296f953a85b4a57a13cb636.pkl + params_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/params.yaml + predictions_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/predictions.json + probabilities_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/probabilities.json + score_dict_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/score_dict.json + test_labels_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/test_labels.json + train_labels_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/train_labels.json + model_dir: models + name: c3179ff58af733b4e2ab50d349e94110 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6e2e9eaefa6c6d6f312cb88e63d3fe12 + trainer: + kwargs: {} +name: c3179ff58af733b4e2ab50d349e94110 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c3880dd98badcd9deaf84cc7e597b066/params.yaml b/examples/security/classification/output/reports/train/c3880dd98badcd9deaf84cc7e597b066/params.yaml new file mode 100644 index 00000000..26891791 --- /dev/null +++ b/examples/security/classification/output/reports/train/c3880dd98badcd9deaf84cc7e597b066/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/adv_predictions.json + adv_probabilities_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/aa2cb68b688add76946be16a4a19ac99.pkl + params_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/params.yaml + predictions_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/predictions.json + probabilities_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/probabilities.json + score_dict_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/score_dict.json + test_labels_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/test_labels.json + train_labels_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/train_labels.json + model_dir: models + name: c3880dd98badcd9deaf84cc7e597b066 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3978278fe37259f3d72b94ac5f4d2d36 + trainer: + kwargs: {} +name: c3880dd98badcd9deaf84cc7e597b066 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c3cc276410121eb872e477e447a6c243/params.yaml b/examples/security/classification/output/reports/train/c3cc276410121eb872e477e447a6c243/params.yaml new file mode 100644 index 00000000..d84096ea --- /dev/null +++ b/examples/security/classification/output/reports/train/c3cc276410121eb872e477e447a6c243/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c3cc276410121eb872e477e447a6c243/adv_predictions.json + adv_probabilities_file: output/reports/train/c3cc276410121eb872e477e447a6c243/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/4b7484f492c7a19cd8ab284160529d92.pkl + params_file: output/reports/train/c3cc276410121eb872e477e447a6c243/params.yaml + predictions_file: output/reports/train/c3cc276410121eb872e477e447a6c243/predictions.json + probabilities_file: output/reports/train/c3cc276410121eb872e477e447a6c243/probabilities.json + score_dict_file: output/reports/train/c3cc276410121eb872e477e447a6c243/score_dict.json + test_labels_file: output/reports/train/c3cc276410121eb872e477e447a6c243/test_labels.json + train_labels_file: output/reports/train/c3cc276410121eb872e477e447a6c243/train_labels.json + model_dir: models + name: c3cc276410121eb872e477e447a6c243 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8e0af9b9d883754a0db13bb56d7b996e + trainer: + kwargs: {} +name: c3cc276410121eb872e477e447a6c243 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/params.yaml b/examples/security/classification/output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/params.yaml new file mode 100644 index 00000000..6ae49dee --- /dev/null +++ b/examples/security/classification/output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/adv_predictions.json + adv_probabilities_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/bf1bbef087e6145c2753d053564dd388.pkl + params_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/params.yaml + predictions_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/predictions.json + probabilities_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/probabilities.json + score_dict_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/score_dict.json + test_labels_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/test_labels.json + train_labels_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/train_labels.json + model_dir: models + name: c40ef8aae5bd9e70669324b48d3e55d8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dc26927794f9adb8c921c790a563327f + trainer: + kwargs: {} +name: c40ef8aae5bd9e70669324b48d3e55d8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/params.yaml b/examples/security/classification/output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/params.yaml new file mode 100644 index 00000000..b18be7f9 --- /dev/null +++ b/examples/security/classification/output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/adv_predictions.json + adv_probabilities_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/18dd322e4c2a8c4f6cca435f504c359d.pkl + params_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/params.yaml + predictions_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/predictions.json + probabilities_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/probabilities.json + score_dict_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/score_dict.json + test_labels_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/test_labels.json + train_labels_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/train_labels.json + model_dir: models + name: c41d54b8f4a993e3dbfcc012ec85026e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 783771c66e7b04fd6a38c64edc67f23c + trainer: + kwargs: {} +name: c41d54b8f4a993e3dbfcc012ec85026e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/params.yaml b/examples/security/classification/output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/params.yaml new file mode 100644 index 00000000..f413ac9a --- /dev/null +++ b/examples/security/classification/output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/adv_predictions.json + adv_probabilities_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/e4723c688d7dde03e8823934b6d848c5.pkl + params_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/params.yaml + predictions_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/predictions.json + probabilities_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/probabilities.json + score_dict_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/score_dict.json + test_labels_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/test_labels.json + train_labels_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/train_labels.json + model_dir: models + name: c425af38d9f73cec9f61fe49cf8f624d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f4bc53c39aae6e2210434ffcd80fee2b + trainer: + kwargs: {} +name: c425af38d9f73cec9f61fe49cf8f624d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/params.yaml b/examples/security/classification/output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/params.yaml new file mode 100644 index 00000000..9cf93e1d --- /dev/null +++ b/examples/security/classification/output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/params.yaml @@ -0,0 +1,104 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/adv_predictions.json + adv_probabilities_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e682d61ad144d6317beb5c1a8b984489.pkl + params_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/params.yaml + predictions_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/predictions.json + probabilities_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/probabilities.json + score_dict_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/score_dict.json + test_labels_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/test_labels.json + train_labels_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/train_labels.json + model_dir: models + name: c44a91cabfaa291cca29708b64ef7bb8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: + - linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 333b37302fd2cc71e052d3cec78aabb0 + trainer: + kwargs: {} +name: c44a91cabfaa291cca29708b64ef7bb8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c4b7ff307d0c1823d6677845862db8f5/params.yaml b/examples/security/classification/output/reports/train/c4b7ff307d0c1823d6677845862db8f5/params.yaml new file mode 100644 index 00000000..1e7abb09 --- /dev/null +++ b/examples/security/classification/output/reports/train/c4b7ff307d0c1823d6677845862db8f5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/adv_predictions.json + adv_probabilities_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/dea31999ad373f637cd5e3aae90630d9.pkl + params_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/params.yaml + predictions_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/predictions.json + probabilities_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/probabilities.json + score_dict_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/score_dict.json + test_labels_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/test_labels.json + train_labels_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/train_labels.json + model_dir: models + name: c4b7ff307d0c1823d6677845862db8f5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 60a3cec71f570dabaa9cdb2df09dd852 + trainer: + kwargs: {} +name: c4b7ff307d0c1823d6677845862db8f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/params.yaml b/examples/security/classification/output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/params.yaml new file mode 100644 index 00000000..36f7c2ec --- /dev/null +++ b/examples/security/classification/output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/adv_predictions.json + adv_probabilities_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/b4e9efa7c3fa1664974f9c4e65b7eeb9.pkl + params_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/params.yaml + predictions_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/predictions.json + probabilities_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/probabilities.json + score_dict_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/score_dict.json + test_labels_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/test_labels.json + train_labels_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/train_labels.json + model_dir: models + name: c4bb673e05bb64bb50724e4be269b4dd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dc026a4c82fe5a96a66fcea2dbad6fbb + trainer: + kwargs: {} +name: c4bb673e05bb64bb50724e4be269b4dd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c4be380e69c798e644e389918fd95506/params.yaml b/examples/security/classification/output/reports/train/c4be380e69c798e644e389918fd95506/params.yaml new file mode 100644 index 00000000..0d426f1d --- /dev/null +++ b/examples/security/classification/output/reports/train/c4be380e69c798e644e389918fd95506/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c4be380e69c798e644e389918fd95506/adv_predictions.json + adv_probabilities_file: output/reports/train/c4be380e69c798e644e389918fd95506/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/23421d4c0df994a7becd5ff990e976ba.pkl + params_file: output/reports/train/c4be380e69c798e644e389918fd95506/params.yaml + predictions_file: output/reports/train/c4be380e69c798e644e389918fd95506/predictions.json + probabilities_file: output/reports/train/c4be380e69c798e644e389918fd95506/probabilities.json + score_dict_file: output/reports/train/c4be380e69c798e644e389918fd95506/score_dict.json + test_labels_file: output/reports/train/c4be380e69c798e644e389918fd95506/test_labels.json + train_labels_file: output/reports/train/c4be380e69c798e644e389918fd95506/train_labels.json + model_dir: models + name: c4be380e69c798e644e389918fd95506 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e647cfb6f4eb5205f20fc3b704d1deff + trainer: + kwargs: {} +name: c4be380e69c798e644e389918fd95506 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c4d8612f4ed7c63403c85942582923c1/params.yaml b/examples/security/classification/output/reports/train/c4d8612f4ed7c63403c85942582923c1/params.yaml new file mode 100644 index 00000000..aa09be53 --- /dev/null +++ b/examples/security/classification/output/reports/train/c4d8612f4ed7c63403c85942582923c1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/adv_predictions.json + adv_probabilities_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/6f37f0c467ff75f77156dd5c96634633.pkl + params_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/params.yaml + predictions_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/predictions.json + probabilities_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/probabilities.json + score_dict_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/score_dict.json + test_labels_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/test_labels.json + train_labels_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/train_labels.json + model_dir: models + name: c4d8612f4ed7c63403c85942582923c1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a255ad841ccf1d8edb944106de6786ac + trainer: + kwargs: {} +name: c4d8612f4ed7c63403c85942582923c1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c4dc93add515096cf5ab0fd530111246/params.yaml b/examples/security/classification/output/reports/train/c4dc93add515096cf5ab0fd530111246/params.yaml new file mode 100644 index 00000000..61cfdd69 --- /dev/null +++ b/examples/security/classification/output/reports/train/c4dc93add515096cf5ab0fd530111246/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/adv_predictions.json + adv_probabilities_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/3f42cfc8ca58293f7a3048fb2bac4771.pkl + params_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/params.yaml + predictions_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/predictions.json + probabilities_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/probabilities.json + score_dict_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/score_dict.json + test_labels_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/test_labels.json + train_labels_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/train_labels.json + model_dir: models + name: c4dc93add515096cf5ab0fd530111246 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 53f24f2808e78155d7698dd0c2b0716d + trainer: + kwargs: {} +name: c4dc93add515096cf5ab0fd530111246 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/params.yaml b/examples/security/classification/output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/params.yaml new file mode 100644 index 00000000..5e7c0374 --- /dev/null +++ b/examples/security/classification/output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/adv_predictions.json + adv_probabilities_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/17788ae0c943cb2a9179c0ac32cbe8ed.pkl + params_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/params.yaml + predictions_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/predictions.json + probabilities_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/probabilities.json + score_dict_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/score_dict.json + test_labels_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/test_labels.json + train_labels_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/train_labels.json + model_dir: models + name: c4f2f7aa19e382606bdcb0984bd2525b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 41bffc67bf912f4faa5d9b34c7b7c006 + trainer: + kwargs: {} +name: c4f2f7aa19e382606bdcb0984bd2525b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/params.yaml b/examples/security/classification/output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/params.yaml new file mode 100644 index 00000000..27a7fbcb --- /dev/null +++ b/examples/security/classification/output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/adv_predictions.json + adv_probabilities_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/19b5f14c9af31ad44e17d0da00e3a6d5.pkl + params_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/params.yaml + predictions_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/predictions.json + probabilities_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/probabilities.json + score_dict_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/score_dict.json + test_labels_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/test_labels.json + train_labels_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/train_labels.json + model_dir: models + name: c52fc173ca8dfedd0cd1745ed734e126 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0bae8a2782f593ce6c104936b63b821d + trainer: + kwargs: {} +name: c52fc173ca8dfedd0cd1745ed734e126 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/params.yaml b/examples/security/classification/output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/params.yaml new file mode 100644 index 00000000..748ccdac --- /dev/null +++ b/examples/security/classification/output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/adv_predictions.json + adv_probabilities_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/c6ef3c87ca472ea11ae291d31ef96d5d.pkl + params_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/params.yaml + predictions_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/predictions.json + probabilities_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/probabilities.json + score_dict_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/score_dict.json + test_labels_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/test_labels.json + train_labels_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/train_labels.json + model_dir: models + name: c56c81f86d8bc58a094acb23dd8fe89d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6a607494632c3d04565d4ee023fa5094 + trainer: + kwargs: {} +name: c56c81f86d8bc58a094acb23dd8fe89d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c572b6af94535d9ced166214dac58c34/params.yaml b/examples/security/classification/output/reports/train/c572b6af94535d9ced166214dac58c34/params.yaml new file mode 100644 index 00000000..c4e3047c --- /dev/null +++ b/examples/security/classification/output/reports/train/c572b6af94535d9ced166214dac58c34/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c572b6af94535d9ced166214dac58c34/adv_predictions.json + adv_probabilities_file: output/reports/train/c572b6af94535d9ced166214dac58c34/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/b2d2e578404ce841911154b0a2ec8579.pkl + params_file: output/reports/train/c572b6af94535d9ced166214dac58c34/params.yaml + predictions_file: output/reports/train/c572b6af94535d9ced166214dac58c34/predictions.json + probabilities_file: output/reports/train/c572b6af94535d9ced166214dac58c34/probabilities.json + score_dict_file: output/reports/train/c572b6af94535d9ced166214dac58c34/score_dict.json + test_labels_file: output/reports/train/c572b6af94535d9ced166214dac58c34/test_labels.json + train_labels_file: output/reports/train/c572b6af94535d9ced166214dac58c34/train_labels.json + model_dir: models + name: c572b6af94535d9ced166214dac58c34 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 404cbc8c24b978d8b9668a7204855a6f + trainer: + kwargs: {} +name: c572b6af94535d9ced166214dac58c34 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/params.yaml b/examples/security/classification/output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/params.yaml new file mode 100644 index 00000000..5b17a5c0 --- /dev/null +++ b/examples/security/classification/output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/adv_predictions.json + adv_probabilities_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/0f8f78ea5bee88612dd86c033909c00a.pkl + params_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/params.yaml + predictions_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/predictions.json + probabilities_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/probabilities.json + score_dict_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/score_dict.json + test_labels_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/test_labels.json + train_labels_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/train_labels.json + model_dir: models + name: c5b4fad4cedf45a5ec25248aec2e600b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6bfa60dd2e7a420c2cbe0e53d984ba20 + trainer: + kwargs: {} +name: c5b4fad4cedf45a5ec25248aec2e600b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c5d5942a08e68e66945ba6593152aba9/params.yaml b/examples/security/classification/output/reports/train/c5d5942a08e68e66945ba6593152aba9/params.yaml new file mode 100644 index 00000000..b7ffe910 --- /dev/null +++ b/examples/security/classification/output/reports/train/c5d5942a08e68e66945ba6593152aba9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/adv_predictions.json + adv_probabilities_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/1bc47ccd2ffe267c41c45170971b9922.pkl + params_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/params.yaml + predictions_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/predictions.json + probabilities_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/probabilities.json + score_dict_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/score_dict.json + test_labels_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/test_labels.json + train_labels_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/train_labels.json + model_dir: models + name: c5d5942a08e68e66945ba6593152aba9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 19dca0679221c2e9bca14c5488e7d720 + trainer: + kwargs: {} +name: c5d5942a08e68e66945ba6593152aba9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/params.yaml b/examples/security/classification/output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/params.yaml new file mode 100644 index 00000000..b8ebf29d --- /dev/null +++ b/examples/security/classification/output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/adv_predictions.json + adv_probabilities_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/affb16ac144694fbb1673065eef35e1e.pkl + params_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/params.yaml + predictions_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/predictions.json + probabilities_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/probabilities.json + score_dict_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/score_dict.json + test_labels_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/test_labels.json + train_labels_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/train_labels.json + model_dir: models + name: c5eb4d7bcdbbe2136d870baa9ce28b42 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 15d9b713fed18f5eeeaa4400d8703f84 + trainer: + kwargs: {} +name: c5eb4d7bcdbbe2136d870baa9ce28b42 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/params.yaml b/examples/security/classification/output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/params.yaml new file mode 100644 index 00000000..cd4c46b5 --- /dev/null +++ b/examples/security/classification/output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/adv_predictions.json + adv_probabilities_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/44bf6c89c039bed4eeac83e29348b397.pkl + params_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/params.yaml + predictions_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/predictions.json + probabilities_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/probabilities.json + score_dict_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/score_dict.json + test_labels_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/test_labels.json + train_labels_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/train_labels.json + model_dir: models + name: c619946dc0b3a0ba8b82c829ee766d32 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e13af8eecc55455cbe9aa420ac043633 + trainer: + kwargs: {} +name: c619946dc0b3a0ba8b82c829ee766d32 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/params.yaml b/examples/security/classification/output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/params.yaml new file mode 100644 index 00000000..5f777932 --- /dev/null +++ b/examples/security/classification/output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/adv_predictions.json + adv_probabilities_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/f1d7fd832d0f611873bed274a588c1b4.pkl + params_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/params.yaml + predictions_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/predictions.json + probabilities_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/probabilities.json + score_dict_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/score_dict.json + test_labels_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/test_labels.json + train_labels_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/train_labels.json + model_dir: models + name: c663f5d9e1f96bf18d72361ccd30fdb9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2a61680d599004323f1514e9f3e38e9 + trainer: + kwargs: {} +name: c663f5d9e1f96bf18d72361ccd30fdb9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c68325a7327591458a8cf1080454e390/params.yaml b/examples/security/classification/output/reports/train/c68325a7327591458a8cf1080454e390/params.yaml new file mode 100644 index 00000000..0f9d926b --- /dev/null +++ b/examples/security/classification/output/reports/train/c68325a7327591458a8cf1080454e390/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c68325a7327591458a8cf1080454e390/adv_predictions.json + adv_probabilities_file: output/reports/train/c68325a7327591458a8cf1080454e390/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/f5cea59a861186eacd56d5b05f20260a.pkl + params_file: output/reports/train/c68325a7327591458a8cf1080454e390/params.yaml + predictions_file: output/reports/train/c68325a7327591458a8cf1080454e390/predictions.json + probabilities_file: output/reports/train/c68325a7327591458a8cf1080454e390/probabilities.json + score_dict_file: output/reports/train/c68325a7327591458a8cf1080454e390/score_dict.json + test_labels_file: output/reports/train/c68325a7327591458a8cf1080454e390/test_labels.json + train_labels_file: output/reports/train/c68325a7327591458a8cf1080454e390/train_labels.json + model_dir: models + name: c68325a7327591458a8cf1080454e390 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e8a88cd6418f4489cfca5df51aa3739a + trainer: + kwargs: {} +name: c68325a7327591458a8cf1080454e390 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c6aee9652a95a8aef27322801bb1da08/params.yaml b/examples/security/classification/output/reports/train/c6aee9652a95a8aef27322801bb1da08/params.yaml new file mode 100644 index 00000000..beba563f --- /dev/null +++ b/examples/security/classification/output/reports/train/c6aee9652a95a8aef27322801bb1da08/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/adv_predictions.json + adv_probabilities_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/8d502efa598346e4304a0620d52459d0.pkl + params_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/params.yaml + predictions_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/predictions.json + probabilities_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/probabilities.json + score_dict_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/score_dict.json + test_labels_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/test_labels.json + train_labels_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/train_labels.json + model_dir: models + name: c6aee9652a95a8aef27322801bb1da08 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ffc0b17085652548f337af104d12d86a + trainer: + kwargs: {} +name: c6aee9652a95a8aef27322801bb1da08 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c713642e21dfd1a7d1812c34550dcb58/params.yaml b/examples/security/classification/output/reports/train/c713642e21dfd1a7d1812c34550dcb58/params.yaml new file mode 100644 index 00000000..f8d4c909 --- /dev/null +++ b/examples/security/classification/output/reports/train/c713642e21dfd1a7d1812c34550dcb58/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/adv_predictions.json + adv_probabilities_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/2ef71d80b43ba6c69ee5b8a40382e5d4.pkl + params_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/params.yaml + predictions_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/predictions.json + probabilities_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/probabilities.json + score_dict_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/score_dict.json + test_labels_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/test_labels.json + train_labels_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/train_labels.json + model_dir: models + name: c713642e21dfd1a7d1812c34550dcb58 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e7635ee802a655152081179539a36aaf + trainer: + kwargs: {} +name: c713642e21dfd1a7d1812c34550dcb58 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/params.yaml b/examples/security/classification/output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/params.yaml new file mode 100644 index 00000000..f2b212ae --- /dev/null +++ b/examples/security/classification/output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/adv_predictions.json + adv_probabilities_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/1931e7f238515f5397f9507797fcc2af.pkl + params_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/params.yaml + predictions_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/predictions.json + probabilities_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/probabilities.json + score_dict_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/score_dict.json + test_labels_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/test_labels.json + train_labels_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/train_labels.json + model_dir: models + name: c754c7a27b024436e136c54e5a1bbd6f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6f88c004e25301a81fa07dc15455db30 + trainer: + kwargs: {} +name: c754c7a27b024436e136c54e5a1bbd6f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c76347883264ae9bfd25d2aea6625673/params.yaml b/examples/security/classification/output/reports/train/c76347883264ae9bfd25d2aea6625673/params.yaml new file mode 100644 index 00000000..857f4618 --- /dev/null +++ b/examples/security/classification/output/reports/train/c76347883264ae9bfd25d2aea6625673/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/adv_predictions.json + adv_probabilities_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/1ad67a0f5512e1948ec3025b46fea351.pkl + params_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/params.yaml + predictions_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/predictions.json + probabilities_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/probabilities.json + score_dict_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/score_dict.json + test_labels_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/test_labels.json + train_labels_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/train_labels.json + model_dir: models + name: c76347883264ae9bfd25d2aea6625673 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d74edef8766dafdfa9a0297c40f8e9a3 + trainer: + kwargs: {} +name: c76347883264ae9bfd25d2aea6625673 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c7735d453acc38058508af0fd2bf6d29/params.yaml b/examples/security/classification/output/reports/train/c7735d453acc38058508af0fd2bf6d29/params.yaml new file mode 100644 index 00000000..1cd91154 --- /dev/null +++ b/examples/security/classification/output/reports/train/c7735d453acc38058508af0fd2bf6d29/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/adv_predictions.json + adv_probabilities_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/d796da9df961faa58fbf3e5cf39e949d.pkl + params_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/params.yaml + predictions_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/predictions.json + probabilities_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/probabilities.json + score_dict_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/score_dict.json + test_labels_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/test_labels.json + train_labels_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/train_labels.json + model_dir: models + name: c7735d453acc38058508af0fd2bf6d29 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 017f23ff5ca8fe41fe4f0c387c5c6ff6 + trainer: + kwargs: {} +name: c7735d453acc38058508af0fd2bf6d29 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/params.yaml b/examples/security/classification/output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/params.yaml new file mode 100644 index 00000000..03802c06 --- /dev/null +++ b/examples/security/classification/output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/adv_predictions.json + adv_probabilities_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/dec4742504a1dea730128e31af0e9f58.pkl + params_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/params.yaml + predictions_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/predictions.json + probabilities_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/probabilities.json + score_dict_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/score_dict.json + test_labels_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/test_labels.json + train_labels_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/train_labels.json + model_dir: models + name: c77cc291dc994c517e3f25d72c5e0d64 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b00a7a6c7fff36cd909ff44aaa9afbbe + trainer: + kwargs: {} +name: c77cc291dc994c517e3f25d72c5e0d64 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/params.yaml b/examples/security/classification/output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/params.yaml new file mode 100644 index 00000000..2e923aa8 --- /dev/null +++ b/examples/security/classification/output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/adv_predictions.json + adv_probabilities_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/36108956b67fe945c5d8b538e51e9b3c.pkl + params_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/params.yaml + predictions_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/predictions.json + probabilities_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/probabilities.json + score_dict_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/score_dict.json + test_labels_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/test_labels.json + train_labels_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/train_labels.json + model_dir: models + name: c7baa82fc45e2ffef8186733f9c155cc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d023ea6ee31bfb1fa37350b07b2ae37 + trainer: + kwargs: {} +name: c7baa82fc45e2ffef8186733f9c155cc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/params.yaml b/examples/security/classification/output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/params.yaml new file mode 100644 index 00000000..4f389d98 --- /dev/null +++ b/examples/security/classification/output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/adv_predictions.json + adv_probabilities_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/c3024472384de101f48455a5f7ef7b8a.pkl + params_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/params.yaml + predictions_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/predictions.json + probabilities_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/probabilities.json + score_dict_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/score_dict.json + test_labels_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/test_labels.json + train_labels_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/train_labels.json + model_dir: models + name: c7c579a3f7c457a8f310cdc662e839a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 710825fca037c09076feb4f2cc413839 + trainer: + kwargs: {} +name: c7c579a3f7c457a8f310cdc662e839a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/params.yaml b/examples/security/classification/output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/params.yaml new file mode 100644 index 00000000..2ddde5a0 --- /dev/null +++ b/examples/security/classification/output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/adv_predictions.json + adv_probabilities_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/8746db937c44a621056b765dd9157ceb.pkl + params_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/params.yaml + predictions_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/predictions.json + probabilities_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/probabilities.json + score_dict_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/score_dict.json + test_labels_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/test_labels.json + train_labels_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/train_labels.json + model_dir: models + name: c7e26acb47d0706ae19cb2329f8a9c84 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0b4925bf7218544c4078d5639bb2913d + trainer: + kwargs: {} +name: c7e26acb47d0706ae19cb2329f8a9c84 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c7f1fd00947e0274a7ad733e71c09628/params.yaml b/examples/security/classification/output/reports/train/c7f1fd00947e0274a7ad733e71c09628/params.yaml new file mode 100644 index 00000000..4f582712 --- /dev/null +++ b/examples/security/classification/output/reports/train/c7f1fd00947e0274a7ad733e71c09628/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/adv_predictions.json + adv_probabilities_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/a916f88df302a97fe8d21ca0ddd4bd2a.pkl + params_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/params.yaml + predictions_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/predictions.json + probabilities_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/probabilities.json + score_dict_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/score_dict.json + test_labels_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/test_labels.json + train_labels_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/train_labels.json + model_dir: models + name: c7f1fd00947e0274a7ad733e71c09628 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6e9cb650c18467d35e13c06470e5882c + trainer: + kwargs: {} +name: c7f1fd00947e0274a7ad733e71c09628 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c800b271cb0c9371607bfab0cfecd358/params.yaml b/examples/security/classification/output/reports/train/c800b271cb0c9371607bfab0cfecd358/params.yaml new file mode 100644 index 00000000..8eea8901 --- /dev/null +++ b/examples/security/classification/output/reports/train/c800b271cb0c9371607bfab0cfecd358/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/adv_predictions.json + adv_probabilities_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/a5f1f712222a43b94f365885b5d56470.pkl + params_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/params.yaml + predictions_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/predictions.json + probabilities_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/probabilities.json + score_dict_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/score_dict.json + test_labels_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/test_labels.json + train_labels_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/train_labels.json + model_dir: models + name: c800b271cb0c9371607bfab0cfecd358 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4e370e6b4c4ec30b5e4538b7064d5e1f + trainer: + kwargs: {} +name: c800b271cb0c9371607bfab0cfecd358 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c841770fcb0303ed13250bd10f45d682/params.yaml b/examples/security/classification/output/reports/train/c841770fcb0303ed13250bd10f45d682/params.yaml new file mode 100644 index 00000000..d33c4431 --- /dev/null +++ b/examples/security/classification/output/reports/train/c841770fcb0303ed13250bd10f45d682/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/adv_predictions.json + adv_probabilities_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/18ffa24c6a59f70804fae1ec19c38308.pkl + params_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/params.yaml + predictions_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/predictions.json + probabilities_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/probabilities.json + score_dict_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/score_dict.json + test_labels_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/test_labels.json + train_labels_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/train_labels.json + model_dir: models + name: c841770fcb0303ed13250bd10f45d682 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b2bd15095a180942e9398ccf73e87b81 + trainer: + kwargs: {} +name: c841770fcb0303ed13250bd10f45d682 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/params.yaml b/examples/security/classification/output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/params.yaml new file mode 100644 index 00000000..50c2ed22 --- /dev/null +++ b/examples/security/classification/output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/adv_predictions.json + adv_probabilities_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/053c453d46905170dfe459a3d7bef7f4.pkl + params_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/params.yaml + predictions_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/predictions.json + probabilities_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/probabilities.json + score_dict_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/score_dict.json + test_labels_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/test_labels.json + train_labels_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/train_labels.json + model_dir: models + name: c8528c4d1aa1968bcbdeb086c6bf162d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 176dde26aee2d3a238115de69df528ad + trainer: + kwargs: {} +name: c8528c4d1aa1968bcbdeb086c6bf162d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/params.yaml b/examples/security/classification/output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/params.yaml new file mode 100644 index 00000000..0af3fee1 --- /dev/null +++ b/examples/security/classification/output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/adv_predictions.json + adv_probabilities_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/a2bb63d7b8a2f0534917ded4c456c274.pkl + params_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/params.yaml + predictions_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/predictions.json + probabilities_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/probabilities.json + score_dict_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/score_dict.json + test_labels_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/test_labels.json + train_labels_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/train_labels.json + model_dir: models + name: c8730d0c6e842f06dfffc0a7220bdef6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8b180c0d034af8aa0cb495c5bed00572 + trainer: + kwargs: {} +name: c8730d0c6e842f06dfffc0a7220bdef6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c88a5df09481a2441a693b48d168feb0/params.yaml b/examples/security/classification/output/reports/train/c88a5df09481a2441a693b48d168feb0/params.yaml new file mode 100644 index 00000000..74d1c057 --- /dev/null +++ b/examples/security/classification/output/reports/train/c88a5df09481a2441a693b48d168feb0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/adv_predictions.json + adv_probabilities_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/d7f48a1e7c80dd94e2064680cc4a6f7c.pkl + params_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/params.yaml + predictions_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/predictions.json + probabilities_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/probabilities.json + score_dict_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/score_dict.json + test_labels_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/test_labels.json + train_labels_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/train_labels.json + model_dir: models + name: c88a5df09481a2441a693b48d168feb0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a64b10c3f9e6d162379dddd797712900 + trainer: + kwargs: {} +name: c88a5df09481a2441a693b48d168feb0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c9215f08fe121a6dab292775db734ba6/params.yaml b/examples/security/classification/output/reports/train/c9215f08fe121a6dab292775db734ba6/params.yaml new file mode 100644 index 00000000..22924e5d --- /dev/null +++ b/examples/security/classification/output/reports/train/c9215f08fe121a6dab292775db734ba6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/adv_predictions.json + adv_probabilities_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/32b261cd6a6ee453b5a108217fb8cd94.pkl + params_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/params.yaml + predictions_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/predictions.json + probabilities_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/probabilities.json + score_dict_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/score_dict.json + test_labels_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/test_labels.json + train_labels_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/train_labels.json + model_dir: models + name: c9215f08fe121a6dab292775db734ba6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3348e8b5aaa937e6076b755271ac8aa3 + trainer: + kwargs: {} +name: c9215f08fe121a6dab292775db734ba6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c94623bc02d36f5738ea88df13863105/params.yaml b/examples/security/classification/output/reports/train/c94623bc02d36f5738ea88df13863105/params.yaml new file mode 100644 index 00000000..4d904b92 --- /dev/null +++ b/examples/security/classification/output/reports/train/c94623bc02d36f5738ea88df13863105/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c94623bc02d36f5738ea88df13863105/adv_predictions.json + adv_probabilities_file: output/reports/train/c94623bc02d36f5738ea88df13863105/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/c549905fc1262fec0a2c92d8015a04e2.pkl + params_file: output/reports/train/c94623bc02d36f5738ea88df13863105/params.yaml + predictions_file: output/reports/train/c94623bc02d36f5738ea88df13863105/predictions.json + probabilities_file: output/reports/train/c94623bc02d36f5738ea88df13863105/probabilities.json + score_dict_file: output/reports/train/c94623bc02d36f5738ea88df13863105/score_dict.json + test_labels_file: output/reports/train/c94623bc02d36f5738ea88df13863105/test_labels.json + train_labels_file: output/reports/train/c94623bc02d36f5738ea88df13863105/train_labels.json + model_dir: models + name: c94623bc02d36f5738ea88df13863105 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 37b47e578d8143f2a17f578134b3f3e1 + trainer: + kwargs: {} +name: c94623bc02d36f5738ea88df13863105 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c9663463051eb5f3c70e82dcba17284d/params.yaml b/examples/security/classification/output/reports/train/c9663463051eb5f3c70e82dcba17284d/params.yaml new file mode 100644 index 00000000..569541cd --- /dev/null +++ b/examples/security/classification/output/reports/train/c9663463051eb5f3c70e82dcba17284d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/adv_predictions.json + adv_probabilities_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/77c254338da712342e843889f3cd2bef.pkl + params_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/params.yaml + predictions_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/predictions.json + probabilities_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/probabilities.json + score_dict_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/score_dict.json + test_labels_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/test_labels.json + train_labels_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/train_labels.json + model_dir: models + name: c9663463051eb5f3c70e82dcba17284d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c6e63dc0a4673e6a45ab9fab85c88387 + trainer: + kwargs: {} +name: c9663463051eb5f3c70e82dcba17284d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c98c713595a08626eac065d0e99806db/params.yaml b/examples/security/classification/output/reports/train/c98c713595a08626eac065d0e99806db/params.yaml new file mode 100644 index 00000000..d348109f --- /dev/null +++ b/examples/security/classification/output/reports/train/c98c713595a08626eac065d0e99806db/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c98c713595a08626eac065d0e99806db/adv_predictions.json + adv_probabilities_file: output/reports/train/c98c713595a08626eac065d0e99806db/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/9dfa4f60bfe73ff961fea13d7bc468d5.pkl + params_file: output/reports/train/c98c713595a08626eac065d0e99806db/params.yaml + predictions_file: output/reports/train/c98c713595a08626eac065d0e99806db/predictions.json + probabilities_file: output/reports/train/c98c713595a08626eac065d0e99806db/probabilities.json + score_dict_file: output/reports/train/c98c713595a08626eac065d0e99806db/score_dict.json + test_labels_file: output/reports/train/c98c713595a08626eac065d0e99806db/test_labels.json + train_labels_file: output/reports/train/c98c713595a08626eac065d0e99806db/train_labels.json + model_dir: models + name: c98c713595a08626eac065d0e99806db + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2f72c2254c85a0187212c11a2bf1aef0 + trainer: + kwargs: {} +name: c98c713595a08626eac065d0e99806db +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/params.yaml b/examples/security/classification/output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/params.yaml new file mode 100644 index 00000000..f4d79a67 --- /dev/null +++ b/examples/security/classification/output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/adv_predictions.json + adv_probabilities_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/34077a43379db203885b94ed0449b350.pkl + params_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/params.yaml + predictions_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/predictions.json + probabilities_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/probabilities.json + score_dict_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/score_dict.json + test_labels_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/test_labels.json + train_labels_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/train_labels.json + model_dir: models + name: c99707732dde1c8c8be93e3e835ec2e5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a340ba61df09dd52e3e4c49cfac5aff6 + trainer: + kwargs: {} +name: c99707732dde1c8c8be93e3e835ec2e5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c99d6e04d0ea11639873d85628b34c97/params.yaml b/examples/security/classification/output/reports/train/c99d6e04d0ea11639873d85628b34c97/params.yaml new file mode 100644 index 00000000..09fdd194 --- /dev/null +++ b/examples/security/classification/output/reports/train/c99d6e04d0ea11639873d85628b34c97/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/adv_predictions.json + adv_probabilities_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/7db0e4dccf6fd82738ad3d074b0b9667.pkl + params_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/params.yaml + predictions_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/predictions.json + probabilities_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/probabilities.json + score_dict_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/score_dict.json + test_labels_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/test_labels.json + train_labels_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/train_labels.json + model_dir: models + name: c99d6e04d0ea11639873d85628b34c97 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6faabe5539e309a03d6b529365d2eeb5 + trainer: + kwargs: {} +name: c99d6e04d0ea11639873d85628b34c97 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/params.yaml b/examples/security/classification/output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/params.yaml new file mode 100644 index 00000000..02465d93 --- /dev/null +++ b/examples/security/classification/output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/adv_predictions.json + adv_probabilities_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/0e885121abf16a962e106c3b50c47284.pkl + params_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/params.yaml + predictions_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/predictions.json + probabilities_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/probabilities.json + score_dict_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/score_dict.json + test_labels_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/test_labels.json + train_labels_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/train_labels.json + model_dir: models + name: c9af6bee16dcb8fcbd93050e219484f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: da764bbe4c7444a0613f1d2ba9f62264 + trainer: + kwargs: {} +name: c9af6bee16dcb8fcbd93050e219484f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c9b0277deb574aaade6538d229f7016f/params.yaml b/examples/security/classification/output/reports/train/c9b0277deb574aaade6538d229f7016f/params.yaml new file mode 100644 index 00000000..dba19048 --- /dev/null +++ b/examples/security/classification/output/reports/train/c9b0277deb574aaade6538d229f7016f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/adv_predictions.json + adv_probabilities_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/bda23311ba0722d23458ddcd6a37331c.pkl + params_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/params.yaml + predictions_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/predictions.json + probabilities_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/probabilities.json + score_dict_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/score_dict.json + test_labels_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/test_labels.json + train_labels_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/train_labels.json + model_dir: models + name: c9b0277deb574aaade6538d229f7016f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b09bb73348411b2f99e9550e5bfe5313 + trainer: + kwargs: {} +name: c9b0277deb574aaade6538d229f7016f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c9c335593ca172f41879092bd01a1616/params.yaml b/examples/security/classification/output/reports/train/c9c335593ca172f41879092bd01a1616/params.yaml new file mode 100644 index 00000000..07d8b430 --- /dev/null +++ b/examples/security/classification/output/reports/train/c9c335593ca172f41879092bd01a1616/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c9c335593ca172f41879092bd01a1616/adv_predictions.json + adv_probabilities_file: output/reports/train/c9c335593ca172f41879092bd01a1616/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/0a4fbea364c5b3e4feeed749b3087c4c.pkl + params_file: output/reports/train/c9c335593ca172f41879092bd01a1616/params.yaml + predictions_file: output/reports/train/c9c335593ca172f41879092bd01a1616/predictions.json + probabilities_file: output/reports/train/c9c335593ca172f41879092bd01a1616/probabilities.json + score_dict_file: output/reports/train/c9c335593ca172f41879092bd01a1616/score_dict.json + test_labels_file: output/reports/train/c9c335593ca172f41879092bd01a1616/test_labels.json + train_labels_file: output/reports/train/c9c335593ca172f41879092bd01a1616/train_labels.json + model_dir: models + name: c9c335593ca172f41879092bd01a1616 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 02acb9420babacf247e44acfa3408304 + trainer: + kwargs: {} +name: c9c335593ca172f41879092bd01a1616 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/params.yaml b/examples/security/classification/output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/params.yaml new file mode 100644 index 00000000..984789d3 --- /dev/null +++ b/examples/security/classification/output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/adv_predictions.json + adv_probabilities_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/072b58d1b6c56560e96e18df32e77de9.pkl + params_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/params.yaml + predictions_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/predictions.json + probabilities_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/probabilities.json + score_dict_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/score_dict.json + test_labels_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/test_labels.json + train_labels_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/train_labels.json + model_dir: models + name: c9f5d6c0d4bcdadba73ca2905d2a9306 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 864ebc09ff89843f4f831db1e333f568 + trainer: + kwargs: {} +name: c9f5d6c0d4bcdadba73ca2905d2a9306 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/params.yaml b/examples/security/classification/output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/params.yaml new file mode 100644 index 00000000..44cca5ec --- /dev/null +++ b/examples/security/classification/output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/adv_predictions.json + adv_probabilities_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/54877cef1f5a0987237c75080b43acfb.pkl + params_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/params.yaml + predictions_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/predictions.json + probabilities_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/probabilities.json + score_dict_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/score_dict.json + test_labels_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/test_labels.json + train_labels_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/train_labels.json + model_dir: models + name: ca1f05f66c4ee40ccaa57002a30c0650 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8b82017f47580a78f3818ae6a04dc1ab + trainer: + kwargs: {} +name: ca1f05f66c4ee40ccaa57002a30c0650 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ca28301efac63cd826688d978b2ff397/params.yaml b/examples/security/classification/output/reports/train/ca28301efac63cd826688d978b2ff397/params.yaml new file mode 100644 index 00000000..fd813d31 --- /dev/null +++ b/examples/security/classification/output/reports/train/ca28301efac63cd826688d978b2ff397/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ca28301efac63cd826688d978b2ff397/adv_predictions.json + adv_probabilities_file: output/reports/train/ca28301efac63cd826688d978b2ff397/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/097db94a754fef177ff8da74fb0ea5f7.pkl + params_file: output/reports/train/ca28301efac63cd826688d978b2ff397/params.yaml + predictions_file: output/reports/train/ca28301efac63cd826688d978b2ff397/predictions.json + probabilities_file: output/reports/train/ca28301efac63cd826688d978b2ff397/probabilities.json + score_dict_file: output/reports/train/ca28301efac63cd826688d978b2ff397/score_dict.json + test_labels_file: output/reports/train/ca28301efac63cd826688d978b2ff397/test_labels.json + train_labels_file: output/reports/train/ca28301efac63cd826688d978b2ff397/train_labels.json + model_dir: models + name: ca28301efac63cd826688d978b2ff397 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: adbc05436ded75471a35beffafde17f6 + trainer: + kwargs: {} +name: ca28301efac63cd826688d978b2ff397 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/params.yaml b/examples/security/classification/output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/params.yaml new file mode 100644 index 00000000..f18f9557 --- /dev/null +++ b/examples/security/classification/output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/adv_predictions.json + adv_probabilities_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/f8d89e93fcdba36931c785190e9a0220.pkl + params_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/params.yaml + predictions_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/predictions.json + probabilities_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/probabilities.json + score_dict_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/score_dict.json + test_labels_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/test_labels.json + train_labels_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/train_labels.json + model_dir: models + name: ca5e7c06cd2a6c84ee902f737fa29880 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 44b4a26e9d91311d64651b2b39a81bf9 + trainer: + kwargs: {} +name: ca5e7c06cd2a6c84ee902f737fa29880 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ca770e528a441faf0764007a10280041/params.yaml b/examples/security/classification/output/reports/train/ca770e528a441faf0764007a10280041/params.yaml new file mode 100644 index 00000000..92b626b9 --- /dev/null +++ b/examples/security/classification/output/reports/train/ca770e528a441faf0764007a10280041/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ca770e528a441faf0764007a10280041/adv_predictions.json + adv_probabilities_file: output/reports/train/ca770e528a441faf0764007a10280041/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/9a51ab2b5239caade33cfdfe1fcdb12f.pkl + params_file: output/reports/train/ca770e528a441faf0764007a10280041/params.yaml + predictions_file: output/reports/train/ca770e528a441faf0764007a10280041/predictions.json + probabilities_file: output/reports/train/ca770e528a441faf0764007a10280041/probabilities.json + score_dict_file: output/reports/train/ca770e528a441faf0764007a10280041/score_dict.json + test_labels_file: output/reports/train/ca770e528a441faf0764007a10280041/test_labels.json + train_labels_file: output/reports/train/ca770e528a441faf0764007a10280041/train_labels.json + model_dir: models + name: ca770e528a441faf0764007a10280041 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 070eb7472a3986bc561a2ac8079d3c6a + trainer: + kwargs: {} +name: ca770e528a441faf0764007a10280041 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/params.yaml b/examples/security/classification/output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/params.yaml new file mode 100644 index 00000000..476ae7df --- /dev/null +++ b/examples/security/classification/output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/adv_predictions.json + adv_probabilities_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/0e845d310bf2057926b58069198c9c77.pkl + params_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/params.yaml + predictions_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/predictions.json + probabilities_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/probabilities.json + score_dict_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/score_dict.json + test_labels_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/test_labels.json + train_labels_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/train_labels.json + model_dir: models + name: ca9aaa92914b0933f6d12dd2f5ee7609 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5c8ff4883352e485ca1670650c89f5e1 + trainer: + kwargs: {} +name: ca9aaa92914b0933f6d12dd2f5ee7609 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/params.yaml b/examples/security/classification/output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/params.yaml new file mode 100644 index 00000000..0f45988a --- /dev/null +++ b/examples/security/classification/output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/adv_predictions.json + adv_probabilities_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/96b0e8d1813f9d1f61bc3cf1ea81fc9e.pkl + params_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/params.yaml + predictions_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/predictions.json + probabilities_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/probabilities.json + score_dict_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/score_dict.json + test_labels_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/test_labels.json + train_labels_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/train_labels.json + model_dir: models + name: cad5efe8f1645d02c831966c1ca5fe8b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bf0ebe8bb2c5e74d4357d87a75448016 + trainer: + kwargs: {} +name: cad5efe8f1645d02c831966c1ca5fe8b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cb29628695165a10ec7327a7ec07ac8f/params.yaml b/examples/security/classification/output/reports/train/cb29628695165a10ec7327a7ec07ac8f/params.yaml new file mode 100644 index 00000000..097750ac --- /dev/null +++ b/examples/security/classification/output/reports/train/cb29628695165a10ec7327a7ec07ac8f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/adv_predictions.json + adv_probabilities_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/a270677414bb56e53b318f4edf7ef661.pkl + params_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/params.yaml + predictions_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/predictions.json + probabilities_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/probabilities.json + score_dict_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/score_dict.json + test_labels_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/test_labels.json + train_labels_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/train_labels.json + model_dir: models + name: cb29628695165a10ec7327a7ec07ac8f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c1f29c0ec091a0d9a3994e93f9dd9f06 + trainer: + kwargs: {} +name: cb29628695165a10ec7327a7ec07ac8f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cb30f6369bd206d04297d6984107d67d/params.yaml b/examples/security/classification/output/reports/train/cb30f6369bd206d04297d6984107d67d/params.yaml new file mode 100644 index 00000000..cc471d57 --- /dev/null +++ b/examples/security/classification/output/reports/train/cb30f6369bd206d04297d6984107d67d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/adv_predictions.json + adv_probabilities_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e0a8354599f3b6d147822b1a43f2db89.pkl + params_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/params.yaml + predictions_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/predictions.json + probabilities_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/probabilities.json + score_dict_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/score_dict.json + test_labels_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/test_labels.json + train_labels_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/train_labels.json + model_dir: models + name: cb30f6369bd206d04297d6984107d67d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 00dd09111603a9b86a347b8e504bfa68 + trainer: + kwargs: {} +name: cb30f6369bd206d04297d6984107d67d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cb386c53bb93b598b3470f8d62db89eb/params.yaml b/examples/security/classification/output/reports/train/cb386c53bb93b598b3470f8d62db89eb/params.yaml new file mode 100644 index 00000000..153badc9 --- /dev/null +++ b/examples/security/classification/output/reports/train/cb386c53bb93b598b3470f8d62db89eb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/adv_predictions.json + adv_probabilities_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/c8c96df1b1928a4e3ef64fd8edb45d69.pkl + params_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/params.yaml + predictions_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/predictions.json + probabilities_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/probabilities.json + score_dict_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/score_dict.json + test_labels_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/test_labels.json + train_labels_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/train_labels.json + model_dir: models + name: cb386c53bb93b598b3470f8d62db89eb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 231e05a541a003d0803f4d4c3baaab13 + trainer: + kwargs: {} +name: cb386c53bb93b598b3470f8d62db89eb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cb5636c1e422beafedfe8fab2dea9805/params.yaml b/examples/security/classification/output/reports/train/cb5636c1e422beafedfe8fab2dea9805/params.yaml new file mode 100644 index 00000000..ad0fd90f --- /dev/null +++ b/examples/security/classification/output/reports/train/cb5636c1e422beafedfe8fab2dea9805/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/adv_predictions.json + adv_probabilities_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/df12bfa20bd02cb3e5ce42d954fca16f.pkl + params_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/params.yaml + predictions_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/predictions.json + probabilities_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/probabilities.json + score_dict_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/score_dict.json + test_labels_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/test_labels.json + train_labels_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/train_labels.json + model_dir: models + name: cb5636c1e422beafedfe8fab2dea9805 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3b9063f63206571668f14c0102b24d5d + trainer: + kwargs: {} +name: cb5636c1e422beafedfe8fab2dea9805 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/params.yaml b/examples/security/classification/output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/params.yaml new file mode 100644 index 00000000..2fc56616 --- /dev/null +++ b/examples/security/classification/output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/adv_predictions.json + adv_probabilities_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/1625bd437b27b22f1c809984b92ff4a9.pkl + params_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/params.yaml + predictions_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/predictions.json + probabilities_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/probabilities.json + score_dict_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/score_dict.json + test_labels_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/test_labels.json + train_labels_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/train_labels.json + model_dir: models + name: cb57bffd3eb4bb3913454f9e30b3a00e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 34b11d88803b73f93fa8384453c4d8a4 + trainer: + kwargs: {} +name: cb57bffd3eb4bb3913454f9e30b3a00e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cb5d836bb6e80f8f1df86907fab57874/params.yaml b/examples/security/classification/output/reports/train/cb5d836bb6e80f8f1df86907fab57874/params.yaml new file mode 100644 index 00000000..1187ab20 --- /dev/null +++ b/examples/security/classification/output/reports/train/cb5d836bb6e80f8f1df86907fab57874/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/adv_predictions.json + adv_probabilities_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/a6ce6af2432002e6b83f205d76d0cdb9.pkl + params_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/params.yaml + predictions_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/predictions.json + probabilities_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/probabilities.json + score_dict_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/score_dict.json + test_labels_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/test_labels.json + train_labels_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/train_labels.json + model_dir: models + name: cb5d836bb6e80f8f1df86907fab57874 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6f5ad5ef79bd95e955401b72f29e020e + trainer: + kwargs: {} +name: cb5d836bb6e80f8f1df86907fab57874 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cb92f4da4330687c48af183808aa2cef/params.yaml b/examples/security/classification/output/reports/train/cb92f4da4330687c48af183808aa2cef/params.yaml new file mode 100644 index 00000000..1a49c9ac --- /dev/null +++ b/examples/security/classification/output/reports/train/cb92f4da4330687c48af183808aa2cef/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/adv_predictions.json + adv_probabilities_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/65ee766a1b39aea866f217ffb90ce67d.pkl + params_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/params.yaml + predictions_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/predictions.json + probabilities_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/probabilities.json + score_dict_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/score_dict.json + test_labels_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/test_labels.json + train_labels_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/train_labels.json + model_dir: models + name: cb92f4da4330687c48af183808aa2cef + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a7b2da5a801394815ca594e1d1581e48 + trainer: + kwargs: {} +name: cb92f4da4330687c48af183808aa2cef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cba0e2b80b4f54a65a675207efc035d3/params.yaml b/examples/security/classification/output/reports/train/cba0e2b80b4f54a65a675207efc035d3/params.yaml new file mode 100644 index 00000000..140b1977 --- /dev/null +++ b/examples/security/classification/output/reports/train/cba0e2b80b4f54a65a675207efc035d3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/adv_predictions.json + adv_probabilities_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/8f9cd00c3531c0df85fc85bdd6a9104d.pkl + params_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/params.yaml + predictions_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/predictions.json + probabilities_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/probabilities.json + score_dict_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/score_dict.json + test_labels_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/test_labels.json + train_labels_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/train_labels.json + model_dir: models + name: cba0e2b80b4f54a65a675207efc035d3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4606f2e414909c23149148a15bdfa0cd + trainer: + kwargs: {} +name: cba0e2b80b4f54a65a675207efc035d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/params.yaml b/examples/security/classification/output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/params.yaml new file mode 100644 index 00000000..9db442b8 --- /dev/null +++ b/examples/security/classification/output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/adv_predictions.json + adv_probabilities_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/cc37ba2baf29dc49f02bcb73458d6e6e.pkl + params_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/params.yaml + predictions_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/predictions.json + probabilities_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/probabilities.json + score_dict_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/score_dict.json + test_labels_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/test_labels.json + train_labels_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/train_labels.json + model_dir: models + name: cbbb252ee3db0f8d1b7802f4b99d7c7e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ba46eca4135a5b0291c725738a9749be + trainer: + kwargs: {} +name: cbbb252ee3db0f8d1b7802f4b99d7c7e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/params.yaml b/examples/security/classification/output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/params.yaml new file mode 100644 index 00000000..809bde5c --- /dev/null +++ b/examples/security/classification/output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/adv_predictions.json + adv_probabilities_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/90643d962f64762ec28bff23fba24b12.pkl + params_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/params.yaml + predictions_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/predictions.json + probabilities_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/probabilities.json + score_dict_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/score_dict.json + test_labels_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/test_labels.json + train_labels_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/train_labels.json + model_dir: models + name: cbe9c552582e901bf2e912e7f2e7d3e3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c533d658861f10f5234b293bbdf0265c + trainer: + kwargs: {} +name: cbe9c552582e901bf2e912e7f2e7d3e3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cbfcec6c464f0758b41ad24c0152af31/params.yaml b/examples/security/classification/output/reports/train/cbfcec6c464f0758b41ad24c0152af31/params.yaml new file mode 100644 index 00000000..39084d45 --- /dev/null +++ b/examples/security/classification/output/reports/train/cbfcec6c464f0758b41ad24c0152af31/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/adv_predictions.json + adv_probabilities_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/780723799a16700e492c8394a6664944.pkl + params_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/params.yaml + predictions_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/predictions.json + probabilities_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/probabilities.json + score_dict_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/score_dict.json + test_labels_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/test_labels.json + train_labels_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/train_labels.json + model_dir: models + name: cbfcec6c464f0758b41ad24c0152af31 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a40428894e8f676759c60b13330c5700 + trainer: + kwargs: {} +name: cbfcec6c464f0758b41ad24c0152af31 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cc094bb166feccb3efe178fefb405edd/params.yaml b/examples/security/classification/output/reports/train/cc094bb166feccb3efe178fefb405edd/params.yaml new file mode 100644 index 00000000..b2535108 --- /dev/null +++ b/examples/security/classification/output/reports/train/cc094bb166feccb3efe178fefb405edd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/adv_predictions.json + adv_probabilities_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/e09abe40aa75cc2397d7392fece9e021.pkl + params_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/params.yaml + predictions_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/predictions.json + probabilities_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/probabilities.json + score_dict_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/score_dict.json + test_labels_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/test_labels.json + train_labels_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/train_labels.json + model_dir: models + name: cc094bb166feccb3efe178fefb405edd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6c8bd8d1545a96c84d9f0e4ff330d90a + trainer: + kwargs: {} +name: cc094bb166feccb3efe178fefb405edd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cc13cf0b31326c94ef410fd16459d282/params.yaml b/examples/security/classification/output/reports/train/cc13cf0b31326c94ef410fd16459d282/params.yaml new file mode 100644 index 00000000..0532fe5f --- /dev/null +++ b/examples/security/classification/output/reports/train/cc13cf0b31326c94ef410fd16459d282/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/adv_predictions.json + adv_probabilities_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/48fdd4e50c9c699a8f15bc1ed3e2753c.pkl + params_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/params.yaml + predictions_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/predictions.json + probabilities_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/probabilities.json + score_dict_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/score_dict.json + test_labels_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/test_labels.json + train_labels_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/train_labels.json + model_dir: models + name: cc13cf0b31326c94ef410fd16459d282 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ca992686f446024c06b59dcea7334f84 + trainer: + kwargs: {} +name: cc13cf0b31326c94ef410fd16459d282 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cc34592ab360c6ec53426d2cf89411ce/params.yaml b/examples/security/classification/output/reports/train/cc34592ab360c6ec53426d2cf89411ce/params.yaml new file mode 100644 index 00000000..e7049565 --- /dev/null +++ b/examples/security/classification/output/reports/train/cc34592ab360c6ec53426d2cf89411ce/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/adv_predictions.json + adv_probabilities_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/a6b6a731989804a3cb393fc2e11e3abf.pkl + params_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/params.yaml + predictions_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/predictions.json + probabilities_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/probabilities.json + score_dict_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/score_dict.json + test_labels_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/test_labels.json + train_labels_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/train_labels.json + model_dir: models + name: cc34592ab360c6ec53426d2cf89411ce + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1d8e8613525bffbe10da768b7fd0b125 + trainer: + kwargs: {} +name: cc34592ab360c6ec53426d2cf89411ce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/params.yaml b/examples/security/classification/output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/params.yaml new file mode 100644 index 00000000..c9d64688 --- /dev/null +++ b/examples/security/classification/output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/adv_predictions.json + adv_probabilities_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/2656c3525aa721a4db542065ca6f2221.pkl + params_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/params.yaml + predictions_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/predictions.json + probabilities_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/probabilities.json + score_dict_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/score_dict.json + test_labels_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/test_labels.json + train_labels_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/train_labels.json + model_dir: models + name: cc4def629ba2c5aa9e6593c2ff495d2e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d27a84b20eb68d5f3e2ea0e04ebc0987 + trainer: + kwargs: {} +name: cc4def629ba2c5aa9e6593c2ff495d2e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/params.yaml b/examples/security/classification/output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/params.yaml new file mode 100644 index 00000000..7f8f639e --- /dev/null +++ b/examples/security/classification/output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/adv_predictions.json + adv_probabilities_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/8467ec3b138dce9f74c694c45031af14.pkl + params_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/params.yaml + predictions_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/predictions.json + probabilities_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/probabilities.json + score_dict_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/score_dict.json + test_labels_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/test_labels.json + train_labels_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/train_labels.json + model_dir: models + name: ccb41d7e06653c094c8ad184c83cadfb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b703c07122e75dd4ecf2dcc4bac9cb35 + trainer: + kwargs: {} +name: ccb41d7e06653c094c8ad184c83cadfb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/params.yaml b/examples/security/classification/output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/params.yaml new file mode 100644 index 00000000..4434e235 --- /dev/null +++ b/examples/security/classification/output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/adv_predictions.json + adv_probabilities_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/2ec265057c462680dfb6597f43931d4a.pkl + params_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/params.yaml + predictions_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/predictions.json + probabilities_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/probabilities.json + score_dict_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/score_dict.json + test_labels_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/test_labels.json + train_labels_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/train_labels.json + model_dir: models + name: ccfb1763ed25eb8fc94c49a232c65109 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c3e1d0f771b6167d283cfb373ab0c9e4 + trainer: + kwargs: {} +name: ccfb1763ed25eb8fc94c49a232c65109 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/params.yaml b/examples/security/classification/output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/params.yaml new file mode 100644 index 00000000..2a91b546 --- /dev/null +++ b/examples/security/classification/output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/adv_predictions.json + adv_probabilities_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/78bcf020d78127e082a778d0f3668c42.pkl + params_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/params.yaml + predictions_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/predictions.json + probabilities_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/probabilities.json + score_dict_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/score_dict.json + test_labels_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/test_labels.json + train_labels_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/train_labels.json + model_dir: models + name: cd052978fbaaeed25c90e6cfb52ce466 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e54d6bfe97b9dc3bc834b441781adc66 + trainer: + kwargs: {} +name: cd052978fbaaeed25c90e6cfb52ce466 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/params.yaml b/examples/security/classification/output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/params.yaml new file mode 100644 index 00000000..d084b055 --- /dev/null +++ b/examples/security/classification/output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/adv_predictions.json + adv_probabilities_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/8d4fdb426e97535530112d1a0e223bf6.pkl + params_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/params.yaml + predictions_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/predictions.json + probabilities_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/probabilities.json + score_dict_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/score_dict.json + test_labels_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/test_labels.json + train_labels_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/train_labels.json + model_dir: models + name: cd4dda188304eb9d197523d5fd90e2bb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 64c7d739e2632ba9da49892616ee4a8c + trainer: + kwargs: {} +name: cd4dda188304eb9d197523d5fd90e2bb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/params.yaml b/examples/security/classification/output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/params.yaml new file mode 100644 index 00000000..4f0ee3ef --- /dev/null +++ b/examples/security/classification/output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/adv_predictions.json + adv_probabilities_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/9019088a022775cedb75d0d40a8e48ee.pkl + params_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/params.yaml + predictions_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/predictions.json + probabilities_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/probabilities.json + score_dict_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/score_dict.json + test_labels_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/test_labels.json + train_labels_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/train_labels.json + model_dir: models + name: cd58fa2cd6c3fa7c502b6e9c77b4f5c3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9eda1f549547f593990e396b34527b2f + trainer: + kwargs: {} +name: cd58fa2cd6c3fa7c502b6e9c77b4f5c3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/params.yaml b/examples/security/classification/output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/params.yaml new file mode 100644 index 00000000..3f25eb80 --- /dev/null +++ b/examples/security/classification/output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/adv_predictions.json + adv_probabilities_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/8534f1fbd6b5438b169f483bf23f71d3.pkl + params_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/params.yaml + predictions_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/predictions.json + probabilities_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/probabilities.json + score_dict_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/score_dict.json + test_labels_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/test_labels.json + train_labels_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/train_labels.json + model_dir: models + name: cd62701c3f51aa7a9c2dc8d5141dadd7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 705280b0ca1ac8bcac43a75760282e4a + trainer: + kwargs: {} +name: cd62701c3f51aa7a9c2dc8d5141dadd7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/params.yaml b/examples/security/classification/output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/params.yaml new file mode 100644 index 00000000..d41ef396 --- /dev/null +++ b/examples/security/classification/output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/adv_predictions.json + adv_probabilities_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/d86ffcb60f72bfc79f200087920f701c.pkl + params_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/params.yaml + predictions_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/predictions.json + probabilities_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/probabilities.json + score_dict_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/score_dict.json + test_labels_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/test_labels.json + train_labels_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/train_labels.json + model_dir: models + name: cd7f8cac43c9bcd36c5058a567c5d6db + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1d4a1311ce2b986aac55f092552e3f40 + trainer: + kwargs: {} +name: cd7f8cac43c9bcd36c5058a567c5d6db +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cdba26940c0ca51dcc485a2284204cc3/params.yaml b/examples/security/classification/output/reports/train/cdba26940c0ca51dcc485a2284204cc3/params.yaml new file mode 100644 index 00000000..b31f8012 --- /dev/null +++ b/examples/security/classification/output/reports/train/cdba26940c0ca51dcc485a2284204cc3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/adv_predictions.json + adv_probabilities_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/6c69455eac38ff2f6f054b15f32fd240.pkl + params_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/params.yaml + predictions_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/predictions.json + probabilities_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/probabilities.json + score_dict_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/score_dict.json + test_labels_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/test_labels.json + train_labels_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/train_labels.json + model_dir: models + name: cdba26940c0ca51dcc485a2284204cc3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 637adf86fd3f9d6e412eed25bd5ced67 + trainer: + kwargs: {} +name: cdba26940c0ca51dcc485a2284204cc3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/params.yaml b/examples/security/classification/output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/params.yaml new file mode 100644 index 00000000..01aae082 --- /dev/null +++ b/examples/security/classification/output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/adv_predictions.json + adv_probabilities_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/ee5903f9f4a677172dbbd46a8b403517.pkl + params_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/params.yaml + predictions_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/predictions.json + probabilities_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/probabilities.json + score_dict_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/score_dict.json + test_labels_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/test_labels.json + train_labels_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/train_labels.json + model_dir: models + name: cdd3f787e4d498bc944d581ed85a17d2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7beb952f7808906b14d4589db41557b4 + trainer: + kwargs: {} +name: cdd3f787e4d498bc944d581ed85a17d2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cde9d43c88023b8eda074d297076c3c8/params.yaml b/examples/security/classification/output/reports/train/cde9d43c88023b8eda074d297076c3c8/params.yaml new file mode 100644 index 00000000..9e169c35 --- /dev/null +++ b/examples/security/classification/output/reports/train/cde9d43c88023b8eda074d297076c3c8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/adv_predictions.json + adv_probabilities_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/5ed31148a44e5c1633c196c2cd586509.pkl + params_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/params.yaml + predictions_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/predictions.json + probabilities_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/probabilities.json + score_dict_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/score_dict.json + test_labels_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/test_labels.json + train_labels_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/train_labels.json + model_dir: models + name: cde9d43c88023b8eda074d297076c3c8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7fb6d571b195c05139745c4b784d93ee + trainer: + kwargs: {} +name: cde9d43c88023b8eda074d297076c3c8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ce1ac0e27372fedb201faff974a1b458/params.yaml b/examples/security/classification/output/reports/train/ce1ac0e27372fedb201faff974a1b458/params.yaml new file mode 100644 index 00000000..c1f53764 --- /dev/null +++ b/examples/security/classification/output/reports/train/ce1ac0e27372fedb201faff974a1b458/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/adv_predictions.json + adv_probabilities_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/1bb5c81d6d394795e0af0f36173c9e6f.pkl + params_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/params.yaml + predictions_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/predictions.json + probabilities_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/probabilities.json + score_dict_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/score_dict.json + test_labels_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/test_labels.json + train_labels_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/train_labels.json + model_dir: models + name: ce1ac0e27372fedb201faff974a1b458 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 06bd44cecc7f853994c96f0c17cfe7ee + trainer: + kwargs: {} +name: ce1ac0e27372fedb201faff974a1b458 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ce2a3cca517437573bad138e7f6d401a/params.yaml b/examples/security/classification/output/reports/train/ce2a3cca517437573bad138e7f6d401a/params.yaml new file mode 100644 index 00000000..9a4ea6c9 --- /dev/null +++ b/examples/security/classification/output/reports/train/ce2a3cca517437573bad138e7f6d401a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/adv_predictions.json + adv_probabilities_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/d0d67a9eec2d3c2c98855a5aec8364c0.pkl + params_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/params.yaml + predictions_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/predictions.json + probabilities_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/probabilities.json + score_dict_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/score_dict.json + test_labels_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/test_labels.json + train_labels_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/train_labels.json + model_dir: models + name: ce2a3cca517437573bad138e7f6d401a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 42ebfa80943d06071871f3d473c54ceb + trainer: + kwargs: {} +name: ce2a3cca517437573bad138e7f6d401a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ce745df6eb5c97da2b947a0438358a0a/params.yaml b/examples/security/classification/output/reports/train/ce745df6eb5c97da2b947a0438358a0a/params.yaml new file mode 100644 index 00000000..5c3b2bd5 --- /dev/null +++ b/examples/security/classification/output/reports/train/ce745df6eb5c97da2b947a0438358a0a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/adv_predictions.json + adv_probabilities_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/80c11180aa645bcfa56ce800c2305645.pkl + params_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/params.yaml + predictions_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/predictions.json + probabilities_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/probabilities.json + score_dict_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/score_dict.json + test_labels_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/test_labels.json + train_labels_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/train_labels.json + model_dir: models + name: ce745df6eb5c97da2b947a0438358a0a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 49b00b24e75ae9ffdce0e9ad711f2244 + trainer: + kwargs: {} +name: ce745df6eb5c97da2b947a0438358a0a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/params.yaml b/examples/security/classification/output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/params.yaml new file mode 100644 index 00000000..7ea71e07 --- /dev/null +++ b/examples/security/classification/output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/adv_predictions.json + adv_probabilities_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/1d3f2cba6d233d181d51a27fde4a7851.pkl + params_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/params.yaml + predictions_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/predictions.json + probabilities_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/probabilities.json + score_dict_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/score_dict.json + test_labels_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/test_labels.json + train_labels_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/train_labels.json + model_dir: models + name: ce8a83a9516b08ad9f8d0f0840aa67c4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 12c130258ae536684b7d4814585fc637 + trainer: + kwargs: {} +name: ce8a83a9516b08ad9f8d0f0840aa67c4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/params.yaml b/examples/security/classification/output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/params.yaml new file mode 100644 index 00000000..1354a159 --- /dev/null +++ b/examples/security/classification/output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/adv_predictions.json + adv_probabilities_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/9fcf5ebb3490f961fe8747d4830dffda.pkl + params_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/params.yaml + predictions_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/predictions.json + probabilities_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/probabilities.json + score_dict_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/score_dict.json + test_labels_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/test_labels.json + train_labels_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/train_labels.json + model_dir: models + name: ce91622ba883a97dea577fa17ed3ae2f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 234a01488e192ac2de4f9985336134ad + trainer: + kwargs: {} +name: ce91622ba883a97dea577fa17ed3ae2f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ceae30d896d3152596480727b5fa4d8e/params.yaml b/examples/security/classification/output/reports/train/ceae30d896d3152596480727b5fa4d8e/params.yaml new file mode 100644 index 00000000..75028768 --- /dev/null +++ b/examples/security/classification/output/reports/train/ceae30d896d3152596480727b5fa4d8e/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/adv_predictions.json + adv_probabilities_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/88523dee6495c67f59da022559e99035.pkl + params_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/params.yaml + predictions_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/predictions.json + probabilities_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/probabilities.json + score_dict_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/score_dict.json + test_labels_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/test_labels.json + train_labels_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/train_labels.json + model_dir: models + name: ceae30d896d3152596480727b5fa4d8e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 3 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: baf9fb7eedabed12dffda9289125ae86 + trainer: + kwargs: {} +name: ceae30d896d3152596480727b5fa4d8e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/params.yaml b/examples/security/classification/output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/params.yaml new file mode 100644 index 00000000..147023f2 --- /dev/null +++ b/examples/security/classification/output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/adv_predictions.json + adv_probabilities_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/054d011bf5e1c701ac9dc22eeabc4280.pkl + params_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/params.yaml + predictions_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/predictions.json + probabilities_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/probabilities.json + score_dict_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/score_dict.json + test_labels_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/test_labels.json + train_labels_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/train_labels.json + model_dir: models + name: ceb8139cfd7cc7b06ab01c01ba242a01 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ab30c7b78af954208bfca8e2abae6568 + trainer: + kwargs: {} +name: ceb8139cfd7cc7b06ab01c01ba242a01 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cec3cd4d92465d84167b1600ce96eecb/params.yaml b/examples/security/classification/output/reports/train/cec3cd4d92465d84167b1600ce96eecb/params.yaml new file mode 100644 index 00000000..fe9e78a7 --- /dev/null +++ b/examples/security/classification/output/reports/train/cec3cd4d92465d84167b1600ce96eecb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/adv_predictions.json + adv_probabilities_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/f8ccd34a698c10337840195251db35b6.pkl + params_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/params.yaml + predictions_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/predictions.json + probabilities_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/probabilities.json + score_dict_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/score_dict.json + test_labels_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/test_labels.json + train_labels_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/train_labels.json + model_dir: models + name: cec3cd4d92465d84167b1600ce96eecb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bc56c8d910d6ba2d6882a2e779c8856a + trainer: + kwargs: {} +name: cec3cd4d92465d84167b1600ce96eecb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/params.yaml b/examples/security/classification/output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/params.yaml new file mode 100644 index 00000000..b27ace6f --- /dev/null +++ b/examples/security/classification/output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/params.yaml @@ -0,0 +1,206 @@ +attack: + attack_size: 10 + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 10.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ef9fcd079cf9bd3744561e6b95d1d3a5 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 03996dd853313e6b73c0088d70c20607 + trainer: + kwargs: {} + name: f6bc509de4b6b6d40203d8d656edc7b4 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/adv_predictions.json + adv_probabilities_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/adv_probabilities.json + attack_file: output/attacks/1a9a1aef11d6fd8596351f69acbd1118.pkl + data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl + model_file: output/models/8990a2dbb057543bbff10dc45b80f44a.pkl + params_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/params.yaml + predictions_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/predictions.json + probabilities_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/probabilities.json + score_dict_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/score_dict.json + test_labels_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/test_labels.json + train_labels_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/train_labels.json + model_dir: models + name: cee0dd1700b6f00fb1d54a2a6eb95a4f + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3be40bc56e6b67f6d9ed5cbcbecca960 + trainer: + kwargs: {} +name: cee0dd1700b6f00fb1d54a2a6eb95a4f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cf21278ba69ff282bf1823598b0a42ba/params.yaml b/examples/security/classification/output/reports/train/cf21278ba69ff282bf1823598b0a42ba/params.yaml new file mode 100644 index 00000000..21498697 --- /dev/null +++ b/examples/security/classification/output/reports/train/cf21278ba69ff282bf1823598b0a42ba/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/adv_predictions.json + adv_probabilities_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/91936c6291d83c9c9e31dd01dae24b54.pkl + params_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/params.yaml + predictions_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/predictions.json + probabilities_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/probabilities.json + score_dict_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/score_dict.json + test_labels_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/test_labels.json + train_labels_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/train_labels.json + model_dir: models + name: cf21278ba69ff282bf1823598b0a42ba + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66d038de73edfd05b81aaffa145d12cc + trainer: + kwargs: {} +name: cf21278ba69ff282bf1823598b0a42ba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/params.yaml b/examples/security/classification/output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/params.yaml new file mode 100644 index 00000000..11066e1e --- /dev/null +++ b/examples/security/classification/output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/adv_predictions.json + adv_probabilities_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/e7d2035c3bed5c47f26813815d4ed2b3.pkl + params_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/params.yaml + predictions_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/predictions.json + probabilities_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/probabilities.json + score_dict_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/score_dict.json + test_labels_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/test_labels.json + train_labels_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/train_labels.json + model_dir: models + name: cf49a841c5efd84a8e0f86a7e58df726 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 95212dc33d6f2dd00981aa92039ac6b8 + trainer: + kwargs: {} +name: cf49a841c5efd84a8e0f86a7e58df726 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/params.yaml b/examples/security/classification/output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/params.yaml new file mode 100644 index 00000000..f7caf182 --- /dev/null +++ b/examples/security/classification/output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/adv_predictions.json + adv_probabilities_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/88cf13df54a0a2182c517a11647a6265.pkl + params_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/params.yaml + predictions_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/predictions.json + probabilities_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/probabilities.json + score_dict_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/score_dict.json + test_labels_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/test_labels.json + train_labels_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/train_labels.json + model_dir: models + name: cfbb7067a11d6fcb16de293a0d1777a3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b3381dc7273130758bc934a618983bf0 + trainer: + kwargs: {} +name: cfbb7067a11d6fcb16de293a0d1777a3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/params.yaml b/examples/security/classification/output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/params.yaml new file mode 100644 index 00000000..8983c4fa --- /dev/null +++ b/examples/security/classification/output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/adv_predictions.json + adv_probabilities_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/48686e897b52d4f2e3c280ed89c88530.pkl + params_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/params.yaml + predictions_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/predictions.json + probabilities_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/probabilities.json + score_dict_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/score_dict.json + test_labels_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/test_labels.json + train_labels_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/train_labels.json + model_dir: models + name: d01ca3c032a155e187c9629e13c9c1e1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 81eb1a5367f791141fed2e882331768b + trainer: + kwargs: {} +name: d01ca3c032a155e187c9629e13c9c1e1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d032d9ac0191463f3e76d966b76e70b7/params.yaml b/examples/security/classification/output/reports/train/d032d9ac0191463f3e76d966b76e70b7/params.yaml new file mode 100644 index 00000000..c541fa2d --- /dev/null +++ b/examples/security/classification/output/reports/train/d032d9ac0191463f3e76d966b76e70b7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/adv_predictions.json + adv_probabilities_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/0af3963ff796091daf4e657dac7f5296.pkl + params_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/params.yaml + predictions_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/predictions.json + probabilities_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/probabilities.json + score_dict_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/score_dict.json + test_labels_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/test_labels.json + train_labels_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/train_labels.json + model_dir: models + name: d032d9ac0191463f3e76d966b76e70b7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2e74f08f2d00459d544eca06f5c747e1 + trainer: + kwargs: {} +name: d032d9ac0191463f3e76d966b76e70b7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d038580b363fcf3e497a008821c88028/params.yaml b/examples/security/classification/output/reports/train/d038580b363fcf3e497a008821c88028/params.yaml new file mode 100644 index 00000000..6e6a560e --- /dev/null +++ b/examples/security/classification/output/reports/train/d038580b363fcf3e497a008821c88028/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d038580b363fcf3e497a008821c88028/adv_predictions.json + adv_probabilities_file: output/reports/train/d038580b363fcf3e497a008821c88028/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/d71bb252ceba88c8ea2b47a1c20e17e6.pkl + params_file: output/reports/train/d038580b363fcf3e497a008821c88028/params.yaml + predictions_file: output/reports/train/d038580b363fcf3e497a008821c88028/predictions.json + probabilities_file: output/reports/train/d038580b363fcf3e497a008821c88028/probabilities.json + score_dict_file: output/reports/train/d038580b363fcf3e497a008821c88028/score_dict.json + test_labels_file: output/reports/train/d038580b363fcf3e497a008821c88028/test_labels.json + train_labels_file: output/reports/train/d038580b363fcf3e497a008821c88028/train_labels.json + model_dir: models + name: d038580b363fcf3e497a008821c88028 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9172bcbad8046d097dd9a9cb044f8f90 + trainer: + kwargs: {} +name: d038580b363fcf3e497a008821c88028 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d076822808110ee8d09f170d953614bb/params.yaml b/examples/security/classification/output/reports/train/d076822808110ee8d09f170d953614bb/params.yaml new file mode 100644 index 00000000..d495cdc2 --- /dev/null +++ b/examples/security/classification/output/reports/train/d076822808110ee8d09f170d953614bb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d076822808110ee8d09f170d953614bb/adv_predictions.json + adv_probabilities_file: output/reports/train/d076822808110ee8d09f170d953614bb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/8f47dce04bf92222b821083963089a9a.pkl + params_file: output/reports/train/d076822808110ee8d09f170d953614bb/params.yaml + predictions_file: output/reports/train/d076822808110ee8d09f170d953614bb/predictions.json + probabilities_file: output/reports/train/d076822808110ee8d09f170d953614bb/probabilities.json + score_dict_file: output/reports/train/d076822808110ee8d09f170d953614bb/score_dict.json + test_labels_file: output/reports/train/d076822808110ee8d09f170d953614bb/test_labels.json + train_labels_file: output/reports/train/d076822808110ee8d09f170d953614bb/train_labels.json + model_dir: models + name: d076822808110ee8d09f170d953614bb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9f6eb5a1981512cd60059054a262381c + trainer: + kwargs: {} +name: d076822808110ee8d09f170d953614bb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/params.yaml b/examples/security/classification/output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/params.yaml new file mode 100644 index 00000000..4683b461 --- /dev/null +++ b/examples/security/classification/output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/adv_predictions.json + adv_probabilities_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/a37828cfe7ffb64d6de58f92f22ac218.pkl + params_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/params.yaml + predictions_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/predictions.json + probabilities_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/probabilities.json + score_dict_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/score_dict.json + test_labels_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/test_labels.json + train_labels_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/train_labels.json + model_dir: models + name: d090c2896ed5218dd8d5fb8f223a11da + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7226d1d80404878e178f0f2d580a11b8 + trainer: + kwargs: {} +name: d090c2896ed5218dd8d5fb8f223a11da +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/params.yaml b/examples/security/classification/output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/params.yaml new file mode 100644 index 00000000..3f6c56d9 --- /dev/null +++ b/examples/security/classification/output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/adv_predictions.json + adv_probabilities_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/2f0a86313defa3cb4a73dceed086e543.pkl + params_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/params.yaml + predictions_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/predictions.json + probabilities_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/probabilities.json + score_dict_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/score_dict.json + test_labels_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/test_labels.json + train_labels_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/train_labels.json + model_dir: models + name: d0a71182dc3ccc0d7511f109c07d59eb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ac532d2f6b3f887484a8cb0a26b10902 + trainer: + kwargs: {} +name: d0a71182dc3ccc0d7511f109c07d59eb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/params.yaml b/examples/security/classification/output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/params.yaml new file mode 100644 index 00000000..29730047 --- /dev/null +++ b/examples/security/classification/output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/adv_predictions.json + adv_probabilities_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/a94cdd1140e2847dc7a68647e00335fd.pkl + params_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/params.yaml + predictions_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/predictions.json + probabilities_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/probabilities.json + score_dict_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/score_dict.json + test_labels_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/test_labels.json + train_labels_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/train_labels.json + model_dir: models + name: d0eeda5c03651a0da8bf1ac50974d966 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ac75d8464c6a9da7683d388aa994323c + trainer: + kwargs: {} +name: d0eeda5c03651a0da8bf1ac50974d966 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d10608ac1d220a86ea60a8041c93966c/params.yaml b/examples/security/classification/output/reports/train/d10608ac1d220a86ea60a8041c93966c/params.yaml new file mode 100644 index 00000000..cc28e03a --- /dev/null +++ b/examples/security/classification/output/reports/train/d10608ac1d220a86ea60a8041c93966c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/adv_predictions.json + adv_probabilities_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/1e478f82dc41606b4aa5825e1a27f992.pkl + params_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/params.yaml + predictions_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/predictions.json + probabilities_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/probabilities.json + score_dict_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/score_dict.json + test_labels_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/test_labels.json + train_labels_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/train_labels.json + model_dir: models + name: d10608ac1d220a86ea60a8041c93966c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1ebc8f9aba5af6f33809a71696f87d81 + trainer: + kwargs: {} +name: d10608ac1d220a86ea60a8041c93966c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d113b9f279cef527b1c6f72d96429c0f/params.yaml b/examples/security/classification/output/reports/train/d113b9f279cef527b1c6f72d96429c0f/params.yaml new file mode 100644 index 00000000..2ad3570f --- /dev/null +++ b/examples/security/classification/output/reports/train/d113b9f279cef527b1c6f72d96429c0f/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/adv_predictions.json + adv_probabilities_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/1dd50524bc8f5a9555467f1f7a7cdc16.pkl + params_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/params.yaml + predictions_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/predictions.json + probabilities_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/probabilities.json + score_dict_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/score_dict.json + test_labels_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/test_labels.json + train_labels_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/train_labels.json + model_dir: models + name: d113b9f279cef527b1c6f72d96429c0f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2fbcf48ede5142cc9568efd285d22d6c + trainer: + kwargs: {} +name: d113b9f279cef527b1c6f72d96429c0f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/params.yaml b/examples/security/classification/output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/params.yaml new file mode 100644 index 00000000..2a27df9d --- /dev/null +++ b/examples/security/classification/output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/adv_predictions.json + adv_probabilities_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/3fc84a2e77d0b26b71edb42410500e02.pkl + params_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/params.yaml + predictions_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/predictions.json + probabilities_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/probabilities.json + score_dict_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/score_dict.json + test_labels_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/test_labels.json + train_labels_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/train_labels.json + model_dir: models + name: d1425c9ef3662e6cd6306ebb0999a253 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 52da7ce6a3dea25467e7cd4f3055f5c7 + trainer: + kwargs: {} +name: d1425c9ef3662e6cd6306ebb0999a253 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/params.yaml b/examples/security/classification/output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/params.yaml new file mode 100644 index 00000000..8213a681 --- /dev/null +++ b/examples/security/classification/output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/adv_predictions.json + adv_probabilities_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/49e7c70f4e213e81c127e6216bb8a6f1.pkl + params_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/params.yaml + predictions_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/predictions.json + probabilities_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/probabilities.json + score_dict_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/score_dict.json + test_labels_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/test_labels.json + train_labels_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/train_labels.json + model_dir: models + name: d16d6cbd4bcc97b48c73a0dab68012cd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 01f9c40b88e93bf33e78b87cbbd39952 + trainer: + kwargs: {} +name: d16d6cbd4bcc97b48c73a0dab68012cd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d182456a01a94832b884fd7aa2dd74ed/params.yaml b/examples/security/classification/output/reports/train/d182456a01a94832b884fd7aa2dd74ed/params.yaml new file mode 100644 index 00000000..c5e9835b --- /dev/null +++ b/examples/security/classification/output/reports/train/d182456a01a94832b884fd7aa2dd74ed/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/adv_predictions.json + adv_probabilities_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/9e38d18c89925a87a45a2abf26f54442.pkl + params_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/params.yaml + predictions_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/predictions.json + probabilities_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/probabilities.json + score_dict_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/score_dict.json + test_labels_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/test_labels.json + train_labels_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/train_labels.json + model_dir: models + name: d182456a01a94832b884fd7aa2dd74ed + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5217ee1a25bb54eac85ce0dfac18f1b6 + trainer: + kwargs: {} +name: d182456a01a94832b884fd7aa2dd74ed +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d192dba1610d64cb83cee0f69a99d58c/params.yaml b/examples/security/classification/output/reports/train/d192dba1610d64cb83cee0f69a99d58c/params.yaml new file mode 100644 index 00000000..11388992 --- /dev/null +++ b/examples/security/classification/output/reports/train/d192dba1610d64cb83cee0f69a99d58c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/adv_predictions.json + adv_probabilities_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/aee30cc2731267dfece42ec8c0d465fa.pkl + params_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/params.yaml + predictions_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/predictions.json + probabilities_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/probabilities.json + score_dict_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/score_dict.json + test_labels_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/test_labels.json + train_labels_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/train_labels.json + model_dir: models + name: d192dba1610d64cb83cee0f69a99d58c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ccb5450b05fd824d19832b24b4a8e635 + trainer: + kwargs: {} +name: d192dba1610d64cb83cee0f69a99d58c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/params.yaml b/examples/security/classification/output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/params.yaml new file mode 100644 index 00000000..eb6b9ba4 --- /dev/null +++ b/examples/security/classification/output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/adv_predictions.json + adv_probabilities_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/a899d1b1b1ccff618af6b91dc21a0270.pkl + params_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/params.yaml + predictions_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/predictions.json + probabilities_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/probabilities.json + score_dict_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/score_dict.json + test_labels_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/test_labels.json + train_labels_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/train_labels.json + model_dir: models + name: d1ab10ac751ae58cd6338fbcc2e92db8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 10805c7fa2e2db5b430795dfc629ac76 + trainer: + kwargs: {} +name: d1ab10ac751ae58cd6338fbcc2e92db8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d1bb97eacd379c65128c9607d04593ad/params.yaml b/examples/security/classification/output/reports/train/d1bb97eacd379c65128c9607d04593ad/params.yaml new file mode 100644 index 00000000..d53e6185 --- /dev/null +++ b/examples/security/classification/output/reports/train/d1bb97eacd379c65128c9607d04593ad/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/adv_predictions.json + adv_probabilities_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/649c45313b02db08a2e3a71b4b36dc09.pkl + params_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/params.yaml + predictions_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/predictions.json + probabilities_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/probabilities.json + score_dict_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/score_dict.json + test_labels_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/test_labels.json + train_labels_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/train_labels.json + model_dir: models + name: d1bb97eacd379c65128c9607d04593ad + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 86b14458a8b07ddbed345b034a5fa516 + trainer: + kwargs: {} +name: d1bb97eacd379c65128c9607d04593ad +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/params.yaml b/examples/security/classification/output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/params.yaml new file mode 100644 index 00000000..d7e685df --- /dev/null +++ b/examples/security/classification/output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/adv_predictions.json + adv_probabilities_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/16fe4280fb662db726bb8f14bba94e3d.pkl + params_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/params.yaml + predictions_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/predictions.json + probabilities_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/probabilities.json + score_dict_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/score_dict.json + test_labels_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/test_labels.json + train_labels_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/train_labels.json + model_dir: models + name: d1bd1e58499df06e68a7f00a1ebc6098 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d491374b86b27bf4bb65ef54ed9644ce + trainer: + kwargs: {} +name: d1bd1e58499df06e68a7f00a1ebc6098 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d1c6ee44152546dd537a953b4a4840dd/params.yaml b/examples/security/classification/output/reports/train/d1c6ee44152546dd537a953b4a4840dd/params.yaml new file mode 100644 index 00000000..e847a211 --- /dev/null +++ b/examples/security/classification/output/reports/train/d1c6ee44152546dd537a953b4a4840dd/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/adv_predictions.json + adv_probabilities_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/2595b097302e5d253166130a9cbada03.pkl + params_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/params.yaml + predictions_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/predictions.json + probabilities_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/probabilities.json + score_dict_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/score_dict.json + test_labels_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/test_labels.json + train_labels_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/train_labels.json + model_dir: models + name: d1c6ee44152546dd537a953b4a4840dd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 11a5e199fab50fc1b2e6a15c49c1f76d + trainer: + kwargs: {} +name: d1c6ee44152546dd537a953b4a4840dd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/params.yaml b/examples/security/classification/output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/params.yaml new file mode 100644 index 00000000..3db2b330 --- /dev/null +++ b/examples/security/classification/output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/adv_predictions.json + adv_probabilities_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/55589676601b8cb7920b2707999d98eb.pkl + params_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/params.yaml + predictions_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/predictions.json + probabilities_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/probabilities.json + score_dict_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/score_dict.json + test_labels_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/test_labels.json + train_labels_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/train_labels.json + model_dir: models + name: d1f4fff9d2b98de31bbdc2e3bb54418d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1c846e6d32a60c2e6fc2c2abc65d3273 + trainer: + kwargs: {} +name: d1f4fff9d2b98de31bbdc2e3bb54418d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/params.yaml b/examples/security/classification/output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/params.yaml new file mode 100644 index 00000000..b785e4b1 --- /dev/null +++ b/examples/security/classification/output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/adv_predictions.json + adv_probabilities_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/94e31afbeeded1085ff82050d62cda5e.pkl + params_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/params.yaml + predictions_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/predictions.json + probabilities_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/probabilities.json + score_dict_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/score_dict.json + test_labels_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/test_labels.json + train_labels_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/train_labels.json + model_dir: models + name: d2200183564b6ab2a1ccd2cfb99d6059 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dc5ba638a59403b47e2a59eaaa73e737 + trainer: + kwargs: {} +name: d2200183564b6ab2a1ccd2cfb99d6059 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d22895293f5459285485749a2cf341ea/params.yaml b/examples/security/classification/output/reports/train/d22895293f5459285485749a2cf341ea/params.yaml new file mode 100644 index 00000000..c1c8a06c --- /dev/null +++ b/examples/security/classification/output/reports/train/d22895293f5459285485749a2cf341ea/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d22895293f5459285485749a2cf341ea/adv_predictions.json + adv_probabilities_file: output/reports/train/d22895293f5459285485749a2cf341ea/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/952e4a8ad672f7689fafa8dfc1511dce.pkl + params_file: output/reports/train/d22895293f5459285485749a2cf341ea/params.yaml + predictions_file: output/reports/train/d22895293f5459285485749a2cf341ea/predictions.json + probabilities_file: output/reports/train/d22895293f5459285485749a2cf341ea/probabilities.json + score_dict_file: output/reports/train/d22895293f5459285485749a2cf341ea/score_dict.json + test_labels_file: output/reports/train/d22895293f5459285485749a2cf341ea/test_labels.json + train_labels_file: output/reports/train/d22895293f5459285485749a2cf341ea/train_labels.json + model_dir: models + name: d22895293f5459285485749a2cf341ea + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a263da5ede41a28bbc152b505dcb642b + trainer: + kwargs: {} +name: d22895293f5459285485749a2cf341ea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d23df2c9d62a9efbd6794b48833db818/params.yaml b/examples/security/classification/output/reports/train/d23df2c9d62a9efbd6794b48833db818/params.yaml new file mode 100644 index 00000000..321321f8 --- /dev/null +++ b/examples/security/classification/output/reports/train/d23df2c9d62a9efbd6794b48833db818/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/adv_predictions.json + adv_probabilities_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/f98a5e6058a1647de758fe9bc2a684cb.pkl + params_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/params.yaml + predictions_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/predictions.json + probabilities_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/probabilities.json + score_dict_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/score_dict.json + test_labels_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/test_labels.json + train_labels_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/train_labels.json + model_dir: models + name: d23df2c9d62a9efbd6794b48833db818 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e6472a4f91a179eefff44b53f644cacd + trainer: + kwargs: {} +name: d23df2c9d62a9efbd6794b48833db818 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d24b08265baed7f690c8edf2e8e94674/params.yaml b/examples/security/classification/output/reports/train/d24b08265baed7f690c8edf2e8e94674/params.yaml new file mode 100644 index 00000000..c2a00f3a --- /dev/null +++ b/examples/security/classification/output/reports/train/d24b08265baed7f690c8edf2e8e94674/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/adv_predictions.json + adv_probabilities_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/55dc37d1618e140e204eb8f4290e5af0.pkl + params_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/params.yaml + predictions_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/predictions.json + probabilities_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/probabilities.json + score_dict_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/score_dict.json + test_labels_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/test_labels.json + train_labels_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/train_labels.json + model_dir: models + name: d24b08265baed7f690c8edf2e8e94674 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 47227629c713c84ca5edf3b6f619825a + trainer: + kwargs: {} +name: d24b08265baed7f690c8edf2e8e94674 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d289eab3d89d08e928e3f6dde343ba34/params.yaml b/examples/security/classification/output/reports/train/d289eab3d89d08e928e3f6dde343ba34/params.yaml new file mode 100644 index 00000000..a737d5ad --- /dev/null +++ b/examples/security/classification/output/reports/train/d289eab3d89d08e928e3f6dde343ba34/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/adv_predictions.json + adv_probabilities_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/42e213c5826b69a18d5ec9a074fedb42.pkl + params_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/params.yaml + predictions_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/predictions.json + probabilities_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/probabilities.json + score_dict_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/score_dict.json + test_labels_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/test_labels.json + train_labels_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/train_labels.json + model_dir: models + name: d289eab3d89d08e928e3f6dde343ba34 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5fe1f375edb67b742c50951dc432a5b6 + trainer: + kwargs: {} +name: d289eab3d89d08e928e3f6dde343ba34 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d3004412a6db49f768427cf19487524c/params.yaml b/examples/security/classification/output/reports/train/d3004412a6db49f768427cf19487524c/params.yaml new file mode 100644 index 00000000..736b9b37 --- /dev/null +++ b/examples/security/classification/output/reports/train/d3004412a6db49f768427cf19487524c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d3004412a6db49f768427cf19487524c/adv_predictions.json + adv_probabilities_file: output/reports/train/d3004412a6db49f768427cf19487524c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/88d4159fbcae76094f28efa179af6864.pkl + params_file: output/reports/train/d3004412a6db49f768427cf19487524c/params.yaml + predictions_file: output/reports/train/d3004412a6db49f768427cf19487524c/predictions.json + probabilities_file: output/reports/train/d3004412a6db49f768427cf19487524c/probabilities.json + score_dict_file: output/reports/train/d3004412a6db49f768427cf19487524c/score_dict.json + test_labels_file: output/reports/train/d3004412a6db49f768427cf19487524c/test_labels.json + train_labels_file: output/reports/train/d3004412a6db49f768427cf19487524c/train_labels.json + model_dir: models + name: d3004412a6db49f768427cf19487524c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 59348541a71bef452b63ec86949edda3 + trainer: + kwargs: {} +name: d3004412a6db49f768427cf19487524c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d30329a4183bb3b7970d530f6c0390d1/params.yaml b/examples/security/classification/output/reports/train/d30329a4183bb3b7970d530f6c0390d1/params.yaml new file mode 100644 index 00000000..109b5184 --- /dev/null +++ b/examples/security/classification/output/reports/train/d30329a4183bb3b7970d530f6c0390d1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/adv_predictions.json + adv_probabilities_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/bbf3d57d17aca51a88368e72ab7fd8cd.pkl + params_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/params.yaml + predictions_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/predictions.json + probabilities_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/probabilities.json + score_dict_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/score_dict.json + test_labels_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/test_labels.json + train_labels_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/train_labels.json + model_dir: models + name: d30329a4183bb3b7970d530f6c0390d1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 49810619ab1a1ab998b68f47642bd8af + trainer: + kwargs: {} +name: d30329a4183bb3b7970d530f6c0390d1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/params.yaml b/examples/security/classification/output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/params.yaml new file mode 100644 index 00000000..7436c8c1 --- /dev/null +++ b/examples/security/classification/output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/adv_predictions.json + adv_probabilities_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/d3748063ae0fc210d48d55780886eb99.pkl + params_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/params.yaml + predictions_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/predictions.json + probabilities_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/probabilities.json + score_dict_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/score_dict.json + test_labels_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/test_labels.json + train_labels_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/train_labels.json + model_dir: models + name: d315b51ce2d5633eb8fd0439df451e1a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 74db705a8d739b030b480900aea5fbd1 + trainer: + kwargs: {} +name: d315b51ce2d5633eb8fd0439df451e1a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d3407ebbe49029496324b7b5acc007f8/params.yaml b/examples/security/classification/output/reports/train/d3407ebbe49029496324b7b5acc007f8/params.yaml new file mode 100644 index 00000000..ea15f1e0 --- /dev/null +++ b/examples/security/classification/output/reports/train/d3407ebbe49029496324b7b5acc007f8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/adv_predictions.json + adv_probabilities_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/78513babe8a6d060d6268ecf813b8e03.pkl + params_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/params.yaml + predictions_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/predictions.json + probabilities_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/probabilities.json + score_dict_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/score_dict.json + test_labels_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/test_labels.json + train_labels_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/train_labels.json + model_dir: models + name: d3407ebbe49029496324b7b5acc007f8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f63b2fd011612fef5472161034c7f163 + trainer: + kwargs: {} +name: d3407ebbe49029496324b7b5acc007f8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d39407340c5f9158c58e09c103a464a6/params.yaml b/examples/security/classification/output/reports/train/d39407340c5f9158c58e09c103a464a6/params.yaml new file mode 100644 index 00000000..5eea90d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/d39407340c5f9158c58e09c103a464a6/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/adv_predictions.json + adv_probabilities_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/0b5b1b328ff600901be8ef2caa146fc3.pkl + params_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/params.yaml + predictions_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/predictions.json + probabilities_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/probabilities.json + score_dict_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/score_dict.json + test_labels_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/test_labels.json + train_labels_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/train_labels.json + model_dir: models + name: d39407340c5f9158c58e09c103a464a6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d25fa7cf386bd3f8f72c7aa73f1586f8 + trainer: + kwargs: {} +name: d39407340c5f9158c58e09c103a464a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/params.yaml b/examples/security/classification/output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/params.yaml new file mode 100644 index 00000000..dc8a94d5 --- /dev/null +++ b/examples/security/classification/output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/adv_predictions.json + adv_probabilities_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/414af31323c218d42e34f9b7a8ae54d5.pkl + params_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/params.yaml + predictions_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/predictions.json + probabilities_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/probabilities.json + score_dict_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/score_dict.json + test_labels_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/test_labels.json + train_labels_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/train_labels.json + model_dir: models + name: d3a34f7ec4a2a8d72a43bb5836451c77 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 264fa35fda6d3558921f776990d281c3 + trainer: + kwargs: {} +name: d3a34f7ec4a2a8d72a43bb5836451c77 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d3a735927356d831ec25c1bdb4322236/params.yaml b/examples/security/classification/output/reports/train/d3a735927356d831ec25c1bdb4322236/params.yaml new file mode 100644 index 00000000..1e3edf2c --- /dev/null +++ b/examples/security/classification/output/reports/train/d3a735927356d831ec25c1bdb4322236/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/adv_predictions.json + adv_probabilities_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/ca855344a8614c14970c1535086fdf93.pkl + params_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/params.yaml + predictions_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/predictions.json + probabilities_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/probabilities.json + score_dict_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/score_dict.json + test_labels_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/test_labels.json + train_labels_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/train_labels.json + model_dir: models + name: d3a735927356d831ec25c1bdb4322236 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8cdc9482054e4fb0270d776b5fa47da8 + trainer: + kwargs: {} +name: d3a735927356d831ec25c1bdb4322236 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/params.yaml b/examples/security/classification/output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/params.yaml new file mode 100644 index 00000000..54520afb --- /dev/null +++ b/examples/security/classification/output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/adv_predictions.json + adv_probabilities_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/1581d6fa25427e9a786e490f3309b18a.pkl + params_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/params.yaml + predictions_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/predictions.json + probabilities_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/probabilities.json + score_dict_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/score_dict.json + test_labels_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/test_labels.json + train_labels_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/train_labels.json + model_dir: models + name: d3b8674a56eb6fa2a19b601d33f5b085 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 94d23de9a61ee30518e249d7f18f146d + trainer: + kwargs: {} +name: d3b8674a56eb6fa2a19b601d33f5b085 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d3c4f2eedaa0282319828cad55ad8326/params.yaml b/examples/security/classification/output/reports/train/d3c4f2eedaa0282319828cad55ad8326/params.yaml new file mode 100644 index 00000000..d83610fb --- /dev/null +++ b/examples/security/classification/output/reports/train/d3c4f2eedaa0282319828cad55ad8326/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/adv_predictions.json + adv_probabilities_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/21a6c72b039bd382f7435bfd02e05294.pkl + params_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/params.yaml + predictions_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/predictions.json + probabilities_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/probabilities.json + score_dict_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/score_dict.json + test_labels_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/test_labels.json + train_labels_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/train_labels.json + model_dir: models + name: d3c4f2eedaa0282319828cad55ad8326 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d37c3e75fa62ffa7a14b23f87c775b1 + trainer: + kwargs: {} +name: d3c4f2eedaa0282319828cad55ad8326 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/params.yaml b/examples/security/classification/output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/params.yaml new file mode 100644 index 00000000..4ff63702 --- /dev/null +++ b/examples/security/classification/output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/adv_predictions.json + adv_probabilities_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/da05ecb7287abb7a83bf29fa43949db3.pkl + params_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/params.yaml + predictions_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/predictions.json + probabilities_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/probabilities.json + score_dict_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/score_dict.json + test_labels_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/test_labels.json + train_labels_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/train_labels.json + model_dir: models + name: d3caa5a1c75941a1b4fbccb312781f42 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 93d75b58f67a181e5dfea06d32566e83 + trainer: + kwargs: {} +name: d3caa5a1c75941a1b4fbccb312781f42 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d3f8127c0841abcf24db6a0dca23f177/params.yaml b/examples/security/classification/output/reports/train/d3f8127c0841abcf24db6a0dca23f177/params.yaml new file mode 100644 index 00000000..af706da8 --- /dev/null +++ b/examples/security/classification/output/reports/train/d3f8127c0841abcf24db6a0dca23f177/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/adv_predictions.json + adv_probabilities_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/ba6916f534db0f2242a7b5dd7e8181c8.pkl + params_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/params.yaml + predictions_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/predictions.json + probabilities_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/probabilities.json + score_dict_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/score_dict.json + test_labels_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/test_labels.json + train_labels_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/train_labels.json + model_dir: models + name: d3f8127c0841abcf24db6a0dca23f177 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aa0c1a90af1a57fa58458b255e077e63 + trainer: + kwargs: {} +name: d3f8127c0841abcf24db6a0dca23f177 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d4552f9038939953684977c4506dba9e/params.yaml b/examples/security/classification/output/reports/train/d4552f9038939953684977c4506dba9e/params.yaml new file mode 100644 index 00000000..eb571482 --- /dev/null +++ b/examples/security/classification/output/reports/train/d4552f9038939953684977c4506dba9e/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d4552f9038939953684977c4506dba9e/adv_predictions.json + adv_probabilities_file: output/reports/train/d4552f9038939953684977c4506dba9e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/46807b9aefbdc2cd0e9628c5eeb9bef8.pkl + params_file: output/reports/train/d4552f9038939953684977c4506dba9e/params.yaml + predictions_file: output/reports/train/d4552f9038939953684977c4506dba9e/predictions.json + probabilities_file: output/reports/train/d4552f9038939953684977c4506dba9e/probabilities.json + score_dict_file: output/reports/train/d4552f9038939953684977c4506dba9e/score_dict.json + test_labels_file: output/reports/train/d4552f9038939953684977c4506dba9e/test_labels.json + train_labels_file: output/reports/train/d4552f9038939953684977c4506dba9e/train_labels.json + model_dir: models + name: d4552f9038939953684977c4506dba9e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 33cfcb8ef0f1b70956296e9056b27dbe + trainer: + kwargs: {} +name: d4552f9038939953684977c4506dba9e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/params.yaml b/examples/security/classification/output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/params.yaml new file mode 100644 index 00000000..87cb775d --- /dev/null +++ b/examples/security/classification/output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/adv_predictions.json + adv_probabilities_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/6a78013331e11b7b142e6204fcce7a0f.pkl + params_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/params.yaml + predictions_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/predictions.json + probabilities_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/probabilities.json + score_dict_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/score_dict.json + test_labels_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/test_labels.json + train_labels_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/train_labels.json + model_dir: models + name: d47405f3d834bbae0eec99e0c92bb7c8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 088f0f8cec605c9efa72614802d7a632 + trainer: + kwargs: {} +name: d47405f3d834bbae0eec99e0c92bb7c8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/params.yaml b/examples/security/classification/output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/params.yaml new file mode 100644 index 00000000..5b836a86 --- /dev/null +++ b/examples/security/classification/output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/adv_predictions.json + adv_probabilities_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/d0e2ab5cd56df8e2e81aa6fbeb755b59.pkl + params_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/params.yaml + predictions_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/predictions.json + probabilities_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/probabilities.json + score_dict_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/score_dict.json + test_labels_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/test_labels.json + train_labels_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/train_labels.json + model_dir: models + name: d4766acf4f9fc80c1bb105dcb3fed799 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8f7d8785a44b3905fff83f10cad7e7f3 + trainer: + kwargs: {} +name: d4766acf4f9fc80c1bb105dcb3fed799 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/params.yaml b/examples/security/classification/output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/params.yaml new file mode 100644 index 00000000..66c128c5 --- /dev/null +++ b/examples/security/classification/output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/adv_predictions.json + adv_probabilities_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/9e79ee75e9be1fcfc53b9a57f940be25.pkl + params_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/params.yaml + predictions_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/predictions.json + probabilities_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/probabilities.json + score_dict_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/score_dict.json + test_labels_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/test_labels.json + train_labels_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/train_labels.json + model_dir: models + name: d4b1d5a940c1b45d766da5e728c8b125 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 663c0a7e4b1d8e2e2a92dce1da1da636 + trainer: + kwargs: {} +name: d4b1d5a940c1b45d766da5e728c8b125 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d4c23768942a93175f0425246b12fec3/params.yaml b/examples/security/classification/output/reports/train/d4c23768942a93175f0425246b12fec3/params.yaml new file mode 100644 index 00000000..c7abb256 --- /dev/null +++ b/examples/security/classification/output/reports/train/d4c23768942a93175f0425246b12fec3/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d4c23768942a93175f0425246b12fec3/adv_predictions.json + adv_probabilities_file: output/reports/train/d4c23768942a93175f0425246b12fec3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/bf77d20bf0fc85cc09fecb4a8d6deb91.pkl + params_file: output/reports/train/d4c23768942a93175f0425246b12fec3/params.yaml + predictions_file: output/reports/train/d4c23768942a93175f0425246b12fec3/predictions.json + probabilities_file: output/reports/train/d4c23768942a93175f0425246b12fec3/probabilities.json + score_dict_file: output/reports/train/d4c23768942a93175f0425246b12fec3/score_dict.json + test_labels_file: output/reports/train/d4c23768942a93175f0425246b12fec3/test_labels.json + train_labels_file: output/reports/train/d4c23768942a93175f0425246b12fec3/train_labels.json + model_dir: models + name: d4c23768942a93175f0425246b12fec3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ed704141c027ca70a10625214cb1c235 + trainer: + kwargs: {} +name: d4c23768942a93175f0425246b12fec3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d5aeca69561c75b7a258b9b64cc64497/params.yaml b/examples/security/classification/output/reports/train/d5aeca69561c75b7a258b9b64cc64497/params.yaml new file mode 100644 index 00000000..7c84e7bc --- /dev/null +++ b/examples/security/classification/output/reports/train/d5aeca69561c75b7a258b9b64cc64497/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/adv_predictions.json + adv_probabilities_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/9dbc392b4e18175e2f3841da25652298.pkl + params_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/params.yaml + predictions_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/predictions.json + probabilities_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/probabilities.json + score_dict_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/score_dict.json + test_labels_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/test_labels.json + train_labels_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/train_labels.json + model_dir: models + name: d5aeca69561c75b7a258b9b64cc64497 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bb32eaf48da27ded87a5280c77ac831d + trainer: + kwargs: {} +name: d5aeca69561c75b7a258b9b64cc64497 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/params.yaml b/examples/security/classification/output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/params.yaml new file mode 100644 index 00000000..47ca4284 --- /dev/null +++ b/examples/security/classification/output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/adv_predictions.json + adv_probabilities_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/272e5ab006102931df9cbf1e6e25bca8.pkl + params_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/params.yaml + predictions_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/predictions.json + probabilities_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/probabilities.json + score_dict_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/score_dict.json + test_labels_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/test_labels.json + train_labels_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/train_labels.json + model_dir: models + name: d5bf055ff08dc82a70bf5fa13a7fa89a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b42b1815eb9b0d9a8987797cd5834bd1 + trainer: + kwargs: {} +name: d5bf055ff08dc82a70bf5fa13a7fa89a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d5dd96727584c85f39041280c7bfbf49/params.yaml b/examples/security/classification/output/reports/train/d5dd96727584c85f39041280c7bfbf49/params.yaml new file mode 100644 index 00000000..7477b746 --- /dev/null +++ b/examples/security/classification/output/reports/train/d5dd96727584c85f39041280c7bfbf49/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/adv_predictions.json + adv_probabilities_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/f995c4eb8d54b602a023ffd6e73dd977.pkl + params_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/params.yaml + predictions_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/predictions.json + probabilities_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/probabilities.json + score_dict_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/score_dict.json + test_labels_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/test_labels.json + train_labels_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/train_labels.json + model_dir: models + name: d5dd96727584c85f39041280c7bfbf49 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9cbdbbfea50641bacdfc102d0f91582e + trainer: + kwargs: {} +name: d5dd96727584c85f39041280c7bfbf49 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/params.yaml b/examples/security/classification/output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/params.yaml new file mode 100644 index 00000000..7f49d197 --- /dev/null +++ b/examples/security/classification/output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/adv_predictions.json + adv_probabilities_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/d772826dc47b2c9e7de27f0181bba076.pkl + params_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/params.yaml + predictions_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/predictions.json + probabilities_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/probabilities.json + score_dict_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/score_dict.json + test_labels_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/test_labels.json + train_labels_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/train_labels.json + model_dir: models + name: d60feadd9c521dcdcd72b88731d66f2c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8e3e5c9688c1b98b21a724d733eb1aec + trainer: + kwargs: {} +name: d60feadd9c521dcdcd72b88731d66f2c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d614cdf8a8126b71eda180882f7e73b2/params.yaml b/examples/security/classification/output/reports/train/d614cdf8a8126b71eda180882f7e73b2/params.yaml new file mode 100644 index 00000000..f83a4812 --- /dev/null +++ b/examples/security/classification/output/reports/train/d614cdf8a8126b71eda180882f7e73b2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/adv_predictions.json + adv_probabilities_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/8d65488a44c9dcdf9ed11ec75238cc77.pkl + params_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/params.yaml + predictions_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/predictions.json + probabilities_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/probabilities.json + score_dict_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/score_dict.json + test_labels_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/test_labels.json + train_labels_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/train_labels.json + model_dir: models + name: d614cdf8a8126b71eda180882f7e73b2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0676a34db2c26f44dab02d9b468dfd25 + trainer: + kwargs: {} +name: d614cdf8a8126b71eda180882f7e73b2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/params.yaml b/examples/security/classification/output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/params.yaml new file mode 100644 index 00000000..a4f91643 --- /dev/null +++ b/examples/security/classification/output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/adv_predictions.json + adv_probabilities_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/97d05be4cc891e9123941cf888114dcf.pkl + params_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/params.yaml + predictions_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/predictions.json + probabilities_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/probabilities.json + score_dict_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/score_dict.json + test_labels_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/test_labels.json + train_labels_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/train_labels.json + model_dir: models + name: d62a5024104ddad2f3b82ebd4c441b1a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c2909709dc950593e6fdd713c0717c03 + trainer: + kwargs: {} +name: d62a5024104ddad2f3b82ebd4c441b1a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d6607a8a2f967005647615d2278114e1/params.yaml b/examples/security/classification/output/reports/train/d6607a8a2f967005647615d2278114e1/params.yaml new file mode 100644 index 00000000..20cfa59e --- /dev/null +++ b/examples/security/classification/output/reports/train/d6607a8a2f967005647615d2278114e1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d6607a8a2f967005647615d2278114e1/adv_predictions.json + adv_probabilities_file: output/reports/train/d6607a8a2f967005647615d2278114e1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/a96257c158ba4838c08dc44fd5f70d5b.pkl + params_file: output/reports/train/d6607a8a2f967005647615d2278114e1/params.yaml + predictions_file: output/reports/train/d6607a8a2f967005647615d2278114e1/predictions.json + probabilities_file: output/reports/train/d6607a8a2f967005647615d2278114e1/probabilities.json + score_dict_file: output/reports/train/d6607a8a2f967005647615d2278114e1/score_dict.json + test_labels_file: output/reports/train/d6607a8a2f967005647615d2278114e1/test_labels.json + train_labels_file: output/reports/train/d6607a8a2f967005647615d2278114e1/train_labels.json + model_dir: models + name: d6607a8a2f967005647615d2278114e1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6095ac2ca8618f4d10238ed783f15e8c + trainer: + kwargs: {} +name: d6607a8a2f967005647615d2278114e1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d69626eef8ef2e524ad475863e6538d8/params.yaml b/examples/security/classification/output/reports/train/d69626eef8ef2e524ad475863e6538d8/params.yaml new file mode 100644 index 00000000..ec7a82bf --- /dev/null +++ b/examples/security/classification/output/reports/train/d69626eef8ef2e524ad475863e6538d8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/adv_predictions.json + adv_probabilities_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/07741404f47ba4122b4ac9d460fa428a.pkl + params_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/params.yaml + predictions_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/predictions.json + probabilities_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/probabilities.json + score_dict_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/score_dict.json + test_labels_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/test_labels.json + train_labels_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/train_labels.json + model_dir: models + name: d69626eef8ef2e524ad475863e6538d8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 52424da337470766b8661db59492f68c + trainer: + kwargs: {} +name: d69626eef8ef2e524ad475863e6538d8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/params.yaml b/examples/security/classification/output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/params.yaml new file mode 100644 index 00000000..d009c768 --- /dev/null +++ b/examples/security/classification/output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/adv_predictions.json + adv_probabilities_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/3676ac980c136d658ada00c4a1cab58e.pkl + params_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/params.yaml + predictions_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/predictions.json + probabilities_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/probabilities.json + score_dict_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/score_dict.json + test_labels_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/test_labels.json + train_labels_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/train_labels.json + model_dir: models + name: d6aa81b04aff72ae7ed0d2fd90b15693 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ef9be58a97d05f0d6b17cd01f74b638b + trainer: + kwargs: {} +name: d6aa81b04aff72ae7ed0d2fd90b15693 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/params.yaml b/examples/security/classification/output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/params.yaml new file mode 100644 index 00000000..ee44ac9a --- /dev/null +++ b/examples/security/classification/output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/adv_predictions.json + adv_probabilities_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/978e847300732de34f9daacefb977122.pkl + params_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/params.yaml + predictions_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/predictions.json + probabilities_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/probabilities.json + score_dict_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/score_dict.json + test_labels_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/test_labels.json + train_labels_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/train_labels.json + model_dir: models + name: d6d5b9675f00563bfa30a0d8f646708a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 85422a74a18a83f4673a5e22a545edf7 + trainer: + kwargs: {} +name: d6d5b9675f00563bfa30a0d8f646708a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/params.yaml b/examples/security/classification/output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/params.yaml new file mode 100644 index 00000000..90aa1557 --- /dev/null +++ b/examples/security/classification/output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/adv_predictions.json + adv_probabilities_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/19b7609f748d217eda2a94a49d3e4441.pkl + params_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/params.yaml + predictions_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/predictions.json + probabilities_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/probabilities.json + score_dict_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/score_dict.json + test_labels_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/test_labels.json + train_labels_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/train_labels.json + model_dir: models + name: d6d9bd2cddc4eee6729c88fcd47ceea8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a0ddbbe113e527ca0a2e9f9967bec2ac + trainer: + kwargs: {} +name: d6d9bd2cddc4eee6729c88fcd47ceea8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d6e019b9de15227df801aad189331c14/params.yaml b/examples/security/classification/output/reports/train/d6e019b9de15227df801aad189331c14/params.yaml new file mode 100644 index 00000000..a58f6a0e --- /dev/null +++ b/examples/security/classification/output/reports/train/d6e019b9de15227df801aad189331c14/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d6e019b9de15227df801aad189331c14/adv_predictions.json + adv_probabilities_file: output/reports/train/d6e019b9de15227df801aad189331c14/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/9906607c6ee58bc262690ea776ce5821.pkl + params_file: output/reports/train/d6e019b9de15227df801aad189331c14/params.yaml + predictions_file: output/reports/train/d6e019b9de15227df801aad189331c14/predictions.json + probabilities_file: output/reports/train/d6e019b9de15227df801aad189331c14/probabilities.json + score_dict_file: output/reports/train/d6e019b9de15227df801aad189331c14/score_dict.json + test_labels_file: output/reports/train/d6e019b9de15227df801aad189331c14/test_labels.json + train_labels_file: output/reports/train/d6e019b9de15227df801aad189331c14/train_labels.json + model_dir: models + name: d6e019b9de15227df801aad189331c14 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4a838644b48b2e193576850e74e1f6c2 + trainer: + kwargs: {} +name: d6e019b9de15227df801aad189331c14 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d6e0575f806620984df51600b5fd0338/params.yaml b/examples/security/classification/output/reports/train/d6e0575f806620984df51600b5fd0338/params.yaml new file mode 100644 index 00000000..dafb2faa --- /dev/null +++ b/examples/security/classification/output/reports/train/d6e0575f806620984df51600b5fd0338/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d6e0575f806620984df51600b5fd0338/adv_predictions.json + adv_probabilities_file: output/reports/train/d6e0575f806620984df51600b5fd0338/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/5f2e20b2deed520b3b17bb1ebe6a3e11.pkl + params_file: output/reports/train/d6e0575f806620984df51600b5fd0338/params.yaml + predictions_file: output/reports/train/d6e0575f806620984df51600b5fd0338/predictions.json + probabilities_file: output/reports/train/d6e0575f806620984df51600b5fd0338/probabilities.json + score_dict_file: output/reports/train/d6e0575f806620984df51600b5fd0338/score_dict.json + test_labels_file: output/reports/train/d6e0575f806620984df51600b5fd0338/test_labels.json + train_labels_file: output/reports/train/d6e0575f806620984df51600b5fd0338/train_labels.json + model_dir: models + name: d6e0575f806620984df51600b5fd0338 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8676672310471ec319075c90b50ca982 + trainer: + kwargs: {} +name: d6e0575f806620984df51600b5fd0338 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/params.yaml b/examples/security/classification/output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/params.yaml new file mode 100644 index 00000000..b2e330fd --- /dev/null +++ b/examples/security/classification/output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/adv_predictions.json + adv_probabilities_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/34e40f9c4e35ea802f4c890210ce903a.pkl + params_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/params.yaml + predictions_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/predictions.json + probabilities_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/probabilities.json + score_dict_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/score_dict.json + test_labels_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/test_labels.json + train_labels_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/train_labels.json + model_dir: models + name: d6e4d512d1897529e2cd7d7f8f7af8b5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 26db99af1048dde4aafdc9b9ac0c5e04 + trainer: + kwargs: {} +name: d6e4d512d1897529e2cd7d7f8f7af8b5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/params.yaml b/examples/security/classification/output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/params.yaml new file mode 100644 index 00000000..8d5df82d --- /dev/null +++ b/examples/security/classification/output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/adv_predictions.json + adv_probabilities_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/86f64b77db9b41382ab919d5aa364bb3.pkl + params_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/params.yaml + predictions_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/predictions.json + probabilities_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/probabilities.json + score_dict_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/score_dict.json + test_labels_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/test_labels.json + train_labels_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/train_labels.json + model_dir: models + name: d6f3102b4147cfe502fcdc9a7f583b3d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c76120f25a3e8572408d7f46c4508584 + trainer: + kwargs: {} +name: d6f3102b4147cfe502fcdc9a7f583b3d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d70e7698c153704e65aad1b0c19c0508/params.yaml b/examples/security/classification/output/reports/train/d70e7698c153704e65aad1b0c19c0508/params.yaml new file mode 100644 index 00000000..92ba3a2c --- /dev/null +++ b/examples/security/classification/output/reports/train/d70e7698c153704e65aad1b0c19c0508/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/adv_predictions.json + adv_probabilities_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/62c378bff4241805dcec5758226f50db.pkl + params_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/params.yaml + predictions_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/predictions.json + probabilities_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/probabilities.json + score_dict_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/score_dict.json + test_labels_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/test_labels.json + train_labels_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/train_labels.json + model_dir: models + name: d70e7698c153704e65aad1b0c19c0508 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 872b42178b6fec57ba5cb2f2451c0903 + trainer: + kwargs: {} +name: d70e7698c153704e65aad1b0c19c0508 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d73cd95cb190e1afda7630e04ef18888/params.yaml b/examples/security/classification/output/reports/train/d73cd95cb190e1afda7630e04ef18888/params.yaml new file mode 100644 index 00000000..50f1f6f4 --- /dev/null +++ b/examples/security/classification/output/reports/train/d73cd95cb190e1afda7630e04ef18888/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/adv_predictions.json + adv_probabilities_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/9d44b85e2c0ff9581c024c56229cb456.pkl + params_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/params.yaml + predictions_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/predictions.json + probabilities_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/probabilities.json + score_dict_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/score_dict.json + test_labels_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/test_labels.json + train_labels_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/train_labels.json + model_dir: models + name: d73cd95cb190e1afda7630e04ef18888 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4a5b04af74e4e541784d14ab4afaf604 + trainer: + kwargs: {} +name: d73cd95cb190e1afda7630e04ef18888 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d74992721b32fad604cf654f92f32f6c/params.yaml b/examples/security/classification/output/reports/train/d74992721b32fad604cf654f92f32f6c/params.yaml new file mode 100644 index 00000000..cdd8efc2 --- /dev/null +++ b/examples/security/classification/output/reports/train/d74992721b32fad604cf654f92f32f6c/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/adv_predictions.json + adv_probabilities_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/bdfe7c5fc60798e5a76182ffcc609b57.pkl + params_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/params.yaml + predictions_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/predictions.json + probabilities_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/probabilities.json + score_dict_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/score_dict.json + test_labels_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/test_labels.json + train_labels_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/train_labels.json + model_dir: models + name: d74992721b32fad604cf654f92f32f6c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2b81b3d95fb629740c71c0fc1fa8f3f5 + trainer: + kwargs: {} +name: d74992721b32fad604cf654f92f32f6c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d78d2329e135c09b475863a510d5bc6c/params.yaml b/examples/security/classification/output/reports/train/d78d2329e135c09b475863a510d5bc6c/params.yaml new file mode 100644 index 00000000..bd5f4219 --- /dev/null +++ b/examples/security/classification/output/reports/train/d78d2329e135c09b475863a510d5bc6c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/adv_predictions.json + adv_probabilities_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/3f25c2eb710ae9434035b17b63571b65.pkl + params_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/params.yaml + predictions_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/predictions.json + probabilities_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/probabilities.json + score_dict_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/score_dict.json + test_labels_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/test_labels.json + train_labels_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/train_labels.json + model_dir: models + name: d78d2329e135c09b475863a510d5bc6c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b8dfaf592bd1d4e7f3c897f048ca92ed + trainer: + kwargs: {} +name: d78d2329e135c09b475863a510d5bc6c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/params.yaml b/examples/security/classification/output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/params.yaml new file mode 100644 index 00000000..f0866131 --- /dev/null +++ b/examples/security/classification/output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/adv_predictions.json + adv_probabilities_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/85096c093381b4e133dc1d7814830020.pkl + params_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/params.yaml + predictions_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/predictions.json + probabilities_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/probabilities.json + score_dict_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/score_dict.json + test_labels_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/test_labels.json + train_labels_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/train_labels.json + model_dir: models + name: d7c2e60cd7e1cffc5661bed77ac8e5f6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3b8e7dbfeeedfe144c2e7aeb165cde99 + trainer: + kwargs: {} +name: d7c2e60cd7e1cffc5661bed77ac8e5f6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d81de434fa8a13193d782cac4a03feba/params.yaml b/examples/security/classification/output/reports/train/d81de434fa8a13193d782cac4a03feba/params.yaml new file mode 100644 index 00000000..be2a578e --- /dev/null +++ b/examples/security/classification/output/reports/train/d81de434fa8a13193d782cac4a03feba/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/adv_predictions.json + adv_probabilities_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/38c1d4371d4326e1b47be7d1e86da82f.pkl + params_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/params.yaml + predictions_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/predictions.json + probabilities_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/probabilities.json + score_dict_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/score_dict.json + test_labels_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/test_labels.json + train_labels_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/train_labels.json + model_dir: models + name: d81de434fa8a13193d782cac4a03feba + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cd95725847a5e370ef2628fe150eaacc + trainer: + kwargs: {} +name: d81de434fa8a13193d782cac4a03feba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d82bce39029e5045373f7fe5766f322a/params.yaml b/examples/security/classification/output/reports/train/d82bce39029e5045373f7fe5766f322a/params.yaml new file mode 100644 index 00000000..7cb6c896 --- /dev/null +++ b/examples/security/classification/output/reports/train/d82bce39029e5045373f7fe5766f322a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/adv_predictions.json + adv_probabilities_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/d73b76aa6c6a704ff38d41f8903fd323.pkl + params_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/params.yaml + predictions_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/predictions.json + probabilities_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/probabilities.json + score_dict_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/score_dict.json + test_labels_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/test_labels.json + train_labels_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/train_labels.json + model_dir: models + name: d82bce39029e5045373f7fe5766f322a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 21c7ee4d330f3d21e37f5a5c7d68d661 + trainer: + kwargs: {} +name: d82bce39029e5045373f7fe5766f322a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d83f76beaeb279c6fb2aba648e120881/params.yaml b/examples/security/classification/output/reports/train/d83f76beaeb279c6fb2aba648e120881/params.yaml new file mode 100644 index 00000000..504b758f --- /dev/null +++ b/examples/security/classification/output/reports/train/d83f76beaeb279c6fb2aba648e120881/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/adv_predictions.json + adv_probabilities_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/72c3e13ee13b9754b15c4db656cb63ba.pkl + params_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/params.yaml + predictions_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/predictions.json + probabilities_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/probabilities.json + score_dict_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/score_dict.json + test_labels_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/test_labels.json + train_labels_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/train_labels.json + model_dir: models + name: d83f76beaeb279c6fb2aba648e120881 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d5be2f4156018f5e72554978f5d6980 + trainer: + kwargs: {} +name: d83f76beaeb279c6fb2aba648e120881 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d86943eb6e94f4f786b35065cec4be64/params.yaml b/examples/security/classification/output/reports/train/d86943eb6e94f4f786b35065cec4be64/params.yaml new file mode 100644 index 00000000..d3eae7d0 --- /dev/null +++ b/examples/security/classification/output/reports/train/d86943eb6e94f4f786b35065cec4be64/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/adv_predictions.json + adv_probabilities_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/43702e2abc3572612290af8b4f0ebc7c.pkl + params_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/params.yaml + predictions_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/predictions.json + probabilities_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/probabilities.json + score_dict_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/score_dict.json + test_labels_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/test_labels.json + train_labels_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/train_labels.json + model_dir: models + name: d86943eb6e94f4f786b35065cec4be64 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 463298c235e6bc83af10cb66283d4ebb + trainer: + kwargs: {} +name: d86943eb6e94f4f786b35065cec4be64 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/params.yaml b/examples/security/classification/output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/params.yaml new file mode 100644 index 00000000..1ac621f0 --- /dev/null +++ b/examples/security/classification/output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/adv_predictions.json + adv_probabilities_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/f3522c7920cb614d156428798eff113d.pkl + params_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/params.yaml + predictions_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/predictions.json + probabilities_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/probabilities.json + score_dict_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/score_dict.json + test_labels_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/test_labels.json + train_labels_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/train_labels.json + model_dir: models + name: d86db3b066a7aaf88d9a48cec787e05f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 799a9d79c8fdc251df41976e0a6065fe + trainer: + kwargs: {} +name: d86db3b066a7aaf88d9a48cec787e05f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d880ead035306efaedd479008c24a797/params.yaml b/examples/security/classification/output/reports/train/d880ead035306efaedd479008c24a797/params.yaml new file mode 100644 index 00000000..901a3707 --- /dev/null +++ b/examples/security/classification/output/reports/train/d880ead035306efaedd479008c24a797/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d880ead035306efaedd479008c24a797/adv_predictions.json + adv_probabilities_file: output/reports/train/d880ead035306efaedd479008c24a797/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e41a764287f114894d85d65c7e66eeea.pkl + params_file: output/reports/train/d880ead035306efaedd479008c24a797/params.yaml + predictions_file: output/reports/train/d880ead035306efaedd479008c24a797/predictions.json + probabilities_file: output/reports/train/d880ead035306efaedd479008c24a797/probabilities.json + score_dict_file: output/reports/train/d880ead035306efaedd479008c24a797/score_dict.json + test_labels_file: output/reports/train/d880ead035306efaedd479008c24a797/test_labels.json + train_labels_file: output/reports/train/d880ead035306efaedd479008c24a797/train_labels.json + model_dir: models + name: d880ead035306efaedd479008c24a797 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0a95c9a07071ec3c927d4c80e0d0bf28 + trainer: + kwargs: {} +name: d880ead035306efaedd479008c24a797 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d88c5872c96eb2d8b11002cf2817b611/params.yaml b/examples/security/classification/output/reports/train/d88c5872c96eb2d8b11002cf2817b611/params.yaml new file mode 100644 index 00000000..cb7fe3fd --- /dev/null +++ b/examples/security/classification/output/reports/train/d88c5872c96eb2d8b11002cf2817b611/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/adv_predictions.json + adv_probabilities_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/8d283e3fdd03bb8921b293224c4c28a8.pkl + params_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/params.yaml + predictions_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/predictions.json + probabilities_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/probabilities.json + score_dict_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/score_dict.json + test_labels_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/test_labels.json + train_labels_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/train_labels.json + model_dir: models + name: d88c5872c96eb2d8b11002cf2817b611 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 683bd3e59ae453df627dd0de42a604df + trainer: + kwargs: {} +name: d88c5872c96eb2d8b11002cf2817b611 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d8bed13ff35520656c812f8ccc49aaea/params.yaml b/examples/security/classification/output/reports/train/d8bed13ff35520656c812f8ccc49aaea/params.yaml new file mode 100644 index 00000000..36c6c44c --- /dev/null +++ b/examples/security/classification/output/reports/train/d8bed13ff35520656c812f8ccc49aaea/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/adv_predictions.json + adv_probabilities_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/2d106d8fbd0e91cdfa325c85d253c6e9.pkl + params_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/params.yaml + predictions_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/predictions.json + probabilities_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/probabilities.json + score_dict_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/score_dict.json + test_labels_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/test_labels.json + train_labels_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/train_labels.json + model_dir: models + name: d8bed13ff35520656c812f8ccc49aaea + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3b89157c4a9164a4f85255ebffd5fa83 + trainer: + kwargs: {} +name: d8bed13ff35520656c812f8ccc49aaea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/params.yaml b/examples/security/classification/output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/params.yaml new file mode 100644 index 00000000..167b77cb --- /dev/null +++ b/examples/security/classification/output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/adv_predictions.json + adv_probabilities_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/f0ef527c9441be9f1d285734188b303d.pkl + params_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/params.yaml + predictions_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/predictions.json + probabilities_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/probabilities.json + score_dict_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/score_dict.json + test_labels_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/test_labels.json + train_labels_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/train_labels.json + model_dir: models + name: d8bee46cc1451ff84b988cf76e97f5db + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c6b945fb2a2a1ed0c58679cda47c5c41 + trainer: + kwargs: {} +name: d8bee46cc1451ff84b988cf76e97f5db +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/params.yaml b/examples/security/classification/output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/params.yaml new file mode 100644 index 00000000..04e3ff86 --- /dev/null +++ b/examples/security/classification/output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/adv_predictions.json + adv_probabilities_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/d3a455e91a81267a2d01bb76579efda2.pkl + params_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/params.yaml + predictions_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/predictions.json + probabilities_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/probabilities.json + score_dict_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/score_dict.json + test_labels_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/test_labels.json + train_labels_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/train_labels.json + model_dir: models + name: d8d5ae64626ab04c3dcdbcb5c1d83f03 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3cc484e3e00cc15d88b604e00d0e2a90 + trainer: + kwargs: {} +name: d8d5ae64626ab04c3dcdbcb5c1d83f03 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d90fabd56fa41732bff7a6819d288ba7/params.yaml b/examples/security/classification/output/reports/train/d90fabd56fa41732bff7a6819d288ba7/params.yaml new file mode 100644 index 00000000..b504e885 --- /dev/null +++ b/examples/security/classification/output/reports/train/d90fabd56fa41732bff7a6819d288ba7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/adv_predictions.json + adv_probabilities_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/8ad2b16827ac2b7c6df0193daedb7883.pkl + params_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/params.yaml + predictions_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/predictions.json + probabilities_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/probabilities.json + score_dict_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/score_dict.json + test_labels_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/test_labels.json + train_labels_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/train_labels.json + model_dir: models + name: d90fabd56fa41732bff7a6819d288ba7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 25eccab226bc95115f0af0c3ad2e3701 + trainer: + kwargs: {} +name: d90fabd56fa41732bff7a6819d288ba7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/params.yaml b/examples/security/classification/output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/params.yaml new file mode 100644 index 00000000..d05e6ba8 --- /dev/null +++ b/examples/security/classification/output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/adv_predictions.json + adv_probabilities_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/639a315945d707e5ee8bcc78450e6420.pkl + params_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/params.yaml + predictions_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/predictions.json + probabilities_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/probabilities.json + score_dict_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/score_dict.json + test_labels_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/test_labels.json + train_labels_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/train_labels.json + model_dir: models + name: d91c29c8132ce5cc7679eba094cebdc8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ef2cb43772e5eaa09c31d4209c89d3b0 + trainer: + kwargs: {} +name: d91c29c8132ce5cc7679eba094cebdc8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d9206212559d0e2bccd51a49b510bd72/params.yaml b/examples/security/classification/output/reports/train/d9206212559d0e2bccd51a49b510bd72/params.yaml new file mode 100644 index 00000000..9adcdfab --- /dev/null +++ b/examples/security/classification/output/reports/train/d9206212559d0e2bccd51a49b510bd72/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/adv_predictions.json + adv_probabilities_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/950ce953b0c3ac5f46281f5879452e2b.pkl + params_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/params.yaml + predictions_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/predictions.json + probabilities_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/probabilities.json + score_dict_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/score_dict.json + test_labels_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/test_labels.json + train_labels_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/train_labels.json + model_dir: models + name: d9206212559d0e2bccd51a49b510bd72 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e65c510acf69a3afe531818f10911c91 + trainer: + kwargs: {} +name: d9206212559d0e2bccd51a49b510bd72 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d9650d811e30357b1d5966445c4b3f48/params.yaml b/examples/security/classification/output/reports/train/d9650d811e30357b1d5966445c4b3f48/params.yaml new file mode 100644 index 00000000..91a26446 --- /dev/null +++ b/examples/security/classification/output/reports/train/d9650d811e30357b1d5966445c4b3f48/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/adv_predictions.json + adv_probabilities_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/870044f83dc40376c4a933683c81c0e3.pkl + params_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/params.yaml + predictions_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/predictions.json + probabilities_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/probabilities.json + score_dict_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/score_dict.json + test_labels_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/test_labels.json + train_labels_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/train_labels.json + model_dir: models + name: d9650d811e30357b1d5966445c4b3f48 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1d39ca817ee5d9629e69d693b121fa80 + trainer: + kwargs: {} +name: d9650d811e30357b1d5966445c4b3f48 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d966569100da275b398e3d13cb4c3d11/params.yaml b/examples/security/classification/output/reports/train/d966569100da275b398e3d13cb4c3d11/params.yaml new file mode 100644 index 00000000..1d1eb7aa --- /dev/null +++ b/examples/security/classification/output/reports/train/d966569100da275b398e3d13cb4c3d11/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/adv_predictions.json + adv_probabilities_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/e24e9b7f2e21df7bc8f9d29e7582f98a.pkl + params_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/params.yaml + predictions_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/predictions.json + probabilities_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/probabilities.json + score_dict_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/score_dict.json + test_labels_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/test_labels.json + train_labels_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/train_labels.json + model_dir: models + name: d966569100da275b398e3d13cb4c3d11 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 94dd7e9b24959c4c735d1e02ab8da679 + trainer: + kwargs: {} +name: d966569100da275b398e3d13cb4c3d11 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d97cf03457f0116b24bf98ce6a938393/params.yaml b/examples/security/classification/output/reports/train/d97cf03457f0116b24bf98ce6a938393/params.yaml new file mode 100644 index 00000000..45a75f73 --- /dev/null +++ b/examples/security/classification/output/reports/train/d97cf03457f0116b24bf98ce6a938393/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/adv_predictions.json + adv_probabilities_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/a9eab5a909ac95b5c58dcbd752d97d8d.pkl + params_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/params.yaml + predictions_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/predictions.json + probabilities_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/probabilities.json + score_dict_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/score_dict.json + test_labels_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/test_labels.json + train_labels_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/train_labels.json + model_dir: models + name: d97cf03457f0116b24bf98ce6a938393 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dccb776c194d3c8e020c502012b99e7b + trainer: + kwargs: {} +name: d97cf03457f0116b24bf98ce6a938393 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/params.yaml b/examples/security/classification/output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/params.yaml new file mode 100644 index 00000000..960188a9 --- /dev/null +++ b/examples/security/classification/output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/adv_predictions.json + adv_probabilities_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/618c226b510456460d4fed36f298c313.pkl + params_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/params.yaml + predictions_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/predictions.json + probabilities_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/probabilities.json + score_dict_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/score_dict.json + test_labels_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/test_labels.json + train_labels_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/train_labels.json + model_dir: models + name: d993aa4a7b84070d5b7a1ed4775e12ee + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4dd101cfc8692f943b8b9a913bb72c91 + trainer: + kwargs: {} +name: d993aa4a7b84070d5b7a1ed4775e12ee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/params.yaml b/examples/security/classification/output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/params.yaml new file mode 100644 index 00000000..e246e2b4 --- /dev/null +++ b/examples/security/classification/output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/adv_predictions.json + adv_probabilities_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/71296dc8add7db59fd6fe61216b88df4.pkl + params_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/params.yaml + predictions_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/predictions.json + probabilities_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/probabilities.json + score_dict_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/score_dict.json + test_labels_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/test_labels.json + train_labels_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/train_labels.json + model_dir: models + name: d99f69e0ddc6d30aad42f99313c5ac0d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 646cbcdb7df1750b3a4164fbef09d370 + trainer: + kwargs: {} +name: d99f69e0ddc6d30aad42f99313c5ac0d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/params.yaml b/examples/security/classification/output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/params.yaml new file mode 100644 index 00000000..0a93c5cd --- /dev/null +++ b/examples/security/classification/output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/adv_predictions.json + adv_probabilities_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/746277262c2e4a9a52a964049d12abeb.pkl + params_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/params.yaml + predictions_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/predictions.json + probabilities_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/probabilities.json + score_dict_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/score_dict.json + test_labels_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/test_labels.json + train_labels_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/train_labels.json + model_dir: models + name: d9bfb70d3d7910a5abc08e8c614e3a9b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2130594750a3cf6cf1a045fc89c9ef5a + trainer: + kwargs: {} +name: d9bfb70d3d7910a5abc08e8c614e3a9b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/params.yaml b/examples/security/classification/output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/params.yaml new file mode 100644 index 00000000..37f5b8a4 --- /dev/null +++ b/examples/security/classification/output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/adv_predictions.json + adv_probabilities_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/4dc88d0a3fd38f298d046d5ed4cadd03.pkl + params_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/params.yaml + predictions_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/predictions.json + probabilities_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/probabilities.json + score_dict_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/score_dict.json + test_labels_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/test_labels.json + train_labels_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/train_labels.json + model_dir: models + name: d9cb771ea2e6d7b0343810e9ecf533d2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6f64f323705976b3665d680292422a4a + trainer: + kwargs: {} +name: d9cb771ea2e6d7b0343810e9ecf533d2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d9cdd499b40a11ee477c598f31f5265c/params.yaml b/examples/security/classification/output/reports/train/d9cdd499b40a11ee477c598f31f5265c/params.yaml new file mode 100644 index 00000000..1a7765fa --- /dev/null +++ b/examples/security/classification/output/reports/train/d9cdd499b40a11ee477c598f31f5265c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/adv_predictions.json + adv_probabilities_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/8805a7367c3989e5197a49b1e74a3edf.pkl + params_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/params.yaml + predictions_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/predictions.json + probabilities_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/probabilities.json + score_dict_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/score_dict.json + test_labels_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/test_labels.json + train_labels_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/train_labels.json + model_dir: models + name: d9cdd499b40a11ee477c598f31f5265c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 58298f82ff6e75dc59993853e6001e4e + trainer: + kwargs: {} +name: d9cdd499b40a11ee477c598f31f5265c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/params.yaml b/examples/security/classification/output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/params.yaml new file mode 100644 index 00000000..0e2f2484 --- /dev/null +++ b/examples/security/classification/output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/adv_predictions.json + adv_probabilities_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/6f50fda6410d6451be3a599b112b8948.pkl + params_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/params.yaml + predictions_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/predictions.json + probabilities_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/probabilities.json + score_dict_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/score_dict.json + test_labels_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/test_labels.json + train_labels_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/train_labels.json + model_dir: models + name: d9d717e9ce9d942cb464b7ff0c2c5c4f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7f066213fdb47fe571d20b05c642f2e3 + trainer: + kwargs: {} +name: d9d717e9ce9d942cb464b7ff0c2c5c4f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/params.yaml b/examples/security/classification/output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/params.yaml new file mode 100644 index 00000000..d825b2cb --- /dev/null +++ b/examples/security/classification/output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/adv_predictions.json + adv_probabilities_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/024ac25f9e6a8004a64cb1e29c343df4.pkl + params_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/params.yaml + predictions_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/predictions.json + probabilities_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/probabilities.json + score_dict_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/score_dict.json + test_labels_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/test_labels.json + train_labels_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/train_labels.json + model_dir: models + name: d9dc0878aa7e697149784a5976e2d6a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3e1124673cd69bc751a24a7401e66fd5 + trainer: + kwargs: {} +name: d9dc0878aa7e697149784a5976e2d6a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/params.yaml b/examples/security/classification/output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/params.yaml new file mode 100644 index 00000000..53a2b8f9 --- /dev/null +++ b/examples/security/classification/output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/adv_predictions.json + adv_probabilities_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/06cbc82db5cf652dd462a20a72492546.pkl + params_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/params.yaml + predictions_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/predictions.json + probabilities_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/probabilities.json + score_dict_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/score_dict.json + test_labels_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/test_labels.json + train_labels_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/train_labels.json + model_dir: models + name: d9e6daa16b5fd44d1287f3b765ec64e7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6e8a4319ab952fc991d8ba21213ee806 + trainer: + kwargs: {} +name: d9e6daa16b5fd44d1287f3b765ec64e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/da26ab0f41802765917aa728a48c644b/params.yaml b/examples/security/classification/output/reports/train/da26ab0f41802765917aa728a48c644b/params.yaml new file mode 100644 index 00000000..e283525a --- /dev/null +++ b/examples/security/classification/output/reports/train/da26ab0f41802765917aa728a48c644b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/da26ab0f41802765917aa728a48c644b/adv_predictions.json + adv_probabilities_file: output/reports/train/da26ab0f41802765917aa728a48c644b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/0fbb9bac008ea2975ab5bb1e2452b21a.pkl + params_file: output/reports/train/da26ab0f41802765917aa728a48c644b/params.yaml + predictions_file: output/reports/train/da26ab0f41802765917aa728a48c644b/predictions.json + probabilities_file: output/reports/train/da26ab0f41802765917aa728a48c644b/probabilities.json + score_dict_file: output/reports/train/da26ab0f41802765917aa728a48c644b/score_dict.json + test_labels_file: output/reports/train/da26ab0f41802765917aa728a48c644b/test_labels.json + train_labels_file: output/reports/train/da26ab0f41802765917aa728a48c644b/train_labels.json + model_dir: models + name: da26ab0f41802765917aa728a48c644b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 28b40c998acb14a852a21f989e6e0b02 + trainer: + kwargs: {} +name: da26ab0f41802765917aa728a48c644b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/da37f3877f5d8396df95a668372dedaa/params.yaml b/examples/security/classification/output/reports/train/da37f3877f5d8396df95a668372dedaa/params.yaml new file mode 100644 index 00000000..46415ce0 --- /dev/null +++ b/examples/security/classification/output/reports/train/da37f3877f5d8396df95a668372dedaa/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/adv_predictions.json + adv_probabilities_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/f49d30c2a42bf73a0bdc61ae4754456c.pkl + params_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/params.yaml + predictions_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/predictions.json + probabilities_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/probabilities.json + score_dict_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/score_dict.json + test_labels_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/test_labels.json + train_labels_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/train_labels.json + model_dir: models + name: da37f3877f5d8396df95a668372dedaa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: abe4d11aacc1819d744cefe54f4f582c + trainer: + kwargs: {} +name: da37f3877f5d8396df95a668372dedaa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/da58f2b53d9cb07759b7e54edfa16354/params.yaml b/examples/security/classification/output/reports/train/da58f2b53d9cb07759b7e54edfa16354/params.yaml new file mode 100644 index 00000000..184c5433 --- /dev/null +++ b/examples/security/classification/output/reports/train/da58f2b53d9cb07759b7e54edfa16354/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/adv_predictions.json + adv_probabilities_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/4d79e47fec549b7d3a5a43babce1b758.pkl + params_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/params.yaml + predictions_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/predictions.json + probabilities_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/probabilities.json + score_dict_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/score_dict.json + test_labels_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/test_labels.json + train_labels_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/train_labels.json + model_dir: models + name: da58f2b53d9cb07759b7e54edfa16354 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0b7714d55df9f96701fa06349d8accf7 + trainer: + kwargs: {} +name: da58f2b53d9cb07759b7e54edfa16354 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/da712422226f466e4d8e5d19c408ee29/params.yaml b/examples/security/classification/output/reports/train/da712422226f466e4d8e5d19c408ee29/params.yaml new file mode 100644 index 00000000..0f180fca --- /dev/null +++ b/examples/security/classification/output/reports/train/da712422226f466e4d8e5d19c408ee29/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/adv_predictions.json + adv_probabilities_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/801304ea563e631a94624d3055f93b43.pkl + params_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/params.yaml + predictions_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/predictions.json + probabilities_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/probabilities.json + score_dict_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/score_dict.json + test_labels_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/test_labels.json + train_labels_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/train_labels.json + model_dir: models + name: da712422226f466e4d8e5d19c408ee29 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 535c08409eef86412cf68b1383514428 + trainer: + kwargs: {} +name: da712422226f466e4d8e5d19c408ee29 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/params.yaml b/examples/security/classification/output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/params.yaml new file mode 100644 index 00000000..fc3e5808 --- /dev/null +++ b/examples/security/classification/output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/adv_predictions.json + adv_probabilities_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/dfc45dd7cb1dd51f941856b1ba566200.pkl + params_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/params.yaml + predictions_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/predictions.json + probabilities_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/probabilities.json + score_dict_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/score_dict.json + test_labels_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/test_labels.json + train_labels_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/train_labels.json + model_dir: models + name: da71de7ea9d71b8fdf18f99b2c87985e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c0f063208dac22499417231a9ff9397 + trainer: + kwargs: {} +name: da71de7ea9d71b8fdf18f99b2c87985e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/params.yaml b/examples/security/classification/output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/params.yaml new file mode 100644 index 00000000..bf5504dd --- /dev/null +++ b/examples/security/classification/output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/adv_predictions.json + adv_probabilities_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/23b03e51b54f01b0af53a46cee3e4c9a.pkl + params_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/params.yaml + predictions_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/predictions.json + probabilities_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/probabilities.json + score_dict_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/score_dict.json + test_labels_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/test_labels.json + train_labels_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/train_labels.json + model_dir: models + name: da75ff85eb626ca35fe29d39e9f648b1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c68b53fa590c319b763dae18f1fbc6b0 + trainer: + kwargs: {} +name: da75ff85eb626ca35fe29d39e9f648b1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/da7b150d51903d6cafba98057b5b76e8/params.yaml b/examples/security/classification/output/reports/train/da7b150d51903d6cafba98057b5b76e8/params.yaml new file mode 100644 index 00000000..88c5e6f4 --- /dev/null +++ b/examples/security/classification/output/reports/train/da7b150d51903d6cafba98057b5b76e8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/adv_predictions.json + adv_probabilities_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/bdacb7efc36b9b93310769e3ae058205.pkl + params_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/params.yaml + predictions_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/predictions.json + probabilities_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/probabilities.json + score_dict_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/score_dict.json + test_labels_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/test_labels.json + train_labels_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/train_labels.json + model_dir: models + name: da7b150d51903d6cafba98057b5b76e8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8fb1bafb1fa57eb892070e39b114062f + trainer: + kwargs: {} +name: da7b150d51903d6cafba98057b5b76e8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/params.yaml b/examples/security/classification/output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/params.yaml new file mode 100644 index 00000000..983b9004 --- /dev/null +++ b/examples/security/classification/output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/adv_predictions.json + adv_probabilities_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/866d845a5b3758a084fe0ac35214fdcf.pkl + params_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/params.yaml + predictions_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/predictions.json + probabilities_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/probabilities.json + score_dict_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/score_dict.json + test_labels_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/test_labels.json + train_labels_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/train_labels.json + model_dir: models + name: da8f8c2d5ae4d40553438db9189fa7e5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 49a0a632eac76d05eac2a28139f01418 + trainer: + kwargs: {} +name: da8f8c2d5ae4d40553438db9189fa7e5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/params.yaml b/examples/security/classification/output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/params.yaml new file mode 100644 index 00000000..a55d08e8 --- /dev/null +++ b/examples/security/classification/output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/adv_predictions.json + adv_probabilities_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/81bc127b1d9579bb013b5a5494bc18cb.pkl + params_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/params.yaml + predictions_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/predictions.json + probabilities_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/probabilities.json + score_dict_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/score_dict.json + test_labels_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/test_labels.json + train_labels_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/train_labels.json + model_dir: models + name: da93939c6d1bf7572cd6b1b0036148b5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ba8e6e16ac8c46ecc82b98611e220ce7 + trainer: + kwargs: {} +name: da93939c6d1bf7572cd6b1b0036148b5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/params.yaml b/examples/security/classification/output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/params.yaml new file mode 100644 index 00000000..d2fe481c --- /dev/null +++ b/examples/security/classification/output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/adv_predictions.json + adv_probabilities_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/35d3b1288e18cb8fa4eb2c3c229eb40f.pkl + params_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/params.yaml + predictions_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/predictions.json + probabilities_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/probabilities.json + score_dict_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/score_dict.json + test_labels_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/test_labels.json + train_labels_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/train_labels.json + model_dir: models + name: dab880c4fc1af5ae082b398b21aa9bbb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3c231a86565a12bed4236f0f9d7c8860 + trainer: + kwargs: {} +name: dab880c4fc1af5ae082b398b21aa9bbb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/params.yaml b/examples/security/classification/output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/params.yaml new file mode 100644 index 00000000..52f2e4fa --- /dev/null +++ b/examples/security/classification/output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/adv_predictions.json + adv_probabilities_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/1168eb21aad2493348a43760824ea08a.pkl + params_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/params.yaml + predictions_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/predictions.json + probabilities_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/probabilities.json + score_dict_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/score_dict.json + test_labels_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/test_labels.json + train_labels_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/train_labels.json + model_dir: models + name: dac4f856b23682a3c5cc734ff30ffcaa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0865fffececfff42c434cd687d9ca622 + trainer: + kwargs: {} +name: dac4f856b23682a3c5cc734ff30ffcaa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dadd4a1f979db30baa31fbbac14642e4/params.yaml b/examples/security/classification/output/reports/train/dadd4a1f979db30baa31fbbac14642e4/params.yaml new file mode 100644 index 00000000..15285a5c --- /dev/null +++ b/examples/security/classification/output/reports/train/dadd4a1f979db30baa31fbbac14642e4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/adv_predictions.json + adv_probabilities_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/1c41dda98d91dbc23c0f87be7407d881.pkl + params_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/params.yaml + predictions_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/predictions.json + probabilities_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/probabilities.json + score_dict_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/score_dict.json + test_labels_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/test_labels.json + train_labels_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/train_labels.json + model_dir: models + name: dadd4a1f979db30baa31fbbac14642e4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: abcc84e8f23ef6c04cd1bafa42b8c5c2 + trainer: + kwargs: {} +name: dadd4a1f979db30baa31fbbac14642e4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/params.yaml b/examples/security/classification/output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/params.yaml new file mode 100644 index 00000000..ec6a2368 --- /dev/null +++ b/examples/security/classification/output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/adv_predictions.json + adv_probabilities_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/a885be38b9cb42864abf32ca17d2562f.pkl + params_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/params.yaml + predictions_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/predictions.json + probabilities_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/probabilities.json + score_dict_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/score_dict.json + test_labels_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/test_labels.json + train_labels_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/train_labels.json + model_dir: models + name: dae5b3f7961f23aa814e3639e3f7630b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1d7a1d3422ad6d92c6000ed0e9e9e0a0 + trainer: + kwargs: {} +name: dae5b3f7961f23aa814e3639e3f7630b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dae9c3187f05766d51d40df5260f1339/params.yaml b/examples/security/classification/output/reports/train/dae9c3187f05766d51d40df5260f1339/params.yaml new file mode 100644 index 00000000..8ac61de4 --- /dev/null +++ b/examples/security/classification/output/reports/train/dae9c3187f05766d51d40df5260f1339/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/adv_predictions.json + adv_probabilities_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/a4fb3fea56d88c3448603bd792e1f1d1.pkl + params_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/params.yaml + predictions_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/predictions.json + probabilities_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/probabilities.json + score_dict_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/score_dict.json + test_labels_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/test_labels.json + train_labels_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/train_labels.json + model_dir: models + name: dae9c3187f05766d51d40df5260f1339 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3e7d81e7ec623e4211d42f68d138daa3 + trainer: + kwargs: {} +name: dae9c3187f05766d51d40df5260f1339 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/daf3e2d05958a0eff858cff4581359e5/params.yaml b/examples/security/classification/output/reports/train/daf3e2d05958a0eff858cff4581359e5/params.yaml new file mode 100644 index 00000000..00cf410e --- /dev/null +++ b/examples/security/classification/output/reports/train/daf3e2d05958a0eff858cff4581359e5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/adv_predictions.json + adv_probabilities_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/f8cb163c62d41c9effbb8fa359cb49ec.pkl + params_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/params.yaml + predictions_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/predictions.json + probabilities_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/probabilities.json + score_dict_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/score_dict.json + test_labels_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/test_labels.json + train_labels_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/train_labels.json + model_dir: models + name: daf3e2d05958a0eff858cff4581359e5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a8fc222b9a5dfd1aa8d50794f846749a + trainer: + kwargs: {} +name: daf3e2d05958a0eff858cff4581359e5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/db338fee14e162ca34ace199264f33b6/params.yaml b/examples/security/classification/output/reports/train/db338fee14e162ca34ace199264f33b6/params.yaml new file mode 100644 index 00000000..95210c95 --- /dev/null +++ b/examples/security/classification/output/reports/train/db338fee14e162ca34ace199264f33b6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/db338fee14e162ca34ace199264f33b6/adv_predictions.json + adv_probabilities_file: output/reports/train/db338fee14e162ca34ace199264f33b6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/3052d459d1057a5b4bd159cf285c8061.pkl + params_file: output/reports/train/db338fee14e162ca34ace199264f33b6/params.yaml + predictions_file: output/reports/train/db338fee14e162ca34ace199264f33b6/predictions.json + probabilities_file: output/reports/train/db338fee14e162ca34ace199264f33b6/probabilities.json + score_dict_file: output/reports/train/db338fee14e162ca34ace199264f33b6/score_dict.json + test_labels_file: output/reports/train/db338fee14e162ca34ace199264f33b6/test_labels.json + train_labels_file: output/reports/train/db338fee14e162ca34ace199264f33b6/train_labels.json + model_dir: models + name: db338fee14e162ca34ace199264f33b6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c1c553f0a99d424a21c6e72290b065d2 + trainer: + kwargs: {} +name: db338fee14e162ca34ace199264f33b6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/params.yaml b/examples/security/classification/output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/params.yaml new file mode 100644 index 00000000..4a151b7d --- /dev/null +++ b/examples/security/classification/output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/adv_predictions.json + adv_probabilities_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/a5495da8b36989883b553fa977b80ee3.pkl + params_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/params.yaml + predictions_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/predictions.json + probabilities_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/probabilities.json + score_dict_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/score_dict.json + test_labels_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/test_labels.json + train_labels_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/train_labels.json + model_dir: models + name: db3e6fc14b575b9a25337dc8000ac9fa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1ae9af6d635624af7ec48f9713554f3f + trainer: + kwargs: {} +name: db3e6fc14b575b9a25337dc8000ac9fa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/params.yaml b/examples/security/classification/output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/params.yaml new file mode 100644 index 00000000..04e2e711 --- /dev/null +++ b/examples/security/classification/output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/adv_predictions.json + adv_probabilities_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/e125b2f8d2f420c706c1cf316a8ffa00.pkl + params_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/params.yaml + predictions_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/predictions.json + probabilities_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/probabilities.json + score_dict_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/score_dict.json + test_labels_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/test_labels.json + train_labels_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/train_labels.json + model_dir: models + name: db8d243ff9ed2b3f32c2c6a1ff18e4b7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7886c06d76eb1814d5d461ec5f797240 + trainer: + kwargs: {} +name: db8d243ff9ed2b3f32c2c6a1ff18e4b7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/db9ca5350d494393a63c29d718392b38/params.yaml b/examples/security/classification/output/reports/train/db9ca5350d494393a63c29d718392b38/params.yaml new file mode 100644 index 00000000..76c59073 --- /dev/null +++ b/examples/security/classification/output/reports/train/db9ca5350d494393a63c29d718392b38/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/db9ca5350d494393a63c29d718392b38/adv_predictions.json + adv_probabilities_file: output/reports/train/db9ca5350d494393a63c29d718392b38/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/b7dce59a9c65f5580f917388b0a8726d.pkl + params_file: output/reports/train/db9ca5350d494393a63c29d718392b38/params.yaml + predictions_file: output/reports/train/db9ca5350d494393a63c29d718392b38/predictions.json + probabilities_file: output/reports/train/db9ca5350d494393a63c29d718392b38/probabilities.json + score_dict_file: output/reports/train/db9ca5350d494393a63c29d718392b38/score_dict.json + test_labels_file: output/reports/train/db9ca5350d494393a63c29d718392b38/test_labels.json + train_labels_file: output/reports/train/db9ca5350d494393a63c29d718392b38/train_labels.json + model_dir: models + name: db9ca5350d494393a63c29d718392b38 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a31f85f9f607bde1cf7a0f993982499d + trainer: + kwargs: {} +name: db9ca5350d494393a63c29d718392b38 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/params.yaml b/examples/security/classification/output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/params.yaml new file mode 100644 index 00000000..73e354ad --- /dev/null +++ b/examples/security/classification/output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/adv_predictions.json + adv_probabilities_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/7778c1132cbcacdc2616d71d9f2a2f6d.pkl + params_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/params.yaml + predictions_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/predictions.json + probabilities_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/probabilities.json + score_dict_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/score_dict.json + test_labels_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/test_labels.json + train_labels_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/train_labels.json + model_dir: models + name: dbda5ff49db9e9b8e34f62384bb4e7f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 28e6ada77decb3c97dafa00d997ff593 + trainer: + kwargs: {} +name: dbda5ff49db9e9b8e34f62384bb4e7f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/params.yaml b/examples/security/classification/output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/params.yaml new file mode 100644 index 00000000..f4c5ab6a --- /dev/null +++ b/examples/security/classification/output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/adv_predictions.json + adv_probabilities_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/cb15408096e11f766358992835291409.pkl + params_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/params.yaml + predictions_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/predictions.json + probabilities_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/probabilities.json + score_dict_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/score_dict.json + test_labels_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/test_labels.json + train_labels_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/train_labels.json + model_dir: models + name: dbdb6dfeb7f88437260a3a373a7ac612 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2fe415441df5a45ed4ba302f163b1c03 + trainer: + kwargs: {} +name: dbdb6dfeb7f88437260a3a373a7ac612 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dc104245a02d6785a9eab221b57a79fe/params.yaml b/examples/security/classification/output/reports/train/dc104245a02d6785a9eab221b57a79fe/params.yaml new file mode 100644 index 00000000..17d24337 --- /dev/null +++ b/examples/security/classification/output/reports/train/dc104245a02d6785a9eab221b57a79fe/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/adv_predictions.json + adv_probabilities_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/3d3b9a094dab4aa5ae562e1ca8a29304.pkl + params_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/params.yaml + predictions_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/predictions.json + probabilities_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/probabilities.json + score_dict_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/score_dict.json + test_labels_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/test_labels.json + train_labels_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/train_labels.json + model_dir: models + name: dc104245a02d6785a9eab221b57a79fe + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 321554c439f6a40c20cb298d5b303edd + trainer: + kwargs: {} +name: dc104245a02d6785a9eab221b57a79fe +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dc2d062910de6f10a723fd731d70675a/params.yaml b/examples/security/classification/output/reports/train/dc2d062910de6f10a723fd731d70675a/params.yaml new file mode 100644 index 00000000..543f74b5 --- /dev/null +++ b/examples/security/classification/output/reports/train/dc2d062910de6f10a723fd731d70675a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/adv_predictions.json + adv_probabilities_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/53b4f132afcd36c648a04be83c3ba2dc.pkl + params_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/params.yaml + predictions_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/predictions.json + probabilities_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/probabilities.json + score_dict_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/score_dict.json + test_labels_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/test_labels.json + train_labels_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/train_labels.json + model_dir: models + name: dc2d062910de6f10a723fd731d70675a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 998b739f545785adbcebc688e892b74f + trainer: + kwargs: {} +name: dc2d062910de6f10a723fd731d70675a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/params.yaml b/examples/security/classification/output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/params.yaml new file mode 100644 index 00000000..8f40eff8 --- /dev/null +++ b/examples/security/classification/output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/adv_predictions.json + adv_probabilities_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/c2768741371def9e57cf6f81a67ada73.pkl + params_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/params.yaml + predictions_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/predictions.json + probabilities_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/probabilities.json + score_dict_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/score_dict.json + test_labels_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/test_labels.json + train_labels_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/train_labels.json + model_dir: models + name: dc7dbdc4075c8743f477d541fa0cdeb4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2702f4eff9c6a31881a01308e4e3f7ca + trainer: + kwargs: {} +name: dc7dbdc4075c8743f477d541fa0cdeb4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/params.yaml b/examples/security/classification/output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/params.yaml new file mode 100644 index 00000000..cb980fe9 --- /dev/null +++ b/examples/security/classification/output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/adv_predictions.json + adv_probabilities_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/49b86ed9fc03b036b1707d35c1478992.pkl + params_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/params.yaml + predictions_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/predictions.json + probabilities_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/probabilities.json + score_dict_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/score_dict.json + test_labels_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/test_labels.json + train_labels_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/train_labels.json + model_dir: models + name: dc9ebdeaafade24bf142efaf02fa350f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6dce779c777f879dea873a46aee1895a + trainer: + kwargs: {} +name: dc9ebdeaafade24bf142efaf02fa350f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/params.yaml b/examples/security/classification/output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/params.yaml new file mode 100644 index 00000000..b41ef485 --- /dev/null +++ b/examples/security/classification/output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/adv_predictions.json + adv_probabilities_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/dcd7349c768d71e83fe0aebcf92c59a2.pkl + params_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/params.yaml + predictions_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/predictions.json + probabilities_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/probabilities.json + score_dict_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/score_dict.json + test_labels_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/test_labels.json + train_labels_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/train_labels.json + model_dir: models + name: dcc0ba6c062ec551d53c748e77901b6e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a8e04de76c82189b2dc1dcaf175c9e71 + trainer: + kwargs: {} +name: dcc0ba6c062ec551d53c748e77901b6e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dd01b2dcb10e40974fcebee053ad031e/params.yaml b/examples/security/classification/output/reports/train/dd01b2dcb10e40974fcebee053ad031e/params.yaml new file mode 100644 index 00000000..b4d6a9d3 --- /dev/null +++ b/examples/security/classification/output/reports/train/dd01b2dcb10e40974fcebee053ad031e/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/adv_predictions.json + adv_probabilities_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/bc9037250671b397efc20a3383a86cb4.pkl + params_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/params.yaml + predictions_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/predictions.json + probabilities_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/probabilities.json + score_dict_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/score_dict.json + test_labels_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/test_labels.json + train_labels_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/train_labels.json + model_dir: models + name: dd01b2dcb10e40974fcebee053ad031e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5a4b02f9abd1669dae68a782ef7168ed + trainer: + kwargs: {} +name: dd01b2dcb10e40974fcebee053ad031e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/params.yaml b/examples/security/classification/output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/params.yaml new file mode 100644 index 00000000..e77011d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/adv_predictions.json + adv_probabilities_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/ee32d8e4949989cffccce27a03c9abca.pkl + params_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/params.yaml + predictions_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/predictions.json + probabilities_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/probabilities.json + score_dict_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/score_dict.json + test_labels_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/test_labels.json + train_labels_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/train_labels.json + model_dir: models + name: dd0f6db3c04fe3dbefa23463406062dd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c3e7db7be025d4475a74a81cc9a6ebab + trainer: + kwargs: {} +name: dd0f6db3c04fe3dbefa23463406062dd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dd20d2da499acc55b5e113862170f8f9/params.yaml b/examples/security/classification/output/reports/train/dd20d2da499acc55b5e113862170f8f9/params.yaml new file mode 100644 index 00000000..44550ab5 --- /dev/null +++ b/examples/security/classification/output/reports/train/dd20d2da499acc55b5e113862170f8f9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/adv_predictions.json + adv_probabilities_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/12ce2b674ecb9ef40a31d5b6ecda336f.pkl + params_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/params.yaml + predictions_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/predictions.json + probabilities_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/probabilities.json + score_dict_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/score_dict.json + test_labels_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/test_labels.json + train_labels_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/train_labels.json + model_dir: models + name: dd20d2da499acc55b5e113862170f8f9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ac816587f961ed904d16a094d19fba7c + trainer: + kwargs: {} +name: dd20d2da499acc55b5e113862170f8f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dd57b0a13c85d92345c35d76db603716/params.yaml b/examples/security/classification/output/reports/train/dd57b0a13c85d92345c35d76db603716/params.yaml new file mode 100644 index 00000000..e655d4cd --- /dev/null +++ b/examples/security/classification/output/reports/train/dd57b0a13c85d92345c35d76db603716/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/adv_predictions.json + adv_probabilities_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/1c092aa3a926b766bcefaa56a1a91c1c.pkl + params_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/params.yaml + predictions_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/predictions.json + probabilities_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/probabilities.json + score_dict_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/score_dict.json + test_labels_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/test_labels.json + train_labels_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/train_labels.json + model_dir: models + name: dd57b0a13c85d92345c35d76db603716 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fb3bd9f446810001f5939e03202de2ff + trainer: + kwargs: {} +name: dd57b0a13c85d92345c35d76db603716 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/params.yaml b/examples/security/classification/output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/params.yaml new file mode 100644 index 00000000..92bd6978 --- /dev/null +++ b/examples/security/classification/output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/adv_predictions.json + adv_probabilities_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/06cb203f98529e70c209c4f6efc792c9.pkl + params_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/params.yaml + predictions_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/predictions.json + probabilities_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/probabilities.json + score_dict_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/score_dict.json + test_labels_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/test_labels.json + train_labels_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/train_labels.json + model_dir: models + name: dd6929921b671b8ba5c5cf27dec3cfeb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b0b668798fed57652342f0383ee6b6f7 + trainer: + kwargs: {} +name: dd6929921b671b8ba5c5cf27dec3cfeb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ddcabac2a19736914917edcf860fd285/params.yaml b/examples/security/classification/output/reports/train/ddcabac2a19736914917edcf860fd285/params.yaml new file mode 100644 index 00000000..60feedbe --- /dev/null +++ b/examples/security/classification/output/reports/train/ddcabac2a19736914917edcf860fd285/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ddcabac2a19736914917edcf860fd285/adv_predictions.json + adv_probabilities_file: output/reports/train/ddcabac2a19736914917edcf860fd285/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/ad5db8bed2ebf76089c08c0d5c0d68ef.pkl + params_file: output/reports/train/ddcabac2a19736914917edcf860fd285/params.yaml + predictions_file: output/reports/train/ddcabac2a19736914917edcf860fd285/predictions.json + probabilities_file: output/reports/train/ddcabac2a19736914917edcf860fd285/probabilities.json + score_dict_file: output/reports/train/ddcabac2a19736914917edcf860fd285/score_dict.json + test_labels_file: output/reports/train/ddcabac2a19736914917edcf860fd285/test_labels.json + train_labels_file: output/reports/train/ddcabac2a19736914917edcf860fd285/train_labels.json + model_dir: models + name: ddcabac2a19736914917edcf860fd285 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 58e7d8163d20c914e05fbc8ec67dce73 + trainer: + kwargs: {} +name: ddcabac2a19736914917edcf860fd285 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ddd06578673f15e7ee26f76969ea5016/params.yaml b/examples/security/classification/output/reports/train/ddd06578673f15e7ee26f76969ea5016/params.yaml new file mode 100644 index 00000000..7cc0d0c4 --- /dev/null +++ b/examples/security/classification/output/reports/train/ddd06578673f15e7ee26f76969ea5016/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/adv_predictions.json + adv_probabilities_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/83bb61c7df2decf1ae194c58fee42762.pkl + params_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/params.yaml + predictions_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/predictions.json + probabilities_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/probabilities.json + score_dict_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/score_dict.json + test_labels_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/test_labels.json + train_labels_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/train_labels.json + model_dir: models + name: ddd06578673f15e7ee26f76969ea5016 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eb263e8454a6299d8c84e0186f791f0a + trainer: + kwargs: {} +name: ddd06578673f15e7ee26f76969ea5016 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/params.yaml b/examples/security/classification/output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/params.yaml new file mode 100644 index 00000000..8e3677eb --- /dev/null +++ b/examples/security/classification/output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/adv_predictions.json + adv_probabilities_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/00c2316f4bab581ae9a4e5a6d1bec592.pkl + params_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/params.yaml + predictions_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/predictions.json + probabilities_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/probabilities.json + score_dict_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/score_dict.json + test_labels_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/test_labels.json + train_labels_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/train_labels.json + model_dir: models + name: ddd44b7d67383b9d6cdbe0b2d6d0214c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 24b94eb724ac3571c6128cbcf180705e + trainer: + kwargs: {} +name: ddd44b7d67383b9d6cdbe0b2d6d0214c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dde0c2540a69515776afa11b1400245f/params.yaml b/examples/security/classification/output/reports/train/dde0c2540a69515776afa11b1400245f/params.yaml new file mode 100644 index 00000000..274b9571 --- /dev/null +++ b/examples/security/classification/output/reports/train/dde0c2540a69515776afa11b1400245f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dde0c2540a69515776afa11b1400245f/adv_predictions.json + adv_probabilities_file: output/reports/train/dde0c2540a69515776afa11b1400245f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/09e6c87c1ca87a086f975d01f6215486.pkl + params_file: output/reports/train/dde0c2540a69515776afa11b1400245f/params.yaml + predictions_file: output/reports/train/dde0c2540a69515776afa11b1400245f/predictions.json + probabilities_file: output/reports/train/dde0c2540a69515776afa11b1400245f/probabilities.json + score_dict_file: output/reports/train/dde0c2540a69515776afa11b1400245f/score_dict.json + test_labels_file: output/reports/train/dde0c2540a69515776afa11b1400245f/test_labels.json + train_labels_file: output/reports/train/dde0c2540a69515776afa11b1400245f/train_labels.json + model_dir: models + name: dde0c2540a69515776afa11b1400245f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 66cb134bc8aee58d81a1fc725873efd3 + trainer: + kwargs: {} +name: dde0c2540a69515776afa11b1400245f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/params.yaml b/examples/security/classification/output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/params.yaml new file mode 100644 index 00000000..7ccadab8 --- /dev/null +++ b/examples/security/classification/output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/adv_predictions.json + adv_probabilities_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/3cb792cdd7f4e58a3127af3ed944edf3.pkl + params_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/params.yaml + predictions_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/predictions.json + probabilities_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/probabilities.json + score_dict_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/score_dict.json + test_labels_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/test_labels.json + train_labels_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/train_labels.json + model_dir: models + name: dde4c164a8eb06ac815e69bb45cf703d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1aa766c86439bf925027b0e5e678c460 + trainer: + kwargs: {} +name: dde4c164a8eb06ac815e69bb45cf703d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/params.yaml b/examples/security/classification/output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/params.yaml new file mode 100644 index 00000000..4b06f1ad --- /dev/null +++ b/examples/security/classification/output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/adv_predictions.json + adv_probabilities_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/a0ea855ab85edcbabb7ce0a38abb11ae.pkl + params_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/params.yaml + predictions_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/predictions.json + probabilities_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/probabilities.json + score_dict_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/score_dict.json + test_labels_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/test_labels.json + train_labels_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/train_labels.json + model_dir: models + name: ddecfdc6997ed6e7c45a32be0e795fb1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e16a2fba93bc3db4ea02429ec2dc35a2 + trainer: + kwargs: {} +name: ddecfdc6997ed6e7c45a32be0e795fb1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/de1100610852082d0df0721803615270/params.yaml b/examples/security/classification/output/reports/train/de1100610852082d0df0721803615270/params.yaml new file mode 100644 index 00000000..b2531b44 --- /dev/null +++ b/examples/security/classification/output/reports/train/de1100610852082d0df0721803615270/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/de1100610852082d0df0721803615270/adv_predictions.json + adv_probabilities_file: output/reports/train/de1100610852082d0df0721803615270/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/a1273d1de33cedf92f9e8e3ae04b9f85.pkl + params_file: output/reports/train/de1100610852082d0df0721803615270/params.yaml + predictions_file: output/reports/train/de1100610852082d0df0721803615270/predictions.json + probabilities_file: output/reports/train/de1100610852082d0df0721803615270/probabilities.json + score_dict_file: output/reports/train/de1100610852082d0df0721803615270/score_dict.json + test_labels_file: output/reports/train/de1100610852082d0df0721803615270/test_labels.json + train_labels_file: output/reports/train/de1100610852082d0df0721803615270/train_labels.json + model_dir: models + name: de1100610852082d0df0721803615270 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d4ce71bde4ca1ab16354077df4749d02 + trainer: + kwargs: {} +name: de1100610852082d0df0721803615270 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/de43c54bf61a5700264fb3fbb6490268/params.yaml b/examples/security/classification/output/reports/train/de43c54bf61a5700264fb3fbb6490268/params.yaml new file mode 100644 index 00000000..debea064 --- /dev/null +++ b/examples/security/classification/output/reports/train/de43c54bf61a5700264fb3fbb6490268/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/adv_predictions.json + adv_probabilities_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/7832d1ace5d9465833248c65715b317c.pkl + params_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/params.yaml + predictions_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/predictions.json + probabilities_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/probabilities.json + score_dict_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/score_dict.json + test_labels_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/test_labels.json + train_labels_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/train_labels.json + model_dir: models + name: de43c54bf61a5700264fb3fbb6490268 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 48f3671ce64e9adc85b6ca5504929b73 + trainer: + kwargs: {} +name: de43c54bf61a5700264fb3fbb6490268 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/de484e6957196344e2f4cb86f700cf4a/params.yaml b/examples/security/classification/output/reports/train/de484e6957196344e2f4cb86f700cf4a/params.yaml new file mode 100644 index 00000000..704b5799 --- /dev/null +++ b/examples/security/classification/output/reports/train/de484e6957196344e2f4cb86f700cf4a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/adv_predictions.json + adv_probabilities_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/a6acacc58e9689f3af131b1f202dae94.pkl + params_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/params.yaml + predictions_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/predictions.json + probabilities_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/probabilities.json + score_dict_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/score_dict.json + test_labels_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/test_labels.json + train_labels_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/train_labels.json + model_dir: models + name: de484e6957196344e2f4cb86f700cf4a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 48910f55c9b32d9acd752334044130a7 + trainer: + kwargs: {} +name: de484e6957196344e2f4cb86f700cf4a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/params.yaml b/examples/security/classification/output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/params.yaml new file mode 100644 index 00000000..c7c74c0a --- /dev/null +++ b/examples/security/classification/output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/adv_predictions.json + adv_probabilities_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/066ee66bab2a0c1517f13d145e64a507.pkl + params_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/params.yaml + predictions_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/predictions.json + probabilities_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/probabilities.json + score_dict_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/score_dict.json + test_labels_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/test_labels.json + train_labels_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/train_labels.json + model_dir: models + name: debf8d0db23b7d8342c25531b1fd18cb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1798a865a607b47f59a83aad8acc7071 + trainer: + kwargs: {} +name: debf8d0db23b7d8342c25531b1fd18cb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/decd1896bc50d5023affaefff2db6e8d/params.yaml b/examples/security/classification/output/reports/train/decd1896bc50d5023affaefff2db6e8d/params.yaml new file mode 100644 index 00000000..da314f5a --- /dev/null +++ b/examples/security/classification/output/reports/train/decd1896bc50d5023affaefff2db6e8d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/adv_predictions.json + adv_probabilities_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/d785197a32bde00575b9cb4563f16e62.pkl + params_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/params.yaml + predictions_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/predictions.json + probabilities_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/probabilities.json + score_dict_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/score_dict.json + test_labels_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/test_labels.json + train_labels_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/train_labels.json + model_dir: models + name: decd1896bc50d5023affaefff2db6e8d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c95ffa41e1bae651d2ba5a4e9f0d24b4 + trainer: + kwargs: {} +name: decd1896bc50d5023affaefff2db6e8d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ded68a9aabaae66fda1123b098c786a1/params.yaml b/examples/security/classification/output/reports/train/ded68a9aabaae66fda1123b098c786a1/params.yaml new file mode 100644 index 00000000..fc3e2e85 --- /dev/null +++ b/examples/security/classification/output/reports/train/ded68a9aabaae66fda1123b098c786a1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/adv_predictions.json + adv_probabilities_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/0a1980c72e084f34e0327142a5c04a31.pkl + params_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/params.yaml + predictions_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/predictions.json + probabilities_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/probabilities.json + score_dict_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/score_dict.json + test_labels_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/test_labels.json + train_labels_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/train_labels.json + model_dir: models + name: ded68a9aabaae66fda1123b098c786a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ca0cb3d62c454d8d36fc161a7605d260 + trainer: + kwargs: {} +name: ded68a9aabaae66fda1123b098c786a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/params.yaml b/examples/security/classification/output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/params.yaml new file mode 100644 index 00000000..ea02211c --- /dev/null +++ b/examples/security/classification/output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/adv_predictions.json + adv_probabilities_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/813387f70a83ef559fa06806ded0ec44.pkl + params_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/params.yaml + predictions_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/predictions.json + probabilities_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/probabilities.json + score_dict_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/score_dict.json + test_labels_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/test_labels.json + train_labels_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/train_labels.json + model_dir: models + name: def04b6f2c5c5a5a9b7ccde5002c24f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c4364980571aceecdd41860f89918f10 + trainer: + kwargs: {} +name: def04b6f2c5c5a5a9b7ccde5002c24f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/default/params.yaml b/examples/security/classification/output/reports/train/default/params.yaml new file mode 100644 index 00000000..28a2396e --- /dev/null +++ b/examples/security/classification/output/reports/train/default/params.yaml @@ -0,0 +1,95 @@ +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + params_file: output/reports/train/default/params.yaml + predictions_file: output/reports/train/default/predictions.json + probabilities_file: output/reports/train/default/probabilities.json + score_dict_file: output/reports/train/default/score_dict.json + model_dir: models + name: default + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b1ae58b464df54ee8918089f870a3ad + trainer: + kwargs: {} +name: default +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/params.yaml b/examples/security/classification/output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/params.yaml new file mode 100644 index 00000000..1485a615 --- /dev/null +++ b/examples/security/classification/output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/adv_predictions.json + adv_probabilities_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/92717b0f6984d1aba5826c1b4941a672.pkl + params_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/params.yaml + predictions_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/predictions.json + probabilities_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/probabilities.json + score_dict_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/score_dict.json + test_labels_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/test_labels.json + train_labels_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/train_labels.json + model_dir: models + name: df3068568b0d05a18a9eefc89e0f4bc0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8aabde0852af66ae8bee8d96c84406dc + trainer: + kwargs: {} +name: df3068568b0d05a18a9eefc89e0f4bc0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/params.yaml b/examples/security/classification/output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/params.yaml new file mode 100644 index 00000000..8be317f3 --- /dev/null +++ b/examples/security/classification/output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/adv_predictions.json + adv_probabilities_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/53467b6a29cc0ad87ef8de2825e7992a.pkl + params_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/params.yaml + predictions_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/predictions.json + probabilities_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/probabilities.json + score_dict_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/score_dict.json + test_labels_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/test_labels.json + train_labels_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/train_labels.json + model_dir: models + name: df3b2ef1fdd263f71e4aa07e88d7411e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: abd31692cfcba9a52f0109243c83b539 + trainer: + kwargs: {} +name: df3b2ef1fdd263f71e4aa07e88d7411e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/df60994cafb11874e063ffde6999fb31/params.yaml b/examples/security/classification/output/reports/train/df60994cafb11874e063ffde6999fb31/params.yaml new file mode 100644 index 00000000..ff14f1e6 --- /dev/null +++ b/examples/security/classification/output/reports/train/df60994cafb11874e063ffde6999fb31/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/df60994cafb11874e063ffde6999fb31/adv_predictions.json + adv_probabilities_file: output/reports/train/df60994cafb11874e063ffde6999fb31/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/fdff2fd1dcc15955a5037f4cbd0b29bb.pkl + params_file: output/reports/train/df60994cafb11874e063ffde6999fb31/params.yaml + predictions_file: output/reports/train/df60994cafb11874e063ffde6999fb31/predictions.json + probabilities_file: output/reports/train/df60994cafb11874e063ffde6999fb31/probabilities.json + score_dict_file: output/reports/train/df60994cafb11874e063ffde6999fb31/score_dict.json + test_labels_file: output/reports/train/df60994cafb11874e063ffde6999fb31/test_labels.json + train_labels_file: output/reports/train/df60994cafb11874e063ffde6999fb31/train_labels.json + model_dir: models + name: df60994cafb11874e063ffde6999fb31 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bb161ebd2a3de24b64bd6264d7c06371 + trainer: + kwargs: {} +name: df60994cafb11874e063ffde6999fb31 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/params.yaml b/examples/security/classification/output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/params.yaml new file mode 100644 index 00000000..46fb5e3c --- /dev/null +++ b/examples/security/classification/output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/adv_predictions.json + adv_probabilities_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/02cbd07a2c853da9ea50df22f8a5a0db.pkl + params_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/params.yaml + predictions_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/predictions.json + probabilities_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/probabilities.json + score_dict_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/score_dict.json + test_labels_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/test_labels.json + train_labels_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/train_labels.json + model_dir: models + name: df8f31b59b1ca9a5a0cc642f17ab3eb7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 62419bc6bcb85abb4000fa0e776f18f4 + trainer: + kwargs: {} +name: df8f31b59b1ca9a5a0cc642f17ab3eb7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/params.yaml b/examples/security/classification/output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/params.yaml new file mode 100644 index 00000000..74643886 --- /dev/null +++ b/examples/security/classification/output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/adv_predictions.json + adv_probabilities_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/3fc61385443097e1b2d187df4246e6f3.pkl + params_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/params.yaml + predictions_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/predictions.json + probabilities_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/probabilities.json + score_dict_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/score_dict.json + test_labels_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/test_labels.json + train_labels_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/train_labels.json + model_dir: models + name: df99cd044742a4a8fbb6b7499a7c562c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a06dc2c994aff70e6c11d28ba3c7fcd1 + trainer: + kwargs: {} +name: df99cd044742a4a8fbb6b7499a7c562c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dfa00dd84be2703d7716b591c491fb5e/params.yaml b/examples/security/classification/output/reports/train/dfa00dd84be2703d7716b591c491fb5e/params.yaml new file mode 100644 index 00000000..cb5681b3 --- /dev/null +++ b/examples/security/classification/output/reports/train/dfa00dd84be2703d7716b591c491fb5e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/adv_predictions.json + adv_probabilities_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/9944e829d483de1ecddf7ac127472dab.pkl + params_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/params.yaml + predictions_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/predictions.json + probabilities_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/probabilities.json + score_dict_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/score_dict.json + test_labels_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/test_labels.json + train_labels_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/train_labels.json + model_dir: models + name: dfa00dd84be2703d7716b591c491fb5e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fe08ef9fc78ecb0522466d5ba30b49fe + trainer: + kwargs: {} +name: dfa00dd84be2703d7716b591c491fb5e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/params.yaml b/examples/security/classification/output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/params.yaml new file mode 100644 index 00000000..66c546cb --- /dev/null +++ b/examples/security/classification/output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/adv_predictions.json + adv_probabilities_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/51fec476dc3bb51ccc75f0836d6a213a.pkl + params_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/params.yaml + predictions_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/predictions.json + probabilities_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/probabilities.json + score_dict_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/score_dict.json + test_labels_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/test_labels.json + train_labels_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/train_labels.json + model_dir: models + name: dfa81153ba3c091ccce22f6c4f1cb282 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 716f00ac5fdc0f4cd329e5a77442657a + trainer: + kwargs: {} +name: dfa81153ba3c091ccce22f6c4f1cb282 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/params.yaml b/examples/security/classification/output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/params.yaml new file mode 100644 index 00000000..9edccc4d --- /dev/null +++ b/examples/security/classification/output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/adv_predictions.json + adv_probabilities_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/1ed2a475a43de3bed6e1fa4c0108c8ca.pkl + params_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/params.yaml + predictions_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/predictions.json + probabilities_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/probabilities.json + score_dict_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/score_dict.json + test_labels_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/test_labels.json + train_labels_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/train_labels.json + model_dir: models + name: dfe9722d30be9c127e8d8c8e9a53bce3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b742b7fe7b17ed434dbcd324ebd1110f + trainer: + kwargs: {} +name: dfe9722d30be9c127e8d8c8e9a53bce3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e001aa2c549d03d559173d888862a2e9/params.yaml b/examples/security/classification/output/reports/train/e001aa2c549d03d559173d888862a2e9/params.yaml new file mode 100644 index 00000000..c02a3847 --- /dev/null +++ b/examples/security/classification/output/reports/train/e001aa2c549d03d559173d888862a2e9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/adv_predictions.json + adv_probabilities_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/7278125a3d54afdb20aa168310e2f8d5.pkl + params_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/params.yaml + predictions_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/predictions.json + probabilities_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/probabilities.json + score_dict_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/score_dict.json + test_labels_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/test_labels.json + train_labels_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/train_labels.json + model_dir: models + name: e001aa2c549d03d559173d888862a2e9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1379c17111c3de3ac6187d9644696a41 + trainer: + kwargs: {} +name: e001aa2c549d03d559173d888862a2e9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e00974b438bc9617c3ded020fa5c5f98/params.yaml b/examples/security/classification/output/reports/train/e00974b438bc9617c3ded020fa5c5f98/params.yaml new file mode 100644 index 00000000..0431d466 --- /dev/null +++ b/examples/security/classification/output/reports/train/e00974b438bc9617c3ded020fa5c5f98/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/adv_predictions.json + adv_probabilities_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/612b79a545c4eac10037916c4203202a.pkl + params_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/params.yaml + predictions_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/predictions.json + probabilities_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/probabilities.json + score_dict_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/score_dict.json + test_labels_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/test_labels.json + train_labels_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/train_labels.json + model_dir: models + name: e00974b438bc9617c3ded020fa5c5f98 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 995a90036d2e0ca4b1690bae1e7af13e + trainer: + kwargs: {} +name: e00974b438bc9617c3ded020fa5c5f98 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/params.yaml b/examples/security/classification/output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/params.yaml new file mode 100644 index 00000000..02ddbac1 --- /dev/null +++ b/examples/security/classification/output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/adv_predictions.json + adv_probabilities_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/d3922dddd8d7dbe1e704af7d6930011b.pkl + params_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/params.yaml + predictions_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/predictions.json + probabilities_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/probabilities.json + score_dict_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/score_dict.json + test_labels_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/test_labels.json + train_labels_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/train_labels.json + model_dir: models + name: e00c86bdd5b732b1baf4750ae9a4fec9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ad249a7d95abfb82c81747ba7d907a7b + trainer: + kwargs: {} +name: e00c86bdd5b732b1baf4750ae9a4fec9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e03b0173a597b67adc552ec7277a0106/params.yaml b/examples/security/classification/output/reports/train/e03b0173a597b67adc552ec7277a0106/params.yaml new file mode 100644 index 00000000..8e378bdb --- /dev/null +++ b/examples/security/classification/output/reports/train/e03b0173a597b67adc552ec7277a0106/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/adv_predictions.json + adv_probabilities_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/a3a19eb73b9c225558ce2bed09f16a4b.pkl + params_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/params.yaml + predictions_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/predictions.json + probabilities_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/probabilities.json + score_dict_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/score_dict.json + test_labels_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/test_labels.json + train_labels_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/train_labels.json + model_dir: models + name: e03b0173a597b67adc552ec7277a0106 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a594b7a25b6a0900030aacb0413a3505 + trainer: + kwargs: {} +name: e03b0173a597b67adc552ec7277a0106 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/params.yaml b/examples/security/classification/output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/params.yaml new file mode 100644 index 00000000..1f85ffe7 --- /dev/null +++ b/examples/security/classification/output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/adv_predictions.json + adv_probabilities_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/57d7e5ae55ba15356ac1d8a89faf581f.pkl + params_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/params.yaml + predictions_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/predictions.json + probabilities_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/probabilities.json + score_dict_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/score_dict.json + test_labels_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/test_labels.json + train_labels_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/train_labels.json + model_dir: models + name: e0517937f383ef37cf1edcb8b6b2c1c8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a0908824ebb6c549df125cc601962459 + trainer: + kwargs: {} +name: e0517937f383ef37cf1edcb8b6b2c1c8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e060805f292ad9afeefab59125a47036/params.yaml b/examples/security/classification/output/reports/train/e060805f292ad9afeefab59125a47036/params.yaml new file mode 100644 index 00000000..6760b85e --- /dev/null +++ b/examples/security/classification/output/reports/train/e060805f292ad9afeefab59125a47036/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e060805f292ad9afeefab59125a47036/adv_predictions.json + adv_probabilities_file: output/reports/train/e060805f292ad9afeefab59125a47036/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/c9dd367dc79e805b2982720843e92f8b.pkl + params_file: output/reports/train/e060805f292ad9afeefab59125a47036/params.yaml + predictions_file: output/reports/train/e060805f292ad9afeefab59125a47036/predictions.json + probabilities_file: output/reports/train/e060805f292ad9afeefab59125a47036/probabilities.json + score_dict_file: output/reports/train/e060805f292ad9afeefab59125a47036/score_dict.json + test_labels_file: output/reports/train/e060805f292ad9afeefab59125a47036/test_labels.json + train_labels_file: output/reports/train/e060805f292ad9afeefab59125a47036/train_labels.json + model_dir: models + name: e060805f292ad9afeefab59125a47036 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8365feafee4d9d425d31d733d7005c19 + trainer: + kwargs: {} +name: e060805f292ad9afeefab59125a47036 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/params.yaml b/examples/security/classification/output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/params.yaml new file mode 100644 index 00000000..93508a3b --- /dev/null +++ b/examples/security/classification/output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/adv_predictions.json + adv_probabilities_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/08088bbf4bcf9cc3fc0b825c3d3c28cf.pkl + params_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/params.yaml + predictions_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/predictions.json + probabilities_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/probabilities.json + score_dict_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/score_dict.json + test_labels_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/test_labels.json + train_labels_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/train_labels.json + model_dir: models + name: e06ce94205c432b4e4de10bd06d6cbb9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1707a07740ae6924b2b7bb2342a73a8c + trainer: + kwargs: {} +name: e06ce94205c432b4e4de10bd06d6cbb9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e07e3048621d157de2f8511723b2c6c8/params.yaml b/examples/security/classification/output/reports/train/e07e3048621d157de2f8511723b2c6c8/params.yaml new file mode 100644 index 00000000..24821b65 --- /dev/null +++ b/examples/security/classification/output/reports/train/e07e3048621d157de2f8511723b2c6c8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/adv_predictions.json + adv_probabilities_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/2747b484cd4fb00c3407306506aa008c.pkl + params_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/params.yaml + predictions_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/predictions.json + probabilities_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/probabilities.json + score_dict_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/score_dict.json + test_labels_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/test_labels.json + train_labels_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/train_labels.json + model_dir: models + name: e07e3048621d157de2f8511723b2c6c8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ce212026cdac76b07d9da9509b4ad769 + trainer: + kwargs: {} +name: e07e3048621d157de2f8511723b2c6c8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e09f70f192f2a44501b505911c862c16/params.yaml b/examples/security/classification/output/reports/train/e09f70f192f2a44501b505911c862c16/params.yaml new file mode 100644 index 00000000..4345a0e3 --- /dev/null +++ b/examples/security/classification/output/reports/train/e09f70f192f2a44501b505911c862c16/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e09f70f192f2a44501b505911c862c16/adv_predictions.json + adv_probabilities_file: output/reports/train/e09f70f192f2a44501b505911c862c16/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/42d97f0cdc4020f802c2ab442a717a7a.pkl + params_file: output/reports/train/e09f70f192f2a44501b505911c862c16/params.yaml + predictions_file: output/reports/train/e09f70f192f2a44501b505911c862c16/predictions.json + probabilities_file: output/reports/train/e09f70f192f2a44501b505911c862c16/probabilities.json + score_dict_file: output/reports/train/e09f70f192f2a44501b505911c862c16/score_dict.json + test_labels_file: output/reports/train/e09f70f192f2a44501b505911c862c16/test_labels.json + train_labels_file: output/reports/train/e09f70f192f2a44501b505911c862c16/train_labels.json + model_dir: models + name: e09f70f192f2a44501b505911c862c16 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a6e3d7d4bda78e161da1a997a03a5285 + trainer: + kwargs: {} +name: e09f70f192f2a44501b505911c862c16 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/params.yaml b/examples/security/classification/output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/params.yaml new file mode 100644 index 00000000..b02f7f19 --- /dev/null +++ b/examples/security/classification/output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/adv_predictions.json + adv_probabilities_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/7987fb8a7980411b777aa941b1c3fcf2.pkl + params_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/params.yaml + predictions_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/predictions.json + probabilities_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/probabilities.json + score_dict_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/score_dict.json + test_labels_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/test_labels.json + train_labels_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/train_labels.json + model_dir: models + name: e0ad25b14c165d6d7eb670652a8fbd79 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 502753766d2841e5e0a96ec746dc698d + trainer: + kwargs: {} +name: e0ad25b14c165d6d7eb670652a8fbd79 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/params.yaml b/examples/security/classification/output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/params.yaml new file mode 100644 index 00000000..1c95c998 --- /dev/null +++ b/examples/security/classification/output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/adv_predictions.json + adv_probabilities_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/5fc9d28314344e2386ba7c7db820fb08.pkl + params_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/params.yaml + predictions_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/predictions.json + probabilities_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/probabilities.json + score_dict_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/score_dict.json + test_labels_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/test_labels.json + train_labels_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/train_labels.json + model_dir: models + name: e0b7b95df2710fe1b60b3c6601c295de + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fdaff09fe7c58ed7119d24bca6ccfa58 + trainer: + kwargs: {} +name: e0b7b95df2710fe1b60b3c6601c295de +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e0c019162949138f503d5a22e5a115b2/params.yaml b/examples/security/classification/output/reports/train/e0c019162949138f503d5a22e5a115b2/params.yaml new file mode 100644 index 00000000..78613570 --- /dev/null +++ b/examples/security/classification/output/reports/train/e0c019162949138f503d5a22e5a115b2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/adv_predictions.json + adv_probabilities_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/df76883ca2db883bcffcfabff372eb15.pkl + params_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/params.yaml + predictions_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/predictions.json + probabilities_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/probabilities.json + score_dict_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/score_dict.json + test_labels_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/test_labels.json + train_labels_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/train_labels.json + model_dir: models + name: e0c019162949138f503d5a22e5a115b2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 65e4f5c0a74576885523e74edf029690 + trainer: + kwargs: {} +name: e0c019162949138f503d5a22e5a115b2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/params.yaml b/examples/security/classification/output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/params.yaml new file mode 100644 index 00000000..acf5ec22 --- /dev/null +++ b/examples/security/classification/output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/adv_predictions.json + adv_probabilities_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/cc475a4e1ba686598cff99614ea0dc5f.pkl + params_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/params.yaml + predictions_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/predictions.json + probabilities_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/probabilities.json + score_dict_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/score_dict.json + test_labels_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/test_labels.json + train_labels_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/train_labels.json + model_dir: models + name: e0d5331c4cf57fa7f543d231a21dfb7a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 29101bbf24243a7ad68d7df18f3eede3 + trainer: + kwargs: {} +name: e0d5331c4cf57fa7f543d231a21dfb7a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/params.yaml b/examples/security/classification/output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/params.yaml new file mode 100644 index 00000000..03beea6e --- /dev/null +++ b/examples/security/classification/output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/adv_predictions.json + adv_probabilities_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/1f649871db8015bdf2a4df3431da2118.pkl + params_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/params.yaml + predictions_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/predictions.json + probabilities_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/probabilities.json + score_dict_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/score_dict.json + test_labels_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/test_labels.json + train_labels_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/train_labels.json + model_dir: models + name: e0e4044e5c6d3c9eb37e881e1fb622d5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a4a91bd2ade5aa1a31f82caf52c7ae42 + trainer: + kwargs: {} +name: e0e4044e5c6d3c9eb37e881e1fb622d5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/params.yaml b/examples/security/classification/output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/params.yaml new file mode 100644 index 00000000..aa495da3 --- /dev/null +++ b/examples/security/classification/output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/adv_predictions.json + adv_probabilities_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/0f967f896d920d89210810b7f005c8f2.pkl + params_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/params.yaml + predictions_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/predictions.json + probabilities_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/probabilities.json + score_dict_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/score_dict.json + test_labels_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/test_labels.json + train_labels_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/train_labels.json + model_dir: models + name: e0e75f5dad6440a9f5c948b763cc2e7b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bc898444f9e49905b737d77d5a996c69 + trainer: + kwargs: {} +name: e0e75f5dad6440a9f5c948b763cc2e7b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/params.yaml b/examples/security/classification/output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/params.yaml new file mode 100644 index 00000000..daf15c3f --- /dev/null +++ b/examples/security/classification/output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/adv_predictions.json + adv_probabilities_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/df35a1a4bf384dd82c1a25d761756e97.pkl + params_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/params.yaml + predictions_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/predictions.json + probabilities_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/probabilities.json + score_dict_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/score_dict.json + test_labels_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/test_labels.json + train_labels_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/train_labels.json + model_dir: models + name: e10bf0b476f3bc4beef5bed7e6bccd25 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: efa37f734ae9bb88c5222c3938427a75 + trainer: + kwargs: {} +name: e10bf0b476f3bc4beef5bed7e6bccd25 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/params.yaml b/examples/security/classification/output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/params.yaml new file mode 100644 index 00000000..16bd6f21 --- /dev/null +++ b/examples/security/classification/output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/adv_predictions.json + adv_probabilities_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/4768316560553131fe7db2aae3722eb0.pkl + params_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/params.yaml + predictions_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/predictions.json + probabilities_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/probabilities.json + score_dict_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/score_dict.json + test_labels_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/test_labels.json + train_labels_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/train_labels.json + model_dir: models + name: e10c8abfdc237a6f8fc22a9c1e15d26b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b5ec859e8a5caca5ea0a9a0140493a32 + trainer: + kwargs: {} +name: e10c8abfdc237a6f8fc22a9c1e15d26b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/params.yaml b/examples/security/classification/output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/params.yaml new file mode 100644 index 00000000..8905f0d5 --- /dev/null +++ b/examples/security/classification/output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/adv_predictions.json + adv_probabilities_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/96ed37ffdd0dd6069993a71aa4a0e66f.pkl + params_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/params.yaml + predictions_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/predictions.json + probabilities_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/probabilities.json + score_dict_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/score_dict.json + test_labels_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/test_labels.json + train_labels_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/train_labels.json + model_dir: models + name: e179ab77b82a6cb576d3c1cfbce39356 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 32221b768f297c17b44429e21972b401 + trainer: + kwargs: {} +name: e179ab77b82a6cb576d3c1cfbce39356 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e1818ced567f2a2897e3c09e058f9ece/params.yaml b/examples/security/classification/output/reports/train/e1818ced567f2a2897e3c09e058f9ece/params.yaml new file mode 100644 index 00000000..12c3e14c --- /dev/null +++ b/examples/security/classification/output/reports/train/e1818ced567f2a2897e3c09e058f9ece/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/adv_predictions.json + adv_probabilities_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/eeca950c325622eb6bfc0fb4a28ca040.pkl + params_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/params.yaml + predictions_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/predictions.json + probabilities_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/probabilities.json + score_dict_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/score_dict.json + test_labels_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/test_labels.json + train_labels_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/train_labels.json + model_dir: models + name: e1818ced567f2a2897e3c09e058f9ece + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2ba2ee92c3e0d2c74a233c4ff9188533 + trainer: + kwargs: {} +name: e1818ced567f2a2897e3c09e058f9ece +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e1a5849afe730c490f0f11da540e1eca/params.yaml b/examples/security/classification/output/reports/train/e1a5849afe730c490f0f11da540e1eca/params.yaml new file mode 100644 index 00000000..a617065d --- /dev/null +++ b/examples/security/classification/output/reports/train/e1a5849afe730c490f0f11da540e1eca/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/adv_predictions.json + adv_probabilities_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/aa4aa365924d82d730ffa5d3999163be.pkl + params_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/params.yaml + predictions_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/predictions.json + probabilities_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/probabilities.json + score_dict_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/score_dict.json + test_labels_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/test_labels.json + train_labels_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/train_labels.json + model_dir: models + name: e1a5849afe730c490f0f11da540e1eca + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 322d0dd265b517056fdd555afe312f8d + trainer: + kwargs: {} +name: e1a5849afe730c490f0f11da540e1eca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e1acb27a552a4bf1428566a9752b2525/params.yaml b/examples/security/classification/output/reports/train/e1acb27a552a4bf1428566a9752b2525/params.yaml new file mode 100644 index 00000000..079640d7 --- /dev/null +++ b/examples/security/classification/output/reports/train/e1acb27a552a4bf1428566a9752b2525/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/adv_predictions.json + adv_probabilities_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/fdcb655b5d9b52120926eba61f38b4bd.pkl + params_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/params.yaml + predictions_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/predictions.json + probabilities_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/probabilities.json + score_dict_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/score_dict.json + test_labels_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/test_labels.json + train_labels_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/train_labels.json + model_dir: models + name: e1acb27a552a4bf1428566a9752b2525 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3e5f5f366ea7f55a1b812d687764b87d + trainer: + kwargs: {} +name: e1acb27a552a4bf1428566a9752b2525 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/params.yaml b/examples/security/classification/output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/params.yaml new file mode 100644 index 00000000..6a77dca8 --- /dev/null +++ b/examples/security/classification/output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/adv_predictions.json + adv_probabilities_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/79dc5842f73399b6968e59d006cd459f.pkl + params_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/params.yaml + predictions_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/predictions.json + probabilities_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/probabilities.json + score_dict_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/score_dict.json + test_labels_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/test_labels.json + train_labels_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/train_labels.json + model_dir: models + name: e23e5b431a29d08dfe0f434ae34da1a2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 098d535b68cf2e6fba2ce8d3c1894879 + trainer: + kwargs: {} +name: e23e5b431a29d08dfe0f434ae34da1a2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e288cf0b630b13d95c76e60da4c36a83/params.yaml b/examples/security/classification/output/reports/train/e288cf0b630b13d95c76e60da4c36a83/params.yaml new file mode 100644 index 00000000..480260c3 --- /dev/null +++ b/examples/security/classification/output/reports/train/e288cf0b630b13d95c76e60da4c36a83/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/adv_predictions.json + adv_probabilities_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/f61a5b07a978b94aab3ddaf572664086.pkl + params_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/params.yaml + predictions_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/predictions.json + probabilities_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/probabilities.json + score_dict_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/score_dict.json + test_labels_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/test_labels.json + train_labels_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/train_labels.json + model_dir: models + name: e288cf0b630b13d95c76e60da4c36a83 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 853e796a3be15032df0567f7ae1caf72 + trainer: + kwargs: {} +name: e288cf0b630b13d95c76e60da4c36a83 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e28f304300b14a528eec6cf97c07cfbb/params.yaml b/examples/security/classification/output/reports/train/e28f304300b14a528eec6cf97c07cfbb/params.yaml new file mode 100644 index 00000000..1391e298 --- /dev/null +++ b/examples/security/classification/output/reports/train/e28f304300b14a528eec6cf97c07cfbb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/adv_predictions.json + adv_probabilities_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/daa854bf9c285efa229f6da1cd7f12af.pkl + params_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/params.yaml + predictions_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/predictions.json + probabilities_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/probabilities.json + score_dict_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/score_dict.json + test_labels_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/test_labels.json + train_labels_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/train_labels.json + model_dir: models + name: e28f304300b14a528eec6cf97c07cfbb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: df7e1001f0fab813a87113c873b54cdd + trainer: + kwargs: {} +name: e28f304300b14a528eec6cf97c07cfbb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e2aade5727f0b370bb23e5414259f55e/params.yaml b/examples/security/classification/output/reports/train/e2aade5727f0b370bb23e5414259f55e/params.yaml new file mode 100644 index 00000000..e12e18ce --- /dev/null +++ b/examples/security/classification/output/reports/train/e2aade5727f0b370bb23e5414259f55e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/adv_predictions.json + adv_probabilities_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/1db202422ff4fe1a855098ac052004ee.pkl + params_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/params.yaml + predictions_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/predictions.json + probabilities_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/probabilities.json + score_dict_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/score_dict.json + test_labels_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/test_labels.json + train_labels_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/train_labels.json + model_dir: models + name: e2aade5727f0b370bb23e5414259f55e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1971f7b7a5cee97a0c4075f9b1953cdb + trainer: + kwargs: {} +name: e2aade5727f0b370bb23e5414259f55e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e300d41ffd94b5a15cff9207676657ab/params.yaml b/examples/security/classification/output/reports/train/e300d41ffd94b5a15cff9207676657ab/params.yaml new file mode 100644 index 00000000..37a0228d --- /dev/null +++ b/examples/security/classification/output/reports/train/e300d41ffd94b5a15cff9207676657ab/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/adv_predictions.json + adv_probabilities_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/cc1a5b795bae005da8ce3889c5d4e069.pkl + params_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/params.yaml + predictions_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/predictions.json + probabilities_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/probabilities.json + score_dict_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/score_dict.json + test_labels_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/test_labels.json + train_labels_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/train_labels.json + model_dir: models + name: e300d41ffd94b5a15cff9207676657ab + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 33e85f232cc125c66b02a53a17205e99 + trainer: + kwargs: {} +name: e300d41ffd94b5a15cff9207676657ab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e3677153c39e6a0d24227435b5c82d78/params.yaml b/examples/security/classification/output/reports/train/e3677153c39e6a0d24227435b5c82d78/params.yaml new file mode 100644 index 00000000..ce90e5e2 --- /dev/null +++ b/examples/security/classification/output/reports/train/e3677153c39e6a0d24227435b5c82d78/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/adv_predictions.json + adv_probabilities_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/6b2d66fb11b5a4b611db73744506de7d.pkl + params_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/params.yaml + predictions_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/predictions.json + probabilities_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/probabilities.json + score_dict_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/score_dict.json + test_labels_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/test_labels.json + train_labels_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/train_labels.json + model_dir: models + name: e3677153c39e6a0d24227435b5c82d78 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7b5bb7cb60db56de1cefe3427a72c450 + trainer: + kwargs: {} +name: e3677153c39e6a0d24227435b5c82d78 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e3691d832eeaeb10574a10582c4ca433/params.yaml b/examples/security/classification/output/reports/train/e3691d832eeaeb10574a10582c4ca433/params.yaml new file mode 100644 index 00000000..22dab848 --- /dev/null +++ b/examples/security/classification/output/reports/train/e3691d832eeaeb10574a10582c4ca433/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/adv_predictions.json + adv_probabilities_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/932af5a4d668905ce66495161d986857.pkl + params_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/params.yaml + predictions_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/predictions.json + probabilities_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/probabilities.json + score_dict_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/score_dict.json + test_labels_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/test_labels.json + train_labels_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/train_labels.json + model_dir: models + name: e3691d832eeaeb10574a10582c4ca433 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 11aceb3f10f39fce40e1a62f367ef6ea + trainer: + kwargs: {} +name: e3691d832eeaeb10574a10582c4ca433 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/params.yaml b/examples/security/classification/output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/params.yaml new file mode 100644 index 00000000..7d40e057 --- /dev/null +++ b/examples/security/classification/output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/adv_predictions.json + adv_probabilities_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/a5d185460a0c32576ebf50998b8f41e8.pkl + params_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/params.yaml + predictions_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/predictions.json + probabilities_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/probabilities.json + score_dict_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/score_dict.json + test_labels_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/test_labels.json + train_labels_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/train_labels.json + model_dir: models + name: e36db73b3632876ebe8018f1ad55bc8d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6ac747d41502fb01b193b88166c80e50 + trainer: + kwargs: {} +name: e36db73b3632876ebe8018f1ad55bc8d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e3701567224e033f74082ae439c250e0/params.yaml b/examples/security/classification/output/reports/train/e3701567224e033f74082ae439c250e0/params.yaml new file mode 100644 index 00000000..304c7e65 --- /dev/null +++ b/examples/security/classification/output/reports/train/e3701567224e033f74082ae439c250e0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e3701567224e033f74082ae439c250e0/adv_predictions.json + adv_probabilities_file: output/reports/train/e3701567224e033f74082ae439c250e0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/85eb02a64113a85f5363848dc933e8da.pkl + params_file: output/reports/train/e3701567224e033f74082ae439c250e0/params.yaml + predictions_file: output/reports/train/e3701567224e033f74082ae439c250e0/predictions.json + probabilities_file: output/reports/train/e3701567224e033f74082ae439c250e0/probabilities.json + score_dict_file: output/reports/train/e3701567224e033f74082ae439c250e0/score_dict.json + test_labels_file: output/reports/train/e3701567224e033f74082ae439c250e0/test_labels.json + train_labels_file: output/reports/train/e3701567224e033f74082ae439c250e0/train_labels.json + model_dir: models + name: e3701567224e033f74082ae439c250e0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5d0c36d016e5e8eb9176f0e77021e26e + trainer: + kwargs: {} +name: e3701567224e033f74082ae439c250e0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e3dd0686331226c1f789f25d882756f1/params.yaml b/examples/security/classification/output/reports/train/e3dd0686331226c1f789f25d882756f1/params.yaml new file mode 100644 index 00000000..49731537 --- /dev/null +++ b/examples/security/classification/output/reports/train/e3dd0686331226c1f789f25d882756f1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/adv_predictions.json + adv_probabilities_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/6a8eb39f8c16fb423451ea8036f63c0b.pkl + params_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/params.yaml + predictions_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/predictions.json + probabilities_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/probabilities.json + score_dict_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/score_dict.json + test_labels_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/test_labels.json + train_labels_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/train_labels.json + model_dir: models + name: e3dd0686331226c1f789f25d882756f1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3df2f23e6547d42c95dd9202a7e9cd18 + trainer: + kwargs: {} +name: e3dd0686331226c1f789f25d882756f1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e4570785a5c863e12886affb36b1ae5f/params.yaml b/examples/security/classification/output/reports/train/e4570785a5c863e12886affb36b1ae5f/params.yaml new file mode 100644 index 00000000..b2eee22a --- /dev/null +++ b/examples/security/classification/output/reports/train/e4570785a5c863e12886affb36b1ae5f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/adv_predictions.json + adv_probabilities_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/fef4659bdbebf0ac8031c0a0f1080fdf.pkl + params_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/params.yaml + predictions_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/predictions.json + probabilities_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/probabilities.json + score_dict_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/score_dict.json + test_labels_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/test_labels.json + train_labels_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/train_labels.json + model_dir: models + name: e4570785a5c863e12886affb36b1ae5f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f70d66d17091e479cecf8bbb8887baac + trainer: + kwargs: {} +name: e4570785a5c863e12886affb36b1ae5f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e472d9728f57a8c9cded66997912c756/params.yaml b/examples/security/classification/output/reports/train/e472d9728f57a8c9cded66997912c756/params.yaml new file mode 100644 index 00000000..c81081ef --- /dev/null +++ b/examples/security/classification/output/reports/train/e472d9728f57a8c9cded66997912c756/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e472d9728f57a8c9cded66997912c756/adv_predictions.json + adv_probabilities_file: output/reports/train/e472d9728f57a8c9cded66997912c756/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/6628b0bb149ce8a848e00a0fda61397e.pkl + params_file: output/reports/train/e472d9728f57a8c9cded66997912c756/params.yaml + predictions_file: output/reports/train/e472d9728f57a8c9cded66997912c756/predictions.json + probabilities_file: output/reports/train/e472d9728f57a8c9cded66997912c756/probabilities.json + score_dict_file: output/reports/train/e472d9728f57a8c9cded66997912c756/score_dict.json + test_labels_file: output/reports/train/e472d9728f57a8c9cded66997912c756/test_labels.json + train_labels_file: output/reports/train/e472d9728f57a8c9cded66997912c756/train_labels.json + model_dir: models + name: e472d9728f57a8c9cded66997912c756 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cdb170ac54b988d68f15708b037ed4e9 + trainer: + kwargs: {} +name: e472d9728f57a8c9cded66997912c756 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e4bb678c3419902d93bdccbab14a98a9/params.yaml b/examples/security/classification/output/reports/train/e4bb678c3419902d93bdccbab14a98a9/params.yaml new file mode 100644 index 00000000..566a78a5 --- /dev/null +++ b/examples/security/classification/output/reports/train/e4bb678c3419902d93bdccbab14a98a9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/adv_predictions.json + adv_probabilities_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/dfef41a9c8b679f9f9bcc3f56d4a36de.pkl + params_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/params.yaml + predictions_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/predictions.json + probabilities_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/probabilities.json + score_dict_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/score_dict.json + test_labels_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/test_labels.json + train_labels_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/train_labels.json + model_dir: models + name: e4bb678c3419902d93bdccbab14a98a9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6c980a25e7ac97aa3d4fd33a7fba910c + trainer: + kwargs: {} +name: e4bb678c3419902d93bdccbab14a98a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/params.yaml b/examples/security/classification/output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/params.yaml new file mode 100644 index 00000000..4579fa79 --- /dev/null +++ b/examples/security/classification/output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/adv_predictions.json + adv_probabilities_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/f8ae3d6da92dc6aacfc3553d51f1c4a7.pkl + params_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/params.yaml + predictions_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/predictions.json + probabilities_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/probabilities.json + score_dict_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/score_dict.json + test_labels_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/test_labels.json + train_labels_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/train_labels.json + model_dir: models + name: e4bf17243244dbe0e8771cb85f8966fc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a94ee2181b2ed8ba1b8f5a5ca441ec4c + trainer: + kwargs: {} +name: e4bf17243244dbe0e8771cb85f8966fc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/params.yaml b/examples/security/classification/output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/params.yaml new file mode 100644 index 00000000..3f0e2e3e --- /dev/null +++ b/examples/security/classification/output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/adv_predictions.json + adv_probabilities_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/52253601f9a98bd03c89c8c4f5c00109.pkl + params_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/params.yaml + predictions_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/predictions.json + probabilities_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/probabilities.json + score_dict_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/score_dict.json + test_labels_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/test_labels.json + train_labels_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/train_labels.json + model_dir: models + name: e4c7fab3ce132a7b02f2693d26b9b58c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0791fb0df9971a5ef0b0f59ef62c525c + trainer: + kwargs: {} +name: e4c7fab3ce132a7b02f2693d26b9b58c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e503f5a0549fa365e8c1c4a064836062/params.yaml b/examples/security/classification/output/reports/train/e503f5a0549fa365e8c1c4a064836062/params.yaml new file mode 100644 index 00000000..923ca5b4 --- /dev/null +++ b/examples/security/classification/output/reports/train/e503f5a0549fa365e8c1c4a064836062/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/adv_predictions.json + adv_probabilities_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/15a0c36475ab2bc5fb6a1616e8049ecc.pkl + params_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/params.yaml + predictions_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/predictions.json + probabilities_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/probabilities.json + score_dict_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/score_dict.json + test_labels_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/test_labels.json + train_labels_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/train_labels.json + model_dir: models + name: e503f5a0549fa365e8c1c4a064836062 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6fa24993eaaee72bdaaed29701b70ffc + trainer: + kwargs: {} +name: e503f5a0549fa365e8c1c4a064836062 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e50479c2a7d5961d4a7738bc94786d66/params.yaml b/examples/security/classification/output/reports/train/e50479c2a7d5961d4a7738bc94786d66/params.yaml new file mode 100644 index 00000000..3ef3724f --- /dev/null +++ b/examples/security/classification/output/reports/train/e50479c2a7d5961d4a7738bc94786d66/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/adv_predictions.json + adv_probabilities_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/464ec1a3cfd7b41e53d67acd9de96ba3.pkl + params_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/params.yaml + predictions_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/predictions.json + probabilities_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/probabilities.json + score_dict_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/score_dict.json + test_labels_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/test_labels.json + train_labels_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/train_labels.json + model_dir: models + name: e50479c2a7d5961d4a7738bc94786d66 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6a382ad5610a7d25de45281f9b8337e2 + trainer: + kwargs: {} +name: e50479c2a7d5961d4a7738bc94786d66 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/params.yaml b/examples/security/classification/output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/params.yaml new file mode 100644 index 00000000..cfb96728 --- /dev/null +++ b/examples/security/classification/output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/adv_predictions.json + adv_probabilities_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/2844f5180915083e8f5fa908bff48aa7.pkl + params_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/params.yaml + predictions_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/predictions.json + probabilities_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/probabilities.json + score_dict_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/score_dict.json + test_labels_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/test_labels.json + train_labels_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/train_labels.json + model_dir: models + name: e50d3d3482f54ae01ff02c99bb0f6276 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 72a151d8b68cfe2f6f1fb0286fd3ffb5 + trainer: + kwargs: {} +name: e50d3d3482f54ae01ff02c99bb0f6276 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e51330ff22301d3535956e21ca511d83/params.yaml b/examples/security/classification/output/reports/train/e51330ff22301d3535956e21ca511d83/params.yaml new file mode 100644 index 00000000..00973622 --- /dev/null +++ b/examples/security/classification/output/reports/train/e51330ff22301d3535956e21ca511d83/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e51330ff22301d3535956e21ca511d83/adv_predictions.json + adv_probabilities_file: output/reports/train/e51330ff22301d3535956e21ca511d83/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/762ee1ffe7e32fdc158c4c30257e46a3.pkl + params_file: output/reports/train/e51330ff22301d3535956e21ca511d83/params.yaml + predictions_file: output/reports/train/e51330ff22301d3535956e21ca511d83/predictions.json + probabilities_file: output/reports/train/e51330ff22301d3535956e21ca511d83/probabilities.json + score_dict_file: output/reports/train/e51330ff22301d3535956e21ca511d83/score_dict.json + test_labels_file: output/reports/train/e51330ff22301d3535956e21ca511d83/test_labels.json + train_labels_file: output/reports/train/e51330ff22301d3535956e21ca511d83/train_labels.json + model_dir: models + name: e51330ff22301d3535956e21ca511d83 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 19ca6a8fb399249b66c79842ee20034e + trainer: + kwargs: {} +name: e51330ff22301d3535956e21ca511d83 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/params.yaml b/examples/security/classification/output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/params.yaml new file mode 100644 index 00000000..f061b9d5 --- /dev/null +++ b/examples/security/classification/output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/adv_predictions.json + adv_probabilities_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/8c7ac66f282cdb9af5d7dc8db9dc3d9f.pkl + params_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/params.yaml + predictions_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/predictions.json + probabilities_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/probabilities.json + score_dict_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/score_dict.json + test_labels_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/test_labels.json + train_labels_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/train_labels.json + model_dir: models + name: e5445e6edc2f2cd889dfa033ee82bf7d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 534fa1ed1a0b3a86331e9a4a3a555f04 + trainer: + kwargs: {} +name: e5445e6edc2f2cd889dfa033ee82bf7d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e547043a9deda6bb57337dac0f54ec52/params.yaml b/examples/security/classification/output/reports/train/e547043a9deda6bb57337dac0f54ec52/params.yaml new file mode 100644 index 00000000..75942e92 --- /dev/null +++ b/examples/security/classification/output/reports/train/e547043a9deda6bb57337dac0f54ec52/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/adv_predictions.json + adv_probabilities_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/9d6b2a3ef9145d2e8870676ca7b2ada7.pkl + params_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/params.yaml + predictions_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/predictions.json + probabilities_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/probabilities.json + score_dict_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/score_dict.json + test_labels_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/test_labels.json + train_labels_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/train_labels.json + model_dir: models + name: e547043a9deda6bb57337dac0f54ec52 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8d9a2157c37ff7577cef3bda9a3fa44b + trainer: + kwargs: {} +name: e547043a9deda6bb57337dac0f54ec52 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e555e957861ac0efa1e4077f429d9622/params.yaml b/examples/security/classification/output/reports/train/e555e957861ac0efa1e4077f429d9622/params.yaml new file mode 100644 index 00000000..7d29c334 --- /dev/null +++ b/examples/security/classification/output/reports/train/e555e957861ac0efa1e4077f429d9622/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/adv_predictions.json + adv_probabilities_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/85fdf1feaf4a37b631a99c29857f9d78.pkl + params_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/params.yaml + predictions_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/predictions.json + probabilities_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/probabilities.json + score_dict_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/score_dict.json + test_labels_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/test_labels.json + train_labels_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/train_labels.json + model_dir: models + name: e555e957861ac0efa1e4077f429d9622 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 978dc4327f3d4d892f5a838ff5b64ad5 + trainer: + kwargs: {} +name: e555e957861ac0efa1e4077f429d9622 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e5960384348f6cb1706c59020a31a5f5/params.yaml b/examples/security/classification/output/reports/train/e5960384348f6cb1706c59020a31a5f5/params.yaml new file mode 100644 index 00000000..a06a1510 --- /dev/null +++ b/examples/security/classification/output/reports/train/e5960384348f6cb1706c59020a31a5f5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/adv_predictions.json + adv_probabilities_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/8ca356fb8cfb59a55e986ed191c748ae.pkl + params_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/params.yaml + predictions_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/predictions.json + probabilities_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/probabilities.json + score_dict_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/score_dict.json + test_labels_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/test_labels.json + train_labels_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/train_labels.json + model_dir: models + name: e5960384348f6cb1706c59020a31a5f5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 22825b403e9b7e978357b38f7b65ceee + trainer: + kwargs: {} +name: e5960384348f6cb1706c59020a31a5f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e5adf83a9939c19778e6061b7641432f/params.yaml b/examples/security/classification/output/reports/train/e5adf83a9939c19778e6061b7641432f/params.yaml new file mode 100644 index 00000000..05046cd9 --- /dev/null +++ b/examples/security/classification/output/reports/train/e5adf83a9939c19778e6061b7641432f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/adv_predictions.json + adv_probabilities_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/01b66947285c28a8eeb31436886514a7.pkl + params_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/params.yaml + predictions_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/predictions.json + probabilities_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/probabilities.json + score_dict_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/score_dict.json + test_labels_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/test_labels.json + train_labels_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/train_labels.json + model_dir: models + name: e5adf83a9939c19778e6061b7641432f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 475285d4f81f8a9ad9ee63c405721a85 + trainer: + kwargs: {} +name: e5adf83a9939c19778e6061b7641432f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/params.yaml b/examples/security/classification/output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/params.yaml new file mode 100644 index 00000000..e78d02b7 --- /dev/null +++ b/examples/security/classification/output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/adv_predictions.json + adv_probabilities_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/68f8e842a08a752c8eedae6dba02c068.pkl + params_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/params.yaml + predictions_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/predictions.json + probabilities_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/probabilities.json + score_dict_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/score_dict.json + test_labels_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/test_labels.json + train_labels_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/train_labels.json + model_dir: models + name: e5ca904fd99fe56b92e8968e40b67c00 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f76e94656920f72fe1c0a207c5a3e55c + trainer: + kwargs: {} +name: e5ca904fd99fe56b92e8968e40b67c00 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/params.yaml b/examples/security/classification/output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/params.yaml new file mode 100644 index 00000000..979b31fe --- /dev/null +++ b/examples/security/classification/output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/adv_predictions.json + adv_probabilities_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/82e00c389d9514b3a4ba4c9055fd4152.pkl + params_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/params.yaml + predictions_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/predictions.json + probabilities_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/probabilities.json + score_dict_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/score_dict.json + test_labels_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/test_labels.json + train_labels_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/train_labels.json + model_dir: models + name: e5e3370e2bd21aeb248ed2c28b0e7e77 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 32d4aaca70b6bfba272b048d6a75d297 + trainer: + kwargs: {} +name: e5e3370e2bd21aeb248ed2c28b0e7e77 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/params.yaml b/examples/security/classification/output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/params.yaml new file mode 100644 index 00000000..cb0831e1 --- /dev/null +++ b/examples/security/classification/output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/adv_predictions.json + adv_probabilities_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/6ea318e136e4da4a5fc685a041b9d349.pkl + params_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/params.yaml + predictions_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/predictions.json + probabilities_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/probabilities.json + score_dict_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/score_dict.json + test_labels_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/test_labels.json + train_labels_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/train_labels.json + model_dir: models + name: e5f1c170bfc8465c8a58c34521a91c08 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ee0c8313d01ac9f326d19c9ab2e9fa8a + trainer: + kwargs: {} +name: e5f1c170bfc8465c8a58c34521a91c08 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e6368532f01acfe507037de593e49b0d/params.yaml b/examples/security/classification/output/reports/train/e6368532f01acfe507037de593e49b0d/params.yaml new file mode 100644 index 00000000..8662b956 --- /dev/null +++ b/examples/security/classification/output/reports/train/e6368532f01acfe507037de593e49b0d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e6368532f01acfe507037de593e49b0d/adv_predictions.json + adv_probabilities_file: output/reports/train/e6368532f01acfe507037de593e49b0d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/b70d617cf6ff28a51e8fe6e2111ce302.pkl + params_file: output/reports/train/e6368532f01acfe507037de593e49b0d/params.yaml + predictions_file: output/reports/train/e6368532f01acfe507037de593e49b0d/predictions.json + probabilities_file: output/reports/train/e6368532f01acfe507037de593e49b0d/probabilities.json + score_dict_file: output/reports/train/e6368532f01acfe507037de593e49b0d/score_dict.json + test_labels_file: output/reports/train/e6368532f01acfe507037de593e49b0d/test_labels.json + train_labels_file: output/reports/train/e6368532f01acfe507037de593e49b0d/train_labels.json + model_dir: models + name: e6368532f01acfe507037de593e49b0d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a699c4fa8376178c431975e29e93287d + trainer: + kwargs: {} +name: e6368532f01acfe507037de593e49b0d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/params.yaml b/examples/security/classification/output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/params.yaml new file mode 100644 index 00000000..3ae3233d --- /dev/null +++ b/examples/security/classification/output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/adv_predictions.json + adv_probabilities_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/7daf258920d483dc1d8562131cb4450d.pkl + params_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/params.yaml + predictions_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/predictions.json + probabilities_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/probabilities.json + score_dict_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/score_dict.json + test_labels_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/test_labels.json + train_labels_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/train_labels.json + model_dir: models + name: e6760775ee6c0b6d8495522112d3a2e1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ef8c4936cfd64262c7957fc686a2850 + trainer: + kwargs: {} +name: e6760775ee6c0b6d8495522112d3a2e1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/params.yaml b/examples/security/classification/output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/params.yaml new file mode 100644 index 00000000..2ffd9dd2 --- /dev/null +++ b/examples/security/classification/output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/adv_predictions.json + adv_probabilities_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/cdebebfc5c3c98ff8e1b503fc42959b4.pkl + params_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/params.yaml + predictions_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/predictions.json + probabilities_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/probabilities.json + score_dict_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/score_dict.json + test_labels_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/test_labels.json + train_labels_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/train_labels.json + model_dir: models + name: e67d39c9c4b0c0e4369650c48de5a29d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 027e7778fdfcfb63335a6081a02acb49 + trainer: + kwargs: {} +name: e67d39c9c4b0c0e4369650c48de5a29d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/params.yaml b/examples/security/classification/output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/params.yaml new file mode 100644 index 00000000..d6578184 --- /dev/null +++ b/examples/security/classification/output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/adv_predictions.json + adv_probabilities_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/a167f3ce89e24fba92f122d49a65fdc9.pkl + params_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/params.yaml + predictions_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/predictions.json + probabilities_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/probabilities.json + score_dict_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/score_dict.json + test_labels_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/test_labels.json + train_labels_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/train_labels.json + model_dir: models + name: e69338e55c6e18eda0500def6ee3e7d9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 782abee3f9858f3706140220e131e599 + trainer: + kwargs: {} +name: e69338e55c6e18eda0500def6ee3e7d9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e69a32fd3c1ef53974cb116290305997/params.yaml b/examples/security/classification/output/reports/train/e69a32fd3c1ef53974cb116290305997/params.yaml new file mode 100644 index 00000000..493143d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/e69a32fd3c1ef53974cb116290305997/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/adv_predictions.json + adv_probabilities_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/09bca0bb136efdb41b3459b71055b90a.pkl + params_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/params.yaml + predictions_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/predictions.json + probabilities_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/probabilities.json + score_dict_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/score_dict.json + test_labels_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/test_labels.json + train_labels_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/train_labels.json + model_dir: models + name: e69a32fd3c1ef53974cb116290305997 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f5f39235648d90d051a80756d6d02dc0 + trainer: + kwargs: {} +name: e69a32fd3c1ef53974cb116290305997 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e69ced39ae1e694d111d39441696eb14/params.yaml b/examples/security/classification/output/reports/train/e69ced39ae1e694d111d39441696eb14/params.yaml new file mode 100644 index 00000000..df7b16c1 --- /dev/null +++ b/examples/security/classification/output/reports/train/e69ced39ae1e694d111d39441696eb14/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/adv_predictions.json + adv_probabilities_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/a2f28b8606c2da8e030c3218fba25cc4.pkl + params_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/params.yaml + predictions_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/predictions.json + probabilities_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/probabilities.json + score_dict_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/score_dict.json + test_labels_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/test_labels.json + train_labels_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/train_labels.json + model_dir: models + name: e69ced39ae1e694d111d39441696eb14 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b3f62e70e30fce3aa2281420fa4311ae + trainer: + kwargs: {} +name: e69ced39ae1e694d111d39441696eb14 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/params.yaml b/examples/security/classification/output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/params.yaml new file mode 100644 index 00000000..190e6b8c --- /dev/null +++ b/examples/security/classification/output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/adv_predictions.json + adv_probabilities_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/74c88875112cc54de1f95719ba3c6f85.pkl + params_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/params.yaml + predictions_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/predictions.json + probabilities_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/probabilities.json + score_dict_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/score_dict.json + test_labels_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/test_labels.json + train_labels_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/train_labels.json + model_dir: models + name: e6c2e24cac4eb73808fe0fc0719ae8da + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4feb2ca07d3da872d00bfb1d927bbaaf + trainer: + kwargs: {} +name: e6c2e24cac4eb73808fe0fc0719ae8da +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/params.yaml b/examples/security/classification/output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/params.yaml new file mode 100644 index 00000000..37426895 --- /dev/null +++ b/examples/security/classification/output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/adv_predictions.json + adv_probabilities_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/89f319e75ea8fc549d73c60d834d57b2.pkl + params_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/params.yaml + predictions_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/predictions.json + probabilities_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/probabilities.json + score_dict_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/score_dict.json + test_labels_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/test_labels.json + train_labels_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/train_labels.json + model_dir: models + name: e6e00b6a7971d0ca7ad1daa6f49bcbd4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6e07216c5cde3a8d7d541ca5b3f591b6 + trainer: + kwargs: {} +name: e6e00b6a7971d0ca7ad1daa6f49bcbd4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/params.yaml b/examples/security/classification/output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/params.yaml new file mode 100644 index 00000000..f88ee2c5 --- /dev/null +++ b/examples/security/classification/output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/adv_predictions.json + adv_probabilities_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/a9ebbefb18758565bd7a4bf289745f9f.pkl + params_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/params.yaml + predictions_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/predictions.json + probabilities_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/probabilities.json + score_dict_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/score_dict.json + test_labels_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/test_labels.json + train_labels_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/train_labels.json + model_dir: models + name: e72c709618dfcc5c67a8cccc967c62e7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 767850b8cd8512df0ccaca645b071962 + trainer: + kwargs: {} +name: e72c709618dfcc5c67a8cccc967c62e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e7307e33c4f6792eb839dbf6d356340c/params.yaml b/examples/security/classification/output/reports/train/e7307e33c4f6792eb839dbf6d356340c/params.yaml new file mode 100644 index 00000000..d1b80364 --- /dev/null +++ b/examples/security/classification/output/reports/train/e7307e33c4f6792eb839dbf6d356340c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/adv_predictions.json + adv_probabilities_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/a15daf11989bce81353e1bd5225dfa6e.pkl + params_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/params.yaml + predictions_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/predictions.json + probabilities_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/probabilities.json + score_dict_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/score_dict.json + test_labels_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/test_labels.json + train_labels_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/train_labels.json + model_dir: models + name: e7307e33c4f6792eb839dbf6d356340c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e57245ec84f577e1683c248dc6d30dcc + trainer: + kwargs: {} +name: e7307e33c4f6792eb839dbf6d356340c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/params.yaml b/examples/security/classification/output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/params.yaml new file mode 100644 index 00000000..859e0407 --- /dev/null +++ b/examples/security/classification/output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/adv_predictions.json + adv_probabilities_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/e2d69ade391f88872085e1b4824df742.pkl + params_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/params.yaml + predictions_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/predictions.json + probabilities_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/probabilities.json + score_dict_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/score_dict.json + test_labels_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/test_labels.json + train_labels_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/train_labels.json + model_dir: models + name: e74b3d32aabfa89daf98d7fe6618076f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b79c88a27bc5a321d4e7935beb3911f + trainer: + kwargs: {} +name: e74b3d32aabfa89daf98d7fe6618076f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e74d7f380c82daea3f3c938e71677d6b/params.yaml b/examples/security/classification/output/reports/train/e74d7f380c82daea3f3c938e71677d6b/params.yaml new file mode 100644 index 00000000..f282b415 --- /dev/null +++ b/examples/security/classification/output/reports/train/e74d7f380c82daea3f3c938e71677d6b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/adv_predictions.json + adv_probabilities_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/48f7e695f1b80de92ade394ee4751c39.pkl + params_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/params.yaml + predictions_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/predictions.json + probabilities_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/probabilities.json + score_dict_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/score_dict.json + test_labels_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/test_labels.json + train_labels_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/train_labels.json + model_dir: models + name: e74d7f380c82daea3f3c938e71677d6b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 843eddbda83bf0999848ff60b09a19ec + trainer: + kwargs: {} +name: e74d7f380c82daea3f3c938e71677d6b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e7c5e291845b14ee05e1de88c676c0af/params.yaml b/examples/security/classification/output/reports/train/e7c5e291845b14ee05e1de88c676c0af/params.yaml new file mode 100644 index 00000000..1538efe7 --- /dev/null +++ b/examples/security/classification/output/reports/train/e7c5e291845b14ee05e1de88c676c0af/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/adv_predictions.json + adv_probabilities_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/738d79050c9f035e572a11408c1bcba4.pkl + params_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/params.yaml + predictions_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/predictions.json + probabilities_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/probabilities.json + score_dict_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/score_dict.json + test_labels_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/test_labels.json + train_labels_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/train_labels.json + model_dir: models + name: e7c5e291845b14ee05e1de88c676c0af + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 21185b2b1f38b12f02a53988359a0983 + trainer: + kwargs: {} +name: e7c5e291845b14ee05e1de88c676c0af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/params.yaml b/examples/security/classification/output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/params.yaml new file mode 100644 index 00000000..219b62ea --- /dev/null +++ b/examples/security/classification/output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/adv_predictions.json + adv_probabilities_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/c1515849d71b42f22875280eba966d1c.pkl + params_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/params.yaml + predictions_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/predictions.json + probabilities_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/probabilities.json + score_dict_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/score_dict.json + test_labels_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/test_labels.json + train_labels_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/train_labels.json + model_dir: models + name: e818bcdb4ed3e4b6f669d9a8ea1ebf10 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 31d9601311030ee58391a702e11b2bdb + trainer: + kwargs: {} +name: e818bcdb4ed3e4b6f669d9a8ea1ebf10 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e81bf0d976c5e716cb16d63e205cf344/params.yaml b/examples/security/classification/output/reports/train/e81bf0d976c5e716cb16d63e205cf344/params.yaml new file mode 100644 index 00000000..ce7c2662 --- /dev/null +++ b/examples/security/classification/output/reports/train/e81bf0d976c5e716cb16d63e205cf344/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/adv_predictions.json + adv_probabilities_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/30bce144aca3da9b3c2fa5dea989f559.pkl + params_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/params.yaml + predictions_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/predictions.json + probabilities_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/probabilities.json + score_dict_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/score_dict.json + test_labels_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/test_labels.json + train_labels_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/train_labels.json + model_dir: models + name: e81bf0d976c5e716cb16d63e205cf344 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7064e3fd57dcc2e76b833915dc29a72f + trainer: + kwargs: {} +name: e81bf0d976c5e716cb16d63e205cf344 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/params.yaml b/examples/security/classification/output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/params.yaml new file mode 100644 index 00000000..bde289fe --- /dev/null +++ b/examples/security/classification/output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/adv_predictions.json + adv_probabilities_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/e146b7a92ad7539f0b645320150e0a9c.pkl + params_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/params.yaml + predictions_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/predictions.json + probabilities_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/probabilities.json + score_dict_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/score_dict.json + test_labels_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/test_labels.json + train_labels_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/train_labels.json + model_dir: models + name: e825df223c6fc9d2403b2e9b1ae5dc78 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 97c029aaddf43603c7dba7757f6a3d19 + trainer: + kwargs: {} +name: e825df223c6fc9d2403b2e9b1ae5dc78 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e846aa4f707855adb801c7f279d67bda/params.yaml b/examples/security/classification/output/reports/train/e846aa4f707855adb801c7f279d67bda/params.yaml new file mode 100644 index 00000000..896e02f2 --- /dev/null +++ b/examples/security/classification/output/reports/train/e846aa4f707855adb801c7f279d67bda/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/adv_predictions.json + adv_probabilities_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/3dd88d6f3f6b7897156d2060843d8383.pkl + params_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/params.yaml + predictions_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/predictions.json + probabilities_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/probabilities.json + score_dict_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/score_dict.json + test_labels_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/test_labels.json + train_labels_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/train_labels.json + model_dir: models + name: e846aa4f707855adb801c7f279d67bda + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cfda25196afacc423519b0584cf89b34 + trainer: + kwargs: {} +name: e846aa4f707855adb801c7f279d67bda +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/params.yaml b/examples/security/classification/output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/params.yaml new file mode 100644 index 00000000..484994fe --- /dev/null +++ b/examples/security/classification/output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/adv_predictions.json + adv_probabilities_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/1d6cadc58e5d38e196f73e3499dd765f.pkl + params_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/params.yaml + predictions_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/predictions.json + probabilities_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/probabilities.json + score_dict_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/score_dict.json + test_labels_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/test_labels.json + train_labels_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/train_labels.json + model_dir: models + name: e8665e7e3392fa1e10dc4185f291ca6a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b06e3d8c90800de747eff86e6f581f49 + trainer: + kwargs: {} +name: e8665e7e3392fa1e10dc4185f291ca6a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e87ef61295a5b27373cdb1319ada9db9/params.yaml b/examples/security/classification/output/reports/train/e87ef61295a5b27373cdb1319ada9db9/params.yaml new file mode 100644 index 00000000..fd6a9f11 --- /dev/null +++ b/examples/security/classification/output/reports/train/e87ef61295a5b27373cdb1319ada9db9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/adv_predictions.json + adv_probabilities_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/e6f3c587a45af8bc7773e92101830d9c.pkl + params_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/params.yaml + predictions_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/predictions.json + probabilities_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/probabilities.json + score_dict_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/score_dict.json + test_labels_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/test_labels.json + train_labels_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/train_labels.json + model_dir: models + name: e87ef61295a5b27373cdb1319ada9db9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 58050d2f22f7fbeee1e61ed950b39e40 + trainer: + kwargs: {} +name: e87ef61295a5b27373cdb1319ada9db9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e899358679c801f6e43a49370a0d7acd/params.yaml b/examples/security/classification/output/reports/train/e899358679c801f6e43a49370a0d7acd/params.yaml new file mode 100644 index 00000000..9fa11fdd --- /dev/null +++ b/examples/security/classification/output/reports/train/e899358679c801f6e43a49370a0d7acd/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/adv_predictions.json + adv_probabilities_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/c4ed5d492da8a2ccb0607d25e58e11f5.pkl + params_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/params.yaml + predictions_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/predictions.json + probabilities_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/probabilities.json + score_dict_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/score_dict.json + test_labels_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/test_labels.json + train_labels_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/train_labels.json + model_dir: models + name: e899358679c801f6e43a49370a0d7acd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1af2b8036db4feb1473d8049812d57b2 + trainer: + kwargs: {} +name: e899358679c801f6e43a49370a0d7acd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/params.yaml b/examples/security/classification/output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/params.yaml new file mode 100644 index 00000000..a2b035e3 --- /dev/null +++ b/examples/security/classification/output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/adv_predictions.json + adv_probabilities_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/ddc9ec8a76c5db57f1f80f3860a73b46.pkl + params_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/params.yaml + predictions_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/predictions.json + probabilities_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/probabilities.json + score_dict_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/score_dict.json + test_labels_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/test_labels.json + train_labels_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/train_labels.json + model_dir: models + name: e8b19e7e62a5e0ae2888cc140e5d3266 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 07bee317568afac49ce8603f2cdda145 + trainer: + kwargs: {} +name: e8b19e7e62a5e0ae2888cc140e5d3266 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e8c117a2334a052205fd5f472aaa3518/params.yaml b/examples/security/classification/output/reports/train/e8c117a2334a052205fd5f472aaa3518/params.yaml new file mode 100644 index 00000000..a107f52e --- /dev/null +++ b/examples/security/classification/output/reports/train/e8c117a2334a052205fd5f472aaa3518/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/adv_predictions.json + adv_probabilities_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/e90dd22e591f680e0d29929bdb086d2d.pkl + params_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/params.yaml + predictions_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/predictions.json + probabilities_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/probabilities.json + score_dict_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/score_dict.json + test_labels_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/test_labels.json + train_labels_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/train_labels.json + model_dir: models + name: e8c117a2334a052205fd5f472aaa3518 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6b2011e44f8db3b21c8484b96db5f2ec + trainer: + kwargs: {} +name: e8c117a2334a052205fd5f472aaa3518 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e8c27be43209312dd147cb4264e59891/params.yaml b/examples/security/classification/output/reports/train/e8c27be43209312dd147cb4264e59891/params.yaml new file mode 100644 index 00000000..49fac461 --- /dev/null +++ b/examples/security/classification/output/reports/train/e8c27be43209312dd147cb4264e59891/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e8c27be43209312dd147cb4264e59891/adv_predictions.json + adv_probabilities_file: output/reports/train/e8c27be43209312dd147cb4264e59891/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/0cc6ad04670a7d5821ae061eeaa0acd5.pkl + params_file: output/reports/train/e8c27be43209312dd147cb4264e59891/params.yaml + predictions_file: output/reports/train/e8c27be43209312dd147cb4264e59891/predictions.json + probabilities_file: output/reports/train/e8c27be43209312dd147cb4264e59891/probabilities.json + score_dict_file: output/reports/train/e8c27be43209312dd147cb4264e59891/score_dict.json + test_labels_file: output/reports/train/e8c27be43209312dd147cb4264e59891/test_labels.json + train_labels_file: output/reports/train/e8c27be43209312dd147cb4264e59891/train_labels.json + model_dir: models + name: e8c27be43209312dd147cb4264e59891 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 816578005ca7119282c6c63d195a5acc + trainer: + kwargs: {} +name: e8c27be43209312dd147cb4264e59891 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/params.yaml b/examples/security/classification/output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/params.yaml new file mode 100644 index 00000000..9843fd20 --- /dev/null +++ b/examples/security/classification/output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/adv_predictions.json + adv_probabilities_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/97ab13a97393ee9fbf29460b8fd1e689.pkl + params_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/params.yaml + predictions_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/predictions.json + probabilities_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/probabilities.json + score_dict_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/score_dict.json + test_labels_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/test_labels.json + train_labels_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/train_labels.json + model_dir: models + name: e8eeb704f17e2acce4c27aa12a6e9d58 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27a9df117275f6e8bba7da6f77316e96 + trainer: + kwargs: {} +name: e8eeb704f17e2acce4c27aa12a6e9d58 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/params.yaml b/examples/security/classification/output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/params.yaml new file mode 100644 index 00000000..d1e7686c --- /dev/null +++ b/examples/security/classification/output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/adv_predictions.json + adv_probabilities_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/155293e1bafe1e04e64003f53c57d0ee.pkl + params_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/params.yaml + predictions_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/predictions.json + probabilities_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/probabilities.json + score_dict_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/score_dict.json + test_labels_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/test_labels.json + train_labels_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/train_labels.json + model_dir: models + name: e9057ae801790ac5b84e40d8e92b3e3c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e7d18d1adca8a9638fe4725dbdeb8fa1 + trainer: + kwargs: {} +name: e9057ae801790ac5b84e40d8e92b3e3c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/params.yaml b/examples/security/classification/output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/params.yaml new file mode 100644 index 00000000..b4ebfbe7 --- /dev/null +++ b/examples/security/classification/output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/adv_predictions.json + adv_probabilities_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/1b833cee6f013f71840c7b973bd31668.pkl + params_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/params.yaml + predictions_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/predictions.json + probabilities_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/probabilities.json + score_dict_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/score_dict.json + test_labels_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/test_labels.json + train_labels_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/train_labels.json + model_dir: models + name: e911d15b5231f4cb8016cc5bf724a8a5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 981ada45c89feb7e164c8430b28caa16 + trainer: + kwargs: {} +name: e911d15b5231f4cb8016cc5bf724a8a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e945cd7d151563162b4a0ed15af65335/params.yaml b/examples/security/classification/output/reports/train/e945cd7d151563162b4a0ed15af65335/params.yaml new file mode 100644 index 00000000..92f19b4e --- /dev/null +++ b/examples/security/classification/output/reports/train/e945cd7d151563162b4a0ed15af65335/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/adv_predictions.json + adv_probabilities_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/ce50e1a25d7e9ac6ba74f66f4d43df35.pkl + params_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/params.yaml + predictions_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/predictions.json + probabilities_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/probabilities.json + score_dict_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/score_dict.json + test_labels_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/test_labels.json + train_labels_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/train_labels.json + model_dir: models + name: e945cd7d151563162b4a0ed15af65335 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2e136962733461d33ddfe9a1a89b830d + trainer: + kwargs: {} +name: e945cd7d151563162b4a0ed15af65335 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/params.yaml b/examples/security/classification/output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/params.yaml new file mode 100644 index 00000000..3396be48 --- /dev/null +++ b/examples/security/classification/output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/adv_predictions.json + adv_probabilities_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/d64f8853edd371dc112f9ba2ab596cc2.pkl + params_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/params.yaml + predictions_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/predictions.json + probabilities_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/probabilities.json + score_dict_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/score_dict.json + test_labels_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/test_labels.json + train_labels_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/train_labels.json + model_dir: models + name: e97e1ef7703bf336edeba5e89e11bfaf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cf63e39b76e8090165b811c80777458a + trainer: + kwargs: {} +name: e97e1ef7703bf336edeba5e89e11bfaf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e980b6b22cf8b2e6904673f6e536d967/params.yaml b/examples/security/classification/output/reports/train/e980b6b22cf8b2e6904673f6e536d967/params.yaml new file mode 100644 index 00000000..963e0c90 --- /dev/null +++ b/examples/security/classification/output/reports/train/e980b6b22cf8b2e6904673f6e536d967/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/adv_predictions.json + adv_probabilities_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/0f6ebb1be2d5f6f35d833441784c8c6c.pkl + params_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/params.yaml + predictions_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/predictions.json + probabilities_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/probabilities.json + score_dict_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/score_dict.json + test_labels_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/test_labels.json + train_labels_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/train_labels.json + model_dir: models + name: e980b6b22cf8b2e6904673f6e536d967 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 77a45de33fe348e531fed4766428b040 + trainer: + kwargs: {} +name: e980b6b22cf8b2e6904673f6e536d967 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/params.yaml b/examples/security/classification/output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/params.yaml new file mode 100644 index 00000000..17663903 --- /dev/null +++ b/examples/security/classification/output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/adv_predictions.json + adv_probabilities_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/b3c69e04754a59f1bc3b7ab66ebea7d0.pkl + params_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/params.yaml + predictions_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/predictions.json + probabilities_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/probabilities.json + score_dict_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/score_dict.json + test_labels_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/test_labels.json + train_labels_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/train_labels.json + model_dir: models + name: e9bc99d6d38f0f0fb467af2af4f83b38 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 26571b7a2b0035a87d8fce5e1c3228f0 + trainer: + kwargs: {} +name: e9bc99d6d38f0f0fb467af2af4f83b38 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e9c7eed98101ac4a0441a7048598653f/params.yaml b/examples/security/classification/output/reports/train/e9c7eed98101ac4a0441a7048598653f/params.yaml new file mode 100644 index 00000000..e0c74ebe --- /dev/null +++ b/examples/security/classification/output/reports/train/e9c7eed98101ac4a0441a7048598653f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/adv_predictions.json + adv_probabilities_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/00ef7b4695bc287d961b7ebae9985534.pkl + params_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/params.yaml + predictions_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/predictions.json + probabilities_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/probabilities.json + score_dict_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/score_dict.json + test_labels_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/test_labels.json + train_labels_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/train_labels.json + model_dir: models + name: e9c7eed98101ac4a0441a7048598653f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6559ac03fd4b46f0089776d62ca9b590 + trainer: + kwargs: {} +name: e9c7eed98101ac4a0441a7048598653f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/params.yaml b/examples/security/classification/output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/params.yaml new file mode 100644 index 00000000..ec037814 --- /dev/null +++ b/examples/security/classification/output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/adv_predictions.json + adv_probabilities_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/b425dae9b39cb9159610e00efb8a9087.pkl + params_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/params.yaml + predictions_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/predictions.json + probabilities_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/probabilities.json + score_dict_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/score_dict.json + test_labels_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/test_labels.json + train_labels_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/train_labels.json + model_dir: models + name: e9e1fdf3a1e29c6952158cbcc84960a7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dcb4335bfd8c0c83200fd86441dd1cd6 + trainer: + kwargs: {} +name: e9e1fdf3a1e29c6952158cbcc84960a7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/params.yaml b/examples/security/classification/output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/params.yaml new file mode 100644 index 00000000..f48c6db0 --- /dev/null +++ b/examples/security/classification/output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/adv_predictions.json + adv_probabilities_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/cff2c811af31d09ada796aa2cce7f688.pkl + params_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/params.yaml + predictions_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/predictions.json + probabilities_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/probabilities.json + score_dict_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/score_dict.json + test_labels_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/test_labels.json + train_labels_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/train_labels.json + model_dir: models + name: e9f9e8a186608a3180226a93afdb2cfe + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d3acb166223257fd354caa358ec7cf65 + trainer: + kwargs: {} +name: e9f9e8a186608a3180226a93afdb2cfe +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ea145a9601bf11b9454253497cbdfd9f/params.yaml b/examples/security/classification/output/reports/train/ea145a9601bf11b9454253497cbdfd9f/params.yaml new file mode 100644 index 00000000..ee451751 --- /dev/null +++ b/examples/security/classification/output/reports/train/ea145a9601bf11b9454253497cbdfd9f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/adv_predictions.json + adv_probabilities_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/7b9e4edf3ad1fbf591c31ee44c0f3b78.pkl + params_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/params.yaml + predictions_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/predictions.json + probabilities_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/probabilities.json + score_dict_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/score_dict.json + test_labels_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/test_labels.json + train_labels_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/train_labels.json + model_dir: models + name: ea145a9601bf11b9454253497cbdfd9f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a87befcf20bc49bc6443ea965c0ee173 + trainer: + kwargs: {} +name: ea145a9601bf11b9454253497cbdfd9f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ea378eed920b312df869e722cb22f52a/params.yaml b/examples/security/classification/output/reports/train/ea378eed920b312df869e722cb22f52a/params.yaml new file mode 100644 index 00000000..1f7b9388 --- /dev/null +++ b/examples/security/classification/output/reports/train/ea378eed920b312df869e722cb22f52a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ea378eed920b312df869e722cb22f52a/adv_predictions.json + adv_probabilities_file: output/reports/train/ea378eed920b312df869e722cb22f52a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/cb2ee7b045d671f85c520e12310068c0.pkl + params_file: output/reports/train/ea378eed920b312df869e722cb22f52a/params.yaml + predictions_file: output/reports/train/ea378eed920b312df869e722cb22f52a/predictions.json + probabilities_file: output/reports/train/ea378eed920b312df869e722cb22f52a/probabilities.json + score_dict_file: output/reports/train/ea378eed920b312df869e722cb22f52a/score_dict.json + test_labels_file: output/reports/train/ea378eed920b312df869e722cb22f52a/test_labels.json + train_labels_file: output/reports/train/ea378eed920b312df869e722cb22f52a/train_labels.json + model_dir: models + name: ea378eed920b312df869e722cb22f52a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cea19640e6b4a19ea977321c3b925459 + trainer: + kwargs: {} +name: ea378eed920b312df869e722cb22f52a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/params.yaml b/examples/security/classification/output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/params.yaml new file mode 100644 index 00000000..47cf3d41 --- /dev/null +++ b/examples/security/classification/output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/adv_predictions.json + adv_probabilities_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/09d537f1b554212a531b4a4c949215aa.pkl + params_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/params.yaml + predictions_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/predictions.json + probabilities_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/probabilities.json + score_dict_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/score_dict.json + test_labels_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/test_labels.json + train_labels_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/train_labels.json + model_dir: models + name: ea7db42fd62cf90ee4bae2cbb561468e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 149a489a42f095f2369d2950676aa3af + trainer: + kwargs: {} +name: ea7db42fd62cf90ee4bae2cbb561468e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/params.yaml b/examples/security/classification/output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/params.yaml new file mode 100644 index 00000000..9ce4d7e3 --- /dev/null +++ b/examples/security/classification/output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/adv_predictions.json + adv_probabilities_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/5255e49f6827262ae609199a2b80125f.pkl + params_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/params.yaml + predictions_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/predictions.json + probabilities_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/probabilities.json + score_dict_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/score_dict.json + test_labels_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/test_labels.json + train_labels_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/train_labels.json + model_dir: models + name: eacde15c490a4776a80e8ff0f05db4bc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 86cd8e861b44dc28c8778951ba30a98d + trainer: + kwargs: {} +name: eacde15c490a4776a80e8ff0f05db4bc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/params.yaml b/examples/security/classification/output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/params.yaml new file mode 100644 index 00000000..ce170fc2 --- /dev/null +++ b/examples/security/classification/output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/adv_predictions.json + adv_probabilities_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/e8cf5fd1f5d3a18771da77166622b2bd.pkl + params_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/params.yaml + predictions_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/predictions.json + probabilities_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/probabilities.json + score_dict_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/score_dict.json + test_labels_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/test_labels.json + train_labels_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/train_labels.json + model_dir: models + name: ead0b6385d8a4b5a288c9eb906c33e15 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b6aa9f6c9f4460f0334e23405dd4b7fe + trainer: + kwargs: {} +name: ead0b6385d8a4b5a288c9eb906c33e15 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/eb40ff16f16a4c348441badffcc94251/params.yaml b/examples/security/classification/output/reports/train/eb40ff16f16a4c348441badffcc94251/params.yaml new file mode 100644 index 00000000..33b27bb7 --- /dev/null +++ b/examples/security/classification/output/reports/train/eb40ff16f16a4c348441badffcc94251/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/adv_predictions.json + adv_probabilities_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/83e8820199c4ba4c1cf78b96a7abf02e.pkl + params_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/params.yaml + predictions_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/predictions.json + probabilities_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/probabilities.json + score_dict_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/score_dict.json + test_labels_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/test_labels.json + train_labels_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/train_labels.json + model_dir: models + name: eb40ff16f16a4c348441badffcc94251 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4993d77a8adea02b9c86920985164777 + trainer: + kwargs: {} +name: eb40ff16f16a4c348441badffcc94251 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/eb4a93f1a67703e587ea895032a7003a/params.yaml b/examples/security/classification/output/reports/train/eb4a93f1a67703e587ea895032a7003a/params.yaml new file mode 100644 index 00000000..2d4a1fa6 --- /dev/null +++ b/examples/security/classification/output/reports/train/eb4a93f1a67703e587ea895032a7003a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/adv_predictions.json + adv_probabilities_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/138ab5acb8edbdad4b835db9ad3a0206.pkl + params_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/params.yaml + predictions_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/predictions.json + probabilities_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/probabilities.json + score_dict_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/score_dict.json + test_labels_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/test_labels.json + train_labels_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/train_labels.json + model_dir: models + name: eb4a93f1a67703e587ea895032a7003a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a8a3ffbd25cc1c77e3be273385e71a4d + trainer: + kwargs: {} +name: eb4a93f1a67703e587ea895032a7003a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/eb68f7a15f8799590ca0fdde540c5640/params.yaml b/examples/security/classification/output/reports/train/eb68f7a15f8799590ca0fdde540c5640/params.yaml new file mode 100644 index 00000000..7a972456 --- /dev/null +++ b/examples/security/classification/output/reports/train/eb68f7a15f8799590ca0fdde540c5640/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/adv_predictions.json + adv_probabilities_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/a11546a1c7ca364c3db0d0af8992c45f.pkl + params_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/params.yaml + predictions_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/predictions.json + probabilities_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/probabilities.json + score_dict_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/score_dict.json + test_labels_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/test_labels.json + train_labels_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/train_labels.json + model_dir: models + name: eb68f7a15f8799590ca0fdde540c5640 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5beef159fe944dc8bc490ab5857ad6e2 + trainer: + kwargs: {} +name: eb68f7a15f8799590ca0fdde540c5640 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/params.yaml b/examples/security/classification/output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/params.yaml new file mode 100644 index 00000000..df542e57 --- /dev/null +++ b/examples/security/classification/output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/adv_predictions.json + adv_probabilities_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/620a7d8b8085d2fbb953987ab84dada2.pkl + params_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/params.yaml + predictions_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/predictions.json + probabilities_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/probabilities.json + score_dict_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/score_dict.json + test_labels_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/test_labels.json + train_labels_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/train_labels.json + model_dir: models + name: eb86ebe2a20746bcd80ee3523f1e2886 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3f8ea926e214e7bb4fc69479b2282f4 + trainer: + kwargs: {} +name: eb86ebe2a20746bcd80ee3523f1e2886 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/params.yaml b/examples/security/classification/output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/params.yaml new file mode 100644 index 00000000..bbfc1bbb --- /dev/null +++ b/examples/security/classification/output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/adv_predictions.json + adv_probabilities_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/d1b7fb50ee0e1a72db4158a93ebd534f.pkl + params_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/params.yaml + predictions_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/predictions.json + probabilities_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/probabilities.json + score_dict_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/score_dict.json + test_labels_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/test_labels.json + train_labels_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/train_labels.json + model_dir: models + name: eb9835c40bb7edbc11bd27aa7591037a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c54a3353cadb3ebfa57d663da3ca23bb + trainer: + kwargs: {} +name: eb9835c40bb7edbc11bd27aa7591037a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/params.yaml b/examples/security/classification/output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/params.yaml new file mode 100644 index 00000000..01dfc437 --- /dev/null +++ b/examples/security/classification/output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/adv_predictions.json + adv_probabilities_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/f6461b87ab9ad3e446d9bb0220f523ae.pkl + params_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/params.yaml + predictions_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/predictions.json + probabilities_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/probabilities.json + score_dict_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/score_dict.json + test_labels_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/test_labels.json + train_labels_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/train_labels.json + model_dir: models + name: ebb0f191822e30c3fba8fe8e4a4d798d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9aeecfd6c8da3f6535f3055361b75fd1 + trainer: + kwargs: {} +name: ebb0f191822e30c3fba8fe8e4a4d798d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ebed79e91a7b458890045c8039133ca9/params.yaml b/examples/security/classification/output/reports/train/ebed79e91a7b458890045c8039133ca9/params.yaml new file mode 100644 index 00000000..38f4de99 --- /dev/null +++ b/examples/security/classification/output/reports/train/ebed79e91a7b458890045c8039133ca9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/adv_predictions.json + adv_probabilities_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/f61a0bf1162bf2ab9fd355d072a94c08.pkl + params_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/params.yaml + predictions_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/predictions.json + probabilities_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/probabilities.json + score_dict_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/score_dict.json + test_labels_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/test_labels.json + train_labels_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/train_labels.json + model_dir: models + name: ebed79e91a7b458890045c8039133ca9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0299eb6f28967305366f33b1d78f9daa + trainer: + kwargs: {} +name: ebed79e91a7b458890045c8039133ca9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ebf48577409cfdaf8fa1d317fde02664/params.yaml b/examples/security/classification/output/reports/train/ebf48577409cfdaf8fa1d317fde02664/params.yaml new file mode 100644 index 00000000..4fbfac44 --- /dev/null +++ b/examples/security/classification/output/reports/train/ebf48577409cfdaf8fa1d317fde02664/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/adv_predictions.json + adv_probabilities_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/3e5372c0782be74c613b613f92cd779b.pkl + params_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/params.yaml + predictions_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/predictions.json + probabilities_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/probabilities.json + score_dict_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/score_dict.json + test_labels_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/test_labels.json + train_labels_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/train_labels.json + model_dir: models + name: ebf48577409cfdaf8fa1d317fde02664 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 009dc769d25c3ffc447d3c098376087b + trainer: + kwargs: {} +name: ebf48577409cfdaf8fa1d317fde02664 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/params.yaml b/examples/security/classification/output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/params.yaml new file mode 100644 index 00000000..04efa776 --- /dev/null +++ b/examples/security/classification/output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/adv_predictions.json + adv_probabilities_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/64e83305feab8a9bb66d278bda023347.pkl + params_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/params.yaml + predictions_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/predictions.json + probabilities_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/probabilities.json + score_dict_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/score_dict.json + test_labels_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/test_labels.json + train_labels_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/train_labels.json + model_dir: models + name: ebffc486a1707d02aff2c7dfc61a4b50 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: acbf4338bb00066312fdbefc718d439e + trainer: + kwargs: {} +name: ebffc486a1707d02aff2c7dfc61a4b50 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ec103ed79e24da73d0fa4284d28054c6/params.yaml b/examples/security/classification/output/reports/train/ec103ed79e24da73d0fa4284d28054c6/params.yaml new file mode 100644 index 00000000..0119dbcd --- /dev/null +++ b/examples/security/classification/output/reports/train/ec103ed79e24da73d0fa4284d28054c6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/adv_predictions.json + adv_probabilities_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/7953a074cb8272be6486d72942d0f5af.pkl + params_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/params.yaml + predictions_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/predictions.json + probabilities_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/probabilities.json + score_dict_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/score_dict.json + test_labels_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/test_labels.json + train_labels_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/train_labels.json + model_dir: models + name: ec103ed79e24da73d0fa4284d28054c6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eb43ba23847684df408bf77a5c7aae54 + trainer: + kwargs: {} +name: ec103ed79e24da73d0fa4284d28054c6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ec130594a417a85d8f4c082270fbc165/params.yaml b/examples/security/classification/output/reports/train/ec130594a417a85d8f4c082270fbc165/params.yaml new file mode 100644 index 00000000..e42963d2 --- /dev/null +++ b/examples/security/classification/output/reports/train/ec130594a417a85d8f4c082270fbc165/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/adv_predictions.json + adv_probabilities_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/93ac07df0663de7c7bce162fdde8f8e9.pkl + params_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/params.yaml + predictions_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/predictions.json + probabilities_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/probabilities.json + score_dict_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/score_dict.json + test_labels_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/test_labels.json + train_labels_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/train_labels.json + model_dir: models + name: ec130594a417a85d8f4c082270fbc165 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ac8a6d4edbc8d812e139f1365812291b + trainer: + kwargs: {} +name: ec130594a417a85d8f4c082270fbc165 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ec1bbba6d3f703c803790644d8a87d52/params.yaml b/examples/security/classification/output/reports/train/ec1bbba6d3f703c803790644d8a87d52/params.yaml new file mode 100644 index 00000000..4133065a --- /dev/null +++ b/examples/security/classification/output/reports/train/ec1bbba6d3f703c803790644d8a87d52/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/adv_predictions.json + adv_probabilities_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/24355d4540e42fea4478b3401c45e396.pkl + params_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/params.yaml + predictions_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/predictions.json + probabilities_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/probabilities.json + score_dict_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/score_dict.json + test_labels_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/test_labels.json + train_labels_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/train_labels.json + model_dir: models + name: ec1bbba6d3f703c803790644d8a87d52 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 39829a54873b0bff9cbaa0c8380f4fbb + trainer: + kwargs: {} +name: ec1bbba6d3f703c803790644d8a87d52 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/params.yaml b/examples/security/classification/output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/params.yaml new file mode 100644 index 00000000..23ead2f2 --- /dev/null +++ b/examples/security/classification/output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/adv_predictions.json + adv_probabilities_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/4c5423c17eafc6d7bf6bf9c273c748c5.pkl + params_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/params.yaml + predictions_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/predictions.json + probabilities_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/probabilities.json + score_dict_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/score_dict.json + test_labels_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/test_labels.json + train_labels_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/train_labels.json + model_dir: models + name: ec213f520ac4ef81e9c8da6a22afdfd9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eee930038d736854721ae1046c91039d + trainer: + kwargs: {} +name: ec213f520ac4ef81e9c8da6a22afdfd9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ec2404a0434fd5af43c0a3b77b668003/params.yaml b/examples/security/classification/output/reports/train/ec2404a0434fd5af43c0a3b77b668003/params.yaml new file mode 100644 index 00000000..27f40b7c --- /dev/null +++ b/examples/security/classification/output/reports/train/ec2404a0434fd5af43c0a3b77b668003/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/adv_predictions.json + adv_probabilities_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/b38efade392b8e04b6c9d5c4c10e4c23.pkl + params_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/params.yaml + predictions_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/predictions.json + probabilities_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/probabilities.json + score_dict_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/score_dict.json + test_labels_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/test_labels.json + train_labels_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/train_labels.json + model_dir: models + name: ec2404a0434fd5af43c0a3b77b668003 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 34478b6c293f404754e4776bf082db8a + trainer: + kwargs: {} +name: ec2404a0434fd5af43c0a3b77b668003 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ec33273879697620f55a0a83a9a3c6f3/params.yaml b/examples/security/classification/output/reports/train/ec33273879697620f55a0a83a9a3c6f3/params.yaml new file mode 100644 index 00000000..7bdafa0e --- /dev/null +++ b/examples/security/classification/output/reports/train/ec33273879697620f55a0a83a9a3c6f3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/adv_predictions.json + adv_probabilities_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/63b095517368dc3e5cfc812e3096d332.pkl + params_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/params.yaml + predictions_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/predictions.json + probabilities_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/probabilities.json + score_dict_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/score_dict.json + test_labels_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/test_labels.json + train_labels_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/train_labels.json + model_dir: models + name: ec33273879697620f55a0a83a9a3c6f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0769f0daeeb4214e943614301fa8cefc + trainer: + kwargs: {} +name: ec33273879697620f55a0a83a9a3c6f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ec7690e362ffe357e03688dc91e073de/params.yaml b/examples/security/classification/output/reports/train/ec7690e362ffe357e03688dc91e073de/params.yaml new file mode 100644 index 00000000..2dad436f --- /dev/null +++ b/examples/security/classification/output/reports/train/ec7690e362ffe357e03688dc91e073de/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/adv_predictions.json + adv_probabilities_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/7eebd27111282e42b7ed577c958b0aba.pkl + params_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/params.yaml + predictions_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/predictions.json + probabilities_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/probabilities.json + score_dict_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/score_dict.json + test_labels_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/test_labels.json + train_labels_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/train_labels.json + model_dir: models + name: ec7690e362ffe357e03688dc91e073de + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0091aed8b516908a93141cbac7a62aa5 + trainer: + kwargs: {} +name: ec7690e362ffe357e03688dc91e073de +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/params.yaml b/examples/security/classification/output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/params.yaml new file mode 100644 index 00000000..84bc81a1 --- /dev/null +++ b/examples/security/classification/output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/adv_predictions.json + adv_probabilities_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/ba348845fc220c8fabad81f830c1aa69.pkl + params_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/params.yaml + predictions_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/predictions.json + probabilities_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/probabilities.json + score_dict_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/score_dict.json + test_labels_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/test_labels.json + train_labels_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/train_labels.json + model_dir: models + name: ec7939361cc53dbca55d18c7be6ae31a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bd4d52461b6506003e337379c5fe1d81 + trainer: + kwargs: {} +name: ec7939361cc53dbca55d18c7be6ae31a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/params.yaml b/examples/security/classification/output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/params.yaml new file mode 100644 index 00000000..d9fc6161 --- /dev/null +++ b/examples/security/classification/output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/adv_predictions.json + adv_probabilities_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/d9106a2d5ba78e76c8fccbae57d41be8.pkl + params_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/params.yaml + predictions_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/predictions.json + probabilities_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/probabilities.json + score_dict_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/score_dict.json + test_labels_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/test_labels.json + train_labels_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/train_labels.json + model_dir: models + name: ec9283d7a922c9eb8b2556a2c42a0ab4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5339f103b4bc2e5064e90e4a57fbc78f + trainer: + kwargs: {} +name: ec9283d7a922c9eb8b2556a2c42a0ab4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/params.yaml b/examples/security/classification/output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/params.yaml new file mode 100644 index 00000000..051f8cf5 --- /dev/null +++ b/examples/security/classification/output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/adv_predictions.json + adv_probabilities_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/9570e9ed0b80974ae3e83bec98a05c63.pkl + params_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/params.yaml + predictions_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/predictions.json + probabilities_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/probabilities.json + score_dict_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/score_dict.json + test_labels_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/test_labels.json + train_labels_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/train_labels.json + model_dir: models + name: ec937bd4e7ddceb9624b7ebb75afef35 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e15fe99437c3e24c6884f2a63f903f65 + trainer: + kwargs: {} +name: ec937bd4e7ddceb9624b7ebb75afef35 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/params.yaml b/examples/security/classification/output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/params.yaml new file mode 100644 index 00000000..8ed44971 --- /dev/null +++ b/examples/security/classification/output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/adv_predictions.json + adv_probabilities_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/5164952bcb5a52a590bde98034177cab.pkl + params_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/params.yaml + predictions_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/predictions.json + probabilities_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/probabilities.json + score_dict_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/score_dict.json + test_labels_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/test_labels.json + train_labels_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/train_labels.json + model_dir: models + name: ec9abc2e1bfe08fe410eeebe865e752b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d110376096062211be6e8ddb879bf36a + trainer: + kwargs: {} +name: ec9abc2e1bfe08fe410eeebe865e752b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/params.yaml b/examples/security/classification/output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/params.yaml new file mode 100644 index 00000000..5f704c03 --- /dev/null +++ b/examples/security/classification/output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/adv_predictions.json + adv_probabilities_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/6c1d43e3f88348e257140d738e693f74.pkl + params_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/params.yaml + predictions_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/predictions.json + probabilities_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/probabilities.json + score_dict_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/score_dict.json + test_labels_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/test_labels.json + train_labels_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/train_labels.json + model_dir: models + name: ecaeb4bebb2631d11ce8290e79f2e168 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1fccb5a5e5d3f6307f70d0d49dd40aa1 + trainer: + kwargs: {} +name: ecaeb4bebb2631d11ce8290e79f2e168 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/params.yaml b/examples/security/classification/output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/params.yaml new file mode 100644 index 00000000..01917910 --- /dev/null +++ b/examples/security/classification/output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/adv_predictions.json + adv_probabilities_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/45ec1f76fe4d8d49232219d5ed58bdda.pkl + params_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/params.yaml + predictions_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/predictions.json + probabilities_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/probabilities.json + score_dict_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/score_dict.json + test_labels_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/test_labels.json + train_labels_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/train_labels.json + model_dir: models + name: ecc586d351e7e3d1f69d372c8274cfe5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c8713eed03b82c79cf96fdb80ebe3e13 + trainer: + kwargs: {} +name: ecc586d351e7e3d1f69d372c8274cfe5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/params.yaml b/examples/security/classification/output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/params.yaml new file mode 100644 index 00000000..beb585d3 --- /dev/null +++ b/examples/security/classification/output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/adv_predictions.json + adv_probabilities_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/38bb6eb7643db9ecb5805c9f4e16798b.pkl + params_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/params.yaml + predictions_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/predictions.json + probabilities_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/probabilities.json + score_dict_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/score_dict.json + test_labels_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/test_labels.json + train_labels_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/train_labels.json + model_dir: models + name: ecd4bbed9e3584dab0fd8feb694791f7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 51398844aca4858f7dc72521173463d8 + trainer: + kwargs: {} +name: ecd4bbed9e3584dab0fd8feb694791f7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ecea211e44cc272689fefa340db04d79/params.yaml b/examples/security/classification/output/reports/train/ecea211e44cc272689fefa340db04d79/params.yaml new file mode 100644 index 00000000..e36b8f7c --- /dev/null +++ b/examples/security/classification/output/reports/train/ecea211e44cc272689fefa340db04d79/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ecea211e44cc272689fefa340db04d79/adv_predictions.json + adv_probabilities_file: output/reports/train/ecea211e44cc272689fefa340db04d79/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/57714f7d42b0d18885baa6f2abe13131.pkl + params_file: output/reports/train/ecea211e44cc272689fefa340db04d79/params.yaml + predictions_file: output/reports/train/ecea211e44cc272689fefa340db04d79/predictions.json + probabilities_file: output/reports/train/ecea211e44cc272689fefa340db04d79/probabilities.json + score_dict_file: output/reports/train/ecea211e44cc272689fefa340db04d79/score_dict.json + test_labels_file: output/reports/train/ecea211e44cc272689fefa340db04d79/test_labels.json + train_labels_file: output/reports/train/ecea211e44cc272689fefa340db04d79/train_labels.json + model_dir: models + name: ecea211e44cc272689fefa340db04d79 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cb96203b414db5dd4f809a6a5b50dc6d + trainer: + kwargs: {} +name: ecea211e44cc272689fefa340db04d79 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/eceeb3632296ca1181e6af1da4b8d236/params.yaml b/examples/security/classification/output/reports/train/eceeb3632296ca1181e6af1da4b8d236/params.yaml new file mode 100644 index 00000000..9bd3c698 --- /dev/null +++ b/examples/security/classification/output/reports/train/eceeb3632296ca1181e6af1da4b8d236/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/adv_predictions.json + adv_probabilities_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/702ec808ffdf13723d0abe124e9c6a1f.pkl + params_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/params.yaml + predictions_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/predictions.json + probabilities_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/probabilities.json + score_dict_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/score_dict.json + test_labels_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/test_labels.json + train_labels_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/train_labels.json + model_dir: models + name: eceeb3632296ca1181e6af1da4b8d236 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fe586ebfad866c217c6975571c098ed3 + trainer: + kwargs: {} +name: eceeb3632296ca1181e6af1da4b8d236 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/params.yaml b/examples/security/classification/output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/params.yaml new file mode 100644 index 00000000..e3fbf68e --- /dev/null +++ b/examples/security/classification/output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/adv_predictions.json + adv_probabilities_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/2c49ff29c5dd958059d8477e79254f13.pkl + params_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/params.yaml + predictions_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/predictions.json + probabilities_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/probabilities.json + score_dict_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/score_dict.json + test_labels_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/test_labels.json + train_labels_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/train_labels.json + model_dir: models + name: ed7e0c762ffda376ef1fa17f933d0203 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1c779f8b41c714a3b1c4443029849947 + trainer: + kwargs: {} +name: ed7e0c762ffda376ef1fa17f933d0203 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/params.yaml b/examples/security/classification/output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/params.yaml new file mode 100644 index 00000000..4ac7a5c8 --- /dev/null +++ b/examples/security/classification/output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/adv_predictions.json + adv_probabilities_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/3e11aba446d7bf611ec9efab3572d48e.pkl + params_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/params.yaml + predictions_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/predictions.json + probabilities_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/probabilities.json + score_dict_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/score_dict.json + test_labels_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/test_labels.json + train_labels_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/train_labels.json + model_dir: models + name: ed8299b68b5945337e7f1f91d1e6641d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b71609cd89e6f095b3d79dd720fc02b1 + trainer: + kwargs: {} +name: ed8299b68b5945337e7f1f91d1e6641d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/params.yaml b/examples/security/classification/output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/params.yaml new file mode 100644 index 00000000..74728812 --- /dev/null +++ b/examples/security/classification/output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/adv_predictions.json + adv_probabilities_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/dbc0adde52a7df74796c9b349b96d7a1.pkl + params_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/params.yaml + predictions_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/predictions.json + probabilities_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/probabilities.json + score_dict_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/score_dict.json + test_labels_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/test_labels.json + train_labels_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/train_labels.json + model_dir: models + name: ed83ac495e7c5159f8a5402ec64ed1f4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d4362692c172d6393dbbe35fb561af95 + trainer: + kwargs: {} +name: ed83ac495e7c5159f8a5402ec64ed1f4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/eda189c060b9856696f99b67af2c80b2/params.yaml b/examples/security/classification/output/reports/train/eda189c060b9856696f99b67af2c80b2/params.yaml new file mode 100644 index 00000000..b4aecec2 --- /dev/null +++ b/examples/security/classification/output/reports/train/eda189c060b9856696f99b67af2c80b2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/adv_predictions.json + adv_probabilities_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/439110f50d4913862250feced8378e22.pkl + params_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/params.yaml + predictions_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/predictions.json + probabilities_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/probabilities.json + score_dict_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/score_dict.json + test_labels_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/test_labels.json + train_labels_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/train_labels.json + model_dir: models + name: eda189c060b9856696f99b67af2c80b2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ed7148696f299881cc04d24fc04c007a + trainer: + kwargs: {} +name: eda189c060b9856696f99b67af2c80b2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/params.yaml b/examples/security/classification/output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/params.yaml new file mode 100644 index 00000000..40dda27c --- /dev/null +++ b/examples/security/classification/output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/adv_predictions.json + adv_probabilities_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/1509dfb4f54093dc69ca302039b8df86.pkl + params_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/params.yaml + predictions_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/predictions.json + probabilities_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/probabilities.json + score_dict_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/score_dict.json + test_labels_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/test_labels.json + train_labels_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/train_labels.json + model_dir: models + name: edb74b9125de21a83b6b01f7ad5b1639 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8cad8738d57bee1e02b27614eca621bd + trainer: + kwargs: {} +name: edb74b9125de21a83b6b01f7ad5b1639 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/params.yaml b/examples/security/classification/output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/params.yaml new file mode 100644 index 00000000..67d1ef6d --- /dev/null +++ b/examples/security/classification/output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/adv_predictions.json + adv_probabilities_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/c73d1ad91169d917adad5177f82a49cb.pkl + params_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/params.yaml + predictions_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/predictions.json + probabilities_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/probabilities.json + score_dict_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/score_dict.json + test_labels_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/test_labels.json + train_labels_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/train_labels.json + model_dir: models + name: edbee5e2358800a90199efc1c3c7bc6f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 91605fb80ca11b9ab84830a67afbd00a + trainer: + kwargs: {} +name: edbee5e2358800a90199efc1c3c7bc6f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/params.yaml b/examples/security/classification/output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/params.yaml new file mode 100644 index 00000000..16ee249b --- /dev/null +++ b/examples/security/classification/output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/adv_predictions.json + adv_probabilities_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/1084f2d4dc4ebeef1a9e3f6dfc800af3.pkl + params_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/params.yaml + predictions_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/predictions.json + probabilities_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/probabilities.json + score_dict_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/score_dict.json + test_labels_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/test_labels.json + train_labels_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/train_labels.json + model_dir: models + name: ee246c4414eadc0dfd4eadb6645a1771 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b6afc84971094b3cac950d5116c85c3f + trainer: + kwargs: {} +name: ee246c4414eadc0dfd4eadb6645a1771 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/params.yaml b/examples/security/classification/output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/params.yaml new file mode 100644 index 00000000..5f2b1bb7 --- /dev/null +++ b/examples/security/classification/output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/adv_predictions.json + adv_probabilities_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl + model_file: output/models/e44125a6bcf2f980e0da3609458b1970.pkl + params_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/params.yaml + predictions_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/predictions.json + probabilities_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/probabilities.json + score_dict_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/score_dict.json + test_labels_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/test_labels.json + train_labels_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/train_labels.json + model_dir: models + name: eec0a40d026c7c2724f535a63bf1d7b8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 1010000 + random_state: 0 + name: classification + name: 4bec4681e85b26448e6272780b649e5d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2fecae8cb1395aef26c5dbb482ed45e7 + trainer: + kwargs: {} +name: eec0a40d026c7c2724f535a63bf1d7b8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/eed8957284b5864041a990a59f4947c9/params.yaml b/examples/security/classification/output/reports/train/eed8957284b5864041a990a59f4947c9/params.yaml new file mode 100644 index 00000000..db2ff03c --- /dev/null +++ b/examples/security/classification/output/reports/train/eed8957284b5864041a990a59f4947c9/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eed8957284b5864041a990a59f4947c9/adv_predictions.json + adv_probabilities_file: output/reports/train/eed8957284b5864041a990a59f4947c9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/b053ac793ee5166579b0bf753daf4bb8.pkl + params_file: output/reports/train/eed8957284b5864041a990a59f4947c9/params.yaml + predictions_file: output/reports/train/eed8957284b5864041a990a59f4947c9/predictions.json + probabilities_file: output/reports/train/eed8957284b5864041a990a59f4947c9/probabilities.json + score_dict_file: output/reports/train/eed8957284b5864041a990a59f4947c9/score_dict.json + test_labels_file: output/reports/train/eed8957284b5864041a990a59f4947c9/test_labels.json + train_labels_file: output/reports/train/eed8957284b5864041a990a59f4947c9/train_labels.json + model_dir: models + name: eed8957284b5864041a990a59f4947c9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4f7afff0e9ad18c0c9bc2e35bf72954d + trainer: + kwargs: {} +name: eed8957284b5864041a990a59f4947c9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/eee21f6d983316f8da2d2d924969ee36/params.yaml b/examples/security/classification/output/reports/train/eee21f6d983316f8da2d2d924969ee36/params.yaml new file mode 100644 index 00000000..90008663 --- /dev/null +++ b/examples/security/classification/output/reports/train/eee21f6d983316f8da2d2d924969ee36/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/adv_predictions.json + adv_probabilities_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/caed8d268b3d279bff66051b588c00f4.pkl + params_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/params.yaml + predictions_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/predictions.json + probabilities_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/probabilities.json + score_dict_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/score_dict.json + test_labels_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/test_labels.json + train_labels_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/train_labels.json + model_dir: models + name: eee21f6d983316f8da2d2d924969ee36 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9138a000ba3186489249c158e1783710 + trainer: + kwargs: {} +name: eee21f6d983316f8da2d2d924969ee36 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/eee295d6504db8f55b9b892f50c387af/params.yaml b/examples/security/classification/output/reports/train/eee295d6504db8f55b9b892f50c387af/params.yaml new file mode 100644 index 00000000..f6b6bedc --- /dev/null +++ b/examples/security/classification/output/reports/train/eee295d6504db8f55b9b892f50c387af/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/adv_predictions.json + adv_probabilities_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/7068e71e3ed8dc965e30e71177e1c8f8.pkl + params_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/params.yaml + predictions_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/predictions.json + probabilities_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/probabilities.json + score_dict_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/score_dict.json + test_labels_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/test_labels.json + train_labels_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/train_labels.json + model_dir: models + name: eee295d6504db8f55b9b892f50c387af + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6dbb596ed6807c3f61b8f9754064e604 + trainer: + kwargs: {} +name: eee295d6504db8f55b9b892f50c387af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/params.yaml b/examples/security/classification/output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/params.yaml new file mode 100644 index 00000000..a7f88533 --- /dev/null +++ b/examples/security/classification/output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/adv_predictions.json + adv_probabilities_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/c7dc4ab15d699757cde44611b20028e4.pkl + params_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/params.yaml + predictions_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/predictions.json + probabilities_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/probabilities.json + score_dict_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/score_dict.json + test_labels_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/test_labels.json + train_labels_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/train_labels.json + model_dir: models + name: ef14a6d45facf3676d1bfe0d180cf3ed + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7a4dd4ccb5a79da84765d552ee7cb62b + trainer: + kwargs: {} +name: ef14a6d45facf3676d1bfe0d180cf3ed +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/params.yaml b/examples/security/classification/output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/params.yaml new file mode 100644 index 00000000..e5d9d9a3 --- /dev/null +++ b/examples/security/classification/output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/adv_predictions.json + adv_probabilities_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/159d350d1a36a8020dce9bfbc2e31aa8.pkl + params_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/params.yaml + predictions_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/predictions.json + probabilities_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/probabilities.json + score_dict_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/score_dict.json + test_labels_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/test_labels.json + train_labels_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/train_labels.json + model_dir: models + name: ef1c3fe772fe3f60f2427cd24e409eed + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ab652966d4f57ded7ec983c7aed9d2e7 + trainer: + kwargs: {} +name: ef1c3fe772fe3f60f2427cd24e409eed +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ef36b1f736cf76d5166331989f2a7190/params.yaml b/examples/security/classification/output/reports/train/ef36b1f736cf76d5166331989f2a7190/params.yaml new file mode 100644 index 00000000..8792067e --- /dev/null +++ b/examples/security/classification/output/reports/train/ef36b1f736cf76d5166331989f2a7190/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/adv_predictions.json + adv_probabilities_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/480a85413f71b4d08b46f29842fb7ab3.pkl + params_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/params.yaml + predictions_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/predictions.json + probabilities_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/probabilities.json + score_dict_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/score_dict.json + test_labels_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/test_labels.json + train_labels_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/train_labels.json + model_dir: models + name: ef36b1f736cf76d5166331989f2a7190 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9388eb06643c614e16cbc4a6a7e7cc7d + trainer: + kwargs: {} +name: ef36b1f736cf76d5166331989f2a7190 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ef462af0f8351e0b47ec657379f3f224/params.yaml b/examples/security/classification/output/reports/train/ef462af0f8351e0b47ec657379f3f224/params.yaml new file mode 100644 index 00000000..6b88d29b --- /dev/null +++ b/examples/security/classification/output/reports/train/ef462af0f8351e0b47ec657379f3f224/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/adv_predictions.json + adv_probabilities_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl + model_file: output/models/e887bcbacb5a14a42a3830bdee5e9946.pkl + params_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/params.yaml + predictions_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/predictions.json + probabilities_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/probabilities.json + score_dict_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/score_dict.json + test_labels_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/test_labels.json + train_labels_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/train_labels.json + model_dir: models + name: ef462af0f8351e0b47ec657379f3f224 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 072fce735f22f879258b106dcee0c0d7 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7a2a2a6992d4557d843667c30b82c962 + trainer: + kwargs: {} +name: ef462af0f8351e0b47ec657379f3f224 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ef511110eca1792d64254fce51bf07b4/params.yaml b/examples/security/classification/output/reports/train/ef511110eca1792d64254fce51bf07b4/params.yaml new file mode 100644 index 00000000..b4bd8422 --- /dev/null +++ b/examples/security/classification/output/reports/train/ef511110eca1792d64254fce51bf07b4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/adv_predictions.json + adv_probabilities_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/31ed65856fe8510a3a88ce438f276cf5.pkl + params_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/params.yaml + predictions_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/predictions.json + probabilities_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/probabilities.json + score_dict_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/score_dict.json + test_labels_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/test_labels.json + train_labels_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/train_labels.json + model_dir: models + name: ef511110eca1792d64254fce51bf07b4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9117ed527a5a58d2e21bfc296ff189a0 + trainer: + kwargs: {} +name: ef511110eca1792d64254fce51bf07b4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/params.yaml b/examples/security/classification/output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/params.yaml new file mode 100644 index 00000000..86edf5ff --- /dev/null +++ b/examples/security/classification/output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/params.yaml @@ -0,0 +1,115 @@ +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/adv_predictions.json + adv_probabilities_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl + model_file: output/models/4bfc23ca7b797d6c10d341b670cfcafc.pkl + params_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/params.yaml + predictions_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/predictions.json + probabilities_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/probabilities.json + score_dict_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/score_dict.json + test_labels_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/test_labels.json + train_labels_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/train_labels.json + model_dir: models + name: ef5698cfbb0d5391ce0b82e6b505966f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7566e9d12d80f8d5b6aea1651b9ee47d + trainer: + kwargs: {} +name: ef5698cfbb0d5391ce0b82e6b505966f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ef60180c5a265fe09902bb2e92d6d969/params.yaml b/examples/security/classification/output/reports/train/ef60180c5a265fe09902bb2e92d6d969/params.yaml new file mode 100644 index 00000000..82a42e08 --- /dev/null +++ b/examples/security/classification/output/reports/train/ef60180c5a265fe09902bb2e92d6d969/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/adv_predictions.json + adv_probabilities_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/6d37fef5c040b50207a6a31e37f1af21.pkl + params_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/params.yaml + predictions_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/predictions.json + probabilities_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/probabilities.json + score_dict_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/score_dict.json + test_labels_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/test_labels.json + train_labels_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/train_labels.json + model_dir: models + name: ef60180c5a265fe09902bb2e92d6d969 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 57d9efbc95fb4604d1113861233adf55 + trainer: + kwargs: {} +name: ef60180c5a265fe09902bb2e92d6d969 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/params.yaml b/examples/security/classification/output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/params.yaml new file mode 100644 index 00000000..5464039a --- /dev/null +++ b/examples/security/classification/output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/adv_predictions.json + adv_probabilities_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/0ce779092f0c55b6ac82ed97e32a7a90.pkl + params_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/params.yaml + predictions_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/predictions.json + probabilities_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/probabilities.json + score_dict_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/score_dict.json + test_labels_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/test_labels.json + train_labels_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/train_labels.json + model_dir: models + name: ef60a9945f2b7aebf5f596acca17b2b2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5f8e74d13e20dde6061af1afb3d91db1 + trainer: + kwargs: {} +name: ef60a9945f2b7aebf5f596acca17b2b2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/params.yaml b/examples/security/classification/output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/params.yaml new file mode 100644 index 00000000..27b796cb --- /dev/null +++ b/examples/security/classification/output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/adv_predictions.json + adv_probabilities_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/8110a6e4635a3e6603b1d7912d8b7871.pkl + params_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/params.yaml + predictions_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/predictions.json + probabilities_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/probabilities.json + score_dict_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/score_dict.json + test_labels_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/test_labels.json + train_labels_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/train_labels.json + model_dir: models + name: ef6f79f2653f2ca602f1d8233e76d485 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3a71b8d32b57a1f484a14961347301a0 + trainer: + kwargs: {} +name: ef6f79f2653f2ca602f1d8233e76d485 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ef72ade688a4aa02a5e15c198038cc46/params.yaml b/examples/security/classification/output/reports/train/ef72ade688a4aa02a5e15c198038cc46/params.yaml new file mode 100644 index 00000000..c3429804 --- /dev/null +++ b/examples/security/classification/output/reports/train/ef72ade688a4aa02a5e15c198038cc46/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/adv_predictions.json + adv_probabilities_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/1febc2d516461b330c5fb33de08f3a6d.pkl + params_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/params.yaml + predictions_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/predictions.json + probabilities_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/probabilities.json + score_dict_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/score_dict.json + test_labels_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/test_labels.json + train_labels_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/train_labels.json + model_dir: models + name: ef72ade688a4aa02a5e15c198038cc46 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7659c7ee759ed84525724367d1774ba7 + trainer: + kwargs: {} +name: ef72ade688a4aa02a5e15c198038cc46 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/efbaaf8f93921ac2f381e18778727004/params.yaml b/examples/security/classification/output/reports/train/efbaaf8f93921ac2f381e18778727004/params.yaml new file mode 100644 index 00000000..b11c3357 --- /dev/null +++ b/examples/security/classification/output/reports/train/efbaaf8f93921ac2f381e18778727004/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/adv_predictions.json + adv_probabilities_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/99de6398bee7e82acc15c19af1950cb2.pkl + params_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/params.yaml + predictions_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/predictions.json + probabilities_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/probabilities.json + score_dict_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/score_dict.json + test_labels_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/test_labels.json + train_labels_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/train_labels.json + model_dir: models + name: efbaaf8f93921ac2f381e18778727004 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 40b521ecc2e0322fbb8563a6bf2b0631 + trainer: + kwargs: {} +name: efbaaf8f93921ac2f381e18778727004 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/params.yaml b/examples/security/classification/output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/params.yaml new file mode 100644 index 00000000..a08e429c --- /dev/null +++ b/examples/security/classification/output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/adv_predictions.json + adv_probabilities_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/79683100f6c87bcb3f3014d2c65e6956.pkl + params_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/params.yaml + predictions_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/predictions.json + probabilities_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/probabilities.json + score_dict_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/score_dict.json + test_labels_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/test_labels.json + train_labels_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/train_labels.json + model_dir: models + name: efbb8be9c28c74b75dff24ea08dfcf94 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3dd6bc837a549ee428cc94e5edcd4462 + trainer: + kwargs: {} +name: efbb8be9c28c74b75dff24ea08dfcf94 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/params.yaml b/examples/security/classification/output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/params.yaml new file mode 100644 index 00000000..96a7d643 --- /dev/null +++ b/examples/security/classification/output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/adv_predictions.json + adv_probabilities_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/a385afd9c6eb27ea6d623488874fc49b.pkl + params_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/params.yaml + predictions_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/predictions.json + probabilities_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/probabilities.json + score_dict_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/score_dict.json + test_labels_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/test_labels.json + train_labels_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/train_labels.json + model_dir: models + name: efd6d4f4a693c0a3a4bbb44eb401245a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c5cd5ccdfbaf4ced6d1e008c16ebabe5 + trainer: + kwargs: {} +name: efd6d4f4a693c0a3a4bbb44eb401245a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/params.yaml b/examples/security/classification/output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/params.yaml new file mode 100644 index 00000000..f446601a --- /dev/null +++ b/examples/security/classification/output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/adv_predictions.json + adv_probabilities_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/14ba88f14bfd9fba05af72eb697dcf8c.pkl + params_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/params.yaml + predictions_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/predictions.json + probabilities_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/probabilities.json + score_dict_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/score_dict.json + test_labels_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/test_labels.json + train_labels_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/train_labels.json + model_dir: models + name: f0040de2f9a1f4a69b22f9aa7f60cd19 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 43b74be48a4d1fadfd1ab81f71f01291 + trainer: + kwargs: {} +name: f0040de2f9a1f4a69b22f9aa7f60cd19 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f034f340114688f10f7e94dcf2d23152/params.yaml b/examples/security/classification/output/reports/train/f034f340114688f10f7e94dcf2d23152/params.yaml new file mode 100644 index 00000000..0f06c4aa --- /dev/null +++ b/examples/security/classification/output/reports/train/f034f340114688f10f7e94dcf2d23152/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/adv_predictions.json + adv_probabilities_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/c865ccd3668046112f52cc5006ae1d6c.pkl + params_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/params.yaml + predictions_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/predictions.json + probabilities_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/probabilities.json + score_dict_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/score_dict.json + test_labels_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/test_labels.json + train_labels_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/train_labels.json + model_dir: models + name: f034f340114688f10f7e94dcf2d23152 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cc31038a670e7c0e82bf3d9a13e14603 + trainer: + kwargs: {} +name: f034f340114688f10f7e94dcf2d23152 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f077397458c52493586d2b466f4ee959/params.yaml b/examples/security/classification/output/reports/train/f077397458c52493586d2b466f4ee959/params.yaml new file mode 100644 index 00000000..3424805a --- /dev/null +++ b/examples/security/classification/output/reports/train/f077397458c52493586d2b466f4ee959/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f077397458c52493586d2b466f4ee959/adv_predictions.json + adv_probabilities_file: output/reports/train/f077397458c52493586d2b466f4ee959/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/7929283f1c067353e61165eede2a2047.pkl + params_file: output/reports/train/f077397458c52493586d2b466f4ee959/params.yaml + predictions_file: output/reports/train/f077397458c52493586d2b466f4ee959/predictions.json + probabilities_file: output/reports/train/f077397458c52493586d2b466f4ee959/probabilities.json + score_dict_file: output/reports/train/f077397458c52493586d2b466f4ee959/score_dict.json + test_labels_file: output/reports/train/f077397458c52493586d2b466f4ee959/test_labels.json + train_labels_file: output/reports/train/f077397458c52493586d2b466f4ee959/train_labels.json + model_dir: models + name: f077397458c52493586d2b466f4ee959 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 393ad91975474bf147e5782b75b3fd3b + trainer: + kwargs: {} +name: f077397458c52493586d2b466f4ee959 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f08744cea1675c1c019ad82513e22aa7/params.yaml b/examples/security/classification/output/reports/train/f08744cea1675c1c019ad82513e22aa7/params.yaml new file mode 100644 index 00000000..bb9331ab --- /dev/null +++ b/examples/security/classification/output/reports/train/f08744cea1675c1c019ad82513e22aa7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/adv_predictions.json + adv_probabilities_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/dcf9f7f238fee5e649e9f4229e9adb77.pkl + params_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/params.yaml + predictions_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/predictions.json + probabilities_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/probabilities.json + score_dict_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/score_dict.json + test_labels_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/test_labels.json + train_labels_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/train_labels.json + model_dir: models + name: f08744cea1675c1c019ad82513e22aa7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: adf0851c561bfd2defcea6fe96f32a10 + trainer: + kwargs: {} +name: f08744cea1675c1c019ad82513e22aa7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f0cba93a472a43c59665957a206ea9f0/params.yaml b/examples/security/classification/output/reports/train/f0cba93a472a43c59665957a206ea9f0/params.yaml new file mode 100644 index 00000000..3343b4a2 --- /dev/null +++ b/examples/security/classification/output/reports/train/f0cba93a472a43c59665957a206ea9f0/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/adv_predictions.json + adv_probabilities_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/4efa479ce2d4f2c2824f45e27cd6ea51.pkl + params_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/params.yaml + predictions_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/predictions.json + probabilities_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/probabilities.json + score_dict_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/score_dict.json + test_labels_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/test_labels.json + train_labels_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/train_labels.json + model_dir: models + name: f0cba93a472a43c59665957a206ea9f0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9463646f56c403846b25ebbea0cf79d0 + trainer: + kwargs: {} +name: f0cba93a472a43c59665957a206ea9f0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f104107b17e79b83761d6faf79e14733/params.yaml b/examples/security/classification/output/reports/train/f104107b17e79b83761d6faf79e14733/params.yaml new file mode 100644 index 00000000..323ec105 --- /dev/null +++ b/examples/security/classification/output/reports/train/f104107b17e79b83761d6faf79e14733/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f104107b17e79b83761d6faf79e14733/adv_predictions.json + adv_probabilities_file: output/reports/train/f104107b17e79b83761d6faf79e14733/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/95cc9b55d2b447f56ccd9fa50221e988.pkl + params_file: output/reports/train/f104107b17e79b83761d6faf79e14733/params.yaml + predictions_file: output/reports/train/f104107b17e79b83761d6faf79e14733/predictions.json + probabilities_file: output/reports/train/f104107b17e79b83761d6faf79e14733/probabilities.json + score_dict_file: output/reports/train/f104107b17e79b83761d6faf79e14733/score_dict.json + test_labels_file: output/reports/train/f104107b17e79b83761d6faf79e14733/test_labels.json + train_labels_file: output/reports/train/f104107b17e79b83761d6faf79e14733/train_labels.json + model_dir: models + name: f104107b17e79b83761d6faf79e14733 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 441fc3b9cd0c30830c6e043ef080732a + trainer: + kwargs: {} +name: f104107b17e79b83761d6faf79e14733 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f11b476501a4d27a0efadb8134297028/params.yaml b/examples/security/classification/output/reports/train/f11b476501a4d27a0efadb8134297028/params.yaml new file mode 100644 index 00000000..2437d8cc --- /dev/null +++ b/examples/security/classification/output/reports/train/f11b476501a4d27a0efadb8134297028/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f11b476501a4d27a0efadb8134297028/adv_predictions.json + adv_probabilities_file: output/reports/train/f11b476501a4d27a0efadb8134297028/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/29deab736eb75ddedf1515a1ebe14e6b.pkl + params_file: output/reports/train/f11b476501a4d27a0efadb8134297028/params.yaml + predictions_file: output/reports/train/f11b476501a4d27a0efadb8134297028/predictions.json + probabilities_file: output/reports/train/f11b476501a4d27a0efadb8134297028/probabilities.json + score_dict_file: output/reports/train/f11b476501a4d27a0efadb8134297028/score_dict.json + test_labels_file: output/reports/train/f11b476501a4d27a0efadb8134297028/test_labels.json + train_labels_file: output/reports/train/f11b476501a4d27a0efadb8134297028/train_labels.json + model_dir: models + name: f11b476501a4d27a0efadb8134297028 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3d0540c2ef4a962c77a72a853b60ac57 + trainer: + kwargs: {} +name: f11b476501a4d27a0efadb8134297028 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f14e1de498be925efa9a2162a1504847/params.yaml b/examples/security/classification/output/reports/train/f14e1de498be925efa9a2162a1504847/params.yaml new file mode 100644 index 00000000..8b6dc519 --- /dev/null +++ b/examples/security/classification/output/reports/train/f14e1de498be925efa9a2162a1504847/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f14e1de498be925efa9a2162a1504847/adv_predictions.json + adv_probabilities_file: output/reports/train/f14e1de498be925efa9a2162a1504847/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/94946013b94e0f4365b10b8ccf7ab0b2.pkl + params_file: output/reports/train/f14e1de498be925efa9a2162a1504847/params.yaml + predictions_file: output/reports/train/f14e1de498be925efa9a2162a1504847/predictions.json + probabilities_file: output/reports/train/f14e1de498be925efa9a2162a1504847/probabilities.json + score_dict_file: output/reports/train/f14e1de498be925efa9a2162a1504847/score_dict.json + test_labels_file: output/reports/train/f14e1de498be925efa9a2162a1504847/test_labels.json + train_labels_file: output/reports/train/f14e1de498be925efa9a2162a1504847/train_labels.json + model_dir: models + name: f14e1de498be925efa9a2162a1504847 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b124272d2e28de266313740bb79126b0 + trainer: + kwargs: {} +name: f14e1de498be925efa9a2162a1504847 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/params.yaml b/examples/security/classification/output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/params.yaml new file mode 100644 index 00000000..76982cb1 --- /dev/null +++ b/examples/security/classification/output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/adv_predictions.json + adv_probabilities_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/320dd42a88d3036ac25b12e2d29d9a94.pkl + params_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/params.yaml + predictions_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/predictions.json + probabilities_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/probabilities.json + score_dict_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/score_dict.json + test_labels_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/test_labels.json + train_labels_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/train_labels.json + model_dir: models + name: f1670a7b0d13c3cdb3023e07c122fd56 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 55577acc7cc139bd420e7f1eaad6b436 + trainer: + kwargs: {} +name: f1670a7b0d13c3cdb3023e07c122fd56 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/params.yaml b/examples/security/classification/output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/params.yaml new file mode 100644 index 00000000..67622dce --- /dev/null +++ b/examples/security/classification/output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/adv_predictions.json + adv_probabilities_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/4c20af50acccc61f0e781fa85949fb12.pkl + params_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/params.yaml + predictions_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/predictions.json + probabilities_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/probabilities.json + score_dict_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/score_dict.json + test_labels_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/test_labels.json + train_labels_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/train_labels.json + model_dir: models + name: f1815572b4f29ff4bb2c11875f705c4d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 310a822840f506f70b8dfe89769d8b51 + trainer: + kwargs: {} +name: f1815572b4f29ff4bb2c11875f705c4d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/params.yaml b/examples/security/classification/output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/params.yaml new file mode 100644 index 00000000..2aabed41 --- /dev/null +++ b/examples/security/classification/output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/adv_predictions.json + adv_probabilities_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/c1adfbb3acfac935077cb09f742e1ee7.pkl + params_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/params.yaml + predictions_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/predictions.json + probabilities_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/probabilities.json + score_dict_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/score_dict.json + test_labels_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/test_labels.json + train_labels_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/train_labels.json + model_dir: models + name: f196c0f1c310309a5eb8d9b034ec636f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 851bb2f4251273a7647f7e08256f5582 + trainer: + kwargs: {} +name: f196c0f1c310309a5eb8d9b034ec636f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/params.yaml b/examples/security/classification/output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/params.yaml new file mode 100644 index 00000000..cefe7cb4 --- /dev/null +++ b/examples/security/classification/output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/adv_predictions.json + adv_probabilities_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/ebeeeacefbe6f16ea6eb1e30049102de.pkl + params_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/params.yaml + predictions_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/predictions.json + probabilities_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/probabilities.json + score_dict_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/score_dict.json + test_labels_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/test_labels.json + train_labels_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/train_labels.json + model_dir: models + name: f1c1e6bb842b1cabfb61cdf9145ff340 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1001d9f34a4241000584692b8db96607 + trainer: + kwargs: {} +name: f1c1e6bb842b1cabfb61cdf9145ff340 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/params.yaml b/examples/security/classification/output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/params.yaml new file mode 100644 index 00000000..ca091fe8 --- /dev/null +++ b/examples/security/classification/output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/adv_predictions.json + adv_probabilities_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/2fd71691533306ee788e0ffc87821e9e.pkl + params_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/params.yaml + predictions_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/predictions.json + probabilities_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/probabilities.json + score_dict_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/score_dict.json + test_labels_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/test_labels.json + train_labels_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/train_labels.json + model_dir: models + name: f1dbe93146259b17a8d8dbc3f5b43e28 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0a173e26ed0b597f859bceeb7953266e + trainer: + kwargs: {} +name: f1dbe93146259b17a8d8dbc3f5b43e28 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/params.yaml b/examples/security/classification/output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/params.yaml new file mode 100644 index 00000000..aa17c109 --- /dev/null +++ b/examples/security/classification/output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/adv_predictions.json + adv_probabilities_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/25d277181032020328157fe64d2b5a17.pkl + params_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/params.yaml + predictions_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/predictions.json + probabilities_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/probabilities.json + score_dict_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/score_dict.json + test_labels_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/test_labels.json + train_labels_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/train_labels.json + model_dir: models + name: f1ed42e533e3ed2bc4c684cd3658daeb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 328068c9978553ad64ad4295a18dd9af + trainer: + kwargs: {} +name: f1ed42e533e3ed2bc4c684cd3658daeb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f22f679839fe36b27d910acc6afaff73/params.yaml b/examples/security/classification/output/reports/train/f22f679839fe36b27d910acc6afaff73/params.yaml new file mode 100644 index 00000000..7f5cc6ae --- /dev/null +++ b/examples/security/classification/output/reports/train/f22f679839fe36b27d910acc6afaff73/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/adv_predictions.json + adv_probabilities_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/7f5aef4493f8a5953a6c5895810eaff2.pkl + params_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/params.yaml + predictions_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/predictions.json + probabilities_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/probabilities.json + score_dict_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/score_dict.json + test_labels_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/test_labels.json + train_labels_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/train_labels.json + model_dir: models + name: f22f679839fe36b27d910acc6afaff73 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d18bf770af24e1b79d1637087d38bc06 + trainer: + kwargs: {} +name: f22f679839fe36b27d910acc6afaff73 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f25721df84630ddc84706c2d82e37ec1/params.yaml b/examples/security/classification/output/reports/train/f25721df84630ddc84706c2d82e37ec1/params.yaml new file mode 100644 index 00000000..487fd070 --- /dev/null +++ b/examples/security/classification/output/reports/train/f25721df84630ddc84706c2d82e37ec1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/adv_predictions.json + adv_probabilities_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/b959918fa1e6d48d780e6e8d277165b0.pkl + params_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/params.yaml + predictions_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/predictions.json + probabilities_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/probabilities.json + score_dict_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/score_dict.json + test_labels_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/test_labels.json + train_labels_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/train_labels.json + model_dir: models + name: f25721df84630ddc84706c2d82e37ec1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 166b5a44818480f55221c527c6f32344 + trainer: + kwargs: {} +name: f25721df84630ddc84706c2d82e37ec1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/params.yaml b/examples/security/classification/output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/params.yaml new file mode 100644 index 00000000..5aef74ee --- /dev/null +++ b/examples/security/classification/output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/adv_predictions.json + adv_probabilities_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/83fdd1e8e56a3b002a9cc3583145e862.pkl + params_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/params.yaml + predictions_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/predictions.json + probabilities_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/probabilities.json + score_dict_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/score_dict.json + test_labels_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/test_labels.json + train_labels_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/train_labels.json + model_dir: models + name: f2647d13bc344b402d42e75d6dc9c9a9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bbda112b0b6b992b4361bc2f6e66d3df + trainer: + kwargs: {} +name: f2647d13bc344b402d42e75d6dc9c9a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f27e63e2fc69334f22edc827da4d9b75/params.yaml b/examples/security/classification/output/reports/train/f27e63e2fc69334f22edc827da4d9b75/params.yaml new file mode 100644 index 00000000..730eedbc --- /dev/null +++ b/examples/security/classification/output/reports/train/f27e63e2fc69334f22edc827da4d9b75/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/adv_predictions.json + adv_probabilities_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/4facd08cb9565d931b4a0d1d6f13b5dc.pkl + params_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/params.yaml + predictions_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/predictions.json + probabilities_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/probabilities.json + score_dict_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/score_dict.json + test_labels_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/test_labels.json + train_labels_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/train_labels.json + model_dir: models + name: f27e63e2fc69334f22edc827da4d9b75 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 73a439f9be242bc9ab65a71d21741851 + trainer: + kwargs: {} +name: f27e63e2fc69334f22edc827da4d9b75 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f27ff349d0e303f877034df0861950c3/params.yaml b/examples/security/classification/output/reports/train/f27ff349d0e303f877034df0861950c3/params.yaml new file mode 100644 index 00000000..1375b934 --- /dev/null +++ b/examples/security/classification/output/reports/train/f27ff349d0e303f877034df0861950c3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f27ff349d0e303f877034df0861950c3/adv_predictions.json + adv_probabilities_file: output/reports/train/f27ff349d0e303f877034df0861950c3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/520945f2ca5adfdfcd79d3f17faee7be.pkl + params_file: output/reports/train/f27ff349d0e303f877034df0861950c3/params.yaml + predictions_file: output/reports/train/f27ff349d0e303f877034df0861950c3/predictions.json + probabilities_file: output/reports/train/f27ff349d0e303f877034df0861950c3/probabilities.json + score_dict_file: output/reports/train/f27ff349d0e303f877034df0861950c3/score_dict.json + test_labels_file: output/reports/train/f27ff349d0e303f877034df0861950c3/test_labels.json + train_labels_file: output/reports/train/f27ff349d0e303f877034df0861950c3/train_labels.json + model_dir: models + name: f27ff349d0e303f877034df0861950c3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2dbaab0f5ed52605a9378e7f2582debf + trainer: + kwargs: {} +name: f27ff349d0e303f877034df0861950c3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f282fc490c80de418fa3b00f30d0a076/params.yaml b/examples/security/classification/output/reports/train/f282fc490c80de418fa3b00f30d0a076/params.yaml new file mode 100644 index 00000000..156aed6d --- /dev/null +++ b/examples/security/classification/output/reports/train/f282fc490c80de418fa3b00f30d0a076/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/adv_predictions.json + adv_probabilities_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/3c0a13e86ce4f2d1fe9ccfe05df27b89.pkl + params_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/params.yaml + predictions_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/predictions.json + probabilities_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/probabilities.json + score_dict_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/score_dict.json + test_labels_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/test_labels.json + train_labels_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/train_labels.json + model_dir: models + name: f282fc490c80de418fa3b00f30d0a076 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 063bd7dbd5bf440830494b019d90d526 + trainer: + kwargs: {} +name: f282fc490c80de418fa3b00f30d0a076 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/params.yaml b/examples/security/classification/output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/params.yaml new file mode 100644 index 00000000..25f55242 --- /dev/null +++ b/examples/security/classification/output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/adv_predictions.json + adv_probabilities_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/f9c8b4eea9db5be42f0ecdc5f04aeda3.pkl + params_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/params.yaml + predictions_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/predictions.json + probabilities_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/probabilities.json + score_dict_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/score_dict.json + test_labels_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/test_labels.json + train_labels_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/train_labels.json + model_dir: models + name: f2aeb82e29cdd0a6a94908724faef20f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c291991d224e173a97b05e0989062ad1 + trainer: + kwargs: {} +name: f2aeb82e29cdd0a6a94908724faef20f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f2be86387711078e61ccdc900fc21a4c/params.yaml b/examples/security/classification/output/reports/train/f2be86387711078e61ccdc900fc21a4c/params.yaml new file mode 100644 index 00000000..15d833a3 --- /dev/null +++ b/examples/security/classification/output/reports/train/f2be86387711078e61ccdc900fc21a4c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/adv_predictions.json + adv_probabilities_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/cf8d769a1c478a476cf794c344839964.pkl + params_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/params.yaml + predictions_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/predictions.json + probabilities_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/probabilities.json + score_dict_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/score_dict.json + test_labels_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/test_labels.json + train_labels_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/train_labels.json + model_dir: models + name: f2be86387711078e61ccdc900fc21a4c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cdd9b8f39284797665b14bce133726b1 + trainer: + kwargs: {} +name: f2be86387711078e61ccdc900fc21a4c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f2c14882610c65baceb0d9ba22f7c324/params.yaml b/examples/security/classification/output/reports/train/f2c14882610c65baceb0d9ba22f7c324/params.yaml new file mode 100644 index 00000000..6bd7049a --- /dev/null +++ b/examples/security/classification/output/reports/train/f2c14882610c65baceb0d9ba22f7c324/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/adv_predictions.json + adv_probabilities_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/2810f0fef4973685865e937a9ca6570a.pkl + params_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/params.yaml + predictions_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/predictions.json + probabilities_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/probabilities.json + score_dict_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/score_dict.json + test_labels_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/test_labels.json + train_labels_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/train_labels.json + model_dir: models + name: f2c14882610c65baceb0d9ba22f7c324 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4bed6ae71a3c5490acfd28e8e9b2363b + trainer: + kwargs: {} +name: f2c14882610c65baceb0d9ba22f7c324 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/params.yaml b/examples/security/classification/output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/params.yaml new file mode 100644 index 00000000..c5a7e474 --- /dev/null +++ b/examples/security/classification/output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/adv_predictions.json + adv_probabilities_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/b4fbc3d12f9b3e921323343960bbec75.pkl + params_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/params.yaml + predictions_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/predictions.json + probabilities_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/probabilities.json + score_dict_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/score_dict.json + test_labels_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/test_labels.json + train_labels_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/train_labels.json + model_dir: models + name: f2c5d5f79d79818cea7a1dd7c9c23292 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c40e7e8465ef51377677bb04d6e95dc9 + trainer: + kwargs: {} +name: f2c5d5f79d79818cea7a1dd7c9c23292 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/params.yaml b/examples/security/classification/output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/params.yaml new file mode 100644 index 00000000..e3968444 --- /dev/null +++ b/examples/security/classification/output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/adv_predictions.json + adv_probabilities_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/77de9335bee959edfddfe9cd4352d32b.pkl + params_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/params.yaml + predictions_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/predictions.json + probabilities_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/probabilities.json + score_dict_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/score_dict.json + test_labels_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/test_labels.json + train_labels_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/train_labels.json + model_dir: models + name: f2ca5b9dbdd1b111b5ec81165efdb594 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fe797ff575349fa4a18af69cc11f36f6 + trainer: + kwargs: {} +name: f2ca5b9dbdd1b111b5ec81165efdb594 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/params.yaml b/examples/security/classification/output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/params.yaml new file mode 100644 index 00000000..d24ea751 --- /dev/null +++ b/examples/security/classification/output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/adv_predictions.json + adv_probabilities_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/f7b673670b83179e7220af6383cf4328.pkl + params_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/params.yaml + predictions_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/predictions.json + probabilities_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/probabilities.json + score_dict_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/score_dict.json + test_labels_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/test_labels.json + train_labels_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/train_labels.json + model_dir: models + name: f2db86e99b3fa72c892868db4dbdf6fb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9008f160188e8eee5023d4cf8e732dae + trainer: + kwargs: {} +name: f2db86e99b3fa72c892868db4dbdf6fb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/params.yaml b/examples/security/classification/output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/params.yaml new file mode 100644 index 00000000..2f54c72f --- /dev/null +++ b/examples/security/classification/output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/adv_predictions.json + adv_probabilities_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/ac9024f94c34fd771c9fd9e7d6b54ee2.pkl + params_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/params.yaml + predictions_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/predictions.json + probabilities_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/probabilities.json + score_dict_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/score_dict.json + test_labels_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/test_labels.json + train_labels_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/train_labels.json + model_dir: models + name: f353ba32c7c78daa4c84e31c15a018ae + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 265f176aa0979c10198201ead903ca4d + trainer: + kwargs: {} +name: f353ba32c7c78daa4c84e31c15a018ae +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f3609e6c3d330413160f865f74edc8e2/params.yaml b/examples/security/classification/output/reports/train/f3609e6c3d330413160f865f74edc8e2/params.yaml new file mode 100644 index 00000000..da208c59 --- /dev/null +++ b/examples/security/classification/output/reports/train/f3609e6c3d330413160f865f74edc8e2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/adv_predictions.json + adv_probabilities_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/3e5f4356c2799ca8d072a5c6efab7b82.pkl + params_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/params.yaml + predictions_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/predictions.json + probabilities_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/probabilities.json + score_dict_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/score_dict.json + test_labels_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/test_labels.json + train_labels_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/train_labels.json + model_dir: models + name: f3609e6c3d330413160f865f74edc8e2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 221ea58af2b7e20f52ab89a077fd33e0 + trainer: + kwargs: {} +name: f3609e6c3d330413160f865f74edc8e2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f3816018f11ffa98afac0839588cb1f8/params.yaml b/examples/security/classification/output/reports/train/f3816018f11ffa98afac0839588cb1f8/params.yaml new file mode 100644 index 00000000..612766e1 --- /dev/null +++ b/examples/security/classification/output/reports/train/f3816018f11ffa98afac0839588cb1f8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/adv_predictions.json + adv_probabilities_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/6c7dd399fefe8d33380bd49cd54efe8a.pkl + params_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/params.yaml + predictions_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/predictions.json + probabilities_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/probabilities.json + score_dict_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/score_dict.json + test_labels_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/test_labels.json + train_labels_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/train_labels.json + model_dir: models + name: f3816018f11ffa98afac0839588cb1f8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 630366fd91184e976247be9b3b257e74 + trainer: + kwargs: {} +name: f3816018f11ffa98afac0839588cb1f8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/params.yaml b/examples/security/classification/output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/params.yaml new file mode 100644 index 00000000..e0ea0090 --- /dev/null +++ b/examples/security/classification/output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/adv_predictions.json + adv_probabilities_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/60c4965686b803cb0453f175e86438ff.pkl + params_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/params.yaml + predictions_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/predictions.json + probabilities_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/probabilities.json + score_dict_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/score_dict.json + test_labels_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/test_labels.json + train_labels_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/train_labels.json + model_dir: models + name: f3cb891c478ca1873ebaa0db9c0f8288 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 135c81a1cb3bb278bb15ac332e80784d + trainer: + kwargs: {} +name: f3cb891c478ca1873ebaa0db9c0f8288 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f40244315167904d4ab5bedcdb4c7750/params.yaml b/examples/security/classification/output/reports/train/f40244315167904d4ab5bedcdb4c7750/params.yaml new file mode 100644 index 00000000..26fa769a --- /dev/null +++ b/examples/security/classification/output/reports/train/f40244315167904d4ab5bedcdb4c7750/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/adv_predictions.json + adv_probabilities_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/e077e8fae71b4ea3d0283ddbb14af9a9.pkl + params_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/params.yaml + predictions_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/predictions.json + probabilities_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/probabilities.json + score_dict_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/score_dict.json + test_labels_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/test_labels.json + train_labels_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/train_labels.json + model_dir: models + name: f40244315167904d4ab5bedcdb4c7750 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 170064bdadaf278c436a4e4f183c2b8b + trainer: + kwargs: {} +name: f40244315167904d4ab5bedcdb4c7750 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/params.yaml b/examples/security/classification/output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/params.yaml new file mode 100644 index 00000000..8e45de79 --- /dev/null +++ b/examples/security/classification/output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/adv_predictions.json + adv_probabilities_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/71644f905c2abe8b3f78a404c0c5701f.pkl + params_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/params.yaml + predictions_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/predictions.json + probabilities_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/probabilities.json + score_dict_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/score_dict.json + test_labels_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/test_labels.json + train_labels_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/train_labels.json + model_dir: models + name: f42021c77a41ba9d3d40c499142b8d3c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 38375080c3d9ac0962ff9f2c930703bf + trainer: + kwargs: {} +name: f42021c77a41ba9d3d40c499142b8d3c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f42ed6406db5252352fc0f8e112661c5/params.yaml b/examples/security/classification/output/reports/train/f42ed6406db5252352fc0f8e112661c5/params.yaml new file mode 100644 index 00000000..cb71a93f --- /dev/null +++ b/examples/security/classification/output/reports/train/f42ed6406db5252352fc0f8e112661c5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/adv_predictions.json + adv_probabilities_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/ea34509e1b59adea4dc868ecc13755e6.pkl + params_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/params.yaml + predictions_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/predictions.json + probabilities_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/probabilities.json + score_dict_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/score_dict.json + test_labels_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/test_labels.json + train_labels_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/train_labels.json + model_dir: models + name: f42ed6406db5252352fc0f8e112661c5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dd6d9dbfe77b63c6ca104bb356844738 + trainer: + kwargs: {} +name: f42ed6406db5252352fc0f8e112661c5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/params.yaml b/examples/security/classification/output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/params.yaml new file mode 100644 index 00000000..c5057a3e --- /dev/null +++ b/examples/security/classification/output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/adv_predictions.json + adv_probabilities_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/bbc013def9362d2c1b2a42f8f859bd9c.pkl + params_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/params.yaml + predictions_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/predictions.json + probabilities_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/probabilities.json + score_dict_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/score_dict.json + test_labels_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/test_labels.json + train_labels_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/train_labels.json + model_dir: models + name: f43aaa1eb79495ee17b38b640f716ba3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 93c4c54bb7641bb58b3ab2e0df27de7c + trainer: + kwargs: {} +name: f43aaa1eb79495ee17b38b640f716ba3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f44642425b9437278df4a76ff706c4b6/params.yaml b/examples/security/classification/output/reports/train/f44642425b9437278df4a76ff706c4b6/params.yaml new file mode 100644 index 00000000..1ff3d7dc --- /dev/null +++ b/examples/security/classification/output/reports/train/f44642425b9437278df4a76ff706c4b6/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/adv_predictions.json + adv_probabilities_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/ed993f2fcdf2e3419766df7be0b02f2d.pkl + params_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/params.yaml + predictions_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/predictions.json + probabilities_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/probabilities.json + score_dict_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/score_dict.json + test_labels_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/test_labels.json + train_labels_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/train_labels.json + model_dir: models + name: f44642425b9437278df4a76ff706c4b6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b53e13b9e2677e8c717e61919665939a + trainer: + kwargs: {} +name: f44642425b9437278df4a76ff706c4b6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/params.yaml b/examples/security/classification/output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/params.yaml new file mode 100644 index 00000000..65b16d55 --- /dev/null +++ b/examples/security/classification/output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/adv_predictions.json + adv_probabilities_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/1cf6da722782fd15baa813b2cb399b60.pkl + params_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/params.yaml + predictions_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/predictions.json + probabilities_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/probabilities.json + score_dict_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/score_dict.json + test_labels_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/test_labels.json + train_labels_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/train_labels.json + model_dir: models + name: f44bea96c537cc71b1dabc3d7ae498a3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 509cc8278549c8cdba32c23ca84d945f + trainer: + kwargs: {} +name: f44bea96c537cc71b1dabc3d7ae498a3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/params.yaml b/examples/security/classification/output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/params.yaml new file mode 100644 index 00000000..4b1af025 --- /dev/null +++ b/examples/security/classification/output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/adv_predictions.json + adv_probabilities_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/babdd08a0b917a156120c9afd88cf82b.pkl + params_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/params.yaml + predictions_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/predictions.json + probabilities_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/probabilities.json + score_dict_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/score_dict.json + test_labels_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/test_labels.json + train_labels_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/train_labels.json + model_dir: models + name: f456ff1740cba6dfaabead9a82f7a6e9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eb424481d348e4f5e8e196883824ad4d + trainer: + kwargs: {} +name: f456ff1740cba6dfaabead9a82f7a6e9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/params.yaml b/examples/security/classification/output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/params.yaml new file mode 100644 index 00000000..967971e6 --- /dev/null +++ b/examples/security/classification/output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/adv_predictions.json + adv_probabilities_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/dc922c5392a071e8cba7ce060a116236.pkl + params_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/params.yaml + predictions_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/predictions.json + probabilities_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/probabilities.json + score_dict_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/score_dict.json + test_labels_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/test_labels.json + train_labels_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/train_labels.json + model_dir: models + name: f473fe326cdfbc7f675f1a6d409180ed + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e4c5b29f997d4551f210333ad0e3069c + trainer: + kwargs: {} +name: f473fe326cdfbc7f675f1a6d409180ed +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/params.yaml b/examples/security/classification/output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/params.yaml new file mode 100644 index 00000000..47543bf8 --- /dev/null +++ b/examples/security/classification/output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/adv_predictions.json + adv_probabilities_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/683fb77c9fd6ed3f40607d0957dfd933.pkl + params_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/params.yaml + predictions_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/predictions.json + probabilities_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/probabilities.json + score_dict_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/score_dict.json + test_labels_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/test_labels.json + train_labels_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/train_labels.json + model_dir: models + name: f49a386efc5ffdfb48e2685792cc4b9e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: df9b7f0d34b24cbb8be022a9368bc779 + trainer: + kwargs: {} +name: f49a386efc5ffdfb48e2685792cc4b9e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/params.yaml b/examples/security/classification/output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/params.yaml new file mode 100644 index 00000000..a150cd17 --- /dev/null +++ b/examples/security/classification/output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/adv_predictions.json + adv_probabilities_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/29563ab911b07c98660f34ae322a043b.pkl + params_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/params.yaml + predictions_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/predictions.json + probabilities_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/probabilities.json + score_dict_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/score_dict.json + test_labels_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/test_labels.json + train_labels_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/train_labels.json + model_dir: models + name: f4b6e2fa405c9eaa7215bc7dc7f48cb4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 19f329b59529e51ddb435dadb776ac7d + trainer: + kwargs: {} +name: f4b6e2fa405c9eaa7215bc7dc7f48cb4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f4c8206cd02321c5153020fb929580f7/params.yaml b/examples/security/classification/output/reports/train/f4c8206cd02321c5153020fb929580f7/params.yaml new file mode 100644 index 00000000..6c75d08f --- /dev/null +++ b/examples/security/classification/output/reports/train/f4c8206cd02321c5153020fb929580f7/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/adv_predictions.json + adv_probabilities_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/e4c1b6794aa27d625e9c50d7692e0947.pkl + params_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/params.yaml + predictions_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/predictions.json + probabilities_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/probabilities.json + score_dict_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/score_dict.json + test_labels_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/test_labels.json + train_labels_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/train_labels.json + model_dir: models + name: f4c8206cd02321c5153020fb929580f7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2584758b070261af9c2886310690e017 + trainer: + kwargs: {} +name: f4c8206cd02321c5153020fb929580f7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/params.yaml b/examples/security/classification/output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/params.yaml new file mode 100644 index 00000000..317b4a15 --- /dev/null +++ b/examples/security/classification/output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/adv_predictions.json + adv_probabilities_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/11631499cbbf7ed3cb78a15fb5624a5b.pkl + params_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/params.yaml + predictions_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/predictions.json + probabilities_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/probabilities.json + score_dict_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/score_dict.json + test_labels_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/test_labels.json + train_labels_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/train_labels.json + model_dir: models + name: f4c8d5033ea577bdc68c3ecc6e67a4c4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 67498ce7d7801a205df2fac1092d872f + trainer: + kwargs: {} +name: f4c8d5033ea577bdc68c3ecc6e67a4c4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/params.yaml b/examples/security/classification/output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/params.yaml new file mode 100644 index 00000000..0d562912 --- /dev/null +++ b/examples/security/classification/output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/adv_predictions.json + adv_probabilities_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/d02036960717fe8e6df983822f1b1bef.pkl + params_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/params.yaml + predictions_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/predictions.json + probabilities_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/probabilities.json + score_dict_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/score_dict.json + test_labels_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/test_labels.json + train_labels_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/train_labels.json + model_dir: models + name: f4e7e9abe73c2ee6927e81da477ea935 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d026c2a67efbed8996e6a03029454d18 + trainer: + kwargs: {} +name: f4e7e9abe73c2ee6927e81da477ea935 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f507bacadd76b963cbe4677061bf4f3d/params.yaml b/examples/security/classification/output/reports/train/f507bacadd76b963cbe4677061bf4f3d/params.yaml new file mode 100644 index 00000000..8d49aaa8 --- /dev/null +++ b/examples/security/classification/output/reports/train/f507bacadd76b963cbe4677061bf4f3d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/adv_predictions.json + adv_probabilities_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/218821fd2a5679d7217ae68f6f5d5e8b.pkl + params_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/params.yaml + predictions_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/predictions.json + probabilities_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/probabilities.json + score_dict_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/score_dict.json + test_labels_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/test_labels.json + train_labels_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/train_labels.json + model_dir: models + name: f507bacadd76b963cbe4677061bf4f3d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d684b2da461b397df704e0342ebf204e + trainer: + kwargs: {} +name: f507bacadd76b963cbe4677061bf4f3d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f53e5dc0431f6532e0db23b2718e841a/params.yaml b/examples/security/classification/output/reports/train/f53e5dc0431f6532e0db23b2718e841a/params.yaml new file mode 100644 index 00000000..b13f0760 --- /dev/null +++ b/examples/security/classification/output/reports/train/f53e5dc0431f6532e0db23b2718e841a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/adv_predictions.json + adv_probabilities_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/272a004d61aa3028c866927e11c0d8c8.pkl + params_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/params.yaml + predictions_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/predictions.json + probabilities_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/probabilities.json + score_dict_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/score_dict.json + test_labels_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/test_labels.json + train_labels_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/train_labels.json + model_dir: models + name: f53e5dc0431f6532e0db23b2718e841a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0551cf895a301de8a0faf9f406fd9dd4 + trainer: + kwargs: {} +name: f53e5dc0431f6532e0db23b2718e841a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/params.yaml b/examples/security/classification/output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/params.yaml new file mode 100644 index 00000000..9ac635dc --- /dev/null +++ b/examples/security/classification/output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/adv_predictions.json + adv_probabilities_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/45e6aa9614385e64b92b59611e80414a.pkl + params_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/params.yaml + predictions_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/predictions.json + probabilities_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/probabilities.json + score_dict_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/score_dict.json + test_labels_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/test_labels.json + train_labels_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/train_labels.json + model_dir: models + name: f5543a7ce23c9ab0cffc2faefa0f45a5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c528844b500281d12667bc8eccd5a6f + trainer: + kwargs: {} +name: f5543a7ce23c9ab0cffc2faefa0f45a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/params.yaml b/examples/security/classification/output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/params.yaml new file mode 100644 index 00000000..687e603b --- /dev/null +++ b/examples/security/classification/output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/adv_predictions.json + adv_probabilities_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/1a6d03a1de9c26f07fee97b188f459d9.pkl + params_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/params.yaml + predictions_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/predictions.json + probabilities_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/probabilities.json + score_dict_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/score_dict.json + test_labels_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/test_labels.json + train_labels_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/train_labels.json + model_dir: models + name: f58f48c3ffe4e69ed9c2be87a9559106 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2127f1f61b5dfd8ce47d5564a47d84dc + trainer: + kwargs: {} +name: f58f48c3ffe4e69ed9c2be87a9559106 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/params.yaml b/examples/security/classification/output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/params.yaml new file mode 100644 index 00000000..eefd0519 --- /dev/null +++ b/examples/security/classification/output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/adv_predictions.json + adv_probabilities_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/c079b4a980997516527b0c7dc1378384.pkl + params_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/params.yaml + predictions_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/predictions.json + probabilities_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/probabilities.json + score_dict_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/score_dict.json + test_labels_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/test_labels.json + train_labels_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/train_labels.json + model_dir: models + name: f5ce28091f1c745fbcdb0eeaefee846d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d09f9157ca5a5d60f2baf92417fc574a + trainer: + kwargs: {} +name: f5ce28091f1c745fbcdb0eeaefee846d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/params.yaml b/examples/security/classification/output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/params.yaml new file mode 100644 index 00000000..f95b4391 --- /dev/null +++ b/examples/security/classification/output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/adv_predictions.json + adv_probabilities_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/0082af42aa5ae8d4000c88292c9cfa1f.pkl + params_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/params.yaml + predictions_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/predictions.json + probabilities_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/probabilities.json + score_dict_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/score_dict.json + test_labels_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/test_labels.json + train_labels_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/train_labels.json + model_dir: models + name: f5f547b2e47eeff36eceeff2a9f395c1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 193be15007b8245f5ef8d26dfeba537f + trainer: + kwargs: {} +name: f5f547b2e47eeff36eceeff2a9f395c1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/params.yaml b/examples/security/classification/output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/params.yaml new file mode 100644 index 00000000..8e8bc21e --- /dev/null +++ b/examples/security/classification/output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/adv_predictions.json + adv_probabilities_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/cc2d1c7f0a61fd19fde2b5e6ade4af6d.pkl + params_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/params.yaml + predictions_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/predictions.json + probabilities_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/probabilities.json + score_dict_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/score_dict.json + test_labels_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/test_labels.json + train_labels_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/train_labels.json + model_dir: models + name: f61711fb2c5bbb39dd8e17626ac71c5a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 140618c85fd4279e6a667ffd793ce483 + trainer: + kwargs: {} +name: f61711fb2c5bbb39dd8e17626ac71c5a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/params.yaml b/examples/security/classification/output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/params.yaml new file mode 100644 index 00000000..808c45a8 --- /dev/null +++ b/examples/security/classification/output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/adv_predictions.json + adv_probabilities_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/2c5fc459436c9ec41c501f4b1e177eb5.pkl + params_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/params.yaml + predictions_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/predictions.json + probabilities_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/probabilities.json + score_dict_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/score_dict.json + test_labels_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/test_labels.json + train_labels_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/train_labels.json + model_dir: models + name: f6672e7f1fd944ab3da9459c0d79f589 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 34ba4c7772c583eb4cbabbece841361a + trainer: + kwargs: {} +name: f6672e7f1fd944ab3da9459c0d79f589 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/params.yaml b/examples/security/classification/output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/params.yaml new file mode 100644 index 00000000..3750e3a5 --- /dev/null +++ b/examples/security/classification/output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/adv_predictions.json + adv_probabilities_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/8ca3c15a207b54882a70e2c0e2a9c22a.pkl + params_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/params.yaml + predictions_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/predictions.json + probabilities_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/probabilities.json + score_dict_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/score_dict.json + test_labels_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/test_labels.json + train_labels_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/train_labels.json + model_dir: models + name: f6951d7c42c2bada3342bad9e9f9fc08 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 055c65e1b23eb1a53c9bd3692e26099d + trainer: + kwargs: {} +name: f6951d7c42c2bada3342bad9e9f9fc08 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/params.yaml b/examples/security/classification/output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/params.yaml new file mode 100644 index 00000000..7853a54f --- /dev/null +++ b/examples/security/classification/output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/adv_predictions.json + adv_probabilities_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/f89e7654843df6652a752cc8b292bb0c.pkl + params_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/params.yaml + predictions_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/predictions.json + probabilities_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/probabilities.json + score_dict_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/score_dict.json + test_labels_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/test_labels.json + train_labels_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/train_labels.json + model_dir: models + name: f69e3e32a09ba64d4227203032ddfa2a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d5d168aab130e43e095165b9dc825b3f + trainer: + kwargs: {} +name: f69e3e32a09ba64d4227203032ddfa2a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/params.yaml b/examples/security/classification/output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/params.yaml new file mode 100644 index 00000000..9e08854f --- /dev/null +++ b/examples/security/classification/output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/adv_predictions.json + adv_probabilities_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/cad2be7faa8e5ba99eabba08b77fad30.pkl + params_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/params.yaml + predictions_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/predictions.json + probabilities_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/probabilities.json + score_dict_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/score_dict.json + test_labels_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/test_labels.json + train_labels_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/train_labels.json + model_dir: models + name: f6a108569171e8f1dc6c420d3d0f3a58 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 871257c85d9bd1a35a5c56b61036b311 + trainer: + kwargs: {} +name: f6a108569171e8f1dc6c420d3d0f3a58 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f6b641491e817c7a16d531945d70d1a1/params.yaml b/examples/security/classification/output/reports/train/f6b641491e817c7a16d531945d70d1a1/params.yaml new file mode 100644 index 00000000..4ba05c8a --- /dev/null +++ b/examples/security/classification/output/reports/train/f6b641491e817c7a16d531945d70d1a1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/adv_predictions.json + adv_probabilities_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/88d4135ab38836dd0e43fc72a72af33f.pkl + params_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/params.yaml + predictions_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/predictions.json + probabilities_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/probabilities.json + score_dict_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/score_dict.json + test_labels_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/test_labels.json + train_labels_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/train_labels.json + model_dir: models + name: f6b641491e817c7a16d531945d70d1a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2628ac8169c5dd027b37ba48547d949a + trainer: + kwargs: {} +name: f6b641491e817c7a16d531945d70d1a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/params.yaml b/examples/security/classification/output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/params.yaml new file mode 100644 index 00000000..3b52ebca --- /dev/null +++ b/examples/security/classification/output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/adv_predictions.json + adv_probabilities_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/65196e1b3c9fc49ff0db2cadeee23483.pkl + params_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/params.yaml + predictions_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/predictions.json + probabilities_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/probabilities.json + score_dict_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/score_dict.json + test_labels_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/test_labels.json + train_labels_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/train_labels.json + model_dir: models + name: f6c4d684820168b9c4f714ec20de0c9c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9e74bbbaf261ab6859194175fa2f3582 + trainer: + kwargs: {} +name: f6c4d684820168b9c4f714ec20de0c9c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/params.yaml b/examples/security/classification/output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/params.yaml new file mode 100644 index 00000000..d6cb5351 --- /dev/null +++ b/examples/security/classification/output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/adv_predictions.json + adv_probabilities_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/9d327ba86411e28960509b163981171c.pkl + params_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/params.yaml + predictions_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/predictions.json + probabilities_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/probabilities.json + score_dict_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/score_dict.json + test_labels_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/test_labels.json + train_labels_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/train_labels.json + model_dir: models + name: f6dccee630cc8d1c8ef418f245699b3d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 87d648fba3547dd06e254d13d3d8a849 + trainer: + kwargs: {} +name: f6dccee630cc8d1c8ef418f245699b3d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f720b75135dfaf2d02b112622d0128f4/params.yaml b/examples/security/classification/output/reports/train/f720b75135dfaf2d02b112622d0128f4/params.yaml new file mode 100644 index 00000000..a9b5503a --- /dev/null +++ b/examples/security/classification/output/reports/train/f720b75135dfaf2d02b112622d0128f4/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/adv_predictions.json + adv_probabilities_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/daf405a67081a1a4add9f1f86a958316.pkl + params_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/params.yaml + predictions_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/predictions.json + probabilities_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/probabilities.json + score_dict_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/score_dict.json + test_labels_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/test_labels.json + train_labels_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/train_labels.json + model_dir: models + name: f720b75135dfaf2d02b112622d0128f4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 50f8d3fe2bd4d2baffab1ce662e26b8f + trainer: + kwargs: {} +name: f720b75135dfaf2d02b112622d0128f4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f72531988b39b77c3e8ec59d40537de9/params.yaml b/examples/security/classification/output/reports/train/f72531988b39b77c3e8ec59d40537de9/params.yaml new file mode 100644 index 00000000..318d9f51 --- /dev/null +++ b/examples/security/classification/output/reports/train/f72531988b39b77c3e8ec59d40537de9/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/adv_predictions.json + adv_probabilities_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/6c03fa9bc14a06f8b5dbb17c7c6882a0.pkl + params_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/params.yaml + predictions_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/predictions.json + probabilities_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/probabilities.json + score_dict_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/score_dict.json + test_labels_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/test_labels.json + train_labels_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/train_labels.json + model_dir: models + name: f72531988b39b77c3e8ec59d40537de9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f3e8a58180c3f9696d7251ac3ce44ef5 + trainer: + kwargs: {} +name: f72531988b39b77c3e8ec59d40537de9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/params.yaml b/examples/security/classification/output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/params.yaml new file mode 100644 index 00000000..3e48bfa1 --- /dev/null +++ b/examples/security/classification/output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/adv_predictions.json + adv_probabilities_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/7bd66a3687debb4c7f916b40319db721.pkl + params_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/params.yaml + predictions_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/predictions.json + probabilities_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/probabilities.json + score_dict_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/score_dict.json + test_labels_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/test_labels.json + train_labels_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/train_labels.json + model_dir: models + name: f7323b696d94bb46e4869bfd92a6ca32 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6bfbaafb18a795f7c0725e0fe25b4a07 + trainer: + kwargs: {} +name: f7323b696d94bb46e4869bfd92a6ca32 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/params.yaml b/examples/security/classification/output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/params.yaml new file mode 100644 index 00000000..43ddf202 --- /dev/null +++ b/examples/security/classification/output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/adv_predictions.json + adv_probabilities_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/50fa78d0c4f616841bd7eb55b89a83e5.pkl + params_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/params.yaml + predictions_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/predictions.json + probabilities_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/probabilities.json + score_dict_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/score_dict.json + test_labels_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/test_labels.json + train_labels_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/train_labels.json + model_dir: models + name: f75ec5623e07bdf5b9b00efe4adc79b8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d671ea3ccbef0c3cba7d1596fca906d7 + trainer: + kwargs: {} +name: f75ec5623e07bdf5b9b00efe4adc79b8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f766e79660c502433ae4d65d022fd9ce/params.yaml b/examples/security/classification/output/reports/train/f766e79660c502433ae4d65d022fd9ce/params.yaml new file mode 100644 index 00000000..e638f700 --- /dev/null +++ b/examples/security/classification/output/reports/train/f766e79660c502433ae4d65d022fd9ce/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/adv_predictions.json + adv_probabilities_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/b14f32d1d382842de638c03599a0c83f.pkl + params_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/params.yaml + predictions_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/predictions.json + probabilities_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/probabilities.json + score_dict_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/score_dict.json + test_labels_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/test_labels.json + train_labels_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/train_labels.json + model_dir: models + name: f766e79660c502433ae4d65d022fd9ce + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1004340d6b4006268764c2bd2ecc7942 + trainer: + kwargs: {} +name: f766e79660c502433ae4d65d022fd9ce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f783e108743c07edaffdc258e0583e2d/params.yaml b/examples/security/classification/output/reports/train/f783e108743c07edaffdc258e0583e2d/params.yaml new file mode 100644 index 00000000..0e2a3d77 --- /dev/null +++ b/examples/security/classification/output/reports/train/f783e108743c07edaffdc258e0583e2d/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/adv_predictions.json + adv_probabilities_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/98753945e8b565b33956a56ec7651b74.pkl + params_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/params.yaml + predictions_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/predictions.json + probabilities_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/probabilities.json + score_dict_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/score_dict.json + test_labels_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/test_labels.json + train_labels_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/train_labels.json + model_dir: models + name: f783e108743c07edaffdc258e0583e2d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: abdb5501a699b4243d8c62d20af59880 + trainer: + kwargs: {} +name: f783e108743c07edaffdc258e0583e2d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f7ad4a0617981515a197e94af180ea36/params.yaml b/examples/security/classification/output/reports/train/f7ad4a0617981515a197e94af180ea36/params.yaml new file mode 100644 index 00000000..d417f382 --- /dev/null +++ b/examples/security/classification/output/reports/train/f7ad4a0617981515a197e94af180ea36/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/adv_predictions.json + adv_probabilities_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/349529b56dc89a08ca197b9206049e10.pkl + params_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/params.yaml + predictions_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/predictions.json + probabilities_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/probabilities.json + score_dict_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/score_dict.json + test_labels_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/test_labels.json + train_labels_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/train_labels.json + model_dir: models + name: f7ad4a0617981515a197e94af180ea36 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 10a30e3c674337c05e9580f7808de5a7 + trainer: + kwargs: {} +name: f7ad4a0617981515a197e94af180ea36 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/params.yaml b/examples/security/classification/output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/params.yaml new file mode 100644 index 00000000..8b2ba613 --- /dev/null +++ b/examples/security/classification/output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/adv_predictions.json + adv_probabilities_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl + model_file: output/models/7b11c28e946be22ef18d2b7f493e41cf.pkl + params_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/params.yaml + predictions_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/predictions.json + probabilities_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/probabilities.json + score_dict_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/score_dict.json + test_labels_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/test_labels.json + train_labels_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/train_labels.json + model_dir: models + name: f7b66b5d3f04f67312dd22a5bdda3262 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 558b40e4a5de01a1816e029b48e75a52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 457612e89e16acbf257358f473459f59 + trainer: + kwargs: {} +name: f7b66b5d3f04f67312dd22a5bdda3262 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f811fe78599038f6ad928729a677d376/params.yaml b/examples/security/classification/output/reports/train/f811fe78599038f6ad928729a677d376/params.yaml new file mode 100644 index 00000000..1813a5c4 --- /dev/null +++ b/examples/security/classification/output/reports/train/f811fe78599038f6ad928729a677d376/params.yaml @@ -0,0 +1,102 @@ +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f811fe78599038f6ad928729a677d376/adv_predictions.json + adv_probabilities_file: output/reports/train/f811fe78599038f6ad928729a677d376/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/2db9ede9d723385cf4052c8e5300be74.pkl + model_file: output/models/ffa17bcdff44b3d99961b5b9ea0db4c8.pkl + params_file: output/reports/train/f811fe78599038f6ad928729a677d376/params.yaml + predictions_file: output/reports/train/f811fe78599038f6ad928729a677d376/predictions.json + probabilities_file: output/reports/train/f811fe78599038f6ad928729a677d376/probabilities.json + score_dict_file: output/reports/train/f811fe78599038f6ad928729a677d376/score_dict.json + test_labels_file: output/reports/train/f811fe78599038f6ad928729a677d376/test_labels.json + train_labels_file: output/reports/train/f811fe78599038f6ad928729a677d376/train_labels.json + model_dir: models + name: f811fe78599038f6ad928729a677d376 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: 0b50a84d7472bc7095f9199da9fa02bc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b1ae58b464df54ee8918089f870a3ad + trainer: + kwargs: {} +name: f811fe78599038f6ad928729a677d376 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/params.yaml b/examples/security/classification/output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/params.yaml new file mode 100644 index 00000000..bfe58ee9 --- /dev/null +++ b/examples/security/classification/output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/adv_predictions.json + adv_probabilities_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/340fbc12d8ebd7ebc2649f184fb1b6c8.pkl + params_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/params.yaml + predictions_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/predictions.json + probabilities_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/probabilities.json + score_dict_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/score_dict.json + test_labels_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/test_labels.json + train_labels_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/train_labels.json + model_dir: models + name: f81cb8acc6909a41d0c17b836c0f1cf8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dfc9731dc44b0da582b2133ccaa4e320 + trainer: + kwargs: {} +name: f81cb8acc6909a41d0c17b836c0f1cf8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/params.yaml b/examples/security/classification/output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/params.yaml new file mode 100644 index 00000000..642ac0d1 --- /dev/null +++ b/examples/security/classification/output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/adv_predictions.json + adv_probabilities_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/7645f001fa544aac7433e3a1ad76b9a7.pkl + params_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/params.yaml + predictions_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/predictions.json + probabilities_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/probabilities.json + score_dict_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/score_dict.json + test_labels_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/test_labels.json + train_labels_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/train_labels.json + model_dir: models + name: f825b6ff31d02f49aec0b03dfdbfbee8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2ddcf810e7e9b714608c657a7893c71a + trainer: + kwargs: {} +name: f825b6ff31d02f49aec0b03dfdbfbee8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f82ba4c202c55c371dbb65594b172c8b/params.yaml b/examples/security/classification/output/reports/train/f82ba4c202c55c371dbb65594b172c8b/params.yaml new file mode 100644 index 00000000..bb5f01eb --- /dev/null +++ b/examples/security/classification/output/reports/train/f82ba4c202c55c371dbb65594b172c8b/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/adv_predictions.json + adv_probabilities_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/06c09c7df9bfc16693763f1df73edb39.pkl + params_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/params.yaml + predictions_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/predictions.json + probabilities_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/probabilities.json + score_dict_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/score_dict.json + test_labels_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/test_labels.json + train_labels_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/train_labels.json + model_dir: models + name: f82ba4c202c55c371dbb65594b172c8b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e7302a8d8c2056b007716b4fc50cdafc + trainer: + kwargs: {} +name: f82ba4c202c55c371dbb65594b172c8b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/params.yaml b/examples/security/classification/output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/params.yaml new file mode 100644 index 00000000..4f966c2a --- /dev/null +++ b/examples/security/classification/output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/adv_predictions.json + adv_probabilities_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/ef26a6953bd1d217f5cf7a0ed7a11a28.pkl + params_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/params.yaml + predictions_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/predictions.json + probabilities_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/probabilities.json + score_dict_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/score_dict.json + test_labels_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/test_labels.json + train_labels_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/train_labels.json + model_dir: models + name: f8445d9a9eaf421d069945f99c8e08c2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8a8dcba8d751d568c3450a04eb59f9fa + trainer: + kwargs: {} +name: f8445d9a9eaf421d069945f99c8e08c2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/params.yaml b/examples/security/classification/output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/params.yaml new file mode 100644 index 00000000..ae6981c4 --- /dev/null +++ b/examples/security/classification/output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/adv_predictions.json + adv_probabilities_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/261a8258ab643f86fbf8af2dd5ba18d7.pkl + params_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/params.yaml + predictions_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/predictions.json + probabilities_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/probabilities.json + score_dict_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/score_dict.json + test_labels_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/test_labels.json + train_labels_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/train_labels.json + model_dir: models + name: f8b2f650a4a2bcafa1ef9e116cd49b5e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b8939f11dc1e4baf59cc778365645b0f + trainer: + kwargs: {} +name: f8b2f650a4a2bcafa1ef9e116cd49b5e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f8db4f1e4eec6c381669e227670cf491/params.yaml b/examples/security/classification/output/reports/train/f8db4f1e4eec6c381669e227670cf491/params.yaml new file mode 100644 index 00000000..9453f248 --- /dev/null +++ b/examples/security/classification/output/reports/train/f8db4f1e4eec6c381669e227670cf491/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/adv_predictions.json + adv_probabilities_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/38cf29096c94c002970b8cff61144089.pkl + params_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/params.yaml + predictions_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/predictions.json + probabilities_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/probabilities.json + score_dict_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/score_dict.json + test_labels_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/test_labels.json + train_labels_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/train_labels.json + model_dir: models + name: f8db4f1e4eec6c381669e227670cf491 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 386b2aa451fd6de145d069ffef80116e + trainer: + kwargs: {} +name: f8db4f1e4eec6c381669e227670cf491 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f8ec690640736b198c9d88be43f0ca9a/params.yaml b/examples/security/classification/output/reports/train/f8ec690640736b198c9d88be43f0ca9a/params.yaml new file mode 100644 index 00000000..a64bd26d --- /dev/null +++ b/examples/security/classification/output/reports/train/f8ec690640736b198c9d88be43f0ca9a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/adv_predictions.json + adv_probabilities_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/018f585ab93d590716ced72c8f3dcd09.pkl + params_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/params.yaml + predictions_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/predictions.json + probabilities_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/probabilities.json + score_dict_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/score_dict.json + test_labels_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/test_labels.json + train_labels_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/train_labels.json + model_dir: models + name: f8ec690640736b198c9d88be43f0ca9a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3e05aa234c039b32f482343e29ce5d1d + trainer: + kwargs: {} +name: f8ec690640736b198c9d88be43f0ca9a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/params.yaml b/examples/security/classification/output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/params.yaml new file mode 100644 index 00000000..9ef505d5 --- /dev/null +++ b/examples/security/classification/output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/adv_predictions.json + adv_probabilities_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/82c79849aa7730daadee6516ab94fe83.pkl + params_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/params.yaml + predictions_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/predictions.json + probabilities_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/probabilities.json + score_dict_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/score_dict.json + test_labels_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/test_labels.json + train_labels_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/train_labels.json + model_dir: models + name: f8ecd1ecd39272b540bbef2511acd55f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 63f45129a1a1d02820be042a425fded3 + trainer: + kwargs: {} +name: f8ecd1ecd39272b540bbef2511acd55f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f902d021581a35b1a9906b1ddffb61e5/params.yaml b/examples/security/classification/output/reports/train/f902d021581a35b1a9906b1ddffb61e5/params.yaml new file mode 100644 index 00000000..0309f20a --- /dev/null +++ b/examples/security/classification/output/reports/train/f902d021581a35b1a9906b1ddffb61e5/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/adv_predictions.json + adv_probabilities_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/10a5aa4f7641729ec91b1f01ffece2a2.pkl + params_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/params.yaml + predictions_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/predictions.json + probabilities_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/probabilities.json + score_dict_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/score_dict.json + test_labels_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/test_labels.json + train_labels_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/train_labels.json + model_dir: models + name: f902d021581a35b1a9906b1ddffb61e5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 04e354b63777df1aa50d0b48d15f1354 + trainer: + kwargs: {} +name: f902d021581a35b1a9906b1ddffb61e5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/params.yaml b/examples/security/classification/output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/params.yaml new file mode 100644 index 00000000..6e262422 --- /dev/null +++ b/examples/security/classification/output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/adv_predictions.json + adv_probabilities_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/0c26909810420725670bdeae6ee1e0ac.pkl + params_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/params.yaml + predictions_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/predictions.json + probabilities_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/probabilities.json + score_dict_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/score_dict.json + test_labels_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/test_labels.json + train_labels_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/train_labels.json + model_dir: models + name: f906b74ab32bfc34f03d43f3d3672d10 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3deeabfc2e1900a86ebfbfd45881a872 + trainer: + kwargs: {} +name: f906b74ab32bfc34f03d43f3d3672d10 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/params.yaml b/examples/security/classification/output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/params.yaml new file mode 100644 index 00000000..1f9a66fe --- /dev/null +++ b/examples/security/classification/output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/adv_predictions.json + adv_probabilities_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/6afad5956da9aa59313a25b1ea2063e5.pkl + params_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/params.yaml + predictions_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/predictions.json + probabilities_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/probabilities.json + score_dict_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/score_dict.json + test_labels_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/test_labels.json + train_labels_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/train_labels.json + model_dir: models + name: f90a0df48a4e6eab8792a789a2c766b1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: caa5a95b6ae5cf502ab3c935331fef3e + trainer: + kwargs: {} +name: f90a0df48a4e6eab8792a789a2c766b1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f95064d252686f686ee7160faf806443/params.yaml b/examples/security/classification/output/reports/train/f95064d252686f686ee7160faf806443/params.yaml new file mode 100644 index 00000000..ee3b88cc --- /dev/null +++ b/examples/security/classification/output/reports/train/f95064d252686f686ee7160faf806443/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f95064d252686f686ee7160faf806443/adv_predictions.json + adv_probabilities_file: output/reports/train/f95064d252686f686ee7160faf806443/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/2850a15da759103fe79db723fc4b33de.pkl + params_file: output/reports/train/f95064d252686f686ee7160faf806443/params.yaml + predictions_file: output/reports/train/f95064d252686f686ee7160faf806443/predictions.json + probabilities_file: output/reports/train/f95064d252686f686ee7160faf806443/probabilities.json + score_dict_file: output/reports/train/f95064d252686f686ee7160faf806443/score_dict.json + test_labels_file: output/reports/train/f95064d252686f686ee7160faf806443/test_labels.json + train_labels_file: output/reports/train/f95064d252686f686ee7160faf806443/train_labels.json + model_dir: models + name: f95064d252686f686ee7160faf806443 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a8709dc27d8955fb3fa90d5cb1458306 + trainer: + kwargs: {} +name: f95064d252686f686ee7160faf806443 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/params.yaml b/examples/security/classification/output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/params.yaml new file mode 100644 index 00000000..1c354e53 --- /dev/null +++ b/examples/security/classification/output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/adv_predictions.json + adv_probabilities_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/abd71b41dc7059012a299d0351f176ac.pkl + params_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/params.yaml + predictions_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/predictions.json + probabilities_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/probabilities.json + score_dict_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/score_dict.json + test_labels_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/test_labels.json + train_labels_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/train_labels.json + model_dir: models + name: f9882e5a34cc7f107d9fc4eb8b604825 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e7b2765ad085176fb193850db76ac43c + trainer: + kwargs: {} +name: f9882e5a34cc7f107d9fc4eb8b604825 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f997096429d1f3d07026c235f61771fa/params.yaml b/examples/security/classification/output/reports/train/f997096429d1f3d07026c235f61771fa/params.yaml new file mode 100644 index 00000000..00d2ade1 --- /dev/null +++ b/examples/security/classification/output/reports/train/f997096429d1f3d07026c235f61771fa/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f997096429d1f3d07026c235f61771fa/adv_predictions.json + adv_probabilities_file: output/reports/train/f997096429d1f3d07026c235f61771fa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/4af95142a2005618103ec75abcc1cb03.pkl + params_file: output/reports/train/f997096429d1f3d07026c235f61771fa/params.yaml + predictions_file: output/reports/train/f997096429d1f3d07026c235f61771fa/predictions.json + probabilities_file: output/reports/train/f997096429d1f3d07026c235f61771fa/probabilities.json + score_dict_file: output/reports/train/f997096429d1f3d07026c235f61771fa/score_dict.json + test_labels_file: output/reports/train/f997096429d1f3d07026c235f61771fa/test_labels.json + train_labels_file: output/reports/train/f997096429d1f3d07026c235f61771fa/train_labels.json + model_dir: models + name: f997096429d1f3d07026c235f61771fa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e8b20fedc129c7fc38da2ca824434428 + trainer: + kwargs: {} +name: f997096429d1f3d07026c235f61771fa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/params.yaml b/examples/security/classification/output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/params.yaml new file mode 100644 index 00000000..2b4fbde2 --- /dev/null +++ b/examples/security/classification/output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/adv_predictions.json + adv_probabilities_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/2f9d25217cd5152a49e9b600065b6988.pkl + params_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/params.yaml + predictions_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/predictions.json + probabilities_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/probabilities.json + score_dict_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/score_dict.json + test_labels_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/test_labels.json + train_labels_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/train_labels.json + model_dir: models + name: f99f13f7c5329652d8f029ebaa6f10b1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b1f86e3caa985e6d603c00e48d52f302 + trainer: + kwargs: {} +name: f99f13f7c5329652d8f029ebaa6f10b1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f9a9e6f313f76c597464dee05a03944f/params.yaml b/examples/security/classification/output/reports/train/f9a9e6f313f76c597464dee05a03944f/params.yaml new file mode 100644 index 00000000..912eaff4 --- /dev/null +++ b/examples/security/classification/output/reports/train/f9a9e6f313f76c597464dee05a03944f/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/adv_predictions.json + adv_probabilities_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/2a8c6baf3e52e8e59b81932712566521.pkl + params_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/params.yaml + predictions_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/predictions.json + probabilities_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/probabilities.json + score_dict_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/score_dict.json + test_labels_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/test_labels.json + train_labels_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/train_labels.json + model_dir: models + name: f9a9e6f313f76c597464dee05a03944f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f68a8c89c834e2c458c2e749b0a05e12 + trainer: + kwargs: {} +name: f9a9e6f313f76c597464dee05a03944f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/params.yaml b/examples/security/classification/output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/params.yaml new file mode 100644 index 00000000..179c9515 --- /dev/null +++ b/examples/security/classification/output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/adv_predictions.json + adv_probabilities_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl + model_file: output/models/04834be10162a484eb0cca74ef0573bd.pkl + params_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/params.yaml + predictions_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/predictions.json + probabilities_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/probabilities.json + score_dict_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/score_dict.json + test_labels_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/test_labels.json + train_labels_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/train_labels.json + model_dir: models + name: f9adf91a581503c76ac3c2b4ee0ee951 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 7d24ace0c72b0d2db1a9a5e41567083c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1cb581c846242874891fb468479f2720 + trainer: + kwargs: {} +name: f9adf91a581503c76ac3c2b4ee0ee951 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f9b41a391d248a16e134009ba1694e9c/params.yaml b/examples/security/classification/output/reports/train/f9b41a391d248a16e134009ba1694e9c/params.yaml new file mode 100644 index 00000000..55494e5e --- /dev/null +++ b/examples/security/classification/output/reports/train/f9b41a391d248a16e134009ba1694e9c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/adv_predictions.json + adv_probabilities_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/c40f0b20272414fcbe10e2e07680c6fa.pkl + params_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/params.yaml + predictions_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/predictions.json + probabilities_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/probabilities.json + score_dict_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/score_dict.json + test_labels_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/test_labels.json + train_labels_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/train_labels.json + model_dir: models + name: f9b41a391d248a16e134009ba1694e9c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ffac1e04c194ee6e067b119eb472fb47 + trainer: + kwargs: {} +name: f9b41a391d248a16e134009ba1694e9c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/params.yaml b/examples/security/classification/output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/params.yaml new file mode 100644 index 00000000..6a27e595 --- /dev/null +++ b/examples/security/classification/output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/adv_predictions.json + adv_probabilities_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/2c71ee06a16b0c42e977a0402d904d38.pkl + params_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/params.yaml + predictions_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/predictions.json + probabilities_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/probabilities.json + score_dict_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/score_dict.json + test_labels_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/test_labels.json + train_labels_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/train_labels.json + model_dir: models + name: f9d6eb9cb4222a4ccf000f4239b829c2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f9fda14b9cfcfd15807d2459adf91cce + trainer: + kwargs: {} +name: f9d6eb9cb4222a4ccf000f4239b829c2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/params.yaml b/examples/security/classification/output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/params.yaml new file mode 100644 index 00000000..4927f00f --- /dev/null +++ b/examples/security/classification/output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/adv_predictions.json + adv_probabilities_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl + model_file: output/models/014a3f1e0e04c8a6bbe3a7093125ef58.pkl + params_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/params.yaml + predictions_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/predictions.json + probabilities_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/probabilities.json + score_dict_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/score_dict.json + test_labels_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/test_labels.json + train_labels_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/train_labels.json + model_dir: models + name: f9f35e4452e13f09e7670252d24b7dd1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: adad2c2f1660f3b976f025cc3901addc + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1be8bf5e13d0311b8d1e631fc09d8376 + trainer: + kwargs: {} +name: f9f35e4452e13f09e7670252d24b7dd1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f9f3caa439887279484339dbe2d05cf2/params.yaml b/examples/security/classification/output/reports/train/f9f3caa439887279484339dbe2d05cf2/params.yaml new file mode 100644 index 00000000..491171a7 --- /dev/null +++ b/examples/security/classification/output/reports/train/f9f3caa439887279484339dbe2d05cf2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/adv_predictions.json + adv_probabilities_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/021b1f7df6fa918344484cbaa29accf7.pkl + params_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/params.yaml + predictions_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/predictions.json + probabilities_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/probabilities.json + score_dict_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/score_dict.json + test_labels_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/test_labels.json + train_labels_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/train_labels.json + model_dir: models + name: f9f3caa439887279484339dbe2d05cf2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 36b6ddea0ead7ac79e68b090873f95d7 + trainer: + kwargs: {} +name: f9f3caa439887279484339dbe2d05cf2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/params.yaml b/examples/security/classification/output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/params.yaml new file mode 100644 index 00000000..276091a9 --- /dev/null +++ b/examples/security/classification/output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/params.yaml @@ -0,0 +1,107 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/adv_predictions.json + adv_probabilities_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/0e805b59ed829e9f5ea6cc4a7fcefc5e.pkl + params_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/params.yaml + predictions_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/predictions.json + probabilities_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/probabilities.json + score_dict_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/score_dict.json + test_labels_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/test_labels.json + train_labels_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/train_labels.json + model_dir: models + name: f9f685a89cdbe694f02acc840bd14ecd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1000 + degree: 5 + gamma: scale + kernel: + - poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c84cc16ceea3d325d4026a8e7d6acb6c + trainer: + kwargs: {} +name: f9f685a89cdbe694f02acc840bd14ecd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/params.yaml b/examples/security/classification/output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/params.yaml new file mode 100644 index 00000000..371fda26 --- /dev/null +++ b/examples/security/classification/output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/adv_predictions.json + adv_probabilities_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/09e4d24d205c842eb40197568c4f0927.pkl + params_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/params.yaml + predictions_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/predictions.json + probabilities_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/probabilities.json + score_dict_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/score_dict.json + test_labels_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/test_labels.json + train_labels_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/train_labels.json + model_dir: models + name: fa0b9ed5cf79088577de74ba70398f9b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 09c863a354d2ecedf53ba4c62df11bb5 + trainer: + kwargs: {} +name: fa0b9ed5cf79088577de74ba70398f9b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/params.yaml b/examples/security/classification/output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/params.yaml new file mode 100644 index 00000000..4243b7f5 --- /dev/null +++ b/examples/security/classification/output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/adv_predictions.json + adv_probabilities_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/bc1c9abc322e684f09f30581e4f6d674.pkl + params_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/params.yaml + predictions_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/predictions.json + probabilities_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/probabilities.json + score_dict_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/score_dict.json + test_labels_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/test_labels.json + train_labels_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/train_labels.json + model_dir: models + name: fa20b52eb5056d1a8ad6d486d23a1f6c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2f5c96adacc55cd0556ddaf7751b5b45 + trainer: + kwargs: {} +name: fa20b52eb5056d1a8ad6d486d23a1f6c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/params.yaml b/examples/security/classification/output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/params.yaml new file mode 100644 index 00000000..1fb123f5 --- /dev/null +++ b/examples/security/classification/output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/adv_predictions.json + adv_probabilities_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/1b88f024917d23e1dc7db82b98982f37.pkl + params_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/params.yaml + predictions_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/predictions.json + probabilities_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/probabilities.json + score_dict_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/score_dict.json + test_labels_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/test_labels.json + train_labels_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/train_labels.json + model_dir: models + name: fa2f50ea72ba5a7e20c2838475586c45 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 77b3a3698e280778a7193395d324ef13 + trainer: + kwargs: {} +name: fa2f50ea72ba5a7e20c2838475586c45 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fa53d45bdb490b035034f11d11176825/params.yaml b/examples/security/classification/output/reports/train/fa53d45bdb490b035034f11d11176825/params.yaml new file mode 100644 index 00000000..3726e820 --- /dev/null +++ b/examples/security/classification/output/reports/train/fa53d45bdb490b035034f11d11176825/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fa53d45bdb490b035034f11d11176825/adv_predictions.json + adv_probabilities_file: output/reports/train/fa53d45bdb490b035034f11d11176825/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/045ae7b0228f2b4bedec55bf6586f9fc.pkl + params_file: output/reports/train/fa53d45bdb490b035034f11d11176825/params.yaml + predictions_file: output/reports/train/fa53d45bdb490b035034f11d11176825/predictions.json + probabilities_file: output/reports/train/fa53d45bdb490b035034f11d11176825/probabilities.json + score_dict_file: output/reports/train/fa53d45bdb490b035034f11d11176825/score_dict.json + test_labels_file: output/reports/train/fa53d45bdb490b035034f11d11176825/test_labels.json + train_labels_file: output/reports/train/fa53d45bdb490b035034f11d11176825/train_labels.json + model_dir: models + name: fa53d45bdb490b035034f11d11176825 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0eb125ddfe465b774ab25c2fa597bf81 + trainer: + kwargs: {} +name: fa53d45bdb490b035034f11d11176825 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/params.yaml b/examples/security/classification/output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/params.yaml new file mode 100644 index 00000000..b88bc16b --- /dev/null +++ b/examples/security/classification/output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/adv_predictions.json + adv_probabilities_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl + model_file: output/models/1bf049137a86510e4fd1ad30f9c1848d.pkl + params_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/params.yaml + predictions_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/predictions.json + probabilities_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/probabilities.json + score_dict_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/score_dict.json + test_labels_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/test_labels.json + train_labels_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/train_labels.json + model_dir: models + name: fa92c0b651467be12fa65ee0f0cb8689 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: c19017431413b39638d3aec6a3342a81 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 45020341d0b49ad56c6730400435aead + trainer: + kwargs: {} +name: fa92c0b651467be12fa65ee0f0cb8689 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/params.yaml b/examples/security/classification/output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/params.yaml new file mode 100644 index 00000000..4e38afa0 --- /dev/null +++ b/examples/security/classification/output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/adv_predictions.json + adv_probabilities_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/fe3f725e2791b3f1243f53d01bf84a61.pkl + params_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/params.yaml + predictions_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/predictions.json + probabilities_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/probabilities.json + score_dict_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/score_dict.json + test_labels_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/test_labels.json + train_labels_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/train_labels.json + model_dir: models + name: faa9c91d771afc95c4ea027fb5e684e4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0e9c33b6bcd29f5421db12b16010f28e + trainer: + kwargs: {} +name: faa9c91d771afc95c4ea027fb5e684e4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fac17f44332a6dd8c3ea807774b6c247/params.yaml b/examples/security/classification/output/reports/train/fac17f44332a6dd8c3ea807774b6c247/params.yaml new file mode 100644 index 00000000..5f6abc01 --- /dev/null +++ b/examples/security/classification/output/reports/train/fac17f44332a6dd8c3ea807774b6c247/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/adv_predictions.json + adv_probabilities_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/0a413dc3a4cf30a6c3aa6c2812dc76e0.pkl + params_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/params.yaml + predictions_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/predictions.json + probabilities_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/probabilities.json + score_dict_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/score_dict.json + test_labels_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/test_labels.json + train_labels_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/train_labels.json + model_dir: models + name: fac17f44332a6dd8c3ea807774b6c247 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1fd99c93fbe2940545bd86b6eb41aeae + trainer: + kwargs: {} +name: fac17f44332a6dd8c3ea807774b6c247 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fadc650bab3c7e833094acfba16068b9/params.yaml b/examples/security/classification/output/reports/train/fadc650bab3c7e833094acfba16068b9/params.yaml new file mode 100644 index 00000000..4e57007a --- /dev/null +++ b/examples/security/classification/output/reports/train/fadc650bab3c7e833094acfba16068b9/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/adv_predictions.json + adv_probabilities_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/645ddc632c726160ad52a12bb1e9f674.pkl + params_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/params.yaml + predictions_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/predictions.json + probabilities_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/probabilities.json + score_dict_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/score_dict.json + test_labels_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/test_labels.json + train_labels_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/train_labels.json + model_dir: models + name: fadc650bab3c7e833094acfba16068b9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bf79b52003937c4d4fba59a95a14ce2e + trainer: + kwargs: {} +name: fadc650bab3c7e833094acfba16068b9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fadf57ffb82b49ad128097e830d4dddc/params.yaml b/examples/security/classification/output/reports/train/fadf57ffb82b49ad128097e830d4dddc/params.yaml new file mode 100644 index 00000000..354dacd7 --- /dev/null +++ b/examples/security/classification/output/reports/train/fadf57ffb82b49ad128097e830d4dddc/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/adv_predictions.json + adv_probabilities_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/30740a89c60c07f6676d137493e3620d.pkl + params_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/params.yaml + predictions_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/predictions.json + probabilities_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/probabilities.json + score_dict_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/score_dict.json + test_labels_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/test_labels.json + train_labels_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/train_labels.json + model_dir: models + name: fadf57ffb82b49ad128097e830d4dddc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bde64ea3f0b55e4b4c57737be33e90f6 + trainer: + kwargs: {} +name: fadf57ffb82b49ad128097e830d4dddc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fb01aec510bdf73ce0a211ab751636ca/params.yaml b/examples/security/classification/output/reports/train/fb01aec510bdf73ce0a211ab751636ca/params.yaml new file mode 100644 index 00000000..a990c153 --- /dev/null +++ b/examples/security/classification/output/reports/train/fb01aec510bdf73ce0a211ab751636ca/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/adv_predictions.json + adv_probabilities_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/14ce1b0ce5c9dbd8ba4cb8b6b78817bc.pkl + params_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/params.yaml + predictions_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/predictions.json + probabilities_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/probabilities.json + score_dict_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/score_dict.json + test_labels_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/test_labels.json + train_labels_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/train_labels.json + model_dir: models + name: fb01aec510bdf73ce0a211ab751636ca + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a793a9edd9c3ecedb7fa998779ddc5ea + trainer: + kwargs: {} +name: fb01aec510bdf73ce0a211ab751636ca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fb08b1ac8d579c576eae21463d2e9883/params.yaml b/examples/security/classification/output/reports/train/fb08b1ac8d579c576eae21463d2e9883/params.yaml new file mode 100644 index 00000000..a1ebad11 --- /dev/null +++ b/examples/security/classification/output/reports/train/fb08b1ac8d579c576eae21463d2e9883/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/adv_predictions.json + adv_probabilities_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/19cb90556e27f7856d79094a7cdbd5d5.pkl + params_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/params.yaml + predictions_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/predictions.json + probabilities_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/probabilities.json + score_dict_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/score_dict.json + test_labels_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/test_labels.json + train_labels_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/train_labels.json + model_dir: models + name: fb08b1ac8d579c576eae21463d2e9883 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ec7ca04cdef37316c9de4d834038eb4c + trainer: + kwargs: {} +name: fb08b1ac8d579c576eae21463d2e9883 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/params.yaml b/examples/security/classification/output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/params.yaml new file mode 100644 index 00000000..d9217bec --- /dev/null +++ b/examples/security/classification/output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/adv_predictions.json + adv_probabilities_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/089fdc6f4a1fd7b09abe2f61d5df8bef.pkl + params_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/params.yaml + predictions_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/predictions.json + probabilities_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/probabilities.json + score_dict_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/score_dict.json + test_labels_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/test_labels.json + train_labels_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/train_labels.json + model_dir: models + name: fb14765f2377c73cbfcf8ba8e9ac04a6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d69d5bd83f887172897b541c0efab437 + trainer: + kwargs: {} +name: fb14765f2377c73cbfcf8ba8e9ac04a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/params.yaml b/examples/security/classification/output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/params.yaml new file mode 100644 index 00000000..158753e6 --- /dev/null +++ b/examples/security/classification/output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/adv_predictions.json + adv_probabilities_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/2577b7551395febb04ae8bd76fddc3c9.pkl + params_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/params.yaml + predictions_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/predictions.json + probabilities_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/probabilities.json + score_dict_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/score_dict.json + test_labels_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/test_labels.json + train_labels_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/train_labels.json + model_dir: models + name: fb179f9342c16a9c9123ed76e3a7bb00 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 73e22735885e3d35005d776101d06b1a + trainer: + kwargs: {} +name: fb179f9342c16a9c9123ed76e3a7bb00 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fb23461e811a7530ab87cbc51427cce8/params.yaml b/examples/security/classification/output/reports/train/fb23461e811a7530ab87cbc51427cce8/params.yaml new file mode 100644 index 00000000..0ce0427c --- /dev/null +++ b/examples/security/classification/output/reports/train/fb23461e811a7530ab87cbc51427cce8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/adv_predictions.json + adv_probabilities_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl + model_file: output/models/62887a6b3b058afa2aa26df7d36d0227.pkl + params_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/params.yaml + predictions_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/predictions.json + probabilities_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/probabilities.json + score_dict_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/score_dict.json + test_labels_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/test_labels.json + train_labels_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/train_labels.json + model_dir: models + name: fb23461e811a7530ab87cbc51427cce8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 0416443d5d0c2adf85c100e28a421762 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f27a2551e9bfea27bf365e29490d72ca + trainer: + kwargs: {} +name: fb23461e811a7530ab87cbc51427cce8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/params.yaml b/examples/security/classification/output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/params.yaml new file mode 100644 index 00000000..efd7fb94 --- /dev/null +++ b/examples/security/classification/output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/adv_predictions.json + adv_probabilities_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/6ae3b5812e657d27256da536a4aaf1ee.pkl + params_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/params.yaml + predictions_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/predictions.json + probabilities_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/probabilities.json + score_dict_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/score_dict.json + test_labels_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/test_labels.json + train_labels_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/train_labels.json + model_dir: models + name: fb4413494a3630a31e0dacf79aaaf33e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cc732dffa5e9e5803a3db4a39a299287 + trainer: + kwargs: {} +name: fb4413494a3630a31e0dacf79aaaf33e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fb4755fa4fe2a11a007381d048cc3142/params.yaml b/examples/security/classification/output/reports/train/fb4755fa4fe2a11a007381d048cc3142/params.yaml new file mode 100644 index 00000000..32353a74 --- /dev/null +++ b/examples/security/classification/output/reports/train/fb4755fa4fe2a11a007381d048cc3142/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/adv_predictions.json + adv_probabilities_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/cda5ef19a3cc4fbbea82dc700924e5ef.pkl + params_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/params.yaml + predictions_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/predictions.json + probabilities_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/probabilities.json + score_dict_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/score_dict.json + test_labels_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/test_labels.json + train_labels_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/train_labels.json + model_dir: models + name: fb4755fa4fe2a11a007381d048cc3142 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 32e5b406cb9b33a029812d46436e2aa8 + trainer: + kwargs: {} +name: fb4755fa4fe2a11a007381d048cc3142 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fb513682740458bd659bea08757c66c0/params.yaml b/examples/security/classification/output/reports/train/fb513682740458bd659bea08757c66c0/params.yaml new file mode 100644 index 00000000..584c6eb1 --- /dev/null +++ b/examples/security/classification/output/reports/train/fb513682740458bd659bea08757c66c0/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fb513682740458bd659bea08757c66c0/adv_predictions.json + adv_probabilities_file: output/reports/train/fb513682740458bd659bea08757c66c0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/bb27e68d68f198637925f23c1561a40c.pkl + params_file: output/reports/train/fb513682740458bd659bea08757c66c0/params.yaml + predictions_file: output/reports/train/fb513682740458bd659bea08757c66c0/predictions.json + probabilities_file: output/reports/train/fb513682740458bd659bea08757c66c0/probabilities.json + score_dict_file: output/reports/train/fb513682740458bd659bea08757c66c0/score_dict.json + test_labels_file: output/reports/train/fb513682740458bd659bea08757c66c0/test_labels.json + train_labels_file: output/reports/train/fb513682740458bd659bea08757c66c0/train_labels.json + model_dir: models + name: fb513682740458bd659bea08757c66c0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bfe59ae11397776036e6083c1343f625 + trainer: + kwargs: {} +name: fb513682740458bd659bea08757c66c0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/params.yaml b/examples/security/classification/output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/params.yaml new file mode 100644 index 00000000..968fd977 --- /dev/null +++ b/examples/security/classification/output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/adv_predictions.json + adv_probabilities_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/2a077604b53d4a4ec9242a953e9dfd5d.pkl + params_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/params.yaml + predictions_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/predictions.json + probabilities_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/probabilities.json + score_dict_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/score_dict.json + test_labels_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/test_labels.json + train_labels_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/train_labels.json + model_dir: models + name: fb70727062b0c2b7d20d049d42ec06f6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: + - rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 920d773ccdc73dd608822667ab08bf51 + trainer: + kwargs: {} +name: fb70727062b0c2b7d20d049d42ec06f6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/params.yaml b/examples/security/classification/output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/params.yaml new file mode 100644 index 00000000..28b50c34 --- /dev/null +++ b/examples/security/classification/output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/adv_predictions.json + adv_probabilities_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/67bafdfbdd424ba32cb3a59195bb01df.pkl + params_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/params.yaml + predictions_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/predictions.json + probabilities_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/probabilities.json + score_dict_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/score_dict.json + test_labels_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/test_labels.json + train_labels_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/train_labels.json + model_dir: models + name: fc397d0184822d72e181f49aa2e7a3ed + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7afc461b580b167ac9aa374a52e6599f + trainer: + kwargs: {} +name: fc397d0184822d72e181f49aa2e7a3ed +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fc54944e029839b6ec676791c41f0eec/params.yaml b/examples/security/classification/output/reports/train/fc54944e029839b6ec676791c41f0eec/params.yaml new file mode 100644 index 00000000..6132f83a --- /dev/null +++ b/examples/security/classification/output/reports/train/fc54944e029839b6ec676791c41f0eec/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/adv_predictions.json + adv_probabilities_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/6c7bad768ceccd0f82cf401c7832620e.pkl + params_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/params.yaml + predictions_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/predictions.json + probabilities_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/probabilities.json + score_dict_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/score_dict.json + test_labels_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/test_labels.json + train_labels_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/train_labels.json + model_dir: models + name: fc54944e029839b6ec676791c41f0eec + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6c627fcd72bef2cb69d6cc1e1ef40bad + trainer: + kwargs: {} +name: fc54944e029839b6ec676791c41f0eec +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fc5dd786b88040fb926d1efbd0da8935/params.yaml b/examples/security/classification/output/reports/train/fc5dd786b88040fb926d1efbd0da8935/params.yaml new file mode 100644 index 00000000..b2fe4da1 --- /dev/null +++ b/examples/security/classification/output/reports/train/fc5dd786b88040fb926d1efbd0da8935/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/adv_predictions.json + adv_probabilities_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/31aa461949cca3435137356ddfd1540c.pkl + params_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/params.yaml + predictions_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/predictions.json + probabilities_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/probabilities.json + score_dict_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/score_dict.json + test_labels_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/test_labels.json + train_labels_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/train_labels.json + model_dir: models + name: fc5dd786b88040fb926d1efbd0da8935 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c33dd02cab635ca7969877b47e862f72 + trainer: + kwargs: {} +name: fc5dd786b88040fb926d1efbd0da8935 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/params.yaml b/examples/security/classification/output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/params.yaml new file mode 100644 index 00000000..c13f5eaf --- /dev/null +++ b/examples/security/classification/output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/adv_predictions.json + adv_probabilities_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl + model_file: output/models/f624e57ef711de2a97a4d0342b87cbe2.pkl + params_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/params.yaml + predictions_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/predictions.json + probabilities_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/probabilities.json + score_dict_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/score_dict.json + test_labels_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/test_labels.json + train_labels_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/train_labels.json + model_dir: models + name: fc6c7262d8f25ad77eda12b8325502d0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 5d7ff5375ad08af05930926f2c0aeb78 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9fa54271a7c33523b5637d57ff44746b + trainer: + kwargs: {} +name: fc6c7262d8f25ad77eda12b8325502d0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/params.yaml b/examples/security/classification/output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/params.yaml new file mode 100644 index 00000000..54d786df --- /dev/null +++ b/examples/security/classification/output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/adv_predictions.json + adv_probabilities_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl + model_file: output/models/db0ef1e5f3feae5e3dea46f55bf3f5a3.pkl + params_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/params.yaml + predictions_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/predictions.json + probabilities_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/probabilities.json + score_dict_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/score_dict.json + test_labels_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/test_labels.json + train_labels_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/train_labels.json + model_dir: models + name: fc6dbf2af2c90df889c98aee11bf8276 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: e25866e5c6b40fe7dad2dddab07f52ff + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.001 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1395033c8a58ac6d3a1b97ae94131c2c + trainer: + kwargs: {} +name: fc6dbf2af2c90df889c98aee11bf8276 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/params.yaml b/examples/security/classification/output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/params.yaml new file mode 100644 index 00000000..7fb61980 --- /dev/null +++ b/examples/security/classification/output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/adv_predictions.json + adv_probabilities_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/deb223244689141c484f46d6350b734c.pkl + params_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/params.yaml + predictions_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/predictions.json + probabilities_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/probabilities.json + score_dict_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/score_dict.json + test_labels_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/test_labels.json + train_labels_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/train_labels.json + model_dir: models + name: fca7de23aa97b9225a2bbd6f8b11ee96 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d7cb0d36725d9cb5e0178be17f125adc + trainer: + kwargs: {} +name: fca7de23aa97b9225a2bbd6f8b11ee96 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fcc9639c47002b9bb83739ab67336ac2/params.yaml b/examples/security/classification/output/reports/train/fcc9639c47002b9bb83739ab67336ac2/params.yaml new file mode 100644 index 00000000..6c198fe3 --- /dev/null +++ b/examples/security/classification/output/reports/train/fcc9639c47002b9bb83739ab67336ac2/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/adv_predictions.json + adv_probabilities_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/4992f1177927ae1f26ceb3d13719b260.pkl + params_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/params.yaml + predictions_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/predictions.json + probabilities_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/probabilities.json + score_dict_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/score_dict.json + test_labels_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/test_labels.json + train_labels_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/train_labels.json + model_dir: models + name: fcc9639c47002b9bb83739ab67336ac2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6bfdc9e274c20663f8b44e4642b26540 + trainer: + kwargs: {} +name: fcc9639c47002b9bb83739ab67336ac2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/params.yaml b/examples/security/classification/output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/params.yaml new file mode 100644 index 00000000..8872a171 --- /dev/null +++ b/examples/security/classification/output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/adv_predictions.json + adv_probabilities_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/9a3064d0570b1d1f9926ba08abf64de6.pkl + params_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/params.yaml + predictions_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/predictions.json + probabilities_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/probabilities.json + score_dict_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/score_dict.json + test_labels_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/test_labels.json + train_labels_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/train_labels.json + model_dir: models + name: fcfa80f7eaa34c8a42310702e2e1d64e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2c21a82c4cb1a1acb0ea08d433b70390 + trainer: + kwargs: {} +name: fcfa80f7eaa34c8a42310702e2e1d64e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/params.yaml b/examples/security/classification/output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/params.yaml new file mode 100644 index 00000000..4e86bd20 --- /dev/null +++ b/examples/security/classification/output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/adv_predictions.json + adv_probabilities_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/2d3c3f4e0bd32f21f8121b38b5337316.pkl + params_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/params.yaml + predictions_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/predictions.json + probabilities_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/probabilities.json + score_dict_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/score_dict.json + test_labels_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/test_labels.json + train_labels_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/train_labels.json + model_dir: models + name: fcfcb64fc3d3c8d6eb5ee3a0c7d396a4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1d435f2fd8685d0eadf1169897119876 + trainer: + kwargs: {} +name: fcfcb64fc3d3c8d6eb5ee3a0c7d396a4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/params.yaml b/examples/security/classification/output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/params.yaml new file mode 100644 index 00000000..6ba35d3b --- /dev/null +++ b/examples/security/classification/output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/adv_predictions.json + adv_probabilities_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl + model_file: output/models/9bbd235b3b785c2e51f810a9b9a5e1f6.pkl + params_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/params.yaml + predictions_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/predictions.json + probabilities_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/probabilities.json + score_dict_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/score_dict.json + test_labels_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/test_labels.json + train_labels_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/train_labels.json + model_dir: models + name: fd1e87b8a402c2d98ead0b7caca67c6a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: df8212985b44252bf2d873135313ad43 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: acd9259363ebdfa5c2feb3c79ac0b1ce + trainer: + kwargs: {} +name: fd1e87b8a402c2d98ead0b7caca67c6a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/params.yaml b/examples/security/classification/output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/params.yaml new file mode 100644 index 00000000..94775c4e --- /dev/null +++ b/examples/security/classification/output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/adv_predictions.json + adv_probabilities_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl + model_file: output/models/880487d23814bb7ce5b15f05d2914dd8.pkl + params_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/params.yaml + predictions_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/predictions.json + probabilities_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/probabilities.json + score_dict_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/score_dict.json + test_labels_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/test_labels.json + train_labels_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/train_labels.json + model_dir: models + name: fd1f6cff410e8d3f807f3d8db97a9ad3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: db374a98e9c571bca6dcbbf091eec40b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 28ad861af92ac15f5d0041a6852635cf + trainer: + kwargs: {} +name: fd1f6cff410e8d3f807f3d8db97a9ad3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/params.yaml b/examples/security/classification/output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/params.yaml new file mode 100644 index 00000000..06e5322d --- /dev/null +++ b/examples/security/classification/output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/adv_predictions.json + adv_probabilities_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl + model_file: output/models/a096a075643b558cbe40adc0d4c03f70.pkl + params_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/params.yaml + predictions_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/predictions.json + probabilities_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/probabilities.json + score_dict_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/score_dict.json + test_labels_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/test_labels.json + train_labels_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/train_labels.json + model_dir: models + name: fd375c576de5cfea71cdd9607a94fa7a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 206841f63cbde0cb85b56364545456c8 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b3291b618ec1328414c741591254b96c + trainer: + kwargs: {} +name: fd375c576de5cfea71cdd9607a94fa7a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fd6435f2e206379615b4b4007d31e167/params.yaml b/examples/security/classification/output/reports/train/fd6435f2e206379615b4b4007d31e167/params.yaml new file mode 100644 index 00000000..fe794ca4 --- /dev/null +++ b/examples/security/classification/output/reports/train/fd6435f2e206379615b4b4007d31e167/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/adv_predictions.json + adv_probabilities_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/47d941b1d7c60bd2b4d268e48442c6bf.pkl + params_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/params.yaml + predictions_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/predictions.json + probabilities_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/probabilities.json + score_dict_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/score_dict.json + test_labels_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/test_labels.json + train_labels_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/train_labels.json + model_dir: models + name: fd6435f2e206379615b4b4007d31e167 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 14ba5b7a1d786a0d9c55849d31e3d240 + trainer: + kwargs: {} +name: fd6435f2e206379615b4b4007d31e167 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/params.yaml b/examples/security/classification/output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/params.yaml new file mode 100644 index 00000000..8fbab9ac --- /dev/null +++ b/examples/security/classification/output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/adv_predictions.json + adv_probabilities_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/9921825c42078f57702c88471cdd1537.pkl + model_file: output/models/b8a5c074b6838ea657b79ede55b71fb7.pkl + params_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/params.yaml + predictions_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/predictions.json + probabilities_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/probabilities.json + score_dict_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/score_dict.json + test_labels_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/test_labels.json + train_labels_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/train_labels.json + model_dir: models + name: fd65f6a0f6463364ef40c056cc5501b1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10000 + n_samples: 110000 + random_state: 0 + name: classification + name: 3036dd9056c364ce4717b01d38bd62c4 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: caf8026e7801d023d74dcf86c912273e + trainer: + kwargs: {} +name: fd65f6a0f6463364ef40c056cc5501b1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/params.yaml b/examples/security/classification/output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/params.yaml new file mode 100644 index 00000000..2d477f2a --- /dev/null +++ b/examples/security/classification/output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/adv_predictions.json + adv_probabilities_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl + model_file: output/models/e0add251e98d8373cbf0b61e67470fd1.pkl + params_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/params.yaml + predictions_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/predictions.json + probabilities_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/probabilities.json + score_dict_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/score_dict.json + test_labels_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/test_labels.json + train_labels_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/train_labels.json + model_dir: models + name: fd6d3610f17b3118e0ed4493e34f5ff8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 0dc7ad7c144259e984975957950b2c52 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 41dcfa133335d798276eb68f8c66b134 + trainer: + kwargs: {} +name: fd6d3610f17b3118e0ed4493e34f5ff8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/params.yaml b/examples/security/classification/output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/params.yaml new file mode 100644 index 00000000..751aae7b --- /dev/null +++ b/examples/security/classification/output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/adv_predictions.json + adv_probabilities_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/7a08d3514a007f4986b7f3ba4171e5c2.pkl + params_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/params.yaml + predictions_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/predictions.json + probabilities_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/probabilities.json + score_dict_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/score_dict.json + test_labels_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/test_labels.json + train_labels_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/train_labels.json + model_dir: models + name: fdb0ee7bf33dfd0fc28bd970705cb208 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1000 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aa0095918ae079390b05ff5a49be1d26 + trainer: + kwargs: {} +name: fdb0ee7bf33dfd0fc28bd970705cb208 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/params.yaml b/examples/security/classification/output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/params.yaml new file mode 100644 index 00000000..68b28867 --- /dev/null +++ b/examples/security/classification/output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/adv_predictions.json + adv_probabilities_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/517012bdc8320af7cb28021220c382e8.pkl + params_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/params.yaml + predictions_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/predictions.json + probabilities_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/probabilities.json + score_dict_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/score_dict.json + test_labels_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/test_labels.json + train_labels_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/train_labels.json + model_dir: models + name: fe13fda00ac7e07fc883a8a5c27dc4be + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 53c98e3f226f76194505a8b98a3f5369 + trainer: + kwargs: {} +name: fe13fda00ac7e07fc883a8a5c27dc4be +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/params.yaml b/examples/security/classification/output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/params.yaml new file mode 100644 index 00000000..c11a1127 --- /dev/null +++ b/examples/security/classification/output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/adv_predictions.json + adv_probabilities_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl + model_file: output/models/8b5dc9823d5b3acd47dff6eae429665c.pkl + params_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/params.yaml + predictions_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/predictions.json + probabilities_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/probabilities.json + score_dict_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/score_dict.json + test_labels_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/test_labels.json + train_labels_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/train_labels.json + model_dir: models + name: fe8ddc0ecbaed829983aabb3f92c249c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 5e7f8fb4d9da05ff8d6a1e6284494318 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7311993522a051bc1381bf68ee626e39 + trainer: + kwargs: {} +name: fe8ddc0ecbaed829983aabb3f92c249c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/params.yaml b/examples/security/classification/output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/params.yaml new file mode 100644 index 00000000..a5ff6bee --- /dev/null +++ b/examples/security/classification/output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/adv_predictions.json + adv_probabilities_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl + model_file: output/models/a237ec48d72ab8b02675cf53941e09ed.pkl + params_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/params.yaml + predictions_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/predictions.json + probabilities_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/probabilities.json + score_dict_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/score_dict.json + test_labels_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/test_labels.json + train_labels_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/train_labels.json + model_dir: models + name: fe9266ea06e93c3c8540bcf631c2564e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 955aa47fcd397e98dc2d7eeb13f7a251 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ef53942ad863739f812a0b3e91d48924 + trainer: + kwargs: {} +name: fe9266ea06e93c3c8540bcf631c2564e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fec6b66375c3ffa0ac639de1eb589356/params.yaml b/examples/security/classification/output/reports/train/fec6b66375c3ffa0ac639de1eb589356/params.yaml new file mode 100644 index 00000000..b782a312 --- /dev/null +++ b/examples/security/classification/output/reports/train/fec6b66375c3ffa0ac639de1eb589356/params.yaml @@ -0,0 +1,206 @@ +attack: + attack_size: 10 + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000.0 + time_series: false + train_size: 10.0 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d3de07647c3505f0dd7f58327724e3a7 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: {} + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 21499fc66c748249dbd6219e76a7fe4f + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d1db5d4ce6d952ed89b5b87e0a2b8eed + trainer: + kwargs: {} + name: f767fdf82eff5d5decbf7a0425adf459 +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/adv_predictions.json + adv_probabilities_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/adv_probabilities.json + attack_file: output/attacks/6820d87929e14a603509d91b807b4dae.pkl + data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl + model_file: output/models/2e2546e74bbb0e4570a91ce5f2b0db12.pkl + params_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/params.yaml + predictions_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/predictions.json + probabilities_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/probabilities.json + score_dict_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/score_dict.json + test_labels_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/test_labels.json + train_labels_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/train_labels.json + model_dir: models + name: fec6b66375c3ffa0ac639de1eb589356 + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv + name: 5724fd0f78a918663d5d84d131787520 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9d65f4c5a39650c61655adfe988b464a + trainer: + kwargs: {} +name: fec6b66375c3ffa0ac639de1eb589356 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/params.yaml b/examples/security/classification/output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/params.yaml new file mode 100644 index 00000000..7dfe698c --- /dev/null +++ b/examples/security/classification/output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/adv_predictions.json + adv_probabilities_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl + model_file: output/models/e94f04f4924c88c4218c0cffeb38be8c.pkl + params_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/params.yaml + predictions_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/predictions.json + probabilities_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/probabilities.json + score_dict_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/score_dict.json + test_labels_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/test_labels.json + train_labels_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/train_labels.json + model_dir: models + name: fed3e0a9c687d6b0ecb6a59cc34c6aea + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 3c658331bf9aa200e4bb240d23c93537 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5ef906bc3a3c1ae6925cad5c82b19078 + trainer: + kwargs: {} +name: fed3e0a9c687d6b0ecb6a59cc34c6aea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fed48412a902a1acf55afbd4f5c05e90/params.yaml b/examples/security/classification/output/reports/train/fed48412a902a1acf55afbd4f5c05e90/params.yaml new file mode 100644 index 00000000..17993033 --- /dev/null +++ b/examples/security/classification/output/reports/train/fed48412a902a1acf55afbd4f5c05e90/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/adv_predictions.json + adv_probabilities_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl + model_file: output/models/d2cdc597b6cb85e8d0f76d6d508df9d8.pkl + params_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/params.yaml + predictions_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/predictions.json + probabilities_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/probabilities.json + score_dict_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/score_dict.json + test_labels_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/test_labels.json + train_labels_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/train_labels.json + model_dir: models + name: fed48412a902a1acf55afbd4f5c05e90 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 764d34f2d7b2544174db30270e130e83 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0d50a1e8330178f028856ad987a10317 + trainer: + kwargs: {} +name: fed48412a902a1acf55afbd4f5c05e90 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/fefee03d9029aa8de6187d838fd95d7c/params.yaml b/examples/security/classification/output/reports/train/fefee03d9029aa8de6187d838fd95d7c/params.yaml new file mode 100644 index 00000000..ba6f8ff8 --- /dev/null +++ b/examples/security/classification/output/reports/train/fefee03d9029aa8de6187d838fd95d7c/params.yaml @@ -0,0 +1,103 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/adv_predictions.json + adv_probabilities_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl + model_file: output/models/324ae9df16d0db7e1e3b4534803da896.pkl + params_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/params.yaml + predictions_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/predictions.json + probabilities_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/probabilities.json + score_dict_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/score_dict.json + test_labels_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/test_labels.json + train_labels_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/train_labels.json + model_dir: models + name: fefee03d9029aa8de6187d838fd95d7c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: 12cc1a19978b591ed330f9a91ea5b2a0 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 99e4cab232281b81ca46cdc23a1d3c72 + trainer: + kwargs: {} +name: fefee03d9029aa8de6187d838fd95d7c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ff0a2a54859b72a53699b2a15d91a818/params.yaml b/examples/security/classification/output/reports/train/ff0a2a54859b72a53699b2a15d91a818/params.yaml new file mode 100644 index 00000000..f38dd830 --- /dev/null +++ b/examples/security/classification/output/reports/train/ff0a2a54859b72a53699b2a15d91a818/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/adv_predictions.json + adv_probabilities_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl + model_file: output/models/f4016278e77b7303bbe9e6ba61df5254.pkl + params_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/params.yaml + predictions_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/predictions.json + probabilities_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/probabilities.json + score_dict_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/score_dict.json + test_labels_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/test_labels.json + train_labels_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/train_labels.json + model_dir: models + name: ff0a2a54859b72a53699b2a15d91a818 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: 2572635b7a115744874d878d0ffcd47b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 91f40ed1f12d2b922664b759a23708ba + trainer: + kwargs: {} +name: ff0a2a54859b72a53699b2a15d91a818 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ff435217cfd79669e2bfa36cf473a30a/params.yaml b/examples/security/classification/output/reports/train/ff435217cfd79669e2bfa36cf473a30a/params.yaml new file mode 100644 index 00000000..5a184820 --- /dev/null +++ b/examples/security/classification/output/reports/train/ff435217cfd79669e2bfa36cf473a30a/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/adv_predictions.json + adv_probabilities_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/4943e578bde3617e86866619d243dee2.pkl + model_file: output/models/8d979d8b4022f03e3a4346f43a761c13.pkl + params_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/params.yaml + predictions_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/predictions.json + probabilities_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/probabilities.json + score_dict_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/score_dict.json + test_labels_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/test_labels.json + train_labels_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/train_labels.json + model_dir: models + name: ff435217cfd79669e2bfa36cf473a30a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 81fa69e4e139f9c390fa6d5bf216069b + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 100000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 39789529ef38c1568d3cb8ee5aa25d29 + trainer: + kwargs: {} +name: ff435217cfd79669e2bfa36cf473a30a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/params.yaml b/examples/security/classification/output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/params.yaml new file mode 100644 index 00000000..4a4ac4b4 --- /dev/null +++ b/examples/security/classification/output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/adv_predictions.json + adv_probabilities_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl + model_file: output/models/6ea0c83e303c880adf69b5c3f73e0dc2.pkl + params_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/params.yaml + predictions_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/predictions.json + probabilities_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/probabilities.json + score_dict_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/score_dict.json + test_labels_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/test_labels.json + train_labels_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/train_labels.json + model_dir: models + name: ff49a54d68d8e432eeb292fd1230bf4f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 1010000 + random_state: 0 + name: classification + name: 6a684319b5187070a58128a5b600489c + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1e660d6fb6ea45dbbba76808d8e89549 + trainer: + kwargs: {} +name: ff49a54d68d8e432eeb292fd1230bf4f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/params.yaml b/examples/security/classification/output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/params.yaml new file mode 100644 index 00000000..75489dcc --- /dev/null +++ b/examples/security/classification/output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/params.yaml @@ -0,0 +1,115 @@ +data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/adv_predictions.json + adv_probabilities_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl + model_file: output/models/ade8ed57191158b3cdb3d18179ba5c70.pkl + params_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/params.yaml + predictions_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/predictions.json + probabilities_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/probabilities.json + score_dict_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/score_dict.json + test_labels_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/test_labels.json + train_labels_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/train_labels.json + model_dir: models + name: ff61521aa5cfeb2dff3957f3c0e6006b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 20 + n_samples: 1100 + random_state: 0 + name: classification + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 0.2 + time_series: false + train_size: 0.8 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: ignore + sparse: false + name: sklearn.preprocessing.OneHotEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c60bca6e3aecbdfee321f2aa473be68b + trainer: + kwargs: {} +name: ff61521aa5cfeb2dff3957f3c0e6006b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ffab8a13ab042999ffae702acd280cdf/params.yaml b/examples/security/classification/output/reports/train/ffab8a13ab042999ffae702acd280cdf/params.yaml new file mode 100644 index 00000000..6a93a450 --- /dev/null +++ b/examples/security/classification/output/reports/train/ffab8a13ab042999ffae702acd280cdf/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/adv_predictions.json + adv_probabilities_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl + model_file: output/models/eaa4843929b5743158570eae7935416a.pkl + params_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/params.yaml + predictions_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/predictions.json + probabilities_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/probabilities.json + score_dict_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/score_dict.json + test_labels_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/test_labels.json + train_labels_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/train_labels.json + model_dir: models + name: ffab8a13ab042999ffae702acd280cdf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 1000 + n_samples: 110000 + random_state: 0 + name: classification + name: e2537bfc84785ae872f4a2df5b87b0c9 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 45d2c4bc3e260a372648dd964d0f5f06 + trainer: + kwargs: {} +name: ffab8a13ab042999ffae702acd280cdf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/params.yaml b/examples/security/classification/output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/params.yaml new file mode 100644 index 00000000..07c4e33e --- /dev/null +++ b/examples/security/classification/output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/adv_predictions.json + adv_probabilities_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl + model_file: output/models/7588f507246b15fcb0249ec6a389ef1f.pkl + params_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/params.yaml + predictions_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/predictions.json + probabilities_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/probabilities.json + score_dict_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/score_dict.json + test_labels_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/test_labels.json + train_labels_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/train_labels.json + model_dir: models + name: ffc6cb1a40072c62411c2c86e4693d61 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 100 + n_samples: 110000 + random_state: 0 + name: classification + name: 6c007e9fe4704d82fb7915f82f581d1d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 10000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 100 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c0d431ba776e252cd655f083cf5931ca + trainer: + kwargs: {} +name: ffc6cb1a40072c62411c2c86e4693d61 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/params.yaml b/examples/security/classification/output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/params.yaml new file mode 100644 index 00000000..f6d77b68 --- /dev/null +++ b/examples/security/classification/output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/params.yaml @@ -0,0 +1,106 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/adv_predictions.json + adv_probabilities_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl + model_file: output/models/1baf2f21a5a38f23fcf3910e9d8de355.pkl + params_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/params.yaml + predictions_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/predictions.json + probabilities_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/probabilities.json + score_dict_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/score_dict.json + test_labels_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/test_labels.json + train_labels_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/train_labels.json + model_dir: models + name: ffc844a02a3e59d94f5ddd6b1c43089e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 1010000 + random_state: 0 + name: classification + name: 1f7903275c72068a778612a686c61d3d + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 10 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: abd9e19303adfe819d6d4f07c8053cc7 + trainer: + kwargs: {} +name: ffc844a02a3e59d94f5ddd6b1c43089e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/classification/output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/params.yaml b/examples/security/classification/output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/params.yaml new file mode 100644 index 00000000..1255dbe2 --- /dev/null +++ b/examples/security/classification/output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/params.yaml @@ -0,0 +1,105 @@ +data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/adv_predictions.json + adv_probabilities_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl + model_file: output/models/d4894dacdc5de6cb562a8bfbbcb1e07c.pkl + params_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/params.yaml + predictions_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/predictions.json + probabilities_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/probabilities.json + score_dict_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/score_dict.json + test_labels_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/test_labels.json + train_labels_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/train_labels.json + model_dir: models + name: ffd7416df4e7ba8f91c2d1ef03ed195e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + generate: + kwargs: + n_features: 10 + n_samples: 110000 + random_state: 0 + name: classification + name: a9530d121484629b59a0240897e7b455 + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 1000000 + sklearn_pipeline: + pipeline: + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + init: + kwargs: + C: 1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 100 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3b7d2e19708501cda689b54f97a4e328 + trainer: + kwargs: {} +name: ffd7416df4e7ba8f91c2d1ef03ed195e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/classification/params.yaml b/examples/security/classification/params.yaml similarity index 100% rename from examples/classification/params.yaml rename to examples/security/classification/params.yaml diff --git a/examples/classification/plots.py b/examples/security/classification/plots.py similarity index 100% rename from examples/classification/plots.py rename to examples/security/classification/plots.py diff --git a/examples/classification/plots/.gitignore b/examples/security/classification/plots/.gitignore similarity index 100% rename from examples/classification/plots/.gitignore rename to examples/security/classification/plots/.gitignore diff --git a/examples/classification/retrain.py b/examples/security/classification/retrain.py similarity index 100% rename from examples/classification/retrain.py rename to examples/security/classification/retrain.py diff --git a/examples/kdd-nsl/.dvc/.gitignore b/examples/security/kdd-nsl/.dvc/.gitignore similarity index 100% rename from examples/kdd-nsl/.dvc/.gitignore rename to examples/security/kdd-nsl/.dvc/.gitignore diff --git a/examples/kdd-nsl/.dvc/config b/examples/security/kdd-nsl/.dvc/config similarity index 100% rename from examples/kdd-nsl/.dvc/config rename to examples/security/kdd-nsl/.dvc/config diff --git a/examples/kdd-nsl/.dvcignore b/examples/security/kdd-nsl/.dvcignore similarity index 100% rename from examples/kdd-nsl/.dvcignore rename to examples/security/kdd-nsl/.dvcignore diff --git a/examples/kdd-nsl/.gitignore b/examples/security/kdd-nsl/.gitignore similarity index 100% rename from examples/kdd-nsl/.gitignore rename to examples/security/kdd-nsl/.gitignore diff --git a/examples/kdd-nsl/README.md b/examples/security/kdd-nsl/README.md similarity index 100% rename from examples/kdd-nsl/README.md rename to examples/security/kdd-nsl/README.md diff --git a/examples/kdd-nsl/attacks.sh b/examples/security/kdd-nsl/attacks.sh similarity index 100% rename from examples/kdd-nsl/attacks.sh rename to examples/security/kdd-nsl/attacks.sh diff --git a/examples/kdd-nsl/conf/RQ.md b/examples/security/kdd-nsl/conf/RQ.md similarity index 100% rename from examples/kdd-nsl/conf/RQ.md rename to examples/security/kdd-nsl/conf/RQ.md diff --git a/examples/kdd-nsl/conf/attack.yaml b/examples/security/kdd-nsl/conf/attack.yaml similarity index 100% rename from examples/kdd-nsl/conf/attack.yaml rename to examples/security/kdd-nsl/conf/attack.yaml diff --git a/examples/kdd-nsl/conf/attack/.gitignore b/examples/security/kdd-nsl/conf/attack/.gitignore similarity index 100% rename from examples/kdd-nsl/conf/attack/.gitignore rename to examples/security/kdd-nsl/conf/attack/.gitignore diff --git a/examples/kdd-nsl/conf/attack/attack_grid.yaml b/examples/security/kdd-nsl/conf/attack/attack_grid.yaml similarity index 100% rename from examples/kdd-nsl/conf/attack/attack_grid.yaml rename to examples/security/kdd-nsl/conf/attack/attack_grid.yaml diff --git a/examples/kdd-nsl/conf/attack/default.yaml b/examples/security/kdd-nsl/conf/attack/default.yaml similarity index 100% rename from examples/kdd-nsl/conf/attack/default.yaml rename to examples/security/kdd-nsl/conf/attack/default.yaml diff --git a/examples/kdd-nsl/conf/attack/hsj.yaml b/examples/security/kdd-nsl/conf/attack/hsj.yaml similarity index 100% rename from examples/kdd-nsl/conf/attack/hsj.yaml rename to examples/security/kdd-nsl/conf/attack/hsj.yaml diff --git a/examples/kdd-nsl/conf/compile.yaml b/examples/security/kdd-nsl/conf/compile.yaml similarity index 100% rename from examples/kdd-nsl/conf/compile.yaml rename to examples/security/kdd-nsl/conf/compile.yaml diff --git a/examples/kdd-nsl/conf/data/attack.yaml b/examples/security/kdd-nsl/conf/data/attack.yaml similarity index 100% rename from examples/kdd-nsl/conf/data/attack.yaml rename to examples/security/kdd-nsl/conf/data/attack.yaml diff --git a/examples/kdd-nsl/conf/data/default.yaml b/examples/security/kdd-nsl/conf/data/default.yaml similarity index 100% rename from examples/kdd-nsl/conf/data/default.yaml rename to examples/security/kdd-nsl/conf/data/default.yaml diff --git a/examples/kdd-nsl/conf/data/kdd_nsl.yaml b/examples/security/kdd-nsl/conf/data/kdd_nsl.yaml similarity index 100% rename from examples/kdd-nsl/conf/data/kdd_nsl.yaml rename to examples/security/kdd-nsl/conf/data/kdd_nsl.yaml diff --git a/examples/kdd-nsl/conf/data/small.yaml b/examples/security/kdd-nsl/conf/data/small.yaml similarity index 100% rename from examples/kdd-nsl/conf/data/small.yaml rename to examples/security/kdd-nsl/conf/data/small.yaml diff --git a/examples/kdd-nsl/conf/data/truthseeker.yaml b/examples/security/kdd-nsl/conf/data/truthseeker.yaml similarity index 100% rename from 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examples/security/kdd-nsl/conf/model/.gitignore diff --git a/examples/kdd-nsl/conf/model/art/defense_grid.yaml b/examples/security/kdd-nsl/conf/model/art/defense_grid.yaml similarity index 100% rename from examples/kdd-nsl/conf/model/art/defense_grid.yaml rename to examples/security/kdd-nsl/conf/model/art/defense_grid.yaml diff --git a/examples/kdd-nsl/conf/model/art/postprocessor.yaml b/examples/security/kdd-nsl/conf/model/art/postprocessor.yaml similarity index 100% rename from examples/kdd-nsl/conf/model/art/postprocessor.yaml rename to examples/security/kdd-nsl/conf/model/art/postprocessor.yaml diff --git a/examples/kdd-nsl/conf/model/art/postprocessor/high_confidence.yaml b/examples/security/kdd-nsl/conf/model/art/postprocessor/high_confidence.yaml similarity index 100% rename from examples/kdd-nsl/conf/model/art/postprocessor/high_confidence.yaml rename to examples/security/kdd-nsl/conf/model/art/postprocessor/high_confidence.yaml diff --git a/examples/kdd-nsl/conf/model/art/postprocessor/labels.yaml b/examples/security/kdd-nsl/conf/model/art/postprocessor/labels.yaml similarity index 100% rename from examples/kdd-nsl/conf/model/art/postprocessor/labels.yaml rename to examples/security/kdd-nsl/conf/model/art/postprocessor/labels.yaml diff --git a/examples/kdd-nsl/conf/model/art/preprocessor.yaml b/examples/security/kdd-nsl/conf/model/art/preprocessor.yaml similarity index 100% rename from examples/kdd-nsl/conf/model/art/preprocessor.yaml rename to examples/security/kdd-nsl/conf/model/art/preprocessor.yaml diff --git a/examples/kdd-nsl/conf/model/art/preprocessor/feature_squeezing.yaml b/examples/security/kdd-nsl/conf/model/art/preprocessor/feature_squeezing.yaml similarity index 100% rename from examples/kdd-nsl/conf/model/art/preprocessor/feature_squeezing.yaml rename to examples/security/kdd-nsl/conf/model/art/preprocessor/feature_squeezing.yaml diff --git a/examples/kdd-nsl/conf/model/default.yaml b/examples/security/kdd-nsl/conf/model/default.yaml similarity index 100% rename from examples/kdd-nsl/conf/model/default.yaml rename to examples/security/kdd-nsl/conf/model/default.yaml diff --git a/examples/kdd-nsl/conf/model/linear.yaml b/examples/security/kdd-nsl/conf/model/linear.yaml similarity index 100% rename from examples/kdd-nsl/conf/model/linear.yaml rename to examples/security/kdd-nsl/conf/model/linear.yaml diff --git a/examples/kdd-nsl/conf/model/poly.yaml b/examples/security/kdd-nsl/conf/model/poly.yaml similarity index 100% rename from examples/kdd-nsl/conf/model/poly.yaml rename to examples/security/kdd-nsl/conf/model/poly.yaml diff --git a/examples/kdd-nsl/conf/model/rbf.yaml b/examples/security/kdd-nsl/conf/model/rbf.yaml similarity index 100% rename from examples/kdd-nsl/conf/model/rbf.yaml rename to examples/security/kdd-nsl/conf/model/rbf.yaml diff --git a/examples/kdd-nsl/conf/rq.yaml b/examples/security/kdd-nsl/conf/rq.yaml similarity index 100% rename from examples/kdd-nsl/conf/rq.yaml rename to examples/security/kdd-nsl/conf/rq.yaml diff --git a/examples/kdd-nsl/conf/scorers/default.yaml b/examples/security/kdd-nsl/conf/scorers/default.yaml similarity index 100% rename from examples/kdd-nsl/conf/scorers/default.yaml rename to examples/security/kdd-nsl/conf/scorers/default.yaml diff --git a/examples/kdd-nsl/dvc.lock b/examples/security/kdd-nsl/dvc.lock similarity index 100% rename from examples/kdd-nsl/dvc.lock rename to examples/security/kdd-nsl/dvc.lock diff --git a/examples/kdd-nsl/dvc.yaml b/examples/security/kdd-nsl/dvc.yaml similarity index 100% rename from examples/kdd-nsl/dvc.yaml rename to examples/security/kdd-nsl/dvc.yaml diff --git a/examples/kdd-nsl/logs/.gitignore b/examples/security/kdd-nsl/logs/.gitignore similarity index 100% rename from examples/kdd-nsl/logs/.gitignore rename to examples/security/kdd-nsl/logs/.gitignore diff --git a/examples/security/kdd-nsl/multirun/2023-10-13/17-07-07/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-13/17-07-07/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-13/17-07-07/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/kdd-nsl/multirun/2023-10-13/17-07-32/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-13/17-07-32/optimization_results.yaml new file mode 100644 index 00000000..42a8bab3 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-13/17-07-32/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.0001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/kdd-nsl/multirun/2023-10-13/17-08-20/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-13/17-08-20/optimization_results.yaml new file mode 100644 index 00000000..e9213595 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-13/17-08-20/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 1 + +model.init.degree: 2 + model.init.C: 10000 +best_value: 0.616 diff --git a/examples/security/kdd-nsl/multirun/2023-10-13/17-09-20/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-13/17-09-20/optimization_results.yaml new file mode 100644 index 00000000..666a9d1c --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-13/17-09-20/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 10 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.3 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-13/17-10-16/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-13/17-10-16/optimization_results.yaml new file mode 100644 index 00000000..9e88e87b --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-13/17-10-16/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 1 + ++attack.init.eps: 1 + ++attack.init.eps_step: 1 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-13/17-11-11/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-13/17-11-11/optimization_results.yaml new file mode 100644 index 00000000..e8523510 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-13/17-11-11/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 100 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.5 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/17-19-34/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/17-19-34/optimization_results.yaml new file mode 100644 index 00000000..0a303cc1 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/17-19-34/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: -10000000000.0 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/17-19-59/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/17-19-59/optimization_results.yaml new file mode 100644 index 00000000..954b6bf7 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/17-19-59/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + model.init.C: 0.0001 +best_value: -10000000000.0 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/17-20-44/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/17-20-44/optimization_results.yaml new file mode 100644 index 00000000..16436bc4 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/17-20-44/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 0.0001 + +model.init.degree: 3 + model.init.C: 100 +best_value: -10000000000.0 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/17-23-54/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/17-23-54/optimization_results.yaml new file mode 100644 index 00000000..666a9d1c --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/17-23-54/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 10 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.3 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/17-24-53/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/17-24-53/optimization_results.yaml new file mode 100644 index 00000000..9e88e87b --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/17-24-53/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 1 + ++attack.init.eps: 1 + ++attack.init.eps_step: 1 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/17-25-49/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/17-25-49/optimization_results.yaml new file mode 100644 index 00000000..e8523510 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/17-25-49/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 100 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.5 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/17-35-44/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/17-35-44/optimization_results.yaml new file mode 100644 index 00000000..0a303cc1 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/17-35-44/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: -10000000000.0 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/17-36-18/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/17-36-18/optimization_results.yaml new file mode 100644 index 00000000..954b6bf7 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/17-36-18/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + model.init.C: 0.0001 +best_value: -10000000000.0 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/17-37-05/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/17-37-05/optimization_results.yaml new file mode 100644 index 00000000..16436bc4 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/17-37-05/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 0.0001 + +model.init.degree: 3 + model.init.C: 100 +best_value: -10000000000.0 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/17-38-17/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/17-38-17/optimization_results.yaml new file mode 100644 index 00000000..666a9d1c --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/17-38-17/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 10 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.3 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/17-39-12/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/17-39-12/optimization_results.yaml new file mode 100644 index 00000000..9e88e87b --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/17-39-12/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 1 + ++attack.init.eps: 1 + ++attack.init.eps_step: 1 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/17-40-09/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/17-40-09/optimization_results.yaml new file mode 100644 index 00000000..f64ab4e9 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/17-40-09/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 50 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.5 + ++attack.init.max_iter: 10 + ++attack.init.norm: .inf +best_value: 0.0 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-11-28/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-11-28/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-11-28/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-11-55/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-11-55/optimization_results.yaml new file mode 100644 index 00000000..cb2dc0ee --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-11-55/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 10 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-12-43/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-12-43/optimization_results.yaml new file mode 100644 index 00000000..5a92f311 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-12-43/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10 + +model.init.degree: 1 + model.init.C: 0.1 +best_value: 0.57 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-13-46/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-13-46/optimization_results.yaml new file mode 100644 index 00000000..666a9d1c --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-13-46/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 10 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.3 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-14-36/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-14-36/optimization_results.yaml new file mode 100644 index 00000000..9e88e87b --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-14-36/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 1 + ++attack.init.eps: 1 + ++attack.init.eps_step: 1 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-16-01/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-16-01/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-16-01/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-16-28/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-16-28/optimization_results.yaml new file mode 100644 index 00000000..42a8bab3 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-16-28/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.0001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-17-13/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-17-13/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-17-13/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-18-18/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-18-18/optimization_results.yaml new file mode 100644 index 00000000..666a9d1c --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-18-18/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 10 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.3 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-19-10/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-19-10/optimization_results.yaml new file mode 100644 index 00000000..9e88e87b --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-19-10/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 1 + ++attack.init.eps: 1 + ++attack.init.eps_step: 1 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-20-04/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-20-04/optimization_results.yaml new file mode 100644 index 00000000..f64ab4e9 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-20-04/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 50 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.5 + ++attack.init.max_iter: 10 + ++attack.init.norm: .inf +best_value: 0.0 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-37-22/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-37-22/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-37-22/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-37-46/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-37-46/optimization_results.yaml new file mode 100644 index 00000000..42a8bab3 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-37-46/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.0001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-38-29/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-38-29/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-38-29/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-39-27/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-39-27/optimization_results.yaml new file mode 100644 index 00000000..666a9d1c --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-39-27/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 10 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.3 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-40-18/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-40-18/optimization_results.yaml new file mode 100644 index 00000000..9e88e87b --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-40-18/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 1 + ++attack.init.eps: 1 + ++attack.init.eps_step: 1 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-41-12/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-41-12/optimization_results.yaml new file mode 100644 index 00000000..f64ab4e9 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-41-12/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 50 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.5 + ++attack.init.max_iter: 10 + ++attack.init.norm: .inf +best_value: 0.0 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-45-36/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-45-36/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-45-36/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-45-59/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-45-59/optimization_results.yaml new file mode 100644 index 00000000..42a8bab3 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-45-59/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.0001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-46-45/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-46-45/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-46-45/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-47-45/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-47-45/optimization_results.yaml new file mode 100644 index 00000000..666a9d1c --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-47-45/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 10 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.3 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-48-33/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-48-33/optimization_results.yaml new file mode 100644 index 00000000..9e88e87b --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-48-33/optimization_results.yaml @@ -0,0 +1,7 @@ +name: optuna +best_params: + ++attack.init.batch_size: 1 + ++attack.init.eps: 1 + ++attack.init.eps_step: 1 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/18-49-24/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/18-49-24/optimization_results.yaml new file mode 100644 index 00000000..f64ab4e9 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/18-49-24/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 50 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.5 + ++attack.init.max_iter: 10 + ++attack.init.norm: .inf +best_value: 0.0 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/19-21-16/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/19-21-16/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/19-21-16/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/19-21-43/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/19-21-43/optimization_results.yaml new file mode 100644 index 00000000..42a8bab3 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/19-21-43/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.0001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/19-22-32/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/19-22-32/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/19-22-32/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/19-30-45/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/19-30-45/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/19-30-45/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/19-31-13/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/19-31-13/optimization_results.yaml new file mode 100644 index 00000000..42a8bab3 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/19-31-13/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.0001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/19-31-58/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/19-31-58/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/19-31-58/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/19-36-22/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/19-36-22/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/19-36-22/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/19-36-47/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/19-36-47/optimization_results.yaml new file mode 100644 index 00000000..42a8bab3 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/19-36-47/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.0001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/19-37-33/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/19-37-33/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/19-37-33/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/23-07-44/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/23-07-44/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/23-07-44/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/23-08-06/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/23-08-06/optimization_results.yaml new file mode 100644 index 00000000..42a8bab3 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/23-08-06/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.0001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/23-08-47/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/23-08-47/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/23-08-47/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/23-15-35/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/23-15-35/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/23-15-35/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/23-15-58/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/23-15-58/optimization_results.yaml new file mode 100644 index 00000000..42a8bab3 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/23-15-58/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.0001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/23-16-38/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/23-16-38/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/23-16-38/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/23-33-59/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/23-33-59/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/23-33-59/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/23-34-21/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/23-34-21/optimization_results.yaml new file mode 100644 index 00000000..42a8bab3 --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/23-34-21/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.0001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/kdd-nsl/multirun/2023-10-14/23-35-01/optimization_results.yaml b/examples/security/kdd-nsl/multirun/2023-10-14/23-35-01/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/kdd-nsl/multirun/2023-10-14/23-35-01/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/kdd-nsl/other_data.sh b/examples/security/kdd-nsl/other_data.sh similarity index 100% rename from examples/kdd-nsl/other_data.sh rename to examples/security/kdd-nsl/other_data.sh diff --git a/examples/security/kdd-nsl/output/reports/attack/0238b99d8598785c9a801fa1de0c2a06/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0238b99d8598785c9a801fa1de0c2a06/params.yaml new file mode 100644 index 00000000..26e25dd1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0238b99d8598785c9a801fa1de0c2a06/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.001 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 4b8cee3d6ca34b5f3be6617c2b6fc080 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0238b99d8598785c9a801fa1de0c2a06/adv_predictions.json + adv_probabilities_file: output/reports/attack/0238b99d8598785c9a801fa1de0c2a06/adv_probabilities.json + attack_file: output/attacks/915cb331ca91c9a55ee2b66e8e65f2ca.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/0238b99d8598785c9a801fa1de0c2a06/params.yaml + predictions_file: output/reports/attack/0238b99d8598785c9a801fa1de0c2a06/predictions.json + probabilities_file: output/reports/attack/0238b99d8598785c9a801fa1de0c2a06/probabilities.json + score_dict_file: output/reports/attack/0238b99d8598785c9a801fa1de0c2a06/score_dict.json + test_labels_file: output/reports/attack/0238b99d8598785c9a801fa1de0c2a06/test_labels.json + train_labels_file: output/reports/attack/0238b99d8598785c9a801fa1de0c2a06/train_labels.json + model_dir: models + name: 0238b99d8598785c9a801fa1de0c2a06 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 0238b99d8598785c9a801fa1de0c2a06 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/039351ea66e38af72e0b9a8a2c777e18/params.yaml b/examples/security/kdd-nsl/output/reports/attack/039351ea66e38af72e0b9a8a2c777e18/params.yaml new file mode 100644 index 00000000..a785603a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/039351ea66e38af72e0b9a8a2c777e18/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: cce1ca96eee1909d9938153fb6785a0b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/039351ea66e38af72e0b9a8a2c777e18/adv_predictions.json + adv_probabilities_file: output/reports/attack/039351ea66e38af72e0b9a8a2c777e18/adv_probabilities.json + attack_file: output/attacks/463451e77a959103770cd091de56f446.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/039351ea66e38af72e0b9a8a2c777e18/params.yaml + predictions_file: output/reports/attack/039351ea66e38af72e0b9a8a2c777e18/predictions.json + probabilities_file: output/reports/attack/039351ea66e38af72e0b9a8a2c777e18/probabilities.json + score_dict_file: output/reports/attack/039351ea66e38af72e0b9a8a2c777e18/score_dict.json + test_labels_file: output/reports/attack/039351ea66e38af72e0b9a8a2c777e18/test_labels.json + train_labels_file: output/reports/attack/039351ea66e38af72e0b9a8a2c777e18/train_labels.json + model_dir: models + name: 039351ea66e38af72e0b9a8a2c777e18 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 039351ea66e38af72e0b9a8a2c777e18 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/041362b0a56204066e63e0e56c74f53b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/041362b0a56204066e63e0e56c74f53b/params.yaml new file mode 100644 index 00000000..a3cfe29b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/041362b0a56204066e63e0e56c74f53b/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 366a0a40000f5e72332b52e83447255f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/041362b0a56204066e63e0e56c74f53b/adv_predictions.json + adv_probabilities_file: output/reports/attack/041362b0a56204066e63e0e56c74f53b/adv_probabilities.json + attack_file: output/attacks/d060df04bd83f969d5116425c9632cbe.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/041362b0a56204066e63e0e56c74f53b/params.yaml + predictions_file: output/reports/attack/041362b0a56204066e63e0e56c74f53b/predictions.json + probabilities_file: output/reports/attack/041362b0a56204066e63e0e56c74f53b/probabilities.json + score_dict_file: output/reports/attack/041362b0a56204066e63e0e56c74f53b/score_dict.json + test_labels_file: output/reports/attack/041362b0a56204066e63e0e56c74f53b/test_labels.json + train_labels_file: output/reports/attack/041362b0a56204066e63e0e56c74f53b/train_labels.json + model_dir: models + name: 041362b0a56204066e63e0e56c74f53b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 041362b0a56204066e63e0e56c74f53b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0456d487a92d8b18a0f166a7fb276ea4/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0456d487a92d8b18a0f166a7fb276ea4/params.yaml new file mode 100644 index 00000000..db68091b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0456d487a92d8b18a0f166a7fb276ea4/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: f296ccb056da2048a05b61506b15a505 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0456d487a92d8b18a0f166a7fb276ea4/adv_predictions.json + adv_probabilities_file: output/reports/attack/0456d487a92d8b18a0f166a7fb276ea4/adv_probabilities.json + attack_file: output/attacks/e410f07c6823c094ea251de761924dbd.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/0456d487a92d8b18a0f166a7fb276ea4/params.yaml + predictions_file: output/reports/attack/0456d487a92d8b18a0f166a7fb276ea4/predictions.json + probabilities_file: output/reports/attack/0456d487a92d8b18a0f166a7fb276ea4/probabilities.json + score_dict_file: output/reports/attack/0456d487a92d8b18a0f166a7fb276ea4/score_dict.json + test_labels_file: output/reports/attack/0456d487a92d8b18a0f166a7fb276ea4/test_labels.json + train_labels_file: output/reports/attack/0456d487a92d8b18a0f166a7fb276ea4/train_labels.json + model_dir: models + name: 0456d487a92d8b18a0f166a7fb276ea4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 0456d487a92d8b18a0f166a7fb276ea4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0629326f97220c80bff229ee2911b705/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0629326f97220c80bff229ee2911b705/params.yaml new file mode 100644 index 00000000..ff599bd3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0629326f97220c80bff229ee2911b705/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 246c8af88993651f7ffaa5a650c2fc05 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0629326f97220c80bff229ee2911b705/adv_predictions.json + adv_probabilities_file: output/reports/attack/0629326f97220c80bff229ee2911b705/adv_probabilities.json + attack_file: output/attacks/10948a1e25103cfc9e4c9cd7a9f5eaa8.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/0629326f97220c80bff229ee2911b705/params.yaml + predictions_file: output/reports/attack/0629326f97220c80bff229ee2911b705/predictions.json + probabilities_file: output/reports/attack/0629326f97220c80bff229ee2911b705/probabilities.json + score_dict_file: output/reports/attack/0629326f97220c80bff229ee2911b705/score_dict.json + test_labels_file: output/reports/attack/0629326f97220c80bff229ee2911b705/test_labels.json + train_labels_file: output/reports/attack/0629326f97220c80bff229ee2911b705/train_labels.json + model_dir: models + name: 0629326f97220c80bff229ee2911b705 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 0629326f97220c80bff229ee2911b705 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0658803e10f8c7ea20322e2c9e703099/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0658803e10f8c7ea20322e2c9e703099/params.yaml new file mode 100644 index 00000000..5d5a96eb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0658803e10f8c7ea20322e2c9e703099/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 2e24ff7b8b2d6891b6b8bd6f5d3d52fa +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0658803e10f8c7ea20322e2c9e703099/adv_predictions.json + adv_probabilities_file: output/reports/attack/0658803e10f8c7ea20322e2c9e703099/adv_probabilities.json + attack_file: output/attacks/803291cf502748fe0faacc45fb9f0ace.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/0658803e10f8c7ea20322e2c9e703099/params.yaml + predictions_file: output/reports/attack/0658803e10f8c7ea20322e2c9e703099/predictions.json + probabilities_file: output/reports/attack/0658803e10f8c7ea20322e2c9e703099/probabilities.json + score_dict_file: output/reports/attack/0658803e10f8c7ea20322e2c9e703099/score_dict.json + test_labels_file: output/reports/attack/0658803e10f8c7ea20322e2c9e703099/test_labels.json + train_labels_file: output/reports/attack/0658803e10f8c7ea20322e2c9e703099/train_labels.json + model_dir: models + name: 0658803e10f8c7ea20322e2c9e703099 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 0658803e10f8c7ea20322e2c9e703099 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/06bd1ad17a0f4e12a9fba6f3b3ca8d63/params.yaml b/examples/security/kdd-nsl/output/reports/attack/06bd1ad17a0f4e12a9fba6f3b3ca8d63/params.yaml new file mode 100644 index 00000000..f5efd598 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/06bd1ad17a0f4e12a9fba6f3b3ca8d63/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: bd9b0ed37b3a4ce9606fae277d87d3f6 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/06bd1ad17a0f4e12a9fba6f3b3ca8d63/adv_predictions.json + adv_probabilities_file: output/reports/attack/06bd1ad17a0f4e12a9fba6f3b3ca8d63/adv_probabilities.json + attack_file: output/attacks/e6bc93e23fa98420966837fcc8ab67b8.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/06bd1ad17a0f4e12a9fba6f3b3ca8d63/params.yaml + predictions_file: output/reports/attack/06bd1ad17a0f4e12a9fba6f3b3ca8d63/predictions.json + probabilities_file: output/reports/attack/06bd1ad17a0f4e12a9fba6f3b3ca8d63/probabilities.json + score_dict_file: output/reports/attack/06bd1ad17a0f4e12a9fba6f3b3ca8d63/score_dict.json + test_labels_file: output/reports/attack/06bd1ad17a0f4e12a9fba6f3b3ca8d63/test_labels.json + train_labels_file: output/reports/attack/06bd1ad17a0f4e12a9fba6f3b3ca8d63/train_labels.json + model_dir: models + name: 06bd1ad17a0f4e12a9fba6f3b3ca8d63 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 06bd1ad17a0f4e12a9fba6f3b3ca8d63 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/074f37e459633e946cef6717f053484c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/074f37e459633e946cef6717f053484c/params.yaml new file mode 100644 index 00000000..f85331b6 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/074f37e459633e946cef6717f053484c/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.5 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: af6444d02d56df441da91cda93928aba +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/074f37e459633e946cef6717f053484c/adv_predictions.json + adv_probabilities_file: output/reports/attack/074f37e459633e946cef6717f053484c/adv_probabilities.json + attack_file: output/attacks/f4ba01cd28021034d9fec52029108222.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/074f37e459633e946cef6717f053484c/params.yaml + predictions_file: output/reports/attack/074f37e459633e946cef6717f053484c/predictions.json + probabilities_file: output/reports/attack/074f37e459633e946cef6717f053484c/probabilities.json + score_dict_file: output/reports/attack/074f37e459633e946cef6717f053484c/score_dict.json + test_labels_file: output/reports/attack/074f37e459633e946cef6717f053484c/test_labels.json + train_labels_file: output/reports/attack/074f37e459633e946cef6717f053484c/train_labels.json + model_dir: models + name: 074f37e459633e946cef6717f053484c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 074f37e459633e946cef6717f053484c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/077b58b86cd510245a1331c38676488b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/077b58b86cd510245a1331c38676488b/params.yaml new file mode 100644 index 00000000..730d1f1d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/077b58b86cd510245a1331c38676488b/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 698361d8091ffb57955372b6634867fa +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/077b58b86cd510245a1331c38676488b/adv_predictions.json + adv_probabilities_file: output/reports/attack/077b58b86cd510245a1331c38676488b/adv_probabilities.json + attack_file: output/attacks/b459f22a6169415cb11a63960a18adef.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/077b58b86cd510245a1331c38676488b/params.yaml + predictions_file: output/reports/attack/077b58b86cd510245a1331c38676488b/predictions.json + probabilities_file: output/reports/attack/077b58b86cd510245a1331c38676488b/probabilities.json + score_dict_file: output/reports/attack/077b58b86cd510245a1331c38676488b/score_dict.json + test_labels_file: output/reports/attack/077b58b86cd510245a1331c38676488b/test_labels.json + train_labels_file: output/reports/attack/077b58b86cd510245a1331c38676488b/train_labels.json + model_dir: models + name: 077b58b86cd510245a1331c38676488b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 077b58b86cd510245a1331c38676488b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0995d5a8f9aa82f92ed1d6996fba3b79/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0995d5a8f9aa82f92ed1d6996fba3b79/params.yaml new file mode 100644 index 00000000..496f2ebc --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0995d5a8f9aa82f92ed1d6996fba3b79/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 84464e44d44206b5d5007567e03cb01d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0995d5a8f9aa82f92ed1d6996fba3b79/adv_predictions.json + adv_probabilities_file: output/reports/attack/0995d5a8f9aa82f92ed1d6996fba3b79/adv_probabilities.json + attack_file: output/attacks/a484a610ab3f3c0610e07bf3738c644b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/0995d5a8f9aa82f92ed1d6996fba3b79/params.yaml + predictions_file: output/reports/attack/0995d5a8f9aa82f92ed1d6996fba3b79/predictions.json + probabilities_file: output/reports/attack/0995d5a8f9aa82f92ed1d6996fba3b79/probabilities.json + score_dict_file: output/reports/attack/0995d5a8f9aa82f92ed1d6996fba3b79/score_dict.json + test_labels_file: output/reports/attack/0995d5a8f9aa82f92ed1d6996fba3b79/test_labels.json + train_labels_file: output/reports/attack/0995d5a8f9aa82f92ed1d6996fba3b79/train_labels.json + model_dir: models + name: 0995d5a8f9aa82f92ed1d6996fba3b79 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 0995d5a8f9aa82f92ed1d6996fba3b79 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0abcf5c1442190a91bd59e4061e73e62/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0abcf5c1442190a91bd59e4061e73e62/params.yaml new file mode 100644 index 00000000..d6e38b10 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0abcf5c1442190a91bd59e4061e73e62/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: d661f070b283b458866b9f148605c11b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0abcf5c1442190a91bd59e4061e73e62/adv_predictions.json + adv_probabilities_file: output/reports/attack/0abcf5c1442190a91bd59e4061e73e62/adv_probabilities.json + attack_file: output/attacks/1d676f97e85c2be66b868f920db38e42.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/0abcf5c1442190a91bd59e4061e73e62/params.yaml + predictions_file: output/reports/attack/0abcf5c1442190a91bd59e4061e73e62/predictions.json + probabilities_file: output/reports/attack/0abcf5c1442190a91bd59e4061e73e62/probabilities.json + score_dict_file: output/reports/attack/0abcf5c1442190a91bd59e4061e73e62/score_dict.json + test_labels_file: output/reports/attack/0abcf5c1442190a91bd59e4061e73e62/test_labels.json + train_labels_file: output/reports/attack/0abcf5c1442190a91bd59e4061e73e62/train_labels.json + model_dir: models + name: 0abcf5c1442190a91bd59e4061e73e62 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 0abcf5c1442190a91bd59e4061e73e62 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0b34e8f8f31e45c3ddba18683154bba2/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0b34e8f8f31e45c3ddba18683154bba2/params.yaml new file mode 100644 index 00000000..7ae0fd2d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0b34e8f8f31e45c3ddba18683154bba2/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: b5578a334ca7101100b4b91cd8202d7b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0b34e8f8f31e45c3ddba18683154bba2/adv_predictions.json + adv_probabilities_file: output/reports/attack/0b34e8f8f31e45c3ddba18683154bba2/adv_probabilities.json + attack_file: output/attacks/9fe149c91b5c36eb6788b6cb2c2f0213.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/0b34e8f8f31e45c3ddba18683154bba2/params.yaml + predictions_file: output/reports/attack/0b34e8f8f31e45c3ddba18683154bba2/predictions.json + probabilities_file: output/reports/attack/0b34e8f8f31e45c3ddba18683154bba2/probabilities.json + score_dict_file: output/reports/attack/0b34e8f8f31e45c3ddba18683154bba2/score_dict.json + test_labels_file: output/reports/attack/0b34e8f8f31e45c3ddba18683154bba2/test_labels.json + train_labels_file: output/reports/attack/0b34e8f8f31e45c3ddba18683154bba2/train_labels.json + model_dir: models + name: 0b34e8f8f31e45c3ddba18683154bba2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 0b34e8f8f31e45c3ddba18683154bba2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0ba742bada03def0ceace68bb09a2f26/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0ba742bada03def0ceace68bb09a2f26/params.yaml new file mode 100644 index 00000000..3c9819f9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0ba742bada03def0ceace68bb09a2f26/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.3 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 13f451d597084dbb276e8b73a55e9937 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0ba742bada03def0ceace68bb09a2f26/adv_predictions.json + adv_probabilities_file: output/reports/attack/0ba742bada03def0ceace68bb09a2f26/adv_probabilities.json + attack_file: output/attacks/b3b6a70d2f995f020bab5c2efbf6fe1c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/0ba742bada03def0ceace68bb09a2f26/params.yaml + predictions_file: output/reports/attack/0ba742bada03def0ceace68bb09a2f26/predictions.json + probabilities_file: output/reports/attack/0ba742bada03def0ceace68bb09a2f26/probabilities.json + score_dict_file: output/reports/attack/0ba742bada03def0ceace68bb09a2f26/score_dict.json + test_labels_file: output/reports/attack/0ba742bada03def0ceace68bb09a2f26/test_labels.json + train_labels_file: output/reports/attack/0ba742bada03def0ceace68bb09a2f26/train_labels.json + model_dir: models + name: 0ba742bada03def0ceace68bb09a2f26 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 0ba742bada03def0ceace68bb09a2f26 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0c2191cc993b55d423502e864a0125de/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0c2191cc993b55d423502e864a0125de/params.yaml new file mode 100644 index 00000000..8ea1d305 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0c2191cc993b55d423502e864a0125de/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.5 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 35582e7d9adcef5e5472342b00e5ad1a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0c2191cc993b55d423502e864a0125de/adv_predictions.json + adv_probabilities_file: output/reports/attack/0c2191cc993b55d423502e864a0125de/adv_probabilities.json + attack_file: output/attacks/1f7bca3028b4764d176d619fb3748a92.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/0c2191cc993b55d423502e864a0125de/params.yaml + predictions_file: output/reports/attack/0c2191cc993b55d423502e864a0125de/predictions.json + probabilities_file: output/reports/attack/0c2191cc993b55d423502e864a0125de/probabilities.json + score_dict_file: output/reports/attack/0c2191cc993b55d423502e864a0125de/score_dict.json + test_labels_file: output/reports/attack/0c2191cc993b55d423502e864a0125de/test_labels.json + train_labels_file: output/reports/attack/0c2191cc993b55d423502e864a0125de/train_labels.json + model_dir: models + name: 0c2191cc993b55d423502e864a0125de + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 0c2191cc993b55d423502e864a0125de +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0d5cf9ce2dcc8a6bf12877bcc1868255/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0d5cf9ce2dcc8a6bf12877bcc1868255/params.yaml new file mode 100644 index 00000000..3a28f1a1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0d5cf9ce2dcc8a6bf12877bcc1868255/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.01 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 3175438a65e3ddb3c2963900311f16d8 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0d5cf9ce2dcc8a6bf12877bcc1868255/adv_predictions.json + adv_probabilities_file: output/reports/attack/0d5cf9ce2dcc8a6bf12877bcc1868255/adv_probabilities.json + attack_file: output/attacks/41968ef9890df5d57ed0e5ee5ee64ae1.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/0d5cf9ce2dcc8a6bf12877bcc1868255/params.yaml + predictions_file: output/reports/attack/0d5cf9ce2dcc8a6bf12877bcc1868255/predictions.json + probabilities_file: output/reports/attack/0d5cf9ce2dcc8a6bf12877bcc1868255/probabilities.json + score_dict_file: output/reports/attack/0d5cf9ce2dcc8a6bf12877bcc1868255/score_dict.json + test_labels_file: output/reports/attack/0d5cf9ce2dcc8a6bf12877bcc1868255/test_labels.json + train_labels_file: output/reports/attack/0d5cf9ce2dcc8a6bf12877bcc1868255/train_labels.json + model_dir: models + name: 0d5cf9ce2dcc8a6bf12877bcc1868255 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 0d5cf9ce2dcc8a6bf12877bcc1868255 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0d979b68270f65eb388da7cdf485b08c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0d979b68270f65eb388da7cdf485b08c/params.yaml new file mode 100644 index 00000000..3ca1f92d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0d979b68270f65eb388da7cdf485b08c/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.01 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 7b1cc7f9bdfa8410785828413aef5fb3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0d979b68270f65eb388da7cdf485b08c/adv_predictions.json + adv_probabilities_file: output/reports/attack/0d979b68270f65eb388da7cdf485b08c/adv_probabilities.json + attack_file: output/attacks/b913d5c2cfb648dd1b864b8b4ce62e23.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/0d979b68270f65eb388da7cdf485b08c/params.yaml + predictions_file: output/reports/attack/0d979b68270f65eb388da7cdf485b08c/predictions.json + probabilities_file: output/reports/attack/0d979b68270f65eb388da7cdf485b08c/probabilities.json + score_dict_file: output/reports/attack/0d979b68270f65eb388da7cdf485b08c/score_dict.json + test_labels_file: output/reports/attack/0d979b68270f65eb388da7cdf485b08c/test_labels.json + train_labels_file: output/reports/attack/0d979b68270f65eb388da7cdf485b08c/train_labels.json + model_dir: models + name: 0d979b68270f65eb388da7cdf485b08c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 0d979b68270f65eb388da7cdf485b08c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0daf3731a6c73ea0e0b989cdf53cb036/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0daf3731a6c73ea0e0b989cdf53cb036/params.yaml new file mode 100644 index 00000000..509c88f3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0daf3731a6c73ea0e0b989cdf53cb036/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: ed91615c3ccf7423d3450f9fa93473b9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0daf3731a6c73ea0e0b989cdf53cb036/adv_predictions.json + adv_probabilities_file: output/reports/attack/0daf3731a6c73ea0e0b989cdf53cb036/adv_probabilities.json + attack_file: output/attacks/0607b6eddc5f8a1e2316d7b0b31d0d1e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/0daf3731a6c73ea0e0b989cdf53cb036/params.yaml + predictions_file: output/reports/attack/0daf3731a6c73ea0e0b989cdf53cb036/predictions.json + probabilities_file: output/reports/attack/0daf3731a6c73ea0e0b989cdf53cb036/probabilities.json + score_dict_file: output/reports/attack/0daf3731a6c73ea0e0b989cdf53cb036/score_dict.json + test_labels_file: output/reports/attack/0daf3731a6c73ea0e0b989cdf53cb036/test_labels.json + train_labels_file: output/reports/attack/0daf3731a6c73ea0e0b989cdf53cb036/train_labels.json + model_dir: models + name: 0daf3731a6c73ea0e0b989cdf53cb036 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 0daf3731a6c73ea0e0b989cdf53cb036 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0e333488709fd1adf203f3de25380473/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0e333488709fd1adf203f3de25380473/params.yaml new file mode 100644 index 00000000..8338acca --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0e333488709fd1adf203f3de25380473/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 7c0736908b8ba78f18f7828e65314355 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0e333488709fd1adf203f3de25380473/adv_predictions.json + adv_probabilities_file: output/reports/attack/0e333488709fd1adf203f3de25380473/adv_probabilities.json + attack_file: output/attacks/370caabae07daf17c61c5f4c76ed0c3a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/0e333488709fd1adf203f3de25380473/params.yaml + predictions_file: output/reports/attack/0e333488709fd1adf203f3de25380473/predictions.json + probabilities_file: output/reports/attack/0e333488709fd1adf203f3de25380473/probabilities.json + score_dict_file: output/reports/attack/0e333488709fd1adf203f3de25380473/score_dict.json + test_labels_file: output/reports/attack/0e333488709fd1adf203f3de25380473/test_labels.json + train_labels_file: output/reports/attack/0e333488709fd1adf203f3de25380473/train_labels.json + model_dir: models + name: 0e333488709fd1adf203f3de25380473 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 0e333488709fd1adf203f3de25380473 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0f3e5916b0a3a65d7933ff5aebb6ad7c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0f3e5916b0a3a65d7933ff5aebb6ad7c/params.yaml new file mode 100644 index 00000000..017aab0f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0f3e5916b0a3a65d7933ff5aebb6ad7c/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.001 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 12f7697bc8da593721bfbe7b5523428c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0f3e5916b0a3a65d7933ff5aebb6ad7c/adv_predictions.json + adv_probabilities_file: output/reports/attack/0f3e5916b0a3a65d7933ff5aebb6ad7c/adv_probabilities.json + attack_file: output/attacks/331209adea32924cafbae214cd5e0ce7.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/0f3e5916b0a3a65d7933ff5aebb6ad7c/params.yaml + predictions_file: output/reports/attack/0f3e5916b0a3a65d7933ff5aebb6ad7c/predictions.json + probabilities_file: output/reports/attack/0f3e5916b0a3a65d7933ff5aebb6ad7c/probabilities.json + score_dict_file: output/reports/attack/0f3e5916b0a3a65d7933ff5aebb6ad7c/score_dict.json + test_labels_file: output/reports/attack/0f3e5916b0a3a65d7933ff5aebb6ad7c/test_labels.json + train_labels_file: output/reports/attack/0f3e5916b0a3a65d7933ff5aebb6ad7c/train_labels.json + model_dir: models + name: 0f3e5916b0a3a65d7933ff5aebb6ad7c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 0f3e5916b0a3a65d7933ff5aebb6ad7c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/0ff65cd9ede8019d777cff2e78cb97c2/params.yaml b/examples/security/kdd-nsl/output/reports/attack/0ff65cd9ede8019d777cff2e78cb97c2/params.yaml new file mode 100644 index 00000000..be37b536 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/0ff65cd9ede8019d777cff2e78cb97c2/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: e97e9743c74828dca68390f256ae4701 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0ff65cd9ede8019d777cff2e78cb97c2/adv_predictions.json + adv_probabilities_file: output/reports/attack/0ff65cd9ede8019d777cff2e78cb97c2/adv_probabilities.json + attack_file: output/attacks/99e04ddcaedb4f1e958fb529c1e75ffb.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/0ff65cd9ede8019d777cff2e78cb97c2/params.yaml + predictions_file: output/reports/attack/0ff65cd9ede8019d777cff2e78cb97c2/predictions.json + probabilities_file: output/reports/attack/0ff65cd9ede8019d777cff2e78cb97c2/probabilities.json + score_dict_file: output/reports/attack/0ff65cd9ede8019d777cff2e78cb97c2/score_dict.json + test_labels_file: output/reports/attack/0ff65cd9ede8019d777cff2e78cb97c2/test_labels.json + train_labels_file: output/reports/attack/0ff65cd9ede8019d777cff2e78cb97c2/train_labels.json + model_dir: models + name: 0ff65cd9ede8019d777cff2e78cb97c2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 0ff65cd9ede8019d777cff2e78cb97c2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/100db9810b27526b069a4ad045623ddc/params.yaml b/examples/security/kdd-nsl/output/reports/attack/100db9810b27526b069a4ad045623ddc/params.yaml new file mode 100644 index 00000000..0d7ec7b9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/100db9810b27526b069a4ad045623ddc/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.5 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 9d248437790a1788bce9ed5d1fc3a1ff +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/100db9810b27526b069a4ad045623ddc/adv_predictions.json + adv_probabilities_file: output/reports/attack/100db9810b27526b069a4ad045623ddc/adv_probabilities.json + attack_file: output/attacks/d3b7f54bb0e994a765fe91ef572c3bae.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/100db9810b27526b069a4ad045623ddc/params.yaml + predictions_file: output/reports/attack/100db9810b27526b069a4ad045623ddc/predictions.json + probabilities_file: output/reports/attack/100db9810b27526b069a4ad045623ddc/probabilities.json + score_dict_file: output/reports/attack/100db9810b27526b069a4ad045623ddc/score_dict.json + test_labels_file: output/reports/attack/100db9810b27526b069a4ad045623ddc/test_labels.json + train_labels_file: output/reports/attack/100db9810b27526b069a4ad045623ddc/train_labels.json + model_dir: models + name: 100db9810b27526b069a4ad045623ddc + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 100db9810b27526b069a4ad045623ddc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/1029a0cd93d1c2d71cd78f5b7be0a256/params.yaml b/examples/security/kdd-nsl/output/reports/attack/1029a0cd93d1c2d71cd78f5b7be0a256/params.yaml new file mode 100644 index 00000000..c8364cc1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/1029a0cd93d1c2d71cd78f5b7be0a256/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 48504e21552975865ce398e4bab15640 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1029a0cd93d1c2d71cd78f5b7be0a256/adv_predictions.json + adv_probabilities_file: output/reports/attack/1029a0cd93d1c2d71cd78f5b7be0a256/adv_probabilities.json + attack_file: output/attacks/938adecedd7e24050c65b7630c6b19bb.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/1029a0cd93d1c2d71cd78f5b7be0a256/params.yaml + predictions_file: output/reports/attack/1029a0cd93d1c2d71cd78f5b7be0a256/predictions.json + probabilities_file: output/reports/attack/1029a0cd93d1c2d71cd78f5b7be0a256/probabilities.json + score_dict_file: output/reports/attack/1029a0cd93d1c2d71cd78f5b7be0a256/score_dict.json + test_labels_file: output/reports/attack/1029a0cd93d1c2d71cd78f5b7be0a256/test_labels.json + train_labels_file: output/reports/attack/1029a0cd93d1c2d71cd78f5b7be0a256/train_labels.json + model_dir: models + name: 1029a0cd93d1c2d71cd78f5b7be0a256 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 1029a0cd93d1c2d71cd78f5b7be0a256 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/11c7ecc3cd6ab0717cc8c9aee7699ad7/params.yaml b/examples/security/kdd-nsl/output/reports/attack/11c7ecc3cd6ab0717cc8c9aee7699ad7/params.yaml new file mode 100644 index 00000000..78f28149 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/11c7ecc3cd6ab0717cc8c9aee7699ad7/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 68060ec915f806167a1ae1c6aa99500f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/11c7ecc3cd6ab0717cc8c9aee7699ad7/adv_predictions.json + adv_probabilities_file: output/reports/attack/11c7ecc3cd6ab0717cc8c9aee7699ad7/adv_probabilities.json + attack_file: output/attacks/690089d6f49e2b05ff7983d1205e3857.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/11c7ecc3cd6ab0717cc8c9aee7699ad7/params.yaml + predictions_file: output/reports/attack/11c7ecc3cd6ab0717cc8c9aee7699ad7/predictions.json + probabilities_file: output/reports/attack/11c7ecc3cd6ab0717cc8c9aee7699ad7/probabilities.json + score_dict_file: output/reports/attack/11c7ecc3cd6ab0717cc8c9aee7699ad7/score_dict.json + test_labels_file: output/reports/attack/11c7ecc3cd6ab0717cc8c9aee7699ad7/test_labels.json + train_labels_file: output/reports/attack/11c7ecc3cd6ab0717cc8c9aee7699ad7/train_labels.json + model_dir: models + name: 11c7ecc3cd6ab0717cc8c9aee7699ad7 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 11c7ecc3cd6ab0717cc8c9aee7699ad7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/13055cc1fea248a5aa7e049c88c5e8ab/params.yaml b/examples/security/kdd-nsl/output/reports/attack/13055cc1fea248a5aa7e049c88c5e8ab/params.yaml new file mode 100644 index 00000000..3ecb4c58 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/13055cc1fea248a5aa7e049c88c5e8ab/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 983aa4ed88a8f5a4873c03deb95642a9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/13055cc1fea248a5aa7e049c88c5e8ab/adv_predictions.json + adv_probabilities_file: output/reports/attack/13055cc1fea248a5aa7e049c88c5e8ab/adv_probabilities.json + attack_file: output/attacks/a253520669c6eaa5577c8e5b489dded0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/13055cc1fea248a5aa7e049c88c5e8ab/params.yaml + predictions_file: output/reports/attack/13055cc1fea248a5aa7e049c88c5e8ab/predictions.json + probabilities_file: output/reports/attack/13055cc1fea248a5aa7e049c88c5e8ab/probabilities.json + score_dict_file: output/reports/attack/13055cc1fea248a5aa7e049c88c5e8ab/score_dict.json + test_labels_file: output/reports/attack/13055cc1fea248a5aa7e049c88c5e8ab/test_labels.json + train_labels_file: output/reports/attack/13055cc1fea248a5aa7e049c88c5e8ab/train_labels.json + model_dir: models + name: 13055cc1fea248a5aa7e049c88c5e8ab + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 13055cc1fea248a5aa7e049c88c5e8ab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/131a3c8223ed4b1e134db8fe4bb10b54/params.yaml b/examples/security/kdd-nsl/output/reports/attack/131a3c8223ed4b1e134db8fe4bb10b54/params.yaml new file mode 100644 index 00000000..9c626150 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/131a3c8223ed4b1e134db8fe4bb10b54/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 30aa7be345366c134ab05d46df75dfe5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/131a3c8223ed4b1e134db8fe4bb10b54/adv_predictions.json + adv_probabilities_file: output/reports/attack/131a3c8223ed4b1e134db8fe4bb10b54/adv_probabilities.json + attack_file: output/attacks/42f8d1f74f1ac824ef57b7564892c76b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/131a3c8223ed4b1e134db8fe4bb10b54/params.yaml + predictions_file: output/reports/attack/131a3c8223ed4b1e134db8fe4bb10b54/predictions.json + probabilities_file: output/reports/attack/131a3c8223ed4b1e134db8fe4bb10b54/probabilities.json + score_dict_file: output/reports/attack/131a3c8223ed4b1e134db8fe4bb10b54/score_dict.json + test_labels_file: output/reports/attack/131a3c8223ed4b1e134db8fe4bb10b54/test_labels.json + train_labels_file: output/reports/attack/131a3c8223ed4b1e134db8fe4bb10b54/train_labels.json + model_dir: models + name: 131a3c8223ed4b1e134db8fe4bb10b54 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 131a3c8223ed4b1e134db8fe4bb10b54 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/params.yaml b/examples/security/kdd-nsl/output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/params.yaml new file mode 100644 index 00000000..2f6f3354 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: cc5584361e821cb4d03df0625df58a65 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/adv_predictions.json + adv_probabilities_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/adv_probabilities.json + attack_file: output/attacks/8320bfd18965f34983368925c17c18e8.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/params.yaml + predictions_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/predictions.json + probabilities_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/probabilities.json + score_dict_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/score_dict.json + test_labels_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/test_labels.json + train_labels_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/train_labels.json + model_dir: models + name: 1362bddc676c1f49785f71fb83dac5f9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 1362bddc676c1f49785f71fb83dac5f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/13e0c285c2ad9aba119264e9283c1600/params.yaml b/examples/security/kdd-nsl/output/reports/attack/13e0c285c2ad9aba119264e9283c1600/params.yaml new file mode 100644 index 00000000..0271cd2b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/13e0c285c2ad9aba119264e9283c1600/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.001 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 173b7e899980e4ac1bb98abe3511a559 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/13e0c285c2ad9aba119264e9283c1600/adv_predictions.json + adv_probabilities_file: output/reports/attack/13e0c285c2ad9aba119264e9283c1600/adv_probabilities.json + attack_file: output/attacks/ccf9eb141a89e37aebd2c962ce33a2db.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/13e0c285c2ad9aba119264e9283c1600/params.yaml + predictions_file: output/reports/attack/13e0c285c2ad9aba119264e9283c1600/predictions.json + probabilities_file: output/reports/attack/13e0c285c2ad9aba119264e9283c1600/probabilities.json + score_dict_file: output/reports/attack/13e0c285c2ad9aba119264e9283c1600/score_dict.json + test_labels_file: output/reports/attack/13e0c285c2ad9aba119264e9283c1600/test_labels.json + train_labels_file: output/reports/attack/13e0c285c2ad9aba119264e9283c1600/train_labels.json + model_dir: models + name: 13e0c285c2ad9aba119264e9283c1600 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 13e0c285c2ad9aba119264e9283c1600 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/141e9dfdc7073af89dc0f7aac640bdda/params.yaml b/examples/security/kdd-nsl/output/reports/attack/141e9dfdc7073af89dc0f7aac640bdda/params.yaml new file mode 100644 index 00000000..02ae8939 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/141e9dfdc7073af89dc0f7aac640bdda/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.5 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 22c6d9beab1b3643cbadf7679f50dc16 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/141e9dfdc7073af89dc0f7aac640bdda/adv_predictions.json + adv_probabilities_file: output/reports/attack/141e9dfdc7073af89dc0f7aac640bdda/adv_probabilities.json + attack_file: output/attacks/691dd34c60e56706436796c9e7bef7e0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/141e9dfdc7073af89dc0f7aac640bdda/params.yaml + predictions_file: output/reports/attack/141e9dfdc7073af89dc0f7aac640bdda/predictions.json + probabilities_file: output/reports/attack/141e9dfdc7073af89dc0f7aac640bdda/probabilities.json + score_dict_file: output/reports/attack/141e9dfdc7073af89dc0f7aac640bdda/score_dict.json + test_labels_file: output/reports/attack/141e9dfdc7073af89dc0f7aac640bdda/test_labels.json + train_labels_file: output/reports/attack/141e9dfdc7073af89dc0f7aac640bdda/train_labels.json + model_dir: models + name: 141e9dfdc7073af89dc0f7aac640bdda + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 141e9dfdc7073af89dc0f7aac640bdda +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/143efd195b1c7bb536c7cbffcc18e822/params.yaml b/examples/security/kdd-nsl/output/reports/attack/143efd195b1c7bb536c7cbffcc18e822/params.yaml new file mode 100644 index 00000000..0ea99242 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/143efd195b1c7bb536c7cbffcc18e822/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 1 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 637335196dfabf504846e7724d4a9d06 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/143efd195b1c7bb536c7cbffcc18e822/adv_predictions.json + adv_probabilities_file: output/reports/attack/143efd195b1c7bb536c7cbffcc18e822/adv_probabilities.json + attack_file: output/attacks/edc0b95ac594f52a3cf2861f23f64113.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/143efd195b1c7bb536c7cbffcc18e822/params.yaml + predictions_file: output/reports/attack/143efd195b1c7bb536c7cbffcc18e822/predictions.json + probabilities_file: output/reports/attack/143efd195b1c7bb536c7cbffcc18e822/probabilities.json + score_dict_file: output/reports/attack/143efd195b1c7bb536c7cbffcc18e822/score_dict.json + test_labels_file: output/reports/attack/143efd195b1c7bb536c7cbffcc18e822/test_labels.json + train_labels_file: output/reports/attack/143efd195b1c7bb536c7cbffcc18e822/train_labels.json + model_dir: models + name: 143efd195b1c7bb536c7cbffcc18e822 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 143efd195b1c7bb536c7cbffcc18e822 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/147573234b378a702dcfc05cdaedab36/params.yaml b/examples/security/kdd-nsl/output/reports/attack/147573234b378a702dcfc05cdaedab36/params.yaml new file mode 100644 index 00000000..f64b0d02 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/147573234b378a702dcfc05cdaedab36/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 1 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: e9ff928abb04dd0afb1aeba8334ffcae +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/147573234b378a702dcfc05cdaedab36/adv_predictions.json + adv_probabilities_file: output/reports/attack/147573234b378a702dcfc05cdaedab36/adv_probabilities.json + attack_file: output/attacks/36cdcf7c8fee1745e95be66b388b518f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/147573234b378a702dcfc05cdaedab36/params.yaml + predictions_file: output/reports/attack/147573234b378a702dcfc05cdaedab36/predictions.json + probabilities_file: output/reports/attack/147573234b378a702dcfc05cdaedab36/probabilities.json + score_dict_file: output/reports/attack/147573234b378a702dcfc05cdaedab36/score_dict.json + test_labels_file: output/reports/attack/147573234b378a702dcfc05cdaedab36/test_labels.json + train_labels_file: output/reports/attack/147573234b378a702dcfc05cdaedab36/train_labels.json + model_dir: models + name: 147573234b378a702dcfc05cdaedab36 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 147573234b378a702dcfc05cdaedab36 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/1599bd460be626068ceb56892a25cc63/params.yaml b/examples/security/kdd-nsl/output/reports/attack/1599bd460be626068ceb56892a25cc63/params.yaml new file mode 100644 index 00000000..feb59bb4 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/1599bd460be626068ceb56892a25cc63/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.001 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 5dfe184da34ebdc7037c50816ccbde6f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1599bd460be626068ceb56892a25cc63/adv_predictions.json + adv_probabilities_file: output/reports/attack/1599bd460be626068ceb56892a25cc63/adv_probabilities.json + attack_file: output/attacks/9fdcbefd82596331a90876837280a8f5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/1599bd460be626068ceb56892a25cc63/params.yaml + predictions_file: output/reports/attack/1599bd460be626068ceb56892a25cc63/predictions.json + probabilities_file: output/reports/attack/1599bd460be626068ceb56892a25cc63/probabilities.json + score_dict_file: output/reports/attack/1599bd460be626068ceb56892a25cc63/score_dict.json + test_labels_file: output/reports/attack/1599bd460be626068ceb56892a25cc63/test_labels.json + train_labels_file: output/reports/attack/1599bd460be626068ceb56892a25cc63/train_labels.json + model_dir: models + name: 1599bd460be626068ceb56892a25cc63 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 1599bd460be626068ceb56892a25cc63 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/16215b8380435b9bffbb71a1a6991ca1/params.yaml b/examples/security/kdd-nsl/output/reports/attack/16215b8380435b9bffbb71a1a6991ca1/params.yaml new file mode 100644 index 00000000..ccbfb0b8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/16215b8380435b9bffbb71a1a6991ca1/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.001 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: c39f4e2727e36ee23488db92e46681b5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/16215b8380435b9bffbb71a1a6991ca1/adv_predictions.json + adv_probabilities_file: output/reports/attack/16215b8380435b9bffbb71a1a6991ca1/adv_probabilities.json + attack_file: output/attacks/08901319fcb2950ef384a6451259e127.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/16215b8380435b9bffbb71a1a6991ca1/params.yaml + predictions_file: output/reports/attack/16215b8380435b9bffbb71a1a6991ca1/predictions.json + probabilities_file: output/reports/attack/16215b8380435b9bffbb71a1a6991ca1/probabilities.json + score_dict_file: output/reports/attack/16215b8380435b9bffbb71a1a6991ca1/score_dict.json + test_labels_file: output/reports/attack/16215b8380435b9bffbb71a1a6991ca1/test_labels.json + train_labels_file: output/reports/attack/16215b8380435b9bffbb71a1a6991ca1/train_labels.json + model_dir: models + name: 16215b8380435b9bffbb71a1a6991ca1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 16215b8380435b9bffbb71a1a6991ca1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/165de532e639d35740a8054dc5c0b8f0/params.yaml b/examples/security/kdd-nsl/output/reports/attack/165de532e639d35740a8054dc5c0b8f0/params.yaml new file mode 100644 index 00000000..e9eed747 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/165de532e639d35740a8054dc5c0b8f0/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.1 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: a374aaeff2eb9e29dea472a612268b81 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/165de532e639d35740a8054dc5c0b8f0/adv_predictions.json + adv_probabilities_file: output/reports/attack/165de532e639d35740a8054dc5c0b8f0/adv_probabilities.json + attack_file: output/attacks/326fa4bbba75de6811c12f30d4dde57d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/165de532e639d35740a8054dc5c0b8f0/params.yaml + predictions_file: output/reports/attack/165de532e639d35740a8054dc5c0b8f0/predictions.json + probabilities_file: output/reports/attack/165de532e639d35740a8054dc5c0b8f0/probabilities.json + score_dict_file: output/reports/attack/165de532e639d35740a8054dc5c0b8f0/score_dict.json + test_labels_file: output/reports/attack/165de532e639d35740a8054dc5c0b8f0/test_labels.json + train_labels_file: output/reports/attack/165de532e639d35740a8054dc5c0b8f0/train_labels.json + model_dir: models + name: 165de532e639d35740a8054dc5c0b8f0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 165de532e639d35740a8054dc5c0b8f0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/16e1ed4ba4c585261599caa8b7b39fe9/params.yaml b/examples/security/kdd-nsl/output/reports/attack/16e1ed4ba4c585261599caa8b7b39fe9/params.yaml new file mode 100644 index 00000000..cb6d60c3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/16e1ed4ba4c585261599caa8b7b39fe9/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 7b95fea0015de6b252c008a3a086f0a8 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/16e1ed4ba4c585261599caa8b7b39fe9/adv_predictions.json + adv_probabilities_file: output/reports/attack/16e1ed4ba4c585261599caa8b7b39fe9/adv_probabilities.json + attack_file: output/attacks/0e0d11f9488918852010195727c405a9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/16e1ed4ba4c585261599caa8b7b39fe9/params.yaml + predictions_file: output/reports/attack/16e1ed4ba4c585261599caa8b7b39fe9/predictions.json + probabilities_file: output/reports/attack/16e1ed4ba4c585261599caa8b7b39fe9/probabilities.json + score_dict_file: output/reports/attack/16e1ed4ba4c585261599caa8b7b39fe9/score_dict.json + test_labels_file: output/reports/attack/16e1ed4ba4c585261599caa8b7b39fe9/test_labels.json + train_labels_file: output/reports/attack/16e1ed4ba4c585261599caa8b7b39fe9/train_labels.json + model_dir: models + name: 16e1ed4ba4c585261599caa8b7b39fe9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 16e1ed4ba4c585261599caa8b7b39fe9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/199d0e9baeff6bec3d424a9b61724877/params.yaml b/examples/security/kdd-nsl/output/reports/attack/199d0e9baeff6bec3d424a9b61724877/params.yaml new file mode 100644 index 00000000..da101f5b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/199d0e9baeff6bec3d424a9b61724877/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.01 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: b297235b98ca69fd72c4780a5e8f06c9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/199d0e9baeff6bec3d424a9b61724877/adv_predictions.json + adv_probabilities_file: output/reports/attack/199d0e9baeff6bec3d424a9b61724877/adv_probabilities.json + attack_file: output/attacks/b0176808cf71395787da7a965ef28e4c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/199d0e9baeff6bec3d424a9b61724877/params.yaml + predictions_file: output/reports/attack/199d0e9baeff6bec3d424a9b61724877/predictions.json + probabilities_file: output/reports/attack/199d0e9baeff6bec3d424a9b61724877/probabilities.json + score_dict_file: output/reports/attack/199d0e9baeff6bec3d424a9b61724877/score_dict.json + test_labels_file: output/reports/attack/199d0e9baeff6bec3d424a9b61724877/test_labels.json + train_labels_file: output/reports/attack/199d0e9baeff6bec3d424a9b61724877/train_labels.json + model_dir: models + name: 199d0e9baeff6bec3d424a9b61724877 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 199d0e9baeff6bec3d424a9b61724877 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/1a839ff45ca1069b08edccd356e22488/params.yaml b/examples/security/kdd-nsl/output/reports/attack/1a839ff45ca1069b08edccd356e22488/params.yaml new file mode 100644 index 00000000..4962108b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/1a839ff45ca1069b08edccd356e22488/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.5 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 7677865fbd8f74b03d3771af89643fab +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1a839ff45ca1069b08edccd356e22488/adv_predictions.json + adv_probabilities_file: output/reports/attack/1a839ff45ca1069b08edccd356e22488/adv_probabilities.json + attack_file: output/attacks/8d0140f71268980162f7541f8962e3a7.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/1a839ff45ca1069b08edccd356e22488/params.yaml + predictions_file: output/reports/attack/1a839ff45ca1069b08edccd356e22488/predictions.json + probabilities_file: output/reports/attack/1a839ff45ca1069b08edccd356e22488/probabilities.json + score_dict_file: output/reports/attack/1a839ff45ca1069b08edccd356e22488/score_dict.json + test_labels_file: output/reports/attack/1a839ff45ca1069b08edccd356e22488/test_labels.json + train_labels_file: output/reports/attack/1a839ff45ca1069b08edccd356e22488/train_labels.json + model_dir: models + name: 1a839ff45ca1069b08edccd356e22488 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 1a839ff45ca1069b08edccd356e22488 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/1a8e2ec9d260c77bfe90b80395cc0de5/params.yaml b/examples/security/kdd-nsl/output/reports/attack/1a8e2ec9d260c77bfe90b80395cc0de5/params.yaml new file mode 100644 index 00000000..9863a5c0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/1a8e2ec9d260c77bfe90b80395cc0de5/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: c8bcd1888e4d2752113f8314797b1e97 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1a8e2ec9d260c77bfe90b80395cc0de5/adv_predictions.json + adv_probabilities_file: output/reports/attack/1a8e2ec9d260c77bfe90b80395cc0de5/adv_probabilities.json + attack_file: output/attacks/be43775b84f069df59ff5f87c8ca54a8.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/1a8e2ec9d260c77bfe90b80395cc0de5/params.yaml + predictions_file: output/reports/attack/1a8e2ec9d260c77bfe90b80395cc0de5/predictions.json + probabilities_file: output/reports/attack/1a8e2ec9d260c77bfe90b80395cc0de5/probabilities.json + score_dict_file: output/reports/attack/1a8e2ec9d260c77bfe90b80395cc0de5/score_dict.json + test_labels_file: output/reports/attack/1a8e2ec9d260c77bfe90b80395cc0de5/test_labels.json + train_labels_file: output/reports/attack/1a8e2ec9d260c77bfe90b80395cc0de5/train_labels.json + model_dir: models + name: 1a8e2ec9d260c77bfe90b80395cc0de5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 1a8e2ec9d260c77bfe90b80395cc0de5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/1c72ad1a2031c20f862752fe0039bff3/params.yaml b/examples/security/kdd-nsl/output/reports/attack/1c72ad1a2031c20f862752fe0039bff3/params.yaml new file mode 100644 index 00000000..27becd3c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/1c72ad1a2031c20f862752fe0039bff3/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.5 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 968941b661b42df1eaec5e1de614c9c8 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1c72ad1a2031c20f862752fe0039bff3/adv_predictions.json + adv_probabilities_file: output/reports/attack/1c72ad1a2031c20f862752fe0039bff3/adv_probabilities.json + attack_file: output/attacks/09eb110b024c62ca75039bf12d6e5b7a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/1c72ad1a2031c20f862752fe0039bff3/params.yaml + predictions_file: output/reports/attack/1c72ad1a2031c20f862752fe0039bff3/predictions.json + probabilities_file: output/reports/attack/1c72ad1a2031c20f862752fe0039bff3/probabilities.json + score_dict_file: output/reports/attack/1c72ad1a2031c20f862752fe0039bff3/score_dict.json + test_labels_file: output/reports/attack/1c72ad1a2031c20f862752fe0039bff3/test_labels.json + train_labels_file: output/reports/attack/1c72ad1a2031c20f862752fe0039bff3/train_labels.json + model_dir: models + name: 1c72ad1a2031c20f862752fe0039bff3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 1c72ad1a2031c20f862752fe0039bff3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/1d77d377de4abe29be75f7d979eb907a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/1d77d377de4abe29be75f7d979eb907a/params.yaml new file mode 100644 index 00000000..df210066 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/1d77d377de4abe29be75f7d979eb907a/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 28fe699d7baf45d42689d9a29f6e9d38 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1d77d377de4abe29be75f7d979eb907a/adv_predictions.json + adv_probabilities_file: output/reports/attack/1d77d377de4abe29be75f7d979eb907a/adv_probabilities.json + attack_file: output/attacks/b8e7811e758714cb21d0990d498d5225.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/1d77d377de4abe29be75f7d979eb907a/params.yaml + predictions_file: output/reports/attack/1d77d377de4abe29be75f7d979eb907a/predictions.json + probabilities_file: output/reports/attack/1d77d377de4abe29be75f7d979eb907a/probabilities.json + score_dict_file: output/reports/attack/1d77d377de4abe29be75f7d979eb907a/score_dict.json + test_labels_file: output/reports/attack/1d77d377de4abe29be75f7d979eb907a/test_labels.json + train_labels_file: output/reports/attack/1d77d377de4abe29be75f7d979eb907a/train_labels.json + model_dir: models + name: 1d77d377de4abe29be75f7d979eb907a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 1d77d377de4abe29be75f7d979eb907a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/1d99be624df1634fb9f6b45f73c61025/params.yaml b/examples/security/kdd-nsl/output/reports/attack/1d99be624df1634fb9f6b45f73c61025/params.yaml new file mode 100644 index 00000000..508e01c9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/1d99be624df1634fb9f6b45f73c61025/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.3 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 39780c1e2b143eaa9932e9645c1a423d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1d99be624df1634fb9f6b45f73c61025/adv_predictions.json + adv_probabilities_file: output/reports/attack/1d99be624df1634fb9f6b45f73c61025/adv_probabilities.json + attack_file: output/attacks/c048c6d24f0867f469240488b15806d1.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/1d99be624df1634fb9f6b45f73c61025/params.yaml + predictions_file: output/reports/attack/1d99be624df1634fb9f6b45f73c61025/predictions.json + probabilities_file: output/reports/attack/1d99be624df1634fb9f6b45f73c61025/probabilities.json + score_dict_file: output/reports/attack/1d99be624df1634fb9f6b45f73c61025/score_dict.json + test_labels_file: output/reports/attack/1d99be624df1634fb9f6b45f73c61025/test_labels.json + train_labels_file: output/reports/attack/1d99be624df1634fb9f6b45f73c61025/train_labels.json + model_dir: models + name: 1d99be624df1634fb9f6b45f73c61025 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 1d99be624df1634fb9f6b45f73c61025 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/1e2bab976bff2db43aeb2997d3996eee/params.yaml b/examples/security/kdd-nsl/output/reports/attack/1e2bab976bff2db43aeb2997d3996eee/params.yaml new file mode 100644 index 00000000..612120e3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/1e2bab976bff2db43aeb2997d3996eee/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.5 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 122c668a5a3a75f9c4f6e1af09a0352f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1e2bab976bff2db43aeb2997d3996eee/adv_predictions.json + adv_probabilities_file: output/reports/attack/1e2bab976bff2db43aeb2997d3996eee/adv_probabilities.json + attack_file: output/attacks/da26a1e7c59e49034a3433040e3ddf87.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/1e2bab976bff2db43aeb2997d3996eee/params.yaml + predictions_file: output/reports/attack/1e2bab976bff2db43aeb2997d3996eee/predictions.json + probabilities_file: output/reports/attack/1e2bab976bff2db43aeb2997d3996eee/probabilities.json + score_dict_file: output/reports/attack/1e2bab976bff2db43aeb2997d3996eee/score_dict.json + test_labels_file: output/reports/attack/1e2bab976bff2db43aeb2997d3996eee/test_labels.json + train_labels_file: output/reports/attack/1e2bab976bff2db43aeb2997d3996eee/train_labels.json + model_dir: models + name: 1e2bab976bff2db43aeb2997d3996eee + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 1e2bab976bff2db43aeb2997d3996eee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/1e49d7af9895123ea1b7cc11c74c3359/params.yaml b/examples/security/kdd-nsl/output/reports/attack/1e49d7af9895123ea1b7cc11c74c3359/params.yaml new file mode 100644 index 00000000..83999c46 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/1e49d7af9895123ea1b7cc11c74c3359/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.3 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 76631da2866b32717dafd448f31e05a6 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1e49d7af9895123ea1b7cc11c74c3359/adv_predictions.json + adv_probabilities_file: output/reports/attack/1e49d7af9895123ea1b7cc11c74c3359/adv_probabilities.json + attack_file: output/attacks/b3f9714f3af6a6c0cf9d77c90b37ae77.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/1e49d7af9895123ea1b7cc11c74c3359/params.yaml + predictions_file: output/reports/attack/1e49d7af9895123ea1b7cc11c74c3359/predictions.json + probabilities_file: output/reports/attack/1e49d7af9895123ea1b7cc11c74c3359/probabilities.json + score_dict_file: output/reports/attack/1e49d7af9895123ea1b7cc11c74c3359/score_dict.json + test_labels_file: output/reports/attack/1e49d7af9895123ea1b7cc11c74c3359/test_labels.json + train_labels_file: output/reports/attack/1e49d7af9895123ea1b7cc11c74c3359/train_labels.json + model_dir: models + name: 1e49d7af9895123ea1b7cc11c74c3359 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 1e49d7af9895123ea1b7cc11c74c3359 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/1e6babb853b265d8bb883c4373d67762/params.yaml b/examples/security/kdd-nsl/output/reports/attack/1e6babb853b265d8bb883c4373d67762/params.yaml new file mode 100644 index 00000000..b73604d7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/1e6babb853b265d8bb883c4373d67762/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 9448244697263bccd60e51e414341b83 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1e6babb853b265d8bb883c4373d67762/adv_predictions.json + adv_probabilities_file: output/reports/attack/1e6babb853b265d8bb883c4373d67762/adv_probabilities.json + attack_file: output/attacks/3addc5ac4f71e5c731a4d63c251c7732.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/1e6babb853b265d8bb883c4373d67762/params.yaml + predictions_file: output/reports/attack/1e6babb853b265d8bb883c4373d67762/predictions.json + probabilities_file: output/reports/attack/1e6babb853b265d8bb883c4373d67762/probabilities.json + score_dict_file: output/reports/attack/1e6babb853b265d8bb883c4373d67762/score_dict.json + test_labels_file: output/reports/attack/1e6babb853b265d8bb883c4373d67762/test_labels.json + train_labels_file: output/reports/attack/1e6babb853b265d8bb883c4373d67762/train_labels.json + model_dir: models + name: 1e6babb853b265d8bb883c4373d67762 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 1e6babb853b265d8bb883c4373d67762 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/200ea8592a51783cc6f44e526f657274/params.yaml b/examples/security/kdd-nsl/output/reports/attack/200ea8592a51783cc6f44e526f657274/params.yaml new file mode 100644 index 00000000..df4f7d05 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/200ea8592a51783cc6f44e526f657274/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.5 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 8e09e642a7d04662531405c024a4213c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/200ea8592a51783cc6f44e526f657274/adv_predictions.json + adv_probabilities_file: output/reports/attack/200ea8592a51783cc6f44e526f657274/adv_probabilities.json + attack_file: output/attacks/0bf4b4446bbf034279ad461920404368.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/200ea8592a51783cc6f44e526f657274/params.yaml + predictions_file: output/reports/attack/200ea8592a51783cc6f44e526f657274/predictions.json + probabilities_file: output/reports/attack/200ea8592a51783cc6f44e526f657274/probabilities.json + score_dict_file: output/reports/attack/200ea8592a51783cc6f44e526f657274/score_dict.json + test_labels_file: output/reports/attack/200ea8592a51783cc6f44e526f657274/test_labels.json + train_labels_file: output/reports/attack/200ea8592a51783cc6f44e526f657274/train_labels.json + model_dir: models + name: 200ea8592a51783cc6f44e526f657274 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 200ea8592a51783cc6f44e526f657274 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/20ab2f548f14f5e75ef103cf638959e6/params.yaml b/examples/security/kdd-nsl/output/reports/attack/20ab2f548f14f5e75ef103cf638959e6/params.yaml new file mode 100644 index 00000000..82f93fc0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/20ab2f548f14f5e75ef103cf638959e6/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.3 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: d17d913aa1b9d057b4dba89220311a9e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/20ab2f548f14f5e75ef103cf638959e6/adv_predictions.json + adv_probabilities_file: output/reports/attack/20ab2f548f14f5e75ef103cf638959e6/adv_probabilities.json + attack_file: output/attacks/8c54312d2afca11bb5325c97de310968.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/20ab2f548f14f5e75ef103cf638959e6/params.yaml + predictions_file: output/reports/attack/20ab2f548f14f5e75ef103cf638959e6/predictions.json + probabilities_file: output/reports/attack/20ab2f548f14f5e75ef103cf638959e6/probabilities.json + score_dict_file: output/reports/attack/20ab2f548f14f5e75ef103cf638959e6/score_dict.json + test_labels_file: output/reports/attack/20ab2f548f14f5e75ef103cf638959e6/test_labels.json + train_labels_file: output/reports/attack/20ab2f548f14f5e75ef103cf638959e6/train_labels.json + model_dir: models + name: 20ab2f548f14f5e75ef103cf638959e6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 20ab2f548f14f5e75ef103cf638959e6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/21271fb5b567d695de6cbfa01a430ac0/params.yaml b/examples/security/kdd-nsl/output/reports/attack/21271fb5b567d695de6cbfa01a430ac0/params.yaml new file mode 100644 index 00000000..880c3caa --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/21271fb5b567d695de6cbfa01a430ac0/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: e5a3302ee5d340b21c138dbdb27415f7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/21271fb5b567d695de6cbfa01a430ac0/adv_predictions.json + adv_probabilities_file: output/reports/attack/21271fb5b567d695de6cbfa01a430ac0/adv_probabilities.json + attack_file: output/attacks/c185bd7a8989ce913ec76f2a9211d401.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/21271fb5b567d695de6cbfa01a430ac0/params.yaml + predictions_file: output/reports/attack/21271fb5b567d695de6cbfa01a430ac0/predictions.json + probabilities_file: output/reports/attack/21271fb5b567d695de6cbfa01a430ac0/probabilities.json + score_dict_file: output/reports/attack/21271fb5b567d695de6cbfa01a430ac0/score_dict.json + test_labels_file: output/reports/attack/21271fb5b567d695de6cbfa01a430ac0/test_labels.json + train_labels_file: output/reports/attack/21271fb5b567d695de6cbfa01a430ac0/train_labels.json + model_dir: models + name: 21271fb5b567d695de6cbfa01a430ac0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 21271fb5b567d695de6cbfa01a430ac0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/2132e216e22c9a20c4dec66b67af1dd5/params.yaml b/examples/security/kdd-nsl/output/reports/attack/2132e216e22c9a20c4dec66b67af1dd5/params.yaml new file mode 100644 index 00000000..fa9eb864 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/2132e216e22c9a20c4dec66b67af1dd5/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.5 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: a2ddc93b0310cdfb430a2c2bd782c39f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2132e216e22c9a20c4dec66b67af1dd5/adv_predictions.json + adv_probabilities_file: output/reports/attack/2132e216e22c9a20c4dec66b67af1dd5/adv_probabilities.json + attack_file: output/attacks/30770f29bcaf9a2f9f28e74cde92333b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/2132e216e22c9a20c4dec66b67af1dd5/params.yaml + predictions_file: output/reports/attack/2132e216e22c9a20c4dec66b67af1dd5/predictions.json + probabilities_file: output/reports/attack/2132e216e22c9a20c4dec66b67af1dd5/probabilities.json + score_dict_file: output/reports/attack/2132e216e22c9a20c4dec66b67af1dd5/score_dict.json + test_labels_file: output/reports/attack/2132e216e22c9a20c4dec66b67af1dd5/test_labels.json + train_labels_file: output/reports/attack/2132e216e22c9a20c4dec66b67af1dd5/train_labels.json + model_dir: models + name: 2132e216e22c9a20c4dec66b67af1dd5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 2132e216e22c9a20c4dec66b67af1dd5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/21e03ef603b3324e7427ea6f47d4d2ed/params.yaml b/examples/security/kdd-nsl/output/reports/attack/21e03ef603b3324e7427ea6f47d4d2ed/params.yaml new file mode 100644 index 00000000..8ceed91e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/21e03ef603b3324e7427ea6f47d4d2ed/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 4bdc5bed865bffbcd5ae70c693b0c605 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/21e03ef603b3324e7427ea6f47d4d2ed/adv_predictions.json + adv_probabilities_file: output/reports/attack/21e03ef603b3324e7427ea6f47d4d2ed/adv_probabilities.json + attack_file: output/attacks/d0dfbf1d08c87963f78d988cf2aafc92.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/21e03ef603b3324e7427ea6f47d4d2ed/params.yaml + predictions_file: output/reports/attack/21e03ef603b3324e7427ea6f47d4d2ed/predictions.json + probabilities_file: output/reports/attack/21e03ef603b3324e7427ea6f47d4d2ed/probabilities.json + score_dict_file: output/reports/attack/21e03ef603b3324e7427ea6f47d4d2ed/score_dict.json + test_labels_file: output/reports/attack/21e03ef603b3324e7427ea6f47d4d2ed/test_labels.json + train_labels_file: output/reports/attack/21e03ef603b3324e7427ea6f47d4d2ed/train_labels.json + model_dir: models + name: 21e03ef603b3324e7427ea6f47d4d2ed + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 21e03ef603b3324e7427ea6f47d4d2ed +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/248abe22c01962c468cb2da36aa5253d/params.yaml b/examples/security/kdd-nsl/output/reports/attack/248abe22c01962c468cb2da36aa5253d/params.yaml new file mode 100644 index 00000000..417c7583 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/248abe22c01962c468cb2da36aa5253d/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 2ecc64ba2aafe196355dc20038c24d7e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/248abe22c01962c468cb2da36aa5253d/adv_predictions.json + adv_probabilities_file: output/reports/attack/248abe22c01962c468cb2da36aa5253d/adv_probabilities.json + attack_file: output/attacks/afd9c1c09b1476e33b56966ac640d331.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/248abe22c01962c468cb2da36aa5253d/params.yaml + predictions_file: output/reports/attack/248abe22c01962c468cb2da36aa5253d/predictions.json + probabilities_file: output/reports/attack/248abe22c01962c468cb2da36aa5253d/probabilities.json + score_dict_file: output/reports/attack/248abe22c01962c468cb2da36aa5253d/score_dict.json + test_labels_file: output/reports/attack/248abe22c01962c468cb2da36aa5253d/test_labels.json + train_labels_file: output/reports/attack/248abe22c01962c468cb2da36aa5253d/train_labels.json + model_dir: models + name: 248abe22c01962c468cb2da36aa5253d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 248abe22c01962c468cb2da36aa5253d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/24d1458d2f1d6ac4038e5bca23f6f5c3/params.yaml b/examples/security/kdd-nsl/output/reports/attack/24d1458d2f1d6ac4038e5bca23f6f5c3/params.yaml new file mode 100644 index 00000000..3c0f74c1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/24d1458d2f1d6ac4038e5bca23f6f5c3/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.5 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: fc79567ed379711b121e4a75dd47a02a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/24d1458d2f1d6ac4038e5bca23f6f5c3/adv_predictions.json + adv_probabilities_file: output/reports/attack/24d1458d2f1d6ac4038e5bca23f6f5c3/adv_probabilities.json + attack_file: output/attacks/2a81a52bf7fdca970d17065cc9a8a99c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/24d1458d2f1d6ac4038e5bca23f6f5c3/params.yaml + predictions_file: output/reports/attack/24d1458d2f1d6ac4038e5bca23f6f5c3/predictions.json + probabilities_file: output/reports/attack/24d1458d2f1d6ac4038e5bca23f6f5c3/probabilities.json + score_dict_file: output/reports/attack/24d1458d2f1d6ac4038e5bca23f6f5c3/score_dict.json + test_labels_file: output/reports/attack/24d1458d2f1d6ac4038e5bca23f6f5c3/test_labels.json + train_labels_file: output/reports/attack/24d1458d2f1d6ac4038e5bca23f6f5c3/train_labels.json + model_dir: models + name: 24d1458d2f1d6ac4038e5bca23f6f5c3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 24d1458d2f1d6ac4038e5bca23f6f5c3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/2582dfa453a798cd80d3df5285e5016c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/2582dfa453a798cd80d3df5285e5016c/params.yaml new file mode 100644 index 00000000..fbcbc2f2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/2582dfa453a798cd80d3df5285e5016c/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.5 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 23ef1621742b4289ec2d107d7377e616 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2582dfa453a798cd80d3df5285e5016c/adv_predictions.json + adv_probabilities_file: output/reports/attack/2582dfa453a798cd80d3df5285e5016c/adv_probabilities.json + attack_file: output/attacks/0015b0f7742bc91179af4555f33d9079.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/2582dfa453a798cd80d3df5285e5016c/params.yaml + predictions_file: output/reports/attack/2582dfa453a798cd80d3df5285e5016c/predictions.json + probabilities_file: output/reports/attack/2582dfa453a798cd80d3df5285e5016c/probabilities.json + score_dict_file: output/reports/attack/2582dfa453a798cd80d3df5285e5016c/score_dict.json + test_labels_file: output/reports/attack/2582dfa453a798cd80d3df5285e5016c/test_labels.json + train_labels_file: output/reports/attack/2582dfa453a798cd80d3df5285e5016c/train_labels.json + model_dir: models + name: 2582dfa453a798cd80d3df5285e5016c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 2582dfa453a798cd80d3df5285e5016c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/25dc8f09030fee079e7efc89c405aa26/params.yaml b/examples/security/kdd-nsl/output/reports/attack/25dc8f09030fee079e7efc89c405aa26/params.yaml new file mode 100644 index 00000000..982ec1cd --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/25dc8f09030fee079e7efc89c405aa26/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: e2db90d384d12cbea361cb6956ec53cc +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/25dc8f09030fee079e7efc89c405aa26/adv_predictions.json + adv_probabilities_file: output/reports/attack/25dc8f09030fee079e7efc89c405aa26/adv_probabilities.json + attack_file: output/attacks/17df02fd0adb609e83bdc539db9dac9c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/25dc8f09030fee079e7efc89c405aa26/params.yaml + predictions_file: output/reports/attack/25dc8f09030fee079e7efc89c405aa26/predictions.json + probabilities_file: output/reports/attack/25dc8f09030fee079e7efc89c405aa26/probabilities.json + score_dict_file: output/reports/attack/25dc8f09030fee079e7efc89c405aa26/score_dict.json + test_labels_file: output/reports/attack/25dc8f09030fee079e7efc89c405aa26/test_labels.json + train_labels_file: output/reports/attack/25dc8f09030fee079e7efc89c405aa26/train_labels.json + model_dir: models + name: 25dc8f09030fee079e7efc89c405aa26 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 25dc8f09030fee079e7efc89c405aa26 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/26e275e17c7229e8f82806dc871e8203/params.yaml b/examples/security/kdd-nsl/output/reports/attack/26e275e17c7229e8f82806dc871e8203/params.yaml new file mode 100644 index 00000000..89265599 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/26e275e17c7229e8f82806dc871e8203/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.3 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 6f3482a89800325825a75efbb291b670 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/26e275e17c7229e8f82806dc871e8203/adv_predictions.json + adv_probabilities_file: output/reports/attack/26e275e17c7229e8f82806dc871e8203/adv_probabilities.json + attack_file: output/attacks/1dd942664370cce79c8e31097c441f46.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/26e275e17c7229e8f82806dc871e8203/params.yaml + predictions_file: output/reports/attack/26e275e17c7229e8f82806dc871e8203/predictions.json + probabilities_file: output/reports/attack/26e275e17c7229e8f82806dc871e8203/probabilities.json + score_dict_file: output/reports/attack/26e275e17c7229e8f82806dc871e8203/score_dict.json + test_labels_file: output/reports/attack/26e275e17c7229e8f82806dc871e8203/test_labels.json + train_labels_file: output/reports/attack/26e275e17c7229e8f82806dc871e8203/train_labels.json + model_dir: models + name: 26e275e17c7229e8f82806dc871e8203 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 26e275e17c7229e8f82806dc871e8203 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/2717aef7b2cccc9b8a67ce7425affa38/params.yaml b/examples/security/kdd-nsl/output/reports/attack/2717aef7b2cccc9b8a67ce7425affa38/params.yaml new file mode 100644 index 00000000..80477044 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/2717aef7b2cccc9b8a67ce7425affa38/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.5 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: d35ffa70b7175c1fe4bd4922d04b4318 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2717aef7b2cccc9b8a67ce7425affa38/adv_predictions.json + adv_probabilities_file: output/reports/attack/2717aef7b2cccc9b8a67ce7425affa38/adv_probabilities.json + attack_file: output/attacks/3da368d8819468c679c233e99fae24f0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/2717aef7b2cccc9b8a67ce7425affa38/params.yaml + predictions_file: output/reports/attack/2717aef7b2cccc9b8a67ce7425affa38/predictions.json + probabilities_file: output/reports/attack/2717aef7b2cccc9b8a67ce7425affa38/probabilities.json + score_dict_file: output/reports/attack/2717aef7b2cccc9b8a67ce7425affa38/score_dict.json + test_labels_file: output/reports/attack/2717aef7b2cccc9b8a67ce7425affa38/test_labels.json + train_labels_file: output/reports/attack/2717aef7b2cccc9b8a67ce7425affa38/train_labels.json + model_dir: models + name: 2717aef7b2cccc9b8a67ce7425affa38 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 2717aef7b2cccc9b8a67ce7425affa38 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/276af6f0c7538ff99f0f2bb4105f72df/params.yaml b/examples/security/kdd-nsl/output/reports/attack/276af6f0c7538ff99f0f2bb4105f72df/params.yaml new file mode 100644 index 00000000..12cfc6b8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/276af6f0c7538ff99f0f2bb4105f72df/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 4a89be27ee06ea6f9ef479412a6199a4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/276af6f0c7538ff99f0f2bb4105f72df/adv_predictions.json + adv_probabilities_file: output/reports/attack/276af6f0c7538ff99f0f2bb4105f72df/adv_probabilities.json + attack_file: output/attacks/78b94b38707cc913a2caf6664255caf3.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/276af6f0c7538ff99f0f2bb4105f72df/params.yaml + predictions_file: output/reports/attack/276af6f0c7538ff99f0f2bb4105f72df/predictions.json + probabilities_file: output/reports/attack/276af6f0c7538ff99f0f2bb4105f72df/probabilities.json + score_dict_file: output/reports/attack/276af6f0c7538ff99f0f2bb4105f72df/score_dict.json + test_labels_file: output/reports/attack/276af6f0c7538ff99f0f2bb4105f72df/test_labels.json + train_labels_file: output/reports/attack/276af6f0c7538ff99f0f2bb4105f72df/train_labels.json + model_dir: models + name: 276af6f0c7538ff99f0f2bb4105f72df + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 276af6f0c7538ff99f0f2bb4105f72df +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/278748b0228de6eb5923ea907b8be357/params.yaml b/examples/security/kdd-nsl/output/reports/attack/278748b0228de6eb5923ea907b8be357/params.yaml new file mode 100644 index 00000000..4abdc74d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/278748b0228de6eb5923ea907b8be357/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.001 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: a4fdb8f14676a348e6a050165e1c9dc8 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/278748b0228de6eb5923ea907b8be357/adv_predictions.json + adv_probabilities_file: output/reports/attack/278748b0228de6eb5923ea907b8be357/adv_probabilities.json + attack_file: output/attacks/5ec1637fbaac99b2f9097a28777a3c0b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/278748b0228de6eb5923ea907b8be357/params.yaml + predictions_file: output/reports/attack/278748b0228de6eb5923ea907b8be357/predictions.json + probabilities_file: output/reports/attack/278748b0228de6eb5923ea907b8be357/probabilities.json + score_dict_file: output/reports/attack/278748b0228de6eb5923ea907b8be357/score_dict.json + test_labels_file: output/reports/attack/278748b0228de6eb5923ea907b8be357/test_labels.json + train_labels_file: output/reports/attack/278748b0228de6eb5923ea907b8be357/train_labels.json + model_dir: models + name: 278748b0228de6eb5923ea907b8be357 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 278748b0228de6eb5923ea907b8be357 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/286495fc1a2d2a187a9baed5ed18ee75/params.yaml b/examples/security/kdd-nsl/output/reports/attack/286495fc1a2d2a187a9baed5ed18ee75/params.yaml new file mode 100644 index 00000000..a4e4bc0c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/286495fc1a2d2a187a9baed5ed18ee75/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.1 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: d6de4dcb157800510d20e87c5e84d8f7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/286495fc1a2d2a187a9baed5ed18ee75/adv_predictions.json + adv_probabilities_file: output/reports/attack/286495fc1a2d2a187a9baed5ed18ee75/adv_probabilities.json + attack_file: output/attacks/cec76b52bdfbc4af557cf0a1e96e2c8b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/286495fc1a2d2a187a9baed5ed18ee75/params.yaml + predictions_file: output/reports/attack/286495fc1a2d2a187a9baed5ed18ee75/predictions.json + probabilities_file: output/reports/attack/286495fc1a2d2a187a9baed5ed18ee75/probabilities.json + score_dict_file: output/reports/attack/286495fc1a2d2a187a9baed5ed18ee75/score_dict.json + test_labels_file: output/reports/attack/286495fc1a2d2a187a9baed5ed18ee75/test_labels.json + train_labels_file: output/reports/attack/286495fc1a2d2a187a9baed5ed18ee75/train_labels.json + model_dir: models + name: 286495fc1a2d2a187a9baed5ed18ee75 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 286495fc1a2d2a187a9baed5ed18ee75 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/29f3f757249038293829b308a9b56b1b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/29f3f757249038293829b308a9b56b1b/params.yaml new file mode 100644 index 00000000..ab425787 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/29f3f757249038293829b308a9b56b1b/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.5 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 387599075db7646d4d14e7d501460428 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/29f3f757249038293829b308a9b56b1b/adv_predictions.json + adv_probabilities_file: output/reports/attack/29f3f757249038293829b308a9b56b1b/adv_probabilities.json + attack_file: output/attacks/04967e1c600c192a70eaf852eb555a7a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/29f3f757249038293829b308a9b56b1b/params.yaml + predictions_file: output/reports/attack/29f3f757249038293829b308a9b56b1b/predictions.json + probabilities_file: output/reports/attack/29f3f757249038293829b308a9b56b1b/probabilities.json + score_dict_file: output/reports/attack/29f3f757249038293829b308a9b56b1b/score_dict.json + test_labels_file: output/reports/attack/29f3f757249038293829b308a9b56b1b/test_labels.json + train_labels_file: output/reports/attack/29f3f757249038293829b308a9b56b1b/train_labels.json + model_dir: models + name: 29f3f757249038293829b308a9b56b1b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 29f3f757249038293829b308a9b56b1b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/2ac5b1d155c8910fe15148e00d008a8b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/2ac5b1d155c8910fe15148e00d008a8b/params.yaml new file mode 100644 index 00000000..06e06a2a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/2ac5b1d155c8910fe15148e00d008a8b/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.001 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 345abbb66663e5e1c08945c8b9349c86 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2ac5b1d155c8910fe15148e00d008a8b/adv_predictions.json + adv_probabilities_file: output/reports/attack/2ac5b1d155c8910fe15148e00d008a8b/adv_probabilities.json + attack_file: output/attacks/d9d957cb58e2efd3fca185990fa18785.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/2ac5b1d155c8910fe15148e00d008a8b/params.yaml + predictions_file: output/reports/attack/2ac5b1d155c8910fe15148e00d008a8b/predictions.json + probabilities_file: output/reports/attack/2ac5b1d155c8910fe15148e00d008a8b/probabilities.json + score_dict_file: output/reports/attack/2ac5b1d155c8910fe15148e00d008a8b/score_dict.json + test_labels_file: output/reports/attack/2ac5b1d155c8910fe15148e00d008a8b/test_labels.json + train_labels_file: output/reports/attack/2ac5b1d155c8910fe15148e00d008a8b/train_labels.json + model_dir: models + name: 2ac5b1d155c8910fe15148e00d008a8b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 2ac5b1d155c8910fe15148e00d008a8b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/2bd334cfcc6bcf3e50e686196199a9f4/params.yaml b/examples/security/kdd-nsl/output/reports/attack/2bd334cfcc6bcf3e50e686196199a9f4/params.yaml new file mode 100644 index 00000000..3e517d86 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/2bd334cfcc6bcf3e50e686196199a9f4/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.3 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: b750a7b2692ddf52227408948ca32e2f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2bd334cfcc6bcf3e50e686196199a9f4/adv_predictions.json + adv_probabilities_file: output/reports/attack/2bd334cfcc6bcf3e50e686196199a9f4/adv_probabilities.json + attack_file: output/attacks/486a769c30f8efc786ae6b5de66f83bb.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/2bd334cfcc6bcf3e50e686196199a9f4/params.yaml + predictions_file: output/reports/attack/2bd334cfcc6bcf3e50e686196199a9f4/predictions.json + probabilities_file: output/reports/attack/2bd334cfcc6bcf3e50e686196199a9f4/probabilities.json + score_dict_file: output/reports/attack/2bd334cfcc6bcf3e50e686196199a9f4/score_dict.json + test_labels_file: output/reports/attack/2bd334cfcc6bcf3e50e686196199a9f4/test_labels.json + train_labels_file: output/reports/attack/2bd334cfcc6bcf3e50e686196199a9f4/train_labels.json + model_dir: models + name: 2bd334cfcc6bcf3e50e686196199a9f4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 2bd334cfcc6bcf3e50e686196199a9f4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/2c19303c3d91ce9375741a7c40b83356/params.yaml b/examples/security/kdd-nsl/output/reports/attack/2c19303c3d91ce9375741a7c40b83356/params.yaml new file mode 100644 index 00000000..ec030922 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/2c19303c3d91ce9375741a7c40b83356/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 6fbff33fc6bd01c713a2588086ddcb97 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2c19303c3d91ce9375741a7c40b83356/adv_predictions.json + adv_probabilities_file: output/reports/attack/2c19303c3d91ce9375741a7c40b83356/adv_probabilities.json + attack_file: output/attacks/958a5186e31ab82d34cac9f3432b9ab6.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/2c19303c3d91ce9375741a7c40b83356/params.yaml + predictions_file: output/reports/attack/2c19303c3d91ce9375741a7c40b83356/predictions.json + probabilities_file: output/reports/attack/2c19303c3d91ce9375741a7c40b83356/probabilities.json + score_dict_file: output/reports/attack/2c19303c3d91ce9375741a7c40b83356/score_dict.json + test_labels_file: output/reports/attack/2c19303c3d91ce9375741a7c40b83356/test_labels.json + train_labels_file: output/reports/attack/2c19303c3d91ce9375741a7c40b83356/train_labels.json + model_dir: models + name: 2c19303c3d91ce9375741a7c40b83356 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 2c19303c3d91ce9375741a7c40b83356 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/2cdcc3be27bee31d706cb8dfa0b2d80b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/2cdcc3be27bee31d706cb8dfa0b2d80b/params.yaml new file mode 100644 index 00000000..af9cdb63 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/2cdcc3be27bee31d706cb8dfa0b2d80b/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: e1eb10598592ebd1e5f2d0e8ed4a66ec +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2cdcc3be27bee31d706cb8dfa0b2d80b/adv_predictions.json + adv_probabilities_file: output/reports/attack/2cdcc3be27bee31d706cb8dfa0b2d80b/adv_probabilities.json + attack_file: output/attacks/affe629d6f1d889c84e55e524bf670aa.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/2cdcc3be27bee31d706cb8dfa0b2d80b/params.yaml + predictions_file: output/reports/attack/2cdcc3be27bee31d706cb8dfa0b2d80b/predictions.json + probabilities_file: output/reports/attack/2cdcc3be27bee31d706cb8dfa0b2d80b/probabilities.json + score_dict_file: output/reports/attack/2cdcc3be27bee31d706cb8dfa0b2d80b/score_dict.json + test_labels_file: output/reports/attack/2cdcc3be27bee31d706cb8dfa0b2d80b/test_labels.json + train_labels_file: output/reports/attack/2cdcc3be27bee31d706cb8dfa0b2d80b/train_labels.json + model_dir: models + name: 2cdcc3be27bee31d706cb8dfa0b2d80b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 2cdcc3be27bee31d706cb8dfa0b2d80b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/2d282e9db68e4d4b5b672fa7096be35d/params.yaml b/examples/security/kdd-nsl/output/reports/attack/2d282e9db68e4d4b5b672fa7096be35d/params.yaml new file mode 100644 index 00000000..099a6bc1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/2d282e9db68e4d4b5b672fa7096be35d/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 683688b13594223572ee737911550a4c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2d282e9db68e4d4b5b672fa7096be35d/adv_predictions.json + adv_probabilities_file: output/reports/attack/2d282e9db68e4d4b5b672fa7096be35d/adv_probabilities.json + attack_file: output/attacks/bd362306cd2d392f14c6e78031f499c9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/2d282e9db68e4d4b5b672fa7096be35d/params.yaml + predictions_file: output/reports/attack/2d282e9db68e4d4b5b672fa7096be35d/predictions.json + probabilities_file: output/reports/attack/2d282e9db68e4d4b5b672fa7096be35d/probabilities.json + score_dict_file: output/reports/attack/2d282e9db68e4d4b5b672fa7096be35d/score_dict.json + test_labels_file: output/reports/attack/2d282e9db68e4d4b5b672fa7096be35d/test_labels.json + train_labels_file: output/reports/attack/2d282e9db68e4d4b5b672fa7096be35d/train_labels.json + model_dir: models + name: 2d282e9db68e4d4b5b672fa7096be35d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 2d282e9db68e4d4b5b672fa7096be35d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/2e57fbc6d7a8b8fd96dbd07c4b96bb19/params.yaml b/examples/security/kdd-nsl/output/reports/attack/2e57fbc6d7a8b8fd96dbd07c4b96bb19/params.yaml new file mode 100644 index 00000000..48d7c99b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/2e57fbc6d7a8b8fd96dbd07c4b96bb19/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 1ac30d39cf812f5de7115397ca286122 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2e57fbc6d7a8b8fd96dbd07c4b96bb19/adv_predictions.json + adv_probabilities_file: output/reports/attack/2e57fbc6d7a8b8fd96dbd07c4b96bb19/adv_probabilities.json + attack_file: output/attacks/72c165099a80a3922f7cc64689dc2228.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/2e57fbc6d7a8b8fd96dbd07c4b96bb19/params.yaml + predictions_file: output/reports/attack/2e57fbc6d7a8b8fd96dbd07c4b96bb19/predictions.json + probabilities_file: output/reports/attack/2e57fbc6d7a8b8fd96dbd07c4b96bb19/probabilities.json + score_dict_file: output/reports/attack/2e57fbc6d7a8b8fd96dbd07c4b96bb19/score_dict.json + test_labels_file: output/reports/attack/2e57fbc6d7a8b8fd96dbd07c4b96bb19/test_labels.json + train_labels_file: output/reports/attack/2e57fbc6d7a8b8fd96dbd07c4b96bb19/train_labels.json + model_dir: models + name: 2e57fbc6d7a8b8fd96dbd07c4b96bb19 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 2e57fbc6d7a8b8fd96dbd07c4b96bb19 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/2f2899fb56696b0d37ea92931804e714/params.yaml b/examples/security/kdd-nsl/output/reports/attack/2f2899fb56696b0d37ea92931804e714/params.yaml new file mode 100644 index 00000000..9153de95 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/2f2899fb56696b0d37ea92931804e714/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: dcf7a76387dd1e5f5f6939dc6b8f78c7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2f2899fb56696b0d37ea92931804e714/adv_predictions.json + adv_probabilities_file: output/reports/attack/2f2899fb56696b0d37ea92931804e714/adv_probabilities.json + attack_file: output/attacks/02c3f36496ed33b1b249818d97f019fb.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/2f2899fb56696b0d37ea92931804e714/params.yaml + predictions_file: output/reports/attack/2f2899fb56696b0d37ea92931804e714/predictions.json + probabilities_file: output/reports/attack/2f2899fb56696b0d37ea92931804e714/probabilities.json + score_dict_file: output/reports/attack/2f2899fb56696b0d37ea92931804e714/score_dict.json + test_labels_file: output/reports/attack/2f2899fb56696b0d37ea92931804e714/test_labels.json + train_labels_file: output/reports/attack/2f2899fb56696b0d37ea92931804e714/train_labels.json + model_dir: models + name: 2f2899fb56696b0d37ea92931804e714 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 2f2899fb56696b0d37ea92931804e714 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/2f76a14620d7c9a9f80d6e387de2eacf/params.yaml b/examples/security/kdd-nsl/output/reports/attack/2f76a14620d7c9a9f80d6e387de2eacf/params.yaml new file mode 100644 index 00000000..66944c61 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/2f76a14620d7c9a9f80d6e387de2eacf/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.01 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 62ab74a15de8301e1c896133841fb876 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2f76a14620d7c9a9f80d6e387de2eacf/adv_predictions.json + adv_probabilities_file: output/reports/attack/2f76a14620d7c9a9f80d6e387de2eacf/adv_probabilities.json + attack_file: output/attacks/62b6dd1a5157dc62c62c59e6618f7359.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/2f76a14620d7c9a9f80d6e387de2eacf/params.yaml + predictions_file: output/reports/attack/2f76a14620d7c9a9f80d6e387de2eacf/predictions.json + probabilities_file: output/reports/attack/2f76a14620d7c9a9f80d6e387de2eacf/probabilities.json + score_dict_file: output/reports/attack/2f76a14620d7c9a9f80d6e387de2eacf/score_dict.json + test_labels_file: output/reports/attack/2f76a14620d7c9a9f80d6e387de2eacf/test_labels.json + train_labels_file: output/reports/attack/2f76a14620d7c9a9f80d6e387de2eacf/train_labels.json + model_dir: models + name: 2f76a14620d7c9a9f80d6e387de2eacf + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 2f76a14620d7c9a9f80d6e387de2eacf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/303e5de088f6d04030a05ac4be89924f/params.yaml b/examples/security/kdd-nsl/output/reports/attack/303e5de088f6d04030a05ac4be89924f/params.yaml new file mode 100644 index 00000000..148b2d12 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/303e5de088f6d04030a05ac4be89924f/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.3 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 09d3738ef138ded0c36e4160b3094040 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/303e5de088f6d04030a05ac4be89924f/adv_predictions.json + adv_probabilities_file: output/reports/attack/303e5de088f6d04030a05ac4be89924f/adv_probabilities.json + attack_file: output/attacks/1ca491d3353921bd057d2ee3f20fd48a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/303e5de088f6d04030a05ac4be89924f/params.yaml + predictions_file: output/reports/attack/303e5de088f6d04030a05ac4be89924f/predictions.json + probabilities_file: output/reports/attack/303e5de088f6d04030a05ac4be89924f/probabilities.json + score_dict_file: output/reports/attack/303e5de088f6d04030a05ac4be89924f/score_dict.json + test_labels_file: output/reports/attack/303e5de088f6d04030a05ac4be89924f/test_labels.json + train_labels_file: output/reports/attack/303e5de088f6d04030a05ac4be89924f/train_labels.json + model_dir: models + name: 303e5de088f6d04030a05ac4be89924f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 303e5de088f6d04030a05ac4be89924f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/307c274de1c157cd94c8eecbda9b2d49/params.yaml b/examples/security/kdd-nsl/output/reports/attack/307c274de1c157cd94c8eecbda9b2d49/params.yaml new file mode 100644 index 00000000..70472efa --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/307c274de1c157cd94c8eecbda9b2d49/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.001 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: befe7895073ce018a224fc3720402cbb +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/307c274de1c157cd94c8eecbda9b2d49/adv_predictions.json + adv_probabilities_file: output/reports/attack/307c274de1c157cd94c8eecbda9b2d49/adv_probabilities.json + attack_file: output/attacks/89c9f99ff70d3a8ca0c9d9fe35b70cf4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/307c274de1c157cd94c8eecbda9b2d49/params.yaml + predictions_file: output/reports/attack/307c274de1c157cd94c8eecbda9b2d49/predictions.json + probabilities_file: output/reports/attack/307c274de1c157cd94c8eecbda9b2d49/probabilities.json + score_dict_file: output/reports/attack/307c274de1c157cd94c8eecbda9b2d49/score_dict.json + test_labels_file: output/reports/attack/307c274de1c157cd94c8eecbda9b2d49/test_labels.json + train_labels_file: output/reports/attack/307c274de1c157cd94c8eecbda9b2d49/train_labels.json + model_dir: models + name: 307c274de1c157cd94c8eecbda9b2d49 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 307c274de1c157cd94c8eecbda9b2d49 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/30aa6fca15709d6a7e5f42f734006e2f/params.yaml b/examples/security/kdd-nsl/output/reports/attack/30aa6fca15709d6a7e5f42f734006e2f/params.yaml new file mode 100644 index 00000000..781ea77b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/30aa6fca15709d6a7e5f42f734006e2f/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.001 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: cf5a0ffb247f30338501c5f8712c39e3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/30aa6fca15709d6a7e5f42f734006e2f/adv_predictions.json + adv_probabilities_file: output/reports/attack/30aa6fca15709d6a7e5f42f734006e2f/adv_probabilities.json + attack_file: output/attacks/78a16eeb0ecaa39fd63c8e32201cdeaf.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/30aa6fca15709d6a7e5f42f734006e2f/params.yaml + predictions_file: output/reports/attack/30aa6fca15709d6a7e5f42f734006e2f/predictions.json + probabilities_file: output/reports/attack/30aa6fca15709d6a7e5f42f734006e2f/probabilities.json + score_dict_file: output/reports/attack/30aa6fca15709d6a7e5f42f734006e2f/score_dict.json + test_labels_file: output/reports/attack/30aa6fca15709d6a7e5f42f734006e2f/test_labels.json + train_labels_file: output/reports/attack/30aa6fca15709d6a7e5f42f734006e2f/train_labels.json + model_dir: models + name: 30aa6fca15709d6a7e5f42f734006e2f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 30aa6fca15709d6a7e5f42f734006e2f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/32feea878652526058fa5ed296617baf/params.yaml b/examples/security/kdd-nsl/output/reports/attack/32feea878652526058fa5ed296617baf/params.yaml new file mode 100644 index 00000000..3c1815ce --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/32feea878652526058fa5ed296617baf/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.001 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 39f512c795fa99b51bb3d13e1e916d30 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/32feea878652526058fa5ed296617baf/adv_predictions.json + adv_probabilities_file: output/reports/attack/32feea878652526058fa5ed296617baf/adv_probabilities.json + attack_file: output/attacks/e1df836fdb6c4656ebf9a69ecd8ae86d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/32feea878652526058fa5ed296617baf/params.yaml + predictions_file: output/reports/attack/32feea878652526058fa5ed296617baf/predictions.json + probabilities_file: output/reports/attack/32feea878652526058fa5ed296617baf/probabilities.json + score_dict_file: output/reports/attack/32feea878652526058fa5ed296617baf/score_dict.json + test_labels_file: output/reports/attack/32feea878652526058fa5ed296617baf/test_labels.json + train_labels_file: output/reports/attack/32feea878652526058fa5ed296617baf/train_labels.json + model_dir: models + name: 32feea878652526058fa5ed296617baf + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 32feea878652526058fa5ed296617baf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/33487a8bffd363b7b09b8d188075db89/params.yaml b/examples/security/kdd-nsl/output/reports/attack/33487a8bffd363b7b09b8d188075db89/params.yaml new file mode 100644 index 00000000..e32a4c6f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/33487a8bffd363b7b09b8d188075db89/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.3 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 9ee4c6bd2e8ad1f6bd7796d9bd87297a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/33487a8bffd363b7b09b8d188075db89/adv_predictions.json + adv_probabilities_file: output/reports/attack/33487a8bffd363b7b09b8d188075db89/adv_probabilities.json + attack_file: output/attacks/01081c58088ed76a6cdb1c09cf05a630.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/33487a8bffd363b7b09b8d188075db89/params.yaml + predictions_file: output/reports/attack/33487a8bffd363b7b09b8d188075db89/predictions.json + probabilities_file: output/reports/attack/33487a8bffd363b7b09b8d188075db89/probabilities.json + score_dict_file: output/reports/attack/33487a8bffd363b7b09b8d188075db89/score_dict.json + test_labels_file: output/reports/attack/33487a8bffd363b7b09b8d188075db89/test_labels.json + train_labels_file: output/reports/attack/33487a8bffd363b7b09b8d188075db89/train_labels.json + model_dir: models + name: 33487a8bffd363b7b09b8d188075db89 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 33487a8bffd363b7b09b8d188075db89 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/357b2b2015916ecc6d65e4c31d72e8b9/params.yaml b/examples/security/kdd-nsl/output/reports/attack/357b2b2015916ecc6d65e4c31d72e8b9/params.yaml new file mode 100644 index 00000000..6e95747b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/357b2b2015916ecc6d65e4c31d72e8b9/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.001 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 1a80f37cc605ab5eac85dc425e596afa +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/357b2b2015916ecc6d65e4c31d72e8b9/adv_predictions.json + adv_probabilities_file: output/reports/attack/357b2b2015916ecc6d65e4c31d72e8b9/adv_probabilities.json + attack_file: output/attacks/236a146cf3f13e3c6c126c6515ed7fba.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/357b2b2015916ecc6d65e4c31d72e8b9/params.yaml + predictions_file: output/reports/attack/357b2b2015916ecc6d65e4c31d72e8b9/predictions.json + probabilities_file: output/reports/attack/357b2b2015916ecc6d65e4c31d72e8b9/probabilities.json + score_dict_file: output/reports/attack/357b2b2015916ecc6d65e4c31d72e8b9/score_dict.json + test_labels_file: output/reports/attack/357b2b2015916ecc6d65e4c31d72e8b9/test_labels.json + train_labels_file: output/reports/attack/357b2b2015916ecc6d65e4c31d72e8b9/train_labels.json + model_dir: models + name: 357b2b2015916ecc6d65e4c31d72e8b9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 357b2b2015916ecc6d65e4c31d72e8b9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/38ad5850daa8f05af589a4817ea9ee70/params.yaml b/examples/security/kdd-nsl/output/reports/attack/38ad5850daa8f05af589a4817ea9ee70/params.yaml new file mode 100644 index 00000000..f50e690d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/38ad5850daa8f05af589a4817ea9ee70/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.3 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 3172764f6bb71ed72103848b5fb831c4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/38ad5850daa8f05af589a4817ea9ee70/adv_predictions.json + adv_probabilities_file: output/reports/attack/38ad5850daa8f05af589a4817ea9ee70/adv_probabilities.json + attack_file: output/attacks/28808dcbd41da439931d78e3513a30e2.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/38ad5850daa8f05af589a4817ea9ee70/params.yaml + predictions_file: output/reports/attack/38ad5850daa8f05af589a4817ea9ee70/predictions.json + probabilities_file: output/reports/attack/38ad5850daa8f05af589a4817ea9ee70/probabilities.json + score_dict_file: output/reports/attack/38ad5850daa8f05af589a4817ea9ee70/score_dict.json + test_labels_file: output/reports/attack/38ad5850daa8f05af589a4817ea9ee70/test_labels.json + train_labels_file: output/reports/attack/38ad5850daa8f05af589a4817ea9ee70/train_labels.json + model_dir: models + name: 38ad5850daa8f05af589a4817ea9ee70 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 38ad5850daa8f05af589a4817ea9ee70 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/38f2132fc376928c9a0f2ec0e9cbf163/params.yaml b/examples/security/kdd-nsl/output/reports/attack/38f2132fc376928c9a0f2ec0e9cbf163/params.yaml new file mode 100644 index 00000000..46e137de --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/38f2132fc376928c9a0f2ec0e9cbf163/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.3 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 272c23c694cf25b7ea5fc123740e174f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/38f2132fc376928c9a0f2ec0e9cbf163/adv_predictions.json + adv_probabilities_file: output/reports/attack/38f2132fc376928c9a0f2ec0e9cbf163/adv_probabilities.json + attack_file: output/attacks/d9636d7e255c248c905c8a744af482fe.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/38f2132fc376928c9a0f2ec0e9cbf163/params.yaml + predictions_file: output/reports/attack/38f2132fc376928c9a0f2ec0e9cbf163/predictions.json + probabilities_file: output/reports/attack/38f2132fc376928c9a0f2ec0e9cbf163/probabilities.json + score_dict_file: output/reports/attack/38f2132fc376928c9a0f2ec0e9cbf163/score_dict.json + test_labels_file: output/reports/attack/38f2132fc376928c9a0f2ec0e9cbf163/test_labels.json + train_labels_file: output/reports/attack/38f2132fc376928c9a0f2ec0e9cbf163/train_labels.json + model_dir: models + name: 38f2132fc376928c9a0f2ec0e9cbf163 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 38f2132fc376928c9a0f2ec0e9cbf163 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/38f7d5687e0c31b5454eaaa674ab5c95/params.yaml b/examples/security/kdd-nsl/output/reports/attack/38f7d5687e0c31b5454eaaa674ab5c95/params.yaml new file mode 100644 index 00000000..bfaa3c03 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/38f7d5687e0c31b5454eaaa674ab5c95/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: a8821ae31ae37fbbcad5254b9afa6acd +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/38f7d5687e0c31b5454eaaa674ab5c95/adv_predictions.json + adv_probabilities_file: output/reports/attack/38f7d5687e0c31b5454eaaa674ab5c95/adv_probabilities.json + attack_file: output/attacks/32976060b9b559b0b6b0138eaad7eb62.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/38f7d5687e0c31b5454eaaa674ab5c95/params.yaml + predictions_file: output/reports/attack/38f7d5687e0c31b5454eaaa674ab5c95/predictions.json + probabilities_file: output/reports/attack/38f7d5687e0c31b5454eaaa674ab5c95/probabilities.json + score_dict_file: output/reports/attack/38f7d5687e0c31b5454eaaa674ab5c95/score_dict.json + test_labels_file: output/reports/attack/38f7d5687e0c31b5454eaaa674ab5c95/test_labels.json + train_labels_file: output/reports/attack/38f7d5687e0c31b5454eaaa674ab5c95/train_labels.json + model_dir: models + name: 38f7d5687e0c31b5454eaaa674ab5c95 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 38f7d5687e0c31b5454eaaa674ab5c95 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/392b18d435d0bae1a95945d52dfd2c5b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/392b18d435d0bae1a95945d52dfd2c5b/params.yaml new file mode 100644 index 00000000..16dc3025 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/392b18d435d0bae1a95945d52dfd2c5b/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 37ab9fe77a0dbce908632e45e4df40e8 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/392b18d435d0bae1a95945d52dfd2c5b/adv_predictions.json + adv_probabilities_file: output/reports/attack/392b18d435d0bae1a95945d52dfd2c5b/adv_probabilities.json + attack_file: output/attacks/6bbacfe898b0567456290abb97dd8b84.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/392b18d435d0bae1a95945d52dfd2c5b/params.yaml + predictions_file: output/reports/attack/392b18d435d0bae1a95945d52dfd2c5b/predictions.json + probabilities_file: output/reports/attack/392b18d435d0bae1a95945d52dfd2c5b/probabilities.json + score_dict_file: output/reports/attack/392b18d435d0bae1a95945d52dfd2c5b/score_dict.json + test_labels_file: output/reports/attack/392b18d435d0bae1a95945d52dfd2c5b/test_labels.json + train_labels_file: output/reports/attack/392b18d435d0bae1a95945d52dfd2c5b/train_labels.json + model_dir: models + name: 392b18d435d0bae1a95945d52dfd2c5b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 392b18d435d0bae1a95945d52dfd2c5b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/3942cdc7371837b898749b37d403953c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/3942cdc7371837b898749b37d403953c/params.yaml new file mode 100644 index 00000000..14b42890 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/3942cdc7371837b898749b37d403953c/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 157bcd52c39dc4d3e73787c0e65450ea +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3942cdc7371837b898749b37d403953c/adv_predictions.json + adv_probabilities_file: output/reports/attack/3942cdc7371837b898749b37d403953c/adv_probabilities.json + attack_file: output/attacks/96d59e4409d027d40495943696f8bbb4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/3942cdc7371837b898749b37d403953c/params.yaml + predictions_file: output/reports/attack/3942cdc7371837b898749b37d403953c/predictions.json + probabilities_file: output/reports/attack/3942cdc7371837b898749b37d403953c/probabilities.json + score_dict_file: output/reports/attack/3942cdc7371837b898749b37d403953c/score_dict.json + test_labels_file: output/reports/attack/3942cdc7371837b898749b37d403953c/test_labels.json + train_labels_file: output/reports/attack/3942cdc7371837b898749b37d403953c/train_labels.json + model_dir: models + name: 3942cdc7371837b898749b37d403953c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 3942cdc7371837b898749b37d403953c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/3a26d6eac7a34934e4d9f91c22ddf1c1/params.yaml b/examples/security/kdd-nsl/output/reports/attack/3a26d6eac7a34934e4d9f91c22ddf1c1/params.yaml new file mode 100644 index 00000000..69f381a3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/3a26d6eac7a34934e4d9f91c22ddf1c1/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.3 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: eb0aacecc10127062db0300d09520f98 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3a26d6eac7a34934e4d9f91c22ddf1c1/adv_predictions.json + adv_probabilities_file: output/reports/attack/3a26d6eac7a34934e4d9f91c22ddf1c1/adv_probabilities.json + attack_file: output/attacks/1a7acd7456537dea9dce0a459ca93882.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/3a26d6eac7a34934e4d9f91c22ddf1c1/params.yaml + predictions_file: output/reports/attack/3a26d6eac7a34934e4d9f91c22ddf1c1/predictions.json + probabilities_file: output/reports/attack/3a26d6eac7a34934e4d9f91c22ddf1c1/probabilities.json + score_dict_file: output/reports/attack/3a26d6eac7a34934e4d9f91c22ddf1c1/score_dict.json + test_labels_file: output/reports/attack/3a26d6eac7a34934e4d9f91c22ddf1c1/test_labels.json + train_labels_file: output/reports/attack/3a26d6eac7a34934e4d9f91c22ddf1c1/train_labels.json + model_dir: models + name: 3a26d6eac7a34934e4d9f91c22ddf1c1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 3a26d6eac7a34934e4d9f91c22ddf1c1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/3a30cac1e4792a4aa67643a975c438dd/params.yaml b/examples/security/kdd-nsl/output/reports/attack/3a30cac1e4792a4aa67643a975c438dd/params.yaml new file mode 100644 index 00000000..aa48ad33 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/3a30cac1e4792a4aa67643a975c438dd/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 651355094e6b520e7f7391b62417a5f5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3a30cac1e4792a4aa67643a975c438dd/adv_predictions.json + adv_probabilities_file: output/reports/attack/3a30cac1e4792a4aa67643a975c438dd/adv_probabilities.json + attack_file: output/attacks/6209f726b07ae3a4c3c594e05c2dd65b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/3a30cac1e4792a4aa67643a975c438dd/params.yaml + predictions_file: output/reports/attack/3a30cac1e4792a4aa67643a975c438dd/predictions.json + probabilities_file: output/reports/attack/3a30cac1e4792a4aa67643a975c438dd/probabilities.json + score_dict_file: output/reports/attack/3a30cac1e4792a4aa67643a975c438dd/score_dict.json + test_labels_file: output/reports/attack/3a30cac1e4792a4aa67643a975c438dd/test_labels.json + train_labels_file: output/reports/attack/3a30cac1e4792a4aa67643a975c438dd/train_labels.json + model_dir: models + name: 3a30cac1e4792a4aa67643a975c438dd + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 3a30cac1e4792a4aa67643a975c438dd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/3b4efd31e7e63741d44423c8d3eb3e21/params.yaml b/examples/security/kdd-nsl/output/reports/attack/3b4efd31e7e63741d44423c8d3eb3e21/params.yaml new file mode 100644 index 00000000..8ed27825 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/3b4efd31e7e63741d44423c8d3eb3e21/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.01 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 67b3d9987d54162e432844be2795e366 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3b4efd31e7e63741d44423c8d3eb3e21/adv_predictions.json + adv_probabilities_file: output/reports/attack/3b4efd31e7e63741d44423c8d3eb3e21/adv_probabilities.json + attack_file: output/attacks/600a97c87e40a4519eb7d3eb9a859edf.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/3b4efd31e7e63741d44423c8d3eb3e21/params.yaml + predictions_file: output/reports/attack/3b4efd31e7e63741d44423c8d3eb3e21/predictions.json + probabilities_file: output/reports/attack/3b4efd31e7e63741d44423c8d3eb3e21/probabilities.json + score_dict_file: output/reports/attack/3b4efd31e7e63741d44423c8d3eb3e21/score_dict.json + test_labels_file: output/reports/attack/3b4efd31e7e63741d44423c8d3eb3e21/test_labels.json + train_labels_file: output/reports/attack/3b4efd31e7e63741d44423c8d3eb3e21/train_labels.json + model_dir: models + name: 3b4efd31e7e63741d44423c8d3eb3e21 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 3b4efd31e7e63741d44423c8d3eb3e21 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/3b68a0c380ef04ddbaac95b553aded6e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/3b68a0c380ef04ddbaac95b553aded6e/params.yaml new file mode 100644 index 00000000..8472874d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/3b68a0c380ef04ddbaac95b553aded6e/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.5 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 6403e4d1bc3505ea7ef8d1fc656ebbd1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3b68a0c380ef04ddbaac95b553aded6e/adv_predictions.json + adv_probabilities_file: output/reports/attack/3b68a0c380ef04ddbaac95b553aded6e/adv_probabilities.json + attack_file: output/attacks/53fad4532c21a95af2e43de4c35372fa.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/3b68a0c380ef04ddbaac95b553aded6e/params.yaml + predictions_file: output/reports/attack/3b68a0c380ef04ddbaac95b553aded6e/predictions.json + probabilities_file: output/reports/attack/3b68a0c380ef04ddbaac95b553aded6e/probabilities.json + score_dict_file: output/reports/attack/3b68a0c380ef04ddbaac95b553aded6e/score_dict.json + test_labels_file: output/reports/attack/3b68a0c380ef04ddbaac95b553aded6e/test_labels.json + train_labels_file: output/reports/attack/3b68a0c380ef04ddbaac95b553aded6e/train_labels.json + model_dir: models + name: 3b68a0c380ef04ddbaac95b553aded6e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 3b68a0c380ef04ddbaac95b553aded6e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/3c95bf00272b7c006d5e1736c84bb0e4/params.yaml b/examples/security/kdd-nsl/output/reports/attack/3c95bf00272b7c006d5e1736c84bb0e4/params.yaml new file mode 100644 index 00000000..899c6e77 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/3c95bf00272b7c006d5e1736c84bb0e4/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: e2c41858a4c26f37a8020e4da3a39fa2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3c95bf00272b7c006d5e1736c84bb0e4/adv_predictions.json + adv_probabilities_file: output/reports/attack/3c95bf00272b7c006d5e1736c84bb0e4/adv_probabilities.json + attack_file: output/attacks/cdc217f30cfab2ada9af32f4ae804cf9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/3c95bf00272b7c006d5e1736c84bb0e4/params.yaml + predictions_file: output/reports/attack/3c95bf00272b7c006d5e1736c84bb0e4/predictions.json + probabilities_file: output/reports/attack/3c95bf00272b7c006d5e1736c84bb0e4/probabilities.json + score_dict_file: output/reports/attack/3c95bf00272b7c006d5e1736c84bb0e4/score_dict.json + test_labels_file: output/reports/attack/3c95bf00272b7c006d5e1736c84bb0e4/test_labels.json + train_labels_file: output/reports/attack/3c95bf00272b7c006d5e1736c84bb0e4/train_labels.json + model_dir: models + name: 3c95bf00272b7c006d5e1736c84bb0e4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 3c95bf00272b7c006d5e1736c84bb0e4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/3cab68a7a7844b989da4af6c83416251/params.yaml b/examples/security/kdd-nsl/output/reports/attack/3cab68a7a7844b989da4af6c83416251/params.yaml new file mode 100644 index 00000000..62a9c0cb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/3cab68a7a7844b989da4af6c83416251/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: a2261173cc2657a5287f759d5ceff745 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3cab68a7a7844b989da4af6c83416251/adv_predictions.json + adv_probabilities_file: output/reports/attack/3cab68a7a7844b989da4af6c83416251/adv_probabilities.json + attack_file: output/attacks/5ecea5367a45c4c77caee0401504c90d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/3cab68a7a7844b989da4af6c83416251/params.yaml + predictions_file: output/reports/attack/3cab68a7a7844b989da4af6c83416251/predictions.json + probabilities_file: output/reports/attack/3cab68a7a7844b989da4af6c83416251/probabilities.json + score_dict_file: output/reports/attack/3cab68a7a7844b989da4af6c83416251/score_dict.json + test_labels_file: output/reports/attack/3cab68a7a7844b989da4af6c83416251/test_labels.json + train_labels_file: output/reports/attack/3cab68a7a7844b989da4af6c83416251/train_labels.json + model_dir: models + name: 3cab68a7a7844b989da4af6c83416251 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 3cab68a7a7844b989da4af6c83416251 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/3d7789f68fdc96f1eb3a567ebdc49c55/params.yaml b/examples/security/kdd-nsl/output/reports/attack/3d7789f68fdc96f1eb3a567ebdc49c55/params.yaml new file mode 100644 index 00000000..e2cc7ccf --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/3d7789f68fdc96f1eb3a567ebdc49c55/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.5 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 5a79425021fef830549207a2e1cff77c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3d7789f68fdc96f1eb3a567ebdc49c55/adv_predictions.json + adv_probabilities_file: output/reports/attack/3d7789f68fdc96f1eb3a567ebdc49c55/adv_probabilities.json + attack_file: output/attacks/8801cc8b581e971729421e196282c76b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/3d7789f68fdc96f1eb3a567ebdc49c55/params.yaml + predictions_file: output/reports/attack/3d7789f68fdc96f1eb3a567ebdc49c55/predictions.json + probabilities_file: output/reports/attack/3d7789f68fdc96f1eb3a567ebdc49c55/probabilities.json + score_dict_file: output/reports/attack/3d7789f68fdc96f1eb3a567ebdc49c55/score_dict.json + test_labels_file: output/reports/attack/3d7789f68fdc96f1eb3a567ebdc49c55/test_labels.json + train_labels_file: output/reports/attack/3d7789f68fdc96f1eb3a567ebdc49c55/train_labels.json + model_dir: models + name: 3d7789f68fdc96f1eb3a567ebdc49c55 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 3d7789f68fdc96f1eb3a567ebdc49c55 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/3e62ec5793803dcc255ea7de4453166a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/3e62ec5793803dcc255ea7de4453166a/params.yaml new file mode 100644 index 00000000..41d31558 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/3e62ec5793803dcc255ea7de4453166a/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.01 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 24df97e20eea395d676319debafd9e2c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3e62ec5793803dcc255ea7de4453166a/adv_predictions.json + adv_probabilities_file: output/reports/attack/3e62ec5793803dcc255ea7de4453166a/adv_probabilities.json + attack_file: output/attacks/781b150c85c8de59fbcd26e6a707fa98.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/3e62ec5793803dcc255ea7de4453166a/params.yaml + predictions_file: output/reports/attack/3e62ec5793803dcc255ea7de4453166a/predictions.json + probabilities_file: output/reports/attack/3e62ec5793803dcc255ea7de4453166a/probabilities.json + score_dict_file: output/reports/attack/3e62ec5793803dcc255ea7de4453166a/score_dict.json + test_labels_file: output/reports/attack/3e62ec5793803dcc255ea7de4453166a/test_labels.json + train_labels_file: output/reports/attack/3e62ec5793803dcc255ea7de4453166a/train_labels.json + model_dir: models + name: 3e62ec5793803dcc255ea7de4453166a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 3e62ec5793803dcc255ea7de4453166a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/3eea8237fe058ed8e338f9377f2975c4/params.yaml b/examples/security/kdd-nsl/output/reports/attack/3eea8237fe058ed8e338f9377f2975c4/params.yaml new file mode 100644 index 00000000..35192575 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/3eea8237fe058ed8e338f9377f2975c4/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.5 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 8aec4ef79cd01b406e37ea8db415598f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3eea8237fe058ed8e338f9377f2975c4/adv_predictions.json + adv_probabilities_file: output/reports/attack/3eea8237fe058ed8e338f9377f2975c4/adv_probabilities.json + attack_file: output/attacks/ddd90fac39d8bde654469f8ffdea710b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/3eea8237fe058ed8e338f9377f2975c4/params.yaml + predictions_file: output/reports/attack/3eea8237fe058ed8e338f9377f2975c4/predictions.json + probabilities_file: output/reports/attack/3eea8237fe058ed8e338f9377f2975c4/probabilities.json + score_dict_file: output/reports/attack/3eea8237fe058ed8e338f9377f2975c4/score_dict.json + test_labels_file: output/reports/attack/3eea8237fe058ed8e338f9377f2975c4/test_labels.json + train_labels_file: output/reports/attack/3eea8237fe058ed8e338f9377f2975c4/train_labels.json + model_dir: models + name: 3eea8237fe058ed8e338f9377f2975c4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 3eea8237fe058ed8e338f9377f2975c4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/3f0c3a4f6d2b9e78f06c523e072d63c8/params.yaml b/examples/security/kdd-nsl/output/reports/attack/3f0c3a4f6d2b9e78f06c523e072d63c8/params.yaml new file mode 100644 index 00000000..e75c5970 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/3f0c3a4f6d2b9e78f06c523e072d63c8/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 8432ff8b2ab1d9b2fdb977ec5bde37d8 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3f0c3a4f6d2b9e78f06c523e072d63c8/adv_predictions.json + adv_probabilities_file: output/reports/attack/3f0c3a4f6d2b9e78f06c523e072d63c8/adv_probabilities.json + attack_file: output/attacks/0972fed1c334b1153e4569bbd6fcc3c0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/3f0c3a4f6d2b9e78f06c523e072d63c8/params.yaml + predictions_file: output/reports/attack/3f0c3a4f6d2b9e78f06c523e072d63c8/predictions.json + probabilities_file: output/reports/attack/3f0c3a4f6d2b9e78f06c523e072d63c8/probabilities.json + score_dict_file: output/reports/attack/3f0c3a4f6d2b9e78f06c523e072d63c8/score_dict.json + test_labels_file: output/reports/attack/3f0c3a4f6d2b9e78f06c523e072d63c8/test_labels.json + train_labels_file: output/reports/attack/3f0c3a4f6d2b9e78f06c523e072d63c8/train_labels.json + model_dir: models + name: 3f0c3a4f6d2b9e78f06c523e072d63c8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 3f0c3a4f6d2b9e78f06c523e072d63c8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/3f99192fce1d56e999c89ac8a6ed20ff/params.yaml b/examples/security/kdd-nsl/output/reports/attack/3f99192fce1d56e999c89ac8a6ed20ff/params.yaml new file mode 100644 index 00000000..0bdab87d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/3f99192fce1d56e999c89ac8a6ed20ff/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.3 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 719809328cf98872c0838fc368d84a98 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3f99192fce1d56e999c89ac8a6ed20ff/adv_predictions.json + adv_probabilities_file: output/reports/attack/3f99192fce1d56e999c89ac8a6ed20ff/adv_probabilities.json + attack_file: output/attacks/e891d88a9bf8d3e1e5bfd292dfd122e6.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/3f99192fce1d56e999c89ac8a6ed20ff/params.yaml + predictions_file: output/reports/attack/3f99192fce1d56e999c89ac8a6ed20ff/predictions.json + probabilities_file: output/reports/attack/3f99192fce1d56e999c89ac8a6ed20ff/probabilities.json + score_dict_file: output/reports/attack/3f99192fce1d56e999c89ac8a6ed20ff/score_dict.json + test_labels_file: output/reports/attack/3f99192fce1d56e999c89ac8a6ed20ff/test_labels.json + train_labels_file: output/reports/attack/3f99192fce1d56e999c89ac8a6ed20ff/train_labels.json + model_dir: models + name: 3f99192fce1d56e999c89ac8a6ed20ff + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 3f99192fce1d56e999c89ac8a6ed20ff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/401e334196a0d11e15c180cfa0814249/params.yaml b/examples/security/kdd-nsl/output/reports/attack/401e334196a0d11e15c180cfa0814249/params.yaml new file mode 100644 index 00000000..a931d563 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/401e334196a0d11e15c180cfa0814249/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.3 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: f47a265a4b7c37251ceb60d87276a4ea +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/401e334196a0d11e15c180cfa0814249/adv_predictions.json + adv_probabilities_file: output/reports/attack/401e334196a0d11e15c180cfa0814249/adv_probabilities.json + attack_file: output/attacks/093f35e590cdcab07b99c28ae09b7a61.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/401e334196a0d11e15c180cfa0814249/params.yaml + predictions_file: output/reports/attack/401e334196a0d11e15c180cfa0814249/predictions.json + probabilities_file: output/reports/attack/401e334196a0d11e15c180cfa0814249/probabilities.json + score_dict_file: output/reports/attack/401e334196a0d11e15c180cfa0814249/score_dict.json + test_labels_file: output/reports/attack/401e334196a0d11e15c180cfa0814249/test_labels.json + train_labels_file: output/reports/attack/401e334196a0d11e15c180cfa0814249/train_labels.json + model_dir: models + name: 401e334196a0d11e15c180cfa0814249 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 401e334196a0d11e15c180cfa0814249 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/40f4b8a06be87c83e631e881fd5f1ab5/params.yaml b/examples/security/kdd-nsl/output/reports/attack/40f4b8a06be87c83e631e881fd5f1ab5/params.yaml new file mode 100644 index 00000000..b9ca0c5c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/40f4b8a06be87c83e631e881fd5f1ab5/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: f20e40aded950497f09f532154598616 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/40f4b8a06be87c83e631e881fd5f1ab5/adv_predictions.json + adv_probabilities_file: output/reports/attack/40f4b8a06be87c83e631e881fd5f1ab5/adv_probabilities.json + attack_file: output/attacks/1c4a4e2283016161490f7a188d7d7951.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/40f4b8a06be87c83e631e881fd5f1ab5/params.yaml + predictions_file: output/reports/attack/40f4b8a06be87c83e631e881fd5f1ab5/predictions.json + probabilities_file: output/reports/attack/40f4b8a06be87c83e631e881fd5f1ab5/probabilities.json + score_dict_file: output/reports/attack/40f4b8a06be87c83e631e881fd5f1ab5/score_dict.json + test_labels_file: output/reports/attack/40f4b8a06be87c83e631e881fd5f1ab5/test_labels.json + train_labels_file: output/reports/attack/40f4b8a06be87c83e631e881fd5f1ab5/train_labels.json + model_dir: models + name: 40f4b8a06be87c83e631e881fd5f1ab5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 40f4b8a06be87c83e631e881fd5f1ab5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/40ff688471f796e2e8674d101d6f6d15/params.yaml b/examples/security/kdd-nsl/output/reports/attack/40ff688471f796e2e8674d101d6f6d15/params.yaml new file mode 100644 index 00000000..1fe00b7d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/40ff688471f796e2e8674d101d6f6d15/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.01 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 3c3d8fa28fa9675e7898797648e6c15c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/40ff688471f796e2e8674d101d6f6d15/adv_predictions.json + adv_probabilities_file: output/reports/attack/40ff688471f796e2e8674d101d6f6d15/adv_probabilities.json + attack_file: output/attacks/f114bed1eb7f9b650aa28a979867dffb.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/40ff688471f796e2e8674d101d6f6d15/params.yaml + predictions_file: output/reports/attack/40ff688471f796e2e8674d101d6f6d15/predictions.json + probabilities_file: output/reports/attack/40ff688471f796e2e8674d101d6f6d15/probabilities.json + score_dict_file: output/reports/attack/40ff688471f796e2e8674d101d6f6d15/score_dict.json + test_labels_file: output/reports/attack/40ff688471f796e2e8674d101d6f6d15/test_labels.json + train_labels_file: output/reports/attack/40ff688471f796e2e8674d101d6f6d15/train_labels.json + model_dir: models + name: 40ff688471f796e2e8674d101d6f6d15 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 40ff688471f796e2e8674d101d6f6d15 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/423774c1716ac33dce7ae60ae5a53b65/params.yaml b/examples/security/kdd-nsl/output/reports/attack/423774c1716ac33dce7ae60ae5a53b65/params.yaml new file mode 100644 index 00000000..349a76d5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/423774c1716ac33dce7ae60ae5a53b65/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.5 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 99166ac5b0ba89f97be7fdcdac9b3344 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/423774c1716ac33dce7ae60ae5a53b65/adv_predictions.json + adv_probabilities_file: output/reports/attack/423774c1716ac33dce7ae60ae5a53b65/adv_probabilities.json + attack_file: output/attacks/b7fa97affe188ffa70737f6d2c9e1b1e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/423774c1716ac33dce7ae60ae5a53b65/params.yaml + predictions_file: output/reports/attack/423774c1716ac33dce7ae60ae5a53b65/predictions.json + probabilities_file: output/reports/attack/423774c1716ac33dce7ae60ae5a53b65/probabilities.json + score_dict_file: output/reports/attack/423774c1716ac33dce7ae60ae5a53b65/score_dict.json + test_labels_file: output/reports/attack/423774c1716ac33dce7ae60ae5a53b65/test_labels.json + train_labels_file: output/reports/attack/423774c1716ac33dce7ae60ae5a53b65/train_labels.json + model_dir: models + name: 423774c1716ac33dce7ae60ae5a53b65 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 423774c1716ac33dce7ae60ae5a53b65 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/424f6ca0b3e71f2b4804b8f850c258e3/params.yaml b/examples/security/kdd-nsl/output/reports/attack/424f6ca0b3e71f2b4804b8f850c258e3/params.yaml new file mode 100644 index 00000000..66324194 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/424f6ca0b3e71f2b4804b8f850c258e3/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.001 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 51ead5b7ed09c4072003c49f8bf2e6ba +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/424f6ca0b3e71f2b4804b8f850c258e3/adv_predictions.json + adv_probabilities_file: output/reports/attack/424f6ca0b3e71f2b4804b8f850c258e3/adv_probabilities.json + attack_file: output/attacks/b6994ed6a23e25fa187c59ba56cfbcff.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/424f6ca0b3e71f2b4804b8f850c258e3/params.yaml + predictions_file: output/reports/attack/424f6ca0b3e71f2b4804b8f850c258e3/predictions.json + probabilities_file: output/reports/attack/424f6ca0b3e71f2b4804b8f850c258e3/probabilities.json + score_dict_file: output/reports/attack/424f6ca0b3e71f2b4804b8f850c258e3/score_dict.json + test_labels_file: output/reports/attack/424f6ca0b3e71f2b4804b8f850c258e3/test_labels.json + train_labels_file: output/reports/attack/424f6ca0b3e71f2b4804b8f850c258e3/train_labels.json + model_dir: models + name: 424f6ca0b3e71f2b4804b8f850c258e3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 424f6ca0b3e71f2b4804b8f850c258e3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/43b1adcbcaeff423d4829be2066bb869/params.yaml b/examples/security/kdd-nsl/output/reports/attack/43b1adcbcaeff423d4829be2066bb869/params.yaml new file mode 100644 index 00000000..34adeb60 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/43b1adcbcaeff423d4829be2066bb869/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: ec477fa908f06909f38bd794c29f8f97 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/43b1adcbcaeff423d4829be2066bb869/adv_predictions.json + adv_probabilities_file: output/reports/attack/43b1adcbcaeff423d4829be2066bb869/adv_probabilities.json + attack_file: output/attacks/d2119a0f4091fe084f0b06cbf8eeb1e0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/43b1adcbcaeff423d4829be2066bb869/params.yaml + predictions_file: output/reports/attack/43b1adcbcaeff423d4829be2066bb869/predictions.json + probabilities_file: output/reports/attack/43b1adcbcaeff423d4829be2066bb869/probabilities.json + score_dict_file: output/reports/attack/43b1adcbcaeff423d4829be2066bb869/score_dict.json + test_labels_file: output/reports/attack/43b1adcbcaeff423d4829be2066bb869/test_labels.json + train_labels_file: output/reports/attack/43b1adcbcaeff423d4829be2066bb869/train_labels.json + model_dir: models + name: 43b1adcbcaeff423d4829be2066bb869 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 43b1adcbcaeff423d4829be2066bb869 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/4440d163784b22beec670596d606b083/params.yaml b/examples/security/kdd-nsl/output/reports/attack/4440d163784b22beec670596d606b083/params.yaml new file mode 100644 index 00000000..a3d097cb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/4440d163784b22beec670596d606b083/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.3 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: e30ae514cd9381e3bdd059a99762b94d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4440d163784b22beec670596d606b083/adv_predictions.json + adv_probabilities_file: output/reports/attack/4440d163784b22beec670596d606b083/adv_probabilities.json + attack_file: output/attacks/f85c83bc4ffcb0a970a0379408444e41.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/4440d163784b22beec670596d606b083/params.yaml + predictions_file: output/reports/attack/4440d163784b22beec670596d606b083/predictions.json + probabilities_file: output/reports/attack/4440d163784b22beec670596d606b083/probabilities.json + score_dict_file: output/reports/attack/4440d163784b22beec670596d606b083/score_dict.json + test_labels_file: output/reports/attack/4440d163784b22beec670596d606b083/test_labels.json + train_labels_file: output/reports/attack/4440d163784b22beec670596d606b083/train_labels.json + model_dir: models + name: 4440d163784b22beec670596d606b083 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 4440d163784b22beec670596d606b083 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/44eefb01bb5c0c092c64b7b78392eb67/params.yaml b/examples/security/kdd-nsl/output/reports/attack/44eefb01bb5c0c092c64b7b78392eb67/params.yaml new file mode 100644 index 00000000..7de0e5fe --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/44eefb01bb5c0c092c64b7b78392eb67/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 16486b5bf156a06be2cf74790cbd2d19 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/44eefb01bb5c0c092c64b7b78392eb67/adv_predictions.json + adv_probabilities_file: output/reports/attack/44eefb01bb5c0c092c64b7b78392eb67/adv_probabilities.json + attack_file: output/attacks/c4caa1e9bab50adca7e46892a03b462a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/44eefb01bb5c0c092c64b7b78392eb67/params.yaml + predictions_file: output/reports/attack/44eefb01bb5c0c092c64b7b78392eb67/predictions.json + probabilities_file: output/reports/attack/44eefb01bb5c0c092c64b7b78392eb67/probabilities.json + score_dict_file: output/reports/attack/44eefb01bb5c0c092c64b7b78392eb67/score_dict.json + test_labels_file: output/reports/attack/44eefb01bb5c0c092c64b7b78392eb67/test_labels.json + train_labels_file: output/reports/attack/44eefb01bb5c0c092c64b7b78392eb67/train_labels.json + model_dir: models + name: 44eefb01bb5c0c092c64b7b78392eb67 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 44eefb01bb5c0c092c64b7b78392eb67 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/46a1710f4dd72503e8d305925e956816/params.yaml b/examples/security/kdd-nsl/output/reports/attack/46a1710f4dd72503e8d305925e956816/params.yaml new file mode 100644 index 00000000..fb2962b1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/46a1710f4dd72503e8d305925e956816/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 63ff4a3e8e310e3b60a3b4235b7a8513 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/46a1710f4dd72503e8d305925e956816/adv_predictions.json + adv_probabilities_file: output/reports/attack/46a1710f4dd72503e8d305925e956816/adv_probabilities.json + attack_file: output/attacks/d4094671bd7bdd70a9882780d594d085.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/46a1710f4dd72503e8d305925e956816/params.yaml + predictions_file: output/reports/attack/46a1710f4dd72503e8d305925e956816/predictions.json + probabilities_file: output/reports/attack/46a1710f4dd72503e8d305925e956816/probabilities.json + score_dict_file: output/reports/attack/46a1710f4dd72503e8d305925e956816/score_dict.json + test_labels_file: output/reports/attack/46a1710f4dd72503e8d305925e956816/test_labels.json + train_labels_file: output/reports/attack/46a1710f4dd72503e8d305925e956816/train_labels.json + model_dir: models + name: 46a1710f4dd72503e8d305925e956816 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 46a1710f4dd72503e8d305925e956816 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/482eccefde6a8056eb38780fa242eb72/params.yaml b/examples/security/kdd-nsl/output/reports/attack/482eccefde6a8056eb38780fa242eb72/params.yaml new file mode 100644 index 00000000..c1957aa3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/482eccefde6a8056eb38780fa242eb72/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: cfde6d77b98cf07098b1bda92c9ec30a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/482eccefde6a8056eb38780fa242eb72/adv_predictions.json + adv_probabilities_file: output/reports/attack/482eccefde6a8056eb38780fa242eb72/adv_probabilities.json + attack_file: output/attacks/896ce7041e26a516608d0d2c704a1fb5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/482eccefde6a8056eb38780fa242eb72/params.yaml + predictions_file: output/reports/attack/482eccefde6a8056eb38780fa242eb72/predictions.json + probabilities_file: output/reports/attack/482eccefde6a8056eb38780fa242eb72/probabilities.json + score_dict_file: output/reports/attack/482eccefde6a8056eb38780fa242eb72/score_dict.json + test_labels_file: output/reports/attack/482eccefde6a8056eb38780fa242eb72/test_labels.json + train_labels_file: output/reports/attack/482eccefde6a8056eb38780fa242eb72/train_labels.json + model_dir: models + name: 482eccefde6a8056eb38780fa242eb72 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 482eccefde6a8056eb38780fa242eb72 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/params.yaml b/examples/security/kdd-nsl/output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/params.yaml new file mode 100644 index 00000000..775a607b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.01 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: d7d041d933cbe3e89c5f88e84468bfb7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/adv_predictions.json + adv_probabilities_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/adv_probabilities.json + attack_file: output/attacks/d807d0d095cfd2b7548024c7a43530b1.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/params.yaml + predictions_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/predictions.json + probabilities_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/probabilities.json + score_dict_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/score_dict.json + test_labels_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/test_labels.json + train_labels_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/train_labels.json + model_dir: models + name: 487b5e1894069577e8fe0f1c8484a8af + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 487b5e1894069577e8fe0f1c8484a8af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/48c6553df82e81d890cb3fc0fc37be57/params.yaml b/examples/security/kdd-nsl/output/reports/attack/48c6553df82e81d890cb3fc0fc37be57/params.yaml new file mode 100644 index 00000000..812ed036 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/48c6553df82e81d890cb3fc0fc37be57/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: c5064fb30e1e4d6ff7ce9945de497ae8 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/48c6553df82e81d890cb3fc0fc37be57/adv_predictions.json + adv_probabilities_file: output/reports/attack/48c6553df82e81d890cb3fc0fc37be57/adv_probabilities.json + attack_file: output/attacks/ae3fbee6e0a81813bc2309f5c917164a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/48c6553df82e81d890cb3fc0fc37be57/params.yaml + predictions_file: output/reports/attack/48c6553df82e81d890cb3fc0fc37be57/predictions.json + probabilities_file: output/reports/attack/48c6553df82e81d890cb3fc0fc37be57/probabilities.json + score_dict_file: output/reports/attack/48c6553df82e81d890cb3fc0fc37be57/score_dict.json + test_labels_file: output/reports/attack/48c6553df82e81d890cb3fc0fc37be57/test_labels.json + train_labels_file: output/reports/attack/48c6553df82e81d890cb3fc0fc37be57/train_labels.json + model_dir: models + name: 48c6553df82e81d890cb3fc0fc37be57 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 48c6553df82e81d890cb3fc0fc37be57 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/495dd865a9936f457656f785e8757a38/params.yaml b/examples/security/kdd-nsl/output/reports/attack/495dd865a9936f457656f785e8757a38/params.yaml new file mode 100644 index 00000000..fe1f5873 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/495dd865a9936f457656f785e8757a38/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: b73bc629aa0b1a6596cbbd7b2891a867 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/495dd865a9936f457656f785e8757a38/adv_predictions.json + adv_probabilities_file: output/reports/attack/495dd865a9936f457656f785e8757a38/adv_probabilities.json + attack_file: output/attacks/a34acaabbc97c3950f9a40060084c755.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/495dd865a9936f457656f785e8757a38/params.yaml + predictions_file: output/reports/attack/495dd865a9936f457656f785e8757a38/predictions.json + probabilities_file: output/reports/attack/495dd865a9936f457656f785e8757a38/probabilities.json + score_dict_file: output/reports/attack/495dd865a9936f457656f785e8757a38/score_dict.json + test_labels_file: output/reports/attack/495dd865a9936f457656f785e8757a38/test_labels.json + train_labels_file: output/reports/attack/495dd865a9936f457656f785e8757a38/train_labels.json + model_dir: models + name: 495dd865a9936f457656f785e8757a38 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 495dd865a9936f457656f785e8757a38 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/4a2ba399310c95f4765cd715752168fe/params.yaml b/examples/security/kdd-nsl/output/reports/attack/4a2ba399310c95f4765cd715752168fe/params.yaml new file mode 100644 index 00000000..66abeafc --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/4a2ba399310c95f4765cd715752168fe/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.01 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: beedf341e47ccdac464b7f0b40ab1932 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4a2ba399310c95f4765cd715752168fe/adv_predictions.json + adv_probabilities_file: output/reports/attack/4a2ba399310c95f4765cd715752168fe/adv_probabilities.json + attack_file: output/attacks/614bbbe7d46e6d0b97d6f936f7d1b4a9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/4a2ba399310c95f4765cd715752168fe/params.yaml + predictions_file: output/reports/attack/4a2ba399310c95f4765cd715752168fe/predictions.json + probabilities_file: output/reports/attack/4a2ba399310c95f4765cd715752168fe/probabilities.json + score_dict_file: output/reports/attack/4a2ba399310c95f4765cd715752168fe/score_dict.json + test_labels_file: output/reports/attack/4a2ba399310c95f4765cd715752168fe/test_labels.json + train_labels_file: output/reports/attack/4a2ba399310c95f4765cd715752168fe/train_labels.json + model_dir: models + name: 4a2ba399310c95f4765cd715752168fe + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 4a2ba399310c95f4765cd715752168fe +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/4a902119e16187039b89587ddac48266/params.yaml b/examples/security/kdd-nsl/output/reports/attack/4a902119e16187039b89587ddac48266/params.yaml new file mode 100644 index 00000000..c2cee05e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/4a902119e16187039b89587ddac48266/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.01 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 6a3cb9ce9e10f1bcef725280dd71f639 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4a902119e16187039b89587ddac48266/adv_predictions.json + adv_probabilities_file: output/reports/attack/4a902119e16187039b89587ddac48266/adv_probabilities.json + attack_file: output/attacks/31cb61c3710fd2a32541efb0b29c1d35.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/4a902119e16187039b89587ddac48266/params.yaml + predictions_file: output/reports/attack/4a902119e16187039b89587ddac48266/predictions.json + probabilities_file: output/reports/attack/4a902119e16187039b89587ddac48266/probabilities.json + score_dict_file: output/reports/attack/4a902119e16187039b89587ddac48266/score_dict.json + test_labels_file: output/reports/attack/4a902119e16187039b89587ddac48266/test_labels.json + train_labels_file: output/reports/attack/4a902119e16187039b89587ddac48266/train_labels.json + model_dir: models + name: 4a902119e16187039b89587ddac48266 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 4a902119e16187039b89587ddac48266 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/4b2394506e2031b13869ca82278b002a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/4b2394506e2031b13869ca82278b002a/params.yaml new file mode 100644 index 00000000..73c2cfb0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/4b2394506e2031b13869ca82278b002a/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 0b2894957af47a0b52db46c7a35f0ca5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4b2394506e2031b13869ca82278b002a/adv_predictions.json + adv_probabilities_file: output/reports/attack/4b2394506e2031b13869ca82278b002a/adv_probabilities.json + attack_file: output/attacks/e9a767d0c07619714a3777ffa45c0b35.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/4b2394506e2031b13869ca82278b002a/params.yaml + predictions_file: output/reports/attack/4b2394506e2031b13869ca82278b002a/predictions.json + probabilities_file: output/reports/attack/4b2394506e2031b13869ca82278b002a/probabilities.json + score_dict_file: output/reports/attack/4b2394506e2031b13869ca82278b002a/score_dict.json + test_labels_file: output/reports/attack/4b2394506e2031b13869ca82278b002a/test_labels.json + train_labels_file: output/reports/attack/4b2394506e2031b13869ca82278b002a/train_labels.json + model_dir: models + name: 4b2394506e2031b13869ca82278b002a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 4b2394506e2031b13869ca82278b002a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/4bd711bcf713820e89ad03fdca50e9f8/params.yaml b/examples/security/kdd-nsl/output/reports/attack/4bd711bcf713820e89ad03fdca50e9f8/params.yaml new file mode 100644 index 00000000..8a1c91e9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/4bd711bcf713820e89ad03fdca50e9f8/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 724113e93d69e11f6716881ef1a988a4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4bd711bcf713820e89ad03fdca50e9f8/adv_predictions.json + adv_probabilities_file: output/reports/attack/4bd711bcf713820e89ad03fdca50e9f8/adv_probabilities.json + attack_file: output/attacks/872c84f30d01525f713ae53a6b49cbd3.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/4bd711bcf713820e89ad03fdca50e9f8/params.yaml + predictions_file: output/reports/attack/4bd711bcf713820e89ad03fdca50e9f8/predictions.json + probabilities_file: output/reports/attack/4bd711bcf713820e89ad03fdca50e9f8/probabilities.json + score_dict_file: output/reports/attack/4bd711bcf713820e89ad03fdca50e9f8/score_dict.json + test_labels_file: output/reports/attack/4bd711bcf713820e89ad03fdca50e9f8/test_labels.json + train_labels_file: output/reports/attack/4bd711bcf713820e89ad03fdca50e9f8/train_labels.json + model_dir: models + name: 4bd711bcf713820e89ad03fdca50e9f8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 4bd711bcf713820e89ad03fdca50e9f8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/4d320fc440c4d8d1717cd0c18b0ad07e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/4d320fc440c4d8d1717cd0c18b0ad07e/params.yaml new file mode 100644 index 00000000..11b726aa --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/4d320fc440c4d8d1717cd0c18b0ad07e/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.3 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: cdf3a5daf759adc6637f42f8b476e26f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4d320fc440c4d8d1717cd0c18b0ad07e/adv_predictions.json + adv_probabilities_file: output/reports/attack/4d320fc440c4d8d1717cd0c18b0ad07e/adv_probabilities.json + attack_file: output/attacks/a4998eb65f050a685670632ae04768f1.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/4d320fc440c4d8d1717cd0c18b0ad07e/params.yaml + predictions_file: output/reports/attack/4d320fc440c4d8d1717cd0c18b0ad07e/predictions.json + probabilities_file: output/reports/attack/4d320fc440c4d8d1717cd0c18b0ad07e/probabilities.json + score_dict_file: output/reports/attack/4d320fc440c4d8d1717cd0c18b0ad07e/score_dict.json + test_labels_file: output/reports/attack/4d320fc440c4d8d1717cd0c18b0ad07e/test_labels.json + train_labels_file: output/reports/attack/4d320fc440c4d8d1717cd0c18b0ad07e/train_labels.json + model_dir: models + name: 4d320fc440c4d8d1717cd0c18b0ad07e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 4d320fc440c4d8d1717cd0c18b0ad07e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/4d59704230ed63568423233dba802656/params.yaml b/examples/security/kdd-nsl/output/reports/attack/4d59704230ed63568423233dba802656/params.yaml new file mode 100644 index 00000000..b7e23713 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/4d59704230ed63568423233dba802656/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: aab1edbc3f961dff70b549440f3e981e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4d59704230ed63568423233dba802656/adv_predictions.json + adv_probabilities_file: output/reports/attack/4d59704230ed63568423233dba802656/adv_probabilities.json + attack_file: output/attacks/f4cc9c13bae23693b8b8b86b023a47ed.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/4d59704230ed63568423233dba802656/params.yaml + predictions_file: output/reports/attack/4d59704230ed63568423233dba802656/predictions.json + probabilities_file: output/reports/attack/4d59704230ed63568423233dba802656/probabilities.json + score_dict_file: output/reports/attack/4d59704230ed63568423233dba802656/score_dict.json + test_labels_file: output/reports/attack/4d59704230ed63568423233dba802656/test_labels.json + train_labels_file: output/reports/attack/4d59704230ed63568423233dba802656/train_labels.json + model_dir: models + name: 4d59704230ed63568423233dba802656 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 4d59704230ed63568423233dba802656 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/4e37b6f74b3d1371c9bc2b56209f1193/params.yaml b/examples/security/kdd-nsl/output/reports/attack/4e37b6f74b3d1371c9bc2b56209f1193/params.yaml new file mode 100644 index 00000000..c183a8d7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/4e37b6f74b3d1371c9bc2b56209f1193/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.001 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: c9043a7c834423af82381bdb3c42c333 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4e37b6f74b3d1371c9bc2b56209f1193/adv_predictions.json + adv_probabilities_file: output/reports/attack/4e37b6f74b3d1371c9bc2b56209f1193/adv_probabilities.json + attack_file: output/attacks/97cc989a8080ed30c98108185b9a4fee.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/4e37b6f74b3d1371c9bc2b56209f1193/params.yaml + predictions_file: output/reports/attack/4e37b6f74b3d1371c9bc2b56209f1193/predictions.json + probabilities_file: output/reports/attack/4e37b6f74b3d1371c9bc2b56209f1193/probabilities.json + score_dict_file: output/reports/attack/4e37b6f74b3d1371c9bc2b56209f1193/score_dict.json + test_labels_file: output/reports/attack/4e37b6f74b3d1371c9bc2b56209f1193/test_labels.json + train_labels_file: output/reports/attack/4e37b6f74b3d1371c9bc2b56209f1193/train_labels.json + model_dir: models + name: 4e37b6f74b3d1371c9bc2b56209f1193 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 4e37b6f74b3d1371c9bc2b56209f1193 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/4ea408ceb05263b7b44fe18f14a76eea/params.yaml b/examples/security/kdd-nsl/output/reports/attack/4ea408ceb05263b7b44fe18f14a76eea/params.yaml new file mode 100644 index 00000000..732f6939 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/4ea408ceb05263b7b44fe18f14a76eea/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 04f00aa96ae78148324bf9a6846a3052 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4ea408ceb05263b7b44fe18f14a76eea/adv_predictions.json + adv_probabilities_file: output/reports/attack/4ea408ceb05263b7b44fe18f14a76eea/adv_probabilities.json + attack_file: output/attacks/bcaf6d11e463f37dc1c7b1af55d61879.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/4ea408ceb05263b7b44fe18f14a76eea/params.yaml + predictions_file: output/reports/attack/4ea408ceb05263b7b44fe18f14a76eea/predictions.json + probabilities_file: output/reports/attack/4ea408ceb05263b7b44fe18f14a76eea/probabilities.json + score_dict_file: output/reports/attack/4ea408ceb05263b7b44fe18f14a76eea/score_dict.json + test_labels_file: output/reports/attack/4ea408ceb05263b7b44fe18f14a76eea/test_labels.json + train_labels_file: output/reports/attack/4ea408ceb05263b7b44fe18f14a76eea/train_labels.json + model_dir: models + name: 4ea408ceb05263b7b44fe18f14a76eea + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 4ea408ceb05263b7b44fe18f14a76eea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/4ee91468f8cbba69334af7f86d5e1f38/params.yaml b/examples/security/kdd-nsl/output/reports/attack/4ee91468f8cbba69334af7f86d5e1f38/params.yaml new file mode 100644 index 00000000..07266cf8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/4ee91468f8cbba69334af7f86d5e1f38/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 54c52b81fbe5894661fd191f3edc4133 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4ee91468f8cbba69334af7f86d5e1f38/adv_predictions.json + adv_probabilities_file: output/reports/attack/4ee91468f8cbba69334af7f86d5e1f38/adv_probabilities.json + attack_file: output/attacks/958b0bd29276e43acbab6421fda2c430.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/4ee91468f8cbba69334af7f86d5e1f38/params.yaml + predictions_file: output/reports/attack/4ee91468f8cbba69334af7f86d5e1f38/predictions.json + probabilities_file: output/reports/attack/4ee91468f8cbba69334af7f86d5e1f38/probabilities.json + score_dict_file: output/reports/attack/4ee91468f8cbba69334af7f86d5e1f38/score_dict.json + test_labels_file: output/reports/attack/4ee91468f8cbba69334af7f86d5e1f38/test_labels.json + train_labels_file: output/reports/attack/4ee91468f8cbba69334af7f86d5e1f38/train_labels.json + model_dir: models + name: 4ee91468f8cbba69334af7f86d5e1f38 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 4ee91468f8cbba69334af7f86d5e1f38 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/4f47057b779e853f12252a3e5573548b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/4f47057b779e853f12252a3e5573548b/params.yaml new file mode 100644 index 00000000..a95b000c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/4f47057b779e853f12252a3e5573548b/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 860e80e67ff6e5359cbd046794d168e0 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4f47057b779e853f12252a3e5573548b/adv_predictions.json + adv_probabilities_file: output/reports/attack/4f47057b779e853f12252a3e5573548b/adv_probabilities.json + attack_file: output/attacks/9c2ef66c60bd80f3dae99dd60fc27955.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/4f47057b779e853f12252a3e5573548b/params.yaml + predictions_file: output/reports/attack/4f47057b779e853f12252a3e5573548b/predictions.json + probabilities_file: output/reports/attack/4f47057b779e853f12252a3e5573548b/probabilities.json + score_dict_file: output/reports/attack/4f47057b779e853f12252a3e5573548b/score_dict.json + test_labels_file: output/reports/attack/4f47057b779e853f12252a3e5573548b/test_labels.json + train_labels_file: output/reports/attack/4f47057b779e853f12252a3e5573548b/train_labels.json + model_dir: models + name: 4f47057b779e853f12252a3e5573548b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 4f47057b779e853f12252a3e5573548b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5099c0486db97aa8f05b582dda8394ed/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5099c0486db97aa8f05b582dda8394ed/params.yaml new file mode 100644 index 00000000..39eac0c1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5099c0486db97aa8f05b582dda8394ed/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.01 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 124d285d0b9fe7468342095562a13898 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5099c0486db97aa8f05b582dda8394ed/adv_predictions.json + adv_probabilities_file: output/reports/attack/5099c0486db97aa8f05b582dda8394ed/adv_probabilities.json + attack_file: output/attacks/04b82bbc28541e53cc82f5e3bbf75490.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/5099c0486db97aa8f05b582dda8394ed/params.yaml + predictions_file: output/reports/attack/5099c0486db97aa8f05b582dda8394ed/predictions.json + probabilities_file: output/reports/attack/5099c0486db97aa8f05b582dda8394ed/probabilities.json + score_dict_file: output/reports/attack/5099c0486db97aa8f05b582dda8394ed/score_dict.json + test_labels_file: output/reports/attack/5099c0486db97aa8f05b582dda8394ed/test_labels.json + train_labels_file: output/reports/attack/5099c0486db97aa8f05b582dda8394ed/train_labels.json + model_dir: models + name: 5099c0486db97aa8f05b582dda8394ed + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 5099c0486db97aa8f05b582dda8394ed +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/50bc942432c5ec3928a04769a91ecc28/params.yaml b/examples/security/kdd-nsl/output/reports/attack/50bc942432c5ec3928a04769a91ecc28/params.yaml new file mode 100644 index 00000000..ba2a8bde --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/50bc942432c5ec3928a04769a91ecc28/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.5 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 4c1ce899ecf2388c8fd37eacbb714b63 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/50bc942432c5ec3928a04769a91ecc28/adv_predictions.json + adv_probabilities_file: output/reports/attack/50bc942432c5ec3928a04769a91ecc28/adv_probabilities.json + attack_file: output/attacks/299ce5e498b19d6ebdb54275693faac7.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/50bc942432c5ec3928a04769a91ecc28/params.yaml + predictions_file: output/reports/attack/50bc942432c5ec3928a04769a91ecc28/predictions.json + probabilities_file: output/reports/attack/50bc942432c5ec3928a04769a91ecc28/probabilities.json + score_dict_file: output/reports/attack/50bc942432c5ec3928a04769a91ecc28/score_dict.json + test_labels_file: output/reports/attack/50bc942432c5ec3928a04769a91ecc28/test_labels.json + train_labels_file: output/reports/attack/50bc942432c5ec3928a04769a91ecc28/train_labels.json + model_dir: models + name: 50bc942432c5ec3928a04769a91ecc28 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 50bc942432c5ec3928a04769a91ecc28 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/511d524086df1d8914e2550897c7c2c9/params.yaml b/examples/security/kdd-nsl/output/reports/attack/511d524086df1d8914e2550897c7c2c9/params.yaml new file mode 100644 index 00000000..68b381a5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/511d524086df1d8914e2550897c7c2c9/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.01 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: fb6e6e7c2aa962f0ae9399c8ba6bd8f9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/511d524086df1d8914e2550897c7c2c9/adv_predictions.json + adv_probabilities_file: output/reports/attack/511d524086df1d8914e2550897c7c2c9/adv_probabilities.json + attack_file: output/attacks/21dfb2a4a757b25061d45ad307427d8d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/511d524086df1d8914e2550897c7c2c9/params.yaml + predictions_file: output/reports/attack/511d524086df1d8914e2550897c7c2c9/predictions.json + probabilities_file: output/reports/attack/511d524086df1d8914e2550897c7c2c9/probabilities.json + score_dict_file: output/reports/attack/511d524086df1d8914e2550897c7c2c9/score_dict.json + test_labels_file: output/reports/attack/511d524086df1d8914e2550897c7c2c9/test_labels.json + train_labels_file: output/reports/attack/511d524086df1d8914e2550897c7c2c9/train_labels.json + model_dir: models + name: 511d524086df1d8914e2550897c7c2c9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 511d524086df1d8914e2550897c7c2c9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/521aac598390d272d93d22afb593a6e2/params.yaml b/examples/security/kdd-nsl/output/reports/attack/521aac598390d272d93d22afb593a6e2/params.yaml new file mode 100644 index 00000000..baf25c70 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/521aac598390d272d93d22afb593a6e2/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.3 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: f8470aa7e59efe2aa87f140407fa1ddc +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/521aac598390d272d93d22afb593a6e2/adv_predictions.json + adv_probabilities_file: output/reports/attack/521aac598390d272d93d22afb593a6e2/adv_probabilities.json + attack_file: output/attacks/7259bc5a7d8345d3d4cae42f8f4b9616.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/521aac598390d272d93d22afb593a6e2/params.yaml + predictions_file: output/reports/attack/521aac598390d272d93d22afb593a6e2/predictions.json + probabilities_file: output/reports/attack/521aac598390d272d93d22afb593a6e2/probabilities.json + score_dict_file: output/reports/attack/521aac598390d272d93d22afb593a6e2/score_dict.json + test_labels_file: output/reports/attack/521aac598390d272d93d22afb593a6e2/test_labels.json + train_labels_file: output/reports/attack/521aac598390d272d93d22afb593a6e2/train_labels.json + model_dir: models + name: 521aac598390d272d93d22afb593a6e2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 521aac598390d272d93d22afb593a6e2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5269a738a559f38eb1459dd2cf1b2c00/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5269a738a559f38eb1459dd2cf1b2c00/params.yaml new file mode 100644 index 00000000..595302aa --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5269a738a559f38eb1459dd2cf1b2c00/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.5 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 4119eff9fe1bba5fc91189fbc0c3822c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5269a738a559f38eb1459dd2cf1b2c00/adv_predictions.json + adv_probabilities_file: output/reports/attack/5269a738a559f38eb1459dd2cf1b2c00/adv_probabilities.json + attack_file: output/attacks/4df07d29a9f36015f8863c0eaaf9af0e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/5269a738a559f38eb1459dd2cf1b2c00/params.yaml + predictions_file: output/reports/attack/5269a738a559f38eb1459dd2cf1b2c00/predictions.json + probabilities_file: output/reports/attack/5269a738a559f38eb1459dd2cf1b2c00/probabilities.json + score_dict_file: output/reports/attack/5269a738a559f38eb1459dd2cf1b2c00/score_dict.json + test_labels_file: output/reports/attack/5269a738a559f38eb1459dd2cf1b2c00/test_labels.json + train_labels_file: output/reports/attack/5269a738a559f38eb1459dd2cf1b2c00/train_labels.json + model_dir: models + name: 5269a738a559f38eb1459dd2cf1b2c00 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 5269a738a559f38eb1459dd2cf1b2c00 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/52bc36f4ef80bca9441d0e4fb9227b30/params.yaml b/examples/security/kdd-nsl/output/reports/attack/52bc36f4ef80bca9441d0e4fb9227b30/params.yaml new file mode 100644 index 00000000..2c317a93 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/52bc36f4ef80bca9441d0e4fb9227b30/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 8c41a6442d2181f93c5d7a29080fe5aa +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/52bc36f4ef80bca9441d0e4fb9227b30/adv_predictions.json + adv_probabilities_file: output/reports/attack/52bc36f4ef80bca9441d0e4fb9227b30/adv_probabilities.json + attack_file: output/attacks/3dfbfe06dd22eb9668ba6a7b310a53d1.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/52bc36f4ef80bca9441d0e4fb9227b30/params.yaml + predictions_file: output/reports/attack/52bc36f4ef80bca9441d0e4fb9227b30/predictions.json + probabilities_file: output/reports/attack/52bc36f4ef80bca9441d0e4fb9227b30/probabilities.json + score_dict_file: output/reports/attack/52bc36f4ef80bca9441d0e4fb9227b30/score_dict.json + test_labels_file: output/reports/attack/52bc36f4ef80bca9441d0e4fb9227b30/test_labels.json + train_labels_file: output/reports/attack/52bc36f4ef80bca9441d0e4fb9227b30/train_labels.json + model_dir: models + name: 52bc36f4ef80bca9441d0e4fb9227b30 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 52bc36f4ef80bca9441d0e4fb9227b30 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/52d8a254bef57338f1c192ffd015e873/params.yaml b/examples/security/kdd-nsl/output/reports/attack/52d8a254bef57338f1c192ffd015e873/params.yaml new file mode 100644 index 00000000..476012b3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/52d8a254bef57338f1c192ffd015e873/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.01 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 6e2647b8830b8e3dc49cd5f220533121 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/52d8a254bef57338f1c192ffd015e873/adv_predictions.json + adv_probabilities_file: output/reports/attack/52d8a254bef57338f1c192ffd015e873/adv_probabilities.json + attack_file: output/attacks/2c2570611a8474baff3d09375d24431c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/52d8a254bef57338f1c192ffd015e873/params.yaml + predictions_file: output/reports/attack/52d8a254bef57338f1c192ffd015e873/predictions.json + probabilities_file: output/reports/attack/52d8a254bef57338f1c192ffd015e873/probabilities.json + score_dict_file: output/reports/attack/52d8a254bef57338f1c192ffd015e873/score_dict.json + test_labels_file: output/reports/attack/52d8a254bef57338f1c192ffd015e873/test_labels.json + train_labels_file: output/reports/attack/52d8a254bef57338f1c192ffd015e873/train_labels.json + model_dir: models + name: 52d8a254bef57338f1c192ffd015e873 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 52d8a254bef57338f1c192ffd015e873 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5350b8410f3c0222da9ac303004de91e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5350b8410f3c0222da9ac303004de91e/params.yaml new file mode 100644 index 00000000..e07251b2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5350b8410f3c0222da9ac303004de91e/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 2ec7964c28b3ff5a5686b8cdfe366e02 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5350b8410f3c0222da9ac303004de91e/adv_predictions.json + adv_probabilities_file: output/reports/attack/5350b8410f3c0222da9ac303004de91e/adv_probabilities.json + attack_file: output/attacks/3cdbf66ccc6775f8d235ed1014d64c00.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/5350b8410f3c0222da9ac303004de91e/params.yaml + predictions_file: output/reports/attack/5350b8410f3c0222da9ac303004de91e/predictions.json + probabilities_file: output/reports/attack/5350b8410f3c0222da9ac303004de91e/probabilities.json + score_dict_file: output/reports/attack/5350b8410f3c0222da9ac303004de91e/score_dict.json + test_labels_file: output/reports/attack/5350b8410f3c0222da9ac303004de91e/test_labels.json + train_labels_file: output/reports/attack/5350b8410f3c0222da9ac303004de91e/train_labels.json + model_dir: models + name: 5350b8410f3c0222da9ac303004de91e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 5350b8410f3c0222da9ac303004de91e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/535b38d88d4ebcfdc2916815db04d075/params.yaml b/examples/security/kdd-nsl/output/reports/attack/535b38d88d4ebcfdc2916815db04d075/params.yaml new file mode 100644 index 00000000..08774045 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/535b38d88d4ebcfdc2916815db04d075/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: db747673a6cf3e79e1a1ea5bc1cb2f76 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/535b38d88d4ebcfdc2916815db04d075/adv_predictions.json + adv_probabilities_file: output/reports/attack/535b38d88d4ebcfdc2916815db04d075/adv_probabilities.json + attack_file: output/attacks/f3b48c9f81021f9d192c2e5aa2f18390.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/535b38d88d4ebcfdc2916815db04d075/params.yaml + predictions_file: output/reports/attack/535b38d88d4ebcfdc2916815db04d075/predictions.json + probabilities_file: output/reports/attack/535b38d88d4ebcfdc2916815db04d075/probabilities.json + score_dict_file: output/reports/attack/535b38d88d4ebcfdc2916815db04d075/score_dict.json + test_labels_file: output/reports/attack/535b38d88d4ebcfdc2916815db04d075/test_labels.json + train_labels_file: output/reports/attack/535b38d88d4ebcfdc2916815db04d075/train_labels.json + model_dir: models + name: 535b38d88d4ebcfdc2916815db04d075 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 535b38d88d4ebcfdc2916815db04d075 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/54085d1ae2457c5c650ab1f0f001a9fd/params.yaml b/examples/security/kdd-nsl/output/reports/attack/54085d1ae2457c5c650ab1f0f001a9fd/params.yaml new file mode 100644 index 00000000..cde500dd --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/54085d1ae2457c5c650ab1f0f001a9fd/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 912e3b38dbe7a687b98ee35316417aa1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/54085d1ae2457c5c650ab1f0f001a9fd/adv_predictions.json + adv_probabilities_file: output/reports/attack/54085d1ae2457c5c650ab1f0f001a9fd/adv_probabilities.json + attack_file: output/attacks/3bfb60a7a0db427be10c04b5c2253e5e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/54085d1ae2457c5c650ab1f0f001a9fd/params.yaml + predictions_file: output/reports/attack/54085d1ae2457c5c650ab1f0f001a9fd/predictions.json + probabilities_file: output/reports/attack/54085d1ae2457c5c650ab1f0f001a9fd/probabilities.json + score_dict_file: output/reports/attack/54085d1ae2457c5c650ab1f0f001a9fd/score_dict.json + test_labels_file: output/reports/attack/54085d1ae2457c5c650ab1f0f001a9fd/test_labels.json + train_labels_file: output/reports/attack/54085d1ae2457c5c650ab1f0f001a9fd/train_labels.json + model_dir: models + name: 54085d1ae2457c5c650ab1f0f001a9fd + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 54085d1ae2457c5c650ab1f0f001a9fd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/543c0e9a00342e0c930307ae6c3eba97/params.yaml b/examples/security/kdd-nsl/output/reports/attack/543c0e9a00342e0c930307ae6c3eba97/params.yaml new file mode 100644 index 00000000..9a9bb209 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/543c0e9a00342e0c930307ae6c3eba97/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.001 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 91e592e7f8913d621eca03db2607db1c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/543c0e9a00342e0c930307ae6c3eba97/adv_predictions.json + adv_probabilities_file: output/reports/attack/543c0e9a00342e0c930307ae6c3eba97/adv_probabilities.json + attack_file: output/attacks/cdc78baf4c0140e038d7cbc0e5ce89cf.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/543c0e9a00342e0c930307ae6c3eba97/params.yaml + predictions_file: output/reports/attack/543c0e9a00342e0c930307ae6c3eba97/predictions.json + probabilities_file: output/reports/attack/543c0e9a00342e0c930307ae6c3eba97/probabilities.json + score_dict_file: output/reports/attack/543c0e9a00342e0c930307ae6c3eba97/score_dict.json + test_labels_file: output/reports/attack/543c0e9a00342e0c930307ae6c3eba97/test_labels.json + train_labels_file: output/reports/attack/543c0e9a00342e0c930307ae6c3eba97/train_labels.json + model_dir: models + name: 543c0e9a00342e0c930307ae6c3eba97 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 543c0e9a00342e0c930307ae6c3eba97 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5608cf260ede727221f8146d23c8fd34/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5608cf260ede727221f8146d23c8fd34/params.yaml new file mode 100644 index 00000000..c77a647b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5608cf260ede727221f8146d23c8fd34/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 069a42ba7b2a9dc5898cae87927653a3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5608cf260ede727221f8146d23c8fd34/adv_predictions.json + adv_probabilities_file: output/reports/attack/5608cf260ede727221f8146d23c8fd34/adv_probabilities.json + attack_file: output/attacks/464e3a6a3d7aa73d739fa57ebe3fb99a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/5608cf260ede727221f8146d23c8fd34/params.yaml + predictions_file: output/reports/attack/5608cf260ede727221f8146d23c8fd34/predictions.json + probabilities_file: output/reports/attack/5608cf260ede727221f8146d23c8fd34/probabilities.json + score_dict_file: output/reports/attack/5608cf260ede727221f8146d23c8fd34/score_dict.json + test_labels_file: output/reports/attack/5608cf260ede727221f8146d23c8fd34/test_labels.json + train_labels_file: output/reports/attack/5608cf260ede727221f8146d23c8fd34/train_labels.json + model_dir: models + name: 5608cf260ede727221f8146d23c8fd34 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 5608cf260ede727221f8146d23c8fd34 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/56276beb5f30b4b2959219f19ebad497/params.yaml b/examples/security/kdd-nsl/output/reports/attack/56276beb5f30b4b2959219f19ebad497/params.yaml new file mode 100644 index 00000000..10efc74c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/56276beb5f30b4b2959219f19ebad497/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.3 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 71fceda52196371ba4b38dd98f8b3a48 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/56276beb5f30b4b2959219f19ebad497/adv_predictions.json + adv_probabilities_file: output/reports/attack/56276beb5f30b4b2959219f19ebad497/adv_probabilities.json + attack_file: output/attacks/7886a16c904c0ddd8ec367d02528d5ac.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/56276beb5f30b4b2959219f19ebad497/params.yaml + predictions_file: output/reports/attack/56276beb5f30b4b2959219f19ebad497/predictions.json + probabilities_file: output/reports/attack/56276beb5f30b4b2959219f19ebad497/probabilities.json + score_dict_file: output/reports/attack/56276beb5f30b4b2959219f19ebad497/score_dict.json + test_labels_file: output/reports/attack/56276beb5f30b4b2959219f19ebad497/test_labels.json + train_labels_file: output/reports/attack/56276beb5f30b4b2959219f19ebad497/train_labels.json + model_dir: models + name: 56276beb5f30b4b2959219f19ebad497 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 56276beb5f30b4b2959219f19ebad497 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5688188510ebc7ccbf6e14032d7f5aba/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5688188510ebc7ccbf6e14032d7f5aba/params.yaml new file mode 100644 index 00000000..ba66f07b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5688188510ebc7ccbf6e14032d7f5aba/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 24a1beba4212acdd8469c9c5ed0b1e01 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5688188510ebc7ccbf6e14032d7f5aba/adv_predictions.json + adv_probabilities_file: output/reports/attack/5688188510ebc7ccbf6e14032d7f5aba/adv_probabilities.json + attack_file: output/attacks/ad8ba055b8f329cd07f82c4c6e302103.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/5688188510ebc7ccbf6e14032d7f5aba/params.yaml + predictions_file: output/reports/attack/5688188510ebc7ccbf6e14032d7f5aba/predictions.json + probabilities_file: output/reports/attack/5688188510ebc7ccbf6e14032d7f5aba/probabilities.json + score_dict_file: output/reports/attack/5688188510ebc7ccbf6e14032d7f5aba/score_dict.json + test_labels_file: output/reports/attack/5688188510ebc7ccbf6e14032d7f5aba/test_labels.json + train_labels_file: output/reports/attack/5688188510ebc7ccbf6e14032d7f5aba/train_labels.json + model_dir: models + name: 5688188510ebc7ccbf6e14032d7f5aba + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 5688188510ebc7ccbf6e14032d7f5aba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/576bf5c65f83821a92e5fff010151b4c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/576bf5c65f83821a92e5fff010151b4c/params.yaml new file mode 100644 index 00000000..2794abf8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/576bf5c65f83821a92e5fff010151b4c/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.001 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 399b2b9b8963f8c0a5f8a7c9f3d80143 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/576bf5c65f83821a92e5fff010151b4c/adv_predictions.json + adv_probabilities_file: output/reports/attack/576bf5c65f83821a92e5fff010151b4c/adv_probabilities.json + attack_file: output/attacks/f3fce9b66ee1bd438db705ed88d1f6f0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/576bf5c65f83821a92e5fff010151b4c/params.yaml + predictions_file: output/reports/attack/576bf5c65f83821a92e5fff010151b4c/predictions.json + probabilities_file: output/reports/attack/576bf5c65f83821a92e5fff010151b4c/probabilities.json + score_dict_file: output/reports/attack/576bf5c65f83821a92e5fff010151b4c/score_dict.json + test_labels_file: output/reports/attack/576bf5c65f83821a92e5fff010151b4c/test_labels.json + train_labels_file: output/reports/attack/576bf5c65f83821a92e5fff010151b4c/train_labels.json + model_dir: models + name: 576bf5c65f83821a92e5fff010151b4c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 576bf5c65f83821a92e5fff010151b4c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/57e2a35acfedc8257ef4ad4530d8e58a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/57e2a35acfedc8257ef4ad4530d8e58a/params.yaml new file mode 100644 index 00000000..ce8224f5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/57e2a35acfedc8257ef4ad4530d8e58a/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.1 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 8d0e00fbe6880ea2a9d004223dcb7e67 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/57e2a35acfedc8257ef4ad4530d8e58a/adv_predictions.json + adv_probabilities_file: output/reports/attack/57e2a35acfedc8257ef4ad4530d8e58a/adv_probabilities.json + attack_file: output/attacks/69a29014738fe6a25f71dce77449f5de.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/57e2a35acfedc8257ef4ad4530d8e58a/params.yaml + predictions_file: output/reports/attack/57e2a35acfedc8257ef4ad4530d8e58a/predictions.json + probabilities_file: output/reports/attack/57e2a35acfedc8257ef4ad4530d8e58a/probabilities.json + score_dict_file: output/reports/attack/57e2a35acfedc8257ef4ad4530d8e58a/score_dict.json + test_labels_file: output/reports/attack/57e2a35acfedc8257ef4ad4530d8e58a/test_labels.json + train_labels_file: output/reports/attack/57e2a35acfedc8257ef4ad4530d8e58a/train_labels.json + model_dir: models + name: 57e2a35acfedc8257ef4ad4530d8e58a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 57e2a35acfedc8257ef4ad4530d8e58a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/580411d9254fad230f091ce00967572e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/580411d9254fad230f091ce00967572e/params.yaml new file mode 100644 index 00000000..8fdc744c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/580411d9254fad230f091ce00967572e/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.01 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: d46de9f8173e0ebbd34bcb0de7916e12 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/580411d9254fad230f091ce00967572e/adv_predictions.json + adv_probabilities_file: output/reports/attack/580411d9254fad230f091ce00967572e/adv_probabilities.json + attack_file: output/attacks/0843af7526ba230f436703ae70432b24.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/580411d9254fad230f091ce00967572e/params.yaml + predictions_file: output/reports/attack/580411d9254fad230f091ce00967572e/predictions.json + probabilities_file: output/reports/attack/580411d9254fad230f091ce00967572e/probabilities.json + score_dict_file: output/reports/attack/580411d9254fad230f091ce00967572e/score_dict.json + test_labels_file: output/reports/attack/580411d9254fad230f091ce00967572e/test_labels.json + train_labels_file: output/reports/attack/580411d9254fad230f091ce00967572e/train_labels.json + model_dir: models + name: 580411d9254fad230f091ce00967572e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 580411d9254fad230f091ce00967572e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5882aa77b8ee80946b5f6df094071e9c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5882aa77b8ee80946b5f6df094071e9c/params.yaml new file mode 100644 index 00000000..1e3d8b54 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5882aa77b8ee80946b5f6df094071e9c/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.001 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 3ab7716388f82709a9c57e417061a6f1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5882aa77b8ee80946b5f6df094071e9c/adv_predictions.json + adv_probabilities_file: output/reports/attack/5882aa77b8ee80946b5f6df094071e9c/adv_probabilities.json + attack_file: output/attacks/f3fca8f95feffb25f5a3fdf4b78ee1ac.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/5882aa77b8ee80946b5f6df094071e9c/params.yaml + predictions_file: output/reports/attack/5882aa77b8ee80946b5f6df094071e9c/predictions.json + probabilities_file: output/reports/attack/5882aa77b8ee80946b5f6df094071e9c/probabilities.json + score_dict_file: output/reports/attack/5882aa77b8ee80946b5f6df094071e9c/score_dict.json + test_labels_file: output/reports/attack/5882aa77b8ee80946b5f6df094071e9c/test_labels.json + train_labels_file: output/reports/attack/5882aa77b8ee80946b5f6df094071e9c/train_labels.json + model_dir: models + name: 5882aa77b8ee80946b5f6df094071e9c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 5882aa77b8ee80946b5f6df094071e9c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/588429921d6e5fe6a164d6457fc26d54/params.yaml b/examples/security/kdd-nsl/output/reports/attack/588429921d6e5fe6a164d6457fc26d54/params.yaml new file mode 100644 index 00000000..905165e9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/588429921d6e5fe6a164d6457fc26d54/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: bfc43de04ca1f4213feaf27614c9422a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/588429921d6e5fe6a164d6457fc26d54/adv_predictions.json + adv_probabilities_file: output/reports/attack/588429921d6e5fe6a164d6457fc26d54/adv_probabilities.json + attack_file: output/attacks/456f25673fee66a880da95239847b81c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/588429921d6e5fe6a164d6457fc26d54/params.yaml + predictions_file: output/reports/attack/588429921d6e5fe6a164d6457fc26d54/predictions.json + probabilities_file: output/reports/attack/588429921d6e5fe6a164d6457fc26d54/probabilities.json + score_dict_file: output/reports/attack/588429921d6e5fe6a164d6457fc26d54/score_dict.json + test_labels_file: output/reports/attack/588429921d6e5fe6a164d6457fc26d54/test_labels.json + train_labels_file: output/reports/attack/588429921d6e5fe6a164d6457fc26d54/train_labels.json + model_dir: models + name: 588429921d6e5fe6a164d6457fc26d54 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 588429921d6e5fe6a164d6457fc26d54 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5961d955c9f718d96e3ecdbc44e2f1c7/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5961d955c9f718d96e3ecdbc44e2f1c7/params.yaml new file mode 100644 index 00000000..50da511b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5961d955c9f718d96e3ecdbc44e2f1c7/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.5 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 92b983a1c97f4c1d4fc950841fc237e1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5961d955c9f718d96e3ecdbc44e2f1c7/adv_predictions.json + adv_probabilities_file: output/reports/attack/5961d955c9f718d96e3ecdbc44e2f1c7/adv_probabilities.json + attack_file: output/attacks/dd7c3bfb4577d04e0a32e939a2b7e978.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/5961d955c9f718d96e3ecdbc44e2f1c7/params.yaml + predictions_file: output/reports/attack/5961d955c9f718d96e3ecdbc44e2f1c7/predictions.json + probabilities_file: output/reports/attack/5961d955c9f718d96e3ecdbc44e2f1c7/probabilities.json + score_dict_file: output/reports/attack/5961d955c9f718d96e3ecdbc44e2f1c7/score_dict.json + test_labels_file: output/reports/attack/5961d955c9f718d96e3ecdbc44e2f1c7/test_labels.json + train_labels_file: output/reports/attack/5961d955c9f718d96e3ecdbc44e2f1c7/train_labels.json + model_dir: models + name: 5961d955c9f718d96e3ecdbc44e2f1c7 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 5961d955c9f718d96e3ecdbc44e2f1c7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/59be6cf15c77976a9bd02aeeb040ffb5/params.yaml b/examples/security/kdd-nsl/output/reports/attack/59be6cf15c77976a9bd02aeeb040ffb5/params.yaml new file mode 100644 index 00000000..6f6af549 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/59be6cf15c77976a9bd02aeeb040ffb5/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.01 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 7d5861a048ade852ea8ae998e283967e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/59be6cf15c77976a9bd02aeeb040ffb5/adv_predictions.json + adv_probabilities_file: output/reports/attack/59be6cf15c77976a9bd02aeeb040ffb5/adv_probabilities.json + attack_file: output/attacks/d9a39f4783d20bdfcf80aedbb6567d2e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/59be6cf15c77976a9bd02aeeb040ffb5/params.yaml + predictions_file: output/reports/attack/59be6cf15c77976a9bd02aeeb040ffb5/predictions.json + probabilities_file: output/reports/attack/59be6cf15c77976a9bd02aeeb040ffb5/probabilities.json + score_dict_file: output/reports/attack/59be6cf15c77976a9bd02aeeb040ffb5/score_dict.json + test_labels_file: output/reports/attack/59be6cf15c77976a9bd02aeeb040ffb5/test_labels.json + train_labels_file: output/reports/attack/59be6cf15c77976a9bd02aeeb040ffb5/train_labels.json + model_dir: models + name: 59be6cf15c77976a9bd02aeeb040ffb5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 59be6cf15c77976a9bd02aeeb040ffb5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5b430901ed042e4451a028640bc2f3d1/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5b430901ed042e4451a028640bc2f3d1/params.yaml new file mode 100644 index 00000000..cd348b94 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5b430901ed042e4451a028640bc2f3d1/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: d096da33894cc636635f84dac61c5e31 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5b430901ed042e4451a028640bc2f3d1/adv_predictions.json + adv_probabilities_file: output/reports/attack/5b430901ed042e4451a028640bc2f3d1/adv_probabilities.json + attack_file: output/attacks/3ba311514b1dbded97d86b7e69e419a3.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/5b430901ed042e4451a028640bc2f3d1/params.yaml + predictions_file: output/reports/attack/5b430901ed042e4451a028640bc2f3d1/predictions.json + probabilities_file: output/reports/attack/5b430901ed042e4451a028640bc2f3d1/probabilities.json + score_dict_file: output/reports/attack/5b430901ed042e4451a028640bc2f3d1/score_dict.json + test_labels_file: output/reports/attack/5b430901ed042e4451a028640bc2f3d1/test_labels.json + train_labels_file: output/reports/attack/5b430901ed042e4451a028640bc2f3d1/train_labels.json + model_dir: models + name: 5b430901ed042e4451a028640bc2f3d1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 5b430901ed042e4451a028640bc2f3d1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5b603ebcb6304c5fd8e32a7f759c6b51/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5b603ebcb6304c5fd8e32a7f759c6b51/params.yaml new file mode 100644 index 00000000..28825c6d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5b603ebcb6304c5fd8e32a7f759c6b51/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: a92e6c523b7beb0857acaec4002371ce +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5b603ebcb6304c5fd8e32a7f759c6b51/adv_predictions.json + adv_probabilities_file: output/reports/attack/5b603ebcb6304c5fd8e32a7f759c6b51/adv_probabilities.json + attack_file: output/attacks/280d39837eb2622799c02c60134de9c0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/5b603ebcb6304c5fd8e32a7f759c6b51/params.yaml + predictions_file: output/reports/attack/5b603ebcb6304c5fd8e32a7f759c6b51/predictions.json + probabilities_file: output/reports/attack/5b603ebcb6304c5fd8e32a7f759c6b51/probabilities.json + score_dict_file: output/reports/attack/5b603ebcb6304c5fd8e32a7f759c6b51/score_dict.json + test_labels_file: output/reports/attack/5b603ebcb6304c5fd8e32a7f759c6b51/test_labels.json + train_labels_file: output/reports/attack/5b603ebcb6304c5fd8e32a7f759c6b51/train_labels.json + model_dir: models + name: 5b603ebcb6304c5fd8e32a7f759c6b51 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 5b603ebcb6304c5fd8e32a7f759c6b51 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/params.yaml new file mode 100644 index 00000000..62be3ba9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 993030eee75baf52bc4f57f4ef9a7ff4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/adv_predictions.json + adv_probabilities_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/adv_probabilities.json + attack_file: output/attacks/1e5b14fd98e3f34b81c514e54a211391.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/params.yaml + predictions_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/predictions.json + probabilities_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/probabilities.json + score_dict_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/score_dict.json + test_labels_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/test_labels.json + train_labels_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/train_labels.json + model_dir: models + name: 5cb9981c36289417ff7f551eda1ebc74 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 5cb9981c36289417ff7f551eda1ebc74 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5d8237fcfa3a113df78542565947c595/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5d8237fcfa3a113df78542565947c595/params.yaml new file mode 100644 index 00000000..ea04938b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5d8237fcfa3a113df78542565947c595/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.3 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 72a82437cc846638e65c70ff28d7c850 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5d8237fcfa3a113df78542565947c595/adv_predictions.json + adv_probabilities_file: output/reports/attack/5d8237fcfa3a113df78542565947c595/adv_probabilities.json + attack_file: output/attacks/fb233e01eaf76fc481bba7221282dce0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/5d8237fcfa3a113df78542565947c595/params.yaml + predictions_file: output/reports/attack/5d8237fcfa3a113df78542565947c595/predictions.json + probabilities_file: output/reports/attack/5d8237fcfa3a113df78542565947c595/probabilities.json + score_dict_file: output/reports/attack/5d8237fcfa3a113df78542565947c595/score_dict.json + test_labels_file: output/reports/attack/5d8237fcfa3a113df78542565947c595/test_labels.json + train_labels_file: output/reports/attack/5d8237fcfa3a113df78542565947c595/train_labels.json + model_dir: models + name: 5d8237fcfa3a113df78542565947c595 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 5d8237fcfa3a113df78542565947c595 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5d8398e598bb134039686c316e43b7a8/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5d8398e598bb134039686c316e43b7a8/params.yaml new file mode 100644 index 00000000..24ea7d5a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5d8398e598bb134039686c316e43b7a8/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.01 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 0ea472e7918d8c0e69b1ad0a2bb70a64 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5d8398e598bb134039686c316e43b7a8/adv_predictions.json + adv_probabilities_file: output/reports/attack/5d8398e598bb134039686c316e43b7a8/adv_probabilities.json + attack_file: output/attacks/2f50cd1a5955edb3669caea27ee075d8.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/5d8398e598bb134039686c316e43b7a8/params.yaml + predictions_file: output/reports/attack/5d8398e598bb134039686c316e43b7a8/predictions.json + probabilities_file: output/reports/attack/5d8398e598bb134039686c316e43b7a8/probabilities.json + score_dict_file: output/reports/attack/5d8398e598bb134039686c316e43b7a8/score_dict.json + test_labels_file: output/reports/attack/5d8398e598bb134039686c316e43b7a8/test_labels.json + train_labels_file: output/reports/attack/5d8398e598bb134039686c316e43b7a8/train_labels.json + model_dir: models + name: 5d8398e598bb134039686c316e43b7a8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 5d8398e598bb134039686c316e43b7a8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5da41aa2bea8000d1dd79e6e422280d6/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5da41aa2bea8000d1dd79e6e422280d6/params.yaml new file mode 100644 index 00000000..16ef871e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5da41aa2bea8000d1dd79e6e422280d6/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.001 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: c393cf636449b4292888fe5f5411c714 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5da41aa2bea8000d1dd79e6e422280d6/adv_predictions.json + adv_probabilities_file: output/reports/attack/5da41aa2bea8000d1dd79e6e422280d6/adv_probabilities.json + attack_file: output/attacks/4f73af32490dc957132118e6ec0a2ba0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/5da41aa2bea8000d1dd79e6e422280d6/params.yaml + predictions_file: output/reports/attack/5da41aa2bea8000d1dd79e6e422280d6/predictions.json + probabilities_file: output/reports/attack/5da41aa2bea8000d1dd79e6e422280d6/probabilities.json + score_dict_file: output/reports/attack/5da41aa2bea8000d1dd79e6e422280d6/score_dict.json + test_labels_file: output/reports/attack/5da41aa2bea8000d1dd79e6e422280d6/test_labels.json + train_labels_file: output/reports/attack/5da41aa2bea8000d1dd79e6e422280d6/train_labels.json + model_dir: models + name: 5da41aa2bea8000d1dd79e6e422280d6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 5da41aa2bea8000d1dd79e6e422280d6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5e3b6f283f36aee1ae196a0da365b786/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5e3b6f283f36aee1ae196a0da365b786/params.yaml new file mode 100644 index 00000000..fee5ba13 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5e3b6f283f36aee1ae196a0da365b786/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 79602b90350f3e363acb04cc28f2d97f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5e3b6f283f36aee1ae196a0da365b786/adv_predictions.json + adv_probabilities_file: output/reports/attack/5e3b6f283f36aee1ae196a0da365b786/adv_probabilities.json + attack_file: output/attacks/919a7a29db0df7886fded0d1f0f97f01.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/5e3b6f283f36aee1ae196a0da365b786/params.yaml + predictions_file: output/reports/attack/5e3b6f283f36aee1ae196a0da365b786/predictions.json + probabilities_file: output/reports/attack/5e3b6f283f36aee1ae196a0da365b786/probabilities.json + score_dict_file: output/reports/attack/5e3b6f283f36aee1ae196a0da365b786/score_dict.json + test_labels_file: output/reports/attack/5e3b6f283f36aee1ae196a0da365b786/test_labels.json + train_labels_file: output/reports/attack/5e3b6f283f36aee1ae196a0da365b786/train_labels.json + model_dir: models + name: 5e3b6f283f36aee1ae196a0da365b786 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 5e3b6f283f36aee1ae196a0da365b786 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5e43155fcd780ac31ae4b75197ad292f/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5e43155fcd780ac31ae4b75197ad292f/params.yaml new file mode 100644 index 00000000..8207913e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5e43155fcd780ac31ae4b75197ad292f/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.01 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 13f2ced6432c66b46cd9c9f317b5e887 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5e43155fcd780ac31ae4b75197ad292f/adv_predictions.json + adv_probabilities_file: output/reports/attack/5e43155fcd780ac31ae4b75197ad292f/adv_probabilities.json + attack_file: output/attacks/56f090156104053c990cf6e31ea14b71.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/5e43155fcd780ac31ae4b75197ad292f/params.yaml + predictions_file: output/reports/attack/5e43155fcd780ac31ae4b75197ad292f/predictions.json + probabilities_file: output/reports/attack/5e43155fcd780ac31ae4b75197ad292f/probabilities.json + score_dict_file: output/reports/attack/5e43155fcd780ac31ae4b75197ad292f/score_dict.json + test_labels_file: output/reports/attack/5e43155fcd780ac31ae4b75197ad292f/test_labels.json + train_labels_file: output/reports/attack/5e43155fcd780ac31ae4b75197ad292f/train_labels.json + model_dir: models + name: 5e43155fcd780ac31ae4b75197ad292f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 5e43155fcd780ac31ae4b75197ad292f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5eaece753592ee33e832d4aa780d2831/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5eaece753592ee33e832d4aa780d2831/params.yaml new file mode 100644 index 00000000..745fe72b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5eaece753592ee33e832d4aa780d2831/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 3c55a46ec1e75137035bb081585ae56c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5eaece753592ee33e832d4aa780d2831/adv_predictions.json + adv_probabilities_file: output/reports/attack/5eaece753592ee33e832d4aa780d2831/adv_probabilities.json + attack_file: output/attacks/ef5662cd24b0e2f5b18b1e1880366627.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/5eaece753592ee33e832d4aa780d2831/params.yaml + predictions_file: output/reports/attack/5eaece753592ee33e832d4aa780d2831/predictions.json + probabilities_file: output/reports/attack/5eaece753592ee33e832d4aa780d2831/probabilities.json + score_dict_file: output/reports/attack/5eaece753592ee33e832d4aa780d2831/score_dict.json + test_labels_file: output/reports/attack/5eaece753592ee33e832d4aa780d2831/test_labels.json + train_labels_file: output/reports/attack/5eaece753592ee33e832d4aa780d2831/train_labels.json + model_dir: models + name: 5eaece753592ee33e832d4aa780d2831 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 5eaece753592ee33e832d4aa780d2831 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5ef3f5e958daca7ae44601d0ddb29bf3/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5ef3f5e958daca7ae44601d0ddb29bf3/params.yaml new file mode 100644 index 00000000..b52cc84b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5ef3f5e958daca7ae44601d0ddb29bf3/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 3022a58729315af9b031855ac0e0afa3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5ef3f5e958daca7ae44601d0ddb29bf3/adv_predictions.json + adv_probabilities_file: output/reports/attack/5ef3f5e958daca7ae44601d0ddb29bf3/adv_probabilities.json + attack_file: output/attacks/3f76bc8f2b51afd54872c3644fb2a07c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/5ef3f5e958daca7ae44601d0ddb29bf3/params.yaml + predictions_file: output/reports/attack/5ef3f5e958daca7ae44601d0ddb29bf3/predictions.json + probabilities_file: output/reports/attack/5ef3f5e958daca7ae44601d0ddb29bf3/probabilities.json + score_dict_file: output/reports/attack/5ef3f5e958daca7ae44601d0ddb29bf3/score_dict.json + test_labels_file: output/reports/attack/5ef3f5e958daca7ae44601d0ddb29bf3/test_labels.json + train_labels_file: output/reports/attack/5ef3f5e958daca7ae44601d0ddb29bf3/train_labels.json + model_dir: models + name: 5ef3f5e958daca7ae44601d0ddb29bf3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 5ef3f5e958daca7ae44601d0ddb29bf3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5f084df8d1099295921f1e6af1d147a8/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5f084df8d1099295921f1e6af1d147a8/params.yaml new file mode 100644 index 00000000..3cc789e6 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5f084df8d1099295921f1e6af1d147a8/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.01 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 6e35354c7da9e759ad2c22c2c23384e9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5f084df8d1099295921f1e6af1d147a8/adv_predictions.json + adv_probabilities_file: output/reports/attack/5f084df8d1099295921f1e6af1d147a8/adv_probabilities.json + attack_file: output/attacks/431dcaf0f0787c8bc24aa1140f26da8a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/5f084df8d1099295921f1e6af1d147a8/params.yaml + predictions_file: output/reports/attack/5f084df8d1099295921f1e6af1d147a8/predictions.json + probabilities_file: output/reports/attack/5f084df8d1099295921f1e6af1d147a8/probabilities.json + score_dict_file: output/reports/attack/5f084df8d1099295921f1e6af1d147a8/score_dict.json + test_labels_file: output/reports/attack/5f084df8d1099295921f1e6af1d147a8/test_labels.json + train_labels_file: output/reports/attack/5f084df8d1099295921f1e6af1d147a8/train_labels.json + model_dir: models + name: 5f084df8d1099295921f1e6af1d147a8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 5f084df8d1099295921f1e6af1d147a8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/5fb49454dafedb0d8710f133cff74951/params.yaml b/examples/security/kdd-nsl/output/reports/attack/5fb49454dafedb0d8710f133cff74951/params.yaml new file mode 100644 index 00000000..548d94ee --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/5fb49454dafedb0d8710f133cff74951/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.01 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 91548144c044e6ec024cb0287f892d5f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5fb49454dafedb0d8710f133cff74951/adv_predictions.json + adv_probabilities_file: output/reports/attack/5fb49454dafedb0d8710f133cff74951/adv_probabilities.json + attack_file: output/attacks/da937e766c8321876dfc2980f0682a69.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/5fb49454dafedb0d8710f133cff74951/params.yaml + predictions_file: output/reports/attack/5fb49454dafedb0d8710f133cff74951/predictions.json + probabilities_file: output/reports/attack/5fb49454dafedb0d8710f133cff74951/probabilities.json + score_dict_file: output/reports/attack/5fb49454dafedb0d8710f133cff74951/score_dict.json + test_labels_file: output/reports/attack/5fb49454dafedb0d8710f133cff74951/test_labels.json + train_labels_file: output/reports/attack/5fb49454dafedb0d8710f133cff74951/train_labels.json + model_dir: models + name: 5fb49454dafedb0d8710f133cff74951 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 5fb49454dafedb0d8710f133cff74951 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/6182325ee435bcf73903a4495de747ab/params.yaml b/examples/security/kdd-nsl/output/reports/attack/6182325ee435bcf73903a4495de747ab/params.yaml new file mode 100644 index 00000000..1811fdb7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/6182325ee435bcf73903a4495de747ab/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: ee289859245100feb6d9fb39d7e75168 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6182325ee435bcf73903a4495de747ab/adv_predictions.json + adv_probabilities_file: output/reports/attack/6182325ee435bcf73903a4495de747ab/adv_probabilities.json + attack_file: output/attacks/64585a708317370e3edf6f018417cc35.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/6182325ee435bcf73903a4495de747ab/params.yaml + predictions_file: output/reports/attack/6182325ee435bcf73903a4495de747ab/predictions.json + probabilities_file: output/reports/attack/6182325ee435bcf73903a4495de747ab/probabilities.json + score_dict_file: output/reports/attack/6182325ee435bcf73903a4495de747ab/score_dict.json + test_labels_file: output/reports/attack/6182325ee435bcf73903a4495de747ab/test_labels.json + train_labels_file: output/reports/attack/6182325ee435bcf73903a4495de747ab/train_labels.json + model_dir: models + name: 6182325ee435bcf73903a4495de747ab + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 6182325ee435bcf73903a4495de747ab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/62a469e7c3ce54124920ef2014645956/params.yaml b/examples/security/kdd-nsl/output/reports/attack/62a469e7c3ce54124920ef2014645956/params.yaml new file mode 100644 index 00000000..4ae03943 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/62a469e7c3ce54124920ef2014645956/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.3 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: b314e4c2a519544036b8a08f0338cf46 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/62a469e7c3ce54124920ef2014645956/adv_predictions.json + adv_probabilities_file: output/reports/attack/62a469e7c3ce54124920ef2014645956/adv_probabilities.json + attack_file: output/attacks/2e203d11fb4d0d8a0096eb91e98d6a62.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/62a469e7c3ce54124920ef2014645956/params.yaml + predictions_file: output/reports/attack/62a469e7c3ce54124920ef2014645956/predictions.json + probabilities_file: output/reports/attack/62a469e7c3ce54124920ef2014645956/probabilities.json + score_dict_file: output/reports/attack/62a469e7c3ce54124920ef2014645956/score_dict.json + test_labels_file: output/reports/attack/62a469e7c3ce54124920ef2014645956/test_labels.json + train_labels_file: output/reports/attack/62a469e7c3ce54124920ef2014645956/train_labels.json + model_dir: models + name: 62a469e7c3ce54124920ef2014645956 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 62a469e7c3ce54124920ef2014645956 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/63bb3f1cd187eaa6a89d27b910b6998a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/63bb3f1cd187eaa6a89d27b910b6998a/params.yaml new file mode 100644 index 00000000..0e80be34 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/63bb3f1cd187eaa6a89d27b910b6998a/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 75d715e6ad9e2a087ce4f4bc7b20cdd4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/63bb3f1cd187eaa6a89d27b910b6998a/adv_predictions.json + adv_probabilities_file: output/reports/attack/63bb3f1cd187eaa6a89d27b910b6998a/adv_probabilities.json + attack_file: output/attacks/c59af849fe38d7e1678966cb5298fdd2.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/63bb3f1cd187eaa6a89d27b910b6998a/params.yaml + predictions_file: output/reports/attack/63bb3f1cd187eaa6a89d27b910b6998a/predictions.json + probabilities_file: output/reports/attack/63bb3f1cd187eaa6a89d27b910b6998a/probabilities.json + score_dict_file: output/reports/attack/63bb3f1cd187eaa6a89d27b910b6998a/score_dict.json + test_labels_file: output/reports/attack/63bb3f1cd187eaa6a89d27b910b6998a/test_labels.json + train_labels_file: output/reports/attack/63bb3f1cd187eaa6a89d27b910b6998a/train_labels.json + model_dir: models + name: 63bb3f1cd187eaa6a89d27b910b6998a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 63bb3f1cd187eaa6a89d27b910b6998a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/63e97fefe4733435e50f59d74b387c4b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/63e97fefe4733435e50f59d74b387c4b/params.yaml new file mode 100644 index 00000000..efd22cef --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/63e97fefe4733435e50f59d74b387c4b/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.001 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 75109df416a35d5b3c1c28679724debb +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/63e97fefe4733435e50f59d74b387c4b/adv_predictions.json + adv_probabilities_file: output/reports/attack/63e97fefe4733435e50f59d74b387c4b/adv_probabilities.json + attack_file: output/attacks/13eee3b2916249e4864e4ec036542a3c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/63e97fefe4733435e50f59d74b387c4b/params.yaml + predictions_file: output/reports/attack/63e97fefe4733435e50f59d74b387c4b/predictions.json + probabilities_file: output/reports/attack/63e97fefe4733435e50f59d74b387c4b/probabilities.json + score_dict_file: output/reports/attack/63e97fefe4733435e50f59d74b387c4b/score_dict.json + test_labels_file: output/reports/attack/63e97fefe4733435e50f59d74b387c4b/test_labels.json + train_labels_file: output/reports/attack/63e97fefe4733435e50f59d74b387c4b/train_labels.json + model_dir: models + name: 63e97fefe4733435e50f59d74b387c4b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 63e97fefe4733435e50f59d74b387c4b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/65985c455ffa19f3c600c2e416643e37/params.yaml b/examples/security/kdd-nsl/output/reports/attack/65985c455ffa19f3c600c2e416643e37/params.yaml new file mode 100644 index 00000000..ab10b3f0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/65985c455ffa19f3c600c2e416643e37/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.5 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: d82d5d13bc7f3616c1960f64689190c8 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/65985c455ffa19f3c600c2e416643e37/adv_predictions.json + adv_probabilities_file: output/reports/attack/65985c455ffa19f3c600c2e416643e37/adv_probabilities.json + attack_file: output/attacks/d1390f311b2b2dd2568ad42b3676a879.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/65985c455ffa19f3c600c2e416643e37/params.yaml + predictions_file: output/reports/attack/65985c455ffa19f3c600c2e416643e37/predictions.json + probabilities_file: output/reports/attack/65985c455ffa19f3c600c2e416643e37/probabilities.json + score_dict_file: output/reports/attack/65985c455ffa19f3c600c2e416643e37/score_dict.json + test_labels_file: output/reports/attack/65985c455ffa19f3c600c2e416643e37/test_labels.json + train_labels_file: output/reports/attack/65985c455ffa19f3c600c2e416643e37/train_labels.json + model_dir: models + name: 65985c455ffa19f3c600c2e416643e37 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 65985c455ffa19f3c600c2e416643e37 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/65a53fa4874a9a7647b5427156dcf53a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/65a53fa4874a9a7647b5427156dcf53a/params.yaml new file mode 100644 index 00000000..318b5675 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/65a53fa4874a9a7647b5427156dcf53a/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 8d26a17306a27145a14c79eca183d241 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/65a53fa4874a9a7647b5427156dcf53a/adv_predictions.json + adv_probabilities_file: output/reports/attack/65a53fa4874a9a7647b5427156dcf53a/adv_probabilities.json + attack_file: output/attacks/657518b2daec3e00aec1b3e0baaacd7d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/65a53fa4874a9a7647b5427156dcf53a/params.yaml + predictions_file: output/reports/attack/65a53fa4874a9a7647b5427156dcf53a/predictions.json + probabilities_file: output/reports/attack/65a53fa4874a9a7647b5427156dcf53a/probabilities.json + score_dict_file: output/reports/attack/65a53fa4874a9a7647b5427156dcf53a/score_dict.json + test_labels_file: output/reports/attack/65a53fa4874a9a7647b5427156dcf53a/test_labels.json + train_labels_file: output/reports/attack/65a53fa4874a9a7647b5427156dcf53a/train_labels.json + model_dir: models + name: 65a53fa4874a9a7647b5427156dcf53a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 65a53fa4874a9a7647b5427156dcf53a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/66945f2ecd9b5fd08ec07417c4081b91/params.yaml b/examples/security/kdd-nsl/output/reports/attack/66945f2ecd9b5fd08ec07417c4081b91/params.yaml new file mode 100644 index 00000000..feead427 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/66945f2ecd9b5fd08ec07417c4081b91/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.5 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: a3a1d2e32975de95fdb07ddc4e6b138a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/66945f2ecd9b5fd08ec07417c4081b91/adv_predictions.json + adv_probabilities_file: output/reports/attack/66945f2ecd9b5fd08ec07417c4081b91/adv_probabilities.json + attack_file: output/attacks/7d6ae86dcdbcb283437628babb1881ed.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/66945f2ecd9b5fd08ec07417c4081b91/params.yaml + predictions_file: output/reports/attack/66945f2ecd9b5fd08ec07417c4081b91/predictions.json + probabilities_file: output/reports/attack/66945f2ecd9b5fd08ec07417c4081b91/probabilities.json + score_dict_file: output/reports/attack/66945f2ecd9b5fd08ec07417c4081b91/score_dict.json + test_labels_file: output/reports/attack/66945f2ecd9b5fd08ec07417c4081b91/test_labels.json + train_labels_file: output/reports/attack/66945f2ecd9b5fd08ec07417c4081b91/train_labels.json + model_dir: models + name: 66945f2ecd9b5fd08ec07417c4081b91 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 66945f2ecd9b5fd08ec07417c4081b91 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/685ecebfa7164f2f17683f88e37ef016/params.yaml b/examples/security/kdd-nsl/output/reports/attack/685ecebfa7164f2f17683f88e37ef016/params.yaml new file mode 100644 index 00000000..883461d2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/685ecebfa7164f2f17683f88e37ef016/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 72d09866580a87bc3f8f5e3f08867a74 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/685ecebfa7164f2f17683f88e37ef016/adv_predictions.json + adv_probabilities_file: output/reports/attack/685ecebfa7164f2f17683f88e37ef016/adv_probabilities.json + attack_file: output/attacks/2d6e1c051f3697f1f117ccd77b932495.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/685ecebfa7164f2f17683f88e37ef016/params.yaml + predictions_file: output/reports/attack/685ecebfa7164f2f17683f88e37ef016/predictions.json + probabilities_file: output/reports/attack/685ecebfa7164f2f17683f88e37ef016/probabilities.json + score_dict_file: output/reports/attack/685ecebfa7164f2f17683f88e37ef016/score_dict.json + test_labels_file: output/reports/attack/685ecebfa7164f2f17683f88e37ef016/test_labels.json + train_labels_file: output/reports/attack/685ecebfa7164f2f17683f88e37ef016/train_labels.json + model_dir: models + name: 685ecebfa7164f2f17683f88e37ef016 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 685ecebfa7164f2f17683f88e37ef016 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/6b03ed3171219f41a7d12038da6f40b2/params.yaml b/examples/security/kdd-nsl/output/reports/attack/6b03ed3171219f41a7d12038da6f40b2/params.yaml new file mode 100644 index 00000000..5844dc16 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/6b03ed3171219f41a7d12038da6f40b2/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: b5c96130bfbef362d1353e1c997dc539 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6b03ed3171219f41a7d12038da6f40b2/adv_predictions.json + adv_probabilities_file: output/reports/attack/6b03ed3171219f41a7d12038da6f40b2/adv_probabilities.json + attack_file: output/attacks/e07fefc39cb477c50be79fa0d67e2a23.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/6b03ed3171219f41a7d12038da6f40b2/params.yaml + predictions_file: output/reports/attack/6b03ed3171219f41a7d12038da6f40b2/predictions.json + probabilities_file: output/reports/attack/6b03ed3171219f41a7d12038da6f40b2/probabilities.json + score_dict_file: output/reports/attack/6b03ed3171219f41a7d12038da6f40b2/score_dict.json + test_labels_file: output/reports/attack/6b03ed3171219f41a7d12038da6f40b2/test_labels.json + train_labels_file: output/reports/attack/6b03ed3171219f41a7d12038da6f40b2/train_labels.json + model_dir: models + name: 6b03ed3171219f41a7d12038da6f40b2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 6b03ed3171219f41a7d12038da6f40b2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/6be7ee9f6f24ce201d7daaac6101300c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/6be7ee9f6f24ce201d7daaac6101300c/params.yaml new file mode 100644 index 00000000..c04cd37b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/6be7ee9f6f24ce201d7daaac6101300c/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.01 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 1749d047b77ddfb1124e8b00df9cea93 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6be7ee9f6f24ce201d7daaac6101300c/adv_predictions.json + adv_probabilities_file: output/reports/attack/6be7ee9f6f24ce201d7daaac6101300c/adv_probabilities.json + attack_file: output/attacks/606e212c779856fb879e4626f6cd1249.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/6be7ee9f6f24ce201d7daaac6101300c/params.yaml + predictions_file: output/reports/attack/6be7ee9f6f24ce201d7daaac6101300c/predictions.json + probabilities_file: output/reports/attack/6be7ee9f6f24ce201d7daaac6101300c/probabilities.json + score_dict_file: output/reports/attack/6be7ee9f6f24ce201d7daaac6101300c/score_dict.json + test_labels_file: output/reports/attack/6be7ee9f6f24ce201d7daaac6101300c/test_labels.json + train_labels_file: output/reports/attack/6be7ee9f6f24ce201d7daaac6101300c/train_labels.json + model_dir: models + name: 6be7ee9f6f24ce201d7daaac6101300c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 6be7ee9f6f24ce201d7daaac6101300c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/6cd27fe28365846427f08f66eb782c52/params.yaml b/examples/security/kdd-nsl/output/reports/attack/6cd27fe28365846427f08f66eb782c52/params.yaml new file mode 100644 index 00000000..48224e22 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/6cd27fe28365846427f08f66eb782c52/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 1 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 081b830b2f00eca8eb69d94b421dcbb0 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6cd27fe28365846427f08f66eb782c52/adv_predictions.json + adv_probabilities_file: output/reports/attack/6cd27fe28365846427f08f66eb782c52/adv_probabilities.json + attack_file: output/attacks/88e7c9afd7a43a53775b1b43bd980419.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/6cd27fe28365846427f08f66eb782c52/params.yaml + predictions_file: output/reports/attack/6cd27fe28365846427f08f66eb782c52/predictions.json + probabilities_file: output/reports/attack/6cd27fe28365846427f08f66eb782c52/probabilities.json + score_dict_file: output/reports/attack/6cd27fe28365846427f08f66eb782c52/score_dict.json + test_labels_file: output/reports/attack/6cd27fe28365846427f08f66eb782c52/test_labels.json + train_labels_file: output/reports/attack/6cd27fe28365846427f08f66eb782c52/train_labels.json + model_dir: models + name: 6cd27fe28365846427f08f66eb782c52 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 6cd27fe28365846427f08f66eb782c52 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/6d2742c3a49d1691c0142404258a227c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/6d2742c3a49d1691c0142404258a227c/params.yaml new file mode 100644 index 00000000..e092ff29 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/6d2742c3a49d1691c0142404258a227c/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.01 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 0723da4419fed18ec5136155e4f19721 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6d2742c3a49d1691c0142404258a227c/adv_predictions.json + adv_probabilities_file: output/reports/attack/6d2742c3a49d1691c0142404258a227c/adv_probabilities.json + attack_file: output/attacks/29c2a0955079586ca866e5b139190c2d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/6d2742c3a49d1691c0142404258a227c/params.yaml + predictions_file: output/reports/attack/6d2742c3a49d1691c0142404258a227c/predictions.json + probabilities_file: output/reports/attack/6d2742c3a49d1691c0142404258a227c/probabilities.json + score_dict_file: output/reports/attack/6d2742c3a49d1691c0142404258a227c/score_dict.json + test_labels_file: output/reports/attack/6d2742c3a49d1691c0142404258a227c/test_labels.json + train_labels_file: output/reports/attack/6d2742c3a49d1691c0142404258a227c/train_labels.json + model_dir: models + name: 6d2742c3a49d1691c0142404258a227c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 6d2742c3a49d1691c0142404258a227c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/6da06a2ef6ff96d720dc6b86fab3cd46/params.yaml b/examples/security/kdd-nsl/output/reports/attack/6da06a2ef6ff96d720dc6b86fab3cd46/params.yaml new file mode 100644 index 00000000..03aada99 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/6da06a2ef6ff96d720dc6b86fab3cd46/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.01 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: d6aea25919e2529af9e88b54932f90e6 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6da06a2ef6ff96d720dc6b86fab3cd46/adv_predictions.json + adv_probabilities_file: output/reports/attack/6da06a2ef6ff96d720dc6b86fab3cd46/adv_probabilities.json + attack_file: output/attacks/52f3ef8722fed9f7f7fc57027c56829b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/6da06a2ef6ff96d720dc6b86fab3cd46/params.yaml + predictions_file: output/reports/attack/6da06a2ef6ff96d720dc6b86fab3cd46/predictions.json + probabilities_file: output/reports/attack/6da06a2ef6ff96d720dc6b86fab3cd46/probabilities.json + score_dict_file: output/reports/attack/6da06a2ef6ff96d720dc6b86fab3cd46/score_dict.json + test_labels_file: output/reports/attack/6da06a2ef6ff96d720dc6b86fab3cd46/test_labels.json + train_labels_file: output/reports/attack/6da06a2ef6ff96d720dc6b86fab3cd46/train_labels.json + model_dir: models + name: 6da06a2ef6ff96d720dc6b86fab3cd46 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 6da06a2ef6ff96d720dc6b86fab3cd46 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/6f15f1476cf50a0bb1fc7e4bc3e21f10/params.yaml b/examples/security/kdd-nsl/output/reports/attack/6f15f1476cf50a0bb1fc7e4bc3e21f10/params.yaml new file mode 100644 index 00000000..c62b5487 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/6f15f1476cf50a0bb1fc7e4bc3e21f10/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: f75626eea1984e4194471690a743e1b6 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6f15f1476cf50a0bb1fc7e4bc3e21f10/adv_predictions.json + adv_probabilities_file: output/reports/attack/6f15f1476cf50a0bb1fc7e4bc3e21f10/adv_probabilities.json + attack_file: output/attacks/326e916e068c558b9c8a061527d1b023.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/6f15f1476cf50a0bb1fc7e4bc3e21f10/params.yaml + predictions_file: output/reports/attack/6f15f1476cf50a0bb1fc7e4bc3e21f10/predictions.json + probabilities_file: output/reports/attack/6f15f1476cf50a0bb1fc7e4bc3e21f10/probabilities.json + score_dict_file: output/reports/attack/6f15f1476cf50a0bb1fc7e4bc3e21f10/score_dict.json + test_labels_file: output/reports/attack/6f15f1476cf50a0bb1fc7e4bc3e21f10/test_labels.json + train_labels_file: output/reports/attack/6f15f1476cf50a0bb1fc7e4bc3e21f10/train_labels.json + model_dir: models + name: 6f15f1476cf50a0bb1fc7e4bc3e21f10 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 6f15f1476cf50a0bb1fc7e4bc3e21f10 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/6faa4c14a51b017b04dcb7e718884d92/params.yaml b/examples/security/kdd-nsl/output/reports/attack/6faa4c14a51b017b04dcb7e718884d92/params.yaml new file mode 100644 index 00000000..cc6cc42a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/6faa4c14a51b017b04dcb7e718884d92/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 8a6830319e29e02326faef7d736438bd +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6faa4c14a51b017b04dcb7e718884d92/adv_predictions.json + adv_probabilities_file: output/reports/attack/6faa4c14a51b017b04dcb7e718884d92/adv_probabilities.json + attack_file: output/attacks/f297d4768ed3507fab42d29a26386170.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/6faa4c14a51b017b04dcb7e718884d92/params.yaml + predictions_file: output/reports/attack/6faa4c14a51b017b04dcb7e718884d92/predictions.json + probabilities_file: output/reports/attack/6faa4c14a51b017b04dcb7e718884d92/probabilities.json + score_dict_file: output/reports/attack/6faa4c14a51b017b04dcb7e718884d92/score_dict.json + test_labels_file: output/reports/attack/6faa4c14a51b017b04dcb7e718884d92/test_labels.json + train_labels_file: output/reports/attack/6faa4c14a51b017b04dcb7e718884d92/train_labels.json + model_dir: models + name: 6faa4c14a51b017b04dcb7e718884d92 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 6faa4c14a51b017b04dcb7e718884d92 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/6fca39c4873a8b9b1dd9460443cfef1f/params.yaml b/examples/security/kdd-nsl/output/reports/attack/6fca39c4873a8b9b1dd9460443cfef1f/params.yaml new file mode 100644 index 00000000..b9fe09ce --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/6fca39c4873a8b9b1dd9460443cfef1f/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.5 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 1c1cbe9753a1645fdbd19b94ecd2a17c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6fca39c4873a8b9b1dd9460443cfef1f/adv_predictions.json + adv_probabilities_file: output/reports/attack/6fca39c4873a8b9b1dd9460443cfef1f/adv_probabilities.json + attack_file: output/attacks/3bd32d94c9f2229294c66cc4d140b894.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/6fca39c4873a8b9b1dd9460443cfef1f/params.yaml + predictions_file: output/reports/attack/6fca39c4873a8b9b1dd9460443cfef1f/predictions.json + probabilities_file: output/reports/attack/6fca39c4873a8b9b1dd9460443cfef1f/probabilities.json + score_dict_file: output/reports/attack/6fca39c4873a8b9b1dd9460443cfef1f/score_dict.json + test_labels_file: output/reports/attack/6fca39c4873a8b9b1dd9460443cfef1f/test_labels.json + train_labels_file: output/reports/attack/6fca39c4873a8b9b1dd9460443cfef1f/train_labels.json + model_dir: models + name: 6fca39c4873a8b9b1dd9460443cfef1f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 6fca39c4873a8b9b1dd9460443cfef1f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/6fdb550d67e543a30001827afd7327e6/params.yaml b/examples/security/kdd-nsl/output/reports/attack/6fdb550d67e543a30001827afd7327e6/params.yaml new file mode 100644 index 00000000..beea89e3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/6fdb550d67e543a30001827afd7327e6/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.3 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: d7f098812e08683e5d067586abd080d5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6fdb550d67e543a30001827afd7327e6/adv_predictions.json + adv_probabilities_file: output/reports/attack/6fdb550d67e543a30001827afd7327e6/adv_probabilities.json + attack_file: output/attacks/fad28faf6dfb6859842a7d384ecda9df.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/6fdb550d67e543a30001827afd7327e6/params.yaml + predictions_file: output/reports/attack/6fdb550d67e543a30001827afd7327e6/predictions.json + probabilities_file: output/reports/attack/6fdb550d67e543a30001827afd7327e6/probabilities.json + score_dict_file: output/reports/attack/6fdb550d67e543a30001827afd7327e6/score_dict.json + test_labels_file: output/reports/attack/6fdb550d67e543a30001827afd7327e6/test_labels.json + train_labels_file: output/reports/attack/6fdb550d67e543a30001827afd7327e6/train_labels.json + model_dir: models + name: 6fdb550d67e543a30001827afd7327e6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 6fdb550d67e543a30001827afd7327e6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/6ff23b3944c6bb8dfef09d9fb023701b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/6ff23b3944c6bb8dfef09d9fb023701b/params.yaml new file mode 100644 index 00000000..1c29a725 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/6ff23b3944c6bb8dfef09d9fb023701b/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.01 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 3df9134cbda9ecf1050a92cacc6a127e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6ff23b3944c6bb8dfef09d9fb023701b/adv_predictions.json + adv_probabilities_file: output/reports/attack/6ff23b3944c6bb8dfef09d9fb023701b/adv_probabilities.json + attack_file: output/attacks/a55dd7a41485d51ae4efbd05f2690f38.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/6ff23b3944c6bb8dfef09d9fb023701b/params.yaml + predictions_file: output/reports/attack/6ff23b3944c6bb8dfef09d9fb023701b/predictions.json + probabilities_file: output/reports/attack/6ff23b3944c6bb8dfef09d9fb023701b/probabilities.json + score_dict_file: output/reports/attack/6ff23b3944c6bb8dfef09d9fb023701b/score_dict.json + test_labels_file: output/reports/attack/6ff23b3944c6bb8dfef09d9fb023701b/test_labels.json + train_labels_file: output/reports/attack/6ff23b3944c6bb8dfef09d9fb023701b/train_labels.json + model_dir: models + name: 6ff23b3944c6bb8dfef09d9fb023701b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 6ff23b3944c6bb8dfef09d9fb023701b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/71120b3830da084d676c68b3fa74b1ba/params.yaml b/examples/security/kdd-nsl/output/reports/attack/71120b3830da084d676c68b3fa74b1ba/params.yaml new file mode 100644 index 00000000..4573de25 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/71120b3830da084d676c68b3fa74b1ba/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.5 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 3fffe1ddc1c8a42e3d0544ad04d52dcc +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/71120b3830da084d676c68b3fa74b1ba/adv_predictions.json + adv_probabilities_file: output/reports/attack/71120b3830da084d676c68b3fa74b1ba/adv_probabilities.json + attack_file: output/attacks/fc09d2c1b74a5bfa6c9e2e72a12c7304.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/71120b3830da084d676c68b3fa74b1ba/params.yaml + predictions_file: output/reports/attack/71120b3830da084d676c68b3fa74b1ba/predictions.json + probabilities_file: output/reports/attack/71120b3830da084d676c68b3fa74b1ba/probabilities.json + score_dict_file: output/reports/attack/71120b3830da084d676c68b3fa74b1ba/score_dict.json + test_labels_file: output/reports/attack/71120b3830da084d676c68b3fa74b1ba/test_labels.json + train_labels_file: output/reports/attack/71120b3830da084d676c68b3fa74b1ba/train_labels.json + model_dir: models + name: 71120b3830da084d676c68b3fa74b1ba + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 71120b3830da084d676c68b3fa74b1ba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/7202a6be7b667596be5389679d7e2df3/params.yaml b/examples/security/kdd-nsl/output/reports/attack/7202a6be7b667596be5389679d7e2df3/params.yaml new file mode 100644 index 00000000..f80f0926 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/7202a6be7b667596be5389679d7e2df3/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 209f3844fbe95ab5a2920886653254d9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/7202a6be7b667596be5389679d7e2df3/adv_predictions.json + adv_probabilities_file: output/reports/attack/7202a6be7b667596be5389679d7e2df3/adv_probabilities.json + attack_file: output/attacks/e2a204e8516c1484de2f47dd9127a049.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/7202a6be7b667596be5389679d7e2df3/params.yaml + predictions_file: output/reports/attack/7202a6be7b667596be5389679d7e2df3/predictions.json + probabilities_file: output/reports/attack/7202a6be7b667596be5389679d7e2df3/probabilities.json + score_dict_file: output/reports/attack/7202a6be7b667596be5389679d7e2df3/score_dict.json + test_labels_file: output/reports/attack/7202a6be7b667596be5389679d7e2df3/test_labels.json + train_labels_file: output/reports/attack/7202a6be7b667596be5389679d7e2df3/train_labels.json + model_dir: models + name: 7202a6be7b667596be5389679d7e2df3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 7202a6be7b667596be5389679d7e2df3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/732a7d2b4ce5645d35a63644ffd5f1b8/params.yaml b/examples/security/kdd-nsl/output/reports/attack/732a7d2b4ce5645d35a63644ffd5f1b8/params.yaml new file mode 100644 index 00000000..f562326d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/732a7d2b4ce5645d35a63644ffd5f1b8/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.001 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: a81245e399250aaba1006fe4284b269b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/732a7d2b4ce5645d35a63644ffd5f1b8/adv_predictions.json + adv_probabilities_file: output/reports/attack/732a7d2b4ce5645d35a63644ffd5f1b8/adv_probabilities.json + attack_file: output/attacks/0a03c4564e17cf3138644d53e5c3bfe8.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/732a7d2b4ce5645d35a63644ffd5f1b8/params.yaml + predictions_file: output/reports/attack/732a7d2b4ce5645d35a63644ffd5f1b8/predictions.json + probabilities_file: output/reports/attack/732a7d2b4ce5645d35a63644ffd5f1b8/probabilities.json + score_dict_file: output/reports/attack/732a7d2b4ce5645d35a63644ffd5f1b8/score_dict.json + test_labels_file: output/reports/attack/732a7d2b4ce5645d35a63644ffd5f1b8/test_labels.json + train_labels_file: output/reports/attack/732a7d2b4ce5645d35a63644ffd5f1b8/train_labels.json + model_dir: models + name: 732a7d2b4ce5645d35a63644ffd5f1b8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 732a7d2b4ce5645d35a63644ffd5f1b8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/7368efdaf75b5ee836a9b3656e1e4fb8/params.yaml b/examples/security/kdd-nsl/output/reports/attack/7368efdaf75b5ee836a9b3656e1e4fb8/params.yaml new file mode 100644 index 00000000..f1270bc4 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/7368efdaf75b5ee836a9b3656e1e4fb8/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.01 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 6da4e362cc625b4a5ba137de22896656 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/7368efdaf75b5ee836a9b3656e1e4fb8/adv_predictions.json + adv_probabilities_file: output/reports/attack/7368efdaf75b5ee836a9b3656e1e4fb8/adv_probabilities.json + attack_file: output/attacks/876acb90412c97e4d702e90c6b3154e0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/7368efdaf75b5ee836a9b3656e1e4fb8/params.yaml + predictions_file: output/reports/attack/7368efdaf75b5ee836a9b3656e1e4fb8/predictions.json + probabilities_file: output/reports/attack/7368efdaf75b5ee836a9b3656e1e4fb8/probabilities.json + score_dict_file: output/reports/attack/7368efdaf75b5ee836a9b3656e1e4fb8/score_dict.json + test_labels_file: output/reports/attack/7368efdaf75b5ee836a9b3656e1e4fb8/test_labels.json + train_labels_file: output/reports/attack/7368efdaf75b5ee836a9b3656e1e4fb8/train_labels.json + model_dir: models + name: 7368efdaf75b5ee836a9b3656e1e4fb8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 7368efdaf75b5ee836a9b3656e1e4fb8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/73ab82d47d53e2052177b75311ad50fc/params.yaml b/examples/security/kdd-nsl/output/reports/attack/73ab82d47d53e2052177b75311ad50fc/params.yaml new file mode 100644 index 00000000..b2eaca1f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/73ab82d47d53e2052177b75311ad50fc/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: d745c1073a4310f8b8ba2a83222601f2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/73ab82d47d53e2052177b75311ad50fc/adv_predictions.json + adv_probabilities_file: output/reports/attack/73ab82d47d53e2052177b75311ad50fc/adv_probabilities.json + attack_file: output/attacks/236b31640c9a527eeba2563943dadad7.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/73ab82d47d53e2052177b75311ad50fc/params.yaml + predictions_file: output/reports/attack/73ab82d47d53e2052177b75311ad50fc/predictions.json + probabilities_file: output/reports/attack/73ab82d47d53e2052177b75311ad50fc/probabilities.json + score_dict_file: output/reports/attack/73ab82d47d53e2052177b75311ad50fc/score_dict.json + test_labels_file: output/reports/attack/73ab82d47d53e2052177b75311ad50fc/test_labels.json + train_labels_file: output/reports/attack/73ab82d47d53e2052177b75311ad50fc/train_labels.json + model_dir: models + name: 73ab82d47d53e2052177b75311ad50fc + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 73ab82d47d53e2052177b75311ad50fc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/7503355b251381b7d14ff8204b962122/params.yaml b/examples/security/kdd-nsl/output/reports/attack/7503355b251381b7d14ff8204b962122/params.yaml new file mode 100644 index 00000000..421ed550 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/7503355b251381b7d14ff8204b962122/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: ce4ab506eeb983258f183ccd752765f7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/7503355b251381b7d14ff8204b962122/adv_predictions.json + adv_probabilities_file: output/reports/attack/7503355b251381b7d14ff8204b962122/adv_probabilities.json + attack_file: output/attacks/a67c681abf8f19d54f5942924d1f1175.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/7503355b251381b7d14ff8204b962122/params.yaml + predictions_file: output/reports/attack/7503355b251381b7d14ff8204b962122/predictions.json + probabilities_file: output/reports/attack/7503355b251381b7d14ff8204b962122/probabilities.json + score_dict_file: output/reports/attack/7503355b251381b7d14ff8204b962122/score_dict.json + test_labels_file: output/reports/attack/7503355b251381b7d14ff8204b962122/test_labels.json + train_labels_file: output/reports/attack/7503355b251381b7d14ff8204b962122/train_labels.json + model_dir: models + name: 7503355b251381b7d14ff8204b962122 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 7503355b251381b7d14ff8204b962122 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/76a15c9ed305f09170e0167c1140b1f4/params.yaml b/examples/security/kdd-nsl/output/reports/attack/76a15c9ed305f09170e0167c1140b1f4/params.yaml new file mode 100644 index 00000000..44bd6e2e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/76a15c9ed305f09170e0167c1140b1f4/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.5 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: c26b7134487fbc3602fdfb06d79159b7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/76a15c9ed305f09170e0167c1140b1f4/adv_predictions.json + adv_probabilities_file: output/reports/attack/76a15c9ed305f09170e0167c1140b1f4/adv_probabilities.json + attack_file: output/attacks/4598f192fa9082446c3ef54224694431.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/76a15c9ed305f09170e0167c1140b1f4/params.yaml + predictions_file: output/reports/attack/76a15c9ed305f09170e0167c1140b1f4/predictions.json + probabilities_file: output/reports/attack/76a15c9ed305f09170e0167c1140b1f4/probabilities.json + score_dict_file: output/reports/attack/76a15c9ed305f09170e0167c1140b1f4/score_dict.json + test_labels_file: output/reports/attack/76a15c9ed305f09170e0167c1140b1f4/test_labels.json + train_labels_file: output/reports/attack/76a15c9ed305f09170e0167c1140b1f4/train_labels.json + model_dir: models + name: 76a15c9ed305f09170e0167c1140b1f4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 76a15c9ed305f09170e0167c1140b1f4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/76a5dacf9ef5a7d2116e0bcbcdc89cef/params.yaml b/examples/security/kdd-nsl/output/reports/attack/76a5dacf9ef5a7d2116e0bcbcdc89cef/params.yaml new file mode 100644 index 00000000..43eac50d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/76a5dacf9ef5a7d2116e0bcbcdc89cef/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.3 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: d59c55d5f3cbadaa9a31348721160d02 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/76a5dacf9ef5a7d2116e0bcbcdc89cef/adv_predictions.json + adv_probabilities_file: output/reports/attack/76a5dacf9ef5a7d2116e0bcbcdc89cef/adv_probabilities.json + attack_file: output/attacks/c624edf6add1a44995214d7ce268362e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/76a5dacf9ef5a7d2116e0bcbcdc89cef/params.yaml + predictions_file: output/reports/attack/76a5dacf9ef5a7d2116e0bcbcdc89cef/predictions.json + probabilities_file: output/reports/attack/76a5dacf9ef5a7d2116e0bcbcdc89cef/probabilities.json + score_dict_file: output/reports/attack/76a5dacf9ef5a7d2116e0bcbcdc89cef/score_dict.json + test_labels_file: output/reports/attack/76a5dacf9ef5a7d2116e0bcbcdc89cef/test_labels.json + train_labels_file: output/reports/attack/76a5dacf9ef5a7d2116e0bcbcdc89cef/train_labels.json + model_dir: models + name: 76a5dacf9ef5a7d2116e0bcbcdc89cef + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 76a5dacf9ef5a7d2116e0bcbcdc89cef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/780e139ee8948b9c00f88da270091344/params.yaml b/examples/security/kdd-nsl/output/reports/attack/780e139ee8948b9c00f88da270091344/params.yaml new file mode 100644 index 00000000..61ffa0ee --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/780e139ee8948b9c00f88da270091344/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 06d75d4bfc525b0692e6e780037cc55f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/780e139ee8948b9c00f88da270091344/adv_predictions.json + adv_probabilities_file: output/reports/attack/780e139ee8948b9c00f88da270091344/adv_probabilities.json + attack_file: output/attacks/459a387b9919524830c39d72dea4a5dc.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/780e139ee8948b9c00f88da270091344/params.yaml + predictions_file: output/reports/attack/780e139ee8948b9c00f88da270091344/predictions.json + probabilities_file: output/reports/attack/780e139ee8948b9c00f88da270091344/probabilities.json + score_dict_file: output/reports/attack/780e139ee8948b9c00f88da270091344/score_dict.json + test_labels_file: output/reports/attack/780e139ee8948b9c00f88da270091344/test_labels.json + train_labels_file: output/reports/attack/780e139ee8948b9c00f88da270091344/train_labels.json + model_dir: models + name: 780e139ee8948b9c00f88da270091344 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 780e139ee8948b9c00f88da270091344 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/797044e719ec52c431f51c7997fe519e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/797044e719ec52c431f51c7997fe519e/params.yaml new file mode 100644 index 00000000..3e78df95 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/797044e719ec52c431f51c7997fe519e/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.5 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 63f6f30e4b384e4e5f9159be07a90313 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/797044e719ec52c431f51c7997fe519e/adv_predictions.json + adv_probabilities_file: output/reports/attack/797044e719ec52c431f51c7997fe519e/adv_probabilities.json + attack_file: output/attacks/5db73e6f5b8b433c75d957f8c6f14c44.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/797044e719ec52c431f51c7997fe519e/params.yaml + predictions_file: output/reports/attack/797044e719ec52c431f51c7997fe519e/predictions.json + probabilities_file: output/reports/attack/797044e719ec52c431f51c7997fe519e/probabilities.json + score_dict_file: output/reports/attack/797044e719ec52c431f51c7997fe519e/score_dict.json + test_labels_file: output/reports/attack/797044e719ec52c431f51c7997fe519e/test_labels.json + train_labels_file: output/reports/attack/797044e719ec52c431f51c7997fe519e/train_labels.json + model_dir: models + name: 797044e719ec52c431f51c7997fe519e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 797044e719ec52c431f51c7997fe519e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/79ecdeb3fbacbcdfdd486e0bf2fafce8/params.yaml b/examples/security/kdd-nsl/output/reports/attack/79ecdeb3fbacbcdfdd486e0bf2fafce8/params.yaml new file mode 100644 index 00000000..204de4f3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/79ecdeb3fbacbcdfdd486e0bf2fafce8/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 36cd7be64989af7bb4f8919f147decd3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/79ecdeb3fbacbcdfdd486e0bf2fafce8/adv_predictions.json + adv_probabilities_file: output/reports/attack/79ecdeb3fbacbcdfdd486e0bf2fafce8/adv_probabilities.json + attack_file: output/attacks/140e251f14d68cd18c84836026d15acb.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/79ecdeb3fbacbcdfdd486e0bf2fafce8/params.yaml + predictions_file: output/reports/attack/79ecdeb3fbacbcdfdd486e0bf2fafce8/predictions.json + probabilities_file: output/reports/attack/79ecdeb3fbacbcdfdd486e0bf2fafce8/probabilities.json + score_dict_file: output/reports/attack/79ecdeb3fbacbcdfdd486e0bf2fafce8/score_dict.json + test_labels_file: output/reports/attack/79ecdeb3fbacbcdfdd486e0bf2fafce8/test_labels.json + train_labels_file: output/reports/attack/79ecdeb3fbacbcdfdd486e0bf2fafce8/train_labels.json + model_dir: models + name: 79ecdeb3fbacbcdfdd486e0bf2fafce8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 79ecdeb3fbacbcdfdd486e0bf2fafce8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/79edccb94006f8074d53894024252094/params.yaml b/examples/security/kdd-nsl/output/reports/attack/79edccb94006f8074d53894024252094/params.yaml new file mode 100644 index 00000000..b2e224cb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/79edccb94006f8074d53894024252094/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.1 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 8e4b464fd95e19deca5b188be40fb8ba +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/79edccb94006f8074d53894024252094/adv_predictions.json + adv_probabilities_file: output/reports/attack/79edccb94006f8074d53894024252094/adv_probabilities.json + attack_file: output/attacks/72479adeae9efaae85a3f0b3469092ce.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/79edccb94006f8074d53894024252094/params.yaml + predictions_file: output/reports/attack/79edccb94006f8074d53894024252094/predictions.json + probabilities_file: output/reports/attack/79edccb94006f8074d53894024252094/probabilities.json + score_dict_file: output/reports/attack/79edccb94006f8074d53894024252094/score_dict.json + test_labels_file: output/reports/attack/79edccb94006f8074d53894024252094/test_labels.json + train_labels_file: output/reports/attack/79edccb94006f8074d53894024252094/train_labels.json + model_dir: models + name: 79edccb94006f8074d53894024252094 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 79edccb94006f8074d53894024252094 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/7a9185c71f18a56a76ea8774c383bc12/params.yaml b/examples/security/kdd-nsl/output/reports/attack/7a9185c71f18a56a76ea8774c383bc12/params.yaml new file mode 100644 index 00000000..ef8a2bf9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/7a9185c71f18a56a76ea8774c383bc12/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 70480ce09787c749d4a6abf62aa505d4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/7a9185c71f18a56a76ea8774c383bc12/adv_predictions.json + adv_probabilities_file: output/reports/attack/7a9185c71f18a56a76ea8774c383bc12/adv_probabilities.json + attack_file: output/attacks/19364e940219b9b62cfa75cc16d31002.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/7a9185c71f18a56a76ea8774c383bc12/params.yaml + predictions_file: output/reports/attack/7a9185c71f18a56a76ea8774c383bc12/predictions.json + probabilities_file: output/reports/attack/7a9185c71f18a56a76ea8774c383bc12/probabilities.json + score_dict_file: output/reports/attack/7a9185c71f18a56a76ea8774c383bc12/score_dict.json + test_labels_file: output/reports/attack/7a9185c71f18a56a76ea8774c383bc12/test_labels.json + train_labels_file: output/reports/attack/7a9185c71f18a56a76ea8774c383bc12/train_labels.json + model_dir: models + name: 7a9185c71f18a56a76ea8774c383bc12 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 7a9185c71f18a56a76ea8774c383bc12 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/7b4f2360cd169c4a10b3d8ec752478df/params.yaml b/examples/security/kdd-nsl/output/reports/attack/7b4f2360cd169c4a10b3d8ec752478df/params.yaml new file mode 100644 index 00000000..b48568a7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/7b4f2360cd169c4a10b3d8ec752478df/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.5 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 7172cd17236b7dc30bea0ea514887186 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/7b4f2360cd169c4a10b3d8ec752478df/adv_predictions.json + adv_probabilities_file: output/reports/attack/7b4f2360cd169c4a10b3d8ec752478df/adv_probabilities.json + attack_file: output/attacks/7c02971e2712252c4ca48726287e52f0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/7b4f2360cd169c4a10b3d8ec752478df/params.yaml + predictions_file: output/reports/attack/7b4f2360cd169c4a10b3d8ec752478df/predictions.json + probabilities_file: output/reports/attack/7b4f2360cd169c4a10b3d8ec752478df/probabilities.json + score_dict_file: output/reports/attack/7b4f2360cd169c4a10b3d8ec752478df/score_dict.json + test_labels_file: output/reports/attack/7b4f2360cd169c4a10b3d8ec752478df/test_labels.json + train_labels_file: output/reports/attack/7b4f2360cd169c4a10b3d8ec752478df/train_labels.json + model_dir: models + name: 7b4f2360cd169c4a10b3d8ec752478df + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 7b4f2360cd169c4a10b3d8ec752478df +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/7d176e9a62de13d9f533dc157532cec1/params.yaml b/examples/security/kdd-nsl/output/reports/attack/7d176e9a62de13d9f533dc157532cec1/params.yaml new file mode 100644 index 00000000..3ae5b6cd --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/7d176e9a62de13d9f533dc157532cec1/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.5 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 7dc83184f5362321ef9069a3035a99b1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/7d176e9a62de13d9f533dc157532cec1/adv_predictions.json + adv_probabilities_file: output/reports/attack/7d176e9a62de13d9f533dc157532cec1/adv_probabilities.json + attack_file: output/attacks/c4295e494e3e561747232aadefd0b2f0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/7d176e9a62de13d9f533dc157532cec1/params.yaml + predictions_file: output/reports/attack/7d176e9a62de13d9f533dc157532cec1/predictions.json + probabilities_file: output/reports/attack/7d176e9a62de13d9f533dc157532cec1/probabilities.json + score_dict_file: output/reports/attack/7d176e9a62de13d9f533dc157532cec1/score_dict.json + test_labels_file: output/reports/attack/7d176e9a62de13d9f533dc157532cec1/test_labels.json + train_labels_file: output/reports/attack/7d176e9a62de13d9f533dc157532cec1/train_labels.json + model_dir: models + name: 7d176e9a62de13d9f533dc157532cec1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 7d176e9a62de13d9f533dc157532cec1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/7d1b987c813947ff2c57b0374ca299f5/params.yaml b/examples/security/kdd-nsl/output/reports/attack/7d1b987c813947ff2c57b0374ca299f5/params.yaml new file mode 100644 index 00000000..a85e1710 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/7d1b987c813947ff2c57b0374ca299f5/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.5 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 541a1fbbda297efd8d5fde46f7b1effa +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/7d1b987c813947ff2c57b0374ca299f5/adv_predictions.json + adv_probabilities_file: output/reports/attack/7d1b987c813947ff2c57b0374ca299f5/adv_probabilities.json + attack_file: output/attacks/be8f6859fdfbd95c5e5993dee1282fa2.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/7d1b987c813947ff2c57b0374ca299f5/params.yaml + predictions_file: output/reports/attack/7d1b987c813947ff2c57b0374ca299f5/predictions.json + probabilities_file: output/reports/attack/7d1b987c813947ff2c57b0374ca299f5/probabilities.json + score_dict_file: output/reports/attack/7d1b987c813947ff2c57b0374ca299f5/score_dict.json + test_labels_file: output/reports/attack/7d1b987c813947ff2c57b0374ca299f5/test_labels.json + train_labels_file: output/reports/attack/7d1b987c813947ff2c57b0374ca299f5/train_labels.json + model_dir: models + name: 7d1b987c813947ff2c57b0374ca299f5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 7d1b987c813947ff2c57b0374ca299f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/7d66f7a3f6523427513f029558c96a09/params.yaml b/examples/security/kdd-nsl/output/reports/attack/7d66f7a3f6523427513f029558c96a09/params.yaml new file mode 100644 index 00000000..17ad9f27 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/7d66f7a3f6523427513f029558c96a09/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 1808682316d6a8fda9ad67c6fa4c5d46 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/7d66f7a3f6523427513f029558c96a09/adv_predictions.json + adv_probabilities_file: output/reports/attack/7d66f7a3f6523427513f029558c96a09/adv_probabilities.json + attack_file: output/attacks/bb1a1c828fcf56df0fe1c44a6feff836.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/7d66f7a3f6523427513f029558c96a09/params.yaml + predictions_file: output/reports/attack/7d66f7a3f6523427513f029558c96a09/predictions.json + probabilities_file: output/reports/attack/7d66f7a3f6523427513f029558c96a09/probabilities.json + score_dict_file: output/reports/attack/7d66f7a3f6523427513f029558c96a09/score_dict.json + test_labels_file: output/reports/attack/7d66f7a3f6523427513f029558c96a09/test_labels.json + train_labels_file: output/reports/attack/7d66f7a3f6523427513f029558c96a09/train_labels.json + model_dir: models + name: 7d66f7a3f6523427513f029558c96a09 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 7d66f7a3f6523427513f029558c96a09 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/7df031f6cf3a3931b3a66f4ebde67385/params.yaml b/examples/security/kdd-nsl/output/reports/attack/7df031f6cf3a3931b3a66f4ebde67385/params.yaml new file mode 100644 index 00000000..b9975fed --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/7df031f6cf3a3931b3a66f4ebde67385/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 51a80f4ee29421782fd7f303ea660603 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/7df031f6cf3a3931b3a66f4ebde67385/adv_predictions.json + adv_probabilities_file: output/reports/attack/7df031f6cf3a3931b3a66f4ebde67385/adv_probabilities.json + attack_file: output/attacks/9606f1e0b593c8606c194c9468cfd4bf.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/7df031f6cf3a3931b3a66f4ebde67385/params.yaml + predictions_file: output/reports/attack/7df031f6cf3a3931b3a66f4ebde67385/predictions.json + probabilities_file: output/reports/attack/7df031f6cf3a3931b3a66f4ebde67385/probabilities.json + score_dict_file: output/reports/attack/7df031f6cf3a3931b3a66f4ebde67385/score_dict.json + test_labels_file: output/reports/attack/7df031f6cf3a3931b3a66f4ebde67385/test_labels.json + train_labels_file: output/reports/attack/7df031f6cf3a3931b3a66f4ebde67385/train_labels.json + model_dir: models + name: 7df031f6cf3a3931b3a66f4ebde67385 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 7df031f6cf3a3931b3a66f4ebde67385 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/7e463eff6d1676bfe882b15590c9ac68/params.yaml b/examples/security/kdd-nsl/output/reports/attack/7e463eff6d1676bfe882b15590c9ac68/params.yaml new file mode 100644 index 00000000..059dd40f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/7e463eff6d1676bfe882b15590c9ac68/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 9390a0538df267b6d2e3171f5cc2d144 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/7e463eff6d1676bfe882b15590c9ac68/adv_predictions.json + adv_probabilities_file: output/reports/attack/7e463eff6d1676bfe882b15590c9ac68/adv_probabilities.json + attack_file: output/attacks/60126b74a00fb440f2c3709c9a96c2cc.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/7e463eff6d1676bfe882b15590c9ac68/params.yaml + predictions_file: output/reports/attack/7e463eff6d1676bfe882b15590c9ac68/predictions.json + probabilities_file: output/reports/attack/7e463eff6d1676bfe882b15590c9ac68/probabilities.json + score_dict_file: output/reports/attack/7e463eff6d1676bfe882b15590c9ac68/score_dict.json + test_labels_file: output/reports/attack/7e463eff6d1676bfe882b15590c9ac68/test_labels.json + train_labels_file: output/reports/attack/7e463eff6d1676bfe882b15590c9ac68/train_labels.json + model_dir: models + name: 7e463eff6d1676bfe882b15590c9ac68 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 7e463eff6d1676bfe882b15590c9ac68 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/8069c5fe4746eaafffabf1e0d6800159/params.yaml b/examples/security/kdd-nsl/output/reports/attack/8069c5fe4746eaafffabf1e0d6800159/params.yaml new file mode 100644 index 00000000..6ef57074 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/8069c5fe4746eaafffabf1e0d6800159/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: be7b49ba97c1828fe48998279c84cb1d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8069c5fe4746eaafffabf1e0d6800159/adv_predictions.json + adv_probabilities_file: output/reports/attack/8069c5fe4746eaafffabf1e0d6800159/adv_probabilities.json + attack_file: output/attacks/fcef282550282b8c48b92e1ddae9326e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/8069c5fe4746eaafffabf1e0d6800159/params.yaml + predictions_file: output/reports/attack/8069c5fe4746eaafffabf1e0d6800159/predictions.json + probabilities_file: output/reports/attack/8069c5fe4746eaafffabf1e0d6800159/probabilities.json + score_dict_file: output/reports/attack/8069c5fe4746eaafffabf1e0d6800159/score_dict.json + test_labels_file: output/reports/attack/8069c5fe4746eaafffabf1e0d6800159/test_labels.json + train_labels_file: output/reports/attack/8069c5fe4746eaafffabf1e0d6800159/train_labels.json + model_dir: models + name: 8069c5fe4746eaafffabf1e0d6800159 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 8069c5fe4746eaafffabf1e0d6800159 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/806d083d8c05b0ff14ef8175a873e5d5/params.yaml b/examples/security/kdd-nsl/output/reports/attack/806d083d8c05b0ff14ef8175a873e5d5/params.yaml new file mode 100644 index 00000000..6b11e805 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/806d083d8c05b0ff14ef8175a873e5d5/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 15d5eae60074c86fb889c635c47dbaa9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/806d083d8c05b0ff14ef8175a873e5d5/adv_predictions.json + adv_probabilities_file: output/reports/attack/806d083d8c05b0ff14ef8175a873e5d5/adv_probabilities.json + attack_file: output/attacks/8620551da1c885e50a3732b4de474e51.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/806d083d8c05b0ff14ef8175a873e5d5/params.yaml + predictions_file: output/reports/attack/806d083d8c05b0ff14ef8175a873e5d5/predictions.json + probabilities_file: output/reports/attack/806d083d8c05b0ff14ef8175a873e5d5/probabilities.json + score_dict_file: output/reports/attack/806d083d8c05b0ff14ef8175a873e5d5/score_dict.json + test_labels_file: output/reports/attack/806d083d8c05b0ff14ef8175a873e5d5/test_labels.json + train_labels_file: output/reports/attack/806d083d8c05b0ff14ef8175a873e5d5/train_labels.json + model_dir: models + name: 806d083d8c05b0ff14ef8175a873e5d5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 806d083d8c05b0ff14ef8175a873e5d5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/81115dee12440c492c089077f11285b1/params.yaml b/examples/security/kdd-nsl/output/reports/attack/81115dee12440c492c089077f11285b1/params.yaml new file mode 100644 index 00000000..83429de5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/81115dee12440c492c089077f11285b1/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.001 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: c6d8d89d688e9a14deab401de856a0c3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/81115dee12440c492c089077f11285b1/adv_predictions.json + adv_probabilities_file: output/reports/attack/81115dee12440c492c089077f11285b1/adv_probabilities.json + attack_file: output/attacks/ad4e64d9c9d2263e893ead10af643c3d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/81115dee12440c492c089077f11285b1/params.yaml + predictions_file: output/reports/attack/81115dee12440c492c089077f11285b1/predictions.json + probabilities_file: output/reports/attack/81115dee12440c492c089077f11285b1/probabilities.json + score_dict_file: output/reports/attack/81115dee12440c492c089077f11285b1/score_dict.json + test_labels_file: output/reports/attack/81115dee12440c492c089077f11285b1/test_labels.json + train_labels_file: output/reports/attack/81115dee12440c492c089077f11285b1/train_labels.json + model_dir: models + name: 81115dee12440c492c089077f11285b1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 81115dee12440c492c089077f11285b1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/8369733a5c3ae8021d70dfb4c45cf131/params.yaml b/examples/security/kdd-nsl/output/reports/attack/8369733a5c3ae8021d70dfb4c45cf131/params.yaml new file mode 100644 index 00000000..738e11ef --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/8369733a5c3ae8021d70dfb4c45cf131/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 3be77a8d8e590f135031422ed6f5e8e1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8369733a5c3ae8021d70dfb4c45cf131/adv_predictions.json + adv_probabilities_file: output/reports/attack/8369733a5c3ae8021d70dfb4c45cf131/adv_probabilities.json + attack_file: output/attacks/87cd480fc86d8f1d2b7d7c171b3422f9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/8369733a5c3ae8021d70dfb4c45cf131/params.yaml + predictions_file: output/reports/attack/8369733a5c3ae8021d70dfb4c45cf131/predictions.json + probabilities_file: output/reports/attack/8369733a5c3ae8021d70dfb4c45cf131/probabilities.json + score_dict_file: output/reports/attack/8369733a5c3ae8021d70dfb4c45cf131/score_dict.json + test_labels_file: output/reports/attack/8369733a5c3ae8021d70dfb4c45cf131/test_labels.json + train_labels_file: output/reports/attack/8369733a5c3ae8021d70dfb4c45cf131/train_labels.json + model_dir: models + name: 8369733a5c3ae8021d70dfb4c45cf131 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 8369733a5c3ae8021d70dfb4c45cf131 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/84bc7ddc74b94067bd0d6fecdeb6be99/params.yaml b/examples/security/kdd-nsl/output/reports/attack/84bc7ddc74b94067bd0d6fecdeb6be99/params.yaml new file mode 100644 index 00000000..06abad17 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/84bc7ddc74b94067bd0d6fecdeb6be99/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 40416cae92231e8153fe1c247f4624ad +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/84bc7ddc74b94067bd0d6fecdeb6be99/adv_predictions.json + adv_probabilities_file: output/reports/attack/84bc7ddc74b94067bd0d6fecdeb6be99/adv_probabilities.json + attack_file: output/attacks/91d650b85a4cbde8c5e6f390b31d443f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/84bc7ddc74b94067bd0d6fecdeb6be99/params.yaml + predictions_file: output/reports/attack/84bc7ddc74b94067bd0d6fecdeb6be99/predictions.json + probabilities_file: output/reports/attack/84bc7ddc74b94067bd0d6fecdeb6be99/probabilities.json + score_dict_file: output/reports/attack/84bc7ddc74b94067bd0d6fecdeb6be99/score_dict.json + test_labels_file: output/reports/attack/84bc7ddc74b94067bd0d6fecdeb6be99/test_labels.json + train_labels_file: output/reports/attack/84bc7ddc74b94067bd0d6fecdeb6be99/train_labels.json + model_dir: models + name: 84bc7ddc74b94067bd0d6fecdeb6be99 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 84bc7ddc74b94067bd0d6fecdeb6be99 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/854e31972f1a1496f39196ade76f36fd/params.yaml b/examples/security/kdd-nsl/output/reports/attack/854e31972f1a1496f39196ade76f36fd/params.yaml new file mode 100644 index 00000000..c6263b7c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/854e31972f1a1496f39196ade76f36fd/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 6d080ed43b9c1ef28b8e108c8dcf203a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/854e31972f1a1496f39196ade76f36fd/adv_predictions.json + adv_probabilities_file: output/reports/attack/854e31972f1a1496f39196ade76f36fd/adv_probabilities.json + attack_file: output/attacks/e033268b615dcb2e958187b70dc8a6d9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/854e31972f1a1496f39196ade76f36fd/params.yaml + predictions_file: output/reports/attack/854e31972f1a1496f39196ade76f36fd/predictions.json + probabilities_file: output/reports/attack/854e31972f1a1496f39196ade76f36fd/probabilities.json + score_dict_file: output/reports/attack/854e31972f1a1496f39196ade76f36fd/score_dict.json + test_labels_file: output/reports/attack/854e31972f1a1496f39196ade76f36fd/test_labels.json + train_labels_file: output/reports/attack/854e31972f1a1496f39196ade76f36fd/train_labels.json + model_dir: models + name: 854e31972f1a1496f39196ade76f36fd + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 854e31972f1a1496f39196ade76f36fd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/85ac0ce9deba7b134ec039ca0e394838/params.yaml b/examples/security/kdd-nsl/output/reports/attack/85ac0ce9deba7b134ec039ca0e394838/params.yaml new file mode 100644 index 00000000..2b51ec64 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/85ac0ce9deba7b134ec039ca0e394838/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 82b4ee7524ae1eafd78bcf9425b34547 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/85ac0ce9deba7b134ec039ca0e394838/adv_predictions.json + adv_probabilities_file: output/reports/attack/85ac0ce9deba7b134ec039ca0e394838/adv_probabilities.json + attack_file: output/attacks/ab7ff6ee5641796909bc592a4f74d517.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/85ac0ce9deba7b134ec039ca0e394838/params.yaml + predictions_file: output/reports/attack/85ac0ce9deba7b134ec039ca0e394838/predictions.json + probabilities_file: output/reports/attack/85ac0ce9deba7b134ec039ca0e394838/probabilities.json + score_dict_file: output/reports/attack/85ac0ce9deba7b134ec039ca0e394838/score_dict.json + test_labels_file: output/reports/attack/85ac0ce9deba7b134ec039ca0e394838/test_labels.json + train_labels_file: output/reports/attack/85ac0ce9deba7b134ec039ca0e394838/train_labels.json + model_dir: models + name: 85ac0ce9deba7b134ec039ca0e394838 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 85ac0ce9deba7b134ec039ca0e394838 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/867873bd4421b2359ce456c1f67731f3/params.yaml b/examples/security/kdd-nsl/output/reports/attack/867873bd4421b2359ce456c1f67731f3/params.yaml new file mode 100644 index 00000000..9612271c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/867873bd4421b2359ce456c1f67731f3/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: fda0bb6af864a6faf198ea5d17c6e7c0 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/867873bd4421b2359ce456c1f67731f3/adv_predictions.json + adv_probabilities_file: output/reports/attack/867873bd4421b2359ce456c1f67731f3/adv_probabilities.json + attack_file: output/attacks/7424c3d2f08a4280f325c3e1cec7a6b9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/867873bd4421b2359ce456c1f67731f3/params.yaml + predictions_file: output/reports/attack/867873bd4421b2359ce456c1f67731f3/predictions.json + probabilities_file: output/reports/attack/867873bd4421b2359ce456c1f67731f3/probabilities.json + score_dict_file: output/reports/attack/867873bd4421b2359ce456c1f67731f3/score_dict.json + test_labels_file: output/reports/attack/867873bd4421b2359ce456c1f67731f3/test_labels.json + train_labels_file: output/reports/attack/867873bd4421b2359ce456c1f67731f3/train_labels.json + model_dir: models + name: 867873bd4421b2359ce456c1f67731f3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 867873bd4421b2359ce456c1f67731f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/867fb4a35c7b41ebf107c5e1903a7c2c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/867fb4a35c7b41ebf107c5e1903a7c2c/params.yaml new file mode 100644 index 00000000..25a043fc --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/867fb4a35c7b41ebf107c5e1903a7c2c/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.001 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 22a713e2eb607e3533c6e20e8255439b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/867fb4a35c7b41ebf107c5e1903a7c2c/adv_predictions.json + adv_probabilities_file: output/reports/attack/867fb4a35c7b41ebf107c5e1903a7c2c/adv_probabilities.json + attack_file: output/attacks/f244fd2438d0f602abf4f094a2936ea6.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/867fb4a35c7b41ebf107c5e1903a7c2c/params.yaml + predictions_file: output/reports/attack/867fb4a35c7b41ebf107c5e1903a7c2c/predictions.json + probabilities_file: output/reports/attack/867fb4a35c7b41ebf107c5e1903a7c2c/probabilities.json + score_dict_file: output/reports/attack/867fb4a35c7b41ebf107c5e1903a7c2c/score_dict.json + test_labels_file: output/reports/attack/867fb4a35c7b41ebf107c5e1903a7c2c/test_labels.json + train_labels_file: output/reports/attack/867fb4a35c7b41ebf107c5e1903a7c2c/train_labels.json + model_dir: models + name: 867fb4a35c7b41ebf107c5e1903a7c2c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 867fb4a35c7b41ebf107c5e1903a7c2c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/869dc3b899a69a89e67aa7eb8971a02e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/869dc3b899a69a89e67aa7eb8971a02e/params.yaml new file mode 100644 index 00000000..a2e429bc --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/869dc3b899a69a89e67aa7eb8971a02e/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: c9030507c630ec1d4720a3fa1905c3b7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/869dc3b899a69a89e67aa7eb8971a02e/adv_predictions.json + adv_probabilities_file: output/reports/attack/869dc3b899a69a89e67aa7eb8971a02e/adv_probabilities.json + attack_file: output/attacks/455f8dffb68c96dacfc939f25b12ea23.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/869dc3b899a69a89e67aa7eb8971a02e/params.yaml + predictions_file: output/reports/attack/869dc3b899a69a89e67aa7eb8971a02e/predictions.json + probabilities_file: output/reports/attack/869dc3b899a69a89e67aa7eb8971a02e/probabilities.json + score_dict_file: output/reports/attack/869dc3b899a69a89e67aa7eb8971a02e/score_dict.json + test_labels_file: output/reports/attack/869dc3b899a69a89e67aa7eb8971a02e/test_labels.json + train_labels_file: output/reports/attack/869dc3b899a69a89e67aa7eb8971a02e/train_labels.json + model_dir: models + name: 869dc3b899a69a89e67aa7eb8971a02e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 869dc3b899a69a89e67aa7eb8971a02e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/87bff36d3f3318ba6d5910d3311ed99e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/87bff36d3f3318ba6d5910d3311ed99e/params.yaml new file mode 100644 index 00000000..4da47381 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/87bff36d3f3318ba6d5910d3311ed99e/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.3 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 3634af17c2e2609c01213560747f0569 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/87bff36d3f3318ba6d5910d3311ed99e/adv_predictions.json + adv_probabilities_file: output/reports/attack/87bff36d3f3318ba6d5910d3311ed99e/adv_probabilities.json + attack_file: output/attacks/f9378f7f1504a3b5a8ebb56ebbf9b4c1.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/87bff36d3f3318ba6d5910d3311ed99e/params.yaml + predictions_file: output/reports/attack/87bff36d3f3318ba6d5910d3311ed99e/predictions.json + probabilities_file: output/reports/attack/87bff36d3f3318ba6d5910d3311ed99e/probabilities.json + score_dict_file: output/reports/attack/87bff36d3f3318ba6d5910d3311ed99e/score_dict.json + test_labels_file: output/reports/attack/87bff36d3f3318ba6d5910d3311ed99e/test_labels.json + train_labels_file: output/reports/attack/87bff36d3f3318ba6d5910d3311ed99e/train_labels.json + model_dir: models + name: 87bff36d3f3318ba6d5910d3311ed99e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 87bff36d3f3318ba6d5910d3311ed99e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/88a99722c06c1b08a44b45135ebcc191/params.yaml b/examples/security/kdd-nsl/output/reports/attack/88a99722c06c1b08a44b45135ebcc191/params.yaml new file mode 100644 index 00000000..a42dd42d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/88a99722c06c1b08a44b45135ebcc191/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.001 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 495cc436e85eb614ad764d54cd7cd0bc +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/88a99722c06c1b08a44b45135ebcc191/adv_predictions.json + adv_probabilities_file: output/reports/attack/88a99722c06c1b08a44b45135ebcc191/adv_probabilities.json + attack_file: output/attacks/c81da7eb9cb02fb6aa40b235c68bf92d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/88a99722c06c1b08a44b45135ebcc191/params.yaml + predictions_file: output/reports/attack/88a99722c06c1b08a44b45135ebcc191/predictions.json + probabilities_file: output/reports/attack/88a99722c06c1b08a44b45135ebcc191/probabilities.json + score_dict_file: output/reports/attack/88a99722c06c1b08a44b45135ebcc191/score_dict.json + test_labels_file: output/reports/attack/88a99722c06c1b08a44b45135ebcc191/test_labels.json + train_labels_file: output/reports/attack/88a99722c06c1b08a44b45135ebcc191/train_labels.json + model_dir: models + name: 88a99722c06c1b08a44b45135ebcc191 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 88a99722c06c1b08a44b45135ebcc191 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/88c2d06a380f2b539f4ea86daa9c61a5/params.yaml b/examples/security/kdd-nsl/output/reports/attack/88c2d06a380f2b539f4ea86daa9c61a5/params.yaml new file mode 100644 index 00000000..3a5eb776 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/88c2d06a380f2b539f4ea86daa9c61a5/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: bbc39cb68dadd4f3e1e4ba5c78c327f4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/88c2d06a380f2b539f4ea86daa9c61a5/adv_predictions.json + adv_probabilities_file: output/reports/attack/88c2d06a380f2b539f4ea86daa9c61a5/adv_probabilities.json + attack_file: output/attacks/5fdedc675cc09930e748ce1dd56a4478.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/88c2d06a380f2b539f4ea86daa9c61a5/params.yaml + predictions_file: output/reports/attack/88c2d06a380f2b539f4ea86daa9c61a5/predictions.json + probabilities_file: output/reports/attack/88c2d06a380f2b539f4ea86daa9c61a5/probabilities.json + score_dict_file: output/reports/attack/88c2d06a380f2b539f4ea86daa9c61a5/score_dict.json + test_labels_file: output/reports/attack/88c2d06a380f2b539f4ea86daa9c61a5/test_labels.json + train_labels_file: output/reports/attack/88c2d06a380f2b539f4ea86daa9c61a5/train_labels.json + model_dir: models + name: 88c2d06a380f2b539f4ea86daa9c61a5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 88c2d06a380f2b539f4ea86daa9c61a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/893003efd7d579ac7f0c553525f2bec8/params.yaml b/examples/security/kdd-nsl/output/reports/attack/893003efd7d579ac7f0c553525f2bec8/params.yaml new file mode 100644 index 00000000..be4bea45 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/893003efd7d579ac7f0c553525f2bec8/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 417f1220756d728810607caac9a8520f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/893003efd7d579ac7f0c553525f2bec8/adv_predictions.json + adv_probabilities_file: output/reports/attack/893003efd7d579ac7f0c553525f2bec8/adv_probabilities.json + attack_file: output/attacks/7d111c0514fa3288d61debeb78659c23.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/893003efd7d579ac7f0c553525f2bec8/params.yaml + predictions_file: output/reports/attack/893003efd7d579ac7f0c553525f2bec8/predictions.json + probabilities_file: output/reports/attack/893003efd7d579ac7f0c553525f2bec8/probabilities.json + score_dict_file: output/reports/attack/893003efd7d579ac7f0c553525f2bec8/score_dict.json + test_labels_file: output/reports/attack/893003efd7d579ac7f0c553525f2bec8/test_labels.json + train_labels_file: output/reports/attack/893003efd7d579ac7f0c553525f2bec8/train_labels.json + model_dir: models + name: 893003efd7d579ac7f0c553525f2bec8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 893003efd7d579ac7f0c553525f2bec8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/8991d1fe2075c6c886d9632b24c3ead1/params.yaml b/examples/security/kdd-nsl/output/reports/attack/8991d1fe2075c6c886d9632b24c3ead1/params.yaml new file mode 100644 index 00000000..88195a14 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/8991d1fe2075c6c886d9632b24c3ead1/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.3 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 0a52b39384c3ef750b6d24a65e2fe5e9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8991d1fe2075c6c886d9632b24c3ead1/adv_predictions.json + adv_probabilities_file: output/reports/attack/8991d1fe2075c6c886d9632b24c3ead1/adv_probabilities.json + attack_file: output/attacks/fb8841430b2fdb1abe04be09e6e8fce9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/8991d1fe2075c6c886d9632b24c3ead1/params.yaml + predictions_file: output/reports/attack/8991d1fe2075c6c886d9632b24c3ead1/predictions.json + probabilities_file: output/reports/attack/8991d1fe2075c6c886d9632b24c3ead1/probabilities.json + score_dict_file: output/reports/attack/8991d1fe2075c6c886d9632b24c3ead1/score_dict.json + test_labels_file: output/reports/attack/8991d1fe2075c6c886d9632b24c3ead1/test_labels.json + train_labels_file: output/reports/attack/8991d1fe2075c6c886d9632b24c3ead1/train_labels.json + model_dir: models + name: 8991d1fe2075c6c886d9632b24c3ead1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 8991d1fe2075c6c886d9632b24c3ead1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/89e105c557714da9267c068324256ade/params.yaml b/examples/security/kdd-nsl/output/reports/attack/89e105c557714da9267c068324256ade/params.yaml new file mode 100644 index 00000000..1824c397 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/89e105c557714da9267c068324256ade/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.3 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 0f4052f6ac6c1a3009fdc59272a13b6c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/89e105c557714da9267c068324256ade/adv_predictions.json + adv_probabilities_file: output/reports/attack/89e105c557714da9267c068324256ade/adv_probabilities.json + attack_file: output/attacks/3200331d272d5baa2cdd168dd6ae75c2.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/89e105c557714da9267c068324256ade/params.yaml + predictions_file: output/reports/attack/89e105c557714da9267c068324256ade/predictions.json + probabilities_file: output/reports/attack/89e105c557714da9267c068324256ade/probabilities.json + score_dict_file: output/reports/attack/89e105c557714da9267c068324256ade/score_dict.json + test_labels_file: output/reports/attack/89e105c557714da9267c068324256ade/test_labels.json + train_labels_file: output/reports/attack/89e105c557714da9267c068324256ade/train_labels.json + model_dir: models + name: 89e105c557714da9267c068324256ade + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 89e105c557714da9267c068324256ade +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/8aeb9d334f42fcee9391aa96fed22473/params.yaml b/examples/security/kdd-nsl/output/reports/attack/8aeb9d334f42fcee9391aa96fed22473/params.yaml new file mode 100644 index 00000000..343cce1a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/8aeb9d334f42fcee9391aa96fed22473/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 2974e772df35451b8b4e2dbb7ab469f3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8aeb9d334f42fcee9391aa96fed22473/adv_predictions.json + adv_probabilities_file: output/reports/attack/8aeb9d334f42fcee9391aa96fed22473/adv_probabilities.json + attack_file: output/attacks/3aee029d8e56ec03791c2fb7ba43d6ce.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/8aeb9d334f42fcee9391aa96fed22473/params.yaml + predictions_file: output/reports/attack/8aeb9d334f42fcee9391aa96fed22473/predictions.json + probabilities_file: output/reports/attack/8aeb9d334f42fcee9391aa96fed22473/probabilities.json + score_dict_file: output/reports/attack/8aeb9d334f42fcee9391aa96fed22473/score_dict.json + test_labels_file: output/reports/attack/8aeb9d334f42fcee9391aa96fed22473/test_labels.json + train_labels_file: output/reports/attack/8aeb9d334f42fcee9391aa96fed22473/train_labels.json + model_dir: models + name: 8aeb9d334f42fcee9391aa96fed22473 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 8aeb9d334f42fcee9391aa96fed22473 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/8b45f8cd323cf3b36012d3c75c43ee42/params.yaml b/examples/security/kdd-nsl/output/reports/attack/8b45f8cd323cf3b36012d3c75c43ee42/params.yaml new file mode 100644 index 00000000..e75a0e92 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/8b45f8cd323cf3b36012d3c75c43ee42/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.001 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 778e713144897330d5e2fd2a94e76167 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8b45f8cd323cf3b36012d3c75c43ee42/adv_predictions.json + adv_probabilities_file: output/reports/attack/8b45f8cd323cf3b36012d3c75c43ee42/adv_probabilities.json + attack_file: output/attacks/c7740e10acac40586662cbed22d821c5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/8b45f8cd323cf3b36012d3c75c43ee42/params.yaml + predictions_file: output/reports/attack/8b45f8cd323cf3b36012d3c75c43ee42/predictions.json + probabilities_file: output/reports/attack/8b45f8cd323cf3b36012d3c75c43ee42/probabilities.json + score_dict_file: output/reports/attack/8b45f8cd323cf3b36012d3c75c43ee42/score_dict.json + test_labels_file: output/reports/attack/8b45f8cd323cf3b36012d3c75c43ee42/test_labels.json + train_labels_file: output/reports/attack/8b45f8cd323cf3b36012d3c75c43ee42/train_labels.json + model_dir: models + name: 8b45f8cd323cf3b36012d3c75c43ee42 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 8b45f8cd323cf3b36012d3c75c43ee42 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/8c9df54a88e9b5aaaecdd90b1ba7167b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/8c9df54a88e9b5aaaecdd90b1ba7167b/params.yaml new file mode 100644 index 00000000..ba64eefe --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/8c9df54a88e9b5aaaecdd90b1ba7167b/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.01 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 1364cbdf0ebe6b7ecf5ac4d7390dab2f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8c9df54a88e9b5aaaecdd90b1ba7167b/adv_predictions.json + adv_probabilities_file: output/reports/attack/8c9df54a88e9b5aaaecdd90b1ba7167b/adv_probabilities.json + attack_file: output/attacks/a21b7f29788302ae0d486d54d5255b65.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/8c9df54a88e9b5aaaecdd90b1ba7167b/params.yaml + predictions_file: output/reports/attack/8c9df54a88e9b5aaaecdd90b1ba7167b/predictions.json + probabilities_file: output/reports/attack/8c9df54a88e9b5aaaecdd90b1ba7167b/probabilities.json + score_dict_file: output/reports/attack/8c9df54a88e9b5aaaecdd90b1ba7167b/score_dict.json + test_labels_file: output/reports/attack/8c9df54a88e9b5aaaecdd90b1ba7167b/test_labels.json + train_labels_file: output/reports/attack/8c9df54a88e9b5aaaecdd90b1ba7167b/train_labels.json + model_dir: models + name: 8c9df54a88e9b5aaaecdd90b1ba7167b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 8c9df54a88e9b5aaaecdd90b1ba7167b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/8d9a56fee0f9154e2b6fd84a01e56973/params.yaml b/examples/security/kdd-nsl/output/reports/attack/8d9a56fee0f9154e2b6fd84a01e56973/params.yaml new file mode 100644 index 00000000..da8fb8d5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/8d9a56fee0f9154e2b6fd84a01e56973/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.01 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 1ab43ed62a1fadda834a58a5a8508a07 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8d9a56fee0f9154e2b6fd84a01e56973/adv_predictions.json + adv_probabilities_file: output/reports/attack/8d9a56fee0f9154e2b6fd84a01e56973/adv_probabilities.json + attack_file: output/attacks/762a211509e114786e0a11b186d6f4b0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/8d9a56fee0f9154e2b6fd84a01e56973/params.yaml + predictions_file: output/reports/attack/8d9a56fee0f9154e2b6fd84a01e56973/predictions.json + probabilities_file: output/reports/attack/8d9a56fee0f9154e2b6fd84a01e56973/probabilities.json + score_dict_file: output/reports/attack/8d9a56fee0f9154e2b6fd84a01e56973/score_dict.json + test_labels_file: output/reports/attack/8d9a56fee0f9154e2b6fd84a01e56973/test_labels.json + train_labels_file: output/reports/attack/8d9a56fee0f9154e2b6fd84a01e56973/train_labels.json + model_dir: models + name: 8d9a56fee0f9154e2b6fd84a01e56973 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 8d9a56fee0f9154e2b6fd84a01e56973 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/8db1df066fc0d908df86a9b30b3b411a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/8db1df066fc0d908df86a9b30b3b411a/params.yaml new file mode 100644 index 00000000..e786e653 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/8db1df066fc0d908df86a9b30b3b411a/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 78ad245f5645ac112983389e6d4825f1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8db1df066fc0d908df86a9b30b3b411a/adv_predictions.json + adv_probabilities_file: output/reports/attack/8db1df066fc0d908df86a9b30b3b411a/adv_probabilities.json + attack_file: output/attacks/629c5510309c754be852a3aebd040783.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/8db1df066fc0d908df86a9b30b3b411a/params.yaml + predictions_file: output/reports/attack/8db1df066fc0d908df86a9b30b3b411a/predictions.json + probabilities_file: output/reports/attack/8db1df066fc0d908df86a9b30b3b411a/probabilities.json + score_dict_file: output/reports/attack/8db1df066fc0d908df86a9b30b3b411a/score_dict.json + test_labels_file: output/reports/attack/8db1df066fc0d908df86a9b30b3b411a/test_labels.json + train_labels_file: output/reports/attack/8db1df066fc0d908df86a9b30b3b411a/train_labels.json + model_dir: models + name: 8db1df066fc0d908df86a9b30b3b411a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 8db1df066fc0d908df86a9b30b3b411a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/8e070ee808968c85e47480c6ce43a517/params.yaml b/examples/security/kdd-nsl/output/reports/attack/8e070ee808968c85e47480c6ce43a517/params.yaml new file mode 100644 index 00000000..d25348f6 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/8e070ee808968c85e47480c6ce43a517/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.5 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 187dda4e9eb91d4c4679ec5d9f716afe +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8e070ee808968c85e47480c6ce43a517/adv_predictions.json + adv_probabilities_file: output/reports/attack/8e070ee808968c85e47480c6ce43a517/adv_probabilities.json + attack_file: output/attacks/70ebbdba52e71c1232ce8a7e92159bf5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/8e070ee808968c85e47480c6ce43a517/params.yaml + predictions_file: output/reports/attack/8e070ee808968c85e47480c6ce43a517/predictions.json + probabilities_file: output/reports/attack/8e070ee808968c85e47480c6ce43a517/probabilities.json + score_dict_file: output/reports/attack/8e070ee808968c85e47480c6ce43a517/score_dict.json + test_labels_file: output/reports/attack/8e070ee808968c85e47480c6ce43a517/test_labels.json + train_labels_file: output/reports/attack/8e070ee808968c85e47480c6ce43a517/train_labels.json + model_dir: models + name: 8e070ee808968c85e47480c6ce43a517 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 8e070ee808968c85e47480c6ce43a517 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/8f78166bb914ce2259481bb9220f1c39/params.yaml b/examples/security/kdd-nsl/output/reports/attack/8f78166bb914ce2259481bb9220f1c39/params.yaml new file mode 100644 index 00000000..cad45ae5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/8f78166bb914ce2259481bb9220f1c39/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.001 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 2ab61baf674c4a014a63b6942f0df55e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8f78166bb914ce2259481bb9220f1c39/adv_predictions.json + adv_probabilities_file: output/reports/attack/8f78166bb914ce2259481bb9220f1c39/adv_probabilities.json + attack_file: output/attacks/210a14515d31ea9e5c968c954a0249cf.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/8f78166bb914ce2259481bb9220f1c39/params.yaml + predictions_file: output/reports/attack/8f78166bb914ce2259481bb9220f1c39/predictions.json + probabilities_file: output/reports/attack/8f78166bb914ce2259481bb9220f1c39/probabilities.json + score_dict_file: output/reports/attack/8f78166bb914ce2259481bb9220f1c39/score_dict.json + test_labels_file: output/reports/attack/8f78166bb914ce2259481bb9220f1c39/test_labels.json + train_labels_file: output/reports/attack/8f78166bb914ce2259481bb9220f1c39/train_labels.json + model_dir: models + name: 8f78166bb914ce2259481bb9220f1c39 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 8f78166bb914ce2259481bb9220f1c39 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/8fa2e9faef48ae8afd776a396dbd6670/params.yaml b/examples/security/kdd-nsl/output/reports/attack/8fa2e9faef48ae8afd776a396dbd6670/params.yaml new file mode 100644 index 00000000..2bb3826f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/8fa2e9faef48ae8afd776a396dbd6670/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.01 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: c80ff6ec695eb4ef0439b35407971484 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8fa2e9faef48ae8afd776a396dbd6670/adv_predictions.json + adv_probabilities_file: output/reports/attack/8fa2e9faef48ae8afd776a396dbd6670/adv_probabilities.json + attack_file: output/attacks/b95a632d7a4c6ef902e0e4157d737f9d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/8fa2e9faef48ae8afd776a396dbd6670/params.yaml + predictions_file: output/reports/attack/8fa2e9faef48ae8afd776a396dbd6670/predictions.json + probabilities_file: output/reports/attack/8fa2e9faef48ae8afd776a396dbd6670/probabilities.json + score_dict_file: output/reports/attack/8fa2e9faef48ae8afd776a396dbd6670/score_dict.json + test_labels_file: output/reports/attack/8fa2e9faef48ae8afd776a396dbd6670/test_labels.json + train_labels_file: output/reports/attack/8fa2e9faef48ae8afd776a396dbd6670/train_labels.json + model_dir: models + name: 8fa2e9faef48ae8afd776a396dbd6670 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 8fa2e9faef48ae8afd776a396dbd6670 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/8ffcbb4d5f07d280ffe43bbf625e60da/params.yaml b/examples/security/kdd-nsl/output/reports/attack/8ffcbb4d5f07d280ffe43bbf625e60da/params.yaml new file mode 100644 index 00000000..7f56b3ae --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/8ffcbb4d5f07d280ffe43bbf625e60da/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.001 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: b5b67e176fe90dbc340604be94cd533c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8ffcbb4d5f07d280ffe43bbf625e60da/adv_predictions.json + adv_probabilities_file: output/reports/attack/8ffcbb4d5f07d280ffe43bbf625e60da/adv_probabilities.json + attack_file: output/attacks/94379eba9ffbca1ea8693c302b58d917.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/8ffcbb4d5f07d280ffe43bbf625e60da/params.yaml + predictions_file: output/reports/attack/8ffcbb4d5f07d280ffe43bbf625e60da/predictions.json + probabilities_file: output/reports/attack/8ffcbb4d5f07d280ffe43bbf625e60da/probabilities.json + score_dict_file: output/reports/attack/8ffcbb4d5f07d280ffe43bbf625e60da/score_dict.json + test_labels_file: output/reports/attack/8ffcbb4d5f07d280ffe43bbf625e60da/test_labels.json + train_labels_file: output/reports/attack/8ffcbb4d5f07d280ffe43bbf625e60da/train_labels.json + model_dir: models + name: 8ffcbb4d5f07d280ffe43bbf625e60da + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 8ffcbb4d5f07d280ffe43bbf625e60da +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/9153498ef97cf4af32afa1da5ff046b9/params.yaml b/examples/security/kdd-nsl/output/reports/attack/9153498ef97cf4af32afa1da5ff046b9/params.yaml new file mode 100644 index 00000000..4e620978 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/9153498ef97cf4af32afa1da5ff046b9/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 6c2b7a5fdfb0a9644d63b903c7dbc32b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9153498ef97cf4af32afa1da5ff046b9/adv_predictions.json + adv_probabilities_file: output/reports/attack/9153498ef97cf4af32afa1da5ff046b9/adv_probabilities.json + attack_file: output/attacks/8f4deab919d394c53a7dd64cd603f414.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/9153498ef97cf4af32afa1da5ff046b9/params.yaml + predictions_file: output/reports/attack/9153498ef97cf4af32afa1da5ff046b9/predictions.json + probabilities_file: output/reports/attack/9153498ef97cf4af32afa1da5ff046b9/probabilities.json + score_dict_file: output/reports/attack/9153498ef97cf4af32afa1da5ff046b9/score_dict.json + test_labels_file: output/reports/attack/9153498ef97cf4af32afa1da5ff046b9/test_labels.json + train_labels_file: output/reports/attack/9153498ef97cf4af32afa1da5ff046b9/train_labels.json + model_dir: models + name: 9153498ef97cf4af32afa1da5ff046b9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 9153498ef97cf4af32afa1da5ff046b9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/94a9e17273a8fedf4c768e19a30be65f/params.yaml b/examples/security/kdd-nsl/output/reports/attack/94a9e17273a8fedf4c768e19a30be65f/params.yaml new file mode 100644 index 00000000..c4ac13de --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/94a9e17273a8fedf4c768e19a30be65f/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 3b7052a6db3100918f2a7e1e6a4ac2a6 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/94a9e17273a8fedf4c768e19a30be65f/adv_predictions.json + adv_probabilities_file: output/reports/attack/94a9e17273a8fedf4c768e19a30be65f/adv_probabilities.json + attack_file: output/attacks/267e79d01546d6b4bdfd95ef8cda1e53.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/94a9e17273a8fedf4c768e19a30be65f/params.yaml + predictions_file: output/reports/attack/94a9e17273a8fedf4c768e19a30be65f/predictions.json + probabilities_file: output/reports/attack/94a9e17273a8fedf4c768e19a30be65f/probabilities.json + score_dict_file: output/reports/attack/94a9e17273a8fedf4c768e19a30be65f/score_dict.json + test_labels_file: output/reports/attack/94a9e17273a8fedf4c768e19a30be65f/test_labels.json + train_labels_file: output/reports/attack/94a9e17273a8fedf4c768e19a30be65f/train_labels.json + model_dir: models + name: 94a9e17273a8fedf4c768e19a30be65f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 94a9e17273a8fedf4c768e19a30be65f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/953c66655d6145c7e721a595e7d94f59/params.yaml b/examples/security/kdd-nsl/output/reports/attack/953c66655d6145c7e721a595e7d94f59/params.yaml new file mode 100644 index 00000000..24d7dbd7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/953c66655d6145c7e721a595e7d94f59/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 8fc5eef4f39ba62265a09a0d051be774 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/953c66655d6145c7e721a595e7d94f59/adv_predictions.json + adv_probabilities_file: output/reports/attack/953c66655d6145c7e721a595e7d94f59/adv_probabilities.json + attack_file: output/attacks/6b596e8c70d89e8cd43e569e16522b38.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/953c66655d6145c7e721a595e7d94f59/params.yaml + predictions_file: output/reports/attack/953c66655d6145c7e721a595e7d94f59/predictions.json + probabilities_file: output/reports/attack/953c66655d6145c7e721a595e7d94f59/probabilities.json + score_dict_file: output/reports/attack/953c66655d6145c7e721a595e7d94f59/score_dict.json + test_labels_file: output/reports/attack/953c66655d6145c7e721a595e7d94f59/test_labels.json + train_labels_file: output/reports/attack/953c66655d6145c7e721a595e7d94f59/train_labels.json + model_dir: models + name: 953c66655d6145c7e721a595e7d94f59 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 953c66655d6145c7e721a595e7d94f59 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/954ac41b99941d54db625398e13573a3/params.yaml b/examples/security/kdd-nsl/output/reports/attack/954ac41b99941d54db625398e13573a3/params.yaml new file mode 100644 index 00000000..7e8e73a3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/954ac41b99941d54db625398e13573a3/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.001 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 7e469f25f590f056f26789327ccb7ca4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/954ac41b99941d54db625398e13573a3/adv_predictions.json + adv_probabilities_file: output/reports/attack/954ac41b99941d54db625398e13573a3/adv_probabilities.json + attack_file: output/attacks/c725f2b610be9685abb6b74966b29665.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/954ac41b99941d54db625398e13573a3/params.yaml + predictions_file: output/reports/attack/954ac41b99941d54db625398e13573a3/predictions.json + probabilities_file: output/reports/attack/954ac41b99941d54db625398e13573a3/probabilities.json + score_dict_file: output/reports/attack/954ac41b99941d54db625398e13573a3/score_dict.json + test_labels_file: output/reports/attack/954ac41b99941d54db625398e13573a3/test_labels.json + train_labels_file: output/reports/attack/954ac41b99941d54db625398e13573a3/train_labels.json + model_dir: models + name: 954ac41b99941d54db625398e13573a3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 954ac41b99941d54db625398e13573a3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/9678a752bdb40d290546c7d57a6ea6ae/params.yaml b/examples/security/kdd-nsl/output/reports/attack/9678a752bdb40d290546c7d57a6ea6ae/params.yaml new file mode 100644 index 00000000..c53ad17a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/9678a752bdb40d290546c7d57a6ea6ae/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 757a4fdfcc03edea3d55806ce1fab7fc +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9678a752bdb40d290546c7d57a6ea6ae/adv_predictions.json + adv_probabilities_file: output/reports/attack/9678a752bdb40d290546c7d57a6ea6ae/adv_probabilities.json + attack_file: output/attacks/30f86cde5ad5118796f462e16c4b273f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/9678a752bdb40d290546c7d57a6ea6ae/params.yaml + predictions_file: output/reports/attack/9678a752bdb40d290546c7d57a6ea6ae/predictions.json + probabilities_file: output/reports/attack/9678a752bdb40d290546c7d57a6ea6ae/probabilities.json + score_dict_file: output/reports/attack/9678a752bdb40d290546c7d57a6ea6ae/score_dict.json + test_labels_file: output/reports/attack/9678a752bdb40d290546c7d57a6ea6ae/test_labels.json + train_labels_file: output/reports/attack/9678a752bdb40d290546c7d57a6ea6ae/train_labels.json + model_dir: models + name: 9678a752bdb40d290546c7d57a6ea6ae + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 9678a752bdb40d290546c7d57a6ea6ae +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/96d789ba8a776e34754a6114d6777687/params.yaml b/examples/security/kdd-nsl/output/reports/attack/96d789ba8a776e34754a6114d6777687/params.yaml new file mode 100644 index 00000000..7cb30493 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/96d789ba8a776e34754a6114d6777687/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.5 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 57ec79529b186ac84d2d0e5c95169c50 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/96d789ba8a776e34754a6114d6777687/adv_predictions.json + adv_probabilities_file: output/reports/attack/96d789ba8a776e34754a6114d6777687/adv_probabilities.json + attack_file: output/attacks/8140efbc21b53cb213ba326c8605d3e3.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/96d789ba8a776e34754a6114d6777687/params.yaml + predictions_file: output/reports/attack/96d789ba8a776e34754a6114d6777687/predictions.json + probabilities_file: output/reports/attack/96d789ba8a776e34754a6114d6777687/probabilities.json + score_dict_file: output/reports/attack/96d789ba8a776e34754a6114d6777687/score_dict.json + test_labels_file: output/reports/attack/96d789ba8a776e34754a6114d6777687/test_labels.json + train_labels_file: output/reports/attack/96d789ba8a776e34754a6114d6777687/train_labels.json + model_dir: models + name: 96d789ba8a776e34754a6114d6777687 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 96d789ba8a776e34754a6114d6777687 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/96dcc5a0f82dc78a159cf1fb0ae23f13/params.yaml b/examples/security/kdd-nsl/output/reports/attack/96dcc5a0f82dc78a159cf1fb0ae23f13/params.yaml new file mode 100644 index 00000000..ebb7881e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/96dcc5a0f82dc78a159cf1fb0ae23f13/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 125e7ee8145f4beb16425623bd40d3ae +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/96dcc5a0f82dc78a159cf1fb0ae23f13/adv_predictions.json + adv_probabilities_file: output/reports/attack/96dcc5a0f82dc78a159cf1fb0ae23f13/adv_probabilities.json + attack_file: output/attacks/f0a4cf7c2419a2ccc3d2fce9c40a92ca.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/96dcc5a0f82dc78a159cf1fb0ae23f13/params.yaml + predictions_file: output/reports/attack/96dcc5a0f82dc78a159cf1fb0ae23f13/predictions.json + probabilities_file: output/reports/attack/96dcc5a0f82dc78a159cf1fb0ae23f13/probabilities.json + score_dict_file: output/reports/attack/96dcc5a0f82dc78a159cf1fb0ae23f13/score_dict.json + test_labels_file: output/reports/attack/96dcc5a0f82dc78a159cf1fb0ae23f13/test_labels.json + train_labels_file: output/reports/attack/96dcc5a0f82dc78a159cf1fb0ae23f13/train_labels.json + model_dir: models + name: 96dcc5a0f82dc78a159cf1fb0ae23f13 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 96dcc5a0f82dc78a159cf1fb0ae23f13 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/96e15e4d68b9ef4a6e63e78c304e9b53/params.yaml b/examples/security/kdd-nsl/output/reports/attack/96e15e4d68b9ef4a6e63e78c304e9b53/params.yaml new file mode 100644 index 00000000..55ba1e16 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/96e15e4d68b9ef4a6e63e78c304e9b53/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 8fef4239d667f92c8dbc7cb7bf2ba222 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/96e15e4d68b9ef4a6e63e78c304e9b53/adv_predictions.json + adv_probabilities_file: output/reports/attack/96e15e4d68b9ef4a6e63e78c304e9b53/adv_probabilities.json + attack_file: output/attacks/f6c3e444065238c7b33f6b2fb1ad071e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/96e15e4d68b9ef4a6e63e78c304e9b53/params.yaml + predictions_file: output/reports/attack/96e15e4d68b9ef4a6e63e78c304e9b53/predictions.json + probabilities_file: output/reports/attack/96e15e4d68b9ef4a6e63e78c304e9b53/probabilities.json + score_dict_file: output/reports/attack/96e15e4d68b9ef4a6e63e78c304e9b53/score_dict.json + test_labels_file: output/reports/attack/96e15e4d68b9ef4a6e63e78c304e9b53/test_labels.json + train_labels_file: output/reports/attack/96e15e4d68b9ef4a6e63e78c304e9b53/train_labels.json + model_dir: models + name: 96e15e4d68b9ef4a6e63e78c304e9b53 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 96e15e4d68b9ef4a6e63e78c304e9b53 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/976b409686726a24c9daddd017996105/params.yaml b/examples/security/kdd-nsl/output/reports/attack/976b409686726a24c9daddd017996105/params.yaml new file mode 100644 index 00000000..db6588ac --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/976b409686726a24c9daddd017996105/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 73ba26f18f0f0f964ba14471d55faee1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/976b409686726a24c9daddd017996105/adv_predictions.json + adv_probabilities_file: output/reports/attack/976b409686726a24c9daddd017996105/adv_probabilities.json + attack_file: output/attacks/95556ef41c3e306a5ea3da8c852e0541.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/976b409686726a24c9daddd017996105/params.yaml + predictions_file: output/reports/attack/976b409686726a24c9daddd017996105/predictions.json + probabilities_file: output/reports/attack/976b409686726a24c9daddd017996105/probabilities.json + score_dict_file: output/reports/attack/976b409686726a24c9daddd017996105/score_dict.json + test_labels_file: output/reports/attack/976b409686726a24c9daddd017996105/test_labels.json + train_labels_file: output/reports/attack/976b409686726a24c9daddd017996105/train_labels.json + model_dir: models + name: 976b409686726a24c9daddd017996105 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 976b409686726a24c9daddd017996105 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/977166f2e405fa6f274186f8a954622b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/977166f2e405fa6f274186f8a954622b/params.yaml new file mode 100644 index 00000000..57fb54b9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/977166f2e405fa6f274186f8a954622b/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 9cb03aed830182097d0e2963e58f14ac +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/977166f2e405fa6f274186f8a954622b/adv_predictions.json + adv_probabilities_file: output/reports/attack/977166f2e405fa6f274186f8a954622b/adv_probabilities.json + attack_file: output/attacks/472a58a5b0656fe8be65848b7eea4b05.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/977166f2e405fa6f274186f8a954622b/params.yaml + predictions_file: output/reports/attack/977166f2e405fa6f274186f8a954622b/predictions.json + probabilities_file: output/reports/attack/977166f2e405fa6f274186f8a954622b/probabilities.json + score_dict_file: output/reports/attack/977166f2e405fa6f274186f8a954622b/score_dict.json + test_labels_file: output/reports/attack/977166f2e405fa6f274186f8a954622b/test_labels.json + train_labels_file: output/reports/attack/977166f2e405fa6f274186f8a954622b/train_labels.json + model_dir: models + name: 977166f2e405fa6f274186f8a954622b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 977166f2e405fa6f274186f8a954622b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/97febc0a29c720f1e14af6160c9e6cef/params.yaml b/examples/security/kdd-nsl/output/reports/attack/97febc0a29c720f1e14af6160c9e6cef/params.yaml new file mode 100644 index 00000000..a8abb021 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/97febc0a29c720f1e14af6160c9e6cef/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 057a0e1a59890e1315ddac2aeeb5b398 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/97febc0a29c720f1e14af6160c9e6cef/adv_predictions.json + adv_probabilities_file: output/reports/attack/97febc0a29c720f1e14af6160c9e6cef/adv_probabilities.json + attack_file: output/attacks/5a416aa2b8bae32e3c44a43fde481824.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/97febc0a29c720f1e14af6160c9e6cef/params.yaml + predictions_file: output/reports/attack/97febc0a29c720f1e14af6160c9e6cef/predictions.json + probabilities_file: output/reports/attack/97febc0a29c720f1e14af6160c9e6cef/probabilities.json + score_dict_file: output/reports/attack/97febc0a29c720f1e14af6160c9e6cef/score_dict.json + test_labels_file: output/reports/attack/97febc0a29c720f1e14af6160c9e6cef/test_labels.json + train_labels_file: output/reports/attack/97febc0a29c720f1e14af6160c9e6cef/train_labels.json + model_dir: models + name: 97febc0a29c720f1e14af6160c9e6cef + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 97febc0a29c720f1e14af6160c9e6cef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/981450421d2c8f4efc8ab48bec11a94b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/981450421d2c8f4efc8ab48bec11a94b/params.yaml new file mode 100644 index 00000000..a4134048 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/981450421d2c8f4efc8ab48bec11a94b/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.01 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: cbf98e95c6b428819f7916db7da35c5c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/981450421d2c8f4efc8ab48bec11a94b/adv_predictions.json + adv_probabilities_file: output/reports/attack/981450421d2c8f4efc8ab48bec11a94b/adv_probabilities.json + attack_file: output/attacks/ce6f16e815fff9e66a390c23c908f1f6.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/981450421d2c8f4efc8ab48bec11a94b/params.yaml + predictions_file: output/reports/attack/981450421d2c8f4efc8ab48bec11a94b/predictions.json + probabilities_file: output/reports/attack/981450421d2c8f4efc8ab48bec11a94b/probabilities.json + score_dict_file: output/reports/attack/981450421d2c8f4efc8ab48bec11a94b/score_dict.json + test_labels_file: output/reports/attack/981450421d2c8f4efc8ab48bec11a94b/test_labels.json + train_labels_file: output/reports/attack/981450421d2c8f4efc8ab48bec11a94b/train_labels.json + model_dir: models + name: 981450421d2c8f4efc8ab48bec11a94b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 981450421d2c8f4efc8ab48bec11a94b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/987da4402b132206ccb45bd43fcbdd37/params.yaml b/examples/security/kdd-nsl/output/reports/attack/987da4402b132206ccb45bd43fcbdd37/params.yaml new file mode 100644 index 00000000..7ed78554 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/987da4402b132206ccb45bd43fcbdd37/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.5 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 5f2e63aa29de6a9242c7b4b3309dfc09 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/987da4402b132206ccb45bd43fcbdd37/adv_predictions.json + adv_probabilities_file: output/reports/attack/987da4402b132206ccb45bd43fcbdd37/adv_probabilities.json + attack_file: output/attacks/1d0eb371fc378d7a00b911a3665c4533.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/987da4402b132206ccb45bd43fcbdd37/params.yaml + predictions_file: output/reports/attack/987da4402b132206ccb45bd43fcbdd37/predictions.json + probabilities_file: output/reports/attack/987da4402b132206ccb45bd43fcbdd37/probabilities.json + score_dict_file: output/reports/attack/987da4402b132206ccb45bd43fcbdd37/score_dict.json + test_labels_file: output/reports/attack/987da4402b132206ccb45bd43fcbdd37/test_labels.json + train_labels_file: output/reports/attack/987da4402b132206ccb45bd43fcbdd37/train_labels.json + model_dir: models + name: 987da4402b132206ccb45bd43fcbdd37 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 987da4402b132206ccb45bd43fcbdd37 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/98d9b9a944fdf4f8925d52b55b00af4c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/98d9b9a944fdf4f8925d52b55b00af4c/params.yaml new file mode 100644 index 00000000..54a95b89 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/98d9b9a944fdf4f8925d52b55b00af4c/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.001 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: ae757b78954afc700cf0e324dc33982a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/98d9b9a944fdf4f8925d52b55b00af4c/adv_predictions.json + adv_probabilities_file: output/reports/attack/98d9b9a944fdf4f8925d52b55b00af4c/adv_probabilities.json + attack_file: output/attacks/b7292fa079f4650148c4b77aa5107239.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/98d9b9a944fdf4f8925d52b55b00af4c/params.yaml + predictions_file: output/reports/attack/98d9b9a944fdf4f8925d52b55b00af4c/predictions.json + probabilities_file: output/reports/attack/98d9b9a944fdf4f8925d52b55b00af4c/probabilities.json + score_dict_file: output/reports/attack/98d9b9a944fdf4f8925d52b55b00af4c/score_dict.json + test_labels_file: output/reports/attack/98d9b9a944fdf4f8925d52b55b00af4c/test_labels.json + train_labels_file: output/reports/attack/98d9b9a944fdf4f8925d52b55b00af4c/train_labels.json + model_dir: models + name: 98d9b9a944fdf4f8925d52b55b00af4c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 98d9b9a944fdf4f8925d52b55b00af4c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/99a7fc545a7dcb57532d1e255b28b3ef/params.yaml b/examples/security/kdd-nsl/output/reports/attack/99a7fc545a7dcb57532d1e255b28b3ef/params.yaml new file mode 100644 index 00000000..ad4c3b9a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/99a7fc545a7dcb57532d1e255b28b3ef/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.3 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 44de70fd49338d1e9f780972f939b39c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/99a7fc545a7dcb57532d1e255b28b3ef/adv_predictions.json + adv_probabilities_file: output/reports/attack/99a7fc545a7dcb57532d1e255b28b3ef/adv_probabilities.json + attack_file: output/attacks/6049d26c8b1f637ce33475a1da27feea.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/99a7fc545a7dcb57532d1e255b28b3ef/params.yaml + predictions_file: output/reports/attack/99a7fc545a7dcb57532d1e255b28b3ef/predictions.json + probabilities_file: output/reports/attack/99a7fc545a7dcb57532d1e255b28b3ef/probabilities.json + score_dict_file: output/reports/attack/99a7fc545a7dcb57532d1e255b28b3ef/score_dict.json + test_labels_file: output/reports/attack/99a7fc545a7dcb57532d1e255b28b3ef/test_labels.json + train_labels_file: output/reports/attack/99a7fc545a7dcb57532d1e255b28b3ef/train_labels.json + model_dir: models + name: 99a7fc545a7dcb57532d1e255b28b3ef + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 99a7fc545a7dcb57532d1e255b28b3ef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/99df5cd90bb51b12bf3a37ec99caee88/params.yaml b/examples/security/kdd-nsl/output/reports/attack/99df5cd90bb51b12bf3a37ec99caee88/params.yaml new file mode 100644 index 00000000..28fa198c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/99df5cd90bb51b12bf3a37ec99caee88/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.3 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 1cc7b1c90c8d9924998663811ff95156 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/99df5cd90bb51b12bf3a37ec99caee88/adv_predictions.json + adv_probabilities_file: output/reports/attack/99df5cd90bb51b12bf3a37ec99caee88/adv_probabilities.json + attack_file: output/attacks/19b178a69d234419610765f86e462916.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/99df5cd90bb51b12bf3a37ec99caee88/params.yaml + predictions_file: output/reports/attack/99df5cd90bb51b12bf3a37ec99caee88/predictions.json + probabilities_file: output/reports/attack/99df5cd90bb51b12bf3a37ec99caee88/probabilities.json + score_dict_file: output/reports/attack/99df5cd90bb51b12bf3a37ec99caee88/score_dict.json + test_labels_file: output/reports/attack/99df5cd90bb51b12bf3a37ec99caee88/test_labels.json + train_labels_file: output/reports/attack/99df5cd90bb51b12bf3a37ec99caee88/train_labels.json + model_dir: models + name: 99df5cd90bb51b12bf3a37ec99caee88 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 99df5cd90bb51b12bf3a37ec99caee88 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/9a0f938fe453e65273e8685ad50856fe/params.yaml b/examples/security/kdd-nsl/output/reports/attack/9a0f938fe453e65273e8685ad50856fe/params.yaml new file mode 100644 index 00000000..493c2e24 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/9a0f938fe453e65273e8685ad50856fe/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: b425a98653900ee6707b4866bbd1b1fd +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9a0f938fe453e65273e8685ad50856fe/adv_predictions.json + adv_probabilities_file: output/reports/attack/9a0f938fe453e65273e8685ad50856fe/adv_probabilities.json + attack_file: output/attacks/fb9652671fa80392e9a2f67f4b6e29da.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/9a0f938fe453e65273e8685ad50856fe/params.yaml + predictions_file: output/reports/attack/9a0f938fe453e65273e8685ad50856fe/predictions.json + probabilities_file: output/reports/attack/9a0f938fe453e65273e8685ad50856fe/probabilities.json + score_dict_file: output/reports/attack/9a0f938fe453e65273e8685ad50856fe/score_dict.json + test_labels_file: output/reports/attack/9a0f938fe453e65273e8685ad50856fe/test_labels.json + train_labels_file: output/reports/attack/9a0f938fe453e65273e8685ad50856fe/train_labels.json + model_dir: models + name: 9a0f938fe453e65273e8685ad50856fe + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 9a0f938fe453e65273e8685ad50856fe +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/9a79794cabc105953847ee5f3635e3dd/params.yaml b/examples/security/kdd-nsl/output/reports/attack/9a79794cabc105953847ee5f3635e3dd/params.yaml new file mode 100644 index 00000000..34215131 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/9a79794cabc105953847ee5f3635e3dd/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 8b927ee27a11934d641dce1a806cef8c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9a79794cabc105953847ee5f3635e3dd/adv_predictions.json + adv_probabilities_file: output/reports/attack/9a79794cabc105953847ee5f3635e3dd/adv_probabilities.json + attack_file: output/attacks/08c47a8c8cd905cdd22eaf404462d6a9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/9a79794cabc105953847ee5f3635e3dd/params.yaml + predictions_file: output/reports/attack/9a79794cabc105953847ee5f3635e3dd/predictions.json + probabilities_file: output/reports/attack/9a79794cabc105953847ee5f3635e3dd/probabilities.json + score_dict_file: output/reports/attack/9a79794cabc105953847ee5f3635e3dd/score_dict.json + test_labels_file: output/reports/attack/9a79794cabc105953847ee5f3635e3dd/test_labels.json + train_labels_file: output/reports/attack/9a79794cabc105953847ee5f3635e3dd/train_labels.json + model_dir: models + name: 9a79794cabc105953847ee5f3635e3dd + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 9a79794cabc105953847ee5f3635e3dd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/9a79ffce452416ce55fed10307216b79/params.yaml b/examples/security/kdd-nsl/output/reports/attack/9a79ffce452416ce55fed10307216b79/params.yaml new file mode 100644 index 00000000..2277cf3d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/9a79ffce452416ce55fed10307216b79/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.01 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 4a1d7cd5e0162445bdd3c8c6a50c638f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9a79ffce452416ce55fed10307216b79/adv_predictions.json + adv_probabilities_file: output/reports/attack/9a79ffce452416ce55fed10307216b79/adv_probabilities.json + attack_file: output/attacks/15436bbea67d6fa987bb7a06df45666d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/9a79ffce452416ce55fed10307216b79/params.yaml + predictions_file: output/reports/attack/9a79ffce452416ce55fed10307216b79/predictions.json + probabilities_file: output/reports/attack/9a79ffce452416ce55fed10307216b79/probabilities.json + score_dict_file: output/reports/attack/9a79ffce452416ce55fed10307216b79/score_dict.json + test_labels_file: output/reports/attack/9a79ffce452416ce55fed10307216b79/test_labels.json + train_labels_file: output/reports/attack/9a79ffce452416ce55fed10307216b79/train_labels.json + model_dir: models + name: 9a79ffce452416ce55fed10307216b79 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 9a79ffce452416ce55fed10307216b79 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/9ba7caa166ad035857ef0056d0dc9733/params.yaml b/examples/security/kdd-nsl/output/reports/attack/9ba7caa166ad035857ef0056d0dc9733/params.yaml new file mode 100644 index 00000000..5d198f32 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/9ba7caa166ad035857ef0056d0dc9733/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.3 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 3613147392b1434d5022d4f5da67886b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9ba7caa166ad035857ef0056d0dc9733/adv_predictions.json + adv_probabilities_file: output/reports/attack/9ba7caa166ad035857ef0056d0dc9733/adv_probabilities.json + attack_file: output/attacks/894834c0935ce4799123f2939b8b1e58.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/9ba7caa166ad035857ef0056d0dc9733/params.yaml + predictions_file: output/reports/attack/9ba7caa166ad035857ef0056d0dc9733/predictions.json + probabilities_file: output/reports/attack/9ba7caa166ad035857ef0056d0dc9733/probabilities.json + score_dict_file: output/reports/attack/9ba7caa166ad035857ef0056d0dc9733/score_dict.json + test_labels_file: output/reports/attack/9ba7caa166ad035857ef0056d0dc9733/test_labels.json + train_labels_file: output/reports/attack/9ba7caa166ad035857ef0056d0dc9733/train_labels.json + model_dir: models + name: 9ba7caa166ad035857ef0056d0dc9733 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 9ba7caa166ad035857ef0056d0dc9733 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/9c2dfdc8e3f229c2d52552262c50351d/params.yaml b/examples/security/kdd-nsl/output/reports/attack/9c2dfdc8e3f229c2d52552262c50351d/params.yaml new file mode 100644 index 00000000..86e10a6b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/9c2dfdc8e3f229c2d52552262c50351d/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 56e85c56fdb98a8218c80295442ac45b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9c2dfdc8e3f229c2d52552262c50351d/adv_predictions.json + adv_probabilities_file: output/reports/attack/9c2dfdc8e3f229c2d52552262c50351d/adv_probabilities.json + attack_file: output/attacks/071806df105c9057de161fbdcf0e7a0d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/9c2dfdc8e3f229c2d52552262c50351d/params.yaml + predictions_file: output/reports/attack/9c2dfdc8e3f229c2d52552262c50351d/predictions.json + probabilities_file: output/reports/attack/9c2dfdc8e3f229c2d52552262c50351d/probabilities.json + score_dict_file: output/reports/attack/9c2dfdc8e3f229c2d52552262c50351d/score_dict.json + test_labels_file: output/reports/attack/9c2dfdc8e3f229c2d52552262c50351d/test_labels.json + train_labels_file: output/reports/attack/9c2dfdc8e3f229c2d52552262c50351d/train_labels.json + model_dir: models + name: 9c2dfdc8e3f229c2d52552262c50351d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 9c2dfdc8e3f229c2d52552262c50351d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/9da00d0f381d7f18caaaaf7420f79bb4/params.yaml b/examples/security/kdd-nsl/output/reports/attack/9da00d0f381d7f18caaaaf7420f79bb4/params.yaml new file mode 100644 index 00000000..693183dc --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/9da00d0f381d7f18caaaaf7420f79bb4/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 5031ba66d1f67ba142f491305a2f7884 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9da00d0f381d7f18caaaaf7420f79bb4/adv_predictions.json + adv_probabilities_file: output/reports/attack/9da00d0f381d7f18caaaaf7420f79bb4/adv_probabilities.json + attack_file: output/attacks/d4c3a41e273915c5fffb799f3bf6f4cf.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/9da00d0f381d7f18caaaaf7420f79bb4/params.yaml + predictions_file: output/reports/attack/9da00d0f381d7f18caaaaf7420f79bb4/predictions.json + probabilities_file: output/reports/attack/9da00d0f381d7f18caaaaf7420f79bb4/probabilities.json + score_dict_file: output/reports/attack/9da00d0f381d7f18caaaaf7420f79bb4/score_dict.json + test_labels_file: output/reports/attack/9da00d0f381d7f18caaaaf7420f79bb4/test_labels.json + train_labels_file: output/reports/attack/9da00d0f381d7f18caaaaf7420f79bb4/train_labels.json + model_dir: models + name: 9da00d0f381d7f18caaaaf7420f79bb4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 9da00d0f381d7f18caaaaf7420f79bb4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/9dea9c251ff060b9302a45d35e0b4a75/params.yaml b/examples/security/kdd-nsl/output/reports/attack/9dea9c251ff060b9302a45d35e0b4a75/params.yaml new file mode 100644 index 00000000..744f2fbe --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/9dea9c251ff060b9302a45d35e0b4a75/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.5 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: bb30c41fdb2bd9581177f911c9908a03 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9dea9c251ff060b9302a45d35e0b4a75/adv_predictions.json + adv_probabilities_file: output/reports/attack/9dea9c251ff060b9302a45d35e0b4a75/adv_probabilities.json + attack_file: output/attacks/fd03f1035f1f0a0894d71ec6dc1e0d9c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/9dea9c251ff060b9302a45d35e0b4a75/params.yaml + predictions_file: output/reports/attack/9dea9c251ff060b9302a45d35e0b4a75/predictions.json + probabilities_file: output/reports/attack/9dea9c251ff060b9302a45d35e0b4a75/probabilities.json + score_dict_file: output/reports/attack/9dea9c251ff060b9302a45d35e0b4a75/score_dict.json + test_labels_file: output/reports/attack/9dea9c251ff060b9302a45d35e0b4a75/test_labels.json + train_labels_file: output/reports/attack/9dea9c251ff060b9302a45d35e0b4a75/train_labels.json + model_dir: models + name: 9dea9c251ff060b9302a45d35e0b4a75 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 9dea9c251ff060b9302a45d35e0b4a75 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/9e09e59e25a99fa73b6a79b664124c0a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/9e09e59e25a99fa73b6a79b664124c0a/params.yaml new file mode 100644 index 00000000..e47494a5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/9e09e59e25a99fa73b6a79b664124c0a/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.01 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: bd65b9a61d66cc80bf744ab25f45eb7a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9e09e59e25a99fa73b6a79b664124c0a/adv_predictions.json + adv_probabilities_file: output/reports/attack/9e09e59e25a99fa73b6a79b664124c0a/adv_probabilities.json + attack_file: output/attacks/e8b3e35a2d72971373eb2ddf65d2b7e3.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/9e09e59e25a99fa73b6a79b664124c0a/params.yaml + predictions_file: output/reports/attack/9e09e59e25a99fa73b6a79b664124c0a/predictions.json + probabilities_file: output/reports/attack/9e09e59e25a99fa73b6a79b664124c0a/probabilities.json + score_dict_file: output/reports/attack/9e09e59e25a99fa73b6a79b664124c0a/score_dict.json + test_labels_file: output/reports/attack/9e09e59e25a99fa73b6a79b664124c0a/test_labels.json + train_labels_file: output/reports/attack/9e09e59e25a99fa73b6a79b664124c0a/train_labels.json + model_dir: models + name: 9e09e59e25a99fa73b6a79b664124c0a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: 9e09e59e25a99fa73b6a79b664124c0a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/params.yaml b/examples/security/kdd-nsl/output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/params.yaml new file mode 100644 index 00000000..d73e982d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: bbda3a0e3450e72196b52dcee4001c4e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/adv_predictions.json + adv_probabilities_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/adv_probabilities.json + attack_file: output/attacks/ddf969f554f4543dc2f125f772a2220d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/params.yaml + predictions_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/predictions.json + probabilities_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/probabilities.json + score_dict_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/score_dict.json + test_labels_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/test_labels.json + train_labels_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/train_labels.json + model_dir: models + name: 9ec8f3e4f43d1b31320f5164422ca1a2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 9ec8f3e4f43d1b31320f5164422ca1a2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/9f0c81089c6aa4a6738e5c79f484bffb/params.yaml b/examples/security/kdd-nsl/output/reports/attack/9f0c81089c6aa4a6738e5c79f484bffb/params.yaml new file mode 100644 index 00000000..cbe9790f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/9f0c81089c6aa4a6738e5c79f484bffb/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 96538db7eb6cbf61b0b8a771f2427fe1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9f0c81089c6aa4a6738e5c79f484bffb/adv_predictions.json + adv_probabilities_file: output/reports/attack/9f0c81089c6aa4a6738e5c79f484bffb/adv_probabilities.json + attack_file: output/attacks/b051ae26d11c4a93e8a66f940bbf5805.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/9f0c81089c6aa4a6738e5c79f484bffb/params.yaml + predictions_file: output/reports/attack/9f0c81089c6aa4a6738e5c79f484bffb/predictions.json + probabilities_file: output/reports/attack/9f0c81089c6aa4a6738e5c79f484bffb/probabilities.json + score_dict_file: output/reports/attack/9f0c81089c6aa4a6738e5c79f484bffb/score_dict.json + test_labels_file: output/reports/attack/9f0c81089c6aa4a6738e5c79f484bffb/test_labels.json + train_labels_file: output/reports/attack/9f0c81089c6aa4a6738e5c79f484bffb/train_labels.json + model_dir: models + name: 9f0c81089c6aa4a6738e5c79f484bffb + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 9f0c81089c6aa4a6738e5c79f484bffb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a03c5b835aaa36ae3fbb7ecada087727/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a03c5b835aaa36ae3fbb7ecada087727/params.yaml new file mode 100644 index 00000000..4386a0d3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a03c5b835aaa36ae3fbb7ecada087727/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: b8572a6d250ace908018ce7e10a4be7f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a03c5b835aaa36ae3fbb7ecada087727/adv_predictions.json + adv_probabilities_file: output/reports/attack/a03c5b835aaa36ae3fbb7ecada087727/adv_probabilities.json + attack_file: output/attacks/e90c95c41fbfa737836ea45e08fab561.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/a03c5b835aaa36ae3fbb7ecada087727/params.yaml + predictions_file: output/reports/attack/a03c5b835aaa36ae3fbb7ecada087727/predictions.json + probabilities_file: output/reports/attack/a03c5b835aaa36ae3fbb7ecada087727/probabilities.json + score_dict_file: output/reports/attack/a03c5b835aaa36ae3fbb7ecada087727/score_dict.json + test_labels_file: output/reports/attack/a03c5b835aaa36ae3fbb7ecada087727/test_labels.json + train_labels_file: output/reports/attack/a03c5b835aaa36ae3fbb7ecada087727/train_labels.json + model_dir: models + name: a03c5b835aaa36ae3fbb7ecada087727 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: a03c5b835aaa36ae3fbb7ecada087727 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a0e9f481d457d859e338ee25197261d1/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a0e9f481d457d859e338ee25197261d1/params.yaml new file mode 100644 index 00000000..b87245de --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a0e9f481d457d859e338ee25197261d1/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 51b4214f88f25314e7ed2a9c4c070878 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a0e9f481d457d859e338ee25197261d1/adv_predictions.json + adv_probabilities_file: output/reports/attack/a0e9f481d457d859e338ee25197261d1/adv_probabilities.json + attack_file: output/attacks/337d7987aae9d3ba60bb547e0778bf39.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/a0e9f481d457d859e338ee25197261d1/params.yaml + predictions_file: output/reports/attack/a0e9f481d457d859e338ee25197261d1/predictions.json + probabilities_file: output/reports/attack/a0e9f481d457d859e338ee25197261d1/probabilities.json + score_dict_file: output/reports/attack/a0e9f481d457d859e338ee25197261d1/score_dict.json + test_labels_file: output/reports/attack/a0e9f481d457d859e338ee25197261d1/test_labels.json + train_labels_file: output/reports/attack/a0e9f481d457d859e338ee25197261d1/train_labels.json + model_dir: models + name: a0e9f481d457d859e338ee25197261d1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: a0e9f481d457d859e338ee25197261d1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a1307f9ba94a77659c80bc64935cc65d/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a1307f9ba94a77659c80bc64935cc65d/params.yaml new file mode 100644 index 00000000..9914ea47 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a1307f9ba94a77659c80bc64935cc65d/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 3ca54fee54896810b53a2eb8c0b7c09c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a1307f9ba94a77659c80bc64935cc65d/adv_predictions.json + adv_probabilities_file: output/reports/attack/a1307f9ba94a77659c80bc64935cc65d/adv_probabilities.json + attack_file: output/attacks/84aa292dcb574a5e6fb49b8fe0c57dde.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/a1307f9ba94a77659c80bc64935cc65d/params.yaml + predictions_file: output/reports/attack/a1307f9ba94a77659c80bc64935cc65d/predictions.json + probabilities_file: output/reports/attack/a1307f9ba94a77659c80bc64935cc65d/probabilities.json + score_dict_file: output/reports/attack/a1307f9ba94a77659c80bc64935cc65d/score_dict.json + test_labels_file: output/reports/attack/a1307f9ba94a77659c80bc64935cc65d/test_labels.json + train_labels_file: output/reports/attack/a1307f9ba94a77659c80bc64935cc65d/train_labels.json + model_dir: models + name: a1307f9ba94a77659c80bc64935cc65d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: a1307f9ba94a77659c80bc64935cc65d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a1c17efbd9dcc1cbfe893550095b970b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a1c17efbd9dcc1cbfe893550095b970b/params.yaml new file mode 100644 index 00000000..89f72e19 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a1c17efbd9dcc1cbfe893550095b970b/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.01 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: dbeeef31dae70a7fd920f5b128753582 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a1c17efbd9dcc1cbfe893550095b970b/adv_predictions.json + adv_probabilities_file: output/reports/attack/a1c17efbd9dcc1cbfe893550095b970b/adv_probabilities.json + attack_file: output/attacks/16abbcb8fa5ce799281718597ec6e950.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/a1c17efbd9dcc1cbfe893550095b970b/params.yaml + predictions_file: output/reports/attack/a1c17efbd9dcc1cbfe893550095b970b/predictions.json + probabilities_file: output/reports/attack/a1c17efbd9dcc1cbfe893550095b970b/probabilities.json + score_dict_file: output/reports/attack/a1c17efbd9dcc1cbfe893550095b970b/score_dict.json + test_labels_file: output/reports/attack/a1c17efbd9dcc1cbfe893550095b970b/test_labels.json + train_labels_file: output/reports/attack/a1c17efbd9dcc1cbfe893550095b970b/train_labels.json + model_dir: models + name: a1c17efbd9dcc1cbfe893550095b970b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: a1c17efbd9dcc1cbfe893550095b970b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a2de4cc0de4e56021d0d5fa121988b82/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a2de4cc0de4e56021d0d5fa121988b82/params.yaml new file mode 100644 index 00000000..5a3e5aac --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a2de4cc0de4e56021d0d5fa121988b82/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.3 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: aa893356fce53aec5beec62abc70bcfb +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a2de4cc0de4e56021d0d5fa121988b82/adv_predictions.json + adv_probabilities_file: output/reports/attack/a2de4cc0de4e56021d0d5fa121988b82/adv_probabilities.json + attack_file: output/attacks/42948356c2f52f5f2044c0eeea3297ab.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/a2de4cc0de4e56021d0d5fa121988b82/params.yaml + predictions_file: output/reports/attack/a2de4cc0de4e56021d0d5fa121988b82/predictions.json + probabilities_file: output/reports/attack/a2de4cc0de4e56021d0d5fa121988b82/probabilities.json + score_dict_file: output/reports/attack/a2de4cc0de4e56021d0d5fa121988b82/score_dict.json + test_labels_file: output/reports/attack/a2de4cc0de4e56021d0d5fa121988b82/test_labels.json + train_labels_file: output/reports/attack/a2de4cc0de4e56021d0d5fa121988b82/train_labels.json + model_dir: models + name: a2de4cc0de4e56021d0d5fa121988b82 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: a2de4cc0de4e56021d0d5fa121988b82 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a3775ff7bf0b1f1f6f1bd5189f33cc53/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a3775ff7bf0b1f1f6f1bd5189f33cc53/params.yaml new file mode 100644 index 00000000..45983263 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a3775ff7bf0b1f1f6f1bd5189f33cc53/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.3 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: e03f204c2536ea3aca6e03facac33fd2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a3775ff7bf0b1f1f6f1bd5189f33cc53/adv_predictions.json + adv_probabilities_file: output/reports/attack/a3775ff7bf0b1f1f6f1bd5189f33cc53/adv_probabilities.json + attack_file: output/attacks/3b5f7f26bd973959e2742eb362165188.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/a3775ff7bf0b1f1f6f1bd5189f33cc53/params.yaml + predictions_file: output/reports/attack/a3775ff7bf0b1f1f6f1bd5189f33cc53/predictions.json + probabilities_file: output/reports/attack/a3775ff7bf0b1f1f6f1bd5189f33cc53/probabilities.json + score_dict_file: output/reports/attack/a3775ff7bf0b1f1f6f1bd5189f33cc53/score_dict.json + test_labels_file: output/reports/attack/a3775ff7bf0b1f1f6f1bd5189f33cc53/test_labels.json + train_labels_file: output/reports/attack/a3775ff7bf0b1f1f6f1bd5189f33cc53/train_labels.json + model_dir: models + name: a3775ff7bf0b1f1f6f1bd5189f33cc53 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: a3775ff7bf0b1f1f6f1bd5189f33cc53 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a38496b64d3d6bf2a9f77589b45aeb28/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a38496b64d3d6bf2a9f77589b45aeb28/params.yaml new file mode 100644 index 00000000..a3b7c3eb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a38496b64d3d6bf2a9f77589b45aeb28/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: ab34238bb4ad84701fb1252f0cafd8da +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a38496b64d3d6bf2a9f77589b45aeb28/adv_predictions.json + adv_probabilities_file: output/reports/attack/a38496b64d3d6bf2a9f77589b45aeb28/adv_probabilities.json + attack_file: output/attacks/650f719ff9d13b0b800f082fb235e4cf.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/a38496b64d3d6bf2a9f77589b45aeb28/params.yaml + predictions_file: output/reports/attack/a38496b64d3d6bf2a9f77589b45aeb28/predictions.json + probabilities_file: output/reports/attack/a38496b64d3d6bf2a9f77589b45aeb28/probabilities.json + score_dict_file: output/reports/attack/a38496b64d3d6bf2a9f77589b45aeb28/score_dict.json + test_labels_file: output/reports/attack/a38496b64d3d6bf2a9f77589b45aeb28/test_labels.json + train_labels_file: output/reports/attack/a38496b64d3d6bf2a9f77589b45aeb28/train_labels.json + model_dir: models + name: a38496b64d3d6bf2a9f77589b45aeb28 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: a38496b64d3d6bf2a9f77589b45aeb28 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a47d13f5637b0150e2ba959cbfcb8276/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a47d13f5637b0150e2ba959cbfcb8276/params.yaml new file mode 100644 index 00000000..26129757 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a47d13f5637b0150e2ba959cbfcb8276/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 0bb4a1d1f1c937f6e67721474216405c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a47d13f5637b0150e2ba959cbfcb8276/adv_predictions.json + adv_probabilities_file: output/reports/attack/a47d13f5637b0150e2ba959cbfcb8276/adv_probabilities.json + attack_file: output/attacks/ab2e1db078f92f9e930a5faa184ae1d1.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/a47d13f5637b0150e2ba959cbfcb8276/params.yaml + predictions_file: output/reports/attack/a47d13f5637b0150e2ba959cbfcb8276/predictions.json + probabilities_file: output/reports/attack/a47d13f5637b0150e2ba959cbfcb8276/probabilities.json + score_dict_file: output/reports/attack/a47d13f5637b0150e2ba959cbfcb8276/score_dict.json + test_labels_file: output/reports/attack/a47d13f5637b0150e2ba959cbfcb8276/test_labels.json + train_labels_file: output/reports/attack/a47d13f5637b0150e2ba959cbfcb8276/train_labels.json + model_dir: models + name: a47d13f5637b0150e2ba959cbfcb8276 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: a47d13f5637b0150e2ba959cbfcb8276 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a4832423a6c938999da62f51e93ddf32/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a4832423a6c938999da62f51e93ddf32/params.yaml new file mode 100644 index 00000000..76c464c7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a4832423a6c938999da62f51e93ddf32/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.5 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 64cc02c995c1644961937b317f0d5b0b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a4832423a6c938999da62f51e93ddf32/adv_predictions.json + adv_probabilities_file: output/reports/attack/a4832423a6c938999da62f51e93ddf32/adv_probabilities.json + attack_file: output/attacks/f9c73a1391a5980e217597c9f27a6b3e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/a4832423a6c938999da62f51e93ddf32/params.yaml + predictions_file: output/reports/attack/a4832423a6c938999da62f51e93ddf32/predictions.json + probabilities_file: output/reports/attack/a4832423a6c938999da62f51e93ddf32/probabilities.json + score_dict_file: output/reports/attack/a4832423a6c938999da62f51e93ddf32/score_dict.json + test_labels_file: output/reports/attack/a4832423a6c938999da62f51e93ddf32/test_labels.json + train_labels_file: output/reports/attack/a4832423a6c938999da62f51e93ddf32/train_labels.json + model_dir: models + name: a4832423a6c938999da62f51e93ddf32 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: a4832423a6c938999da62f51e93ddf32 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a564ec40bef84ac71880e20efa84b0a1/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a564ec40bef84ac71880e20efa84b0a1/params.yaml new file mode 100644 index 00000000..81413a0d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a564ec40bef84ac71880e20efa84b0a1/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 099f423b797a6adfcd29f2cd47b0ef1b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a564ec40bef84ac71880e20efa84b0a1/adv_predictions.json + adv_probabilities_file: output/reports/attack/a564ec40bef84ac71880e20efa84b0a1/adv_probabilities.json + attack_file: output/attacks/27daa3427c24b6550f39f739ab64b02f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/a564ec40bef84ac71880e20efa84b0a1/params.yaml + predictions_file: output/reports/attack/a564ec40bef84ac71880e20efa84b0a1/predictions.json + probabilities_file: output/reports/attack/a564ec40bef84ac71880e20efa84b0a1/probabilities.json + score_dict_file: output/reports/attack/a564ec40bef84ac71880e20efa84b0a1/score_dict.json + test_labels_file: output/reports/attack/a564ec40bef84ac71880e20efa84b0a1/test_labels.json + train_labels_file: output/reports/attack/a564ec40bef84ac71880e20efa84b0a1/train_labels.json + model_dir: models + name: a564ec40bef84ac71880e20efa84b0a1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: a564ec40bef84ac71880e20efa84b0a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a65a1cc911565b10f75d1dc03eb108d3/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a65a1cc911565b10f75d1dc03eb108d3/params.yaml new file mode 100644 index 00000000..b73a489f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a65a1cc911565b10f75d1dc03eb108d3/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.001 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 50a48663cc728e6f6a7aca1433bda36f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a65a1cc911565b10f75d1dc03eb108d3/adv_predictions.json + adv_probabilities_file: output/reports/attack/a65a1cc911565b10f75d1dc03eb108d3/adv_probabilities.json + attack_file: output/attacks/a762f9cb3dacd4755aa06d09a143cf69.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/a65a1cc911565b10f75d1dc03eb108d3/params.yaml + predictions_file: output/reports/attack/a65a1cc911565b10f75d1dc03eb108d3/predictions.json + probabilities_file: output/reports/attack/a65a1cc911565b10f75d1dc03eb108d3/probabilities.json + score_dict_file: output/reports/attack/a65a1cc911565b10f75d1dc03eb108d3/score_dict.json + test_labels_file: output/reports/attack/a65a1cc911565b10f75d1dc03eb108d3/test_labels.json + train_labels_file: output/reports/attack/a65a1cc911565b10f75d1dc03eb108d3/train_labels.json + model_dir: models + name: a65a1cc911565b10f75d1dc03eb108d3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: a65a1cc911565b10f75d1dc03eb108d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a76cf1c3caa3d6f16154b877790ce43e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a76cf1c3caa3d6f16154b877790ce43e/params.yaml new file mode 100644 index 00000000..a6920f47 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a76cf1c3caa3d6f16154b877790ce43e/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.001 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 09584d915c7bcc3f0ad9c17ba484ba37 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a76cf1c3caa3d6f16154b877790ce43e/adv_predictions.json + adv_probabilities_file: output/reports/attack/a76cf1c3caa3d6f16154b877790ce43e/adv_probabilities.json + attack_file: output/attacks/7fac6ac10788a70e2f1523f7fee02ae4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/a76cf1c3caa3d6f16154b877790ce43e/params.yaml + predictions_file: output/reports/attack/a76cf1c3caa3d6f16154b877790ce43e/predictions.json + probabilities_file: output/reports/attack/a76cf1c3caa3d6f16154b877790ce43e/probabilities.json + score_dict_file: output/reports/attack/a76cf1c3caa3d6f16154b877790ce43e/score_dict.json + test_labels_file: output/reports/attack/a76cf1c3caa3d6f16154b877790ce43e/test_labels.json + train_labels_file: output/reports/attack/a76cf1c3caa3d6f16154b877790ce43e/train_labels.json + model_dir: models + name: a76cf1c3caa3d6f16154b877790ce43e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: a76cf1c3caa3d6f16154b877790ce43e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a841ee521f7b96a7725be5e7335d04cf/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a841ee521f7b96a7725be5e7335d04cf/params.yaml new file mode 100644 index 00000000..685829f0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a841ee521f7b96a7725be5e7335d04cf/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: b1e2feb014f4f283f9c2581bd8fdf962 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a841ee521f7b96a7725be5e7335d04cf/adv_predictions.json + adv_probabilities_file: output/reports/attack/a841ee521f7b96a7725be5e7335d04cf/adv_probabilities.json + attack_file: output/attacks/e163345e784516e5ba8857036a0d83d1.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/a841ee521f7b96a7725be5e7335d04cf/params.yaml + predictions_file: output/reports/attack/a841ee521f7b96a7725be5e7335d04cf/predictions.json + probabilities_file: output/reports/attack/a841ee521f7b96a7725be5e7335d04cf/probabilities.json + score_dict_file: output/reports/attack/a841ee521f7b96a7725be5e7335d04cf/score_dict.json + test_labels_file: output/reports/attack/a841ee521f7b96a7725be5e7335d04cf/test_labels.json + train_labels_file: output/reports/attack/a841ee521f7b96a7725be5e7335d04cf/train_labels.json + model_dir: models + name: a841ee521f7b96a7725be5e7335d04cf + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: a841ee521f7b96a7725be5e7335d04cf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/params.yaml new file mode 100644 index 00000000..08512600 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.001 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: c5987a2c5d270afe698e11b3256f0f64 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/adv_predictions.json + adv_probabilities_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/adv_probabilities.json + attack_file: output/attacks/2a65f4c64a48dcb2ddea2f4244985311.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/params.yaml + predictions_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/predictions.json + probabilities_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/probabilities.json + score_dict_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/score_dict.json + test_labels_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/test_labels.json + train_labels_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/train_labels.json + model_dir: models + name: a85cb94d158ef3b8ad1c777803ed8dbe + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: a85cb94d158ef3b8ad1c777803ed8dbe +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a85d6c8a9e9a8df6c3e32f5540f451a6/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a85d6c8a9e9a8df6c3e32f5540f451a6/params.yaml new file mode 100644 index 00000000..a2c8a88c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a85d6c8a9e9a8df6c3e32f5540f451a6/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 84910837e6390984da698324c04713f6 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a85d6c8a9e9a8df6c3e32f5540f451a6/adv_predictions.json + adv_probabilities_file: output/reports/attack/a85d6c8a9e9a8df6c3e32f5540f451a6/adv_probabilities.json + attack_file: output/attacks/d19ac1f93a2986ea830818e4c59d58a3.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/a85d6c8a9e9a8df6c3e32f5540f451a6/params.yaml + predictions_file: output/reports/attack/a85d6c8a9e9a8df6c3e32f5540f451a6/predictions.json + probabilities_file: output/reports/attack/a85d6c8a9e9a8df6c3e32f5540f451a6/probabilities.json + score_dict_file: output/reports/attack/a85d6c8a9e9a8df6c3e32f5540f451a6/score_dict.json + test_labels_file: output/reports/attack/a85d6c8a9e9a8df6c3e32f5540f451a6/test_labels.json + train_labels_file: output/reports/attack/a85d6c8a9e9a8df6c3e32f5540f451a6/train_labels.json + model_dir: models + name: a85d6c8a9e9a8df6c3e32f5540f451a6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: a85d6c8a9e9a8df6c3e32f5540f451a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a8d61cb045027ac7ddb44030cb4bcb5b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a8d61cb045027ac7ddb44030cb4bcb5b/params.yaml new file mode 100644 index 00000000..4313b1c6 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a8d61cb045027ac7ddb44030cb4bcb5b/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 8a8102aed13605df4f93f8b22680d2ae +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a8d61cb045027ac7ddb44030cb4bcb5b/adv_predictions.json + adv_probabilities_file: output/reports/attack/a8d61cb045027ac7ddb44030cb4bcb5b/adv_probabilities.json + attack_file: output/attacks/dcb7f9df7362c3ac97aab0932cee5960.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/a8d61cb045027ac7ddb44030cb4bcb5b/params.yaml + predictions_file: output/reports/attack/a8d61cb045027ac7ddb44030cb4bcb5b/predictions.json + probabilities_file: output/reports/attack/a8d61cb045027ac7ddb44030cb4bcb5b/probabilities.json + score_dict_file: output/reports/attack/a8d61cb045027ac7ddb44030cb4bcb5b/score_dict.json + test_labels_file: output/reports/attack/a8d61cb045027ac7ddb44030cb4bcb5b/test_labels.json + train_labels_file: output/reports/attack/a8d61cb045027ac7ddb44030cb4bcb5b/train_labels.json + model_dir: models + name: a8d61cb045027ac7ddb44030cb4bcb5b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: a8d61cb045027ac7ddb44030cb4bcb5b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a9663d4d4f12fbd9ce11a43467b02246/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a9663d4d4f12fbd9ce11a43467b02246/params.yaml new file mode 100644 index 00000000..7b1fd0d2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a9663d4d4f12fbd9ce11a43467b02246/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 8226d85ef0308aebd3e0a2b4c6b8babc +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a9663d4d4f12fbd9ce11a43467b02246/adv_predictions.json + adv_probabilities_file: output/reports/attack/a9663d4d4f12fbd9ce11a43467b02246/adv_probabilities.json + attack_file: output/attacks/361a181553deb7d8cf417bf730a09015.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/a9663d4d4f12fbd9ce11a43467b02246/params.yaml + predictions_file: output/reports/attack/a9663d4d4f12fbd9ce11a43467b02246/predictions.json + probabilities_file: output/reports/attack/a9663d4d4f12fbd9ce11a43467b02246/probabilities.json + score_dict_file: output/reports/attack/a9663d4d4f12fbd9ce11a43467b02246/score_dict.json + test_labels_file: output/reports/attack/a9663d4d4f12fbd9ce11a43467b02246/test_labels.json + train_labels_file: output/reports/attack/a9663d4d4f12fbd9ce11a43467b02246/train_labels.json + model_dir: models + name: a9663d4d4f12fbd9ce11a43467b02246 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: a9663d4d4f12fbd9ce11a43467b02246 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a989b34a75335e1abc79ff584530649e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a989b34a75335e1abc79ff584530649e/params.yaml new file mode 100644 index 00000000..12fe26a6 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a989b34a75335e1abc79ff584530649e/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: fbc53bdd22815505609a8713352c2958 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a989b34a75335e1abc79ff584530649e/adv_predictions.json + adv_probabilities_file: output/reports/attack/a989b34a75335e1abc79ff584530649e/adv_probabilities.json + attack_file: output/attacks/4d928586238ac4866898277561246d0c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/a989b34a75335e1abc79ff584530649e/params.yaml + predictions_file: output/reports/attack/a989b34a75335e1abc79ff584530649e/predictions.json + probabilities_file: output/reports/attack/a989b34a75335e1abc79ff584530649e/probabilities.json + score_dict_file: output/reports/attack/a989b34a75335e1abc79ff584530649e/score_dict.json + test_labels_file: output/reports/attack/a989b34a75335e1abc79ff584530649e/test_labels.json + train_labels_file: output/reports/attack/a989b34a75335e1abc79ff584530649e/train_labels.json + model_dir: models + name: a989b34a75335e1abc79ff584530649e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: a989b34a75335e1abc79ff584530649e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/a98eb4f6ed8b12e447e1d00f9d35edb7/params.yaml b/examples/security/kdd-nsl/output/reports/attack/a98eb4f6ed8b12e447e1d00f9d35edb7/params.yaml new file mode 100644 index 00000000..204c3518 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/a98eb4f6ed8b12e447e1d00f9d35edb7/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 31da113cc448a6eee5a5bed9d2da5aad +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a98eb4f6ed8b12e447e1d00f9d35edb7/adv_predictions.json + adv_probabilities_file: output/reports/attack/a98eb4f6ed8b12e447e1d00f9d35edb7/adv_probabilities.json + attack_file: output/attacks/15ac8e3d0d1ae4f5d133f8f07594b15a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/a98eb4f6ed8b12e447e1d00f9d35edb7/params.yaml + predictions_file: output/reports/attack/a98eb4f6ed8b12e447e1d00f9d35edb7/predictions.json + probabilities_file: output/reports/attack/a98eb4f6ed8b12e447e1d00f9d35edb7/probabilities.json + score_dict_file: output/reports/attack/a98eb4f6ed8b12e447e1d00f9d35edb7/score_dict.json + test_labels_file: output/reports/attack/a98eb4f6ed8b12e447e1d00f9d35edb7/test_labels.json + train_labels_file: output/reports/attack/a98eb4f6ed8b12e447e1d00f9d35edb7/train_labels.json + model_dir: models + name: a98eb4f6ed8b12e447e1d00f9d35edb7 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: a98eb4f6ed8b12e447e1d00f9d35edb7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ab6c947d043a56ee5c08d9a9250f301b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ab6c947d043a56ee5c08d9a9250f301b/params.yaml new file mode 100644 index 00000000..a29ab3d6 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ab6c947d043a56ee5c08d9a9250f301b/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 1 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 8a3936b43224e983d6e71c5fbd83afd0 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ab6c947d043a56ee5c08d9a9250f301b/adv_predictions.json + adv_probabilities_file: output/reports/attack/ab6c947d043a56ee5c08d9a9250f301b/adv_probabilities.json + attack_file: output/attacks/f90aaa703a1c06fff620eb9a4f4ddb70.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/ab6c947d043a56ee5c08d9a9250f301b/params.yaml + predictions_file: output/reports/attack/ab6c947d043a56ee5c08d9a9250f301b/predictions.json + probabilities_file: output/reports/attack/ab6c947d043a56ee5c08d9a9250f301b/probabilities.json + score_dict_file: output/reports/attack/ab6c947d043a56ee5c08d9a9250f301b/score_dict.json + test_labels_file: output/reports/attack/ab6c947d043a56ee5c08d9a9250f301b/test_labels.json + train_labels_file: output/reports/attack/ab6c947d043a56ee5c08d9a9250f301b/train_labels.json + model_dir: models + name: ab6c947d043a56ee5c08d9a9250f301b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: ab6c947d043a56ee5c08d9a9250f301b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/abe93806a3930a3495fb4f4b84dd52b4/params.yaml b/examples/security/kdd-nsl/output/reports/attack/abe93806a3930a3495fb4f4b84dd52b4/params.yaml new file mode 100644 index 00000000..dd5fa924 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/abe93806a3930a3495fb4f4b84dd52b4/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.01 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: c91aaa3960cb4cdac0743df88a5e7c7e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/abe93806a3930a3495fb4f4b84dd52b4/adv_predictions.json + adv_probabilities_file: output/reports/attack/abe93806a3930a3495fb4f4b84dd52b4/adv_probabilities.json + attack_file: output/attacks/3a1c4736fd3eaf5e251746b33feb8f22.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/abe93806a3930a3495fb4f4b84dd52b4/params.yaml + predictions_file: output/reports/attack/abe93806a3930a3495fb4f4b84dd52b4/predictions.json + probabilities_file: output/reports/attack/abe93806a3930a3495fb4f4b84dd52b4/probabilities.json + score_dict_file: output/reports/attack/abe93806a3930a3495fb4f4b84dd52b4/score_dict.json + test_labels_file: output/reports/attack/abe93806a3930a3495fb4f4b84dd52b4/test_labels.json + train_labels_file: output/reports/attack/abe93806a3930a3495fb4f4b84dd52b4/train_labels.json + model_dir: models + name: abe93806a3930a3495fb4f4b84dd52b4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: abe93806a3930a3495fb4f4b84dd52b4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/aca4d7487cbd9f947ed4e52092b5a30c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/aca4d7487cbd9f947ed4e52092b5a30c/params.yaml new file mode 100644 index 00000000..be41613c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/aca4d7487cbd9f947ed4e52092b5a30c/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 3da81c4f1a805bbca6d7280f0dde273c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/aca4d7487cbd9f947ed4e52092b5a30c/adv_predictions.json + adv_probabilities_file: output/reports/attack/aca4d7487cbd9f947ed4e52092b5a30c/adv_probabilities.json + attack_file: output/attacks/b71fd61a0ac5c44090e76c2dd7218f1d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/aca4d7487cbd9f947ed4e52092b5a30c/params.yaml + predictions_file: output/reports/attack/aca4d7487cbd9f947ed4e52092b5a30c/predictions.json + probabilities_file: output/reports/attack/aca4d7487cbd9f947ed4e52092b5a30c/probabilities.json + score_dict_file: output/reports/attack/aca4d7487cbd9f947ed4e52092b5a30c/score_dict.json + test_labels_file: output/reports/attack/aca4d7487cbd9f947ed4e52092b5a30c/test_labels.json + train_labels_file: output/reports/attack/aca4d7487cbd9f947ed4e52092b5a30c/train_labels.json + model_dir: models + name: aca4d7487cbd9f947ed4e52092b5a30c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: aca4d7487cbd9f947ed4e52092b5a30c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ad51468e66ba478f8ad41275e0666094/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ad51468e66ba478f8ad41275e0666094/params.yaml new file mode 100644 index 00000000..962e2aa9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ad51468e66ba478f8ad41275e0666094/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 3919600a1a4438b8b2d1da07162858da +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ad51468e66ba478f8ad41275e0666094/adv_predictions.json + adv_probabilities_file: output/reports/attack/ad51468e66ba478f8ad41275e0666094/adv_probabilities.json + attack_file: output/attacks/93619ddf078024cd363fdd00dfaedf32.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/ad51468e66ba478f8ad41275e0666094/params.yaml + predictions_file: output/reports/attack/ad51468e66ba478f8ad41275e0666094/predictions.json + probabilities_file: output/reports/attack/ad51468e66ba478f8ad41275e0666094/probabilities.json + score_dict_file: output/reports/attack/ad51468e66ba478f8ad41275e0666094/score_dict.json + test_labels_file: output/reports/attack/ad51468e66ba478f8ad41275e0666094/test_labels.json + train_labels_file: output/reports/attack/ad51468e66ba478f8ad41275e0666094/train_labels.json + model_dir: models + name: ad51468e66ba478f8ad41275e0666094 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: ad51468e66ba478f8ad41275e0666094 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ad631d0df89a1d28aa84ab43e3c90314/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ad631d0df89a1d28aa84ab43e3c90314/params.yaml new file mode 100644 index 00000000..8484c674 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ad631d0df89a1d28aa84ab43e3c90314/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: b4f93015ceefc472d6694f321c640bec +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ad631d0df89a1d28aa84ab43e3c90314/adv_predictions.json + adv_probabilities_file: output/reports/attack/ad631d0df89a1d28aa84ab43e3c90314/adv_probabilities.json + attack_file: output/attacks/6678bfc2b9ce3ab85026740080e14285.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/ad631d0df89a1d28aa84ab43e3c90314/params.yaml + predictions_file: output/reports/attack/ad631d0df89a1d28aa84ab43e3c90314/predictions.json + probabilities_file: output/reports/attack/ad631d0df89a1d28aa84ab43e3c90314/probabilities.json + score_dict_file: output/reports/attack/ad631d0df89a1d28aa84ab43e3c90314/score_dict.json + test_labels_file: output/reports/attack/ad631d0df89a1d28aa84ab43e3c90314/test_labels.json + train_labels_file: output/reports/attack/ad631d0df89a1d28aa84ab43e3c90314/train_labels.json + model_dir: models + name: ad631d0df89a1d28aa84ab43e3c90314 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: ad631d0df89a1d28aa84ab43e3c90314 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ae4bed02831bab239464a5762f4990d2/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ae4bed02831bab239464a5762f4990d2/params.yaml new file mode 100644 index 00000000..675f424f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ae4bed02831bab239464a5762f4990d2/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.3 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: bbd17484a4cbc424d19f63a4c07bcf07 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ae4bed02831bab239464a5762f4990d2/adv_predictions.json + adv_probabilities_file: output/reports/attack/ae4bed02831bab239464a5762f4990d2/adv_probabilities.json + attack_file: output/attacks/71a0302bbd0e0ef8322a7726d9514512.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/ae4bed02831bab239464a5762f4990d2/params.yaml + predictions_file: output/reports/attack/ae4bed02831bab239464a5762f4990d2/predictions.json + probabilities_file: output/reports/attack/ae4bed02831bab239464a5762f4990d2/probabilities.json + score_dict_file: output/reports/attack/ae4bed02831bab239464a5762f4990d2/score_dict.json + test_labels_file: output/reports/attack/ae4bed02831bab239464a5762f4990d2/test_labels.json + train_labels_file: output/reports/attack/ae4bed02831bab239464a5762f4990d2/train_labels.json + model_dir: models + name: ae4bed02831bab239464a5762f4990d2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: ae4bed02831bab239464a5762f4990d2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/af59e9921b524ef312f4191286670a48/params.yaml b/examples/security/kdd-nsl/output/reports/attack/af59e9921b524ef312f4191286670a48/params.yaml new file mode 100644 index 00000000..a924b143 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/af59e9921b524ef312f4191286670a48/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 4417bd0f418f15fe48645d1f6c08391e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/af59e9921b524ef312f4191286670a48/adv_predictions.json + adv_probabilities_file: output/reports/attack/af59e9921b524ef312f4191286670a48/adv_probabilities.json + attack_file: output/attacks/209d46ccc64d7ab9f6ceb1f85a0b7fc0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/af59e9921b524ef312f4191286670a48/params.yaml + predictions_file: output/reports/attack/af59e9921b524ef312f4191286670a48/predictions.json + probabilities_file: output/reports/attack/af59e9921b524ef312f4191286670a48/probabilities.json + score_dict_file: output/reports/attack/af59e9921b524ef312f4191286670a48/score_dict.json + test_labels_file: output/reports/attack/af59e9921b524ef312f4191286670a48/test_labels.json + train_labels_file: output/reports/attack/af59e9921b524ef312f4191286670a48/train_labels.json + model_dir: models + name: af59e9921b524ef312f4191286670a48 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: af59e9921b524ef312f4191286670a48 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/af703a1c4e7123ca599621be17082a90/params.yaml b/examples/security/kdd-nsl/output/reports/attack/af703a1c4e7123ca599621be17082a90/params.yaml new file mode 100644 index 00000000..22b135cc --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/af703a1c4e7123ca599621be17082a90/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.5 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 0c5fbb72ee4ed98fd93b5e3e492aa168 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/af703a1c4e7123ca599621be17082a90/adv_predictions.json + adv_probabilities_file: output/reports/attack/af703a1c4e7123ca599621be17082a90/adv_probabilities.json + attack_file: output/attacks/2deb48e16946bfb73df47005e58797db.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/af703a1c4e7123ca599621be17082a90/params.yaml + predictions_file: output/reports/attack/af703a1c4e7123ca599621be17082a90/predictions.json + probabilities_file: output/reports/attack/af703a1c4e7123ca599621be17082a90/probabilities.json + score_dict_file: output/reports/attack/af703a1c4e7123ca599621be17082a90/score_dict.json + test_labels_file: output/reports/attack/af703a1c4e7123ca599621be17082a90/test_labels.json + train_labels_file: output/reports/attack/af703a1c4e7123ca599621be17082a90/train_labels.json + model_dir: models + name: af703a1c4e7123ca599621be17082a90 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: af703a1c4e7123ca599621be17082a90 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/aff0287b09fa0484c891031c0cbc3fb5/params.yaml b/examples/security/kdd-nsl/output/reports/attack/aff0287b09fa0484c891031c0cbc3fb5/params.yaml new file mode 100644 index 00000000..26e329c1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/aff0287b09fa0484c891031c0cbc3fb5/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 1a2828cb686219091bd8c52862bc8b90 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/aff0287b09fa0484c891031c0cbc3fb5/adv_predictions.json + adv_probabilities_file: output/reports/attack/aff0287b09fa0484c891031c0cbc3fb5/adv_probabilities.json + attack_file: output/attacks/afc5f943a3260b368960014b9e16367a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/aff0287b09fa0484c891031c0cbc3fb5/params.yaml + predictions_file: output/reports/attack/aff0287b09fa0484c891031c0cbc3fb5/predictions.json + probabilities_file: output/reports/attack/aff0287b09fa0484c891031c0cbc3fb5/probabilities.json + score_dict_file: output/reports/attack/aff0287b09fa0484c891031c0cbc3fb5/score_dict.json + test_labels_file: output/reports/attack/aff0287b09fa0484c891031c0cbc3fb5/test_labels.json + train_labels_file: output/reports/attack/aff0287b09fa0484c891031c0cbc3fb5/train_labels.json + model_dir: models + name: aff0287b09fa0484c891031c0cbc3fb5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: aff0287b09fa0484c891031c0cbc3fb5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b0c3b1c6f8aee91ee3ac3c989bcd9c46/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b0c3b1c6f8aee91ee3ac3c989bcd9c46/params.yaml new file mode 100644 index 00000000..efc337f1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b0c3b1c6f8aee91ee3ac3c989bcd9c46/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 2fab8983d667765eddc7d2e12937f510 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b0c3b1c6f8aee91ee3ac3c989bcd9c46/adv_predictions.json + adv_probabilities_file: output/reports/attack/b0c3b1c6f8aee91ee3ac3c989bcd9c46/adv_probabilities.json + attack_file: output/attacks/98e494af23bae78915d2b7ebb581d156.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/b0c3b1c6f8aee91ee3ac3c989bcd9c46/params.yaml + predictions_file: output/reports/attack/b0c3b1c6f8aee91ee3ac3c989bcd9c46/predictions.json + probabilities_file: output/reports/attack/b0c3b1c6f8aee91ee3ac3c989bcd9c46/probabilities.json + score_dict_file: output/reports/attack/b0c3b1c6f8aee91ee3ac3c989bcd9c46/score_dict.json + test_labels_file: output/reports/attack/b0c3b1c6f8aee91ee3ac3c989bcd9c46/test_labels.json + train_labels_file: output/reports/attack/b0c3b1c6f8aee91ee3ac3c989bcd9c46/train_labels.json + model_dir: models + name: b0c3b1c6f8aee91ee3ac3c989bcd9c46 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: b0c3b1c6f8aee91ee3ac3c989bcd9c46 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b1154229a59324417ba1404b09e89bd3/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b1154229a59324417ba1404b09e89bd3/params.yaml new file mode 100644 index 00000000..0f60c095 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b1154229a59324417ba1404b09e89bd3/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 45b702cea8b6b69198773ac6345369e7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b1154229a59324417ba1404b09e89bd3/adv_predictions.json + adv_probabilities_file: output/reports/attack/b1154229a59324417ba1404b09e89bd3/adv_probabilities.json + attack_file: output/attacks/fc9a5286597188b7dc179aed86e7398f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/b1154229a59324417ba1404b09e89bd3/params.yaml + predictions_file: output/reports/attack/b1154229a59324417ba1404b09e89bd3/predictions.json + probabilities_file: output/reports/attack/b1154229a59324417ba1404b09e89bd3/probabilities.json + score_dict_file: output/reports/attack/b1154229a59324417ba1404b09e89bd3/score_dict.json + test_labels_file: output/reports/attack/b1154229a59324417ba1404b09e89bd3/test_labels.json + train_labels_file: output/reports/attack/b1154229a59324417ba1404b09e89bd3/train_labels.json + model_dir: models + name: b1154229a59324417ba1404b09e89bd3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: b1154229a59324417ba1404b09e89bd3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b1a64ba18701daa3067398a4942138c0/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b1a64ba18701daa3067398a4942138c0/params.yaml new file mode 100644 index 00000000..174e8abc --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b1a64ba18701daa3067398a4942138c0/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.5 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 781fde85dd398ffc24c51799ca69c46c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b1a64ba18701daa3067398a4942138c0/adv_predictions.json + adv_probabilities_file: output/reports/attack/b1a64ba18701daa3067398a4942138c0/adv_probabilities.json + attack_file: output/attacks/a47b9092f010871a968a288c9f984a35.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/b1a64ba18701daa3067398a4942138c0/params.yaml + predictions_file: output/reports/attack/b1a64ba18701daa3067398a4942138c0/predictions.json + probabilities_file: output/reports/attack/b1a64ba18701daa3067398a4942138c0/probabilities.json + score_dict_file: output/reports/attack/b1a64ba18701daa3067398a4942138c0/score_dict.json + test_labels_file: output/reports/attack/b1a64ba18701daa3067398a4942138c0/test_labels.json + train_labels_file: output/reports/attack/b1a64ba18701daa3067398a4942138c0/train_labels.json + model_dir: models + name: b1a64ba18701daa3067398a4942138c0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: b1a64ba18701daa3067398a4942138c0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b2311a312b71e57ba5092e0395685f83/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b2311a312b71e57ba5092e0395685f83/params.yaml new file mode 100644 index 00000000..638df47e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b2311a312b71e57ba5092e0395685f83/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.5 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 3a3a7b5c2d13d1e10a9a6f1b63cf4eb9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b2311a312b71e57ba5092e0395685f83/adv_predictions.json + adv_probabilities_file: output/reports/attack/b2311a312b71e57ba5092e0395685f83/adv_probabilities.json + attack_file: output/attacks/df0d93db333c07aded8f5c696bfc0296.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/b2311a312b71e57ba5092e0395685f83/params.yaml + predictions_file: output/reports/attack/b2311a312b71e57ba5092e0395685f83/predictions.json + probabilities_file: output/reports/attack/b2311a312b71e57ba5092e0395685f83/probabilities.json + score_dict_file: output/reports/attack/b2311a312b71e57ba5092e0395685f83/score_dict.json + test_labels_file: output/reports/attack/b2311a312b71e57ba5092e0395685f83/test_labels.json + train_labels_file: output/reports/attack/b2311a312b71e57ba5092e0395685f83/train_labels.json + model_dir: models + name: b2311a312b71e57ba5092e0395685f83 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: b2311a312b71e57ba5092e0395685f83 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b24316048048b1b5911e7d14f3a756b4/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b24316048048b1b5911e7d14f3a756b4/params.yaml new file mode 100644 index 00000000..1a1b45c8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b24316048048b1b5911e7d14f3a756b4/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 375e2952f6ec4e6ce693236deaafaeaa +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b24316048048b1b5911e7d14f3a756b4/adv_predictions.json + adv_probabilities_file: output/reports/attack/b24316048048b1b5911e7d14f3a756b4/adv_probabilities.json + attack_file: output/attacks/0556c527c1b5e112ba726b0aa02c6111.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/b24316048048b1b5911e7d14f3a756b4/params.yaml + predictions_file: output/reports/attack/b24316048048b1b5911e7d14f3a756b4/predictions.json + probabilities_file: output/reports/attack/b24316048048b1b5911e7d14f3a756b4/probabilities.json + score_dict_file: output/reports/attack/b24316048048b1b5911e7d14f3a756b4/score_dict.json + test_labels_file: output/reports/attack/b24316048048b1b5911e7d14f3a756b4/test_labels.json + train_labels_file: output/reports/attack/b24316048048b1b5911e7d14f3a756b4/train_labels.json + model_dir: models + name: b24316048048b1b5911e7d14f3a756b4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: b24316048048b1b5911e7d14f3a756b4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b28adc552658c6995198a2c5291f85b2/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b28adc552658c6995198a2c5291f85b2/params.yaml new file mode 100644 index 00000000..87270265 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b28adc552658c6995198a2c5291f85b2/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.5 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 7da15ad4a4db0b9ffca257e310bedcfa +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b28adc552658c6995198a2c5291f85b2/adv_predictions.json + adv_probabilities_file: output/reports/attack/b28adc552658c6995198a2c5291f85b2/adv_probabilities.json + attack_file: output/attacks/b1ab1f75f27eaf46b3d4a397e144b088.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/b28adc552658c6995198a2c5291f85b2/params.yaml + predictions_file: output/reports/attack/b28adc552658c6995198a2c5291f85b2/predictions.json + probabilities_file: output/reports/attack/b28adc552658c6995198a2c5291f85b2/probabilities.json + score_dict_file: output/reports/attack/b28adc552658c6995198a2c5291f85b2/score_dict.json + test_labels_file: output/reports/attack/b28adc552658c6995198a2c5291f85b2/test_labels.json + train_labels_file: output/reports/attack/b28adc552658c6995198a2c5291f85b2/train_labels.json + model_dir: models + name: b28adc552658c6995198a2c5291f85b2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: b28adc552658c6995198a2c5291f85b2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b352195dad116ccea32c4072c5fb4ace/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b352195dad116ccea32c4072c5fb4ace/params.yaml new file mode 100644 index 00000000..cbdf20bd --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b352195dad116ccea32c4072c5fb4ace/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.001 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 66226d287171c6016a248ee4e55f3753 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b352195dad116ccea32c4072c5fb4ace/adv_predictions.json + adv_probabilities_file: output/reports/attack/b352195dad116ccea32c4072c5fb4ace/adv_probabilities.json + attack_file: output/attacks/0175e7f68cb9d186c46a344bf52ee6b8.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/b352195dad116ccea32c4072c5fb4ace/params.yaml + predictions_file: output/reports/attack/b352195dad116ccea32c4072c5fb4ace/predictions.json + probabilities_file: output/reports/attack/b352195dad116ccea32c4072c5fb4ace/probabilities.json + score_dict_file: output/reports/attack/b352195dad116ccea32c4072c5fb4ace/score_dict.json + test_labels_file: output/reports/attack/b352195dad116ccea32c4072c5fb4ace/test_labels.json + train_labels_file: output/reports/attack/b352195dad116ccea32c4072c5fb4ace/train_labels.json + model_dir: models + name: b352195dad116ccea32c4072c5fb4ace + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: b352195dad116ccea32c4072c5fb4ace +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b4bd77770c09fb48f1921f6dfc989b4d/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b4bd77770c09fb48f1921f6dfc989b4d/params.yaml new file mode 100644 index 00000000..1d217a59 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b4bd77770c09fb48f1921f6dfc989b4d/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.5 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: bcc5fa458956888209a1fc4f5ab7744e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b4bd77770c09fb48f1921f6dfc989b4d/adv_predictions.json + adv_probabilities_file: output/reports/attack/b4bd77770c09fb48f1921f6dfc989b4d/adv_probabilities.json + attack_file: output/attacks/76178431d6dcd0be6ea2e7403c8491a9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/b4bd77770c09fb48f1921f6dfc989b4d/params.yaml + predictions_file: output/reports/attack/b4bd77770c09fb48f1921f6dfc989b4d/predictions.json + probabilities_file: output/reports/attack/b4bd77770c09fb48f1921f6dfc989b4d/probabilities.json + score_dict_file: output/reports/attack/b4bd77770c09fb48f1921f6dfc989b4d/score_dict.json + test_labels_file: output/reports/attack/b4bd77770c09fb48f1921f6dfc989b4d/test_labels.json + train_labels_file: output/reports/attack/b4bd77770c09fb48f1921f6dfc989b4d/train_labels.json + model_dir: models + name: b4bd77770c09fb48f1921f6dfc989b4d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: b4bd77770c09fb48f1921f6dfc989b4d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b596934f8d427303b832947675c73c2c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b596934f8d427303b832947675c73c2c/params.yaml new file mode 100644 index 00000000..05d34c8f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b596934f8d427303b832947675c73c2c/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.001 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 701be93d7958ed07082c5a19686db800 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b596934f8d427303b832947675c73c2c/adv_predictions.json + adv_probabilities_file: output/reports/attack/b596934f8d427303b832947675c73c2c/adv_probabilities.json + attack_file: output/attacks/b55928d3ad9e42da3cf49f0885e3bc20.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/b596934f8d427303b832947675c73c2c/params.yaml + predictions_file: output/reports/attack/b596934f8d427303b832947675c73c2c/predictions.json + probabilities_file: output/reports/attack/b596934f8d427303b832947675c73c2c/probabilities.json + score_dict_file: output/reports/attack/b596934f8d427303b832947675c73c2c/score_dict.json + test_labels_file: output/reports/attack/b596934f8d427303b832947675c73c2c/test_labels.json + train_labels_file: output/reports/attack/b596934f8d427303b832947675c73c2c/train_labels.json + model_dir: models + name: b596934f8d427303b832947675c73c2c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: b596934f8d427303b832947675c73c2c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b62079d325c2e2bc7e39c920acaaeec8/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b62079d325c2e2bc7e39c920acaaeec8/params.yaml new file mode 100644 index 00000000..287ebe05 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b62079d325c2e2bc7e39c920acaaeec8/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 615ca0fa083ffa681f7ee3db1f272edd +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b62079d325c2e2bc7e39c920acaaeec8/adv_predictions.json + adv_probabilities_file: output/reports/attack/b62079d325c2e2bc7e39c920acaaeec8/adv_probabilities.json + attack_file: output/attacks/4b0560e2def4ddc08bbb905e5d7c7e48.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/b62079d325c2e2bc7e39c920acaaeec8/params.yaml + predictions_file: output/reports/attack/b62079d325c2e2bc7e39c920acaaeec8/predictions.json + probabilities_file: output/reports/attack/b62079d325c2e2bc7e39c920acaaeec8/probabilities.json + score_dict_file: output/reports/attack/b62079d325c2e2bc7e39c920acaaeec8/score_dict.json + test_labels_file: output/reports/attack/b62079d325c2e2bc7e39c920acaaeec8/test_labels.json + train_labels_file: output/reports/attack/b62079d325c2e2bc7e39c920acaaeec8/train_labels.json + model_dir: models + name: b62079d325c2e2bc7e39c920acaaeec8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: b62079d325c2e2bc7e39c920acaaeec8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b65de78c7ccb0e9662155ada562b010c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b65de78c7ccb0e9662155ada562b010c/params.yaml new file mode 100644 index 00000000..56a2d0b1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b65de78c7ccb0e9662155ada562b010c/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 15e97b9a2648d8915001bc893dee41fa +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b65de78c7ccb0e9662155ada562b010c/adv_predictions.json + adv_probabilities_file: output/reports/attack/b65de78c7ccb0e9662155ada562b010c/adv_probabilities.json + attack_file: output/attacks/adc61e037363e86e8c9a17c0b8b10520.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/b65de78c7ccb0e9662155ada562b010c/params.yaml + predictions_file: output/reports/attack/b65de78c7ccb0e9662155ada562b010c/predictions.json + probabilities_file: output/reports/attack/b65de78c7ccb0e9662155ada562b010c/probabilities.json + score_dict_file: output/reports/attack/b65de78c7ccb0e9662155ada562b010c/score_dict.json + test_labels_file: output/reports/attack/b65de78c7ccb0e9662155ada562b010c/test_labels.json + train_labels_file: output/reports/attack/b65de78c7ccb0e9662155ada562b010c/train_labels.json + model_dir: models + name: b65de78c7ccb0e9662155ada562b010c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: b65de78c7ccb0e9662155ada562b010c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b65eecca754b5698e072b3d75198a51e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b65eecca754b5698e072b3d75198a51e/params.yaml new file mode 100644 index 00000000..a1479134 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b65eecca754b5698e072b3d75198a51e/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 8c29534447d25ded232955e5894a3057 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b65eecca754b5698e072b3d75198a51e/adv_predictions.json + adv_probabilities_file: output/reports/attack/b65eecca754b5698e072b3d75198a51e/adv_probabilities.json + attack_file: output/attacks/e55d015bf9d4c63cdea48255fd10a583.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/b65eecca754b5698e072b3d75198a51e/params.yaml + predictions_file: output/reports/attack/b65eecca754b5698e072b3d75198a51e/predictions.json + probabilities_file: output/reports/attack/b65eecca754b5698e072b3d75198a51e/probabilities.json + score_dict_file: output/reports/attack/b65eecca754b5698e072b3d75198a51e/score_dict.json + test_labels_file: output/reports/attack/b65eecca754b5698e072b3d75198a51e/test_labels.json + train_labels_file: output/reports/attack/b65eecca754b5698e072b3d75198a51e/train_labels.json + model_dir: models + name: b65eecca754b5698e072b3d75198a51e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: b65eecca754b5698e072b3d75198a51e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b6c514e550e85dae6827082622998345/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b6c514e550e85dae6827082622998345/params.yaml new file mode 100644 index 00000000..d482879c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b6c514e550e85dae6827082622998345/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 2dcaee59684191f21741f3ea05204efd +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b6c514e550e85dae6827082622998345/adv_predictions.json + adv_probabilities_file: output/reports/attack/b6c514e550e85dae6827082622998345/adv_probabilities.json + attack_file: output/attacks/05cc0a99799a51bc5f56fbf6a4e4b089.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/b6c514e550e85dae6827082622998345/params.yaml + predictions_file: output/reports/attack/b6c514e550e85dae6827082622998345/predictions.json + probabilities_file: output/reports/attack/b6c514e550e85dae6827082622998345/probabilities.json + score_dict_file: output/reports/attack/b6c514e550e85dae6827082622998345/score_dict.json + test_labels_file: output/reports/attack/b6c514e550e85dae6827082622998345/test_labels.json + train_labels_file: output/reports/attack/b6c514e550e85dae6827082622998345/train_labels.json + model_dir: models + name: b6c514e550e85dae6827082622998345 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: b6c514e550e85dae6827082622998345 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b758fc17ff52a482ef6392e819a852cb/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b758fc17ff52a482ef6392e819a852cb/params.yaml new file mode 100644 index 00000000..1f867e52 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b758fc17ff52a482ef6392e819a852cb/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.5 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 804662389a65f5397b44b447ee51959b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b758fc17ff52a482ef6392e819a852cb/adv_predictions.json + adv_probabilities_file: output/reports/attack/b758fc17ff52a482ef6392e819a852cb/adv_probabilities.json + attack_file: output/attacks/cd244d7c6e2d328e1ff91706f4837d65.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/b758fc17ff52a482ef6392e819a852cb/params.yaml + predictions_file: output/reports/attack/b758fc17ff52a482ef6392e819a852cb/predictions.json + probabilities_file: output/reports/attack/b758fc17ff52a482ef6392e819a852cb/probabilities.json + score_dict_file: output/reports/attack/b758fc17ff52a482ef6392e819a852cb/score_dict.json + test_labels_file: output/reports/attack/b758fc17ff52a482ef6392e819a852cb/test_labels.json + train_labels_file: output/reports/attack/b758fc17ff52a482ef6392e819a852cb/train_labels.json + model_dir: models + name: b758fc17ff52a482ef6392e819a852cb + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: b758fc17ff52a482ef6392e819a852cb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b7cead25b18215e91eb4cfbeaa6ddfa2/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b7cead25b18215e91eb4cfbeaa6ddfa2/params.yaml new file mode 100644 index 00000000..8a8f5465 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b7cead25b18215e91eb4cfbeaa6ddfa2/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 60873cc34d53632fabcc28881c7948ba +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b7cead25b18215e91eb4cfbeaa6ddfa2/adv_predictions.json + adv_probabilities_file: output/reports/attack/b7cead25b18215e91eb4cfbeaa6ddfa2/adv_probabilities.json + attack_file: output/attacks/3eeb0e1095b23b7f2072228c968d9b50.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/b7cead25b18215e91eb4cfbeaa6ddfa2/params.yaml + predictions_file: output/reports/attack/b7cead25b18215e91eb4cfbeaa6ddfa2/predictions.json + probabilities_file: output/reports/attack/b7cead25b18215e91eb4cfbeaa6ddfa2/probabilities.json + score_dict_file: output/reports/attack/b7cead25b18215e91eb4cfbeaa6ddfa2/score_dict.json + test_labels_file: output/reports/attack/b7cead25b18215e91eb4cfbeaa6ddfa2/test_labels.json + train_labels_file: output/reports/attack/b7cead25b18215e91eb4cfbeaa6ddfa2/train_labels.json + model_dir: models + name: b7cead25b18215e91eb4cfbeaa6ddfa2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: b7cead25b18215e91eb4cfbeaa6ddfa2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b834c7759c2c1ac77ab36fef62f05648/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b834c7759c2c1ac77ab36fef62f05648/params.yaml new file mode 100644 index 00000000..bba7c7c6 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b834c7759c2c1ac77ab36fef62f05648/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.3 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: f79faba62dad883e3165bfdb0f2cfa82 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b834c7759c2c1ac77ab36fef62f05648/adv_predictions.json + adv_probabilities_file: output/reports/attack/b834c7759c2c1ac77ab36fef62f05648/adv_probabilities.json + attack_file: output/attacks/a5d1290d87f9cc5bfbcc91dc43c3626b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/b834c7759c2c1ac77ab36fef62f05648/params.yaml + predictions_file: output/reports/attack/b834c7759c2c1ac77ab36fef62f05648/predictions.json + probabilities_file: output/reports/attack/b834c7759c2c1ac77ab36fef62f05648/probabilities.json + score_dict_file: output/reports/attack/b834c7759c2c1ac77ab36fef62f05648/score_dict.json + test_labels_file: output/reports/attack/b834c7759c2c1ac77ab36fef62f05648/test_labels.json + train_labels_file: output/reports/attack/b834c7759c2c1ac77ab36fef62f05648/train_labels.json + model_dir: models + name: b834c7759c2c1ac77ab36fef62f05648 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: b834c7759c2c1ac77ab36fef62f05648 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/b926015d14c99e64a0608d06e62c2bab/params.yaml b/examples/security/kdd-nsl/output/reports/attack/b926015d14c99e64a0608d06e62c2bab/params.yaml new file mode 100644 index 00000000..c724cd39 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/b926015d14c99e64a0608d06e62c2bab/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: a50e4393ae792f8b4c383e5116e4c95e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b926015d14c99e64a0608d06e62c2bab/adv_predictions.json + adv_probabilities_file: output/reports/attack/b926015d14c99e64a0608d06e62c2bab/adv_probabilities.json + attack_file: output/attacks/e505b85c6a8cb04fd7ca06254fa088f3.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/b926015d14c99e64a0608d06e62c2bab/params.yaml + predictions_file: output/reports/attack/b926015d14c99e64a0608d06e62c2bab/predictions.json + probabilities_file: output/reports/attack/b926015d14c99e64a0608d06e62c2bab/probabilities.json + score_dict_file: output/reports/attack/b926015d14c99e64a0608d06e62c2bab/score_dict.json + test_labels_file: output/reports/attack/b926015d14c99e64a0608d06e62c2bab/test_labels.json + train_labels_file: output/reports/attack/b926015d14c99e64a0608d06e62c2bab/train_labels.json + model_dir: models + name: b926015d14c99e64a0608d06e62c2bab + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: b926015d14c99e64a0608d06e62c2bab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ba13cfc3ca80082548d15329799f74c8/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ba13cfc3ca80082548d15329799f74c8/params.yaml new file mode 100644 index 00000000..90bd29c2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ba13cfc3ca80082548d15329799f74c8/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.5 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 77dfd99cfc4d18f88311098984b38fec +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ba13cfc3ca80082548d15329799f74c8/adv_predictions.json + adv_probabilities_file: output/reports/attack/ba13cfc3ca80082548d15329799f74c8/adv_probabilities.json + attack_file: output/attacks/3808a020ee67872d744fa4faf253d5ca.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/ba13cfc3ca80082548d15329799f74c8/params.yaml + predictions_file: output/reports/attack/ba13cfc3ca80082548d15329799f74c8/predictions.json + probabilities_file: output/reports/attack/ba13cfc3ca80082548d15329799f74c8/probabilities.json + score_dict_file: output/reports/attack/ba13cfc3ca80082548d15329799f74c8/score_dict.json + test_labels_file: output/reports/attack/ba13cfc3ca80082548d15329799f74c8/test_labels.json + train_labels_file: output/reports/attack/ba13cfc3ca80082548d15329799f74c8/train_labels.json + model_dir: models + name: ba13cfc3ca80082548d15329799f74c8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: ba13cfc3ca80082548d15329799f74c8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ba30478c39feaba7e8c1f76da431170a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ba30478c39feaba7e8c1f76da431170a/params.yaml new file mode 100644 index 00000000..cc7bd053 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ba30478c39feaba7e8c1f76da431170a/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.3 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 2ed99d6dd6cd64647da7d4b8dfab6086 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ba30478c39feaba7e8c1f76da431170a/adv_predictions.json + adv_probabilities_file: output/reports/attack/ba30478c39feaba7e8c1f76da431170a/adv_probabilities.json + attack_file: output/attacks/46bedd4cc57d06655e7ba4594c6b8d9d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/ba30478c39feaba7e8c1f76da431170a/params.yaml + predictions_file: output/reports/attack/ba30478c39feaba7e8c1f76da431170a/predictions.json + probabilities_file: output/reports/attack/ba30478c39feaba7e8c1f76da431170a/probabilities.json + score_dict_file: output/reports/attack/ba30478c39feaba7e8c1f76da431170a/score_dict.json + test_labels_file: output/reports/attack/ba30478c39feaba7e8c1f76da431170a/test_labels.json + train_labels_file: output/reports/attack/ba30478c39feaba7e8c1f76da431170a/train_labels.json + model_dir: models + name: ba30478c39feaba7e8c1f76da431170a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: ba30478c39feaba7e8c1f76da431170a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ba9e4cfe6356a1c4450ce9d6bc75c158/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ba9e4cfe6356a1c4450ce9d6bc75c158/params.yaml new file mode 100644 index 00000000..8b6130bf --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ba9e4cfe6356a1c4450ce9d6bc75c158/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: c4189125734a79c317eacbf02eb5d15c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ba9e4cfe6356a1c4450ce9d6bc75c158/adv_predictions.json + adv_probabilities_file: output/reports/attack/ba9e4cfe6356a1c4450ce9d6bc75c158/adv_probabilities.json + attack_file: output/attacks/c9277c2cb7ad442b2859c4f5b1d2db28.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/ba9e4cfe6356a1c4450ce9d6bc75c158/params.yaml + predictions_file: output/reports/attack/ba9e4cfe6356a1c4450ce9d6bc75c158/predictions.json + probabilities_file: output/reports/attack/ba9e4cfe6356a1c4450ce9d6bc75c158/probabilities.json + score_dict_file: output/reports/attack/ba9e4cfe6356a1c4450ce9d6bc75c158/score_dict.json + test_labels_file: output/reports/attack/ba9e4cfe6356a1c4450ce9d6bc75c158/test_labels.json + train_labels_file: output/reports/attack/ba9e4cfe6356a1c4450ce9d6bc75c158/train_labels.json + model_dir: models + name: ba9e4cfe6356a1c4450ce9d6bc75c158 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: ba9e4cfe6356a1c4450ce9d6bc75c158 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/bb199c73957d584fddda055aca7f81e2/params.yaml b/examples/security/kdd-nsl/output/reports/attack/bb199c73957d584fddda055aca7f81e2/params.yaml new file mode 100644 index 00000000..3653495d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/bb199c73957d584fddda055aca7f81e2/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.001 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: b78b0fb2b2d3f8284553ba2842c2495b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bb199c73957d584fddda055aca7f81e2/adv_predictions.json + adv_probabilities_file: output/reports/attack/bb199c73957d584fddda055aca7f81e2/adv_probabilities.json + attack_file: output/attacks/c2a617424740d1e82e26147efc2e5953.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/bb199c73957d584fddda055aca7f81e2/params.yaml + predictions_file: output/reports/attack/bb199c73957d584fddda055aca7f81e2/predictions.json + probabilities_file: output/reports/attack/bb199c73957d584fddda055aca7f81e2/probabilities.json + score_dict_file: output/reports/attack/bb199c73957d584fddda055aca7f81e2/score_dict.json + test_labels_file: output/reports/attack/bb199c73957d584fddda055aca7f81e2/test_labels.json + train_labels_file: output/reports/attack/bb199c73957d584fddda055aca7f81e2/train_labels.json + model_dir: models + name: bb199c73957d584fddda055aca7f81e2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: bb199c73957d584fddda055aca7f81e2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/bc11a363bedf2e3bbe21ba4e91ffc68a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/bc11a363bedf2e3bbe21ba4e91ffc68a/params.yaml new file mode 100644 index 00000000..ad5c1905 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/bc11a363bedf2e3bbe21ba4e91ffc68a/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.3 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 51dacab51aac8812b19a061e32bb1828 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bc11a363bedf2e3bbe21ba4e91ffc68a/adv_predictions.json + adv_probabilities_file: output/reports/attack/bc11a363bedf2e3bbe21ba4e91ffc68a/adv_probabilities.json + attack_file: output/attacks/a899e6801cd2a00c90dd8cf3d0ca488a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/bc11a363bedf2e3bbe21ba4e91ffc68a/params.yaml + predictions_file: output/reports/attack/bc11a363bedf2e3bbe21ba4e91ffc68a/predictions.json + probabilities_file: output/reports/attack/bc11a363bedf2e3bbe21ba4e91ffc68a/probabilities.json + score_dict_file: output/reports/attack/bc11a363bedf2e3bbe21ba4e91ffc68a/score_dict.json + test_labels_file: output/reports/attack/bc11a363bedf2e3bbe21ba4e91ffc68a/test_labels.json + train_labels_file: output/reports/attack/bc11a363bedf2e3bbe21ba4e91ffc68a/train_labels.json + model_dir: models + name: bc11a363bedf2e3bbe21ba4e91ffc68a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: bc11a363bedf2e3bbe21ba4e91ffc68a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/bc2101d2414315102457f32c14e966d6/params.yaml b/examples/security/kdd-nsl/output/reports/attack/bc2101d2414315102457f32c14e966d6/params.yaml new file mode 100644 index 00000000..34a66247 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/bc2101d2414315102457f32c14e966d6/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.3 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: ece182746eb3fd47f3f59006fd6eba63 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bc2101d2414315102457f32c14e966d6/adv_predictions.json + adv_probabilities_file: output/reports/attack/bc2101d2414315102457f32c14e966d6/adv_probabilities.json + attack_file: output/attacks/14f12f89fb5d6c477d8833dc3d94295b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/bc2101d2414315102457f32c14e966d6/params.yaml + predictions_file: output/reports/attack/bc2101d2414315102457f32c14e966d6/predictions.json + probabilities_file: output/reports/attack/bc2101d2414315102457f32c14e966d6/probabilities.json + score_dict_file: output/reports/attack/bc2101d2414315102457f32c14e966d6/score_dict.json + test_labels_file: output/reports/attack/bc2101d2414315102457f32c14e966d6/test_labels.json + train_labels_file: output/reports/attack/bc2101d2414315102457f32c14e966d6/train_labels.json + model_dir: models + name: bc2101d2414315102457f32c14e966d6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: bc2101d2414315102457f32c14e966d6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/bcaa1d8f2491d50733c379127754e24f/params.yaml b/examples/security/kdd-nsl/output/reports/attack/bcaa1d8f2491d50733c379127754e24f/params.yaml new file mode 100644 index 00000000..373bbeef --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/bcaa1d8f2491d50733c379127754e24f/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: baf53b89e351f419c4e447965cddf7b7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bcaa1d8f2491d50733c379127754e24f/adv_predictions.json + adv_probabilities_file: output/reports/attack/bcaa1d8f2491d50733c379127754e24f/adv_probabilities.json + attack_file: output/attacks/4c252f48685d339d3aca3c907287240b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/bcaa1d8f2491d50733c379127754e24f/params.yaml + predictions_file: output/reports/attack/bcaa1d8f2491d50733c379127754e24f/predictions.json + probabilities_file: output/reports/attack/bcaa1d8f2491d50733c379127754e24f/probabilities.json + score_dict_file: output/reports/attack/bcaa1d8f2491d50733c379127754e24f/score_dict.json + test_labels_file: output/reports/attack/bcaa1d8f2491d50733c379127754e24f/test_labels.json + train_labels_file: output/reports/attack/bcaa1d8f2491d50733c379127754e24f/train_labels.json + model_dir: models + name: bcaa1d8f2491d50733c379127754e24f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: bcaa1d8f2491d50733c379127754e24f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/bef815a05279459635e94e0ec3e18a8c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/bef815a05279459635e94e0ec3e18a8c/params.yaml new file mode 100644 index 00000000..d8500a68 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/bef815a05279459635e94e0ec3e18a8c/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.001 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: a078431142d7f202c14a2ba420962ffc +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/adv_predictions.json + adv_probabilities_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/adv_probabilities.json + attack_file: output/attacks/488456acf643c8c0dc0fc14e6edba835.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/params.yaml + predictions_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/predictions.json + probabilities_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/probabilities.json + score_dict_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/score_dict.json + test_labels_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/test_labels.json + train_labels_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/train_labels.json + model_dir: models + name: bef815a05279459635e94e0ec3e18a8c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: bef815a05279459635e94e0ec3e18a8c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/bfe8bcb483120e206512cd39b84484ba/params.yaml b/examples/security/kdd-nsl/output/reports/attack/bfe8bcb483120e206512cd39b84484ba/params.yaml new file mode 100644 index 00000000..deb3d7fd --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/bfe8bcb483120e206512cd39b84484ba/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: e1d754452ef44221aa2063f458b2c3d2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bfe8bcb483120e206512cd39b84484ba/adv_predictions.json + adv_probabilities_file: output/reports/attack/bfe8bcb483120e206512cd39b84484ba/adv_probabilities.json + attack_file: output/attacks/096c9e477bf4fb7f209670b200d3c951.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/bfe8bcb483120e206512cd39b84484ba/params.yaml + predictions_file: output/reports/attack/bfe8bcb483120e206512cd39b84484ba/predictions.json + probabilities_file: output/reports/attack/bfe8bcb483120e206512cd39b84484ba/probabilities.json + score_dict_file: output/reports/attack/bfe8bcb483120e206512cd39b84484ba/score_dict.json + test_labels_file: output/reports/attack/bfe8bcb483120e206512cd39b84484ba/test_labels.json + train_labels_file: output/reports/attack/bfe8bcb483120e206512cd39b84484ba/train_labels.json + model_dir: models + name: bfe8bcb483120e206512cd39b84484ba + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: bfe8bcb483120e206512cd39b84484ba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c006627ccead78b621b9cae40044c0aa/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c006627ccead78b621b9cae40044c0aa/params.yaml new file mode 100644 index 00000000..e5e8bb67 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c006627ccead78b621b9cae40044c0aa/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 6051c3e2f8f0bab0722f523bd1d6c8b9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c006627ccead78b621b9cae40044c0aa/adv_predictions.json + adv_probabilities_file: output/reports/attack/c006627ccead78b621b9cae40044c0aa/adv_probabilities.json + attack_file: output/attacks/5a60d1939b685582333183fcf9b896be.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/c006627ccead78b621b9cae40044c0aa/params.yaml + predictions_file: output/reports/attack/c006627ccead78b621b9cae40044c0aa/predictions.json + probabilities_file: output/reports/attack/c006627ccead78b621b9cae40044c0aa/probabilities.json + score_dict_file: output/reports/attack/c006627ccead78b621b9cae40044c0aa/score_dict.json + test_labels_file: output/reports/attack/c006627ccead78b621b9cae40044c0aa/test_labels.json + train_labels_file: output/reports/attack/c006627ccead78b621b9cae40044c0aa/train_labels.json + model_dir: models + name: c006627ccead78b621b9cae40044c0aa + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: c006627ccead78b621b9cae40044c0aa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c0c6d6a288845e60ac6c4ec6b12a1495/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c0c6d6a288845e60ac6c4ec6b12a1495/params.yaml new file mode 100644 index 00000000..c33cc56e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c0c6d6a288845e60ac6c4ec6b12a1495/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 56b94ee53bb17eb73d589fcf99bd5325 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c0c6d6a288845e60ac6c4ec6b12a1495/adv_predictions.json + adv_probabilities_file: output/reports/attack/c0c6d6a288845e60ac6c4ec6b12a1495/adv_probabilities.json + attack_file: output/attacks/26d2321be02c5dc38680b8f4a09cc471.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/c0c6d6a288845e60ac6c4ec6b12a1495/params.yaml + predictions_file: output/reports/attack/c0c6d6a288845e60ac6c4ec6b12a1495/predictions.json + probabilities_file: output/reports/attack/c0c6d6a288845e60ac6c4ec6b12a1495/probabilities.json + score_dict_file: output/reports/attack/c0c6d6a288845e60ac6c4ec6b12a1495/score_dict.json + test_labels_file: output/reports/attack/c0c6d6a288845e60ac6c4ec6b12a1495/test_labels.json + train_labels_file: output/reports/attack/c0c6d6a288845e60ac6c4ec6b12a1495/train_labels.json + model_dir: models + name: c0c6d6a288845e60ac6c4ec6b12a1495 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: c0c6d6a288845e60ac6c4ec6b12a1495 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c162607ec7bd41b161f1e608b25c39d9/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c162607ec7bd41b161f1e608b25c39d9/params.yaml new file mode 100644 index 00000000..7d33e5f3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c162607ec7bd41b161f1e608b25c39d9/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: d88a8b19149f7f9e0d29b672c3bf712d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c162607ec7bd41b161f1e608b25c39d9/adv_predictions.json + adv_probabilities_file: output/reports/attack/c162607ec7bd41b161f1e608b25c39d9/adv_probabilities.json + attack_file: output/attacks/2df04d7d042c235537454e6763c21aae.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/c162607ec7bd41b161f1e608b25c39d9/params.yaml + predictions_file: output/reports/attack/c162607ec7bd41b161f1e608b25c39d9/predictions.json + probabilities_file: output/reports/attack/c162607ec7bd41b161f1e608b25c39d9/probabilities.json + score_dict_file: output/reports/attack/c162607ec7bd41b161f1e608b25c39d9/score_dict.json + test_labels_file: output/reports/attack/c162607ec7bd41b161f1e608b25c39d9/test_labels.json + train_labels_file: output/reports/attack/c162607ec7bd41b161f1e608b25c39d9/train_labels.json + model_dir: models + name: c162607ec7bd41b161f1e608b25c39d9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: c162607ec7bd41b161f1e608b25c39d9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c1a723c1ca82eb753145abb64ebdfab0/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c1a723c1ca82eb753145abb64ebdfab0/params.yaml new file mode 100644 index 00000000..3c44b981 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c1a723c1ca82eb753145abb64ebdfab0/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.3 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 8a9f50aa29f70c34a3d4c88d18cfc8d0 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c1a723c1ca82eb753145abb64ebdfab0/adv_predictions.json + adv_probabilities_file: output/reports/attack/c1a723c1ca82eb753145abb64ebdfab0/adv_probabilities.json + attack_file: output/attacks/10942a6de8cf45e49521ebbed5710d42.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/c1a723c1ca82eb753145abb64ebdfab0/params.yaml + predictions_file: output/reports/attack/c1a723c1ca82eb753145abb64ebdfab0/predictions.json + probabilities_file: output/reports/attack/c1a723c1ca82eb753145abb64ebdfab0/probabilities.json + score_dict_file: output/reports/attack/c1a723c1ca82eb753145abb64ebdfab0/score_dict.json + test_labels_file: output/reports/attack/c1a723c1ca82eb753145abb64ebdfab0/test_labels.json + train_labels_file: output/reports/attack/c1a723c1ca82eb753145abb64ebdfab0/train_labels.json + model_dir: models + name: c1a723c1ca82eb753145abb64ebdfab0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: c1a723c1ca82eb753145abb64ebdfab0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c214fbe5a422403f8758d3897dfbc28d/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c214fbe5a422403f8758d3897dfbc28d/params.yaml new file mode 100644 index 00000000..f7a06739 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c214fbe5a422403f8758d3897dfbc28d/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.01 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 618633ac9f27702b37df40af27faf23a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c214fbe5a422403f8758d3897dfbc28d/adv_predictions.json + adv_probabilities_file: output/reports/attack/c214fbe5a422403f8758d3897dfbc28d/adv_probabilities.json + attack_file: output/attacks/da2d38f9508e8360611a2da79da52553.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/c214fbe5a422403f8758d3897dfbc28d/params.yaml + predictions_file: output/reports/attack/c214fbe5a422403f8758d3897dfbc28d/predictions.json + probabilities_file: output/reports/attack/c214fbe5a422403f8758d3897dfbc28d/probabilities.json + score_dict_file: output/reports/attack/c214fbe5a422403f8758d3897dfbc28d/score_dict.json + test_labels_file: output/reports/attack/c214fbe5a422403f8758d3897dfbc28d/test_labels.json + train_labels_file: output/reports/attack/c214fbe5a422403f8758d3897dfbc28d/train_labels.json + model_dir: models + name: c214fbe5a422403f8758d3897dfbc28d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: c214fbe5a422403f8758d3897dfbc28d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c36e9ec0c4e10f4dfe2bd5904a9aa839/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c36e9ec0c4e10f4dfe2bd5904a9aa839/params.yaml new file mode 100644 index 00000000..26c6366e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c36e9ec0c4e10f4dfe2bd5904a9aa839/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: bf3528f474500a9147c5d5ff88a1ee02 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c36e9ec0c4e10f4dfe2bd5904a9aa839/adv_predictions.json + adv_probabilities_file: output/reports/attack/c36e9ec0c4e10f4dfe2bd5904a9aa839/adv_probabilities.json + attack_file: output/attacks/6c8ed647f66675604b654b6b6f14fc21.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/c36e9ec0c4e10f4dfe2bd5904a9aa839/params.yaml + predictions_file: output/reports/attack/c36e9ec0c4e10f4dfe2bd5904a9aa839/predictions.json + probabilities_file: output/reports/attack/c36e9ec0c4e10f4dfe2bd5904a9aa839/probabilities.json + score_dict_file: output/reports/attack/c36e9ec0c4e10f4dfe2bd5904a9aa839/score_dict.json + test_labels_file: output/reports/attack/c36e9ec0c4e10f4dfe2bd5904a9aa839/test_labels.json + train_labels_file: output/reports/attack/c36e9ec0c4e10f4dfe2bd5904a9aa839/train_labels.json + model_dir: models + name: c36e9ec0c4e10f4dfe2bd5904a9aa839 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: c36e9ec0c4e10f4dfe2bd5904a9aa839 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c39194a23a5f953e17677d04a8dafa7b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c39194a23a5f953e17677d04a8dafa7b/params.yaml new file mode 100644 index 00000000..2e2e11ac --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c39194a23a5f953e17677d04a8dafa7b/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.001 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 3e47e6854ef828c0327d914f33a20ba7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c39194a23a5f953e17677d04a8dafa7b/adv_predictions.json + adv_probabilities_file: output/reports/attack/c39194a23a5f953e17677d04a8dafa7b/adv_probabilities.json + attack_file: output/attacks/81277d8b8dbd59ce467857e410c82fd4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/c39194a23a5f953e17677d04a8dafa7b/params.yaml + predictions_file: output/reports/attack/c39194a23a5f953e17677d04a8dafa7b/predictions.json + probabilities_file: output/reports/attack/c39194a23a5f953e17677d04a8dafa7b/probabilities.json + score_dict_file: output/reports/attack/c39194a23a5f953e17677d04a8dafa7b/score_dict.json + test_labels_file: output/reports/attack/c39194a23a5f953e17677d04a8dafa7b/test_labels.json + train_labels_file: output/reports/attack/c39194a23a5f953e17677d04a8dafa7b/train_labels.json + model_dir: models + name: c39194a23a5f953e17677d04a8dafa7b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: c39194a23a5f953e17677d04a8dafa7b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c455b68009a8344e275ed4c165fcb73c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c455b68009a8344e275ed4c165fcb73c/params.yaml new file mode 100644 index 00000000..6d4defa1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c455b68009a8344e275ed4c165fcb73c/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: d1c9eb5a6f05ba250c5ed8c0064929fd +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c455b68009a8344e275ed4c165fcb73c/adv_predictions.json + adv_probabilities_file: output/reports/attack/c455b68009a8344e275ed4c165fcb73c/adv_probabilities.json + attack_file: output/attacks/02fcffa909a7ce233718184ce4f92fcb.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/c455b68009a8344e275ed4c165fcb73c/params.yaml + predictions_file: output/reports/attack/c455b68009a8344e275ed4c165fcb73c/predictions.json + probabilities_file: output/reports/attack/c455b68009a8344e275ed4c165fcb73c/probabilities.json + score_dict_file: output/reports/attack/c455b68009a8344e275ed4c165fcb73c/score_dict.json + test_labels_file: output/reports/attack/c455b68009a8344e275ed4c165fcb73c/test_labels.json + train_labels_file: output/reports/attack/c455b68009a8344e275ed4c165fcb73c/train_labels.json + model_dir: models + name: c455b68009a8344e275ed4c165fcb73c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: c455b68009a8344e275ed4c165fcb73c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c497b00911001699fab16384de3698ce/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c497b00911001699fab16384de3698ce/params.yaml new file mode 100644 index 00000000..53a3b0fe --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c497b00911001699fab16384de3698ce/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: a6991dcd122f22ce11b428343c473437 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c497b00911001699fab16384de3698ce/adv_predictions.json + adv_probabilities_file: output/reports/attack/c497b00911001699fab16384de3698ce/adv_probabilities.json + attack_file: output/attacks/9ef3241b91b135fff0397134eec824ee.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/c497b00911001699fab16384de3698ce/params.yaml + predictions_file: output/reports/attack/c497b00911001699fab16384de3698ce/predictions.json + probabilities_file: output/reports/attack/c497b00911001699fab16384de3698ce/probabilities.json + score_dict_file: output/reports/attack/c497b00911001699fab16384de3698ce/score_dict.json + test_labels_file: output/reports/attack/c497b00911001699fab16384de3698ce/test_labels.json + train_labels_file: output/reports/attack/c497b00911001699fab16384de3698ce/train_labels.json + model_dir: models + name: c497b00911001699fab16384de3698ce + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: c497b00911001699fab16384de3698ce +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c4acafc32e84d709c6044df6c0ea345f/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c4acafc32e84d709c6044df6c0ea345f/params.yaml new file mode 100644 index 00000000..9879287e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c4acafc32e84d709c6044df6c0ea345f/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 5ff5ee3b46b247dd1774dd29298028e2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c4acafc32e84d709c6044df6c0ea345f/adv_predictions.json + adv_probabilities_file: output/reports/attack/c4acafc32e84d709c6044df6c0ea345f/adv_probabilities.json + attack_file: output/attacks/cffe01e8f888d8011c35e3cbf302ce4e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/c4acafc32e84d709c6044df6c0ea345f/params.yaml + predictions_file: output/reports/attack/c4acafc32e84d709c6044df6c0ea345f/predictions.json + probabilities_file: output/reports/attack/c4acafc32e84d709c6044df6c0ea345f/probabilities.json + score_dict_file: output/reports/attack/c4acafc32e84d709c6044df6c0ea345f/score_dict.json + test_labels_file: output/reports/attack/c4acafc32e84d709c6044df6c0ea345f/test_labels.json + train_labels_file: output/reports/attack/c4acafc32e84d709c6044df6c0ea345f/train_labels.json + model_dir: models + name: c4acafc32e84d709c6044df6c0ea345f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: c4acafc32e84d709c6044df6c0ea345f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c4ce1203b3b1ca81f2da8b2bb51c149f/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c4ce1203b3b1ca81f2da8b2bb51c149f/params.yaml new file mode 100644 index 00000000..cd5ebf64 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c4ce1203b3b1ca81f2da8b2bb51c149f/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.01 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 539e29701f7425e6f341f08d4c0e39ac +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c4ce1203b3b1ca81f2da8b2bb51c149f/adv_predictions.json + adv_probabilities_file: output/reports/attack/c4ce1203b3b1ca81f2da8b2bb51c149f/adv_probabilities.json + attack_file: output/attacks/76ecf632127a8adf54bc02517cb6e28c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/c4ce1203b3b1ca81f2da8b2bb51c149f/params.yaml + predictions_file: output/reports/attack/c4ce1203b3b1ca81f2da8b2bb51c149f/predictions.json + probabilities_file: output/reports/attack/c4ce1203b3b1ca81f2da8b2bb51c149f/probabilities.json + score_dict_file: output/reports/attack/c4ce1203b3b1ca81f2da8b2bb51c149f/score_dict.json + test_labels_file: output/reports/attack/c4ce1203b3b1ca81f2da8b2bb51c149f/test_labels.json + train_labels_file: output/reports/attack/c4ce1203b3b1ca81f2da8b2bb51c149f/train_labels.json + model_dir: models + name: c4ce1203b3b1ca81f2da8b2bb51c149f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: c4ce1203b3b1ca81f2da8b2bb51c149f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c66346596b374348535da83dbfeda759/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c66346596b374348535da83dbfeda759/params.yaml new file mode 100644 index 00000000..0ab5f293 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c66346596b374348535da83dbfeda759/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 4315b4394690b5a3fa26e5c68b9abdf2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c66346596b374348535da83dbfeda759/adv_predictions.json + adv_probabilities_file: output/reports/attack/c66346596b374348535da83dbfeda759/adv_probabilities.json + attack_file: output/attacks/9602c36eba67a8a9dbcbaa38342398b0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/c66346596b374348535da83dbfeda759/params.yaml + predictions_file: output/reports/attack/c66346596b374348535da83dbfeda759/predictions.json + probabilities_file: output/reports/attack/c66346596b374348535da83dbfeda759/probabilities.json + score_dict_file: output/reports/attack/c66346596b374348535da83dbfeda759/score_dict.json + test_labels_file: output/reports/attack/c66346596b374348535da83dbfeda759/test_labels.json + train_labels_file: output/reports/attack/c66346596b374348535da83dbfeda759/train_labels.json + model_dir: models + name: c66346596b374348535da83dbfeda759 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: c66346596b374348535da83dbfeda759 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c72dd0c8fe0a3aae5be8b58c705b0faa/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c72dd0c8fe0a3aae5be8b58c705b0faa/params.yaml new file mode 100644 index 00000000..51490ba5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c72dd0c8fe0a3aae5be8b58c705b0faa/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.3 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 5135489d7e1bb7486c09b960751eaf4d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c72dd0c8fe0a3aae5be8b58c705b0faa/adv_predictions.json + adv_probabilities_file: output/reports/attack/c72dd0c8fe0a3aae5be8b58c705b0faa/adv_probabilities.json + attack_file: output/attacks/ee43c51048632827fc91dd0920cff4e5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/c72dd0c8fe0a3aae5be8b58c705b0faa/params.yaml + predictions_file: output/reports/attack/c72dd0c8fe0a3aae5be8b58c705b0faa/predictions.json + probabilities_file: output/reports/attack/c72dd0c8fe0a3aae5be8b58c705b0faa/probabilities.json + score_dict_file: output/reports/attack/c72dd0c8fe0a3aae5be8b58c705b0faa/score_dict.json + test_labels_file: output/reports/attack/c72dd0c8fe0a3aae5be8b58c705b0faa/test_labels.json + train_labels_file: output/reports/attack/c72dd0c8fe0a3aae5be8b58c705b0faa/train_labels.json + model_dir: models + name: c72dd0c8fe0a3aae5be8b58c705b0faa + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: c72dd0c8fe0a3aae5be8b58c705b0faa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c7dafd1f1e087053757e0f31bc6d6581/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c7dafd1f1e087053757e0f31bc6d6581/params.yaml new file mode 100644 index 00000000..235ecd5e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c7dafd1f1e087053757e0f31bc6d6581/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: ebb239dd46aada01474eec826cc016d6 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c7dafd1f1e087053757e0f31bc6d6581/adv_predictions.json + adv_probabilities_file: output/reports/attack/c7dafd1f1e087053757e0f31bc6d6581/adv_probabilities.json + attack_file: output/attacks/8b1a9633a7a8220ce6eb7b8cb4c26059.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/c7dafd1f1e087053757e0f31bc6d6581/params.yaml + predictions_file: output/reports/attack/c7dafd1f1e087053757e0f31bc6d6581/predictions.json + probabilities_file: output/reports/attack/c7dafd1f1e087053757e0f31bc6d6581/probabilities.json + score_dict_file: output/reports/attack/c7dafd1f1e087053757e0f31bc6d6581/score_dict.json + test_labels_file: output/reports/attack/c7dafd1f1e087053757e0f31bc6d6581/test_labels.json + train_labels_file: output/reports/attack/c7dafd1f1e087053757e0f31bc6d6581/train_labels.json + model_dir: models + name: c7dafd1f1e087053757e0f31bc6d6581 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: c7dafd1f1e087053757e0f31bc6d6581 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c83bb46944ba65357d3aa624f7b84478/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c83bb46944ba65357d3aa624f7b84478/params.yaml new file mode 100644 index 00000000..327eb35a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c83bb46944ba65357d3aa624f7b84478/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 539968a42317399276f418ce6c9ab2dc +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c83bb46944ba65357d3aa624f7b84478/adv_predictions.json + adv_probabilities_file: output/reports/attack/c83bb46944ba65357d3aa624f7b84478/adv_probabilities.json + attack_file: output/attacks/862f2eb123b3048a63cd41526bee4308.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/c83bb46944ba65357d3aa624f7b84478/params.yaml + predictions_file: output/reports/attack/c83bb46944ba65357d3aa624f7b84478/predictions.json + probabilities_file: output/reports/attack/c83bb46944ba65357d3aa624f7b84478/probabilities.json + score_dict_file: output/reports/attack/c83bb46944ba65357d3aa624f7b84478/score_dict.json + test_labels_file: output/reports/attack/c83bb46944ba65357d3aa624f7b84478/test_labels.json + train_labels_file: output/reports/attack/c83bb46944ba65357d3aa624f7b84478/train_labels.json + model_dir: models + name: c83bb46944ba65357d3aa624f7b84478 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: c83bb46944ba65357d3aa624f7b84478 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c8ade817514890246113b3b18c6eae9e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c8ade817514890246113b3b18c6eae9e/params.yaml new file mode 100644 index 00000000..c541d2c7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c8ade817514890246113b3b18c6eae9e/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: deb9cb23cb67fd393a7952e00e2fffd1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c8ade817514890246113b3b18c6eae9e/adv_predictions.json + adv_probabilities_file: output/reports/attack/c8ade817514890246113b3b18c6eae9e/adv_probabilities.json + attack_file: output/attacks/0490d85f9a26c65395d99810c5e14973.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/c8ade817514890246113b3b18c6eae9e/params.yaml + predictions_file: output/reports/attack/c8ade817514890246113b3b18c6eae9e/predictions.json + probabilities_file: output/reports/attack/c8ade817514890246113b3b18c6eae9e/probabilities.json + score_dict_file: output/reports/attack/c8ade817514890246113b3b18c6eae9e/score_dict.json + test_labels_file: output/reports/attack/c8ade817514890246113b3b18c6eae9e/test_labels.json + train_labels_file: output/reports/attack/c8ade817514890246113b3b18c6eae9e/train_labels.json + model_dir: models + name: c8ade817514890246113b3b18c6eae9e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: c8ade817514890246113b3b18c6eae9e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c8e739d8c0f8f28d65a7ef7306c36527/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c8e739d8c0f8f28d65a7ef7306c36527/params.yaml new file mode 100644 index 00000000..3d6d917d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c8e739d8c0f8f28d65a7ef7306c36527/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.01 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 0a35fc84c8e4b215671d1b7812235050 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c8e739d8c0f8f28d65a7ef7306c36527/adv_predictions.json + adv_probabilities_file: output/reports/attack/c8e739d8c0f8f28d65a7ef7306c36527/adv_probabilities.json + attack_file: output/attacks/7473b0c87fad4c314f0503a7d74c7ad8.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/c8e739d8c0f8f28d65a7ef7306c36527/params.yaml + predictions_file: output/reports/attack/c8e739d8c0f8f28d65a7ef7306c36527/predictions.json + probabilities_file: output/reports/attack/c8e739d8c0f8f28d65a7ef7306c36527/probabilities.json + score_dict_file: output/reports/attack/c8e739d8c0f8f28d65a7ef7306c36527/score_dict.json + test_labels_file: output/reports/attack/c8e739d8c0f8f28d65a7ef7306c36527/test_labels.json + train_labels_file: output/reports/attack/c8e739d8c0f8f28d65a7ef7306c36527/train_labels.json + model_dir: models + name: c8e739d8c0f8f28d65a7ef7306c36527 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: c8e739d8c0f8f28d65a7ef7306c36527 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c903e890e2573daba824b70da815a284/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c903e890e2573daba824b70da815a284/params.yaml new file mode 100644 index 00000000..31fe5dc3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c903e890e2573daba824b70da815a284/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 726bc8527ab0445276c718affe6908f0 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c903e890e2573daba824b70da815a284/adv_predictions.json + adv_probabilities_file: output/reports/attack/c903e890e2573daba824b70da815a284/adv_probabilities.json + attack_file: output/attacks/54f13f7820c2bc1019321520739e6b79.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/c903e890e2573daba824b70da815a284/params.yaml + predictions_file: output/reports/attack/c903e890e2573daba824b70da815a284/predictions.json + probabilities_file: output/reports/attack/c903e890e2573daba824b70da815a284/probabilities.json + score_dict_file: output/reports/attack/c903e890e2573daba824b70da815a284/score_dict.json + test_labels_file: output/reports/attack/c903e890e2573daba824b70da815a284/test_labels.json + train_labels_file: output/reports/attack/c903e890e2573daba824b70da815a284/train_labels.json + model_dir: models + name: c903e890e2573daba824b70da815a284 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: c903e890e2573daba824b70da815a284 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/c98b413e898aab86d0cb1f282d8a189c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/c98b413e898aab86d0cb1f282d8a189c/params.yaml new file mode 100644 index 00000000..1b4c8721 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/c98b413e898aab86d0cb1f282d8a189c/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: b333d63b1faa20c1a081a562eebca7f6 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c98b413e898aab86d0cb1f282d8a189c/adv_predictions.json + adv_probabilities_file: output/reports/attack/c98b413e898aab86d0cb1f282d8a189c/adv_probabilities.json + attack_file: output/attacks/19cba4453e7e1c3a8f677275efaf18a4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/c98b413e898aab86d0cb1f282d8a189c/params.yaml + predictions_file: output/reports/attack/c98b413e898aab86d0cb1f282d8a189c/predictions.json + probabilities_file: output/reports/attack/c98b413e898aab86d0cb1f282d8a189c/probabilities.json + score_dict_file: output/reports/attack/c98b413e898aab86d0cb1f282d8a189c/score_dict.json + test_labels_file: output/reports/attack/c98b413e898aab86d0cb1f282d8a189c/test_labels.json + train_labels_file: output/reports/attack/c98b413e898aab86d0cb1f282d8a189c/train_labels.json + model_dir: models + name: c98b413e898aab86d0cb1f282d8a189c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: c98b413e898aab86d0cb1f282d8a189c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ca0f7c847d284de355f9429328cf8393/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ca0f7c847d284de355f9429328cf8393/params.yaml new file mode 100644 index 00000000..6660a107 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ca0f7c847d284de355f9429328cf8393/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.5 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 29f76b78b2a35aa216423155520d5d1e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ca0f7c847d284de355f9429328cf8393/adv_predictions.json + adv_probabilities_file: output/reports/attack/ca0f7c847d284de355f9429328cf8393/adv_probabilities.json + attack_file: output/attacks/f3bb4a73004d9fde8d01477239addb50.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/ca0f7c847d284de355f9429328cf8393/params.yaml + predictions_file: output/reports/attack/ca0f7c847d284de355f9429328cf8393/predictions.json + probabilities_file: output/reports/attack/ca0f7c847d284de355f9429328cf8393/probabilities.json + score_dict_file: output/reports/attack/ca0f7c847d284de355f9429328cf8393/score_dict.json + test_labels_file: output/reports/attack/ca0f7c847d284de355f9429328cf8393/test_labels.json + train_labels_file: output/reports/attack/ca0f7c847d284de355f9429328cf8393/train_labels.json + model_dir: models + name: ca0f7c847d284de355f9429328cf8393 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: ca0f7c847d284de355f9429328cf8393 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/cbf6ee80eb64107fa02847245c68dd15/params.yaml b/examples/security/kdd-nsl/output/reports/attack/cbf6ee80eb64107fa02847245c68dd15/params.yaml new file mode 100644 index 00000000..a6f31bb8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/cbf6ee80eb64107fa02847245c68dd15/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 1c65d5bcab61743f6e5b2f33deb73696 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/cbf6ee80eb64107fa02847245c68dd15/adv_predictions.json + adv_probabilities_file: output/reports/attack/cbf6ee80eb64107fa02847245c68dd15/adv_probabilities.json + attack_file: output/attacks/181432dac12889c3951fbab1c72abdc0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/cbf6ee80eb64107fa02847245c68dd15/params.yaml + predictions_file: output/reports/attack/cbf6ee80eb64107fa02847245c68dd15/predictions.json + probabilities_file: output/reports/attack/cbf6ee80eb64107fa02847245c68dd15/probabilities.json + score_dict_file: output/reports/attack/cbf6ee80eb64107fa02847245c68dd15/score_dict.json + test_labels_file: output/reports/attack/cbf6ee80eb64107fa02847245c68dd15/test_labels.json + train_labels_file: output/reports/attack/cbf6ee80eb64107fa02847245c68dd15/train_labels.json + model_dir: models + name: cbf6ee80eb64107fa02847245c68dd15 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: cbf6ee80eb64107fa02847245c68dd15 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/cd33d207b94c556e7b0791c9d0ac5b6e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/cd33d207b94c556e7b0791c9d0ac5b6e/params.yaml new file mode 100644 index 00000000..676f3c30 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/cd33d207b94c556e7b0791c9d0ac5b6e/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.5 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 47d373dece4ef95e22f5d2765b38f915 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/cd33d207b94c556e7b0791c9d0ac5b6e/adv_predictions.json + adv_probabilities_file: output/reports/attack/cd33d207b94c556e7b0791c9d0ac5b6e/adv_probabilities.json + attack_file: output/attacks/1666dd9291d4b345f04d6bc2ddfaf0ba.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/cd33d207b94c556e7b0791c9d0ac5b6e/params.yaml + predictions_file: output/reports/attack/cd33d207b94c556e7b0791c9d0ac5b6e/predictions.json + probabilities_file: output/reports/attack/cd33d207b94c556e7b0791c9d0ac5b6e/probabilities.json + score_dict_file: output/reports/attack/cd33d207b94c556e7b0791c9d0ac5b6e/score_dict.json + test_labels_file: output/reports/attack/cd33d207b94c556e7b0791c9d0ac5b6e/test_labels.json + train_labels_file: output/reports/attack/cd33d207b94c556e7b0791c9d0ac5b6e/train_labels.json + model_dir: models + name: cd33d207b94c556e7b0791c9d0ac5b6e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: cd33d207b94c556e7b0791c9d0ac5b6e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/cdffb012ef379c33df059bc181112f1d/params.yaml b/examples/security/kdd-nsl/output/reports/attack/cdffb012ef379c33df059bc181112f1d/params.yaml new file mode 100644 index 00000000..eaed2acf --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/cdffb012ef379c33df059bc181112f1d/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 1e1798ecefadbeafa59f33674b3eb086 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/cdffb012ef379c33df059bc181112f1d/adv_predictions.json + adv_probabilities_file: output/reports/attack/cdffb012ef379c33df059bc181112f1d/adv_probabilities.json + attack_file: output/attacks/1a2c00d94dfb97fe80c2319d7ca30350.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/cdffb012ef379c33df059bc181112f1d/params.yaml + predictions_file: output/reports/attack/cdffb012ef379c33df059bc181112f1d/predictions.json + probabilities_file: output/reports/attack/cdffb012ef379c33df059bc181112f1d/probabilities.json + score_dict_file: output/reports/attack/cdffb012ef379c33df059bc181112f1d/score_dict.json + test_labels_file: output/reports/attack/cdffb012ef379c33df059bc181112f1d/test_labels.json + train_labels_file: output/reports/attack/cdffb012ef379c33df059bc181112f1d/train_labels.json + model_dir: models + name: cdffb012ef379c33df059bc181112f1d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: cdffb012ef379c33df059bc181112f1d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ce4bda1537ee46782425f92eb642e5ca/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ce4bda1537ee46782425f92eb642e5ca/params.yaml new file mode 100644 index 00000000..bd5c6126 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ce4bda1537ee46782425f92eb642e5ca/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 7dacb6b7686d16f331ed636db4a53d11 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ce4bda1537ee46782425f92eb642e5ca/adv_predictions.json + adv_probabilities_file: output/reports/attack/ce4bda1537ee46782425f92eb642e5ca/adv_probabilities.json + attack_file: output/attacks/90e2c0e449b5cb60782c499f6654bb4e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/ce4bda1537ee46782425f92eb642e5ca/params.yaml + predictions_file: output/reports/attack/ce4bda1537ee46782425f92eb642e5ca/predictions.json + probabilities_file: output/reports/attack/ce4bda1537ee46782425f92eb642e5ca/probabilities.json + score_dict_file: output/reports/attack/ce4bda1537ee46782425f92eb642e5ca/score_dict.json + test_labels_file: output/reports/attack/ce4bda1537ee46782425f92eb642e5ca/test_labels.json + train_labels_file: output/reports/attack/ce4bda1537ee46782425f92eb642e5ca/train_labels.json + model_dir: models + name: ce4bda1537ee46782425f92eb642e5ca + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: ce4bda1537ee46782425f92eb642e5ca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/cf72693a6237f0f1bb3022a3498c2f88/params.yaml b/examples/security/kdd-nsl/output/reports/attack/cf72693a6237f0f1bb3022a3498c2f88/params.yaml new file mode 100644 index 00000000..428dfeda --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/cf72693a6237f0f1bb3022a3498c2f88/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 6f7239c71edd6a57a52b574e026caf49 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/cf72693a6237f0f1bb3022a3498c2f88/adv_predictions.json + adv_probabilities_file: output/reports/attack/cf72693a6237f0f1bb3022a3498c2f88/adv_probabilities.json + attack_file: output/attacks/e32eff60e0444c5444ebd2c05678bcf6.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/cf72693a6237f0f1bb3022a3498c2f88/params.yaml + predictions_file: output/reports/attack/cf72693a6237f0f1bb3022a3498c2f88/predictions.json + probabilities_file: output/reports/attack/cf72693a6237f0f1bb3022a3498c2f88/probabilities.json + score_dict_file: output/reports/attack/cf72693a6237f0f1bb3022a3498c2f88/score_dict.json + test_labels_file: output/reports/attack/cf72693a6237f0f1bb3022a3498c2f88/test_labels.json + train_labels_file: output/reports/attack/cf72693a6237f0f1bb3022a3498c2f88/train_labels.json + model_dir: models + name: cf72693a6237f0f1bb3022a3498c2f88 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: cf72693a6237f0f1bb3022a3498c2f88 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d04aa3671650b2ee407dca9d349b9cfc/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d04aa3671650b2ee407dca9d349b9cfc/params.yaml new file mode 100644 index 00000000..3349b440 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d04aa3671650b2ee407dca9d349b9cfc/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.001 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 5d7e8f477961817bf254596b99ae6b53 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d04aa3671650b2ee407dca9d349b9cfc/adv_predictions.json + adv_probabilities_file: output/reports/attack/d04aa3671650b2ee407dca9d349b9cfc/adv_probabilities.json + attack_file: output/attacks/54f3f168ea27db1c261f62452e661e55.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/d04aa3671650b2ee407dca9d349b9cfc/params.yaml + predictions_file: output/reports/attack/d04aa3671650b2ee407dca9d349b9cfc/predictions.json + probabilities_file: output/reports/attack/d04aa3671650b2ee407dca9d349b9cfc/probabilities.json + score_dict_file: output/reports/attack/d04aa3671650b2ee407dca9d349b9cfc/score_dict.json + test_labels_file: output/reports/attack/d04aa3671650b2ee407dca9d349b9cfc/test_labels.json + train_labels_file: output/reports/attack/d04aa3671650b2ee407dca9d349b9cfc/train_labels.json + model_dir: models + name: d04aa3671650b2ee407dca9d349b9cfc + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: d04aa3671650b2ee407dca9d349b9cfc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d0e5466c212c28cc6154e507db6cc0a3/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d0e5466c212c28cc6154e507db6cc0a3/params.yaml new file mode 100644 index 00000000..7261b585 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d0e5466c212c28cc6154e507db6cc0a3/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 12c39ce9d8da20b3df497dc81e7adbcd +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d0e5466c212c28cc6154e507db6cc0a3/adv_predictions.json + adv_probabilities_file: output/reports/attack/d0e5466c212c28cc6154e507db6cc0a3/adv_probabilities.json + attack_file: output/attacks/6f4dd60f882bea42379f84f5eab98353.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/d0e5466c212c28cc6154e507db6cc0a3/params.yaml + predictions_file: output/reports/attack/d0e5466c212c28cc6154e507db6cc0a3/predictions.json + probabilities_file: output/reports/attack/d0e5466c212c28cc6154e507db6cc0a3/probabilities.json + score_dict_file: output/reports/attack/d0e5466c212c28cc6154e507db6cc0a3/score_dict.json + test_labels_file: output/reports/attack/d0e5466c212c28cc6154e507db6cc0a3/test_labels.json + train_labels_file: output/reports/attack/d0e5466c212c28cc6154e507db6cc0a3/train_labels.json + model_dir: models + name: d0e5466c212c28cc6154e507db6cc0a3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: d0e5466c212c28cc6154e507db6cc0a3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d11a1fd79a5988dda2c3e53c6f44a75e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d11a1fd79a5988dda2c3e53c6f44a75e/params.yaml new file mode 100644 index 00000000..1cc26d94 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d11a1fd79a5988dda2c3e53c6f44a75e/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 5cca416b747780435adade23a9086c05 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d11a1fd79a5988dda2c3e53c6f44a75e/adv_predictions.json + adv_probabilities_file: output/reports/attack/d11a1fd79a5988dda2c3e53c6f44a75e/adv_probabilities.json + attack_file: output/attacks/5f33b8e2d2a99bd01ae40275fe129239.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/d11a1fd79a5988dda2c3e53c6f44a75e/params.yaml + predictions_file: output/reports/attack/d11a1fd79a5988dda2c3e53c6f44a75e/predictions.json + probabilities_file: output/reports/attack/d11a1fd79a5988dda2c3e53c6f44a75e/probabilities.json + score_dict_file: output/reports/attack/d11a1fd79a5988dda2c3e53c6f44a75e/score_dict.json + test_labels_file: output/reports/attack/d11a1fd79a5988dda2c3e53c6f44a75e/test_labels.json + train_labels_file: output/reports/attack/d11a1fd79a5988dda2c3e53c6f44a75e/train_labels.json + model_dir: models + name: d11a1fd79a5988dda2c3e53c6f44a75e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: d11a1fd79a5988dda2c3e53c6f44a75e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d12fccf3a472ae914e0008a9124c67e9/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d12fccf3a472ae914e0008a9124c67e9/params.yaml new file mode 100644 index 00000000..d5a7c759 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d12fccf3a472ae914e0008a9124c67e9/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: d6fc11a1ed82458c4b9a793b56a0f972 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d12fccf3a472ae914e0008a9124c67e9/adv_predictions.json + adv_probabilities_file: output/reports/attack/d12fccf3a472ae914e0008a9124c67e9/adv_probabilities.json + attack_file: output/attacks/55c409e07bc85ba37118a838e9a35fe5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/d12fccf3a472ae914e0008a9124c67e9/params.yaml + predictions_file: output/reports/attack/d12fccf3a472ae914e0008a9124c67e9/predictions.json + probabilities_file: output/reports/attack/d12fccf3a472ae914e0008a9124c67e9/probabilities.json + score_dict_file: output/reports/attack/d12fccf3a472ae914e0008a9124c67e9/score_dict.json + test_labels_file: output/reports/attack/d12fccf3a472ae914e0008a9124c67e9/test_labels.json + train_labels_file: output/reports/attack/d12fccf3a472ae914e0008a9124c67e9/train_labels.json + model_dir: models + name: d12fccf3a472ae914e0008a9124c67e9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: d12fccf3a472ae914e0008a9124c67e9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d3bc99715bd540fb1132f89c69daf7b6/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d3bc99715bd540fb1132f89c69daf7b6/params.yaml new file mode 100644 index 00000000..49015468 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d3bc99715bd540fb1132f89c69daf7b6/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 9bebe8ce572994eafd25d00f19e02e32 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d3bc99715bd540fb1132f89c69daf7b6/adv_predictions.json + adv_probabilities_file: output/reports/attack/d3bc99715bd540fb1132f89c69daf7b6/adv_probabilities.json + attack_file: output/attacks/2ce786f40ab4ab29605e633be9751d87.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d3bc99715bd540fb1132f89c69daf7b6/params.yaml + predictions_file: output/reports/attack/d3bc99715bd540fb1132f89c69daf7b6/predictions.json + probabilities_file: output/reports/attack/d3bc99715bd540fb1132f89c69daf7b6/probabilities.json + score_dict_file: output/reports/attack/d3bc99715bd540fb1132f89c69daf7b6/score_dict.json + test_labels_file: output/reports/attack/d3bc99715bd540fb1132f89c69daf7b6/test_labels.json + train_labels_file: output/reports/attack/d3bc99715bd540fb1132f89c69daf7b6/train_labels.json + model_dir: models + name: d3bc99715bd540fb1132f89c69daf7b6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d3bc99715bd540fb1132f89c69daf7b6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d41a21642595ec6eeee05b04d5aa4f0e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d41a21642595ec6eeee05b04d5aa4f0e/params.yaml new file mode 100644 index 00000000..9454b63d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d41a21642595ec6eeee05b04d5aa4f0e/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.5 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: ddacbde3b9a83f71465a57eb313a8788 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d41a21642595ec6eeee05b04d5aa4f0e/adv_predictions.json + adv_probabilities_file: output/reports/attack/d41a21642595ec6eeee05b04d5aa4f0e/adv_probabilities.json + attack_file: output/attacks/614bdb35f58ed65b55e7bc5c4af7c22c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d41a21642595ec6eeee05b04d5aa4f0e/params.yaml + predictions_file: output/reports/attack/d41a21642595ec6eeee05b04d5aa4f0e/predictions.json + probabilities_file: output/reports/attack/d41a21642595ec6eeee05b04d5aa4f0e/probabilities.json + score_dict_file: output/reports/attack/d41a21642595ec6eeee05b04d5aa4f0e/score_dict.json + test_labels_file: output/reports/attack/d41a21642595ec6eeee05b04d5aa4f0e/test_labels.json + train_labels_file: output/reports/attack/d41a21642595ec6eeee05b04d5aa4f0e/train_labels.json + model_dir: models + name: d41a21642595ec6eeee05b04d5aa4f0e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d41a21642595ec6eeee05b04d5aa4f0e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d42c1bc807cc1530c8662a3044d34f9c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d42c1bc807cc1530c8662a3044d34f9c/params.yaml new file mode 100644 index 00000000..633c30db --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d42c1bc807cc1530c8662a3044d34f9c/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.5 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 4358f52e25f8076bb0c38d3de906ce57 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d42c1bc807cc1530c8662a3044d34f9c/adv_predictions.json + adv_probabilities_file: output/reports/attack/d42c1bc807cc1530c8662a3044d34f9c/adv_probabilities.json + attack_file: output/attacks/81d7d0d7e53daf91fc4f95719e300278.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d42c1bc807cc1530c8662a3044d34f9c/params.yaml + predictions_file: output/reports/attack/d42c1bc807cc1530c8662a3044d34f9c/predictions.json + probabilities_file: output/reports/attack/d42c1bc807cc1530c8662a3044d34f9c/probabilities.json + score_dict_file: output/reports/attack/d42c1bc807cc1530c8662a3044d34f9c/score_dict.json + test_labels_file: output/reports/attack/d42c1bc807cc1530c8662a3044d34f9c/test_labels.json + train_labels_file: output/reports/attack/d42c1bc807cc1530c8662a3044d34f9c/train_labels.json + model_dir: models + name: d42c1bc807cc1530c8662a3044d34f9c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d42c1bc807cc1530c8662a3044d34f9c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d4317911258d877431aec4a46b1d71f4/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d4317911258d877431aec4a46b1d71f4/params.yaml new file mode 100644 index 00000000..9a46eb32 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d4317911258d877431aec4a46b1d71f4/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.001 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 38f08deda8a26fba91063f24f7430cef +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d4317911258d877431aec4a46b1d71f4/adv_predictions.json + adv_probabilities_file: output/reports/attack/d4317911258d877431aec4a46b1d71f4/adv_probabilities.json + attack_file: output/attacks/d20e005bc8d224417e3ca5098a6361ca.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d4317911258d877431aec4a46b1d71f4/params.yaml + predictions_file: output/reports/attack/d4317911258d877431aec4a46b1d71f4/predictions.json + probabilities_file: output/reports/attack/d4317911258d877431aec4a46b1d71f4/probabilities.json + score_dict_file: output/reports/attack/d4317911258d877431aec4a46b1d71f4/score_dict.json + test_labels_file: output/reports/attack/d4317911258d877431aec4a46b1d71f4/test_labels.json + train_labels_file: output/reports/attack/d4317911258d877431aec4a46b1d71f4/train_labels.json + model_dir: models + name: d4317911258d877431aec4a46b1d71f4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d4317911258d877431aec4a46b1d71f4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d4358911e7fe0fc4a4f975ff83aefe54/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d4358911e7fe0fc4a4f975ff83aefe54/params.yaml new file mode 100644 index 00000000..6c95cce8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d4358911e7fe0fc4a4f975ff83aefe54/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.001 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: ba3bf59f4bbc5a8dc305ad11bf34fe76 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d4358911e7fe0fc4a4f975ff83aefe54/adv_predictions.json + adv_probabilities_file: output/reports/attack/d4358911e7fe0fc4a4f975ff83aefe54/adv_probabilities.json + attack_file: output/attacks/129ea09995284a10cd9607124bf97d39.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/d4358911e7fe0fc4a4f975ff83aefe54/params.yaml + predictions_file: output/reports/attack/d4358911e7fe0fc4a4f975ff83aefe54/predictions.json + probabilities_file: output/reports/attack/d4358911e7fe0fc4a4f975ff83aefe54/probabilities.json + score_dict_file: output/reports/attack/d4358911e7fe0fc4a4f975ff83aefe54/score_dict.json + test_labels_file: output/reports/attack/d4358911e7fe0fc4a4f975ff83aefe54/test_labels.json + train_labels_file: output/reports/attack/d4358911e7fe0fc4a4f975ff83aefe54/train_labels.json + model_dir: models + name: d4358911e7fe0fc4a4f975ff83aefe54 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: d4358911e7fe0fc4a4f975ff83aefe54 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d4c4306a49eb372c9f7a0a8e1955a0a0/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d4c4306a49eb372c9f7a0a8e1955a0a0/params.yaml new file mode 100644 index 00000000..8cca236a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d4c4306a49eb372c9f7a0a8e1955a0a0/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.3 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 66cdbefc8db1e5abb75723459668d2c9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d4c4306a49eb372c9f7a0a8e1955a0a0/adv_predictions.json + adv_probabilities_file: output/reports/attack/d4c4306a49eb372c9f7a0a8e1955a0a0/adv_probabilities.json + attack_file: output/attacks/86057ab0662654d83f2f1ef37fb45495.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/d4c4306a49eb372c9f7a0a8e1955a0a0/params.yaml + predictions_file: output/reports/attack/d4c4306a49eb372c9f7a0a8e1955a0a0/predictions.json + probabilities_file: output/reports/attack/d4c4306a49eb372c9f7a0a8e1955a0a0/probabilities.json + score_dict_file: output/reports/attack/d4c4306a49eb372c9f7a0a8e1955a0a0/score_dict.json + test_labels_file: output/reports/attack/d4c4306a49eb372c9f7a0a8e1955a0a0/test_labels.json + train_labels_file: output/reports/attack/d4c4306a49eb372c9f7a0a8e1955a0a0/train_labels.json + model_dir: models + name: d4c4306a49eb372c9f7a0a8e1955a0a0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: d4c4306a49eb372c9f7a0a8e1955a0a0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d509563ba45c4d201754a7ff82b8758c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d509563ba45c4d201754a7ff82b8758c/params.yaml new file mode 100644 index 00000000..2ba97fc8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d509563ba45c4d201754a7ff82b8758c/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 1 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 2ea04b784f94d018ca821682621472a1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d509563ba45c4d201754a7ff82b8758c/adv_predictions.json + adv_probabilities_file: output/reports/attack/d509563ba45c4d201754a7ff82b8758c/adv_probabilities.json + attack_file: output/attacks/a6256de953e18057e1d2f2ad78e968a5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d509563ba45c4d201754a7ff82b8758c/params.yaml + predictions_file: output/reports/attack/d509563ba45c4d201754a7ff82b8758c/predictions.json + probabilities_file: output/reports/attack/d509563ba45c4d201754a7ff82b8758c/probabilities.json + score_dict_file: output/reports/attack/d509563ba45c4d201754a7ff82b8758c/score_dict.json + test_labels_file: output/reports/attack/d509563ba45c4d201754a7ff82b8758c/test_labels.json + train_labels_file: output/reports/attack/d509563ba45c4d201754a7ff82b8758c/train_labels.json + model_dir: models + name: d509563ba45c4d201754a7ff82b8758c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d509563ba45c4d201754a7ff82b8758c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d5409f1ead9ffc49d991f8afb528beee/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d5409f1ead9ffc49d991f8afb528beee/params.yaml new file mode 100644 index 00000000..60841103 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d5409f1ead9ffc49d991f8afb528beee/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 0d29099e17b5c60c9e2f6faa2589076f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d5409f1ead9ffc49d991f8afb528beee/adv_predictions.json + adv_probabilities_file: output/reports/attack/d5409f1ead9ffc49d991f8afb528beee/adv_probabilities.json + attack_file: output/attacks/e7a47c5313be03673d05b4d336ab4e20.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d5409f1ead9ffc49d991f8afb528beee/params.yaml + predictions_file: output/reports/attack/d5409f1ead9ffc49d991f8afb528beee/predictions.json + probabilities_file: output/reports/attack/d5409f1ead9ffc49d991f8afb528beee/probabilities.json + score_dict_file: output/reports/attack/d5409f1ead9ffc49d991f8afb528beee/score_dict.json + test_labels_file: output/reports/attack/d5409f1ead9ffc49d991f8afb528beee/test_labels.json + train_labels_file: output/reports/attack/d5409f1ead9ffc49d991f8afb528beee/train_labels.json + model_dir: models + name: d5409f1ead9ffc49d991f8afb528beee + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d5409f1ead9ffc49d991f8afb528beee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d596a384a5c608673592f3e769eaac5a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d596a384a5c608673592f3e769eaac5a/params.yaml new file mode 100644 index 00000000..4a4f0e50 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d596a384a5c608673592f3e769eaac5a/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: c71f7f31b5aa79b37bfb0544610b1184 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d596a384a5c608673592f3e769eaac5a/adv_predictions.json + adv_probabilities_file: output/reports/attack/d596a384a5c608673592f3e769eaac5a/adv_probabilities.json + attack_file: output/attacks/9e459d837e39e5bd04a4a79a8b5f840a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d596a384a5c608673592f3e769eaac5a/params.yaml + predictions_file: output/reports/attack/d596a384a5c608673592f3e769eaac5a/predictions.json + probabilities_file: output/reports/attack/d596a384a5c608673592f3e769eaac5a/probabilities.json + score_dict_file: output/reports/attack/d596a384a5c608673592f3e769eaac5a/score_dict.json + test_labels_file: output/reports/attack/d596a384a5c608673592f3e769eaac5a/test_labels.json + train_labels_file: output/reports/attack/d596a384a5c608673592f3e769eaac5a/train_labels.json + model_dir: models + name: d596a384a5c608673592f3e769eaac5a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d596a384a5c608673592f3e769eaac5a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d63d71eae2bd57671acc6c28dccd01c9/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d63d71eae2bd57671acc6c28dccd01c9/params.yaml new file mode 100644 index 00000000..f1bbd785 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d63d71eae2bd57671acc6c28dccd01c9/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: f9201cbc5d820eac29858535891ae522 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d63d71eae2bd57671acc6c28dccd01c9/adv_predictions.json + adv_probabilities_file: output/reports/attack/d63d71eae2bd57671acc6c28dccd01c9/adv_probabilities.json + attack_file: output/attacks/6a7cd8181188fa2e2ec91c7b6533b965.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d63d71eae2bd57671acc6c28dccd01c9/params.yaml + predictions_file: output/reports/attack/d63d71eae2bd57671acc6c28dccd01c9/predictions.json + probabilities_file: output/reports/attack/d63d71eae2bd57671acc6c28dccd01c9/probabilities.json + score_dict_file: output/reports/attack/d63d71eae2bd57671acc6c28dccd01c9/score_dict.json + test_labels_file: output/reports/attack/d63d71eae2bd57671acc6c28dccd01c9/test_labels.json + train_labels_file: output/reports/attack/d63d71eae2bd57671acc6c28dccd01c9/train_labels.json + model_dir: models + name: d63d71eae2bd57671acc6c28dccd01c9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d63d71eae2bd57671acc6c28dccd01c9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d679e69eba07ea0dbe859f8c6f1ec8f2/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d679e69eba07ea0dbe859f8c6f1ec8f2/params.yaml new file mode 100644 index 00000000..99542e7d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d679e69eba07ea0dbe859f8c6f1ec8f2/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.5 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: ce3c9570943e56bdbf691e75442b0806 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d679e69eba07ea0dbe859f8c6f1ec8f2/adv_predictions.json + adv_probabilities_file: output/reports/attack/d679e69eba07ea0dbe859f8c6f1ec8f2/adv_probabilities.json + attack_file: output/attacks/cfae32e267257ca61d96c56d24883357.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/d679e69eba07ea0dbe859f8c6f1ec8f2/params.yaml + predictions_file: output/reports/attack/d679e69eba07ea0dbe859f8c6f1ec8f2/predictions.json + probabilities_file: output/reports/attack/d679e69eba07ea0dbe859f8c6f1ec8f2/probabilities.json + score_dict_file: output/reports/attack/d679e69eba07ea0dbe859f8c6f1ec8f2/score_dict.json + test_labels_file: output/reports/attack/d679e69eba07ea0dbe859f8c6f1ec8f2/test_labels.json + train_labels_file: output/reports/attack/d679e69eba07ea0dbe859f8c6f1ec8f2/train_labels.json + model_dir: models + name: d679e69eba07ea0dbe859f8c6f1ec8f2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: d679e69eba07ea0dbe859f8c6f1ec8f2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d6e320216e4cafc2b8de4e37c4de554a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d6e320216e4cafc2b8de4e37c4de554a/params.yaml new file mode 100644 index 00000000..8d20cca2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d6e320216e4cafc2b8de4e37c4de554a/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.3 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 093912d3785e6e3c8329397abd34cbf6 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d6e320216e4cafc2b8de4e37c4de554a/adv_predictions.json + adv_probabilities_file: output/reports/attack/d6e320216e4cafc2b8de4e37c4de554a/adv_probabilities.json + attack_file: output/attacks/481bca2d3d96f6c943e0df0b21f25d0a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/d6e320216e4cafc2b8de4e37c4de554a/params.yaml + predictions_file: output/reports/attack/d6e320216e4cafc2b8de4e37c4de554a/predictions.json + probabilities_file: output/reports/attack/d6e320216e4cafc2b8de4e37c4de554a/probabilities.json + score_dict_file: output/reports/attack/d6e320216e4cafc2b8de4e37c4de554a/score_dict.json + test_labels_file: output/reports/attack/d6e320216e4cafc2b8de4e37c4de554a/test_labels.json + train_labels_file: output/reports/attack/d6e320216e4cafc2b8de4e37c4de554a/train_labels.json + model_dir: models + name: d6e320216e4cafc2b8de4e37c4de554a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: d6e320216e4cafc2b8de4e37c4de554a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d7b88670854cf6370e5faa254ed12036/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d7b88670854cf6370e5faa254ed12036/params.yaml new file mode 100644 index 00000000..181c1672 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d7b88670854cf6370e5faa254ed12036/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 706f49045bb0dd1d22fe32459d6999d8 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d7b88670854cf6370e5faa254ed12036/adv_predictions.json + adv_probabilities_file: output/reports/attack/d7b88670854cf6370e5faa254ed12036/adv_probabilities.json + attack_file: output/attacks/f2d415b374d640feba66096babea5210.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d7b88670854cf6370e5faa254ed12036/params.yaml + predictions_file: output/reports/attack/d7b88670854cf6370e5faa254ed12036/predictions.json + probabilities_file: output/reports/attack/d7b88670854cf6370e5faa254ed12036/probabilities.json + score_dict_file: output/reports/attack/d7b88670854cf6370e5faa254ed12036/score_dict.json + test_labels_file: output/reports/attack/d7b88670854cf6370e5faa254ed12036/test_labels.json + train_labels_file: output/reports/attack/d7b88670854cf6370e5faa254ed12036/train_labels.json + model_dir: models + name: d7b88670854cf6370e5faa254ed12036 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d7b88670854cf6370e5faa254ed12036 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d7c03b8a4642e6eb1f56f5619f02e27e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d7c03b8a4642e6eb1f56f5619f02e27e/params.yaml new file mode 100644 index 00000000..41bcdecb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d7c03b8a4642e6eb1f56f5619f02e27e/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.001 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 6e3ab220127db841aff764958c0d1ac8 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d7c03b8a4642e6eb1f56f5619f02e27e/adv_predictions.json + adv_probabilities_file: output/reports/attack/d7c03b8a4642e6eb1f56f5619f02e27e/adv_probabilities.json + attack_file: output/attacks/a03402ca5322b66ff9175531e58bae2c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/d7c03b8a4642e6eb1f56f5619f02e27e/params.yaml + predictions_file: output/reports/attack/d7c03b8a4642e6eb1f56f5619f02e27e/predictions.json + probabilities_file: output/reports/attack/d7c03b8a4642e6eb1f56f5619f02e27e/probabilities.json + score_dict_file: output/reports/attack/d7c03b8a4642e6eb1f56f5619f02e27e/score_dict.json + test_labels_file: output/reports/attack/d7c03b8a4642e6eb1f56f5619f02e27e/test_labels.json + train_labels_file: output/reports/attack/d7c03b8a4642e6eb1f56f5619f02e27e/train_labels.json + model_dir: models + name: d7c03b8a4642e6eb1f56f5619f02e27e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: d7c03b8a4642e6eb1f56f5619f02e27e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d7ef2ef4c326fc6965a9070964ff9c08/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d7ef2ef4c326fc6965a9070964ff9c08/params.yaml new file mode 100644 index 00000000..e49666fc --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d7ef2ef4c326fc6965a9070964ff9c08/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.01 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: d467d4f341a1a8b1073c685823f46ca1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d7ef2ef4c326fc6965a9070964ff9c08/adv_predictions.json + adv_probabilities_file: output/reports/attack/d7ef2ef4c326fc6965a9070964ff9c08/adv_probabilities.json + attack_file: output/attacks/cb05a2693232c4a644d79a673b2bcdc9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/d7ef2ef4c326fc6965a9070964ff9c08/params.yaml + predictions_file: output/reports/attack/d7ef2ef4c326fc6965a9070964ff9c08/predictions.json + probabilities_file: output/reports/attack/d7ef2ef4c326fc6965a9070964ff9c08/probabilities.json + score_dict_file: output/reports/attack/d7ef2ef4c326fc6965a9070964ff9c08/score_dict.json + test_labels_file: output/reports/attack/d7ef2ef4c326fc6965a9070964ff9c08/test_labels.json + train_labels_file: output/reports/attack/d7ef2ef4c326fc6965a9070964ff9c08/train_labels.json + model_dir: models + name: d7ef2ef4c326fc6965a9070964ff9c08 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: d7ef2ef4c326fc6965a9070964ff9c08 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/params.yaml new file mode 100644 index 00000000..7139f5ec --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.5 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: e584bceb92d5a7120baa32b60cb00b3d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/adv_predictions.json + adv_probabilities_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/adv_probabilities.json + attack_file: output/attacks/26f5779125a73c5e0fb0eccb22aa924a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/params.yaml + predictions_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/predictions.json + probabilities_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/probabilities.json + score_dict_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/score_dict.json + test_labels_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/test_labels.json + train_labels_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/train_labels.json + model_dir: models + name: d8dbc76017ce0953ca4ba4fda002be83 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d8dbc76017ce0953ca4ba4fda002be83 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/d93fbbe70441e086600b44c19d9dd951/params.yaml b/examples/security/kdd-nsl/output/reports/attack/d93fbbe70441e086600b44c19d9dd951/params.yaml new file mode 100644 index 00000000..5f1affab --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/d93fbbe70441e086600b44c19d9dd951/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: d6aaa3feecf13ec1d38a3f431f50bd92 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d93fbbe70441e086600b44c19d9dd951/adv_predictions.json + adv_probabilities_file: output/reports/attack/d93fbbe70441e086600b44c19d9dd951/adv_probabilities.json + attack_file: output/attacks/9c8ee9261bbcadc463e57e120a56021b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d93fbbe70441e086600b44c19d9dd951/params.yaml + predictions_file: output/reports/attack/d93fbbe70441e086600b44c19d9dd951/predictions.json + probabilities_file: output/reports/attack/d93fbbe70441e086600b44c19d9dd951/probabilities.json + score_dict_file: output/reports/attack/d93fbbe70441e086600b44c19d9dd951/score_dict.json + test_labels_file: output/reports/attack/d93fbbe70441e086600b44c19d9dd951/test_labels.json + train_labels_file: output/reports/attack/d93fbbe70441e086600b44c19d9dd951/train_labels.json + model_dir: models + name: d93fbbe70441e086600b44c19d9dd951 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d93fbbe70441e086600b44c19d9dd951 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/db7e5a14a0cebb11ae53dacb321ce0f3/params.yaml b/examples/security/kdd-nsl/output/reports/attack/db7e5a14a0cebb11ae53dacb321ce0f3/params.yaml new file mode 100644 index 00000000..5ac9a306 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/db7e5a14a0cebb11ae53dacb321ce0f3/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 3b27c6d828c7b38c8e64fd32d378cde5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/db7e5a14a0cebb11ae53dacb321ce0f3/adv_predictions.json + adv_probabilities_file: output/reports/attack/db7e5a14a0cebb11ae53dacb321ce0f3/adv_probabilities.json + attack_file: output/attacks/2840eee6ce15e51992fb75654869e8fd.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/db7e5a14a0cebb11ae53dacb321ce0f3/params.yaml + predictions_file: output/reports/attack/db7e5a14a0cebb11ae53dacb321ce0f3/predictions.json + probabilities_file: output/reports/attack/db7e5a14a0cebb11ae53dacb321ce0f3/probabilities.json + score_dict_file: output/reports/attack/db7e5a14a0cebb11ae53dacb321ce0f3/score_dict.json + test_labels_file: output/reports/attack/db7e5a14a0cebb11ae53dacb321ce0f3/test_labels.json + train_labels_file: output/reports/attack/db7e5a14a0cebb11ae53dacb321ce0f3/train_labels.json + model_dir: models + name: db7e5a14a0cebb11ae53dacb321ce0f3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: db7e5a14a0cebb11ae53dacb321ce0f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/dcd043ce57fe9dbf97ee3e1ce27fb53d/params.yaml b/examples/security/kdd-nsl/output/reports/attack/dcd043ce57fe9dbf97ee3e1ce27fb53d/params.yaml new file mode 100644 index 00000000..8cb38783 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/dcd043ce57fe9dbf97ee3e1ce27fb53d/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.5 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: b8f8166da959b414e060ade06078ce8d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/dcd043ce57fe9dbf97ee3e1ce27fb53d/adv_predictions.json + adv_probabilities_file: output/reports/attack/dcd043ce57fe9dbf97ee3e1ce27fb53d/adv_probabilities.json + attack_file: output/attacks/1b490f8691221aa69acc99a5dc1cf888.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/dcd043ce57fe9dbf97ee3e1ce27fb53d/params.yaml + predictions_file: output/reports/attack/dcd043ce57fe9dbf97ee3e1ce27fb53d/predictions.json + probabilities_file: output/reports/attack/dcd043ce57fe9dbf97ee3e1ce27fb53d/probabilities.json + score_dict_file: output/reports/attack/dcd043ce57fe9dbf97ee3e1ce27fb53d/score_dict.json + test_labels_file: output/reports/attack/dcd043ce57fe9dbf97ee3e1ce27fb53d/test_labels.json + train_labels_file: output/reports/attack/dcd043ce57fe9dbf97ee3e1ce27fb53d/train_labels.json + model_dir: models + name: dcd043ce57fe9dbf97ee3e1ce27fb53d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: dcd043ce57fe9dbf97ee3e1ce27fb53d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/de04485b29f56b99efa879587c9908fa/params.yaml b/examples/security/kdd-nsl/output/reports/attack/de04485b29f56b99efa879587c9908fa/params.yaml new file mode 100644 index 00000000..775527c3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/de04485b29f56b99efa879587c9908fa/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.01 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 3b40d568ccd9b9466c9a570a88442bbd +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/de04485b29f56b99efa879587c9908fa/adv_predictions.json + adv_probabilities_file: output/reports/attack/de04485b29f56b99efa879587c9908fa/adv_probabilities.json + attack_file: output/attacks/0980332d9bdd91b2670d042abc4fef6d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/de04485b29f56b99efa879587c9908fa/params.yaml + predictions_file: output/reports/attack/de04485b29f56b99efa879587c9908fa/predictions.json + probabilities_file: output/reports/attack/de04485b29f56b99efa879587c9908fa/probabilities.json + score_dict_file: output/reports/attack/de04485b29f56b99efa879587c9908fa/score_dict.json + test_labels_file: output/reports/attack/de04485b29f56b99efa879587c9908fa/test_labels.json + train_labels_file: output/reports/attack/de04485b29f56b99efa879587c9908fa/train_labels.json + model_dir: models + name: de04485b29f56b99efa879587c9908fa + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: de04485b29f56b99efa879587c9908fa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/default/params.yaml b/examples/security/kdd-nsl/output/reports/attack/default/params.yaml new file mode 100644 index 00000000..653ef908 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/default/params.yaml @@ -0,0 +1,211 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 5be6efb2a95e92834d747594b415d983 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/default/adv_predictions.json + adv_probabilities_file: output/reports/attack/default/adv_probabilities.json + attack_file: output/attacks/attack.pkl + params_file: output/reports/attack/default/params.yaml + predictions_file: output/reports/attack/default/predictions.json + probabilities_file: output/reports/attack/default/probabilities.json + score_dict_file: output/reports/attack/default/score_dict.json + model_dir: models + name: default + reports: reports + stage: attack +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: default +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e07e357a8f0d3f53d715e2442725816d/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e07e357a8f0d3f53d715e2442725816d/params.yaml new file mode 100644 index 00000000..96e901c8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e07e357a8f0d3f53d715e2442725816d/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.3 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 24ec6fecd03f5fa6d922e382ee1c346b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e07e357a8f0d3f53d715e2442725816d/adv_predictions.json + adv_probabilities_file: output/reports/attack/e07e357a8f0d3f53d715e2442725816d/adv_probabilities.json + attack_file: output/attacks/ce127cd447da0e3e10164b52d02e6d8f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/e07e357a8f0d3f53d715e2442725816d/params.yaml + predictions_file: output/reports/attack/e07e357a8f0d3f53d715e2442725816d/predictions.json + probabilities_file: output/reports/attack/e07e357a8f0d3f53d715e2442725816d/probabilities.json + score_dict_file: output/reports/attack/e07e357a8f0d3f53d715e2442725816d/score_dict.json + test_labels_file: output/reports/attack/e07e357a8f0d3f53d715e2442725816d/test_labels.json + train_labels_file: output/reports/attack/e07e357a8f0d3f53d715e2442725816d/train_labels.json + model_dir: models + name: e07e357a8f0d3f53d715e2442725816d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: e07e357a8f0d3f53d715e2442725816d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e092a572c4c88ee03b2b9bb471ef2dea/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e092a572c4c88ee03b2b9bb471ef2dea/params.yaml new file mode 100644 index 00000000..d1cfd103 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e092a572c4c88ee03b2b9bb471ef2dea/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 20104f30c8aa4445635af06882ae27be +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e092a572c4c88ee03b2b9bb471ef2dea/adv_predictions.json + adv_probabilities_file: output/reports/attack/e092a572c4c88ee03b2b9bb471ef2dea/adv_probabilities.json + attack_file: output/attacks/72a39da63b422d2dd4c7270e6a79141a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/e092a572c4c88ee03b2b9bb471ef2dea/params.yaml + predictions_file: output/reports/attack/e092a572c4c88ee03b2b9bb471ef2dea/predictions.json + probabilities_file: output/reports/attack/e092a572c4c88ee03b2b9bb471ef2dea/probabilities.json + score_dict_file: output/reports/attack/e092a572c4c88ee03b2b9bb471ef2dea/score_dict.json + test_labels_file: output/reports/attack/e092a572c4c88ee03b2b9bb471ef2dea/test_labels.json + train_labels_file: output/reports/attack/e092a572c4c88ee03b2b9bb471ef2dea/train_labels.json + model_dir: models + name: e092a572c4c88ee03b2b9bb471ef2dea + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: e092a572c4c88ee03b2b9bb471ef2dea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e0c3333e1fc28205d5dfb8c0637323e9/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e0c3333e1fc28205d5dfb8c0637323e9/params.yaml new file mode 100644 index 00000000..aea649ce --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e0c3333e1fc28205d5dfb8c0637323e9/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.3 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: bd4360e8470b9ea37538ad1b41bf2607 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e0c3333e1fc28205d5dfb8c0637323e9/adv_predictions.json + adv_probabilities_file: output/reports/attack/e0c3333e1fc28205d5dfb8c0637323e9/adv_probabilities.json + attack_file: output/attacks/9b5a12aba26be807921768aaea0cde01.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/e0c3333e1fc28205d5dfb8c0637323e9/params.yaml + predictions_file: output/reports/attack/e0c3333e1fc28205d5dfb8c0637323e9/predictions.json + probabilities_file: output/reports/attack/e0c3333e1fc28205d5dfb8c0637323e9/probabilities.json + score_dict_file: output/reports/attack/e0c3333e1fc28205d5dfb8c0637323e9/score_dict.json + test_labels_file: output/reports/attack/e0c3333e1fc28205d5dfb8c0637323e9/test_labels.json + train_labels_file: output/reports/attack/e0c3333e1fc28205d5dfb8c0637323e9/train_labels.json + model_dir: models + name: e0c3333e1fc28205d5dfb8c0637323e9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: e0c3333e1fc28205d5dfb8c0637323e9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e0db694b5fb994f84523d51db4fa989d/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e0db694b5fb994f84523d51db4fa989d/params.yaml new file mode 100644 index 00000000..05e97981 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e0db694b5fb994f84523d51db4fa989d/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.01 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 2389dae8f1cce4424dd2fa66dbd52e7b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e0db694b5fb994f84523d51db4fa989d/adv_predictions.json + adv_probabilities_file: output/reports/attack/e0db694b5fb994f84523d51db4fa989d/adv_probabilities.json + attack_file: output/attacks/1059125526e79b87737371f40569c563.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/e0db694b5fb994f84523d51db4fa989d/params.yaml + predictions_file: output/reports/attack/e0db694b5fb994f84523d51db4fa989d/predictions.json + probabilities_file: output/reports/attack/e0db694b5fb994f84523d51db4fa989d/probabilities.json + score_dict_file: output/reports/attack/e0db694b5fb994f84523d51db4fa989d/score_dict.json + test_labels_file: output/reports/attack/e0db694b5fb994f84523d51db4fa989d/test_labels.json + train_labels_file: output/reports/attack/e0db694b5fb994f84523d51db4fa989d/train_labels.json + model_dir: models + name: e0db694b5fb994f84523d51db4fa989d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: e0db694b5fb994f84523d51db4fa989d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e11ccda8e89e04aeafff1527453c552b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e11ccda8e89e04aeafff1527453c552b/params.yaml new file mode 100644 index 00000000..0c5537c1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e11ccda8e89e04aeafff1527453c552b/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 4e6845859d28c8726d534b8d02fb60b2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e11ccda8e89e04aeafff1527453c552b/adv_predictions.json + adv_probabilities_file: output/reports/attack/e11ccda8e89e04aeafff1527453c552b/adv_probabilities.json + attack_file: output/attacks/8783bb5dbdd6321867dbfe41618dad01.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/e11ccda8e89e04aeafff1527453c552b/params.yaml + predictions_file: output/reports/attack/e11ccda8e89e04aeafff1527453c552b/predictions.json + probabilities_file: output/reports/attack/e11ccda8e89e04aeafff1527453c552b/probabilities.json + score_dict_file: output/reports/attack/e11ccda8e89e04aeafff1527453c552b/score_dict.json + test_labels_file: output/reports/attack/e11ccda8e89e04aeafff1527453c552b/test_labels.json + train_labels_file: output/reports/attack/e11ccda8e89e04aeafff1527453c552b/train_labels.json + model_dir: models + name: e11ccda8e89e04aeafff1527453c552b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: e11ccda8e89e04aeafff1527453c552b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e23dba46d17283524ee5ec8b53327516/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e23dba46d17283524ee5ec8b53327516/params.yaml new file mode 100644 index 00000000..4945060f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e23dba46d17283524ee5ec8b53327516/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 96a78e3c7c6744bc5de6b19fec48d252 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e23dba46d17283524ee5ec8b53327516/adv_predictions.json + adv_probabilities_file: output/reports/attack/e23dba46d17283524ee5ec8b53327516/adv_probabilities.json + attack_file: output/attacks/0aa12edb9809dd525b1a43d91b7da9fd.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/e23dba46d17283524ee5ec8b53327516/params.yaml + predictions_file: output/reports/attack/e23dba46d17283524ee5ec8b53327516/predictions.json + probabilities_file: output/reports/attack/e23dba46d17283524ee5ec8b53327516/probabilities.json + score_dict_file: output/reports/attack/e23dba46d17283524ee5ec8b53327516/score_dict.json + test_labels_file: output/reports/attack/e23dba46d17283524ee5ec8b53327516/test_labels.json + train_labels_file: output/reports/attack/e23dba46d17283524ee5ec8b53327516/train_labels.json + model_dir: models + name: e23dba46d17283524ee5ec8b53327516 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: e23dba46d17283524ee5ec8b53327516 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e370c2853c1ea6b6fd76416ab0b52384/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e370c2853c1ea6b6fd76416ab0b52384/params.yaml new file mode 100644 index 00000000..aebe45f0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e370c2853c1ea6b6fd76416ab0b52384/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.001 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 0092487d3d8922cac1433fa2da27deb0 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e370c2853c1ea6b6fd76416ab0b52384/adv_predictions.json + adv_probabilities_file: output/reports/attack/e370c2853c1ea6b6fd76416ab0b52384/adv_probabilities.json + attack_file: output/attacks/07aa091186cd4da5e6771629fc998f04.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/e370c2853c1ea6b6fd76416ab0b52384/params.yaml + predictions_file: output/reports/attack/e370c2853c1ea6b6fd76416ab0b52384/predictions.json + probabilities_file: output/reports/attack/e370c2853c1ea6b6fd76416ab0b52384/probabilities.json + score_dict_file: output/reports/attack/e370c2853c1ea6b6fd76416ab0b52384/score_dict.json + test_labels_file: output/reports/attack/e370c2853c1ea6b6fd76416ab0b52384/test_labels.json + train_labels_file: output/reports/attack/e370c2853c1ea6b6fd76416ab0b52384/train_labels.json + model_dir: models + name: e370c2853c1ea6b6fd76416ab0b52384 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: e370c2853c1ea6b6fd76416ab0b52384 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e3ecf36b65677133401919c4a67f3ed8/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e3ecf36b65677133401919c4a67f3ed8/params.yaml new file mode 100644 index 00000000..ae71dd52 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e3ecf36b65677133401919c4a67f3ed8/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.5 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: d1b7b9054a48f6a2acb02f12537b0804 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e3ecf36b65677133401919c4a67f3ed8/adv_predictions.json + adv_probabilities_file: output/reports/attack/e3ecf36b65677133401919c4a67f3ed8/adv_probabilities.json + attack_file: output/attacks/11359896944c1ba7799dcb72a9b6bd7e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/e3ecf36b65677133401919c4a67f3ed8/params.yaml + predictions_file: output/reports/attack/e3ecf36b65677133401919c4a67f3ed8/predictions.json + probabilities_file: output/reports/attack/e3ecf36b65677133401919c4a67f3ed8/probabilities.json + score_dict_file: output/reports/attack/e3ecf36b65677133401919c4a67f3ed8/score_dict.json + test_labels_file: output/reports/attack/e3ecf36b65677133401919c4a67f3ed8/test_labels.json + train_labels_file: output/reports/attack/e3ecf36b65677133401919c4a67f3ed8/train_labels.json + model_dir: models + name: e3ecf36b65677133401919c4a67f3ed8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: e3ecf36b65677133401919c4a67f3ed8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e400b63e2ecaf7a12e7c7530869c5c4d/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e400b63e2ecaf7a12e7c7530869c5c4d/params.yaml new file mode 100644 index 00000000..bb5eb277 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e400b63e2ecaf7a12e7c7530869c5c4d/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 486de360f006fdd3ec145c72b4a7ebc2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e400b63e2ecaf7a12e7c7530869c5c4d/adv_predictions.json + adv_probabilities_file: output/reports/attack/e400b63e2ecaf7a12e7c7530869c5c4d/adv_probabilities.json + attack_file: output/attacks/e32ba100fca3d069be933acd192a1fb7.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/e400b63e2ecaf7a12e7c7530869c5c4d/params.yaml + predictions_file: output/reports/attack/e400b63e2ecaf7a12e7c7530869c5c4d/predictions.json + probabilities_file: output/reports/attack/e400b63e2ecaf7a12e7c7530869c5c4d/probabilities.json + score_dict_file: output/reports/attack/e400b63e2ecaf7a12e7c7530869c5c4d/score_dict.json + test_labels_file: output/reports/attack/e400b63e2ecaf7a12e7c7530869c5c4d/test_labels.json + train_labels_file: output/reports/attack/e400b63e2ecaf7a12e7c7530869c5c4d/train_labels.json + model_dir: models + name: e400b63e2ecaf7a12e7c7530869c5c4d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: e400b63e2ecaf7a12e7c7530869c5c4d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e4d0db01c5ba70ea8be87d82f4e04f51/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e4d0db01c5ba70ea8be87d82f4e04f51/params.yaml new file mode 100644 index 00000000..00c12ea8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e4d0db01c5ba70ea8be87d82f4e04f51/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.01 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 509dc434f21c345bf8570328fcf792da +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e4d0db01c5ba70ea8be87d82f4e04f51/adv_predictions.json + adv_probabilities_file: output/reports/attack/e4d0db01c5ba70ea8be87d82f4e04f51/adv_probabilities.json + attack_file: output/attacks/73111cf459ad6ec9c8937eee4ed07233.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/e4d0db01c5ba70ea8be87d82f4e04f51/params.yaml + predictions_file: output/reports/attack/e4d0db01c5ba70ea8be87d82f4e04f51/predictions.json + probabilities_file: output/reports/attack/e4d0db01c5ba70ea8be87d82f4e04f51/probabilities.json + score_dict_file: output/reports/attack/e4d0db01c5ba70ea8be87d82f4e04f51/score_dict.json + test_labels_file: output/reports/attack/e4d0db01c5ba70ea8be87d82f4e04f51/test_labels.json + train_labels_file: output/reports/attack/e4d0db01c5ba70ea8be87d82f4e04f51/train_labels.json + model_dir: models + name: e4d0db01c5ba70ea8be87d82f4e04f51 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: e4d0db01c5ba70ea8be87d82f4e04f51 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e5de0341d37c664c7dbf8f23498ea41b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e5de0341d37c664c7dbf8f23498ea41b/params.yaml new file mode 100644 index 00000000..2cd707ba --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e5de0341d37c664c7dbf8f23498ea41b/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: c2575c146646d53da262aaaa0b889701 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e5de0341d37c664c7dbf8f23498ea41b/adv_predictions.json + adv_probabilities_file: output/reports/attack/e5de0341d37c664c7dbf8f23498ea41b/adv_probabilities.json + attack_file: output/attacks/a43325fdd47e87b28dd3207f0f990842.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/e5de0341d37c664c7dbf8f23498ea41b/params.yaml + predictions_file: output/reports/attack/e5de0341d37c664c7dbf8f23498ea41b/predictions.json + probabilities_file: output/reports/attack/e5de0341d37c664c7dbf8f23498ea41b/probabilities.json + score_dict_file: output/reports/attack/e5de0341d37c664c7dbf8f23498ea41b/score_dict.json + test_labels_file: output/reports/attack/e5de0341d37c664c7dbf8f23498ea41b/test_labels.json + train_labels_file: output/reports/attack/e5de0341d37c664c7dbf8f23498ea41b/train_labels.json + model_dir: models + name: e5de0341d37c664c7dbf8f23498ea41b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: e5de0341d37c664c7dbf8f23498ea41b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e6e0c40ec206188a3ac27c633da9ac59/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e6e0c40ec206188a3ac27c633da9ac59/params.yaml new file mode 100644 index 00000000..89196fa1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e6e0c40ec206188a3ac27c633da9ac59/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.3 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 29ba03292bf41bbdc2eaa28b974ffee9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e6e0c40ec206188a3ac27c633da9ac59/adv_predictions.json + adv_probabilities_file: output/reports/attack/e6e0c40ec206188a3ac27c633da9ac59/adv_probabilities.json + attack_file: output/attacks/c61294333b3cac1cf82aae8b3bdcecb4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/e6e0c40ec206188a3ac27c633da9ac59/params.yaml + predictions_file: output/reports/attack/e6e0c40ec206188a3ac27c633da9ac59/predictions.json + probabilities_file: output/reports/attack/e6e0c40ec206188a3ac27c633da9ac59/probabilities.json + score_dict_file: output/reports/attack/e6e0c40ec206188a3ac27c633da9ac59/score_dict.json + test_labels_file: output/reports/attack/e6e0c40ec206188a3ac27c633da9ac59/test_labels.json + train_labels_file: output/reports/attack/e6e0c40ec206188a3ac27c633da9ac59/train_labels.json + model_dir: models + name: e6e0c40ec206188a3ac27c633da9ac59 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: e6e0c40ec206188a3ac27c633da9ac59 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e70605b5af8b913c1940fef5c88efdef/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e70605b5af8b913c1940fef5c88efdef/params.yaml new file mode 100644 index 00000000..8ec3f212 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e70605b5af8b913c1940fef5c88efdef/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.5 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: f530854cbcbac32da929dcd99d1f5491 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e70605b5af8b913c1940fef5c88efdef/adv_predictions.json + adv_probabilities_file: output/reports/attack/e70605b5af8b913c1940fef5c88efdef/adv_probabilities.json + attack_file: output/attacks/8e91916a005e078879dadb68d021e220.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/e70605b5af8b913c1940fef5c88efdef/params.yaml + predictions_file: output/reports/attack/e70605b5af8b913c1940fef5c88efdef/predictions.json + probabilities_file: output/reports/attack/e70605b5af8b913c1940fef5c88efdef/probabilities.json + score_dict_file: output/reports/attack/e70605b5af8b913c1940fef5c88efdef/score_dict.json + test_labels_file: output/reports/attack/e70605b5af8b913c1940fef5c88efdef/test_labels.json + train_labels_file: output/reports/attack/e70605b5af8b913c1940fef5c88efdef/train_labels.json + model_dir: models + name: e70605b5af8b913c1940fef5c88efdef + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: e70605b5af8b913c1940fef5c88efdef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e7980e180cc4573759b289a3a80e384e/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e7980e180cc4573759b289a3a80e384e/params.yaml new file mode 100644 index 00000000..48f861fe --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e7980e180cc4573759b289a3a80e384e/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.5 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 8cc289e4d719ca11ad7f16d09d660637 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e7980e180cc4573759b289a3a80e384e/adv_predictions.json + adv_probabilities_file: output/reports/attack/e7980e180cc4573759b289a3a80e384e/adv_probabilities.json + attack_file: output/attacks/36582330f86d5b59ef8783bb6d137813.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/e7980e180cc4573759b289a3a80e384e/params.yaml + predictions_file: output/reports/attack/e7980e180cc4573759b289a3a80e384e/predictions.json + probabilities_file: output/reports/attack/e7980e180cc4573759b289a3a80e384e/probabilities.json + score_dict_file: output/reports/attack/e7980e180cc4573759b289a3a80e384e/score_dict.json + test_labels_file: output/reports/attack/e7980e180cc4573759b289a3a80e384e/test_labels.json + train_labels_file: output/reports/attack/e7980e180cc4573759b289a3a80e384e/train_labels.json + model_dir: models + name: e7980e180cc4573759b289a3a80e384e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: e7980e180cc4573759b289a3a80e384e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/params.yaml new file mode 100644 index 00000000..46d48030 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: e335084cff06f2ea7b22699485bb98b3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/adv_predictions.json + adv_probabilities_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/adv_probabilities.json + attack_file: output/attacks/19cd7be04ecc0a1bb96d31ec590337db.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/params.yaml + predictions_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/predictions.json + probabilities_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/probabilities.json + score_dict_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/score_dict.json + test_labels_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/test_labels.json + train_labels_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/train_labels.json + model_dir: models + name: e88189571dc2e7f742f71a82870cc3d0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: e88189571dc2e7f742f71a82870cc3d0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e8a1a053afb0e7d6bc669d60b025e7b8/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e8a1a053afb0e7d6bc669d60b025e7b8/params.yaml new file mode 100644 index 00000000..27605f09 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e8a1a053afb0e7d6bc669d60b025e7b8/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: b354526267a60d1e30a1f2c79bf161f3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e8a1a053afb0e7d6bc669d60b025e7b8/adv_predictions.json + adv_probabilities_file: output/reports/attack/e8a1a053afb0e7d6bc669d60b025e7b8/adv_probabilities.json + attack_file: output/attacks/1c474f9b1e0c8b1bba02518a62063344.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/e8a1a053afb0e7d6bc669d60b025e7b8/params.yaml + predictions_file: output/reports/attack/e8a1a053afb0e7d6bc669d60b025e7b8/predictions.json + probabilities_file: output/reports/attack/e8a1a053afb0e7d6bc669d60b025e7b8/probabilities.json + score_dict_file: output/reports/attack/e8a1a053afb0e7d6bc669d60b025e7b8/score_dict.json + test_labels_file: output/reports/attack/e8a1a053afb0e7d6bc669d60b025e7b8/test_labels.json + train_labels_file: output/reports/attack/e8a1a053afb0e7d6bc669d60b025e7b8/train_labels.json + model_dir: models + name: e8a1a053afb0e7d6bc669d60b025e7b8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: e8a1a053afb0e7d6bc669d60b025e7b8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e8edfae5cfd824a76ae75fc3c6c21fd7/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e8edfae5cfd824a76ae75fc3c6c21fd7/params.yaml new file mode 100644 index 00000000..5b4314db --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e8edfae5cfd824a76ae75fc3c6c21fd7/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 4f4e9bf4c11d01fadf8e1129a6b9789d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e8edfae5cfd824a76ae75fc3c6c21fd7/adv_predictions.json + adv_probabilities_file: output/reports/attack/e8edfae5cfd824a76ae75fc3c6c21fd7/adv_probabilities.json + attack_file: output/attacks/310daa49a27920aee16b43d3fae07167.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/e8edfae5cfd824a76ae75fc3c6c21fd7/params.yaml + predictions_file: output/reports/attack/e8edfae5cfd824a76ae75fc3c6c21fd7/predictions.json + probabilities_file: output/reports/attack/e8edfae5cfd824a76ae75fc3c6c21fd7/probabilities.json + score_dict_file: output/reports/attack/e8edfae5cfd824a76ae75fc3c6c21fd7/score_dict.json + test_labels_file: output/reports/attack/e8edfae5cfd824a76ae75fc3c6c21fd7/test_labels.json + train_labels_file: output/reports/attack/e8edfae5cfd824a76ae75fc3c6c21fd7/train_labels.json + model_dir: models + name: e8edfae5cfd824a76ae75fc3c6c21fd7 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: e8edfae5cfd824a76ae75fc3c6c21fd7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e9169d05210566221ab50bd3af0b5755/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e9169d05210566221ab50bd3af0b5755/params.yaml new file mode 100644 index 00000000..7c6c7d9a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e9169d05210566221ab50bd3af0b5755/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: e7601f78885c69b548930da45b2d22ea +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e9169d05210566221ab50bd3af0b5755/adv_predictions.json + adv_probabilities_file: output/reports/attack/e9169d05210566221ab50bd3af0b5755/adv_probabilities.json + attack_file: output/attacks/12bbe47ca9dc068fd3a1d588701947c2.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/e9169d05210566221ab50bd3af0b5755/params.yaml + predictions_file: output/reports/attack/e9169d05210566221ab50bd3af0b5755/predictions.json + probabilities_file: output/reports/attack/e9169d05210566221ab50bd3af0b5755/probabilities.json + score_dict_file: output/reports/attack/e9169d05210566221ab50bd3af0b5755/score_dict.json + test_labels_file: output/reports/attack/e9169d05210566221ab50bd3af0b5755/test_labels.json + train_labels_file: output/reports/attack/e9169d05210566221ab50bd3af0b5755/train_labels.json + model_dir: models + name: e9169d05210566221ab50bd3af0b5755 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: e9169d05210566221ab50bd3af0b5755 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e97d1ad31ac4fcf3abf7b89b4c06dd80/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e97d1ad31ac4fcf3abf7b89b4c06dd80/params.yaml new file mode 100644 index 00000000..088278e4 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e97d1ad31ac4fcf3abf7b89b4c06dd80/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: dbba56511636a46453ad9827e3113f2d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e97d1ad31ac4fcf3abf7b89b4c06dd80/adv_predictions.json + adv_probabilities_file: output/reports/attack/e97d1ad31ac4fcf3abf7b89b4c06dd80/adv_probabilities.json + attack_file: output/attacks/06436da915aa76379812f04efbf44fd1.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/e97d1ad31ac4fcf3abf7b89b4c06dd80/params.yaml + predictions_file: output/reports/attack/e97d1ad31ac4fcf3abf7b89b4c06dd80/predictions.json + probabilities_file: output/reports/attack/e97d1ad31ac4fcf3abf7b89b4c06dd80/probabilities.json + score_dict_file: output/reports/attack/e97d1ad31ac4fcf3abf7b89b4c06dd80/score_dict.json + test_labels_file: output/reports/attack/e97d1ad31ac4fcf3abf7b89b4c06dd80/test_labels.json + train_labels_file: output/reports/attack/e97d1ad31ac4fcf3abf7b89b4c06dd80/train_labels.json + model_dir: models + name: e97d1ad31ac4fcf3abf7b89b4c06dd80 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: e97d1ad31ac4fcf3abf7b89b4c06dd80 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/e9c6df4230285f888cdc6ff3003e3ff9/params.yaml b/examples/security/kdd-nsl/output/reports/attack/e9c6df4230285f888cdc6ff3003e3ff9/params.yaml new file mode 100644 index 00000000..825cabcc --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/e9c6df4230285f888cdc6ff3003e3ff9/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.3 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 98173fcdc19fc81cb9a51cbf6a2b91ad +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e9c6df4230285f888cdc6ff3003e3ff9/adv_predictions.json + adv_probabilities_file: output/reports/attack/e9c6df4230285f888cdc6ff3003e3ff9/adv_probabilities.json + attack_file: output/attacks/434240cb2099f6739e4caec4712ffe25.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/e9c6df4230285f888cdc6ff3003e3ff9/params.yaml + predictions_file: output/reports/attack/e9c6df4230285f888cdc6ff3003e3ff9/predictions.json + probabilities_file: output/reports/attack/e9c6df4230285f888cdc6ff3003e3ff9/probabilities.json + score_dict_file: output/reports/attack/e9c6df4230285f888cdc6ff3003e3ff9/score_dict.json + test_labels_file: output/reports/attack/e9c6df4230285f888cdc6ff3003e3ff9/test_labels.json + train_labels_file: output/reports/attack/e9c6df4230285f888cdc6ff3003e3ff9/train_labels.json + model_dir: models + name: e9c6df4230285f888cdc6ff3003e3ff9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: e9c6df4230285f888cdc6ff3003e3ff9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ea2beb6e0768050b1f49803300abe007/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ea2beb6e0768050b1f49803300abe007/params.yaml new file mode 100644 index 00000000..50f2feac --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ea2beb6e0768050b1f49803300abe007/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.5 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: a9e260177f55775236b6f4a916bf22d7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ea2beb6e0768050b1f49803300abe007/adv_predictions.json + adv_probabilities_file: output/reports/attack/ea2beb6e0768050b1f49803300abe007/adv_probabilities.json + attack_file: output/attacks/666d27e92aabbff377e02a073cacc8ab.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/ea2beb6e0768050b1f49803300abe007/params.yaml + predictions_file: output/reports/attack/ea2beb6e0768050b1f49803300abe007/predictions.json + probabilities_file: output/reports/attack/ea2beb6e0768050b1f49803300abe007/probabilities.json + score_dict_file: output/reports/attack/ea2beb6e0768050b1f49803300abe007/score_dict.json + test_labels_file: output/reports/attack/ea2beb6e0768050b1f49803300abe007/test_labels.json + train_labels_file: output/reports/attack/ea2beb6e0768050b1f49803300abe007/train_labels.json + model_dir: models + name: ea2beb6e0768050b1f49803300abe007 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: ea2beb6e0768050b1f49803300abe007 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ea70b235670218798ddf88e954c73191/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ea70b235670218798ddf88e954c73191/params.yaml new file mode 100644 index 00000000..235e6e8a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ea70b235670218798ddf88e954c73191/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 1 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 592ce9c690ba6707b4fd1ffcf100f446 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ea70b235670218798ddf88e954c73191/adv_predictions.json + adv_probabilities_file: output/reports/attack/ea70b235670218798ddf88e954c73191/adv_probabilities.json + attack_file: output/attacks/0a060d7c9f5da95760be673b1f4b5597.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/ea70b235670218798ddf88e954c73191/params.yaml + predictions_file: output/reports/attack/ea70b235670218798ddf88e954c73191/predictions.json + probabilities_file: output/reports/attack/ea70b235670218798ddf88e954c73191/probabilities.json + score_dict_file: output/reports/attack/ea70b235670218798ddf88e954c73191/score_dict.json + test_labels_file: output/reports/attack/ea70b235670218798ddf88e954c73191/test_labels.json + train_labels_file: output/reports/attack/ea70b235670218798ddf88e954c73191/train_labels.json + model_dir: models + name: ea70b235670218798ddf88e954c73191 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: ea70b235670218798ddf88e954c73191 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/eb4d77ceed96051cc71eb0338c9ce022/params.yaml b/examples/security/kdd-nsl/output/reports/attack/eb4d77ceed96051cc71eb0338c9ce022/params.yaml new file mode 100644 index 00000000..bc4231f1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/eb4d77ceed96051cc71eb0338c9ce022/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 3b6a5772fd84884dad042936a50d495e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/eb4d77ceed96051cc71eb0338c9ce022/adv_predictions.json + adv_probabilities_file: output/reports/attack/eb4d77ceed96051cc71eb0338c9ce022/adv_probabilities.json + attack_file: output/attacks/5e2443082ac0d321eec86fc63ed22976.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/eb4d77ceed96051cc71eb0338c9ce022/params.yaml + predictions_file: output/reports/attack/eb4d77ceed96051cc71eb0338c9ce022/predictions.json + probabilities_file: output/reports/attack/eb4d77ceed96051cc71eb0338c9ce022/probabilities.json + score_dict_file: output/reports/attack/eb4d77ceed96051cc71eb0338c9ce022/score_dict.json + test_labels_file: output/reports/attack/eb4d77ceed96051cc71eb0338c9ce022/test_labels.json + train_labels_file: output/reports/attack/eb4d77ceed96051cc71eb0338c9ce022/train_labels.json + model_dir: models + name: eb4d77ceed96051cc71eb0338c9ce022 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: eb4d77ceed96051cc71eb0338c9ce022 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ebeb829580d7de3f5ac7d6edcf06daae/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ebeb829580d7de3f5ac7d6edcf06daae/params.yaml new file mode 100644 index 00000000..57e349eb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ebeb829580d7de3f5ac7d6edcf06daae/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.5 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 98c4199004878febb96046b61d0605e3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ebeb829580d7de3f5ac7d6edcf06daae/adv_predictions.json + adv_probabilities_file: output/reports/attack/ebeb829580d7de3f5ac7d6edcf06daae/adv_probabilities.json + attack_file: output/attacks/e505a3c01239f2fcf6a7a9821894586d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/ebeb829580d7de3f5ac7d6edcf06daae/params.yaml + predictions_file: output/reports/attack/ebeb829580d7de3f5ac7d6edcf06daae/predictions.json + probabilities_file: output/reports/attack/ebeb829580d7de3f5ac7d6edcf06daae/probabilities.json + score_dict_file: output/reports/attack/ebeb829580d7de3f5ac7d6edcf06daae/score_dict.json + test_labels_file: output/reports/attack/ebeb829580d7de3f5ac7d6edcf06daae/test_labels.json + train_labels_file: output/reports/attack/ebeb829580d7de3f5ac7d6edcf06daae/train_labels.json + model_dir: models + name: ebeb829580d7de3f5ac7d6edcf06daae + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: ebeb829580d7de3f5ac7d6edcf06daae +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ee881a2f5e9ad420f1d2d6ec9152e24f/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ee881a2f5e9ad420f1d2d6ec9152e24f/params.yaml new file mode 100644 index 00000000..fcd67c3f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ee881a2f5e9ad420f1d2d6ec9152e24f/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 58e8eb210ab056d2ccece5989f11d0b1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ee881a2f5e9ad420f1d2d6ec9152e24f/adv_predictions.json + adv_probabilities_file: output/reports/attack/ee881a2f5e9ad420f1d2d6ec9152e24f/adv_probabilities.json + attack_file: output/attacks/173e42ed0586a76b536c3f6f5b7cf7f6.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/ee881a2f5e9ad420f1d2d6ec9152e24f/params.yaml + predictions_file: output/reports/attack/ee881a2f5e9ad420f1d2d6ec9152e24f/predictions.json + probabilities_file: output/reports/attack/ee881a2f5e9ad420f1d2d6ec9152e24f/probabilities.json + score_dict_file: output/reports/attack/ee881a2f5e9ad420f1d2d6ec9152e24f/score_dict.json + test_labels_file: output/reports/attack/ee881a2f5e9ad420f1d2d6ec9152e24f/test_labels.json + train_labels_file: output/reports/attack/ee881a2f5e9ad420f1d2d6ec9152e24f/train_labels.json + model_dir: models + name: ee881a2f5e9ad420f1d2d6ec9152e24f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: ee881a2f5e9ad420f1d2d6ec9152e24f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/efcdf585efd8770ba868e1f4b9f7cd84/params.yaml b/examples/security/kdd-nsl/output/reports/attack/efcdf585efd8770ba868e1f4b9f7cd84/params.yaml new file mode 100644 index 00000000..4c39eb01 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/efcdf585efd8770ba868e1f4b9f7cd84/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.001 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: cb8b2ac19d0977fd13817036a3450f28 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/efcdf585efd8770ba868e1f4b9f7cd84/adv_predictions.json + adv_probabilities_file: output/reports/attack/efcdf585efd8770ba868e1f4b9f7cd84/adv_probabilities.json + attack_file: output/attacks/11cbeb52c1e0b3e6492f6ef9c6ef2063.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/efcdf585efd8770ba868e1f4b9f7cd84/params.yaml + predictions_file: output/reports/attack/efcdf585efd8770ba868e1f4b9f7cd84/predictions.json + probabilities_file: output/reports/attack/efcdf585efd8770ba868e1f4b9f7cd84/probabilities.json + score_dict_file: output/reports/attack/efcdf585efd8770ba868e1f4b9f7cd84/score_dict.json + test_labels_file: output/reports/attack/efcdf585efd8770ba868e1f4b9f7cd84/test_labels.json + train_labels_file: output/reports/attack/efcdf585efd8770ba868e1f4b9f7cd84/train_labels.json + model_dir: models + name: efcdf585efd8770ba868e1f4b9f7cd84 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: efcdf585efd8770ba868e1f4b9f7cd84 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/eff8a00a65a1430e5f25b39f10fa5122/params.yaml b/examples/security/kdd-nsl/output/reports/attack/eff8a00a65a1430e5f25b39f10fa5122/params.yaml new file mode 100644 index 00000000..1d88decb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/eff8a00a65a1430e5f25b39f10fa5122/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.001 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: d80ecad481a9823075738fa1c5858c76 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/eff8a00a65a1430e5f25b39f10fa5122/adv_predictions.json + adv_probabilities_file: output/reports/attack/eff8a00a65a1430e5f25b39f10fa5122/adv_probabilities.json + attack_file: output/attacks/9b109e680c4ff5926966ac12a916b7a7.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/eff8a00a65a1430e5f25b39f10fa5122/params.yaml + predictions_file: output/reports/attack/eff8a00a65a1430e5f25b39f10fa5122/predictions.json + probabilities_file: output/reports/attack/eff8a00a65a1430e5f25b39f10fa5122/probabilities.json + score_dict_file: output/reports/attack/eff8a00a65a1430e5f25b39f10fa5122/score_dict.json + test_labels_file: output/reports/attack/eff8a00a65a1430e5f25b39f10fa5122/test_labels.json + train_labels_file: output/reports/attack/eff8a00a65a1430e5f25b39f10fa5122/train_labels.json + model_dir: models + name: eff8a00a65a1430e5f25b39f10fa5122 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: eff8a00a65a1430e5f25b39f10fa5122 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f000cfd66aa7dc915c5189d158ddf05b/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f000cfd66aa7dc915c5189d158ddf05b/params.yaml new file mode 100644 index 00000000..d563e9e0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f000cfd66aa7dc915c5189d158ddf05b/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.001 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: c7766e7781d04de6eeefbcd561c6355c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f000cfd66aa7dc915c5189d158ddf05b/adv_predictions.json + adv_probabilities_file: output/reports/attack/f000cfd66aa7dc915c5189d158ddf05b/adv_probabilities.json + attack_file: output/attacks/b396ba3e3dcccc385e5fad4ce3b29c6b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/f000cfd66aa7dc915c5189d158ddf05b/params.yaml + predictions_file: output/reports/attack/f000cfd66aa7dc915c5189d158ddf05b/predictions.json + probabilities_file: output/reports/attack/f000cfd66aa7dc915c5189d158ddf05b/probabilities.json + score_dict_file: output/reports/attack/f000cfd66aa7dc915c5189d158ddf05b/score_dict.json + test_labels_file: output/reports/attack/f000cfd66aa7dc915c5189d158ddf05b/test_labels.json + train_labels_file: output/reports/attack/f000cfd66aa7dc915c5189d158ddf05b/train_labels.json + model_dir: models + name: f000cfd66aa7dc915c5189d158ddf05b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: f000cfd66aa7dc915c5189d158ddf05b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f07d332292352d880212858818064ee1/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f07d332292352d880212858818064ee1/params.yaml new file mode 100644 index 00000000..14437d04 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f07d332292352d880212858818064ee1/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.001 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 4dafcc3c0777ba3112b445980286e8a7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f07d332292352d880212858818064ee1/adv_predictions.json + adv_probabilities_file: output/reports/attack/f07d332292352d880212858818064ee1/adv_probabilities.json + attack_file: output/attacks/6b1edb77668260e0aad9143d4802eac5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/f07d332292352d880212858818064ee1/params.yaml + predictions_file: output/reports/attack/f07d332292352d880212858818064ee1/predictions.json + probabilities_file: output/reports/attack/f07d332292352d880212858818064ee1/probabilities.json + score_dict_file: output/reports/attack/f07d332292352d880212858818064ee1/score_dict.json + test_labels_file: output/reports/attack/f07d332292352d880212858818064ee1/test_labels.json + train_labels_file: output/reports/attack/f07d332292352d880212858818064ee1/train_labels.json + model_dir: models + name: f07d332292352d880212858818064ee1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: f07d332292352d880212858818064ee1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f116079a2c305c405c00341c4dfd71ab/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f116079a2c305c405c00341c4dfd71ab/params.yaml new file mode 100644 index 00000000..c2192903 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f116079a2c305c405c00341c4dfd71ab/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 545badf45ae39f9fa57519eb3353ab2f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f116079a2c305c405c00341c4dfd71ab/adv_predictions.json + adv_probabilities_file: output/reports/attack/f116079a2c305c405c00341c4dfd71ab/adv_probabilities.json + attack_file: output/attacks/5c9e0c9ebf5ccf1557af6cac05c573bf.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/f116079a2c305c405c00341c4dfd71ab/params.yaml + predictions_file: output/reports/attack/f116079a2c305c405c00341c4dfd71ab/predictions.json + probabilities_file: output/reports/attack/f116079a2c305c405c00341c4dfd71ab/probabilities.json + score_dict_file: output/reports/attack/f116079a2c305c405c00341c4dfd71ab/score_dict.json + test_labels_file: output/reports/attack/f116079a2c305c405c00341c4dfd71ab/test_labels.json + train_labels_file: output/reports/attack/f116079a2c305c405c00341c4dfd71ab/train_labels.json + model_dir: models + name: f116079a2c305c405c00341c4dfd71ab + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: f116079a2c305c405c00341c4dfd71ab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f20c18f046c5a3fcbbce24dcb7de4297/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f20c18f046c5a3fcbbce24dcb7de4297/params.yaml new file mode 100644 index 00000000..7105dde8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f20c18f046c5a3fcbbce24dcb7de4297/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 901b554e3c84acccfa9d7a0b01759ade +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f20c18f046c5a3fcbbce24dcb7de4297/adv_predictions.json + adv_probabilities_file: output/reports/attack/f20c18f046c5a3fcbbce24dcb7de4297/adv_probabilities.json + attack_file: output/attacks/b7493cdb115ce27d4f34d87bd7f904f3.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/f20c18f046c5a3fcbbce24dcb7de4297/params.yaml + predictions_file: output/reports/attack/f20c18f046c5a3fcbbce24dcb7de4297/predictions.json + probabilities_file: output/reports/attack/f20c18f046c5a3fcbbce24dcb7de4297/probabilities.json + score_dict_file: output/reports/attack/f20c18f046c5a3fcbbce24dcb7de4297/score_dict.json + test_labels_file: output/reports/attack/f20c18f046c5a3fcbbce24dcb7de4297/test_labels.json + train_labels_file: output/reports/attack/f20c18f046c5a3fcbbce24dcb7de4297/train_labels.json + model_dir: models + name: f20c18f046c5a3fcbbce24dcb7de4297 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: f20c18f046c5a3fcbbce24dcb7de4297 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f3247255d065a336ca68402774edc5c7/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f3247255d065a336ca68402774edc5c7/params.yaml new file mode 100644 index 00000000..9ad65e0e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f3247255d065a336ca68402774edc5c7/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 63becaa5c8e6cbcfb2cb4ad2ff9bd43c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f3247255d065a336ca68402774edc5c7/adv_predictions.json + adv_probabilities_file: output/reports/attack/f3247255d065a336ca68402774edc5c7/adv_probabilities.json + attack_file: output/attacks/6295c04b92abb0f21c7ee7c169ed2ea2.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/f3247255d065a336ca68402774edc5c7/params.yaml + predictions_file: output/reports/attack/f3247255d065a336ca68402774edc5c7/predictions.json + probabilities_file: output/reports/attack/f3247255d065a336ca68402774edc5c7/probabilities.json + score_dict_file: output/reports/attack/f3247255d065a336ca68402774edc5c7/score_dict.json + test_labels_file: output/reports/attack/f3247255d065a336ca68402774edc5c7/test_labels.json + train_labels_file: output/reports/attack/f3247255d065a336ca68402774edc5c7/train_labels.json + model_dir: models + name: f3247255d065a336ca68402774edc5c7 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: f3247255d065a336ca68402774edc5c7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f3383f5624d4d05057bb7bd40080344a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f3383f5624d4d05057bb7bd40080344a/params.yaml new file mode 100644 index 00000000..b187f3b8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f3383f5624d4d05057bb7bd40080344a/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.5 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: a30540d71b024aa78ec5069c0970d099 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f3383f5624d4d05057bb7bd40080344a/adv_predictions.json + adv_probabilities_file: output/reports/attack/f3383f5624d4d05057bb7bd40080344a/adv_probabilities.json + attack_file: output/attacks/3e50909c5e8123ee577c0fc72cb26865.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/f3383f5624d4d05057bb7bd40080344a/params.yaml + predictions_file: output/reports/attack/f3383f5624d4d05057bb7bd40080344a/predictions.json + probabilities_file: output/reports/attack/f3383f5624d4d05057bb7bd40080344a/probabilities.json + score_dict_file: output/reports/attack/f3383f5624d4d05057bb7bd40080344a/score_dict.json + test_labels_file: output/reports/attack/f3383f5624d4d05057bb7bd40080344a/test_labels.json + train_labels_file: output/reports/attack/f3383f5624d4d05057bb7bd40080344a/train_labels.json + model_dir: models + name: f3383f5624d4d05057bb7bd40080344a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: f3383f5624d4d05057bb7bd40080344a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f3d92a61035a3bb8a0b255f6d9d2b44a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f3d92a61035a3bb8a0b255f6d9d2b44a/params.yaml new file mode 100644 index 00000000..b25fabfb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f3d92a61035a3bb8a0b255f6d9d2b44a/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.001 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: fb8d54a16cdb076947fe5e7d6c654944 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f3d92a61035a3bb8a0b255f6d9d2b44a/adv_predictions.json + adv_probabilities_file: output/reports/attack/f3d92a61035a3bb8a0b255f6d9d2b44a/adv_probabilities.json + attack_file: output/attacks/569392cee255f10ad4a57d17d6d91be2.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/f3d92a61035a3bb8a0b255f6d9d2b44a/params.yaml + predictions_file: output/reports/attack/f3d92a61035a3bb8a0b255f6d9d2b44a/predictions.json + probabilities_file: output/reports/attack/f3d92a61035a3bb8a0b255f6d9d2b44a/probabilities.json + score_dict_file: output/reports/attack/f3d92a61035a3bb8a0b255f6d9d2b44a/score_dict.json + test_labels_file: output/reports/attack/f3d92a61035a3bb8a0b255f6d9d2b44a/test_labels.json + train_labels_file: output/reports/attack/f3d92a61035a3bb8a0b255f6d9d2b44a/train_labels.json + model_dir: models + name: f3d92a61035a3bb8a0b255f6d9d2b44a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: f3d92a61035a3bb8a0b255f6d9d2b44a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f401c198d3a2023a9c67a3761766b5cd/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f401c198d3a2023a9c67a3761766b5cd/params.yaml new file mode 100644 index 00000000..6ab405e2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f401c198d3a2023a9c67a3761766b5cd/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.5 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 44ca14870938c21576cb9f6647dfe726 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f401c198d3a2023a9c67a3761766b5cd/adv_predictions.json + adv_probabilities_file: output/reports/attack/f401c198d3a2023a9c67a3761766b5cd/adv_probabilities.json + attack_file: output/attacks/1f2d3d00389c86157bcca09646e45d27.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/f401c198d3a2023a9c67a3761766b5cd/params.yaml + predictions_file: output/reports/attack/f401c198d3a2023a9c67a3761766b5cd/predictions.json + probabilities_file: output/reports/attack/f401c198d3a2023a9c67a3761766b5cd/probabilities.json + score_dict_file: output/reports/attack/f401c198d3a2023a9c67a3761766b5cd/score_dict.json + test_labels_file: output/reports/attack/f401c198d3a2023a9c67a3761766b5cd/test_labels.json + train_labels_file: output/reports/attack/f401c198d3a2023a9c67a3761766b5cd/train_labels.json + model_dir: models + name: f401c198d3a2023a9c67a3761766b5cd + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: f401c198d3a2023a9c67a3761766b5cd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f52aaeaf5f2f53c9c18a8a56b9b50fc1/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f52aaeaf5f2f53c9c18a8a56b9b50fc1/params.yaml new file mode 100644 index 00000000..e0c8161c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f52aaeaf5f2f53c9c18a8a56b9b50fc1/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 8e6657495834f545f73baec5e9aa4bb4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f52aaeaf5f2f53c9c18a8a56b9b50fc1/adv_predictions.json + adv_probabilities_file: output/reports/attack/f52aaeaf5f2f53c9c18a8a56b9b50fc1/adv_probabilities.json + attack_file: output/attacks/5a35a29f13192fa16e1df77f303d4e33.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/f52aaeaf5f2f53c9c18a8a56b9b50fc1/params.yaml + predictions_file: output/reports/attack/f52aaeaf5f2f53c9c18a8a56b9b50fc1/predictions.json + probabilities_file: output/reports/attack/f52aaeaf5f2f53c9c18a8a56b9b50fc1/probabilities.json + score_dict_file: output/reports/attack/f52aaeaf5f2f53c9c18a8a56b9b50fc1/score_dict.json + test_labels_file: output/reports/attack/f52aaeaf5f2f53c9c18a8a56b9b50fc1/test_labels.json + train_labels_file: output/reports/attack/f52aaeaf5f2f53c9c18a8a56b9b50fc1/train_labels.json + model_dir: models + name: f52aaeaf5f2f53c9c18a8a56b9b50fc1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: f52aaeaf5f2f53c9c18a8a56b9b50fc1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f52b38768de72d58c064ab0ee6e260c5/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f52b38768de72d58c064ab0ee6e260c5/params.yaml new file mode 100644 index 00000000..26aba39a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f52b38768de72d58c064ab0ee6e260c5/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.5 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 91270ec8bd0a424bedaa448f9e794593 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f52b38768de72d58c064ab0ee6e260c5/adv_predictions.json + adv_probabilities_file: output/reports/attack/f52b38768de72d58c064ab0ee6e260c5/adv_probabilities.json + attack_file: output/attacks/bb4acd762dfeeb1fb95ff78e6ba1b36f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/f52b38768de72d58c064ab0ee6e260c5/params.yaml + predictions_file: output/reports/attack/f52b38768de72d58c064ab0ee6e260c5/predictions.json + probabilities_file: output/reports/attack/f52b38768de72d58c064ab0ee6e260c5/probabilities.json + score_dict_file: output/reports/attack/f52b38768de72d58c064ab0ee6e260c5/score_dict.json + test_labels_file: output/reports/attack/f52b38768de72d58c064ab0ee6e260c5/test_labels.json + train_labels_file: output/reports/attack/f52b38768de72d58c064ab0ee6e260c5/train_labels.json + model_dir: models + name: f52b38768de72d58c064ab0ee6e260c5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: f52b38768de72d58c064ab0ee6e260c5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f563d67e50019ce5417222b3a05b0f33/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f563d67e50019ce5417222b3a05b0f33/params.yaml new file mode 100644 index 00000000..0b2d19c3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f563d67e50019ce5417222b3a05b0f33/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.5 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: f02675b88640ac665e87a2d2b83ad133 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f563d67e50019ce5417222b3a05b0f33/adv_predictions.json + adv_probabilities_file: output/reports/attack/f563d67e50019ce5417222b3a05b0f33/adv_probabilities.json + attack_file: output/attacks/588a579be4bc4f0a3d3daecc89d0c334.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/f563d67e50019ce5417222b3a05b0f33/params.yaml + predictions_file: output/reports/attack/f563d67e50019ce5417222b3a05b0f33/predictions.json + probabilities_file: output/reports/attack/f563d67e50019ce5417222b3a05b0f33/probabilities.json + score_dict_file: output/reports/attack/f563d67e50019ce5417222b3a05b0f33/score_dict.json + test_labels_file: output/reports/attack/f563d67e50019ce5417222b3a05b0f33/test_labels.json + train_labels_file: output/reports/attack/f563d67e50019ce5417222b3a05b0f33/train_labels.json + model_dir: models + name: f563d67e50019ce5417222b3a05b0f33 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: f563d67e50019ce5417222b3a05b0f33 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f585b257d686988ef8274c5b9977864c/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f585b257d686988ef8274c5b9977864c/params.yaml new file mode 100644 index 00000000..94fb1e41 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f585b257d686988ef8274c5b9977864c/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 3b3c401dcdebd29879f667a85bea974a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f585b257d686988ef8274c5b9977864c/adv_predictions.json + adv_probabilities_file: output/reports/attack/f585b257d686988ef8274c5b9977864c/adv_probabilities.json + attack_file: output/attacks/4a546389094da6c31df92a53d15317b1.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/f585b257d686988ef8274c5b9977864c/params.yaml + predictions_file: output/reports/attack/f585b257d686988ef8274c5b9977864c/predictions.json + probabilities_file: output/reports/attack/f585b257d686988ef8274c5b9977864c/probabilities.json + score_dict_file: output/reports/attack/f585b257d686988ef8274c5b9977864c/score_dict.json + test_labels_file: output/reports/attack/f585b257d686988ef8274c5b9977864c/test_labels.json + train_labels_file: output/reports/attack/f585b257d686988ef8274c5b9977864c/train_labels.json + model_dir: models + name: f585b257d686988ef8274c5b9977864c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: f585b257d686988ef8274c5b9977864c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f6084ece660d2ae490a572f1f5a100a6/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f6084ece660d2ae490a572f1f5a100a6/params.yaml new file mode 100644 index 00000000..464f0065 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f6084ece660d2ae490a572f1f5a100a6/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 52b751414f3f38c0418304596eaad1b4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f6084ece660d2ae490a572f1f5a100a6/adv_predictions.json + adv_probabilities_file: output/reports/attack/f6084ece660d2ae490a572f1f5a100a6/adv_probabilities.json + attack_file: output/attacks/be4196cd5d5105b4893ee86476aa7af5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/f6084ece660d2ae490a572f1f5a100a6/params.yaml + predictions_file: output/reports/attack/f6084ece660d2ae490a572f1f5a100a6/predictions.json + probabilities_file: output/reports/attack/f6084ece660d2ae490a572f1f5a100a6/probabilities.json + score_dict_file: output/reports/attack/f6084ece660d2ae490a572f1f5a100a6/score_dict.json + test_labels_file: output/reports/attack/f6084ece660d2ae490a572f1f5a100a6/test_labels.json + train_labels_file: output/reports/attack/f6084ece660d2ae490a572f1f5a100a6/train_labels.json + model_dir: models + name: f6084ece660d2ae490a572f1f5a100a6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: f6084ece660d2ae490a572f1f5a100a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f62d5eb9705ca34291999ed32e4791ef/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f62d5eb9705ca34291999ed32e4791ef/params.yaml new file mode 100644 index 00000000..cafe7ffe --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f62d5eb9705ca34291999ed32e4791ef/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 85dba4fb2a02810e7846fae42ba97f39 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f62d5eb9705ca34291999ed32e4791ef/adv_predictions.json + adv_probabilities_file: output/reports/attack/f62d5eb9705ca34291999ed32e4791ef/adv_probabilities.json + attack_file: output/attacks/26c86d7222f8216af80b0ac98b1a1746.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/f62d5eb9705ca34291999ed32e4791ef/params.yaml + predictions_file: output/reports/attack/f62d5eb9705ca34291999ed32e4791ef/predictions.json + probabilities_file: output/reports/attack/f62d5eb9705ca34291999ed32e4791ef/probabilities.json + score_dict_file: output/reports/attack/f62d5eb9705ca34291999ed32e4791ef/score_dict.json + test_labels_file: output/reports/attack/f62d5eb9705ca34291999ed32e4791ef/test_labels.json + train_labels_file: output/reports/attack/f62d5eb9705ca34291999ed32e4791ef/train_labels.json + model_dir: models + name: f62d5eb9705ca34291999ed32e4791ef + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: f62d5eb9705ca34291999ed32e4791ef +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f6438ecbc38d7125dd3f65aa4f5412df/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f6438ecbc38d7125dd3f65aa4f5412df/params.yaml new file mode 100644 index 00000000..5f5074d3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f6438ecbc38d7125dd3f65aa4f5412df/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.1 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 076f3c78ace7b0d4feeb3540138f18a5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f6438ecbc38d7125dd3f65aa4f5412df/adv_predictions.json + adv_probabilities_file: output/reports/attack/f6438ecbc38d7125dd3f65aa4f5412df/adv_probabilities.json + attack_file: output/attacks/b24bf9758de6ae729482e0f2cba13432.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/f6438ecbc38d7125dd3f65aa4f5412df/params.yaml + predictions_file: output/reports/attack/f6438ecbc38d7125dd3f65aa4f5412df/predictions.json + probabilities_file: output/reports/attack/f6438ecbc38d7125dd3f65aa4f5412df/probabilities.json + score_dict_file: output/reports/attack/f6438ecbc38d7125dd3f65aa4f5412df/score_dict.json + test_labels_file: output/reports/attack/f6438ecbc38d7125dd3f65aa4f5412df/test_labels.json + train_labels_file: output/reports/attack/f6438ecbc38d7125dd3f65aa4f5412df/train_labels.json + model_dir: models + name: f6438ecbc38d7125dd3f65aa4f5412df + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: f6438ecbc38d7125dd3f65aa4f5412df +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f753a81814eaef1c6d10d2c3f10ba9d1/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f753a81814eaef1c6d10d2c3f10ba9d1/params.yaml new file mode 100644 index 00000000..d86d249f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f753a81814eaef1c6d10d2c3f10ba9d1/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.5 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 2dc98e2b14f5d3701fedb374bdd8e992 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f753a81814eaef1c6d10d2c3f10ba9d1/adv_predictions.json + adv_probabilities_file: output/reports/attack/f753a81814eaef1c6d10d2c3f10ba9d1/adv_probabilities.json + attack_file: output/attacks/fb320a305b62c3f74520693b71912f8d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/f753a81814eaef1c6d10d2c3f10ba9d1/params.yaml + predictions_file: output/reports/attack/f753a81814eaef1c6d10d2c3f10ba9d1/predictions.json + probabilities_file: output/reports/attack/f753a81814eaef1c6d10d2c3f10ba9d1/probabilities.json + score_dict_file: output/reports/attack/f753a81814eaef1c6d10d2c3f10ba9d1/score_dict.json + test_labels_file: output/reports/attack/f753a81814eaef1c6d10d2c3f10ba9d1/test_labels.json + train_labels_file: output/reports/attack/f753a81814eaef1c6d10d2c3f10ba9d1/train_labels.json + model_dir: models + name: f753a81814eaef1c6d10d2c3f10ba9d1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: f753a81814eaef1c6d10d2c3f10ba9d1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f8616c0bf4a3c73ac486cc2888506ab3/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f8616c0bf4a3c73ac486cc2888506ab3/params.yaml new file mode 100644 index 00000000..5667eae9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f8616c0bf4a3c73ac486cc2888506ab3/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 3c75eb92b86a43367320c3cd358ec71f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f8616c0bf4a3c73ac486cc2888506ab3/adv_predictions.json + adv_probabilities_file: output/reports/attack/f8616c0bf4a3c73ac486cc2888506ab3/adv_probabilities.json + attack_file: output/attacks/a1aa250073bcb902ea18261a78a9616a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/f8616c0bf4a3c73ac486cc2888506ab3/params.yaml + predictions_file: output/reports/attack/f8616c0bf4a3c73ac486cc2888506ab3/predictions.json + probabilities_file: output/reports/attack/f8616c0bf4a3c73ac486cc2888506ab3/probabilities.json + score_dict_file: output/reports/attack/f8616c0bf4a3c73ac486cc2888506ab3/score_dict.json + test_labels_file: output/reports/attack/f8616c0bf4a3c73ac486cc2888506ab3/test_labels.json + train_labels_file: output/reports/attack/f8616c0bf4a3c73ac486cc2888506ab3/train_labels.json + model_dir: models + name: f8616c0bf4a3c73ac486cc2888506ab3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: f8616c0bf4a3c73ac486cc2888506ab3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/params.yaml new file mode 100644 index 00000000..ddb553e7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.01 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 3c226c158c87b8d8a7fbf755ae490633 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/adv_predictions.json + adv_probabilities_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/adv_probabilities.json + attack_file: output/attacks/68d3f6642b5e16f7781a54759c851c54.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/params.yaml + predictions_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/predictions.json + probabilities_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/probabilities.json + score_dict_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/score_dict.json + test_labels_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/test_labels.json + train_labels_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/train_labels.json + model_dir: models + name: f8bd24762b80cf8d103def5b2d0ec48f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: f8bd24762b80cf8d103def5b2d0ec48f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f96a0a33a4eed43f2c1697d1676ff478/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f96a0a33a4eed43f2c1697d1676ff478/params.yaml new file mode 100644 index 00000000..c9daa8eb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f96a0a33a4eed43f2c1697d1676ff478/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 68f70ccab90fd9d5cb72ef31ac1b225f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f96a0a33a4eed43f2c1697d1676ff478/adv_predictions.json + adv_probabilities_file: output/reports/attack/f96a0a33a4eed43f2c1697d1676ff478/adv_probabilities.json + attack_file: output/attacks/3217ad8063ba2914594143d97c8d2414.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/f96a0a33a4eed43f2c1697d1676ff478/params.yaml + predictions_file: output/reports/attack/f96a0a33a4eed43f2c1697d1676ff478/predictions.json + probabilities_file: output/reports/attack/f96a0a33a4eed43f2c1697d1676ff478/probabilities.json + score_dict_file: output/reports/attack/f96a0a33a4eed43f2c1697d1676ff478/score_dict.json + test_labels_file: output/reports/attack/f96a0a33a4eed43f2c1697d1676ff478/test_labels.json + train_labels_file: output/reports/attack/f96a0a33a4eed43f2c1697d1676ff478/train_labels.json + model_dir: models + name: f96a0a33a4eed43f2c1697d1676ff478 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: f96a0a33a4eed43f2c1697d1676ff478 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/f9d1fc058b7a3024567345732ec0d8eb/params.yaml b/examples/security/kdd-nsl/output/reports/attack/f9d1fc058b7a3024567345732ec0d8eb/params.yaml new file mode 100644 index 00000000..00286b41 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/f9d1fc058b7a3024567345732ec0d8eb/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.3 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 3c124a6e38c6b8abf143aa89e8ef9fb7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f9d1fc058b7a3024567345732ec0d8eb/adv_predictions.json + adv_probabilities_file: output/reports/attack/f9d1fc058b7a3024567345732ec0d8eb/adv_probabilities.json + attack_file: output/attacks/e4caf9b8a4cccc533638d6eef379131c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/f9d1fc058b7a3024567345732ec0d8eb/params.yaml + predictions_file: output/reports/attack/f9d1fc058b7a3024567345732ec0d8eb/predictions.json + probabilities_file: output/reports/attack/f9d1fc058b7a3024567345732ec0d8eb/probabilities.json + score_dict_file: output/reports/attack/f9d1fc058b7a3024567345732ec0d8eb/score_dict.json + test_labels_file: output/reports/attack/f9d1fc058b7a3024567345732ec0d8eb/test_labels.json + train_labels_file: output/reports/attack/f9d1fc058b7a3024567345732ec0d8eb/train_labels.json + model_dir: models + name: f9d1fc058b7a3024567345732ec0d8eb + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: f9d1fc058b7a3024567345732ec0d8eb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/fb170b19de4faaacdeab1a095fc0f0aa/params.yaml b/examples/security/kdd-nsl/output/reports/attack/fb170b19de4faaacdeab1a095fc0f0aa/params.yaml new file mode 100644 index 00000000..87dccc83 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/fb170b19de4faaacdeab1a095fc0f0aa/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 6a710e6f9a5eaaca20f619aa12d3c307 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/fb170b19de4faaacdeab1a095fc0f0aa/adv_predictions.json + adv_probabilities_file: output/reports/attack/fb170b19de4faaacdeab1a095fc0f0aa/adv_probabilities.json + attack_file: output/attacks/b53f24afec43312aa3ff394411dfea34.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/fb170b19de4faaacdeab1a095fc0f0aa/params.yaml + predictions_file: output/reports/attack/fb170b19de4faaacdeab1a095fc0f0aa/predictions.json + probabilities_file: output/reports/attack/fb170b19de4faaacdeab1a095fc0f0aa/probabilities.json + score_dict_file: output/reports/attack/fb170b19de4faaacdeab1a095fc0f0aa/score_dict.json + test_labels_file: output/reports/attack/fb170b19de4faaacdeab1a095fc0f0aa/test_labels.json + train_labels_file: output/reports/attack/fb170b19de4faaacdeab1a095fc0f0aa/train_labels.json + model_dir: models + name: fb170b19de4faaacdeab1a095fc0f0aa + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: fb170b19de4faaacdeab1a095fc0f0aa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/fc4dcf0724bb4f69fbc10568562ab608/params.yaml b/examples/security/kdd-nsl/output/reports/attack/fc4dcf0724bb4f69fbc10568562ab608/params.yaml new file mode 100644 index 00000000..3bd4a904 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/fc4dcf0724bb4f69fbc10568562ab608/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.01 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 81340894a5a5e659801f8af2b22c6236 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/fc4dcf0724bb4f69fbc10568562ab608/adv_predictions.json + adv_probabilities_file: output/reports/attack/fc4dcf0724bb4f69fbc10568562ab608/adv_probabilities.json + attack_file: output/attacks/57c6f04a0be5614de6cc49a96378d7e7.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/fc4dcf0724bb4f69fbc10568562ab608/params.yaml + predictions_file: output/reports/attack/fc4dcf0724bb4f69fbc10568562ab608/predictions.json + probabilities_file: output/reports/attack/fc4dcf0724bb4f69fbc10568562ab608/probabilities.json + score_dict_file: output/reports/attack/fc4dcf0724bb4f69fbc10568562ab608/score_dict.json + test_labels_file: output/reports/attack/fc4dcf0724bb4f69fbc10568562ab608/test_labels.json + train_labels_file: output/reports/attack/fc4dcf0724bb4f69fbc10568562ab608/train_labels.json + model_dir: models + name: fc4dcf0724bb4f69fbc10568562ab608 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: fc4dcf0724bb4f69fbc10568562ab608 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/fc59becfe81b566544b6cf4d98967a47/params.yaml b/examples/security/kdd-nsl/output/reports/attack/fc59becfe81b566544b6cf4d98967a47/params.yaml new file mode 100644 index 00000000..6b0d0bd4 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/fc59becfe81b566544b6cf4d98967a47/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.3 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 7ea9ae24d9e8e392e709c4a0fe233e24 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/fc59becfe81b566544b6cf4d98967a47/adv_predictions.json + adv_probabilities_file: output/reports/attack/fc59becfe81b566544b6cf4d98967a47/adv_probabilities.json + attack_file: output/attacks/790e05ccf870ffc698b948797306581c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/fc59becfe81b566544b6cf4d98967a47/params.yaml + predictions_file: output/reports/attack/fc59becfe81b566544b6cf4d98967a47/predictions.json + probabilities_file: output/reports/attack/fc59becfe81b566544b6cf4d98967a47/probabilities.json + score_dict_file: output/reports/attack/fc59becfe81b566544b6cf4d98967a47/score_dict.json + test_labels_file: output/reports/attack/fc59becfe81b566544b6cf4d98967a47/test_labels.json + train_labels_file: output/reports/attack/fc59becfe81b566544b6cf4d98967a47/train_labels.json + model_dir: models + name: fc59becfe81b566544b6cf4d98967a47 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: fc59becfe81b566544b6cf4d98967a47 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/fc6c3260904c04940865645415f5d3a7/params.yaml b/examples/security/kdd-nsl/output/reports/attack/fc6c3260904c04940865645415f5d3a7/params.yaml new file mode 100644 index 00000000..11b43856 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/fc6c3260904c04940865645415f5d3a7/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.5 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 231cfa7371415d0d4ace5dfff610ccb0 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/fc6c3260904c04940865645415f5d3a7/adv_predictions.json + adv_probabilities_file: output/reports/attack/fc6c3260904c04940865645415f5d3a7/adv_probabilities.json + attack_file: output/attacks/0a18f24b8c6c68744834d888f6c3dadf.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/fc6c3260904c04940865645415f5d3a7/params.yaml + predictions_file: output/reports/attack/fc6c3260904c04940865645415f5d3a7/predictions.json + probabilities_file: output/reports/attack/fc6c3260904c04940865645415f5d3a7/probabilities.json + score_dict_file: output/reports/attack/fc6c3260904c04940865645415f5d3a7/score_dict.json + test_labels_file: output/reports/attack/fc6c3260904c04940865645415f5d3a7/test_labels.json + train_labels_file: output/reports/attack/fc6c3260904c04940865645415f5d3a7/train_labels.json + model_dir: models + name: fc6c3260904c04940865645415f5d3a7 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: fc6c3260904c04940865645415f5d3a7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/fce239e50fdebd96647b994ddf4a35ca/params.yaml b/examples/security/kdd-nsl/output/reports/attack/fce239e50fdebd96647b994ddf4a35ca/params.yaml new file mode 100644 index 00000000..a3029b97 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/fce239e50fdebd96647b994ddf4a35ca/params.yaml @@ -0,0 +1,220 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.01 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 02bf135b7675547c560a8d643948b773 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/fce239e50fdebd96647b994ddf4a35ca/adv_predictions.json + adv_probabilities_file: output/reports/attack/fce239e50fdebd96647b994ddf4a35ca/adv_probabilities.json + attack_file: output/attacks/2b81a1a8274aa899d34dbcb32ad681ce.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/31e4372a5bedb2c83b770bba6d47ca62.pkl + params_file: output/reports/attack/fce239e50fdebd96647b994ddf4a35ca/params.yaml + predictions_file: output/reports/attack/fce239e50fdebd96647b994ddf4a35ca/predictions.json + probabilities_file: output/reports/attack/fce239e50fdebd96647b994ddf4a35ca/probabilities.json + score_dict_file: output/reports/attack/fce239e50fdebd96647b994ddf4a35ca/score_dict.json + test_labels_file: output/reports/attack/fce239e50fdebd96647b994ddf4a35ca/test_labels.json + train_labels_file: output/reports/attack/fce239e50fdebd96647b994ddf4a35ca/train_labels.json + model_dir: models + name: fce239e50fdebd96647b994ddf4a35ca + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: fce239e50fdebd96647b994ddf4a35ca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/fd61b12a2072480bb55a413bacc3cc7f/params.yaml b/examples/security/kdd-nsl/output/reports/attack/fd61b12a2072480bb55a413bacc3cc7f/params.yaml new file mode 100644 index 00000000..308689b0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/fd61b12a2072480bb55a413bacc3cc7f/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: be8a3721261c9d2e137ad0898a1391d9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/fd61b12a2072480bb55a413bacc3cc7f/adv_predictions.json + adv_probabilities_file: output/reports/attack/fd61b12a2072480bb55a413bacc3cc7f/adv_probabilities.json + attack_file: output/attacks/7a16a45ab19a9f20df18908802e1b49d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/fd61b12a2072480bb55a413bacc3cc7f/params.yaml + predictions_file: output/reports/attack/fd61b12a2072480bb55a413bacc3cc7f/predictions.json + probabilities_file: output/reports/attack/fd61b12a2072480bb55a413bacc3cc7f/probabilities.json + score_dict_file: output/reports/attack/fd61b12a2072480bb55a413bacc3cc7f/score_dict.json + test_labels_file: output/reports/attack/fd61b12a2072480bb55a413bacc3cc7f/test_labels.json + train_labels_file: output/reports/attack/fd61b12a2072480bb55a413bacc3cc7f/train_labels.json + model_dir: models + name: fd61b12a2072480bb55a413bacc3cc7f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: fd61b12a2072480bb55a413bacc3cc7f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/fe150a6bdc59f04da3c85524bc2a517f/params.yaml b/examples/security/kdd-nsl/output/reports/attack/fe150a6bdc59f04da3c85524bc2a517f/params.yaml new file mode 100644 index 00000000..52cf4554 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/fe150a6bdc59f04da3c85524bc2a517f/params.yaml @@ -0,0 +1,226 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.01 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 13cc3edbec35afc3dd6ff2cbd4481c82 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/fe150a6bdc59f04da3c85524bc2a517f/adv_predictions.json + adv_probabilities_file: output/reports/attack/fe150a6bdc59f04da3c85524bc2a517f/adv_probabilities.json + attack_file: output/attacks/0a8575d2132a39e0b731a7cbafef86f3.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/fe150a6bdc59f04da3c85524bc2a517f/params.yaml + predictions_file: output/reports/attack/fe150a6bdc59f04da3c85524bc2a517f/predictions.json + probabilities_file: output/reports/attack/fe150a6bdc59f04da3c85524bc2a517f/probabilities.json + score_dict_file: output/reports/attack/fe150a6bdc59f04da3c85524bc2a517f/score_dict.json + test_labels_file: output/reports/attack/fe150a6bdc59f04da3c85524bc2a517f/test_labels.json + train_labels_file: output/reports/attack/fe150a6bdc59f04da3c85524bc2a517f/train_labels.json + model_dir: models + name: fe150a6bdc59f04da3c85524bc2a517f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: fe150a6bdc59f04da3c85524bc2a517f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/fe96072b542053e27013797b7f5416bb/params.yaml b/examples/security/kdd-nsl/output/reports/attack/fe96072b542053e27013797b7f5416bb/params.yaml new file mode 100644 index 00000000..6b0959d4 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/fe96072b542053e27013797b7f5416bb/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: a3a6358e43c36ea6974892bc24622745 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/fe96072b542053e27013797b7f5416bb/adv_predictions.json + adv_probabilities_file: output/reports/attack/fe96072b542053e27013797b7f5416bb/adv_probabilities.json + attack_file: output/attacks/7e6ea3b5cd0cfb5089019e05c27ac34b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/fe96072b542053e27013797b7f5416bb/params.yaml + predictions_file: output/reports/attack/fe96072b542053e27013797b7f5416bb/predictions.json + probabilities_file: output/reports/attack/fe96072b542053e27013797b7f5416bb/probabilities.json + score_dict_file: output/reports/attack/fe96072b542053e27013797b7f5416bb/score_dict.json + test_labels_file: output/reports/attack/fe96072b542053e27013797b7f5416bb/test_labels.json + train_labels_file: output/reports/attack/fe96072b542053e27013797b7f5416bb/train_labels.json + model_dir: models + name: fe96072b542053e27013797b7f5416bb + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: fe96072b542053e27013797b7f5416bb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ff2fbbadae2f1fbe3a6656f739b4bdf3/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ff2fbbadae2f1fbe3a6656f739b4bdf3/params.yaml new file mode 100644 index 00000000..aa351f40 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ff2fbbadae2f1fbe3a6656f739b4bdf3/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.5 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: 7c638b12415c29654614bf097ff47c85 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ff2fbbadae2f1fbe3a6656f739b4bdf3/adv_predictions.json + adv_probabilities_file: output/reports/attack/ff2fbbadae2f1fbe3a6656f739b4bdf3/adv_probabilities.json + attack_file: output/attacks/9930f4574b0a35550d8b38ec9d5cc67e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/ff2fbbadae2f1fbe3a6656f739b4bdf3/params.yaml + predictions_file: output/reports/attack/ff2fbbadae2f1fbe3a6656f739b4bdf3/predictions.json + probabilities_file: output/reports/attack/ff2fbbadae2f1fbe3a6656f739b4bdf3/probabilities.json + score_dict_file: output/reports/attack/ff2fbbadae2f1fbe3a6656f739b4bdf3/score_dict.json + test_labels_file: output/reports/attack/ff2fbbadae2f1fbe3a6656f739b4bdf3/test_labels.json + train_labels_file: output/reports/attack/ff2fbbadae2f1fbe3a6656f739b4bdf3/train_labels.json + model_dir: models + name: ff2fbbadae2f1fbe3a6656f739b4bdf3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: ff2fbbadae2f1fbe3a6656f739b4bdf3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ff5d63e1690da1bbcf9111fc8eb7a0f7/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ff5d63e1690da1bbcf9111fc8eb7a0f7/params.yaml new file mode 100644 index 00000000..ec3bab0d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ff5d63e1690da1bbcf9111fc8eb7a0f7/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: b6a8515cd4553ee3ab71e4b0c8be0a1a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ff5d63e1690da1bbcf9111fc8eb7a0f7/adv_predictions.json + adv_probabilities_file: output/reports/attack/ff5d63e1690da1bbcf9111fc8eb7a0f7/adv_probabilities.json + attack_file: output/attacks/d9547eb3eb55d01d664115d4dbc23f72.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/ff5d63e1690da1bbcf9111fc8eb7a0f7/params.yaml + predictions_file: output/reports/attack/ff5d63e1690da1bbcf9111fc8eb7a0f7/predictions.json + probabilities_file: output/reports/attack/ff5d63e1690da1bbcf9111fc8eb7a0f7/probabilities.json + score_dict_file: output/reports/attack/ff5d63e1690da1bbcf9111fc8eb7a0f7/score_dict.json + test_labels_file: output/reports/attack/ff5d63e1690da1bbcf9111fc8eb7a0f7/test_labels.json + train_labels_file: output/reports/attack/ff5d63e1690da1bbcf9111fc8eb7a0f7/train_labels.json + model_dir: models + name: ff5d63e1690da1bbcf9111fc8eb7a0f7 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: ff5d63e1690da1bbcf9111fc8eb7a0f7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ff69df12c3734e7648591fcf52a4377a/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ff69df12c3734e7648591fcf52a4377a/params.yaml new file mode 100644 index 00000000..678c573c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ff69df12c3734e7648591fcf52a4377a/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.001 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ffa3f7036ce27b2be224c85d89d106a + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} + name: 09f86f9f084b63c26bae908e978273c0 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ff69df12c3734e7648591fcf52a4377a/adv_predictions.json + adv_probabilities_file: output/reports/attack/ff69df12c3734e7648591fcf52a4377a/adv_probabilities.json + attack_file: output/attacks/bd49e575d7bd5db209b7e71293a0005a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/attack/ff69df12c3734e7648591fcf52a4377a/params.yaml + predictions_file: output/reports/attack/ff69df12c3734e7648591fcf52a4377a/predictions.json + probabilities_file: output/reports/attack/ff69df12c3734e7648591fcf52a4377a/probabilities.json + score_dict_file: output/reports/attack/ff69df12c3734e7648591fcf52a4377a/score_dict.json + test_labels_file: output/reports/attack/ff69df12c3734e7648591fcf52a4377a/test_labels.json + train_labels_file: output/reports/attack/ff69df12c3734e7648591fcf52a4377a/train_labels.json + model_dir: models + name: ff69df12c3734e7648591fcf52a4377a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: ff69df12c3734e7648591fcf52a4377a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ffbe070a3d733491a646bcbb79f893f1/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ffbe070a3d733491a646bcbb79f893f1/params.yaml new file mode 100644 index 00000000..1962bb0f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ffbe070a3d733491a646bcbb79f893f1/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 081fcc7c9015e66d890a2b2a264ffb98 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ffbe070a3d733491a646bcbb79f893f1/adv_predictions.json + adv_probabilities_file: output/reports/attack/ffbe070a3d733491a646bcbb79f893f1/adv_probabilities.json + attack_file: output/attacks/8537d8cad6b59fd3c86261e85ce020e5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/ffbe070a3d733491a646bcbb79f893f1/params.yaml + predictions_file: output/reports/attack/ffbe070a3d733491a646bcbb79f893f1/predictions.json + probabilities_file: output/reports/attack/ffbe070a3d733491a646bcbb79f893f1/probabilities.json + score_dict_file: output/reports/attack/ffbe070a3d733491a646bcbb79f893f1/score_dict.json + test_labels_file: output/reports/attack/ffbe070a3d733491a646bcbb79f893f1/test_labels.json + train_labels_file: output/reports/attack/ffbe070a3d733491a646bcbb79f893f1/train_labels.json + model_dir: models + name: ffbe070a3d733491a646bcbb79f893f1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: ffbe070a3d733491a646bcbb79f893f1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/attack/ffefd1410efc523b5775470ee1a8b476/params.yaml b/examples/security/kdd-nsl/output/reports/attack/ffefd1410efc523b5775470ee1a8b476/params.yaml new file mode 100644 index 00000000..36ede6e0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/attack/ffefd1410efc523b5775470ee1a8b476/params.yaml @@ -0,0 +1,229 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.3 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4c02a06f02dae58c7d039ecfd9239fa2 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} + name: dcbd12e71ff0fe1bd02ed1ec2379602a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ffefd1410efc523b5775470ee1a8b476/adv_predictions.json + adv_probabilities_file: output/reports/attack/ffefd1410efc523b5775470ee1a8b476/adv_probabilities.json + attack_file: output/attacks/84cbabaa3e5700d733d09195d781ec65.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/attack/ffefd1410efc523b5775470ee1a8b476/params.yaml + predictions_file: output/reports/attack/ffefd1410efc523b5775470ee1a8b476/predictions.json + probabilities_file: output/reports/attack/ffefd1410efc523b5775470ee1a8b476/probabilities.json + score_dict_file: output/reports/attack/ffefd1410efc523b5775470ee1a8b476/score_dict.json + test_labels_file: output/reports/attack/ffefd1410efc523b5775470ee1a8b476/test_labels.json + train_labels_file: output/reports/attack/ffefd1410efc523b5775470ee1a8b476/train_labels.json + model_dir: models + name: ffefd1410efc523b5775470ee1a8b476 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: ffefd1410efc523b5775470ee1a8b476 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/params.yaml b/examples/security/kdd-nsl/output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/params.yaml new file mode 100644 index 00000000..c0fb505b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/adv_predictions.json + adv_probabilities_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7ee6ee2b1768a2f927f774f000663b31.pkl + params_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/params.yaml + predictions_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/predictions.json + probabilities_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/probabilities.json + score_dict_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/score_dict.json + test_labels_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/test_labels.json + train_labels_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/train_labels.json + model_dir: models + name: 0030dc74ce75d68c788d83e4edf5b61f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: de7140be97c1ae249218b2b43de057db + trainer: + kwargs: {} +name: 0030dc74ce75d68c788d83e4edf5b61f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/0205708475e713a1883527369d90dcac/params.yaml b/examples/security/kdd-nsl/output/reports/train/0205708475e713a1883527369d90dcac/params.yaml new file mode 100644 index 00000000..08285660 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/0205708475e713a1883527369d90dcac/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0205708475e713a1883527369d90dcac/adv_predictions.json + adv_probabilities_file: output/reports/train/0205708475e713a1883527369d90dcac/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7d79009ea646253e5913af3da463c793.pkl + params_file: output/reports/train/0205708475e713a1883527369d90dcac/params.yaml + predictions_file: output/reports/train/0205708475e713a1883527369d90dcac/predictions.json + probabilities_file: output/reports/train/0205708475e713a1883527369d90dcac/probabilities.json + score_dict_file: output/reports/train/0205708475e713a1883527369d90dcac/score_dict.json + test_labels_file: output/reports/train/0205708475e713a1883527369d90dcac/test_labels.json + train_labels_file: output/reports/train/0205708475e713a1883527369d90dcac/train_labels.json + model_dir: models + name: 0205708475e713a1883527369d90dcac + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bf064e856f97d50058115c3305ddf84b + trainer: + kwargs: {} +name: 0205708475e713a1883527369d90dcac +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/params.yaml b/examples/security/kdd-nsl/output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/params.yaml new file mode 100644 index 00000000..8d41d403 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/adv_predictions.json + adv_probabilities_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/11bec5b40d872042de508e1b56daae11.pkl + params_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/params.yaml + predictions_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/predictions.json + probabilities_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/probabilities.json + score_dict_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/score_dict.json + test_labels_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/test_labels.json + train_labels_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/train_labels.json + model_dir: models + name: 02a5a9ba99c99d47dfc1064ea8bdefd0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 50cc38ab0df54a361715e71a22b7e05c + trainer: + kwargs: {} +name: 02a5a9ba99c99d47dfc1064ea8bdefd0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/params.yaml b/examples/security/kdd-nsl/output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/params.yaml new file mode 100644 index 00000000..34ebb7c3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/adv_predictions.json + adv_probabilities_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/37a6d4e18b5d2e989f67d25206037cf7.pkl + params_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/params.yaml + predictions_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/predictions.json + probabilities_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/probabilities.json + score_dict_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/score_dict.json + test_labels_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/test_labels.json + train_labels_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/train_labels.json + model_dir: models + name: 02c3b951c3452df6ceedce6f3e432b9d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9a020cc91c89312191e273e2f322a0c4 + trainer: + kwargs: {} +name: 02c3b951c3452df6ceedce6f3e432b9d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/params.yaml b/examples/security/kdd-nsl/output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/params.yaml new file mode 100644 index 00000000..59c99c14 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/adv_predictions.json + adv_probabilities_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/00a30ea0312f812679adba4c78904149.pkl + params_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/params.yaml + predictions_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/predictions.json + probabilities_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/probabilities.json + score_dict_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/score_dict.json + test_labels_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/test_labels.json + train_labels_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/train_labels.json + model_dir: models + name: 03c722c9d204e3b94fc1d2d7d4650ad4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ce3ec64e768be4f01f87541270276e4 + trainer: + kwargs: {} +name: 03c722c9d204e3b94fc1d2d7d4650ad4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/params.yaml b/examples/security/kdd-nsl/output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/params.yaml new file mode 100644 index 00000000..e38e3dae --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/adv_predictions.json + adv_probabilities_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5269adde7bba7b9b74d0eb99711b9668.pkl + params_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/params.yaml + predictions_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/predictions.json + probabilities_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/probabilities.json + score_dict_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/score_dict.json + test_labels_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/test_labels.json + train_labels_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/train_labels.json + model_dir: models + name: 040c3712f9a736f0a98a4cf954c6edc7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0b22caac6d4015ded883ec1e7efe8be3 + trainer: + kwargs: {} +name: 040c3712f9a736f0a98a4cf954c6edc7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/0511b94f838b9c69621e2aaa3ef20935/params.yaml b/examples/security/kdd-nsl/output/reports/train/0511b94f838b9c69621e2aaa3ef20935/params.yaml new file mode 100644 index 00000000..15e40226 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/0511b94f838b9c69621e2aaa3ef20935/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0511b94f838b9c69621e2aaa3ef20935/adv_predictions.json + adv_probabilities_file: output/reports/train/0511b94f838b9c69621e2aaa3ef20935/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b8619d342b1092c21d438976eb4d11ed.pkl + params_file: output/reports/train/0511b94f838b9c69621e2aaa3ef20935/params.yaml + predictions_file: output/reports/train/0511b94f838b9c69621e2aaa3ef20935/predictions.json + probabilities_file: output/reports/train/0511b94f838b9c69621e2aaa3ef20935/probabilities.json + score_dict_file: output/reports/train/0511b94f838b9c69621e2aaa3ef20935/score_dict.json + test_labels_file: output/reports/train/0511b94f838b9c69621e2aaa3ef20935/test_labels.json + train_labels_file: output/reports/train/0511b94f838b9c69621e2aaa3ef20935/train_labels.json + model_dir: models + name: 0511b94f838b9c69621e2aaa3ef20935 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 969bebfe5048235cb5a7266c7497155d + trainer: + kwargs: {} +name: 0511b94f838b9c69621e2aaa3ef20935 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/05b85969e6c66e487e5f58d66b433f0d/params.yaml b/examples/security/kdd-nsl/output/reports/train/05b85969e6c66e487e5f58d66b433f0d/params.yaml new file mode 100644 index 00000000..e6e376f7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/05b85969e6c66e487e5f58d66b433f0d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/05b85969e6c66e487e5f58d66b433f0d/adv_predictions.json + adv_probabilities_file: output/reports/train/05b85969e6c66e487e5f58d66b433f0d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5839552dca3dd563226f9ad3309bbc9c.pkl + params_file: output/reports/train/05b85969e6c66e487e5f58d66b433f0d/params.yaml + predictions_file: output/reports/train/05b85969e6c66e487e5f58d66b433f0d/predictions.json + probabilities_file: output/reports/train/05b85969e6c66e487e5f58d66b433f0d/probabilities.json + score_dict_file: output/reports/train/05b85969e6c66e487e5f58d66b433f0d/score_dict.json + test_labels_file: output/reports/train/05b85969e6c66e487e5f58d66b433f0d/test_labels.json + train_labels_file: output/reports/train/05b85969e6c66e487e5f58d66b433f0d/train_labels.json + model_dir: models + name: 05b85969e6c66e487e5f58d66b433f0d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 90c269e917320f308e5d05e9f57e3b6a + trainer: + kwargs: {} +name: 05b85969e6c66e487e5f58d66b433f0d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/params.yaml b/examples/security/kdd-nsl/output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/params.yaml new file mode 100644 index 00000000..cc7b10e3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/adv_predictions.json + adv_probabilities_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1e8e3d1ddab0e3d42123d927891b0036.pkl + params_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/params.yaml + predictions_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/predictions.json + probabilities_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/probabilities.json + score_dict_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/score_dict.json + test_labels_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/test_labels.json + train_labels_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/train_labels.json + model_dir: models + name: 06eb0ddb3dd08490e30378ee5edeac4b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6c4993c3157b337b6f0a5e1bc2314cdc + trainer: + kwargs: {} +name: 06eb0ddb3dd08490e30378ee5edeac4b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/077babf97bfc42126994cb509c43b627/params.yaml b/examples/security/kdd-nsl/output/reports/train/077babf97bfc42126994cb509c43b627/params.yaml new file mode 100644 index 00000000..df6a5927 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/077babf97bfc42126994cb509c43b627/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/077babf97bfc42126994cb509c43b627/adv_predictions.json + adv_probabilities_file: output/reports/train/077babf97bfc42126994cb509c43b627/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/49b15883633a55ea10b93b4395f0e83c.pkl + params_file: output/reports/train/077babf97bfc42126994cb509c43b627/params.yaml + predictions_file: output/reports/train/077babf97bfc42126994cb509c43b627/predictions.json + probabilities_file: output/reports/train/077babf97bfc42126994cb509c43b627/probabilities.json + score_dict_file: output/reports/train/077babf97bfc42126994cb509c43b627/score_dict.json + test_labels_file: output/reports/train/077babf97bfc42126994cb509c43b627/test_labels.json + train_labels_file: output/reports/train/077babf97bfc42126994cb509c43b627/train_labels.json + model_dir: models + name: 077babf97bfc42126994cb509c43b627 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e321dab140a8a6fda732cfcd1a2087e4 + trainer: + kwargs: {} +name: 077babf97bfc42126994cb509c43b627 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/07c96c5f7368bedc384a4ebd1535897f/params.yaml b/examples/security/kdd-nsl/output/reports/train/07c96c5f7368bedc384a4ebd1535897f/params.yaml new file mode 100644 index 00000000..0826eea0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/07c96c5f7368bedc384a4ebd1535897f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/07c96c5f7368bedc384a4ebd1535897f/adv_predictions.json + adv_probabilities_file: output/reports/train/07c96c5f7368bedc384a4ebd1535897f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b85ad36299416054dd0e911698425597.pkl + params_file: output/reports/train/07c96c5f7368bedc384a4ebd1535897f/params.yaml + predictions_file: output/reports/train/07c96c5f7368bedc384a4ebd1535897f/predictions.json + probabilities_file: output/reports/train/07c96c5f7368bedc384a4ebd1535897f/probabilities.json + score_dict_file: output/reports/train/07c96c5f7368bedc384a4ebd1535897f/score_dict.json + test_labels_file: output/reports/train/07c96c5f7368bedc384a4ebd1535897f/test_labels.json + train_labels_file: output/reports/train/07c96c5f7368bedc384a4ebd1535897f/train_labels.json + model_dir: models + name: 07c96c5f7368bedc384a4ebd1535897f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2ebb960345c5bb2113f78a510714e52a + trainer: + kwargs: {} +name: 07c96c5f7368bedc384a4ebd1535897f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/09976d544db729e4f9f620a3c2c0f612/params.yaml b/examples/security/kdd-nsl/output/reports/train/09976d544db729e4f9f620a3c2c0f612/params.yaml new file mode 100644 index 00000000..02341f5b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/09976d544db729e4f9f620a3c2c0f612/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/adv_predictions.json + adv_probabilities_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/770fd572edda8bb5459aed13fa63ad1a.pkl + params_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/params.yaml + predictions_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/predictions.json + probabilities_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/probabilities.json + score_dict_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/score_dict.json + test_labels_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/test_labels.json + train_labels_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/train_labels.json + model_dir: models + name: 09976d544db729e4f9f620a3c2c0f612 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 361c1f6954772b05ff16df071fd76df0 + trainer: + kwargs: {} +name: 09976d544db729e4f9f620a3c2c0f612 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/0a710e486d0b1008ad483189c0d49637/params.yaml b/examples/security/kdd-nsl/output/reports/train/0a710e486d0b1008ad483189c0d49637/params.yaml new file mode 100644 index 00000000..56d74b3c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/0a710e486d0b1008ad483189c0d49637/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0a710e486d0b1008ad483189c0d49637/adv_predictions.json + adv_probabilities_file: output/reports/train/0a710e486d0b1008ad483189c0d49637/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/86ef62b335112f77892a0fac18efc48b.pkl + params_file: output/reports/train/0a710e486d0b1008ad483189c0d49637/params.yaml + predictions_file: output/reports/train/0a710e486d0b1008ad483189c0d49637/predictions.json + probabilities_file: output/reports/train/0a710e486d0b1008ad483189c0d49637/probabilities.json + score_dict_file: output/reports/train/0a710e486d0b1008ad483189c0d49637/score_dict.json + test_labels_file: output/reports/train/0a710e486d0b1008ad483189c0d49637/test_labels.json + train_labels_file: output/reports/train/0a710e486d0b1008ad483189c0d49637/train_labels.json + model_dir: models + name: 0a710e486d0b1008ad483189c0d49637 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3160692180da0e6d2d465e019dfc3a0b + trainer: + kwargs: {} +name: 0a710e486d0b1008ad483189c0d49637 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/0c76514387fa074fff4aa4f987f2d482/params.yaml b/examples/security/kdd-nsl/output/reports/train/0c76514387fa074fff4aa4f987f2d482/params.yaml new file mode 100644 index 00000000..5c785df8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/0c76514387fa074fff4aa4f987f2d482/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0c76514387fa074fff4aa4f987f2d482/adv_predictions.json + adv_probabilities_file: output/reports/train/0c76514387fa074fff4aa4f987f2d482/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/a45dd243543cbcb96d6d16724c90ed2f.pkl + params_file: output/reports/train/0c76514387fa074fff4aa4f987f2d482/params.yaml + predictions_file: output/reports/train/0c76514387fa074fff4aa4f987f2d482/predictions.json + probabilities_file: output/reports/train/0c76514387fa074fff4aa4f987f2d482/probabilities.json + score_dict_file: output/reports/train/0c76514387fa074fff4aa4f987f2d482/score_dict.json + test_labels_file: output/reports/train/0c76514387fa074fff4aa4f987f2d482/test_labels.json + train_labels_file: output/reports/train/0c76514387fa074fff4aa4f987f2d482/train_labels.json + model_dir: models + name: 0c76514387fa074fff4aa4f987f2d482 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d2a9eb9b8c2149cc5064d84043e6878a + trainer: + kwargs: {} +name: 0c76514387fa074fff4aa4f987f2d482 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/0c934d7bbc08041f024d98de1886184a/params.yaml b/examples/security/kdd-nsl/output/reports/train/0c934d7bbc08041f024d98de1886184a/params.yaml new file mode 100644 index 00000000..0bde5909 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/0c934d7bbc08041f024d98de1886184a/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0c934d7bbc08041f024d98de1886184a/adv_predictions.json + adv_probabilities_file: output/reports/train/0c934d7bbc08041f024d98de1886184a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/fa799b32c6e37070e5c27116b3a7345e.pkl + params_file: output/reports/train/0c934d7bbc08041f024d98de1886184a/params.yaml + predictions_file: output/reports/train/0c934d7bbc08041f024d98de1886184a/predictions.json + probabilities_file: output/reports/train/0c934d7bbc08041f024d98de1886184a/probabilities.json + score_dict_file: output/reports/train/0c934d7bbc08041f024d98de1886184a/score_dict.json + test_labels_file: output/reports/train/0c934d7bbc08041f024d98de1886184a/test_labels.json + train_labels_file: output/reports/train/0c934d7bbc08041f024d98de1886184a/train_labels.json + model_dir: models + name: 0c934d7bbc08041f024d98de1886184a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6d7cbe60eb33cd12e7039b662c0d3d67 + trainer: + kwargs: {} +name: 0c934d7bbc08041f024d98de1886184a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/0d224e1da2d5e90555b6e677bec214e4/params.yaml b/examples/security/kdd-nsl/output/reports/train/0d224e1da2d5e90555b6e677bec214e4/params.yaml new file mode 100644 index 00000000..d3439f2d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/0d224e1da2d5e90555b6e677bec214e4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0d224e1da2d5e90555b6e677bec214e4/adv_predictions.json + adv_probabilities_file: output/reports/train/0d224e1da2d5e90555b6e677bec214e4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/055996a38b1a24e249191fc586f121b8.pkl + params_file: output/reports/train/0d224e1da2d5e90555b6e677bec214e4/params.yaml + predictions_file: output/reports/train/0d224e1da2d5e90555b6e677bec214e4/predictions.json + probabilities_file: output/reports/train/0d224e1da2d5e90555b6e677bec214e4/probabilities.json + score_dict_file: output/reports/train/0d224e1da2d5e90555b6e677bec214e4/score_dict.json + test_labels_file: output/reports/train/0d224e1da2d5e90555b6e677bec214e4/test_labels.json + train_labels_file: output/reports/train/0d224e1da2d5e90555b6e677bec214e4/train_labels.json + model_dir: models + name: 0d224e1da2d5e90555b6e677bec214e4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b58da23673bf8cc60ae0ea7dbeb242e + trainer: + kwargs: {} +name: 0d224e1da2d5e90555b6e677bec214e4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/10561060ddf61af191eed0124840f632/params.yaml b/examples/security/kdd-nsl/output/reports/train/10561060ddf61af191eed0124840f632/params.yaml new file mode 100644 index 00000000..5a67515e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/10561060ddf61af191eed0124840f632/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/10561060ddf61af191eed0124840f632/adv_predictions.json + adv_probabilities_file: output/reports/train/10561060ddf61af191eed0124840f632/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5a17ea36b8f1ebd8c2d57d1aee9fea8c.pkl + params_file: output/reports/train/10561060ddf61af191eed0124840f632/params.yaml + predictions_file: output/reports/train/10561060ddf61af191eed0124840f632/predictions.json + probabilities_file: output/reports/train/10561060ddf61af191eed0124840f632/probabilities.json + score_dict_file: output/reports/train/10561060ddf61af191eed0124840f632/score_dict.json + test_labels_file: output/reports/train/10561060ddf61af191eed0124840f632/test_labels.json + train_labels_file: output/reports/train/10561060ddf61af191eed0124840f632/train_labels.json + model_dir: models + name: 10561060ddf61af191eed0124840f632 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 68e1faf53c845356f62102d1bc15b0d8 + trainer: + kwargs: {} +name: 10561060ddf61af191eed0124840f632 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/1070b713d9d0148acf12445b0d28d89f/params.yaml b/examples/security/kdd-nsl/output/reports/train/1070b713d9d0148acf12445b0d28d89f/params.yaml new file mode 100644 index 00000000..827e579e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/1070b713d9d0148acf12445b0d28d89f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/adv_predictions.json + adv_probabilities_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/177aebbec5a2a36bbe39a4bf5caf399d.pkl + params_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/params.yaml + predictions_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/predictions.json + probabilities_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/probabilities.json + score_dict_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/score_dict.json + test_labels_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/test_labels.json + train_labels_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/train_labels.json + model_dir: models + name: 1070b713d9d0148acf12445b0d28d89f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 870bc742236aaf2e6368747e7b4efb7d + trainer: + kwargs: {} +name: 1070b713d9d0148acf12445b0d28d89f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/10d59a528c08fe3d790c347783b9f6ab/params.yaml b/examples/security/kdd-nsl/output/reports/train/10d59a528c08fe3d790c347783b9f6ab/params.yaml new file mode 100644 index 00000000..cf0b664b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/10d59a528c08fe3d790c347783b9f6ab/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/adv_predictions.json + adv_probabilities_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5b5764b42873128207f58d4aacbdc72e.pkl + params_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/params.yaml + predictions_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/predictions.json + probabilities_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/probabilities.json + score_dict_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/score_dict.json + test_labels_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/test_labels.json + train_labels_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/train_labels.json + model_dir: models + name: 10d59a528c08fe3d790c347783b9f6ab + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aca628860614b3809bd822f66a220811 + trainer: + kwargs: {} +name: 10d59a528c08fe3d790c347783b9f6ab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/12f1d3a01e5caec3841e16c42c905d95/params.yaml b/examples/security/kdd-nsl/output/reports/train/12f1d3a01e5caec3841e16c42c905d95/params.yaml new file mode 100644 index 00000000..5d577490 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/12f1d3a01e5caec3841e16c42c905d95/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/12f1d3a01e5caec3841e16c42c905d95/adv_predictions.json + adv_probabilities_file: output/reports/train/12f1d3a01e5caec3841e16c42c905d95/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5ee37340357a45bb08d9e20416a31820.pkl + params_file: output/reports/train/12f1d3a01e5caec3841e16c42c905d95/params.yaml + predictions_file: output/reports/train/12f1d3a01e5caec3841e16c42c905d95/predictions.json + probabilities_file: output/reports/train/12f1d3a01e5caec3841e16c42c905d95/probabilities.json + score_dict_file: output/reports/train/12f1d3a01e5caec3841e16c42c905d95/score_dict.json + test_labels_file: output/reports/train/12f1d3a01e5caec3841e16c42c905d95/test_labels.json + train_labels_file: output/reports/train/12f1d3a01e5caec3841e16c42c905d95/train_labels.json + model_dir: models + name: 12f1d3a01e5caec3841e16c42c905d95 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7f80347614b0e53cc09d18c45d9a121b + trainer: + kwargs: {} +name: 12f1d3a01e5caec3841e16c42c905d95 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/140e3ea8043442464fb0fc85998bcd3e/params.yaml b/examples/security/kdd-nsl/output/reports/train/140e3ea8043442464fb0fc85998bcd3e/params.yaml new file mode 100644 index 00000000..deee986e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/140e3ea8043442464fb0fc85998bcd3e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/adv_predictions.json + adv_probabilities_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b5a63088d3f966bc96c3be6e7f7bda71.pkl + params_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/params.yaml + predictions_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/predictions.json + probabilities_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/probabilities.json + score_dict_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/score_dict.json + test_labels_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/test_labels.json + train_labels_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/train_labels.json + model_dir: models + name: 140e3ea8043442464fb0fc85998bcd3e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c4929bea0ac54cb611443b3adf0833c0 + trainer: + kwargs: {} +name: 140e3ea8043442464fb0fc85998bcd3e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/147254efe4665420abc4252bd87d170b/params.yaml b/examples/security/kdd-nsl/output/reports/train/147254efe4665420abc4252bd87d170b/params.yaml new file mode 100644 index 00000000..59033e4a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/147254efe4665420abc4252bd87d170b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/147254efe4665420abc4252bd87d170b/adv_predictions.json + adv_probabilities_file: output/reports/train/147254efe4665420abc4252bd87d170b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/737399c9d766e38d97476aec9b12dabf.pkl + params_file: output/reports/train/147254efe4665420abc4252bd87d170b/params.yaml + predictions_file: output/reports/train/147254efe4665420abc4252bd87d170b/predictions.json + probabilities_file: output/reports/train/147254efe4665420abc4252bd87d170b/probabilities.json + score_dict_file: output/reports/train/147254efe4665420abc4252bd87d170b/score_dict.json + test_labels_file: output/reports/train/147254efe4665420abc4252bd87d170b/test_labels.json + train_labels_file: output/reports/train/147254efe4665420abc4252bd87d170b/train_labels.json + model_dir: models + name: 147254efe4665420abc4252bd87d170b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e9b64b79e61f65b949cb0d3c1cff0b47 + trainer: + kwargs: {} +name: 147254efe4665420abc4252bd87d170b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/1514d720564018af5e3c6ccd730cbc22/params.yaml b/examples/security/kdd-nsl/output/reports/train/1514d720564018af5e3c6ccd730cbc22/params.yaml new file mode 100644 index 00000000..87fba3eb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/1514d720564018af5e3c6ccd730cbc22/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/adv_predictions.json + adv_probabilities_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/eef762e590551c23bacc3d34f798de78.pkl + params_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/params.yaml + predictions_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/predictions.json + probabilities_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/probabilities.json + score_dict_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/score_dict.json + test_labels_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/test_labels.json + train_labels_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/train_labels.json + model_dir: models + name: 1514d720564018af5e3c6ccd730cbc22 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c436d789ea5cc1202a29cdcdea713b63 + trainer: + kwargs: {} +name: 1514d720564018af5e3c6ccd730cbc22 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/159be054c555f989fe871c8eb4e5d55f/params.yaml b/examples/security/kdd-nsl/output/reports/train/159be054c555f989fe871c8eb4e5d55f/params.yaml new file mode 100644 index 00000000..162b6965 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/159be054c555f989fe871c8eb4e5d55f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/159be054c555f989fe871c8eb4e5d55f/adv_predictions.json + adv_probabilities_file: output/reports/train/159be054c555f989fe871c8eb4e5d55f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/51f8aacedfe18affcd9e3f376eb8eb3e.pkl + params_file: output/reports/train/159be054c555f989fe871c8eb4e5d55f/params.yaml + predictions_file: output/reports/train/159be054c555f989fe871c8eb4e5d55f/predictions.json + probabilities_file: output/reports/train/159be054c555f989fe871c8eb4e5d55f/probabilities.json + score_dict_file: output/reports/train/159be054c555f989fe871c8eb4e5d55f/score_dict.json + test_labels_file: output/reports/train/159be054c555f989fe871c8eb4e5d55f/test_labels.json + train_labels_file: output/reports/train/159be054c555f989fe871c8eb4e5d55f/train_labels.json + model_dir: models + name: 159be054c555f989fe871c8eb4e5d55f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4cc52f522861cae9080628025a2efbd6 + trainer: + kwargs: {} +name: 159be054c555f989fe871c8eb4e5d55f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/params.yaml b/examples/security/kdd-nsl/output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/params.yaml new file mode 100644 index 00000000..45259b48 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/adv_predictions.json + adv_probabilities_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f7d8f54b901cc8d9febfe06fcf240f56.pkl + params_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/params.yaml + predictions_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/predictions.json + probabilities_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/probabilities.json + score_dict_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/score_dict.json + test_labels_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/test_labels.json + train_labels_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/train_labels.json + model_dir: models + name: 18ea77af1a2e14ddf5048a37a9768d3b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 87cbc4e3bc8efcb86b661b3cbe147b22 + trainer: + kwargs: {} +name: 18ea77af1a2e14ddf5048a37a9768d3b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/1a5f6163db9b78ea97d43171083908f5/params.yaml b/examples/security/kdd-nsl/output/reports/train/1a5f6163db9b78ea97d43171083908f5/params.yaml new file mode 100644 index 00000000..e39bd25f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/1a5f6163db9b78ea97d43171083908f5/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/adv_predictions.json + adv_probabilities_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e50835e256d443333227096eea217419.pkl + params_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/params.yaml + predictions_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/predictions.json + probabilities_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/probabilities.json + score_dict_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/score_dict.json + test_labels_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/test_labels.json + train_labels_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/train_labels.json + model_dir: models + name: 1a5f6163db9b78ea97d43171083908f5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ad9bae639eaea56957358f4ac96b72da + trainer: + kwargs: {} +name: 1a5f6163db9b78ea97d43171083908f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/1adb0a1c5c970750c39ab237bba072a6/params.yaml b/examples/security/kdd-nsl/output/reports/train/1adb0a1c5c970750c39ab237bba072a6/params.yaml new file mode 100644 index 00000000..5031801a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/1adb0a1c5c970750c39ab237bba072a6/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1adb0a1c5c970750c39ab237bba072a6/adv_predictions.json + adv_probabilities_file: output/reports/train/1adb0a1c5c970750c39ab237bba072a6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0754d2614037b7cce56f1c2769832d11.pkl + params_file: output/reports/train/1adb0a1c5c970750c39ab237bba072a6/params.yaml + predictions_file: output/reports/train/1adb0a1c5c970750c39ab237bba072a6/predictions.json + probabilities_file: output/reports/train/1adb0a1c5c970750c39ab237bba072a6/probabilities.json + score_dict_file: output/reports/train/1adb0a1c5c970750c39ab237bba072a6/score_dict.json + test_labels_file: output/reports/train/1adb0a1c5c970750c39ab237bba072a6/test_labels.json + train_labels_file: output/reports/train/1adb0a1c5c970750c39ab237bba072a6/train_labels.json + model_dir: models + name: 1adb0a1c5c970750c39ab237bba072a6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 31d8bf410add58be6240597d01829314 + trainer: + kwargs: {} +name: 1adb0a1c5c970750c39ab237bba072a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/1bd1f9c7406f1934e28c75637dde51a7/params.yaml b/examples/security/kdd-nsl/output/reports/train/1bd1f9c7406f1934e28c75637dde51a7/params.yaml new file mode 100644 index 00000000..2da86dba --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/1bd1f9c7406f1934e28c75637dde51a7/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1bd1f9c7406f1934e28c75637dde51a7/adv_predictions.json + adv_probabilities_file: output/reports/train/1bd1f9c7406f1934e28c75637dde51a7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/521a06522ffefdee9a1d165ff902298a.pkl + params_file: output/reports/train/1bd1f9c7406f1934e28c75637dde51a7/params.yaml + predictions_file: output/reports/train/1bd1f9c7406f1934e28c75637dde51a7/predictions.json + probabilities_file: output/reports/train/1bd1f9c7406f1934e28c75637dde51a7/probabilities.json + score_dict_file: output/reports/train/1bd1f9c7406f1934e28c75637dde51a7/score_dict.json + test_labels_file: output/reports/train/1bd1f9c7406f1934e28c75637dde51a7/test_labels.json + train_labels_file: output/reports/train/1bd1f9c7406f1934e28c75637dde51a7/train_labels.json + model_dir: models + name: 1bd1f9c7406f1934e28c75637dde51a7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 56f25e5e673376959b93762ae2979cdc + trainer: + kwargs: {} +name: 1bd1f9c7406f1934e28c75637dde51a7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/1c28d7c2341e7b4e443ae611bc8236e1/params.yaml b/examples/security/kdd-nsl/output/reports/train/1c28d7c2341e7b4e443ae611bc8236e1/params.yaml new file mode 100644 index 00000000..22eacaeb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/1c28d7c2341e7b4e443ae611bc8236e1/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1c28d7c2341e7b4e443ae611bc8236e1/adv_predictions.json + adv_probabilities_file: output/reports/train/1c28d7c2341e7b4e443ae611bc8236e1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/422460f541ee4526e0171edabb964ab6.pkl + params_file: output/reports/train/1c28d7c2341e7b4e443ae611bc8236e1/params.yaml + predictions_file: output/reports/train/1c28d7c2341e7b4e443ae611bc8236e1/predictions.json + probabilities_file: output/reports/train/1c28d7c2341e7b4e443ae611bc8236e1/probabilities.json + score_dict_file: output/reports/train/1c28d7c2341e7b4e443ae611bc8236e1/score_dict.json + test_labels_file: output/reports/train/1c28d7c2341e7b4e443ae611bc8236e1/test_labels.json + train_labels_file: output/reports/train/1c28d7c2341e7b4e443ae611bc8236e1/train_labels.json + model_dir: models + name: 1c28d7c2341e7b4e443ae611bc8236e1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a1d3ccace5402a0c315ce610f9d558e8 + trainer: + kwargs: {} +name: 1c28d7c2341e7b4e443ae611bc8236e1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/params.yaml b/examples/security/kdd-nsl/output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/params.yaml new file mode 100644 index 00000000..6e3cfc62 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/adv_predictions.json + adv_probabilities_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/10507edb9690b8a5ab47de076471881b.pkl + params_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/params.yaml + predictions_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/predictions.json + probabilities_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/probabilities.json + score_dict_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/score_dict.json + test_labels_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/test_labels.json + train_labels_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/train_labels.json + model_dir: models + name: 1c657e2ad2382e6a0a7bfac40929a79f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ecaf66bf55743cd0b4fa76a8b25061d7 + trainer: + kwargs: {} +name: 1c657e2ad2382e6a0a7bfac40929a79f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/1c8b869c60e7c3321c243aa03210c010/params.yaml b/examples/security/kdd-nsl/output/reports/train/1c8b869c60e7c3321c243aa03210c010/params.yaml new file mode 100644 index 00000000..d68f7e9a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/1c8b869c60e7c3321c243aa03210c010/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/adv_predictions.json + adv_probabilities_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9d6e6a64e05d8cb2a553ae6458421538.pkl + params_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/params.yaml + predictions_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/predictions.json + probabilities_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/probabilities.json + score_dict_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/score_dict.json + test_labels_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/test_labels.json + train_labels_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/train_labels.json + model_dir: models + name: 1c8b869c60e7c3321c243aa03210c010 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a3dd108221326a9a20591f595dd050a5 + trainer: + kwargs: {} +name: 1c8b869c60e7c3321c243aa03210c010 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/205d8a308bd1d11757e2bd30e59d1197/params.yaml b/examples/security/kdd-nsl/output/reports/train/205d8a308bd1d11757e2bd30e59d1197/params.yaml new file mode 100644 index 00000000..84be0a48 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/205d8a308bd1d11757e2bd30e59d1197/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/205d8a308bd1d11757e2bd30e59d1197/adv_predictions.json + adv_probabilities_file: output/reports/train/205d8a308bd1d11757e2bd30e59d1197/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2b8b0efe8b3dc895ff74677062bf9ad8.pkl + params_file: output/reports/train/205d8a308bd1d11757e2bd30e59d1197/params.yaml + predictions_file: output/reports/train/205d8a308bd1d11757e2bd30e59d1197/predictions.json + probabilities_file: output/reports/train/205d8a308bd1d11757e2bd30e59d1197/probabilities.json + score_dict_file: output/reports/train/205d8a308bd1d11757e2bd30e59d1197/score_dict.json + test_labels_file: output/reports/train/205d8a308bd1d11757e2bd30e59d1197/test_labels.json + train_labels_file: output/reports/train/205d8a308bd1d11757e2bd30e59d1197/train_labels.json + model_dir: models + name: 205d8a308bd1d11757e2bd30e59d1197 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fdb0bf2f4b0379e838ff4632271aeddf + trainer: + kwargs: {} +name: 205d8a308bd1d11757e2bd30e59d1197 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/20cfddabd539ef218bf9ab0359e446e7/params.yaml b/examples/security/kdd-nsl/output/reports/train/20cfddabd539ef218bf9ab0359e446e7/params.yaml new file mode 100644 index 00000000..af0fb784 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/20cfddabd539ef218bf9ab0359e446e7/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/adv_predictions.json + adv_probabilities_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/11ded58c91e5638cf7a4c0376c497b6b.pkl + params_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/params.yaml + predictions_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/predictions.json + probabilities_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/probabilities.json + score_dict_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/score_dict.json + test_labels_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/test_labels.json + train_labels_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/train_labels.json + model_dir: models + name: 20cfddabd539ef218bf9ab0359e446e7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cf57ddd1d132de91efd1df7267dae6b2 + trainer: + kwargs: {} +name: 20cfddabd539ef218bf9ab0359e446e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/22032ebace8e257964123b1c5606e468/params.yaml b/examples/security/kdd-nsl/output/reports/train/22032ebace8e257964123b1c5606e468/params.yaml new file mode 100644 index 00000000..5d2181a4 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/22032ebace8e257964123b1c5606e468/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/22032ebace8e257964123b1c5606e468/adv_predictions.json + adv_probabilities_file: output/reports/train/22032ebace8e257964123b1c5606e468/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/219840e8657ab386b3dd0b99cb5c3b01.pkl + params_file: output/reports/train/22032ebace8e257964123b1c5606e468/params.yaml + predictions_file: output/reports/train/22032ebace8e257964123b1c5606e468/predictions.json + probabilities_file: output/reports/train/22032ebace8e257964123b1c5606e468/probabilities.json + score_dict_file: output/reports/train/22032ebace8e257964123b1c5606e468/score_dict.json + test_labels_file: output/reports/train/22032ebace8e257964123b1c5606e468/test_labels.json + train_labels_file: output/reports/train/22032ebace8e257964123b1c5606e468/train_labels.json + model_dir: models + name: 22032ebace8e257964123b1c5606e468 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 89e4128b2be0e7530a38c062dd202cb3 + trainer: + kwargs: {} +name: 22032ebace8e257964123b1c5606e468 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/22d9dd9a0f3f761fcb6bae7eefcd02d3/params.yaml b/examples/security/kdd-nsl/output/reports/train/22d9dd9a0f3f761fcb6bae7eefcd02d3/params.yaml new file mode 100644 index 00000000..135f0186 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/22d9dd9a0f3f761fcb6bae7eefcd02d3/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/22d9dd9a0f3f761fcb6bae7eefcd02d3/adv_predictions.json + adv_probabilities_file: output/reports/train/22d9dd9a0f3f761fcb6bae7eefcd02d3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9aaf5b0975ba06c87146c881afeb178d.pkl + params_file: output/reports/train/22d9dd9a0f3f761fcb6bae7eefcd02d3/params.yaml + predictions_file: output/reports/train/22d9dd9a0f3f761fcb6bae7eefcd02d3/predictions.json + probabilities_file: output/reports/train/22d9dd9a0f3f761fcb6bae7eefcd02d3/probabilities.json + score_dict_file: output/reports/train/22d9dd9a0f3f761fcb6bae7eefcd02d3/score_dict.json + test_labels_file: output/reports/train/22d9dd9a0f3f761fcb6bae7eefcd02d3/test_labels.json + train_labels_file: output/reports/train/22d9dd9a0f3f761fcb6bae7eefcd02d3/train_labels.json + model_dir: models + name: 22d9dd9a0f3f761fcb6bae7eefcd02d3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d029c3aa35c9632393567123681716aa + trainer: + kwargs: {} +name: 22d9dd9a0f3f761fcb6bae7eefcd02d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/23878a268b692d1c02041cd32f32046d/params.yaml b/examples/security/kdd-nsl/output/reports/train/23878a268b692d1c02041cd32f32046d/params.yaml new file mode 100644 index 00000000..2df05e82 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/23878a268b692d1c02041cd32f32046d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/23878a268b692d1c02041cd32f32046d/adv_predictions.json + adv_probabilities_file: output/reports/train/23878a268b692d1c02041cd32f32046d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bcd96e0c9cd9d4d41051087fb4a66be7.pkl + params_file: output/reports/train/23878a268b692d1c02041cd32f32046d/params.yaml + predictions_file: output/reports/train/23878a268b692d1c02041cd32f32046d/predictions.json + probabilities_file: output/reports/train/23878a268b692d1c02041cd32f32046d/probabilities.json + score_dict_file: output/reports/train/23878a268b692d1c02041cd32f32046d/score_dict.json + test_labels_file: output/reports/train/23878a268b692d1c02041cd32f32046d/test_labels.json + train_labels_file: output/reports/train/23878a268b692d1c02041cd32f32046d/train_labels.json + model_dir: models + name: 23878a268b692d1c02041cd32f32046d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: af308e5290c6ac5751b160d3b673ce06 + trainer: + kwargs: {} +name: 23878a268b692d1c02041cd32f32046d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/238a4f033c631da932fdf69268851850/params.yaml b/examples/security/kdd-nsl/output/reports/train/238a4f033c631da932fdf69268851850/params.yaml new file mode 100644 index 00000000..d42e6182 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/238a4f033c631da932fdf69268851850/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/238a4f033c631da932fdf69268851850/adv_predictions.json + adv_probabilities_file: output/reports/train/238a4f033c631da932fdf69268851850/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/301b53e58f8903b008830f37fdb97088.pkl + params_file: output/reports/train/238a4f033c631da932fdf69268851850/params.yaml + predictions_file: output/reports/train/238a4f033c631da932fdf69268851850/predictions.json + probabilities_file: output/reports/train/238a4f033c631da932fdf69268851850/probabilities.json + score_dict_file: output/reports/train/238a4f033c631da932fdf69268851850/score_dict.json + test_labels_file: output/reports/train/238a4f033c631da932fdf69268851850/test_labels.json + train_labels_file: output/reports/train/238a4f033c631da932fdf69268851850/train_labels.json + model_dir: models + name: 238a4f033c631da932fdf69268851850 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b18354fde9bbb121ddf9af63adee7cd9 + trainer: + kwargs: {} +name: 238a4f033c631da932fdf69268851850 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/24abc428c50a102d4fd52e148ee26d78/params.yaml b/examples/security/kdd-nsl/output/reports/train/24abc428c50a102d4fd52e148ee26d78/params.yaml new file mode 100644 index 00000000..287378e2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/24abc428c50a102d4fd52e148ee26d78/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/24abc428c50a102d4fd52e148ee26d78/adv_predictions.json + adv_probabilities_file: output/reports/train/24abc428c50a102d4fd52e148ee26d78/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/4be9dd8ba7498c1dc6e0a24668aab0c7.pkl + params_file: output/reports/train/24abc428c50a102d4fd52e148ee26d78/params.yaml + predictions_file: output/reports/train/24abc428c50a102d4fd52e148ee26d78/predictions.json + probabilities_file: output/reports/train/24abc428c50a102d4fd52e148ee26d78/probabilities.json + score_dict_file: output/reports/train/24abc428c50a102d4fd52e148ee26d78/score_dict.json + test_labels_file: output/reports/train/24abc428c50a102d4fd52e148ee26d78/test_labels.json + train_labels_file: output/reports/train/24abc428c50a102d4fd52e148ee26d78/train_labels.json + model_dir: models + name: 24abc428c50a102d4fd52e148ee26d78 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 598d38bccc36a4b276b59ea6b043a256 + trainer: + kwargs: {} +name: 24abc428c50a102d4fd52e148ee26d78 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/params.yaml b/examples/security/kdd-nsl/output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/params.yaml new file mode 100644 index 00000000..40cb5dd7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/adv_predictions.json + adv_probabilities_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/adc10b7bf3d5b933316130fbf72efa9d.pkl + params_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/params.yaml + predictions_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/predictions.json + probabilities_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/probabilities.json + score_dict_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/score_dict.json + test_labels_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/test_labels.json + train_labels_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/train_labels.json + model_dir: models + name: 26855e98dcd2b3fe07fad9d2b9bc6809 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 678d9e5aa40a04b090fc35fff9f9d3d1 + trainer: + kwargs: {} +name: 26855e98dcd2b3fe07fad9d2b9bc6809 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/27d7246fe5b1a304955421d778bdecf9/params.yaml b/examples/security/kdd-nsl/output/reports/train/27d7246fe5b1a304955421d778bdecf9/params.yaml new file mode 100644 index 00000000..e54f2f12 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/27d7246fe5b1a304955421d778bdecf9/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/27d7246fe5b1a304955421d778bdecf9/adv_predictions.json + adv_probabilities_file: output/reports/train/27d7246fe5b1a304955421d778bdecf9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/29b3aef655cc58fd61ea1d3672f5fbc7.pkl + params_file: output/reports/train/27d7246fe5b1a304955421d778bdecf9/params.yaml + predictions_file: output/reports/train/27d7246fe5b1a304955421d778bdecf9/predictions.json + probabilities_file: output/reports/train/27d7246fe5b1a304955421d778bdecf9/probabilities.json + score_dict_file: output/reports/train/27d7246fe5b1a304955421d778bdecf9/score_dict.json + test_labels_file: output/reports/train/27d7246fe5b1a304955421d778bdecf9/test_labels.json + train_labels_file: output/reports/train/27d7246fe5b1a304955421d778bdecf9/train_labels.json + model_dir: models + name: 27d7246fe5b1a304955421d778bdecf9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4353d17c5ed1d37dd5a795d08c962d3d + trainer: + kwargs: {} +name: 27d7246fe5b1a304955421d778bdecf9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/params.yaml b/examples/security/kdd-nsl/output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/params.yaml new file mode 100644 index 00000000..36c91106 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/adv_predictions.json + adv_probabilities_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/34392cd7c05a4952636031d4e6f95f53.pkl + params_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/params.yaml + predictions_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/predictions.json + probabilities_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/probabilities.json + score_dict_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/score_dict.json + test_labels_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/test_labels.json + train_labels_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/train_labels.json + model_dir: models + name: 28d2b1e6a076b09265cbc6aaf36576f7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5a69109e0d33dc05cd08763922cb4ed7 + trainer: + kwargs: {} +name: 28d2b1e6a076b09265cbc6aaf36576f7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/2951bfff0c883560a02f3a378c9f80b5/params.yaml b/examples/security/kdd-nsl/output/reports/train/2951bfff0c883560a02f3a378c9f80b5/params.yaml new file mode 100644 index 00000000..f3f08621 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/2951bfff0c883560a02f3a378c9f80b5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2951bfff0c883560a02f3a378c9f80b5/adv_predictions.json + adv_probabilities_file: output/reports/train/2951bfff0c883560a02f3a378c9f80b5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3542aafdbd6c06efb5c2a23197ad7033.pkl + params_file: output/reports/train/2951bfff0c883560a02f3a378c9f80b5/params.yaml + predictions_file: output/reports/train/2951bfff0c883560a02f3a378c9f80b5/predictions.json + probabilities_file: output/reports/train/2951bfff0c883560a02f3a378c9f80b5/probabilities.json + score_dict_file: output/reports/train/2951bfff0c883560a02f3a378c9f80b5/score_dict.json + test_labels_file: output/reports/train/2951bfff0c883560a02f3a378c9f80b5/test_labels.json + train_labels_file: output/reports/train/2951bfff0c883560a02f3a378c9f80b5/train_labels.json + model_dir: models + name: 2951bfff0c883560a02f3a378c9f80b5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8af5ff5a4d40bdc4b35c68f40393323c + trainer: + kwargs: {} +name: 2951bfff0c883560a02f3a378c9f80b5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/params.yaml b/examples/security/kdd-nsl/output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/params.yaml new file mode 100644 index 00000000..2ed4093c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/adv_predictions.json + adv_probabilities_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/10fd48e70d83b435a335f410e7ec9370.pkl + params_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/params.yaml + predictions_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/predictions.json + probabilities_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/probabilities.json + score_dict_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/score_dict.json + test_labels_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/test_labels.json + train_labels_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/train_labels.json + model_dir: models + name: 2d62a4dec4eaee1d6a83958c9f33c45e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6f9a605b303c3727fbc43feba149f9a1 + trainer: + kwargs: {} +name: 2d62a4dec4eaee1d6a83958c9f33c45e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/params.yaml b/examples/security/kdd-nsl/output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/params.yaml new file mode 100644 index 00000000..88fb6ff7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/adv_predictions.json + adv_probabilities_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9f2b218bec18da0eba413c01f4d7511d.pkl + params_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/params.yaml + predictions_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/predictions.json + probabilities_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/probabilities.json + score_dict_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/score_dict.json + test_labels_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/test_labels.json + train_labels_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/train_labels.json + model_dir: models + name: 2d79b8d1b8d2de7cfab3005e6f3bfc00 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 76ca09d6d60d2f10fe23d8622d8b453d + trainer: + kwargs: {} +name: 2d79b8d1b8d2de7cfab3005e6f3bfc00 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/params.yaml b/examples/security/kdd-nsl/output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/params.yaml new file mode 100644 index 00000000..f980d0d2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/adv_predictions.json + adv_probabilities_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b2e232e2d28d1b8aa418761172d5e4cb.pkl + params_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/params.yaml + predictions_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/predictions.json + probabilities_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/probabilities.json + score_dict_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/score_dict.json + test_labels_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/test_labels.json + train_labels_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/train_labels.json + model_dir: models + name: 2ebf71f050ecf6ccb53977ad5d490ea5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bae527781ce5b10d74e7efef4d14e881 + trainer: + kwargs: {} +name: 2ebf71f050ecf6ccb53977ad5d490ea5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/params.yaml b/examples/security/kdd-nsl/output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/params.yaml new file mode 100644 index 00000000..77c08478 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/adv_predictions.json + adv_probabilities_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7b68ba783521ebd5a43586a0565b6c20.pkl + params_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/params.yaml + predictions_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/predictions.json + probabilities_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/probabilities.json + score_dict_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/score_dict.json + test_labels_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/test_labels.json + train_labels_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/train_labels.json + model_dir: models + name: 2edf7fbffb2095b81593cd881e3d55f4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2c6708e1faf264da72e7eab15c24e599 + trainer: + kwargs: {} +name: 2edf7fbffb2095b81593cd881e3d55f4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/2f151c43b96ed42acd34d949654ad38e/params.yaml b/examples/security/kdd-nsl/output/reports/train/2f151c43b96ed42acd34d949654ad38e/params.yaml new file mode 100644 index 00000000..1af4850a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/2f151c43b96ed42acd34d949654ad38e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/adv_predictions.json + adv_probabilities_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2403d7220f06bc1d92c6dd53a20908a6.pkl + params_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/params.yaml + predictions_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/predictions.json + probabilities_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/probabilities.json + score_dict_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/score_dict.json + test_labels_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/test_labels.json + train_labels_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/train_labels.json + model_dir: models + name: 2f151c43b96ed42acd34d949654ad38e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e95e21ef2b149b76d57a512186b584e1 + trainer: + kwargs: {} +name: 2f151c43b96ed42acd34d949654ad38e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/2f455c2a58c918d7fbb7989194473ec5/params.yaml b/examples/security/kdd-nsl/output/reports/train/2f455c2a58c918d7fbb7989194473ec5/params.yaml new file mode 100644 index 00000000..418910ab --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/2f455c2a58c918d7fbb7989194473ec5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2f455c2a58c918d7fbb7989194473ec5/adv_predictions.json + adv_probabilities_file: output/reports/train/2f455c2a58c918d7fbb7989194473ec5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/370a5da8f03744761367b468c49f82b7.pkl + params_file: output/reports/train/2f455c2a58c918d7fbb7989194473ec5/params.yaml + predictions_file: output/reports/train/2f455c2a58c918d7fbb7989194473ec5/predictions.json + probabilities_file: output/reports/train/2f455c2a58c918d7fbb7989194473ec5/probabilities.json + score_dict_file: output/reports/train/2f455c2a58c918d7fbb7989194473ec5/score_dict.json + test_labels_file: output/reports/train/2f455c2a58c918d7fbb7989194473ec5/test_labels.json + train_labels_file: output/reports/train/2f455c2a58c918d7fbb7989194473ec5/train_labels.json + model_dir: models + name: 2f455c2a58c918d7fbb7989194473ec5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 242fc3cdeecbeb66bafb4803c94cd5f2 + trainer: + kwargs: {} +name: 2f455c2a58c918d7fbb7989194473ec5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/2faf21648c8c259b7c15a2a402ec2f00/params.yaml b/examples/security/kdd-nsl/output/reports/train/2faf21648c8c259b7c15a2a402ec2f00/params.yaml new file mode 100644 index 00000000..796efbff --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/2faf21648c8c259b7c15a2a402ec2f00/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2faf21648c8c259b7c15a2a402ec2f00/adv_predictions.json + adv_probabilities_file: output/reports/train/2faf21648c8c259b7c15a2a402ec2f00/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5f044ae68e41e4bb193c55ba08729524.pkl + params_file: output/reports/train/2faf21648c8c259b7c15a2a402ec2f00/params.yaml + predictions_file: output/reports/train/2faf21648c8c259b7c15a2a402ec2f00/predictions.json + probabilities_file: output/reports/train/2faf21648c8c259b7c15a2a402ec2f00/probabilities.json + score_dict_file: output/reports/train/2faf21648c8c259b7c15a2a402ec2f00/score_dict.json + test_labels_file: output/reports/train/2faf21648c8c259b7c15a2a402ec2f00/test_labels.json + train_labels_file: output/reports/train/2faf21648c8c259b7c15a2a402ec2f00/train_labels.json + model_dir: models + name: 2faf21648c8c259b7c15a2a402ec2f00 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ed342bbebf11cf0543ca89fcc4cc92af + trainer: + kwargs: {} +name: 2faf21648c8c259b7c15a2a402ec2f00 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/300818f45fdbb759c52b20caab7efa6d/params.yaml b/examples/security/kdd-nsl/output/reports/train/300818f45fdbb759c52b20caab7efa6d/params.yaml new file mode 100644 index 00000000..b08e4c9d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/300818f45fdbb759c52b20caab7efa6d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/adv_predictions.json + adv_probabilities_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/da7219bc65c766746afd0529b73bb595.pkl + params_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/params.yaml + predictions_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/predictions.json + probabilities_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/probabilities.json + score_dict_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/score_dict.json + test_labels_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/test_labels.json + train_labels_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/train_labels.json + model_dir: models + name: 300818f45fdbb759c52b20caab7efa6d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c9a716f5efbea2b07117cfe0eba719d0 + trainer: + kwargs: {} +name: 300818f45fdbb759c52b20caab7efa6d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/30a16a14336e218ab5d2a77537183a89/params.yaml b/examples/security/kdd-nsl/output/reports/train/30a16a14336e218ab5d2a77537183a89/params.yaml new file mode 100644 index 00000000..48fc8a7c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/30a16a14336e218ab5d2a77537183a89/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/adv_predictions.json + adv_probabilities_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0b5f523f1ded2d07be700dab2ae695d2.pkl + params_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/params.yaml + predictions_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/predictions.json + probabilities_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/probabilities.json + score_dict_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/score_dict.json + test_labels_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/test_labels.json + train_labels_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/train_labels.json + model_dir: models + name: 30a16a14336e218ab5d2a77537183a89 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 92afcf079ee83598b56f00569ea6a591 + trainer: + kwargs: {} +name: 30a16a14336e218ab5d2a77537183a89 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/31f202d2104091f32ae68dffc326a15d/params.yaml b/examples/security/kdd-nsl/output/reports/train/31f202d2104091f32ae68dffc326a15d/params.yaml new file mode 100644 index 00000000..27c42ab7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/31f202d2104091f32ae68dffc326a15d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/31f202d2104091f32ae68dffc326a15d/adv_predictions.json + adv_probabilities_file: output/reports/train/31f202d2104091f32ae68dffc326a15d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1528dfc67a62b8d70dce5fa59b7fc121.pkl + params_file: output/reports/train/31f202d2104091f32ae68dffc326a15d/params.yaml + predictions_file: output/reports/train/31f202d2104091f32ae68dffc326a15d/predictions.json + probabilities_file: output/reports/train/31f202d2104091f32ae68dffc326a15d/probabilities.json + score_dict_file: output/reports/train/31f202d2104091f32ae68dffc326a15d/score_dict.json + test_labels_file: output/reports/train/31f202d2104091f32ae68dffc326a15d/test_labels.json + train_labels_file: output/reports/train/31f202d2104091f32ae68dffc326a15d/train_labels.json + model_dir: models + name: 31f202d2104091f32ae68dffc326a15d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4e76bda8e9e53042cd593b3815946008 + trainer: + kwargs: {} +name: 31f202d2104091f32ae68dffc326a15d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/3367cd5393fa9341b73f921829d20f2c/params.yaml b/examples/security/kdd-nsl/output/reports/train/3367cd5393fa9341b73f921829d20f2c/params.yaml new file mode 100644 index 00000000..7c39bc93 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/3367cd5393fa9341b73f921829d20f2c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/adv_predictions.json + adv_probabilities_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9ff854beebbb32ab9523d8c6c48ac1d0.pkl + params_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/params.yaml + predictions_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/predictions.json + probabilities_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/probabilities.json + score_dict_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/score_dict.json + test_labels_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/test_labels.json + train_labels_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/train_labels.json + model_dir: models + name: 3367cd5393fa9341b73f921829d20f2c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e436c91a2baa3c17bf9de14d3cdffedf + trainer: + kwargs: {} +name: 3367cd5393fa9341b73f921829d20f2c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/params.yaml b/examples/security/kdd-nsl/output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/params.yaml new file mode 100644 index 00000000..54adab1a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/adv_predictions.json + adv_probabilities_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7eb569ee99c3620bdb3e0ac93e1ee0ea.pkl + params_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/params.yaml + predictions_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/predictions.json + probabilities_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/probabilities.json + score_dict_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/score_dict.json + test_labels_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/test_labels.json + train_labels_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/train_labels.json + model_dir: models + name: 33d7d5e6f0770f6266e4d9cc7291a3a9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ef374abcda025fca916552f8d2af690a + trainer: + kwargs: {} +name: 33d7d5e6f0770f6266e4d9cc7291a3a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/params.yaml b/examples/security/kdd-nsl/output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/params.yaml new file mode 100644 index 00000000..bc0c4a19 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/adv_predictions.json + adv_probabilities_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/90d2b4942579dc38bb8d89e9e04139da.pkl + params_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/params.yaml + predictions_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/predictions.json + probabilities_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/probabilities.json + score_dict_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/score_dict.json + test_labels_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/test_labels.json + train_labels_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/train_labels.json + model_dir: models + name: 34c4d88419d4e5fdb7a86356bd9f545e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8b960960862e39839a2c04542d91754e + trainer: + kwargs: {} +name: 34c4d88419d4e5fdb7a86356bd9f545e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/34e0f86886978f933a03736e0435ff4b/params.yaml b/examples/security/kdd-nsl/output/reports/train/34e0f86886978f933a03736e0435ff4b/params.yaml new file mode 100644 index 00000000..f0f68ed0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/34e0f86886978f933a03736e0435ff4b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/34e0f86886978f933a03736e0435ff4b/adv_predictions.json + adv_probabilities_file: output/reports/train/34e0f86886978f933a03736e0435ff4b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/dd0f30b5326564c4c312fc9fca914624.pkl + params_file: output/reports/train/34e0f86886978f933a03736e0435ff4b/params.yaml + predictions_file: output/reports/train/34e0f86886978f933a03736e0435ff4b/predictions.json + probabilities_file: output/reports/train/34e0f86886978f933a03736e0435ff4b/probabilities.json + score_dict_file: output/reports/train/34e0f86886978f933a03736e0435ff4b/score_dict.json + test_labels_file: output/reports/train/34e0f86886978f933a03736e0435ff4b/test_labels.json + train_labels_file: output/reports/train/34e0f86886978f933a03736e0435ff4b/train_labels.json + model_dir: models + name: 34e0f86886978f933a03736e0435ff4b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2d1565ec46adf5f28ce2f70cb04f4677 + trainer: + kwargs: {} +name: 34e0f86886978f933a03736e0435ff4b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/3509add9839f7d882e4a7660560922a6/params.yaml b/examples/security/kdd-nsl/output/reports/train/3509add9839f7d882e4a7660560922a6/params.yaml new file mode 100644 index 00000000..cdbfd588 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/3509add9839f7d882e4a7660560922a6/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3509add9839f7d882e4a7660560922a6/adv_predictions.json + adv_probabilities_file: output/reports/train/3509add9839f7d882e4a7660560922a6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ef0a2f08c2112ffec0c54e39024ca4d5.pkl + params_file: output/reports/train/3509add9839f7d882e4a7660560922a6/params.yaml + predictions_file: output/reports/train/3509add9839f7d882e4a7660560922a6/predictions.json + probabilities_file: output/reports/train/3509add9839f7d882e4a7660560922a6/probabilities.json + score_dict_file: output/reports/train/3509add9839f7d882e4a7660560922a6/score_dict.json + test_labels_file: output/reports/train/3509add9839f7d882e4a7660560922a6/test_labels.json + train_labels_file: output/reports/train/3509add9839f7d882e4a7660560922a6/train_labels.json + model_dir: models + name: 3509add9839f7d882e4a7660560922a6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a3a311fd5b5d543bbc8946edb8a9f6fd + trainer: + kwargs: {} +name: 3509add9839f7d882e4a7660560922a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/36ca3736c72fb2d9596bcb50fc193986/params.yaml b/examples/security/kdd-nsl/output/reports/train/36ca3736c72fb2d9596bcb50fc193986/params.yaml new file mode 100644 index 00000000..daffa4df --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/36ca3736c72fb2d9596bcb50fc193986/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/adv_predictions.json + adv_probabilities_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1257b7ae0100b0c769e51f2378b370dd.pkl + params_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/params.yaml + predictions_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/predictions.json + probabilities_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/probabilities.json + score_dict_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/score_dict.json + test_labels_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/test_labels.json + train_labels_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/train_labels.json + model_dir: models + name: 36ca3736c72fb2d9596bcb50fc193986 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 16392046b7322ad33e80d9685e4349aa + trainer: + kwargs: {} +name: 36ca3736c72fb2d9596bcb50fc193986 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/params.yaml b/examples/security/kdd-nsl/output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/params.yaml new file mode 100644 index 00000000..94438948 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/adv_predictions.json + adv_probabilities_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1c9d1794f00f0c8f0fff10b6b2296a81.pkl + params_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/params.yaml + predictions_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/predictions.json + probabilities_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/probabilities.json + score_dict_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/score_dict.json + test_labels_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/test_labels.json + train_labels_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/train_labels.json + model_dir: models + name: 37d8f3738fc2d4506d4879bfab32b16e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 85f2dc5f2e25567eacdf5da05cfffcff + trainer: + kwargs: {} +name: 37d8f3738fc2d4506d4879bfab32b16e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/params.yaml b/examples/security/kdd-nsl/output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/params.yaml new file mode 100644 index 00000000..3287d1f0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/adv_predictions.json + adv_probabilities_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/982495bc6ec42cd51fd441cbc87c5b31.pkl + params_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/params.yaml + predictions_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/predictions.json + probabilities_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/probabilities.json + score_dict_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/score_dict.json + test_labels_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/test_labels.json + train_labels_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/train_labels.json + model_dir: models + name: 38cbadd5bd0f0ed9075db54e8fe29549 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c6b08258ec4dd4570e0482eeb5135e98 + trainer: + kwargs: {} +name: 38cbadd5bd0f0ed9075db54e8fe29549 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/3afeab54c9fe76fa72a91597d9fa25c9/params.yaml b/examples/security/kdd-nsl/output/reports/train/3afeab54c9fe76fa72a91597d9fa25c9/params.yaml new file mode 100644 index 00000000..36fd2f9f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/3afeab54c9fe76fa72a91597d9fa25c9/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3afeab54c9fe76fa72a91597d9fa25c9/adv_predictions.json + adv_probabilities_file: output/reports/train/3afeab54c9fe76fa72a91597d9fa25c9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/a48de948047ac0b500ec46dc101a78ce.pkl + params_file: output/reports/train/3afeab54c9fe76fa72a91597d9fa25c9/params.yaml + predictions_file: output/reports/train/3afeab54c9fe76fa72a91597d9fa25c9/predictions.json + probabilities_file: output/reports/train/3afeab54c9fe76fa72a91597d9fa25c9/probabilities.json + score_dict_file: output/reports/train/3afeab54c9fe76fa72a91597d9fa25c9/score_dict.json + test_labels_file: output/reports/train/3afeab54c9fe76fa72a91597d9fa25c9/test_labels.json + train_labels_file: output/reports/train/3afeab54c9fe76fa72a91597d9fa25c9/train_labels.json + model_dir: models + name: 3afeab54c9fe76fa72a91597d9fa25c9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 03a2d9cd7fe3f2f6c33421107a8c5890 + trainer: + kwargs: {} +name: 3afeab54c9fe76fa72a91597d9fa25c9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/3b18e3df106a88d9964ada47862d04dd/params.yaml b/examples/security/kdd-nsl/output/reports/train/3b18e3df106a88d9964ada47862d04dd/params.yaml new file mode 100644 index 00000000..6f6e25cf --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/3b18e3df106a88d9964ada47862d04dd/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/adv_predictions.json + adv_probabilities_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d5d1b1eadf2da74309e7bd9b776f5065.pkl + params_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/params.yaml + predictions_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/predictions.json + probabilities_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/probabilities.json + score_dict_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/score_dict.json + test_labels_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/test_labels.json + train_labels_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/train_labels.json + model_dir: models + name: 3b18e3df106a88d9964ada47862d04dd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 80e33669706b0d566b0fa09d87676451 + trainer: + kwargs: {} +name: 3b18e3df106a88d9964ada47862d04dd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/params.yaml b/examples/security/kdd-nsl/output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/params.yaml new file mode 100644 index 00000000..d251de5f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/adv_predictions.json + adv_probabilities_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/params.yaml + predictions_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/predictions.json + probabilities_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/probabilities.json + score_dict_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/score_dict.json + test_labels_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/test_labels.json + train_labels_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/train_labels.json + model_dir: models + name: 3bbfcc937eb9bb07e91608bf6787b5a3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 3bbfcc937eb9bb07e91608bf6787b5a3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/3cfa4f658adfad11f1f08936c08221cc/params.yaml b/examples/security/kdd-nsl/output/reports/train/3cfa4f658adfad11f1f08936c08221cc/params.yaml new file mode 100644 index 00000000..ee06ae0f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/3cfa4f658adfad11f1f08936c08221cc/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3cfa4f658adfad11f1f08936c08221cc/adv_predictions.json + adv_probabilities_file: output/reports/train/3cfa4f658adfad11f1f08936c08221cc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3868c16e2d5808a364a0788bf2eaee2d.pkl + params_file: output/reports/train/3cfa4f658adfad11f1f08936c08221cc/params.yaml + predictions_file: output/reports/train/3cfa4f658adfad11f1f08936c08221cc/predictions.json + probabilities_file: output/reports/train/3cfa4f658adfad11f1f08936c08221cc/probabilities.json + score_dict_file: output/reports/train/3cfa4f658adfad11f1f08936c08221cc/score_dict.json + test_labels_file: output/reports/train/3cfa4f658adfad11f1f08936c08221cc/test_labels.json + train_labels_file: output/reports/train/3cfa4f658adfad11f1f08936c08221cc/train_labels.json + model_dir: models + name: 3cfa4f658adfad11f1f08936c08221cc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b4598e3aee42ae746e5770f4ee5afe8 + trainer: + kwargs: {} +name: 3cfa4f658adfad11f1f08936c08221cc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/params.yaml b/examples/security/kdd-nsl/output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/params.yaml new file mode 100644 index 00000000..d58e4fac --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/adv_predictions.json + adv_probabilities_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3fd6bfb90887de957edc0cd9eac71e41.pkl + params_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/params.yaml + predictions_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/predictions.json + probabilities_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/probabilities.json + score_dict_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/score_dict.json + test_labels_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/test_labels.json + train_labels_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/train_labels.json + model_dir: models + name: 3e0ea6dda2c6c2e32d987b38d4b119fb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 11f87b6c0031745c1f1a47dfbc566801 + trainer: + kwargs: {} +name: 3e0ea6dda2c6c2e32d987b38d4b119fb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/4207f1106f1bc54828ff279ffb6ef085/params.yaml b/examples/security/kdd-nsl/output/reports/train/4207f1106f1bc54828ff279ffb6ef085/params.yaml new file mode 100644 index 00000000..c3d6c79b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/4207f1106f1bc54828ff279ffb6ef085/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/adv_predictions.json + adv_probabilities_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f89416975555df8ce1be7600e51f1e5e.pkl + params_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/params.yaml + predictions_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/predictions.json + probabilities_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/probabilities.json + score_dict_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/score_dict.json + test_labels_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/test_labels.json + train_labels_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/train_labels.json + model_dir: models + name: 4207f1106f1bc54828ff279ffb6ef085 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f50dbdc91553ed54ffdbc56d000f88ec + trainer: + kwargs: {} +name: 4207f1106f1bc54828ff279ffb6ef085 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/427ca300d1efd15d2b264a2999429d28/params.yaml b/examples/security/kdd-nsl/output/reports/train/427ca300d1efd15d2b264a2999429d28/params.yaml new file mode 100644 index 00000000..8ce596c9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/427ca300d1efd15d2b264a2999429d28/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/adv_predictions.json + adv_probabilities_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d834f00d680f9ec0ce4e5d3061f1b6c6.pkl + params_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/params.yaml + predictions_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/predictions.json + probabilities_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/probabilities.json + score_dict_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/score_dict.json + test_labels_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/test_labels.json + train_labels_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/train_labels.json + model_dir: models + name: 427ca300d1efd15d2b264a2999429d28 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aec915cac7c5aa9d007f4909127040d2 + trainer: + kwargs: {} +name: 427ca300d1efd15d2b264a2999429d28 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/42b8305345b144d4bfd886e273ebece8/params.yaml b/examples/security/kdd-nsl/output/reports/train/42b8305345b144d4bfd886e273ebece8/params.yaml new file mode 100644 index 00000000..d7c31c44 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/42b8305345b144d4bfd886e273ebece8/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/adv_predictions.json + adv_probabilities_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d18617117e734742bf5dd9b1b4ec1cb0.pkl + params_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/params.yaml + predictions_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/predictions.json + probabilities_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/probabilities.json + score_dict_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/score_dict.json + test_labels_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/test_labels.json + train_labels_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/train_labels.json + model_dir: models + name: 42b8305345b144d4bfd886e273ebece8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eafc1c49cd7737d2fe5d56b1b6b326fc + trainer: + kwargs: {} +name: 42b8305345b144d4bfd886e273ebece8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/445c69c2cfa70a63fcca7812c72010ff/params.yaml b/examples/security/kdd-nsl/output/reports/train/445c69c2cfa70a63fcca7812c72010ff/params.yaml new file mode 100644 index 00000000..0e95103f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/445c69c2cfa70a63fcca7812c72010ff/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/adv_predictions.json + adv_probabilities_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/cadef0d5cad7400e0247fb362eb13c68.pkl + params_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/params.yaml + predictions_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/predictions.json + probabilities_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/probabilities.json + score_dict_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/score_dict.json + test_labels_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/test_labels.json + train_labels_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/train_labels.json + model_dir: models + name: 445c69c2cfa70a63fcca7812c72010ff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6cd9af412b716d0ecc9e3261f08dc769 + trainer: + kwargs: {} +name: 445c69c2cfa70a63fcca7812c72010ff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/45663a2197f7adcee1eed3f885fcb723/params.yaml b/examples/security/kdd-nsl/output/reports/train/45663a2197f7adcee1eed3f885fcb723/params.yaml new file mode 100644 index 00000000..ddf35129 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/45663a2197f7adcee1eed3f885fcb723/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/adv_predictions.json + adv_probabilities_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/df380ce86fe6c64d1e134374efef84d8.pkl + params_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/params.yaml + predictions_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/predictions.json + probabilities_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/probabilities.json + score_dict_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/score_dict.json + test_labels_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/test_labels.json + train_labels_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/train_labels.json + model_dir: models + name: 45663a2197f7adcee1eed3f885fcb723 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ca5f122887eb77c223f0d90525c9311b + trainer: + kwargs: {} +name: 45663a2197f7adcee1eed3f885fcb723 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/45707959a63165fa79b3235e6ff5679a/params.yaml b/examples/security/kdd-nsl/output/reports/train/45707959a63165fa79b3235e6ff5679a/params.yaml new file mode 100644 index 00000000..02cbd9f2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/45707959a63165fa79b3235e6ff5679a/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/45707959a63165fa79b3235e6ff5679a/adv_predictions.json + adv_probabilities_file: output/reports/train/45707959a63165fa79b3235e6ff5679a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1faccca4705394cf0743c7bed2f883aa.pkl + params_file: output/reports/train/45707959a63165fa79b3235e6ff5679a/params.yaml + predictions_file: output/reports/train/45707959a63165fa79b3235e6ff5679a/predictions.json + probabilities_file: output/reports/train/45707959a63165fa79b3235e6ff5679a/probabilities.json + score_dict_file: output/reports/train/45707959a63165fa79b3235e6ff5679a/score_dict.json + test_labels_file: output/reports/train/45707959a63165fa79b3235e6ff5679a/test_labels.json + train_labels_file: output/reports/train/45707959a63165fa79b3235e6ff5679a/train_labels.json + model_dir: models + name: 45707959a63165fa79b3235e6ff5679a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ff95b73d1cfd623375b596c7216413c + trainer: + kwargs: {} +name: 45707959a63165fa79b3235e6ff5679a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/466c0d9f44ce5895346877114e96bb54/params.yaml b/examples/security/kdd-nsl/output/reports/train/466c0d9f44ce5895346877114e96bb54/params.yaml new file mode 100644 index 00000000..979f2d69 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/466c0d9f44ce5895346877114e96bb54/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/adv_predictions.json + adv_probabilities_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/562b48ff040331e4eadf29d8802c3f1f.pkl + params_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/params.yaml + predictions_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/predictions.json + probabilities_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/probabilities.json + score_dict_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/score_dict.json + test_labels_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/test_labels.json + train_labels_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/train_labels.json + model_dir: models + name: 466c0d9f44ce5895346877114e96bb54 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2780731cb11dfc200aee0a4da9c4e82 + trainer: + kwargs: {} +name: 466c0d9f44ce5895346877114e96bb54 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/467e594a1bca659bd50ffe576de4358c/params.yaml b/examples/security/kdd-nsl/output/reports/train/467e594a1bca659bd50ffe576de4358c/params.yaml new file mode 100644 index 00000000..4727e7a4 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/467e594a1bca659bd50ffe576de4358c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/467e594a1bca659bd50ffe576de4358c/adv_predictions.json + adv_probabilities_file: output/reports/train/467e594a1bca659bd50ffe576de4358c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bea45129f566a030a887c1edf8e492b2.pkl + params_file: output/reports/train/467e594a1bca659bd50ffe576de4358c/params.yaml + predictions_file: output/reports/train/467e594a1bca659bd50ffe576de4358c/predictions.json + probabilities_file: output/reports/train/467e594a1bca659bd50ffe576de4358c/probabilities.json + score_dict_file: output/reports/train/467e594a1bca659bd50ffe576de4358c/score_dict.json + test_labels_file: output/reports/train/467e594a1bca659bd50ffe576de4358c/test_labels.json + train_labels_file: output/reports/train/467e594a1bca659bd50ffe576de4358c/train_labels.json + model_dir: models + name: 467e594a1bca659bd50ffe576de4358c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aec9711d6588e0ccafa3c7c810e3e057 + trainer: + kwargs: {} +name: 467e594a1bca659bd50ffe576de4358c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/49d4abe6fae586627c61a8636b07ec14/params.yaml b/examples/security/kdd-nsl/output/reports/train/49d4abe6fae586627c61a8636b07ec14/params.yaml new file mode 100644 index 00000000..d96abc22 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/49d4abe6fae586627c61a8636b07ec14/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/49d4abe6fae586627c61a8636b07ec14/adv_predictions.json + adv_probabilities_file: output/reports/train/49d4abe6fae586627c61a8636b07ec14/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/19306d3ee7ba89852153aac0235eac7c.pkl + params_file: output/reports/train/49d4abe6fae586627c61a8636b07ec14/params.yaml + predictions_file: output/reports/train/49d4abe6fae586627c61a8636b07ec14/predictions.json + probabilities_file: output/reports/train/49d4abe6fae586627c61a8636b07ec14/probabilities.json + score_dict_file: output/reports/train/49d4abe6fae586627c61a8636b07ec14/score_dict.json + test_labels_file: output/reports/train/49d4abe6fae586627c61a8636b07ec14/test_labels.json + train_labels_file: output/reports/train/49d4abe6fae586627c61a8636b07ec14/train_labels.json + model_dir: models + name: 49d4abe6fae586627c61a8636b07ec14 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f1c031c50f34a0fe8f6ae81307cc2f1b + trainer: + kwargs: {} +name: 49d4abe6fae586627c61a8636b07ec14 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/params.yaml b/examples/security/kdd-nsl/output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/params.yaml new file mode 100644 index 00000000..3909337e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/adv_predictions.json + adv_probabilities_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/15e2158cda7c5bfe4a04a20ad2e556a4.pkl + params_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/params.yaml + predictions_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/predictions.json + probabilities_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/probabilities.json + score_dict_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/score_dict.json + test_labels_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/test_labels.json + train_labels_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/train_labels.json + model_dir: models + name: 4a6c4162ed49f656650f27a02bbc7e9d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8a62edecee845292df0d496647eab2a9 + trainer: + kwargs: {} +name: 4a6c4162ed49f656650f27a02bbc7e9d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/4bf0fa06f98f8b3d7dd5aac04690dc54/params.yaml b/examples/security/kdd-nsl/output/reports/train/4bf0fa06f98f8b3d7dd5aac04690dc54/params.yaml new file mode 100644 index 00000000..6065ee6b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/4bf0fa06f98f8b3d7dd5aac04690dc54/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4bf0fa06f98f8b3d7dd5aac04690dc54/adv_predictions.json + adv_probabilities_file: output/reports/train/4bf0fa06f98f8b3d7dd5aac04690dc54/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3e67212233875d558667b2e69c5b50bf.pkl + params_file: output/reports/train/4bf0fa06f98f8b3d7dd5aac04690dc54/params.yaml + predictions_file: output/reports/train/4bf0fa06f98f8b3d7dd5aac04690dc54/predictions.json + probabilities_file: output/reports/train/4bf0fa06f98f8b3d7dd5aac04690dc54/probabilities.json + score_dict_file: output/reports/train/4bf0fa06f98f8b3d7dd5aac04690dc54/score_dict.json + test_labels_file: output/reports/train/4bf0fa06f98f8b3d7dd5aac04690dc54/test_labels.json + train_labels_file: output/reports/train/4bf0fa06f98f8b3d7dd5aac04690dc54/train_labels.json + model_dir: models + name: 4bf0fa06f98f8b3d7dd5aac04690dc54 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 05aebc2a93c94eda902353fc27a366b9 + trainer: + kwargs: {} +name: 4bf0fa06f98f8b3d7dd5aac04690dc54 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/4c6550ac38d6c379aa10266c6fa11757/params.yaml b/examples/security/kdd-nsl/output/reports/train/4c6550ac38d6c379aa10266c6fa11757/params.yaml new file mode 100644 index 00000000..68b04251 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/4c6550ac38d6c379aa10266c6fa11757/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4c6550ac38d6c379aa10266c6fa11757/adv_predictions.json + adv_probabilities_file: output/reports/train/4c6550ac38d6c379aa10266c6fa11757/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/abf6a6b02f81ee72c4098a85d8c92749.pkl + params_file: output/reports/train/4c6550ac38d6c379aa10266c6fa11757/params.yaml + predictions_file: output/reports/train/4c6550ac38d6c379aa10266c6fa11757/predictions.json + probabilities_file: output/reports/train/4c6550ac38d6c379aa10266c6fa11757/probabilities.json + score_dict_file: output/reports/train/4c6550ac38d6c379aa10266c6fa11757/score_dict.json + test_labels_file: output/reports/train/4c6550ac38d6c379aa10266c6fa11757/test_labels.json + train_labels_file: output/reports/train/4c6550ac38d6c379aa10266c6fa11757/train_labels.json + model_dir: models + name: 4c6550ac38d6c379aa10266c6fa11757 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 558fdee112698ceb5701d3243e0f70f9 + trainer: + kwargs: {} +name: 4c6550ac38d6c379aa10266c6fa11757 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/4d71a71b5f6297ad2ae1ed4409f9540e/params.yaml b/examples/security/kdd-nsl/output/reports/train/4d71a71b5f6297ad2ae1ed4409f9540e/params.yaml new file mode 100644 index 00000000..0ee870d4 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/4d71a71b5f6297ad2ae1ed4409f9540e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4d71a71b5f6297ad2ae1ed4409f9540e/adv_predictions.json + adv_probabilities_file: output/reports/train/4d71a71b5f6297ad2ae1ed4409f9540e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/da5bdbc037e05a181d1c11684ab79d8d.pkl + params_file: output/reports/train/4d71a71b5f6297ad2ae1ed4409f9540e/params.yaml + predictions_file: output/reports/train/4d71a71b5f6297ad2ae1ed4409f9540e/predictions.json + probabilities_file: output/reports/train/4d71a71b5f6297ad2ae1ed4409f9540e/probabilities.json + score_dict_file: output/reports/train/4d71a71b5f6297ad2ae1ed4409f9540e/score_dict.json + test_labels_file: output/reports/train/4d71a71b5f6297ad2ae1ed4409f9540e/test_labels.json + train_labels_file: output/reports/train/4d71a71b5f6297ad2ae1ed4409f9540e/train_labels.json + model_dir: models + name: 4d71a71b5f6297ad2ae1ed4409f9540e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b15183d19771cf5d8315cc5b6ff26973 + trainer: + kwargs: {} +name: 4d71a71b5f6297ad2ae1ed4409f9540e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/4db223acd3619e9599e2d408a1fd7ea0/params.yaml b/examples/security/kdd-nsl/output/reports/train/4db223acd3619e9599e2d408a1fd7ea0/params.yaml new file mode 100644 index 00000000..eecaf6c7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/4db223acd3619e9599e2d408a1fd7ea0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4db223acd3619e9599e2d408a1fd7ea0/adv_predictions.json + adv_probabilities_file: output/reports/train/4db223acd3619e9599e2d408a1fd7ea0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/88adb5b3a8e752aa810c898c340e9a65.pkl + params_file: output/reports/train/4db223acd3619e9599e2d408a1fd7ea0/params.yaml + predictions_file: output/reports/train/4db223acd3619e9599e2d408a1fd7ea0/predictions.json + probabilities_file: output/reports/train/4db223acd3619e9599e2d408a1fd7ea0/probabilities.json + score_dict_file: output/reports/train/4db223acd3619e9599e2d408a1fd7ea0/score_dict.json + test_labels_file: output/reports/train/4db223acd3619e9599e2d408a1fd7ea0/test_labels.json + train_labels_file: output/reports/train/4db223acd3619e9599e2d408a1fd7ea0/train_labels.json + model_dir: models + name: 4db223acd3619e9599e2d408a1fd7ea0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ae50486dbcbff2e94bc56e00d72fc217 + trainer: + kwargs: {} +name: 4db223acd3619e9599e2d408a1fd7ea0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/4e38a5516956a808ff0c9de39edb3db1/params.yaml b/examples/security/kdd-nsl/output/reports/train/4e38a5516956a808ff0c9de39edb3db1/params.yaml new file mode 100644 index 00000000..74715835 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/4e38a5516956a808ff0c9de39edb3db1/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/adv_predictions.json + adv_probabilities_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/53ce45c654e1eedeac7a8a1b30b4a578.pkl + params_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/params.yaml + predictions_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/predictions.json + probabilities_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/probabilities.json + score_dict_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/score_dict.json + test_labels_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/test_labels.json + train_labels_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/train_labels.json + model_dir: models + name: 4e38a5516956a808ff0c9de39edb3db1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 71cb9639017dc304cd9e967491fca65f + trainer: + kwargs: {} +name: 4e38a5516956a808ff0c9de39edb3db1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/4f3af39c8b482bc6250a73661348b2c1/params.yaml b/examples/security/kdd-nsl/output/reports/train/4f3af39c8b482bc6250a73661348b2c1/params.yaml new file mode 100644 index 00000000..b5f74ac5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/4f3af39c8b482bc6250a73661348b2c1/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/adv_predictions.json + adv_probabilities_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d072e65aba2f4626a24e0f5982e49de7.pkl + params_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/params.yaml + predictions_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/predictions.json + probabilities_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/probabilities.json + score_dict_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/score_dict.json + test_labels_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/test_labels.json + train_labels_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/train_labels.json + model_dir: models + name: 4f3af39c8b482bc6250a73661348b2c1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1997cdf1fcc1bf068ecd31c8f6a45f71 + trainer: + kwargs: {} +name: 4f3af39c8b482bc6250a73661348b2c1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/506340393c45926ec4abb265766a3ee2/params.yaml b/examples/security/kdd-nsl/output/reports/train/506340393c45926ec4abb265766a3ee2/params.yaml new file mode 100644 index 00000000..c1a5b588 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/506340393c45926ec4abb265766a3ee2/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/506340393c45926ec4abb265766a3ee2/adv_predictions.json + adv_probabilities_file: output/reports/train/506340393c45926ec4abb265766a3ee2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/6ccc30b03e289e621982c1320795074d.pkl + params_file: output/reports/train/506340393c45926ec4abb265766a3ee2/params.yaml + predictions_file: output/reports/train/506340393c45926ec4abb265766a3ee2/predictions.json + probabilities_file: output/reports/train/506340393c45926ec4abb265766a3ee2/probabilities.json + score_dict_file: output/reports/train/506340393c45926ec4abb265766a3ee2/score_dict.json + test_labels_file: output/reports/train/506340393c45926ec4abb265766a3ee2/test_labels.json + train_labels_file: output/reports/train/506340393c45926ec4abb265766a3ee2/train_labels.json + model_dir: models + name: 506340393c45926ec4abb265766a3ee2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 713f9de83e06bea4f4bcf59acfaf3124 + trainer: + kwargs: {} +name: 506340393c45926ec4abb265766a3ee2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/511806a2e49f2bc030de9e791942b1bd/params.yaml b/examples/security/kdd-nsl/output/reports/train/511806a2e49f2bc030de9e791942b1bd/params.yaml new file mode 100644 index 00000000..31500683 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/511806a2e49f2bc030de9e791942b1bd/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/adv_predictions.json + adv_probabilities_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7ed24b12667263231a65d3b7310ca5df.pkl + params_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/params.yaml + predictions_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/predictions.json + probabilities_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/probabilities.json + score_dict_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/score_dict.json + test_labels_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/test_labels.json + train_labels_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/train_labels.json + model_dir: models + name: 511806a2e49f2bc030de9e791942b1bd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fc17611f5d80a034c8f141ff2ee5dda4 + trainer: + kwargs: {} +name: 511806a2e49f2bc030de9e791942b1bd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/5287e665c6fe1264b5af22ebc17c488e/params.yaml b/examples/security/kdd-nsl/output/reports/train/5287e665c6fe1264b5af22ebc17c488e/params.yaml new file mode 100644 index 00000000..8acae18f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/5287e665c6fe1264b5af22ebc17c488e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5287e665c6fe1264b5af22ebc17c488e/adv_predictions.json + adv_probabilities_file: output/reports/train/5287e665c6fe1264b5af22ebc17c488e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7089d6aeb852119cf5b189f549237e8d.pkl + params_file: output/reports/train/5287e665c6fe1264b5af22ebc17c488e/params.yaml + predictions_file: output/reports/train/5287e665c6fe1264b5af22ebc17c488e/predictions.json + probabilities_file: output/reports/train/5287e665c6fe1264b5af22ebc17c488e/probabilities.json + score_dict_file: output/reports/train/5287e665c6fe1264b5af22ebc17c488e/score_dict.json + test_labels_file: output/reports/train/5287e665c6fe1264b5af22ebc17c488e/test_labels.json + train_labels_file: output/reports/train/5287e665c6fe1264b5af22ebc17c488e/train_labels.json + model_dir: models + name: 5287e665c6fe1264b5af22ebc17c488e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e67e0fbc1960d587cf41c97217f1567d + trainer: + kwargs: {} +name: 5287e665c6fe1264b5af22ebc17c488e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/53cc9bede24a0a1d8d30920288e533b2/params.yaml b/examples/security/kdd-nsl/output/reports/train/53cc9bede24a0a1d8d30920288e533b2/params.yaml new file mode 100644 index 00000000..c1f866b1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/53cc9bede24a0a1d8d30920288e533b2/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/adv_predictions.json + adv_probabilities_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/656b4fad65a460a6dbf8a6805f91da39.pkl + params_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/params.yaml + predictions_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/predictions.json + probabilities_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/probabilities.json + score_dict_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/score_dict.json + test_labels_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/test_labels.json + train_labels_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/train_labels.json + model_dir: models + name: 53cc9bede24a0a1d8d30920288e533b2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f9c8d5c50c166cce7ee00d729310d010 + trainer: + kwargs: {} +name: 53cc9bede24a0a1d8d30920288e533b2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/549b97dd9328b88ec5c39c368a301c07/params.yaml b/examples/security/kdd-nsl/output/reports/train/549b97dd9328b88ec5c39c368a301c07/params.yaml new file mode 100644 index 00000000..830c42ac --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/549b97dd9328b88ec5c39c368a301c07/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/adv_predictions.json + adv_probabilities_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7755d1ed6ad6ba65552d835a8d8f8b92.pkl + params_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/params.yaml + predictions_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/predictions.json + probabilities_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/probabilities.json + score_dict_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/score_dict.json + test_labels_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/test_labels.json + train_labels_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/train_labels.json + model_dir: models + name: 549b97dd9328b88ec5c39c368a301c07 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5f20fcbdc942526c009bb51d413673b3 + trainer: + kwargs: {} +name: 549b97dd9328b88ec5c39c368a301c07 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/54d748a08284e71a98c3adb0284bd313/params.yaml b/examples/security/kdd-nsl/output/reports/train/54d748a08284e71a98c3adb0284bd313/params.yaml new file mode 100644 index 00000000..56011a10 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/54d748a08284e71a98c3adb0284bd313/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/adv_predictions.json + adv_probabilities_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/73d704f29491799d6c60233f3c486885.pkl + params_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/params.yaml + predictions_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/predictions.json + probabilities_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/probabilities.json + score_dict_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/score_dict.json + test_labels_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/test_labels.json + train_labels_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/train_labels.json + model_dir: models + name: 54d748a08284e71a98c3adb0284bd313 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4787ad0f4346d07f549151f200153ba4 + trainer: + kwargs: {} +name: 54d748a08284e71a98c3adb0284bd313 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/552a43864c60d62c35491ab9c0348232/params.yaml b/examples/security/kdd-nsl/output/reports/train/552a43864c60d62c35491ab9c0348232/params.yaml new file mode 100644 index 00000000..6e795804 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/552a43864c60d62c35491ab9c0348232/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/552a43864c60d62c35491ab9c0348232/adv_predictions.json + adv_probabilities_file: output/reports/train/552a43864c60d62c35491ab9c0348232/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2b61ebac5d2659f1aaf6f64c0aaefce8.pkl + params_file: output/reports/train/552a43864c60d62c35491ab9c0348232/params.yaml + predictions_file: output/reports/train/552a43864c60d62c35491ab9c0348232/predictions.json + probabilities_file: output/reports/train/552a43864c60d62c35491ab9c0348232/probabilities.json + score_dict_file: output/reports/train/552a43864c60d62c35491ab9c0348232/score_dict.json + test_labels_file: output/reports/train/552a43864c60d62c35491ab9c0348232/test_labels.json + train_labels_file: output/reports/train/552a43864c60d62c35491ab9c0348232/train_labels.json + model_dir: models + name: 552a43864c60d62c35491ab9c0348232 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9d673cbbd613c64d0e1db2aa21e444f2 + trainer: + kwargs: {} +name: 552a43864c60d62c35491ab9c0348232 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/params.yaml b/examples/security/kdd-nsl/output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/params.yaml new file mode 100644 index 00000000..968976c4 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/adv_predictions.json + adv_probabilities_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ef0100b4abffaab73d24efbf92feec2e.pkl + params_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/params.yaml + predictions_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/predictions.json + probabilities_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/probabilities.json + score_dict_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/score_dict.json + test_labels_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/test_labels.json + train_labels_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/train_labels.json + model_dir: models + name: 555ffb82e7eb8d05c9a7bbb1bffb181e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3d4e3192794abd81b21bad56bbf70156 + trainer: + kwargs: {} +name: 555ffb82e7eb8d05c9a7bbb1bffb181e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/557b2353dc56a8a7e6243148d37830e0/params.yaml b/examples/security/kdd-nsl/output/reports/train/557b2353dc56a8a7e6243148d37830e0/params.yaml new file mode 100644 index 00000000..225b2ceb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/557b2353dc56a8a7e6243148d37830e0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/557b2353dc56a8a7e6243148d37830e0/adv_predictions.json + adv_probabilities_file: output/reports/train/557b2353dc56a8a7e6243148d37830e0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e60514559334cc828e40c0e3d3e772fc.pkl + params_file: output/reports/train/557b2353dc56a8a7e6243148d37830e0/params.yaml + predictions_file: output/reports/train/557b2353dc56a8a7e6243148d37830e0/predictions.json + probabilities_file: output/reports/train/557b2353dc56a8a7e6243148d37830e0/probabilities.json + score_dict_file: output/reports/train/557b2353dc56a8a7e6243148d37830e0/score_dict.json + test_labels_file: output/reports/train/557b2353dc56a8a7e6243148d37830e0/test_labels.json + train_labels_file: output/reports/train/557b2353dc56a8a7e6243148d37830e0/train_labels.json + model_dir: models + name: 557b2353dc56a8a7e6243148d37830e0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 53beac2c47226c0a03af732e19e2eda0 + trainer: + kwargs: {} +name: 557b2353dc56a8a7e6243148d37830e0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/557d519534611dcc9c4a7e8e38ae9068/params.yaml b/examples/security/kdd-nsl/output/reports/train/557d519534611dcc9c4a7e8e38ae9068/params.yaml new file mode 100644 index 00000000..49dd4cc5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/557d519534611dcc9c4a7e8e38ae9068/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/557d519534611dcc9c4a7e8e38ae9068/adv_predictions.json + adv_probabilities_file: output/reports/train/557d519534611dcc9c4a7e8e38ae9068/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c8431c656aed4ffb32898a9e953e4423.pkl + params_file: output/reports/train/557d519534611dcc9c4a7e8e38ae9068/params.yaml + predictions_file: output/reports/train/557d519534611dcc9c4a7e8e38ae9068/predictions.json + probabilities_file: output/reports/train/557d519534611dcc9c4a7e8e38ae9068/probabilities.json + score_dict_file: output/reports/train/557d519534611dcc9c4a7e8e38ae9068/score_dict.json + test_labels_file: output/reports/train/557d519534611dcc9c4a7e8e38ae9068/test_labels.json + train_labels_file: output/reports/train/557d519534611dcc9c4a7e8e38ae9068/train_labels.json + model_dir: models + name: 557d519534611dcc9c4a7e8e38ae9068 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0f45af90337cd5991e56ea47ab78c707 + trainer: + kwargs: {} +name: 557d519534611dcc9c4a7e8e38ae9068 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/params.yaml b/examples/security/kdd-nsl/output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/params.yaml new file mode 100644 index 00000000..c0314a69 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/adv_predictions.json + adv_probabilities_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1406754c5034f882d58a3bbd344a5456.pkl + params_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/params.yaml + predictions_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/predictions.json + probabilities_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/probabilities.json + score_dict_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/score_dict.json + test_labels_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/test_labels.json + train_labels_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/train_labels.json + model_dir: models + name: 55f1e95d4cda115ac5943aed4acbefc3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b44c94b4e6997dec80efb770a5efa14 + trainer: + kwargs: {} +name: 55f1e95d4cda115ac5943aed4acbefc3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/58761309abdb0fd761612c03655f9f4a/params.yaml b/examples/security/kdd-nsl/output/reports/train/58761309abdb0fd761612c03655f9f4a/params.yaml new file mode 100644 index 00000000..f804f550 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/58761309abdb0fd761612c03655f9f4a/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/adv_predictions.json + adv_probabilities_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/params.yaml + predictions_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/predictions.json + probabilities_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/probabilities.json + score_dict_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/score_dict.json + test_labels_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/test_labels.json + train_labels_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/train_labels.json + model_dir: models + name: 58761309abdb0fd761612c03655f9f4a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 58761309abdb0fd761612c03655f9f4a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/58cf9f707cde129be1bf0188d52ea396/params.yaml b/examples/security/kdd-nsl/output/reports/train/58cf9f707cde129be1bf0188d52ea396/params.yaml new file mode 100644 index 00000000..216ed133 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/58cf9f707cde129be1bf0188d52ea396/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/adv_predictions.json + adv_probabilities_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/90e1d0e418f98f4b0b5593b52580efe2.pkl + params_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/params.yaml + predictions_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/predictions.json + probabilities_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/probabilities.json + score_dict_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/score_dict.json + test_labels_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/test_labels.json + train_labels_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/train_labels.json + model_dir: models + name: 58cf9f707cde129be1bf0188d52ea396 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 01f3b66ae9d53723c1c36c359e0546ff + trainer: + kwargs: {} +name: 58cf9f707cde129be1bf0188d52ea396 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/59d53bf6f4ada837956a3b493770723b/params.yaml b/examples/security/kdd-nsl/output/reports/train/59d53bf6f4ada837956a3b493770723b/params.yaml new file mode 100644 index 00000000..11e2a2da --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/59d53bf6f4ada837956a3b493770723b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/adv_predictions.json + adv_probabilities_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ab8dc94016bfa0fb0394a5320380868c.pkl + params_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/params.yaml + predictions_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/predictions.json + probabilities_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/probabilities.json + score_dict_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/score_dict.json + test_labels_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/test_labels.json + train_labels_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/train_labels.json + model_dir: models + name: 59d53bf6f4ada837956a3b493770723b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3f00f34e434f1803d06beb9dfe59a4b8 + trainer: + kwargs: {} +name: 59d53bf6f4ada837956a3b493770723b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/5aac1825bac9ed3ac531e140505f9b17/params.yaml b/examples/security/kdd-nsl/output/reports/train/5aac1825bac9ed3ac531e140505f9b17/params.yaml new file mode 100644 index 00000000..8bd3b43c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/5aac1825bac9ed3ac531e140505f9b17/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/adv_predictions.json + adv_probabilities_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3e4e6ad0dbe0897041e3e0b744b59866.pkl + params_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/params.yaml + predictions_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/predictions.json + probabilities_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/probabilities.json + score_dict_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/score_dict.json + test_labels_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/test_labels.json + train_labels_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/train_labels.json + model_dir: models + name: 5aac1825bac9ed3ac531e140505f9b17 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6d32dd58a7931879d5947002da530bfa + trainer: + kwargs: {} +name: 5aac1825bac9ed3ac531e140505f9b17 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/params.yaml b/examples/security/kdd-nsl/output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/params.yaml new file mode 100644 index 00000000..1db3b8e9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/adv_predictions.json + adv_probabilities_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/cafdc5851a55a5ee5987a47faeb7b3de.pkl + params_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/params.yaml + predictions_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/predictions.json + probabilities_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/probabilities.json + score_dict_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/score_dict.json + test_labels_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/test_labels.json + train_labels_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/train_labels.json + model_dir: models + name: 5b5da2ae35f855cfd1cce86d30b78a24 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: faf3776487510d40fe97061d07d7f5ba + trainer: + kwargs: {} +name: 5b5da2ae35f855cfd1cce86d30b78a24 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/5cc95a6b75c3593d0b1b200d0d024818/params.yaml b/examples/security/kdd-nsl/output/reports/train/5cc95a6b75c3593d0b1b200d0d024818/params.yaml new file mode 100644 index 00000000..c8639115 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/5cc95a6b75c3593d0b1b200d0d024818/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5cc95a6b75c3593d0b1b200d0d024818/adv_predictions.json + adv_probabilities_file: output/reports/train/5cc95a6b75c3593d0b1b200d0d024818/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/edf64c43da81188c7f72007a24dd2d3f.pkl + params_file: output/reports/train/5cc95a6b75c3593d0b1b200d0d024818/params.yaml + predictions_file: output/reports/train/5cc95a6b75c3593d0b1b200d0d024818/predictions.json + probabilities_file: output/reports/train/5cc95a6b75c3593d0b1b200d0d024818/probabilities.json + score_dict_file: output/reports/train/5cc95a6b75c3593d0b1b200d0d024818/score_dict.json + test_labels_file: output/reports/train/5cc95a6b75c3593d0b1b200d0d024818/test_labels.json + train_labels_file: output/reports/train/5cc95a6b75c3593d0b1b200d0d024818/train_labels.json + model_dir: models + name: 5cc95a6b75c3593d0b1b200d0d024818 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 348a994163abdd564adaa0a883e2c76b + trainer: + kwargs: {} +name: 5cc95a6b75c3593d0b1b200d0d024818 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/5e640871a2254700b402a45bddcefb5f/params.yaml b/examples/security/kdd-nsl/output/reports/train/5e640871a2254700b402a45bddcefb5f/params.yaml new file mode 100644 index 00000000..9959a3ff --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/5e640871a2254700b402a45bddcefb5f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5e640871a2254700b402a45bddcefb5f/adv_predictions.json + adv_probabilities_file: output/reports/train/5e640871a2254700b402a45bddcefb5f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/11b99338ece013c1e24751d5e454775f.pkl + params_file: output/reports/train/5e640871a2254700b402a45bddcefb5f/params.yaml + predictions_file: output/reports/train/5e640871a2254700b402a45bddcefb5f/predictions.json + probabilities_file: output/reports/train/5e640871a2254700b402a45bddcefb5f/probabilities.json + score_dict_file: output/reports/train/5e640871a2254700b402a45bddcefb5f/score_dict.json + test_labels_file: output/reports/train/5e640871a2254700b402a45bddcefb5f/test_labels.json + train_labels_file: output/reports/train/5e640871a2254700b402a45bddcefb5f/train_labels.json + model_dir: models + name: 5e640871a2254700b402a45bddcefb5f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 40829b5f44b8750221d88ec7d1bac974 + trainer: + kwargs: {} +name: 5e640871a2254700b402a45bddcefb5f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/5eba6a2839cf8b55019790659e9b2166/params.yaml b/examples/security/kdd-nsl/output/reports/train/5eba6a2839cf8b55019790659e9b2166/params.yaml new file mode 100644 index 00000000..c7d59288 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/5eba6a2839cf8b55019790659e9b2166/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5eba6a2839cf8b55019790659e9b2166/adv_predictions.json + adv_probabilities_file: output/reports/train/5eba6a2839cf8b55019790659e9b2166/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/98f9865212ffebcfc8b78539767d31d1.pkl + params_file: output/reports/train/5eba6a2839cf8b55019790659e9b2166/params.yaml + predictions_file: output/reports/train/5eba6a2839cf8b55019790659e9b2166/predictions.json + probabilities_file: output/reports/train/5eba6a2839cf8b55019790659e9b2166/probabilities.json + score_dict_file: output/reports/train/5eba6a2839cf8b55019790659e9b2166/score_dict.json + test_labels_file: output/reports/train/5eba6a2839cf8b55019790659e9b2166/test_labels.json + train_labels_file: output/reports/train/5eba6a2839cf8b55019790659e9b2166/train_labels.json + model_dir: models + name: 5eba6a2839cf8b55019790659e9b2166 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f9a67d943a7a04b2eef3eca93c1a7e26 + trainer: + kwargs: {} +name: 5eba6a2839cf8b55019790659e9b2166 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/5ede44a042312a2e725cc52ae7d568cf/params.yaml b/examples/security/kdd-nsl/output/reports/train/5ede44a042312a2e725cc52ae7d568cf/params.yaml new file mode 100644 index 00000000..cc09b597 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/5ede44a042312a2e725cc52ae7d568cf/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/adv_predictions.json + adv_probabilities_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/06d6a6ce6be148e1928a2aca0a2a61e8.pkl + params_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/params.yaml + predictions_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/predictions.json + probabilities_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/probabilities.json + score_dict_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/score_dict.json + test_labels_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/test_labels.json + train_labels_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/train_labels.json + model_dir: models + name: 5ede44a042312a2e725cc52ae7d568cf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 85f0ea0cffdbdb3782ed6c82eb494f1f + trainer: + kwargs: {} +name: 5ede44a042312a2e725cc52ae7d568cf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/611292eeb90817651d149951783f6576/params.yaml b/examples/security/kdd-nsl/output/reports/train/611292eeb90817651d149951783f6576/params.yaml new file mode 100644 index 00000000..1906cb02 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/611292eeb90817651d149951783f6576/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/611292eeb90817651d149951783f6576/adv_predictions.json + adv_probabilities_file: output/reports/train/611292eeb90817651d149951783f6576/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/a286af221d9bcfeb3071bb860ab1254a.pkl + params_file: output/reports/train/611292eeb90817651d149951783f6576/params.yaml + predictions_file: output/reports/train/611292eeb90817651d149951783f6576/predictions.json + probabilities_file: output/reports/train/611292eeb90817651d149951783f6576/probabilities.json + score_dict_file: output/reports/train/611292eeb90817651d149951783f6576/score_dict.json + test_labels_file: output/reports/train/611292eeb90817651d149951783f6576/test_labels.json + train_labels_file: output/reports/train/611292eeb90817651d149951783f6576/train_labels.json + model_dir: models + name: 611292eeb90817651d149951783f6576 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c167830d8a9f5e6464eaaf990b0ecec9 + trainer: + kwargs: {} +name: 611292eeb90817651d149951783f6576 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/618da6200217ac542d9d5720c6d3bd78/params.yaml b/examples/security/kdd-nsl/output/reports/train/618da6200217ac542d9d5720c6d3bd78/params.yaml new file mode 100644 index 00000000..994a9d25 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/618da6200217ac542d9d5720c6d3bd78/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/618da6200217ac542d9d5720c6d3bd78/adv_predictions.json + adv_probabilities_file: output/reports/train/618da6200217ac542d9d5720c6d3bd78/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9f6bb1fe8dc2b8ad1a35059b3f54f4e8.pkl + params_file: output/reports/train/618da6200217ac542d9d5720c6d3bd78/params.yaml + predictions_file: output/reports/train/618da6200217ac542d9d5720c6d3bd78/predictions.json + probabilities_file: output/reports/train/618da6200217ac542d9d5720c6d3bd78/probabilities.json + score_dict_file: output/reports/train/618da6200217ac542d9d5720c6d3bd78/score_dict.json + test_labels_file: output/reports/train/618da6200217ac542d9d5720c6d3bd78/test_labels.json + train_labels_file: output/reports/train/618da6200217ac542d9d5720c6d3bd78/train_labels.json + model_dir: models + name: 618da6200217ac542d9d5720c6d3bd78 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2366dee8a4a29061c41f8b62502ff9c5 + trainer: + kwargs: {} +name: 618da6200217ac542d9d5720c6d3bd78 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/621eb7a0514e574dd0685177dae27538/params.yaml b/examples/security/kdd-nsl/output/reports/train/621eb7a0514e574dd0685177dae27538/params.yaml new file mode 100644 index 00000000..3049141a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/621eb7a0514e574dd0685177dae27538/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/621eb7a0514e574dd0685177dae27538/adv_predictions.json + adv_probabilities_file: output/reports/train/621eb7a0514e574dd0685177dae27538/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/fb786b647549e79874f57eebe4701e40.pkl + params_file: output/reports/train/621eb7a0514e574dd0685177dae27538/params.yaml + predictions_file: output/reports/train/621eb7a0514e574dd0685177dae27538/predictions.json + probabilities_file: output/reports/train/621eb7a0514e574dd0685177dae27538/probabilities.json + score_dict_file: output/reports/train/621eb7a0514e574dd0685177dae27538/score_dict.json + test_labels_file: output/reports/train/621eb7a0514e574dd0685177dae27538/test_labels.json + train_labels_file: output/reports/train/621eb7a0514e574dd0685177dae27538/train_labels.json + model_dir: models + name: 621eb7a0514e574dd0685177dae27538 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1bfbc5c8cc020ca239f45c5ea4ca58e6 + trainer: + kwargs: {} +name: 621eb7a0514e574dd0685177dae27538 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/params.yaml b/examples/security/kdd-nsl/output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/params.yaml new file mode 100644 index 00000000..71781f10 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/adv_predictions.json + adv_probabilities_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/a241625090477c7551b003e128ff79fe.pkl + params_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/params.yaml + predictions_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/predictions.json + probabilities_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/probabilities.json + score_dict_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/score_dict.json + test_labels_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/test_labels.json + train_labels_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/train_labels.json + model_dir: models + name: 63d649e4850b33dc9ec8e248d38bba5d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7da7fa701b06782c619cb7fd7faa41da + trainer: + kwargs: {} +name: 63d649e4850b33dc9ec8e248d38bba5d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/64afdff8e84fc92eed6abd11920b6913/params.yaml b/examples/security/kdd-nsl/output/reports/train/64afdff8e84fc92eed6abd11920b6913/params.yaml new file mode 100644 index 00000000..fcc2142d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/64afdff8e84fc92eed6abd11920b6913/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/adv_predictions.json + adv_probabilities_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2d20bd576021d8bbe07c9bca9a5f5788.pkl + params_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/params.yaml + predictions_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/predictions.json + probabilities_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/probabilities.json + score_dict_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/score_dict.json + test_labels_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/test_labels.json + train_labels_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/train_labels.json + model_dir: models + name: 64afdff8e84fc92eed6abd11920b6913 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3c4b5e813fe2ec1c34d5fa91da1c6ba9 + trainer: + kwargs: {} +name: 64afdff8e84fc92eed6abd11920b6913 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/652ca55ca0ac347048327b23feea5d9e/params.yaml b/examples/security/kdd-nsl/output/reports/train/652ca55ca0ac347048327b23feea5d9e/params.yaml new file mode 100644 index 00000000..6d681dc5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/652ca55ca0ac347048327b23feea5d9e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/652ca55ca0ac347048327b23feea5d9e/adv_predictions.json + adv_probabilities_file: output/reports/train/652ca55ca0ac347048327b23feea5d9e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/78e767b1812c1ef480b3154538059ef2.pkl + params_file: output/reports/train/652ca55ca0ac347048327b23feea5d9e/params.yaml + predictions_file: output/reports/train/652ca55ca0ac347048327b23feea5d9e/predictions.json + probabilities_file: output/reports/train/652ca55ca0ac347048327b23feea5d9e/probabilities.json + score_dict_file: output/reports/train/652ca55ca0ac347048327b23feea5d9e/score_dict.json + test_labels_file: output/reports/train/652ca55ca0ac347048327b23feea5d9e/test_labels.json + train_labels_file: output/reports/train/652ca55ca0ac347048327b23feea5d9e/train_labels.json + model_dir: models + name: 652ca55ca0ac347048327b23feea5d9e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 01bdfdd0f007074eb5e5e453d86c87ef + trainer: + kwargs: {} +name: 652ca55ca0ac347048327b23feea5d9e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/661881b850787455013a6840ab5ea9f2/params.yaml b/examples/security/kdd-nsl/output/reports/train/661881b850787455013a6840ab5ea9f2/params.yaml new file mode 100644 index 00000000..a52131b7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/661881b850787455013a6840ab5ea9f2/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/661881b850787455013a6840ab5ea9f2/adv_predictions.json + adv_probabilities_file: output/reports/train/661881b850787455013a6840ab5ea9f2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ca29bd05d9515dbf63b37bc906c31a10.pkl + params_file: output/reports/train/661881b850787455013a6840ab5ea9f2/params.yaml + predictions_file: output/reports/train/661881b850787455013a6840ab5ea9f2/predictions.json + probabilities_file: output/reports/train/661881b850787455013a6840ab5ea9f2/probabilities.json + score_dict_file: output/reports/train/661881b850787455013a6840ab5ea9f2/score_dict.json + test_labels_file: output/reports/train/661881b850787455013a6840ab5ea9f2/test_labels.json + train_labels_file: output/reports/train/661881b850787455013a6840ab5ea9f2/train_labels.json + model_dir: models + name: 661881b850787455013a6840ab5ea9f2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 328419c6f2fb0176cea18629967a6695 + trainer: + kwargs: {} +name: 661881b850787455013a6840ab5ea9f2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/66fb4f269c900efdea61ed0c729ccd7b/params.yaml b/examples/security/kdd-nsl/output/reports/train/66fb4f269c900efdea61ed0c729ccd7b/params.yaml new file mode 100644 index 00000000..aa82fd29 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/66fb4f269c900efdea61ed0c729ccd7b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/66fb4f269c900efdea61ed0c729ccd7b/adv_predictions.json + adv_probabilities_file: output/reports/train/66fb4f269c900efdea61ed0c729ccd7b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d1a77ca0a7f2b67d527ff6032e0e8d43.pkl + params_file: output/reports/train/66fb4f269c900efdea61ed0c729ccd7b/params.yaml + predictions_file: output/reports/train/66fb4f269c900efdea61ed0c729ccd7b/predictions.json + probabilities_file: output/reports/train/66fb4f269c900efdea61ed0c729ccd7b/probabilities.json + score_dict_file: output/reports/train/66fb4f269c900efdea61ed0c729ccd7b/score_dict.json + test_labels_file: output/reports/train/66fb4f269c900efdea61ed0c729ccd7b/test_labels.json + train_labels_file: output/reports/train/66fb4f269c900efdea61ed0c729ccd7b/train_labels.json + model_dir: models + name: 66fb4f269c900efdea61ed0c729ccd7b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b5cc20ded0b992c27d0136a5c11076d6 + trainer: + kwargs: {} +name: 66fb4f269c900efdea61ed0c729ccd7b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/674f484006a7a1010dc48ce7eaf27a72/params.yaml b/examples/security/kdd-nsl/output/reports/train/674f484006a7a1010dc48ce7eaf27a72/params.yaml new file mode 100644 index 00000000..b6ac0ad9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/674f484006a7a1010dc48ce7eaf27a72/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/adv_predictions.json + adv_probabilities_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/05d9a48f0f88ddb2bff9effcbdc01905.pkl + params_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/params.yaml + predictions_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/predictions.json + probabilities_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/probabilities.json + score_dict_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/score_dict.json + test_labels_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/test_labels.json + train_labels_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/train_labels.json + model_dir: models + name: 674f484006a7a1010dc48ce7eaf27a72 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 70b54f1b8875f1ef4069cce4af9728ec + trainer: + kwargs: {} +name: 674f484006a7a1010dc48ce7eaf27a72 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/68deb269d9740e651b6cf1908b242721/params.yaml b/examples/security/kdd-nsl/output/reports/train/68deb269d9740e651b6cf1908b242721/params.yaml new file mode 100644 index 00000000..d455cd45 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/68deb269d9740e651b6cf1908b242721/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/68deb269d9740e651b6cf1908b242721/adv_predictions.json + adv_probabilities_file: output/reports/train/68deb269d9740e651b6cf1908b242721/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7ab55b12570b8f758e538636ccf67d38.pkl + params_file: output/reports/train/68deb269d9740e651b6cf1908b242721/params.yaml + predictions_file: output/reports/train/68deb269d9740e651b6cf1908b242721/predictions.json + probabilities_file: output/reports/train/68deb269d9740e651b6cf1908b242721/probabilities.json + score_dict_file: output/reports/train/68deb269d9740e651b6cf1908b242721/score_dict.json + test_labels_file: output/reports/train/68deb269d9740e651b6cf1908b242721/test_labels.json + train_labels_file: output/reports/train/68deb269d9740e651b6cf1908b242721/train_labels.json + model_dir: models + name: 68deb269d9740e651b6cf1908b242721 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e6a61c13939bcb4994e0607f251edcec + trainer: + kwargs: {} +name: 68deb269d9740e651b6cf1908b242721 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/68f48931c2c23e5f931d45f41f40d824/params.yaml b/examples/security/kdd-nsl/output/reports/train/68f48931c2c23e5f931d45f41f40d824/params.yaml new file mode 100644 index 00000000..1f63d4fe --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/68f48931c2c23e5f931d45f41f40d824/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/adv_predictions.json + adv_probabilities_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/a7b55e9c92494750ceace81f4579d67a.pkl + params_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/params.yaml + predictions_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/predictions.json + probabilities_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/probabilities.json + score_dict_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/score_dict.json + test_labels_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/test_labels.json + train_labels_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/train_labels.json + model_dir: models + name: 68f48931c2c23e5f931d45f41f40d824 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e5f88a07896e7114062de8eef5275d8d + trainer: + kwargs: {} +name: 68f48931c2c23e5f931d45f41f40d824 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/692f354e6436a28b322675737183039d/params.yaml b/examples/security/kdd-nsl/output/reports/train/692f354e6436a28b322675737183039d/params.yaml new file mode 100644 index 00000000..614ba5f5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/692f354e6436a28b322675737183039d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/692f354e6436a28b322675737183039d/adv_predictions.json + adv_probabilities_file: output/reports/train/692f354e6436a28b322675737183039d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/08f372d3591ed22d74a4d8ab0229670c.pkl + params_file: output/reports/train/692f354e6436a28b322675737183039d/params.yaml + predictions_file: output/reports/train/692f354e6436a28b322675737183039d/predictions.json + probabilities_file: output/reports/train/692f354e6436a28b322675737183039d/probabilities.json + score_dict_file: output/reports/train/692f354e6436a28b322675737183039d/score_dict.json + test_labels_file: output/reports/train/692f354e6436a28b322675737183039d/test_labels.json + train_labels_file: output/reports/train/692f354e6436a28b322675737183039d/train_labels.json + model_dir: models + name: 692f354e6436a28b322675737183039d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9944a6830d46df64b31f0454c49ff6eb + trainer: + kwargs: {} +name: 692f354e6436a28b322675737183039d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/6bb925e4db561981ec3e5344978a247c/params.yaml b/examples/security/kdd-nsl/output/reports/train/6bb925e4db561981ec3e5344978a247c/params.yaml new file mode 100644 index 00000000..41eb1e02 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/6bb925e4db561981ec3e5344978a247c/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/adv_predictions.json + adv_probabilities_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/89d0a670f9432e51c1c345cc73506348.pkl + params_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/params.yaml + predictions_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/predictions.json + probabilities_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/probabilities.json + score_dict_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/score_dict.json + test_labels_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/test_labels.json + train_labels_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/train_labels.json + model_dir: models + name: 6bb925e4db561981ec3e5344978a247c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: af1a4e9a46c42f4c38a6148e9965963c + trainer: + kwargs: {} +name: 6bb925e4db561981ec3e5344978a247c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/706e7460210257c5b259626c82c6d32e/params.yaml b/examples/security/kdd-nsl/output/reports/train/706e7460210257c5b259626c82c6d32e/params.yaml new file mode 100644 index 00000000..f49d4c26 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/706e7460210257c5b259626c82c6d32e/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/706e7460210257c5b259626c82c6d32e/adv_predictions.json + adv_probabilities_file: output/reports/train/706e7460210257c5b259626c82c6d32e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0c2a459231c9967e61f4528a05b00e29.pkl + params_file: output/reports/train/706e7460210257c5b259626c82c6d32e/params.yaml + predictions_file: output/reports/train/706e7460210257c5b259626c82c6d32e/predictions.json + probabilities_file: output/reports/train/706e7460210257c5b259626c82c6d32e/probabilities.json + score_dict_file: output/reports/train/706e7460210257c5b259626c82c6d32e/score_dict.json + test_labels_file: output/reports/train/706e7460210257c5b259626c82c6d32e/test_labels.json + train_labels_file: output/reports/train/706e7460210257c5b259626c82c6d32e/train_labels.json + model_dir: models + name: 706e7460210257c5b259626c82c6d32e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 22f8f9e6176f4d365f13ebb01f80c55e + trainer: + kwargs: {} +name: 706e7460210257c5b259626c82c6d32e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/7152f5f671f4b6f2e116322a92b9adea/params.yaml b/examples/security/kdd-nsl/output/reports/train/7152f5f671f4b6f2e116322a92b9adea/params.yaml new file mode 100644 index 00000000..686b03f2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/7152f5f671f4b6f2e116322a92b9adea/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/adv_predictions.json + adv_probabilities_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/42fd313171585dbbe9bdcb00f083545a.pkl + params_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/params.yaml + predictions_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/predictions.json + probabilities_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/probabilities.json + score_dict_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/score_dict.json + test_labels_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/test_labels.json + train_labels_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/train_labels.json + model_dir: models + name: 7152f5f671f4b6f2e116322a92b9adea + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: de5ff357e2bb09bb4c2a766e339aae8d + trainer: + kwargs: {} +name: 7152f5f671f4b6f2e116322a92b9adea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/71cb2c6d236b75fd92f07471e3309947/params.yaml b/examples/security/kdd-nsl/output/reports/train/71cb2c6d236b75fd92f07471e3309947/params.yaml new file mode 100644 index 00000000..9c36c85b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/71cb2c6d236b75fd92f07471e3309947/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/adv_predictions.json + adv_probabilities_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c6466682a0bf488ccb57f596b483e88a.pkl + params_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/params.yaml + predictions_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/predictions.json + probabilities_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/probabilities.json + score_dict_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/score_dict.json + test_labels_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/test_labels.json + train_labels_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/train_labels.json + model_dir: models + name: 71cb2c6d236b75fd92f07471e3309947 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 041fb0fe62ce798cab87816f19095966 + trainer: + kwargs: {} +name: 71cb2c6d236b75fd92f07471e3309947 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/737e56cc54e153dcc0946f414ec80f55/params.yaml b/examples/security/kdd-nsl/output/reports/train/737e56cc54e153dcc0946f414ec80f55/params.yaml new file mode 100644 index 00000000..29ca2698 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/737e56cc54e153dcc0946f414ec80f55/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/737e56cc54e153dcc0946f414ec80f55/adv_predictions.json + adv_probabilities_file: output/reports/train/737e56cc54e153dcc0946f414ec80f55/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e1922bd737494e581d322b8222d0ca45.pkl + params_file: output/reports/train/737e56cc54e153dcc0946f414ec80f55/params.yaml + predictions_file: output/reports/train/737e56cc54e153dcc0946f414ec80f55/predictions.json + probabilities_file: output/reports/train/737e56cc54e153dcc0946f414ec80f55/probabilities.json + score_dict_file: output/reports/train/737e56cc54e153dcc0946f414ec80f55/score_dict.json + test_labels_file: output/reports/train/737e56cc54e153dcc0946f414ec80f55/test_labels.json + train_labels_file: output/reports/train/737e56cc54e153dcc0946f414ec80f55/train_labels.json + model_dir: models + name: 737e56cc54e153dcc0946f414ec80f55 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 020f1832e600dbe5c78482fd117d9778 + trainer: + kwargs: {} +name: 737e56cc54e153dcc0946f414ec80f55 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/74d2456076ac0a06fe96d31fe60cbaca/params.yaml b/examples/security/kdd-nsl/output/reports/train/74d2456076ac0a06fe96d31fe60cbaca/params.yaml new file mode 100644 index 00000000..edad688c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/74d2456076ac0a06fe96d31fe60cbaca/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/74d2456076ac0a06fe96d31fe60cbaca/adv_predictions.json + adv_probabilities_file: output/reports/train/74d2456076ac0a06fe96d31fe60cbaca/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/337eafc66f20a4108121e3d3c851d1ba.pkl + params_file: output/reports/train/74d2456076ac0a06fe96d31fe60cbaca/params.yaml + predictions_file: output/reports/train/74d2456076ac0a06fe96d31fe60cbaca/predictions.json + probabilities_file: output/reports/train/74d2456076ac0a06fe96d31fe60cbaca/probabilities.json + score_dict_file: output/reports/train/74d2456076ac0a06fe96d31fe60cbaca/score_dict.json + test_labels_file: output/reports/train/74d2456076ac0a06fe96d31fe60cbaca/test_labels.json + train_labels_file: output/reports/train/74d2456076ac0a06fe96d31fe60cbaca/train_labels.json + model_dir: models + name: 74d2456076ac0a06fe96d31fe60cbaca + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 55d993a4dd2d72a86a4e59e3ea3b9555 + trainer: + kwargs: {} +name: 74d2456076ac0a06fe96d31fe60cbaca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/76cc06b5cc914383e1164f805abae21b/params.yaml b/examples/security/kdd-nsl/output/reports/train/76cc06b5cc914383e1164f805abae21b/params.yaml new file mode 100644 index 00000000..3841c731 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/76cc06b5cc914383e1164f805abae21b/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/adv_predictions.json + adv_probabilities_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/22d012a4f572c48239a27385de960696.pkl + params_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/params.yaml + predictions_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/predictions.json + probabilities_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/probabilities.json + score_dict_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/score_dict.json + test_labels_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/test_labels.json + train_labels_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/train_labels.json + model_dir: models + name: 76cc06b5cc914383e1164f805abae21b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1ebeed32613201096dba6fc696387514 + trainer: + kwargs: {} +name: 76cc06b5cc914383e1164f805abae21b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/779fab079c75f81e635b329b550703a6/params.yaml b/examples/security/kdd-nsl/output/reports/train/779fab079c75f81e635b329b550703a6/params.yaml new file mode 100644 index 00000000..f232eb62 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/779fab079c75f81e635b329b550703a6/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/779fab079c75f81e635b329b550703a6/adv_predictions.json + adv_probabilities_file: output/reports/train/779fab079c75f81e635b329b550703a6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/84bffdddad088ab8775c6e8e0e14b579.pkl + params_file: output/reports/train/779fab079c75f81e635b329b550703a6/params.yaml + predictions_file: output/reports/train/779fab079c75f81e635b329b550703a6/predictions.json + probabilities_file: output/reports/train/779fab079c75f81e635b329b550703a6/probabilities.json + score_dict_file: output/reports/train/779fab079c75f81e635b329b550703a6/score_dict.json + test_labels_file: output/reports/train/779fab079c75f81e635b329b550703a6/test_labels.json + train_labels_file: output/reports/train/779fab079c75f81e635b329b550703a6/train_labels.json + model_dir: models + name: 779fab079c75f81e635b329b550703a6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3740e7e7754bec4e0943b5dee56511d2 + trainer: + kwargs: {} +name: 779fab079c75f81e635b329b550703a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/77ba18f3d5e800bb7119cb153cb89948/params.yaml b/examples/security/kdd-nsl/output/reports/train/77ba18f3d5e800bb7119cb153cb89948/params.yaml new file mode 100644 index 00000000..78f2a4fa --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/77ba18f3d5e800bb7119cb153cb89948/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/adv_predictions.json + adv_probabilities_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/eb385de13928d460fb49b48754815e68.pkl + params_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/params.yaml + predictions_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/predictions.json + probabilities_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/probabilities.json + score_dict_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/score_dict.json + test_labels_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/test_labels.json + train_labels_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/train_labels.json + model_dir: models + name: 77ba18f3d5e800bb7119cb153cb89948 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6f8603c0c2813e093a756466e91d495c + trainer: + kwargs: {} +name: 77ba18f3d5e800bb7119cb153cb89948 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/782dd07ae0169b7e378db74c244bcf49/params.yaml b/examples/security/kdd-nsl/output/reports/train/782dd07ae0169b7e378db74c244bcf49/params.yaml new file mode 100644 index 00000000..d95a7dd1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/782dd07ae0169b7e378db74c244bcf49/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/adv_predictions.json + adv_probabilities_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d64f709e1bcb17862630f80196a24651.pkl + params_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/params.yaml + predictions_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/predictions.json + probabilities_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/probabilities.json + score_dict_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/score_dict.json + test_labels_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/test_labels.json + train_labels_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/train_labels.json + model_dir: models + name: 782dd07ae0169b7e378db74c244bcf49 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e7e10a3fe830a9a56394e28b8d69b005 + trainer: + kwargs: {} +name: 782dd07ae0169b7e378db74c244bcf49 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/7c41aefff4aae5034eafca9754fff8cd/params.yaml b/examples/security/kdd-nsl/output/reports/train/7c41aefff4aae5034eafca9754fff8cd/params.yaml new file mode 100644 index 00000000..da2559a7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/7c41aefff4aae5034eafca9754fff8cd/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/adv_predictions.json + adv_probabilities_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2d330748565264893d0dcf217255da5e.pkl + params_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/params.yaml + predictions_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/predictions.json + probabilities_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/probabilities.json + score_dict_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/score_dict.json + test_labels_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/test_labels.json + train_labels_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/train_labels.json + model_dir: models + name: 7c41aefff4aae5034eafca9754fff8cd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4bb263d05af2e590c6929fe92b9c63c6 + trainer: + kwargs: {} +name: 7c41aefff4aae5034eafca9754fff8cd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/7ee42a29581572a853e586d2c651e8a1/params.yaml b/examples/security/kdd-nsl/output/reports/train/7ee42a29581572a853e586d2c651e8a1/params.yaml new file mode 100644 index 00000000..61b9fff5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/7ee42a29581572a853e586d2c651e8a1/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/adv_predictions.json + adv_probabilities_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c4ac8fbf40a205f35e2da9f567343332.pkl + params_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/params.yaml + predictions_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/predictions.json + probabilities_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/probabilities.json + score_dict_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/score_dict.json + test_labels_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/test_labels.json + train_labels_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/train_labels.json + model_dir: models + name: 7ee42a29581572a853e586d2c651e8a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7c677332f4e3eebde90af8d1328822d7 + trainer: + kwargs: {} +name: 7ee42a29581572a853e586d2c651e8a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/7f3620980ea71b094fda7efee7dcf71e/params.yaml b/examples/security/kdd-nsl/output/reports/train/7f3620980ea71b094fda7efee7dcf71e/params.yaml new file mode 100644 index 00000000..ab64977c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/7f3620980ea71b094fda7efee7dcf71e/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/adv_predictions.json + adv_probabilities_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b7033344658616a25bbf77babdf79e7e.pkl + params_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/params.yaml + predictions_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/predictions.json + probabilities_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/probabilities.json + score_dict_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/score_dict.json + test_labels_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/test_labels.json + train_labels_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/train_labels.json + model_dir: models + name: 7f3620980ea71b094fda7efee7dcf71e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 200728ea3bee5e055569e0faca746b1e + trainer: + kwargs: {} +name: 7f3620980ea71b094fda7efee7dcf71e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/8049aaed12c0914063c26b3c438398b7/params.yaml b/examples/security/kdd-nsl/output/reports/train/8049aaed12c0914063c26b3c438398b7/params.yaml new file mode 100644 index 00000000..00bf500e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/8049aaed12c0914063c26b3c438398b7/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8049aaed12c0914063c26b3c438398b7/adv_predictions.json + adv_probabilities_file: output/reports/train/8049aaed12c0914063c26b3c438398b7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d20e5610f52eed859258dec5206d63e4.pkl + params_file: output/reports/train/8049aaed12c0914063c26b3c438398b7/params.yaml + predictions_file: output/reports/train/8049aaed12c0914063c26b3c438398b7/predictions.json + probabilities_file: output/reports/train/8049aaed12c0914063c26b3c438398b7/probabilities.json + score_dict_file: output/reports/train/8049aaed12c0914063c26b3c438398b7/score_dict.json + test_labels_file: output/reports/train/8049aaed12c0914063c26b3c438398b7/test_labels.json + train_labels_file: output/reports/train/8049aaed12c0914063c26b3c438398b7/train_labels.json + model_dir: models + name: 8049aaed12c0914063c26b3c438398b7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cb2c7f0170d56dd9751aafce0d031b07 + trainer: + kwargs: {} +name: 8049aaed12c0914063c26b3c438398b7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/818c206c6397dee7f8b3d638f8c821bb/params.yaml b/examples/security/kdd-nsl/output/reports/train/818c206c6397dee7f8b3d638f8c821bb/params.yaml new file mode 100644 index 00000000..dc8aa9e9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/818c206c6397dee7f8b3d638f8c821bb/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/818c206c6397dee7f8b3d638f8c821bb/adv_predictions.json + adv_probabilities_file: output/reports/train/818c206c6397dee7f8b3d638f8c821bb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0e37ca110a97826074916900e23a62ef.pkl + params_file: output/reports/train/818c206c6397dee7f8b3d638f8c821bb/params.yaml + predictions_file: output/reports/train/818c206c6397dee7f8b3d638f8c821bb/predictions.json + probabilities_file: output/reports/train/818c206c6397dee7f8b3d638f8c821bb/probabilities.json + score_dict_file: output/reports/train/818c206c6397dee7f8b3d638f8c821bb/score_dict.json + test_labels_file: output/reports/train/818c206c6397dee7f8b3d638f8c821bb/test_labels.json + train_labels_file: output/reports/train/818c206c6397dee7f8b3d638f8c821bb/train_labels.json + model_dir: models + name: 818c206c6397dee7f8b3d638f8c821bb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 932c54920849d53aca33d581fd7c8ccb + trainer: + kwargs: {} +name: 818c206c6397dee7f8b3d638f8c821bb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/82f316869ca511c9ca4d33e059fd104b/params.yaml b/examples/security/kdd-nsl/output/reports/train/82f316869ca511c9ca4d33e059fd104b/params.yaml new file mode 100644 index 00000000..81819da2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/82f316869ca511c9ca4d33e059fd104b/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/adv_predictions.json + adv_probabilities_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e141b4724ffe2abb3160d334f09ca3b1.pkl + params_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/params.yaml + predictions_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/predictions.json + probabilities_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/probabilities.json + score_dict_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/score_dict.json + test_labels_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/test_labels.json + train_labels_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/train_labels.json + model_dir: models + name: 82f316869ca511c9ca4d33e059fd104b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b76a0c552ac0a583e9e2edae2640d38d + trainer: + kwargs: {} +name: 82f316869ca511c9ca4d33e059fd104b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/837d7db3db3355b101bb1595a4e1a926/params.yaml b/examples/security/kdd-nsl/output/reports/train/837d7db3db3355b101bb1595a4e1a926/params.yaml new file mode 100644 index 00000000..dee37013 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/837d7db3db3355b101bb1595a4e1a926/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/837d7db3db3355b101bb1595a4e1a926/adv_predictions.json + adv_probabilities_file: output/reports/train/837d7db3db3355b101bb1595a4e1a926/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c5533d78207868b448daafb01be41003.pkl + params_file: output/reports/train/837d7db3db3355b101bb1595a4e1a926/params.yaml + predictions_file: output/reports/train/837d7db3db3355b101bb1595a4e1a926/predictions.json + probabilities_file: output/reports/train/837d7db3db3355b101bb1595a4e1a926/probabilities.json + score_dict_file: output/reports/train/837d7db3db3355b101bb1595a4e1a926/score_dict.json + test_labels_file: output/reports/train/837d7db3db3355b101bb1595a4e1a926/test_labels.json + train_labels_file: output/reports/train/837d7db3db3355b101bb1595a4e1a926/train_labels.json + model_dir: models + name: 837d7db3db3355b101bb1595a4e1a926 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b83bc2242f9cc853e468e9ece0cb8a43 + trainer: + kwargs: {} +name: 837d7db3db3355b101bb1595a4e1a926 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/84d83d958c95dd783c7d187d78e46fc9/params.yaml b/examples/security/kdd-nsl/output/reports/train/84d83d958c95dd783c7d187d78e46fc9/params.yaml new file mode 100644 index 00000000..035f8d38 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/84d83d958c95dd783c7d187d78e46fc9/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/adv_predictions.json + adv_probabilities_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/90196996615b8659025526c526c3fb29.pkl + params_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/params.yaml + predictions_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/predictions.json + probabilities_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/probabilities.json + score_dict_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/score_dict.json + test_labels_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/test_labels.json + train_labels_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/train_labels.json + model_dir: models + name: 84d83d958c95dd783c7d187d78e46fc9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 81355cdde7c0c430298ba3fc1c511381 + trainer: + kwargs: {} +name: 84d83d958c95dd783c7d187d78e46fc9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/84e6fca3461c90d0cafa5aff840174a0/params.yaml b/examples/security/kdd-nsl/output/reports/train/84e6fca3461c90d0cafa5aff840174a0/params.yaml new file mode 100644 index 00000000..6e4a2ed2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/84e6fca3461c90d0cafa5aff840174a0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/adv_predictions.json + adv_probabilities_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1cddf23e83a6c39caed92d019bb66c83.pkl + params_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/params.yaml + predictions_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/predictions.json + probabilities_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/probabilities.json + score_dict_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/score_dict.json + test_labels_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/test_labels.json + train_labels_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/train_labels.json + model_dir: models + name: 84e6fca3461c90d0cafa5aff840174a0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 11f979ac41f22ec0c88a08ce206385a3 + trainer: + kwargs: {} +name: 84e6fca3461c90d0cafa5aff840174a0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/85f53577d10b98ca944834793126f95b/params.yaml b/examples/security/kdd-nsl/output/reports/train/85f53577d10b98ca944834793126f95b/params.yaml new file mode 100644 index 00000000..bfe382b2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/85f53577d10b98ca944834793126f95b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/85f53577d10b98ca944834793126f95b/adv_predictions.json + adv_probabilities_file: output/reports/train/85f53577d10b98ca944834793126f95b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/dfcbeba1599d9ea21359edf86436df2e.pkl + params_file: output/reports/train/85f53577d10b98ca944834793126f95b/params.yaml + predictions_file: output/reports/train/85f53577d10b98ca944834793126f95b/predictions.json + probabilities_file: output/reports/train/85f53577d10b98ca944834793126f95b/probabilities.json + score_dict_file: output/reports/train/85f53577d10b98ca944834793126f95b/score_dict.json + test_labels_file: output/reports/train/85f53577d10b98ca944834793126f95b/test_labels.json + train_labels_file: output/reports/train/85f53577d10b98ca944834793126f95b/train_labels.json + model_dir: models + name: 85f53577d10b98ca944834793126f95b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0a6c17fce5baa906a19793a3a49796d2 + trainer: + kwargs: {} +name: 85f53577d10b98ca944834793126f95b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/860a9a3bd0730834b768e6a38081c1a9/params.yaml b/examples/security/kdd-nsl/output/reports/train/860a9a3bd0730834b768e6a38081c1a9/params.yaml new file mode 100644 index 00000000..22d90a72 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/860a9a3bd0730834b768e6a38081c1a9/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/adv_predictions.json + adv_probabilities_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/82f9617be2283b54226f1f4cb01a2d39.pkl + params_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/params.yaml + predictions_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/predictions.json + probabilities_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/probabilities.json + score_dict_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/score_dict.json + test_labels_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/test_labels.json + train_labels_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/train_labels.json + model_dir: models + name: 860a9a3bd0730834b768e6a38081c1a9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e9d07b2c6acf28e3fb2dfa5122dd9a95 + trainer: + kwargs: {} +name: 860a9a3bd0730834b768e6a38081c1a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/params.yaml b/examples/security/kdd-nsl/output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/params.yaml new file mode 100644 index 00000000..e3091e1d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/adv_predictions.json + adv_probabilities_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d6031f071a912dcbf78d29e355a6f9a9.pkl + params_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/params.yaml + predictions_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/predictions.json + probabilities_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/probabilities.json + score_dict_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/score_dict.json + test_labels_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/test_labels.json + train_labels_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/train_labels.json + model_dir: models + name: 86c1cc0fee35c935cc66b050d4a1f412 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 86e09315f804cc2656b3496b91417e29 + trainer: + kwargs: {} +name: 86c1cc0fee35c935cc66b050d4a1f412 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/87384318e64acca7c5091009637c4299/params.yaml b/examples/security/kdd-nsl/output/reports/train/87384318e64acca7c5091009637c4299/params.yaml new file mode 100644 index 00000000..cf832edb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/87384318e64acca7c5091009637c4299/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/87384318e64acca7c5091009637c4299/adv_predictions.json + adv_probabilities_file: output/reports/train/87384318e64acca7c5091009637c4299/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/244ee3d86ce2e3ab4eb25589933c3753.pkl + params_file: output/reports/train/87384318e64acca7c5091009637c4299/params.yaml + predictions_file: output/reports/train/87384318e64acca7c5091009637c4299/predictions.json + probabilities_file: output/reports/train/87384318e64acca7c5091009637c4299/probabilities.json + score_dict_file: output/reports/train/87384318e64acca7c5091009637c4299/score_dict.json + test_labels_file: output/reports/train/87384318e64acca7c5091009637c4299/test_labels.json + train_labels_file: output/reports/train/87384318e64acca7c5091009637c4299/train_labels.json + model_dir: models + name: 87384318e64acca7c5091009637c4299 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b783edaed0bc4f91c81c8204d9f8bb5e + trainer: + kwargs: {} +name: 87384318e64acca7c5091009637c4299 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/8795b51bbb44d40edca644ea03e5f277/params.yaml b/examples/security/kdd-nsl/output/reports/train/8795b51bbb44d40edca644ea03e5f277/params.yaml new file mode 100644 index 00000000..a2fc40c9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/8795b51bbb44d40edca644ea03e5f277/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/adv_predictions.json + adv_probabilities_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/361084365ff77568270a8c55f831aea9.pkl + params_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/params.yaml + predictions_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/predictions.json + probabilities_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/probabilities.json + score_dict_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/score_dict.json + test_labels_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/test_labels.json + train_labels_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/train_labels.json + model_dir: models + name: 8795b51bbb44d40edca644ea03e5f277 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0ec18cab85e720600c210333de81cbf6 + trainer: + kwargs: {} +name: 8795b51bbb44d40edca644ea03e5f277 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/8816698a6a16f21c14cc15ab34ca584e/params.yaml b/examples/security/kdd-nsl/output/reports/train/8816698a6a16f21c14cc15ab34ca584e/params.yaml new file mode 100644 index 00000000..1c394e7d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/8816698a6a16f21c14cc15ab34ca584e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/adv_predictions.json + adv_probabilities_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/208020f3631375bd5e8e8e67d8f6101e.pkl + params_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/params.yaml + predictions_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/predictions.json + probabilities_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/probabilities.json + score_dict_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/score_dict.json + test_labels_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/test_labels.json + train_labels_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/train_labels.json + model_dir: models + name: 8816698a6a16f21c14cc15ab34ca584e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0d7be139ef573a55db065b72ad210dea + trainer: + kwargs: {} +name: 8816698a6a16f21c14cc15ab34ca584e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/params.yaml b/examples/security/kdd-nsl/output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/params.yaml new file mode 100644 index 00000000..5f84f191 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/adv_predictions.json + adv_probabilities_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/95c45dd019f4bb553d27761382f00bd7.pkl + params_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/params.yaml + predictions_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/predictions.json + probabilities_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/probabilities.json + score_dict_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/score_dict.json + test_labels_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/test_labels.json + train_labels_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/train_labels.json + model_dir: models + name: 889ed2f2bcd91e3219ecadc03eb2c80b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f9ffae8c6fa0d26df2df092569559a18 + trainer: + kwargs: {} +name: 889ed2f2bcd91e3219ecadc03eb2c80b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/89de9539606329a8874fcd4ce683828a/params.yaml b/examples/security/kdd-nsl/output/reports/train/89de9539606329a8874fcd4ce683828a/params.yaml new file mode 100644 index 00000000..55e9e02d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/89de9539606329a8874fcd4ce683828a/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/89de9539606329a8874fcd4ce683828a/adv_predictions.json + adv_probabilities_file: output/reports/train/89de9539606329a8874fcd4ce683828a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bab082410d16849aba08451d9b93e136.pkl + params_file: output/reports/train/89de9539606329a8874fcd4ce683828a/params.yaml + predictions_file: output/reports/train/89de9539606329a8874fcd4ce683828a/predictions.json + probabilities_file: output/reports/train/89de9539606329a8874fcd4ce683828a/probabilities.json + score_dict_file: output/reports/train/89de9539606329a8874fcd4ce683828a/score_dict.json + test_labels_file: output/reports/train/89de9539606329a8874fcd4ce683828a/test_labels.json + train_labels_file: output/reports/train/89de9539606329a8874fcd4ce683828a/train_labels.json + model_dir: models + name: 89de9539606329a8874fcd4ce683828a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6033d7ecae6038a6241254a869cd81ad + trainer: + kwargs: {} +name: 89de9539606329a8874fcd4ce683828a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/8a6903d24bc4431ef2b5805888a72802/params.yaml b/examples/security/kdd-nsl/output/reports/train/8a6903d24bc4431ef2b5805888a72802/params.yaml new file mode 100644 index 00000000..a3dfff79 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/8a6903d24bc4431ef2b5805888a72802/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/adv_predictions.json + adv_probabilities_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1574cbac3beba3f7526935a2147e0d3e.pkl + params_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/params.yaml + predictions_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/predictions.json + probabilities_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/probabilities.json + score_dict_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/score_dict.json + test_labels_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/test_labels.json + train_labels_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/train_labels.json + model_dir: models + name: 8a6903d24bc4431ef2b5805888a72802 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 500525a43140917eae46d6037625e53c + trainer: + kwargs: {} +name: 8a6903d24bc4431ef2b5805888a72802 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/params.yaml b/examples/security/kdd-nsl/output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/params.yaml new file mode 100644 index 00000000..3a527c69 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/adv_predictions.json + adv_probabilities_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/84fe807edaa7330e58091270908f8099.pkl + params_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/params.yaml + predictions_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/predictions.json + probabilities_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/probabilities.json + score_dict_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/score_dict.json + test_labels_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/test_labels.json + train_labels_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/train_labels.json + model_dir: models + name: 8bc3572e65778bb6ea8b519d7a91c476 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0d69263260f77291d38dbe8d8c97c145 + trainer: + kwargs: {} +name: 8bc3572e65778bb6ea8b519d7a91c476 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/params.yaml b/examples/security/kdd-nsl/output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/params.yaml new file mode 100644 index 00000000..af53a1c0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/adv_predictions.json + adv_probabilities_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/49e8b6971ac0e87e8586d0e3471606f8.pkl + params_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/params.yaml + predictions_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/predictions.json + probabilities_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/probabilities.json + score_dict_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/score_dict.json + test_labels_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/test_labels.json + train_labels_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/train_labels.json + model_dir: models + name: 8bd609c97c47ce1ee390d76da1fe5b42 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d062c309fba85516e6be5305905669e0 + trainer: + kwargs: {} +name: 8bd609c97c47ce1ee390d76da1fe5b42 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/905f8f492a02f653e3cb6616942d8b04/params.yaml b/examples/security/kdd-nsl/output/reports/train/905f8f492a02f653e3cb6616942d8b04/params.yaml new file mode 100644 index 00000000..10f3e691 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/905f8f492a02f653e3cb6616942d8b04/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/adv_predictions.json + adv_probabilities_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3c91cb775686e3281c2f0fd98d6a8dcd.pkl + params_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/params.yaml + predictions_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/predictions.json + probabilities_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/probabilities.json + score_dict_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/score_dict.json + test_labels_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/test_labels.json + train_labels_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/train_labels.json + model_dir: models + name: 905f8f492a02f653e3cb6616942d8b04 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1b9515ff1f9ee54122648f356726d99a + trainer: + kwargs: {} +name: 905f8f492a02f653e3cb6616942d8b04 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/91ab830bc8c897fd606ae1cec526ee84/params.yaml b/examples/security/kdd-nsl/output/reports/train/91ab830bc8c897fd606ae1cec526ee84/params.yaml new file mode 100644 index 00000000..8c534222 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/91ab830bc8c897fd606ae1cec526ee84/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/adv_predictions.json + adv_probabilities_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/aed1c02e033ee1e92829ff322b9753ff.pkl + params_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/params.yaml + predictions_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/predictions.json + probabilities_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/probabilities.json + score_dict_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/score_dict.json + test_labels_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/test_labels.json + train_labels_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/train_labels.json + model_dir: models + name: 91ab830bc8c897fd606ae1cec526ee84 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9913b0d20830fbe35b016c25b249dd25 + trainer: + kwargs: {} +name: 91ab830bc8c897fd606ae1cec526ee84 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/params.yaml b/examples/security/kdd-nsl/output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/params.yaml new file mode 100644 index 00000000..72695d0d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/adv_predictions.json + adv_probabilities_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c52308bdb7097625851509bde64ae9fe.pkl + params_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/params.yaml + predictions_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/predictions.json + probabilities_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/probabilities.json + score_dict_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/score_dict.json + test_labels_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/test_labels.json + train_labels_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/train_labels.json + model_dir: models + name: 9365d63e7bba7e6290257c8a201cb2d6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0f763e2f47024db878c0e59575bd7d70 + trainer: + kwargs: {} +name: 9365d63e7bba7e6290257c8a201cb2d6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/93d6f960454e7837e84b673384c598ba/params.yaml b/examples/security/kdd-nsl/output/reports/train/93d6f960454e7837e84b673384c598ba/params.yaml new file mode 100644 index 00000000..6a5fadf2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/93d6f960454e7837e84b673384c598ba/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/93d6f960454e7837e84b673384c598ba/adv_predictions.json + adv_probabilities_file: output/reports/train/93d6f960454e7837e84b673384c598ba/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c290ac0a38e1ea25a1f3da344ede6580.pkl + params_file: output/reports/train/93d6f960454e7837e84b673384c598ba/params.yaml + predictions_file: output/reports/train/93d6f960454e7837e84b673384c598ba/predictions.json + probabilities_file: output/reports/train/93d6f960454e7837e84b673384c598ba/probabilities.json + score_dict_file: output/reports/train/93d6f960454e7837e84b673384c598ba/score_dict.json + test_labels_file: output/reports/train/93d6f960454e7837e84b673384c598ba/test_labels.json + train_labels_file: output/reports/train/93d6f960454e7837e84b673384c598ba/train_labels.json + model_dir: models + name: 93d6f960454e7837e84b673384c598ba + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 965b4f57e09a82d12e924d0fa95aa05a + trainer: + kwargs: {} +name: 93d6f960454e7837e84b673384c598ba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/943c577d16570eb1f57bee91124c8720/params.yaml b/examples/security/kdd-nsl/output/reports/train/943c577d16570eb1f57bee91124c8720/params.yaml new file mode 100644 index 00000000..2c463820 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/943c577d16570eb1f57bee91124c8720/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/943c577d16570eb1f57bee91124c8720/adv_predictions.json + adv_probabilities_file: output/reports/train/943c577d16570eb1f57bee91124c8720/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1cc355dccdda02959d6979b95d56bb53.pkl + params_file: output/reports/train/943c577d16570eb1f57bee91124c8720/params.yaml + predictions_file: output/reports/train/943c577d16570eb1f57bee91124c8720/predictions.json + probabilities_file: output/reports/train/943c577d16570eb1f57bee91124c8720/probabilities.json + score_dict_file: output/reports/train/943c577d16570eb1f57bee91124c8720/score_dict.json + test_labels_file: output/reports/train/943c577d16570eb1f57bee91124c8720/test_labels.json + train_labels_file: output/reports/train/943c577d16570eb1f57bee91124c8720/train_labels.json + model_dir: models + name: 943c577d16570eb1f57bee91124c8720 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b590421be19f0d98abbb5772d2a18cb8 + trainer: + kwargs: {} +name: 943c577d16570eb1f57bee91124c8720 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/95e2df263fdfcf00feece4e43edaf291/params.yaml b/examples/security/kdd-nsl/output/reports/train/95e2df263fdfcf00feece4e43edaf291/params.yaml new file mode 100644 index 00000000..5ac95b02 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/95e2df263fdfcf00feece4e43edaf291/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/adv_predictions.json + adv_probabilities_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/params.yaml + predictions_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/predictions.json + probabilities_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/probabilities.json + score_dict_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/score_dict.json + test_labels_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/test_labels.json + train_labels_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/train_labels.json + model_dir: models + name: 95e2df263fdfcf00feece4e43edaf291 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 95e2df263fdfcf00feece4e43edaf291 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/params.yaml b/examples/security/kdd-nsl/output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/params.yaml new file mode 100644 index 00000000..bbf1af49 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/adv_predictions.json + adv_probabilities_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/params.yaml + predictions_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/predictions.json + probabilities_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/probabilities.json + score_dict_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/score_dict.json + test_labels_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/test_labels.json + train_labels_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/train_labels.json + model_dir: models + name: 96c88f6adfa37ab6f3eb63042d1b3e73 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 96c88f6adfa37ab6f3eb63042d1b3e73 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/params.yaml b/examples/security/kdd-nsl/output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/params.yaml new file mode 100644 index 00000000..9cbe3869 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/adv_predictions.json + adv_probabilities_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fdaf1c214cd6a782c215644097d3c69.pkl + params_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/params.yaml + predictions_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/predictions.json + probabilities_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/probabilities.json + score_dict_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/score_dict.json + test_labels_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/test_labels.json + train_labels_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/train_labels.json + model_dir: models + name: 9836579d277e189d5b9c8f4aa4a399a0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edc33615699e2d68200161d9ed2c3684 + trainer: + kwargs: {} +name: 9836579d277e189d5b9c8f4aa4a399a0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/9893d0a2753784d80d6075311001d407/params.yaml b/examples/security/kdd-nsl/output/reports/train/9893d0a2753784d80d6075311001d407/params.yaml new file mode 100644 index 00000000..effa6d38 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/9893d0a2753784d80d6075311001d407/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9893d0a2753784d80d6075311001d407/adv_predictions.json + adv_probabilities_file: output/reports/train/9893d0a2753784d80d6075311001d407/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f3883347bfdc0e430736b8e0b6e65cec.pkl + params_file: output/reports/train/9893d0a2753784d80d6075311001d407/params.yaml + predictions_file: output/reports/train/9893d0a2753784d80d6075311001d407/predictions.json + probabilities_file: output/reports/train/9893d0a2753784d80d6075311001d407/probabilities.json + score_dict_file: output/reports/train/9893d0a2753784d80d6075311001d407/score_dict.json + test_labels_file: output/reports/train/9893d0a2753784d80d6075311001d407/test_labels.json + train_labels_file: output/reports/train/9893d0a2753784d80d6075311001d407/train_labels.json + model_dir: models + name: 9893d0a2753784d80d6075311001d407 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2c289ac904500e2cf35198378f8717ff + trainer: + kwargs: {} +name: 9893d0a2753784d80d6075311001d407 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/98d29e28126b4d6f63b8be8c4d5b6ffd/params.yaml b/examples/security/kdd-nsl/output/reports/train/98d29e28126b4d6f63b8be8c4d5b6ffd/params.yaml new file mode 100644 index 00000000..14411aee --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/98d29e28126b4d6f63b8be8c4d5b6ffd/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/98d29e28126b4d6f63b8be8c4d5b6ffd/adv_predictions.json + adv_probabilities_file: output/reports/train/98d29e28126b4d6f63b8be8c4d5b6ffd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/8c933635595127586b05beda1995d128.pkl + params_file: output/reports/train/98d29e28126b4d6f63b8be8c4d5b6ffd/params.yaml + predictions_file: output/reports/train/98d29e28126b4d6f63b8be8c4d5b6ffd/predictions.json + probabilities_file: output/reports/train/98d29e28126b4d6f63b8be8c4d5b6ffd/probabilities.json + score_dict_file: output/reports/train/98d29e28126b4d6f63b8be8c4d5b6ffd/score_dict.json + test_labels_file: output/reports/train/98d29e28126b4d6f63b8be8c4d5b6ffd/test_labels.json + train_labels_file: output/reports/train/98d29e28126b4d6f63b8be8c4d5b6ffd/train_labels.json + model_dir: models + name: 98d29e28126b4d6f63b8be8c4d5b6ffd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8a1845a0e9bad093ca2e99ba365e3806 + trainer: + kwargs: {} +name: 98d29e28126b4d6f63b8be8c4d5b6ffd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/params.yaml b/examples/security/kdd-nsl/output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/params.yaml new file mode 100644 index 00000000..d5194ca9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/adv_predictions.json + adv_probabilities_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5dd86492a6cbbf2a7e14bada9289ec49.pkl + params_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/params.yaml + predictions_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/predictions.json + probabilities_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/probabilities.json + score_dict_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/score_dict.json + test_labels_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/test_labels.json + train_labels_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/train_labels.json + model_dir: models + name: 9c7e9a28ab9e24a1c2159d743f7d7908 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 537c7565c6f401b821b243ff38ec023a + trainer: + kwargs: {} +name: 9c7e9a28ab9e24a1c2159d743f7d7908 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/params.yaml b/examples/security/kdd-nsl/output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/params.yaml new file mode 100644 index 00000000..9fc46b34 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/adv_predictions.json + adv_probabilities_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/394ad8e1ae1b6d55421de3a49138d43f.pkl + params_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/params.yaml + predictions_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/predictions.json + probabilities_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/probabilities.json + score_dict_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/score_dict.json + test_labels_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/test_labels.json + train_labels_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/train_labels.json + model_dir: models + name: 9dc1caf993f2f97edaae2f893a3c8984 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 72c794cd2d57355205b8ff7c9668bf2d + trainer: + kwargs: {} +name: 9dc1caf993f2f97edaae2f893a3c8984 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/9f36dea9f543144d55c096487c6df3c5/params.yaml b/examples/security/kdd-nsl/output/reports/train/9f36dea9f543144d55c096487c6df3c5/params.yaml new file mode 100644 index 00000000..73d70763 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/9f36dea9f543144d55c096487c6df3c5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/adv_predictions.json + adv_probabilities_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/46028503695aab4300983ca3db084769.pkl + params_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/params.yaml + predictions_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/predictions.json + probabilities_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/probabilities.json + score_dict_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/score_dict.json + test_labels_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/test_labels.json + train_labels_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/train_labels.json + model_dir: models + name: 9f36dea9f543144d55c096487c6df3c5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6021281724e72d73f3988d9e814f08f8 + trainer: + kwargs: {} +name: 9f36dea9f543144d55c096487c6df3c5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/9f6254697c52aa6b7861db36551ef5fb/params.yaml b/examples/security/kdd-nsl/output/reports/train/9f6254697c52aa6b7861db36551ef5fb/params.yaml new file mode 100644 index 00000000..9257b6e4 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/9f6254697c52aa6b7861db36551ef5fb/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/adv_predictions.json + adv_probabilities_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7778eaac0301fc8206593f78a9e659a2.pkl + params_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/params.yaml + predictions_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/predictions.json + probabilities_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/probabilities.json + score_dict_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/score_dict.json + test_labels_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/test_labels.json + train_labels_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/train_labels.json + model_dir: models + name: 9f6254697c52aa6b7861db36551ef5fb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b1ac88da5ee0f374a5179995ff2cadee + trainer: + kwargs: {} +name: 9f6254697c52aa6b7861db36551ef5fb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/9ffdf06cbbbee8c9ad53ecd2024c1c47/params.yaml b/examples/security/kdd-nsl/output/reports/train/9ffdf06cbbbee8c9ad53ecd2024c1c47/params.yaml new file mode 100644 index 00000000..69047efb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/9ffdf06cbbbee8c9ad53ecd2024c1c47/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9ffdf06cbbbee8c9ad53ecd2024c1c47/adv_predictions.json + adv_probabilities_file: output/reports/train/9ffdf06cbbbee8c9ad53ecd2024c1c47/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d9291358a1d1395903e298fd206c4706.pkl + params_file: output/reports/train/9ffdf06cbbbee8c9ad53ecd2024c1c47/params.yaml + predictions_file: output/reports/train/9ffdf06cbbbee8c9ad53ecd2024c1c47/predictions.json + probabilities_file: output/reports/train/9ffdf06cbbbee8c9ad53ecd2024c1c47/probabilities.json + score_dict_file: output/reports/train/9ffdf06cbbbee8c9ad53ecd2024c1c47/score_dict.json + test_labels_file: output/reports/train/9ffdf06cbbbee8c9ad53ecd2024c1c47/test_labels.json + train_labels_file: output/reports/train/9ffdf06cbbbee8c9ad53ecd2024c1c47/train_labels.json + model_dir: models + name: 9ffdf06cbbbee8c9ad53ecd2024c1c47 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8048b67d6414423d7bb5e5ed0c26b2ff + trainer: + kwargs: {} +name: 9ffdf06cbbbee8c9ad53ecd2024c1c47 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/params.yaml b/examples/security/kdd-nsl/output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/params.yaml new file mode 100644 index 00000000..619aed14 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/adv_predictions.json + adv_probabilities_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/36eecdd1163862cc72097a011a0bb54f.pkl + params_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/params.yaml + predictions_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/predictions.json + probabilities_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/probabilities.json + score_dict_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/score_dict.json + test_labels_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/test_labels.json + train_labels_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/train_labels.json + model_dir: models + name: a007a4b353dc9bbadd10aafd8f9d687d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b492bf10d680f6d06927a6df4ca0f171 + trainer: + kwargs: {} +name: a007a4b353dc9bbadd10aafd8f9d687d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/params.yaml b/examples/security/kdd-nsl/output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/params.yaml new file mode 100644 index 00000000..fe53fdd1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/adv_predictions.json + adv_probabilities_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/10dfc126a600c8994aed911c14e48db9.pkl + params_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/params.yaml + predictions_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/predictions.json + probabilities_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/probabilities.json + score_dict_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/score_dict.json + test_labels_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/test_labels.json + train_labels_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/train_labels.json + model_dir: models + name: a1fb169f96a8444a23cdd39a80ee6d47 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7076851e723c3a6f54ad275b429ae9e0 + trainer: + kwargs: {} +name: a1fb169f96a8444a23cdd39a80ee6d47 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/a31024a88b71ed0f4b421f352dca4ab1/params.yaml b/examples/security/kdd-nsl/output/reports/train/a31024a88b71ed0f4b421f352dca4ab1/params.yaml new file mode 100644 index 00000000..01be2082 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/a31024a88b71ed0f4b421f352dca4ab1/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a31024a88b71ed0f4b421f352dca4ab1/adv_predictions.json + adv_probabilities_file: output/reports/train/a31024a88b71ed0f4b421f352dca4ab1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/96c7c27df8ac673f59e2f26992306e01.pkl + params_file: output/reports/train/a31024a88b71ed0f4b421f352dca4ab1/params.yaml + predictions_file: output/reports/train/a31024a88b71ed0f4b421f352dca4ab1/predictions.json + probabilities_file: output/reports/train/a31024a88b71ed0f4b421f352dca4ab1/probabilities.json + score_dict_file: output/reports/train/a31024a88b71ed0f4b421f352dca4ab1/score_dict.json + test_labels_file: output/reports/train/a31024a88b71ed0f4b421f352dca4ab1/test_labels.json + train_labels_file: output/reports/train/a31024a88b71ed0f4b421f352dca4ab1/train_labels.json + model_dir: models + name: a31024a88b71ed0f4b421f352dca4ab1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 048ace38081f0f1682e4095a49328f84 + trainer: + kwargs: {} +name: a31024a88b71ed0f4b421f352dca4ab1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/params.yaml b/examples/security/kdd-nsl/output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/params.yaml new file mode 100644 index 00000000..95956a6a --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/adv_predictions.json + adv_probabilities_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/6612d4c2407cca3c3e51abbe04890492.pkl + params_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/params.yaml + predictions_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/predictions.json + probabilities_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/probabilities.json + score_dict_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/score_dict.json + test_labels_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/test_labels.json + train_labels_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/train_labels.json + model_dir: models + name: a460f6be5c0a6a2754e9e251bc569ed9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3d819e8fd68cd4cbb78507ca5dc981d2 + trainer: + kwargs: {} +name: a460f6be5c0a6a2754e9e251bc569ed9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/a488c2e7396a0909134398bb74b77e13/params.yaml b/examples/security/kdd-nsl/output/reports/train/a488c2e7396a0909134398bb74b77e13/params.yaml new file mode 100644 index 00000000..fee05420 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/a488c2e7396a0909134398bb74b77e13/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a488c2e7396a0909134398bb74b77e13/adv_predictions.json + adv_probabilities_file: output/reports/train/a488c2e7396a0909134398bb74b77e13/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/151e2433ae219430435decd66f696ecf.pkl + params_file: output/reports/train/a488c2e7396a0909134398bb74b77e13/params.yaml + predictions_file: output/reports/train/a488c2e7396a0909134398bb74b77e13/predictions.json + probabilities_file: output/reports/train/a488c2e7396a0909134398bb74b77e13/probabilities.json + score_dict_file: output/reports/train/a488c2e7396a0909134398bb74b77e13/score_dict.json + test_labels_file: output/reports/train/a488c2e7396a0909134398bb74b77e13/test_labels.json + train_labels_file: output/reports/train/a488c2e7396a0909134398bb74b77e13/train_labels.json + model_dir: models + name: a488c2e7396a0909134398bb74b77e13 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 418120c864d8db0877f4d206d124dbee + trainer: + kwargs: {} +name: a488c2e7396a0909134398bb74b77e13 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/a532b7776cd833c0f3998b72c9e91271/params.yaml b/examples/security/kdd-nsl/output/reports/train/a532b7776cd833c0f3998b72c9e91271/params.yaml new file mode 100644 index 00000000..df8ef4f4 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/a532b7776cd833c0f3998b72c9e91271/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/adv_predictions.json + adv_probabilities_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5b24c80b5d8f8501afcdbeb1aa749818.pkl + params_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/params.yaml + predictions_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/predictions.json + probabilities_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/probabilities.json + score_dict_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/score_dict.json + test_labels_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/test_labels.json + train_labels_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/train_labels.json + model_dir: models + name: a532b7776cd833c0f3998b72c9e91271 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: debd35766f31aeea8351900c315a874b + trainer: + kwargs: {} +name: a532b7776cd833c0f3998b72c9e91271 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/a5a243acd41213e404b822e4c1258340/params.yaml b/examples/security/kdd-nsl/output/reports/train/a5a243acd41213e404b822e4c1258340/params.yaml new file mode 100644 index 00000000..167fe970 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/a5a243acd41213e404b822e4c1258340/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a5a243acd41213e404b822e4c1258340/adv_predictions.json + adv_probabilities_file: output/reports/train/a5a243acd41213e404b822e4c1258340/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/622869a0696f375552778d3877f1726a.pkl + params_file: output/reports/train/a5a243acd41213e404b822e4c1258340/params.yaml + predictions_file: output/reports/train/a5a243acd41213e404b822e4c1258340/predictions.json + probabilities_file: output/reports/train/a5a243acd41213e404b822e4c1258340/probabilities.json + score_dict_file: output/reports/train/a5a243acd41213e404b822e4c1258340/score_dict.json + test_labels_file: output/reports/train/a5a243acd41213e404b822e4c1258340/test_labels.json + train_labels_file: output/reports/train/a5a243acd41213e404b822e4c1258340/train_labels.json + model_dir: models + name: a5a243acd41213e404b822e4c1258340 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1b17b167b30bad032fa0a10e73d82d4c + trainer: + kwargs: {} +name: a5a243acd41213e404b822e4c1258340 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/params.yaml b/examples/security/kdd-nsl/output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/params.yaml new file mode 100644 index 00000000..98240115 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/adv_predictions.json + adv_probabilities_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/cf1f628b162783c52830fb1eaae0a2be.pkl + params_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/params.yaml + predictions_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/predictions.json + probabilities_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/probabilities.json + score_dict_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/score_dict.json + test_labels_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/test_labels.json + train_labels_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/train_labels.json + model_dir: models + name: a61f42f974129fdfeb8fa2f97a4368a8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 826c8ac76156b0219e52fd85a659f3c2 + trainer: + kwargs: {} +name: a61f42f974129fdfeb8fa2f97a4368a8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/a7320d911ecff8fc080839dc5b3aad26/params.yaml b/examples/security/kdd-nsl/output/reports/train/a7320d911ecff8fc080839dc5b3aad26/params.yaml new file mode 100644 index 00000000..d1586eb4 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/a7320d911ecff8fc080839dc5b3aad26/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/adv_predictions.json + adv_probabilities_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7b60136ba6a4dc09e998c342f162fc9c.pkl + params_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/params.yaml + predictions_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/predictions.json + probabilities_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/probabilities.json + score_dict_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/score_dict.json + test_labels_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/test_labels.json + train_labels_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/train_labels.json + model_dir: models + name: a7320d911ecff8fc080839dc5b3aad26 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3af2177580d586c1759ab626a4fbf5f6 + trainer: + kwargs: {} +name: a7320d911ecff8fc080839dc5b3aad26 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/a87a8b5b183011ec945a9517f2673853/params.yaml b/examples/security/kdd-nsl/output/reports/train/a87a8b5b183011ec945a9517f2673853/params.yaml new file mode 100644 index 00000000..c5e28ffc --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/a87a8b5b183011ec945a9517f2673853/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a87a8b5b183011ec945a9517f2673853/adv_predictions.json + adv_probabilities_file: output/reports/train/a87a8b5b183011ec945a9517f2673853/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/db2795f0777ba97d38875f995a0e3895.pkl + params_file: output/reports/train/a87a8b5b183011ec945a9517f2673853/params.yaml + predictions_file: output/reports/train/a87a8b5b183011ec945a9517f2673853/predictions.json + probabilities_file: output/reports/train/a87a8b5b183011ec945a9517f2673853/probabilities.json + score_dict_file: output/reports/train/a87a8b5b183011ec945a9517f2673853/score_dict.json + test_labels_file: output/reports/train/a87a8b5b183011ec945a9517f2673853/test_labels.json + train_labels_file: output/reports/train/a87a8b5b183011ec945a9517f2673853/train_labels.json + model_dir: models + name: a87a8b5b183011ec945a9517f2673853 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 712678e6988f24915c65273ec01dd70d + trainer: + kwargs: {} +name: a87a8b5b183011ec945a9517f2673853 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/params.yaml b/examples/security/kdd-nsl/output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/params.yaml new file mode 100644 index 00000000..0327c2a6 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/adv_predictions.json + adv_probabilities_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3fe69eae40c98998e036a6fd2f5fd577.pkl + params_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/params.yaml + predictions_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/predictions.json + probabilities_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/probabilities.json + score_dict_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/score_dict.json + test_labels_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/test_labels.json + train_labels_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/train_labels.json + model_dir: models + name: a94ba93fdd86d30246a2a372c73b50bd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cef5a66166a703b9c01e61722b497763 + trainer: + kwargs: {} +name: a94ba93fdd86d30246a2a372c73b50bd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/ab644d466da7bb5264bb230c9323827d/params.yaml b/examples/security/kdd-nsl/output/reports/train/ab644d466da7bb5264bb230c9323827d/params.yaml new file mode 100644 index 00000000..5a5945d8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/ab644d466da7bb5264bb230c9323827d/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/adv_predictions.json + adv_probabilities_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/37783d2e2cfdb1dad06abb06462574af.pkl + params_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/params.yaml + predictions_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/predictions.json + probabilities_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/probabilities.json + score_dict_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/score_dict.json + test_labels_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/test_labels.json + train_labels_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/train_labels.json + model_dir: models + name: ab644d466da7bb5264bb230c9323827d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9858ad55b247dc1840750194b1beeb10 + trainer: + kwargs: {} +name: ab644d466da7bb5264bb230c9323827d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/ad6a617bb7bb75f3f34c0484fcb1d1a3/params.yaml b/examples/security/kdd-nsl/output/reports/train/ad6a617bb7bb75f3f34c0484fcb1d1a3/params.yaml new file mode 100644 index 00000000..fab4740d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/ad6a617bb7bb75f3f34c0484fcb1d1a3/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ad6a617bb7bb75f3f34c0484fcb1d1a3/adv_predictions.json + adv_probabilities_file: output/reports/train/ad6a617bb7bb75f3f34c0484fcb1d1a3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/96e7683d2f781f446f3bffd42271ddbb.pkl + params_file: output/reports/train/ad6a617bb7bb75f3f34c0484fcb1d1a3/params.yaml + predictions_file: output/reports/train/ad6a617bb7bb75f3f34c0484fcb1d1a3/predictions.json + probabilities_file: output/reports/train/ad6a617bb7bb75f3f34c0484fcb1d1a3/probabilities.json + score_dict_file: output/reports/train/ad6a617bb7bb75f3f34c0484fcb1d1a3/score_dict.json + test_labels_file: output/reports/train/ad6a617bb7bb75f3f34c0484fcb1d1a3/test_labels.json + train_labels_file: output/reports/train/ad6a617bb7bb75f3f34c0484fcb1d1a3/train_labels.json + model_dir: models + name: ad6a617bb7bb75f3f34c0484fcb1d1a3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4bb945c3fb7f6fab8cbc18cca9028bac + trainer: + kwargs: {} +name: ad6a617bb7bb75f3f34c0484fcb1d1a3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b13570da2a0a0f8669c00ac526a15876/params.yaml b/examples/security/kdd-nsl/output/reports/train/b13570da2a0a0f8669c00ac526a15876/params.yaml new file mode 100644 index 00000000..a065c160 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b13570da2a0a0f8669c00ac526a15876/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b13570da2a0a0f8669c00ac526a15876/adv_predictions.json + adv_probabilities_file: output/reports/train/b13570da2a0a0f8669c00ac526a15876/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1476d350464fcac9bc19b7fd3190cf9a.pkl + params_file: output/reports/train/b13570da2a0a0f8669c00ac526a15876/params.yaml + predictions_file: output/reports/train/b13570da2a0a0f8669c00ac526a15876/predictions.json + probabilities_file: output/reports/train/b13570da2a0a0f8669c00ac526a15876/probabilities.json + score_dict_file: output/reports/train/b13570da2a0a0f8669c00ac526a15876/score_dict.json + test_labels_file: output/reports/train/b13570da2a0a0f8669c00ac526a15876/test_labels.json + train_labels_file: output/reports/train/b13570da2a0a0f8669c00ac526a15876/train_labels.json + model_dir: models + name: b13570da2a0a0f8669c00ac526a15876 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3d6831eb6beab079998f187df879aa97 + trainer: + kwargs: {} +name: b13570da2a0a0f8669c00ac526a15876 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b177be5f7b6fbb6e2ac33401c0ea3a12/params.yaml b/examples/security/kdd-nsl/output/reports/train/b177be5f7b6fbb6e2ac33401c0ea3a12/params.yaml new file mode 100644 index 00000000..995a8873 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b177be5f7b6fbb6e2ac33401c0ea3a12/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b177be5f7b6fbb6e2ac33401c0ea3a12/adv_predictions.json + adv_probabilities_file: output/reports/train/b177be5f7b6fbb6e2ac33401c0ea3a12/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/6469125ad20952a54643ad6a604e7b44.pkl + params_file: output/reports/train/b177be5f7b6fbb6e2ac33401c0ea3a12/params.yaml + predictions_file: output/reports/train/b177be5f7b6fbb6e2ac33401c0ea3a12/predictions.json + probabilities_file: output/reports/train/b177be5f7b6fbb6e2ac33401c0ea3a12/probabilities.json + score_dict_file: output/reports/train/b177be5f7b6fbb6e2ac33401c0ea3a12/score_dict.json + test_labels_file: output/reports/train/b177be5f7b6fbb6e2ac33401c0ea3a12/test_labels.json + train_labels_file: output/reports/train/b177be5f7b6fbb6e2ac33401c0ea3a12/train_labels.json + model_dir: models + name: b177be5f7b6fbb6e2ac33401c0ea3a12 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2e88b311a2e940cac4325a81161f2d13 + trainer: + kwargs: {} +name: b177be5f7b6fbb6e2ac33401c0ea3a12 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b256d9a2f40f2305f8153f05cb492790/params.yaml b/examples/security/kdd-nsl/output/reports/train/b256d9a2f40f2305f8153f05cb492790/params.yaml new file mode 100644 index 00000000..f85d3366 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b256d9a2f40f2305f8153f05cb492790/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b256d9a2f40f2305f8153f05cb492790/adv_predictions.json + adv_probabilities_file: output/reports/train/b256d9a2f40f2305f8153f05cb492790/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/fd45349d7566b14733b0b867b368cadb.pkl + params_file: output/reports/train/b256d9a2f40f2305f8153f05cb492790/params.yaml + predictions_file: output/reports/train/b256d9a2f40f2305f8153f05cb492790/predictions.json + probabilities_file: output/reports/train/b256d9a2f40f2305f8153f05cb492790/probabilities.json + score_dict_file: output/reports/train/b256d9a2f40f2305f8153f05cb492790/score_dict.json + test_labels_file: output/reports/train/b256d9a2f40f2305f8153f05cb492790/test_labels.json + train_labels_file: output/reports/train/b256d9a2f40f2305f8153f05cb492790/train_labels.json + model_dir: models + name: b256d9a2f40f2305f8153f05cb492790 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 71182ee40cee41ded5614d2fc9cc1fd3 + trainer: + kwargs: {} +name: b256d9a2f40f2305f8153f05cb492790 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/params.yaml b/examples/security/kdd-nsl/output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/params.yaml new file mode 100644 index 00000000..f80c84c7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/adv_predictions.json + adv_probabilities_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1748f23f29b38c3c86027d0da919446a.pkl + params_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/params.yaml + predictions_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/predictions.json + probabilities_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/probabilities.json + score_dict_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/score_dict.json + test_labels_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/test_labels.json + train_labels_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/train_labels.json + model_dir: models + name: b26aa7d4768207144c3dbdc5f2f68674 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 31ac0c5cdaf73e02f236a9260a955b2f + trainer: + kwargs: {} +name: b26aa7d4768207144c3dbdc5f2f68674 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b29e6d300cbf7b137acd3f127ec06b29/params.yaml b/examples/security/kdd-nsl/output/reports/train/b29e6d300cbf7b137acd3f127ec06b29/params.yaml new file mode 100644 index 00000000..9a0d5d6f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b29e6d300cbf7b137acd3f127ec06b29/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b29e6d300cbf7b137acd3f127ec06b29/adv_predictions.json + adv_probabilities_file: output/reports/train/b29e6d300cbf7b137acd3f127ec06b29/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c5d50609cbb76f9e04d7d8f941ffa08e.pkl + params_file: output/reports/train/b29e6d300cbf7b137acd3f127ec06b29/params.yaml + predictions_file: output/reports/train/b29e6d300cbf7b137acd3f127ec06b29/predictions.json + probabilities_file: output/reports/train/b29e6d300cbf7b137acd3f127ec06b29/probabilities.json + score_dict_file: output/reports/train/b29e6d300cbf7b137acd3f127ec06b29/score_dict.json + test_labels_file: output/reports/train/b29e6d300cbf7b137acd3f127ec06b29/test_labels.json + train_labels_file: output/reports/train/b29e6d300cbf7b137acd3f127ec06b29/train_labels.json + model_dir: models + name: b29e6d300cbf7b137acd3f127ec06b29 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 690281edbe82f7e7fe4d91ab26ffd13a + trainer: + kwargs: {} +name: b29e6d300cbf7b137acd3f127ec06b29 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b3303c8af30e0aa7b58ea71fe8d0ec89/params.yaml b/examples/security/kdd-nsl/output/reports/train/b3303c8af30e0aa7b58ea71fe8d0ec89/params.yaml new file mode 100644 index 00000000..b9ca1718 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b3303c8af30e0aa7b58ea71fe8d0ec89/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b3303c8af30e0aa7b58ea71fe8d0ec89/adv_predictions.json + adv_probabilities_file: output/reports/train/b3303c8af30e0aa7b58ea71fe8d0ec89/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0b55e9510bd7b91291fbf42f90b9e1bd.pkl + params_file: output/reports/train/b3303c8af30e0aa7b58ea71fe8d0ec89/params.yaml + predictions_file: output/reports/train/b3303c8af30e0aa7b58ea71fe8d0ec89/predictions.json + probabilities_file: output/reports/train/b3303c8af30e0aa7b58ea71fe8d0ec89/probabilities.json + score_dict_file: output/reports/train/b3303c8af30e0aa7b58ea71fe8d0ec89/score_dict.json + test_labels_file: output/reports/train/b3303c8af30e0aa7b58ea71fe8d0ec89/test_labels.json + train_labels_file: output/reports/train/b3303c8af30e0aa7b58ea71fe8d0ec89/train_labels.json + model_dir: models + name: b3303c8af30e0aa7b58ea71fe8d0ec89 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 42f7d4240f81a7265a25516198b23c8e + trainer: + kwargs: {} +name: b3303c8af30e0aa7b58ea71fe8d0ec89 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/params.yaml b/examples/security/kdd-nsl/output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/params.yaml new file mode 100644 index 00000000..b28e5318 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/adv_predictions.json + adv_probabilities_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f97fc57baa6fa4c68e532627416d06fe.pkl + params_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/params.yaml + predictions_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/predictions.json + probabilities_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/probabilities.json + score_dict_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/score_dict.json + test_labels_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/test_labels.json + train_labels_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/train_labels.json + model_dir: models + name: b3c29be5f02d2e88e5e87518b4bb9ab6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ad66332874116a834e214201816c382 + trainer: + kwargs: {} +name: b3c29be5f02d2e88e5e87518b4bb9ab6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/params.yaml b/examples/security/kdd-nsl/output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/params.yaml new file mode 100644 index 00000000..1fe54aba --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/adv_predictions.json + adv_probabilities_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/052941eab1c68be79a5161d61c2ad8bb.pkl + params_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/params.yaml + predictions_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/predictions.json + probabilities_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/probabilities.json + score_dict_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/score_dict.json + test_labels_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/test_labels.json + train_labels_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/train_labels.json + model_dir: models + name: b456f1e1b350ae5b3c3153a1dc39c6e6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5a1ebf217bf39bb73441f14b0c723a9c + trainer: + kwargs: {} +name: b456f1e1b350ae5b3c3153a1dc39c6e6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/params.yaml b/examples/security/kdd-nsl/output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/params.yaml new file mode 100644 index 00000000..d124c26b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/adv_predictions.json + adv_probabilities_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2de4130231bc00bdfdf9deff95044623.pkl + params_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/params.yaml + predictions_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/predictions.json + probabilities_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/probabilities.json + score_dict_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/score_dict.json + test_labels_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/test_labels.json + train_labels_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/train_labels.json + model_dir: models + name: b5ffc5cfd4ad1ba608e1401d7cbb422a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0ef97d34cec7b3fcd70973c36a51e9ff + trainer: + kwargs: {} +name: b5ffc5cfd4ad1ba608e1401d7cbb422a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b67bde2439f5b0f04f839c05b45db561/params.yaml b/examples/security/kdd-nsl/output/reports/train/b67bde2439f5b0f04f839c05b45db561/params.yaml new file mode 100644 index 00000000..36c81212 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b67bde2439f5b0f04f839c05b45db561/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/adv_predictions.json + adv_probabilities_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f053cb7f67a015c66fd57339405921e6.pkl + params_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/params.yaml + predictions_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/predictions.json + probabilities_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/probabilities.json + score_dict_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/score_dict.json + test_labels_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/test_labels.json + train_labels_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/train_labels.json + model_dir: models + name: b67bde2439f5b0f04f839c05b45db561 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9256873a9a17b532dfddf66dde9e6dd2 + trainer: + kwargs: {} +name: b67bde2439f5b0f04f839c05b45db561 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b6eb721a4a67544442afd1bb186b45b5/params.yaml b/examples/security/kdd-nsl/output/reports/train/b6eb721a4a67544442afd1bb186b45b5/params.yaml new file mode 100644 index 00000000..73c5ba75 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b6eb721a4a67544442afd1bb186b45b5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/adv_predictions.json + adv_probabilities_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2ef58e3ba8f059afa8d16e4f91782a14.pkl + params_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/params.yaml + predictions_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/predictions.json + probabilities_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/probabilities.json + score_dict_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/score_dict.json + test_labels_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/test_labels.json + train_labels_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/train_labels.json + model_dir: models + name: b6eb721a4a67544442afd1bb186b45b5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 96d8331d3acfeaa958f1646fe649c4c9 + trainer: + kwargs: {} +name: b6eb721a4a67544442afd1bb186b45b5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b987d828072a185ac7a7dfc1f61bae73/params.yaml b/examples/security/kdd-nsl/output/reports/train/b987d828072a185ac7a7dfc1f61bae73/params.yaml new file mode 100644 index 00000000..46db0562 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b987d828072a185ac7a7dfc1f61bae73/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/adv_predictions.json + adv_probabilities_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e14307ec9f51404bd430620a25be3592.pkl + params_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/params.yaml + predictions_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/predictions.json + probabilities_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/probabilities.json + score_dict_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/score_dict.json + test_labels_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/test_labels.json + train_labels_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/train_labels.json + model_dir: models + name: b987d828072a185ac7a7dfc1f61bae73 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 943a5662d10d6511fe7520e97767d613 + trainer: + kwargs: {} +name: b987d828072a185ac7a7dfc1f61bae73 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b9cfd4121447cfcbd13f78c84be82825/params.yaml b/examples/security/kdd-nsl/output/reports/train/b9cfd4121447cfcbd13f78c84be82825/params.yaml new file mode 100644 index 00000000..28058f4e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b9cfd4121447cfcbd13f78c84be82825/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b9cfd4121447cfcbd13f78c84be82825/adv_predictions.json + adv_probabilities_file: output/reports/train/b9cfd4121447cfcbd13f78c84be82825/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c62dc7d7cf16d385b186403b0e35f51c.pkl + params_file: output/reports/train/b9cfd4121447cfcbd13f78c84be82825/params.yaml + predictions_file: output/reports/train/b9cfd4121447cfcbd13f78c84be82825/predictions.json + probabilities_file: output/reports/train/b9cfd4121447cfcbd13f78c84be82825/probabilities.json + score_dict_file: output/reports/train/b9cfd4121447cfcbd13f78c84be82825/score_dict.json + test_labels_file: output/reports/train/b9cfd4121447cfcbd13f78c84be82825/test_labels.json + train_labels_file: output/reports/train/b9cfd4121447cfcbd13f78c84be82825/train_labels.json + model_dir: models + name: b9cfd4121447cfcbd13f78c84be82825 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bb24d980fc3b7b326377e39ec7d196eb + trainer: + kwargs: {} +name: b9cfd4121447cfcbd13f78c84be82825 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/b9d9bc6723dae511ee463243e1b538e6/params.yaml b/examples/security/kdd-nsl/output/reports/train/b9d9bc6723dae511ee463243e1b538e6/params.yaml new file mode 100644 index 00000000..4e894e79 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/b9d9bc6723dae511ee463243e1b538e6/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b9d9bc6723dae511ee463243e1b538e6/adv_predictions.json + adv_probabilities_file: output/reports/train/b9d9bc6723dae511ee463243e1b538e6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2a808adeb0f9fc6ff77019099c8e8e77.pkl + params_file: output/reports/train/b9d9bc6723dae511ee463243e1b538e6/params.yaml + predictions_file: output/reports/train/b9d9bc6723dae511ee463243e1b538e6/predictions.json + probabilities_file: output/reports/train/b9d9bc6723dae511ee463243e1b538e6/probabilities.json + score_dict_file: output/reports/train/b9d9bc6723dae511ee463243e1b538e6/score_dict.json + test_labels_file: output/reports/train/b9d9bc6723dae511ee463243e1b538e6/test_labels.json + train_labels_file: output/reports/train/b9d9bc6723dae511ee463243e1b538e6/train_labels.json + model_dir: models + name: b9d9bc6723dae511ee463243e1b538e6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 15b98b2d0d5fce0d209f7d4040802a3a + trainer: + kwargs: {} +name: b9d9bc6723dae511ee463243e1b538e6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/bc8c103fff32832cb7c457e79ea83d57/params.yaml b/examples/security/kdd-nsl/output/reports/train/bc8c103fff32832cb7c457e79ea83d57/params.yaml new file mode 100644 index 00000000..9fcdd6ca --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/bc8c103fff32832cb7c457e79ea83d57/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/adv_predictions.json + adv_probabilities_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9618e9465779c20f8c0e961adc3cbb9f.pkl + params_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/params.yaml + predictions_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/predictions.json + probabilities_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/probabilities.json + score_dict_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/score_dict.json + test_labels_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/test_labels.json + train_labels_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/train_labels.json + model_dir: models + name: bc8c103fff32832cb7c457e79ea83d57 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 20653d15201d4a14fa1b119d6045cfac + trainer: + kwargs: {} +name: bc8c103fff32832cb7c457e79ea83d57 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/bdc67cee6da2519c37545a11183aec8e/params.yaml b/examples/security/kdd-nsl/output/reports/train/bdc67cee6da2519c37545a11183aec8e/params.yaml new file mode 100644 index 00000000..877f25af --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/bdc67cee6da2519c37545a11183aec8e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bdc67cee6da2519c37545a11183aec8e/adv_predictions.json + adv_probabilities_file: output/reports/train/bdc67cee6da2519c37545a11183aec8e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9171cc10a1914d9be1865171cc9ec61d.pkl + params_file: output/reports/train/bdc67cee6da2519c37545a11183aec8e/params.yaml + predictions_file: output/reports/train/bdc67cee6da2519c37545a11183aec8e/predictions.json + probabilities_file: output/reports/train/bdc67cee6da2519c37545a11183aec8e/probabilities.json + score_dict_file: output/reports/train/bdc67cee6da2519c37545a11183aec8e/score_dict.json + test_labels_file: output/reports/train/bdc67cee6da2519c37545a11183aec8e/test_labels.json + train_labels_file: output/reports/train/bdc67cee6da2519c37545a11183aec8e/train_labels.json + model_dir: models + name: bdc67cee6da2519c37545a11183aec8e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 19ac673aeee3f03c25d616d6865c52ab + trainer: + kwargs: {} +name: bdc67cee6da2519c37545a11183aec8e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/params.yaml b/examples/security/kdd-nsl/output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/params.yaml new file mode 100644 index 00000000..607bcd30 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/adv_predictions.json + adv_probabilities_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/87cbb404ea36b265aa10895903c3c616.pkl + params_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/params.yaml + predictions_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/predictions.json + probabilities_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/probabilities.json + score_dict_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/score_dict.json + test_labels_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/test_labels.json + train_labels_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/train_labels.json + model_dir: models + name: bfeaf64981a9f786a3cabdb03a5e5529 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7fbbf8c0f80d0412c51685c719a5331c + trainer: + kwargs: {} +name: bfeaf64981a9f786a3cabdb03a5e5529 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/params.yaml b/examples/security/kdd-nsl/output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/params.yaml new file mode 100644 index 00000000..b24411f8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/adv_predictions.json + adv_probabilities_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/11d13bfb1ca2d25578c7d480b894f028.pkl + params_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/params.yaml + predictions_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/predictions.json + probabilities_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/probabilities.json + score_dict_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/score_dict.json + test_labels_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/test_labels.json + train_labels_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/train_labels.json + model_dir: models + name: bff2856ba43e1b45d5711b4666eb4c8c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d052800adb8863e016d1c434f92c6944 + trainer: + kwargs: {} +name: bff2856ba43e1b45d5711b4666eb4c8c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/params.yaml b/examples/security/kdd-nsl/output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/params.yaml new file mode 100644 index 00000000..afa46753 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/adv_predictions.json + adv_probabilities_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/4a8b0655c53194e767e4631e167ba87a.pkl + params_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/params.yaml + predictions_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/predictions.json + probabilities_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/probabilities.json + score_dict_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/score_dict.json + test_labels_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/test_labels.json + train_labels_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/train_labels.json + model_dir: models + name: c0368c0d8c2b9caf9cfb5c0392f6208b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5bd518699a2b65e5cfc1f59fc82101df + trainer: + kwargs: {} +name: c0368c0d8c2b9caf9cfb5c0392f6208b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/c1a0e8feec35f4df59ad86842c35576e/params.yaml b/examples/security/kdd-nsl/output/reports/train/c1a0e8feec35f4df59ad86842c35576e/params.yaml new file mode 100644 index 00000000..a0a91777 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/c1a0e8feec35f4df59ad86842c35576e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/adv_predictions.json + adv_probabilities_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2625f99f7d7b0baaa5fe649abf0aa7ce.pkl + params_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/params.yaml + predictions_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/predictions.json + probabilities_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/probabilities.json + score_dict_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/score_dict.json + test_labels_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/test_labels.json + train_labels_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/train_labels.json + model_dir: models + name: c1a0e8feec35f4df59ad86842c35576e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1f1cee128f4cb487f539c3922dc8e6ec + trainer: + kwargs: {} +name: c1a0e8feec35f4df59ad86842c35576e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/c211eba2164db6e8a9d102c5fe059d9f/params.yaml b/examples/security/kdd-nsl/output/reports/train/c211eba2164db6e8a9d102c5fe059d9f/params.yaml new file mode 100644 index 00000000..38f71722 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/c211eba2164db6e8a9d102c5fe059d9f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c211eba2164db6e8a9d102c5fe059d9f/adv_predictions.json + adv_probabilities_file: output/reports/train/c211eba2164db6e8a9d102c5fe059d9f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b71575ed441e7a20f9f29b117adc8344.pkl + params_file: output/reports/train/c211eba2164db6e8a9d102c5fe059d9f/params.yaml + predictions_file: output/reports/train/c211eba2164db6e8a9d102c5fe059d9f/predictions.json + probabilities_file: output/reports/train/c211eba2164db6e8a9d102c5fe059d9f/probabilities.json + score_dict_file: output/reports/train/c211eba2164db6e8a9d102c5fe059d9f/score_dict.json + test_labels_file: output/reports/train/c211eba2164db6e8a9d102c5fe059d9f/test_labels.json + train_labels_file: output/reports/train/c211eba2164db6e8a9d102c5fe059d9f/train_labels.json + model_dir: models + name: c211eba2164db6e8a9d102c5fe059d9f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 08289250b31996d288693bf36f7894ec + trainer: + kwargs: {} +name: c211eba2164db6e8a9d102c5fe059d9f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/c41e15007c281163dc5e65df5dafc408/params.yaml b/examples/security/kdd-nsl/output/reports/train/c41e15007c281163dc5e65df5dafc408/params.yaml new file mode 100644 index 00000000..f90911c8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/c41e15007c281163dc5e65df5dafc408/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/adv_predictions.json + adv_probabilities_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/078ac8572ce6fde705ec0f407068b1d6.pkl + params_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/params.yaml + predictions_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/predictions.json + probabilities_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/probabilities.json + score_dict_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/score_dict.json + test_labels_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/test_labels.json + train_labels_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/train_labels.json + model_dir: models + name: c41e15007c281163dc5e65df5dafc408 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8abd3826f95572aea9089f8ac3054975 + trainer: + kwargs: {} +name: c41e15007c281163dc5e65df5dafc408 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/c483e689d1f4d951b0c2e901fcd10b55/params.yaml b/examples/security/kdd-nsl/output/reports/train/c483e689d1f4d951b0c2e901fcd10b55/params.yaml new file mode 100644 index 00000000..72bec60f --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/c483e689d1f4d951b0c2e901fcd10b55/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c483e689d1f4d951b0c2e901fcd10b55/adv_predictions.json + adv_probabilities_file: output/reports/train/c483e689d1f4d951b0c2e901fcd10b55/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3bdbaca9bab2594b18cc8de1bdc943fb.pkl + params_file: output/reports/train/c483e689d1f4d951b0c2e901fcd10b55/params.yaml + predictions_file: output/reports/train/c483e689d1f4d951b0c2e901fcd10b55/predictions.json + probabilities_file: output/reports/train/c483e689d1f4d951b0c2e901fcd10b55/probabilities.json + score_dict_file: output/reports/train/c483e689d1f4d951b0c2e901fcd10b55/score_dict.json + test_labels_file: output/reports/train/c483e689d1f4d951b0c2e901fcd10b55/test_labels.json + train_labels_file: output/reports/train/c483e689d1f4d951b0c2e901fcd10b55/train_labels.json + model_dir: models + name: c483e689d1f4d951b0c2e901fcd10b55 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ee1d45ffb14a01337c183d4c1bde373b + trainer: + kwargs: {} +name: c483e689d1f4d951b0c2e901fcd10b55 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/c74de38aec54d28c2a192c3a581a601d/params.yaml b/examples/security/kdd-nsl/output/reports/train/c74de38aec54d28c2a192c3a581a601d/params.yaml new file mode 100644 index 00000000..27c31b32 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/c74de38aec54d28c2a192c3a581a601d/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/adv_predictions.json + adv_probabilities_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/64ea70deffcd4c8f21aa2cb0b3d339f0.pkl + params_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/params.yaml + predictions_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/predictions.json + probabilities_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/probabilities.json + score_dict_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/score_dict.json + test_labels_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/test_labels.json + train_labels_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/train_labels.json + model_dir: models + name: c74de38aec54d28c2a192c3a581a601d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 63a4caee522ebeea2b0ec79b1fab9ad3 + trainer: + kwargs: {} +name: c74de38aec54d28c2a192c3a581a601d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/c7aa4a9bedad4d02f9a5d920120948b6/params.yaml b/examples/security/kdd-nsl/output/reports/train/c7aa4a9bedad4d02f9a5d920120948b6/params.yaml new file mode 100644 index 00000000..3220d270 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/c7aa4a9bedad4d02f9a5d920120948b6/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c7aa4a9bedad4d02f9a5d920120948b6/adv_predictions.json + adv_probabilities_file: output/reports/train/c7aa4a9bedad4d02f9a5d920120948b6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/4d46bce97116f92947af1e05c7dd3e49.pkl + params_file: output/reports/train/c7aa4a9bedad4d02f9a5d920120948b6/params.yaml + predictions_file: output/reports/train/c7aa4a9bedad4d02f9a5d920120948b6/predictions.json + probabilities_file: output/reports/train/c7aa4a9bedad4d02f9a5d920120948b6/probabilities.json + score_dict_file: output/reports/train/c7aa4a9bedad4d02f9a5d920120948b6/score_dict.json + test_labels_file: output/reports/train/c7aa4a9bedad4d02f9a5d920120948b6/test_labels.json + train_labels_file: output/reports/train/c7aa4a9bedad4d02f9a5d920120948b6/train_labels.json + model_dir: models + name: c7aa4a9bedad4d02f9a5d920120948b6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 16e6c18f2e2c8d49bcbae7fd782beda6 + trainer: + kwargs: {} +name: c7aa4a9bedad4d02f9a5d920120948b6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/params.yaml b/examples/security/kdd-nsl/output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/params.yaml new file mode 100644 index 00000000..a7e0e1c5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/adv_predictions.json + adv_probabilities_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/68e90a82e492c860d8b21bb09ca31afa.pkl + params_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/params.yaml + predictions_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/predictions.json + probabilities_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/probabilities.json + score_dict_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/score_dict.json + test_labels_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/test_labels.json + train_labels_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/train_labels.json + model_dir: models + name: c7d588601d5d718c9bbdb9524c2bdacd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cf999a11a909a51a4fadfc1a1fca6e9f + trainer: + kwargs: {} +name: c7d588601d5d718c9bbdb9524c2bdacd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/c838f9c9062797f67b2d2d48854cfcca/params.yaml b/examples/security/kdd-nsl/output/reports/train/c838f9c9062797f67b2d2d48854cfcca/params.yaml new file mode 100644 index 00000000..0b0ecfda --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/c838f9c9062797f67b2d2d48854cfcca/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/adv_predictions.json + adv_probabilities_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b000e021d7332160eb8bb9b3a9d84a60.pkl + params_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/params.yaml + predictions_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/predictions.json + probabilities_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/probabilities.json + score_dict_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/score_dict.json + test_labels_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/test_labels.json + train_labels_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/train_labels.json + model_dir: models + name: c838f9c9062797f67b2d2d48854cfcca + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0f149e7fff1520e2f03c1da81a16410b + trainer: + kwargs: {} +name: c838f9c9062797f67b2d2d48854cfcca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/c85beab37ca6ebdb3f0c9417ba64295d/params.yaml b/examples/security/kdd-nsl/output/reports/train/c85beab37ca6ebdb3f0c9417ba64295d/params.yaml new file mode 100644 index 00000000..554e3142 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/c85beab37ca6ebdb3f0c9417ba64295d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c85beab37ca6ebdb3f0c9417ba64295d/adv_predictions.json + adv_probabilities_file: output/reports/train/c85beab37ca6ebdb3f0c9417ba64295d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b5ee76ffd1838efcb70dece50b89df4c.pkl + params_file: output/reports/train/c85beab37ca6ebdb3f0c9417ba64295d/params.yaml + predictions_file: output/reports/train/c85beab37ca6ebdb3f0c9417ba64295d/predictions.json + probabilities_file: output/reports/train/c85beab37ca6ebdb3f0c9417ba64295d/probabilities.json + score_dict_file: output/reports/train/c85beab37ca6ebdb3f0c9417ba64295d/score_dict.json + test_labels_file: output/reports/train/c85beab37ca6ebdb3f0c9417ba64295d/test_labels.json + train_labels_file: output/reports/train/c85beab37ca6ebdb3f0c9417ba64295d/train_labels.json + model_dir: models + name: c85beab37ca6ebdb3f0c9417ba64295d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: df3d4e9844ebe7d04abdd22ee805742c + trainer: + kwargs: {} +name: c85beab37ca6ebdb3f0c9417ba64295d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/c8a382adcdaac158f09c27008ff45b3c/params.yaml b/examples/security/kdd-nsl/output/reports/train/c8a382adcdaac158f09c27008ff45b3c/params.yaml new file mode 100644 index 00000000..013a6c78 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/c8a382adcdaac158f09c27008ff45b3c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c8a382adcdaac158f09c27008ff45b3c/adv_predictions.json + adv_probabilities_file: output/reports/train/c8a382adcdaac158f09c27008ff45b3c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/03decb91ea7f67f214bf3a33dc0a5f03.pkl + params_file: output/reports/train/c8a382adcdaac158f09c27008ff45b3c/params.yaml + predictions_file: output/reports/train/c8a382adcdaac158f09c27008ff45b3c/predictions.json + probabilities_file: output/reports/train/c8a382adcdaac158f09c27008ff45b3c/probabilities.json + score_dict_file: output/reports/train/c8a382adcdaac158f09c27008ff45b3c/score_dict.json + test_labels_file: output/reports/train/c8a382adcdaac158f09c27008ff45b3c/test_labels.json + train_labels_file: output/reports/train/c8a382adcdaac158f09c27008ff45b3c/train_labels.json + model_dir: models + name: c8a382adcdaac158f09c27008ff45b3c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 030ac5707aeef8b03e9174428637eae0 + trainer: + kwargs: {} +name: c8a382adcdaac158f09c27008ff45b3c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/ca59084cb49796d8b15444affe1a695d/params.yaml b/examples/security/kdd-nsl/output/reports/train/ca59084cb49796d8b15444affe1a695d/params.yaml new file mode 100644 index 00000000..1ff029ce --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/ca59084cb49796d8b15444affe1a695d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/adv_predictions.json + adv_probabilities_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/997d30ee47fd3eb727a822a66622a2d1.pkl + params_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/params.yaml + predictions_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/predictions.json + probabilities_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/probabilities.json + score_dict_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/score_dict.json + test_labels_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/test_labels.json + train_labels_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/train_labels.json + model_dir: models + name: ca59084cb49796d8b15444affe1a695d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 365af44ffb96290c3bd4e58e4da2d3c5 + trainer: + kwargs: {} +name: ca59084cb49796d8b15444affe1a695d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/ce7589d3f412f02d2e7497d990b788af/params.yaml b/examples/security/kdd-nsl/output/reports/train/ce7589d3f412f02d2e7497d990b788af/params.yaml new file mode 100644 index 00000000..584e5fca --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/ce7589d3f412f02d2e7497d990b788af/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/adv_predictions.json + adv_probabilities_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/fb02a6b0d9ee6c12b1eee4b0871148f3.pkl + params_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/params.yaml + predictions_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/predictions.json + probabilities_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/probabilities.json + score_dict_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/score_dict.json + test_labels_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/test_labels.json + train_labels_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/train_labels.json + model_dir: models + name: ce7589d3f412f02d2e7497d990b788af + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c546f2fc86d80740f40dbbb6e3714513 + trainer: + kwargs: {} +name: ce7589d3f412f02d2e7497d990b788af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/ce90ac7c7611eca12e68a456e7206829/params.yaml b/examples/security/kdd-nsl/output/reports/train/ce90ac7c7611eca12e68a456e7206829/params.yaml new file mode 100644 index 00000000..8e8089c2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/ce90ac7c7611eca12e68a456e7206829/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/adv_predictions.json + adv_probabilities_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e1f2dcf7521ad0d86b32cedd62df20a1.pkl + params_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/params.yaml + predictions_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/predictions.json + probabilities_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/probabilities.json + score_dict_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/score_dict.json + test_labels_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/test_labels.json + train_labels_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/train_labels.json + model_dir: models + name: ce90ac7c7611eca12e68a456e7206829 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 514764e5b2bfc4e70108698b19d1634a + trainer: + kwargs: {} +name: ce90ac7c7611eca12e68a456e7206829 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/params.yaml b/examples/security/kdd-nsl/output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/params.yaml new file mode 100644 index 00000000..cefc5bf9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/adv_predictions.json + adv_probabilities_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0de0b40d2110e895aa5b49daf7a6ed34.pkl + params_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/params.yaml + predictions_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/predictions.json + probabilities_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/probabilities.json + score_dict_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/score_dict.json + test_labels_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/test_labels.json + train_labels_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/train_labels.json + model_dir: models + name: d0d89d5ffa698b6d26756aad31734e5f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ed11c48df3ee48ac230abdee1a9e91d9 + trainer: + kwargs: {} +name: d0d89d5ffa698b6d26756aad31734e5f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d150bacefe5937d49344aca500eeb7ba/params.yaml b/examples/security/kdd-nsl/output/reports/train/d150bacefe5937d49344aca500eeb7ba/params.yaml new file mode 100644 index 00000000..ee905e94 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d150bacefe5937d49344aca500eeb7ba/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/adv_predictions.json + adv_probabilities_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/97f7e601bda1da916eb09b4ba768eded.pkl + params_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/params.yaml + predictions_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/predictions.json + probabilities_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/probabilities.json + score_dict_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/score_dict.json + test_labels_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/test_labels.json + train_labels_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/train_labels.json + model_dir: models + name: d150bacefe5937d49344aca500eeb7ba + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 11c903041fc2f23439bcc44ee63fe40b + trainer: + kwargs: {} +name: d150bacefe5937d49344aca500eeb7ba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d189d5ce3c76d1a043fa4a8391a3a08a/params.yaml b/examples/security/kdd-nsl/output/reports/train/d189d5ce3c76d1a043fa4a8391a3a08a/params.yaml new file mode 100644 index 00000000..75b9637e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d189d5ce3c76d1a043fa4a8391a3a08a/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d189d5ce3c76d1a043fa4a8391a3a08a/adv_predictions.json + adv_probabilities_file: output/reports/train/d189d5ce3c76d1a043fa4a8391a3a08a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0a7930e43b0afdd969849ae9cc188329.pkl + params_file: output/reports/train/d189d5ce3c76d1a043fa4a8391a3a08a/params.yaml + predictions_file: output/reports/train/d189d5ce3c76d1a043fa4a8391a3a08a/predictions.json + probabilities_file: output/reports/train/d189d5ce3c76d1a043fa4a8391a3a08a/probabilities.json + score_dict_file: output/reports/train/d189d5ce3c76d1a043fa4a8391a3a08a/score_dict.json + test_labels_file: output/reports/train/d189d5ce3c76d1a043fa4a8391a3a08a/test_labels.json + train_labels_file: output/reports/train/d189d5ce3c76d1a043fa4a8391a3a08a/train_labels.json + model_dir: models + name: d189d5ce3c76d1a043fa4a8391a3a08a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a7082de624504a984d1cbde09d96c3c1 + trainer: + kwargs: {} +name: d189d5ce3c76d1a043fa4a8391a3a08a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d30ee5d4f98f42b1537975175db76cb9/params.yaml b/examples/security/kdd-nsl/output/reports/train/d30ee5d4f98f42b1537975175db76cb9/params.yaml new file mode 100644 index 00000000..fa538091 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d30ee5d4f98f42b1537975175db76cb9/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d30ee5d4f98f42b1537975175db76cb9/adv_predictions.json + adv_probabilities_file: output/reports/train/d30ee5d4f98f42b1537975175db76cb9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9f6390ac41408039a8708def453fc1fb.pkl + params_file: output/reports/train/d30ee5d4f98f42b1537975175db76cb9/params.yaml + predictions_file: output/reports/train/d30ee5d4f98f42b1537975175db76cb9/predictions.json + probabilities_file: output/reports/train/d30ee5d4f98f42b1537975175db76cb9/probabilities.json + score_dict_file: output/reports/train/d30ee5d4f98f42b1537975175db76cb9/score_dict.json + test_labels_file: output/reports/train/d30ee5d4f98f42b1537975175db76cb9/test_labels.json + train_labels_file: output/reports/train/d30ee5d4f98f42b1537975175db76cb9/train_labels.json + model_dir: models + name: d30ee5d4f98f42b1537975175db76cb9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b1a450856b3f51288e1454a026b779de + trainer: + kwargs: {} +name: d30ee5d4f98f42b1537975175db76cb9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d35cecb2665111a11d66cf1f438a5d8e/params.yaml b/examples/security/kdd-nsl/output/reports/train/d35cecb2665111a11d66cf1f438a5d8e/params.yaml new file mode 100644 index 00000000..6ce855c2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d35cecb2665111a11d66cf1f438a5d8e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d35cecb2665111a11d66cf1f438a5d8e/adv_predictions.json + adv_probabilities_file: output/reports/train/d35cecb2665111a11d66cf1f438a5d8e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/fe6cf1bc452cd045c4b8c9dbcc867e56.pkl + params_file: output/reports/train/d35cecb2665111a11d66cf1f438a5d8e/params.yaml + predictions_file: output/reports/train/d35cecb2665111a11d66cf1f438a5d8e/predictions.json + probabilities_file: output/reports/train/d35cecb2665111a11d66cf1f438a5d8e/probabilities.json + score_dict_file: output/reports/train/d35cecb2665111a11d66cf1f438a5d8e/score_dict.json + test_labels_file: output/reports/train/d35cecb2665111a11d66cf1f438a5d8e/test_labels.json + train_labels_file: output/reports/train/d35cecb2665111a11d66cf1f438a5d8e/train_labels.json + model_dir: models + name: d35cecb2665111a11d66cf1f438a5d8e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d2654a8c2946a2f02345d37ef234e2c9 + trainer: + kwargs: {} +name: d35cecb2665111a11d66cf1f438a5d8e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d38d785cea9a0f57209331e6b20aa393/params.yaml b/examples/security/kdd-nsl/output/reports/train/d38d785cea9a0f57209331e6b20aa393/params.yaml new file mode 100644 index 00000000..513504b8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d38d785cea9a0f57209331e6b20aa393/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d38d785cea9a0f57209331e6b20aa393/adv_predictions.json + adv_probabilities_file: output/reports/train/d38d785cea9a0f57209331e6b20aa393/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/a02e9cf59273edfa8363a731c8be1e08.pkl + params_file: output/reports/train/d38d785cea9a0f57209331e6b20aa393/params.yaml + predictions_file: output/reports/train/d38d785cea9a0f57209331e6b20aa393/predictions.json + probabilities_file: output/reports/train/d38d785cea9a0f57209331e6b20aa393/probabilities.json + score_dict_file: output/reports/train/d38d785cea9a0f57209331e6b20aa393/score_dict.json + test_labels_file: output/reports/train/d38d785cea9a0f57209331e6b20aa393/test_labels.json + train_labels_file: output/reports/train/d38d785cea9a0f57209331e6b20aa393/train_labels.json + model_dir: models + name: d38d785cea9a0f57209331e6b20aa393 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 40d58efd3ee652bce02d79770382e086 + trainer: + kwargs: {} +name: d38d785cea9a0f57209331e6b20aa393 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/params.yaml b/examples/security/kdd-nsl/output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/params.yaml new file mode 100644 index 00000000..a18f39c6 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/adv_predictions.json + adv_probabilities_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/44647811760ab022c038cb0c06240164.pkl + params_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/params.yaml + predictions_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/predictions.json + probabilities_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/probabilities.json + score_dict_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/score_dict.json + test_labels_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/test_labels.json + train_labels_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/train_labels.json + model_dir: models + name: d43e7cdb8c13dcea0d7d19130b3b4f64 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9805c0bb6292e21c6cee24a95aa26e98 + trainer: + kwargs: {} +name: d43e7cdb8c13dcea0d7d19130b3b4f64 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d4b2e1c44cd43beb788586b852a7d933/params.yaml b/examples/security/kdd-nsl/output/reports/train/d4b2e1c44cd43beb788586b852a7d933/params.yaml new file mode 100644 index 00000000..e26012bc --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d4b2e1c44cd43beb788586b852a7d933/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/adv_predictions.json + adv_probabilities_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c281c21383bb54e3bddcd9b60140bb76.pkl + params_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/params.yaml + predictions_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/predictions.json + probabilities_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/probabilities.json + score_dict_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/score_dict.json + test_labels_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/test_labels.json + train_labels_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/train_labels.json + model_dir: models + name: d4b2e1c44cd43beb788586b852a7d933 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 83e8e5c09d50e6e353d376b77db06617 + trainer: + kwargs: {} +name: d4b2e1c44cd43beb788586b852a7d933 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/params.yaml b/examples/security/kdd-nsl/output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/params.yaml new file mode 100644 index 00000000..556e9f86 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/adv_predictions.json + adv_probabilities_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/4ad573b6ac1aa917fe35f75e12d2cd9d.pkl + params_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/params.yaml + predictions_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/predictions.json + probabilities_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/probabilities.json + score_dict_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/score_dict.json + test_labels_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/test_labels.json + train_labels_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/train_labels.json + model_dir: models + name: d4ba4227db48fa64339e8878f8e1fa87 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cfc34ed8a593351e39430a8c3c1f4816 + trainer: + kwargs: {} +name: d4ba4227db48fa64339e8878f8e1fa87 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d6377d0487556f02d0fe0230fd0d855b/params.yaml b/examples/security/kdd-nsl/output/reports/train/d6377d0487556f02d0fe0230fd0d855b/params.yaml new file mode 100644 index 00000000..202db2f3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d6377d0487556f02d0fe0230fd0d855b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d6377d0487556f02d0fe0230fd0d855b/adv_predictions.json + adv_probabilities_file: output/reports/train/d6377d0487556f02d0fe0230fd0d855b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/44d8fdc65f704d4cc2c55dd94ef432e1.pkl + params_file: output/reports/train/d6377d0487556f02d0fe0230fd0d855b/params.yaml + predictions_file: output/reports/train/d6377d0487556f02d0fe0230fd0d855b/predictions.json + probabilities_file: output/reports/train/d6377d0487556f02d0fe0230fd0d855b/probabilities.json + score_dict_file: output/reports/train/d6377d0487556f02d0fe0230fd0d855b/score_dict.json + test_labels_file: output/reports/train/d6377d0487556f02d0fe0230fd0d855b/test_labels.json + train_labels_file: output/reports/train/d6377d0487556f02d0fe0230fd0d855b/train_labels.json + model_dir: models + name: d6377d0487556f02d0fe0230fd0d855b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7311b91f62494b35f38a5cd758d026b4 + trainer: + kwargs: {} +name: d6377d0487556f02d0fe0230fd0d855b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/params.yaml b/examples/security/kdd-nsl/output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/params.yaml new file mode 100644 index 00000000..35d4eb4e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/adv_predictions.json + adv_probabilities_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/a2918feb31bef710907ab77ca216d0a5.pkl + params_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/params.yaml + predictions_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/predictions.json + probabilities_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/probabilities.json + score_dict_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/score_dict.json + test_labels_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/test_labels.json + train_labels_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/train_labels.json + model_dir: models + name: d6f9198c705d3c1efbb8c6b653934fd0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bdff7cd509c12a6ae99d5829829ee5df + trainer: + kwargs: {} +name: d6f9198c705d3c1efbb8c6b653934fd0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/params.yaml b/examples/security/kdd-nsl/output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/params.yaml new file mode 100644 index 00000000..4b7f64f2 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/adv_predictions.json + adv_probabilities_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/55f4a2e72a7485d088fc92b50c5af7b4.pkl + params_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/params.yaml + predictions_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/predictions.json + probabilities_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/probabilities.json + score_dict_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/score_dict.json + test_labels_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/test_labels.json + train_labels_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/train_labels.json + model_dir: models + name: d71272a64d9e1b3f6f1a692528a8a124 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7b256a9129939eeae6c9165cac0a655a + trainer: + kwargs: {} +name: d71272a64d9e1b3f6f1a692528a8a124 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/params.yaml b/examples/security/kdd-nsl/output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/params.yaml new file mode 100644 index 00000000..0c1b00d0 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/adv_predictions.json + adv_probabilities_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/235536360cec1f14dd2feafd69ddf49b.pkl + params_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/params.yaml + predictions_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/predictions.json + probabilities_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/probabilities.json + score_dict_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/score_dict.json + test_labels_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/test_labels.json + train_labels_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/train_labels.json + model_dir: models + name: d8865cad7eb1409aeabe4a94d275ba3b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8078dd5202f2cde1c157ac459bcca3b3 + trainer: + kwargs: {} +name: d8865cad7eb1409aeabe4a94d275ba3b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/d90ad91dbb6dc43dccc905021bee1685/params.yaml b/examples/security/kdd-nsl/output/reports/train/d90ad91dbb6dc43dccc905021bee1685/params.yaml new file mode 100644 index 00000000..b567f02b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/d90ad91dbb6dc43dccc905021bee1685/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d90ad91dbb6dc43dccc905021bee1685/adv_predictions.json + adv_probabilities_file: output/reports/train/d90ad91dbb6dc43dccc905021bee1685/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ca14a2bb131f8962bfb32ab1bc825dd2.pkl + params_file: output/reports/train/d90ad91dbb6dc43dccc905021bee1685/params.yaml + predictions_file: output/reports/train/d90ad91dbb6dc43dccc905021bee1685/predictions.json + probabilities_file: output/reports/train/d90ad91dbb6dc43dccc905021bee1685/probabilities.json + score_dict_file: output/reports/train/d90ad91dbb6dc43dccc905021bee1685/score_dict.json + test_labels_file: output/reports/train/d90ad91dbb6dc43dccc905021bee1685/test_labels.json + train_labels_file: output/reports/train/d90ad91dbb6dc43dccc905021bee1685/train_labels.json + model_dir: models + name: d90ad91dbb6dc43dccc905021bee1685 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 09e833369b71bacf6d9be1898b8af9e4 + trainer: + kwargs: {} +name: d90ad91dbb6dc43dccc905021bee1685 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/params.yaml b/examples/security/kdd-nsl/output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/params.yaml new file mode 100644 index 00000000..4ce64f18 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/adv_predictions.json + adv_probabilities_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ee3d610cc23ab4fdea63d276dc48b03c.pkl + params_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/params.yaml + predictions_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/predictions.json + probabilities_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/probabilities.json + score_dict_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/score_dict.json + test_labels_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/test_labels.json + train_labels_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/train_labels.json + model_dir: models + name: dc25085a9c071c2a82cebd1da83f33dc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d607c615c7571d5cbe469daacf75f865 + trainer: + kwargs: {} +name: dc25085a9c071c2a82cebd1da83f33dc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/params.yaml b/examples/security/kdd-nsl/output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/params.yaml new file mode 100644 index 00000000..d6eefcfb --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/adv_predictions.json + adv_probabilities_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9307239fd3ef26a1f19baad71fc5efcf.pkl + params_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/params.yaml + predictions_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/predictions.json + probabilities_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/probabilities.json + score_dict_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/score_dict.json + test_labels_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/test_labels.json + train_labels_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/train_labels.json + model_dir: models + name: dd22bff6b5a6fc06f9ac78108f93cbb4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5ef9fc2489b9e287aeac251f405c8ead + trainer: + kwargs: {} +name: dd22bff6b5a6fc06f9ac78108f93cbb4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/dd2f0d8beeab97ed79ee873b86467093/params.yaml b/examples/security/kdd-nsl/output/reports/train/dd2f0d8beeab97ed79ee873b86467093/params.yaml new file mode 100644 index 00000000..43977f8e --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/dd2f0d8beeab97ed79ee873b86467093/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/adv_predictions.json + adv_probabilities_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/8810a7e72155f7b57aba7b04f8a9f2ae.pkl + params_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/params.yaml + predictions_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/predictions.json + probabilities_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/probabilities.json + score_dict_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/score_dict.json + test_labels_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/test_labels.json + train_labels_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/train_labels.json + model_dir: models + name: dd2f0d8beeab97ed79ee873b86467093 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea9d43bcdf4e0555019f8a80c231cc7d + trainer: + kwargs: {} +name: dd2f0d8beeab97ed79ee873b86467093 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/dd9edce55d2789737b6b89d09ab89256/params.yaml b/examples/security/kdd-nsl/output/reports/train/dd9edce55d2789737b6b89d09ab89256/params.yaml new file mode 100644 index 00000000..044f54c1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/dd9edce55d2789737b6b89d09ab89256/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/adv_predictions.json + adv_probabilities_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5767636fc16e2fc05f81774bd0ef1cd4.pkl + params_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/params.yaml + predictions_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/predictions.json + probabilities_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/probabilities.json + score_dict_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/score_dict.json + test_labels_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/test_labels.json + train_labels_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/train_labels.json + model_dir: models + name: dd9edce55d2789737b6b89d09ab89256 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 61334077c332cbca643773013f6438a3 + trainer: + kwargs: {} +name: dd9edce55d2789737b6b89d09ab89256 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/dda059f957faeeec8cd138407943ff17/params.yaml b/examples/security/kdd-nsl/output/reports/train/dda059f957faeeec8cd138407943ff17/params.yaml new file mode 100644 index 00000000..16a794f7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/dda059f957faeeec8cd138407943ff17/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dda059f957faeeec8cd138407943ff17/adv_predictions.json + adv_probabilities_file: output/reports/train/dda059f957faeeec8cd138407943ff17/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bbb8e1a1eb93a847193856f525b729db.pkl + params_file: output/reports/train/dda059f957faeeec8cd138407943ff17/params.yaml + predictions_file: output/reports/train/dda059f957faeeec8cd138407943ff17/predictions.json + probabilities_file: output/reports/train/dda059f957faeeec8cd138407943ff17/probabilities.json + score_dict_file: output/reports/train/dda059f957faeeec8cd138407943ff17/score_dict.json + test_labels_file: output/reports/train/dda059f957faeeec8cd138407943ff17/test_labels.json + train_labels_file: output/reports/train/dda059f957faeeec8cd138407943ff17/train_labels.json + model_dir: models + name: dda059f957faeeec8cd138407943ff17 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c3fd1971e655ce3702477769f1015c2b + trainer: + kwargs: {} +name: dda059f957faeeec8cd138407943ff17 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/params.yaml b/examples/security/kdd-nsl/output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/params.yaml new file mode 100644 index 00000000..ca9cd0cd --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/adv_predictions.json + adv_probabilities_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/066a5c482399ab441a2e97334fc2138e.pkl + params_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/params.yaml + predictions_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/predictions.json + probabilities_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/probabilities.json + score_dict_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/score_dict.json + test_labels_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/test_labels.json + train_labels_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/train_labels.json + model_dir: models + name: de4165aa67d9a710c2fa8629fc9584aa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6cd9edd4e4bbcc8af49060588061bf40 + trainer: + kwargs: {} +name: de4165aa67d9a710c2fa8629fc9584aa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/de43012c89b8b45375784c2d7bb5c502/params.yaml b/examples/security/kdd-nsl/output/reports/train/de43012c89b8b45375784c2d7bb5c502/params.yaml new file mode 100644 index 00000000..6654d64d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/de43012c89b8b45375784c2d7bb5c502/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/adv_predictions.json + adv_probabilities_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/35ffdaf55dda26675b5a44f5d49f9c7e.pkl + params_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/params.yaml + predictions_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/predictions.json + probabilities_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/probabilities.json + score_dict_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/score_dict.json + test_labels_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/test_labels.json + train_labels_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/train_labels.json + model_dir: models + name: de43012c89b8b45375784c2d7bb5c502 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 797c292bb693422638e2ebf3000c7aa5 + trainer: + kwargs: {} +name: de43012c89b8b45375784c2d7bb5c502 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/de93fbf1633671443b118afb75c0b283/params.yaml b/examples/security/kdd-nsl/output/reports/train/de93fbf1633671443b118afb75c0b283/params.yaml new file mode 100644 index 00000000..f3bf06f7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/de93fbf1633671443b118afb75c0b283/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/de93fbf1633671443b118afb75c0b283/adv_predictions.json + adv_probabilities_file: output/reports/train/de93fbf1633671443b118afb75c0b283/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b327fbb4a4e279902bb9f832fc0c23d3.pkl + params_file: output/reports/train/de93fbf1633671443b118afb75c0b283/params.yaml + predictions_file: output/reports/train/de93fbf1633671443b118afb75c0b283/predictions.json + probabilities_file: output/reports/train/de93fbf1633671443b118afb75c0b283/probabilities.json + score_dict_file: output/reports/train/de93fbf1633671443b118afb75c0b283/score_dict.json + test_labels_file: output/reports/train/de93fbf1633671443b118afb75c0b283/test_labels.json + train_labels_file: output/reports/train/de93fbf1633671443b118afb75c0b283/train_labels.json + model_dir: models + name: de93fbf1633671443b118afb75c0b283 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d83f17846eb4962877e69470147c918b + trainer: + kwargs: {} +name: de93fbf1633671443b118afb75c0b283 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/default/params.yaml b/examples/security/kdd-nsl/output/reports/train/default/params.yaml new file mode 100644 index 00000000..144fc869 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/default/params.yaml @@ -0,0 +1,95 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + params_file: output/reports/train/default/params.yaml + predictions_file: output/reports/train/default/predictions.json + probabilities_file: output/reports/train/default/probabilities.json + score_dict_file: output/reports/train/default/score_dict.json + model_dir: models + name: default + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: default +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/df00be1aa168cb442af1cd29a27c9746/params.yaml b/examples/security/kdd-nsl/output/reports/train/df00be1aa168cb442af1cd29a27c9746/params.yaml new file mode 100644 index 00000000..a50fb307 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/df00be1aa168cb442af1cd29a27c9746/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/df00be1aa168cb442af1cd29a27c9746/adv_predictions.json + adv_probabilities_file: output/reports/train/df00be1aa168cb442af1cd29a27c9746/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/713fb4426af603c7ccd7e7186a4f4df8.pkl + params_file: output/reports/train/df00be1aa168cb442af1cd29a27c9746/params.yaml + predictions_file: output/reports/train/df00be1aa168cb442af1cd29a27c9746/predictions.json + probabilities_file: output/reports/train/df00be1aa168cb442af1cd29a27c9746/probabilities.json + score_dict_file: output/reports/train/df00be1aa168cb442af1cd29a27c9746/score_dict.json + test_labels_file: output/reports/train/df00be1aa168cb442af1cd29a27c9746/test_labels.json + train_labels_file: output/reports/train/df00be1aa168cb442af1cd29a27c9746/train_labels.json + model_dir: models + name: df00be1aa168cb442af1cd29a27c9746 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bcb57e6ea19a54707cbdf8402e22ff9a + trainer: + kwargs: {} +name: df00be1aa168cb442af1cd29a27c9746 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/df32c7282a6b1c866ccd25af964ab28f/params.yaml b/examples/security/kdd-nsl/output/reports/train/df32c7282a6b1c866ccd25af964ab28f/params.yaml new file mode 100644 index 00000000..af023bc1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/df32c7282a6b1c866ccd25af964ab28f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/adv_predictions.json + adv_probabilities_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1a57864cc40c4df0ee9b44c1901ee6d7.pkl + params_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/params.yaml + predictions_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/predictions.json + probabilities_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/probabilities.json + score_dict_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/score_dict.json + test_labels_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/test_labels.json + train_labels_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/train_labels.json + model_dir: models + name: df32c7282a6b1c866ccd25af964ab28f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 782df5ad027e449f31e1ea28b233fe77 + trainer: + kwargs: {} +name: df32c7282a6b1c866ccd25af964ab28f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/e0df68be0e38236ff09f73642252afde/params.yaml b/examples/security/kdd-nsl/output/reports/train/e0df68be0e38236ff09f73642252afde/params.yaml new file mode 100644 index 00000000..86fbe8fc --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/e0df68be0e38236ff09f73642252afde/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e0df68be0e38236ff09f73642252afde/adv_predictions.json + adv_probabilities_file: output/reports/train/e0df68be0e38236ff09f73642252afde/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9db307db4a8a8c9402b4b7b6963ec6c7.pkl + params_file: output/reports/train/e0df68be0e38236ff09f73642252afde/params.yaml + predictions_file: output/reports/train/e0df68be0e38236ff09f73642252afde/predictions.json + probabilities_file: output/reports/train/e0df68be0e38236ff09f73642252afde/probabilities.json + score_dict_file: output/reports/train/e0df68be0e38236ff09f73642252afde/score_dict.json + test_labels_file: output/reports/train/e0df68be0e38236ff09f73642252afde/test_labels.json + train_labels_file: output/reports/train/e0df68be0e38236ff09f73642252afde/train_labels.json + model_dir: models + name: e0df68be0e38236ff09f73642252afde + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3353bd47ab4344387cb55a4dbb332c46 + trainer: + kwargs: {} +name: e0df68be0e38236ff09f73642252afde +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/e196cfbced4984e5dc33ba9f6eab5e87/params.yaml b/examples/security/kdd-nsl/output/reports/train/e196cfbced4984e5dc33ba9f6eab5e87/params.yaml new file mode 100644 index 00000000..41a16cf7 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/e196cfbced4984e5dc33ba9f6eab5e87/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e196cfbced4984e5dc33ba9f6eab5e87/adv_predictions.json + adv_probabilities_file: output/reports/train/e196cfbced4984e5dc33ba9f6eab5e87/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ba65c9685ead0f1e831254c5971283cc.pkl + params_file: output/reports/train/e196cfbced4984e5dc33ba9f6eab5e87/params.yaml + predictions_file: output/reports/train/e196cfbced4984e5dc33ba9f6eab5e87/predictions.json + probabilities_file: output/reports/train/e196cfbced4984e5dc33ba9f6eab5e87/probabilities.json + score_dict_file: output/reports/train/e196cfbced4984e5dc33ba9f6eab5e87/score_dict.json + test_labels_file: output/reports/train/e196cfbced4984e5dc33ba9f6eab5e87/test_labels.json + train_labels_file: output/reports/train/e196cfbced4984e5dc33ba9f6eab5e87/train_labels.json + model_dir: models + name: e196cfbced4984e5dc33ba9f6eab5e87 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: daff3582170fbf137399854ed442153e + trainer: + kwargs: {} +name: e196cfbced4984e5dc33ba9f6eab5e87 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/e5ab567a8cedf2ded5a476c5218bdd88/params.yaml b/examples/security/kdd-nsl/output/reports/train/e5ab567a8cedf2ded5a476c5218bdd88/params.yaml new file mode 100644 index 00000000..b009d789 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/e5ab567a8cedf2ded5a476c5218bdd88/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e5ab567a8cedf2ded5a476c5218bdd88/adv_predictions.json + adv_probabilities_file: output/reports/train/e5ab567a8cedf2ded5a476c5218bdd88/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bde0a0cea8d162ee006eae961bc43c50.pkl + params_file: output/reports/train/e5ab567a8cedf2ded5a476c5218bdd88/params.yaml + predictions_file: output/reports/train/e5ab567a8cedf2ded5a476c5218bdd88/predictions.json + probabilities_file: output/reports/train/e5ab567a8cedf2ded5a476c5218bdd88/probabilities.json + score_dict_file: output/reports/train/e5ab567a8cedf2ded5a476c5218bdd88/score_dict.json + test_labels_file: output/reports/train/e5ab567a8cedf2ded5a476c5218bdd88/test_labels.json + train_labels_file: output/reports/train/e5ab567a8cedf2ded5a476c5218bdd88/train_labels.json + model_dir: models + name: e5ab567a8cedf2ded5a476c5218bdd88 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 01387b0110ee9e76e631cc12e5803faf + trainer: + kwargs: {} +name: e5ab567a8cedf2ded5a476c5218bdd88 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/e88abe964cb272442dd2e97c8551737e/params.yaml b/examples/security/kdd-nsl/output/reports/train/e88abe964cb272442dd2e97c8551737e/params.yaml new file mode 100644 index 00000000..a7df8ebe --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/e88abe964cb272442dd2e97c8551737e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/adv_predictions.json + adv_probabilities_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/387b7990a5aa73864fcf50b0c5a121b4.pkl + params_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/params.yaml + predictions_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/predictions.json + probabilities_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/probabilities.json + score_dict_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/score_dict.json + test_labels_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/test_labels.json + train_labels_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/train_labels.json + model_dir: models + name: e88abe964cb272442dd2e97c8551737e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 65cebe6573a5b844a9aa7938616ad3d3 + trainer: + kwargs: {} +name: e88abe964cb272442dd2e97c8551737e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/e91057b4c338c70b4216b35f1a591bea/params.yaml b/examples/security/kdd-nsl/output/reports/train/e91057b4c338c70b4216b35f1a591bea/params.yaml new file mode 100644 index 00000000..9a1e2cda --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/e91057b4c338c70b4216b35f1a591bea/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e91057b4c338c70b4216b35f1a591bea/adv_predictions.json + adv_probabilities_file: output/reports/train/e91057b4c338c70b4216b35f1a591bea/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7a19d50487f8d6299d02a2f39963366c.pkl + params_file: output/reports/train/e91057b4c338c70b4216b35f1a591bea/params.yaml + predictions_file: output/reports/train/e91057b4c338c70b4216b35f1a591bea/predictions.json + probabilities_file: output/reports/train/e91057b4c338c70b4216b35f1a591bea/probabilities.json + score_dict_file: output/reports/train/e91057b4c338c70b4216b35f1a591bea/score_dict.json + test_labels_file: output/reports/train/e91057b4c338c70b4216b35f1a591bea/test_labels.json + train_labels_file: output/reports/train/e91057b4c338c70b4216b35f1a591bea/train_labels.json + model_dir: models + name: e91057b4c338c70b4216b35f1a591bea + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a4db4708b8e969b1b80b40a1c50130e4 + trainer: + kwargs: {} +name: e91057b4c338c70b4216b35f1a591bea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/ec245700c43f018b45adf84e3eaa28aa/params.yaml b/examples/security/kdd-nsl/output/reports/train/ec245700c43f018b45adf84e3eaa28aa/params.yaml new file mode 100644 index 00000000..9bda38e5 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/ec245700c43f018b45adf84e3eaa28aa/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/adv_predictions.json + adv_probabilities_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f22037fd146bb977203c9b5c09a4ff98.pkl + params_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/params.yaml + predictions_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/predictions.json + probabilities_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/probabilities.json + score_dict_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/score_dict.json + test_labels_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/test_labels.json + train_labels_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/train_labels.json + model_dir: models + name: ec245700c43f018b45adf84e3eaa28aa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d512c6c95bd99aa3801425889014ec6a + trainer: + kwargs: {} +name: ec245700c43f018b45adf84e3eaa28aa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/eeb8a635f633415117e0b9bd4be03dc1/params.yaml b/examples/security/kdd-nsl/output/reports/train/eeb8a635f633415117e0b9bd4be03dc1/params.yaml new file mode 100644 index 00000000..34cb8cb9 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/eeb8a635f633415117e0b9bd4be03dc1/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eeb8a635f633415117e0b9bd4be03dc1/adv_predictions.json + adv_probabilities_file: output/reports/train/eeb8a635f633415117e0b9bd4be03dc1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/6664c70678301d04c7c56463f357f1d6.pkl + params_file: output/reports/train/eeb8a635f633415117e0b9bd4be03dc1/params.yaml + predictions_file: output/reports/train/eeb8a635f633415117e0b9bd4be03dc1/predictions.json + probabilities_file: output/reports/train/eeb8a635f633415117e0b9bd4be03dc1/probabilities.json + score_dict_file: output/reports/train/eeb8a635f633415117e0b9bd4be03dc1/score_dict.json + test_labels_file: output/reports/train/eeb8a635f633415117e0b9bd4be03dc1/test_labels.json + train_labels_file: output/reports/train/eeb8a635f633415117e0b9bd4be03dc1/train_labels.json + model_dir: models + name: eeb8a635f633415117e0b9bd4be03dc1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2047a1b904062ca35af2d47ddce6a198 + trainer: + kwargs: {} +name: eeb8a635f633415117e0b9bd4be03dc1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f05b17164e87b1148e24f9df9e51fec2/params.yaml b/examples/security/kdd-nsl/output/reports/train/f05b17164e87b1148e24f9df9e51fec2/params.yaml new file mode 100644 index 00000000..395e1542 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f05b17164e87b1148e24f9df9e51fec2/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/adv_predictions.json + adv_probabilities_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5093d371892c3a6a7ee9292ab8b94253.pkl + params_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/params.yaml + predictions_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/predictions.json + probabilities_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/probabilities.json + score_dict_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/score_dict.json + test_labels_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/test_labels.json + train_labels_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/train_labels.json + model_dir: models + name: f05b17164e87b1148e24f9df9e51fec2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1c6dd8cb719752e44361e6794ed712d8 + trainer: + kwargs: {} +name: f05b17164e87b1148e24f9df9e51fec2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/params.yaml b/examples/security/kdd-nsl/output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/params.yaml new file mode 100644 index 00000000..d393d842 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/adv_predictions.json + adv_probabilities_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c8a766691a146281d9f12baa9976ecf3.pkl + params_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/params.yaml + predictions_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/predictions.json + probabilities_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/probabilities.json + score_dict_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/score_dict.json + test_labels_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/test_labels.json + train_labels_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/train_labels.json + model_dir: models + name: f0d37f0757baf7c419b7e01d7d02ebd5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 68755efc740e1fc17bb7d4ea8acc24ee + trainer: + kwargs: {} +name: f0d37f0757baf7c419b7e01d7d02ebd5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f0fb6406f33807a1101696f9f664dddf/params.yaml b/examples/security/kdd-nsl/output/reports/train/f0fb6406f33807a1101696f9f664dddf/params.yaml new file mode 100644 index 00000000..bccb44d8 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f0fb6406f33807a1101696f9f664dddf/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/adv_predictions.json + adv_probabilities_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/8727ee1b289644aaae5eff304cd623f7.pkl + params_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/params.yaml + predictions_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/predictions.json + probabilities_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/probabilities.json + score_dict_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/score_dict.json + test_labels_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/test_labels.json + train_labels_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/train_labels.json + model_dir: models + name: f0fb6406f33807a1101696f9f664dddf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1d8c4d1238c3dab30dea7d6fd3efaf6c + trainer: + kwargs: {} +name: f0fb6406f33807a1101696f9f664dddf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f131c09b697f873a2a28c7cc94b494c9/params.yaml b/examples/security/kdd-nsl/output/reports/train/f131c09b697f873a2a28c7cc94b494c9/params.yaml new file mode 100644 index 00000000..6ddb2a68 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f131c09b697f873a2a28c7cc94b494c9/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f131c09b697f873a2a28c7cc94b494c9/adv_predictions.json + adv_probabilities_file: output/reports/train/f131c09b697f873a2a28c7cc94b494c9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/64ce43d6142543ad12362a585c7647a9.pkl + params_file: output/reports/train/f131c09b697f873a2a28c7cc94b494c9/params.yaml + predictions_file: output/reports/train/f131c09b697f873a2a28c7cc94b494c9/predictions.json + probabilities_file: output/reports/train/f131c09b697f873a2a28c7cc94b494c9/probabilities.json + score_dict_file: output/reports/train/f131c09b697f873a2a28c7cc94b494c9/score_dict.json + test_labels_file: output/reports/train/f131c09b697f873a2a28c7cc94b494c9/test_labels.json + train_labels_file: output/reports/train/f131c09b697f873a2a28c7cc94b494c9/train_labels.json + model_dir: models + name: f131c09b697f873a2a28c7cc94b494c9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 795fd31e8033552735b0084f961d0c84 + trainer: + kwargs: {} +name: f131c09b697f873a2a28c7cc94b494c9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f2f901c7752c2d0e204f6f8614617423/params.yaml b/examples/security/kdd-nsl/output/reports/train/f2f901c7752c2d0e204f6f8614617423/params.yaml new file mode 100644 index 00000000..e9695e38 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f2f901c7752c2d0e204f6f8614617423/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f2f901c7752c2d0e204f6f8614617423/adv_predictions.json + adv_probabilities_file: output/reports/train/f2f901c7752c2d0e204f6f8614617423/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/cff8f4e4abc951ffbf3467251388c119.pkl + params_file: output/reports/train/f2f901c7752c2d0e204f6f8614617423/params.yaml + predictions_file: output/reports/train/f2f901c7752c2d0e204f6f8614617423/predictions.json + probabilities_file: output/reports/train/f2f901c7752c2d0e204f6f8614617423/probabilities.json + score_dict_file: output/reports/train/f2f901c7752c2d0e204f6f8614617423/score_dict.json + test_labels_file: output/reports/train/f2f901c7752c2d0e204f6f8614617423/test_labels.json + train_labels_file: output/reports/train/f2f901c7752c2d0e204f6f8614617423/train_labels.json + model_dir: models + name: f2f901c7752c2d0e204f6f8614617423 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 128951b0e0ecd3dccc5efa091b2ae7ef + trainer: + kwargs: {} +name: f2f901c7752c2d0e204f6f8614617423 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f41f5ef042bb1f88c9b2c558c28e8ddf/params.yaml b/examples/security/kdd-nsl/output/reports/train/f41f5ef042bb1f88c9b2c558c28e8ddf/params.yaml new file mode 100644 index 00000000..cda0d0cf --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f41f5ef042bb1f88c9b2c558c28e8ddf/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f41f5ef042bb1f88c9b2c558c28e8ddf/adv_predictions.json + adv_probabilities_file: output/reports/train/f41f5ef042bb1f88c9b2c558c28e8ddf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/75c30d45a3b475220699ad4cf8b5c169.pkl + params_file: output/reports/train/f41f5ef042bb1f88c9b2c558c28e8ddf/params.yaml + predictions_file: output/reports/train/f41f5ef042bb1f88c9b2c558c28e8ddf/predictions.json + probabilities_file: output/reports/train/f41f5ef042bb1f88c9b2c558c28e8ddf/probabilities.json + score_dict_file: output/reports/train/f41f5ef042bb1f88c9b2c558c28e8ddf/score_dict.json + test_labels_file: output/reports/train/f41f5ef042bb1f88c9b2c558c28e8ddf/test_labels.json + train_labels_file: output/reports/train/f41f5ef042bb1f88c9b2c558c28e8ddf/train_labels.json + model_dir: models + name: f41f5ef042bb1f88c9b2c558c28e8ddf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3525f063d6c97af081798aecf15e8d34 + trainer: + kwargs: {} +name: f41f5ef042bb1f88c9b2c558c28e8ddf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/params.yaml b/examples/security/kdd-nsl/output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/params.yaml new file mode 100644 index 00000000..379f3e18 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/adv_predictions.json + adv_probabilities_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e50f4c5bfc99a6c37d5287e22221f521.pkl + params_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/params.yaml + predictions_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/predictions.json + probabilities_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/probabilities.json + score_dict_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/score_dict.json + test_labels_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/test_labels.json + train_labels_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/train_labels.json + model_dir: models + name: f47e538760a5680fb0f2f2b0460e1dad + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d3fd72c06fee7850b2776f693ef2e25 + trainer: + kwargs: {} +name: f47e538760a5680fb0f2f2b0460e1dad +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/params.yaml b/examples/security/kdd-nsl/output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/params.yaml new file mode 100644 index 00000000..f89b4905 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/adv_predictions.json + adv_probabilities_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3c099f7d35a561775856e570f99ab671.pkl + params_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/params.yaml + predictions_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/predictions.json + probabilities_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/probabilities.json + score_dict_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/score_dict.json + test_labels_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/test_labels.json + train_labels_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/train_labels.json + model_dir: models + name: f57619ef894e7c8d9407fb8e4070abe5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 802d4e141f5d7f0649aa64caf1cc8ce6 + trainer: + kwargs: {} +name: f57619ef894e7c8d9407fb8e4070abe5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f5d2acc94c5f6953f277ce9ff12ac989/params.yaml b/examples/security/kdd-nsl/output/reports/train/f5d2acc94c5f6953f277ce9ff12ac989/params.yaml new file mode 100644 index 00000000..98a5d20c --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f5d2acc94c5f6953f277ce9ff12ac989/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f5d2acc94c5f6953f277ce9ff12ac989/adv_predictions.json + adv_probabilities_file: output/reports/train/f5d2acc94c5f6953f277ce9ff12ac989/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7546475e53d884820a24a23baa1c702a.pkl + params_file: output/reports/train/f5d2acc94c5f6953f277ce9ff12ac989/params.yaml + predictions_file: output/reports/train/f5d2acc94c5f6953f277ce9ff12ac989/predictions.json + probabilities_file: output/reports/train/f5d2acc94c5f6953f277ce9ff12ac989/probabilities.json + score_dict_file: output/reports/train/f5d2acc94c5f6953f277ce9ff12ac989/score_dict.json + test_labels_file: output/reports/train/f5d2acc94c5f6953f277ce9ff12ac989/test_labels.json + train_labels_file: output/reports/train/f5d2acc94c5f6953f277ce9ff12ac989/train_labels.json + model_dir: models + name: f5d2acc94c5f6953f277ce9ff12ac989 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7577f28aff6a0bd443b956497e53cbf7 + trainer: + kwargs: {} +name: f5d2acc94c5f6953f277ce9ff12ac989 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/params.yaml b/examples/security/kdd-nsl/output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/params.yaml new file mode 100644 index 00000000..2457d79d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/adv_predictions.json + adv_probabilities_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/4bb1b9b293a4125e22ed3c86bd74f9d8.pkl + params_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/params.yaml + predictions_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/predictions.json + probabilities_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/probabilities.json + score_dict_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/score_dict.json + test_labels_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/test_labels.json + train_labels_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/train_labels.json + model_dir: models + name: f5f6879d55a59ef2713cf5d242b67e44 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2be23235cd542bf8c370083a68d1c4af + trainer: + kwargs: {} +name: f5f6879d55a59ef2713cf5d242b67e44 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f60d06aa3efadbc1757ff989003323a5/params.yaml b/examples/security/kdd-nsl/output/reports/train/f60d06aa3efadbc1757ff989003323a5/params.yaml new file mode 100644 index 00000000..b3c3f015 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f60d06aa3efadbc1757ff989003323a5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/adv_predictions.json + adv_probabilities_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/6f814be8b0f1277068c18650de92ee9a.pkl + params_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/params.yaml + predictions_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/predictions.json + probabilities_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/probabilities.json + score_dict_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/score_dict.json + test_labels_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/test_labels.json + train_labels_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/train_labels.json + model_dir: models + name: f60d06aa3efadbc1757ff989003323a5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e5821b1f69713fad232d39d2f92629de + trainer: + kwargs: {} +name: f60d06aa3efadbc1757ff989003323a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f6a420bd66357f3a33017e36aa3aff00/params.yaml b/examples/security/kdd-nsl/output/reports/train/f6a420bd66357f3a33017e36aa3aff00/params.yaml new file mode 100644 index 00000000..3cbe729d --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f6a420bd66357f3a33017e36aa3aff00/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/adv_predictions.json + adv_probabilities_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/404db9b50b978eec1223c4c83e2cb552.pkl + params_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/params.yaml + predictions_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/predictions.json + probabilities_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/probabilities.json + score_dict_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/score_dict.json + test_labels_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/test_labels.json + train_labels_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/train_labels.json + model_dir: models + name: f6a420bd66357f3a33017e36aa3aff00 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e568cfb9e0fb8c421e0ca463ffd0ca2a + trainer: + kwargs: {} +name: f6a420bd66357f3a33017e36aa3aff00 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f8553451873d6ba64dd128609edff255/params.yaml b/examples/security/kdd-nsl/output/reports/train/f8553451873d6ba64dd128609edff255/params.yaml new file mode 100644 index 00000000..df759ff1 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f8553451873d6ba64dd128609edff255/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f8553451873d6ba64dd128609edff255/adv_predictions.json + adv_probabilities_file: output/reports/train/f8553451873d6ba64dd128609edff255/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bd19f0f939ba8af43ef8cf8d21b6d7a3.pkl + params_file: output/reports/train/f8553451873d6ba64dd128609edff255/params.yaml + predictions_file: output/reports/train/f8553451873d6ba64dd128609edff255/predictions.json + probabilities_file: output/reports/train/f8553451873d6ba64dd128609edff255/probabilities.json + score_dict_file: output/reports/train/f8553451873d6ba64dd128609edff255/score_dict.json + test_labels_file: output/reports/train/f8553451873d6ba64dd128609edff255/test_labels.json + train_labels_file: output/reports/train/f8553451873d6ba64dd128609edff255/train_labels.json + model_dir: models + name: f8553451873d6ba64dd128609edff255 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 61fb1856c33aaed54316c2b6db22b2de + trainer: + kwargs: {} +name: f8553451873d6ba64dd128609edff255 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/f8f24230b6a7f6ec93fae9691fd47b24/params.yaml b/examples/security/kdd-nsl/output/reports/train/f8f24230b6a7f6ec93fae9691fd47b24/params.yaml new file mode 100644 index 00000000..69eeb40b --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/f8f24230b6a7f6ec93fae9691fd47b24/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f8f24230b6a7f6ec93fae9691fd47b24/adv_predictions.json + adv_probabilities_file: output/reports/train/f8f24230b6a7f6ec93fae9691fd47b24/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/71f565c44c1b1c2d5e41291fd3491b72.pkl + params_file: output/reports/train/f8f24230b6a7f6ec93fae9691fd47b24/params.yaml + predictions_file: output/reports/train/f8f24230b6a7f6ec93fae9691fd47b24/predictions.json + probabilities_file: output/reports/train/f8f24230b6a7f6ec93fae9691fd47b24/probabilities.json + score_dict_file: output/reports/train/f8f24230b6a7f6ec93fae9691fd47b24/score_dict.json + test_labels_file: output/reports/train/f8f24230b6a7f6ec93fae9691fd47b24/test_labels.json + train_labels_file: output/reports/train/f8f24230b6a7f6ec93fae9691fd47b24/train_labels.json + model_dir: models + name: f8f24230b6a7f6ec93fae9691fd47b24 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ba06014bc721e4a9797337c7b2bd7195 + trainer: + kwargs: {} +name: f8f24230b6a7f6ec93fae9691fd47b24 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/fa3c62f4a9544a63018848a931ab559a/params.yaml b/examples/security/kdd-nsl/output/reports/train/fa3c62f4a9544a63018848a931ab559a/params.yaml new file mode 100644 index 00000000..08c282df --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/fa3c62f4a9544a63018848a931ab559a/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/adv_predictions.json + adv_probabilities_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c0a77730cb0dacdbe00f6f79808eb356.pkl + params_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/params.yaml + predictions_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/predictions.json + probabilities_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/probabilities.json + score_dict_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/score_dict.json + test_labels_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/test_labels.json + train_labels_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/train_labels.json + model_dir: models + name: fa3c62f4a9544a63018848a931ab559a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a130b62263caf44429cd13d6d4ab5b5f + trainer: + kwargs: {} +name: fa3c62f4a9544a63018848a931ab559a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/fab432dff2bfa28ba848daef4ee8e247/params.yaml b/examples/security/kdd-nsl/output/reports/train/fab432dff2bfa28ba848daef4ee8e247/params.yaml new file mode 100644 index 00000000..5f5b8190 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/fab432dff2bfa28ba848daef4ee8e247/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/adv_predictions.json + adv_probabilities_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c1e1c409082581259254f5d0488f6249.pkl + params_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/params.yaml + predictions_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/predictions.json + probabilities_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/probabilities.json + score_dict_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/score_dict.json + test_labels_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/test_labels.json + train_labels_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/train_labels.json + model_dir: models + name: fab432dff2bfa28ba848daef4ee8e247 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ae1ac2e219102b34cb4f6a5958a3265 + trainer: + kwargs: {} +name: fab432dff2bfa28ba848daef4ee8e247 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/params.yaml b/examples/security/kdd-nsl/output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/params.yaml new file mode 100644 index 00000000..6119d273 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/adv_predictions.json + adv_probabilities_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9b7c23c21523a4270ac9ca5c3c91d5d.pkl + params_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/params.yaml + predictions_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/predictions.json + probabilities_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/probabilities.json + score_dict_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/score_dict.json + test_labels_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/test_labels.json + train_labels_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/train_labels.json + model_dir: models + name: fac893616a6d51f6983cf1ed1c5845e2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d180ea53c9ce0b1d7a6b85ff6982c244 + trainer: + kwargs: {} +name: fac893616a6d51f6983cf1ed1c5845e2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/params.yaml b/examples/security/kdd-nsl/output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/params.yaml new file mode 100644 index 00000000..25da0f70 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/adv_predictions.json + adv_probabilities_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ebe4a26e8e3793bd4867d3616bf37d06.pkl + params_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/params.yaml + predictions_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/predictions.json + probabilities_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/probabilities.json + score_dict_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/score_dict.json + test_labels_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/test_labels.json + train_labels_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/train_labels.json + model_dir: models + name: fb6dc710a19c8feed52059f69c2f92f5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 258790f91e3a1de52882242139e7f793 + trainer: + kwargs: {} +name: fb6dc710a19c8feed52059f69c2f92f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/fb75727696520d8b26653805a882806e/params.yaml b/examples/security/kdd-nsl/output/reports/train/fb75727696520d8b26653805a882806e/params.yaml new file mode 100644 index 00000000..e8afb993 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/fb75727696520d8b26653805a882806e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fb75727696520d8b26653805a882806e/adv_predictions.json + adv_probabilities_file: output/reports/train/fb75727696520d8b26653805a882806e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9cfe37978102d80d60f35fc2e7439788.pkl + params_file: output/reports/train/fb75727696520d8b26653805a882806e/params.yaml + predictions_file: output/reports/train/fb75727696520d8b26653805a882806e/predictions.json + probabilities_file: output/reports/train/fb75727696520d8b26653805a882806e/probabilities.json + score_dict_file: output/reports/train/fb75727696520d8b26653805a882806e/score_dict.json + test_labels_file: output/reports/train/fb75727696520d8b26653805a882806e/test_labels.json + train_labels_file: output/reports/train/fb75727696520d8b26653805a882806e/train_labels.json + model_dir: models + name: fb75727696520d8b26653805a882806e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a5bf8a4f6f619ab283928101a3c9b760 + trainer: + kwargs: {} +name: fb75727696520d8b26653805a882806e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/fcaf5bf60becf909634771c964aaceb8/params.yaml b/examples/security/kdd-nsl/output/reports/train/fcaf5bf60becf909634771c964aaceb8/params.yaml new file mode 100644 index 00000000..41873ee3 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/fcaf5bf60becf909634771c964aaceb8/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/adv_predictions.json + adv_probabilities_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bbc568c01fbf3b31b130ef4437a7f8a4.pkl + params_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/params.yaml + predictions_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/predictions.json + probabilities_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/probabilities.json + score_dict_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/score_dict.json + test_labels_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/test_labels.json + train_labels_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/train_labels.json + model_dir: models + name: fcaf5bf60becf909634771c964aaceb8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 02c4f4c6f38657d9d00a12c4010b740b + trainer: + kwargs: {} +name: fcaf5bf60becf909634771c964aaceb8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/fcb13835349e3c8cb40246dfded8ac47/params.yaml b/examples/security/kdd-nsl/output/reports/train/fcb13835349e3c8cb40246dfded8ac47/params.yaml new file mode 100644 index 00000000..d8db3aaf --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/fcb13835349e3c8cb40246dfded8ac47/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/adv_predictions.json + adv_probabilities_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d92494cd29ba28727b7f2c9c34f8d4b2.pkl + params_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/params.yaml + predictions_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/predictions.json + probabilities_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/probabilities.json + score_dict_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/score_dict.json + test_labels_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/test_labels.json + train_labels_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/train_labels.json + model_dir: models + name: fcb13835349e3c8cb40246dfded8ac47 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2e7c2770e3e726cd9690b917643a0387 + trainer: + kwargs: {} +name: fcb13835349e3c8cb40246dfded8ac47 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/fd7615c8e89c458cae4c39a5b921f363/params.yaml b/examples/security/kdd-nsl/output/reports/train/fd7615c8e89c458cae4c39a5b921f363/params.yaml new file mode 100644 index 00000000..63acdfee --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/fd7615c8e89c458cae4c39a5b921f363/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fd7615c8e89c458cae4c39a5b921f363/adv_predictions.json + adv_probabilities_file: output/reports/train/fd7615c8e89c458cae4c39a5b921f363/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/abb43539da5dea5cf9766cf910805464.pkl + params_file: output/reports/train/fd7615c8e89c458cae4c39a5b921f363/params.yaml + predictions_file: output/reports/train/fd7615c8e89c458cae4c39a5b921f363/predictions.json + probabilities_file: output/reports/train/fd7615c8e89c458cae4c39a5b921f363/probabilities.json + score_dict_file: output/reports/train/fd7615c8e89c458cae4c39a5b921f363/score_dict.json + test_labels_file: output/reports/train/fd7615c8e89c458cae4c39a5b921f363/test_labels.json + train_labels_file: output/reports/train/fd7615c8e89c458cae4c39a5b921f363/train_labels.json + model_dir: models + name: fd7615c8e89c458cae4c39a5b921f363 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 85e3a2d452c681b4fd1220b95b36362a + trainer: + kwargs: {} +name: fd7615c8e89c458cae4c39a5b921f363 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/fdf6a170e0b4394f5bdda5e581b6129d/params.yaml b/examples/security/kdd-nsl/output/reports/train/fdf6a170e0b4394f5bdda5e581b6129d/params.yaml new file mode 100644 index 00000000..8f6137b4 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/fdf6a170e0b4394f5bdda5e581b6129d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fdf6a170e0b4394f5bdda5e581b6129d/adv_predictions.json + adv_probabilities_file: output/reports/train/fdf6a170e0b4394f5bdda5e581b6129d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/cc6f0a72648101290e56f3912231511b.pkl + params_file: output/reports/train/fdf6a170e0b4394f5bdda5e581b6129d/params.yaml + predictions_file: output/reports/train/fdf6a170e0b4394f5bdda5e581b6129d/predictions.json + probabilities_file: output/reports/train/fdf6a170e0b4394f5bdda5e581b6129d/probabilities.json + score_dict_file: output/reports/train/fdf6a170e0b4394f5bdda5e581b6129d/score_dict.json + test_labels_file: output/reports/train/fdf6a170e0b4394f5bdda5e581b6129d/test_labels.json + train_labels_file: output/reports/train/fdf6a170e0b4394f5bdda5e581b6129d/train_labels.json + model_dir: models + name: fdf6a170e0b4394f5bdda5e581b6129d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7f03e278ac7273aa7f62a78a33fdaef5 + trainer: + kwargs: {} +name: fdf6a170e0b4394f5bdda5e581b6129d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/kdd-nsl/output/reports/train/ff55c0252050e50ba16a1abf018752c0/params.yaml b/examples/security/kdd-nsl/output/reports/train/ff55c0252050e50ba16a1abf018752c0/params.yaml new file mode 100644 index 00000000..ff44b415 --- /dev/null +++ b/examples/security/kdd-nsl/output/reports/train/ff55c0252050e50ba16a1abf018752c0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ff55c0252050e50ba16a1abf018752c0/adv_predictions.json + adv_probabilities_file: output/reports/train/ff55c0252050e50ba16a1abf018752c0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/44ff509d008a57733ab91393119cfc0c.pkl + params_file: output/reports/train/ff55c0252050e50ba16a1abf018752c0/params.yaml + predictions_file: output/reports/train/ff55c0252050e50ba16a1abf018752c0/predictions.json + probabilities_file: output/reports/train/ff55c0252050e50ba16a1abf018752c0/probabilities.json + score_dict_file: output/reports/train/ff55c0252050e50ba16a1abf018752c0/score_dict.json + test_labels_file: output/reports/train/ff55c0252050e50ba16a1abf018752c0/test_labels.json + train_labels_file: output/reports/train/ff55c0252050e50ba16a1abf018752c0/train_labels.json + model_dir: models + name: ff55c0252050e50ba16a1abf018752c0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 42f561e358c14ea7c49833fa77b2b8a1 + trainer: + kwargs: {} +name: ff55c0252050e50ba16a1abf018752c0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/kdd-nsl/params.yaml b/examples/security/kdd-nsl/params.yaml similarity index 100% rename from examples/kdd-nsl/params.yaml rename to examples/security/kdd-nsl/params.yaml diff --git a/examples/kdd-nsl/plots.py b/examples/security/kdd-nsl/plots.py similarity index 100% rename from examples/kdd-nsl/plots.py rename to examples/security/kdd-nsl/plots.py diff --git a/examples/kdd-nsl/plots/.gitignore b/examples/security/kdd-nsl/plots/.gitignore similarity index 100% rename from examples/kdd-nsl/plots/.gitignore rename to examples/security/kdd-nsl/plots/.gitignore diff --git a/examples/kdd-nsl/retrain.py b/examples/security/kdd-nsl/retrain.py similarity index 100% rename from examples/kdd-nsl/retrain.py rename to examples/security/kdd-nsl/retrain.py diff --git a/examples/truthseeker/.dvc/.gitignore b/examples/security/truthseeker/.dvc/.gitignore similarity index 100% rename from examples/truthseeker/.dvc/.gitignore rename to examples/security/truthseeker/.dvc/.gitignore diff --git a/examples/truthseeker/.dvc/config b/examples/security/truthseeker/.dvc/config similarity index 100% rename from examples/truthseeker/.dvc/config rename to examples/security/truthseeker/.dvc/config diff --git a/examples/truthseeker/.dvcignore b/examples/security/truthseeker/.dvcignore similarity index 100% rename from examples/truthseeker/.dvcignore rename to examples/security/truthseeker/.dvcignore diff --git a/examples/truthseeker/.gitignore b/examples/security/truthseeker/.gitignore similarity index 100% rename from examples/truthseeker/.gitignore rename to examples/security/truthseeker/.gitignore diff --git a/examples/truthseeker/README.md b/examples/security/truthseeker/README.md similarity index 100% rename from examples/truthseeker/README.md rename to examples/security/truthseeker/README.md diff --git a/examples/truthseeker/attacks.sh b/examples/security/truthseeker/attacks.sh similarity index 100% rename from examples/truthseeker/attacks.sh rename to examples/security/truthseeker/attacks.sh diff --git a/examples/truthseeker/conf/attack.yaml b/examples/security/truthseeker/conf/attack.yaml similarity index 100% rename from examples/truthseeker/conf/attack.yaml rename to examples/security/truthseeker/conf/attack.yaml diff --git a/examples/truthseeker/conf/attack/.gitignore b/examples/security/truthseeker/conf/attack/.gitignore similarity index 100% rename from examples/truthseeker/conf/attack/.gitignore rename to examples/security/truthseeker/conf/attack/.gitignore diff --git a/examples/truthseeker/conf/attack/attack_grid.yaml b/examples/security/truthseeker/conf/attack/attack_grid.yaml similarity index 100% rename from examples/truthseeker/conf/attack/attack_grid.yaml rename to examples/security/truthseeker/conf/attack/attack_grid.yaml diff --git a/examples/truthseeker/conf/attack/default.yaml b/examples/security/truthseeker/conf/attack/default.yaml similarity index 100% rename from examples/truthseeker/conf/attack/default.yaml rename to examples/security/truthseeker/conf/attack/default.yaml diff --git a/examples/truthseeker/conf/attack/hsj.yaml b/examples/security/truthseeker/conf/attack/hsj.yaml similarity index 100% rename from examples/truthseeker/conf/attack/hsj.yaml rename to examples/security/truthseeker/conf/attack/hsj.yaml diff --git a/examples/truthseeker/conf/compile.yaml b/examples/security/truthseeker/conf/compile.yaml similarity index 100% rename from examples/truthseeker/conf/compile.yaml rename to examples/security/truthseeker/conf/compile.yaml diff --git a/examples/truthseeker/conf/data/attack.yaml b/examples/security/truthseeker/conf/data/attack.yaml similarity index 100% rename from examples/truthseeker/conf/data/attack.yaml rename to examples/security/truthseeker/conf/data/attack.yaml diff --git a/examples/truthseeker/conf/data/default.yaml b/examples/security/truthseeker/conf/data/default.yaml similarity index 100% rename from examples/truthseeker/conf/data/default.yaml rename to examples/security/truthseeker/conf/data/default.yaml diff --git a/examples/truthseeker/conf/data/kdd_nsl.yaml b/examples/security/truthseeker/conf/data/kdd_nsl.yaml similarity index 100% rename from examples/truthseeker/conf/data/kdd_nsl.yaml rename to examples/security/truthseeker/conf/data/kdd_nsl.yaml diff --git a/examples/truthseeker/conf/data/small.yaml b/examples/security/truthseeker/conf/data/small.yaml similarity index 100% rename from examples/truthseeker/conf/data/small.yaml rename to examples/security/truthseeker/conf/data/small.yaml diff --git a/examples/truthseeker/conf/data/truthseeker.yaml b/examples/security/truthseeker/conf/data/truthseeker.yaml similarity index 100% rename from examples/truthseeker/conf/data/truthseeker.yaml rename to examples/security/truthseeker/conf/data/truthseeker.yaml diff --git a/examples/truthseeker/conf/default.yaml b/examples/security/truthseeker/conf/default.yaml similarity index 100% rename from examples/truthseeker/conf/default.yaml rename to examples/security/truthseeker/conf/default.yaml diff --git a/examples/truthseeker/conf/files/default.yaml b/examples/security/truthseeker/conf/files/default.yaml similarity index 100% rename from examples/truthseeker/conf/files/default.yaml rename to examples/security/truthseeker/conf/files/default.yaml diff --git a/examples/truthseeker/conf/model.yaml b/examples/security/truthseeker/conf/model.yaml similarity index 100% rename from examples/truthseeker/conf/model.yaml rename to examples/security/truthseeker/conf/model.yaml diff --git a/examples/truthseeker/conf/model/.gitignore b/examples/security/truthseeker/conf/model/.gitignore similarity index 100% rename from examples/truthseeker/conf/model/.gitignore rename to examples/security/truthseeker/conf/model/.gitignore diff --git a/examples/truthseeker/conf/model/art/defense_grid.yaml b/examples/security/truthseeker/conf/model/art/defense_grid.yaml similarity index 100% rename from examples/truthseeker/conf/model/art/defense_grid.yaml rename to examples/security/truthseeker/conf/model/art/defense_grid.yaml diff --git a/examples/truthseeker/conf/model/art/postprocessor.yaml b/examples/security/truthseeker/conf/model/art/postprocessor.yaml similarity index 100% rename from examples/truthseeker/conf/model/art/postprocessor.yaml rename to examples/security/truthseeker/conf/model/art/postprocessor.yaml diff --git a/examples/truthseeker/conf/model/art/postprocessor/high_confidence.yaml b/examples/security/truthseeker/conf/model/art/postprocessor/high_confidence.yaml similarity index 100% rename from examples/truthseeker/conf/model/art/postprocessor/high_confidence.yaml rename to examples/security/truthseeker/conf/model/art/postprocessor/high_confidence.yaml diff --git a/examples/truthseeker/conf/model/art/postprocessor/labels.yaml b/examples/security/truthseeker/conf/model/art/postprocessor/labels.yaml similarity index 100% rename from examples/truthseeker/conf/model/art/postprocessor/labels.yaml rename to examples/security/truthseeker/conf/model/art/postprocessor/labels.yaml diff --git a/examples/truthseeker/conf/model/art/preprocessor.yaml b/examples/security/truthseeker/conf/model/art/preprocessor.yaml similarity index 100% rename from examples/truthseeker/conf/model/art/preprocessor.yaml rename to examples/security/truthseeker/conf/model/art/preprocessor.yaml diff --git a/examples/truthseeker/conf/model/art/preprocessor/feature_squeezing.yaml b/examples/security/truthseeker/conf/model/art/preprocessor/feature_squeezing.yaml similarity index 100% rename from examples/truthseeker/conf/model/art/preprocessor/feature_squeezing.yaml rename to examples/security/truthseeker/conf/model/art/preprocessor/feature_squeezing.yaml diff --git a/examples/truthseeker/conf/model/default.yaml b/examples/security/truthseeker/conf/model/default.yaml similarity index 100% rename from examples/truthseeker/conf/model/default.yaml rename to examples/security/truthseeker/conf/model/default.yaml diff --git a/examples/truthseeker/conf/model/linear.yaml b/examples/security/truthseeker/conf/model/linear.yaml similarity index 100% rename from examples/truthseeker/conf/model/linear.yaml rename to examples/security/truthseeker/conf/model/linear.yaml diff --git a/examples/truthseeker/conf/model/poly.yaml b/examples/security/truthseeker/conf/model/poly.yaml similarity index 100% rename from examples/truthseeker/conf/model/poly.yaml rename to examples/security/truthseeker/conf/model/poly.yaml diff --git a/examples/truthseeker/conf/model/rbf.yaml b/examples/security/truthseeker/conf/model/rbf.yaml similarity index 100% rename from examples/truthseeker/conf/model/rbf.yaml rename to examples/security/truthseeker/conf/model/rbf.yaml diff --git a/examples/truthseeker/conf/scorers/default.yaml b/examples/security/truthseeker/conf/scorers/default.yaml similarity index 100% rename from examples/truthseeker/conf/scorers/default.yaml rename to examples/security/truthseeker/conf/scorers/default.yaml diff --git a/examples/truthseeker/dvc.lock b/examples/security/truthseeker/dvc.lock similarity index 100% rename from examples/truthseeker/dvc.lock rename to examples/security/truthseeker/dvc.lock diff --git a/examples/truthseeker/dvc.yaml b/examples/security/truthseeker/dvc.yaml similarity index 100% rename from examples/truthseeker/dvc.yaml rename to examples/security/truthseeker/dvc.yaml diff --git a/examples/truthseeker/logs/.gitignore b/examples/security/truthseeker/logs/.gitignore similarity index 100% rename from examples/truthseeker/logs/.gitignore rename to examples/security/truthseeker/logs/.gitignore diff --git a/examples/truthseeker/models.sh b/examples/security/truthseeker/models.sh similarity index 100% rename from examples/truthseeker/models.sh rename to examples/security/truthseeker/models.sh diff --git a/examples/security/truthseeker/models/reports/train/00c98c5927bba3b932302ea09bc2384c/params.yaml b/examples/security/truthseeker/models/reports/train/00c98c5927bba3b932302ea09bc2384c/params.yaml new file mode 100644 index 00000000..23a11959 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/00c98c5927bba3b932302ea09bc2384c/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/00c98c5927bba3b932302ea09bc2384c/adv_predictions.json + adv_probabilities_file: models/reports/train/00c98c5927bba3b932302ea09bc2384c/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/0c2a459231c9967e61f4528a05b00e29.pkl + params_file: models/reports/train/00c98c5927bba3b932302ea09bc2384c/params.yaml + predictions_file: models/reports/train/00c98c5927bba3b932302ea09bc2384c/predictions.json + probabilities_file: models/reports/train/00c98c5927bba3b932302ea09bc2384c/probabilities.json + score_dict_file: models/reports/train/00c98c5927bba3b932302ea09bc2384c/score_dict.json + test_labels_file: models/reports/train/00c98c5927bba3b932302ea09bc2384c/test_labels.json + train_labels_file: models/reports/train/00c98c5927bba3b932302ea09bc2384c/train_labels.json + model_dir: models + name: 00c98c5927bba3b932302ea09bc2384c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 22f8f9e6176f4d365f13ebb01f80c55e + trainer: + kwargs: {} +name: 00c98c5927bba3b932302ea09bc2384c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/01ed95f9e99746dcb4b4d158303813be/params.yaml b/examples/security/truthseeker/models/reports/train/01ed95f9e99746dcb4b4d158303813be/params.yaml new file mode 100644 index 00000000..d8f6ac75 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/01ed95f9e99746dcb4b4d158303813be/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/01ed95f9e99746dcb4b4d158303813be/adv_predictions.json + adv_probabilities_file: models/reports/train/01ed95f9e99746dcb4b4d158303813be/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/81e0346ed42031336ca586e889f91b9a.pkl + params_file: models/reports/train/01ed95f9e99746dcb4b4d158303813be/params.yaml + predictions_file: models/reports/train/01ed95f9e99746dcb4b4d158303813be/predictions.json + probabilities_file: models/reports/train/01ed95f9e99746dcb4b4d158303813be/probabilities.json + score_dict_file: models/reports/train/01ed95f9e99746dcb4b4d158303813be/score_dict.json + test_labels_file: models/reports/train/01ed95f9e99746dcb4b4d158303813be/test_labels.json + train_labels_file: models/reports/train/01ed95f9e99746dcb4b4d158303813be/train_labels.json + model_dir: models + name: 01ed95f9e99746dcb4b4d158303813be + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 511a178dabcd54a7cb49be0a84d35c0f + trainer: + kwargs: {} +name: 01ed95f9e99746dcb4b4d158303813be +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/022a458419f3ff04886a7cad38810b47/params.yaml b/examples/security/truthseeker/models/reports/train/022a458419f3ff04886a7cad38810b47/params.yaml new file mode 100644 index 00000000..e89cbe2c --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/022a458419f3ff04886a7cad38810b47/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/022a458419f3ff04886a7cad38810b47/adv_predictions.json + adv_probabilities_file: models/reports/train/022a458419f3ff04886a7cad38810b47/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/f24752a1cc00c553312ef7ab75c0c96f.pkl + params_file: models/reports/train/022a458419f3ff04886a7cad38810b47/params.yaml + predictions_file: models/reports/train/022a458419f3ff04886a7cad38810b47/predictions.json + probabilities_file: models/reports/train/022a458419f3ff04886a7cad38810b47/probabilities.json + score_dict_file: models/reports/train/022a458419f3ff04886a7cad38810b47/score_dict.json + test_labels_file: models/reports/train/022a458419f3ff04886a7cad38810b47/test_labels.json + train_labels_file: models/reports/train/022a458419f3ff04886a7cad38810b47/train_labels.json + model_dir: models + name: 022a458419f3ff04886a7cad38810b47 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9f48740672e890755eef25c071fe3dc1 + trainer: + kwargs: {} +name: 022a458419f3ff04886a7cad38810b47 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/03252a58cfec0a03a0a9ef6fc90a1401/params.yaml b/examples/security/truthseeker/models/reports/train/03252a58cfec0a03a0a9ef6fc90a1401/params.yaml new file mode 100644 index 00000000..50b83e37 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/03252a58cfec0a03a0a9ef6fc90a1401/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/03252a58cfec0a03a0a9ef6fc90a1401/adv_predictions.json + adv_probabilities_file: models/reports/train/03252a58cfec0a03a0a9ef6fc90a1401/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/adc10b7bf3d5b933316130fbf72efa9d.pkl + params_file: models/reports/train/03252a58cfec0a03a0a9ef6fc90a1401/params.yaml + predictions_file: models/reports/train/03252a58cfec0a03a0a9ef6fc90a1401/predictions.json + probabilities_file: models/reports/train/03252a58cfec0a03a0a9ef6fc90a1401/probabilities.json + score_dict_file: models/reports/train/03252a58cfec0a03a0a9ef6fc90a1401/score_dict.json + test_labels_file: models/reports/train/03252a58cfec0a03a0a9ef6fc90a1401/test_labels.json + train_labels_file: models/reports/train/03252a58cfec0a03a0a9ef6fc90a1401/train_labels.json + model_dir: models + name: 03252a58cfec0a03a0a9ef6fc90a1401 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 678d9e5aa40a04b090fc35fff9f9d3d1 + trainer: + kwargs: {} +name: 03252a58cfec0a03a0a9ef6fc90a1401 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/0387674b4768c888a5214c150db07aaf/params.yaml b/examples/security/truthseeker/models/reports/train/0387674b4768c888a5214c150db07aaf/params.yaml new file mode 100644 index 00000000..55f73847 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/0387674b4768c888a5214c150db07aaf/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/0387674b4768c888a5214c150db07aaf/adv_predictions.json + adv_probabilities_file: models/reports/train/0387674b4768c888a5214c150db07aaf/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/f1e06be38fd0f810b7a6f9c93314fd9e.pkl + params_file: models/reports/train/0387674b4768c888a5214c150db07aaf/params.yaml + predictions_file: models/reports/train/0387674b4768c888a5214c150db07aaf/predictions.json + probabilities_file: models/reports/train/0387674b4768c888a5214c150db07aaf/probabilities.json + score_dict_file: models/reports/train/0387674b4768c888a5214c150db07aaf/score_dict.json + test_labels_file: models/reports/train/0387674b4768c888a5214c150db07aaf/test_labels.json + train_labels_file: models/reports/train/0387674b4768c888a5214c150db07aaf/train_labels.json + model_dir: models + name: 0387674b4768c888a5214c150db07aaf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 772ac003fcba6608cedf7e716dd059aa + trainer: + kwargs: {} +name: 0387674b4768c888a5214c150db07aaf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/047e8657d7ea6052a720d37e0c75a9c6/params.yaml b/examples/security/truthseeker/models/reports/train/047e8657d7ea6052a720d37e0c75a9c6/params.yaml new file mode 100644 index 00000000..500afc85 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/047e8657d7ea6052a720d37e0c75a9c6/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/047e8657d7ea6052a720d37e0c75a9c6/adv_predictions.json + adv_probabilities_file: models/reports/train/047e8657d7ea6052a720d37e0c75a9c6/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/b00dbaa545444bb693c0e56468f04b7e.pkl + params_file: models/reports/train/047e8657d7ea6052a720d37e0c75a9c6/params.yaml + predictions_file: models/reports/train/047e8657d7ea6052a720d37e0c75a9c6/predictions.json + probabilities_file: models/reports/train/047e8657d7ea6052a720d37e0c75a9c6/probabilities.json + score_dict_file: models/reports/train/047e8657d7ea6052a720d37e0c75a9c6/score_dict.json + test_labels_file: models/reports/train/047e8657d7ea6052a720d37e0c75a9c6/test_labels.json + train_labels_file: models/reports/train/047e8657d7ea6052a720d37e0c75a9c6/train_labels.json + model_dir: models + name: 047e8657d7ea6052a720d37e0c75a9c6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c9d876473356435922f99a77deeb44d6 + trainer: + kwargs: {} +name: 047e8657d7ea6052a720d37e0c75a9c6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/04b7e32318a06fd2e3cb15f350c101bf/params.yaml b/examples/security/truthseeker/models/reports/train/04b7e32318a06fd2e3cb15f350c101bf/params.yaml new file mode 100644 index 00000000..346907b2 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/04b7e32318a06fd2e3cb15f350c101bf/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/04b7e32318a06fd2e3cb15f350c101bf/adv_predictions.json + adv_probabilities_file: models/reports/train/04b7e32318a06fd2e3cb15f350c101bf/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/022d2d5db21fb187dda0211179f4aba1.pkl + params_file: models/reports/train/04b7e32318a06fd2e3cb15f350c101bf/params.yaml + predictions_file: models/reports/train/04b7e32318a06fd2e3cb15f350c101bf/predictions.json + probabilities_file: models/reports/train/04b7e32318a06fd2e3cb15f350c101bf/probabilities.json + score_dict_file: models/reports/train/04b7e32318a06fd2e3cb15f350c101bf/score_dict.json + test_labels_file: models/reports/train/04b7e32318a06fd2e3cb15f350c101bf/test_labels.json + train_labels_file: models/reports/train/04b7e32318a06fd2e3cb15f350c101bf/train_labels.json + model_dir: models + name: 04b7e32318a06fd2e3cb15f350c101bf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: afacdbf424f2802f9de79c189bd9666b + trainer: + kwargs: {} +name: 04b7e32318a06fd2e3cb15f350c101bf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/0775c636d5f1dedc93feea8ca5ec8b3e/params.yaml b/examples/security/truthseeker/models/reports/train/0775c636d5f1dedc93feea8ca5ec8b3e/params.yaml new file mode 100644 index 00000000..5b74094d --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/0775c636d5f1dedc93feea8ca5ec8b3e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/0775c636d5f1dedc93feea8ca5ec8b3e/adv_predictions.json + adv_probabilities_file: models/reports/train/0775c636d5f1dedc93feea8ca5ec8b3e/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/8797cc43ab66b2fb274938200d4b71b6.pkl + params_file: models/reports/train/0775c636d5f1dedc93feea8ca5ec8b3e/params.yaml + predictions_file: models/reports/train/0775c636d5f1dedc93feea8ca5ec8b3e/predictions.json + probabilities_file: models/reports/train/0775c636d5f1dedc93feea8ca5ec8b3e/probabilities.json + score_dict_file: models/reports/train/0775c636d5f1dedc93feea8ca5ec8b3e/score_dict.json + test_labels_file: models/reports/train/0775c636d5f1dedc93feea8ca5ec8b3e/test_labels.json + train_labels_file: models/reports/train/0775c636d5f1dedc93feea8ca5ec8b3e/train_labels.json + model_dir: models + name: 0775c636d5f1dedc93feea8ca5ec8b3e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fba3359414134d06c3cde4c74bcbd4a8 + trainer: + kwargs: {} +name: 0775c636d5f1dedc93feea8ca5ec8b3e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/0bb6d6c79877d0005c6a16b139c4d25c/params.yaml b/examples/security/truthseeker/models/reports/train/0bb6d6c79877d0005c6a16b139c4d25c/params.yaml new file mode 100644 index 00000000..f4cace89 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/0bb6d6c79877d0005c6a16b139c4d25c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/0bb6d6c79877d0005c6a16b139c4d25c/adv_predictions.json + adv_probabilities_file: models/reports/train/0bb6d6c79877d0005c6a16b139c4d25c/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/fc68b0293dc52e8e50093d9acc61ffbe.pkl + params_file: models/reports/train/0bb6d6c79877d0005c6a16b139c4d25c/params.yaml + predictions_file: models/reports/train/0bb6d6c79877d0005c6a16b139c4d25c/predictions.json + probabilities_file: models/reports/train/0bb6d6c79877d0005c6a16b139c4d25c/probabilities.json + score_dict_file: models/reports/train/0bb6d6c79877d0005c6a16b139c4d25c/score_dict.json + test_labels_file: models/reports/train/0bb6d6c79877d0005c6a16b139c4d25c/test_labels.json + train_labels_file: models/reports/train/0bb6d6c79877d0005c6a16b139c4d25c/train_labels.json + model_dir: models + name: 0bb6d6c79877d0005c6a16b139c4d25c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 02f334070cdc85e3cf02d8fe24eb60fa + trainer: + kwargs: {} +name: 0bb6d6c79877d0005c6a16b139c4d25c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/0cf5cb04049663c6deb3cbbb5648e6bc/params.yaml b/examples/security/truthseeker/models/reports/train/0cf5cb04049663c6deb3cbbb5648e6bc/params.yaml new file mode 100644 index 00000000..7eb6fd38 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/0cf5cb04049663c6deb3cbbb5648e6bc/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/0cf5cb04049663c6deb3cbbb5648e6bc/adv_predictions.json + adv_probabilities_file: models/reports/train/0cf5cb04049663c6deb3cbbb5648e6bc/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/b4c0e0e7594a00d8c9a0a919da5def80.pkl + params_file: models/reports/train/0cf5cb04049663c6deb3cbbb5648e6bc/params.yaml + predictions_file: models/reports/train/0cf5cb04049663c6deb3cbbb5648e6bc/predictions.json + probabilities_file: models/reports/train/0cf5cb04049663c6deb3cbbb5648e6bc/probabilities.json + score_dict_file: models/reports/train/0cf5cb04049663c6deb3cbbb5648e6bc/score_dict.json + test_labels_file: models/reports/train/0cf5cb04049663c6deb3cbbb5648e6bc/test_labels.json + train_labels_file: models/reports/train/0cf5cb04049663c6deb3cbbb5648e6bc/train_labels.json + model_dir: models + name: 0cf5cb04049663c6deb3cbbb5648e6bc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fc1e8ed8dcd0dd3bba148ae248e25b2d + trainer: + kwargs: {} +name: 0cf5cb04049663c6deb3cbbb5648e6bc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/0d0039d72504c5713c1a84635146fada/params.yaml b/examples/security/truthseeker/models/reports/train/0d0039d72504c5713c1a84635146fada/params.yaml new file mode 100644 index 00000000..e3ff986b --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/0d0039d72504c5713c1a84635146fada/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/0d0039d72504c5713c1a84635146fada/adv_predictions.json + adv_probabilities_file: models/reports/train/0d0039d72504c5713c1a84635146fada/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/be98deeb8e136b385fb4075f1d8505aa.pkl + params_file: models/reports/train/0d0039d72504c5713c1a84635146fada/params.yaml + predictions_file: models/reports/train/0d0039d72504c5713c1a84635146fada/predictions.json + probabilities_file: models/reports/train/0d0039d72504c5713c1a84635146fada/probabilities.json + score_dict_file: models/reports/train/0d0039d72504c5713c1a84635146fada/score_dict.json + test_labels_file: models/reports/train/0d0039d72504c5713c1a84635146fada/test_labels.json + train_labels_file: models/reports/train/0d0039d72504c5713c1a84635146fada/train_labels.json + model_dir: models + name: 0d0039d72504c5713c1a84635146fada + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fff75ab415d4b6079278d8e60e34075b + trainer: + kwargs: {} +name: 0d0039d72504c5713c1a84635146fada +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/0ee06c288bc5bf0414c9ce089dcdb984/params.yaml b/examples/security/truthseeker/models/reports/train/0ee06c288bc5bf0414c9ce089dcdb984/params.yaml new file mode 100644 index 00000000..f312c7e6 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/0ee06c288bc5bf0414c9ce089dcdb984/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/0ee06c288bc5bf0414c9ce089dcdb984/adv_predictions.json + adv_probabilities_file: models/reports/train/0ee06c288bc5bf0414c9ce089dcdb984/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/44bfb3accca1f55117dc4f4eb865e2fa.pkl + params_file: models/reports/train/0ee06c288bc5bf0414c9ce089dcdb984/params.yaml + predictions_file: models/reports/train/0ee06c288bc5bf0414c9ce089dcdb984/predictions.json + probabilities_file: models/reports/train/0ee06c288bc5bf0414c9ce089dcdb984/probabilities.json + score_dict_file: models/reports/train/0ee06c288bc5bf0414c9ce089dcdb984/score_dict.json + test_labels_file: models/reports/train/0ee06c288bc5bf0414c9ce089dcdb984/test_labels.json + train_labels_file: models/reports/train/0ee06c288bc5bf0414c9ce089dcdb984/train_labels.json + model_dir: models + name: 0ee06c288bc5bf0414c9ce089dcdb984 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3dbc7903116033d0a6ec5d0b3d557e1b + trainer: + kwargs: {} +name: 0ee06c288bc5bf0414c9ce089dcdb984 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/0ef34102c61ab7ff8ab98ce93b90a2bc/params.yaml b/examples/security/truthseeker/models/reports/train/0ef34102c61ab7ff8ab98ce93b90a2bc/params.yaml new file mode 100644 index 00000000..3e553ad2 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/0ef34102c61ab7ff8ab98ce93b90a2bc/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/0ef34102c61ab7ff8ab98ce93b90a2bc/adv_predictions.json + adv_probabilities_file: models/reports/train/0ef34102c61ab7ff8ab98ce93b90a2bc/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/0de0b40d2110e895aa5b49daf7a6ed34.pkl + params_file: models/reports/train/0ef34102c61ab7ff8ab98ce93b90a2bc/params.yaml + predictions_file: models/reports/train/0ef34102c61ab7ff8ab98ce93b90a2bc/predictions.json + probabilities_file: models/reports/train/0ef34102c61ab7ff8ab98ce93b90a2bc/probabilities.json + score_dict_file: models/reports/train/0ef34102c61ab7ff8ab98ce93b90a2bc/score_dict.json + test_labels_file: models/reports/train/0ef34102c61ab7ff8ab98ce93b90a2bc/test_labels.json + train_labels_file: models/reports/train/0ef34102c61ab7ff8ab98ce93b90a2bc/train_labels.json + model_dir: models + name: 0ef34102c61ab7ff8ab98ce93b90a2bc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ed11c48df3ee48ac230abdee1a9e91d9 + trainer: + kwargs: {} +name: 0ef34102c61ab7ff8ab98ce93b90a2bc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/12dd318564ea3fa1ec65a775016dc3e4/params.yaml b/examples/security/truthseeker/models/reports/train/12dd318564ea3fa1ec65a775016dc3e4/params.yaml new file mode 100644 index 00000000..01384a1f --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/12dd318564ea3fa1ec65a775016dc3e4/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/12dd318564ea3fa1ec65a775016dc3e4/adv_predictions.json + adv_probabilities_file: models/reports/train/12dd318564ea3fa1ec65a775016dc3e4/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/40e0b11fe9973d964bb1aee6b22f719e.pkl + params_file: models/reports/train/12dd318564ea3fa1ec65a775016dc3e4/params.yaml + predictions_file: models/reports/train/12dd318564ea3fa1ec65a775016dc3e4/predictions.json + probabilities_file: models/reports/train/12dd318564ea3fa1ec65a775016dc3e4/probabilities.json + score_dict_file: models/reports/train/12dd318564ea3fa1ec65a775016dc3e4/score_dict.json + test_labels_file: models/reports/train/12dd318564ea3fa1ec65a775016dc3e4/test_labels.json + train_labels_file: models/reports/train/12dd318564ea3fa1ec65a775016dc3e4/train_labels.json + model_dir: models + name: 12dd318564ea3fa1ec65a775016dc3e4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a34136cda3feeac3aacad9e2bb5971d0 + trainer: + kwargs: {} +name: 12dd318564ea3fa1ec65a775016dc3e4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/142515a4044e4799a1f80542f3d99b9e/params.yaml b/examples/security/truthseeker/models/reports/train/142515a4044e4799a1f80542f3d99b9e/params.yaml new file mode 100644 index 00000000..19d9a7c6 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/142515a4044e4799a1f80542f3d99b9e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/142515a4044e4799a1f80542f3d99b9e/adv_predictions.json + adv_probabilities_file: models/reports/train/142515a4044e4799a1f80542f3d99b9e/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/23dbe4a8009b044e8bf81e1a8012dbad.pkl + params_file: models/reports/train/142515a4044e4799a1f80542f3d99b9e/params.yaml + predictions_file: models/reports/train/142515a4044e4799a1f80542f3d99b9e/predictions.json + probabilities_file: models/reports/train/142515a4044e4799a1f80542f3d99b9e/probabilities.json + score_dict_file: models/reports/train/142515a4044e4799a1f80542f3d99b9e/score_dict.json + test_labels_file: models/reports/train/142515a4044e4799a1f80542f3d99b9e/test_labels.json + train_labels_file: models/reports/train/142515a4044e4799a1f80542f3d99b9e/train_labels.json + model_dir: models + name: 142515a4044e4799a1f80542f3d99b9e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fa44141ac6855cd0713c39732da17838 + trainer: + kwargs: {} +name: 142515a4044e4799a1f80542f3d99b9e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/14840508ccbd7a9e54e1f325f2ce3727/params.yaml b/examples/security/truthseeker/models/reports/train/14840508ccbd7a9e54e1f325f2ce3727/params.yaml new file mode 100644 index 00000000..7ff66d66 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/14840508ccbd7a9e54e1f325f2ce3727/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/14840508ccbd7a9e54e1f325f2ce3727/adv_predictions.json + adv_probabilities_file: models/reports/train/14840508ccbd7a9e54e1f325f2ce3727/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/e50835e256d443333227096eea217419.pkl + params_file: models/reports/train/14840508ccbd7a9e54e1f325f2ce3727/params.yaml + predictions_file: models/reports/train/14840508ccbd7a9e54e1f325f2ce3727/predictions.json + probabilities_file: models/reports/train/14840508ccbd7a9e54e1f325f2ce3727/probabilities.json + score_dict_file: models/reports/train/14840508ccbd7a9e54e1f325f2ce3727/score_dict.json + test_labels_file: models/reports/train/14840508ccbd7a9e54e1f325f2ce3727/test_labels.json + train_labels_file: models/reports/train/14840508ccbd7a9e54e1f325f2ce3727/train_labels.json + model_dir: models + name: 14840508ccbd7a9e54e1f325f2ce3727 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ad9bae639eaea56957358f4ac96b72da + trainer: + kwargs: {} +name: 14840508ccbd7a9e54e1f325f2ce3727 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/1639996792245a636da33ea778c90d33/params.yaml b/examples/security/truthseeker/models/reports/train/1639996792245a636da33ea778c90d33/params.yaml new file mode 100644 index 00000000..ccf14c5f --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/1639996792245a636da33ea778c90d33/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/1639996792245a636da33ea778c90d33/adv_predictions.json + adv_probabilities_file: models/reports/train/1639996792245a636da33ea778c90d33/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/08f372d3591ed22d74a4d8ab0229670c.pkl + params_file: models/reports/train/1639996792245a636da33ea778c90d33/params.yaml + predictions_file: models/reports/train/1639996792245a636da33ea778c90d33/predictions.json + probabilities_file: models/reports/train/1639996792245a636da33ea778c90d33/probabilities.json + score_dict_file: models/reports/train/1639996792245a636da33ea778c90d33/score_dict.json + test_labels_file: models/reports/train/1639996792245a636da33ea778c90d33/test_labels.json + train_labels_file: models/reports/train/1639996792245a636da33ea778c90d33/train_labels.json + model_dir: models + name: 1639996792245a636da33ea778c90d33 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9944a6830d46df64b31f0454c49ff6eb + trainer: + kwargs: {} +name: 1639996792245a636da33ea778c90d33 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/168fe962a58284c7e4e08a063655e4ad/params.yaml b/examples/security/truthseeker/models/reports/train/168fe962a58284c7e4e08a063655e4ad/params.yaml new file mode 100644 index 00000000..5933750b --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/168fe962a58284c7e4e08a063655e4ad/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/168fe962a58284c7e4e08a063655e4ad/adv_predictions.json + adv_probabilities_file: models/reports/train/168fe962a58284c7e4e08a063655e4ad/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/684ff8b980781a16560dcd489fa55953.pkl + params_file: models/reports/train/168fe962a58284c7e4e08a063655e4ad/params.yaml + predictions_file: models/reports/train/168fe962a58284c7e4e08a063655e4ad/predictions.json + probabilities_file: models/reports/train/168fe962a58284c7e4e08a063655e4ad/probabilities.json + score_dict_file: models/reports/train/168fe962a58284c7e4e08a063655e4ad/score_dict.json + test_labels_file: models/reports/train/168fe962a58284c7e4e08a063655e4ad/test_labels.json + train_labels_file: models/reports/train/168fe962a58284c7e4e08a063655e4ad/train_labels.json + model_dir: models + name: 168fe962a58284c7e4e08a063655e4ad + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 42bf76327db1b54183f984673411996a + trainer: + kwargs: {} +name: 168fe962a58284c7e4e08a063655e4ad +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/18d4786f11fac5839b38ea657e87f354/params.yaml b/examples/security/truthseeker/models/reports/train/18d4786f11fac5839b38ea657e87f354/params.yaml new file mode 100644 index 00000000..d30ebb04 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/18d4786f11fac5839b38ea657e87f354/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/18d4786f11fac5839b38ea657e87f354/adv_predictions.json + adv_probabilities_file: models/reports/train/18d4786f11fac5839b38ea657e87f354/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/42fd313171585dbbe9bdcb00f083545a.pkl + params_file: models/reports/train/18d4786f11fac5839b38ea657e87f354/params.yaml + predictions_file: models/reports/train/18d4786f11fac5839b38ea657e87f354/predictions.json + probabilities_file: models/reports/train/18d4786f11fac5839b38ea657e87f354/probabilities.json + score_dict_file: models/reports/train/18d4786f11fac5839b38ea657e87f354/score_dict.json + test_labels_file: models/reports/train/18d4786f11fac5839b38ea657e87f354/test_labels.json + train_labels_file: models/reports/train/18d4786f11fac5839b38ea657e87f354/train_labels.json + model_dir: models + name: 18d4786f11fac5839b38ea657e87f354 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: de5ff357e2bb09bb4c2a766e339aae8d + trainer: + kwargs: {} +name: 18d4786f11fac5839b38ea657e87f354 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/19bda18cec9e1d668b4fde5ac66d9c48/params.yaml b/examples/security/truthseeker/models/reports/train/19bda18cec9e1d668b4fde5ac66d9c48/params.yaml new file mode 100644 index 00000000..21bcfcd4 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/19bda18cec9e1d668b4fde5ac66d9c48/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/19bda18cec9e1d668b4fde5ac66d9c48/adv_predictions.json + adv_probabilities_file: models/reports/train/19bda18cec9e1d668b4fde5ac66d9c48/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/7b08f460debbb0e925bd9c394883ef1a.pkl + params_file: models/reports/train/19bda18cec9e1d668b4fde5ac66d9c48/params.yaml + predictions_file: models/reports/train/19bda18cec9e1d668b4fde5ac66d9c48/predictions.json + probabilities_file: models/reports/train/19bda18cec9e1d668b4fde5ac66d9c48/probabilities.json + score_dict_file: models/reports/train/19bda18cec9e1d668b4fde5ac66d9c48/score_dict.json + test_labels_file: models/reports/train/19bda18cec9e1d668b4fde5ac66d9c48/test_labels.json + train_labels_file: models/reports/train/19bda18cec9e1d668b4fde5ac66d9c48/train_labels.json + model_dir: models + name: 19bda18cec9e1d668b4fde5ac66d9c48 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7028fc5091f36247ae13d56d79a8b246 + trainer: + kwargs: {} +name: 19bda18cec9e1d668b4fde5ac66d9c48 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/1b55a0505db01cb4b642be2bf0324b44/params.yaml b/examples/security/truthseeker/models/reports/train/1b55a0505db01cb4b642be2bf0324b44/params.yaml new file mode 100644 index 00000000..52ba0be0 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/1b55a0505db01cb4b642be2bf0324b44/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/1b55a0505db01cb4b642be2bf0324b44/adv_predictions.json + adv_probabilities_file: models/reports/train/1b55a0505db01cb4b642be2bf0324b44/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/05d9a48f0f88ddb2bff9effcbdc01905.pkl + params_file: models/reports/train/1b55a0505db01cb4b642be2bf0324b44/params.yaml + predictions_file: models/reports/train/1b55a0505db01cb4b642be2bf0324b44/predictions.json + probabilities_file: models/reports/train/1b55a0505db01cb4b642be2bf0324b44/probabilities.json + score_dict_file: models/reports/train/1b55a0505db01cb4b642be2bf0324b44/score_dict.json + test_labels_file: models/reports/train/1b55a0505db01cb4b642be2bf0324b44/test_labels.json + train_labels_file: models/reports/train/1b55a0505db01cb4b642be2bf0324b44/train_labels.json + model_dir: models + name: 1b55a0505db01cb4b642be2bf0324b44 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 70b54f1b8875f1ef4069cce4af9728ec + trainer: + kwargs: {} +name: 1b55a0505db01cb4b642be2bf0324b44 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/1cf44ce76964d067f6f6b27f8a4a6e75/params.yaml b/examples/security/truthseeker/models/reports/train/1cf44ce76964d067f6f6b27f8a4a6e75/params.yaml new file mode 100644 index 00000000..3a92ca93 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/1cf44ce76964d067f6f6b27f8a4a6e75/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/1cf44ce76964d067f6f6b27f8a4a6e75/adv_predictions.json + adv_probabilities_file: models/reports/train/1cf44ce76964d067f6f6b27f8a4a6e75/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/9f6390ac41408039a8708def453fc1fb.pkl + params_file: models/reports/train/1cf44ce76964d067f6f6b27f8a4a6e75/params.yaml + predictions_file: models/reports/train/1cf44ce76964d067f6f6b27f8a4a6e75/predictions.json + probabilities_file: models/reports/train/1cf44ce76964d067f6f6b27f8a4a6e75/probabilities.json + score_dict_file: models/reports/train/1cf44ce76964d067f6f6b27f8a4a6e75/score_dict.json + test_labels_file: models/reports/train/1cf44ce76964d067f6f6b27f8a4a6e75/test_labels.json + train_labels_file: models/reports/train/1cf44ce76964d067f6f6b27f8a4a6e75/train_labels.json + model_dir: models + name: 1cf44ce76964d067f6f6b27f8a4a6e75 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b1a450856b3f51288e1454a026b779de + trainer: + kwargs: {} +name: 1cf44ce76964d067f6f6b27f8a4a6e75 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/1ee3a8f26b50c3c76a83233cd09ae386/params.yaml b/examples/security/truthseeker/models/reports/train/1ee3a8f26b50c3c76a83233cd09ae386/params.yaml new file mode 100644 index 00000000..c7d9e0fd --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/1ee3a8f26b50c3c76a83233cd09ae386/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/1ee3a8f26b50c3c76a83233cd09ae386/adv_predictions.json + adv_probabilities_file: models/reports/train/1ee3a8f26b50c3c76a83233cd09ae386/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/dd0f30b5326564c4c312fc9fca914624.pkl + params_file: models/reports/train/1ee3a8f26b50c3c76a83233cd09ae386/params.yaml + predictions_file: models/reports/train/1ee3a8f26b50c3c76a83233cd09ae386/predictions.json + probabilities_file: models/reports/train/1ee3a8f26b50c3c76a83233cd09ae386/probabilities.json + score_dict_file: models/reports/train/1ee3a8f26b50c3c76a83233cd09ae386/score_dict.json + test_labels_file: models/reports/train/1ee3a8f26b50c3c76a83233cd09ae386/test_labels.json + train_labels_file: models/reports/train/1ee3a8f26b50c3c76a83233cd09ae386/train_labels.json + model_dir: models + name: 1ee3a8f26b50c3c76a83233cd09ae386 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2d1565ec46adf5f28ce2f70cb04f4677 + trainer: + kwargs: {} +name: 1ee3a8f26b50c3c76a83233cd09ae386 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/1f15141f205f3ceda63b76d92f5880c2/params.yaml b/examples/security/truthseeker/models/reports/train/1f15141f205f3ceda63b76d92f5880c2/params.yaml new file mode 100644 index 00000000..813a03b3 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/1f15141f205f3ceda63b76d92f5880c2/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/1f15141f205f3ceda63b76d92f5880c2/adv_predictions.json + adv_probabilities_file: models/reports/train/1f15141f205f3ceda63b76d92f5880c2/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/89ecd48f4e544572ae76e453f04b98d0.pkl + params_file: models/reports/train/1f15141f205f3ceda63b76d92f5880c2/params.yaml + predictions_file: models/reports/train/1f15141f205f3ceda63b76d92f5880c2/predictions.json + probabilities_file: models/reports/train/1f15141f205f3ceda63b76d92f5880c2/probabilities.json + score_dict_file: models/reports/train/1f15141f205f3ceda63b76d92f5880c2/score_dict.json + test_labels_file: models/reports/train/1f15141f205f3ceda63b76d92f5880c2/test_labels.json + train_labels_file: models/reports/train/1f15141f205f3ceda63b76d92f5880c2/train_labels.json + model_dir: models + name: 1f15141f205f3ceda63b76d92f5880c2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 72a8e6fc77f9f7df69073b6ef628ce2d + trainer: + kwargs: {} +name: 1f15141f205f3ceda63b76d92f5880c2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/2023505c83418897d008f5e439d3d58d/params.yaml b/examples/security/truthseeker/models/reports/train/2023505c83418897d008f5e439d3d58d/params.yaml new file mode 100644 index 00000000..acc1162f --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/2023505c83418897d008f5e439d3d58d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/2023505c83418897d008f5e439d3d58d/adv_predictions.json + adv_probabilities_file: models/reports/train/2023505c83418897d008f5e439d3d58d/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/2b8b0efe8b3dc895ff74677062bf9ad8.pkl + params_file: models/reports/train/2023505c83418897d008f5e439d3d58d/params.yaml + predictions_file: models/reports/train/2023505c83418897d008f5e439d3d58d/predictions.json + probabilities_file: models/reports/train/2023505c83418897d008f5e439d3d58d/probabilities.json + score_dict_file: models/reports/train/2023505c83418897d008f5e439d3d58d/score_dict.json + test_labels_file: models/reports/train/2023505c83418897d008f5e439d3d58d/test_labels.json + train_labels_file: models/reports/train/2023505c83418897d008f5e439d3d58d/train_labels.json + model_dir: models + name: 2023505c83418897d008f5e439d3d58d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fdb0bf2f4b0379e838ff4632271aeddf + trainer: + kwargs: {} +name: 2023505c83418897d008f5e439d3d58d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/20c24d9dc3a4468a9a8ad49f74cc0cac/params.yaml b/examples/security/truthseeker/models/reports/train/20c24d9dc3a4468a9a8ad49f74cc0cac/params.yaml new file mode 100644 index 00000000..21c55a54 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/20c24d9dc3a4468a9a8ad49f74cc0cac/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/20c24d9dc3a4468a9a8ad49f74cc0cac/adv_predictions.json + adv_probabilities_file: models/reports/train/20c24d9dc3a4468a9a8ad49f74cc0cac/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/4546fbb97654d8663ea802400e861725.pkl + params_file: models/reports/train/20c24d9dc3a4468a9a8ad49f74cc0cac/params.yaml + predictions_file: models/reports/train/20c24d9dc3a4468a9a8ad49f74cc0cac/predictions.json + probabilities_file: models/reports/train/20c24d9dc3a4468a9a8ad49f74cc0cac/probabilities.json + score_dict_file: models/reports/train/20c24d9dc3a4468a9a8ad49f74cc0cac/score_dict.json + test_labels_file: models/reports/train/20c24d9dc3a4468a9a8ad49f74cc0cac/test_labels.json + train_labels_file: models/reports/train/20c24d9dc3a4468a9a8ad49f74cc0cac/train_labels.json + model_dir: models + name: 20c24d9dc3a4468a9a8ad49f74cc0cac + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 69c4bbd74188d9ba8cf0f5318f46780e + trainer: + kwargs: {} +name: 20c24d9dc3a4468a9a8ad49f74cc0cac +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/243dd8a48f9354533bbe244661d05ae9/params.yaml b/examples/security/truthseeker/models/reports/train/243dd8a48f9354533bbe244661d05ae9/params.yaml new file mode 100644 index 00000000..abfaa105 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/243dd8a48f9354533bbe244661d05ae9/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/243dd8a48f9354533bbe244661d05ae9/adv_predictions.json + adv_probabilities_file: models/reports/train/243dd8a48f9354533bbe244661d05ae9/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/97f7e601bda1da916eb09b4ba768eded.pkl + params_file: models/reports/train/243dd8a48f9354533bbe244661d05ae9/params.yaml + predictions_file: models/reports/train/243dd8a48f9354533bbe244661d05ae9/predictions.json + probabilities_file: models/reports/train/243dd8a48f9354533bbe244661d05ae9/probabilities.json + score_dict_file: models/reports/train/243dd8a48f9354533bbe244661d05ae9/score_dict.json + test_labels_file: models/reports/train/243dd8a48f9354533bbe244661d05ae9/test_labels.json + train_labels_file: models/reports/train/243dd8a48f9354533bbe244661d05ae9/train_labels.json + model_dir: models + name: 243dd8a48f9354533bbe244661d05ae9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 11c903041fc2f23439bcc44ee63fe40b + trainer: + kwargs: {} +name: 243dd8a48f9354533bbe244661d05ae9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/2472e1a6418ae6f388d0285d49221934/params.yaml b/examples/security/truthseeker/models/reports/train/2472e1a6418ae6f388d0285d49221934/params.yaml new file mode 100644 index 00000000..93db3445 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/2472e1a6418ae6f388d0285d49221934/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/2472e1a6418ae6f388d0285d49221934/adv_predictions.json + adv_probabilities_file: models/reports/train/2472e1a6418ae6f388d0285d49221934/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/87cbb404ea36b265aa10895903c3c616.pkl + params_file: models/reports/train/2472e1a6418ae6f388d0285d49221934/params.yaml + predictions_file: models/reports/train/2472e1a6418ae6f388d0285d49221934/predictions.json + probabilities_file: models/reports/train/2472e1a6418ae6f388d0285d49221934/probabilities.json + score_dict_file: models/reports/train/2472e1a6418ae6f388d0285d49221934/score_dict.json + test_labels_file: models/reports/train/2472e1a6418ae6f388d0285d49221934/test_labels.json + train_labels_file: models/reports/train/2472e1a6418ae6f388d0285d49221934/train_labels.json + model_dir: models + name: 2472e1a6418ae6f388d0285d49221934 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7fbbf8c0f80d0412c51685c719a5331c + trainer: + kwargs: {} +name: 2472e1a6418ae6f388d0285d49221934 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/2489b51de9fe76c54555f3e1aa0f1e5c/params.yaml b/examples/security/truthseeker/models/reports/train/2489b51de9fe76c54555f3e1aa0f1e5c/params.yaml new file mode 100644 index 00000000..140e29ae --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/2489b51de9fe76c54555f3e1aa0f1e5c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/2489b51de9fe76c54555f3e1aa0f1e5c/adv_predictions.json + adv_probabilities_file: models/reports/train/2489b51de9fe76c54555f3e1aa0f1e5c/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/3bdbaca9bab2594b18cc8de1bdc943fb.pkl + params_file: models/reports/train/2489b51de9fe76c54555f3e1aa0f1e5c/params.yaml + predictions_file: models/reports/train/2489b51de9fe76c54555f3e1aa0f1e5c/predictions.json + probabilities_file: models/reports/train/2489b51de9fe76c54555f3e1aa0f1e5c/probabilities.json + score_dict_file: models/reports/train/2489b51de9fe76c54555f3e1aa0f1e5c/score_dict.json + test_labels_file: models/reports/train/2489b51de9fe76c54555f3e1aa0f1e5c/test_labels.json + train_labels_file: models/reports/train/2489b51de9fe76c54555f3e1aa0f1e5c/train_labels.json + model_dir: models + name: 2489b51de9fe76c54555f3e1aa0f1e5c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ee1d45ffb14a01337c183d4c1bde373b + trainer: + kwargs: {} +name: 2489b51de9fe76c54555f3e1aa0f1e5c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/26c4df390320939046a857de0272704c/params.yaml b/examples/security/truthseeker/models/reports/train/26c4df390320939046a857de0272704c/params.yaml new file mode 100644 index 00000000..c22eba4a --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/26c4df390320939046a857de0272704c/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/26c4df390320939046a857de0272704c/adv_predictions.json + adv_probabilities_file: models/reports/train/26c4df390320939046a857de0272704c/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/c8a766691a146281d9f12baa9976ecf3.pkl + params_file: models/reports/train/26c4df390320939046a857de0272704c/params.yaml + predictions_file: models/reports/train/26c4df390320939046a857de0272704c/predictions.json + probabilities_file: models/reports/train/26c4df390320939046a857de0272704c/probabilities.json + score_dict_file: models/reports/train/26c4df390320939046a857de0272704c/score_dict.json + test_labels_file: models/reports/train/26c4df390320939046a857de0272704c/test_labels.json + train_labels_file: models/reports/train/26c4df390320939046a857de0272704c/train_labels.json + model_dir: models + name: 26c4df390320939046a857de0272704c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 68755efc740e1fc17bb7d4ea8acc24ee + trainer: + kwargs: {} +name: 26c4df390320939046a857de0272704c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/29f5b43e2d1b97caf7903b68bf57b481/params.yaml b/examples/security/truthseeker/models/reports/train/29f5b43e2d1b97caf7903b68bf57b481/params.yaml new file mode 100644 index 00000000..5b8ca7fa --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/29f5b43e2d1b97caf7903b68bf57b481/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/29f5b43e2d1b97caf7903b68bf57b481/adv_predictions.json + adv_probabilities_file: models/reports/train/29f5b43e2d1b97caf7903b68bf57b481/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/fd00dc2d23be5145c7e48316f6205c65.pkl + params_file: models/reports/train/29f5b43e2d1b97caf7903b68bf57b481/params.yaml + predictions_file: models/reports/train/29f5b43e2d1b97caf7903b68bf57b481/predictions.json + probabilities_file: models/reports/train/29f5b43e2d1b97caf7903b68bf57b481/probabilities.json + score_dict_file: models/reports/train/29f5b43e2d1b97caf7903b68bf57b481/score_dict.json + test_labels_file: models/reports/train/29f5b43e2d1b97caf7903b68bf57b481/test_labels.json + train_labels_file: models/reports/train/29f5b43e2d1b97caf7903b68bf57b481/train_labels.json + model_dir: models + name: 29f5b43e2d1b97caf7903b68bf57b481 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a68166ddf6234fbd13a556006057a2ab + trainer: + kwargs: {} +name: 29f5b43e2d1b97caf7903b68bf57b481 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/2c8c1bc445dde49683947c466ab562d4/params.yaml b/examples/security/truthseeker/models/reports/train/2c8c1bc445dde49683947c466ab562d4/params.yaml new file mode 100644 index 00000000..36ed567e --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/2c8c1bc445dde49683947c466ab562d4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/2c8c1bc445dde49683947c466ab562d4/adv_predictions.json + adv_probabilities_file: models/reports/train/2c8c1bc445dde49683947c466ab562d4/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/52c85ec0452a733a0a8f6950c551c7a2.pkl + params_file: models/reports/train/2c8c1bc445dde49683947c466ab562d4/params.yaml + predictions_file: models/reports/train/2c8c1bc445dde49683947c466ab562d4/predictions.json + probabilities_file: models/reports/train/2c8c1bc445dde49683947c466ab562d4/probabilities.json + score_dict_file: models/reports/train/2c8c1bc445dde49683947c466ab562d4/score_dict.json + test_labels_file: models/reports/train/2c8c1bc445dde49683947c466ab562d4/test_labels.json + train_labels_file: models/reports/train/2c8c1bc445dde49683947c466ab562d4/train_labels.json + model_dir: models + name: 2c8c1bc445dde49683947c466ab562d4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e1a98969a1144d5060ac200c0e8e2547 + trainer: + kwargs: {} +name: 2c8c1bc445dde49683947c466ab562d4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/2cdef178fe9252725aeb0337896d196d/params.yaml b/examples/security/truthseeker/models/reports/train/2cdef178fe9252725aeb0337896d196d/params.yaml new file mode 100644 index 00000000..d5792043 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/2cdef178fe9252725aeb0337896d196d/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/2cdef178fe9252725aeb0337896d196d/adv_predictions.json + adv_probabilities_file: models/reports/train/2cdef178fe9252725aeb0337896d196d/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/64ea70deffcd4c8f21aa2cb0b3d339f0.pkl + params_file: models/reports/train/2cdef178fe9252725aeb0337896d196d/params.yaml + predictions_file: models/reports/train/2cdef178fe9252725aeb0337896d196d/predictions.json + probabilities_file: models/reports/train/2cdef178fe9252725aeb0337896d196d/probabilities.json + score_dict_file: models/reports/train/2cdef178fe9252725aeb0337896d196d/score_dict.json + test_labels_file: models/reports/train/2cdef178fe9252725aeb0337896d196d/test_labels.json + train_labels_file: models/reports/train/2cdef178fe9252725aeb0337896d196d/train_labels.json + model_dir: models + name: 2cdef178fe9252725aeb0337896d196d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 63a4caee522ebeea2b0ec79b1fab9ad3 + trainer: + kwargs: {} +name: 2cdef178fe9252725aeb0337896d196d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/2d30ceb6949781c44ff552b6ceaf5f25/params.yaml b/examples/security/truthseeker/models/reports/train/2d30ceb6949781c44ff552b6ceaf5f25/params.yaml new file mode 100644 index 00000000..1e84b9e9 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/2d30ceb6949781c44ff552b6ceaf5f25/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/2d30ceb6949781c44ff552b6ceaf5f25/adv_predictions.json + adv_probabilities_file: models/reports/train/2d30ceb6949781c44ff552b6ceaf5f25/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/3175b23780be5fe6fcbfbc50d8b05ebf.pkl + params_file: models/reports/train/2d30ceb6949781c44ff552b6ceaf5f25/params.yaml + predictions_file: models/reports/train/2d30ceb6949781c44ff552b6ceaf5f25/predictions.json + probabilities_file: models/reports/train/2d30ceb6949781c44ff552b6ceaf5f25/probabilities.json + score_dict_file: models/reports/train/2d30ceb6949781c44ff552b6ceaf5f25/score_dict.json + test_labels_file: models/reports/train/2d30ceb6949781c44ff552b6ceaf5f25/test_labels.json + train_labels_file: models/reports/train/2d30ceb6949781c44ff552b6ceaf5f25/train_labels.json + model_dir: models + name: 2d30ceb6949781c44ff552b6ceaf5f25 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 51423b81552a3b40c6eb89009bb4964f + trainer: + kwargs: {} +name: 2d30ceb6949781c44ff552b6ceaf5f25 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/2df302c4c4ca0e319a922084c0dced35/params.yaml b/examples/security/truthseeker/models/reports/train/2df302c4c4ca0e319a922084c0dced35/params.yaml new file mode 100644 index 00000000..189b06c3 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/2df302c4c4ca0e319a922084c0dced35/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/2df302c4c4ca0e319a922084c0dced35/adv_predictions.json + adv_probabilities_file: models/reports/train/2df302c4c4ca0e319a922084c0dced35/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/982495bc6ec42cd51fd441cbc87c5b31.pkl + params_file: models/reports/train/2df302c4c4ca0e319a922084c0dced35/params.yaml + predictions_file: models/reports/train/2df302c4c4ca0e319a922084c0dced35/predictions.json + probabilities_file: models/reports/train/2df302c4c4ca0e319a922084c0dced35/probabilities.json + score_dict_file: models/reports/train/2df302c4c4ca0e319a922084c0dced35/score_dict.json + test_labels_file: models/reports/train/2df302c4c4ca0e319a922084c0dced35/test_labels.json + train_labels_file: models/reports/train/2df302c4c4ca0e319a922084c0dced35/train_labels.json + model_dir: models + name: 2df302c4c4ca0e319a922084c0dced35 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c6b08258ec4dd4570e0482eeb5135e98 + trainer: + kwargs: {} +name: 2df302c4c4ca0e319a922084c0dced35 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/2e39570574e4c94493945949fe30c69b/params.yaml b/examples/security/truthseeker/models/reports/train/2e39570574e4c94493945949fe30c69b/params.yaml new file mode 100644 index 00000000..2a436ea1 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/2e39570574e4c94493945949fe30c69b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/2e39570574e4c94493945949fe30c69b/adv_predictions.json + adv_probabilities_file: models/reports/train/2e39570574e4c94493945949fe30c69b/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/b0d405c204020c880a56d39eb01eb48a.pkl + params_file: models/reports/train/2e39570574e4c94493945949fe30c69b/params.yaml + predictions_file: models/reports/train/2e39570574e4c94493945949fe30c69b/predictions.json + probabilities_file: models/reports/train/2e39570574e4c94493945949fe30c69b/probabilities.json + score_dict_file: models/reports/train/2e39570574e4c94493945949fe30c69b/score_dict.json + test_labels_file: models/reports/train/2e39570574e4c94493945949fe30c69b/test_labels.json + train_labels_file: models/reports/train/2e39570574e4c94493945949fe30c69b/train_labels.json + model_dir: models + name: 2e39570574e4c94493945949fe30c69b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7f648d33c79c5e978edb679430708919 + trainer: + kwargs: {} +name: 2e39570574e4c94493945949fe30c69b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/34953b35993d64e53669dc2e273a4a6c/params.yaml b/examples/security/truthseeker/models/reports/train/34953b35993d64e53669dc2e273a4a6c/params.yaml new file mode 100644 index 00000000..23300195 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/34953b35993d64e53669dc2e273a4a6c/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/34953b35993d64e53669dc2e273a4a6c/adv_predictions.json + adv_probabilities_file: models/reports/train/34953b35993d64e53669dc2e273a4a6c/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/afc4207fb07abc9c1a42ae741b383cd2.pkl + params_file: models/reports/train/34953b35993d64e53669dc2e273a4a6c/params.yaml + predictions_file: models/reports/train/34953b35993d64e53669dc2e273a4a6c/predictions.json + probabilities_file: models/reports/train/34953b35993d64e53669dc2e273a4a6c/probabilities.json + score_dict_file: models/reports/train/34953b35993d64e53669dc2e273a4a6c/score_dict.json + test_labels_file: models/reports/train/34953b35993d64e53669dc2e273a4a6c/test_labels.json + train_labels_file: models/reports/train/34953b35993d64e53669dc2e273a4a6c/train_labels.json + model_dir: models + name: 34953b35993d64e53669dc2e273a4a6c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b9b9419060b0a6f5ef69dc1c7e79aa67 + trainer: + kwargs: {} +name: 34953b35993d64e53669dc2e273a4a6c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/356158920e3c90ed0b743e766d70f161/params.yaml b/examples/security/truthseeker/models/reports/train/356158920e3c90ed0b743e766d70f161/params.yaml new file mode 100644 index 00000000..c5c4fd23 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/356158920e3c90ed0b743e766d70f161/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/356158920e3c90ed0b743e766d70f161/adv_predictions.json + adv_probabilities_file: models/reports/train/356158920e3c90ed0b743e766d70f161/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/dfcbeba1599d9ea21359edf86436df2e.pkl + params_file: models/reports/train/356158920e3c90ed0b743e766d70f161/params.yaml + predictions_file: models/reports/train/356158920e3c90ed0b743e766d70f161/predictions.json + probabilities_file: models/reports/train/356158920e3c90ed0b743e766d70f161/probabilities.json + score_dict_file: models/reports/train/356158920e3c90ed0b743e766d70f161/score_dict.json + test_labels_file: models/reports/train/356158920e3c90ed0b743e766d70f161/test_labels.json + train_labels_file: models/reports/train/356158920e3c90ed0b743e766d70f161/train_labels.json + model_dir: models + name: 356158920e3c90ed0b743e766d70f161 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0a6c17fce5baa906a19793a3a49796d2 + trainer: + kwargs: {} +name: 356158920e3c90ed0b743e766d70f161 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/3719252be804722b4e1744fdf9fa4336/params.yaml b/examples/security/truthseeker/models/reports/train/3719252be804722b4e1744fdf9fa4336/params.yaml new file mode 100644 index 00000000..e97fb7a6 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/3719252be804722b4e1744fdf9fa4336/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/3719252be804722b4e1744fdf9fa4336/adv_predictions.json + adv_probabilities_file: models/reports/train/3719252be804722b4e1744fdf9fa4336/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/fae93075c5192acb7cf606149b6ee593.pkl + params_file: models/reports/train/3719252be804722b4e1744fdf9fa4336/params.yaml + predictions_file: models/reports/train/3719252be804722b4e1744fdf9fa4336/predictions.json + probabilities_file: models/reports/train/3719252be804722b4e1744fdf9fa4336/probabilities.json + score_dict_file: models/reports/train/3719252be804722b4e1744fdf9fa4336/score_dict.json + test_labels_file: models/reports/train/3719252be804722b4e1744fdf9fa4336/test_labels.json + train_labels_file: models/reports/train/3719252be804722b4e1744fdf9fa4336/train_labels.json + model_dir: models + name: 3719252be804722b4e1744fdf9fa4336 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4ed1d0e210968dfc57ec529fbf48d96c + trainer: + kwargs: {} +name: 3719252be804722b4e1744fdf9fa4336 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/37ce0f654c464b2aa899b6e842e4a1bc/params.yaml b/examples/security/truthseeker/models/reports/train/37ce0f654c464b2aa899b6e842e4a1bc/params.yaml new file mode 100644 index 00000000..a647c288 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/37ce0f654c464b2aa899b6e842e4a1bc/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/37ce0f654c464b2aa899b6e842e4a1bc/adv_predictions.json + adv_probabilities_file: models/reports/train/37ce0f654c464b2aa899b6e842e4a1bc/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/3fd6bfb90887de957edc0cd9eac71e41.pkl + params_file: models/reports/train/37ce0f654c464b2aa899b6e842e4a1bc/params.yaml + predictions_file: models/reports/train/37ce0f654c464b2aa899b6e842e4a1bc/predictions.json + probabilities_file: models/reports/train/37ce0f654c464b2aa899b6e842e4a1bc/probabilities.json + score_dict_file: models/reports/train/37ce0f654c464b2aa899b6e842e4a1bc/score_dict.json + test_labels_file: models/reports/train/37ce0f654c464b2aa899b6e842e4a1bc/test_labels.json + train_labels_file: models/reports/train/37ce0f654c464b2aa899b6e842e4a1bc/train_labels.json + model_dir: models + name: 37ce0f654c464b2aa899b6e842e4a1bc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 11f87b6c0031745c1f1a47dfbc566801 + trainer: + kwargs: {} +name: 37ce0f654c464b2aa899b6e842e4a1bc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/397167736d4602e9d5aa52a18a74ec69/params.yaml b/examples/security/truthseeker/models/reports/train/397167736d4602e9d5aa52a18a74ec69/params.yaml new file mode 100644 index 00000000..090f9abb --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/397167736d4602e9d5aa52a18a74ec69/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/397167736d4602e9d5aa52a18a74ec69/adv_predictions.json + adv_probabilities_file: models/reports/train/397167736d4602e9d5aa52a18a74ec69/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/ee3d610cc23ab4fdea63d276dc48b03c.pkl + params_file: models/reports/train/397167736d4602e9d5aa52a18a74ec69/params.yaml + predictions_file: models/reports/train/397167736d4602e9d5aa52a18a74ec69/predictions.json + probabilities_file: models/reports/train/397167736d4602e9d5aa52a18a74ec69/probabilities.json + score_dict_file: models/reports/train/397167736d4602e9d5aa52a18a74ec69/score_dict.json + test_labels_file: models/reports/train/397167736d4602e9d5aa52a18a74ec69/test_labels.json + train_labels_file: models/reports/train/397167736d4602e9d5aa52a18a74ec69/train_labels.json + model_dir: models + name: 397167736d4602e9d5aa52a18a74ec69 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d607c615c7571d5cbe469daacf75f865 + trainer: + kwargs: {} +name: 397167736d4602e9d5aa52a18a74ec69 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/39ccc1fb63319798fbc9ae9004736cc5/params.yaml b/examples/security/truthseeker/models/reports/train/39ccc1fb63319798fbc9ae9004736cc5/params.yaml new file mode 100644 index 00000000..867e6afe --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/39ccc1fb63319798fbc9ae9004736cc5/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/39ccc1fb63319798fbc9ae9004736cc5/adv_predictions.json + adv_probabilities_file: models/reports/train/39ccc1fb63319798fbc9ae9004736cc5/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/d64f709e1bcb17862630f80196a24651.pkl + params_file: models/reports/train/39ccc1fb63319798fbc9ae9004736cc5/params.yaml + predictions_file: models/reports/train/39ccc1fb63319798fbc9ae9004736cc5/predictions.json + probabilities_file: models/reports/train/39ccc1fb63319798fbc9ae9004736cc5/probabilities.json + score_dict_file: models/reports/train/39ccc1fb63319798fbc9ae9004736cc5/score_dict.json + test_labels_file: models/reports/train/39ccc1fb63319798fbc9ae9004736cc5/test_labels.json + train_labels_file: models/reports/train/39ccc1fb63319798fbc9ae9004736cc5/train_labels.json + model_dir: models + name: 39ccc1fb63319798fbc9ae9004736cc5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e7e10a3fe830a9a56394e28b8d69b005 + trainer: + kwargs: {} +name: 39ccc1fb63319798fbc9ae9004736cc5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/3a61b20ef689ae3cd5379c90c7922963/params.yaml b/examples/security/truthseeker/models/reports/train/3a61b20ef689ae3cd5379c90c7922963/params.yaml new file mode 100644 index 00000000..6923c234 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/3a61b20ef689ae3cd5379c90c7922963/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/3a61b20ef689ae3cd5379c90c7922963/adv_predictions.json + adv_probabilities_file: models/reports/train/3a61b20ef689ae3cd5379c90c7922963/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/d381e90e3207deedc0998b53d4ce1944.pkl + params_file: models/reports/train/3a61b20ef689ae3cd5379c90c7922963/params.yaml + predictions_file: models/reports/train/3a61b20ef689ae3cd5379c90c7922963/predictions.json + probabilities_file: models/reports/train/3a61b20ef689ae3cd5379c90c7922963/probabilities.json + score_dict_file: models/reports/train/3a61b20ef689ae3cd5379c90c7922963/score_dict.json + test_labels_file: models/reports/train/3a61b20ef689ae3cd5379c90c7922963/test_labels.json + train_labels_file: models/reports/train/3a61b20ef689ae3cd5379c90c7922963/train_labels.json + model_dir: models + name: 3a61b20ef689ae3cd5379c90c7922963 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 76b92fcf7e44da5caf1e228cddc905ab + trainer: + kwargs: {} +name: 3a61b20ef689ae3cd5379c90c7922963 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/3c1fb6ff6fc3aec3066b50ea8617c89a/params.yaml b/examples/security/truthseeker/models/reports/train/3c1fb6ff6fc3aec3066b50ea8617c89a/params.yaml new file mode 100644 index 00000000..eace186f --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/3c1fb6ff6fc3aec3066b50ea8617c89a/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/3c1fb6ff6fc3aec3066b50ea8617c89a/adv_predictions.json + adv_probabilities_file: models/reports/train/3c1fb6ff6fc3aec3066b50ea8617c89a/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: models/reports/train/3c1fb6ff6fc3aec3066b50ea8617c89a/params.yaml + predictions_file: models/reports/train/3c1fb6ff6fc3aec3066b50ea8617c89a/predictions.json + probabilities_file: models/reports/train/3c1fb6ff6fc3aec3066b50ea8617c89a/probabilities.json + score_dict_file: models/reports/train/3c1fb6ff6fc3aec3066b50ea8617c89a/score_dict.json + test_labels_file: models/reports/train/3c1fb6ff6fc3aec3066b50ea8617c89a/test_labels.json + train_labels_file: models/reports/train/3c1fb6ff6fc3aec3066b50ea8617c89a/train_labels.json + model_dir: models + name: 3c1fb6ff6fc3aec3066b50ea8617c89a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 3c1fb6ff6fc3aec3066b50ea8617c89a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/3d44332c3ec61db301f3d226f39dc831/params.yaml b/examples/security/truthseeker/models/reports/train/3d44332c3ec61db301f3d226f39dc831/params.yaml new file mode 100644 index 00000000..682be814 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/3d44332c3ec61db301f3d226f39dc831/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/3d44332c3ec61db301f3d226f39dc831/adv_predictions.json + adv_probabilities_file: models/reports/train/3d44332c3ec61db301f3d226f39dc831/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/34392cd7c05a4952636031d4e6f95f53.pkl + params_file: models/reports/train/3d44332c3ec61db301f3d226f39dc831/params.yaml + predictions_file: models/reports/train/3d44332c3ec61db301f3d226f39dc831/predictions.json + probabilities_file: models/reports/train/3d44332c3ec61db301f3d226f39dc831/probabilities.json + score_dict_file: models/reports/train/3d44332c3ec61db301f3d226f39dc831/score_dict.json + test_labels_file: models/reports/train/3d44332c3ec61db301f3d226f39dc831/test_labels.json + train_labels_file: models/reports/train/3d44332c3ec61db301f3d226f39dc831/train_labels.json + model_dir: models + name: 3d44332c3ec61db301f3d226f39dc831 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5a69109e0d33dc05cd08763922cb4ed7 + trainer: + kwargs: {} +name: 3d44332c3ec61db301f3d226f39dc831 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/3e58a9ff3991431525feafc99a95953c/params.yaml b/examples/security/truthseeker/models/reports/train/3e58a9ff3991431525feafc99a95953c/params.yaml new file mode 100644 index 00000000..4f9825ca --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/3e58a9ff3991431525feafc99a95953c/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/3e58a9ff3991431525feafc99a95953c/adv_predictions.json + adv_probabilities_file: models/reports/train/3e58a9ff3991431525feafc99a95953c/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/bc53afff90cfaedf96601a96947f52c0.pkl + params_file: models/reports/train/3e58a9ff3991431525feafc99a95953c/params.yaml + predictions_file: models/reports/train/3e58a9ff3991431525feafc99a95953c/predictions.json + probabilities_file: models/reports/train/3e58a9ff3991431525feafc99a95953c/probabilities.json + score_dict_file: models/reports/train/3e58a9ff3991431525feafc99a95953c/score_dict.json + test_labels_file: models/reports/train/3e58a9ff3991431525feafc99a95953c/test_labels.json + train_labels_file: models/reports/train/3e58a9ff3991431525feafc99a95953c/train_labels.json + model_dir: models + name: 3e58a9ff3991431525feafc99a95953c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f5034fd235a829080f301696f8a0b860 + trainer: + kwargs: {} +name: 3e58a9ff3991431525feafc99a95953c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/434b3903afdd391cdea9eacdedb48ac0/params.yaml b/examples/security/truthseeker/models/reports/train/434b3903afdd391cdea9eacdedb48ac0/params.yaml new file mode 100644 index 00000000..06d5597e --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/434b3903afdd391cdea9eacdedb48ac0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/434b3903afdd391cdea9eacdedb48ac0/adv_predictions.json + adv_probabilities_file: models/reports/train/434b3903afdd391cdea9eacdedb48ac0/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/708c99b5cb835bd1377e07f34ce67e1e.pkl + params_file: models/reports/train/434b3903afdd391cdea9eacdedb48ac0/params.yaml + predictions_file: models/reports/train/434b3903afdd391cdea9eacdedb48ac0/predictions.json + probabilities_file: models/reports/train/434b3903afdd391cdea9eacdedb48ac0/probabilities.json + score_dict_file: models/reports/train/434b3903afdd391cdea9eacdedb48ac0/score_dict.json + test_labels_file: models/reports/train/434b3903afdd391cdea9eacdedb48ac0/test_labels.json + train_labels_file: models/reports/train/434b3903afdd391cdea9eacdedb48ac0/train_labels.json + model_dir: models + name: 434b3903afdd391cdea9eacdedb48ac0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d1e64da14376b014638fe992123dd7e6 + trainer: + kwargs: {} +name: 434b3903afdd391cdea9eacdedb48ac0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/45fba2309f8eb54b0f20b4ae46a50ac9/params.yaml b/examples/security/truthseeker/models/reports/train/45fba2309f8eb54b0f20b4ae46a50ac9/params.yaml new file mode 100644 index 00000000..51eafe7e --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/45fba2309f8eb54b0f20b4ae46a50ac9/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/45fba2309f8eb54b0f20b4ae46a50ac9/adv_predictions.json + adv_probabilities_file: models/reports/train/45fba2309f8eb54b0f20b4ae46a50ac9/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/eae53cb6efaeaedc8b2c35203b1977f5.pkl + params_file: models/reports/train/45fba2309f8eb54b0f20b4ae46a50ac9/params.yaml + predictions_file: models/reports/train/45fba2309f8eb54b0f20b4ae46a50ac9/predictions.json + probabilities_file: models/reports/train/45fba2309f8eb54b0f20b4ae46a50ac9/probabilities.json + score_dict_file: models/reports/train/45fba2309f8eb54b0f20b4ae46a50ac9/score_dict.json + test_labels_file: models/reports/train/45fba2309f8eb54b0f20b4ae46a50ac9/test_labels.json + train_labels_file: models/reports/train/45fba2309f8eb54b0f20b4ae46a50ac9/train_labels.json + model_dir: models + name: 45fba2309f8eb54b0f20b4ae46a50ac9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e4704f38a85f7353c97655440219d125 + trainer: + kwargs: {} +name: 45fba2309f8eb54b0f20b4ae46a50ac9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/46dbd3f17d436ea3db3e0fe86b3cfa67/params.yaml b/examples/security/truthseeker/models/reports/train/46dbd3f17d436ea3db3e0fe86b3cfa67/params.yaml new file mode 100644 index 00000000..e477591e --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/46dbd3f17d436ea3db3e0fe86b3cfa67/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/46dbd3f17d436ea3db3e0fe86b3cfa67/adv_predictions.json + adv_probabilities_file: models/reports/train/46dbd3f17d436ea3db3e0fe86b3cfa67/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/06d6a6ce6be148e1928a2aca0a2a61e8.pkl + params_file: models/reports/train/46dbd3f17d436ea3db3e0fe86b3cfa67/params.yaml + predictions_file: models/reports/train/46dbd3f17d436ea3db3e0fe86b3cfa67/predictions.json + probabilities_file: models/reports/train/46dbd3f17d436ea3db3e0fe86b3cfa67/probabilities.json + score_dict_file: models/reports/train/46dbd3f17d436ea3db3e0fe86b3cfa67/score_dict.json + test_labels_file: models/reports/train/46dbd3f17d436ea3db3e0fe86b3cfa67/test_labels.json + train_labels_file: models/reports/train/46dbd3f17d436ea3db3e0fe86b3cfa67/train_labels.json + model_dir: models + name: 46dbd3f17d436ea3db3e0fe86b3cfa67 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 85f0ea0cffdbdb3782ed6c82eb494f1f + trainer: + kwargs: {} +name: 46dbd3f17d436ea3db3e0fe86b3cfa67 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/473c06636d926f518ef77d47d9ce2387/params.yaml b/examples/security/truthseeker/models/reports/train/473c06636d926f518ef77d47d9ce2387/params.yaml new file mode 100644 index 00000000..4900636a --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/473c06636d926f518ef77d47d9ce2387/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/473c06636d926f518ef77d47d9ce2387/adv_predictions.json + adv_probabilities_file: models/reports/train/473c06636d926f518ef77d47d9ce2387/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/464cc68fd081dc2acce319d5da69f76b.pkl + params_file: models/reports/train/473c06636d926f518ef77d47d9ce2387/params.yaml + predictions_file: models/reports/train/473c06636d926f518ef77d47d9ce2387/predictions.json + probabilities_file: models/reports/train/473c06636d926f518ef77d47d9ce2387/probabilities.json + score_dict_file: models/reports/train/473c06636d926f518ef77d47d9ce2387/score_dict.json + test_labels_file: models/reports/train/473c06636d926f518ef77d47d9ce2387/test_labels.json + train_labels_file: models/reports/train/473c06636d926f518ef77d47d9ce2387/train_labels.json + model_dir: models + name: 473c06636d926f518ef77d47d9ce2387 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74ae777e788813e03f6f2948cc025c4 + trainer: + kwargs: {} +name: 473c06636d926f518ef77d47d9ce2387 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/4a0139a776291cce2e409708b2b88fd9/params.yaml b/examples/security/truthseeker/models/reports/train/4a0139a776291cce2e409708b2b88fd9/params.yaml new file mode 100644 index 00000000..73892a84 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/4a0139a776291cce2e409708b2b88fd9/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/4a0139a776291cce2e409708b2b88fd9/adv_predictions.json + adv_probabilities_file: models/reports/train/4a0139a776291cce2e409708b2b88fd9/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/216357492833cb17e231de3b9005b1e1.pkl + params_file: models/reports/train/4a0139a776291cce2e409708b2b88fd9/params.yaml + predictions_file: models/reports/train/4a0139a776291cce2e409708b2b88fd9/predictions.json + probabilities_file: models/reports/train/4a0139a776291cce2e409708b2b88fd9/probabilities.json + score_dict_file: models/reports/train/4a0139a776291cce2e409708b2b88fd9/score_dict.json + test_labels_file: models/reports/train/4a0139a776291cce2e409708b2b88fd9/test_labels.json + train_labels_file: models/reports/train/4a0139a776291cce2e409708b2b88fd9/train_labels.json + model_dir: models + name: 4a0139a776291cce2e409708b2b88fd9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 79b254765f7ce1aa63c93b58b2b26def + trainer: + kwargs: {} +name: 4a0139a776291cce2e409708b2b88fd9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/4b74e6a0a7d800c82424e9cb004a6b41/params.yaml b/examples/security/truthseeker/models/reports/train/4b74e6a0a7d800c82424e9cb004a6b41/params.yaml new file mode 100644 index 00000000..0c0854f9 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/4b74e6a0a7d800c82424e9cb004a6b41/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/4b74e6a0a7d800c82424e9cb004a6b41/adv_predictions.json + adv_probabilities_file: models/reports/train/4b74e6a0a7d800c82424e9cb004a6b41/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/080fbe5a2d667ad76d454bd4f7caa919.pkl + params_file: models/reports/train/4b74e6a0a7d800c82424e9cb004a6b41/params.yaml + predictions_file: models/reports/train/4b74e6a0a7d800c82424e9cb004a6b41/predictions.json + probabilities_file: models/reports/train/4b74e6a0a7d800c82424e9cb004a6b41/probabilities.json + score_dict_file: models/reports/train/4b74e6a0a7d800c82424e9cb004a6b41/score_dict.json + test_labels_file: models/reports/train/4b74e6a0a7d800c82424e9cb004a6b41/test_labels.json + train_labels_file: models/reports/train/4b74e6a0a7d800c82424e9cb004a6b41/train_labels.json + model_dir: models + name: 4b74e6a0a7d800c82424e9cb004a6b41 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 17e5c85eb146f39bc8e3ab11a31b866f + trainer: + kwargs: {} +name: 4b74e6a0a7d800c82424e9cb004a6b41 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/4ea5e5743025594459d020511afa30f9/params.yaml b/examples/security/truthseeker/models/reports/train/4ea5e5743025594459d020511afa30f9/params.yaml new file mode 100644 index 00000000..d4cda19d --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/4ea5e5743025594459d020511afa30f9/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/4ea5e5743025594459d020511afa30f9/adv_predictions.json + adv_probabilities_file: models/reports/train/4ea5e5743025594459d020511afa30f9/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/d072e65aba2f4626a24e0f5982e49de7.pkl + params_file: models/reports/train/4ea5e5743025594459d020511afa30f9/params.yaml + predictions_file: models/reports/train/4ea5e5743025594459d020511afa30f9/predictions.json + probabilities_file: models/reports/train/4ea5e5743025594459d020511afa30f9/probabilities.json + score_dict_file: models/reports/train/4ea5e5743025594459d020511afa30f9/score_dict.json + test_labels_file: models/reports/train/4ea5e5743025594459d020511afa30f9/test_labels.json + train_labels_file: models/reports/train/4ea5e5743025594459d020511afa30f9/train_labels.json + model_dir: models + name: 4ea5e5743025594459d020511afa30f9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1997cdf1fcc1bf068ecd31c8f6a45f71 + trainer: + kwargs: {} +name: 4ea5e5743025594459d020511afa30f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/4fe4962b71e688d2968d89477e107b06/params.yaml b/examples/security/truthseeker/models/reports/train/4fe4962b71e688d2968d89477e107b06/params.yaml new file mode 100644 index 00000000..98367899 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/4fe4962b71e688d2968d89477e107b06/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/4fe4962b71e688d2968d89477e107b06/adv_predictions.json + adv_probabilities_file: models/reports/train/4fe4962b71e688d2968d89477e107b06/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/7d2ee72eda0ed67387ae9f0fe6f485a3.pkl + params_file: models/reports/train/4fe4962b71e688d2968d89477e107b06/params.yaml + predictions_file: models/reports/train/4fe4962b71e688d2968d89477e107b06/predictions.json + probabilities_file: models/reports/train/4fe4962b71e688d2968d89477e107b06/probabilities.json + score_dict_file: models/reports/train/4fe4962b71e688d2968d89477e107b06/score_dict.json + test_labels_file: models/reports/train/4fe4962b71e688d2968d89477e107b06/test_labels.json + train_labels_file: models/reports/train/4fe4962b71e688d2968d89477e107b06/train_labels.json + model_dir: models + name: 4fe4962b71e688d2968d89477e107b06 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5e9b42600af28928b205905fc7fcecf8 + trainer: + kwargs: {} +name: 4fe4962b71e688d2968d89477e107b06 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/50e4b7685b95130c593b5fc8dfb489f5/params.yaml b/examples/security/truthseeker/models/reports/train/50e4b7685b95130c593b5fc8dfb489f5/params.yaml new file mode 100644 index 00000000..5ce34e64 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/50e4b7685b95130c593b5fc8dfb489f5/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/50e4b7685b95130c593b5fc8dfb489f5/adv_predictions.json + adv_probabilities_file: models/reports/train/50e4b7685b95130c593b5fc8dfb489f5/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/3b3ee4a51cf602aba4f6d5db8e0147ff.pkl + params_file: models/reports/train/50e4b7685b95130c593b5fc8dfb489f5/params.yaml + predictions_file: models/reports/train/50e4b7685b95130c593b5fc8dfb489f5/predictions.json + probabilities_file: models/reports/train/50e4b7685b95130c593b5fc8dfb489f5/probabilities.json + score_dict_file: models/reports/train/50e4b7685b95130c593b5fc8dfb489f5/score_dict.json + test_labels_file: models/reports/train/50e4b7685b95130c593b5fc8dfb489f5/test_labels.json + train_labels_file: models/reports/train/50e4b7685b95130c593b5fc8dfb489f5/train_labels.json + model_dir: models + name: 50e4b7685b95130c593b5fc8dfb489f5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0d80cfae516e96e6befcee96e06897db + trainer: + kwargs: {} +name: 50e4b7685b95130c593b5fc8dfb489f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/54cd9b1999feb86eb8e20b9a9a291847/params.yaml b/examples/security/truthseeker/models/reports/train/54cd9b1999feb86eb8e20b9a9a291847/params.yaml new file mode 100644 index 00000000..3f0a7a99 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/54cd9b1999feb86eb8e20b9a9a291847/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/54cd9b1999feb86eb8e20b9a9a291847/adv_predictions.json + adv_probabilities_file: models/reports/train/54cd9b1999feb86eb8e20b9a9a291847/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/51948ae4b92445a146b7a90ed6898714.pkl + params_file: models/reports/train/54cd9b1999feb86eb8e20b9a9a291847/params.yaml + predictions_file: models/reports/train/54cd9b1999feb86eb8e20b9a9a291847/predictions.json + probabilities_file: models/reports/train/54cd9b1999feb86eb8e20b9a9a291847/probabilities.json + score_dict_file: models/reports/train/54cd9b1999feb86eb8e20b9a9a291847/score_dict.json + test_labels_file: models/reports/train/54cd9b1999feb86eb8e20b9a9a291847/test_labels.json + train_labels_file: models/reports/train/54cd9b1999feb86eb8e20b9a9a291847/train_labels.json + model_dir: models + name: 54cd9b1999feb86eb8e20b9a9a291847 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cce8e2e72282aca010c9220d1651c1f8 + trainer: + kwargs: {} +name: 54cd9b1999feb86eb8e20b9a9a291847 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/55e48b3070f27b7fea44ff66f0082578/params.yaml b/examples/security/truthseeker/models/reports/train/55e48b3070f27b7fea44ff66f0082578/params.yaml new file mode 100644 index 00000000..4952ec33 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/55e48b3070f27b7fea44ff66f0082578/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/55e48b3070f27b7fea44ff66f0082578/adv_predictions.json + adv_probabilities_file: models/reports/train/55e48b3070f27b7fea44ff66f0082578/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/9171cc10a1914d9be1865171cc9ec61d.pkl + params_file: models/reports/train/55e48b3070f27b7fea44ff66f0082578/params.yaml + predictions_file: models/reports/train/55e48b3070f27b7fea44ff66f0082578/predictions.json + probabilities_file: models/reports/train/55e48b3070f27b7fea44ff66f0082578/probabilities.json + score_dict_file: models/reports/train/55e48b3070f27b7fea44ff66f0082578/score_dict.json + test_labels_file: models/reports/train/55e48b3070f27b7fea44ff66f0082578/test_labels.json + train_labels_file: models/reports/train/55e48b3070f27b7fea44ff66f0082578/train_labels.json + model_dir: models + name: 55e48b3070f27b7fea44ff66f0082578 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 19ac673aeee3f03c25d616d6865c52ab + trainer: + kwargs: {} +name: 55e48b3070f27b7fea44ff66f0082578 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/58de2870ab371a01082b5325a1f3609e/params.yaml b/examples/security/truthseeker/models/reports/train/58de2870ab371a01082b5325a1f3609e/params.yaml new file mode 100644 index 00000000..158e7af3 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/58de2870ab371a01082b5325a1f3609e/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/58de2870ab371a01082b5325a1f3609e/adv_predictions.json + adv_probabilities_file: models/reports/train/58de2870ab371a01082b5325a1f3609e/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/4d8c0746420df8a191c689d470d3a3a8.pkl + params_file: models/reports/train/58de2870ab371a01082b5325a1f3609e/params.yaml + predictions_file: models/reports/train/58de2870ab371a01082b5325a1f3609e/predictions.json + probabilities_file: models/reports/train/58de2870ab371a01082b5325a1f3609e/probabilities.json + score_dict_file: models/reports/train/58de2870ab371a01082b5325a1f3609e/score_dict.json + test_labels_file: models/reports/train/58de2870ab371a01082b5325a1f3609e/test_labels.json + train_labels_file: models/reports/train/58de2870ab371a01082b5325a1f3609e/train_labels.json + model_dir: models + name: 58de2870ab371a01082b5325a1f3609e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ef9dd1b04eed153638de5fb0ffd1888e + trainer: + kwargs: {} +name: 58de2870ab371a01082b5325a1f3609e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/594b6bc641b04d9fbf9b4fc5ffefa631/params.yaml b/examples/security/truthseeker/models/reports/train/594b6bc641b04d9fbf9b4fc5ffefa631/params.yaml new file mode 100644 index 00000000..9e0c8fef --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/594b6bc641b04d9fbf9b4fc5ffefa631/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/594b6bc641b04d9fbf9b4fc5ffefa631/adv_predictions.json + adv_probabilities_file: models/reports/train/594b6bc641b04d9fbf9b4fc5ffefa631/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/73d704f29491799d6c60233f3c486885.pkl + params_file: models/reports/train/594b6bc641b04d9fbf9b4fc5ffefa631/params.yaml + predictions_file: models/reports/train/594b6bc641b04d9fbf9b4fc5ffefa631/predictions.json + probabilities_file: models/reports/train/594b6bc641b04d9fbf9b4fc5ffefa631/probabilities.json + score_dict_file: models/reports/train/594b6bc641b04d9fbf9b4fc5ffefa631/score_dict.json + test_labels_file: models/reports/train/594b6bc641b04d9fbf9b4fc5ffefa631/test_labels.json + train_labels_file: models/reports/train/594b6bc641b04d9fbf9b4fc5ffefa631/train_labels.json + model_dir: models + name: 594b6bc641b04d9fbf9b4fc5ffefa631 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4787ad0f4346d07f549151f200153ba4 + trainer: + kwargs: {} +name: 594b6bc641b04d9fbf9b4fc5ffefa631 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/59ea8e41a199f60badbde1821c811a21/params.yaml b/examples/security/truthseeker/models/reports/train/59ea8e41a199f60badbde1821c811a21/params.yaml new file mode 100644 index 00000000..0e07596c --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/59ea8e41a199f60badbde1821c811a21/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/59ea8e41a199f60badbde1821c811a21/adv_predictions.json + adv_probabilities_file: models/reports/train/59ea8e41a199f60badbde1821c811a21/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/e5d94f6159b4609a5873ad35eca792be.pkl + params_file: models/reports/train/59ea8e41a199f60badbde1821c811a21/params.yaml + predictions_file: models/reports/train/59ea8e41a199f60badbde1821c811a21/predictions.json + probabilities_file: models/reports/train/59ea8e41a199f60badbde1821c811a21/probabilities.json + score_dict_file: models/reports/train/59ea8e41a199f60badbde1821c811a21/score_dict.json + test_labels_file: models/reports/train/59ea8e41a199f60badbde1821c811a21/test_labels.json + train_labels_file: models/reports/train/59ea8e41a199f60badbde1821c811a21/train_labels.json + model_dir: models + name: 59ea8e41a199f60badbde1821c811a21 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9e5d164c1b01c6cbcd62069b2cf33255 + trainer: + kwargs: {} +name: 59ea8e41a199f60badbde1821c811a21 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/5a84577de89da5756fe519d78ab035f2/params.yaml b/examples/security/truthseeker/models/reports/train/5a84577de89da5756fe519d78ab035f2/params.yaml new file mode 100644 index 00000000..ac4f6f8b --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/5a84577de89da5756fe519d78ab035f2/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/5a84577de89da5756fe519d78ab035f2/adv_predictions.json + adv_probabilities_file: models/reports/train/5a84577de89da5756fe519d78ab035f2/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/6469125ad20952a54643ad6a604e7b44.pkl + params_file: models/reports/train/5a84577de89da5756fe519d78ab035f2/params.yaml + predictions_file: models/reports/train/5a84577de89da5756fe519d78ab035f2/predictions.json + probabilities_file: models/reports/train/5a84577de89da5756fe519d78ab035f2/probabilities.json + score_dict_file: models/reports/train/5a84577de89da5756fe519d78ab035f2/score_dict.json + test_labels_file: models/reports/train/5a84577de89da5756fe519d78ab035f2/test_labels.json + train_labels_file: models/reports/train/5a84577de89da5756fe519d78ab035f2/train_labels.json + model_dir: models + name: 5a84577de89da5756fe519d78ab035f2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2e88b311a2e940cac4325a81161f2d13 + trainer: + kwargs: {} +name: 5a84577de89da5756fe519d78ab035f2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/5a938db1b33c3016da2f4ab85cf16c1d/params.yaml b/examples/security/truthseeker/models/reports/train/5a938db1b33c3016da2f4ab85cf16c1d/params.yaml new file mode 100644 index 00000000..d564afe0 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/5a938db1b33c3016da2f4ab85cf16c1d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/5a938db1b33c3016da2f4ab85cf16c1d/adv_predictions.json + adv_probabilities_file: models/reports/train/5a938db1b33c3016da2f4ab85cf16c1d/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/95c45dd019f4bb553d27761382f00bd7.pkl + params_file: models/reports/train/5a938db1b33c3016da2f4ab85cf16c1d/params.yaml + predictions_file: models/reports/train/5a938db1b33c3016da2f4ab85cf16c1d/predictions.json + probabilities_file: models/reports/train/5a938db1b33c3016da2f4ab85cf16c1d/probabilities.json + score_dict_file: models/reports/train/5a938db1b33c3016da2f4ab85cf16c1d/score_dict.json + test_labels_file: models/reports/train/5a938db1b33c3016da2f4ab85cf16c1d/test_labels.json + train_labels_file: models/reports/train/5a938db1b33c3016da2f4ab85cf16c1d/train_labels.json + model_dir: models + name: 5a938db1b33c3016da2f4ab85cf16c1d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f9ffae8c6fa0d26df2df092569559a18 + trainer: + kwargs: {} +name: 5a938db1b33c3016da2f4ab85cf16c1d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/5ad373540d77c5b07af271dd71aeaaea/params.yaml b/examples/security/truthseeker/models/reports/train/5ad373540d77c5b07af271dd71aeaaea/params.yaml new file mode 100644 index 00000000..7bf86658 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/5ad373540d77c5b07af271dd71aeaaea/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/5ad373540d77c5b07af271dd71aeaaea/adv_predictions.json + adv_probabilities_file: models/reports/train/5ad373540d77c5b07af271dd71aeaaea/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/0b5f523f1ded2d07be700dab2ae695d2.pkl + params_file: models/reports/train/5ad373540d77c5b07af271dd71aeaaea/params.yaml + predictions_file: models/reports/train/5ad373540d77c5b07af271dd71aeaaea/predictions.json + probabilities_file: models/reports/train/5ad373540d77c5b07af271dd71aeaaea/probabilities.json + score_dict_file: models/reports/train/5ad373540d77c5b07af271dd71aeaaea/score_dict.json + test_labels_file: models/reports/train/5ad373540d77c5b07af271dd71aeaaea/test_labels.json + train_labels_file: models/reports/train/5ad373540d77c5b07af271dd71aeaaea/train_labels.json + model_dir: models + name: 5ad373540d77c5b07af271dd71aeaaea + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 92afcf079ee83598b56f00569ea6a591 + trainer: + kwargs: {} +name: 5ad373540d77c5b07af271dd71aeaaea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/5b9d1793c6c1245d858428a43b3a9388/params.yaml b/examples/security/truthseeker/models/reports/train/5b9d1793c6c1245d858428a43b3a9388/params.yaml new file mode 100644 index 00000000..347ac092 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/5b9d1793c6c1245d858428a43b3a9388/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/5b9d1793c6c1245d858428a43b3a9388/adv_predictions.json + adv_probabilities_file: models/reports/train/5b9d1793c6c1245d858428a43b3a9388/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/5dfc84df1ab82feb6d9f94e84e6348b8.pkl + params_file: models/reports/train/5b9d1793c6c1245d858428a43b3a9388/params.yaml + predictions_file: models/reports/train/5b9d1793c6c1245d858428a43b3a9388/predictions.json + probabilities_file: models/reports/train/5b9d1793c6c1245d858428a43b3a9388/probabilities.json + score_dict_file: models/reports/train/5b9d1793c6c1245d858428a43b3a9388/score_dict.json + test_labels_file: models/reports/train/5b9d1793c6c1245d858428a43b3a9388/test_labels.json + train_labels_file: models/reports/train/5b9d1793c6c1245d858428a43b3a9388/train_labels.json + model_dir: models + name: 5b9d1793c6c1245d858428a43b3a9388 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d64ac64c8ee5ca48edbc63e3d284c989 + trainer: + kwargs: {} +name: 5b9d1793c6c1245d858428a43b3a9388 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/5c342a7a485034ff7562109f441ba891/params.yaml b/examples/security/truthseeker/models/reports/train/5c342a7a485034ff7562109f441ba891/params.yaml new file mode 100644 index 00000000..1a09767f --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/5c342a7a485034ff7562109f441ba891/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/5c342a7a485034ff7562109f441ba891/adv_predictions.json + adv_probabilities_file: models/reports/train/5c342a7a485034ff7562109f441ba891/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/b83a6e838127456c96c2c5f5316b8baa.pkl + params_file: models/reports/train/5c342a7a485034ff7562109f441ba891/params.yaml + predictions_file: models/reports/train/5c342a7a485034ff7562109f441ba891/predictions.json + probabilities_file: models/reports/train/5c342a7a485034ff7562109f441ba891/probabilities.json + score_dict_file: models/reports/train/5c342a7a485034ff7562109f441ba891/score_dict.json + test_labels_file: models/reports/train/5c342a7a485034ff7562109f441ba891/test_labels.json + train_labels_file: models/reports/train/5c342a7a485034ff7562109f441ba891/train_labels.json + model_dir: models + name: 5c342a7a485034ff7562109f441ba891 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1b02739dbb0f7c9b64318e0a58fdc6f4 + trainer: + kwargs: {} +name: 5c342a7a485034ff7562109f441ba891 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/60cf80e287fd54056d676a9750228e0b/params.yaml b/examples/security/truthseeker/models/reports/train/60cf80e287fd54056d676a9750228e0b/params.yaml new file mode 100644 index 00000000..ff0aedec --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/60cf80e287fd54056d676a9750228e0b/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/60cf80e287fd54056d676a9750228e0b/adv_predictions.json + adv_probabilities_file: models/reports/train/60cf80e287fd54056d676a9750228e0b/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/929e31ab4c901dd666bd84cae95589af.pkl + params_file: models/reports/train/60cf80e287fd54056d676a9750228e0b/params.yaml + predictions_file: models/reports/train/60cf80e287fd54056d676a9750228e0b/predictions.json + probabilities_file: models/reports/train/60cf80e287fd54056d676a9750228e0b/probabilities.json + score_dict_file: models/reports/train/60cf80e287fd54056d676a9750228e0b/score_dict.json + test_labels_file: models/reports/train/60cf80e287fd54056d676a9750228e0b/test_labels.json + train_labels_file: models/reports/train/60cf80e287fd54056d676a9750228e0b/train_labels.json + model_dir: models + name: 60cf80e287fd54056d676a9750228e0b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d77baab0d306d22dbd15473c778b1eb + trainer: + kwargs: {} +name: 60cf80e287fd54056d676a9750228e0b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/61aa2f4a5eecf2f76f86400b9766770e/params.yaml b/examples/security/truthseeker/models/reports/train/61aa2f4a5eecf2f76f86400b9766770e/params.yaml new file mode 100644 index 00000000..19602646 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/61aa2f4a5eecf2f76f86400b9766770e/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/61aa2f4a5eecf2f76f86400b9766770e/adv_predictions.json + adv_probabilities_file: models/reports/train/61aa2f4a5eecf2f76f86400b9766770e/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/f54fcd53cd16200ee63d0eee2d88a37e.pkl + params_file: models/reports/train/61aa2f4a5eecf2f76f86400b9766770e/params.yaml + predictions_file: models/reports/train/61aa2f4a5eecf2f76f86400b9766770e/predictions.json + probabilities_file: models/reports/train/61aa2f4a5eecf2f76f86400b9766770e/probabilities.json + score_dict_file: models/reports/train/61aa2f4a5eecf2f76f86400b9766770e/score_dict.json + test_labels_file: models/reports/train/61aa2f4a5eecf2f76f86400b9766770e/test_labels.json + train_labels_file: models/reports/train/61aa2f4a5eecf2f76f86400b9766770e/train_labels.json + model_dir: models + name: 61aa2f4a5eecf2f76f86400b9766770e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ebb53de5457f2a8bb8e4ce9bd3e0b1d3 + trainer: + kwargs: {} +name: 61aa2f4a5eecf2f76f86400b9766770e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/6373d6f94632e2a082a9888486ad891b/params.yaml b/examples/security/truthseeker/models/reports/train/6373d6f94632e2a082a9888486ad891b/params.yaml new file mode 100644 index 00000000..82c56499 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/6373d6f94632e2a082a9888486ad891b/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/6373d6f94632e2a082a9888486ad891b/adv_predictions.json + adv_probabilities_file: models/reports/train/6373d6f94632e2a082a9888486ad891b/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/55188554697d2e2c31141bb9151e59a6.pkl + params_file: models/reports/train/6373d6f94632e2a082a9888486ad891b/params.yaml + predictions_file: models/reports/train/6373d6f94632e2a082a9888486ad891b/predictions.json + probabilities_file: models/reports/train/6373d6f94632e2a082a9888486ad891b/probabilities.json + score_dict_file: models/reports/train/6373d6f94632e2a082a9888486ad891b/score_dict.json + test_labels_file: models/reports/train/6373d6f94632e2a082a9888486ad891b/test_labels.json + train_labels_file: models/reports/train/6373d6f94632e2a082a9888486ad891b/train_labels.json + model_dir: models + name: 6373d6f94632e2a082a9888486ad891b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8d99a717e3017b92573b5b10de2cae34 + trainer: + kwargs: {} +name: 6373d6f94632e2a082a9888486ad891b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/64358803c01360ef01526da4e6c4eb72/params.yaml b/examples/security/truthseeker/models/reports/train/64358803c01360ef01526da4e6c4eb72/params.yaml new file mode 100644 index 00000000..02790f54 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/64358803c01360ef01526da4e6c4eb72/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/64358803c01360ef01526da4e6c4eb72/adv_predictions.json + adv_probabilities_file: models/reports/train/64358803c01360ef01526da4e6c4eb72/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/078ac8572ce6fde705ec0f407068b1d6.pkl + params_file: models/reports/train/64358803c01360ef01526da4e6c4eb72/params.yaml + predictions_file: models/reports/train/64358803c01360ef01526da4e6c4eb72/predictions.json + probabilities_file: models/reports/train/64358803c01360ef01526da4e6c4eb72/probabilities.json + score_dict_file: models/reports/train/64358803c01360ef01526da4e6c4eb72/score_dict.json + test_labels_file: models/reports/train/64358803c01360ef01526da4e6c4eb72/test_labels.json + train_labels_file: models/reports/train/64358803c01360ef01526da4e6c4eb72/train_labels.json + model_dir: models + name: 64358803c01360ef01526da4e6c4eb72 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8abd3826f95572aea9089f8ac3054975 + trainer: + kwargs: {} +name: 64358803c01360ef01526da4e6c4eb72 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/6471813dbe85140496565de9ecbd4e0e/params.yaml b/examples/security/truthseeker/models/reports/train/6471813dbe85140496565de9ecbd4e0e/params.yaml new file mode 100644 index 00000000..e0109060 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/6471813dbe85140496565de9ecbd4e0e/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/6471813dbe85140496565de9ecbd4e0e/adv_predictions.json + adv_probabilities_file: models/reports/train/6471813dbe85140496565de9ecbd4e0e/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/d834f00d680f9ec0ce4e5d3061f1b6c6.pkl + params_file: models/reports/train/6471813dbe85140496565de9ecbd4e0e/params.yaml + predictions_file: models/reports/train/6471813dbe85140496565de9ecbd4e0e/predictions.json + probabilities_file: models/reports/train/6471813dbe85140496565de9ecbd4e0e/probabilities.json + score_dict_file: models/reports/train/6471813dbe85140496565de9ecbd4e0e/score_dict.json + test_labels_file: models/reports/train/6471813dbe85140496565de9ecbd4e0e/test_labels.json + train_labels_file: models/reports/train/6471813dbe85140496565de9ecbd4e0e/train_labels.json + model_dir: models + name: 6471813dbe85140496565de9ecbd4e0e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aec915cac7c5aa9d007f4909127040d2 + trainer: + kwargs: {} +name: 6471813dbe85140496565de9ecbd4e0e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/6626469344e9a992e4488d8c85669417/params.yaml b/examples/security/truthseeker/models/reports/train/6626469344e9a992e4488d8c85669417/params.yaml new file mode 100644 index 00000000..aa703780 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/6626469344e9a992e4488d8c85669417/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/6626469344e9a992e4488d8c85669417/adv_predictions.json + adv_probabilities_file: models/reports/train/6626469344e9a992e4488d8c85669417/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/e60514559334cc828e40c0e3d3e772fc.pkl + params_file: models/reports/train/6626469344e9a992e4488d8c85669417/params.yaml + predictions_file: models/reports/train/6626469344e9a992e4488d8c85669417/predictions.json + probabilities_file: models/reports/train/6626469344e9a992e4488d8c85669417/probabilities.json + score_dict_file: models/reports/train/6626469344e9a992e4488d8c85669417/score_dict.json + test_labels_file: models/reports/train/6626469344e9a992e4488d8c85669417/test_labels.json + train_labels_file: models/reports/train/6626469344e9a992e4488d8c85669417/train_labels.json + model_dir: models + name: 6626469344e9a992e4488d8c85669417 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 53beac2c47226c0a03af732e19e2eda0 + trainer: + kwargs: {} +name: 6626469344e9a992e4488d8c85669417 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/677d3d80df1fc3858992e0542b46ba29/params.yaml b/examples/security/truthseeker/models/reports/train/677d3d80df1fc3858992e0542b46ba29/params.yaml new file mode 100644 index 00000000..93895ca8 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/677d3d80df1fc3858992e0542b46ba29/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/677d3d80df1fc3858992e0542b46ba29/adv_predictions.json + adv_probabilities_file: models/reports/train/677d3d80df1fc3858992e0542b46ba29/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/2567db0cda6b08a95f1fb92f639ff341.pkl + params_file: models/reports/train/677d3d80df1fc3858992e0542b46ba29/params.yaml + predictions_file: models/reports/train/677d3d80df1fc3858992e0542b46ba29/predictions.json + probabilities_file: models/reports/train/677d3d80df1fc3858992e0542b46ba29/probabilities.json + score_dict_file: models/reports/train/677d3d80df1fc3858992e0542b46ba29/score_dict.json + test_labels_file: models/reports/train/677d3d80df1fc3858992e0542b46ba29/test_labels.json + train_labels_file: models/reports/train/677d3d80df1fc3858992e0542b46ba29/train_labels.json + model_dir: models + name: 677d3d80df1fc3858992e0542b46ba29 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27620aaf8311bda4052a04b407e0bbdc + trainer: + kwargs: {} +name: 677d3d80df1fc3858992e0542b46ba29 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/67d4ed1fa1a1dfdf65717f6252d8f368/params.yaml b/examples/security/truthseeker/models/reports/train/67d4ed1fa1a1dfdf65717f6252d8f368/params.yaml new file mode 100644 index 00000000..ceed63f6 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/67d4ed1fa1a1dfdf65717f6252d8f368/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/67d4ed1fa1a1dfdf65717f6252d8f368/adv_predictions.json + adv_probabilities_file: models/reports/train/67d4ed1fa1a1dfdf65717f6252d8f368/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/a89f830aca99982a871607e644c7bae0.pkl + params_file: models/reports/train/67d4ed1fa1a1dfdf65717f6252d8f368/params.yaml + predictions_file: models/reports/train/67d4ed1fa1a1dfdf65717f6252d8f368/predictions.json + probabilities_file: models/reports/train/67d4ed1fa1a1dfdf65717f6252d8f368/probabilities.json + score_dict_file: models/reports/train/67d4ed1fa1a1dfdf65717f6252d8f368/score_dict.json + test_labels_file: models/reports/train/67d4ed1fa1a1dfdf65717f6252d8f368/test_labels.json + train_labels_file: models/reports/train/67d4ed1fa1a1dfdf65717f6252d8f368/train_labels.json + model_dir: models + name: 67d4ed1fa1a1dfdf65717f6252d8f368 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: abf930383fdc6e1212a3f9c8409af765 + trainer: + kwargs: {} +name: 67d4ed1fa1a1dfdf65717f6252d8f368 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/6bbc85128e252671cd55df82277ffd80/params.yaml b/examples/security/truthseeker/models/reports/train/6bbc85128e252671cd55df82277ffd80/params.yaml new file mode 100644 index 00000000..c5b901bd --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/6bbc85128e252671cd55df82277ffd80/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/6bbc85128e252671cd55df82277ffd80/adv_predictions.json + adv_probabilities_file: models/reports/train/6bbc85128e252671cd55df82277ffd80/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/9618e9465779c20f8c0e961adc3cbb9f.pkl + params_file: models/reports/train/6bbc85128e252671cd55df82277ffd80/params.yaml + predictions_file: models/reports/train/6bbc85128e252671cd55df82277ffd80/predictions.json + probabilities_file: models/reports/train/6bbc85128e252671cd55df82277ffd80/probabilities.json + score_dict_file: models/reports/train/6bbc85128e252671cd55df82277ffd80/score_dict.json + test_labels_file: models/reports/train/6bbc85128e252671cd55df82277ffd80/test_labels.json + train_labels_file: models/reports/train/6bbc85128e252671cd55df82277ffd80/train_labels.json + model_dir: models + name: 6bbc85128e252671cd55df82277ffd80 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 20653d15201d4a14fa1b119d6045cfac + trainer: + kwargs: {} +name: 6bbc85128e252671cd55df82277ffd80 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/6e18bdbf1a0fee572f58624297f433e1/params.yaml b/examples/security/truthseeker/models/reports/train/6e18bdbf1a0fee572f58624297f433e1/params.yaml new file mode 100644 index 00000000..6b6dd0ec --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/6e18bdbf1a0fee572f58624297f433e1/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/6e18bdbf1a0fee572f58624297f433e1/adv_predictions.json + adv_probabilities_file: models/reports/train/6e18bdbf1a0fee572f58624297f433e1/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/9e93810134d5c8168b69bbbb3a9578b3.pkl + params_file: models/reports/train/6e18bdbf1a0fee572f58624297f433e1/params.yaml + predictions_file: models/reports/train/6e18bdbf1a0fee572f58624297f433e1/predictions.json + probabilities_file: models/reports/train/6e18bdbf1a0fee572f58624297f433e1/probabilities.json + score_dict_file: models/reports/train/6e18bdbf1a0fee572f58624297f433e1/score_dict.json + test_labels_file: models/reports/train/6e18bdbf1a0fee572f58624297f433e1/test_labels.json + train_labels_file: models/reports/train/6e18bdbf1a0fee572f58624297f433e1/train_labels.json + model_dir: models + name: 6e18bdbf1a0fee572f58624297f433e1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 350c6b1067f08a5bcb66d6b8cb8aeecf + trainer: + kwargs: {} +name: 6e18bdbf1a0fee572f58624297f433e1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/70a008c8bb67d180fef952511934dec2/params.yaml b/examples/security/truthseeker/models/reports/train/70a008c8bb67d180fef952511934dec2/params.yaml new file mode 100644 index 00000000..2ca299fb --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/70a008c8bb67d180fef952511934dec2/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/70a008c8bb67d180fef952511934dec2/adv_predictions.json + adv_probabilities_file: models/reports/train/70a008c8bb67d180fef952511934dec2/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/38327d2036128a118ecce0468fe52259.pkl + params_file: models/reports/train/70a008c8bb67d180fef952511934dec2/params.yaml + predictions_file: models/reports/train/70a008c8bb67d180fef952511934dec2/predictions.json + probabilities_file: models/reports/train/70a008c8bb67d180fef952511934dec2/probabilities.json + score_dict_file: models/reports/train/70a008c8bb67d180fef952511934dec2/score_dict.json + test_labels_file: models/reports/train/70a008c8bb67d180fef952511934dec2/test_labels.json + train_labels_file: models/reports/train/70a008c8bb67d180fef952511934dec2/train_labels.json + model_dir: models + name: 70a008c8bb67d180fef952511934dec2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 70a008c8bb67d180fef952511934dec2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/710fdd184a0aac012e23e1124b85ac1b/params.yaml b/examples/security/truthseeker/models/reports/train/710fdd184a0aac012e23e1124b85ac1b/params.yaml new file mode 100644 index 00000000..95924246 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/710fdd184a0aac012e23e1124b85ac1b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/710fdd184a0aac012e23e1124b85ac1b/adv_predictions.json + adv_probabilities_file: models/reports/train/710fdd184a0aac012e23e1124b85ac1b/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/ee908376214ec276935679453f64bae1.pkl + params_file: models/reports/train/710fdd184a0aac012e23e1124b85ac1b/params.yaml + predictions_file: models/reports/train/710fdd184a0aac012e23e1124b85ac1b/predictions.json + probabilities_file: models/reports/train/710fdd184a0aac012e23e1124b85ac1b/probabilities.json + score_dict_file: models/reports/train/710fdd184a0aac012e23e1124b85ac1b/score_dict.json + test_labels_file: models/reports/train/710fdd184a0aac012e23e1124b85ac1b/test_labels.json + train_labels_file: models/reports/train/710fdd184a0aac012e23e1124b85ac1b/train_labels.json + model_dir: models + name: 710fdd184a0aac012e23e1124b85ac1b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a91f1225860a4aeb18b8a401f69d16d1 + trainer: + kwargs: {} +name: 710fdd184a0aac012e23e1124b85ac1b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/725c39c7c5de1a8090682f1eac8ea8d6/params.yaml b/examples/security/truthseeker/models/reports/train/725c39c7c5de1a8090682f1eac8ea8d6/params.yaml new file mode 100644 index 00000000..72468cbc --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/725c39c7c5de1a8090682f1eac8ea8d6/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/725c39c7c5de1a8090682f1eac8ea8d6/adv_predictions.json + adv_probabilities_file: models/reports/train/725c39c7c5de1a8090682f1eac8ea8d6/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/37783d2e2cfdb1dad06abb06462574af.pkl + params_file: models/reports/train/725c39c7c5de1a8090682f1eac8ea8d6/params.yaml + predictions_file: models/reports/train/725c39c7c5de1a8090682f1eac8ea8d6/predictions.json + probabilities_file: models/reports/train/725c39c7c5de1a8090682f1eac8ea8d6/probabilities.json + score_dict_file: models/reports/train/725c39c7c5de1a8090682f1eac8ea8d6/score_dict.json + test_labels_file: models/reports/train/725c39c7c5de1a8090682f1eac8ea8d6/test_labels.json + train_labels_file: models/reports/train/725c39c7c5de1a8090682f1eac8ea8d6/train_labels.json + model_dir: models + name: 725c39c7c5de1a8090682f1eac8ea8d6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9858ad55b247dc1840750194b1beeb10 + trainer: + kwargs: {} +name: 725c39c7c5de1a8090682f1eac8ea8d6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/72bfa8cbd5d4b2d6b1ed37cd743fe4eb/params.yaml b/examples/security/truthseeker/models/reports/train/72bfa8cbd5d4b2d6b1ed37cd743fe4eb/params.yaml new file mode 100644 index 00000000..2f40d254 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/72bfa8cbd5d4b2d6b1ed37cd743fe4eb/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/72bfa8cbd5d4b2d6b1ed37cd743fe4eb/adv_predictions.json + adv_probabilities_file: models/reports/train/72bfa8cbd5d4b2d6b1ed37cd743fe4eb/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/ef0100b4abffaab73d24efbf92feec2e.pkl + params_file: models/reports/train/72bfa8cbd5d4b2d6b1ed37cd743fe4eb/params.yaml + predictions_file: models/reports/train/72bfa8cbd5d4b2d6b1ed37cd743fe4eb/predictions.json + probabilities_file: models/reports/train/72bfa8cbd5d4b2d6b1ed37cd743fe4eb/probabilities.json + score_dict_file: models/reports/train/72bfa8cbd5d4b2d6b1ed37cd743fe4eb/score_dict.json + test_labels_file: models/reports/train/72bfa8cbd5d4b2d6b1ed37cd743fe4eb/test_labels.json + train_labels_file: models/reports/train/72bfa8cbd5d4b2d6b1ed37cd743fe4eb/train_labels.json + model_dir: models + name: 72bfa8cbd5d4b2d6b1ed37cd743fe4eb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3d4e3192794abd81b21bad56bbf70156 + trainer: + kwargs: {} +name: 72bfa8cbd5d4b2d6b1ed37cd743fe4eb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/749c4a5200c8ddf35bb2fe05c43343d9/params.yaml b/examples/security/truthseeker/models/reports/train/749c4a5200c8ddf35bb2fe05c43343d9/params.yaml new file mode 100644 index 00000000..44a07c1e --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/749c4a5200c8ddf35bb2fe05c43343d9/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/749c4a5200c8ddf35bb2fe05c43343d9/adv_predictions.json + adv_probabilities_file: models/reports/train/749c4a5200c8ddf35bb2fe05c43343d9/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/127c07f49be31fb9880cbd07f74a802b.pkl + params_file: models/reports/train/749c4a5200c8ddf35bb2fe05c43343d9/params.yaml + predictions_file: models/reports/train/749c4a5200c8ddf35bb2fe05c43343d9/predictions.json + probabilities_file: models/reports/train/749c4a5200c8ddf35bb2fe05c43343d9/probabilities.json + score_dict_file: models/reports/train/749c4a5200c8ddf35bb2fe05c43343d9/score_dict.json + test_labels_file: models/reports/train/749c4a5200c8ddf35bb2fe05c43343d9/test_labels.json + train_labels_file: models/reports/train/749c4a5200c8ddf35bb2fe05c43343d9/train_labels.json + model_dir: models + name: 749c4a5200c8ddf35bb2fe05c43343d9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 694435cfd539f3697b97d85c40686d23 + trainer: + kwargs: {} +name: 749c4a5200c8ddf35bb2fe05c43343d9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/76e6af8fab38c226ecbe9685bf1b9768/params.yaml b/examples/security/truthseeker/models/reports/train/76e6af8fab38c226ecbe9685bf1b9768/params.yaml new file mode 100644 index 00000000..234edb37 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/76e6af8fab38c226ecbe9685bf1b9768/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/76e6af8fab38c226ecbe9685bf1b9768/adv_predictions.json + adv_probabilities_file: models/reports/train/76e6af8fab38c226ecbe9685bf1b9768/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/4f8b6ba30e7fd80a875a5c152ee7782d.pkl + params_file: models/reports/train/76e6af8fab38c226ecbe9685bf1b9768/params.yaml + predictions_file: models/reports/train/76e6af8fab38c226ecbe9685bf1b9768/predictions.json + probabilities_file: models/reports/train/76e6af8fab38c226ecbe9685bf1b9768/probabilities.json + score_dict_file: models/reports/train/76e6af8fab38c226ecbe9685bf1b9768/score_dict.json + test_labels_file: models/reports/train/76e6af8fab38c226ecbe9685bf1b9768/test_labels.json + train_labels_file: models/reports/train/76e6af8fab38c226ecbe9685bf1b9768/train_labels.json + model_dir: models + name: 76e6af8fab38c226ecbe9685bf1b9768 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 24a1530387b48f4ec13b0cbd9445b326 + trainer: + kwargs: {} +name: 76e6af8fab38c226ecbe9685bf1b9768 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/77557fa7df379aff4a65669ba2801d07/params.yaml b/examples/security/truthseeker/models/reports/train/77557fa7df379aff4a65669ba2801d07/params.yaml new file mode 100644 index 00000000..24dc7cfb --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/77557fa7df379aff4a65669ba2801d07/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/77557fa7df379aff4a65669ba2801d07/adv_predictions.json + adv_probabilities_file: models/reports/train/77557fa7df379aff4a65669ba2801d07/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/eaa35f42c2fd88937f0447ff7ef19506.pkl + params_file: models/reports/train/77557fa7df379aff4a65669ba2801d07/params.yaml + predictions_file: models/reports/train/77557fa7df379aff4a65669ba2801d07/predictions.json + probabilities_file: models/reports/train/77557fa7df379aff4a65669ba2801d07/probabilities.json + score_dict_file: models/reports/train/77557fa7df379aff4a65669ba2801d07/score_dict.json + test_labels_file: models/reports/train/77557fa7df379aff4a65669ba2801d07/test_labels.json + train_labels_file: models/reports/train/77557fa7df379aff4a65669ba2801d07/train_labels.json + model_dir: models + name: 77557fa7df379aff4a65669ba2801d07 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9e1b5029576f3ea01f9bfe741176bb7a + trainer: + kwargs: {} +name: 77557fa7df379aff4a65669ba2801d07 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/77e00e0a8a6af2a4409731cb21fecfeb/params.yaml b/examples/security/truthseeker/models/reports/train/77e00e0a8a6af2a4409731cb21fecfeb/params.yaml new file mode 100644 index 00000000..400776f4 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/77e00e0a8a6af2a4409731cb21fecfeb/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/77e00e0a8a6af2a4409731cb21fecfeb/adv_predictions.json + adv_probabilities_file: models/reports/train/77e00e0a8a6af2a4409731cb21fecfeb/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/84fe807edaa7330e58091270908f8099.pkl + params_file: models/reports/train/77e00e0a8a6af2a4409731cb21fecfeb/params.yaml + predictions_file: models/reports/train/77e00e0a8a6af2a4409731cb21fecfeb/predictions.json + probabilities_file: models/reports/train/77e00e0a8a6af2a4409731cb21fecfeb/probabilities.json + score_dict_file: models/reports/train/77e00e0a8a6af2a4409731cb21fecfeb/score_dict.json + test_labels_file: models/reports/train/77e00e0a8a6af2a4409731cb21fecfeb/test_labels.json + train_labels_file: models/reports/train/77e00e0a8a6af2a4409731cb21fecfeb/train_labels.json + model_dir: models + name: 77e00e0a8a6af2a4409731cb21fecfeb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0d69263260f77291d38dbe8d8c97c145 + trainer: + kwargs: {} +name: 77e00e0a8a6af2a4409731cb21fecfeb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/7870285be6f6310d36def97c5172339d/params.yaml b/examples/security/truthseeker/models/reports/train/7870285be6f6310d36def97c5172339d/params.yaml new file mode 100644 index 00000000..0ffa79db --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/7870285be6f6310d36def97c5172339d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/7870285be6f6310d36def97c5172339d/adv_predictions.json + adv_probabilities_file: models/reports/train/7870285be6f6310d36def97c5172339d/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/4475b67af2915cd2b4e47a8cb5a70a26.pkl + params_file: models/reports/train/7870285be6f6310d36def97c5172339d/params.yaml + predictions_file: models/reports/train/7870285be6f6310d36def97c5172339d/predictions.json + probabilities_file: models/reports/train/7870285be6f6310d36def97c5172339d/probabilities.json + score_dict_file: models/reports/train/7870285be6f6310d36def97c5172339d/score_dict.json + test_labels_file: models/reports/train/7870285be6f6310d36def97c5172339d/test_labels.json + train_labels_file: models/reports/train/7870285be6f6310d36def97c5172339d/train_labels.json + model_dir: models + name: 7870285be6f6310d36def97c5172339d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cbea2fe31bb798a1eb7f595bacb7168f + trainer: + kwargs: {} +name: 7870285be6f6310d36def97c5172339d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/7bbfd5512129723101c2d305f3e2ef39/params.yaml b/examples/security/truthseeker/models/reports/train/7bbfd5512129723101c2d305f3e2ef39/params.yaml new file mode 100644 index 00000000..d314b458 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/7bbfd5512129723101c2d305f3e2ef39/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/7bbfd5512129723101c2d305f3e2ef39/adv_predictions.json + adv_probabilities_file: models/reports/train/7bbfd5512129723101c2d305f3e2ef39/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/114a410a869dc144ed130bc0b5ad553e.pkl + params_file: models/reports/train/7bbfd5512129723101c2d305f3e2ef39/params.yaml + predictions_file: models/reports/train/7bbfd5512129723101c2d305f3e2ef39/predictions.json + probabilities_file: models/reports/train/7bbfd5512129723101c2d305f3e2ef39/probabilities.json + score_dict_file: models/reports/train/7bbfd5512129723101c2d305f3e2ef39/score_dict.json + test_labels_file: models/reports/train/7bbfd5512129723101c2d305f3e2ef39/test_labels.json + train_labels_file: models/reports/train/7bbfd5512129723101c2d305f3e2ef39/train_labels.json + model_dir: models + name: 7bbfd5512129723101c2d305f3e2ef39 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8af38fe9b4170fb7130ae6bda7066653 + trainer: + kwargs: {} +name: 7bbfd5512129723101c2d305f3e2ef39 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/7e0d6a94cb1a3f01a2bace82c7da5973/params.yaml b/examples/security/truthseeker/models/reports/train/7e0d6a94cb1a3f01a2bace82c7da5973/params.yaml new file mode 100644 index 00000000..26bef90f --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/7e0d6a94cb1a3f01a2bace82c7da5973/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/7e0d6a94cb1a3f01a2bace82c7da5973/adv_predictions.json + adv_probabilities_file: models/reports/train/7e0d6a94cb1a3f01a2bace82c7da5973/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/177bbe848973712f41eea15fbb0dcd37.pkl + params_file: models/reports/train/7e0d6a94cb1a3f01a2bace82c7da5973/params.yaml + predictions_file: models/reports/train/7e0d6a94cb1a3f01a2bace82c7da5973/predictions.json + probabilities_file: models/reports/train/7e0d6a94cb1a3f01a2bace82c7da5973/probabilities.json + score_dict_file: models/reports/train/7e0d6a94cb1a3f01a2bace82c7da5973/score_dict.json + test_labels_file: models/reports/train/7e0d6a94cb1a3f01a2bace82c7da5973/test_labels.json + train_labels_file: models/reports/train/7e0d6a94cb1a3f01a2bace82c7da5973/train_labels.json + model_dir: models + name: 7e0d6a94cb1a3f01a2bace82c7da5973 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4782679c1ffd51174c7ea3adbc184c1e + trainer: + kwargs: {} +name: 7e0d6a94cb1a3f01a2bace82c7da5973 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/7f2bb063ea85616a427ed14ec024784b/params.yaml b/examples/security/truthseeker/models/reports/train/7f2bb063ea85616a427ed14ec024784b/params.yaml new file mode 100644 index 00000000..179992c7 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/7f2bb063ea85616a427ed14ec024784b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/7f2bb063ea85616a427ed14ec024784b/adv_predictions.json + adv_probabilities_file: models/reports/train/7f2bb063ea85616a427ed14ec024784b/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/28178e193d230d25986db63a6a42ace3.pkl + params_file: models/reports/train/7f2bb063ea85616a427ed14ec024784b/params.yaml + predictions_file: models/reports/train/7f2bb063ea85616a427ed14ec024784b/predictions.json + probabilities_file: models/reports/train/7f2bb063ea85616a427ed14ec024784b/probabilities.json + score_dict_file: models/reports/train/7f2bb063ea85616a427ed14ec024784b/score_dict.json + test_labels_file: models/reports/train/7f2bb063ea85616a427ed14ec024784b/test_labels.json + train_labels_file: models/reports/train/7f2bb063ea85616a427ed14ec024784b/train_labels.json + model_dir: models + name: 7f2bb063ea85616a427ed14ec024784b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3de4feb00a9880cb6f0e7fef636b9ca9 + trainer: + kwargs: {} +name: 7f2bb063ea85616a427ed14ec024784b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/842b6ac2f48a861841be14c8df53ccae/params.yaml b/examples/security/truthseeker/models/reports/train/842b6ac2f48a861841be14c8df53ccae/params.yaml new file mode 100644 index 00000000..78bcb5e4 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/842b6ac2f48a861841be14c8df53ccae/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/842b6ac2f48a861841be14c8df53ccae/adv_predictions.json + adv_probabilities_file: models/reports/train/842b6ac2f48a861841be14c8df53ccae/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/90e1d0e418f98f4b0b5593b52580efe2.pkl + params_file: models/reports/train/842b6ac2f48a861841be14c8df53ccae/params.yaml + predictions_file: models/reports/train/842b6ac2f48a861841be14c8df53ccae/predictions.json + probabilities_file: models/reports/train/842b6ac2f48a861841be14c8df53ccae/probabilities.json + score_dict_file: models/reports/train/842b6ac2f48a861841be14c8df53ccae/score_dict.json + test_labels_file: models/reports/train/842b6ac2f48a861841be14c8df53ccae/test_labels.json + train_labels_file: models/reports/train/842b6ac2f48a861841be14c8df53ccae/train_labels.json + model_dir: models + name: 842b6ac2f48a861841be14c8df53ccae + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 01f3b66ae9d53723c1c36c359e0546ff + trainer: + kwargs: {} +name: 842b6ac2f48a861841be14c8df53ccae +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/875041e5b64f40a1655bed61cbf59884/params.yaml b/examples/security/truthseeker/models/reports/train/875041e5b64f40a1655bed61cbf59884/params.yaml new file mode 100644 index 00000000..28c6123f --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/875041e5b64f40a1655bed61cbf59884/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/875041e5b64f40a1655bed61cbf59884/adv_predictions.json + adv_probabilities_file: models/reports/train/875041e5b64f40a1655bed61cbf59884/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/151b26212ac1a1bd0ffe17b8c185d63b.pkl + params_file: models/reports/train/875041e5b64f40a1655bed61cbf59884/params.yaml + predictions_file: models/reports/train/875041e5b64f40a1655bed61cbf59884/predictions.json + probabilities_file: models/reports/train/875041e5b64f40a1655bed61cbf59884/probabilities.json + score_dict_file: models/reports/train/875041e5b64f40a1655bed61cbf59884/score_dict.json + test_labels_file: models/reports/train/875041e5b64f40a1655bed61cbf59884/test_labels.json + train_labels_file: models/reports/train/875041e5b64f40a1655bed61cbf59884/train_labels.json + model_dir: models + name: 875041e5b64f40a1655bed61cbf59884 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fd250212c56ee0bbd2c2f2f41393c7db + trainer: + kwargs: {} +name: 875041e5b64f40a1655bed61cbf59884 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/8bff77430aca13fd6652cbce39e2533d/params.yaml b/examples/security/truthseeker/models/reports/train/8bff77430aca13fd6652cbce39e2533d/params.yaml new file mode 100644 index 00000000..040dd4b0 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/8bff77430aca13fd6652cbce39e2533d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/8bff77430aca13fd6652cbce39e2533d/adv_predictions.json + adv_probabilities_file: models/reports/train/8bff77430aca13fd6652cbce39e2533d/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/fe197254557aef04993be2b7968bd258.pkl + params_file: models/reports/train/8bff77430aca13fd6652cbce39e2533d/params.yaml + predictions_file: models/reports/train/8bff77430aca13fd6652cbce39e2533d/predictions.json + probabilities_file: models/reports/train/8bff77430aca13fd6652cbce39e2533d/probabilities.json + score_dict_file: models/reports/train/8bff77430aca13fd6652cbce39e2533d/score_dict.json + test_labels_file: models/reports/train/8bff77430aca13fd6652cbce39e2533d/test_labels.json + train_labels_file: models/reports/train/8bff77430aca13fd6652cbce39e2533d/train_labels.json + model_dir: models + name: 8bff77430aca13fd6652cbce39e2533d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d38bf7d12bd6000ecc761d6f479b09bc + trainer: + kwargs: {} +name: 8bff77430aca13fd6652cbce39e2533d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/8ea6613bb87b65f08c230e1cd59087a3/params.yaml b/examples/security/truthseeker/models/reports/train/8ea6613bb87b65f08c230e1cd59087a3/params.yaml new file mode 100644 index 00000000..cc98154e --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/8ea6613bb87b65f08c230e1cd59087a3/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/8ea6613bb87b65f08c230e1cd59087a3/adv_predictions.json + adv_probabilities_file: models/reports/train/8ea6613bb87b65f08c230e1cd59087a3/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/1528dfc67a62b8d70dce5fa59b7fc121.pkl + params_file: models/reports/train/8ea6613bb87b65f08c230e1cd59087a3/params.yaml + predictions_file: models/reports/train/8ea6613bb87b65f08c230e1cd59087a3/predictions.json + probabilities_file: models/reports/train/8ea6613bb87b65f08c230e1cd59087a3/probabilities.json + score_dict_file: models/reports/train/8ea6613bb87b65f08c230e1cd59087a3/score_dict.json + test_labels_file: models/reports/train/8ea6613bb87b65f08c230e1cd59087a3/test_labels.json + train_labels_file: models/reports/train/8ea6613bb87b65f08c230e1cd59087a3/train_labels.json + model_dir: models + name: 8ea6613bb87b65f08c230e1cd59087a3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4e76bda8e9e53042cd593b3815946008 + trainer: + kwargs: {} +name: 8ea6613bb87b65f08c230e1cd59087a3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/8fb04f58628c05d5f8d2ff61b3cc6be3/params.yaml b/examples/security/truthseeker/models/reports/train/8fb04f58628c05d5f8d2ff61b3cc6be3/params.yaml new file mode 100644 index 00000000..6c718eed --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/8fb04f58628c05d5f8d2ff61b3cc6be3/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/8fb04f58628c05d5f8d2ff61b3cc6be3/adv_predictions.json + adv_probabilities_file: models/reports/train/8fb04f58628c05d5f8d2ff61b3cc6be3/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/70432c7554de8103d2b685f1519570d6.pkl + params_file: models/reports/train/8fb04f58628c05d5f8d2ff61b3cc6be3/params.yaml + predictions_file: models/reports/train/8fb04f58628c05d5f8d2ff61b3cc6be3/predictions.json + probabilities_file: models/reports/train/8fb04f58628c05d5f8d2ff61b3cc6be3/probabilities.json + score_dict_file: models/reports/train/8fb04f58628c05d5f8d2ff61b3cc6be3/score_dict.json + test_labels_file: models/reports/train/8fb04f58628c05d5f8d2ff61b3cc6be3/test_labels.json + train_labels_file: models/reports/train/8fb04f58628c05d5f8d2ff61b3cc6be3/train_labels.json + model_dir: models + name: 8fb04f58628c05d5f8d2ff61b3cc6be3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d8b58523682f6ba7f37a27f1c42fe903 + trainer: + kwargs: {} +name: 8fb04f58628c05d5f8d2ff61b3cc6be3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/964634600a0f6170ec52bd70e1c5f206/params.yaml b/examples/security/truthseeker/models/reports/train/964634600a0f6170ec52bd70e1c5f206/params.yaml new file mode 100644 index 00000000..4409e920 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/964634600a0f6170ec52bd70e1c5f206/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/964634600a0f6170ec52bd70e1c5f206/adv_predictions.json + adv_probabilities_file: models/reports/train/964634600a0f6170ec52bd70e1c5f206/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/2f7c97f6d1662117d5dd25d5384b588a.pkl + params_file: models/reports/train/964634600a0f6170ec52bd70e1c5f206/params.yaml + predictions_file: models/reports/train/964634600a0f6170ec52bd70e1c5f206/predictions.json + probabilities_file: models/reports/train/964634600a0f6170ec52bd70e1c5f206/probabilities.json + score_dict_file: models/reports/train/964634600a0f6170ec52bd70e1c5f206/score_dict.json + test_labels_file: models/reports/train/964634600a0f6170ec52bd70e1c5f206/test_labels.json + train_labels_file: models/reports/train/964634600a0f6170ec52bd70e1c5f206/train_labels.json + model_dir: models + name: 964634600a0f6170ec52bd70e1c5f206 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b06102d2c90fe3e3007505ecace1d1b9 + trainer: + kwargs: {} +name: 964634600a0f6170ec52bd70e1c5f206 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/9ac113ba0d4d1adf29d38fe198c892c1/params.yaml b/examples/security/truthseeker/models/reports/train/9ac113ba0d4d1adf29d38fe198c892c1/params.yaml new file mode 100644 index 00000000..0c53e1c1 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/9ac113ba0d4d1adf29d38fe198c892c1/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/9ac113ba0d4d1adf29d38fe198c892c1/adv_predictions.json + adv_probabilities_file: models/reports/train/9ac113ba0d4d1adf29d38fe198c892c1/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/03decb91ea7f67f214bf3a33dc0a5f03.pkl + params_file: models/reports/train/9ac113ba0d4d1adf29d38fe198c892c1/params.yaml + predictions_file: models/reports/train/9ac113ba0d4d1adf29d38fe198c892c1/predictions.json + probabilities_file: models/reports/train/9ac113ba0d4d1adf29d38fe198c892c1/probabilities.json + score_dict_file: models/reports/train/9ac113ba0d4d1adf29d38fe198c892c1/score_dict.json + test_labels_file: models/reports/train/9ac113ba0d4d1adf29d38fe198c892c1/test_labels.json + train_labels_file: models/reports/train/9ac113ba0d4d1adf29d38fe198c892c1/train_labels.json + model_dir: models + name: 9ac113ba0d4d1adf29d38fe198c892c1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 030ac5707aeef8b03e9174428637eae0 + trainer: + kwargs: {} +name: 9ac113ba0d4d1adf29d38fe198c892c1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/9c1edf44d6f589261b6bf244a38e685d/params.yaml b/examples/security/truthseeker/models/reports/train/9c1edf44d6f589261b6bf244a38e685d/params.yaml new file mode 100644 index 00000000..61a39cec --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/9c1edf44d6f589261b6bf244a38e685d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/9c1edf44d6f589261b6bf244a38e685d/adv_predictions.json + adv_probabilities_file: models/reports/train/9c1edf44d6f589261b6bf244a38e685d/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/b875e92faac7d54be6a94dd8abbd6f75.pkl + params_file: models/reports/train/9c1edf44d6f589261b6bf244a38e685d/params.yaml + predictions_file: models/reports/train/9c1edf44d6f589261b6bf244a38e685d/predictions.json + probabilities_file: models/reports/train/9c1edf44d6f589261b6bf244a38e685d/probabilities.json + score_dict_file: models/reports/train/9c1edf44d6f589261b6bf244a38e685d/score_dict.json + test_labels_file: models/reports/train/9c1edf44d6f589261b6bf244a38e685d/test_labels.json + train_labels_file: models/reports/train/9c1edf44d6f589261b6bf244a38e685d/train_labels.json + model_dir: models + name: 9c1edf44d6f589261b6bf244a38e685d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5bd783b11b295f51ec2c503b3383070c + trainer: + kwargs: {} +name: 9c1edf44d6f589261b6bf244a38e685d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/9d31676506ea5194ac1f226eb2f90f9f/params.yaml b/examples/security/truthseeker/models/reports/train/9d31676506ea5194ac1f226eb2f90f9f/params.yaml new file mode 100644 index 00000000..b850e53d --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/9d31676506ea5194ac1f226eb2f90f9f/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/9d31676506ea5194ac1f226eb2f90f9f/adv_predictions.json + adv_probabilities_file: models/reports/train/9d31676506ea5194ac1f226eb2f90f9f/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/cebff5413aaa48385398f404b3ef5b59.pkl + params_file: models/reports/train/9d31676506ea5194ac1f226eb2f90f9f/params.yaml + predictions_file: models/reports/train/9d31676506ea5194ac1f226eb2f90f9f/predictions.json + probabilities_file: models/reports/train/9d31676506ea5194ac1f226eb2f90f9f/probabilities.json + score_dict_file: models/reports/train/9d31676506ea5194ac1f226eb2f90f9f/score_dict.json + test_labels_file: models/reports/train/9d31676506ea5194ac1f226eb2f90f9f/test_labels.json + train_labels_file: models/reports/train/9d31676506ea5194ac1f226eb2f90f9f/train_labels.json + model_dir: models + name: 9d31676506ea5194ac1f226eb2f90f9f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2e4b4cc394881ca3a965ce0fd1ea7c97 + trainer: + kwargs: {} +name: 9d31676506ea5194ac1f226eb2f90f9f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/9d9fc1a28c1ac70c2f83b86f089d3abc/params.yaml b/examples/security/truthseeker/models/reports/train/9d9fc1a28c1ac70c2f83b86f089d3abc/params.yaml new file mode 100644 index 00000000..74ad419d --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/9d9fc1a28c1ac70c2f83b86f089d3abc/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/9d9fc1a28c1ac70c2f83b86f089d3abc/adv_predictions.json + adv_probabilities_file: models/reports/train/9d9fc1a28c1ac70c2f83b86f089d3abc/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/35cc1b1d9388843778e43ebf9ca6a15a.pkl + params_file: models/reports/train/9d9fc1a28c1ac70c2f83b86f089d3abc/params.yaml + predictions_file: models/reports/train/9d9fc1a28c1ac70c2f83b86f089d3abc/predictions.json + probabilities_file: models/reports/train/9d9fc1a28c1ac70c2f83b86f089d3abc/probabilities.json + score_dict_file: models/reports/train/9d9fc1a28c1ac70c2f83b86f089d3abc/score_dict.json + test_labels_file: models/reports/train/9d9fc1a28c1ac70c2f83b86f089d3abc/test_labels.json + train_labels_file: models/reports/train/9d9fc1a28c1ac70c2f83b86f089d3abc/train_labels.json + model_dir: models + name: 9d9fc1a28c1ac70c2f83b86f089d3abc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 87cfe177602b7c46c12ef7247e6349b4 + trainer: + kwargs: {} +name: 9d9fc1a28c1ac70c2f83b86f089d3abc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/a45c08bbe40dadac6b77d79d5cbec5f4/params.yaml b/examples/security/truthseeker/models/reports/train/a45c08bbe40dadac6b77d79d5cbec5f4/params.yaml new file mode 100644 index 00000000..8801057a --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/a45c08bbe40dadac6b77d79d5cbec5f4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/a45c08bbe40dadac6b77d79d5cbec5f4/adv_predictions.json + adv_probabilities_file: models/reports/train/a45c08bbe40dadac6b77d79d5cbec5f4/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: models/reports/train/a45c08bbe40dadac6b77d79d5cbec5f4/params.yaml + predictions_file: models/reports/train/a45c08bbe40dadac6b77d79d5cbec5f4/predictions.json + probabilities_file: models/reports/train/a45c08bbe40dadac6b77d79d5cbec5f4/probabilities.json + score_dict_file: models/reports/train/a45c08bbe40dadac6b77d79d5cbec5f4/score_dict.json + test_labels_file: models/reports/train/a45c08bbe40dadac6b77d79d5cbec5f4/test_labels.json + train_labels_file: models/reports/train/a45c08bbe40dadac6b77d79d5cbec5f4/train_labels.json + model_dir: models + name: a45c08bbe40dadac6b77d79d5cbec5f4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: a45c08bbe40dadac6b77d79d5cbec5f4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/a48a808e2d12fe18a871b6adcbbec010/params.yaml b/examples/security/truthseeker/models/reports/train/a48a808e2d12fe18a871b6adcbbec010/params.yaml new file mode 100644 index 00000000..522e2c0c --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/a48a808e2d12fe18a871b6adcbbec010/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/a48a808e2d12fe18a871b6adcbbec010/adv_predictions.json + adv_probabilities_file: models/reports/train/a48a808e2d12fe18a871b6adcbbec010/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: models/reports/train/a48a808e2d12fe18a871b6adcbbec010/params.yaml + predictions_file: models/reports/train/a48a808e2d12fe18a871b6adcbbec010/predictions.json + probabilities_file: models/reports/train/a48a808e2d12fe18a871b6adcbbec010/probabilities.json + score_dict_file: models/reports/train/a48a808e2d12fe18a871b6adcbbec010/score_dict.json + test_labels_file: models/reports/train/a48a808e2d12fe18a871b6adcbbec010/test_labels.json + train_labels_file: models/reports/train/a48a808e2d12fe18a871b6adcbbec010/train_labels.json + model_dir: models + name: a48a808e2d12fe18a871b6adcbbec010 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: a48a808e2d12fe18a871b6adcbbec010 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/a61089914ba3e62de8b60fe64f5135dc/params.yaml b/examples/security/truthseeker/models/reports/train/a61089914ba3e62de8b60fe64f5135dc/params.yaml new file mode 100644 index 00000000..11956b61 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/a61089914ba3e62de8b60fe64f5135dc/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/a61089914ba3e62de8b60fe64f5135dc/adv_predictions.json + adv_probabilities_file: models/reports/train/a61089914ba3e62de8b60fe64f5135dc/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/3fe9c4eb66750243cf307983621d4328.pkl + params_file: models/reports/train/a61089914ba3e62de8b60fe64f5135dc/params.yaml + predictions_file: models/reports/train/a61089914ba3e62de8b60fe64f5135dc/predictions.json + probabilities_file: models/reports/train/a61089914ba3e62de8b60fe64f5135dc/probabilities.json + score_dict_file: models/reports/train/a61089914ba3e62de8b60fe64f5135dc/score_dict.json + test_labels_file: models/reports/train/a61089914ba3e62de8b60fe64f5135dc/test_labels.json + train_labels_file: models/reports/train/a61089914ba3e62de8b60fe64f5135dc/train_labels.json + model_dir: models + name: a61089914ba3e62de8b60fe64f5135dc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7e21f8f37a6f6d01f318f6c8fbd2d202 + trainer: + kwargs: {} +name: a61089914ba3e62de8b60fe64f5135dc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/a79d24d0a24ddc3eeba8d5abb5082a57/params.yaml b/examples/security/truthseeker/models/reports/train/a79d24d0a24ddc3eeba8d5abb5082a57/params.yaml new file mode 100644 index 00000000..a3b49807 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/a79d24d0a24ddc3eeba8d5abb5082a57/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/a79d24d0a24ddc3eeba8d5abb5082a57/adv_predictions.json + adv_probabilities_file: models/reports/train/a79d24d0a24ddc3eeba8d5abb5082a57/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/7a2152ce650e449737106c7664ddefcc.pkl + params_file: models/reports/train/a79d24d0a24ddc3eeba8d5abb5082a57/params.yaml + predictions_file: models/reports/train/a79d24d0a24ddc3eeba8d5abb5082a57/predictions.json + probabilities_file: models/reports/train/a79d24d0a24ddc3eeba8d5abb5082a57/probabilities.json + score_dict_file: models/reports/train/a79d24d0a24ddc3eeba8d5abb5082a57/score_dict.json + test_labels_file: models/reports/train/a79d24d0a24ddc3eeba8d5abb5082a57/test_labels.json + train_labels_file: models/reports/train/a79d24d0a24ddc3eeba8d5abb5082a57/train_labels.json + model_dir: models + name: a79d24d0a24ddc3eeba8d5abb5082a57 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9501bd0e77696fa6cb198f4c3b7854ba + trainer: + kwargs: {} +name: a79d24d0a24ddc3eeba8d5abb5082a57 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/aa1a29e0c292defb89d6893a1b29c5bc/params.yaml b/examples/security/truthseeker/models/reports/train/aa1a29e0c292defb89d6893a1b29c5bc/params.yaml new file mode 100644 index 00000000..17d942cb --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/aa1a29e0c292defb89d6893a1b29c5bc/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/aa1a29e0c292defb89d6893a1b29c5bc/adv_predictions.json + adv_probabilities_file: models/reports/train/aa1a29e0c292defb89d6893a1b29c5bc/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/2dcf13371923b37b52f3b897972f081c.pkl + params_file: models/reports/train/aa1a29e0c292defb89d6893a1b29c5bc/params.yaml + predictions_file: models/reports/train/aa1a29e0c292defb89d6893a1b29c5bc/predictions.json + probabilities_file: models/reports/train/aa1a29e0c292defb89d6893a1b29c5bc/probabilities.json + score_dict_file: models/reports/train/aa1a29e0c292defb89d6893a1b29c5bc/score_dict.json + test_labels_file: models/reports/train/aa1a29e0c292defb89d6893a1b29c5bc/test_labels.json + train_labels_file: models/reports/train/aa1a29e0c292defb89d6893a1b29c5bc/train_labels.json + model_dir: models + name: aa1a29e0c292defb89d6893a1b29c5bc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 24cf3a44e148907ad937868867c0a5f0 + trainer: + kwargs: {} +name: aa1a29e0c292defb89d6893a1b29c5bc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/aae67a3ad26713a1a53c7c974d620f2f/params.yaml b/examples/security/truthseeker/models/reports/train/aae67a3ad26713a1a53c7c974d620f2f/params.yaml new file mode 100644 index 00000000..bc5bc422 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/aae67a3ad26713a1a53c7c974d620f2f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/aae67a3ad26713a1a53c7c974d620f2f/adv_predictions.json + adv_probabilities_file: models/reports/train/aae67a3ad26713a1a53c7c974d620f2f/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/0d8bfdc46e89f087ff701c964cc8e0ad.pkl + params_file: models/reports/train/aae67a3ad26713a1a53c7c974d620f2f/params.yaml + predictions_file: models/reports/train/aae67a3ad26713a1a53c7c974d620f2f/predictions.json + probabilities_file: models/reports/train/aae67a3ad26713a1a53c7c974d620f2f/probabilities.json + score_dict_file: models/reports/train/aae67a3ad26713a1a53c7c974d620f2f/score_dict.json + test_labels_file: models/reports/train/aae67a3ad26713a1a53c7c974d620f2f/test_labels.json + train_labels_file: models/reports/train/aae67a3ad26713a1a53c7c974d620f2f/train_labels.json + model_dir: models + name: aae67a3ad26713a1a53c7c974d620f2f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a1f1a26657d9c783e154dd19b003e859 + trainer: + kwargs: {} +name: aae67a3ad26713a1a53c7c974d620f2f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/ad546633d2819a86daa9cbb65453c943/params.yaml b/examples/security/truthseeker/models/reports/train/ad546633d2819a86daa9cbb65453c943/params.yaml new file mode 100644 index 00000000..2d6bf180 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/ad546633d2819a86daa9cbb65453c943/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/ad546633d2819a86daa9cbb65453c943/adv_predictions.json + adv_probabilities_file: models/reports/train/ad546633d2819a86daa9cbb65453c943/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/fb786b647549e79874f57eebe4701e40.pkl + params_file: models/reports/train/ad546633d2819a86daa9cbb65453c943/params.yaml + predictions_file: models/reports/train/ad546633d2819a86daa9cbb65453c943/predictions.json + probabilities_file: models/reports/train/ad546633d2819a86daa9cbb65453c943/probabilities.json + score_dict_file: models/reports/train/ad546633d2819a86daa9cbb65453c943/score_dict.json + test_labels_file: models/reports/train/ad546633d2819a86daa9cbb65453c943/test_labels.json + train_labels_file: models/reports/train/ad546633d2819a86daa9cbb65453c943/train_labels.json + model_dir: models + name: ad546633d2819a86daa9cbb65453c943 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1bfbc5c8cc020ca239f45c5ea4ca58e6 + trainer: + kwargs: {} +name: ad546633d2819a86daa9cbb65453c943 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/af20a808686bb5ad0c548be1b44329d8/params.yaml b/examples/security/truthseeker/models/reports/train/af20a808686bb5ad0c548be1b44329d8/params.yaml new file mode 100644 index 00000000..187c2f0e --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/af20a808686bb5ad0c548be1b44329d8/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/af20a808686bb5ad0c548be1b44329d8/adv_predictions.json + adv_probabilities_file: models/reports/train/af20a808686bb5ad0c548be1b44329d8/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/eef921bb9ad37bce1046824220b2a3a2.pkl + params_file: models/reports/train/af20a808686bb5ad0c548be1b44329d8/params.yaml + predictions_file: models/reports/train/af20a808686bb5ad0c548be1b44329d8/predictions.json + probabilities_file: models/reports/train/af20a808686bb5ad0c548be1b44329d8/probabilities.json + score_dict_file: models/reports/train/af20a808686bb5ad0c548be1b44329d8/score_dict.json + test_labels_file: models/reports/train/af20a808686bb5ad0c548be1b44329d8/test_labels.json + train_labels_file: models/reports/train/af20a808686bb5ad0c548be1b44329d8/train_labels.json + model_dir: models + name: af20a808686bb5ad0c548be1b44329d8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6aaba297ccf4727b7d5d5d60ba24112e + trainer: + kwargs: {} +name: af20a808686bb5ad0c548be1b44329d8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/b265b8bdb49caef71a03028fb16ddae6/params.yaml b/examples/security/truthseeker/models/reports/train/b265b8bdb49caef71a03028fb16ddae6/params.yaml new file mode 100644 index 00000000..79e5652c --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/b265b8bdb49caef71a03028fb16ddae6/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/b265b8bdb49caef71a03028fb16ddae6/adv_predictions.json + adv_probabilities_file: models/reports/train/b265b8bdb49caef71a03028fb16ddae6/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/c281c21383bb54e3bddcd9b60140bb76.pkl + params_file: models/reports/train/b265b8bdb49caef71a03028fb16ddae6/params.yaml + predictions_file: models/reports/train/b265b8bdb49caef71a03028fb16ddae6/predictions.json + probabilities_file: models/reports/train/b265b8bdb49caef71a03028fb16ddae6/probabilities.json + score_dict_file: models/reports/train/b265b8bdb49caef71a03028fb16ddae6/score_dict.json + test_labels_file: models/reports/train/b265b8bdb49caef71a03028fb16ddae6/test_labels.json + train_labels_file: models/reports/train/b265b8bdb49caef71a03028fb16ddae6/train_labels.json + model_dir: models + name: b265b8bdb49caef71a03028fb16ddae6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 83e8e5c09d50e6e353d376b77db06617 + trainer: + kwargs: {} +name: b265b8bdb49caef71a03028fb16ddae6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/b266f140b55a5c6cfb753a7ba0793c58/params.yaml b/examples/security/truthseeker/models/reports/train/b266f140b55a5c6cfb753a7ba0793c58/params.yaml new file mode 100644 index 00000000..e88f8779 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/b266f140b55a5c6cfb753a7ba0793c58/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/b266f140b55a5c6cfb753a7ba0793c58/adv_predictions.json + adv_probabilities_file: models/reports/train/b266f140b55a5c6cfb753a7ba0793c58/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/35ffdaf55dda26675b5a44f5d49f9c7e.pkl + params_file: models/reports/train/b266f140b55a5c6cfb753a7ba0793c58/params.yaml + predictions_file: models/reports/train/b266f140b55a5c6cfb753a7ba0793c58/predictions.json + probabilities_file: models/reports/train/b266f140b55a5c6cfb753a7ba0793c58/probabilities.json + score_dict_file: models/reports/train/b266f140b55a5c6cfb753a7ba0793c58/score_dict.json + test_labels_file: models/reports/train/b266f140b55a5c6cfb753a7ba0793c58/test_labels.json + train_labels_file: models/reports/train/b266f140b55a5c6cfb753a7ba0793c58/train_labels.json + model_dir: models + name: b266f140b55a5c6cfb753a7ba0793c58 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 797c292bb693422638e2ebf3000c7aa5 + trainer: + kwargs: {} +name: b266f140b55a5c6cfb753a7ba0793c58 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/b689d7b177f544f793c4da095fd284df/params.yaml b/examples/security/truthseeker/models/reports/train/b689d7b177f544f793c4da095fd284df/params.yaml new file mode 100644 index 00000000..3c02d0ba --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/b689d7b177f544f793c4da095fd284df/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/b689d7b177f544f793c4da095fd284df/adv_predictions.json + adv_probabilities_file: models/reports/train/b689d7b177f544f793c4da095fd284df/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/f3883347bfdc0e430736b8e0b6e65cec.pkl + params_file: models/reports/train/b689d7b177f544f793c4da095fd284df/params.yaml + predictions_file: models/reports/train/b689d7b177f544f793c4da095fd284df/predictions.json + probabilities_file: models/reports/train/b689d7b177f544f793c4da095fd284df/probabilities.json + score_dict_file: models/reports/train/b689d7b177f544f793c4da095fd284df/score_dict.json + test_labels_file: models/reports/train/b689d7b177f544f793c4da095fd284df/test_labels.json + train_labels_file: models/reports/train/b689d7b177f544f793c4da095fd284df/train_labels.json + model_dir: models + name: b689d7b177f544f793c4da095fd284df + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2c289ac904500e2cf35198378f8717ff + trainer: + kwargs: {} +name: b689d7b177f544f793c4da095fd284df +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/b7129007c3ca72f1652a786c6e5ed934/params.yaml b/examples/security/truthseeker/models/reports/train/b7129007c3ca72f1652a786c6e5ed934/params.yaml new file mode 100644 index 00000000..92692c21 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/b7129007c3ca72f1652a786c6e5ed934/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/b7129007c3ca72f1652a786c6e5ed934/adv_predictions.json + adv_probabilities_file: models/reports/train/b7129007c3ca72f1652a786c6e5ed934/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/6664c70678301d04c7c56463f357f1d6.pkl + params_file: models/reports/train/b7129007c3ca72f1652a786c6e5ed934/params.yaml + predictions_file: models/reports/train/b7129007c3ca72f1652a786c6e5ed934/predictions.json + probabilities_file: models/reports/train/b7129007c3ca72f1652a786c6e5ed934/probabilities.json + score_dict_file: models/reports/train/b7129007c3ca72f1652a786c6e5ed934/score_dict.json + test_labels_file: models/reports/train/b7129007c3ca72f1652a786c6e5ed934/test_labels.json + train_labels_file: models/reports/train/b7129007c3ca72f1652a786c6e5ed934/train_labels.json + model_dir: models + name: b7129007c3ca72f1652a786c6e5ed934 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2047a1b904062ca35af2d47ddce6a198 + trainer: + kwargs: {} +name: b7129007c3ca72f1652a786c6e5ed934 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/b9efb7eaf85fdd217e9c3849e918eab5/params.yaml b/examples/security/truthseeker/models/reports/train/b9efb7eaf85fdd217e9c3849e918eab5/params.yaml new file mode 100644 index 00000000..763b680a --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/b9efb7eaf85fdd217e9c3849e918eab5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/b9efb7eaf85fdd217e9c3849e918eab5/adv_predictions.json + adv_probabilities_file: models/reports/train/b9efb7eaf85fdd217e9c3849e918eab5/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/0485643e48405264a3521847baef977c.pkl + params_file: models/reports/train/b9efb7eaf85fdd217e9c3849e918eab5/params.yaml + predictions_file: models/reports/train/b9efb7eaf85fdd217e9c3849e918eab5/predictions.json + probabilities_file: models/reports/train/b9efb7eaf85fdd217e9c3849e918eab5/probabilities.json + score_dict_file: models/reports/train/b9efb7eaf85fdd217e9c3849e918eab5/score_dict.json + test_labels_file: models/reports/train/b9efb7eaf85fdd217e9c3849e918eab5/test_labels.json + train_labels_file: models/reports/train/b9efb7eaf85fdd217e9c3849e918eab5/train_labels.json + model_dir: models + name: b9efb7eaf85fdd217e9c3849e918eab5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 759b8c59e7d1bc2ca5cdabd0cf5d4582 + trainer: + kwargs: {} +name: b9efb7eaf85fdd217e9c3849e918eab5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/ba2f9c5380ee6802d009bbe800bcc4cc/params.yaml b/examples/security/truthseeker/models/reports/train/ba2f9c5380ee6802d009bbe800bcc4cc/params.yaml new file mode 100644 index 00000000..a3bb4318 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/ba2f9c5380ee6802d009bbe800bcc4cc/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/ba2f9c5380ee6802d009bbe800bcc4cc/adv_predictions.json + adv_probabilities_file: models/reports/train/ba2f9c5380ee6802d009bbe800bcc4cc/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/6afe78812eb2f712f4ac2a4ee7b5541b.pkl + params_file: models/reports/train/ba2f9c5380ee6802d009bbe800bcc4cc/params.yaml + predictions_file: models/reports/train/ba2f9c5380ee6802d009bbe800bcc4cc/predictions.json + probabilities_file: models/reports/train/ba2f9c5380ee6802d009bbe800bcc4cc/probabilities.json + score_dict_file: models/reports/train/ba2f9c5380ee6802d009bbe800bcc4cc/score_dict.json + test_labels_file: models/reports/train/ba2f9c5380ee6802d009bbe800bcc4cc/test_labels.json + train_labels_file: models/reports/train/ba2f9c5380ee6802d009bbe800bcc4cc/train_labels.json + model_dir: models + name: ba2f9c5380ee6802d009bbe800bcc4cc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7dbd8bda69c522f51b4bc23f61a09aa8 + trainer: + kwargs: {} +name: ba2f9c5380ee6802d009bbe800bcc4cc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/baf500d84d12273ea65f84a5f71b4242/params.yaml b/examples/security/truthseeker/models/reports/train/baf500d84d12273ea65f84a5f71b4242/params.yaml new file mode 100644 index 00000000..ccd59a72 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/baf500d84d12273ea65f84a5f71b4242/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/baf500d84d12273ea65f84a5f71b4242/adv_predictions.json + adv_probabilities_file: models/reports/train/baf500d84d12273ea65f84a5f71b4242/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/e96ef25bfddbcd5bb37c2ee8e6517454.pkl + params_file: models/reports/train/baf500d84d12273ea65f84a5f71b4242/params.yaml + predictions_file: models/reports/train/baf500d84d12273ea65f84a5f71b4242/predictions.json + probabilities_file: models/reports/train/baf500d84d12273ea65f84a5f71b4242/probabilities.json + score_dict_file: models/reports/train/baf500d84d12273ea65f84a5f71b4242/score_dict.json + test_labels_file: models/reports/train/baf500d84d12273ea65f84a5f71b4242/test_labels.json + train_labels_file: models/reports/train/baf500d84d12273ea65f84a5f71b4242/train_labels.json + model_dir: models + name: baf500d84d12273ea65f84a5f71b4242 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0fa61e9f04b44af7927bcef371f13fe2 + trainer: + kwargs: {} +name: baf500d84d12273ea65f84a5f71b4242 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/bd2a520f1ae97e250c5729b606ebec32/params.yaml b/examples/security/truthseeker/models/reports/train/bd2a520f1ae97e250c5729b606ebec32/params.yaml new file mode 100644 index 00000000..44af0844 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/bd2a520f1ae97e250c5729b606ebec32/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/bd2a520f1ae97e250c5729b606ebec32/adv_predictions.json + adv_probabilities_file: models/reports/train/bd2a520f1ae97e250c5729b606ebec32/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/3e4e6ad0dbe0897041e3e0b744b59866.pkl + params_file: models/reports/train/bd2a520f1ae97e250c5729b606ebec32/params.yaml + predictions_file: models/reports/train/bd2a520f1ae97e250c5729b606ebec32/predictions.json + probabilities_file: models/reports/train/bd2a520f1ae97e250c5729b606ebec32/probabilities.json + score_dict_file: models/reports/train/bd2a520f1ae97e250c5729b606ebec32/score_dict.json + test_labels_file: models/reports/train/bd2a520f1ae97e250c5729b606ebec32/test_labels.json + train_labels_file: models/reports/train/bd2a520f1ae97e250c5729b606ebec32/train_labels.json + model_dir: models + name: bd2a520f1ae97e250c5729b606ebec32 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6d32dd58a7931879d5947002da530bfa + trainer: + kwargs: {} +name: bd2a520f1ae97e250c5729b606ebec32 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/bec5bbdee6042e234f90ba22d60fdeee/params.yaml b/examples/security/truthseeker/models/reports/train/bec5bbdee6042e234f90ba22d60fdeee/params.yaml new file mode 100644 index 00000000..a123e69e --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/bec5bbdee6042e234f90ba22d60fdeee/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/bec5bbdee6042e234f90ba22d60fdeee/adv_predictions.json + adv_probabilities_file: models/reports/train/bec5bbdee6042e234f90ba22d60fdeee/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/b5b3389252121ae82a7dcb01f02be6c8.pkl + params_file: models/reports/train/bec5bbdee6042e234f90ba22d60fdeee/params.yaml + predictions_file: models/reports/train/bec5bbdee6042e234f90ba22d60fdeee/predictions.json + probabilities_file: models/reports/train/bec5bbdee6042e234f90ba22d60fdeee/probabilities.json + score_dict_file: models/reports/train/bec5bbdee6042e234f90ba22d60fdeee/score_dict.json + test_labels_file: models/reports/train/bec5bbdee6042e234f90ba22d60fdeee/test_labels.json + train_labels_file: models/reports/train/bec5bbdee6042e234f90ba22d60fdeee/train_labels.json + model_dir: models + name: bec5bbdee6042e234f90ba22d60fdeee + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4b649f8968240718034393875d672fab + trainer: + kwargs: {} +name: bec5bbdee6042e234f90ba22d60fdeee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/c13adc2ae608554c509cdc0d97abd13a/params.yaml b/examples/security/truthseeker/models/reports/train/c13adc2ae608554c509cdc0d97abd13a/params.yaml new file mode 100644 index 00000000..cf1a20cc --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/c13adc2ae608554c509cdc0d97abd13a/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/c13adc2ae608554c509cdc0d97abd13a/adv_predictions.json + adv_probabilities_file: models/reports/train/c13adc2ae608554c509cdc0d97abd13a/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/4c407982bcb002dfd7aad6ff3b54852b.pkl + params_file: models/reports/train/c13adc2ae608554c509cdc0d97abd13a/params.yaml + predictions_file: models/reports/train/c13adc2ae608554c509cdc0d97abd13a/predictions.json + probabilities_file: models/reports/train/c13adc2ae608554c509cdc0d97abd13a/probabilities.json + score_dict_file: models/reports/train/c13adc2ae608554c509cdc0d97abd13a/score_dict.json + test_labels_file: models/reports/train/c13adc2ae608554c509cdc0d97abd13a/test_labels.json + train_labels_file: models/reports/train/c13adc2ae608554c509cdc0d97abd13a/train_labels.json + model_dir: models + name: c13adc2ae608554c509cdc0d97abd13a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bdf30155ada5f775b2ebba750ddb011d + trainer: + kwargs: {} +name: c13adc2ae608554c509cdc0d97abd13a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/c418229ff2a96954491ae052683c0cc0/params.yaml b/examples/security/truthseeker/models/reports/train/c418229ff2a96954491ae052683c0cc0/params.yaml new file mode 100644 index 00000000..7f2ed842 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/c418229ff2a96954491ae052683c0cc0/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/c418229ff2a96954491ae052683c0cc0/adv_predictions.json + adv_probabilities_file: models/reports/train/c418229ff2a96954491ae052683c0cc0/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/d45a6b73772b5c43c2069dc34aa3689b.pkl + params_file: models/reports/train/c418229ff2a96954491ae052683c0cc0/params.yaml + predictions_file: models/reports/train/c418229ff2a96954491ae052683c0cc0/predictions.json + probabilities_file: models/reports/train/c418229ff2a96954491ae052683c0cc0/probabilities.json + score_dict_file: models/reports/train/c418229ff2a96954491ae052683c0cc0/score_dict.json + test_labels_file: models/reports/train/c418229ff2a96954491ae052683c0cc0/test_labels.json + train_labels_file: models/reports/train/c418229ff2a96954491ae052683c0cc0/train_labels.json + model_dir: models + name: c418229ff2a96954491ae052683c0cc0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bb5d6424fd2597278efad04fbce9628b + trainer: + kwargs: {} +name: c418229ff2a96954491ae052683c0cc0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/c8120549bcae2dbef8a2a15f6e9e4136/params.yaml b/examples/security/truthseeker/models/reports/train/c8120549bcae2dbef8a2a15f6e9e4136/params.yaml new file mode 100644 index 00000000..1890d8d5 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/c8120549bcae2dbef8a2a15f6e9e4136/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/c8120549bcae2dbef8a2a15f6e9e4136/adv_predictions.json + adv_probabilities_file: models/reports/train/c8120549bcae2dbef8a2a15f6e9e4136/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/214bfc98b1eb8bdc91244bbe737327b1.pkl + params_file: models/reports/train/c8120549bcae2dbef8a2a15f6e9e4136/params.yaml + predictions_file: models/reports/train/c8120549bcae2dbef8a2a15f6e9e4136/predictions.json + probabilities_file: models/reports/train/c8120549bcae2dbef8a2a15f6e9e4136/probabilities.json + score_dict_file: models/reports/train/c8120549bcae2dbef8a2a15f6e9e4136/score_dict.json + test_labels_file: models/reports/train/c8120549bcae2dbef8a2a15f6e9e4136/test_labels.json + train_labels_file: models/reports/train/c8120549bcae2dbef8a2a15f6e9e4136/train_labels.json + model_dir: models + name: c8120549bcae2dbef8a2a15f6e9e4136 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7da7d28fa5eea7fde689adedc780ca35 + trainer: + kwargs: {} +name: c8120549bcae2dbef8a2a15f6e9e4136 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/c907623efe69b9a6aefa4da32aa32fc4/params.yaml b/examples/security/truthseeker/models/reports/train/c907623efe69b9a6aefa4da32aa32fc4/params.yaml new file mode 100644 index 00000000..77a67735 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/c907623efe69b9a6aefa4da32aa32fc4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/c907623efe69b9a6aefa4da32aa32fc4/adv_predictions.json + adv_probabilities_file: models/reports/train/c907623efe69b9a6aefa4da32aa32fc4/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/01be86c48c4e81cf249eea8de6f7ebad.pkl + params_file: models/reports/train/c907623efe69b9a6aefa4da32aa32fc4/params.yaml + predictions_file: models/reports/train/c907623efe69b9a6aefa4da32aa32fc4/predictions.json + probabilities_file: models/reports/train/c907623efe69b9a6aefa4da32aa32fc4/probabilities.json + score_dict_file: models/reports/train/c907623efe69b9a6aefa4da32aa32fc4/score_dict.json + test_labels_file: models/reports/train/c907623efe69b9a6aefa4da32aa32fc4/test_labels.json + train_labels_file: models/reports/train/c907623efe69b9a6aefa4da32aa32fc4/train_labels.json + model_dir: models + name: c907623efe69b9a6aefa4da32aa32fc4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 547223fdf0bef44c29118eb548447143 + trainer: + kwargs: {} +name: c907623efe69b9a6aefa4da32aa32fc4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/c9a22e973f44ec246b07667f957bd454/params.yaml b/examples/security/truthseeker/models/reports/train/c9a22e973f44ec246b07667f957bd454/params.yaml new file mode 100644 index 00000000..32109886 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/c9a22e973f44ec246b07667f957bd454/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/c9a22e973f44ec246b07667f957bd454/adv_predictions.json + adv_probabilities_file: models/reports/train/c9a22e973f44ec246b07667f957bd454/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/df380ce86fe6c64d1e134374efef84d8.pkl + params_file: models/reports/train/c9a22e973f44ec246b07667f957bd454/params.yaml + predictions_file: models/reports/train/c9a22e973f44ec246b07667f957bd454/predictions.json + probabilities_file: models/reports/train/c9a22e973f44ec246b07667f957bd454/probabilities.json + score_dict_file: models/reports/train/c9a22e973f44ec246b07667f957bd454/score_dict.json + test_labels_file: models/reports/train/c9a22e973f44ec246b07667f957bd454/test_labels.json + train_labels_file: models/reports/train/c9a22e973f44ec246b07667f957bd454/train_labels.json + model_dir: models + name: c9a22e973f44ec246b07667f957bd454 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ca5f122887eb77c223f0d90525c9311b + trainer: + kwargs: {} +name: c9a22e973f44ec246b07667f957bd454 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/cb7941e5b9fec027225fda13c1a8447c/params.yaml b/examples/security/truthseeker/models/reports/train/cb7941e5b9fec027225fda13c1a8447c/params.yaml new file mode 100644 index 00000000..8440fc02 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/cb7941e5b9fec027225fda13c1a8447c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/cb7941e5b9fec027225fda13c1a8447c/adv_predictions.json + adv_probabilities_file: models/reports/train/cb7941e5b9fec027225fda13c1a8447c/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/45549240f11c26be6d9797bab35a338e.pkl + params_file: models/reports/train/cb7941e5b9fec027225fda13c1a8447c/params.yaml + predictions_file: models/reports/train/cb7941e5b9fec027225fda13c1a8447c/predictions.json + probabilities_file: models/reports/train/cb7941e5b9fec027225fda13c1a8447c/probabilities.json + score_dict_file: models/reports/train/cb7941e5b9fec027225fda13c1a8447c/score_dict.json + test_labels_file: models/reports/train/cb7941e5b9fec027225fda13c1a8447c/test_labels.json + train_labels_file: models/reports/train/cb7941e5b9fec027225fda13c1a8447c/train_labels.json + model_dir: models + name: cb7941e5b9fec027225fda13c1a8447c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3f0de67350567e1545f0fa4642dd995d + trainer: + kwargs: {} +name: cb7941e5b9fec027225fda13c1a8447c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/ce3bbfca00b0dbaf948fb863d4d17228/params.yaml b/examples/security/truthseeker/models/reports/train/ce3bbfca00b0dbaf948fb863d4d17228/params.yaml new file mode 100644 index 00000000..b4dc3d08 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/ce3bbfca00b0dbaf948fb863d4d17228/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/ce3bbfca00b0dbaf948fb863d4d17228/adv_predictions.json + adv_probabilities_file: models/reports/train/ce3bbfca00b0dbaf948fb863d4d17228/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/957550396e3699c2c0f1101246493458.pkl + params_file: models/reports/train/ce3bbfca00b0dbaf948fb863d4d17228/params.yaml + predictions_file: models/reports/train/ce3bbfca00b0dbaf948fb863d4d17228/predictions.json + probabilities_file: models/reports/train/ce3bbfca00b0dbaf948fb863d4d17228/probabilities.json + score_dict_file: models/reports/train/ce3bbfca00b0dbaf948fb863d4d17228/score_dict.json + test_labels_file: models/reports/train/ce3bbfca00b0dbaf948fb863d4d17228/test_labels.json + train_labels_file: models/reports/train/ce3bbfca00b0dbaf948fb863d4d17228/train_labels.json + model_dir: models + name: ce3bbfca00b0dbaf948fb863d4d17228 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7381d906984a1ba4dbf903f23f46d290 + trainer: + kwargs: {} +name: ce3bbfca00b0dbaf948fb863d4d17228 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/ce900987b58d230adda4f2a89175b77c/params.yaml b/examples/security/truthseeker/models/reports/train/ce900987b58d230adda4f2a89175b77c/params.yaml new file mode 100644 index 00000000..ba330296 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/ce900987b58d230adda4f2a89175b77c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/ce900987b58d230adda4f2a89175b77c/adv_predictions.json + adv_probabilities_file: models/reports/train/ce900987b58d230adda4f2a89175b77c/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/a45dd243543cbcb96d6d16724c90ed2f.pkl + params_file: models/reports/train/ce900987b58d230adda4f2a89175b77c/params.yaml + predictions_file: models/reports/train/ce900987b58d230adda4f2a89175b77c/predictions.json + probabilities_file: models/reports/train/ce900987b58d230adda4f2a89175b77c/probabilities.json + score_dict_file: models/reports/train/ce900987b58d230adda4f2a89175b77c/score_dict.json + test_labels_file: models/reports/train/ce900987b58d230adda4f2a89175b77c/test_labels.json + train_labels_file: models/reports/train/ce900987b58d230adda4f2a89175b77c/train_labels.json + model_dir: models + name: ce900987b58d230adda4f2a89175b77c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d2a9eb9b8c2149cc5064d84043e6878a + trainer: + kwargs: {} +name: ce900987b58d230adda4f2a89175b77c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/cfd0324d4893b41c2fe018d0a33edde4/params.yaml b/examples/security/truthseeker/models/reports/train/cfd0324d4893b41c2fe018d0a33edde4/params.yaml new file mode 100644 index 00000000..49a953ca --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/cfd0324d4893b41c2fe018d0a33edde4/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/cfd0324d4893b41c2fe018d0a33edde4/adv_predictions.json + adv_probabilities_file: models/reports/train/cfd0324d4893b41c2fe018d0a33edde4/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/a7b55e9c92494750ceace81f4579d67a.pkl + params_file: models/reports/train/cfd0324d4893b41c2fe018d0a33edde4/params.yaml + predictions_file: models/reports/train/cfd0324d4893b41c2fe018d0a33edde4/predictions.json + probabilities_file: models/reports/train/cfd0324d4893b41c2fe018d0a33edde4/probabilities.json + score_dict_file: models/reports/train/cfd0324d4893b41c2fe018d0a33edde4/score_dict.json + test_labels_file: models/reports/train/cfd0324d4893b41c2fe018d0a33edde4/test_labels.json + train_labels_file: models/reports/train/cfd0324d4893b41c2fe018d0a33edde4/train_labels.json + model_dir: models + name: cfd0324d4893b41c2fe018d0a33edde4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e5f88a07896e7114062de8eef5275d8d + trainer: + kwargs: {} +name: cfd0324d4893b41c2fe018d0a33edde4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/d2597783e4ac61266393df542ac991a6/params.yaml b/examples/security/truthseeker/models/reports/train/d2597783e4ac61266393df542ac991a6/params.yaml new file mode 100644 index 00000000..802a5b17 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/d2597783e4ac61266393df542ac991a6/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/d2597783e4ac61266393df542ac991a6/adv_predictions.json + adv_probabilities_file: models/reports/train/d2597783e4ac61266393df542ac991a6/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/14513fa094ef4283e1f4263c94713e14.pkl + params_file: models/reports/train/d2597783e4ac61266393df542ac991a6/params.yaml + predictions_file: models/reports/train/d2597783e4ac61266393df542ac991a6/predictions.json + probabilities_file: models/reports/train/d2597783e4ac61266393df542ac991a6/probabilities.json + score_dict_file: models/reports/train/d2597783e4ac61266393df542ac991a6/score_dict.json + test_labels_file: models/reports/train/d2597783e4ac61266393df542ac991a6/test_labels.json + train_labels_file: models/reports/train/d2597783e4ac61266393df542ac991a6/train_labels.json + model_dir: models + name: d2597783e4ac61266393df542ac991a6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 16311b978bc905817528e88f9a85e467 + trainer: + kwargs: {} +name: d2597783e4ac61266393df542ac991a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/d2651ef64bb0821a4c82211475ba1225/params.yaml b/examples/security/truthseeker/models/reports/train/d2651ef64bb0821a4c82211475ba1225/params.yaml new file mode 100644 index 00000000..2ad05117 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/d2651ef64bb0821a4c82211475ba1225/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/d2651ef64bb0821a4c82211475ba1225/adv_predictions.json + adv_probabilities_file: models/reports/train/d2651ef64bb0821a4c82211475ba1225/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/622869a0696f375552778d3877f1726a.pkl + params_file: models/reports/train/d2651ef64bb0821a4c82211475ba1225/params.yaml + predictions_file: models/reports/train/d2651ef64bb0821a4c82211475ba1225/predictions.json + probabilities_file: models/reports/train/d2651ef64bb0821a4c82211475ba1225/probabilities.json + score_dict_file: models/reports/train/d2651ef64bb0821a4c82211475ba1225/score_dict.json + test_labels_file: models/reports/train/d2651ef64bb0821a4c82211475ba1225/test_labels.json + train_labels_file: models/reports/train/d2651ef64bb0821a4c82211475ba1225/train_labels.json + model_dir: models + name: d2651ef64bb0821a4c82211475ba1225 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1b17b167b30bad032fa0a10e73d82d4c + trainer: + kwargs: {} +name: d2651ef64bb0821a4c82211475ba1225 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/d2b6b5e8db7fbd5f1d3f8f372d3889db/params.yaml b/examples/security/truthseeker/models/reports/train/d2b6b5e8db7fbd5f1d3f8f372d3889db/params.yaml new file mode 100644 index 00000000..74dd8d81 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/d2b6b5e8db7fbd5f1d3f8f372d3889db/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/d2b6b5e8db7fbd5f1d3f8f372d3889db/adv_predictions.json + adv_probabilities_file: models/reports/train/d2b6b5e8db7fbd5f1d3f8f372d3889db/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/9995096db7ef58f427a7c098ca8aecfd.pkl + params_file: models/reports/train/d2b6b5e8db7fbd5f1d3f8f372d3889db/params.yaml + predictions_file: models/reports/train/d2b6b5e8db7fbd5f1d3f8f372d3889db/predictions.json + probabilities_file: models/reports/train/d2b6b5e8db7fbd5f1d3f8f372d3889db/probabilities.json + score_dict_file: models/reports/train/d2b6b5e8db7fbd5f1d3f8f372d3889db/score_dict.json + test_labels_file: models/reports/train/d2b6b5e8db7fbd5f1d3f8f372d3889db/test_labels.json + train_labels_file: models/reports/train/d2b6b5e8db7fbd5f1d3f8f372d3889db/train_labels.json + model_dir: models + name: d2b6b5e8db7fbd5f1d3f8f372d3889db + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 87ea7d1d89d17d4ea6dc4f172a68abe1 + trainer: + kwargs: {} +name: d2b6b5e8db7fbd5f1d3f8f372d3889db +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/d6e47db9de75d0c384fa4bc2f6bbd02d/params.yaml b/examples/security/truthseeker/models/reports/train/d6e47db9de75d0c384fa4bc2f6bbd02d/params.yaml new file mode 100644 index 00000000..26e00050 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/d6e47db9de75d0c384fa4bc2f6bbd02d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/d6e47db9de75d0c384fa4bc2f6bbd02d/adv_predictions.json + adv_probabilities_file: models/reports/train/d6e47db9de75d0c384fa4bc2f6bbd02d/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/6633b96101d0bb0d6f08214ba64affdf.pkl + params_file: models/reports/train/d6e47db9de75d0c384fa4bc2f6bbd02d/params.yaml + predictions_file: models/reports/train/d6e47db9de75d0c384fa4bc2f6bbd02d/predictions.json + probabilities_file: models/reports/train/d6e47db9de75d0c384fa4bc2f6bbd02d/probabilities.json + score_dict_file: models/reports/train/d6e47db9de75d0c384fa4bc2f6bbd02d/score_dict.json + test_labels_file: models/reports/train/d6e47db9de75d0c384fa4bc2f6bbd02d/test_labels.json + train_labels_file: models/reports/train/d6e47db9de75d0c384fa4bc2f6bbd02d/train_labels.json + model_dir: models + name: d6e47db9de75d0c384fa4bc2f6bbd02d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d314cfc7c678b6f3e9d6587cab44504 + trainer: + kwargs: {} +name: d6e47db9de75d0c384fa4bc2f6bbd02d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/dc27e947cb8b1346b3978aeca6494813/params.yaml b/examples/security/truthseeker/models/reports/train/dc27e947cb8b1346b3978aeca6494813/params.yaml new file mode 100644 index 00000000..5fb961e1 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/dc27e947cb8b1346b3978aeca6494813/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/dc27e947cb8b1346b3978aeca6494813/adv_predictions.json + adv_probabilities_file: models/reports/train/dc27e947cb8b1346b3978aeca6494813/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/4d60afabbac8120f0628cbc701609a29.pkl + params_file: models/reports/train/dc27e947cb8b1346b3978aeca6494813/params.yaml + predictions_file: models/reports/train/dc27e947cb8b1346b3978aeca6494813/predictions.json + probabilities_file: models/reports/train/dc27e947cb8b1346b3978aeca6494813/probabilities.json + score_dict_file: models/reports/train/dc27e947cb8b1346b3978aeca6494813/score_dict.json + test_labels_file: models/reports/train/dc27e947cb8b1346b3978aeca6494813/test_labels.json + train_labels_file: models/reports/train/dc27e947cb8b1346b3978aeca6494813/train_labels.json + model_dir: models + name: dc27e947cb8b1346b3978aeca6494813 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 21300ba249bd809f0aa5becda7426a21 + trainer: + kwargs: {} +name: dc27e947cb8b1346b3978aeca6494813 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/dcf97d25a83fffb00d8902ccba9cb108/params.yaml b/examples/security/truthseeker/models/reports/train/dcf97d25a83fffb00d8902ccba9cb108/params.yaml new file mode 100644 index 00000000..52aedd1d --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/dcf97d25a83fffb00d8902ccba9cb108/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/dcf97d25a83fffb00d8902ccba9cb108/adv_predictions.json + adv_probabilities_file: models/reports/train/dcf97d25a83fffb00d8902ccba9cb108/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/0f32e2b21bf79ab51938e9c00f39460e.pkl + params_file: models/reports/train/dcf97d25a83fffb00d8902ccba9cb108/params.yaml + predictions_file: models/reports/train/dcf97d25a83fffb00d8902ccba9cb108/predictions.json + probabilities_file: models/reports/train/dcf97d25a83fffb00d8902ccba9cb108/probabilities.json + score_dict_file: models/reports/train/dcf97d25a83fffb00d8902ccba9cb108/score_dict.json + test_labels_file: models/reports/train/dcf97d25a83fffb00d8902ccba9cb108/test_labels.json + train_labels_file: models/reports/train/dcf97d25a83fffb00d8902ccba9cb108/train_labels.json + model_dir: models + name: dcf97d25a83fffb00d8902ccba9cb108 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 04bd3489fed16bfc647ec1f2acd7b800 + trainer: + kwargs: {} +name: dcf97d25a83fffb00d8902ccba9cb108 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/dd30444c32b8f5ee1168ac0d79057b36/params.yaml b/examples/security/truthseeker/models/reports/train/dd30444c32b8f5ee1168ac0d79057b36/params.yaml new file mode 100644 index 00000000..28290093 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/dd30444c32b8f5ee1168ac0d79057b36/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/dd30444c32b8f5ee1168ac0d79057b36/adv_predictions.json + adv_probabilities_file: models/reports/train/dd30444c32b8f5ee1168ac0d79057b36/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/15e2158cda7c5bfe4a04a20ad2e556a4.pkl + params_file: models/reports/train/dd30444c32b8f5ee1168ac0d79057b36/params.yaml + predictions_file: models/reports/train/dd30444c32b8f5ee1168ac0d79057b36/predictions.json + probabilities_file: models/reports/train/dd30444c32b8f5ee1168ac0d79057b36/probabilities.json + score_dict_file: models/reports/train/dd30444c32b8f5ee1168ac0d79057b36/score_dict.json + test_labels_file: models/reports/train/dd30444c32b8f5ee1168ac0d79057b36/test_labels.json + train_labels_file: models/reports/train/dd30444c32b8f5ee1168ac0d79057b36/train_labels.json + model_dir: models + name: dd30444c32b8f5ee1168ac0d79057b36 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8a62edecee845292df0d496647eab2a9 + trainer: + kwargs: {} +name: dd30444c32b8f5ee1168ac0d79057b36 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/ddebb8086d9632ffbc4a90971ef1b06d/params.yaml b/examples/security/truthseeker/models/reports/train/ddebb8086d9632ffbc4a90971ef1b06d/params.yaml new file mode 100644 index 00000000..1a11f1c2 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/ddebb8086d9632ffbc4a90971ef1b06d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/ddebb8086d9632ffbc4a90971ef1b06d/adv_predictions.json + adv_probabilities_file: models/reports/train/ddebb8086d9632ffbc4a90971ef1b06d/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/56edfc04fe4a5a0c8ceaf333d93de605.pkl + params_file: models/reports/train/ddebb8086d9632ffbc4a90971ef1b06d/params.yaml + predictions_file: models/reports/train/ddebb8086d9632ffbc4a90971ef1b06d/predictions.json + probabilities_file: models/reports/train/ddebb8086d9632ffbc4a90971ef1b06d/probabilities.json + score_dict_file: models/reports/train/ddebb8086d9632ffbc4a90971ef1b06d/score_dict.json + test_labels_file: models/reports/train/ddebb8086d9632ffbc4a90971ef1b06d/test_labels.json + train_labels_file: models/reports/train/ddebb8086d9632ffbc4a90971ef1b06d/train_labels.json + model_dir: models + name: ddebb8086d9632ffbc4a90971ef1b06d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 718cd25e5f316fbf21cd9703c5e0571e + trainer: + kwargs: {} +name: ddebb8086d9632ffbc4a90971ef1b06d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/dfac19bb7dd81c4b32bbffed86d4f208/params.yaml b/examples/security/truthseeker/models/reports/train/dfac19bb7dd81c4b32bbffed86d4f208/params.yaml new file mode 100644 index 00000000..fabce143 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/dfac19bb7dd81c4b32bbffed86d4f208/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/dfac19bb7dd81c4b32bbffed86d4f208/adv_predictions.json + adv_probabilities_file: models/reports/train/dfac19bb7dd81c4b32bbffed86d4f208/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/ade18121fe47a2184bb775cf95ef8651.pkl + params_file: models/reports/train/dfac19bb7dd81c4b32bbffed86d4f208/params.yaml + predictions_file: models/reports/train/dfac19bb7dd81c4b32bbffed86d4f208/predictions.json + probabilities_file: models/reports/train/dfac19bb7dd81c4b32bbffed86d4f208/probabilities.json + score_dict_file: models/reports/train/dfac19bb7dd81c4b32bbffed86d4f208/score_dict.json + test_labels_file: models/reports/train/dfac19bb7dd81c4b32bbffed86d4f208/test_labels.json + train_labels_file: models/reports/train/dfac19bb7dd81c4b32bbffed86d4f208/train_labels.json + model_dir: models + name: dfac19bb7dd81c4b32bbffed86d4f208 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2d5f2ce892cf99d62b915f46964338a3 + trainer: + kwargs: {} +name: dfac19bb7dd81c4b32bbffed86d4f208 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/e11a3e02b61ac3d6bc124d3e78b57774/params.yaml b/examples/security/truthseeker/models/reports/train/e11a3e02b61ac3d6bc124d3e78b57774/params.yaml new file mode 100644 index 00000000..76c0b8e3 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/e11a3e02b61ac3d6bc124d3e78b57774/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/e11a3e02b61ac3d6bc124d3e78b57774/adv_predictions.json + adv_probabilities_file: models/reports/train/e11a3e02b61ac3d6bc124d3e78b57774/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/5dd86492a6cbbf2a7e14bada9289ec49.pkl + params_file: models/reports/train/e11a3e02b61ac3d6bc124d3e78b57774/params.yaml + predictions_file: models/reports/train/e11a3e02b61ac3d6bc124d3e78b57774/predictions.json + probabilities_file: models/reports/train/e11a3e02b61ac3d6bc124d3e78b57774/probabilities.json + score_dict_file: models/reports/train/e11a3e02b61ac3d6bc124d3e78b57774/score_dict.json + test_labels_file: models/reports/train/e11a3e02b61ac3d6bc124d3e78b57774/test_labels.json + train_labels_file: models/reports/train/e11a3e02b61ac3d6bc124d3e78b57774/train_labels.json + model_dir: models + name: e11a3e02b61ac3d6bc124d3e78b57774 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 537c7565c6f401b821b243ff38ec023a + trainer: + kwargs: {} +name: e11a3e02b61ac3d6bc124d3e78b57774 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/e35955020d58d6987b08cc2348b6d127/params.yaml b/examples/security/truthseeker/models/reports/train/e35955020d58d6987b08cc2348b6d127/params.yaml new file mode 100644 index 00000000..4ae1fccd --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/e35955020d58d6987b08cc2348b6d127/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/e35955020d58d6987b08cc2348b6d127/adv_predictions.json + adv_probabilities_file: models/reports/train/e35955020d58d6987b08cc2348b6d127/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/1d454dc6eaf707b6fd46f19874a48ca1.pkl + params_file: models/reports/train/e35955020d58d6987b08cc2348b6d127/params.yaml + predictions_file: models/reports/train/e35955020d58d6987b08cc2348b6d127/predictions.json + probabilities_file: models/reports/train/e35955020d58d6987b08cc2348b6d127/probabilities.json + score_dict_file: models/reports/train/e35955020d58d6987b08cc2348b6d127/score_dict.json + test_labels_file: models/reports/train/e35955020d58d6987b08cc2348b6d127/test_labels.json + train_labels_file: models/reports/train/e35955020d58d6987b08cc2348b6d127/train_labels.json + model_dir: models + name: e35955020d58d6987b08cc2348b6d127 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3464e1a1eed56424198b0f596defb46e + trainer: + kwargs: {} +name: e35955020d58d6987b08cc2348b6d127 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/e7347b27a29f206f794f0ae217807751/params.yaml b/examples/security/truthseeker/models/reports/train/e7347b27a29f206f794f0ae217807751/params.yaml new file mode 100644 index 00000000..9b3f9664 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/e7347b27a29f206f794f0ae217807751/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/e7347b27a29f206f794f0ae217807751/adv_predictions.json + adv_probabilities_file: models/reports/train/e7347b27a29f206f794f0ae217807751/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/fe5037e971a9b3c6f85ca578f22f83e5.pkl + params_file: models/reports/train/e7347b27a29f206f794f0ae217807751/params.yaml + predictions_file: models/reports/train/e7347b27a29f206f794f0ae217807751/predictions.json + probabilities_file: models/reports/train/e7347b27a29f206f794f0ae217807751/probabilities.json + score_dict_file: models/reports/train/e7347b27a29f206f794f0ae217807751/score_dict.json + test_labels_file: models/reports/train/e7347b27a29f206f794f0ae217807751/test_labels.json + train_labels_file: models/reports/train/e7347b27a29f206f794f0ae217807751/train_labels.json + model_dir: models + name: e7347b27a29f206f794f0ae217807751 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: df60e9fa3ee76173c1f01f8347024c4e + trainer: + kwargs: {} +name: e7347b27a29f206f794f0ae217807751 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/e745877fcf1e29f92e139d3d41017a8a/params.yaml b/examples/security/truthseeker/models/reports/train/e745877fcf1e29f92e139d3d41017a8a/params.yaml new file mode 100644 index 00000000..13bd054c --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/e745877fcf1e29f92e139d3d41017a8a/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/e745877fcf1e29f92e139d3d41017a8a/adv_predictions.json + adv_probabilities_file: models/reports/train/e745877fcf1e29f92e139d3d41017a8a/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/7b60136ba6a4dc09e998c342f162fc9c.pkl + params_file: models/reports/train/e745877fcf1e29f92e139d3d41017a8a/params.yaml + predictions_file: models/reports/train/e745877fcf1e29f92e139d3d41017a8a/predictions.json + probabilities_file: models/reports/train/e745877fcf1e29f92e139d3d41017a8a/probabilities.json + score_dict_file: models/reports/train/e745877fcf1e29f92e139d3d41017a8a/score_dict.json + test_labels_file: models/reports/train/e745877fcf1e29f92e139d3d41017a8a/test_labels.json + train_labels_file: models/reports/train/e745877fcf1e29f92e139d3d41017a8a/train_labels.json + model_dir: models + name: e745877fcf1e29f92e139d3d41017a8a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3af2177580d586c1759ab626a4fbf5f6 + trainer: + kwargs: {} +name: e745877fcf1e29f92e139d3d41017a8a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/e78328f0c651b7579f64b623f71b1157/params.yaml b/examples/security/truthseeker/models/reports/train/e78328f0c651b7579f64b623f71b1157/params.yaml new file mode 100644 index 00000000..16d01ba3 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/e78328f0c651b7579f64b623f71b1157/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/e78328f0c651b7579f64b623f71b1157/adv_predictions.json + adv_probabilities_file: models/reports/train/e78328f0c651b7579f64b623f71b1157/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/d04330a804dc97f418232a338e05cfec.pkl + params_file: models/reports/train/e78328f0c651b7579f64b623f71b1157/params.yaml + predictions_file: models/reports/train/e78328f0c651b7579f64b623f71b1157/predictions.json + probabilities_file: models/reports/train/e78328f0c651b7579f64b623f71b1157/probabilities.json + score_dict_file: models/reports/train/e78328f0c651b7579f64b623f71b1157/score_dict.json + test_labels_file: models/reports/train/e78328f0c651b7579f64b623f71b1157/test_labels.json + train_labels_file: models/reports/train/e78328f0c651b7579f64b623f71b1157/train_labels.json + model_dir: models + name: e78328f0c651b7579f64b623f71b1157 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 60b0e01d3a154a8340716fc430860886 + trainer: + kwargs: {} +name: e78328f0c651b7579f64b623f71b1157 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/e8e5dec5611d50bbe53764d0ef0768d4/params.yaml b/examples/security/truthseeker/models/reports/train/e8e5dec5611d50bbe53764d0ef0768d4/params.yaml new file mode 100644 index 00000000..d32a7267 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/e8e5dec5611d50bbe53764d0ef0768d4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/e8e5dec5611d50bbe53764d0ef0768d4/adv_predictions.json + adv_probabilities_file: models/reports/train/e8e5dec5611d50bbe53764d0ef0768d4/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/a1c45e4d1bdba0ad6b25a47d2e16c944.pkl + params_file: models/reports/train/e8e5dec5611d50bbe53764d0ef0768d4/params.yaml + predictions_file: models/reports/train/e8e5dec5611d50bbe53764d0ef0768d4/predictions.json + probabilities_file: models/reports/train/e8e5dec5611d50bbe53764d0ef0768d4/probabilities.json + score_dict_file: models/reports/train/e8e5dec5611d50bbe53764d0ef0768d4/score_dict.json + test_labels_file: models/reports/train/e8e5dec5611d50bbe53764d0ef0768d4/test_labels.json + train_labels_file: models/reports/train/e8e5dec5611d50bbe53764d0ef0768d4/train_labels.json + model_dir: models + name: e8e5dec5611d50bbe53764d0ef0768d4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 775dd0893a2a1884fd0e050a10379db9 + trainer: + kwargs: {} +name: e8e5dec5611d50bbe53764d0ef0768d4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/e9b3fceb3404567098d4b0891e138d25/params.yaml b/examples/security/truthseeker/models/reports/train/e9b3fceb3404567098d4b0891e138d25/params.yaml new file mode 100644 index 00000000..437ebfab --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/e9b3fceb3404567098d4b0891e138d25/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/e9b3fceb3404567098d4b0891e138d25/adv_predictions.json + adv_probabilities_file: models/reports/train/e9b3fceb3404567098d4b0891e138d25/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/b0a6af33fabdf50d796848291f3ad162.pkl + params_file: models/reports/train/e9b3fceb3404567098d4b0891e138d25/params.yaml + predictions_file: models/reports/train/e9b3fceb3404567098d4b0891e138d25/predictions.json + probabilities_file: models/reports/train/e9b3fceb3404567098d4b0891e138d25/probabilities.json + score_dict_file: models/reports/train/e9b3fceb3404567098d4b0891e138d25/score_dict.json + test_labels_file: models/reports/train/e9b3fceb3404567098d4b0891e138d25/test_labels.json + train_labels_file: models/reports/train/e9b3fceb3404567098d4b0891e138d25/train_labels.json + model_dir: models + name: e9b3fceb3404567098d4b0891e138d25 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 69d5874108f3f337ab5587fac40ed215 + trainer: + kwargs: {} +name: e9b3fceb3404567098d4b0891e138d25 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/eb0a6cd1c25fbc1c4ed802abf93cfa49/params.yaml b/examples/security/truthseeker/models/reports/train/eb0a6cd1c25fbc1c4ed802abf93cfa49/params.yaml new file mode 100644 index 00000000..f6779e0d --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/eb0a6cd1c25fbc1c4ed802abf93cfa49/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/eb0a6cd1c25fbc1c4ed802abf93cfa49/adv_predictions.json + adv_probabilities_file: models/reports/train/eb0a6cd1c25fbc1c4ed802abf93cfa49/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/2780e361b3b97564f145977479bd5dc7.pkl + params_file: models/reports/train/eb0a6cd1c25fbc1c4ed802abf93cfa49/params.yaml + predictions_file: models/reports/train/eb0a6cd1c25fbc1c4ed802abf93cfa49/predictions.json + probabilities_file: models/reports/train/eb0a6cd1c25fbc1c4ed802abf93cfa49/probabilities.json + score_dict_file: models/reports/train/eb0a6cd1c25fbc1c4ed802abf93cfa49/score_dict.json + test_labels_file: models/reports/train/eb0a6cd1c25fbc1c4ed802abf93cfa49/test_labels.json + train_labels_file: models/reports/train/eb0a6cd1c25fbc1c4ed802abf93cfa49/train_labels.json + model_dir: models + name: eb0a6cd1c25fbc1c4ed802abf93cfa49 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7c898ce7912f37753adf1117066d7b35 + trainer: + kwargs: {} +name: eb0a6cd1c25fbc1c4ed802abf93cfa49 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/eed6ff0f278024596f8fbc23b30e9f5c/params.yaml b/examples/security/truthseeker/models/reports/train/eed6ff0f278024596f8fbc23b30e9f5c/params.yaml new file mode 100644 index 00000000..f5cc99da --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/eed6ff0f278024596f8fbc23b30e9f5c/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/eed6ff0f278024596f8fbc23b30e9f5c/adv_predictions.json + adv_probabilities_file: models/reports/train/eed6ff0f278024596f8fbc23b30e9f5c/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/aed1c02e033ee1e92829ff322b9753ff.pkl + params_file: models/reports/train/eed6ff0f278024596f8fbc23b30e9f5c/params.yaml + predictions_file: models/reports/train/eed6ff0f278024596f8fbc23b30e9f5c/predictions.json + probabilities_file: models/reports/train/eed6ff0f278024596f8fbc23b30e9f5c/probabilities.json + score_dict_file: models/reports/train/eed6ff0f278024596f8fbc23b30e9f5c/score_dict.json + test_labels_file: models/reports/train/eed6ff0f278024596f8fbc23b30e9f5c/test_labels.json + train_labels_file: models/reports/train/eed6ff0f278024596f8fbc23b30e9f5c/train_labels.json + model_dir: models + name: eed6ff0f278024596f8fbc23b30e9f5c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9913b0d20830fbe35b016c25b249dd25 + trainer: + kwargs: {} +name: eed6ff0f278024596f8fbc23b30e9f5c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/eef076fef7356d17d110eb6a736bf6bb/params.yaml b/examples/security/truthseeker/models/reports/train/eef076fef7356d17d110eb6a736bf6bb/params.yaml new file mode 100644 index 00000000..38fc8b3c --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/eef076fef7356d17d110eb6a736bf6bb/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/eef076fef7356d17d110eb6a736bf6bb/adv_predictions.json + adv_probabilities_file: models/reports/train/eef076fef7356d17d110eb6a736bf6bb/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/7d70c23c3ae5cf16ea966f1fe5c29ded.pkl + params_file: models/reports/train/eef076fef7356d17d110eb6a736bf6bb/params.yaml + predictions_file: models/reports/train/eef076fef7356d17d110eb6a736bf6bb/predictions.json + probabilities_file: models/reports/train/eef076fef7356d17d110eb6a736bf6bb/probabilities.json + score_dict_file: models/reports/train/eef076fef7356d17d110eb6a736bf6bb/score_dict.json + test_labels_file: models/reports/train/eef076fef7356d17d110eb6a736bf6bb/test_labels.json + train_labels_file: models/reports/train/eef076fef7356d17d110eb6a736bf6bb/train_labels.json + model_dir: models + name: eef076fef7356d17d110eb6a736bf6bb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ce00e2b68ca3d9077318277e711fed04 + trainer: + kwargs: {} +name: eef076fef7356d17d110eb6a736bf6bb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/f15cbc60e3c4bf0d197cd78b062ab9e8/params.yaml b/examples/security/truthseeker/models/reports/train/f15cbc60e3c4bf0d197cd78b062ab9e8/params.yaml new file mode 100644 index 00000000..7d5ad987 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/f15cbc60e3c4bf0d197cd78b062ab9e8/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/f15cbc60e3c4bf0d197cd78b062ab9e8/adv_predictions.json + adv_probabilities_file: models/reports/train/f15cbc60e3c4bf0d197cd78b062ab9e8/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/10fd48e70d83b435a335f410e7ec9370.pkl + params_file: models/reports/train/f15cbc60e3c4bf0d197cd78b062ab9e8/params.yaml + predictions_file: models/reports/train/f15cbc60e3c4bf0d197cd78b062ab9e8/predictions.json + probabilities_file: models/reports/train/f15cbc60e3c4bf0d197cd78b062ab9e8/probabilities.json + score_dict_file: models/reports/train/f15cbc60e3c4bf0d197cd78b062ab9e8/score_dict.json + test_labels_file: models/reports/train/f15cbc60e3c4bf0d197cd78b062ab9e8/test_labels.json + train_labels_file: models/reports/train/f15cbc60e3c4bf0d197cd78b062ab9e8/train_labels.json + model_dir: models + name: f15cbc60e3c4bf0d197cd78b062ab9e8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6f9a605b303c3727fbc43feba149f9a1 + trainer: + kwargs: {} +name: f15cbc60e3c4bf0d197cd78b062ab9e8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/f23090e91e5357ebc25aa353edecf41b/params.yaml b/examples/security/truthseeker/models/reports/train/f23090e91e5357ebc25aa353edecf41b/params.yaml new file mode 100644 index 00000000..0f9af415 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/f23090e91e5357ebc25aa353edecf41b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/f23090e91e5357ebc25aa353edecf41b/adv_predictions.json + adv_probabilities_file: models/reports/train/f23090e91e5357ebc25aa353edecf41b/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/44d8fdc65f704d4cc2c55dd94ef432e1.pkl + params_file: models/reports/train/f23090e91e5357ebc25aa353edecf41b/params.yaml + predictions_file: models/reports/train/f23090e91e5357ebc25aa353edecf41b/predictions.json + probabilities_file: models/reports/train/f23090e91e5357ebc25aa353edecf41b/probabilities.json + score_dict_file: models/reports/train/f23090e91e5357ebc25aa353edecf41b/score_dict.json + test_labels_file: models/reports/train/f23090e91e5357ebc25aa353edecf41b/test_labels.json + train_labels_file: models/reports/train/f23090e91e5357ebc25aa353edecf41b/train_labels.json + model_dir: models + name: f23090e91e5357ebc25aa353edecf41b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7311b91f62494b35f38a5cd758d026b4 + trainer: + kwargs: {} +name: f23090e91e5357ebc25aa353edecf41b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/f70eb2fe3fceb19ebc057369687006ae/params.yaml b/examples/security/truthseeker/models/reports/train/f70eb2fe3fceb19ebc057369687006ae/params.yaml new file mode 100644 index 00000000..2edae690 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/f70eb2fe3fceb19ebc057369687006ae/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/f70eb2fe3fceb19ebc057369687006ae/adv_predictions.json + adv_probabilities_file: models/reports/train/f70eb2fe3fceb19ebc057369687006ae/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/e62cbb1dd251885f09791eeca799cc61.pkl + params_file: models/reports/train/f70eb2fe3fceb19ebc057369687006ae/params.yaml + predictions_file: models/reports/train/f70eb2fe3fceb19ebc057369687006ae/predictions.json + probabilities_file: models/reports/train/f70eb2fe3fceb19ebc057369687006ae/probabilities.json + score_dict_file: models/reports/train/f70eb2fe3fceb19ebc057369687006ae/score_dict.json + test_labels_file: models/reports/train/f70eb2fe3fceb19ebc057369687006ae/test_labels.json + train_labels_file: models/reports/train/f70eb2fe3fceb19ebc057369687006ae/train_labels.json + model_dir: models + name: f70eb2fe3fceb19ebc057369687006ae + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 31ea69b4923237b92dceeb00462db4b8 + trainer: + kwargs: {} +name: f70eb2fe3fceb19ebc057369687006ae +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/f95baa6b04d3af30722d909961c3f6e2/params.yaml b/examples/security/truthseeker/models/reports/train/f95baa6b04d3af30722d909961c3f6e2/params.yaml new file mode 100644 index 00000000..8e6d43cc --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/f95baa6b04d3af30722d909961c3f6e2/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/f95baa6b04d3af30722d909961c3f6e2/adv_predictions.json + adv_probabilities_file: models/reports/train/f95baa6b04d3af30722d909961c3f6e2/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/7778eaac0301fc8206593f78a9e659a2.pkl + params_file: models/reports/train/f95baa6b04d3af30722d909961c3f6e2/params.yaml + predictions_file: models/reports/train/f95baa6b04d3af30722d909961c3f6e2/predictions.json + probabilities_file: models/reports/train/f95baa6b04d3af30722d909961c3f6e2/probabilities.json + score_dict_file: models/reports/train/f95baa6b04d3af30722d909961c3f6e2/score_dict.json + test_labels_file: models/reports/train/f95baa6b04d3af30722d909961c3f6e2/test_labels.json + train_labels_file: models/reports/train/f95baa6b04d3af30722d909961c3f6e2/train_labels.json + model_dir: models + name: f95baa6b04d3af30722d909961c3f6e2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b1ac88da5ee0f374a5179995ff2cadee + trainer: + kwargs: {} +name: f95baa6b04d3af30722d909961c3f6e2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/fc2958fbad6e7138ac84d5f21b115838/params.yaml b/examples/security/truthseeker/models/reports/train/fc2958fbad6e7138ac84d5f21b115838/params.yaml new file mode 100644 index 00000000..3abc41da --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/fc2958fbad6e7138ac84d5f21b115838/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/fc2958fbad6e7138ac84d5f21b115838/adv_predictions.json + adv_probabilities_file: models/reports/train/fc2958fbad6e7138ac84d5f21b115838/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: models/models/d20e5610f52eed859258dec5206d63e4.pkl + params_file: models/reports/train/fc2958fbad6e7138ac84d5f21b115838/params.yaml + predictions_file: models/reports/train/fc2958fbad6e7138ac84d5f21b115838/predictions.json + probabilities_file: models/reports/train/fc2958fbad6e7138ac84d5f21b115838/probabilities.json + score_dict_file: models/reports/train/fc2958fbad6e7138ac84d5f21b115838/score_dict.json + test_labels_file: models/reports/train/fc2958fbad6e7138ac84d5f21b115838/test_labels.json + train_labels_file: models/reports/train/fc2958fbad6e7138ac84d5f21b115838/train_labels.json + model_dir: models + name: fc2958fbad6e7138ac84d5f21b115838 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cb2c7f0170d56dd9751aafce0d031b07 + trainer: + kwargs: {} +name: fc2958fbad6e7138ac84d5f21b115838 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/models/reports/train/fec247c792f3097dcb62cc4dce7c5ac4/params.yaml b/examples/security/truthseeker/models/reports/train/fec247c792f3097dcb62cc4dce7c5ac4/params.yaml new file mode 100644 index 00000000..27012d78 --- /dev/null +++ b/examples/security/truthseeker/models/reports/train/fec247c792f3097dcb62cc4dce7c5ac4/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: models/reports/train/fec247c792f3097dcb62cc4dce7c5ac4/adv_predictions.json + adv_probabilities_file: models/reports/train/fec247c792f3097dcb62cc4dce7c5ac4/adv_probabilities.json + attack_file: models/attacks/attack.pkl + data_file: models/data/3215b3a3af8e08a80db4f8db75618bd6.pkl + model_file: models/models/055071adc2a7635662ca5454a2f75630.pkl + params_file: models/reports/train/fec247c792f3097dcb62cc4dce7c5ac4/params.yaml + predictions_file: models/reports/train/fec247c792f3097dcb62cc4dce7c5ac4/predictions.json + probabilities_file: models/reports/train/fec247c792f3097dcb62cc4dce7c5ac4/probabilities.json + score_dict_file: models/reports/train/fec247c792f3097dcb62cc4dce7c5ac4/score_dict.json + test_labels_file: models/reports/train/fec247c792f3097dcb62cc4dce7c5ac4/test_labels.json + train_labels_file: models/reports/train/fec247c792f3097dcb62cc4dce7c5ac4/train_labels.json + model_dir: models + name: fec247c792f3097dcb62cc4dce7c5ac4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: '' + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2dd8e2b32ef68137163fc8efba4ee6a1 + trainer: + kwargs: {} +name: fec247c792f3097dcb62cc4dce7c5ac4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-06-30/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-06-30/optimization_results.yaml new file mode 100644 index 00000000..e5dd8bc8 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-06-30/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 1 +best_value: -10000000000.0 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-06-56/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-06-56/optimization_results.yaml new file mode 100644 index 00000000..bf491f56 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-06-56/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + model.init.C: 10 +best_value: -10000000000.0 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-07-43/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-07-43/optimization_results.yaml new file mode 100644 index 00000000..67f2771c --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-07-43/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 0.001 + +model.init.degree: 3 + model.init.C: 10 +best_value: -10000000000.0 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-09-41/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-09-41/optimization_results.yaml new file mode 100644 index 00000000..ed21ab1a --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-09-41/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 10000 +best_value: -10000000000.0 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-10-09/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-10-09/optimization_results.yaml new file mode 100644 index 00000000..e0fc3fb3 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-10-09/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + model.init.C: 0.01 +best_value: -10000000000.0 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-13-00/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-13-00/optimization_results.yaml new file mode 100644 index 00000000..d5d4214a --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-13-00/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 10000 +best_value: 0.556 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-13-25/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-13-25/optimization_results.yaml new file mode 100644 index 00000000..9785c939 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-13-25/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 100 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-14-12/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-14-12/optimization_results.yaml new file mode 100644 index 00000000..87aae5e4 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-14-12/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 0.01 + +model.init.degree: 1 + model.init.C: 100 +best_value: 0.557 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-15-46/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-15-46/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-15-46/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-16-17/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-16-17/optimization_results.yaml new file mode 100644 index 00000000..173df743 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-16-17/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-17-02/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-17-02/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-17-02/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-18-03/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-18-03/optimization_results.yaml new file mode 100644 index 00000000..88d6984a --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-18-03/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 1 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.1 + ++attack.init.max_iter: 10 + ++attack.init.norm: .inf +best_value: 0.2 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-19-00/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-19-00/optimization_results.yaml new file mode 100644 index 00000000..7838df53 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-19-00/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 10 + ++attack.init.eps: 0.5 + ++attack.init.eps_step: 0.3 + ++attack.init.max_iter: 100 + ++attack.init.norm: .inf +best_value: 0.3 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-19-52/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-19-52/optimization_results.yaml new file mode 100644 index 00000000..fa678a91 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-19-52/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 100 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.5 + ++attack.init.max_iter: 10 + ++attack.init.norm: .inf +best_value: 0.0 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-37-26/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-37-26/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-37-26/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-37-50/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-37-50/optimization_results.yaml new file mode 100644 index 00000000..173df743 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-37-50/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-38-33/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-38-33/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-38-33/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-39-31/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-39-31/optimization_results.yaml new file mode 100644 index 00000000..88d6984a --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-39-31/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 1 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.1 + ++attack.init.max_iter: 10 + ++attack.init.norm: .inf +best_value: 0.2 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-40-21/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-40-21/optimization_results.yaml new file mode 100644 index 00000000..7838df53 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-40-21/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 10 + ++attack.init.eps: 0.5 + ++attack.init.eps_step: 0.3 + ++attack.init.max_iter: 100 + ++attack.init.norm: .inf +best_value: 0.3 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-41-15/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-41-15/optimization_results.yaml new file mode 100644 index 00000000..fa678a91 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-41-15/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 100 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.5 + ++attack.init.max_iter: 10 + ++attack.init.norm: .inf +best_value: 0.0 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-45-23/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-45-23/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-45-23/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-45-48/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-45-48/optimization_results.yaml new file mode 100644 index 00000000..173df743 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-45-48/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-46-33/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-46-33/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-46-33/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-47-32/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-47-32/optimization_results.yaml new file mode 100644 index 00000000..88d6984a --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-47-32/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 1 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.1 + ++attack.init.max_iter: 10 + ++attack.init.norm: .inf +best_value: 0.2 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-48-22/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-48-22/optimization_results.yaml new file mode 100644 index 00000000..30332364 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-48-22/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 50 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.1 + ++attack.init.max_iter: 100 + ++attack.init.norm: .inf +best_value: 0.1 diff --git a/examples/security/truthseeker/multirun/2023-10-14/18-49-12/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/18-49-12/optimization_results.yaml new file mode 100644 index 00000000..fa678a91 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/18-49-12/optimization_results.yaml @@ -0,0 +1,8 @@ +name: optuna +best_params: + ++attack.init.batch_size: 100 + ++attack.init.eps: 1 + ++attack.init.eps_step: 0.5 + ++attack.init.max_iter: 10 + ++attack.init.norm: .inf +best_value: 0.0 diff --git a/examples/security/truthseeker/multirun/2023-10-14/19-21-16/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/19-21-16/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/19-21-16/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/truthseeker/multirun/2023-10-14/19-21-43/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/19-21-43/optimization_results.yaml new file mode 100644 index 00000000..173df743 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/19-21-43/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/truthseeker/multirun/2023-10-14/19-22-32/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/19-22-32/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/19-22-32/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/truthseeker/multirun/2023-10-14/19-30-32/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/19-30-32/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/19-30-32/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/truthseeker/multirun/2023-10-14/19-31-00/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/19-31-00/optimization_results.yaml new file mode 100644 index 00000000..173df743 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/19-31-00/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/truthseeker/multirun/2023-10-14/19-31-48/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/19-31-48/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/19-31-48/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/truthseeker/multirun/2023-10-14/19-36-28/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/19-36-28/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/19-36-28/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/truthseeker/multirun/2023-10-14/19-36-54/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/19-36-54/optimization_results.yaml new file mode 100644 index 00000000..173df743 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/19-36-54/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/truthseeker/multirun/2023-10-14/19-37-40/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/19-37-40/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/19-37-40/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/truthseeker/multirun/2023-10-14/23-08-02/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/23-08-02/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/23-08-02/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/truthseeker/multirun/2023-10-14/23-08-24/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/23-08-24/optimization_results.yaml new file mode 100644 index 00000000..173df743 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/23-08-24/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/truthseeker/multirun/2023-10-14/23-09-06/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/23-09-06/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/23-09-06/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/truthseeker/multirun/2023-10-14/23-15-38/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/23-15-38/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/23-15-38/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/truthseeker/multirun/2023-10-14/23-16-01/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/23-16-01/optimization_results.yaml new file mode 100644 index 00000000..173df743 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/23-16-01/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/truthseeker/multirun/2023-10-14/23-16-41/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/23-16-41/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/23-16-41/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/truthseeker/multirun/2023-10-14/23-34-04/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/23-34-04/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/23-34-04/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/truthseeker/multirun/2023-10-14/23-34-26/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/23-34-26/optimization_results.yaml new file mode 100644 index 00000000..173df743 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/23-34-26/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/truthseeker/multirun/2023-10-14/23-35-06/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/23-35-06/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/23-35-06/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/security/truthseeker/multirun/2023-10-14/23-41-47/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/23-41-47/optimization_results.yaml new file mode 100644 index 00000000..441da37e --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/23-41-47/optimization_results.yaml @@ -0,0 +1,4 @@ +name: optuna +best_params: + model.init.C: 0.1 +best_value: 0.745 diff --git a/examples/security/truthseeker/multirun/2023-10-14/23-42-08/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/23-42-08/optimization_results.yaml new file mode 100644 index 00000000..173df743 --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/23-42-08/optimization_results.yaml @@ -0,0 +1,5 @@ +name: optuna +best_params: + +model.init.coef0: 0.001 + model.init.C: 10000 +best_value: 0.97 diff --git a/examples/security/truthseeker/multirun/2023-10-14/23-42-49/optimization_results.yaml b/examples/security/truthseeker/multirun/2023-10-14/23-42-49/optimization_results.yaml new file mode 100644 index 00000000..a2235daa --- /dev/null +++ b/examples/security/truthseeker/multirun/2023-10-14/23-42-49/optimization_results.yaml @@ -0,0 +1,6 @@ +name: optuna +best_params: + +model.init.coef0: 10000 + +model.init.degree: 2 + model.init.C: 0.001 +best_value: 0.857 diff --git a/examples/truthseeker/other_data.sh b/examples/security/truthseeker/other_data.sh similarity index 100% rename from examples/truthseeker/other_data.sh rename to examples/security/truthseeker/other_data.sh diff --git a/examples/security/truthseeker/output/reports/attack/03db035a26848fb76b798944069218c0/params.yaml b/examples/security/truthseeker/output/reports/attack/03db035a26848fb76b798944069218c0/params.yaml new file mode 100644 index 00000000..b083d7ce --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/03db035a26848fb76b798944069218c0/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 42f73c7e3377fc1af51951d27606b1d7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/03db035a26848fb76b798944069218c0/adv_predictions.json + adv_probabilities_file: output/reports/attack/03db035a26848fb76b798944069218c0/adv_probabilities.json + attack_file: output/attacks/8a793f3ea48309c01e0fb9e61186d594.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/03db035a26848fb76b798944069218c0/params.yaml + predictions_file: output/reports/attack/03db035a26848fb76b798944069218c0/predictions.json + probabilities_file: output/reports/attack/03db035a26848fb76b798944069218c0/probabilities.json + score_dict_file: output/reports/attack/03db035a26848fb76b798944069218c0/score_dict.json + test_labels_file: output/reports/attack/03db035a26848fb76b798944069218c0/test_labels.json + train_labels_file: output/reports/attack/03db035a26848fb76b798944069218c0/train_labels.json + model_dir: models + name: 03db035a26848fb76b798944069218c0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 03db035a26848fb76b798944069218c0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/04c1b805453355560606a70a7e4401ec/params.yaml b/examples/security/truthseeker/output/reports/attack/04c1b805453355560606a70a7e4401ec/params.yaml new file mode 100644 index 00000000..d8dd4da5 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/04c1b805453355560606a70a7e4401ec/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.01 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: e7cbca7ebb1551c54d51d003a5f3dfd1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/04c1b805453355560606a70a7e4401ec/adv_predictions.json + adv_probabilities_file: output/reports/attack/04c1b805453355560606a70a7e4401ec/adv_probabilities.json + attack_file: output/attacks/398d7d06d339d19ba3716d3eb5489542.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/04c1b805453355560606a70a7e4401ec/params.yaml + predictions_file: output/reports/attack/04c1b805453355560606a70a7e4401ec/predictions.json + probabilities_file: output/reports/attack/04c1b805453355560606a70a7e4401ec/probabilities.json + score_dict_file: output/reports/attack/04c1b805453355560606a70a7e4401ec/score_dict.json + test_labels_file: output/reports/attack/04c1b805453355560606a70a7e4401ec/test_labels.json + train_labels_file: output/reports/attack/04c1b805453355560606a70a7e4401ec/train_labels.json + model_dir: models + name: 04c1b805453355560606a70a7e4401ec + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 04c1b805453355560606a70a7e4401ec +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/04db388482c6eea6374c260226431135/params.yaml b/examples/security/truthseeker/output/reports/attack/04db388482c6eea6374c260226431135/params.yaml new file mode 100644 index 00000000..085362ac --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/04db388482c6eea6374c260226431135/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.001 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 670558a3d0ab27b1c1c427a7d37dbc1f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/04db388482c6eea6374c260226431135/adv_predictions.json + adv_probabilities_file: output/reports/attack/04db388482c6eea6374c260226431135/adv_probabilities.json + attack_file: output/attacks/2e4dffd59fa4d995c3a80fd519b3cc69.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/04db388482c6eea6374c260226431135/params.yaml + predictions_file: output/reports/attack/04db388482c6eea6374c260226431135/predictions.json + probabilities_file: output/reports/attack/04db388482c6eea6374c260226431135/probabilities.json + score_dict_file: output/reports/attack/04db388482c6eea6374c260226431135/score_dict.json + test_labels_file: output/reports/attack/04db388482c6eea6374c260226431135/test_labels.json + train_labels_file: output/reports/attack/04db388482c6eea6374c260226431135/train_labels.json + model_dir: models + name: 04db388482c6eea6374c260226431135 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 04db388482c6eea6374c260226431135 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/0729bb76cb487b84aa7d4b92f2053a09/params.yaml b/examples/security/truthseeker/output/reports/attack/0729bb76cb487b84aa7d4b92f2053a09/params.yaml new file mode 100644 index 00000000..ad5a9d2a --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/0729bb76cb487b84aa7d4b92f2053a09/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.01 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: f31a11de5993033735eb67537f56e87b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0729bb76cb487b84aa7d4b92f2053a09/adv_predictions.json + adv_probabilities_file: output/reports/attack/0729bb76cb487b84aa7d4b92f2053a09/adv_probabilities.json + attack_file: output/attacks/36c180b15bd756b119c055451699a831.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/0729bb76cb487b84aa7d4b92f2053a09/params.yaml + predictions_file: output/reports/attack/0729bb76cb487b84aa7d4b92f2053a09/predictions.json + probabilities_file: output/reports/attack/0729bb76cb487b84aa7d4b92f2053a09/probabilities.json + score_dict_file: output/reports/attack/0729bb76cb487b84aa7d4b92f2053a09/score_dict.json + test_labels_file: output/reports/attack/0729bb76cb487b84aa7d4b92f2053a09/test_labels.json + train_labels_file: output/reports/attack/0729bb76cb487b84aa7d4b92f2053a09/train_labels.json + model_dir: models + name: 0729bb76cb487b84aa7d4b92f2053a09 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 0729bb76cb487b84aa7d4b92f2053a09 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/0909923a9f3ab71ffe6f6b53b068ab1f/params.yaml b/examples/security/truthseeker/output/reports/attack/0909923a9f3ab71ffe6f6b53b068ab1f/params.yaml new file mode 100644 index 00000000..2c05190b --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/0909923a9f3ab71ffe6f6b53b068ab1f/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: a436b3ec79ed4772692dd61caa904f23 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0909923a9f3ab71ffe6f6b53b068ab1f/adv_predictions.json + adv_probabilities_file: output/reports/attack/0909923a9f3ab71ffe6f6b53b068ab1f/adv_probabilities.json + attack_file: output/attacks/f14d72ac4e67b6d9e279417986463f62.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/0909923a9f3ab71ffe6f6b53b068ab1f/params.yaml + predictions_file: output/reports/attack/0909923a9f3ab71ffe6f6b53b068ab1f/predictions.json + probabilities_file: output/reports/attack/0909923a9f3ab71ffe6f6b53b068ab1f/probabilities.json + score_dict_file: output/reports/attack/0909923a9f3ab71ffe6f6b53b068ab1f/score_dict.json + test_labels_file: output/reports/attack/0909923a9f3ab71ffe6f6b53b068ab1f/test_labels.json + train_labels_file: output/reports/attack/0909923a9f3ab71ffe6f6b53b068ab1f/train_labels.json + model_dir: models + name: 0909923a9f3ab71ffe6f6b53b068ab1f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 0909923a9f3ab71ffe6f6b53b068ab1f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/0a992a4ac32cc31be87641fe61a3f9a9/params.yaml b/examples/security/truthseeker/output/reports/attack/0a992a4ac32cc31be87641fe61a3f9a9/params.yaml new file mode 100644 index 00000000..36f508ad --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/0a992a4ac32cc31be87641fe61a3f9a9/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.3 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: ae76535cf09525f6dd2c65680a796ccd +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0a992a4ac32cc31be87641fe61a3f9a9/adv_predictions.json + adv_probabilities_file: output/reports/attack/0a992a4ac32cc31be87641fe61a3f9a9/adv_probabilities.json + attack_file: output/attacks/8f835dc1f1dc96ef89cb83f19af481ec.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/0a992a4ac32cc31be87641fe61a3f9a9/params.yaml + predictions_file: output/reports/attack/0a992a4ac32cc31be87641fe61a3f9a9/predictions.json + probabilities_file: output/reports/attack/0a992a4ac32cc31be87641fe61a3f9a9/probabilities.json + score_dict_file: output/reports/attack/0a992a4ac32cc31be87641fe61a3f9a9/score_dict.json + test_labels_file: output/reports/attack/0a992a4ac32cc31be87641fe61a3f9a9/test_labels.json + train_labels_file: output/reports/attack/0a992a4ac32cc31be87641fe61a3f9a9/train_labels.json + model_dir: models + name: 0a992a4ac32cc31be87641fe61a3f9a9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 0a992a4ac32cc31be87641fe61a3f9a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/0d3c9403a090b7ad6a6021e18df00962/params.yaml b/examples/security/truthseeker/output/reports/attack/0d3c9403a090b7ad6a6021e18df00962/params.yaml new file mode 100644 index 00000000..7c63ca07 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/0d3c9403a090b7ad6a6021e18df00962/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.5 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 259b78124a0437ed3c0e94ece2512bef +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/0d3c9403a090b7ad6a6021e18df00962/adv_predictions.json + adv_probabilities_file: output/reports/attack/0d3c9403a090b7ad6a6021e18df00962/adv_probabilities.json + attack_file: output/attacks/9039e05a8d5f2a8f92198ac21295c910.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/0d3c9403a090b7ad6a6021e18df00962/params.yaml + predictions_file: output/reports/attack/0d3c9403a090b7ad6a6021e18df00962/predictions.json + probabilities_file: output/reports/attack/0d3c9403a090b7ad6a6021e18df00962/probabilities.json + score_dict_file: output/reports/attack/0d3c9403a090b7ad6a6021e18df00962/score_dict.json + test_labels_file: output/reports/attack/0d3c9403a090b7ad6a6021e18df00962/test_labels.json + train_labels_file: output/reports/attack/0d3c9403a090b7ad6a6021e18df00962/train_labels.json + model_dir: models + name: 0d3c9403a090b7ad6a6021e18df00962 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 0d3c9403a090b7ad6a6021e18df00962 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/13161ad1c5ad43aede1b4d106e806272/params.yaml b/examples/security/truthseeker/output/reports/attack/13161ad1c5ad43aede1b4d106e806272/params.yaml new file mode 100644 index 00000000..175f669d --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/13161ad1c5ad43aede1b4d106e806272/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 7bab64db4c6922affa6b12d43da7d400 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/13161ad1c5ad43aede1b4d106e806272/adv_predictions.json + adv_probabilities_file: output/reports/attack/13161ad1c5ad43aede1b4d106e806272/adv_probabilities.json + attack_file: output/attacks/d9c5100e84606dfe38103d216659d13c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/13161ad1c5ad43aede1b4d106e806272/params.yaml + predictions_file: output/reports/attack/13161ad1c5ad43aede1b4d106e806272/predictions.json + probabilities_file: output/reports/attack/13161ad1c5ad43aede1b4d106e806272/probabilities.json + score_dict_file: output/reports/attack/13161ad1c5ad43aede1b4d106e806272/score_dict.json + test_labels_file: output/reports/attack/13161ad1c5ad43aede1b4d106e806272/test_labels.json + train_labels_file: output/reports/attack/13161ad1c5ad43aede1b4d106e806272/train_labels.json + model_dir: models + name: 13161ad1c5ad43aede1b4d106e806272 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 13161ad1c5ad43aede1b4d106e806272 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/131bbf480afbbcba007a07b0cba6bcfd/params.yaml b/examples/security/truthseeker/output/reports/attack/131bbf480afbbcba007a07b0cba6bcfd/params.yaml new file mode 100644 index 00000000..a5911cf2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/131bbf480afbbcba007a07b0cba6bcfd/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 0446a73b8ab0c8dceabe6d36bf82737b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/131bbf480afbbcba007a07b0cba6bcfd/adv_predictions.json + adv_probabilities_file: output/reports/attack/131bbf480afbbcba007a07b0cba6bcfd/adv_probabilities.json + attack_file: output/attacks/2aa94b57a8152b7beca16f89328e1bb0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/131bbf480afbbcba007a07b0cba6bcfd/params.yaml + predictions_file: output/reports/attack/131bbf480afbbcba007a07b0cba6bcfd/predictions.json + probabilities_file: output/reports/attack/131bbf480afbbcba007a07b0cba6bcfd/probabilities.json + score_dict_file: output/reports/attack/131bbf480afbbcba007a07b0cba6bcfd/score_dict.json + test_labels_file: output/reports/attack/131bbf480afbbcba007a07b0cba6bcfd/test_labels.json + train_labels_file: output/reports/attack/131bbf480afbbcba007a07b0cba6bcfd/train_labels.json + model_dir: models + name: 131bbf480afbbcba007a07b0cba6bcfd + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 131bbf480afbbcba007a07b0cba6bcfd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/params.yaml b/examples/security/truthseeker/output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/params.yaml new file mode 100644 index 00000000..2f6f3354 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: cc5584361e821cb4d03df0625df58a65 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/adv_predictions.json + adv_probabilities_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/adv_probabilities.json + attack_file: output/attacks/8320bfd18965f34983368925c17c18e8.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/params.yaml + predictions_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/predictions.json + probabilities_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/probabilities.json + score_dict_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/score_dict.json + test_labels_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/test_labels.json + train_labels_file: output/reports/attack/1362bddc676c1f49785f71fb83dac5f9/train_labels.json + model_dir: models + name: 1362bddc676c1f49785f71fb83dac5f9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 1362bddc676c1f49785f71fb83dac5f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/1a4a0b27b85dd351168e7b4d134e32d6/params.yaml b/examples/security/truthseeker/output/reports/attack/1a4a0b27b85dd351168e7b4d134e32d6/params.yaml new file mode 100644 index 00000000..4bc60857 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/1a4a0b27b85dd351168e7b4d134e32d6/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.001 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 64cbe70351ea4127c5d5628656815421 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1a4a0b27b85dd351168e7b4d134e32d6/adv_predictions.json + adv_probabilities_file: output/reports/attack/1a4a0b27b85dd351168e7b4d134e32d6/adv_probabilities.json + attack_file: output/attacks/d0e28db18b904808d0c31f6eb8545ba4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/1a4a0b27b85dd351168e7b4d134e32d6/params.yaml + predictions_file: output/reports/attack/1a4a0b27b85dd351168e7b4d134e32d6/predictions.json + probabilities_file: output/reports/attack/1a4a0b27b85dd351168e7b4d134e32d6/probabilities.json + score_dict_file: output/reports/attack/1a4a0b27b85dd351168e7b4d134e32d6/score_dict.json + test_labels_file: output/reports/attack/1a4a0b27b85dd351168e7b4d134e32d6/test_labels.json + train_labels_file: output/reports/attack/1a4a0b27b85dd351168e7b4d134e32d6/train_labels.json + model_dir: models + name: 1a4a0b27b85dd351168e7b4d134e32d6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 1a4a0b27b85dd351168e7b4d134e32d6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/1b1cc9ee5402526eb78188db792ffab0/params.yaml b/examples/security/truthseeker/output/reports/attack/1b1cc9ee5402526eb78188db792ffab0/params.yaml new file mode 100644 index 00000000..0fc2e86f --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/1b1cc9ee5402526eb78188db792ffab0/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.3 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 3762419e14ea1af2b08233211f722e3e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1b1cc9ee5402526eb78188db792ffab0/adv_predictions.json + adv_probabilities_file: output/reports/attack/1b1cc9ee5402526eb78188db792ffab0/adv_probabilities.json + attack_file: output/attacks/7b2ff75b050c0e8f46720142b4107e54.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/1b1cc9ee5402526eb78188db792ffab0/params.yaml + predictions_file: output/reports/attack/1b1cc9ee5402526eb78188db792ffab0/predictions.json + probabilities_file: output/reports/attack/1b1cc9ee5402526eb78188db792ffab0/probabilities.json + score_dict_file: output/reports/attack/1b1cc9ee5402526eb78188db792ffab0/score_dict.json + test_labels_file: output/reports/attack/1b1cc9ee5402526eb78188db792ffab0/test_labels.json + train_labels_file: output/reports/attack/1b1cc9ee5402526eb78188db792ffab0/train_labels.json + model_dir: models + name: 1b1cc9ee5402526eb78188db792ffab0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 1b1cc9ee5402526eb78188db792ffab0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/1bd2197229552fec748f6941677f3dab/params.yaml b/examples/security/truthseeker/output/reports/attack/1bd2197229552fec748f6941677f3dab/params.yaml new file mode 100644 index 00000000..fe9d9043 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/1bd2197229552fec748f6941677f3dab/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.01 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: a0c60cc5539d03f8ae69561db5d16fe3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1bd2197229552fec748f6941677f3dab/adv_predictions.json + adv_probabilities_file: output/reports/attack/1bd2197229552fec748f6941677f3dab/adv_probabilities.json + attack_file: output/attacks/6e0ec5639bff2058612e48524dda0dc2.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/1bd2197229552fec748f6941677f3dab/params.yaml + predictions_file: output/reports/attack/1bd2197229552fec748f6941677f3dab/predictions.json + probabilities_file: output/reports/attack/1bd2197229552fec748f6941677f3dab/probabilities.json + score_dict_file: output/reports/attack/1bd2197229552fec748f6941677f3dab/score_dict.json + test_labels_file: output/reports/attack/1bd2197229552fec748f6941677f3dab/test_labels.json + train_labels_file: output/reports/attack/1bd2197229552fec748f6941677f3dab/train_labels.json + model_dir: models + name: 1bd2197229552fec748f6941677f3dab + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 1bd2197229552fec748f6941677f3dab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/1d257638f9a27451cf3ff7906e4045e7/params.yaml b/examples/security/truthseeker/output/reports/attack/1d257638f9a27451cf3ff7906e4045e7/params.yaml new file mode 100644 index 00000000..bfaea9c0 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/1d257638f9a27451cf3ff7906e4045e7/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 5939ca4eed55980eece8b0dbfdd99311 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1d257638f9a27451cf3ff7906e4045e7/adv_predictions.json + adv_probabilities_file: output/reports/attack/1d257638f9a27451cf3ff7906e4045e7/adv_probabilities.json + attack_file: output/attacks/cabb7342452cf59dfd19a3bbd6bdb64d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/1d257638f9a27451cf3ff7906e4045e7/params.yaml + predictions_file: output/reports/attack/1d257638f9a27451cf3ff7906e4045e7/predictions.json + probabilities_file: output/reports/attack/1d257638f9a27451cf3ff7906e4045e7/probabilities.json + score_dict_file: output/reports/attack/1d257638f9a27451cf3ff7906e4045e7/score_dict.json + test_labels_file: output/reports/attack/1d257638f9a27451cf3ff7906e4045e7/test_labels.json + train_labels_file: output/reports/attack/1d257638f9a27451cf3ff7906e4045e7/train_labels.json + model_dir: models + name: 1d257638f9a27451cf3ff7906e4045e7 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 1d257638f9a27451cf3ff7906e4045e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/1e3605d6ec8fa5d998b8986aab198994/params.yaml b/examples/security/truthseeker/output/reports/attack/1e3605d6ec8fa5d998b8986aab198994/params.yaml new file mode 100644 index 00000000..62686961 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/1e3605d6ec8fa5d998b8986aab198994/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 204d5b76672fb8bd62cd8f5d9cb78f87 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/1e3605d6ec8fa5d998b8986aab198994/adv_predictions.json + adv_probabilities_file: output/reports/attack/1e3605d6ec8fa5d998b8986aab198994/adv_probabilities.json + attack_file: output/attacks/eb2a78fc42fb01540bfa4f1f4d63e2d4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/1e3605d6ec8fa5d998b8986aab198994/params.yaml + predictions_file: output/reports/attack/1e3605d6ec8fa5d998b8986aab198994/predictions.json + probabilities_file: output/reports/attack/1e3605d6ec8fa5d998b8986aab198994/probabilities.json + score_dict_file: output/reports/attack/1e3605d6ec8fa5d998b8986aab198994/score_dict.json + test_labels_file: output/reports/attack/1e3605d6ec8fa5d998b8986aab198994/test_labels.json + train_labels_file: output/reports/attack/1e3605d6ec8fa5d998b8986aab198994/train_labels.json + model_dir: models + name: 1e3605d6ec8fa5d998b8986aab198994 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 1e3605d6ec8fa5d998b8986aab198994 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/20e00a3faaaebee89378166e9a31b659/params.yaml b/examples/security/truthseeker/output/reports/attack/20e00a3faaaebee89378166e9a31b659/params.yaml new file mode 100644 index 00000000..b1e3b9bd --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/20e00a3faaaebee89378166e9a31b659/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.001 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: e10f701d6dedd8289efbb72fa6fa7916 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/20e00a3faaaebee89378166e9a31b659/adv_predictions.json + adv_probabilities_file: output/reports/attack/20e00a3faaaebee89378166e9a31b659/adv_probabilities.json + attack_file: output/attacks/7c210fc39ff3c7afcd4f96fc9e3b154f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/20e00a3faaaebee89378166e9a31b659/params.yaml + predictions_file: output/reports/attack/20e00a3faaaebee89378166e9a31b659/predictions.json + probabilities_file: output/reports/attack/20e00a3faaaebee89378166e9a31b659/probabilities.json + score_dict_file: output/reports/attack/20e00a3faaaebee89378166e9a31b659/score_dict.json + test_labels_file: output/reports/attack/20e00a3faaaebee89378166e9a31b659/test_labels.json + train_labels_file: output/reports/attack/20e00a3faaaebee89378166e9a31b659/train_labels.json + model_dir: models + name: 20e00a3faaaebee89378166e9a31b659 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 20e00a3faaaebee89378166e9a31b659 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/21ed03382908f7cb125596ca6b0eb3db/params.yaml b/examples/security/truthseeker/output/reports/attack/21ed03382908f7cb125596ca6b0eb3db/params.yaml new file mode 100644 index 00000000..e876fe75 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/21ed03382908f7cb125596ca6b0eb3db/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 0851d10a0cb79230fe5458c8148c9f45 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/21ed03382908f7cb125596ca6b0eb3db/adv_predictions.json + adv_probabilities_file: output/reports/attack/21ed03382908f7cb125596ca6b0eb3db/adv_probabilities.json + attack_file: output/attacks/2dd10a9521ef94cb9593c0712cc73779.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/21ed03382908f7cb125596ca6b0eb3db/params.yaml + predictions_file: output/reports/attack/21ed03382908f7cb125596ca6b0eb3db/predictions.json + probabilities_file: output/reports/attack/21ed03382908f7cb125596ca6b0eb3db/probabilities.json + score_dict_file: output/reports/attack/21ed03382908f7cb125596ca6b0eb3db/score_dict.json + test_labels_file: output/reports/attack/21ed03382908f7cb125596ca6b0eb3db/test_labels.json + train_labels_file: output/reports/attack/21ed03382908f7cb125596ca6b0eb3db/train_labels.json + model_dir: models + name: 21ed03382908f7cb125596ca6b0eb3db + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 21ed03382908f7cb125596ca6b0eb3db +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/263182b6067f041407c0b17519d1a159/params.yaml b/examples/security/truthseeker/output/reports/attack/263182b6067f041407c0b17519d1a159/params.yaml new file mode 100644 index 00000000..47bbbb5e --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/263182b6067f041407c0b17519d1a159/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: a7b947dc2f1602340415bbfe3db6bfe9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/263182b6067f041407c0b17519d1a159/adv_predictions.json + adv_probabilities_file: output/reports/attack/263182b6067f041407c0b17519d1a159/adv_probabilities.json + attack_file: output/attacks/bedd6a4d7f13f0e56f16729d531c9775.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/263182b6067f041407c0b17519d1a159/params.yaml + predictions_file: output/reports/attack/263182b6067f041407c0b17519d1a159/predictions.json + probabilities_file: output/reports/attack/263182b6067f041407c0b17519d1a159/probabilities.json + score_dict_file: output/reports/attack/263182b6067f041407c0b17519d1a159/score_dict.json + test_labels_file: output/reports/attack/263182b6067f041407c0b17519d1a159/test_labels.json + train_labels_file: output/reports/attack/263182b6067f041407c0b17519d1a159/train_labels.json + model_dir: models + name: 263182b6067f041407c0b17519d1a159 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 263182b6067f041407c0b17519d1a159 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/269890c7e5933983994b299145b47593/params.yaml b/examples/security/truthseeker/output/reports/attack/269890c7e5933983994b299145b47593/params.yaml new file mode 100644 index 00000000..f94e8f8f --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/269890c7e5933983994b299145b47593/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 405c9c1d1f50d806a47b590af83b33ac +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/269890c7e5933983994b299145b47593/adv_predictions.json + adv_probabilities_file: output/reports/attack/269890c7e5933983994b299145b47593/adv_probabilities.json + attack_file: output/attacks/61ae30326fe9c2f6bbd6661264f3a656.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/269890c7e5933983994b299145b47593/params.yaml + predictions_file: output/reports/attack/269890c7e5933983994b299145b47593/predictions.json + probabilities_file: output/reports/attack/269890c7e5933983994b299145b47593/probabilities.json + score_dict_file: output/reports/attack/269890c7e5933983994b299145b47593/score_dict.json + test_labels_file: output/reports/attack/269890c7e5933983994b299145b47593/test_labels.json + train_labels_file: output/reports/attack/269890c7e5933983994b299145b47593/train_labels.json + model_dir: models + name: 269890c7e5933983994b299145b47593 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 269890c7e5933983994b299145b47593 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/29177d957443346a252676df965f016b/params.yaml b/examples/security/truthseeker/output/reports/attack/29177d957443346a252676df965f016b/params.yaml new file mode 100644 index 00000000..d78977cb --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/29177d957443346a252676df965f016b/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.5 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 5406999559dc5eda61df355cc9c644cb +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/29177d957443346a252676df965f016b/adv_predictions.json + adv_probabilities_file: output/reports/attack/29177d957443346a252676df965f016b/adv_probabilities.json + attack_file: output/attacks/ba1ba7a069498e296d8fc69b29a05171.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/29177d957443346a252676df965f016b/params.yaml + predictions_file: output/reports/attack/29177d957443346a252676df965f016b/predictions.json + probabilities_file: output/reports/attack/29177d957443346a252676df965f016b/probabilities.json + score_dict_file: output/reports/attack/29177d957443346a252676df965f016b/score_dict.json + test_labels_file: output/reports/attack/29177d957443346a252676df965f016b/test_labels.json + train_labels_file: output/reports/attack/29177d957443346a252676df965f016b/train_labels.json + model_dir: models + name: 29177d957443346a252676df965f016b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 29177d957443346a252676df965f016b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/2aa84bf8eb2a09a8c7c073e84faf242d/params.yaml b/examples/security/truthseeker/output/reports/attack/2aa84bf8eb2a09a8c7c073e84faf242d/params.yaml new file mode 100644 index 00000000..a77df0ba --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/2aa84bf8eb2a09a8c7c073e84faf242d/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 12f39eeee10e401b040717898f0fc77d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2aa84bf8eb2a09a8c7c073e84faf242d/adv_predictions.json + adv_probabilities_file: output/reports/attack/2aa84bf8eb2a09a8c7c073e84faf242d/adv_probabilities.json + attack_file: output/attacks/7bfc9ed33fd8523c0c794ac7bcc6902f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/2aa84bf8eb2a09a8c7c073e84faf242d/params.yaml + predictions_file: output/reports/attack/2aa84bf8eb2a09a8c7c073e84faf242d/predictions.json + probabilities_file: output/reports/attack/2aa84bf8eb2a09a8c7c073e84faf242d/probabilities.json + score_dict_file: output/reports/attack/2aa84bf8eb2a09a8c7c073e84faf242d/score_dict.json + test_labels_file: output/reports/attack/2aa84bf8eb2a09a8c7c073e84faf242d/test_labels.json + train_labels_file: output/reports/attack/2aa84bf8eb2a09a8c7c073e84faf242d/train_labels.json + model_dir: models + name: 2aa84bf8eb2a09a8c7c073e84faf242d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 2aa84bf8eb2a09a8c7c073e84faf242d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/2cf4b12d06c74595367293d6a6837136/params.yaml b/examples/security/truthseeker/output/reports/attack/2cf4b12d06c74595367293d6a6837136/params.yaml new file mode 100644 index 00000000..138c24c5 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/2cf4b12d06c74595367293d6a6837136/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: c2d5cb78eef902ef0d516b09ce20cfc9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2cf4b12d06c74595367293d6a6837136/adv_predictions.json + adv_probabilities_file: output/reports/attack/2cf4b12d06c74595367293d6a6837136/adv_probabilities.json + attack_file: output/attacks/4f2ffd6c708b751989b37b2f24f8c652.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/2cf4b12d06c74595367293d6a6837136/params.yaml + predictions_file: output/reports/attack/2cf4b12d06c74595367293d6a6837136/predictions.json + probabilities_file: output/reports/attack/2cf4b12d06c74595367293d6a6837136/probabilities.json + score_dict_file: output/reports/attack/2cf4b12d06c74595367293d6a6837136/score_dict.json + test_labels_file: output/reports/attack/2cf4b12d06c74595367293d6a6837136/test_labels.json + train_labels_file: output/reports/attack/2cf4b12d06c74595367293d6a6837136/train_labels.json + model_dir: models + name: 2cf4b12d06c74595367293d6a6837136 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 2cf4b12d06c74595367293d6a6837136 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/2e2583456f2a5bedc5a59d23ac982964/params.yaml b/examples/security/truthseeker/output/reports/attack/2e2583456f2a5bedc5a59d23ac982964/params.yaml new file mode 100644 index 00000000..673e771d --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/2e2583456f2a5bedc5a59d23ac982964/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.5 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 720403802f3d42f5c71a8d81ecb9be62 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2e2583456f2a5bedc5a59d23ac982964/adv_predictions.json + adv_probabilities_file: output/reports/attack/2e2583456f2a5bedc5a59d23ac982964/adv_probabilities.json + attack_file: output/attacks/120abd387d9e8b04152cb8c3d9676d87.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/2e2583456f2a5bedc5a59d23ac982964/params.yaml + predictions_file: output/reports/attack/2e2583456f2a5bedc5a59d23ac982964/predictions.json + probabilities_file: output/reports/attack/2e2583456f2a5bedc5a59d23ac982964/probabilities.json + score_dict_file: output/reports/attack/2e2583456f2a5bedc5a59d23ac982964/score_dict.json + test_labels_file: output/reports/attack/2e2583456f2a5bedc5a59d23ac982964/test_labels.json + train_labels_file: output/reports/attack/2e2583456f2a5bedc5a59d23ac982964/train_labels.json + model_dir: models + name: 2e2583456f2a5bedc5a59d23ac982964 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 2e2583456f2a5bedc5a59d23ac982964 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/2e947a3246b024be66b0b2e83f434b2b/params.yaml b/examples/security/truthseeker/output/reports/attack/2e947a3246b024be66b0b2e83f434b2b/params.yaml new file mode 100644 index 00000000..33269fd2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/2e947a3246b024be66b0b2e83f434b2b/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: f144fd6ddccf343276eea65dae5c5e47 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2e947a3246b024be66b0b2e83f434b2b/adv_predictions.json + adv_probabilities_file: output/reports/attack/2e947a3246b024be66b0b2e83f434b2b/adv_probabilities.json + attack_file: output/attacks/a874ad1db24b3991adc62ab5da5fa7f2.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/2e947a3246b024be66b0b2e83f434b2b/params.yaml + predictions_file: output/reports/attack/2e947a3246b024be66b0b2e83f434b2b/predictions.json + probabilities_file: output/reports/attack/2e947a3246b024be66b0b2e83f434b2b/probabilities.json + score_dict_file: output/reports/attack/2e947a3246b024be66b0b2e83f434b2b/score_dict.json + test_labels_file: output/reports/attack/2e947a3246b024be66b0b2e83f434b2b/test_labels.json + train_labels_file: output/reports/attack/2e947a3246b024be66b0b2e83f434b2b/train_labels.json + model_dir: models + name: 2e947a3246b024be66b0b2e83f434b2b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 2e947a3246b024be66b0b2e83f434b2b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/2eff9ce3f662d9c05d79b529216b111c/params.yaml b/examples/security/truthseeker/output/reports/attack/2eff9ce3f662d9c05d79b529216b111c/params.yaml new file mode 100644 index 00000000..7961be63 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/2eff9ce3f662d9c05d79b529216b111c/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.001 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: f5979b088ed1515651e38eca65f52e02 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2eff9ce3f662d9c05d79b529216b111c/adv_predictions.json + adv_probabilities_file: output/reports/attack/2eff9ce3f662d9c05d79b529216b111c/adv_probabilities.json + attack_file: output/attacks/a103329f5e4aba8d1b4198ab1f0bd9aa.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/2eff9ce3f662d9c05d79b529216b111c/params.yaml + predictions_file: output/reports/attack/2eff9ce3f662d9c05d79b529216b111c/predictions.json + probabilities_file: output/reports/attack/2eff9ce3f662d9c05d79b529216b111c/probabilities.json + score_dict_file: output/reports/attack/2eff9ce3f662d9c05d79b529216b111c/score_dict.json + test_labels_file: output/reports/attack/2eff9ce3f662d9c05d79b529216b111c/test_labels.json + train_labels_file: output/reports/attack/2eff9ce3f662d9c05d79b529216b111c/train_labels.json + model_dir: models + name: 2eff9ce3f662d9c05d79b529216b111c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 2eff9ce3f662d9c05d79b529216b111c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/2fdfbea325585d6da58236ef3a3680ae/params.yaml b/examples/security/truthseeker/output/reports/attack/2fdfbea325585d6da58236ef3a3680ae/params.yaml new file mode 100644 index 00000000..581551dc --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/2fdfbea325585d6da58236ef3a3680ae/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.001 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: e4ce85a4f57c91845f2c2eb21524f891 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/2fdfbea325585d6da58236ef3a3680ae/adv_predictions.json + adv_probabilities_file: output/reports/attack/2fdfbea325585d6da58236ef3a3680ae/adv_probabilities.json + attack_file: output/attacks/5e875a5bb374c8e08f43495197bddf1d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/2fdfbea325585d6da58236ef3a3680ae/params.yaml + predictions_file: output/reports/attack/2fdfbea325585d6da58236ef3a3680ae/predictions.json + probabilities_file: output/reports/attack/2fdfbea325585d6da58236ef3a3680ae/probabilities.json + score_dict_file: output/reports/attack/2fdfbea325585d6da58236ef3a3680ae/score_dict.json + test_labels_file: output/reports/attack/2fdfbea325585d6da58236ef3a3680ae/test_labels.json + train_labels_file: output/reports/attack/2fdfbea325585d6da58236ef3a3680ae/train_labels.json + model_dir: models + name: 2fdfbea325585d6da58236ef3a3680ae + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 2fdfbea325585d6da58236ef3a3680ae +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/30842ef793dc00503c9c390b61012424/params.yaml b/examples/security/truthseeker/output/reports/attack/30842ef793dc00503c9c390b61012424/params.yaml new file mode 100644 index 00000000..09bff6bd --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/30842ef793dc00503c9c390b61012424/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 50960f79999dc1d89d58615e60acd21c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/30842ef793dc00503c9c390b61012424/adv_predictions.json + adv_probabilities_file: output/reports/attack/30842ef793dc00503c9c390b61012424/adv_probabilities.json + attack_file: output/attacks/9a6a834f896f9977dec117f680780479.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/30842ef793dc00503c9c390b61012424/params.yaml + predictions_file: output/reports/attack/30842ef793dc00503c9c390b61012424/predictions.json + probabilities_file: output/reports/attack/30842ef793dc00503c9c390b61012424/probabilities.json + score_dict_file: output/reports/attack/30842ef793dc00503c9c390b61012424/score_dict.json + test_labels_file: output/reports/attack/30842ef793dc00503c9c390b61012424/test_labels.json + train_labels_file: output/reports/attack/30842ef793dc00503c9c390b61012424/train_labels.json + model_dir: models + name: 30842ef793dc00503c9c390b61012424 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 30842ef793dc00503c9c390b61012424 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/318f076caaca5e5b505e7244cfa3833b/params.yaml b/examples/security/truthseeker/output/reports/attack/318f076caaca5e5b505e7244cfa3833b/params.yaml new file mode 100644 index 00000000..7574e69e --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/318f076caaca5e5b505e7244cfa3833b/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 07fc37775784eb0d607860f92e137807 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/318f076caaca5e5b505e7244cfa3833b/adv_predictions.json + adv_probabilities_file: output/reports/attack/318f076caaca5e5b505e7244cfa3833b/adv_probabilities.json + attack_file: output/attacks/652e970070245c922ee921bd8300072b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/318f076caaca5e5b505e7244cfa3833b/params.yaml + predictions_file: output/reports/attack/318f076caaca5e5b505e7244cfa3833b/predictions.json + probabilities_file: output/reports/attack/318f076caaca5e5b505e7244cfa3833b/probabilities.json + score_dict_file: output/reports/attack/318f076caaca5e5b505e7244cfa3833b/score_dict.json + test_labels_file: output/reports/attack/318f076caaca5e5b505e7244cfa3833b/test_labels.json + train_labels_file: output/reports/attack/318f076caaca5e5b505e7244cfa3833b/train_labels.json + model_dir: models + name: 318f076caaca5e5b505e7244cfa3833b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 318f076caaca5e5b505e7244cfa3833b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/330f85312885f525f4338f23f55f9722/params.yaml b/examples/security/truthseeker/output/reports/attack/330f85312885f525f4338f23f55f9722/params.yaml new file mode 100644 index 00000000..55b6d7cb --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/330f85312885f525f4338f23f55f9722/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: da790f10073411090cf866a2f09adb7a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/330f85312885f525f4338f23f55f9722/adv_predictions.json + adv_probabilities_file: output/reports/attack/330f85312885f525f4338f23f55f9722/adv_probabilities.json + attack_file: output/attacks/a5ef294d3d0851d27a8a744ceadc926a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/330f85312885f525f4338f23f55f9722/params.yaml + predictions_file: output/reports/attack/330f85312885f525f4338f23f55f9722/predictions.json + probabilities_file: output/reports/attack/330f85312885f525f4338f23f55f9722/probabilities.json + score_dict_file: output/reports/attack/330f85312885f525f4338f23f55f9722/score_dict.json + test_labels_file: output/reports/attack/330f85312885f525f4338f23f55f9722/test_labels.json + train_labels_file: output/reports/attack/330f85312885f525f4338f23f55f9722/train_labels.json + model_dir: models + name: 330f85312885f525f4338f23f55f9722 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 330f85312885f525f4338f23f55f9722 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/347f6cecab00930a6d6818601559b6f5/params.yaml b/examples/security/truthseeker/output/reports/attack/347f6cecab00930a6d6818601559b6f5/params.yaml new file mode 100644 index 00000000..bf68bf56 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/347f6cecab00930a6d6818601559b6f5/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.5 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 26d054e8ce79c5a910964872461b7647 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/347f6cecab00930a6d6818601559b6f5/adv_predictions.json + adv_probabilities_file: output/reports/attack/347f6cecab00930a6d6818601559b6f5/adv_probabilities.json + attack_file: output/attacks/29ecb25961db2525b47fb5210583a738.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/347f6cecab00930a6d6818601559b6f5/params.yaml + predictions_file: output/reports/attack/347f6cecab00930a6d6818601559b6f5/predictions.json + probabilities_file: output/reports/attack/347f6cecab00930a6d6818601559b6f5/probabilities.json + score_dict_file: output/reports/attack/347f6cecab00930a6d6818601559b6f5/score_dict.json + test_labels_file: output/reports/attack/347f6cecab00930a6d6818601559b6f5/test_labels.json + train_labels_file: output/reports/attack/347f6cecab00930a6d6818601559b6f5/train_labels.json + model_dir: models + name: 347f6cecab00930a6d6818601559b6f5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 347f6cecab00930a6d6818601559b6f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/35955df1bdffc0f4202fd668a5e9d972/params.yaml b/examples/security/truthseeker/output/reports/attack/35955df1bdffc0f4202fd668a5e9d972/params.yaml new file mode 100644 index 00000000..c417a2a4 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/35955df1bdffc0f4202fd668a5e9d972/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 164341d3757ef53270d740892c730d87 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/35955df1bdffc0f4202fd668a5e9d972/adv_predictions.json + adv_probabilities_file: output/reports/attack/35955df1bdffc0f4202fd668a5e9d972/adv_probabilities.json + attack_file: output/attacks/5e2991c400185f2326cb7b5b3ac18b7e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/35955df1bdffc0f4202fd668a5e9d972/params.yaml + predictions_file: output/reports/attack/35955df1bdffc0f4202fd668a5e9d972/predictions.json + probabilities_file: output/reports/attack/35955df1bdffc0f4202fd668a5e9d972/probabilities.json + score_dict_file: output/reports/attack/35955df1bdffc0f4202fd668a5e9d972/score_dict.json + test_labels_file: output/reports/attack/35955df1bdffc0f4202fd668a5e9d972/test_labels.json + train_labels_file: output/reports/attack/35955df1bdffc0f4202fd668a5e9d972/train_labels.json + model_dir: models + name: 35955df1bdffc0f4202fd668a5e9d972 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 35955df1bdffc0f4202fd668a5e9d972 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/38450961939f7db44c8c90e160b4f3e3/params.yaml b/examples/security/truthseeker/output/reports/attack/38450961939f7db44c8c90e160b4f3e3/params.yaml new file mode 100644 index 00000000..41344a4f --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/38450961939f7db44c8c90e160b4f3e3/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.001 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 365ac4e10bb24f44279d893b65771c15 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/38450961939f7db44c8c90e160b4f3e3/adv_predictions.json + adv_probabilities_file: output/reports/attack/38450961939f7db44c8c90e160b4f3e3/adv_probabilities.json + attack_file: output/attacks/d6d1bc0ef42415894c4fff5a1b79ac6e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/38450961939f7db44c8c90e160b4f3e3/params.yaml + predictions_file: output/reports/attack/38450961939f7db44c8c90e160b4f3e3/predictions.json + probabilities_file: output/reports/attack/38450961939f7db44c8c90e160b4f3e3/probabilities.json + score_dict_file: output/reports/attack/38450961939f7db44c8c90e160b4f3e3/score_dict.json + test_labels_file: output/reports/attack/38450961939f7db44c8c90e160b4f3e3/test_labels.json + train_labels_file: output/reports/attack/38450961939f7db44c8c90e160b4f3e3/train_labels.json + model_dir: models + name: 38450961939f7db44c8c90e160b4f3e3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 38450961939f7db44c8c90e160b4f3e3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/3865da1d410bd67549608edec2e09927/params.yaml b/examples/security/truthseeker/output/reports/attack/3865da1d410bd67549608edec2e09927/params.yaml new file mode 100644 index 00000000..ff0e306c --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/3865da1d410bd67549608edec2e09927/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 06ad7b55416d955929c6e269c600f4f7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3865da1d410bd67549608edec2e09927/adv_predictions.json + adv_probabilities_file: output/reports/attack/3865da1d410bd67549608edec2e09927/adv_probabilities.json + attack_file: output/attacks/4b0019059685b578658cbf2ad188aef1.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/3865da1d410bd67549608edec2e09927/params.yaml + predictions_file: output/reports/attack/3865da1d410bd67549608edec2e09927/predictions.json + probabilities_file: output/reports/attack/3865da1d410bd67549608edec2e09927/probabilities.json + score_dict_file: output/reports/attack/3865da1d410bd67549608edec2e09927/score_dict.json + test_labels_file: output/reports/attack/3865da1d410bd67549608edec2e09927/test_labels.json + train_labels_file: output/reports/attack/3865da1d410bd67549608edec2e09927/train_labels.json + model_dir: models + name: 3865da1d410bd67549608edec2e09927 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 3865da1d410bd67549608edec2e09927 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/3a52ef8c0e39dd174369d170cc553b4c/params.yaml b/examples/security/truthseeker/output/reports/attack/3a52ef8c0e39dd174369d170cc553b4c/params.yaml new file mode 100644 index 00000000..9db5f982 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/3a52ef8c0e39dd174369d170cc553b4c/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 75aea319523e91af5affa45158b92d65 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3a52ef8c0e39dd174369d170cc553b4c/adv_predictions.json + adv_probabilities_file: output/reports/attack/3a52ef8c0e39dd174369d170cc553b4c/adv_probabilities.json + attack_file: output/attacks/c1cfb025b4102d17cf980afd7abbd6b4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/3a52ef8c0e39dd174369d170cc553b4c/params.yaml + predictions_file: output/reports/attack/3a52ef8c0e39dd174369d170cc553b4c/predictions.json + probabilities_file: output/reports/attack/3a52ef8c0e39dd174369d170cc553b4c/probabilities.json + score_dict_file: output/reports/attack/3a52ef8c0e39dd174369d170cc553b4c/score_dict.json + test_labels_file: output/reports/attack/3a52ef8c0e39dd174369d170cc553b4c/test_labels.json + train_labels_file: output/reports/attack/3a52ef8c0e39dd174369d170cc553b4c/train_labels.json + model_dir: models + name: 3a52ef8c0e39dd174369d170cc553b4c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 3a52ef8c0e39dd174369d170cc553b4c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/3ab94bc6fa771e0ea982b62e6bc01ba0/params.yaml b/examples/security/truthseeker/output/reports/attack/3ab94bc6fa771e0ea982b62e6bc01ba0/params.yaml new file mode 100644 index 00000000..c3352b5f --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/3ab94bc6fa771e0ea982b62e6bc01ba0/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.5 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 04434f42a65cc09f92303986b7f83afe +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3ab94bc6fa771e0ea982b62e6bc01ba0/adv_predictions.json + adv_probabilities_file: output/reports/attack/3ab94bc6fa771e0ea982b62e6bc01ba0/adv_probabilities.json + attack_file: output/attacks/1d45e0cfaf89022dd79df1b5eabf26db.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/3ab94bc6fa771e0ea982b62e6bc01ba0/params.yaml + predictions_file: output/reports/attack/3ab94bc6fa771e0ea982b62e6bc01ba0/predictions.json + probabilities_file: output/reports/attack/3ab94bc6fa771e0ea982b62e6bc01ba0/probabilities.json + score_dict_file: output/reports/attack/3ab94bc6fa771e0ea982b62e6bc01ba0/score_dict.json + test_labels_file: output/reports/attack/3ab94bc6fa771e0ea982b62e6bc01ba0/test_labels.json + train_labels_file: output/reports/attack/3ab94bc6fa771e0ea982b62e6bc01ba0/train_labels.json + model_dir: models + name: 3ab94bc6fa771e0ea982b62e6bc01ba0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 3ab94bc6fa771e0ea982b62e6bc01ba0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/3b6f0ac67445c78315681b2cf50858f8/params.yaml b/examples/security/truthseeker/output/reports/attack/3b6f0ac67445c78315681b2cf50858f8/params.yaml new file mode 100644 index 00000000..6e7a070a --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/3b6f0ac67445c78315681b2cf50858f8/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.001 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 4f588ab7e9f29786cea857138e8e2adb +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3b6f0ac67445c78315681b2cf50858f8/adv_predictions.json + adv_probabilities_file: output/reports/attack/3b6f0ac67445c78315681b2cf50858f8/adv_probabilities.json + attack_file: output/attacks/b59cecc843d22a63b7d47bd2e9d6c88f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/3b6f0ac67445c78315681b2cf50858f8/params.yaml + predictions_file: output/reports/attack/3b6f0ac67445c78315681b2cf50858f8/predictions.json + probabilities_file: output/reports/attack/3b6f0ac67445c78315681b2cf50858f8/probabilities.json + score_dict_file: output/reports/attack/3b6f0ac67445c78315681b2cf50858f8/score_dict.json + test_labels_file: output/reports/attack/3b6f0ac67445c78315681b2cf50858f8/test_labels.json + train_labels_file: output/reports/attack/3b6f0ac67445c78315681b2cf50858f8/train_labels.json + model_dir: models + name: 3b6f0ac67445c78315681b2cf50858f8 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 3b6f0ac67445c78315681b2cf50858f8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/3c07a9c2e52b2a54fac037def1634b39/params.yaml b/examples/security/truthseeker/output/reports/attack/3c07a9c2e52b2a54fac037def1634b39/params.yaml new file mode 100644 index 00000000..afcea24d --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/3c07a9c2e52b2a54fac037def1634b39/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 88208a5b8544d9d856776b4104ec78ab +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3c07a9c2e52b2a54fac037def1634b39/adv_predictions.json + adv_probabilities_file: output/reports/attack/3c07a9c2e52b2a54fac037def1634b39/adv_probabilities.json + attack_file: output/attacks/26b138572593989dff5a6dfeeebc5228.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/3c07a9c2e52b2a54fac037def1634b39/params.yaml + predictions_file: output/reports/attack/3c07a9c2e52b2a54fac037def1634b39/predictions.json + probabilities_file: output/reports/attack/3c07a9c2e52b2a54fac037def1634b39/probabilities.json + score_dict_file: output/reports/attack/3c07a9c2e52b2a54fac037def1634b39/score_dict.json + test_labels_file: output/reports/attack/3c07a9c2e52b2a54fac037def1634b39/test_labels.json + train_labels_file: output/reports/attack/3c07a9c2e52b2a54fac037def1634b39/train_labels.json + model_dir: models + name: 3c07a9c2e52b2a54fac037def1634b39 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 3c07a9c2e52b2a54fac037def1634b39 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/3e2b271232ee1da0959e3f27c3d1d057/params.yaml b/examples/security/truthseeker/output/reports/attack/3e2b271232ee1da0959e3f27c3d1d057/params.yaml new file mode 100644 index 00000000..1487bd38 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/3e2b271232ee1da0959e3f27c3d1d057/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 1989048c9d824b7ea2361340abc7fa38 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3e2b271232ee1da0959e3f27c3d1d057/adv_predictions.json + adv_probabilities_file: output/reports/attack/3e2b271232ee1da0959e3f27c3d1d057/adv_probabilities.json + attack_file: output/attacks/7d1fee14a493b21b919780a99b771e73.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/3e2b271232ee1da0959e3f27c3d1d057/params.yaml + predictions_file: output/reports/attack/3e2b271232ee1da0959e3f27c3d1d057/predictions.json + probabilities_file: output/reports/attack/3e2b271232ee1da0959e3f27c3d1d057/probabilities.json + score_dict_file: output/reports/attack/3e2b271232ee1da0959e3f27c3d1d057/score_dict.json + test_labels_file: output/reports/attack/3e2b271232ee1da0959e3f27c3d1d057/test_labels.json + train_labels_file: output/reports/attack/3e2b271232ee1da0959e3f27c3d1d057/train_labels.json + model_dir: models + name: 3e2b271232ee1da0959e3f27c3d1d057 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 3e2b271232ee1da0959e3f27c3d1d057 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/3e444e94367ef699eba3674913404ca3/params.yaml b/examples/security/truthseeker/output/reports/attack/3e444e94367ef699eba3674913404ca3/params.yaml new file mode 100644 index 00000000..9834e325 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/3e444e94367ef699eba3674913404ca3/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.3 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 7f422b7712ad8a259c47dfbbb8ae567e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/3e444e94367ef699eba3674913404ca3/adv_predictions.json + adv_probabilities_file: output/reports/attack/3e444e94367ef699eba3674913404ca3/adv_probabilities.json + attack_file: output/attacks/2a30b52a3a9eb865128119f21904ce55.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/3e444e94367ef699eba3674913404ca3/params.yaml + predictions_file: output/reports/attack/3e444e94367ef699eba3674913404ca3/predictions.json + probabilities_file: output/reports/attack/3e444e94367ef699eba3674913404ca3/probabilities.json + score_dict_file: output/reports/attack/3e444e94367ef699eba3674913404ca3/score_dict.json + test_labels_file: output/reports/attack/3e444e94367ef699eba3674913404ca3/test_labels.json + train_labels_file: output/reports/attack/3e444e94367ef699eba3674913404ca3/train_labels.json + model_dir: models + name: 3e444e94367ef699eba3674913404ca3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 3e444e94367ef699eba3674913404ca3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/4157f61d95a983b0d0b9bb483fe9c33f/params.yaml b/examples/security/truthseeker/output/reports/attack/4157f61d95a983b0d0b9bb483fe9c33f/params.yaml new file mode 100644 index 00000000..aa0d6230 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/4157f61d95a983b0d0b9bb483fe9c33f/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 81106658b35b75e3cfe0b447f8bd65e9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4157f61d95a983b0d0b9bb483fe9c33f/adv_predictions.json + adv_probabilities_file: output/reports/attack/4157f61d95a983b0d0b9bb483fe9c33f/adv_probabilities.json + attack_file: output/attacks/5ebdbdfa774202882f291811b61c2006.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/4157f61d95a983b0d0b9bb483fe9c33f/params.yaml + predictions_file: output/reports/attack/4157f61d95a983b0d0b9bb483fe9c33f/predictions.json + probabilities_file: output/reports/attack/4157f61d95a983b0d0b9bb483fe9c33f/probabilities.json + score_dict_file: output/reports/attack/4157f61d95a983b0d0b9bb483fe9c33f/score_dict.json + test_labels_file: output/reports/attack/4157f61d95a983b0d0b9bb483fe9c33f/test_labels.json + train_labels_file: output/reports/attack/4157f61d95a983b0d0b9bb483fe9c33f/train_labels.json + model_dir: models + name: 4157f61d95a983b0d0b9bb483fe9c33f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 4157f61d95a983b0d0b9bb483fe9c33f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/41fff93bca2610606d4ab414101aae02/params.yaml b/examples/security/truthseeker/output/reports/attack/41fff93bca2610606d4ab414101aae02/params.yaml new file mode 100644 index 00000000..4b8b7a81 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/41fff93bca2610606d4ab414101aae02/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 292704a00e3fb8dafdef5e150b6e5205 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/41fff93bca2610606d4ab414101aae02/adv_predictions.json + adv_probabilities_file: output/reports/attack/41fff93bca2610606d4ab414101aae02/adv_probabilities.json + attack_file: output/attacks/5af2a577be8341092e312553f2edcc58.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/41fff93bca2610606d4ab414101aae02/params.yaml + predictions_file: output/reports/attack/41fff93bca2610606d4ab414101aae02/predictions.json + probabilities_file: output/reports/attack/41fff93bca2610606d4ab414101aae02/probabilities.json + score_dict_file: output/reports/attack/41fff93bca2610606d4ab414101aae02/score_dict.json + test_labels_file: output/reports/attack/41fff93bca2610606d4ab414101aae02/test_labels.json + train_labels_file: output/reports/attack/41fff93bca2610606d4ab414101aae02/train_labels.json + model_dir: models + name: 41fff93bca2610606d4ab414101aae02 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 41fff93bca2610606d4ab414101aae02 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/42cb6d53d75cdf696fb177a8a307ea48/params.yaml b/examples/security/truthseeker/output/reports/attack/42cb6d53d75cdf696fb177a8a307ea48/params.yaml new file mode 100644 index 00000000..75348a55 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/42cb6d53d75cdf696fb177a8a307ea48/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 3e647597b79c0eee0a707fa67bcc9646 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/42cb6d53d75cdf696fb177a8a307ea48/adv_predictions.json + adv_probabilities_file: output/reports/attack/42cb6d53d75cdf696fb177a8a307ea48/adv_probabilities.json + attack_file: output/attacks/e5c5146ea7c1baca49cc54e10304c8d4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/42cb6d53d75cdf696fb177a8a307ea48/params.yaml + predictions_file: output/reports/attack/42cb6d53d75cdf696fb177a8a307ea48/predictions.json + probabilities_file: output/reports/attack/42cb6d53d75cdf696fb177a8a307ea48/probabilities.json + score_dict_file: output/reports/attack/42cb6d53d75cdf696fb177a8a307ea48/score_dict.json + test_labels_file: output/reports/attack/42cb6d53d75cdf696fb177a8a307ea48/test_labels.json + train_labels_file: output/reports/attack/42cb6d53d75cdf696fb177a8a307ea48/train_labels.json + model_dir: models + name: 42cb6d53d75cdf696fb177a8a307ea48 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 42cb6d53d75cdf696fb177a8a307ea48 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/436475d22b3d3cd8e4a7693bb3ac183c/params.yaml b/examples/security/truthseeker/output/reports/attack/436475d22b3d3cd8e4a7693bb3ac183c/params.yaml new file mode 100644 index 00000000..79f551fc --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/436475d22b3d3cd8e4a7693bb3ac183c/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 0331037053bc63f5b548ad9630931280 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/436475d22b3d3cd8e4a7693bb3ac183c/adv_predictions.json + adv_probabilities_file: output/reports/attack/436475d22b3d3cd8e4a7693bb3ac183c/adv_probabilities.json + attack_file: output/attacks/026b7f1925c7042eadc68862b1d4b66a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/436475d22b3d3cd8e4a7693bb3ac183c/params.yaml + predictions_file: output/reports/attack/436475d22b3d3cd8e4a7693bb3ac183c/predictions.json + probabilities_file: output/reports/attack/436475d22b3d3cd8e4a7693bb3ac183c/probabilities.json + score_dict_file: output/reports/attack/436475d22b3d3cd8e4a7693bb3ac183c/score_dict.json + test_labels_file: output/reports/attack/436475d22b3d3cd8e4a7693bb3ac183c/test_labels.json + train_labels_file: output/reports/attack/436475d22b3d3cd8e4a7693bb3ac183c/train_labels.json + model_dir: models + name: 436475d22b3d3cd8e4a7693bb3ac183c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 436475d22b3d3cd8e4a7693bb3ac183c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/4397e393e388842271edf1d5f2d3772c/params.yaml b/examples/security/truthseeker/output/reports/attack/4397e393e388842271edf1d5f2d3772c/params.yaml new file mode 100644 index 00000000..831ffa14 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/4397e393e388842271edf1d5f2d3772c/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.001 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: f662eb9c3c27d11866cc8df092ba300f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4397e393e388842271edf1d5f2d3772c/adv_predictions.json + adv_probabilities_file: output/reports/attack/4397e393e388842271edf1d5f2d3772c/adv_probabilities.json + attack_file: output/attacks/91c8605ceadf9b9ef6447a5f58c69145.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/4397e393e388842271edf1d5f2d3772c/params.yaml + predictions_file: output/reports/attack/4397e393e388842271edf1d5f2d3772c/predictions.json + probabilities_file: output/reports/attack/4397e393e388842271edf1d5f2d3772c/probabilities.json + score_dict_file: output/reports/attack/4397e393e388842271edf1d5f2d3772c/score_dict.json + test_labels_file: output/reports/attack/4397e393e388842271edf1d5f2d3772c/test_labels.json + train_labels_file: output/reports/attack/4397e393e388842271edf1d5f2d3772c/train_labels.json + model_dir: models + name: 4397e393e388842271edf1d5f2d3772c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 4397e393e388842271edf1d5f2d3772c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/4495849ae1f7abfb0769c3f37b33e455/params.yaml b/examples/security/truthseeker/output/reports/attack/4495849ae1f7abfb0769c3f37b33e455/params.yaml new file mode 100644 index 00000000..c84518a5 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/4495849ae1f7abfb0769c3f37b33e455/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 26e9b7e1c35e51923899ea1c3ac4b88e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4495849ae1f7abfb0769c3f37b33e455/adv_predictions.json + adv_probabilities_file: output/reports/attack/4495849ae1f7abfb0769c3f37b33e455/adv_probabilities.json + attack_file: output/attacks/ee24a2defcba4e23ac4ee42f346d1f55.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/4495849ae1f7abfb0769c3f37b33e455/params.yaml + predictions_file: output/reports/attack/4495849ae1f7abfb0769c3f37b33e455/predictions.json + probabilities_file: output/reports/attack/4495849ae1f7abfb0769c3f37b33e455/probabilities.json + score_dict_file: output/reports/attack/4495849ae1f7abfb0769c3f37b33e455/score_dict.json + test_labels_file: output/reports/attack/4495849ae1f7abfb0769c3f37b33e455/test_labels.json + train_labels_file: output/reports/attack/4495849ae1f7abfb0769c3f37b33e455/train_labels.json + model_dir: models + name: 4495849ae1f7abfb0769c3f37b33e455 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 4495849ae1f7abfb0769c3f37b33e455 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/45611f9d03febe64e4a03ccf8ea58620/params.yaml b/examples/security/truthseeker/output/reports/attack/45611f9d03febe64e4a03ccf8ea58620/params.yaml new file mode 100644 index 00000000..a4f41526 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/45611f9d03febe64e4a03ccf8ea58620/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 668c0fe36f4da9baacb3335c7e511e5c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/45611f9d03febe64e4a03ccf8ea58620/adv_predictions.json + adv_probabilities_file: output/reports/attack/45611f9d03febe64e4a03ccf8ea58620/adv_probabilities.json + attack_file: output/attacks/9c6bef6fe7c83443d2d74be36bf217e8.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/45611f9d03febe64e4a03ccf8ea58620/params.yaml + predictions_file: output/reports/attack/45611f9d03febe64e4a03ccf8ea58620/predictions.json + probabilities_file: output/reports/attack/45611f9d03febe64e4a03ccf8ea58620/probabilities.json + score_dict_file: output/reports/attack/45611f9d03febe64e4a03ccf8ea58620/score_dict.json + test_labels_file: output/reports/attack/45611f9d03febe64e4a03ccf8ea58620/test_labels.json + train_labels_file: output/reports/attack/45611f9d03febe64e4a03ccf8ea58620/train_labels.json + model_dir: models + name: 45611f9d03febe64e4a03ccf8ea58620 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 45611f9d03febe64e4a03ccf8ea58620 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/46f9196611908b92d57bf889ac37e58c/params.yaml b/examples/security/truthseeker/output/reports/attack/46f9196611908b92d57bf889ac37e58c/params.yaml new file mode 100644 index 00000000..fd6e4063 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/46f9196611908b92d57bf889ac37e58c/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.01 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: bc378b25a71bf4fd9b5385e595a1bb7c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/46f9196611908b92d57bf889ac37e58c/adv_predictions.json + adv_probabilities_file: output/reports/attack/46f9196611908b92d57bf889ac37e58c/adv_probabilities.json + attack_file: output/attacks/a588997f80d9b66f5e983de44b7bc0d6.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/46f9196611908b92d57bf889ac37e58c/params.yaml + predictions_file: output/reports/attack/46f9196611908b92d57bf889ac37e58c/predictions.json + probabilities_file: output/reports/attack/46f9196611908b92d57bf889ac37e58c/probabilities.json + score_dict_file: output/reports/attack/46f9196611908b92d57bf889ac37e58c/score_dict.json + test_labels_file: output/reports/attack/46f9196611908b92d57bf889ac37e58c/test_labels.json + train_labels_file: output/reports/attack/46f9196611908b92d57bf889ac37e58c/train_labels.json + model_dir: models + name: 46f9196611908b92d57bf889ac37e58c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 46f9196611908b92d57bf889ac37e58c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/params.yaml b/examples/security/truthseeker/output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/params.yaml new file mode 100644 index 00000000..775a607b --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.01 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: d7d041d933cbe3e89c5f88e84468bfb7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/adv_predictions.json + adv_probabilities_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/adv_probabilities.json + attack_file: output/attacks/d807d0d095cfd2b7548024c7a43530b1.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/params.yaml + predictions_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/predictions.json + probabilities_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/probabilities.json + score_dict_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/score_dict.json + test_labels_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/test_labels.json + train_labels_file: output/reports/attack/487b5e1894069577e8fe0f1c8484a8af/train_labels.json + model_dir: models + name: 487b5e1894069577e8fe0f1c8484a8af + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 487b5e1894069577e8fe0f1c8484a8af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/489b9b7b28ebe573ab4187e99b5c499f/params.yaml b/examples/security/truthseeker/output/reports/attack/489b9b7b28ebe573ab4187e99b5c499f/params.yaml new file mode 100644 index 00000000..80ff83ef --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/489b9b7b28ebe573ab4187e99b5c499f/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 31f8cc3dc1f9bfcf85cd3f4690372758 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/489b9b7b28ebe573ab4187e99b5c499f/adv_predictions.json + adv_probabilities_file: output/reports/attack/489b9b7b28ebe573ab4187e99b5c499f/adv_probabilities.json + attack_file: output/attacks/1c2c35175a23172f5a8187bbbf05d36a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/489b9b7b28ebe573ab4187e99b5c499f/params.yaml + predictions_file: output/reports/attack/489b9b7b28ebe573ab4187e99b5c499f/predictions.json + probabilities_file: output/reports/attack/489b9b7b28ebe573ab4187e99b5c499f/probabilities.json + score_dict_file: output/reports/attack/489b9b7b28ebe573ab4187e99b5c499f/score_dict.json + test_labels_file: output/reports/attack/489b9b7b28ebe573ab4187e99b5c499f/test_labels.json + train_labels_file: output/reports/attack/489b9b7b28ebe573ab4187e99b5c499f/train_labels.json + model_dir: models + name: 489b9b7b28ebe573ab4187e99b5c499f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 489b9b7b28ebe573ab4187e99b5c499f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/48dbdc03a7ff0292b33e167a7831f222/params.yaml b/examples/security/truthseeker/output/reports/attack/48dbdc03a7ff0292b33e167a7831f222/params.yaml new file mode 100644 index 00000000..d59cc712 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/48dbdc03a7ff0292b33e167a7831f222/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.01 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: c642f868e49b3ab588e77f3d31420dc1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/48dbdc03a7ff0292b33e167a7831f222/adv_predictions.json + adv_probabilities_file: output/reports/attack/48dbdc03a7ff0292b33e167a7831f222/adv_probabilities.json + attack_file: output/attacks/181a81827aa9d3e102632787bd0e99e8.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/48dbdc03a7ff0292b33e167a7831f222/params.yaml + predictions_file: output/reports/attack/48dbdc03a7ff0292b33e167a7831f222/predictions.json + probabilities_file: output/reports/attack/48dbdc03a7ff0292b33e167a7831f222/probabilities.json + score_dict_file: output/reports/attack/48dbdc03a7ff0292b33e167a7831f222/score_dict.json + test_labels_file: output/reports/attack/48dbdc03a7ff0292b33e167a7831f222/test_labels.json + train_labels_file: output/reports/attack/48dbdc03a7ff0292b33e167a7831f222/train_labels.json + model_dir: models + name: 48dbdc03a7ff0292b33e167a7831f222 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 48dbdc03a7ff0292b33e167a7831f222 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/4a902119e16187039b89587ddac48266/params.yaml b/examples/security/truthseeker/output/reports/attack/4a902119e16187039b89587ddac48266/params.yaml new file mode 100644 index 00000000..c2cee05e --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/4a902119e16187039b89587ddac48266/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.01 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 6a3cb9ce9e10f1bcef725280dd71f639 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/4a902119e16187039b89587ddac48266/adv_predictions.json + adv_probabilities_file: output/reports/attack/4a902119e16187039b89587ddac48266/adv_probabilities.json + attack_file: output/attacks/31cb61c3710fd2a32541efb0b29c1d35.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/4a902119e16187039b89587ddac48266/params.yaml + predictions_file: output/reports/attack/4a902119e16187039b89587ddac48266/predictions.json + probabilities_file: output/reports/attack/4a902119e16187039b89587ddac48266/probabilities.json + score_dict_file: output/reports/attack/4a902119e16187039b89587ddac48266/score_dict.json + test_labels_file: output/reports/attack/4a902119e16187039b89587ddac48266/test_labels.json + train_labels_file: output/reports/attack/4a902119e16187039b89587ddac48266/train_labels.json + model_dir: models + name: 4a902119e16187039b89587ddac48266 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 4a902119e16187039b89587ddac48266 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/5278f6bfdbf3b838e89375259db1b6a5/params.yaml b/examples/security/truthseeker/output/reports/attack/5278f6bfdbf3b838e89375259db1b6a5/params.yaml new file mode 100644 index 00000000..753393cc --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/5278f6bfdbf3b838e89375259db1b6a5/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.01 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: eda864753c3c16f842df64c457e03a89 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5278f6bfdbf3b838e89375259db1b6a5/adv_predictions.json + adv_probabilities_file: output/reports/attack/5278f6bfdbf3b838e89375259db1b6a5/adv_probabilities.json + attack_file: output/attacks/9ba684ae7cc9cb5b79b2df3b098ed048.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/5278f6bfdbf3b838e89375259db1b6a5/params.yaml + predictions_file: output/reports/attack/5278f6bfdbf3b838e89375259db1b6a5/predictions.json + probabilities_file: output/reports/attack/5278f6bfdbf3b838e89375259db1b6a5/probabilities.json + score_dict_file: output/reports/attack/5278f6bfdbf3b838e89375259db1b6a5/score_dict.json + test_labels_file: output/reports/attack/5278f6bfdbf3b838e89375259db1b6a5/test_labels.json + train_labels_file: output/reports/attack/5278f6bfdbf3b838e89375259db1b6a5/train_labels.json + model_dir: models + name: 5278f6bfdbf3b838e89375259db1b6a5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 5278f6bfdbf3b838e89375259db1b6a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/5410cedcc0efd1758abfcc308c2af532/params.yaml b/examples/security/truthseeker/output/reports/attack/5410cedcc0efd1758abfcc308c2af532/params.yaml new file mode 100644 index 00000000..50cf4f92 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/5410cedcc0efd1758abfcc308c2af532/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.5 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 470fa21d1314399b33d6887d6ef810f9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5410cedcc0efd1758abfcc308c2af532/adv_predictions.json + adv_probabilities_file: output/reports/attack/5410cedcc0efd1758abfcc308c2af532/adv_probabilities.json + attack_file: output/attacks/f89a23879722bb71451a749c2e20d909.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/5410cedcc0efd1758abfcc308c2af532/params.yaml + predictions_file: output/reports/attack/5410cedcc0efd1758abfcc308c2af532/predictions.json + probabilities_file: output/reports/attack/5410cedcc0efd1758abfcc308c2af532/probabilities.json + score_dict_file: output/reports/attack/5410cedcc0efd1758abfcc308c2af532/score_dict.json + test_labels_file: output/reports/attack/5410cedcc0efd1758abfcc308c2af532/test_labels.json + train_labels_file: output/reports/attack/5410cedcc0efd1758abfcc308c2af532/train_labels.json + model_dir: models + name: 5410cedcc0efd1758abfcc308c2af532 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 5410cedcc0efd1758abfcc308c2af532 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/560c8f5914e323225edeb973e14a896c/params.yaml b/examples/security/truthseeker/output/reports/attack/560c8f5914e323225edeb973e14a896c/params.yaml new file mode 100644 index 00000000..f15c07fc --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/560c8f5914e323225edeb973e14a896c/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 7cf390b7068171e289dabbcdb7fbd3d2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/560c8f5914e323225edeb973e14a896c/adv_predictions.json + adv_probabilities_file: output/reports/attack/560c8f5914e323225edeb973e14a896c/adv_probabilities.json + attack_file: output/attacks/f5c95ac4864a2017d91d7ccdfe32f99c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/560c8f5914e323225edeb973e14a896c/params.yaml + predictions_file: output/reports/attack/560c8f5914e323225edeb973e14a896c/predictions.json + probabilities_file: output/reports/attack/560c8f5914e323225edeb973e14a896c/probabilities.json + score_dict_file: output/reports/attack/560c8f5914e323225edeb973e14a896c/score_dict.json + test_labels_file: output/reports/attack/560c8f5914e323225edeb973e14a896c/test_labels.json + train_labels_file: output/reports/attack/560c8f5914e323225edeb973e14a896c/train_labels.json + model_dir: models + name: 560c8f5914e323225edeb973e14a896c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 560c8f5914e323225edeb973e14a896c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/56ac9ffcc44d962357cf6c156f54cdfd/params.yaml b/examples/security/truthseeker/output/reports/attack/56ac9ffcc44d962357cf6c156f54cdfd/params.yaml new file mode 100644 index 00000000..28a26999 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/56ac9ffcc44d962357cf6c156f54cdfd/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 9e90c42488fb133d4272d3d526aef1d6 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/56ac9ffcc44d962357cf6c156f54cdfd/adv_predictions.json + adv_probabilities_file: output/reports/attack/56ac9ffcc44d962357cf6c156f54cdfd/adv_probabilities.json + attack_file: output/attacks/73220b31b309aa8c5134b0d9bb8f6d3f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/56ac9ffcc44d962357cf6c156f54cdfd/params.yaml + predictions_file: output/reports/attack/56ac9ffcc44d962357cf6c156f54cdfd/predictions.json + probabilities_file: output/reports/attack/56ac9ffcc44d962357cf6c156f54cdfd/probabilities.json + score_dict_file: output/reports/attack/56ac9ffcc44d962357cf6c156f54cdfd/score_dict.json + test_labels_file: output/reports/attack/56ac9ffcc44d962357cf6c156f54cdfd/test_labels.json + train_labels_file: output/reports/attack/56ac9ffcc44d962357cf6c156f54cdfd/train_labels.json + model_dir: models + name: 56ac9ffcc44d962357cf6c156f54cdfd + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 56ac9ffcc44d962357cf6c156f54cdfd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/578c6ea73b70e11f2d8362db3ceee8b3/params.yaml b/examples/security/truthseeker/output/reports/attack/578c6ea73b70e11f2d8362db3ceee8b3/params.yaml new file mode 100644 index 00000000..04201d0c --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/578c6ea73b70e11f2d8362db3ceee8b3/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.001 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: e796b65df47674c34965d39be8b46cdc +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/578c6ea73b70e11f2d8362db3ceee8b3/adv_predictions.json + adv_probabilities_file: output/reports/attack/578c6ea73b70e11f2d8362db3ceee8b3/adv_probabilities.json + attack_file: output/attacks/bd6ab688985f6e4d50b50f6a65b404b9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/578c6ea73b70e11f2d8362db3ceee8b3/params.yaml + predictions_file: output/reports/attack/578c6ea73b70e11f2d8362db3ceee8b3/predictions.json + probabilities_file: output/reports/attack/578c6ea73b70e11f2d8362db3ceee8b3/probabilities.json + score_dict_file: output/reports/attack/578c6ea73b70e11f2d8362db3ceee8b3/score_dict.json + test_labels_file: output/reports/attack/578c6ea73b70e11f2d8362db3ceee8b3/test_labels.json + train_labels_file: output/reports/attack/578c6ea73b70e11f2d8362db3ceee8b3/train_labels.json + model_dir: models + name: 578c6ea73b70e11f2d8362db3ceee8b3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 578c6ea73b70e11f2d8362db3ceee8b3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/57c10e753a2fcebddd25e9e1ca12f49d/params.yaml b/examples/security/truthseeker/output/reports/attack/57c10e753a2fcebddd25e9e1ca12f49d/params.yaml new file mode 100644 index 00000000..45c84593 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/57c10e753a2fcebddd25e9e1ca12f49d/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 242c7c625fe0eb10635e5f766ac1c8a3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/57c10e753a2fcebddd25e9e1ca12f49d/adv_predictions.json + adv_probabilities_file: output/reports/attack/57c10e753a2fcebddd25e9e1ca12f49d/adv_probabilities.json + attack_file: output/attacks/c036a045dc88be11c75dbcb8d7fc48da.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/57c10e753a2fcebddd25e9e1ca12f49d/params.yaml + predictions_file: output/reports/attack/57c10e753a2fcebddd25e9e1ca12f49d/predictions.json + probabilities_file: output/reports/attack/57c10e753a2fcebddd25e9e1ca12f49d/probabilities.json + score_dict_file: output/reports/attack/57c10e753a2fcebddd25e9e1ca12f49d/score_dict.json + test_labels_file: output/reports/attack/57c10e753a2fcebddd25e9e1ca12f49d/test_labels.json + train_labels_file: output/reports/attack/57c10e753a2fcebddd25e9e1ca12f49d/train_labels.json + model_dir: models + name: 57c10e753a2fcebddd25e9e1ca12f49d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 57c10e753a2fcebddd25e9e1ca12f49d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/57f9ca4bde811a6d75f801ae497359e3/params.yaml b/examples/security/truthseeker/output/reports/attack/57f9ca4bde811a6d75f801ae497359e3/params.yaml new file mode 100644 index 00000000..3d147f73 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/57f9ca4bde811a6d75f801ae497359e3/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 41ca91da55d856260a808fababba2bc0 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/57f9ca4bde811a6d75f801ae497359e3/adv_predictions.json + adv_probabilities_file: output/reports/attack/57f9ca4bde811a6d75f801ae497359e3/adv_probabilities.json + attack_file: output/attacks/e3f52f40e1a17c0b3cf1efbed88fa77f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/57f9ca4bde811a6d75f801ae497359e3/params.yaml + predictions_file: output/reports/attack/57f9ca4bde811a6d75f801ae497359e3/predictions.json + probabilities_file: output/reports/attack/57f9ca4bde811a6d75f801ae497359e3/probabilities.json + score_dict_file: output/reports/attack/57f9ca4bde811a6d75f801ae497359e3/score_dict.json + test_labels_file: output/reports/attack/57f9ca4bde811a6d75f801ae497359e3/test_labels.json + train_labels_file: output/reports/attack/57f9ca4bde811a6d75f801ae497359e3/train_labels.json + model_dir: models + name: 57f9ca4bde811a6d75f801ae497359e3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 57f9ca4bde811a6d75f801ae497359e3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/592c83765f2e53c394101bf9cca74e0e/params.yaml b/examples/security/truthseeker/output/reports/attack/592c83765f2e53c394101bf9cca74e0e/params.yaml new file mode 100644 index 00000000..08350515 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/592c83765f2e53c394101bf9cca74e0e/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.3 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: d24af7564c4fc3a7fc8ea7a55f04ae23 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/592c83765f2e53c394101bf9cca74e0e/adv_predictions.json + adv_probabilities_file: output/reports/attack/592c83765f2e53c394101bf9cca74e0e/adv_probabilities.json + attack_file: output/attacks/f4a6853f7f5f58d22677790f67d09309.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/592c83765f2e53c394101bf9cca74e0e/params.yaml + predictions_file: output/reports/attack/592c83765f2e53c394101bf9cca74e0e/predictions.json + probabilities_file: output/reports/attack/592c83765f2e53c394101bf9cca74e0e/probabilities.json + score_dict_file: output/reports/attack/592c83765f2e53c394101bf9cca74e0e/score_dict.json + test_labels_file: output/reports/attack/592c83765f2e53c394101bf9cca74e0e/test_labels.json + train_labels_file: output/reports/attack/592c83765f2e53c394101bf9cca74e0e/train_labels.json + model_dir: models + name: 592c83765f2e53c394101bf9cca74e0e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 592c83765f2e53c394101bf9cca74e0e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/596d59f4dda5809a92ec454111f2980a/params.yaml b/examples/security/truthseeker/output/reports/attack/596d59f4dda5809a92ec454111f2980a/params.yaml new file mode 100644 index 00000000..1ca4725d --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/596d59f4dda5809a92ec454111f2980a/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.001 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 36e8b0a07023b184fa54a90855be8771 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/596d59f4dda5809a92ec454111f2980a/adv_predictions.json + adv_probabilities_file: output/reports/attack/596d59f4dda5809a92ec454111f2980a/adv_probabilities.json + attack_file: output/attacks/525d9f598fac82e593da4027158b4989.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/596d59f4dda5809a92ec454111f2980a/params.yaml + predictions_file: output/reports/attack/596d59f4dda5809a92ec454111f2980a/predictions.json + probabilities_file: output/reports/attack/596d59f4dda5809a92ec454111f2980a/probabilities.json + score_dict_file: output/reports/attack/596d59f4dda5809a92ec454111f2980a/score_dict.json + test_labels_file: output/reports/attack/596d59f4dda5809a92ec454111f2980a/test_labels.json + train_labels_file: output/reports/attack/596d59f4dda5809a92ec454111f2980a/train_labels.json + model_dir: models + name: 596d59f4dda5809a92ec454111f2980a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 596d59f4dda5809a92ec454111f2980a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/5992839972ed59261e5817977451d3dc/params.yaml b/examples/security/truthseeker/output/reports/attack/5992839972ed59261e5817977451d3dc/params.yaml new file mode 100644 index 00000000..87331510 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/5992839972ed59261e5817977451d3dc/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.001 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: c77a67428935cb06c04fd1d4f07c8472 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5992839972ed59261e5817977451d3dc/adv_predictions.json + adv_probabilities_file: output/reports/attack/5992839972ed59261e5817977451d3dc/adv_probabilities.json + attack_file: output/attacks/94430db169af5a8d4d16439f1c109411.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/5992839972ed59261e5817977451d3dc/params.yaml + predictions_file: output/reports/attack/5992839972ed59261e5817977451d3dc/predictions.json + probabilities_file: output/reports/attack/5992839972ed59261e5817977451d3dc/probabilities.json + score_dict_file: output/reports/attack/5992839972ed59261e5817977451d3dc/score_dict.json + test_labels_file: output/reports/attack/5992839972ed59261e5817977451d3dc/test_labels.json + train_labels_file: output/reports/attack/5992839972ed59261e5817977451d3dc/train_labels.json + model_dir: models + name: 5992839972ed59261e5817977451d3dc + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 5992839972ed59261e5817977451d3dc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/5bc17544f32163cd45e55b14bd4f7472/params.yaml b/examples/security/truthseeker/output/reports/attack/5bc17544f32163cd45e55b14bd4f7472/params.yaml new file mode 100644 index 00000000..bcc2382d --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/5bc17544f32163cd45e55b14bd4f7472/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 2977a5aadef4380d10a4eb5eacad7875 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5bc17544f32163cd45e55b14bd4f7472/adv_predictions.json + adv_probabilities_file: output/reports/attack/5bc17544f32163cd45e55b14bd4f7472/adv_probabilities.json + attack_file: output/attacks/105bca548a1cd98707605b46053c62f6.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/5bc17544f32163cd45e55b14bd4f7472/params.yaml + predictions_file: output/reports/attack/5bc17544f32163cd45e55b14bd4f7472/predictions.json + probabilities_file: output/reports/attack/5bc17544f32163cd45e55b14bd4f7472/probabilities.json + score_dict_file: output/reports/attack/5bc17544f32163cd45e55b14bd4f7472/score_dict.json + test_labels_file: output/reports/attack/5bc17544f32163cd45e55b14bd4f7472/test_labels.json + train_labels_file: output/reports/attack/5bc17544f32163cd45e55b14bd4f7472/train_labels.json + model_dir: models + name: 5bc17544f32163cd45e55b14bd4f7472 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 5bc17544f32163cd45e55b14bd4f7472 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/params.yaml b/examples/security/truthseeker/output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/params.yaml new file mode 100644 index 00000000..62be3ba9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.1 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 993030eee75baf52bc4f57f4ef9a7ff4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/adv_predictions.json + adv_probabilities_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/adv_probabilities.json + attack_file: output/attacks/1e5b14fd98e3f34b81c514e54a211391.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/params.yaml + predictions_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/predictions.json + probabilities_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/probabilities.json + score_dict_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/score_dict.json + test_labels_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/test_labels.json + train_labels_file: output/reports/attack/5cb9981c36289417ff7f551eda1ebc74/train_labels.json + model_dir: models + name: 5cb9981c36289417ff7f551eda1ebc74 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 5cb9981c36289417ff7f551eda1ebc74 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/5ef19231261305c5c9a177e0f4e8bb77/params.yaml b/examples/security/truthseeker/output/reports/attack/5ef19231261305c5c9a177e0f4e8bb77/params.yaml new file mode 100644 index 00000000..50cfcae6 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/5ef19231261305c5c9a177e0f4e8bb77/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 01d20a9217fc6fca5bfd3b028997c604 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5ef19231261305c5c9a177e0f4e8bb77/adv_predictions.json + adv_probabilities_file: output/reports/attack/5ef19231261305c5c9a177e0f4e8bb77/adv_probabilities.json + attack_file: output/attacks/aec8876931de0889424d14575d89a4be.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/5ef19231261305c5c9a177e0f4e8bb77/params.yaml + predictions_file: output/reports/attack/5ef19231261305c5c9a177e0f4e8bb77/predictions.json + probabilities_file: output/reports/attack/5ef19231261305c5c9a177e0f4e8bb77/probabilities.json + score_dict_file: output/reports/attack/5ef19231261305c5c9a177e0f4e8bb77/score_dict.json + test_labels_file: output/reports/attack/5ef19231261305c5c9a177e0f4e8bb77/test_labels.json + train_labels_file: output/reports/attack/5ef19231261305c5c9a177e0f4e8bb77/train_labels.json + model_dir: models + name: 5ef19231261305c5c9a177e0f4e8bb77 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 5ef19231261305c5c9a177e0f4e8bb77 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/5f2e97c75be855bdb753891b48b30af1/params.yaml b/examples/security/truthseeker/output/reports/attack/5f2e97c75be855bdb753891b48b30af1/params.yaml new file mode 100644 index 00000000..d07711d6 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/5f2e97c75be855bdb753891b48b30af1/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: b8693d91c5fa9e8f1f1dee388b294211 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/5f2e97c75be855bdb753891b48b30af1/adv_predictions.json + adv_probabilities_file: output/reports/attack/5f2e97c75be855bdb753891b48b30af1/adv_probabilities.json + attack_file: output/attacks/626519748a4fd7aa9c3f160d1d732382.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/5f2e97c75be855bdb753891b48b30af1/params.yaml + predictions_file: output/reports/attack/5f2e97c75be855bdb753891b48b30af1/predictions.json + probabilities_file: output/reports/attack/5f2e97c75be855bdb753891b48b30af1/probabilities.json + score_dict_file: output/reports/attack/5f2e97c75be855bdb753891b48b30af1/score_dict.json + test_labels_file: output/reports/attack/5f2e97c75be855bdb753891b48b30af1/test_labels.json + train_labels_file: output/reports/attack/5f2e97c75be855bdb753891b48b30af1/train_labels.json + model_dir: models + name: 5f2e97c75be855bdb753891b48b30af1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 5f2e97c75be855bdb753891b48b30af1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/63e66c41552053839f6dc1d545206dda/params.yaml b/examples/security/truthseeker/output/reports/attack/63e66c41552053839f6dc1d545206dda/params.yaml new file mode 100644 index 00000000..0f3d7910 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/63e66c41552053839f6dc1d545206dda/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 2ec17b98015f3ef04fd290e89d6e6dc5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/63e66c41552053839f6dc1d545206dda/adv_predictions.json + adv_probabilities_file: output/reports/attack/63e66c41552053839f6dc1d545206dda/adv_probabilities.json + attack_file: output/attacks/7bf4c12d2d7b4d3658ae76859054d595.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/63e66c41552053839f6dc1d545206dda/params.yaml + predictions_file: output/reports/attack/63e66c41552053839f6dc1d545206dda/predictions.json + probabilities_file: output/reports/attack/63e66c41552053839f6dc1d545206dda/probabilities.json + score_dict_file: output/reports/attack/63e66c41552053839f6dc1d545206dda/score_dict.json + test_labels_file: output/reports/attack/63e66c41552053839f6dc1d545206dda/test_labels.json + train_labels_file: output/reports/attack/63e66c41552053839f6dc1d545206dda/train_labels.json + model_dir: models + name: 63e66c41552053839f6dc1d545206dda + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 63e66c41552053839f6dc1d545206dda +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/64321d110e8f751f662517a02d4fec8b/params.yaml b/examples/security/truthseeker/output/reports/attack/64321d110e8f751f662517a02d4fec8b/params.yaml new file mode 100644 index 00000000..41dd879f --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/64321d110e8f751f662517a02d4fec8b/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.3 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 055244ef24c9196b7c63269ff46213f5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/64321d110e8f751f662517a02d4fec8b/adv_predictions.json + adv_probabilities_file: output/reports/attack/64321d110e8f751f662517a02d4fec8b/adv_probabilities.json + attack_file: output/attacks/f88939e33a67e838a0a128a589b91c62.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/64321d110e8f751f662517a02d4fec8b/params.yaml + predictions_file: output/reports/attack/64321d110e8f751f662517a02d4fec8b/predictions.json + probabilities_file: output/reports/attack/64321d110e8f751f662517a02d4fec8b/probabilities.json + score_dict_file: output/reports/attack/64321d110e8f751f662517a02d4fec8b/score_dict.json + test_labels_file: output/reports/attack/64321d110e8f751f662517a02d4fec8b/test_labels.json + train_labels_file: output/reports/attack/64321d110e8f751f662517a02d4fec8b/train_labels.json + model_dir: models + name: 64321d110e8f751f662517a02d4fec8b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 64321d110e8f751f662517a02d4fec8b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/650e9a2d71f1199d65b7d935ea44ab87/params.yaml b/examples/security/truthseeker/output/reports/attack/650e9a2d71f1199d65b7d935ea44ab87/params.yaml new file mode 100644 index 00000000..3b0cfb0e --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/650e9a2d71f1199d65b7d935ea44ab87/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: a1722c54a03dc94038fbde9c99267028 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/650e9a2d71f1199d65b7d935ea44ab87/adv_predictions.json + adv_probabilities_file: output/reports/attack/650e9a2d71f1199d65b7d935ea44ab87/adv_probabilities.json + attack_file: output/attacks/be0cbc1d7f00ba541efd465c56b9ad21.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/650e9a2d71f1199d65b7d935ea44ab87/params.yaml + predictions_file: output/reports/attack/650e9a2d71f1199d65b7d935ea44ab87/predictions.json + probabilities_file: output/reports/attack/650e9a2d71f1199d65b7d935ea44ab87/probabilities.json + score_dict_file: output/reports/attack/650e9a2d71f1199d65b7d935ea44ab87/score_dict.json + test_labels_file: output/reports/attack/650e9a2d71f1199d65b7d935ea44ab87/test_labels.json + train_labels_file: output/reports/attack/650e9a2d71f1199d65b7d935ea44ab87/train_labels.json + model_dir: models + name: 650e9a2d71f1199d65b7d935ea44ab87 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 650e9a2d71f1199d65b7d935ea44ab87 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/6699cb57d4719c3486f161e1a20e4108/params.yaml b/examples/security/truthseeker/output/reports/attack/6699cb57d4719c3486f161e1a20e4108/params.yaml new file mode 100644 index 00000000..42d8c8ca --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/6699cb57d4719c3486f161e1a20e4108/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: c640869244bde77bd0972415173f4571 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6699cb57d4719c3486f161e1a20e4108/adv_predictions.json + adv_probabilities_file: output/reports/attack/6699cb57d4719c3486f161e1a20e4108/adv_probabilities.json + attack_file: output/attacks/0a0f03504eb41644c418128a722302f5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/6699cb57d4719c3486f161e1a20e4108/params.yaml + predictions_file: output/reports/attack/6699cb57d4719c3486f161e1a20e4108/predictions.json + probabilities_file: output/reports/attack/6699cb57d4719c3486f161e1a20e4108/probabilities.json + score_dict_file: output/reports/attack/6699cb57d4719c3486f161e1a20e4108/score_dict.json + test_labels_file: output/reports/attack/6699cb57d4719c3486f161e1a20e4108/test_labels.json + train_labels_file: output/reports/attack/6699cb57d4719c3486f161e1a20e4108/train_labels.json + model_dir: models + name: 6699cb57d4719c3486f161e1a20e4108 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 6699cb57d4719c3486f161e1a20e4108 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/68ad3737f3ea27fdd965747b877af599/params.yaml b/examples/security/truthseeker/output/reports/attack/68ad3737f3ea27fdd965747b877af599/params.yaml new file mode 100644 index 00000000..e8d6f600 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/68ad3737f3ea27fdd965747b877af599/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.001 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 811333200c26d5895aa44900c1c376d5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/68ad3737f3ea27fdd965747b877af599/adv_predictions.json + adv_probabilities_file: output/reports/attack/68ad3737f3ea27fdd965747b877af599/adv_probabilities.json + attack_file: output/attacks/ce53d9d97e79fb40c644365c166f6062.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/68ad3737f3ea27fdd965747b877af599/params.yaml + predictions_file: output/reports/attack/68ad3737f3ea27fdd965747b877af599/predictions.json + probabilities_file: output/reports/attack/68ad3737f3ea27fdd965747b877af599/probabilities.json + score_dict_file: output/reports/attack/68ad3737f3ea27fdd965747b877af599/score_dict.json + test_labels_file: output/reports/attack/68ad3737f3ea27fdd965747b877af599/test_labels.json + train_labels_file: output/reports/attack/68ad3737f3ea27fdd965747b877af599/train_labels.json + model_dir: models + name: 68ad3737f3ea27fdd965747b877af599 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 68ad3737f3ea27fdd965747b877af599 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/6b1a958296890ebab7570824c6d9f90c/params.yaml b/examples/security/truthseeker/output/reports/attack/6b1a958296890ebab7570824c6d9f90c/params.yaml new file mode 100644 index 00000000..97ab4043 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/6b1a958296890ebab7570824c6d9f90c/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.001 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 4a92e4872ab61ec10be11062ea673289 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6b1a958296890ebab7570824c6d9f90c/adv_predictions.json + adv_probabilities_file: output/reports/attack/6b1a958296890ebab7570824c6d9f90c/adv_probabilities.json + attack_file: output/attacks/452ae756597adef221bf86a157143c2d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/6b1a958296890ebab7570824c6d9f90c/params.yaml + predictions_file: output/reports/attack/6b1a958296890ebab7570824c6d9f90c/predictions.json + probabilities_file: output/reports/attack/6b1a958296890ebab7570824c6d9f90c/probabilities.json + score_dict_file: output/reports/attack/6b1a958296890ebab7570824c6d9f90c/score_dict.json + test_labels_file: output/reports/attack/6b1a958296890ebab7570824c6d9f90c/test_labels.json + train_labels_file: output/reports/attack/6b1a958296890ebab7570824c6d9f90c/train_labels.json + model_dir: models + name: 6b1a958296890ebab7570824c6d9f90c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 6b1a958296890ebab7570824c6d9f90c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/6b92c68425ed25adadbd5fd0ac101bd7/params.yaml b/examples/security/truthseeker/output/reports/attack/6b92c68425ed25adadbd5fd0ac101bd7/params.yaml new file mode 100644 index 00000000..09d2e260 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/6b92c68425ed25adadbd5fd0ac101bd7/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 1 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: b20047a924683ed516a9d4c20b94d3ba +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6b92c68425ed25adadbd5fd0ac101bd7/adv_predictions.json + adv_probabilities_file: output/reports/attack/6b92c68425ed25adadbd5fd0ac101bd7/adv_probabilities.json + attack_file: output/attacks/dd4130b00ff640057159f169fab610a4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/6b92c68425ed25adadbd5fd0ac101bd7/params.yaml + predictions_file: output/reports/attack/6b92c68425ed25adadbd5fd0ac101bd7/predictions.json + probabilities_file: output/reports/attack/6b92c68425ed25adadbd5fd0ac101bd7/probabilities.json + score_dict_file: output/reports/attack/6b92c68425ed25adadbd5fd0ac101bd7/score_dict.json + test_labels_file: output/reports/attack/6b92c68425ed25adadbd5fd0ac101bd7/test_labels.json + train_labels_file: output/reports/attack/6b92c68425ed25adadbd5fd0ac101bd7/train_labels.json + model_dir: models + name: 6b92c68425ed25adadbd5fd0ac101bd7 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 6b92c68425ed25adadbd5fd0ac101bd7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/6c04676f691de4b5f3f668e260be8127/params.yaml b/examples/security/truthseeker/output/reports/attack/6c04676f691de4b5f3f668e260be8127/params.yaml new file mode 100644 index 00000000..5949d2cf --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/6c04676f691de4b5f3f668e260be8127/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.01 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: d772ec3365971e489ec81889c61fbe04 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/6c04676f691de4b5f3f668e260be8127/adv_predictions.json + adv_probabilities_file: output/reports/attack/6c04676f691de4b5f3f668e260be8127/adv_probabilities.json + attack_file: output/attacks/910677845af0ebdcc7c7d788083f5b34.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/6c04676f691de4b5f3f668e260be8127/params.yaml + predictions_file: output/reports/attack/6c04676f691de4b5f3f668e260be8127/predictions.json + probabilities_file: output/reports/attack/6c04676f691de4b5f3f668e260be8127/probabilities.json + score_dict_file: output/reports/attack/6c04676f691de4b5f3f668e260be8127/score_dict.json + test_labels_file: output/reports/attack/6c04676f691de4b5f3f668e260be8127/test_labels.json + train_labels_file: output/reports/attack/6c04676f691de4b5f3f668e260be8127/train_labels.json + model_dir: models + name: 6c04676f691de4b5f3f668e260be8127 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 6c04676f691de4b5f3f668e260be8127 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/706e946bb17839da7232c94030d7b8b1/params.yaml b/examples/security/truthseeker/output/reports/attack/706e946bb17839da7232c94030d7b8b1/params.yaml new file mode 100644 index 00000000..b1b9f007 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/706e946bb17839da7232c94030d7b8b1/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 996f3ca237c020013436ff0e0d6ebff5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/706e946bb17839da7232c94030d7b8b1/adv_predictions.json + adv_probabilities_file: output/reports/attack/706e946bb17839da7232c94030d7b8b1/adv_probabilities.json + attack_file: output/attacks/7384df334fffd99b29f2f9fa564ba71e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/706e946bb17839da7232c94030d7b8b1/params.yaml + predictions_file: output/reports/attack/706e946bb17839da7232c94030d7b8b1/predictions.json + probabilities_file: output/reports/attack/706e946bb17839da7232c94030d7b8b1/probabilities.json + score_dict_file: output/reports/attack/706e946bb17839da7232c94030d7b8b1/score_dict.json + test_labels_file: output/reports/attack/706e946bb17839da7232c94030d7b8b1/test_labels.json + train_labels_file: output/reports/attack/706e946bb17839da7232c94030d7b8b1/train_labels.json + model_dir: models + name: 706e946bb17839da7232c94030d7b8b1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 706e946bb17839da7232c94030d7b8b1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/7261330ed1840614a49848d50029db79/params.yaml b/examples/security/truthseeker/output/reports/attack/7261330ed1840614a49848d50029db79/params.yaml new file mode 100644 index 00000000..9b13a143 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/7261330ed1840614a49848d50029db79/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 9ad7d70f14b1977a1dff0aaf0d77710c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/7261330ed1840614a49848d50029db79/adv_predictions.json + adv_probabilities_file: output/reports/attack/7261330ed1840614a49848d50029db79/adv_probabilities.json + attack_file: output/attacks/2ef03272a04ee6d8b4a59471344adaeb.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/7261330ed1840614a49848d50029db79/params.yaml + predictions_file: output/reports/attack/7261330ed1840614a49848d50029db79/predictions.json + probabilities_file: output/reports/attack/7261330ed1840614a49848d50029db79/probabilities.json + score_dict_file: output/reports/attack/7261330ed1840614a49848d50029db79/score_dict.json + test_labels_file: output/reports/attack/7261330ed1840614a49848d50029db79/test_labels.json + train_labels_file: output/reports/attack/7261330ed1840614a49848d50029db79/train_labels.json + model_dir: models + name: 7261330ed1840614a49848d50029db79 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 7261330ed1840614a49848d50029db79 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/73993efbc1c8d07d8503b72d86d00e66/params.yaml b/examples/security/truthseeker/output/reports/attack/73993efbc1c8d07d8503b72d86d00e66/params.yaml new file mode 100644 index 00000000..a26ac999 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/73993efbc1c8d07d8503b72d86d00e66/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 5d232371bbe7e17e83ee86a978bebc75 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/73993efbc1c8d07d8503b72d86d00e66/adv_predictions.json + adv_probabilities_file: output/reports/attack/73993efbc1c8d07d8503b72d86d00e66/adv_probabilities.json + attack_file: output/attacks/26193c8531d926986d5841222b169b3b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/73993efbc1c8d07d8503b72d86d00e66/params.yaml + predictions_file: output/reports/attack/73993efbc1c8d07d8503b72d86d00e66/predictions.json + probabilities_file: output/reports/attack/73993efbc1c8d07d8503b72d86d00e66/probabilities.json + score_dict_file: output/reports/attack/73993efbc1c8d07d8503b72d86d00e66/score_dict.json + test_labels_file: output/reports/attack/73993efbc1c8d07d8503b72d86d00e66/test_labels.json + train_labels_file: output/reports/attack/73993efbc1c8d07d8503b72d86d00e66/train_labels.json + model_dir: models + name: 73993efbc1c8d07d8503b72d86d00e66 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 73993efbc1c8d07d8503b72d86d00e66 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/739b9d5b19671d49feac77c0a0f68e8e/params.yaml b/examples/security/truthseeker/output/reports/attack/739b9d5b19671d49feac77c0a0f68e8e/params.yaml new file mode 100644 index 00000000..d4c246f8 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/739b9d5b19671d49feac77c0a0f68e8e/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 1 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 66ee508112a6d8ac7e48fb8375dfc985 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/739b9d5b19671d49feac77c0a0f68e8e/adv_predictions.json + adv_probabilities_file: output/reports/attack/739b9d5b19671d49feac77c0a0f68e8e/adv_probabilities.json + attack_file: output/attacks/325857e0ec6e6a36b2bcc04501b148d2.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/739b9d5b19671d49feac77c0a0f68e8e/params.yaml + predictions_file: output/reports/attack/739b9d5b19671d49feac77c0a0f68e8e/predictions.json + probabilities_file: output/reports/attack/739b9d5b19671d49feac77c0a0f68e8e/probabilities.json + score_dict_file: output/reports/attack/739b9d5b19671d49feac77c0a0f68e8e/score_dict.json + test_labels_file: output/reports/attack/739b9d5b19671d49feac77c0a0f68e8e/test_labels.json + train_labels_file: output/reports/attack/739b9d5b19671d49feac77c0a0f68e8e/train_labels.json + model_dir: models + name: 739b9d5b19671d49feac77c0a0f68e8e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 739b9d5b19671d49feac77c0a0f68e8e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/768e4182d7b86012c60aa33e1b4a72e1/params.yaml b/examples/security/truthseeker/output/reports/attack/768e4182d7b86012c60aa33e1b4a72e1/params.yaml new file mode 100644 index 00000000..53a04101 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/768e4182d7b86012c60aa33e1b4a72e1/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.3 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 3cf4d1179f01346980d8b006a5cea595 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/768e4182d7b86012c60aa33e1b4a72e1/adv_predictions.json + adv_probabilities_file: output/reports/attack/768e4182d7b86012c60aa33e1b4a72e1/adv_probabilities.json + attack_file: output/attacks/df1a56654365fc858930fe10e8953b2c.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/768e4182d7b86012c60aa33e1b4a72e1/params.yaml + predictions_file: output/reports/attack/768e4182d7b86012c60aa33e1b4a72e1/predictions.json + probabilities_file: output/reports/attack/768e4182d7b86012c60aa33e1b4a72e1/probabilities.json + score_dict_file: output/reports/attack/768e4182d7b86012c60aa33e1b4a72e1/score_dict.json + test_labels_file: output/reports/attack/768e4182d7b86012c60aa33e1b4a72e1/test_labels.json + train_labels_file: output/reports/attack/768e4182d7b86012c60aa33e1b4a72e1/train_labels.json + model_dir: models + name: 768e4182d7b86012c60aa33e1b4a72e1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 768e4182d7b86012c60aa33e1b4a72e1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/7edb7f490f3d60d6f44c886cd1637193/params.yaml b/examples/security/truthseeker/output/reports/attack/7edb7f490f3d60d6f44c886cd1637193/params.yaml new file mode 100644 index 00000000..a01c7078 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/7edb7f490f3d60d6f44c886cd1637193/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 9bda2db80e0c7c7e4868b480a82c0da5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/7edb7f490f3d60d6f44c886cd1637193/adv_predictions.json + adv_probabilities_file: output/reports/attack/7edb7f490f3d60d6f44c886cd1637193/adv_probabilities.json + attack_file: output/attacks/c0d14dfb63f2fdad0b7ee86964235005.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/7edb7f490f3d60d6f44c886cd1637193/params.yaml + predictions_file: output/reports/attack/7edb7f490f3d60d6f44c886cd1637193/predictions.json + probabilities_file: output/reports/attack/7edb7f490f3d60d6f44c886cd1637193/probabilities.json + score_dict_file: output/reports/attack/7edb7f490f3d60d6f44c886cd1637193/score_dict.json + test_labels_file: output/reports/attack/7edb7f490f3d60d6f44c886cd1637193/test_labels.json + train_labels_file: output/reports/attack/7edb7f490f3d60d6f44c886cd1637193/train_labels.json + model_dir: models + name: 7edb7f490f3d60d6f44c886cd1637193 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 7edb7f490f3d60d6f44c886cd1637193 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/8239ed7deecd57e969fddaf06a1e3e56/params.yaml b/examples/security/truthseeker/output/reports/attack/8239ed7deecd57e969fddaf06a1e3e56/params.yaml new file mode 100644 index 00000000..37806892 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/8239ed7deecd57e969fddaf06a1e3e56/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.001 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 980c696c4f97751752e477f201fd53bb +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8239ed7deecd57e969fddaf06a1e3e56/adv_predictions.json + adv_probabilities_file: output/reports/attack/8239ed7deecd57e969fddaf06a1e3e56/adv_probabilities.json + attack_file: output/attacks/bfc4fce2fbd269138e5589f805e08622.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/8239ed7deecd57e969fddaf06a1e3e56/params.yaml + predictions_file: output/reports/attack/8239ed7deecd57e969fddaf06a1e3e56/predictions.json + probabilities_file: output/reports/attack/8239ed7deecd57e969fddaf06a1e3e56/probabilities.json + score_dict_file: output/reports/attack/8239ed7deecd57e969fddaf06a1e3e56/score_dict.json + test_labels_file: output/reports/attack/8239ed7deecd57e969fddaf06a1e3e56/test_labels.json + train_labels_file: output/reports/attack/8239ed7deecd57e969fddaf06a1e3e56/train_labels.json + model_dir: models + name: 8239ed7deecd57e969fddaf06a1e3e56 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 8239ed7deecd57e969fddaf06a1e3e56 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/839afcd475c3032801fb6ff681ce286a/params.yaml b/examples/security/truthseeker/output/reports/attack/839afcd475c3032801fb6ff681ce286a/params.yaml new file mode 100644 index 00000000..98b20da7 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/839afcd475c3032801fb6ff681ce286a/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.001 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 6865b9a50c5c6968329f3694d446444e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/839afcd475c3032801fb6ff681ce286a/adv_predictions.json + adv_probabilities_file: output/reports/attack/839afcd475c3032801fb6ff681ce286a/adv_probabilities.json + attack_file: output/attacks/5e9be40a6a339476cb113286fbb55194.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/839afcd475c3032801fb6ff681ce286a/params.yaml + predictions_file: output/reports/attack/839afcd475c3032801fb6ff681ce286a/predictions.json + probabilities_file: output/reports/attack/839afcd475c3032801fb6ff681ce286a/probabilities.json + score_dict_file: output/reports/attack/839afcd475c3032801fb6ff681ce286a/score_dict.json + test_labels_file: output/reports/attack/839afcd475c3032801fb6ff681ce286a/test_labels.json + train_labels_file: output/reports/attack/839afcd475c3032801fb6ff681ce286a/train_labels.json + model_dir: models + name: 839afcd475c3032801fb6ff681ce286a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 839afcd475c3032801fb6ff681ce286a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/83c972bbfc40155e179f1de0392a4130/params.yaml b/examples/security/truthseeker/output/reports/attack/83c972bbfc40155e179f1de0392a4130/params.yaml new file mode 100644 index 00000000..936cf295 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/83c972bbfc40155e179f1de0392a4130/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.5 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 86aaee3cd233feedb46350666841cff1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/83c972bbfc40155e179f1de0392a4130/adv_predictions.json + adv_probabilities_file: output/reports/attack/83c972bbfc40155e179f1de0392a4130/adv_probabilities.json + attack_file: output/attacks/eed3a415a3e01c4d921f76c37da86dc2.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/83c972bbfc40155e179f1de0392a4130/params.yaml + predictions_file: output/reports/attack/83c972bbfc40155e179f1de0392a4130/predictions.json + probabilities_file: output/reports/attack/83c972bbfc40155e179f1de0392a4130/probabilities.json + score_dict_file: output/reports/attack/83c972bbfc40155e179f1de0392a4130/score_dict.json + test_labels_file: output/reports/attack/83c972bbfc40155e179f1de0392a4130/test_labels.json + train_labels_file: output/reports/attack/83c972bbfc40155e179f1de0392a4130/train_labels.json + model_dir: models + name: 83c972bbfc40155e179f1de0392a4130 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 83c972bbfc40155e179f1de0392a4130 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/83d448ecd6570f8eef80e7bf4f7383fd/params.yaml b/examples/security/truthseeker/output/reports/attack/83d448ecd6570f8eef80e7bf4f7383fd/params.yaml new file mode 100644 index 00000000..62f26e93 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/83d448ecd6570f8eef80e7bf4f7383fd/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 2042695730293bcf8ae19e48703269e6 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/83d448ecd6570f8eef80e7bf4f7383fd/adv_predictions.json + adv_probabilities_file: output/reports/attack/83d448ecd6570f8eef80e7bf4f7383fd/adv_probabilities.json + attack_file: output/attacks/6f1ef4918cf617c33b10d82ea5b0e40f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/83d448ecd6570f8eef80e7bf4f7383fd/params.yaml + predictions_file: output/reports/attack/83d448ecd6570f8eef80e7bf4f7383fd/predictions.json + probabilities_file: output/reports/attack/83d448ecd6570f8eef80e7bf4f7383fd/probabilities.json + score_dict_file: output/reports/attack/83d448ecd6570f8eef80e7bf4f7383fd/score_dict.json + test_labels_file: output/reports/attack/83d448ecd6570f8eef80e7bf4f7383fd/test_labels.json + train_labels_file: output/reports/attack/83d448ecd6570f8eef80e7bf4f7383fd/train_labels.json + model_dir: models + name: 83d448ecd6570f8eef80e7bf4f7383fd + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 83d448ecd6570f8eef80e7bf4f7383fd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/8438bfaa4a07eaa55b73551d11379fe9/params.yaml b/examples/security/truthseeker/output/reports/attack/8438bfaa4a07eaa55b73551d11379fe9/params.yaml new file mode 100644 index 00000000..e4f48dab --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/8438bfaa4a07eaa55b73551d11379fe9/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.001 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 7ab38090003a2a6915c4f46c9f5aafb2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8438bfaa4a07eaa55b73551d11379fe9/adv_predictions.json + adv_probabilities_file: output/reports/attack/8438bfaa4a07eaa55b73551d11379fe9/adv_probabilities.json + attack_file: output/attacks/80862e866088174710435c76201c0dc5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/8438bfaa4a07eaa55b73551d11379fe9/params.yaml + predictions_file: output/reports/attack/8438bfaa4a07eaa55b73551d11379fe9/predictions.json + probabilities_file: output/reports/attack/8438bfaa4a07eaa55b73551d11379fe9/probabilities.json + score_dict_file: output/reports/attack/8438bfaa4a07eaa55b73551d11379fe9/score_dict.json + test_labels_file: output/reports/attack/8438bfaa4a07eaa55b73551d11379fe9/test_labels.json + train_labels_file: output/reports/attack/8438bfaa4a07eaa55b73551d11379fe9/train_labels.json + model_dir: models + name: 8438bfaa4a07eaa55b73551d11379fe9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 8438bfaa4a07eaa55b73551d11379fe9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/84a91ccdefd989d76c4ca437905fe2f6/params.yaml b/examples/security/truthseeker/output/reports/attack/84a91ccdefd989d76c4ca437905fe2f6/params.yaml new file mode 100644 index 00000000..85f3d713 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/84a91ccdefd989d76c4ca437905fe2f6/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: b15372dcaff0d732d78c20a3c30b3f8c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/84a91ccdefd989d76c4ca437905fe2f6/adv_predictions.json + adv_probabilities_file: output/reports/attack/84a91ccdefd989d76c4ca437905fe2f6/adv_probabilities.json + attack_file: output/attacks/ed2d403ac910e126eb896916bb9eb5f3.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/84a91ccdefd989d76c4ca437905fe2f6/params.yaml + predictions_file: output/reports/attack/84a91ccdefd989d76c4ca437905fe2f6/predictions.json + probabilities_file: output/reports/attack/84a91ccdefd989d76c4ca437905fe2f6/probabilities.json + score_dict_file: output/reports/attack/84a91ccdefd989d76c4ca437905fe2f6/score_dict.json + test_labels_file: output/reports/attack/84a91ccdefd989d76c4ca437905fe2f6/test_labels.json + train_labels_file: output/reports/attack/84a91ccdefd989d76c4ca437905fe2f6/train_labels.json + model_dir: models + name: 84a91ccdefd989d76c4ca437905fe2f6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 84a91ccdefd989d76c4ca437905fe2f6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/87af630f72cd0b196b480b030b90235a/params.yaml b/examples/security/truthseeker/output/reports/attack/87af630f72cd0b196b480b030b90235a/params.yaml new file mode 100644 index 00000000..b3ea45a2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/87af630f72cd0b196b480b030b90235a/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 5c694e6e9e9baeda9779aaa59d185cc4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/87af630f72cd0b196b480b030b90235a/adv_predictions.json + adv_probabilities_file: output/reports/attack/87af630f72cd0b196b480b030b90235a/adv_probabilities.json + attack_file: output/attacks/eb9406e8260ecb08ca0119bd68aba88f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/87af630f72cd0b196b480b030b90235a/params.yaml + predictions_file: output/reports/attack/87af630f72cd0b196b480b030b90235a/predictions.json + probabilities_file: output/reports/attack/87af630f72cd0b196b480b030b90235a/probabilities.json + score_dict_file: output/reports/attack/87af630f72cd0b196b480b030b90235a/score_dict.json + test_labels_file: output/reports/attack/87af630f72cd0b196b480b030b90235a/test_labels.json + train_labels_file: output/reports/attack/87af630f72cd0b196b480b030b90235a/train_labels.json + model_dir: models + name: 87af630f72cd0b196b480b030b90235a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 87af630f72cd0b196b480b030b90235a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/89fb21886eec82a7079b422231c94f30/params.yaml b/examples/security/truthseeker/output/reports/attack/89fb21886eec82a7079b422231c94f30/params.yaml new file mode 100644 index 00000000..da83c4d6 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/89fb21886eec82a7079b422231c94f30/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: c728664dc1331ce514d69c07ca73e8ad +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/89fb21886eec82a7079b422231c94f30/adv_predictions.json + adv_probabilities_file: output/reports/attack/89fb21886eec82a7079b422231c94f30/adv_probabilities.json + attack_file: output/attacks/9a38d4c0cc852e6eaf16a12467c99ba0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/89fb21886eec82a7079b422231c94f30/params.yaml + predictions_file: output/reports/attack/89fb21886eec82a7079b422231c94f30/predictions.json + probabilities_file: output/reports/attack/89fb21886eec82a7079b422231c94f30/probabilities.json + score_dict_file: output/reports/attack/89fb21886eec82a7079b422231c94f30/score_dict.json + test_labels_file: output/reports/attack/89fb21886eec82a7079b422231c94f30/test_labels.json + train_labels_file: output/reports/attack/89fb21886eec82a7079b422231c94f30/train_labels.json + model_dir: models + name: 89fb21886eec82a7079b422231c94f30 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 89fb21886eec82a7079b422231c94f30 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/8c56a2c19c61c0f29334128256d10ba4/params.yaml b/examples/security/truthseeker/output/reports/attack/8c56a2c19c61c0f29334128256d10ba4/params.yaml new file mode 100644 index 00000000..9091ec72 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/8c56a2c19c61c0f29334128256d10ba4/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 930f67fc12dec6e2e76ae6ed8a516a07 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8c56a2c19c61c0f29334128256d10ba4/adv_predictions.json + adv_probabilities_file: output/reports/attack/8c56a2c19c61c0f29334128256d10ba4/adv_probabilities.json + attack_file: output/attacks/44d500209c3808c5a148efd8de280245.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/8c56a2c19c61c0f29334128256d10ba4/params.yaml + predictions_file: output/reports/attack/8c56a2c19c61c0f29334128256d10ba4/predictions.json + probabilities_file: output/reports/attack/8c56a2c19c61c0f29334128256d10ba4/probabilities.json + score_dict_file: output/reports/attack/8c56a2c19c61c0f29334128256d10ba4/score_dict.json + test_labels_file: output/reports/attack/8c56a2c19c61c0f29334128256d10ba4/test_labels.json + train_labels_file: output/reports/attack/8c56a2c19c61c0f29334128256d10ba4/train_labels.json + model_dir: models + name: 8c56a2c19c61c0f29334128256d10ba4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 8c56a2c19c61c0f29334128256d10ba4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/8dacb1e7acd7edb2395ba52f82ec1656/params.yaml b/examples/security/truthseeker/output/reports/attack/8dacb1e7acd7edb2395ba52f82ec1656/params.yaml new file mode 100644 index 00000000..e09f5ad6 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/8dacb1e7acd7edb2395ba52f82ec1656/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.001 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: e308664fc85c0650c2db57e6c9448d1a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/8dacb1e7acd7edb2395ba52f82ec1656/adv_predictions.json + adv_probabilities_file: output/reports/attack/8dacb1e7acd7edb2395ba52f82ec1656/adv_probabilities.json + attack_file: output/attacks/de6502b7c9fe59ace6bf4718f201c347.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/8dacb1e7acd7edb2395ba52f82ec1656/params.yaml + predictions_file: output/reports/attack/8dacb1e7acd7edb2395ba52f82ec1656/predictions.json + probabilities_file: output/reports/attack/8dacb1e7acd7edb2395ba52f82ec1656/probabilities.json + score_dict_file: output/reports/attack/8dacb1e7acd7edb2395ba52f82ec1656/score_dict.json + test_labels_file: output/reports/attack/8dacb1e7acd7edb2395ba52f82ec1656/test_labels.json + train_labels_file: output/reports/attack/8dacb1e7acd7edb2395ba52f82ec1656/train_labels.json + model_dir: models + name: 8dacb1e7acd7edb2395ba52f82ec1656 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 8dacb1e7acd7edb2395ba52f82ec1656 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/90abd8f7564398fdad00f9dd70b5ff1a/params.yaml b/examples/security/truthseeker/output/reports/attack/90abd8f7564398fdad00f9dd70b5ff1a/params.yaml new file mode 100644 index 00000000..5e4a7ec9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/90abd8f7564398fdad00f9dd70b5ff1a/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.5 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 2eed44f2d7ea049d79a0eb3c9d87f993 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/90abd8f7564398fdad00f9dd70b5ff1a/adv_predictions.json + adv_probabilities_file: output/reports/attack/90abd8f7564398fdad00f9dd70b5ff1a/adv_probabilities.json + attack_file: output/attacks/f1a33599a1858f942611e7bb6cba87fb.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/90abd8f7564398fdad00f9dd70b5ff1a/params.yaml + predictions_file: output/reports/attack/90abd8f7564398fdad00f9dd70b5ff1a/predictions.json + probabilities_file: output/reports/attack/90abd8f7564398fdad00f9dd70b5ff1a/probabilities.json + score_dict_file: output/reports/attack/90abd8f7564398fdad00f9dd70b5ff1a/score_dict.json + test_labels_file: output/reports/attack/90abd8f7564398fdad00f9dd70b5ff1a/test_labels.json + train_labels_file: output/reports/attack/90abd8f7564398fdad00f9dd70b5ff1a/train_labels.json + model_dir: models + name: 90abd8f7564398fdad00f9dd70b5ff1a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 90abd8f7564398fdad00f9dd70b5ff1a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/92136dcb21bcff9ce5ecedf031a56c61/params.yaml b/examples/security/truthseeker/output/reports/attack/92136dcb21bcff9ce5ecedf031a56c61/params.yaml new file mode 100644 index 00000000..a89de72e --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/92136dcb21bcff9ce5ecedf031a56c61/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.5 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: def15f966fd9b0120104c5fee0b70039 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/92136dcb21bcff9ce5ecedf031a56c61/adv_predictions.json + adv_probabilities_file: output/reports/attack/92136dcb21bcff9ce5ecedf031a56c61/adv_probabilities.json + attack_file: output/attacks/7fce1da53175b01de6397ab9fab434df.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/92136dcb21bcff9ce5ecedf031a56c61/params.yaml + predictions_file: output/reports/attack/92136dcb21bcff9ce5ecedf031a56c61/predictions.json + probabilities_file: output/reports/attack/92136dcb21bcff9ce5ecedf031a56c61/probabilities.json + score_dict_file: output/reports/attack/92136dcb21bcff9ce5ecedf031a56c61/score_dict.json + test_labels_file: output/reports/attack/92136dcb21bcff9ce5ecedf031a56c61/test_labels.json + train_labels_file: output/reports/attack/92136dcb21bcff9ce5ecedf031a56c61/train_labels.json + model_dir: models + name: 92136dcb21bcff9ce5ecedf031a56c61 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 92136dcb21bcff9ce5ecedf031a56c61 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/92194152bb21c6ffaf4c300230a8b88c/params.yaml b/examples/security/truthseeker/output/reports/attack/92194152bb21c6ffaf4c300230a8b88c/params.yaml new file mode 100644 index 00000000..725fc2e4 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/92194152bb21c6ffaf4c300230a8b88c/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.001 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: c711a443e1c4335e6022adc20635c429 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/92194152bb21c6ffaf4c300230a8b88c/adv_predictions.json + adv_probabilities_file: output/reports/attack/92194152bb21c6ffaf4c300230a8b88c/adv_probabilities.json + attack_file: output/attacks/9dca82aff4f2dc3ae8b09f1abe118d76.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/92194152bb21c6ffaf4c300230a8b88c/params.yaml + predictions_file: output/reports/attack/92194152bb21c6ffaf4c300230a8b88c/predictions.json + probabilities_file: output/reports/attack/92194152bb21c6ffaf4c300230a8b88c/probabilities.json + score_dict_file: output/reports/attack/92194152bb21c6ffaf4c300230a8b88c/score_dict.json + test_labels_file: output/reports/attack/92194152bb21c6ffaf4c300230a8b88c/test_labels.json + train_labels_file: output/reports/attack/92194152bb21c6ffaf4c300230a8b88c/train_labels.json + model_dir: models + name: 92194152bb21c6ffaf4c300230a8b88c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 92194152bb21c6ffaf4c300230a8b88c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/92915f682a0d8b9b912686a951ee5069/params.yaml b/examples/security/truthseeker/output/reports/attack/92915f682a0d8b9b912686a951ee5069/params.yaml new file mode 100644 index 00000000..4f990112 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/92915f682a0d8b9b912686a951ee5069/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.3 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 7b90cd2c638c68c9cc76adcb7f8e1fb3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/92915f682a0d8b9b912686a951ee5069/adv_predictions.json + adv_probabilities_file: output/reports/attack/92915f682a0d8b9b912686a951ee5069/adv_probabilities.json + attack_file: output/attacks/9d63a318e70f57ba4636e9241b428c22.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/92915f682a0d8b9b912686a951ee5069/params.yaml + predictions_file: output/reports/attack/92915f682a0d8b9b912686a951ee5069/predictions.json + probabilities_file: output/reports/attack/92915f682a0d8b9b912686a951ee5069/probabilities.json + score_dict_file: output/reports/attack/92915f682a0d8b9b912686a951ee5069/score_dict.json + test_labels_file: output/reports/attack/92915f682a0d8b9b912686a951ee5069/test_labels.json + train_labels_file: output/reports/attack/92915f682a0d8b9b912686a951ee5069/train_labels.json + model_dir: models + name: 92915f682a0d8b9b912686a951ee5069 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 92915f682a0d8b9b912686a951ee5069 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/9373eec80192a26ff87aa4aa803c8c9e/params.yaml b/examples/security/truthseeker/output/reports/attack/9373eec80192a26ff87aa4aa803c8c9e/params.yaml new file mode 100644 index 00000000..ca6867b8 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/9373eec80192a26ff87aa4aa803c8c9e/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: ab771f7a6f0f1a84ca9bab940d547a2a +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9373eec80192a26ff87aa4aa803c8c9e/adv_predictions.json + adv_probabilities_file: output/reports/attack/9373eec80192a26ff87aa4aa803c8c9e/adv_probabilities.json + attack_file: output/attacks/5ccb6bfdd12dd43dd2884785dae1f357.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/9373eec80192a26ff87aa4aa803c8c9e/params.yaml + predictions_file: output/reports/attack/9373eec80192a26ff87aa4aa803c8c9e/predictions.json + probabilities_file: output/reports/attack/9373eec80192a26ff87aa4aa803c8c9e/probabilities.json + score_dict_file: output/reports/attack/9373eec80192a26ff87aa4aa803c8c9e/score_dict.json + test_labels_file: output/reports/attack/9373eec80192a26ff87aa4aa803c8c9e/test_labels.json + train_labels_file: output/reports/attack/9373eec80192a26ff87aa4aa803c8c9e/train_labels.json + model_dir: models + name: 9373eec80192a26ff87aa4aa803c8c9e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 9373eec80192a26ff87aa4aa803c8c9e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/94287f347a6c3c2d677e139afa73d5ab/params.yaml b/examples/security/truthseeker/output/reports/attack/94287f347a6c3c2d677e139afa73d5ab/params.yaml new file mode 100644 index 00000000..685630b6 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/94287f347a6c3c2d677e139afa73d5ab/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.5 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 5f97739d199c3308d59be77ca13f64d7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/94287f347a6c3c2d677e139afa73d5ab/adv_predictions.json + adv_probabilities_file: output/reports/attack/94287f347a6c3c2d677e139afa73d5ab/adv_probabilities.json + attack_file: output/attacks/93031d19d3e9d66c1c35b7136ad4d125.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/94287f347a6c3c2d677e139afa73d5ab/params.yaml + predictions_file: output/reports/attack/94287f347a6c3c2d677e139afa73d5ab/predictions.json + probabilities_file: output/reports/attack/94287f347a6c3c2d677e139afa73d5ab/probabilities.json + score_dict_file: output/reports/attack/94287f347a6c3c2d677e139afa73d5ab/score_dict.json + test_labels_file: output/reports/attack/94287f347a6c3c2d677e139afa73d5ab/test_labels.json + train_labels_file: output/reports/attack/94287f347a6c3c2d677e139afa73d5ab/train_labels.json + model_dir: models + name: 94287f347a6c3c2d677e139afa73d5ab + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 94287f347a6c3c2d677e139afa73d5ab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/96a2a9b4edf05116e45578ca6b34c901/params.yaml b/examples/security/truthseeker/output/reports/attack/96a2a9b4edf05116e45578ca6b34c901/params.yaml new file mode 100644 index 00000000..ca2b1853 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/96a2a9b4edf05116e45578ca6b34c901/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 7a65d4403dbd5220210d4a99daf767c8 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/96a2a9b4edf05116e45578ca6b34c901/adv_predictions.json + adv_probabilities_file: output/reports/attack/96a2a9b4edf05116e45578ca6b34c901/adv_probabilities.json + attack_file: output/attacks/7e9e579a891afa0df62ef4266a8d88e8.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/96a2a9b4edf05116e45578ca6b34c901/params.yaml + predictions_file: output/reports/attack/96a2a9b4edf05116e45578ca6b34c901/predictions.json + probabilities_file: output/reports/attack/96a2a9b4edf05116e45578ca6b34c901/probabilities.json + score_dict_file: output/reports/attack/96a2a9b4edf05116e45578ca6b34c901/score_dict.json + test_labels_file: output/reports/attack/96a2a9b4edf05116e45578ca6b34c901/test_labels.json + train_labels_file: output/reports/attack/96a2a9b4edf05116e45578ca6b34c901/train_labels.json + model_dir: models + name: 96a2a9b4edf05116e45578ca6b34c901 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 96a2a9b4edf05116e45578ca6b34c901 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/98ab16c2c301daf1454384a2231623c1/params.yaml b/examples/security/truthseeker/output/reports/attack/98ab16c2c301daf1454384a2231623c1/params.yaml new file mode 100644 index 00000000..29dca17b --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/98ab16c2c301daf1454384a2231623c1/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.3 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: a978d9dfb237db6c5108f48e697a1712 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/98ab16c2c301daf1454384a2231623c1/adv_predictions.json + adv_probabilities_file: output/reports/attack/98ab16c2c301daf1454384a2231623c1/adv_probabilities.json + attack_file: output/attacks/d0c6bdabbb4f1999a265643eb77a6309.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/98ab16c2c301daf1454384a2231623c1/params.yaml + predictions_file: output/reports/attack/98ab16c2c301daf1454384a2231623c1/predictions.json + probabilities_file: output/reports/attack/98ab16c2c301daf1454384a2231623c1/probabilities.json + score_dict_file: output/reports/attack/98ab16c2c301daf1454384a2231623c1/score_dict.json + test_labels_file: output/reports/attack/98ab16c2c301daf1454384a2231623c1/test_labels.json + train_labels_file: output/reports/attack/98ab16c2c301daf1454384a2231623c1/train_labels.json + model_dir: models + name: 98ab16c2c301daf1454384a2231623c1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 98ab16c2c301daf1454384a2231623c1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/9ccceb399eb69d4cbe902e881cd655f9/params.yaml b/examples/security/truthseeker/output/reports/attack/9ccceb399eb69d4cbe902e881cd655f9/params.yaml new file mode 100644 index 00000000..536ced91 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/9ccceb399eb69d4cbe902e881cd655f9/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.5 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 8316f9db181a2e1b7d2139e3f47c424c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9ccceb399eb69d4cbe902e881cd655f9/adv_predictions.json + adv_probabilities_file: output/reports/attack/9ccceb399eb69d4cbe902e881cd655f9/adv_probabilities.json + attack_file: output/attacks/1629f17f1a6396c463ec5e4690be9369.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/9ccceb399eb69d4cbe902e881cd655f9/params.yaml + predictions_file: output/reports/attack/9ccceb399eb69d4cbe902e881cd655f9/predictions.json + probabilities_file: output/reports/attack/9ccceb399eb69d4cbe902e881cd655f9/probabilities.json + score_dict_file: output/reports/attack/9ccceb399eb69d4cbe902e881cd655f9/score_dict.json + test_labels_file: output/reports/attack/9ccceb399eb69d4cbe902e881cd655f9/test_labels.json + train_labels_file: output/reports/attack/9ccceb399eb69d4cbe902e881cd655f9/train_labels.json + model_dir: models + name: 9ccceb399eb69d4cbe902e881cd655f9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 9ccceb399eb69d4cbe902e881cd655f9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/params.yaml b/examples/security/truthseeker/output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/params.yaml new file mode 100644 index 00000000..d73e982d --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: bbda3a0e3450e72196b52dcee4001c4e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/adv_predictions.json + adv_probabilities_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/adv_probabilities.json + attack_file: output/attacks/ddf969f554f4543dc2f125f772a2220d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/params.yaml + predictions_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/predictions.json + probabilities_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/probabilities.json + score_dict_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/score_dict.json + test_labels_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/test_labels.json + train_labels_file: output/reports/attack/9ec8f3e4f43d1b31320f5164422ca1a2/train_labels.json + model_dir: models + name: 9ec8f3e4f43d1b31320f5164422ca1a2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 9ec8f3e4f43d1b31320f5164422ca1a2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/9ee6ed48648ecc79aa3e15bec80d0e7e/params.yaml b/examples/security/truthseeker/output/reports/attack/9ee6ed48648ecc79aa3e15bec80d0e7e/params.yaml new file mode 100644 index 00000000..2e50bbf5 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/9ee6ed48648ecc79aa3e15bec80d0e7e/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: deeee3cde963e545e1893b8b1f44e43d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9ee6ed48648ecc79aa3e15bec80d0e7e/adv_predictions.json + adv_probabilities_file: output/reports/attack/9ee6ed48648ecc79aa3e15bec80d0e7e/adv_probabilities.json + attack_file: output/attacks/d0af42d0d077c36f7b414dc26143dec0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/9ee6ed48648ecc79aa3e15bec80d0e7e/params.yaml + predictions_file: output/reports/attack/9ee6ed48648ecc79aa3e15bec80d0e7e/predictions.json + probabilities_file: output/reports/attack/9ee6ed48648ecc79aa3e15bec80d0e7e/probabilities.json + score_dict_file: output/reports/attack/9ee6ed48648ecc79aa3e15bec80d0e7e/score_dict.json + test_labels_file: output/reports/attack/9ee6ed48648ecc79aa3e15bec80d0e7e/test_labels.json + train_labels_file: output/reports/attack/9ee6ed48648ecc79aa3e15bec80d0e7e/train_labels.json + model_dir: models + name: 9ee6ed48648ecc79aa3e15bec80d0e7e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 9ee6ed48648ecc79aa3e15bec80d0e7e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/9f1a3ffd8c921f7ab6bad892ef2b2ac0/params.yaml b/examples/security/truthseeker/output/reports/attack/9f1a3ffd8c921f7ab6bad892ef2b2ac0/params.yaml new file mode 100644 index 00000000..30144e97 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/9f1a3ffd8c921f7ab6bad892ef2b2ac0/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.1 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 23da9ace779f3a9b69d21be2611eb7dd +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9f1a3ffd8c921f7ab6bad892ef2b2ac0/adv_predictions.json + adv_probabilities_file: output/reports/attack/9f1a3ffd8c921f7ab6bad892ef2b2ac0/adv_probabilities.json + attack_file: output/attacks/a28d0dbf0da6e35a1fc8724b5af23cc5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/9f1a3ffd8c921f7ab6bad892ef2b2ac0/params.yaml + predictions_file: output/reports/attack/9f1a3ffd8c921f7ab6bad892ef2b2ac0/predictions.json + probabilities_file: output/reports/attack/9f1a3ffd8c921f7ab6bad892ef2b2ac0/probabilities.json + score_dict_file: output/reports/attack/9f1a3ffd8c921f7ab6bad892ef2b2ac0/score_dict.json + test_labels_file: output/reports/attack/9f1a3ffd8c921f7ab6bad892ef2b2ac0/test_labels.json + train_labels_file: output/reports/attack/9f1a3ffd8c921f7ab6bad892ef2b2ac0/train_labels.json + model_dir: models + name: 9f1a3ffd8c921f7ab6bad892ef2b2ac0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 9f1a3ffd8c921f7ab6bad892ef2b2ac0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/9fbc1e498275a56c4f035376f0bfcd69/params.yaml b/examples/security/truthseeker/output/reports/attack/9fbc1e498275a56c4f035376f0bfcd69/params.yaml new file mode 100644 index 00000000..114f3bc8 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/9fbc1e498275a56c4f035376f0bfcd69/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 656950226cb6aa0b17221575380cb8f4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/9fbc1e498275a56c4f035376f0bfcd69/adv_predictions.json + adv_probabilities_file: output/reports/attack/9fbc1e498275a56c4f035376f0bfcd69/adv_probabilities.json + attack_file: output/attacks/747dff24c86b3761b8f80c62ce75f8d0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/9fbc1e498275a56c4f035376f0bfcd69/params.yaml + predictions_file: output/reports/attack/9fbc1e498275a56c4f035376f0bfcd69/predictions.json + probabilities_file: output/reports/attack/9fbc1e498275a56c4f035376f0bfcd69/probabilities.json + score_dict_file: output/reports/attack/9fbc1e498275a56c4f035376f0bfcd69/score_dict.json + test_labels_file: output/reports/attack/9fbc1e498275a56c4f035376f0bfcd69/test_labels.json + train_labels_file: output/reports/attack/9fbc1e498275a56c4f035376f0bfcd69/train_labels.json + model_dir: models + name: 9fbc1e498275a56c4f035376f0bfcd69 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 9fbc1e498275a56c4f035376f0bfcd69 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/a52ba90d90930b56ad07bb4afd4aa9ea/params.yaml b/examples/security/truthseeker/output/reports/attack/a52ba90d90930b56ad07bb4afd4aa9ea/params.yaml new file mode 100644 index 00000000..57834b75 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/a52ba90d90930b56ad07bb4afd4aa9ea/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: a2315d7f38babb2f201e6f9bf01c889b +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a52ba90d90930b56ad07bb4afd4aa9ea/adv_predictions.json + adv_probabilities_file: output/reports/attack/a52ba90d90930b56ad07bb4afd4aa9ea/adv_probabilities.json + attack_file: output/attacks/a277fe8fcc81571782d48ee6abe39e88.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/a52ba90d90930b56ad07bb4afd4aa9ea/params.yaml + predictions_file: output/reports/attack/a52ba90d90930b56ad07bb4afd4aa9ea/predictions.json + probabilities_file: output/reports/attack/a52ba90d90930b56ad07bb4afd4aa9ea/probabilities.json + score_dict_file: output/reports/attack/a52ba90d90930b56ad07bb4afd4aa9ea/score_dict.json + test_labels_file: output/reports/attack/a52ba90d90930b56ad07bb4afd4aa9ea/test_labels.json + train_labels_file: output/reports/attack/a52ba90d90930b56ad07bb4afd4aa9ea/train_labels.json + model_dir: models + name: a52ba90d90930b56ad07bb4afd4aa9ea + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: a52ba90d90930b56ad07bb4afd4aa9ea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/a61643558a23ca207f77b329e43b90df/params.yaml b/examples/security/truthseeker/output/reports/attack/a61643558a23ca207f77b329e43b90df/params.yaml new file mode 100644 index 00000000..42e6bfd9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/a61643558a23ca207f77b329e43b90df/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: f4f45aa1cbc640ac9149a2704fce985e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a61643558a23ca207f77b329e43b90df/adv_predictions.json + adv_probabilities_file: output/reports/attack/a61643558a23ca207f77b329e43b90df/adv_probabilities.json + attack_file: output/attacks/ba63bc8255f608c61b45ae72a7d2a225.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/a61643558a23ca207f77b329e43b90df/params.yaml + predictions_file: output/reports/attack/a61643558a23ca207f77b329e43b90df/predictions.json + probabilities_file: output/reports/attack/a61643558a23ca207f77b329e43b90df/probabilities.json + score_dict_file: output/reports/attack/a61643558a23ca207f77b329e43b90df/score_dict.json + test_labels_file: output/reports/attack/a61643558a23ca207f77b329e43b90df/test_labels.json + train_labels_file: output/reports/attack/a61643558a23ca207f77b329e43b90df/train_labels.json + model_dir: models + name: a61643558a23ca207f77b329e43b90df + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: a61643558a23ca207f77b329e43b90df +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/params.yaml b/examples/security/truthseeker/output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/params.yaml new file mode 100644 index 00000000..08512600 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.001 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: c5987a2c5d270afe698e11b3256f0f64 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/adv_predictions.json + adv_probabilities_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/adv_probabilities.json + attack_file: output/attacks/2a65f4c64a48dcb2ddea2f4244985311.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/params.yaml + predictions_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/predictions.json + probabilities_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/probabilities.json + score_dict_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/score_dict.json + test_labels_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/test_labels.json + train_labels_file: output/reports/attack/a85cb94d158ef3b8ad1c777803ed8dbe/train_labels.json + model_dir: models + name: a85cb94d158ef3b8ad1c777803ed8dbe + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: a85cb94d158ef3b8ad1c777803ed8dbe +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/a893fd958080ecd39087e3671d4ffec5/params.yaml b/examples/security/truthseeker/output/reports/attack/a893fd958080ecd39087e3671d4ffec5/params.yaml new file mode 100644 index 00000000..481b5635 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/a893fd958080ecd39087e3671d4ffec5/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.001 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: edc096962fa3ec4c356477b042b16827 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a893fd958080ecd39087e3671d4ffec5/adv_predictions.json + adv_probabilities_file: output/reports/attack/a893fd958080ecd39087e3671d4ffec5/adv_probabilities.json + attack_file: output/attacks/895af78d970c681d962b32ea7dc4e366.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/a893fd958080ecd39087e3671d4ffec5/params.yaml + predictions_file: output/reports/attack/a893fd958080ecd39087e3671d4ffec5/predictions.json + probabilities_file: output/reports/attack/a893fd958080ecd39087e3671d4ffec5/probabilities.json + score_dict_file: output/reports/attack/a893fd958080ecd39087e3671d4ffec5/score_dict.json + test_labels_file: output/reports/attack/a893fd958080ecd39087e3671d4ffec5/test_labels.json + train_labels_file: output/reports/attack/a893fd958080ecd39087e3671d4ffec5/train_labels.json + model_dir: models + name: a893fd958080ecd39087e3671d4ffec5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: a893fd958080ecd39087e3671d4ffec5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/a9832bd6341564e1a48989483c54a8de/params.yaml b/examples/security/truthseeker/output/reports/attack/a9832bd6341564e1a48989483c54a8de/params.yaml new file mode 100644 index 00000000..ab13c723 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/a9832bd6341564e1a48989483c54a8de/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.01 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: c251ae5987c885cfbb415bf7542860e4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/a9832bd6341564e1a48989483c54a8de/adv_predictions.json + adv_probabilities_file: output/reports/attack/a9832bd6341564e1a48989483c54a8de/adv_probabilities.json + attack_file: output/attacks/6cd048f9fab1131259bcc3ed900269b6.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/a9832bd6341564e1a48989483c54a8de/params.yaml + predictions_file: output/reports/attack/a9832bd6341564e1a48989483c54a8de/predictions.json + probabilities_file: output/reports/attack/a9832bd6341564e1a48989483c54a8de/probabilities.json + score_dict_file: output/reports/attack/a9832bd6341564e1a48989483c54a8de/score_dict.json + test_labels_file: output/reports/attack/a9832bd6341564e1a48989483c54a8de/test_labels.json + train_labels_file: output/reports/attack/a9832bd6341564e1a48989483c54a8de/train_labels.json + model_dir: models + name: a9832bd6341564e1a48989483c54a8de + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: a9832bd6341564e1a48989483c54a8de +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/ab72c325eddccaa60dce7823a73492b5/params.yaml b/examples/security/truthseeker/output/reports/attack/ab72c325eddccaa60dce7823a73492b5/params.yaml new file mode 100644 index 00000000..fd8a4538 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/ab72c325eddccaa60dce7823a73492b5/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 0.001 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 085c40ce825d079f2a6d4af553dca117 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ab72c325eddccaa60dce7823a73492b5/adv_predictions.json + adv_probabilities_file: output/reports/attack/ab72c325eddccaa60dce7823a73492b5/adv_probabilities.json + attack_file: output/attacks/0c8a1af3dd82b313c4afb15e5c2c743b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/ab72c325eddccaa60dce7823a73492b5/params.yaml + predictions_file: output/reports/attack/ab72c325eddccaa60dce7823a73492b5/predictions.json + probabilities_file: output/reports/attack/ab72c325eddccaa60dce7823a73492b5/probabilities.json + score_dict_file: output/reports/attack/ab72c325eddccaa60dce7823a73492b5/score_dict.json + test_labels_file: output/reports/attack/ab72c325eddccaa60dce7823a73492b5/test_labels.json + train_labels_file: output/reports/attack/ab72c325eddccaa60dce7823a73492b5/train_labels.json + model_dir: models + name: ab72c325eddccaa60dce7823a73492b5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: ab72c325eddccaa60dce7823a73492b5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/ab80718cf2f6ba3d61ba7f343d8078f1/params.yaml b/examples/security/truthseeker/output/reports/attack/ab80718cf2f6ba3d61ba7f343d8078f1/params.yaml new file mode 100644 index 00000000..6902266e --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/ab80718cf2f6ba3d61ba7f343d8078f1/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 0.5 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: bbce1239a16eaf6e5f8420da3532cb66 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ab80718cf2f6ba3d61ba7f343d8078f1/adv_predictions.json + adv_probabilities_file: output/reports/attack/ab80718cf2f6ba3d61ba7f343d8078f1/adv_probabilities.json + attack_file: output/attacks/9e71e1d9ad6a6a8b139909e187faeefe.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/ab80718cf2f6ba3d61ba7f343d8078f1/params.yaml + predictions_file: output/reports/attack/ab80718cf2f6ba3d61ba7f343d8078f1/predictions.json + probabilities_file: output/reports/attack/ab80718cf2f6ba3d61ba7f343d8078f1/probabilities.json + score_dict_file: output/reports/attack/ab80718cf2f6ba3d61ba7f343d8078f1/score_dict.json + test_labels_file: output/reports/attack/ab80718cf2f6ba3d61ba7f343d8078f1/test_labels.json + train_labels_file: output/reports/attack/ab80718cf2f6ba3d61ba7f343d8078f1/train_labels.json + model_dir: models + name: ab80718cf2f6ba3d61ba7f343d8078f1 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: ab80718cf2f6ba3d61ba7f343d8078f1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/ae05537ce4e28a222bc60b9c41479047/params.yaml b/examples/security/truthseeker/output/reports/attack/ae05537ce4e28a222bc60b9c41479047/params.yaml new file mode 100644 index 00000000..64b3a9b1 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/ae05537ce4e28a222bc60b9c41479047/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 1 + eps_step: 1 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 9d0f99293c7401bcaf1803354be6eaf4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ae05537ce4e28a222bc60b9c41479047/adv_predictions.json + adv_probabilities_file: output/reports/attack/ae05537ce4e28a222bc60b9c41479047/adv_probabilities.json + attack_file: output/attacks/0c0f0720d0b56e0cf2dd42e58b01285e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/ae05537ce4e28a222bc60b9c41479047/params.yaml + predictions_file: output/reports/attack/ae05537ce4e28a222bc60b9c41479047/predictions.json + probabilities_file: output/reports/attack/ae05537ce4e28a222bc60b9c41479047/probabilities.json + score_dict_file: output/reports/attack/ae05537ce4e28a222bc60b9c41479047/score_dict.json + test_labels_file: output/reports/attack/ae05537ce4e28a222bc60b9c41479047/test_labels.json + train_labels_file: output/reports/attack/ae05537ce4e28a222bc60b9c41479047/train_labels.json + model_dir: models + name: ae05537ce4e28a222bc60b9c41479047 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: ae05537ce4e28a222bc60b9c41479047 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/ae59def53ef8ef7623c06b01b56c8980/params.yaml b/examples/security/truthseeker/output/reports/attack/ae59def53ef8ef7623c06b01b56c8980/params.yaml new file mode 100644 index 00000000..1f0dda54 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/ae59def53ef8ef7623c06b01b56c8980/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.01 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: f4fcc1a4fcbe6af54ff3416e6a4fe0d4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ae59def53ef8ef7623c06b01b56c8980/adv_predictions.json + adv_probabilities_file: output/reports/attack/ae59def53ef8ef7623c06b01b56c8980/adv_probabilities.json + attack_file: output/attacks/2722c77eef0200282858687975345a47.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/ae59def53ef8ef7623c06b01b56c8980/params.yaml + predictions_file: output/reports/attack/ae59def53ef8ef7623c06b01b56c8980/predictions.json + probabilities_file: output/reports/attack/ae59def53ef8ef7623c06b01b56c8980/probabilities.json + score_dict_file: output/reports/attack/ae59def53ef8ef7623c06b01b56c8980/score_dict.json + test_labels_file: output/reports/attack/ae59def53ef8ef7623c06b01b56c8980/test_labels.json + train_labels_file: output/reports/attack/ae59def53ef8ef7623c06b01b56c8980/train_labels.json + model_dir: models + name: ae59def53ef8ef7623c06b01b56c8980 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: ae59def53ef8ef7623c06b01b56c8980 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/af2c02ff8f4222cbabc8bd188d3d5f5c/params.yaml b/examples/security/truthseeker/output/reports/attack/af2c02ff8f4222cbabc8bd188d3d5f5c/params.yaml new file mode 100644 index 00000000..e307e03b --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/af2c02ff8f4222cbabc8bd188d3d5f5c/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: b33c25f65424f4f7c5097eddb6483015 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/af2c02ff8f4222cbabc8bd188d3d5f5c/adv_predictions.json + adv_probabilities_file: output/reports/attack/af2c02ff8f4222cbabc8bd188d3d5f5c/adv_probabilities.json + attack_file: output/attacks/6d2f73a961c679844d488826ae1f75dc.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/af2c02ff8f4222cbabc8bd188d3d5f5c/params.yaml + predictions_file: output/reports/attack/af2c02ff8f4222cbabc8bd188d3d5f5c/predictions.json + probabilities_file: output/reports/attack/af2c02ff8f4222cbabc8bd188d3d5f5c/probabilities.json + score_dict_file: output/reports/attack/af2c02ff8f4222cbabc8bd188d3d5f5c/score_dict.json + test_labels_file: output/reports/attack/af2c02ff8f4222cbabc8bd188d3d5f5c/test_labels.json + train_labels_file: output/reports/attack/af2c02ff8f4222cbabc8bd188d3d5f5c/train_labels.json + model_dir: models + name: af2c02ff8f4222cbabc8bd188d3d5f5c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: af2c02ff8f4222cbabc8bd188d3d5f5c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/b219d5c153814411ccc9ab837777d367/params.yaml b/examples/security/truthseeker/output/reports/attack/b219d5c153814411ccc9ab837777d367/params.yaml new file mode 100644 index 00000000..e6f22301 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/b219d5c153814411ccc9ab837777d367/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.1 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 1029bb9f75d68cb511d431feeb800d58 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b219d5c153814411ccc9ab837777d367/adv_predictions.json + adv_probabilities_file: output/reports/attack/b219d5c153814411ccc9ab837777d367/adv_probabilities.json + attack_file: output/attacks/23531f7b26a1d8914d48e84eedc7425b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/b219d5c153814411ccc9ab837777d367/params.yaml + predictions_file: output/reports/attack/b219d5c153814411ccc9ab837777d367/predictions.json + probabilities_file: output/reports/attack/b219d5c153814411ccc9ab837777d367/probabilities.json + score_dict_file: output/reports/attack/b219d5c153814411ccc9ab837777d367/score_dict.json + test_labels_file: output/reports/attack/b219d5c153814411ccc9ab837777d367/test_labels.json + train_labels_file: output/reports/attack/b219d5c153814411ccc9ab837777d367/train_labels.json + model_dir: models + name: b219d5c153814411ccc9ab837777d367 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: b219d5c153814411ccc9ab837777d367 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/b22710f440492fe13f3f8ad9c1c51a40/params.yaml b/examples/security/truthseeker/output/reports/attack/b22710f440492fe13f3f8ad9c1c51a40/params.yaml new file mode 100644 index 00000000..5c210635 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/b22710f440492fe13f3f8ad9c1c51a40/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.01 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 78fdafef5108b3332260cda42feed617 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b22710f440492fe13f3f8ad9c1c51a40/adv_predictions.json + adv_probabilities_file: output/reports/attack/b22710f440492fe13f3f8ad9c1c51a40/adv_probabilities.json + attack_file: output/attacks/c66bd02d017eed719b26feb59f837ba5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/b22710f440492fe13f3f8ad9c1c51a40/params.yaml + predictions_file: output/reports/attack/b22710f440492fe13f3f8ad9c1c51a40/predictions.json + probabilities_file: output/reports/attack/b22710f440492fe13f3f8ad9c1c51a40/probabilities.json + score_dict_file: output/reports/attack/b22710f440492fe13f3f8ad9c1c51a40/score_dict.json + test_labels_file: output/reports/attack/b22710f440492fe13f3f8ad9c1c51a40/test_labels.json + train_labels_file: output/reports/attack/b22710f440492fe13f3f8ad9c1c51a40/train_labels.json + model_dir: models + name: b22710f440492fe13f3f8ad9c1c51a40 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: b22710f440492fe13f3f8ad9c1c51a40 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/b32724cab0662ad5a61eb6d3d39f282a/params.yaml b/examples/security/truthseeker/output/reports/attack/b32724cab0662ad5a61eb6d3d39f282a/params.yaml new file mode 100644 index 00000000..cc43d309 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/b32724cab0662ad5a61eb6d3d39f282a/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 5f2f4b3f92d40688bb5f9d2d3b4a28be +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b32724cab0662ad5a61eb6d3d39f282a/adv_predictions.json + adv_probabilities_file: output/reports/attack/b32724cab0662ad5a61eb6d3d39f282a/adv_probabilities.json + attack_file: output/attacks/b40c0e0d0f119f5c70d95a77cea29bb4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/b32724cab0662ad5a61eb6d3d39f282a/params.yaml + predictions_file: output/reports/attack/b32724cab0662ad5a61eb6d3d39f282a/predictions.json + probabilities_file: output/reports/attack/b32724cab0662ad5a61eb6d3d39f282a/probabilities.json + score_dict_file: output/reports/attack/b32724cab0662ad5a61eb6d3d39f282a/score_dict.json + test_labels_file: output/reports/attack/b32724cab0662ad5a61eb6d3d39f282a/test_labels.json + train_labels_file: output/reports/attack/b32724cab0662ad5a61eb6d3d39f282a/train_labels.json + model_dir: models + name: b32724cab0662ad5a61eb6d3d39f282a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: b32724cab0662ad5a61eb6d3d39f282a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/b56f18ff7c0d06963a32c0ec8e4a978f/params.yaml b/examples/security/truthseeker/output/reports/attack/b56f18ff7c0d06963a32c0ec8e4a978f/params.yaml new file mode 100644 index 00000000..9d69d75d --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/b56f18ff7c0d06963a32c0ec8e4a978f/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: b5b9f29cbc066d9edf3bc9229a6d48eb +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b56f18ff7c0d06963a32c0ec8e4a978f/adv_predictions.json + adv_probabilities_file: output/reports/attack/b56f18ff7c0d06963a32c0ec8e4a978f/adv_probabilities.json + attack_file: output/attacks/f27be838a45d3af19f6c216e46ea2eec.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/b56f18ff7c0d06963a32c0ec8e4a978f/params.yaml + predictions_file: output/reports/attack/b56f18ff7c0d06963a32c0ec8e4a978f/predictions.json + probabilities_file: output/reports/attack/b56f18ff7c0d06963a32c0ec8e4a978f/probabilities.json + score_dict_file: output/reports/attack/b56f18ff7c0d06963a32c0ec8e4a978f/score_dict.json + test_labels_file: output/reports/attack/b56f18ff7c0d06963a32c0ec8e4a978f/test_labels.json + train_labels_file: output/reports/attack/b56f18ff7c0d06963a32c0ec8e4a978f/train_labels.json + model_dir: models + name: b56f18ff7c0d06963a32c0ec8e4a978f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: b56f18ff7c0d06963a32c0ec8e4a978f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/b59e8dd2e5f60299078ca1a09cb91cfa/params.yaml b/examples/security/truthseeker/output/reports/attack/b59e8dd2e5f60299078ca1a09cb91cfa/params.yaml new file mode 100644 index 00000000..0f4dfc8d --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/b59e8dd2e5f60299078ca1a09cb91cfa/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.3 + eps_step: 0.001 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 30ba1263987e0664dce25b113544c332 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b59e8dd2e5f60299078ca1a09cb91cfa/adv_predictions.json + adv_probabilities_file: output/reports/attack/b59e8dd2e5f60299078ca1a09cb91cfa/adv_probabilities.json + attack_file: output/attacks/2cc4ce417dceca68d5ef89ac0eddf3a1.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/b59e8dd2e5f60299078ca1a09cb91cfa/params.yaml + predictions_file: output/reports/attack/b59e8dd2e5f60299078ca1a09cb91cfa/predictions.json + probabilities_file: output/reports/attack/b59e8dd2e5f60299078ca1a09cb91cfa/probabilities.json + score_dict_file: output/reports/attack/b59e8dd2e5f60299078ca1a09cb91cfa/score_dict.json + test_labels_file: output/reports/attack/b59e8dd2e5f60299078ca1a09cb91cfa/test_labels.json + train_labels_file: output/reports/attack/b59e8dd2e5f60299078ca1a09cb91cfa/train_labels.json + model_dir: models + name: b59e8dd2e5f60299078ca1a09cb91cfa + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: b59e8dd2e5f60299078ca1a09cb91cfa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/b66c8d74968bb279c394d0f26126beda/params.yaml b/examples/security/truthseeker/output/reports/attack/b66c8d74968bb279c394d0f26126beda/params.yaml new file mode 100644 index 00000000..9af7a027 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/b66c8d74968bb279c394d0f26126beda/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.5 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: af99de04cc3823ea5d4f93c4657c1d14 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b66c8d74968bb279c394d0f26126beda/adv_predictions.json + adv_probabilities_file: output/reports/attack/b66c8d74968bb279c394d0f26126beda/adv_probabilities.json + attack_file: output/attacks/39f997a6a7860ae01cfe4bb215bf1ede.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/b66c8d74968bb279c394d0f26126beda/params.yaml + predictions_file: output/reports/attack/b66c8d74968bb279c394d0f26126beda/predictions.json + probabilities_file: output/reports/attack/b66c8d74968bb279c394d0f26126beda/probabilities.json + score_dict_file: output/reports/attack/b66c8d74968bb279c394d0f26126beda/score_dict.json + test_labels_file: output/reports/attack/b66c8d74968bb279c394d0f26126beda/test_labels.json + train_labels_file: output/reports/attack/b66c8d74968bb279c394d0f26126beda/train_labels.json + model_dir: models + name: b66c8d74968bb279c394d0f26126beda + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: b66c8d74968bb279c394d0f26126beda +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/b7818b15146787153c3cfa5f8f713af6/params.yaml b/examples/security/truthseeker/output/reports/attack/b7818b15146787153c3cfa5f8f713af6/params.yaml new file mode 100644 index 00000000..4e64850b --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/b7818b15146787153c3cfa5f8f713af6/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 281adcb47c8424e1a9e1f8f51180cbe4 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/b7818b15146787153c3cfa5f8f713af6/adv_predictions.json + adv_probabilities_file: output/reports/attack/b7818b15146787153c3cfa5f8f713af6/adv_probabilities.json + attack_file: output/attacks/f40be134cf7cdc8ce8a72ff73f8ce03b.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/b7818b15146787153c3cfa5f8f713af6/params.yaml + predictions_file: output/reports/attack/b7818b15146787153c3cfa5f8f713af6/predictions.json + probabilities_file: output/reports/attack/b7818b15146787153c3cfa5f8f713af6/probabilities.json + score_dict_file: output/reports/attack/b7818b15146787153c3cfa5f8f713af6/score_dict.json + test_labels_file: output/reports/attack/b7818b15146787153c3cfa5f8f713af6/test_labels.json + train_labels_file: output/reports/attack/b7818b15146787153c3cfa5f8f713af6/train_labels.json + model_dir: models + name: b7818b15146787153c3cfa5f8f713af6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: b7818b15146787153c3cfa5f8f713af6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/ba19b71dade8f0ea5b67ab63e7bb0dea/params.yaml b/examples/security/truthseeker/output/reports/attack/ba19b71dade8f0ea5b67ab63e7bb0dea/params.yaml new file mode 100644 index 00000000..b82b50a3 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/ba19b71dade8f0ea5b67ab63e7bb0dea/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.5 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 9eef2265742c5fec7e3a91f37ee4a7d6 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ba19b71dade8f0ea5b67ab63e7bb0dea/adv_predictions.json + adv_probabilities_file: output/reports/attack/ba19b71dade8f0ea5b67ab63e7bb0dea/adv_probabilities.json + attack_file: output/attacks/547a9089a8ddc4930bdd62d2b64611ba.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/ba19b71dade8f0ea5b67ab63e7bb0dea/params.yaml + predictions_file: output/reports/attack/ba19b71dade8f0ea5b67ab63e7bb0dea/predictions.json + probabilities_file: output/reports/attack/ba19b71dade8f0ea5b67ab63e7bb0dea/probabilities.json + score_dict_file: output/reports/attack/ba19b71dade8f0ea5b67ab63e7bb0dea/score_dict.json + test_labels_file: output/reports/attack/ba19b71dade8f0ea5b67ab63e7bb0dea/test_labels.json + train_labels_file: output/reports/attack/ba19b71dade8f0ea5b67ab63e7bb0dea/train_labels.json + model_dir: models + name: ba19b71dade8f0ea5b67ab63e7bb0dea + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: ba19b71dade8f0ea5b67ab63e7bb0dea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/ba29811fd1479a149e267771afda62e7/params.yaml b/examples/security/truthseeker/output/reports/attack/ba29811fd1479a149e267771afda62e7/params.yaml new file mode 100644 index 00000000..a47d920d --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/ba29811fd1479a149e267771afda62e7/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.5 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 1e7f65e545613d6e567c4c2e2e2ff59f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ba29811fd1479a149e267771afda62e7/adv_predictions.json + adv_probabilities_file: output/reports/attack/ba29811fd1479a149e267771afda62e7/adv_probabilities.json + attack_file: output/attacks/277ac48260b1eaae04a5aab80e626fca.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/ba29811fd1479a149e267771afda62e7/params.yaml + predictions_file: output/reports/attack/ba29811fd1479a149e267771afda62e7/predictions.json + probabilities_file: output/reports/attack/ba29811fd1479a149e267771afda62e7/probabilities.json + score_dict_file: output/reports/attack/ba29811fd1479a149e267771afda62e7/score_dict.json + test_labels_file: output/reports/attack/ba29811fd1479a149e267771afda62e7/test_labels.json + train_labels_file: output/reports/attack/ba29811fd1479a149e267771afda62e7/train_labels.json + model_dir: models + name: ba29811fd1479a149e267771afda62e7 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: ba29811fd1479a149e267771afda62e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/bb00ec7d3d80f44148d9ee0d61f9fca6/params.yaml b/examples/security/truthseeker/output/reports/attack/bb00ec7d3d80f44148d9ee0d61f9fca6/params.yaml new file mode 100644 index 00000000..0ef2d1e6 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/bb00ec7d3d80f44148d9ee0d61f9fca6/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.001 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 2a466a2039432d3f68a56a3a0a94d649 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bb00ec7d3d80f44148d9ee0d61f9fca6/adv_predictions.json + adv_probabilities_file: output/reports/attack/bb00ec7d3d80f44148d9ee0d61f9fca6/adv_probabilities.json + attack_file: output/attacks/6dc8329fa130a6bd98e9cf62374ce883.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/bb00ec7d3d80f44148d9ee0d61f9fca6/params.yaml + predictions_file: output/reports/attack/bb00ec7d3d80f44148d9ee0d61f9fca6/predictions.json + probabilities_file: output/reports/attack/bb00ec7d3d80f44148d9ee0d61f9fca6/probabilities.json + score_dict_file: output/reports/attack/bb00ec7d3d80f44148d9ee0d61f9fca6/score_dict.json + test_labels_file: output/reports/attack/bb00ec7d3d80f44148d9ee0d61f9fca6/test_labels.json + train_labels_file: output/reports/attack/bb00ec7d3d80f44148d9ee0d61f9fca6/train_labels.json + model_dir: models + name: bb00ec7d3d80f44148d9ee0d61f9fca6 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: bb00ec7d3d80f44148d9ee0d61f9fca6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/bb30b95d748bb2f8f1e0c28011b893cd/params.yaml b/examples/security/truthseeker/output/reports/attack/bb30b95d748bb2f8f1e0c28011b893cd/params.yaml new file mode 100644 index 00000000..5df404a2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/bb30b95d748bb2f8f1e0c28011b893cd/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.3 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 3e3db78a0582da217c3e6517a1744938 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bb30b95d748bb2f8f1e0c28011b893cd/adv_predictions.json + adv_probabilities_file: output/reports/attack/bb30b95d748bb2f8f1e0c28011b893cd/adv_probabilities.json + attack_file: output/attacks/b0c4aa1712949caf289cc5a31c660cda.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/bb30b95d748bb2f8f1e0c28011b893cd/params.yaml + predictions_file: output/reports/attack/bb30b95d748bb2f8f1e0c28011b893cd/predictions.json + probabilities_file: output/reports/attack/bb30b95d748bb2f8f1e0c28011b893cd/probabilities.json + score_dict_file: output/reports/attack/bb30b95d748bb2f8f1e0c28011b893cd/score_dict.json + test_labels_file: output/reports/attack/bb30b95d748bb2f8f1e0c28011b893cd/test_labels.json + train_labels_file: output/reports/attack/bb30b95d748bb2f8f1e0c28011b893cd/train_labels.json + model_dir: models + name: bb30b95d748bb2f8f1e0c28011b893cd + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: bb30b95d748bb2f8f1e0c28011b893cd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/bef815a05279459635e94e0ec3e18a8c/params.yaml b/examples/security/truthseeker/output/reports/attack/bef815a05279459635e94e0ec3e18a8c/params.yaml new file mode 100644 index 00000000..d8500a68 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/bef815a05279459635e94e0ec3e18a8c/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.001 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: a078431142d7f202c14a2ba420962ffc +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/adv_predictions.json + adv_probabilities_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/adv_probabilities.json + attack_file: output/attacks/488456acf643c8c0dc0fc14e6edba835.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/params.yaml + predictions_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/predictions.json + probabilities_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/probabilities.json + score_dict_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/score_dict.json + test_labels_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/test_labels.json + train_labels_file: output/reports/attack/bef815a05279459635e94e0ec3e18a8c/train_labels.json + model_dir: models + name: bef815a05279459635e94e0ec3e18a8c + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: bef815a05279459635e94e0ec3e18a8c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/c0d21a7d6d907878d252e33200f5ea27/params.yaml b/examples/security/truthseeker/output/reports/attack/c0d21a7d6d907878d252e33200f5ea27/params.yaml new file mode 100644 index 00000000..a0d82840 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/c0d21a7d6d907878d252e33200f5ea27/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.01 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: edd3d2bcf867b310fcee53affaac6ac6 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c0d21a7d6d907878d252e33200f5ea27/adv_predictions.json + adv_probabilities_file: output/reports/attack/c0d21a7d6d907878d252e33200f5ea27/adv_probabilities.json + attack_file: output/attacks/526b3d89cbbd18819103d62441af1a31.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/c0d21a7d6d907878d252e33200f5ea27/params.yaml + predictions_file: output/reports/attack/c0d21a7d6d907878d252e33200f5ea27/predictions.json + probabilities_file: output/reports/attack/c0d21a7d6d907878d252e33200f5ea27/probabilities.json + score_dict_file: output/reports/attack/c0d21a7d6d907878d252e33200f5ea27/score_dict.json + test_labels_file: output/reports/attack/c0d21a7d6d907878d252e33200f5ea27/test_labels.json + train_labels_file: output/reports/attack/c0d21a7d6d907878d252e33200f5ea27/train_labels.json + model_dir: models + name: c0d21a7d6d907878d252e33200f5ea27 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: c0d21a7d6d907878d252e33200f5ea27 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/c286178b29703d67e3d6114f57450c30/params.yaml b/examples/security/truthseeker/output/reports/attack/c286178b29703d67e3d6114f57450c30/params.yaml new file mode 100644 index 00000000..18a068cb --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/c286178b29703d67e3d6114f57450c30/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.001 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 243ba87fabeee9cfb6fce141a34b42bb +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c286178b29703d67e3d6114f57450c30/adv_predictions.json + adv_probabilities_file: output/reports/attack/c286178b29703d67e3d6114f57450c30/adv_probabilities.json + attack_file: output/attacks/840a5a0e5074cc9c678d661a7bc24ea8.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/c286178b29703d67e3d6114f57450c30/params.yaml + predictions_file: output/reports/attack/c286178b29703d67e3d6114f57450c30/predictions.json + probabilities_file: output/reports/attack/c286178b29703d67e3d6114f57450c30/probabilities.json + score_dict_file: output/reports/attack/c286178b29703d67e3d6114f57450c30/score_dict.json + test_labels_file: output/reports/attack/c286178b29703d67e3d6114f57450c30/test_labels.json + train_labels_file: output/reports/attack/c286178b29703d67e3d6114f57450c30/train_labels.json + model_dir: models + name: c286178b29703d67e3d6114f57450c30 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: c286178b29703d67e3d6114f57450c30 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/c44ad01a6f862cae098a6bf1ceb3e122/params.yaml b/examples/security/truthseeker/output/reports/attack/c44ad01a6f862cae098a6bf1ceb3e122/params.yaml new file mode 100644 index 00000000..ee8ecaa9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/c44ad01a6f862cae098a6bf1ceb3e122/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.01 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 1c746148bb36416da4a94a3cded97018 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c44ad01a6f862cae098a6bf1ceb3e122/adv_predictions.json + adv_probabilities_file: output/reports/attack/c44ad01a6f862cae098a6bf1ceb3e122/adv_probabilities.json + attack_file: output/attacks/daaac0de0c289d97c459759729bddceb.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/c44ad01a6f862cae098a6bf1ceb3e122/params.yaml + predictions_file: output/reports/attack/c44ad01a6f862cae098a6bf1ceb3e122/predictions.json + probabilities_file: output/reports/attack/c44ad01a6f862cae098a6bf1ceb3e122/probabilities.json + score_dict_file: output/reports/attack/c44ad01a6f862cae098a6bf1ceb3e122/score_dict.json + test_labels_file: output/reports/attack/c44ad01a6f862cae098a6bf1ceb3e122/test_labels.json + train_labels_file: output/reports/attack/c44ad01a6f862cae098a6bf1ceb3e122/train_labels.json + model_dir: models + name: c44ad01a6f862cae098a6bf1ceb3e122 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: c44ad01a6f862cae098a6bf1ceb3e122 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/c555640cbe0b2223c50f6712a3b27be0/params.yaml b/examples/security/truthseeker/output/reports/attack/c555640cbe0b2223c50f6712a3b27be0/params.yaml new file mode 100644 index 00000000..4b1cf64a --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/c555640cbe0b2223c50f6712a3b27be0/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 7f785126d4a581500df43c34844b3cc2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c555640cbe0b2223c50f6712a3b27be0/adv_predictions.json + adv_probabilities_file: output/reports/attack/c555640cbe0b2223c50f6712a3b27be0/adv_probabilities.json + attack_file: output/attacks/5a4e63f662b7df9f82fb078a3bd8afa4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/c555640cbe0b2223c50f6712a3b27be0/params.yaml + predictions_file: output/reports/attack/c555640cbe0b2223c50f6712a3b27be0/predictions.json + probabilities_file: output/reports/attack/c555640cbe0b2223c50f6712a3b27be0/probabilities.json + score_dict_file: output/reports/attack/c555640cbe0b2223c50f6712a3b27be0/score_dict.json + test_labels_file: output/reports/attack/c555640cbe0b2223c50f6712a3b27be0/test_labels.json + train_labels_file: output/reports/attack/c555640cbe0b2223c50f6712a3b27be0/train_labels.json + model_dir: models + name: c555640cbe0b2223c50f6712a3b27be0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: c555640cbe0b2223c50f6712a3b27be0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/c693614d5340772b1167fadb1f29019d/params.yaml b/examples/security/truthseeker/output/reports/attack/c693614d5340772b1167fadb1f29019d/params.yaml new file mode 100644 index 00000000..fe0e7055 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/c693614d5340772b1167fadb1f29019d/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.1 + eps_step: 1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: b2e87c992b728b9712e4cde99e218166 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c693614d5340772b1167fadb1f29019d/adv_predictions.json + adv_probabilities_file: output/reports/attack/c693614d5340772b1167fadb1f29019d/adv_probabilities.json + attack_file: output/attacks/6ed431aa4bf7d3fa1d442d8b8085f5c4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/c693614d5340772b1167fadb1f29019d/params.yaml + predictions_file: output/reports/attack/c693614d5340772b1167fadb1f29019d/predictions.json + probabilities_file: output/reports/attack/c693614d5340772b1167fadb1f29019d/probabilities.json + score_dict_file: output/reports/attack/c693614d5340772b1167fadb1f29019d/score_dict.json + test_labels_file: output/reports/attack/c693614d5340772b1167fadb1f29019d/test_labels.json + train_labels_file: output/reports/attack/c693614d5340772b1167fadb1f29019d/train_labels.json + model_dir: models + name: c693614d5340772b1167fadb1f29019d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: c693614d5340772b1167fadb1f29019d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/c6d363c416b62e74eefe0e4c5586feb3/params.yaml b/examples/security/truthseeker/output/reports/attack/c6d363c416b62e74eefe0e4c5586feb3/params.yaml new file mode 100644 index 00000000..a8aea2a6 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/c6d363c416b62e74eefe0e4c5586feb3/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 1c8ca5a099075fcc866277c6df41fb86 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c6d363c416b62e74eefe0e4c5586feb3/adv_predictions.json + adv_probabilities_file: output/reports/attack/c6d363c416b62e74eefe0e4c5586feb3/adv_probabilities.json + attack_file: output/attacks/aeac295102f08c0de9dd128814514aad.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/c6d363c416b62e74eefe0e4c5586feb3/params.yaml + predictions_file: output/reports/attack/c6d363c416b62e74eefe0e4c5586feb3/predictions.json + probabilities_file: output/reports/attack/c6d363c416b62e74eefe0e4c5586feb3/probabilities.json + score_dict_file: output/reports/attack/c6d363c416b62e74eefe0e4c5586feb3/score_dict.json + test_labels_file: output/reports/attack/c6d363c416b62e74eefe0e4c5586feb3/test_labels.json + train_labels_file: output/reports/attack/c6d363c416b62e74eefe0e4c5586feb3/train_labels.json + model_dir: models + name: c6d363c416b62e74eefe0e4c5586feb3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: c6d363c416b62e74eefe0e4c5586feb3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/c8027a4d541b13d66d3cd5a8dbce9ea7/params.yaml b/examples/security/truthseeker/output/reports/attack/c8027a4d541b13d66d3cd5a8dbce9ea7/params.yaml new file mode 100644 index 00000000..e2336497 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/c8027a4d541b13d66d3cd5a8dbce9ea7/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.001 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: de12b06714e334c8e518cde57fa152c3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c8027a4d541b13d66d3cd5a8dbce9ea7/adv_predictions.json + adv_probabilities_file: output/reports/attack/c8027a4d541b13d66d3cd5a8dbce9ea7/adv_probabilities.json + attack_file: output/attacks/d94e0830dc24cb99590ff7038bcd1cee.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/c8027a4d541b13d66d3cd5a8dbce9ea7/params.yaml + predictions_file: output/reports/attack/c8027a4d541b13d66d3cd5a8dbce9ea7/predictions.json + probabilities_file: output/reports/attack/c8027a4d541b13d66d3cd5a8dbce9ea7/probabilities.json + score_dict_file: output/reports/attack/c8027a4d541b13d66d3cd5a8dbce9ea7/score_dict.json + test_labels_file: output/reports/attack/c8027a4d541b13d66d3cd5a8dbce9ea7/test_labels.json + train_labels_file: output/reports/attack/c8027a4d541b13d66d3cd5a8dbce9ea7/train_labels.json + model_dir: models + name: c8027a4d541b13d66d3cd5a8dbce9ea7 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: c8027a4d541b13d66d3cd5a8dbce9ea7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/c8a131737105448b80bbb08b59e6d468/params.yaml b/examples/security/truthseeker/output/reports/attack/c8a131737105448b80bbb08b59e6d468/params.yaml new file mode 100644 index 00000000..d253c1f3 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/c8a131737105448b80bbb08b59e6d468/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.001 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: f14dca0fd2f3f76ee9aff8e1996c66b1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c8a131737105448b80bbb08b59e6d468/adv_predictions.json + adv_probabilities_file: output/reports/attack/c8a131737105448b80bbb08b59e6d468/adv_probabilities.json + attack_file: output/attacks/62feb231bf3f5aaac1bd154d2a0f456a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/c8a131737105448b80bbb08b59e6d468/params.yaml + predictions_file: output/reports/attack/c8a131737105448b80bbb08b59e6d468/predictions.json + probabilities_file: output/reports/attack/c8a131737105448b80bbb08b59e6d468/probabilities.json + score_dict_file: output/reports/attack/c8a131737105448b80bbb08b59e6d468/score_dict.json + test_labels_file: output/reports/attack/c8a131737105448b80bbb08b59e6d468/test_labels.json + train_labels_file: output/reports/attack/c8a131737105448b80bbb08b59e6d468/train_labels.json + model_dir: models + name: c8a131737105448b80bbb08b59e6d468 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: c8a131737105448b80bbb08b59e6d468 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/c8b5535b3f1e53d878770e4e6bb555b4/params.yaml b/examples/security/truthseeker/output/reports/attack/c8b5535b3f1e53d878770e4e6bb555b4/params.yaml new file mode 100644 index 00000000..9f57f948 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/c8b5535b3f1e53d878770e4e6bb555b4/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: efcbf5b4a936e218ba6320cdd8026645 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c8b5535b3f1e53d878770e4e6bb555b4/adv_predictions.json + adv_probabilities_file: output/reports/attack/c8b5535b3f1e53d878770e4e6bb555b4/adv_probabilities.json + attack_file: output/attacks/3ec993bf4da514575e52f5c4407f3b9a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/c8b5535b3f1e53d878770e4e6bb555b4/params.yaml + predictions_file: output/reports/attack/c8b5535b3f1e53d878770e4e6bb555b4/predictions.json + probabilities_file: output/reports/attack/c8b5535b3f1e53d878770e4e6bb555b4/probabilities.json + score_dict_file: output/reports/attack/c8b5535b3f1e53d878770e4e6bb555b4/score_dict.json + test_labels_file: output/reports/attack/c8b5535b3f1e53d878770e4e6bb555b4/test_labels.json + train_labels_file: output/reports/attack/c8b5535b3f1e53d878770e4e6bb555b4/train_labels.json + model_dir: models + name: c8b5535b3f1e53d878770e4e6bb555b4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: c8b5535b3f1e53d878770e4e6bb555b4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/c90969dd3fea4226c8686040ebb01a2d/params.yaml b/examples/security/truthseeker/output/reports/attack/c90969dd3fea4226c8686040ebb01a2d/params.yaml new file mode 100644 index 00000000..7541d933 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/c90969dd3fea4226c8686040ebb01a2d/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.5 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: de63c994e568d7fdc7d30a049cbf86a8 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/c90969dd3fea4226c8686040ebb01a2d/adv_predictions.json + adv_probabilities_file: output/reports/attack/c90969dd3fea4226c8686040ebb01a2d/adv_probabilities.json + attack_file: output/attacks/12c32b2b79937cede561f44d36bf28fd.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/c90969dd3fea4226c8686040ebb01a2d/params.yaml + predictions_file: output/reports/attack/c90969dd3fea4226c8686040ebb01a2d/predictions.json + probabilities_file: output/reports/attack/c90969dd3fea4226c8686040ebb01a2d/probabilities.json + score_dict_file: output/reports/attack/c90969dd3fea4226c8686040ebb01a2d/score_dict.json + test_labels_file: output/reports/attack/c90969dd3fea4226c8686040ebb01a2d/test_labels.json + train_labels_file: output/reports/attack/c90969dd3fea4226c8686040ebb01a2d/train_labels.json + model_dir: models + name: c90969dd3fea4226c8686040ebb01a2d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: c90969dd3fea4226c8686040ebb01a2d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/cd07f9acea4691cdb86bf001f51f4c40/params.yaml b/examples/security/truthseeker/output/reports/attack/cd07f9acea4691cdb86bf001f51f4c40/params.yaml new file mode 100644 index 00000000..0fdb32f3 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/cd07f9acea4691cdb86bf001f51f4c40/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.3 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 8876a4d1c3903c7c513a7408a89cc5e2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/cd07f9acea4691cdb86bf001f51f4c40/adv_predictions.json + adv_probabilities_file: output/reports/attack/cd07f9acea4691cdb86bf001f51f4c40/adv_probabilities.json + attack_file: output/attacks/7ce71c3588fb5491619ea09f9bdba5ce.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/cd07f9acea4691cdb86bf001f51f4c40/params.yaml + predictions_file: output/reports/attack/cd07f9acea4691cdb86bf001f51f4c40/predictions.json + probabilities_file: output/reports/attack/cd07f9acea4691cdb86bf001f51f4c40/probabilities.json + score_dict_file: output/reports/attack/cd07f9acea4691cdb86bf001f51f4c40/score_dict.json + test_labels_file: output/reports/attack/cd07f9acea4691cdb86bf001f51f4c40/test_labels.json + train_labels_file: output/reports/attack/cd07f9acea4691cdb86bf001f51f4c40/train_labels.json + model_dir: models + name: cd07f9acea4691cdb86bf001f51f4c40 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: cd07f9acea4691cdb86bf001f51f4c40 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/cd5b6862e0fef608bd1c6430f9a2611d/params.yaml b/examples/security/truthseeker/output/reports/attack/cd5b6862e0fef608bd1c6430f9a2611d/params.yaml new file mode 100644 index 00000000..c403c792 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/cd5b6862e0fef608bd1c6430f9a2611d/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 1bd9b3533b3eabc68ea7f32059669317 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/cd5b6862e0fef608bd1c6430f9a2611d/adv_predictions.json + adv_probabilities_file: output/reports/attack/cd5b6862e0fef608bd1c6430f9a2611d/adv_probabilities.json + attack_file: output/attacks/6dd2a7512ef027d058b0b7b463202040.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/cd5b6862e0fef608bd1c6430f9a2611d/params.yaml + predictions_file: output/reports/attack/cd5b6862e0fef608bd1c6430f9a2611d/predictions.json + probabilities_file: output/reports/attack/cd5b6862e0fef608bd1c6430f9a2611d/probabilities.json + score_dict_file: output/reports/attack/cd5b6862e0fef608bd1c6430f9a2611d/score_dict.json + test_labels_file: output/reports/attack/cd5b6862e0fef608bd1c6430f9a2611d/test_labels.json + train_labels_file: output/reports/attack/cd5b6862e0fef608bd1c6430f9a2611d/train_labels.json + model_dir: models + name: cd5b6862e0fef608bd1c6430f9a2611d + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: cd5b6862e0fef608bd1c6430f9a2611d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/d04764ad4fc5b865426e550558c8cbc7/params.yaml b/examples/security/truthseeker/output/reports/attack/d04764ad4fc5b865426e550558c8cbc7/params.yaml new file mode 100644 index 00000000..b71dcbc4 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/d04764ad4fc5b865426e550558c8cbc7/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: d51ce6ae09a117743ac6f4ea56cb9a69 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d04764ad4fc5b865426e550558c8cbc7/adv_predictions.json + adv_probabilities_file: output/reports/attack/d04764ad4fc5b865426e550558c8cbc7/adv_probabilities.json + attack_file: output/attacks/9128c6835f6939b3d0c7f82f473d8f83.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/d04764ad4fc5b865426e550558c8cbc7/params.yaml + predictions_file: output/reports/attack/d04764ad4fc5b865426e550558c8cbc7/predictions.json + probabilities_file: output/reports/attack/d04764ad4fc5b865426e550558c8cbc7/probabilities.json + score_dict_file: output/reports/attack/d04764ad4fc5b865426e550558c8cbc7/score_dict.json + test_labels_file: output/reports/attack/d04764ad4fc5b865426e550558c8cbc7/test_labels.json + train_labels_file: output/reports/attack/d04764ad4fc5b865426e550558c8cbc7/train_labels.json + model_dir: models + name: d04764ad4fc5b865426e550558c8cbc7 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: d04764ad4fc5b865426e550558c8cbc7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/d14d8beb4f5951b4cb2dae1170be11d3/params.yaml b/examples/security/truthseeker/output/reports/attack/d14d8beb4f5951b4cb2dae1170be11d3/params.yaml new file mode 100644 index 00000000..74090013 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/d14d8beb4f5951b4cb2dae1170be11d3/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 0.01 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 5703419b40f4367311d59ac2fb76878f +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d14d8beb4f5951b4cb2dae1170be11d3/adv_predictions.json + adv_probabilities_file: output/reports/attack/d14d8beb4f5951b4cb2dae1170be11d3/adv_probabilities.json + attack_file: output/attacks/c817e394a37855df01bca0111c62e781.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d14d8beb4f5951b4cb2dae1170be11d3/params.yaml + predictions_file: output/reports/attack/d14d8beb4f5951b4cb2dae1170be11d3/predictions.json + probabilities_file: output/reports/attack/d14d8beb4f5951b4cb2dae1170be11d3/probabilities.json + score_dict_file: output/reports/attack/d14d8beb4f5951b4cb2dae1170be11d3/score_dict.json + test_labels_file: output/reports/attack/d14d8beb4f5951b4cb2dae1170be11d3/test_labels.json + train_labels_file: output/reports/attack/d14d8beb4f5951b4cb2dae1170be11d3/train_labels.json + model_dir: models + name: d14d8beb4f5951b4cb2dae1170be11d3 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d14d8beb4f5951b4cb2dae1170be11d3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/d17989d63df1a6c13d90b39fb4da7130/params.yaml b/examples/security/truthseeker/output/reports/attack/d17989d63df1a6c13d90b39fb4da7130/params.yaml new file mode 100644 index 00000000..e4ec6da9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/d17989d63df1a6c13d90b39fb4da7130/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 0.5 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: f6f3ee130ed8ab92e3601ed8b36e22e7 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d17989d63df1a6c13d90b39fb4da7130/adv_predictions.json + adv_probabilities_file: output/reports/attack/d17989d63df1a6c13d90b39fb4da7130/adv_probabilities.json + attack_file: output/attacks/a91591b9be6bf3eaaf49c125d755cf0a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d17989d63df1a6c13d90b39fb4da7130/params.yaml + predictions_file: output/reports/attack/d17989d63df1a6c13d90b39fb4da7130/predictions.json + probabilities_file: output/reports/attack/d17989d63df1a6c13d90b39fb4da7130/probabilities.json + score_dict_file: output/reports/attack/d17989d63df1a6c13d90b39fb4da7130/score_dict.json + test_labels_file: output/reports/attack/d17989d63df1a6c13d90b39fb4da7130/test_labels.json + train_labels_file: output/reports/attack/d17989d63df1a6c13d90b39fb4da7130/train_labels.json + model_dir: models + name: d17989d63df1a6c13d90b39fb4da7130 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d17989d63df1a6c13d90b39fb4da7130 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/d62d5747d2eede605e29fb3f264709e2/params.yaml b/examples/security/truthseeker/output/reports/attack/d62d5747d2eede605e29fb3f264709e2/params.yaml new file mode 100644 index 00000000..860f0511 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/d62d5747d2eede605e29fb3f264709e2/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.5 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: a62d5c1a4a4ea63fe34e0e551c94ee93 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d62d5747d2eede605e29fb3f264709e2/adv_predictions.json + adv_probabilities_file: output/reports/attack/d62d5747d2eede605e29fb3f264709e2/adv_probabilities.json + attack_file: output/attacks/7fe1e8bf1d586ab27b94a3304f858ff9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/d62d5747d2eede605e29fb3f264709e2/params.yaml + predictions_file: output/reports/attack/d62d5747d2eede605e29fb3f264709e2/predictions.json + probabilities_file: output/reports/attack/d62d5747d2eede605e29fb3f264709e2/probabilities.json + score_dict_file: output/reports/attack/d62d5747d2eede605e29fb3f264709e2/score_dict.json + test_labels_file: output/reports/attack/d62d5747d2eede605e29fb3f264709e2/test_labels.json + train_labels_file: output/reports/attack/d62d5747d2eede605e29fb3f264709e2/train_labels.json + model_dir: models + name: d62d5747d2eede605e29fb3f264709e2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: d62d5747d2eede605e29fb3f264709e2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/d8b5d591d1b149c323a2e9fe4dccacd0/params.yaml b/examples/security/truthseeker/output/reports/attack/d8b5d591d1b149c323a2e9fe4dccacd0/params.yaml new file mode 100644 index 00000000..1abede59 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/d8b5d591d1b149c323a2e9fe4dccacd0/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.001 + eps_step: 1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: f0ffd44dd01e3a9e2f9fc67ef0aea30c +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d8b5d591d1b149c323a2e9fe4dccacd0/adv_predictions.json + adv_probabilities_file: output/reports/attack/d8b5d591d1b149c323a2e9fe4dccacd0/adv_probabilities.json + attack_file: output/attacks/e588f076087a84a4202fb6a395b611d4.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/d8b5d591d1b149c323a2e9fe4dccacd0/params.yaml + predictions_file: output/reports/attack/d8b5d591d1b149c323a2e9fe4dccacd0/predictions.json + probabilities_file: output/reports/attack/d8b5d591d1b149c323a2e9fe4dccacd0/probabilities.json + score_dict_file: output/reports/attack/d8b5d591d1b149c323a2e9fe4dccacd0/score_dict.json + test_labels_file: output/reports/attack/d8b5d591d1b149c323a2e9fe4dccacd0/test_labels.json + train_labels_file: output/reports/attack/d8b5d591d1b149c323a2e9fe4dccacd0/train_labels.json + model_dir: models + name: d8b5d591d1b149c323a2e9fe4dccacd0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: d8b5d591d1b149c323a2e9fe4dccacd0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/params.yaml b/examples/security/truthseeker/output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/params.yaml new file mode 100644 index 00000000..7139f5ec --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.5 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: e584bceb92d5a7120baa32b60cb00b3d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/adv_predictions.json + adv_probabilities_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/adv_probabilities.json + attack_file: output/attacks/26f5779125a73c5e0fb0eccb22aa924a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/params.yaml + predictions_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/predictions.json + probabilities_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/probabilities.json + score_dict_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/score_dict.json + test_labels_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/test_labels.json + train_labels_file: output/reports/attack/d8dbc76017ce0953ca4ba4fda002be83/train_labels.json + model_dir: models + name: d8dbc76017ce0953ca4ba4fda002be83 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: d8dbc76017ce0953ca4ba4fda002be83 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/d935cc0246c6c54933e6f1fe0f892256/params.yaml b/examples/security/truthseeker/output/reports/attack/d935cc0246c6c54933e6f1fe0f892256/params.yaml new file mode 100644 index 00000000..1a7061f1 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/d935cc0246c6c54933e6f1fe0f892256/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.01 + eps_step: 0.1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 5a1eddc9edde73b6db4928930f6314dd +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/d935cc0246c6c54933e6f1fe0f892256/adv_predictions.json + adv_probabilities_file: output/reports/attack/d935cc0246c6c54933e6f1fe0f892256/adv_probabilities.json + attack_file: output/attacks/d317e79a5d430815f625bcf788736d58.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/d935cc0246c6c54933e6f1fe0f892256/params.yaml + predictions_file: output/reports/attack/d935cc0246c6c54933e6f1fe0f892256/predictions.json + probabilities_file: output/reports/attack/d935cc0246c6c54933e6f1fe0f892256/probabilities.json + score_dict_file: output/reports/attack/d935cc0246c6c54933e6f1fe0f892256/score_dict.json + test_labels_file: output/reports/attack/d935cc0246c6c54933e6f1fe0f892256/test_labels.json + train_labels_file: output/reports/attack/d935cc0246c6c54933e6f1fe0f892256/train_labels.json + model_dir: models + name: d935cc0246c6c54933e6f1fe0f892256 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: d935cc0246c6c54933e6f1fe0f892256 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/dadfbcf84c5a65111cd5f6b167899810/params.yaml b/examples/security/truthseeker/output/reports/attack/dadfbcf84c5a65111cd5f6b167899810/params.yaml new file mode 100644 index 00000000..fab198ae --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/dadfbcf84c5a65111cd5f6b167899810/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 0.5 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 1b2e479c4a6713b1dcc43284e568c449 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/dadfbcf84c5a65111cd5f6b167899810/adv_predictions.json + adv_probabilities_file: output/reports/attack/dadfbcf84c5a65111cd5f6b167899810/adv_probabilities.json + attack_file: output/attacks/7cdba8640898e1043404e8253d7cf5da.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/dadfbcf84c5a65111cd5f6b167899810/params.yaml + predictions_file: output/reports/attack/dadfbcf84c5a65111cd5f6b167899810/predictions.json + probabilities_file: output/reports/attack/dadfbcf84c5a65111cd5f6b167899810/probabilities.json + score_dict_file: output/reports/attack/dadfbcf84c5a65111cd5f6b167899810/score_dict.json + test_labels_file: output/reports/attack/dadfbcf84c5a65111cd5f6b167899810/test_labels.json + train_labels_file: output/reports/attack/dadfbcf84c5a65111cd5f6b167899810/train_labels.json + model_dir: models + name: dadfbcf84c5a65111cd5f6b167899810 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: dadfbcf84c5a65111cd5f6b167899810 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/dbf362c5889381463f90c9736c796c34/params.yaml b/examples/security/truthseeker/output/reports/attack/dbf362c5889381463f90c9736c796c34/params.yaml new file mode 100644 index 00000000..899ab71c --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/dbf362c5889381463f90c9736c796c34/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.5 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 3119041a9a19da6846df35f4ac511618 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/dbf362c5889381463f90c9736c796c34/adv_predictions.json + adv_probabilities_file: output/reports/attack/dbf362c5889381463f90c9736c796c34/adv_probabilities.json + attack_file: output/attacks/22e2ab47fb3a53e338099c75709c35fb.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/dbf362c5889381463f90c9736c796c34/params.yaml + predictions_file: output/reports/attack/dbf362c5889381463f90c9736c796c34/predictions.json + probabilities_file: output/reports/attack/dbf362c5889381463f90c9736c796c34/probabilities.json + score_dict_file: output/reports/attack/dbf362c5889381463f90c9736c796c34/score_dict.json + test_labels_file: output/reports/attack/dbf362c5889381463f90c9736c796c34/test_labels.json + train_labels_file: output/reports/attack/dbf362c5889381463f90c9736c796c34/train_labels.json + model_dir: models + name: dbf362c5889381463f90c9736c796c34 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: dbf362c5889381463f90c9736c796c34 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/de3281bda288929474098d1822fef5ca/params.yaml b/examples/security/truthseeker/output/reports/attack/de3281bda288929474098d1822fef5ca/params.yaml new file mode 100644 index 00000000..5234a041 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/de3281bda288929474098d1822fef5ca/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.01 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 0760ae4de84abacb1bdad050c9fc6ee9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/de3281bda288929474098d1822fef5ca/adv_predictions.json + adv_probabilities_file: output/reports/attack/de3281bda288929474098d1822fef5ca/adv_probabilities.json + attack_file: output/attacks/7b66d1d8cd668f193b9045f7f0ff2704.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/de3281bda288929474098d1822fef5ca/params.yaml + predictions_file: output/reports/attack/de3281bda288929474098d1822fef5ca/predictions.json + probabilities_file: output/reports/attack/de3281bda288929474098d1822fef5ca/probabilities.json + score_dict_file: output/reports/attack/de3281bda288929474098d1822fef5ca/score_dict.json + test_labels_file: output/reports/attack/de3281bda288929474098d1822fef5ca/test_labels.json + train_labels_file: output/reports/attack/de3281bda288929474098d1822fef5ca/train_labels.json + model_dir: models + name: de3281bda288929474098d1822fef5ca + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: de3281bda288929474098d1822fef5ca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/deacb0c911310f9c55ecdd48a8c2550a/params.yaml b/examples/security/truthseeker/output/reports/attack/deacb0c911310f9c55ecdd48a8c2550a/params.yaml new file mode 100644 index 00000000..419fa7bd --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/deacb0c911310f9c55ecdd48a8c2550a/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 983c0bb3f84d81d44f8095e52458d2f2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/deacb0c911310f9c55ecdd48a8c2550a/adv_predictions.json + adv_probabilities_file: output/reports/attack/deacb0c911310f9c55ecdd48a8c2550a/adv_probabilities.json + attack_file: output/attacks/10b2a557ce261ef5cbccf246d08062ab.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/deacb0c911310f9c55ecdd48a8c2550a/params.yaml + predictions_file: output/reports/attack/deacb0c911310f9c55ecdd48a8c2550a/predictions.json + probabilities_file: output/reports/attack/deacb0c911310f9c55ecdd48a8c2550a/probabilities.json + score_dict_file: output/reports/attack/deacb0c911310f9c55ecdd48a8c2550a/score_dict.json + test_labels_file: output/reports/attack/deacb0c911310f9c55ecdd48a8c2550a/test_labels.json + train_labels_file: output/reports/attack/deacb0c911310f9c55ecdd48a8c2550a/train_labels.json + model_dir: models + name: deacb0c911310f9c55ecdd48a8c2550a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: deacb0c911310f9c55ecdd48a8c2550a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/default/params.yaml b/examples/security/truthseeker/output/reports/attack/default/params.yaml new file mode 100644 index 00000000..653ef908 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/default/params.yaml @@ -0,0 +1,211 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: {} + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 46154306408d55fc055f71371ba79689 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.HopSkipJump + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} + name: 5be6efb2a95e92834d747594b415d983 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/default/adv_predictions.json + adv_probabilities_file: output/reports/attack/default/adv_probabilities.json + attack_file: output/attacks/attack.pkl + params_file: output/reports/attack/default/params.yaml + predictions_file: output/reports/attack/default/predictions.json + probabilities_file: output/reports/attack/default/probabilities.json + score_dict_file: output/reports/attack/default/score_dict.json + model_dir: models + name: default + reports: reports + stage: attack +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: default +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/e0408e2222e1b5a5290dec9d2af6a3a4/params.yaml b/examples/security/truthseeker/output/reports/attack/e0408e2222e1b5a5290dec9d2af6a3a4/params.yaml new file mode 100644 index 00000000..7d4759bf --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/e0408e2222e1b5a5290dec9d2af6a3a4/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.001 + eps_step: 1 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 5665bedc3551677b470bc8008af43982 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e0408e2222e1b5a5290dec9d2af6a3a4/adv_predictions.json + adv_probabilities_file: output/reports/attack/e0408e2222e1b5a5290dec9d2af6a3a4/adv_probabilities.json + attack_file: output/attacks/93fa6a3cf3b0e0347a72177036ac4585.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/e0408e2222e1b5a5290dec9d2af6a3a4/params.yaml + predictions_file: output/reports/attack/e0408e2222e1b5a5290dec9d2af6a3a4/predictions.json + probabilities_file: output/reports/attack/e0408e2222e1b5a5290dec9d2af6a3a4/probabilities.json + score_dict_file: output/reports/attack/e0408e2222e1b5a5290dec9d2af6a3a4/score_dict.json + test_labels_file: output/reports/attack/e0408e2222e1b5a5290dec9d2af6a3a4/test_labels.json + train_labels_file: output/reports/attack/e0408e2222e1b5a5290dec9d2af6a3a4/train_labels.json + model_dir: models + name: e0408e2222e1b5a5290dec9d2af6a3a4 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: e0408e2222e1b5a5290dec9d2af6a3a4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/e0df1244f35007de2568e8bb641728ff/params.yaml b/examples/security/truthseeker/output/reports/attack/e0df1244f35007de2568e8bb641728ff/params.yaml new file mode 100644 index 00000000..8666f5d4 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/e0df1244f35007de2568e8bb641728ff/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.01 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 2d33edd4ab8f7af70c7d93a3ac5d0cdc +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e0df1244f35007de2568e8bb641728ff/adv_predictions.json + adv_probabilities_file: output/reports/attack/e0df1244f35007de2568e8bb641728ff/adv_probabilities.json + attack_file: output/attacks/35d7e94aca45ffeca2c14a013b2dddb7.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/e0df1244f35007de2568e8bb641728ff/params.yaml + predictions_file: output/reports/attack/e0df1244f35007de2568e8bb641728ff/predictions.json + probabilities_file: output/reports/attack/e0df1244f35007de2568e8bb641728ff/probabilities.json + score_dict_file: output/reports/attack/e0df1244f35007de2568e8bb641728ff/score_dict.json + test_labels_file: output/reports/attack/e0df1244f35007de2568e8bb641728ff/test_labels.json + train_labels_file: output/reports/attack/e0df1244f35007de2568e8bb641728ff/train_labels.json + model_dir: models + name: e0df1244f35007de2568e8bb641728ff + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: e0df1244f35007de2568e8bb641728ff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/e12cb34d9249409cd6d47aaae16472fb/params.yaml b/examples/security/truthseeker/output/reports/attack/e12cb34d9249409cd6d47aaae16472fb/params.yaml new file mode 100644 index 00000000..08c9e379 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/e12cb34d9249409cd6d47aaae16472fb/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 1 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 1c09cc422d0c1552237ed75c47923a48 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e12cb34d9249409cd6d47aaae16472fb/adv_predictions.json + adv_probabilities_file: output/reports/attack/e12cb34d9249409cd6d47aaae16472fb/adv_probabilities.json + attack_file: output/attacks/7b942d7f2ff744e2439a341540acc5a9.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/e12cb34d9249409cd6d47aaae16472fb/params.yaml + predictions_file: output/reports/attack/e12cb34d9249409cd6d47aaae16472fb/predictions.json + probabilities_file: output/reports/attack/e12cb34d9249409cd6d47aaae16472fb/probabilities.json + score_dict_file: output/reports/attack/e12cb34d9249409cd6d47aaae16472fb/score_dict.json + test_labels_file: output/reports/attack/e12cb34d9249409cd6d47aaae16472fb/test_labels.json + train_labels_file: output/reports/attack/e12cb34d9249409cd6d47aaae16472fb/train_labels.json + model_dir: models + name: e12cb34d9249409cd6d47aaae16472fb + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: e12cb34d9249409cd6d47aaae16472fb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/e1ab1d3712358d5ff1df26093de89763/params.yaml b/examples/security/truthseeker/output/reports/attack/e1ab1d3712358d5ff1df26093de89763/params.yaml new file mode 100644 index 00000000..48436f77 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/e1ab1d3712358d5ff1df26093de89763/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.3 + eps_step: 0.001 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: e088b1129afe67c0d5add0bde3246285 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e1ab1d3712358d5ff1df26093de89763/adv_predictions.json + adv_probabilities_file: output/reports/attack/e1ab1d3712358d5ff1df26093de89763/adv_probabilities.json + attack_file: output/attacks/e0de70ea8d2a94083da277984912a0ef.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/e1ab1d3712358d5ff1df26093de89763/params.yaml + predictions_file: output/reports/attack/e1ab1d3712358d5ff1df26093de89763/predictions.json + probabilities_file: output/reports/attack/e1ab1d3712358d5ff1df26093de89763/probabilities.json + score_dict_file: output/reports/attack/e1ab1d3712358d5ff1df26093de89763/score_dict.json + test_labels_file: output/reports/attack/e1ab1d3712358d5ff1df26093de89763/test_labels.json + train_labels_file: output/reports/attack/e1ab1d3712358d5ff1df26093de89763/train_labels.json + model_dir: models + name: e1ab1d3712358d5ff1df26093de89763 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: e1ab1d3712358d5ff1df26093de89763 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/e2ddef2e054e0aabf3d0fbaff727de61/params.yaml b/examples/security/truthseeker/output/reports/attack/e2ddef2e054e0aabf3d0fbaff727de61/params.yaml new file mode 100644 index 00000000..975bb4e1 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/e2ddef2e054e0aabf3d0fbaff727de61/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 9fb33ce78569646e84430f59a96059cb +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e2ddef2e054e0aabf3d0fbaff727de61/adv_predictions.json + adv_probabilities_file: output/reports/attack/e2ddef2e054e0aabf3d0fbaff727de61/adv_probabilities.json + attack_file: output/attacks/888cc393e27500343c7fb66c61453f85.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/e2ddef2e054e0aabf3d0fbaff727de61/params.yaml + predictions_file: output/reports/attack/e2ddef2e054e0aabf3d0fbaff727de61/predictions.json + probabilities_file: output/reports/attack/e2ddef2e054e0aabf3d0fbaff727de61/probabilities.json + score_dict_file: output/reports/attack/e2ddef2e054e0aabf3d0fbaff727de61/score_dict.json + test_labels_file: output/reports/attack/e2ddef2e054e0aabf3d0fbaff727de61/test_labels.json + train_labels_file: output/reports/attack/e2ddef2e054e0aabf3d0fbaff727de61/train_labels.json + model_dir: models + name: e2ddef2e054e0aabf3d0fbaff727de61 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: e2ddef2e054e0aabf3d0fbaff727de61 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/e3fdccb5cd6f41124da533cccc7e00ee/params.yaml b/examples/security/truthseeker/output/reports/attack/e3fdccb5cd6f41124da533cccc7e00ee/params.yaml new file mode 100644 index 00000000..01c29136 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/e3fdccb5cd6f41124da533cccc7e00ee/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.3 + eps_step: 0.3 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 77c97badf34de19460c9783a524cd466 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e3fdccb5cd6f41124da533cccc7e00ee/adv_predictions.json + adv_probabilities_file: output/reports/attack/e3fdccb5cd6f41124da533cccc7e00ee/adv_probabilities.json + attack_file: output/attacks/ed142b8266d51117ffbb006fff374455.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/e3fdccb5cd6f41124da533cccc7e00ee/params.yaml + predictions_file: output/reports/attack/e3fdccb5cd6f41124da533cccc7e00ee/predictions.json + probabilities_file: output/reports/attack/e3fdccb5cd6f41124da533cccc7e00ee/probabilities.json + score_dict_file: output/reports/attack/e3fdccb5cd6f41124da533cccc7e00ee/score_dict.json + test_labels_file: output/reports/attack/e3fdccb5cd6f41124da533cccc7e00ee/test_labels.json + train_labels_file: output/reports/attack/e3fdccb5cd6f41124da533cccc7e00ee/train_labels.json + model_dir: models + name: e3fdccb5cd6f41124da533cccc7e00ee + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: e3fdccb5cd6f41124da533cccc7e00ee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/e47bc1829900115a0285d32bafbd9b88/params.yaml b/examples/security/truthseeker/output/reports/attack/e47bc1829900115a0285d32bafbd9b88/params.yaml new file mode 100644 index 00000000..aaf342e9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/e47bc1829900115a0285d32bafbd9b88/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.5 + eps_step: 1 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 4439fb5f99b8357dca47a07c3361abc5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e47bc1829900115a0285d32bafbd9b88/adv_predictions.json + adv_probabilities_file: output/reports/attack/e47bc1829900115a0285d32bafbd9b88/adv_probabilities.json + attack_file: output/attacks/58bf59d202aadba94839f2f45412eb36.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/e47bc1829900115a0285d32bafbd9b88/params.yaml + predictions_file: output/reports/attack/e47bc1829900115a0285d32bafbd9b88/predictions.json + probabilities_file: output/reports/attack/e47bc1829900115a0285d32bafbd9b88/probabilities.json + score_dict_file: output/reports/attack/e47bc1829900115a0285d32bafbd9b88/score_dict.json + test_labels_file: output/reports/attack/e47bc1829900115a0285d32bafbd9b88/test_labels.json + train_labels_file: output/reports/attack/e47bc1829900115a0285d32bafbd9b88/train_labels.json + model_dir: models + name: e47bc1829900115a0285d32bafbd9b88 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: e47bc1829900115a0285d32bafbd9b88 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/e715396dd6eb8bd75464b41c62088e25/params.yaml b/examples/security/truthseeker/output/reports/attack/e715396dd6eb8bd75464b41c62088e25/params.yaml new file mode 100644 index 00000000..ebcf22cb --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/e715396dd6eb8bd75464b41c62088e25/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.3 + eps_step: 0.001 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 59048eee69793361813089aaf36e0ef3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e715396dd6eb8bd75464b41c62088e25/adv_predictions.json + adv_probabilities_file: output/reports/attack/e715396dd6eb8bd75464b41c62088e25/adv_probabilities.json + attack_file: output/attacks/55cc96356188deccc5a7daf9943aa51f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/e715396dd6eb8bd75464b41c62088e25/params.yaml + predictions_file: output/reports/attack/e715396dd6eb8bd75464b41c62088e25/predictions.json + probabilities_file: output/reports/attack/e715396dd6eb8bd75464b41c62088e25/probabilities.json + score_dict_file: output/reports/attack/e715396dd6eb8bd75464b41c62088e25/score_dict.json + test_labels_file: output/reports/attack/e715396dd6eb8bd75464b41c62088e25/test_labels.json + train_labels_file: output/reports/attack/e715396dd6eb8bd75464b41c62088e25/train_labels.json + model_dir: models + name: e715396dd6eb8bd75464b41c62088e25 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: e715396dd6eb8bd75464b41c62088e25 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/e7e7e44a2a6c744630d9cace3be600e5/params.yaml b/examples/security/truthseeker/output/reports/attack/e7e7e44a2a6c744630d9cace3be600e5/params.yaml new file mode 100644 index 00000000..80192753 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/e7e7e44a2a6c744630d9cace3be600e5/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.3 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 1b7b9ed82baaf40599acbf7e3adad5c0 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e7e7e44a2a6c744630d9cace3be600e5/adv_predictions.json + adv_probabilities_file: output/reports/attack/e7e7e44a2a6c744630d9cace3be600e5/adv_probabilities.json + attack_file: output/attacks/fade480aa54d004f82742fbee303e729.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/e7e7e44a2a6c744630d9cace3be600e5/params.yaml + predictions_file: output/reports/attack/e7e7e44a2a6c744630d9cace3be600e5/predictions.json + probabilities_file: output/reports/attack/e7e7e44a2a6c744630d9cace3be600e5/probabilities.json + score_dict_file: output/reports/attack/e7e7e44a2a6c744630d9cace3be600e5/score_dict.json + test_labels_file: output/reports/attack/e7e7e44a2a6c744630d9cace3be600e5/test_labels.json + train_labels_file: output/reports/attack/e7e7e44a2a6c744630d9cace3be600e5/train_labels.json + model_dir: models + name: e7e7e44a2a6c744630d9cace3be600e5 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: e7e7e44a2a6c744630d9cace3be600e5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/params.yaml b/examples/security/truthseeker/output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/params.yaml new file mode 100644 index 00000000..46d48030 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.3 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: e335084cff06f2ea7b22699485bb98b3 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/adv_predictions.json + adv_probabilities_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/adv_probabilities.json + attack_file: output/attacks/19cd7be04ecc0a1bb96d31ec590337db.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/params.yaml + predictions_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/predictions.json + probabilities_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/probabilities.json + score_dict_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/score_dict.json + test_labels_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/test_labels.json + train_labels_file: output/reports/attack/e88189571dc2e7f742f71a82870cc3d0/train_labels.json + model_dir: models + name: e88189571dc2e7f742f71a82870cc3d0 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: e88189571dc2e7f742f71a82870cc3d0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/e8911025a4faac9b5898faa7159ceb8e/params.yaml b/examples/security/truthseeker/output/reports/attack/e8911025a4faac9b5898faa7159ceb8e/params.yaml new file mode 100644 index 00000000..38ce7a03 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/e8911025a4faac9b5898faa7159ceb8e/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 1 + eps_step: 0.1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 1f3695b759d401218a2f87578176a23e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e8911025a4faac9b5898faa7159ceb8e/adv_predictions.json + adv_probabilities_file: output/reports/attack/e8911025a4faac9b5898faa7159ceb8e/adv_probabilities.json + attack_file: output/attacks/7684d7848bf7329995b4e6cdd403ad33.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/e8911025a4faac9b5898faa7159ceb8e/params.yaml + predictions_file: output/reports/attack/e8911025a4faac9b5898faa7159ceb8e/predictions.json + probabilities_file: output/reports/attack/e8911025a4faac9b5898faa7159ceb8e/probabilities.json + score_dict_file: output/reports/attack/e8911025a4faac9b5898faa7159ceb8e/score_dict.json + test_labels_file: output/reports/attack/e8911025a4faac9b5898faa7159ceb8e/test_labels.json + train_labels_file: output/reports/attack/e8911025a4faac9b5898faa7159ceb8e/train_labels.json + model_dir: models + name: e8911025a4faac9b5898faa7159ceb8e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: e8911025a4faac9b5898faa7159ceb8e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/e8a017cc0047960d9f6c258c918b4b83/params.yaml b/examples/security/truthseeker/output/reports/attack/e8a017cc0047960d9f6c258c918b4b83/params.yaml new file mode 100644 index 00000000..e47a224b --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/e8a017cc0047960d9f6c258c918b4b83/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.01 + eps_step: 0.5 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 4f722d27b4d2925b6dab304b3a6ff938 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/e8a017cc0047960d9f6c258c918b4b83/adv_predictions.json + adv_probabilities_file: output/reports/attack/e8a017cc0047960d9f6c258c918b4b83/adv_probabilities.json + attack_file: output/attacks/a565b1721c72d0d510cefe4e1d7c09e3.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/e8a017cc0047960d9f6c258c918b4b83/params.yaml + predictions_file: output/reports/attack/e8a017cc0047960d9f6c258c918b4b83/predictions.json + probabilities_file: output/reports/attack/e8a017cc0047960d9f6c258c918b4b83/probabilities.json + score_dict_file: output/reports/attack/e8a017cc0047960d9f6c258c918b4b83/score_dict.json + test_labels_file: output/reports/attack/e8a017cc0047960d9f6c258c918b4b83/test_labels.json + train_labels_file: output/reports/attack/e8a017cc0047960d9f6c258c918b4b83/train_labels.json + model_dir: models + name: e8a017cc0047960d9f6c258c918b4b83 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: e8a017cc0047960d9f6c258c918b4b83 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/eaee330e26d4c67a460bc538af527438/params.yaml b/examples/security/truthseeker/output/reports/attack/eaee330e26d4c67a460bc538af527438/params.yaml new file mode 100644 index 00000000..40a78db6 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/eaee330e26d4c67a460bc538af527438/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 1 + eps_step: 0.1 + max_iter: 100 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: a168b70bd6a5fdf60400d6dfb209cfbf +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/eaee330e26d4c67a460bc538af527438/adv_predictions.json + adv_probabilities_file: output/reports/attack/eaee330e26d4c67a460bc538af527438/adv_probabilities.json + attack_file: output/attacks/4bd3af634ca9d197de82720156fab1b6.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/eaee330e26d4c67a460bc538af527438/params.yaml + predictions_file: output/reports/attack/eaee330e26d4c67a460bc538af527438/predictions.json + probabilities_file: output/reports/attack/eaee330e26d4c67a460bc538af527438/probabilities.json + score_dict_file: output/reports/attack/eaee330e26d4c67a460bc538af527438/score_dict.json + test_labels_file: output/reports/attack/eaee330e26d4c67a460bc538af527438/test_labels.json + train_labels_file: output/reports/attack/eaee330e26d4c67a460bc538af527438/train_labels.json + model_dir: models + name: eaee330e26d4c67a460bc538af527438 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: eaee330e26d4c67a460bc538af527438 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/ed19efad9153b983749901a78112291a/params.yaml b/examples/security/truthseeker/output/reports/attack/ed19efad9153b983749901a78112291a/params.yaml new file mode 100644 index 00000000..777c72e8 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/ed19efad9153b983749901a78112291a/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 1 + max_iter: 1 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: b0873a91987a585f68762a2d27bf47ce +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/ed19efad9153b983749901a78112291a/adv_predictions.json + adv_probabilities_file: output/reports/attack/ed19efad9153b983749901a78112291a/adv_probabilities.json + attack_file: output/attacks/b96dcaca8e2dc8672eb73457272803b5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/ed19efad9153b983749901a78112291a/params.yaml + predictions_file: output/reports/attack/ed19efad9153b983749901a78112291a/predictions.json + probabilities_file: output/reports/attack/ed19efad9153b983749901a78112291a/probabilities.json + score_dict_file: output/reports/attack/ed19efad9153b983749901a78112291a/score_dict.json + test_labels_file: output/reports/attack/ed19efad9153b983749901a78112291a/test_labels.json + train_labels_file: output/reports/attack/ed19efad9153b983749901a78112291a/train_labels.json + model_dir: models + name: ed19efad9153b983749901a78112291a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: ed19efad9153b983749901a78112291a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f12d7b6e141422527e3a8652bbef3b95/params.yaml b/examples/security/truthseeker/output/reports/attack/f12d7b6e141422527e3a8652bbef3b95/params.yaml new file mode 100644 index 00000000..e94d9614 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f12d7b6e141422527e3a8652bbef3b95/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.5 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 7d08f5f2a4d9dba88e515d8f76b663fc +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f12d7b6e141422527e3a8652bbef3b95/adv_predictions.json + adv_probabilities_file: output/reports/attack/f12d7b6e141422527e3a8652bbef3b95/adv_probabilities.json + attack_file: output/attacks/80e6512be8ded6b0ebb222f074ac7111.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/f12d7b6e141422527e3a8652bbef3b95/params.yaml + predictions_file: output/reports/attack/f12d7b6e141422527e3a8652bbef3b95/predictions.json + probabilities_file: output/reports/attack/f12d7b6e141422527e3a8652bbef3b95/probabilities.json + score_dict_file: output/reports/attack/f12d7b6e141422527e3a8652bbef3b95/score_dict.json + test_labels_file: output/reports/attack/f12d7b6e141422527e3a8652bbef3b95/test_labels.json + train_labels_file: output/reports/attack/f12d7b6e141422527e3a8652bbef3b95/train_labels.json + model_dir: models + name: f12d7b6e141422527e3a8652bbef3b95 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: f12d7b6e141422527e3a8652bbef3b95 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f1551d8aed5fef23958fc1803fc154f7/params.yaml b/examples/security/truthseeker/output/reports/attack/f1551d8aed5fef23958fc1803fc154f7/params.yaml new file mode 100644 index 00000000..0018ec3d --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f1551d8aed5fef23958fc1803fc154f7/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.01 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 21a6b1df155d362ad7c438439223629d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f1551d8aed5fef23958fc1803fc154f7/adv_predictions.json + adv_probabilities_file: output/reports/attack/f1551d8aed5fef23958fc1803fc154f7/adv_probabilities.json + attack_file: output/attacks/c128a9db39f8956a2503056bdfe7ba42.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/f1551d8aed5fef23958fc1803fc154f7/params.yaml + predictions_file: output/reports/attack/f1551d8aed5fef23958fc1803fc154f7/predictions.json + probabilities_file: output/reports/attack/f1551d8aed5fef23958fc1803fc154f7/probabilities.json + score_dict_file: output/reports/attack/f1551d8aed5fef23958fc1803fc154f7/score_dict.json + test_labels_file: output/reports/attack/f1551d8aed5fef23958fc1803fc154f7/test_labels.json + train_labels_file: output/reports/attack/f1551d8aed5fef23958fc1803fc154f7/train_labels.json + model_dir: models + name: f1551d8aed5fef23958fc1803fc154f7 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: f1551d8aed5fef23958fc1803fc154f7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f1ecbaed1b13191809027f070f5e5a07/params.yaml b/examples/security/truthseeker/output/reports/attack/f1ecbaed1b13191809027f070f5e5a07/params.yaml new file mode 100644 index 00000000..4bd00bbe --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f1ecbaed1b13191809027f070f5e5a07/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 1 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 5eb8a05092d05343583743a15c3ce069 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f1ecbaed1b13191809027f070f5e5a07/adv_predictions.json + adv_probabilities_file: output/reports/attack/f1ecbaed1b13191809027f070f5e5a07/adv_probabilities.json + attack_file: output/attacks/68d13d40ec6c730bff3b388a76d6e697.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/f1ecbaed1b13191809027f070f5e5a07/params.yaml + predictions_file: output/reports/attack/f1ecbaed1b13191809027f070f5e5a07/predictions.json + probabilities_file: output/reports/attack/f1ecbaed1b13191809027f070f5e5a07/probabilities.json + score_dict_file: output/reports/attack/f1ecbaed1b13191809027f070f5e5a07/score_dict.json + test_labels_file: output/reports/attack/f1ecbaed1b13191809027f070f5e5a07/test_labels.json + train_labels_file: output/reports/attack/f1ecbaed1b13191809027f070f5e5a07/train_labels.json + model_dir: models + name: f1ecbaed1b13191809027f070f5e5a07 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: f1ecbaed1b13191809027f070f5e5a07 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f59bf796f71129282c9c6c9c923f1afc/params.yaml b/examples/security/truthseeker/output/reports/attack/f59bf796f71129282c9c6c9c923f1afc/params.yaml new file mode 100644 index 00000000..3eb19a6c --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f59bf796f71129282c9c6c9c923f1afc/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.3 + max_iter: 10 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 6f92029604a6ef4e73abdbd9643ac3fb +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f59bf796f71129282c9c6c9c923f1afc/adv_predictions.json + adv_probabilities_file: output/reports/attack/f59bf796f71129282c9c6c9c923f1afc/adv_probabilities.json + attack_file: output/attacks/91e3d29d19c53ca597c9afe6e796d3d6.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/f59bf796f71129282c9c6c9c923f1afc/params.yaml + predictions_file: output/reports/attack/f59bf796f71129282c9c6c9c923f1afc/predictions.json + probabilities_file: output/reports/attack/f59bf796f71129282c9c6c9c923f1afc/probabilities.json + score_dict_file: output/reports/attack/f59bf796f71129282c9c6c9c923f1afc/score_dict.json + test_labels_file: output/reports/attack/f59bf796f71129282c9c6c9c923f1afc/test_labels.json + train_labels_file: output/reports/attack/f59bf796f71129282c9c6c9c923f1afc/train_labels.json + model_dir: models + name: f59bf796f71129282c9c6c9c923f1afc + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: f59bf796f71129282c9c6c9c923f1afc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f5b09e1e125e23dbcb9041bbef4b0347/params.yaml b/examples/security/truthseeker/output/reports/attack/f5b09e1e125e23dbcb9041bbef4b0347/params.yaml new file mode 100644 index 00000000..cc621c78 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f5b09e1e125e23dbcb9041bbef4b0347/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 0.3 + max_iter: 10 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: ac69c278ad6298e5673963b411e0c2c1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f5b09e1e125e23dbcb9041bbef4b0347/adv_predictions.json + adv_probabilities_file: output/reports/attack/f5b09e1e125e23dbcb9041bbef4b0347/adv_probabilities.json + attack_file: output/attacks/978884d44a610c7fe01806667a14d305.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/f5b09e1e125e23dbcb9041bbef4b0347/params.yaml + predictions_file: output/reports/attack/f5b09e1e125e23dbcb9041bbef4b0347/predictions.json + probabilities_file: output/reports/attack/f5b09e1e125e23dbcb9041bbef4b0347/probabilities.json + score_dict_file: output/reports/attack/f5b09e1e125e23dbcb9041bbef4b0347/score_dict.json + test_labels_file: output/reports/attack/f5b09e1e125e23dbcb9041bbef4b0347/test_labels.json + train_labels_file: output/reports/attack/f5b09e1e125e23dbcb9041bbef4b0347/train_labels.json + model_dir: models + name: f5b09e1e125e23dbcb9041bbef4b0347 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: f5b09e1e125e23dbcb9041bbef4b0347 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f5c7e0bc70c433d984c26e623005fb70/params.yaml b/examples/security/truthseeker/output/reports/attack/f5c7e0bc70c433d984c26e623005fb70/params.yaml new file mode 100644 index 00000000..42c049cb --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f5c7e0bc70c433d984c26e623005fb70/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 1 + eps_step: 0.01 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 53d488c1da25f2860a00605410d006ca +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f5c7e0bc70c433d984c26e623005fb70/adv_predictions.json + adv_probabilities_file: output/reports/attack/f5c7e0bc70c433d984c26e623005fb70/adv_probabilities.json + attack_file: output/attacks/6f639e13b1efcad04418b08fb28e034a.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/f5c7e0bc70c433d984c26e623005fb70/params.yaml + predictions_file: output/reports/attack/f5c7e0bc70c433d984c26e623005fb70/predictions.json + probabilities_file: output/reports/attack/f5c7e0bc70c433d984c26e623005fb70/probabilities.json + score_dict_file: output/reports/attack/f5c7e0bc70c433d984c26e623005fb70/score_dict.json + test_labels_file: output/reports/attack/f5c7e0bc70c433d984c26e623005fb70/test_labels.json + train_labels_file: output/reports/attack/f5c7e0bc70c433d984c26e623005fb70/train_labels.json + model_dir: models + name: f5c7e0bc70c433d984c26e623005fb70 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: f5c7e0bc70c433d984c26e623005fb70 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f5dd4aeb3c950505b5e05b6b0ee144d9/params.yaml b/examples/security/truthseeker/output/reports/attack/f5dd4aeb3c950505b5e05b6b0ee144d9/params.yaml new file mode 100644 index 00000000..a1bd426f --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f5dd4aeb3c950505b5e05b6b0ee144d9/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.1 + eps_step: 1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: d3a81ec75ef33ae87b56d762de4817e0 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f5dd4aeb3c950505b5e05b6b0ee144d9/adv_predictions.json + adv_probabilities_file: output/reports/attack/f5dd4aeb3c950505b5e05b6b0ee144d9/adv_probabilities.json + attack_file: output/attacks/60a5c33d47b6fa8e4b49364bdf6dd529.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/f5dd4aeb3c950505b5e05b6b0ee144d9/params.yaml + predictions_file: output/reports/attack/f5dd4aeb3c950505b5e05b6b0ee144d9/predictions.json + probabilities_file: output/reports/attack/f5dd4aeb3c950505b5e05b6b0ee144d9/probabilities.json + score_dict_file: output/reports/attack/f5dd4aeb3c950505b5e05b6b0ee144d9/score_dict.json + test_labels_file: output/reports/attack/f5dd4aeb3c950505b5e05b6b0ee144d9/test_labels.json + train_labels_file: output/reports/attack/f5dd4aeb3c950505b5e05b6b0ee144d9/train_labels.json + model_dir: models + name: f5dd4aeb3c950505b5e05b6b0ee144d9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: f5dd4aeb3c950505b5e05b6b0ee144d9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f61f742c0c357e3892a876ea28631ea9/params.yaml b/examples/security/truthseeker/output/reports/attack/f61f742c0c357e3892a876ea28631ea9/params.yaml new file mode 100644 index 00000000..670dffa3 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f61f742c0c357e3892a876ea28631ea9/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.5 + eps_step: 0.5 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 14bec5872bf70b97b533c32aab2c5e0d +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f61f742c0c357e3892a876ea28631ea9/adv_predictions.json + adv_probabilities_file: output/reports/attack/f61f742c0c357e3892a876ea28631ea9/adv_probabilities.json + attack_file: output/attacks/2fa8e3b419e3733b752c232eb6c4208f.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/f61f742c0c357e3892a876ea28631ea9/params.yaml + predictions_file: output/reports/attack/f61f742c0c357e3892a876ea28631ea9/predictions.json + probabilities_file: output/reports/attack/f61f742c0c357e3892a876ea28631ea9/probabilities.json + score_dict_file: output/reports/attack/f61f742c0c357e3892a876ea28631ea9/score_dict.json + test_labels_file: output/reports/attack/f61f742c0c357e3892a876ea28631ea9/test_labels.json + train_labels_file: output/reports/attack/f61f742c0c357e3892a876ea28631ea9/train_labels.json + model_dir: models + name: f61f742c0c357e3892a876ea28631ea9 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: f61f742c0c357e3892a876ea28631ea9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f6eb7d073d5f67c8b9c88b28f62bb096/params.yaml b/examples/security/truthseeker/output/reports/attack/f6eb7d073d5f67c8b9c88b28f62bb096/params.yaml new file mode 100644 index 00000000..602accb8 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f6eb7d073d5f67c8b9c88b28f62bb096/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 1 + eps: 0.1 + eps_step: 0.3 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 2eb26aa4bb4ec1e4b4d6148e1693b9c2 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f6eb7d073d5f67c8b9c88b28f62bb096/adv_predictions.json + adv_probabilities_file: output/reports/attack/f6eb7d073d5f67c8b9c88b28f62bb096/adv_probabilities.json + attack_file: output/attacks/312be3cf67d56eedf7b38df3a8397668.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/f6eb7d073d5f67c8b9c88b28f62bb096/params.yaml + predictions_file: output/reports/attack/f6eb7d073d5f67c8b9c88b28f62bb096/predictions.json + probabilities_file: output/reports/attack/f6eb7d073d5f67c8b9c88b28f62bb096/probabilities.json + score_dict_file: output/reports/attack/f6eb7d073d5f67c8b9c88b28f62bb096/score_dict.json + test_labels_file: output/reports/attack/f6eb7d073d5f67c8b9c88b28f62bb096/test_labels.json + train_labels_file: output/reports/attack/f6eb7d073d5f67c8b9c88b28f62bb096/train_labels.json + model_dir: models + name: f6eb7d073d5f67c8b9c88b28f62bb096 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: f6eb7d073d5f67c8b9c88b28f62bb096 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f72c931e729c4c6efe02b13a7a656a26/params.yaml b/examples/security/truthseeker/output/reports/attack/f72c931e729c4c6efe02b13a7a656a26/params.yaml new file mode 100644 index 00000000..3c964fb1 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f72c931e729c4c6efe02b13a7a656a26/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.01 + eps_step: 0.1 + max_iter: 100 + norm: .inf + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 57fee3c5b271313a93a125e4f3e90318 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f72c931e729c4c6efe02b13a7a656a26/adv_predictions.json + adv_probabilities_file: output/reports/attack/f72c931e729c4c6efe02b13a7a656a26/adv_probabilities.json + attack_file: output/attacks/3c30c79a6108ae936075111106a0d32e.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/f72c931e729c4c6efe02b13a7a656a26/params.yaml + predictions_file: output/reports/attack/f72c931e729c4c6efe02b13a7a656a26/predictions.json + probabilities_file: output/reports/attack/f72c931e729c4c6efe02b13a7a656a26/probabilities.json + score_dict_file: output/reports/attack/f72c931e729c4c6efe02b13a7a656a26/score_dict.json + test_labels_file: output/reports/attack/f72c931e729c4c6efe02b13a7a656a26/test_labels.json + train_labels_file: output/reports/attack/f72c931e729c4c6efe02b13a7a656a26/train_labels.json + model_dir: models + name: f72c931e729c4c6efe02b13a7a656a26 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: f72c931e729c4c6efe02b13a7a656a26 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f7b66facbb4590e469fb0b28369a26c2/params.yaml b/examples/security/truthseeker/output/reports/attack/f7b66facbb4590e469fb0b28369a26c2/params.yaml new file mode 100644 index 00000000..b194929b --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f7b66facbb4590e469fb0b28369a26c2/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: fa9d5e0aa5626dfe29d1b7cb48393be1 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f7b66facbb4590e469fb0b28369a26c2/adv_predictions.json + adv_probabilities_file: output/reports/attack/f7b66facbb4590e469fb0b28369a26c2/adv_probabilities.json + attack_file: output/attacks/877d167279b0a8aebc7626ebefcb25f5.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/f7b66facbb4590e469fb0b28369a26c2/params.yaml + predictions_file: output/reports/attack/f7b66facbb4590e469fb0b28369a26c2/predictions.json + probabilities_file: output/reports/attack/f7b66facbb4590e469fb0b28369a26c2/probabilities.json + score_dict_file: output/reports/attack/f7b66facbb4590e469fb0b28369a26c2/score_dict.json + test_labels_file: output/reports/attack/f7b66facbb4590e469fb0b28369a26c2/test_labels.json + train_labels_file: output/reports/attack/f7b66facbb4590e469fb0b28369a26c2/train_labels.json + model_dir: models + name: f7b66facbb4590e469fb0b28369a26c2 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: f7b66facbb4590e469fb0b28369a26c2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f7bfa06beec71e4d6a9da34e9457ca3a/params.yaml b/examples/security/truthseeker/output/reports/attack/f7bfa06beec71e4d6a9da34e9457ca3a/params.yaml new file mode 100644 index 00000000..9c2be2da --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f7bfa06beec71e4d6a9da34e9457ca3a/params.yaml @@ -0,0 +1,227 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.001 + eps_step: 0.3 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2aaa084953d7e17acde422cfeae5e25 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} + name: 52cd7e7e80dfbf8e96fd412c0a5847b9 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f7bfa06beec71e4d6a9da34e9457ca3a/adv_predictions.json + adv_probabilities_file: output/reports/attack/f7bfa06beec71e4d6a9da34e9457ca3a/adv_probabilities.json + attack_file: output/attacks/3719aa72ac9c2654609a287869edce88.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/attack/f7bfa06beec71e4d6a9da34e9457ca3a/params.yaml + predictions_file: output/reports/attack/f7bfa06beec71e4d6a9da34e9457ca3a/predictions.json + probabilities_file: output/reports/attack/f7bfa06beec71e4d6a9da34e9457ca3a/probabilities.json + score_dict_file: output/reports/attack/f7bfa06beec71e4d6a9da34e9457ca3a/score_dict.json + test_labels_file: output/reports/attack/f7bfa06beec71e4d6a9da34e9457ca3a/test_labels.json + train_labels_file: output/reports/attack/f7bfa06beec71e4d6a9da34e9457ca3a/train_labels.json + model_dir: models + name: f7bfa06beec71e4d6a9da34e9457ca3a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: f7bfa06beec71e4d6a9da34e9457ca3a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f813de7484cc7f4e8a459bf66e459a57/params.yaml b/examples/security/truthseeker/output/reports/attack/f813de7484cc7f4e8a459bf66e459a57/params.yaml new file mode 100644 index 00000000..b0a3f599 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f813de7484cc7f4e8a459bf66e459a57/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.5 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 9d9da92271971034660e0e0ec51cadf5 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f813de7484cc7f4e8a459bf66e459a57/adv_predictions.json + adv_probabilities_file: output/reports/attack/f813de7484cc7f4e8a459bf66e459a57/adv_probabilities.json + attack_file: output/attacks/194819eb6c0db80f0d3d33d057659ba0.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/f813de7484cc7f4e8a459bf66e459a57/params.yaml + predictions_file: output/reports/attack/f813de7484cc7f4e8a459bf66e459a57/predictions.json + probabilities_file: output/reports/attack/f813de7484cc7f4e8a459bf66e459a57/probabilities.json + score_dict_file: output/reports/attack/f813de7484cc7f4e8a459bf66e459a57/score_dict.json + test_labels_file: output/reports/attack/f813de7484cc7f4e8a459bf66e459a57/test_labels.json + train_labels_file: output/reports/attack/f813de7484cc7f4e8a459bf66e459a57/train_labels.json + model_dir: models + name: f813de7484cc7f4e8a459bf66e459a57 + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: f813de7484cc7f4e8a459bf66e459a57 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f8769cbaae1d077ca7a3f044d911055b/params.yaml b/examples/security/truthseeker/output/reports/attack/f8769cbaae1d077ca7a3f044d911055b/params.yaml new file mode 100644 index 00000000..6ab10421 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f8769cbaae1d077ca7a3f044d911055b/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.001 + eps_step: 0.1 + max_iter: 1 + norm: 2 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 08ffc0db236912a2cc1630ceb8d0280e +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f8769cbaae1d077ca7a3f044d911055b/adv_predictions.json + adv_probabilities_file: output/reports/attack/f8769cbaae1d077ca7a3f044d911055b/adv_probabilities.json + attack_file: output/attacks/772441f2df3c3d25d41f36ea62445477.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/f8769cbaae1d077ca7a3f044d911055b/params.yaml + predictions_file: output/reports/attack/f8769cbaae1d077ca7a3f044d911055b/predictions.json + probabilities_file: output/reports/attack/f8769cbaae1d077ca7a3f044d911055b/probabilities.json + score_dict_file: output/reports/attack/f8769cbaae1d077ca7a3f044d911055b/score_dict.json + test_labels_file: output/reports/attack/f8769cbaae1d077ca7a3f044d911055b/test_labels.json + train_labels_file: output/reports/attack/f8769cbaae1d077ca7a3f044d911055b/train_labels.json + model_dir: models + name: f8769cbaae1d077ca7a3f044d911055b + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: f8769cbaae1d077ca7a3f044d911055b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/params.yaml b/examples/security/truthseeker/output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/params.yaml new file mode 100644 index 00000000..ddb553e7 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 10 + eps: 0.5 + eps_step: 0.01 + max_iter: 10 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 3c226c158c87b8d8a7fbf755ae490633 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/adv_predictions.json + adv_probabilities_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/adv_probabilities.json + attack_file: output/attacks/68d3f6642b5e16f7781a54759c851c54.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/params.yaml + predictions_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/predictions.json + probabilities_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/probabilities.json + score_dict_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/score_dict.json + test_labels_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/test_labels.json + train_labels_file: output/reports/attack/f8bd24762b80cf8d103def5b2d0ec48f/train_labels.json + model_dir: models + name: f8bd24762b80cf8d103def5b2d0ec48f + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: f8bd24762b80cf8d103def5b2d0ec48f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/fbaf22b2316530f5eb14b374ee61202e/params.yaml b/examples/security/truthseeker/output/reports/attack/fbaf22b2316530f5eb14b374ee61202e/params.yaml new file mode 100644 index 00000000..5b8aa3b7 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/fbaf22b2316530f5eb14b374ee61202e/params.yaml @@ -0,0 +1,230 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 50 + eps: 0.5 + eps_step: 0.01 + max_iter: 1 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d315f58f87b2ac3c1fa36a4e69d247bc + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} + name: 998e9feacf90cf5dcac378688229a055 +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/fbaf22b2316530f5eb14b374ee61202e/adv_predictions.json + adv_probabilities_file: output/reports/attack/fbaf22b2316530f5eb14b374ee61202e/adv_probabilities.json + attack_file: output/attacks/7c9b04b0d86008d63b941ba12010ba0d.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/attack/fbaf22b2316530f5eb14b374ee61202e/params.yaml + predictions_file: output/reports/attack/fbaf22b2316530f5eb14b374ee61202e/predictions.json + probabilities_file: output/reports/attack/fbaf22b2316530f5eb14b374ee61202e/probabilities.json + score_dict_file: output/reports/attack/fbaf22b2316530f5eb14b374ee61202e/score_dict.json + test_labels_file: output/reports/attack/fbaf22b2316530f5eb14b374ee61202e/test_labels.json + train_labels_file: output/reports/attack/fbaf22b2316530f5eb14b374ee61202e/train_labels.json + model_dir: models + name: fbaf22b2316530f5eb14b374ee61202e + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: fbaf22b2316530f5eb14b374ee61202e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/attack/fe57fa04a87981b1e3759c796f26fe3a/params.yaml b/examples/security/truthseeker/output/reports/attack/fe57fa04a87981b1e3759c796f26fe3a/params.yaml new file mode 100644 index 00000000..9e247ed7 --- /dev/null +++ b/examples/security/truthseeker/output/reports/attack/fe57fa04a87981b1e3759c796f26fe3a/params.yaml @@ -0,0 +1,221 @@ +attack: + attack_size: 10 + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + batch_size: 100 + eps: 0.1 + eps_step: 1 + max_iter: 100 + norm: 1 + model: + art: + library: sklearn-svc + name: 37e40fba19cda476dad12a95c79baf44 + pipeline: {} + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 517e86f372dd91f33490d9e1bf044375 + trainer: + kwargs: + kwargs: {} + name: art.attacks.evasion.ProjectedGradientDescent + kwargs: {} + method: evasion + model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} + name: 4a3b5f5aacce8442aaaa0cab0762c7df +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/attack/fe57fa04a87981b1e3759c796f26fe3a/adv_predictions.json + adv_probabilities_file: output/reports/attack/fe57fa04a87981b1e3759c796f26fe3a/adv_probabilities.json + attack_file: output/attacks/a4ee17dd48b095095cf76edc39babdd2.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/attack/fe57fa04a87981b1e3759c796f26fe3a/params.yaml + predictions_file: output/reports/attack/fe57fa04a87981b1e3759c796f26fe3a/predictions.json + probabilities_file: output/reports/attack/fe57fa04a87981b1e3759c796f26fe3a/probabilities.json + score_dict_file: output/reports/attack/fe57fa04a87981b1e3759c796f26fe3a/score_dict.json + test_labels_file: output/reports/attack/fe57fa04a87981b1e3759c796f26fe3a/test_labels.json + train_labels_file: output/reports/attack/fe57fa04a87981b1e3759c796f26fe3a/train_labels.json + model_dir: models + name: fe57fa04a87981b1e3759c796f26fe3a + reports: reports + stage: attack +kwargs: + direction: minimize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: fe57fa04a87981b1e3759c796f26fe3a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/0027b6fba30f0b43e702fe1cace5c2c9/params.yaml b/examples/security/truthseeker/output/reports/train/0027b6fba30f0b43e702fe1cace5c2c9/params.yaml new file mode 100644 index 00000000..0f000cd7 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/0027b6fba30f0b43e702fe1cace5c2c9/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0027b6fba30f0b43e702fe1cace5c2c9/adv_predictions.json + adv_probabilities_file: output/reports/train/0027b6fba30f0b43e702fe1cace5c2c9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f61eebee621a8a48273b9149a0295db0.pkl + params_file: output/reports/train/0027b6fba30f0b43e702fe1cace5c2c9/params.yaml + predictions_file: output/reports/train/0027b6fba30f0b43e702fe1cace5c2c9/predictions.json + probabilities_file: output/reports/train/0027b6fba30f0b43e702fe1cace5c2c9/probabilities.json + score_dict_file: output/reports/train/0027b6fba30f0b43e702fe1cace5c2c9/score_dict.json + test_labels_file: output/reports/train/0027b6fba30f0b43e702fe1cace5c2c9/test_labels.json + train_labels_file: output/reports/train/0027b6fba30f0b43e702fe1cace5c2c9/train_labels.json + model_dir: models + name: 0027b6fba30f0b43e702fe1cace5c2c9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d44adb8cb721e16994be37e68c6cd5d4 + trainer: + kwargs: {} +name: 0027b6fba30f0b43e702fe1cace5c2c9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/params.yaml b/examples/security/truthseeker/output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/params.yaml new file mode 100644 index 00000000..c0fb505b --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/adv_predictions.json + adv_probabilities_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7ee6ee2b1768a2f927f774f000663b31.pkl + params_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/params.yaml + predictions_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/predictions.json + probabilities_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/probabilities.json + score_dict_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/score_dict.json + test_labels_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/test_labels.json + train_labels_file: output/reports/train/0030dc74ce75d68c788d83e4edf5b61f/train_labels.json + model_dir: models + name: 0030dc74ce75d68c788d83e4edf5b61f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: de7140be97c1ae249218b2b43de057db + trainer: + kwargs: {} +name: 0030dc74ce75d68c788d83e4edf5b61f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/025a800ef6edd0166800b39e26cf268c/params.yaml b/examples/security/truthseeker/output/reports/train/025a800ef6edd0166800b39e26cf268c/params.yaml new file mode 100644 index 00000000..b779ad24 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/025a800ef6edd0166800b39e26cf268c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/025a800ef6edd0166800b39e26cf268c/adv_predictions.json + adv_probabilities_file: output/reports/train/025a800ef6edd0166800b39e26cf268c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b90b1d0e9300239d089153b73b55b285.pkl + params_file: output/reports/train/025a800ef6edd0166800b39e26cf268c/params.yaml + predictions_file: output/reports/train/025a800ef6edd0166800b39e26cf268c/predictions.json + probabilities_file: output/reports/train/025a800ef6edd0166800b39e26cf268c/probabilities.json + score_dict_file: output/reports/train/025a800ef6edd0166800b39e26cf268c/score_dict.json + test_labels_file: output/reports/train/025a800ef6edd0166800b39e26cf268c/test_labels.json + train_labels_file: output/reports/train/025a800ef6edd0166800b39e26cf268c/train_labels.json + model_dir: models + name: 025a800ef6edd0166800b39e26cf268c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2362e64a0f9ace6ce9f297b2d66689e6 + trainer: + kwargs: {} +name: 025a800ef6edd0166800b39e26cf268c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/params.yaml b/examples/security/truthseeker/output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/params.yaml new file mode 100644 index 00000000..8d41d403 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/adv_predictions.json + adv_probabilities_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/11bec5b40d872042de508e1b56daae11.pkl + params_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/params.yaml + predictions_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/predictions.json + probabilities_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/probabilities.json + score_dict_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/score_dict.json + test_labels_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/test_labels.json + train_labels_file: output/reports/train/02a5a9ba99c99d47dfc1064ea8bdefd0/train_labels.json + model_dir: models + name: 02a5a9ba99c99d47dfc1064ea8bdefd0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 50cc38ab0df54a361715e71a22b7e05c + trainer: + kwargs: {} +name: 02a5a9ba99c99d47dfc1064ea8bdefd0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/params.yaml b/examples/security/truthseeker/output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/params.yaml new file mode 100644 index 00000000..34ebb7c3 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/adv_predictions.json + adv_probabilities_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/37a6d4e18b5d2e989f67d25206037cf7.pkl + params_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/params.yaml + predictions_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/predictions.json + probabilities_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/probabilities.json + score_dict_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/score_dict.json + test_labels_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/test_labels.json + train_labels_file: output/reports/train/02c3b951c3452df6ceedce6f3e432b9d/train_labels.json + model_dir: models + name: 02c3b951c3452df6ceedce6f3e432b9d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9a020cc91c89312191e273e2f322a0c4 + trainer: + kwargs: {} +name: 02c3b951c3452df6ceedce6f3e432b9d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/03129654f3245d637415613921f0f030/params.yaml b/examples/security/truthseeker/output/reports/train/03129654f3245d637415613921f0f030/params.yaml new file mode 100644 index 00000000..a64fc345 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/03129654f3245d637415613921f0f030/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/03129654f3245d637415613921f0f030/adv_predictions.json + adv_probabilities_file: output/reports/train/03129654f3245d637415613921f0f030/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d45cad15577bbd2a085eda76645a5744.pkl + params_file: output/reports/train/03129654f3245d637415613921f0f030/params.yaml + predictions_file: output/reports/train/03129654f3245d637415613921f0f030/predictions.json + probabilities_file: output/reports/train/03129654f3245d637415613921f0f030/probabilities.json + score_dict_file: output/reports/train/03129654f3245d637415613921f0f030/score_dict.json + test_labels_file: output/reports/train/03129654f3245d637415613921f0f030/test_labels.json + train_labels_file: output/reports/train/03129654f3245d637415613921f0f030/train_labels.json + model_dir: models + name: 03129654f3245d637415613921f0f030 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 92ba6bbfac37174fb4ddbc1e94cb5c82 + trainer: + kwargs: {} +name: 03129654f3245d637415613921f0f030 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/params.yaml b/examples/security/truthseeker/output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/params.yaml new file mode 100644 index 00000000..59c99c14 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/adv_predictions.json + adv_probabilities_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/00a30ea0312f812679adba4c78904149.pkl + params_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/params.yaml + predictions_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/predictions.json + probabilities_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/probabilities.json + score_dict_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/score_dict.json + test_labels_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/test_labels.json + train_labels_file: output/reports/train/03c722c9d204e3b94fc1d2d7d4650ad4/train_labels.json + model_dir: models + name: 03c722c9d204e3b94fc1d2d7d4650ad4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ce3ec64e768be4f01f87541270276e4 + trainer: + kwargs: {} +name: 03c722c9d204e3b94fc1d2d7d4650ad4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/params.yaml b/examples/security/truthseeker/output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/params.yaml new file mode 100644 index 00000000..e38e3dae --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/adv_predictions.json + adv_probabilities_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5269adde7bba7b9b74d0eb99711b9668.pkl + params_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/params.yaml + predictions_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/predictions.json + probabilities_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/probabilities.json + score_dict_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/score_dict.json + test_labels_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/test_labels.json + train_labels_file: output/reports/train/040c3712f9a736f0a98a4cf954c6edc7/train_labels.json + model_dir: models + name: 040c3712f9a736f0a98a4cf954c6edc7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0b22caac6d4015ded883ec1e7efe8be3 + trainer: + kwargs: {} +name: 040c3712f9a736f0a98a4cf954c6edc7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/params.yaml b/examples/security/truthseeker/output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/params.yaml new file mode 100644 index 00000000..cc7b10e3 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/adv_predictions.json + adv_probabilities_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1e8e3d1ddab0e3d42123d927891b0036.pkl + params_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/params.yaml + predictions_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/predictions.json + probabilities_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/probabilities.json + score_dict_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/score_dict.json + test_labels_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/test_labels.json + train_labels_file: output/reports/train/06eb0ddb3dd08490e30378ee5edeac4b/train_labels.json + model_dir: models + name: 06eb0ddb3dd08490e30378ee5edeac4b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6c4993c3157b337b6f0a5e1bc2314cdc + trainer: + kwargs: {} +name: 06eb0ddb3dd08490e30378ee5edeac4b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/089e5a336b7c621c2a3f082e4dd06502/params.yaml b/examples/security/truthseeker/output/reports/train/089e5a336b7c621c2a3f082e4dd06502/params.yaml new file mode 100644 index 00000000..dc12da7e --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/089e5a336b7c621c2a3f082e4dd06502/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/089e5a336b7c621c2a3f082e4dd06502/adv_predictions.json + adv_probabilities_file: output/reports/train/089e5a336b7c621c2a3f082e4dd06502/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38c4234bc37aea3facd3a6226bdd29a7.pkl + params_file: output/reports/train/089e5a336b7c621c2a3f082e4dd06502/params.yaml + predictions_file: output/reports/train/089e5a336b7c621c2a3f082e4dd06502/predictions.json + probabilities_file: output/reports/train/089e5a336b7c621c2a3f082e4dd06502/probabilities.json + score_dict_file: output/reports/train/089e5a336b7c621c2a3f082e4dd06502/score_dict.json + test_labels_file: output/reports/train/089e5a336b7c621c2a3f082e4dd06502/test_labels.json + train_labels_file: output/reports/train/089e5a336b7c621c2a3f082e4dd06502/train_labels.json + model_dir: models + name: 089e5a336b7c621c2a3f082e4dd06502 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 78d8b1f5d6a873e5f21bb33f63905e47 + trainer: + kwargs: {} +name: 089e5a336b7c621c2a3f082e4dd06502 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/09976d544db729e4f9f620a3c2c0f612/params.yaml b/examples/security/truthseeker/output/reports/train/09976d544db729e4f9f620a3c2c0f612/params.yaml new file mode 100644 index 00000000..02341f5b --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/09976d544db729e4f9f620a3c2c0f612/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/adv_predictions.json + adv_probabilities_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/770fd572edda8bb5459aed13fa63ad1a.pkl + params_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/params.yaml + predictions_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/predictions.json + probabilities_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/probabilities.json + score_dict_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/score_dict.json + test_labels_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/test_labels.json + train_labels_file: output/reports/train/09976d544db729e4f9f620a3c2c0f612/train_labels.json + model_dir: models + name: 09976d544db729e4f9f620a3c2c0f612 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 361c1f6954772b05ff16df071fd76df0 + trainer: + kwargs: {} +name: 09976d544db729e4f9f620a3c2c0f612 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/09c89c6dca9bc3e4944cdee3289b40b4/params.yaml b/examples/security/truthseeker/output/reports/train/09c89c6dca9bc3e4944cdee3289b40b4/params.yaml new file mode 100644 index 00000000..715356d9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/09c89c6dca9bc3e4944cdee3289b40b4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/09c89c6dca9bc3e4944cdee3289b40b4/adv_predictions.json + adv_probabilities_file: output/reports/train/09c89c6dca9bc3e4944cdee3289b40b4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/75ff5343281933535cd3364c2c22024a.pkl + params_file: output/reports/train/09c89c6dca9bc3e4944cdee3289b40b4/params.yaml + predictions_file: output/reports/train/09c89c6dca9bc3e4944cdee3289b40b4/predictions.json + probabilities_file: output/reports/train/09c89c6dca9bc3e4944cdee3289b40b4/probabilities.json + score_dict_file: output/reports/train/09c89c6dca9bc3e4944cdee3289b40b4/score_dict.json + test_labels_file: output/reports/train/09c89c6dca9bc3e4944cdee3289b40b4/test_labels.json + train_labels_file: output/reports/train/09c89c6dca9bc3e4944cdee3289b40b4/train_labels.json + model_dir: models + name: 09c89c6dca9bc3e4944cdee3289b40b4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1efe46acb60eba55d8523f5a9c8a91eb + trainer: + kwargs: {} +name: 09c89c6dca9bc3e4944cdee3289b40b4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/0a938c46271698dafde4865f0ac4c9a5/params.yaml b/examples/security/truthseeker/output/reports/train/0a938c46271698dafde4865f0ac4c9a5/params.yaml new file mode 100644 index 00000000..7182cc99 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/0a938c46271698dafde4865f0ac4c9a5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0a938c46271698dafde4865f0ac4c9a5/adv_predictions.json + adv_probabilities_file: output/reports/train/0a938c46271698dafde4865f0ac4c9a5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/56edfc04fe4a5a0c8ceaf333d93de605.pkl + params_file: output/reports/train/0a938c46271698dafde4865f0ac4c9a5/params.yaml + predictions_file: output/reports/train/0a938c46271698dafde4865f0ac4c9a5/predictions.json + probabilities_file: output/reports/train/0a938c46271698dafde4865f0ac4c9a5/probabilities.json + score_dict_file: output/reports/train/0a938c46271698dafde4865f0ac4c9a5/score_dict.json + test_labels_file: output/reports/train/0a938c46271698dafde4865f0ac4c9a5/test_labels.json + train_labels_file: output/reports/train/0a938c46271698dafde4865f0ac4c9a5/train_labels.json + model_dir: models + name: 0a938c46271698dafde4865f0ac4c9a5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 718cd25e5f316fbf21cd9703c5e0571e + trainer: + kwargs: {} +name: 0a938c46271698dafde4865f0ac4c9a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/0da4b75a6f5df78ad10bf077cb395779/params.yaml b/examples/security/truthseeker/output/reports/train/0da4b75a6f5df78ad10bf077cb395779/params.yaml new file mode 100644 index 00000000..dc6b2dc0 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/0da4b75a6f5df78ad10bf077cb395779/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/0da4b75a6f5df78ad10bf077cb395779/adv_predictions.json + adv_probabilities_file: output/reports/train/0da4b75a6f5df78ad10bf077cb395779/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/251fee3611442a7fd58468d6c890b9b0.pkl + params_file: output/reports/train/0da4b75a6f5df78ad10bf077cb395779/params.yaml + predictions_file: output/reports/train/0da4b75a6f5df78ad10bf077cb395779/predictions.json + probabilities_file: output/reports/train/0da4b75a6f5df78ad10bf077cb395779/probabilities.json + score_dict_file: output/reports/train/0da4b75a6f5df78ad10bf077cb395779/score_dict.json + test_labels_file: output/reports/train/0da4b75a6f5df78ad10bf077cb395779/test_labels.json + train_labels_file: output/reports/train/0da4b75a6f5df78ad10bf077cb395779/train_labels.json + model_dir: models + name: 0da4b75a6f5df78ad10bf077cb395779 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 34ab067865d9b9b97bb4a799cdbce51c + trainer: + kwargs: {} +name: 0da4b75a6f5df78ad10bf077cb395779 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/1070b713d9d0148acf12445b0d28d89f/params.yaml b/examples/security/truthseeker/output/reports/train/1070b713d9d0148acf12445b0d28d89f/params.yaml new file mode 100644 index 00000000..827e579e --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/1070b713d9d0148acf12445b0d28d89f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/adv_predictions.json + adv_probabilities_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/177aebbec5a2a36bbe39a4bf5caf399d.pkl + params_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/params.yaml + predictions_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/predictions.json + probabilities_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/probabilities.json + score_dict_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/score_dict.json + test_labels_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/test_labels.json + train_labels_file: output/reports/train/1070b713d9d0148acf12445b0d28d89f/train_labels.json + model_dir: models + name: 1070b713d9d0148acf12445b0d28d89f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 870bc742236aaf2e6368747e7b4efb7d + trainer: + kwargs: {} +name: 1070b713d9d0148acf12445b0d28d89f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/10d59a528c08fe3d790c347783b9f6ab/params.yaml b/examples/security/truthseeker/output/reports/train/10d59a528c08fe3d790c347783b9f6ab/params.yaml new file mode 100644 index 00000000..cf0b664b --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/10d59a528c08fe3d790c347783b9f6ab/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/adv_predictions.json + adv_probabilities_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5b5764b42873128207f58d4aacbdc72e.pkl + params_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/params.yaml + predictions_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/predictions.json + probabilities_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/probabilities.json + score_dict_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/score_dict.json + test_labels_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/test_labels.json + train_labels_file: output/reports/train/10d59a528c08fe3d790c347783b9f6ab/train_labels.json + model_dir: models + name: 10d59a528c08fe3d790c347783b9f6ab + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aca628860614b3809bd822f66a220811 + trainer: + kwargs: {} +name: 10d59a528c08fe3d790c347783b9f6ab +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/137877b38c03a644d6699fc29713db07/params.yaml b/examples/security/truthseeker/output/reports/train/137877b38c03a644d6699fc29713db07/params.yaml new file mode 100644 index 00000000..c23a3468 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/137877b38c03a644d6699fc29713db07/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/137877b38c03a644d6699fc29713db07/adv_predictions.json + adv_probabilities_file: output/reports/train/137877b38c03a644d6699fc29713db07/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b53ccb085a6f574e5a9e77481a9a4ca6.pkl + params_file: output/reports/train/137877b38c03a644d6699fc29713db07/params.yaml + predictions_file: output/reports/train/137877b38c03a644d6699fc29713db07/predictions.json + probabilities_file: output/reports/train/137877b38c03a644d6699fc29713db07/probabilities.json + score_dict_file: output/reports/train/137877b38c03a644d6699fc29713db07/score_dict.json + test_labels_file: output/reports/train/137877b38c03a644d6699fc29713db07/test_labels.json + train_labels_file: output/reports/train/137877b38c03a644d6699fc29713db07/train_labels.json + model_dir: models + name: 137877b38c03a644d6699fc29713db07 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f6bdaca6b40982398585366105022432 + trainer: + kwargs: {} +name: 137877b38c03a644d6699fc29713db07 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/140e3ea8043442464fb0fc85998bcd3e/params.yaml b/examples/security/truthseeker/output/reports/train/140e3ea8043442464fb0fc85998bcd3e/params.yaml new file mode 100644 index 00000000..deee986e --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/140e3ea8043442464fb0fc85998bcd3e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/adv_predictions.json + adv_probabilities_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b5a63088d3f966bc96c3be6e7f7bda71.pkl + params_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/params.yaml + predictions_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/predictions.json + probabilities_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/probabilities.json + score_dict_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/score_dict.json + test_labels_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/test_labels.json + train_labels_file: output/reports/train/140e3ea8043442464fb0fc85998bcd3e/train_labels.json + model_dir: models + name: 140e3ea8043442464fb0fc85998bcd3e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c4929bea0ac54cb611443b3adf0833c0 + trainer: + kwargs: {} +name: 140e3ea8043442464fb0fc85998bcd3e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/1514d720564018af5e3c6ccd730cbc22/params.yaml b/examples/security/truthseeker/output/reports/train/1514d720564018af5e3c6ccd730cbc22/params.yaml new file mode 100644 index 00000000..87fba3eb --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/1514d720564018af5e3c6ccd730cbc22/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/adv_predictions.json + adv_probabilities_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/eef762e590551c23bacc3d34f798de78.pkl + params_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/params.yaml + predictions_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/predictions.json + probabilities_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/probabilities.json + score_dict_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/score_dict.json + test_labels_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/test_labels.json + train_labels_file: output/reports/train/1514d720564018af5e3c6ccd730cbc22/train_labels.json + model_dir: models + name: 1514d720564018af5e3c6ccd730cbc22 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c436d789ea5cc1202a29cdcdea713b63 + trainer: + kwargs: {} +name: 1514d720564018af5e3c6ccd730cbc22 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/179a3aa1be954c538f25e2fc68a8fca6/params.yaml b/examples/security/truthseeker/output/reports/train/179a3aa1be954c538f25e2fc68a8fca6/params.yaml new file mode 100644 index 00000000..36e058dd --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/179a3aa1be954c538f25e2fc68a8fca6/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/179a3aa1be954c538f25e2fc68a8fca6/adv_predictions.json + adv_probabilities_file: output/reports/train/179a3aa1be954c538f25e2fc68a8fca6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0f205de2762b6b242407fe7f1fb3af21.pkl + params_file: output/reports/train/179a3aa1be954c538f25e2fc68a8fca6/params.yaml + predictions_file: output/reports/train/179a3aa1be954c538f25e2fc68a8fca6/predictions.json + probabilities_file: output/reports/train/179a3aa1be954c538f25e2fc68a8fca6/probabilities.json + score_dict_file: output/reports/train/179a3aa1be954c538f25e2fc68a8fca6/score_dict.json + test_labels_file: output/reports/train/179a3aa1be954c538f25e2fc68a8fca6/test_labels.json + train_labels_file: output/reports/train/179a3aa1be954c538f25e2fc68a8fca6/train_labels.json + model_dir: models + name: 179a3aa1be954c538f25e2fc68a8fca6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4ad95e4a9fa127cf7fe3cdd9c98851e8 + trainer: + kwargs: {} +name: 179a3aa1be954c538f25e2fc68a8fca6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/params.yaml b/examples/security/truthseeker/output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/params.yaml new file mode 100644 index 00000000..45259b48 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/adv_predictions.json + adv_probabilities_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f7d8f54b901cc8d9febfe06fcf240f56.pkl + params_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/params.yaml + predictions_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/predictions.json + probabilities_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/probabilities.json + score_dict_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/score_dict.json + test_labels_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/test_labels.json + train_labels_file: output/reports/train/18ea77af1a2e14ddf5048a37a9768d3b/train_labels.json + model_dir: models + name: 18ea77af1a2e14ddf5048a37a9768d3b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 87cbc4e3bc8efcb86b661b3cbe147b22 + trainer: + kwargs: {} +name: 18ea77af1a2e14ddf5048a37a9768d3b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/1a5f6163db9b78ea97d43171083908f5/params.yaml b/examples/security/truthseeker/output/reports/train/1a5f6163db9b78ea97d43171083908f5/params.yaml new file mode 100644 index 00000000..e39bd25f --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/1a5f6163db9b78ea97d43171083908f5/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/adv_predictions.json + adv_probabilities_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e50835e256d443333227096eea217419.pkl + params_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/params.yaml + predictions_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/predictions.json + probabilities_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/probabilities.json + score_dict_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/score_dict.json + test_labels_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/test_labels.json + train_labels_file: output/reports/train/1a5f6163db9b78ea97d43171083908f5/train_labels.json + model_dir: models + name: 1a5f6163db9b78ea97d43171083908f5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ad9bae639eaea56957358f4ac96b72da + trainer: + kwargs: {} +name: 1a5f6163db9b78ea97d43171083908f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/params.yaml b/examples/security/truthseeker/output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/params.yaml new file mode 100644 index 00000000..6e3cfc62 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/adv_predictions.json + adv_probabilities_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/10507edb9690b8a5ab47de076471881b.pkl + params_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/params.yaml + predictions_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/predictions.json + probabilities_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/probabilities.json + score_dict_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/score_dict.json + test_labels_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/test_labels.json + train_labels_file: output/reports/train/1c657e2ad2382e6a0a7bfac40929a79f/train_labels.json + model_dir: models + name: 1c657e2ad2382e6a0a7bfac40929a79f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ecaf66bf55743cd0b4fa76a8b25061d7 + trainer: + kwargs: {} +name: 1c657e2ad2382e6a0a7bfac40929a79f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/1c8b869c60e7c3321c243aa03210c010/params.yaml b/examples/security/truthseeker/output/reports/train/1c8b869c60e7c3321c243aa03210c010/params.yaml new file mode 100644 index 00000000..d68f7e9a --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/1c8b869c60e7c3321c243aa03210c010/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/adv_predictions.json + adv_probabilities_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9d6e6a64e05d8cb2a553ae6458421538.pkl + params_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/params.yaml + predictions_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/predictions.json + probabilities_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/probabilities.json + score_dict_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/score_dict.json + test_labels_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/test_labels.json + train_labels_file: output/reports/train/1c8b869c60e7c3321c243aa03210c010/train_labels.json + model_dir: models + name: 1c8b869c60e7c3321c243aa03210c010 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a3dd108221326a9a20591f595dd050a5 + trainer: + kwargs: {} +name: 1c8b869c60e7c3321c243aa03210c010 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/1e8cd0659cd4f4bbc335f2ba6f968132/params.yaml b/examples/security/truthseeker/output/reports/train/1e8cd0659cd4f4bbc335f2ba6f968132/params.yaml new file mode 100644 index 00000000..e74afa1f --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/1e8cd0659cd4f4bbc335f2ba6f968132/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1e8cd0659cd4f4bbc335f2ba6f968132/adv_predictions.json + adv_probabilities_file: output/reports/train/1e8cd0659cd4f4bbc335f2ba6f968132/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/884696c4736e4a52ea054588610c0b28.pkl + params_file: output/reports/train/1e8cd0659cd4f4bbc335f2ba6f968132/params.yaml + predictions_file: output/reports/train/1e8cd0659cd4f4bbc335f2ba6f968132/predictions.json + probabilities_file: output/reports/train/1e8cd0659cd4f4bbc335f2ba6f968132/probabilities.json + score_dict_file: output/reports/train/1e8cd0659cd4f4bbc335f2ba6f968132/score_dict.json + test_labels_file: output/reports/train/1e8cd0659cd4f4bbc335f2ba6f968132/test_labels.json + train_labels_file: output/reports/train/1e8cd0659cd4f4bbc335f2ba6f968132/train_labels.json + model_dir: models + name: 1e8cd0659cd4f4bbc335f2ba6f968132 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 83ba8bcbd7eb25551ca390a9dc51a965 + trainer: + kwargs: {} +name: 1e8cd0659cd4f4bbc335f2ba6f968132 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/1f9970ecfb6e0875eda50e40e61788cf/params.yaml b/examples/security/truthseeker/output/reports/train/1f9970ecfb6e0875eda50e40e61788cf/params.yaml new file mode 100644 index 00000000..cafe8cca --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/1f9970ecfb6e0875eda50e40e61788cf/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/1f9970ecfb6e0875eda50e40e61788cf/adv_predictions.json + adv_probabilities_file: output/reports/train/1f9970ecfb6e0875eda50e40e61788cf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/889d5fefe3225e29a4e99f55d8d79b2d.pkl + params_file: output/reports/train/1f9970ecfb6e0875eda50e40e61788cf/params.yaml + predictions_file: output/reports/train/1f9970ecfb6e0875eda50e40e61788cf/predictions.json + probabilities_file: output/reports/train/1f9970ecfb6e0875eda50e40e61788cf/probabilities.json + score_dict_file: output/reports/train/1f9970ecfb6e0875eda50e40e61788cf/score_dict.json + test_labels_file: output/reports/train/1f9970ecfb6e0875eda50e40e61788cf/test_labels.json + train_labels_file: output/reports/train/1f9970ecfb6e0875eda50e40e61788cf/train_labels.json + model_dir: models + name: 1f9970ecfb6e0875eda50e40e61788cf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1b76ee83e9930ea1fe18c43ccc0e943b + trainer: + kwargs: {} +name: 1f9970ecfb6e0875eda50e40e61788cf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/20cfddabd539ef218bf9ab0359e446e7/params.yaml b/examples/security/truthseeker/output/reports/train/20cfddabd539ef218bf9ab0359e446e7/params.yaml new file mode 100644 index 00000000..af0fb784 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/20cfddabd539ef218bf9ab0359e446e7/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/adv_predictions.json + adv_probabilities_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/11ded58c91e5638cf7a4c0376c497b6b.pkl + params_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/params.yaml + predictions_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/predictions.json + probabilities_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/probabilities.json + score_dict_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/score_dict.json + test_labels_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/test_labels.json + train_labels_file: output/reports/train/20cfddabd539ef218bf9ab0359e446e7/train_labels.json + model_dir: models + name: 20cfddabd539ef218bf9ab0359e446e7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cf57ddd1d132de91efd1df7267dae6b2 + trainer: + kwargs: {} +name: 20cfddabd539ef218bf9ab0359e446e7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/21d0fae6062839e902ac64c899823363/params.yaml b/examples/security/truthseeker/output/reports/train/21d0fae6062839e902ac64c899823363/params.yaml new file mode 100644 index 00000000..77bbc9e7 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/21d0fae6062839e902ac64c899823363/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/21d0fae6062839e902ac64c899823363/adv_predictions.json + adv_probabilities_file: output/reports/train/21d0fae6062839e902ac64c899823363/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2a50d0d292f6cdb95e9debdce19f71e2.pkl + params_file: output/reports/train/21d0fae6062839e902ac64c899823363/params.yaml + predictions_file: output/reports/train/21d0fae6062839e902ac64c899823363/predictions.json + probabilities_file: output/reports/train/21d0fae6062839e902ac64c899823363/probabilities.json + score_dict_file: output/reports/train/21d0fae6062839e902ac64c899823363/score_dict.json + test_labels_file: output/reports/train/21d0fae6062839e902ac64c899823363/test_labels.json + train_labels_file: output/reports/train/21d0fae6062839e902ac64c899823363/train_labels.json + model_dir: models + name: 21d0fae6062839e902ac64c899823363 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d654d1ffb869c66118bb307c1a7c105e + trainer: + kwargs: {} +name: 21d0fae6062839e902ac64c899823363 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/22032ebace8e257964123b1c5606e468/params.yaml b/examples/security/truthseeker/output/reports/train/22032ebace8e257964123b1c5606e468/params.yaml new file mode 100644 index 00000000..5d2181a4 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/22032ebace8e257964123b1c5606e468/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/22032ebace8e257964123b1c5606e468/adv_predictions.json + adv_probabilities_file: output/reports/train/22032ebace8e257964123b1c5606e468/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/219840e8657ab386b3dd0b99cb5c3b01.pkl + params_file: output/reports/train/22032ebace8e257964123b1c5606e468/params.yaml + predictions_file: output/reports/train/22032ebace8e257964123b1c5606e468/predictions.json + probabilities_file: output/reports/train/22032ebace8e257964123b1c5606e468/probabilities.json + score_dict_file: output/reports/train/22032ebace8e257964123b1c5606e468/score_dict.json + test_labels_file: output/reports/train/22032ebace8e257964123b1c5606e468/test_labels.json + train_labels_file: output/reports/train/22032ebace8e257964123b1c5606e468/train_labels.json + model_dir: models + name: 22032ebace8e257964123b1c5606e468 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 89e4128b2be0e7530a38c062dd202cb3 + trainer: + kwargs: {} +name: 22032ebace8e257964123b1c5606e468 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/22ede3e6c5b20009c01797bd041f1266/params.yaml b/examples/security/truthseeker/output/reports/train/22ede3e6c5b20009c01797bd041f1266/params.yaml new file mode 100644 index 00000000..733c58a1 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/22ede3e6c5b20009c01797bd041f1266/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/22ede3e6c5b20009c01797bd041f1266/adv_predictions.json + adv_probabilities_file: output/reports/train/22ede3e6c5b20009c01797bd041f1266/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e5334f24d66865193bfdb2112365ec2b.pkl + params_file: output/reports/train/22ede3e6c5b20009c01797bd041f1266/params.yaml + predictions_file: output/reports/train/22ede3e6c5b20009c01797bd041f1266/predictions.json + probabilities_file: output/reports/train/22ede3e6c5b20009c01797bd041f1266/probabilities.json + score_dict_file: output/reports/train/22ede3e6c5b20009c01797bd041f1266/score_dict.json + test_labels_file: output/reports/train/22ede3e6c5b20009c01797bd041f1266/test_labels.json + train_labels_file: output/reports/train/22ede3e6c5b20009c01797bd041f1266/train_labels.json + model_dir: models + name: 22ede3e6c5b20009c01797bd041f1266 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 58d231a5567051d4a9a2fadd788d3c00 + trainer: + kwargs: {} +name: 22ede3e6c5b20009c01797bd041f1266 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/23878a268b692d1c02041cd32f32046d/params.yaml b/examples/security/truthseeker/output/reports/train/23878a268b692d1c02041cd32f32046d/params.yaml new file mode 100644 index 00000000..2df05e82 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/23878a268b692d1c02041cd32f32046d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/23878a268b692d1c02041cd32f32046d/adv_predictions.json + adv_probabilities_file: output/reports/train/23878a268b692d1c02041cd32f32046d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bcd96e0c9cd9d4d41051087fb4a66be7.pkl + params_file: output/reports/train/23878a268b692d1c02041cd32f32046d/params.yaml + predictions_file: output/reports/train/23878a268b692d1c02041cd32f32046d/predictions.json + probabilities_file: output/reports/train/23878a268b692d1c02041cd32f32046d/probabilities.json + score_dict_file: output/reports/train/23878a268b692d1c02041cd32f32046d/score_dict.json + test_labels_file: output/reports/train/23878a268b692d1c02041cd32f32046d/test_labels.json + train_labels_file: output/reports/train/23878a268b692d1c02041cd32f32046d/train_labels.json + model_dir: models + name: 23878a268b692d1c02041cd32f32046d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: af308e5290c6ac5751b160d3b673ce06 + trainer: + kwargs: {} +name: 23878a268b692d1c02041cd32f32046d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/238a4f033c631da932fdf69268851850/params.yaml b/examples/security/truthseeker/output/reports/train/238a4f033c631da932fdf69268851850/params.yaml new file mode 100644 index 00000000..d42e6182 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/238a4f033c631da932fdf69268851850/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/238a4f033c631da932fdf69268851850/adv_predictions.json + adv_probabilities_file: output/reports/train/238a4f033c631da932fdf69268851850/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/301b53e58f8903b008830f37fdb97088.pkl + params_file: output/reports/train/238a4f033c631da932fdf69268851850/params.yaml + predictions_file: output/reports/train/238a4f033c631da932fdf69268851850/predictions.json + probabilities_file: output/reports/train/238a4f033c631da932fdf69268851850/probabilities.json + score_dict_file: output/reports/train/238a4f033c631da932fdf69268851850/score_dict.json + test_labels_file: output/reports/train/238a4f033c631da932fdf69268851850/test_labels.json + train_labels_file: output/reports/train/238a4f033c631da932fdf69268851850/train_labels.json + model_dir: models + name: 238a4f033c631da932fdf69268851850 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b18354fde9bbb121ddf9af63adee7cd9 + trainer: + kwargs: {} +name: 238a4f033c631da932fdf69268851850 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/2430f5d8b1d115c941f0b2c36e1df719/params.yaml b/examples/security/truthseeker/output/reports/train/2430f5d8b1d115c941f0b2c36e1df719/params.yaml new file mode 100644 index 00000000..950d7338 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/2430f5d8b1d115c941f0b2c36e1df719/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2430f5d8b1d115c941f0b2c36e1df719/adv_predictions.json + adv_probabilities_file: output/reports/train/2430f5d8b1d115c941f0b2c36e1df719/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5fc8e10c56562a500c18ba35edfd83f0.pkl + params_file: output/reports/train/2430f5d8b1d115c941f0b2c36e1df719/params.yaml + predictions_file: output/reports/train/2430f5d8b1d115c941f0b2c36e1df719/predictions.json + probabilities_file: output/reports/train/2430f5d8b1d115c941f0b2c36e1df719/probabilities.json + score_dict_file: output/reports/train/2430f5d8b1d115c941f0b2c36e1df719/score_dict.json + test_labels_file: output/reports/train/2430f5d8b1d115c941f0b2c36e1df719/test_labels.json + train_labels_file: output/reports/train/2430f5d8b1d115c941f0b2c36e1df719/train_labels.json + model_dir: models + name: 2430f5d8b1d115c941f0b2c36e1df719 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a727842f5808f0f5861f64f55374fa53 + trainer: + kwargs: {} +name: 2430f5d8b1d115c941f0b2c36e1df719 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/params.yaml b/examples/security/truthseeker/output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/params.yaml new file mode 100644 index 00000000..40cb5dd7 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/adv_predictions.json + adv_probabilities_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/adc10b7bf3d5b933316130fbf72efa9d.pkl + params_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/params.yaml + predictions_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/predictions.json + probabilities_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/probabilities.json + score_dict_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/score_dict.json + test_labels_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/test_labels.json + train_labels_file: output/reports/train/26855e98dcd2b3fe07fad9d2b9bc6809/train_labels.json + model_dir: models + name: 26855e98dcd2b3fe07fad9d2b9bc6809 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 678d9e5aa40a04b090fc35fff9f9d3d1 + trainer: + kwargs: {} +name: 26855e98dcd2b3fe07fad9d2b9bc6809 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/28468687bd1466204eab9b9ed14b315b/params.yaml b/examples/security/truthseeker/output/reports/train/28468687bd1466204eab9b9ed14b315b/params.yaml new file mode 100644 index 00000000..82d68a51 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/28468687bd1466204eab9b9ed14b315b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/28468687bd1466204eab9b9ed14b315b/adv_predictions.json + adv_probabilities_file: output/reports/train/28468687bd1466204eab9b9ed14b315b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bee5199d9c9b6e0e861916b0d0b44ddb.pkl + params_file: output/reports/train/28468687bd1466204eab9b9ed14b315b/params.yaml + predictions_file: output/reports/train/28468687bd1466204eab9b9ed14b315b/predictions.json + probabilities_file: output/reports/train/28468687bd1466204eab9b9ed14b315b/probabilities.json + score_dict_file: output/reports/train/28468687bd1466204eab9b9ed14b315b/score_dict.json + test_labels_file: output/reports/train/28468687bd1466204eab9b9ed14b315b/test_labels.json + train_labels_file: output/reports/train/28468687bd1466204eab9b9ed14b315b/train_labels.json + model_dir: models + name: 28468687bd1466204eab9b9ed14b315b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0089eec482b3d6ed6a54fac50d7c84aa + trainer: + kwargs: {} +name: 28468687bd1466204eab9b9ed14b315b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/params.yaml b/examples/security/truthseeker/output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/params.yaml new file mode 100644 index 00000000..36c91106 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/adv_predictions.json + adv_probabilities_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/34392cd7c05a4952636031d4e6f95f53.pkl + params_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/params.yaml + predictions_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/predictions.json + probabilities_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/probabilities.json + score_dict_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/score_dict.json + test_labels_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/test_labels.json + train_labels_file: output/reports/train/28d2b1e6a076b09265cbc6aaf36576f7/train_labels.json + model_dir: models + name: 28d2b1e6a076b09265cbc6aaf36576f7 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5a69109e0d33dc05cd08763922cb4ed7 + trainer: + kwargs: {} +name: 28d2b1e6a076b09265cbc6aaf36576f7 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/2b3652543bc260dec413233cae0f7cf0/params.yaml b/examples/security/truthseeker/output/reports/train/2b3652543bc260dec413233cae0f7cf0/params.yaml new file mode 100644 index 00000000..30a6d4d3 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/2b3652543bc260dec413233cae0f7cf0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2b3652543bc260dec413233cae0f7cf0/adv_predictions.json + adv_probabilities_file: output/reports/train/2b3652543bc260dec413233cae0f7cf0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1f63acc6c0705294320c9e8615786ee6.pkl + params_file: output/reports/train/2b3652543bc260dec413233cae0f7cf0/params.yaml + predictions_file: output/reports/train/2b3652543bc260dec413233cae0f7cf0/predictions.json + probabilities_file: output/reports/train/2b3652543bc260dec413233cae0f7cf0/probabilities.json + score_dict_file: output/reports/train/2b3652543bc260dec413233cae0f7cf0/score_dict.json + test_labels_file: output/reports/train/2b3652543bc260dec413233cae0f7cf0/test_labels.json + train_labels_file: output/reports/train/2b3652543bc260dec413233cae0f7cf0/train_labels.json + model_dir: models + name: 2b3652543bc260dec413233cae0f7cf0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e0c3b6d6cf42c29a3b7e653de7035185 + trainer: + kwargs: {} +name: 2b3652543bc260dec413233cae0f7cf0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/2bd69dfcc750e72a77c54336a76bee0b/params.yaml b/examples/security/truthseeker/output/reports/train/2bd69dfcc750e72a77c54336a76bee0b/params.yaml new file mode 100644 index 00000000..495216a3 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/2bd69dfcc750e72a77c54336a76bee0b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2bd69dfcc750e72a77c54336a76bee0b/adv_predictions.json + adv_probabilities_file: output/reports/train/2bd69dfcc750e72a77c54336a76bee0b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bce9ba59bdfb4d9e2f601f0284a30a10.pkl + params_file: output/reports/train/2bd69dfcc750e72a77c54336a76bee0b/params.yaml + predictions_file: output/reports/train/2bd69dfcc750e72a77c54336a76bee0b/predictions.json + probabilities_file: output/reports/train/2bd69dfcc750e72a77c54336a76bee0b/probabilities.json + score_dict_file: output/reports/train/2bd69dfcc750e72a77c54336a76bee0b/score_dict.json + test_labels_file: output/reports/train/2bd69dfcc750e72a77c54336a76bee0b/test_labels.json + train_labels_file: output/reports/train/2bd69dfcc750e72a77c54336a76bee0b/train_labels.json + model_dir: models + name: 2bd69dfcc750e72a77c54336a76bee0b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1210294953e4cf35b4b12ff49b0b88e2 + trainer: + kwargs: {} +name: 2bd69dfcc750e72a77c54336a76bee0b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/2d1d5c0d0ef445f6f24c8cb9d9a156e1/params.yaml b/examples/security/truthseeker/output/reports/train/2d1d5c0d0ef445f6f24c8cb9d9a156e1/params.yaml new file mode 100644 index 00000000..d7d7d763 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/2d1d5c0d0ef445f6f24c8cb9d9a156e1/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2d1d5c0d0ef445f6f24c8cb9d9a156e1/adv_predictions.json + adv_probabilities_file: output/reports/train/2d1d5c0d0ef445f6f24c8cb9d9a156e1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/4f0a4309e2e8b4c2b80c3e14b6a4b1bf.pkl + params_file: output/reports/train/2d1d5c0d0ef445f6f24c8cb9d9a156e1/params.yaml + predictions_file: output/reports/train/2d1d5c0d0ef445f6f24c8cb9d9a156e1/predictions.json + probabilities_file: output/reports/train/2d1d5c0d0ef445f6f24c8cb9d9a156e1/probabilities.json + score_dict_file: output/reports/train/2d1d5c0d0ef445f6f24c8cb9d9a156e1/score_dict.json + test_labels_file: output/reports/train/2d1d5c0d0ef445f6f24c8cb9d9a156e1/test_labels.json + train_labels_file: output/reports/train/2d1d5c0d0ef445f6f24c8cb9d9a156e1/train_labels.json + model_dir: models + name: 2d1d5c0d0ef445f6f24c8cb9d9a156e1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a8aea913e2f1d745b34a71244da06a0a + trainer: + kwargs: {} +name: 2d1d5c0d0ef445f6f24c8cb9d9a156e1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/params.yaml b/examples/security/truthseeker/output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/params.yaml new file mode 100644 index 00000000..2ed4093c --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/adv_predictions.json + adv_probabilities_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/10fd48e70d83b435a335f410e7ec9370.pkl + params_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/params.yaml + predictions_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/predictions.json + probabilities_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/probabilities.json + score_dict_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/score_dict.json + test_labels_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/test_labels.json + train_labels_file: output/reports/train/2d62a4dec4eaee1d6a83958c9f33c45e/train_labels.json + model_dir: models + name: 2d62a4dec4eaee1d6a83958c9f33c45e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6f9a605b303c3727fbc43feba149f9a1 + trainer: + kwargs: {} +name: 2d62a4dec4eaee1d6a83958c9f33c45e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/params.yaml b/examples/security/truthseeker/output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/params.yaml new file mode 100644 index 00000000..88fb6ff7 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/adv_predictions.json + adv_probabilities_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9f2b218bec18da0eba413c01f4d7511d.pkl + params_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/params.yaml + predictions_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/predictions.json + probabilities_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/probabilities.json + score_dict_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/score_dict.json + test_labels_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/test_labels.json + train_labels_file: output/reports/train/2d79b8d1b8d2de7cfab3005e6f3bfc00/train_labels.json + model_dir: models + name: 2d79b8d1b8d2de7cfab3005e6f3bfc00 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 76ca09d6d60d2f10fe23d8622d8b453d + trainer: + kwargs: {} +name: 2d79b8d1b8d2de7cfab3005e6f3bfc00 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/2df96496cb0c4b1bc58770f77d045185/params.yaml b/examples/security/truthseeker/output/reports/train/2df96496cb0c4b1bc58770f77d045185/params.yaml new file mode 100644 index 00000000..30b46ddc --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/2df96496cb0c4b1bc58770f77d045185/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2df96496cb0c4b1bc58770f77d045185/adv_predictions.json + adv_probabilities_file: output/reports/train/2df96496cb0c4b1bc58770f77d045185/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/070f84e5c3eefd8dae47cd25b28fee5e.pkl + params_file: output/reports/train/2df96496cb0c4b1bc58770f77d045185/params.yaml + predictions_file: output/reports/train/2df96496cb0c4b1bc58770f77d045185/predictions.json + probabilities_file: output/reports/train/2df96496cb0c4b1bc58770f77d045185/probabilities.json + score_dict_file: output/reports/train/2df96496cb0c4b1bc58770f77d045185/score_dict.json + test_labels_file: output/reports/train/2df96496cb0c4b1bc58770f77d045185/test_labels.json + train_labels_file: output/reports/train/2df96496cb0c4b1bc58770f77d045185/train_labels.json + model_dir: models + name: 2df96496cb0c4b1bc58770f77d045185 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 58d1de094dbb1e290fdf4cfd6c50e81e + trainer: + kwargs: {} +name: 2df96496cb0c4b1bc58770f77d045185 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/params.yaml b/examples/security/truthseeker/output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/params.yaml new file mode 100644 index 00000000..f980d0d2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/adv_predictions.json + adv_probabilities_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b2e232e2d28d1b8aa418761172d5e4cb.pkl + params_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/params.yaml + predictions_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/predictions.json + probabilities_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/probabilities.json + score_dict_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/score_dict.json + test_labels_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/test_labels.json + train_labels_file: output/reports/train/2ebf71f050ecf6ccb53977ad5d490ea5/train_labels.json + model_dir: models + name: 2ebf71f050ecf6ccb53977ad5d490ea5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bae527781ce5b10d74e7efef4d14e881 + trainer: + kwargs: {} +name: 2ebf71f050ecf6ccb53977ad5d490ea5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/params.yaml b/examples/security/truthseeker/output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/params.yaml new file mode 100644 index 00000000..77c08478 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/adv_predictions.json + adv_probabilities_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7b68ba783521ebd5a43586a0565b6c20.pkl + params_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/params.yaml + predictions_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/predictions.json + probabilities_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/probabilities.json + score_dict_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/score_dict.json + test_labels_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/test_labels.json + train_labels_file: output/reports/train/2edf7fbffb2095b81593cd881e3d55f4/train_labels.json + model_dir: models + name: 2edf7fbffb2095b81593cd881e3d55f4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2c6708e1faf264da72e7eab15c24e599 + trainer: + kwargs: {} +name: 2edf7fbffb2095b81593cd881e3d55f4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/2f151c43b96ed42acd34d949654ad38e/params.yaml b/examples/security/truthseeker/output/reports/train/2f151c43b96ed42acd34d949654ad38e/params.yaml new file mode 100644 index 00000000..1af4850a --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/2f151c43b96ed42acd34d949654ad38e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/adv_predictions.json + adv_probabilities_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2403d7220f06bc1d92c6dd53a20908a6.pkl + params_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/params.yaml + predictions_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/predictions.json + probabilities_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/probabilities.json + score_dict_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/score_dict.json + test_labels_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/test_labels.json + train_labels_file: output/reports/train/2f151c43b96ed42acd34d949654ad38e/train_labels.json + model_dir: models + name: 2f151c43b96ed42acd34d949654ad38e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e95e21ef2b149b76d57a512186b584e1 + trainer: + kwargs: {} +name: 2f151c43b96ed42acd34d949654ad38e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/300818f45fdbb759c52b20caab7efa6d/params.yaml b/examples/security/truthseeker/output/reports/train/300818f45fdbb759c52b20caab7efa6d/params.yaml new file mode 100644 index 00000000..b08e4c9d --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/300818f45fdbb759c52b20caab7efa6d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/adv_predictions.json + adv_probabilities_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/da7219bc65c766746afd0529b73bb595.pkl + params_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/params.yaml + predictions_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/predictions.json + probabilities_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/probabilities.json + score_dict_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/score_dict.json + test_labels_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/test_labels.json + train_labels_file: output/reports/train/300818f45fdbb759c52b20caab7efa6d/train_labels.json + model_dir: models + name: 300818f45fdbb759c52b20caab7efa6d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c9a716f5efbea2b07117cfe0eba719d0 + trainer: + kwargs: {} +name: 300818f45fdbb759c52b20caab7efa6d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/30a16a14336e218ab5d2a77537183a89/params.yaml b/examples/security/truthseeker/output/reports/train/30a16a14336e218ab5d2a77537183a89/params.yaml new file mode 100644 index 00000000..48fc8a7c --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/30a16a14336e218ab5d2a77537183a89/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/adv_predictions.json + adv_probabilities_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0b5f523f1ded2d07be700dab2ae695d2.pkl + params_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/params.yaml + predictions_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/predictions.json + probabilities_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/probabilities.json + score_dict_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/score_dict.json + test_labels_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/test_labels.json + train_labels_file: output/reports/train/30a16a14336e218ab5d2a77537183a89/train_labels.json + model_dir: models + name: 30a16a14336e218ab5d2a77537183a89 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 92afcf079ee83598b56f00569ea6a591 + trainer: + kwargs: {} +name: 30a16a14336e218ab5d2a77537183a89 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/3367cd5393fa9341b73f921829d20f2c/params.yaml b/examples/security/truthseeker/output/reports/train/3367cd5393fa9341b73f921829d20f2c/params.yaml new file mode 100644 index 00000000..7c39bc93 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/3367cd5393fa9341b73f921829d20f2c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/adv_predictions.json + adv_probabilities_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9ff854beebbb32ab9523d8c6c48ac1d0.pkl + params_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/params.yaml + predictions_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/predictions.json + probabilities_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/probabilities.json + score_dict_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/score_dict.json + test_labels_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/test_labels.json + train_labels_file: output/reports/train/3367cd5393fa9341b73f921829d20f2c/train_labels.json + model_dir: models + name: 3367cd5393fa9341b73f921829d20f2c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e436c91a2baa3c17bf9de14d3cdffedf + trainer: + kwargs: {} +name: 3367cd5393fa9341b73f921829d20f2c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/33893f3834c2efe45dbac7ab1909480b/params.yaml b/examples/security/truthseeker/output/reports/train/33893f3834c2efe45dbac7ab1909480b/params.yaml new file mode 100644 index 00000000..5708aee1 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/33893f3834c2efe45dbac7ab1909480b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/33893f3834c2efe45dbac7ab1909480b/adv_predictions.json + adv_probabilities_file: output/reports/train/33893f3834c2efe45dbac7ab1909480b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3142b6bbcb24f5784c072d4a86f0c9af.pkl + params_file: output/reports/train/33893f3834c2efe45dbac7ab1909480b/params.yaml + predictions_file: output/reports/train/33893f3834c2efe45dbac7ab1909480b/predictions.json + probabilities_file: output/reports/train/33893f3834c2efe45dbac7ab1909480b/probabilities.json + score_dict_file: output/reports/train/33893f3834c2efe45dbac7ab1909480b/score_dict.json + test_labels_file: output/reports/train/33893f3834c2efe45dbac7ab1909480b/test_labels.json + train_labels_file: output/reports/train/33893f3834c2efe45dbac7ab1909480b/train_labels.json + model_dir: models + name: 33893f3834c2efe45dbac7ab1909480b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27567920cb18297fb7a9986c6018fcbb + trainer: + kwargs: {} +name: 33893f3834c2efe45dbac7ab1909480b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/33a72f153f1b3ac6f74902045cd7fd2c/params.yaml b/examples/security/truthseeker/output/reports/train/33a72f153f1b3ac6f74902045cd7fd2c/params.yaml new file mode 100644 index 00000000..55710024 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/33a72f153f1b3ac6f74902045cd7fd2c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/33a72f153f1b3ac6f74902045cd7fd2c/adv_predictions.json + adv_probabilities_file: output/reports/train/33a72f153f1b3ac6f74902045cd7fd2c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f0183ff4a617ad623b6595370bf1c021.pkl + params_file: output/reports/train/33a72f153f1b3ac6f74902045cd7fd2c/params.yaml + predictions_file: output/reports/train/33a72f153f1b3ac6f74902045cd7fd2c/predictions.json + probabilities_file: output/reports/train/33a72f153f1b3ac6f74902045cd7fd2c/probabilities.json + score_dict_file: output/reports/train/33a72f153f1b3ac6f74902045cd7fd2c/score_dict.json + test_labels_file: output/reports/train/33a72f153f1b3ac6f74902045cd7fd2c/test_labels.json + train_labels_file: output/reports/train/33a72f153f1b3ac6f74902045cd7fd2c/train_labels.json + model_dir: models + name: 33a72f153f1b3ac6f74902045cd7fd2c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 92a802591dddce30dd4f7d13735e16c4 + trainer: + kwargs: {} +name: 33a72f153f1b3ac6f74902045cd7fd2c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/params.yaml b/examples/security/truthseeker/output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/params.yaml new file mode 100644 index 00000000..54adab1a --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/adv_predictions.json + adv_probabilities_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7eb569ee99c3620bdb3e0ac93e1ee0ea.pkl + params_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/params.yaml + predictions_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/predictions.json + probabilities_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/probabilities.json + score_dict_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/score_dict.json + test_labels_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/test_labels.json + train_labels_file: output/reports/train/33d7d5e6f0770f6266e4d9cc7291a3a9/train_labels.json + model_dir: models + name: 33d7d5e6f0770f6266e4d9cc7291a3a9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ef374abcda025fca916552f8d2af690a + trainer: + kwargs: {} +name: 33d7d5e6f0770f6266e4d9cc7291a3a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/params.yaml b/examples/security/truthseeker/output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/params.yaml new file mode 100644 index 00000000..bc0c4a19 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/adv_predictions.json + adv_probabilities_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/90d2b4942579dc38bb8d89e9e04139da.pkl + params_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/params.yaml + predictions_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/predictions.json + probabilities_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/probabilities.json + score_dict_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/score_dict.json + test_labels_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/test_labels.json + train_labels_file: output/reports/train/34c4d88419d4e5fdb7a86356bd9f545e/train_labels.json + model_dir: models + name: 34c4d88419d4e5fdb7a86356bd9f545e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8b960960862e39839a2c04542d91754e + trainer: + kwargs: {} +name: 34c4d88419d4e5fdb7a86356bd9f545e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/36ca3736c72fb2d9596bcb50fc193986/params.yaml b/examples/security/truthseeker/output/reports/train/36ca3736c72fb2d9596bcb50fc193986/params.yaml new file mode 100644 index 00000000..daffa4df --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/36ca3736c72fb2d9596bcb50fc193986/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/adv_predictions.json + adv_probabilities_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1257b7ae0100b0c769e51f2378b370dd.pkl + params_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/params.yaml + predictions_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/predictions.json + probabilities_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/probabilities.json + score_dict_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/score_dict.json + test_labels_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/test_labels.json + train_labels_file: output/reports/train/36ca3736c72fb2d9596bcb50fc193986/train_labels.json + model_dir: models + name: 36ca3736c72fb2d9596bcb50fc193986 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 16392046b7322ad33e80d9685e4349aa + trainer: + kwargs: {} +name: 36ca3736c72fb2d9596bcb50fc193986 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/params.yaml b/examples/security/truthseeker/output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/params.yaml new file mode 100644 index 00000000..94438948 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/adv_predictions.json + adv_probabilities_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1c9d1794f00f0c8f0fff10b6b2296a81.pkl + params_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/params.yaml + predictions_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/predictions.json + probabilities_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/probabilities.json + score_dict_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/score_dict.json + test_labels_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/test_labels.json + train_labels_file: output/reports/train/37d8f3738fc2d4506d4879bfab32b16e/train_labels.json + model_dir: models + name: 37d8f3738fc2d4506d4879bfab32b16e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 85f2dc5f2e25567eacdf5da05cfffcff + trainer: + kwargs: {} +name: 37d8f3738fc2d4506d4879bfab32b16e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/params.yaml b/examples/security/truthseeker/output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/params.yaml new file mode 100644 index 00000000..3287d1f0 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/adv_predictions.json + adv_probabilities_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/982495bc6ec42cd51fd441cbc87c5b31.pkl + params_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/params.yaml + predictions_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/predictions.json + probabilities_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/probabilities.json + score_dict_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/score_dict.json + test_labels_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/test_labels.json + train_labels_file: output/reports/train/38cbadd5bd0f0ed9075db54e8fe29549/train_labels.json + model_dir: models + name: 38cbadd5bd0f0ed9075db54e8fe29549 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c6b08258ec4dd4570e0482eeb5135e98 + trainer: + kwargs: {} +name: 38cbadd5bd0f0ed9075db54e8fe29549 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/38ef77106de280be0204b46b146fa36a/params.yaml b/examples/security/truthseeker/output/reports/train/38ef77106de280be0204b46b146fa36a/params.yaml new file mode 100644 index 00000000..7070db19 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/38ef77106de280be0204b46b146fa36a/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/38ef77106de280be0204b46b146fa36a/adv_predictions.json + adv_probabilities_file: output/reports/train/38ef77106de280be0204b46b146fa36a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/a2e8e5fccb2aaa097a106a74e0bcce44.pkl + params_file: output/reports/train/38ef77106de280be0204b46b146fa36a/params.yaml + predictions_file: output/reports/train/38ef77106de280be0204b46b146fa36a/predictions.json + probabilities_file: output/reports/train/38ef77106de280be0204b46b146fa36a/probabilities.json + score_dict_file: output/reports/train/38ef77106de280be0204b46b146fa36a/score_dict.json + test_labels_file: output/reports/train/38ef77106de280be0204b46b146fa36a/test_labels.json + train_labels_file: output/reports/train/38ef77106de280be0204b46b146fa36a/train_labels.json + model_dir: models + name: 38ef77106de280be0204b46b146fa36a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f0076c6f590b7cd8116906e6102454bd + trainer: + kwargs: {} +name: 38ef77106de280be0204b46b146fa36a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/3b18e3df106a88d9964ada47862d04dd/params.yaml b/examples/security/truthseeker/output/reports/train/3b18e3df106a88d9964ada47862d04dd/params.yaml new file mode 100644 index 00000000..6f6e25cf --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/3b18e3df106a88d9964ada47862d04dd/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/adv_predictions.json + adv_probabilities_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d5d1b1eadf2da74309e7bd9b776f5065.pkl + params_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/params.yaml + predictions_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/predictions.json + probabilities_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/probabilities.json + score_dict_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/score_dict.json + test_labels_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/test_labels.json + train_labels_file: output/reports/train/3b18e3df106a88d9964ada47862d04dd/train_labels.json + model_dir: models + name: 3b18e3df106a88d9964ada47862d04dd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 80e33669706b0d566b0fa09d87676451 + trainer: + kwargs: {} +name: 3b18e3df106a88d9964ada47862d04dd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/params.yaml b/examples/security/truthseeker/output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/params.yaml new file mode 100644 index 00000000..d251de5f --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/adv_predictions.json + adv_probabilities_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fb99ca99a6a6e718a0510f6a6b75fd3.pkl + params_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/params.yaml + predictions_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/predictions.json + probabilities_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/probabilities.json + score_dict_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/score_dict.json + test_labels_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/test_labels.json + train_labels_file: output/reports/train/3bbfcc937eb9bb07e91608bf6787b5a3/train_labels.json + model_dir: models + name: 3bbfcc937eb9bb07e91608bf6787b5a3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ec79ffd12963a60b7d7368124179159 + trainer: + kwargs: {} +name: 3bbfcc937eb9bb07e91608bf6787b5a3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/params.yaml b/examples/security/truthseeker/output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/params.yaml new file mode 100644 index 00000000..d58e4fac --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/adv_predictions.json + adv_probabilities_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3fd6bfb90887de957edc0cd9eac71e41.pkl + params_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/params.yaml + predictions_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/predictions.json + probabilities_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/probabilities.json + score_dict_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/score_dict.json + test_labels_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/test_labels.json + train_labels_file: output/reports/train/3e0ea6dda2c6c2e32d987b38d4b119fb/train_labels.json + model_dir: models + name: 3e0ea6dda2c6c2e32d987b38d4b119fb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 11f87b6c0031745c1f1a47dfbc566801 + trainer: + kwargs: {} +name: 3e0ea6dda2c6c2e32d987b38d4b119fb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/4034e6beefa6a270539fa177d197f696/params.yaml b/examples/security/truthseeker/output/reports/train/4034e6beefa6a270539fa177d197f696/params.yaml new file mode 100644 index 00000000..7b4749fd --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/4034e6beefa6a270539fa177d197f696/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4034e6beefa6a270539fa177d197f696/adv_predictions.json + adv_probabilities_file: output/reports/train/4034e6beefa6a270539fa177d197f696/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5e79a84f5d5c69c9b4292a70a9385852.pkl + params_file: output/reports/train/4034e6beefa6a270539fa177d197f696/params.yaml + predictions_file: output/reports/train/4034e6beefa6a270539fa177d197f696/predictions.json + probabilities_file: output/reports/train/4034e6beefa6a270539fa177d197f696/probabilities.json + score_dict_file: output/reports/train/4034e6beefa6a270539fa177d197f696/score_dict.json + test_labels_file: output/reports/train/4034e6beefa6a270539fa177d197f696/test_labels.json + train_labels_file: output/reports/train/4034e6beefa6a270539fa177d197f696/train_labels.json + model_dir: models + name: 4034e6beefa6a270539fa177d197f696 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 75518cf31db1a3804c99a85c9863027a + trainer: + kwargs: {} +name: 4034e6beefa6a270539fa177d197f696 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/40a4d43d93b7c47b96ad363c3ef5f7b9/params.yaml b/examples/security/truthseeker/output/reports/train/40a4d43d93b7c47b96ad363c3ef5f7b9/params.yaml new file mode 100644 index 00000000..aae5aa0c --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/40a4d43d93b7c47b96ad363c3ef5f7b9/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/40a4d43d93b7c47b96ad363c3ef5f7b9/adv_predictions.json + adv_probabilities_file: output/reports/train/40a4d43d93b7c47b96ad363c3ef5f7b9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5bfe4f8518fa1df9db8a319f4d6b0e2d.pkl + params_file: output/reports/train/40a4d43d93b7c47b96ad363c3ef5f7b9/params.yaml + predictions_file: output/reports/train/40a4d43d93b7c47b96ad363c3ef5f7b9/predictions.json + probabilities_file: output/reports/train/40a4d43d93b7c47b96ad363c3ef5f7b9/probabilities.json + score_dict_file: output/reports/train/40a4d43d93b7c47b96ad363c3ef5f7b9/score_dict.json + test_labels_file: output/reports/train/40a4d43d93b7c47b96ad363c3ef5f7b9/test_labels.json + train_labels_file: output/reports/train/40a4d43d93b7c47b96ad363c3ef5f7b9/train_labels.json + model_dir: models + name: 40a4d43d93b7c47b96ad363c3ef5f7b9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 670952c1ff5b97d5c6e2d9e1eff6aa81 + trainer: + kwargs: {} +name: 40a4d43d93b7c47b96ad363c3ef5f7b9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/4207f1106f1bc54828ff279ffb6ef085/params.yaml b/examples/security/truthseeker/output/reports/train/4207f1106f1bc54828ff279ffb6ef085/params.yaml new file mode 100644 index 00000000..c3d6c79b --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/4207f1106f1bc54828ff279ffb6ef085/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/adv_predictions.json + adv_probabilities_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f89416975555df8ce1be7600e51f1e5e.pkl + params_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/params.yaml + predictions_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/predictions.json + probabilities_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/probabilities.json + score_dict_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/score_dict.json + test_labels_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/test_labels.json + train_labels_file: output/reports/train/4207f1106f1bc54828ff279ffb6ef085/train_labels.json + model_dir: models + name: 4207f1106f1bc54828ff279ffb6ef085 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f50dbdc91553ed54ffdbc56d000f88ec + trainer: + kwargs: {} +name: 4207f1106f1bc54828ff279ffb6ef085 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/42738fae780305a0553250c16c36e77b/params.yaml b/examples/security/truthseeker/output/reports/train/42738fae780305a0553250c16c36e77b/params.yaml new file mode 100644 index 00000000..4c84717a --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/42738fae780305a0553250c16c36e77b/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/42738fae780305a0553250c16c36e77b/adv_predictions.json + adv_probabilities_file: output/reports/train/42738fae780305a0553250c16c36e77b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/193126bd4fbb718baef183650cc71fca.pkl + params_file: output/reports/train/42738fae780305a0553250c16c36e77b/params.yaml + predictions_file: output/reports/train/42738fae780305a0553250c16c36e77b/predictions.json + probabilities_file: output/reports/train/42738fae780305a0553250c16c36e77b/probabilities.json + score_dict_file: output/reports/train/42738fae780305a0553250c16c36e77b/score_dict.json + test_labels_file: output/reports/train/42738fae780305a0553250c16c36e77b/test_labels.json + train_labels_file: output/reports/train/42738fae780305a0553250c16c36e77b/train_labels.json + model_dir: models + name: 42738fae780305a0553250c16c36e77b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 74faa5c66ac4cd91ae727731e6138b75 + trainer: + kwargs: {} +name: 42738fae780305a0553250c16c36e77b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/427ca300d1efd15d2b264a2999429d28/params.yaml b/examples/security/truthseeker/output/reports/train/427ca300d1efd15d2b264a2999429d28/params.yaml new file mode 100644 index 00000000..8ce596c9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/427ca300d1efd15d2b264a2999429d28/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/adv_predictions.json + adv_probabilities_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d834f00d680f9ec0ce4e5d3061f1b6c6.pkl + params_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/params.yaml + predictions_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/predictions.json + probabilities_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/probabilities.json + score_dict_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/score_dict.json + test_labels_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/test_labels.json + train_labels_file: output/reports/train/427ca300d1efd15d2b264a2999429d28/train_labels.json + model_dir: models + name: 427ca300d1efd15d2b264a2999429d28 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: aec915cac7c5aa9d007f4909127040d2 + trainer: + kwargs: {} +name: 427ca300d1efd15d2b264a2999429d28 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/42b8305345b144d4bfd886e273ebece8/params.yaml b/examples/security/truthseeker/output/reports/train/42b8305345b144d4bfd886e273ebece8/params.yaml new file mode 100644 index 00000000..d7c31c44 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/42b8305345b144d4bfd886e273ebece8/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/adv_predictions.json + adv_probabilities_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d18617117e734742bf5dd9b1b4ec1cb0.pkl + params_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/params.yaml + predictions_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/predictions.json + probabilities_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/probabilities.json + score_dict_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/score_dict.json + test_labels_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/test_labels.json + train_labels_file: output/reports/train/42b8305345b144d4bfd886e273ebece8/train_labels.json + model_dir: models + name: 42b8305345b144d4bfd886e273ebece8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: eafc1c49cd7737d2fe5d56b1b6b326fc + trainer: + kwargs: {} +name: 42b8305345b144d4bfd886e273ebece8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/445c69c2cfa70a63fcca7812c72010ff/params.yaml b/examples/security/truthseeker/output/reports/train/445c69c2cfa70a63fcca7812c72010ff/params.yaml new file mode 100644 index 00000000..0e95103f --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/445c69c2cfa70a63fcca7812c72010ff/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/adv_predictions.json + adv_probabilities_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/cadef0d5cad7400e0247fb362eb13c68.pkl + params_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/params.yaml + predictions_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/predictions.json + probabilities_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/probabilities.json + score_dict_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/score_dict.json + test_labels_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/test_labels.json + train_labels_file: output/reports/train/445c69c2cfa70a63fcca7812c72010ff/train_labels.json + model_dir: models + name: 445c69c2cfa70a63fcca7812c72010ff + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6cd9af412b716d0ecc9e3261f08dc769 + trainer: + kwargs: {} +name: 445c69c2cfa70a63fcca7812c72010ff +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/45663a2197f7adcee1eed3f885fcb723/params.yaml b/examples/security/truthseeker/output/reports/train/45663a2197f7adcee1eed3f885fcb723/params.yaml new file mode 100644 index 00000000..ddf35129 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/45663a2197f7adcee1eed3f885fcb723/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/adv_predictions.json + adv_probabilities_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/df380ce86fe6c64d1e134374efef84d8.pkl + params_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/params.yaml + predictions_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/predictions.json + probabilities_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/probabilities.json + score_dict_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/score_dict.json + test_labels_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/test_labels.json + train_labels_file: output/reports/train/45663a2197f7adcee1eed3f885fcb723/train_labels.json + model_dir: models + name: 45663a2197f7adcee1eed3f885fcb723 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ca5f122887eb77c223f0d90525c9311b + trainer: + kwargs: {} +name: 45663a2197f7adcee1eed3f885fcb723 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/466c0d9f44ce5895346877114e96bb54/params.yaml b/examples/security/truthseeker/output/reports/train/466c0d9f44ce5895346877114e96bb54/params.yaml new file mode 100644 index 00000000..979f2d69 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/466c0d9f44ce5895346877114e96bb54/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/adv_predictions.json + adv_probabilities_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/562b48ff040331e4eadf29d8802c3f1f.pkl + params_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/params.yaml + predictions_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/predictions.json + probabilities_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/probabilities.json + score_dict_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/score_dict.json + test_labels_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/test_labels.json + train_labels_file: output/reports/train/466c0d9f44ce5895346877114e96bb54/train_labels.json + model_dir: models + name: 466c0d9f44ce5895346877114e96bb54 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e2780731cb11dfc200aee0a4da9c4e82 + trainer: + kwargs: {} +name: 466c0d9f44ce5895346877114e96bb54 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/params.yaml b/examples/security/truthseeker/output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/params.yaml new file mode 100644 index 00000000..3909337e --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/adv_predictions.json + adv_probabilities_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/15e2158cda7c5bfe4a04a20ad2e556a4.pkl + params_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/params.yaml + predictions_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/predictions.json + probabilities_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/probabilities.json + score_dict_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/score_dict.json + test_labels_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/test_labels.json + train_labels_file: output/reports/train/4a6c4162ed49f656650f27a02bbc7e9d/train_labels.json + model_dir: models + name: 4a6c4162ed49f656650f27a02bbc7e9d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8a62edecee845292df0d496647eab2a9 + trainer: + kwargs: {} +name: 4a6c4162ed49f656650f27a02bbc7e9d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/4c29676318b8f511f6254240e2ca8cb0/params.yaml b/examples/security/truthseeker/output/reports/train/4c29676318b8f511f6254240e2ca8cb0/params.yaml new file mode 100644 index 00000000..5f4e52f3 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/4c29676318b8f511f6254240e2ca8cb0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4c29676318b8f511f6254240e2ca8cb0/adv_predictions.json + adv_probabilities_file: output/reports/train/4c29676318b8f511f6254240e2ca8cb0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/8357dc3e83cf91590980444ca9e379ee.pkl + params_file: output/reports/train/4c29676318b8f511f6254240e2ca8cb0/params.yaml + predictions_file: output/reports/train/4c29676318b8f511f6254240e2ca8cb0/predictions.json + probabilities_file: output/reports/train/4c29676318b8f511f6254240e2ca8cb0/probabilities.json + score_dict_file: output/reports/train/4c29676318b8f511f6254240e2ca8cb0/score_dict.json + test_labels_file: output/reports/train/4c29676318b8f511f6254240e2ca8cb0/test_labels.json + train_labels_file: output/reports/train/4c29676318b8f511f6254240e2ca8cb0/train_labels.json + model_dir: models + name: 4c29676318b8f511f6254240e2ca8cb0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dcb803567b3e7cffa6e989d8f526041e + trainer: + kwargs: {} +name: 4c29676318b8f511f6254240e2ca8cb0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/4cadded87e5f2fb4f25741aa2705b462/params.yaml b/examples/security/truthseeker/output/reports/train/4cadded87e5f2fb4f25741aa2705b462/params.yaml new file mode 100644 index 00000000..f8489fb9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/4cadded87e5f2fb4f25741aa2705b462/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4cadded87e5f2fb4f25741aa2705b462/adv_predictions.json + adv_probabilities_file: output/reports/train/4cadded87e5f2fb4f25741aa2705b462/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/158c1dd38c25db0e7564a047a43ce0d3.pkl + params_file: output/reports/train/4cadded87e5f2fb4f25741aa2705b462/params.yaml + predictions_file: output/reports/train/4cadded87e5f2fb4f25741aa2705b462/predictions.json + probabilities_file: output/reports/train/4cadded87e5f2fb4f25741aa2705b462/probabilities.json + score_dict_file: output/reports/train/4cadded87e5f2fb4f25741aa2705b462/score_dict.json + test_labels_file: output/reports/train/4cadded87e5f2fb4f25741aa2705b462/test_labels.json + train_labels_file: output/reports/train/4cadded87e5f2fb4f25741aa2705b462/train_labels.json + model_dir: models + name: 4cadded87e5f2fb4f25741aa2705b462 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 467b8d2a7a18c96dab77780f4f70e392 + trainer: + kwargs: {} +name: 4cadded87e5f2fb4f25741aa2705b462 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/4e38a5516956a808ff0c9de39edb3db1/params.yaml b/examples/security/truthseeker/output/reports/train/4e38a5516956a808ff0c9de39edb3db1/params.yaml new file mode 100644 index 00000000..74715835 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/4e38a5516956a808ff0c9de39edb3db1/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/adv_predictions.json + adv_probabilities_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/53ce45c654e1eedeac7a8a1b30b4a578.pkl + params_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/params.yaml + predictions_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/predictions.json + probabilities_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/probabilities.json + score_dict_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/score_dict.json + test_labels_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/test_labels.json + train_labels_file: output/reports/train/4e38a5516956a808ff0c9de39edb3db1/train_labels.json + model_dir: models + name: 4e38a5516956a808ff0c9de39edb3db1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 71cb9639017dc304cd9e967491fca65f + trainer: + kwargs: {} +name: 4e38a5516956a808ff0c9de39edb3db1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/4ed440797c89fe7ea76089ccd51943bb/params.yaml b/examples/security/truthseeker/output/reports/train/4ed440797c89fe7ea76089ccd51943bb/params.yaml new file mode 100644 index 00000000..ebb80524 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/4ed440797c89fe7ea76089ccd51943bb/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4ed440797c89fe7ea76089ccd51943bb/adv_predictions.json + adv_probabilities_file: output/reports/train/4ed440797c89fe7ea76089ccd51943bb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/12b6f9e1e58dd10b7f0a341947a0a06d.pkl + params_file: output/reports/train/4ed440797c89fe7ea76089ccd51943bb/params.yaml + predictions_file: output/reports/train/4ed440797c89fe7ea76089ccd51943bb/predictions.json + probabilities_file: output/reports/train/4ed440797c89fe7ea76089ccd51943bb/probabilities.json + score_dict_file: output/reports/train/4ed440797c89fe7ea76089ccd51943bb/score_dict.json + test_labels_file: output/reports/train/4ed440797c89fe7ea76089ccd51943bb/test_labels.json + train_labels_file: output/reports/train/4ed440797c89fe7ea76089ccd51943bb/train_labels.json + model_dir: models + name: 4ed440797c89fe7ea76089ccd51943bb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4494cc8f1cccdc61b6e32289c32a911e + trainer: + kwargs: {} +name: 4ed440797c89fe7ea76089ccd51943bb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/4f3af39c8b482bc6250a73661348b2c1/params.yaml b/examples/security/truthseeker/output/reports/train/4f3af39c8b482bc6250a73661348b2c1/params.yaml new file mode 100644 index 00000000..b5f74ac5 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/4f3af39c8b482bc6250a73661348b2c1/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/adv_predictions.json + adv_probabilities_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d072e65aba2f4626a24e0f5982e49de7.pkl + params_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/params.yaml + predictions_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/predictions.json + probabilities_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/probabilities.json + score_dict_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/score_dict.json + test_labels_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/test_labels.json + train_labels_file: output/reports/train/4f3af39c8b482bc6250a73661348b2c1/train_labels.json + model_dir: models + name: 4f3af39c8b482bc6250a73661348b2c1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1997cdf1fcc1bf068ecd31c8f6a45f71 + trainer: + kwargs: {} +name: 4f3af39c8b482bc6250a73661348b2c1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/4f6d3e79518de37978bbea9983a8ce92/params.yaml b/examples/security/truthseeker/output/reports/train/4f6d3e79518de37978bbea9983a8ce92/params.yaml new file mode 100644 index 00000000..91086eef --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/4f6d3e79518de37978bbea9983a8ce92/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/4f6d3e79518de37978bbea9983a8ce92/adv_predictions.json + adv_probabilities_file: output/reports/train/4f6d3e79518de37978bbea9983a8ce92/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7597f5fffaa37161d8b6b59c07f052d7.pkl + params_file: output/reports/train/4f6d3e79518de37978bbea9983a8ce92/params.yaml + predictions_file: output/reports/train/4f6d3e79518de37978bbea9983a8ce92/predictions.json + probabilities_file: output/reports/train/4f6d3e79518de37978bbea9983a8ce92/probabilities.json + score_dict_file: output/reports/train/4f6d3e79518de37978bbea9983a8ce92/score_dict.json + test_labels_file: output/reports/train/4f6d3e79518de37978bbea9983a8ce92/test_labels.json + train_labels_file: output/reports/train/4f6d3e79518de37978bbea9983a8ce92/train_labels.json + model_dir: models + name: 4f6d3e79518de37978bbea9983a8ce92 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e89e1ce623be571e30635332d755bff4 + trainer: + kwargs: {} +name: 4f6d3e79518de37978bbea9983a8ce92 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/506340393c45926ec4abb265766a3ee2/params.yaml b/examples/security/truthseeker/output/reports/train/506340393c45926ec4abb265766a3ee2/params.yaml new file mode 100644 index 00000000..c1a5b588 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/506340393c45926ec4abb265766a3ee2/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/506340393c45926ec4abb265766a3ee2/adv_predictions.json + adv_probabilities_file: output/reports/train/506340393c45926ec4abb265766a3ee2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/6ccc30b03e289e621982c1320795074d.pkl + params_file: output/reports/train/506340393c45926ec4abb265766a3ee2/params.yaml + predictions_file: output/reports/train/506340393c45926ec4abb265766a3ee2/predictions.json + probabilities_file: output/reports/train/506340393c45926ec4abb265766a3ee2/probabilities.json + score_dict_file: output/reports/train/506340393c45926ec4abb265766a3ee2/score_dict.json + test_labels_file: output/reports/train/506340393c45926ec4abb265766a3ee2/test_labels.json + train_labels_file: output/reports/train/506340393c45926ec4abb265766a3ee2/train_labels.json + model_dir: models + name: 506340393c45926ec4abb265766a3ee2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 713f9de83e06bea4f4bcf59acfaf3124 + trainer: + kwargs: {} +name: 506340393c45926ec4abb265766a3ee2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/511806a2e49f2bc030de9e791942b1bd/params.yaml b/examples/security/truthseeker/output/reports/train/511806a2e49f2bc030de9e791942b1bd/params.yaml new file mode 100644 index 00000000..31500683 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/511806a2e49f2bc030de9e791942b1bd/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/adv_predictions.json + adv_probabilities_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7ed24b12667263231a65d3b7310ca5df.pkl + params_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/params.yaml + predictions_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/predictions.json + probabilities_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/probabilities.json + score_dict_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/score_dict.json + test_labels_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/test_labels.json + train_labels_file: output/reports/train/511806a2e49f2bc030de9e791942b1bd/train_labels.json + model_dir: models + name: 511806a2e49f2bc030de9e791942b1bd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fc17611f5d80a034c8f141ff2ee5dda4 + trainer: + kwargs: {} +name: 511806a2e49f2bc030de9e791942b1bd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/53cc9bede24a0a1d8d30920288e533b2/params.yaml b/examples/security/truthseeker/output/reports/train/53cc9bede24a0a1d8d30920288e533b2/params.yaml new file mode 100644 index 00000000..c1f866b1 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/53cc9bede24a0a1d8d30920288e533b2/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/adv_predictions.json + adv_probabilities_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/656b4fad65a460a6dbf8a6805f91da39.pkl + params_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/params.yaml + predictions_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/predictions.json + probabilities_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/probabilities.json + score_dict_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/score_dict.json + test_labels_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/test_labels.json + train_labels_file: output/reports/train/53cc9bede24a0a1d8d30920288e533b2/train_labels.json + model_dir: models + name: 53cc9bede24a0a1d8d30920288e533b2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f9c8d5c50c166cce7ee00d729310d010 + trainer: + kwargs: {} +name: 53cc9bede24a0a1d8d30920288e533b2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/549b97dd9328b88ec5c39c368a301c07/params.yaml b/examples/security/truthseeker/output/reports/train/549b97dd9328b88ec5c39c368a301c07/params.yaml new file mode 100644 index 00000000..830c42ac --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/549b97dd9328b88ec5c39c368a301c07/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/adv_predictions.json + adv_probabilities_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7755d1ed6ad6ba65552d835a8d8f8b92.pkl + params_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/params.yaml + predictions_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/predictions.json + probabilities_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/probabilities.json + score_dict_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/score_dict.json + test_labels_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/test_labels.json + train_labels_file: output/reports/train/549b97dd9328b88ec5c39c368a301c07/train_labels.json + model_dir: models + name: 549b97dd9328b88ec5c39c368a301c07 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5f20fcbdc942526c009bb51d413673b3 + trainer: + kwargs: {} +name: 549b97dd9328b88ec5c39c368a301c07 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/54d748a08284e71a98c3adb0284bd313/params.yaml b/examples/security/truthseeker/output/reports/train/54d748a08284e71a98c3adb0284bd313/params.yaml new file mode 100644 index 00000000..56011a10 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/54d748a08284e71a98c3adb0284bd313/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/adv_predictions.json + adv_probabilities_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/73d704f29491799d6c60233f3c486885.pkl + params_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/params.yaml + predictions_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/predictions.json + probabilities_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/probabilities.json + score_dict_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/score_dict.json + test_labels_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/test_labels.json + train_labels_file: output/reports/train/54d748a08284e71a98c3adb0284bd313/train_labels.json + model_dir: models + name: 54d748a08284e71a98c3adb0284bd313 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4787ad0f4346d07f549151f200153ba4 + trainer: + kwargs: {} +name: 54d748a08284e71a98c3adb0284bd313 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/params.yaml b/examples/security/truthseeker/output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/params.yaml new file mode 100644 index 00000000..968976c4 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/adv_predictions.json + adv_probabilities_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ef0100b4abffaab73d24efbf92feec2e.pkl + params_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/params.yaml + predictions_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/predictions.json + probabilities_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/probabilities.json + score_dict_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/score_dict.json + test_labels_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/test_labels.json + train_labels_file: output/reports/train/555ffb82e7eb8d05c9a7bbb1bffb181e/train_labels.json + model_dir: models + name: 555ffb82e7eb8d05c9a7bbb1bffb181e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3d4e3192794abd81b21bad56bbf70156 + trainer: + kwargs: {} +name: 555ffb82e7eb8d05c9a7bbb1bffb181e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/params.yaml b/examples/security/truthseeker/output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/params.yaml new file mode 100644 index 00000000..c0314a69 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/adv_predictions.json + adv_probabilities_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1406754c5034f882d58a3bbd344a5456.pkl + params_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/params.yaml + predictions_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/predictions.json + probabilities_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/probabilities.json + score_dict_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/score_dict.json + test_labels_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/test_labels.json + train_labels_file: output/reports/train/55f1e95d4cda115ac5943aed4acbefc3/train_labels.json + model_dir: models + name: 55f1e95d4cda115ac5943aed4acbefc3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9b44c94b4e6997dec80efb770a5efa14 + trainer: + kwargs: {} +name: 55f1e95d4cda115ac5943aed4acbefc3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/56ffc311ae0acb0063d96bf129fcbc1c/params.yaml b/examples/security/truthseeker/output/reports/train/56ffc311ae0acb0063d96bf129fcbc1c/params.yaml new file mode 100644 index 00000000..1c105342 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/56ffc311ae0acb0063d96bf129fcbc1c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/56ffc311ae0acb0063d96bf129fcbc1c/adv_predictions.json + adv_probabilities_file: output/reports/train/56ffc311ae0acb0063d96bf129fcbc1c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/74177ff62348b5a17afc951f4afa37c8.pkl + params_file: output/reports/train/56ffc311ae0acb0063d96bf129fcbc1c/params.yaml + predictions_file: output/reports/train/56ffc311ae0acb0063d96bf129fcbc1c/predictions.json + probabilities_file: output/reports/train/56ffc311ae0acb0063d96bf129fcbc1c/probabilities.json + score_dict_file: output/reports/train/56ffc311ae0acb0063d96bf129fcbc1c/score_dict.json + test_labels_file: output/reports/train/56ffc311ae0acb0063d96bf129fcbc1c/test_labels.json + train_labels_file: output/reports/train/56ffc311ae0acb0063d96bf129fcbc1c/train_labels.json + model_dir: models + name: 56ffc311ae0acb0063d96bf129fcbc1c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b426b224724dbdad02b519a225dd6d46 + trainer: + kwargs: {} +name: 56ffc311ae0acb0063d96bf129fcbc1c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/57c3e8d7d82d615a10c47ebcfe1934f3/params.yaml b/examples/security/truthseeker/output/reports/train/57c3e8d7d82d615a10c47ebcfe1934f3/params.yaml new file mode 100644 index 00000000..a013e3c0 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/57c3e8d7d82d615a10c47ebcfe1934f3/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/57c3e8d7d82d615a10c47ebcfe1934f3/adv_predictions.json + adv_probabilities_file: output/reports/train/57c3e8d7d82d615a10c47ebcfe1934f3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/a4ef1840b0959b51aaaafc9d3b416775.pkl + params_file: output/reports/train/57c3e8d7d82d615a10c47ebcfe1934f3/params.yaml + predictions_file: output/reports/train/57c3e8d7d82d615a10c47ebcfe1934f3/predictions.json + probabilities_file: output/reports/train/57c3e8d7d82d615a10c47ebcfe1934f3/probabilities.json + score_dict_file: output/reports/train/57c3e8d7d82d615a10c47ebcfe1934f3/score_dict.json + test_labels_file: output/reports/train/57c3e8d7d82d615a10c47ebcfe1934f3/test_labels.json + train_labels_file: output/reports/train/57c3e8d7d82d615a10c47ebcfe1934f3/train_labels.json + model_dir: models + name: 57c3e8d7d82d615a10c47ebcfe1934f3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c15174576e1b0b21adf6d85df38723ae + trainer: + kwargs: {} +name: 57c3e8d7d82d615a10c47ebcfe1934f3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/58761309abdb0fd761612c03655f9f4a/params.yaml b/examples/security/truthseeker/output/reports/train/58761309abdb0fd761612c03655f9f4a/params.yaml new file mode 100644 index 00000000..f804f550 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/58761309abdb0fd761612c03655f9f4a/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/adv_predictions.json + adv_probabilities_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/38327d2036128a118ecce0468fe52259.pkl + params_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/params.yaml + predictions_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/predictions.json + probabilities_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/probabilities.json + score_dict_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/score_dict.json + test_labels_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/test_labels.json + train_labels_file: output/reports/train/58761309abdb0fd761612c03655f9f4a/train_labels.json + model_dir: models + name: 58761309abdb0fd761612c03655f9f4a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea5893115d8ebefb50c6855e2cc64558 + trainer: + kwargs: {} +name: 58761309abdb0fd761612c03655f9f4a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/58cf9f707cde129be1bf0188d52ea396/params.yaml b/examples/security/truthseeker/output/reports/train/58cf9f707cde129be1bf0188d52ea396/params.yaml new file mode 100644 index 00000000..216ed133 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/58cf9f707cde129be1bf0188d52ea396/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/adv_predictions.json + adv_probabilities_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/90e1d0e418f98f4b0b5593b52580efe2.pkl + params_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/params.yaml + predictions_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/predictions.json + probabilities_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/probabilities.json + score_dict_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/score_dict.json + test_labels_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/test_labels.json + train_labels_file: output/reports/train/58cf9f707cde129be1bf0188d52ea396/train_labels.json + model_dir: models + name: 58cf9f707cde129be1bf0188d52ea396 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 01f3b66ae9d53723c1c36c359e0546ff + trainer: + kwargs: {} +name: 58cf9f707cde129be1bf0188d52ea396 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/59d53bf6f4ada837956a3b493770723b/params.yaml b/examples/security/truthseeker/output/reports/train/59d53bf6f4ada837956a3b493770723b/params.yaml new file mode 100644 index 00000000..11e2a2da --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/59d53bf6f4ada837956a3b493770723b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/adv_predictions.json + adv_probabilities_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ab8dc94016bfa0fb0394a5320380868c.pkl + params_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/params.yaml + predictions_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/predictions.json + probabilities_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/probabilities.json + score_dict_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/score_dict.json + test_labels_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/test_labels.json + train_labels_file: output/reports/train/59d53bf6f4ada837956a3b493770723b/train_labels.json + model_dir: models + name: 59d53bf6f4ada837956a3b493770723b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3f00f34e434f1803d06beb9dfe59a4b8 + trainer: + kwargs: {} +name: 59d53bf6f4ada837956a3b493770723b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/5aac1825bac9ed3ac531e140505f9b17/params.yaml b/examples/security/truthseeker/output/reports/train/5aac1825bac9ed3ac531e140505f9b17/params.yaml new file mode 100644 index 00000000..8bd3b43c --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/5aac1825bac9ed3ac531e140505f9b17/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/adv_predictions.json + adv_probabilities_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3e4e6ad0dbe0897041e3e0b744b59866.pkl + params_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/params.yaml + predictions_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/predictions.json + probabilities_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/probabilities.json + score_dict_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/score_dict.json + test_labels_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/test_labels.json + train_labels_file: output/reports/train/5aac1825bac9ed3ac531e140505f9b17/train_labels.json + model_dir: models + name: 5aac1825bac9ed3ac531e140505f9b17 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6d32dd58a7931879d5947002da530bfa + trainer: + kwargs: {} +name: 5aac1825bac9ed3ac531e140505f9b17 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/params.yaml b/examples/security/truthseeker/output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/params.yaml new file mode 100644 index 00000000..1db3b8e9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/adv_predictions.json + adv_probabilities_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/cafdc5851a55a5ee5987a47faeb7b3de.pkl + params_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/params.yaml + predictions_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/predictions.json + probabilities_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/probabilities.json + score_dict_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/score_dict.json + test_labels_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/test_labels.json + train_labels_file: output/reports/train/5b5da2ae35f855cfd1cce86d30b78a24/train_labels.json + model_dir: models + name: 5b5da2ae35f855cfd1cce86d30b78a24 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: faf3776487510d40fe97061d07d7f5ba + trainer: + kwargs: {} +name: 5b5da2ae35f855cfd1cce86d30b78a24 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/5c0c536cb920a4d03b65b12fd73feed1/params.yaml b/examples/security/truthseeker/output/reports/train/5c0c536cb920a4d03b65b12fd73feed1/params.yaml new file mode 100644 index 00000000..8c7eb741 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/5c0c536cb920a4d03b65b12fd73feed1/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5c0c536cb920a4d03b65b12fd73feed1/adv_predictions.json + adv_probabilities_file: output/reports/train/5c0c536cb920a4d03b65b12fd73feed1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0d2e74ac8c5a31cdddf0f4baebaa3cdf.pkl + params_file: output/reports/train/5c0c536cb920a4d03b65b12fd73feed1/params.yaml + predictions_file: output/reports/train/5c0c536cb920a4d03b65b12fd73feed1/predictions.json + probabilities_file: output/reports/train/5c0c536cb920a4d03b65b12fd73feed1/probabilities.json + score_dict_file: output/reports/train/5c0c536cb920a4d03b65b12fd73feed1/score_dict.json + test_labels_file: output/reports/train/5c0c536cb920a4d03b65b12fd73feed1/test_labels.json + train_labels_file: output/reports/train/5c0c536cb920a4d03b65b12fd73feed1/train_labels.json + model_dir: models + name: 5c0c536cb920a4d03b65b12fd73feed1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4e32048040ac1f371185473a1ef9b017 + trainer: + kwargs: {} +name: 5c0c536cb920a4d03b65b12fd73feed1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/5ede44a042312a2e725cc52ae7d568cf/params.yaml b/examples/security/truthseeker/output/reports/train/5ede44a042312a2e725cc52ae7d568cf/params.yaml new file mode 100644 index 00000000..cc09b597 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/5ede44a042312a2e725cc52ae7d568cf/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/adv_predictions.json + adv_probabilities_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/06d6a6ce6be148e1928a2aca0a2a61e8.pkl + params_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/params.yaml + predictions_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/predictions.json + probabilities_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/probabilities.json + score_dict_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/score_dict.json + test_labels_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/test_labels.json + train_labels_file: output/reports/train/5ede44a042312a2e725cc52ae7d568cf/train_labels.json + model_dir: models + name: 5ede44a042312a2e725cc52ae7d568cf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 85f0ea0cffdbdb3782ed6c82eb494f1f + trainer: + kwargs: {} +name: 5ede44a042312a2e725cc52ae7d568cf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/params.yaml b/examples/security/truthseeker/output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/params.yaml new file mode 100644 index 00000000..71781f10 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/adv_predictions.json + adv_probabilities_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/a241625090477c7551b003e128ff79fe.pkl + params_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/params.yaml + predictions_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/predictions.json + probabilities_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/probabilities.json + score_dict_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/score_dict.json + test_labels_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/test_labels.json + train_labels_file: output/reports/train/63d649e4850b33dc9ec8e248d38bba5d/train_labels.json + model_dir: models + name: 63d649e4850b33dc9ec8e248d38bba5d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7da7fa701b06782c619cb7fd7faa41da + trainer: + kwargs: {} +name: 63d649e4850b33dc9ec8e248d38bba5d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/64afdff8e84fc92eed6abd11920b6913/params.yaml b/examples/security/truthseeker/output/reports/train/64afdff8e84fc92eed6abd11920b6913/params.yaml new file mode 100644 index 00000000..fcc2142d --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/64afdff8e84fc92eed6abd11920b6913/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/adv_predictions.json + adv_probabilities_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2d20bd576021d8bbe07c9bca9a5f5788.pkl + params_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/params.yaml + predictions_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/predictions.json + probabilities_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/probabilities.json + score_dict_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/score_dict.json + test_labels_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/test_labels.json + train_labels_file: output/reports/train/64afdff8e84fc92eed6abd11920b6913/train_labels.json + model_dir: models + name: 64afdff8e84fc92eed6abd11920b6913 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3c4b5e813fe2ec1c34d5fa91da1c6ba9 + trainer: + kwargs: {} +name: 64afdff8e84fc92eed6abd11920b6913 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/651e2775e55bef828c544d84737dea41/params.yaml b/examples/security/truthseeker/output/reports/train/651e2775e55bef828c544d84737dea41/params.yaml new file mode 100644 index 00000000..86b295a0 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/651e2775e55bef828c544d84737dea41/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/651e2775e55bef828c544d84737dea41/adv_predictions.json + adv_probabilities_file: output/reports/train/651e2775e55bef828c544d84737dea41/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/acf96b5955cbfd527ccedbac2607cebc.pkl + params_file: output/reports/train/651e2775e55bef828c544d84737dea41/params.yaml + predictions_file: output/reports/train/651e2775e55bef828c544d84737dea41/predictions.json + probabilities_file: output/reports/train/651e2775e55bef828c544d84737dea41/probabilities.json + score_dict_file: output/reports/train/651e2775e55bef828c544d84737dea41/score_dict.json + test_labels_file: output/reports/train/651e2775e55bef828c544d84737dea41/test_labels.json + train_labels_file: output/reports/train/651e2775e55bef828c544d84737dea41/train_labels.json + model_dir: models + name: 651e2775e55bef828c544d84737dea41 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: feeee48cde303e8f9e525f9e639d03ac + trainer: + kwargs: {} +name: 651e2775e55bef828c544d84737dea41 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/674f484006a7a1010dc48ce7eaf27a72/params.yaml b/examples/security/truthseeker/output/reports/train/674f484006a7a1010dc48ce7eaf27a72/params.yaml new file mode 100644 index 00000000..b6ac0ad9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/674f484006a7a1010dc48ce7eaf27a72/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/adv_predictions.json + adv_probabilities_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/05d9a48f0f88ddb2bff9effcbdc01905.pkl + params_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/params.yaml + predictions_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/predictions.json + probabilities_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/probabilities.json + score_dict_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/score_dict.json + test_labels_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/test_labels.json + train_labels_file: output/reports/train/674f484006a7a1010dc48ce7eaf27a72/train_labels.json + model_dir: models + name: 674f484006a7a1010dc48ce7eaf27a72 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 70b54f1b8875f1ef4069cce4af9728ec + trainer: + kwargs: {} +name: 674f484006a7a1010dc48ce7eaf27a72 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/68deb269d9740e651b6cf1908b242721/params.yaml b/examples/security/truthseeker/output/reports/train/68deb269d9740e651b6cf1908b242721/params.yaml new file mode 100644 index 00000000..d455cd45 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/68deb269d9740e651b6cf1908b242721/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/68deb269d9740e651b6cf1908b242721/adv_predictions.json + adv_probabilities_file: output/reports/train/68deb269d9740e651b6cf1908b242721/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7ab55b12570b8f758e538636ccf67d38.pkl + params_file: output/reports/train/68deb269d9740e651b6cf1908b242721/params.yaml + predictions_file: output/reports/train/68deb269d9740e651b6cf1908b242721/predictions.json + probabilities_file: output/reports/train/68deb269d9740e651b6cf1908b242721/probabilities.json + score_dict_file: output/reports/train/68deb269d9740e651b6cf1908b242721/score_dict.json + test_labels_file: output/reports/train/68deb269d9740e651b6cf1908b242721/test_labels.json + train_labels_file: output/reports/train/68deb269d9740e651b6cf1908b242721/train_labels.json + model_dir: models + name: 68deb269d9740e651b6cf1908b242721 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e6a61c13939bcb4994e0607f251edcec + trainer: + kwargs: {} +name: 68deb269d9740e651b6cf1908b242721 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/68f48931c2c23e5f931d45f41f40d824/params.yaml b/examples/security/truthseeker/output/reports/train/68f48931c2c23e5f931d45f41f40d824/params.yaml new file mode 100644 index 00000000..1f63d4fe --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/68f48931c2c23e5f931d45f41f40d824/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/adv_predictions.json + adv_probabilities_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/a7b55e9c92494750ceace81f4579d67a.pkl + params_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/params.yaml + predictions_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/predictions.json + probabilities_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/probabilities.json + score_dict_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/score_dict.json + test_labels_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/test_labels.json + train_labels_file: output/reports/train/68f48931c2c23e5f931d45f41f40d824/train_labels.json + model_dir: models + name: 68f48931c2c23e5f931d45f41f40d824 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e5f88a07896e7114062de8eef5275d8d + trainer: + kwargs: {} +name: 68f48931c2c23e5f931d45f41f40d824 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/6bb925e4db561981ec3e5344978a247c/params.yaml b/examples/security/truthseeker/output/reports/train/6bb925e4db561981ec3e5344978a247c/params.yaml new file mode 100644 index 00000000..41eb1e02 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/6bb925e4db561981ec3e5344978a247c/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/adv_predictions.json + adv_probabilities_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/89d0a670f9432e51c1c345cc73506348.pkl + params_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/params.yaml + predictions_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/predictions.json + probabilities_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/probabilities.json + score_dict_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/score_dict.json + test_labels_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/test_labels.json + train_labels_file: output/reports/train/6bb925e4db561981ec3e5344978a247c/train_labels.json + model_dir: models + name: 6bb925e4db561981ec3e5344978a247c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: af1a4e9a46c42f4c38a6148e9965963c + trainer: + kwargs: {} +name: 6bb925e4db561981ec3e5344978a247c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/6e945967e2aaf6fc8f1329f69393c978/params.yaml b/examples/security/truthseeker/output/reports/train/6e945967e2aaf6fc8f1329f69393c978/params.yaml new file mode 100644 index 00000000..82014178 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/6e945967e2aaf6fc8f1329f69393c978/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6e945967e2aaf6fc8f1329f69393c978/adv_predictions.json + adv_probabilities_file: output/reports/train/6e945967e2aaf6fc8f1329f69393c978/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/464cc68fd081dc2acce319d5da69f76b.pkl + params_file: output/reports/train/6e945967e2aaf6fc8f1329f69393c978/params.yaml + predictions_file: output/reports/train/6e945967e2aaf6fc8f1329f69393c978/predictions.json + probabilities_file: output/reports/train/6e945967e2aaf6fc8f1329f69393c978/probabilities.json + score_dict_file: output/reports/train/6e945967e2aaf6fc8f1329f69393c978/score_dict.json + test_labels_file: output/reports/train/6e945967e2aaf6fc8f1329f69393c978/test_labels.json + train_labels_file: output/reports/train/6e945967e2aaf6fc8f1329f69393c978/train_labels.json + model_dir: models + name: 6e945967e2aaf6fc8f1329f69393c978 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c74ae777e788813e03f6f2948cc025c4 + trainer: + kwargs: {} +name: 6e945967e2aaf6fc8f1329f69393c978 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/6eeacb43502f547d76258cdb257c9e88/params.yaml b/examples/security/truthseeker/output/reports/train/6eeacb43502f547d76258cdb257c9e88/params.yaml new file mode 100644 index 00000000..d2796cb2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/6eeacb43502f547d76258cdb257c9e88/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6eeacb43502f547d76258cdb257c9e88/adv_predictions.json + adv_probabilities_file: output/reports/train/6eeacb43502f547d76258cdb257c9e88/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c44f2b8bf51474952d5a45a75e8579ec.pkl + params_file: output/reports/train/6eeacb43502f547d76258cdb257c9e88/params.yaml + predictions_file: output/reports/train/6eeacb43502f547d76258cdb257c9e88/predictions.json + probabilities_file: output/reports/train/6eeacb43502f547d76258cdb257c9e88/probabilities.json + score_dict_file: output/reports/train/6eeacb43502f547d76258cdb257c9e88/score_dict.json + test_labels_file: output/reports/train/6eeacb43502f547d76258cdb257c9e88/test_labels.json + train_labels_file: output/reports/train/6eeacb43502f547d76258cdb257c9e88/train_labels.json + model_dir: models + name: 6eeacb43502f547d76258cdb257c9e88 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 928157d14235d65cb42cc815b220df83 + trainer: + kwargs: {} +name: 6eeacb43502f547d76258cdb257c9e88 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/6f20314a20ad8b793773b4ee78491be8/params.yaml b/examples/security/truthseeker/output/reports/train/6f20314a20ad8b793773b4ee78491be8/params.yaml new file mode 100644 index 00000000..0e32805c --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/6f20314a20ad8b793773b4ee78491be8/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/6f20314a20ad8b793773b4ee78491be8/adv_predictions.json + adv_probabilities_file: output/reports/train/6f20314a20ad8b793773b4ee78491be8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f416c433d615f02eea1820a15630781f.pkl + params_file: output/reports/train/6f20314a20ad8b793773b4ee78491be8/params.yaml + predictions_file: output/reports/train/6f20314a20ad8b793773b4ee78491be8/predictions.json + probabilities_file: output/reports/train/6f20314a20ad8b793773b4ee78491be8/probabilities.json + score_dict_file: output/reports/train/6f20314a20ad8b793773b4ee78491be8/score_dict.json + test_labels_file: output/reports/train/6f20314a20ad8b793773b4ee78491be8/test_labels.json + train_labels_file: output/reports/train/6f20314a20ad8b793773b4ee78491be8/train_labels.json + model_dir: models + name: 6f20314a20ad8b793773b4ee78491be8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 052756bf4089129e60dfb21cdfce9059 + trainer: + kwargs: {} +name: 6f20314a20ad8b793773b4ee78491be8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/706e7460210257c5b259626c82c6d32e/params.yaml b/examples/security/truthseeker/output/reports/train/706e7460210257c5b259626c82c6d32e/params.yaml new file mode 100644 index 00000000..f49d4c26 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/706e7460210257c5b259626c82c6d32e/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/706e7460210257c5b259626c82c6d32e/adv_predictions.json + adv_probabilities_file: output/reports/train/706e7460210257c5b259626c82c6d32e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0c2a459231c9967e61f4528a05b00e29.pkl + params_file: output/reports/train/706e7460210257c5b259626c82c6d32e/params.yaml + predictions_file: output/reports/train/706e7460210257c5b259626c82c6d32e/predictions.json + probabilities_file: output/reports/train/706e7460210257c5b259626c82c6d32e/probabilities.json + score_dict_file: output/reports/train/706e7460210257c5b259626c82c6d32e/score_dict.json + test_labels_file: output/reports/train/706e7460210257c5b259626c82c6d32e/test_labels.json + train_labels_file: output/reports/train/706e7460210257c5b259626c82c6d32e/train_labels.json + model_dir: models + name: 706e7460210257c5b259626c82c6d32e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 22f8f9e6176f4d365f13ebb01f80c55e + trainer: + kwargs: {} +name: 706e7460210257c5b259626c82c6d32e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/70fa7d718478d41166470a8c72b4bbd0/params.yaml b/examples/security/truthseeker/output/reports/train/70fa7d718478d41166470a8c72b4bbd0/params.yaml new file mode 100644 index 00000000..61daa56f --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/70fa7d718478d41166470a8c72b4bbd0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/70fa7d718478d41166470a8c72b4bbd0/adv_predictions.json + adv_probabilities_file: output/reports/train/70fa7d718478d41166470a8c72b4bbd0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/92f8acdd1b53d0ecf847c8d56965f4e6.pkl + params_file: output/reports/train/70fa7d718478d41166470a8c72b4bbd0/params.yaml + predictions_file: output/reports/train/70fa7d718478d41166470a8c72b4bbd0/predictions.json + probabilities_file: output/reports/train/70fa7d718478d41166470a8c72b4bbd0/probabilities.json + score_dict_file: output/reports/train/70fa7d718478d41166470a8c72b4bbd0/score_dict.json + test_labels_file: output/reports/train/70fa7d718478d41166470a8c72b4bbd0/test_labels.json + train_labels_file: output/reports/train/70fa7d718478d41166470a8c72b4bbd0/train_labels.json + model_dir: models + name: 70fa7d718478d41166470a8c72b4bbd0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3c1847e741e292dea22c2946fa2420e8 + trainer: + kwargs: {} +name: 70fa7d718478d41166470a8c72b4bbd0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/711589e68af9c9bd9c41810a979d0a25/params.yaml b/examples/security/truthseeker/output/reports/train/711589e68af9c9bd9c41810a979d0a25/params.yaml new file mode 100644 index 00000000..8da1df20 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/711589e68af9c9bd9c41810a979d0a25/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/711589e68af9c9bd9c41810a979d0a25/adv_predictions.json + adv_probabilities_file: output/reports/train/711589e68af9c9bd9c41810a979d0a25/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/4dc3ca8863432666aa786c33567632a1.pkl + params_file: output/reports/train/711589e68af9c9bd9c41810a979d0a25/params.yaml + predictions_file: output/reports/train/711589e68af9c9bd9c41810a979d0a25/predictions.json + probabilities_file: output/reports/train/711589e68af9c9bd9c41810a979d0a25/probabilities.json + score_dict_file: output/reports/train/711589e68af9c9bd9c41810a979d0a25/score_dict.json + test_labels_file: output/reports/train/711589e68af9c9bd9c41810a979d0a25/test_labels.json + train_labels_file: output/reports/train/711589e68af9c9bd9c41810a979d0a25/train_labels.json + model_dir: models + name: 711589e68af9c9bd9c41810a979d0a25 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e6fb63752bd8e6ee635160de4dca5d1d + trainer: + kwargs: {} +name: 711589e68af9c9bd9c41810a979d0a25 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/7152f5f671f4b6f2e116322a92b9adea/params.yaml b/examples/security/truthseeker/output/reports/train/7152f5f671f4b6f2e116322a92b9adea/params.yaml new file mode 100644 index 00000000..686b03f2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/7152f5f671f4b6f2e116322a92b9adea/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/adv_predictions.json + adv_probabilities_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/42fd313171585dbbe9bdcb00f083545a.pkl + params_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/params.yaml + predictions_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/predictions.json + probabilities_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/probabilities.json + score_dict_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/score_dict.json + test_labels_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/test_labels.json + train_labels_file: output/reports/train/7152f5f671f4b6f2e116322a92b9adea/train_labels.json + model_dir: models + name: 7152f5f671f4b6f2e116322a92b9adea + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: de5ff357e2bb09bb4c2a766e339aae8d + trainer: + kwargs: {} +name: 7152f5f671f4b6f2e116322a92b9adea +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/71cb2c6d236b75fd92f07471e3309947/params.yaml b/examples/security/truthseeker/output/reports/train/71cb2c6d236b75fd92f07471e3309947/params.yaml new file mode 100644 index 00000000..9c36c85b --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/71cb2c6d236b75fd92f07471e3309947/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/adv_predictions.json + adv_probabilities_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c6466682a0bf488ccb57f596b483e88a.pkl + params_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/params.yaml + predictions_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/predictions.json + probabilities_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/probabilities.json + score_dict_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/score_dict.json + test_labels_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/test_labels.json + train_labels_file: output/reports/train/71cb2c6d236b75fd92f07471e3309947/train_labels.json + model_dir: models + name: 71cb2c6d236b75fd92f07471e3309947 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 041fb0fe62ce798cab87816f19095966 + trainer: + kwargs: {} +name: 71cb2c6d236b75fd92f07471e3309947 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/72096c1fda4cc00bc318dfb14ef8c842/params.yaml b/examples/security/truthseeker/output/reports/train/72096c1fda4cc00bc318dfb14ef8c842/params.yaml new file mode 100644 index 00000000..3ee11ec2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/72096c1fda4cc00bc318dfb14ef8c842/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/72096c1fda4cc00bc318dfb14ef8c842/adv_predictions.json + adv_probabilities_file: output/reports/train/72096c1fda4cc00bc318dfb14ef8c842/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/20b0729c0aeb86db88e94e2c1a92a35e.pkl + params_file: output/reports/train/72096c1fda4cc00bc318dfb14ef8c842/params.yaml + predictions_file: output/reports/train/72096c1fda4cc00bc318dfb14ef8c842/predictions.json + probabilities_file: output/reports/train/72096c1fda4cc00bc318dfb14ef8c842/probabilities.json + score_dict_file: output/reports/train/72096c1fda4cc00bc318dfb14ef8c842/score_dict.json + test_labels_file: output/reports/train/72096c1fda4cc00bc318dfb14ef8c842/test_labels.json + train_labels_file: output/reports/train/72096c1fda4cc00bc318dfb14ef8c842/train_labels.json + model_dir: models + name: 72096c1fda4cc00bc318dfb14ef8c842 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 492ba5bf00511eb88347a9b62ce4eedb + trainer: + kwargs: {} +name: 72096c1fda4cc00bc318dfb14ef8c842 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/741fbca4a4207856ba7b7e9e687c9734/params.yaml b/examples/security/truthseeker/output/reports/train/741fbca4a4207856ba7b7e9e687c9734/params.yaml new file mode 100644 index 00000000..40fe3043 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/741fbca4a4207856ba7b7e9e687c9734/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/741fbca4a4207856ba7b7e9e687c9734/adv_predictions.json + adv_probabilities_file: output/reports/train/741fbca4a4207856ba7b7e9e687c9734/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/013b9ac428d6e7ef3bf5ccf979d01170.pkl + params_file: output/reports/train/741fbca4a4207856ba7b7e9e687c9734/params.yaml + predictions_file: output/reports/train/741fbca4a4207856ba7b7e9e687c9734/predictions.json + probabilities_file: output/reports/train/741fbca4a4207856ba7b7e9e687c9734/probabilities.json + score_dict_file: output/reports/train/741fbca4a4207856ba7b7e9e687c9734/score_dict.json + test_labels_file: output/reports/train/741fbca4a4207856ba7b7e9e687c9734/test_labels.json + train_labels_file: output/reports/train/741fbca4a4207856ba7b7e9e687c9734/train_labels.json + model_dir: models + name: 741fbca4a4207856ba7b7e9e687c9734 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c98084cc09b52410e8871e3e560dec67 + trainer: + kwargs: {} +name: 741fbca4a4207856ba7b7e9e687c9734 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/76cc06b5cc914383e1164f805abae21b/params.yaml b/examples/security/truthseeker/output/reports/train/76cc06b5cc914383e1164f805abae21b/params.yaml new file mode 100644 index 00000000..3841c731 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/76cc06b5cc914383e1164f805abae21b/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/adv_predictions.json + adv_probabilities_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/22d012a4f572c48239a27385de960696.pkl + params_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/params.yaml + predictions_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/predictions.json + probabilities_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/probabilities.json + score_dict_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/score_dict.json + test_labels_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/test_labels.json + train_labels_file: output/reports/train/76cc06b5cc914383e1164f805abae21b/train_labels.json + model_dir: models + name: 76cc06b5cc914383e1164f805abae21b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1ebeed32613201096dba6fc696387514 + trainer: + kwargs: {} +name: 76cc06b5cc914383e1164f805abae21b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/76df55691cc6a46b76d679df5f2f5f49/params.yaml b/examples/security/truthseeker/output/reports/train/76df55691cc6a46b76d679df5f2f5f49/params.yaml new file mode 100644 index 00000000..111eec2d --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/76df55691cc6a46b76d679df5f2f5f49/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/76df55691cc6a46b76d679df5f2f5f49/adv_predictions.json + adv_probabilities_file: output/reports/train/76df55691cc6a46b76d679df5f2f5f49/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d2d02f49936f494a0e2680270535e59b.pkl + params_file: output/reports/train/76df55691cc6a46b76d679df5f2f5f49/params.yaml + predictions_file: output/reports/train/76df55691cc6a46b76d679df5f2f5f49/predictions.json + probabilities_file: output/reports/train/76df55691cc6a46b76d679df5f2f5f49/probabilities.json + score_dict_file: output/reports/train/76df55691cc6a46b76d679df5f2f5f49/score_dict.json + test_labels_file: output/reports/train/76df55691cc6a46b76d679df5f2f5f49/test_labels.json + train_labels_file: output/reports/train/76df55691cc6a46b76d679df5f2f5f49/train_labels.json + model_dir: models + name: 76df55691cc6a46b76d679df5f2f5f49 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ab88d9b48c25d4fa3909e09795c8c053 + trainer: + kwargs: {} +name: 76df55691cc6a46b76d679df5f2f5f49 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/771bf25c14a489546dd0e3474c34f0ee/params.yaml b/examples/security/truthseeker/output/reports/train/771bf25c14a489546dd0e3474c34f0ee/params.yaml new file mode 100644 index 00000000..952f53e7 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/771bf25c14a489546dd0e3474c34f0ee/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/771bf25c14a489546dd0e3474c34f0ee/adv_predictions.json + adv_probabilities_file: output/reports/train/771bf25c14a489546dd0e3474c34f0ee/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ec5346e9b69d233d9429865d70e8554c.pkl + params_file: output/reports/train/771bf25c14a489546dd0e3474c34f0ee/params.yaml + predictions_file: output/reports/train/771bf25c14a489546dd0e3474c34f0ee/predictions.json + probabilities_file: output/reports/train/771bf25c14a489546dd0e3474c34f0ee/probabilities.json + score_dict_file: output/reports/train/771bf25c14a489546dd0e3474c34f0ee/score_dict.json + test_labels_file: output/reports/train/771bf25c14a489546dd0e3474c34f0ee/test_labels.json + train_labels_file: output/reports/train/771bf25c14a489546dd0e3474c34f0ee/train_labels.json + model_dir: models + name: 771bf25c14a489546dd0e3474c34f0ee + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d9092db3ddf6c669048b485f6e3b2486 + trainer: + kwargs: {} +name: 771bf25c14a489546dd0e3474c34f0ee +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/779fab079c75f81e635b329b550703a6/params.yaml b/examples/security/truthseeker/output/reports/train/779fab079c75f81e635b329b550703a6/params.yaml new file mode 100644 index 00000000..f232eb62 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/779fab079c75f81e635b329b550703a6/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/779fab079c75f81e635b329b550703a6/adv_predictions.json + adv_probabilities_file: output/reports/train/779fab079c75f81e635b329b550703a6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/84bffdddad088ab8775c6e8e0e14b579.pkl + params_file: output/reports/train/779fab079c75f81e635b329b550703a6/params.yaml + predictions_file: output/reports/train/779fab079c75f81e635b329b550703a6/predictions.json + probabilities_file: output/reports/train/779fab079c75f81e635b329b550703a6/probabilities.json + score_dict_file: output/reports/train/779fab079c75f81e635b329b550703a6/score_dict.json + test_labels_file: output/reports/train/779fab079c75f81e635b329b550703a6/test_labels.json + train_labels_file: output/reports/train/779fab079c75f81e635b329b550703a6/train_labels.json + model_dir: models + name: 779fab079c75f81e635b329b550703a6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3740e7e7754bec4e0943b5dee56511d2 + trainer: + kwargs: {} +name: 779fab079c75f81e635b329b550703a6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/77ba18f3d5e800bb7119cb153cb89948/params.yaml b/examples/security/truthseeker/output/reports/train/77ba18f3d5e800bb7119cb153cb89948/params.yaml new file mode 100644 index 00000000..78f2a4fa --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/77ba18f3d5e800bb7119cb153cb89948/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/adv_predictions.json + adv_probabilities_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/eb385de13928d460fb49b48754815e68.pkl + params_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/params.yaml + predictions_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/predictions.json + probabilities_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/probabilities.json + score_dict_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/score_dict.json + test_labels_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/test_labels.json + train_labels_file: output/reports/train/77ba18f3d5e800bb7119cb153cb89948/train_labels.json + model_dir: models + name: 77ba18f3d5e800bb7119cb153cb89948 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6f8603c0c2813e093a756466e91d495c + trainer: + kwargs: {} +name: 77ba18f3d5e800bb7119cb153cb89948 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/782dd07ae0169b7e378db74c244bcf49/params.yaml b/examples/security/truthseeker/output/reports/train/782dd07ae0169b7e378db74c244bcf49/params.yaml new file mode 100644 index 00000000..d95a7dd1 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/782dd07ae0169b7e378db74c244bcf49/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/adv_predictions.json + adv_probabilities_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d64f709e1bcb17862630f80196a24651.pkl + params_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/params.yaml + predictions_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/predictions.json + probabilities_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/probabilities.json + score_dict_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/score_dict.json + test_labels_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/test_labels.json + train_labels_file: output/reports/train/782dd07ae0169b7e378db74c244bcf49/train_labels.json + model_dir: models + name: 782dd07ae0169b7e378db74c244bcf49 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e7e10a3fe830a9a56394e28b8d69b005 + trainer: + kwargs: {} +name: 782dd07ae0169b7e378db74c244bcf49 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/7c41aefff4aae5034eafca9754fff8cd/params.yaml b/examples/security/truthseeker/output/reports/train/7c41aefff4aae5034eafca9754fff8cd/params.yaml new file mode 100644 index 00000000..da2559a7 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/7c41aefff4aae5034eafca9754fff8cd/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/adv_predictions.json + adv_probabilities_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2d330748565264893d0dcf217255da5e.pkl + params_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/params.yaml + predictions_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/predictions.json + probabilities_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/probabilities.json + score_dict_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/score_dict.json + test_labels_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/test_labels.json + train_labels_file: output/reports/train/7c41aefff4aae5034eafca9754fff8cd/train_labels.json + model_dir: models + name: 7c41aefff4aae5034eafca9754fff8cd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4bb263d05af2e590c6929fe92b9c63c6 + trainer: + kwargs: {} +name: 7c41aefff4aae5034eafca9754fff8cd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/7de041bf1676eee97539e07675dd80ec/params.yaml b/examples/security/truthseeker/output/reports/train/7de041bf1676eee97539e07675dd80ec/params.yaml new file mode 100644 index 00000000..c5819723 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/7de041bf1676eee97539e07675dd80ec/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7de041bf1676eee97539e07675dd80ec/adv_predictions.json + adv_probabilities_file: output/reports/train/7de041bf1676eee97539e07675dd80ec/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/51948ae4b92445a146b7a90ed6898714.pkl + params_file: output/reports/train/7de041bf1676eee97539e07675dd80ec/params.yaml + predictions_file: output/reports/train/7de041bf1676eee97539e07675dd80ec/predictions.json + probabilities_file: output/reports/train/7de041bf1676eee97539e07675dd80ec/probabilities.json + score_dict_file: output/reports/train/7de041bf1676eee97539e07675dd80ec/score_dict.json + test_labels_file: output/reports/train/7de041bf1676eee97539e07675dd80ec/test_labels.json + train_labels_file: output/reports/train/7de041bf1676eee97539e07675dd80ec/train_labels.json + model_dir: models + name: 7de041bf1676eee97539e07675dd80ec + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cce8e2e72282aca010c9220d1651c1f8 + trainer: + kwargs: {} +name: 7de041bf1676eee97539e07675dd80ec +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/7ee42a29581572a853e586d2c651e8a1/params.yaml b/examples/security/truthseeker/output/reports/train/7ee42a29581572a853e586d2c651e8a1/params.yaml new file mode 100644 index 00000000..61b9fff5 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/7ee42a29581572a853e586d2c651e8a1/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/adv_predictions.json + adv_probabilities_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c4ac8fbf40a205f35e2da9f567343332.pkl + params_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/params.yaml + predictions_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/predictions.json + probabilities_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/probabilities.json + score_dict_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/score_dict.json + test_labels_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/test_labels.json + train_labels_file: output/reports/train/7ee42a29581572a853e586d2c651e8a1/train_labels.json + model_dir: models + name: 7ee42a29581572a853e586d2c651e8a1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7c677332f4e3eebde90af8d1328822d7 + trainer: + kwargs: {} +name: 7ee42a29581572a853e586d2c651e8a1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/7f3620980ea71b094fda7efee7dcf71e/params.yaml b/examples/security/truthseeker/output/reports/train/7f3620980ea71b094fda7efee7dcf71e/params.yaml new file mode 100644 index 00000000..ab64977c --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/7f3620980ea71b094fda7efee7dcf71e/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/adv_predictions.json + adv_probabilities_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b7033344658616a25bbf77babdf79e7e.pkl + params_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/params.yaml + predictions_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/predictions.json + probabilities_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/probabilities.json + score_dict_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/score_dict.json + test_labels_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/test_labels.json + train_labels_file: output/reports/train/7f3620980ea71b094fda7efee7dcf71e/train_labels.json + model_dir: models + name: 7f3620980ea71b094fda7efee7dcf71e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 200728ea3bee5e055569e0faca746b1e + trainer: + kwargs: {} +name: 7f3620980ea71b094fda7efee7dcf71e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/7ff973419de24d9a906b24dd5f0cbc18/params.yaml b/examples/security/truthseeker/output/reports/train/7ff973419de24d9a906b24dd5f0cbc18/params.yaml new file mode 100644 index 00000000..7aefb567 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/7ff973419de24d9a906b24dd5f0cbc18/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/7ff973419de24d9a906b24dd5f0cbc18/adv_predictions.json + adv_probabilities_file: output/reports/train/7ff973419de24d9a906b24dd5f0cbc18/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/80c2f85b6da0c3f55df3318623b35c52.pkl + params_file: output/reports/train/7ff973419de24d9a906b24dd5f0cbc18/params.yaml + predictions_file: output/reports/train/7ff973419de24d9a906b24dd5f0cbc18/predictions.json + probabilities_file: output/reports/train/7ff973419de24d9a906b24dd5f0cbc18/probabilities.json + score_dict_file: output/reports/train/7ff973419de24d9a906b24dd5f0cbc18/score_dict.json + test_labels_file: output/reports/train/7ff973419de24d9a906b24dd5f0cbc18/test_labels.json + train_labels_file: output/reports/train/7ff973419de24d9a906b24dd5f0cbc18/train_labels.json + model_dir: models + name: 7ff973419de24d9a906b24dd5f0cbc18 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 971910627fbe7d99fc891e85c47c2030 + trainer: + kwargs: {} +name: 7ff973419de24d9a906b24dd5f0cbc18 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/817d81b6b6be4507003cb17740a7c9dd/params.yaml b/examples/security/truthseeker/output/reports/train/817d81b6b6be4507003cb17740a7c9dd/params.yaml new file mode 100644 index 00000000..b37ad96e --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/817d81b6b6be4507003cb17740a7c9dd/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/817d81b6b6be4507003cb17740a7c9dd/adv_predictions.json + adv_probabilities_file: output/reports/train/817d81b6b6be4507003cb17740a7c9dd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1b6b79445cc2b51acf48731d891f3546.pkl + params_file: output/reports/train/817d81b6b6be4507003cb17740a7c9dd/params.yaml + predictions_file: output/reports/train/817d81b6b6be4507003cb17740a7c9dd/predictions.json + probabilities_file: output/reports/train/817d81b6b6be4507003cb17740a7c9dd/probabilities.json + score_dict_file: output/reports/train/817d81b6b6be4507003cb17740a7c9dd/score_dict.json + test_labels_file: output/reports/train/817d81b6b6be4507003cb17740a7c9dd/test_labels.json + train_labels_file: output/reports/train/817d81b6b6be4507003cb17740a7c9dd/train_labels.json + model_dir: models + name: 817d81b6b6be4507003cb17740a7c9dd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1c8679822882040c162219c176a17f77 + trainer: + kwargs: {} +name: 817d81b6b6be4507003cb17740a7c9dd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/82f316869ca511c9ca4d33e059fd104b/params.yaml b/examples/security/truthseeker/output/reports/train/82f316869ca511c9ca4d33e059fd104b/params.yaml new file mode 100644 index 00000000..81819da2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/82f316869ca511c9ca4d33e059fd104b/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/adv_predictions.json + adv_probabilities_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e141b4724ffe2abb3160d334f09ca3b1.pkl + params_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/params.yaml + predictions_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/predictions.json + probabilities_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/probabilities.json + score_dict_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/score_dict.json + test_labels_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/test_labels.json + train_labels_file: output/reports/train/82f316869ca511c9ca4d33e059fd104b/train_labels.json + model_dir: models + name: 82f316869ca511c9ca4d33e059fd104b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b76a0c552ac0a583e9e2edae2640d38d + trainer: + kwargs: {} +name: 82f316869ca511c9ca4d33e059fd104b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/84d83d958c95dd783c7d187d78e46fc9/params.yaml b/examples/security/truthseeker/output/reports/train/84d83d958c95dd783c7d187d78e46fc9/params.yaml new file mode 100644 index 00000000..035f8d38 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/84d83d958c95dd783c7d187d78e46fc9/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/adv_predictions.json + adv_probabilities_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/90196996615b8659025526c526c3fb29.pkl + params_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/params.yaml + predictions_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/predictions.json + probabilities_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/probabilities.json + score_dict_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/score_dict.json + test_labels_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/test_labels.json + train_labels_file: output/reports/train/84d83d958c95dd783c7d187d78e46fc9/train_labels.json + model_dir: models + name: 84d83d958c95dd783c7d187d78e46fc9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 81355cdde7c0c430298ba3fc1c511381 + trainer: + kwargs: {} +name: 84d83d958c95dd783c7d187d78e46fc9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/84e6fca3461c90d0cafa5aff840174a0/params.yaml b/examples/security/truthseeker/output/reports/train/84e6fca3461c90d0cafa5aff840174a0/params.yaml new file mode 100644 index 00000000..6e4a2ed2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/84e6fca3461c90d0cafa5aff840174a0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/adv_predictions.json + adv_probabilities_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1cddf23e83a6c39caed92d019bb66c83.pkl + params_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/params.yaml + predictions_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/predictions.json + probabilities_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/probabilities.json + score_dict_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/score_dict.json + test_labels_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/test_labels.json + train_labels_file: output/reports/train/84e6fca3461c90d0cafa5aff840174a0/train_labels.json + model_dir: models + name: 84e6fca3461c90d0cafa5aff840174a0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 11f979ac41f22ec0c88a08ce206385a3 + trainer: + kwargs: {} +name: 84e6fca3461c90d0cafa5aff840174a0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/85f53577d10b98ca944834793126f95b/params.yaml b/examples/security/truthseeker/output/reports/train/85f53577d10b98ca944834793126f95b/params.yaml new file mode 100644 index 00000000..bfe382b2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/85f53577d10b98ca944834793126f95b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/85f53577d10b98ca944834793126f95b/adv_predictions.json + adv_probabilities_file: output/reports/train/85f53577d10b98ca944834793126f95b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/dfcbeba1599d9ea21359edf86436df2e.pkl + params_file: output/reports/train/85f53577d10b98ca944834793126f95b/params.yaml + predictions_file: output/reports/train/85f53577d10b98ca944834793126f95b/predictions.json + probabilities_file: output/reports/train/85f53577d10b98ca944834793126f95b/probabilities.json + score_dict_file: output/reports/train/85f53577d10b98ca944834793126f95b/score_dict.json + test_labels_file: output/reports/train/85f53577d10b98ca944834793126f95b/test_labels.json + train_labels_file: output/reports/train/85f53577d10b98ca944834793126f95b/train_labels.json + model_dir: models + name: 85f53577d10b98ca944834793126f95b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0a6c17fce5baa906a19793a3a49796d2 + trainer: + kwargs: {} +name: 85f53577d10b98ca944834793126f95b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/860a9a3bd0730834b768e6a38081c1a9/params.yaml b/examples/security/truthseeker/output/reports/train/860a9a3bd0730834b768e6a38081c1a9/params.yaml new file mode 100644 index 00000000..22d90a72 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/860a9a3bd0730834b768e6a38081c1a9/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/adv_predictions.json + adv_probabilities_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/82f9617be2283b54226f1f4cb01a2d39.pkl + params_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/params.yaml + predictions_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/predictions.json + probabilities_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/probabilities.json + score_dict_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/score_dict.json + test_labels_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/test_labels.json + train_labels_file: output/reports/train/860a9a3bd0730834b768e6a38081c1a9/train_labels.json + model_dir: models + name: 860a9a3bd0730834b768e6a38081c1a9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e9d07b2c6acf28e3fb2dfa5122dd9a95 + trainer: + kwargs: {} +name: 860a9a3bd0730834b768e6a38081c1a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/params.yaml b/examples/security/truthseeker/output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/params.yaml new file mode 100644 index 00000000..e3091e1d --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/adv_predictions.json + adv_probabilities_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d6031f071a912dcbf78d29e355a6f9a9.pkl + params_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/params.yaml + predictions_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/predictions.json + probabilities_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/probabilities.json + score_dict_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/score_dict.json + test_labels_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/test_labels.json + train_labels_file: output/reports/train/86c1cc0fee35c935cc66b050d4a1f412/train_labels.json + model_dir: models + name: 86c1cc0fee35c935cc66b050d4a1f412 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 86e09315f804cc2656b3496b91417e29 + trainer: + kwargs: {} +name: 86c1cc0fee35c935cc66b050d4a1f412 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/8795b51bbb44d40edca644ea03e5f277/params.yaml b/examples/security/truthseeker/output/reports/train/8795b51bbb44d40edca644ea03e5f277/params.yaml new file mode 100644 index 00000000..a2fc40c9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/8795b51bbb44d40edca644ea03e5f277/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/adv_predictions.json + adv_probabilities_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/361084365ff77568270a8c55f831aea9.pkl + params_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/params.yaml + predictions_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/predictions.json + probabilities_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/probabilities.json + score_dict_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/score_dict.json + test_labels_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/test_labels.json + train_labels_file: output/reports/train/8795b51bbb44d40edca644ea03e5f277/train_labels.json + model_dir: models + name: 8795b51bbb44d40edca644ea03e5f277 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0ec18cab85e720600c210333de81cbf6 + trainer: + kwargs: {} +name: 8795b51bbb44d40edca644ea03e5f277 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/8816698a6a16f21c14cc15ab34ca584e/params.yaml b/examples/security/truthseeker/output/reports/train/8816698a6a16f21c14cc15ab34ca584e/params.yaml new file mode 100644 index 00000000..1c394e7d --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/8816698a6a16f21c14cc15ab34ca584e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/adv_predictions.json + adv_probabilities_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/208020f3631375bd5e8e8e67d8f6101e.pkl + params_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/params.yaml + predictions_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/predictions.json + probabilities_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/probabilities.json + score_dict_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/score_dict.json + test_labels_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/test_labels.json + train_labels_file: output/reports/train/8816698a6a16f21c14cc15ab34ca584e/train_labels.json + model_dir: models + name: 8816698a6a16f21c14cc15ab34ca584e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0d7be139ef573a55db065b72ad210dea + trainer: + kwargs: {} +name: 8816698a6a16f21c14cc15ab34ca584e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/886aeccbbf920e34d90eb401749d3f2c/params.yaml b/examples/security/truthseeker/output/reports/train/886aeccbbf920e34d90eb401749d3f2c/params.yaml new file mode 100644 index 00000000..5cd8ae1b --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/886aeccbbf920e34d90eb401749d3f2c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/886aeccbbf920e34d90eb401749d3f2c/adv_predictions.json + adv_probabilities_file: output/reports/train/886aeccbbf920e34d90eb401749d3f2c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/127c07f49be31fb9880cbd07f74a802b.pkl + params_file: output/reports/train/886aeccbbf920e34d90eb401749d3f2c/params.yaml + predictions_file: output/reports/train/886aeccbbf920e34d90eb401749d3f2c/predictions.json + probabilities_file: output/reports/train/886aeccbbf920e34d90eb401749d3f2c/probabilities.json + score_dict_file: output/reports/train/886aeccbbf920e34d90eb401749d3f2c/score_dict.json + test_labels_file: output/reports/train/886aeccbbf920e34d90eb401749d3f2c/test_labels.json + train_labels_file: output/reports/train/886aeccbbf920e34d90eb401749d3f2c/train_labels.json + model_dir: models + name: 886aeccbbf920e34d90eb401749d3f2c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 694435cfd539f3697b97d85c40686d23 + trainer: + kwargs: {} +name: 886aeccbbf920e34d90eb401749d3f2c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/params.yaml b/examples/security/truthseeker/output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/params.yaml new file mode 100644 index 00000000..5f84f191 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/adv_predictions.json + adv_probabilities_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/95c45dd019f4bb553d27761382f00bd7.pkl + params_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/params.yaml + predictions_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/predictions.json + probabilities_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/probabilities.json + score_dict_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/score_dict.json + test_labels_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/test_labels.json + train_labels_file: output/reports/train/889ed2f2bcd91e3219ecadc03eb2c80b/train_labels.json + model_dir: models + name: 889ed2f2bcd91e3219ecadc03eb2c80b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f9ffae8c6fa0d26df2df092569559a18 + trainer: + kwargs: {} +name: 889ed2f2bcd91e3219ecadc03eb2c80b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/894740d1fb58a95c47568baffb9c62a8/params.yaml b/examples/security/truthseeker/output/reports/train/894740d1fb58a95c47568baffb9c62a8/params.yaml new file mode 100644 index 00000000..3621f143 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/894740d1fb58a95c47568baffb9c62a8/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/894740d1fb58a95c47568baffb9c62a8/adv_predictions.json + adv_probabilities_file: output/reports/train/894740d1fb58a95c47568baffb9c62a8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3630f594f487326a6a28ec7f16e1d55c.pkl + params_file: output/reports/train/894740d1fb58a95c47568baffb9c62a8/params.yaml + predictions_file: output/reports/train/894740d1fb58a95c47568baffb9c62a8/predictions.json + probabilities_file: output/reports/train/894740d1fb58a95c47568baffb9c62a8/probabilities.json + score_dict_file: output/reports/train/894740d1fb58a95c47568baffb9c62a8/score_dict.json + test_labels_file: output/reports/train/894740d1fb58a95c47568baffb9c62a8/test_labels.json + train_labels_file: output/reports/train/894740d1fb58a95c47568baffb9c62a8/train_labels.json + model_dir: models + name: 894740d1fb58a95c47568baffb9c62a8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2d51c39b0185ce1378603ce78f741868 + trainer: + kwargs: {} +name: 894740d1fb58a95c47568baffb9c62a8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/895a70795ce683ecb2abaad3c84896a5/params.yaml b/examples/security/truthseeker/output/reports/train/895a70795ce683ecb2abaad3c84896a5/params.yaml new file mode 100644 index 00000000..8349f4e2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/895a70795ce683ecb2abaad3c84896a5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/895a70795ce683ecb2abaad3c84896a5/adv_predictions.json + adv_probabilities_file: output/reports/train/895a70795ce683ecb2abaad3c84896a5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/629f83e05321f4874ea2e81b47fa5788.pkl + params_file: output/reports/train/895a70795ce683ecb2abaad3c84896a5/params.yaml + predictions_file: output/reports/train/895a70795ce683ecb2abaad3c84896a5/predictions.json + probabilities_file: output/reports/train/895a70795ce683ecb2abaad3c84896a5/probabilities.json + score_dict_file: output/reports/train/895a70795ce683ecb2abaad3c84896a5/score_dict.json + test_labels_file: output/reports/train/895a70795ce683ecb2abaad3c84896a5/test_labels.json + train_labels_file: output/reports/train/895a70795ce683ecb2abaad3c84896a5/train_labels.json + model_dir: models + name: 895a70795ce683ecb2abaad3c84896a5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2d61f031dcbdd59934fd183be7e09c88 + trainer: + kwargs: {} +name: 895a70795ce683ecb2abaad3c84896a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/89de9539606329a8874fcd4ce683828a/params.yaml b/examples/security/truthseeker/output/reports/train/89de9539606329a8874fcd4ce683828a/params.yaml new file mode 100644 index 00000000..55e9e02d --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/89de9539606329a8874fcd4ce683828a/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/89de9539606329a8874fcd4ce683828a/adv_predictions.json + adv_probabilities_file: output/reports/train/89de9539606329a8874fcd4ce683828a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bab082410d16849aba08451d9b93e136.pkl + params_file: output/reports/train/89de9539606329a8874fcd4ce683828a/params.yaml + predictions_file: output/reports/train/89de9539606329a8874fcd4ce683828a/predictions.json + probabilities_file: output/reports/train/89de9539606329a8874fcd4ce683828a/probabilities.json + score_dict_file: output/reports/train/89de9539606329a8874fcd4ce683828a/score_dict.json + test_labels_file: output/reports/train/89de9539606329a8874fcd4ce683828a/test_labels.json + train_labels_file: output/reports/train/89de9539606329a8874fcd4ce683828a/train_labels.json + model_dir: models + name: 89de9539606329a8874fcd4ce683828a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6033d7ecae6038a6241254a869cd81ad + trainer: + kwargs: {} +name: 89de9539606329a8874fcd4ce683828a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/8a6903d24bc4431ef2b5805888a72802/params.yaml b/examples/security/truthseeker/output/reports/train/8a6903d24bc4431ef2b5805888a72802/params.yaml new file mode 100644 index 00000000..a3dfff79 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/8a6903d24bc4431ef2b5805888a72802/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/adv_predictions.json + adv_probabilities_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1574cbac3beba3f7526935a2147e0d3e.pkl + params_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/params.yaml + predictions_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/predictions.json + probabilities_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/probabilities.json + score_dict_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/score_dict.json + test_labels_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/test_labels.json + train_labels_file: output/reports/train/8a6903d24bc4431ef2b5805888a72802/train_labels.json + model_dir: models + name: 8a6903d24bc4431ef2b5805888a72802 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 500525a43140917eae46d6037625e53c + trainer: + kwargs: {} +name: 8a6903d24bc4431ef2b5805888a72802 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/params.yaml b/examples/security/truthseeker/output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/params.yaml new file mode 100644 index 00000000..3a527c69 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/adv_predictions.json + adv_probabilities_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/84fe807edaa7330e58091270908f8099.pkl + params_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/params.yaml + predictions_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/predictions.json + probabilities_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/probabilities.json + score_dict_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/score_dict.json + test_labels_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/test_labels.json + train_labels_file: output/reports/train/8bc3572e65778bb6ea8b519d7a91c476/train_labels.json + model_dir: models + name: 8bc3572e65778bb6ea8b519d7a91c476 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0d69263260f77291d38dbe8d8c97c145 + trainer: + kwargs: {} +name: 8bc3572e65778bb6ea8b519d7a91c476 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/params.yaml b/examples/security/truthseeker/output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/params.yaml new file mode 100644 index 00000000..af53a1c0 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/adv_predictions.json + adv_probabilities_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/49e8b6971ac0e87e8586d0e3471606f8.pkl + params_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/params.yaml + predictions_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/predictions.json + probabilities_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/probabilities.json + score_dict_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/score_dict.json + test_labels_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/test_labels.json + train_labels_file: output/reports/train/8bd609c97c47ce1ee390d76da1fe5b42/train_labels.json + model_dir: models + name: 8bd609c97c47ce1ee390d76da1fe5b42 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d062c309fba85516e6be5305905669e0 + trainer: + kwargs: {} +name: 8bd609c97c47ce1ee390d76da1fe5b42 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/8d248d4158337f94047c6f153fb2fc4a/params.yaml b/examples/security/truthseeker/output/reports/train/8d248d4158337f94047c6f153fb2fc4a/params.yaml new file mode 100644 index 00000000..349d1f8d --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/8d248d4158337f94047c6f153fb2fc4a/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8d248d4158337f94047c6f153fb2fc4a/adv_predictions.json + adv_probabilities_file: output/reports/train/8d248d4158337f94047c6f153fb2fc4a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3887bd2486676318b1fa9d6650ecb035.pkl + params_file: output/reports/train/8d248d4158337f94047c6f153fb2fc4a/params.yaml + predictions_file: output/reports/train/8d248d4158337f94047c6f153fb2fc4a/predictions.json + probabilities_file: output/reports/train/8d248d4158337f94047c6f153fb2fc4a/probabilities.json + score_dict_file: output/reports/train/8d248d4158337f94047c6f153fb2fc4a/score_dict.json + test_labels_file: output/reports/train/8d248d4158337f94047c6f153fb2fc4a/test_labels.json + train_labels_file: output/reports/train/8d248d4158337f94047c6f153fb2fc4a/train_labels.json + model_dir: models + name: 8d248d4158337f94047c6f153fb2fc4a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: dccb6cdc553fd3953663df22876de630 + trainer: + kwargs: {} +name: 8d248d4158337f94047c6f153fb2fc4a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/8fed9e14a84f2e7e8e8aa272181cbd60/params.yaml b/examples/security/truthseeker/output/reports/train/8fed9e14a84f2e7e8e8aa272181cbd60/params.yaml new file mode 100644 index 00000000..d77bac1a --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/8fed9e14a84f2e7e8e8aa272181cbd60/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/8fed9e14a84f2e7e8e8aa272181cbd60/adv_predictions.json + adv_probabilities_file: output/reports/train/8fed9e14a84f2e7e8e8aa272181cbd60/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/45dde22f4dc7cff68a4071fe07402b67.pkl + params_file: output/reports/train/8fed9e14a84f2e7e8e8aa272181cbd60/params.yaml + predictions_file: output/reports/train/8fed9e14a84f2e7e8e8aa272181cbd60/predictions.json + probabilities_file: output/reports/train/8fed9e14a84f2e7e8e8aa272181cbd60/probabilities.json + score_dict_file: output/reports/train/8fed9e14a84f2e7e8e8aa272181cbd60/score_dict.json + test_labels_file: output/reports/train/8fed9e14a84f2e7e8e8aa272181cbd60/test_labels.json + train_labels_file: output/reports/train/8fed9e14a84f2e7e8e8aa272181cbd60/train_labels.json + model_dir: models + name: 8fed9e14a84f2e7e8e8aa272181cbd60 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fb388692017b5f765f682185f8ee8223 + trainer: + kwargs: {} +name: 8fed9e14a84f2e7e8e8aa272181cbd60 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/905f8f492a02f653e3cb6616942d8b04/params.yaml b/examples/security/truthseeker/output/reports/train/905f8f492a02f653e3cb6616942d8b04/params.yaml new file mode 100644 index 00000000..10f3e691 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/905f8f492a02f653e3cb6616942d8b04/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/adv_predictions.json + adv_probabilities_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3c91cb775686e3281c2f0fd98d6a8dcd.pkl + params_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/params.yaml + predictions_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/predictions.json + probabilities_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/probabilities.json + score_dict_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/score_dict.json + test_labels_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/test_labels.json + train_labels_file: output/reports/train/905f8f492a02f653e3cb6616942d8b04/train_labels.json + model_dir: models + name: 905f8f492a02f653e3cb6616942d8b04 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1b9515ff1f9ee54122648f356726d99a + trainer: + kwargs: {} +name: 905f8f492a02f653e3cb6616942d8b04 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/91ab830bc8c897fd606ae1cec526ee84/params.yaml b/examples/security/truthseeker/output/reports/train/91ab830bc8c897fd606ae1cec526ee84/params.yaml new file mode 100644 index 00000000..8c534222 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/91ab830bc8c897fd606ae1cec526ee84/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/adv_predictions.json + adv_probabilities_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/aed1c02e033ee1e92829ff322b9753ff.pkl + params_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/params.yaml + predictions_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/predictions.json + probabilities_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/probabilities.json + score_dict_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/score_dict.json + test_labels_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/test_labels.json + train_labels_file: output/reports/train/91ab830bc8c897fd606ae1cec526ee84/train_labels.json + model_dir: models + name: 91ab830bc8c897fd606ae1cec526ee84 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9913b0d20830fbe35b016c25b249dd25 + trainer: + kwargs: {} +name: 91ab830bc8c897fd606ae1cec526ee84 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/params.yaml b/examples/security/truthseeker/output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/params.yaml new file mode 100644 index 00000000..72695d0d --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/adv_predictions.json + adv_probabilities_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c52308bdb7097625851509bde64ae9fe.pkl + params_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/params.yaml + predictions_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/predictions.json + probabilities_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/probabilities.json + score_dict_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/score_dict.json + test_labels_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/test_labels.json + train_labels_file: output/reports/train/9365d63e7bba7e6290257c8a201cb2d6/train_labels.json + model_dir: models + name: 9365d63e7bba7e6290257c8a201cb2d6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0f763e2f47024db878c0e59575bd7d70 + trainer: + kwargs: {} +name: 9365d63e7bba7e6290257c8a201cb2d6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/939f0efcca7f09392404bbe0294ad209/params.yaml b/examples/security/truthseeker/output/reports/train/939f0efcca7f09392404bbe0294ad209/params.yaml new file mode 100644 index 00000000..25d902a0 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/939f0efcca7f09392404bbe0294ad209/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/939f0efcca7f09392404bbe0294ad209/adv_predictions.json + adv_probabilities_file: output/reports/train/939f0efcca7f09392404bbe0294ad209/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/fe08c1ae902c5700dd4a17ab229cc08e.pkl + params_file: output/reports/train/939f0efcca7f09392404bbe0294ad209/params.yaml + predictions_file: output/reports/train/939f0efcca7f09392404bbe0294ad209/predictions.json + probabilities_file: output/reports/train/939f0efcca7f09392404bbe0294ad209/probabilities.json + score_dict_file: output/reports/train/939f0efcca7f09392404bbe0294ad209/score_dict.json + test_labels_file: output/reports/train/939f0efcca7f09392404bbe0294ad209/test_labels.json + train_labels_file: output/reports/train/939f0efcca7f09392404bbe0294ad209/train_labels.json + model_dir: models + name: 939f0efcca7f09392404bbe0294ad209 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 30686c973d292fabb3d64f0bba7b4dcd + trainer: + kwargs: {} +name: 939f0efcca7f09392404bbe0294ad209 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/94e912564db08e8922421ab25e529f95/params.yaml b/examples/security/truthseeker/output/reports/train/94e912564db08e8922421ab25e529f95/params.yaml new file mode 100644 index 00000000..e5f5e89a --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/94e912564db08e8922421ab25e529f95/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/94e912564db08e8922421ab25e529f95/adv_predictions.json + adv_probabilities_file: output/reports/train/94e912564db08e8922421ab25e529f95/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/8673dd6265b8743752d6eac29fbcb406.pkl + params_file: output/reports/train/94e912564db08e8922421ab25e529f95/params.yaml + predictions_file: output/reports/train/94e912564db08e8922421ab25e529f95/predictions.json + probabilities_file: output/reports/train/94e912564db08e8922421ab25e529f95/probabilities.json + score_dict_file: output/reports/train/94e912564db08e8922421ab25e529f95/score_dict.json + test_labels_file: output/reports/train/94e912564db08e8922421ab25e529f95/test_labels.json + train_labels_file: output/reports/train/94e912564db08e8922421ab25e529f95/train_labels.json + model_dir: models + name: 94e912564db08e8922421ab25e529f95 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 100 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f0cd99ec56a0ed883cd2f740eeee63a3 + trainer: + kwargs: {} +name: 94e912564db08e8922421ab25e529f95 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/9504efd91fbef222af5e9a3f70ee33a9/params.yaml b/examples/security/truthseeker/output/reports/train/9504efd91fbef222af5e9a3f70ee33a9/params.yaml new file mode 100644 index 00000000..20e35798 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/9504efd91fbef222af5e9a3f70ee33a9/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9504efd91fbef222af5e9a3f70ee33a9/adv_predictions.json + adv_probabilities_file: output/reports/train/9504efd91fbef222af5e9a3f70ee33a9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/4131481e91f75f1e11b5b79deb7defc2.pkl + params_file: output/reports/train/9504efd91fbef222af5e9a3f70ee33a9/params.yaml + predictions_file: output/reports/train/9504efd91fbef222af5e9a3f70ee33a9/predictions.json + probabilities_file: output/reports/train/9504efd91fbef222af5e9a3f70ee33a9/probabilities.json + score_dict_file: output/reports/train/9504efd91fbef222af5e9a3f70ee33a9/score_dict.json + test_labels_file: output/reports/train/9504efd91fbef222af5e9a3f70ee33a9/test_labels.json + train_labels_file: output/reports/train/9504efd91fbef222af5e9a3f70ee33a9/train_labels.json + model_dir: models + name: 9504efd91fbef222af5e9a3f70ee33a9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5fc7d443ffdac82b6ed6092183b62aeb + trainer: + kwargs: {} +name: 9504efd91fbef222af5e9a3f70ee33a9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/95e2df263fdfcf00feece4e43edaf291/params.yaml b/examples/security/truthseeker/output/reports/train/95e2df263fdfcf00feece4e43edaf291/params.yaml new file mode 100644 index 00000000..5ac95b02 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/95e2df263fdfcf00feece4e43edaf291/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/adv_predictions.json + adv_probabilities_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9860b91cbc79b48719fb2ab6b238cf1.pkl + params_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/params.yaml + predictions_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/predictions.json + probabilities_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/probabilities.json + score_dict_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/score_dict.json + test_labels_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/test_labels.json + train_labels_file: output/reports/train/95e2df263fdfcf00feece4e43edaf291/train_labels.json + model_dir: models + name: 95e2df263fdfcf00feece4e43edaf291 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5cfd37fc59648b545f9433cb143b9d52 + trainer: + kwargs: {} +name: 95e2df263fdfcf00feece4e43edaf291 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/params.yaml b/examples/security/truthseeker/output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/params.yaml new file mode 100644 index 00000000..bbf1af49 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/adv_predictions.json + adv_probabilities_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f638ff359c33db1e8354ead8e8c0c814.pkl + params_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/params.yaml + predictions_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/predictions.json + probabilities_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/probabilities.json + score_dict_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/score_dict.json + test_labels_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/test_labels.json + train_labels_file: output/reports/train/96c88f6adfa37ab6f3eb63042d1b3e73/train_labels.json + model_dir: models + name: 96c88f6adfa37ab6f3eb63042d1b3e73 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 293f50a97bbc1c38d991d7ccacc781d1 + trainer: + kwargs: {} +name: 96c88f6adfa37ab6f3eb63042d1b3e73 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/97cf2551f4fd1dc935c0d233557dd268/params.yaml b/examples/security/truthseeker/output/reports/train/97cf2551f4fd1dc935c0d233557dd268/params.yaml new file mode 100644 index 00000000..5a8fc6c7 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/97cf2551f4fd1dc935c0d233557dd268/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/97cf2551f4fd1dc935c0d233557dd268/adv_predictions.json + adv_probabilities_file: output/reports/train/97cf2551f4fd1dc935c0d233557dd268/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/8797cc43ab66b2fb274938200d4b71b6.pkl + params_file: output/reports/train/97cf2551f4fd1dc935c0d233557dd268/params.yaml + predictions_file: output/reports/train/97cf2551f4fd1dc935c0d233557dd268/predictions.json + probabilities_file: output/reports/train/97cf2551f4fd1dc935c0d233557dd268/probabilities.json + score_dict_file: output/reports/train/97cf2551f4fd1dc935c0d233557dd268/score_dict.json + test_labels_file: output/reports/train/97cf2551f4fd1dc935c0d233557dd268/test_labels.json + train_labels_file: output/reports/train/97cf2551f4fd1dc935c0d233557dd268/train_labels.json + model_dir: models + name: 97cf2551f4fd1dc935c0d233557dd268 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fba3359414134d06c3cde4c74bcbd4a8 + trainer: + kwargs: {} +name: 97cf2551f4fd1dc935c0d233557dd268 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/params.yaml b/examples/security/truthseeker/output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/params.yaml new file mode 100644 index 00000000..9cbe3869 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/adv_predictions.json + adv_probabilities_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1fdaf1c214cd6a782c215644097d3c69.pkl + params_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/params.yaml + predictions_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/predictions.json + probabilities_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/probabilities.json + score_dict_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/score_dict.json + test_labels_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/test_labels.json + train_labels_file: output/reports/train/9836579d277e189d5b9c8f4aa4a399a0/train_labels.json + model_dir: models + name: 9836579d277e189d5b9c8f4aa4a399a0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: edc33615699e2d68200161d9ed2c3684 + trainer: + kwargs: {} +name: 9836579d277e189d5b9c8f4aa4a399a0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/984b9e871e0a8d03aa6c354e3cfadd7c/params.yaml b/examples/security/truthseeker/output/reports/train/984b9e871e0a8d03aa6c354e3cfadd7c/params.yaml new file mode 100644 index 00000000..3c83062f --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/984b9e871e0a8d03aa6c354e3cfadd7c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/984b9e871e0a8d03aa6c354e3cfadd7c/adv_predictions.json + adv_probabilities_file: output/reports/train/984b9e871e0a8d03aa6c354e3cfadd7c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/601dbccca404cba6aeaef504e92fbf6f.pkl + params_file: output/reports/train/984b9e871e0a8d03aa6c354e3cfadd7c/params.yaml + predictions_file: output/reports/train/984b9e871e0a8d03aa6c354e3cfadd7c/predictions.json + probabilities_file: output/reports/train/984b9e871e0a8d03aa6c354e3cfadd7c/probabilities.json + score_dict_file: output/reports/train/984b9e871e0a8d03aa6c354e3cfadd7c/score_dict.json + test_labels_file: output/reports/train/984b9e871e0a8d03aa6c354e3cfadd7c/test_labels.json + train_labels_file: output/reports/train/984b9e871e0a8d03aa6c354e3cfadd7c/train_labels.json + model_dir: models + name: 984b9e871e0a8d03aa6c354e3cfadd7c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 27711f3fd4cb01d815dfac7bdcd013c3 + trainer: + kwargs: {} +name: 984b9e871e0a8d03aa6c354e3cfadd7c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/9899b7a1d732e2999e7729a0aa8a5905/params.yaml b/examples/security/truthseeker/output/reports/train/9899b7a1d732e2999e7729a0aa8a5905/params.yaml new file mode 100644 index 00000000..7b46f5a9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/9899b7a1d732e2999e7729a0aa8a5905/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9899b7a1d732e2999e7729a0aa8a5905/adv_predictions.json + adv_probabilities_file: output/reports/train/9899b7a1d732e2999e7729a0aa8a5905/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7d2a498d135b4da1aa53b4b20f9e4c3c.pkl + params_file: output/reports/train/9899b7a1d732e2999e7729a0aa8a5905/params.yaml + predictions_file: output/reports/train/9899b7a1d732e2999e7729a0aa8a5905/predictions.json + probabilities_file: output/reports/train/9899b7a1d732e2999e7729a0aa8a5905/probabilities.json + score_dict_file: output/reports/train/9899b7a1d732e2999e7729a0aa8a5905/score_dict.json + test_labels_file: output/reports/train/9899b7a1d732e2999e7729a0aa8a5905/test_labels.json + train_labels_file: output/reports/train/9899b7a1d732e2999e7729a0aa8a5905/train_labels.json + model_dir: models + name: 9899b7a1d732e2999e7729a0aa8a5905 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: fb144d76e62f62c20e2fb87dfb28e6f7 + trainer: + kwargs: {} +name: 9899b7a1d732e2999e7729a0aa8a5905 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/99a6d8a970e156953c1e9343870af288/params.yaml b/examples/security/truthseeker/output/reports/train/99a6d8a970e156953c1e9343870af288/params.yaml new file mode 100644 index 00000000..136307dd --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/99a6d8a970e156953c1e9343870af288/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/99a6d8a970e156953c1e9343870af288/adv_predictions.json + adv_probabilities_file: output/reports/train/99a6d8a970e156953c1e9343870af288/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9401397685bc42b7c7f9b7d7748e4ebd.pkl + params_file: output/reports/train/99a6d8a970e156953c1e9343870af288/params.yaml + predictions_file: output/reports/train/99a6d8a970e156953c1e9343870af288/predictions.json + probabilities_file: output/reports/train/99a6d8a970e156953c1e9343870af288/probabilities.json + score_dict_file: output/reports/train/99a6d8a970e156953c1e9343870af288/score_dict.json + test_labels_file: output/reports/train/99a6d8a970e156953c1e9343870af288/test_labels.json + train_labels_file: output/reports/train/99a6d8a970e156953c1e9343870af288/train_labels.json + model_dir: models + name: 99a6d8a970e156953c1e9343870af288 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: db9a0d3d7e35ab226ce51f4b137bcf14 + trainer: + kwargs: {} +name: 99a6d8a970e156953c1e9343870af288 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/9be10eea1dca9d3ed3b674a2c52e6d0a/params.yaml b/examples/security/truthseeker/output/reports/train/9be10eea1dca9d3ed3b674a2c52e6d0a/params.yaml new file mode 100644 index 00000000..18df8237 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/9be10eea1dca9d3ed3b674a2c52e6d0a/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9be10eea1dca9d3ed3b674a2c52e6d0a/adv_predictions.json + adv_probabilities_file: output/reports/train/9be10eea1dca9d3ed3b674a2c52e6d0a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/96b1eef9db649ace3c9e0ffa22a430f7.pkl + params_file: output/reports/train/9be10eea1dca9d3ed3b674a2c52e6d0a/params.yaml + predictions_file: output/reports/train/9be10eea1dca9d3ed3b674a2c52e6d0a/predictions.json + probabilities_file: output/reports/train/9be10eea1dca9d3ed3b674a2c52e6d0a/probabilities.json + score_dict_file: output/reports/train/9be10eea1dca9d3ed3b674a2c52e6d0a/score_dict.json + test_labels_file: output/reports/train/9be10eea1dca9d3ed3b674a2c52e6d0a/test_labels.json + train_labels_file: output/reports/train/9be10eea1dca9d3ed3b674a2c52e6d0a/train_labels.json + model_dir: models + name: 9be10eea1dca9d3ed3b674a2c52e6d0a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f3bdeecc1e31df9a1de281afb82f442e + trainer: + kwargs: {} +name: 9be10eea1dca9d3ed3b674a2c52e6d0a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/9c2bd0aa6eff458cb9db017a47cb58a4/params.yaml b/examples/security/truthseeker/output/reports/train/9c2bd0aa6eff458cb9db017a47cb58a4/params.yaml new file mode 100644 index 00000000..4c11704c --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/9c2bd0aa6eff458cb9db017a47cb58a4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9c2bd0aa6eff458cb9db017a47cb58a4/adv_predictions.json + adv_probabilities_file: output/reports/train/9c2bd0aa6eff458cb9db017a47cb58a4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/eb25999ad1475a3848258ff0de935db7.pkl + params_file: output/reports/train/9c2bd0aa6eff458cb9db017a47cb58a4/params.yaml + predictions_file: output/reports/train/9c2bd0aa6eff458cb9db017a47cb58a4/predictions.json + probabilities_file: output/reports/train/9c2bd0aa6eff458cb9db017a47cb58a4/probabilities.json + score_dict_file: output/reports/train/9c2bd0aa6eff458cb9db017a47cb58a4/score_dict.json + test_labels_file: output/reports/train/9c2bd0aa6eff458cb9db017a47cb58a4/test_labels.json + train_labels_file: output/reports/train/9c2bd0aa6eff458cb9db017a47cb58a4/train_labels.json + model_dir: models + name: 9c2bd0aa6eff458cb9db017a47cb58a4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c98b85c3d07073b7b3782d3156045e4f + trainer: + kwargs: {} +name: 9c2bd0aa6eff458cb9db017a47cb58a4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/params.yaml b/examples/security/truthseeker/output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/params.yaml new file mode 100644 index 00000000..d5194ca9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/adv_predictions.json + adv_probabilities_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5dd86492a6cbbf2a7e14bada9289ec49.pkl + params_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/params.yaml + predictions_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/predictions.json + probabilities_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/probabilities.json + score_dict_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/score_dict.json + test_labels_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/test_labels.json + train_labels_file: output/reports/train/9c7e9a28ab9e24a1c2159d743f7d7908/train_labels.json + model_dir: models + name: 9c7e9a28ab9e24a1c2159d743f7d7908 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 537c7565c6f401b821b243ff38ec023a + trainer: + kwargs: {} +name: 9c7e9a28ab9e24a1c2159d743f7d7908 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/9d7077d23dc6e8b109f3885671431be8/params.yaml b/examples/security/truthseeker/output/reports/train/9d7077d23dc6e8b109f3885671431be8/params.yaml new file mode 100644 index 00000000..f7c897c0 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/9d7077d23dc6e8b109f3885671431be8/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9d7077d23dc6e8b109f3885671431be8/adv_predictions.json + adv_probabilities_file: output/reports/train/9d7077d23dc6e8b109f3885671431be8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/8c596e66e1d2a61060d11afcdd7468db.pkl + params_file: output/reports/train/9d7077d23dc6e8b109f3885671431be8/params.yaml + predictions_file: output/reports/train/9d7077d23dc6e8b109f3885671431be8/predictions.json + probabilities_file: output/reports/train/9d7077d23dc6e8b109f3885671431be8/probabilities.json + score_dict_file: output/reports/train/9d7077d23dc6e8b109f3885671431be8/score_dict.json + test_labels_file: output/reports/train/9d7077d23dc6e8b109f3885671431be8/test_labels.json + train_labels_file: output/reports/train/9d7077d23dc6e8b109f3885671431be8/train_labels.json + model_dir: models + name: 9d7077d23dc6e8b109f3885671431be8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 777cc4f3a8b24e6a66971b4cc98e7479 + trainer: + kwargs: {} +name: 9d7077d23dc6e8b109f3885671431be8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/params.yaml b/examples/security/truthseeker/output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/params.yaml new file mode 100644 index 00000000..9fc46b34 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/adv_predictions.json + adv_probabilities_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/394ad8e1ae1b6d55421de3a49138d43f.pkl + params_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/params.yaml + predictions_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/predictions.json + probabilities_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/probabilities.json + score_dict_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/score_dict.json + test_labels_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/test_labels.json + train_labels_file: output/reports/train/9dc1caf993f2f97edaae2f893a3c8984/train_labels.json + model_dir: models + name: 9dc1caf993f2f97edaae2f893a3c8984 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 72c794cd2d57355205b8ff7c9668bf2d + trainer: + kwargs: {} +name: 9dc1caf993f2f97edaae2f893a3c8984 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/9f36dea9f543144d55c096487c6df3c5/params.yaml b/examples/security/truthseeker/output/reports/train/9f36dea9f543144d55c096487c6df3c5/params.yaml new file mode 100644 index 00000000..73d70763 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/9f36dea9f543144d55c096487c6df3c5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/adv_predictions.json + adv_probabilities_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/46028503695aab4300983ca3db084769.pkl + params_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/params.yaml + predictions_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/predictions.json + probabilities_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/probabilities.json + score_dict_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/score_dict.json + test_labels_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/test_labels.json + train_labels_file: output/reports/train/9f36dea9f543144d55c096487c6df3c5/train_labels.json + model_dir: models + name: 9f36dea9f543144d55c096487c6df3c5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6021281724e72d73f3988d9e814f08f8 + trainer: + kwargs: {} +name: 9f36dea9f543144d55c096487c6df3c5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/9f6254697c52aa6b7861db36551ef5fb/params.yaml b/examples/security/truthseeker/output/reports/train/9f6254697c52aa6b7861db36551ef5fb/params.yaml new file mode 100644 index 00000000..9257b6e4 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/9f6254697c52aa6b7861db36551ef5fb/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/adv_predictions.json + adv_probabilities_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7778eaac0301fc8206593f78a9e659a2.pkl + params_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/params.yaml + predictions_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/predictions.json + probabilities_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/probabilities.json + score_dict_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/score_dict.json + test_labels_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/test_labels.json + train_labels_file: output/reports/train/9f6254697c52aa6b7861db36551ef5fb/train_labels.json + model_dir: models + name: 9f6254697c52aa6b7861db36551ef5fb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b1ac88da5ee0f374a5179995ff2cadee + trainer: + kwargs: {} +name: 9f6254697c52aa6b7861db36551ef5fb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/params.yaml b/examples/security/truthseeker/output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/params.yaml new file mode 100644 index 00000000..619aed14 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/adv_predictions.json + adv_probabilities_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/36eecdd1163862cc72097a011a0bb54f.pkl + params_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/params.yaml + predictions_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/predictions.json + probabilities_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/probabilities.json + score_dict_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/score_dict.json + test_labels_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/test_labels.json + train_labels_file: output/reports/train/a007a4b353dc9bbadd10aafd8f9d687d/train_labels.json + model_dir: models + name: a007a4b353dc9bbadd10aafd8f9d687d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b492bf10d680f6d06927a6df4ca0f171 + trainer: + kwargs: {} +name: a007a4b353dc9bbadd10aafd8f9d687d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/params.yaml b/examples/security/truthseeker/output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/params.yaml new file mode 100644 index 00000000..fe53fdd1 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/adv_predictions.json + adv_probabilities_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/10dfc126a600c8994aed911c14e48db9.pkl + params_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/params.yaml + predictions_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/predictions.json + probabilities_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/probabilities.json + score_dict_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/score_dict.json + test_labels_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/test_labels.json + train_labels_file: output/reports/train/a1fb169f96a8444a23cdd39a80ee6d47/train_labels.json + model_dir: models + name: a1fb169f96a8444a23cdd39a80ee6d47 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7076851e723c3a6f54ad275b429ae9e0 + trainer: + kwargs: {} +name: a1fb169f96a8444a23cdd39a80ee6d47 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/a4273c83bed31a588ee42160649df2d9/params.yaml b/examples/security/truthseeker/output/reports/train/a4273c83bed31a588ee42160649df2d9/params.yaml new file mode 100644 index 00000000..9e8a0f6d --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/a4273c83bed31a588ee42160649df2d9/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a4273c83bed31a588ee42160649df2d9/adv_predictions.json + adv_probabilities_file: output/reports/train/a4273c83bed31a588ee42160649df2d9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bff8cb7011034f27f0bef3ce89994a78.pkl + params_file: output/reports/train/a4273c83bed31a588ee42160649df2d9/params.yaml + predictions_file: output/reports/train/a4273c83bed31a588ee42160649df2d9/predictions.json + probabilities_file: output/reports/train/a4273c83bed31a588ee42160649df2d9/probabilities.json + score_dict_file: output/reports/train/a4273c83bed31a588ee42160649df2d9/score_dict.json + test_labels_file: output/reports/train/a4273c83bed31a588ee42160649df2d9/test_labels.json + train_labels_file: output/reports/train/a4273c83bed31a588ee42160649df2d9/train_labels.json + model_dir: models + name: a4273c83bed31a588ee42160649df2d9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2aab8faab3644cd7a55cf62370ef29e6 + trainer: + kwargs: {} +name: a4273c83bed31a588ee42160649df2d9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/a452f7c99f542bd960e246aae13fb85e/params.yaml b/examples/security/truthseeker/output/reports/train/a452f7c99f542bd960e246aae13fb85e/params.yaml new file mode 100644 index 00000000..d20b8c7d --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/a452f7c99f542bd960e246aae13fb85e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a452f7c99f542bd960e246aae13fb85e/adv_predictions.json + adv_probabilities_file: output/reports/train/a452f7c99f542bd960e246aae13fb85e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/04d944bbdf24af132508b5ca71b14d7a.pkl + params_file: output/reports/train/a452f7c99f542bd960e246aae13fb85e/params.yaml + predictions_file: output/reports/train/a452f7c99f542bd960e246aae13fb85e/predictions.json + probabilities_file: output/reports/train/a452f7c99f542bd960e246aae13fb85e/probabilities.json + score_dict_file: output/reports/train/a452f7c99f542bd960e246aae13fb85e/score_dict.json + test_labels_file: output/reports/train/a452f7c99f542bd960e246aae13fb85e/test_labels.json + train_labels_file: output/reports/train/a452f7c99f542bd960e246aae13fb85e/train_labels.json + model_dir: models + name: a452f7c99f542bd960e246aae13fb85e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a5632742af7ba95d554951225e6b1915 + trainer: + kwargs: {} +name: a452f7c99f542bd960e246aae13fb85e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/params.yaml b/examples/security/truthseeker/output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/params.yaml new file mode 100644 index 00000000..95956a6a --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/adv_predictions.json + adv_probabilities_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/6612d4c2407cca3c3e51abbe04890492.pkl + params_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/params.yaml + predictions_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/predictions.json + probabilities_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/probabilities.json + score_dict_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/score_dict.json + test_labels_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/test_labels.json + train_labels_file: output/reports/train/a460f6be5c0a6a2754e9e251bc569ed9/train_labels.json + model_dir: models + name: a460f6be5c0a6a2754e9e251bc569ed9 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3d819e8fd68cd4cbb78507ca5dc981d2 + trainer: + kwargs: {} +name: a460f6be5c0a6a2754e9e251bc569ed9 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/a532b7776cd833c0f3998b72c9e91271/params.yaml b/examples/security/truthseeker/output/reports/train/a532b7776cd833c0f3998b72c9e91271/params.yaml new file mode 100644 index 00000000..df8ef4f4 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/a532b7776cd833c0f3998b72c9e91271/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/adv_predictions.json + adv_probabilities_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5b24c80b5d8f8501afcdbeb1aa749818.pkl + params_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/params.yaml + predictions_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/predictions.json + probabilities_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/probabilities.json + score_dict_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/score_dict.json + test_labels_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/test_labels.json + train_labels_file: output/reports/train/a532b7776cd833c0f3998b72c9e91271/train_labels.json + model_dir: models + name: a532b7776cd833c0f3998b72c9e91271 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: debd35766f31aeea8351900c315a874b + trainer: + kwargs: {} +name: a532b7776cd833c0f3998b72c9e91271 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/a5a243acd41213e404b822e4c1258340/params.yaml b/examples/security/truthseeker/output/reports/train/a5a243acd41213e404b822e4c1258340/params.yaml new file mode 100644 index 00000000..167fe970 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/a5a243acd41213e404b822e4c1258340/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a5a243acd41213e404b822e4c1258340/adv_predictions.json + adv_probabilities_file: output/reports/train/a5a243acd41213e404b822e4c1258340/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/622869a0696f375552778d3877f1726a.pkl + params_file: output/reports/train/a5a243acd41213e404b822e4c1258340/params.yaml + predictions_file: output/reports/train/a5a243acd41213e404b822e4c1258340/predictions.json + probabilities_file: output/reports/train/a5a243acd41213e404b822e4c1258340/probabilities.json + score_dict_file: output/reports/train/a5a243acd41213e404b822e4c1258340/score_dict.json + test_labels_file: output/reports/train/a5a243acd41213e404b822e4c1258340/test_labels.json + train_labels_file: output/reports/train/a5a243acd41213e404b822e4c1258340/train_labels.json + model_dir: models + name: a5a243acd41213e404b822e4c1258340 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1b17b167b30bad032fa0a10e73d82d4c + trainer: + kwargs: {} +name: a5a243acd41213e404b822e4c1258340 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/a5af84388fe0471ed25f3443d60957e6/params.yaml b/examples/security/truthseeker/output/reports/train/a5af84388fe0471ed25f3443d60957e6/params.yaml new file mode 100644 index 00000000..fd6cf08a --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/a5af84388fe0471ed25f3443d60957e6/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a5af84388fe0471ed25f3443d60957e6/adv_predictions.json + adv_probabilities_file: output/reports/train/a5af84388fe0471ed25f3443d60957e6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e9f689537321062cfa343f4fdb4f169b.pkl + params_file: output/reports/train/a5af84388fe0471ed25f3443d60957e6/params.yaml + predictions_file: output/reports/train/a5af84388fe0471ed25f3443d60957e6/predictions.json + probabilities_file: output/reports/train/a5af84388fe0471ed25f3443d60957e6/probabilities.json + score_dict_file: output/reports/train/a5af84388fe0471ed25f3443d60957e6/score_dict.json + test_labels_file: output/reports/train/a5af84388fe0471ed25f3443d60957e6/test_labels.json + train_labels_file: output/reports/train/a5af84388fe0471ed25f3443d60957e6/train_labels.json + model_dir: models + name: a5af84388fe0471ed25f3443d60957e6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d794c4d7dfdebb22a9dca19939e35a49 + trainer: + kwargs: {} +name: a5af84388fe0471ed25f3443d60957e6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/a5afbec4c71f27b6cb0d8e4f20a77b4a/params.yaml b/examples/security/truthseeker/output/reports/train/a5afbec4c71f27b6cb0d8e4f20a77b4a/params.yaml new file mode 100644 index 00000000..e6f0308b --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/a5afbec4c71f27b6cb0d8e4f20a77b4a/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a5afbec4c71f27b6cb0d8e4f20a77b4a/adv_predictions.json + adv_probabilities_file: output/reports/train/a5afbec4c71f27b6cb0d8e4f20a77b4a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/09bc4e20726efbaa3a2c9ca68c92d4ed.pkl + params_file: output/reports/train/a5afbec4c71f27b6cb0d8e4f20a77b4a/params.yaml + predictions_file: output/reports/train/a5afbec4c71f27b6cb0d8e4f20a77b4a/predictions.json + probabilities_file: output/reports/train/a5afbec4c71f27b6cb0d8e4f20a77b4a/probabilities.json + score_dict_file: output/reports/train/a5afbec4c71f27b6cb0d8e4f20a77b4a/score_dict.json + test_labels_file: output/reports/train/a5afbec4c71f27b6cb0d8e4f20a77b4a/test_labels.json + train_labels_file: output/reports/train/a5afbec4c71f27b6cb0d8e4f20a77b4a/train_labels.json + model_dir: models + name: a5afbec4c71f27b6cb0d8e4f20a77b4a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 75b308d6e1b752fa08fba813346d0381 + trainer: + kwargs: {} +name: a5afbec4c71f27b6cb0d8e4f20a77b4a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/params.yaml b/examples/security/truthseeker/output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/params.yaml new file mode 100644 index 00000000..98240115 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/adv_predictions.json + adv_probabilities_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/cf1f628b162783c52830fb1eaae0a2be.pkl + params_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/params.yaml + predictions_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/predictions.json + probabilities_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/probabilities.json + score_dict_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/score_dict.json + test_labels_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/test_labels.json + train_labels_file: output/reports/train/a61f42f974129fdfeb8fa2f97a4368a8/train_labels.json + model_dir: models + name: a61f42f974129fdfeb8fa2f97a4368a8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 826c8ac76156b0219e52fd85a659f3c2 + trainer: + kwargs: {} +name: a61f42f974129fdfeb8fa2f97a4368a8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/a6b08025bf8d01141601f3a636161bd8/params.yaml b/examples/security/truthseeker/output/reports/train/a6b08025bf8d01141601f3a636161bd8/params.yaml new file mode 100644 index 00000000..0562fd50 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/a6b08025bf8d01141601f3a636161bd8/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a6b08025bf8d01141601f3a636161bd8/adv_predictions.json + adv_probabilities_file: output/reports/train/a6b08025bf8d01141601f3a636161bd8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0fb1e77d057c35de5dbb569a3320e0d5.pkl + params_file: output/reports/train/a6b08025bf8d01141601f3a636161bd8/params.yaml + predictions_file: output/reports/train/a6b08025bf8d01141601f3a636161bd8/predictions.json + probabilities_file: output/reports/train/a6b08025bf8d01141601f3a636161bd8/probabilities.json + score_dict_file: output/reports/train/a6b08025bf8d01141601f3a636161bd8/score_dict.json + test_labels_file: output/reports/train/a6b08025bf8d01141601f3a636161bd8/test_labels.json + train_labels_file: output/reports/train/a6b08025bf8d01141601f3a636161bd8/train_labels.json + model_dir: models + name: a6b08025bf8d01141601f3a636161bd8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 41f96e4f4b9a7d6a83dd34646179af6a + trainer: + kwargs: {} +name: a6b08025bf8d01141601f3a636161bd8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/a7320d911ecff8fc080839dc5b3aad26/params.yaml b/examples/security/truthseeker/output/reports/train/a7320d911ecff8fc080839dc5b3aad26/params.yaml new file mode 100644 index 00000000..d1586eb4 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/a7320d911ecff8fc080839dc5b3aad26/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/adv_predictions.json + adv_probabilities_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/7b60136ba6a4dc09e998c342f162fc9c.pkl + params_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/params.yaml + predictions_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/predictions.json + probabilities_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/probabilities.json + score_dict_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/score_dict.json + test_labels_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/test_labels.json + train_labels_file: output/reports/train/a7320d911ecff8fc080839dc5b3aad26/train_labels.json + model_dir: models + name: a7320d911ecff8fc080839dc5b3aad26 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3af2177580d586c1759ab626a4fbf5f6 + trainer: + kwargs: {} +name: a7320d911ecff8fc080839dc5b3aad26 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/params.yaml b/examples/security/truthseeker/output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/params.yaml new file mode 100644 index 00000000..0327c2a6 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/adv_predictions.json + adv_probabilities_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3fe69eae40c98998e036a6fd2f5fd577.pkl + params_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/params.yaml + predictions_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/predictions.json + probabilities_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/probabilities.json + score_dict_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/score_dict.json + test_labels_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/test_labels.json + train_labels_file: output/reports/train/a94ba93fdd86d30246a2a372c73b50bd/train_labels.json + model_dir: models + name: a94ba93fdd86d30246a2a372c73b50bd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cef5a66166a703b9c01e61722b497763 + trainer: + kwargs: {} +name: a94ba93fdd86d30246a2a372c73b50bd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ab644d466da7bb5264bb230c9323827d/params.yaml b/examples/security/truthseeker/output/reports/train/ab644d466da7bb5264bb230c9323827d/params.yaml new file mode 100644 index 00000000..5a5945d8 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ab644d466da7bb5264bb230c9323827d/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/adv_predictions.json + adv_probabilities_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/37783d2e2cfdb1dad06abb06462574af.pkl + params_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/params.yaml + predictions_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/predictions.json + probabilities_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/probabilities.json + score_dict_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/score_dict.json + test_labels_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/test_labels.json + train_labels_file: output/reports/train/ab644d466da7bb5264bb230c9323827d/train_labels.json + model_dir: models + name: ab644d466da7bb5264bb230c9323827d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9858ad55b247dc1840750194b1beeb10 + trainer: + kwargs: {} +name: ab644d466da7bb5264bb230c9323827d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ac4121da7e69fce23340a0ea84ad2cb3/params.yaml b/examples/security/truthseeker/output/reports/train/ac4121da7e69fce23340a0ea84ad2cb3/params.yaml new file mode 100644 index 00000000..551e6aee --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ac4121da7e69fce23340a0ea84ad2cb3/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ac4121da7e69fce23340a0ea84ad2cb3/adv_predictions.json + adv_probabilities_file: output/reports/train/ac4121da7e69fce23340a0ea84ad2cb3/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e1799ef9fa41eb330f2343f79b47874f.pkl + params_file: output/reports/train/ac4121da7e69fce23340a0ea84ad2cb3/params.yaml + predictions_file: output/reports/train/ac4121da7e69fce23340a0ea84ad2cb3/predictions.json + probabilities_file: output/reports/train/ac4121da7e69fce23340a0ea84ad2cb3/probabilities.json + score_dict_file: output/reports/train/ac4121da7e69fce23340a0ea84ad2cb3/score_dict.json + test_labels_file: output/reports/train/ac4121da7e69fce23340a0ea84ad2cb3/test_labels.json + train_labels_file: output/reports/train/ac4121da7e69fce23340a0ea84ad2cb3/train_labels.json + model_dir: models + name: ac4121da7e69fce23340a0ea84ad2cb3 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 080e48a9f361e7dcd2f77cf5372c7463 + trainer: + kwargs: {} +name: ac4121da7e69fce23340a0ea84ad2cb3 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/acaae2f083bb2605dbe7adb1b3595437/params.yaml b/examples/security/truthseeker/output/reports/train/acaae2f083bb2605dbe7adb1b3595437/params.yaml new file mode 100644 index 00000000..b09265b1 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/acaae2f083bb2605dbe7adb1b3595437/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/acaae2f083bb2605dbe7adb1b3595437/adv_predictions.json + adv_probabilities_file: output/reports/train/acaae2f083bb2605dbe7adb1b3595437/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/589775dce1fc38177ad72264fd45f3d0.pkl + params_file: output/reports/train/acaae2f083bb2605dbe7adb1b3595437/params.yaml + predictions_file: output/reports/train/acaae2f083bb2605dbe7adb1b3595437/predictions.json + probabilities_file: output/reports/train/acaae2f083bb2605dbe7adb1b3595437/probabilities.json + score_dict_file: output/reports/train/acaae2f083bb2605dbe7adb1b3595437/score_dict.json + test_labels_file: output/reports/train/acaae2f083bb2605dbe7adb1b3595437/test_labels.json + train_labels_file: output/reports/train/acaae2f083bb2605dbe7adb1b3595437/train_labels.json + model_dir: models + name: acaae2f083bb2605dbe7adb1b3595437 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c5ed432791c831896a8e0209af56c4d6 + trainer: + kwargs: {} +name: acaae2f083bb2605dbe7adb1b3595437 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ae84977dfba189ac6aaddeab03f0950d/params.yaml b/examples/security/truthseeker/output/reports/train/ae84977dfba189ac6aaddeab03f0950d/params.yaml new file mode 100644 index 00000000..cb99a640 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ae84977dfba189ac6aaddeab03f0950d/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ae84977dfba189ac6aaddeab03f0950d/adv_predictions.json + adv_probabilities_file: output/reports/train/ae84977dfba189ac6aaddeab03f0950d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f24752a1cc00c553312ef7ab75c0c96f.pkl + params_file: output/reports/train/ae84977dfba189ac6aaddeab03f0950d/params.yaml + predictions_file: output/reports/train/ae84977dfba189ac6aaddeab03f0950d/predictions.json + probabilities_file: output/reports/train/ae84977dfba189ac6aaddeab03f0950d/probabilities.json + score_dict_file: output/reports/train/ae84977dfba189ac6aaddeab03f0950d/score_dict.json + test_labels_file: output/reports/train/ae84977dfba189ac6aaddeab03f0950d/test_labels.json + train_labels_file: output/reports/train/ae84977dfba189ac6aaddeab03f0950d/train_labels.json + model_dir: models + name: ae84977dfba189ac6aaddeab03f0950d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9f48740672e890755eef25c071fe3dc1 + trainer: + kwargs: {} +name: ae84977dfba189ac6aaddeab03f0950d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/aee36ea7bf0e038d5e28b2a7cfc2b66f/params.yaml b/examples/security/truthseeker/output/reports/train/aee36ea7bf0e038d5e28b2a7cfc2b66f/params.yaml new file mode 100644 index 00000000..8324aa36 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/aee36ea7bf0e038d5e28b2a7cfc2b66f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/aee36ea7bf0e038d5e28b2a7cfc2b66f/adv_predictions.json + adv_probabilities_file: output/reports/train/aee36ea7bf0e038d5e28b2a7cfc2b66f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/75ad03bcf283f8ca74f02b9c9471d92c.pkl + params_file: output/reports/train/aee36ea7bf0e038d5e28b2a7cfc2b66f/params.yaml + predictions_file: output/reports/train/aee36ea7bf0e038d5e28b2a7cfc2b66f/predictions.json + probabilities_file: output/reports/train/aee36ea7bf0e038d5e28b2a7cfc2b66f/probabilities.json + score_dict_file: output/reports/train/aee36ea7bf0e038d5e28b2a7cfc2b66f/score_dict.json + test_labels_file: output/reports/train/aee36ea7bf0e038d5e28b2a7cfc2b66f/test_labels.json + train_labels_file: output/reports/train/aee36ea7bf0e038d5e28b2a7cfc2b66f/train_labels.json + model_dir: models + name: aee36ea7bf0e038d5e28b2a7cfc2b66f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2ce446453a06697907275f9311d36688 + trainer: + kwargs: {} +name: aee36ea7bf0e038d5e28b2a7cfc2b66f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/params.yaml b/examples/security/truthseeker/output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/params.yaml new file mode 100644 index 00000000..f80c84c7 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/adv_predictions.json + adv_probabilities_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1748f23f29b38c3c86027d0da919446a.pkl + params_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/params.yaml + predictions_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/predictions.json + probabilities_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/probabilities.json + score_dict_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/score_dict.json + test_labels_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/test_labels.json + train_labels_file: output/reports/train/b26aa7d4768207144c3dbdc5f2f68674/train_labels.json + model_dir: models + name: b26aa7d4768207144c3dbdc5f2f68674 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 31ac0c5cdaf73e02f236a9260a955b2f + trainer: + kwargs: {} +name: b26aa7d4768207144c3dbdc5f2f68674 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/params.yaml b/examples/security/truthseeker/output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/params.yaml new file mode 100644 index 00000000..b28e5318 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/adv_predictions.json + adv_probabilities_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f97fc57baa6fa4c68e532627416d06fe.pkl + params_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/params.yaml + predictions_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/predictions.json + probabilities_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/probabilities.json + score_dict_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/score_dict.json + test_labels_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/test_labels.json + train_labels_file: output/reports/train/b3c29be5f02d2e88e5e87518b4bb9ab6/train_labels.json + model_dir: models + name: b3c29be5f02d2e88e5e87518b4bb9ab6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9ad66332874116a834e214201816c382 + trainer: + kwargs: {} +name: b3c29be5f02d2e88e5e87518b4bb9ab6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/params.yaml b/examples/security/truthseeker/output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/params.yaml new file mode 100644 index 00000000..1fe54aba --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/adv_predictions.json + adv_probabilities_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/052941eab1c68be79a5161d61c2ad8bb.pkl + params_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/params.yaml + predictions_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/predictions.json + probabilities_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/probabilities.json + score_dict_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/score_dict.json + test_labels_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/test_labels.json + train_labels_file: output/reports/train/b456f1e1b350ae5b3c3153a1dc39c6e6/train_labels.json + model_dir: models + name: b456f1e1b350ae5b3c3153a1dc39c6e6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5a1ebf217bf39bb73441f14b0c723a9c + trainer: + kwargs: {} +name: b456f1e1b350ae5b3c3153a1dc39c6e6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/params.yaml b/examples/security/truthseeker/output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/params.yaml new file mode 100644 index 00000000..d124c26b --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/adv_predictions.json + adv_probabilities_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2de4130231bc00bdfdf9deff95044623.pkl + params_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/params.yaml + predictions_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/predictions.json + probabilities_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/probabilities.json + score_dict_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/score_dict.json + test_labels_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/test_labels.json + train_labels_file: output/reports/train/b5ffc5cfd4ad1ba608e1401d7cbb422a/train_labels.json + model_dir: models + name: b5ffc5cfd4ad1ba608e1401d7cbb422a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0ef97d34cec7b3fcd70973c36a51e9ff + trainer: + kwargs: {} +name: b5ffc5cfd4ad1ba608e1401d7cbb422a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/b67bde2439f5b0f04f839c05b45db561/params.yaml b/examples/security/truthseeker/output/reports/train/b67bde2439f5b0f04f839c05b45db561/params.yaml new file mode 100644 index 00000000..36c81212 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/b67bde2439f5b0f04f839c05b45db561/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/adv_predictions.json + adv_probabilities_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f053cb7f67a015c66fd57339405921e6.pkl + params_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/params.yaml + predictions_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/predictions.json + probabilities_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/probabilities.json + score_dict_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/score_dict.json + test_labels_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/test_labels.json + train_labels_file: output/reports/train/b67bde2439f5b0f04f839c05b45db561/train_labels.json + model_dir: models + name: b67bde2439f5b0f04f839c05b45db561 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9256873a9a17b532dfddf66dde9e6dd2 + trainer: + kwargs: {} +name: b67bde2439f5b0f04f839c05b45db561 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/b6eb721a4a67544442afd1bb186b45b5/params.yaml b/examples/security/truthseeker/output/reports/train/b6eb721a4a67544442afd1bb186b45b5/params.yaml new file mode 100644 index 00000000..73c5ba75 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/b6eb721a4a67544442afd1bb186b45b5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/adv_predictions.json + adv_probabilities_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2ef58e3ba8f059afa8d16e4f91782a14.pkl + params_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/params.yaml + predictions_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/predictions.json + probabilities_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/probabilities.json + score_dict_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/score_dict.json + test_labels_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/test_labels.json + train_labels_file: output/reports/train/b6eb721a4a67544442afd1bb186b45b5/train_labels.json + model_dir: models + name: b6eb721a4a67544442afd1bb186b45b5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 96d8331d3acfeaa958f1646fe649c4c9 + trainer: + kwargs: {} +name: b6eb721a4a67544442afd1bb186b45b5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/b709b9d6956b2f92a8296786e78d13ba/params.yaml b/examples/security/truthseeker/output/reports/train/b709b9d6956b2f92a8296786e78d13ba/params.yaml new file mode 100644 index 00000000..d21335ba --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/b709b9d6956b2f92a8296786e78d13ba/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b709b9d6956b2f92a8296786e78d13ba/adv_predictions.json + adv_probabilities_file: output/reports/train/b709b9d6956b2f92a8296786e78d13ba/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f45f8e8ad230195a19f83c86c3015644.pkl + params_file: output/reports/train/b709b9d6956b2f92a8296786e78d13ba/params.yaml + predictions_file: output/reports/train/b709b9d6956b2f92a8296786e78d13ba/predictions.json + probabilities_file: output/reports/train/b709b9d6956b2f92a8296786e78d13ba/probabilities.json + score_dict_file: output/reports/train/b709b9d6956b2f92a8296786e78d13ba/score_dict.json + test_labels_file: output/reports/train/b709b9d6956b2f92a8296786e78d13ba/test_labels.json + train_labels_file: output/reports/train/b709b9d6956b2f92a8296786e78d13ba/train_labels.json + model_dir: models + name: b709b9d6956b2f92a8296786e78d13ba + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 83a0d82781a9dc97d29b26c8246019da + trainer: + kwargs: {} +name: b709b9d6956b2f92a8296786e78d13ba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/b987d828072a185ac7a7dfc1f61bae73/params.yaml b/examples/security/truthseeker/output/reports/train/b987d828072a185ac7a7dfc1f61bae73/params.yaml new file mode 100644 index 00000000..46db0562 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/b987d828072a185ac7a7dfc1f61bae73/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/adv_predictions.json + adv_probabilities_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e14307ec9f51404bd430620a25be3592.pkl + params_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/params.yaml + predictions_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/predictions.json + probabilities_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/probabilities.json + score_dict_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/score_dict.json + test_labels_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/test_labels.json + train_labels_file: output/reports/train/b987d828072a185ac7a7dfc1f61bae73/train_labels.json + model_dir: models + name: b987d828072a185ac7a7dfc1f61bae73 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 943a5662d10d6511fe7520e97767d613 + trainer: + kwargs: {} +name: b987d828072a185ac7a7dfc1f61bae73 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/b9b6b6433eb4ef76049649ab079b3cc6/params.yaml b/examples/security/truthseeker/output/reports/train/b9b6b6433eb4ef76049649ab079b3cc6/params.yaml new file mode 100644 index 00000000..924c334e --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/b9b6b6433eb4ef76049649ab079b3cc6/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/b9b6b6433eb4ef76049649ab079b3cc6/adv_predictions.json + adv_probabilities_file: output/reports/train/b9b6b6433eb4ef76049649ab079b3cc6/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/91222c54129c002eed70e03c781250fd.pkl + params_file: output/reports/train/b9b6b6433eb4ef76049649ab079b3cc6/params.yaml + predictions_file: output/reports/train/b9b6b6433eb4ef76049649ab079b3cc6/predictions.json + probabilities_file: output/reports/train/b9b6b6433eb4ef76049649ab079b3cc6/probabilities.json + score_dict_file: output/reports/train/b9b6b6433eb4ef76049649ab079b3cc6/score_dict.json + test_labels_file: output/reports/train/b9b6b6433eb4ef76049649ab079b3cc6/test_labels.json + train_labels_file: output/reports/train/b9b6b6433eb4ef76049649ab079b3cc6/train_labels.json + model_dir: models + name: b9b6b6433eb4ef76049649ab079b3cc6 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 79546f1432055f2f7ffe05cbdab5ba9a + trainer: + kwargs: {} +name: b9b6b6433eb4ef76049649ab079b3cc6 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/bc672b117b23327da6735787f75d126c/params.yaml b/examples/security/truthseeker/output/reports/train/bc672b117b23327da6735787f75d126c/params.yaml new file mode 100644 index 00000000..feaadd8f --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/bc672b117b23327da6735787f75d126c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bc672b117b23327da6735787f75d126c/adv_predictions.json + adv_probabilities_file: output/reports/train/bc672b117b23327da6735787f75d126c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2bd03dd8e0a8a9f18f46ebc04b6a1c69.pkl + params_file: output/reports/train/bc672b117b23327da6735787f75d126c/params.yaml + predictions_file: output/reports/train/bc672b117b23327da6735787f75d126c/predictions.json + probabilities_file: output/reports/train/bc672b117b23327da6735787f75d126c/probabilities.json + score_dict_file: output/reports/train/bc672b117b23327da6735787f75d126c/score_dict.json + test_labels_file: output/reports/train/bc672b117b23327da6735787f75d126c/test_labels.json + train_labels_file: output/reports/train/bc672b117b23327da6735787f75d126c/train_labels.json + model_dir: models + name: bc672b117b23327da6735787f75d126c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e664f11e5c7d2047cbb736381a799345 + trainer: + kwargs: {} +name: bc672b117b23327da6735787f75d126c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/bc8c103fff32832cb7c457e79ea83d57/params.yaml b/examples/security/truthseeker/output/reports/train/bc8c103fff32832cb7c457e79ea83d57/params.yaml new file mode 100644 index 00000000..9fcdd6ca --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/bc8c103fff32832cb7c457e79ea83d57/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/adv_predictions.json + adv_probabilities_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9618e9465779c20f8c0e961adc3cbb9f.pkl + params_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/params.yaml + predictions_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/predictions.json + probabilities_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/probabilities.json + score_dict_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/score_dict.json + test_labels_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/test_labels.json + train_labels_file: output/reports/train/bc8c103fff32832cb7c457e79ea83d57/train_labels.json + model_dir: models + name: bc8c103fff32832cb7c457e79ea83d57 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 20653d15201d4a14fa1b119d6045cfac + trainer: + kwargs: {} +name: bc8c103fff32832cb7c457e79ea83d57 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/bdf12fea2681b0719826964f0b1d5d32/params.yaml b/examples/security/truthseeker/output/reports/train/bdf12fea2681b0719826964f0b1d5d32/params.yaml new file mode 100644 index 00000000..6c6389f6 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/bdf12fea2681b0719826964f0b1d5d32/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bdf12fea2681b0719826964f0b1d5d32/adv_predictions.json + adv_probabilities_file: output/reports/train/bdf12fea2681b0719826964f0b1d5d32/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/aedb1ac08a0b50b89abd701aa6e68ef9.pkl + params_file: output/reports/train/bdf12fea2681b0719826964f0b1d5d32/params.yaml + predictions_file: output/reports/train/bdf12fea2681b0719826964f0b1d5d32/predictions.json + probabilities_file: output/reports/train/bdf12fea2681b0719826964f0b1d5d32/probabilities.json + score_dict_file: output/reports/train/bdf12fea2681b0719826964f0b1d5d32/score_dict.json + test_labels_file: output/reports/train/bdf12fea2681b0719826964f0b1d5d32/test_labels.json + train_labels_file: output/reports/train/bdf12fea2681b0719826964f0b1d5d32/train_labels.json + model_dir: models + name: bdf12fea2681b0719826964f0b1d5d32 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.1 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b37757a9328472c072e6eb2f644bfa98 + trainer: + kwargs: {} +name: bdf12fea2681b0719826964f0b1d5d32 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/bdf160010301fd7cb5e471982ff91d40/params.yaml b/examples/security/truthseeker/output/reports/train/bdf160010301fd7cb5e471982ff91d40/params.yaml new file mode 100644 index 00000000..c2c15d3a --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/bdf160010301fd7cb5e471982ff91d40/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bdf160010301fd7cb5e471982ff91d40/adv_predictions.json + adv_probabilities_file: output/reports/train/bdf160010301fd7cb5e471982ff91d40/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/168eec2e3630176bf26c99b9e0b756c6.pkl + params_file: output/reports/train/bdf160010301fd7cb5e471982ff91d40/params.yaml + predictions_file: output/reports/train/bdf160010301fd7cb5e471982ff91d40/predictions.json + probabilities_file: output/reports/train/bdf160010301fd7cb5e471982ff91d40/probabilities.json + score_dict_file: output/reports/train/bdf160010301fd7cb5e471982ff91d40/score_dict.json + test_labels_file: output/reports/train/bdf160010301fd7cb5e471982ff91d40/test_labels.json + train_labels_file: output/reports/train/bdf160010301fd7cb5e471982ff91d40/train_labels.json + model_dir: models + name: bdf160010301fd7cb5e471982ff91d40 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3db3ad4f759b2d9d66607ea4934b6399 + trainer: + kwargs: {} +name: bdf160010301fd7cb5e471982ff91d40 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/params.yaml b/examples/security/truthseeker/output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/params.yaml new file mode 100644 index 00000000..607bcd30 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/adv_predictions.json + adv_probabilities_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/87cbb404ea36b265aa10895903c3c616.pkl + params_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/params.yaml + predictions_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/predictions.json + probabilities_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/probabilities.json + score_dict_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/score_dict.json + test_labels_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/test_labels.json + train_labels_file: output/reports/train/bfeaf64981a9f786a3cabdb03a5e5529/train_labels.json + model_dir: models + name: bfeaf64981a9f786a3cabdb03a5e5529 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7fbbf8c0f80d0412c51685c719a5331c + trainer: + kwargs: {} +name: bfeaf64981a9f786a3cabdb03a5e5529 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/params.yaml b/examples/security/truthseeker/output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/params.yaml new file mode 100644 index 00000000..b24411f8 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/adv_predictions.json + adv_probabilities_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/11d13bfb1ca2d25578c7d480b894f028.pkl + params_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/params.yaml + predictions_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/predictions.json + probabilities_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/probabilities.json + score_dict_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/score_dict.json + test_labels_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/test_labels.json + train_labels_file: output/reports/train/bff2856ba43e1b45d5711b4666eb4c8c/train_labels.json + model_dir: models + name: bff2856ba43e1b45d5711b4666eb4c8c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d052800adb8863e016d1c434f92c6944 + trainer: + kwargs: {} +name: bff2856ba43e1b45d5711b4666eb4c8c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/params.yaml b/examples/security/truthseeker/output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/params.yaml new file mode 100644 index 00000000..afa46753 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/adv_predictions.json + adv_probabilities_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/4a8b0655c53194e767e4631e167ba87a.pkl + params_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/params.yaml + predictions_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/predictions.json + probabilities_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/probabilities.json + score_dict_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/score_dict.json + test_labels_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/test_labels.json + train_labels_file: output/reports/train/c0368c0d8c2b9caf9cfb5c0392f6208b/train_labels.json + model_dir: models + name: c0368c0d8c2b9caf9cfb5c0392f6208b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5bd518699a2b65e5cfc1f59fc82101df + trainer: + kwargs: {} +name: c0368c0d8c2b9caf9cfb5c0392f6208b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/c1a0e8feec35f4df59ad86842c35576e/params.yaml b/examples/security/truthseeker/output/reports/train/c1a0e8feec35f4df59ad86842c35576e/params.yaml new file mode 100644 index 00000000..a0a91777 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/c1a0e8feec35f4df59ad86842c35576e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/adv_predictions.json + adv_probabilities_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/2625f99f7d7b0baaa5fe649abf0aa7ce.pkl + params_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/params.yaml + predictions_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/predictions.json + probabilities_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/probabilities.json + score_dict_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/score_dict.json + test_labels_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/test_labels.json + train_labels_file: output/reports/train/c1a0e8feec35f4df59ad86842c35576e/train_labels.json + model_dir: models + name: c1a0e8feec35f4df59ad86842c35576e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1f1cee128f4cb487f539c3922dc8e6ec + trainer: + kwargs: {} +name: c1a0e8feec35f4df59ad86842c35576e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/c41e15007c281163dc5e65df5dafc408/params.yaml b/examples/security/truthseeker/output/reports/train/c41e15007c281163dc5e65df5dafc408/params.yaml new file mode 100644 index 00000000..f90911c8 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/c41e15007c281163dc5e65df5dafc408/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/adv_predictions.json + adv_probabilities_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/078ac8572ce6fde705ec0f407068b1d6.pkl + params_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/params.yaml + predictions_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/predictions.json + probabilities_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/probabilities.json + score_dict_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/score_dict.json + test_labels_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/test_labels.json + train_labels_file: output/reports/train/c41e15007c281163dc5e65df5dafc408/train_labels.json + model_dir: models + name: c41e15007c281163dc5e65df5dafc408 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8abd3826f95572aea9089f8ac3054975 + trainer: + kwargs: {} +name: c41e15007c281163dc5e65df5dafc408 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/c74de38aec54d28c2a192c3a581a601d/params.yaml b/examples/security/truthseeker/output/reports/train/c74de38aec54d28c2a192c3a581a601d/params.yaml new file mode 100644 index 00000000..27c31b32 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/c74de38aec54d28c2a192c3a581a601d/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/adv_predictions.json + adv_probabilities_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/64ea70deffcd4c8f21aa2cb0b3d339f0.pkl + params_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/params.yaml + predictions_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/predictions.json + probabilities_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/probabilities.json + score_dict_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/score_dict.json + test_labels_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/test_labels.json + train_labels_file: output/reports/train/c74de38aec54d28c2a192c3a581a601d/train_labels.json + model_dir: models + name: c74de38aec54d28c2a192c3a581a601d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 63a4caee522ebeea2b0ec79b1fab9ad3 + trainer: + kwargs: {} +name: c74de38aec54d28c2a192c3a581a601d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/params.yaml b/examples/security/truthseeker/output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/params.yaml new file mode 100644 index 00000000..a7e0e1c5 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/adv_predictions.json + adv_probabilities_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/68e90a82e492c860d8b21bb09ca31afa.pkl + params_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/params.yaml + predictions_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/predictions.json + probabilities_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/probabilities.json + score_dict_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/score_dict.json + test_labels_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/test_labels.json + train_labels_file: output/reports/train/c7d588601d5d718c9bbdb9524c2bdacd/train_labels.json + model_dir: models + name: c7d588601d5d718c9bbdb9524c2bdacd + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cf999a11a909a51a4fadfc1a1fca6e9f + trainer: + kwargs: {} +name: c7d588601d5d718c9bbdb9524c2bdacd +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/c838f9c9062797f67b2d2d48854cfcca/params.yaml b/examples/security/truthseeker/output/reports/train/c838f9c9062797f67b2d2d48854cfcca/params.yaml new file mode 100644 index 00000000..0b0ecfda --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/c838f9c9062797f67b2d2d48854cfcca/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/adv_predictions.json + adv_probabilities_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b000e021d7332160eb8bb9b3a9d84a60.pkl + params_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/params.yaml + predictions_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/predictions.json + probabilities_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/probabilities.json + score_dict_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/score_dict.json + test_labels_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/test_labels.json + train_labels_file: output/reports/train/c838f9c9062797f67b2d2d48854cfcca/train_labels.json + model_dir: models + name: c838f9c9062797f67b2d2d48854cfcca + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 0f149e7fff1520e2f03c1da81a16410b + trainer: + kwargs: {} +name: c838f9c9062797f67b2d2d48854cfcca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ca59084cb49796d8b15444affe1a695d/params.yaml b/examples/security/truthseeker/output/reports/train/ca59084cb49796d8b15444affe1a695d/params.yaml new file mode 100644 index 00000000..1ff029ce --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ca59084cb49796d8b15444affe1a695d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/adv_predictions.json + adv_probabilities_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/997d30ee47fd3eb727a822a66622a2d1.pkl + params_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/params.yaml + predictions_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/predictions.json + probabilities_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/probabilities.json + score_dict_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/score_dict.json + test_labels_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/test_labels.json + train_labels_file: output/reports/train/ca59084cb49796d8b15444affe1a695d/train_labels.json + model_dir: models + name: ca59084cb49796d8b15444affe1a695d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 365af44ffb96290c3bd4e58e4da2d3c5 + trainer: + kwargs: {} +name: ca59084cb49796d8b15444affe1a695d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/cccb2dd6c78d7c8953eea69adc54839c/params.yaml b/examples/security/truthseeker/output/reports/train/cccb2dd6c78d7c8953eea69adc54839c/params.yaml new file mode 100644 index 00000000..788f3108 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/cccb2dd6c78d7c8953eea69adc54839c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cccb2dd6c78d7c8953eea69adc54839c/adv_predictions.json + adv_probabilities_file: output/reports/train/cccb2dd6c78d7c8953eea69adc54839c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/632730a2acca52c98bce69e22bb0d4f8.pkl + params_file: output/reports/train/cccb2dd6c78d7c8953eea69adc54839c/params.yaml + predictions_file: output/reports/train/cccb2dd6c78d7c8953eea69adc54839c/predictions.json + probabilities_file: output/reports/train/cccb2dd6c78d7c8953eea69adc54839c/probabilities.json + score_dict_file: output/reports/train/cccb2dd6c78d7c8953eea69adc54839c/score_dict.json + test_labels_file: output/reports/train/cccb2dd6c78d7c8953eea69adc54839c/test_labels.json + train_labels_file: output/reports/train/cccb2dd6c78d7c8953eea69adc54839c/train_labels.json + model_dir: models + name: cccb2dd6c78d7c8953eea69adc54839c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ae287229802e87eadf1d39c63b7375cf + trainer: + kwargs: {} +name: cccb2dd6c78d7c8953eea69adc54839c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ce0e754a912eafef48bc7f38af3cd8ca/params.yaml b/examples/security/truthseeker/output/reports/train/ce0e754a912eafef48bc7f38af3cd8ca/params.yaml new file mode 100644 index 00000000..05694557 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ce0e754a912eafef48bc7f38af3cd8ca/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ce0e754a912eafef48bc7f38af3cd8ca/adv_predictions.json + adv_probabilities_file: output/reports/train/ce0e754a912eafef48bc7f38af3cd8ca/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/70f6a1ea2c0a09e2591cc3d8cf9fc964.pkl + params_file: output/reports/train/ce0e754a912eafef48bc7f38af3cd8ca/params.yaml + predictions_file: output/reports/train/ce0e754a912eafef48bc7f38af3cd8ca/predictions.json + probabilities_file: output/reports/train/ce0e754a912eafef48bc7f38af3cd8ca/probabilities.json + score_dict_file: output/reports/train/ce0e754a912eafef48bc7f38af3cd8ca/score_dict.json + test_labels_file: output/reports/train/ce0e754a912eafef48bc7f38af3cd8ca/test_labels.json + train_labels_file: output/reports/train/ce0e754a912eafef48bc7f38af3cd8ca/train_labels.json + model_dir: models + name: ce0e754a912eafef48bc7f38af3cd8ca + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5b82747552ab991b59a34b02b4e73198 + trainer: + kwargs: {} +name: ce0e754a912eafef48bc7f38af3cd8ca +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ce664b34b714d9cc7060ceeffcc9d762/params.yaml b/examples/security/truthseeker/output/reports/train/ce664b34b714d9cc7060ceeffcc9d762/params.yaml new file mode 100644 index 00000000..fc906957 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ce664b34b714d9cc7060ceeffcc9d762/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ce664b34b714d9cc7060ceeffcc9d762/adv_predictions.json + adv_probabilities_file: output/reports/train/ce664b34b714d9cc7060ceeffcc9d762/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5f985b0586695f4539456f1fbd04b848.pkl + params_file: output/reports/train/ce664b34b714d9cc7060ceeffcc9d762/params.yaml + predictions_file: output/reports/train/ce664b34b714d9cc7060ceeffcc9d762/predictions.json + probabilities_file: output/reports/train/ce664b34b714d9cc7060ceeffcc9d762/probabilities.json + score_dict_file: output/reports/train/ce664b34b714d9cc7060ceeffcc9d762/score_dict.json + test_labels_file: output/reports/train/ce664b34b714d9cc7060ceeffcc9d762/test_labels.json + train_labels_file: output/reports/train/ce664b34b714d9cc7060ceeffcc9d762/train_labels.json + model_dir: models + name: ce664b34b714d9cc7060ceeffcc9d762 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 44493e3c513733a2fa443104fe086556 + trainer: + kwargs: {} +name: ce664b34b714d9cc7060ceeffcc9d762 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ce7589d3f412f02d2e7497d990b788af/params.yaml b/examples/security/truthseeker/output/reports/train/ce7589d3f412f02d2e7497d990b788af/params.yaml new file mode 100644 index 00000000..584e5fca --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ce7589d3f412f02d2e7497d990b788af/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/adv_predictions.json + adv_probabilities_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/fb02a6b0d9ee6c12b1eee4b0871148f3.pkl + params_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/params.yaml + predictions_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/predictions.json + probabilities_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/probabilities.json + score_dict_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/score_dict.json + test_labels_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/test_labels.json + train_labels_file: output/reports/train/ce7589d3f412f02d2e7497d990b788af/train_labels.json + model_dir: models + name: ce7589d3f412f02d2e7497d990b788af + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: c546f2fc86d80740f40dbbb6e3714513 + trainer: + kwargs: {} +name: ce7589d3f412f02d2e7497d990b788af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ce90ac7c7611eca12e68a456e7206829/params.yaml b/examples/security/truthseeker/output/reports/train/ce90ac7c7611eca12e68a456e7206829/params.yaml new file mode 100644 index 00000000..8e8089c2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ce90ac7c7611eca12e68a456e7206829/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/adv_predictions.json + adv_probabilities_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e1f2dcf7521ad0d86b32cedd62df20a1.pkl + params_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/params.yaml + predictions_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/predictions.json + probabilities_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/probabilities.json + score_dict_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/score_dict.json + test_labels_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/test_labels.json + train_labels_file: output/reports/train/ce90ac7c7611eca12e68a456e7206829/train_labels.json + model_dir: models + name: ce90ac7c7611eca12e68a456e7206829 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 514764e5b2bfc4e70108698b19d1634a + trainer: + kwargs: {} +name: ce90ac7c7611eca12e68a456e7206829 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/cee7ef55b5ba0ac8439c2ea7742e0cb1/params.yaml b/examples/security/truthseeker/output/reports/train/cee7ef55b5ba0ac8439c2ea7742e0cb1/params.yaml new file mode 100644 index 00000000..15b9314c --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/cee7ef55b5ba0ac8439c2ea7742e0cb1/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cee7ef55b5ba0ac8439c2ea7742e0cb1/adv_predictions.json + adv_probabilities_file: output/reports/train/cee7ef55b5ba0ac8439c2ea7742e0cb1/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/a8614f0bfe65eace8165d6e3840333cd.pkl + params_file: output/reports/train/cee7ef55b5ba0ac8439c2ea7742e0cb1/params.yaml + predictions_file: output/reports/train/cee7ef55b5ba0ac8439c2ea7742e0cb1/predictions.json + probabilities_file: output/reports/train/cee7ef55b5ba0ac8439c2ea7742e0cb1/probabilities.json + score_dict_file: output/reports/train/cee7ef55b5ba0ac8439c2ea7742e0cb1/score_dict.json + test_labels_file: output/reports/train/cee7ef55b5ba0ac8439c2ea7742e0cb1/test_labels.json + train_labels_file: output/reports/train/cee7ef55b5ba0ac8439c2ea7742e0cb1/train_labels.json + model_dir: models + name: cee7ef55b5ba0ac8439c2ea7742e0cb1 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 74d88c856979e889ddd93462ecc17550 + trainer: + kwargs: {} +name: cee7ef55b5ba0ac8439c2ea7742e0cb1 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/cefc494c453297029b77d380daebeead/params.yaml b/examples/security/truthseeker/output/reports/train/cefc494c453297029b77d380daebeead/params.yaml new file mode 100644 index 00000000..7c808ea4 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/cefc494c453297029b77d380daebeead/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cefc494c453297029b77d380daebeead/adv_predictions.json + adv_probabilities_file: output/reports/train/cefc494c453297029b77d380daebeead/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b3e33e9d6e7e790b8fe0f8855439b018.pkl + params_file: output/reports/train/cefc494c453297029b77d380daebeead/params.yaml + predictions_file: output/reports/train/cefc494c453297029b77d380daebeead/predictions.json + probabilities_file: output/reports/train/cefc494c453297029b77d380daebeead/probabilities.json + score_dict_file: output/reports/train/cefc494c453297029b77d380daebeead/score_dict.json + test_labels_file: output/reports/train/cefc494c453297029b77d380daebeead/test_labels.json + train_labels_file: output/reports/train/cefc494c453297029b77d380daebeead/train_labels.json + model_dir: models + name: cefc494c453297029b77d380daebeead + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a49139b222dfb1023a9d54d3552abd76 + trainer: + kwargs: {} +name: cefc494c453297029b77d380daebeead +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/cfc3fc6b8af382181fa0426a8fe75c0d/params.yaml b/examples/security/truthseeker/output/reports/train/cfc3fc6b8af382181fa0426a8fe75c0d/params.yaml new file mode 100644 index 00000000..8d80bd55 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/cfc3fc6b8af382181fa0426a8fe75c0d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/cfc3fc6b8af382181fa0426a8fe75c0d/adv_predictions.json + adv_probabilities_file: output/reports/train/cfc3fc6b8af382181fa0426a8fe75c0d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/13b774a3325cd3013385be4d57995b7f.pkl + params_file: output/reports/train/cfc3fc6b8af382181fa0426a8fe75c0d/params.yaml + predictions_file: output/reports/train/cfc3fc6b8af382181fa0426a8fe75c0d/predictions.json + probabilities_file: output/reports/train/cfc3fc6b8af382181fa0426a8fe75c0d/probabilities.json + score_dict_file: output/reports/train/cfc3fc6b8af382181fa0426a8fe75c0d/score_dict.json + test_labels_file: output/reports/train/cfc3fc6b8af382181fa0426a8fe75c0d/test_labels.json + train_labels_file: output/reports/train/cfc3fc6b8af382181fa0426a8fe75c0d/train_labels.json + model_dir: models + name: cfc3fc6b8af382181fa0426a8fe75c0d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.01 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f2a2a56afc4c0f7d72f403a4635fcaaa + trainer: + kwargs: {} +name: cfc3fc6b8af382181fa0426a8fe75c0d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/d0a77a7d81b3e19b2c06150aa90f3a6c/params.yaml b/examples/security/truthseeker/output/reports/train/d0a77a7d81b3e19b2c06150aa90f3a6c/params.yaml new file mode 100644 index 00000000..9016e511 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/d0a77a7d81b3e19b2c06150aa90f3a6c/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d0a77a7d81b3e19b2c06150aa90f3a6c/adv_predictions.json + adv_probabilities_file: output/reports/train/d0a77a7d81b3e19b2c06150aa90f3a6c/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/8d8af0781f32293b29c2a217f914e12b.pkl + params_file: output/reports/train/d0a77a7d81b3e19b2c06150aa90f3a6c/params.yaml + predictions_file: output/reports/train/d0a77a7d81b3e19b2c06150aa90f3a6c/predictions.json + probabilities_file: output/reports/train/d0a77a7d81b3e19b2c06150aa90f3a6c/probabilities.json + score_dict_file: output/reports/train/d0a77a7d81b3e19b2c06150aa90f3a6c/score_dict.json + test_labels_file: output/reports/train/d0a77a7d81b3e19b2c06150aa90f3a6c/test_labels.json + train_labels_file: output/reports/train/d0a77a7d81b3e19b2c06150aa90f3a6c/train_labels.json + model_dir: models + name: d0a77a7d81b3e19b2c06150aa90f3a6c + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 64d6c7b2b2281b684cc00831abcb1d6e + trainer: + kwargs: {} +name: d0a77a7d81b3e19b2c06150aa90f3a6c +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/params.yaml b/examples/security/truthseeker/output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/params.yaml new file mode 100644 index 00000000..cefc5bf9 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/adv_predictions.json + adv_probabilities_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0de0b40d2110e895aa5b49daf7a6ed34.pkl + params_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/params.yaml + predictions_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/predictions.json + probabilities_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/probabilities.json + score_dict_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/score_dict.json + test_labels_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/test_labels.json + train_labels_file: output/reports/train/d0d89d5ffa698b6d26756aad31734e5f/train_labels.json + model_dir: models + name: d0d89d5ffa698b6d26756aad31734e5f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 100 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ed11c48df3ee48ac230abdee1a9e91d9 + trainer: + kwargs: {} +name: d0d89d5ffa698b6d26756aad31734e5f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/d150bacefe5937d49344aca500eeb7ba/params.yaml b/examples/security/truthseeker/output/reports/train/d150bacefe5937d49344aca500eeb7ba/params.yaml new file mode 100644 index 00000000..ee905e94 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/d150bacefe5937d49344aca500eeb7ba/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/adv_predictions.json + adv_probabilities_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/97f7e601bda1da916eb09b4ba768eded.pkl + params_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/params.yaml + predictions_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/predictions.json + probabilities_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/probabilities.json + score_dict_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/score_dict.json + test_labels_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/test_labels.json + train_labels_file: output/reports/train/d150bacefe5937d49344aca500eeb7ba/train_labels.json + model_dir: models + name: d150bacefe5937d49344aca500eeb7ba + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 11c903041fc2f23439bcc44ee63fe40b + trainer: + kwargs: {} +name: d150bacefe5937d49344aca500eeb7ba +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/d27ba6a249364ce1ac0519022d9ecd13/params.yaml b/examples/security/truthseeker/output/reports/train/d27ba6a249364ce1ac0519022d9ecd13/params.yaml new file mode 100644 index 00000000..61190ed3 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/d27ba6a249364ce1ac0519022d9ecd13/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d27ba6a249364ce1ac0519022d9ecd13/adv_predictions.json + adv_probabilities_file: output/reports/train/d27ba6a249364ce1ac0519022d9ecd13/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/64a5f83e697a9fbb2c14f34b61a176a6.pkl + params_file: output/reports/train/d27ba6a249364ce1ac0519022d9ecd13/params.yaml + predictions_file: output/reports/train/d27ba6a249364ce1ac0519022d9ecd13/predictions.json + probabilities_file: output/reports/train/d27ba6a249364ce1ac0519022d9ecd13/probabilities.json + score_dict_file: output/reports/train/d27ba6a249364ce1ac0519022d9ecd13/score_dict.json + test_labels_file: output/reports/train/d27ba6a249364ce1ac0519022d9ecd13/test_labels.json + train_labels_file: output/reports/train/d27ba6a249364ce1ac0519022d9ecd13/train_labels.json + model_dir: models + name: d27ba6a249364ce1ac0519022d9ecd13 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 100 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5ef8f845b93232605537d744742a6f1a + trainer: + kwargs: {} +name: d27ba6a249364ce1ac0519022d9ecd13 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/params.yaml b/examples/security/truthseeker/output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/params.yaml new file mode 100644 index 00000000..a18f39c6 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/adv_predictions.json + adv_probabilities_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/44647811760ab022c038cb0c06240164.pkl + params_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/params.yaml + predictions_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/predictions.json + probabilities_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/probabilities.json + score_dict_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/score_dict.json + test_labels_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/test_labels.json + train_labels_file: output/reports/train/d43e7cdb8c13dcea0d7d19130b3b4f64/train_labels.json + model_dir: models + name: d43e7cdb8c13dcea0d7d19130b3b4f64 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 100 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 9805c0bb6292e21c6cee24a95aa26e98 + trainer: + kwargs: {} +name: d43e7cdb8c13dcea0d7d19130b3b4f64 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/d4b2e1c44cd43beb788586b852a7d933/params.yaml b/examples/security/truthseeker/output/reports/train/d4b2e1c44cd43beb788586b852a7d933/params.yaml new file mode 100644 index 00000000..e26012bc --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/d4b2e1c44cd43beb788586b852a7d933/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/adv_predictions.json + adv_probabilities_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c281c21383bb54e3bddcd9b60140bb76.pkl + params_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/params.yaml + predictions_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/predictions.json + probabilities_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/probabilities.json + score_dict_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/score_dict.json + test_labels_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/test_labels.json + train_labels_file: output/reports/train/d4b2e1c44cd43beb788586b852a7d933/train_labels.json + model_dir: models + name: d4b2e1c44cd43beb788586b852a7d933 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 83e8e5c09d50e6e353d376b77db06617 + trainer: + kwargs: {} +name: d4b2e1c44cd43beb788586b852a7d933 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/params.yaml b/examples/security/truthseeker/output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/params.yaml new file mode 100644 index 00000000..556e9f86 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/adv_predictions.json + adv_probabilities_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/4ad573b6ac1aa917fe35f75e12d2cd9d.pkl + params_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/params.yaml + predictions_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/predictions.json + probabilities_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/probabilities.json + score_dict_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/score_dict.json + test_labels_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/test_labels.json + train_labels_file: output/reports/train/d4ba4227db48fa64339e8878f8e1fa87/train_labels.json + model_dir: models + name: d4ba4227db48fa64339e8878f8e1fa87 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 100 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: cfc34ed8a593351e39430a8c3c1f4816 + trainer: + kwargs: {} +name: d4ba4227db48fa64339e8878f8e1fa87 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/params.yaml b/examples/security/truthseeker/output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/params.yaml new file mode 100644 index 00000000..35d4eb4e --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/adv_predictions.json + adv_probabilities_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/a2918feb31bef710907ab77ca216d0a5.pkl + params_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/params.yaml + predictions_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/predictions.json + probabilities_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/probabilities.json + score_dict_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/score_dict.json + test_labels_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/test_labels.json + train_labels_file: output/reports/train/d6f9198c705d3c1efbb8c6b653934fd0/train_labels.json + model_dir: models + name: d6f9198c705d3c1efbb8c6b653934fd0 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bdff7cd509c12a6ae99d5829829ee5df + trainer: + kwargs: {} +name: d6f9198c705d3c1efbb8c6b653934fd0 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/params.yaml b/examples/security/truthseeker/output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/params.yaml new file mode 100644 index 00000000..4b7f64f2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/adv_predictions.json + adv_probabilities_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/55f4a2e72a7485d088fc92b50c5af7b4.pkl + params_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/params.yaml + predictions_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/predictions.json + probabilities_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/probabilities.json + score_dict_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/score_dict.json + test_labels_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/test_labels.json + train_labels_file: output/reports/train/d71272a64d9e1b3f6f1a692528a8a124/train_labels.json + model_dir: models + name: d71272a64d9e1b3f6f1a692528a8a124 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 1000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7b256a9129939eeae6c9165cac0a655a + trainer: + kwargs: {} +name: d71272a64d9e1b3f6f1a692528a8a124 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/params.yaml b/examples/security/truthseeker/output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/params.yaml new file mode 100644 index 00000000..0c1b00d0 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/adv_predictions.json + adv_probabilities_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/235536360cec1f14dd2feafd69ddf49b.pkl + params_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/params.yaml + predictions_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/predictions.json + probabilities_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/probabilities.json + score_dict_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/score_dict.json + test_labels_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/test_labels.json + train_labels_file: output/reports/train/d8865cad7eb1409aeabe4a94d275ba3b/train_labels.json + model_dir: models + name: d8865cad7eb1409aeabe4a94d275ba3b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8078dd5202f2cde1c157ac459bcca3b3 + trainer: + kwargs: {} +name: d8865cad7eb1409aeabe4a94d275ba3b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/d96587d060597cf100ede77d61d90763/params.yaml b/examples/security/truthseeker/output/reports/train/d96587d060597cf100ede77d61d90763/params.yaml new file mode 100644 index 00000000..268fd3b0 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/d96587d060597cf100ede77d61d90763/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/d96587d060597cf100ede77d61d90763/adv_predictions.json + adv_probabilities_file: output/reports/train/d96587d060597cf100ede77d61d90763/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/01fe6cb1b4361d20f886aa73c944b0e6.pkl + params_file: output/reports/train/d96587d060597cf100ede77d61d90763/params.yaml + predictions_file: output/reports/train/d96587d060597cf100ede77d61d90763/predictions.json + probabilities_file: output/reports/train/d96587d060597cf100ede77d61d90763/probabilities.json + score_dict_file: output/reports/train/d96587d060597cf100ede77d61d90763/score_dict.json + test_labels_file: output/reports/train/d96587d060597cf100ede77d61d90763/test_labels.json + train_labels_file: output/reports/train/d96587d060597cf100ede77d61d90763/train_labels.json + model_dir: models + name: d96587d060597cf100ede77d61d90763 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2b54d6ba5e6d62b2c539f26908e285d8 + trainer: + kwargs: {} +name: d96587d060597cf100ede77d61d90763 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/db8bddea8c1977ba5f24e9d70e6cad85/params.yaml b/examples/security/truthseeker/output/reports/train/db8bddea8c1977ba5f24e9d70e6cad85/params.yaml new file mode 100644 index 00000000..571d9083 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/db8bddea8c1977ba5f24e9d70e6cad85/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/db8bddea8c1977ba5f24e9d70e6cad85/adv_predictions.json + adv_probabilities_file: output/reports/train/db8bddea8c1977ba5f24e9d70e6cad85/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/251a558f750e7423133ff7499ddee2a8.pkl + params_file: output/reports/train/db8bddea8c1977ba5f24e9d70e6cad85/params.yaml + predictions_file: output/reports/train/db8bddea8c1977ba5f24e9d70e6cad85/predictions.json + probabilities_file: output/reports/train/db8bddea8c1977ba5f24e9d70e6cad85/probabilities.json + score_dict_file: output/reports/train/db8bddea8c1977ba5f24e9d70e6cad85/score_dict.json + test_labels_file: output/reports/train/db8bddea8c1977ba5f24e9d70e6cad85/test_labels.json + train_labels_file: output/reports/train/db8bddea8c1977ba5f24e9d70e6cad85/train_labels.json + model_dir: models + name: db8bddea8c1977ba5f24e9d70e6cad85 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3161aabe6594a2c739071344bcb24c78 + trainer: + kwargs: {} +name: db8bddea8c1977ba5f24e9d70e6cad85 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/dbab3bfac33e9554b4a622a8bf1ca818/params.yaml b/examples/security/truthseeker/output/reports/train/dbab3bfac33e9554b4a622a8bf1ca818/params.yaml new file mode 100644 index 00000000..0378aa3f --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/dbab3bfac33e9554b4a622a8bf1ca818/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dbab3bfac33e9554b4a622a8bf1ca818/adv_predictions.json + adv_probabilities_file: output/reports/train/dbab3bfac33e9554b4a622a8bf1ca818/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/11e303993f8cf1eb233ad69f5353e953.pkl + params_file: output/reports/train/dbab3bfac33e9554b4a622a8bf1ca818/params.yaml + predictions_file: output/reports/train/dbab3bfac33e9554b4a622a8bf1ca818/predictions.json + probabilities_file: output/reports/train/dbab3bfac33e9554b4a622a8bf1ca818/probabilities.json + score_dict_file: output/reports/train/dbab3bfac33e9554b4a622a8bf1ca818/score_dict.json + test_labels_file: output/reports/train/dbab3bfac33e9554b4a622a8bf1ca818/test_labels.json + train_labels_file: output/reports/train/dbab3bfac33e9554b4a622a8bf1ca818/train_labels.json + model_dir: models + name: dbab3bfac33e9554b4a622a8bf1ca818 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 03ca4ca70a61db32d0a3112581ae2965 + trainer: + kwargs: {} +name: dbab3bfac33e9554b4a622a8bf1ca818 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/dbc22fe421452a6fbdc1dde6acc0eb8d/params.yaml b/examples/security/truthseeker/output/reports/train/dbc22fe421452a6fbdc1dde6acc0eb8d/params.yaml new file mode 100644 index 00000000..36960a99 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/dbc22fe421452a6fbdc1dde6acc0eb8d/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dbc22fe421452a6fbdc1dde6acc0eb8d/adv_predictions.json + adv_probabilities_file: output/reports/train/dbc22fe421452a6fbdc1dde6acc0eb8d/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/fb353b94ad77f4ab29daeb1883edb904.pkl + params_file: output/reports/train/dbc22fe421452a6fbdc1dde6acc0eb8d/params.yaml + predictions_file: output/reports/train/dbc22fe421452a6fbdc1dde6acc0eb8d/predictions.json + probabilities_file: output/reports/train/dbc22fe421452a6fbdc1dde6acc0eb8d/probabilities.json + score_dict_file: output/reports/train/dbc22fe421452a6fbdc1dde6acc0eb8d/score_dict.json + test_labels_file: output/reports/train/dbc22fe421452a6fbdc1dde6acc0eb8d/test_labels.json + train_labels_file: output/reports/train/dbc22fe421452a6fbdc1dde6acc0eb8d/train_labels.json + model_dir: models + name: dbc22fe421452a6fbdc1dde6acc0eb8d + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 7b0aed2575c1670d26b95ec7972d5187 + trainer: + kwargs: {} +name: dbc22fe421452a6fbdc1dde6acc0eb8d +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/params.yaml b/examples/security/truthseeker/output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/params.yaml new file mode 100644 index 00000000..4ce64f18 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/adv_predictions.json + adv_probabilities_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ee3d610cc23ab4fdea63d276dc48b03c.pkl + params_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/params.yaml + predictions_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/predictions.json + probabilities_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/probabilities.json + score_dict_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/score_dict.json + test_labels_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/test_labels.json + train_labels_file: output/reports/train/dc25085a9c071c2a82cebd1da83f33dc/train_labels.json + model_dir: models + name: dc25085a9c071c2a82cebd1da83f33dc + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d607c615c7571d5cbe469daacf75f865 + trainer: + kwargs: {} +name: dc25085a9c071c2a82cebd1da83f33dc +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/params.yaml b/examples/security/truthseeker/output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/params.yaml new file mode 100644 index 00000000..d6eefcfb --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/adv_predictions.json + adv_probabilities_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9307239fd3ef26a1f19baad71fc5efcf.pkl + params_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/params.yaml + predictions_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/predictions.json + probabilities_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/probabilities.json + score_dict_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/score_dict.json + test_labels_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/test_labels.json + train_labels_file: output/reports/train/dd22bff6b5a6fc06f9ac78108f93cbb4/train_labels.json + model_dir: models + name: dd22bff6b5a6fc06f9ac78108f93cbb4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 10000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 5ef9fc2489b9e287aeac251f405c8ead + trainer: + kwargs: {} +name: dd22bff6b5a6fc06f9ac78108f93cbb4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/dd2f0d8beeab97ed79ee873b86467093/params.yaml b/examples/security/truthseeker/output/reports/train/dd2f0d8beeab97ed79ee873b86467093/params.yaml new file mode 100644 index 00000000..43977f8e --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/dd2f0d8beeab97ed79ee873b86467093/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/adv_predictions.json + adv_probabilities_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/8810a7e72155f7b57aba7b04f8a9f2ae.pkl + params_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/params.yaml + predictions_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/predictions.json + probabilities_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/probabilities.json + score_dict_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/score_dict.json + test_labels_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/test_labels.json + train_labels_file: output/reports/train/dd2f0d8beeab97ed79ee873b86467093/train_labels.json + model_dir: models + name: dd2f0d8beeab97ed79ee873b86467093 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: ea9d43bcdf4e0555019f8a80c231cc7d + trainer: + kwargs: {} +name: dd2f0d8beeab97ed79ee873b86467093 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/dd9edce55d2789737b6b89d09ab89256/params.yaml b/examples/security/truthseeker/output/reports/train/dd9edce55d2789737b6b89d09ab89256/params.yaml new file mode 100644 index 00000000..044f54c1 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/dd9edce55d2789737b6b89d09ab89256/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/adv_predictions.json + adv_probabilities_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5767636fc16e2fc05f81774bd0ef1cd4.pkl + params_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/params.yaml + predictions_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/predictions.json + probabilities_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/probabilities.json + score_dict_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/score_dict.json + test_labels_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/test_labels.json + train_labels_file: output/reports/train/dd9edce55d2789737b6b89d09ab89256/train_labels.json + model_dir: models + name: dd9edce55d2789737b6b89d09ab89256 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.01 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 61334077c332cbca643773013f6438a3 + trainer: + kwargs: {} +name: dd9edce55d2789737b6b89d09ab89256 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/params.yaml b/examples/security/truthseeker/output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/params.yaml new file mode 100644 index 00000000..ca9cd0cd --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/adv_predictions.json + adv_probabilities_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/066a5c482399ab441a2e97334fc2138e.pkl + params_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/params.yaml + predictions_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/predictions.json + probabilities_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/probabilities.json + score_dict_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/score_dict.json + test_labels_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/test_labels.json + train_labels_file: output/reports/train/de4165aa67d9a710c2fa8629fc9584aa/train_labels.json + model_dir: models + name: de4165aa67d9a710c2fa8629fc9584aa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 1 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 6cd9edd4e4bbcc8af49060588061bf40 + trainer: + kwargs: {} +name: de4165aa67d9a710c2fa8629fc9584aa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/de43012c89b8b45375784c2d7bb5c502/params.yaml b/examples/security/truthseeker/output/reports/train/de43012c89b8b45375784c2d7bb5c502/params.yaml new file mode 100644 index 00000000..6654d64d --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/de43012c89b8b45375784c2d7bb5c502/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/adv_predictions.json + adv_probabilities_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/35ffdaf55dda26675b5a44f5d49f9c7e.pkl + params_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/params.yaml + predictions_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/predictions.json + probabilities_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/probabilities.json + score_dict_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/score_dict.json + test_labels_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/test_labels.json + train_labels_file: output/reports/train/de43012c89b8b45375784c2d7bb5c502/train_labels.json + model_dir: models + name: de43012c89b8b45375784c2d7bb5c502 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 10 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 797c292bb693422638e2ebf3000c7aa5 + trainer: + kwargs: {} +name: de43012c89b8b45375784c2d7bb5c502 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/de93fbf1633671443b118afb75c0b283/params.yaml b/examples/security/truthseeker/output/reports/train/de93fbf1633671443b118afb75c0b283/params.yaml new file mode 100644 index 00000000..f3bf06f7 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/de93fbf1633671443b118afb75c0b283/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/de93fbf1633671443b118afb75c0b283/adv_predictions.json + adv_probabilities_file: output/reports/train/de93fbf1633671443b118afb75c0b283/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b327fbb4a4e279902bb9f832fc0c23d3.pkl + params_file: output/reports/train/de93fbf1633671443b118afb75c0b283/params.yaml + predictions_file: output/reports/train/de93fbf1633671443b118afb75c0b283/predictions.json + probabilities_file: output/reports/train/de93fbf1633671443b118afb75c0b283/probabilities.json + score_dict_file: output/reports/train/de93fbf1633671443b118afb75c0b283/score_dict.json + test_labels_file: output/reports/train/de93fbf1633671443b118afb75c0b283/test_labels.json + train_labels_file: output/reports/train/de93fbf1633671443b118afb75c0b283/train_labels.json + model_dir: models + name: de93fbf1633671443b118afb75c0b283 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d83f17846eb4962877e69470147c918b + trainer: + kwargs: {} +name: de93fbf1633671443b118afb75c0b283 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/default/params.yaml b/examples/security/truthseeker/output/reports/train/default/params.yaml new file mode 100644 index 00000000..144fc869 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/default/params.yaml @@ -0,0 +1,95 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + params_file: output/reports/train/default/params.yaml + predictions_file: output/reports/train/default/predictions.json + probabilities_file: output/reports/train/default/probabilities.json + score_dict_file: output/reports/train/default/score_dict.json + model_dir: models + name: default + reports: reports + stage: train +kwargs: {} +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1.0 + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 + trainer: + kwargs: {} +name: default +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/df32c7282a6b1c866ccd25af964ab28f/params.yaml b/examples/security/truthseeker/output/reports/train/df32c7282a6b1c866ccd25af964ab28f/params.yaml new file mode 100644 index 00000000..af023bc1 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/df32c7282a6b1c866ccd25af964ab28f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/adv_predictions.json + adv_probabilities_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1a57864cc40c4df0ee9b44c1901ee6d7.pkl + params_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/params.yaml + predictions_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/predictions.json + probabilities_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/probabilities.json + score_dict_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/score_dict.json + test_labels_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/test_labels.json + train_labels_file: output/reports/train/df32c7282a6b1c866ccd25af964ab28f/train_labels.json + model_dir: models + name: df32c7282a6b1c866ccd25af964ab28f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 782df5ad027e449f31e1ea28b233fe77 + trainer: + kwargs: {} +name: df32c7282a6b1c866ccd25af964ab28f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/e27c1bd50dc8e8aada94e62c41ecd5ac/params.yaml b/examples/security/truthseeker/output/reports/train/e27c1bd50dc8e8aada94e62c41ecd5ac/params.yaml new file mode 100644 index 00000000..ed2691a0 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/e27c1bd50dc8e8aada94e62c41ecd5ac/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e27c1bd50dc8e8aada94e62c41ecd5ac/adv_predictions.json + adv_probabilities_file: output/reports/train/e27c1bd50dc8e8aada94e62c41ecd5ac/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/abb04cddbcda7a6bee6a3c33b3a48a06.pkl + params_file: output/reports/train/e27c1bd50dc8e8aada94e62c41ecd5ac/params.yaml + predictions_file: output/reports/train/e27c1bd50dc8e8aada94e62c41ecd5ac/predictions.json + probabilities_file: output/reports/train/e27c1bd50dc8e8aada94e62c41ecd5ac/probabilities.json + score_dict_file: output/reports/train/e27c1bd50dc8e8aada94e62c41ecd5ac/score_dict.json + test_labels_file: output/reports/train/e27c1bd50dc8e8aada94e62c41ecd5ac/test_labels.json + train_labels_file: output/reports/train/e27c1bd50dc8e8aada94e62c41ecd5ac/train_labels.json + model_dir: models + name: e27c1bd50dc8e8aada94e62c41ecd5ac + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 1000 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e0b9b4b0614705fb4f4e872d47dbe80a + trainer: + kwargs: {} +name: e27c1bd50dc8e8aada94e62c41ecd5ac +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/e88abe964cb272442dd2e97c8551737e/params.yaml b/examples/security/truthseeker/output/reports/train/e88abe964cb272442dd2e97c8551737e/params.yaml new file mode 100644 index 00000000..a7df8ebe --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/e88abe964cb272442dd2e97c8551737e/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/adv_predictions.json + adv_probabilities_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/387b7990a5aa73864fcf50b0c5a121b4.pkl + params_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/params.yaml + predictions_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/predictions.json + probabilities_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/probabilities.json + score_dict_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/score_dict.json + test_labels_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/test_labels.json + train_labels_file: output/reports/train/e88abe964cb272442dd2e97c8551737e/train_labels.json + model_dir: models + name: e88abe964cb272442dd2e97c8551737e + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 65cebe6573a5b844a9aa7938616ad3d3 + trainer: + kwargs: {} +name: e88abe964cb272442dd2e97c8551737e +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/eb2bb56555c4e88230c14991a9f0df1f/params.yaml b/examples/security/truthseeker/output/reports/train/eb2bb56555c4e88230c14991a9f0df1f/params.yaml new file mode 100644 index 00000000..204c8035 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/eb2bb56555c4e88230c14991a9f0df1f/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/eb2bb56555c4e88230c14991a9f0df1f/adv_predictions.json + adv_probabilities_file: output/reports/train/eb2bb56555c4e88230c14991a9f0df1f/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/0206d4cb6dfcdfeee6548880cbf606d7.pkl + params_file: output/reports/train/eb2bb56555c4e88230c14991a9f0df1f/params.yaml + predictions_file: output/reports/train/eb2bb56555c4e88230c14991a9f0df1f/predictions.json + probabilities_file: output/reports/train/eb2bb56555c4e88230c14991a9f0df1f/probabilities.json + score_dict_file: output/reports/train/eb2bb56555c4e88230c14991a9f0df1f/score_dict.json + test_labels_file: output/reports/train/eb2bb56555c4e88230c14991a9f0df1f/test_labels.json + train_labels_file: output/reports/train/eb2bb56555c4e88230c14991a9f0df1f/train_labels.json + model_dir: models + name: eb2bb56555c4e88230c14991a9f0df1f + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.0001 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: bfb1304ea33c4b8cbbc41a5b0873f03d + trainer: + kwargs: {} +name: eb2bb56555c4e88230c14991a9f0df1f +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ec245700c43f018b45adf84e3eaa28aa/params.yaml b/examples/security/truthseeker/output/reports/train/ec245700c43f018b45adf84e3eaa28aa/params.yaml new file mode 100644 index 00000000..9bda38e5 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ec245700c43f018b45adf84e3eaa28aa/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/adv_predictions.json + adv_probabilities_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f22037fd146bb977203c9b5c09a4ff98.pkl + params_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/params.yaml + predictions_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/predictions.json + probabilities_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/probabilities.json + score_dict_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/score_dict.json + test_labels_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/test_labels.json + train_labels_file: output/reports/train/ec245700c43f018b45adf84e3eaa28aa/train_labels.json + model_dir: models + name: ec245700c43f018b45adf84e3eaa28aa + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d512c6c95bd99aa3801425889014ec6a + trainer: + kwargs: {} +name: ec245700c43f018b45adf84e3eaa28aa +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ec2d5af38d1245a491b674d3b339dcb5/params.yaml b/examples/security/truthseeker/output/reports/train/ec2d5af38d1245a491b674d3b339dcb5/params.yaml new file mode 100644 index 00000000..dfec38d3 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ec2d5af38d1245a491b674d3b339dcb5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec2d5af38d1245a491b674d3b339dcb5/adv_predictions.json + adv_probabilities_file: output/reports/train/ec2d5af38d1245a491b674d3b339dcb5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/f57611841a2c2100af806b2168c0a563.pkl + params_file: output/reports/train/ec2d5af38d1245a491b674d3b339dcb5/params.yaml + predictions_file: output/reports/train/ec2d5af38d1245a491b674d3b339dcb5/predictions.json + probabilities_file: output/reports/train/ec2d5af38d1245a491b674d3b339dcb5/probabilities.json + score_dict_file: output/reports/train/ec2d5af38d1245a491b674d3b339dcb5/score_dict.json + test_labels_file: output/reports/train/ec2d5af38d1245a491b674d3b339dcb5/test_labels.json + train_labels_file: output/reports/train/ec2d5af38d1245a491b674d3b339dcb5/train_labels.json + model_dir: models + name: ec2d5af38d1245a491b674d3b339dcb5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 0.001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a5876676511f7ac1d8bca0d1101859bc + trainer: + kwargs: {} +name: ec2d5af38d1245a491b674d3b339dcb5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ec71fff75e507869a5bed6e6d0cbf235/params.yaml b/examples/security/truthseeker/output/reports/train/ec71fff75e507869a5bed6e6d0cbf235/params.yaml new file mode 100644 index 00000000..361d58b2 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ec71fff75e507869a5bed6e6d0cbf235/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ec71fff75e507869a5bed6e6d0cbf235/adv_predictions.json + adv_probabilities_file: output/reports/train/ec71fff75e507869a5bed6e6d0cbf235/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/1f97094aad8f73f7351797ebda7c2a7e.pkl + params_file: output/reports/train/ec71fff75e507869a5bed6e6d0cbf235/params.yaml + predictions_file: output/reports/train/ec71fff75e507869a5bed6e6d0cbf235/predictions.json + probabilities_file: output/reports/train/ec71fff75e507869a5bed6e6d0cbf235/probabilities.json + score_dict_file: output/reports/train/ec71fff75e507869a5bed6e6d0cbf235/score_dict.json + test_labels_file: output/reports/train/ec71fff75e507869a5bed6e6d0cbf235/test_labels.json + train_labels_file: output/reports/train/ec71fff75e507869a5bed6e6d0cbf235/train_labels.json + model_dir: models + name: ec71fff75e507869a5bed6e6d0cbf235 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.0001 + coef0: 1000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2c0c9ad03d00d42e6d57c4aa9528b1e7 + trainer: + kwargs: {} +name: ec71fff75e507869a5bed6e6d0cbf235 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ece3f839b68ccc4177d3e98ae6077e3a/params.yaml b/examples/security/truthseeker/output/reports/train/ece3f839b68ccc4177d3e98ae6077e3a/params.yaml new file mode 100644 index 00000000..48993110 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ece3f839b68ccc4177d3e98ae6077e3a/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ece3f839b68ccc4177d3e98ae6077e3a/adv_predictions.json + adv_probabilities_file: output/reports/train/ece3f839b68ccc4177d3e98ae6077e3a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ff990ed0f57f174b7590b758201f256b.pkl + params_file: output/reports/train/ece3f839b68ccc4177d3e98ae6077e3a/params.yaml + predictions_file: output/reports/train/ece3f839b68ccc4177d3e98ae6077e3a/predictions.json + probabilities_file: output/reports/train/ece3f839b68ccc4177d3e98ae6077e3a/probabilities.json + score_dict_file: output/reports/train/ece3f839b68ccc4177d3e98ae6077e3a/score_dict.json + test_labels_file: output/reports/train/ece3f839b68ccc4177d3e98ae6077e3a/test_labels.json + train_labels_file: output/reports/train/ece3f839b68ccc4177d3e98ae6077e3a/train_labels.json + model_dir: models + name: ece3f839b68ccc4177d3e98ae6077e3a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 3c139e6fef821923ed36d605d6f54dfe + trainer: + kwargs: {} +name: ece3f839b68ccc4177d3e98ae6077e3a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ed08ab9d5d87d0669de17b24b8d113e4/params.yaml b/examples/security/truthseeker/output/reports/train/ed08ab9d5d87d0669de17b24b8d113e4/params.yaml new file mode 100644 index 00000000..3e98854e --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ed08ab9d5d87d0669de17b24b8d113e4/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ed08ab9d5d87d0669de17b24b8d113e4/adv_predictions.json + adv_probabilities_file: output/reports/train/ed08ab9d5d87d0669de17b24b8d113e4/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c1ec9746f558e7ddfb5f6b9dc8ac966c.pkl + params_file: output/reports/train/ed08ab9d5d87d0669de17b24b8d113e4/params.yaml + predictions_file: output/reports/train/ed08ab9d5d87d0669de17b24b8d113e4/predictions.json + probabilities_file: output/reports/train/ed08ab9d5d87d0669de17b24b8d113e4/probabilities.json + score_dict_file: output/reports/train/ed08ab9d5d87d0669de17b24b8d113e4/score_dict.json + test_labels_file: output/reports/train/ed08ab9d5d87d0669de17b24b8d113e4/test_labels.json + train_labels_file: output/reports/train/ed08ab9d5d87d0669de17b24b8d113e4/train_labels.json + model_dir: models + name: ed08ab9d5d87d0669de17b24b8d113e4 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 1000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: b73f34002b9304b094fc1452ba43ebc0 + trainer: + kwargs: {} +name: ed08ab9d5d87d0669de17b24b8d113e4 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ef50b82603186c7b1464761a888be5af/params.yaml b/examples/security/truthseeker/output/reports/train/ef50b82603186c7b1464761a888be5af/params.yaml new file mode 100644 index 00000000..8449b502 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ef50b82603186c7b1464761a888be5af/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ef50b82603186c7b1464761a888be5af/adv_predictions.json + adv_probabilities_file: output/reports/train/ef50b82603186c7b1464761a888be5af/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bfa04c65d73dda65570ec10c321b23aa.pkl + params_file: output/reports/train/ef50b82603186c7b1464761a888be5af/params.yaml + predictions_file: output/reports/train/ef50b82603186c7b1464761a888be5af/predictions.json + probabilities_file: output/reports/train/ef50b82603186c7b1464761a888be5af/probabilities.json + score_dict_file: output/reports/train/ef50b82603186c7b1464761a888be5af/score_dict.json + test_labels_file: output/reports/train/ef50b82603186c7b1464761a888be5af/test_labels.json + train_labels_file: output/reports/train/ef50b82603186c7b1464761a888be5af/train_labels.json + model_dir: models + name: ef50b82603186c7b1464761a888be5af + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e664d1caf5ec8bd8f9c343c17e2f01c5 + trainer: + kwargs: {} +name: ef50b82603186c7b1464761a888be5af +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/f05b17164e87b1148e24f9df9e51fec2/params.yaml b/examples/security/truthseeker/output/reports/train/f05b17164e87b1148e24f9df9e51fec2/params.yaml new file mode 100644 index 00000000..395e1542 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/f05b17164e87b1148e24f9df9e51fec2/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/adv_predictions.json + adv_probabilities_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/5093d371892c3a6a7ee9292ab8b94253.pkl + params_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/params.yaml + predictions_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/predictions.json + probabilities_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/probabilities.json + score_dict_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/score_dict.json + test_labels_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/test_labels.json + train_labels_file: output/reports/train/f05b17164e87b1148e24f9df9e51fec2/train_labels.json + model_dir: models + name: f05b17164e87b1148e24f9df9e51fec2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1c6dd8cb719752e44361e6794ed712d8 + trainer: + kwargs: {} +name: f05b17164e87b1148e24f9df9e51fec2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/params.yaml b/examples/security/truthseeker/output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/params.yaml new file mode 100644 index 00000000..d393d842 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/params.yaml @@ -0,0 +1,103 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/adv_predictions.json + adv_probabilities_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c8a766691a146281d9f12baa9976ecf3.pkl + params_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/params.yaml + predictions_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/predictions.json + probabilities_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/probabilities.json + score_dict_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/score_dict.json + test_labels_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/test_labels.json + train_labels_file: output/reports/train/f0d37f0757baf7c419b7e01d7d02ebd5/train_labels.json + model_dir: models + name: f0d37f0757baf7c419b7e01d7d02ebd5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + kernel: linear + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 68755efc740e1fc17bb7d4ea8acc24ee + trainer: + kwargs: {} +name: f0d37f0757baf7c419b7e01d7d02ebd5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/f0fb6406f33807a1101696f9f664dddf/params.yaml b/examples/security/truthseeker/output/reports/train/f0fb6406f33807a1101696f9f664dddf/params.yaml new file mode 100644 index 00000000..bccb44d8 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/f0fb6406f33807a1101696f9f664dddf/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/adv_predictions.json + adv_probabilities_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/8727ee1b289644aaae5eff304cd623f7.pkl + params_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/params.yaml + predictions_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/predictions.json + probabilities_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/probabilities.json + score_dict_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/score_dict.json + test_labels_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/test_labels.json + train_labels_file: output/reports/train/f0fb6406f33807a1101696f9f664dddf/train_labels.json + model_dir: models + name: f0fb6406f33807a1101696f9f664dddf + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.0001 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 1d8c4d1238c3dab30dea7d6fd3efaf6c + trainer: + kwargs: {} +name: f0fb6406f33807a1101696f9f664dddf +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/params.yaml b/examples/security/truthseeker/output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/params.yaml new file mode 100644 index 00000000..379f3e18 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/adv_predictions.json + adv_probabilities_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/e50f4c5bfc99a6c37d5287e22221f521.pkl + params_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/params.yaml + predictions_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/predictions.json + probabilities_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/probabilities.json + score_dict_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/score_dict.json + test_labels_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/test_labels.json + train_labels_file: output/reports/train/f47e538760a5680fb0f2f2b0460e1dad/train_labels.json + model_dir: models + name: f47e538760a5680fb0f2f2b0460e1dad + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 1 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4d3fd72c06fee7850b2776f693ef2e25 + trainer: + kwargs: {} +name: f47e538760a5680fb0f2f2b0460e1dad +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/params.yaml b/examples/security/truthseeker/output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/params.yaml new file mode 100644 index 00000000..f89b4905 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/adv_predictions.json + adv_probabilities_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3c099f7d35a561775856e570f99ab671.pkl + params_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/params.yaml + predictions_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/predictions.json + probabilities_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/probabilities.json + score_dict_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/score_dict.json + test_labels_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/test_labels.json + train_labels_file: output/reports/train/f57619ef894e7c8d9407fb8e4070abe5/train_labels.json + model_dir: models + name: f57619ef894e7c8d9407fb8e4070abe5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.001 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 802d4e141f5d7f0649aa64caf1cc8ce6 + trainer: + kwargs: {} +name: f57619ef894e7c8d9407fb8e4070abe5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/params.yaml b/examples/security/truthseeker/output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/params.yaml new file mode 100644 index 00000000..2457d79d --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/adv_predictions.json + adv_probabilities_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/4bb1b9b293a4125e22ed3c86bd74f9d8.pkl + params_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/params.yaml + predictions_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/predictions.json + probabilities_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/probabilities.json + score_dict_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/score_dict.json + test_labels_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/test_labels.json + train_labels_file: output/reports/train/f5f6879d55a59ef2713cf5d242b67e44/train_labels.json + model_dir: models + name: f5f6879d55a59ef2713cf5d242b67e44 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2be23235cd542bf8c370083a68d1c4af + trainer: + kwargs: {} +name: f5f6879d55a59ef2713cf5d242b67e44 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/f60d06aa3efadbc1757ff989003323a5/params.yaml b/examples/security/truthseeker/output/reports/train/f60d06aa3efadbc1757ff989003323a5/params.yaml new file mode 100644 index 00000000..b3c3f015 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/f60d06aa3efadbc1757ff989003323a5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/adv_predictions.json + adv_probabilities_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/6f814be8b0f1277068c18650de92ee9a.pkl + params_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/params.yaml + predictions_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/predictions.json + probabilities_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/probabilities.json + score_dict_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/score_dict.json + test_labels_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/test_labels.json + train_labels_file: output/reports/train/f60d06aa3efadbc1757ff989003323a5/train_labels.json + model_dir: models + name: f60d06aa3efadbc1757ff989003323a5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e5821b1f69713fad232d39d2f92629de + trainer: + kwargs: {} +name: f60d06aa3efadbc1757ff989003323a5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/f6a420bd66357f3a33017e36aa3aff00/params.yaml b/examples/security/truthseeker/output/reports/train/f6a420bd66357f3a33017e36aa3aff00/params.yaml new file mode 100644 index 00000000..3cbe729d --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/f6a420bd66357f3a33017e36aa3aff00/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/adv_predictions.json + adv_probabilities_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/404db9b50b978eec1223c4c83e2cb552.pkl + params_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/params.yaml + predictions_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/predictions.json + probabilities_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/probabilities.json + score_dict_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/score_dict.json + test_labels_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/test_labels.json + train_labels_file: output/reports/train/f6a420bd66357f3a33017e36aa3aff00/train_labels.json + model_dir: models + name: f6a420bd66357f3a33017e36aa3aff00 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10000 + coef0: 10000 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: e568cfb9e0fb8c421e0ca463ffd0ca2a + trainer: + kwargs: {} +name: f6a420bd66357f3a33017e36aa3aff00 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/f8553451873d6ba64dd128609edff255/params.yaml b/examples/security/truthseeker/output/reports/train/f8553451873d6ba64dd128609edff255/params.yaml new file mode 100644 index 00000000..df759ff1 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/f8553451873d6ba64dd128609edff255/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f8553451873d6ba64dd128609edff255/adv_predictions.json + adv_probabilities_file: output/reports/train/f8553451873d6ba64dd128609edff255/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bd19f0f939ba8af43ef8cf8d21b6d7a3.pkl + params_file: output/reports/train/f8553451873d6ba64dd128609edff255/params.yaml + predictions_file: output/reports/train/f8553451873d6ba64dd128609edff255/predictions.json + probabilities_file: output/reports/train/f8553451873d6ba64dd128609edff255/probabilities.json + score_dict_file: output/reports/train/f8553451873d6ba64dd128609edff255/score_dict.json + test_labels_file: output/reports/train/f8553451873d6ba64dd128609edff255/test_labels.json + train_labels_file: output/reports/train/f8553451873d6ba64dd128609edff255/train_labels.json + model_dir: models + name: f8553451873d6ba64dd128609edff255 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.1 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 61fb1856c33aaed54316c2b6db22b2de + trainer: + kwargs: {} +name: f8553451873d6ba64dd128609edff255 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/f864d8f0f479e23ec088a5a18649686b/params.yaml b/examples/security/truthseeker/output/reports/train/f864d8f0f479e23ec088a5a18649686b/params.yaml new file mode 100644 index 00000000..4344f1df --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/f864d8f0f479e23ec088a5a18649686b/params.yaml @@ -0,0 +1,105 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/f864d8f0f479e23ec088a5a18649686b/adv_predictions.json + adv_probabilities_file: output/reports/train/f864d8f0f479e23ec088a5a18649686b/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c015fb0c7d00783f3ed57f0d616f069e.pkl + params_file: output/reports/train/f864d8f0f479e23ec088a5a18649686b/params.yaml + predictions_file: output/reports/train/f864d8f0f479e23ec088a5a18649686b/predictions.json + probabilities_file: output/reports/train/f864d8f0f479e23ec088a5a18649686b/probabilities.json + score_dict_file: output/reports/train/f864d8f0f479e23ec088a5a18649686b/score_dict.json + test_labels_file: output/reports/train/f864d8f0f479e23ec088a5a18649686b/test_labels.json + train_labels_file: output/reports/train/f864d8f0f479e23ec088a5a18649686b/train_labels.json + model_dir: models + name: f864d8f0f479e23ec088a5a18649686b + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.01 + gamma: scale + kernel: rbf + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: f34695ec15a3a015ca43fd48473ab07d + trainer: + kwargs: {} +name: f864d8f0f479e23ec088a5a18649686b +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/fa3c62f4a9544a63018848a931ab559a/params.yaml b/examples/security/truthseeker/output/reports/train/fa3c62f4a9544a63018848a931ab559a/params.yaml new file mode 100644 index 00000000..08c282df --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/fa3c62f4a9544a63018848a931ab559a/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/adv_predictions.json + adv_probabilities_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c0a77730cb0dacdbe00f6f79808eb356.pkl + params_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/params.yaml + predictions_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/predictions.json + probabilities_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/probabilities.json + score_dict_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/score_dict.json + test_labels_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/test_labels.json + train_labels_file: output/reports/train/fa3c62f4a9544a63018848a931ab559a/train_labels.json + model_dir: models + name: fa3c62f4a9544a63018848a931ab559a + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 100 + coef0: 1 + degree: 2 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: a130b62263caf44429cd13d6d4ab5b5f + trainer: + kwargs: {} +name: fa3c62f4a9544a63018848a931ab559a +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/fa9c60a66514839cb62f3a6be1b53f54/params.yaml b/examples/security/truthseeker/output/reports/train/fa9c60a66514839cb62f3a6be1b53f54/params.yaml new file mode 100644 index 00000000..e13af905 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/fa9c60a66514839cb62f3a6be1b53f54/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fa9c60a66514839cb62f3a6be1b53f54/adv_predictions.json + adv_probabilities_file: output/reports/train/fa9c60a66514839cb62f3a6be1b53f54/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/9ab448b0a1644493dd4eb9f7450cec18.pkl + params_file: output/reports/train/fa9c60a66514839cb62f3a6be1b53f54/params.yaml + predictions_file: output/reports/train/fa9c60a66514839cb62f3a6be1b53f54/predictions.json + probabilities_file: output/reports/train/fa9c60a66514839cb62f3a6be1b53f54/probabilities.json + score_dict_file: output/reports/train/fa9c60a66514839cb62f3a6be1b53f54/score_dict.json + test_labels_file: output/reports/train/fa9c60a66514839cb62f3a6be1b53f54/test_labels.json + train_labels_file: output/reports/train/fa9c60a66514839cb62f3a6be1b53f54/train_labels.json + model_dir: models + name: fa9c60a66514839cb62f3a6be1b53f54 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 10 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 4fb4e1bea0c5f173a80f13a24fcb257a + trainer: + kwargs: {} +name: fa9c60a66514839cb62f3a6be1b53f54 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/fab432dff2bfa28ba848daef4ee8e247/params.yaml b/examples/security/truthseeker/output/reports/train/fab432dff2bfa28ba848daef4ee8e247/params.yaml new file mode 100644 index 00000000..5f5b8190 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/fab432dff2bfa28ba848daef4ee8e247/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/adv_predictions.json + adv_probabilities_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/c1e1c409082581259254f5d0488f6249.pkl + params_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/params.yaml + predictions_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/predictions.json + probabilities_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/probabilities.json + score_dict_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/score_dict.json + test_labels_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/test_labels.json + train_labels_file: output/reports/train/fab432dff2bfa28ba848daef4ee8e247/train_labels.json + model_dir: models + name: fab432dff2bfa28ba848daef4ee8e247 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1000 + coef0: 10000 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8ae1ac2e219102b34cb4f6a5958a3265 + trainer: + kwargs: {} +name: fab432dff2bfa28ba848daef4ee8e247 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/params.yaml b/examples/security/truthseeker/output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/params.yaml new file mode 100644 index 00000000..6119d273 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/adv_predictions.json + adv_probabilities_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/b9b7c23c21523a4270ac9ca5c3c91d5d.pkl + params_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/params.yaml + predictions_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/predictions.json + probabilities_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/probabilities.json + score_dict_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/score_dict.json + test_labels_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/test_labels.json + train_labels_file: output/reports/train/fac893616a6d51f6983cf1ed1c5845e2/train_labels.json + model_dir: models + name: fac893616a6d51f6983cf1ed1c5845e2 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.01 + coef0: 0.01 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: d180ea53c9ce0b1d7a6b85ff6982c244 + trainer: + kwargs: {} +name: fac893616a6d51f6983cf1ed1c5845e2 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/params.yaml b/examples/security/truthseeker/output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/params.yaml new file mode 100644 index 00000000..25da0f70 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/adv_predictions.json + adv_probabilities_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/ebe4a26e8e3793bd4867d3616bf37d06.pkl + params_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/params.yaml + predictions_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/predictions.json + probabilities_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/probabilities.json + score_dict_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/score_dict.json + test_labels_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/test_labels.json + train_labels_file: output/reports/train/fb6dc710a19c8feed52059f69c2f92f5/train_labels.json + model_dir: models + name: fb6dc710a19c8feed52059f69c2f92f5 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 0.001 + coef0: 100 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 258790f91e3a1de52882242139e7f793 + trainer: + kwargs: {} +name: fb6dc710a19c8feed52059f69c2f92f5 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/fcaf5bf60becf909634771c964aaceb8/params.yaml b/examples/security/truthseeker/output/reports/train/fcaf5bf60becf909634771c964aaceb8/params.yaml new file mode 100644 index 00000000..41873ee3 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/fcaf5bf60becf909634771c964aaceb8/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/adv_predictions.json + adv_probabilities_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/bbc568c01fbf3b31b130ef4437a7f8a4.pkl + params_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/params.yaml + predictions_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/predictions.json + probabilities_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/probabilities.json + score_dict_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/score_dict.json + test_labels_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/test_labels.json + train_labels_file: output/reports/train/fcaf5bf60becf909634771c964aaceb8/train_labels.json + model_dir: models + name: fcaf5bf60becf909634771c964aaceb8 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 10 + coef0: 0.0001 + degree: 4 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 02c4f4c6f38657d9d00a12c4010b740b + trainer: + kwargs: {} +name: fcaf5bf60becf909634771c964aaceb8 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/fcb13835349e3c8cb40246dfded8ac47/params.yaml b/examples/security/truthseeker/output/reports/train/fcb13835349e3c8cb40246dfded8ac47/params.yaml new file mode 100644 index 00000000..d8db3aaf --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/fcb13835349e3c8cb40246dfded8ac47/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/adv_predictions.json + adv_probabilities_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/d92494cd29ba28727b7f2c9c34f8d4b2.pkl + params_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/params.yaml + predictions_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/predictions.json + probabilities_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/probabilities.json + score_dict_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/score_dict.json + test_labels_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/test_labels.json + train_labels_file: output/reports/train/fcb13835349e3c8cb40246dfded8ac47/train_labels.json + model_dir: models + name: fcb13835349e3c8cb40246dfded8ac47 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10000 + degree: 3 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 2e7c2770e3e726cd9690b917643a0387 + trainer: + kwargs: {} +name: fcb13835349e3c8cb40246dfded8ac47 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/fdfe4a1f5767ad07946b5388af43d7cb/params.yaml b/examples/security/truthseeker/output/reports/train/fdfe4a1f5767ad07946b5388af43d7cb/params.yaml new file mode 100644 index 00000000..252e0adc --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/fdfe4a1f5767ad07946b5388af43d7cb/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/fdfe4a1f5767ad07946b5388af43d7cb/adv_predictions.json + adv_probabilities_file: output/reports/train/fdfe4a1f5767ad07946b5388af43d7cb/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/19f20444cd1b4c8840565ed269bfbc22.pkl + params_file: output/reports/train/fdfe4a1f5767ad07946b5388af43d7cb/params.yaml + predictions_file: output/reports/train/fdfe4a1f5767ad07946b5388af43d7cb/predictions.json + probabilities_file: output/reports/train/fdfe4a1f5767ad07946b5388af43d7cb/probabilities.json + score_dict_file: output/reports/train/fdfe4a1f5767ad07946b5388af43d7cb/score_dict.json + test_labels_file: output/reports/train/fdfe4a1f5767ad07946b5388af43d7cb/test_labels.json + train_labels_file: output/reports/train/fdfe4a1f5767ad07946b5388af43d7cb/train_labels.json + model_dir: models + name: fdfe4a1f5767ad07946b5388af43d7cb + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 0.1 + degree: 1 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 8b4e340177c3f6913ea2cacc35ffdf2b + trainer: + kwargs: {} +name: fdfe4a1f5767ad07946b5388af43d7cb +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/security/truthseeker/output/reports/train/ff770fb00fcb06171fff0938828ab204/params.yaml b/examples/security/truthseeker/output/reports/train/ff770fb00fcb06171fff0938828ab204/params.yaml new file mode 100644 index 00000000..ad3da980 --- /dev/null +++ b/examples/security/truthseeker/output/reports/train/ff770fb00fcb06171fff0938828ab204/params.yaml @@ -0,0 +1,106 @@ +data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label +files: + data_dir: data + files: + adv_predictions_file: output/reports/train/ff770fb00fcb06171fff0938828ab204/adv_predictions.json + adv_probabilities_file: output/reports/train/ff770fb00fcb06171fff0938828ab204/adv_probabilities.json + attack_file: output/attacks/attack.pkl + data_file: output/data/8079bd842507e3e2304c81e0360db636.pkl + model_file: output/models/3929e9ff40e4d37cf4f2e707f2c6076c.pkl + params_file: output/reports/train/ff770fb00fcb06171fff0938828ab204/params.yaml + predictions_file: output/reports/train/ff770fb00fcb06171fff0938828ab204/predictions.json + probabilities_file: output/reports/train/ff770fb00fcb06171fff0938828ab204/probabilities.json + score_dict_file: output/reports/train/ff770fb00fcb06171fff0938828ab204/score_dict.json + test_labels_file: output/reports/train/ff770fb00fcb06171fff0938828ab204/test_labels.json + train_labels_file: output/reports/train/ff770fb00fcb06171fff0938828ab204/train_labels.json + model_dir: models + name: ff770fb00fcb06171fff0938828ab204 + reports: reports + stage: train +kwargs: + direction: maximize +model: + art: + library: sklearn-svc + name: 9b86c9b0cfb8d5e872a0126c0611580a + pipeline: + initialize: + kwargs: {} + name: initialize + data: + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + random_state: 0 + shuffle: true + stratify: true + test_size: 1000 + time_series: false + train_size: 5000 + sklearn_pipeline: + pipeline: + encoder: + kwargs: + handle_unknown: use_encoded_value + unknown_value: -1 + name: sklearn.preprocessing.OrdinalEncoder + preprocessor: + kwargs: + with_mean: true + with_std: true + name: sklearn.preprocessing.StandardScaler + target: label + init: + kwargs: + C: 1 + coef0: 10 + degree: 5 + gamma: scale + kernel: poly + max_iter: 10 + probability: true + random_state: 0 + name: sklearn.svm.SVC + library: sklearn-svc + name: 04a1cc6a88d84ce84c5068afe2e86702 + trainer: + kwargs: {} +name: ff770fb00fcb06171fff0938828ab204 +scorers: + scorers: + accuracy: + alias: accuracy_score + args: + - y_true + - y_pred + direction: maximize + name: sklearn.metrics.accuracy_score + params: {} + log_loss: + alias: log_loss + args: + - y_true + - y_pred + direction: minimize + name: sklearn.metrics.log_loss + params: {} diff --git a/examples/truthseeker/params.yaml b/examples/security/truthseeker/params.yaml similarity index 100% rename from examples/truthseeker/params.yaml rename to examples/security/truthseeker/params.yaml diff --git a/examples/truthseeker/plots.py b/examples/security/truthseeker/plots.py similarity index 100% rename from examples/truthseeker/plots.py rename to examples/security/truthseeker/plots.py diff --git a/examples/truthseeker/plots/.gitignore b/examples/security/truthseeker/plots/.gitignore similarity index 100% rename from examples/truthseeker/plots/.gitignore rename to examples/security/truthseeker/plots/.gitignore diff --git a/examples/truthseeker/retrain.py b/examples/security/truthseeker/retrain.py similarity index 100% rename from examples/truthseeker/retrain.py rename to examples/security/truthseeker/retrain.py From 02ac7ae6a95b52bad87c429ffa05d9e197655078 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Sun, 15 Oct 2023 01:03:40 +0200 Subject: [PATCH 106/148] linting --- deckard/base/attack/attack.py | 14 ++-- deckard/base/data/data.py | 4 +- deckard/layers/afr.py | 6 +- deckard/layers/compile.py | 4 +- deckard/layers/find_best.py | 16 +++-- deckard/layers/optimise.py | 8 ++- .../security/classification/conf/compile.yaml | 2 +- .../classification/conf/data/small.yaml | 2 +- examples/security/classification/models.sh | 2 +- examples/security/classification/plots.py | 11 +-- examples/security/classification/retrain.py | 68 +++++++++++++------ examples/security/kdd-nsl/conf/compile.yaml | 2 +- .../security/kdd-nsl/conf/data/default.yaml | 2 +- .../security/kdd-nsl/conf/data/small.yaml | 2 +- examples/security/kdd-nsl/conf/model/rbf.yaml | 2 +- examples/security/kdd-nsl/other_data.sh | 2 +- examples/security/kdd-nsl/plots.py | 2 +- examples/security/kdd-nsl/retrain.py | 63 +++++++++++------ .../security/truthseeker/conf/compile.yaml | 2 +- .../truthseeker/conf/data/default.yaml | 2 +- .../security/truthseeker/conf/data/small.yaml | 2 +- .../security/truthseeker/conf/model/rbf.yaml | 2 +- examples/security/truthseeker/models.sh | 2 +- examples/security/truthseeker/other_data.sh | 2 +- examples/security/truthseeker/plots.py | 2 +- examples/security/truthseeker/retrain.py | 63 +++++++++++------ 26 files changed, 193 insertions(+), 96 deletions(-) diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index d6309433..daaf16bf 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -98,11 +98,11 @@ def __call__(self, model=None, data=None, attack_size=-1): else: raise e except Exception as e: - if "has not been fitted correctly" in str(e): - model, _ = self.model.fit(data=data, model=model) - attack = instantiate(config, model) - else: - raise e + if "has not been fitted correctly" in str(e): + model, _ = self.model.fit(data=data, model=model) + attack = instantiate(config, model) + else: + raise e return attack @@ -155,7 +155,9 @@ def __call__( if "AdversarialPatch" in self.name: start = process_time_ns() patches, _ = atk.generate(ben_samples, **kwargs) - samples = atk.apply_patch(ben_samples, scale=scale_max, patch_external=patches) + samples = atk.apply_patch( + ben_samples, scale=scale_max, patch_external=patches + ) else: start = process_time_ns() samples = atk.generate(ben_samples, **kwargs) diff --git a/deckard/base/data/data.py b/deckard/base/data/data.py index 0f1d7431..57248a80 100644 --- a/deckard/base/data/data.py +++ b/deckard/base/data/data.py @@ -102,7 +102,9 @@ def initialize(self): elif isinstance(result, DataFrame) and self.target is not None: if not isinstance(result, DataFrame): result = DataFrame(result) - assert self.target in result, f"Target {self.target} not in data with columns {result.columns}" + assert ( + self.target in result + ), f"Target {self.target} not in data with columns {result.columns}" y = result[self.target] if isinstance(result, DataFrame): X = result.drop(self.target, axis=1) diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index 3c8393da..a40f4150 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -52,10 +52,12 @@ data.dropna(axis=0, subset=["atk_value", "atk_param"], inplace=True) data.dropna(axis=0, subset=["def_value", "def_param"], inplace=True) data.loc[:, "adv_failures"] = (1 - data.loc[:, "adv_accuracy"]) * data.loc[ - :, "attack.attack_size" + :, + "attack.attack_size", ] data.loc[:, "ben_failures"] = (1 - data.loc[:, "accuracy"]) * data.loc[ - :, "attack.attack_size" + :, + "attack.attack_size", ] def plot_aft( diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 18c80caf..d3121187 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -139,7 +139,7 @@ def parse_results(folder, files=["score_dict.json", "params.yaml"]): def format_control_parameter(data, control_dict): logger.info("Formatting control parameters...") - + if hasattr(data, "def_gen"): defences = data.def_gen.unique() else: @@ -148,7 +148,7 @@ def format_control_parameter(data, control_dict): attacks = data.atk_gen.unique() else: attacks = [] - + for defence in defences: if defence in control_dict: param = control_dict[defence] diff --git a/deckard/layers/find_best.py b/deckard/layers/find_best.py index 71982efb..aa3fbba4 100644 --- a/deckard/layers/find_best.py +++ b/deckard/layers/find_best.py @@ -49,14 +49,18 @@ def find_optuna_best( if params_file is not None: if params_file is True: if config_subdir is not None: - params_file = Path(config_folder, f"{config_subdir}", f"{default_config}.yaml") + params_file = Path( + config_folder, f"{config_subdir}", f"{default_config}.yaml" + ) params = cfg.get(config_subdir) else: params_file = Path(config_folder, f"{default_config}.yaml") params = cfg else: if config_subdir is not None: - params_file = Path(config_folder, f"{config_subdir}", f"{params_file}.yaml") + params_file = Path( + config_folder, f"{config_subdir}", f"{params_file}.yaml" + ) params = cfg.get(config_subdir) else: params = cfg @@ -64,7 +68,9 @@ def find_optuna_best( params_file.parent.mkdir(parents=True, exist_ok=True) with open(params_file.with_suffix(".yaml"), "w") as f: yaml.dump(params, f) - assert params_file.exists(), f"{params_file.resolve().as_posix()} does not exist." + assert ( + params_file.exists() + ), f"{params_file.resolve().as_posix()} does not exist." return params @@ -86,7 +92,9 @@ def find_optuna_best( args.config_folder = Path(args.config_folder).resolve().as_posix() if args.study_type == "optuna": - with open(Path(args.config_folder, args.default_config).with_suffix(".yaml"), "r") as f: + with open( + Path(args.config_folder, args.default_config).with_suffix(".yaml"), "r" + ) as f: default_params = yaml.load(f, Loader=yaml.FullLoader) if "hydra" in default_params: hydra_params = default_params.pop("hydra") diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index fbb80e25..29626fba 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -84,7 +84,7 @@ def merge_params(default, params) -> dict: """ for key, value in params.items(): if key in default and isinstance(value, dict) and value is not None: - default[key] = merge_params(default[key], value) + default[key] = merge_params(default[key], value) elif ( isinstance(value, (list, tuple, int, float, str, bool, dict)) and value is not None @@ -157,7 +157,8 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: with open(Path(path, "params.yaml"), "r") as f: default_params = yaml.load(f, Loader=yaml.FullLoader) default_params = OmegaConf.to_container( - OmegaConf.create(default_params), resolve=True, + OmegaConf.create(default_params), + resolve=True, ) key_list = [] for stage in stages: @@ -172,7 +173,8 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: with open(Path(params), "r") as f: params = yaml.load(f, Loader=yaml.FullLoader) params = OmegaConf.to_container( - OmegaConf.create(params), resolve=True, + OmegaConf.create(params), + resolve=True, ) assert isinstance( params, diff --git a/examples/security/classification/conf/compile.yaml b/examples/security/classification/conf/compile.yaml index 109994af..817d2b4e 100644 --- a/examples/security/classification/conf/compile.yaml +++ b/examples/security/classification/conf/compile.yaml @@ -5,4 +5,4 @@ defences: Control: Control params: Control: model.init.kwargs.kernel - PGD: attack.init.kwargs.eps \ No newline at end of file + PGD: attack.init.kwargs.eps diff --git a/examples/security/classification/conf/data/small.yaml b/examples/security/classification/conf/data/small.yaml index ff01d089..2207d717 100644 --- a/examples/security/classification/conf/data/small.yaml +++ b/examples/security/classification/conf/data/small.yaml @@ -17,4 +17,4 @@ sklearn_pipeline: # name: sklearn.preprocessing.StandardScaler with_mean: True - with_std: True \ No newline at end of file + with_std: True diff --git a/examples/security/classification/models.sh b/examples/security/classification/models.sh index 6934ee1a..2d5a7b8a 100644 --- a/examples/security/classification/models.sh +++ b/examples/security/classification/models.sh @@ -53,4 +53,4 @@ for train_size in ${TRAIN_SIZES[@]}; do echo "Successfully completed experiment ${i} of ${TOTAL}" >> log.txt done; done; -done; \ No newline at end of file +done; diff --git a/examples/security/classification/plots.py b/examples/security/classification/plots.py index ba7aac20..b5779ff3 100644 --- a/examples/security/classification/plots.py +++ b/examples/security/classification/plots.py @@ -20,7 +20,10 @@ # else: # results = parse_results("reports/model_queue/") results = pd.read_csv("output/train.csv") -input_size = results["data.generate.kwargs.n_samples"] * results["data.generate.kwargs.n_features"] +input_size = ( + results["data.generate.kwargs.n_samples"] + * results["data.generate.kwargs.n_features"] +) results["Kernel"] = results["model.init.kwargs.kernel"].copy() results["Features"] = results["data.generate.kwargs.n_features"].copy() results["Samples"] = results["data.sample.train_size"].copy() @@ -56,7 +59,7 @@ graph1.set_ylabel("Accuracy") graph1.set_xscale("log") graph1.get_figure().tight_layout() -graph1.set(xlim=(10,1e6)) +graph1.set(xlim=(10, 1e6)) graph1.get_figure().savefig("plots/accuracy_vs_samples.pdf") plt.gcf().clear() @@ -100,7 +103,7 @@ ) graph4.set_xlabel("Number of Samples") graph4.set_ylabel("Training Time") -graph4.set(yscale="log", xscale="log", xlim=(10,1e6)) +graph4.set(yscale="log", xscale="log", xlim=(10, 1e6)) graph4.legend(title="Kernel") graph4.get_figure().tight_layout() graph4.get_figure().savefig("plots/train_time_vs_samples.pdf") @@ -366,4 +369,4 @@ graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") fig.tight_layout(h_pad=0.5) fig.savefig("plots/retrain_confidence_vs_attack_parameters.pdf") -plt.gcf().clear() \ No newline at end of file +plt.gcf().clear() diff --git a/examples/security/classification/retrain.py b/examples/security/classification/retrain.py index 1fa27e49..b8a0ea7e 100644 --- a/examples/security/classification/retrain.py +++ b/examples/security/classification/retrain.py @@ -21,6 +21,8 @@ logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) + + def parse_folder( folder, exclude=[ @@ -64,6 +66,7 @@ def flatten_results(results): new_results = pd.concat([new_results, tmp], axis=1) return new_results + def parse_results(result_dir, flatten=True): """ Recursively parse a directory containing json files and return a dataframe with the results. @@ -130,6 +133,8 @@ def unflatten_results(df, sep=".") -> List[dict]: parsed_row = set_for_keys(parsed_row, keys, val) result.append(parsed_row) return result + + def retrain_loop( clf, X_train, @@ -232,7 +237,10 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: # Parse Model Results results = pd.read_csv("output/train.csv") # Some convenient variable names -input_size = results["data.generate.kwargs.n_samples"] * results["data.generate.kwargs.n_features"] +input_size = ( + results["data.generate.kwargs.n_samples"] + * results["data.generate.kwargs.n_features"] +) results["Kernel"] = results["model.init.kwargs.kernel"].copy() results["Features"] = results["data.generate.kwargs.n_features"].copy() results["Samples"] = results["data.sample.train_size"].copy() @@ -248,16 +256,16 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: subset = subset[subset["data.generate.kwargs.n_features"] == 100] with open("conf/model/best_rbf.yaml", "r") as f: best_rbf = yaml.safe_load(f) -best_rbf['init'].pop("_target_", None) -best_rbf['init'].pop("name", None) +best_rbf["init"].pop("_target_", None) +best_rbf["init"].pop("name", None) with open("conf/model/best_poly.yaml", "r") as f: best_poly = yaml.safe_load(f) -best_poly['init'].pop("_target_", None) -best_poly['init'].pop("name", None) +best_poly["init"].pop("_target_", None) +best_poly["init"].pop("name", None) with open("conf/model/best_linear.yaml", "r") as f: best_lin = yaml.safe_load(f) -best_lin['init'].pop("_target_", None) -best_lin['init'].pop("name", None) +best_lin["init"].pop("_target_", None) +best_lin["init"].pop("name", None) rbf_model = SVC(**best_rbf["init"]) # Poly poly_model = SVC(**best_poly["init"]) @@ -267,7 +275,7 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: with open("conf/data/attack.yaml", "r") as f: data = yaml.safe_load(f) data = instantiate(data) -X_train, X_test, y_train, y_test = data() +X_train, X_test, y_train, y_test = data() # Fit Models rbf_model.fit(X_train, y_train) poly_model.fit(X_train, y_train) @@ -301,12 +309,16 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: for folder in tqdm(os.listdir("output/reports/attack")): confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): - with open(Path("output/reports/attack", folder, "adv_probabilities.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "adv_probabilities.json"), "r" + ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) - with open(Path("output/reports/attack", folder, "score_dict.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "score_dict.json"), "r" + ) as f: try: scores = json.load(f) except: # noqa E722 @@ -314,7 +326,9 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob - with open(Path("output/reports/attack", folder, "score_dict.json"), "w") as f: + with open( + Path("output/reports/attack", folder, "score_dict.json"), "w" + ) as f: json.dump(scores, f) yaml_file = Path("output/reports/attack", folder, "params.yaml") json_file = Path("output/reports/attack", folder, "params.json") @@ -328,8 +342,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: params = json.load(f) else: raise ValueError(f"No params file found for {folder}") - attack_params = params["attack"]["init"]['kwargs'] - attack_params.update({"name" : params["attack"]["init"]["name"]}) + attack_params = params["attack"]["init"]["kwargs"] + attack_params.update({"name": params["attack"]["init"]["name"]}) confidence_ser["Kernel"] = name confidence_ser["Average False Confidence"] = avg_prob # print(f"Shape of confidence ser: {confidence_ser.shape}") @@ -373,32 +387,46 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: for folder in tqdm(os.listdir("output/reports/attack")): confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): - with open(Path("output/reports/attack", folder, "adv_probabilities.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "adv_probabilities.json"), "r" + ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) pd.DataFrame(probs).to_csv( - Path("output/reports/attack", folder, f"probs_after_retraining_{name}.csv"), + Path( + "output/reports/attack", + folder, + f"probs_after_retraining_{name}.csv", + ), ) - with open(Path("output/reports/attack", folder, "score_dict.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "score_dict.json"), "r" + ) as f: scores = json.load(f) if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence {name.capitalize()}"] = avg_prob - with open(Path("output/reports/attack", folder, "score_dict.json"), "w") as f: + with open( + Path("output/reports/attack", folder, "score_dict.json"), "w" + ) as f: json.dump(scores, f) if Path("output/reports/attack", folder, "params.yaml").exists(): - with open(Path("output/reports/attack", folder, "params.yaml"), "r") as f: + with open( + Path("output/reports/attack", folder, "params.yaml"), "r" + ) as f: params = yaml.safe_load(f) elif Path("output/reports/attack", folder, "params.json").exists(): - with open(Path("output/reports/attack", folder, "params.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "params.json"), "r" + ) as f: params = json.load(f) else: logger.warning(f"No params file found for {folder}") continue attack_params = params["attack"]["init"]["kwargs"] - attack_params.update({"name" : params["attack"]["init"]["name"]}) + attack_params.update({"name": params["attack"]["init"]["name"]}) confidence_ser["Kernel"] = name confidence_ser["Average False Confidence After Retraining"] = avg_prob attack_ser = pd.Series(attack_params) diff --git a/examples/security/kdd-nsl/conf/compile.yaml b/examples/security/kdd-nsl/conf/compile.yaml index 109994af..817d2b4e 100644 --- a/examples/security/kdd-nsl/conf/compile.yaml +++ b/examples/security/kdd-nsl/conf/compile.yaml @@ -5,4 +5,4 @@ defences: Control: Control params: Control: model.init.kwargs.kernel - PGD: attack.init.kwargs.eps \ No newline at end of file + PGD: attack.init.kwargs.eps diff --git a/examples/security/kdd-nsl/conf/data/default.yaml b/examples/security/kdd-nsl/conf/data/default.yaml index b9487f7e..b3d67001 100644 --- a/examples/security/kdd-nsl/conf/data/default.yaml +++ b/examples/security/kdd-nsl/conf/data/default.yaml @@ -1,2 +1,2 @@ defaults: - - kdd_nsl \ No newline at end of file + - kdd_nsl diff --git a/examples/security/kdd-nsl/conf/data/small.yaml b/examples/security/kdd-nsl/conf/data/small.yaml index ff01d089..2207d717 100644 --- a/examples/security/kdd-nsl/conf/data/small.yaml +++ b/examples/security/kdd-nsl/conf/data/small.yaml @@ -17,4 +17,4 @@ sklearn_pipeline: # name: sklearn.preprocessing.StandardScaler with_mean: True - with_std: True \ No newline at end of file + with_std: True diff --git a/examples/security/kdd-nsl/conf/model/rbf.yaml b/examples/security/kdd-nsl/conf/model/rbf.yaml index 036bc65f..8b2d61af 100644 --- a/examples/security/kdd-nsl/conf/model/rbf.yaml +++ b/examples/security/kdd-nsl/conf/model/rbf.yaml @@ -12,4 +12,4 @@ _target_: deckard.base.model.Model art: _target_ : deckard.base.model.art_pipeline.ArtPipeline library : sklearn-svc - initialize: \ No newline at end of file + initialize: diff --git a/examples/security/kdd-nsl/other_data.sh b/examples/security/kdd-nsl/other_data.sh index 0d3ddcc3..88f33d4c 100644 --- a/examples/security/kdd-nsl/other_data.sh +++ b/examples/security/kdd-nsl/other_data.sh @@ -32,4 +32,4 @@ model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ ++hydra.sweeper.study_name=poly "$@" --multirun \ >| logs/models/poly.log echo "Poly Kernel Done" >> model_log.txt -echo "Successfully completed experiment ${i} of ${TOTAL}" >> model_log.txt \ No newline at end of file +echo "Successfully completed experiment ${i} of ${TOTAL}" >> model_log.txt diff --git a/examples/security/kdd-nsl/plots.py b/examples/security/kdd-nsl/plots.py index 98e7bd21..a5f130c6 100644 --- a/examples/security/kdd-nsl/plots.py +++ b/examples/security/kdd-nsl/plots.py @@ -375,4 +375,4 @@ graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") fig.tight_layout(h_pad=0.5) fig.savefig("plots/retrain_confidence_vs_attack_parameters.pdf") -plt.gcf().clear() \ No newline at end of file +plt.gcf().clear() diff --git a/examples/security/kdd-nsl/retrain.py b/examples/security/kdd-nsl/retrain.py index a359b01b..7722ed24 100644 --- a/examples/security/kdd-nsl/retrain.py +++ b/examples/security/kdd-nsl/retrain.py @@ -21,6 +21,8 @@ logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) + + def parse_folder( folder, exclude=[ @@ -64,6 +66,7 @@ def flatten_results(results): new_results = pd.concat([new_results, tmp], axis=1) return new_results + def parse_results(result_dir, flatten=True): """ Recursively parse a directory containing json files and return a dataframe with the results. @@ -130,6 +133,8 @@ def unflatten_results(df, sep=".") -> List[dict]: parsed_row = set_for_keys(parsed_row, keys, val) result.append(parsed_row) return result + + def retrain_loop( clf, X_train, @@ -248,16 +253,16 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: # subset = subset[subset["data.generate.kwargs.n_features"] == 100] with open("conf/model/best_rbf.yaml", "r") as f: best_rbf = yaml.safe_load(f) -best_rbf['init'].pop("_target_", None) -best_rbf['init'].pop("name", None) +best_rbf["init"].pop("_target_", None) +best_rbf["init"].pop("name", None) with open("conf/model/best_poly.yaml", "r") as f: best_poly = yaml.safe_load(f) -best_poly['init'].pop("_target_", None) -best_poly['init'].pop("name", None) +best_poly["init"].pop("_target_", None) +best_poly["init"].pop("name", None) with open("conf/model/best_linear.yaml", "r") as f: best_lin = yaml.safe_load(f) -best_lin['init'].pop("_target_", None) -best_lin['init'].pop("name", None) +best_lin["init"].pop("_target_", None) +best_lin["init"].pop("name", None) rbf_model = SVC(**best_rbf["init"]) # Poly poly_model = SVC(**best_poly["init"]) @@ -267,7 +272,7 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: with open("conf/data/attack.yaml", "r") as f: data = yaml.safe_load(f) data = instantiate(data) -X_train, X_test, y_train, y_test = data() +X_train, X_test, y_train, y_test = data() # Fit Models rbf_model.fit(X_train, y_train) poly_model.fit(X_train, y_train) @@ -301,12 +306,16 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: for folder in tqdm(os.listdir("output/reports/attack")): confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): - with open(Path("output/reports/attack", folder, "adv_probabilities.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "adv_probabilities.json"), "r" + ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) - with open(Path("output/reports/attack", folder, "score_dict.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "score_dict.json"), "r" + ) as f: try: scores = json.load(f) except: # noqa E722 @@ -314,7 +323,9 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob - with open(Path("output/reports/attack", folder, "score_dict.json"), "w") as f: + with open( + Path("output/reports/attack", folder, "score_dict.json"), "w" + ) as f: json.dump(scores, f) yaml_file = Path("output/reports/attack", folder, "params.yaml") json_file = Path("output/reports/attack", folder, "params.json") @@ -328,8 +339,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: params = json.load(f) else: raise ValueError(f"No params file found for {folder}") - attack_params = params["attack"]["init"]['kwargs'] - attack_params.update({"name" : params["attack"]["init"]["name"]}) + attack_params = params["attack"]["init"]["kwargs"] + attack_params.update({"name": params["attack"]["init"]["name"]}) confidence_ser["Kernel"] = name confidence_ser["Average False Confidence"] = avg_prob # print(f"Shape of confidence ser: {confidence_ser.shape}") @@ -373,32 +384,46 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: for folder in tqdm(os.listdir("output/reports/attack")): confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): - with open(Path("output/reports/attack", folder, "adv_probabilities.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "adv_probabilities.json"), "r" + ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) pd.DataFrame(probs).to_csv( - Path("output/reports/attack", folder, f"probs_after_retraining_{name}.csv"), + Path( + "output/reports/attack", + folder, + f"probs_after_retraining_{name}.csv", + ), ) - with open(Path("output/reports/attack", folder, "score_dict.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "score_dict.json"), "r" + ) as f: scores = json.load(f) if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence {name.capitalize()}"] = avg_prob - with open(Path("output/reports/attack", folder, "score_dict.json"), "w") as f: + with open( + Path("output/reports/attack", folder, "score_dict.json"), "w" + ) as f: json.dump(scores, f) if Path("output/reports/attack", folder, "params.yaml").exists(): - with open(Path("output/reports/attack", folder, "params.yaml"), "r") as f: + with open( + Path("output/reports/attack", folder, "params.yaml"), "r" + ) as f: params = yaml.safe_load(f) elif Path("output/reports/attack", folder, "params.json").exists(): - with open(Path("output/reports/attack", folder, "params.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "params.json"), "r" + ) as f: params = json.load(f) else: logger.warning(f"No params file found for {folder}") continue attack_params = params["attack"]["init"]["kwargs"] - attack_params.update({"name" : params["attack"]["init"]["name"]}) + attack_params.update({"name": params["attack"]["init"]["name"]}) confidence_ser["Kernel"] = name confidence_ser["Average False Confidence After Retraining"] = avg_prob attack_ser = pd.Series(attack_params) diff --git a/examples/security/truthseeker/conf/compile.yaml b/examples/security/truthseeker/conf/compile.yaml index 109994af..817d2b4e 100644 --- a/examples/security/truthseeker/conf/compile.yaml +++ b/examples/security/truthseeker/conf/compile.yaml @@ -5,4 +5,4 @@ defences: Control: Control params: Control: model.init.kwargs.kernel - PGD: attack.init.kwargs.eps \ No newline at end of file + PGD: attack.init.kwargs.eps diff --git a/examples/security/truthseeker/conf/data/default.yaml b/examples/security/truthseeker/conf/data/default.yaml index b9487f7e..b3d67001 100644 --- a/examples/security/truthseeker/conf/data/default.yaml +++ b/examples/security/truthseeker/conf/data/default.yaml @@ -1,2 +1,2 @@ defaults: - - kdd_nsl \ No newline at end of file + - kdd_nsl diff --git a/examples/security/truthseeker/conf/data/small.yaml b/examples/security/truthseeker/conf/data/small.yaml index ff01d089..2207d717 100644 --- a/examples/security/truthseeker/conf/data/small.yaml +++ b/examples/security/truthseeker/conf/data/small.yaml @@ -17,4 +17,4 @@ sklearn_pipeline: # name: sklearn.preprocessing.StandardScaler with_mean: True - with_std: True \ No newline at end of file + with_std: True diff --git a/examples/security/truthseeker/conf/model/rbf.yaml b/examples/security/truthseeker/conf/model/rbf.yaml index 036bc65f..8b2d61af 100644 --- a/examples/security/truthseeker/conf/model/rbf.yaml +++ b/examples/security/truthseeker/conf/model/rbf.yaml @@ -12,4 +12,4 @@ _target_: deckard.base.model.Model art: _target_ : deckard.base.model.art_pipeline.ArtPipeline library : sklearn-svc - initialize: \ No newline at end of file + initialize: diff --git a/examples/security/truthseeker/models.sh b/examples/security/truthseeker/models.sh index 2cd3d313..fb8c7cad 100644 --- a/examples/security/truthseeker/models.sh +++ b/examples/security/truthseeker/models.sh @@ -53,4 +53,4 @@ for train_size in ${TRAIN_SIZES[@]}; do echo "Successfully completed experiment ${i} of ${TOTAL}" >> log.txt done; done; -done; \ No newline at end of file +done; diff --git a/examples/security/truthseeker/other_data.sh b/examples/security/truthseeker/other_data.sh index 0d3ddcc3..88f33d4c 100644 --- a/examples/security/truthseeker/other_data.sh +++ b/examples/security/truthseeker/other_data.sh @@ -32,4 +32,4 @@ model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ ++hydra.sweeper.study_name=poly "$@" --multirun \ >| logs/models/poly.log echo "Poly Kernel Done" >> model_log.txt -echo "Successfully completed experiment ${i} of ${TOTAL}" >> model_log.txt \ No newline at end of file +echo "Successfully completed experiment ${i} of ${TOTAL}" >> model_log.txt diff --git a/examples/security/truthseeker/plots.py b/examples/security/truthseeker/plots.py index 30664f22..e2b6f973 100644 --- a/examples/security/truthseeker/plots.py +++ b/examples/security/truthseeker/plots.py @@ -375,4 +375,4 @@ graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") fig.tight_layout(h_pad=0.5) fig.savefig("plots/retrain_confidence_vs_attack_parameters.pdf") -plt.gcf().clear() \ No newline at end of file +plt.gcf().clear() diff --git a/examples/security/truthseeker/retrain.py b/examples/security/truthseeker/retrain.py index a359b01b..7722ed24 100644 --- a/examples/security/truthseeker/retrain.py +++ b/examples/security/truthseeker/retrain.py @@ -21,6 +21,8 @@ logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) + + def parse_folder( folder, exclude=[ @@ -64,6 +66,7 @@ def flatten_results(results): new_results = pd.concat([new_results, tmp], axis=1) return new_results + def parse_results(result_dir, flatten=True): """ Recursively parse a directory containing json files and return a dataframe with the results. @@ -130,6 +133,8 @@ def unflatten_results(df, sep=".") -> List[dict]: parsed_row = set_for_keys(parsed_row, keys, val) result.append(parsed_row) return result + + def retrain_loop( clf, X_train, @@ -248,16 +253,16 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: # subset = subset[subset["data.generate.kwargs.n_features"] == 100] with open("conf/model/best_rbf.yaml", "r") as f: best_rbf = yaml.safe_load(f) -best_rbf['init'].pop("_target_", None) -best_rbf['init'].pop("name", None) +best_rbf["init"].pop("_target_", None) +best_rbf["init"].pop("name", None) with open("conf/model/best_poly.yaml", "r") as f: best_poly = yaml.safe_load(f) -best_poly['init'].pop("_target_", None) -best_poly['init'].pop("name", None) +best_poly["init"].pop("_target_", None) +best_poly["init"].pop("name", None) with open("conf/model/best_linear.yaml", "r") as f: best_lin = yaml.safe_load(f) -best_lin['init'].pop("_target_", None) -best_lin['init'].pop("name", None) +best_lin["init"].pop("_target_", None) +best_lin["init"].pop("name", None) rbf_model = SVC(**best_rbf["init"]) # Poly poly_model = SVC(**best_poly["init"]) @@ -267,7 +272,7 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: with open("conf/data/attack.yaml", "r") as f: data = yaml.safe_load(f) data = instantiate(data) -X_train, X_test, y_train, y_test = data() +X_train, X_test, y_train, y_test = data() # Fit Models rbf_model.fit(X_train, y_train) poly_model.fit(X_train, y_train) @@ -301,12 +306,16 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: for folder in tqdm(os.listdir("output/reports/attack")): confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): - with open(Path("output/reports/attack", folder, "adv_probabilities.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "adv_probabilities.json"), "r" + ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) - with open(Path("output/reports/attack", folder, "score_dict.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "score_dict.json"), "r" + ) as f: try: scores = json.load(f) except: # noqa E722 @@ -314,7 +323,9 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob - with open(Path("output/reports/attack", folder, "score_dict.json"), "w") as f: + with open( + Path("output/reports/attack", folder, "score_dict.json"), "w" + ) as f: json.dump(scores, f) yaml_file = Path("output/reports/attack", folder, "params.yaml") json_file = Path("output/reports/attack", folder, "params.json") @@ -328,8 +339,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: params = json.load(f) else: raise ValueError(f"No params file found for {folder}") - attack_params = params["attack"]["init"]['kwargs'] - attack_params.update({"name" : params["attack"]["init"]["name"]}) + attack_params = params["attack"]["init"]["kwargs"] + attack_params.update({"name": params["attack"]["init"]["name"]}) confidence_ser["Kernel"] = name confidence_ser["Average False Confidence"] = avg_prob # print(f"Shape of confidence ser: {confidence_ser.shape}") @@ -373,32 +384,46 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: for folder in tqdm(os.listdir("output/reports/attack")): confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): - with open(Path("output/reports/attack", folder, "adv_probabilities.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "adv_probabilities.json"), "r" + ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) pd.DataFrame(probs).to_csv( - Path("output/reports/attack", folder, f"probs_after_retraining_{name}.csv"), + Path( + "output/reports/attack", + folder, + f"probs_after_retraining_{name}.csv", + ), ) - with open(Path("output/reports/attack", folder, "score_dict.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "score_dict.json"), "r" + ) as f: scores = json.load(f) if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence {name.capitalize()}"] = avg_prob - with open(Path("output/reports/attack", folder, "score_dict.json"), "w") as f: + with open( + Path("output/reports/attack", folder, "score_dict.json"), "w" + ) as f: json.dump(scores, f) if Path("output/reports/attack", folder, "params.yaml").exists(): - with open(Path("output/reports/attack", folder, "params.yaml"), "r") as f: + with open( + Path("output/reports/attack", folder, "params.yaml"), "r" + ) as f: params = yaml.safe_load(f) elif Path("output/reports/attack", folder, "params.json").exists(): - with open(Path("output/reports/attack", folder, "params.json"), "r") as f: + with open( + Path("output/reports/attack", folder, "params.json"), "r" + ) as f: params = json.load(f) else: logger.warning(f"No params file found for {folder}") continue attack_params = params["attack"]["init"]["kwargs"] - attack_params.update({"name" : params["attack"]["init"]["name"]}) + attack_params.update({"name": params["attack"]["init"]["name"]}) confidence_ser["Kernel"] = name confidence_ser["Average False Confidence After Retraining"] = avg_prob attack_ser = pd.Series(attack_params) From 8d0bba67f12f528d2d706b76577584cd6b2767b7 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Sun, 15 Oct 2023 01:07:05 +0200 Subject: [PATCH 107/148] linting --- deckard/base/attack/attack.py | 4 +++- deckard/layers/find_best.py | 13 +++++++---- examples/security/classification/plots.py | 2 -- examples/security/classification/retrain.py | 25 +++++++++++++-------- examples/security/kdd-nsl/plots.py | 2 -- examples/security/kdd-nsl/retrain.py | 25 +++++++++++++-------- examples/security/truthseeker/plots.py | 1 - examples/security/truthseeker/retrain.py | 25 +++++++++++++-------- 8 files changed, 60 insertions(+), 37 deletions(-) diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index daaf16bf..27e0a32b 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -156,7 +156,9 @@ def __call__( start = process_time_ns() patches, _ = atk.generate(ben_samples, **kwargs) samples = atk.apply_patch( - ben_samples, scale=scale_max, patch_external=patches + ben_samples, + scale=scale_max, + patch_external=patches, ) else: start = process_time_ns() diff --git a/deckard/layers/find_best.py b/deckard/layers/find_best.py index aa3fbba4..0461f28e 100644 --- a/deckard/layers/find_best.py +++ b/deckard/layers/find_best.py @@ -4,7 +4,7 @@ from hydra import initialize_config_dir, compose from omegaconf import OmegaConf import yaml -from ..base.utils import flatten_dict, unflatten_dict +from ..base.utils import flatten_dict logger = logging.getLogger(__name__) @@ -50,7 +50,9 @@ def find_optuna_best( if params_file is True: if config_subdir is not None: params_file = Path( - config_folder, f"{config_subdir}", f"{default_config}.yaml" + config_folder, + f"{config_subdir}", + f"{default_config}.yaml", ) params = cfg.get(config_subdir) else: @@ -59,7 +61,9 @@ def find_optuna_best( else: if config_subdir is not None: params_file = Path( - config_folder, f"{config_subdir}", f"{params_file}.yaml" + config_folder, + f"{config_subdir}", + f"{params_file}.yaml", ) params = cfg.get(config_subdir) else: @@ -93,7 +97,8 @@ def find_optuna_best( if args.study_type == "optuna": with open( - Path(args.config_folder, args.default_config).with_suffix(".yaml"), "r" + Path(args.config_folder, args.default_config).with_suffix(".yaml"), + "r", ) as f: default_params = yaml.load(f, Loader=yaml.FullLoader) if "hydra" in default_params: diff --git a/examples/security/classification/plots.py b/examples/security/classification/plots.py index b5779ff3..a381f887 100644 --- a/examples/security/classification/plots.py +++ b/examples/security/classification/plots.py @@ -1,8 +1,6 @@ import pandas as pd import seaborn as sns from pathlib import Path -import matplotlib.pyplot as plt - import matplotlib.pyplot as plt import logging diff --git a/examples/security/classification/retrain.py b/examples/security/classification/retrain.py index b8a0ea7e..9623e19d 100644 --- a/examples/security/classification/retrain.py +++ b/examples/security/classification/retrain.py @@ -1,4 +1,3 @@ -import json import logging import os import pickle @@ -310,14 +309,16 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), "r" + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) with open( - Path("output/reports/attack", folder, "score_dict.json"), "r" + Path("output/reports/attack", folder, "score_dict.json"), + "r", ) as f: try: scores = json.load(f) @@ -327,7 +328,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: del scores["False Confidence"] scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), "w" + Path("output/reports/attack", folder, "score_dict.json"), + "w", ) as f: json.dump(scores, f) yaml_file = Path("output/reports/attack", folder, "params.yaml") @@ -388,7 +390,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), "r" + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", ) as f: probs = json.load(f) probs = np.array(probs) @@ -402,24 +405,28 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: ), ) with open( - Path("output/reports/attack", folder, "score_dict.json"), "r" + Path("output/reports/attack", folder, "score_dict.json"), + "r", ) as f: scores = json.load(f) if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), "w" + Path("output/reports/attack", folder, "score_dict.json"), + "w", ) as f: json.dump(scores, f) if Path("output/reports/attack", folder, "params.yaml").exists(): with open( - Path("output/reports/attack", folder, "params.yaml"), "r" + Path("output/reports/attack", folder, "params.yaml"), + "r", ) as f: params = yaml.safe_load(f) elif Path("output/reports/attack", folder, "params.json").exists(): with open( - Path("output/reports/attack", folder, "params.json"), "r" + Path("output/reports/attack", folder, "params.json"), + "r", ) as f: params = json.load(f) else: diff --git a/examples/security/kdd-nsl/plots.py b/examples/security/kdd-nsl/plots.py index a5f130c6..06375d98 100644 --- a/examples/security/kdd-nsl/plots.py +++ b/examples/security/kdd-nsl/plots.py @@ -1,8 +1,6 @@ import pandas as pd import seaborn as sns from pathlib import Path -import matplotlib.pyplot as plt - import matplotlib.pyplot as plt import logging diff --git a/examples/security/kdd-nsl/retrain.py b/examples/security/kdd-nsl/retrain.py index 7722ed24..a7dbac4f 100644 --- a/examples/security/kdd-nsl/retrain.py +++ b/examples/security/kdd-nsl/retrain.py @@ -1,4 +1,3 @@ -import json import logging import os import pickle @@ -307,14 +306,16 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), "r" + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) with open( - Path("output/reports/attack", folder, "score_dict.json"), "r" + Path("output/reports/attack", folder, "score_dict.json"), + "r", ) as f: try: scores = json.load(f) @@ -324,7 +325,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: del scores["False Confidence"] scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), "w" + Path("output/reports/attack", folder, "score_dict.json"), + "w", ) as f: json.dump(scores, f) yaml_file = Path("output/reports/attack", folder, "params.yaml") @@ -385,7 +387,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), "r" + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", ) as f: probs = json.load(f) probs = np.array(probs) @@ -399,24 +402,28 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: ), ) with open( - Path("output/reports/attack", folder, "score_dict.json"), "r" + Path("output/reports/attack", folder, "score_dict.json"), + "r", ) as f: scores = json.load(f) if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), "w" + Path("output/reports/attack", folder, "score_dict.json"), + "w", ) as f: json.dump(scores, f) if Path("output/reports/attack", folder, "params.yaml").exists(): with open( - Path("output/reports/attack", folder, "params.yaml"), "r" + Path("output/reports/attack", folder, "params.yaml"), + "r", ) as f: params = yaml.safe_load(f) elif Path("output/reports/attack", folder, "params.json").exists(): with open( - Path("output/reports/attack", folder, "params.json"), "r" + Path("output/reports/attack", folder, "params.json"), + "r", ) as f: params = json.load(f) else: diff --git a/examples/security/truthseeker/plots.py b/examples/security/truthseeker/plots.py index e2b6f973..c5ae8ac3 100644 --- a/examples/security/truthseeker/plots.py +++ b/examples/security/truthseeker/plots.py @@ -3,7 +3,6 @@ from pathlib import Path import matplotlib.pyplot as plt -import matplotlib.pyplot as plt import logging sns.set_style("whitegrid") diff --git a/examples/security/truthseeker/retrain.py b/examples/security/truthseeker/retrain.py index 7722ed24..6b91b13c 100644 --- a/examples/security/truthseeker/retrain.py +++ b/examples/security/truthseeker/retrain.py @@ -4,7 +4,6 @@ import pickle from pathlib import Path from time import process_time -import json from typing import List import numpy as np import pandas as pd @@ -307,14 +306,16 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), "r" + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) with open( - Path("output/reports/attack", folder, "score_dict.json"), "r" + Path("output/reports/attack", folder, "score_dict.json"), + "r", ) as f: try: scores = json.load(f) @@ -324,7 +325,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: del scores["False Confidence"] scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), "w" + Path("output/reports/attack", folder, "score_dict.json"), + "w", ) as f: json.dump(scores, f) yaml_file = Path("output/reports/attack", folder, "params.yaml") @@ -385,7 +387,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), "r" + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", ) as f: probs = json.load(f) probs = np.array(probs) @@ -399,24 +402,28 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: ), ) with open( - Path("output/reports/attack", folder, "score_dict.json"), "r" + Path("output/reports/attack", folder, "score_dict.json"), + "r", ) as f: scores = json.load(f) if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), "w" + Path("output/reports/attack", folder, "score_dict.json"), + "w", ) as f: json.dump(scores, f) if Path("output/reports/attack", folder, "params.yaml").exists(): with open( - Path("output/reports/attack", folder, "params.yaml"), "r" + Path("output/reports/attack", folder, "params.yaml"), + "r", ) as f: params = yaml.safe_load(f) elif Path("output/reports/attack", folder, "params.json").exists(): with open( - Path("output/reports/attack", folder, "params.json"), "r" + Path("output/reports/attack", folder, "params.json"), + "r", ) as f: params = json.load(f) else: From 78a716deae9dd0a150cbbe20b92919977ebe0450 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Sun, 15 Oct 2023 01:14:00 +0200 Subject: [PATCH 108/148] linting --- deckard/base/attack/attack.py | 1 - deckard/layers/afr.py | 1 - deckard/layers/plots.py | 1 - examples/classification/plots.ipynb | 252 ++++++++++++++++++++++++++++ 4 files changed, 252 insertions(+), 3 deletions(-) create mode 100644 examples/classification/plots.ipynb diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index 27e0a32b..4c1dc1f0 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -147,7 +147,6 @@ def __call__( if attack_file is not None and Path(attack_file).exists(): samples = self.data.load(attack_file) else: - atk = self.init(model=model, attack_size=self.attack_size) if targeted is True: diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index a40f4150..f7d073ce 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -21,7 +21,6 @@ logger = logging.getLogger(__name__) if "__main__" == __name__: - afr_parser = argparse.ArgumentParser() afr_parser.add_argument("--target", type=str, default="adv_failures") afr_parser.add_argument("--duration_col", type=str, default="adv_fit_time") diff --git a/deckard/layers/plots.py b/deckard/layers/plots.py index f18261f7..93bbba5c 100644 --- a/deckard/layers/plots.py +++ b/deckard/layers/plots.py @@ -98,7 +98,6 @@ def scatter_plot( hue_order=None, **kwargs, ): - # plt.gcf().clear() data = data.sort_values(by=[hue, x, y]) graph = sns.scatterplot( diff --git a/examples/classification/plots.ipynb b/examples/classification/plots.ipynb new file mode 100644 index 00000000..1ef9111e --- /dev/null +++ b/examples/classification/plots.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "\n", + "# Load data\n", + "df = pd.read_csv(\"output/attack.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['attacks', 'defences', 'params'])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3723846/651469242.py:12: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " attack_results['Kernel'] = attack_results['model.init.kwargs.kernel']\n" + ] + } + ], + "source": [ + "from deckard.layers.compile import clean_data_for_plotting\n", + "import yaml\n", + "\n", + "with open(\"conf/compile.yaml\", \"r\") as f:\n", + " config = yaml.load(f, Loader=yaml.FullLoader)\n", + "print(config.keys())\n", + "def_gen_dict = config[\"defences\"]\n", + "atk_gen_dict = config[\"attacks\"]\n", + "control_dict = config[\"params\"]\n", + "\n", + "df = clean_data_for_plotting(df, def_gen_dict, atk_gen_dict, control_dict)\n", + "attack_results = df.dropna(subset=[\"accuracy\", \"adv_accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 2)\n", + "graph5 = sns.lineplot(\n", + " x=\"attack.init.kwargs.eps\",\n", + " y=\"accuracy\",\n", + " data=attack_results,\n", + " style=\"model.init.kwargs.kernel\",\n", + " ax=ax[0, 0],\n", + " legend=False,\n", + " color=\"darkred\",\n", + " style_order=[\"rbf\", \"poly\", \"linear\"],\n", + ")\n", + "graph5.set(xscale=\"log\", xlabel=\"Perturbation Distance\", ylabel=\"Accuracy\")\n", + "graph6 = sns.lineplot(\n", + " x=\"attack.init.kwargs.eps_step\",\n", + " y=\"accuracy\",\n", + " data=attack_results,\n", + " style=\"model.init.kwargs.kernel\",\n", + " ax=ax[0, 1],\n", + " color=\"darkred\",\n", + " style_order=[\"rbf\", \"poly\", \"linear\"],\n", + ")\n", + "graph6.set(xscale=\"log\", xlabel=\"Perturbation Step\", ylabel=\"Accuracy\")\n", + "graph7 = sns.lineplot(\n", + " x=\"attack.init.kwargs.max_iter\",\n", + " y=\"accuracy\",\n", + " data=attack_results,\n", + " style=\"Kernel\",\n", + " ax=ax[1, 0],\n", + " legend=False,\n", + " color=\"darkred\",\n", + " style_order=[\"rbf\", \"poly\", \"linear\"],\n", + ")\n", + "graph7.set(xscale=\"log\", xlabel=\"Maximum Iterations\", ylabel=\"Accuracy\")\n", + "graph8 = sns.lineplot(\n", + " x=\"attack.init.kwargs.batch_size\",\n", + " y=\"accuracy\",\n", + " data=attack_results,\n", + " style=\"Kernel\",\n", + " ax=ax[1, 1],\n", + " legend=False,\n", + " color=\"darkred\",\n", + " style_order=[\"rbf\", \"poly\", \"linear\"],\n", + ")\n", + "graph8.set(xscale=\"log\", xlabel=\"Batch Size\", ylabel=\"Accuracy\")\n", + "graph6.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5), ncol=1, title=\"Kernel\")\n", + "fig.tight_layout()\n", + "fig.savefig(\"plots/accuracy_vs_attack_parameters.pdf\")\n", + "plt.gcf().clear()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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/dLvtthunnXYaZ5xxBi+++CKrVq3i008/Zd68efzzn/9s8nk//fRTRo8ezYYNG9rttQwZMoQlS5awfPlytm3bltpYs+Fq3XXb/c477/DCCy/UK0BO1gE9/fTTqbAzbtw4IpEICxYsSAU/cOpdFi9ezL///W++++47rrnmmiaDTEOnnnoqtm1z/vnns3TpUv7973+ngk3yg/Paa6/llVdeYcWKFfzvf//jtddeqzec1JrXddppp1FcXMxPf/pT3n//fVatWsXChQu5+OKLWb9+/U7buyNXXHEFH330EbNnz+bLL7/k+++/55VXXmH27Nm7dN5MppQiHIixbX0NpSurqCwNoBuQU+hNFTiLXaNpTgG3YeqYbgOXx8DtNfH4TbxZLrzZLnw5bvx5HrLyPWQXeMku9JBd4CGnwEt2gXM5K9+DP9eNN8vE7TVwuZ3AA2DbinjUIhKMOb13ZWEqNgcp2xBg6/oatq6tYcvqajavqkrN3Nu4opJNKyrZ+F0lG76vTMzmq2DDdxVsWlnJ5tVVbF1XQ9nGWio3B6jaGqK6LERtRZhAVYRgdZRQrTOLLxqKEw3HiUctrLjtFMLvZG060X1ID1CaDRgwgNdff53LL7+c8ePHU1hYyKxZs1JF1AC33347tbW1zJgxg5ycHC677DKqqqrqneexxx7j5ptv5rLLLmPDhg0UFxez7777cswxxzT5vMFgkOXLl7fr7t/nnXceCxcuZNKkSdTW1vLOO+9w8MEHN1qtu65Ro0bx9ttvp3pMklP8DzroIF5++eVUANJ1nR/96Ef885//rFf/84tf/IL//ve/nHzyyWiaxsyZM7nwwgvrLTPQlNzcXP7xj39wwQUXMGHCBMaOHcu1117LqaeemqoLcrvdXHXVVaxevRqfz8eBBx5Yb6HN1r6u9957jyuuuILjjz+empoaBgwYwLRp03a5R2jcuHG8++67XH311Rx44IEopRg+fDgnn3zyLp03EynbCT615WEC1VFsS+Hxm/hyXDt/sEiLVGE3Ghg7P35Hmio8d3qedlB4nuiVQqlU4XlTvVL1Cs+NxPCeFJ53a5qSuNtIdXU1eXl5VFVVNfpwCofDrFq1iqFDh/bY2TXd1dNPP83ZZ59NVVUVPp8v3c3pVJn+e23bilBNlNpyZyo7mobHb2C6dvETVfRYzRWep4b6lBSed0U7+vxuSHqARI/1xBNPMGzYMAYMGMBXX33FFVdcwUknndTjwk8msyybUGIqe6g2hq6DN9uFYcrovdg1GVN47pwkVXieDEDJwvNktUOq8DyxJIIUnu86CUCixyotLeXaa6+ltLSUfv36ceKJJ3LLLbeku1kCiMes1Bo+kUAMw6Xjz22/NXyEaC9SeN51SQASPdZvfvOb1IKKIjPEohaBygi15WEiIQuXWycr39Pl32iFaAlN09CM9glS1AlP7bbieRPLIWh1Vzw3dQydFq94bph6Wv/blgAkhEi7aCieWMMnOZXdILvALXUPQrRBuxeeN+iNSgYsy7JRu1B4nlPopbBf69doay8SgIQQaaGUIhKME6gMU1sZxYpZzho+XWTxQiF6gvbqlYL6heehmhhWvH0WA24rCUBCiE6VXMOntjxMoCqKHVd4skx82TKVXYjurG7hud5OoWpXSAASQnQK21aEa2PUlIcIVkUBZ1d20y1T2YUQnU8CkBCiQ9mWTagmRnVZmFBtFE2TqexCiPSTdyDRasnNSsHZ/uLuu+9Oa3tEZrJiNjXlYUp/qGbz6moiwRj+HBdZeR4JP0KItJMeILFLPvvsszbttC66r3jUSs3oigRjmG4df55bFmATQmQUCUBil/Tq1SvdTQAgFovhckkRbTpFw3GClRFqKiJEQ3FcXkNmdAkhMpb0Q4td0nAITNM0Hn74YY477jj8fj8jR47k1VdfrfeYb775hiOPPJLs7Gz69OnD6aefXm9T0TfeeIOpU6eSn59PUVERxxxzDCtXrkzdv3r1ajRN47nnnuOggw7C6/Xy9NNPd/hrFU2LhOKUbQqwaWUVZZuCaBpkF3rwZrkk/AghMpYEoAyilCIYjaflqz33xL3hhhs46aSTWLJkCUcddRSnnXYa5eXlAFRWVnLooYcyceJEFi9ezBtvvMHmzZs56aSTUo8PBALMmTOHxYsXs2DBAnRd57jjjsO2668ZceWVV3LJJZewdOlSpk+f3m7tFzunlDOja+u6GkpXVlG5OYBpauQUenD7TAk+QoiMJ0NgGSQUs9j92n+n5bm/vXE6fnf7/DqcddZZzJw5E4Bbb72Ve++9l08//ZQjjjiC++67j4kTJ3Lrrbemjn/00UcpKSnhu+++Y7fdduNnP/tZvfM9+uij9OrVi2+//ZY999wzdfull17K8ccf3y5tFi2jbEWoNkZtRZhAVQRlgzfLxJcjw49CiK5FeoBEuxs3blzqclZWFrm5uWzZsgWAr776infeeYfs7OzU1+jRowFSw1zff/89M2fOZNiwYeTm5jJkyBAA1q5dW+95Jk2a1AmvRoAzlT1QFWHzmmpKV1URqIri9bvILvDIOj5CiC5JeoAyiM9l8O2N6RnK8bna70OsYTGypmmp4ava2lpmzJjBbbfd1uhx/fr1A2DGjBkMHjyYhx56iP79+2PbNnvuuSfRaLTe8TL7rONZcZtQTZTqsjDh2hi6oeHPkV3ZhRBdnwSgDKJpWrsNQ2WqvfbaixdeeIEhQ4Zgmo1fa1lZGcuXL+ehhx7iwAMPBOCDDz7o7Gb2ePGYRbAqSk15mEgghuGSqexCiO5F/owTnepXv/oV5eXlzJw5k88++4yVK1fy73//m7PPPhvLsigoKKCoqIi//OUvrFixgrfffps5c+aku9k9RixqUbklyKaVVWxdV4MVt8nK9+DLkfAjhOheJACJTtW/f38+/PBDLMvi8MMPZ+zYsVx66aXk5+ej6zq6rvPss8/y+eefs+eee/LrX/+a22+/Pd3N7vZsW2FbNlvX1lC+oRaUIrsgMZVdgo8QohvSVHvOf+4mqqurycvLo6qqitzc3Hr3hcNhVq1axdChQ/F6vWlqoRC7TimFshW2pQgGQ6xes5pcdzE+n0+msQshOlSwOkpWvodeJTntet4dfX431L0LToQQjSSDjxV3en0A0EDXNVxuWcNHCNEzSAASoodQyuntsS0b23I6fjVdQ9M0tLiEHiFEzyIBSIhuLhl8rLiNsusHHyGE6KkkAAnRTSlbYdt1go8GuqEBEnyEEEICkBDdTHJGlx13ApCmaxJ8hBCiAQlAQnQTyeBjxRVKKXRNwzAl+AghRFMkAAnRxSWLmi1re/CRHh8hhNgxCUBCdEGpqeyJWV0op7BZ12VtUyGEaAkJQEJ0IUo5dT12vMFUdlmtWQghWkUCkBBdQGoqu2WjLJnKLoQQuyqt/eXvvfceM2bMoH///miaxssvv1zv/rPOOstZpK3O1xFHHLHT895///0MGTIEr9fLlClT+PTTTzvoFfRMBx98cOrf48svv2x0/+OPP05+fn6nt6szNPV72hpDhgxJ/ewqKyt3erxSzjT2WMQiHrVQlkI3NHRDl/AjhBC7IK0BKBAIMH78eO6///5mjzniiCPYtGlT6uuvf/3rDs/53HPPMWfOHK677jq++OILxo8fz/Tp09myZUt7N79HO++889i0aRN77rknq1ev7vQP4yFDhnD33Xd36nO2xcEHH8zjjz+euv7ZZ5/xwgsv7PRxylZYMSf4xKKWU9ycCD5S3CyEELsurUNgRx55JEceeeQOj/F4PPTt27fF5/zjH//Ieeedx9lnnw3Agw8+yD//+U8effRRrrzyyiYfE4lEiEQiqevV1dUtfr6eyu/3t+rfRTh69epFYWFhs/c3tYaPITO6hBCi3WX8lJGFCxfSu3dvRo0axQUXXEBZWVmzx0ajUT7//HMOO+yw1G26rnPYYYexaNGiZh83b9488vLyUl8lJSXt+hpaTCmIBtLzpVS7v5yXX36ZkSNH4vV6mT59OuvWrat3/yuvvMJee+2F1+tl2LBh3HDDDcTj8cSPQnH99dczaNAgPB4P/fv35+KLLwacXpU1a9bw61//OjWctDPJYbmdtemBBx5g+PDhuN1uRo0axZNPPtnsOQ899FBmz55d77atW7fidrtZsGDBTtuULGi24jbxmEUsEneGumLOBqWGqaHrEn6EEKIjZHQR9BFHHMHxxx/P0KFDWblyJb/97W858sgjWbRoEYZhNDp+27ZtWJZFnz596t3ep08fli1b1uzzXHXVVcyZMyd1vbq6Oj0hKBaEW/t3/vMC/HYjuLPa7XTBYJBbbrmFJ554ArfbzYUXXsgpp5zChx9+CMD777/PGWecwb333suBBx7IypUrOf/88wG47rrreOGFF7jrrrt49tln2WOPPSgtLeWrr74C4MUXX2T8+PGcf/75nHfeee3WppdeeolLLrmEu+++m8MOO4zXXnuNs88+m4EDB3LIIYc0Ot+5557L7NmzufPOO/F4PAA89dRTDBgwgEMPPbTesUoplNpe0wMQC1vEvHGokz1lKrsQQnSOjA5Ap5xySury2LFjGTduHMOHD2fhwoVMmzat3Z7H4/GkPsBE6w0ZMgTVoAcpFotx3333MWXKFADmz5/PmDFj+PTTT5k8eTI33HADV155JWeeeSYAw4YN46abbuI3v/kN1113HWvXrqVv374cdthhuFwuBg0axOTJkwEoLCzEMAxycnJaNQy3szbdcccdnHXWWVx44YUAzJkzh48//pg77rijyQB0/PHHM3v2bF555RVOOukkwOlpOvPMM1EKbNtmwZtvYytFLGw5OadOAAKnqFqmsAshROfL6ADU0LBhwyguLmbFihVNBqDi4mIMw2Dz5s31bt+8eXPXqFdx+Z2emHQ9dzsyTZN99tkndX306NHk5+ezdOlSJk+ezFdffcWHH37ILbfckjrGsizC4TDBYJATTzyRu+++m2HDhnHEEUdw1FFHMWPGDEyz7b+yO2vT0qVLU71QSQcccAD33HNPk+fzer38/Oc/55FHHuWEn53A559/wTfffMPfn3+RWMSqN6zoDNUle3icwCPT2IUQIn26VABav349ZWVl9OvXr8n73W43e++9NwsWLODYY48FEn+FL1jQqFYjI2lauw5DZbLa2lpuuOEGjj/++Eb3eb1eSkpKWL58OW+99RZvvvkmF154IbfffjvvvvsuLpcrDS0m1ctl23Zqh/WzzjibfaZMYtUPa3j0scc4+KBDGDxoMCTCjtTvCCFEZkprsUFtbS1ffvllai2ZVatW8eWXX7J27Vpqa2u5/PLL+fjjj1m9ejULFizgpz/9KSNGjGD69Ompc0ybNo377rsvdX3OnDk89NBDzJ8/n6VLl3LBBRcQCARSs8JE54jH4yxevDh1ffny5VRWVjJmzBgA9tprL5YvX86IESMafSVrYHw+HzNmzODee+9l4cKFLFq0iK+//hpwwq5lWe3apjFjxqTqgcCZiv7BBx8wZswY4lHL6dUBrJhNPGphxW322GMse++1N489/ijPP/8sZ511dp2eHQk/QgiRqdLaA7R48eJ6tRXJQuQzzzyTBx54gCVLljB//nwqKyvp378/hx9+ODfddFO9ep2VK1eybdu21PWTTz6ZrVu3cu2111JaWsqECRN44403GhVGi47lcrm46KKLuPfeezFNk9mzZ7Pvvvum6niuvfZajjnmGAYNGsQJJ5yArut89dVXfPPNN9x88808/vjjWJbFlClT8Pv9PPXUU/h8PgYPHgw4dUfvvfcep5xyCh6Ph+Li4l1qk1KKyy67jFNOOYVxY8dzyCGH8s9/vsZLL73E66+94dTtaNuHrpz1eBznnD2LS359MVlZWRz702Pb/4cphBCi3aU1AB188MGNimfr+ve//73Tc6xevbrRbbNnz+4aQ17dmN/v54orruDUU09lw4YNHHjggTzyyCOp+6dPn85rr73GjTfeyG233YbL5WL06NGce+65AOTn5/P73/+eOXPmYFkWY8eO5R//+AdFRUUA3HjjjfziF79g+PDhRCKRHf4eNdemqVOn8pc/P0QsEkfZcPQRM7jj9j/yx7v+yJzLfs2QIUN56C8PN1EAXb9n5+STT+Gyy+dw8kmn4PV6d+0HJ4QQolNoqiWfHD1MdXU1eXl5VFVVkZubW+++cDjMqlWrGDp0aI/9sDv44IOZMGFCl1iJOTn9/PHHHmPOZXPYunkbyk7cTiLKaNuLlNsybLV69WpG774biz78mIkT99rp8e++u5AfTz+MLaXbMmbLkHAkzJo1qynw98FlutPdHCFENxesjpKV76FXSU67nndHn98NyYIjok3+7//+j+zs7FRNTiaov7Cg7SwsGLaIheNY8WQBs/NdNzQMQ0c3dHRdb1PNTiwWo7S0lOuuv5Ypk6e0KPyMnziOGT89ptWvTQghRPvqUrPARGZ4+umnCYVCAAwaNCgtbai7sCA2HH3MUXzw4QdNHnvFb66kfz9ngcn2XGTwo48+5MfTD2PkyN149q/Ptegxr778D2LxGMBO/zoRQgjRcSQAiVYbMGBApz5fMuygFMoGWymUvf02gP+7/0HC4XBiOAvq9uYUFhRSWFjIGWec2a7tOuigg4mG4616TLKIWwghRHpJABIZRSkFqk4Pj+0Ma9UNO1B/YUHQ0rd/mxBCiC5JApBIm7aGHSGEEGJXSQASncYZtmo67GyfkSVhRwghRMeTACQ6hKpTp6OUwrYk7AghhMgcEoDELqvbq5MKPs2ttSNhRwghRAaQdYBEq9Rfa8dKrbVjGDovvvAiVsx21trRGq+1s/y75Uz90QHk5GUxafLe6X4pQgghejDpARI7ZVs2tp3o4Un08qRooJHcI0uvt0dWQzfedANZ/iy+WfIt2dnZHd1sIYQQolkSgMQO2bYiHrWxlUJj+zCWpm0fxopGoy061w8//MCRRxwpa+EIIYRIOxkCE81StiIetVBKpYayNF3jx4dP45JLL+ayuXPoN6APRx9zJAClpZuY8ZOjyc3PZtTokbzw4gupc7m9Jl988Tm33Hozbq/JjTfdkK6XJYQQQkgPUCZRShGKh9Ly3D7TV69XRylFPGajbIVuNC5afvKpJzj//F+w8J33ABg7bg+uv+E6brnpVu688y6efvopfn76qey++5eMGT2GtavXc8RR05l++OH8+tLLZAhMCCFEWkkAyiCheIgpz0xJy3N/cuon+F1+wAk/VszGsmwMo+lZWyNGjOT3t95W77afHX8C55wzC4Abrr+RBW+/xf/93/386d776Nu3L6ZpkpWVTd++fTv89QghhBA7IkNgohErbmPFbfQdTFnfq4mdz6dM2bfe9X2n7MuyZUs7oolCCCHELpEeoAziM318cuonaXtuSISfmN2o0LmhrKyszmqaEEII0e4kAGUQTdNSw1DpYFs28ZidWKG59YsVfvrpJ5z+89NT1z/59BMmjJ/Ynk0UQggh2oUEIAEkprvHbFBqh2v57MgLL/6dvffam/0POIC//vUZPvvsM/784EPt3FIhhBBi10kAEihbYcUsbFslip7b5tprruP5vz3HRZfMpl/ffjz5xNPsPmb3dmypEEII0T4kAPVwSinicRvLUs3O+GrorTffbnRbNBwH4Je/uKDZxy3+9PM2t1MIIYRoTzILrAdTytnTy97JjC8hhBCiu5EA1IPZlmrRjC8hhBCiu5EA1ENZiRlfWhtnfAkhhBBdmQSgHsi2FVbUmfGl6RJ+hBBC9DwSgHqYuhucNrXHlxBCCNETSADqQZIbnNqpDU4lAAkhhOiZJAD1EC3Z4FQIIYToKSQA9RAt2eBUCCGE6CkkAPUAqQ1OZcaXEEIIAUgA6vbqbXAqM76EEEIIQAJQt1Zvg9N2DD+H/fhQ3F4Tt9fky6++bHT/E0/Mp1efonZ7vvbQsE033nQDkybvncYW7Vjy55tpP0chhOguJAB1U07Rs1Vnxlf7mnXOuaxdvZ4999iT1atX4/Z27rZyI3cbzr1/uqfNj5/z68v497/+044t2jUjdxvOu+8uTF1fu3o9d97xx/Q1SAghujnZDLUbSk53b80Gp63l9/vp27dvu5+3s2RnZ5OdnZ3uZhCNRnG73Y1u79u3L3m5eWlokRBC9AzSA5RBlFLYweAufVmBALHqWuI1tRAJYQdDLXqcUqrdX88rr77C7nuMJicvi6OPOZJ169bVu//Vf7zK5H33IScvi1GjR3LTzTcSj8dTP4sbb7qB4SOGkp3rZ/DQEn4951LAGYJbs3YNcy+/LDVU1FoNh8BmnXsOPzvxeP54150MGjKQvv17c/ElFxGLxVLHRCIRrrjycoYMG0R+YS4HHLhfvV6bsrIyfn76aQwZNoi8ghwm7j2BZ597tt7zHvbjQ7nk0ou5bO4c+g3ow9HHHNnqtgshhNh10gOUQVQoxPK90lOXMmzRp2h+f7udLxgM8vvb5vHoI4/hdru56JLZ/Pz0U3l34fsAfPDB+5wz6yz+eOfdTD1gKj/8sJILf3UBANf87lpefOlF7v3TPTz15NPsPmYPNm8uZcmSJQA8/9zfmbTPXsyadS6zzjm33dr87rsL6de3H//591usXLmC035+KuPHjWfWLOc5Lrn0YpYu/Zannniafv3688qrL3PMT47mi8+/ZOSIkYTDYfbaay/mzr2c3Jxc/vXG65x9zpkMHzaMffaZnHqeJ596gvPP/wUL33mv3douhBCidSQAiV02ZMgQouF4vdtisRj33HUPkydPAeCRhx9j3Pg9+eyzT9lnn8ncfMtNXD73N5xx+hkADBs2jOuuu4HfXn0l1/zuWtatW0ufPn2ZduhhuFwuBg0alAoRhYWFGIZBTk5Ouw7DFeQXcM/d92IYBqNHjebII4/i7YVvM2vWuaxdu5b5TzzOyu9X0b9/f8CpI/rPf/7N/PmPc/NNtzBgwADm/Pqy1Pl+deFs3nzzP/z9hb/VC0AjRozk97feVu+5v/9uZbu9DiGEEDsnASiDaD4fo774vE2PdWZ8WShboeutH9nUfL42PW9zTNNk0qR9UtdHjxpNfn4+S5ctY599JrPk6yV8tOgjfn/bvNQxlmURDocJBoP87PgT+NOf7mXU6JEcfvh0jjjiSI45+hhMs+N+ZXfffXcMw0hd79e3H9988zUA3/zvayzLYo+xY+o9JhKJUFhUlGr/72+bx99f+DsbN24gGo0SiUTwNehZ22viXh32GoQQQrSMBKAMomlam4ahlK2woxaa3nFFz+2ttraWa6+5jmOPPa7RfV6vl5KSEr75+lsWvP0WCxYs4OJLZvPHu+5gwZvv4HK5OqRNZoPzapqGbduJ9gYwDIOPF31aLyQBZGc5xdR3/vEO7rv/T9xx+x/Zc889ycrKYu7cOUSj0XrHZ2VldUj7hRBCtFxai6Dfe+89ZsyYQf/+/dE0jZdffjl1XywW44orrmDs2LFkZWXRv39/zjjjDDZu3LjDc15//fWpFY+TX6NHj+7gV5I+mbrBaTwe5/PPF6euL/9uOZWVlYxJ/FtMnDCR7777jhHDRzT6SvZg+Xw+jjl6Bnf98W7e/M8CPv7441SPjMvtxrKsTns9EyZMwLIstm7Z0qi9yWG4jxZ9xIxjfsJpp57G+HHjGTZ0GN99/32ntVEIIUTLpbUHKBAIMH78eM455xyOP/74evcFg0G++OILrrnmGsaPH09FRQWXXHIJP/nJT1i8eHEzZ3TssccevPXWW6nrHTlskk6ZvMGpy+Xi0jmXcNedd2OaJpf8+mKmTJmSqoW5+urfcexxP6WkpITjj/8Zuq6zZMkS/ve/b7jxhpt44on5WJbFPpMn4/f5eeaZp/H5fAwaNBiAIYMH8/4H73PSiSfj8XgoLi7u0Nez28jdmHnKqZwz62xuu+12JoyfwLZtW3n7nbcZO3YsRx15NCNHjODFF19k0aKPyC8o4J577mbLls2MGTNm508ghBCiU6U1GRx55JEceWTT04Dz8vJ4880369123333MXnyZNauXcugQYOaPa9pml16jZqWyuQNTv1+P3Mv+w1nnHk6GzZuYOoBU/nzgw+l7j/8x9N5+aVXuOWWm7njzttxuVyMGjWKc86aBUBefj6333Ebl18xF8uy2HPPPXnphZcpStTbXHft9Vw4+0JG774bkUikURF2R3j4oUe4dd4tXHHF5WzYuIHi4mImT57CUUcdDcBVV17ND6tWcfSMo/D7/cw651x+MuOnVFVXdXjbhBBCtI6mOmIBmDbQNI2XXnqJY489ttlj3nrrLQ4//HAqKyvJzc1t8pjrr7+e22+/nby8PLxeL/vttx/z5s3bYWCKRCJEIpHU9erqakpKSqiqqmr0POFwmFWrVjF06FC8Xm/rXmQ7suI28ajlDPN18h5fh/34UMaPnyArFXewJ56Yz2WXz2Hr5rIOf65wJMyaNasp8PfBZTZemFEIIdpTsDpKVr6HXiU57Xre6upq8vLymvz8bqjLLIQYDoe54oormDlz5g5f1JQpU3j88cd54403eOCBB1i1ahUHHnggNTU1zT5m3rx55OXlpb5KSko64iW0G9tydndHI20bnD745wcoKMrj60RNjmhfBUV5/OqiC9PdDCGE6La6RHFMLBbjpJNOQinFAw88sMNj6w6pjRs3jilTpjB48GCef/55Zs2a1eRjrrrqKubMmZO6nuwBykTJDU6VUuhGevLr/MefJBQOATCopPmetc404ydH88GHHzR53xW/uZIrr7iqk1u0az771FkOwdCNnRwphBCiLTI+ACXDz5o1a3j77bd32qXVUH5+PrvtthsrVqxo9hiPx4PH49nVpna4uhucGh2wwWlLDRgwIG3P3ZwHH/hLKpQ1VFhQ2Mmt2XUjho9IdxOEEKJby+gAlAw/33//Pe+8806qALY1amtrWblyJaeffnoHtLDzpDY4jSsMM/OKntMtE0OZEEKIzJXWGqDa2lq+/PJLvvzySwBWrVrFl19+ydq1a4nFYpxwwgksXryYp59+GsuyKC0tpbS0tN7CctOmTeO+++5LXZ87dy7vvvsuq1ev5qOPPuK4447DMAxmzpzZ2S+v3SilsOI2dtzOqLV+hBBCiK4qrT1Aixcv5pBDDkldT9bhnHnmmVx//fW8+uqrgLMIXV3vvPMOBx98MAArV65k27ZtqfvWr1/PzJkzKSsro1evXkydOpWPP/6YXr16deyL6UC25az3o+nOwo5CCCGE2DVpDUAHH3wwO5qF35IZ+qtXr653/dlnn93VZmUU27KJx+zUqtZCCCGE2HVdZhp8T5Sc8YVSaZvuLoQQQnRHEoAylLIV8Whid/c0zvgSQgghuiMJQBkoUzc4bS9PPDGfXn1aP6NPCCGEaC8SgDJMaoPTeOZtcCqEEEJ0FxKAMkxqg1MJP0IIIUSHkQCUQeIxi3BtjHjUJh61iUWsTvtqzZ64h/34UC659GIuufRiinsX0m9AH667/trUOSoqKjj7nLPo3beYvIIcZvzkaL5f8X2T51q9ejUen4vPP19c7/Z7/3QPI0YOw7bttv9AhRBCiGZk9ErQPYlt2UQCceZf9VFanv/sP0zF5Wn5vlNPPvUEZ591Dh9+sIjPP/+cC3/1SwaVDGLWrHOZdd45rFixghf//hI5ublcffVV/PSnM/jqy69xuVz1zjNkyBCmHTqN+U/MZ++9J6Vun//EfE4//Qx0XTK6EEKI9iefLhmg7ganXcXAgSXccfudjNptFKfOPJULL/wV9/zpHr5f8T2vvfYP/vzAn5k69UDGjxvP/MefZMPGDbzy6itNnuvss2fx3PPPEolEAPjvf7/gm2++5swzzurEVySEEKInkR6gNKu7wanbZ3D2H6ampR2mu3VZeMrkKfUWZtx3yn7cffddLF26FNM0mTx5Suq+oqIidtttFMuWLW3yXD/9yU+55NKLePmVlzn5pJN54sknOPiggxkyZEibXosQQgixMxKA0sy2FbalUjO+XJm/KX27c7vd/Py0n/PEE49z3LHH8exzf+XOO+5Kd7OEEEJ0YzIElm4KnIGvrjXj69PPPq13/ZNPPmbEiJGMGTOGeDzOp59+krqvrKyM775bzpgxuzd7vrPPnsWCtxfw4J8fIB6Pc9yxx3VY24UQQggJQBmga0Ufx7p1a7n8N5ex/LvlPPvcs/zfA/dz0a8uYuSIkcyY8RN+eeEv+fDDD/hqyVecdfYZDOg/gJ/M+Emz5xszegxTJk/ht1dfxcknnYLP5+vEVyOEEKKnkQCUZl2p8Lmun592OqFQmAOm7scll17E7F9dxLnnngfAw395hL0m7sWxx/+UHx00FaUUr7zyj0YzwBo6+6xziEajnHXmWZ3wCoQQQvRkUgOUbl0z/+Byubjzjj9y35/ub3RfQUEBjz36eLOPPeOMMznjjDMb3b5h4wb23HMskybt055NFUIIIRqRHqA0U4quOQbWjmpra/nmf9/wwIP/x68u+FW6myOEEKIHkACUZkoSEJdcejH77jeZH/3oIM466+x0N0cIIUQPIENgaaSUMwWsq8Wft958u13P98jDj/LIw4+26zmFEEKIHZEeoDRT0PUSkBBCCNHFSQBKoy46AUwIIYTo8iQApZtSaNIDJIQQQnQqCUDpJF1AQgghRFpIAEqj7flHuoCEEEKIziQBKM2kD0gIIYTofBKA0qmLpp/Dfnwol82dk+5mNDJyt+Hc+6d70t0MIYQQXYAEoDRSKBn8EkIIIdJAAlAaSQ00RKPRdDdBCCFEDyQBKJ1U/WWglVLEIuG0fO3KrvSv/+ufFPcu5Jm/PsO6deuYedop9OpTRJ9+vTj+hONYvXp16thZ557Dz048nnm/v5XBQ0vYc+zurF69GrfX5KWXX+LHh08jryCHvffZi48/XlTveT788AMOOfQgcvOzGTZ8CL+ecymBQKBNbXZ7TR599BFOOOln5BXksPseo/nHa/9I3W9ZFuf/4jx2GzWC3Pxs9hi7O3+6795650i+lt/fNo+Bg/rTq08RN99yE/F4nCuv+g19+vVi6PDBzJ//eL3H7exnJIQQouPJVhhppBosAx2PRnjgvJPT0pYLHnoOl8fb6sf99dm/MvuiC3li/pMc/uPp7L3PXuw7ZV/eXrAQ0zSZN+9WjvnJ0Xyx+L+43W4A3nnnbXJzcnn9n2/UO9e1113Dbb+/jREjRnLtdddw+hk/Z+m3yzFNk5UrV3LMT47mhutv5C9/eZhtW7dyya8v4ZJLL+bhhx5p02u++ZabuPXW3/P7ebfxf/93P2eedTorvvuBwsJCbNtmwIAB/PWZZyksLGLRx4u48Fe/pG/ffpx4wompcyxc+A4DBwxkwVvvsOijjzj/l+fx8ceLmDr1QD54/yP+9rfnuXD2BUybdhgDBw4kFotx9IyjdvozEkII0bGkByhNuuo+YHU98OD/cfEls3nphZc5+qhjeP5vz2PbNn9+8C+M3XMsY0aP4eGHHmHdurW8++7C1OOysrL484N/YY/d92CP3fdI3T7n0jkcdeTR7DZyN6695jrWrF3DipUrAPjD7bcx85RTufiiSxg5YiT77bc/d915F089/SThcLhN7T/99DM45eRTGDF8BDfdeDO1tbV8tvhTAFwuF9ddez177z2JoUOHcurMUznzjLP4+wt/q3eOwoJC7vrj3YzabRRnnXU2u+02imAwyJVXXMXIESO54jdX4na7+fCjDwFa/DMSQgjRsdrcA/T+++/z5z//mZUrV/L3v/+dAQMG8OSTTzJ06FCmTp3anm3snpz8U28VaNPt4YKHnktLc0y3p1XHv/jSC2zZsoV333mPSZP2AeDrr79i5coVFBbn1zs2HA7zw6ofUtf33GPPJns6xo4dm7rcr28/ALZu2cLoUaNZ8vUSvv56CX999pnUMUopbNtm1epVjBk9plXtd55vXOpyVlYWubm5bNmyNXXbAw/+H4/Pf5x169YSCoWIRqOMHz++3jl23313dH373xF9evdmjz32TF03DIOiwiK2bt0CtPxnJIQQomO1KQC98MILnH766Zx22mn897//JRKJAFBVVcWtt97K66+/3q6N7I6aqrjRNK1Nw1DpMH78BL788r88Pv9x9t57EpqmUVsbYK+99mL+4082Or5Xca/UZX9WVpPnNF2u1GUtkQxt2wagtraW8849n1/9anajxw0qGdSm1+Cq83zJ51SJ53vu+ee44srf8IfbbmfKlH3Jycnhj3+8k08/+7TZNifP0dR5t7+Olv2MhBBCdKw2BaCbb76ZBx98kDPOOINnn302dfsBBxzAzTff3G6N69aUcvYB07vmINjwYcP5w2238+PDp2EYBvfcfS8TJ0zkb39/nt69epObm9uuzzdxwkSWLv2WEcNHtOt5m7No0Ufst+9+/PIXF6Ru++GHlbt83o78GQkhhGi5NtUALV++nB/96EeNbs/Ly6OysnJX2yS6iN1G7sZ//v0WL738IpfNncPMmadSVFTMz044jg8+eJ9Vq1bx7rsL+fWcS1m/fv0uPdfcuZez6ONFXHLpxXz51Zd8v+J7Xv3Hq1xy6cXt9GrqGzFiBJ9/8Tn/efPffPf9d1x3/bUs/nzxLp+3I39GQgghWq5NAahv376sWLGi0e0ffPABw4YN2+VG9QTdZR+wUbuN4t9vvMlzzz/L9Tdcy9tvvUNJySBOOuVExk3Yk1/88nzC4fAu93aMGzuOBW++zffff8eh0w5m8pRJ3HDj9fTr1699XkgD5517Psf+9DhO+/mpTD1wf8rLy/nF+b/c5fP6/f4O+xkJIYRoOU21YQGYefPm8dRTT/Hoo4/y4x//mNdff501a9bw61//mmuuuYaLLrqoI9raaaqrq8nLy6OqqqrRh1I4HGbVqlUMHToUr7ft9TpW3CYetdANmYgn0i8cCbNmzWoK/H1wmTIVXwjRsQJVEXy5bvoOyWvX8+7o87uhNtUAXXnlldi2zbRp0wgGg/zoRz/C4/Ewd+7cLh9+Oo2sAi2EEKIbseI2kWCcSCBGOPE9EowTDsaIBOJEgsnrzn2D9iji6AvH7fzEHaRNAUjTNK6++mouv/xyVqxYQW1tLbvvvjvZ2dnt3b5uS0kC6jDP/PUZfjX7gibvGzRoMF/9d0knt0gIIboeZSsioXiLQ008YrXq/JFgrINa3jK7tBK02+1m9913b6+29CiyD1jHmXHMDCZPntzkfS7T1eTtQgjR3TnbLVlOoEkEmOZCTTgQJxqKt/o5NF3Dm2Xi8bvw+E28Wc53j9/Ek+XC63fus21FQb+ml0TpLG0KQOFwmD/96U+88847bNmyJbXGSdIXX3zRLo3r1lQXXwY6g+Xk5JCTk5PuZgghRIezYnYTvTHJ63HCiXCTDDXKbv1f326fuT3UZJl4E+HGkwg3yVDjyTJxeYzUOm47EqyOYrrSWwPbpgA0a9Ys/vOf/3DCCScwefLkFr1YUZ+Tf+TnJoQQYjvbVkSbHGJqOtTEo/bOT9qA6dbx+F3NhhpvnR4bt89E76Lr1e1MmwLQa6+9xuuvv84BBxywS0/+3nvvcfvtt/P555+zadMmXnrpJY499tjU/UoprrvuOh566CEqKys54IADeOCBBxg5cuQOz3v//fdz++23U1payvjx4/nTn/7U7JBIOiT3ARNCCNG9KaWIha0mh5iS4Wb79bYNO+mG1miIqclQk+ixMUyZfQxtDEADBgxolyGGQCDA+PHjOeecczj++OMb3f+HP/yBe++9l/nz5zN06FCuueYapk+fzrffftvsFPTnnnuOOXPm8OCDDzJlyhTuvvtupk+fzvLly+ndu/cut7ldNLEPmBBCiK4hHrMaBJkdh5pWDztp4PE1rptJhZpEz00y1JhuXUZi2qBN6wD961//4t577+XBBx9k8ODB7dMQTavXA6SUon///lx22WXMnTsXcPYa69OnD48//jinnHJKk+eZMmUK++yzD/fddx/g7CVVUlLCRRddxJVXXtnkYyKRSGo/M3DWESgpKemwdYBsWxGLxNE0TX5pRUaQdYBET2ZbtjPbqYnemdTlQJxIyLnPirV+2MnlMZqtm6kXarJM3F6zy26T1FLB6ihZ+R56lbRvvWaHrwM0adIkwuEww4YNw+/3N9r8sby8vC2nrWfVqlWUlpZy2GGHpW7Ly8tjypQpLFq0qMkAFI1G+fzzz7nqqqtSt+m6zmGHHcaiRYuafa558+Zxww037HKbWywxBKZJL6QQQrQ7pRTRkEUk1MSQU3LmU+pyjFi4ddO3wRl2Ss1walA34wSc7aFGhp0yU5sC0MyZM9mwYQO33norffr06ZBejNLSUgD69OlT7/Y+ffqk7mto27ZtWJbV5GOWLVvW7HNdddVVzJkzJ3U92QPU8TRspYhZNqauY3SRxH/Yjw/lvfffA+DTTxYzYfyEevc/8cR8Lrt8Dls3l6Whda0z69xzqKyq5IW/vdjsMUopLvzVBbz40gtUVFQ0+Zp39jMRQrSdUsqZ7dTkENP2IOOsV+Pc1uqxDY1GwWVHoUaGnbq+NgWgjz76iEWLFjF+/Pj2bk9aeDwePB5Ppz1f3RpopRQxSxG3LUxdw2Xo6F3gP6pZ55zLdddeT3FxMatXr2a30SOIhltfvNdWI3cbzkUXXczFF13S4c/17/+8wRNPzuet/yxg6NBhFBcXM+vccxg8eDDXXnMdAM8/93d++GEl+0/dr8PbI0R3YFt2s0NMTYUaK96GYSev0XSoqbM2TbIXx+0zJdD0MG0KQKNHjyYUCrV3W+rp27cvAJs3b6634eXmzZuZMGFCk48pLi7GMAw2b95c7/bNmzenzpcpkv+ZqcRlQ9OIWwrLtjANHVPXMjoI+f3+jPuZtoZlWS1+s/vhhx/o17cf++23f7PHFBYWUl1d3V7NE6LLcYadGkzTrhtiGoSaNg07mduHnRrWzSSHmrZfN2WvRbFDbQpAv//977nsssu45ZZbGDt2bKMaoPbY1Xro0KH07duXBQsWpAJPdXU1n3zyCRdc0PQ2B263m7333psFCxakiqlt22bBggXMnj17l9vUXup2zSYvaxoYOthRm2jEIqaBy9AxDa1z1gtytX937iuvvsJVV13BuvXr+NGBP+LBB/5Sb2jx1X+8ys233MTSpd/Sv19/fv7z07nqyt9imiZKKW66+Ubmz3+czVs2U1RUxPHH/Yy7/ng3h/34UNasXcPcyy9j7uWXAey09yk5LPfoI49z9e9+y/fff8fS/y1P3X/TzTfywIP/RyQS4ZSTZ3LXH+/G7XYz69xzePKpJwBwe00GDxrM99+tbNefkxCZSClFPGo30RuT3BIhnqqxSd7X2uU9NI1UIXBLQo3RAe9ToudqUwA64ogjAJg2bVq925VSaJqGZbUs2dfW1rJixYrU9VWrVvHll19SWFjIoEGDuPTSS7n55psZOXJkahp8//79660VNG3aNI477rhUwJkzZw5nnnkmkyZNYvLkydx9990EAgHOPvvstrzUjlEnAdV7v4jZ1Pxhcac3B6D31VPAbbTb+YLBIL+/bR6PPvIYbrebiy6Zzc9PP5V3F74PwAcfvM85s87ij3fezdQDpvLDDyu58FdOsL3md9fy4ksvcu+f7uGpJ59m9zF7sHlzKUuWOHt4Pf/c35m0z17MmnUus845t1VtuuOOP/DnB/5MYVFRalmEd955G6/Xy5v/WcCaNas57/xzKSws5KYbb+aPd97FsGHDeOSRh/now48xjPb7GQnR2VKbVTYbapK9Nc59drwNqwZ7jTqhJjnk1CDUJGY+ubwtWzVYiI7QpgD0zjvvtMuTL168mEMOOSR1PVmIfOaZZ/L444/zm9/8hkAgwPnnn09lZSVTp07ljTfeqDf9fOXKlWzbti11/eSTT2br1q1ce+21lJaWMmHCBN54441GhdHpVL8HqOtviTFkyJBGPTCxWIx77rqHyZOnAPDIw48xbvyefPbZp+yzz2RuvuUmLp/7G844/QwAhg0bxnXX3cBvr76Sa353LevWraVPn75MO/QwXC4XgwYNYp99nMUsCwsLMQyDnJycVg3DxWIx7r33PsaPq1+75na7eejPD+P3+9lj9z247trrufKqK7jh+hvJy8sjJycHwzDqPdcjDz/app+VEO2p3maVdUNNcuZTvX2eWr9ZJYDh0psdYmoYajw+GXYSXUebAtBBBx3ULk9+8MEHs6NliDRN48Ybb+TGG29s9pjVq1c3um327NkZNeTV2PbQUy//uHTyrpjU+GgFVuLn1GGF0u28J4tpmkyatE/q+uhRo8nPz2fpsmXss89klny9hI8WfcTvb5uXOsayLMLhMMFgkJ8dfwJ/+tO9jBo9ksMPn84RRxzJMUcfg2m2ff9et9vNuLHjGt0+buw4/H5/6vqUKftSW1vLunXr2m2dKyFaQilFPGLVm6bdaAXhBj01raXpWqL4t6lQ42p0n9mOPcNCZJIWf5osWbKEPffcE13XU0MRzRk3rvGHjHAopertA1Y3/mma1uQwlAboJIKQrbA1MA0Nl951F1Ksra3l2muu49hjj2t0n9frpaSkhG++/pYFb7/FggULuPiS2fzxrjtY8OY7jWrOWsrn83XZn5fouqy43WiIqeGU7br32VbbNqvcYaipsy2CDDsJ4WhxAJowYQKlpaX07t2bCRMmoGlak703rakB6rGSU78AlGrxCJiWCD62gljcxtLAZTrrB2XaxqrxeJzPP1+cGrZa/t1yKisrGTN6NAATJ0zku+++Y8TwEc2ew+fzcczRMzjm6Bn88pcXMHbcHnzzzddMnLgXLre73X7Plny9hFAohM/nA+DTTz4hOzu7k9aCEl2NbSdmOzUVaprYFmFXNqusO0273t5OdUKN2999N6sUoiO1OACtWrWKXr16pS6LNlLb849CJfYEa92bl645q5BatiISszF0DZehYeiZM/bucrm4dM4l3HXn3ZimySW/vjixTYkTiK6++ncce9xPKSkp4fjjf5bqWfzf/77hxhtu4okn5mNZFvtMnozf5+eZZ57G5/MxaJAzJDVk8GDe/+B9TjrxZDweD8XFxW1uazQa5fxfnMdVV/2WNWtWc+PNN3DBLy9Ez6Cfp+g4SiliEavpupk6u24n16xpy2aVmq41GGJqPtR4/CamS4adhOhoLQ5AdWsh1qxZw/7779+oHiMej/PRRx9J3cQOqNT/a9unwLfxXIbunMNWinBMYRoqY1aU9vv9zL3sN5xx5uls2LiBqQdM5c8PPpS6//AfT+fll17hlltu5o47b8flcjFq1CjOOWsWAHn5+dx+x21cfsVcLMtizz335KUXXqaoqAiA6669ngtnX8jo3XcjEons0iKMhxxyKCNGjGDaYYcQiUQ4+aRTUgsciu5D2Ypt62tZv7yCmrJwvVDT6s0qcYadmhpi2r7ztonH59zn8siwkxCZpk2boRqGwaZNmxrtrl5WVkbv3r27/BDYjjZT29XNUG3LJhax0BNDWaGYhaFpu7wzfN1CaZeuYXbgitKH/fhQxo+fwJ13/LFDzt9VJVfE7opbYXTXzVCVSoSepRWsX15BuDbW7LGmx8Cz01CzfdVgGXYSou267GaoyfV+GiorKyMrK6stp+wxtqfNRA1VO22Kqmlgaon6oMTWGq7EitId8Zfng39+gEcfe4T33v2AsXuObffzdzUzfnI073/wfrqbIXDen8rW17JuWQXrl9UPPS6PwYDd8uk1KKdRqJHNKoXoWVoVgI4//njAqVk566yz6u2fZVkWS5YsYf/9m98uQNDqlVJbK1kfZNsQjdvEU/VB7VcoPf/xJwmFna1QBpUMapdz7qoZPzmaDz78oMn7rvjNlVx5xVUd+vwPPvCXjPuZ9CRKKco2BFi/tJz1yysI1dQPPf13y6dkdAF9hubKOjVCCKCVASgvLw9w3mxycnJSs2bAWWNl33335bzzzmvfFnZjqgPTkK6DTrJQWrVrofSAAQPaoYXtq24AaaiwoLDDnz8TfybdnVKK8g0B1i0rZ/2y+qHH9BgMGJnPwETokd4dIURDrQpAjz32GOCs/Dt37tydDnd9+OGHTJo0qVN3Whf1JQulLaWw4gpTV11mx/nWkADSMyilKN8YYP2yCtYtqyBUHU3dZ7p1+o/Mp2RMoYQeIcROtakG6LrrWjZD5sgjj+TLL79k2LBhbXmajGbbrV/bo6HWl5+3TbI+SCm27zivOxutdrcgJNpGJX+fM/DXQSlFxaYA65Y6NT1BCT1CiHbQ9n0FWqANE8wyntvtRtd1Nm7cSK9evXC73a0qMrbiNlbMQjN0YpZN3FZYbZ/B3SZKQUQlgpGuYXTWjvMi8yhFLBZjW9lWUBqm3rZVttubE3qCqeGtYFWD0DMin4FjCug7NA+jnbdxEUL0DB0agLojXdcZOnQomzZtYuPGja1+vG0r7LiNpmvELYWtVNp6YWzlVCHpmjNUJr1BPZQC03BTmN03rWvVKKWoKA2yfmk56xqEHsPlhJ6SMQX0HSahRwix6yQAtYHb7WbQoEHE4/FWr3kUrIlStr4Gf7aHlVtricZssrzp+2dQCgKROFHLIs/npijHTY7HJf1BPYUGuqaja+lZqE8pRWVpMDFlvZxAZcPQk8fAMYX0k9AjhGhnEoDaSNM0XC5XqzfmtCIahhbGNNzELANb01Hp/GfQIMtr4rUUVaEYlaEIvbIVvXK9ZHvk10O0P6UUlZtDrF9WzrqlFQQqI6n7DJdOvxF5lIwupO/wXNkSQgjRYTr0E06Wfm+epRSWUhgZ8jMyDI2CLDcxy6a0JkJ5MEbvXA+9s7145C9vsYuUUlRtCbFuqVPTU1tRJ/SYTugZOLqAfsPzMN0SeoTo6mwFccvGUoq4pZx6V9vGshVRy6a2MkKxruhF+64E3RpSBJ0mtu3U/7gybMNNl6HTK9tDKGqxvjxIWW2UfnleirI9mLL0v2iFZOhZv6yCdUvL64Ue3dToN9yp6ZHQI0TXsbNgE47ZzgSfuI2lwLJt7MR3Z88nha7phKujeHPTO+miTQHo0EMP5cUXXyQ/P7/e7dXV1Rx77LG8/fbbANTU1OxyA7srSylsW6G7MjNU+NwGXpdBIBrnh621BKNxhhRl7/KeZaJ7U0pRvTXEuqUVrFtWTm15w9DjDG/1GyGhR4hMUi/YxBVxZSeWTVHELJtwvJlgk+zoUKDrGobm7DxgaOAyNLwuF4am0fBv/a2B9O8Z2qYAtHDhQqLRaKPbw+Ew778v+yG1hGUpbEXGDIE1RdMg22PiMXVKqyNke1z0ypFFLUV9Simqt4VTw1s1ZeHUfbqh0Xd4HiWjC+g3Ih+XR0KPEJ3Jsp0QE7dtLIsmg000bjm3Ked22wabpoONqWm4DQ3DZThbLGXuR9hOtSoALVmyJHX522+/pbS0NHXdsizeeOMNWZG3hSyVmAKfWSNgTXIZOj6XwbqKID6XQXYaZ62JzFG1dXshc6PQMyzPGd6S0CNEh2gu2MRtRTRuEY0rInHLOa5BsHEGopw/wJ3eGSfYmIaO4dK6fLBpqVZ9kk2YMAFNc3YXP/TQQxvd7/P5+NOf/tRujevObLsjdwJrf9kek/JAlHUVQUb0zsYlG0r2SNXbQokVmcup3tY49AwcXUD/kRJ6hGgry07U1ign2Fi2s2Buk8GmTrhxPk8UoPX4YNNSrQpAq1atQinFsGHD+PTTT+nVq1fqPrfbTe/evTEMeeNribitutxaO/k+N2WBMBsqQgwuypL/kHqI6rIQ65c6e29Vb92+4ayma/QdlkvJ6EL6j8zDJT2DQjQptR9jM8EmEreIxhQRy0r01Ow42BiG893lcvZ1lGDTNq16xxo8eDDQPvtg9XR2F5whp+uQ53NTWh0my2NKPVA3VlMWdrahWFpBVcPQMzSXgWOcnh63hB7RgzUMNsnamuSwVDhuEYs3HWxAoRKbEBmasxJ/Mti4Jdh0iha/e7366qsceeSRuFwuXn311R0e+5Of/GSXG9bdxS3VJfffcps6HtNgfUUIn9uQxRK7kZqysFPTs6yCqi31Q0+fobmUJIa33D75Nxfdm1KkpnfHk0NNiWATjysilu0Em7iVCjTJL03bcbBJDk1JsEm/Fr+THXvssZSWltK7d2+OPfbYZo/TNK3V20P0RFHLpqsuq5PjNSkLRFhXHmRE7xxcRhd9IYLa8nBqG4rKzQ1Cz5AcBo4pZICEHtFN7CjYxOJOjU0y3NQNNk6Pff1gY+g6uo4Emy6sxe9qdYe9ZAhs18Wt9G2C2h4KfB7KAhE2VYYoKfTLf/RdSG1FOLE4YQWVm4Op2zUNeg/JpWRMIQN2k9Ajuo5ksHGGnhTx5LCU7QxLNRdsLJWYEaWc338JNj1Li9/hCgsL+e677yguLuacc87hnnvuIScnfUtYd3VRy0Lvql1AJOuBXGyqDuH3GBRnSz1QJqutiKSGtypLmwo9BfQfWYDHL6FHZA4n2CR6a5oINpG4RSTuDEvFUdjJtWyaCDZmItiYuobH1DE0vUssQyI6Tovf7aLRKNXV1RQXFzN//nxuu+02CUBtZNvOQohdfeTIbeq44wbrEvVAWW758MwkgcpIqpC5okHo6TU4J9XT4/Gndzl60fM0FWzi1vbtFKJx56thsLHV9llROk6hsKk7qwy7dA1Dgo1ohRZ/Yu23334ce+yx7L333iiluPjii/H5fE0e++ijj7ZbA7sjWyksnP9gu7ocr8m22gjry0MM65Ut9UBpFqiKpPbeqti0PfSgQe/BOZSMLmTAKAk9omPsbJ+oesFGqab3iWoy2BhNbqcgxK5ocQB66qmnuOuuu1i5ciWaplFVVUU4HN75A0UjqX3AusligoV+px7I7w4xsEDqgTpbMvSsX1pB+abA9js06D0oUci8Wz7eLAk9om12GGziO9gnqsEGmC3dJ0qIztDiANSnTx9+//vfAzB06FCefPJJioqKOqxh3VlyVkFXLoKuS9ch1+tiU1UYv9ukKNud7iZ1e8GqCOuXO4XM5Rvrh55eg3IoGV3AgFEFEnrEDrVlA0xrB/tESbARXUmbijZWrVrVouPGjh3L66+/TklJSVueptuyE3uyGN1gCCzJ49KJxnVnvzC3jl/qgdpdsDrK+mXOhqNlGwL17us1KJuBowsZOKoAb7aEnp5ONsAUYuc69FNq9erVxGKxjnyKLskp5rO7xJtIxapqtv6vAhRohoama+iJ75rhfOnJy7pGyLKp8VRSlOfBZeropo5uOAuB6YZzWTecYkW9zm2GoaWOda7r6Kaz71xPFqqJpmp6Goae4pJsp5B5VD4+6XXrEWQDTCHaj/yZngbOsHjmv9NsW1rBuo82t/pxNUBpO7UhGbhSoShxWTf1RKiqE6LqBCjdqHN/UyEsg4NZKvQsq6BsfW29+4pLshk4uoCBowrw5Ujo6S62z4aSDTCF6CwSgNLAUpm/E/zmr8rYuHgrAEWj8skdmIWynemoylIo2/lu2wpl2XUuK+Ixm1jcJstlYGoadtzGthJTWa3E5bjCtm2suErct/2YulTyDT8GMTJnhfFGwcx0Pnh2JZjZtqL0hyq2rasfeooGZlMyuoCBoyX0dCWyAaYQmU0CUBoolbk7wSul2PT5NjZ/VQZAn/FF9Nu7uNU9HtWhOIYOu/XNwecyWvX8ySCUDEVWIizZcec2K3l/IlhZdQNUvE7Iqvt4q6kQtrPHJ54v7gS+eu3s4GBWNCArMbxVgD9XQk8m2dEGmPX2iZINMIXIaBKA0iBu2Rk5A0wpxfpFm9m2tBKA/pN60Wd822b61V0faGivLMwWFnxrmoZhahgmQMuDU0drNpjVCVtWsmfLqhOs4rZT9N6CYGbbisJ+fgaOLpTQkwayAaYQPYsEoDSIWQrdzKx3QmUr1r6/ifIV1QAM3L8PvcYUtPl8mgaFfjfbasP43AYDC5peNLOryNRgJlrPVlAVjBJr4QaYJPprZZ8oIbqXNgWgdevWtWhq+5///Gf69OnTlqfo1mK2jaFlTva0LZvV72ykak0taDD4R/0oHJG3y+c1DI0cr4uNlSGyPAYFfunVEOmlFGyoDLKhIrTTDTANWdVciG6tTctUDRkyhIMOOoiHHnqIioqKZo879dRTycrKanPjuiOVWHBMy5AFwqyYzQ//WU/Vmlo0XWPotAHtEn6SvC5nCft1ZUFCscwpYhY9U2l1mA0VIXI8LnrleCjO9lCQ5SbXZ5LtMfG5DdyJQnYhRPfWpo/hxYsXM3nyZG688Ub69evHsccey9///ncikUh7t48hQ4agaVqjr1/96ldNHv/44483Otbr9bZ7u9rKsp19wIwM6DOPRyxW/nsdNRuD6KbG8OkDyR/c/hvc5vpcBKJx1leEEoWgQnS+rTUR1pUHyXKbeFwZ8heIECJt2vQuMHHiRG6//XbWrl3Lv/71L3r16sX5559Pnz59OOecc9q1gZ999hmbNm1Kfb355psAnHjiic0+Jjc3t95j1qxZ065t2hWWrVAZsA1GLBRnxetrCWwOYbh1RhwxiJz+HdNbp2lQ4PewrTbM5irZP050vopglLVlQdymjs8tNVxCiDYGoCRN0zjkkEN46KGHeOuttxg6dCjz589vr7YB0KtXL/r27Zv6eu211xg+fDgHHXTQDttV9zGZVIcUT0yL1dO4DUY0EOP7f64lVB7B9BqMOGoQWX06tkjZNDSy3S42VIWoCMrq4KLz1ITirC4LApDtyZzaOyFEeu1SAFq/fj1/+MMfmDBhApMnTyY7O5v777+/vdrWSDQa5amnnuKcc87Z4bo0tbW1DB48mJKSEn7605/yv//9b4fnjUQiVFdX1/vqKJbtzEJJ1xBYpDrK96+tIVIVxZVlMvKYwfiLOmeI0Oc20NBYXx4kLPVAohMEonFWlwWIx23y/LJHmhBiuzYFoD//+c8cdNBBDBkyhCeeeIKTTz6ZlStX8v777/PLX/6yvduY8vLLL1NZWclZZ53V7DGjRo3i0Ucf5ZVXXuGpp57Ctm32339/1q9f3+xj5s2bR15eXuqrIzdvtWzb2Qk+DSUIofII3722hmhtHE+ui92OGYw3r3NnZuX5XNRGYqyrCCHlQKIjhWMWa8qCBKJx8mUGohCiAU0p1eqPoZKSEmbOnMlpp53G+PHjO6JdTZo+fTput5t//OMfLX5MLBZjzJgxzJw5k5tuuqnJYyKRSL0C7urqakpKSqiqqiI3N3eX213Xmg3VLP68lH79stv1vDsT2Bpi5b/XYUVsvAUeRhxRgsufnuGAuKWoCEYYUpRFv/yuvT6QyEwxy2bV1gBlgQjF2V5Zp0eIDLN1S4i+/XxM2at/u563urqavLy8Fn1+t+kTcO3atZ2+meeaNWt46623ePHFF1v1OJfLxcSJE1mxYkWzx3g8Hjwez642sWXS0OtRsynAD29uwI7Z+Ht5GT69BNOTvkJQ09DI9rhYXxnC5zHJ98nQhGg/cVuxtjxIWSBCUZaEHyFE01ocgJYsWdLik44bN65NjdmRxx57jN69e3P00Ue36nGWZfH1119z1FFHtXubuoKqtbWsensDylJk9/Mz7LABGBkwC8bnNgjHbNaVB/H1zpFpyaJd2ArWlwfZUh2mwO9Jy1CzEKJraHEAmjBhApqmkRwx21EPkGW1b4Grbds89thjnHnmmZhm/SafccYZDBgwgHnz5gFw4403su+++zJixAgqKyu5/fbbWbNmDeeee267tqkrqPihmtULN4KC3EHZDD2kP7qZOZ8I+X4XW2vDrK8MMrQ4mzROjBPdQHKV501VYfL9bkxZzFAIsQMtDkCrVq1KXf7vf//L3Llzufzyy9lvv/0AWLRoEXfeeSd/+MMf2r2Rb731FmvXrm1yjaG1a9ei1/kzr6KigvPOO4/S0lIKCgrYe++9+eijj9h9993bvV2ZbNvyStZ9UApAwbBcBh/UDy3DEoazX5iHLdUR/G6TfnmZs2Cl6Ho2J1d59pq4jMwJ+kKIzNSmIujJkydz/fXXNxpWev3117nmmmv4/PPP262B6dCaIqrWWrO+msVfdGwR9Javy9nw6RYAikbnU7Jfn4wLP3WFIhYRy2JknxzypB5ItMG22gg/bA3gMw18aaxvE0K0TCYUQbfpz6Svv/6aoUOHNrp96NChfPvtt205pWgHSik2fbE1FX56jy2kZP/MDj8APo+BUrC2PEgkZqe7OaKLqQjGWFMWxGXoEn6EEC3WpgA0ZswY5s2bRzQaTd0WjUaZN28eY8aMabfGiZZTSrHhky2U/rcMgH57F9N/n16dPluvrfL9bmrDcTZUBmV9INFiNeE4a8oCoCDHK6s8CyFark3vGA8++CAzZsxg4MCBqRlfyVlir732Wvu1TrSIshVrPyyl/LsqAAbu25teexSmuVWto2lOCNpcHcbvNukr9UBiJ4LROKu3BYjGbQqzZKFDIUTrtCkATZ48mR9++IGnn36aZcuWAXDyySdz6qmnkpXVMRtqiqbZlmLNuxupXFUDGgw6sB9FI/PS3aw2cRkaWW6TDZUh/G6TXJ/8RS+aFonZqVWei7I6aQ0vIUS30uZPmKysLKZOncqgQYNSQ2ELFiwA4Cc/+Un7tE7skB23WbVgA9XrA2g6DDm4P/lD27dou7P5PSYVgSjrKgKMdOXgzqBp+yIzxCybNWUBKoNRWeVZCNFmbQpAP/zwA8cddxxff/11am2gurUm7b0OkGjMilr88OZ6aktDaIbGsMMGkDuwc7fX6Cj5fjfbasJsqAgypDhbPuBEiiWrPAsh2kmb/ry+5JJLGDp0KFu2bMHv9/PNN9/w7rvvMmnSJBYuXNjOTRQNxcMWK/61jtrSELpLZ8QRJd0m/ECiHijLQ2l1mC014XQ3R2QIpWCdrPIshGgnbeoBWrRoEW+//TbFxcXouo5hGEydOpV58+Zx8cUX89///re92ykSYoEYK95YR7gyiuk1GD69BH9x9ysYdhkafrfJhooQfpdJjtQD9WjOKs8hNlWHyfPJKs9CiF3Xpr+hLMsiJycHgOLiYjZu3AjA4MGDWb58efu1TtQTqYny3T/XEq6M4vKbjDx6ULcMP0lZHpO4rVhXGSQal/WBerLN1WE2VAbJ8ZhSFyaEaBdt+rN6zz335KuvvmLo0KFMmTKFP/zhD7jdbv7yl78wbNiw9m6jAMKVEVb8ax2xYBx3josRR5bgyen+U38L/G621ToffkOKpB6oJyqrjbK2PIjfNPG6ZKFDIUT7aFMA+t3vfkcgEACczUePOeYYDjzwQIqKinjuuefatYECgtvCrPz3OuJhC2++mxFHlODK6hlbRmxfHyhClsekd0737fESjVWGYqwuC8gqz0J0cUrZxOwoMTtO3I5SEa3BG8kF2ncrjNZoUwCaPn166vKIESNYtmwZ5eXlFBQUdJmVh9MlakUIxwNAy4qWa0uDrPzPeuyYjb/Yy/DpAzF72Iq3LkPH6zJYXxHC5zJlxd8eoibsLHSIQmrAhOgilFLE7ChxFSNmxYjZEcJWmKgVJq5iWHYcBVRFIuTF05sX2u1dpbCwa608nC6BeIDaeBXQZ6fHVq+v5Ye3NqAsRXZfH8N+PBDD3TP/Cs72mJQHoqyvCDKid7bs9t3NhWIWq8sCxOI2BbLKsxAZRylF3I4RU1HiVpyoHSFshYhaYSxixK146lhDNzE0E4/hQzcNdE0jZFSksfUO+bMqDWwslLLRtOY/xCtXVbN64UaUDbkDsxg6bQB6Dy/+zPe5KQuE2VARYnBRltQDdVORmM3qbQFqI3GKZZVnIdJKKYjbMeIqmujRiRK2gk6PDjHidhylQGN70HFpXrxuEz3D36QlAKWBrSzittVsL0bZ91WsfX8TKMgfmsPgg/qjy7RfdB3yfG5Kq8NkeUx65ciHY3cTs2zWlgepCEbpJas8C9Fp6gYdp2cnRjgeJBKvG3RsQMPQje1Bx5X5Qac5EoDSQmFh4aJxIfPW/5Wz/uMtABTtlkfJAX3R9K75y9UR3GbdeiCDbKkH6jYsW7GuPMi22jDFssqzEB1ie9CJEbejxFSMSDxM2AoSV3EsO4atFBqgaQambmJqbryurC4bdJojnx5pEFcWtorXu00pxeavytj0+TYAeu1RwIApvaWovAnJeqB1Ug/UbSgFGypCbJZVnoVoN3E7nihGdoqSw6mg4xQj28pZX03XdEzdham58Lp86Dsoz+hOJAClgVIKy47Xu77xs61s+bocgL4Ti+g7sVjCzw4k64E2VYYpKfRLb0EXphRsrAyxoTIoqzwL0QaWHSemYsQtpyg5HA8TscLEVDTRo+MEHU3TEz06LrymF13vmZNqkiQApYHCwlLOhrHKVqxbtJmyZZUADJjSm957yoy6nUnWA22qDuH3GBRnSz1QV7WlJsz6yiA5Xpes8izEDli2lQg6ztBVNB4iYoeJJqad28oGBZqmpYKOx/Rg9PCg0xwJQGlgKxtb2SilWPPeJipWVgNQMrUvxaPy09u4LsRt6rjjBusqQvjcBllu+XXuapKrPPtMQ1Z5FiLBTgSdmO3U6UStiDPzyo5hqRiWstAUkAw6mLiNbAk6rSSfGGkSsSJsXltDxcpqbBTVo7MZObT77OjeWXK8JmWBCOvKQgzvnY1Lhk+6jMpQjDVlAUxdx++RtyLR89i25dTo2LHEKskxQlYgUbMTT5VKaBqYugsDE7fhR9dMGfZvB/KukyZxFWPzxloMYJVp82LpNua/XsZ+AwuYNqyY4QV+qQFqoQKfh7JAhNKqEAMLpB6oK6hNrPJsK8iXVZ5FN+dsA1GnR8eObl80MBF0FAoNp0fH0Fx4DR+GKUGnI8k7T5rE7SjhGo0sIO7VGZTnZW1VmHfXlPPumnKG5Ps4bFgx+5cU4DWlW3NHdB1yvS42VoXxu02KsmXl4EwWilmsKgsQjdsUyirPohvZ0TYQydWRVeLYVI2OBJ20kQCUJpaysAJO92Z2vod504byfXmAt37YxifrK1ldGeLhL9bx9JINHDi4kMOGFTMw15fmVmcuj0snGtdZVxHE59bxSz1QRorGZZVn0fUlt4FIBp2oHSFihYgkg44dJ5l0utrqyD2JfEqkicJCCzkzwdzZJpqmsVtRNrsVZfPzcXHeW1PGgh+2sTkQ5T8rt/GfldsYXZzFtKHFTB6QL2vfNCHHZ7KtNsL68hDDemdjygKSGSVmKdaUBakMxijK8shfvCLjNVod2Y4Rigea3AZC1w1MzdXlV0fuSSQApYllW7ijzp8Ivpz6wwC5HpNjduvDUSN7878tNbz1wzY+31TFsm0Blm0L8OSSDRw0uJBDhxbTR6Z/11Po91AWCOOrMhiYL/VAmcKyFevLA2yrDVMoCx2KDNPsNhCJHcydoKPQNNBwVkeWoNP1SQBKAx0NK6bwOB1A5BR4mz5O0xjbJ5exfXIpD0V5Z1UZ76wuozwU4x/fbeG177Ywrk8O04YVM7FvHkYH9XjE7Ti2iqNhYGh6Ri+e5dQDudlUGSbLbUqNSQZIrvJcmljl2ZCZeiJNlAJLxZ06HduZaRWOhxpsA2GjoXX7bSCEBKC0UUHnP6YIin45O/+QLvS5+dnu/Th2dF++2FTFglXbWLK5hq8SX4U+F4cOLeKQIcUU+BrvMdYWtm1RHaukPLyVmIqhaxo6BrqmY+gmpmbi0t0YuomOjqEZaFoiJGlG6lhdMzq1J8bj0onEdWd9GbeBT9aXSRulYJOs8izSoKltICJWiJiKYdmJtXTQeuw2EEICUNpYAeeDoEpX7O5reS+FoWvsMyCffQbks7k2woJV21iY6BX6+7elvLi0lEn985k2tIg9eue06a8WpSAQr6E8vJmaWDVuw43X8CUWb7SxlEU8HiWoFAorNQaenN2gaVoi+GhomBhoqUJAUzNxGR40TcdIBiTdCUlGvcC0ax+UOd7t9UBDe2VJPVCabK0Ns64yJKs8iw7jbAMRT6yOHCUaDxNKbgOhYti2jSK535XzHuQ1PRndky06hwSgNIlUa7iAakOR427bf4h9sj2cOnYAJ+zej083VLLgh20sLwvw6YZKPt1QSd9sD9OGFvGjwUXktHChuYgVpjy8jaroNhQaue78Vv9FZCuFUtb27yjidji1AjZKoUiEJo3ErsM6hqaDE4PQNTOxHoaJSzcTvUzbA5KeOF7XTAxNR2vQRk2DQr+bbbVhvG5d6oHSoDwQZU1ZEK+pyyrPYpeltoFILBrYom0gdA+G/O6JZkgASpN4rY4LiLi1Xe7tcBs6UwcVMnVQIWurQiz4YRsfrC2ntDbC019v5Pn/bWLfxAKLIwubXmAxbsepipRTHt1CzIrhN7NxGW0bStM1DTQT521n5+dwglJiexCc7zEVIRoPYSV6nZKSLdc0HT0RfHScUOR0Y5vOiqm606OEASu3BdC0XIqyfRiajqE7w3MSiDpOVSjG6rIApqaTJas8i1Zo+TYQzurIzjYQWRi6/J6J1pHfmDRRQafHwvK277DAoDwfZ08sYebY/ny4toK3ftjGmqoQ768t5/215QzO277Aos9loJSiJlpFWWQzwXgAr+Ejz9O5W3I4gSkRWFpAKVI9S9uH5eLE45F6gSk5JBeMxtm23qCkMMuZtYGBrmu4dBcu3Y1LN3HprsQ01mSNU2JITndqmwxZer7FaiPOKs+WBQVZ8hYjmuasjhxNBJ0YUTva/DYQmrM6smwDIdqTvDuliRZKfPd3zD+B1zSYNqyYQ4cWsbIiyFs/bGPRugrWVIV45L/reObrDUwZmMPE/jZZ3hpMzSTXXdAlZjpoGmiaQUujY54bygNhaoIG2Xlu0J1eplA8REAFsJSFshNxKVHMpGma01OU6GHSNA2X4UqEJufL6UlKhKXEUFyyQDx5uacJxSxWbwsQkVWeRUJz20DErAhxFUtsA+H8pyfbQIjOJAEoTcyw81+2mdWx49OapjGiMIsRhVn8fNwA3ltTzls/bKW0NsrC1VUsXA2D8w32G+RmXB/olnWBGuT7vVSFotR4oHcLVtR2epYUcWUlephswvEwQRXCVlai3qBe6TeGnijoJhGENBO37sI0TdyaOxGUjMRwXbKOyUgFLUPr2j/85CrPNeE4xbI+VY+TXB05pqKJbSDq7HfVxDYQhmbKNhAirSQApUMcXJbzX7wnp/P+CXymxn6DdEb20viuzOarjS6+3WqxptJiTWWQV10h9hngZt8SD8UdHMw6m65Dlsdka20Er0sndydLBeiaDhqtGJZzhuNsbCzbCUgxO0rYDqNiNpZtA4rt1d+kZr/pmoaBE4Tcuhufy4fH8CR6nNy4DGfmSiaLWYo15UEqA1GKsr3ygdaNyTYQorvI7HfVbkqLOEMjIU2R0wlBQymojVVRHt5KIF6N2/Ayvk8RE/pCddjm0w0RPlkXoTKseHd1hHdXRxhZZLLfIA+793J12AKLnc1t6kTjNltrInhMA4+r/YaoNE1zenMwcOk7L/x2ApPCxpktZ9lx4nacsBWhPFKeCEoaZmJ5fY/pxmf68Jre1BCc2/BgZkA9hK1wVnmuiVCYJas8dxdNbQMRtoJE4qHENhAWSjlJx5BtIEQXJAEoHcLb1wDK93TsG0UoHqQyso3KaBkaBjkN6nxyvTqHDfdx6DAvy7bGWLQ2wvJtcb4vc75yPRpTSjwcPMSL2+z6b2rZHpPKUJQtNWH65/vSFu6cwKSRqmQyGtfLOENwTjAKxUNUR6tx6ruVM5ymG7h1D37Th9flxa27U3VKbt29y7MLW8JZ5TlIaU2YfFnluUuSbSBETyUBKA20sPOhV60rBnVQAIpZMSqjZVRGtxK34/jNHMwdTBPVNY3de7vZvbeb8qDFx+uifLohQnVE8eaKMEu3xDh772xyPV38z3sNcrxuKgJRfC6D4pzMrVXRNA2X5mqyR8myLSccWTHKIkGsoAVoaBq4DBeGZuAzfPhdftyGOzWc5k7c1x6UgtKqEBsqQ+R53bgk/GS05reBCCWKkWPYSqXW5XKWlXDjdfl7ZEG/6P4kAKVDyHkzqdJt2rtWVCmb6mglZeEthKwgfsOP353TqnMU+g2OGuXj8JFevi6N8crSIOurLe77uIZZe2fTJ7tr1wcZiXqgLTURPC6DHG/X+88gOU3fY9T/BXJqj5xeo+pYDeWRCueTLzGc5tJcuE03ftOPx/Ti0s1Ez5G71cNp22ojrKsIkeU2ZZXnDOOsjtyybSCMxKKBsg2E6Gm63jt/N2CFNEycHiC3HmuXcyoFwXgN5ZGt1ESrcBku8tyFu1QfYuoaE/u7KckzeOTzWrYFbe7/uIYz98pieGH77DeWLm5TJxK32VIdxm368XSTD3Bd0/EYbjwNhtTqDqcF48HEcFqd+g3d2det4XCaW3fj0l2NhtMqAlHWlAVwmzq+Nq5kLnZdS7aBgGSPjlOnI9tACOHI+AB0/fXXc8MNN9S7bdSoUSxbtqzZx/ztb3/jmmuuYfXq1YwcOZLbbruNo446qqOb2mLJIbCoR2Fh7fL5IlaYirBT56NQ5Lhy2/UNrjjLYPa+OTz2RS1rKi0e+qyWk8dmMbF/117nJcdjUhGMsrU6TP98f7cu3q07nOaj/jIAlm0l1mmJNjmcZuomPtOHz/DjNlyEYxrryqIYmotsWeW5UzTcBiJmhZ0p5nYUS8WxlAVKJYKOiYmJx8iWbSCE2IEu8e61xx578NZbb6Wum2bzzf7oo4+YOXMm8+bN45hjjuGZZ57h2GOP5YsvvmDPPffsjObulBF1PmltL8TsSJvPY9lxqqIVlEW2ELWiZO3C9hU7k+XW+cU+Ofx1SYCvN8d4ZkmAipDFIcO8nVJs2yE0yPW5KQ/E8LmiFOV07UDXVsmC6oa2D6fFqIpUUWaXE47FKa0KYyuDfK+Xmlo3Xt2Hy/Dg0lyYhhuXZsq2BG1kN9zvyooQtkNELadHx7ItZzuYettA+OXnLUQbdIn/akzTpG/fvi069p577uGII47g8ssvB+Cmm27izTff5L777uPBBx9s8jGRSIRIZHsQqa6u3vVGNyMetjASawDpWWApZ92Ypj6AmqOUojZWRVl4K8F4NR7DT76noKOanOIyNH4+IYt/Lg/x3uoI//o+THnI5rjd/V12qryhg99tsLkmjMelk90F64E6SsPhtEjMprImiFtzke01sFScSDxIQFU7SxxpiR23Nad41mN48Zre1P5spubG1F1pn7afCZraBiJsBYlaEeIqjt3E6sguwy+LBgrRjrrEu/33339P//798Xq97LfffsybN49BgwY1eeyiRYuYM2dOvdumT5/Oyy+/3Oz5582b12iYraOEq5z9bQKaIsuno4hjK6vFC+6F4gHKI1upjlRg6CY57sJOnYqqaxozRvsp9Om8sjTEJ+ujVIZtfj4hG28XnSbvcelELJvNiXogKehtLGbZlFaHCUTi5Ps9zv5MmIC33nGWbWHZMWIqTCgaQEWcGhRd05xiW0zchheP4Uss8uhy1o/RXWjdsAC3/jYQzqKBzW0DkSpGltWRhegUGR+ApkyZwuOPP86oUaPYtGkTN9xwAwceeCDffPMNOTmNZzeVlpbSp0+ferf16dOH0tLSZp/jqquuqheaqqurKSkpab8XUUek2glA1boi12M4MzKwdrpnesyKUhHZRkW0DNuOk+XKSWu39wGDveT7dJ7+MsDybXEe+KSGc/bOJq+dN3ftLDkek8pgjC09oB6otSxbUVoVpioYJc/v3uEHc2p9oga328ombsexVJzaeDXV0QoUCg0N0zAxcOEyPHgNHy7dhak7vUVdZTitpdtAJIOObAMhRPpl/DvLkUcembo8btw4pkyZwuDBg3n++eeZNWtWuzyHx+PB4+mc9WDCVc6sryrdJs/rQqmIs+txMx1Atm1RFaukPLyFiBXCZ2bhdrVuWntH2aO3mwum6Dz6eS0bayzu+7iaWXvn0Den6xVeahrk+lyUB6L4XGaPrQdqyLZhc3WIikCMXJ+bto506pqO23BDg2iUXAXbUnFC8Vpqo5Wp/aKSm8w2Hk5zO8NsaRhOa8k2EEo5wU62gRAis2V8AGooPz+f3XbbjRUrVjR5f9++fdm8eXO92zZv3tziGqKOFkkMgVXpihFeHQXYqvFMMKUgEKumPLKFmlg1bsND7i5Oa+8IJXkmF+2bw8Of17I1YHP/J9WcMTGbkUVdb5q8oYPPbbClJozXbZDl6XpBrj0pG7bWhtlWEyXX68bogF4xXdPQDReuBn2gSoGt4olp3s5wmh1x9lNzNpB19kfzGF48ujOcZhpmuw2ntWwbCBtnE1xje9CR1ZGF6DK6XACqra1l5cqVnH766U3ev99++7FgwQIuvfTS1G1vvvkm++23Xye1cMfCdQJQbmIV6HiDABS2Qqlp7Roaue78jF6grNBvMHtKDvP/G+CHijgPL67lxD39TBqQuassN8frMqiOxymtCjGoyI+rIz71uwIF5YEoW6ojZHlcGJ2cBTUNjMTwV6PhNNsiriwsFac6WoWtylOPMfXGw2muxHCaqbkaTTZoahuISL3VkeN1Vkd21ksyNQ9el0uCjhBdXMYHoLlz5zJjxgwGDx7Mxo0bue666zAMg5kzZwJwxhlnMGDAAObNmwfAJZdcwkEHHcSdd97J0UcfzbPPPsvixYv5y1/+ks6XkRJJDYEpcj06NmDZzm0xK0ZVtJyK6BbiVhy/a8fbV2QSv1vnvH2yeW5JgC9LYzz3dZDykM2Ph3e9afI5XpPKYNSpB8rzk8HZs8NUhKKUVofxuY2M2wNO1w3cGLRsOM2pvEkOR7l1Fx7di8vwEEmspSPbQAjR+Zw/PtTOD+xAGf/pun79embOnElZWRm9evVi6tSpfPzxx/Tq1QuAtWvXotepWN1///155pln+N3vfsdvf/tbRo4cycsvv5wRawAppVI9QDWGIsutEYjpxOwoVZEKysNbCFoBfIYfvycz6nxaw9Q1Zo7PosAX5p1VYd5cEaYiZPOzPfyYXWiavKZBrtdNeW0Ur8ukKLtn1QNVh2JsqgxjGlqXWiF7R8NpVmI4LWKHCcRrUcqu06Pjwmt6ZXVkIXZB1FIEoopA1CYQUwTrXA5EFYGYnbhfEYzZ1EZh7wEBTjwofW3O+AD07LPP7vD+hQsXNrrtxBNP5MQTT+ygFrVdJBDHjjmJV3mTU4MNAvEAFdEyXJpJboPd2rsaXdM4apSPAp/OS98GWbwhSlXY5vQJ2fhcXed1GQZ4XAZbqsN4XT2nHigQsdhUFUZHw99NtrjQNBLF0xn/didERojbiQATaxBiojbBWP2gE4g6wSZmt/55AtE2PKgdyTtCJ6ouCwFQqyn8ieniHt1L2AqRbea2ajHETLffIA/5Pp2nvqzl+7I4//eJM0Ms39d1ehR8boPqUJzN1WFKCn3dvh4oFLUorQoRtxS5PnlrEKI7sJVqFFp21jsTjrftuQwNstwaWW6dLJdGllvDX+dytlvHn7gcrKlh+ID0jnTIu1wnqt4WBpwp8LmJAGToJll61xvuaokxvVxcMDmHR7+opbTW5k8fV3PO3tkMyO06v3bJeqCtNRr9crtvPVAkbrOpKkwoZpHn7Xoz+IToCWylCMdVvbASqNtTU+dysgcnFFO0pdJG10iFlSyXjt+tJYKMnrit/mW/W8dj0OKaz9IIuI30jgp0nU+ibiC7wENtP5OVFTFyPd30k7SBgXkmF+2byyOf17C51uaBT2r4+YRsRvfqGh+ymgY5XhdltTG8riiFWd2vHihm2ZRWhQlEYuT53M5qfUKIDqWUImrRqCcmGN1+uTaaHIqyE4FH0da6Yb8rGVTq99DU661xJQKNW8Nral26HKMlJAB1ok3ZP/DG4PlsyMrhIM+MdDen0xT4dH6VmCa/sjzOY1/UcvzufqaUdI1p8qah4Tb1VD1Qd6mNgdat8iyEaF7Mqt8TE2yiVyYZdIKJy/E2lsB4DBr1vjQVaJKXfS6ty+7X2JEkAHWibaFtbNM+xcwaRF4P6QFK8rl0zp2Uzd++CfLFxih//58zTf6IkV1jmrw/WQ9UFWZgN6kH2r7Kc3SXVnkWoruxbLXTWpmGvTXRxuvZtoipQ3YirNQdckr2xCQv++tc7kqzajOZBKBO1C+rHwCaq5IcT8/7BTZ1jVPGOhupvrUyzNs/ONPkTxrbNabJZ3tNqoJRttbo9Mv1de16IJVc5TlGjtfVIas8C5EJbOXUwdSrj0lcr23YW5O4PRxv2ziTniwCdm3vffEnin+TtzWspUl3HUxPJgGoE/XNcrbj0Mwast3pXQAqXTRNY/pIZ5r8C/8L8t9NzjT5M/fKwu/K7E9hXYNsj4uy2ig+t0GBv+vWA5XVJld5NjHlDVh0EUopwnEa9b4EGlwONhiKasu7rUadIuAW9s54zZYXAfdklopTbZdREZOFEHuMPHcByjbR9Diaqxrole4mpc3kgR7yvTpP/LeWHyri3P9xDbP2zqbQn9n1NS5Tw23rbKmK4DG7Zj1QZTBzV3kWPYdSiphFkwGm4Uymur0zbS0C9pnJQt+mZzL5XfXrZ3yu7l8E3BGUUgTtGqriFVRb5VTFE19WOdXxCqqscmqtakAxoWI/zuGYtLVVAlAnqgjEUPE8NHcZcb2CnhyAAHYrdnHhvjk8uriWLQGbP31cwzl7Z1OSl9m/lsl6oC3VYQYW+DC70PhRTSjeJVd5FpkvbjeujandybozbS0Cdhs0WRtTr4emwZCTFAG3j5gdpSoRbKoTwaYqXk61VZH6HlexnZ7HwKRtfXPtJ7M/abqZzdUR7Fg+uruMGqs83c3JCP1zTGbvl8ujn9eyqcbigU9r+Pn4LHbvndnDS6l6INOgX563S0wdD0QsNlY5i3F2xZ4r0XksO1E309RMpmiyQLj+CsGRNhYBG5pTBOxv2CvT1PBT4naXDNt2CFvZ1FpVid6a+sEmGXRCdqBF58rW8sg1CskzCpzv7kLyPYXke4rI9xRSEQ4xvHf/Dn5FOyYBqBNtqQmjYvkAVEkASsn36lw4JYcn/1vLd2VxHv8iwLG72+w/yJvupjUrVQ8UiOBz6+RneD1QWFZ57rFspQinwsyOZzIlw02ojbUZTS2el93MTKZk0HG3YvE8sWvCdig1JFVt1f9eFa+gxqrAZufdcm485OpF28ONUUiuWUi+1/nK9RbgMl1oBmgG0MS/cWVVGC3NQ/DyTtiJonEbN4UooDpeke7mZBSvqXHO3tm8+G2QT9dHeenbEOVBm6NG+TJ2HN5largsnc3VEdwZXA8UjdtsrAoTjFrk+7rGApSiaUopIsnF8xr0yjScyVR324O2DjTI4nldh6XiVMcrt/feJGtuUpfLiajwTs+joZOr55OrF5KrF5CnF5JnFpJrFpHnKSTfW4DP7Uc3NTC07SFH73pBVgJQJzpybD+WV4/mkRVvUhWXHqCGDF3jhD38FPh0/v19mHdXR6gI25wyNitju7z9HoOqUIwt1RFKCnwYGdbOuOVscVEbjjm9VJnVvB4vZjXdK1NvmKlB0LHamGa8JvXCSnO1MsmiYJ8pdTOZwiksrm00JFV3mKrGqoIWRF2flpUKN7l6YSrg5JmF5LoKyfXkYXh0NFNDaxhwutnvgwSgTlbkcQqfZQisaZqmcdhwZ5r8374OsqQ0RnW4hrP2yibLnZlFuzleF1XBGFtNnb4ZVA8kqzx3rtbtoO0EnVgnLJ6XnRiK6gprbfVUycLi5Cyp7QXGFanenJYWFucZiXCjFTpBxygkTy8gz+UMU3ncHjQX6C4tMTzVtXtxdoUEoE5WnAxA8TKUUj3ql6019u7vIc+jM/+/AVZXWtz3cQ2zJmVTnIHT5J16IJNtAWd9oDx/+oeZnFWew5TLKs9tko4dtFvTOyOL53UdtrIJWNWpYLO97mb7NPGgXduic2XreeQaiZ4brZAcLXHZKCTPLCDLzEUzNHQXaG6cXhwdNENz6nCM7teLsyskAHWyQk8xAFEVIaJCeDV/mluUuUYUufjVlBwe+byWbUGb+z6u4ey9shmcn3m/tk49kEZpdRi3qeNLZz2Qgq21Ecpqo7LKcxPWV8XZVGM1OZMpebmtO2g3tXheo56aBrU0rdlBW2SeSLKwONVzU78Xp7qlhcWaZ3tBsV5IrlbQoCcnH5fhAl1DM3F6cdx1enH05guORdMy75Okm/MYXrz4CROkKl6O1y0BaEf65hhctG8Oj35Ry4Zqiwc/reHU8VmM7ZN5s678HoPKYIwtNREG5qevHqisNsrWmjB+tyGrPNdRWmPxr+9CfLt150MJSbJ4Xs9mKYuaZM1NM/U3LS4sNvK3TwvXnd6bPL2QXArI04vwaL5EzY0GOuguwAW6mRyikl6c9iYBKA2yyE8FoD7ugeluTsbL9epcMDmHp76qZdnWOE/+N8CM0TYHDsm8afK5PhdVwSheU6dPbufXA1UGo5TWhPGaBm5Z6BCAypDNf1aEWLwhisIZshxeaJLjaXomU6p+RhbP69aUUoTsQP1AU2+YqhWFxXpWYiq4E2pyErOncjUn4GRpueiaiabh7CFYtxfHlQw22g6njYv2JwEoDbK0PMrURimEbgWPqXHWxGxeXhrk43VRXl0WojxkM2N0Zk2T1zXI8rjYVhvF6+rceqDUKs+ahifD91XrDMGozdurwny4JpJacXjPPi6OHOmjd3bm1ZKJ9hWzo3VWJ96+1k3dsNPiwmIzEW6MQnKNZMhJ1OFQgEu50dBQsL3mRnpxMp4EoDTI1vJB0SOnwiulAIWNcv5fqe3XlPM9eV0phZ267Hw/aKSN1xfmvdVhPiq12RIzmD7Sg6FT77iGj1PY2HWeq/nn3tE5WtZGG5to3IKAIttjoOvUe/7kOewmzlvgKqCPtx/9PP3p4+1HnpnXor8Eg9E6qzx7evaHe8xSfLAmwjs/hAkldvUeVmBy1ChfRtaPidZTyqbWqq43S6phgXHQrmnRubKNXGdoykyEGzNZg+PU4fjJRrM1lALQpBenG5F3gzTIIg+A6gwPQNXxCj6rWcjK8P+wlNVEMNhReLGbDDntsveLAVnDnYubgMe37PopO0zLVo1vllf30sfbj76efvT19qOPpx99PX3p7e2LR/cAzirPmypDxG1Frrfn/idt2YrPN0b5z/chqiLO71nfbIOjRvkYXWzKh1EX4hQWVzS7JUN1vAKbna8h4NI85NcNNnWCTq5WSI6Wj6lcKAuUrbb34miJ3hrNCTh1p41LL0730XPfLdMoW8sHMnctoI2R1XxcvYBvg4tbNHuhY2joaM7/a9r2a5rzXUPHVhCKgVI6uqaR4zYwdR09cWxzj3Nu3/F928+x/T69zvkaXa932Xk0Sicat8n2uslxu9B1ffu5k8+j6Ylz6ShlUxYtozSyidLwRrZFtxK2w6wJrmJNcFWjn1CRu5je7r5k0YssiinxD0DF+5BrFPSoD3ulFP/bEuNf34XYEnB+X/O9OtNHetmrvzujhkhFsrC4st5mmnVnTjmFxaGdnidVWJwcmkos5pesxcnVC/DYfjSloWzAUk4vjtLQrPq9OLoPNJeWKkKWXpyeQQJQGiR7gDJpCMxWNsuDX/JxzVusi6xM3T7IM4K9cw4iW89NhQG9QSjRtKYCQ+Pj6gWNOkGhYchJnrMlttRaPPJ5LeUhG8ulcdZe2QwtyJxf62jcJhyzKcn3kdvKeqCYHWNrZAulkU1sDm9yvkc2URouJWDVUhbdRll02/YHJJYScWluisw+FLn6UOTqS7GrD0VmX4pcvXHrmVc4vitWVcT55/Igayqd3gC/S+PQYV72H+TJ2NXDu7NUYXGq56aiUYFxrVXVop5gn56V6rnZ3ntTkOrFydZz0ZWBsgCb7b04loaykr04gK6he5yAk1r8T98ecjT5PemxMueTogdJ9gDVWJXYykLX0lezEbZD/Lf2Az6rfodKqwwAHZ09svZhSs40+nsGp61tLdE722D2vjk89kUt66os/vJZDaeMy2J838yYJu82nV6g0powHpfRquJkl+6iv28A/X0DGt1XFa3mf9tWsyawgZC2jbL4ZspipVTEtxJTUUpj6yiNrWv0uBwjnyJXH4rNvomA1IdiV1/yjEI0resUTjec0u7S4cAhXg4e6sXnkg+0jhJXsUZTwqsbzJyKqehOz2NgpnpunALjxPTwVC9OAW7di7LV9oCT7MWJATFneEolenHQwfAmenEabuEgvTiiGRKA0sBHFjoGNhY1VhV5ZmGnt6E8tpVPa97my9oPiaqI0y49i72zf8Q+OQeTY+Z3epvaKsej88vJOTzzVYD/bYnx1JcBKkbZHDTEkxFvfNkek8pgjM3VYQYU+HZ5arWyIRR0k2MNYr+CEfUWOrSURWV8G9tipZTFNlMW35y6HLRrqLEqqbEqWc3yeuc0NReFZm8nFJlOKEr2IHl13y61tz01NaV9nwFufjzCR5636wS4TOQUFtc02EyzvF7dTaCFhcVZem6dnpuCOkNTzm1ZejaapjuTIhK9N6lenLBTixNHNd2LYzbewkF6cURbSABKA03TyTMLqIhvoype3mkBSCnFmsj3fFK9gOWhr0iub9HL1Y8pOdMYmzUFl16/50TZCjvojJ3rHs0pCMyAUNGQ29A4Y2IWry4N8eHaCP9c7kyT/+noXQ8cu0xz1geqDEbxuAz65Hrafi4FW2ojbK2NkOVpvMqzoRmpnp2GQlYg0VOUCEWJy+WxLcRVjC2xDWyJbWj0uCw9t04gcobTil19yDeLOq33Uqa077qIHW5yrZu6M6daWlic7KWpV1icCjn5mNr24V5lqzpDVEAosaGrcgIOdaaNSy+O6EwSgNIk1yh0AlAnFEJbKs43gc/4pGYBpdHtwyLDvXuwb+5hDPOOafLNxY4q7DCY2TqYYAcUVhg0l3L+Gkt3sGhA1zR+OsZHoV/ntWUhFq2NUBmy+fn4LNxmetuq65DlMdlWG8Hn0sn1tW19oLLA9lWeXa18TT4ji4HGMAZ6htW73VY2VfEytsUTvUZ1AlKtVUXAriYQqWZN5Lv6rwmDQlfvVL1RMiQVm33xGVlten0NyZT2lrGV05tcf0uG+gXGYTu40/NoaOQY+fWKieuGnDyjEK/ur/d+kerFsQELVAzilt1gRhVOL44bNLf04ojMIO8gaZJnFkKkYwuhA1YNn9e8x+LahdRa1YAz1DE+az8m5x5KL1e/Jh+nlMIOAQpcRRpmno6ma9h5CjuksGqTvUJ2xvUKaZrGj4Z4yffq/HVJgKVbYzzwaQ1n751Nrie9QyTJeqAtNWE8ZuvqgQCqgjFKq8N4TL1dV3nWNZ0CVy8KXL0Y6Rtb776IHdoeiuoEpLL4ZuIqxrbYJrbFNkGDSTt+PTvVW+SEI2c4rcDshdGCXiOZ0r6dUoqwHWxyrZvkMFWNVdmiwmKv7t9ed9OowLiQHCOvyV69VC9O3NloF1uhLEVyXZx6vTgeJ+Q06sXRM++PJtGzSQBKk+SwV0esBbQluoFPahawpPYTLJwtqnOMfPbJOZi9sg/Eb2Q3+1hlKayAQvfquAp1DP/2Nyw9MYvCyHZ6hqygjR0Eq0Y5vULuzPkrblxfN7kence/qGV9tcV9i5zd5Pukebgk22NSGYqypSZM//yWD8/VhONsqgxhahpeV+e9Bo/uo79nCP09Q+rdrpRNlVWRCESliVoj53K1VUHQriUYqa03oxCcAvsCs1e94bRkQPLrOQA9bkp7XMWcXpompoQne3FaW1ic7LlJ1d0kCow9zcwCTPXixMG2FMomFXCgQS9OYqdx3aU36sVBz5w/hoTYGQlAaZJnOAGovYbAlLJZEf4fn1Qv4Ifw0tTt/d2DmZJ7GLv798LQdvzPbUcUdhTMXB2zQHdWN22CpmsYfjD8BnasTq9QKLN6hYYUmMzed/tu8vd/XMOZe2UxvLDztqdoRIMcr5vKYBSvadCrBfVAyVWebSAnQ1Z51jSdfLOIfLOI4b7d690XtSOp+qKyesXYm4mpxH3xzY16jdz4sKK9CAeLsb298Lt6M6XPAA4pGYDPzIxZfa2llE3ArtnhZpqtKywuSNXaNBymytJzdjiTT9kKFU+GGxJ1OS3oxUneLov/iW5GAlCa5CZ6gHZ1CCxqR1gS+JhPqhc4Hyo44/ij/ROZkjONEs/wnQYRpRR20Pkrz1WkYebqLX6Tq9crFAErkFm9QsVZ26fJr6m0eOizWk4a62ev/rtQiLyLDB38bpOttRG8LoMcX/P/GUZitrPKc1yRu4PjMolb99DPPYh+7kH1bldKUWNVNpidVsqW6GZqrHKiWgjca3G516Yesxj4fINGvlmU6i1K1RuZfck2ctMatKN2OBFomtqSoYLqeEWqF3ZHXJq70WJ+9bZmMAvqFRY3RSlnWErZ1JlZpVCqTi9OYtq4nlzd2F2nF0eXgmPRs3SNd9RuKM8oANoegJLbVHxe+16quNGjeZmYPZV9cg6hwFXcovOouMIKguHXMAt0jP9v78zjoyrv/f95nrPNTGYmG5AECKTsyCKyCqigYmnxouDtBZeL6C3aVvRaeGmLgheXVqxSa92VVvH2V0XxIm0REMWibIJioiiLslchrFlnMss55/n9cWYmM8kEMiGZySTft68xM+c855znPCTn+cz3+S72pj34GGeQ7IBkD1mFfAJGVeuwCmWoHD8b4cIbX3qw83gQb3zpRVmNiSt62FL2oFdlDn/IH0hVHNDi+PQEDRPHKmrgDRjIaqLTdGuCMWZl6JWz8QP0s0Lav6/Bse8DECwISTuNPvllKOxQBg9ORASSX/hQpp9CmX4K+3xfxZxTZbaQf1FejEDKlfPqRTQmSrRjcXSum6Y6Ftdabs7tWNwQEV+caCtOqIQDgFgrjh1AVNg4WXEIIhYSQCkibAHyixr4zJpG51r53n8Q2yrXY5d3R6RMRbbcASNdV2CIcwy0BHK2mD4rqZicxaBkcbBmipSK6yvkSa1VSJEY/nNIBt7dW4OPD/mx9lsfztSYuO4CR8rC5F2ajDJvACcqfeiS5QCPzudjCBwr96GyRrcqyreh+SpuSHsnB37cO7deSLsQAh6zMuJfFLYenQ4eR5l+EgHhw9HAYRwNHK53nUwppzYTdpRAcoe+fEQ7FldGfG5q/W8SdSyOV5LhbI7F8YiJqIqx4gB1C3FGrDgKp0KcBNEESAClCI3bYOMO+EwvKvUzsKn1s/2GMYWBPd4SfFK1Ht9FOZV21/pglPtK9LEPBk8gi68wQ47OCofSiUFyNr70RCIwFmUVcqfeKsQZw+R+DuTYOf62uwbbvwug3GdixhAnbKkIk2eA266izBOEXfGjg8taljNMgdLKGpR7A8i0q2grX9abEtLOGINTyoRTykSRrU/MPkPoOKOfxOlgaa1AComjcDmGCuMMDvh2xRynMBUAQzCUAPRscEgx5ReiI6fCYqchx+KGiLbixJRwCIeNkxWHIJICCaAUkinlRL6FdkJ9AeQzvSiu3oztlf9ERaRMhYSBoTIVBVq3esecC8tpGZAyrCgvriVJfJzNKiSH8golySo0trsNWXaOv5Z48M0pHS9sq8J/DXOmJJOwxAGHJuFElR+aIsGpyjhZ6cfp6gDcdjXGKpSutFRIu8RkdFQK4qZz8BrVUckeawVSuFRImAzuql+KQaoNDXdKZ3csbogYX5zoQpx1rDjhQpxkxSGI5EMCKIVkyjk4Hvyunh/QmeAJbK/6EMXVWyLfUh3ciWGucRjuHAeXnJnwtYSwhAcMQM4OLXmlwDk52iokMgWMmtRYhQZ0UvGLURyv7KjG0SoDz3xSiZ8Oc6HAlfwoKy2cH6jSB69NxskqH5xxsjynG6ms0u6QnOgm9UI39IrZHi4VAgBuKbvJfkJCCCvp37msOAz1CnGSFYcgWgckgFJIZlQkmBACh/zfYFvlenxT8yVqy1R0DpWpGNn0h7Vphalz1Vry4hkts+SVKExmkF2pswoVZsq462IX/rSjGic9Jp7fVokZQ5zo0yH5DseWP1AQ3oABhyYnnOW5tdFaq7SHS4U0hriFOBuy4thCYeMSlXAgiHSBBFAKcYdyAe2r+Qr7a76Oqd7dyz4Qo1xXNlimorFEl7OQczi42voexnGtQknKK5TjkHDnKBdeK/bgQJmOP++oxk8GODCia5LD5BmQ6VBgGCKtxU9plYE139Zg14m6Vdo12BPMfN3SxC3EGW3FiVeIM2TFAacSDgSR7pAASiFhC9Dx4HcAastUjHJfiQ5K/nmdu6FyFq2dVFiFHCrHbSOceHOnByXHgnjrKytM/qpeyQ2T5wzgaSp+0qFKuzCsqEcrGaCV9was1u+GUyFOgmhXtHoBtGjRIqxYsQJ79uyB3W7HmDFj8Lvf/Q59+/Zt8JilS5fi1ltvjdmmaRp8Pl9Ldzchumm9oDINGrdjhOtyDHNe2ixFJIUhYHgFuFa/nEW6cFarkGmC25rXKiRzhhsGZyDH7sOHB3x4f78VJv+TgQ7IaSAcU0VrrtIuDAGhAyIYEjySJXCkTAZJY1SIkyDaOa1eAH300UeYPXs2RowYAV3Xcf/99+OHP/whdu3ahYyMhsWC2+3G3r17I59b4zc4t5yNX3V9CoYAFM6bJddLpJyF6+zlLNKJaKuQ8AO6J2QVqhZgUvNZhThj+HEfO7JsHO/s8mLH0QAq/CZuHuKEvQ2MY3PSGqu0xxU8cq3gYQoDU1vns4AgiOTT6gXQ2rVrYz4vXboUnTp1wo4dO3DZZZc1eBxjDPn557eMlAx8ukBQFxBCBxggcQ6ZM6iSZEUBNfJZfT7lLNIFxhiYDVBtDViFtOaZ4EZ305Bl5/h/JdXYd1rH89usCLEse+tYykkl8UPaOSb1dSS9Srsww0taAExYy1kKg+Ri4DYGroashG3s74AgiOah1QugulRUVAAAcnJyztquuroa3bt3h2maGDp0KB599FEMGDAgblu/3w+/vzYpWmVlZfN1+BzopkCWQ0amXUVAN+EN6vAFDXiDOgzDjBFFimT9rCuKmqucRTrR0lah/h0V/GKkC698Xo3SahPPfFKJ/xrmRBd32v3JNAvhkPa139bgeHVqqrRbxTwBEQRgwCr7oDBITpDgIQgiYdLqaW6aJn75y19i7NixGDhwYIPt+vbti1deeQWDBw9GRUUFFi9ejDFjxuDrr79G165d67VftGgRHnrooZbsegSZSQBjMIUAZwymacKuypGCmLlQYZpWHaiAbkZEkT9ooiZo1BNFkm69mrucRbpwVquQMK1JsYlWoa6ZMu662I0/76jC8WoTL2yrwn8OcaJfx/Svy5UIqQppjyt4ZAYpA+D2UESWSoKHIIimwYQQ5y5200r4xS9+gTVr1mDTpk1xhUxDBINB9O/fHzfccAMeeeSRevvjWYAKCwtRUVEBt9vdLH0Ps/fQcazd+hk65bkgcxnl3iC65zjgdpx9Uo0RRYYJjy+ImgoTOhMQLhMiA1BlGYrEoMq82XyK0hEhQlYhrwmz2koFEInyacKEXRM08VqxB/vP6OAMuO4CB0YVpq6afLI4Xm1g9TfJC2mPFjzCDIWhy1YIOrdHWXjIYZkg0p4jFcfRM7szJvUb3qznraysRGZmZqPm77SxAN15551YtWoVPv7444TEDwAoioKLLroI+/bti7tf0zRoWnImNIUrkLgMw9TBmQzOGOQ4lcDrwjmgcQ5N4TCCAg5dgtKNQ85hMCTArxuo9uvw+g14AwaCRhBCCMgShyrxdiWKYqxC7iirkC/kK5SgVciucMwa7sTyr7z4/GgAb3/txZkaEz/qnbpq8i1JskLaI4JHt5ZxGbdC0CWHJXiYwlJSOJcgiPZBqxdAQgjcddddeOedd7Bhwwb84Ac/SPgchmFg586dmDRpUgv0MDEkLkOCAkMEwQw15NvTuAe8EAJ6jYCpAxkdJNhz5ai8MQryYFmK/LoBf9CMK4ogAEmy/Im0diCK6voKGTUmjKpQXqGwr1Ajlg1lznD9IKuQ6gf7rVD5shoT0wa1nTB5b9DEPw/4sKmFQtqFiLLwGFbCQaYwSDaA2zmYGqpw3s6WcQmivWEKE61h8anVC6DZs2fj9ddfx9/+9je4XC6UlpYCADIzM2G32wEAN998M7p06YJFixYBAB5++GFcfPHF6NWrF8rLy/HEE0/g8OHDmDVrVsruIxpFUqELHyAEZIlZIuQcCEPAX21CUhlcXWSoLh7X+sA5YFcl2FUJ0aIooBvwhUSRx6/D04AoUkOvtiaKwlYhbpMgu0KV6ast65CoaZxViDGGib3tyLZz/N/XXhQfC6DCZ2Lm0Aw4WlmW40QIGgKbj/jx4f7mDWmPCJ6whSckeLgGSA5ea+EhwUMQaY0pzNDLsF4wYYTfh/YxhAs8AZxxMDDIPLUSpNULoBdeeAEAMH78+Jjtr776Km655RYAwJEjR8CjRERZWRluu+02lJaWIjs7G8OGDcOWLVtwwQUXJKvbZ0XhKoJCwBACGap0TrFh+E0EawQ0twRHJwmylthkyzlgUyXYQqIIiBJFugl/MCSKAgZqggYqfW1bFDGZQXIy8AwBuQlWoZFdNWTZOP63uBoHynQ890kVfjrMiRxHahP/JUokpH1fDSp85x/SHiN4ggJgDFwGuMrAMxm4yknwEEQaEE/QhMWMIQwIYUbEDBjAwCAxCZxxcEiQmARV0qBwNfLiTILMZXBm7S9TDBQ4XKm8zfRygk4WiThRJcrh7yrx8fZv4c8ohdCd6JxlR64rfpFTIQSCHqsAoyOXw5Yjg7do1I21fFZXFAV0E0HdgCJLyFBlyG3QJ0PodaxCpjinVeholY5XPqtGhV/AqTL81zAnCjNb/XcKCCGw66RVpf18QtrDFdHNUHkJwBI8TGHgdoBrHExBm0jGSRDpjBACpjAsqwyihI0wYQgTptBhyRiE6uAxcMYtUQNL2MhchcIUKFyBKtkiQkbickj8hD4z6/O5vkCdrPIjP1PDBZ0zm/Ve26QTdFtCCjk/B2E0KCZMQyBQZUK2M2R0kqEmoawAi2MpCouiKr+O09V+VPqCVui+IsOhymgrPsAxVqGAVYPMqArlFeLxrUKdXTLuHO3GKzuqcazKwAvbq/CfF2bggk7xBW1r4GCZjtV7vTjUhJD2sOAROmAGQ4JHCll43KEoLdWqjt4WncMJorUQFjSWmDFhCD1itQkvPQGoFTRg4NwSNAwSJMahcjsUrkQsNDKXI+KF81ohExY3nKXvMn9DkABKATKXwSBBwIASJwJM95nQfQK2LAmOjjKkFFZwjxZFHTI0eAI6yr0BnPYEcNrjh8QYMjQZaiMi2dIBxhiYBnBNguy28gmdzVcoy8ZxxygX/lJcjW9O61j6uQfX9jcxtrstxXcSS1ND2oUuagWPqLXwyM5QeQkSPARx3gghImIm1jpT+17ULjpZgobxkDCxBI3CVahcg8xVqFyBzJW4VhqZyW1W0CQKCaAUwJgEYcpgTI/51i2EQKDaBOMMGfky7NlSq0ryxjjgtMlw2mTkuW2o8uk44wmgoiaIipoAbIqMDE1GK+ryecGkOFah6vpWIZtsLX+t2OXF9u8CWLm7BmU1Jib1tSclQ/LZKPeZWPdtbUg7AzCya8Mh7TEV04VVMZ0pVqJNyRaqp9WMRWgJoq1iRByAo52BjdCSkwHESBpAYjyUGoWHlpwU2HlGSNioEREjMbmesAmLGyIxSAClAA4OiSkIMj/kkAo3dYFAtQElgyOjkwLF0brVuSJz5DhV5GSo8AZ0VNboOFXtR5nXSiiZocqwyed28E4HzmoV8lo1yLgC/GSAA9l2jve+9eGjQ36U+UxcPyijRbMlN0RjQ9rPVTGdqSR4CAJAlICJFTaGMGHCAEStoLEsNAwsZJ0JOwDL3AGVq5Y/DVeifGdihUzYWZhoWUgApQiJaZB4FRgHgjUmjICALUeGo4MMKZ2cRhng0GQ4NBkdXRqq/DrKvH6Ue3RU+XxQZQkZmtxmcuXUtQrpHsMSQ9UC4AyXd1ORpQFvf+3Dl6VBVPiqMGOIHdGJvmsfkyLmoSkgQg9O65tdU6xH8ULaf5At4+pQSLswBEy/oIrpRLunVsSY9RyErUgnUS90O7zcxGD9jWohPxpr6UmJcQKOXm4KW2uI1gX9i6QKoUCTGXSfCWECrs4KtDSv4C5JDFkOBVkOBTVuHVU+HaeqfCiv8cM0TNg1CTZZAmPRpl8RSYgV3irqCANrS2hfTNuoNiL6aCtyLvqMteetPTY66gGoNVZFf442UTMwINz30A7mYBA2AD4GeBiEh6G3g+HGAQxv7RE4XG7g+W3VmHGRhBwHC52VRa7Gas8MBgYTAobwwRc0YIbulcFaNpU4h4TQA5XHruGbQuCz7+uHtP+4tx19M2XAYDCqBFVMJ9osDYVuhy02QlimUAEAzLLEh5ebOCzRonEbFEmFwizHYInXWXKqY6mhLwrpDQmgFCFzGUKSYPgF7LkSbFmtz9xpmAa8uhfeoNcSHnXUAhNWZfqwsAhvhwhN6RKQkwnYAyaqfEGUeYM46RWQGCzHaak2ioxF5AeL+sSselBR/4GFlqTAgZCZGYyBg9e2Yiy2DQDGOBizWiHkQBjOX1F7/drrIepcQG0frT5E949FziGEgAgIBL0Gulca6OGowTNfHsOZGh2vfAb88uJC9MrJsI5kda7Mas+hCx2G0GGYBnQRhG7qCJoB+A0/DBFEMCyQIAAT+OY08M8DwEmP1c9MjeGqbhqGdtDAJauPnCqmE2mGEKLWCfhcodus1jE4OnTbJjkgMwWqpELh2lmdghsTuk20LUgApQATAjKTISky9KAB2dZ6qovrpg5v0BI9nHE4FAe6u7rDoTgiD4fI1M1iRQKLEgTRbcI/g7qJcq+Ok1UBlHsD8AdNZKgyXDYVciiRZa2giJJELL4gaZWoAJyAmWsiO8/AI7mZeHzzPhyu9OGxTYcwe0R3jOya3eDhjAEqJADxa9OZpvWNVhdB7D1Vjf/bdRr7yyy/K7sMXNZFxvCuApItAK9WA6YIQAFkqfahLwsFkkkmeSK5RIduGw04BkdbYePlolG5HWrIf0aRtBiLjMSjLTWWtabVPy+IlEJPwFQgAJusggkFJtchaan9Iw0aQXh0D3xBHyQuIUPJQI+sHsjSsuBUnVB4Mwk0Fch1AD1yBar8Os5U+1Fa6UeFNwjOBFw2K7dQW4BLHJqLo9CZiUWdBuOJ9/ei+GgF/rjtEK4v92FS3zxITSifwcBxtMyHt3YfQ/GJKgCAyhl+1LsTrruwM5xOGaZswmQ6dKFDNwPQTR0+owZ+o8ayJJk1MEwDBoyI5S7seBmeOGQmk0Aizkpt6LYR10HYCOWiCROx0PDoXDQ2KFwJhW6rEedfmcmhXDSxPjUUuk00J/SESxE2WQWECkMOpiTPT8AIwBP0wKf7oHAFTsWJwuxCuFU3XKoLEm+5JTnGGNw2BW6bgq7ZDmtprMqHk9V+lHmDsMkcbrsCRUr/hx1jDC6XioVTBuLFDfuxdlcp3thbilO+IKb3yoOiSZC1htMdCFPA1E0YQROnqgP424ET2HS0PPQNGZjQuxNuGFWIjpn2RvXHFEZkSc0SSEHoZhA+wwe/WYOg4UfQ9KNGeGCYoW/koeVEObJ0UJswjb5hty0aDt223gOxwdvRwkRiUlTpAw0qV6J+X2Kjm2rDudP/b5xIX0gApQi7KsPwa6ix1yTNH8On++ANeuE3/FAlFW7VjSJ3EVyqCy7VlZKHkSxxdHRp6OjS4A1YeYVKK3w44wnAEAIuLZxbKL0nWokz3HF5T+Rn2bB0yyG8f/g0yg0Dtw/qCrM6CDBAtsngMoMZtASP0K16WjUwsfrQKbx34CSChjX5jO6Rixmju6Mw25FQPziToEpWnZ54hAWSLoIIhsSRLoLw6fUFkmmaCJuQGOMRqxEJpNZD3WR6DYduW0vOnLE6uWhkKDwjkjFYbiB0O9qnhiDSBRJAqYABGaqEQNAGr2K22GWEEPAZPniCHuiGbokezY2O9o5wqS44FWermqAcqrUE1jnTjoqaIE5V+3G8yo9jFT6oEofLJsOmpO8DljGGfx/aFZ1cGp58/xt8+l05KgI65l3eB7agQNCrQ/ea4AqHYpchVAnv7T+BFV8ehSdgLScM6OzGLWOK0C+/eWvUhYkIpIZ8kIQZEUWW9UhHUAQQ0H3wm/7IMltYIIVD+xlj1tJalEWAfDQSJzp0O9pB2DANmAiFbjMWiZaUGI/JRRMO3dZCGYNlLseGakdZayh0m2jr0G93CpAZh8QAVVPAm7lslBACNXoNPEFrCcMm29DB1gG59ly4VBccsqPVTzqcM2RnqMjOUFGY40C5N4jjlT6c9vhx2hOAU5Ph1GRIaRrJdGnvjsjJUPGbd3fjmxPVuH/NLiycfAHyc50wdROQOT7efwp/3X4Yp6oDAIDuOQ7MHFOE4d2zU/rvxxmHKmmNEEi1/ke6CMKv18Bv+hEwfAiaAfhCUW5h+4O1hBJy0o7Kp9Laf1fPl3ih27EWGzNOLpromk4SNClU04mpUCQ1jpCh0G2CiAcJoBQgSwyywaDYVEgqgynM81p+MoVpRW7pXpimCZtsQ54jDzm2HEv0KIktk7QmbIqE/EwJeW4NlT4dZR4/Siv8OFHlC/kSpafj9IDOmXjiJ4Px4D++RmmlD796+0vMv7o/qv06Xtt6GP864wUAdHBq+M9R3TC+b6e0EHyxAimj3v5w4cbI8lrImhQw/PAZNSGBFIRPeGMEEmPMWmKLWWZrfU6xMYKmXui2US8XDQOL3AcPCRpV0kKh2xoUrsb1nYm20pCgIYimkX4zRxtA4QwwTbjcDqhcgW5ay1OJYAoTnqAH3qA1UdplOzo7OyNHs0SPTW5dxTjPF8YYMu0KMu0KumQ7UOYN4GSlH6eq/Sj3BmFTJLhsclo5TnfNduCJn1yI37y7C98cr8a8FTsj+1yajGnDCzFpUEGbKTQLhC0YlqNsPKw8SLW5j4yQo7bf8Fkv0wfd1OEXPhhCjyz1tJRACoduGzGCpm5Np1BCy9D/6oZua5IDClOsjMGSLTZUO46wIUFDEMmBBFAKkCQGmXFkOG1QfAqCZrBRAiico6cmWAPGGByyA91c3ZBly4JbdScsotIVReLo5LKhk8sGjz/kOF1Z6zjt1hRkaOkxkWQ7VPx2yiAsXrcX2w6egSpzXHthZ1w3tCucWvv782SMRbLw2lHfchktkGr9kIIImAH4dC8CDQkksBj/I84kCAgYQo9YbcJLT1b72lzhnEcvOdWGbodrOsWcl9dfcmptViqCICza3xM2xagyh12WoWocDrsNNt0GT9ADNJBqRzd1VAer4dN94ODIUDJQlFWELC0LLtXVfDl60pSMUJRY56yQ43SVtTx2rDIAhUtw22Vocut2nLYpEu77cX+U/KscRbkO5Drj+9cQsQIpHlb2YMtqFKwjkCw/pJqI43ZY0FjVtrVQLhoFMlfOmjGYBA1BtA1IACUZt11BQaYNsipB0SRk+DJQ5i+LaRPO0ePX/ZC5jAwlA10zuyJTy4RTdUKmyIx6SJwhJ8OqTt8t11oiK63wocwbQFAPIKOVO05LnGFY94YzRBONw1oKs0RMvEXgsEAyhFG7VEWh2wTRLqGZNEXYXCoYZ7DLdpjChN/wwxPwRHL0OBUnuru6w6254VScLZqYsK1hUyQUZNqR77ah0mdlnD5W4cOJKh94KAmjXaXxbI9EBFJDJleCINoNJIBSgKRw2OzW0GuSBg4OT8ADl+pCD0cPuFU3MpQMMrWfJ3Udp8trAjgRcpw+4/XDochw2WTIaeQ4TRAEQTQPJIBSgKxwKJplgcjUMnFB7gVwqs60yNGTrqhyreN0tV9HmSeAYxU1OOUJQAgBl01BhpoejtMEQRDE+UMCKAUomhQRQKqkIi8jL8U9al+EEyl2zrKj3BvAySo/Tlb7cbQiAE2W4LYpbSr0nCAIgqgPCaAkY8tQIMk8afW/iIaROEOuU0OuU0NR0MAZTwDHK3044w1AN0xkqApctvSvQ0YQBEHUhwRQkpFkDomsC60OmyKhc5YdBZk2VNboVh2ySh9KK2ogcw4XOU4TBEG0KUgAEUQUjDFkOhRkOpRQHbLaJItnvH44VBkujRynCYIg0h0SQATRAKrM0cltQye3DVW+IMo8ARyt8OGUJwBAwKUpcJDjNEEQRFpCAoggGoHLpsBlUyzH6UjGaT/KKwKwyRLcdiWt6pARBJFehMu6CACht5HtIqZd6CdEnXa126O3RT6J2n0x2+teI2qfiLpAdL9EVCOB+tcQAgjoJoDUZr0nAUQQCSBLHB2cGjo4NXTPNXDGG0BpRQ3OeAMwTBNOVYGTHKcJolFET6zNPqnHOaY5JnURtS/eNcKF5BgLH8NC762WDAzRTWvPEPXMCJ+s9kAwZtWmi3m0sOh9tZtYZEfUNVjtdhazndU7NpyCLtyOMWa9wtsYwMEjfQm34zxyBXAePp+1HaFzcxY+H1Je75AEEEE0EbsqoYtqR4HbhkpfMOQ47UdppQ8yY3DbFdgUcpxubzT3pB4zeTfjpC5qT3bWST0yBzdxUmeR8wowxmLOZbVv4qQe53gWPhC1ky8A6wtJnfPWndQ5Y5Fzhc8XmaxRO2mz0Plq29TdXysyYn9GCY0GxUj87Q0dC9a4dtHj29C16x3bDr7EkQAiiPOEc4Ysh4oshxpynA5adcg8AZz2WI7TbpvSauuQpTNCCJgCMIWAKawJ2wxvM60p3jRF7bZQGxGe/qMmYmGVf7cmdhGuBR+uCh+5YpMn9ehJunZij5qYgaj3jZ/Uo9txzmv389B5mmlSrzuJAjjrhNvYST16zOKKnyZO6ix6zKL6SxBhSAARRDOiyRLy3BI6uTRU+3Wc8QRwrMKHk9U+MDA4NbldO06bQkSEiWHWChYhAEOIiKCxipZG7QdixUrofDw0MXMees+tyTvy4oCicavqu8QgcQZF4pA4a5ZJPUbUxJtwmzipxxNSNKkTRPNCAoggWgDGWMRxukvIcfpElQ8nq/yoqAxCk3ird5wWdawmYStLWKBELC/RlpbQz/DyiDU1W++EsJJPggFSyAphCRVLtEicQZY4ZB56zy3RYn3mlsgJtWMMkCIixzqHxKOET+gziQOCIBqCBBBBtDDRjtPeXB1l3iCOldfgjCcAwxRw2WRkaOfvOF13Ocg0rQUb02xgiSj8OSpOI+zLEb3MwjggRSwtlqiQwCBLgMy59ZIQES+KxGOFScjXQgp95iGhIoWWV2qFC1kzCIJIHiSACCKJOFQZDlVGgduGipogTnv8KK3041iFD4rE4FBloIHloPD76GiTiC8K4i8HhcUH5wwyY1BkFnc5KCxYJFZfpJzN8kIQBJGukAAiiBTAOUN2horsDMtxuswTxPFKH6r8OjhAy0EEQRAtDAkggkgxmiwhP1NCnluDbgpaDiIIgkgCJIAIopXAGIMikeghCIJIBq03BIUgCIIgCKKFIAFEEARBEES7Iy0E0HPPPYeioiLYbDaMGjUK27dvP2v75cuXo1+/frDZbBg0aBBWr16dpJ4SBEEQBJEOtHoB9Oabb2Lu3LlYuHAhPv/8c1x44YWYOHEiTpw4Ebf9li1bcMMNN+CnP/0piouLMWXKFEyZMgVfffVVkntOEARBEERrhYnoKnmtkFGjRmHEiBF49tlnAQCmaaKwsBB33XUX5s2bV6/99OnT4fF4sGrVqsi2iy++GEOGDMGLL77YqGtWVlYiMzMTFRUVcLvdzXMjBEEQBEG0KInM363aAhQIBLBjxw5MmDAhso1zjgkTJmDr1q1xj9m6dWtMewCYOHFig+0BwO/3o7KyMuZFEARBEETbpVULoFOnTsEwDOTl5cVsz8vLQ2lpadxjSktLE2oPAIsWLUJmZmbkVVhYeP6dJwiCIAii1dKqBVCyuO+++1BRURF5/etf/0p1lwiCIAiCaEFadSLEDh06QJIkHD9+PGb78ePHkZ+fH/eY/Pz8hNoDgKZp0DTt/DtMEARBEERa0KotQKqqYtiwYVi/fn1km2maWL9+PUaPHh33mNGjR8e0B4D333+/wfYEQRAEQbQ/WrUFCADmzp2LmTNnYvjw4Rg5ciSeeuopeDwe3HrrrQCAm2++GV26dMGiRYsAAHfffTfGjRuH3//+97j66quxbNkyfPbZZ3j55ZdTeRsEQRAEQbQiWr0Amj59Ok6ePIn/+Z//QWlpKYYMGYK1a9dGHJ2PHDkCzmsNWWPGjMHrr7+OBQsW4P7770fv3r2xcuVKDBw4MFW3QBAEQRBEK6PV5wFKBZQHiCAIgiDSj0Tm71ZvAUoFYU1I+YAIgiAIIn0Iz9uNse2QAIpDVVUVAFA+IIIgCIJIQ6qqqpCZmXnWNrQEFgfTNHH06FG4XC4wxprtvJWVlSgsLMS//vUvWlprYWiskwONc3KgcU4ONM7Jo6XGWgiBqqoqdO7cOcY/OB5kAYoD5xxdu3ZtsfO73W7640oSNNbJgcY5OdA4Jwca5+TREmN9LstPmFadB4ggCIIgCKIlIAFEEARBEES7gwRQEtE0DQsXLqSyG0mAxjo50DgnBxrn5EDjnDxaw1iTEzRBEARBEO0OsgARBEEQBNHuIAFEEARBEES7gwQQQRAEQRDtDhJABEEQBEG0O0gANTPPPfccioqKYLPZMGrUKGzfvv2s7ZcvX45+/frBZrNh0KBBWL16dZJ6mt4kMs5LlizBpZdeiuzsbGRnZ2PChAnn/Hchakn0dzrMsmXLwBjDlClTWraDbYREx7m8vByzZ89GQUEBNE1Dnz596PnRCBId56eeegp9+/aF3W5HYWEh5syZA5/Pl6Tepicff/wxJk+ejM6dO4MxhpUrV57zmA0bNmDo0KHQNA29evXC0qVLW7yfEESzsWzZMqGqqnjllVfE119/LW677TaRlZUljh8/Hrf95s2bhSRJ4vHHHxe7du0SCxYsEIqiiJ07dya55+lFouN84403iueee04UFxeL3bt3i1tuuUVkZmaK7777Lsk9Tz8SHeswBw8eFF26dBGXXnqpuPbaa5PT2TQm0XH2+/1i+PDhYtKkSWLTpk3i4MGDYsOGDaKkpCTJPU8vEh3nv/71r0LTNPHXv/5VHDx4ULz33nuioKBAzJkzJ8k9Ty9Wr14t5s+fL1asWCEAiHfeeees7Q8cOCAcDoeYO3eu2LVrl3jmmWeEJEli7dq1LdpPEkDNyMiRI8Xs2bMjnw3DEJ07dxaLFi2K237atGni6quvjtk2atQo8bOf/axF+5nuJDrOddF1XbhcLvHaa6+1VBfbDE0Za13XxZgxY8Sf/vQnMXPmTBJAjSDRcX7hhRdEjx49RCAQSFYX2wSJjvPs2bPFFVdcEbNt7ty5YuzYsS3az7ZEYwTQr371KzFgwICYbdOnTxcTJ05swZ4JQUtgzUQgEMCOHTswYcKEyDbOOSZMmICtW7fGPWbr1q0x7QFg4sSJDbYnmjbOdfF6vQgGg8jJyWmpbrYJmjrWDz/8MDp16oSf/vSnyehm2tOUcf773/+O0aNHY/bs2cjLy8PAgQPx6KOPwjCMZHU77WjKOI8ZMwY7duyILJMdOHAAq1evxqRJk5LS5/ZCquZCKobaTJw6dQqGYSAvLy9me15eHvbs2RP3mNLS0rjtS0tLW6yf6U5Txrkuv/71r9G5c+d6f3BELE0Z602bNuHPf/4zSkpKktDDtkFTxvnAgQP48MMPcdNNN2H16tXYt28f7rjjDgSDQSxcuDAZ3U47mjLON954I06dOoVLLrkEQgjouo6f//znuP/++5PR5XZDQ3NhZWUlampqYLfbW+S6ZAEi2hWPPfYYli1bhnfeeQc2my3V3WlTVFVVYcaMGViyZAk6dOiQ6u60aUzTRKdOnfDyyy9j2LBhmD59OubPn48XX3wx1V1rU2zYsAGPPvoonn/+eXz++edYsWIF3n33XTzyyCOp7hrRDJAFqJno0KEDJEnC8ePHY7YfP34c+fn5cY/Jz89PqD3RtHEOs3jxYjz22GP44IMPMHjw4JbsZpsg0bHev38/Dh06hMmTJ0e2maYJAJBlGXv37kXPnj1bttNpSFN+pwsKCqAoCiRJimzr378/SktLEQgEoKpqi/Y5HWnKOD/wwAOYMWMGZs2aBQAYNGgQPB4Pbr/9dsyfPx+ckw2hOWhoLnS73S1m/QHIAtRsqKqKYcOGYf369ZFtpmli/fr1GD16dNxjRo8eHdMeAN5///0G2xNNG2cAePzxx/HII49g7dq1GD58eDK6mvYkOtb9+vXDzp07UVJSEnldc801uPzyy1FSUoLCwsJkdj9taMrv9NixY7Fv376IwASAb775BgUFBSR+GqAp4+z1euuJnLDoFFRGs9lI2VzYoi7W7Yxly5YJTdPE0qVLxa5du8Ttt98usrKyRGlpqRBCiBkzZoh58+ZF2m/evFnIsiwWL14sdu/eLRYuXEhh8I0g0XF+7LHHhKqq4u233xbHjh2LvKqqqlJ1C2lDomNdF4oCaxyJjvORI0eEy+USd955p9i7d69YtWqV6NSpk/jNb36TqltICxId54ULFwqXyyXeeOMNceDAAbFu3TrRs2dPMW3atFTdQlpQVVUliouLRXFxsQAgnnzySVFcXCwOHz4shBBi3rx5YsaMGZH24TD4e++9V+zevVs899xzFAafjjzzzDOiW7duQlVVMXLkSPHJJ59E9o0bN07MnDkzpv1bb70l+vTpI1RVFQMGDBDvvvtuknucniQyzt27dxcA6r0WLlyY/I6nIYn+TkdDAqjxJDrOW7ZsEaNGjRKapokePXqI3/72t0LX9ST3Ov1IZJyDwaB48MEHRc+ePYXNZhOFhYXijjvuEGVlZcnveBrxz3/+M+4zNzy2M2fOFOPGjat3zJAhQ4SqqqJHjx7i1VdfbfF+MiHIjkcQBEEQRPuCfIAIgiAIgmh3kAAiCIIgCKLdQQKIIAiCIIh2BwkggiAIgiDaHSSACIIgCIJod5AAIgiCIAii3UECiCAIgiCIdgcJIIIgCIIg2h0kgAiCaHaKiorw1FNPNbr90qVLkZWVldA1xo8fj1/+8pcJHQMAjDGsXLky4eMIgmhbkAAiiHbAoUOHwBhDSUlJzPZbbrkFU6ZMSUmfopk+fTq++eabhI5ZsWIFHnnkkcjnREUXQRDtGznVHSAIgrDb7bDb7Qkdk5OT00K9aVkCgQBVbCeIVgBZgAiijbB27VpccsklyMrKQm5uLv7t3/4N+/fvBwD84Ac/AABcdNFFYIxh/PjxePDBB/Haa6/hb3/7GxhjYIxhw4YNAIBf//rX6NOnDxwOB3r06IEHHngAwWAw5nr/+Mc/MGLECNhsNnTo0AFTp05tsG9/+tOfkJWVhfXr18fdX3cJ7MEHH8SQIUPwl7/8BUVFRcjMzMT111+PqqqqSJvoJbDx48fj8OHDmDNnTuReGsvChQtRUFCAL7/8Es8++ywGDhwY2bdy5UowxvDiiy9Gtk2YMAELFiwAAOzfvx/XXnst8vLy4HQ6MWLECHzwwQcx5y8qKsIjjzyCm2++GW63G7fffjsAYMmSJSgsLITD4cDUqVPx5JNPxozBF198gcsvvxwulwtutxvDhg3DZ5991uB9lJeXY9asWejYsSPcbjeuuOIKfPHFF/XG9KWXXopcd9q0aaioqIi02bBhA0aOHImMjAxkZWVh7NixOHz4cKPHkiDSCRJABNFG8Hg8mDt3Lj777DOsX78enHNMnToVpmli+/btAIAPPvgAx44dw4oVK3DPPfdg2rRp+NGPfoRjx47h2LFjGDNmDADA5XJh6dKl2LVrF/74xz9iyZIl+MMf/hC51rvvvoupU6di0qRJKC4uxvr16zFy5Mi4/Xr88ccxb948rFu3DldeeWWj72f//v1YuXIlVq1ahVWrVuGjjz7CY489FrftihUr0LVrVzz88MORezkXQgjcdddd+N///V9s3LgRgwcPxrhx47Br1y6cPHkSAPDRRx+hQ4cOEWEYDAaxdetWjB8/HgBQXV2NSZMmYf369SguLsaPfvQjTJ48GUeOHIm51uLFi3HhhReiuLgYDzzwADZv3oyf//znuPvuu1FSUoKrrroKv/3tb2OOuemmm9C1a1d8+umn2LFjB+bNmwdFURq8n//4j//AiRMnsGbNGuzYsQNDhw7FlVdeiTNnzkTa7Nu3D2+99Rb+8Y9/YO3atSguLsYdd9wBANB1HVOmTMG4cePw5ZdfYuvWrbj99tsTEpMEkVa0eL15giBSwsmTJwUAsXPnTnHw4EEBQBQXF8e0mTlzprj22mvPea4nnnhCDBs2LPJ59OjR4qabbmqwfffu3cUf/vAH8atf/UoUFBSIr7766qznf/XVV0VmZmbk88KFC4XD4RCVlZWRbffee68YNWpU5PO4cePE3XffXe+a5wKAWL58ubjxxhtF//79xXfffRfZZ5qmyM3NFcuXLxdCCDFkyBCxaNEikZ+fL4QQYtOmTUJRFOHxeBo8/4ABA8QzzzwT068pU6bEtJk+fbq4+uqrY7bddNNNMWPgcrnE0qVLz3k/QgixceNG4Xa7hc/ni9nes2dP8dJLLwkhrDGVJCnmftesWSM45+LYsWPi9OnTAoDYsGFDo65JEOkOWYAIoo3w7bff4oYbbkCPHj3gdrtRVFQEAPWsEY3hzTffxNixY5Gfnw+n04kFCxbEnKekpOSc1pzf//73WLJkCTZt2oQBAwYk3IeioiK4XK7I54KCApw4cSLh88Rjzpw52LZtGz7++GN06dIlsp0xhssuuwwbNmxAeXk5du3ahTvuuAN+vx979uzBRx99hBEjRsDhcACwLED33HMP+vfvj6ysLDidTuzevbvemA8fPjzm8969e+tZzOp+njt3LmbNmoUJEybgscceiyxnxuOLL75AdXU1cnNz4XQ6I6+DBw/GHNetW7eY+x09ejRM08TevXuRk5ODW265BRMnTsTkyZPxxz/+sVGWNIJIV0gAEUQbYfLkyThz5gyWLFmCbdu2Ydu2bQAsp9tE2Lp1K2666SZMmjQJq1atQnFxMebPnx9znsY4LF966aUwDANvvfVWYjcSou5yD2MMpmk26Vx1ueqqq/D999/jvffeq7dv/Pjx2LBhAzZu3IiLLroIbrc7Ioo++ugjjBs3LtL2nnvuwTvvvINHH30UGzduRElJCQYNGlRvzDMyMhLu44MPPoivv/4aV199NT788ENccMEFeOedd+K2ra6uRkFBAUpKSmJee/fuxb333tvoa7766qvYunUrxowZgzfffBN9+vTBJ598knDfCSIdIAFEEG2A06dPY+/evViwYAGuvPJK9O/fH2VlZZH94agjwzBijlNVtd62LVu2oHv37pg/fz6GDx+O3r1713OEHTx4cIMOzWFGjhyJNWvW4NFHH8XixYvP5/YaRbx7aYhrrrkGr7/+OmbNmoVly5bF7Av7AS1fvjzi6zN+/Hh88MEH2Lx5c2QbAGzevBm33HILpk6dikGDBiE/Px+HDh065/X79u2LTz/9NGZb3c8A0KdPH8yZMwfr1q3Dddddh1dffTXu+YYOHYrS0lLIsoxevXrFvDp06BBpd+TIERw9ejTy+ZNPPgHnHH379o1su+iii3Dfffdhy5YtGDhwIF5//fVz3g9BpCMkgAiiDZCdnY3c3Fy8/PLL2LdvHz788EPMnTs3sr9Tp06w2+1Yu3Ytjh8/Hon8KSoqwpdffom9e/fi1KlTCAaD6N27N44cOYJly5Zh//79ePrpp+tZHhYuXIg33ngDCxcuxO7du7Fz50787ne/q9evMWPGYPXq1XjooYdicvQ8++yzCTlEN4aioiJ8/PHH+P7773Hq1CkAwPfff49+/fpFnMCjmTp1Kv7yl7/g1ltvxdtvvx3ZPnjwYGRnZ+P111+PEUArV66E3+/H2LFjI2179+6NFStWoKSkBF988QVuvPHGRlmp7rrrLqxevRpPPvkkvv32W7z00ktYs2ZNxOG4pqYGd955JzZs2IDDhw9j8+bN+PTTT9G/f/+49zVhwgSMHj0aU6ZMwbp163Do0CFs2bIF8+fPj4kcs9lsmDlzJr744gts3LgR//3f/41p06YhPz8fBw8exH333YetW7fi8OHDWLduHb799tvINQmirUECiCDaAJxzLFu2DDt27MDAgQMxZ84cPPHEE5H9sizj6aefxksvvYTOnTvj2muvBQDcdttt6Nu3L4YPH46OHTti8+bNuOaaazBnzhzceeedGDJkCLZs2YIHHngg5nrjx4/H8uXL8fe//x1DhgzBFVdcEVdkAMAll1yCd999FwsWLMAzzzwDADh16tRZfVqawsMPP4xDhw6hZ8+e6NixIwAramvv3r3wer1xj/nJT36C1157DTNmzMCKFSsAWEttl156KRhjuOSSSwBYosjtdmP48OExy1lPPvkksrOzMWbMGEyePBkTJ07E0KFDz9nXsWPH4sUXX8STTz6JCy+8EGvXrsWcOXNgs9kAAJIk4fTp07j55pvRp08fTJs2DT/+8Y/x0EMPxb0vxhhWr16Nyy67DLfeeiv69OmD66+/HocPH0ZeXl7kur169cJ1112HSZMm4Yc//CEGDx6M559/HgDgcDiwZ88e/Pu//zv69OmD22+/HbNnz8bPfvazhP4dCCJdYEIIkepOEARBtHduu+027NmzBxs3bmyR8z/44INYuXJlvWzgBNFeoUzQBEEQKWDx4sW46qqrkJGRgTVr1uC1116LWGMIgmh5SAARBEGkgO3bt+Pxxx9HVVUVevTogaeffhqzZs1KdbcIot1AS2AEQRAEQbQ7yAmaIAiCIIh2BwkggiAIgiDaHSSACIIgCIJod5AAIgiCIAii3UECiCAIgiCIdgcJIIIgCIIg2h0kgAiCIAiCaHeQACIIgiAIot3x/wFGbGWj6klUbgAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lineplot(\n", + " data=df,\n", + " y=\"adv_fit_time\",\n", + " x=\"attack.init.kwargs.eps\",\n", + " hue=\"model.init.kwargs.kernel\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lineplot(\n", + " data=df,\n", + " y=\"adv_fit_time\",\n", + " x=\"attack.init.kwargs.eps_step\",\n", + " hue=\"model.init.kwargs.kernel\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lineplot(\n", + " data=df,\n", + " y=\"adv_fit_time\",\n", + " x=\"attack.init.kwargs.batch_size\",\n", + " hue=\"model.init.kwargs.kernel\",\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 06238c10670a7974e9a0294ab65f7664ba493ed1 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Sun, 15 Oct 2023 01:23:34 +0200 Subject: [PATCH 109/148] fixed dockerfile --- Dockerfile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Dockerfile b/Dockerfile index 64d129ae..8d8cc562 100644 --- a/Dockerfile +++ b/Dockerfile @@ -5,7 +5,7 @@ RUN apt-get install -y python3 python3-distutils python3-pip ffmpeg libavcodec-e RUN python3 -m pip install nvidia-pyindex nvidia-cuda-runtime-cu11 RUN git clone https://github.com/simplymathematics/deckard.git WORKDIR /deckard -RUN python3 -m pip install --editable .[pytorch_image] +RUN python3 -m pip install --editable .[test] RUN git clone https://github.com/Trusted-AI/adversarial-robustness-toolbox.git RUN cd adversarial-robustness-toolbox && python3 -m pip install . RUN apt install python-is-python3 From 59c9e741d5e4106e36e6ccc81a420176d66cd43d Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Sun, 15 Oct 2023 01:49:00 +0200 Subject: [PATCH 110/148] fixed dockerfile, tests --- Dockerfile | 3 ++- test/conf/data/keras_cifar.yaml | 7 ------- test/conf/data/keras_mnist.yaml | 7 ------- test/conf/data/tensorflow_cifar.yaml | 7 ------- test/conf/data/tensorflow_mnist.yaml | 6 ------ test/conf/data/torch_cifar.yaml | 7 ------- test/conf/data/torch_mnist.yaml | 6 ------ test/conf/model/keras_mnist.yaml | 7 ------- test/conf/model/tf_mnist.yaml | 7 ------- test/conf/model/torch_mnist.yaml | 7 ------- 10 files changed, 2 insertions(+), 62 deletions(-) diff --git a/Dockerfile b/Dockerfile index 8d8cc562..d8f8401f 100644 --- a/Dockerfile +++ b/Dockerfile @@ -5,7 +5,8 @@ RUN apt-get install -y python3 python3-distutils python3-pip ffmpeg libavcodec-e RUN python3 -m pip install nvidia-pyindex nvidia-cuda-runtime-cu11 RUN git clone https://github.com/simplymathematics/deckard.git WORKDIR /deckard -RUN python3 -m pip install --editable .[test] +RUN python3 -m pip install --editable .[torch,torchvision,tensorflow] +RUN python3 -m pip install pytest RUN git clone https://github.com/Trusted-AI/adversarial-robustness-toolbox.git RUN cd adversarial-robustness-toolbox && python3 -m pip install . RUN apt install python-is-python3 diff --git a/test/conf/data/keras_cifar.yaml b/test/conf/data/keras_cifar.yaml index ce5cd4c9..7b3cfb0d 100644 --- a/test/conf/data/keras_cifar.yaml +++ b/test/conf/data/keras_cifar.yaml @@ -6,10 +6,3 @@ sample: _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - # - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/test/conf/data/keras_mnist.yaml b/test/conf/data/keras_mnist.yaml index ad09362e..3fd63fef 100644 --- a/test/conf/data/keras_mnist.yaml +++ b/test/conf/data/keras_mnist.yaml @@ -6,10 +6,3 @@ sample: _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/test/conf/data/tensorflow_cifar.yaml b/test/conf/data/tensorflow_cifar.yaml index c4674928..6567f19f 100644 --- a/test/conf/data/tensorflow_cifar.yaml +++ b/test/conf/data/tensorflow_cifar.yaml @@ -6,10 +6,3 @@ sample: _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/test/conf/data/tensorflow_mnist.yaml b/test/conf/data/tensorflow_mnist.yaml index 42ba394e..09dfde89 100644 --- a/test/conf/data/tensorflow_mnist.yaml +++ b/test/conf/data/tensorflow_mnist.yaml @@ -6,9 +6,3 @@ sample: _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/test/conf/data/torch_cifar.yaml b/test/conf/data/torch_cifar.yaml index 09d489e4..36f29c69 100644 --- a/test/conf/data/torch_cifar.yaml +++ b/test/conf/data/torch_cifar.yaml @@ -6,10 +6,3 @@ sample: _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/test/conf/data/torch_mnist.yaml b/test/conf/data/torch_mnist.yaml index 56e9c38b..3b77487f 100644 --- a/test/conf/data/torch_mnist.yaml +++ b/test/conf/data/torch_mnist.yaml @@ -6,9 +6,3 @@ sample: _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/test/conf/model/keras_mnist.yaml b/test/conf/model/keras_mnist.yaml index 40c87042..d4eee24a 100644 --- a/test/conf/model/keras_mnist.yaml +++ b/test/conf/model/keras_mnist.yaml @@ -9,13 +9,6 @@ data: stratify: True train_size : .01 test_size : .01 - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True init: name : keras_example.MNISTNet loss: categorical_crossentropy diff --git a/test/conf/model/tf_mnist.yaml b/test/conf/model/tf_mnist.yaml index 71152f57..0fcbe654 100644 --- a/test/conf/model/tf_mnist.yaml +++ b/test/conf/model/tf_mnist.yaml @@ -9,13 +9,6 @@ data: stratify: True train_size : .01 test_size : .01 - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True init: _target_: deckard.base.model.ModelInitializer name : tfv2_example.TFNet diff --git a/test/conf/model/torch_mnist.yaml b/test/conf/model/torch_mnist.yaml index f00031b4..62869f35 100644 --- a/test/conf/model/torch_mnist.yaml +++ b/test/conf/model/torch_mnist.yaml @@ -9,13 +9,6 @@ data: stratify: True train_size : .01 test_size : .01 - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True init: _target_: deckard.base.model.ModelInitializer name: torch_example.MNISTNet From 6e5b0ba7b12c5eb6207d44ffd45db5b227963af5 Mon Sep 17 00:00:00 2001 From: simplymathematics <15224968+simplymathematics@users.noreply.github.com> Date: Sat, 14 Oct 2023 23:58:41 +0000 Subject: [PATCH 111/148] fixed bug in data --- deckard/base/data/data.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deckard/base/data/data.py b/deckard/base/data/data.py index 57248a80..143e9a70 100644 --- a/deckard/base/data/data.py +++ b/deckard/base/data/data.py @@ -113,7 +113,8 @@ def initialize(self): result = self.sample(X, y) else: assert len(result) == 4 - result = self.sklearn_pipeline(*result) + if self.sklearn_pipeline is not None: + result = self.sklearn_pipeline(*result) return result def load(self, filename) -> DataFrame: From d956ab1f915112fc5069d75b6edb45c5ab0adbd4 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 31 Oct 2023 13:11:54 +0100 Subject: [PATCH 112/148] stop tracking params.yaml --- examples/security/.dvc/.gitignore | 3 + .../output/reports/train/default/params.yaml | 95 ------------------- 2 files changed, 3 insertions(+), 95 deletions(-) create mode 100644 examples/security/.dvc/.gitignore delete mode 100644 examples/security/classification/output/reports/train/default/params.yaml diff --git a/examples/security/.dvc/.gitignore b/examples/security/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/security/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/security/classification/output/reports/train/default/params.yaml b/examples/security/classification/output/reports/train/default/params.yaml deleted file mode 100644 index 28a2396e..00000000 --- a/examples/security/classification/output/reports/train/default/params.yaml +++ /dev/null @@ -1,95 +0,0 @@ -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - params_file: output/reports/train/default/params.yaml - predictions_file: output/reports/train/default/predictions.json - probabilities_file: output/reports/train/default/probabilities.json - score_dict_file: output/reports/train/default/score_dict.json - model_dir: models - name: default - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9b1ae58b464df54ee8918089f870a3ad - trainer: - kwargs: {} -name: default -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} From 10c9bfaf9410437ec39d46ff7023e644bc493cd0 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 31 Oct 2023 13:12:43 +0100 Subject: [PATCH 113/148] stop tracking params.yaml --- .../output/reports/attack/default/params.yaml | 211 ------------------ 1 file changed, 211 deletions(-) delete mode 100644 examples/security/classification/output/reports/attack/default/params.yaml diff --git a/examples/security/classification/output/reports/attack/default/params.yaml b/examples/security/classification/output/reports/attack/default/params.yaml deleted file mode 100644 index 36fda048..00000000 --- a/examples/security/classification/output/reports/attack/default/params.yaml +++ /dev/null @@ -1,211 +0,0 @@ -attack: - attack_size: 10 - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 433fc7aa2f77199ae99841829bb02ec9 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9b1ae58b464df54ee8918089f870a3ad - trainer: - kwargs: {} - name: 31e0c762a66b2663b2fca3765877aab3 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/default/adv_predictions.json - adv_probabilities_file: output/reports/attack/default/adv_probabilities.json - attack_file: output/attacks/attack.pkl - params_file: output/reports/attack/default/params.yaml - predictions_file: output/reports/attack/default/predictions.json - probabilities_file: output/reports/attack/default/probabilities.json - score_dict_file: output/reports/attack/default/score_dict.json - model_dir: models - name: default - reports: reports - stage: attack -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9b1ae58b464df54ee8918089f870a3ad - trainer: - kwargs: {} -name: default -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} From cfd755c81e0030a825310e6488108b62b99311fa Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 31 Oct 2023 14:03:41 +0100 Subject: [PATCH 114/148] cleanup reports file for security example --- examples/.dvc/.gitignore | 3 +++ examples/.dvc/config | 0 2 files changed, 3 insertions(+) create mode 100644 examples/.dvc/.gitignore create mode 100644 examples/.dvc/config diff --git a/examples/.dvc/.gitignore b/examples/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/.dvc/config b/examples/.dvc/config new file mode 100644 index 00000000..e69de29b From 69a3b8c753d19ab9446a83a96605c6b5dc95d943 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 31 Oct 2023 14:09:24 +0100 Subject: [PATCH 115/148] clean up repo --- .gitignore | 3 +- examples/security/.dvc/.gitignore | 3 - examples/security/classification/dvc.lock | 230 ++++++++-------- .../classification/output/plots/.gitignore | 10 - .../params.yaml | 245 ------------------ .../params.yaml | 242 ----------------- .../params.yaml | 245 ------------------ .../params.yaml | 236 ----------------- .../params.yaml | 245 ------------------ .../params.yaml | 242 ----------------- .../params.yaml | 236 ----------------- .../params.yaml | 245 ------------------ .../params.yaml | 236 ----------------- .../params.yaml | 245 ------------------ .../params.yaml | 236 ----------------- .../params.yaml | 242 ----------------- .../params.yaml | 242 ----------------- .../params.yaml | 236 ----------------- .../params.yaml | 242 ----------------- .../params.yaml | 245 ------------------ .../params.yaml | 236 ----------------- .../params.yaml | 236 ----------------- .../params.yaml | 245 ------------------ .../params.yaml | 242 ----------------- .../params.yaml | 242 ----------------- .../params.yaml | 245 ------------------ .../params.yaml | 236 ----------------- .../params.yaml | 245 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+examples/**/multirun/* diff --git a/examples/security/.dvc/.gitignore b/examples/security/.dvc/.gitignore deleted file mode 100644 index 528f30c7..00000000 --- a/examples/security/.dvc/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -/config.local -/tmp -/cache diff --git a/examples/security/classification/dvc.lock b/examples/security/classification/dvc.lock index 6ac79df2..d87303ca 100644 --- a/examples/security/classification/dvc.lock +++ b/examples/security/classification/dvc.lock @@ -100,8 +100,8 @@ stages: size: 42667 - path: output/reports/train/default/score_dict.json hash: md5 - md5: 195e2e518f502d3307629fb721f9583b - size: 357 + md5: 5df33da7e8d1e12c45432edcba0a010f + size: 355 attack: cmd: python -m deckard.layers.experiment attack deps: @@ -119,8 +119,8 @@ stages: size: 42667 - path: output/reports/train/default/score_dict.json hash: md5 - md5: 195e2e518f502d3307629fb721f9583b - size: 357 + md5: 5df33da7e8d1e12c45432edcba0a010f + size: 355 params: params.yaml: attack: @@ -318,8 +318,8 @@ stages: size: 42667 - path: output/reports/attack/default/score_dict.json hash: md5 - md5: 41e353b3926d5482bea344665f6bf93f - size: 579 + md5: 474014d6f0eae42d3b65e5840cea2dfe + size: 577 models: cmd: bash models.sh +stage=train --config-name=model.yaml deps: @@ -329,8 +329,8 @@ stages: size: 950 - path: models.sh hash: md5 - md5: 98694a486e52e950882ada2d2ce51377 - size: 2973 + md5: 034c94712f10de83e562ffa58ba204f9 + size: 2974 - path: output/reports/train/default/params.yaml hash: md5 md5: d4e0a34b2b15765ca71fa5ecaf7e3826 @@ -425,53 +425,53 @@ stages: outs: - path: logs/models/ hash: md5 - md5: d9dd105b6ca60d5a6ff624e9a021d170.dir - size: 4938450 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 nfiles: 60 - path: model.db hash: md5 - md5: 08787c64b2b8e9d66873b6728c37632d - size: 1843200 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 compile_models: cmd: python -m deckard.layers.compile --report_folder output/reports/train/ --results_file output/train.csv deps: - path: logs/models/ hash: md5 - md5: d9dd105b6ca60d5a6ff624e9a021d170.dir - size: 4938450 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 nfiles: 60 - path: model.db hash: md5 - md5: 08787c64b2b8e9d66873b6728c37632d - size: 1843200 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 - path: output/reports/train/ hash: md5 - md5: 3cbcd641b8792e062a5e3f554b1ed2c8.dir - size: 4311984765 - nfiles: 11139 + md5: 58131db53b5c1975bc969ee083a14602.dir + size: 87802 + nfiles: 5 outs: - path: output/train.csv hash: md5 - md5: 64c56ee3b82fc42f885338d5a42bbd1c - size: 4323243 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 find_best_model@rbf: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model --params_file best_rbf --study_name=rbf_100_10000 --default_config model.yaml deps: - path: logs/models/ hash: md5 - md5: d9dd105b6ca60d5a6ff624e9a021d170.dir - size: 4938450 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 nfiles: 60 - path: model.db hash: md5 - md5: 08787c64b2b8e9d66873b6728c37632d - size: 1843200 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 - path: output/train.csv hash: md5 - md5: 64c56ee3b82fc42f885338d5a42bbd1c - size: 4323243 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 outs: - path: conf/model/best_rbf.yaml hash: md5 @@ -483,17 +483,17 @@ stages: deps: - path: logs/models/ hash: md5 - md5: d9dd105b6ca60d5a6ff624e9a021d170.dir - size: 4938450 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 nfiles: 60 - path: model.db hash: md5 - md5: 08787c64b2b8e9d66873b6728c37632d - size: 1843200 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 - path: output/train.csv hash: md5 - md5: 64c56ee3b82fc42f885338d5a42bbd1c - size: 4323243 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 outs: - path: conf/model/best_linear.yaml hash: md5 @@ -505,17 +505,17 @@ stages: deps: - path: logs/models/ hash: md5 - md5: d9dd105b6ca60d5a6ff624e9a021d170.dir - size: 4938450 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 nfiles: 60 - path: model.db hash: md5 - md5: 08787c64b2b8e9d66873b6728c37632d - size: 1843200 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 - path: output/train.csv hash: md5 - md5: 64c56ee3b82fc42f885338d5a42bbd1c - size: 4323243 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 outs: - path: conf/model/best_poly.yaml hash: md5 @@ -538,26 +538,26 @@ stages: size: 357 - path: logs/models/ hash: md5 - md5: d9dd105b6ca60d5a6ff624e9a021d170.dir - size: 4938450 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 nfiles: 60 - path: model.db hash: md5 - md5: 08787c64b2b8e9d66873b6728c37632d - size: 1843200 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 - path: output/train.csv hash: md5 - md5: 64c56ee3b82fc42f885338d5a42bbd1c - size: 4323243 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 outs: - path: attack.db hash: md5 - md5: df10d4e743f0a815bfd83744eb7ca4f3 - size: 2039808 + md5: c67caa2e3ab1e72e19f441f93472cab8 + size: 2236416 - path: logs/attacks/ hash: md5 - md5: 79f819648d0ac189f0007dd29dc9ca35.dir - size: 17291838 + md5: 8012d759b75d0670b7f4db74037aa13a.dir + size: 19145346 nfiles: 3 compile_attacks: cmd: python -m deckard.layers.compile --report_folder output/reports/attack/ --results_file @@ -565,40 +565,40 @@ stages: deps: - path: attack.db hash: md5 - md5: df10d4e743f0a815bfd83744eb7ca4f3 - size: 2039808 + md5: c67caa2e3ab1e72e19f441f93472cab8 + size: 2236416 - path: logs/attacks/ hash: md5 - md5: 79f819648d0ac189f0007dd29dc9ca35.dir - size: 17291838 + md5: 8012d759b75d0670b7f4db74037aa13a.dir + size: 19145346 nfiles: 3 - path: output/reports/attack/ hash: md5 - md5: 93e02cdd34be3720bba985edabd2fc6b.dir - size: 17374464 - nfiles: 1329 + md5: 1130569bbd62ca77a62f12313fb38aa0.dir + size: 91788 + nfiles: 7 outs: - path: output/attack.csv hash: md5 - md5: a8b90230af3b4c71da865f2d4b0e689e - size: 398932 + md5: b8ea071d580c07f2ea03c74a02947c6e + size: 7276 find_best_attack@linear: cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack --params_file best_linear --study_name=best_linear --default_config attack.yaml deps: - path: logs/models/ hash: md5 - md5: d9dd105b6ca60d5a6ff624e9a021d170.dir - size: 4938450 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 nfiles: 60 - path: model.db hash: md5 - md5: 08787c64b2b8e9d66873b6728c37632d - size: 1843200 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 - path: output/train.csv hash: md5 - md5: 64c56ee3b82fc42f885338d5a42bbd1c - size: 4323243 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 outs: - path: conf/attack/best_linear.yaml hash: md5 @@ -610,17 +610,17 @@ stages: deps: - path: logs/models/ hash: md5 - md5: d9dd105b6ca60d5a6ff624e9a021d170.dir - size: 4938450 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 nfiles: 60 - path: model.db hash: md5 - md5: 08787c64b2b8e9d66873b6728c37632d - size: 1843200 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 - path: output/train.csv hash: md5 - md5: 64c56ee3b82fc42f885338d5a42bbd1c - size: 4323243 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 outs: - path: conf/attack/best_rbf.yaml hash: md5 @@ -632,17 +632,17 @@ stages: deps: - path: logs/models/ hash: md5 - md5: d9dd105b6ca60d5a6ff624e9a021d170.dir - size: 4938450 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 nfiles: 60 - path: model.db hash: md5 - md5: 08787c64b2b8e9d66873b6728c37632d - size: 1843200 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 - path: output/train.csv hash: md5 - md5: 64c56ee3b82fc42f885338d5a42bbd1c - size: 4323243 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 outs: - path: conf/attack/best_poly.yaml hash: md5 @@ -707,85 +707,85 @@ stages: size: 357 - path: output/attacks/ hash: md5 - md5: feacaf410b6d02d31060ca2eb7dda7e2.dir - size: 4819192 - nfiles: 121 + md5: ac2eb814d85404eb185c98af5f129ccf.dir + size: 2410072 + nfiles: 61 - path: output/models/ hash: md5 - md5: a1ae09e3b875da75aff46e8508e0e7d9.dir - size: 7290058 - nfiles: 613 + md5: c98656661f89d049ad99de3948c07c9e.dir + size: 54772 + nfiles: 3 outs: - path: plots/after_retrain_confidence.csv hash: md5 - md5: 439d2511d6be751a3b370a98792a8ba0 - size: 35819 + md5: 85b6c20f3db9f521dc19697f1804b1de + size: 18008 - path: plots/before_retrain_confidence.csv hash: md5 - md5: d319aabcfcb3ad48ae8456cafd9db2c0 - size: 35802 + md5: 4cf5f55f22c167803b9e8965d28d13c5 + size: 17991 - path: retrain/ hash: md5 - md5: 5e416b4ed718b3a4404e48048930282c.dir - size: 176889 + md5: 4d533cad670b054775f1ad0ddb241370.dir + size: 176885 nfiles: 12 plots: cmd: python plots.py deps: - path: output/attack.csv hash: md5 - md5: a8b90230af3b4c71da865f2d4b0e689e - size: 398932 + md5: feef3638a92cb9ec2fe9c361f0ac8aee + size: 204088 - path: output/train.csv hash: md5 - md5: 64c56ee3b82fc42f885338d5a42bbd1c - size: 4323243 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 - path: plots/after_retrain_confidence.csv hash: md5 - md5: 439d2511d6be751a3b370a98792a8ba0 - size: 35819 + md5: 85b6c20f3db9f521dc19697f1804b1de + size: 18008 - path: plots/before_retrain_confidence.csv hash: md5 - md5: d319aabcfcb3ad48ae8456cafd9db2c0 - size: 35802 + md5: 4cf5f55f22c167803b9e8965d28d13c5 + size: 17991 outs: - path: plots/accuracy_vs_attack_parameters.eps hash: md5 - md5: ba869c3e82249de5f28ca823744a5405 - size: 41516 + md5: 3d2dd3aac0d156e1fd9c33e36af4ba8d + size: 41230 - path: plots/accuracy_vs_features.eps hash: md5 - md5: 3249e773665ec8fe050ab414a8d48d33 - size: 22707 + md5: fd4589a083f49c1867c8266f65079042 + size: 21386 - path: plots/accuracy_vs_samples.eps hash: md5 - md5: be0c4724a0a10928ef696cb2a9c3661e - size: 24443 + md5: 68cac57cb36da240f16c59de0cd6fab8 + size: 23933 - path: plots/confidence_vs_attack_parameters.eps hash: md5 - md5: 625ddf8a03f34e361efe4b5ad0dbe727 - size: 42243 + md5: 4bed1ae75648072e60b1de618c3a458d + size: 41285 - path: plots/retrain_accuracy.eps hash: md5 - md5: 7fe23f4cc126f9135fa05941c9783eb9 + md5: 8f31fb8e6bfde27e322e94d3ca403e2d size: 23419 - path: plots/retrain_confidence_vs_attack_parameters.eps hash: md5 - md5: 50105ac743b6900c8f75bd08e4e935ad - size: 42192 + md5: af27dbbce36fe7118d2ab36e5f5e51d2 + size: 41304 - path: plots/retrain_time.eps hash: md5 - md5: d2cdf4c0f9266e87af87b427d835c5e9 - size: 20952 + md5: 173c20ed56ff30f749652814fb6e6b3a + size: 20953 - path: plots/train_time_vs_attack_parameters.eps hash: md5 - md5: a04c540e19c01816f422d5ff65dc0885 - size: 38897 + md5: f65d32d2dccabac8b992a2f7fe1a029f + size: 38831 - path: plots/train_time_vs_features.eps hash: md5 - md5: 87cb1eafd89d657890de704d184b78e2 - size: 21125 + md5: f093fffdb45713b11a9a302b7f6765b8 + size: 17543 - path: plots/train_time_vs_samples.eps hash: md5 - md5: 5ebae11c0fb43d301298274b657b8996 - size: 24477 + md5: c359b1e7c36783d590a57df80ed0dc0a + size: 20679 diff --git a/examples/security/classification/output/plots/.gitignore b/examples/security/classification/output/plots/.gitignore deleted file mode 100644 index e7f7378a..00000000 --- a/examples/security/classification/output/plots/.gitignore +++ /dev/null @@ -1,10 +0,0 @@ -/accuracy_vs_attack_parameters.pdf -/accuracy_vs_features.pdf -/accuracy_vs_samples.pdf -/confidence_vs_attack_parameters.pdf -/train_time_vs_attack_parameters.pdf -/train_time_vs_features.pdf -/train_time_vs_samples.pdf -/retrain_accuracy.pdf -/retrain_confidence_vs_attack_parameters.pdf -/retrain_time.pdf diff --git a/examples/security/classification/output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/params.yaml b/examples/security/classification/output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/params.yaml deleted file mode 100644 index a4b3e6c9..00000000 --- a/examples/security/classification/output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 1 - eps_step: 1 - max_iter: 1000 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 3b5765f02de2c9ac351847de6f923a06 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/adv_predictions.json - adv_probabilities_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/adv_probabilities.json - attack_file: output/attacks/904bbdc1b81b6891328139bd6f03c17b.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/params.yaml - predictions_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/predictions.json - probabilities_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/probabilities.json - score_dict_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/score_dict.json - test_labels_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/test_labels.json - train_labels_file: output/reports/attack/04ea26bc380fe6b10ad454dfec2e14ba/train_labels.json - model_dir: models - name: 04ea26bc380fe6b10ad454dfec2e14ba - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 04ea26bc380fe6b10ad454dfec2e14ba -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/params.yaml b/examples/security/classification/output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/params.yaml deleted file mode 100644 index 24bc36b2..00000000 --- a/examples/security/classification/output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.3 - eps_step: 0.3 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: fbc9130bfa39d88145f62f64fe4b5f81 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/adv_predictions.json - adv_probabilities_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/adv_probabilities.json - attack_file: output/attacks/d3e607aa42c0a4222c8a20acc88c7f5f.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/params.yaml - predictions_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/predictions.json - probabilities_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/probabilities.json - score_dict_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/score_dict.json - test_labels_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/test_labels.json - train_labels_file: output/reports/attack/05cd19bd97879eb733669370dcaa4bf9/train_labels.json - model_dir: models - name: 05cd19bd97879eb733669370dcaa4bf9 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 05cd19bd97879eb733669370dcaa4bf9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/params.yaml b/examples/security/classification/output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/params.yaml deleted file mode 100644 index 9f027c50..00000000 --- a/examples/security/classification/output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.01 - eps_step: 0.001 - max_iter: 100 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 42aa05d8e56e837209c3a5bb38f04283 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/adv_predictions.json - adv_probabilities_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/adv_probabilities.json - attack_file: output/attacks/9fc323308ffd4047482789e31e82b088.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/params.yaml - predictions_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/predictions.json - probabilities_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/probabilities.json - score_dict_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/score_dict.json - test_labels_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/test_labels.json - train_labels_file: output/reports/attack/05e7e23ca25d2ca1042b8edef9ad1548/train_labels.json - model_dir: models - name: 05e7e23ca25d2ca1042b8edef9ad1548 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 05e7e23ca25d2ca1042b8edef9ad1548 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/params.yaml b/examples/security/classification/output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/params.yaml deleted file mode 100644 index ad2f5ba1..00000000 --- a/examples/security/classification/output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.01 - eps_step: 0.1 - max_iter: 100 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: f173cafe7a1c8d4d7eb14602d0c5752d -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/adv_predictions.json - adv_probabilities_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/adv_probabilities.json - attack_file: output/attacks/ce1e6a061ea5a4647ecce0c4d6570628.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/params.yaml - predictions_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/predictions.json - probabilities_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/probabilities.json - score_dict_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/score_dict.json - test_labels_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/test_labels.json - train_labels_file: output/reports/attack/06b2f80df6dfa53eb83cde7cd02ea085/train_labels.json - model_dir: models - name: 06b2f80df6dfa53eb83cde7cd02ea085 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 06b2f80df6dfa53eb83cde7cd02ea085 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/params.yaml b/examples/security/classification/output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/params.yaml deleted file mode 100644 index b8c1621d..00000000 --- a/examples/security/classification/output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.01 - eps_step: 0.01 - max_iter: 1000 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 627235d7f8b29cf00b882303dcdf29be -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/adv_predictions.json - adv_probabilities_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/adv_probabilities.json - attack_file: output/attacks/abf34efaec9bef7d8fc932c42239de9b.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/params.yaml - predictions_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/predictions.json - probabilities_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/probabilities.json - score_dict_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/score_dict.json - test_labels_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/test_labels.json - train_labels_file: output/reports/attack/07157c4727b477e9c69bb9885e27a2a0/train_labels.json - model_dir: models - name: 07157c4727b477e9c69bb9885e27a2a0 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 07157c4727b477e9c69bb9885e27a2a0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/0cdc224d197c450388cef6965763be7c/params.yaml b/examples/security/classification/output/reports/attack/0cdc224d197c450388cef6965763be7c/params.yaml deleted file mode 100644 index 2db5e17a..00000000 --- a/examples/security/classification/output/reports/attack/0cdc224d197c450388cef6965763be7c/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.5 - eps_step: 0.001 - max_iter: 1000 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 50e968a462215f622e220d58363a37f4 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/adv_predictions.json - adv_probabilities_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/adv_probabilities.json - attack_file: output/attacks/7a80d64427a17cd62f12faab5a5923b0.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/params.yaml - predictions_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/predictions.json - probabilities_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/probabilities.json - score_dict_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/score_dict.json - test_labels_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/test_labels.json - train_labels_file: output/reports/attack/0cdc224d197c450388cef6965763be7c/train_labels.json - model_dir: models - name: 0cdc224d197c450388cef6965763be7c - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 0cdc224d197c450388cef6965763be7c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/params.yaml b/examples/security/classification/output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/params.yaml deleted file mode 100644 index 100f811a..00000000 --- a/examples/security/classification/output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.01 - eps_step: 0.5 - max_iter: 100 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: dcda6d29b01e01a5e5c7e75365a1a35f -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/adv_predictions.json - adv_probabilities_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/adv_probabilities.json - attack_file: output/attacks/890452ce08811807bcaffccb876ff2fc.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/params.yaml - predictions_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/predictions.json - probabilities_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/probabilities.json - score_dict_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/score_dict.json - test_labels_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/test_labels.json - train_labels_file: output/reports/attack/0f1d476ab623be19fa4a7fcd01d17cf0/train_labels.json - model_dir: models - name: 0f1d476ab623be19fa4a7fcd01d17cf0 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 0f1d476ab623be19fa4a7fcd01d17cf0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/params.yaml b/examples/security/classification/output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/params.yaml deleted file mode 100644 index 5532f5ea..00000000 --- a/examples/security/classification/output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.01 - eps_step: 0.3 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 8df9cc2e1f94da15e5a9771498972220 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/adv_predictions.json - adv_probabilities_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/adv_probabilities.json - attack_file: output/attacks/79adc798c19d54d7cb59dd6e38c5dc64.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/params.yaml - predictions_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/predictions.json - probabilities_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/probabilities.json - score_dict_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/score_dict.json - test_labels_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/test_labels.json - train_labels_file: output/reports/attack/1222b5d1f13ffc0ecb68b6ddf1cfa544/train_labels.json - model_dir: models - name: 1222b5d1f13ffc0ecb68b6ddf1cfa544 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 1222b5d1f13ffc0ecb68b6ddf1cfa544 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/params.yaml b/examples/security/classification/output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/params.yaml deleted file mode 100644 index bb0f6ffb..00000000 --- a/examples/security/classification/output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.001 - eps_step: 0.3 - max_iter: 1000 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 7bae9c379f60b2550294fd5f88e861a4 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/adv_predictions.json - adv_probabilities_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/adv_probabilities.json - attack_file: output/attacks/e079fcffb852f0e1aeaafa48d5018b29.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/params.yaml - predictions_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/predictions.json - probabilities_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/probabilities.json - score_dict_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/score_dict.json - test_labels_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/test_labels.json - train_labels_file: output/reports/attack/12e0471fdb18fe6a2f348b2b4ea480fc/train_labels.json - model_dir: models - name: 12e0471fdb18fe6a2f348b2b4ea480fc - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 12e0471fdb18fe6a2f348b2b4ea480fc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/13f80fb91b7c61a65e59eb7106099446/params.yaml b/examples/security/classification/output/reports/attack/13f80fb91b7c61a65e59eb7106099446/params.yaml deleted file mode 100644 index 351341bb..00000000 --- a/examples/security/classification/output/reports/attack/13f80fb91b7c61a65e59eb7106099446/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.1 - eps_step: 0.01 - max_iter: 1 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 24a90609f13365a76327e52aff4188a7 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/adv_predictions.json - adv_probabilities_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/adv_probabilities.json - attack_file: output/attacks/0b6b66ac476aa99b0de2b0af2f609ad0.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/params.yaml - predictions_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/predictions.json - probabilities_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/probabilities.json - score_dict_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/score_dict.json - test_labels_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/test_labels.json - train_labels_file: output/reports/attack/13f80fb91b7c61a65e59eb7106099446/train_labels.json - model_dir: models - name: 13f80fb91b7c61a65e59eb7106099446 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 13f80fb91b7c61a65e59eb7106099446 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/14e23414b1089257270338b9ed3dd54a/params.yaml b/examples/security/classification/output/reports/attack/14e23414b1089257270338b9ed3dd54a/params.yaml deleted file mode 100644 index 4531cf68..00000000 --- a/examples/security/classification/output/reports/attack/14e23414b1089257270338b9ed3dd54a/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.5 - eps_step: 0.001 - max_iter: 1 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 6c95b6147748247b708706f373d59b11 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/adv_predictions.json - adv_probabilities_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/adv_probabilities.json - attack_file: output/attacks/e1fdc76d1cbdae1ada95c24bb1a58583.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/params.yaml - predictions_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/predictions.json - probabilities_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/probabilities.json - score_dict_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/score_dict.json - test_labels_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/test_labels.json - train_labels_file: output/reports/attack/14e23414b1089257270338b9ed3dd54a/train_labels.json - model_dir: models - name: 14e23414b1089257270338b9ed3dd54a - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 14e23414b1089257270338b9ed3dd54a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/params.yaml b/examples/security/classification/output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/params.yaml deleted file mode 100644 index d0b7df24..00000000 --- a/examples/security/classification/output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.5 - eps_step: 1 - max_iter: 10 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: a91b432012fe6732156ce758b7be91af -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/adv_predictions.json - adv_probabilities_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/adv_probabilities.json - attack_file: output/attacks/9bdecd56e8e471e223c965072717fddd.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/params.yaml - predictions_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/predictions.json - probabilities_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/probabilities.json - score_dict_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/score_dict.json - test_labels_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/test_labels.json - train_labels_file: output/reports/attack/1545316a183b2a5c6ea3cbe01d777ee6/train_labels.json - model_dir: models - name: 1545316a183b2a5c6ea3cbe01d777ee6 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 1545316a183b2a5c6ea3cbe01d777ee6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/186e46c92ef66070a825683786db0871/params.yaml b/examples/security/classification/output/reports/attack/186e46c92ef66070a825683786db0871/params.yaml deleted file mode 100644 index e2f47504..00000000 --- a/examples/security/classification/output/reports/attack/186e46c92ef66070a825683786db0871/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.01 - eps_step: 0.001 - max_iter: 100 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: d5beab1477c9e97f4b088e27bed90307 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/186e46c92ef66070a825683786db0871/adv_predictions.json - adv_probabilities_file: output/reports/attack/186e46c92ef66070a825683786db0871/adv_probabilities.json - attack_file: output/attacks/cd65a2449f25f362cb9a8651c006b6d8.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/186e46c92ef66070a825683786db0871/params.yaml - predictions_file: output/reports/attack/186e46c92ef66070a825683786db0871/predictions.json - probabilities_file: output/reports/attack/186e46c92ef66070a825683786db0871/probabilities.json - score_dict_file: output/reports/attack/186e46c92ef66070a825683786db0871/score_dict.json - test_labels_file: output/reports/attack/186e46c92ef66070a825683786db0871/test_labels.json - train_labels_file: output/reports/attack/186e46c92ef66070a825683786db0871/train_labels.json - model_dir: models - name: 186e46c92ef66070a825683786db0871 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 186e46c92ef66070a825683786db0871 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/params.yaml b/examples/security/classification/output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/params.yaml deleted file mode 100644 index fa688e4f..00000000 --- a/examples/security/classification/output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.1 - eps_step: 0.1 - max_iter: 100 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: c48392ef1dfabbe2569554f97e9c0c7b -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/adv_predictions.json - adv_probabilities_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/adv_probabilities.json - attack_file: output/attacks/b660ba4969aa2cb1f6c064cb0924de72.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/params.yaml - predictions_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/predictions.json - probabilities_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/probabilities.json - score_dict_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/score_dict.json - test_labels_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/test_labels.json - train_labels_file: output/reports/attack/224ea87e6d3c0563c9859a6235fb6b3a/train_labels.json - model_dir: models - name: 224ea87e6d3c0563c9859a6235fb6b3a - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 224ea87e6d3c0563c9859a6235fb6b3a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/29d1387e691db094542db2b51279840b/params.yaml b/examples/security/classification/output/reports/attack/29d1387e691db094542db2b51279840b/params.yaml deleted file mode 100644 index 97d40f8a..00000000 --- a/examples/security/classification/output/reports/attack/29d1387e691db094542db2b51279840b/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.5 - eps_step: 0.01 - max_iter: 100 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: baa9a7e500dee5cda429b8e49896a5a0 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/29d1387e691db094542db2b51279840b/adv_predictions.json - adv_probabilities_file: output/reports/attack/29d1387e691db094542db2b51279840b/adv_probabilities.json - attack_file: output/attacks/8045b98a0c76f7d3c3f365cd7c8fbf7b.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/29d1387e691db094542db2b51279840b/params.yaml - predictions_file: output/reports/attack/29d1387e691db094542db2b51279840b/predictions.json - probabilities_file: output/reports/attack/29d1387e691db094542db2b51279840b/probabilities.json - score_dict_file: output/reports/attack/29d1387e691db094542db2b51279840b/score_dict.json - test_labels_file: output/reports/attack/29d1387e691db094542db2b51279840b/test_labels.json - train_labels_file: output/reports/attack/29d1387e691db094542db2b51279840b/train_labels.json - model_dir: models - name: 29d1387e691db094542db2b51279840b - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 29d1387e691db094542db2b51279840b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/params.yaml b/examples/security/classification/output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/params.yaml deleted file mode 100644 index d489dd01..00000000 --- a/examples/security/classification/output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.1 - eps_step: 0.1 - max_iter: 10 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: c6e4741768d33da4569c0f4d55e0c95a -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/adv_predictions.json - adv_probabilities_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/adv_probabilities.json - attack_file: output/attacks/f0ba8b6a253cc4c60063b5aee9447bce.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/params.yaml - predictions_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/predictions.json - probabilities_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/probabilities.json - score_dict_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/score_dict.json - test_labels_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/test_labels.json - train_labels_file: output/reports/attack/29e49c9f6dc5e062b1ff4fd1af5c7b6d/train_labels.json - model_dir: models - name: 29e49c9f6dc5e062b1ff4fd1af5c7b6d - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 29e49c9f6dc5e062b1ff4fd1af5c7b6d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/params.yaml b/examples/security/classification/output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/params.yaml deleted file mode 100644 index 67b78832..00000000 --- a/examples/security/classification/output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.001 - eps_step: 0.001 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 54af8c7d59186e57be11b9d0d174e1e8 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/adv_predictions.json - adv_probabilities_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/adv_probabilities.json - attack_file: output/attacks/da756aa4105e9814b4c9da56c126b456.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/params.yaml - predictions_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/predictions.json - probabilities_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/probabilities.json - score_dict_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/score_dict.json - test_labels_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/test_labels.json - train_labels_file: output/reports/attack/2b1db37c6d88356f910abbb892f78b8f/train_labels.json - model_dir: models - name: 2b1db37c6d88356f910abbb892f78b8f - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 2b1db37c6d88356f910abbb892f78b8f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/params.yaml b/examples/security/classification/output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/params.yaml deleted file mode 100644 index 6c483d29..00000000 --- a/examples/security/classification/output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.5 - eps_step: 0.01 - max_iter: 1 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: bd0553ddf61498da2dfa495e7fb514de -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/adv_predictions.json - adv_probabilities_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/adv_probabilities.json - attack_file: output/attacks/3042406749910eb46affce0b11a11cfe.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/params.yaml - predictions_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/predictions.json - probabilities_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/probabilities.json - score_dict_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/score_dict.json - test_labels_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/test_labels.json - train_labels_file: output/reports/attack/2b5b145c8d1804ec7419123bbd4ef258/train_labels.json - model_dir: models - name: 2b5b145c8d1804ec7419123bbd4ef258 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 2b5b145c8d1804ec7419123bbd4ef258 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/params.yaml b/examples/security/classification/output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/params.yaml deleted file mode 100644 index 2274df05..00000000 --- a/examples/security/classification/output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.01 - eps_step: 0.1 - max_iter: 10 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 6e55d0685e917bedcdfc07c38dd39801 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/adv_predictions.json - adv_probabilities_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/adv_probabilities.json - attack_file: output/attacks/4f3ed4b4ed660cc92f42b4ff87e24063.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/params.yaml - predictions_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/predictions.json - probabilities_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/probabilities.json - score_dict_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/score_dict.json - test_labels_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/test_labels.json - train_labels_file: output/reports/attack/2d60289ca42435ce4696ba71b9fb641f/train_labels.json - model_dir: models - name: 2d60289ca42435ce4696ba71b9fb641f - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 2d60289ca42435ce4696ba71b9fb641f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/params.yaml b/examples/security/classification/output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/params.yaml deleted file mode 100644 index 5dba2e80..00000000 --- a/examples/security/classification/output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.01 - eps_step: 1 - max_iter: 1000 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 64cd1942d14dad4a3418d89fce4dd509 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/adv_predictions.json - adv_probabilities_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/adv_probabilities.json - attack_file: output/attacks/c2ec56d18b15b58662507efe7657c776.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/params.yaml - predictions_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/predictions.json - probabilities_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/probabilities.json - score_dict_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/score_dict.json - test_labels_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/test_labels.json - train_labels_file: output/reports/attack/2d8533955630bf2c5f174b4a65abbac1/train_labels.json - model_dir: models - name: 2d8533955630bf2c5f174b4a65abbac1 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 2d8533955630bf2c5f174b4a65abbac1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/params.yaml b/examples/security/classification/output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/params.yaml deleted file mode 100644 index efb534c7..00000000 --- a/examples/security/classification/output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.001 - eps_step: 0.5 - max_iter: 1 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 26fa2acf9368518c36dfe9c8ce782ddd -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/adv_predictions.json - adv_probabilities_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/adv_probabilities.json - attack_file: output/attacks/ab91fd1b63c5be4ae2ae0f542e50353e.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/params.yaml - predictions_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/predictions.json - probabilities_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/probabilities.json - score_dict_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/score_dict.json - test_labels_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/test_labels.json - train_labels_file: output/reports/attack/348a212e2fce7fe3d8749d8b1a38efee/train_labels.json - model_dir: models - name: 348a212e2fce7fe3d8749d8b1a38efee - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 348a212e2fce7fe3d8749d8b1a38efee -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/348b7a17642342880be2c8e4776e71ca/params.yaml b/examples/security/classification/output/reports/attack/348b7a17642342880be2c8e4776e71ca/params.yaml deleted file mode 100644 index 24c4e5d4..00000000 --- a/examples/security/classification/output/reports/attack/348b7a17642342880be2c8e4776e71ca/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.1 - eps_step: 0.5 - max_iter: 100 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 3131459e6596691b2eec9c9301ad41f1 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/adv_predictions.json - adv_probabilities_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/adv_probabilities.json - attack_file: output/attacks/acc17c51bbf330f7c191ee0d094c2fd9.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/params.yaml - predictions_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/predictions.json - probabilities_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/probabilities.json - score_dict_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/score_dict.json - test_labels_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/test_labels.json - train_labels_file: output/reports/attack/348b7a17642342880be2c8e4776e71ca/train_labels.json - model_dir: models - name: 348b7a17642342880be2c8e4776e71ca - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 348b7a17642342880be2c8e4776e71ca -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/params.yaml b/examples/security/classification/output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/params.yaml deleted file mode 100644 index 9ae1fedc..00000000 --- a/examples/security/classification/output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 1 - eps_step: 0.001 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: c64e8eb8a9676a1addfd961047b57c45 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/adv_predictions.json - adv_probabilities_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/adv_probabilities.json - attack_file: output/attacks/84de5c16b1fbf141fcdfab370390d5a7.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/params.yaml - predictions_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/predictions.json - probabilities_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/probabilities.json - score_dict_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/score_dict.json - test_labels_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/test_labels.json - train_labels_file: output/reports/attack/356c1286c4cf03fd40257074c3f47a9f/train_labels.json - model_dir: models - name: 356c1286c4cf03fd40257074c3f47a9f - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 356c1286c4cf03fd40257074c3f47a9f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/params.yaml b/examples/security/classification/output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/params.yaml deleted file mode 100644 index 2829ef43..00000000 --- a/examples/security/classification/output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.01 - eps_step: 0.01 - max_iter: 1 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: a072cc72b1cd1ba4e52fea3795bc4552 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/adv_predictions.json - adv_probabilities_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/adv_probabilities.json - attack_file: output/attacks/23a3d0771eed1a54f187fbe0357baf60.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/params.yaml - predictions_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/predictions.json - probabilities_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/probabilities.json - score_dict_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/score_dict.json - test_labels_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/test_labels.json - train_labels_file: output/reports/attack/377569dcddfbe3c7ff187f38895ccaed/train_labels.json - model_dir: models - name: 377569dcddfbe3c7ff187f38895ccaed - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 377569dcddfbe3c7ff187f38895ccaed -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/386a4dec58c199035136189958f04673/params.yaml b/examples/security/classification/output/reports/attack/386a4dec58c199035136189958f04673/params.yaml deleted file mode 100644 index 9151d0e7..00000000 --- a/examples/security/classification/output/reports/attack/386a4dec58c199035136189958f04673/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.3 - eps_step: 1 - max_iter: 100 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: df4ccdd86f3c6c74faa9b38c524660a3 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/386a4dec58c199035136189958f04673/adv_predictions.json - adv_probabilities_file: output/reports/attack/386a4dec58c199035136189958f04673/adv_probabilities.json - attack_file: output/attacks/71525f0cc9c947339ab34a43b4fed35e.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/386a4dec58c199035136189958f04673/params.yaml - predictions_file: output/reports/attack/386a4dec58c199035136189958f04673/predictions.json - probabilities_file: output/reports/attack/386a4dec58c199035136189958f04673/probabilities.json - score_dict_file: output/reports/attack/386a4dec58c199035136189958f04673/score_dict.json - test_labels_file: output/reports/attack/386a4dec58c199035136189958f04673/test_labels.json - train_labels_file: output/reports/attack/386a4dec58c199035136189958f04673/train_labels.json - model_dir: models - name: 386a4dec58c199035136189958f04673 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 386a4dec58c199035136189958f04673 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/params.yaml b/examples/security/classification/output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/params.yaml deleted file mode 100644 index 19d5e3cc..00000000 --- a/examples/security/classification/output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.001 - eps_step: 0.01 - max_iter: 1000 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 8b63374d147c6e0cb4aa4f0e281150f2 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/adv_predictions.json - adv_probabilities_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/adv_probabilities.json - attack_file: output/attacks/a8fb177802053dd54c1e2d81c72e3653.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/params.yaml - predictions_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/predictions.json - probabilities_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/probabilities.json - score_dict_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/score_dict.json - test_labels_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/test_labels.json - train_labels_file: output/reports/attack/3a9de2601dec0634564f418e84cf7d2d/train_labels.json - model_dir: models - name: 3a9de2601dec0634564f418e84cf7d2d - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 3a9de2601dec0634564f418e84cf7d2d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/3bcf141cfab3423c15f99efa66385e18/params.yaml b/examples/security/classification/output/reports/attack/3bcf141cfab3423c15f99efa66385e18/params.yaml deleted file mode 100644 index fe6c2a5e..00000000 --- a/examples/security/classification/output/reports/attack/3bcf141cfab3423c15f99efa66385e18/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.1 - eps_step: 0.001 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 171df0fd503cb89fb546449d9e4b6454 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/adv_predictions.json - adv_probabilities_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/adv_probabilities.json - attack_file: output/attacks/849c6e41c61ebfb7e25c7e29c4f4535b.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/params.yaml - predictions_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/predictions.json - probabilities_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/probabilities.json - score_dict_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/score_dict.json - test_labels_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/test_labels.json - train_labels_file: output/reports/attack/3bcf141cfab3423c15f99efa66385e18/train_labels.json - model_dir: models - name: 3bcf141cfab3423c15f99efa66385e18 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 3bcf141cfab3423c15f99efa66385e18 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/3e972baeee5735882b06a7615a911841/params.yaml b/examples/security/classification/output/reports/attack/3e972baeee5735882b06a7615a911841/params.yaml deleted file mode 100644 index 3f11d377..00000000 --- a/examples/security/classification/output/reports/attack/3e972baeee5735882b06a7615a911841/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 1 - eps_step: 0.1 - max_iter: 1000 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: f3fbf263e81bf94d4a39b565cfd7e847 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/3e972baeee5735882b06a7615a911841/adv_predictions.json - adv_probabilities_file: output/reports/attack/3e972baeee5735882b06a7615a911841/adv_probabilities.json - attack_file: output/attacks/c14aee97098c5c7a68a694738ff9b52a.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/3e972baeee5735882b06a7615a911841/params.yaml - predictions_file: output/reports/attack/3e972baeee5735882b06a7615a911841/predictions.json - probabilities_file: output/reports/attack/3e972baeee5735882b06a7615a911841/probabilities.json - score_dict_file: output/reports/attack/3e972baeee5735882b06a7615a911841/score_dict.json - test_labels_file: output/reports/attack/3e972baeee5735882b06a7615a911841/test_labels.json - train_labels_file: output/reports/attack/3e972baeee5735882b06a7615a911841/train_labels.json - model_dir: models - name: 3e972baeee5735882b06a7615a911841 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 3e972baeee5735882b06a7615a911841 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/params.yaml b/examples/security/classification/output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/params.yaml deleted file mode 100644 index 50622d17..00000000 --- a/examples/security/classification/output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 1 - eps_step: 0.001 - max_iter: 10 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 503b4a409fec9023703815d74886b9c6 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/adv_predictions.json - adv_probabilities_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/adv_probabilities.json - attack_file: output/attacks/c4e896f3d4615f003f22070065bec4d3.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/params.yaml - predictions_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/predictions.json - probabilities_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/probabilities.json - score_dict_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/score_dict.json - test_labels_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/test_labels.json - train_labels_file: output/reports/attack/40efb445118fe8ce98d27bfe156ac9ba/train_labels.json - model_dir: models - name: 40efb445118fe8ce98d27bfe156ac9ba - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 40efb445118fe8ce98d27bfe156ac9ba -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/41a52a88333b166590d0783a69190134/params.yaml b/examples/security/classification/output/reports/attack/41a52a88333b166590d0783a69190134/params.yaml deleted file mode 100644 index 7844dca0..00000000 --- a/examples/security/classification/output/reports/attack/41a52a88333b166590d0783a69190134/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.001 - eps_step: 1 - max_iter: 100 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: f4fe8486040c4a065de70f6ad5334960 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/41a52a88333b166590d0783a69190134/adv_predictions.json - adv_probabilities_file: output/reports/attack/41a52a88333b166590d0783a69190134/adv_probabilities.json - attack_file: output/attacks/9223032732e02b80c604acc216940795.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/41a52a88333b166590d0783a69190134/params.yaml - predictions_file: output/reports/attack/41a52a88333b166590d0783a69190134/predictions.json - probabilities_file: output/reports/attack/41a52a88333b166590d0783a69190134/probabilities.json - score_dict_file: output/reports/attack/41a52a88333b166590d0783a69190134/score_dict.json - test_labels_file: output/reports/attack/41a52a88333b166590d0783a69190134/test_labels.json - train_labels_file: output/reports/attack/41a52a88333b166590d0783a69190134/train_labels.json - model_dir: models - name: 41a52a88333b166590d0783a69190134 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 41a52a88333b166590d0783a69190134 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/params.yaml b/examples/security/classification/output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/params.yaml deleted file mode 100644 index 72c5818e..00000000 --- a/examples/security/classification/output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.1 - eps_step: 0.1 - max_iter: 100 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 53c6220162dd8fc7bc44e0958e5a6978 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/adv_predictions.json - adv_probabilities_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/adv_probabilities.json - attack_file: output/attacks/f18c097a1cfcffbfdeda36b4ba6da575.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/params.yaml - predictions_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/predictions.json - probabilities_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/probabilities.json - score_dict_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/score_dict.json - test_labels_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/test_labels.json - train_labels_file: output/reports/attack/431a4054e5b497c68b9fc3ac480265e5/train_labels.json - model_dir: models - name: 431a4054e5b497c68b9fc3ac480265e5 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 431a4054e5b497c68b9fc3ac480265e5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/43609b18b6ecdcf78243575061377063/params.yaml b/examples/security/classification/output/reports/attack/43609b18b6ecdcf78243575061377063/params.yaml deleted file mode 100644 index 3655735e..00000000 --- a/examples/security/classification/output/reports/attack/43609b18b6ecdcf78243575061377063/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.01 - eps_step: 0.5 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 3766aaf766df04d12680c28dd2f2bca3 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/43609b18b6ecdcf78243575061377063/adv_predictions.json - adv_probabilities_file: output/reports/attack/43609b18b6ecdcf78243575061377063/adv_probabilities.json - attack_file: output/attacks/e25b35f91068de91354cc0c25417623a.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/43609b18b6ecdcf78243575061377063/params.yaml - predictions_file: output/reports/attack/43609b18b6ecdcf78243575061377063/predictions.json - probabilities_file: output/reports/attack/43609b18b6ecdcf78243575061377063/probabilities.json - score_dict_file: output/reports/attack/43609b18b6ecdcf78243575061377063/score_dict.json - test_labels_file: output/reports/attack/43609b18b6ecdcf78243575061377063/test_labels.json - train_labels_file: output/reports/attack/43609b18b6ecdcf78243575061377063/train_labels.json - model_dir: models - name: 43609b18b6ecdcf78243575061377063 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 43609b18b6ecdcf78243575061377063 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/params.yaml b/examples/security/classification/output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/params.yaml deleted file mode 100644 index 0d8e8a7e..00000000 --- a/examples/security/classification/output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.01 - eps_step: 0.01 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 413c592278b6f97c670c5b2272b07c6d -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/adv_predictions.json - adv_probabilities_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/adv_probabilities.json - attack_file: output/attacks/3fc761d3203b932a15586c45445c1607.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/params.yaml - predictions_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/predictions.json - probabilities_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/probabilities.json - score_dict_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/score_dict.json - test_labels_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/test_labels.json - train_labels_file: output/reports/attack/45f15c74e96501d6c7e0b565b07acca6/train_labels.json - model_dir: models - name: 45f15c74e96501d6c7e0b565b07acca6 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 45f15c74e96501d6c7e0b565b07acca6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/490168005f33f8cf056a8f514bac8125/params.yaml b/examples/security/classification/output/reports/attack/490168005f33f8cf056a8f514bac8125/params.yaml deleted file mode 100644 index 6667babc..00000000 --- a/examples/security/classification/output/reports/attack/490168005f33f8cf056a8f514bac8125/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.1 - eps_step: 1 - max_iter: 100 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: f59990c91c11f6837cd229afecbe9f0d -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/adv_predictions.json - adv_probabilities_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/adv_probabilities.json - attack_file: output/attacks/17c0cae5082a884f66fb5cde4751d143.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/params.yaml - predictions_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/predictions.json - probabilities_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/probabilities.json - score_dict_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/score_dict.json - test_labels_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/test_labels.json - train_labels_file: output/reports/attack/490168005f33f8cf056a8f514bac8125/train_labels.json - model_dir: models - name: 490168005f33f8cf056a8f514bac8125 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 490168005f33f8cf056a8f514bac8125 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/49568c81416f48f42e29e47a3a4295ef/params.yaml b/examples/security/classification/output/reports/attack/49568c81416f48f42e29e47a3a4295ef/params.yaml deleted file mode 100644 index 82a7130d..00000000 --- a/examples/security/classification/output/reports/attack/49568c81416f48f42e29e47a3a4295ef/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.3 - eps_step: 1 - max_iter: 100 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: ca8552b46511a42c6ea0063bd2cacc38 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/adv_predictions.json - adv_probabilities_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/adv_probabilities.json - attack_file: output/attacks/4dd66410e32eb3409ca8d429dc0c2ca2.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/params.yaml - predictions_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/predictions.json - probabilities_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/probabilities.json - score_dict_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/score_dict.json - test_labels_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/test_labels.json - train_labels_file: output/reports/attack/49568c81416f48f42e29e47a3a4295ef/train_labels.json - model_dir: models - name: 49568c81416f48f42e29e47a3a4295ef - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 49568c81416f48f42e29e47a3a4295ef -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/params.yaml b/examples/security/classification/output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/params.yaml deleted file mode 100644 index 5cf3c8f1..00000000 --- a/examples/security/classification/output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.01 - eps_step: 0.5 - max_iter: 1 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 926961aeecf1d3ea072210e900d62505 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/adv_predictions.json - adv_probabilities_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/adv_probabilities.json - attack_file: output/attacks/5977b859c4307dd02b904b87c2af2b43.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/params.yaml - predictions_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/predictions.json - probabilities_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/probabilities.json - score_dict_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/score_dict.json - test_labels_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/test_labels.json - train_labels_file: output/reports/attack/4b727c670ac84e5e7e31fb4b88fc18a0/train_labels.json - model_dir: models - name: 4b727c670ac84e5e7e31fb4b88fc18a0 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 4b727c670ac84e5e7e31fb4b88fc18a0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/params.yaml b/examples/security/classification/output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/params.yaml deleted file mode 100644 index d976f27b..00000000 --- a/examples/security/classification/output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.01 - eps_step: 0.5 - max_iter: 100 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 1b9ab5baae1529cdf8e6e5eb59b163c7 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/adv_predictions.json - adv_probabilities_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/adv_probabilities.json - attack_file: output/attacks/7f9e25a70d7c5e132f0f07705b507de3.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/params.yaml - predictions_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/predictions.json - probabilities_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/probabilities.json - score_dict_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/score_dict.json - test_labels_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/test_labels.json - train_labels_file: output/reports/attack/4c5aabb5448357744bbed60c3ece4fd6/train_labels.json - model_dir: models - name: 4c5aabb5448357744bbed60c3ece4fd6 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 4c5aabb5448357744bbed60c3ece4fd6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/params.yaml b/examples/security/classification/output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/params.yaml deleted file mode 100644 index 71bb6455..00000000 --- a/examples/security/classification/output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.3 - eps_step: 0.1 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 08be4209293859526bf26bb631cd3052 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/adv_predictions.json - adv_probabilities_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/adv_probabilities.json - attack_file: output/attacks/519edd5db419e1756c387a967b9e01c6.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/params.yaml - predictions_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/predictions.json - probabilities_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/probabilities.json - score_dict_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/score_dict.json - test_labels_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/test_labels.json - train_labels_file: output/reports/attack/4ce1e69ec2ec2df5f3d7458246c1e38e/train_labels.json - model_dir: models - name: 4ce1e69ec2ec2df5f3d7458246c1e38e - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 4ce1e69ec2ec2df5f3d7458246c1e38e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/params.yaml b/examples/security/classification/output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/params.yaml deleted file mode 100644 index e0889827..00000000 --- a/examples/security/classification/output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.5 - eps_step: 0.3 - max_iter: 1000 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 55545b33d6ac6e2994a43e8b5a9c5e48 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/adv_predictions.json - adv_probabilities_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/adv_probabilities.json - attack_file: output/attacks/6ed39cfdb81030ef37fc5049f7edbd9c.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/params.yaml - predictions_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/predictions.json - probabilities_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/probabilities.json - score_dict_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/score_dict.json - test_labels_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/test_labels.json - train_labels_file: output/reports/attack/4fb90a14df596f581a170fa6fbe03f54/train_labels.json - model_dir: models - name: 4fb90a14df596f581a170fa6fbe03f54 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 4fb90a14df596f581a170fa6fbe03f54 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/params.yaml b/examples/security/classification/output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/params.yaml deleted file mode 100644 index 31fc83e9..00000000 --- a/examples/security/classification/output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 1 - eps_step: 0.3 - max_iter: 1 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: a98a15974a5391e7aac1d1d7bb19d83a -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/adv_predictions.json - adv_probabilities_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/adv_probabilities.json - attack_file: output/attacks/7409e714128e1dc3111a076fe1226994.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/params.yaml - predictions_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/predictions.json - probabilities_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/probabilities.json - score_dict_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/score_dict.json - test_labels_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/test_labels.json - train_labels_file: output/reports/attack/5125babc339b2b55a4a997a30aeb5f7d/train_labels.json - model_dir: models - name: 5125babc339b2b55a4a997a30aeb5f7d - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 5125babc339b2b55a4a997a30aeb5f7d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/54373cbda6df703a7bbd92d496bad236/params.yaml b/examples/security/classification/output/reports/attack/54373cbda6df703a7bbd92d496bad236/params.yaml deleted file mode 100644 index 100dfd17..00000000 --- a/examples/security/classification/output/reports/attack/54373cbda6df703a7bbd92d496bad236/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.1 - eps_step: 0.01 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 927b7d2025d4afd02f28ea528afdabe5 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/adv_predictions.json - adv_probabilities_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/adv_probabilities.json - attack_file: output/attacks/62545688420782143047b4ab7c8d7dee.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/params.yaml - predictions_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/predictions.json - probabilities_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/probabilities.json - score_dict_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/score_dict.json - test_labels_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/test_labels.json - train_labels_file: output/reports/attack/54373cbda6df703a7bbd92d496bad236/train_labels.json - model_dir: models - name: 54373cbda6df703a7bbd92d496bad236 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 54373cbda6df703a7bbd92d496bad236 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/58ae06ebc35f26c157bac01373d365ab/params.yaml b/examples/security/classification/output/reports/attack/58ae06ebc35f26c157bac01373d365ab/params.yaml deleted file mode 100644 index 1ddd4e8b..00000000 --- a/examples/security/classification/output/reports/attack/58ae06ebc35f26c157bac01373d365ab/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.001 - eps_step: 0.001 - max_iter: 100 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 3ed8f172c1ffc23c7785e33d047c46f8 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/adv_predictions.json - adv_probabilities_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/adv_probabilities.json - attack_file: output/attacks/5cae022a5f76b0b6c78b71021215913d.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/params.yaml - predictions_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/predictions.json - probabilities_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/probabilities.json - score_dict_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/score_dict.json - test_labels_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/test_labels.json - train_labels_file: output/reports/attack/58ae06ebc35f26c157bac01373d365ab/train_labels.json - model_dir: models - name: 58ae06ebc35f26c157bac01373d365ab - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 58ae06ebc35f26c157bac01373d365ab -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/58f3502acbdf096a0606ac8e3d264122/params.yaml b/examples/security/classification/output/reports/attack/58f3502acbdf096a0606ac8e3d264122/params.yaml deleted file mode 100644 index ec67d53b..00000000 --- a/examples/security/classification/output/reports/attack/58f3502acbdf096a0606ac8e3d264122/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 1 - eps_step: 0.5 - max_iter: 10 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 9642bc90d44e9bef34cf2e236c74a39f -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/adv_predictions.json - adv_probabilities_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/adv_probabilities.json - attack_file: output/attacks/5df27b5dacdeb54d9beefcd23afb6819.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/params.yaml - predictions_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/predictions.json - probabilities_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/probabilities.json - score_dict_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/score_dict.json - test_labels_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/test_labels.json - train_labels_file: output/reports/attack/58f3502acbdf096a0606ac8e3d264122/train_labels.json - model_dir: models - name: 58f3502acbdf096a0606ac8e3d264122 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 58f3502acbdf096a0606ac8e3d264122 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/params.yaml b/examples/security/classification/output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/params.yaml deleted file mode 100644 index e97ea8aa..00000000 --- a/examples/security/classification/output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.01 - eps_step: 0.01 - max_iter: 100 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: deedc83fd69c492eef98fa89f671d37d -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/adv_predictions.json - adv_probabilities_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/adv_probabilities.json - attack_file: output/attacks/1ae65e5906fd221835d94f864880d325.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/params.yaml - predictions_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/predictions.json - probabilities_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/probabilities.json - score_dict_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/score_dict.json - test_labels_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/test_labels.json - train_labels_file: output/reports/attack/5a0fa0e337eba06ce1764286fcc12ef0/train_labels.json - model_dir: models - name: 5a0fa0e337eba06ce1764286fcc12ef0 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 5a0fa0e337eba06ce1764286fcc12ef0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/params.yaml b/examples/security/classification/output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/params.yaml deleted file mode 100644 index fd6fa2a3..00000000 --- a/examples/security/classification/output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.1 - eps_step: 0.001 - max_iter: 100 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 210857021ebcc10f933284804aa1b44d -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/adv_predictions.json - adv_probabilities_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/adv_probabilities.json - attack_file: output/attacks/611b089dd9172792a94bef28e888f5a3.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/params.yaml - predictions_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/predictions.json - probabilities_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/probabilities.json - score_dict_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/score_dict.json - test_labels_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/test_labels.json - train_labels_file: output/reports/attack/5d946b787415af772eaf5bd2c1a2269f/train_labels.json - model_dir: models - name: 5d946b787415af772eaf5bd2c1a2269f - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 5d946b787415af772eaf5bd2c1a2269f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/params.yaml b/examples/security/classification/output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/params.yaml deleted file mode 100644 index f244734f..00000000 --- a/examples/security/classification/output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.5 - eps_step: 0.001 - max_iter: 1000 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 47b871c4ad24779b9f5c51b61a8bcace -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/adv_predictions.json - adv_probabilities_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/adv_probabilities.json - attack_file: output/attacks/fc3dc5f6546493f3721e09a9a141bdc3.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/params.yaml - predictions_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/predictions.json - probabilities_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/probabilities.json - score_dict_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/score_dict.json - test_labels_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/test_labels.json - train_labels_file: output/reports/attack/60e86854e9773ca0ca4180010d4b1e5f/train_labels.json - model_dir: models - name: 60e86854e9773ca0ca4180010d4b1e5f - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 60e86854e9773ca0ca4180010d4b1e5f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/params.yaml b/examples/security/classification/output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/params.yaml deleted file mode 100644 index fb2cdc1b..00000000 --- a/examples/security/classification/output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 1 - eps_step: 0.1 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 96aaedc7c33cb5f04c85adfac613aa9c -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/adv_predictions.json - adv_probabilities_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/adv_probabilities.json - attack_file: output/attacks/0c0e190fbca487faae269ad51bd858fd.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/params.yaml - predictions_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/predictions.json - probabilities_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/probabilities.json - score_dict_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/score_dict.json - test_labels_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/test_labels.json - train_labels_file: output/reports/attack/6200b3eb8c52dbd93b42de2c53d49d66/train_labels.json - model_dir: models - name: 6200b3eb8c52dbd93b42de2c53d49d66 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 6200b3eb8c52dbd93b42de2c53d49d66 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/65789ef42fab964d5d1f4b522c842307/params.yaml b/examples/security/classification/output/reports/attack/65789ef42fab964d5d1f4b522c842307/params.yaml deleted file mode 100644 index c406a7fe..00000000 --- a/examples/security/classification/output/reports/attack/65789ef42fab964d5d1f4b522c842307/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.5 - eps_step: 0.5 - max_iter: 1000 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 9ead075d9a95cc48fc37f085db586280 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/adv_predictions.json - adv_probabilities_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/adv_probabilities.json - attack_file: output/attacks/cdddc3440fd9dfd79fee720927274703.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/params.yaml - predictions_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/predictions.json - probabilities_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/probabilities.json - score_dict_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/score_dict.json - test_labels_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/test_labels.json - train_labels_file: output/reports/attack/65789ef42fab964d5d1f4b522c842307/train_labels.json - model_dir: models - name: 65789ef42fab964d5d1f4b522c842307 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 65789ef42fab964d5d1f4b522c842307 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/66778716c19365040a3c5661875c9384/params.yaml b/examples/security/classification/output/reports/attack/66778716c19365040a3c5661875c9384/params.yaml deleted file mode 100644 index 42992ff8..00000000 --- a/examples/security/classification/output/reports/attack/66778716c19365040a3c5661875c9384/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.001 - eps_step: 0.001 - max_iter: 10 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 8ee820e67bf0d80a5e556550bea25cfd -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/66778716c19365040a3c5661875c9384/adv_predictions.json - adv_probabilities_file: output/reports/attack/66778716c19365040a3c5661875c9384/adv_probabilities.json - attack_file: output/attacks/5088cd82720ef34e6c6e78041fa0d1e4.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/66778716c19365040a3c5661875c9384/params.yaml - predictions_file: output/reports/attack/66778716c19365040a3c5661875c9384/predictions.json - probabilities_file: output/reports/attack/66778716c19365040a3c5661875c9384/probabilities.json - score_dict_file: output/reports/attack/66778716c19365040a3c5661875c9384/score_dict.json - test_labels_file: output/reports/attack/66778716c19365040a3c5661875c9384/test_labels.json - train_labels_file: output/reports/attack/66778716c19365040a3c5661875c9384/train_labels.json - model_dir: models - name: 66778716c19365040a3c5661875c9384 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 66778716c19365040a3c5661875c9384 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/66ec144d4307a9a702145e83a16b92bd/params.yaml b/examples/security/classification/output/reports/attack/66ec144d4307a9a702145e83a16b92bd/params.yaml deleted file mode 100644 index 7ff6f306..00000000 --- a/examples/security/classification/output/reports/attack/66ec144d4307a9a702145e83a16b92bd/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.1 - eps_step: 0.1 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: eab6f9ab7e94cb67a54dce6f22f529e9 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/adv_predictions.json - adv_probabilities_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/adv_probabilities.json - attack_file: output/attacks/95b7cff21924f1b9d4fccd6d9d608292.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/params.yaml - predictions_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/predictions.json - probabilities_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/probabilities.json - score_dict_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/score_dict.json - test_labels_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/test_labels.json - train_labels_file: output/reports/attack/66ec144d4307a9a702145e83a16b92bd/train_labels.json - model_dir: models - name: 66ec144d4307a9a702145e83a16b92bd - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 66ec144d4307a9a702145e83a16b92bd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/68079f2892b8606cc3451033e13878aa/params.yaml b/examples/security/classification/output/reports/attack/68079f2892b8606cc3451033e13878aa/params.yaml deleted file mode 100644 index 9f38e840..00000000 --- a/examples/security/classification/output/reports/attack/68079f2892b8606cc3451033e13878aa/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.1 - eps_step: 0.5 - max_iter: 10 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: eed6d9b1ba45e38c749ac63408552e93 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/adv_predictions.json - adv_probabilities_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/adv_probabilities.json - attack_file: output/attacks/9476ffebd526259dccb15c7e7261550e.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/params.yaml - predictions_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/predictions.json - probabilities_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/probabilities.json - score_dict_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/score_dict.json - test_labels_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/test_labels.json - train_labels_file: output/reports/attack/68079f2892b8606cc3451033e13878aa/train_labels.json - model_dir: models - name: 68079f2892b8606cc3451033e13878aa - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 68079f2892b8606cc3451033e13878aa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/68ac7e04e44143769c116c6989a829f6/params.yaml b/examples/security/classification/output/reports/attack/68ac7e04e44143769c116c6989a829f6/params.yaml deleted file mode 100644 index 82989790..00000000 --- a/examples/security/classification/output/reports/attack/68ac7e04e44143769c116c6989a829f6/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.5 - eps_step: 0.001 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: a44ddb9d69de3905002c42b7b85285a3 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/adv_predictions.json - adv_probabilities_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/adv_probabilities.json - attack_file: output/attacks/78e52f4a6c167fdbef97cbf2640c848a.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/params.yaml - predictions_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/predictions.json - probabilities_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/probabilities.json - score_dict_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/score_dict.json - test_labels_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/test_labels.json - train_labels_file: output/reports/attack/68ac7e04e44143769c116c6989a829f6/train_labels.json - model_dir: models - name: 68ac7e04e44143769c116c6989a829f6 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 68ac7e04e44143769c116c6989a829f6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/params.yaml b/examples/security/classification/output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/params.yaml deleted file mode 100644 index 8204fb49..00000000 --- a/examples/security/classification/output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 1 - eps_step: 0.5 - max_iter: 10 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: bfc645599bb1ff75d05caf2b8771762d -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/adv_predictions.json - adv_probabilities_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/adv_probabilities.json - attack_file: output/attacks/f5afb5bfffbaf3c232f3c69279b5d8bd.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/params.yaml - predictions_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/predictions.json - probabilities_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/probabilities.json - score_dict_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/score_dict.json - test_labels_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/test_labels.json - train_labels_file: output/reports/attack/6de2ee4c7c4d542bd6e00156f49024a6/train_labels.json - model_dir: models - name: 6de2ee4c7c4d542bd6e00156f49024a6 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 6de2ee4c7c4d542bd6e00156f49024a6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/7412d66f284793d574d73a7c93853353/params.yaml b/examples/security/classification/output/reports/attack/7412d66f284793d574d73a7c93853353/params.yaml deleted file mode 100644 index 5005720c..00000000 --- a/examples/security/classification/output/reports/attack/7412d66f284793d574d73a7c93853353/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.01 - eps_step: 0.001 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: bc50e98b2c96fd8a38e01bbefdd40ceb -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/7412d66f284793d574d73a7c93853353/adv_predictions.json - adv_probabilities_file: output/reports/attack/7412d66f284793d574d73a7c93853353/adv_probabilities.json - attack_file: output/attacks/293cea47f185f12bcd1c23f0eb3fdc7f.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/7412d66f284793d574d73a7c93853353/params.yaml - predictions_file: output/reports/attack/7412d66f284793d574d73a7c93853353/predictions.json - probabilities_file: output/reports/attack/7412d66f284793d574d73a7c93853353/probabilities.json - score_dict_file: output/reports/attack/7412d66f284793d574d73a7c93853353/score_dict.json - test_labels_file: output/reports/attack/7412d66f284793d574d73a7c93853353/test_labels.json - train_labels_file: output/reports/attack/7412d66f284793d574d73a7c93853353/train_labels.json - model_dir: models - name: 7412d66f284793d574d73a7c93853353 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: 7412d66f284793d574d73a7c93853353 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/8069811604c366444e374c8a5fea3227/params.yaml b/examples/security/classification/output/reports/attack/8069811604c366444e374c8a5fea3227/params.yaml deleted file mode 100644 index ebcbbbd3..00000000 --- a/examples/security/classification/output/reports/attack/8069811604c366444e374c8a5fea3227/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 1 - eps_step: 0.5 - max_iter: 1 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: a24e4c3bf1b721aaa6573c0551f7a318 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/8069811604c366444e374c8a5fea3227/adv_predictions.json - adv_probabilities_file: output/reports/attack/8069811604c366444e374c8a5fea3227/adv_probabilities.json - attack_file: output/attacks/22935140b76f01c109ba35cb4a1f4011.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/8069811604c366444e374c8a5fea3227/params.yaml - predictions_file: output/reports/attack/8069811604c366444e374c8a5fea3227/predictions.json - probabilities_file: output/reports/attack/8069811604c366444e374c8a5fea3227/probabilities.json - score_dict_file: output/reports/attack/8069811604c366444e374c8a5fea3227/score_dict.json - test_labels_file: output/reports/attack/8069811604c366444e374c8a5fea3227/test_labels.json - train_labels_file: output/reports/attack/8069811604c366444e374c8a5fea3227/train_labels.json - model_dir: models - name: 8069811604c366444e374c8a5fea3227 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 8069811604c366444e374c8a5fea3227 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/84a6c965b79c86855b822d7dddba2693/params.yaml b/examples/security/classification/output/reports/attack/84a6c965b79c86855b822d7dddba2693/params.yaml deleted file mode 100644 index fdd5a97d..00000000 --- a/examples/security/classification/output/reports/attack/84a6c965b79c86855b822d7dddba2693/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.3 - eps_step: 0.01 - max_iter: 1 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 2d235690b32db725ba0da28fff809038 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/adv_predictions.json - adv_probabilities_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/adv_probabilities.json - attack_file: output/attacks/5016185a9530e81c5b77406f17ec2fe7.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/params.yaml - predictions_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/predictions.json - probabilities_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/probabilities.json - score_dict_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/score_dict.json - test_labels_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/test_labels.json - train_labels_file: output/reports/attack/84a6c965b79c86855b822d7dddba2693/train_labels.json - model_dir: models - name: 84a6c965b79c86855b822d7dddba2693 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 84a6c965b79c86855b822d7dddba2693 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/889abbbd16cd836932303c02b0033666/params.yaml b/examples/security/classification/output/reports/attack/889abbbd16cd836932303c02b0033666/params.yaml deleted file mode 100644 index 5ad0a189..00000000 --- a/examples/security/classification/output/reports/attack/889abbbd16cd836932303c02b0033666/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.3 - eps_step: 1 - max_iter: 1 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 47f23b1abb623d944891ae7947623acc -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/889abbbd16cd836932303c02b0033666/adv_predictions.json - adv_probabilities_file: output/reports/attack/889abbbd16cd836932303c02b0033666/adv_probabilities.json - attack_file: output/attacks/509111db4c9b75851b65533265a9c8c3.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/889abbbd16cd836932303c02b0033666/params.yaml - predictions_file: output/reports/attack/889abbbd16cd836932303c02b0033666/predictions.json - probabilities_file: output/reports/attack/889abbbd16cd836932303c02b0033666/probabilities.json - score_dict_file: output/reports/attack/889abbbd16cd836932303c02b0033666/score_dict.json - test_labels_file: output/reports/attack/889abbbd16cd836932303c02b0033666/test_labels.json - train_labels_file: output/reports/attack/889abbbd16cd836932303c02b0033666/train_labels.json - model_dir: models - name: 889abbbd16cd836932303c02b0033666 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 889abbbd16cd836932303c02b0033666 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/params.yaml b/examples/security/classification/output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/params.yaml deleted file mode 100644 index 31970a50..00000000 --- a/examples/security/classification/output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 1 - eps_step: 0.3 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 97e175a6a542c4f4258404e6898b0495 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/adv_predictions.json - adv_probabilities_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/adv_probabilities.json - attack_file: output/attacks/74f4763d9916645a1e7caff6cb8efa8b.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/params.yaml - predictions_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/predictions.json - probabilities_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/probabilities.json - score_dict_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/score_dict.json - test_labels_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/test_labels.json - train_labels_file: output/reports/attack/8cc99206eaab3140d9f6e7e6cdd31859/train_labels.json - model_dir: models - name: 8cc99206eaab3140d9f6e7e6cdd31859 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 8cc99206eaab3140d9f6e7e6cdd31859 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/params.yaml b/examples/security/classification/output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/params.yaml deleted file mode 100644 index b39441cb..00000000 --- a/examples/security/classification/output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.3 - eps_step: 0.3 - max_iter: 1 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: fa303fd1a3c4c0090c1aacda08f90ee3 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/adv_predictions.json - adv_probabilities_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/adv_probabilities.json - attack_file: output/attacks/281f4e847cc2f38a93067bb0b337f62b.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/params.yaml - predictions_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/predictions.json - probabilities_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/probabilities.json - score_dict_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/score_dict.json - test_labels_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/test_labels.json - train_labels_file: output/reports/attack/92d6e5c964485a2cde78f204c83e7d31/train_labels.json - model_dir: models - name: 92d6e5c964485a2cde78f204c83e7d31 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 92d6e5c964485a2cde78f204c83e7d31 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/949c89b39bb29cbbb87650da48c577c1/params.yaml b/examples/security/classification/output/reports/attack/949c89b39bb29cbbb87650da48c577c1/params.yaml deleted file mode 100644 index aacab083..00000000 --- a/examples/security/classification/output/reports/attack/949c89b39bb29cbbb87650da48c577c1/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.01 - eps_step: 0.1 - max_iter: 1 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 6058fe3daee844c2e454cf75f17be062 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/adv_predictions.json - adv_probabilities_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/adv_probabilities.json - attack_file: output/attacks/5e2964cfbba51090971decbd93631c29.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/params.yaml - predictions_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/predictions.json - probabilities_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/probabilities.json - score_dict_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/score_dict.json - test_labels_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/test_labels.json - train_labels_file: output/reports/attack/949c89b39bb29cbbb87650da48c577c1/train_labels.json - model_dir: models - name: 949c89b39bb29cbbb87650da48c577c1 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: 949c89b39bb29cbbb87650da48c577c1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/params.yaml b/examples/security/classification/output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/params.yaml deleted file mode 100644 index b3d19d07..00000000 --- a/examples/security/classification/output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 1 - eps_step: 0.1 - max_iter: 1000 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 184c99a34218e996b8b9906cd44978d0 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/adv_predictions.json - adv_probabilities_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/adv_probabilities.json - attack_file: output/attacks/3fa9b85c35b5b110ce083d6cfd7ed83c.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/params.yaml - predictions_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/predictions.json - probabilities_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/probabilities.json - score_dict_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/score_dict.json - test_labels_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/test_labels.json - train_labels_file: output/reports/attack/95ab58b1f03230de7b5978581e63c4b9/train_labels.json - model_dir: models - name: 95ab58b1f03230de7b5978581e63c4b9 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 95ab58b1f03230de7b5978581e63c4b9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/params.yaml b/examples/security/classification/output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/params.yaml deleted file mode 100644 index f3dafe8d..00000000 --- a/examples/security/classification/output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.001 - eps_step: 0.3 - max_iter: 1 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 703892d07fbbb0ace9a0951239a2bbdf -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/adv_predictions.json - adv_probabilities_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/adv_probabilities.json - attack_file: output/attacks/b83295cdd0fd9ae704a9500c9abdc831.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/params.yaml - predictions_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/predictions.json - probabilities_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/probabilities.json - score_dict_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/score_dict.json - test_labels_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/test_labels.json - train_labels_file: output/reports/attack/9832b1f47d43d263ed23664ebf3ee5eb/train_labels.json - model_dir: models - name: 9832b1f47d43d263ed23664ebf3ee5eb - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: 9832b1f47d43d263ed23664ebf3ee5eb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/a02603189d68a31a1e95098e4abd3cca/params.yaml b/examples/security/classification/output/reports/attack/a02603189d68a31a1e95098e4abd3cca/params.yaml deleted file mode 100644 index 1420f2e8..00000000 --- a/examples/security/classification/output/reports/attack/a02603189d68a31a1e95098e4abd3cca/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.1 - eps_step: 0.001 - max_iter: 10 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: b31f442a86aad0da8d29da829ab31098 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/adv_predictions.json - adv_probabilities_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/adv_probabilities.json - attack_file: output/attacks/d0b76872fbfeeab26b2e8c3a750c7287.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/params.yaml - predictions_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/predictions.json - probabilities_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/probabilities.json - score_dict_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/score_dict.json - test_labels_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/test_labels.json - train_labels_file: output/reports/attack/a02603189d68a31a1e95098e4abd3cca/train_labels.json - model_dir: models - name: a02603189d68a31a1e95098e4abd3cca - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: a02603189d68a31a1e95098e4abd3cca -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/params.yaml b/examples/security/classification/output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/params.yaml deleted file mode 100644 index c137c2cf..00000000 --- a/examples/security/classification/output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.1 - eps_step: 0.1 - max_iter: 10 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 7d2f1480dfb249baedfe4b7d6b435014 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/adv_predictions.json - adv_probabilities_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/adv_probabilities.json - attack_file: output/attacks/670c3ff351829ae34f21a64961161d3f.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/params.yaml - predictions_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/predictions.json - probabilities_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/probabilities.json - score_dict_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/score_dict.json - test_labels_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/test_labels.json - train_labels_file: output/reports/attack/a0e744ac00fed019ee24d862d1a8c629/train_labels.json - model_dir: models - name: a0e744ac00fed019ee24d862d1a8c629 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: a0e744ac00fed019ee24d862d1a8c629 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/params.yaml b/examples/security/classification/output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/params.yaml deleted file mode 100644 index 446b00ca..00000000 --- a/examples/security/classification/output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.01 - eps_step: 0.3 - max_iter: 1 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: ffab9fc382d672759ee9451bb91a61a4 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/adv_predictions.json - adv_probabilities_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/adv_probabilities.json - attack_file: output/attacks/ea12fcc688eb964dda99d4a5e6caba81.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/params.yaml - predictions_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/predictions.json - probabilities_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/probabilities.json - score_dict_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/score_dict.json - test_labels_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/test_labels.json - train_labels_file: output/reports/attack/a2f91a6f21c4f4ccc56264a00f481d89/train_labels.json - model_dir: models - name: a2f91a6f21c4f4ccc56264a00f481d89 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: a2f91a6f21c4f4ccc56264a00f481d89 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/params.yaml b/examples/security/classification/output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/params.yaml deleted file mode 100644 index 134125d5..00000000 --- a/examples/security/classification/output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.3 - eps_step: 0.01 - max_iter: 100 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 6f05150cbc56fa307f098a5a67b9a71d -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/adv_predictions.json - adv_probabilities_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/adv_probabilities.json - attack_file: output/attacks/58af1859c5bef58532e70223314ab69c.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/params.yaml - predictions_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/predictions.json - probabilities_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/probabilities.json - score_dict_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/score_dict.json - test_labels_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/test_labels.json - train_labels_file: output/reports/attack/a49a6ffd24ce8950bdf7f1a9e940fb12/train_labels.json - model_dir: models - name: a49a6ffd24ce8950bdf7f1a9e940fb12 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: a49a6ffd24ce8950bdf7f1a9e940fb12 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/params.yaml b/examples/security/classification/output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/params.yaml deleted file mode 100644 index 6ccae740..00000000 --- a/examples/security/classification/output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.3 - eps_step: 0.5 - max_iter: 10 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 3fe34b691cb27d7840c530c9e6d54dd1 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/adv_predictions.json - adv_probabilities_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/adv_probabilities.json - attack_file: output/attacks/d8f8162fa2d39af956b0d939a71d7706.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/params.yaml - predictions_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/predictions.json - probabilities_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/probabilities.json - score_dict_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/score_dict.json - test_labels_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/test_labels.json - train_labels_file: output/reports/attack/a66b2ffb3d703b399cb1d012ab167d36/train_labels.json - model_dir: models - name: a66b2ffb3d703b399cb1d012ab167d36 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: a66b2ffb3d703b399cb1d012ab167d36 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/a6f2e63544f4039fef7d33345209f171/params.yaml b/examples/security/classification/output/reports/attack/a6f2e63544f4039fef7d33345209f171/params.yaml deleted file mode 100644 index 6a8b65c2..00000000 --- a/examples/security/classification/output/reports/attack/a6f2e63544f4039fef7d33345209f171/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 1 - eps_step: 0.1 - max_iter: 100 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 8530fb8620d8c33d2da88c2b6f914191 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/adv_predictions.json - adv_probabilities_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/adv_probabilities.json - attack_file: output/attacks/7f1d1a0d6c0a3f99b9c1ecced72c1587.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/params.yaml - predictions_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/predictions.json - probabilities_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/probabilities.json - score_dict_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/score_dict.json - test_labels_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/test_labels.json - train_labels_file: output/reports/attack/a6f2e63544f4039fef7d33345209f171/train_labels.json - model_dir: models - name: a6f2e63544f4039fef7d33345209f171 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: a6f2e63544f4039fef7d33345209f171 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/params.yaml b/examples/security/classification/output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/params.yaml deleted file mode 100644 index 5d770e8a..00000000 --- a/examples/security/classification/output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.3 - eps_step: 0.01 - max_iter: 10 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 7d96e6be3acec53dfef02cc8a2dd86e2 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/adv_predictions.json - adv_probabilities_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/adv_probabilities.json - attack_file: output/attacks/27727e6e87a2957b3c4c3fc3da85b480.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/params.yaml - predictions_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/predictions.json - probabilities_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/probabilities.json - score_dict_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/score_dict.json - test_labels_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/test_labels.json - train_labels_file: output/reports/attack/a98844e1bd450ddc9131e9c862242fb3/train_labels.json - model_dir: models - name: a98844e1bd450ddc9131e9c862242fb3 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: a98844e1bd450ddc9131e9c862242fb3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/params.yaml b/examples/security/classification/output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/params.yaml deleted file mode 100644 index 56d07227..00000000 --- a/examples/security/classification/output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.5 - eps_step: 0.01 - max_iter: 1000 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: b1bf52dbeee53a9f1421ff7c54d2b123 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/adv_predictions.json - adv_probabilities_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/adv_probabilities.json - attack_file: output/attacks/43ee52a9e4424269138d4ed7258de40e.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/params.yaml - predictions_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/predictions.json - probabilities_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/probabilities.json - score_dict_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/score_dict.json - test_labels_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/test_labels.json - train_labels_file: output/reports/attack/aa5063f3f3c57711a57af27c5a0b4c32/train_labels.json - model_dir: models - name: aa5063f3f3c57711a57af27c5a0b4c32 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: aa5063f3f3c57711a57af27c5a0b4c32 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/abc1a630c8d7055b732170695ef77168/params.yaml b/examples/security/classification/output/reports/attack/abc1a630c8d7055b732170695ef77168/params.yaml deleted file mode 100644 index d3249fbb..00000000 --- a/examples/security/classification/output/reports/attack/abc1a630c8d7055b732170695ef77168/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 1 - eps_step: 0.001 - max_iter: 1000 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: bb2ed9272cfce72db44b199041537486 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/adv_predictions.json - adv_probabilities_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/adv_probabilities.json - attack_file: output/attacks/e5a42d82c7d5d10794fe2680538ae016.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/params.yaml - predictions_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/predictions.json - probabilities_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/probabilities.json - score_dict_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/score_dict.json - test_labels_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/test_labels.json - train_labels_file: output/reports/attack/abc1a630c8d7055b732170695ef77168/train_labels.json - model_dir: models - name: abc1a630c8d7055b732170695ef77168 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: abc1a630c8d7055b732170695ef77168 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/params.yaml b/examples/security/classification/output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/params.yaml deleted file mode 100644 index 996f219b..00000000 --- a/examples/security/classification/output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.3 - eps_step: 0.01 - max_iter: 1000 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: bf2373223c67bf0db20206f61218457e -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/adv_predictions.json - adv_probabilities_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/adv_probabilities.json - attack_file: output/attacks/71677d4447e49ef57a4aaf8735f2b88a.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/params.yaml - predictions_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/predictions.json - probabilities_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/probabilities.json - score_dict_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/score_dict.json - test_labels_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/test_labels.json - train_labels_file: output/reports/attack/ac1f19994da7ed0f5580e1e2e49ef91a/train_labels.json - model_dir: models - name: ac1f19994da7ed0f5580e1e2e49ef91a - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: ac1f19994da7ed0f5580e1e2e49ef91a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/params.yaml b/examples/security/classification/output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/params.yaml deleted file mode 100644 index 706eb828..00000000 --- a/examples/security/classification/output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.001 - eps_step: 0.001 - max_iter: 1 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: f312716c1b96333aa9e82133d3c9dc3f -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/adv_predictions.json - adv_probabilities_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/adv_probabilities.json - attack_file: output/attacks/50e84aacf02abe678d990702a3b267d8.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/params.yaml - predictions_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/predictions.json - probabilities_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/probabilities.json - score_dict_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/score_dict.json - test_labels_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/test_labels.json - train_labels_file: output/reports/attack/b18b3b59888dd75c351d61436a55a6a5/train_labels.json - model_dir: models - name: b18b3b59888dd75c351d61436a55a6a5 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: b18b3b59888dd75c351d61436a55a6a5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/b52301d69894bf716f00e081a5a824db/params.yaml b/examples/security/classification/output/reports/attack/b52301d69894bf716f00e081a5a824db/params.yaml deleted file mode 100644 index 54faf1a4..00000000 --- a/examples/security/classification/output/reports/attack/b52301d69894bf716f00e081a5a824db/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.01 - eps_step: 0.3 - max_iter: 1000 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: d9661a8e48a627de0627b0cefc662cfa -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/adv_predictions.json - adv_probabilities_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/adv_probabilities.json - attack_file: output/attacks/45177b30597b7f692d32dc71350c8572.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/params.yaml - predictions_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/predictions.json - probabilities_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/probabilities.json - score_dict_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/score_dict.json - test_labels_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/test_labels.json - train_labels_file: output/reports/attack/b52301d69894bf716f00e081a5a824db/train_labels.json - model_dir: models - name: b52301d69894bf716f00e081a5a824db - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: b52301d69894bf716f00e081a5a824db -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/params.yaml b/examples/security/classification/output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/params.yaml deleted file mode 100644 index a56939f9..00000000 --- a/examples/security/classification/output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.01 - eps_step: 0.3 - max_iter: 1 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: d4c3c2c7f6cfe40bf0d1545ce9a08f6b -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/adv_predictions.json - adv_probabilities_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/adv_probabilities.json - attack_file: output/attacks/c0474a20834597090a9dc368ea22db12.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/params.yaml - predictions_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/predictions.json - probabilities_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/probabilities.json - score_dict_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/score_dict.json - test_labels_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/test_labels.json - train_labels_file: output/reports/attack/b5b8f6ecc5195c2571295b2ddb5c88c1/train_labels.json - model_dir: models - name: b5b8f6ecc5195c2571295b2ddb5c88c1 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: b5b8f6ecc5195c2571295b2ddb5c88c1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/params.yaml b/examples/security/classification/output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/params.yaml deleted file mode 100644 index fa203edf..00000000 --- a/examples/security/classification/output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.01 - eps_step: 1 - max_iter: 100 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: c32ce349c2746e0bc6e88e6eecbca055 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/adv_predictions.json - adv_probabilities_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/adv_probabilities.json - attack_file: output/attacks/2a75d11854438fbde78bf82dbe56c530.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/params.yaml - predictions_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/predictions.json - probabilities_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/probabilities.json - score_dict_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/score_dict.json - test_labels_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/test_labels.json - train_labels_file: output/reports/attack/b5cf3d0fef008d99f856ba99fb5c10b0/train_labels.json - model_dir: models - name: b5cf3d0fef008d99f856ba99fb5c10b0 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: b5cf3d0fef008d99f856ba99fb5c10b0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/b697176935d5caaecdc9123084f0efbd/params.yaml b/examples/security/classification/output/reports/attack/b697176935d5caaecdc9123084f0efbd/params.yaml deleted file mode 100644 index a0c7ce8f..00000000 --- a/examples/security/classification/output/reports/attack/b697176935d5caaecdc9123084f0efbd/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 1 - eps_step: 1 - max_iter: 1 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 9efa6eec98f841bee110e72d147e25a8 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/adv_predictions.json - adv_probabilities_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/adv_probabilities.json - attack_file: output/attacks/2fdbe53885ed551a348e2d707a699448.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/params.yaml - predictions_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/predictions.json - probabilities_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/probabilities.json - score_dict_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/score_dict.json - test_labels_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/test_labels.json - train_labels_file: output/reports/attack/b697176935d5caaecdc9123084f0efbd/train_labels.json - model_dir: models - name: b697176935d5caaecdc9123084f0efbd - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: b697176935d5caaecdc9123084f0efbd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/params.yaml b/examples/security/classification/output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/params.yaml deleted file mode 100644 index 5a5e3249..00000000 --- a/examples/security/classification/output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 1 - eps_step: 0.5 - max_iter: 1 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 95d97a653998836885d11ece958e24d1 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/adv_predictions.json - adv_probabilities_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/adv_probabilities.json - attack_file: output/attacks/178937285fd082a4766e2db123ad0adf.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/params.yaml - predictions_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/predictions.json - probabilities_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/probabilities.json - score_dict_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/score_dict.json - test_labels_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/test_labels.json - train_labels_file: output/reports/attack/b6d921b9f67933888fc05cfaeb74a091/train_labels.json - model_dir: models - name: b6d921b9f67933888fc05cfaeb74a091 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: b6d921b9f67933888fc05cfaeb74a091 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/params.yaml b/examples/security/classification/output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/params.yaml deleted file mode 100644 index 75b77c53..00000000 --- a/examples/security/classification/output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.1 - eps_step: 1 - max_iter: 10 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 432dac4801a936fc09026dbbb90b5df8 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/adv_predictions.json - adv_probabilities_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/adv_probabilities.json - attack_file: output/attacks/a09d1b64fcabeb450942064ed850155e.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/params.yaml - predictions_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/predictions.json - probabilities_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/probabilities.json - score_dict_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/score_dict.json - test_labels_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/test_labels.json - train_labels_file: output/reports/attack/ba2dd71a65dd0cb5b310a7852273f70a/train_labels.json - model_dir: models - name: ba2dd71a65dd0cb5b310a7852273f70a - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: ba2dd71a65dd0cb5b310a7852273f70a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/bb959ad4d6313759872120214cbe7a6e/params.yaml b/examples/security/classification/output/reports/attack/bb959ad4d6313759872120214cbe7a6e/params.yaml deleted file mode 100644 index 6c8b2a25..00000000 --- a/examples/security/classification/output/reports/attack/bb959ad4d6313759872120214cbe7a6e/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.1 - eps_step: 0.5 - max_iter: 100 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: a2023bda340686331312ff0ae27dd4ea -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/adv_predictions.json - adv_probabilities_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/adv_probabilities.json - attack_file: output/attacks/4feef1230c39cda652f3617a9161b690.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/params.yaml - predictions_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/predictions.json - probabilities_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/probabilities.json - score_dict_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/score_dict.json - test_labels_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/test_labels.json - train_labels_file: output/reports/attack/bb959ad4d6313759872120214cbe7a6e/train_labels.json - model_dir: models - name: bb959ad4d6313759872120214cbe7a6e - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: bb959ad4d6313759872120214cbe7a6e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/bbb5777995a8d71f12831358700cba37/params.yaml b/examples/security/classification/output/reports/attack/bbb5777995a8d71f12831358700cba37/params.yaml deleted file mode 100644 index c9d2a4f8..00000000 --- a/examples/security/classification/output/reports/attack/bbb5777995a8d71f12831358700cba37/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.1 - eps_step: 0.5 - max_iter: 1 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: cbda2c8f09efbb72d794d3cb03b1a287 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/adv_predictions.json - adv_probabilities_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/adv_probabilities.json - attack_file: output/attacks/b737f7a57f738b60df1f3ad3d763f323.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/params.yaml - predictions_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/predictions.json - probabilities_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/probabilities.json - score_dict_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/score_dict.json - test_labels_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/test_labels.json - train_labels_file: output/reports/attack/bbb5777995a8d71f12831358700cba37/train_labels.json - model_dir: models - name: bbb5777995a8d71f12831358700cba37 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: bbb5777995a8d71f12831358700cba37 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/bbbb9f8821393a0a5915960139269aab/params.yaml b/examples/security/classification/output/reports/attack/bbbb9f8821393a0a5915960139269aab/params.yaml deleted file mode 100644 index 8017a1c7..00000000 --- a/examples/security/classification/output/reports/attack/bbbb9f8821393a0a5915960139269aab/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.001 - eps_step: 0.3 - max_iter: 1000 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 95e661cde9dcc16835c35a40cd7442e6 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/adv_predictions.json - adv_probabilities_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/adv_probabilities.json - attack_file: output/attacks/fce346eaf2d6f78609034db7f61d7033.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/params.yaml - predictions_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/predictions.json - probabilities_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/probabilities.json - score_dict_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/score_dict.json - test_labels_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/test_labels.json - train_labels_file: output/reports/attack/bbbb9f8821393a0a5915960139269aab/train_labels.json - model_dir: models - name: bbbb9f8821393a0a5915960139269aab - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: bbbb9f8821393a0a5915960139269aab -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/params.yaml b/examples/security/classification/output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/params.yaml deleted file mode 100644 index 4f1e5ddb..00000000 --- a/examples/security/classification/output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 1 - eps_step: 0.5 - max_iter: 1 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 7680233d11aa441703740f57ee2b1966 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/adv_predictions.json - adv_probabilities_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/adv_probabilities.json - attack_file: output/attacks/8e4b673a505f4c1f531d3136ac309b34.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/params.yaml - predictions_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/predictions.json - probabilities_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/probabilities.json - score_dict_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/score_dict.json - test_labels_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/test_labels.json - train_labels_file: output/reports/attack/bc0efd09cb94d5a79d8f16c503147e4f/train_labels.json - model_dir: models - name: bc0efd09cb94d5a79d8f16c503147e4f - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: bc0efd09cb94d5a79d8f16c503147e4f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/bc3d1ce32db16998772fe8079460a133/params.yaml b/examples/security/classification/output/reports/attack/bc3d1ce32db16998772fe8079460a133/params.yaml deleted file mode 100644 index b1be400e..00000000 --- a/examples/security/classification/output/reports/attack/bc3d1ce32db16998772fe8079460a133/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.001 - eps_step: 0.1 - max_iter: 100 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: aedabba049c64c1a2171000b226e931b -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/adv_predictions.json - adv_probabilities_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/adv_probabilities.json - attack_file: output/attacks/3a42325515cf726c492904fdd23117a0.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/params.yaml - predictions_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/predictions.json - probabilities_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/probabilities.json - score_dict_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/score_dict.json - test_labels_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/test_labels.json - train_labels_file: output/reports/attack/bc3d1ce32db16998772fe8079460a133/train_labels.json - model_dir: models - name: bc3d1ce32db16998772fe8079460a133 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: bc3d1ce32db16998772fe8079460a133 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/params.yaml b/examples/security/classification/output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/params.yaml deleted file mode 100644 index 1003207f..00000000 --- a/examples/security/classification/output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.1 - eps_step: 0.1 - max_iter: 10 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: e0483c15c2d3d1f4b00ab464e1596e6a -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/adv_predictions.json - adv_probabilities_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/adv_probabilities.json - attack_file: output/attacks/2a092ec393809dc37ec1b2cac2c48219.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/params.yaml - predictions_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/predictions.json - probabilities_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/probabilities.json - score_dict_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/score_dict.json - test_labels_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/test_labels.json - train_labels_file: output/reports/attack/bce5a6b06c963e348aefdf0e910d5159/train_labels.json - model_dir: models - name: bce5a6b06c963e348aefdf0e910d5159 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: bce5a6b06c963e348aefdf0e910d5159 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/be923088aa789394a4d643fb4b67836b/params.yaml b/examples/security/classification/output/reports/attack/be923088aa789394a4d643fb4b67836b/params.yaml deleted file mode 100644 index 1cedc0d0..00000000 --- a/examples/security/classification/output/reports/attack/be923088aa789394a4d643fb4b67836b/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 1 - eps_step: 1 - max_iter: 1 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: c10e7f76f70d7d1aaeaf7ee1404d44f1 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/adv_predictions.json - adv_probabilities_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/adv_probabilities.json - attack_file: output/attacks/aa07c5f8ed3478b59ade749db50ff1c2.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/params.yaml - predictions_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/predictions.json - probabilities_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/probabilities.json - score_dict_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/score_dict.json - test_labels_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/test_labels.json - train_labels_file: output/reports/attack/be923088aa789394a4d643fb4b67836b/train_labels.json - model_dir: models - name: be923088aa789394a4d643fb4b67836b - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: be923088aa789394a4d643fb4b67836b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/c1102547c2f304a47e17dedf53dcad42/params.yaml b/examples/security/classification/output/reports/attack/c1102547c2f304a47e17dedf53dcad42/params.yaml deleted file mode 100644 index e579e0fd..00000000 --- a/examples/security/classification/output/reports/attack/c1102547c2f304a47e17dedf53dcad42/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 1 - eps_step: 0.1 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 3f11ed77d945d8765963f78fe519a1bf -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/adv_predictions.json - adv_probabilities_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/adv_probabilities.json - attack_file: output/attacks/fa8e9590d68c07b0f3134a5080de8cec.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/params.yaml - predictions_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/predictions.json - probabilities_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/probabilities.json - score_dict_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/score_dict.json - test_labels_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/test_labels.json - train_labels_file: output/reports/attack/c1102547c2f304a47e17dedf53dcad42/train_labels.json - model_dir: models - name: c1102547c2f304a47e17dedf53dcad42 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: c1102547c2f304a47e17dedf53dcad42 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/params.yaml b/examples/security/classification/output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/params.yaml deleted file mode 100644 index 9dc472e7..00000000 --- a/examples/security/classification/output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.1 - eps_step: 0.1 - max_iter: 1 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 4b590da1b52ac672c836580ba8b9a45d -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/adv_predictions.json - adv_probabilities_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/adv_probabilities.json - attack_file: output/attacks/08870d8783916c1fdb8bf31cade453e7.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/params.yaml - predictions_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/predictions.json - probabilities_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/probabilities.json - score_dict_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/score_dict.json - test_labels_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/test_labels.json - train_labels_file: output/reports/attack/c20bef1e31d6a4ac60aa724611735deb/train_labels.json - model_dir: models - name: c20bef1e31d6a4ac60aa724611735deb - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: c20bef1e31d6a4ac60aa724611735deb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/params.yaml b/examples/security/classification/output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/params.yaml deleted file mode 100644 index c620f5b8..00000000 --- a/examples/security/classification/output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.3 - eps_step: 0.1 - max_iter: 10 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 92ab049ea9e6df935a4f919a7d7ec370 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/adv_predictions.json - adv_probabilities_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/adv_probabilities.json - attack_file: output/attacks/c063f351f831bdfb1f657164bd2f24eb.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/params.yaml - predictions_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/predictions.json - probabilities_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/probabilities.json - score_dict_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/score_dict.json - test_labels_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/test_labels.json - train_labels_file: output/reports/attack/c64d3383ed0989439cb0eeecdb47457b/train_labels.json - model_dir: models - name: c64d3383ed0989439cb0eeecdb47457b - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: c64d3383ed0989439cb0eeecdb47457b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/params.yaml b/examples/security/classification/output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/params.yaml deleted file mode 100644 index 723ad2f9..00000000 --- a/examples/security/classification/output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.1 - eps_step: 1 - max_iter: 1 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 3f17b8511d8d47852053b2588e09c24e -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/adv_predictions.json - adv_probabilities_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/adv_probabilities.json - attack_file: output/attacks/c7123fa902f348f66852b56b928b070b.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/params.yaml - predictions_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/predictions.json - probabilities_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/probabilities.json - score_dict_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/score_dict.json - test_labels_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/test_labels.json - train_labels_file: output/reports/attack/c679004c91a6290c44902a9ddad1bb3a/train_labels.json - model_dir: models - name: c679004c91a6290c44902a9ddad1bb3a - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: c679004c91a6290c44902a9ddad1bb3a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/params.yaml b/examples/security/classification/output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/params.yaml deleted file mode 100644 index 018137bc..00000000 --- a/examples/security/classification/output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.001 - eps_step: 1 - max_iter: 10 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: dec9555092c668af6c0b61c24597317c -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/adv_predictions.json - adv_probabilities_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/adv_probabilities.json - attack_file: output/attacks/df76e5067db3a205e47852b5e970592a.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/params.yaml - predictions_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/predictions.json - probabilities_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/probabilities.json - score_dict_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/score_dict.json - test_labels_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/test_labels.json - train_labels_file: output/reports/attack/c6db7e0fbfd632f201ce1f826c8d1f29/train_labels.json - model_dir: models - name: c6db7e0fbfd632f201ce1f826c8d1f29 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: c6db7e0fbfd632f201ce1f826c8d1f29 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/params.yaml b/examples/security/classification/output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/params.yaml deleted file mode 100644 index 720e5c36..00000000 --- a/examples/security/classification/output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 1 - eps_step: 0.01 - max_iter: 1 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: e3425ec1a558c80902d9df4710ac1f9a -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/adv_predictions.json - adv_probabilities_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/adv_probabilities.json - attack_file: output/attacks/bdf902a0ea756d6ec7710994c20fefc5.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/params.yaml - predictions_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/predictions.json - probabilities_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/probabilities.json - score_dict_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/score_dict.json - test_labels_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/test_labels.json - train_labels_file: output/reports/attack/c86efe6fff1474c1b735b66d36bc9d07/train_labels.json - model_dir: models - name: c86efe6fff1474c1b735b66d36bc9d07 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: c86efe6fff1474c1b735b66d36bc9d07 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/params.yaml b/examples/security/classification/output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/params.yaml deleted file mode 100644 index 0ea84150..00000000 --- a/examples/security/classification/output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.3 - eps_step: 0.01 - max_iter: 1 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 2d54c9e7f035bf5c5529fa00c1d655c8 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/adv_predictions.json - adv_probabilities_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/adv_probabilities.json - attack_file: output/attacks/7adf2e7262ff9601792f0e0b1eca6415.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/params.yaml - predictions_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/predictions.json - probabilities_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/probabilities.json - score_dict_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/score_dict.json - test_labels_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/test_labels.json - train_labels_file: output/reports/attack/c8d769fbaf37149f8537a4e9ab28a166/train_labels.json - model_dir: models - name: c8d769fbaf37149f8537a4e9ab28a166 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: c8d769fbaf37149f8537a4e9ab28a166 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/params.yaml b/examples/security/classification/output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/params.yaml deleted file mode 100644 index 5a7589af..00000000 --- a/examples/security/classification/output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.001 - eps_step: 1 - max_iter: 1 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 04218b81316f60cb73615231ec778a53 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/adv_predictions.json - adv_probabilities_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/adv_probabilities.json - attack_file: output/attacks/03465bd1303e919e0e057925f35d11c7.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/params.yaml - predictions_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/predictions.json - probabilities_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/probabilities.json - score_dict_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/score_dict.json - test_labels_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/test_labels.json - train_labels_file: output/reports/attack/caec4fd4d685c0f099ca3b242eca1b27/train_labels.json - model_dir: models - name: caec4fd4d685c0f099ca3b242eca1b27 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: caec4fd4d685c0f099ca3b242eca1b27 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/cb28258195e5d12352131d1fde7e255f/params.yaml b/examples/security/classification/output/reports/attack/cb28258195e5d12352131d1fde7e255f/params.yaml deleted file mode 100644 index 2d0d90c8..00000000 --- a/examples/security/classification/output/reports/attack/cb28258195e5d12352131d1fde7e255f/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.5 - eps_step: 1 - max_iter: 1 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: f2c7e58618863804e47458c1884d55e0 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/adv_predictions.json - adv_probabilities_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/adv_probabilities.json - attack_file: output/attacks/dde0c187c66a9589e585ce5806019e15.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/params.yaml - predictions_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/predictions.json - probabilities_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/probabilities.json - score_dict_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/score_dict.json - test_labels_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/test_labels.json - train_labels_file: output/reports/attack/cb28258195e5d12352131d1fde7e255f/train_labels.json - model_dir: models - name: cb28258195e5d12352131d1fde7e255f - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: cb28258195e5d12352131d1fde7e255f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/cd3d3333255b48575620fdb8ddd38072/params.yaml b/examples/security/classification/output/reports/attack/cd3d3333255b48575620fdb8ddd38072/params.yaml deleted file mode 100644 index 011f188a..00000000 --- a/examples/security/classification/output/reports/attack/cd3d3333255b48575620fdb8ddd38072/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.3 - eps_step: 0.001 - max_iter: 1 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 89a3e5039d98b956ec53ede8e8002dc5 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/adv_predictions.json - adv_probabilities_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/adv_probabilities.json - attack_file: output/attacks/b67690cb49f5283368efa4847ce6d29a.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/params.yaml - predictions_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/predictions.json - probabilities_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/probabilities.json - score_dict_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/score_dict.json - test_labels_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/test_labels.json - train_labels_file: output/reports/attack/cd3d3333255b48575620fdb8ddd38072/train_labels.json - model_dir: models - name: cd3d3333255b48575620fdb8ddd38072 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: cd3d3333255b48575620fdb8ddd38072 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/params.yaml b/examples/security/classification/output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/params.yaml deleted file mode 100644 index a77453d3..00000000 --- a/examples/security/classification/output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.001 - eps_step: 0.01 - max_iter: 1000 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 2c19b04676e7b92b53810534fb630673 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/adv_predictions.json - adv_probabilities_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/adv_probabilities.json - attack_file: output/attacks/69412dfe021077f35475803b7dc18786.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/params.yaml - predictions_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/predictions.json - probabilities_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/probabilities.json - score_dict_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/score_dict.json - test_labels_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/test_labels.json - train_labels_file: output/reports/attack/cde8e7e4defa31c0c9313c7a17900d10/train_labels.json - model_dir: models - name: cde8e7e4defa31c0c9313c7a17900d10 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: cde8e7e4defa31c0c9313c7a17900d10 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/params.yaml b/examples/security/classification/output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/params.yaml deleted file mode 100644 index 2184812a..00000000 --- a/examples/security/classification/output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.1 - eps_step: 0.3 - max_iter: 1 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 28b4a67b9ba37fca532276214a393d1d -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/adv_predictions.json - adv_probabilities_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/adv_probabilities.json - attack_file: output/attacks/1aad1c5a5971e30a7d5ffd5ab747954d.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/params.yaml - predictions_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/predictions.json - probabilities_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/probabilities.json - score_dict_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/score_dict.json - test_labels_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/test_labels.json - train_labels_file: output/reports/attack/d23ef1fc5c7a2c220ac3da33a5f0f543/train_labels.json - model_dir: models - name: d23ef1fc5c7a2c220ac3da33a5f0f543 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: d23ef1fc5c7a2c220ac3da33a5f0f543 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/params.yaml b/examples/security/classification/output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/params.yaml deleted file mode 100644 index 831513f7..00000000 --- a/examples/security/classification/output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.1 - eps_step: 1 - max_iter: 100 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 6d569196024fed3733c7669f71b5a78c -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/adv_predictions.json - adv_probabilities_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/adv_probabilities.json - attack_file: output/attacks/aa20c87a460458baa500291f095562d1.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/params.yaml - predictions_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/predictions.json - probabilities_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/probabilities.json - score_dict_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/score_dict.json - test_labels_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/test_labels.json - train_labels_file: output/reports/attack/d83f9b2c942764715bffdfad6b2bdaf4/train_labels.json - model_dir: models - name: d83f9b2c942764715bffdfad6b2bdaf4 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: d83f9b2c942764715bffdfad6b2bdaf4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/params.yaml b/examples/security/classification/output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/params.yaml deleted file mode 100644 index d12cc2c3..00000000 --- a/examples/security/classification/output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 1 - eps_step: 0.5 - max_iter: 1000 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 773292464bcbff341e81ee91c2148601 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/adv_predictions.json - adv_probabilities_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/adv_probabilities.json - attack_file: output/attacks/86b22f5841967b4ec54b97fc1f013d8d.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/params.yaml - predictions_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/predictions.json - probabilities_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/probabilities.json - score_dict_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/score_dict.json - test_labels_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/test_labels.json - train_labels_file: output/reports/attack/da4e0555455650a1ca606aa53c6cdfb8/train_labels.json - model_dir: models - name: da4e0555455650a1ca606aa53c6cdfb8 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: da4e0555455650a1ca606aa53c6cdfb8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/params.yaml b/examples/security/classification/output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/params.yaml deleted file mode 100644 index e07b54a6..00000000 --- a/examples/security/classification/output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.001 - eps_step: 0.001 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 1b011238e89838778980f452e536e8e7 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/adv_predictions.json - adv_probabilities_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/adv_probabilities.json - attack_file: output/attacks/850e948e6e2047c866757ff585487275.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/params.yaml - predictions_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/predictions.json - probabilities_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/probabilities.json - score_dict_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/score_dict.json - test_labels_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/test_labels.json - train_labels_file: output/reports/attack/de39c594b2bb0387b8ca45d41e951c96/train_labels.json - model_dir: models - name: de39c594b2bb0387b8ca45d41e951c96 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: de39c594b2bb0387b8ca45d41e951c96 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/df7b493149f4daf0d9543018014d39e4/params.yaml b/examples/security/classification/output/reports/attack/df7b493149f4daf0d9543018014d39e4/params.yaml deleted file mode 100644 index 4633f153..00000000 --- a/examples/security/classification/output/reports/attack/df7b493149f4daf0d9543018014d39e4/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.5 - eps_step: 0.3 - max_iter: 10 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 6cf63a8c333ea3596724465bbd2fa795 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/adv_predictions.json - adv_probabilities_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/adv_probabilities.json - attack_file: output/attacks/6a046ef983c586991c08309c9168c1bf.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/params.yaml - predictions_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/predictions.json - probabilities_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/probabilities.json - score_dict_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/score_dict.json - test_labels_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/test_labels.json - train_labels_file: output/reports/attack/df7b493149f4daf0d9543018014d39e4/train_labels.json - model_dir: models - name: df7b493149f4daf0d9543018014d39e4 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: df7b493149f4daf0d9543018014d39e4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/params.yaml b/examples/security/classification/output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/params.yaml deleted file mode 100644 index 39ac519a..00000000 --- a/examples/security/classification/output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.001 - eps_step: 0.1 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 0d5e7ef801a17bed48e26b45bdfa1531 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/adv_predictions.json - adv_probabilities_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/adv_probabilities.json - attack_file: output/attacks/8bcbc68cdd71cd47763da0ad81e0ef5e.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/params.yaml - predictions_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/predictions.json - probabilities_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/probabilities.json - score_dict_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/score_dict.json - test_labels_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/test_labels.json - train_labels_file: output/reports/attack/dfbaf52d895803745c7a06e181e4f7e5/train_labels.json - model_dir: models - name: dfbaf52d895803745c7a06e181e4f7e5 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: dfbaf52d895803745c7a06e181e4f7e5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/params.yaml b/examples/security/classification/output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/params.yaml deleted file mode 100644 index 8f47dd19..00000000 --- a/examples/security/classification/output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.001 - eps_step: 1 - max_iter: 100 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 8f24e25c6a18645e0d6cc1543310c423 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/adv_predictions.json - adv_probabilities_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/adv_probabilities.json - attack_file: output/attacks/721876caa3295c865895dbe808a27e08.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/params.yaml - predictions_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/predictions.json - probabilities_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/probabilities.json - score_dict_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/score_dict.json - test_labels_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/test_labels.json - train_labels_file: output/reports/attack/e0fcc0b40a4b12272661a9b2612ff5e9/train_labels.json - model_dir: models - name: e0fcc0b40a4b12272661a9b2612ff5e9 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: e0fcc0b40a4b12272661a9b2612ff5e9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/e2b9bcec824489e09088cab69982352e/params.yaml b/examples/security/classification/output/reports/attack/e2b9bcec824489e09088cab69982352e/params.yaml deleted file mode 100644 index 37b45c16..00000000 --- a/examples/security/classification/output/reports/attack/e2b9bcec824489e09088cab69982352e/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.5 - eps_step: 0.1 - max_iter: 100 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: df20c3829ec256b768bbc6c846070284 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/adv_predictions.json - adv_probabilities_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/adv_probabilities.json - attack_file: output/attacks/f8be3ba4865a5b563ba586058200e643.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/params.yaml - predictions_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/predictions.json - probabilities_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/probabilities.json - score_dict_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/score_dict.json - test_labels_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/test_labels.json - train_labels_file: output/reports/attack/e2b9bcec824489e09088cab69982352e/train_labels.json - model_dir: models - name: e2b9bcec824489e09088cab69982352e - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: e2b9bcec824489e09088cab69982352e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/params.yaml b/examples/security/classification/output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/params.yaml deleted file mode 100644 index 6bdcc66c..00000000 --- a/examples/security/classification/output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.001 - eps_step: 0.001 - max_iter: 1000 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 9ece1d70c06add89cfc37bedb5ace006 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/adv_predictions.json - adv_probabilities_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/adv_probabilities.json - attack_file: output/attacks/70856d956f6156b06f73060da2886584.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/params.yaml - predictions_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/predictions.json - probabilities_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/probabilities.json - score_dict_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/score_dict.json - test_labels_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/test_labels.json - train_labels_file: output/reports/attack/e579a3109a16a2a33e979c697ab63a4a/train_labels.json - model_dir: models - name: e579a3109a16a2a33e979c697ab63a4a - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: e579a3109a16a2a33e979c697ab63a4a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/params.yaml b/examples/security/classification/output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/params.yaml deleted file mode 100644 index 2a9d337d..00000000 --- a/examples/security/classification/output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.1 - eps_step: 1 - max_iter: 10 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 1b8cd4f679b74cba64501ac11183678c -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/adv_predictions.json - adv_probabilities_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/adv_probabilities.json - attack_file: output/attacks/77208c589b0433f375db6785f40658d5.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/params.yaml - predictions_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/predictions.json - probabilities_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/probabilities.json - score_dict_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/score_dict.json - test_labels_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/test_labels.json - train_labels_file: output/reports/attack/e66b2f45d279f68b3cb2c3106d9f636c/train_labels.json - model_dir: models - name: e66b2f45d279f68b3cb2c3106d9f636c - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: e66b2f45d279f68b3cb2c3106d9f636c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/params.yaml b/examples/security/classification/output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/params.yaml deleted file mode 100644 index e431c059..00000000 --- a/examples/security/classification/output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.5 - eps_step: 0.001 - max_iter: 10 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 13d718c9fc44b2eed8829fad741ba879 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/adv_predictions.json - adv_probabilities_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/adv_probabilities.json - attack_file: output/attacks/c1bf6baf84573280df84497dd82a7d0c.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/params.yaml - predictions_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/predictions.json - probabilities_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/probabilities.json - score_dict_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/score_dict.json - test_labels_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/test_labels.json - train_labels_file: output/reports/attack/e72bd8fe158bacd0f30f7269cdeb1719/train_labels.json - model_dir: models - name: e72bd8fe158bacd0f30f7269cdeb1719 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: e72bd8fe158bacd0f30f7269cdeb1719 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/params.yaml b/examples/security/classification/output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/params.yaml deleted file mode 100644 index 767fd2a7..00000000 --- a/examples/security/classification/output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.5 - eps_step: 0.01 - max_iter: 100 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: eefd01477aad3c812ab28e0141636c01 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/adv_predictions.json - adv_probabilities_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/adv_probabilities.json - attack_file: output/attacks/40750f51ad6641c76bdc6a575bc8d985.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/params.yaml - predictions_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/predictions.json - probabilities_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/probabilities.json - score_dict_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/score_dict.json - test_labels_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/test_labels.json - train_labels_file: output/reports/attack/e7d69a830f033e4d16780cf4c88dab19/train_labels.json - model_dir: models - name: e7d69a830f033e4d16780cf4c88dab19 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: e7d69a830f033e4d16780cf4c88dab19 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/e89f1df76a9accbd7505f3771f97be27/params.yaml b/examples/security/classification/output/reports/attack/e89f1df76a9accbd7505f3771f97be27/params.yaml deleted file mode 100644 index 56060daf..00000000 --- a/examples/security/classification/output/reports/attack/e89f1df76a9accbd7505f3771f97be27/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.5 - eps_step: 0.3 - max_iter: 1 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 66c90b82419d06ce73372f3f68a3a158 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/adv_predictions.json - adv_probabilities_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/adv_probabilities.json - attack_file: output/attacks/db9ffeb9f1e3c36c71fdeba20ac15379.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/params.yaml - predictions_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/predictions.json - probabilities_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/probabilities.json - score_dict_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/score_dict.json - test_labels_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/test_labels.json - train_labels_file: output/reports/attack/e89f1df76a9accbd7505f3771f97be27/train_labels.json - model_dir: models - name: e89f1df76a9accbd7505f3771f97be27 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: e89f1df76a9accbd7505f3771f97be27 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/params.yaml b/examples/security/classification/output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/params.yaml deleted file mode 100644 index 3dd3306d..00000000 --- a/examples/security/classification/output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.1 - eps_step: 1 - max_iter: 10 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: e1f9ba8cacc2754374e228d7e4a86632 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/adv_predictions.json - adv_probabilities_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/adv_probabilities.json - attack_file: output/attacks/19111b6c90a02d20638d7ec1054e486f.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/params.yaml - predictions_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/predictions.json - probabilities_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/probabilities.json - score_dict_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/score_dict.json - test_labels_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/test_labels.json - train_labels_file: output/reports/attack/e9b3408407a9c92df60dcdbcc5e175bb/train_labels.json - model_dir: models - name: e9b3408407a9c92df60dcdbcc5e175bb - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: e9b3408407a9c92df60dcdbcc5e175bb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/params.yaml b/examples/security/classification/output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/params.yaml deleted file mode 100644 index af11eebb..00000000 --- a/examples/security/classification/output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.001 - eps_step: 0.1 - max_iter: 10 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 2f7d4cae092d148b50f5ccbfe67c4107 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/adv_predictions.json - adv_probabilities_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/adv_probabilities.json - attack_file: output/attacks/e9cc974ee78fcbaf3b5181da7a1fc91e.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/params.yaml - predictions_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/predictions.json - probabilities_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/probabilities.json - score_dict_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/score_dict.json - test_labels_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/test_labels.json - train_labels_file: output/reports/attack/ea88aca97b2572dff5a51f0b5c9f3473/train_labels.json - model_dir: models - name: ea88aca97b2572dff5a51f0b5c9f3473 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: ea88aca97b2572dff5a51f0b5c9f3473 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/params.yaml b/examples/security/classification/output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/params.yaml deleted file mode 100644 index 61e512ad..00000000 --- a/examples/security/classification/output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.01 - eps_step: 0.01 - max_iter: 100 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 5e984e132966df2e705f65fd75269e45 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/adv_predictions.json - adv_probabilities_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/adv_probabilities.json - attack_file: output/attacks/2eb89d26564fe4f822417313135409f6.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/params.yaml - predictions_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/predictions.json - probabilities_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/probabilities.json - score_dict_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/score_dict.json - test_labels_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/test_labels.json - train_labels_file: output/reports/attack/f10fb48933cb9fb310b30cfe6ef8972f/train_labels.json - model_dir: models - name: f10fb48933cb9fb310b30cfe6ef8972f - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: f10fb48933cb9fb310b30cfe6ef8972f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/params.yaml b/examples/security/classification/output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/params.yaml deleted file mode 100644 index a5370062..00000000 --- a/examples/security/classification/output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/params.yaml +++ /dev/null @@ -1,245 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.3 - eps_step: 1 - max_iter: 10 - norm: .inf - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c74376130120af776c27955c98e35448 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} - name: 8942e4b566dffd963d6c1feb135a31a7 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/adv_predictions.json - adv_probabilities_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/adv_probabilities.json - attack_file: output/attacks/740a50a7eea7ec81ab85dd3bca573b13.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/bb05c27a45e2e3b9cea863a4c5cdfca3.pkl - params_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/params.yaml - predictions_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/predictions.json - probabilities_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/probabilities.json - score_dict_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/score_dict.json - test_labels_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/test_labels.json - train_labels_file: output/reports/attack/f41c4a587a1eefd82f6bffad8cf20ae2/train_labels.json - model_dir: models - name: f41c4a587a1eefd82f6bffad8cf20ae2 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f23663db59297764839458062c5f97f - trainer: - kwargs: {} -name: f41c4a587a1eefd82f6bffad8cf20ae2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/params.yaml b/examples/security/classification/output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/params.yaml deleted file mode 100644 index 44fe1322..00000000 --- a/examples/security/classification/output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 50 - eps: 0.5 - eps_step: 1 - max_iter: 10 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 38579dfb39e79ad44de95e995613d1fe -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/adv_predictions.json - adv_probabilities_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/adv_probabilities.json - attack_file: output/attacks/8ff8664ee7ad2da87d55433db70f0b1b.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/params.yaml - predictions_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/predictions.json - probabilities_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/probabilities.json - score_dict_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/score_dict.json - test_labels_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/test_labels.json - train_labels_file: output/reports/attack/f5a7a716b340c343b84eeebda368fc5f/train_labels.json - model_dir: models - name: f5a7a716b340c343b84eeebda368fc5f - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: f5a7a716b340c343b84eeebda368fc5f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/params.yaml b/examples/security/classification/output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/params.yaml deleted file mode 100644 index 694cb862..00000000 --- a/examples/security/classification/output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.3 - eps_step: 1 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: fc0c615709f951ea2ec3ff3a6fad8297 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/adv_predictions.json - adv_probabilities_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/adv_probabilities.json - attack_file: output/attacks/615ba380c284d996a8400f27c9cef11d.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/params.yaml - predictions_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/predictions.json - probabilities_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/probabilities.json - score_dict_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/score_dict.json - test_labels_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/test_labels.json - train_labels_file: output/reports/attack/f5f45477cf75f8e5c7fa86de2da18bb6/train_labels.json - model_dir: models - name: f5f45477cf75f8e5c7fa86de2da18bb6 - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: f5f45477cf75f8e5c7fa86de2da18bb6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/params.yaml b/examples/security/classification/output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/params.yaml deleted file mode 100644 index c58248a5..00000000 --- a/examples/security/classification/output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/params.yaml +++ /dev/null @@ -1,236 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 10 - eps: 0.5 - eps_step: 0.1 - max_iter: 1000 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66528d318bd7cfc7b1ab7e6f233b17e6 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} - name: 8234927e0b19ff2bb44319394b82d494 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/adv_predictions.json - adv_probabilities_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/adv_probabilities.json - attack_file: output/attacks/3b802398c0c9971a37ae11a29c920917.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/0eec5c638c50302a2616e3884d711254.pkl - params_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/params.yaml - predictions_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/predictions.json - probabilities_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/probabilities.json - score_dict_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/score_dict.json - test_labels_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/test_labels.json - train_labels_file: output/reports/attack/f6bc9387f2ee85f4db62ba3bde014fdc/train_labels.json - model_dir: models - name: f6bc9387f2ee85f4db62ba3bde014fdc - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27857f11e234ec6587e9ab699619368f - trainer: - kwargs: {} -name: f6bc9387f2ee85f4db62ba3bde014fdc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/f9376ab290106335589016c44ad6bb9c/params.yaml b/examples/security/classification/output/reports/attack/f9376ab290106335589016c44ad6bb9c/params.yaml deleted file mode 100644 index 95042dfc..00000000 --- a/examples/security/classification/output/reports/attack/f9376ab290106335589016c44ad6bb9c/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.001 - eps_step: 0.01 - max_iter: 1 - norm: 1 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 003337a433b309b239ed4e268d08a894 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/adv_predictions.json - adv_probabilities_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/adv_probabilities.json - attack_file: output/attacks/4d119dfca0c668cb732afd4f7302fbf3.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/params.yaml - predictions_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/predictions.json - probabilities_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/probabilities.json - score_dict_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/score_dict.json - test_labels_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/test_labels.json - train_labels_file: output/reports/attack/f9376ab290106335589016c44ad6bb9c/train_labels.json - model_dir: models - name: f9376ab290106335589016c44ad6bb9c - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: f9376ab290106335589016c44ad6bb9c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/f99979a98f927382eb45efb02e76703f/params.yaml b/examples/security/classification/output/reports/attack/f99979a98f927382eb45efb02e76703f/params.yaml deleted file mode 100644 index 88801e2c..00000000 --- a/examples/security/classification/output/reports/attack/f99979a98f927382eb45efb02e76703f/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 100 - eps: 0.001 - eps_step: 1 - max_iter: 10 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 7fd5cb6fd5ca4ceb499d4e0628330bd5 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/adv_predictions.json - adv_probabilities_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/adv_probabilities.json - attack_file: output/attacks/13e5d0da7d0ce62cf68adf7b16c0ef11.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/params.yaml - predictions_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/predictions.json - probabilities_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/probabilities.json - score_dict_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/score_dict.json - test_labels_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/test_labels.json - train_labels_file: output/reports/attack/f99979a98f927382eb45efb02e76703f/train_labels.json - model_dir: models - name: f99979a98f927382eb45efb02e76703f - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: f99979a98f927382eb45efb02e76703f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/attack/fed73ab02f758325b690937b5242841c/params.yaml b/examples/security/classification/output/reports/attack/fed73ab02f758325b690937b5242841c/params.yaml deleted file mode 100644 index 9d73a2db..00000000 --- a/examples/security/classification/output/reports/attack/fed73ab02f758325b690937b5242841c/params.yaml +++ /dev/null @@ -1,242 +0,0 @@ -attack: - attack_size: 100 - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - batch_size: 1 - eps: 0.01 - eps_step: 0.5 - max_iter: 1 - norm: 2 - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbc3781be6645662ab8fa6536aefcb46 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.ProjectedGradientDescent - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} - name: 35ad9ef0391113d6952b248b23b09641 -data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/attack/fed73ab02f758325b690937b5242841c/adv_predictions.json - adv_probabilities_file: output/reports/attack/fed73ab02f758325b690937b5242841c/adv_probabilities.json - attack_file: output/attacks/60f3045b7faf9bdcb750ebbc8dba73d6.pkl - data_file: output/data/a858116080834fd47b74ae783316fef9.pkl - model_file: output/models/a15d66ce38a9235de45d6361fa42b01a.pkl - params_file: output/reports/attack/fed73ab02f758325b690937b5242841c/params.yaml - predictions_file: output/reports/attack/fed73ab02f758325b690937b5242841c/predictions.json - probabilities_file: output/reports/attack/fed73ab02f758325b690937b5242841c/probabilities.json - score_dict_file: output/reports/attack/fed73ab02f758325b690937b5242841c/score_dict.json - test_labels_file: output/reports/attack/fed73ab02f758325b690937b5242841c/test_labels.json - train_labels_file: output/reports/attack/fed73ab02f758325b690937b5242841c/train_labels.json - model_dir: models - name: fed73ab02f758325b690937b5242841c - reports: reports - stage: attack -kwargs: - direction: minimize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_informative: 99 - n_redundant: 0 - n_repeated: 0 - n_samples: 11100 - random_state: 0 - name: classification - name: a64858fac5ed02f8fc79116d3cdf6c22 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edd1e5ee431b09141af50a5714b2a5f7 - trainer: - kwargs: {} -name: fed73ab02f758325b690937b5242841c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/000424de3cdde01eb26cd9e434dc736e/params.yaml b/examples/security/classification/output/reports/train/000424de3cdde01eb26cd9e434dc736e/params.yaml deleted file mode 100644 index 3b9077b4..00000000 --- a/examples/security/classification/output/reports/train/000424de3cdde01eb26cd9e434dc736e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/adv_predictions.json - adv_probabilities_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/177dac86eec16b1573c4556b0a10a669.pkl - params_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/params.yaml - predictions_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/predictions.json - probabilities_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/probabilities.json - score_dict_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/score_dict.json - test_labels_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/test_labels.json - train_labels_file: output/reports/train/000424de3cdde01eb26cd9e434dc736e/train_labels.json - model_dir: models - name: 000424de3cdde01eb26cd9e434dc736e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 875c7597e496443fb8702099c7cae816 - trainer: - kwargs: {} -name: 000424de3cdde01eb26cd9e434dc736e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/001ba8df2a743772c43daad225a5f7db/params.yaml b/examples/security/classification/output/reports/train/001ba8df2a743772c43daad225a5f7db/params.yaml deleted file mode 100644 index e505eadd..00000000 --- a/examples/security/classification/output/reports/train/001ba8df2a743772c43daad225a5f7db/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/adv_predictions.json - adv_probabilities_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/945fd15deb89aee1828b06f5942b3b55.pkl - params_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/params.yaml - predictions_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/predictions.json - probabilities_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/probabilities.json - score_dict_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/score_dict.json - test_labels_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/test_labels.json - train_labels_file: output/reports/train/001ba8df2a743772c43daad225a5f7db/train_labels.json - model_dir: models - name: 001ba8df2a743772c43daad225a5f7db - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0bea9914d91fb38084cac1887ac4a091 - trainer: - kwargs: {} -name: 001ba8df2a743772c43daad225a5f7db -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/params.yaml b/examples/security/classification/output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/params.yaml deleted file mode 100644 index acb6ce41..00000000 --- a/examples/security/classification/output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/adv_predictions.json - adv_probabilities_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/f05e1737bfbac75c769b7a8ced069c4c.pkl - params_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/params.yaml - predictions_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/predictions.json - probabilities_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/probabilities.json - score_dict_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/score_dict.json - test_labels_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/test_labels.json - train_labels_file: output/reports/train/001df026f0fc9e0e28c26e8b4a4cd29c/train_labels.json - model_dir: models - name: 001df026f0fc9e0e28c26e8b4a4cd29c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2940bd3c4ea11032878b290ba0fb581b - trainer: - kwargs: {} -name: 001df026f0fc9e0e28c26e8b4a4cd29c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/params.yaml b/examples/security/classification/output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/params.yaml deleted file mode 100644 index 1497ccb7..00000000 --- a/examples/security/classification/output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/adv_predictions.json - adv_probabilities_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/92fd54df0d4cd191dbb4697086028432.pkl - params_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/params.yaml - predictions_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/predictions.json - probabilities_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/probabilities.json - score_dict_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/score_dict.json - test_labels_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/test_labels.json - train_labels_file: output/reports/train/00261e7a9a5007a0513f43d613f7d7d2/train_labels.json - model_dir: models - name: 00261e7a9a5007a0513f43d613f7d7d2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b134377327f48f9d86f95850773d815b - trainer: - kwargs: {} -name: 00261e7a9a5007a0513f43d613f7d7d2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/002de43ae63fed842b522a51cdbaaef5/params.yaml b/examples/security/classification/output/reports/train/002de43ae63fed842b522a51cdbaaef5/params.yaml deleted file mode 100644 index e2f212d7..00000000 --- a/examples/security/classification/output/reports/train/002de43ae63fed842b522a51cdbaaef5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/adv_predictions.json - adv_probabilities_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/bb5fc61cd21a967ab1856ff457f31fdb.pkl - params_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/params.yaml - predictions_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/predictions.json - probabilities_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/probabilities.json - score_dict_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/score_dict.json - test_labels_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/test_labels.json - train_labels_file: output/reports/train/002de43ae63fed842b522a51cdbaaef5/train_labels.json - model_dir: models - name: 002de43ae63fed842b522a51cdbaaef5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 94f3dcf4fafa34f2a4f6cbeae7e7537f - trainer: - kwargs: {} -name: 002de43ae63fed842b522a51cdbaaef5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/00471b964a87636526493b1efcd11d4d/params.yaml b/examples/security/classification/output/reports/train/00471b964a87636526493b1efcd11d4d/params.yaml deleted file mode 100644 index c6e562a1..00000000 --- a/examples/security/classification/output/reports/train/00471b964a87636526493b1efcd11d4d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/00471b964a87636526493b1efcd11d4d/adv_predictions.json - adv_probabilities_file: output/reports/train/00471b964a87636526493b1efcd11d4d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/a8e7d6025e21d685bb61a2ca84fe034f.pkl - params_file: output/reports/train/00471b964a87636526493b1efcd11d4d/params.yaml - predictions_file: output/reports/train/00471b964a87636526493b1efcd11d4d/predictions.json - probabilities_file: output/reports/train/00471b964a87636526493b1efcd11d4d/probabilities.json - score_dict_file: output/reports/train/00471b964a87636526493b1efcd11d4d/score_dict.json - test_labels_file: output/reports/train/00471b964a87636526493b1efcd11d4d/test_labels.json - train_labels_file: output/reports/train/00471b964a87636526493b1efcd11d4d/train_labels.json - model_dir: models - name: 00471b964a87636526493b1efcd11d4d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5cf1c13c3eb57fe9f01185f24ca6af67 - trainer: - kwargs: {} -name: 00471b964a87636526493b1efcd11d4d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/006eec1797100faea81a121088f5dfb4/params.yaml b/examples/security/classification/output/reports/train/006eec1797100faea81a121088f5dfb4/params.yaml deleted file mode 100644 index 35856261..00000000 --- a/examples/security/classification/output/reports/train/006eec1797100faea81a121088f5dfb4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/006eec1797100faea81a121088f5dfb4/adv_predictions.json - adv_probabilities_file: output/reports/train/006eec1797100faea81a121088f5dfb4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/a0e2067041c3e3f1507176a98b9b5dd4.pkl - params_file: output/reports/train/006eec1797100faea81a121088f5dfb4/params.yaml - predictions_file: output/reports/train/006eec1797100faea81a121088f5dfb4/predictions.json - probabilities_file: output/reports/train/006eec1797100faea81a121088f5dfb4/probabilities.json - score_dict_file: output/reports/train/006eec1797100faea81a121088f5dfb4/score_dict.json - test_labels_file: output/reports/train/006eec1797100faea81a121088f5dfb4/test_labels.json - train_labels_file: output/reports/train/006eec1797100faea81a121088f5dfb4/train_labels.json - model_dir: models - name: 006eec1797100faea81a121088f5dfb4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 58d5228ee47e78a802cecd6f2ee6a3a8 - trainer: - kwargs: {} -name: 006eec1797100faea81a121088f5dfb4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/params.yaml b/examples/security/classification/output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/params.yaml deleted file mode 100644 index f542aa42..00000000 --- a/examples/security/classification/output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/adv_predictions.json - adv_probabilities_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/490683787733493416e24281a6c98272.pkl - params_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/params.yaml - predictions_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/predictions.json - probabilities_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/probabilities.json - score_dict_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/score_dict.json - test_labels_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/test_labels.json - train_labels_file: output/reports/train/0074f756b6a4f89a8571af22fedcbaa1/train_labels.json - model_dir: models - name: 0074f756b6a4f89a8571af22fedcbaa1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0c9de328e271cbbc0ee5158e30ace715 - trainer: - kwargs: {} -name: 0074f756b6a4f89a8571af22fedcbaa1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/008145b71e35b55b6ac5df92bcae3978/params.yaml b/examples/security/classification/output/reports/train/008145b71e35b55b6ac5df92bcae3978/params.yaml deleted file mode 100644 index 4fab4055..00000000 --- a/examples/security/classification/output/reports/train/008145b71e35b55b6ac5df92bcae3978/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/adv_predictions.json - adv_probabilities_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/25bf05a8920385b43c3b4526db047941.pkl - params_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/params.yaml - predictions_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/predictions.json - probabilities_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/probabilities.json - score_dict_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/score_dict.json - test_labels_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/test_labels.json - train_labels_file: output/reports/train/008145b71e35b55b6ac5df92bcae3978/train_labels.json - model_dir: models - name: 008145b71e35b55b6ac5df92bcae3978 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7983d126b4a1b0fb0adac43b8ee4d576 - trainer: - kwargs: {} -name: 008145b71e35b55b6ac5df92bcae3978 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/008b521057d46a0f84bf73a195f7623c/params.yaml b/examples/security/classification/output/reports/train/008b521057d46a0f84bf73a195f7623c/params.yaml deleted file mode 100644 index b387456c..00000000 --- a/examples/security/classification/output/reports/train/008b521057d46a0f84bf73a195f7623c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/adv_predictions.json - adv_probabilities_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/0b1efa3cb7a5404964f0c22429304bf7.pkl - params_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/params.yaml - predictions_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/predictions.json - probabilities_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/probabilities.json - score_dict_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/score_dict.json - test_labels_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/test_labels.json - train_labels_file: output/reports/train/008b521057d46a0f84bf73a195f7623c/train_labels.json - model_dir: models - name: 008b521057d46a0f84bf73a195f7623c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a0492376b03bb08b86cf35f4b8d37ee3 - trainer: - kwargs: {} -name: 008b521057d46a0f84bf73a195f7623c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/008f1387800af7f30f2a036481cc5885/params.yaml b/examples/security/classification/output/reports/train/008f1387800af7f30f2a036481cc5885/params.yaml deleted file mode 100644 index b91142f4..00000000 --- a/examples/security/classification/output/reports/train/008f1387800af7f30f2a036481cc5885/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/008f1387800af7f30f2a036481cc5885/adv_predictions.json - adv_probabilities_file: output/reports/train/008f1387800af7f30f2a036481cc5885/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/f7a9545548fed18bcae1d59b8c7e8c25.pkl - params_file: output/reports/train/008f1387800af7f30f2a036481cc5885/params.yaml - predictions_file: output/reports/train/008f1387800af7f30f2a036481cc5885/predictions.json - probabilities_file: output/reports/train/008f1387800af7f30f2a036481cc5885/probabilities.json - score_dict_file: output/reports/train/008f1387800af7f30f2a036481cc5885/score_dict.json - test_labels_file: output/reports/train/008f1387800af7f30f2a036481cc5885/test_labels.json - train_labels_file: output/reports/train/008f1387800af7f30f2a036481cc5885/train_labels.json - model_dir: models - name: 008f1387800af7f30f2a036481cc5885 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 04bef9cb8e2106bfbe64de598da03419 - trainer: - kwargs: {} -name: 008f1387800af7f30f2a036481cc5885 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/params.yaml b/examples/security/classification/output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/params.yaml deleted file mode 100644 index 02d9de88..00000000 --- a/examples/security/classification/output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/adv_predictions.json - adv_probabilities_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/938caa3b57ce89db67da1d9a53bb3038.pkl - params_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/params.yaml - predictions_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/predictions.json - probabilities_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/probabilities.json - score_dict_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/score_dict.json - test_labels_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/test_labels.json - train_labels_file: output/reports/train/00cbc264f1ebfdabe46e414e4a6e5386/train_labels.json - model_dir: models - name: 00cbc264f1ebfdabe46e414e4a6e5386 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4609858b9c10e33d4085204e5a566247 - trainer: - kwargs: {} -name: 00cbc264f1ebfdabe46e414e4a6e5386 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/params.yaml b/examples/security/classification/output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/params.yaml deleted file mode 100644 index a29ad21f..00000000 --- a/examples/security/classification/output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/adv_predictions.json - adv_probabilities_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/48cbf9ee0ba36ec09dc6a2234e37aa2f.pkl - params_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/params.yaml - predictions_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/predictions.json - probabilities_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/probabilities.json - score_dict_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/score_dict.json - test_labels_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/test_labels.json - train_labels_file: output/reports/train/00e2a0bd64fd7fbef3976fb8e325524b/train_labels.json - model_dir: models - name: 00e2a0bd64fd7fbef3976fb8e325524b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 650ec8be400df5380d5d9e303ff7554f - trainer: - kwargs: {} -name: 00e2a0bd64fd7fbef3976fb8e325524b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/00ed86574d5c0674be5c6d72009187a8/params.yaml b/examples/security/classification/output/reports/train/00ed86574d5c0674be5c6d72009187a8/params.yaml deleted file mode 100644 index 0dc1d693..00000000 --- a/examples/security/classification/output/reports/train/00ed86574d5c0674be5c6d72009187a8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/adv_predictions.json - adv_probabilities_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/e03e3030178ef221dd83d0647d20206e.pkl - params_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/params.yaml - predictions_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/predictions.json - probabilities_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/probabilities.json - score_dict_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/score_dict.json - test_labels_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/test_labels.json - train_labels_file: output/reports/train/00ed86574d5c0674be5c6d72009187a8/train_labels.json - model_dir: models - name: 00ed86574d5c0674be5c6d72009187a8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 41e60c8855ff4d4d19fce6d30109d958 - trainer: - kwargs: {} -name: 00ed86574d5c0674be5c6d72009187a8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/00fe05bd296c637755d060d6c1f728d3/params.yaml b/examples/security/classification/output/reports/train/00fe05bd296c637755d060d6c1f728d3/params.yaml deleted file mode 100644 index 4fa7bd8d..00000000 --- a/examples/security/classification/output/reports/train/00fe05bd296c637755d060d6c1f728d3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/adv_predictions.json - adv_probabilities_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/72848ba8c94b0a2c3f3881e6ee773e07.pkl - params_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/params.yaml - predictions_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/predictions.json - probabilities_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/probabilities.json - score_dict_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/score_dict.json - test_labels_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/test_labels.json - train_labels_file: output/reports/train/00fe05bd296c637755d060d6c1f728d3/train_labels.json - model_dir: models - name: 00fe05bd296c637755d060d6c1f728d3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 60e768dacf6f146cfe083fbf3f753c83 - trainer: - kwargs: {} -name: 00fe05bd296c637755d060d6c1f728d3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/01471934c0109c91667d61a37485e898/params.yaml b/examples/security/classification/output/reports/train/01471934c0109c91667d61a37485e898/params.yaml deleted file mode 100644 index 06142ea6..00000000 --- a/examples/security/classification/output/reports/train/01471934c0109c91667d61a37485e898/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/01471934c0109c91667d61a37485e898/adv_predictions.json - adv_probabilities_file: output/reports/train/01471934c0109c91667d61a37485e898/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/737717676d2e8a864103321840161e14.pkl - params_file: output/reports/train/01471934c0109c91667d61a37485e898/params.yaml - predictions_file: output/reports/train/01471934c0109c91667d61a37485e898/predictions.json - probabilities_file: output/reports/train/01471934c0109c91667d61a37485e898/probabilities.json - score_dict_file: output/reports/train/01471934c0109c91667d61a37485e898/score_dict.json - test_labels_file: output/reports/train/01471934c0109c91667d61a37485e898/test_labels.json - train_labels_file: output/reports/train/01471934c0109c91667d61a37485e898/train_labels.json - model_dir: models - name: 01471934c0109c91667d61a37485e898 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c8a2e002f2cbe6fc4b754e58981755b3 - trainer: - kwargs: {} -name: 01471934c0109c91667d61a37485e898 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/params.yaml b/examples/security/classification/output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/params.yaml deleted file mode 100644 index 9c26ca70..00000000 --- a/examples/security/classification/output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/adv_predictions.json - adv_probabilities_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/bc5b71f4c086473c00ef015a9ad097b8.pkl - params_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/params.yaml - predictions_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/predictions.json - probabilities_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/probabilities.json - score_dict_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/score_dict.json - test_labels_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/test_labels.json - train_labels_file: output/reports/train/0148a59aff967b5fc6421f3f7706fcb0/train_labels.json - model_dir: models - name: 0148a59aff967b5fc6421f3f7706fcb0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 212d1ced2bbf8de9a324b8d50dc753d5 - trainer: - kwargs: {} -name: 0148a59aff967b5fc6421f3f7706fcb0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0155216b09479199346ab27988db9489/params.yaml b/examples/security/classification/output/reports/train/0155216b09479199346ab27988db9489/params.yaml deleted file mode 100644 index 70b33543..00000000 --- a/examples/security/classification/output/reports/train/0155216b09479199346ab27988db9489/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0155216b09479199346ab27988db9489/adv_predictions.json - adv_probabilities_file: output/reports/train/0155216b09479199346ab27988db9489/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/0039d6c512d4cb62c7c94b7fedadc907.pkl - params_file: output/reports/train/0155216b09479199346ab27988db9489/params.yaml - predictions_file: output/reports/train/0155216b09479199346ab27988db9489/predictions.json - probabilities_file: output/reports/train/0155216b09479199346ab27988db9489/probabilities.json - score_dict_file: output/reports/train/0155216b09479199346ab27988db9489/score_dict.json - test_labels_file: output/reports/train/0155216b09479199346ab27988db9489/test_labels.json - train_labels_file: output/reports/train/0155216b09479199346ab27988db9489/train_labels.json - model_dir: models - name: 0155216b09479199346ab27988db9489 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dd59594e2b119b9e381687331cd8e6b7 - trainer: - kwargs: {} -name: 0155216b09479199346ab27988db9489 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/016861b2726fd64689bf970e77642548/params.yaml b/examples/security/classification/output/reports/train/016861b2726fd64689bf970e77642548/params.yaml deleted file mode 100644 index ca421610..00000000 --- a/examples/security/classification/output/reports/train/016861b2726fd64689bf970e77642548/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/016861b2726fd64689bf970e77642548/adv_predictions.json - adv_probabilities_file: output/reports/train/016861b2726fd64689bf970e77642548/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/b7d38ef5dd72d10476995dd5fef247ec.pkl - params_file: output/reports/train/016861b2726fd64689bf970e77642548/params.yaml - predictions_file: output/reports/train/016861b2726fd64689bf970e77642548/predictions.json - probabilities_file: output/reports/train/016861b2726fd64689bf970e77642548/probabilities.json - score_dict_file: output/reports/train/016861b2726fd64689bf970e77642548/score_dict.json - test_labels_file: output/reports/train/016861b2726fd64689bf970e77642548/test_labels.json - train_labels_file: output/reports/train/016861b2726fd64689bf970e77642548/train_labels.json - model_dir: models - name: 016861b2726fd64689bf970e77642548 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d9dd354051953a3e2057028659b61689 - trainer: - kwargs: {} -name: 016861b2726fd64689bf970e77642548 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0181b2e981b7acacc65369ebb8f6e074/params.yaml b/examples/security/classification/output/reports/train/0181b2e981b7acacc65369ebb8f6e074/params.yaml deleted file mode 100644 index f2db664b..00000000 --- a/examples/security/classification/output/reports/train/0181b2e981b7acacc65369ebb8f6e074/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/adv_predictions.json - adv_probabilities_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/4a48eae37138aefbe83394c833ee1381.pkl - params_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/params.yaml - predictions_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/predictions.json - probabilities_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/probabilities.json - score_dict_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/score_dict.json - test_labels_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/test_labels.json - train_labels_file: output/reports/train/0181b2e981b7acacc65369ebb8f6e074/train_labels.json - model_dir: models - name: 0181b2e981b7acacc65369ebb8f6e074 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f65ba873428a25dd0482fefb01c9ab37 - trainer: - kwargs: {} -name: 0181b2e981b7acacc65369ebb8f6e074 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/01a14cb96e201a11a44c6b38cc145175/params.yaml b/examples/security/classification/output/reports/train/01a14cb96e201a11a44c6b38cc145175/params.yaml deleted file mode 100644 index f517746d..00000000 --- a/examples/security/classification/output/reports/train/01a14cb96e201a11a44c6b38cc145175/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/adv_predictions.json - adv_probabilities_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/20eb94a05097e8dcfaac1da3806f30b6.pkl - params_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/params.yaml - predictions_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/predictions.json - probabilities_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/probabilities.json - score_dict_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/score_dict.json - test_labels_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/test_labels.json - train_labels_file: output/reports/train/01a14cb96e201a11a44c6b38cc145175/train_labels.json - model_dir: models - name: 01a14cb96e201a11a44c6b38cc145175 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eae8ea137c70fe37a4208ce09f82c94f - trainer: - kwargs: {} -name: 01a14cb96e201a11a44c6b38cc145175 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/01b37293d605577cb762a1a61096c10f/params.yaml b/examples/security/classification/output/reports/train/01b37293d605577cb762a1a61096c10f/params.yaml deleted file mode 100644 index 151f61ef..00000000 --- a/examples/security/classification/output/reports/train/01b37293d605577cb762a1a61096c10f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/01b37293d605577cb762a1a61096c10f/adv_predictions.json - adv_probabilities_file: output/reports/train/01b37293d605577cb762a1a61096c10f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/df4c278f791033fb73e0f7fac93086b1.pkl - params_file: output/reports/train/01b37293d605577cb762a1a61096c10f/params.yaml - predictions_file: output/reports/train/01b37293d605577cb762a1a61096c10f/predictions.json - probabilities_file: output/reports/train/01b37293d605577cb762a1a61096c10f/probabilities.json - score_dict_file: output/reports/train/01b37293d605577cb762a1a61096c10f/score_dict.json - test_labels_file: output/reports/train/01b37293d605577cb762a1a61096c10f/test_labels.json - train_labels_file: output/reports/train/01b37293d605577cb762a1a61096c10f/train_labels.json - model_dir: models - name: 01b37293d605577cb762a1a61096c10f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d1a6db9abd7a1345633864eaa130555f - trainer: - kwargs: {} -name: 01b37293d605577cb762a1a61096c10f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/01bd4b56a941e92ea4628dee68e518e1/params.yaml b/examples/security/classification/output/reports/train/01bd4b56a941e92ea4628dee68e518e1/params.yaml deleted file mode 100644 index 19aeaddd..00000000 --- a/examples/security/classification/output/reports/train/01bd4b56a941e92ea4628dee68e518e1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/adv_predictions.json - adv_probabilities_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/39df3c965f62c1a041bff9ebf7caf01d.pkl - params_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/params.yaml - predictions_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/predictions.json - probabilities_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/probabilities.json - score_dict_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/score_dict.json - test_labels_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/test_labels.json - train_labels_file: output/reports/train/01bd4b56a941e92ea4628dee68e518e1/train_labels.json - model_dir: models - name: 01bd4b56a941e92ea4628dee68e518e1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b8f1962ccd367f146adaff4a1f0bc518 - trainer: - kwargs: {} -name: 01bd4b56a941e92ea4628dee68e518e1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/01be3ec9555691345657367f5d0c8989/params.yaml b/examples/security/classification/output/reports/train/01be3ec9555691345657367f5d0c8989/params.yaml deleted file mode 100644 index b27def1d..00000000 --- a/examples/security/classification/output/reports/train/01be3ec9555691345657367f5d0c8989/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/01be3ec9555691345657367f5d0c8989/adv_predictions.json - adv_probabilities_file: output/reports/train/01be3ec9555691345657367f5d0c8989/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/b8e3a99c73fae36d132d6e504e11aa98.pkl - params_file: output/reports/train/01be3ec9555691345657367f5d0c8989/params.yaml - predictions_file: output/reports/train/01be3ec9555691345657367f5d0c8989/predictions.json - probabilities_file: output/reports/train/01be3ec9555691345657367f5d0c8989/probabilities.json - score_dict_file: output/reports/train/01be3ec9555691345657367f5d0c8989/score_dict.json - test_labels_file: output/reports/train/01be3ec9555691345657367f5d0c8989/test_labels.json - train_labels_file: output/reports/train/01be3ec9555691345657367f5d0c8989/train_labels.json - model_dir: models - name: 01be3ec9555691345657367f5d0c8989 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2bcb9f370e42be373149d438cd6518d3 - trainer: - kwargs: {} -name: 01be3ec9555691345657367f5d0c8989 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/021112bafd0156a3bb03070cdb433ca0/params.yaml b/examples/security/classification/output/reports/train/021112bafd0156a3bb03070cdb433ca0/params.yaml deleted file mode 100644 index e62930fc..00000000 --- a/examples/security/classification/output/reports/train/021112bafd0156a3bb03070cdb433ca0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/adv_predictions.json - adv_probabilities_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/be4730abe350898f9a0c7831eb436ba2.pkl - params_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/params.yaml - predictions_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/predictions.json - probabilities_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/probabilities.json - score_dict_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/score_dict.json - test_labels_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/test_labels.json - train_labels_file: output/reports/train/021112bafd0156a3bb03070cdb433ca0/train_labels.json - model_dir: models - name: 021112bafd0156a3bb03070cdb433ca0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 75528a8bd08aef980e5324a2333badd2 - trainer: - kwargs: {} -name: 021112bafd0156a3bb03070cdb433ca0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/02201a32b9fe1dac953027105bdd01ef/params.yaml b/examples/security/classification/output/reports/train/02201a32b9fe1dac953027105bdd01ef/params.yaml deleted file mode 100644 index f804261a..00000000 --- a/examples/security/classification/output/reports/train/02201a32b9fe1dac953027105bdd01ef/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/adv_predictions.json - adv_probabilities_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/b1d0d763928294e1969cd163ae8bf17b.pkl - params_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/params.yaml - predictions_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/predictions.json - probabilities_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/probabilities.json - score_dict_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/score_dict.json - test_labels_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/test_labels.json - train_labels_file: output/reports/train/02201a32b9fe1dac953027105bdd01ef/train_labels.json - model_dir: models - name: 02201a32b9fe1dac953027105bdd01ef - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0feecf1215cd263350c5383e32d5e36b - trainer: - kwargs: {} -name: 02201a32b9fe1dac953027105bdd01ef -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0240d3b9381b3703113c7645711007bf/params.yaml b/examples/security/classification/output/reports/train/0240d3b9381b3703113c7645711007bf/params.yaml deleted file mode 100644 index 9f8cb11a..00000000 --- a/examples/security/classification/output/reports/train/0240d3b9381b3703113c7645711007bf/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0240d3b9381b3703113c7645711007bf/adv_predictions.json - adv_probabilities_file: output/reports/train/0240d3b9381b3703113c7645711007bf/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/0ffe7b07456724bc1ae10c78fdb9efd3.pkl - params_file: output/reports/train/0240d3b9381b3703113c7645711007bf/params.yaml - predictions_file: output/reports/train/0240d3b9381b3703113c7645711007bf/predictions.json - probabilities_file: output/reports/train/0240d3b9381b3703113c7645711007bf/probabilities.json - score_dict_file: output/reports/train/0240d3b9381b3703113c7645711007bf/score_dict.json - test_labels_file: output/reports/train/0240d3b9381b3703113c7645711007bf/test_labels.json - train_labels_file: output/reports/train/0240d3b9381b3703113c7645711007bf/train_labels.json - model_dir: models - name: 0240d3b9381b3703113c7645711007bf - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 53356695c6b385b1e26545bfe70cb50e - trainer: - kwargs: {} -name: 0240d3b9381b3703113c7645711007bf -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/026c53739fb01e9460401f3b1946c0d6/params.yaml b/examples/security/classification/output/reports/train/026c53739fb01e9460401f3b1946c0d6/params.yaml deleted file mode 100644 index ae0f6c60..00000000 --- a/examples/security/classification/output/reports/train/026c53739fb01e9460401f3b1946c0d6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/adv_predictions.json - adv_probabilities_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/f77211aad4b98d180f469fc485adf3a0.pkl - params_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/params.yaml - predictions_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/predictions.json - probabilities_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/probabilities.json - score_dict_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/score_dict.json - test_labels_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/test_labels.json - train_labels_file: output/reports/train/026c53739fb01e9460401f3b1946c0d6/train_labels.json - model_dir: models - name: 026c53739fb01e9460401f3b1946c0d6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 114c7a3ff26cf38834bb6b2f12565d9f - trainer: - kwargs: {} -name: 026c53739fb01e9460401f3b1946c0d6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/031df9824b8979901f18bdf9b8159d85/params.yaml b/examples/security/classification/output/reports/train/031df9824b8979901f18bdf9b8159d85/params.yaml deleted file mode 100644 index 6c00421b..00000000 --- a/examples/security/classification/output/reports/train/031df9824b8979901f18bdf9b8159d85/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/adv_predictions.json - adv_probabilities_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/d893aff69b99d8a0656eabe4623d2a9b.pkl - params_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/params.yaml - predictions_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/predictions.json - probabilities_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/probabilities.json - score_dict_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/score_dict.json - test_labels_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/test_labels.json - train_labels_file: output/reports/train/031df9824b8979901f18bdf9b8159d85/train_labels.json - model_dir: models - name: 031df9824b8979901f18bdf9b8159d85 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 276c24b9bb999be326e90b3e93f5ae27 - trainer: - kwargs: {} -name: 031df9824b8979901f18bdf9b8159d85 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0323097f0be1a89f479a736b437ecb1c/params.yaml b/examples/security/classification/output/reports/train/0323097f0be1a89f479a736b437ecb1c/params.yaml deleted file mode 100644 index 583c8cc7..00000000 --- a/examples/security/classification/output/reports/train/0323097f0be1a89f479a736b437ecb1c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/adv_predictions.json - adv_probabilities_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/af8bcfb8184c1a8b3c7d95fed35dd2da.pkl - params_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/params.yaml - predictions_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/predictions.json - probabilities_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/probabilities.json - score_dict_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/score_dict.json - test_labels_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/test_labels.json - train_labels_file: output/reports/train/0323097f0be1a89f479a736b437ecb1c/train_labels.json - model_dir: models - name: 0323097f0be1a89f479a736b437ecb1c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 29bf6b793eea7e667942609024e247c6 - trainer: - kwargs: {} -name: 0323097f0be1a89f479a736b437ecb1c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/params.yaml b/examples/security/classification/output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/params.yaml deleted file mode 100644 index 493ecd40..00000000 --- a/examples/security/classification/output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/adv_predictions.json - adv_probabilities_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/5a4c03bf9a9325b5ad1ac33b5e21af88.pkl - params_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/params.yaml - predictions_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/predictions.json - probabilities_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/probabilities.json - score_dict_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/score_dict.json - test_labels_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/test_labels.json - train_labels_file: output/reports/train/032f4f38d95aed3f5f46ea62dc66405a/train_labels.json - model_dir: models - name: 032f4f38d95aed3f5f46ea62dc66405a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9f040e30221f260cf9ecdbe6445a52bd - trainer: - kwargs: {} -name: 032f4f38d95aed3f5f46ea62dc66405a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0393497ffc75be7428b5645d371c7e27/params.yaml b/examples/security/classification/output/reports/train/0393497ffc75be7428b5645d371c7e27/params.yaml deleted file mode 100644 index f5dae230..00000000 --- a/examples/security/classification/output/reports/train/0393497ffc75be7428b5645d371c7e27/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/adv_predictions.json - adv_probabilities_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/582b95d980d47f35c736914e9e758dfd.pkl - params_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/params.yaml - predictions_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/predictions.json - probabilities_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/probabilities.json - score_dict_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/score_dict.json - test_labels_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/test_labels.json - train_labels_file: output/reports/train/0393497ffc75be7428b5645d371c7e27/train_labels.json - model_dir: models - name: 0393497ffc75be7428b5645d371c7e27 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3bd90c07370827fb9be49db2e973795e - trainer: - kwargs: {} -name: 0393497ffc75be7428b5645d371c7e27 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/params.yaml b/examples/security/classification/output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/params.yaml deleted file mode 100644 index b39af030..00000000 --- a/examples/security/classification/output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/adv_predictions.json - adv_probabilities_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/b0a00fff0bc1c93db57025df150dca42.pkl - params_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/params.yaml - predictions_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/predictions.json - probabilities_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/probabilities.json - score_dict_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/score_dict.json - test_labels_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/test_labels.json - train_labels_file: output/reports/train/03c140faf2f2f80fad5f7e692c0a52cd/train_labels.json - model_dir: models - name: 03c140faf2f2f80fad5f7e692c0a52cd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c27d632e7e519d0431a83585beadd47c - trainer: - kwargs: {} -name: 03c140faf2f2f80fad5f7e692c0a52cd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/params.yaml b/examples/security/classification/output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/params.yaml deleted file mode 100644 index 57f8b9ee..00000000 --- a/examples/security/classification/output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/adv_predictions.json - adv_probabilities_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/b5c542a4bb2d00903552b010b86a69b3.pkl - params_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/params.yaml - predictions_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/predictions.json - probabilities_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/probabilities.json - score_dict_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/score_dict.json - test_labels_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/test_labels.json - train_labels_file: output/reports/train/03d0d6302f2054943d5e21ea3a43b8b4/train_labels.json - model_dir: models - name: 03d0d6302f2054943d5e21ea3a43b8b4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3f890e8f0bf35832c6ac19c80207e138 - trainer: - kwargs: {} -name: 03d0d6302f2054943d5e21ea3a43b8b4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/params.yaml b/examples/security/classification/output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/params.yaml deleted file mode 100644 index 548d9694..00000000 --- a/examples/security/classification/output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/adv_predictions.json - adv_probabilities_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/ee5a712b7f6bb3c9b7bddbb3df1ab373.pkl - params_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/params.yaml - predictions_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/predictions.json - probabilities_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/probabilities.json - score_dict_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/score_dict.json - test_labels_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/test_labels.json - train_labels_file: output/reports/train/03e6a6818409ad68ab3f3ed95b956b3b/train_labels.json - model_dir: models - name: 03e6a6818409ad68ab3f3ed95b956b3b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a8a4ff5cac4db8ae63df2cf3ec15a155 - trainer: - kwargs: {} -name: 03e6a6818409ad68ab3f3ed95b956b3b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/03f75cf135cf9823e46cd5f688577b1f/params.yaml b/examples/security/classification/output/reports/train/03f75cf135cf9823e46cd5f688577b1f/params.yaml deleted file mode 100644 index 4122d35a..00000000 --- a/examples/security/classification/output/reports/train/03f75cf135cf9823e46cd5f688577b1f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/adv_predictions.json - adv_probabilities_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/3e1e1b2d6bff719d0fa851f3ffe6ad16.pkl - params_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/params.yaml - predictions_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/predictions.json - probabilities_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/probabilities.json - score_dict_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/score_dict.json - test_labels_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/test_labels.json - train_labels_file: output/reports/train/03f75cf135cf9823e46cd5f688577b1f/train_labels.json - model_dir: models - name: 03f75cf135cf9823e46cd5f688577b1f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4608fba19d2a9477a76cf7df077fc6a5 - trainer: - kwargs: {} -name: 03f75cf135cf9823e46cd5f688577b1f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/params.yaml b/examples/security/classification/output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/params.yaml deleted file mode 100644 index efa86159..00000000 --- a/examples/security/classification/output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/adv_predictions.json - adv_probabilities_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/5a616db39c5bd0fd2e3884d34717b39d.pkl - params_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/params.yaml - predictions_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/predictions.json - probabilities_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/probabilities.json - score_dict_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/score_dict.json - test_labels_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/test_labels.json - train_labels_file: output/reports/train/041f10bfcfc310c67dac0c6f7732bbbd/train_labels.json - model_dir: models - name: 041f10bfcfc310c67dac0c6f7732bbbd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d67bffb97fea864a98bf0250f4f95127 - trainer: - kwargs: {} -name: 041f10bfcfc310c67dac0c6f7732bbbd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0460181507e6ceba946d67ce510dd727/params.yaml b/examples/security/classification/output/reports/train/0460181507e6ceba946d67ce510dd727/params.yaml deleted file mode 100644 index b69ddece..00000000 --- a/examples/security/classification/output/reports/train/0460181507e6ceba946d67ce510dd727/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0460181507e6ceba946d67ce510dd727/adv_predictions.json - adv_probabilities_file: output/reports/train/0460181507e6ceba946d67ce510dd727/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/89f716654010f7f9bfedf26981390785.pkl - params_file: output/reports/train/0460181507e6ceba946d67ce510dd727/params.yaml - predictions_file: output/reports/train/0460181507e6ceba946d67ce510dd727/predictions.json - probabilities_file: output/reports/train/0460181507e6ceba946d67ce510dd727/probabilities.json - score_dict_file: output/reports/train/0460181507e6ceba946d67ce510dd727/score_dict.json - test_labels_file: output/reports/train/0460181507e6ceba946d67ce510dd727/test_labels.json - train_labels_file: output/reports/train/0460181507e6ceba946d67ce510dd727/train_labels.json - model_dir: models - name: 0460181507e6ceba946d67ce510dd727 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 47ea74fea7d88f83d62f05686d98d6e1 - trainer: - kwargs: {} -name: 0460181507e6ceba946d67ce510dd727 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0477f59fcf1306e99f441bee8627816d/params.yaml b/examples/security/classification/output/reports/train/0477f59fcf1306e99f441bee8627816d/params.yaml deleted file mode 100644 index 02f539a7..00000000 --- a/examples/security/classification/output/reports/train/0477f59fcf1306e99f441bee8627816d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/adv_predictions.json - adv_probabilities_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/d4eedf98458a3e4ad6e7aff70f01f7ec.pkl - params_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/params.yaml - predictions_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/predictions.json - probabilities_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/probabilities.json - score_dict_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/score_dict.json - test_labels_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/test_labels.json - train_labels_file: output/reports/train/0477f59fcf1306e99f441bee8627816d/train_labels.json - model_dir: models - name: 0477f59fcf1306e99f441bee8627816d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7cdfecef3bcef80b70cc074851fe6d1c - trainer: - kwargs: {} -name: 0477f59fcf1306e99f441bee8627816d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/047819a06e8118218f9ee6a8f7467c41/params.yaml b/examples/security/classification/output/reports/train/047819a06e8118218f9ee6a8f7467c41/params.yaml deleted file mode 100644 index dfc15536..00000000 --- a/examples/security/classification/output/reports/train/047819a06e8118218f9ee6a8f7467c41/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/adv_predictions.json - adv_probabilities_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/3dabc3fc8e673125f7dd678ad38f7451.pkl - params_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/params.yaml - predictions_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/predictions.json - probabilities_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/probabilities.json - score_dict_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/score_dict.json - test_labels_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/test_labels.json - train_labels_file: output/reports/train/047819a06e8118218f9ee6a8f7467c41/train_labels.json - model_dir: models - name: 047819a06e8118218f9ee6a8f7467c41 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b4c865ac157b4b0711fa99a67272d382 - trainer: - kwargs: {} -name: 047819a06e8118218f9ee6a8f7467c41 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/047faf13591d5356bb0ef80306542a13/params.yaml b/examples/security/classification/output/reports/train/047faf13591d5356bb0ef80306542a13/params.yaml deleted file mode 100644 index 6459cf48..00000000 --- a/examples/security/classification/output/reports/train/047faf13591d5356bb0ef80306542a13/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/047faf13591d5356bb0ef80306542a13/adv_predictions.json - adv_probabilities_file: output/reports/train/047faf13591d5356bb0ef80306542a13/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/275b367c6bbc39d89bd36776a7fedcb5.pkl - params_file: output/reports/train/047faf13591d5356bb0ef80306542a13/params.yaml - predictions_file: output/reports/train/047faf13591d5356bb0ef80306542a13/predictions.json - probabilities_file: output/reports/train/047faf13591d5356bb0ef80306542a13/probabilities.json - score_dict_file: output/reports/train/047faf13591d5356bb0ef80306542a13/score_dict.json - test_labels_file: output/reports/train/047faf13591d5356bb0ef80306542a13/test_labels.json - train_labels_file: output/reports/train/047faf13591d5356bb0ef80306542a13/train_labels.json - model_dir: models - name: 047faf13591d5356bb0ef80306542a13 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fdd9959b9c68db7433ca614da7f3471e - trainer: - kwargs: {} -name: 047faf13591d5356bb0ef80306542a13 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/049958f7506b171feec2530a213ab1b3/params.yaml b/examples/security/classification/output/reports/train/049958f7506b171feec2530a213ab1b3/params.yaml deleted file mode 100644 index f2395956..00000000 --- a/examples/security/classification/output/reports/train/049958f7506b171feec2530a213ab1b3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/049958f7506b171feec2530a213ab1b3/adv_predictions.json - adv_probabilities_file: output/reports/train/049958f7506b171feec2530a213ab1b3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/de4752eab610e9e19d24a069af024d27.pkl - params_file: output/reports/train/049958f7506b171feec2530a213ab1b3/params.yaml - predictions_file: output/reports/train/049958f7506b171feec2530a213ab1b3/predictions.json - probabilities_file: output/reports/train/049958f7506b171feec2530a213ab1b3/probabilities.json - score_dict_file: output/reports/train/049958f7506b171feec2530a213ab1b3/score_dict.json - test_labels_file: output/reports/train/049958f7506b171feec2530a213ab1b3/test_labels.json - train_labels_file: output/reports/train/049958f7506b171feec2530a213ab1b3/train_labels.json - model_dir: models - name: 049958f7506b171feec2530a213ab1b3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3bfffbca2965a0fc0e6a025a151dfe02 - trainer: - kwargs: {} -name: 049958f7506b171feec2530a213ab1b3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/04a1e4e7bbe21809db38d0e372623fce/params.yaml b/examples/security/classification/output/reports/train/04a1e4e7bbe21809db38d0e372623fce/params.yaml deleted file mode 100644 index a96793e7..00000000 --- a/examples/security/classification/output/reports/train/04a1e4e7bbe21809db38d0e372623fce/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/adv_predictions.json - adv_probabilities_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/3ccb5368f36837270e365a3ebd2a1caa.pkl - params_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/params.yaml - predictions_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/predictions.json - probabilities_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/probabilities.json - score_dict_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/score_dict.json - test_labels_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/test_labels.json - train_labels_file: output/reports/train/04a1e4e7bbe21809db38d0e372623fce/train_labels.json - model_dir: models - name: 04a1e4e7bbe21809db38d0e372623fce - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 093bef12529077f0ad020335c6f93d24 - trainer: - kwargs: {} -name: 04a1e4e7bbe21809db38d0e372623fce -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/04a848664aa8493454d7ea565e1ad0cf/params.yaml b/examples/security/classification/output/reports/train/04a848664aa8493454d7ea565e1ad0cf/params.yaml deleted file mode 100644 index c029115b..00000000 --- a/examples/security/classification/output/reports/train/04a848664aa8493454d7ea565e1ad0cf/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/adv_predictions.json - adv_probabilities_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/533827f1e4903800b129f41fd9433a7d.pkl - params_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/params.yaml - predictions_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/predictions.json - probabilities_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/probabilities.json - score_dict_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/score_dict.json - test_labels_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/test_labels.json - train_labels_file: output/reports/train/04a848664aa8493454d7ea565e1ad0cf/train_labels.json - model_dir: models - name: 04a848664aa8493454d7ea565e1ad0cf - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4080d9008fc51b90a39d6df117e9eb7a - trainer: - kwargs: {} -name: 04a848664aa8493454d7ea565e1ad0cf -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/params.yaml b/examples/security/classification/output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/params.yaml deleted file mode 100644 index 04822018..00000000 --- a/examples/security/classification/output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/adv_predictions.json - adv_probabilities_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/c2b3c7ef6c6a6ec5820aee6ce0bb3fb8.pkl - params_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/params.yaml - predictions_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/predictions.json - probabilities_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/probabilities.json - score_dict_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/score_dict.json - test_labels_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/test_labels.json - train_labels_file: output/reports/train/04e62a80b8f389ceff0d5f43ed9306ba/train_labels.json - model_dir: models - name: 04e62a80b8f389ceff0d5f43ed9306ba - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e12915077f434f70a0c519f007a072cc - trainer: - kwargs: {} -name: 04e62a80b8f389ceff0d5f43ed9306ba -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/params.yaml b/examples/security/classification/output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/params.yaml deleted file mode 100644 index 69ce352f..00000000 --- a/examples/security/classification/output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/adv_predictions.json - adv_probabilities_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/f78ae74584efee26dbe71cee4daa7a07.pkl - params_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/params.yaml - predictions_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/predictions.json - probabilities_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/probabilities.json - score_dict_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/score_dict.json - test_labels_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/test_labels.json - train_labels_file: output/reports/train/04f3e3cadb943916fa155b42fa84c1ed/train_labels.json - model_dir: models - name: 04f3e3cadb943916fa155b42fa84c1ed - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b132baee274945bc59b4987713764bf6 - trainer: - kwargs: {} -name: 04f3e3cadb943916fa155b42fa84c1ed -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/051d7b953c51025aa631b63e71156053/params.yaml b/examples/security/classification/output/reports/train/051d7b953c51025aa631b63e71156053/params.yaml deleted file mode 100644 index 6c761020..00000000 --- a/examples/security/classification/output/reports/train/051d7b953c51025aa631b63e71156053/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/051d7b953c51025aa631b63e71156053/adv_predictions.json - adv_probabilities_file: output/reports/train/051d7b953c51025aa631b63e71156053/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/4e4d36dc0cdad9a420cf80973591fa93.pkl - params_file: output/reports/train/051d7b953c51025aa631b63e71156053/params.yaml - predictions_file: output/reports/train/051d7b953c51025aa631b63e71156053/predictions.json - probabilities_file: output/reports/train/051d7b953c51025aa631b63e71156053/probabilities.json - score_dict_file: output/reports/train/051d7b953c51025aa631b63e71156053/score_dict.json - test_labels_file: output/reports/train/051d7b953c51025aa631b63e71156053/test_labels.json - train_labels_file: output/reports/train/051d7b953c51025aa631b63e71156053/train_labels.json - model_dir: models - name: 051d7b953c51025aa631b63e71156053 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2c53805eb51960727861029aa8083579 - trainer: - kwargs: {} -name: 051d7b953c51025aa631b63e71156053 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/params.yaml b/examples/security/classification/output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/params.yaml deleted file mode 100644 index ec82b65b..00000000 --- a/examples/security/classification/output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/adv_predictions.json - adv_probabilities_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/9c7a8eb1af32a3884121db3ce5b1c948.pkl - params_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/params.yaml - predictions_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/predictions.json - probabilities_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/probabilities.json - score_dict_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/score_dict.json - test_labels_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/test_labels.json - train_labels_file: output/reports/train/052579c18fc160b2f989c8bc8bd4ce46/train_labels.json - model_dir: models - name: 052579c18fc160b2f989c8bc8bd4ce46 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: df27bf01a5ca7f99c32b4f2ce712bb9c - trainer: - kwargs: {} -name: 052579c18fc160b2f989c8bc8bd4ce46 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/params.yaml b/examples/security/classification/output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/params.yaml deleted file mode 100644 index 3d42cb9f..00000000 --- a/examples/security/classification/output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/adv_predictions.json - adv_probabilities_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/88d5262c6d651b127e09451ccfb33170.pkl - params_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/params.yaml - predictions_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/predictions.json - probabilities_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/probabilities.json - score_dict_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/score_dict.json - test_labels_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/test_labels.json - train_labels_file: output/reports/train/052ceb5699bc60d366f5a2eaa2b318c2/train_labels.json - model_dir: models - name: 052ceb5699bc60d366f5a2eaa2b318c2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eb2fef68b3e708caf268f139e938d8a4 - trainer: - kwargs: {} -name: 052ceb5699bc60d366f5a2eaa2b318c2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/05355e9deed6b39b89c091d2fe0ed808/params.yaml b/examples/security/classification/output/reports/train/05355e9deed6b39b89c091d2fe0ed808/params.yaml deleted file mode 100644 index 12d7a4e2..00000000 --- a/examples/security/classification/output/reports/train/05355e9deed6b39b89c091d2fe0ed808/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/adv_predictions.json - adv_probabilities_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/b59e273ab528742d946f2c067f821db7.pkl - params_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/params.yaml - predictions_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/predictions.json - probabilities_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/probabilities.json - score_dict_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/score_dict.json - test_labels_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/test_labels.json - train_labels_file: output/reports/train/05355e9deed6b39b89c091d2fe0ed808/train_labels.json - model_dir: models - name: 05355e9deed6b39b89c091d2fe0ed808 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 60fa5c2e4c81db949e8d9087f7b211e6 - trainer: - kwargs: {} -name: 05355e9deed6b39b89c091d2fe0ed808 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/05aa1cb25083b4032299377ef9751dc0/params.yaml b/examples/security/classification/output/reports/train/05aa1cb25083b4032299377ef9751dc0/params.yaml deleted file mode 100644 index fbd107ff..00000000 --- a/examples/security/classification/output/reports/train/05aa1cb25083b4032299377ef9751dc0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/adv_predictions.json - adv_probabilities_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/0bb16481b7669ec43848e47e8994db84.pkl - params_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/params.yaml - predictions_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/predictions.json - probabilities_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/probabilities.json - score_dict_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/score_dict.json - test_labels_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/test_labels.json - train_labels_file: output/reports/train/05aa1cb25083b4032299377ef9751dc0/train_labels.json - model_dir: models - name: 05aa1cb25083b4032299377ef9751dc0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cfffaa48f425e132d70209806d32c14a - trainer: - kwargs: {} -name: 05aa1cb25083b4032299377ef9751dc0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/05b3ceb278df68fe087212ae5e90e807/params.yaml b/examples/security/classification/output/reports/train/05b3ceb278df68fe087212ae5e90e807/params.yaml deleted file mode 100644 index e2aa1318..00000000 --- a/examples/security/classification/output/reports/train/05b3ceb278df68fe087212ae5e90e807/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/adv_predictions.json - adv_probabilities_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/8e90b59c78885a696c95578f777244e2.pkl - params_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/params.yaml - predictions_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/predictions.json - probabilities_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/probabilities.json - score_dict_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/score_dict.json - test_labels_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/test_labels.json - train_labels_file: output/reports/train/05b3ceb278df68fe087212ae5e90e807/train_labels.json - model_dir: models - name: 05b3ceb278df68fe087212ae5e90e807 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 85ebb22769419b40a336057a6e70c930 - trainer: - kwargs: {} -name: 05b3ceb278df68fe087212ae5e90e807 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/05b3eb565faba25505e5cfd3540af88f/params.yaml b/examples/security/classification/output/reports/train/05b3eb565faba25505e5cfd3540af88f/params.yaml deleted file mode 100644 index 8c628675..00000000 --- a/examples/security/classification/output/reports/train/05b3eb565faba25505e5cfd3540af88f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/adv_predictions.json - adv_probabilities_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/30029ffce8a66c184b09c99a30058a36.pkl - params_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/params.yaml - predictions_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/predictions.json - probabilities_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/probabilities.json - score_dict_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/score_dict.json - test_labels_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/test_labels.json - train_labels_file: output/reports/train/05b3eb565faba25505e5cfd3540af88f/train_labels.json - model_dir: models - name: 05b3eb565faba25505e5cfd3540af88f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0b737774f34d0bcca110348772af3e19 - trainer: - kwargs: {} -name: 05b3eb565faba25505e5cfd3540af88f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/05dba9648a6235a5636edf43ccaddd0e/params.yaml b/examples/security/classification/output/reports/train/05dba9648a6235a5636edf43ccaddd0e/params.yaml deleted file mode 100644 index 3764f46e..00000000 --- a/examples/security/classification/output/reports/train/05dba9648a6235a5636edf43ccaddd0e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/adv_predictions.json - adv_probabilities_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/a388a867da56ec921ec9a073df18d536.pkl - params_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/params.yaml - predictions_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/predictions.json - probabilities_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/probabilities.json - score_dict_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/score_dict.json - test_labels_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/test_labels.json - train_labels_file: output/reports/train/05dba9648a6235a5636edf43ccaddd0e/train_labels.json - model_dir: models - name: 05dba9648a6235a5636edf43ccaddd0e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 79bd8a2c365caa4a8e356ff6728ea129 - trainer: - kwargs: {} -name: 05dba9648a6235a5636edf43ccaddd0e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/05faf2d4786e2e295fb724a115914bd9/params.yaml b/examples/security/classification/output/reports/train/05faf2d4786e2e295fb724a115914bd9/params.yaml deleted file mode 100644 index 438645d2..00000000 --- a/examples/security/classification/output/reports/train/05faf2d4786e2e295fb724a115914bd9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/adv_predictions.json - adv_probabilities_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/de3e65d0af964b21f335c7c2718852b0.pkl - params_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/params.yaml - predictions_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/predictions.json - probabilities_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/probabilities.json - score_dict_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/score_dict.json - test_labels_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/test_labels.json - train_labels_file: output/reports/train/05faf2d4786e2e295fb724a115914bd9/train_labels.json - model_dir: models - name: 05faf2d4786e2e295fb724a115914bd9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7556c916769c015350c5eec031de1651 - trainer: - kwargs: {} -name: 05faf2d4786e2e295fb724a115914bd9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0620ab72d6a4cf3938aa437549c9d596/params.yaml b/examples/security/classification/output/reports/train/0620ab72d6a4cf3938aa437549c9d596/params.yaml deleted file mode 100644 index cb85a9f9..00000000 --- a/examples/security/classification/output/reports/train/0620ab72d6a4cf3938aa437549c9d596/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/adv_predictions.json - adv_probabilities_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/4239b06e9f2bc392f531e7f520022308.pkl - params_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/params.yaml - predictions_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/predictions.json - probabilities_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/probabilities.json - score_dict_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/score_dict.json - test_labels_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/test_labels.json - train_labels_file: output/reports/train/0620ab72d6a4cf3938aa437549c9d596/train_labels.json - model_dir: models - name: 0620ab72d6a4cf3938aa437549c9d596 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4931e7f4350d552ed813565c4e9be2bd - trainer: - kwargs: {} -name: 0620ab72d6a4cf3938aa437549c9d596 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/params.yaml b/examples/security/classification/output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/params.yaml deleted file mode 100644 index 1dcaa777..00000000 --- a/examples/security/classification/output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/adv_predictions.json - adv_probabilities_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/482d5675da43062d0cb20a6b0ed59687.pkl - params_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/params.yaml - predictions_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/predictions.json - probabilities_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/probabilities.json - score_dict_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/score_dict.json - test_labels_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/test_labels.json - train_labels_file: output/reports/train/0621dfbdfbeb02be1dab12e4939ba248/train_labels.json - model_dir: models - name: 0621dfbdfbeb02be1dab12e4939ba248 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fe0724ebac4195f912541399c82f60aa - trainer: - kwargs: {} -name: 0621dfbdfbeb02be1dab12e4939ba248 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/063193340a3bc4b8869e007236e37e18/params.yaml b/examples/security/classification/output/reports/train/063193340a3bc4b8869e007236e37e18/params.yaml deleted file mode 100644 index 5c3feccc..00000000 --- a/examples/security/classification/output/reports/train/063193340a3bc4b8869e007236e37e18/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/063193340a3bc4b8869e007236e37e18/adv_predictions.json - adv_probabilities_file: output/reports/train/063193340a3bc4b8869e007236e37e18/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/8d2dea3a0a2a6c28719f9ff6f36ea9a8.pkl - params_file: output/reports/train/063193340a3bc4b8869e007236e37e18/params.yaml - predictions_file: output/reports/train/063193340a3bc4b8869e007236e37e18/predictions.json - probabilities_file: output/reports/train/063193340a3bc4b8869e007236e37e18/probabilities.json - score_dict_file: output/reports/train/063193340a3bc4b8869e007236e37e18/score_dict.json - test_labels_file: output/reports/train/063193340a3bc4b8869e007236e37e18/test_labels.json - train_labels_file: output/reports/train/063193340a3bc4b8869e007236e37e18/train_labels.json - model_dir: models - name: 063193340a3bc4b8869e007236e37e18 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ed44d59edcc1893831ff15649df37874 - trainer: - kwargs: {} -name: 063193340a3bc4b8869e007236e37e18 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/06478db734b52b527c25a208d18e173f/params.yaml b/examples/security/classification/output/reports/train/06478db734b52b527c25a208d18e173f/params.yaml deleted file mode 100644 index ab833ad5..00000000 --- a/examples/security/classification/output/reports/train/06478db734b52b527c25a208d18e173f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/06478db734b52b527c25a208d18e173f/adv_predictions.json - adv_probabilities_file: output/reports/train/06478db734b52b527c25a208d18e173f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/b3c23137953069dfb4662e8160278a4a.pkl - params_file: output/reports/train/06478db734b52b527c25a208d18e173f/params.yaml - predictions_file: output/reports/train/06478db734b52b527c25a208d18e173f/predictions.json - probabilities_file: output/reports/train/06478db734b52b527c25a208d18e173f/probabilities.json - score_dict_file: output/reports/train/06478db734b52b527c25a208d18e173f/score_dict.json - test_labels_file: output/reports/train/06478db734b52b527c25a208d18e173f/test_labels.json - train_labels_file: output/reports/train/06478db734b52b527c25a208d18e173f/train_labels.json - model_dir: models - name: 06478db734b52b527c25a208d18e173f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5c7769bb08e4fa023cf3b8ff91c7ba2f - trainer: - kwargs: {} -name: 06478db734b52b527c25a208d18e173f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/065511fdb5df3608c2c388875f1d7333/params.yaml b/examples/security/classification/output/reports/train/065511fdb5df3608c2c388875f1d7333/params.yaml deleted file mode 100644 index dba1329a..00000000 --- a/examples/security/classification/output/reports/train/065511fdb5df3608c2c388875f1d7333/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/adv_predictions.json - adv_probabilities_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/c54d59135336e913266800cced576432.pkl - params_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/params.yaml - predictions_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/predictions.json - probabilities_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/probabilities.json - score_dict_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/score_dict.json - test_labels_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/test_labels.json - train_labels_file: output/reports/train/065511fdb5df3608c2c388875f1d7333/train_labels.json - model_dir: models - name: 065511fdb5df3608c2c388875f1d7333 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b2df69d984d1866fcca2e24ce8ec63b6 - trainer: - kwargs: {} -name: 065511fdb5df3608c2c388875f1d7333 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/params.yaml b/examples/security/classification/output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/params.yaml deleted file mode 100644 index 7f100c45..00000000 --- a/examples/security/classification/output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/adv_predictions.json - adv_probabilities_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/351ddf0395aafd332a2bee60f91d1213.pkl - params_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/params.yaml - predictions_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/predictions.json - probabilities_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/probabilities.json - score_dict_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/score_dict.json - test_labels_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/test_labels.json - train_labels_file: output/reports/train/065731fc30bfd6f2d894dd1ea00091c6/train_labels.json - model_dir: models - name: 065731fc30bfd6f2d894dd1ea00091c6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1f08735c7fd96973ae5fa878b2f98103 - trainer: - kwargs: {} -name: 065731fc30bfd6f2d894dd1ea00091c6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/065d4aeea58ff7f4425520158554c858/params.yaml b/examples/security/classification/output/reports/train/065d4aeea58ff7f4425520158554c858/params.yaml deleted file mode 100644 index ab7adb9e..00000000 --- a/examples/security/classification/output/reports/train/065d4aeea58ff7f4425520158554c858/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/065d4aeea58ff7f4425520158554c858/adv_predictions.json - adv_probabilities_file: output/reports/train/065d4aeea58ff7f4425520158554c858/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/6153611f9f92137883b7ab2f7540d122.pkl - params_file: output/reports/train/065d4aeea58ff7f4425520158554c858/params.yaml - predictions_file: output/reports/train/065d4aeea58ff7f4425520158554c858/predictions.json - probabilities_file: output/reports/train/065d4aeea58ff7f4425520158554c858/probabilities.json - score_dict_file: output/reports/train/065d4aeea58ff7f4425520158554c858/score_dict.json - test_labels_file: output/reports/train/065d4aeea58ff7f4425520158554c858/test_labels.json - train_labels_file: output/reports/train/065d4aeea58ff7f4425520158554c858/train_labels.json - model_dir: models - name: 065d4aeea58ff7f4425520158554c858 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3376ca5f768651409ac859517418faa9 - trainer: - kwargs: {} -name: 065d4aeea58ff7f4425520158554c858 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/params.yaml b/examples/security/classification/output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/params.yaml deleted file mode 100644 index 851ee070..00000000 --- a/examples/security/classification/output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/adv_predictions.json - adv_probabilities_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/c4870e2cb0427727316b623dc9b4e674.pkl - params_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/params.yaml - predictions_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/predictions.json - probabilities_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/probabilities.json - score_dict_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/score_dict.json - test_labels_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/test_labels.json - train_labels_file: output/reports/train/06f2ce56e8981de8ce6e3acbad656cc8/train_labels.json - model_dir: models - name: 06f2ce56e8981de8ce6e3acbad656cc8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dcb5d447796b78fc3046a7df842caf97 - trainer: - kwargs: {} -name: 06f2ce56e8981de8ce6e3acbad656cc8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/params.yaml b/examples/security/classification/output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/params.yaml deleted file mode 100644 index ef1235e9..00000000 --- a/examples/security/classification/output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/adv_predictions.json - adv_probabilities_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/bf35fb29c148f62f8c2f418f6f6c5175.pkl - params_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/params.yaml - predictions_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/predictions.json - probabilities_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/probabilities.json - score_dict_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/score_dict.json - test_labels_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/test_labels.json - train_labels_file: output/reports/train/06f8c1d52a1a5795e86e9186ebbacddc/train_labels.json - model_dir: models - name: 06f8c1d52a1a5795e86e9186ebbacddc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e80f36224038b0b3ad6b2f0e1f2d7af8 - trainer: - kwargs: {} -name: 06f8c1d52a1a5795e86e9186ebbacddc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/070bda0a997206d128055ab43ad8c5f8/params.yaml b/examples/security/classification/output/reports/train/070bda0a997206d128055ab43ad8c5f8/params.yaml deleted file mode 100644 index 9de3a931..00000000 --- a/examples/security/classification/output/reports/train/070bda0a997206d128055ab43ad8c5f8/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/adv_predictions.json - adv_probabilities_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/f47de12bc3ec4e68e36d5e33828ff7cb.pkl - params_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/params.yaml - predictions_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/predictions.json - probabilities_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/probabilities.json - score_dict_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/score_dict.json - test_labels_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/test_labels.json - train_labels_file: output/reports/train/070bda0a997206d128055ab43ad8c5f8/train_labels.json - model_dir: models - name: 070bda0a997206d128055ab43ad8c5f8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 220ff2e81f8cb10b8b55b0df849a1dd3 - trainer: - kwargs: {} -name: 070bda0a997206d128055ab43ad8c5f8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/071dcf00b8185db311ba3e94652c9151/params.yaml b/examples/security/classification/output/reports/train/071dcf00b8185db311ba3e94652c9151/params.yaml deleted file mode 100644 index 11858e7a..00000000 --- a/examples/security/classification/output/reports/train/071dcf00b8185db311ba3e94652c9151/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/adv_predictions.json - adv_probabilities_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/71e6ae42dd420b0a9e0188c181089bdf.pkl - params_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/params.yaml - predictions_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/predictions.json - probabilities_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/probabilities.json - score_dict_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/score_dict.json - test_labels_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/test_labels.json - train_labels_file: output/reports/train/071dcf00b8185db311ba3e94652c9151/train_labels.json - model_dir: models - name: 071dcf00b8185db311ba3e94652c9151 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a2192aa99a4e0bb1e8afef86ab21d71d - trainer: - kwargs: {} -name: 071dcf00b8185db311ba3e94652c9151 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/072341877f0ab788171ed528b8bfda9d/params.yaml b/examples/security/classification/output/reports/train/072341877f0ab788171ed528b8bfda9d/params.yaml deleted file mode 100644 index aa716b50..00000000 --- a/examples/security/classification/output/reports/train/072341877f0ab788171ed528b8bfda9d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/adv_predictions.json - adv_probabilities_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/b9638468b479cdd22b15d8227277f9aa.pkl - params_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/params.yaml - predictions_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/predictions.json - probabilities_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/probabilities.json - score_dict_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/score_dict.json - test_labels_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/test_labels.json - train_labels_file: output/reports/train/072341877f0ab788171ed528b8bfda9d/train_labels.json - model_dir: models - name: 072341877f0ab788171ed528b8bfda9d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 417400b9095b735ef68098c3b8ab3fd8 - trainer: - kwargs: {} -name: 072341877f0ab788171ed528b8bfda9d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/07604af93db3690a7acfdbadb2fab8a3/params.yaml b/examples/security/classification/output/reports/train/07604af93db3690a7acfdbadb2fab8a3/params.yaml deleted file mode 100644 index b2039fa2..00000000 --- a/examples/security/classification/output/reports/train/07604af93db3690a7acfdbadb2fab8a3/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/adv_predictions.json - adv_probabilities_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/18f2b75a3c495bf83909b9b29b28618d.pkl - params_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/params.yaml - predictions_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/predictions.json - probabilities_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/probabilities.json - score_dict_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/score_dict.json - test_labels_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/test_labels.json - train_labels_file: output/reports/train/07604af93db3690a7acfdbadb2fab8a3/train_labels.json - model_dir: models - name: 07604af93db3690a7acfdbadb2fab8a3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 01bbac10afdf0b3c636a6602f485180c - trainer: - kwargs: {} -name: 07604af93db3690a7acfdbadb2fab8a3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/076440432993b653b79e17b5522c918c/params.yaml b/examples/security/classification/output/reports/train/076440432993b653b79e17b5522c918c/params.yaml deleted file mode 100644 index e22fcebc..00000000 --- a/examples/security/classification/output/reports/train/076440432993b653b79e17b5522c918c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/076440432993b653b79e17b5522c918c/adv_predictions.json - adv_probabilities_file: output/reports/train/076440432993b653b79e17b5522c918c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/bb71fb0c5a2676ceb43d4784694e4d69.pkl - params_file: output/reports/train/076440432993b653b79e17b5522c918c/params.yaml - predictions_file: output/reports/train/076440432993b653b79e17b5522c918c/predictions.json - probabilities_file: output/reports/train/076440432993b653b79e17b5522c918c/probabilities.json - score_dict_file: output/reports/train/076440432993b653b79e17b5522c918c/score_dict.json - test_labels_file: output/reports/train/076440432993b653b79e17b5522c918c/test_labels.json - train_labels_file: output/reports/train/076440432993b653b79e17b5522c918c/train_labels.json - model_dir: models - name: 076440432993b653b79e17b5522c918c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ca2abe1a4c549eee02c929357e38ab08 - trainer: - kwargs: {} -name: 076440432993b653b79e17b5522c918c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/077664147e5c73f126a8b889416552b1/params.yaml b/examples/security/classification/output/reports/train/077664147e5c73f126a8b889416552b1/params.yaml deleted file mode 100644 index 51887c89..00000000 --- a/examples/security/classification/output/reports/train/077664147e5c73f126a8b889416552b1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/077664147e5c73f126a8b889416552b1/adv_predictions.json - adv_probabilities_file: output/reports/train/077664147e5c73f126a8b889416552b1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/2dd546dcaad15f6c1163d1324c4dcb51.pkl - params_file: output/reports/train/077664147e5c73f126a8b889416552b1/params.yaml - predictions_file: output/reports/train/077664147e5c73f126a8b889416552b1/predictions.json - probabilities_file: output/reports/train/077664147e5c73f126a8b889416552b1/probabilities.json - score_dict_file: output/reports/train/077664147e5c73f126a8b889416552b1/score_dict.json - test_labels_file: output/reports/train/077664147e5c73f126a8b889416552b1/test_labels.json - train_labels_file: output/reports/train/077664147e5c73f126a8b889416552b1/train_labels.json - model_dir: models - name: 077664147e5c73f126a8b889416552b1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d0407bf0d5fa25d6bd8a4aa591294209 - trainer: - kwargs: {} -name: 077664147e5c73f126a8b889416552b1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/078779c88602dacbda4033ac87a09dc5/params.yaml b/examples/security/classification/output/reports/train/078779c88602dacbda4033ac87a09dc5/params.yaml deleted file mode 100644 index 2341ae4c..00000000 --- a/examples/security/classification/output/reports/train/078779c88602dacbda4033ac87a09dc5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/adv_predictions.json - adv_probabilities_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/3f9a40d7ff21c1f10dd818fffef30148.pkl - params_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/params.yaml - predictions_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/predictions.json - probabilities_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/probabilities.json - score_dict_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/score_dict.json - test_labels_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/test_labels.json - train_labels_file: output/reports/train/078779c88602dacbda4033ac87a09dc5/train_labels.json - model_dir: models - name: 078779c88602dacbda4033ac87a09dc5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ce5b3f517b53b8c7aa7d3571b4054c33 - trainer: - kwargs: {} -name: 078779c88602dacbda4033ac87a09dc5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/07a65af5708a2bc4620c687fe6a42755/params.yaml b/examples/security/classification/output/reports/train/07a65af5708a2bc4620c687fe6a42755/params.yaml deleted file mode 100644 index c3821246..00000000 --- a/examples/security/classification/output/reports/train/07a65af5708a2bc4620c687fe6a42755/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/adv_predictions.json - adv_probabilities_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/d3a467d4520f6b7481449af0576ace88.pkl - params_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/params.yaml - predictions_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/predictions.json - probabilities_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/probabilities.json - score_dict_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/score_dict.json - test_labels_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/test_labels.json - train_labels_file: output/reports/train/07a65af5708a2bc4620c687fe6a42755/train_labels.json - model_dir: models - name: 07a65af5708a2bc4620c687fe6a42755 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 548cf1336f135ec87db2749f7f30fcdf - trainer: - kwargs: {} -name: 07a65af5708a2bc4620c687fe6a42755 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/07ace1ff12f9536010330bb193223333/params.yaml b/examples/security/classification/output/reports/train/07ace1ff12f9536010330bb193223333/params.yaml deleted file mode 100644 index 942d73f0..00000000 --- a/examples/security/classification/output/reports/train/07ace1ff12f9536010330bb193223333/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/07ace1ff12f9536010330bb193223333/adv_predictions.json - adv_probabilities_file: output/reports/train/07ace1ff12f9536010330bb193223333/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/7acbb4a037ce4690dae33acd3bbec713.pkl - params_file: output/reports/train/07ace1ff12f9536010330bb193223333/params.yaml - predictions_file: output/reports/train/07ace1ff12f9536010330bb193223333/predictions.json - probabilities_file: output/reports/train/07ace1ff12f9536010330bb193223333/probabilities.json - score_dict_file: output/reports/train/07ace1ff12f9536010330bb193223333/score_dict.json - test_labels_file: output/reports/train/07ace1ff12f9536010330bb193223333/test_labels.json - train_labels_file: output/reports/train/07ace1ff12f9536010330bb193223333/train_labels.json - model_dir: models - name: 07ace1ff12f9536010330bb193223333 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dfc2eb397e91c6e35969d2c7c34f66d5 - trainer: - kwargs: {} -name: 07ace1ff12f9536010330bb193223333 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/params.yaml b/examples/security/classification/output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/params.yaml deleted file mode 100644 index 7c8379ed..00000000 --- a/examples/security/classification/output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/adv_predictions.json - adv_probabilities_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/6ad727474abd42317012a664da16957a.pkl - params_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/params.yaml - predictions_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/predictions.json - probabilities_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/probabilities.json - score_dict_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/score_dict.json - test_labels_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/test_labels.json - train_labels_file: output/reports/train/07b498f47eb70a50b6a1361ee5dd54ec/train_labels.json - model_dir: models - name: 07b498f47eb70a50b6a1361ee5dd54ec - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d35661daae835192474c59a92045313a - trainer: - kwargs: {} -name: 07b498f47eb70a50b6a1361ee5dd54ec -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/07b61d280a3c423bc97b57f75e2570b2/params.yaml b/examples/security/classification/output/reports/train/07b61d280a3c423bc97b57f75e2570b2/params.yaml deleted file mode 100644 index 80d20ec9..00000000 --- a/examples/security/classification/output/reports/train/07b61d280a3c423bc97b57f75e2570b2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/adv_predictions.json - adv_probabilities_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/be05b31b3ae97d7211a92b8f72dc862b.pkl - params_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/params.yaml - predictions_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/predictions.json - probabilities_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/probabilities.json - score_dict_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/score_dict.json - test_labels_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/test_labels.json - train_labels_file: output/reports/train/07b61d280a3c423bc97b57f75e2570b2/train_labels.json - model_dir: models - name: 07b61d280a3c423bc97b57f75e2570b2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f1ab9be6e0d7b69fc0a5b351c809edab - trainer: - kwargs: {} -name: 07b61d280a3c423bc97b57f75e2570b2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/params.yaml b/examples/security/classification/output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/params.yaml deleted file mode 100644 index dc152a83..00000000 --- a/examples/security/classification/output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/adv_predictions.json - adv_probabilities_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/71fda9607654711ffdfad031e4289418.pkl - params_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/params.yaml - predictions_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/predictions.json - probabilities_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/probabilities.json - score_dict_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/score_dict.json - test_labels_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/test_labels.json - train_labels_file: output/reports/train/07ed8b6e219b1ae8ea98960e666531fa/train_labels.json - model_dir: models - name: 07ed8b6e219b1ae8ea98960e666531fa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c046962576e28ad021a32a342acb43c4 - trainer: - kwargs: {} -name: 07ed8b6e219b1ae8ea98960e666531fa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/07fa5ff14bc1f5725cdebda879544932/params.yaml b/examples/security/classification/output/reports/train/07fa5ff14bc1f5725cdebda879544932/params.yaml deleted file mode 100644 index 716da861..00000000 --- a/examples/security/classification/output/reports/train/07fa5ff14bc1f5725cdebda879544932/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/adv_predictions.json - adv_probabilities_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/e38f4e9500e94e036b2dc5e29f1ed172.pkl - params_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/params.yaml - predictions_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/predictions.json - probabilities_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/probabilities.json - score_dict_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/score_dict.json - test_labels_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/test_labels.json - train_labels_file: output/reports/train/07fa5ff14bc1f5725cdebda879544932/train_labels.json - model_dir: models - name: 07fa5ff14bc1f5725cdebda879544932 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a32676ba665b647d451ad593d4d3b096 - trainer: - kwargs: {} -name: 07fa5ff14bc1f5725cdebda879544932 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/07fea681075d007e481eaf6c3e9942f1/params.yaml b/examples/security/classification/output/reports/train/07fea681075d007e481eaf6c3e9942f1/params.yaml deleted file mode 100644 index 948d43fe..00000000 --- a/examples/security/classification/output/reports/train/07fea681075d007e481eaf6c3e9942f1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/adv_predictions.json - adv_probabilities_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/a28f12e6e5f3f2f65b7d50859eeb0e37.pkl - params_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/params.yaml - predictions_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/predictions.json - probabilities_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/probabilities.json - score_dict_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/score_dict.json - test_labels_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/test_labels.json - train_labels_file: output/reports/train/07fea681075d007e481eaf6c3e9942f1/train_labels.json - model_dir: models - name: 07fea681075d007e481eaf6c3e9942f1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9d9c769e3b91fffbcff2572e00c71482 - trainer: - kwargs: {} -name: 07fea681075d007e481eaf6c3e9942f1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/08014a44ce06788947f6b5412256b2c6/params.yaml b/examples/security/classification/output/reports/train/08014a44ce06788947f6b5412256b2c6/params.yaml deleted file mode 100644 index 2cf870d4..00000000 --- a/examples/security/classification/output/reports/train/08014a44ce06788947f6b5412256b2c6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/adv_predictions.json - adv_probabilities_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/91bbb469aab625a51312329f5d8b4530.pkl - params_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/params.yaml - predictions_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/predictions.json - probabilities_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/probabilities.json - score_dict_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/score_dict.json - test_labels_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/test_labels.json - train_labels_file: output/reports/train/08014a44ce06788947f6b5412256b2c6/train_labels.json - model_dir: models - name: 08014a44ce06788947f6b5412256b2c6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 37defb03f5c81e7c754c0690547b3e60 - trainer: - kwargs: {} -name: 08014a44ce06788947f6b5412256b2c6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/08110612fd27c41fb306e567774016a9/params.yaml b/examples/security/classification/output/reports/train/08110612fd27c41fb306e567774016a9/params.yaml deleted file mode 100644 index fd882f3c..00000000 --- a/examples/security/classification/output/reports/train/08110612fd27c41fb306e567774016a9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/08110612fd27c41fb306e567774016a9/adv_predictions.json - adv_probabilities_file: output/reports/train/08110612fd27c41fb306e567774016a9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/9620d00cd060c401500118f1a15a491b.pkl - params_file: output/reports/train/08110612fd27c41fb306e567774016a9/params.yaml - predictions_file: output/reports/train/08110612fd27c41fb306e567774016a9/predictions.json - probabilities_file: output/reports/train/08110612fd27c41fb306e567774016a9/probabilities.json - score_dict_file: output/reports/train/08110612fd27c41fb306e567774016a9/score_dict.json - test_labels_file: output/reports/train/08110612fd27c41fb306e567774016a9/test_labels.json - train_labels_file: output/reports/train/08110612fd27c41fb306e567774016a9/train_labels.json - model_dir: models - name: 08110612fd27c41fb306e567774016a9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 75549469b405784b79725701acce1a9e - trainer: - kwargs: {} -name: 08110612fd27c41fb306e567774016a9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/083f639490166ed37e53c55111d80037/params.yaml b/examples/security/classification/output/reports/train/083f639490166ed37e53c55111d80037/params.yaml deleted file mode 100644 index d7f8c0bd..00000000 --- a/examples/security/classification/output/reports/train/083f639490166ed37e53c55111d80037/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/083f639490166ed37e53c55111d80037/adv_predictions.json - adv_probabilities_file: output/reports/train/083f639490166ed37e53c55111d80037/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/edd638b67bbbc98ac2ccd68c6f72f0a9.pkl - params_file: output/reports/train/083f639490166ed37e53c55111d80037/params.yaml - predictions_file: output/reports/train/083f639490166ed37e53c55111d80037/predictions.json - probabilities_file: output/reports/train/083f639490166ed37e53c55111d80037/probabilities.json - score_dict_file: output/reports/train/083f639490166ed37e53c55111d80037/score_dict.json - test_labels_file: output/reports/train/083f639490166ed37e53c55111d80037/test_labels.json - train_labels_file: output/reports/train/083f639490166ed37e53c55111d80037/train_labels.json - model_dir: models - name: 083f639490166ed37e53c55111d80037 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eb8cc8abe0b579589a9280ef084cb6b2 - trainer: - kwargs: {} -name: 083f639490166ed37e53c55111d80037 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/083fb0126b1f373ca7fdf49ed205c869/params.yaml b/examples/security/classification/output/reports/train/083fb0126b1f373ca7fdf49ed205c869/params.yaml deleted file mode 100644 index b1dd80db..00000000 --- a/examples/security/classification/output/reports/train/083fb0126b1f373ca7fdf49ed205c869/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/adv_predictions.json - adv_probabilities_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/879836a8bfd7504b9d0b75307a825b5e.pkl - params_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/params.yaml - predictions_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/predictions.json - probabilities_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/probabilities.json - score_dict_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/score_dict.json - test_labels_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/test_labels.json - train_labels_file: output/reports/train/083fb0126b1f373ca7fdf49ed205c869/train_labels.json - model_dir: models - name: 083fb0126b1f373ca7fdf49ed205c869 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 399e641f05c0534e8135e90e8cbdb928 - trainer: - kwargs: {} -name: 083fb0126b1f373ca7fdf49ed205c869 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0842d77ea3dc797f305e84654dd2728e/params.yaml b/examples/security/classification/output/reports/train/0842d77ea3dc797f305e84654dd2728e/params.yaml deleted file mode 100644 index 89292277..00000000 --- a/examples/security/classification/output/reports/train/0842d77ea3dc797f305e84654dd2728e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/adv_predictions.json - adv_probabilities_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/e7d4cea33dcc0d8476d2d8faa1fd5198.pkl - params_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/params.yaml - predictions_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/predictions.json - probabilities_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/probabilities.json - score_dict_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/score_dict.json - test_labels_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/test_labels.json - train_labels_file: output/reports/train/0842d77ea3dc797f305e84654dd2728e/train_labels.json - model_dir: models - name: 0842d77ea3dc797f305e84654dd2728e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5c9c6e52d453ebb2e325389da4d40f8a - trainer: - kwargs: {} -name: 0842d77ea3dc797f305e84654dd2728e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/084d4beebdaf4302b14dbd94000189b5/params.yaml b/examples/security/classification/output/reports/train/084d4beebdaf4302b14dbd94000189b5/params.yaml deleted file mode 100644 index e3e00b9c..00000000 --- a/examples/security/classification/output/reports/train/084d4beebdaf4302b14dbd94000189b5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/adv_predictions.json - adv_probabilities_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/17b2a38926dd6cb57171f4d2fa598d50.pkl - params_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/params.yaml - predictions_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/predictions.json - probabilities_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/probabilities.json - score_dict_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/score_dict.json - test_labels_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/test_labels.json - train_labels_file: output/reports/train/084d4beebdaf4302b14dbd94000189b5/train_labels.json - model_dir: models - name: 084d4beebdaf4302b14dbd94000189b5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 69487662be8955559bcf70be16e5fdf0 - trainer: - kwargs: {} -name: 084d4beebdaf4302b14dbd94000189b5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0854ebb3208784b4dedf370b6041acc6/params.yaml b/examples/security/classification/output/reports/train/0854ebb3208784b4dedf370b6041acc6/params.yaml deleted file mode 100644 index 4b8f9ad2..00000000 --- a/examples/security/classification/output/reports/train/0854ebb3208784b4dedf370b6041acc6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/adv_predictions.json - adv_probabilities_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/8ecc0669ab0d167cce256a4cb1a91fde.pkl - params_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/params.yaml - predictions_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/predictions.json - probabilities_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/probabilities.json - score_dict_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/score_dict.json - test_labels_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/test_labels.json - train_labels_file: output/reports/train/0854ebb3208784b4dedf370b6041acc6/train_labels.json - model_dir: models - name: 0854ebb3208784b4dedf370b6041acc6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 171a69bf26cc399fd653b6900191464f - trainer: - kwargs: {} -name: 0854ebb3208784b4dedf370b6041acc6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/085854032908dc2eacffbdd920e7d0e0/params.yaml b/examples/security/classification/output/reports/train/085854032908dc2eacffbdd920e7d0e0/params.yaml deleted file mode 100644 index fa3a5f22..00000000 --- a/examples/security/classification/output/reports/train/085854032908dc2eacffbdd920e7d0e0/params.yaml +++ /dev/null @@ -1,212 +0,0 @@ -attack: - attack_size: 10 - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 10.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6bd56a46ece28ceefd80f97967e041fd - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e82c16ae1bb5e334fc90c4434c4fb4f6 - trainer: - kwargs: {} - name: 170cbbf53b5289592070bb89ee6791d4 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/adv_predictions.json - adv_probabilities_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/adv_probabilities.json - attack_file: output/attacks/c85c6fc5ed8cde9f23606bfe9be8333b.pkl - data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl - model_file: output/models/63abcb7db4605ee026da8c8cbd8cec27.pkl - params_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/params.yaml - predictions_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/predictions.json - probabilities_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/probabilities.json - score_dict_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/score_dict.json - test_labels_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/test_labels.json - train_labels_file: output/reports/train/085854032908dc2eacffbdd920e7d0e0/train_labels.json - model_dir: models - name: 085854032908dc2eacffbdd920e7d0e0 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 84ea0798f74e7e66ca2f2140fe1653f6 - trainer: - kwargs: {} -name: 085854032908dc2eacffbdd920e7d0e0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/085ab58aee8cc449fc2371f050ce9d72/params.yaml b/examples/security/classification/output/reports/train/085ab58aee8cc449fc2371f050ce9d72/params.yaml deleted file mode 100644 index 1edc63c6..00000000 --- a/examples/security/classification/output/reports/train/085ab58aee8cc449fc2371f050ce9d72/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/adv_predictions.json - adv_probabilities_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/ee64a2c87683436f1ec711ed8dca9b9a.pkl - params_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/params.yaml - predictions_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/predictions.json - probabilities_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/probabilities.json - score_dict_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/score_dict.json - test_labels_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/test_labels.json - train_labels_file: output/reports/train/085ab58aee8cc449fc2371f050ce9d72/train_labels.json - model_dir: models - name: 085ab58aee8cc449fc2371f050ce9d72 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 732cea3d76ccc44908543ca4c97e1435 - trainer: - kwargs: {} -name: 085ab58aee8cc449fc2371f050ce9d72 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/params.yaml b/examples/security/classification/output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/params.yaml deleted file mode 100644 index 61ee2c85..00000000 --- a/examples/security/classification/output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/adv_predictions.json - adv_probabilities_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/d7c04244bec2bfc716024eb8abba66a4.pkl - params_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/params.yaml - predictions_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/predictions.json - probabilities_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/probabilities.json - score_dict_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/score_dict.json - test_labels_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/test_labels.json - train_labels_file: output/reports/train/087ddedfb88f2b19d4f5afcb147ce9db/train_labels.json - model_dir: models - name: 087ddedfb88f2b19d4f5afcb147ce9db - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 76caa6ec10d76007e7f5fdf4b596035e - trainer: - kwargs: {} -name: 087ddedfb88f2b19d4f5afcb147ce9db -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0886ff81f71f1abb574fa325b213d257/params.yaml b/examples/security/classification/output/reports/train/0886ff81f71f1abb574fa325b213d257/params.yaml deleted file mode 100644 index c47f66d3..00000000 --- a/examples/security/classification/output/reports/train/0886ff81f71f1abb574fa325b213d257/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/adv_predictions.json - adv_probabilities_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/b616c9e5a9b6141a360082fbc6946dfc.pkl - params_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/params.yaml - predictions_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/predictions.json - probabilities_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/probabilities.json - score_dict_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/score_dict.json - test_labels_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/test_labels.json - train_labels_file: output/reports/train/0886ff81f71f1abb574fa325b213d257/train_labels.json - model_dir: models - name: 0886ff81f71f1abb574fa325b213d257 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 54eda3d494c992528c52ac471b957113 - trainer: - kwargs: {} -name: 0886ff81f71f1abb574fa325b213d257 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/08b32344810163dd917651045576a6c8/params.yaml b/examples/security/classification/output/reports/train/08b32344810163dd917651045576a6c8/params.yaml deleted file mode 100644 index 8c021a14..00000000 --- a/examples/security/classification/output/reports/train/08b32344810163dd917651045576a6c8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/08b32344810163dd917651045576a6c8/adv_predictions.json - adv_probabilities_file: output/reports/train/08b32344810163dd917651045576a6c8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/93362ee1b9d55333a6871a2d597adc3a.pkl - params_file: output/reports/train/08b32344810163dd917651045576a6c8/params.yaml - predictions_file: output/reports/train/08b32344810163dd917651045576a6c8/predictions.json - probabilities_file: output/reports/train/08b32344810163dd917651045576a6c8/probabilities.json - score_dict_file: output/reports/train/08b32344810163dd917651045576a6c8/score_dict.json - test_labels_file: output/reports/train/08b32344810163dd917651045576a6c8/test_labels.json - train_labels_file: output/reports/train/08b32344810163dd917651045576a6c8/train_labels.json - model_dir: models - name: 08b32344810163dd917651045576a6c8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ee9aa74a503c8f1943e9d26dc8012f26 - trainer: - kwargs: {} -name: 08b32344810163dd917651045576a6c8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/params.yaml b/examples/security/classification/output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/params.yaml deleted file mode 100644 index d79b8306..00000000 --- a/examples/security/classification/output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/adv_predictions.json - adv_probabilities_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/1d43cb97d58b9ecd5d9adc8913e36628.pkl - params_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/params.yaml - predictions_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/predictions.json - probabilities_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/probabilities.json - score_dict_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/score_dict.json - test_labels_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/test_labels.json - train_labels_file: output/reports/train/08ec835d594dcc0be01fd8f96ce20aec/train_labels.json - model_dir: models - name: 08ec835d594dcc0be01fd8f96ce20aec - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1b97ea29a6dfcac05781db345b23ebaa - trainer: - kwargs: {} -name: 08ec835d594dcc0be01fd8f96ce20aec -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/08f77eeb41da360916ecb8625c7e7895/params.yaml b/examples/security/classification/output/reports/train/08f77eeb41da360916ecb8625c7e7895/params.yaml deleted file mode 100644 index cda77513..00000000 --- a/examples/security/classification/output/reports/train/08f77eeb41da360916ecb8625c7e7895/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/adv_predictions.json - adv_probabilities_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/1bd4e9497ecc38cd041860758478528d.pkl - params_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/params.yaml - predictions_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/predictions.json - probabilities_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/probabilities.json - score_dict_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/score_dict.json - test_labels_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/test_labels.json - train_labels_file: output/reports/train/08f77eeb41da360916ecb8625c7e7895/train_labels.json - model_dir: models - name: 08f77eeb41da360916ecb8625c7e7895 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ff93cfffe57fd98fb37cf07056347ea8 - trainer: - kwargs: {} -name: 08f77eeb41da360916ecb8625c7e7895 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0919742d7c92f35471b55f8911c0fc0e/params.yaml b/examples/security/classification/output/reports/train/0919742d7c92f35471b55f8911c0fc0e/params.yaml deleted file mode 100644 index 702fb19d..00000000 --- a/examples/security/classification/output/reports/train/0919742d7c92f35471b55f8911c0fc0e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/adv_predictions.json - adv_probabilities_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/3f967968730a28826694c0bb6d329979.pkl - params_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/params.yaml - predictions_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/predictions.json - probabilities_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/probabilities.json - score_dict_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/score_dict.json - test_labels_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/test_labels.json - train_labels_file: output/reports/train/0919742d7c92f35471b55f8911c0fc0e/train_labels.json - model_dir: models - name: 0919742d7c92f35471b55f8911c0fc0e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3161f5e54c3a52461d53235b3f14a4a4 - trainer: - kwargs: {} -name: 0919742d7c92f35471b55f8911c0fc0e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/091bb98634c741162fbafc0765d079f4/params.yaml b/examples/security/classification/output/reports/train/091bb98634c741162fbafc0765d079f4/params.yaml deleted file mode 100644 index eaef3482..00000000 --- a/examples/security/classification/output/reports/train/091bb98634c741162fbafc0765d079f4/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/091bb98634c741162fbafc0765d079f4/adv_predictions.json - adv_probabilities_file: output/reports/train/091bb98634c741162fbafc0765d079f4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/dbb795859836bc04925cde441ec672da.pkl - params_file: output/reports/train/091bb98634c741162fbafc0765d079f4/params.yaml - predictions_file: output/reports/train/091bb98634c741162fbafc0765d079f4/predictions.json - probabilities_file: output/reports/train/091bb98634c741162fbafc0765d079f4/probabilities.json - score_dict_file: output/reports/train/091bb98634c741162fbafc0765d079f4/score_dict.json - test_labels_file: output/reports/train/091bb98634c741162fbafc0765d079f4/test_labels.json - train_labels_file: output/reports/train/091bb98634c741162fbafc0765d079f4/train_labels.json - model_dir: models - name: 091bb98634c741162fbafc0765d079f4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5691780d9c02f826c873b287ad485bd7 - trainer: - kwargs: {} -name: 091bb98634c741162fbafc0765d079f4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/params.yaml b/examples/security/classification/output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/params.yaml deleted file mode 100644 index ab512115..00000000 --- a/examples/security/classification/output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/adv_predictions.json - adv_probabilities_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/5840a5b77474ddd5f8ef9a7b5be226a2.pkl - params_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/params.yaml - predictions_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/predictions.json - probabilities_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/probabilities.json - score_dict_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/score_dict.json - test_labels_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/test_labels.json - train_labels_file: output/reports/train/093a05bb4ce7518cf247d8e8c14619ce/train_labels.json - model_dir: models - name: 093a05bb4ce7518cf247d8e8c14619ce - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: efe9a5f8c205b993b1f3eb1868fe29f8 - trainer: - kwargs: {} -name: 093a05bb4ce7518cf247d8e8c14619ce -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0947a7182a239fca0cd2c459fad9856f/params.yaml b/examples/security/classification/output/reports/train/0947a7182a239fca0cd2c459fad9856f/params.yaml deleted file mode 100644 index 754520d4..00000000 --- a/examples/security/classification/output/reports/train/0947a7182a239fca0cd2c459fad9856f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/adv_predictions.json - adv_probabilities_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/06adf4fc44408a0ab95b9d6ef71b8177.pkl - params_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/params.yaml - predictions_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/predictions.json - probabilities_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/probabilities.json - score_dict_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/score_dict.json - test_labels_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/test_labels.json - train_labels_file: output/reports/train/0947a7182a239fca0cd2c459fad9856f/train_labels.json - model_dir: models - name: 0947a7182a239fca0cd2c459fad9856f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b6f37722de458b364fd072f29e4b7c51 - trainer: - kwargs: {} -name: 0947a7182a239fca0cd2c459fad9856f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/params.yaml b/examples/security/classification/output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/params.yaml deleted file mode 100644 index 79f3fe82..00000000 --- a/examples/security/classification/output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/adv_predictions.json - adv_probabilities_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/e949874212e636fbad2e3a9de1375b9e.pkl - params_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/params.yaml - predictions_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/predictions.json - probabilities_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/probabilities.json - score_dict_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/score_dict.json - test_labels_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/test_labels.json - train_labels_file: output/reports/train/096b5e37cd5ff5d8b5027e23b00d9eec/train_labels.json - model_dir: models - name: 096b5e37cd5ff5d8b5027e23b00d9eec - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2604fb3cafdbc5317b77dea00f2d1c5c - trainer: - kwargs: {} -name: 096b5e37cd5ff5d8b5027e23b00d9eec -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/096e58ee22745e7daf06474d1d7cfae3/params.yaml b/examples/security/classification/output/reports/train/096e58ee22745e7daf06474d1d7cfae3/params.yaml deleted file mode 100644 index e670e8cf..00000000 --- a/examples/security/classification/output/reports/train/096e58ee22745e7daf06474d1d7cfae3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/adv_predictions.json - adv_probabilities_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/21e517ce30c65333da35f8173ef343f8.pkl - params_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/params.yaml - predictions_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/predictions.json - probabilities_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/probabilities.json - score_dict_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/score_dict.json - test_labels_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/test_labels.json - train_labels_file: output/reports/train/096e58ee22745e7daf06474d1d7cfae3/train_labels.json - model_dir: models - name: 096e58ee22745e7daf06474d1d7cfae3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c7652445822689439e0f7a9946c13571 - trainer: - kwargs: {} -name: 096e58ee22745e7daf06474d1d7cfae3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/09cc402be03919bddc12fb77adad24f1/params.yaml b/examples/security/classification/output/reports/train/09cc402be03919bddc12fb77adad24f1/params.yaml deleted file mode 100644 index 1b8ffc92..00000000 --- a/examples/security/classification/output/reports/train/09cc402be03919bddc12fb77adad24f1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/adv_predictions.json - adv_probabilities_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/9062424fd8a207655eb060ac8bca50ef.pkl - params_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/params.yaml - predictions_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/predictions.json - probabilities_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/probabilities.json - score_dict_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/score_dict.json - test_labels_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/test_labels.json - train_labels_file: output/reports/train/09cc402be03919bddc12fb77adad24f1/train_labels.json - model_dir: models - name: 09cc402be03919bddc12fb77adad24f1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fd6064511fa76af00bcf35fcd3df94f7 - trainer: - kwargs: {} -name: 09cc402be03919bddc12fb77adad24f1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/params.yaml b/examples/security/classification/output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/params.yaml deleted file mode 100644 index 914e7b19..00000000 --- a/examples/security/classification/output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/params.yaml +++ /dev/null @@ -1,197 +0,0 @@ -attack: - attack_size: 10 - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0fb6062c51cb64aca2a63524ab4637cc - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 905d11203fc8a3d26d729322bee9a795 - trainer: - kwargs: {} - name: 216db53eb0cad0928debfeef94e933bb -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/adv_predictions.json - adv_probabilities_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/adv_probabilities.json - attack_file: output/attacks/9f0dd8aaacf0385dfa29fa6e360ce27e.pkl - data_file: output/data/f34e0bf4a34ceef1b8d68462b86685e1.pkl - model_file: output/models/9c2940e6a514fb9de16018be07c76754.pkl - params_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/params.yaml - predictions_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/predictions.json - probabilities_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/probabilities.json - score_dict_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/score_dict.json - test_labels_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/test_labels.json - train_labels_file: output/reports/train/0a1b224f20a18b9e018aaa5eb61ab9a4/train_labels.json - model_dir: models - name: 0a1b224f20a18b9e018aaa5eb61ab9a4 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 31307ed00c1b52640db29f72b1b97167 - trainer: - kwargs: {} -name: 0a1b224f20a18b9e018aaa5eb61ab9a4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/params.yaml b/examples/security/classification/output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/params.yaml deleted file mode 100644 index 0a3c20bc..00000000 --- a/examples/security/classification/output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/adv_predictions.json - adv_probabilities_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/7aadc948fab68539409800f0b0257df2.pkl - params_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/params.yaml - predictions_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/predictions.json - probabilities_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/probabilities.json - score_dict_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/score_dict.json - test_labels_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/test_labels.json - train_labels_file: output/reports/train/0a44e058b2fabf5fad8b2636f1ccfcde/train_labels.json - model_dir: models - name: 0a44e058b2fabf5fad8b2636f1ccfcde - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a893072df276a0bfcbcdc1842f681487 - trainer: - kwargs: {} -name: 0a44e058b2fabf5fad8b2636f1ccfcde -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0a7131aaaa4a858247d64b830e288ddd/params.yaml b/examples/security/classification/output/reports/train/0a7131aaaa4a858247d64b830e288ddd/params.yaml deleted file mode 100644 index 0ee61843..00000000 --- a/examples/security/classification/output/reports/train/0a7131aaaa4a858247d64b830e288ddd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/adv_predictions.json - adv_probabilities_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/000764cb05ab06d75204a688e74793a4.pkl - params_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/params.yaml - predictions_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/predictions.json - probabilities_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/probabilities.json - score_dict_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/score_dict.json - test_labels_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/test_labels.json - train_labels_file: output/reports/train/0a7131aaaa4a858247d64b830e288ddd/train_labels.json - model_dir: models - name: 0a7131aaaa4a858247d64b830e288ddd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e069cc23542169a068ddc9b8ea97fcb2 - trainer: - kwargs: {} -name: 0a7131aaaa4a858247d64b830e288ddd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/params.yaml b/examples/security/classification/output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/params.yaml deleted file mode 100644 index d5ad70ea..00000000 --- a/examples/security/classification/output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/adv_predictions.json - adv_probabilities_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/8553efcd357407f28d7af66cd328f762.pkl - params_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/params.yaml - predictions_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/predictions.json - probabilities_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/probabilities.json - score_dict_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/score_dict.json - test_labels_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/test_labels.json - train_labels_file: output/reports/train/0ac29c7012a3a5daa9b556e34773aef7/train_labels.json - model_dir: models - name: 0ac29c7012a3a5daa9b556e34773aef7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ed67a5c2b6b1fc2adcaf1077cc501845 - trainer: - kwargs: {} -name: 0ac29c7012a3a5daa9b556e34773aef7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0ae20830b42d77fbee347f78babb49aa/params.yaml b/examples/security/classification/output/reports/train/0ae20830b42d77fbee347f78babb49aa/params.yaml deleted file mode 100644 index 91fcf97b..00000000 --- a/examples/security/classification/output/reports/train/0ae20830b42d77fbee347f78babb49aa/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/adv_predictions.json - adv_probabilities_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/f7dc2d22eb53248e18b6f704a887c3e9.pkl - params_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/params.yaml - predictions_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/predictions.json - probabilities_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/probabilities.json - score_dict_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/score_dict.json - test_labels_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/test_labels.json - train_labels_file: output/reports/train/0ae20830b42d77fbee347f78babb49aa/train_labels.json - model_dir: models - name: 0ae20830b42d77fbee347f78babb49aa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1c739c1b0f12d33b1973ff5c83e9b328 - trainer: - kwargs: {} -name: 0ae20830b42d77fbee347f78babb49aa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0aea350411f67f3a70931b1e640eeb9d/params.yaml b/examples/security/classification/output/reports/train/0aea350411f67f3a70931b1e640eeb9d/params.yaml deleted file mode 100644 index ad818f0d..00000000 --- a/examples/security/classification/output/reports/train/0aea350411f67f3a70931b1e640eeb9d/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/adv_predictions.json - adv_probabilities_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/391252c6ea47e9abe6d860cf248d6839.pkl - params_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/params.yaml - predictions_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/predictions.json - probabilities_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/probabilities.json - score_dict_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/score_dict.json - test_labels_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/test_labels.json - train_labels_file: output/reports/train/0aea350411f67f3a70931b1e640eeb9d/train_labels.json - model_dir: models - name: 0aea350411f67f3a70931b1e640eeb9d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: acd0be5fe8036b86ff0eb338aa97b961 - trainer: - kwargs: {} -name: 0aea350411f67f3a70931b1e640eeb9d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0af2ec7717f053b3e38343d64f90d49d/params.yaml b/examples/security/classification/output/reports/train/0af2ec7717f053b3e38343d64f90d49d/params.yaml deleted file mode 100644 index ced0b11b..00000000 --- a/examples/security/classification/output/reports/train/0af2ec7717f053b3e38343d64f90d49d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/adv_predictions.json - adv_probabilities_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/36ac21e26acc298b952f7c83e67bff6a.pkl - params_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/params.yaml - predictions_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/predictions.json - probabilities_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/probabilities.json - score_dict_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/score_dict.json - test_labels_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/test_labels.json - train_labels_file: output/reports/train/0af2ec7717f053b3e38343d64f90d49d/train_labels.json - model_dir: models - name: 0af2ec7717f053b3e38343d64f90d49d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: aa782a11c13b3f125431703961864d8d - trainer: - kwargs: {} -name: 0af2ec7717f053b3e38343d64f90d49d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0b530a25e0e56937b372b2b98fff33fa/params.yaml b/examples/security/classification/output/reports/train/0b530a25e0e56937b372b2b98fff33fa/params.yaml deleted file mode 100644 index 75e6482d..00000000 --- a/examples/security/classification/output/reports/train/0b530a25e0e56937b372b2b98fff33fa/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/adv_predictions.json - adv_probabilities_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/2ffe1488d8d3363c4a8b078b1669be3a.pkl - params_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/params.yaml - predictions_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/predictions.json - probabilities_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/probabilities.json - score_dict_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/score_dict.json - test_labels_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/test_labels.json - train_labels_file: output/reports/train/0b530a25e0e56937b372b2b98fff33fa/train_labels.json - model_dir: models - name: 0b530a25e0e56937b372b2b98fff33fa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 755de1b257acd5b3ab16280283921762 - trainer: - kwargs: {} -name: 0b530a25e0e56937b372b2b98fff33fa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0b5758be778f032989bccadc9abac950/params.yaml b/examples/security/classification/output/reports/train/0b5758be778f032989bccadc9abac950/params.yaml deleted file mode 100644 index 6a00073e..00000000 --- a/examples/security/classification/output/reports/train/0b5758be778f032989bccadc9abac950/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0b5758be778f032989bccadc9abac950/adv_predictions.json - adv_probabilities_file: output/reports/train/0b5758be778f032989bccadc9abac950/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/4d8f0a1cef2a094485bb3c88ebe79a68.pkl - params_file: output/reports/train/0b5758be778f032989bccadc9abac950/params.yaml - predictions_file: output/reports/train/0b5758be778f032989bccadc9abac950/predictions.json - probabilities_file: output/reports/train/0b5758be778f032989bccadc9abac950/probabilities.json - score_dict_file: output/reports/train/0b5758be778f032989bccadc9abac950/score_dict.json - test_labels_file: output/reports/train/0b5758be778f032989bccadc9abac950/test_labels.json - train_labels_file: output/reports/train/0b5758be778f032989bccadc9abac950/train_labels.json - model_dir: models - name: 0b5758be778f032989bccadc9abac950 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3caa82f8dbb76d570630325e0a3c1c51 - trainer: - kwargs: {} -name: 0b5758be778f032989bccadc9abac950 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0b6497f5dfed6229cddf97354471167c/params.yaml b/examples/security/classification/output/reports/train/0b6497f5dfed6229cddf97354471167c/params.yaml deleted file mode 100644 index dd43ff8a..00000000 --- a/examples/security/classification/output/reports/train/0b6497f5dfed6229cddf97354471167c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/adv_predictions.json - adv_probabilities_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/4786f65e345be645f6b8c04dcaa86257.pkl - params_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/params.yaml - predictions_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/predictions.json - probabilities_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/probabilities.json - score_dict_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/score_dict.json - test_labels_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/test_labels.json - train_labels_file: output/reports/train/0b6497f5dfed6229cddf97354471167c/train_labels.json - model_dir: models - name: 0b6497f5dfed6229cddf97354471167c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 356e0759dfd492606a81a652adc9fddf - trainer: - kwargs: {} -name: 0b6497f5dfed6229cddf97354471167c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0b664b81a7779144482e86c041abab3e/params.yaml b/examples/security/classification/output/reports/train/0b664b81a7779144482e86c041abab3e/params.yaml deleted file mode 100644 index 569886c5..00000000 --- a/examples/security/classification/output/reports/train/0b664b81a7779144482e86c041abab3e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0b664b81a7779144482e86c041abab3e/adv_predictions.json - adv_probabilities_file: output/reports/train/0b664b81a7779144482e86c041abab3e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/8a5f5827eb1527b452a39ec66bd0f96e.pkl - params_file: output/reports/train/0b664b81a7779144482e86c041abab3e/params.yaml - predictions_file: output/reports/train/0b664b81a7779144482e86c041abab3e/predictions.json - probabilities_file: output/reports/train/0b664b81a7779144482e86c041abab3e/probabilities.json - score_dict_file: output/reports/train/0b664b81a7779144482e86c041abab3e/score_dict.json - test_labels_file: output/reports/train/0b664b81a7779144482e86c041abab3e/test_labels.json - train_labels_file: output/reports/train/0b664b81a7779144482e86c041abab3e/train_labels.json - model_dir: models - name: 0b664b81a7779144482e86c041abab3e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e560f64b9626b6ecbd08ae38b2871e88 - trainer: - kwargs: {} -name: 0b664b81a7779144482e86c041abab3e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0b7c164c5b4352f671ff99988820254e/params.yaml b/examples/security/classification/output/reports/train/0b7c164c5b4352f671ff99988820254e/params.yaml deleted file mode 100644 index 1f259a52..00000000 --- a/examples/security/classification/output/reports/train/0b7c164c5b4352f671ff99988820254e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/adv_predictions.json - adv_probabilities_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/e3bdbc5aa2e20006d13a92bf530e290d.pkl - params_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/params.yaml - predictions_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/predictions.json - probabilities_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/probabilities.json - score_dict_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/score_dict.json - test_labels_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/test_labels.json - train_labels_file: output/reports/train/0b7c164c5b4352f671ff99988820254e/train_labels.json - model_dir: models - name: 0b7c164c5b4352f671ff99988820254e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e2b4258e923666579595aa43a2e1e34f - trainer: - kwargs: {} -name: 0b7c164c5b4352f671ff99988820254e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/params.yaml b/examples/security/classification/output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/params.yaml deleted file mode 100644 index f3d2a45e..00000000 --- a/examples/security/classification/output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/adv_predictions.json - adv_probabilities_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/225e8d86e685e1291e01642174077ec7.pkl - params_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/params.yaml - predictions_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/predictions.json - probabilities_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/probabilities.json - score_dict_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/score_dict.json - test_labels_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/test_labels.json - train_labels_file: output/reports/train/0bc33dbd5c67dc25e800003f03eb4d26/train_labels.json - model_dir: models - name: 0bc33dbd5c67dc25e800003f03eb4d26 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9f62bb96b3efaa775cf846642dc92d75 - trainer: - kwargs: {} -name: 0bc33dbd5c67dc25e800003f03eb4d26 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0bcf8fce10809531407948840bf8b95e/params.yaml b/examples/security/classification/output/reports/train/0bcf8fce10809531407948840bf8b95e/params.yaml deleted file mode 100644 index 4c682b9d..00000000 --- a/examples/security/classification/output/reports/train/0bcf8fce10809531407948840bf8b95e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/adv_predictions.json - adv_probabilities_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/6ec66ad8d116c5659b6546e2cbb34f68.pkl - params_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/params.yaml - predictions_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/predictions.json - probabilities_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/probabilities.json - score_dict_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/score_dict.json - test_labels_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/test_labels.json - train_labels_file: output/reports/train/0bcf8fce10809531407948840bf8b95e/train_labels.json - model_dir: models - name: 0bcf8fce10809531407948840bf8b95e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 507bd8de80936d5163133fbb7f9f73da - trainer: - kwargs: {} -name: 0bcf8fce10809531407948840bf8b95e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0bd09d7200925230d7db75c319b8c567/params.yaml b/examples/security/classification/output/reports/train/0bd09d7200925230d7db75c319b8c567/params.yaml deleted file mode 100644 index 93b4a024..00000000 --- a/examples/security/classification/output/reports/train/0bd09d7200925230d7db75c319b8c567/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/adv_predictions.json - adv_probabilities_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/15646283734df88918f39d49591fa1cf.pkl - params_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/params.yaml - predictions_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/predictions.json - probabilities_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/probabilities.json - score_dict_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/score_dict.json - test_labels_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/test_labels.json - train_labels_file: output/reports/train/0bd09d7200925230d7db75c319b8c567/train_labels.json - model_dir: models - name: 0bd09d7200925230d7db75c319b8c567 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e5c12d68c9768a74749e793cf697eefb - trainer: - kwargs: {} -name: 0bd09d7200925230d7db75c319b8c567 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0be30eb5d95330f4ed9d28221825337d/params.yaml b/examples/security/classification/output/reports/train/0be30eb5d95330f4ed9d28221825337d/params.yaml deleted file mode 100644 index 1ace5425..00000000 --- a/examples/security/classification/output/reports/train/0be30eb5d95330f4ed9d28221825337d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/adv_predictions.json - adv_probabilities_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/9f8db3bf76806491ec75ac830b8bd3bb.pkl - params_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/params.yaml - predictions_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/predictions.json - probabilities_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/probabilities.json - score_dict_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/score_dict.json - test_labels_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/test_labels.json - train_labels_file: output/reports/train/0be30eb5d95330f4ed9d28221825337d/train_labels.json - model_dir: models - name: 0be30eb5d95330f4ed9d28221825337d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c04d8232dd73ee97b5b888f40dc9d7ad - trainer: - kwargs: {} -name: 0be30eb5d95330f4ed9d28221825337d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0be7948bbf44ee058d4de41456663815/params.yaml b/examples/security/classification/output/reports/train/0be7948bbf44ee058d4de41456663815/params.yaml deleted file mode 100644 index 8ed9b0e5..00000000 --- a/examples/security/classification/output/reports/train/0be7948bbf44ee058d4de41456663815/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0be7948bbf44ee058d4de41456663815/adv_predictions.json - adv_probabilities_file: output/reports/train/0be7948bbf44ee058d4de41456663815/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/48d627e6943770b5c66ca44fe44fb20d.pkl - params_file: output/reports/train/0be7948bbf44ee058d4de41456663815/params.yaml - predictions_file: output/reports/train/0be7948bbf44ee058d4de41456663815/predictions.json - probabilities_file: output/reports/train/0be7948bbf44ee058d4de41456663815/probabilities.json - score_dict_file: output/reports/train/0be7948bbf44ee058d4de41456663815/score_dict.json - test_labels_file: output/reports/train/0be7948bbf44ee058d4de41456663815/test_labels.json - train_labels_file: output/reports/train/0be7948bbf44ee058d4de41456663815/train_labels.json - model_dir: models - name: 0be7948bbf44ee058d4de41456663815 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fcf587e1a9d988bd3a7b021266451677 - trainer: - kwargs: {} -name: 0be7948bbf44ee058d4de41456663815 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/params.yaml b/examples/security/classification/output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/params.yaml deleted file mode 100644 index 0cc50a0f..00000000 --- a/examples/security/classification/output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/adv_predictions.json - adv_probabilities_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/178d08a45d21c6ecd4e3a7dda8d2d7cf.pkl - params_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/params.yaml - predictions_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/predictions.json - probabilities_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/probabilities.json - score_dict_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/score_dict.json - test_labels_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/test_labels.json - train_labels_file: output/reports/train/0bee96996f3c26c261a4e91af93fa0c7/train_labels.json - model_dir: models - name: 0bee96996f3c26c261a4e91af93fa0c7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f97b0bac67ec2ed4cf0c393930315279 - trainer: - kwargs: {} -name: 0bee96996f3c26c261a4e91af93fa0c7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/params.yaml b/examples/security/classification/output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/params.yaml deleted file mode 100644 index 54b70ef5..00000000 --- a/examples/security/classification/output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/adv_predictions.json - adv_probabilities_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/7412524733f33755f6e6bf7fc9bfc12d.pkl - params_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/params.yaml - predictions_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/predictions.json - probabilities_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/probabilities.json - score_dict_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/score_dict.json - test_labels_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/test_labels.json - train_labels_file: output/reports/train/0bf1a99077756c90d5ee407ba1bd5bb2/train_labels.json - model_dir: models - name: 0bf1a99077756c90d5ee407ba1bd5bb2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d160a0311929c1de16d1551732b57f1f - trainer: - kwargs: {} -name: 0bf1a99077756c90d5ee407ba1bd5bb2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0c02ea4a215d61673a3b7a7e62751476/params.yaml b/examples/security/classification/output/reports/train/0c02ea4a215d61673a3b7a7e62751476/params.yaml deleted file mode 100644 index f775f88e..00000000 --- a/examples/security/classification/output/reports/train/0c02ea4a215d61673a3b7a7e62751476/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/adv_predictions.json - adv_probabilities_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/49e4bbe040cc8612f860a48e409b00e8.pkl - params_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/params.yaml - predictions_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/predictions.json - probabilities_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/probabilities.json - score_dict_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/score_dict.json - test_labels_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/test_labels.json - train_labels_file: output/reports/train/0c02ea4a215d61673a3b7a7e62751476/train_labels.json - model_dir: models - name: 0c02ea4a215d61673a3b7a7e62751476 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1a24ceceee013fe17ad8a1d5dc49442b - trainer: - kwargs: {} -name: 0c02ea4a215d61673a3b7a7e62751476 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/params.yaml b/examples/security/classification/output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/params.yaml deleted file mode 100644 index 650507a9..00000000 --- a/examples/security/classification/output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/adv_predictions.json - adv_probabilities_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/b348b04c9d0f18cd6c8592a7c0851fa7.pkl - params_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/params.yaml - predictions_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/predictions.json - probabilities_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/probabilities.json - score_dict_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/score_dict.json - test_labels_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/test_labels.json - train_labels_file: output/reports/train/0c1a1a96c8a563b64aa21cc3e74b84f3/train_labels.json - model_dir: models - name: 0c1a1a96c8a563b64aa21cc3e74b84f3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2ef085a547de8700af3aa0ad8ac7a42d - trainer: - kwargs: {} -name: 0c1a1a96c8a563b64aa21cc3e74b84f3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/params.yaml b/examples/security/classification/output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/params.yaml deleted file mode 100644 index ee30bb72..00000000 --- a/examples/security/classification/output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/params.yaml +++ /dev/null @@ -1,115 +0,0 @@ -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/adv_predictions.json - adv_probabilities_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl - model_file: output/models/17dd2599534253c240a74baffdd03273.pkl - params_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/params.yaml - predictions_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/predictions.json - probabilities_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/probabilities.json - score_dict_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/score_dict.json - test_labels_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/test_labels.json - train_labels_file: output/reports/train/0c1fa0d07797ba21c91426b746d5ff9c/train_labels.json - model_dir: models - name: 0c1fa0d07797ba21c91426b746d5ff9c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8f2ca3159ecefb60740f8fc10b8fb6a4 - trainer: - kwargs: {} -name: 0c1fa0d07797ba21c91426b746d5ff9c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/params.yaml b/examples/security/classification/output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/params.yaml deleted file mode 100644 index 6b1ecf8f..00000000 --- a/examples/security/classification/output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/adv_predictions.json - adv_probabilities_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/a4d68dfc100dbe08f278f61a91edf587.pkl - params_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/params.yaml - predictions_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/predictions.json - probabilities_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/probabilities.json - score_dict_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/score_dict.json - test_labels_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/test_labels.json - train_labels_file: output/reports/train/0c27e60fa5e3ff10bc0a5994ddb248c5/train_labels.json - model_dir: models - name: 0c27e60fa5e3ff10bc0a5994ddb248c5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6949463f4e71db738baf5787d82e361e - trainer: - kwargs: {} -name: 0c27e60fa5e3ff10bc0a5994ddb248c5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0c39316193b548fbe0bf524f4c8e7367/params.yaml b/examples/security/classification/output/reports/train/0c39316193b548fbe0bf524f4c8e7367/params.yaml deleted file mode 100644 index 41b76b5e..00000000 --- a/examples/security/classification/output/reports/train/0c39316193b548fbe0bf524f4c8e7367/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/adv_predictions.json - adv_probabilities_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/923273f417aa098bbb964ac30ca2fa20.pkl - params_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/params.yaml - predictions_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/predictions.json - probabilities_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/probabilities.json - score_dict_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/score_dict.json - test_labels_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/test_labels.json - train_labels_file: output/reports/train/0c39316193b548fbe0bf524f4c8e7367/train_labels.json - model_dir: models - name: 0c39316193b548fbe0bf524f4c8e7367 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7a05abad56e9b81cfc4b458fffb2d451 - trainer: - kwargs: {} -name: 0c39316193b548fbe0bf524f4c8e7367 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0c8061515992a72c3132639ae99fa34e/params.yaml b/examples/security/classification/output/reports/train/0c8061515992a72c3132639ae99fa34e/params.yaml deleted file mode 100644 index a8e0faf1..00000000 --- a/examples/security/classification/output/reports/train/0c8061515992a72c3132639ae99fa34e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/adv_predictions.json - adv_probabilities_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/abc00bc8e3ac060c8395905fa17d3c4b.pkl - params_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/params.yaml - predictions_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/predictions.json - probabilities_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/probabilities.json - score_dict_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/score_dict.json - test_labels_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/test_labels.json - train_labels_file: output/reports/train/0c8061515992a72c3132639ae99fa34e/train_labels.json - model_dir: models - name: 0c8061515992a72c3132639ae99fa34e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4b168ae1c3750de5d9da3ef11f89025b - trainer: - kwargs: {} -name: 0c8061515992a72c3132639ae99fa34e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/params.yaml b/examples/security/classification/output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/params.yaml deleted file mode 100644 index 5ac3fe81..00000000 --- a/examples/security/classification/output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/adv_predictions.json - adv_probabilities_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/c8f5e1ba7825a78f5dc1b20916d63014.pkl - params_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/params.yaml - predictions_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/predictions.json - probabilities_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/probabilities.json - score_dict_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/score_dict.json - test_labels_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/test_labels.json - train_labels_file: output/reports/train/0cadfbac2d03200b52fab0a4fc05d41f/train_labels.json - model_dir: models - name: 0cadfbac2d03200b52fab0a4fc05d41f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d9ef872ad910f06aaadc01631d15c5f3 - trainer: - kwargs: {} -name: 0cadfbac2d03200b52fab0a4fc05d41f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/params.yaml b/examples/security/classification/output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/params.yaml deleted file mode 100644 index 3eedac60..00000000 --- a/examples/security/classification/output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/adv_predictions.json - adv_probabilities_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/72fe98c80b55c8d08c2e2fd9ef4f4044.pkl - params_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/params.yaml - predictions_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/predictions.json - probabilities_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/probabilities.json - score_dict_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/score_dict.json - test_labels_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/test_labels.json - train_labels_file: output/reports/train/0ce49a1bf2a29dea6d5b79c16fd33e3b/train_labels.json - model_dir: models - name: 0ce49a1bf2a29dea6d5b79c16fd33e3b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3689eae4c5799ed94f3624a9f44bd163 - trainer: - kwargs: {} -name: 0ce49a1bf2a29dea6d5b79c16fd33e3b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0d0549d4634421a342b5c5080adf670b/params.yaml b/examples/security/classification/output/reports/train/0d0549d4634421a342b5c5080adf670b/params.yaml deleted file mode 100644 index a85e0f6a..00000000 --- a/examples/security/classification/output/reports/train/0d0549d4634421a342b5c5080adf670b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/adv_predictions.json - adv_probabilities_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/5d67fd2d76b6bd0f83473d71ae62be32.pkl - params_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/params.yaml - predictions_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/predictions.json - probabilities_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/probabilities.json - score_dict_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/score_dict.json - test_labels_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/test_labels.json - train_labels_file: output/reports/train/0d0549d4634421a342b5c5080adf670b/train_labels.json - model_dir: models - name: 0d0549d4634421a342b5c5080adf670b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e2feca5ce7af775c35d427b18d5609fe - trainer: - kwargs: {} -name: 0d0549d4634421a342b5c5080adf670b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/params.yaml b/examples/security/classification/output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/params.yaml deleted file mode 100644 index cbcab762..00000000 --- a/examples/security/classification/output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/adv_predictions.json - adv_probabilities_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/a63791701b434878552f6f61b12c7187.pkl - params_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/params.yaml - predictions_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/predictions.json - probabilities_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/probabilities.json - score_dict_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/score_dict.json - test_labels_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/test_labels.json - train_labels_file: output/reports/train/0d18dc180db870fa70a3b8c70f856ae9/train_labels.json - model_dir: models - name: 0d18dc180db870fa70a3b8c70f856ae9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c205269193ff8fd0292cfc0519bc39c7 - trainer: - kwargs: {} -name: 0d18dc180db870fa70a3b8c70f856ae9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0d18e2f53098fd32f854ddecce780de0/params.yaml b/examples/security/classification/output/reports/train/0d18e2f53098fd32f854ddecce780de0/params.yaml deleted file mode 100644 index 850cd0af..00000000 --- a/examples/security/classification/output/reports/train/0d18e2f53098fd32f854ddecce780de0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/adv_predictions.json - adv_probabilities_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/8fa478857a3a0e15f186da97471660c5.pkl - params_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/params.yaml - predictions_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/predictions.json - probabilities_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/probabilities.json - score_dict_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/score_dict.json - test_labels_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/test_labels.json - train_labels_file: output/reports/train/0d18e2f53098fd32f854ddecce780de0/train_labels.json - model_dir: models - name: 0d18e2f53098fd32f854ddecce780de0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 12f4994f481446aaeb1b9a535eebb2fb - trainer: - kwargs: {} -name: 0d18e2f53098fd32f854ddecce780de0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/params.yaml b/examples/security/classification/output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/params.yaml deleted file mode 100644 index 1dd9a837..00000000 --- a/examples/security/classification/output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/adv_predictions.json - adv_probabilities_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/e7a0064fc4e65bedf92c4423990dddf6.pkl - params_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/params.yaml - predictions_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/predictions.json - probabilities_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/probabilities.json - score_dict_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/score_dict.json - test_labels_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/test_labels.json - train_labels_file: output/reports/train/0d30acae9f8cd9b29b0145ce697a0e45/train_labels.json - model_dir: models - name: 0d30acae9f8cd9b29b0145ce697a0e45 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9313474e4bd081890219bbbc978f8d98 - trainer: - kwargs: {} -name: 0d30acae9f8cd9b29b0145ce697a0e45 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/params.yaml b/examples/security/classification/output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/params.yaml deleted file mode 100644 index 092b93a0..00000000 --- a/examples/security/classification/output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/adv_predictions.json - adv_probabilities_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/9ede3e3855c6487b6c8903f0113a35d7.pkl - params_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/params.yaml - predictions_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/predictions.json - probabilities_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/probabilities.json - score_dict_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/score_dict.json - test_labels_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/test_labels.json - train_labels_file: output/reports/train/0d4068b0d80543031ea46f95fdc2b48c/train_labels.json - model_dir: models - name: 0d4068b0d80543031ea46f95fdc2b48c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 53520543d262d56f44d864c06a97274d - trainer: - kwargs: {} -name: 0d4068b0d80543031ea46f95fdc2b48c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/params.yaml b/examples/security/classification/output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/params.yaml deleted file mode 100644 index addd0dc5..00000000 --- a/examples/security/classification/output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/adv_predictions.json - adv_probabilities_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/f685424f7eb9581a1ce0176666b370a6.pkl - params_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/params.yaml - predictions_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/predictions.json - probabilities_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/probabilities.json - score_dict_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/score_dict.json - test_labels_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/test_labels.json - train_labels_file: output/reports/train/0d6fcce87274bddbef93dd553f90fa3f/train_labels.json - model_dir: models - name: 0d6fcce87274bddbef93dd553f90fa3f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cfaedb7eb32aec29a3d06e64d112d4dd - trainer: - kwargs: {} -name: 0d6fcce87274bddbef93dd553f90fa3f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/params.yaml b/examples/security/classification/output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/params.yaml deleted file mode 100644 index ca430c38..00000000 --- a/examples/security/classification/output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/adv_predictions.json - adv_probabilities_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/1273a34205a1b632838dddac6f55c606.pkl - params_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/params.yaml - predictions_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/predictions.json - probabilities_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/probabilities.json - score_dict_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/score_dict.json - test_labels_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/test_labels.json - train_labels_file: output/reports/train/0d8d2aca7209fa1142cb57fc2ed23f46/train_labels.json - model_dir: models - name: 0d8d2aca7209fa1142cb57fc2ed23f46 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 58dfc74d7f06ef8431da989923b0fec9 - trainer: - kwargs: {} -name: 0d8d2aca7209fa1142cb57fc2ed23f46 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/params.yaml b/examples/security/classification/output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/params.yaml deleted file mode 100644 index acb9fd11..00000000 --- a/examples/security/classification/output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/adv_predictions.json - adv_probabilities_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/5d7e4954b48aed1eeb2874922f290311.pkl - params_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/params.yaml - predictions_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/predictions.json - probabilities_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/probabilities.json - score_dict_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/score_dict.json - test_labels_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/test_labels.json - train_labels_file: output/reports/train/0dc5e4cb61a5f503c57dee87a7ca88c4/train_labels.json - model_dir: models - name: 0dc5e4cb61a5f503c57dee87a7ca88c4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e5eb3c0e8b2e59312b28728492b39933 - trainer: - kwargs: {} -name: 0dc5e4cb61a5f503c57dee87a7ca88c4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0dd33efd478e4261942441610a42e04e/params.yaml b/examples/security/classification/output/reports/train/0dd33efd478e4261942441610a42e04e/params.yaml deleted file mode 100644 index 066772e0..00000000 --- a/examples/security/classification/output/reports/train/0dd33efd478e4261942441610a42e04e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0dd33efd478e4261942441610a42e04e/adv_predictions.json - adv_probabilities_file: output/reports/train/0dd33efd478e4261942441610a42e04e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/c276cc01da1158fb34e9a529301d75cb.pkl - params_file: output/reports/train/0dd33efd478e4261942441610a42e04e/params.yaml - predictions_file: output/reports/train/0dd33efd478e4261942441610a42e04e/predictions.json - probabilities_file: output/reports/train/0dd33efd478e4261942441610a42e04e/probabilities.json - score_dict_file: output/reports/train/0dd33efd478e4261942441610a42e04e/score_dict.json - test_labels_file: output/reports/train/0dd33efd478e4261942441610a42e04e/test_labels.json - train_labels_file: output/reports/train/0dd33efd478e4261942441610a42e04e/train_labels.json - model_dir: models - name: 0dd33efd478e4261942441610a42e04e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 849d6809c44d6d53ef634fc5784041f2 - trainer: - kwargs: {} -name: 0dd33efd478e4261942441610a42e04e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0def827fc0bb207b2693e3beb54e772c/params.yaml b/examples/security/classification/output/reports/train/0def827fc0bb207b2693e3beb54e772c/params.yaml deleted file mode 100644 index 7da0d978..00000000 --- a/examples/security/classification/output/reports/train/0def827fc0bb207b2693e3beb54e772c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/adv_predictions.json - adv_probabilities_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/1c57dc9f4efab09313669d888d6d0dab.pkl - params_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/params.yaml - predictions_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/predictions.json - probabilities_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/probabilities.json - score_dict_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/score_dict.json - test_labels_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/test_labels.json - train_labels_file: output/reports/train/0def827fc0bb207b2693e3beb54e772c/train_labels.json - model_dir: models - name: 0def827fc0bb207b2693e3beb54e772c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 164d07e1f3e8ffa2a7e5f4f7690592ec - trainer: - kwargs: {} -name: 0def827fc0bb207b2693e3beb54e772c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/params.yaml b/examples/security/classification/output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/params.yaml deleted file mode 100644 index b9b15543..00000000 --- a/examples/security/classification/output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/adv_predictions.json - adv_probabilities_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/180988d29511a978881ddd64cc5453e8.pkl - params_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/params.yaml - predictions_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/predictions.json - probabilities_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/probabilities.json - score_dict_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/score_dict.json - test_labels_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/test_labels.json - train_labels_file: output/reports/train/0e0047344db5c6cb636c4ec18b14c7f9/train_labels.json - model_dir: models - name: 0e0047344db5c6cb636c4ec18b14c7f9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 20c8c86d096aa4847b26a3a5a00b8a61 - trainer: - kwargs: {} -name: 0e0047344db5c6cb636c4ec18b14c7f9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/params.yaml b/examples/security/classification/output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/params.yaml deleted file mode 100644 index bccb2337..00000000 --- a/examples/security/classification/output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/adv_predictions.json - adv_probabilities_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/409e04438d927d2e3a37b8c6cc8a2103.pkl - params_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/params.yaml - predictions_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/predictions.json - probabilities_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/probabilities.json - score_dict_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/score_dict.json - test_labels_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/test_labels.json - train_labels_file: output/reports/train/0e0c3c8d02d83e3d7e1ea10ac24a7167/train_labels.json - model_dir: models - name: 0e0c3c8d02d83e3d7e1ea10ac24a7167 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 61c620a0c465d8d4e744508f1bc539d7 - trainer: - kwargs: {} -name: 0e0c3c8d02d83e3d7e1ea10ac24a7167 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0e206cd6447950cdede4c83ad03bec08/params.yaml b/examples/security/classification/output/reports/train/0e206cd6447950cdede4c83ad03bec08/params.yaml deleted file mode 100644 index cb67ae82..00000000 --- a/examples/security/classification/output/reports/train/0e206cd6447950cdede4c83ad03bec08/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/adv_predictions.json - adv_probabilities_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/a833ec031491192a2fd536baf97e1409.pkl - params_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/params.yaml - predictions_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/predictions.json - probabilities_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/probabilities.json - score_dict_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/score_dict.json - test_labels_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/test_labels.json - train_labels_file: output/reports/train/0e206cd6447950cdede4c83ad03bec08/train_labels.json - model_dir: models - name: 0e206cd6447950cdede4c83ad03bec08 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e5e95fa45d6f3b3a28e94962367e9497 - trainer: - kwargs: {} -name: 0e206cd6447950cdede4c83ad03bec08 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0e228977c9e009dd73a125f58393de88/params.yaml b/examples/security/classification/output/reports/train/0e228977c9e009dd73a125f58393de88/params.yaml deleted file mode 100644 index d85c8b94..00000000 --- a/examples/security/classification/output/reports/train/0e228977c9e009dd73a125f58393de88/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0e228977c9e009dd73a125f58393de88/adv_predictions.json - adv_probabilities_file: output/reports/train/0e228977c9e009dd73a125f58393de88/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/7f0263a3e317993633f64351f4166b04.pkl - params_file: output/reports/train/0e228977c9e009dd73a125f58393de88/params.yaml - predictions_file: output/reports/train/0e228977c9e009dd73a125f58393de88/predictions.json - probabilities_file: output/reports/train/0e228977c9e009dd73a125f58393de88/probabilities.json - score_dict_file: output/reports/train/0e228977c9e009dd73a125f58393de88/score_dict.json - test_labels_file: output/reports/train/0e228977c9e009dd73a125f58393de88/test_labels.json - train_labels_file: output/reports/train/0e228977c9e009dd73a125f58393de88/train_labels.json - model_dir: models - name: 0e228977c9e009dd73a125f58393de88 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d3728c2c59ebb23d0be1a5dfc2f48a98 - trainer: - kwargs: {} -name: 0e228977c9e009dd73a125f58393de88 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0e282965a48347c32a618209a80845ff/params.yaml b/examples/security/classification/output/reports/train/0e282965a48347c32a618209a80845ff/params.yaml deleted file mode 100644 index 880960b7..00000000 --- a/examples/security/classification/output/reports/train/0e282965a48347c32a618209a80845ff/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0e282965a48347c32a618209a80845ff/adv_predictions.json - adv_probabilities_file: output/reports/train/0e282965a48347c32a618209a80845ff/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/d15a3410c58d1a48291caf8ef6bb3462.pkl - params_file: output/reports/train/0e282965a48347c32a618209a80845ff/params.yaml - predictions_file: output/reports/train/0e282965a48347c32a618209a80845ff/predictions.json - probabilities_file: output/reports/train/0e282965a48347c32a618209a80845ff/probabilities.json - score_dict_file: output/reports/train/0e282965a48347c32a618209a80845ff/score_dict.json - test_labels_file: output/reports/train/0e282965a48347c32a618209a80845ff/test_labels.json - train_labels_file: output/reports/train/0e282965a48347c32a618209a80845ff/train_labels.json - model_dir: models - name: 0e282965a48347c32a618209a80845ff - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b2c14edfa9778152e04b73b9fbd24102 - trainer: - kwargs: {} -name: 0e282965a48347c32a618209a80845ff -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/params.yaml b/examples/security/classification/output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/params.yaml deleted file mode 100644 index 95075a26..00000000 --- a/examples/security/classification/output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/adv_predictions.json - adv_probabilities_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/81ab94da7a46b0f47beedb1d40f52fc4.pkl - params_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/params.yaml - predictions_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/predictions.json - probabilities_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/probabilities.json - score_dict_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/score_dict.json - test_labels_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/test_labels.json - train_labels_file: output/reports/train/0e2cb367af60d92a7e73720bce5bd3f0/train_labels.json - model_dir: models - name: 0e2cb367af60d92a7e73720bce5bd3f0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f1c6e4a6e4c50ac60eaa9a97292b68e - trainer: - kwargs: {} -name: 0e2cb367af60d92a7e73720bce5bd3f0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0e347f170a0697a5e87fdd068ca4a398/params.yaml b/examples/security/classification/output/reports/train/0e347f170a0697a5e87fdd068ca4a398/params.yaml deleted file mode 100644 index 6610a85b..00000000 --- a/examples/security/classification/output/reports/train/0e347f170a0697a5e87fdd068ca4a398/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/adv_predictions.json - adv_probabilities_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/93845a378d2f3ceea660d8a4c92231f8.pkl - params_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/params.yaml - predictions_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/predictions.json - probabilities_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/probabilities.json - score_dict_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/score_dict.json - test_labels_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/test_labels.json - train_labels_file: output/reports/train/0e347f170a0697a5e87fdd068ca4a398/train_labels.json - model_dir: models - name: 0e347f170a0697a5e87fdd068ca4a398 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3c01123ce1b1917d73d88771a83a52b9 - trainer: - kwargs: {} -name: 0e347f170a0697a5e87fdd068ca4a398 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/params.yaml b/examples/security/classification/output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/params.yaml deleted file mode 100644 index 6e996443..00000000 --- a/examples/security/classification/output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/adv_predictions.json - adv_probabilities_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/1189593d2dadad50863e99d2a764848f.pkl - params_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/params.yaml - predictions_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/predictions.json - probabilities_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/probabilities.json - score_dict_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/score_dict.json - test_labels_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/test_labels.json - train_labels_file: output/reports/train/0e38cab7e1d79fda0cad75bf1ea1607d/train_labels.json - model_dir: models - name: 0e38cab7e1d79fda0cad75bf1ea1607d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1d7b4b9af60a1db1212c0975fd974142 - trainer: - kwargs: {} -name: 0e38cab7e1d79fda0cad75bf1ea1607d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0f2b1c2c742e2503ceace020d6a16114/params.yaml b/examples/security/classification/output/reports/train/0f2b1c2c742e2503ceace020d6a16114/params.yaml deleted file mode 100644 index 2075a65a..00000000 --- a/examples/security/classification/output/reports/train/0f2b1c2c742e2503ceace020d6a16114/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/adv_predictions.json - adv_probabilities_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/f873166f78313a898578258c372096a6.pkl - params_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/params.yaml - predictions_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/predictions.json - probabilities_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/probabilities.json - score_dict_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/score_dict.json - test_labels_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/test_labels.json - train_labels_file: output/reports/train/0f2b1c2c742e2503ceace020d6a16114/train_labels.json - model_dir: models - name: 0f2b1c2c742e2503ceace020d6a16114 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0e97ea8d3862d96e40da5d0691d42ecc - trainer: - kwargs: {} -name: 0f2b1c2c742e2503ceace020d6a16114 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/params.yaml b/examples/security/classification/output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/params.yaml deleted file mode 100644 index ee2792e2..00000000 --- a/examples/security/classification/output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/adv_predictions.json - adv_probabilities_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/c63f7af6eb028edc19c26784f86556dd.pkl - params_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/params.yaml - predictions_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/predictions.json - probabilities_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/probabilities.json - score_dict_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/score_dict.json - test_labels_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/test_labels.json - train_labels_file: output/reports/train/0f4451942d7932ac8de9d90738c2f2f7/train_labels.json - model_dir: models - name: 0f4451942d7932ac8de9d90738c2f2f7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9510dea77f928ddafa5d1d87bfc5d501 - trainer: - kwargs: {} -name: 0f4451942d7932ac8de9d90738c2f2f7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/params.yaml b/examples/security/classification/output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/params.yaml deleted file mode 100644 index aa395cb0..00000000 --- a/examples/security/classification/output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/adv_predictions.json - adv_probabilities_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/744fde506838d018739ec503693098a3.pkl - params_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/params.yaml - predictions_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/predictions.json - probabilities_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/probabilities.json - score_dict_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/score_dict.json - test_labels_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/test_labels.json - train_labels_file: output/reports/train/0f5eb2d36927f272b7d1120cb7300fa7/train_labels.json - model_dir: models - name: 0f5eb2d36927f272b7d1120cb7300fa7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f3e53cf4aa1489b1f524387b0ed54e14 - trainer: - kwargs: {} -name: 0f5eb2d36927f272b7d1120cb7300fa7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0f7a887deb670c4d82b67a7b0295b448/params.yaml b/examples/security/classification/output/reports/train/0f7a887deb670c4d82b67a7b0295b448/params.yaml deleted file mode 100644 index 888a70cb..00000000 --- a/examples/security/classification/output/reports/train/0f7a887deb670c4d82b67a7b0295b448/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/adv_predictions.json - adv_probabilities_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/8de6098b6787faf64766b20e8363d617.pkl - params_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/params.yaml - predictions_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/predictions.json - probabilities_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/probabilities.json - score_dict_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/score_dict.json - test_labels_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/test_labels.json - train_labels_file: output/reports/train/0f7a887deb670c4d82b67a7b0295b448/train_labels.json - model_dir: models - name: 0f7a887deb670c4d82b67a7b0295b448 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9e94d379ace1cabb4df3d526a609e0bd - trainer: - kwargs: {} -name: 0f7a887deb670c4d82b67a7b0295b448 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/params.yaml b/examples/security/classification/output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/params.yaml deleted file mode 100644 index 27f8ef6e..00000000 --- a/examples/security/classification/output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/adv_predictions.json - adv_probabilities_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/1dd57086ca7cad679531f5341a4fd8c4.pkl - params_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/params.yaml - predictions_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/predictions.json - probabilities_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/probabilities.json - score_dict_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/score_dict.json - test_labels_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/test_labels.json - train_labels_file: output/reports/train/0f8ea54d75fa08c1bbb133a1f23815fb/train_labels.json - model_dir: models - name: 0f8ea54d75fa08c1bbb133a1f23815fb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7f467caeb2ae4fef0ca7fd7e81434097 - trainer: - kwargs: {} -name: 0f8ea54d75fa08c1bbb133a1f23815fb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0f9c218967a186ad7ce4f9af038ff173/params.yaml b/examples/security/classification/output/reports/train/0f9c218967a186ad7ce4f9af038ff173/params.yaml deleted file mode 100644 index 0e227fbc..00000000 --- a/examples/security/classification/output/reports/train/0f9c218967a186ad7ce4f9af038ff173/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/adv_predictions.json - adv_probabilities_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/b3332ffb6a4bd25a27cc162c296102d0.pkl - params_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/params.yaml - predictions_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/predictions.json - probabilities_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/probabilities.json - score_dict_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/score_dict.json - test_labels_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/test_labels.json - train_labels_file: output/reports/train/0f9c218967a186ad7ce4f9af038ff173/train_labels.json - model_dir: models - name: 0f9c218967a186ad7ce4f9af038ff173 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 17300090d914d3e80d8cc455d0a10b6f - trainer: - kwargs: {} -name: 0f9c218967a186ad7ce4f9af038ff173 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/params.yaml b/examples/security/classification/output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/params.yaml deleted file mode 100644 index a60f4daf..00000000 --- a/examples/security/classification/output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/adv_predictions.json - adv_probabilities_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/35b63cc28a9a9f8d031d43197433a360.pkl - params_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/params.yaml - predictions_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/predictions.json - probabilities_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/probabilities.json - score_dict_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/score_dict.json - test_labels_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/test_labels.json - train_labels_file: output/reports/train/0fad3c30765b8753b10bf5814fc6e8b9/train_labels.json - model_dir: models - name: 0fad3c30765b8753b10bf5814fc6e8b9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 76f59f34d9b0984f771bb252b6925627 - trainer: - kwargs: {} -name: 0fad3c30765b8753b10bf5814fc6e8b9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0fb22992fa8091ea012f506c730e942b/params.yaml b/examples/security/classification/output/reports/train/0fb22992fa8091ea012f506c730e942b/params.yaml deleted file mode 100644 index f25e5966..00000000 --- a/examples/security/classification/output/reports/train/0fb22992fa8091ea012f506c730e942b/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/adv_predictions.json - adv_probabilities_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/4c86a5fb2523fe885baa54416502eda0.pkl - params_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/params.yaml - predictions_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/predictions.json - probabilities_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/probabilities.json - score_dict_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/score_dict.json - test_labels_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/test_labels.json - train_labels_file: output/reports/train/0fb22992fa8091ea012f506c730e942b/train_labels.json - model_dir: models - name: 0fb22992fa8091ea012f506c730e942b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e37a1fda7e5387485c9698f5ca8bf5e1 - trainer: - kwargs: {} -name: 0fb22992fa8091ea012f506c730e942b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/params.yaml b/examples/security/classification/output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/params.yaml deleted file mode 100644 index b2b1cefc..00000000 --- a/examples/security/classification/output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/adv_predictions.json - adv_probabilities_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/1b46c2f6d5ea07bc635893aea9d341a9.pkl - params_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/params.yaml - predictions_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/predictions.json - probabilities_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/probabilities.json - score_dict_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/score_dict.json - test_labels_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/test_labels.json - train_labels_file: output/reports/train/0fc23b8b42a7e9e6cfada58a9d0fc957/train_labels.json - model_dir: models - name: 0fc23b8b42a7e9e6cfada58a9d0fc957 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f32b04d9dba7ad93727512acc93e1242 - trainer: - kwargs: {} -name: 0fc23b8b42a7e9e6cfada58a9d0fc957 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/0fe5811dffeb87eadc74552310808f68/params.yaml b/examples/security/classification/output/reports/train/0fe5811dffeb87eadc74552310808f68/params.yaml deleted file mode 100644 index a27178d0..00000000 --- a/examples/security/classification/output/reports/train/0fe5811dffeb87eadc74552310808f68/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/adv_predictions.json - adv_probabilities_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/69466392620719c414c341eafcce2ddb.pkl - params_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/params.yaml - predictions_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/predictions.json - probabilities_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/probabilities.json - score_dict_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/score_dict.json - test_labels_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/test_labels.json - train_labels_file: output/reports/train/0fe5811dffeb87eadc74552310808f68/train_labels.json - model_dir: models - name: 0fe5811dffeb87eadc74552310808f68 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4fc58c79b8e840e45a6eb0a43e2ccb75 - trainer: - kwargs: {} -name: 0fe5811dffeb87eadc74552310808f68 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/100318610dfae98f40dd89f78e5f8605/params.yaml b/examples/security/classification/output/reports/train/100318610dfae98f40dd89f78e5f8605/params.yaml deleted file mode 100644 index 43b8a4c7..00000000 --- a/examples/security/classification/output/reports/train/100318610dfae98f40dd89f78e5f8605/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/adv_predictions.json - adv_probabilities_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/8edb47491421578b3783002edb121041.pkl - params_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/params.yaml - predictions_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/predictions.json - probabilities_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/probabilities.json - score_dict_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/score_dict.json - test_labels_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/test_labels.json - train_labels_file: output/reports/train/100318610dfae98f40dd89f78e5f8605/train_labels.json - model_dir: models - name: 100318610dfae98f40dd89f78e5f8605 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8ac28c961f06663a8e08bab8b17f6861 - trainer: - kwargs: {} -name: 100318610dfae98f40dd89f78e5f8605 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/10358493e0bebb2e34330590fe5e69db/params.yaml b/examples/security/classification/output/reports/train/10358493e0bebb2e34330590fe5e69db/params.yaml deleted file mode 100644 index 3d4802f2..00000000 --- a/examples/security/classification/output/reports/train/10358493e0bebb2e34330590fe5e69db/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/adv_predictions.json - adv_probabilities_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/5cea2e292f1e10664890b39f5f26a09a.pkl - params_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/params.yaml - predictions_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/predictions.json - probabilities_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/probabilities.json - score_dict_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/score_dict.json - test_labels_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/test_labels.json - train_labels_file: output/reports/train/10358493e0bebb2e34330590fe5e69db/train_labels.json - model_dir: models - name: 10358493e0bebb2e34330590fe5e69db - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e7b0a398149dc506173006db21952a39 - trainer: - kwargs: {} -name: 10358493e0bebb2e34330590fe5e69db -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/106341a777e70710628b5eed27ee7528/params.yaml b/examples/security/classification/output/reports/train/106341a777e70710628b5eed27ee7528/params.yaml deleted file mode 100644 index e55c6bea..00000000 --- a/examples/security/classification/output/reports/train/106341a777e70710628b5eed27ee7528/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/106341a777e70710628b5eed27ee7528/adv_predictions.json - adv_probabilities_file: output/reports/train/106341a777e70710628b5eed27ee7528/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/5f0326361c060dce17a8f0396b94a761.pkl - params_file: output/reports/train/106341a777e70710628b5eed27ee7528/params.yaml - predictions_file: output/reports/train/106341a777e70710628b5eed27ee7528/predictions.json - probabilities_file: output/reports/train/106341a777e70710628b5eed27ee7528/probabilities.json - score_dict_file: output/reports/train/106341a777e70710628b5eed27ee7528/score_dict.json - test_labels_file: output/reports/train/106341a777e70710628b5eed27ee7528/test_labels.json - train_labels_file: output/reports/train/106341a777e70710628b5eed27ee7528/train_labels.json - model_dir: models - name: 106341a777e70710628b5eed27ee7528 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 13bb9f56463fb8039c39b1a08ac759da - trainer: - kwargs: {} -name: 106341a777e70710628b5eed27ee7528 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/10826a8147a6ae548686397a04bd8bf6/params.yaml b/examples/security/classification/output/reports/train/10826a8147a6ae548686397a04bd8bf6/params.yaml deleted file mode 100644 index 4cdfa39c..00000000 --- a/examples/security/classification/output/reports/train/10826a8147a6ae548686397a04bd8bf6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/adv_predictions.json - adv_probabilities_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/5674a69a6bdb6bdb998ba71926157d2e.pkl - params_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/params.yaml - predictions_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/predictions.json - probabilities_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/probabilities.json - score_dict_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/score_dict.json - test_labels_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/test_labels.json - train_labels_file: output/reports/train/10826a8147a6ae548686397a04bd8bf6/train_labels.json - model_dir: models - name: 10826a8147a6ae548686397a04bd8bf6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b47e5e24ca2df60dbb3997a713f172ef - trainer: - kwargs: {} -name: 10826a8147a6ae548686397a04bd8bf6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1090f25236da98c8be007ee98daadfcb/params.yaml b/examples/security/classification/output/reports/train/1090f25236da98c8be007ee98daadfcb/params.yaml deleted file mode 100644 index f7628441..00000000 --- a/examples/security/classification/output/reports/train/1090f25236da98c8be007ee98daadfcb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/adv_predictions.json - adv_probabilities_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/3d26dbd2821b58098d4326a1518f6904.pkl - params_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/params.yaml - predictions_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/predictions.json - probabilities_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/probabilities.json - score_dict_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/score_dict.json - test_labels_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/test_labels.json - train_labels_file: output/reports/train/1090f25236da98c8be007ee98daadfcb/train_labels.json - model_dir: models - name: 1090f25236da98c8be007ee98daadfcb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 99c94c94c4ce71bb574e3b9782973b82 - trainer: - kwargs: {} -name: 1090f25236da98c8be007ee98daadfcb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/params.yaml b/examples/security/classification/output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/params.yaml deleted file mode 100644 index 0539d445..00000000 --- a/examples/security/classification/output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/adv_predictions.json - adv_probabilities_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/d5c230e672f708b46a52a90e72e04b7a.pkl - params_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/params.yaml - predictions_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/predictions.json - probabilities_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/probabilities.json - score_dict_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/score_dict.json - test_labels_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/test_labels.json - train_labels_file: output/reports/train/111c6bab346fa1fa02d8d381dd5025e7/train_labels.json - model_dir: models - name: 111c6bab346fa1fa02d8d381dd5025e7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fd3db90368ff22ced0b65d15a5340645 - trainer: - kwargs: {} -name: 111c6bab346fa1fa02d8d381dd5025e7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/118484e3b214b11f7f4967c1c20406f3/params.yaml b/examples/security/classification/output/reports/train/118484e3b214b11f7f4967c1c20406f3/params.yaml deleted file mode 100644 index b0e4f2ec..00000000 --- a/examples/security/classification/output/reports/train/118484e3b214b11f7f4967c1c20406f3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/adv_predictions.json - adv_probabilities_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/48096e1a553f449695d4e35a0fd4e437.pkl - params_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/params.yaml - predictions_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/predictions.json - probabilities_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/probabilities.json - score_dict_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/score_dict.json - test_labels_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/test_labels.json - train_labels_file: output/reports/train/118484e3b214b11f7f4967c1c20406f3/train_labels.json - model_dir: models - name: 118484e3b214b11f7f4967c1c20406f3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dfdd288da267e5407432af125092414f - trainer: - kwargs: {} -name: 118484e3b214b11f7f4967c1c20406f3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/params.yaml b/examples/security/classification/output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/params.yaml deleted file mode 100644 index 532591d6..00000000 --- a/examples/security/classification/output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/adv_predictions.json - adv_probabilities_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/f5fc29a44e0782cfa33444950119645e.pkl - params_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/params.yaml - predictions_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/predictions.json - probabilities_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/probabilities.json - score_dict_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/score_dict.json - test_labels_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/test_labels.json - train_labels_file: output/reports/train/11bc02c732f4c7e9aa085a7867293a3b/train_labels.json - model_dir: models - name: 11bc02c732f4c7e9aa085a7867293a3b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 97f1698801cf8443fd9f71e3714c1196 - trainer: - kwargs: {} -name: 11bc02c732f4c7e9aa085a7867293a3b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/11ea8d77c98f31eb034e9607965700c3/params.yaml b/examples/security/classification/output/reports/train/11ea8d77c98f31eb034e9607965700c3/params.yaml deleted file mode 100644 index ac2db0e2..00000000 --- a/examples/security/classification/output/reports/train/11ea8d77c98f31eb034e9607965700c3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/adv_predictions.json - adv_probabilities_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/ea5a48359bfd57927cea43101a9187ac.pkl - params_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/params.yaml - predictions_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/predictions.json - probabilities_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/probabilities.json - score_dict_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/score_dict.json - test_labels_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/test_labels.json - train_labels_file: output/reports/train/11ea8d77c98f31eb034e9607965700c3/train_labels.json - model_dir: models - name: 11ea8d77c98f31eb034e9607965700c3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f91eab9df5889a56e1f6e16bfa2979b9 - trainer: - kwargs: {} -name: 11ea8d77c98f31eb034e9607965700c3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/params.yaml b/examples/security/classification/output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/params.yaml deleted file mode 100644 index 59686814..00000000 --- a/examples/security/classification/output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/adv_predictions.json - adv_probabilities_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/6f6f36f093175809b6b188e8da0483a8.pkl - params_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/params.yaml - predictions_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/predictions.json - probabilities_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/probabilities.json - score_dict_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/score_dict.json - test_labels_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/test_labels.json - train_labels_file: output/reports/train/11eb99ceec7cf8701bcbe395e7cf1dc3/train_labels.json - model_dir: models - name: 11eb99ceec7cf8701bcbe395e7cf1dc3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 21be5c2198f8c3b44a9fb8ee490fd5cf - trainer: - kwargs: {} -name: 11eb99ceec7cf8701bcbe395e7cf1dc3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/params.yaml b/examples/security/classification/output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/params.yaml deleted file mode 100644 index 3a5cedfa..00000000 --- a/examples/security/classification/output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/adv_predictions.json - adv_probabilities_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/7f7044ad2f5845c2d94334361dd565a5.pkl - params_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/params.yaml - predictions_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/predictions.json - probabilities_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/probabilities.json - score_dict_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/score_dict.json - test_labels_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/test_labels.json - train_labels_file: output/reports/train/11f81e53db4f1c1af2e39a804277a2ac/train_labels.json - model_dir: models - name: 11f81e53db4f1c1af2e39a804277a2ac - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 307ab2d765e6c68899cbc49a25c7d5f5 - trainer: - kwargs: {} -name: 11f81e53db4f1c1af2e39a804277a2ac -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/120f2e30cb41dce0befdd069db85fbe7/params.yaml b/examples/security/classification/output/reports/train/120f2e30cb41dce0befdd069db85fbe7/params.yaml deleted file mode 100644 index 59b074cf..00000000 --- a/examples/security/classification/output/reports/train/120f2e30cb41dce0befdd069db85fbe7/params.yaml +++ /dev/null @@ -1,104 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/adv_predictions.json - adv_probabilities_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/41dbc609bdf64827dc179690126cb28d.pkl - params_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/params.yaml - predictions_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/predictions.json - probabilities_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/probabilities.json - score_dict_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/score_dict.json - test_labels_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/test_labels.json - train_labels_file: output/reports/train/120f2e30cb41dce0befdd069db85fbe7/train_labels.json - model_dir: models - name: 120f2e30cb41dce0befdd069db85fbe7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: - - linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ea6d7a3e5079e5a6e5195670f738b448 - trainer: - kwargs: {} -name: 120f2e30cb41dce0befdd069db85fbe7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/121009943678cb5de20daa9dae3a7d5a/params.yaml b/examples/security/classification/output/reports/train/121009943678cb5de20daa9dae3a7d5a/params.yaml deleted file mode 100644 index 577466dd..00000000 --- a/examples/security/classification/output/reports/train/121009943678cb5de20daa9dae3a7d5a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/adv_predictions.json - adv_probabilities_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/70374df26979aac80bd1761ed19fbef6.pkl - params_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/params.yaml - predictions_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/predictions.json - probabilities_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/probabilities.json - score_dict_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/score_dict.json - test_labels_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/test_labels.json - train_labels_file: output/reports/train/121009943678cb5de20daa9dae3a7d5a/train_labels.json - model_dir: models - name: 121009943678cb5de20daa9dae3a7d5a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 542fbef9e2bf4cf950c8fb135fa783a9 - trainer: - kwargs: {} -name: 121009943678cb5de20daa9dae3a7d5a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1275eb949779a1cb479617cb83181524/params.yaml b/examples/security/classification/output/reports/train/1275eb949779a1cb479617cb83181524/params.yaml deleted file mode 100644 index 8c80d1ed..00000000 --- a/examples/security/classification/output/reports/train/1275eb949779a1cb479617cb83181524/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1275eb949779a1cb479617cb83181524/adv_predictions.json - adv_probabilities_file: output/reports/train/1275eb949779a1cb479617cb83181524/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/bba30c14494ac319508e55329f50a4ce.pkl - params_file: output/reports/train/1275eb949779a1cb479617cb83181524/params.yaml - predictions_file: output/reports/train/1275eb949779a1cb479617cb83181524/predictions.json - probabilities_file: output/reports/train/1275eb949779a1cb479617cb83181524/probabilities.json - score_dict_file: output/reports/train/1275eb949779a1cb479617cb83181524/score_dict.json - test_labels_file: output/reports/train/1275eb949779a1cb479617cb83181524/test_labels.json - train_labels_file: output/reports/train/1275eb949779a1cb479617cb83181524/train_labels.json - model_dir: models - name: 1275eb949779a1cb479617cb83181524 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9a8efb6959dc9426d83350d98cd95998 - trainer: - kwargs: {} -name: 1275eb949779a1cb479617cb83181524 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/params.yaml b/examples/security/classification/output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/params.yaml deleted file mode 100644 index d0c090f6..00000000 --- a/examples/security/classification/output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/adv_predictions.json - adv_probabilities_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/7540c22fd98f6ebb5445a4075b208f41.pkl - params_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/params.yaml - predictions_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/predictions.json - probabilities_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/probabilities.json - score_dict_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/score_dict.json - test_labels_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/test_labels.json - train_labels_file: output/reports/train/1276bcf7673322a2ad6ec736fd0b9968/train_labels.json - model_dir: models - name: 1276bcf7673322a2ad6ec736fd0b9968 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - degree: 5 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1a19e02570637690f439cc8381bca86c - trainer: - kwargs: {} -name: 1276bcf7673322a2ad6ec736fd0b9968 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/12b6db77866de920c570921245988134/params.yaml b/examples/security/classification/output/reports/train/12b6db77866de920c570921245988134/params.yaml deleted file mode 100644 index 54380053..00000000 --- a/examples/security/classification/output/reports/train/12b6db77866de920c570921245988134/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/12b6db77866de920c570921245988134/adv_predictions.json - adv_probabilities_file: output/reports/train/12b6db77866de920c570921245988134/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/bdf5bda7db5bdd61bdb3b31fce8c22c3.pkl - params_file: output/reports/train/12b6db77866de920c570921245988134/params.yaml - predictions_file: output/reports/train/12b6db77866de920c570921245988134/predictions.json - probabilities_file: output/reports/train/12b6db77866de920c570921245988134/probabilities.json - score_dict_file: output/reports/train/12b6db77866de920c570921245988134/score_dict.json - test_labels_file: output/reports/train/12b6db77866de920c570921245988134/test_labels.json - train_labels_file: output/reports/train/12b6db77866de920c570921245988134/train_labels.json - model_dir: models - name: 12b6db77866de920c570921245988134 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 790f125b64769c85f526687bdbbfcce1 - trainer: - kwargs: {} -name: 12b6db77866de920c570921245988134 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/12d3e989d4e665423bf4fef63c096d12/params.yaml b/examples/security/classification/output/reports/train/12d3e989d4e665423bf4fef63c096d12/params.yaml deleted file mode 100644 index 859b1b7c..00000000 --- a/examples/security/classification/output/reports/train/12d3e989d4e665423bf4fef63c096d12/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/adv_predictions.json - adv_probabilities_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/a9892ad628074772eca263cd6791e2e8.pkl - params_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/params.yaml - predictions_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/predictions.json - probabilities_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/probabilities.json - score_dict_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/score_dict.json - test_labels_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/test_labels.json - train_labels_file: output/reports/train/12d3e989d4e665423bf4fef63c096d12/train_labels.json - model_dir: models - name: 12d3e989d4e665423bf4fef63c096d12 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 718d2dd949f6242b1a1b72a2de696fbe - trainer: - kwargs: {} -name: 12d3e989d4e665423bf4fef63c096d12 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/params.yaml b/examples/security/classification/output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/params.yaml deleted file mode 100644 index f724f5ec..00000000 --- a/examples/security/classification/output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/adv_predictions.json - adv_probabilities_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/466ade6c1824e50cf5238401f4eb91ef.pkl - params_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/params.yaml - predictions_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/predictions.json - probabilities_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/probabilities.json - score_dict_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/score_dict.json - test_labels_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/test_labels.json - train_labels_file: output/reports/train/1332e4ea5ace8d9bec5a60237a51c4f7/train_labels.json - model_dir: models - name: 1332e4ea5ace8d9bec5a60237a51c4f7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b56cca625133a3f0cd392daffce0aa9c - trainer: - kwargs: {} -name: 1332e4ea5ace8d9bec5a60237a51c4f7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/134e17a661938c84607f12e0cc6e7d5f/params.yaml b/examples/security/classification/output/reports/train/134e17a661938c84607f12e0cc6e7d5f/params.yaml deleted file mode 100644 index 9e30e05a..00000000 --- a/examples/security/classification/output/reports/train/134e17a661938c84607f12e0cc6e7d5f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/adv_predictions.json - adv_probabilities_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/15f325bf6cb87b78d099dfd09439d49c.pkl - params_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/params.yaml - predictions_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/predictions.json - probabilities_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/probabilities.json - score_dict_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/score_dict.json - test_labels_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/test_labels.json - train_labels_file: output/reports/train/134e17a661938c84607f12e0cc6e7d5f/train_labels.json - model_dir: models - name: 134e17a661938c84607f12e0cc6e7d5f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 067a52cb119cffbada52271c39482a8e - trainer: - kwargs: {} -name: 134e17a661938c84607f12e0cc6e7d5f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/137f7bbd7a5dce39d486fba49f78d225/params.yaml b/examples/security/classification/output/reports/train/137f7bbd7a5dce39d486fba49f78d225/params.yaml deleted file mode 100644 index d1b132b6..00000000 --- a/examples/security/classification/output/reports/train/137f7bbd7a5dce39d486fba49f78d225/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/adv_predictions.json - adv_probabilities_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/b910ff668784f88a0622fc06b0bd5718.pkl - params_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/params.yaml - predictions_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/predictions.json - probabilities_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/probabilities.json - score_dict_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/score_dict.json - test_labels_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/test_labels.json - train_labels_file: output/reports/train/137f7bbd7a5dce39d486fba49f78d225/train_labels.json - model_dir: models - name: 137f7bbd7a5dce39d486fba49f78d225 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c47ebe2fefc687989fbe1fbaac8f4e27 - trainer: - kwargs: {} -name: 137f7bbd7a5dce39d486fba49f78d225 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/138b6f0393949a4fd99dc0f0a7237825/params.yaml b/examples/security/classification/output/reports/train/138b6f0393949a4fd99dc0f0a7237825/params.yaml deleted file mode 100644 index e690e629..00000000 --- a/examples/security/classification/output/reports/train/138b6f0393949a4fd99dc0f0a7237825/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/adv_predictions.json - adv_probabilities_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/38e696a735037eb509b1cd77e487c8dc.pkl - params_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/params.yaml - predictions_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/predictions.json - probabilities_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/probabilities.json - score_dict_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/score_dict.json - test_labels_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/test_labels.json - train_labels_file: output/reports/train/138b6f0393949a4fd99dc0f0a7237825/train_labels.json - model_dir: models - name: 138b6f0393949a4fd99dc0f0a7237825 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cb5be94ecfefebd9f2809e188b952066 - trainer: - kwargs: {} -name: 138b6f0393949a4fd99dc0f0a7237825 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/params.yaml b/examples/security/classification/output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/params.yaml deleted file mode 100644 index 6073eac7..00000000 --- a/examples/security/classification/output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/adv_predictions.json - adv_probabilities_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/707a384f777297e8e0f9ff69102f38e3.pkl - params_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/params.yaml - predictions_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/predictions.json - probabilities_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/probabilities.json - score_dict_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/score_dict.json - test_labels_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/test_labels.json - train_labels_file: output/reports/train/13a9afea89d9ba8ea9e1548f2ab56a92/train_labels.json - model_dir: models - name: 13a9afea89d9ba8ea9e1548f2ab56a92 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - degree: 3 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8443ae9f4793b0bf767d966206b90d1f - trainer: - kwargs: {} -name: 13a9afea89d9ba8ea9e1548f2ab56a92 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/13add0b2800a21855748642f0da14485/params.yaml b/examples/security/classification/output/reports/train/13add0b2800a21855748642f0da14485/params.yaml deleted file mode 100644 index 0f1c946c..00000000 --- a/examples/security/classification/output/reports/train/13add0b2800a21855748642f0da14485/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/13add0b2800a21855748642f0da14485/adv_predictions.json - adv_probabilities_file: output/reports/train/13add0b2800a21855748642f0da14485/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/44d71f6f690ba77d76f8f6ece59456cc.pkl - params_file: output/reports/train/13add0b2800a21855748642f0da14485/params.yaml - predictions_file: output/reports/train/13add0b2800a21855748642f0da14485/predictions.json - probabilities_file: output/reports/train/13add0b2800a21855748642f0da14485/probabilities.json - score_dict_file: output/reports/train/13add0b2800a21855748642f0da14485/score_dict.json - test_labels_file: output/reports/train/13add0b2800a21855748642f0da14485/test_labels.json - train_labels_file: output/reports/train/13add0b2800a21855748642f0da14485/train_labels.json - model_dir: models - name: 13add0b2800a21855748642f0da14485 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 260d7e18a94d2d7b8652ef05c7c3e039 - trainer: - kwargs: {} -name: 13add0b2800a21855748642f0da14485 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/13da55851dd21ca361c6952d9e299ba4/params.yaml b/examples/security/classification/output/reports/train/13da55851dd21ca361c6952d9e299ba4/params.yaml deleted file mode 100644 index e76a0336..00000000 --- a/examples/security/classification/output/reports/train/13da55851dd21ca361c6952d9e299ba4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/adv_predictions.json - adv_probabilities_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/b3c09a83b3bfaee19a350c03da2fc0a1.pkl - params_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/params.yaml - predictions_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/predictions.json - probabilities_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/probabilities.json - score_dict_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/score_dict.json - test_labels_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/test_labels.json - train_labels_file: output/reports/train/13da55851dd21ca361c6952d9e299ba4/train_labels.json - model_dir: models - name: 13da55851dd21ca361c6952d9e299ba4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7cd3ddd0dcd13a877686fe470f3b9f73 - trainer: - kwargs: {} -name: 13da55851dd21ca361c6952d9e299ba4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1403c0b709f069b5d268dc2180be68c7/params.yaml b/examples/security/classification/output/reports/train/1403c0b709f069b5d268dc2180be68c7/params.yaml deleted file mode 100644 index 104c5fca..00000000 --- a/examples/security/classification/output/reports/train/1403c0b709f069b5d268dc2180be68c7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/adv_predictions.json - adv_probabilities_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/e36837df2432b15cdd7cfb9c8308ea7d.pkl - params_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/params.yaml - predictions_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/predictions.json - probabilities_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/probabilities.json - score_dict_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/score_dict.json - test_labels_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/test_labels.json - train_labels_file: output/reports/train/1403c0b709f069b5d268dc2180be68c7/train_labels.json - model_dir: models - name: 1403c0b709f069b5d268dc2180be68c7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2df8e6a6ec8a9179739a691f84766240 - trainer: - kwargs: {} -name: 1403c0b709f069b5d268dc2180be68c7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/141f9842c9bd134ff224303f8ffe4776/params.yaml b/examples/security/classification/output/reports/train/141f9842c9bd134ff224303f8ffe4776/params.yaml deleted file mode 100644 index f6269207..00000000 --- a/examples/security/classification/output/reports/train/141f9842c9bd134ff224303f8ffe4776/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/adv_predictions.json - adv_probabilities_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/f9dcbe528a650ea0d2a0cda22d603e3e.pkl - params_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/params.yaml - predictions_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/predictions.json - probabilities_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/probabilities.json - score_dict_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/score_dict.json - test_labels_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/test_labels.json - train_labels_file: output/reports/train/141f9842c9bd134ff224303f8ffe4776/train_labels.json - model_dir: models - name: 141f9842c9bd134ff224303f8ffe4776 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 19c9423408b4f1f16543458c6279152f - trainer: - kwargs: {} -name: 141f9842c9bd134ff224303f8ffe4776 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1439debd522cec47886736238e68a397/params.yaml b/examples/security/classification/output/reports/train/1439debd522cec47886736238e68a397/params.yaml deleted file mode 100644 index 2bb13a7c..00000000 --- a/examples/security/classification/output/reports/train/1439debd522cec47886736238e68a397/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1439debd522cec47886736238e68a397/adv_predictions.json - adv_probabilities_file: output/reports/train/1439debd522cec47886736238e68a397/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/e0602cd2730e0c364fe71c47363d992f.pkl - params_file: output/reports/train/1439debd522cec47886736238e68a397/params.yaml - predictions_file: output/reports/train/1439debd522cec47886736238e68a397/predictions.json - probabilities_file: output/reports/train/1439debd522cec47886736238e68a397/probabilities.json - score_dict_file: output/reports/train/1439debd522cec47886736238e68a397/score_dict.json - test_labels_file: output/reports/train/1439debd522cec47886736238e68a397/test_labels.json - train_labels_file: output/reports/train/1439debd522cec47886736238e68a397/train_labels.json - model_dir: models - name: 1439debd522cec47886736238e68a397 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 54172b2e3517e6413b20757de89ab848 - trainer: - kwargs: {} -name: 1439debd522cec47886736238e68a397 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/143df2901130c73517781cf8701305e1/params.yaml b/examples/security/classification/output/reports/train/143df2901130c73517781cf8701305e1/params.yaml deleted file mode 100644 index bc387bae..00000000 --- a/examples/security/classification/output/reports/train/143df2901130c73517781cf8701305e1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/143df2901130c73517781cf8701305e1/adv_predictions.json - adv_probabilities_file: output/reports/train/143df2901130c73517781cf8701305e1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/e1e7c85dd85f3f5809f907f163a81d62.pkl - params_file: output/reports/train/143df2901130c73517781cf8701305e1/params.yaml - predictions_file: output/reports/train/143df2901130c73517781cf8701305e1/predictions.json - probabilities_file: output/reports/train/143df2901130c73517781cf8701305e1/probabilities.json - score_dict_file: output/reports/train/143df2901130c73517781cf8701305e1/score_dict.json - test_labels_file: output/reports/train/143df2901130c73517781cf8701305e1/test_labels.json - train_labels_file: output/reports/train/143df2901130c73517781cf8701305e1/train_labels.json - model_dir: models - name: 143df2901130c73517781cf8701305e1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 59e65d050302e413f95c5902fedcef43 - trainer: - kwargs: {} -name: 143df2901130c73517781cf8701305e1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/147865c09e0df810777ef7b08cc1cce1/params.yaml b/examples/security/classification/output/reports/train/147865c09e0df810777ef7b08cc1cce1/params.yaml deleted file mode 100644 index b3942573..00000000 --- a/examples/security/classification/output/reports/train/147865c09e0df810777ef7b08cc1cce1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/adv_predictions.json - adv_probabilities_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/5eb8346f718e3e349c4dc6a3c782b31b.pkl - params_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/params.yaml - predictions_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/predictions.json - probabilities_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/probabilities.json - score_dict_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/score_dict.json - test_labels_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/test_labels.json - train_labels_file: output/reports/train/147865c09e0df810777ef7b08cc1cce1/train_labels.json - model_dir: models - name: 147865c09e0df810777ef7b08cc1cce1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6b050b87eb9a1c9637dab12ce92ac266 - trainer: - kwargs: {} -name: 147865c09e0df810777ef7b08cc1cce1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/148d30dc884128bf35f91943a72b0055/params.yaml b/examples/security/classification/output/reports/train/148d30dc884128bf35f91943a72b0055/params.yaml deleted file mode 100644 index a3e7e1e6..00000000 --- a/examples/security/classification/output/reports/train/148d30dc884128bf35f91943a72b0055/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/148d30dc884128bf35f91943a72b0055/adv_predictions.json - adv_probabilities_file: output/reports/train/148d30dc884128bf35f91943a72b0055/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/1b0d0a5a49e78c5e5f6004539de9ac7b.pkl - params_file: output/reports/train/148d30dc884128bf35f91943a72b0055/params.yaml - predictions_file: output/reports/train/148d30dc884128bf35f91943a72b0055/predictions.json - probabilities_file: output/reports/train/148d30dc884128bf35f91943a72b0055/probabilities.json - score_dict_file: output/reports/train/148d30dc884128bf35f91943a72b0055/score_dict.json - test_labels_file: output/reports/train/148d30dc884128bf35f91943a72b0055/test_labels.json - train_labels_file: output/reports/train/148d30dc884128bf35f91943a72b0055/train_labels.json - model_dir: models - name: 148d30dc884128bf35f91943a72b0055 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 63d7ae73f0fd299c7782cfcf4e8d90f3 - trainer: - kwargs: {} -name: 148d30dc884128bf35f91943a72b0055 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/params.yaml b/examples/security/classification/output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/params.yaml deleted file mode 100644 index 0293de23..00000000 --- a/examples/security/classification/output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/adv_predictions.json - adv_probabilities_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/d2d2feaf959775bddab0a59121a4da57.pkl - params_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/params.yaml - predictions_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/predictions.json - probabilities_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/probabilities.json - score_dict_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/score_dict.json - test_labels_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/test_labels.json - train_labels_file: output/reports/train/14a157c3cd09eaff8ca2d09cda767e92/train_labels.json - model_dir: models - name: 14a157c3cd09eaff8ca2d09cda767e92 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6095a76063b03f7fa108fc94a0963971 - trainer: - kwargs: {} -name: 14a157c3cd09eaff8ca2d09cda767e92 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/params.yaml b/examples/security/classification/output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/params.yaml deleted file mode 100644 index fe342148..00000000 --- a/examples/security/classification/output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/adv_predictions.json - adv_probabilities_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/ff0923d0a09e2de20f5be4e451694461.pkl - params_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/params.yaml - predictions_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/predictions.json - probabilities_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/probabilities.json - score_dict_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/score_dict.json - test_labels_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/test_labels.json - train_labels_file: output/reports/train/14c19f51b442c6b1a12ebe5491c377c8/train_labels.json - model_dir: models - name: 14c19f51b442c6b1a12ebe5491c377c8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 37ddc1ab6995a6e7ba83478e41eaf5fb - trainer: - kwargs: {} -name: 14c19f51b442c6b1a12ebe5491c377c8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/15098fe67344bd8982332022be92b018/params.yaml b/examples/security/classification/output/reports/train/15098fe67344bd8982332022be92b018/params.yaml deleted file mode 100644 index 521011e9..00000000 --- a/examples/security/classification/output/reports/train/15098fe67344bd8982332022be92b018/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/15098fe67344bd8982332022be92b018/adv_predictions.json - adv_probabilities_file: output/reports/train/15098fe67344bd8982332022be92b018/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/2a665aed0ea985fd5b98fc14ebd7dc48.pkl - params_file: output/reports/train/15098fe67344bd8982332022be92b018/params.yaml - predictions_file: output/reports/train/15098fe67344bd8982332022be92b018/predictions.json - probabilities_file: output/reports/train/15098fe67344bd8982332022be92b018/probabilities.json - score_dict_file: output/reports/train/15098fe67344bd8982332022be92b018/score_dict.json - test_labels_file: output/reports/train/15098fe67344bd8982332022be92b018/test_labels.json - train_labels_file: output/reports/train/15098fe67344bd8982332022be92b018/train_labels.json - model_dir: models - name: 15098fe67344bd8982332022be92b018 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8f33f761e2d88992fb0154e6cf18ceee - trainer: - kwargs: {} -name: 15098fe67344bd8982332022be92b018 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/151e3deb462099cdf4496c4796b293c2/params.yaml b/examples/security/classification/output/reports/train/151e3deb462099cdf4496c4796b293c2/params.yaml deleted file mode 100644 index e9618304..00000000 --- a/examples/security/classification/output/reports/train/151e3deb462099cdf4496c4796b293c2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/adv_predictions.json - adv_probabilities_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/5324c4c2c39310a7b3161a373a9f8313.pkl - params_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/params.yaml - predictions_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/predictions.json - probabilities_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/probabilities.json - score_dict_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/score_dict.json - test_labels_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/test_labels.json - train_labels_file: output/reports/train/151e3deb462099cdf4496c4796b293c2/train_labels.json - model_dir: models - name: 151e3deb462099cdf4496c4796b293c2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cd25b2328adb62e604c082638a497800 - trainer: - kwargs: {} -name: 151e3deb462099cdf4496c4796b293c2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/15201afc0b8e43514ad879971941fa1e/params.yaml b/examples/security/classification/output/reports/train/15201afc0b8e43514ad879971941fa1e/params.yaml deleted file mode 100644 index a8d00780..00000000 --- a/examples/security/classification/output/reports/train/15201afc0b8e43514ad879971941fa1e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/adv_predictions.json - adv_probabilities_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/bbc9bbb57da59d9742afd9e11ffbb81f.pkl - params_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/params.yaml - predictions_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/predictions.json - probabilities_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/probabilities.json - score_dict_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/score_dict.json - test_labels_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/test_labels.json - train_labels_file: output/reports/train/15201afc0b8e43514ad879971941fa1e/train_labels.json - model_dir: models - name: 15201afc0b8e43514ad879971941fa1e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: da830d2f4a02a7d53d3b71ea968ab5a8 - trainer: - kwargs: {} -name: 15201afc0b8e43514ad879971941fa1e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/params.yaml b/examples/security/classification/output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/params.yaml deleted file mode 100644 index 93879ec1..00000000 --- a/examples/security/classification/output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/params.yaml +++ /dev/null @@ -1,206 +0,0 @@ -attack: - attack_size: 10 - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 10.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 100 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4a64a27887eddfba72101a2193a0427e - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a7503df302b92df27cf7049fdedde437 - trainer: - kwargs: {} - name: 6596818bd280cf9fb05bd0cbe16436aa -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/adv_predictions.json - adv_probabilities_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/adv_probabilities.json - attack_file: output/attacks/1d7a29e561ce6bc38bf891d1b54cd2f4.pkl - data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl - model_file: output/models/260dffdba880f065a90befb1e5c06a91.pkl - params_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/params.yaml - predictions_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/predictions.json - probabilities_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/probabilities.json - score_dict_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/score_dict.json - test_labels_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/test_labels.json - train_labels_file: output/reports/train/152178b0f0c8ce53a2d7d562004b1ca7/train_labels.json - model_dir: models - name: 152178b0f0c8ce53a2d7d562004b1ca7 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8b2bbe7eb707d9f98887cfa8e8e8c70b - trainer: - kwargs: {} -name: 152178b0f0c8ce53a2d7d562004b1ca7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/params.yaml b/examples/security/classification/output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/params.yaml deleted file mode 100644 index 63b46ab4..00000000 --- a/examples/security/classification/output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/adv_predictions.json - adv_probabilities_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/efad4e9a55c7c4938aab5b756e373e78.pkl - params_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/params.yaml - predictions_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/predictions.json - probabilities_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/probabilities.json - score_dict_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/score_dict.json - test_labels_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/test_labels.json - train_labels_file: output/reports/train/1579105c4e9ce3f2aadbc7f43887906b/train_labels.json - model_dir: models - name: 1579105c4e9ce3f2aadbc7f43887906b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5548c9a85a09dcb8f060565244f4f75d - trainer: - kwargs: {} -name: 1579105c4e9ce3f2aadbc7f43887906b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/params.yaml b/examples/security/classification/output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/params.yaml deleted file mode 100644 index 0cb69955..00000000 --- a/examples/security/classification/output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/adv_predictions.json - adv_probabilities_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/a6629ff8c914806188cf99f326a0f20d.pkl - params_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/params.yaml - predictions_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/predictions.json - probabilities_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/probabilities.json - score_dict_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/score_dict.json - test_labels_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/test_labels.json - train_labels_file: output/reports/train/15b32c7b634e6ed64a1a6477cf926a1a/train_labels.json - model_dir: models - name: 15b32c7b634e6ed64a1a6477cf926a1a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 71f54a45ae44efcc908900566b306085 - trainer: - kwargs: {} -name: 15b32c7b634e6ed64a1a6477cf926a1a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/15c048e332626e1e59aee6e65c2e98b5/params.yaml b/examples/security/classification/output/reports/train/15c048e332626e1e59aee6e65c2e98b5/params.yaml deleted file mode 100644 index cbeae6cb..00000000 --- a/examples/security/classification/output/reports/train/15c048e332626e1e59aee6e65c2e98b5/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/adv_predictions.json - adv_probabilities_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/10329f085c30b024374b9afc06a5bc17.pkl - params_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/params.yaml - predictions_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/predictions.json - probabilities_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/probabilities.json - score_dict_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/score_dict.json - test_labels_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/test_labels.json - train_labels_file: output/reports/train/15c048e332626e1e59aee6e65c2e98b5/train_labels.json - model_dir: models - name: 15c048e332626e1e59aee6e65c2e98b5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5040e28503542a88f963e33e9bae8564 - trainer: - kwargs: {} -name: 15c048e332626e1e59aee6e65c2e98b5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/params.yaml b/examples/security/classification/output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/params.yaml deleted file mode 100644 index 26d920c4..00000000 --- a/examples/security/classification/output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/adv_predictions.json - adv_probabilities_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/9c3514d534f8b810c8ac7db47a8f75f0.pkl - params_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/params.yaml - predictions_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/predictions.json - probabilities_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/probabilities.json - score_dict_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/score_dict.json - test_labels_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/test_labels.json - train_labels_file: output/reports/train/15d2b3ea07a8a69be454b7c33d75a3d1/train_labels.json - model_dir: models - name: 15d2b3ea07a8a69be454b7c33d75a3d1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ff3242790ef596122728249f14822fd1 - trainer: - kwargs: {} -name: 15d2b3ea07a8a69be454b7c33d75a3d1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/params.yaml b/examples/security/classification/output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/params.yaml deleted file mode 100644 index 9e34173a..00000000 --- a/examples/security/classification/output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/adv_predictions.json - adv_probabilities_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/8eb1e9d34f755dc4a5bb749ccd5d162a.pkl - params_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/params.yaml - predictions_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/predictions.json - probabilities_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/probabilities.json - score_dict_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/score_dict.json - test_labels_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/test_labels.json - train_labels_file: output/reports/train/15eb7e57e29435c26a27d6fb9cbdceab/train_labels.json - model_dir: models - name: 15eb7e57e29435c26a27d6fb9cbdceab - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4b04510e6201ef58cf3f25d11a5c9dde - trainer: - kwargs: {} -name: 15eb7e57e29435c26a27d6fb9cbdceab -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/params.yaml b/examples/security/classification/output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/params.yaml deleted file mode 100644 index 5cb75825..00000000 --- a/examples/security/classification/output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/adv_predictions.json - adv_probabilities_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/3738416970fdcb44c35242dd4a558655.pkl - params_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/params.yaml - predictions_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/predictions.json - probabilities_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/probabilities.json - score_dict_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/score_dict.json - test_labels_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/test_labels.json - train_labels_file: output/reports/train/1613a6c768c3023775d37f8c8cf4c09c/train_labels.json - model_dir: models - name: 1613a6c768c3023775d37f8c8cf4c09c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fdf913427db728fb0c90069a93f93f43 - trainer: - kwargs: {} -name: 1613a6c768c3023775d37f8c8cf4c09c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1614987e5279a2031253809bb2547a9c/params.yaml b/examples/security/classification/output/reports/train/1614987e5279a2031253809bb2547a9c/params.yaml deleted file mode 100644 index f77270b4..00000000 --- a/examples/security/classification/output/reports/train/1614987e5279a2031253809bb2547a9c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1614987e5279a2031253809bb2547a9c/adv_predictions.json - adv_probabilities_file: output/reports/train/1614987e5279a2031253809bb2547a9c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/b09f451313031bea79441d0f77a7889e.pkl - params_file: output/reports/train/1614987e5279a2031253809bb2547a9c/params.yaml - predictions_file: output/reports/train/1614987e5279a2031253809bb2547a9c/predictions.json - probabilities_file: output/reports/train/1614987e5279a2031253809bb2547a9c/probabilities.json - score_dict_file: output/reports/train/1614987e5279a2031253809bb2547a9c/score_dict.json - test_labels_file: output/reports/train/1614987e5279a2031253809bb2547a9c/test_labels.json - train_labels_file: output/reports/train/1614987e5279a2031253809bb2547a9c/train_labels.json - model_dir: models - name: 1614987e5279a2031253809bb2547a9c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a2b46d609858a3e79dda3f29566f94c8 - trainer: - kwargs: {} -name: 1614987e5279a2031253809bb2547a9c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/16150a4a676ba686f1870b001a0f4fbd/params.yaml b/examples/security/classification/output/reports/train/16150a4a676ba686f1870b001a0f4fbd/params.yaml deleted file mode 100644 index 99729d80..00000000 --- a/examples/security/classification/output/reports/train/16150a4a676ba686f1870b001a0f4fbd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/adv_predictions.json - adv_probabilities_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/5897cb23f18dcc6760df7bf0be722a63.pkl - params_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/params.yaml - predictions_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/predictions.json - probabilities_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/probabilities.json - score_dict_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/score_dict.json - test_labels_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/test_labels.json - train_labels_file: output/reports/train/16150a4a676ba686f1870b001a0f4fbd/train_labels.json - model_dir: models - name: 16150a4a676ba686f1870b001a0f4fbd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c0d10cc9291bed70387629aa6e2b7786 - trainer: - kwargs: {} -name: 16150a4a676ba686f1870b001a0f4fbd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/161b361d9f34e0133d4fc3856ece499c/params.yaml b/examples/security/classification/output/reports/train/161b361d9f34e0133d4fc3856ece499c/params.yaml deleted file mode 100644 index dd086cf3..00000000 --- a/examples/security/classification/output/reports/train/161b361d9f34e0133d4fc3856ece499c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/adv_predictions.json - adv_probabilities_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/6dc7dfd8c04684d8705802e01eb6c239.pkl - params_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/params.yaml - predictions_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/predictions.json - probabilities_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/probabilities.json - score_dict_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/score_dict.json - test_labels_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/test_labels.json - train_labels_file: output/reports/train/161b361d9f34e0133d4fc3856ece499c/train_labels.json - model_dir: models - name: 161b361d9f34e0133d4fc3856ece499c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ea43b510c676803030f23fc49c56d331 - trainer: - kwargs: {} -name: 161b361d9f34e0133d4fc3856ece499c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/163d2a9c46f06251509b446886270151/params.yaml b/examples/security/classification/output/reports/train/163d2a9c46f06251509b446886270151/params.yaml deleted file mode 100644 index cf2524a4..00000000 --- a/examples/security/classification/output/reports/train/163d2a9c46f06251509b446886270151/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/163d2a9c46f06251509b446886270151/adv_predictions.json - adv_probabilities_file: output/reports/train/163d2a9c46f06251509b446886270151/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e2ac301c2212d0f75b7d935b728a6ba0.pkl - params_file: output/reports/train/163d2a9c46f06251509b446886270151/params.yaml - predictions_file: output/reports/train/163d2a9c46f06251509b446886270151/predictions.json - probabilities_file: output/reports/train/163d2a9c46f06251509b446886270151/probabilities.json - score_dict_file: output/reports/train/163d2a9c46f06251509b446886270151/score_dict.json - test_labels_file: output/reports/train/163d2a9c46f06251509b446886270151/test_labels.json - train_labels_file: output/reports/train/163d2a9c46f06251509b446886270151/train_labels.json - model_dir: models - name: 163d2a9c46f06251509b446886270151 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3b6e652200da4b8d9f409f3176286662 - trainer: - kwargs: {} -name: 163d2a9c46f06251509b446886270151 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/16442d640ef7b6020a37bbaf081f39db/params.yaml b/examples/security/classification/output/reports/train/16442d640ef7b6020a37bbaf081f39db/params.yaml deleted file mode 100644 index 1bf9e504..00000000 --- a/examples/security/classification/output/reports/train/16442d640ef7b6020a37bbaf081f39db/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/adv_predictions.json - adv_probabilities_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/3261493abbddc2629f6d06931e3a3bb5.pkl - params_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/params.yaml - predictions_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/predictions.json - probabilities_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/probabilities.json - score_dict_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/score_dict.json - test_labels_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/test_labels.json - train_labels_file: output/reports/train/16442d640ef7b6020a37bbaf081f39db/train_labels.json - model_dir: models - name: 16442d640ef7b6020a37bbaf081f39db - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 26e3fec9e84eac268d7450d6ffcbf55a - trainer: - kwargs: {} -name: 16442d640ef7b6020a37bbaf081f39db -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/16776c299566237d0345fd043fb687e5/params.yaml b/examples/security/classification/output/reports/train/16776c299566237d0345fd043fb687e5/params.yaml deleted file mode 100644 index 088f8b2b..00000000 --- a/examples/security/classification/output/reports/train/16776c299566237d0345fd043fb687e5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/16776c299566237d0345fd043fb687e5/adv_predictions.json - adv_probabilities_file: output/reports/train/16776c299566237d0345fd043fb687e5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/723c3ed800566fdd5c53f3786ff8b79a.pkl - params_file: output/reports/train/16776c299566237d0345fd043fb687e5/params.yaml - predictions_file: output/reports/train/16776c299566237d0345fd043fb687e5/predictions.json - probabilities_file: output/reports/train/16776c299566237d0345fd043fb687e5/probabilities.json - score_dict_file: output/reports/train/16776c299566237d0345fd043fb687e5/score_dict.json - test_labels_file: output/reports/train/16776c299566237d0345fd043fb687e5/test_labels.json - train_labels_file: output/reports/train/16776c299566237d0345fd043fb687e5/train_labels.json - model_dir: models - name: 16776c299566237d0345fd043fb687e5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4d06767b543e62bfcff9459bd760f46a - trainer: - kwargs: {} -name: 16776c299566237d0345fd043fb687e5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1679b897bcd7505034923d74482eb325/params.yaml b/examples/security/classification/output/reports/train/1679b897bcd7505034923d74482eb325/params.yaml deleted file mode 100644 index ebd7cd2e..00000000 --- a/examples/security/classification/output/reports/train/1679b897bcd7505034923d74482eb325/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1679b897bcd7505034923d74482eb325/adv_predictions.json - adv_probabilities_file: output/reports/train/1679b897bcd7505034923d74482eb325/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/a9d897cc49153e0f1baf87c585ea925a.pkl - params_file: output/reports/train/1679b897bcd7505034923d74482eb325/params.yaml - predictions_file: output/reports/train/1679b897bcd7505034923d74482eb325/predictions.json - probabilities_file: output/reports/train/1679b897bcd7505034923d74482eb325/probabilities.json - score_dict_file: output/reports/train/1679b897bcd7505034923d74482eb325/score_dict.json - test_labels_file: output/reports/train/1679b897bcd7505034923d74482eb325/test_labels.json - train_labels_file: output/reports/train/1679b897bcd7505034923d74482eb325/train_labels.json - model_dir: models - name: 1679b897bcd7505034923d74482eb325 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cfae4572b8d127750c0558df1c8ff309 - trainer: - kwargs: {} -name: 1679b897bcd7505034923d74482eb325 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1698f6d6dae06a20fac185ec06c77d04/params.yaml b/examples/security/classification/output/reports/train/1698f6d6dae06a20fac185ec06c77d04/params.yaml deleted file mode 100644 index 6881eb34..00000000 --- a/examples/security/classification/output/reports/train/1698f6d6dae06a20fac185ec06c77d04/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/adv_predictions.json - adv_probabilities_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/68bd15058b5ad135e31aed14acb881f6.pkl - params_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/params.yaml - predictions_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/predictions.json - probabilities_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/probabilities.json - score_dict_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/score_dict.json - test_labels_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/test_labels.json - train_labels_file: output/reports/train/1698f6d6dae06a20fac185ec06c77d04/train_labels.json - model_dir: models - name: 1698f6d6dae06a20fac185ec06c77d04 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 23b86d93a2967e5160b4295cadd991fa - trainer: - kwargs: {} -name: 1698f6d6dae06a20fac185ec06c77d04 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/169d516458f33f7ff7a8666a242242f9/params.yaml b/examples/security/classification/output/reports/train/169d516458f33f7ff7a8666a242242f9/params.yaml deleted file mode 100644 index c46e4711..00000000 --- a/examples/security/classification/output/reports/train/169d516458f33f7ff7a8666a242242f9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/adv_predictions.json - adv_probabilities_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/6cad2eed0e7a321b218a8eda391ca44b.pkl - params_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/params.yaml - predictions_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/predictions.json - probabilities_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/probabilities.json - score_dict_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/score_dict.json - test_labels_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/test_labels.json - train_labels_file: output/reports/train/169d516458f33f7ff7a8666a242242f9/train_labels.json - model_dir: models - name: 169d516458f33f7ff7a8666a242242f9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 46432c6097b6ee2568fc3a42d57769ab - trainer: - kwargs: {} -name: 169d516458f33f7ff7a8666a242242f9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/params.yaml b/examples/security/classification/output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/params.yaml deleted file mode 100644 index 8f433bfa..00000000 --- a/examples/security/classification/output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/adv_predictions.json - adv_probabilities_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/3bc47e7d1d1c9da6b769d81250f0ce48.pkl - params_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/params.yaml - predictions_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/predictions.json - probabilities_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/probabilities.json - score_dict_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/score_dict.json - test_labels_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/test_labels.json - train_labels_file: output/reports/train/16ae3eeb0124fb64e236b4890343f5a0/train_labels.json - model_dir: models - name: 16ae3eeb0124fb64e236b4890343f5a0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4afa4dd465ade2c222e34e67237e0d03 - trainer: - kwargs: {} -name: 16ae3eeb0124fb64e236b4890343f5a0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/16b14fbb166aab92432f45644a4be395/params.yaml b/examples/security/classification/output/reports/train/16b14fbb166aab92432f45644a4be395/params.yaml deleted file mode 100644 index b455816a..00000000 --- a/examples/security/classification/output/reports/train/16b14fbb166aab92432f45644a4be395/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/16b14fbb166aab92432f45644a4be395/adv_predictions.json - adv_probabilities_file: output/reports/train/16b14fbb166aab92432f45644a4be395/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/d6aa5210725a5ff17ded701863bc3a2d.pkl - params_file: output/reports/train/16b14fbb166aab92432f45644a4be395/params.yaml - predictions_file: output/reports/train/16b14fbb166aab92432f45644a4be395/predictions.json - probabilities_file: output/reports/train/16b14fbb166aab92432f45644a4be395/probabilities.json - score_dict_file: output/reports/train/16b14fbb166aab92432f45644a4be395/score_dict.json - test_labels_file: output/reports/train/16b14fbb166aab92432f45644a4be395/test_labels.json - train_labels_file: output/reports/train/16b14fbb166aab92432f45644a4be395/train_labels.json - model_dir: models - name: 16b14fbb166aab92432f45644a4be395 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 07e83b3df352bff110d9908acae6287f - trainer: - kwargs: {} -name: 16b14fbb166aab92432f45644a4be395 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/16babebec4f814699b12b1aa1d195ab2/params.yaml b/examples/security/classification/output/reports/train/16babebec4f814699b12b1aa1d195ab2/params.yaml deleted file mode 100644 index 33fa6ef7..00000000 --- a/examples/security/classification/output/reports/train/16babebec4f814699b12b1aa1d195ab2/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/adv_predictions.json - adv_probabilities_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/c61bd5e623a15071ca4a6ea9d9b6cb2a.pkl - params_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/params.yaml - predictions_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/predictions.json - probabilities_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/probabilities.json - score_dict_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/score_dict.json - test_labels_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/test_labels.json - train_labels_file: output/reports/train/16babebec4f814699b12b1aa1d195ab2/train_labels.json - model_dir: models - name: 16babebec4f814699b12b1aa1d195ab2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ddfa56158ec7763927b80c525a4470ff - trainer: - kwargs: {} -name: 16babebec4f814699b12b1aa1d195ab2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/params.yaml b/examples/security/classification/output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/params.yaml deleted file mode 100644 index a6e77283..00000000 --- a/examples/security/classification/output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/adv_predictions.json - adv_probabilities_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/bfc3ca7b5e74ae55e63770b494e360cd.pkl - params_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/params.yaml - predictions_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/predictions.json - probabilities_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/probabilities.json - score_dict_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/score_dict.json - test_labels_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/test_labels.json - train_labels_file: output/reports/train/16c775c8ed7c91bf03cd7d01dd245ea4/train_labels.json - model_dir: models - name: 16c775c8ed7c91bf03cd7d01dd245ea4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ea99efec069344d81bb4d4d16429f09f - trainer: - kwargs: {} -name: 16c775c8ed7c91bf03cd7d01dd245ea4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/16cd98524388a650a5b27a6d7c9b4968/params.yaml b/examples/security/classification/output/reports/train/16cd98524388a650a5b27a6d7c9b4968/params.yaml deleted file mode 100644 index 1c889bde..00000000 --- a/examples/security/classification/output/reports/train/16cd98524388a650a5b27a6d7c9b4968/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/adv_predictions.json - adv_probabilities_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/4aaf589b7a309911b5dceb08b4a79525.pkl - params_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/params.yaml - predictions_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/predictions.json - probabilities_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/probabilities.json - score_dict_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/score_dict.json - test_labels_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/test_labels.json - train_labels_file: output/reports/train/16cd98524388a650a5b27a6d7c9b4968/train_labels.json - model_dir: models - name: 16cd98524388a650a5b27a6d7c9b4968 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b41d57de4c248ba42ad50fc0ddb8079e - trainer: - kwargs: {} -name: 16cd98524388a650a5b27a6d7c9b4968 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/params.yaml b/examples/security/classification/output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/params.yaml deleted file mode 100644 index c590330b..00000000 --- a/examples/security/classification/output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/adv_predictions.json - adv_probabilities_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/57812ffff14e4c703113d2f95f331256.pkl - params_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/params.yaml - predictions_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/predictions.json - probabilities_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/probabilities.json - score_dict_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/score_dict.json - test_labels_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/test_labels.json - train_labels_file: output/reports/train/16e6867a809a9970cf13cea3e3ed3a8a/train_labels.json - model_dir: models - name: 16e6867a809a9970cf13cea3e3ed3a8a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3ab78ed0da18c4a6729b9976c1a01678 - trainer: - kwargs: {} -name: 16e6867a809a9970cf13cea3e3ed3a8a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1731528739084d8d9391cb93695bfe0b/params.yaml b/examples/security/classification/output/reports/train/1731528739084d8d9391cb93695bfe0b/params.yaml deleted file mode 100644 index 41a3eac0..00000000 --- a/examples/security/classification/output/reports/train/1731528739084d8d9391cb93695bfe0b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/adv_predictions.json - adv_probabilities_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/9f44707b0cdc4e8b57dc5313c205d279.pkl - params_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/params.yaml - predictions_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/predictions.json - probabilities_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/probabilities.json - score_dict_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/score_dict.json - test_labels_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/test_labels.json - train_labels_file: output/reports/train/1731528739084d8d9391cb93695bfe0b/train_labels.json - model_dir: models - name: 1731528739084d8d9391cb93695bfe0b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1421da396afef651905a7a8b695e5598 - trainer: - kwargs: {} -name: 1731528739084d8d9391cb93695bfe0b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/params.yaml b/examples/security/classification/output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/params.yaml deleted file mode 100644 index 53a61133..00000000 --- a/examples/security/classification/output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/adv_predictions.json - adv_probabilities_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/949971ff0fa9494df897ab5594e135dd.pkl - params_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/params.yaml - predictions_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/predictions.json - probabilities_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/probabilities.json - score_dict_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/score_dict.json - test_labels_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/test_labels.json - train_labels_file: output/reports/train/1746a5c21ecd31a2f9287a260d0b48c6/train_labels.json - model_dir: models - name: 1746a5c21ecd31a2f9287a260d0b48c6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9d2c5e4bea8d92a4e3e10832ced42b2a - trainer: - kwargs: {} -name: 1746a5c21ecd31a2f9287a260d0b48c6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/175165f3c2a7c11edf1ea77931622459/params.yaml b/examples/security/classification/output/reports/train/175165f3c2a7c11edf1ea77931622459/params.yaml deleted file mode 100644 index 138cf921..00000000 --- a/examples/security/classification/output/reports/train/175165f3c2a7c11edf1ea77931622459/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/adv_predictions.json - adv_probabilities_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/4a7007a799d43d8625e0e52940c77ac0.pkl - params_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/params.yaml - predictions_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/predictions.json - probabilities_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/probabilities.json - score_dict_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/score_dict.json - test_labels_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/test_labels.json - train_labels_file: output/reports/train/175165f3c2a7c11edf1ea77931622459/train_labels.json - model_dir: models - name: 175165f3c2a7c11edf1ea77931622459 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1ce1a8257d04e1b6f715f82e65524846 - trainer: - kwargs: {} -name: 175165f3c2a7c11edf1ea77931622459 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/176d6f297cef3b7d607e91463f840d39/params.yaml b/examples/security/classification/output/reports/train/176d6f297cef3b7d607e91463f840d39/params.yaml deleted file mode 100644 index c9859254..00000000 --- a/examples/security/classification/output/reports/train/176d6f297cef3b7d607e91463f840d39/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/adv_predictions.json - adv_probabilities_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/b039c7dca78f9da2dc66a3267ab32901.pkl - params_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/params.yaml - predictions_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/predictions.json - probabilities_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/probabilities.json - score_dict_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/score_dict.json - test_labels_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/test_labels.json - train_labels_file: output/reports/train/176d6f297cef3b7d607e91463f840d39/train_labels.json - model_dir: models - name: 176d6f297cef3b7d607e91463f840d39 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d2d1c3d8f2f90fca5869d5837068dc64 - trainer: - kwargs: {} -name: 176d6f297cef3b7d607e91463f840d39 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1777d439d081300005b926fbd130450e/params.yaml b/examples/security/classification/output/reports/train/1777d439d081300005b926fbd130450e/params.yaml deleted file mode 100644 index 404e0d56..00000000 --- a/examples/security/classification/output/reports/train/1777d439d081300005b926fbd130450e/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1777d439d081300005b926fbd130450e/adv_predictions.json - adv_probabilities_file: output/reports/train/1777d439d081300005b926fbd130450e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/95c40c84b8f65bb8fb949df3567a993d.pkl - params_file: output/reports/train/1777d439d081300005b926fbd130450e/params.yaml - predictions_file: output/reports/train/1777d439d081300005b926fbd130450e/predictions.json - probabilities_file: output/reports/train/1777d439d081300005b926fbd130450e/probabilities.json - score_dict_file: output/reports/train/1777d439d081300005b926fbd130450e/score_dict.json - test_labels_file: output/reports/train/1777d439d081300005b926fbd130450e/test_labels.json - train_labels_file: output/reports/train/1777d439d081300005b926fbd130450e/train_labels.json - model_dir: models - name: 1777d439d081300005b926fbd130450e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: db950bee681fb8ac4af10100791e258d - trainer: - kwargs: {} -name: 1777d439d081300005b926fbd130450e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/177faa4762399452a5521e988600082b/params.yaml b/examples/security/classification/output/reports/train/177faa4762399452a5521e988600082b/params.yaml deleted file mode 100644 index 8f940a01..00000000 --- a/examples/security/classification/output/reports/train/177faa4762399452a5521e988600082b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/177faa4762399452a5521e988600082b/adv_predictions.json - adv_probabilities_file: output/reports/train/177faa4762399452a5521e988600082b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/5fa2817e73c574a87f0c9cb2b55acf80.pkl - params_file: output/reports/train/177faa4762399452a5521e988600082b/params.yaml - predictions_file: output/reports/train/177faa4762399452a5521e988600082b/predictions.json - probabilities_file: output/reports/train/177faa4762399452a5521e988600082b/probabilities.json - score_dict_file: output/reports/train/177faa4762399452a5521e988600082b/score_dict.json - test_labels_file: output/reports/train/177faa4762399452a5521e988600082b/test_labels.json - train_labels_file: output/reports/train/177faa4762399452a5521e988600082b/train_labels.json - model_dir: models - name: 177faa4762399452a5521e988600082b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5f346f6765081e2d8d2d9de6e6c3a3d2 - trainer: - kwargs: {} -name: 177faa4762399452a5521e988600082b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/params.yaml b/examples/security/classification/output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/params.yaml deleted file mode 100644 index a9866ab7..00000000 --- a/examples/security/classification/output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/adv_predictions.json - adv_probabilities_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/af9738fe361e2849c998bc142c84387d.pkl - params_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/params.yaml - predictions_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/predictions.json - probabilities_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/probabilities.json - score_dict_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/score_dict.json - test_labels_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/test_labels.json - train_labels_file: output/reports/train/178f38821f00bf6d57bb3dcf0766efbb/train_labels.json - model_dir: models - name: 178f38821f00bf6d57bb3dcf0766efbb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b75475adc212df574e0348b17bf85854 - trainer: - kwargs: {} -name: 178f38821f00bf6d57bb3dcf0766efbb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/17a83e782b44c0d733a65a60ee262fe1/params.yaml b/examples/security/classification/output/reports/train/17a83e782b44c0d733a65a60ee262fe1/params.yaml deleted file mode 100644 index a21f8d1a..00000000 --- a/examples/security/classification/output/reports/train/17a83e782b44c0d733a65a60ee262fe1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/adv_predictions.json - adv_probabilities_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/181a25515829fdfb04c0a6711e663c86.pkl - params_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/params.yaml - predictions_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/predictions.json - probabilities_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/probabilities.json - score_dict_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/score_dict.json - test_labels_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/test_labels.json - train_labels_file: output/reports/train/17a83e782b44c0d733a65a60ee262fe1/train_labels.json - model_dir: models - name: 17a83e782b44c0d733a65a60ee262fe1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5cb169d32132bd7b902e544fa30947fd - trainer: - kwargs: {} -name: 17a83e782b44c0d733a65a60ee262fe1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/17b18d9aa005b219865bef1bb524e022/params.yaml b/examples/security/classification/output/reports/train/17b18d9aa005b219865bef1bb524e022/params.yaml deleted file mode 100644 index b00971a8..00000000 --- a/examples/security/classification/output/reports/train/17b18d9aa005b219865bef1bb524e022/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/adv_predictions.json - adv_probabilities_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/918a3b53a315b11017c84706333ed3e8.pkl - params_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/params.yaml - predictions_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/predictions.json - probabilities_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/probabilities.json - score_dict_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/score_dict.json - test_labels_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/test_labels.json - train_labels_file: output/reports/train/17b18d9aa005b219865bef1bb524e022/train_labels.json - model_dir: models - name: 17b18d9aa005b219865bef1bb524e022 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 06f43f37e381f4bf8b21dd95490e0831 - trainer: - kwargs: {} -name: 17b18d9aa005b219865bef1bb524e022 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/17c7e130c453765d6b61756d176e4d1a/params.yaml b/examples/security/classification/output/reports/train/17c7e130c453765d6b61756d176e4d1a/params.yaml deleted file mode 100644 index 6f1c473f..00000000 --- a/examples/security/classification/output/reports/train/17c7e130c453765d6b61756d176e4d1a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/adv_predictions.json - adv_probabilities_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/fa8b47e5d5edd775165df0992b22b2fd.pkl - params_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/params.yaml - predictions_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/predictions.json - probabilities_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/probabilities.json - score_dict_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/score_dict.json - test_labels_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/test_labels.json - train_labels_file: output/reports/train/17c7e130c453765d6b61756d176e4d1a/train_labels.json - model_dir: models - name: 17c7e130c453765d6b61756d176e4d1a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9a00b09d3f237376b0e8e81f821ab70f - trainer: - kwargs: {} -name: 17c7e130c453765d6b61756d176e4d1a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/180f842ba0fbcb28a0c331905f1669ef/params.yaml b/examples/security/classification/output/reports/train/180f842ba0fbcb28a0c331905f1669ef/params.yaml deleted file mode 100644 index bcf5c351..00000000 --- a/examples/security/classification/output/reports/train/180f842ba0fbcb28a0c331905f1669ef/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/adv_predictions.json - adv_probabilities_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/44330192ef99a49d450da422f2905108.pkl - params_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/params.yaml - predictions_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/predictions.json - probabilities_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/probabilities.json - score_dict_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/score_dict.json - test_labels_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/test_labels.json - train_labels_file: output/reports/train/180f842ba0fbcb28a0c331905f1669ef/train_labels.json - model_dir: models - name: 180f842ba0fbcb28a0c331905f1669ef - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3097146caf098c56315cc1772a595a73 - trainer: - kwargs: {} -name: 180f842ba0fbcb28a0c331905f1669ef -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1812b08ac9553675a1f5f43791897e07/params.yaml b/examples/security/classification/output/reports/train/1812b08ac9553675a1f5f43791897e07/params.yaml deleted file mode 100644 index 040c5fa3..00000000 --- a/examples/security/classification/output/reports/train/1812b08ac9553675a1f5f43791897e07/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/adv_predictions.json - adv_probabilities_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/9e087900962ee4dcd653639f5d1e7cbf.pkl - params_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/params.yaml - predictions_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/predictions.json - probabilities_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/probabilities.json - score_dict_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/score_dict.json - test_labels_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/test_labels.json - train_labels_file: output/reports/train/1812b08ac9553675a1f5f43791897e07/train_labels.json - model_dir: models - name: 1812b08ac9553675a1f5f43791897e07 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bc366863451f268c89cacbf04b31c347 - trainer: - kwargs: {} -name: 1812b08ac9553675a1f5f43791897e07 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/181d543700a7085e11643be4c67faea3/params.yaml b/examples/security/classification/output/reports/train/181d543700a7085e11643be4c67faea3/params.yaml deleted file mode 100644 index 4328e27c..00000000 --- a/examples/security/classification/output/reports/train/181d543700a7085e11643be4c67faea3/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/181d543700a7085e11643be4c67faea3/adv_predictions.json - adv_probabilities_file: output/reports/train/181d543700a7085e11643be4c67faea3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/1de8603a2a5ad1cec3a2d209e41e8b7f.pkl - params_file: output/reports/train/181d543700a7085e11643be4c67faea3/params.yaml - predictions_file: output/reports/train/181d543700a7085e11643be4c67faea3/predictions.json - probabilities_file: output/reports/train/181d543700a7085e11643be4c67faea3/probabilities.json - score_dict_file: output/reports/train/181d543700a7085e11643be4c67faea3/score_dict.json - test_labels_file: output/reports/train/181d543700a7085e11643be4c67faea3/test_labels.json - train_labels_file: output/reports/train/181d543700a7085e11643be4c67faea3/train_labels.json - model_dir: models - name: 181d543700a7085e11643be4c67faea3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 14e4e058c5eda5d47ba6cdad701c6619 - trainer: - kwargs: {} -name: 181d543700a7085e11643be4c67faea3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1838b492b7cbe374b8d24336b97a5427/params.yaml b/examples/security/classification/output/reports/train/1838b492b7cbe374b8d24336b97a5427/params.yaml deleted file mode 100644 index a68e378a..00000000 --- a/examples/security/classification/output/reports/train/1838b492b7cbe374b8d24336b97a5427/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/adv_predictions.json - adv_probabilities_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/b5099248e5271e881663a9fbb85de6ef.pkl - params_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/params.yaml - predictions_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/predictions.json - probabilities_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/probabilities.json - score_dict_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/score_dict.json - test_labels_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/test_labels.json - train_labels_file: output/reports/train/1838b492b7cbe374b8d24336b97a5427/train_labels.json - model_dir: models - name: 1838b492b7cbe374b8d24336b97a5427 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 616c428a1b8aceccad66882047a0e729 - trainer: - kwargs: {} -name: 1838b492b7cbe374b8d24336b97a5427 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/params.yaml b/examples/security/classification/output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/params.yaml deleted file mode 100644 index f4815e72..00000000 --- a/examples/security/classification/output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/adv_predictions.json - adv_probabilities_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/ec7b562faee79c0c1ecb6c4b2e8aa7a8.pkl - params_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/params.yaml - predictions_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/predictions.json - probabilities_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/probabilities.json - score_dict_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/score_dict.json - test_labels_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/test_labels.json - train_labels_file: output/reports/train/18734d419b6aed5650b7f469dfa0bc3d/train_labels.json - model_dir: models - name: 18734d419b6aed5650b7f469dfa0bc3d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9ee69aa809a4f3c3129c4646b0a083e4 - trainer: - kwargs: {} -name: 18734d419b6aed5650b7f469dfa0bc3d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/params.yaml b/examples/security/classification/output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/params.yaml deleted file mode 100644 index bac9c72c..00000000 --- a/examples/security/classification/output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/adv_predictions.json - adv_probabilities_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/d15e252cc949ff3656999d7b6c3f5bce.pkl - params_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/params.yaml - predictions_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/predictions.json - probabilities_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/probabilities.json - score_dict_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/score_dict.json - test_labels_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/test_labels.json - train_labels_file: output/reports/train/18c3e40cfeb85daa878bee2117cf53ea/train_labels.json - model_dir: models - name: 18c3e40cfeb85daa878bee2117cf53ea - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4ded67cb5fa0606e3f78fb8a93633cf2 - trainer: - kwargs: {} -name: 18c3e40cfeb85daa878bee2117cf53ea -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/18eab89ce02f5feac8951def13c627c7/params.yaml b/examples/security/classification/output/reports/train/18eab89ce02f5feac8951def13c627c7/params.yaml deleted file mode 100644 index de75dc95..00000000 --- a/examples/security/classification/output/reports/train/18eab89ce02f5feac8951def13c627c7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/adv_predictions.json - adv_probabilities_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/79b82cf6d0ac773a62e6dc5343e0b8f6.pkl - params_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/params.yaml - predictions_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/predictions.json - probabilities_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/probabilities.json - score_dict_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/score_dict.json - test_labels_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/test_labels.json - train_labels_file: output/reports/train/18eab89ce02f5feac8951def13c627c7/train_labels.json - model_dir: models - name: 18eab89ce02f5feac8951def13c627c7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 605935d00cded169a0cca1ba913dd123 - trainer: - kwargs: {} -name: 18eab89ce02f5feac8951def13c627c7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/params.yaml b/examples/security/classification/output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/params.yaml deleted file mode 100644 index 56328129..00000000 --- a/examples/security/classification/output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/adv_predictions.json - adv_probabilities_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/824222634f3f998709333d90e03ccd84.pkl - params_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/params.yaml - predictions_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/predictions.json - probabilities_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/probabilities.json - score_dict_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/score_dict.json - test_labels_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/test_labels.json - train_labels_file: output/reports/train/18f83b1131ac2c4e765a7b003ef4a406/train_labels.json - model_dir: models - name: 18f83b1131ac2c4e765a7b003ef4a406 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 61bea0ee57be328b5089ccf5f9317d0a - trainer: - kwargs: {} -name: 18f83b1131ac2c4e765a7b003ef4a406 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/194b684de23730ce579d0c776e3a6dc6/params.yaml b/examples/security/classification/output/reports/train/194b684de23730ce579d0c776e3a6dc6/params.yaml deleted file mode 100644 index bb0c1087..00000000 --- a/examples/security/classification/output/reports/train/194b684de23730ce579d0c776e3a6dc6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/adv_predictions.json - adv_probabilities_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/8cd4c014a7fb5f8cb253dd8657cdd8ab.pkl - params_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/params.yaml - predictions_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/predictions.json - probabilities_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/probabilities.json - score_dict_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/score_dict.json - test_labels_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/test_labels.json - train_labels_file: output/reports/train/194b684de23730ce579d0c776e3a6dc6/train_labels.json - model_dir: models - name: 194b684de23730ce579d0c776e3a6dc6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7db9ce3de40ec336b2e2159dab8538c9 - trainer: - kwargs: {} -name: 194b684de23730ce579d0c776e3a6dc6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/params.yaml b/examples/security/classification/output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/params.yaml deleted file mode 100644 index 5076fec0..00000000 --- a/examples/security/classification/output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/adv_predictions.json - adv_probabilities_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/22e04e9e6359826aac485f0e9bb1e7ee.pkl - params_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/params.yaml - predictions_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/predictions.json - probabilities_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/probabilities.json - score_dict_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/score_dict.json - test_labels_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/test_labels.json - train_labels_file: output/reports/train/1953aa0cd09d78bbb09d91ddd3f1b522/train_labels.json - model_dir: models - name: 1953aa0cd09d78bbb09d91ddd3f1b522 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 58bd6e86953e8b9a33f7bb4efc6cd255 - trainer: - kwargs: {} -name: 1953aa0cd09d78bbb09d91ddd3f1b522 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/197d0205e1744e737f2f69618aa32668/params.yaml b/examples/security/classification/output/reports/train/197d0205e1744e737f2f69618aa32668/params.yaml deleted file mode 100644 index 417455d2..00000000 --- a/examples/security/classification/output/reports/train/197d0205e1744e737f2f69618aa32668/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/197d0205e1744e737f2f69618aa32668/adv_predictions.json - adv_probabilities_file: output/reports/train/197d0205e1744e737f2f69618aa32668/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/1a15aff91c9a97fa7631111c0c34fd18.pkl - params_file: output/reports/train/197d0205e1744e737f2f69618aa32668/params.yaml - predictions_file: output/reports/train/197d0205e1744e737f2f69618aa32668/predictions.json - probabilities_file: output/reports/train/197d0205e1744e737f2f69618aa32668/probabilities.json - score_dict_file: output/reports/train/197d0205e1744e737f2f69618aa32668/score_dict.json - test_labels_file: output/reports/train/197d0205e1744e737f2f69618aa32668/test_labels.json - train_labels_file: output/reports/train/197d0205e1744e737f2f69618aa32668/train_labels.json - model_dir: models - name: 197d0205e1744e737f2f69618aa32668 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9f7469456f6e94fce8ac2e0655b3439c - trainer: - kwargs: {} -name: 197d0205e1744e737f2f69618aa32668 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/19a8d802acae97d43955369971a0b255/params.yaml b/examples/security/classification/output/reports/train/19a8d802acae97d43955369971a0b255/params.yaml deleted file mode 100644 index 9884cb18..00000000 --- a/examples/security/classification/output/reports/train/19a8d802acae97d43955369971a0b255/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/19a8d802acae97d43955369971a0b255/adv_predictions.json - adv_probabilities_file: output/reports/train/19a8d802acae97d43955369971a0b255/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/3cf258dff0c311070ad2b0f84f144c5f.pkl - params_file: output/reports/train/19a8d802acae97d43955369971a0b255/params.yaml - predictions_file: output/reports/train/19a8d802acae97d43955369971a0b255/predictions.json - probabilities_file: output/reports/train/19a8d802acae97d43955369971a0b255/probabilities.json - score_dict_file: output/reports/train/19a8d802acae97d43955369971a0b255/score_dict.json - test_labels_file: output/reports/train/19a8d802acae97d43955369971a0b255/test_labels.json - train_labels_file: output/reports/train/19a8d802acae97d43955369971a0b255/train_labels.json - model_dir: models - name: 19a8d802acae97d43955369971a0b255 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6a9c9ec1156b48d8138edea003a1df09 - trainer: - kwargs: {} -name: 19a8d802acae97d43955369971a0b255 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/params.yaml b/examples/security/classification/output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/params.yaml deleted file mode 100644 index 4f1c545b..00000000 --- a/examples/security/classification/output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/adv_predictions.json - adv_probabilities_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/d20eb8cbd700e08f17b016a782e3ae35.pkl - params_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/params.yaml - predictions_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/predictions.json - probabilities_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/probabilities.json - score_dict_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/score_dict.json - test_labels_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/test_labels.json - train_labels_file: output/reports/train/19cefde76c33fa4a4d7a08919fde30f5/train_labels.json - model_dir: models - name: 19cefde76c33fa4a4d7a08919fde30f5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 84aaf0006dad72bd396519070077631c - trainer: - kwargs: {} -name: 19cefde76c33fa4a4d7a08919fde30f5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/params.yaml b/examples/security/classification/output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/params.yaml deleted file mode 100644 index 76835a30..00000000 --- a/examples/security/classification/output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/adv_predictions.json - adv_probabilities_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/466bb3f38a908176ce42bb6cdc5666f2.pkl - params_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/params.yaml - predictions_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/predictions.json - probabilities_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/probabilities.json - score_dict_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/score_dict.json - test_labels_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/test_labels.json - train_labels_file: output/reports/train/19d115fa8e298f84c908ccc6fdc67b65/train_labels.json - model_dir: models - name: 19d115fa8e298f84c908ccc6fdc67b65 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 78c6b819b37e7f16b0ad5db0bf3b2c5f - trainer: - kwargs: {} -name: 19d115fa8e298f84c908ccc6fdc67b65 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/params.yaml b/examples/security/classification/output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/params.yaml deleted file mode 100644 index d55b480f..00000000 --- a/examples/security/classification/output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/params.yaml +++ /dev/null @@ -1,206 +0,0 @@ -attack: - attack_size: 10 - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 10.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 10 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6c89d09b982e41e1a128873c92bce386 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 63893db59d8e6a9fbf4c5947b48a7f8c - trainer: - kwargs: {} - name: 552429a58690906f2fcd046cfd184ed2 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/adv_predictions.json - adv_probabilities_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/adv_probabilities.json - attack_file: output/attacks/ab015de719d2b03fc6a81480559e384d.pkl - data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl - model_file: output/models/3cdbf74c840304670801560dc2ca5233.pkl - params_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/params.yaml - predictions_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/predictions.json - probabilities_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/probabilities.json - score_dict_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/score_dict.json - test_labels_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/test_labels.json - train_labels_file: output/reports/train/19e9066a52d41d2d8fb503400ae65ff5/train_labels.json - model_dir: models - name: 19e9066a52d41d2d8fb503400ae65ff5 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 605297d2df5f46e6932de76ce5e1b650 - trainer: - kwargs: {} -name: 19e9066a52d41d2d8fb503400ae65ff5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/params.yaml b/examples/security/classification/output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/params.yaml deleted file mode 100644 index 5f14bbf9..00000000 --- a/examples/security/classification/output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/adv_predictions.json - adv_probabilities_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/07ad5ac0e8b3fbe4e56d03c0edb0ef28.pkl - params_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/params.yaml - predictions_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/predictions.json - probabilities_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/probabilities.json - score_dict_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/score_dict.json - test_labels_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/test_labels.json - train_labels_file: output/reports/train/19fad42d2bbcd20b06bddd7477b0f7ca/train_labels.json - model_dir: models - name: 19fad42d2bbcd20b06bddd7477b0f7ca - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 965b7a605defa035f92dd8f3c484c5e3 - trainer: - kwargs: {} -name: 19fad42d2bbcd20b06bddd7477b0f7ca -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1a09b6c7acaee30db106668759a2c3b0/params.yaml b/examples/security/classification/output/reports/train/1a09b6c7acaee30db106668759a2c3b0/params.yaml deleted file mode 100644 index 0832d062..00000000 --- a/examples/security/classification/output/reports/train/1a09b6c7acaee30db106668759a2c3b0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/adv_predictions.json - adv_probabilities_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/2cc588d48e50af4b7e68a901710ccf13.pkl - params_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/params.yaml - predictions_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/predictions.json - probabilities_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/probabilities.json - score_dict_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/score_dict.json - test_labels_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/test_labels.json - train_labels_file: output/reports/train/1a09b6c7acaee30db106668759a2c3b0/train_labels.json - model_dir: models - name: 1a09b6c7acaee30db106668759a2c3b0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b6b7d0e3fc01eec81d845dd597d35811 - trainer: - kwargs: {} -name: 1a09b6c7acaee30db106668759a2c3b0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1a17b597e2fcaba117c5d2358c291688/params.yaml b/examples/security/classification/output/reports/train/1a17b597e2fcaba117c5d2358c291688/params.yaml deleted file mode 100644 index 7f78c26a..00000000 --- a/examples/security/classification/output/reports/train/1a17b597e2fcaba117c5d2358c291688/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/adv_predictions.json - adv_probabilities_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/135d674502dc16db3258d6d81efc6253.pkl - params_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/params.yaml - predictions_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/predictions.json - probabilities_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/probabilities.json - score_dict_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/score_dict.json - test_labels_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/test_labels.json - train_labels_file: output/reports/train/1a17b597e2fcaba117c5d2358c291688/train_labels.json - model_dir: models - name: 1a17b597e2fcaba117c5d2358c291688 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ec82bb52c2c841e197f9bd2ebb5e5165 - trainer: - kwargs: {} -name: 1a17b597e2fcaba117c5d2358c291688 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/params.yaml b/examples/security/classification/output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/params.yaml deleted file mode 100644 index a035582a..00000000 --- a/examples/security/classification/output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/adv_predictions.json - adv_probabilities_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/2fd84220927379902f4f1f41eb0bdba4.pkl - params_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/params.yaml - predictions_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/predictions.json - probabilities_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/probabilities.json - score_dict_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/score_dict.json - test_labels_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/test_labels.json - train_labels_file: output/reports/train/1a1f01dc57c29e8cd1c1e178a3b89273/train_labels.json - model_dir: models - name: 1a1f01dc57c29e8cd1c1e178a3b89273 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 44adc7a7cf2094481acd6e82817e2b28 - trainer: - kwargs: {} -name: 1a1f01dc57c29e8cd1c1e178a3b89273 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1a255223760650fa354344bb111a47f5/params.yaml b/examples/security/classification/output/reports/train/1a255223760650fa354344bb111a47f5/params.yaml deleted file mode 100644 index 9dd7bd33..00000000 --- a/examples/security/classification/output/reports/train/1a255223760650fa354344bb111a47f5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1a255223760650fa354344bb111a47f5/adv_predictions.json - adv_probabilities_file: output/reports/train/1a255223760650fa354344bb111a47f5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/fc6dabefc9ca44266092c70d3aad313f.pkl - params_file: output/reports/train/1a255223760650fa354344bb111a47f5/params.yaml - predictions_file: output/reports/train/1a255223760650fa354344bb111a47f5/predictions.json - probabilities_file: output/reports/train/1a255223760650fa354344bb111a47f5/probabilities.json - score_dict_file: output/reports/train/1a255223760650fa354344bb111a47f5/score_dict.json - test_labels_file: output/reports/train/1a255223760650fa354344bb111a47f5/test_labels.json - train_labels_file: output/reports/train/1a255223760650fa354344bb111a47f5/train_labels.json - model_dir: models - name: 1a255223760650fa354344bb111a47f5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e251914fe67a33765ddbc593789f949f - trainer: - kwargs: {} -name: 1a255223760650fa354344bb111a47f5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/params.yaml b/examples/security/classification/output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/params.yaml deleted file mode 100644 index 8570201f..00000000 --- a/examples/security/classification/output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/adv_predictions.json - adv_probabilities_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/c78788600e2ca734aed1e3d5ffa5d592.pkl - params_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/params.yaml - predictions_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/predictions.json - probabilities_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/probabilities.json - score_dict_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/score_dict.json - test_labels_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/test_labels.json - train_labels_file: output/reports/train/1a2f5766f7d856b1e7c825ea10b518be/train_labels.json - model_dir: models - name: 1a2f5766f7d856b1e7c825ea10b518be - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dd0ee86f14693134639ec0179dbb60b3 - trainer: - kwargs: {} -name: 1a2f5766f7d856b1e7c825ea10b518be -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1a4dbcceba7b0811b9e181f642105415/params.yaml b/examples/security/classification/output/reports/train/1a4dbcceba7b0811b9e181f642105415/params.yaml deleted file mode 100644 index 79a85ff0..00000000 --- a/examples/security/classification/output/reports/train/1a4dbcceba7b0811b9e181f642105415/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/adv_predictions.json - adv_probabilities_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/32c04a863dc3ee9a863b6f00adcf9632.pkl - params_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/params.yaml - predictions_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/predictions.json - probabilities_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/probabilities.json - score_dict_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/score_dict.json - test_labels_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/test_labels.json - train_labels_file: output/reports/train/1a4dbcceba7b0811b9e181f642105415/train_labels.json - model_dir: models - name: 1a4dbcceba7b0811b9e181f642105415 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 887a0c2fb59ce612b6c4aa96743ae7da - trainer: - kwargs: {} -name: 1a4dbcceba7b0811b9e181f642105415 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/params.yaml b/examples/security/classification/output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/params.yaml deleted file mode 100644 index 46e95e08..00000000 --- a/examples/security/classification/output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/adv_predictions.json - adv_probabilities_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/6af9cce678867288841d9d64ee3e495d.pkl - params_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/params.yaml - predictions_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/predictions.json - probabilities_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/probabilities.json - score_dict_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/score_dict.json - test_labels_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/test_labels.json - train_labels_file: output/reports/train/1a4f7d44e8aa3bc55db4956f9992d678/train_labels.json - model_dir: models - name: 1a4f7d44e8aa3bc55db4956f9992d678 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1132ae8706e071833a5077d97a3ac06f - trainer: - kwargs: {} -name: 1a4f7d44e8aa3bc55db4956f9992d678 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/params.yaml b/examples/security/classification/output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/params.yaml deleted file mode 100644 index 36add8f7..00000000 --- a/examples/security/classification/output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/adv_predictions.json - adv_probabilities_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/a3f58847265f143d463acbcfe6c36c41.pkl - params_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/params.yaml - predictions_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/predictions.json - probabilities_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/probabilities.json - score_dict_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/score_dict.json - test_labels_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/test_labels.json - train_labels_file: output/reports/train/1a6ecc5a5cc955c2ecb1421827497e8e/train_labels.json - model_dir: models - name: 1a6ecc5a5cc955c2ecb1421827497e8e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4b4ab785d4dc0f98be1e479221d6d4ea - trainer: - kwargs: {} -name: 1a6ecc5a5cc955c2ecb1421827497e8e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1a741b88bedb5bbb4034329f132ff6df/params.yaml b/examples/security/classification/output/reports/train/1a741b88bedb5bbb4034329f132ff6df/params.yaml deleted file mode 100644 index eb653080..00000000 --- a/examples/security/classification/output/reports/train/1a741b88bedb5bbb4034329f132ff6df/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/adv_predictions.json - adv_probabilities_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/908646417c286c217f12dd1322dc9523.pkl - params_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/params.yaml - predictions_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/predictions.json - probabilities_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/probabilities.json - score_dict_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/score_dict.json - test_labels_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/test_labels.json - train_labels_file: output/reports/train/1a741b88bedb5bbb4034329f132ff6df/train_labels.json - model_dir: models - name: 1a741b88bedb5bbb4034329f132ff6df - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4453b006a09492bc69d3c3d63ac84a16 - trainer: - kwargs: {} -name: 1a741b88bedb5bbb4034329f132ff6df -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/params.yaml b/examples/security/classification/output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/params.yaml deleted file mode 100644 index 5c4bb9b4..00000000 --- a/examples/security/classification/output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/adv_predictions.json - adv_probabilities_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/3cd529582cc11b527c68ed77920202bc.pkl - params_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/params.yaml - predictions_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/predictions.json - probabilities_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/probabilities.json - score_dict_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/score_dict.json - test_labels_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/test_labels.json - train_labels_file: output/reports/train/1a9897a1f6453cc473bd3165c74f2dd1/train_labels.json - model_dir: models - name: 1a9897a1f6453cc473bd3165c74f2dd1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f98d5fb0a2673540ff51cf6d64115fd6 - trainer: - kwargs: {} -name: 1a9897a1f6453cc473bd3165c74f2dd1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/params.yaml b/examples/security/classification/output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/params.yaml deleted file mode 100644 index df9b75af..00000000 --- a/examples/security/classification/output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/adv_predictions.json - adv_probabilities_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/8cff1e7ca30b0f0124f772eebd7e2bcb.pkl - params_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/params.yaml - predictions_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/predictions.json - probabilities_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/probabilities.json - score_dict_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/score_dict.json - test_labels_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/test_labels.json - train_labels_file: output/reports/train/1abe763b2412d4e6335fc63097ea4ee9/train_labels.json - model_dir: models - name: 1abe763b2412d4e6335fc63097ea4ee9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6533cad81a73aa999e5a3423daddd2a0 - trainer: - kwargs: {} -name: 1abe763b2412d4e6335fc63097ea4ee9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/params.yaml b/examples/security/classification/output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/params.yaml deleted file mode 100644 index 6381cdb7..00000000 --- a/examples/security/classification/output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/adv_predictions.json - adv_probabilities_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/ef75076912895882cacd26103344c5b4.pkl - params_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/params.yaml - predictions_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/predictions.json - probabilities_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/probabilities.json - score_dict_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/score_dict.json - test_labels_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/test_labels.json - train_labels_file: output/reports/train/1aca8ec2bc805f2fbc5cbefd7235c353/train_labels.json - model_dir: models - name: 1aca8ec2bc805f2fbc5cbefd7235c353 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1f65f4b47a96859853c761f907b82f10 - trainer: - kwargs: {} -name: 1aca8ec2bc805f2fbc5cbefd7235c353 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/params.yaml b/examples/security/classification/output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/params.yaml deleted file mode 100644 index 47a0b24d..00000000 --- a/examples/security/classification/output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/adv_predictions.json - adv_probabilities_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/f0518cb73de9b240066fbfcf0714db29.pkl - params_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/params.yaml - predictions_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/predictions.json - probabilities_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/probabilities.json - score_dict_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/score_dict.json - test_labels_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/test_labels.json - train_labels_file: output/reports/train/1ad213e466e7452b14cfb9be2bdcf9c6/train_labels.json - model_dir: models - name: 1ad213e466e7452b14cfb9be2bdcf9c6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 59be1057793f3537e578d614537349c7 - trainer: - kwargs: {} -name: 1ad213e466e7452b14cfb9be2bdcf9c6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1ad581cbe2cce02651a47ed0646485a1/params.yaml b/examples/security/classification/output/reports/train/1ad581cbe2cce02651a47ed0646485a1/params.yaml deleted file mode 100644 index ab04d531..00000000 --- a/examples/security/classification/output/reports/train/1ad581cbe2cce02651a47ed0646485a1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/adv_predictions.json - adv_probabilities_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/df7977eff0949b0b5960d4b72c2a2c9e.pkl - params_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/params.yaml - predictions_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/predictions.json - probabilities_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/probabilities.json - score_dict_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/score_dict.json - test_labels_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/test_labels.json - train_labels_file: output/reports/train/1ad581cbe2cce02651a47ed0646485a1/train_labels.json - model_dir: models - name: 1ad581cbe2cce02651a47ed0646485a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 876450348a867a59fd3a667869025442 - trainer: - kwargs: {} -name: 1ad581cbe2cce02651a47ed0646485a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/params.yaml b/examples/security/classification/output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/params.yaml deleted file mode 100644 index 45c9473b..00000000 --- a/examples/security/classification/output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/adv_predictions.json - adv_probabilities_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/08f7472718aa8b78003253dbb9256868.pkl - params_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/params.yaml - predictions_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/predictions.json - probabilities_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/probabilities.json - score_dict_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/score_dict.json - test_labels_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/test_labels.json - train_labels_file: output/reports/train/1addf88bbd5e84ab5ec6c6251ed8b8e0/train_labels.json - model_dir: models - name: 1addf88bbd5e84ab5ec6c6251ed8b8e0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: afb1ddde23a2e15bff4399a5b46a8756 - trainer: - kwargs: {} -name: 1addf88bbd5e84ab5ec6c6251ed8b8e0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1af66f632700dd40699eb2f6230a0235/params.yaml b/examples/security/classification/output/reports/train/1af66f632700dd40699eb2f6230a0235/params.yaml deleted file mode 100644 index 16f6e161..00000000 --- a/examples/security/classification/output/reports/train/1af66f632700dd40699eb2f6230a0235/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/adv_predictions.json - adv_probabilities_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/001df357ed056c1a879f8621fbc26421.pkl - params_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/params.yaml - predictions_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/predictions.json - probabilities_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/probabilities.json - score_dict_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/score_dict.json - test_labels_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/test_labels.json - train_labels_file: output/reports/train/1af66f632700dd40699eb2f6230a0235/train_labels.json - model_dir: models - name: 1af66f632700dd40699eb2f6230a0235 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2d15fbfdb5603a2437cdc5511063d123 - trainer: - kwargs: {} -name: 1af66f632700dd40699eb2f6230a0235 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/params.yaml b/examples/security/classification/output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/params.yaml deleted file mode 100644 index 2cc34f63..00000000 --- a/examples/security/classification/output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/adv_predictions.json - adv_probabilities_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/93fea5a76402043b56faf47efb1e2b1a.pkl - params_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/params.yaml - predictions_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/predictions.json - probabilities_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/probabilities.json - score_dict_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/score_dict.json - test_labels_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/test_labels.json - train_labels_file: output/reports/train/1b373cfd6d673b3a09a3a71661e7b4fe/train_labels.json - model_dir: models - name: 1b373cfd6d673b3a09a3a71661e7b4fe - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7b8e27408d6fe4e4875065dbfe77df16 - trainer: - kwargs: {} -name: 1b373cfd6d673b3a09a3a71661e7b4fe -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/params.yaml b/examples/security/classification/output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/params.yaml deleted file mode 100644 index 4a6e2d5b..00000000 --- a/examples/security/classification/output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/adv_predictions.json - adv_probabilities_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/0efb8cd200ffdbd16919f37bf3dcce84.pkl - params_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/params.yaml - predictions_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/predictions.json - probabilities_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/probabilities.json - score_dict_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/score_dict.json - test_labels_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/test_labels.json - train_labels_file: output/reports/train/1b86bfe1a2beee971fb0dc206dd56d8f/train_labels.json - model_dir: models - name: 1b86bfe1a2beee971fb0dc206dd56d8f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6f88b9d84bd024ad73c3a0ed1153cb54 - trainer: - kwargs: {} -name: 1b86bfe1a2beee971fb0dc206dd56d8f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/params.yaml b/examples/security/classification/output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/params.yaml deleted file mode 100644 index 1a4c2cff..00000000 --- a/examples/security/classification/output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/adv_predictions.json - adv_probabilities_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/554590ce9a7b644429ff853a664b40ac.pkl - params_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/params.yaml - predictions_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/predictions.json - probabilities_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/probabilities.json - score_dict_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/score_dict.json - test_labels_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/test_labels.json - train_labels_file: output/reports/train/1b96903f63f4419e05bd8e47ff85f4bc/train_labels.json - model_dir: models - name: 1b96903f63f4419e05bd8e47ff85f4bc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27c9693ced23ec5318216fec31ea7291 - trainer: - kwargs: {} -name: 1b96903f63f4419e05bd8e47ff85f4bc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/params.yaml b/examples/security/classification/output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/params.yaml deleted file mode 100644 index 4d613be2..00000000 --- a/examples/security/classification/output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/adv_predictions.json - adv_probabilities_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/2c3ebbdfb6212851a5fb31be9c4fbb1e.pkl - params_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/params.yaml - predictions_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/predictions.json - probabilities_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/probabilities.json - score_dict_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/score_dict.json - test_labels_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/test_labels.json - train_labels_file: output/reports/train/1bbe4cd9430e4d44e270d499ef359a13/train_labels.json - model_dir: models - name: 1bbe4cd9430e4d44e270d499ef359a13 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c9a3e00263c41be1db00ced3b3ea71a5 - trainer: - kwargs: {} -name: 1bbe4cd9430e4d44e270d499ef359a13 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/params.yaml b/examples/security/classification/output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/params.yaml deleted file mode 100644 index f7215dfe..00000000 --- a/examples/security/classification/output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/adv_predictions.json - adv_probabilities_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/0cb4e05cbe83796b8930094bfb238ee6.pkl - params_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/params.yaml - predictions_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/predictions.json - probabilities_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/probabilities.json - score_dict_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/score_dict.json - test_labels_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/test_labels.json - train_labels_file: output/reports/train/1bd21df442e6e88124f96d5e734dcbf0/train_labels.json - model_dir: models - name: 1bd21df442e6e88124f96d5e734dcbf0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8931aeb31c207e2ed71cdc0464b62082 - trainer: - kwargs: {} -name: 1bd21df442e6e88124f96d5e734dcbf0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1bfe374b834b53991360cdafeb6ff76e/params.yaml b/examples/security/classification/output/reports/train/1bfe374b834b53991360cdafeb6ff76e/params.yaml deleted file mode 100644 index 740f62f3..00000000 --- a/examples/security/classification/output/reports/train/1bfe374b834b53991360cdafeb6ff76e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/adv_predictions.json - adv_probabilities_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/0a7ad961a5e89d673183c17d7f204f8b.pkl - params_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/params.yaml - predictions_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/predictions.json - probabilities_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/probabilities.json - score_dict_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/score_dict.json - test_labels_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/test_labels.json - train_labels_file: output/reports/train/1bfe374b834b53991360cdafeb6ff76e/train_labels.json - model_dir: models - name: 1bfe374b834b53991360cdafeb6ff76e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7768c55257caea75bf406ebf1b92d60e - trainer: - kwargs: {} -name: 1bfe374b834b53991360cdafeb6ff76e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1c753e398eb607f7bb0639f03cc89d91/params.yaml b/examples/security/classification/output/reports/train/1c753e398eb607f7bb0639f03cc89d91/params.yaml deleted file mode 100644 index a17981c4..00000000 --- a/examples/security/classification/output/reports/train/1c753e398eb607f7bb0639f03cc89d91/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/adv_predictions.json - adv_probabilities_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/b0a7e619451b44475300ee978855dee9.pkl - params_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/params.yaml - predictions_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/predictions.json - probabilities_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/probabilities.json - score_dict_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/score_dict.json - test_labels_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/test_labels.json - train_labels_file: output/reports/train/1c753e398eb607f7bb0639f03cc89d91/train_labels.json - model_dir: models - name: 1c753e398eb607f7bb0639f03cc89d91 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b4884f2906be91a0e4edf0d31acaab4b - trainer: - kwargs: {} -name: 1c753e398eb607f7bb0639f03cc89d91 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/params.yaml b/examples/security/classification/output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/params.yaml deleted file mode 100644 index 18b2f66e..00000000 --- a/examples/security/classification/output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/adv_predictions.json - adv_probabilities_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/94bbfd715a170fc7eb4acd8ba0522d42.pkl - params_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/params.yaml - predictions_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/predictions.json - probabilities_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/probabilities.json - score_dict_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/score_dict.json - test_labels_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/test_labels.json - train_labels_file: output/reports/train/1c875851dd4dec6c2aa014f854bb13e6/train_labels.json - model_dir: models - name: 1c875851dd4dec6c2aa014f854bb13e6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6c80a18654d6e31a80146434dc2ada86 - trainer: - kwargs: {} -name: 1c875851dd4dec6c2aa014f854bb13e6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/params.yaml b/examples/security/classification/output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/params.yaml deleted file mode 100644 index ddf939bb..00000000 --- a/examples/security/classification/output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/adv_predictions.json - adv_probabilities_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/0b11b9ccf5ae97f0e28e2b864424cba8.pkl - params_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/params.yaml - predictions_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/predictions.json - probabilities_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/probabilities.json - score_dict_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/score_dict.json - test_labels_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/test_labels.json - train_labels_file: output/reports/train/1c8f81bf2ab6b59051254e5d02ebd847/train_labels.json - model_dir: models - name: 1c8f81bf2ab6b59051254e5d02ebd847 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 23057c816983d717986d69cdf72161bf - trainer: - kwargs: {} -name: 1c8f81bf2ab6b59051254e5d02ebd847 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1cb887b76ef0544accec908123aa13d9/params.yaml b/examples/security/classification/output/reports/train/1cb887b76ef0544accec908123aa13d9/params.yaml deleted file mode 100644 index 5852d149..00000000 --- a/examples/security/classification/output/reports/train/1cb887b76ef0544accec908123aa13d9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/adv_predictions.json - adv_probabilities_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/92228e6287172b35de4c82249c762483.pkl - params_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/params.yaml - predictions_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/predictions.json - probabilities_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/probabilities.json - score_dict_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/score_dict.json - test_labels_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/test_labels.json - train_labels_file: output/reports/train/1cb887b76ef0544accec908123aa13d9/train_labels.json - model_dir: models - name: 1cb887b76ef0544accec908123aa13d9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ed18c09119a2f24f04ccb5406f80fc74 - trainer: - kwargs: {} -name: 1cb887b76ef0544accec908123aa13d9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1cf63da57a4b760baa61cb3259690799/params.yaml b/examples/security/classification/output/reports/train/1cf63da57a4b760baa61cb3259690799/params.yaml deleted file mode 100644 index 9461334e..00000000 --- a/examples/security/classification/output/reports/train/1cf63da57a4b760baa61cb3259690799/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/adv_predictions.json - adv_probabilities_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/a9c7582e54ced8a79b4d7db5d65bee54.pkl - params_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/params.yaml - predictions_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/predictions.json - probabilities_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/probabilities.json - score_dict_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/score_dict.json - test_labels_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/test_labels.json - train_labels_file: output/reports/train/1cf63da57a4b760baa61cb3259690799/train_labels.json - model_dir: models - name: 1cf63da57a4b760baa61cb3259690799 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a5d17741e3e213f27a1ed2ac0f0f8e06 - trainer: - kwargs: {} -name: 1cf63da57a4b760baa61cb3259690799 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1d1f8317a243341a1803e9ea2405db8f/params.yaml b/examples/security/classification/output/reports/train/1d1f8317a243341a1803e9ea2405db8f/params.yaml deleted file mode 100644 index 8a6e3159..00000000 --- a/examples/security/classification/output/reports/train/1d1f8317a243341a1803e9ea2405db8f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/adv_predictions.json - adv_probabilities_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/171ed42a3d35381bf7f01f8542da6961.pkl - params_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/params.yaml - predictions_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/predictions.json - probabilities_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/probabilities.json - score_dict_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/score_dict.json - test_labels_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/test_labels.json - train_labels_file: output/reports/train/1d1f8317a243341a1803e9ea2405db8f/train_labels.json - model_dir: models - name: 1d1f8317a243341a1803e9ea2405db8f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bd538b9500e1224ba3c97d962db8d3b3 - trainer: - kwargs: {} -name: 1d1f8317a243341a1803e9ea2405db8f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1d1fc09a81c3f08febb6699908905fc7/params.yaml b/examples/security/classification/output/reports/train/1d1fc09a81c3f08febb6699908905fc7/params.yaml deleted file mode 100644 index bb4df27e..00000000 --- a/examples/security/classification/output/reports/train/1d1fc09a81c3f08febb6699908905fc7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/adv_predictions.json - adv_probabilities_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/c4ebeef92bc58427ac45e3e9330c8fce.pkl - params_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/params.yaml - predictions_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/predictions.json - probabilities_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/probabilities.json - score_dict_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/score_dict.json - test_labels_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/test_labels.json - train_labels_file: output/reports/train/1d1fc09a81c3f08febb6699908905fc7/train_labels.json - model_dir: models - name: 1d1fc09a81c3f08febb6699908905fc7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 82b99f4eecb8809034de987f3212b6a8 - trainer: - kwargs: {} -name: 1d1fc09a81c3f08febb6699908905fc7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/params.yaml b/examples/security/classification/output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/params.yaml deleted file mode 100644 index 19654c6b..00000000 --- a/examples/security/classification/output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/adv_predictions.json - adv_probabilities_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/2de3771e06ccdc0d9599e931959cd9e4.pkl - params_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/params.yaml - predictions_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/predictions.json - probabilities_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/probabilities.json - score_dict_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/score_dict.json - test_labels_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/test_labels.json - train_labels_file: output/reports/train/1d3544f6d3fc0e5d73b318df927b945c/train_labels.json - model_dir: models - name: 1d3544f6d3fc0e5d73b318df927b945c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9f8d7ac155d185bb3653be0c98b2e8b9 - trainer: - kwargs: {} -name: 1d3544f6d3fc0e5d73b318df927b945c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/params.yaml b/examples/security/classification/output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/params.yaml deleted file mode 100644 index 9bb37d62..00000000 --- a/examples/security/classification/output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/adv_predictions.json - adv_probabilities_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/c13e7022579d3a1009ace6d232600df3.pkl - params_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/params.yaml - predictions_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/predictions.json - probabilities_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/probabilities.json - score_dict_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/score_dict.json - test_labels_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/test_labels.json - train_labels_file: output/reports/train/1d51317e90442e3a6f4636e5d40f45c7/train_labels.json - model_dir: models - name: 1d51317e90442e3a6f4636e5d40f45c7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 449a4b0231d826d78177a35fdc08ad9a - trainer: - kwargs: {} -name: 1d51317e90442e3a6f4636e5d40f45c7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/params.yaml b/examples/security/classification/output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/params.yaml deleted file mode 100644 index 6b0f88fd..00000000 --- a/examples/security/classification/output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/adv_predictions.json - adv_probabilities_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/dd82ee472748d3d2e991fd342640de81.pkl - params_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/params.yaml - predictions_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/predictions.json - probabilities_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/probabilities.json - score_dict_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/score_dict.json - test_labels_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/test_labels.json - train_labels_file: output/reports/train/1d634f3fea42c7769f7b115d504fb1fd/train_labels.json - model_dir: models - name: 1d634f3fea42c7769f7b115d504fb1fd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3ceb12fdf847ee7ec7704a5a246ab829 - trainer: - kwargs: {} -name: 1d634f3fea42c7769f7b115d504fb1fd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/params.yaml b/examples/security/classification/output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/params.yaml deleted file mode 100644 index eff18934..00000000 --- a/examples/security/classification/output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/adv_predictions.json - adv_probabilities_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/0cff640d1d158f9e64921df61acc84e3.pkl - params_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/params.yaml - predictions_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/predictions.json - probabilities_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/probabilities.json - score_dict_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/score_dict.json - test_labels_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/test_labels.json - train_labels_file: output/reports/train/1d6eb1048a4ccf7c1e055acf882994a1/train_labels.json - model_dir: models - name: 1d6eb1048a4ccf7c1e055acf882994a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbdfa127f99e9b9c66061ce73b7985b0 - trainer: - kwargs: {} -name: 1d6eb1048a4ccf7c1e055acf882994a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1d7b8e8ac3eff83935f702634d5447af/params.yaml b/examples/security/classification/output/reports/train/1d7b8e8ac3eff83935f702634d5447af/params.yaml deleted file mode 100644 index 2dad6efc..00000000 --- a/examples/security/classification/output/reports/train/1d7b8e8ac3eff83935f702634d5447af/params.yaml +++ /dev/null @@ -1,206 +0,0 @@ -attack: - attack_size: 10 - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 10.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 99034aefb7fff584d81427ae2a1f5f27 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3d6cda61d3c13d4bb0cee27f0e2c8da5 - trainer: - kwargs: {} - name: 78aa87101cdd06b0cf83e07e3a949900 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/adv_predictions.json - adv_probabilities_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/adv_probabilities.json - attack_file: output/attacks/d87e96c976e0063eb50515b6d064f86f.pkl - data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl - model_file: output/models/c33f7a6117fc095b356f3a35dd8646a3.pkl - params_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/params.yaml - predictions_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/predictions.json - probabilities_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/probabilities.json - score_dict_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/score_dict.json - test_labels_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/test_labels.json - train_labels_file: output/reports/train/1d7b8e8ac3eff83935f702634d5447af/train_labels.json - model_dir: models - name: 1d7b8e8ac3eff83935f702634d5447af - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6e6f54d79165d97f4d5a424cf6825820 - trainer: - kwargs: {} -name: 1d7b8e8ac3eff83935f702634d5447af -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1dcce671dd15b880537dfcf7f5288804/params.yaml b/examples/security/classification/output/reports/train/1dcce671dd15b880537dfcf7f5288804/params.yaml deleted file mode 100644 index c660b1ab..00000000 --- a/examples/security/classification/output/reports/train/1dcce671dd15b880537dfcf7f5288804/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/adv_predictions.json - adv_probabilities_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/903a1cc8fce6c53e20096ed42f5c569e.pkl - params_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/params.yaml - predictions_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/predictions.json - probabilities_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/probabilities.json - score_dict_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/score_dict.json - test_labels_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/test_labels.json - train_labels_file: output/reports/train/1dcce671dd15b880537dfcf7f5288804/train_labels.json - model_dir: models - name: 1dcce671dd15b880537dfcf7f5288804 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: af0934f44c7bd3fa3e61fce9a2627b8c - trainer: - kwargs: {} -name: 1dcce671dd15b880537dfcf7f5288804 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1dd62be2af8f41619dece68599e591d6/params.yaml b/examples/security/classification/output/reports/train/1dd62be2af8f41619dece68599e591d6/params.yaml deleted file mode 100644 index 3c0371fd..00000000 --- a/examples/security/classification/output/reports/train/1dd62be2af8f41619dece68599e591d6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/adv_predictions.json - adv_probabilities_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/dbc2c12eb930e911c389ba8a0fac55d9.pkl - params_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/params.yaml - predictions_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/predictions.json - probabilities_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/probabilities.json - score_dict_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/score_dict.json - test_labels_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/test_labels.json - train_labels_file: output/reports/train/1dd62be2af8f41619dece68599e591d6/train_labels.json - model_dir: models - name: 1dd62be2af8f41619dece68599e591d6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6fdbdbcdcf55f119411fe85be434a65c - trainer: - kwargs: {} -name: 1dd62be2af8f41619dece68599e591d6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/params.yaml b/examples/security/classification/output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/params.yaml deleted file mode 100644 index 6810fe02..00000000 --- a/examples/security/classification/output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/adv_predictions.json - adv_probabilities_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/b520456a45e84f9d9a181bd8ee0b5bfc.pkl - params_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/params.yaml - predictions_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/predictions.json - probabilities_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/probabilities.json - score_dict_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/score_dict.json - test_labels_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/test_labels.json - train_labels_file: output/reports/train/1dee5bc9654534ded4352de2ad6d4ebe/train_labels.json - model_dir: models - name: 1dee5bc9654534ded4352de2ad6d4ebe - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 56e992acfd4509fd9f2715decf95f3a3 - trainer: - kwargs: {} -name: 1dee5bc9654534ded4352de2ad6d4ebe -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/params.yaml b/examples/security/classification/output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/params.yaml deleted file mode 100644 index 117b68ad..00000000 --- a/examples/security/classification/output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/adv_predictions.json - adv_probabilities_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/412ccd0ed53caf4029d6c648db5e5c2f.pkl - params_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/params.yaml - predictions_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/predictions.json - probabilities_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/probabilities.json - score_dict_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/score_dict.json - test_labels_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/test_labels.json - train_labels_file: output/reports/train/1df389a20c6e88d33e18d0be63bff0b6/train_labels.json - model_dir: models - name: 1df389a20c6e88d33e18d0be63bff0b6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0c34172e0cd57ff953b12b8fa65a6df6 - trainer: - kwargs: {} -name: 1df389a20c6e88d33e18d0be63bff0b6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/params.yaml b/examples/security/classification/output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/params.yaml deleted file mode 100644 index f84c5615..00000000 --- a/examples/security/classification/output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/adv_predictions.json - adv_probabilities_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/f1899d4ca1cf54b4a93a29ba62bfb9f2.pkl - params_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/params.yaml - predictions_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/predictions.json - probabilities_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/probabilities.json - score_dict_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/score_dict.json - test_labels_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/test_labels.json - train_labels_file: output/reports/train/1dfbefe8210f30073f8c752a88d2dbff/train_labels.json - model_dir: models - name: 1dfbefe8210f30073f8c752a88d2dbff - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 07692e306f6446f3f1611af9e2a6b376 - trainer: - kwargs: {} -name: 1dfbefe8210f30073f8c752a88d2dbff -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1e0358564026367d3e932e39416ec8ef/params.yaml b/examples/security/classification/output/reports/train/1e0358564026367d3e932e39416ec8ef/params.yaml deleted file mode 100644 index 2ff1f1cc..00000000 --- a/examples/security/classification/output/reports/train/1e0358564026367d3e932e39416ec8ef/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/adv_predictions.json - adv_probabilities_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/d897d45e22cd7caeee2beb44ac5ba598.pkl - params_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/params.yaml - predictions_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/predictions.json - probabilities_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/probabilities.json - score_dict_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/score_dict.json - test_labels_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/test_labels.json - train_labels_file: output/reports/train/1e0358564026367d3e932e39416ec8ef/train_labels.json - model_dir: models - name: 1e0358564026367d3e932e39416ec8ef - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4aea27bd7471f098da3b85fa92e4c339 - trainer: - kwargs: {} -name: 1e0358564026367d3e932e39416ec8ef -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1e0fa20fe1180c30965df2be58b71d33/params.yaml b/examples/security/classification/output/reports/train/1e0fa20fe1180c30965df2be58b71d33/params.yaml deleted file mode 100644 index 3f706176..00000000 --- a/examples/security/classification/output/reports/train/1e0fa20fe1180c30965df2be58b71d33/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/adv_predictions.json - adv_probabilities_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/d7dc9664b0d7577bdfa9a8964f47606e.pkl - params_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/params.yaml - predictions_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/predictions.json - probabilities_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/probabilities.json - score_dict_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/score_dict.json - test_labels_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/test_labels.json - train_labels_file: output/reports/train/1e0fa20fe1180c30965df2be58b71d33/train_labels.json - model_dir: models - name: 1e0fa20fe1180c30965df2be58b71d33 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 505d2d1be225fe2f454bfe8b91576e20 - trainer: - kwargs: {} -name: 1e0fa20fe1180c30965df2be58b71d33 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/params.yaml b/examples/security/classification/output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/params.yaml deleted file mode 100644 index d08dc235..00000000 --- a/examples/security/classification/output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/adv_predictions.json - adv_probabilities_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/9fc4cc62facdfaaa0447e770fa032eae.pkl - params_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/params.yaml - predictions_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/predictions.json - probabilities_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/probabilities.json - score_dict_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/score_dict.json - test_labels_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/test_labels.json - train_labels_file: output/reports/train/1e9c3da99a5d00fb15faf995f414d2b3/train_labels.json - model_dir: models - name: 1e9c3da99a5d00fb15faf995f414d2b3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5c2d25dc6ca69c6ea2c4a6b0db953c42 - trainer: - kwargs: {} -name: 1e9c3da99a5d00fb15faf995f414d2b3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/params.yaml b/examples/security/classification/output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/params.yaml deleted file mode 100644 index 61434562..00000000 --- a/examples/security/classification/output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/adv_predictions.json - adv_probabilities_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/e33e0c60877d52b2ae4ae0cc980c57f4.pkl - params_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/params.yaml - predictions_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/predictions.json - probabilities_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/probabilities.json - score_dict_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/score_dict.json - test_labels_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/test_labels.json - train_labels_file: output/reports/train/1e9c3db1f7cfaeb0264fb2e2f9edd7a6/train_labels.json - model_dir: models - name: 1e9c3db1f7cfaeb0264fb2e2f9edd7a6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b27aae93d620f4a9b6617d385f847d4f - trainer: - kwargs: {} -name: 1e9c3db1f7cfaeb0264fb2e2f9edd7a6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/params.yaml b/examples/security/classification/output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/params.yaml deleted file mode 100644 index 0fee5d24..00000000 --- a/examples/security/classification/output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/adv_predictions.json - adv_probabilities_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/5f2108b4407985fc0163aac802d0bae4.pkl - params_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/params.yaml - predictions_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/predictions.json - probabilities_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/probabilities.json - score_dict_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/score_dict.json - test_labels_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/test_labels.json - train_labels_file: output/reports/train/1e9fe8b1f3fd7fd72e16f1a6acb3af7b/train_labels.json - model_dir: models - name: 1e9fe8b1f3fd7fd72e16f1a6acb3af7b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b252615fcddffc8d63a49aac5da37660 - trainer: - kwargs: {} -name: 1e9fe8b1f3fd7fd72e16f1a6acb3af7b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1eda6ef43474ca10739b491f45cb81dd/params.yaml b/examples/security/classification/output/reports/train/1eda6ef43474ca10739b491f45cb81dd/params.yaml deleted file mode 100644 index 7979e612..00000000 --- a/examples/security/classification/output/reports/train/1eda6ef43474ca10739b491f45cb81dd/params.yaml +++ /dev/null @@ -1,104 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/adv_predictions.json - adv_probabilities_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/94976d8632a33d866fec774289b78144.pkl - params_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/params.yaml - predictions_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/predictions.json - probabilities_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/probabilities.json - score_dict_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/score_dict.json - test_labels_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/test_labels.json - train_labels_file: output/reports/train/1eda6ef43474ca10739b491f45cb81dd/train_labels.json - model_dir: models - name: 1eda6ef43474ca10739b491f45cb81dd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: - - linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2685c1f7b4fe814c0d7e3604b6b19e31 - trainer: - kwargs: {} -name: 1eda6ef43474ca10739b491f45cb81dd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/params.yaml b/examples/security/classification/output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/params.yaml deleted file mode 100644 index 7c5fd947..00000000 --- a/examples/security/classification/output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/adv_predictions.json - adv_probabilities_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/3edee2f340eb2a2468f4ecffeee68213.pkl - params_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/params.yaml - predictions_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/predictions.json - probabilities_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/probabilities.json - score_dict_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/score_dict.json - test_labels_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/test_labels.json - train_labels_file: output/reports/train/1ef453cfe87d6aa7e247c11b2b2a7516/train_labels.json - model_dir: models - name: 1ef453cfe87d6aa7e247c11b2b2a7516 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d820e9d9fac5f1c2438550f7431cadf5 - trainer: - kwargs: {} -name: 1ef453cfe87d6aa7e247c11b2b2a7516 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1f4726d533012f54440d6fa1b01c458a/params.yaml b/examples/security/classification/output/reports/train/1f4726d533012f54440d6fa1b01c458a/params.yaml deleted file mode 100644 index 4b065d98..00000000 --- a/examples/security/classification/output/reports/train/1f4726d533012f54440d6fa1b01c458a/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/adv_predictions.json - adv_probabilities_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/b226392f4a9ba54197f8a61deb5cbd3b.pkl - params_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/params.yaml - predictions_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/predictions.json - probabilities_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/probabilities.json - score_dict_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/score_dict.json - test_labels_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/test_labels.json - train_labels_file: output/reports/train/1f4726d533012f54440d6fa1b01c458a/train_labels.json - model_dir: models - name: 1f4726d533012f54440d6fa1b01c458a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 62ec7bc40ead19ecf3a4bde7f612ffd0 - trainer: - kwargs: {} -name: 1f4726d533012f54440d6fa1b01c458a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1f53b056514e00641604afb744be33ff/params.yaml b/examples/security/classification/output/reports/train/1f53b056514e00641604afb744be33ff/params.yaml deleted file mode 100644 index 6140c6a1..00000000 --- a/examples/security/classification/output/reports/train/1f53b056514e00641604afb744be33ff/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1f53b056514e00641604afb744be33ff/adv_predictions.json - adv_probabilities_file: output/reports/train/1f53b056514e00641604afb744be33ff/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/66267d181e05ff8c83319674bab3b61a.pkl - params_file: output/reports/train/1f53b056514e00641604afb744be33ff/params.yaml - predictions_file: output/reports/train/1f53b056514e00641604afb744be33ff/predictions.json - probabilities_file: output/reports/train/1f53b056514e00641604afb744be33ff/probabilities.json - score_dict_file: output/reports/train/1f53b056514e00641604afb744be33ff/score_dict.json - test_labels_file: output/reports/train/1f53b056514e00641604afb744be33ff/test_labels.json - train_labels_file: output/reports/train/1f53b056514e00641604afb744be33ff/train_labels.json - model_dir: models - name: 1f53b056514e00641604afb744be33ff - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f8b54c1bd2623c6b40196d4c9a9267ae - trainer: - kwargs: {} -name: 1f53b056514e00641604afb744be33ff -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/params.yaml b/examples/security/classification/output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/params.yaml deleted file mode 100644 index 5c0a347b..00000000 --- a/examples/security/classification/output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/adv_predictions.json - adv_probabilities_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/4b6289a822107c0bc4bcb7fdd1af0886.pkl - params_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/params.yaml - predictions_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/predictions.json - probabilities_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/probabilities.json - score_dict_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/score_dict.json - test_labels_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/test_labels.json - train_labels_file: output/reports/train/1f8ed4f0a1be217e03cfa8cbde7c2212/train_labels.json - model_dir: models - name: 1f8ed4f0a1be217e03cfa8cbde7c2212 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b501eac51fed25d4f36ce28ccc101a7c - trainer: - kwargs: {} -name: 1f8ed4f0a1be217e03cfa8cbde7c2212 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/params.yaml b/examples/security/classification/output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/params.yaml deleted file mode 100644 index 7bd41555..00000000 --- a/examples/security/classification/output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/adv_predictions.json - adv_probabilities_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/b7af521d7ffc9136245394fa98ce49d0.pkl - params_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/params.yaml - predictions_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/predictions.json - probabilities_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/probabilities.json - score_dict_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/score_dict.json - test_labels_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/test_labels.json - train_labels_file: output/reports/train/1fcfee5f9a3b07f73d91dd0bb81c8589/train_labels.json - model_dir: models - name: 1fcfee5f9a3b07f73d91dd0bb81c8589 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dfba054c00577fb0d661d239181b26ea - trainer: - kwargs: {} -name: 1fcfee5f9a3b07f73d91dd0bb81c8589 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1fdc26234b001a45b92f2301167ac2fd/params.yaml b/examples/security/classification/output/reports/train/1fdc26234b001a45b92f2301167ac2fd/params.yaml deleted file mode 100644 index 8e688103..00000000 --- a/examples/security/classification/output/reports/train/1fdc26234b001a45b92f2301167ac2fd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/adv_predictions.json - adv_probabilities_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/6871f63bfc591a4b6dd8de369593a7c9.pkl - params_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/params.yaml - predictions_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/predictions.json - probabilities_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/probabilities.json - score_dict_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/score_dict.json - test_labels_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/test_labels.json - train_labels_file: output/reports/train/1fdc26234b001a45b92f2301167ac2fd/train_labels.json - model_dir: models - name: 1fdc26234b001a45b92f2301167ac2fd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a5d6ff64fccb4dc8a10a7c3f16c3416f - trainer: - kwargs: {} -name: 1fdc26234b001a45b92f2301167ac2fd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/params.yaml b/examples/security/classification/output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/params.yaml deleted file mode 100644 index 226ee0fe..00000000 --- a/examples/security/classification/output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/adv_predictions.json - adv_probabilities_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/a75543c527e1f5edea75d33b09c54cb6.pkl - params_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/params.yaml - predictions_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/predictions.json - probabilities_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/probabilities.json - score_dict_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/score_dict.json - test_labels_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/test_labels.json - train_labels_file: output/reports/train/1fe641cdc1f0bad587cdf2fa10c63019/train_labels.json - model_dir: models - name: 1fe641cdc1f0bad587cdf2fa10c63019 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2bb427dc56b6ce4ae99d8898f7acbc62 - trainer: - kwargs: {} -name: 1fe641cdc1f0bad587cdf2fa10c63019 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/201901a6f538910a91c05bf319e9e1d6/params.yaml b/examples/security/classification/output/reports/train/201901a6f538910a91c05bf319e9e1d6/params.yaml deleted file mode 100644 index f260dbaf..00000000 --- a/examples/security/classification/output/reports/train/201901a6f538910a91c05bf319e9e1d6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/adv_predictions.json - adv_probabilities_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/906671ca0ffa2c2140cfaf845d48445b.pkl - params_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/params.yaml - predictions_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/predictions.json - probabilities_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/probabilities.json - score_dict_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/score_dict.json - test_labels_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/test_labels.json - train_labels_file: output/reports/train/201901a6f538910a91c05bf319e9e1d6/train_labels.json - model_dir: models - name: 201901a6f538910a91c05bf319e9e1d6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cab64fa567e6b564c1b1563c6a64f67a - trainer: - kwargs: {} -name: 201901a6f538910a91c05bf319e9e1d6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2023196316784228f70b823bf00377ef/params.yaml b/examples/security/classification/output/reports/train/2023196316784228f70b823bf00377ef/params.yaml deleted file mode 100644 index 2fbdadfe..00000000 --- a/examples/security/classification/output/reports/train/2023196316784228f70b823bf00377ef/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2023196316784228f70b823bf00377ef/adv_predictions.json - adv_probabilities_file: output/reports/train/2023196316784228f70b823bf00377ef/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/35f51ac0709d61df0f722097d7c90719.pkl - params_file: output/reports/train/2023196316784228f70b823bf00377ef/params.yaml - predictions_file: output/reports/train/2023196316784228f70b823bf00377ef/predictions.json - probabilities_file: output/reports/train/2023196316784228f70b823bf00377ef/probabilities.json - score_dict_file: output/reports/train/2023196316784228f70b823bf00377ef/score_dict.json - test_labels_file: output/reports/train/2023196316784228f70b823bf00377ef/test_labels.json - train_labels_file: output/reports/train/2023196316784228f70b823bf00377ef/train_labels.json - model_dir: models - name: 2023196316784228f70b823bf00377ef - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5f8e6e1c2ab2d91cda80c64c983b368c - trainer: - kwargs: {} -name: 2023196316784228f70b823bf00377ef -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/params.yaml b/examples/security/classification/output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/params.yaml deleted file mode 100644 index fc9224f6..00000000 --- a/examples/security/classification/output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/adv_predictions.json - adv_probabilities_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/58bc211add00b180bd583d86ec5cb5ab.pkl - params_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/params.yaml - predictions_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/predictions.json - probabilities_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/probabilities.json - score_dict_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/score_dict.json - test_labels_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/test_labels.json - train_labels_file: output/reports/train/2038f9f46e6e693ee453b30ca8644a1a/train_labels.json - model_dir: models - name: 2038f9f46e6e693ee453b30ca8644a1a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3129843977272e0190b237edfd37519e - trainer: - kwargs: {} -name: 2038f9f46e6e693ee453b30ca8644a1a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/params.yaml b/examples/security/classification/output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/params.yaml deleted file mode 100644 index dda7c779..00000000 --- a/examples/security/classification/output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/adv_predictions.json - adv_probabilities_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/4d1a9156877d700701629e84f9ef1b28.pkl - params_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/params.yaml - predictions_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/predictions.json - probabilities_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/probabilities.json - score_dict_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/score_dict.json - test_labels_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/test_labels.json - train_labels_file: output/reports/train/203cfc3c53e14cfdc893f04ebbb78f56/train_labels.json - model_dir: models - name: 203cfc3c53e14cfdc893f04ebbb78f56 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c85b64cd66739734131a9c25c45075fa - trainer: - kwargs: {} -name: 203cfc3c53e14cfdc893f04ebbb78f56 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/203dc1484db3435ac5991e6e785f0585/params.yaml b/examples/security/classification/output/reports/train/203dc1484db3435ac5991e6e785f0585/params.yaml deleted file mode 100644 index 833c6106..00000000 --- a/examples/security/classification/output/reports/train/203dc1484db3435ac5991e6e785f0585/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/adv_predictions.json - adv_probabilities_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/f156e61a175da8f5ed681393a0269263.pkl - params_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/params.yaml - predictions_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/predictions.json - probabilities_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/probabilities.json - score_dict_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/score_dict.json - test_labels_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/test_labels.json - train_labels_file: output/reports/train/203dc1484db3435ac5991e6e785f0585/train_labels.json - model_dir: models - name: 203dc1484db3435ac5991e6e785f0585 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ba38bdf8f7c9f740ceab05354ce843d1 - trainer: - kwargs: {} -name: 203dc1484db3435ac5991e6e785f0585 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/204c686f63cdd905f681fe2131da0f5a/params.yaml b/examples/security/classification/output/reports/train/204c686f63cdd905f681fe2131da0f5a/params.yaml deleted file mode 100644 index d127cf7b..00000000 --- a/examples/security/classification/output/reports/train/204c686f63cdd905f681fe2131da0f5a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/adv_predictions.json - adv_probabilities_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/4f7f2f60623f1d793353bf2354c45f61.pkl - params_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/params.yaml - predictions_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/predictions.json - probabilities_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/probabilities.json - score_dict_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/score_dict.json - test_labels_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/test_labels.json - train_labels_file: output/reports/train/204c686f63cdd905f681fe2131da0f5a/train_labels.json - model_dir: models - name: 204c686f63cdd905f681fe2131da0f5a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 03aa057c897c8cdcf647f448feec892b - trainer: - kwargs: {} -name: 204c686f63cdd905f681fe2131da0f5a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/params.yaml b/examples/security/classification/output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/params.yaml deleted file mode 100644 index 023ea09c..00000000 --- a/examples/security/classification/output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/adv_predictions.json - adv_probabilities_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/f8c39cb1e70a95fa580da4414f88d5ee.pkl - params_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/params.yaml - predictions_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/predictions.json - probabilities_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/probabilities.json - score_dict_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/score_dict.json - test_labels_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/test_labels.json - train_labels_file: output/reports/train/20585a62e351b7ab3f6c6e04ddb2a9d3/train_labels.json - model_dir: models - name: 20585a62e351b7ab3f6c6e04ddb2a9d3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6de6eb233af0a114f8ccd525871073a9 - trainer: - kwargs: {} -name: 20585a62e351b7ab3f6c6e04ddb2a9d3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/params.yaml b/examples/security/classification/output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/params.yaml deleted file mode 100644 index 5d8eaa80..00000000 --- a/examples/security/classification/output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/adv_predictions.json - adv_probabilities_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/06802fd7632df2d36155b9f22dca79ef.pkl - params_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/params.yaml - predictions_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/predictions.json - probabilities_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/probabilities.json - score_dict_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/score_dict.json - test_labels_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/test_labels.json - train_labels_file: output/reports/train/2058d12f3d246a16e816e3d43bd02ef5/train_labels.json - model_dir: models - name: 2058d12f3d246a16e816e3d43bd02ef5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a39f0d2f5a0f3844a65574fc53790148 - trainer: - kwargs: {} -name: 2058d12f3d246a16e816e3d43bd02ef5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/20848218acb6d9d8a97650479de68e3c/params.yaml b/examples/security/classification/output/reports/train/20848218acb6d9d8a97650479de68e3c/params.yaml deleted file mode 100644 index f2901853..00000000 --- a/examples/security/classification/output/reports/train/20848218acb6d9d8a97650479de68e3c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/adv_predictions.json - adv_probabilities_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/63a15f4a7dc699f707a9145be96fa6ad.pkl - params_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/params.yaml - predictions_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/predictions.json - probabilities_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/probabilities.json - score_dict_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/score_dict.json - test_labels_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/test_labels.json - train_labels_file: output/reports/train/20848218acb6d9d8a97650479de68e3c/train_labels.json - model_dir: models - name: 20848218acb6d9d8a97650479de68e3c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ec781c8bdc47953f0459b2461fcaf97d - trainer: - kwargs: {} -name: 20848218acb6d9d8a97650479de68e3c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/params.yaml b/examples/security/classification/output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/params.yaml deleted file mode 100644 index 7239ae69..00000000 --- a/examples/security/classification/output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/adv_predictions.json - adv_probabilities_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/59db51caec2d8c90fc5de6242e439f31.pkl - params_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/params.yaml - predictions_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/predictions.json - probabilities_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/probabilities.json - score_dict_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/score_dict.json - test_labels_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/test_labels.json - train_labels_file: output/reports/train/2097bbd38174b1c6ec1d3932fcec4d41/train_labels.json - model_dir: models - name: 2097bbd38174b1c6ec1d3932fcec4d41 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d7f1694d3201fde9156d0c23bc9605e6 - trainer: - kwargs: {} -name: 2097bbd38174b1c6ec1d3932fcec4d41 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/20b4be760499a6df6f8c70f668d3197b/params.yaml b/examples/security/classification/output/reports/train/20b4be760499a6df6f8c70f668d3197b/params.yaml deleted file mode 100644 index eae42a73..00000000 --- a/examples/security/classification/output/reports/train/20b4be760499a6df6f8c70f668d3197b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/adv_predictions.json - adv_probabilities_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/dd3ad907419cd11a5add56b1ef78917a.pkl - params_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/params.yaml - predictions_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/predictions.json - probabilities_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/probabilities.json - score_dict_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/score_dict.json - test_labels_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/test_labels.json - train_labels_file: output/reports/train/20b4be760499a6df6f8c70f668d3197b/train_labels.json - model_dir: models - name: 20b4be760499a6df6f8c70f668d3197b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2dc324a81beaeec38f5fd0e9e673e9cf - trainer: - kwargs: {} -name: 20b4be760499a6df6f8c70f668d3197b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/20ba096a9032e7810982d74015a1bcf2/params.yaml b/examples/security/classification/output/reports/train/20ba096a9032e7810982d74015a1bcf2/params.yaml deleted file mode 100644 index da75fda1..00000000 --- a/examples/security/classification/output/reports/train/20ba096a9032e7810982d74015a1bcf2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/adv_predictions.json - adv_probabilities_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/918f92fb552fc82782fb703627305310.pkl - params_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/params.yaml - predictions_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/predictions.json - probabilities_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/probabilities.json - score_dict_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/score_dict.json - test_labels_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/test_labels.json - train_labels_file: output/reports/train/20ba096a9032e7810982d74015a1bcf2/train_labels.json - model_dir: models - name: 20ba096a9032e7810982d74015a1bcf2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 44f5f3f8e955e4c0cc6eee014f22d953 - trainer: - kwargs: {} -name: 20ba096a9032e7810982d74015a1bcf2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/params.yaml b/examples/security/classification/output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/params.yaml deleted file mode 100644 index 06fd83dd..00000000 --- a/examples/security/classification/output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/adv_predictions.json - adv_probabilities_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/e3d6c67a3c0d48d863f4ff8114a3cdb8.pkl - params_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/params.yaml - predictions_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/predictions.json - probabilities_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/probabilities.json - score_dict_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/score_dict.json - test_labels_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/test_labels.json - train_labels_file: output/reports/train/2105f0388f4501b96f1c744fd7a46bc0/train_labels.json - model_dir: models - name: 2105f0388f4501b96f1c744fd7a46bc0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0038196db91603a1a5d038baf4d8c7b9 - trainer: - kwargs: {} -name: 2105f0388f4501b96f1c744fd7a46bc0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/210d1ed09e98b4deb406f28a19762b7b/params.yaml b/examples/security/classification/output/reports/train/210d1ed09e98b4deb406f28a19762b7b/params.yaml deleted file mode 100644 index fb199be0..00000000 --- a/examples/security/classification/output/reports/train/210d1ed09e98b4deb406f28a19762b7b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/adv_predictions.json - adv_probabilities_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/337b28219161b6cb029b7854ad5a73be.pkl - params_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/params.yaml - predictions_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/predictions.json - probabilities_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/probabilities.json - score_dict_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/score_dict.json - test_labels_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/test_labels.json - train_labels_file: output/reports/train/210d1ed09e98b4deb406f28a19762b7b/train_labels.json - model_dir: models - name: 210d1ed09e98b4deb406f28a19762b7b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6b2d410520aecba4b9ee8e44b952d994 - trainer: - kwargs: {} -name: 210d1ed09e98b4deb406f28a19762b7b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/params.yaml b/examples/security/classification/output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/params.yaml deleted file mode 100644 index b2e5e3f6..00000000 --- a/examples/security/classification/output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/adv_predictions.json - adv_probabilities_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/5a37631368766fc1c7aae816bbb416b5.pkl - params_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/params.yaml - predictions_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/predictions.json - probabilities_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/probabilities.json - score_dict_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/score_dict.json - test_labels_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/test_labels.json - train_labels_file: output/reports/train/2178ee306ee4c35e03c7d25cefa72b96/train_labels.json - model_dir: models - name: 2178ee306ee4c35e03c7d25cefa72b96 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 02caf44f6b313ae4deebe9b02b700c13 - trainer: - kwargs: {} -name: 2178ee306ee4c35e03c7d25cefa72b96 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/21aa1a6474ef2302489522aa03bbfdec/params.yaml b/examples/security/classification/output/reports/train/21aa1a6474ef2302489522aa03bbfdec/params.yaml deleted file mode 100644 index dd774787..00000000 --- a/examples/security/classification/output/reports/train/21aa1a6474ef2302489522aa03bbfdec/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/adv_predictions.json - adv_probabilities_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/f21a36afe02b703d15daab0c25521e4b.pkl - params_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/params.yaml - predictions_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/predictions.json - probabilities_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/probabilities.json - score_dict_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/score_dict.json - test_labels_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/test_labels.json - train_labels_file: output/reports/train/21aa1a6474ef2302489522aa03bbfdec/train_labels.json - model_dir: models - name: 21aa1a6474ef2302489522aa03bbfdec - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c4a8831e9a44475446784875641b875c - trainer: - kwargs: {} -name: 21aa1a6474ef2302489522aa03bbfdec -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/21b691c0215f5e32c19674892a67ad03/params.yaml b/examples/security/classification/output/reports/train/21b691c0215f5e32c19674892a67ad03/params.yaml deleted file mode 100644 index 6f807c90..00000000 --- a/examples/security/classification/output/reports/train/21b691c0215f5e32c19674892a67ad03/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/adv_predictions.json - adv_probabilities_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/c9725d97c6fa22a8a7f5fb3fbb25cb51.pkl - params_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/params.yaml - predictions_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/predictions.json - probabilities_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/probabilities.json - score_dict_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/score_dict.json - test_labels_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/test_labels.json - train_labels_file: output/reports/train/21b691c0215f5e32c19674892a67ad03/train_labels.json - model_dir: models - name: 21b691c0215f5e32c19674892a67ad03 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6e6709404ee69cc734a7f51972d89fa0 - trainer: - kwargs: {} -name: 21b691c0215f5e32c19674892a67ad03 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/params.yaml b/examples/security/classification/output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/params.yaml deleted file mode 100644 index aa71e0ca..00000000 --- a/examples/security/classification/output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/adv_predictions.json - adv_probabilities_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/95c3083585731a9e2300eb6351977c06.pkl - params_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/params.yaml - predictions_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/predictions.json - probabilities_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/probabilities.json - score_dict_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/score_dict.json - test_labels_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/test_labels.json - train_labels_file: output/reports/train/21bd68754a82f38b2cdd94b08c3b4228/train_labels.json - model_dir: models - name: 21bd68754a82f38b2cdd94b08c3b4228 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5ff585f82691974e8e0f3b8a708d3ea2 - trainer: - kwargs: {} -name: 21bd68754a82f38b2cdd94b08c3b4228 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/21beea562d9e7ba37e10b0967cb455d3/params.yaml b/examples/security/classification/output/reports/train/21beea562d9e7ba37e10b0967cb455d3/params.yaml deleted file mode 100644 index 9e39d708..00000000 --- a/examples/security/classification/output/reports/train/21beea562d9e7ba37e10b0967cb455d3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/adv_predictions.json - adv_probabilities_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/9e4d9a663301de71e21b861a8b207494.pkl - params_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/params.yaml - predictions_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/predictions.json - probabilities_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/probabilities.json - score_dict_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/score_dict.json - test_labels_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/test_labels.json - train_labels_file: output/reports/train/21beea562d9e7ba37e10b0967cb455d3/train_labels.json - model_dir: models - name: 21beea562d9e7ba37e10b0967cb455d3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9c81aeba470c413a2c5696dc5ce46b1d - trainer: - kwargs: {} -name: 21beea562d9e7ba37e10b0967cb455d3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/params.yaml b/examples/security/classification/output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/params.yaml deleted file mode 100644 index e9721360..00000000 --- a/examples/security/classification/output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/adv_predictions.json - adv_probabilities_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/7ab5630c5a2c492039b4fad7dcc82290.pkl - params_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/params.yaml - predictions_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/predictions.json - probabilities_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/probabilities.json - score_dict_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/score_dict.json - test_labels_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/test_labels.json - train_labels_file: output/reports/train/21c933c4066bddc40f34e9ad7ce092a0/train_labels.json - model_dir: models - name: 21c933c4066bddc40f34e9ad7ce092a0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8ec9971d0a95bb0501c1144b1f8cc608 - trainer: - kwargs: {} -name: 21c933c4066bddc40f34e9ad7ce092a0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/params.yaml b/examples/security/classification/output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/params.yaml deleted file mode 100644 index 8fc82dc7..00000000 --- a/examples/security/classification/output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/adv_predictions.json - adv_probabilities_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/7ad385d8d3b153b1aa3f4d4159bea64b.pkl - params_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/params.yaml - predictions_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/predictions.json - probabilities_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/probabilities.json - score_dict_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/score_dict.json - test_labels_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/test_labels.json - train_labels_file: output/reports/train/21d2d0b23198b7d6d52feada1d198cc8/train_labels.json - model_dir: models - name: 21d2d0b23198b7d6d52feada1d198cc8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7c7424280845c194479015160c411f76 - trainer: - kwargs: {} -name: 21d2d0b23198b7d6d52feada1d198cc8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/params.yaml b/examples/security/classification/output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/params.yaml deleted file mode 100644 index abb24ca0..00000000 --- a/examples/security/classification/output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/adv_predictions.json - adv_probabilities_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/2400ebbf590a04d23c750fd2fb82b8b7.pkl - params_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/params.yaml - predictions_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/predictions.json - probabilities_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/probabilities.json - score_dict_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/score_dict.json - test_labels_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/test_labels.json - train_labels_file: output/reports/train/21e0a6c3704c832b5cd89cf85f29c37d/train_labels.json - model_dir: models - name: 21e0a6c3704c832b5cd89cf85f29c37d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f051c99a958014c8c312cc971c6470fe - trainer: - kwargs: {} -name: 21e0a6c3704c832b5cd89cf85f29c37d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2204dad29eb62fed362787907bde90f7/params.yaml b/examples/security/classification/output/reports/train/2204dad29eb62fed362787907bde90f7/params.yaml deleted file mode 100644 index 35cce751..00000000 --- a/examples/security/classification/output/reports/train/2204dad29eb62fed362787907bde90f7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2204dad29eb62fed362787907bde90f7/adv_predictions.json - adv_probabilities_file: output/reports/train/2204dad29eb62fed362787907bde90f7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/2b23b154e2b16dc45106de8a3b78f62d.pkl - params_file: output/reports/train/2204dad29eb62fed362787907bde90f7/params.yaml - predictions_file: output/reports/train/2204dad29eb62fed362787907bde90f7/predictions.json - probabilities_file: output/reports/train/2204dad29eb62fed362787907bde90f7/probabilities.json - score_dict_file: output/reports/train/2204dad29eb62fed362787907bde90f7/score_dict.json - test_labels_file: output/reports/train/2204dad29eb62fed362787907bde90f7/test_labels.json - train_labels_file: output/reports/train/2204dad29eb62fed362787907bde90f7/train_labels.json - model_dir: models - name: 2204dad29eb62fed362787907bde90f7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1d79335216e7f359573eb9d6fbf7e060 - trainer: - kwargs: {} -name: 2204dad29eb62fed362787907bde90f7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/params.yaml b/examples/security/classification/output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/params.yaml deleted file mode 100644 index ef419cd1..00000000 --- a/examples/security/classification/output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/adv_predictions.json - adv_probabilities_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/988f9b510798c97f27c920f22076c364.pkl - params_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/params.yaml - predictions_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/predictions.json - probabilities_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/probabilities.json - score_dict_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/score_dict.json - test_labels_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/test_labels.json - train_labels_file: output/reports/train/220b49b803cdab7bbc0d482d2ea943f2/train_labels.json - model_dir: models - name: 220b49b803cdab7bbc0d482d2ea943f2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fee03cfccffcf97b97758eaa04cb09bd - trainer: - kwargs: {} -name: 220b49b803cdab7bbc0d482d2ea943f2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/22267c7169a4eae20dbe53341a34ee32/params.yaml b/examples/security/classification/output/reports/train/22267c7169a4eae20dbe53341a34ee32/params.yaml deleted file mode 100644 index 7ad5e42a..00000000 --- a/examples/security/classification/output/reports/train/22267c7169a4eae20dbe53341a34ee32/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/adv_predictions.json - adv_probabilities_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/36c2c6e1470fb8c079a77e7f001e3fe5.pkl - params_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/params.yaml - predictions_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/predictions.json - probabilities_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/probabilities.json - score_dict_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/score_dict.json - test_labels_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/test_labels.json - train_labels_file: output/reports/train/22267c7169a4eae20dbe53341a34ee32/train_labels.json - model_dir: models - name: 22267c7169a4eae20dbe53341a34ee32 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 351562a1c59304e012fb9d08371bc3f3 - trainer: - kwargs: {} -name: 22267c7169a4eae20dbe53341a34ee32 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2228151e789606916babaed9208c2eac/params.yaml b/examples/security/classification/output/reports/train/2228151e789606916babaed9208c2eac/params.yaml deleted file mode 100644 index 728c5a8e..00000000 --- a/examples/security/classification/output/reports/train/2228151e789606916babaed9208c2eac/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2228151e789606916babaed9208c2eac/adv_predictions.json - adv_probabilities_file: output/reports/train/2228151e789606916babaed9208c2eac/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/54738d1214cbdaf5161a9e4ecf01af2b.pkl - params_file: output/reports/train/2228151e789606916babaed9208c2eac/params.yaml - predictions_file: output/reports/train/2228151e789606916babaed9208c2eac/predictions.json - probabilities_file: output/reports/train/2228151e789606916babaed9208c2eac/probabilities.json - score_dict_file: output/reports/train/2228151e789606916babaed9208c2eac/score_dict.json - test_labels_file: output/reports/train/2228151e789606916babaed9208c2eac/test_labels.json - train_labels_file: output/reports/train/2228151e789606916babaed9208c2eac/train_labels.json - model_dir: models - name: 2228151e789606916babaed9208c2eac - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c9a6af40cddc01da82cbba0191baac25 - trainer: - kwargs: {} -name: 2228151e789606916babaed9208c2eac -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/params.yaml b/examples/security/classification/output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/params.yaml deleted file mode 100644 index aa489761..00000000 --- a/examples/security/classification/output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/adv_predictions.json - adv_probabilities_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/7fc914c0cdd9d25ea1b15da195a72abc.pkl - params_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/params.yaml - predictions_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/predictions.json - probabilities_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/probabilities.json - score_dict_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/score_dict.json - test_labels_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/test_labels.json - train_labels_file: output/reports/train/222d7b510ba8e8d081bbbe4aa4309e7d/train_labels.json - model_dir: models - name: 222d7b510ba8e8d081bbbe4aa4309e7d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c0cd3d2630bab4d133dac254f3bb7ecf - trainer: - kwargs: {} -name: 222d7b510ba8e8d081bbbe4aa4309e7d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/params.yaml b/examples/security/classification/output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/params.yaml deleted file mode 100644 index 1cd0989a..00000000 --- a/examples/security/classification/output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/adv_predictions.json - adv_probabilities_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/f352653c999d32bfcdf092c1d5618b57.pkl - params_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/params.yaml - predictions_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/predictions.json - probabilities_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/probabilities.json - score_dict_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/score_dict.json - test_labels_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/test_labels.json - train_labels_file: output/reports/train/223a5012f76f9dea980b6e2d1d0f74cb/train_labels.json - model_dir: models - name: 223a5012f76f9dea980b6e2d1d0f74cb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e059cfc2e0446d58abdeb86129a447b2 - trainer: - kwargs: {} -name: 223a5012f76f9dea980b6e2d1d0f74cb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/22bed80730d16c5890d549b09008f4d5/params.yaml b/examples/security/classification/output/reports/train/22bed80730d16c5890d549b09008f4d5/params.yaml deleted file mode 100644 index c8bdca1b..00000000 --- a/examples/security/classification/output/reports/train/22bed80730d16c5890d549b09008f4d5/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/adv_predictions.json - adv_probabilities_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/638eda91c54f332cc4b8f6648f4d6402.pkl - params_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/params.yaml - predictions_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/predictions.json - probabilities_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/probabilities.json - score_dict_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/score_dict.json - test_labels_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/test_labels.json - train_labels_file: output/reports/train/22bed80730d16c5890d549b09008f4d5/train_labels.json - model_dir: models - name: 22bed80730d16c5890d549b09008f4d5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b1028c83ee50a57b9c4aa806da838e57 - trainer: - kwargs: {} -name: 22bed80730d16c5890d549b09008f4d5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/22c117f29070010d9c2988d3f4644323/params.yaml b/examples/security/classification/output/reports/train/22c117f29070010d9c2988d3f4644323/params.yaml deleted file mode 100644 index 019ad329..00000000 --- a/examples/security/classification/output/reports/train/22c117f29070010d9c2988d3f4644323/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/22c117f29070010d9c2988d3f4644323/adv_predictions.json - adv_probabilities_file: output/reports/train/22c117f29070010d9c2988d3f4644323/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/c90af7c05abf993ec9db88237f612412.pkl - params_file: output/reports/train/22c117f29070010d9c2988d3f4644323/params.yaml - predictions_file: output/reports/train/22c117f29070010d9c2988d3f4644323/predictions.json - probabilities_file: output/reports/train/22c117f29070010d9c2988d3f4644323/probabilities.json - score_dict_file: output/reports/train/22c117f29070010d9c2988d3f4644323/score_dict.json - test_labels_file: output/reports/train/22c117f29070010d9c2988d3f4644323/test_labels.json - train_labels_file: output/reports/train/22c117f29070010d9c2988d3f4644323/train_labels.json - model_dir: models - name: 22c117f29070010d9c2988d3f4644323 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bd5204d93f8adeb773755e087133f4c1 - trainer: - kwargs: {} -name: 22c117f29070010d9c2988d3f4644323 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/22d745717f86f83a940098580ea66f47/params.yaml b/examples/security/classification/output/reports/train/22d745717f86f83a940098580ea66f47/params.yaml deleted file mode 100644 index a197988c..00000000 --- a/examples/security/classification/output/reports/train/22d745717f86f83a940098580ea66f47/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/22d745717f86f83a940098580ea66f47/adv_predictions.json - adv_probabilities_file: output/reports/train/22d745717f86f83a940098580ea66f47/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/c0124dd8e10bb8e4a0f8719f3df438d7.pkl - params_file: output/reports/train/22d745717f86f83a940098580ea66f47/params.yaml - predictions_file: output/reports/train/22d745717f86f83a940098580ea66f47/predictions.json - probabilities_file: output/reports/train/22d745717f86f83a940098580ea66f47/probabilities.json - score_dict_file: output/reports/train/22d745717f86f83a940098580ea66f47/score_dict.json - test_labels_file: output/reports/train/22d745717f86f83a940098580ea66f47/test_labels.json - train_labels_file: output/reports/train/22d745717f86f83a940098580ea66f47/train_labels.json - model_dir: models - name: 22d745717f86f83a940098580ea66f47 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8f3970e2cf09d72bc4d2c576785f9145 - trainer: - kwargs: {} -name: 22d745717f86f83a940098580ea66f47 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/22e2541d8b26abcd71b11204329eb966/params.yaml b/examples/security/classification/output/reports/train/22e2541d8b26abcd71b11204329eb966/params.yaml deleted file mode 100644 index 6ab8bfb4..00000000 --- a/examples/security/classification/output/reports/train/22e2541d8b26abcd71b11204329eb966/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/adv_predictions.json - adv_probabilities_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/66b097704a410e1f0651800813cb86b3.pkl - params_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/params.yaml - predictions_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/predictions.json - probabilities_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/probabilities.json - score_dict_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/score_dict.json - test_labels_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/test_labels.json - train_labels_file: output/reports/train/22e2541d8b26abcd71b11204329eb966/train_labels.json - model_dir: models - name: 22e2541d8b26abcd71b11204329eb966 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f94d26c35c6b4226766b890f9611c00b - trainer: - kwargs: {} -name: 22e2541d8b26abcd71b11204329eb966 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/22e557335f9d6517219356840b403d54/params.yaml b/examples/security/classification/output/reports/train/22e557335f9d6517219356840b403d54/params.yaml deleted file mode 100644 index d059ddee..00000000 --- a/examples/security/classification/output/reports/train/22e557335f9d6517219356840b403d54/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/22e557335f9d6517219356840b403d54/adv_predictions.json - adv_probabilities_file: output/reports/train/22e557335f9d6517219356840b403d54/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/a52364aa46be5601fbda07a7e7437aaa.pkl - params_file: output/reports/train/22e557335f9d6517219356840b403d54/params.yaml - predictions_file: output/reports/train/22e557335f9d6517219356840b403d54/predictions.json - probabilities_file: output/reports/train/22e557335f9d6517219356840b403d54/probabilities.json - score_dict_file: output/reports/train/22e557335f9d6517219356840b403d54/score_dict.json - test_labels_file: output/reports/train/22e557335f9d6517219356840b403d54/test_labels.json - train_labels_file: output/reports/train/22e557335f9d6517219356840b403d54/train_labels.json - model_dir: models - name: 22e557335f9d6517219356840b403d54 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8491f24f67d683a4a1cfbb562200ffe4 - trainer: - kwargs: {} -name: 22e557335f9d6517219356840b403d54 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/params.yaml b/examples/security/classification/output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/params.yaml deleted file mode 100644 index 13996407..00000000 --- a/examples/security/classification/output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/adv_predictions.json - adv_probabilities_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/22a74f78db76883c2b063f2654696acc.pkl - params_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/params.yaml - predictions_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/predictions.json - probabilities_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/probabilities.json - score_dict_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/score_dict.json - test_labels_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/test_labels.json - train_labels_file: output/reports/train/22f8a3ccaabe3fad4e6665a0d6039847/train_labels.json - model_dir: models - name: 22f8a3ccaabe3fad4e6665a0d6039847 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7e848e301f3b39c49f7c26821f5c9100 - trainer: - kwargs: {} -name: 22f8a3ccaabe3fad4e6665a0d6039847 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/23014d7c01c296b3a315f2c330049d5f/params.yaml b/examples/security/classification/output/reports/train/23014d7c01c296b3a315f2c330049d5f/params.yaml deleted file mode 100644 index 3c7b09e5..00000000 --- a/examples/security/classification/output/reports/train/23014d7c01c296b3a315f2c330049d5f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/adv_predictions.json - adv_probabilities_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/5c84019b8f6e2335dce1cf4a3874bc99.pkl - params_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/params.yaml - predictions_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/predictions.json - probabilities_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/probabilities.json - score_dict_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/score_dict.json - test_labels_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/test_labels.json - train_labels_file: output/reports/train/23014d7c01c296b3a315f2c330049d5f/train_labels.json - model_dir: models - name: 23014d7c01c296b3a315f2c330049d5f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5d7c6f447ec5a286cddeb1d94c92b515 - trainer: - kwargs: {} -name: 23014d7c01c296b3a315f2c330049d5f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2302e1bf1d5b825f906618bff8d060cd/params.yaml b/examples/security/classification/output/reports/train/2302e1bf1d5b825f906618bff8d060cd/params.yaml deleted file mode 100644 index 5c63094f..00000000 --- a/examples/security/classification/output/reports/train/2302e1bf1d5b825f906618bff8d060cd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/adv_predictions.json - adv_probabilities_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/30f3d7391f77591555686145ca17e58d.pkl - params_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/params.yaml - predictions_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/predictions.json - probabilities_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/probabilities.json - score_dict_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/score_dict.json - test_labels_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/test_labels.json - train_labels_file: output/reports/train/2302e1bf1d5b825f906618bff8d060cd/train_labels.json - model_dir: models - name: 2302e1bf1d5b825f906618bff8d060cd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eb02acff58cb3df0d8f0e6c87bace2e2 - trainer: - kwargs: {} -name: 2302e1bf1d5b825f906618bff8d060cd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/237b9e7d8072653de4a04915f79ba88b/params.yaml b/examples/security/classification/output/reports/train/237b9e7d8072653de4a04915f79ba88b/params.yaml deleted file mode 100644 index c1ee3bca..00000000 --- a/examples/security/classification/output/reports/train/237b9e7d8072653de4a04915f79ba88b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/adv_predictions.json - adv_probabilities_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/74ba6bd9f20bd9b13094d7826927f932.pkl - params_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/params.yaml - predictions_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/predictions.json - probabilities_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/probabilities.json - score_dict_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/score_dict.json - test_labels_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/test_labels.json - train_labels_file: output/reports/train/237b9e7d8072653de4a04915f79ba88b/train_labels.json - model_dir: models - name: 237b9e7d8072653de4a04915f79ba88b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ab960b1d9c74bac37cfa1d0bfd3f8d59 - trainer: - kwargs: {} -name: 237b9e7d8072653de4a04915f79ba88b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/params.yaml b/examples/security/classification/output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/params.yaml deleted file mode 100644 index 3a6dfc73..00000000 --- a/examples/security/classification/output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/adv_predictions.json - adv_probabilities_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/28e183b5f5f797cb282bbf9a3d8cdf0a.pkl - params_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/params.yaml - predictions_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/predictions.json - probabilities_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/probabilities.json - score_dict_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/score_dict.json - test_labels_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/test_labels.json - train_labels_file: output/reports/train/2391d861fe9fc4eb73eb0138a7419ef6/train_labels.json - model_dir: models - name: 2391d861fe9fc4eb73eb0138a7419ef6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2f5f3c056a4a9ae73bd734f074a98e96 - trainer: - kwargs: {} -name: 2391d861fe9fc4eb73eb0138a7419ef6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2393a3f569a3788985867ee25e26fc6b/params.yaml b/examples/security/classification/output/reports/train/2393a3f569a3788985867ee25e26fc6b/params.yaml deleted file mode 100644 index f5315675..00000000 --- a/examples/security/classification/output/reports/train/2393a3f569a3788985867ee25e26fc6b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/adv_predictions.json - adv_probabilities_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/cee5e6334c37437f6b433781d47414ff.pkl - params_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/params.yaml - predictions_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/predictions.json - probabilities_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/probabilities.json - score_dict_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/score_dict.json - test_labels_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/test_labels.json - train_labels_file: output/reports/train/2393a3f569a3788985867ee25e26fc6b/train_labels.json - model_dir: models - name: 2393a3f569a3788985867ee25e26fc6b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 574a498855560c0d2fb38034b93680bc - trainer: - kwargs: {} -name: 2393a3f569a3788985867ee25e26fc6b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/23965b4e0e7e8bbafe925342c55bef46/params.yaml b/examples/security/classification/output/reports/train/23965b4e0e7e8bbafe925342c55bef46/params.yaml deleted file mode 100644 index 9361a082..00000000 --- a/examples/security/classification/output/reports/train/23965b4e0e7e8bbafe925342c55bef46/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/adv_predictions.json - adv_probabilities_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/0d049d97c0f6432315cf8a18b37e32b1.pkl - params_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/params.yaml - predictions_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/predictions.json - probabilities_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/probabilities.json - score_dict_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/score_dict.json - test_labels_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/test_labels.json - train_labels_file: output/reports/train/23965b4e0e7e8bbafe925342c55bef46/train_labels.json - model_dir: models - name: 23965b4e0e7e8bbafe925342c55bef46 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8fb14482be4d7c812df7e71e906f5e1b - trainer: - kwargs: {} -name: 23965b4e0e7e8bbafe925342c55bef46 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/params.yaml b/examples/security/classification/output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/params.yaml deleted file mode 100644 index 5a235c4e..00000000 --- a/examples/security/classification/output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/adv_predictions.json - adv_probabilities_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/80f9dcd562628e7a80fea933c3a03460.pkl - params_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/params.yaml - predictions_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/predictions.json - probabilities_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/probabilities.json - score_dict_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/score_dict.json - test_labels_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/test_labels.json - train_labels_file: output/reports/train/23d84444ddf67a2bddf1f48728eeccdd/train_labels.json - model_dir: models - name: 23d84444ddf67a2bddf1f48728eeccdd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5bc2a553793a961aa6e415239988deec - trainer: - kwargs: {} -name: 23d84444ddf67a2bddf1f48728eeccdd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2400f7ee5c89b7022a39d215a62496a8/params.yaml b/examples/security/classification/output/reports/train/2400f7ee5c89b7022a39d215a62496a8/params.yaml deleted file mode 100644 index 7f76635b..00000000 --- a/examples/security/classification/output/reports/train/2400f7ee5c89b7022a39d215a62496a8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/adv_predictions.json - adv_probabilities_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/d9c173fd8931e4ed616e9b0976b9799a.pkl - params_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/params.yaml - predictions_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/predictions.json - probabilities_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/probabilities.json - score_dict_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/score_dict.json - test_labels_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/test_labels.json - train_labels_file: output/reports/train/2400f7ee5c89b7022a39d215a62496a8/train_labels.json - model_dir: models - name: 2400f7ee5c89b7022a39d215a62496a8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2191b1fa9d7c0f2b827d67c03d93b334 - trainer: - kwargs: {} -name: 2400f7ee5c89b7022a39d215a62496a8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/params.yaml b/examples/security/classification/output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/params.yaml deleted file mode 100644 index 3c650ff1..00000000 --- a/examples/security/classification/output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/adv_predictions.json - adv_probabilities_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/294a559325d7e7cc19bc4f2f1f9e1f68.pkl - params_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/params.yaml - predictions_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/predictions.json - probabilities_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/probabilities.json - score_dict_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/score_dict.json - test_labels_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/test_labels.json - train_labels_file: output/reports/train/2440b0f6b2a7e2dead33a66d3081c6cf/train_labels.json - model_dir: models - name: 2440b0f6b2a7e2dead33a66d3081c6cf - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 71a5d7a4fad07ab05d30c01cc804c21f - trainer: - kwargs: {} -name: 2440b0f6b2a7e2dead33a66d3081c6cf -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/params.yaml b/examples/security/classification/output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/params.yaml deleted file mode 100644 index 4a151ad9..00000000 --- a/examples/security/classification/output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/adv_predictions.json - adv_probabilities_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/664d2e2814316f5d7972e33a977e62e4.pkl - params_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/params.yaml - predictions_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/predictions.json - probabilities_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/probabilities.json - score_dict_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/score_dict.json - test_labels_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/test_labels.json - train_labels_file: output/reports/train/244ed219d17d1d2a0c41ff8c11846c2d/train_labels.json - model_dir: models - name: 244ed219d17d1d2a0c41ff8c11846c2d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a799ef8fd65eb83c1c24a44f00a8c35a - trainer: - kwargs: {} -name: 244ed219d17d1d2a0c41ff8c11846c2d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/245f396ab17242f597de35c3c5263296/params.yaml b/examples/security/classification/output/reports/train/245f396ab17242f597de35c3c5263296/params.yaml deleted file mode 100644 index eddc5b5b..00000000 --- a/examples/security/classification/output/reports/train/245f396ab17242f597de35c3c5263296/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/245f396ab17242f597de35c3c5263296/adv_predictions.json - adv_probabilities_file: output/reports/train/245f396ab17242f597de35c3c5263296/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/0f095ecd4fe07cd1bfae40e292ba4f46.pkl - params_file: output/reports/train/245f396ab17242f597de35c3c5263296/params.yaml - predictions_file: output/reports/train/245f396ab17242f597de35c3c5263296/predictions.json - probabilities_file: output/reports/train/245f396ab17242f597de35c3c5263296/probabilities.json - score_dict_file: output/reports/train/245f396ab17242f597de35c3c5263296/score_dict.json - test_labels_file: output/reports/train/245f396ab17242f597de35c3c5263296/test_labels.json - train_labels_file: output/reports/train/245f396ab17242f597de35c3c5263296/train_labels.json - model_dir: models - name: 245f396ab17242f597de35c3c5263296 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3f7b506e22baac3967574c0303f194bb - trainer: - kwargs: {} -name: 245f396ab17242f597de35c3c5263296 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/24628cb9061a750c7d27b9e041034a59/params.yaml b/examples/security/classification/output/reports/train/24628cb9061a750c7d27b9e041034a59/params.yaml deleted file mode 100644 index 2b45e090..00000000 --- a/examples/security/classification/output/reports/train/24628cb9061a750c7d27b9e041034a59/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/adv_predictions.json - adv_probabilities_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/8ea37d8e6305e5049b4e38b25daead49.pkl - params_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/params.yaml - predictions_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/predictions.json - probabilities_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/probabilities.json - score_dict_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/score_dict.json - test_labels_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/test_labels.json - train_labels_file: output/reports/train/24628cb9061a750c7d27b9e041034a59/train_labels.json - model_dir: models - name: 24628cb9061a750c7d27b9e041034a59 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a2ae0db4711e2fc6e666adc1e3a77574 - trainer: - kwargs: {} -name: 24628cb9061a750c7d27b9e041034a59 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2491e00acdec08f14722ca1f5868dca0/params.yaml b/examples/security/classification/output/reports/train/2491e00acdec08f14722ca1f5868dca0/params.yaml deleted file mode 100644 index 8695cb02..00000000 --- a/examples/security/classification/output/reports/train/2491e00acdec08f14722ca1f5868dca0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/adv_predictions.json - adv_probabilities_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/de5cf2f37d3db9f69a30a8df1a2938c2.pkl - params_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/params.yaml - predictions_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/predictions.json - probabilities_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/probabilities.json - score_dict_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/score_dict.json - test_labels_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/test_labels.json - train_labels_file: output/reports/train/2491e00acdec08f14722ca1f5868dca0/train_labels.json - model_dir: models - name: 2491e00acdec08f14722ca1f5868dca0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 819af591f36e3fa0731a023863877cdb - trainer: - kwargs: {} -name: 2491e00acdec08f14722ca1f5868dca0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/24b242b3f30d6fce541b1a147de54b27/params.yaml b/examples/security/classification/output/reports/train/24b242b3f30d6fce541b1a147de54b27/params.yaml deleted file mode 100644 index 6a11c458..00000000 --- a/examples/security/classification/output/reports/train/24b242b3f30d6fce541b1a147de54b27/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/adv_predictions.json - adv_probabilities_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/1c578e7c99902a0ee757ec77a1749f81.pkl - params_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/params.yaml - predictions_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/predictions.json - probabilities_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/probabilities.json - score_dict_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/score_dict.json - test_labels_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/test_labels.json - train_labels_file: output/reports/train/24b242b3f30d6fce541b1a147de54b27/train_labels.json - model_dir: models - name: 24b242b3f30d6fce541b1a147de54b27 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8d7f3668045e6ddc1469ed3cb3c342c4 - trainer: - kwargs: {} -name: 24b242b3f30d6fce541b1a147de54b27 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/24b9e85744451176b67cf0f1190045e7/params.yaml b/examples/security/classification/output/reports/train/24b9e85744451176b67cf0f1190045e7/params.yaml deleted file mode 100644 index e144a48a..00000000 --- a/examples/security/classification/output/reports/train/24b9e85744451176b67cf0f1190045e7/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/adv_predictions.json - adv_probabilities_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/f46dce2e83951d8d225b6b77f87e6e14.pkl - params_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/params.yaml - predictions_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/predictions.json - probabilities_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/probabilities.json - score_dict_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/score_dict.json - test_labels_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/test_labels.json - train_labels_file: output/reports/train/24b9e85744451176b67cf0f1190045e7/train_labels.json - model_dir: models - name: 24b9e85744451176b67cf0f1190045e7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 435bd9b90a487ccfc8021b51a6c2f433 - trainer: - kwargs: {} -name: 24b9e85744451176b67cf0f1190045e7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/254bf08d27d75291ad6b93993fd66f40/params.yaml b/examples/security/classification/output/reports/train/254bf08d27d75291ad6b93993fd66f40/params.yaml deleted file mode 100644 index 7a940288..00000000 --- a/examples/security/classification/output/reports/train/254bf08d27d75291ad6b93993fd66f40/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/adv_predictions.json - adv_probabilities_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/1d512e07e9647269adb8b804acb1acc4.pkl - params_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/params.yaml - predictions_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/predictions.json - probabilities_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/probabilities.json - score_dict_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/score_dict.json - test_labels_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/test_labels.json - train_labels_file: output/reports/train/254bf08d27d75291ad6b93993fd66f40/train_labels.json - model_dir: models - name: 254bf08d27d75291ad6b93993fd66f40 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c651b51ad2b1fb86d3666aade08bd61f - trainer: - kwargs: {} -name: 254bf08d27d75291ad6b93993fd66f40 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2567d9833fde91492442a051998f7a31/params.yaml b/examples/security/classification/output/reports/train/2567d9833fde91492442a051998f7a31/params.yaml deleted file mode 100644 index be0b7bf1..00000000 --- a/examples/security/classification/output/reports/train/2567d9833fde91492442a051998f7a31/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2567d9833fde91492442a051998f7a31/adv_predictions.json - adv_probabilities_file: output/reports/train/2567d9833fde91492442a051998f7a31/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/1be582c061242cf5e3648f004d3a7e65.pkl - params_file: output/reports/train/2567d9833fde91492442a051998f7a31/params.yaml - predictions_file: output/reports/train/2567d9833fde91492442a051998f7a31/predictions.json - probabilities_file: output/reports/train/2567d9833fde91492442a051998f7a31/probabilities.json - score_dict_file: output/reports/train/2567d9833fde91492442a051998f7a31/score_dict.json - test_labels_file: output/reports/train/2567d9833fde91492442a051998f7a31/test_labels.json - train_labels_file: output/reports/train/2567d9833fde91492442a051998f7a31/train_labels.json - model_dir: models - name: 2567d9833fde91492442a051998f7a31 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2aaa0b6ce06ff69bc0768975d4708668 - trainer: - kwargs: {} -name: 2567d9833fde91492442a051998f7a31 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/25c04dff949ea2e149d2342d7cd76674/params.yaml b/examples/security/classification/output/reports/train/25c04dff949ea2e149d2342d7cd76674/params.yaml deleted file mode 100644 index d83eb27c..00000000 --- a/examples/security/classification/output/reports/train/25c04dff949ea2e149d2342d7cd76674/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/adv_predictions.json - adv_probabilities_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/99206763e294ac79092035b8ce312dbf.pkl - params_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/params.yaml - predictions_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/predictions.json - probabilities_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/probabilities.json - score_dict_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/score_dict.json - test_labels_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/test_labels.json - train_labels_file: output/reports/train/25c04dff949ea2e149d2342d7cd76674/train_labels.json - model_dir: models - name: 25c04dff949ea2e149d2342d7cd76674 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0c3c56e32fed3f89434033e0c562dbcd - trainer: - kwargs: {} -name: 25c04dff949ea2e149d2342d7cd76674 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/25cc0c5ff3fa612310c071a127299653/params.yaml b/examples/security/classification/output/reports/train/25cc0c5ff3fa612310c071a127299653/params.yaml deleted file mode 100644 index af111d44..00000000 --- a/examples/security/classification/output/reports/train/25cc0c5ff3fa612310c071a127299653/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/adv_predictions.json - adv_probabilities_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/0eb644a4e51cce0cf40d2e4f0bc2c67a.pkl - params_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/params.yaml - predictions_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/predictions.json - probabilities_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/probabilities.json - score_dict_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/score_dict.json - test_labels_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/test_labels.json - train_labels_file: output/reports/train/25cc0c5ff3fa612310c071a127299653/train_labels.json - model_dir: models - name: 25cc0c5ff3fa612310c071a127299653 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a920bceacc5d95401e81b2e1c0e893e7 - trainer: - kwargs: {} -name: 25cc0c5ff3fa612310c071a127299653 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/25ef88397c717ba7adafb1dc5552a01c/params.yaml b/examples/security/classification/output/reports/train/25ef88397c717ba7adafb1dc5552a01c/params.yaml deleted file mode 100644 index a708738e..00000000 --- a/examples/security/classification/output/reports/train/25ef88397c717ba7adafb1dc5552a01c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/adv_predictions.json - adv_probabilities_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/01b68909648850d96221a6e9ff05a421.pkl - params_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/params.yaml - predictions_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/predictions.json - probabilities_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/probabilities.json - score_dict_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/score_dict.json - test_labels_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/test_labels.json - train_labels_file: output/reports/train/25ef88397c717ba7adafb1dc5552a01c/train_labels.json - model_dir: models - name: 25ef88397c717ba7adafb1dc5552a01c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8de395b4817680b889d38a6726c5e7a5 - trainer: - kwargs: {} -name: 25ef88397c717ba7adafb1dc5552a01c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/params.yaml b/examples/security/classification/output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/params.yaml deleted file mode 100644 index 40eaa108..00000000 --- a/examples/security/classification/output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/adv_predictions.json - adv_probabilities_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/e5ea60ca1181c0cdb2e1ddb08b757471.pkl - params_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/params.yaml - predictions_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/predictions.json - probabilities_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/probabilities.json - score_dict_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/score_dict.json - test_labels_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/test_labels.json - train_labels_file: output/reports/train/25f5a04ae65b6284aea60e7b466c47f6/train_labels.json - model_dir: models - name: 25f5a04ae65b6284aea60e7b466c47f6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3a7de0cf1743a1cc0261d21eb7a2dba8 - trainer: - kwargs: {} -name: 25f5a04ae65b6284aea60e7b466c47f6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/26580811893cd34c38bce5fa1dad47c1/params.yaml b/examples/security/classification/output/reports/train/26580811893cd34c38bce5fa1dad47c1/params.yaml deleted file mode 100644 index 01f6e466..00000000 --- a/examples/security/classification/output/reports/train/26580811893cd34c38bce5fa1dad47c1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/adv_predictions.json - adv_probabilities_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/8d25369115ce7556a729f7550e8733fc.pkl - params_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/params.yaml - predictions_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/predictions.json - probabilities_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/probabilities.json - score_dict_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/score_dict.json - test_labels_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/test_labels.json - train_labels_file: output/reports/train/26580811893cd34c38bce5fa1dad47c1/train_labels.json - model_dir: models - name: 26580811893cd34c38bce5fa1dad47c1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fc0321c149fb1d4dad1db5b8f8710d0c - trainer: - kwargs: {} -name: 26580811893cd34c38bce5fa1dad47c1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/params.yaml b/examples/security/classification/output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/params.yaml deleted file mode 100644 index 8a7ea350..00000000 --- a/examples/security/classification/output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/adv_predictions.json - adv_probabilities_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/b608d8645d7361079d3361ea38534677.pkl - params_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/params.yaml - predictions_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/predictions.json - probabilities_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/probabilities.json - score_dict_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/score_dict.json - test_labels_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/test_labels.json - train_labels_file: output/reports/train/268c1c9e9fa7fcb8cea8f378660b36d4/train_labels.json - model_dir: models - name: 268c1c9e9fa7fcb8cea8f378660b36d4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d13b45f41a381c14787f592d62f94d0d - trainer: - kwargs: {} -name: 268c1c9e9fa7fcb8cea8f378660b36d4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/26c67d8513e5968566458339e9583a58/params.yaml b/examples/security/classification/output/reports/train/26c67d8513e5968566458339e9583a58/params.yaml deleted file mode 100644 index 51c49365..00000000 --- a/examples/security/classification/output/reports/train/26c67d8513e5968566458339e9583a58/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/26c67d8513e5968566458339e9583a58/adv_predictions.json - adv_probabilities_file: output/reports/train/26c67d8513e5968566458339e9583a58/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/33af021ad84faa1eaf2ea30053cc6cf5.pkl - params_file: output/reports/train/26c67d8513e5968566458339e9583a58/params.yaml - predictions_file: output/reports/train/26c67d8513e5968566458339e9583a58/predictions.json - probabilities_file: output/reports/train/26c67d8513e5968566458339e9583a58/probabilities.json - score_dict_file: output/reports/train/26c67d8513e5968566458339e9583a58/score_dict.json - test_labels_file: output/reports/train/26c67d8513e5968566458339e9583a58/test_labels.json - train_labels_file: output/reports/train/26c67d8513e5968566458339e9583a58/train_labels.json - model_dir: models - name: 26c67d8513e5968566458339e9583a58 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 924bf661398ccbbf2278b48976e4e795 - trainer: - kwargs: {} -name: 26c67d8513e5968566458339e9583a58 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/26f77f3c328baf8131a349e59ecf7786/params.yaml b/examples/security/classification/output/reports/train/26f77f3c328baf8131a349e59ecf7786/params.yaml deleted file mode 100644 index 65aae660..00000000 --- a/examples/security/classification/output/reports/train/26f77f3c328baf8131a349e59ecf7786/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/adv_predictions.json - adv_probabilities_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/0c261d5690be3ce9daef912cd89ba599.pkl - params_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/params.yaml - predictions_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/predictions.json - probabilities_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/probabilities.json - score_dict_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/score_dict.json - test_labels_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/test_labels.json - train_labels_file: output/reports/train/26f77f3c328baf8131a349e59ecf7786/train_labels.json - model_dir: models - name: 26f77f3c328baf8131a349e59ecf7786 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1cf3c52e341ab9c13b881535d3ea39f6 - trainer: - kwargs: {} -name: 26f77f3c328baf8131a349e59ecf7786 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/26fa1f634f55f3f026923a83f37dca63/params.yaml b/examples/security/classification/output/reports/train/26fa1f634f55f3f026923a83f37dca63/params.yaml deleted file mode 100644 index 00fe3e80..00000000 --- a/examples/security/classification/output/reports/train/26fa1f634f55f3f026923a83f37dca63/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/adv_predictions.json - adv_probabilities_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/231ee01f38b4708092d7a174f236a7f0.pkl - params_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/params.yaml - predictions_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/predictions.json - probabilities_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/probabilities.json - score_dict_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/score_dict.json - test_labels_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/test_labels.json - train_labels_file: output/reports/train/26fa1f634f55f3f026923a83f37dca63/train_labels.json - model_dir: models - name: 26fa1f634f55f3f026923a83f37dca63 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 691d572ab4fc9ee2565f91cae49bfc62 - trainer: - kwargs: {} -name: 26fa1f634f55f3f026923a83f37dca63 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2769e675d76d611d4d5fe41391debdea/params.yaml b/examples/security/classification/output/reports/train/2769e675d76d611d4d5fe41391debdea/params.yaml deleted file mode 100644 index 9042a055..00000000 --- a/examples/security/classification/output/reports/train/2769e675d76d611d4d5fe41391debdea/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/adv_predictions.json - adv_probabilities_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/9dae70e9aaee3ed2713aee4d10c083c1.pkl - params_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/params.yaml - predictions_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/predictions.json - probabilities_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/probabilities.json - score_dict_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/score_dict.json - test_labels_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/test_labels.json - train_labels_file: output/reports/train/2769e675d76d611d4d5fe41391debdea/train_labels.json - model_dir: models - name: 2769e675d76d611d4d5fe41391debdea - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 50514f87f4e6a4f25419a3908ec948be - trainer: - kwargs: {} -name: 2769e675d76d611d4d5fe41391debdea -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/277cd1437819fd36d5d2185efa6c0822/params.yaml b/examples/security/classification/output/reports/train/277cd1437819fd36d5d2185efa6c0822/params.yaml deleted file mode 100644 index 111e44e7..00000000 --- a/examples/security/classification/output/reports/train/277cd1437819fd36d5d2185efa6c0822/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/adv_predictions.json - adv_probabilities_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/5a521a8b49face7f585106d1498156be.pkl - params_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/params.yaml - predictions_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/predictions.json - probabilities_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/probabilities.json - score_dict_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/score_dict.json - test_labels_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/test_labels.json - train_labels_file: output/reports/train/277cd1437819fd36d5d2185efa6c0822/train_labels.json - model_dir: models - name: 277cd1437819fd36d5d2185efa6c0822 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 236e35d3781effc7351aee7e7cab275b - trainer: - kwargs: {} -name: 277cd1437819fd36d5d2185efa6c0822 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/params.yaml b/examples/security/classification/output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/params.yaml deleted file mode 100644 index 01318be0..00000000 --- a/examples/security/classification/output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/adv_predictions.json - adv_probabilities_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/c9dbf43974b8dc89757aa79eb8b6d5db.pkl - params_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/params.yaml - predictions_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/predictions.json - probabilities_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/probabilities.json - score_dict_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/score_dict.json - test_labels_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/test_labels.json - train_labels_file: output/reports/train/279d0dbecdc4d2201238fa7cb85bc4a5/train_labels.json - model_dir: models - name: 279d0dbecdc4d2201238fa7cb85bc4a5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fcc0190ec071feec2fe5c636f8f8fabe - trainer: - kwargs: {} -name: 279d0dbecdc4d2201238fa7cb85bc4a5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/279fe902def2f3b5f027d0d1fd187671/params.yaml b/examples/security/classification/output/reports/train/279fe902def2f3b5f027d0d1fd187671/params.yaml deleted file mode 100644 index b8cd11ea..00000000 --- a/examples/security/classification/output/reports/train/279fe902def2f3b5f027d0d1fd187671/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/adv_predictions.json - adv_probabilities_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/1b008e8de2abc3aa5db2dba589681655.pkl - params_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/params.yaml - predictions_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/predictions.json - probabilities_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/probabilities.json - score_dict_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/score_dict.json - test_labels_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/test_labels.json - train_labels_file: output/reports/train/279fe902def2f3b5f027d0d1fd187671/train_labels.json - model_dir: models - name: 279fe902def2f3b5f027d0d1fd187671 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f28fc2227a3b6442e1d0cd3fade28584 - trainer: - kwargs: {} -name: 279fe902def2f3b5f027d0d1fd187671 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/27da50a91ddbab3f85806e78db8111f4/params.yaml b/examples/security/classification/output/reports/train/27da50a91ddbab3f85806e78db8111f4/params.yaml deleted file mode 100644 index 2555e1f4..00000000 --- a/examples/security/classification/output/reports/train/27da50a91ddbab3f85806e78db8111f4/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/adv_predictions.json - adv_probabilities_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/c34e9eeab7d515e918bdb6faf834aedd.pkl - params_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/params.yaml - predictions_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/predictions.json - probabilities_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/probabilities.json - score_dict_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/score_dict.json - test_labels_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/test_labels.json - train_labels_file: output/reports/train/27da50a91ddbab3f85806e78db8111f4/train_labels.json - model_dir: models - name: 27da50a91ddbab3f85806e78db8111f4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0957264b4d47595ede94e7adaecbb035 - trainer: - kwargs: {} -name: 27da50a91ddbab3f85806e78db8111f4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/params.yaml b/examples/security/classification/output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/params.yaml deleted file mode 100644 index 0071caeb..00000000 --- a/examples/security/classification/output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/adv_predictions.json - adv_probabilities_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/14826f369662f52a9a74189260e6b7f5.pkl - params_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/params.yaml - predictions_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/predictions.json - probabilities_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/probabilities.json - score_dict_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/score_dict.json - test_labels_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/test_labels.json - train_labels_file: output/reports/train/27dd741cbb968b499eeb7bfebdc38b0b/train_labels.json - model_dir: models - name: 27dd741cbb968b499eeb7bfebdc38b0b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4302c203d46a36740d29592038251134 - trainer: - kwargs: {} -name: 27dd741cbb968b499eeb7bfebdc38b0b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/27ebc09ea09efca57118309d42196b5c/params.yaml b/examples/security/classification/output/reports/train/27ebc09ea09efca57118309d42196b5c/params.yaml deleted file mode 100644 index 9f9ff082..00000000 --- a/examples/security/classification/output/reports/train/27ebc09ea09efca57118309d42196b5c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/adv_predictions.json - adv_probabilities_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/d50d5f30fe4666bb53bbf9dd25bbbcd6.pkl - params_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/params.yaml - predictions_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/predictions.json - probabilities_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/probabilities.json - score_dict_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/score_dict.json - test_labels_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/test_labels.json - train_labels_file: output/reports/train/27ebc09ea09efca57118309d42196b5c/train_labels.json - model_dir: models - name: 27ebc09ea09efca57118309d42196b5c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 69669d697856397074f68a4d458449f7 - trainer: - kwargs: {} -name: 27ebc09ea09efca57118309d42196b5c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/params.yaml b/examples/security/classification/output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/params.yaml deleted file mode 100644 index ef919476..00000000 --- a/examples/security/classification/output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/adv_predictions.json - adv_probabilities_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/ffc851be294d112120ecccef8c56bfb2.pkl - params_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/params.yaml - predictions_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/predictions.json - probabilities_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/probabilities.json - score_dict_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/score_dict.json - test_labels_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/test_labels.json - train_labels_file: output/reports/train/28178d3a16f1e7dd303c2c61c409bbe5/train_labels.json - model_dir: models - name: 28178d3a16f1e7dd303c2c61c409bbe5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f34334fe0a323aefc94002df68478b9a - trainer: - kwargs: {} -name: 28178d3a16f1e7dd303c2c61c409bbe5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/289d9e160ccade9d48d1cab737e99116/params.yaml b/examples/security/classification/output/reports/train/289d9e160ccade9d48d1cab737e99116/params.yaml deleted file mode 100644 index bd1b3af1..00000000 --- a/examples/security/classification/output/reports/train/289d9e160ccade9d48d1cab737e99116/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/adv_predictions.json - adv_probabilities_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/4d268492d3d5a7788487894c41782ff4.pkl - params_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/params.yaml - predictions_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/predictions.json - probabilities_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/probabilities.json - score_dict_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/score_dict.json - test_labels_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/test_labels.json - train_labels_file: output/reports/train/289d9e160ccade9d48d1cab737e99116/train_labels.json - model_dir: models - name: 289d9e160ccade9d48d1cab737e99116 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f8c80e5431587761a69a5eef37ee002b - trainer: - kwargs: {} -name: 289d9e160ccade9d48d1cab737e99116 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/28b3bc4a40ff16f17151140f076c19ac/params.yaml b/examples/security/classification/output/reports/train/28b3bc4a40ff16f17151140f076c19ac/params.yaml deleted file mode 100644 index b0bf85bc..00000000 --- a/examples/security/classification/output/reports/train/28b3bc4a40ff16f17151140f076c19ac/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 93cd2ed2f65fde8ccf01e9ac6343c024 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/adv_predictions.json - adv_probabilities_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/04ed3d2b113dd268ca610c98b0b6ac69.pkl - model_file: output/models/7df2162c93c6e061f1869c5a1cd9f89d.pkl - params_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/params.yaml - predictions_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/predictions.json - probabilities_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/probabilities.json - score_dict_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/score_dict.json - test_labels_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/test_labels.json - train_labels_file: output/reports/train/28b3bc4a40ff16f17151140f076c19ac/train_labels.json - model_dir: models - name: 28b3bc4a40ff16f17151140f076c19ac - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 93cd2ed2f65fde8ccf01e9ac6343c024 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f4298700d88fe2ae8cab0055630464c1 - trainer: - kwargs: {} -name: 28b3bc4a40ff16f17151140f076c19ac -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/28c44ee44232b2da59080438589a75a1/params.yaml b/examples/security/classification/output/reports/train/28c44ee44232b2da59080438589a75a1/params.yaml deleted file mode 100644 index 6ee951b7..00000000 --- a/examples/security/classification/output/reports/train/28c44ee44232b2da59080438589a75a1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/28c44ee44232b2da59080438589a75a1/adv_predictions.json - adv_probabilities_file: output/reports/train/28c44ee44232b2da59080438589a75a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/d4e848466bc3af643bc5c10f4c12b468.pkl - params_file: output/reports/train/28c44ee44232b2da59080438589a75a1/params.yaml - predictions_file: output/reports/train/28c44ee44232b2da59080438589a75a1/predictions.json - probabilities_file: output/reports/train/28c44ee44232b2da59080438589a75a1/probabilities.json - score_dict_file: output/reports/train/28c44ee44232b2da59080438589a75a1/score_dict.json - test_labels_file: output/reports/train/28c44ee44232b2da59080438589a75a1/test_labels.json - train_labels_file: output/reports/train/28c44ee44232b2da59080438589a75a1/train_labels.json - model_dir: models - name: 28c44ee44232b2da59080438589a75a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 453b5962240515fb5bb7f328bfea1e7a - trainer: - kwargs: {} -name: 28c44ee44232b2da59080438589a75a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/28c4d2041797b00de1e2e346173af6d0/params.yaml b/examples/security/classification/output/reports/train/28c4d2041797b00de1e2e346173af6d0/params.yaml deleted file mode 100644 index 29420048..00000000 --- a/examples/security/classification/output/reports/train/28c4d2041797b00de1e2e346173af6d0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/adv_predictions.json - adv_probabilities_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/9f037f95b9047d49cfd8b846782900f1.pkl - params_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/params.yaml - predictions_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/predictions.json - probabilities_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/probabilities.json - score_dict_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/score_dict.json - test_labels_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/test_labels.json - train_labels_file: output/reports/train/28c4d2041797b00de1e2e346173af6d0/train_labels.json - model_dir: models - name: 28c4d2041797b00de1e2e346173af6d0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cd94fcd7c033a3531bacbe2494d6c396 - trainer: - kwargs: {} -name: 28c4d2041797b00de1e2e346173af6d0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/28e9bf78985ed99b2a3f2794d049318a/params.yaml b/examples/security/classification/output/reports/train/28e9bf78985ed99b2a3f2794d049318a/params.yaml deleted file mode 100644 index 44ff70f7..00000000 --- a/examples/security/classification/output/reports/train/28e9bf78985ed99b2a3f2794d049318a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/adv_predictions.json - adv_probabilities_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/cf8f6b6b4d3039a1bc6b14f8a63a80e2.pkl - params_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/params.yaml - predictions_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/predictions.json - probabilities_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/probabilities.json - score_dict_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/score_dict.json - test_labels_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/test_labels.json - train_labels_file: output/reports/train/28e9bf78985ed99b2a3f2794d049318a/train_labels.json - model_dir: models - name: 28e9bf78985ed99b2a3f2794d049318a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 74c7a1fbb8bc4125d3b73523ec327828 - trainer: - kwargs: {} -name: 28e9bf78985ed99b2a3f2794d049318a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/28f178446fb4d239c77246ee3096625c/params.yaml b/examples/security/classification/output/reports/train/28f178446fb4d239c77246ee3096625c/params.yaml deleted file mode 100644 index 0c35ef65..00000000 --- a/examples/security/classification/output/reports/train/28f178446fb4d239c77246ee3096625c/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/28f178446fb4d239c77246ee3096625c/adv_predictions.json - adv_probabilities_file: output/reports/train/28f178446fb4d239c77246ee3096625c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/8c76ef167b2ceb1c85d5c27814d954e5.pkl - params_file: output/reports/train/28f178446fb4d239c77246ee3096625c/params.yaml - predictions_file: output/reports/train/28f178446fb4d239c77246ee3096625c/predictions.json - probabilities_file: output/reports/train/28f178446fb4d239c77246ee3096625c/probabilities.json - score_dict_file: output/reports/train/28f178446fb4d239c77246ee3096625c/score_dict.json - test_labels_file: output/reports/train/28f178446fb4d239c77246ee3096625c/test_labels.json - train_labels_file: output/reports/train/28f178446fb4d239c77246ee3096625c/train_labels.json - model_dir: models - name: 28f178446fb4d239c77246ee3096625c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e84d39fb7cb06f45fbd3f2f824163534 - trainer: - kwargs: {} -name: 28f178446fb4d239c77246ee3096625c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/params.yaml b/examples/security/classification/output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/params.yaml deleted file mode 100644 index 73240b5b..00000000 --- a/examples/security/classification/output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/adv_predictions.json - adv_probabilities_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/c6cc6bc6c157f43cfc6219c5b84d1249.pkl - params_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/params.yaml - predictions_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/predictions.json - probabilities_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/probabilities.json - score_dict_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/score_dict.json - test_labels_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/test_labels.json - train_labels_file: output/reports/train/2919e60a66c2d1c95ae3f81d9b757ef4/train_labels.json - model_dir: models - name: 2919e60a66c2d1c95ae3f81d9b757ef4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 455808040cbc3cd56c6b0f7cbb338aa9 - trainer: - kwargs: {} -name: 2919e60a66c2d1c95ae3f81d9b757ef4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2926639bdd57bec76e0117b4433ad6e1/params.yaml b/examples/security/classification/output/reports/train/2926639bdd57bec76e0117b4433ad6e1/params.yaml deleted file mode 100644 index cebc60d9..00000000 --- a/examples/security/classification/output/reports/train/2926639bdd57bec76e0117b4433ad6e1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/adv_predictions.json - adv_probabilities_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/a3b5c8cf25cc87b5b35c88f50c9bd128.pkl - params_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/params.yaml - predictions_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/predictions.json - probabilities_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/probabilities.json - score_dict_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/score_dict.json - test_labels_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/test_labels.json - train_labels_file: output/reports/train/2926639bdd57bec76e0117b4433ad6e1/train_labels.json - model_dir: models - name: 2926639bdd57bec76e0117b4433ad6e1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4ecb41b53d7dff6d105d8fa24b5367d3 - trainer: - kwargs: {} -name: 2926639bdd57bec76e0117b4433ad6e1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/292de071d7660631dfffa5fa9f6640e1/params.yaml b/examples/security/classification/output/reports/train/292de071d7660631dfffa5fa9f6640e1/params.yaml deleted file mode 100644 index 0bc7751f..00000000 --- a/examples/security/classification/output/reports/train/292de071d7660631dfffa5fa9f6640e1/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/adv_predictions.json - adv_probabilities_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/c72170b70e8a7f35fbb53cc80057e795.pkl - params_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/params.yaml - predictions_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/predictions.json - probabilities_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/probabilities.json - score_dict_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/score_dict.json - test_labels_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/test_labels.json - train_labels_file: output/reports/train/292de071d7660631dfffa5fa9f6640e1/train_labels.json - model_dir: models - name: 292de071d7660631dfffa5fa9f6640e1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: be65a989c4372cb8d808c1f6d9f97e7f - trainer: - kwargs: {} -name: 292de071d7660631dfffa5fa9f6640e1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/params.yaml b/examples/security/classification/output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/params.yaml deleted file mode 100644 index 4c6b0b24..00000000 --- a/examples/security/classification/output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/adv_predictions.json - adv_probabilities_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/c3a377c40f17a4ab9505c75d6dcae60c.pkl - params_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/params.yaml - predictions_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/predictions.json - probabilities_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/probabilities.json - score_dict_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/score_dict.json - test_labels_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/test_labels.json - train_labels_file: output/reports/train/296aab2ea5fa8cd853b9dbb8e0c688f6/train_labels.json - model_dir: models - name: 296aab2ea5fa8cd853b9dbb8e0c688f6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 126c95a4996cfad7a7d7948ecbf12f04 - trainer: - kwargs: {} -name: 296aab2ea5fa8cd853b9dbb8e0c688f6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/params.yaml b/examples/security/classification/output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/params.yaml deleted file mode 100644 index 99c6a164..00000000 --- a/examples/security/classification/output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/adv_predictions.json - adv_probabilities_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/e27b8b9b487e6c52952011482ecf8de8.pkl - params_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/params.yaml - predictions_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/predictions.json - probabilities_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/probabilities.json - score_dict_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/score_dict.json - test_labels_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/test_labels.json - train_labels_file: output/reports/train/297abfdc09a14ac480e864ccb67c7ba2/train_labels.json - model_dir: models - name: 297abfdc09a14ac480e864ccb67c7ba2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d958e80453df3305992ee261a94313e0 - trainer: - kwargs: {} -name: 297abfdc09a14ac480e864ccb67c7ba2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/params.yaml b/examples/security/classification/output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/params.yaml deleted file mode 100644 index d385fa74..00000000 --- a/examples/security/classification/output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/adv_predictions.json - adv_probabilities_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/11687a40f8ea6c2f32d37fa67691fcac.pkl - params_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/params.yaml - predictions_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/predictions.json - probabilities_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/probabilities.json - score_dict_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/score_dict.json - test_labels_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/test_labels.json - train_labels_file: output/reports/train/29831e7f1cb93fecbc64228b1d2020f7/train_labels.json - model_dir: models - name: 29831e7f1cb93fecbc64228b1d2020f7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 61a3943d3ef375d80d42843f1ccfb431 - trainer: - kwargs: {} -name: 29831e7f1cb93fecbc64228b1d2020f7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/298ecd75ae285d9a504cec89c7ea1976/params.yaml b/examples/security/classification/output/reports/train/298ecd75ae285d9a504cec89c7ea1976/params.yaml deleted file mode 100644 index a9363948..00000000 --- a/examples/security/classification/output/reports/train/298ecd75ae285d9a504cec89c7ea1976/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/adv_predictions.json - adv_probabilities_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/5b9d6ff0ed75136bfebc8d55dbe1ae1e.pkl - params_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/params.yaml - predictions_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/predictions.json - probabilities_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/probabilities.json - score_dict_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/score_dict.json - test_labels_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/test_labels.json - train_labels_file: output/reports/train/298ecd75ae285d9a504cec89c7ea1976/train_labels.json - model_dir: models - name: 298ecd75ae285d9a504cec89c7ea1976 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f5fbeb8b4aa03ccee5d2471f6d269470 - trainer: - kwargs: {} -name: 298ecd75ae285d9a504cec89c7ea1976 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/29a98de777550258271d96313bc0756b/params.yaml b/examples/security/classification/output/reports/train/29a98de777550258271d96313bc0756b/params.yaml deleted file mode 100644 index 5c17d3c8..00000000 --- a/examples/security/classification/output/reports/train/29a98de777550258271d96313bc0756b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/29a98de777550258271d96313bc0756b/adv_predictions.json - adv_probabilities_file: output/reports/train/29a98de777550258271d96313bc0756b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/f9782076caca16f2266973375b405003.pkl - params_file: output/reports/train/29a98de777550258271d96313bc0756b/params.yaml - predictions_file: output/reports/train/29a98de777550258271d96313bc0756b/predictions.json - probabilities_file: output/reports/train/29a98de777550258271d96313bc0756b/probabilities.json - score_dict_file: output/reports/train/29a98de777550258271d96313bc0756b/score_dict.json - test_labels_file: output/reports/train/29a98de777550258271d96313bc0756b/test_labels.json - train_labels_file: output/reports/train/29a98de777550258271d96313bc0756b/train_labels.json - model_dir: models - name: 29a98de777550258271d96313bc0756b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0ab59824fe36ff2dd580085e6211af4d - trainer: - kwargs: {} -name: 29a98de777550258271d96313bc0756b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/29cc9099f81a02b8ac62dbbd73810273/params.yaml b/examples/security/classification/output/reports/train/29cc9099f81a02b8ac62dbbd73810273/params.yaml deleted file mode 100644 index e1f517df..00000000 --- a/examples/security/classification/output/reports/train/29cc9099f81a02b8ac62dbbd73810273/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/adv_predictions.json - adv_probabilities_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/5fde4b9e540ac578e226ca08ed2971f2.pkl - params_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/params.yaml - predictions_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/predictions.json - probabilities_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/probabilities.json - score_dict_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/score_dict.json - test_labels_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/test_labels.json - train_labels_file: output/reports/train/29cc9099f81a02b8ac62dbbd73810273/train_labels.json - model_dir: models - name: 29cc9099f81a02b8ac62dbbd73810273 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 83f61a32e06c2af5fc69783cdabf1f58 - trainer: - kwargs: {} -name: 29cc9099f81a02b8ac62dbbd73810273 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/29db12ee4020e8a8c22b72d158eabc31/params.yaml b/examples/security/classification/output/reports/train/29db12ee4020e8a8c22b72d158eabc31/params.yaml deleted file mode 100644 index d5cc5462..00000000 --- a/examples/security/classification/output/reports/train/29db12ee4020e8a8c22b72d158eabc31/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/adv_predictions.json - adv_probabilities_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/54a42451d549dc23448a00116871c68b.pkl - params_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/params.yaml - predictions_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/predictions.json - probabilities_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/probabilities.json - score_dict_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/score_dict.json - test_labels_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/test_labels.json - train_labels_file: output/reports/train/29db12ee4020e8a8c22b72d158eabc31/train_labels.json - model_dir: models - name: 29db12ee4020e8a8c22b72d158eabc31 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 761dffd9a60fdc02c1691fbce0a35066 - trainer: - kwargs: {} -name: 29db12ee4020e8a8c22b72d158eabc31 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/params.yaml b/examples/security/classification/output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/params.yaml deleted file mode 100644 index a8752741..00000000 --- a/examples/security/classification/output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/adv_predictions.json - adv_probabilities_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/34c6b2928c337122b6f1094c41a0ad18.pkl - params_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/params.yaml - predictions_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/predictions.json - probabilities_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/probabilities.json - score_dict_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/score_dict.json - test_labels_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/test_labels.json - train_labels_file: output/reports/train/29ec94e61f9372dded9f1b9673ec0da2/train_labels.json - model_dir: models - name: 29ec94e61f9372dded9f1b9673ec0da2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a4a76b32a69b5fdb43ac110575723fd8 - trainer: - kwargs: {} -name: 29ec94e61f9372dded9f1b9673ec0da2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2a1bac4e90303a9f77cdefb21815049e/params.yaml b/examples/security/classification/output/reports/train/2a1bac4e90303a9f77cdefb21815049e/params.yaml deleted file mode 100644 index f47ef6e5..00000000 --- a/examples/security/classification/output/reports/train/2a1bac4e90303a9f77cdefb21815049e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/adv_predictions.json - adv_probabilities_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/2b0c66c50a8ef1a755fa4fc7f96801f0.pkl - params_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/params.yaml - predictions_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/predictions.json - probabilities_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/probabilities.json - score_dict_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/score_dict.json - test_labels_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/test_labels.json - train_labels_file: output/reports/train/2a1bac4e90303a9f77cdefb21815049e/train_labels.json - model_dir: models - name: 2a1bac4e90303a9f77cdefb21815049e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 446ae6ca58c96e93d604886fefc758a4 - trainer: - kwargs: {} -name: 2a1bac4e90303a9f77cdefb21815049e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/params.yaml b/examples/security/classification/output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/params.yaml deleted file mode 100644 index 17f02fce..00000000 --- a/examples/security/classification/output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/adv_predictions.json - adv_probabilities_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/4eda0697553cd9623da26ea025896c06.pkl - params_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/params.yaml - predictions_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/predictions.json - probabilities_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/probabilities.json - score_dict_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/score_dict.json - test_labels_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/test_labels.json - train_labels_file: output/reports/train/2a38f424d51d0d2baa108a5062ae85ea/train_labels.json - model_dir: models - name: 2a38f424d51d0d2baa108a5062ae85ea - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f0559f2676761fe2bd72c8f9790753d9 - trainer: - kwargs: {} -name: 2a38f424d51d0d2baa108a5062ae85ea -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2a8b954f363c2dead575ed8e0953de84/params.yaml b/examples/security/classification/output/reports/train/2a8b954f363c2dead575ed8e0953de84/params.yaml deleted file mode 100644 index f74738f1..00000000 --- a/examples/security/classification/output/reports/train/2a8b954f363c2dead575ed8e0953de84/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/adv_predictions.json - adv_probabilities_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/026a3c245f7e427df43c929e98a4db99.pkl - params_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/params.yaml - predictions_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/predictions.json - probabilities_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/probabilities.json - score_dict_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/score_dict.json - test_labels_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/test_labels.json - train_labels_file: output/reports/train/2a8b954f363c2dead575ed8e0953de84/train_labels.json - model_dir: models - name: 2a8b954f363c2dead575ed8e0953de84 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d0399e13f6ac55c86c2b57fc6face066 - trainer: - kwargs: {} -name: 2a8b954f363c2dead575ed8e0953de84 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/params.yaml b/examples/security/classification/output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/params.yaml deleted file mode 100644 index cbe820d4..00000000 --- a/examples/security/classification/output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/adv_predictions.json - adv_probabilities_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/22bffe8cf630ab977b038005783fbd50.pkl - params_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/params.yaml - predictions_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/predictions.json - probabilities_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/probabilities.json - score_dict_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/score_dict.json - test_labels_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/test_labels.json - train_labels_file: output/reports/train/2a8c8a6bb587e40579fe1ae7b4746e36/train_labels.json - model_dir: models - name: 2a8c8a6bb587e40579fe1ae7b4746e36 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 88b21d614783a0e8ec2f7d5b78863121 - trainer: - kwargs: {} -name: 2a8c8a6bb587e40579fe1ae7b4746e36 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/params.yaml b/examples/security/classification/output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/params.yaml deleted file mode 100644 index ff86381f..00000000 --- a/examples/security/classification/output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/adv_predictions.json - adv_probabilities_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/f12f8884fd896247a3d5b21a1f9040df.pkl - params_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/params.yaml - predictions_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/predictions.json - probabilities_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/probabilities.json - score_dict_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/score_dict.json - test_labels_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/test_labels.json - train_labels_file: output/reports/train/2a8e0cf2eaef57b2fe8c5a04545fa0f8/train_labels.json - model_dir: models - name: 2a8e0cf2eaef57b2fe8c5a04545fa0f8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c39da44a8f2e56106fbf94b0b075850a - trainer: - kwargs: {} -name: 2a8e0cf2eaef57b2fe8c5a04545fa0f8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/params.yaml b/examples/security/classification/output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/params.yaml deleted file mode 100644 index 7ba08186..00000000 --- a/examples/security/classification/output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/adv_predictions.json - adv_probabilities_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/b65325a00720b17ec8808fd6fb6250f8.pkl - params_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/params.yaml - predictions_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/predictions.json - probabilities_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/probabilities.json - score_dict_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/score_dict.json - test_labels_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/test_labels.json - train_labels_file: output/reports/train/2aa3f040bd4f3fce3990b9eeb841e4b8/train_labels.json - model_dir: models - name: 2aa3f040bd4f3fce3990b9eeb841e4b8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8dc10f42858cdbec289d8e6a8d87fad9 - trainer: - kwargs: {} -name: 2aa3f040bd4f3fce3990b9eeb841e4b8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/params.yaml b/examples/security/classification/output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/params.yaml deleted file mode 100644 index 1c09db0d..00000000 --- a/examples/security/classification/output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/adv_predictions.json - adv_probabilities_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/9964166fb090e47bd8a80b77be22bbea.pkl - params_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/params.yaml - predictions_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/predictions.json - probabilities_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/probabilities.json - score_dict_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/score_dict.json - test_labels_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/test_labels.json - train_labels_file: output/reports/train/2aa8c66f01be34d4c20f144ab5b30bb3/train_labels.json - model_dir: models - name: 2aa8c66f01be34d4c20f144ab5b30bb3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 2 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f0db94537112139b5ca53e87aaa4244b - trainer: - kwargs: {} -name: 2aa8c66f01be34d4c20f144ab5b30bb3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2abda47c8c0200484d3c4b1213377fe5/params.yaml b/examples/security/classification/output/reports/train/2abda47c8c0200484d3c4b1213377fe5/params.yaml deleted file mode 100644 index 4ccfa8b4..00000000 --- a/examples/security/classification/output/reports/train/2abda47c8c0200484d3c4b1213377fe5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/adv_predictions.json - adv_probabilities_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/3113a2485ce1d9e35c554ab81cd0f3fd.pkl - params_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/params.yaml - predictions_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/predictions.json - probabilities_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/probabilities.json - score_dict_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/score_dict.json - test_labels_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/test_labels.json - train_labels_file: output/reports/train/2abda47c8c0200484d3c4b1213377fe5/train_labels.json - model_dir: models - name: 2abda47c8c0200484d3c4b1213377fe5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d72a9731a870b8d3f26e09c2149c4cfd - trainer: - kwargs: {} -name: 2abda47c8c0200484d3c4b1213377fe5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2acd707460101a05cedbfd71b2cfd738/params.yaml b/examples/security/classification/output/reports/train/2acd707460101a05cedbfd71b2cfd738/params.yaml deleted file mode 100644 index 18d843ba..00000000 --- a/examples/security/classification/output/reports/train/2acd707460101a05cedbfd71b2cfd738/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/adv_predictions.json - adv_probabilities_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/955f87e691e69bb15425d26e5f986725.pkl - params_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/params.yaml - predictions_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/predictions.json - probabilities_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/probabilities.json - score_dict_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/score_dict.json - test_labels_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/test_labels.json - train_labels_file: output/reports/train/2acd707460101a05cedbfd71b2cfd738/train_labels.json - model_dir: models - name: 2acd707460101a05cedbfd71b2cfd738 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f343d092a6c9bbbd953a4ab5e7c85e28 - trainer: - kwargs: {} -name: 2acd707460101a05cedbfd71b2cfd738 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/params.yaml b/examples/security/classification/output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/params.yaml deleted file mode 100644 index 605eb034..00000000 --- a/examples/security/classification/output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/params.yaml +++ /dev/null @@ -1,104 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/adv_predictions.json - adv_probabilities_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/648b5feba6d079f672b922a8416bf6e1.pkl - params_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/params.yaml - predictions_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/predictions.json - probabilities_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/probabilities.json - score_dict_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/score_dict.json - test_labels_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/test_labels.json - train_labels_file: output/reports/train/2b19558c12180d2a8ba8d1fa6468d6ff/train_labels.json - model_dir: models - name: 2b19558c12180d2a8ba8d1fa6468d6ff - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: - - linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 92900985f53ec6777849af598192203c - trainer: - kwargs: {} -name: 2b19558c12180d2a8ba8d1fa6468d6ff -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2b23c217e60bd5606583944cded1a342/params.yaml b/examples/security/classification/output/reports/train/2b23c217e60bd5606583944cded1a342/params.yaml deleted file mode 100644 index a63b8df8..00000000 --- a/examples/security/classification/output/reports/train/2b23c217e60bd5606583944cded1a342/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2b23c217e60bd5606583944cded1a342/adv_predictions.json - adv_probabilities_file: output/reports/train/2b23c217e60bd5606583944cded1a342/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/ec7892e8e7818e430940e85db9341a23.pkl - params_file: output/reports/train/2b23c217e60bd5606583944cded1a342/params.yaml - predictions_file: output/reports/train/2b23c217e60bd5606583944cded1a342/predictions.json - probabilities_file: output/reports/train/2b23c217e60bd5606583944cded1a342/probabilities.json - score_dict_file: output/reports/train/2b23c217e60bd5606583944cded1a342/score_dict.json - test_labels_file: output/reports/train/2b23c217e60bd5606583944cded1a342/test_labels.json - train_labels_file: output/reports/train/2b23c217e60bd5606583944cded1a342/train_labels.json - model_dir: models - name: 2b23c217e60bd5606583944cded1a342 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e57eee8d7d7f6ebe66891d94323c6123 - trainer: - kwargs: {} -name: 2b23c217e60bd5606583944cded1a342 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/params.yaml b/examples/security/classification/output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/params.yaml deleted file mode 100644 index 6b1975a4..00000000 --- a/examples/security/classification/output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/adv_predictions.json - adv_probabilities_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/3c5a4392dfcb9c751853bf7dfeeaa7fb.pkl - params_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/params.yaml - predictions_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/predictions.json - probabilities_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/probabilities.json - score_dict_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/score_dict.json - test_labels_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/test_labels.json - train_labels_file: output/reports/train/2b2a88ebe2ca322635cfb75c144f6f02/train_labels.json - model_dir: models - name: 2b2a88ebe2ca322635cfb75c144f6f02 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 07b46e11414b564a2097abb1f5462246 - trainer: - kwargs: {} -name: 2b2a88ebe2ca322635cfb75c144f6f02 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/params.yaml b/examples/security/classification/output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/params.yaml deleted file mode 100644 index 3340b8dc..00000000 --- a/examples/security/classification/output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/adv_predictions.json - adv_probabilities_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/e2337ac2f6ed10b1380ef42ef613bad2.pkl - params_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/params.yaml - predictions_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/predictions.json - probabilities_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/probabilities.json - score_dict_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/score_dict.json - test_labels_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/test_labels.json - train_labels_file: output/reports/train/2b9141ff5c4affd8539ce41e92a53f35/train_labels.json - model_dir: models - name: 2b9141ff5c4affd8539ce41e92a53f35 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d7dbb226922b5296eeb7a017c1f235da - trainer: - kwargs: {} -name: 2b9141ff5c4affd8539ce41e92a53f35 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/params.yaml b/examples/security/classification/output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/params.yaml deleted file mode 100644 index f47a6acf..00000000 --- a/examples/security/classification/output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/adv_predictions.json - adv_probabilities_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/bc0fb80e459bd55ab3dc12c9197f5e4d.pkl - params_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/params.yaml - predictions_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/predictions.json - probabilities_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/probabilities.json - score_dict_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/score_dict.json - test_labels_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/test_labels.json - train_labels_file: output/reports/train/2bb4b5206bcbe5a7cdb0c6253edb962e/train_labels.json - model_dir: models - name: 2bb4b5206bcbe5a7cdb0c6253edb962e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1e31a5666e63aee6af60b21888ae9496 - trainer: - kwargs: {} -name: 2bb4b5206bcbe5a7cdb0c6253edb962e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2bb80bdf250fa4cbe25b49b119571207/params.yaml b/examples/security/classification/output/reports/train/2bb80bdf250fa4cbe25b49b119571207/params.yaml deleted file mode 100644 index 9a0b09b5..00000000 --- a/examples/security/classification/output/reports/train/2bb80bdf250fa4cbe25b49b119571207/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/adv_predictions.json - adv_probabilities_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/fe4198447c0925f6c51c0e2bc2d3df57.pkl - params_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/params.yaml - predictions_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/predictions.json - probabilities_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/probabilities.json - score_dict_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/score_dict.json - test_labels_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/test_labels.json - train_labels_file: output/reports/train/2bb80bdf250fa4cbe25b49b119571207/train_labels.json - model_dir: models - name: 2bb80bdf250fa4cbe25b49b119571207 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bae036ffb2115aaca50e151d819d9c26 - trainer: - kwargs: {} -name: 2bb80bdf250fa4cbe25b49b119571207 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/params.yaml b/examples/security/classification/output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/params.yaml deleted file mode 100644 index 399824fb..00000000 --- a/examples/security/classification/output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/adv_predictions.json - adv_probabilities_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/fa8582eec429ce1a6b34be7abf4cf311.pkl - params_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/params.yaml - predictions_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/predictions.json - probabilities_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/probabilities.json - score_dict_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/score_dict.json - test_labels_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/test_labels.json - train_labels_file: output/reports/train/2bd96dbef483ba16a8b5f8c8ab6e3d87/train_labels.json - model_dir: models - name: 2bd96dbef483ba16a8b5f8c8ab6e3d87 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 997a9ee108e1e2c95e049ef254caf9cc - trainer: - kwargs: {} -name: 2bd96dbef483ba16a8b5f8c8ab6e3d87 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/params.yaml b/examples/security/classification/output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/params.yaml deleted file mode 100644 index 44a958d2..00000000 --- a/examples/security/classification/output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/adv_predictions.json - adv_probabilities_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/e600ee5a29ca2089e37f3e25ed76a0ab.pkl - params_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/params.yaml - predictions_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/predictions.json - probabilities_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/probabilities.json - score_dict_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/score_dict.json - test_labels_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/test_labels.json - train_labels_file: output/reports/train/2bdf60e019d05068b74b8cbd83c0fea5/train_labels.json - model_dir: models - name: 2bdf60e019d05068b74b8cbd83c0fea5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 056e430c05129605f09932ff302a5dd7 - trainer: - kwargs: {} -name: 2bdf60e019d05068b74b8cbd83c0fea5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2be2c1245575c399281364ea78574561/params.yaml b/examples/security/classification/output/reports/train/2be2c1245575c399281364ea78574561/params.yaml deleted file mode 100644 index 6eebf8ee..00000000 --- a/examples/security/classification/output/reports/train/2be2c1245575c399281364ea78574561/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2be2c1245575c399281364ea78574561/adv_predictions.json - adv_probabilities_file: output/reports/train/2be2c1245575c399281364ea78574561/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/e3a8067d1279785e83ae489cde6619e5.pkl - params_file: output/reports/train/2be2c1245575c399281364ea78574561/params.yaml - predictions_file: output/reports/train/2be2c1245575c399281364ea78574561/predictions.json - probabilities_file: output/reports/train/2be2c1245575c399281364ea78574561/probabilities.json - score_dict_file: output/reports/train/2be2c1245575c399281364ea78574561/score_dict.json - test_labels_file: output/reports/train/2be2c1245575c399281364ea78574561/test_labels.json - train_labels_file: output/reports/train/2be2c1245575c399281364ea78574561/train_labels.json - model_dir: models - name: 2be2c1245575c399281364ea78574561 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f8211806c18aed8fe108655e885aee9a - trainer: - kwargs: {} -name: 2be2c1245575c399281364ea78574561 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2bfe7d4221e67015786634d92dabbc7b/params.yaml b/examples/security/classification/output/reports/train/2bfe7d4221e67015786634d92dabbc7b/params.yaml deleted file mode 100644 index 4e11a289..00000000 --- a/examples/security/classification/output/reports/train/2bfe7d4221e67015786634d92dabbc7b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/adv_predictions.json - adv_probabilities_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/bc67f6fb590ed50d343e01d2e8d5c3e9.pkl - params_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/params.yaml - predictions_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/predictions.json - probabilities_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/probabilities.json - score_dict_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/score_dict.json - test_labels_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/test_labels.json - train_labels_file: output/reports/train/2bfe7d4221e67015786634d92dabbc7b/train_labels.json - model_dir: models - name: 2bfe7d4221e67015786634d92dabbc7b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7bb1ace5b31ffb3c008a385887ce3f67 - trainer: - kwargs: {} -name: 2bfe7d4221e67015786634d92dabbc7b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2c0f4b93d068963376753fb3c572ef15/params.yaml b/examples/security/classification/output/reports/train/2c0f4b93d068963376753fb3c572ef15/params.yaml deleted file mode 100644 index 99ca30aa..00000000 --- a/examples/security/classification/output/reports/train/2c0f4b93d068963376753fb3c572ef15/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/adv_predictions.json - adv_probabilities_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/4d6a688cc2747af609661bee1c0c3dd4.pkl - params_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/params.yaml - predictions_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/predictions.json - probabilities_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/probabilities.json - score_dict_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/score_dict.json - test_labels_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/test_labels.json - train_labels_file: output/reports/train/2c0f4b93d068963376753fb3c572ef15/train_labels.json - model_dir: models - name: 2c0f4b93d068963376753fb3c572ef15 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 78a991c538df8e537727d6c17b5d212b - trainer: - kwargs: {} -name: 2c0f4b93d068963376753fb3c572ef15 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/params.yaml b/examples/security/classification/output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/params.yaml deleted file mode 100644 index cfa0c2bd..00000000 --- a/examples/security/classification/output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/adv_predictions.json - adv_probabilities_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/416bdda04a0dd4c3dd5e39946b7b0297.pkl - params_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/params.yaml - predictions_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/predictions.json - probabilities_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/probabilities.json - score_dict_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/score_dict.json - test_labels_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/test_labels.json - train_labels_file: output/reports/train/2c1bfe2bf07120188c6e7dae682f53ce/train_labels.json - model_dir: models - name: 2c1bfe2bf07120188c6e7dae682f53ce - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 48443cf76a5ad05b3c4f7481b0b594cd - trainer: - kwargs: {} -name: 2c1bfe2bf07120188c6e7dae682f53ce -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2c1da1457d61af82da49196442dbdf22/params.yaml b/examples/security/classification/output/reports/train/2c1da1457d61af82da49196442dbdf22/params.yaml deleted file mode 100644 index fd4015fe..00000000 --- a/examples/security/classification/output/reports/train/2c1da1457d61af82da49196442dbdf22/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/adv_predictions.json - adv_probabilities_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/bbc71f80a49e06cb3e9a60862fae8d42.pkl - params_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/params.yaml - predictions_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/predictions.json - probabilities_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/probabilities.json - score_dict_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/score_dict.json - test_labels_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/test_labels.json - train_labels_file: output/reports/train/2c1da1457d61af82da49196442dbdf22/train_labels.json - model_dir: models - name: 2c1da1457d61af82da49196442dbdf22 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4c3fc161e55153cc7a8a9c72a870d57a - trainer: - kwargs: {} -name: 2c1da1457d61af82da49196442dbdf22 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/params.yaml b/examples/security/classification/output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/params.yaml deleted file mode 100644 index a697331e..00000000 --- a/examples/security/classification/output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/adv_predictions.json - adv_probabilities_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/ebd581c41222fcefc2ad27d122ed0b24.pkl - params_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/params.yaml - predictions_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/predictions.json - probabilities_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/probabilities.json - score_dict_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/score_dict.json - test_labels_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/test_labels.json - train_labels_file: output/reports/train/2c52223a5e625229b07a4cdff7c52d6e/train_labels.json - model_dir: models - name: 2c52223a5e625229b07a4cdff7c52d6e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7517932302c428a2e68523deaa746de2 - trainer: - kwargs: {} -name: 2c52223a5e625229b07a4cdff7c52d6e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2c74137ba96612419f9a80927ffcdff4/params.yaml b/examples/security/classification/output/reports/train/2c74137ba96612419f9a80927ffcdff4/params.yaml deleted file mode 100644 index b285fde8..00000000 --- a/examples/security/classification/output/reports/train/2c74137ba96612419f9a80927ffcdff4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/adv_predictions.json - adv_probabilities_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/7fe51a7625eca5f1a5423a8f198fe70c.pkl - params_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/params.yaml - predictions_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/predictions.json - probabilities_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/probabilities.json - score_dict_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/score_dict.json - test_labels_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/test_labels.json - train_labels_file: output/reports/train/2c74137ba96612419f9a80927ffcdff4/train_labels.json - model_dir: models - name: 2c74137ba96612419f9a80927ffcdff4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 00b72f5a36701cc6ecac2645b35c7f49 - trainer: - kwargs: {} -name: 2c74137ba96612419f9a80927ffcdff4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/params.yaml b/examples/security/classification/output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/params.yaml deleted file mode 100644 index cec14777..00000000 --- a/examples/security/classification/output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/adv_predictions.json - adv_probabilities_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/d0d898b14d02f50435660ec10809b56f.pkl - params_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/params.yaml - predictions_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/predictions.json - probabilities_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/probabilities.json - score_dict_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/score_dict.json - test_labels_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/test_labels.json - train_labels_file: output/reports/train/2c788fb032659ddd77ee7af4ee7f7984/train_labels.json - model_dir: models - name: 2c788fb032659ddd77ee7af4ee7f7984 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8a625e8264ec76c686ae23f308d2cf64 - trainer: - kwargs: {} -name: 2c788fb032659ddd77ee7af4ee7f7984 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/params.yaml b/examples/security/classification/output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/params.yaml deleted file mode 100644 index 68a14648..00000000 --- a/examples/security/classification/output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/adv_predictions.json - adv_probabilities_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/d8e9ee421c462b120eebceb51780847b.pkl - params_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/params.yaml - predictions_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/predictions.json - probabilities_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/probabilities.json - score_dict_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/score_dict.json - test_labels_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/test_labels.json - train_labels_file: output/reports/train/2c86e2e530c17e1794e091d7003d4cb8/train_labels.json - model_dir: models - name: 2c86e2e530c17e1794e091d7003d4cb8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 99d1a7fc327a1605ed1d93551a84d361 - trainer: - kwargs: {} -name: 2c86e2e530c17e1794e091d7003d4cb8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2ca4538e355964ee71de087f88cadf1e/params.yaml b/examples/security/classification/output/reports/train/2ca4538e355964ee71de087f88cadf1e/params.yaml deleted file mode 100644 index b3d918e7..00000000 --- a/examples/security/classification/output/reports/train/2ca4538e355964ee71de087f88cadf1e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/adv_predictions.json - adv_probabilities_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/3b20ffd6ef1d9b5f4ef00da05dda3138.pkl - params_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/params.yaml - predictions_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/predictions.json - probabilities_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/probabilities.json - score_dict_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/score_dict.json - test_labels_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/test_labels.json - train_labels_file: output/reports/train/2ca4538e355964ee71de087f88cadf1e/train_labels.json - model_dir: models - name: 2ca4538e355964ee71de087f88cadf1e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5353a2bfbd03fe2f11bc5e9596e08b6b - trainer: - kwargs: {} -name: 2ca4538e355964ee71de087f88cadf1e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/params.yaml b/examples/security/classification/output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/params.yaml deleted file mode 100644 index 746d7950..00000000 --- a/examples/security/classification/output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/adv_predictions.json - adv_probabilities_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/5b6e22ce8117caaea664616a157ff252.pkl - params_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/params.yaml - predictions_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/predictions.json - probabilities_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/probabilities.json - score_dict_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/score_dict.json - test_labels_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/test_labels.json - train_labels_file: output/reports/train/2cc6d95a79e2670b4eb45879ec8516b3/train_labels.json - model_dir: models - name: 2cc6d95a79e2670b4eb45879ec8516b3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 54e94b7119bd0e4663dd9a7001f33122 - trainer: - kwargs: {} -name: 2cc6d95a79e2670b4eb45879ec8516b3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/params.yaml b/examples/security/classification/output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/params.yaml deleted file mode 100644 index fd7c4ae2..00000000 --- a/examples/security/classification/output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/adv_predictions.json - adv_probabilities_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/b2c6ad559b3017ea716b3ab7da8cdb6f.pkl - params_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/params.yaml - predictions_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/predictions.json - probabilities_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/probabilities.json - score_dict_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/score_dict.json - test_labels_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/test_labels.json - train_labels_file: output/reports/train/2cf9dd3f68b9bae359ef96cc6ad395ca/train_labels.json - model_dir: models - name: 2cf9dd3f68b9bae359ef96cc6ad395ca - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 83a5c46e5fb6d2c3db2bd4237b7a805b - trainer: - kwargs: {} -name: 2cf9dd3f68b9bae359ef96cc6ad395ca -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2d1a5615b04fb97c029aa168d96b026c/params.yaml b/examples/security/classification/output/reports/train/2d1a5615b04fb97c029aa168d96b026c/params.yaml deleted file mode 100644 index b7835313..00000000 --- a/examples/security/classification/output/reports/train/2d1a5615b04fb97c029aa168d96b026c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/adv_predictions.json - adv_probabilities_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/897b4387de6ad4835eeabef66d49d86a.pkl - params_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/params.yaml - predictions_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/predictions.json - probabilities_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/probabilities.json - score_dict_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/score_dict.json - test_labels_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/test_labels.json - train_labels_file: output/reports/train/2d1a5615b04fb97c029aa168d96b026c/train_labels.json - model_dir: models - name: 2d1a5615b04fb97c029aa168d96b026c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9bb04938e5ef82295fa855129579004a - trainer: - kwargs: {} -name: 2d1a5615b04fb97c029aa168d96b026c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2d23cda7d9caa9810859f5e01af7688b/params.yaml b/examples/security/classification/output/reports/train/2d23cda7d9caa9810859f5e01af7688b/params.yaml deleted file mode 100644 index 8e54ca6a..00000000 --- a/examples/security/classification/output/reports/train/2d23cda7d9caa9810859f5e01af7688b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/adv_predictions.json - adv_probabilities_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/12192e902e45a51a5f0f4bbbe5133e5d.pkl - params_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/params.yaml - predictions_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/predictions.json - probabilities_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/probabilities.json - score_dict_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/score_dict.json - test_labels_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/test_labels.json - train_labels_file: output/reports/train/2d23cda7d9caa9810859f5e01af7688b/train_labels.json - model_dir: models - name: 2d23cda7d9caa9810859f5e01af7688b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 24f69c2650dcbd83a1313205c9a77ffb - trainer: - kwargs: {} -name: 2d23cda7d9caa9810859f5e01af7688b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2d3cad31399616f0e2121d765709e7d2/params.yaml b/examples/security/classification/output/reports/train/2d3cad31399616f0e2121d765709e7d2/params.yaml deleted file mode 100644 index 95e9a19d..00000000 --- a/examples/security/classification/output/reports/train/2d3cad31399616f0e2121d765709e7d2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/adv_predictions.json - adv_probabilities_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/c20ec340606a0eae4a3927b7032a3322.pkl - params_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/params.yaml - predictions_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/predictions.json - probabilities_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/probabilities.json - score_dict_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/score_dict.json - test_labels_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/test_labels.json - train_labels_file: output/reports/train/2d3cad31399616f0e2121d765709e7d2/train_labels.json - model_dir: models - name: 2d3cad31399616f0e2121d765709e7d2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 283b034356b667510deae1386a114da9 - trainer: - kwargs: {} -name: 2d3cad31399616f0e2121d765709e7d2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/params.yaml b/examples/security/classification/output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/params.yaml deleted file mode 100644 index ef6129f9..00000000 --- a/examples/security/classification/output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/adv_predictions.json - adv_probabilities_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/297bcd0c57be89308374e8dfde0b232f.pkl - params_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/params.yaml - predictions_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/predictions.json - probabilities_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/probabilities.json - score_dict_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/score_dict.json - test_labels_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/test_labels.json - train_labels_file: output/reports/train/2d9b9b8afe4ec3cc205f5d6da82f5197/train_labels.json - model_dir: models - name: 2d9b9b8afe4ec3cc205f5d6da82f5197 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bb2fae02daa9c49d46d91533490d5704 - trainer: - kwargs: {} -name: 2d9b9b8afe4ec3cc205f5d6da82f5197 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/params.yaml b/examples/security/classification/output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/params.yaml deleted file mode 100644 index 7292e912..00000000 --- a/examples/security/classification/output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/adv_predictions.json - adv_probabilities_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/c7967e68b9d27eed548508dcf76b2128.pkl - params_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/params.yaml - predictions_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/predictions.json - probabilities_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/probabilities.json - score_dict_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/score_dict.json - test_labels_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/test_labels.json - train_labels_file: output/reports/train/2dd4d21b3b599023ecc83e579ab0b947/train_labels.json - model_dir: models - name: 2dd4d21b3b599023ecc83e579ab0b947 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3362097ad0843295d2bbad9147957dc3 - trainer: - kwargs: {} -name: 2dd4d21b3b599023ecc83e579ab0b947 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2de99cbec06dda15d2bd1236e76573ca/params.yaml b/examples/security/classification/output/reports/train/2de99cbec06dda15d2bd1236e76573ca/params.yaml deleted file mode 100644 index 53511d77..00000000 --- a/examples/security/classification/output/reports/train/2de99cbec06dda15d2bd1236e76573ca/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/adv_predictions.json - adv_probabilities_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/661fa373207a2b56f7d7b9942fe0fab3.pkl - params_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/params.yaml - predictions_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/predictions.json - probabilities_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/probabilities.json - score_dict_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/score_dict.json - test_labels_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/test_labels.json - train_labels_file: output/reports/train/2de99cbec06dda15d2bd1236e76573ca/train_labels.json - model_dir: models - name: 2de99cbec06dda15d2bd1236e76573ca - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b5991b964e23e6cabcbb413f301622f7 - trainer: - kwargs: {} -name: 2de99cbec06dda15d2bd1236e76573ca -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2dea64d650d37afaebe312aece96a8f5/params.yaml b/examples/security/classification/output/reports/train/2dea64d650d37afaebe312aece96a8f5/params.yaml deleted file mode 100644 index 704e8cf0..00000000 --- a/examples/security/classification/output/reports/train/2dea64d650d37afaebe312aece96a8f5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/adv_predictions.json - adv_probabilities_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/357eb9dc9ca3ea4741fec857b933a736.pkl - params_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/params.yaml - predictions_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/predictions.json - probabilities_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/probabilities.json - score_dict_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/score_dict.json - test_labels_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/test_labels.json - train_labels_file: output/reports/train/2dea64d650d37afaebe312aece96a8f5/train_labels.json - model_dir: models - name: 2dea64d650d37afaebe312aece96a8f5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6588b79d1da0e005ba1dbdad5669ecd9 - trainer: - kwargs: {} -name: 2dea64d650d37afaebe312aece96a8f5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/params.yaml b/examples/security/classification/output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/params.yaml deleted file mode 100644 index 4a377aad..00000000 --- a/examples/security/classification/output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/adv_predictions.json - adv_probabilities_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/0de28f76035d410b6b8657e91bd94d58.pkl - params_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/params.yaml - predictions_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/predictions.json - probabilities_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/probabilities.json - score_dict_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/score_dict.json - test_labels_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/test_labels.json - train_labels_file: output/reports/train/2e15cb4aba3e11a47abb1c96cedec857/train_labels.json - model_dir: models - name: 2e15cb4aba3e11a47abb1c96cedec857 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 476f2690c14d41490eb0c22cc968725f - trainer: - kwargs: {} -name: 2e15cb4aba3e11a47abb1c96cedec857 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2e2bdcf47903073081179bb6a806f697/params.yaml b/examples/security/classification/output/reports/train/2e2bdcf47903073081179bb6a806f697/params.yaml deleted file mode 100644 index 511ff744..00000000 --- a/examples/security/classification/output/reports/train/2e2bdcf47903073081179bb6a806f697/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/adv_predictions.json - adv_probabilities_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/6ef9beaaa8cfc751299137c0024ba122.pkl - params_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/params.yaml - predictions_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/predictions.json - probabilities_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/probabilities.json - score_dict_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/score_dict.json - test_labels_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/test_labels.json - train_labels_file: output/reports/train/2e2bdcf47903073081179bb6a806f697/train_labels.json - model_dir: models - name: 2e2bdcf47903073081179bb6a806f697 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 777f17d5acaf89e62dcb4619977348e1 - trainer: - kwargs: {} -name: 2e2bdcf47903073081179bb6a806f697 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/params.yaml b/examples/security/classification/output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/params.yaml deleted file mode 100644 index fcc696bb..00000000 --- a/examples/security/classification/output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/adv_predictions.json - adv_probabilities_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/a18667b6a89d9b80825d18f6fd6b3fb0.pkl - params_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/params.yaml - predictions_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/predictions.json - probabilities_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/probabilities.json - score_dict_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/score_dict.json - test_labels_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/test_labels.json - train_labels_file: output/reports/train/2e69bc9e6a911131bf8e042ca88e1f9e/train_labels.json - model_dir: models - name: 2e69bc9e6a911131bf8e042ca88e1f9e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 883430d010e4148958bb1f51ae1fc5bf - trainer: - kwargs: {} -name: 2e69bc9e6a911131bf8e042ca88e1f9e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2e95144b56948be11857005b4776042d/params.yaml b/examples/security/classification/output/reports/train/2e95144b56948be11857005b4776042d/params.yaml deleted file mode 100644 index ca1aa9d7..00000000 --- a/examples/security/classification/output/reports/train/2e95144b56948be11857005b4776042d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2e95144b56948be11857005b4776042d/adv_predictions.json - adv_probabilities_file: output/reports/train/2e95144b56948be11857005b4776042d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/dfa2e4580a9c5892020bba43126ccb32.pkl - params_file: output/reports/train/2e95144b56948be11857005b4776042d/params.yaml - predictions_file: output/reports/train/2e95144b56948be11857005b4776042d/predictions.json - probabilities_file: output/reports/train/2e95144b56948be11857005b4776042d/probabilities.json - score_dict_file: output/reports/train/2e95144b56948be11857005b4776042d/score_dict.json - test_labels_file: output/reports/train/2e95144b56948be11857005b4776042d/test_labels.json - train_labels_file: output/reports/train/2e95144b56948be11857005b4776042d/train_labels.json - model_dir: models - name: 2e95144b56948be11857005b4776042d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d5453b6a676478c7c84960e66d95329d - trainer: - kwargs: {} -name: 2e95144b56948be11857005b4776042d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2ef3014aa128150ce8976abe446d5c27/params.yaml b/examples/security/classification/output/reports/train/2ef3014aa128150ce8976abe446d5c27/params.yaml deleted file mode 100644 index 01beed0c..00000000 --- a/examples/security/classification/output/reports/train/2ef3014aa128150ce8976abe446d5c27/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/adv_predictions.json - adv_probabilities_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/d4bf7c88af4493f192b8908a6c1d7a24.pkl - params_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/params.yaml - predictions_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/predictions.json - probabilities_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/probabilities.json - score_dict_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/score_dict.json - test_labels_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/test_labels.json - train_labels_file: output/reports/train/2ef3014aa128150ce8976abe446d5c27/train_labels.json - model_dir: models - name: 2ef3014aa128150ce8976abe446d5c27 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a9f0db7d04c445e7b9fde0d34fe0ab7f - trainer: - kwargs: {} -name: 2ef3014aa128150ce8976abe446d5c27 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2f01abc53302e53883bbe34ee09be1f3/params.yaml b/examples/security/classification/output/reports/train/2f01abc53302e53883bbe34ee09be1f3/params.yaml deleted file mode 100644 index 3d059a27..00000000 --- a/examples/security/classification/output/reports/train/2f01abc53302e53883bbe34ee09be1f3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/adv_predictions.json - adv_probabilities_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/1e78ef7fa3b1414ca3fb0f4b65173042.pkl - params_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/params.yaml - predictions_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/predictions.json - probabilities_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/probabilities.json - score_dict_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/score_dict.json - test_labels_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/test_labels.json - train_labels_file: output/reports/train/2f01abc53302e53883bbe34ee09be1f3/train_labels.json - model_dir: models - name: 2f01abc53302e53883bbe34ee09be1f3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0bf9350b0a8556b53d62c5e8ce1ce73e - trainer: - kwargs: {} -name: 2f01abc53302e53883bbe34ee09be1f3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2f1d3426ad5e3453cbe068da99332726/params.yaml b/examples/security/classification/output/reports/train/2f1d3426ad5e3453cbe068da99332726/params.yaml deleted file mode 100644 index 353ee90d..00000000 --- a/examples/security/classification/output/reports/train/2f1d3426ad5e3453cbe068da99332726/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/adv_predictions.json - adv_probabilities_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/3d169e4f9fac61f7e2b94fce98faefff.pkl - params_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/params.yaml - predictions_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/predictions.json - probabilities_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/probabilities.json - score_dict_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/score_dict.json - test_labels_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/test_labels.json - train_labels_file: output/reports/train/2f1d3426ad5e3453cbe068da99332726/train_labels.json - model_dir: models - name: 2f1d3426ad5e3453cbe068da99332726 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f4c2d004c1f709e136639ea6c9129b8c - trainer: - kwargs: {} -name: 2f1d3426ad5e3453cbe068da99332726 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2f368d21cae45f44bc83332d80ea6a97/params.yaml b/examples/security/classification/output/reports/train/2f368d21cae45f44bc83332d80ea6a97/params.yaml deleted file mode 100644 index 015153a8..00000000 --- a/examples/security/classification/output/reports/train/2f368d21cae45f44bc83332d80ea6a97/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/adv_predictions.json - adv_probabilities_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/4dd0792e703396eb8e774095b9ec9114.pkl - params_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/params.yaml - predictions_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/predictions.json - probabilities_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/probabilities.json - score_dict_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/score_dict.json - test_labels_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/test_labels.json - train_labels_file: output/reports/train/2f368d21cae45f44bc83332d80ea6a97/train_labels.json - model_dir: models - name: 2f368d21cae45f44bc83332d80ea6a97 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e013c2b561460f35f9a4cb8db47bdd12 - trainer: - kwargs: {} -name: 2f368d21cae45f44bc83332d80ea6a97 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/params.yaml b/examples/security/classification/output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/params.yaml deleted file mode 100644 index 26904572..00000000 --- a/examples/security/classification/output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/adv_predictions.json - adv_probabilities_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/a13e941d41a69dde15d5bd65b687dc17.pkl - params_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/params.yaml - predictions_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/predictions.json - probabilities_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/probabilities.json - score_dict_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/score_dict.json - test_labels_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/test_labels.json - train_labels_file: output/reports/train/2f84b5e759023d6ea3cd6ebc53bcfd2c/train_labels.json - model_dir: models - name: 2f84b5e759023d6ea3cd6ebc53bcfd2c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ad4ff47ee79adcf86117b4d69177af6a - trainer: - kwargs: {} -name: 2f84b5e759023d6ea3cd6ebc53bcfd2c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/params.yaml b/examples/security/classification/output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/params.yaml deleted file mode 100644 index 206d9277..00000000 --- a/examples/security/classification/output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/adv_predictions.json - adv_probabilities_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/4574fe34ce0bae5b50c926b37eb588e0.pkl - params_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/params.yaml - predictions_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/predictions.json - probabilities_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/probabilities.json - score_dict_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/score_dict.json - test_labels_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/test_labels.json - train_labels_file: output/reports/train/2fa579aa4ca7712d33e2527c4d53a7b1/train_labels.json - model_dir: models - name: 2fa579aa4ca7712d33e2527c4d53a7b1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 24621bdd4a7a5d5c39664e5191298f3b - trainer: - kwargs: {} -name: 2fa579aa4ca7712d33e2527c4d53a7b1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/params.yaml b/examples/security/classification/output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/params.yaml deleted file mode 100644 index 51bbe66b..00000000 --- a/examples/security/classification/output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/adv_predictions.json - adv_probabilities_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/b17967e3f9d5ffb2d4406121e64c46ea.pkl - params_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/params.yaml - predictions_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/predictions.json - probabilities_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/probabilities.json - score_dict_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/score_dict.json - test_labels_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/test_labels.json - train_labels_file: output/reports/train/2fd2ff90e1b1b703b7038349c78309a6/train_labels.json - model_dir: models - name: 2fd2ff90e1b1b703b7038349c78309a6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fcda614a10f2357b648e8b1a6ed7885b - trainer: - kwargs: {} -name: 2fd2ff90e1b1b703b7038349c78309a6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3020459442d6be1c259fd23b6d10efa2/params.yaml b/examples/security/classification/output/reports/train/3020459442d6be1c259fd23b6d10efa2/params.yaml deleted file mode 100644 index 399c88b8..00000000 --- a/examples/security/classification/output/reports/train/3020459442d6be1c259fd23b6d10efa2/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/adv_predictions.json - adv_probabilities_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/1f0c3b53e22a0d231b1ba729dcece189.pkl - params_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/params.yaml - predictions_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/predictions.json - probabilities_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/probabilities.json - score_dict_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/score_dict.json - test_labels_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/test_labels.json - train_labels_file: output/reports/train/3020459442d6be1c259fd23b6d10efa2/train_labels.json - model_dir: models - name: 3020459442d6be1c259fd23b6d10efa2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2414b1ed9d00ae65dc60f952583551d3 - trainer: - kwargs: {} -name: 3020459442d6be1c259fd23b6d10efa2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/params.yaml b/examples/security/classification/output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/params.yaml deleted file mode 100644 index e01a8ba7..00000000 --- a/examples/security/classification/output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/adv_predictions.json - adv_probabilities_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/7fb1a9b838c1c9656ac9f39267537017.pkl - params_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/params.yaml - predictions_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/predictions.json - probabilities_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/probabilities.json - score_dict_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/score_dict.json - test_labels_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/test_labels.json - train_labels_file: output/reports/train/304ebb1d266c37028e624c2a48c3c9a0/train_labels.json - model_dir: models - name: 304ebb1d266c37028e624c2a48c3c9a0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8f1c34ddc7fdf1aaa286e8e7dfd6cb3d - trainer: - kwargs: {} -name: 304ebb1d266c37028e624c2a48c3c9a0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/params.yaml b/examples/security/classification/output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/params.yaml deleted file mode 100644 index 71bc05d0..00000000 --- a/examples/security/classification/output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/params.yaml +++ /dev/null @@ -1,104 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/adv_predictions.json - adv_probabilities_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/eac24411eb9fe7a4e67d5e85052443a6.pkl - params_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/params.yaml - predictions_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/predictions.json - probabilities_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/probabilities.json - score_dict_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/score_dict.json - test_labels_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/test_labels.json - train_labels_file: output/reports/train/306253fb0fa6c13ad69d36a3a3dbdb2b/train_labels.json - model_dir: models - name: 306253fb0fa6c13ad69d36a3a3dbdb2b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: - - linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3dc5adbacb5bfd3906f898770bd5d386 - trainer: - kwargs: {} -name: 306253fb0fa6c13ad69d36a3a3dbdb2b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/307747c6684535da55fa8cb093951b14/params.yaml b/examples/security/classification/output/reports/train/307747c6684535da55fa8cb093951b14/params.yaml deleted file mode 100644 index c9b012e8..00000000 --- a/examples/security/classification/output/reports/train/307747c6684535da55fa8cb093951b14/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/307747c6684535da55fa8cb093951b14/adv_predictions.json - adv_probabilities_file: output/reports/train/307747c6684535da55fa8cb093951b14/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/82f05b31c4767755b97c61ff4d719886.pkl - params_file: output/reports/train/307747c6684535da55fa8cb093951b14/params.yaml - predictions_file: output/reports/train/307747c6684535da55fa8cb093951b14/predictions.json - probabilities_file: output/reports/train/307747c6684535da55fa8cb093951b14/probabilities.json - score_dict_file: output/reports/train/307747c6684535da55fa8cb093951b14/score_dict.json - test_labels_file: output/reports/train/307747c6684535da55fa8cb093951b14/test_labels.json - train_labels_file: output/reports/train/307747c6684535da55fa8cb093951b14/train_labels.json - model_dir: models - name: 307747c6684535da55fa8cb093951b14 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5ccb916ce318ad32a54f2e3062332fb9 - trainer: - kwargs: {} -name: 307747c6684535da55fa8cb093951b14 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3088ccd20b5f1680b36983eeb26501d5/params.yaml b/examples/security/classification/output/reports/train/3088ccd20b5f1680b36983eeb26501d5/params.yaml deleted file mode 100644 index 2bf5788d..00000000 --- a/examples/security/classification/output/reports/train/3088ccd20b5f1680b36983eeb26501d5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/adv_predictions.json - adv_probabilities_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/0b2fde7c5c0cd45ba813273415781afa.pkl - params_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/params.yaml - predictions_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/predictions.json - probabilities_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/probabilities.json - score_dict_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/score_dict.json - test_labels_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/test_labels.json - train_labels_file: output/reports/train/3088ccd20b5f1680b36983eeb26501d5/train_labels.json - model_dir: models - name: 3088ccd20b5f1680b36983eeb26501d5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b517877ab4037b43d594c1e2569645ff - trainer: - kwargs: {} -name: 3088ccd20b5f1680b36983eeb26501d5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/params.yaml b/examples/security/classification/output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/params.yaml deleted file mode 100644 index c94a9956..00000000 --- a/examples/security/classification/output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/adv_predictions.json - adv_probabilities_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/ce58b9779e467c500e3b3f4a46bcccd0.pkl - params_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/params.yaml - predictions_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/predictions.json - probabilities_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/probabilities.json - score_dict_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/score_dict.json - test_labels_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/test_labels.json - train_labels_file: output/reports/train/30e0c9ebd74a72376ad2f2819a3e9b52/train_labels.json - model_dir: models - name: 30e0c9ebd74a72376ad2f2819a3e9b52 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cea87939676f7393dfd85df2c4ab2247 - trainer: - kwargs: {} -name: 30e0c9ebd74a72376ad2f2819a3e9b52 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/30f33fd7b3d833eb4357f33b620688ca/params.yaml b/examples/security/classification/output/reports/train/30f33fd7b3d833eb4357f33b620688ca/params.yaml deleted file mode 100644 index 30bd3095..00000000 --- a/examples/security/classification/output/reports/train/30f33fd7b3d833eb4357f33b620688ca/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/adv_predictions.json - adv_probabilities_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/6bfb1290dfe912393ffe885971ce25b0.pkl - params_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/params.yaml - predictions_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/predictions.json - probabilities_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/probabilities.json - score_dict_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/score_dict.json - test_labels_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/test_labels.json - train_labels_file: output/reports/train/30f33fd7b3d833eb4357f33b620688ca/train_labels.json - model_dir: models - name: 30f33fd7b3d833eb4357f33b620688ca - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b71e5e8e5635222dc773dcb297caf3b4 - trainer: - kwargs: {} -name: 30f33fd7b3d833eb4357f33b620688ca -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/params.yaml b/examples/security/classification/output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/params.yaml deleted file mode 100644 index 222279da..00000000 --- a/examples/security/classification/output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/adv_predictions.json - adv_probabilities_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/acfd5333c05168195e62f2b40152929f.pkl - params_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/params.yaml - predictions_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/predictions.json - probabilities_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/probabilities.json - score_dict_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/score_dict.json - test_labels_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/test_labels.json - train_labels_file: output/reports/train/315c8019ac2fcbb87f67bea61cfbd2a7/train_labels.json - model_dir: models - name: 315c8019ac2fcbb87f67bea61cfbd2a7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8a9fcb6fe1916c2a4eaba1225ba9a97c - trainer: - kwargs: {} -name: 315c8019ac2fcbb87f67bea61cfbd2a7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/315e6a9123260085b7f359ec265dde8d/params.yaml b/examples/security/classification/output/reports/train/315e6a9123260085b7f359ec265dde8d/params.yaml deleted file mode 100644 index e887cf13..00000000 --- a/examples/security/classification/output/reports/train/315e6a9123260085b7f359ec265dde8d/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/adv_predictions.json - adv_probabilities_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/652089893b58c56df09ee4487d461265.pkl - params_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/params.yaml - predictions_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/predictions.json - probabilities_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/probabilities.json - score_dict_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/score_dict.json - test_labels_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/test_labels.json - train_labels_file: output/reports/train/315e6a9123260085b7f359ec265dde8d/train_labels.json - model_dir: models - name: 315e6a9123260085b7f359ec265dde8d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fadcb463ab1e7d469b254615c05b9db8 - trainer: - kwargs: {} -name: 315e6a9123260085b7f359ec265dde8d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/params.yaml b/examples/security/classification/output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/params.yaml deleted file mode 100644 index 084cdd16..00000000 --- a/examples/security/classification/output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/adv_predictions.json - adv_probabilities_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/4c755aef3e9ec368cbda2431159ecb2b.pkl - params_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/params.yaml - predictions_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/predictions.json - probabilities_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/probabilities.json - score_dict_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/score_dict.json - test_labels_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/test_labels.json - train_labels_file: output/reports/train/31600d8f4ddc4b7460f2956ced05ddfd/train_labels.json - model_dir: models - name: 31600d8f4ddc4b7460f2956ced05ddfd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d0f4dd962a239713b223e3c2d8ef304b - trainer: - kwargs: {} -name: 31600d8f4ddc4b7460f2956ced05ddfd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/params.yaml b/examples/security/classification/output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/params.yaml deleted file mode 100644 index 7c643947..00000000 --- a/examples/security/classification/output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/adv_predictions.json - adv_probabilities_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/3179998fc7ea554ef3136c9dee35eddf.pkl - params_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/params.yaml - predictions_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/predictions.json - probabilities_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/probabilities.json - score_dict_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/score_dict.json - test_labels_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/test_labels.json - train_labels_file: output/reports/train/317ea5d62b40c67fa32cadfa32577b1f/train_labels.json - model_dir: models - name: 317ea5d62b40c67fa32cadfa32577b1f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b1683c82540df3d144579221b4882499 - trainer: - kwargs: {} -name: 317ea5d62b40c67fa32cadfa32577b1f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/31ba5203e81f2206fcbc45695731a787/params.yaml b/examples/security/classification/output/reports/train/31ba5203e81f2206fcbc45695731a787/params.yaml deleted file mode 100644 index 829e1322..00000000 --- a/examples/security/classification/output/reports/train/31ba5203e81f2206fcbc45695731a787/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/adv_predictions.json - adv_probabilities_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/9e6ee169768ef23b51eed581cbe1f9fa.pkl - params_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/params.yaml - predictions_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/predictions.json - probabilities_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/probabilities.json - score_dict_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/score_dict.json - test_labels_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/test_labels.json - train_labels_file: output/reports/train/31ba5203e81f2206fcbc45695731a787/train_labels.json - model_dir: models - name: 31ba5203e81f2206fcbc45695731a787 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 195eaabd6150bfc05120a49205e63612 - trainer: - kwargs: {} -name: 31ba5203e81f2206fcbc45695731a787 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3203e953de84465b703f23d47cf7e9eb/params.yaml b/examples/security/classification/output/reports/train/3203e953de84465b703f23d47cf7e9eb/params.yaml deleted file mode 100644 index 787c12c1..00000000 --- a/examples/security/classification/output/reports/train/3203e953de84465b703f23d47cf7e9eb/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/adv_predictions.json - adv_probabilities_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/968bceb816c562ec3f6d370f5ffe7d3d.pkl - params_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/params.yaml - predictions_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/predictions.json - probabilities_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/probabilities.json - score_dict_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/score_dict.json - test_labels_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/test_labels.json - train_labels_file: output/reports/train/3203e953de84465b703f23d47cf7e9eb/train_labels.json - model_dir: models - name: 3203e953de84465b703f23d47cf7e9eb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 3 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5fd621796b4fcc6091b7494db781c3d3 - trainer: - kwargs: {} -name: 3203e953de84465b703f23d47cf7e9eb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/321678724c0e7dcdb097146a482ba6ce/params.yaml b/examples/security/classification/output/reports/train/321678724c0e7dcdb097146a482ba6ce/params.yaml deleted file mode 100644 index bb60a56b..00000000 --- a/examples/security/classification/output/reports/train/321678724c0e7dcdb097146a482ba6ce/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/adv_predictions.json - adv_probabilities_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/3ea725cb3a7b56943d91c9e73e9b74cd.pkl - params_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/params.yaml - predictions_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/predictions.json - probabilities_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/probabilities.json - score_dict_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/score_dict.json - test_labels_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/test_labels.json - train_labels_file: output/reports/train/321678724c0e7dcdb097146a482ba6ce/train_labels.json - model_dir: models - name: 321678724c0e7dcdb097146a482ba6ce - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f5fda853f3fb3ab0b2e33d373232f020 - trainer: - kwargs: {} -name: 321678724c0e7dcdb097146a482ba6ce -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/params.yaml b/examples/security/classification/output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/params.yaml deleted file mode 100644 index e72f3cc6..00000000 --- a/examples/security/classification/output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/adv_predictions.json - adv_probabilities_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/a4cde4f2b84c7d280e0b18d35132f29f.pkl - params_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/params.yaml - predictions_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/predictions.json - probabilities_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/probabilities.json - score_dict_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/score_dict.json - test_labels_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/test_labels.json - train_labels_file: output/reports/train/321a3b66225abdcea2d7659e3e7fc81f/train_labels.json - model_dir: models - name: 321a3b66225abdcea2d7659e3e7fc81f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1a646011cc51ad081e0bb4eaec1c17b9 - trainer: - kwargs: {} -name: 321a3b66225abdcea2d7659e3e7fc81f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/321c5a63e7770fba48a455b181a556a1/params.yaml b/examples/security/classification/output/reports/train/321c5a63e7770fba48a455b181a556a1/params.yaml deleted file mode 100644 index 41b150a7..00000000 --- a/examples/security/classification/output/reports/train/321c5a63e7770fba48a455b181a556a1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/adv_predictions.json - adv_probabilities_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/348f85e12d7f2ed3812e2616bd5d9d62.pkl - params_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/params.yaml - predictions_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/predictions.json - probabilities_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/probabilities.json - score_dict_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/score_dict.json - test_labels_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/test_labels.json - train_labels_file: output/reports/train/321c5a63e7770fba48a455b181a556a1/train_labels.json - model_dir: models - name: 321c5a63e7770fba48a455b181a556a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d167aff11b69ac076dae88217a0ffdf1 - trainer: - kwargs: {} -name: 321c5a63e7770fba48a455b181a556a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/params.yaml b/examples/security/classification/output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/params.yaml deleted file mode 100644 index 666128f4..00000000 --- a/examples/security/classification/output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/adv_predictions.json - adv_probabilities_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e334af916256e6cad46af35b3e3d5b52.pkl - params_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/params.yaml - predictions_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/predictions.json - probabilities_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/probabilities.json - score_dict_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/score_dict.json - test_labels_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/test_labels.json - train_labels_file: output/reports/train/32608cc6d6a3d7e8ee33e4c6e636e6b5/train_labels.json - model_dir: models - name: 32608cc6d6a3d7e8ee33e4c6e636e6b5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e0105e564a5f2e40d01add7a19968f77 - trainer: - kwargs: {} -name: 32608cc6d6a3d7e8ee33e4c6e636e6b5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/32757ca9337f3ea672ca91785e7bd75f/params.yaml b/examples/security/classification/output/reports/train/32757ca9337f3ea672ca91785e7bd75f/params.yaml deleted file mode 100644 index dfea1234..00000000 --- a/examples/security/classification/output/reports/train/32757ca9337f3ea672ca91785e7bd75f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/adv_predictions.json - adv_probabilities_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/d71825da132d338c7fb603828e842ebd.pkl - params_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/params.yaml - predictions_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/predictions.json - probabilities_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/probabilities.json - score_dict_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/score_dict.json - test_labels_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/test_labels.json - train_labels_file: output/reports/train/32757ca9337f3ea672ca91785e7bd75f/train_labels.json - model_dir: models - name: 32757ca9337f3ea672ca91785e7bd75f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7f991320a1cfffda2c822abbca9d8b3a - trainer: - kwargs: {} -name: 32757ca9337f3ea672ca91785e7bd75f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/328433f0f3127f2b99a1f1c9924545b3/params.yaml b/examples/security/classification/output/reports/train/328433f0f3127f2b99a1f1c9924545b3/params.yaml deleted file mode 100644 index 75573154..00000000 --- a/examples/security/classification/output/reports/train/328433f0f3127f2b99a1f1c9924545b3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/adv_predictions.json - adv_probabilities_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/5901b927f19fe4c86f279e5a8ecae56e.pkl - params_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/params.yaml - predictions_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/predictions.json - probabilities_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/probabilities.json - score_dict_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/score_dict.json - test_labels_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/test_labels.json - train_labels_file: output/reports/train/328433f0f3127f2b99a1f1c9924545b3/train_labels.json - model_dir: models - name: 328433f0f3127f2b99a1f1c9924545b3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 63afcb96d22148ad9d20f6732e7fcbb6 - trainer: - kwargs: {} -name: 328433f0f3127f2b99a1f1c9924545b3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/329d127d698a9743ccb6b555b93304e4/params.yaml b/examples/security/classification/output/reports/train/329d127d698a9743ccb6b555b93304e4/params.yaml deleted file mode 100644 index 081a943e..00000000 --- a/examples/security/classification/output/reports/train/329d127d698a9743ccb6b555b93304e4/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/adv_predictions.json - adv_probabilities_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/a9ec29625ea672e1ce2dce0ed2e157c0.pkl - params_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/params.yaml - predictions_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/predictions.json - probabilities_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/probabilities.json - score_dict_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/score_dict.json - test_labels_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/test_labels.json - train_labels_file: output/reports/train/329d127d698a9743ccb6b555b93304e4/train_labels.json - model_dir: models - name: 329d127d698a9743ccb6b555b93304e4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fa32e2509ca18344f2471a0ee85676ca - trainer: - kwargs: {} -name: 329d127d698a9743ccb6b555b93304e4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/32a47571f3883f288c2f7689544c39d0/params.yaml b/examples/security/classification/output/reports/train/32a47571f3883f288c2f7689544c39d0/params.yaml deleted file mode 100644 index 551578dd..00000000 --- a/examples/security/classification/output/reports/train/32a47571f3883f288c2f7689544c39d0/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/adv_predictions.json - adv_probabilities_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/3c67ec07db526873b1fa7a8824edc675.pkl - params_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/params.yaml - predictions_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/predictions.json - probabilities_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/probabilities.json - score_dict_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/score_dict.json - test_labels_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/test_labels.json - train_labels_file: output/reports/train/32a47571f3883f288c2f7689544c39d0/train_labels.json - model_dir: models - name: 32a47571f3883f288c2f7689544c39d0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbcf1958a3c96e6fefa6341a2dc28741 - trainer: - kwargs: {} -name: 32a47571f3883f288c2f7689544c39d0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/params.yaml b/examples/security/classification/output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/params.yaml deleted file mode 100644 index ef611198..00000000 --- a/examples/security/classification/output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/adv_predictions.json - adv_probabilities_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/e5d404cdda3c075627c5e0d9e400c958.pkl - params_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/params.yaml - predictions_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/predictions.json - probabilities_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/probabilities.json - score_dict_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/score_dict.json - test_labels_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/test_labels.json - train_labels_file: output/reports/train/32b17a903e18ce0bfda6ffe79dca0ce5/train_labels.json - model_dir: models - name: 32b17a903e18ce0bfda6ffe79dca0ce5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8eebfb1abe881743ec33ae21195cd3fe - trainer: - kwargs: {} -name: 32b17a903e18ce0bfda6ffe79dca0ce5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/32b2b493215258dddfc2c691160dafd8/params.yaml b/examples/security/classification/output/reports/train/32b2b493215258dddfc2c691160dafd8/params.yaml deleted file mode 100644 index 6c96d9c5..00000000 --- a/examples/security/classification/output/reports/train/32b2b493215258dddfc2c691160dafd8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/adv_predictions.json - adv_probabilities_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/546ed47d4e99e395e3f8310cb08135a6.pkl - params_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/params.yaml - predictions_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/predictions.json - probabilities_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/probabilities.json - score_dict_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/score_dict.json - test_labels_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/test_labels.json - train_labels_file: output/reports/train/32b2b493215258dddfc2c691160dafd8/train_labels.json - model_dir: models - name: 32b2b493215258dddfc2c691160dafd8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8b1afdcbdc14d43f8222e2149fe884b0 - trainer: - kwargs: {} -name: 32b2b493215258dddfc2c691160dafd8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/params.yaml b/examples/security/classification/output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/params.yaml deleted file mode 100644 index 13284292..00000000 --- a/examples/security/classification/output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/adv_predictions.json - adv_probabilities_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/817a2d473b19dd7302d346d74577f1ee.pkl - params_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/params.yaml - predictions_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/predictions.json - probabilities_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/probabilities.json - score_dict_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/score_dict.json - test_labels_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/test_labels.json - train_labels_file: output/reports/train/32b8757d5f90c3fab71fcd2f3b23d7d0/train_labels.json - model_dir: models - name: 32b8757d5f90c3fab71fcd2f3b23d7d0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3410091966e2b8f4d0fee3e790a8b700 - trainer: - kwargs: {} -name: 32b8757d5f90c3fab71fcd2f3b23d7d0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/params.yaml b/examples/security/classification/output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/params.yaml deleted file mode 100644 index 0344513f..00000000 --- a/examples/security/classification/output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/adv_predictions.json - adv_probabilities_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/51b1929bbdf3d8b87d1b3e9685cde3e6.pkl - params_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/params.yaml - predictions_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/predictions.json - probabilities_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/probabilities.json - score_dict_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/score_dict.json - test_labels_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/test_labels.json - train_labels_file: output/reports/train/32be84f99d9e0bab5791d3b27ee994a7/train_labels.json - model_dir: models - name: 32be84f99d9e0bab5791d3b27ee994a7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5a1c5883c9895f19ad624ee61174c6d7 - trainer: - kwargs: {} -name: 32be84f99d9e0bab5791d3b27ee994a7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/330f5b93c916e35d13d3f22b40899acc/params.yaml b/examples/security/classification/output/reports/train/330f5b93c916e35d13d3f22b40899acc/params.yaml deleted file mode 100644 index 4296f42b..00000000 --- a/examples/security/classification/output/reports/train/330f5b93c916e35d13d3f22b40899acc/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/adv_predictions.json - adv_probabilities_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/8e83ad92aa67144502187be39af5658f.pkl - params_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/params.yaml - predictions_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/predictions.json - probabilities_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/probabilities.json - score_dict_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/score_dict.json - test_labels_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/test_labels.json - train_labels_file: output/reports/train/330f5b93c916e35d13d3f22b40899acc/train_labels.json - model_dir: models - name: 330f5b93c916e35d13d3f22b40899acc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 11cf524f756a8f4b78d50790d3bd2a82 - trainer: - kwargs: {} -name: 330f5b93c916e35d13d3f22b40899acc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/332cba091c4ca5ed17a37d86c30e7217/params.yaml b/examples/security/classification/output/reports/train/332cba091c4ca5ed17a37d86c30e7217/params.yaml deleted file mode 100644 index 25dcab95..00000000 --- a/examples/security/classification/output/reports/train/332cba091c4ca5ed17a37d86c30e7217/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/adv_predictions.json - adv_probabilities_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/1a94a7b5cd02600b5a70e5224fae4a40.pkl - params_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/params.yaml - predictions_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/predictions.json - probabilities_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/probabilities.json - score_dict_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/score_dict.json - test_labels_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/test_labels.json - train_labels_file: output/reports/train/332cba091c4ca5ed17a37d86c30e7217/train_labels.json - model_dir: models - name: 332cba091c4ca5ed17a37d86c30e7217 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8d824f048979e91f8d6e8c1bf42eedad - trainer: - kwargs: {} -name: 332cba091c4ca5ed17a37d86c30e7217 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/params.yaml b/examples/security/classification/output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/params.yaml deleted file mode 100644 index 6e46547c..00000000 --- a/examples/security/classification/output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/adv_predictions.json - adv_probabilities_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/75471bc816a753916ff8ea2a0781d5e8.pkl - params_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/params.yaml - predictions_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/predictions.json - probabilities_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/probabilities.json - score_dict_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/score_dict.json - test_labels_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/test_labels.json - train_labels_file: output/reports/train/33716127245a5ffa9a2a6b76cdaac7f8/train_labels.json - model_dir: models - name: 33716127245a5ffa9a2a6b76cdaac7f8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eb5731f85e28d958d6f2b73c1917a4fc - trainer: - kwargs: {} -name: 33716127245a5ffa9a2a6b76cdaac7f8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/339d05acb8eaa27f93098e3f7b036537/params.yaml b/examples/security/classification/output/reports/train/339d05acb8eaa27f93098e3f7b036537/params.yaml deleted file mode 100644 index 894da02d..00000000 --- a/examples/security/classification/output/reports/train/339d05acb8eaa27f93098e3f7b036537/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/adv_predictions.json - adv_probabilities_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/c898dcd83bfa5b321dbf1e51a3c1c7f5.pkl - params_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/params.yaml - predictions_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/predictions.json - probabilities_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/probabilities.json - score_dict_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/score_dict.json - test_labels_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/test_labels.json - train_labels_file: output/reports/train/339d05acb8eaa27f93098e3f7b036537/train_labels.json - model_dir: models - name: 339d05acb8eaa27f93098e3f7b036537 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 75bc210717ec37301124f50f19eb20bc - trainer: - kwargs: {} -name: 339d05acb8eaa27f93098e3f7b036537 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/params.yaml b/examples/security/classification/output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/params.yaml deleted file mode 100644 index 71184084..00000000 --- a/examples/security/classification/output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/adv_predictions.json - adv_probabilities_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/05fa8f89d438c98ac91576761043f631.pkl - params_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/params.yaml - predictions_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/predictions.json - probabilities_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/probabilities.json - score_dict_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/score_dict.json - test_labels_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/test_labels.json - train_labels_file: output/reports/train/33cb1885a22b157bf766aaf34a7e1b92/train_labels.json - model_dir: models - name: 33cb1885a22b157bf766aaf34a7e1b92 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bfa326c8a06c5f3c892fc7d1277576b8 - trainer: - kwargs: {} -name: 33cb1885a22b157bf766aaf34a7e1b92 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/33fd828f7cb968c5863c0eb345ff1208/params.yaml b/examples/security/classification/output/reports/train/33fd828f7cb968c5863c0eb345ff1208/params.yaml deleted file mode 100644 index 3fcf27a2..00000000 --- a/examples/security/classification/output/reports/train/33fd828f7cb968c5863c0eb345ff1208/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/adv_predictions.json - adv_probabilities_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/abeb9bf3634c81abd2494a36c033eb02.pkl - params_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/params.yaml - predictions_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/predictions.json - probabilities_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/probabilities.json - score_dict_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/score_dict.json - test_labels_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/test_labels.json - train_labels_file: output/reports/train/33fd828f7cb968c5863c0eb345ff1208/train_labels.json - model_dir: models - name: 33fd828f7cb968c5863c0eb345ff1208 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6ea0a10c8cbade874397668e0548b70c - trainer: - kwargs: {} -name: 33fd828f7cb968c5863c0eb345ff1208 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/34027642a91f58bce884f30fb4dc1ed5/params.yaml b/examples/security/classification/output/reports/train/34027642a91f58bce884f30fb4dc1ed5/params.yaml deleted file mode 100644 index 9efb57a6..00000000 --- a/examples/security/classification/output/reports/train/34027642a91f58bce884f30fb4dc1ed5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/adv_predictions.json - adv_probabilities_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/12f6dbab638de13fdf043b6f8739cec5.pkl - params_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/params.yaml - predictions_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/predictions.json - probabilities_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/probabilities.json - score_dict_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/score_dict.json - test_labels_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/test_labels.json - train_labels_file: output/reports/train/34027642a91f58bce884f30fb4dc1ed5/train_labels.json - model_dir: models - name: 34027642a91f58bce884f30fb4dc1ed5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b0456e646452e31a19896c62b56dbe57 - trainer: - kwargs: {} -name: 34027642a91f58bce884f30fb4dc1ed5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/params.yaml b/examples/security/classification/output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/params.yaml deleted file mode 100644 index 40140212..00000000 --- a/examples/security/classification/output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/adv_predictions.json - adv_probabilities_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/c41b72f7205bb23cdc94b8ed08b0318e.pkl - params_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/params.yaml - predictions_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/predictions.json - probabilities_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/probabilities.json - score_dict_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/score_dict.json - test_labels_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/test_labels.json - train_labels_file: output/reports/train/3413df4bc2eaecea54077eba5a3bd4a2/train_labels.json - model_dir: models - name: 3413df4bc2eaecea54077eba5a3bd4a2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d391d43c5de3dbc37e71fdcb60cffb81 - trainer: - kwargs: {} -name: 3413df4bc2eaecea54077eba5a3bd4a2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/341df9f6f04225cbb4939e0b947653f3/params.yaml b/examples/security/classification/output/reports/train/341df9f6f04225cbb4939e0b947653f3/params.yaml deleted file mode 100644 index 6d2edaf1..00000000 --- a/examples/security/classification/output/reports/train/341df9f6f04225cbb4939e0b947653f3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/adv_predictions.json - adv_probabilities_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/04e6cda0f7c3ed9b5199d56323be8e52.pkl - params_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/params.yaml - predictions_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/predictions.json - probabilities_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/probabilities.json - score_dict_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/score_dict.json - test_labels_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/test_labels.json - train_labels_file: output/reports/train/341df9f6f04225cbb4939e0b947653f3/train_labels.json - model_dir: models - name: 341df9f6f04225cbb4939e0b947653f3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 53f6a2d0e8241d22e972ea2e3d6bf121 - trainer: - kwargs: {} -name: 341df9f6f04225cbb4939e0b947653f3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/params.yaml b/examples/security/classification/output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/params.yaml deleted file mode 100644 index c2e0afc1..00000000 --- a/examples/security/classification/output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/adv_predictions.json - adv_probabilities_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/e04ae05ae304fcfcd3f1e8d4e040cdc6.pkl - params_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/params.yaml - predictions_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/predictions.json - probabilities_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/probabilities.json - score_dict_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/score_dict.json - test_labels_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/test_labels.json - train_labels_file: output/reports/train/3456cfd57f7157cc275d73e0b4ff7804/train_labels.json - model_dir: models - name: 3456cfd57f7157cc275d73e0b4ff7804 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ce14d8de794b87924f0169e174dff045 - trainer: - kwargs: {} -name: 3456cfd57f7157cc275d73e0b4ff7804 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/34bae851ec52d1169a6efced36fb6d31/params.yaml b/examples/security/classification/output/reports/train/34bae851ec52d1169a6efced36fb6d31/params.yaml deleted file mode 100644 index 38ca8af9..00000000 --- a/examples/security/classification/output/reports/train/34bae851ec52d1169a6efced36fb6d31/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/adv_predictions.json - adv_probabilities_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/129249985d0ab6db4e6661af5385b171.pkl - params_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/params.yaml - predictions_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/predictions.json - probabilities_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/probabilities.json - score_dict_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/score_dict.json - test_labels_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/test_labels.json - train_labels_file: output/reports/train/34bae851ec52d1169a6efced36fb6d31/train_labels.json - model_dir: models - name: 34bae851ec52d1169a6efced36fb6d31 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0195e7d30029335ab59b3d7336e25fda - trainer: - kwargs: {} -name: 34bae851ec52d1169a6efced36fb6d31 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/34c36c3df17f351194635add21754c2e/params.yaml b/examples/security/classification/output/reports/train/34c36c3df17f351194635add21754c2e/params.yaml deleted file mode 100644 index 4c6a4656..00000000 --- a/examples/security/classification/output/reports/train/34c36c3df17f351194635add21754c2e/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/34c36c3df17f351194635add21754c2e/adv_predictions.json - adv_probabilities_file: output/reports/train/34c36c3df17f351194635add21754c2e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/4538f407b43f2a29826079d953608168.pkl - params_file: output/reports/train/34c36c3df17f351194635add21754c2e/params.yaml - predictions_file: output/reports/train/34c36c3df17f351194635add21754c2e/predictions.json - probabilities_file: output/reports/train/34c36c3df17f351194635add21754c2e/probabilities.json - score_dict_file: output/reports/train/34c36c3df17f351194635add21754c2e/score_dict.json - test_labels_file: output/reports/train/34c36c3df17f351194635add21754c2e/test_labels.json - train_labels_file: output/reports/train/34c36c3df17f351194635add21754c2e/train_labels.json - model_dir: models - name: 34c36c3df17f351194635add21754c2e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8070a75328dadff62aa3ed2c5258d91c - trainer: - kwargs: {} -name: 34c36c3df17f351194635add21754c2e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/34f5cfc9c57ffd6da4eb959122859851/params.yaml b/examples/security/classification/output/reports/train/34f5cfc9c57ffd6da4eb959122859851/params.yaml deleted file mode 100644 index ff40d8f1..00000000 --- a/examples/security/classification/output/reports/train/34f5cfc9c57ffd6da4eb959122859851/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/adv_predictions.json - adv_probabilities_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/898878a6d2ed5379ccd8022b4ed3a9a5.pkl - params_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/params.yaml - predictions_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/predictions.json - probabilities_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/probabilities.json - score_dict_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/score_dict.json - test_labels_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/test_labels.json - train_labels_file: output/reports/train/34f5cfc9c57ffd6da4eb959122859851/train_labels.json - model_dir: models - name: 34f5cfc9c57ffd6da4eb959122859851 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 79996fe89c4fa5e05f05957dfa5f9315 - trainer: - kwargs: {} -name: 34f5cfc9c57ffd6da4eb959122859851 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/350f844b1dbe2a52451767796ccec180/params.yaml b/examples/security/classification/output/reports/train/350f844b1dbe2a52451767796ccec180/params.yaml deleted file mode 100644 index 0811c74c..00000000 --- a/examples/security/classification/output/reports/train/350f844b1dbe2a52451767796ccec180/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/350f844b1dbe2a52451767796ccec180/adv_predictions.json - adv_probabilities_file: output/reports/train/350f844b1dbe2a52451767796ccec180/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/7a1f4c7fc39913dc4d41fbf10710d782.pkl - params_file: output/reports/train/350f844b1dbe2a52451767796ccec180/params.yaml - predictions_file: output/reports/train/350f844b1dbe2a52451767796ccec180/predictions.json - probabilities_file: output/reports/train/350f844b1dbe2a52451767796ccec180/probabilities.json - score_dict_file: output/reports/train/350f844b1dbe2a52451767796ccec180/score_dict.json - test_labels_file: output/reports/train/350f844b1dbe2a52451767796ccec180/test_labels.json - train_labels_file: output/reports/train/350f844b1dbe2a52451767796ccec180/train_labels.json - model_dir: models - name: 350f844b1dbe2a52451767796ccec180 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 95f78d9fd8c5cf84729a1bdb77b962da - trainer: - kwargs: {} -name: 350f844b1dbe2a52451767796ccec180 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/352522088c73c8bce48a31b2d4317b94/params.yaml b/examples/security/classification/output/reports/train/352522088c73c8bce48a31b2d4317b94/params.yaml deleted file mode 100644 index d6e9521c..00000000 --- a/examples/security/classification/output/reports/train/352522088c73c8bce48a31b2d4317b94/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/adv_predictions.json - adv_probabilities_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/e12182af6bb0334868e45003a2f91f66.pkl - params_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/params.yaml - predictions_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/predictions.json - probabilities_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/probabilities.json - score_dict_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/score_dict.json - test_labels_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/test_labels.json - train_labels_file: output/reports/train/352522088c73c8bce48a31b2d4317b94/train_labels.json - model_dir: models - name: 352522088c73c8bce48a31b2d4317b94 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f99da6148f11b8b9a2feabf5b473c2ec - trainer: - kwargs: {} -name: 352522088c73c8bce48a31b2d4317b94 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/35464b3444fcded662bb6474fc09a0b4/params.yaml b/examples/security/classification/output/reports/train/35464b3444fcded662bb6474fc09a0b4/params.yaml deleted file mode 100644 index a3159242..00000000 --- a/examples/security/classification/output/reports/train/35464b3444fcded662bb6474fc09a0b4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/adv_predictions.json - adv_probabilities_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/09b07d2ffa4d6c71833928bdd99764df.pkl - params_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/params.yaml - predictions_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/predictions.json - probabilities_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/probabilities.json - score_dict_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/score_dict.json - test_labels_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/test_labels.json - train_labels_file: output/reports/train/35464b3444fcded662bb6474fc09a0b4/train_labels.json - model_dir: models - name: 35464b3444fcded662bb6474fc09a0b4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d36d9db2ca7a1ea9b8fcfaadded17656 - trainer: - kwargs: {} -name: 35464b3444fcded662bb6474fc09a0b4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/params.yaml b/examples/security/classification/output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/params.yaml deleted file mode 100644 index 82862717..00000000 --- a/examples/security/classification/output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/adv_predictions.json - adv_probabilities_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/ff0eb9b74a562964ff93a998aa2a9e2b.pkl - params_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/params.yaml - predictions_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/predictions.json - probabilities_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/probabilities.json - score_dict_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/score_dict.json - test_labels_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/test_labels.json - train_labels_file: output/reports/train/356533f7e13ae428ea1bcb9639d3bce8/train_labels.json - model_dir: models - name: 356533f7e13ae428ea1bcb9639d3bce8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0458efef597b9bc90e9a3c318566cfef - trainer: - kwargs: {} -name: 356533f7e13ae428ea1bcb9639d3bce8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3567913fcfd71d3c949f766e68810080/params.yaml b/examples/security/classification/output/reports/train/3567913fcfd71d3c949f766e68810080/params.yaml deleted file mode 100644 index 0244ba52..00000000 --- a/examples/security/classification/output/reports/train/3567913fcfd71d3c949f766e68810080/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3567913fcfd71d3c949f766e68810080/adv_predictions.json - adv_probabilities_file: output/reports/train/3567913fcfd71d3c949f766e68810080/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/737d6e8c36b65476c12b3bd386261903.pkl - params_file: output/reports/train/3567913fcfd71d3c949f766e68810080/params.yaml - predictions_file: output/reports/train/3567913fcfd71d3c949f766e68810080/predictions.json - probabilities_file: output/reports/train/3567913fcfd71d3c949f766e68810080/probabilities.json - score_dict_file: output/reports/train/3567913fcfd71d3c949f766e68810080/score_dict.json - test_labels_file: output/reports/train/3567913fcfd71d3c949f766e68810080/test_labels.json - train_labels_file: output/reports/train/3567913fcfd71d3c949f766e68810080/train_labels.json - model_dir: models - name: 3567913fcfd71d3c949f766e68810080 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0e48ee2ead9dc9ff7f17346df7e76af5 - trainer: - kwargs: {} -name: 3567913fcfd71d3c949f766e68810080 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/35c135be29b612b3845d2213116c1cda/params.yaml b/examples/security/classification/output/reports/train/35c135be29b612b3845d2213116c1cda/params.yaml deleted file mode 100644 index 864345ad..00000000 --- a/examples/security/classification/output/reports/train/35c135be29b612b3845d2213116c1cda/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/35c135be29b612b3845d2213116c1cda/adv_predictions.json - adv_probabilities_file: output/reports/train/35c135be29b612b3845d2213116c1cda/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/f2de0a95f7980e866191e4b3fa02b9c1.pkl - params_file: output/reports/train/35c135be29b612b3845d2213116c1cda/params.yaml - predictions_file: output/reports/train/35c135be29b612b3845d2213116c1cda/predictions.json - probabilities_file: output/reports/train/35c135be29b612b3845d2213116c1cda/probabilities.json - score_dict_file: output/reports/train/35c135be29b612b3845d2213116c1cda/score_dict.json - test_labels_file: output/reports/train/35c135be29b612b3845d2213116c1cda/test_labels.json - train_labels_file: output/reports/train/35c135be29b612b3845d2213116c1cda/train_labels.json - model_dir: models - name: 35c135be29b612b3845d2213116c1cda - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5c0755e5552570a8d78f993b5d046751 - trainer: - kwargs: {} -name: 35c135be29b612b3845d2213116c1cda -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/params.yaml b/examples/security/classification/output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/params.yaml deleted file mode 100644 index e1111e6f..00000000 --- a/examples/security/classification/output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/adv_predictions.json - adv_probabilities_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/c9e1088d6b2b6d194e3fbc320876aa7f.pkl - params_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/params.yaml - predictions_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/predictions.json - probabilities_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/probabilities.json - score_dict_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/score_dict.json - test_labels_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/test_labels.json - train_labels_file: output/reports/train/35c19ec9fce2d71c43079c649d0d65d2/train_labels.json - model_dir: models - name: 35c19ec9fce2d71c43079c649d0d65d2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b3b8234b827fbe101418ffe2ec37cc97 - trainer: - kwargs: {} -name: 35c19ec9fce2d71c43079c649d0d65d2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3609049669911c261cb5afe0c624d331/params.yaml b/examples/security/classification/output/reports/train/3609049669911c261cb5afe0c624d331/params.yaml deleted file mode 100644 index 623d4965..00000000 --- a/examples/security/classification/output/reports/train/3609049669911c261cb5afe0c624d331/params.yaml +++ /dev/null @@ -1,115 +0,0 @@ -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3609049669911c261cb5afe0c624d331/adv_predictions.json - adv_probabilities_file: output/reports/train/3609049669911c261cb5afe0c624d331/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl - model_file: output/models/fecc8e06394fdec43bcedc921ee5198b.pkl - params_file: output/reports/train/3609049669911c261cb5afe0c624d331/params.yaml - predictions_file: output/reports/train/3609049669911c261cb5afe0c624d331/predictions.json - probabilities_file: output/reports/train/3609049669911c261cb5afe0c624d331/probabilities.json - score_dict_file: output/reports/train/3609049669911c261cb5afe0c624d331/score_dict.json - test_labels_file: output/reports/train/3609049669911c261cb5afe0c624d331/test_labels.json - train_labels_file: output/reports/train/3609049669911c261cb5afe0c624d331/train_labels.json - model_dir: models - name: 3609049669911c261cb5afe0c624d331 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 10 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b76e4586d91b64181c5c7e206a3fc040 - trainer: - kwargs: {} -name: 3609049669911c261cb5afe0c624d331 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/params.yaml b/examples/security/classification/output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/params.yaml deleted file mode 100644 index ca145668..00000000 --- a/examples/security/classification/output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/adv_predictions.json - adv_probabilities_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/f2178e22647feab33e329e548b3a1b83.pkl - params_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/params.yaml - predictions_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/predictions.json - probabilities_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/probabilities.json - score_dict_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/score_dict.json - test_labels_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/test_labels.json - train_labels_file: output/reports/train/360dc7d2bbf4f17faa370ac38e6808f8/train_labels.json - model_dir: models - name: 360dc7d2bbf4f17faa370ac38e6808f8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1d130719b7019d78d4c2310c8ba886e0 - trainer: - kwargs: {} -name: 360dc7d2bbf4f17faa370ac38e6808f8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/362abf2367321024876f2ef624268d94/params.yaml b/examples/security/classification/output/reports/train/362abf2367321024876f2ef624268d94/params.yaml deleted file mode 100644 index a20d4c47..00000000 --- a/examples/security/classification/output/reports/train/362abf2367321024876f2ef624268d94/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/362abf2367321024876f2ef624268d94/adv_predictions.json - adv_probabilities_file: output/reports/train/362abf2367321024876f2ef624268d94/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/bfd58136929a630f09c0a70e0062215b.pkl - params_file: output/reports/train/362abf2367321024876f2ef624268d94/params.yaml - predictions_file: output/reports/train/362abf2367321024876f2ef624268d94/predictions.json - probabilities_file: output/reports/train/362abf2367321024876f2ef624268d94/probabilities.json - score_dict_file: output/reports/train/362abf2367321024876f2ef624268d94/score_dict.json - test_labels_file: output/reports/train/362abf2367321024876f2ef624268d94/test_labels.json - train_labels_file: output/reports/train/362abf2367321024876f2ef624268d94/train_labels.json - model_dir: models - name: 362abf2367321024876f2ef624268d94 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 78b4b1ce568d2a3871d07e28c786e30c - trainer: - kwargs: {} -name: 362abf2367321024876f2ef624268d94 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/363f20c853ef9ec45f81d108ca8665c3/params.yaml b/examples/security/classification/output/reports/train/363f20c853ef9ec45f81d108ca8665c3/params.yaml deleted file mode 100644 index 3a57478d..00000000 --- a/examples/security/classification/output/reports/train/363f20c853ef9ec45f81d108ca8665c3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/adv_predictions.json - adv_probabilities_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/f01287f63b9720126ed847fe99b4f2f8.pkl - params_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/params.yaml - predictions_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/predictions.json - probabilities_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/probabilities.json - score_dict_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/score_dict.json - test_labels_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/test_labels.json - train_labels_file: output/reports/train/363f20c853ef9ec45f81d108ca8665c3/train_labels.json - model_dir: models - name: 363f20c853ef9ec45f81d108ca8665c3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 36dd16ed4498705d85911855ddac78fe - trainer: - kwargs: {} -name: 363f20c853ef9ec45f81d108ca8665c3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3659b64f748408e2494d084cf5dc512e/params.yaml b/examples/security/classification/output/reports/train/3659b64f748408e2494d084cf5dc512e/params.yaml deleted file mode 100644 index 8fa69490..00000000 --- a/examples/security/classification/output/reports/train/3659b64f748408e2494d084cf5dc512e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/adv_predictions.json - adv_probabilities_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/7e94fa9086f444d7cba8d62a9ae3321b.pkl - params_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/params.yaml - predictions_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/predictions.json - probabilities_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/probabilities.json - score_dict_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/score_dict.json - test_labels_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/test_labels.json - train_labels_file: output/reports/train/3659b64f748408e2494d084cf5dc512e/train_labels.json - model_dir: models - name: 3659b64f748408e2494d084cf5dc512e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d6f37e652b3e62dd33892e04f489fa82 - trainer: - kwargs: {} -name: 3659b64f748408e2494d084cf5dc512e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/params.yaml b/examples/security/classification/output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/params.yaml deleted file mode 100644 index 548ed1ac..00000000 --- a/examples/security/classification/output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/adv_predictions.json - adv_probabilities_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/0377d14c8a80d22ddf4d906573624059.pkl - params_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/params.yaml - predictions_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/predictions.json - probabilities_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/probabilities.json - score_dict_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/score_dict.json - test_labels_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/test_labels.json - train_labels_file: output/reports/train/36858219ae4e4cb3bf21e75a4d8a55fb/train_labels.json - model_dir: models - name: 36858219ae4e4cb3bf21e75a4d8a55fb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 65d4983f276069db3d9e773a4499664a - trainer: - kwargs: {} -name: 36858219ae4e4cb3bf21e75a4d8a55fb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/36a6cb189a7014b30ff961bce09e1244/params.yaml b/examples/security/classification/output/reports/train/36a6cb189a7014b30ff961bce09e1244/params.yaml deleted file mode 100644 index 7551ec6b..00000000 --- a/examples/security/classification/output/reports/train/36a6cb189a7014b30ff961bce09e1244/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/adv_predictions.json - adv_probabilities_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/fb384ae8bc54af51ab99d68bd9482343.pkl - params_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/params.yaml - predictions_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/predictions.json - probabilities_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/probabilities.json - score_dict_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/score_dict.json - test_labels_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/test_labels.json - train_labels_file: output/reports/train/36a6cb189a7014b30ff961bce09e1244/train_labels.json - model_dir: models - name: 36a6cb189a7014b30ff961bce09e1244 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bf1c1e6b3e1213c9da9f2b2ff5a1e272 - trainer: - kwargs: {} -name: 36a6cb189a7014b30ff961bce09e1244 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/36b2136d76b62e2cd833fc13a592df26/params.yaml b/examples/security/classification/output/reports/train/36b2136d76b62e2cd833fc13a592df26/params.yaml deleted file mode 100644 index 36b253ab..00000000 --- a/examples/security/classification/output/reports/train/36b2136d76b62e2cd833fc13a592df26/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/adv_predictions.json - adv_probabilities_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/08a20bc06f34c87f5be8ef60e9c1ef10.pkl - params_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/params.yaml - predictions_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/predictions.json - probabilities_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/probabilities.json - score_dict_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/score_dict.json - test_labels_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/test_labels.json - train_labels_file: output/reports/train/36b2136d76b62e2cd833fc13a592df26/train_labels.json - model_dir: models - name: 36b2136d76b62e2cd833fc13a592df26 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 952dab583acc0ef81666ecf1209260e7 - trainer: - kwargs: {} -name: 36b2136d76b62e2cd833fc13a592df26 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/params.yaml b/examples/security/classification/output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/params.yaml deleted file mode 100644 index c0d8a194..00000000 --- a/examples/security/classification/output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/adv_predictions.json - adv_probabilities_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/3cf187e8f4b1642510ed739a24614a36.pkl - params_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/params.yaml - predictions_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/predictions.json - probabilities_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/probabilities.json - score_dict_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/score_dict.json - test_labels_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/test_labels.json - train_labels_file: output/reports/train/36b265eaaf1e3520caf424133fc1f5ae/train_labels.json - model_dir: models - name: 36b265eaaf1e3520caf424133fc1f5ae - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ae604d236b9ff2d73f5cc21f30f1b3e6 - trainer: - kwargs: {} -name: 36b265eaaf1e3520caf424133fc1f5ae -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/36b33b3c37839e72cda79d84a45976c3/params.yaml b/examples/security/classification/output/reports/train/36b33b3c37839e72cda79d84a45976c3/params.yaml deleted file mode 100644 index 0a8766ad..00000000 --- a/examples/security/classification/output/reports/train/36b33b3c37839e72cda79d84a45976c3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/adv_predictions.json - adv_probabilities_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/37216796bd69dd76fb3116da8fee0d7c.pkl - params_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/params.yaml - predictions_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/predictions.json - probabilities_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/probabilities.json - score_dict_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/score_dict.json - test_labels_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/test_labels.json - train_labels_file: output/reports/train/36b33b3c37839e72cda79d84a45976c3/train_labels.json - model_dir: models - name: 36b33b3c37839e72cda79d84a45976c3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ab2d526493b2fb4f4ca112f97ee90143 - trainer: - kwargs: {} -name: 36b33b3c37839e72cda79d84a45976c3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/36c90131fe1155a959004e5f29be11c9/params.yaml b/examples/security/classification/output/reports/train/36c90131fe1155a959004e5f29be11c9/params.yaml deleted file mode 100644 index a4367cf7..00000000 --- a/examples/security/classification/output/reports/train/36c90131fe1155a959004e5f29be11c9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/adv_predictions.json - adv_probabilities_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/59220608526086bc48ff76f059f88aa1.pkl - params_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/params.yaml - predictions_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/predictions.json - probabilities_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/probabilities.json - score_dict_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/score_dict.json - test_labels_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/test_labels.json - train_labels_file: output/reports/train/36c90131fe1155a959004e5f29be11c9/train_labels.json - model_dir: models - name: 36c90131fe1155a959004e5f29be11c9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e0aa88cc5d71776b72961bcf10961e2d - trainer: - kwargs: {} -name: 36c90131fe1155a959004e5f29be11c9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3716c7a07f7a7db09d32221454640a38/params.yaml b/examples/security/classification/output/reports/train/3716c7a07f7a7db09d32221454640a38/params.yaml deleted file mode 100644 index 6d58a07a..00000000 --- a/examples/security/classification/output/reports/train/3716c7a07f7a7db09d32221454640a38/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/adv_predictions.json - adv_probabilities_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/31dabecfb66ea8ec42dbad054acb0dc7.pkl - params_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/params.yaml - predictions_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/predictions.json - probabilities_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/probabilities.json - score_dict_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/score_dict.json - test_labels_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/test_labels.json - train_labels_file: output/reports/train/3716c7a07f7a7db09d32221454640a38/train_labels.json - model_dir: models - name: 3716c7a07f7a7db09d32221454640a38 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 60ad47e13a95f20ba6cb53f994b8faa1 - trainer: - kwargs: {} -name: 3716c7a07f7a7db09d32221454640a38 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/374880df812ca475b1883f868f2ed07d/params.yaml b/examples/security/classification/output/reports/train/374880df812ca475b1883f868f2ed07d/params.yaml deleted file mode 100644 index 6fadccca..00000000 --- a/examples/security/classification/output/reports/train/374880df812ca475b1883f868f2ed07d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/374880df812ca475b1883f868f2ed07d/adv_predictions.json - adv_probabilities_file: output/reports/train/374880df812ca475b1883f868f2ed07d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/d7d1e8800d934967aa08b7aec165e641.pkl - params_file: output/reports/train/374880df812ca475b1883f868f2ed07d/params.yaml - predictions_file: output/reports/train/374880df812ca475b1883f868f2ed07d/predictions.json - probabilities_file: output/reports/train/374880df812ca475b1883f868f2ed07d/probabilities.json - score_dict_file: output/reports/train/374880df812ca475b1883f868f2ed07d/score_dict.json - test_labels_file: output/reports/train/374880df812ca475b1883f868f2ed07d/test_labels.json - train_labels_file: output/reports/train/374880df812ca475b1883f868f2ed07d/train_labels.json - model_dir: models - name: 374880df812ca475b1883f868f2ed07d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4a06f5e44c354bdf20e6ac9d6f0c6f53 - trainer: - kwargs: {} -name: 374880df812ca475b1883f868f2ed07d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/params.yaml b/examples/security/classification/output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/params.yaml deleted file mode 100644 index d4fff480..00000000 --- a/examples/security/classification/output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/adv_predictions.json - adv_probabilities_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/304c9fa1c9e06de865874ca1b21df377.pkl - params_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/params.yaml - predictions_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/predictions.json - probabilities_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/probabilities.json - score_dict_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/score_dict.json - test_labels_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/test_labels.json - train_labels_file: output/reports/train/3774dd8cb4c0a59f44a1e6d4719d0775/train_labels.json - model_dir: models - name: 3774dd8cb4c0a59f44a1e6d4719d0775 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 21b33b4495735d11384003659737ce10 - trainer: - kwargs: {} -name: 3774dd8cb4c0a59f44a1e6d4719d0775 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/37850cdf8f35db2213565fa9519e21d4/params.yaml b/examples/security/classification/output/reports/train/37850cdf8f35db2213565fa9519e21d4/params.yaml deleted file mode 100644 index 7e42bf93..00000000 --- a/examples/security/classification/output/reports/train/37850cdf8f35db2213565fa9519e21d4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/adv_predictions.json - adv_probabilities_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/9fe312d6c650c5c6892fb1911a84643b.pkl - params_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/params.yaml - predictions_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/predictions.json - probabilities_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/probabilities.json - score_dict_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/score_dict.json - test_labels_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/test_labels.json - train_labels_file: output/reports/train/37850cdf8f35db2213565fa9519e21d4/train_labels.json - model_dir: models - name: 37850cdf8f35db2213565fa9519e21d4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 47293dfb9ccf9b23afd8d9c6e32d45fe - trainer: - kwargs: {} -name: 37850cdf8f35db2213565fa9519e21d4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/37d6322e2a28e3132134bcb8739c821d/params.yaml b/examples/security/classification/output/reports/train/37d6322e2a28e3132134bcb8739c821d/params.yaml deleted file mode 100644 index 399ffc72..00000000 --- a/examples/security/classification/output/reports/train/37d6322e2a28e3132134bcb8739c821d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/adv_predictions.json - adv_probabilities_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/a2a13a8d41cdbaa2a5e628a8053ec1d8.pkl - params_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/params.yaml - predictions_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/predictions.json - probabilities_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/probabilities.json - score_dict_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/score_dict.json - test_labels_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/test_labels.json - train_labels_file: output/reports/train/37d6322e2a28e3132134bcb8739c821d/train_labels.json - model_dir: models - name: 37d6322e2a28e3132134bcb8739c821d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1900ac8c1a8d6494a58e32aa6cc22f8a - trainer: - kwargs: {} -name: 37d6322e2a28e3132134bcb8739c821d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/37d986c18aa82788e4bec09689f873da/params.yaml b/examples/security/classification/output/reports/train/37d986c18aa82788e4bec09689f873da/params.yaml deleted file mode 100644 index eef4bbca..00000000 --- a/examples/security/classification/output/reports/train/37d986c18aa82788e4bec09689f873da/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/37d986c18aa82788e4bec09689f873da/adv_predictions.json - adv_probabilities_file: output/reports/train/37d986c18aa82788e4bec09689f873da/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/cd24671c5bc8d49b5910e482bf36e989.pkl - params_file: output/reports/train/37d986c18aa82788e4bec09689f873da/params.yaml - predictions_file: output/reports/train/37d986c18aa82788e4bec09689f873da/predictions.json - probabilities_file: output/reports/train/37d986c18aa82788e4bec09689f873da/probabilities.json - score_dict_file: output/reports/train/37d986c18aa82788e4bec09689f873da/score_dict.json - test_labels_file: output/reports/train/37d986c18aa82788e4bec09689f873da/test_labels.json - train_labels_file: output/reports/train/37d986c18aa82788e4bec09689f873da/train_labels.json - model_dir: models - name: 37d986c18aa82788e4bec09689f873da - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9f42ac16522de476dcc61e0b52c7a33f - trainer: - kwargs: {} -name: 37d986c18aa82788e4bec09689f873da -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/37e34d86125aa84f8f2a1df8782912f4/params.yaml b/examples/security/classification/output/reports/train/37e34d86125aa84f8f2a1df8782912f4/params.yaml deleted file mode 100644 index 0d4e941c..00000000 --- a/examples/security/classification/output/reports/train/37e34d86125aa84f8f2a1df8782912f4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/adv_predictions.json - adv_probabilities_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/7fbc197c2c4da06907c83d0421662132.pkl - params_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/params.yaml - predictions_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/predictions.json - probabilities_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/probabilities.json - score_dict_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/score_dict.json - test_labels_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/test_labels.json - train_labels_file: output/reports/train/37e34d86125aa84f8f2a1df8782912f4/train_labels.json - model_dir: models - name: 37e34d86125aa84f8f2a1df8782912f4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ad2fbe238fea0ccd7d8cdec6f701da9f - trainer: - kwargs: {} -name: 37e34d86125aa84f8f2a1df8782912f4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/37f515200391547010a8b410e5013924/params.yaml b/examples/security/classification/output/reports/train/37f515200391547010a8b410e5013924/params.yaml deleted file mode 100644 index 766db738..00000000 --- a/examples/security/classification/output/reports/train/37f515200391547010a8b410e5013924/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/37f515200391547010a8b410e5013924/adv_predictions.json - adv_probabilities_file: output/reports/train/37f515200391547010a8b410e5013924/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/431722f0bb483d61f6fb9d0e5ac5d7d0.pkl - params_file: output/reports/train/37f515200391547010a8b410e5013924/params.yaml - predictions_file: output/reports/train/37f515200391547010a8b410e5013924/predictions.json - probabilities_file: output/reports/train/37f515200391547010a8b410e5013924/probabilities.json - score_dict_file: output/reports/train/37f515200391547010a8b410e5013924/score_dict.json - test_labels_file: output/reports/train/37f515200391547010a8b410e5013924/test_labels.json - train_labels_file: output/reports/train/37f515200391547010a8b410e5013924/train_labels.json - model_dir: models - name: 37f515200391547010a8b410e5013924 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b3b9250d269aaedf1e6c59794e122509 - trainer: - kwargs: {} -name: 37f515200391547010a8b410e5013924 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/381b7eeeb9bee4bed578e059617c1da3/params.yaml b/examples/security/classification/output/reports/train/381b7eeeb9bee4bed578e059617c1da3/params.yaml deleted file mode 100644 index e3c9cceb..00000000 --- a/examples/security/classification/output/reports/train/381b7eeeb9bee4bed578e059617c1da3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/adv_predictions.json - adv_probabilities_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/1ed162b62c29ff7a7f913c87433121ed.pkl - params_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/params.yaml - predictions_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/predictions.json - probabilities_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/probabilities.json - score_dict_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/score_dict.json - test_labels_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/test_labels.json - train_labels_file: output/reports/train/381b7eeeb9bee4bed578e059617c1da3/train_labels.json - model_dir: models - name: 381b7eeeb9bee4bed578e059617c1da3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 196069c24fa732264cadd297df2e1eb0 - trainer: - kwargs: {} -name: 381b7eeeb9bee4bed578e059617c1da3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/382badbb4db3f2efd20d3042272a7405/params.yaml b/examples/security/classification/output/reports/train/382badbb4db3f2efd20d3042272a7405/params.yaml deleted file mode 100644 index 6d4d8f46..00000000 --- a/examples/security/classification/output/reports/train/382badbb4db3f2efd20d3042272a7405/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/adv_predictions.json - adv_probabilities_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/8361a3af07c5916dfb645af670a62986.pkl - params_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/params.yaml - predictions_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/predictions.json - probabilities_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/probabilities.json - score_dict_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/score_dict.json - test_labels_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/test_labels.json - train_labels_file: output/reports/train/382badbb4db3f2efd20d3042272a7405/train_labels.json - model_dir: models - name: 382badbb4db3f2efd20d3042272a7405 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6aade8363bb5ebef234a42bee13435cb - trainer: - kwargs: {} -name: 382badbb4db3f2efd20d3042272a7405 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/384e745ec2d19b78bdf13a6c8a590006/params.yaml b/examples/security/classification/output/reports/train/384e745ec2d19b78bdf13a6c8a590006/params.yaml deleted file mode 100644 index 95748ae7..00000000 --- a/examples/security/classification/output/reports/train/384e745ec2d19b78bdf13a6c8a590006/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/adv_predictions.json - adv_probabilities_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/16d917b9942a8a92a8f820514e5e399b.pkl - params_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/params.yaml - predictions_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/predictions.json - probabilities_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/probabilities.json - score_dict_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/score_dict.json - test_labels_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/test_labels.json - train_labels_file: output/reports/train/384e745ec2d19b78bdf13a6c8a590006/train_labels.json - model_dir: models - name: 384e745ec2d19b78bdf13a6c8a590006 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 68e70a4e181a40c6fd31ae8c81a9db68 - trainer: - kwargs: {} -name: 384e745ec2d19b78bdf13a6c8a590006 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/params.yaml b/examples/security/classification/output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/params.yaml deleted file mode 100644 index 1e5dea14..00000000 --- a/examples/security/classification/output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/adv_predictions.json - adv_probabilities_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/553e3fad7d217bb938f266f05edac390.pkl - params_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/params.yaml - predictions_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/predictions.json - probabilities_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/probabilities.json - score_dict_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/score_dict.json - test_labels_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/test_labels.json - train_labels_file: output/reports/train/38500784bea2a23fa4bc6f7df860d7d3/train_labels.json - model_dir: models - name: 38500784bea2a23fa4bc6f7df860d7d3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3030b87d2c62e0a7685eed730fff9197 - trainer: - kwargs: {} -name: 38500784bea2a23fa4bc6f7df860d7d3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3855f158433350f0cbf4c155b4e92a4d/params.yaml b/examples/security/classification/output/reports/train/3855f158433350f0cbf4c155b4e92a4d/params.yaml deleted file mode 100644 index 2b8fd8f8..00000000 --- a/examples/security/classification/output/reports/train/3855f158433350f0cbf4c155b4e92a4d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/adv_predictions.json - adv_probabilities_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/62514d4a9a5cd6b9e20f2596aa503e48.pkl - params_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/params.yaml - predictions_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/predictions.json - probabilities_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/probabilities.json - score_dict_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/score_dict.json - test_labels_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/test_labels.json - train_labels_file: output/reports/train/3855f158433350f0cbf4c155b4e92a4d/train_labels.json - model_dir: models - name: 3855f158433350f0cbf4c155b4e92a4d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 68114c6603d539867177d70341dfdf57 - trainer: - kwargs: {} -name: 3855f158433350f0cbf4c155b4e92a4d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/38615915ae1660ceb384f35b8aef05ce/params.yaml b/examples/security/classification/output/reports/train/38615915ae1660ceb384f35b8aef05ce/params.yaml deleted file mode 100644 index 212d6c98..00000000 --- a/examples/security/classification/output/reports/train/38615915ae1660ceb384f35b8aef05ce/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/adv_predictions.json - adv_probabilities_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/8abde679f673e00c8ec6ed5b4c0f71d1.pkl - params_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/params.yaml - predictions_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/predictions.json - probabilities_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/probabilities.json - score_dict_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/score_dict.json - test_labels_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/test_labels.json - train_labels_file: output/reports/train/38615915ae1660ceb384f35b8aef05ce/train_labels.json - model_dir: models - name: 38615915ae1660ceb384f35b8aef05ce - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a94ee6a3d228f78c6204db35108caf53 - trainer: - kwargs: {} -name: 38615915ae1660ceb384f35b8aef05ce -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/params.yaml b/examples/security/classification/output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/params.yaml deleted file mode 100644 index 6af1ea36..00000000 --- a/examples/security/classification/output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/adv_predictions.json - adv_probabilities_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/3e10fdfe8b848c74802f13a703f803f1.pkl - params_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/params.yaml - predictions_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/predictions.json - probabilities_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/probabilities.json - score_dict_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/score_dict.json - test_labels_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/test_labels.json - train_labels_file: output/reports/train/3870ebfbfe65fe1034a9d7ee0b8b77d3/train_labels.json - model_dir: models - name: 3870ebfbfe65fe1034a9d7ee0b8b77d3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 18d64393a844b9cb238777f4b4bebe2b - trainer: - kwargs: {} -name: 3870ebfbfe65fe1034a9d7ee0b8b77d3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/params.yaml b/examples/security/classification/output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/params.yaml deleted file mode 100644 index e887ca99..00000000 --- a/examples/security/classification/output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/adv_predictions.json - adv_probabilities_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/47c4e6167e56fb1b8d4314ef6d3d780d.pkl - params_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/params.yaml - predictions_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/predictions.json - probabilities_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/probabilities.json - score_dict_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/score_dict.json - test_labels_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/test_labels.json - train_labels_file: output/reports/train/38aaebee930ce21eb3e72266b6bdf1b2/train_labels.json - model_dir: models - name: 38aaebee930ce21eb3e72266b6bdf1b2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 764e079e2e85f123d14b2d83effb9eda - trainer: - kwargs: {} -name: 38aaebee930ce21eb3e72266b6bdf1b2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/38b06c39262826eed42d62e30b5cac39/params.yaml b/examples/security/classification/output/reports/train/38b06c39262826eed42d62e30b5cac39/params.yaml deleted file mode 100644 index 58494651..00000000 --- a/examples/security/classification/output/reports/train/38b06c39262826eed42d62e30b5cac39/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/adv_predictions.json - adv_probabilities_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e3adb677335aa1852966f0934d516bce.pkl - params_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/params.yaml - predictions_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/predictions.json - probabilities_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/probabilities.json - score_dict_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/score_dict.json - test_labels_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/test_labels.json - train_labels_file: output/reports/train/38b06c39262826eed42d62e30b5cac39/train_labels.json - model_dir: models - name: 38b06c39262826eed42d62e30b5cac39 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 460a00690f992003a9b01b0ad7723b08 - trainer: - kwargs: {} -name: 38b06c39262826eed42d62e30b5cac39 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/38d60af3b4864dcf49496951ab96a6e8/params.yaml b/examples/security/classification/output/reports/train/38d60af3b4864dcf49496951ab96a6e8/params.yaml deleted file mode 100644 index c76806df..00000000 --- a/examples/security/classification/output/reports/train/38d60af3b4864dcf49496951ab96a6e8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/adv_predictions.json - adv_probabilities_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/8d1cdd2901f26b196f059b43bc54893a.pkl - params_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/params.yaml - predictions_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/predictions.json - probabilities_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/probabilities.json - score_dict_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/score_dict.json - test_labels_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/test_labels.json - train_labels_file: output/reports/train/38d60af3b4864dcf49496951ab96a6e8/train_labels.json - model_dir: models - name: 38d60af3b4864dcf49496951ab96a6e8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c5f2a4f69eddf5d0a84e691d3097a55c - trainer: - kwargs: {} -name: 38d60af3b4864dcf49496951ab96a6e8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/39029b80bfe201b59fc657c29627a775/params.yaml b/examples/security/classification/output/reports/train/39029b80bfe201b59fc657c29627a775/params.yaml deleted file mode 100644 index bfd26b6f..00000000 --- a/examples/security/classification/output/reports/train/39029b80bfe201b59fc657c29627a775/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/39029b80bfe201b59fc657c29627a775/adv_predictions.json - adv_probabilities_file: output/reports/train/39029b80bfe201b59fc657c29627a775/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/c12dd3da448ec5c32fc08bf022836446.pkl - params_file: output/reports/train/39029b80bfe201b59fc657c29627a775/params.yaml - predictions_file: output/reports/train/39029b80bfe201b59fc657c29627a775/predictions.json - probabilities_file: output/reports/train/39029b80bfe201b59fc657c29627a775/probabilities.json - score_dict_file: output/reports/train/39029b80bfe201b59fc657c29627a775/score_dict.json - test_labels_file: output/reports/train/39029b80bfe201b59fc657c29627a775/test_labels.json - train_labels_file: output/reports/train/39029b80bfe201b59fc657c29627a775/train_labels.json - model_dir: models - name: 39029b80bfe201b59fc657c29627a775 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4c6f7481d899514edf6e58a835a44fe2 - trainer: - kwargs: {} -name: 39029b80bfe201b59fc657c29627a775 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/params.yaml b/examples/security/classification/output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/params.yaml deleted file mode 100644 index 37720f5d..00000000 --- a/examples/security/classification/output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/adv_predictions.json - adv_probabilities_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/d74a86267ad0ba567cda3a27ad743667.pkl - params_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/params.yaml - predictions_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/predictions.json - probabilities_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/probabilities.json - score_dict_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/score_dict.json - test_labels_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/test_labels.json - train_labels_file: output/reports/train/392edce3f4ad1f2c98e2c8e35323b4d1/train_labels.json - model_dir: models - name: 392edce3f4ad1f2c98e2c8e35323b4d1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3503d27e3f9cdf027ff709c5be6713d3 - trainer: - kwargs: {} -name: 392edce3f4ad1f2c98e2c8e35323b4d1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3933221e8fafe07633adf25b26281c6e/params.yaml b/examples/security/classification/output/reports/train/3933221e8fafe07633adf25b26281c6e/params.yaml deleted file mode 100644 index 2a2949c7..00000000 --- a/examples/security/classification/output/reports/train/3933221e8fafe07633adf25b26281c6e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/adv_predictions.json - adv_probabilities_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/70dbcb3a114750cc5c26c0248f5163c2.pkl - params_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/params.yaml - predictions_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/predictions.json - probabilities_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/probabilities.json - score_dict_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/score_dict.json - test_labels_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/test_labels.json - train_labels_file: output/reports/train/3933221e8fafe07633adf25b26281c6e/train_labels.json - model_dir: models - name: 3933221e8fafe07633adf25b26281c6e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eac366c6f5f45e44766b2769fd96b9a5 - trainer: - kwargs: {} -name: 3933221e8fafe07633adf25b26281c6e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/394af7a247cba6260f1c4e6b41504970/params.yaml b/examples/security/classification/output/reports/train/394af7a247cba6260f1c4e6b41504970/params.yaml deleted file mode 100644 index 40cf5a79..00000000 --- a/examples/security/classification/output/reports/train/394af7a247cba6260f1c4e6b41504970/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/adv_predictions.json - adv_probabilities_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/d0c013e38f8d9d032327e9b63bf4c235.pkl - params_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/params.yaml - predictions_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/predictions.json - probabilities_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/probabilities.json - score_dict_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/score_dict.json - test_labels_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/test_labels.json - train_labels_file: output/reports/train/394af7a247cba6260f1c4e6b41504970/train_labels.json - model_dir: models - name: 394af7a247cba6260f1c4e6b41504970 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2ba8cf6cbbab37c81a5b95d73132d263 - trainer: - kwargs: {} -name: 394af7a247cba6260f1c4e6b41504970 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/params.yaml b/examples/security/classification/output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/params.yaml deleted file mode 100644 index d353e324..00000000 --- a/examples/security/classification/output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/adv_predictions.json - adv_probabilities_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/6240e3886c9066247918afa3423469eb.pkl - params_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/params.yaml - predictions_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/predictions.json - probabilities_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/probabilities.json - score_dict_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/score_dict.json - test_labels_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/test_labels.json - train_labels_file: output/reports/train/395a2050f923b6ec80cb3fda4aec56bc/train_labels.json - model_dir: models - name: 395a2050f923b6ec80cb3fda4aec56bc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a2b788ae2f0a5c292915f34d7ed3eb32 - trainer: - kwargs: {} -name: 395a2050f923b6ec80cb3fda4aec56bc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/396a7d7c21d8069aab788aecbce79856/params.yaml b/examples/security/classification/output/reports/train/396a7d7c21d8069aab788aecbce79856/params.yaml deleted file mode 100644 index 15d3a43f..00000000 --- a/examples/security/classification/output/reports/train/396a7d7c21d8069aab788aecbce79856/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/adv_predictions.json - adv_probabilities_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/665cebf49dead51bbe26a21022e5b2d2.pkl - params_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/params.yaml - predictions_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/predictions.json - probabilities_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/probabilities.json - score_dict_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/score_dict.json - test_labels_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/test_labels.json - train_labels_file: output/reports/train/396a7d7c21d8069aab788aecbce79856/train_labels.json - model_dir: models - name: 396a7d7c21d8069aab788aecbce79856 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 17a32aefc767c5adf843b5390b6f46de - trainer: - kwargs: {} -name: 396a7d7c21d8069aab788aecbce79856 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/39754482e5242e72aa325834ac26f3df/params.yaml b/examples/security/classification/output/reports/train/39754482e5242e72aa325834ac26f3df/params.yaml deleted file mode 100644 index ed2c8cef..00000000 --- a/examples/security/classification/output/reports/train/39754482e5242e72aa325834ac26f3df/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/39754482e5242e72aa325834ac26f3df/adv_predictions.json - adv_probabilities_file: output/reports/train/39754482e5242e72aa325834ac26f3df/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/3d0a5a347e3dbd4241d7dddb99c1b54e.pkl - params_file: output/reports/train/39754482e5242e72aa325834ac26f3df/params.yaml - predictions_file: output/reports/train/39754482e5242e72aa325834ac26f3df/predictions.json - probabilities_file: output/reports/train/39754482e5242e72aa325834ac26f3df/probabilities.json - score_dict_file: output/reports/train/39754482e5242e72aa325834ac26f3df/score_dict.json - test_labels_file: output/reports/train/39754482e5242e72aa325834ac26f3df/test_labels.json - train_labels_file: output/reports/train/39754482e5242e72aa325834ac26f3df/train_labels.json - model_dir: models - name: 39754482e5242e72aa325834ac26f3df - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ead9452cb872d5904673199345aae4bf - trainer: - kwargs: {} -name: 39754482e5242e72aa325834ac26f3df -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/params.yaml b/examples/security/classification/output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/params.yaml deleted file mode 100644 index 1f60e117..00000000 --- a/examples/security/classification/output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/adv_predictions.json - adv_probabilities_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/12b0ed387c6424c46374a2306bb15cfb.pkl - params_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/params.yaml - predictions_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/predictions.json - probabilities_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/probabilities.json - score_dict_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/score_dict.json - test_labels_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/test_labels.json - train_labels_file: output/reports/train/3983a67b8a4057ead42dfdb69ededaeb/train_labels.json - model_dir: models - name: 3983a67b8a4057ead42dfdb69ededaeb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bc4f514b67c3833261bbd3679592c64d - trainer: - kwargs: {} -name: 3983a67b8a4057ead42dfdb69ededaeb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/params.yaml b/examples/security/classification/output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/params.yaml deleted file mode 100644 index 38757300..00000000 --- a/examples/security/classification/output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/adv_predictions.json - adv_probabilities_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/fd6c84444cbdbf1106f8f2f770d2ba6d.pkl - params_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/params.yaml - predictions_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/predictions.json - probabilities_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/probabilities.json - score_dict_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/score_dict.json - test_labels_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/test_labels.json - train_labels_file: output/reports/train/398bd4cbc0112d8584b1ec453f9f2a01/train_labels.json - model_dir: models - name: 398bd4cbc0112d8584b1ec453f9f2a01 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8ef956373218b58daa6dba737f4b429f - trainer: - kwargs: {} -name: 398bd4cbc0112d8584b1ec453f9f2a01 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/39a8e006e9521a8a3ac8aedcad223869/params.yaml b/examples/security/classification/output/reports/train/39a8e006e9521a8a3ac8aedcad223869/params.yaml deleted file mode 100644 index dcc15e67..00000000 --- a/examples/security/classification/output/reports/train/39a8e006e9521a8a3ac8aedcad223869/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/adv_predictions.json - adv_probabilities_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/e5cfba1dbb9849cc90de4879402d7acb.pkl - params_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/params.yaml - predictions_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/predictions.json - probabilities_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/probabilities.json - score_dict_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/score_dict.json - test_labels_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/test_labels.json - train_labels_file: output/reports/train/39a8e006e9521a8a3ac8aedcad223869/train_labels.json - model_dir: models - name: 39a8e006e9521a8a3ac8aedcad223869 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: da6dc0bdcbea2bb90f45eedc7c925e9e - trainer: - kwargs: {} -name: 39a8e006e9521a8a3ac8aedcad223869 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/39aeae586c478d7560fd778adce6b062/params.yaml b/examples/security/classification/output/reports/train/39aeae586c478d7560fd778adce6b062/params.yaml deleted file mode 100644 index 5b0d9c66..00000000 --- a/examples/security/classification/output/reports/train/39aeae586c478d7560fd778adce6b062/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/39aeae586c478d7560fd778adce6b062/adv_predictions.json - adv_probabilities_file: output/reports/train/39aeae586c478d7560fd778adce6b062/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/ec648fa4e65374e220e58c296531a3f0.pkl - params_file: output/reports/train/39aeae586c478d7560fd778adce6b062/params.yaml - predictions_file: output/reports/train/39aeae586c478d7560fd778adce6b062/predictions.json - probabilities_file: output/reports/train/39aeae586c478d7560fd778adce6b062/probabilities.json - score_dict_file: output/reports/train/39aeae586c478d7560fd778adce6b062/score_dict.json - test_labels_file: output/reports/train/39aeae586c478d7560fd778adce6b062/test_labels.json - train_labels_file: output/reports/train/39aeae586c478d7560fd778adce6b062/train_labels.json - model_dir: models - name: 39aeae586c478d7560fd778adce6b062 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 381edc7996245e261dfeb940095c554f - trainer: - kwargs: {} -name: 39aeae586c478d7560fd778adce6b062 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/39f7317b84740b245937f9e6a6d5975c/params.yaml b/examples/security/classification/output/reports/train/39f7317b84740b245937f9e6a6d5975c/params.yaml deleted file mode 100644 index 2c7d2cae..00000000 --- a/examples/security/classification/output/reports/train/39f7317b84740b245937f9e6a6d5975c/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/adv_predictions.json - adv_probabilities_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/a2f2854cbcf6ca2bddd52e86ff2f4508.pkl - params_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/params.yaml - predictions_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/predictions.json - probabilities_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/probabilities.json - score_dict_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/score_dict.json - test_labels_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/test_labels.json - train_labels_file: output/reports/train/39f7317b84740b245937f9e6a6d5975c/train_labels.json - model_dir: models - name: 39f7317b84740b245937f9e6a6d5975c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 64b8b531961e682308ed04348c6466ae - trainer: - kwargs: {} -name: 39f7317b84740b245937f9e6a6d5975c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3a087746b68cdb9c323f1389fe04d063/params.yaml b/examples/security/classification/output/reports/train/3a087746b68cdb9c323f1389fe04d063/params.yaml deleted file mode 100644 index 78469b92..00000000 --- a/examples/security/classification/output/reports/train/3a087746b68cdb9c323f1389fe04d063/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/adv_predictions.json - adv_probabilities_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/393a03a91f1ec0dfe60c9a1aa2f5e4ae.pkl - params_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/params.yaml - predictions_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/predictions.json - probabilities_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/probabilities.json - score_dict_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/score_dict.json - test_labels_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/test_labels.json - train_labels_file: output/reports/train/3a087746b68cdb9c323f1389fe04d063/train_labels.json - model_dir: models - name: 3a087746b68cdb9c323f1389fe04d063 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ee35ac27960dd5f604aad6e4b02377a1 - trainer: - kwargs: {} -name: 3a087746b68cdb9c323f1389fe04d063 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3a44dae705aeb56908b85440cf50cf50/params.yaml b/examples/security/classification/output/reports/train/3a44dae705aeb56908b85440cf50cf50/params.yaml deleted file mode 100644 index 47d0b119..00000000 --- a/examples/security/classification/output/reports/train/3a44dae705aeb56908b85440cf50cf50/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/adv_predictions.json - adv_probabilities_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/1c2d5eb5c19992c826514236fbf68909.pkl - params_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/params.yaml - predictions_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/predictions.json - probabilities_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/probabilities.json - score_dict_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/score_dict.json - test_labels_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/test_labels.json - train_labels_file: output/reports/train/3a44dae705aeb56908b85440cf50cf50/train_labels.json - model_dir: models - name: 3a44dae705aeb56908b85440cf50cf50 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ee91604df36dc1e3d1659c361b93dfb8 - trainer: - kwargs: {} -name: 3a44dae705aeb56908b85440cf50cf50 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/params.yaml b/examples/security/classification/output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/params.yaml deleted file mode 100644 index c9e6d8cf..00000000 --- a/examples/security/classification/output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/adv_predictions.json - adv_probabilities_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/925e2baf0531247779d638f85a11f038.pkl - params_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/params.yaml - predictions_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/predictions.json - probabilities_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/probabilities.json - score_dict_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/score_dict.json - test_labels_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/test_labels.json - train_labels_file: output/reports/train/3a4760299155c3b3caf615a7aa97eb0b/train_labels.json - model_dir: models - name: 3a4760299155c3b3caf615a7aa97eb0b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 97d6b5c11af5d984c2379d04d45959a3 - trainer: - kwargs: {} -name: 3a4760299155c3b3caf615a7aa97eb0b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3a4fed2414f2e89395b1169db67b06f1/params.yaml b/examples/security/classification/output/reports/train/3a4fed2414f2e89395b1169db67b06f1/params.yaml deleted file mode 100644 index 6893df89..00000000 --- a/examples/security/classification/output/reports/train/3a4fed2414f2e89395b1169db67b06f1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/adv_predictions.json - adv_probabilities_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/69e6d0deb97aac908b7224fc90b658dc.pkl - params_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/params.yaml - predictions_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/predictions.json - probabilities_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/probabilities.json - score_dict_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/score_dict.json - test_labels_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/test_labels.json - train_labels_file: output/reports/train/3a4fed2414f2e89395b1169db67b06f1/train_labels.json - model_dir: models - name: 3a4fed2414f2e89395b1169db67b06f1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b0115ca567bc4af716207c89591da7e0 - trainer: - kwargs: {} -name: 3a4fed2414f2e89395b1169db67b06f1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3a7426b1d77943ea16b0532f56666d10/params.yaml b/examples/security/classification/output/reports/train/3a7426b1d77943ea16b0532f56666d10/params.yaml deleted file mode 100644 index 917976ef..00000000 --- a/examples/security/classification/output/reports/train/3a7426b1d77943ea16b0532f56666d10/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/adv_predictions.json - adv_probabilities_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/03b62f20c6616d60c9a97812c87e8b5a.pkl - params_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/params.yaml - predictions_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/predictions.json - probabilities_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/probabilities.json - score_dict_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/score_dict.json - test_labels_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/test_labels.json - train_labels_file: output/reports/train/3a7426b1d77943ea16b0532f56666d10/train_labels.json - model_dir: models - name: 3a7426b1d77943ea16b0532f56666d10 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7fd0d60b1a6e1a67d39203a89146a0c6 - trainer: - kwargs: {} -name: 3a7426b1d77943ea16b0532f56666d10 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/params.yaml b/examples/security/classification/output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/params.yaml deleted file mode 100644 index b148c7a9..00000000 --- a/examples/security/classification/output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/adv_predictions.json - adv_probabilities_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/cbb175a98a9488c1b93a0429f1fbff48.pkl - params_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/params.yaml - predictions_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/predictions.json - probabilities_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/probabilities.json - score_dict_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/score_dict.json - test_labels_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/test_labels.json - train_labels_file: output/reports/train/3a832dfaaced25bcde1bbe57baa0b2ac/train_labels.json - model_dir: models - name: 3a832dfaaced25bcde1bbe57baa0b2ac - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8f40344262bbabf6471e62064ba4256d - trainer: - kwargs: {} -name: 3a832dfaaced25bcde1bbe57baa0b2ac -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/params.yaml b/examples/security/classification/output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/params.yaml deleted file mode 100644 index 35c9ee02..00000000 --- a/examples/security/classification/output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/adv_predictions.json - adv_probabilities_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/d3da0e36374c561648c85b2eaf3a63ae.pkl - params_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/params.yaml - predictions_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/predictions.json - probabilities_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/probabilities.json - score_dict_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/score_dict.json - test_labels_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/test_labels.json - train_labels_file: output/reports/train/3a90a0d2d8f67cc9ec7c945a798428c2/train_labels.json - model_dir: models - name: 3a90a0d2d8f67cc9ec7c945a798428c2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b23e6b7df8109b1eb3b46f11e9f30a48 - trainer: - kwargs: {} -name: 3a90a0d2d8f67cc9ec7c945a798428c2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3ab98552789ce09182cfa2f13ecf763e/params.yaml b/examples/security/classification/output/reports/train/3ab98552789ce09182cfa2f13ecf763e/params.yaml deleted file mode 100644 index e3ed47ce..00000000 --- a/examples/security/classification/output/reports/train/3ab98552789ce09182cfa2f13ecf763e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/adv_predictions.json - adv_probabilities_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/ca6e418b702e6b3c57e4b31a1e4e1d10.pkl - params_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/params.yaml - predictions_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/predictions.json - probabilities_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/probabilities.json - score_dict_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/score_dict.json - test_labels_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/test_labels.json - train_labels_file: output/reports/train/3ab98552789ce09182cfa2f13ecf763e/train_labels.json - model_dir: models - name: 3ab98552789ce09182cfa2f13ecf763e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 54fb62afb21412efc5a581b56ed84e58 - trainer: - kwargs: {} -name: 3ab98552789ce09182cfa2f13ecf763e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3afb64b04178a88f25d5922ad930b19c/params.yaml b/examples/security/classification/output/reports/train/3afb64b04178a88f25d5922ad930b19c/params.yaml deleted file mode 100644 index bb9b420d..00000000 --- a/examples/security/classification/output/reports/train/3afb64b04178a88f25d5922ad930b19c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/adv_predictions.json - adv_probabilities_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/f916d2385b9c4eb4584b3a19d94a97e4.pkl - params_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/params.yaml - predictions_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/predictions.json - probabilities_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/probabilities.json - score_dict_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/score_dict.json - test_labels_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/test_labels.json - train_labels_file: output/reports/train/3afb64b04178a88f25d5922ad930b19c/train_labels.json - model_dir: models - name: 3afb64b04178a88f25d5922ad930b19c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5db70d81d5a8b13688d3a405f97a73c7 - trainer: - kwargs: {} -name: 3afb64b04178a88f25d5922ad930b19c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/params.yaml b/examples/security/classification/output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/params.yaml deleted file mode 100644 index 55a5f1fa..00000000 --- a/examples/security/classification/output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/adv_predictions.json - adv_probabilities_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/f48083be8b0c06dbac9a897526bcebdf.pkl - params_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/params.yaml - predictions_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/predictions.json - probabilities_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/probabilities.json - score_dict_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/score_dict.json - test_labels_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/test_labels.json - train_labels_file: output/reports/train/3b0f971da7bf95ffa96f88c2fb3124c3/train_labels.json - model_dir: models - name: 3b0f971da7bf95ffa96f88c2fb3124c3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7abc26bb2bc35b8608d92327f014fdc0 - trainer: - kwargs: {} -name: 3b0f971da7bf95ffa96f88c2fb3124c3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/params.yaml b/examples/security/classification/output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/params.yaml deleted file mode 100644 index 28c0a8d8..00000000 --- a/examples/security/classification/output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/adv_predictions.json - adv_probabilities_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/cf6c0909071aa18655e3d32145963b39.pkl - params_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/params.yaml - predictions_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/predictions.json - probabilities_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/probabilities.json - score_dict_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/score_dict.json - test_labels_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/test_labels.json - train_labels_file: output/reports/train/3b5be3a5d99d3f8266edddc5062dea3c/train_labels.json - model_dir: models - name: 3b5be3a5d99d3f8266edddc5062dea3c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 487dca9c32b7d27d6c57556515cabf30 - trainer: - kwargs: {} -name: 3b5be3a5d99d3f8266edddc5062dea3c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3baf817ca53374775736e3770c5ac72d/params.yaml b/examples/security/classification/output/reports/train/3baf817ca53374775736e3770c5ac72d/params.yaml deleted file mode 100644 index 74b3f0cf..00000000 --- a/examples/security/classification/output/reports/train/3baf817ca53374775736e3770c5ac72d/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/adv_predictions.json - adv_probabilities_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/5765ce95d1abe7251689c33d30d08aca.pkl - params_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/params.yaml - predictions_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/predictions.json - probabilities_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/probabilities.json - score_dict_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/score_dict.json - test_labels_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/test_labels.json - train_labels_file: output/reports/train/3baf817ca53374775736e3770c5ac72d/train_labels.json - model_dir: models - name: 3baf817ca53374775736e3770c5ac72d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0c43afb6dd4853ea7248386f9ae4c6d8 - trainer: - kwargs: {} -name: 3baf817ca53374775736e3770c5ac72d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3bb1154f2c86770440b01814ec8a97c1/params.yaml b/examples/security/classification/output/reports/train/3bb1154f2c86770440b01814ec8a97c1/params.yaml deleted file mode 100644 index 7205bf11..00000000 --- a/examples/security/classification/output/reports/train/3bb1154f2c86770440b01814ec8a97c1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/adv_predictions.json - adv_probabilities_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/4dff07915f66876bc115b5b6f5607969.pkl - params_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/params.yaml - predictions_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/predictions.json - probabilities_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/probabilities.json - score_dict_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/score_dict.json - test_labels_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/test_labels.json - train_labels_file: output/reports/train/3bb1154f2c86770440b01814ec8a97c1/train_labels.json - model_dir: models - name: 3bb1154f2c86770440b01814ec8a97c1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b8b9b31a045fdb1c50c345fe08a9e61e - trainer: - kwargs: {} -name: 3bb1154f2c86770440b01814ec8a97c1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3bbc457d3368df994b8adc841370677c/params.yaml b/examples/security/classification/output/reports/train/3bbc457d3368df994b8adc841370677c/params.yaml deleted file mode 100644 index 5e399480..00000000 --- a/examples/security/classification/output/reports/train/3bbc457d3368df994b8adc841370677c/params.yaml +++ /dev/null @@ -1,104 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3bbc457d3368df994b8adc841370677c/adv_predictions.json - adv_probabilities_file: output/reports/train/3bbc457d3368df994b8adc841370677c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/4715bf9063d084b48e5ac97a1023585b.pkl - params_file: output/reports/train/3bbc457d3368df994b8adc841370677c/params.yaml - predictions_file: output/reports/train/3bbc457d3368df994b8adc841370677c/predictions.json - probabilities_file: output/reports/train/3bbc457d3368df994b8adc841370677c/probabilities.json - score_dict_file: output/reports/train/3bbc457d3368df994b8adc841370677c/score_dict.json - test_labels_file: output/reports/train/3bbc457d3368df994b8adc841370677c/test_labels.json - train_labels_file: output/reports/train/3bbc457d3368df994b8adc841370677c/train_labels.json - model_dir: models - name: 3bbc457d3368df994b8adc841370677c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: - - linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e27ce573b383c335e3d87918133b0936 - trainer: - kwargs: {} -name: 3bbc457d3368df994b8adc841370677c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/params.yaml b/examples/security/classification/output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/params.yaml deleted file mode 100644 index 9868f9e2..00000000 --- a/examples/security/classification/output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/adv_predictions.json - adv_probabilities_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/baad3a54c719895e0061af9252ad46d1.pkl - params_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/params.yaml - predictions_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/predictions.json - probabilities_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/probabilities.json - score_dict_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/score_dict.json - test_labels_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/test_labels.json - train_labels_file: output/reports/train/3bc2c5a6b8f5078e80371570e0d32ebb/train_labels.json - model_dir: models - name: 3bc2c5a6b8f5078e80371570e0d32ebb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: df90dd1b96e8188a1c102e2e65ccac14 - trainer: - kwargs: {} -name: 3bc2c5a6b8f5078e80371570e0d32ebb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3bc2f581bb163fbad21055479cd7e345/params.yaml b/examples/security/classification/output/reports/train/3bc2f581bb163fbad21055479cd7e345/params.yaml deleted file mode 100644 index 676a09cb..00000000 --- a/examples/security/classification/output/reports/train/3bc2f581bb163fbad21055479cd7e345/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/adv_predictions.json - adv_probabilities_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/31e504167c1f673368672ebdfcd0a389.pkl - params_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/params.yaml - predictions_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/predictions.json - probabilities_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/probabilities.json - score_dict_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/score_dict.json - test_labels_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/test_labels.json - train_labels_file: output/reports/train/3bc2f581bb163fbad21055479cd7e345/train_labels.json - model_dir: models - name: 3bc2f581bb163fbad21055479cd7e345 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9a51bd02c684c6f9163d1406253ff7a2 - trainer: - kwargs: {} -name: 3bc2f581bb163fbad21055479cd7e345 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3be959c29466ab668a29991179516fa6/params.yaml b/examples/security/classification/output/reports/train/3be959c29466ab668a29991179516fa6/params.yaml deleted file mode 100644 index 476fb77a..00000000 --- a/examples/security/classification/output/reports/train/3be959c29466ab668a29991179516fa6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3be959c29466ab668a29991179516fa6/adv_predictions.json - adv_probabilities_file: output/reports/train/3be959c29466ab668a29991179516fa6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/70baa7fa5b76590041a4b5a35505a8cf.pkl - params_file: output/reports/train/3be959c29466ab668a29991179516fa6/params.yaml - predictions_file: output/reports/train/3be959c29466ab668a29991179516fa6/predictions.json - probabilities_file: output/reports/train/3be959c29466ab668a29991179516fa6/probabilities.json - score_dict_file: output/reports/train/3be959c29466ab668a29991179516fa6/score_dict.json - test_labels_file: output/reports/train/3be959c29466ab668a29991179516fa6/test_labels.json - train_labels_file: output/reports/train/3be959c29466ab668a29991179516fa6/train_labels.json - model_dir: models - name: 3be959c29466ab668a29991179516fa6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0d0e10a5c338be73f10571b69e4d07bf - trainer: - kwargs: {} -name: 3be959c29466ab668a29991179516fa6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/params.yaml b/examples/security/classification/output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/params.yaml deleted file mode 100644 index f4522964..00000000 --- a/examples/security/classification/output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/adv_predictions.json - adv_probabilities_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/5ef6bec184bc7c3d2a2da382d09df218.pkl - params_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/params.yaml - predictions_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/predictions.json - probabilities_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/probabilities.json - score_dict_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/score_dict.json - test_labels_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/test_labels.json - train_labels_file: output/reports/train/3c4a98cda5429230b0a7c9f54ec2a184/train_labels.json - model_dir: models - name: 3c4a98cda5429230b0a7c9f54ec2a184 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 150e114477c9b8dd8f3bfd1ae4526420 - trainer: - kwargs: {} -name: 3c4a98cda5429230b0a7c9f54ec2a184 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/params.yaml b/examples/security/classification/output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/params.yaml deleted file mode 100644 index 4b49af79..00000000 --- a/examples/security/classification/output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/adv_predictions.json - adv_probabilities_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/03fb2824516e4bb9ab027882b08972a2.pkl - params_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/params.yaml - predictions_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/predictions.json - probabilities_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/probabilities.json - score_dict_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/score_dict.json - test_labels_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/test_labels.json - train_labels_file: output/reports/train/3cc1f549a964f618fcc84cf381cdafbc/train_labels.json - model_dir: models - name: 3cc1f549a964f618fcc84cf381cdafbc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2501dd3abb6312086ef451689d064d52 - trainer: - kwargs: {} -name: 3cc1f549a964f618fcc84cf381cdafbc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3cc284269a48628f343f463d712e0b45/params.yaml b/examples/security/classification/output/reports/train/3cc284269a48628f343f463d712e0b45/params.yaml deleted file mode 100644 index 2409b233..00000000 --- a/examples/security/classification/output/reports/train/3cc284269a48628f343f463d712e0b45/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3cc284269a48628f343f463d712e0b45/adv_predictions.json - adv_probabilities_file: output/reports/train/3cc284269a48628f343f463d712e0b45/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/a334e53fc4c95ce44160e2b0550a9f4b.pkl - params_file: output/reports/train/3cc284269a48628f343f463d712e0b45/params.yaml - predictions_file: output/reports/train/3cc284269a48628f343f463d712e0b45/predictions.json - probabilities_file: output/reports/train/3cc284269a48628f343f463d712e0b45/probabilities.json - score_dict_file: output/reports/train/3cc284269a48628f343f463d712e0b45/score_dict.json - test_labels_file: output/reports/train/3cc284269a48628f343f463d712e0b45/test_labels.json - train_labels_file: output/reports/train/3cc284269a48628f343f463d712e0b45/train_labels.json - model_dir: models - name: 3cc284269a48628f343f463d712e0b45 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4a4a88c6d5a796252ce63e00443dbdea - trainer: - kwargs: {} -name: 3cc284269a48628f343f463d712e0b45 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3ceedf9a8259cd4f916c248073618c74/params.yaml b/examples/security/classification/output/reports/train/3ceedf9a8259cd4f916c248073618c74/params.yaml deleted file mode 100644 index 74a101be..00000000 --- a/examples/security/classification/output/reports/train/3ceedf9a8259cd4f916c248073618c74/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/adv_predictions.json - adv_probabilities_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/d268811b068b0a8a493d91c15d69f027.pkl - params_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/params.yaml - predictions_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/predictions.json - probabilities_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/probabilities.json - score_dict_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/score_dict.json - test_labels_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/test_labels.json - train_labels_file: output/reports/train/3ceedf9a8259cd4f916c248073618c74/train_labels.json - model_dir: models - name: 3ceedf9a8259cd4f916c248073618c74 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c7d7adb4d30e546ec5547f4ea88872a3 - trainer: - kwargs: {} -name: 3ceedf9a8259cd4f916c248073618c74 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3d3fead877f029a67100a0c159461ede/params.yaml b/examples/security/classification/output/reports/train/3d3fead877f029a67100a0c159461ede/params.yaml deleted file mode 100644 index 9603dbb3..00000000 --- a/examples/security/classification/output/reports/train/3d3fead877f029a67100a0c159461ede/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3d3fead877f029a67100a0c159461ede/adv_predictions.json - adv_probabilities_file: output/reports/train/3d3fead877f029a67100a0c159461ede/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/03d3eb9ed0f6e9fe94348ce968ce80b0.pkl - params_file: output/reports/train/3d3fead877f029a67100a0c159461ede/params.yaml - predictions_file: output/reports/train/3d3fead877f029a67100a0c159461ede/predictions.json - probabilities_file: output/reports/train/3d3fead877f029a67100a0c159461ede/probabilities.json - score_dict_file: output/reports/train/3d3fead877f029a67100a0c159461ede/score_dict.json - test_labels_file: output/reports/train/3d3fead877f029a67100a0c159461ede/test_labels.json - train_labels_file: output/reports/train/3d3fead877f029a67100a0c159461ede/train_labels.json - model_dir: models - name: 3d3fead877f029a67100a0c159461ede - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7fcc3613f38fd1abdf64cde82e6763eb - trainer: - kwargs: {} -name: 3d3fead877f029a67100a0c159461ede -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/params.yaml b/examples/security/classification/output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/params.yaml deleted file mode 100644 index c8b3ea05..00000000 --- a/examples/security/classification/output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/adv_predictions.json - adv_probabilities_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/ac766c1c3c24180ad584fa1cd77f3d41.pkl - params_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/params.yaml - predictions_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/predictions.json - probabilities_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/probabilities.json - score_dict_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/score_dict.json - test_labels_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/test_labels.json - train_labels_file: output/reports/train/3da8efbcbcf79f16f84d53631a8a0682/train_labels.json - model_dir: models - name: 3da8efbcbcf79f16f84d53631a8a0682 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ed287e0c47b0dfc17b4c8bf69ddbd0c5 - trainer: - kwargs: {} -name: 3da8efbcbcf79f16f84d53631a8a0682 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3dd352d032922a57816d4f639b582aa8/params.yaml b/examples/security/classification/output/reports/train/3dd352d032922a57816d4f639b582aa8/params.yaml deleted file mode 100644 index 7e815298..00000000 --- a/examples/security/classification/output/reports/train/3dd352d032922a57816d4f639b582aa8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/adv_predictions.json - adv_probabilities_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/415f63c6e42d7bda27e5162a4685a667.pkl - params_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/params.yaml - predictions_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/predictions.json - probabilities_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/probabilities.json - score_dict_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/score_dict.json - test_labels_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/test_labels.json - train_labels_file: output/reports/train/3dd352d032922a57816d4f639b582aa8/train_labels.json - model_dir: models - name: 3dd352d032922a57816d4f639b582aa8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 196fc62543dad198c810b791e9980641 - trainer: - kwargs: {} -name: 3dd352d032922a57816d4f639b582aa8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3e156ec6d9cf161bf16d231966609c0e/params.yaml b/examples/security/classification/output/reports/train/3e156ec6d9cf161bf16d231966609c0e/params.yaml deleted file mode 100644 index ca281de2..00000000 --- a/examples/security/classification/output/reports/train/3e156ec6d9cf161bf16d231966609c0e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/adv_predictions.json - adv_probabilities_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/dd876dff7890b7c94fc97431cb06f515.pkl - params_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/params.yaml - predictions_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/predictions.json - probabilities_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/probabilities.json - score_dict_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/score_dict.json - test_labels_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/test_labels.json - train_labels_file: output/reports/train/3e156ec6d9cf161bf16d231966609c0e/train_labels.json - model_dir: models - name: 3e156ec6d9cf161bf16d231966609c0e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8432e8d39a726d14dd6d2e3f88f0fb75 - trainer: - kwargs: {} -name: 3e156ec6d9cf161bf16d231966609c0e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/params.yaml b/examples/security/classification/output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/params.yaml deleted file mode 100644 index a2366ba3..00000000 --- a/examples/security/classification/output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/adv_predictions.json - adv_probabilities_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/a0d66cc783a953380be2ef8c17faa18f.pkl - params_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/params.yaml - predictions_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/predictions.json - probabilities_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/probabilities.json - score_dict_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/score_dict.json - test_labels_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/test_labels.json - train_labels_file: output/reports/train/3e2af0c3fea57bb2069e436ef8a5b7f9/train_labels.json - model_dir: models - name: 3e2af0c3fea57bb2069e436ef8a5b7f9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4cfea40759ec87d53be5e91683cd12c4 - trainer: - kwargs: {} -name: 3e2af0c3fea57bb2069e436ef8a5b7f9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3e3b77428f9ba3246184c368ca84703a/params.yaml b/examples/security/classification/output/reports/train/3e3b77428f9ba3246184c368ca84703a/params.yaml deleted file mode 100644 index f448044d..00000000 --- a/examples/security/classification/output/reports/train/3e3b77428f9ba3246184c368ca84703a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/adv_predictions.json - adv_probabilities_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/50bfae8bcfcbd9383d0d35165214c06f.pkl - params_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/params.yaml - predictions_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/predictions.json - probabilities_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/probabilities.json - score_dict_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/score_dict.json - test_labels_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/test_labels.json - train_labels_file: output/reports/train/3e3b77428f9ba3246184c368ca84703a/train_labels.json - model_dir: models - name: 3e3b77428f9ba3246184c368ca84703a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 412ceb96c9463264950ff219084f28da - trainer: - kwargs: {} -name: 3e3b77428f9ba3246184c368ca84703a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/params.yaml b/examples/security/classification/output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/params.yaml deleted file mode 100644 index 778c5433..00000000 --- a/examples/security/classification/output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/adv_predictions.json - adv_probabilities_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/f04487af3de4c1514059cad89b689967.pkl - params_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/params.yaml - predictions_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/predictions.json - probabilities_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/probabilities.json - score_dict_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/score_dict.json - test_labels_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/test_labels.json - train_labels_file: output/reports/train/3e4b01d604e413a6a27d1f4c8b3efc6b/train_labels.json - model_dir: models - name: 3e4b01d604e413a6a27d1f4c8b3efc6b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5357fcb7a4ff8ffe09090402302c0d87 - trainer: - kwargs: {} -name: 3e4b01d604e413a6a27d1f4c8b3efc6b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/params.yaml b/examples/security/classification/output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/params.yaml deleted file mode 100644 index d255a95b..00000000 --- a/examples/security/classification/output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/adv_predictions.json - adv_probabilities_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/4555c7e68cef92d75a13d6d07a714015.pkl - params_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/params.yaml - predictions_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/predictions.json - probabilities_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/probabilities.json - score_dict_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/score_dict.json - test_labels_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/test_labels.json - train_labels_file: output/reports/train/3ea061edcdc266e1857ee59a1b5a1136/train_labels.json - model_dir: models - name: 3ea061edcdc266e1857ee59a1b5a1136 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f6173122ee2eba0e135f9365ad5b4720 - trainer: - kwargs: {} -name: 3ea061edcdc266e1857ee59a1b5a1136 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3eb24df17a345052a55bde11ae217078/params.yaml b/examples/security/classification/output/reports/train/3eb24df17a345052a55bde11ae217078/params.yaml deleted file mode 100644 index c372bde8..00000000 --- a/examples/security/classification/output/reports/train/3eb24df17a345052a55bde11ae217078/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3eb24df17a345052a55bde11ae217078/adv_predictions.json - adv_probabilities_file: output/reports/train/3eb24df17a345052a55bde11ae217078/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/8010e355c988185b13368fa92a703e83.pkl - params_file: output/reports/train/3eb24df17a345052a55bde11ae217078/params.yaml - predictions_file: output/reports/train/3eb24df17a345052a55bde11ae217078/predictions.json - probabilities_file: output/reports/train/3eb24df17a345052a55bde11ae217078/probabilities.json - score_dict_file: output/reports/train/3eb24df17a345052a55bde11ae217078/score_dict.json - test_labels_file: output/reports/train/3eb24df17a345052a55bde11ae217078/test_labels.json - train_labels_file: output/reports/train/3eb24df17a345052a55bde11ae217078/train_labels.json - model_dir: models - name: 3eb24df17a345052a55bde11ae217078 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e12d4d46de451706700670fd21622471 - trainer: - kwargs: {} -name: 3eb24df17a345052a55bde11ae217078 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/params.yaml b/examples/security/classification/output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/params.yaml deleted file mode 100644 index 299b330e..00000000 --- a/examples/security/classification/output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/adv_predictions.json - adv_probabilities_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/0a358fe37bf78b37e8a037431fc03796.pkl - params_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/params.yaml - predictions_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/predictions.json - probabilities_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/probabilities.json - score_dict_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/score_dict.json - test_labels_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/test_labels.json - train_labels_file: output/reports/train/3ecca09e4bd0bfb13014a44fd81caf53/train_labels.json - model_dir: models - name: 3ecca09e4bd0bfb13014a44fd81caf53 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 396372720711e10d71e9e30009eea90f - trainer: - kwargs: {} -name: 3ecca09e4bd0bfb13014a44fd81caf53 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3ecff33ec1285ab4414c5211101281f9/params.yaml b/examples/security/classification/output/reports/train/3ecff33ec1285ab4414c5211101281f9/params.yaml deleted file mode 100644 index 2802d247..00000000 --- a/examples/security/classification/output/reports/train/3ecff33ec1285ab4414c5211101281f9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/adv_predictions.json - adv_probabilities_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/d1f1644991d7653f5f8e0d5c8509634a.pkl - params_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/params.yaml - predictions_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/predictions.json - probabilities_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/probabilities.json - score_dict_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/score_dict.json - test_labels_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/test_labels.json - train_labels_file: output/reports/train/3ecff33ec1285ab4414c5211101281f9/train_labels.json - model_dir: models - name: 3ecff33ec1285ab4414c5211101281f9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3b545b941f219278e9e23603610c2b1a - trainer: - kwargs: {} -name: 3ecff33ec1285ab4414c5211101281f9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3eddf0e349f03334974b18f5e609afa6/params.yaml b/examples/security/classification/output/reports/train/3eddf0e349f03334974b18f5e609afa6/params.yaml deleted file mode 100644 index fac2dfc0..00000000 --- a/examples/security/classification/output/reports/train/3eddf0e349f03334974b18f5e609afa6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/adv_predictions.json - adv_probabilities_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/2d0f8ae31410468f4925ec7eba463f77.pkl - params_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/params.yaml - predictions_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/predictions.json - probabilities_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/probabilities.json - score_dict_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/score_dict.json - test_labels_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/test_labels.json - train_labels_file: output/reports/train/3eddf0e349f03334974b18f5e609afa6/train_labels.json - model_dir: models - name: 3eddf0e349f03334974b18f5e609afa6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fcc06390ce9d509f1b5cd1d93851c84d - trainer: - kwargs: {} -name: 3eddf0e349f03334974b18f5e609afa6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/params.yaml b/examples/security/classification/output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/params.yaml deleted file mode 100644 index 3a33abab..00000000 --- a/examples/security/classification/output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/adv_predictions.json - adv_probabilities_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/eabcb156e5a779c46fbca7898d2db5f4.pkl - params_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/params.yaml - predictions_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/predictions.json - probabilities_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/probabilities.json - score_dict_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/score_dict.json - test_labels_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/test_labels.json - train_labels_file: output/reports/train/3ef48cfd3aca6f2ec70da1afeea96998/train_labels.json - model_dir: models - name: 3ef48cfd3aca6f2ec70da1afeea96998 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f9df8704aaef136cc187df9d8909be2f - trainer: - kwargs: {} -name: 3ef48cfd3aca6f2ec70da1afeea96998 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/params.yaml b/examples/security/classification/output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/params.yaml deleted file mode 100644 index c83e56c4..00000000 --- a/examples/security/classification/output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/adv_predictions.json - adv_probabilities_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/26f813e552d18eee58129ec8373ce0a3.pkl - params_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/params.yaml - predictions_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/predictions.json - probabilities_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/probabilities.json - score_dict_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/score_dict.json - test_labels_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/test_labels.json - train_labels_file: output/reports/train/3f1b1dd81e14b86fcef44aa611846e89/train_labels.json - model_dir: models - name: 3f1b1dd81e14b86fcef44aa611846e89 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6156791587e731402cc6635c5538947c - trainer: - kwargs: {} -name: 3f1b1dd81e14b86fcef44aa611846e89 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/params.yaml b/examples/security/classification/output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/params.yaml deleted file mode 100644 index 86539af5..00000000 --- a/examples/security/classification/output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/adv_predictions.json - adv_probabilities_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/25e37d84b96855a3e5065cba79a14e5d.pkl - params_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/params.yaml - predictions_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/predictions.json - probabilities_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/probabilities.json - score_dict_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/score_dict.json - test_labels_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/test_labels.json - train_labels_file: output/reports/train/3f3d8335016ac87d9454fe25ed28b3e8/train_labels.json - model_dir: models - name: 3f3d8335016ac87d9454fe25ed28b3e8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e9e1138302f58e0f257552487515107c - trainer: - kwargs: {} -name: 3f3d8335016ac87d9454fe25ed28b3e8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3f465aa09fb7b7450034fce3c46941e2/params.yaml b/examples/security/classification/output/reports/train/3f465aa09fb7b7450034fce3c46941e2/params.yaml deleted file mode 100644 index 224eb2f6..00000000 --- a/examples/security/classification/output/reports/train/3f465aa09fb7b7450034fce3c46941e2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/adv_predictions.json - adv_probabilities_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/e05c1f754d86f6c251d7bbae6f12269e.pkl - params_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/params.yaml - predictions_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/predictions.json - probabilities_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/probabilities.json - score_dict_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/score_dict.json - test_labels_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/test_labels.json - train_labels_file: output/reports/train/3f465aa09fb7b7450034fce3c46941e2/train_labels.json - model_dir: models - name: 3f465aa09fb7b7450034fce3c46941e2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 17368a86eda2d1933a23668e5a423e04 - trainer: - kwargs: {} -name: 3f465aa09fb7b7450034fce3c46941e2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/params.yaml b/examples/security/classification/output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/params.yaml deleted file mode 100644 index 5f8e6827..00000000 --- a/examples/security/classification/output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/adv_predictions.json - adv_probabilities_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/9cf4726086e0159eb8eeeb129651d4b9.pkl - params_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/params.yaml - predictions_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/predictions.json - probabilities_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/probabilities.json - score_dict_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/score_dict.json - test_labels_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/test_labels.json - train_labels_file: output/reports/train/3f652c9408c3f6c726d17c1eb54b2dc5/train_labels.json - model_dir: models - name: 3f652c9408c3f6c726d17c1eb54b2dc5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 49607d4de06f05ea0f03759f93e24538 - trainer: - kwargs: {} -name: 3f652c9408c3f6c726d17c1eb54b2dc5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/params.yaml b/examples/security/classification/output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/params.yaml deleted file mode 100644 index 9096f314..00000000 --- a/examples/security/classification/output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/adv_predictions.json - adv_probabilities_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/a1ba0690b7e301d3b837137d83802e20.pkl - params_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/params.yaml - predictions_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/predictions.json - probabilities_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/probabilities.json - score_dict_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/score_dict.json - test_labels_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/test_labels.json - train_labels_file: output/reports/train/3f9ce81fd04e84d3f41bc2508a21d05a/train_labels.json - model_dir: models - name: 3f9ce81fd04e84d3f41bc2508a21d05a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8beec78980903429edee17bf860659e4 - trainer: - kwargs: {} -name: 3f9ce81fd04e84d3f41bc2508a21d05a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3f9e13c157c200fdce8eeec0f513105d/params.yaml b/examples/security/classification/output/reports/train/3f9e13c157c200fdce8eeec0f513105d/params.yaml deleted file mode 100644 index de51ec0d..00000000 --- a/examples/security/classification/output/reports/train/3f9e13c157c200fdce8eeec0f513105d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/adv_predictions.json - adv_probabilities_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/9c54d62914f9486151bbcf17651bc2c2.pkl - params_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/params.yaml - predictions_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/predictions.json - probabilities_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/probabilities.json - score_dict_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/score_dict.json - test_labels_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/test_labels.json - train_labels_file: output/reports/train/3f9e13c157c200fdce8eeec0f513105d/train_labels.json - model_dir: models - name: 3f9e13c157c200fdce8eeec0f513105d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 26b6f45cc053ad4ee15c418a19129221 - trainer: - kwargs: {} -name: 3f9e13c157c200fdce8eeec0f513105d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3fafa88075220a240d35b37300145252/params.yaml b/examples/security/classification/output/reports/train/3fafa88075220a240d35b37300145252/params.yaml deleted file mode 100644 index dd396dea..00000000 --- a/examples/security/classification/output/reports/train/3fafa88075220a240d35b37300145252/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3fafa88075220a240d35b37300145252/adv_predictions.json - adv_probabilities_file: output/reports/train/3fafa88075220a240d35b37300145252/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/66ccc3b5d3f4c7053435a39bff7169d1.pkl - params_file: output/reports/train/3fafa88075220a240d35b37300145252/params.yaml - predictions_file: output/reports/train/3fafa88075220a240d35b37300145252/predictions.json - probabilities_file: output/reports/train/3fafa88075220a240d35b37300145252/probabilities.json - score_dict_file: output/reports/train/3fafa88075220a240d35b37300145252/score_dict.json - test_labels_file: output/reports/train/3fafa88075220a240d35b37300145252/test_labels.json - train_labels_file: output/reports/train/3fafa88075220a240d35b37300145252/train_labels.json - model_dir: models - name: 3fafa88075220a240d35b37300145252 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: df6a8f789dbcb8e62dd976b755e179b8 - trainer: - kwargs: {} -name: 3fafa88075220a240d35b37300145252 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/params.yaml b/examples/security/classification/output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/params.yaml deleted file mode 100644 index 6e57b249..00000000 --- a/examples/security/classification/output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/adv_predictions.json - adv_probabilities_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/d084527dbdeba1d0be716245ebe4c8df.pkl - params_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/params.yaml - predictions_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/predictions.json - probabilities_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/probabilities.json - score_dict_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/score_dict.json - test_labels_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/test_labels.json - train_labels_file: output/reports/train/3fb3bcda0a9ee278fa2bebaff94857d4/train_labels.json - model_dir: models - name: 3fb3bcda0a9ee278fa2bebaff94857d4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6238300f97dde8c2027321ff25052bbe - trainer: - kwargs: {} -name: 3fb3bcda0a9ee278fa2bebaff94857d4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/3fcad77159a037762651201029459a10/params.yaml b/examples/security/classification/output/reports/train/3fcad77159a037762651201029459a10/params.yaml deleted file mode 100644 index aad60265..00000000 --- a/examples/security/classification/output/reports/train/3fcad77159a037762651201029459a10/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/3fcad77159a037762651201029459a10/adv_predictions.json - adv_probabilities_file: output/reports/train/3fcad77159a037762651201029459a10/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/9ba0e261ca67239f2eb030eed036db8f.pkl - params_file: output/reports/train/3fcad77159a037762651201029459a10/params.yaml - predictions_file: output/reports/train/3fcad77159a037762651201029459a10/predictions.json - probabilities_file: output/reports/train/3fcad77159a037762651201029459a10/probabilities.json - score_dict_file: output/reports/train/3fcad77159a037762651201029459a10/score_dict.json - test_labels_file: output/reports/train/3fcad77159a037762651201029459a10/test_labels.json - train_labels_file: output/reports/train/3fcad77159a037762651201029459a10/train_labels.json - model_dir: models - name: 3fcad77159a037762651201029459a10 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ac8af7d0b42136fd3025e25bf035f1ca - trainer: - kwargs: {} -name: 3fcad77159a037762651201029459a10 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4000882a9b95df6f032f2cc816bd52fc/params.yaml b/examples/security/classification/output/reports/train/4000882a9b95df6f032f2cc816bd52fc/params.yaml deleted file mode 100644 index c4fe1da7..00000000 --- a/examples/security/classification/output/reports/train/4000882a9b95df6f032f2cc816bd52fc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/adv_predictions.json - adv_probabilities_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e66b8296cc0f1f0cfd7a2760172e2ade.pkl - params_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/params.yaml - predictions_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/predictions.json - probabilities_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/probabilities.json - score_dict_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/score_dict.json - test_labels_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/test_labels.json - train_labels_file: output/reports/train/4000882a9b95df6f032f2cc816bd52fc/train_labels.json - model_dir: models - name: 4000882a9b95df6f032f2cc816bd52fc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7de0532ed39cbe2b69a6e9cdf4f909ca - trainer: - kwargs: {} -name: 4000882a9b95df6f032f2cc816bd52fc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4018bf629f1204680c23d1a912d8497e/params.yaml b/examples/security/classification/output/reports/train/4018bf629f1204680c23d1a912d8497e/params.yaml deleted file mode 100644 index 69f2f526..00000000 --- a/examples/security/classification/output/reports/train/4018bf629f1204680c23d1a912d8497e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/adv_predictions.json - adv_probabilities_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/7acd15690cccdaeb361add0f209f4f16.pkl - params_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/params.yaml - predictions_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/predictions.json - probabilities_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/probabilities.json - score_dict_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/score_dict.json - test_labels_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/test_labels.json - train_labels_file: output/reports/train/4018bf629f1204680c23d1a912d8497e/train_labels.json - model_dir: models - name: 4018bf629f1204680c23d1a912d8497e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 72421d47118dcfc94c0ce6fd890d9a63 - trainer: - kwargs: {} -name: 4018bf629f1204680c23d1a912d8497e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/403576dbc35f575f739cb11a571066c7/params.yaml b/examples/security/classification/output/reports/train/403576dbc35f575f739cb11a571066c7/params.yaml deleted file mode 100644 index 32a1cffd..00000000 --- a/examples/security/classification/output/reports/train/403576dbc35f575f739cb11a571066c7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/403576dbc35f575f739cb11a571066c7/adv_predictions.json - adv_probabilities_file: output/reports/train/403576dbc35f575f739cb11a571066c7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/cf19be4be54b659d38575392ff7f5330.pkl - params_file: output/reports/train/403576dbc35f575f739cb11a571066c7/params.yaml - predictions_file: output/reports/train/403576dbc35f575f739cb11a571066c7/predictions.json - probabilities_file: output/reports/train/403576dbc35f575f739cb11a571066c7/probabilities.json - score_dict_file: output/reports/train/403576dbc35f575f739cb11a571066c7/score_dict.json - test_labels_file: output/reports/train/403576dbc35f575f739cb11a571066c7/test_labels.json - train_labels_file: output/reports/train/403576dbc35f575f739cb11a571066c7/train_labels.json - model_dir: models - name: 403576dbc35f575f739cb11a571066c7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5e7f7e836cceca63868378b7a6ab0989 - trainer: - kwargs: {} -name: 403576dbc35f575f739cb11a571066c7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4046b0c93b02898761533cdd1a198086/params.yaml b/examples/security/classification/output/reports/train/4046b0c93b02898761533cdd1a198086/params.yaml deleted file mode 100644 index c53096c4..00000000 --- a/examples/security/classification/output/reports/train/4046b0c93b02898761533cdd1a198086/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4046b0c93b02898761533cdd1a198086/adv_predictions.json - adv_probabilities_file: output/reports/train/4046b0c93b02898761533cdd1a198086/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/d6194862ff4cd0cda5755dc69f0a9095.pkl - params_file: output/reports/train/4046b0c93b02898761533cdd1a198086/params.yaml - predictions_file: output/reports/train/4046b0c93b02898761533cdd1a198086/predictions.json - probabilities_file: output/reports/train/4046b0c93b02898761533cdd1a198086/probabilities.json - score_dict_file: output/reports/train/4046b0c93b02898761533cdd1a198086/score_dict.json - test_labels_file: output/reports/train/4046b0c93b02898761533cdd1a198086/test_labels.json - train_labels_file: output/reports/train/4046b0c93b02898761533cdd1a198086/train_labels.json - model_dir: models - name: 4046b0c93b02898761533cdd1a198086 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ad73d769bb663ce706a1543743ab6de8 - trainer: - kwargs: {} -name: 4046b0c93b02898761533cdd1a198086 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/params.yaml b/examples/security/classification/output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/params.yaml deleted file mode 100644 index ec9d2166..00000000 --- a/examples/security/classification/output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/adv_predictions.json - adv_probabilities_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/295009ae54afa9237f87cffe94041a6a.pkl - params_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/params.yaml - predictions_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/predictions.json - probabilities_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/probabilities.json - score_dict_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/score_dict.json - test_labels_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/test_labels.json - train_labels_file: output/reports/train/40556ac210d2c1a7c2d9e5f97d4bedf1/train_labels.json - model_dir: models - name: 40556ac210d2c1a7c2d9e5f97d4bedf1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e5cea785d18e9b9d6b20415d5340ddf2 - trainer: - kwargs: {} -name: 40556ac210d2c1a7c2d9e5f97d4bedf1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/405a2425f5df778b62a938602eeb1828/params.yaml b/examples/security/classification/output/reports/train/405a2425f5df778b62a938602eeb1828/params.yaml deleted file mode 100644 index e48b0996..00000000 --- a/examples/security/classification/output/reports/train/405a2425f5df778b62a938602eeb1828/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/405a2425f5df778b62a938602eeb1828/adv_predictions.json - adv_probabilities_file: output/reports/train/405a2425f5df778b62a938602eeb1828/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/ea8de26535cf8f5a6ad1dd4e50298a0e.pkl - params_file: output/reports/train/405a2425f5df778b62a938602eeb1828/params.yaml - predictions_file: output/reports/train/405a2425f5df778b62a938602eeb1828/predictions.json - probabilities_file: output/reports/train/405a2425f5df778b62a938602eeb1828/probabilities.json - score_dict_file: output/reports/train/405a2425f5df778b62a938602eeb1828/score_dict.json - test_labels_file: output/reports/train/405a2425f5df778b62a938602eeb1828/test_labels.json - train_labels_file: output/reports/train/405a2425f5df778b62a938602eeb1828/train_labels.json - model_dir: models - name: 405a2425f5df778b62a938602eeb1828 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bd2ba3cfc35d201ad65ef81a8d59b1e3 - trainer: - kwargs: {} -name: 405a2425f5df778b62a938602eeb1828 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4070982537f3337f45d894ab43747302/params.yaml b/examples/security/classification/output/reports/train/4070982537f3337f45d894ab43747302/params.yaml deleted file mode 100644 index 981046b0..00000000 --- a/examples/security/classification/output/reports/train/4070982537f3337f45d894ab43747302/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4070982537f3337f45d894ab43747302/adv_predictions.json - adv_probabilities_file: output/reports/train/4070982537f3337f45d894ab43747302/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/1639140e0558ed1564642a6c424aee96.pkl - params_file: output/reports/train/4070982537f3337f45d894ab43747302/params.yaml - predictions_file: output/reports/train/4070982537f3337f45d894ab43747302/predictions.json - probabilities_file: output/reports/train/4070982537f3337f45d894ab43747302/probabilities.json - score_dict_file: output/reports/train/4070982537f3337f45d894ab43747302/score_dict.json - test_labels_file: output/reports/train/4070982537f3337f45d894ab43747302/test_labels.json - train_labels_file: output/reports/train/4070982537f3337f45d894ab43747302/train_labels.json - model_dir: models - name: 4070982537f3337f45d894ab43747302 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 516eeb5d8746a41f64cde47ec827870c - trainer: - kwargs: {} -name: 4070982537f3337f45d894ab43747302 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/408485404f987fdec98cbc4d440efae0/params.yaml b/examples/security/classification/output/reports/train/408485404f987fdec98cbc4d440efae0/params.yaml deleted file mode 100644 index a9ca35a3..00000000 --- a/examples/security/classification/output/reports/train/408485404f987fdec98cbc4d440efae0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/408485404f987fdec98cbc4d440efae0/adv_predictions.json - adv_probabilities_file: output/reports/train/408485404f987fdec98cbc4d440efae0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/72504deb5d00c64d2fc95d2849f70080.pkl - params_file: output/reports/train/408485404f987fdec98cbc4d440efae0/params.yaml - predictions_file: output/reports/train/408485404f987fdec98cbc4d440efae0/predictions.json - probabilities_file: output/reports/train/408485404f987fdec98cbc4d440efae0/probabilities.json - score_dict_file: output/reports/train/408485404f987fdec98cbc4d440efae0/score_dict.json - test_labels_file: output/reports/train/408485404f987fdec98cbc4d440efae0/test_labels.json - train_labels_file: output/reports/train/408485404f987fdec98cbc4d440efae0/train_labels.json - model_dir: models - name: 408485404f987fdec98cbc4d440efae0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 435fbb819cf8ae63ee814b705b9392d3 - trainer: - kwargs: {} -name: 408485404f987fdec98cbc4d440efae0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/408c7f605ce24ebf9a1f82c955316632/params.yaml b/examples/security/classification/output/reports/train/408c7f605ce24ebf9a1f82c955316632/params.yaml deleted file mode 100644 index 8c5dfa73..00000000 --- a/examples/security/classification/output/reports/train/408c7f605ce24ebf9a1f82c955316632/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/adv_predictions.json - adv_probabilities_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/9f2527914e86b057e2d8bf2a54b03baf.pkl - params_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/params.yaml - predictions_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/predictions.json - probabilities_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/probabilities.json - score_dict_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/score_dict.json - test_labels_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/test_labels.json - train_labels_file: output/reports/train/408c7f605ce24ebf9a1f82c955316632/train_labels.json - model_dir: models - name: 408c7f605ce24ebf9a1f82c955316632 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7094b11d04f0b7f1a4ebdf4bf25e883e - trainer: - kwargs: {} -name: 408c7f605ce24ebf9a1f82c955316632 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4099fb6e786d86420030035f1ae61f12/params.yaml b/examples/security/classification/output/reports/train/4099fb6e786d86420030035f1ae61f12/params.yaml deleted file mode 100644 index 85ee19ae..00000000 --- a/examples/security/classification/output/reports/train/4099fb6e786d86420030035f1ae61f12/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/adv_predictions.json - adv_probabilities_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/166e3b5e1a67c66e88a5deb48bc7de09.pkl - params_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/params.yaml - predictions_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/predictions.json - probabilities_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/probabilities.json - score_dict_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/score_dict.json - test_labels_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/test_labels.json - train_labels_file: output/reports/train/4099fb6e786d86420030035f1ae61f12/train_labels.json - model_dir: models - name: 4099fb6e786d86420030035f1ae61f12 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 450b13aa903401a6f3cc8b79cdce01c1 - trainer: - kwargs: {} -name: 4099fb6e786d86420030035f1ae61f12 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/params.yaml b/examples/security/classification/output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/params.yaml deleted file mode 100644 index e781fc6d..00000000 --- a/examples/security/classification/output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/adv_predictions.json - adv_probabilities_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/f6cd5301abd18e1256e0ea6a8e425ac3.pkl - params_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/params.yaml - predictions_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/predictions.json - probabilities_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/probabilities.json - score_dict_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/score_dict.json - test_labels_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/test_labels.json - train_labels_file: output/reports/train/40a838a1a63c38151dfbf8e5f71c6ccb/train_labels.json - model_dir: models - name: 40a838a1a63c38151dfbf8e5f71c6ccb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3c691fccb245073eb5010b03f71ec067 - trainer: - kwargs: {} -name: 40a838a1a63c38151dfbf8e5f71c6ccb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/params.yaml b/examples/security/classification/output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/params.yaml deleted file mode 100644 index 147a3290..00000000 --- a/examples/security/classification/output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/adv_predictions.json - adv_probabilities_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/35f4778e4ea0bf93490ba9b3b699ede2.pkl - params_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/params.yaml - predictions_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/predictions.json - probabilities_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/probabilities.json - score_dict_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/score_dict.json - test_labels_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/test_labels.json - train_labels_file: output/reports/train/40ab3d23ed2a73180f718ec40d1d3748/train_labels.json - model_dir: models - name: 40ab3d23ed2a73180f718ec40d1d3748 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bab47fcc5abe8b9ee3c0922cd936bbfa - trainer: - kwargs: {} -name: 40ab3d23ed2a73180f718ec40d1d3748 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/params.yaml b/examples/security/classification/output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/params.yaml deleted file mode 100644 index e2128d5d..00000000 --- a/examples/security/classification/output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/adv_predictions.json - adv_probabilities_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/12d3bd3f647ab5c4a65951441a8fb843.pkl - params_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/params.yaml - predictions_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/predictions.json - probabilities_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/probabilities.json - score_dict_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/score_dict.json - test_labels_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/test_labels.json - train_labels_file: output/reports/train/40b0e13eb5b9ec64a869d86f13643b5f/train_labels.json - model_dir: models - name: 40b0e13eb5b9ec64a869d86f13643b5f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 990f26f6eb24d056a36e38ca335eb44f - trainer: - kwargs: {} -name: 40b0e13eb5b9ec64a869d86f13643b5f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/params.yaml b/examples/security/classification/output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/params.yaml deleted file mode 100644 index 41e2b7a8..00000000 --- a/examples/security/classification/output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/adv_predictions.json - adv_probabilities_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/49986782145ea06d0d64a773203447b9.pkl - params_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/params.yaml - predictions_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/predictions.json - probabilities_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/probabilities.json - score_dict_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/score_dict.json - test_labels_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/test_labels.json - train_labels_file: output/reports/train/40bcd2b44f372726d82df8e999b0ca9e/train_labels.json - model_dir: models - name: 40bcd2b44f372726d82df8e999b0ca9e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d7e549771940cba2dd7e7f65137552a4 - trainer: - kwargs: {} -name: 40bcd2b44f372726d82df8e999b0ca9e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/40cf75c347eb1f60315a0d9571697aae/params.yaml b/examples/security/classification/output/reports/train/40cf75c347eb1f60315a0d9571697aae/params.yaml deleted file mode 100644 index 12255d09..00000000 --- a/examples/security/classification/output/reports/train/40cf75c347eb1f60315a0d9571697aae/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/adv_predictions.json - adv_probabilities_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/7de9df1f5ba0bbc0de9968d6819c0805.pkl - params_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/params.yaml - predictions_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/predictions.json - probabilities_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/probabilities.json - score_dict_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/score_dict.json - test_labels_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/test_labels.json - train_labels_file: output/reports/train/40cf75c347eb1f60315a0d9571697aae/train_labels.json - model_dir: models - name: 40cf75c347eb1f60315a0d9571697aae - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6d22f71c4faa4c466e84bc7bfebc531c - trainer: - kwargs: {} -name: 40cf75c347eb1f60315a0d9571697aae -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/params.yaml b/examples/security/classification/output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/params.yaml deleted file mode 100644 index e754b2f0..00000000 --- a/examples/security/classification/output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/adv_predictions.json - adv_probabilities_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/3dc14ee9dbd3c61da7bb840fd430cba9.pkl - params_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/params.yaml - predictions_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/predictions.json - probabilities_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/probabilities.json - score_dict_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/score_dict.json - test_labels_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/test_labels.json - train_labels_file: output/reports/train/40e0c6f8b058393a2c6342ff7bfe8d5c/train_labels.json - model_dir: models - name: 40e0c6f8b058393a2c6342ff7bfe8d5c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6f9f47bf5009f2d8ff5b47f9d13eabc7 - trainer: - kwargs: {} -name: 40e0c6f8b058393a2c6342ff7bfe8d5c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/40ff562d939537dab5edb864c80551cb/params.yaml b/examples/security/classification/output/reports/train/40ff562d939537dab5edb864c80551cb/params.yaml deleted file mode 100644 index cd7d8816..00000000 --- a/examples/security/classification/output/reports/train/40ff562d939537dab5edb864c80551cb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/40ff562d939537dab5edb864c80551cb/adv_predictions.json - adv_probabilities_file: output/reports/train/40ff562d939537dab5edb864c80551cb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/b7a794cef75e95d66b71436499396783.pkl - params_file: output/reports/train/40ff562d939537dab5edb864c80551cb/params.yaml - predictions_file: output/reports/train/40ff562d939537dab5edb864c80551cb/predictions.json - probabilities_file: output/reports/train/40ff562d939537dab5edb864c80551cb/probabilities.json - score_dict_file: output/reports/train/40ff562d939537dab5edb864c80551cb/score_dict.json - test_labels_file: output/reports/train/40ff562d939537dab5edb864c80551cb/test_labels.json - train_labels_file: output/reports/train/40ff562d939537dab5edb864c80551cb/train_labels.json - model_dir: models - name: 40ff562d939537dab5edb864c80551cb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9944aef248b0566adf354a4f60301ecc - trainer: - kwargs: {} -name: 40ff562d939537dab5edb864c80551cb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/410627d17d0eaa117243d2e7596d5460/params.yaml b/examples/security/classification/output/reports/train/410627d17d0eaa117243d2e7596d5460/params.yaml deleted file mode 100644 index a3b5dc54..00000000 --- a/examples/security/classification/output/reports/train/410627d17d0eaa117243d2e7596d5460/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/adv_predictions.json - adv_probabilities_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/41a1be4f9881335cd712c4b45a6e568f.pkl - params_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/params.yaml - predictions_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/predictions.json - probabilities_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/probabilities.json - score_dict_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/score_dict.json - test_labels_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/test_labels.json - train_labels_file: output/reports/train/410627d17d0eaa117243d2e7596d5460/train_labels.json - model_dir: models - name: 410627d17d0eaa117243d2e7596d5460 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 280cae68f27bcd07d364064fe9ceed8e - trainer: - kwargs: {} -name: 410627d17d0eaa117243d2e7596d5460 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/41142fd5b8692344f65219e8ee9a8346/params.yaml b/examples/security/classification/output/reports/train/41142fd5b8692344f65219e8ee9a8346/params.yaml deleted file mode 100644 index 35d462e1..00000000 --- a/examples/security/classification/output/reports/train/41142fd5b8692344f65219e8ee9a8346/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/adv_predictions.json - adv_probabilities_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/96e9fe53055b0d4d2cc7c81c96e9a527.pkl - params_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/params.yaml - predictions_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/predictions.json - probabilities_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/probabilities.json - score_dict_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/score_dict.json - test_labels_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/test_labels.json - train_labels_file: output/reports/train/41142fd5b8692344f65219e8ee9a8346/train_labels.json - model_dir: models - name: 41142fd5b8692344f65219e8ee9a8346 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 801fb39780c97f3bbddb1b91c4c8183f - trainer: - kwargs: {} -name: 41142fd5b8692344f65219e8ee9a8346 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/params.yaml b/examples/security/classification/output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/params.yaml deleted file mode 100644 index 2838bb2d..00000000 --- a/examples/security/classification/output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/adv_predictions.json - adv_probabilities_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/43d5b6dce72e6f574bb1def0673ce1cd.pkl - params_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/params.yaml - predictions_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/predictions.json - probabilities_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/probabilities.json - score_dict_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/score_dict.json - test_labels_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/test_labels.json - train_labels_file: output/reports/train/411a80c29685e9b6417ae1d99ad25ddd/train_labels.json - model_dir: models - name: 411a80c29685e9b6417ae1d99ad25ddd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 706d7c0eb90208ac90374230344b2130 - trainer: - kwargs: {} -name: 411a80c29685e9b6417ae1d99ad25ddd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/411cea795a6cfc4b650f66573481dfae/params.yaml b/examples/security/classification/output/reports/train/411cea795a6cfc4b650f66573481dfae/params.yaml deleted file mode 100644 index a0c44d49..00000000 --- a/examples/security/classification/output/reports/train/411cea795a6cfc4b650f66573481dfae/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/adv_predictions.json - adv_probabilities_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/1da12fcb827626f290a5a20b721db355.pkl - params_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/params.yaml - predictions_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/predictions.json - probabilities_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/probabilities.json - score_dict_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/score_dict.json - test_labels_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/test_labels.json - train_labels_file: output/reports/train/411cea795a6cfc4b650f66573481dfae/train_labels.json - model_dir: models - name: 411cea795a6cfc4b650f66573481dfae - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 74ba3906dd281f9bed97ab0bffe1e76f - trainer: - kwargs: {} -name: 411cea795a6cfc4b650f66573481dfae -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/params.yaml b/examples/security/classification/output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/params.yaml deleted file mode 100644 index c4aea24d..00000000 --- a/examples/security/classification/output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/adv_predictions.json - adv_probabilities_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/010abfc6521851d94d05ef59d0455e3d.pkl - params_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/params.yaml - predictions_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/predictions.json - probabilities_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/probabilities.json - score_dict_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/score_dict.json - test_labels_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/test_labels.json - train_labels_file: output/reports/train/4154738737ca7fbbc7da27ef1f0ddea6/train_labels.json - model_dir: models - name: 4154738737ca7fbbc7da27ef1f0ddea6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8c59fd4d96ab20360bf4105471a26b6b - trainer: - kwargs: {} -name: 4154738737ca7fbbc7da27ef1f0ddea6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4162ded92efae510b9d94490b49eeb03/params.yaml b/examples/security/classification/output/reports/train/4162ded92efae510b9d94490b49eeb03/params.yaml deleted file mode 100644 index 2348b81c..00000000 --- a/examples/security/classification/output/reports/train/4162ded92efae510b9d94490b49eeb03/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/adv_predictions.json - adv_probabilities_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/3f170bf856ae6d3e39b0ad7e91a07f37.pkl - params_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/params.yaml - predictions_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/predictions.json - probabilities_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/probabilities.json - score_dict_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/score_dict.json - test_labels_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/test_labels.json - train_labels_file: output/reports/train/4162ded92efae510b9d94490b49eeb03/train_labels.json - model_dir: models - name: 4162ded92efae510b9d94490b49eeb03 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 71431171bfd75e49473794fc10ebfba3 - trainer: - kwargs: {} -name: 4162ded92efae510b9d94490b49eeb03 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/41a797a45ec373b9f2aecd9363ad356c/params.yaml b/examples/security/classification/output/reports/train/41a797a45ec373b9f2aecd9363ad356c/params.yaml deleted file mode 100644 index ea3c8f44..00000000 --- a/examples/security/classification/output/reports/train/41a797a45ec373b9f2aecd9363ad356c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/adv_predictions.json - adv_probabilities_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/330d2c529888cac811ee4ddd1443387c.pkl - params_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/params.yaml - predictions_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/predictions.json - probabilities_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/probabilities.json - score_dict_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/score_dict.json - test_labels_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/test_labels.json - train_labels_file: output/reports/train/41a797a45ec373b9f2aecd9363ad356c/train_labels.json - model_dir: models - name: 41a797a45ec373b9f2aecd9363ad356c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eaa0550daee009d8d3b26303f67dac99 - trainer: - kwargs: {} -name: 41a797a45ec373b9f2aecd9363ad356c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/41b2c60f95fe6635566543b753934c26/params.yaml b/examples/security/classification/output/reports/train/41b2c60f95fe6635566543b753934c26/params.yaml deleted file mode 100644 index 081c3ee3..00000000 --- a/examples/security/classification/output/reports/train/41b2c60f95fe6635566543b753934c26/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/41b2c60f95fe6635566543b753934c26/adv_predictions.json - adv_probabilities_file: output/reports/train/41b2c60f95fe6635566543b753934c26/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/a20af723ea23cebd6197fb002009303c.pkl - params_file: output/reports/train/41b2c60f95fe6635566543b753934c26/params.yaml - predictions_file: output/reports/train/41b2c60f95fe6635566543b753934c26/predictions.json - probabilities_file: output/reports/train/41b2c60f95fe6635566543b753934c26/probabilities.json - score_dict_file: output/reports/train/41b2c60f95fe6635566543b753934c26/score_dict.json - test_labels_file: output/reports/train/41b2c60f95fe6635566543b753934c26/test_labels.json - train_labels_file: output/reports/train/41b2c60f95fe6635566543b753934c26/train_labels.json - model_dir: models - name: 41b2c60f95fe6635566543b753934c26 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ba04619b6786f270294ca6a5c05db222 - trainer: - kwargs: {} -name: 41b2c60f95fe6635566543b753934c26 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/41bba8e14155b4a6895a782912617774/params.yaml b/examples/security/classification/output/reports/train/41bba8e14155b4a6895a782912617774/params.yaml deleted file mode 100644 index fcfd3210..00000000 --- a/examples/security/classification/output/reports/train/41bba8e14155b4a6895a782912617774/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/41bba8e14155b4a6895a782912617774/adv_predictions.json - adv_probabilities_file: output/reports/train/41bba8e14155b4a6895a782912617774/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/947432a0273c3fa04a5decd549248c3e.pkl - params_file: output/reports/train/41bba8e14155b4a6895a782912617774/params.yaml - predictions_file: output/reports/train/41bba8e14155b4a6895a782912617774/predictions.json - probabilities_file: output/reports/train/41bba8e14155b4a6895a782912617774/probabilities.json - score_dict_file: output/reports/train/41bba8e14155b4a6895a782912617774/score_dict.json - test_labels_file: output/reports/train/41bba8e14155b4a6895a782912617774/test_labels.json - train_labels_file: output/reports/train/41bba8e14155b4a6895a782912617774/train_labels.json - model_dir: models - name: 41bba8e14155b4a6895a782912617774 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dd3caa07eeda3ea746bd26ab7c87aba5 - trainer: - kwargs: {} -name: 41bba8e14155b4a6895a782912617774 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/41ea5091d916842904adfedcd57c0998/params.yaml b/examples/security/classification/output/reports/train/41ea5091d916842904adfedcd57c0998/params.yaml deleted file mode 100644 index 2c518a55..00000000 --- a/examples/security/classification/output/reports/train/41ea5091d916842904adfedcd57c0998/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/41ea5091d916842904adfedcd57c0998/adv_predictions.json - adv_probabilities_file: output/reports/train/41ea5091d916842904adfedcd57c0998/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/43cb1d2bb2b822b4f6551462b99cb2cf.pkl - params_file: output/reports/train/41ea5091d916842904adfedcd57c0998/params.yaml - predictions_file: output/reports/train/41ea5091d916842904adfedcd57c0998/predictions.json - probabilities_file: output/reports/train/41ea5091d916842904adfedcd57c0998/probabilities.json - score_dict_file: output/reports/train/41ea5091d916842904adfedcd57c0998/score_dict.json - test_labels_file: output/reports/train/41ea5091d916842904adfedcd57c0998/test_labels.json - train_labels_file: output/reports/train/41ea5091d916842904adfedcd57c0998/train_labels.json - model_dir: models - name: 41ea5091d916842904adfedcd57c0998 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b78d71e58cf95b9281e9ef0ecd5729c5 - trainer: - kwargs: {} -name: 41ea5091d916842904adfedcd57c0998 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/41f60de5b4a3b89a7cd79204ed374984/params.yaml b/examples/security/classification/output/reports/train/41f60de5b4a3b89a7cd79204ed374984/params.yaml deleted file mode 100644 index b58ea017..00000000 --- a/examples/security/classification/output/reports/train/41f60de5b4a3b89a7cd79204ed374984/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/adv_predictions.json - adv_probabilities_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/5cd7aa456fdfd052473396b08b0b4802.pkl - params_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/params.yaml - predictions_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/predictions.json - probabilities_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/probabilities.json - score_dict_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/score_dict.json - test_labels_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/test_labels.json - train_labels_file: output/reports/train/41f60de5b4a3b89a7cd79204ed374984/train_labels.json - model_dir: models - name: 41f60de5b4a3b89a7cd79204ed374984 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f204b3d1e7c20a7da0c0e27fba47437f - trainer: - kwargs: {} -name: 41f60de5b4a3b89a7cd79204ed374984 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/params.yaml b/examples/security/classification/output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/params.yaml deleted file mode 100644 index d068f1a7..00000000 --- a/examples/security/classification/output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/adv_predictions.json - adv_probabilities_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/83a3cab7666747da6c94b9919851207c.pkl - params_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/params.yaml - predictions_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/predictions.json - probabilities_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/probabilities.json - score_dict_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/score_dict.json - test_labels_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/test_labels.json - train_labels_file: output/reports/train/41fa7e6f5a57fd2efe95ff77601a87ee/train_labels.json - model_dir: models - name: 41fa7e6f5a57fd2efe95ff77601a87ee - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5c9bf7c1fb4b94f7b3ff34654858fc5d - trainer: - kwargs: {} -name: 41fa7e6f5a57fd2efe95ff77601a87ee -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/42266d5a97a33692d4e16226b64f23fa/params.yaml b/examples/security/classification/output/reports/train/42266d5a97a33692d4e16226b64f23fa/params.yaml deleted file mode 100644 index e02a011d..00000000 --- a/examples/security/classification/output/reports/train/42266d5a97a33692d4e16226b64f23fa/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/adv_predictions.json - adv_probabilities_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/fb7baaebf9b7d1c43238947813c03a54.pkl - params_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/params.yaml - predictions_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/predictions.json - probabilities_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/probabilities.json - score_dict_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/score_dict.json - test_labels_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/test_labels.json - train_labels_file: output/reports/train/42266d5a97a33692d4e16226b64f23fa/train_labels.json - model_dir: models - name: 42266d5a97a33692d4e16226b64f23fa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ab62a7eb2a76d892fc2b3fc2fd4dedcb - trainer: - kwargs: {} -name: 42266d5a97a33692d4e16226b64f23fa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4234a74902abc64414c468a7a9022253/params.yaml b/examples/security/classification/output/reports/train/4234a74902abc64414c468a7a9022253/params.yaml deleted file mode 100644 index 2fdc529d..00000000 --- a/examples/security/classification/output/reports/train/4234a74902abc64414c468a7a9022253/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4234a74902abc64414c468a7a9022253/adv_predictions.json - adv_probabilities_file: output/reports/train/4234a74902abc64414c468a7a9022253/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/12a245af57522f24675cfeee1d669282.pkl - params_file: output/reports/train/4234a74902abc64414c468a7a9022253/params.yaml - predictions_file: output/reports/train/4234a74902abc64414c468a7a9022253/predictions.json - probabilities_file: output/reports/train/4234a74902abc64414c468a7a9022253/probabilities.json - score_dict_file: output/reports/train/4234a74902abc64414c468a7a9022253/score_dict.json - test_labels_file: output/reports/train/4234a74902abc64414c468a7a9022253/test_labels.json - train_labels_file: output/reports/train/4234a74902abc64414c468a7a9022253/train_labels.json - model_dir: models - name: 4234a74902abc64414c468a7a9022253 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 43d6610c2a7b9bf65b43153f5f8c1447 - trainer: - kwargs: {} -name: 4234a74902abc64414c468a7a9022253 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/42b7b63522472c072078968261c157f8/params.yaml b/examples/security/classification/output/reports/train/42b7b63522472c072078968261c157f8/params.yaml deleted file mode 100644 index 427fe5fd..00000000 --- a/examples/security/classification/output/reports/train/42b7b63522472c072078968261c157f8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/42b7b63522472c072078968261c157f8/adv_predictions.json - adv_probabilities_file: output/reports/train/42b7b63522472c072078968261c157f8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/74d2b31201042c35b610d59603d51ef1.pkl - params_file: output/reports/train/42b7b63522472c072078968261c157f8/params.yaml - predictions_file: output/reports/train/42b7b63522472c072078968261c157f8/predictions.json - probabilities_file: output/reports/train/42b7b63522472c072078968261c157f8/probabilities.json - score_dict_file: output/reports/train/42b7b63522472c072078968261c157f8/score_dict.json - test_labels_file: output/reports/train/42b7b63522472c072078968261c157f8/test_labels.json - train_labels_file: output/reports/train/42b7b63522472c072078968261c157f8/train_labels.json - model_dir: models - name: 42b7b63522472c072078968261c157f8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b986ead8a8b8c4596f270d3755b5962d - trainer: - kwargs: {} -name: 42b7b63522472c072078968261c157f8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4302ba616617db9b9742f199638dd708/params.yaml b/examples/security/classification/output/reports/train/4302ba616617db9b9742f199638dd708/params.yaml deleted file mode 100644 index 9ed4fd7f..00000000 --- a/examples/security/classification/output/reports/train/4302ba616617db9b9742f199638dd708/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4302ba616617db9b9742f199638dd708/adv_predictions.json - adv_probabilities_file: output/reports/train/4302ba616617db9b9742f199638dd708/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/0a19443c78920504262b2996d90e50f7.pkl - params_file: output/reports/train/4302ba616617db9b9742f199638dd708/params.yaml - predictions_file: output/reports/train/4302ba616617db9b9742f199638dd708/predictions.json - probabilities_file: output/reports/train/4302ba616617db9b9742f199638dd708/probabilities.json - score_dict_file: output/reports/train/4302ba616617db9b9742f199638dd708/score_dict.json - test_labels_file: output/reports/train/4302ba616617db9b9742f199638dd708/test_labels.json - train_labels_file: output/reports/train/4302ba616617db9b9742f199638dd708/train_labels.json - model_dir: models - name: 4302ba616617db9b9742f199638dd708 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2b791b48ce956a63bd9014d45e8d5340 - trainer: - kwargs: {} -name: 4302ba616617db9b9742f199638dd708 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/437382e1b3a3b038239723041109900f/params.yaml b/examples/security/classification/output/reports/train/437382e1b3a3b038239723041109900f/params.yaml deleted file mode 100644 index ddcef22c..00000000 --- a/examples/security/classification/output/reports/train/437382e1b3a3b038239723041109900f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/437382e1b3a3b038239723041109900f/adv_predictions.json - adv_probabilities_file: output/reports/train/437382e1b3a3b038239723041109900f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/a5567560fa586194be16017d8e02b0c5.pkl - params_file: output/reports/train/437382e1b3a3b038239723041109900f/params.yaml - predictions_file: output/reports/train/437382e1b3a3b038239723041109900f/predictions.json - probabilities_file: output/reports/train/437382e1b3a3b038239723041109900f/probabilities.json - score_dict_file: output/reports/train/437382e1b3a3b038239723041109900f/score_dict.json - test_labels_file: output/reports/train/437382e1b3a3b038239723041109900f/test_labels.json - train_labels_file: output/reports/train/437382e1b3a3b038239723041109900f/train_labels.json - model_dir: models - name: 437382e1b3a3b038239723041109900f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6633ffaaf8210b2937a38fd140af3ff5 - trainer: - kwargs: {} -name: 437382e1b3a3b038239723041109900f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/438228aad785e28784a085dc4507ba85/params.yaml b/examples/security/classification/output/reports/train/438228aad785e28784a085dc4507ba85/params.yaml deleted file mode 100644 index b0d83dae..00000000 --- a/examples/security/classification/output/reports/train/438228aad785e28784a085dc4507ba85/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/438228aad785e28784a085dc4507ba85/adv_predictions.json - adv_probabilities_file: output/reports/train/438228aad785e28784a085dc4507ba85/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/c45fceeb4a140fa935dff14d50c68c0c.pkl - params_file: output/reports/train/438228aad785e28784a085dc4507ba85/params.yaml - predictions_file: output/reports/train/438228aad785e28784a085dc4507ba85/predictions.json - probabilities_file: output/reports/train/438228aad785e28784a085dc4507ba85/probabilities.json - score_dict_file: output/reports/train/438228aad785e28784a085dc4507ba85/score_dict.json - test_labels_file: output/reports/train/438228aad785e28784a085dc4507ba85/test_labels.json - train_labels_file: output/reports/train/438228aad785e28784a085dc4507ba85/train_labels.json - model_dir: models - name: 438228aad785e28784a085dc4507ba85 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0da48bf1d74c9b6f8ec64c47c4247282 - trainer: - kwargs: {} -name: 438228aad785e28784a085dc4507ba85 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4384e894ef431c73873837bb4604f12c/params.yaml b/examples/security/classification/output/reports/train/4384e894ef431c73873837bb4604f12c/params.yaml deleted file mode 100644 index b19e5607..00000000 --- a/examples/security/classification/output/reports/train/4384e894ef431c73873837bb4604f12c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4384e894ef431c73873837bb4604f12c/adv_predictions.json - adv_probabilities_file: output/reports/train/4384e894ef431c73873837bb4604f12c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/0c988975597954c22658b21b3743b74f.pkl - params_file: output/reports/train/4384e894ef431c73873837bb4604f12c/params.yaml - predictions_file: output/reports/train/4384e894ef431c73873837bb4604f12c/predictions.json - probabilities_file: output/reports/train/4384e894ef431c73873837bb4604f12c/probabilities.json - score_dict_file: output/reports/train/4384e894ef431c73873837bb4604f12c/score_dict.json - test_labels_file: output/reports/train/4384e894ef431c73873837bb4604f12c/test_labels.json - train_labels_file: output/reports/train/4384e894ef431c73873837bb4604f12c/train_labels.json - model_dir: models - name: 4384e894ef431c73873837bb4604f12c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2a1cc90c762b83f79a0a1fe052a16a5c - trainer: - kwargs: {} -name: 4384e894ef431c73873837bb4604f12c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/438ad8eaac22fc24f522621f66afb307/params.yaml b/examples/security/classification/output/reports/train/438ad8eaac22fc24f522621f66afb307/params.yaml deleted file mode 100644 index 71e6c6d8..00000000 --- a/examples/security/classification/output/reports/train/438ad8eaac22fc24f522621f66afb307/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/adv_predictions.json - adv_probabilities_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/40b1b6785306a5e999f6947ab9f75b9e.pkl - params_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/params.yaml - predictions_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/predictions.json - probabilities_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/probabilities.json - score_dict_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/score_dict.json - test_labels_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/test_labels.json - train_labels_file: output/reports/train/438ad8eaac22fc24f522621f66afb307/train_labels.json - model_dir: models - name: 438ad8eaac22fc24f522621f66afb307 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1fd6e7666ac21155b0a27d0b5a53f5d6 - trainer: - kwargs: {} -name: 438ad8eaac22fc24f522621f66afb307 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/438e9ce8557724ce2ec010f664bea12e/params.yaml b/examples/security/classification/output/reports/train/438e9ce8557724ce2ec010f664bea12e/params.yaml deleted file mode 100644 index 30fe0b34..00000000 --- a/examples/security/classification/output/reports/train/438e9ce8557724ce2ec010f664bea12e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/adv_predictions.json - adv_probabilities_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/26bdb7598ee3755701118309378fa801.pkl - params_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/params.yaml - predictions_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/predictions.json - probabilities_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/probabilities.json - score_dict_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/score_dict.json - test_labels_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/test_labels.json - train_labels_file: output/reports/train/438e9ce8557724ce2ec010f664bea12e/train_labels.json - model_dir: models - name: 438e9ce8557724ce2ec010f664bea12e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 41a94b5b1346656623c2b9e5a77d0d24 - trainer: - kwargs: {} -name: 438e9ce8557724ce2ec010f664bea12e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/439cb6115a2ed3387acccbb27382aa78/params.yaml b/examples/security/classification/output/reports/train/439cb6115a2ed3387acccbb27382aa78/params.yaml deleted file mode 100644 index d81f54e0..00000000 --- a/examples/security/classification/output/reports/train/439cb6115a2ed3387acccbb27382aa78/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/adv_predictions.json - adv_probabilities_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/e6ab19df51b69bbbb9c0d0cd42b54b98.pkl - params_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/params.yaml - predictions_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/predictions.json - probabilities_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/probabilities.json - score_dict_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/score_dict.json - test_labels_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/test_labels.json - train_labels_file: output/reports/train/439cb6115a2ed3387acccbb27382aa78/train_labels.json - model_dir: models - name: 439cb6115a2ed3387acccbb27382aa78 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9a6c594303863fc9fdcaafc5035652cf - trainer: - kwargs: {} -name: 439cb6115a2ed3387acccbb27382aa78 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/params.yaml b/examples/security/classification/output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/params.yaml deleted file mode 100644 index 68b7ea0b..00000000 --- a/examples/security/classification/output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/adv_predictions.json - adv_probabilities_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/6bd2dea59612511625685d89e1bfa6ec.pkl - params_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/params.yaml - predictions_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/predictions.json - probabilities_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/probabilities.json - score_dict_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/score_dict.json - test_labels_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/test_labels.json - train_labels_file: output/reports/train/43bcba5db33490fd14e8cdd251b72d5f/train_labels.json - model_dir: models - name: 43bcba5db33490fd14e8cdd251b72d5f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 10bb9e5164dd50d4eb0f3ffa015e2237 - trainer: - kwargs: {} -name: 43bcba5db33490fd14e8cdd251b72d5f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/params.yaml b/examples/security/classification/output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/params.yaml deleted file mode 100644 index 76addd49..00000000 --- a/examples/security/classification/output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/adv_predictions.json - adv_probabilities_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/fa220dbdd1c286ea07301a18ba2f5e5e.pkl - params_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/params.yaml - predictions_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/predictions.json - probabilities_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/probabilities.json - score_dict_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/score_dict.json - test_labels_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/test_labels.json - train_labels_file: output/reports/train/43c3d8ce0e7bf69684f3d4fd92fe2b41/train_labels.json - model_dir: models - name: 43c3d8ce0e7bf69684f3d4fd92fe2b41 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 853b2ad05c6957fcb40224c40d2dfd58 - trainer: - kwargs: {} -name: 43c3d8ce0e7bf69684f3d4fd92fe2b41 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/params.yaml b/examples/security/classification/output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/params.yaml deleted file mode 100644 index a5ad616d..00000000 --- a/examples/security/classification/output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/adv_predictions.json - adv_probabilities_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/a663194e85e7b2de0bb47dddd60588c3.pkl - params_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/params.yaml - predictions_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/predictions.json - probabilities_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/probabilities.json - score_dict_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/score_dict.json - test_labels_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/test_labels.json - train_labels_file: output/reports/train/43c75b43c5da43e3d3f9a9ea5ee9468d/train_labels.json - model_dir: models - name: 43c75b43c5da43e3d3f9a9ea5ee9468d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0948bb3c542491a4aa0f64214d212baa - trainer: - kwargs: {} -name: 43c75b43c5da43e3d3f9a9ea5ee9468d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/43e420d203554c01f49af05ef285d123/params.yaml b/examples/security/classification/output/reports/train/43e420d203554c01f49af05ef285d123/params.yaml deleted file mode 100644 index 6c03a5be..00000000 --- a/examples/security/classification/output/reports/train/43e420d203554c01f49af05ef285d123/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/43e420d203554c01f49af05ef285d123/adv_predictions.json - adv_probabilities_file: output/reports/train/43e420d203554c01f49af05ef285d123/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/58f5e53516442571bd1a92beaf65d225.pkl - params_file: output/reports/train/43e420d203554c01f49af05ef285d123/params.yaml - predictions_file: output/reports/train/43e420d203554c01f49af05ef285d123/predictions.json - probabilities_file: output/reports/train/43e420d203554c01f49af05ef285d123/probabilities.json - score_dict_file: output/reports/train/43e420d203554c01f49af05ef285d123/score_dict.json - test_labels_file: output/reports/train/43e420d203554c01f49af05ef285d123/test_labels.json - train_labels_file: output/reports/train/43e420d203554c01f49af05ef285d123/train_labels.json - model_dir: models - name: 43e420d203554c01f49af05ef285d123 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0f0e10d4411ff76ecd6a9eb640da2f36 - trainer: - kwargs: {} -name: 43e420d203554c01f49af05ef285d123 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/params.yaml b/examples/security/classification/output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/params.yaml deleted file mode 100644 index 9750607d..00000000 --- a/examples/security/classification/output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/adv_predictions.json - adv_probabilities_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/97267e27baded7f5f3fcf17b3f95fb6b.pkl - params_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/params.yaml - predictions_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/predictions.json - probabilities_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/probabilities.json - score_dict_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/score_dict.json - test_labels_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/test_labels.json - train_labels_file: output/reports/train/4408fb8be4c8201adb9a38bcf2979f30/train_labels.json - model_dir: models - name: 4408fb8be4c8201adb9a38bcf2979f30 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 56d695f59553c606c211d3bef3fdd7c1 - trainer: - kwargs: {} -name: 4408fb8be4c8201adb9a38bcf2979f30 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/params.yaml b/examples/security/classification/output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/params.yaml deleted file mode 100644 index b1d74348..00000000 --- a/examples/security/classification/output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/adv_predictions.json - adv_probabilities_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/73fa35ef00c16c29561c92bae4a05a26.pkl - params_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/params.yaml - predictions_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/predictions.json - probabilities_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/probabilities.json - score_dict_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/score_dict.json - test_labels_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/test_labels.json - train_labels_file: output/reports/train/440f0bf2ae2b5d4266c70ae7cffaf649/train_labels.json - model_dir: models - name: 440f0bf2ae2b5d4266c70ae7cffaf649 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 78c64b8e677462a2d667f1b5c6df06ee - trainer: - kwargs: {} -name: 440f0bf2ae2b5d4266c70ae7cffaf649 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/442b739179fc2666864fff8fb101f2f9/params.yaml b/examples/security/classification/output/reports/train/442b739179fc2666864fff8fb101f2f9/params.yaml deleted file mode 100644 index 1471bdae..00000000 --- a/examples/security/classification/output/reports/train/442b739179fc2666864fff8fb101f2f9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/adv_predictions.json - adv_probabilities_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/c9c690c9799855c8b984741872d3c783.pkl - params_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/params.yaml - predictions_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/predictions.json - probabilities_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/probabilities.json - score_dict_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/score_dict.json - test_labels_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/test_labels.json - train_labels_file: output/reports/train/442b739179fc2666864fff8fb101f2f9/train_labels.json - model_dir: models - name: 442b739179fc2666864fff8fb101f2f9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 313fd574892eaf1c2b7df29898200039 - trainer: - kwargs: {} -name: 442b739179fc2666864fff8fb101f2f9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4437f589d0eb882916c56b785bf305fc/params.yaml b/examples/security/classification/output/reports/train/4437f589d0eb882916c56b785bf305fc/params.yaml deleted file mode 100644 index a319b0fb..00000000 --- a/examples/security/classification/output/reports/train/4437f589d0eb882916c56b785bf305fc/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/adv_predictions.json - adv_probabilities_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/5ce996f249935bec5fa8778420871b6a.pkl - params_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/params.yaml - predictions_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/predictions.json - probabilities_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/probabilities.json - score_dict_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/score_dict.json - test_labels_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/test_labels.json - train_labels_file: output/reports/train/4437f589d0eb882916c56b785bf305fc/train_labels.json - model_dir: models - name: 4437f589d0eb882916c56b785bf305fc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bca1a03392f805f1114fc0db84d536aa - trainer: - kwargs: {} -name: 4437f589d0eb882916c56b785bf305fc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4496eb6a7558cb466b5704171731d183/params.yaml b/examples/security/classification/output/reports/train/4496eb6a7558cb466b5704171731d183/params.yaml deleted file mode 100644 index 974ea906..00000000 --- a/examples/security/classification/output/reports/train/4496eb6a7558cb466b5704171731d183/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4496eb6a7558cb466b5704171731d183/adv_predictions.json - adv_probabilities_file: output/reports/train/4496eb6a7558cb466b5704171731d183/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/7eb6afc3868559db205a84fd69952984.pkl - params_file: output/reports/train/4496eb6a7558cb466b5704171731d183/params.yaml - predictions_file: output/reports/train/4496eb6a7558cb466b5704171731d183/predictions.json - probabilities_file: output/reports/train/4496eb6a7558cb466b5704171731d183/probabilities.json - score_dict_file: output/reports/train/4496eb6a7558cb466b5704171731d183/score_dict.json - test_labels_file: output/reports/train/4496eb6a7558cb466b5704171731d183/test_labels.json - train_labels_file: output/reports/train/4496eb6a7558cb466b5704171731d183/train_labels.json - model_dir: models - name: 4496eb6a7558cb466b5704171731d183 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cc5f4b2ec1e4f54480b6aa18e9c9af26 - trainer: - kwargs: {} -name: 4496eb6a7558cb466b5704171731d183 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/44bfaa22b4aa46a46883e2f05d042405/params.yaml b/examples/security/classification/output/reports/train/44bfaa22b4aa46a46883e2f05d042405/params.yaml deleted file mode 100644 index 6784bed6..00000000 --- a/examples/security/classification/output/reports/train/44bfaa22b4aa46a46883e2f05d042405/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/adv_predictions.json - adv_probabilities_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/5987986ed2c279a850f459dcbb943d7c.pkl - params_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/params.yaml - predictions_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/predictions.json - probabilities_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/probabilities.json - score_dict_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/score_dict.json - test_labels_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/test_labels.json - train_labels_file: output/reports/train/44bfaa22b4aa46a46883e2f05d042405/train_labels.json - model_dir: models - name: 44bfaa22b4aa46a46883e2f05d042405 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f7f55648a48ead0ab9da47eb1ffaa8b6 - trainer: - kwargs: {} -name: 44bfaa22b4aa46a46883e2f05d042405 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/44c2efed1499b01693aefc27375cb521/params.yaml b/examples/security/classification/output/reports/train/44c2efed1499b01693aefc27375cb521/params.yaml deleted file mode 100644 index a00c0166..00000000 --- a/examples/security/classification/output/reports/train/44c2efed1499b01693aefc27375cb521/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/44c2efed1499b01693aefc27375cb521/adv_predictions.json - adv_probabilities_file: output/reports/train/44c2efed1499b01693aefc27375cb521/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/8a778862dd65e9a4ede7fd57711ca6ae.pkl - params_file: output/reports/train/44c2efed1499b01693aefc27375cb521/params.yaml - predictions_file: output/reports/train/44c2efed1499b01693aefc27375cb521/predictions.json - probabilities_file: output/reports/train/44c2efed1499b01693aefc27375cb521/probabilities.json - score_dict_file: output/reports/train/44c2efed1499b01693aefc27375cb521/score_dict.json - test_labels_file: output/reports/train/44c2efed1499b01693aefc27375cb521/test_labels.json - train_labels_file: output/reports/train/44c2efed1499b01693aefc27375cb521/train_labels.json - model_dir: models - name: 44c2efed1499b01693aefc27375cb521 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9968c281daffc94a6499d5041031cb66 - trainer: - kwargs: {} -name: 44c2efed1499b01693aefc27375cb521 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/453f04bfed02a38cdccb0191772bfcd2/params.yaml b/examples/security/classification/output/reports/train/453f04bfed02a38cdccb0191772bfcd2/params.yaml deleted file mode 100644 index 5064af0e..00000000 --- a/examples/security/classification/output/reports/train/453f04bfed02a38cdccb0191772bfcd2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/adv_predictions.json - adv_probabilities_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/15581c0219df49b4312dbfaa74308270.pkl - params_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/params.yaml - predictions_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/predictions.json - probabilities_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/probabilities.json - score_dict_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/score_dict.json - test_labels_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/test_labels.json - train_labels_file: output/reports/train/453f04bfed02a38cdccb0191772bfcd2/train_labels.json - model_dir: models - name: 453f04bfed02a38cdccb0191772bfcd2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1ee76a4795f2a76cc7800ff849c02f13 - trainer: - kwargs: {} -name: 453f04bfed02a38cdccb0191772bfcd2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4558027c552ec5d355df56f6d52a39e2/params.yaml b/examples/security/classification/output/reports/train/4558027c552ec5d355df56f6d52a39e2/params.yaml deleted file mode 100644 index ee977d4e..00000000 --- a/examples/security/classification/output/reports/train/4558027c552ec5d355df56f6d52a39e2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/adv_predictions.json - adv_probabilities_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/a253b94f03a4e0cff5bf65b74b1e0015.pkl - params_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/params.yaml - predictions_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/predictions.json - probabilities_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/probabilities.json - score_dict_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/score_dict.json - test_labels_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/test_labels.json - train_labels_file: output/reports/train/4558027c552ec5d355df56f6d52a39e2/train_labels.json - model_dir: models - name: 4558027c552ec5d355df56f6d52a39e2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ea95bbcd13bce6d202717a0265953913 - trainer: - kwargs: {} -name: 4558027c552ec5d355df56f6d52a39e2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/params.yaml b/examples/security/classification/output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/params.yaml deleted file mode 100644 index 190f9a3e..00000000 --- a/examples/security/classification/output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/adv_predictions.json - adv_probabilities_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/9def92d47515befe20db14d8f9d6699d.pkl - params_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/params.yaml - predictions_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/predictions.json - probabilities_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/probabilities.json - score_dict_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/score_dict.json - test_labels_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/test_labels.json - train_labels_file: output/reports/train/45777ba1f9dfe0c3216bccda7593bb0e/train_labels.json - model_dir: models - name: 45777ba1f9dfe0c3216bccda7593bb0e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 49ccf0e6b27b463bf57829fc3a270df5 - trainer: - kwargs: {} -name: 45777ba1f9dfe0c3216bccda7593bb0e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/45882e8c3acad894666fe57d8f2f73f5/params.yaml b/examples/security/classification/output/reports/train/45882e8c3acad894666fe57d8f2f73f5/params.yaml deleted file mode 100644 index 1b8b04ed..00000000 --- a/examples/security/classification/output/reports/train/45882e8c3acad894666fe57d8f2f73f5/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/adv_predictions.json - adv_probabilities_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/b76cc2775ac05de2063f050708b51048.pkl - params_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/params.yaml - predictions_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/predictions.json - probabilities_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/probabilities.json - score_dict_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/score_dict.json - test_labels_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/test_labels.json - train_labels_file: output/reports/train/45882e8c3acad894666fe57d8f2f73f5/train_labels.json - model_dir: models - name: 45882e8c3acad894666fe57d8f2f73f5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fe753c92dc18f012d313e7d917ea4482 - trainer: - kwargs: {} -name: 45882e8c3acad894666fe57d8f2f73f5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/45a686218387f42cbf755abbc309fdf8/params.yaml b/examples/security/classification/output/reports/train/45a686218387f42cbf755abbc309fdf8/params.yaml deleted file mode 100644 index c6e6905c..00000000 --- a/examples/security/classification/output/reports/train/45a686218387f42cbf755abbc309fdf8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/adv_predictions.json - adv_probabilities_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/51631478706c85b1bd76609307f83ab3.pkl - params_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/params.yaml - predictions_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/predictions.json - probabilities_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/probabilities.json - score_dict_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/score_dict.json - test_labels_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/test_labels.json - train_labels_file: output/reports/train/45a686218387f42cbf755abbc309fdf8/train_labels.json - model_dir: models - name: 45a686218387f42cbf755abbc309fdf8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c14c38629b7b1250d86f183725b1fd7e - trainer: - kwargs: {} -name: 45a686218387f42cbf755abbc309fdf8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/45c2df00872d50f0df473b9ffbff5207/params.yaml b/examples/security/classification/output/reports/train/45c2df00872d50f0df473b9ffbff5207/params.yaml deleted file mode 100644 index 66b3c8a9..00000000 --- a/examples/security/classification/output/reports/train/45c2df00872d50f0df473b9ffbff5207/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/adv_predictions.json - adv_probabilities_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/318928c7aea32f7d987b0b14550fea5e.pkl - params_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/params.yaml - predictions_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/predictions.json - probabilities_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/probabilities.json - score_dict_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/score_dict.json - test_labels_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/test_labels.json - train_labels_file: output/reports/train/45c2df00872d50f0df473b9ffbff5207/train_labels.json - model_dir: models - name: 45c2df00872d50f0df473b9ffbff5207 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1f7b8d623889a8ffa878f9625985ec69 - trainer: - kwargs: {} -name: 45c2df00872d50f0df473b9ffbff5207 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/params.yaml b/examples/security/classification/output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/params.yaml deleted file mode 100644 index ea25803f..00000000 --- a/examples/security/classification/output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/adv_predictions.json - adv_probabilities_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/4b000bceb6776a613d60e8e70680f2ce.pkl - params_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/params.yaml - predictions_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/predictions.json - probabilities_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/probabilities.json - score_dict_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/score_dict.json - test_labels_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/test_labels.json - train_labels_file: output/reports/train/45c4cc6d21760a24f684a5de5b68fb94/train_labels.json - model_dir: models - name: 45c4cc6d21760a24f684a5de5b68fb94 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6ecb462a9e04ce45a5836a907b5f6129 - trainer: - kwargs: {} -name: 45c4cc6d21760a24f684a5de5b68fb94 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/params.yaml b/examples/security/classification/output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/params.yaml deleted file mode 100644 index e05b5a0d..00000000 --- a/examples/security/classification/output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/adv_predictions.json - adv_probabilities_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/27e93fe0b69318baa4a504296a90c074.pkl - params_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/params.yaml - predictions_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/predictions.json - probabilities_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/probabilities.json - score_dict_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/score_dict.json - test_labels_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/test_labels.json - train_labels_file: output/reports/train/45ca4342031d7a6ca4a9fe4b9b8b4a03/train_labels.json - model_dir: models - name: 45ca4342031d7a6ca4a9fe4b9b8b4a03 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 719563ccfc4b69e0ea0ca1ba53ed5902 - trainer: - kwargs: {} -name: 45ca4342031d7a6ca4a9fe4b9b8b4a03 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/params.yaml b/examples/security/classification/output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/params.yaml deleted file mode 100644 index 2ba9d53e..00000000 --- a/examples/security/classification/output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/adv_predictions.json - adv_probabilities_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/72948e4247959357104a0d346355ad9b.pkl - params_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/params.yaml - predictions_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/predictions.json - probabilities_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/probabilities.json - score_dict_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/score_dict.json - test_labels_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/test_labels.json - train_labels_file: output/reports/train/45f069d3ec3a7b74d81212c294df5bcc/train_labels.json - model_dir: models - name: 45f069d3ec3a7b74d81212c294df5bcc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 45c5a2c5229d084046b1d40c7cce444c - trainer: - kwargs: {} -name: 45f069d3ec3a7b74d81212c294df5bcc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4600ee75f171e5595764bf91c4d113e6/params.yaml b/examples/security/classification/output/reports/train/4600ee75f171e5595764bf91c4d113e6/params.yaml deleted file mode 100644 index 1edb45b5..00000000 --- a/examples/security/classification/output/reports/train/4600ee75f171e5595764bf91c4d113e6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/adv_predictions.json - adv_probabilities_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/1a144bab686653a0824082f179fe3edc.pkl - params_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/params.yaml - predictions_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/predictions.json - probabilities_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/probabilities.json - score_dict_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/score_dict.json - test_labels_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/test_labels.json - train_labels_file: output/reports/train/4600ee75f171e5595764bf91c4d113e6/train_labels.json - model_dir: models - name: 4600ee75f171e5595764bf91c4d113e6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1acb4605ba214b18ccca948afc3493c3 - trainer: - kwargs: {} -name: 4600ee75f171e5595764bf91c4d113e6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/464324bc0527840737f4e0f18210ab5f/params.yaml b/examples/security/classification/output/reports/train/464324bc0527840737f4e0f18210ab5f/params.yaml deleted file mode 100644 index 4bec0166..00000000 --- a/examples/security/classification/output/reports/train/464324bc0527840737f4e0f18210ab5f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/adv_predictions.json - adv_probabilities_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/f71af7336b5ecddba9c933c8cfc85ede.pkl - params_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/params.yaml - predictions_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/predictions.json - probabilities_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/probabilities.json - score_dict_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/score_dict.json - test_labels_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/test_labels.json - train_labels_file: output/reports/train/464324bc0527840737f4e0f18210ab5f/train_labels.json - model_dir: models - name: 464324bc0527840737f4e0f18210ab5f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 37f69ea72800ae8332ad59ceae3edaa1 - trainer: - kwargs: {} -name: 464324bc0527840737f4e0f18210ab5f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/465257a5f6f0d49830c194a2ff8952a1/params.yaml b/examples/security/classification/output/reports/train/465257a5f6f0d49830c194a2ff8952a1/params.yaml deleted file mode 100644 index 878272e1..00000000 --- a/examples/security/classification/output/reports/train/465257a5f6f0d49830c194a2ff8952a1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/adv_predictions.json - adv_probabilities_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/9506109e157eeb36fd293ac5b5660775.pkl - params_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/params.yaml - predictions_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/predictions.json - probabilities_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/probabilities.json - score_dict_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/score_dict.json - test_labels_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/test_labels.json - train_labels_file: output/reports/train/465257a5f6f0d49830c194a2ff8952a1/train_labels.json - model_dir: models - name: 465257a5f6f0d49830c194a2ff8952a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c6d8fea82e3ad6a57939c36db2d6616d - trainer: - kwargs: {} -name: 465257a5f6f0d49830c194a2ff8952a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/465d01e6814ac90042fb095146b24937/params.yaml b/examples/security/classification/output/reports/train/465d01e6814ac90042fb095146b24937/params.yaml deleted file mode 100644 index 625cf6f0..00000000 --- a/examples/security/classification/output/reports/train/465d01e6814ac90042fb095146b24937/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/465d01e6814ac90042fb095146b24937/adv_predictions.json - adv_probabilities_file: output/reports/train/465d01e6814ac90042fb095146b24937/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/e36bf197de936d2d10404517a7f3550c.pkl - params_file: output/reports/train/465d01e6814ac90042fb095146b24937/params.yaml - predictions_file: output/reports/train/465d01e6814ac90042fb095146b24937/predictions.json - probabilities_file: output/reports/train/465d01e6814ac90042fb095146b24937/probabilities.json - score_dict_file: output/reports/train/465d01e6814ac90042fb095146b24937/score_dict.json - test_labels_file: output/reports/train/465d01e6814ac90042fb095146b24937/test_labels.json - train_labels_file: output/reports/train/465d01e6814ac90042fb095146b24937/train_labels.json - model_dir: models - name: 465d01e6814ac90042fb095146b24937 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7af8e3da9edcb299b61eea9349c2568e - trainer: - kwargs: {} -name: 465d01e6814ac90042fb095146b24937 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/466dbfd77747a48e681757500b40d2cc/params.yaml b/examples/security/classification/output/reports/train/466dbfd77747a48e681757500b40d2cc/params.yaml deleted file mode 100644 index 5a78ce68..00000000 --- a/examples/security/classification/output/reports/train/466dbfd77747a48e681757500b40d2cc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/adv_predictions.json - adv_probabilities_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/7b09577b589ef13bde7e2a1f7bb2716e.pkl - params_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/params.yaml - predictions_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/predictions.json - probabilities_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/probabilities.json - score_dict_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/score_dict.json - test_labels_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/test_labels.json - train_labels_file: output/reports/train/466dbfd77747a48e681757500b40d2cc/train_labels.json - model_dir: models - name: 466dbfd77747a48e681757500b40d2cc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a421893b18786a88118373f9152e80da - trainer: - kwargs: {} -name: 466dbfd77747a48e681757500b40d2cc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/467a01440d7ebae95a7bda2e79514fd1/params.yaml b/examples/security/classification/output/reports/train/467a01440d7ebae95a7bda2e79514fd1/params.yaml deleted file mode 100644 index d5ccb8f6..00000000 --- a/examples/security/classification/output/reports/train/467a01440d7ebae95a7bda2e79514fd1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/adv_predictions.json - adv_probabilities_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/7f8c2dc24e9d6ee48eb1daa71a25c997.pkl - params_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/params.yaml - predictions_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/predictions.json - probabilities_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/probabilities.json - score_dict_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/score_dict.json - test_labels_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/test_labels.json - train_labels_file: output/reports/train/467a01440d7ebae95a7bda2e79514fd1/train_labels.json - model_dir: models - name: 467a01440d7ebae95a7bda2e79514fd1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e73c01bd707ee72f211e7835000d402d - trainer: - kwargs: {} -name: 467a01440d7ebae95a7bda2e79514fd1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/params.yaml b/examples/security/classification/output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/params.yaml deleted file mode 100644 index af85a7ca..00000000 --- a/examples/security/classification/output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/adv_predictions.json - adv_probabilities_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/51ca3fd480e63309a4a3eee8d4045681.pkl - params_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/params.yaml - predictions_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/predictions.json - probabilities_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/probabilities.json - score_dict_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/score_dict.json - test_labels_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/test_labels.json - train_labels_file: output/reports/train/468f6ae008906ab73d56c6bb172dc7c2/train_labels.json - model_dir: models - name: 468f6ae008906ab73d56c6bb172dc7c2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e96c79c5f3cde1b795188ad46e65a00b - trainer: - kwargs: {} -name: 468f6ae008906ab73d56c6bb172dc7c2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/params.yaml b/examples/security/classification/output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/params.yaml deleted file mode 100644 index 855d485d..00000000 --- a/examples/security/classification/output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/adv_predictions.json - adv_probabilities_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/2bef32d46acfd65addf4196915407249.pkl - params_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/params.yaml - predictions_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/predictions.json - probabilities_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/probabilities.json - score_dict_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/score_dict.json - test_labels_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/test_labels.json - train_labels_file: output/reports/train/46977a64fe4466f14e4c7629b83a1e5c/train_labels.json - model_dir: models - name: 46977a64fe4466f14e4c7629b83a1e5c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d65a1e9a53e71f871ccb51344c7db029 - trainer: - kwargs: {} -name: 46977a64fe4466f14e4c7629b83a1e5c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/46b0be9993b62cd919482c0dca3a80cb/params.yaml b/examples/security/classification/output/reports/train/46b0be9993b62cd919482c0dca3a80cb/params.yaml deleted file mode 100644 index daef0d8d..00000000 --- a/examples/security/classification/output/reports/train/46b0be9993b62cd919482c0dca3a80cb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/adv_predictions.json - adv_probabilities_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e60785c743d5a9c63b4c5a37daacdab7.pkl - params_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/params.yaml - predictions_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/predictions.json - probabilities_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/probabilities.json - score_dict_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/score_dict.json - test_labels_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/test_labels.json - train_labels_file: output/reports/train/46b0be9993b62cd919482c0dca3a80cb/train_labels.json - model_dir: models - name: 46b0be9993b62cd919482c0dca3a80cb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 97ff54b48d485c54569d4ff8e448bca8 - trainer: - kwargs: {} -name: 46b0be9993b62cd919482c0dca3a80cb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/params.yaml b/examples/security/classification/output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/params.yaml deleted file mode 100644 index e8e07704..00000000 --- a/examples/security/classification/output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/adv_predictions.json - adv_probabilities_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/0108cb346fc463290944c3413212a958.pkl - params_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/params.yaml - predictions_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/predictions.json - probabilities_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/probabilities.json - score_dict_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/score_dict.json - test_labels_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/test_labels.json - train_labels_file: output/reports/train/46b1b795c4b7fdb5395d2f7359d2adf9/train_labels.json - model_dir: models - name: 46b1b795c4b7fdb5395d2f7359d2adf9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2bbc5753c91f1ff3cde034c2c9ee83f2 - trainer: - kwargs: {} -name: 46b1b795c4b7fdb5395d2f7359d2adf9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/params.yaml b/examples/security/classification/output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/params.yaml deleted file mode 100644 index 7ca4f19f..00000000 --- a/examples/security/classification/output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/adv_predictions.json - adv_probabilities_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/2a06273cd7a33f7e5182a0e45b6619a7.pkl - params_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/params.yaml - predictions_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/predictions.json - probabilities_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/probabilities.json - score_dict_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/score_dict.json - test_labels_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/test_labels.json - train_labels_file: output/reports/train/46bcdbf1782f8950660c1c4d9f8f7148/train_labels.json - model_dir: models - name: 46bcdbf1782f8950660c1c4d9f8f7148 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fd4662af1f507c7ba7802a873842e527 - trainer: - kwargs: {} -name: 46bcdbf1782f8950660c1c4d9f8f7148 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/params.yaml b/examples/security/classification/output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/params.yaml deleted file mode 100644 index c68058e3..00000000 --- a/examples/security/classification/output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/adv_predictions.json - adv_probabilities_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/b91658c5aaeac0b9a36968441979149f.pkl - params_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/params.yaml - predictions_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/predictions.json - probabilities_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/probabilities.json - score_dict_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/score_dict.json - test_labels_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/test_labels.json - train_labels_file: output/reports/train/46d08e1a731fd799d4fa91e376a4f1b7/train_labels.json - model_dir: models - name: 46d08e1a731fd799d4fa91e376a4f1b7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 93d1a28dc6131296be4a88b62e9bb1bf - trainer: - kwargs: {} -name: 46d08e1a731fd799d4fa91e376a4f1b7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/46f70529a9f0f544ce24f73c678a336e/params.yaml b/examples/security/classification/output/reports/train/46f70529a9f0f544ce24f73c678a336e/params.yaml deleted file mode 100644 index b8de4e04..00000000 --- a/examples/security/classification/output/reports/train/46f70529a9f0f544ce24f73c678a336e/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/adv_predictions.json - adv_probabilities_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/41327749dfd591b3f2e01ea6fc060cd9.pkl - params_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/params.yaml - predictions_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/predictions.json - probabilities_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/probabilities.json - score_dict_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/score_dict.json - test_labels_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/test_labels.json - train_labels_file: output/reports/train/46f70529a9f0f544ce24f73c678a336e/train_labels.json - model_dir: models - name: 46f70529a9f0f544ce24f73c678a336e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5f2699eb9ff974089a7371bc4cb7b084 - trainer: - kwargs: {} -name: 46f70529a9f0f544ce24f73c678a336e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/46f9420cf2030f179a0765b6664f9fe1/params.yaml b/examples/security/classification/output/reports/train/46f9420cf2030f179a0765b6664f9fe1/params.yaml deleted file mode 100644 index 93cd1c5e..00000000 --- a/examples/security/classification/output/reports/train/46f9420cf2030f179a0765b6664f9fe1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/adv_predictions.json - adv_probabilities_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/bfbf774824f20c607865dfea5dd2d950.pkl - params_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/params.yaml - predictions_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/predictions.json - probabilities_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/probabilities.json - score_dict_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/score_dict.json - test_labels_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/test_labels.json - train_labels_file: output/reports/train/46f9420cf2030f179a0765b6664f9fe1/train_labels.json - model_dir: models - name: 46f9420cf2030f179a0765b6664f9fe1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 50cb85544282cbb66ba27a58e7e225b0 - trainer: - kwargs: {} -name: 46f9420cf2030f179a0765b6664f9fe1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4710dcde54e43b16d21cb6e262167b8d/params.yaml b/examples/security/classification/output/reports/train/4710dcde54e43b16d21cb6e262167b8d/params.yaml deleted file mode 100644 index 29f5a897..00000000 --- a/examples/security/classification/output/reports/train/4710dcde54e43b16d21cb6e262167b8d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/adv_predictions.json - adv_probabilities_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/b8143720098411a8533486ad0cdd75f5.pkl - params_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/params.yaml - predictions_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/predictions.json - probabilities_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/probabilities.json - score_dict_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/score_dict.json - test_labels_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/test_labels.json - train_labels_file: output/reports/train/4710dcde54e43b16d21cb6e262167b8d/train_labels.json - model_dir: models - name: 4710dcde54e43b16d21cb6e262167b8d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 26741c72dee967b6e3cfb3358822813f - trainer: - kwargs: {} -name: 4710dcde54e43b16d21cb6e262167b8d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/params.yaml b/examples/security/classification/output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/params.yaml deleted file mode 100644 index d36b46bd..00000000 --- a/examples/security/classification/output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/adv_predictions.json - adv_probabilities_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/3a54681ed6b01354230efd7a500b5f4a.pkl - params_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/params.yaml - predictions_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/predictions.json - probabilities_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/probabilities.json - score_dict_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/score_dict.json - test_labels_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/test_labels.json - train_labels_file: output/reports/train/471ceedcc8f090aaf9f689f557dd2af2/train_labels.json - model_dir: models - name: 471ceedcc8f090aaf9f689f557dd2af2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1f42352fadd4e5f61e6411f502c0492b - trainer: - kwargs: {} -name: 471ceedcc8f090aaf9f689f557dd2af2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/471e042f03cd18a02f726f81b210b868/params.yaml b/examples/security/classification/output/reports/train/471e042f03cd18a02f726f81b210b868/params.yaml deleted file mode 100644 index 2f9e880c..00000000 --- a/examples/security/classification/output/reports/train/471e042f03cd18a02f726f81b210b868/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/471e042f03cd18a02f726f81b210b868/adv_predictions.json - adv_probabilities_file: output/reports/train/471e042f03cd18a02f726f81b210b868/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/1b0cbc6b1c0cc7359ba0bd6f7354d64e.pkl - params_file: output/reports/train/471e042f03cd18a02f726f81b210b868/params.yaml - predictions_file: output/reports/train/471e042f03cd18a02f726f81b210b868/predictions.json - probabilities_file: output/reports/train/471e042f03cd18a02f726f81b210b868/probabilities.json - score_dict_file: output/reports/train/471e042f03cd18a02f726f81b210b868/score_dict.json - test_labels_file: output/reports/train/471e042f03cd18a02f726f81b210b868/test_labels.json - train_labels_file: output/reports/train/471e042f03cd18a02f726f81b210b868/train_labels.json - model_dir: models - name: 471e042f03cd18a02f726f81b210b868 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 45ce3d753eaec683cd92cee1f4f1caa8 - trainer: - kwargs: {} -name: 471e042f03cd18a02f726f81b210b868 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/475ae02b72cb52d9e668b87fdbe17881/params.yaml b/examples/security/classification/output/reports/train/475ae02b72cb52d9e668b87fdbe17881/params.yaml deleted file mode 100644 index 28ef81f4..00000000 --- a/examples/security/classification/output/reports/train/475ae02b72cb52d9e668b87fdbe17881/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/adv_predictions.json - adv_probabilities_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/f02b4634d34477f1afa788b6f9697581.pkl - params_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/params.yaml - predictions_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/predictions.json - probabilities_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/probabilities.json - score_dict_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/score_dict.json - test_labels_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/test_labels.json - train_labels_file: output/reports/train/475ae02b72cb52d9e668b87fdbe17881/train_labels.json - model_dir: models - name: 475ae02b72cb52d9e668b87fdbe17881 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fac62bd132c867ea7d931fdfe3517a42 - trainer: - kwargs: {} -name: 475ae02b72cb52d9e668b87fdbe17881 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/params.yaml b/examples/security/classification/output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/params.yaml deleted file mode 100644 index 036cda63..00000000 --- a/examples/security/classification/output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/adv_predictions.json - adv_probabilities_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/dfae5e590f0886fbd3d4b3814e8c086c.pkl - params_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/params.yaml - predictions_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/predictions.json - probabilities_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/probabilities.json - score_dict_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/score_dict.json - test_labels_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/test_labels.json - train_labels_file: output/reports/train/478243d5b163ae6c8cfee47f3b4b068c/train_labels.json - model_dir: models - name: 478243d5b163ae6c8cfee47f3b4b068c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 455cd059dd96765a99bce1e1d164ad32 - trainer: - kwargs: {} -name: 478243d5b163ae6c8cfee47f3b4b068c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/params.yaml b/examples/security/classification/output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/params.yaml deleted file mode 100644 index 273c995d..00000000 --- a/examples/security/classification/output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/adv_predictions.json - adv_probabilities_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/eac370f71c6bfae3bf2e0f80b575a971.pkl - params_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/params.yaml - predictions_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/predictions.json - probabilities_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/probabilities.json - score_dict_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/score_dict.json - test_labels_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/test_labels.json - train_labels_file: output/reports/train/4795d4b29df05009c3ba0cd454d2e4e3/train_labels.json - model_dir: models - name: 4795d4b29df05009c3ba0cd454d2e4e3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8912e931bd6bb82e13288588fdc9855e - trainer: - kwargs: {} -name: 4795d4b29df05009c3ba0cd454d2e4e3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/479d54ea9ea8dffeb01102e950d90145/params.yaml b/examples/security/classification/output/reports/train/479d54ea9ea8dffeb01102e950d90145/params.yaml deleted file mode 100644 index 33700542..00000000 --- a/examples/security/classification/output/reports/train/479d54ea9ea8dffeb01102e950d90145/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/adv_predictions.json - adv_probabilities_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/9020f07193cf3a3b70b6038bd5f7d496.pkl - params_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/params.yaml - predictions_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/predictions.json - probabilities_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/probabilities.json - score_dict_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/score_dict.json - test_labels_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/test_labels.json - train_labels_file: output/reports/train/479d54ea9ea8dffeb01102e950d90145/train_labels.json - model_dir: models - name: 479d54ea9ea8dffeb01102e950d90145 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a9567a39a5d93cc09973e0e6748fbf46 - trainer: - kwargs: {} -name: 479d54ea9ea8dffeb01102e950d90145 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/params.yaml b/examples/security/classification/output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/params.yaml deleted file mode 100644 index d109711d..00000000 --- a/examples/security/classification/output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/adv_predictions.json - adv_probabilities_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/73e1d59253d3e593c062c586196bf3a7.pkl - params_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/params.yaml - predictions_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/predictions.json - probabilities_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/probabilities.json - score_dict_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/score_dict.json - test_labels_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/test_labels.json - train_labels_file: output/reports/train/47a20378a3170e54c739dfc7f9dc89e2/train_labels.json - model_dir: models - name: 47a20378a3170e54c739dfc7f9dc89e2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b88b7c2bf990f8888b0fff8f05e10bc9 - trainer: - kwargs: {} -name: 47a20378a3170e54c739dfc7f9dc89e2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/params.yaml b/examples/security/classification/output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/params.yaml deleted file mode 100644 index 477ae6cf..00000000 --- a/examples/security/classification/output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/adv_predictions.json - adv_probabilities_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/d5cec6027aadae31490771c8d61e1d05.pkl - params_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/params.yaml - predictions_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/predictions.json - probabilities_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/probabilities.json - score_dict_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/score_dict.json - test_labels_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/test_labels.json - train_labels_file: output/reports/train/480d8c3c7f2caefcde15b4e7d2830aec/train_labels.json - model_dir: models - name: 480d8c3c7f2caefcde15b4e7d2830aec - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 769cbbeaaaabf9fd85a1f1c91f565ba1 - trainer: - kwargs: {} -name: 480d8c3c7f2caefcde15b4e7d2830aec -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/481440de2cea4312f503f2e2337c54de/params.yaml b/examples/security/classification/output/reports/train/481440de2cea4312f503f2e2337c54de/params.yaml deleted file mode 100644 index ba12d587..00000000 --- a/examples/security/classification/output/reports/train/481440de2cea4312f503f2e2337c54de/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/481440de2cea4312f503f2e2337c54de/adv_predictions.json - adv_probabilities_file: output/reports/train/481440de2cea4312f503f2e2337c54de/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/c535e6f44e4276e7e60d3fcd09863676.pkl - params_file: output/reports/train/481440de2cea4312f503f2e2337c54de/params.yaml - predictions_file: output/reports/train/481440de2cea4312f503f2e2337c54de/predictions.json - probabilities_file: output/reports/train/481440de2cea4312f503f2e2337c54de/probabilities.json - score_dict_file: output/reports/train/481440de2cea4312f503f2e2337c54de/score_dict.json - test_labels_file: output/reports/train/481440de2cea4312f503f2e2337c54de/test_labels.json - train_labels_file: output/reports/train/481440de2cea4312f503f2e2337c54de/train_labels.json - model_dir: models - name: 481440de2cea4312f503f2e2337c54de - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4384760d135b4adc9278c65f2b712d9e - trainer: - kwargs: {} -name: 481440de2cea4312f503f2e2337c54de -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/params.yaml b/examples/security/classification/output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/params.yaml deleted file mode 100644 index 624faf85..00000000 --- a/examples/security/classification/output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/adv_predictions.json - adv_probabilities_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/811c05123651f5f69a26e753edd964f4.pkl - params_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/params.yaml - predictions_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/predictions.json - probabilities_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/probabilities.json - score_dict_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/score_dict.json - test_labels_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/test_labels.json - train_labels_file: output/reports/train/48a10e94f91fd7dad7084096dbfcc8d4/train_labels.json - model_dir: models - name: 48a10e94f91fd7dad7084096dbfcc8d4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b7f06aa015631270839f8d55d98a627a - trainer: - kwargs: {} -name: 48a10e94f91fd7dad7084096dbfcc8d4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/48d719aebe8462cb3905089498acdb35/params.yaml b/examples/security/classification/output/reports/train/48d719aebe8462cb3905089498acdb35/params.yaml deleted file mode 100644 index 93a8fd5b..00000000 --- a/examples/security/classification/output/reports/train/48d719aebe8462cb3905089498acdb35/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/48d719aebe8462cb3905089498acdb35/adv_predictions.json - adv_probabilities_file: output/reports/train/48d719aebe8462cb3905089498acdb35/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/a4cea0e6f6d64308919e24254128a782.pkl - params_file: output/reports/train/48d719aebe8462cb3905089498acdb35/params.yaml - predictions_file: output/reports/train/48d719aebe8462cb3905089498acdb35/predictions.json - probabilities_file: output/reports/train/48d719aebe8462cb3905089498acdb35/probabilities.json - score_dict_file: output/reports/train/48d719aebe8462cb3905089498acdb35/score_dict.json - test_labels_file: output/reports/train/48d719aebe8462cb3905089498acdb35/test_labels.json - train_labels_file: output/reports/train/48d719aebe8462cb3905089498acdb35/train_labels.json - model_dir: models - name: 48d719aebe8462cb3905089498acdb35 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 23d22e793face3f263a9ea2400489d1a - trainer: - kwargs: {} -name: 48d719aebe8462cb3905089498acdb35 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/params.yaml b/examples/security/classification/output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/params.yaml deleted file mode 100644 index ed4397e2..00000000 --- a/examples/security/classification/output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/adv_predictions.json - adv_probabilities_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/2192a6c204731bb37c2c4e00a96a525e.pkl - params_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/params.yaml - predictions_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/predictions.json - probabilities_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/probabilities.json - score_dict_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/score_dict.json - test_labels_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/test_labels.json - train_labels_file: output/reports/train/48e59e1bb1f93b4e3cea60447659c38e/train_labels.json - model_dir: models - name: 48e59e1bb1f93b4e3cea60447659c38e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 09c56571729267efeab9f25a57111f94 - trainer: - kwargs: {} -name: 48e59e1bb1f93b4e3cea60447659c38e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/48f8c553f2ef76ba24323a21e6956e70/params.yaml b/examples/security/classification/output/reports/train/48f8c553f2ef76ba24323a21e6956e70/params.yaml deleted file mode 100644 index 24797965..00000000 --- a/examples/security/classification/output/reports/train/48f8c553f2ef76ba24323a21e6956e70/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/adv_predictions.json - adv_probabilities_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/af0d9fe014c646ece2830c99ad46a7bd.pkl - params_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/params.yaml - predictions_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/predictions.json - probabilities_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/probabilities.json - score_dict_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/score_dict.json - test_labels_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/test_labels.json - train_labels_file: output/reports/train/48f8c553f2ef76ba24323a21e6956e70/train_labels.json - model_dir: models - name: 48f8c553f2ef76ba24323a21e6956e70 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4af1dbf6fd855b7f1506c5c85102b227 - trainer: - kwargs: {} -name: 48f8c553f2ef76ba24323a21e6956e70 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/490627ffb7697dcc195c78612da1572a/params.yaml b/examples/security/classification/output/reports/train/490627ffb7697dcc195c78612da1572a/params.yaml deleted file mode 100644 index 48e5135e..00000000 --- a/examples/security/classification/output/reports/train/490627ffb7697dcc195c78612da1572a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/490627ffb7697dcc195c78612da1572a/adv_predictions.json - adv_probabilities_file: output/reports/train/490627ffb7697dcc195c78612da1572a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/0085b5acbf8377fda3550637101cbe79.pkl - params_file: output/reports/train/490627ffb7697dcc195c78612da1572a/params.yaml - predictions_file: output/reports/train/490627ffb7697dcc195c78612da1572a/predictions.json - probabilities_file: output/reports/train/490627ffb7697dcc195c78612da1572a/probabilities.json - score_dict_file: output/reports/train/490627ffb7697dcc195c78612da1572a/score_dict.json - test_labels_file: output/reports/train/490627ffb7697dcc195c78612da1572a/test_labels.json - train_labels_file: output/reports/train/490627ffb7697dcc195c78612da1572a/train_labels.json - model_dir: models - name: 490627ffb7697dcc195c78612da1572a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0a2df038054c0bcd3cf651787001ca58 - trainer: - kwargs: {} -name: 490627ffb7697dcc195c78612da1572a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/49533869f7a66c04df94ff5e9244a8df/params.yaml b/examples/security/classification/output/reports/train/49533869f7a66c04df94ff5e9244a8df/params.yaml deleted file mode 100644 index 3b334526..00000000 --- a/examples/security/classification/output/reports/train/49533869f7a66c04df94ff5e9244a8df/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/adv_predictions.json - adv_probabilities_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/a9521020014633daa12533900a52f30e.pkl - params_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/params.yaml - predictions_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/predictions.json - probabilities_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/probabilities.json - score_dict_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/score_dict.json - test_labels_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/test_labels.json - train_labels_file: output/reports/train/49533869f7a66c04df94ff5e9244a8df/train_labels.json - model_dir: models - name: 49533869f7a66c04df94ff5e9244a8df - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 97b58c43f21605a4b1d11b23554018c0 - trainer: - kwargs: {} -name: 49533869f7a66c04df94ff5e9244a8df -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/params.yaml b/examples/security/classification/output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/params.yaml deleted file mode 100644 index 4e50152a..00000000 --- a/examples/security/classification/output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/adv_predictions.json - adv_probabilities_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/0117c744a008e1161328302cd158f07d.pkl - params_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/params.yaml - predictions_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/predictions.json - probabilities_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/probabilities.json - score_dict_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/score_dict.json - test_labels_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/test_labels.json - train_labels_file: output/reports/train/49724daa63c7e5059d18ecdb050f0bfb/train_labels.json - model_dir: models - name: 49724daa63c7e5059d18ecdb050f0bfb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e1181bf04f8d5d8ea050d983905418d1 - trainer: - kwargs: {} -name: 49724daa63c7e5059d18ecdb050f0bfb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/499dfc79cd805a6f91258d189ee32680/params.yaml b/examples/security/classification/output/reports/train/499dfc79cd805a6f91258d189ee32680/params.yaml deleted file mode 100644 index 736796ff..00000000 --- a/examples/security/classification/output/reports/train/499dfc79cd805a6f91258d189ee32680/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/adv_predictions.json - adv_probabilities_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/f60d591c8bad442f025ba7a0e56d950b.pkl - params_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/params.yaml - predictions_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/predictions.json - probabilities_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/probabilities.json - score_dict_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/score_dict.json - test_labels_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/test_labels.json - train_labels_file: output/reports/train/499dfc79cd805a6f91258d189ee32680/train_labels.json - model_dir: models - name: 499dfc79cd805a6f91258d189ee32680 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9d2d452126302a91cd4bd42a4f094ae5 - trainer: - kwargs: {} -name: 499dfc79cd805a6f91258d189ee32680 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/params.yaml b/examples/security/classification/output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/params.yaml deleted file mode 100644 index 13b09369..00000000 --- a/examples/security/classification/output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/adv_predictions.json - adv_probabilities_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/9e985dcba2681b28c95f479c7ec728eb.pkl - params_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/params.yaml - predictions_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/predictions.json - probabilities_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/probabilities.json - score_dict_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/score_dict.json - test_labels_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/test_labels.json - train_labels_file: output/reports/train/49a0a9c37a43a5d1422d78cc97814e87/train_labels.json - model_dir: models - name: 49a0a9c37a43a5d1422d78cc97814e87 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bba7eaee925931e267c04a018df0a204 - trainer: - kwargs: {} -name: 49a0a9c37a43a5d1422d78cc97814e87 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/49b8b52fb40d7f4f575096353dcf2961/params.yaml b/examples/security/classification/output/reports/train/49b8b52fb40d7f4f575096353dcf2961/params.yaml deleted file mode 100644 index 196728b3..00000000 --- a/examples/security/classification/output/reports/train/49b8b52fb40d7f4f575096353dcf2961/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/adv_predictions.json - adv_probabilities_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/dc248f770b14d61ed4eac281cbe9a11a.pkl - params_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/params.yaml - predictions_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/predictions.json - probabilities_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/probabilities.json - score_dict_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/score_dict.json - test_labels_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/test_labels.json - train_labels_file: output/reports/train/49b8b52fb40d7f4f575096353dcf2961/train_labels.json - model_dir: models - name: 49b8b52fb40d7f4f575096353dcf2961 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bc80f254c6e729ca53cdde39c3d2a5ad - trainer: - kwargs: {} -name: 49b8b52fb40d7f4f575096353dcf2961 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/49c447fd2490e5db9a11b318722981b2/params.yaml b/examples/security/classification/output/reports/train/49c447fd2490e5db9a11b318722981b2/params.yaml deleted file mode 100644 index c022d50a..00000000 --- a/examples/security/classification/output/reports/train/49c447fd2490e5db9a11b318722981b2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/adv_predictions.json - adv_probabilities_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/9695b095f781a7e25a258fc80fcf773e.pkl - params_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/params.yaml - predictions_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/predictions.json - probabilities_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/probabilities.json - score_dict_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/score_dict.json - test_labels_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/test_labels.json - train_labels_file: output/reports/train/49c447fd2490e5db9a11b318722981b2/train_labels.json - model_dir: models - name: 49c447fd2490e5db9a11b318722981b2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 69e2378adf8d7b561e593d9fb371e905 - trainer: - kwargs: {} -name: 49c447fd2490e5db9a11b318722981b2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/49ce7b6409043c14babce3d1ae6b8085/params.yaml b/examples/security/classification/output/reports/train/49ce7b6409043c14babce3d1ae6b8085/params.yaml deleted file mode 100644 index 7e35330c..00000000 --- a/examples/security/classification/output/reports/train/49ce7b6409043c14babce3d1ae6b8085/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/adv_predictions.json - adv_probabilities_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/edc1b2e753a132a3c7a05949b1fd307f.pkl - params_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/params.yaml - predictions_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/predictions.json - probabilities_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/probabilities.json - score_dict_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/score_dict.json - test_labels_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/test_labels.json - train_labels_file: output/reports/train/49ce7b6409043c14babce3d1ae6b8085/train_labels.json - model_dir: models - name: 49ce7b6409043c14babce3d1ae6b8085 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c2dd3ca08c4cfb70b316df15ba963d65 - trainer: - kwargs: {} -name: 49ce7b6409043c14babce3d1ae6b8085 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/49f91bad1c44419aab51ce19c297e070/params.yaml b/examples/security/classification/output/reports/train/49f91bad1c44419aab51ce19c297e070/params.yaml deleted file mode 100644 index 429ab776..00000000 --- a/examples/security/classification/output/reports/train/49f91bad1c44419aab51ce19c297e070/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/adv_predictions.json - adv_probabilities_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/95d4990f4aa5539e982ff1f20af86196.pkl - params_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/params.yaml - predictions_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/predictions.json - probabilities_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/probabilities.json - score_dict_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/score_dict.json - test_labels_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/test_labels.json - train_labels_file: output/reports/train/49f91bad1c44419aab51ce19c297e070/train_labels.json - model_dir: models - name: 49f91bad1c44419aab51ce19c297e070 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 095692ec7248ac12f5f1efb8bde130c8 - trainer: - kwargs: {} -name: 49f91bad1c44419aab51ce19c297e070 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/params.yaml b/examples/security/classification/output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/params.yaml deleted file mode 100644 index 7abb3f14..00000000 --- a/examples/security/classification/output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/params.yaml +++ /dev/null @@ -1,227 +0,0 @@ -attack: - attack_size: 10 - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 253171c8fbfdc2625fe7ee98386b57db - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 29970b61a2e8f9c08d02ce5c21fcb20b - trainer: - kwargs: {} - name: c48f3b8e63385e866731aaa803c06776 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/adv_predictions.json - adv_probabilities_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/adv_probabilities.json - attack_file: output/attacks/b0dbd6b9ffce3957ff44d85bc4bf620a.pkl - data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl - model_file: output/models/17dd2599534253c240a74baffdd03273.pkl - params_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/params.yaml - predictions_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/predictions.json - probabilities_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/probabilities.json - score_dict_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/score_dict.json - test_labels_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/test_labels.json - train_labels_file: output/reports/train/4a0ffcecf5d72c142085d3ee450af9b6/train_labels.json - model_dir: models - name: 4a0ffcecf5d72c142085d3ee450af9b6 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8f2ca3159ecefb60740f8fc10b8fb6a4 - trainer: - kwargs: {} -name: 4a0ffcecf5d72c142085d3ee450af9b6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/params.yaml b/examples/security/classification/output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/params.yaml deleted file mode 100644 index 5ced7b44..00000000 --- a/examples/security/classification/output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/adv_predictions.json - adv_probabilities_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/cc8ed8b3b067f385d7f540c3292a6328.pkl - params_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/params.yaml - predictions_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/predictions.json - probabilities_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/probabilities.json - score_dict_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/score_dict.json - test_labels_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/test_labels.json - train_labels_file: output/reports/train/4a14ffca850a0111d8325a99f9eeaff3/train_labels.json - model_dir: models - name: 4a14ffca850a0111d8325a99f9eeaff3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3f85e1d1ab7ceb0e597b0d00c0b5fdb3 - trainer: - kwargs: {} -name: 4a14ffca850a0111d8325a99f9eeaff3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4a184ef8a58879acbc0885d28fc521d4/params.yaml b/examples/security/classification/output/reports/train/4a184ef8a58879acbc0885d28fc521d4/params.yaml deleted file mode 100644 index e71a71dd..00000000 --- a/examples/security/classification/output/reports/train/4a184ef8a58879acbc0885d28fc521d4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/adv_predictions.json - adv_probabilities_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/a277fdee93338d631a8e08cdd30f8ddd.pkl - params_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/params.yaml - predictions_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/predictions.json - probabilities_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/probabilities.json - score_dict_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/score_dict.json - test_labels_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/test_labels.json - train_labels_file: output/reports/train/4a184ef8a58879acbc0885d28fc521d4/train_labels.json - model_dir: models - name: 4a184ef8a58879acbc0885d28fc521d4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 160ada13ef944fc23ecdcbb4fd65b116 - trainer: - kwargs: {} -name: 4a184ef8a58879acbc0885d28fc521d4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4a3566901b3aa4609faf9b713d0eebea/params.yaml b/examples/security/classification/output/reports/train/4a3566901b3aa4609faf9b713d0eebea/params.yaml deleted file mode 100644 index 17f585b0..00000000 --- a/examples/security/classification/output/reports/train/4a3566901b3aa4609faf9b713d0eebea/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/adv_predictions.json - adv_probabilities_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/bb0cc30ddcc4f77212b75c2f73d7381d.pkl - params_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/params.yaml - predictions_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/predictions.json - probabilities_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/probabilities.json - score_dict_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/score_dict.json - test_labels_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/test_labels.json - train_labels_file: output/reports/train/4a3566901b3aa4609faf9b713d0eebea/train_labels.json - model_dir: models - name: 4a3566901b3aa4609faf9b713d0eebea - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f22824c61657bf8883cade917a2173c4 - trainer: - kwargs: {} -name: 4a3566901b3aa4609faf9b713d0eebea -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4a3cbd42be8bd19341c92beed59de6be/params.yaml b/examples/security/classification/output/reports/train/4a3cbd42be8bd19341c92beed59de6be/params.yaml deleted file mode 100644 index 589a92e1..00000000 --- a/examples/security/classification/output/reports/train/4a3cbd42be8bd19341c92beed59de6be/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/adv_predictions.json - adv_probabilities_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/a5aa6d9b1944e8a2a298cb7797f78a03.pkl - params_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/params.yaml - predictions_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/predictions.json - probabilities_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/probabilities.json - score_dict_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/score_dict.json - test_labels_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/test_labels.json - train_labels_file: output/reports/train/4a3cbd42be8bd19341c92beed59de6be/train_labels.json - model_dir: models - name: 4a3cbd42be8bd19341c92beed59de6be - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 94bc5724d180932bc0a56e0e5c4703c8 - trainer: - kwargs: {} -name: 4a3cbd42be8bd19341c92beed59de6be -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4a50097467dc60eb39758204db4a87a8/params.yaml b/examples/security/classification/output/reports/train/4a50097467dc60eb39758204db4a87a8/params.yaml deleted file mode 100644 index dfdfcac8..00000000 --- a/examples/security/classification/output/reports/train/4a50097467dc60eb39758204db4a87a8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/adv_predictions.json - adv_probabilities_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/0a2bfe759d65defc3ee38716a6fe3e8d.pkl - params_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/params.yaml - predictions_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/predictions.json - probabilities_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/probabilities.json - score_dict_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/score_dict.json - test_labels_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/test_labels.json - train_labels_file: output/reports/train/4a50097467dc60eb39758204db4a87a8/train_labels.json - model_dir: models - name: 4a50097467dc60eb39758204db4a87a8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0478de635d75660f96aca91f440f8e5f - trainer: - kwargs: {} -name: 4a50097467dc60eb39758204db4a87a8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/params.yaml b/examples/security/classification/output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/params.yaml deleted file mode 100644 index 40e66948..00000000 --- a/examples/security/classification/output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/adv_predictions.json - adv_probabilities_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/ec932da709a051f7e990a7d60d200677.pkl - params_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/params.yaml - predictions_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/predictions.json - probabilities_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/probabilities.json - score_dict_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/score_dict.json - test_labels_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/test_labels.json - train_labels_file: output/reports/train/4a65fa18361d694c72b9b51c757a6cc0/train_labels.json - model_dir: models - name: 4a65fa18361d694c72b9b51c757a6cc0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 819a2c747383b35c79a07aa66a55dfb4 - trainer: - kwargs: {} -name: 4a65fa18361d694c72b9b51c757a6cc0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4a9bd9e3571884361e5494d12cf73f52/params.yaml b/examples/security/classification/output/reports/train/4a9bd9e3571884361e5494d12cf73f52/params.yaml deleted file mode 100644 index e73ab107..00000000 --- a/examples/security/classification/output/reports/train/4a9bd9e3571884361e5494d12cf73f52/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/adv_predictions.json - adv_probabilities_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/53e4cc9f95747c561cc2c841d0706c05.pkl - params_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/params.yaml - predictions_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/predictions.json - probabilities_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/probabilities.json - score_dict_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/score_dict.json - test_labels_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/test_labels.json - train_labels_file: output/reports/train/4a9bd9e3571884361e5494d12cf73f52/train_labels.json - model_dir: models - name: 4a9bd9e3571884361e5494d12cf73f52 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 90d99eb0dcc583b82cd3c01124077275 - trainer: - kwargs: {} -name: 4a9bd9e3571884361e5494d12cf73f52 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4ab0f95a641a6c322711711f819494f1/params.yaml b/examples/security/classification/output/reports/train/4ab0f95a641a6c322711711f819494f1/params.yaml deleted file mode 100644 index baaf8299..00000000 --- a/examples/security/classification/output/reports/train/4ab0f95a641a6c322711711f819494f1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/adv_predictions.json - adv_probabilities_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/530ffd4e33869d3bebe9905aec07338d.pkl - params_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/params.yaml - predictions_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/predictions.json - probabilities_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/probabilities.json - score_dict_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/score_dict.json - test_labels_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/test_labels.json - train_labels_file: output/reports/train/4ab0f95a641a6c322711711f819494f1/train_labels.json - model_dir: models - name: 4ab0f95a641a6c322711711f819494f1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7c9a85f1e28ba5531b88a5b7e457578d - trainer: - kwargs: {} -name: 4ab0f95a641a6c322711711f819494f1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4ad45e711e936495f1f17b4796821856/params.yaml b/examples/security/classification/output/reports/train/4ad45e711e936495f1f17b4796821856/params.yaml deleted file mode 100644 index dbb45679..00000000 --- a/examples/security/classification/output/reports/train/4ad45e711e936495f1f17b4796821856/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4ad45e711e936495f1f17b4796821856/adv_predictions.json - adv_probabilities_file: output/reports/train/4ad45e711e936495f1f17b4796821856/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/0e93385aba61430548ce1a63f7ac0e44.pkl - params_file: output/reports/train/4ad45e711e936495f1f17b4796821856/params.yaml - predictions_file: output/reports/train/4ad45e711e936495f1f17b4796821856/predictions.json - probabilities_file: output/reports/train/4ad45e711e936495f1f17b4796821856/probabilities.json - score_dict_file: output/reports/train/4ad45e711e936495f1f17b4796821856/score_dict.json - test_labels_file: output/reports/train/4ad45e711e936495f1f17b4796821856/test_labels.json - train_labels_file: output/reports/train/4ad45e711e936495f1f17b4796821856/train_labels.json - model_dir: models - name: 4ad45e711e936495f1f17b4796821856 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 36d84b04df112cc48e7aab8b0eba1574 - trainer: - kwargs: {} -name: 4ad45e711e936495f1f17b4796821856 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4b28e94538699157eb430f7a89a80e73/params.yaml b/examples/security/classification/output/reports/train/4b28e94538699157eb430f7a89a80e73/params.yaml deleted file mode 100644 index 1b7defd7..00000000 --- a/examples/security/classification/output/reports/train/4b28e94538699157eb430f7a89a80e73/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/adv_predictions.json - adv_probabilities_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/7165635722a5741e0c5d8e83184036ab.pkl - params_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/params.yaml - predictions_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/predictions.json - probabilities_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/probabilities.json - score_dict_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/score_dict.json - test_labels_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/test_labels.json - train_labels_file: output/reports/train/4b28e94538699157eb430f7a89a80e73/train_labels.json - model_dir: models - name: 4b28e94538699157eb430f7a89a80e73 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e03a32042d4d8fa1e221e80320408708 - trainer: - kwargs: {} -name: 4b28e94538699157eb430f7a89a80e73 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4b63ac2e506474fae341d0e75ad69b36/params.yaml b/examples/security/classification/output/reports/train/4b63ac2e506474fae341d0e75ad69b36/params.yaml deleted file mode 100644 index 65771825..00000000 --- a/examples/security/classification/output/reports/train/4b63ac2e506474fae341d0e75ad69b36/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/adv_predictions.json - adv_probabilities_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/2d048baf82b5939928c653683a44aba3.pkl - params_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/params.yaml - predictions_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/predictions.json - probabilities_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/probabilities.json - score_dict_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/score_dict.json - test_labels_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/test_labels.json - train_labels_file: output/reports/train/4b63ac2e506474fae341d0e75ad69b36/train_labels.json - model_dir: models - name: 4b63ac2e506474fae341d0e75ad69b36 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 85ede6dce7618835df4515bdde44fb67 - trainer: - kwargs: {} -name: 4b63ac2e506474fae341d0e75ad69b36 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4bb675259c051574dab809a7dcec7555/params.yaml b/examples/security/classification/output/reports/train/4bb675259c051574dab809a7dcec7555/params.yaml deleted file mode 100644 index 113dc3af..00000000 --- a/examples/security/classification/output/reports/train/4bb675259c051574dab809a7dcec7555/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4bb675259c051574dab809a7dcec7555/adv_predictions.json - adv_probabilities_file: output/reports/train/4bb675259c051574dab809a7dcec7555/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/6ebc435b2d49ae94e7db8fb10e742603.pkl - params_file: output/reports/train/4bb675259c051574dab809a7dcec7555/params.yaml - predictions_file: output/reports/train/4bb675259c051574dab809a7dcec7555/predictions.json - probabilities_file: output/reports/train/4bb675259c051574dab809a7dcec7555/probabilities.json - score_dict_file: output/reports/train/4bb675259c051574dab809a7dcec7555/score_dict.json - test_labels_file: output/reports/train/4bb675259c051574dab809a7dcec7555/test_labels.json - train_labels_file: output/reports/train/4bb675259c051574dab809a7dcec7555/train_labels.json - model_dir: models - name: 4bb675259c051574dab809a7dcec7555 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f5f0640390fb2a4dbda150c4eaa63ece - trainer: - kwargs: {} -name: 4bb675259c051574dab809a7dcec7555 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/params.yaml b/examples/security/classification/output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/params.yaml deleted file mode 100644 index af2b62eb..00000000 --- a/examples/security/classification/output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/adv_predictions.json - adv_probabilities_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/e623aff809ac4ee6fac43903ca8391a7.pkl - params_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/params.yaml - predictions_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/predictions.json - probabilities_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/probabilities.json - score_dict_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/score_dict.json - test_labels_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/test_labels.json - train_labels_file: output/reports/train/4bc9608568a694b0ea9f9075ad824f4c/train_labels.json - model_dir: models - name: 4bc9608568a694b0ea9f9075ad824f4c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a27b94b8634b0f16b0ce96b25358c128 - trainer: - kwargs: {} -name: 4bc9608568a694b0ea9f9075ad824f4c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4bd55917487242c160c07f478bccaef2/params.yaml b/examples/security/classification/output/reports/train/4bd55917487242c160c07f478bccaef2/params.yaml deleted file mode 100644 index 67d0fa58..00000000 --- a/examples/security/classification/output/reports/train/4bd55917487242c160c07f478bccaef2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4bd55917487242c160c07f478bccaef2/adv_predictions.json - adv_probabilities_file: output/reports/train/4bd55917487242c160c07f478bccaef2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/342060e97c2c293ded1f31520eb5ae03.pkl - params_file: output/reports/train/4bd55917487242c160c07f478bccaef2/params.yaml - predictions_file: output/reports/train/4bd55917487242c160c07f478bccaef2/predictions.json - probabilities_file: output/reports/train/4bd55917487242c160c07f478bccaef2/probabilities.json - score_dict_file: output/reports/train/4bd55917487242c160c07f478bccaef2/score_dict.json - test_labels_file: output/reports/train/4bd55917487242c160c07f478bccaef2/test_labels.json - train_labels_file: output/reports/train/4bd55917487242c160c07f478bccaef2/train_labels.json - model_dir: models - name: 4bd55917487242c160c07f478bccaef2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 64c0d8899ebf648e252a68c8c86141a9 - trainer: - kwargs: {} -name: 4bd55917487242c160c07f478bccaef2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4bd6646f03c6f58060ea195a46ebac23/params.yaml b/examples/security/classification/output/reports/train/4bd6646f03c6f58060ea195a46ebac23/params.yaml deleted file mode 100644 index 4b40eed0..00000000 --- a/examples/security/classification/output/reports/train/4bd6646f03c6f58060ea195a46ebac23/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/adv_predictions.json - adv_probabilities_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/9efeca7d3aea0b6db3032f1c8661d9d0.pkl - params_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/params.yaml - predictions_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/predictions.json - probabilities_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/probabilities.json - score_dict_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/score_dict.json - test_labels_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/test_labels.json - train_labels_file: output/reports/train/4bd6646f03c6f58060ea195a46ebac23/train_labels.json - model_dir: models - name: 4bd6646f03c6f58060ea195a46ebac23 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9d2a56482c0575227301151a43ad4270 - trainer: - kwargs: {} -name: 4bd6646f03c6f58060ea195a46ebac23 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/params.yaml b/examples/security/classification/output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/params.yaml deleted file mode 100644 index 712aa827..00000000 --- a/examples/security/classification/output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/adv_predictions.json - adv_probabilities_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/b66011dc4f5021746f924ebd7466d8e9.pkl - params_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/params.yaml - predictions_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/predictions.json - probabilities_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/probabilities.json - score_dict_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/score_dict.json - test_labels_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/test_labels.json - train_labels_file: output/reports/train/4bdd1f9dbc8266ebc86ed6eb4f451578/train_labels.json - model_dir: models - name: 4bdd1f9dbc8266ebc86ed6eb4f451578 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dd8cf3a112c84647c788490eccd552ed - trainer: - kwargs: {} -name: 4bdd1f9dbc8266ebc86ed6eb4f451578 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/params.yaml b/examples/security/classification/output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/params.yaml deleted file mode 100644 index 98128884..00000000 --- a/examples/security/classification/output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/adv_predictions.json - adv_probabilities_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/eb87d2119a0ac9159268dd00db5663ea.pkl - params_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/params.yaml - predictions_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/predictions.json - probabilities_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/probabilities.json - score_dict_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/score_dict.json - test_labels_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/test_labels.json - train_labels_file: output/reports/train/4be2832f85c1d507df9d0f6cccc174f6/train_labels.json - model_dir: models - name: 4be2832f85c1d507df9d0f6cccc174f6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1ae99243fe9ebe0582bb5e56b35eee78 - trainer: - kwargs: {} -name: 4be2832f85c1d507df9d0f6cccc174f6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4bf49eb696a915f2ef7750f6669bd774/params.yaml b/examples/security/classification/output/reports/train/4bf49eb696a915f2ef7750f6669bd774/params.yaml deleted file mode 100644 index 8e88fd76..00000000 --- a/examples/security/classification/output/reports/train/4bf49eb696a915f2ef7750f6669bd774/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/adv_predictions.json - adv_probabilities_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/1613d2ae2fb8e0120c7fae1983863ab2.pkl - params_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/params.yaml - predictions_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/predictions.json - probabilities_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/probabilities.json - score_dict_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/score_dict.json - test_labels_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/test_labels.json - train_labels_file: output/reports/train/4bf49eb696a915f2ef7750f6669bd774/train_labels.json - model_dir: models - name: 4bf49eb696a915f2ef7750f6669bd774 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9268e481a6e31db6c18a15b7b221e071 - trainer: - kwargs: {} -name: 4bf49eb696a915f2ef7750f6669bd774 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4bf8cbc4120f965398b1816546f12988/params.yaml b/examples/security/classification/output/reports/train/4bf8cbc4120f965398b1816546f12988/params.yaml deleted file mode 100644 index 24fdae24..00000000 --- a/examples/security/classification/output/reports/train/4bf8cbc4120f965398b1816546f12988/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/adv_predictions.json - adv_probabilities_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/67a643efd2609b313541b5d76d1d5f66.pkl - params_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/params.yaml - predictions_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/predictions.json - probabilities_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/probabilities.json - score_dict_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/score_dict.json - test_labels_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/test_labels.json - train_labels_file: output/reports/train/4bf8cbc4120f965398b1816546f12988/train_labels.json - model_dir: models - name: 4bf8cbc4120f965398b1816546f12988 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1f0b2917b0a1f4b1500ae7bfe8cd6bee - trainer: - kwargs: {} -name: 4bf8cbc4120f965398b1816546f12988 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4c43cad4a0eec4727551367449b62da1/params.yaml b/examples/security/classification/output/reports/train/4c43cad4a0eec4727551367449b62da1/params.yaml deleted file mode 100644 index 08516807..00000000 --- a/examples/security/classification/output/reports/train/4c43cad4a0eec4727551367449b62da1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/adv_predictions.json - adv_probabilities_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/2b6aad3dd4cca985f51c484a39f94ea0.pkl - params_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/params.yaml - predictions_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/predictions.json - probabilities_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/probabilities.json - score_dict_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/score_dict.json - test_labels_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/test_labels.json - train_labels_file: output/reports/train/4c43cad4a0eec4727551367449b62da1/train_labels.json - model_dir: models - name: 4c43cad4a0eec4727551367449b62da1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c1f5eea2b5f1121bd01e876b5abbddbf - trainer: - kwargs: {} -name: 4c43cad4a0eec4727551367449b62da1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4c5571df958e0111899315f7334f5eef/params.yaml b/examples/security/classification/output/reports/train/4c5571df958e0111899315f7334f5eef/params.yaml deleted file mode 100644 index 4b0a56fa..00000000 --- a/examples/security/classification/output/reports/train/4c5571df958e0111899315f7334f5eef/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4c5571df958e0111899315f7334f5eef/adv_predictions.json - adv_probabilities_file: output/reports/train/4c5571df958e0111899315f7334f5eef/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/39f8403f780f1de47732d1453b458936.pkl - params_file: output/reports/train/4c5571df958e0111899315f7334f5eef/params.yaml - predictions_file: output/reports/train/4c5571df958e0111899315f7334f5eef/predictions.json - probabilities_file: output/reports/train/4c5571df958e0111899315f7334f5eef/probabilities.json - score_dict_file: output/reports/train/4c5571df958e0111899315f7334f5eef/score_dict.json - test_labels_file: output/reports/train/4c5571df958e0111899315f7334f5eef/test_labels.json - train_labels_file: output/reports/train/4c5571df958e0111899315f7334f5eef/train_labels.json - model_dir: models - name: 4c5571df958e0111899315f7334f5eef - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ddf86ebbfcc664f00f510326103ebf2f - trainer: - kwargs: {} -name: 4c5571df958e0111899315f7334f5eef -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/params.yaml b/examples/security/classification/output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/params.yaml deleted file mode 100644 index 5687174e..00000000 --- a/examples/security/classification/output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/adv_predictions.json - adv_probabilities_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/d3bbfdb5f521b3170d9023bbc262e92a.pkl - params_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/params.yaml - predictions_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/predictions.json - probabilities_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/probabilities.json - score_dict_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/score_dict.json - test_labels_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/test_labels.json - train_labels_file: output/reports/train/4c6a110a656aa7361b2b5155a80b22ff/train_labels.json - model_dir: models - name: 4c6a110a656aa7361b2b5155a80b22ff - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c029f113fc1f27fe46c49fb994277aa5 - trainer: - kwargs: {} -name: 4c6a110a656aa7361b2b5155a80b22ff -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4c89819e206a145deddedf45c09e7d4d/params.yaml b/examples/security/classification/output/reports/train/4c89819e206a145deddedf45c09e7d4d/params.yaml deleted file mode 100644 index 2c89f6f9..00000000 --- a/examples/security/classification/output/reports/train/4c89819e206a145deddedf45c09e7d4d/params.yaml +++ /dev/null @@ -1,115 +0,0 @@ -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/adv_predictions.json - adv_probabilities_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl - model_file: output/models/77dc9e5c469b115080f769aef30a9bab.pkl - params_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/params.yaml - predictions_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/predictions.json - probabilities_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/probabilities.json - score_dict_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/score_dict.json - test_labels_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/test_labels.json - train_labels_file: output/reports/train/4c89819e206a145deddedf45c09e7d4d/train_labels.json - model_dir: models - name: 4c89819e206a145deddedf45c09e7d4d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 100 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e2f0f4e7c0aa94810059ba180d68b630 - trainer: - kwargs: {} -name: 4c89819e206a145deddedf45c09e7d4d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/params.yaml b/examples/security/classification/output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/params.yaml deleted file mode 100644 index a01940f0..00000000 --- a/examples/security/classification/output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/adv_predictions.json - adv_probabilities_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/bf75e535a2c26f3535acdb29473d0231.pkl - params_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/params.yaml - predictions_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/predictions.json - probabilities_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/probabilities.json - score_dict_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/score_dict.json - test_labels_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/test_labels.json - train_labels_file: output/reports/train/4c8c6f36f7f931fb9163354ff8e72ac6/train_labels.json - model_dir: models - name: 4c8c6f36f7f931fb9163354ff8e72ac6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d930c78d094aefaedb7c78f41fe2b71a - trainer: - kwargs: {} -name: 4c8c6f36f7f931fb9163354ff8e72ac6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4ca1d3df012951326e2562828e13acf9/params.yaml b/examples/security/classification/output/reports/train/4ca1d3df012951326e2562828e13acf9/params.yaml deleted file mode 100644 index b3997701..00000000 --- a/examples/security/classification/output/reports/train/4ca1d3df012951326e2562828e13acf9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/adv_predictions.json - adv_probabilities_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/c3e7d5021e8d00760ccb7bba13ee2bdc.pkl - params_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/params.yaml - predictions_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/predictions.json - probabilities_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/probabilities.json - score_dict_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/score_dict.json - test_labels_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/test_labels.json - train_labels_file: output/reports/train/4ca1d3df012951326e2562828e13acf9/train_labels.json - model_dir: models - name: 4ca1d3df012951326e2562828e13acf9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 056b73cbd56989bd4772eb44b6ede007 - trainer: - kwargs: {} -name: 4ca1d3df012951326e2562828e13acf9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/params.yaml b/examples/security/classification/output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/params.yaml deleted file mode 100644 index de90c2cf..00000000 --- a/examples/security/classification/output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/adv_predictions.json - adv_probabilities_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/73fd44df0ab5c0fb148a84a629c057b5.pkl - params_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/params.yaml - predictions_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/predictions.json - probabilities_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/probabilities.json - score_dict_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/score_dict.json - test_labels_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/test_labels.json - train_labels_file: output/reports/train/4cbdfbf71b75b299b4d02397e74da2d6/train_labels.json - model_dir: models - name: 4cbdfbf71b75b299b4d02397e74da2d6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 643ef1d4111596e5553b06e3b34d2ce1 - trainer: - kwargs: {} -name: 4cbdfbf71b75b299b4d02397e74da2d6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4cd2f0aa194ad793b830df9027887840/params.yaml b/examples/security/classification/output/reports/train/4cd2f0aa194ad793b830df9027887840/params.yaml deleted file mode 100644 index 8329f101..00000000 --- a/examples/security/classification/output/reports/train/4cd2f0aa194ad793b830df9027887840/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/adv_predictions.json - adv_probabilities_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/7b7a952e18a01d889c3ba21fc3c3be3e.pkl - params_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/params.yaml - predictions_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/predictions.json - probabilities_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/probabilities.json - score_dict_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/score_dict.json - test_labels_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/test_labels.json - train_labels_file: output/reports/train/4cd2f0aa194ad793b830df9027887840/train_labels.json - model_dir: models - name: 4cd2f0aa194ad793b830df9027887840 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 591f4ba263a02435db44a60eff440cc4 - trainer: - kwargs: {} -name: 4cd2f0aa194ad793b830df9027887840 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4cefa6721039bc48f820386c707ebe80/params.yaml b/examples/security/classification/output/reports/train/4cefa6721039bc48f820386c707ebe80/params.yaml deleted file mode 100644 index 67a4d687..00000000 --- a/examples/security/classification/output/reports/train/4cefa6721039bc48f820386c707ebe80/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/adv_predictions.json - adv_probabilities_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/0d44265e3fd476d634e9758c74deb15f.pkl - params_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/params.yaml - predictions_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/predictions.json - probabilities_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/probabilities.json - score_dict_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/score_dict.json - test_labels_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/test_labels.json - train_labels_file: output/reports/train/4cefa6721039bc48f820386c707ebe80/train_labels.json - model_dir: models - name: 4cefa6721039bc48f820386c707ebe80 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cba6dfbc8a87a4cf483abba6c2ce7267 - trainer: - kwargs: {} -name: 4cefa6721039bc48f820386c707ebe80 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/params.yaml b/examples/security/classification/output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/params.yaml deleted file mode 100644 index a527970d..00000000 --- a/examples/security/classification/output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/adv_predictions.json - adv_probabilities_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/988ed0f02812824bad6112e26a7b3c0b.pkl - params_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/params.yaml - predictions_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/predictions.json - probabilities_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/probabilities.json - score_dict_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/score_dict.json - test_labels_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/test_labels.json - train_labels_file: output/reports/train/4d05d57cb58f07efe77044d4b0fb4691/train_labels.json - model_dir: models - name: 4d05d57cb58f07efe77044d4b0fb4691 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2d4e5e40c641e9fbf0e5e31daaa62826 - trainer: - kwargs: {} -name: 4d05d57cb58f07efe77044d4b0fb4691 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4d160b72628465117cb363a4873cab65/params.yaml b/examples/security/classification/output/reports/train/4d160b72628465117cb363a4873cab65/params.yaml deleted file mode 100644 index 96019df1..00000000 --- a/examples/security/classification/output/reports/train/4d160b72628465117cb363a4873cab65/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4d160b72628465117cb363a4873cab65/adv_predictions.json - adv_probabilities_file: output/reports/train/4d160b72628465117cb363a4873cab65/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/f44b17b7df23486e6a92bc8bb3d9e986.pkl - params_file: output/reports/train/4d160b72628465117cb363a4873cab65/params.yaml - predictions_file: output/reports/train/4d160b72628465117cb363a4873cab65/predictions.json - probabilities_file: output/reports/train/4d160b72628465117cb363a4873cab65/probabilities.json - score_dict_file: output/reports/train/4d160b72628465117cb363a4873cab65/score_dict.json - test_labels_file: output/reports/train/4d160b72628465117cb363a4873cab65/test_labels.json - train_labels_file: output/reports/train/4d160b72628465117cb363a4873cab65/train_labels.json - model_dir: models - name: 4d160b72628465117cb363a4873cab65 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 681841b07170a27581b80fdb19df2fe8 - trainer: - kwargs: {} -name: 4d160b72628465117cb363a4873cab65 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/params.yaml b/examples/security/classification/output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/params.yaml deleted file mode 100644 index fd6a74e8..00000000 --- a/examples/security/classification/output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/adv_predictions.json - adv_probabilities_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/84d5d0b6479867b95826073cc703b2d3.pkl - params_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/params.yaml - predictions_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/predictions.json - probabilities_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/probabilities.json - score_dict_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/score_dict.json - test_labels_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/test_labels.json - train_labels_file: output/reports/train/4d30d8dc66a47f463d7b9cb36d1000a1/train_labels.json - model_dir: models - name: 4d30d8dc66a47f463d7b9cb36d1000a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 683664c69d6cb42ad75a1198bd3c863e - trainer: - kwargs: {} -name: 4d30d8dc66a47f463d7b9cb36d1000a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4d3b5374c31d6b43e807630e08d990fc/params.yaml b/examples/security/classification/output/reports/train/4d3b5374c31d6b43e807630e08d990fc/params.yaml deleted file mode 100644 index d2f7b867..00000000 --- a/examples/security/classification/output/reports/train/4d3b5374c31d6b43e807630e08d990fc/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/adv_predictions.json - adv_probabilities_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/bd7a94a66e57e07c01324faba7f7728f.pkl - params_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/params.yaml - predictions_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/predictions.json - probabilities_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/probabilities.json - score_dict_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/score_dict.json - test_labels_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/test_labels.json - train_labels_file: output/reports/train/4d3b5374c31d6b43e807630e08d990fc/train_labels.json - model_dir: models - name: 4d3b5374c31d6b43e807630e08d990fc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 140fc861ebefbffbfa137b0ec828adeb - trainer: - kwargs: {} -name: 4d3b5374c31d6b43e807630e08d990fc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/params.yaml b/examples/security/classification/output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/params.yaml deleted file mode 100644 index 86868486..00000000 --- a/examples/security/classification/output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/adv_predictions.json - adv_probabilities_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/96cc05350fc39dae77c385142f7ece58.pkl - params_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/params.yaml - predictions_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/predictions.json - probabilities_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/probabilities.json - score_dict_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/score_dict.json - test_labels_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/test_labels.json - train_labels_file: output/reports/train/4d6228938ae4018bf70cc9aafe0b280c/train_labels.json - model_dir: models - name: 4d6228938ae4018bf70cc9aafe0b280c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 22cc8abcf8d43ed992f149b679457c53 - trainer: - kwargs: {} -name: 4d6228938ae4018bf70cc9aafe0b280c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/params.yaml b/examples/security/classification/output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/params.yaml deleted file mode 100644 index 82c5a13c..00000000 --- a/examples/security/classification/output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/adv_predictions.json - adv_probabilities_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/1410e177bc8f7a943ffcedf148720ab5.pkl - params_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/params.yaml - predictions_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/predictions.json - probabilities_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/probabilities.json - score_dict_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/score_dict.json - test_labels_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/test_labels.json - train_labels_file: output/reports/train/4d6a192fce463121e20d9fd6eee99ce8/train_labels.json - model_dir: models - name: 4d6a192fce463121e20d9fd6eee99ce8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 237456c5dcf6a23c9c5846f672ef1298 - trainer: - kwargs: {} -name: 4d6a192fce463121e20d9fd6eee99ce8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4db579196909d67ccb0df50b461e2660/params.yaml b/examples/security/classification/output/reports/train/4db579196909d67ccb0df50b461e2660/params.yaml deleted file mode 100644 index a651c50f..00000000 --- a/examples/security/classification/output/reports/train/4db579196909d67ccb0df50b461e2660/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4db579196909d67ccb0df50b461e2660/adv_predictions.json - adv_probabilities_file: output/reports/train/4db579196909d67ccb0df50b461e2660/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/01f960a226a44356397b49701cdec2b2.pkl - params_file: output/reports/train/4db579196909d67ccb0df50b461e2660/params.yaml - predictions_file: output/reports/train/4db579196909d67ccb0df50b461e2660/predictions.json - probabilities_file: output/reports/train/4db579196909d67ccb0df50b461e2660/probabilities.json - score_dict_file: output/reports/train/4db579196909d67ccb0df50b461e2660/score_dict.json - test_labels_file: output/reports/train/4db579196909d67ccb0df50b461e2660/test_labels.json - train_labels_file: output/reports/train/4db579196909d67ccb0df50b461e2660/train_labels.json - model_dir: models - name: 4db579196909d67ccb0df50b461e2660 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7c38dc4dd0d183d25e5896c830575157 - trainer: - kwargs: {} -name: 4db579196909d67ccb0df50b461e2660 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/params.yaml b/examples/security/classification/output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/params.yaml deleted file mode 100644 index 312ae0fa..00000000 --- a/examples/security/classification/output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/adv_predictions.json - adv_probabilities_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e0d8a281b1cdd731ea2b89c346c869ca.pkl - params_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/params.yaml - predictions_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/predictions.json - probabilities_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/probabilities.json - score_dict_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/score_dict.json - test_labels_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/test_labels.json - train_labels_file: output/reports/train/4db9de4939a2750d5413ad1a4c8c1471/train_labels.json - model_dir: models - name: 4db9de4939a2750d5413ad1a4c8c1471 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e3401a06e9656d7cf5f152a63a590c65 - trainer: - kwargs: {} -name: 4db9de4939a2750d5413ad1a4c8c1471 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/params.yaml b/examples/security/classification/output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/params.yaml deleted file mode 100644 index 9cc1f9e1..00000000 --- a/examples/security/classification/output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/adv_predictions.json - adv_probabilities_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/37b18daf9db03e42cc2072242dcee3d4.pkl - params_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/params.yaml - predictions_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/predictions.json - probabilities_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/probabilities.json - score_dict_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/score_dict.json - test_labels_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/test_labels.json - train_labels_file: output/reports/train/4deccaefd1ef20f2e9341c080527d9a7/train_labels.json - model_dir: models - name: 4deccaefd1ef20f2e9341c080527d9a7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ed56935caf239cec50696ce3b3a486a8 - trainer: - kwargs: {} -name: 4deccaefd1ef20f2e9341c080527d9a7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4e1f26073cf636e4c870909008f8c194/params.yaml b/examples/security/classification/output/reports/train/4e1f26073cf636e4c870909008f8c194/params.yaml deleted file mode 100644 index dc43a8a4..00000000 --- a/examples/security/classification/output/reports/train/4e1f26073cf636e4c870909008f8c194/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/adv_predictions.json - adv_probabilities_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/1b197a8fa118085952a288d7f5c9be72.pkl - params_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/params.yaml - predictions_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/predictions.json - probabilities_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/probabilities.json - score_dict_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/score_dict.json - test_labels_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/test_labels.json - train_labels_file: output/reports/train/4e1f26073cf636e4c870909008f8c194/train_labels.json - model_dir: models - name: 4e1f26073cf636e4c870909008f8c194 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 890a86b3ee04ca5c7375ef4032259e72 - trainer: - kwargs: {} -name: 4e1f26073cf636e4c870909008f8c194 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/params.yaml b/examples/security/classification/output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/params.yaml deleted file mode 100644 index 2dca7924..00000000 --- a/examples/security/classification/output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/adv_predictions.json - adv_probabilities_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/50bda82bcb3ece227bb2132d7e22bab1.pkl - params_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/params.yaml - predictions_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/predictions.json - probabilities_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/probabilities.json - score_dict_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/score_dict.json - test_labels_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/test_labels.json - train_labels_file: output/reports/train/4e22743d35c879b6ba1ed74d56b218e3/train_labels.json - model_dir: models - name: 4e22743d35c879b6ba1ed74d56b218e3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edad89f67e5c3b616da36aedd92d2bbf - trainer: - kwargs: {} -name: 4e22743d35c879b6ba1ed74d56b218e3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/params.yaml b/examples/security/classification/output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/params.yaml deleted file mode 100644 index 7b68ca96..00000000 --- a/examples/security/classification/output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/adv_predictions.json - adv_probabilities_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/ada67058f947d28fc8caa9967b3d2a87.pkl - params_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/params.yaml - predictions_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/predictions.json - probabilities_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/probabilities.json - score_dict_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/score_dict.json - test_labels_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/test_labels.json - train_labels_file: output/reports/train/4e29b7a31cc375a26d053512e6b58b4b/train_labels.json - model_dir: models - name: 4e29b7a31cc375a26d053512e6b58b4b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 932e8ac92bdc655c47162a516f75db68 - trainer: - kwargs: {} -name: 4e29b7a31cc375a26d053512e6b58b4b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4e3083c4dbb4889a70011b341072ae1f/params.yaml b/examples/security/classification/output/reports/train/4e3083c4dbb4889a70011b341072ae1f/params.yaml deleted file mode 100644 index 5f4ed328..00000000 --- a/examples/security/classification/output/reports/train/4e3083c4dbb4889a70011b341072ae1f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/adv_predictions.json - adv_probabilities_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/d5568192ddf3c805cee66e394b5dde43.pkl - params_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/params.yaml - predictions_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/predictions.json - probabilities_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/probabilities.json - score_dict_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/score_dict.json - test_labels_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/test_labels.json - train_labels_file: output/reports/train/4e3083c4dbb4889a70011b341072ae1f/train_labels.json - model_dir: models - name: 4e3083c4dbb4889a70011b341072ae1f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e7c5dac7bd6b4bdc852d9771063ef9d7 - trainer: - kwargs: {} -name: 4e3083c4dbb4889a70011b341072ae1f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4e57e62f413620cbc8a5492a6b729d32/params.yaml b/examples/security/classification/output/reports/train/4e57e62f413620cbc8a5492a6b729d32/params.yaml deleted file mode 100644 index 982449ca..00000000 --- a/examples/security/classification/output/reports/train/4e57e62f413620cbc8a5492a6b729d32/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/adv_predictions.json - adv_probabilities_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/12d30dac7ceca154db885eff8483b961.pkl - params_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/params.yaml - predictions_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/predictions.json - probabilities_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/probabilities.json - score_dict_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/score_dict.json - test_labels_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/test_labels.json - train_labels_file: output/reports/train/4e57e62f413620cbc8a5492a6b729d32/train_labels.json - model_dir: models - name: 4e57e62f413620cbc8a5492a6b729d32 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fbb142f83f5b415ed64a8e0757663d95 - trainer: - kwargs: {} -name: 4e57e62f413620cbc8a5492a6b729d32 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4e6624d695112a44c9134fe2d6537069/params.yaml b/examples/security/classification/output/reports/train/4e6624d695112a44c9134fe2d6537069/params.yaml deleted file mode 100644 index 9da5410a..00000000 --- a/examples/security/classification/output/reports/train/4e6624d695112a44c9134fe2d6537069/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/adv_predictions.json - adv_probabilities_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/bc316f2920a2f5b89223ee5823d81f5e.pkl - params_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/params.yaml - predictions_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/predictions.json - probabilities_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/probabilities.json - score_dict_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/score_dict.json - test_labels_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/test_labels.json - train_labels_file: output/reports/train/4e6624d695112a44c9134fe2d6537069/train_labels.json - model_dir: models - name: 4e6624d695112a44c9134fe2d6537069 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3ae3abb620bf3b5f981f7510e4d4aa39 - trainer: - kwargs: {} -name: 4e6624d695112a44c9134fe2d6537069 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/params.yaml b/examples/security/classification/output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/params.yaml deleted file mode 100644 index 67747872..00000000 --- a/examples/security/classification/output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/adv_predictions.json - adv_probabilities_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/008504ba1fbcc5d1c6f6481d476b144d.pkl - params_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/params.yaml - predictions_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/predictions.json - probabilities_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/probabilities.json - score_dict_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/score_dict.json - test_labels_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/test_labels.json - train_labels_file: output/reports/train/4e8acfbced6c7f5db4daf8817d05de9d/train_labels.json - model_dir: models - name: 4e8acfbced6c7f5db4daf8817d05de9d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8119c10261edf4b8493238f49aec087e - trainer: - kwargs: {} -name: 4e8acfbced6c7f5db4daf8817d05de9d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/params.yaml b/examples/security/classification/output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/params.yaml deleted file mode 100644 index 20c3b7ce..00000000 --- a/examples/security/classification/output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/adv_predictions.json - adv_probabilities_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/cdfcfd004745865e944e62e1fcd73bf8.pkl - params_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/params.yaml - predictions_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/predictions.json - probabilities_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/probabilities.json - score_dict_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/score_dict.json - test_labels_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/test_labels.json - train_labels_file: output/reports/train/4ea9ebf2cf74bd2da81f1465c1471352/train_labels.json - model_dir: models - name: 4ea9ebf2cf74bd2da81f1465c1471352 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 325ebb3fbcdc7c3e4ba741af5527b253 - trainer: - kwargs: {} -name: 4ea9ebf2cf74bd2da81f1465c1471352 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4ed057afb0e707bdc57a345848d2731e/params.yaml b/examples/security/classification/output/reports/train/4ed057afb0e707bdc57a345848d2731e/params.yaml deleted file mode 100644 index 7623c899..00000000 --- a/examples/security/classification/output/reports/train/4ed057afb0e707bdc57a345848d2731e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/adv_predictions.json - adv_probabilities_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/0c5f58f8da9828e48e022ae15f7f575a.pkl - params_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/params.yaml - predictions_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/predictions.json - probabilities_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/probabilities.json - score_dict_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/score_dict.json - test_labels_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/test_labels.json - train_labels_file: output/reports/train/4ed057afb0e707bdc57a345848d2731e/train_labels.json - model_dir: models - name: 4ed057afb0e707bdc57a345848d2731e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e2acffbc41974dd82ca307feb1051a01 - trainer: - kwargs: {} -name: 4ed057afb0e707bdc57a345848d2731e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/params.yaml b/examples/security/classification/output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/params.yaml deleted file mode 100644 index 392b4bac..00000000 --- a/examples/security/classification/output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/adv_predictions.json - adv_probabilities_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/5058f8390f2a820c99d0dc078d717b04.pkl - params_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/params.yaml - predictions_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/predictions.json - probabilities_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/probabilities.json - score_dict_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/score_dict.json - test_labels_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/test_labels.json - train_labels_file: output/reports/train/4ed7359106236f89d4a1c3bea6a1ac5b/train_labels.json - model_dir: models - name: 4ed7359106236f89d4a1c3bea6a1ac5b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8bf6aa279ded50348713bd58384564a1 - trainer: - kwargs: {} -name: 4ed7359106236f89d4a1c3bea6a1ac5b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/params.yaml b/examples/security/classification/output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/params.yaml deleted file mode 100644 index 35967e23..00000000 --- a/examples/security/classification/output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/adv_predictions.json - adv_probabilities_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/2cd056af4e68a3f993fe9fde6ea858ba.pkl - params_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/params.yaml - predictions_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/predictions.json - probabilities_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/probabilities.json - score_dict_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/score_dict.json - test_labels_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/test_labels.json - train_labels_file: output/reports/train/4ef3363f80f316ce7e14ac752210c4d6/train_labels.json - model_dir: models - name: 4ef3363f80f316ce7e14ac752210c4d6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: af969c080c765821f72c0323f477351d - trainer: - kwargs: {} -name: 4ef3363f80f316ce7e14ac752210c4d6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4efc464c07bb46317e18d14a4567c758/params.yaml b/examples/security/classification/output/reports/train/4efc464c07bb46317e18d14a4567c758/params.yaml deleted file mode 100644 index a1f6032d..00000000 --- a/examples/security/classification/output/reports/train/4efc464c07bb46317e18d14a4567c758/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/adv_predictions.json - adv_probabilities_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/d2e38e7c1692ff2b5b930b148ee0b357.pkl - params_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/params.yaml - predictions_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/predictions.json - probabilities_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/probabilities.json - score_dict_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/score_dict.json - test_labels_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/test_labels.json - train_labels_file: output/reports/train/4efc464c07bb46317e18d14a4567c758/train_labels.json - model_dir: models - name: 4efc464c07bb46317e18d14a4567c758 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 303f59ee6c50b571b51e8035d15c6360 - trainer: - kwargs: {} -name: 4efc464c07bb46317e18d14a4567c758 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4efe796bd7d31a9229a576d72e32b554/params.yaml b/examples/security/classification/output/reports/train/4efe796bd7d31a9229a576d72e32b554/params.yaml deleted file mode 100644 index a1cbdb28..00000000 --- a/examples/security/classification/output/reports/train/4efe796bd7d31a9229a576d72e32b554/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/adv_predictions.json - adv_probabilities_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/588d8a53b628534ee8b918b884c550a4.pkl - params_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/params.yaml - predictions_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/predictions.json - probabilities_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/probabilities.json - score_dict_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/score_dict.json - test_labels_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/test_labels.json - train_labels_file: output/reports/train/4efe796bd7d31a9229a576d72e32b554/train_labels.json - model_dir: models - name: 4efe796bd7d31a9229a576d72e32b554 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 44331a725f2711f0ed803e41aaa81f57 - trainer: - kwargs: {} -name: 4efe796bd7d31a9229a576d72e32b554 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/params.yaml b/examples/security/classification/output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/params.yaml deleted file mode 100644 index e3d1ac3e..00000000 --- a/examples/security/classification/output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/adv_predictions.json - adv_probabilities_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/1d4b15aae20ba48780ade0ab37a9a4ac.pkl - params_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/params.yaml - predictions_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/predictions.json - probabilities_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/probabilities.json - score_dict_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/score_dict.json - test_labels_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/test_labels.json - train_labels_file: output/reports/train/4f08a3c0e8af2116d4e89473fcc67728/train_labels.json - model_dir: models - name: 4f08a3c0e8af2116d4e89473fcc67728 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 772882005d13bee33e171b00c38b0099 - trainer: - kwargs: {} -name: 4f08a3c0e8af2116d4e89473fcc67728 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/params.yaml b/examples/security/classification/output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/params.yaml deleted file mode 100644 index 41b11ddd..00000000 --- a/examples/security/classification/output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/adv_predictions.json - adv_probabilities_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/036ae6433b8a31caad59bcc2e9652d45.pkl - params_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/params.yaml - predictions_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/predictions.json - probabilities_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/probabilities.json - score_dict_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/score_dict.json - test_labels_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/test_labels.json - train_labels_file: output/reports/train/4f08ea1caf4e3b6f966cfc4915fb7729/train_labels.json - model_dir: models - name: 4f08ea1caf4e3b6f966cfc4915fb7729 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3e729fb747a015cfb4d147fc972220ce - trainer: - kwargs: {} -name: 4f08ea1caf4e3b6f966cfc4915fb7729 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/params.yaml b/examples/security/classification/output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/params.yaml deleted file mode 100644 index 9d5f0456..00000000 --- a/examples/security/classification/output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/adv_predictions.json - adv_probabilities_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/09a7f200a8d3c809fac1c35e8c57ef10.pkl - params_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/params.yaml - predictions_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/predictions.json - probabilities_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/probabilities.json - score_dict_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/score_dict.json - test_labels_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/test_labels.json - train_labels_file: output/reports/train/4f0abb44b6d14787b7c0e14e3c62c325/train_labels.json - model_dir: models - name: 4f0abb44b6d14787b7c0e14e3c62c325 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7625340c6b88a9c02f5f4e6a957e0a58 - trainer: - kwargs: {} -name: 4f0abb44b6d14787b7c0e14e3c62c325 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/params.yaml b/examples/security/classification/output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/params.yaml deleted file mode 100644 index 9a8623ff..00000000 --- a/examples/security/classification/output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/adv_predictions.json - adv_probabilities_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/fa8dce5a08080c69cd91acae9fa22c24.pkl - params_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/params.yaml - predictions_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/predictions.json - probabilities_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/probabilities.json - score_dict_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/score_dict.json - test_labels_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/test_labels.json - train_labels_file: output/reports/train/4f26c8b1d60fca5ae0e35553479b4359/train_labels.json - model_dir: models - name: 4f26c8b1d60fca5ae0e35553479b4359 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 04ea1887ad1ce85040c97225d613ebfb - trainer: - kwargs: {} -name: 4f26c8b1d60fca5ae0e35553479b4359 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/params.yaml b/examples/security/classification/output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/params.yaml deleted file mode 100644 index e62364fb..00000000 --- a/examples/security/classification/output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/adv_predictions.json - adv_probabilities_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/90c0316667c9fee400b2faa50e8f313b.pkl - params_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/params.yaml - predictions_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/predictions.json - probabilities_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/probabilities.json - score_dict_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/score_dict.json - test_labels_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/test_labels.json - train_labels_file: output/reports/train/4f3871fe5b7b5b80cfd79d1c11e27d6f/train_labels.json - model_dir: models - name: 4f3871fe5b7b5b80cfd79d1c11e27d6f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 17b7b9bf1c7508f70734238ab59b633e - trainer: - kwargs: {} -name: 4f3871fe5b7b5b80cfd79d1c11e27d6f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4f3e64c1f768341f70c48568c10f8df6/params.yaml b/examples/security/classification/output/reports/train/4f3e64c1f768341f70c48568c10f8df6/params.yaml deleted file mode 100644 index 3a11e4ca..00000000 --- a/examples/security/classification/output/reports/train/4f3e64c1f768341f70c48568c10f8df6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/adv_predictions.json - adv_probabilities_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/5b97b43d3365ae41d3cedd58d974dc0a.pkl - params_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/params.yaml - predictions_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/predictions.json - probabilities_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/probabilities.json - score_dict_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/score_dict.json - test_labels_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/test_labels.json - train_labels_file: output/reports/train/4f3e64c1f768341f70c48568c10f8df6/train_labels.json - model_dir: models - name: 4f3e64c1f768341f70c48568c10f8df6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 63deddcf992fe530f27e4783e7a969ec - trainer: - kwargs: {} -name: 4f3e64c1f768341f70c48568c10f8df6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4f66bf1ac70407f6b675cffee2710c33/params.yaml b/examples/security/classification/output/reports/train/4f66bf1ac70407f6b675cffee2710c33/params.yaml deleted file mode 100644 index 49289a2c..00000000 --- a/examples/security/classification/output/reports/train/4f66bf1ac70407f6b675cffee2710c33/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/adv_predictions.json - adv_probabilities_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/b7f4d74ca559632b2ac061089c4d475b.pkl - params_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/params.yaml - predictions_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/predictions.json - probabilities_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/probabilities.json - score_dict_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/score_dict.json - test_labels_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/test_labels.json - train_labels_file: output/reports/train/4f66bf1ac70407f6b675cffee2710c33/train_labels.json - model_dir: models - name: 4f66bf1ac70407f6b675cffee2710c33 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2fb640502965c573f5b89af9251f7359 - trainer: - kwargs: {} -name: 4f66bf1ac70407f6b675cffee2710c33 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/params.yaml b/examples/security/classification/output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/params.yaml deleted file mode 100644 index 839de9a6..00000000 --- a/examples/security/classification/output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/adv_predictions.json - adv_probabilities_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/262de918a03ea293a99280bd04790e88.pkl - params_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/params.yaml - predictions_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/predictions.json - probabilities_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/probabilities.json - score_dict_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/score_dict.json - test_labels_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/test_labels.json - train_labels_file: output/reports/train/4fb0a5670414ebeda04a5e5ea3b7bcba/train_labels.json - model_dir: models - name: 4fb0a5670414ebeda04a5e5ea3b7bcba - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9b8d6305d061d3c02db9e68da1f6491c - trainer: - kwargs: {} -name: 4fb0a5670414ebeda04a5e5ea3b7bcba -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/params.yaml b/examples/security/classification/output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/params.yaml deleted file mode 100644 index e6cb0e7f..00000000 --- a/examples/security/classification/output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/adv_predictions.json - adv_probabilities_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/c0f0e38a724ece6a6f45466888903265.pkl - params_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/params.yaml - predictions_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/predictions.json - probabilities_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/probabilities.json - score_dict_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/score_dict.json - test_labels_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/test_labels.json - train_labels_file: output/reports/train/4fbe4ad9b9ad9c174e81c2021df6f523/train_labels.json - model_dir: models - name: 4fbe4ad9b9ad9c174e81c2021df6f523 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 852fa618c649f5ec7028aace31a1316e - trainer: - kwargs: {} -name: 4fbe4ad9b9ad9c174e81c2021df6f523 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4fdc1308c5041b700640e1aec5a38de0/params.yaml b/examples/security/classification/output/reports/train/4fdc1308c5041b700640e1aec5a38de0/params.yaml deleted file mode 100644 index 0815e07a..00000000 --- a/examples/security/classification/output/reports/train/4fdc1308c5041b700640e1aec5a38de0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/adv_predictions.json - adv_probabilities_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/50c6233f809609251e61cfd582decd2c.pkl - params_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/params.yaml - predictions_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/predictions.json - probabilities_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/probabilities.json - score_dict_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/score_dict.json - test_labels_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/test_labels.json - train_labels_file: output/reports/train/4fdc1308c5041b700640e1aec5a38de0/train_labels.json - model_dir: models - name: 4fdc1308c5041b700640e1aec5a38de0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a62ef65179613a98666ca678295fcf96 - trainer: - kwargs: {} -name: 4fdc1308c5041b700640e1aec5a38de0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4fe12f069d4d34ad852561df03e8c50a/params.yaml b/examples/security/classification/output/reports/train/4fe12f069d4d34ad852561df03e8c50a/params.yaml deleted file mode 100644 index 1b179d50..00000000 --- a/examples/security/classification/output/reports/train/4fe12f069d4d34ad852561df03e8c50a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/adv_predictions.json - adv_probabilities_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/62ee45bf69e18c8867bda719f8541ed3.pkl - params_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/params.yaml - predictions_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/predictions.json - probabilities_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/probabilities.json - score_dict_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/score_dict.json - test_labels_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/test_labels.json - train_labels_file: output/reports/train/4fe12f069d4d34ad852561df03e8c50a/train_labels.json - model_dir: models - name: 4fe12f069d4d34ad852561df03e8c50a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f1d9f3cedfe8488de313fa434c5fa8ab - trainer: - kwargs: {} -name: 4fe12f069d4d34ad852561df03e8c50a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/4fec0ceb8e977702930bca93e62e91b3/params.yaml b/examples/security/classification/output/reports/train/4fec0ceb8e977702930bca93e62e91b3/params.yaml deleted file mode 100644 index f194d8d3..00000000 --- a/examples/security/classification/output/reports/train/4fec0ceb8e977702930bca93e62e91b3/params.yaml +++ /dev/null @@ -1,206 +0,0 @@ -attack: - attack_size: 10 - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 10.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cb07a4eeb8b1b7d80ac33d2d4ee67b92 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d1eb6b6992361f23a0b90e848b0f6165 - trainer: - kwargs: {} - name: 25e875ffa58457f925d14640bfe10268 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/adv_predictions.json - adv_probabilities_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/adv_probabilities.json - attack_file: output/attacks/1eba92e25c9b4202e89ec2958fb6ff56.pkl - data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl - model_file: output/models/1697e64c20873080433ffaf85cc99b44.pkl - params_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/params.yaml - predictions_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/predictions.json - probabilities_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/probabilities.json - score_dict_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/score_dict.json - test_labels_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/test_labels.json - train_labels_file: output/reports/train/4fec0ceb8e977702930bca93e62e91b3/train_labels.json - model_dir: models - name: 4fec0ceb8e977702930bca93e62e91b3 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: aafb5936b55f8df520328a275ef4f4bb - trainer: - kwargs: {} -name: 4fec0ceb8e977702930bca93e62e91b3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/500feef99ec799208d2da5272207dceb/params.yaml b/examples/security/classification/output/reports/train/500feef99ec799208d2da5272207dceb/params.yaml deleted file mode 100644 index 1347a01c..00000000 --- a/examples/security/classification/output/reports/train/500feef99ec799208d2da5272207dceb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/500feef99ec799208d2da5272207dceb/adv_predictions.json - adv_probabilities_file: output/reports/train/500feef99ec799208d2da5272207dceb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/d1c9521dd4dae638f55b4a0a6606f595.pkl - params_file: output/reports/train/500feef99ec799208d2da5272207dceb/params.yaml - predictions_file: output/reports/train/500feef99ec799208d2da5272207dceb/predictions.json - probabilities_file: output/reports/train/500feef99ec799208d2da5272207dceb/probabilities.json - score_dict_file: output/reports/train/500feef99ec799208d2da5272207dceb/score_dict.json - test_labels_file: output/reports/train/500feef99ec799208d2da5272207dceb/test_labels.json - train_labels_file: output/reports/train/500feef99ec799208d2da5272207dceb/train_labels.json - model_dir: models - name: 500feef99ec799208d2da5272207dceb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 833eac1f35a64864eda71180648d01d4 - trainer: - kwargs: {} -name: 500feef99ec799208d2da5272207dceb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/505b2e3d036b398852e4940b845db76b/params.yaml b/examples/security/classification/output/reports/train/505b2e3d036b398852e4940b845db76b/params.yaml deleted file mode 100644 index 96c29fdf..00000000 --- a/examples/security/classification/output/reports/train/505b2e3d036b398852e4940b845db76b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/505b2e3d036b398852e4940b845db76b/adv_predictions.json - adv_probabilities_file: output/reports/train/505b2e3d036b398852e4940b845db76b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/e10cc234486ee4a017f612ea128ced6b.pkl - params_file: output/reports/train/505b2e3d036b398852e4940b845db76b/params.yaml - predictions_file: output/reports/train/505b2e3d036b398852e4940b845db76b/predictions.json - probabilities_file: output/reports/train/505b2e3d036b398852e4940b845db76b/probabilities.json - score_dict_file: output/reports/train/505b2e3d036b398852e4940b845db76b/score_dict.json - test_labels_file: output/reports/train/505b2e3d036b398852e4940b845db76b/test_labels.json - train_labels_file: output/reports/train/505b2e3d036b398852e4940b845db76b/train_labels.json - model_dir: models - name: 505b2e3d036b398852e4940b845db76b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d51a58b4f648cbe58ea464445f9e71b7 - trainer: - kwargs: {} -name: 505b2e3d036b398852e4940b845db76b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/params.yaml b/examples/security/classification/output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/params.yaml deleted file mode 100644 index 829df363..00000000 --- a/examples/security/classification/output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/adv_predictions.json - adv_probabilities_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/1c462aa56219a93242f4f8cb8612eadc.pkl - params_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/params.yaml - predictions_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/predictions.json - probabilities_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/probabilities.json - score_dict_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/score_dict.json - test_labels_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/test_labels.json - train_labels_file: output/reports/train/505caf9a0de97bd7b4080a0b5fcf299d/train_labels.json - model_dir: models - name: 505caf9a0de97bd7b4080a0b5fcf299d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e77256e323c3ce3080e40c2540275e47 - trainer: - kwargs: {} -name: 505caf9a0de97bd7b4080a0b5fcf299d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/50bfc88fcc83250789358a47b3419065/params.yaml b/examples/security/classification/output/reports/train/50bfc88fcc83250789358a47b3419065/params.yaml deleted file mode 100644 index 8dd195d3..00000000 --- a/examples/security/classification/output/reports/train/50bfc88fcc83250789358a47b3419065/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/50bfc88fcc83250789358a47b3419065/adv_predictions.json - adv_probabilities_file: output/reports/train/50bfc88fcc83250789358a47b3419065/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/2cf3acf742d748aa12a28e6359c18f7b.pkl - params_file: output/reports/train/50bfc88fcc83250789358a47b3419065/params.yaml - predictions_file: output/reports/train/50bfc88fcc83250789358a47b3419065/predictions.json - probabilities_file: output/reports/train/50bfc88fcc83250789358a47b3419065/probabilities.json - score_dict_file: output/reports/train/50bfc88fcc83250789358a47b3419065/score_dict.json - test_labels_file: output/reports/train/50bfc88fcc83250789358a47b3419065/test_labels.json - train_labels_file: output/reports/train/50bfc88fcc83250789358a47b3419065/train_labels.json - model_dir: models - name: 50bfc88fcc83250789358a47b3419065 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e438e27f9dc80832a945ef360fada0dc - trainer: - kwargs: {} -name: 50bfc88fcc83250789358a47b3419065 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/50f31bda43c11dce50597912f9efe1c7/params.yaml b/examples/security/classification/output/reports/train/50f31bda43c11dce50597912f9efe1c7/params.yaml deleted file mode 100644 index d5903efb..00000000 --- a/examples/security/classification/output/reports/train/50f31bda43c11dce50597912f9efe1c7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/adv_predictions.json - adv_probabilities_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/13a78e4a031a90ff5dafbce93e30d1d3.pkl - params_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/params.yaml - predictions_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/predictions.json - probabilities_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/probabilities.json - score_dict_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/score_dict.json - test_labels_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/test_labels.json - train_labels_file: output/reports/train/50f31bda43c11dce50597912f9efe1c7/train_labels.json - model_dir: models - name: 50f31bda43c11dce50597912f9efe1c7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2550926a35e93c272b7fb22eec89fc1f - trainer: - kwargs: {} -name: 50f31bda43c11dce50597912f9efe1c7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/512470107636657930baca09f1c42dba/params.yaml b/examples/security/classification/output/reports/train/512470107636657930baca09f1c42dba/params.yaml deleted file mode 100644 index 47f096f7..00000000 --- a/examples/security/classification/output/reports/train/512470107636657930baca09f1c42dba/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/512470107636657930baca09f1c42dba/adv_predictions.json - adv_probabilities_file: output/reports/train/512470107636657930baca09f1c42dba/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/6b34aa33cc377762ec2637e28764b351.pkl - params_file: output/reports/train/512470107636657930baca09f1c42dba/params.yaml - predictions_file: output/reports/train/512470107636657930baca09f1c42dba/predictions.json - probabilities_file: output/reports/train/512470107636657930baca09f1c42dba/probabilities.json - score_dict_file: output/reports/train/512470107636657930baca09f1c42dba/score_dict.json - test_labels_file: output/reports/train/512470107636657930baca09f1c42dba/test_labels.json - train_labels_file: output/reports/train/512470107636657930baca09f1c42dba/train_labels.json - model_dir: models - name: 512470107636657930baca09f1c42dba - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a59409a9e4765c5a5db884efe4bb5ef3 - trainer: - kwargs: {} -name: 512470107636657930baca09f1c42dba -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/51563400f5265942ec72e69b4c865349/params.yaml b/examples/security/classification/output/reports/train/51563400f5265942ec72e69b4c865349/params.yaml deleted file mode 100644 index d4f93dab..00000000 --- a/examples/security/classification/output/reports/train/51563400f5265942ec72e69b4c865349/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/51563400f5265942ec72e69b4c865349/adv_predictions.json - adv_probabilities_file: output/reports/train/51563400f5265942ec72e69b4c865349/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/ae64b23e157846f7dc9f972f99246877.pkl - params_file: output/reports/train/51563400f5265942ec72e69b4c865349/params.yaml - predictions_file: output/reports/train/51563400f5265942ec72e69b4c865349/predictions.json - probabilities_file: output/reports/train/51563400f5265942ec72e69b4c865349/probabilities.json - score_dict_file: output/reports/train/51563400f5265942ec72e69b4c865349/score_dict.json - test_labels_file: output/reports/train/51563400f5265942ec72e69b4c865349/test_labels.json - train_labels_file: output/reports/train/51563400f5265942ec72e69b4c865349/train_labels.json - model_dir: models - name: 51563400f5265942ec72e69b4c865349 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f3407df1b980e72510e86747c8d4c78e - trainer: - kwargs: {} -name: 51563400f5265942ec72e69b4c865349 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5162008249ff15d48bc4a286e67aceed/params.yaml b/examples/security/classification/output/reports/train/5162008249ff15d48bc4a286e67aceed/params.yaml deleted file mode 100644 index 7f6f0219..00000000 --- a/examples/security/classification/output/reports/train/5162008249ff15d48bc4a286e67aceed/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/adv_predictions.json - adv_probabilities_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/7d27d0fdea10678ef7cfb9ecf9257dc6.pkl - params_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/params.yaml - predictions_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/predictions.json - probabilities_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/probabilities.json - score_dict_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/score_dict.json - test_labels_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/test_labels.json - train_labels_file: output/reports/train/5162008249ff15d48bc4a286e67aceed/train_labels.json - model_dir: models - name: 5162008249ff15d48bc4a286e67aceed - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66cd21b8241c9a8ae3027c1edd8044dd - trainer: - kwargs: {} -name: 5162008249ff15d48bc4a286e67aceed -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/51870231da6307cfe310b432aa7e8be7/params.yaml b/examples/security/classification/output/reports/train/51870231da6307cfe310b432aa7e8be7/params.yaml deleted file mode 100644 index ffe88b74..00000000 --- a/examples/security/classification/output/reports/train/51870231da6307cfe310b432aa7e8be7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/adv_predictions.json - adv_probabilities_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/a6b5309e713d7b0dc90babb11e4e14c5.pkl - params_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/params.yaml - predictions_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/predictions.json - probabilities_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/probabilities.json - score_dict_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/score_dict.json - test_labels_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/test_labels.json - train_labels_file: output/reports/train/51870231da6307cfe310b432aa7e8be7/train_labels.json - model_dir: models - name: 51870231da6307cfe310b432aa7e8be7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ac28bdb2df50598e355911dab1494ecf - trainer: - kwargs: {} -name: 51870231da6307cfe310b432aa7e8be7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/519717be015b1b1306fd5cbeda4b180c/params.yaml b/examples/security/classification/output/reports/train/519717be015b1b1306fd5cbeda4b180c/params.yaml deleted file mode 100644 index bee92fcd..00000000 --- a/examples/security/classification/output/reports/train/519717be015b1b1306fd5cbeda4b180c/params.yaml +++ /dev/null @@ -1,104 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/adv_predictions.json - adv_probabilities_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/5c0a8f32fd9b960dd388cba791e507bb.pkl - params_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/params.yaml - predictions_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/predictions.json - probabilities_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/probabilities.json - score_dict_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/score_dict.json - test_labels_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/test_labels.json - train_labels_file: output/reports/train/519717be015b1b1306fd5cbeda4b180c/train_labels.json - model_dir: models - name: 519717be015b1b1306fd5cbeda4b180c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: - - linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 09472754bc810e84bba25f75f677ed1c - trainer: - kwargs: {} -name: 519717be015b1b1306fd5cbeda4b180c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/51975394c79a93069f090eea6ddaefff/params.yaml b/examples/security/classification/output/reports/train/51975394c79a93069f090eea6ddaefff/params.yaml deleted file mode 100644 index 479ce6a0..00000000 --- a/examples/security/classification/output/reports/train/51975394c79a93069f090eea6ddaefff/params.yaml +++ /dev/null @@ -1,202 +0,0 @@ -attack: - attack_size: 10 - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cb7496c7ad411f4919f150fb4033c5f8 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: adf238bc307f919f46f2ba43c35ab80d - trainer: - kwargs: {} - name: f968f36593e83a37633f1ea3beb48d11 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/51975394c79a93069f090eea6ddaefff/adv_predictions.json - adv_probabilities_file: output/reports/train/51975394c79a93069f090eea6ddaefff/adv_probabilities.json - attack_file: output/attacks/79aaf01298f1b324fe5a76f24f62d5ca.pkl - data_file: output/data/051f101486d067e3f403ac87eba325c2.pkl - model_file: output/models/c29bd639be38539e177c574845e347ec.pkl - params_file: output/reports/train/51975394c79a93069f090eea6ddaefff/params.yaml - predictions_file: output/reports/train/51975394c79a93069f090eea6ddaefff/predictions.json - probabilities_file: output/reports/train/51975394c79a93069f090eea6ddaefff/probabilities.json - score_dict_file: output/reports/train/51975394c79a93069f090eea6ddaefff/score_dict.json - test_labels_file: output/reports/train/51975394c79a93069f090eea6ddaefff/test_labels.json - train_labels_file: output/reports/train/51975394c79a93069f090eea6ddaefff/train_labels.json - model_dir: models - name: 51975394c79a93069f090eea6ddaefff - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1852678c950600b1beb8613d89bf2950 - trainer: - kwargs: {} -name: 51975394c79a93069f090eea6ddaefff -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/params.yaml b/examples/security/classification/output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/params.yaml deleted file mode 100644 index 8edd62e4..00000000 --- a/examples/security/classification/output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/params.yaml +++ /dev/null @@ -1,202 +0,0 @@ -attack: - attack_size: 10 - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 10000.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 752225dad0d39491658535ea793d9b86 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0b0c4a6366d9c9ed73841d305d1deaec - trainer: - kwargs: {} - name: e9683fce3c41b02dca23381997568dee -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/adv_predictions.json - adv_probabilities_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/adv_probabilities.json - attack_file: output/attacks/f141c591385aab10903b4bb653346673.pkl - data_file: output/data/579db4bb6f9d6b612a1cf6bf1f51ea05.pkl - model_file: output/models/66b5d604d3fd6b8f31ba0326c10583ad.pkl - params_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/params.yaml - predictions_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/predictions.json - probabilities_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/probabilities.json - score_dict_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/score_dict.json - test_labels_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/test_labels.json - train_labels_file: output/reports/train/51b8e289b8f3a49d0b88468c8049f8d1/train_labels.json - model_dir: models - name: 51b8e289b8f3a49d0b88468c8049f8d1 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: aae9e0d6c72fb30e904ad431759aa466 - trainer: - kwargs: {} -name: 51b8e289b8f3a49d0b88468c8049f8d1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/params.yaml b/examples/security/classification/output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/params.yaml deleted file mode 100644 index 4b43845a..00000000 --- a/examples/security/classification/output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/adv_predictions.json - adv_probabilities_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/06a1d1cc348b09f925cd55d69747e8cd.pkl - params_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/params.yaml - predictions_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/predictions.json - probabilities_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/probabilities.json - score_dict_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/score_dict.json - test_labels_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/test_labels.json - train_labels_file: output/reports/train/51e1d8aa6dce349741a6ccf84e4c0af8/train_labels.json - model_dir: models - name: 51e1d8aa6dce349741a6ccf84e4c0af8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 83bcc411c7ec34a7ec7f40eba46e3a52 - trainer: - kwargs: {} -name: 51e1d8aa6dce349741a6ccf84e4c0af8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/51e8593194ae68c4b62208f654d6c811/params.yaml b/examples/security/classification/output/reports/train/51e8593194ae68c4b62208f654d6c811/params.yaml deleted file mode 100644 index 88473b05..00000000 --- a/examples/security/classification/output/reports/train/51e8593194ae68c4b62208f654d6c811/params.yaml +++ /dev/null @@ -1,202 +0,0 @@ -attack: - attack_size: 10 - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 2000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 2000.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 858b009d4cbb08fd74fc49e78b15b430 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 2000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7192300522bbc22ed827a454cd9dd5c3 - trainer: - kwargs: {} - name: 73a00c1ddb74dd9f132a16dd533e8034 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 2000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/adv_predictions.json - adv_probabilities_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/adv_probabilities.json - attack_file: output/attacks/5aa4baffe2159155393b7e70433cc149.pkl - data_file: output/data/66c37c36c533a0007a8deb95b9d0974c.pkl - model_file: output/models/1a15fc5c0a5fc62b2d80a0245a4667e1.pkl - params_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/params.yaml - predictions_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/predictions.json - probabilities_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/probabilities.json - score_dict_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/score_dict.json - test_labels_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/test_labels.json - train_labels_file: output/reports/train/51e8593194ae68c4b62208f654d6c811/train_labels.json - model_dir: models - name: 51e8593194ae68c4b62208f654d6c811 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 2000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2d77f2a4494339d76dda10ca26c6046b - trainer: - kwargs: {} -name: 51e8593194ae68c4b62208f654d6c811 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/params.yaml b/examples/security/classification/output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/params.yaml deleted file mode 100644 index c0b7f7bd..00000000 --- a/examples/security/classification/output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/adv_predictions.json - adv_probabilities_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/d5f1f21907ff22d356090c497e141f97.pkl - params_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/params.yaml - predictions_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/predictions.json - probabilities_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/probabilities.json - score_dict_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/score_dict.json - test_labels_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/test_labels.json - train_labels_file: output/reports/train/521dffa0b87f393212ddd76d6ab4a5d0/train_labels.json - model_dir: models - name: 521dffa0b87f393212ddd76d6ab4a5d0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 97e45dfd831024503e8a4e1e6d7dae51 - trainer: - kwargs: {} -name: 521dffa0b87f393212ddd76d6ab4a5d0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/52496aca8af2cf71adedf23baca703f2/params.yaml b/examples/security/classification/output/reports/train/52496aca8af2cf71adedf23baca703f2/params.yaml deleted file mode 100644 index 64d104da..00000000 --- a/examples/security/classification/output/reports/train/52496aca8af2cf71adedf23baca703f2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/adv_predictions.json - adv_probabilities_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/d988ff3f9c844f3e12e77b93b026ebc4.pkl - params_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/params.yaml - predictions_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/predictions.json - probabilities_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/probabilities.json - score_dict_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/score_dict.json - test_labels_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/test_labels.json - train_labels_file: output/reports/train/52496aca8af2cf71adedf23baca703f2/train_labels.json - model_dir: models - name: 52496aca8af2cf71adedf23baca703f2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 48b2bfe1f05dc5fbc5257006ed6bf2c4 - trainer: - kwargs: {} -name: 52496aca8af2cf71adedf23baca703f2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/52a3bd3871354f37d02314a0be918e5e/params.yaml b/examples/security/classification/output/reports/train/52a3bd3871354f37d02314a0be918e5e/params.yaml deleted file mode 100644 index 4749e931..00000000 --- a/examples/security/classification/output/reports/train/52a3bd3871354f37d02314a0be918e5e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/adv_predictions.json - adv_probabilities_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/0dbf24656c9d3a506ee0f5f06e246f45.pkl - params_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/params.yaml - predictions_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/predictions.json - probabilities_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/probabilities.json - score_dict_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/score_dict.json - test_labels_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/test_labels.json - train_labels_file: output/reports/train/52a3bd3871354f37d02314a0be918e5e/train_labels.json - model_dir: models - name: 52a3bd3871354f37d02314a0be918e5e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: de63cf365af34fa42101b349b9635f27 - trainer: - kwargs: {} -name: 52a3bd3871354f37d02314a0be918e5e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/530ba5a34f581cbaa41454a505b5a691/params.yaml b/examples/security/classification/output/reports/train/530ba5a34f581cbaa41454a505b5a691/params.yaml deleted file mode 100644 index 3d510acc..00000000 --- a/examples/security/classification/output/reports/train/530ba5a34f581cbaa41454a505b5a691/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/adv_predictions.json - adv_probabilities_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/dda6105923860b29d3fd3821ab53b43d.pkl - params_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/params.yaml - predictions_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/predictions.json - probabilities_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/probabilities.json - score_dict_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/score_dict.json - test_labels_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/test_labels.json - train_labels_file: output/reports/train/530ba5a34f581cbaa41454a505b5a691/train_labels.json - model_dir: models - name: 530ba5a34f581cbaa41454a505b5a691 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bd5be377fa53ae26e10ebdeabd32110e - trainer: - kwargs: {} -name: 530ba5a34f581cbaa41454a505b5a691 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/536fe070398da6c64253898981cf8b12/params.yaml b/examples/security/classification/output/reports/train/536fe070398da6c64253898981cf8b12/params.yaml deleted file mode 100644 index 64070a0a..00000000 --- a/examples/security/classification/output/reports/train/536fe070398da6c64253898981cf8b12/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/536fe070398da6c64253898981cf8b12/adv_predictions.json - adv_probabilities_file: output/reports/train/536fe070398da6c64253898981cf8b12/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/c75b8859d1efd2e02fa79f751e2ba4e5.pkl - params_file: output/reports/train/536fe070398da6c64253898981cf8b12/params.yaml - predictions_file: output/reports/train/536fe070398da6c64253898981cf8b12/predictions.json - probabilities_file: output/reports/train/536fe070398da6c64253898981cf8b12/probabilities.json - score_dict_file: output/reports/train/536fe070398da6c64253898981cf8b12/score_dict.json - test_labels_file: output/reports/train/536fe070398da6c64253898981cf8b12/test_labels.json - train_labels_file: output/reports/train/536fe070398da6c64253898981cf8b12/train_labels.json - model_dir: models - name: 536fe070398da6c64253898981cf8b12 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7b873eb5e0ca8608f7f6c2dd515a2449 - trainer: - kwargs: {} -name: 536fe070398da6c64253898981cf8b12 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/params.yaml b/examples/security/classification/output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/params.yaml deleted file mode 100644 index fb1bdf3c..00000000 --- a/examples/security/classification/output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/adv_predictions.json - adv_probabilities_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/f64bf59e951afc3cda3dcd10c5092188.pkl - params_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/params.yaml - predictions_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/predictions.json - probabilities_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/probabilities.json - score_dict_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/score_dict.json - test_labels_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/test_labels.json - train_labels_file: output/reports/train/53a41bb216684a29d3a0ed9c9a6c3a8a/train_labels.json - model_dir: models - name: 53a41bb216684a29d3a0ed9c9a6c3a8a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 982a17a1342ebeffcdefff43b8dd322c - trainer: - kwargs: {} -name: 53a41bb216684a29d3a0ed9c9a6c3a8a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/541162c11026a972e6e13ccb030eb0c4/params.yaml b/examples/security/classification/output/reports/train/541162c11026a972e6e13ccb030eb0c4/params.yaml deleted file mode 100644 index f18700bd..00000000 --- a/examples/security/classification/output/reports/train/541162c11026a972e6e13ccb030eb0c4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/adv_predictions.json - adv_probabilities_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/bd80a7ecf2ca32bbd543335d1ab764df.pkl - params_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/params.yaml - predictions_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/predictions.json - probabilities_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/probabilities.json - score_dict_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/score_dict.json - test_labels_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/test_labels.json - train_labels_file: output/reports/train/541162c11026a972e6e13ccb030eb0c4/train_labels.json - model_dir: models - name: 541162c11026a972e6e13ccb030eb0c4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2250f02c23b9046fce268cb5b703406f - trainer: - kwargs: {} -name: 541162c11026a972e6e13ccb030eb0c4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/541b02f8f11691bae75333d27e5bdf7f/params.yaml b/examples/security/classification/output/reports/train/541b02f8f11691bae75333d27e5bdf7f/params.yaml deleted file mode 100644 index 5b4e740e..00000000 --- a/examples/security/classification/output/reports/train/541b02f8f11691bae75333d27e5bdf7f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/adv_predictions.json - adv_probabilities_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/a190affa2966eff55af1627e05d3fbf9.pkl - params_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/params.yaml - predictions_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/predictions.json - probabilities_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/probabilities.json - score_dict_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/score_dict.json - test_labels_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/test_labels.json - train_labels_file: output/reports/train/541b02f8f11691bae75333d27e5bdf7f/train_labels.json - model_dir: models - name: 541b02f8f11691bae75333d27e5bdf7f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d3ba31fa78c1098bc9780253712102bc - trainer: - kwargs: {} -name: 541b02f8f11691bae75333d27e5bdf7f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/542a18b06a05de137140fdda913ea368/params.yaml b/examples/security/classification/output/reports/train/542a18b06a05de137140fdda913ea368/params.yaml deleted file mode 100644 index 3f3cb317..00000000 --- a/examples/security/classification/output/reports/train/542a18b06a05de137140fdda913ea368/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/542a18b06a05de137140fdda913ea368/adv_predictions.json - adv_probabilities_file: output/reports/train/542a18b06a05de137140fdda913ea368/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/0052686f802e285eaa90abecea5b9be9.pkl - params_file: output/reports/train/542a18b06a05de137140fdda913ea368/params.yaml - predictions_file: output/reports/train/542a18b06a05de137140fdda913ea368/predictions.json - probabilities_file: output/reports/train/542a18b06a05de137140fdda913ea368/probabilities.json - score_dict_file: output/reports/train/542a18b06a05de137140fdda913ea368/score_dict.json - test_labels_file: output/reports/train/542a18b06a05de137140fdda913ea368/test_labels.json - train_labels_file: output/reports/train/542a18b06a05de137140fdda913ea368/train_labels.json - model_dir: models - name: 542a18b06a05de137140fdda913ea368 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 32dd2c04d8d4e2669bb159f87742920d - trainer: - kwargs: {} -name: 542a18b06a05de137140fdda913ea368 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5444a560e035b3366a3a444f932069bb/params.yaml b/examples/security/classification/output/reports/train/5444a560e035b3366a3a444f932069bb/params.yaml deleted file mode 100644 index cb951860..00000000 --- a/examples/security/classification/output/reports/train/5444a560e035b3366a3a444f932069bb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5444a560e035b3366a3a444f932069bb/adv_predictions.json - adv_probabilities_file: output/reports/train/5444a560e035b3366a3a444f932069bb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/24b86cf67064c94f1bb610392302af1a.pkl - params_file: output/reports/train/5444a560e035b3366a3a444f932069bb/params.yaml - predictions_file: output/reports/train/5444a560e035b3366a3a444f932069bb/predictions.json - probabilities_file: output/reports/train/5444a560e035b3366a3a444f932069bb/probabilities.json - score_dict_file: output/reports/train/5444a560e035b3366a3a444f932069bb/score_dict.json - test_labels_file: output/reports/train/5444a560e035b3366a3a444f932069bb/test_labels.json - train_labels_file: output/reports/train/5444a560e035b3366a3a444f932069bb/train_labels.json - model_dir: models - name: 5444a560e035b3366a3a444f932069bb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e15c6c41595b6897779cd221cec92b37 - trainer: - kwargs: {} -name: 5444a560e035b3366a3a444f932069bb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/544ce392628e03c9bf5076a0622e3eba/params.yaml b/examples/security/classification/output/reports/train/544ce392628e03c9bf5076a0622e3eba/params.yaml deleted file mode 100644 index 1c5b93d0..00000000 --- a/examples/security/classification/output/reports/train/544ce392628e03c9bf5076a0622e3eba/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/adv_predictions.json - adv_probabilities_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/3a790b8f5cf957cd279076897d709352.pkl - params_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/params.yaml - predictions_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/predictions.json - probabilities_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/probabilities.json - score_dict_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/score_dict.json - test_labels_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/test_labels.json - train_labels_file: output/reports/train/544ce392628e03c9bf5076a0622e3eba/train_labels.json - model_dir: models - name: 544ce392628e03c9bf5076a0622e3eba - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fb1284e1002014e858fd3b57eb8944fa - trainer: - kwargs: {} -name: 544ce392628e03c9bf5076a0622e3eba -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/54563396dec51efe6c557ee8abb32a5b/params.yaml b/examples/security/classification/output/reports/train/54563396dec51efe6c557ee8abb32a5b/params.yaml deleted file mode 100644 index 7c6ef115..00000000 --- a/examples/security/classification/output/reports/train/54563396dec51efe6c557ee8abb32a5b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/adv_predictions.json - adv_probabilities_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/b26bf27122c304743429e4c36bc339a7.pkl - params_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/params.yaml - predictions_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/predictions.json - probabilities_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/probabilities.json - score_dict_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/score_dict.json - test_labels_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/test_labels.json - train_labels_file: output/reports/train/54563396dec51efe6c557ee8abb32a5b/train_labels.json - model_dir: models - name: 54563396dec51efe6c557ee8abb32a5b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 68533208e4d4bd8c5d95c6ca83cb5e96 - trainer: - kwargs: {} -name: 54563396dec51efe6c557ee8abb32a5b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/54584c096f30ce10bfe766a6300a8999/params.yaml b/examples/security/classification/output/reports/train/54584c096f30ce10bfe766a6300a8999/params.yaml deleted file mode 100644 index bb8710ef..00000000 --- a/examples/security/classification/output/reports/train/54584c096f30ce10bfe766a6300a8999/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/adv_predictions.json - adv_probabilities_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/1a99a7951baa052999be54b6501f7c46.pkl - params_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/params.yaml - predictions_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/predictions.json - probabilities_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/probabilities.json - score_dict_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/score_dict.json - test_labels_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/test_labels.json - train_labels_file: output/reports/train/54584c096f30ce10bfe766a6300a8999/train_labels.json - model_dir: models - name: 54584c096f30ce10bfe766a6300a8999 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 76d852d2eef62aa4d5946c3fc3fa396f - trainer: - kwargs: {} -name: 54584c096f30ce10bfe766a6300a8999 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5466cbbf5f7df59abda71db227dce684/params.yaml b/examples/security/classification/output/reports/train/5466cbbf5f7df59abda71db227dce684/params.yaml deleted file mode 100644 index 5e859700..00000000 --- a/examples/security/classification/output/reports/train/5466cbbf5f7df59abda71db227dce684/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/adv_predictions.json - adv_probabilities_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/4b791db8c2f242d44793293cc3e579a0.pkl - params_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/params.yaml - predictions_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/predictions.json - probabilities_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/probabilities.json - score_dict_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/score_dict.json - test_labels_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/test_labels.json - train_labels_file: output/reports/train/5466cbbf5f7df59abda71db227dce684/train_labels.json - model_dir: models - name: 5466cbbf5f7df59abda71db227dce684 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9dcd786f16121bc0ed939453d02618f7 - trainer: - kwargs: {} -name: 5466cbbf5f7df59abda71db227dce684 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5466fffc76af713231184777c391496c/params.yaml b/examples/security/classification/output/reports/train/5466fffc76af713231184777c391496c/params.yaml deleted file mode 100644 index 9fd22833..00000000 --- a/examples/security/classification/output/reports/train/5466fffc76af713231184777c391496c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5466fffc76af713231184777c391496c/adv_predictions.json - adv_probabilities_file: output/reports/train/5466fffc76af713231184777c391496c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/d7435d809bf61e0e34e4eccd4d340867.pkl - params_file: output/reports/train/5466fffc76af713231184777c391496c/params.yaml - predictions_file: output/reports/train/5466fffc76af713231184777c391496c/predictions.json - probabilities_file: output/reports/train/5466fffc76af713231184777c391496c/probabilities.json - score_dict_file: output/reports/train/5466fffc76af713231184777c391496c/score_dict.json - test_labels_file: output/reports/train/5466fffc76af713231184777c391496c/test_labels.json - train_labels_file: output/reports/train/5466fffc76af713231184777c391496c/train_labels.json - model_dir: models - name: 5466fffc76af713231184777c391496c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c249dc5b94c95668891ae8cd355141fb - trainer: - kwargs: {} -name: 5466fffc76af713231184777c391496c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/params.yaml b/examples/security/classification/output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/params.yaml deleted file mode 100644 index 479f1764..00000000 --- a/examples/security/classification/output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/adv_predictions.json - adv_probabilities_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/c028a4e31641bafb6bb46bae9999c44e.pkl - params_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/params.yaml - predictions_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/predictions.json - probabilities_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/probabilities.json - score_dict_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/score_dict.json - test_labels_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/test_labels.json - train_labels_file: output/reports/train/5469d9e9fac6347eb1f80c7070433bf9/train_labels.json - model_dir: models - name: 5469d9e9fac6347eb1f80c7070433bf9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f270c043039adb0ec6207b9f068583b5 - trainer: - kwargs: {} -name: 5469d9e9fac6347eb1f80c7070433bf9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/54a5bc198907525664154e5745898160/params.yaml b/examples/security/classification/output/reports/train/54a5bc198907525664154e5745898160/params.yaml deleted file mode 100644 index a455c8d2..00000000 --- a/examples/security/classification/output/reports/train/54a5bc198907525664154e5745898160/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/54a5bc198907525664154e5745898160/adv_predictions.json - adv_probabilities_file: output/reports/train/54a5bc198907525664154e5745898160/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/f652e0662b583e8ab96f076439a664a7.pkl - params_file: output/reports/train/54a5bc198907525664154e5745898160/params.yaml - predictions_file: output/reports/train/54a5bc198907525664154e5745898160/predictions.json - probabilities_file: output/reports/train/54a5bc198907525664154e5745898160/probabilities.json - score_dict_file: output/reports/train/54a5bc198907525664154e5745898160/score_dict.json - test_labels_file: output/reports/train/54a5bc198907525664154e5745898160/test_labels.json - train_labels_file: output/reports/train/54a5bc198907525664154e5745898160/train_labels.json - model_dir: models - name: 54a5bc198907525664154e5745898160 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0b5f5fc1bd73a5783f35c7923e3e49df - trainer: - kwargs: {} -name: 54a5bc198907525664154e5745898160 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/54b542cb61014be495eae3660cdcc097/params.yaml b/examples/security/classification/output/reports/train/54b542cb61014be495eae3660cdcc097/params.yaml deleted file mode 100644 index 1a9705cc..00000000 --- a/examples/security/classification/output/reports/train/54b542cb61014be495eae3660cdcc097/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/54b542cb61014be495eae3660cdcc097/adv_predictions.json - adv_probabilities_file: output/reports/train/54b542cb61014be495eae3660cdcc097/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/69af359a860b689154ed1c715c3e3a1d.pkl - params_file: output/reports/train/54b542cb61014be495eae3660cdcc097/params.yaml - predictions_file: output/reports/train/54b542cb61014be495eae3660cdcc097/predictions.json - probabilities_file: output/reports/train/54b542cb61014be495eae3660cdcc097/probabilities.json - score_dict_file: output/reports/train/54b542cb61014be495eae3660cdcc097/score_dict.json - test_labels_file: output/reports/train/54b542cb61014be495eae3660cdcc097/test_labels.json - train_labels_file: output/reports/train/54b542cb61014be495eae3660cdcc097/train_labels.json - model_dir: models - name: 54b542cb61014be495eae3660cdcc097 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 386f7dfd47d6b4ac005173ec68b094de - trainer: - kwargs: {} -name: 54b542cb61014be495eae3660cdcc097 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/params.yaml b/examples/security/classification/output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/params.yaml deleted file mode 100644 index 0cc47dc3..00000000 --- a/examples/security/classification/output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/adv_predictions.json - adv_probabilities_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/571c1a856ef36b4f15cf0e7f3c16ab94.pkl - params_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/params.yaml - predictions_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/predictions.json - probabilities_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/probabilities.json - score_dict_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/score_dict.json - test_labels_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/test_labels.json - train_labels_file: output/reports/train/54d90ec4ff1d45f363a14f866c05ed85/train_labels.json - model_dir: models - name: 54d90ec4ff1d45f363a14f866c05ed85 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3eb653572dd7f8f9c49c165c049324a2 - trainer: - kwargs: {} -name: 54d90ec4ff1d45f363a14f866c05ed85 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/params.yaml b/examples/security/classification/output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/params.yaml deleted file mode 100644 index 0139eb70..00000000 --- a/examples/security/classification/output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/adv_predictions.json - adv_probabilities_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/a4f5425260fb74a61eb0c4096459b1b6.pkl - params_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/params.yaml - predictions_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/predictions.json - probabilities_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/probabilities.json - score_dict_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/score_dict.json - test_labels_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/test_labels.json - train_labels_file: output/reports/train/54de4d912b625b9a9ea9ddea9c7f7872/train_labels.json - model_dir: models - name: 54de4d912b625b9a9ea9ddea9c7f7872 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5e577bd3c9de09b3238c459e2215bd27 - trainer: - kwargs: {} -name: 54de4d912b625b9a9ea9ddea9c7f7872 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/550369fba54fa78c9b49848e05a568ed/params.yaml b/examples/security/classification/output/reports/train/550369fba54fa78c9b49848e05a568ed/params.yaml deleted file mode 100644 index 3f9adc87..00000000 --- a/examples/security/classification/output/reports/train/550369fba54fa78c9b49848e05a568ed/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/adv_predictions.json - adv_probabilities_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/67babded34fe6d7d6f47509a4465750c.pkl - params_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/params.yaml - predictions_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/predictions.json - probabilities_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/probabilities.json - score_dict_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/score_dict.json - test_labels_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/test_labels.json - train_labels_file: output/reports/train/550369fba54fa78c9b49848e05a568ed/train_labels.json - model_dir: models - name: 550369fba54fa78c9b49848e05a568ed - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 95f925cfb9ed291467f5457bd0c93328 - trainer: - kwargs: {} -name: 550369fba54fa78c9b49848e05a568ed -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5506730a7b97e5c200cfcd45ff721eac/params.yaml b/examples/security/classification/output/reports/train/5506730a7b97e5c200cfcd45ff721eac/params.yaml deleted file mode 100644 index 8f1ad482..00000000 --- a/examples/security/classification/output/reports/train/5506730a7b97e5c200cfcd45ff721eac/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/adv_predictions.json - adv_probabilities_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/fb2e205cce24dd210e39c130cab310e2.pkl - params_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/params.yaml - predictions_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/predictions.json - probabilities_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/probabilities.json - score_dict_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/score_dict.json - test_labels_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/test_labels.json - train_labels_file: output/reports/train/5506730a7b97e5c200cfcd45ff721eac/train_labels.json - model_dir: models - name: 5506730a7b97e5c200cfcd45ff721eac - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 460de0b53bcb698e012d935e7e9d7744 - trainer: - kwargs: {} -name: 5506730a7b97e5c200cfcd45ff721eac -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/params.yaml b/examples/security/classification/output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/params.yaml deleted file mode 100644 index 0ad6d5c1..00000000 --- a/examples/security/classification/output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/adv_predictions.json - adv_probabilities_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/27b8f44f0ba7517a0a4c2ef1af5e827c.pkl - params_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/params.yaml - predictions_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/predictions.json - probabilities_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/probabilities.json - score_dict_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/score_dict.json - test_labels_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/test_labels.json - train_labels_file: output/reports/train/551cfb2c0c7f1c847dfce30baecc6e94/train_labels.json - model_dir: models - name: 551cfb2c0c7f1c847dfce30baecc6e94 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e777f9eb42fb6c1a1edde4f8c91a295a - trainer: - kwargs: {} -name: 551cfb2c0c7f1c847dfce30baecc6e94 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5524b354a74912549a2b2c6f248666df/params.yaml b/examples/security/classification/output/reports/train/5524b354a74912549a2b2c6f248666df/params.yaml deleted file mode 100644 index b1869bef..00000000 --- a/examples/security/classification/output/reports/train/5524b354a74912549a2b2c6f248666df/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5524b354a74912549a2b2c6f248666df/adv_predictions.json - adv_probabilities_file: output/reports/train/5524b354a74912549a2b2c6f248666df/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/c78c7807c4beb689054404868db4593e.pkl - params_file: output/reports/train/5524b354a74912549a2b2c6f248666df/params.yaml - predictions_file: output/reports/train/5524b354a74912549a2b2c6f248666df/predictions.json - probabilities_file: output/reports/train/5524b354a74912549a2b2c6f248666df/probabilities.json - score_dict_file: output/reports/train/5524b354a74912549a2b2c6f248666df/score_dict.json - test_labels_file: output/reports/train/5524b354a74912549a2b2c6f248666df/test_labels.json - train_labels_file: output/reports/train/5524b354a74912549a2b2c6f248666df/train_labels.json - model_dir: models - name: 5524b354a74912549a2b2c6f248666df - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4e5b3733d7c0a33479a1dd9f2f0af8a6 - trainer: - kwargs: {} -name: 5524b354a74912549a2b2c6f248666df -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/552bcfbc5e8669a23d481b26819924f3/params.yaml b/examples/security/classification/output/reports/train/552bcfbc5e8669a23d481b26819924f3/params.yaml deleted file mode 100644 index baeebfa4..00000000 --- a/examples/security/classification/output/reports/train/552bcfbc5e8669a23d481b26819924f3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/adv_predictions.json - adv_probabilities_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/519878df3a7614b562663704ef0c8391.pkl - params_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/params.yaml - predictions_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/predictions.json - probabilities_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/probabilities.json - score_dict_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/score_dict.json - test_labels_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/test_labels.json - train_labels_file: output/reports/train/552bcfbc5e8669a23d481b26819924f3/train_labels.json - model_dir: models - name: 552bcfbc5e8669a23d481b26819924f3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 76226f7f6719ae19fd603411cb94aef7 - trainer: - kwargs: {} -name: 552bcfbc5e8669a23d481b26819924f3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/params.yaml b/examples/security/classification/output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/params.yaml deleted file mode 100644 index c534dee4..00000000 --- a/examples/security/classification/output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/adv_predictions.json - adv_probabilities_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/757496301a02714a003b0a8dc40af92b.pkl - params_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/params.yaml - predictions_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/predictions.json - probabilities_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/probabilities.json - score_dict_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/score_dict.json - test_labels_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/test_labels.json - train_labels_file: output/reports/train/553b3a11c7ffb91f5d0a6f4674e82b0f/train_labels.json - model_dir: models - name: 553b3a11c7ffb91f5d0a6f4674e82b0f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fff508c20f214f4d470278d7938117cf - trainer: - kwargs: {} -name: 553b3a11c7ffb91f5d0a6f4674e82b0f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/55572fd810232ce801aff52f0fd94377/params.yaml b/examples/security/classification/output/reports/train/55572fd810232ce801aff52f0fd94377/params.yaml deleted file mode 100644 index f6749533..00000000 --- a/examples/security/classification/output/reports/train/55572fd810232ce801aff52f0fd94377/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/55572fd810232ce801aff52f0fd94377/adv_predictions.json - adv_probabilities_file: output/reports/train/55572fd810232ce801aff52f0fd94377/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/ee13061f1cf7ae0be3d7cc13c46e7459.pkl - params_file: output/reports/train/55572fd810232ce801aff52f0fd94377/params.yaml - predictions_file: output/reports/train/55572fd810232ce801aff52f0fd94377/predictions.json - probabilities_file: output/reports/train/55572fd810232ce801aff52f0fd94377/probabilities.json - score_dict_file: output/reports/train/55572fd810232ce801aff52f0fd94377/score_dict.json - test_labels_file: output/reports/train/55572fd810232ce801aff52f0fd94377/test_labels.json - train_labels_file: output/reports/train/55572fd810232ce801aff52f0fd94377/train_labels.json - model_dir: models - name: 55572fd810232ce801aff52f0fd94377 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9ecd586abd5b7fee4a2a3abde2bba8ae - trainer: - kwargs: {} -name: 55572fd810232ce801aff52f0fd94377 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/557af1520f334a69d65b1aecd340fa2d/params.yaml b/examples/security/classification/output/reports/train/557af1520f334a69d65b1aecd340fa2d/params.yaml deleted file mode 100644 index bbb5f8d1..00000000 --- a/examples/security/classification/output/reports/train/557af1520f334a69d65b1aecd340fa2d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/adv_predictions.json - adv_probabilities_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/a7f9ebb5662d07b8ef59aa8fd9c6fb88.pkl - params_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/params.yaml - predictions_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/predictions.json - probabilities_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/probabilities.json - score_dict_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/score_dict.json - test_labels_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/test_labels.json - train_labels_file: output/reports/train/557af1520f334a69d65b1aecd340fa2d/train_labels.json - model_dir: models - name: 557af1520f334a69d65b1aecd340fa2d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5a49e798eaab0d544a588096b9bbf9c0 - trainer: - kwargs: {} -name: 557af1520f334a69d65b1aecd340fa2d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/557b033314915cc8329e071797a03250/params.yaml b/examples/security/classification/output/reports/train/557b033314915cc8329e071797a03250/params.yaml deleted file mode 100644 index bb6d903c..00000000 --- a/examples/security/classification/output/reports/train/557b033314915cc8329e071797a03250/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/557b033314915cc8329e071797a03250/adv_predictions.json - adv_probabilities_file: output/reports/train/557b033314915cc8329e071797a03250/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/ed964cc5033a2380ddabe562f3b9e460.pkl - params_file: output/reports/train/557b033314915cc8329e071797a03250/params.yaml - predictions_file: output/reports/train/557b033314915cc8329e071797a03250/predictions.json - probabilities_file: output/reports/train/557b033314915cc8329e071797a03250/probabilities.json - score_dict_file: output/reports/train/557b033314915cc8329e071797a03250/score_dict.json - test_labels_file: output/reports/train/557b033314915cc8329e071797a03250/test_labels.json - train_labels_file: output/reports/train/557b033314915cc8329e071797a03250/train_labels.json - model_dir: models - name: 557b033314915cc8329e071797a03250 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: efd1813dd903aa109896662440f276d8 - trainer: - kwargs: {} -name: 557b033314915cc8329e071797a03250 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/558003aec4c2abe27cea646204db0adc/params.yaml b/examples/security/classification/output/reports/train/558003aec4c2abe27cea646204db0adc/params.yaml deleted file mode 100644 index 7ebba8cb..00000000 --- a/examples/security/classification/output/reports/train/558003aec4c2abe27cea646204db0adc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/558003aec4c2abe27cea646204db0adc/adv_predictions.json - adv_probabilities_file: output/reports/train/558003aec4c2abe27cea646204db0adc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/21d7b299c94b45b746d23305a33715e1.pkl - params_file: output/reports/train/558003aec4c2abe27cea646204db0adc/params.yaml - predictions_file: output/reports/train/558003aec4c2abe27cea646204db0adc/predictions.json - probabilities_file: output/reports/train/558003aec4c2abe27cea646204db0adc/probabilities.json - score_dict_file: output/reports/train/558003aec4c2abe27cea646204db0adc/score_dict.json - test_labels_file: output/reports/train/558003aec4c2abe27cea646204db0adc/test_labels.json - train_labels_file: output/reports/train/558003aec4c2abe27cea646204db0adc/train_labels.json - model_dir: models - name: 558003aec4c2abe27cea646204db0adc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 87bade3b71a6117ef3010b2dd409178d - trainer: - kwargs: {} -name: 558003aec4c2abe27cea646204db0adc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/params.yaml b/examples/security/classification/output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/params.yaml deleted file mode 100644 index 1c95acd9..00000000 --- a/examples/security/classification/output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/adv_predictions.json - adv_probabilities_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/9dc0681f225ba6fe140538b297749b4f.pkl - params_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/params.yaml - predictions_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/predictions.json - probabilities_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/probabilities.json - score_dict_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/score_dict.json - test_labels_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/test_labels.json - train_labels_file: output/reports/train/55986d2db564aef8d02ce90ceb39a1a0/train_labels.json - model_dir: models - name: 55986d2db564aef8d02ce90ceb39a1a0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 02d8e826addb70520e536ca713ae3992 - trainer: - kwargs: {} -name: 55986d2db564aef8d02ce90ceb39a1a0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/params.yaml b/examples/security/classification/output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/params.yaml deleted file mode 100644 index c0a2b28b..00000000 --- a/examples/security/classification/output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/adv_predictions.json - adv_probabilities_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/58abdfef416473fe39308a0def75c3cd.pkl - params_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/params.yaml - predictions_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/predictions.json - probabilities_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/probabilities.json - score_dict_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/score_dict.json - test_labels_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/test_labels.json - train_labels_file: output/reports/train/55bfae96534bccc2b9fdf8e6b3bf5467/train_labels.json - model_dir: models - name: 55bfae96534bccc2b9fdf8e6b3bf5467 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c25dc6902c5b756fc60e27bfee597178 - trainer: - kwargs: {} -name: 55bfae96534bccc2b9fdf8e6b3bf5467 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/params.yaml b/examples/security/classification/output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/params.yaml deleted file mode 100644 index cb7b6b82..00000000 --- a/examples/security/classification/output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/adv_predictions.json - adv_probabilities_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/edfcd1bc4121d2543e682455ee2c82f0.pkl - params_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/params.yaml - predictions_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/predictions.json - probabilities_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/probabilities.json - score_dict_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/score_dict.json - test_labels_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/test_labels.json - train_labels_file: output/reports/train/55cba176b52b8ce0ab7b8df37107e63d/train_labels.json - model_dir: models - name: 55cba176b52b8ce0ab7b8df37107e63d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ec0d661ca3897485fd5aace0e465597d - trainer: - kwargs: {} -name: 55cba176b52b8ce0ab7b8df37107e63d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/55d24251b3799994488d2090a80973d3/params.yaml b/examples/security/classification/output/reports/train/55d24251b3799994488d2090a80973d3/params.yaml deleted file mode 100644 index bfeaedda..00000000 --- a/examples/security/classification/output/reports/train/55d24251b3799994488d2090a80973d3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/55d24251b3799994488d2090a80973d3/adv_predictions.json - adv_probabilities_file: output/reports/train/55d24251b3799994488d2090a80973d3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/7b50b147c9ded2eb22318ad6e15c3b60.pkl - params_file: output/reports/train/55d24251b3799994488d2090a80973d3/params.yaml - predictions_file: output/reports/train/55d24251b3799994488d2090a80973d3/predictions.json - probabilities_file: output/reports/train/55d24251b3799994488d2090a80973d3/probabilities.json - score_dict_file: output/reports/train/55d24251b3799994488d2090a80973d3/score_dict.json - test_labels_file: output/reports/train/55d24251b3799994488d2090a80973d3/test_labels.json - train_labels_file: output/reports/train/55d24251b3799994488d2090a80973d3/train_labels.json - model_dir: models - name: 55d24251b3799994488d2090a80973d3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e4464064146066f71d18c5e18cc995fc - trainer: - kwargs: {} -name: 55d24251b3799994488d2090a80973d3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/params.yaml b/examples/security/classification/output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/params.yaml deleted file mode 100644 index 03441b7f..00000000 --- a/examples/security/classification/output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/adv_predictions.json - adv_probabilities_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/49abe4dc6f154bd59830e92121d6d84e.pkl - params_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/params.yaml - predictions_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/predictions.json - probabilities_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/probabilities.json - score_dict_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/score_dict.json - test_labels_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/test_labels.json - train_labels_file: output/reports/train/55df3aa6582b2dce6cb6608c0e7d8dce/train_labels.json - model_dir: models - name: 55df3aa6582b2dce6cb6608c0e7d8dce - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 29e85514f11575806204275ea33be48b - trainer: - kwargs: {} -name: 55df3aa6582b2dce6cb6608c0e7d8dce -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/params.yaml b/examples/security/classification/output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/params.yaml deleted file mode 100644 index faf57262..00000000 --- a/examples/security/classification/output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/adv_predictions.json - adv_probabilities_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/9a8f3655adabfdd5be99c19c0f4ec743.pkl - params_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/params.yaml - predictions_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/predictions.json - probabilities_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/probabilities.json - score_dict_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/score_dict.json - test_labels_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/test_labels.json - train_labels_file: output/reports/train/55f99863f858e2989b4a2ac5c8d5e16f/train_labels.json - model_dir: models - name: 55f99863f858e2989b4a2ac5c8d5e16f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 91d345a42b81a151f384f67757d40d0a - trainer: - kwargs: {} -name: 55f99863f858e2989b4a2ac5c8d5e16f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/params.yaml b/examples/security/classification/output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/params.yaml deleted file mode 100644 index 2431e133..00000000 --- a/examples/security/classification/output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/adv_predictions.json - adv_probabilities_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/f952beb729317a3cfca2aace0062dd9f.pkl - params_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/params.yaml - predictions_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/predictions.json - probabilities_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/probabilities.json - score_dict_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/score_dict.json - test_labels_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/test_labels.json - train_labels_file: output/reports/train/561fa37abe2dab0a256861e0fb71d9ac/train_labels.json - model_dir: models - name: 561fa37abe2dab0a256861e0fb71d9ac - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1ac68b81ace77f12d35053d162117de7 - trainer: - kwargs: {} -name: 561fa37abe2dab0a256861e0fb71d9ac -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/56484c2a2da13b617eb5ec199eee371b/params.yaml b/examples/security/classification/output/reports/train/56484c2a2da13b617eb5ec199eee371b/params.yaml deleted file mode 100644 index e592e6df..00000000 --- a/examples/security/classification/output/reports/train/56484c2a2da13b617eb5ec199eee371b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/adv_predictions.json - adv_probabilities_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/3cd54e4f63b0762da9b25ee04f6f2e95.pkl - params_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/params.yaml - predictions_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/predictions.json - probabilities_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/probabilities.json - score_dict_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/score_dict.json - test_labels_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/test_labels.json - train_labels_file: output/reports/train/56484c2a2da13b617eb5ec199eee371b/train_labels.json - model_dir: models - name: 56484c2a2da13b617eb5ec199eee371b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4d3f3f21ba4df836693b7d5b307d4d96 - trainer: - kwargs: {} -name: 56484c2a2da13b617eb5ec199eee371b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5660948910fe72e243b0123b0add14af/params.yaml b/examples/security/classification/output/reports/train/5660948910fe72e243b0123b0add14af/params.yaml deleted file mode 100644 index c90912ff..00000000 --- a/examples/security/classification/output/reports/train/5660948910fe72e243b0123b0add14af/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5660948910fe72e243b0123b0add14af/adv_predictions.json - adv_probabilities_file: output/reports/train/5660948910fe72e243b0123b0add14af/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/0f4fbac76f8c2f3c32d4a110c2d8cea0.pkl - params_file: output/reports/train/5660948910fe72e243b0123b0add14af/params.yaml - predictions_file: output/reports/train/5660948910fe72e243b0123b0add14af/predictions.json - probabilities_file: output/reports/train/5660948910fe72e243b0123b0add14af/probabilities.json - score_dict_file: output/reports/train/5660948910fe72e243b0123b0add14af/score_dict.json - test_labels_file: output/reports/train/5660948910fe72e243b0123b0add14af/test_labels.json - train_labels_file: output/reports/train/5660948910fe72e243b0123b0add14af/train_labels.json - model_dir: models - name: 5660948910fe72e243b0123b0add14af - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3e6426437bf7409206bb397823350e5f - trainer: - kwargs: {} -name: 5660948910fe72e243b0123b0add14af -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/56af31d4fd9e4d1d947f621613070020/params.yaml b/examples/security/classification/output/reports/train/56af31d4fd9e4d1d947f621613070020/params.yaml deleted file mode 100644 index 1143f48b..00000000 --- a/examples/security/classification/output/reports/train/56af31d4fd9e4d1d947f621613070020/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/adv_predictions.json - adv_probabilities_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/2dbea57763c74e74706be166c3442b33.pkl - params_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/params.yaml - predictions_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/predictions.json - probabilities_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/probabilities.json - score_dict_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/score_dict.json - test_labels_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/test_labels.json - train_labels_file: output/reports/train/56af31d4fd9e4d1d947f621613070020/train_labels.json - model_dir: models - name: 56af31d4fd9e4d1d947f621613070020 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5dced44973f4c7a79bfebc18f151b5e5 - trainer: - kwargs: {} -name: 56af31d4fd9e4d1d947f621613070020 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/56afef145a18f473e6d8be24fb11bbfd/params.yaml b/examples/security/classification/output/reports/train/56afef145a18f473e6d8be24fb11bbfd/params.yaml deleted file mode 100644 index 0adff983..00000000 --- a/examples/security/classification/output/reports/train/56afef145a18f473e6d8be24fb11bbfd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/adv_predictions.json - adv_probabilities_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/282f9964b55565161551d844d1cda033.pkl - params_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/params.yaml - predictions_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/predictions.json - probabilities_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/probabilities.json - score_dict_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/score_dict.json - test_labels_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/test_labels.json - train_labels_file: output/reports/train/56afef145a18f473e6d8be24fb11bbfd/train_labels.json - model_dir: models - name: 56afef145a18f473e6d8be24fb11bbfd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9ee49abd17d900939ef1c8369c28fdd6 - trainer: - kwargs: {} -name: 56afef145a18f473e6d8be24fb11bbfd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/params.yaml b/examples/security/classification/output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/params.yaml deleted file mode 100644 index f4da2bad..00000000 --- a/examples/security/classification/output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/adv_predictions.json - adv_probabilities_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/49a2df4832437fdb7157ce9ab742e10e.pkl - params_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/params.yaml - predictions_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/predictions.json - probabilities_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/probabilities.json - score_dict_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/score_dict.json - test_labels_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/test_labels.json - train_labels_file: output/reports/train/56cd9d4444dc965474c1b2ab4bd3b617/train_labels.json - model_dir: models - name: 56cd9d4444dc965474c1b2ab4bd3b617 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 52de23d438d1b9bcca72671823ca43bd - trainer: - kwargs: {} -name: 56cd9d4444dc965474c1b2ab4bd3b617 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/56e7f08fc318f05d1c248326d5dff55c/params.yaml b/examples/security/classification/output/reports/train/56e7f08fc318f05d1c248326d5dff55c/params.yaml deleted file mode 100644 index 79d2500b..00000000 --- a/examples/security/classification/output/reports/train/56e7f08fc318f05d1c248326d5dff55c/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/adv_predictions.json - adv_probabilities_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/11e9d205776f6d181cc9c5f542094f21.pkl - params_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/params.yaml - predictions_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/predictions.json - probabilities_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/probabilities.json - score_dict_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/score_dict.json - test_labels_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/test_labels.json - train_labels_file: output/reports/train/56e7f08fc318f05d1c248326d5dff55c/train_labels.json - model_dir: models - name: 56e7f08fc318f05d1c248326d5dff55c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4dcf06f63fc90d0bfdced4ed9e883ac5 - trainer: - kwargs: {} -name: 56e7f08fc318f05d1c248326d5dff55c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/params.yaml b/examples/security/classification/output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/params.yaml deleted file mode 100644 index 9437b4ab..00000000 --- a/examples/security/classification/output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/adv_predictions.json - adv_probabilities_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/5556b002ac4ce1e149d88f7cba4a9697.pkl - params_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/params.yaml - predictions_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/predictions.json - probabilities_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/probabilities.json - score_dict_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/score_dict.json - test_labels_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/test_labels.json - train_labels_file: output/reports/train/5704a91f1db5161e5e2b2bba6ef0dbc4/train_labels.json - model_dir: models - name: 5704a91f1db5161e5e2b2bba6ef0dbc4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4b963967744c09f9cd14e9590572430a - trainer: - kwargs: {} -name: 5704a91f1db5161e5e2b2bba6ef0dbc4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/params.yaml b/examples/security/classification/output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/params.yaml deleted file mode 100644 index f32008a6..00000000 --- a/examples/security/classification/output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/adv_predictions.json - adv_probabilities_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/f7c98d7e4586679d591f0e254be758ed.pkl - params_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/params.yaml - predictions_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/predictions.json - probabilities_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/probabilities.json - score_dict_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/score_dict.json - test_labels_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/test_labels.json - train_labels_file: output/reports/train/570cfe7df0b46c03494b539cf0b17dc3/train_labels.json - model_dir: models - name: 570cfe7df0b46c03494b539cf0b17dc3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2e2f830839349004e95c2e03f7bfa662 - trainer: - kwargs: {} -name: 570cfe7df0b46c03494b539cf0b17dc3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/params.yaml b/examples/security/classification/output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/params.yaml deleted file mode 100644 index 4254b55e..00000000 --- a/examples/security/classification/output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/adv_predictions.json - adv_probabilities_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/1c85fcf4fa12a46e4c48310a761e6213.pkl - params_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/params.yaml - predictions_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/predictions.json - probabilities_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/probabilities.json - score_dict_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/score_dict.json - test_labels_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/test_labels.json - train_labels_file: output/reports/train/572c9ad35fe58ed4efec0c95ccff76f1/train_labels.json - model_dir: models - name: 572c9ad35fe58ed4efec0c95ccff76f1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fcb1a41ff02f3bf6a0ffe4d8ef0582e4 - trainer: - kwargs: {} -name: 572c9ad35fe58ed4efec0c95ccff76f1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/params.yaml b/examples/security/classification/output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/params.yaml deleted file mode 100644 index 45669834..00000000 --- a/examples/security/classification/output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/adv_predictions.json - adv_probabilities_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/d4f50f9989ea52364f8863c38296825b.pkl - params_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/params.yaml - predictions_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/predictions.json - probabilities_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/probabilities.json - score_dict_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/score_dict.json - test_labels_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/test_labels.json - train_labels_file: output/reports/train/572e8ea7b0d31d54e7801d2ea8d9c539/train_labels.json - model_dir: models - name: 572e8ea7b0d31d54e7801d2ea8d9c539 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7131e14f30da55323b37a9f8326d7a1c - trainer: - kwargs: {} -name: 572e8ea7b0d31d54e7801d2ea8d9c539 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/57452003995f5259e4fa8395fdef4ec2/params.yaml b/examples/security/classification/output/reports/train/57452003995f5259e4fa8395fdef4ec2/params.yaml deleted file mode 100644 index 2084c0f5..00000000 --- a/examples/security/classification/output/reports/train/57452003995f5259e4fa8395fdef4ec2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/adv_predictions.json - adv_probabilities_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/07ac1128cdcf7dd4e16e8c8fc94dfdeb.pkl - params_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/params.yaml - predictions_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/predictions.json - probabilities_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/probabilities.json - score_dict_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/score_dict.json - test_labels_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/test_labels.json - train_labels_file: output/reports/train/57452003995f5259e4fa8395fdef4ec2/train_labels.json - model_dir: models - name: 57452003995f5259e4fa8395fdef4ec2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3110caae82aedc90f09bf2cc7253a541 - trainer: - kwargs: {} -name: 57452003995f5259e4fa8395fdef4ec2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/575ea89433a37bea6083ac19ea04051d/params.yaml b/examples/security/classification/output/reports/train/575ea89433a37bea6083ac19ea04051d/params.yaml deleted file mode 100644 index a6936c65..00000000 --- a/examples/security/classification/output/reports/train/575ea89433a37bea6083ac19ea04051d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/adv_predictions.json - adv_probabilities_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/6c7edfc9d384b05331035c92236fa7b4.pkl - params_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/params.yaml - predictions_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/predictions.json - probabilities_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/probabilities.json - score_dict_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/score_dict.json - test_labels_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/test_labels.json - train_labels_file: output/reports/train/575ea89433a37bea6083ac19ea04051d/train_labels.json - model_dir: models - name: 575ea89433a37bea6083ac19ea04051d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 90cc5cfa125c8bb48e525d8f84fd869c - trainer: - kwargs: {} -name: 575ea89433a37bea6083ac19ea04051d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5780927248d62f25dfac023435bfa3b0/params.yaml b/examples/security/classification/output/reports/train/5780927248d62f25dfac023435bfa3b0/params.yaml deleted file mode 100644 index 8e77d30d..00000000 --- a/examples/security/classification/output/reports/train/5780927248d62f25dfac023435bfa3b0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/adv_predictions.json - adv_probabilities_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/fd97afb480628d0eef97b4b73da9cf2d.pkl - params_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/params.yaml - predictions_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/predictions.json - probabilities_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/probabilities.json - score_dict_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/score_dict.json - test_labels_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/test_labels.json - train_labels_file: output/reports/train/5780927248d62f25dfac023435bfa3b0/train_labels.json - model_dir: models - name: 5780927248d62f25dfac023435bfa3b0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a884a323a1720c87dc81e7beaf0fa2b5 - trainer: - kwargs: {} -name: 5780927248d62f25dfac023435bfa3b0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/57a991c65041f180d0984a2d0168ce40/params.yaml b/examples/security/classification/output/reports/train/57a991c65041f180d0984a2d0168ce40/params.yaml deleted file mode 100644 index ef7de1f8..00000000 --- a/examples/security/classification/output/reports/train/57a991c65041f180d0984a2d0168ce40/params.yaml +++ /dev/null @@ -1,197 +0,0 @@ -attack: - attack_size: 10 - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d5294e8ab1041edae33534ea52ca8ac1 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5807854823e97434b6fcd7b2a82346e8 - trainer: - kwargs: {} - name: 4b63f8fa43b854ff1187842f7138f4ba -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/adv_predictions.json - adv_probabilities_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/adv_probabilities.json - attack_file: output/attacks/5cfe3fccdcdb0024b76e93cf446f66cd.pkl - data_file: output/data/1d9e337636137b595d73f82016ba0ef9.pkl - model_file: output/models/19e91e79d76ceed771ccd133e61b542b.pkl - params_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/params.yaml - predictions_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/predictions.json - probabilities_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/probabilities.json - score_dict_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/score_dict.json - test_labels_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/test_labels.json - train_labels_file: output/reports/train/57a991c65041f180d0984a2d0168ce40/train_labels.json - model_dir: models - name: 57a991c65041f180d0984a2d0168ce40 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 85285c84d7f24301c3d23f9a50635d2e - trainer: - kwargs: {} -name: 57a991c65041f180d0984a2d0168ce40 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/57ae32094abbffc372f3b575d2851070/params.yaml b/examples/security/classification/output/reports/train/57ae32094abbffc372f3b575d2851070/params.yaml deleted file mode 100644 index 4197b332..00000000 --- a/examples/security/classification/output/reports/train/57ae32094abbffc372f3b575d2851070/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/57ae32094abbffc372f3b575d2851070/adv_predictions.json - adv_probabilities_file: output/reports/train/57ae32094abbffc372f3b575d2851070/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/d17445375deb83426a8dad0dae9fd059.pkl - params_file: output/reports/train/57ae32094abbffc372f3b575d2851070/params.yaml - predictions_file: output/reports/train/57ae32094abbffc372f3b575d2851070/predictions.json - probabilities_file: output/reports/train/57ae32094abbffc372f3b575d2851070/probabilities.json - score_dict_file: output/reports/train/57ae32094abbffc372f3b575d2851070/score_dict.json - test_labels_file: output/reports/train/57ae32094abbffc372f3b575d2851070/test_labels.json - train_labels_file: output/reports/train/57ae32094abbffc372f3b575d2851070/train_labels.json - model_dir: models - name: 57ae32094abbffc372f3b575d2851070 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 175a21466a0c9c753784c8be66c558d9 - trainer: - kwargs: {} -name: 57ae32094abbffc372f3b575d2851070 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/57c66da7ec54232190a9f7a2f75884a5/params.yaml b/examples/security/classification/output/reports/train/57c66da7ec54232190a9f7a2f75884a5/params.yaml deleted file mode 100644 index cab6e07e..00000000 --- a/examples/security/classification/output/reports/train/57c66da7ec54232190a9f7a2f75884a5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/adv_predictions.json - adv_probabilities_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/7558a4ebc7f5e58268ee2b5868244650.pkl - params_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/params.yaml - predictions_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/predictions.json - probabilities_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/probabilities.json - score_dict_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/score_dict.json - test_labels_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/test_labels.json - train_labels_file: output/reports/train/57c66da7ec54232190a9f7a2f75884a5/train_labels.json - model_dir: models - name: 57c66da7ec54232190a9f7a2f75884a5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: db052b22178e8f939bf743a619afad14 - trainer: - kwargs: {} -name: 57c66da7ec54232190a9f7a2f75884a5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/params.yaml b/examples/security/classification/output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/params.yaml deleted file mode 100644 index 292605ef..00000000 --- a/examples/security/classification/output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/adv_predictions.json - adv_probabilities_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e5922e8e921f0a5cba98516d2e0a4bcf.pkl - params_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/params.yaml - predictions_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/predictions.json - probabilities_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/probabilities.json - score_dict_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/score_dict.json - test_labels_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/test_labels.json - train_labels_file: output/reports/train/57eb253e0ce8cdba3c8df8261abb2cd9/train_labels.json - model_dir: models - name: 57eb253e0ce8cdba3c8df8261abb2cd9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a7867024b4f3f7361dcf921009a670ee - trainer: - kwargs: {} -name: 57eb253e0ce8cdba3c8df8261abb2cd9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/params.yaml b/examples/security/classification/output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/params.yaml deleted file mode 100644 index 0fab8b61..00000000 --- a/examples/security/classification/output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/adv_predictions.json - adv_probabilities_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/a6d5cb75097cbac7c170f2ebc2d855cd.pkl - params_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/params.yaml - predictions_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/predictions.json - probabilities_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/probabilities.json - score_dict_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/score_dict.json - test_labels_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/test_labels.json - train_labels_file: output/reports/train/580c758bb53f1e06dfdf5e42b4ef3b43/train_labels.json - model_dir: models - name: 580c758bb53f1e06dfdf5e42b4ef3b43 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bf6b076570b4528d8a85ac3dbf37c10c - trainer: - kwargs: {} -name: 580c758bb53f1e06dfdf5e42b4ef3b43 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5835a6450617b3c7e24da11c7213a993/params.yaml b/examples/security/classification/output/reports/train/5835a6450617b3c7e24da11c7213a993/params.yaml deleted file mode 100644 index c21c5e86..00000000 --- a/examples/security/classification/output/reports/train/5835a6450617b3c7e24da11c7213a993/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/adv_predictions.json - adv_probabilities_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/71d826871fae11236bb3e178be73f591.pkl - params_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/params.yaml - predictions_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/predictions.json - probabilities_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/probabilities.json - score_dict_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/score_dict.json - test_labels_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/test_labels.json - train_labels_file: output/reports/train/5835a6450617b3c7e24da11c7213a993/train_labels.json - model_dir: models - name: 5835a6450617b3c7e24da11c7213a993 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b6c840376aa619e1363c2487029cd7c7 - trainer: - kwargs: {} -name: 5835a6450617b3c7e24da11c7213a993 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/583768f9400fb4fc5a5caa2819c21671/params.yaml b/examples/security/classification/output/reports/train/583768f9400fb4fc5a5caa2819c21671/params.yaml deleted file mode 100644 index b21066c8..00000000 --- a/examples/security/classification/output/reports/train/583768f9400fb4fc5a5caa2819c21671/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/adv_predictions.json - adv_probabilities_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/a557fa366512755791d00d7ea8a2a35f.pkl - params_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/params.yaml - predictions_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/predictions.json - probabilities_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/probabilities.json - score_dict_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/score_dict.json - test_labels_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/test_labels.json - train_labels_file: output/reports/train/583768f9400fb4fc5a5caa2819c21671/train_labels.json - model_dir: models - name: 583768f9400fb4fc5a5caa2819c21671 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b0032ecbf264b5943796e0eea2edae3c - trainer: - kwargs: {} -name: 583768f9400fb4fc5a5caa2819c21671 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/params.yaml b/examples/security/classification/output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/params.yaml deleted file mode 100644 index 509b0fc2..00000000 --- a/examples/security/classification/output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/adv_predictions.json - adv_probabilities_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/28c1ba7b220dae3cf28e0656292ac096.pkl - params_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/params.yaml - predictions_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/predictions.json - probabilities_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/probabilities.json - score_dict_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/score_dict.json - test_labels_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/test_labels.json - train_labels_file: output/reports/train/584cd0cba01d7c5be0fa7ce246b73f6a/train_labels.json - model_dir: models - name: 584cd0cba01d7c5be0fa7ce246b73f6a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3c47488388eb9715e7ffd8116d9d0b55 - trainer: - kwargs: {} -name: 584cd0cba01d7c5be0fa7ce246b73f6a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5872219c935df616bcd1b8fcc784327e/params.yaml b/examples/security/classification/output/reports/train/5872219c935df616bcd1b8fcc784327e/params.yaml deleted file mode 100644 index 519c50c9..00000000 --- a/examples/security/classification/output/reports/train/5872219c935df616bcd1b8fcc784327e/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/adv_predictions.json - adv_probabilities_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/63887f6e311a03d562ae798714ec6131.pkl - params_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/params.yaml - predictions_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/predictions.json - probabilities_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/probabilities.json - score_dict_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/score_dict.json - test_labels_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/test_labels.json - train_labels_file: output/reports/train/5872219c935df616bcd1b8fcc784327e/train_labels.json - model_dir: models - name: 5872219c935df616bcd1b8fcc784327e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: be6a34ec1655e74741233b51bc1de590 - trainer: - kwargs: {} -name: 5872219c935df616bcd1b8fcc784327e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/params.yaml b/examples/security/classification/output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/params.yaml deleted file mode 100644 index 33749ee7..00000000 --- a/examples/security/classification/output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/adv_predictions.json - adv_probabilities_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e7981dc921475926a9ef59fc936585a7.pkl - params_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/params.yaml - predictions_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/predictions.json - probabilities_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/probabilities.json - score_dict_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/score_dict.json - test_labels_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/test_labels.json - train_labels_file: output/reports/train/588ecc2ffd6817942d1b286b15c2bc80/train_labels.json - model_dir: models - name: 588ecc2ffd6817942d1b286b15c2bc80 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 743dfa7ccef3f360bd7cab6ba01758e5 - trainer: - kwargs: {} -name: 588ecc2ffd6817942d1b286b15c2bc80 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/params.yaml b/examples/security/classification/output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/params.yaml deleted file mode 100644 index 41298655..00000000 --- a/examples/security/classification/output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/adv_predictions.json - adv_probabilities_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/30f8b68e2259a0b36aecc7a160739ddd.pkl - params_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/params.yaml - predictions_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/predictions.json - probabilities_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/probabilities.json - score_dict_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/score_dict.json - test_labels_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/test_labels.json - train_labels_file: output/reports/train/58a308c6b4f8d86cce582b8f96b6c968/train_labels.json - model_dir: models - name: 58a308c6b4f8d86cce582b8f96b6c968 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - degree: 2 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: aca1cea26720fe3c197964bb8df66a82 - trainer: - kwargs: {} -name: 58a308c6b4f8d86cce582b8f96b6c968 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/58af91b8cbc10584b119000cb3504f89/params.yaml b/examples/security/classification/output/reports/train/58af91b8cbc10584b119000cb3504f89/params.yaml deleted file mode 100644 index 85159395..00000000 --- a/examples/security/classification/output/reports/train/58af91b8cbc10584b119000cb3504f89/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/adv_predictions.json - adv_probabilities_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/9104b28ec4af8f39bb09389c438e1a31.pkl - params_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/params.yaml - predictions_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/predictions.json - probabilities_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/probabilities.json - score_dict_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/score_dict.json - test_labels_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/test_labels.json - train_labels_file: output/reports/train/58af91b8cbc10584b119000cb3504f89/train_labels.json - model_dir: models - name: 58af91b8cbc10584b119000cb3504f89 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ebc45224568fc3bc22f41ca2e0dd620d - trainer: - kwargs: {} -name: 58af91b8cbc10584b119000cb3504f89 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/params.yaml b/examples/security/classification/output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/params.yaml deleted file mode 100644 index ba8837e0..00000000 --- a/examples/security/classification/output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/adv_predictions.json - adv_probabilities_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/e7560f6d1375c96a703dba9e5ec7eff2.pkl - params_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/params.yaml - predictions_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/predictions.json - probabilities_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/probabilities.json - score_dict_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/score_dict.json - test_labels_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/test_labels.json - train_labels_file: output/reports/train/58dd1e85c6e0d4ccafe772d90402135b/train_labels.json - model_dir: models - name: 58dd1e85c6e0d4ccafe772d90402135b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1cafc4043d22ded1c0297e93a79dfbba - trainer: - kwargs: {} -name: 58dd1e85c6e0d4ccafe772d90402135b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5926d09c350b655e12a954d4bbbde90f/params.yaml b/examples/security/classification/output/reports/train/5926d09c350b655e12a954d4bbbde90f/params.yaml deleted file mode 100644 index 13a5722e..00000000 --- a/examples/security/classification/output/reports/train/5926d09c350b655e12a954d4bbbde90f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/adv_predictions.json - adv_probabilities_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/49f1b0c8ea99b7e5d8ece22adf692668.pkl - params_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/params.yaml - predictions_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/predictions.json - probabilities_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/probabilities.json - score_dict_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/score_dict.json - test_labels_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/test_labels.json - train_labels_file: output/reports/train/5926d09c350b655e12a954d4bbbde90f/train_labels.json - model_dir: models - name: 5926d09c350b655e12a954d4bbbde90f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4fce58fcc41960a1f64db10ad56c02aa - trainer: - kwargs: {} -name: 5926d09c350b655e12a954d4bbbde90f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/params.yaml b/examples/security/classification/output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/params.yaml deleted file mode 100644 index a844bb3b..00000000 --- a/examples/security/classification/output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/adv_predictions.json - adv_probabilities_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/40b624523fa394d7c82f3fdb2bef3b1b.pkl - params_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/params.yaml - predictions_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/predictions.json - probabilities_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/probabilities.json - score_dict_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/score_dict.json - test_labels_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/test_labels.json - train_labels_file: output/reports/train/593bd5d3ab8ddb25fd7eece1dfcb959c/train_labels.json - model_dir: models - name: 593bd5d3ab8ddb25fd7eece1dfcb959c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 49b47e193965c7edb0981eb1798a2581 - trainer: - kwargs: {} -name: 593bd5d3ab8ddb25fd7eece1dfcb959c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/59a342d161702ff023e091b0127435f5/params.yaml b/examples/security/classification/output/reports/train/59a342d161702ff023e091b0127435f5/params.yaml deleted file mode 100644 index c922d215..00000000 --- a/examples/security/classification/output/reports/train/59a342d161702ff023e091b0127435f5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/59a342d161702ff023e091b0127435f5/adv_predictions.json - adv_probabilities_file: output/reports/train/59a342d161702ff023e091b0127435f5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/55e59759d2a44038e3b76355a1957f35.pkl - params_file: output/reports/train/59a342d161702ff023e091b0127435f5/params.yaml - predictions_file: output/reports/train/59a342d161702ff023e091b0127435f5/predictions.json - probabilities_file: output/reports/train/59a342d161702ff023e091b0127435f5/probabilities.json - score_dict_file: output/reports/train/59a342d161702ff023e091b0127435f5/score_dict.json - test_labels_file: output/reports/train/59a342d161702ff023e091b0127435f5/test_labels.json - train_labels_file: output/reports/train/59a342d161702ff023e091b0127435f5/train_labels.json - model_dir: models - name: 59a342d161702ff023e091b0127435f5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e15416d24902c668169a44f797b2c3eb - trainer: - kwargs: {} -name: 59a342d161702ff023e091b0127435f5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/params.yaml b/examples/security/classification/output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/params.yaml deleted file mode 100644 index 31231a0b..00000000 --- a/examples/security/classification/output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/adv_predictions.json - adv_probabilities_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/98d86a4996e28469dcd8aa1618d36653.pkl - params_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/params.yaml - predictions_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/predictions.json - probabilities_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/probabilities.json - score_dict_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/score_dict.json - test_labels_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/test_labels.json - train_labels_file: output/reports/train/59cb5011c5c2e9fcb0b289af27ef0ca6/train_labels.json - model_dir: models - name: 59cb5011c5c2e9fcb0b289af27ef0ca6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fe896a29d41011c9f3216829a5e081d3 - trainer: - kwargs: {} -name: 59cb5011c5c2e9fcb0b289af27ef0ca6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/59d05917adffc4028f2edfe08f12ca58/params.yaml b/examples/security/classification/output/reports/train/59d05917adffc4028f2edfe08f12ca58/params.yaml deleted file mode 100644 index 2a642f9e..00000000 --- a/examples/security/classification/output/reports/train/59d05917adffc4028f2edfe08f12ca58/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/adv_predictions.json - adv_probabilities_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/6b2ad0ce8d01f10c1c387039dff2fa2c.pkl - params_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/params.yaml - predictions_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/predictions.json - probabilities_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/probabilities.json - score_dict_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/score_dict.json - test_labels_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/test_labels.json - train_labels_file: output/reports/train/59d05917adffc4028f2edfe08f12ca58/train_labels.json - model_dir: models - name: 59d05917adffc4028f2edfe08f12ca58 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7e00af5c8d8f132fff2ed7bc20881543 - trainer: - kwargs: {} -name: 59d05917adffc4028f2edfe08f12ca58 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/59e076256bb93556b5556dea0921ced8/params.yaml b/examples/security/classification/output/reports/train/59e076256bb93556b5556dea0921ced8/params.yaml deleted file mode 100644 index b7f2986b..00000000 --- a/examples/security/classification/output/reports/train/59e076256bb93556b5556dea0921ced8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/59e076256bb93556b5556dea0921ced8/adv_predictions.json - adv_probabilities_file: output/reports/train/59e076256bb93556b5556dea0921ced8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/c42dca68dac6c33dd457d375ae52596b.pkl - params_file: output/reports/train/59e076256bb93556b5556dea0921ced8/params.yaml - predictions_file: output/reports/train/59e076256bb93556b5556dea0921ced8/predictions.json - probabilities_file: output/reports/train/59e076256bb93556b5556dea0921ced8/probabilities.json - score_dict_file: output/reports/train/59e076256bb93556b5556dea0921ced8/score_dict.json - test_labels_file: output/reports/train/59e076256bb93556b5556dea0921ced8/test_labels.json - train_labels_file: output/reports/train/59e076256bb93556b5556dea0921ced8/train_labels.json - model_dir: models - name: 59e076256bb93556b5556dea0921ced8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1fe38f9edc9ac24cbc3a2489696e56d3 - trainer: - kwargs: {} -name: 59e076256bb93556b5556dea0921ced8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5a1265a80aad627121ba59274d74f4e2/params.yaml b/examples/security/classification/output/reports/train/5a1265a80aad627121ba59274d74f4e2/params.yaml deleted file mode 100644 index 92881290..00000000 --- a/examples/security/classification/output/reports/train/5a1265a80aad627121ba59274d74f4e2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/adv_predictions.json - adv_probabilities_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/deb03b3606e16b7d8367abd9e4ef8d7a.pkl - params_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/params.yaml - predictions_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/predictions.json - probabilities_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/probabilities.json - score_dict_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/score_dict.json - test_labels_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/test_labels.json - train_labels_file: output/reports/train/5a1265a80aad627121ba59274d74f4e2/train_labels.json - model_dir: models - name: 5a1265a80aad627121ba59274d74f4e2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6f56587fcff38cd93bf1e954ece15512 - trainer: - kwargs: {} -name: 5a1265a80aad627121ba59274d74f4e2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5a248cd148bb2159127facb48a775f20/params.yaml b/examples/security/classification/output/reports/train/5a248cd148bb2159127facb48a775f20/params.yaml deleted file mode 100644 index fc1528da..00000000 --- a/examples/security/classification/output/reports/train/5a248cd148bb2159127facb48a775f20/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5a248cd148bb2159127facb48a775f20/adv_predictions.json - adv_probabilities_file: output/reports/train/5a248cd148bb2159127facb48a775f20/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/05c07f97049a2f09e0d0aac2d7e3fa42.pkl - params_file: output/reports/train/5a248cd148bb2159127facb48a775f20/params.yaml - predictions_file: output/reports/train/5a248cd148bb2159127facb48a775f20/predictions.json - probabilities_file: output/reports/train/5a248cd148bb2159127facb48a775f20/probabilities.json - score_dict_file: output/reports/train/5a248cd148bb2159127facb48a775f20/score_dict.json - test_labels_file: output/reports/train/5a248cd148bb2159127facb48a775f20/test_labels.json - train_labels_file: output/reports/train/5a248cd148bb2159127facb48a775f20/train_labels.json - model_dir: models - name: 5a248cd148bb2159127facb48a775f20 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a65903b246b36217851027ebacaf672d - trainer: - kwargs: {} -name: 5a248cd148bb2159127facb48a775f20 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/params.yaml b/examples/security/classification/output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/params.yaml deleted file mode 100644 index 3f36b549..00000000 --- a/examples/security/classification/output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/adv_predictions.json - adv_probabilities_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/920b828921304454150b08f635fcc91f.pkl - params_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/params.yaml - predictions_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/predictions.json - probabilities_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/probabilities.json - score_dict_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/score_dict.json - test_labels_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/test_labels.json - train_labels_file: output/reports/train/5a491de32a531a80cb9fbc3c47693bf0/train_labels.json - model_dir: models - name: 5a491de32a531a80cb9fbc3c47693bf0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ce635c7c2d8beceed4ae53764c96b146 - trainer: - kwargs: {} -name: 5a491de32a531a80cb9fbc3c47693bf0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5aa510975bb5dede04c709d24e5d8d67/params.yaml b/examples/security/classification/output/reports/train/5aa510975bb5dede04c709d24e5d8d67/params.yaml deleted file mode 100644 index bdaa912d..00000000 --- a/examples/security/classification/output/reports/train/5aa510975bb5dede04c709d24e5d8d67/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/adv_predictions.json - adv_probabilities_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/46b0a438f17991cd112e9650b44e73ae.pkl - params_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/params.yaml - predictions_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/predictions.json - probabilities_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/probabilities.json - score_dict_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/score_dict.json - test_labels_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/test_labels.json - train_labels_file: output/reports/train/5aa510975bb5dede04c709d24e5d8d67/train_labels.json - model_dir: models - name: 5aa510975bb5dede04c709d24e5d8d67 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a1bc05ce82af211b2791a34f193f9d8d - trainer: - kwargs: {} -name: 5aa510975bb5dede04c709d24e5d8d67 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/params.yaml b/examples/security/classification/output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/params.yaml deleted file mode 100644 index 37f76bb3..00000000 --- a/examples/security/classification/output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/adv_predictions.json - adv_probabilities_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/0c66fdfed260a80e281036e99af68c28.pkl - params_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/params.yaml - predictions_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/predictions.json - probabilities_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/probabilities.json - score_dict_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/score_dict.json - test_labels_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/test_labels.json - train_labels_file: output/reports/train/5aed33fc23dcf9b1ef3eff84c5f871e6/train_labels.json - model_dir: models - name: 5aed33fc23dcf9b1ef3eff84c5f871e6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 16b9f39457ad69e1220510b8ba53f066 - trainer: - kwargs: {} -name: 5aed33fc23dcf9b1ef3eff84c5f871e6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/params.yaml b/examples/security/classification/output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/params.yaml deleted file mode 100644 index 0d0f61b1..00000000 --- a/examples/security/classification/output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/adv_predictions.json - adv_probabilities_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/42f9ca78397ae1650e5335e7090e598b.pkl - params_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/params.yaml - predictions_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/predictions.json - probabilities_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/probabilities.json - score_dict_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/score_dict.json - test_labels_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/test_labels.json - train_labels_file: output/reports/train/5af01270f7ad34b1d96420bdc6dcbd2c/train_labels.json - model_dir: models - name: 5af01270f7ad34b1d96420bdc6dcbd2c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e8a3b70ffabcca3b3358d472e603c81f - trainer: - kwargs: {} -name: 5af01270f7ad34b1d96420bdc6dcbd2c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/params.yaml b/examples/security/classification/output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/params.yaml deleted file mode 100644 index 35e67da0..00000000 --- a/examples/security/classification/output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/adv_predictions.json - adv_probabilities_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/9be1c5d8fb4bb6fec67840a525476dcb.pkl - params_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/params.yaml - predictions_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/predictions.json - probabilities_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/probabilities.json - score_dict_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/score_dict.json - test_labels_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/test_labels.json - train_labels_file: output/reports/train/5b11c0dad77ee6ca9690f0e82d4ac899/train_labels.json - model_dir: models - name: 5b11c0dad77ee6ca9690f0e82d4ac899 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5e5687ebe37b126f6f52625845c44c16 - trainer: - kwargs: {} -name: 5b11c0dad77ee6ca9690f0e82d4ac899 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/params.yaml b/examples/security/classification/output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/params.yaml deleted file mode 100644 index 25650854..00000000 --- a/examples/security/classification/output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/adv_predictions.json - adv_probabilities_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/2b99e6e49aeaadddbd4d747f0d61b761.pkl - params_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/params.yaml - predictions_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/predictions.json - probabilities_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/probabilities.json - score_dict_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/score_dict.json - test_labels_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/test_labels.json - train_labels_file: output/reports/train/5b2ae5bb19da8592e8cbda34e0a6c729/train_labels.json - model_dir: models - name: 5b2ae5bb19da8592e8cbda34e0a6c729 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 73d0e5b328aff62c9947579fbfeca1f1 - trainer: - kwargs: {} -name: 5b2ae5bb19da8592e8cbda34e0a6c729 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5b2b76d281531e3a12936d640e47a35b/params.yaml b/examples/security/classification/output/reports/train/5b2b76d281531e3a12936d640e47a35b/params.yaml deleted file mode 100644 index 95de609e..00000000 --- a/examples/security/classification/output/reports/train/5b2b76d281531e3a12936d640e47a35b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/adv_predictions.json - adv_probabilities_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/df2368a325c0fdecb4879132f8a00550.pkl - params_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/params.yaml - predictions_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/predictions.json - probabilities_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/probabilities.json - score_dict_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/score_dict.json - test_labels_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/test_labels.json - train_labels_file: output/reports/train/5b2b76d281531e3a12936d640e47a35b/train_labels.json - model_dir: models - name: 5b2b76d281531e3a12936d640e47a35b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d58c0e60139488b426fac924110f5ffb - trainer: - kwargs: {} -name: 5b2b76d281531e3a12936d640e47a35b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/params.yaml b/examples/security/classification/output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/params.yaml deleted file mode 100644 index 59885c28..00000000 --- a/examples/security/classification/output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/adv_predictions.json - adv_probabilities_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/40c25263acd2154f7d926e34f55e5a49.pkl - params_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/params.yaml - predictions_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/predictions.json - probabilities_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/probabilities.json - score_dict_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/score_dict.json - test_labels_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/test_labels.json - train_labels_file: output/reports/train/5b2eb9093b173d4a366fc33bb429b4f1/train_labels.json - model_dir: models - name: 5b2eb9093b173d4a366fc33bb429b4f1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: db3f843cbcd9c69ed56643aaa22cedf4 - trainer: - kwargs: {} -name: 5b2eb9093b173d4a366fc33bb429b4f1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5b605083b28c329736a3bfd2117539fd/params.yaml b/examples/security/classification/output/reports/train/5b605083b28c329736a3bfd2117539fd/params.yaml deleted file mode 100644 index d1b56480..00000000 --- a/examples/security/classification/output/reports/train/5b605083b28c329736a3bfd2117539fd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/adv_predictions.json - adv_probabilities_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/d0263cdd60c085e05c071e7221b84c4a.pkl - params_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/params.yaml - predictions_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/predictions.json - probabilities_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/probabilities.json - score_dict_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/score_dict.json - test_labels_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/test_labels.json - train_labels_file: output/reports/train/5b605083b28c329736a3bfd2117539fd/train_labels.json - model_dir: models - name: 5b605083b28c329736a3bfd2117539fd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 759caa752ecb0ddae8347687c5ac4992 - trainer: - kwargs: {} -name: 5b605083b28c329736a3bfd2117539fd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5b67bab7191c57c5e394f4a5147a9932/params.yaml b/examples/security/classification/output/reports/train/5b67bab7191c57c5e394f4a5147a9932/params.yaml deleted file mode 100644 index 4baabf57..00000000 --- a/examples/security/classification/output/reports/train/5b67bab7191c57c5e394f4a5147a9932/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/adv_predictions.json - adv_probabilities_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/b600126b63eefb6ed2030a691febe52e.pkl - params_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/params.yaml - predictions_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/predictions.json - probabilities_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/probabilities.json - score_dict_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/score_dict.json - test_labels_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/test_labels.json - train_labels_file: output/reports/train/5b67bab7191c57c5e394f4a5147a9932/train_labels.json - model_dir: models - name: 5b67bab7191c57c5e394f4a5147a9932 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1200c32804f6d9be0eb2f2382dc7d59e - trainer: - kwargs: {} -name: 5b67bab7191c57c5e394f4a5147a9932 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5b828111dce5318f832aecdd2f42aad3/params.yaml b/examples/security/classification/output/reports/train/5b828111dce5318f832aecdd2f42aad3/params.yaml deleted file mode 100644 index f9939261..00000000 --- a/examples/security/classification/output/reports/train/5b828111dce5318f832aecdd2f42aad3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/adv_predictions.json - adv_probabilities_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/a4a8c3f2acb36f703615e4df414207c7.pkl - params_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/params.yaml - predictions_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/predictions.json - probabilities_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/probabilities.json - score_dict_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/score_dict.json - test_labels_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/test_labels.json - train_labels_file: output/reports/train/5b828111dce5318f832aecdd2f42aad3/train_labels.json - model_dir: models - name: 5b828111dce5318f832aecdd2f42aad3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a8ee2a26128ec7fcd99f17741ce4b8af - trainer: - kwargs: {} -name: 5b828111dce5318f832aecdd2f42aad3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/params.yaml b/examples/security/classification/output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/params.yaml deleted file mode 100644 index 95195f58..00000000 --- a/examples/security/classification/output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/adv_predictions.json - adv_probabilities_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/7ada70915c09e942addca3f544cc289b.pkl - params_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/params.yaml - predictions_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/predictions.json - probabilities_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/probabilities.json - score_dict_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/score_dict.json - test_labels_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/test_labels.json - train_labels_file: output/reports/train/5b88c78462c6b872c4bd02cb7738fe4e/train_labels.json - model_dir: models - name: 5b88c78462c6b872c4bd02cb7738fe4e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 49ad25fbcb3b50b0ac663ac8dd1ee85e - trainer: - kwargs: {} -name: 5b88c78462c6b872c4bd02cb7738fe4e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/params.yaml b/examples/security/classification/output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/params.yaml deleted file mode 100644 index e1be215b..00000000 --- a/examples/security/classification/output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/adv_predictions.json - adv_probabilities_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/a2202a8b4047ae1852372e8b2fbb51f3.pkl - params_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/params.yaml - predictions_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/predictions.json - probabilities_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/probabilities.json - score_dict_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/score_dict.json - test_labels_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/test_labels.json - train_labels_file: output/reports/train/5b8b1d3c7a22d6fefd196842edd8503a/train_labels.json - model_dir: models - name: 5b8b1d3c7a22d6fefd196842edd8503a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d56098f70c72df6897d86d3985e53e27 - trainer: - kwargs: {} -name: 5b8b1d3c7a22d6fefd196842edd8503a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/params.yaml b/examples/security/classification/output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/params.yaml deleted file mode 100644 index 616dfe87..00000000 --- a/examples/security/classification/output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/adv_predictions.json - adv_probabilities_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/c58bd7cdf955f55f29e5ccfde09eac3c.pkl - params_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/params.yaml - predictions_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/predictions.json - probabilities_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/probabilities.json - score_dict_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/score_dict.json - test_labels_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/test_labels.json - train_labels_file: output/reports/train/5b8c67e63f3e7db96b4e853ee4da4841/train_labels.json - model_dir: models - name: 5b8c67e63f3e7db96b4e853ee4da4841 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 57d680eb972e4126493b2956b083b2e6 - trainer: - kwargs: {} -name: 5b8c67e63f3e7db96b4e853ee4da4841 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/params.yaml b/examples/security/classification/output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/params.yaml deleted file mode 100644 index 0510f0ea..00000000 --- a/examples/security/classification/output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/adv_predictions.json - adv_probabilities_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/568fd9eb22d2df4069b1a867ab1fe9c0.pkl - params_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/params.yaml - predictions_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/predictions.json - probabilities_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/probabilities.json - score_dict_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/score_dict.json - test_labels_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/test_labels.json - train_labels_file: output/reports/train/5b8ee8d1519e149f06b2dc58473d01d3/train_labels.json - model_dir: models - name: 5b8ee8d1519e149f06b2dc58473d01d3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: db9a87d063df5ee4ff79184d18f4eff7 - trainer: - kwargs: {} -name: 5b8ee8d1519e149f06b2dc58473d01d3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/params.yaml b/examples/security/classification/output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/params.yaml deleted file mode 100644 index a899c943..00000000 --- a/examples/security/classification/output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/adv_predictions.json - adv_probabilities_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/6043bd94b0ab5a431cb6cb6037cd090b.pkl - params_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/params.yaml - predictions_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/predictions.json - probabilities_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/probabilities.json - score_dict_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/score_dict.json - test_labels_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/test_labels.json - train_labels_file: output/reports/train/5c2bf92f22a09f2b6189003a7f53ba97/train_labels.json - model_dir: models - name: 5c2bf92f22a09f2b6189003a7f53ba97 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ad11e1fb6fb33eb9501f57afa22021c2 - trainer: - kwargs: {} -name: 5c2bf92f22a09f2b6189003a7f53ba97 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/params.yaml b/examples/security/classification/output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/params.yaml deleted file mode 100644 index 2cd2bfd5..00000000 --- a/examples/security/classification/output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/adv_predictions.json - adv_probabilities_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/236f63eb872672956270e6be9da91a92.pkl - params_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/params.yaml - predictions_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/predictions.json - probabilities_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/probabilities.json - score_dict_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/score_dict.json - test_labels_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/test_labels.json - train_labels_file: output/reports/train/5c640d2ab1bee9e55681ef5641a8e7fe/train_labels.json - model_dir: models - name: 5c640d2ab1bee9e55681ef5641a8e7fe - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cff99e1e3e85f71dd1f142f5b43dd9e5 - trainer: - kwargs: {} -name: 5c640d2ab1bee9e55681ef5641a8e7fe -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/params.yaml b/examples/security/classification/output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/params.yaml deleted file mode 100644 index f07775d3..00000000 --- a/examples/security/classification/output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/adv_predictions.json - adv_probabilities_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/71d506dd8cf2b110d3ab4376a39462fb.pkl - params_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/params.yaml - predictions_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/predictions.json - probabilities_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/probabilities.json - score_dict_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/score_dict.json - test_labels_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/test_labels.json - train_labels_file: output/reports/train/5c99d60b8fe3ee587bbb716c286ede4b/train_labels.json - model_dir: models - name: 5c99d60b8fe3ee587bbb716c286ede4b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4d9da5263a8f28b34a2dbae53acc184e - trainer: - kwargs: {} -name: 5c99d60b8fe3ee587bbb716c286ede4b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/params.yaml b/examples/security/classification/output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/params.yaml deleted file mode 100644 index cb212c44..00000000 --- a/examples/security/classification/output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/adv_predictions.json - adv_probabilities_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/484a31bff7a247c77b7ca2af9c01cf56.pkl - params_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/params.yaml - predictions_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/predictions.json - probabilities_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/probabilities.json - score_dict_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/score_dict.json - test_labels_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/test_labels.json - train_labels_file: output/reports/train/5cb66bac32991399d0dd0a2a438dba6f/train_labels.json - model_dir: models - name: 5cb66bac32991399d0dd0a2a438dba6f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d3a14b88fb753d113409c10a5ece8059 - trainer: - kwargs: {} -name: 5cb66bac32991399d0dd0a2a438dba6f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5cc77cd0464054cf2666377741f05f88/params.yaml b/examples/security/classification/output/reports/train/5cc77cd0464054cf2666377741f05f88/params.yaml deleted file mode 100644 index 5bf6fa1e..00000000 --- a/examples/security/classification/output/reports/train/5cc77cd0464054cf2666377741f05f88/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/adv_predictions.json - adv_probabilities_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/3a5ed244e667b09c135bf3b57675a322.pkl - params_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/params.yaml - predictions_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/predictions.json - probabilities_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/probabilities.json - score_dict_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/score_dict.json - test_labels_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/test_labels.json - train_labels_file: output/reports/train/5cc77cd0464054cf2666377741f05f88/train_labels.json - model_dir: models - name: 5cc77cd0464054cf2666377741f05f88 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 98ac1f6581d235d6f7e76da001d8340c - trainer: - kwargs: {} -name: 5cc77cd0464054cf2666377741f05f88 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5cd488954437aa0400e9b8602007b50c/params.yaml b/examples/security/classification/output/reports/train/5cd488954437aa0400e9b8602007b50c/params.yaml deleted file mode 100644 index 6879d607..00000000 --- a/examples/security/classification/output/reports/train/5cd488954437aa0400e9b8602007b50c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/adv_predictions.json - adv_probabilities_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/3d51a05f55561e96e9edfdf86e678ec8.pkl - params_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/params.yaml - predictions_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/predictions.json - probabilities_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/probabilities.json - score_dict_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/score_dict.json - test_labels_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/test_labels.json - train_labels_file: output/reports/train/5cd488954437aa0400e9b8602007b50c/train_labels.json - model_dir: models - name: 5cd488954437aa0400e9b8602007b50c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dadfc1dae5746ba8bfcb3609f603232f - trainer: - kwargs: {} -name: 5cd488954437aa0400e9b8602007b50c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/params.yaml b/examples/security/classification/output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/params.yaml deleted file mode 100644 index f99c57a1..00000000 --- a/examples/security/classification/output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/adv_predictions.json - adv_probabilities_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/4bdb2268d66046d72d460fc2f89268e1.pkl - params_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/params.yaml - predictions_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/predictions.json - probabilities_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/probabilities.json - score_dict_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/score_dict.json - test_labels_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/test_labels.json - train_labels_file: output/reports/train/5cdf31e755d5b774fd498b40ca1dc4f7/train_labels.json - model_dir: models - name: 5cdf31e755d5b774fd498b40ca1dc4f7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0d28ac388e34c6968f6a960cc43a6f4b - trainer: - kwargs: {} -name: 5cdf31e755d5b774fd498b40ca1dc4f7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/params.yaml b/examples/security/classification/output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/params.yaml deleted file mode 100644 index cdbde087..00000000 --- a/examples/security/classification/output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/adv_predictions.json - adv_probabilities_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/f8c435fd023a490f0125e49ea001e0ba.pkl - params_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/params.yaml - predictions_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/predictions.json - probabilities_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/probabilities.json - score_dict_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/score_dict.json - test_labels_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/test_labels.json - train_labels_file: output/reports/train/5cf660ef3af069c1af26c29e8d7188d5/train_labels.json - model_dir: models - name: 5cf660ef3af069c1af26c29e8d7188d5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 679b3f68b2307f6d0cb65a835b7345ee - trainer: - kwargs: {} -name: 5cf660ef3af069c1af26c29e8d7188d5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5d25b172041462b0bd0b5251f0fb1356/params.yaml b/examples/security/classification/output/reports/train/5d25b172041462b0bd0b5251f0fb1356/params.yaml deleted file mode 100644 index 838c7cea..00000000 --- a/examples/security/classification/output/reports/train/5d25b172041462b0bd0b5251f0fb1356/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/adv_predictions.json - adv_probabilities_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/57977b61336f11ba026337a4b34a465e.pkl - params_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/params.yaml - predictions_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/predictions.json - probabilities_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/probabilities.json - score_dict_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/score_dict.json - test_labels_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/test_labels.json - train_labels_file: output/reports/train/5d25b172041462b0bd0b5251f0fb1356/train_labels.json - model_dir: models - name: 5d25b172041462b0bd0b5251f0fb1356 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e58657180e3c261883b7f7734819b2b1 - trainer: - kwargs: {} -name: 5d25b172041462b0bd0b5251f0fb1356 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5d54e901b94761d8676b74e82f790ce5/params.yaml b/examples/security/classification/output/reports/train/5d54e901b94761d8676b74e82f790ce5/params.yaml deleted file mode 100644 index c27dfa15..00000000 --- a/examples/security/classification/output/reports/train/5d54e901b94761d8676b74e82f790ce5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/adv_predictions.json - adv_probabilities_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/74843a40073dd1b5934801085e12fd6e.pkl - params_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/params.yaml - predictions_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/predictions.json - probabilities_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/probabilities.json - score_dict_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/score_dict.json - test_labels_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/test_labels.json - train_labels_file: output/reports/train/5d54e901b94761d8676b74e82f790ce5/train_labels.json - model_dir: models - name: 5d54e901b94761d8676b74e82f790ce5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eccf126d130cfea0fd70025fe0983025 - trainer: - kwargs: {} -name: 5d54e901b94761d8676b74e82f790ce5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5d717740c85f5ea364a99f1aedb8f098/params.yaml b/examples/security/classification/output/reports/train/5d717740c85f5ea364a99f1aedb8f098/params.yaml deleted file mode 100644 index 21fe4114..00000000 --- a/examples/security/classification/output/reports/train/5d717740c85f5ea364a99f1aedb8f098/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/adv_predictions.json - adv_probabilities_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/d6438daefb5b15f26c6725c3732c871d.pkl - params_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/params.yaml - predictions_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/predictions.json - probabilities_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/probabilities.json - score_dict_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/score_dict.json - test_labels_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/test_labels.json - train_labels_file: output/reports/train/5d717740c85f5ea364a99f1aedb8f098/train_labels.json - model_dir: models - name: 5d717740c85f5ea364a99f1aedb8f098 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a7dfa7ef1e02cdead37b808e71ebabac - trainer: - kwargs: {} -name: 5d717740c85f5ea364a99f1aedb8f098 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5d76f06cf217bc627f379fad3cf2782a/params.yaml b/examples/security/classification/output/reports/train/5d76f06cf217bc627f379fad3cf2782a/params.yaml deleted file mode 100644 index 88daa9c7..00000000 --- a/examples/security/classification/output/reports/train/5d76f06cf217bc627f379fad3cf2782a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/adv_predictions.json - adv_probabilities_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/b62514ba6ba1b22c55be2a6ef58b8370.pkl - params_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/params.yaml - predictions_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/predictions.json - probabilities_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/probabilities.json - score_dict_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/score_dict.json - test_labels_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/test_labels.json - train_labels_file: output/reports/train/5d76f06cf217bc627f379fad3cf2782a/train_labels.json - model_dir: models - name: 5d76f06cf217bc627f379fad3cf2782a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9403233a23dea1058b32a50aa751299d - trainer: - kwargs: {} -name: 5d76f06cf217bc627f379fad3cf2782a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/params.yaml b/examples/security/classification/output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/params.yaml deleted file mode 100644 index cb4b8d17..00000000 --- a/examples/security/classification/output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/adv_predictions.json - adv_probabilities_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/29f323c68f0d35f8b3ffa94e552c1656.pkl - params_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/params.yaml - predictions_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/predictions.json - probabilities_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/probabilities.json - score_dict_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/score_dict.json - test_labels_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/test_labels.json - train_labels_file: output/reports/train/5d770808449c7ad5d13ac2badbbffdb5/train_labels.json - model_dir: models - name: 5d770808449c7ad5d13ac2badbbffdb5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b8d4794872c8628b7ccd468671a30f5f - trainer: - kwargs: {} -name: 5d770808449c7ad5d13ac2badbbffdb5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/params.yaml b/examples/security/classification/output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/params.yaml deleted file mode 100644 index 81444a62..00000000 --- a/examples/security/classification/output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/adv_predictions.json - adv_probabilities_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/b9a42431e0f476db0708c4000695b923.pkl - params_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/params.yaml - predictions_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/predictions.json - probabilities_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/probabilities.json - score_dict_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/score_dict.json - test_labels_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/test_labels.json - train_labels_file: output/reports/train/5d7d0dd6803049a2de8271aea0e0e1a5/train_labels.json - model_dir: models - name: 5d7d0dd6803049a2de8271aea0e0e1a5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ba25cf0266b7c2363b1cbdb94ad50291 - trainer: - kwargs: {} -name: 5d7d0dd6803049a2de8271aea0e0e1a5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/params.yaml b/examples/security/classification/output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/params.yaml deleted file mode 100644 index 5276e983..00000000 --- a/examples/security/classification/output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/adv_predictions.json - adv_probabilities_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/4b79e86ed87943dacba19f94a55e8aa1.pkl - params_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/params.yaml - predictions_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/predictions.json - probabilities_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/probabilities.json - score_dict_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/score_dict.json - test_labels_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/test_labels.json - train_labels_file: output/reports/train/5d98e0ff9fda42c6c7f14a0f5f09cdd7/train_labels.json - model_dir: models - name: 5d98e0ff9fda42c6c7f14a0f5f09cdd7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f11c65bab235b59c7f10577a54142fc9 - trainer: - kwargs: {} -name: 5d98e0ff9fda42c6c7f14a0f5f09cdd7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/params.yaml b/examples/security/classification/output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/params.yaml deleted file mode 100644 index 97e8c145..00000000 --- a/examples/security/classification/output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/adv_predictions.json - adv_probabilities_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/7e3b7140dc31fee2a9aa532682d513c2.pkl - params_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/params.yaml - predictions_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/predictions.json - probabilities_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/probabilities.json - score_dict_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/score_dict.json - test_labels_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/test_labels.json - train_labels_file: output/reports/train/5da1e61d9b31bb3fa8f57a2ed62fe55f/train_labels.json - model_dir: models - name: 5da1e61d9b31bb3fa8f57a2ed62fe55f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9b27a8f166909e2b83a794953fe61608 - trainer: - kwargs: {} -name: 5da1e61d9b31bb3fa8f57a2ed62fe55f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5da22cb7c862afbf92015179328a80dd/params.yaml b/examples/security/classification/output/reports/train/5da22cb7c862afbf92015179328a80dd/params.yaml deleted file mode 100644 index d4f44c82..00000000 --- a/examples/security/classification/output/reports/train/5da22cb7c862afbf92015179328a80dd/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/adv_predictions.json - adv_probabilities_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/be3b0245e5a0df7558b30e32a4945aba.pkl - params_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/params.yaml - predictions_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/predictions.json - probabilities_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/probabilities.json - score_dict_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/score_dict.json - test_labels_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/test_labels.json - train_labels_file: output/reports/train/5da22cb7c862afbf92015179328a80dd/train_labels.json - model_dir: models - name: 5da22cb7c862afbf92015179328a80dd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 92c0b687769e86f207e80899050daded - trainer: - kwargs: {} -name: 5da22cb7c862afbf92015179328a80dd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5db7b823a7455b921b2aff827f94f0de/params.yaml b/examples/security/classification/output/reports/train/5db7b823a7455b921b2aff827f94f0de/params.yaml deleted file mode 100644 index 4687bbde..00000000 --- a/examples/security/classification/output/reports/train/5db7b823a7455b921b2aff827f94f0de/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/adv_predictions.json - adv_probabilities_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/a3e705af09fe43a5b34ef3c908d1f34a.pkl - params_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/params.yaml - predictions_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/predictions.json - probabilities_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/probabilities.json - score_dict_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/score_dict.json - test_labels_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/test_labels.json - train_labels_file: output/reports/train/5db7b823a7455b921b2aff827f94f0de/train_labels.json - model_dir: models - name: 5db7b823a7455b921b2aff827f94f0de - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f1a462df8875f64f3904ba3b3706cfa1 - trainer: - kwargs: {} -name: 5db7b823a7455b921b2aff827f94f0de -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5dec8633d23ee575a63d31ad2b45a383/params.yaml b/examples/security/classification/output/reports/train/5dec8633d23ee575a63d31ad2b45a383/params.yaml deleted file mode 100644 index defa578a..00000000 --- a/examples/security/classification/output/reports/train/5dec8633d23ee575a63d31ad2b45a383/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/adv_predictions.json - adv_probabilities_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/73a4b73bc8b9ae29ae983f9c08261843.pkl - params_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/params.yaml - predictions_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/predictions.json - probabilities_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/probabilities.json - score_dict_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/score_dict.json - test_labels_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/test_labels.json - train_labels_file: output/reports/train/5dec8633d23ee575a63d31ad2b45a383/train_labels.json - model_dir: models - name: 5dec8633d23ee575a63d31ad2b45a383 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c8b5ebd90549148e17e72bda04c64e87 - trainer: - kwargs: {} -name: 5dec8633d23ee575a63d31ad2b45a383 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/params.yaml b/examples/security/classification/output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/params.yaml deleted file mode 100644 index cba42fba..00000000 --- a/examples/security/classification/output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/adv_predictions.json - adv_probabilities_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/988d35deaffe9a54275aaea279234b10.pkl - params_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/params.yaml - predictions_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/predictions.json - probabilities_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/probabilities.json - score_dict_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/score_dict.json - test_labels_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/test_labels.json - train_labels_file: output/reports/train/5dfab1a52dcfd17f51d7938ef131674c/train_labels.json - model_dir: models - name: 5dfab1a52dcfd17f51d7938ef131674c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3f5cf8f29b51a62607ec547a3d2f4fb8 - trainer: - kwargs: {} -name: 5dfab1a52dcfd17f51d7938ef131674c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/params.yaml b/examples/security/classification/output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/params.yaml deleted file mode 100644 index f53f532c..00000000 --- a/examples/security/classification/output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/adv_predictions.json - adv_probabilities_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/d77c66006c1b01d86fdb97b82f742008.pkl - params_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/params.yaml - predictions_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/predictions.json - probabilities_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/probabilities.json - score_dict_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/score_dict.json - test_labels_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/test_labels.json - train_labels_file: output/reports/train/5e00c8a58ba6c13b75f2690ddfe81191/train_labels.json - model_dir: models - name: 5e00c8a58ba6c13b75f2690ddfe81191 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 706ba90254a9dcf4c5998d8eb2c86195 - trainer: - kwargs: {} -name: 5e00c8a58ba6c13b75f2690ddfe81191 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5e0b1396f8eebed981104f9085baa8fc/params.yaml b/examples/security/classification/output/reports/train/5e0b1396f8eebed981104f9085baa8fc/params.yaml deleted file mode 100644 index 4469309c..00000000 --- a/examples/security/classification/output/reports/train/5e0b1396f8eebed981104f9085baa8fc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/adv_predictions.json - adv_probabilities_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/7ce075cfe3884e4228b6e8dea5c237e4.pkl - params_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/params.yaml - predictions_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/predictions.json - probabilities_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/probabilities.json - score_dict_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/score_dict.json - test_labels_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/test_labels.json - train_labels_file: output/reports/train/5e0b1396f8eebed981104f9085baa8fc/train_labels.json - model_dir: models - name: 5e0b1396f8eebed981104f9085baa8fc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fdc0adc38833d6e1b3aa06fe665bc187 - trainer: - kwargs: {} -name: 5e0b1396f8eebed981104f9085baa8fc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5e198e68433d01a6e6b8b4e808215a91/params.yaml b/examples/security/classification/output/reports/train/5e198e68433d01a6e6b8b4e808215a91/params.yaml deleted file mode 100644 index 4a3c86cc..00000000 --- a/examples/security/classification/output/reports/train/5e198e68433d01a6e6b8b4e808215a91/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/adv_predictions.json - adv_probabilities_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/4db06fd15af4902b4db8444b652f196c.pkl - params_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/params.yaml - predictions_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/predictions.json - probabilities_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/probabilities.json - score_dict_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/score_dict.json - test_labels_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/test_labels.json - train_labels_file: output/reports/train/5e198e68433d01a6e6b8b4e808215a91/train_labels.json - model_dir: models - name: 5e198e68433d01a6e6b8b4e808215a91 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4b27b251eb51a551140866ea87c32d70 - trainer: - kwargs: {} -name: 5e198e68433d01a6e6b8b4e808215a91 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5e1c99729aff25ae394714609531adeb/params.yaml b/examples/security/classification/output/reports/train/5e1c99729aff25ae394714609531adeb/params.yaml deleted file mode 100644 index 7c472d5d..00000000 --- a/examples/security/classification/output/reports/train/5e1c99729aff25ae394714609531adeb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5e1c99729aff25ae394714609531adeb/adv_predictions.json - adv_probabilities_file: output/reports/train/5e1c99729aff25ae394714609531adeb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/192dc6940fe9eefdf0a6096abc53253a.pkl - params_file: output/reports/train/5e1c99729aff25ae394714609531adeb/params.yaml - predictions_file: output/reports/train/5e1c99729aff25ae394714609531adeb/predictions.json - probabilities_file: output/reports/train/5e1c99729aff25ae394714609531adeb/probabilities.json - score_dict_file: output/reports/train/5e1c99729aff25ae394714609531adeb/score_dict.json - test_labels_file: output/reports/train/5e1c99729aff25ae394714609531adeb/test_labels.json - train_labels_file: output/reports/train/5e1c99729aff25ae394714609531adeb/train_labels.json - model_dir: models - name: 5e1c99729aff25ae394714609531adeb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1db7a1f3e919f3ebaf611100901e40a3 - trainer: - kwargs: {} -name: 5e1c99729aff25ae394714609531adeb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/params.yaml b/examples/security/classification/output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/params.yaml deleted file mode 100644 index 82f45dc5..00000000 --- a/examples/security/classification/output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/params.yaml +++ /dev/null @@ -1,115 +0,0 @@ -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/adv_predictions.json - adv_probabilities_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl - model_file: output/models/67b83ea4c8aab1c331a042d155307cbe.pkl - params_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/params.yaml - predictions_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/predictions.json - probabilities_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/probabilities.json - score_dict_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/score_dict.json - test_labels_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/test_labels.json - train_labels_file: output/reports/train/5e1eeefe8424bc2a666dc324b03a1132/train_labels.json - model_dir: models - name: 5e1eeefe8424bc2a666dc324b03a1132 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 10000 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 300ba1d558197ae7fbefd3d5dc4b1276 - trainer: - kwargs: {} -name: 5e1eeefe8424bc2a666dc324b03a1132 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/params.yaml b/examples/security/classification/output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/params.yaml deleted file mode 100644 index bdf888e8..00000000 --- a/examples/security/classification/output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/adv_predictions.json - adv_probabilities_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/46a75840227d3b2a60ff7721e2ed448d.pkl - params_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/params.yaml - predictions_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/predictions.json - probabilities_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/probabilities.json - score_dict_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/score_dict.json - test_labels_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/test_labels.json - train_labels_file: output/reports/train/5e2759c9050bfffcd0f0a4eddcec3273/train_labels.json - model_dir: models - name: 5e2759c9050bfffcd0f0a4eddcec3273 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: aab43c1e1ce00337fe7145cdc0c3bfbe - trainer: - kwargs: {} -name: 5e2759c9050bfffcd0f0a4eddcec3273 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5e400872ab68a5605560126944d063d4/params.yaml b/examples/security/classification/output/reports/train/5e400872ab68a5605560126944d063d4/params.yaml deleted file mode 100644 index edc6a610..00000000 --- a/examples/security/classification/output/reports/train/5e400872ab68a5605560126944d063d4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5e400872ab68a5605560126944d063d4/adv_predictions.json - adv_probabilities_file: output/reports/train/5e400872ab68a5605560126944d063d4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/bd7cf51b2af1bc95400df1302a76f813.pkl - params_file: output/reports/train/5e400872ab68a5605560126944d063d4/params.yaml - predictions_file: output/reports/train/5e400872ab68a5605560126944d063d4/predictions.json - probabilities_file: output/reports/train/5e400872ab68a5605560126944d063d4/probabilities.json - score_dict_file: output/reports/train/5e400872ab68a5605560126944d063d4/score_dict.json - test_labels_file: output/reports/train/5e400872ab68a5605560126944d063d4/test_labels.json - train_labels_file: output/reports/train/5e400872ab68a5605560126944d063d4/train_labels.json - model_dir: models - name: 5e400872ab68a5605560126944d063d4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6926894ff468b8c4a27ce7fc8a831ff8 - trainer: - kwargs: {} -name: 5e400872ab68a5605560126944d063d4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/params.yaml b/examples/security/classification/output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/params.yaml deleted file mode 100644 index 736a27fa..00000000 --- a/examples/security/classification/output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/adv_predictions.json - adv_probabilities_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/52c24d1356fcab4aa35d2572f19df910.pkl - params_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/params.yaml - predictions_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/predictions.json - probabilities_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/probabilities.json - score_dict_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/score_dict.json - test_labels_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/test_labels.json - train_labels_file: output/reports/train/5eb0a2d0436af2ecced061e724db3dc9/train_labels.json - model_dir: models - name: 5eb0a2d0436af2ecced061e724db3dc9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 77583d0a9f988dba343eaec468a9684b - trainer: - kwargs: {} -name: 5eb0a2d0436af2ecced061e724db3dc9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5edccfe72d7c6531749aa56f561203c8/params.yaml b/examples/security/classification/output/reports/train/5edccfe72d7c6531749aa56f561203c8/params.yaml deleted file mode 100644 index f199b279..00000000 --- a/examples/security/classification/output/reports/train/5edccfe72d7c6531749aa56f561203c8/params.yaml +++ /dev/null @@ -1,115 +0,0 @@ -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/adv_predictions.json - adv_probabilities_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl - model_file: output/models/6433f0d8d89ca1b86c01b23723145f0e.pkl - params_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/params.yaml - predictions_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/predictions.json - probabilities_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/probabilities.json - score_dict_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/score_dict.json - test_labels_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/test_labels.json - train_labels_file: output/reports/train/5edccfe72d7c6531749aa56f561203c8/train_labels.json - model_dir: models - name: 5edccfe72d7c6531749aa56f561203c8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 0.01 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: adbfdc19580f6fb87929a6fb20dfa2ab - trainer: - kwargs: {} -name: 5edccfe72d7c6531749aa56f561203c8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5f0a5925daa47e3754249380223f92e7/params.yaml b/examples/security/classification/output/reports/train/5f0a5925daa47e3754249380223f92e7/params.yaml deleted file mode 100644 index 93de0711..00000000 --- a/examples/security/classification/output/reports/train/5f0a5925daa47e3754249380223f92e7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/adv_predictions.json - adv_probabilities_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/d6672a6902778bf5d075dcd27415d119.pkl - params_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/params.yaml - predictions_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/predictions.json - probabilities_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/probabilities.json - score_dict_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/score_dict.json - test_labels_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/test_labels.json - train_labels_file: output/reports/train/5f0a5925daa47e3754249380223f92e7/train_labels.json - model_dir: models - name: 5f0a5925daa47e3754249380223f92e7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 37195eb9f417ec03f1acaee2b3ca234a - trainer: - kwargs: {} -name: 5f0a5925daa47e3754249380223f92e7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5f1a06dc64155a526d615a0f1c52805a/params.yaml b/examples/security/classification/output/reports/train/5f1a06dc64155a526d615a0f1c52805a/params.yaml deleted file mode 100644 index 6bdb0921..00000000 --- a/examples/security/classification/output/reports/train/5f1a06dc64155a526d615a0f1c52805a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/adv_predictions.json - adv_probabilities_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/6091c2e88ae75f8da01d4343d71e933b.pkl - params_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/params.yaml - predictions_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/predictions.json - probabilities_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/probabilities.json - score_dict_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/score_dict.json - test_labels_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/test_labels.json - train_labels_file: output/reports/train/5f1a06dc64155a526d615a0f1c52805a/train_labels.json - model_dir: models - name: 5f1a06dc64155a526d615a0f1c52805a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8bfaa512cfafeeafbfafaff70436e406 - trainer: - kwargs: {} -name: 5f1a06dc64155a526d615a0f1c52805a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5f23f8fbbb12da531af951221649e1ee/params.yaml b/examples/security/classification/output/reports/train/5f23f8fbbb12da531af951221649e1ee/params.yaml deleted file mode 100644 index 471afaf0..00000000 --- a/examples/security/classification/output/reports/train/5f23f8fbbb12da531af951221649e1ee/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/adv_predictions.json - adv_probabilities_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/e830ccab712c566cfc7d847915dda5a5.pkl - params_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/params.yaml - predictions_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/predictions.json - probabilities_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/probabilities.json - score_dict_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/score_dict.json - test_labels_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/test_labels.json - train_labels_file: output/reports/train/5f23f8fbbb12da531af951221649e1ee/train_labels.json - model_dir: models - name: 5f23f8fbbb12da531af951221649e1ee - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4dfc53485313915966686ffa77100413 - trainer: - kwargs: {} -name: 5f23f8fbbb12da531af951221649e1ee -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/params.yaml b/examples/security/classification/output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/params.yaml deleted file mode 100644 index ad7d3480..00000000 --- a/examples/security/classification/output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/adv_predictions.json - adv_probabilities_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/2b8957412e3e5b673278f047b07feb64.pkl - params_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/params.yaml - predictions_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/predictions.json - probabilities_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/probabilities.json - score_dict_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/score_dict.json - test_labels_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/test_labels.json - train_labels_file: output/reports/train/5f27b5f6c9fa8e19ef48f43889078576/train_labels.json - model_dir: models - name: 5f27b5f6c9fa8e19ef48f43889078576 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 723b44b4666cc3e9c9b809dbae57c8e5 - trainer: - kwargs: {} -name: 5f27b5f6c9fa8e19ef48f43889078576 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5f45f4c19bebedb91d14d30c702860f9/params.yaml b/examples/security/classification/output/reports/train/5f45f4c19bebedb91d14d30c702860f9/params.yaml deleted file mode 100644 index b10064f3..00000000 --- a/examples/security/classification/output/reports/train/5f45f4c19bebedb91d14d30c702860f9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/adv_predictions.json - adv_probabilities_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/d545900108a0a84c304a3ddc287ceca1.pkl - params_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/params.yaml - predictions_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/predictions.json - probabilities_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/probabilities.json - score_dict_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/score_dict.json - test_labels_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/test_labels.json - train_labels_file: output/reports/train/5f45f4c19bebedb91d14d30c702860f9/train_labels.json - model_dir: models - name: 5f45f4c19bebedb91d14d30c702860f9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 86c934eb9767ae6f0839e128460f0f2d - trainer: - kwargs: {} -name: 5f45f4c19bebedb91d14d30c702860f9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5f6ce96752f483675e53f5ac7923fb17/params.yaml b/examples/security/classification/output/reports/train/5f6ce96752f483675e53f5ac7923fb17/params.yaml deleted file mode 100644 index b54b1986..00000000 --- a/examples/security/classification/output/reports/train/5f6ce96752f483675e53f5ac7923fb17/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/adv_predictions.json - adv_probabilities_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/957ff0f4a34400638451bcef2a6f2aa4.pkl - params_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/params.yaml - predictions_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/predictions.json - probabilities_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/probabilities.json - score_dict_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/score_dict.json - test_labels_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/test_labels.json - train_labels_file: output/reports/train/5f6ce96752f483675e53f5ac7923fb17/train_labels.json - model_dir: models - name: 5f6ce96752f483675e53f5ac7923fb17 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2c5eb1e8961bd51979c6feca868ac287 - trainer: - kwargs: {} -name: 5f6ce96752f483675e53f5ac7923fb17 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5f749bcf5a2db94b85403558cdba1944/params.yaml b/examples/security/classification/output/reports/train/5f749bcf5a2db94b85403558cdba1944/params.yaml deleted file mode 100644 index 02c41a0a..00000000 --- a/examples/security/classification/output/reports/train/5f749bcf5a2db94b85403558cdba1944/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/adv_predictions.json - adv_probabilities_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/b77b0c383dac6b5d8c21830a43e5b275.pkl - params_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/params.yaml - predictions_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/predictions.json - probabilities_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/probabilities.json - score_dict_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/score_dict.json - test_labels_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/test_labels.json - train_labels_file: output/reports/train/5f749bcf5a2db94b85403558cdba1944/train_labels.json - model_dir: models - name: 5f749bcf5a2db94b85403558cdba1944 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6059d354b02ab28cbc48f5fe7f8124d4 - trainer: - kwargs: {} -name: 5f749bcf5a2db94b85403558cdba1944 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/params.yaml b/examples/security/classification/output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/params.yaml deleted file mode 100644 index 5801890c..00000000 --- a/examples/security/classification/output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/adv_predictions.json - adv_probabilities_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/69e8dcfdb6e3ee44c90c2d8174df7afb.pkl - params_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/params.yaml - predictions_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/predictions.json - probabilities_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/probabilities.json - score_dict_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/score_dict.json - test_labels_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/test_labels.json - train_labels_file: output/reports/train/5f9447342e6eee9aaa4b503e9f9d6c9e/train_labels.json - model_dir: models - name: 5f9447342e6eee9aaa4b503e9f9d6c9e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8c2e6fdba1ca69194ce4debc51863af6 - trainer: - kwargs: {} -name: 5f9447342e6eee9aaa4b503e9f9d6c9e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/params.yaml b/examples/security/classification/output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/params.yaml deleted file mode 100644 index 35da6005..00000000 --- a/examples/security/classification/output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/adv_predictions.json - adv_probabilities_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/4ee652ecd3d27847164455fae99ef356.pkl - params_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/params.yaml - predictions_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/predictions.json - probabilities_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/probabilities.json - score_dict_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/score_dict.json - test_labels_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/test_labels.json - train_labels_file: output/reports/train/5f9b17742b26c6cc62d2b50c532c2283/train_labels.json - model_dir: models - name: 5f9b17742b26c6cc62d2b50c532c2283 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5c235ba96542f438d69190b21af34116 - trainer: - kwargs: {} -name: 5f9b17742b26c6cc62d2b50c532c2283 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5fa4bb01d405687e810727f33d5d8631/params.yaml b/examples/security/classification/output/reports/train/5fa4bb01d405687e810727f33d5d8631/params.yaml deleted file mode 100644 index f2340abd..00000000 --- a/examples/security/classification/output/reports/train/5fa4bb01d405687e810727f33d5d8631/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/adv_predictions.json - adv_probabilities_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/346aa775a703d0d92ebcc8b6cacdecf7.pkl - params_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/params.yaml - predictions_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/predictions.json - probabilities_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/probabilities.json - score_dict_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/score_dict.json - test_labels_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/test_labels.json - train_labels_file: output/reports/train/5fa4bb01d405687e810727f33d5d8631/train_labels.json - model_dir: models - name: 5fa4bb01d405687e810727f33d5d8631 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4b8879f947f0321d30409dec58519bcd - trainer: - kwargs: {} -name: 5fa4bb01d405687e810727f33d5d8631 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/params.yaml b/examples/security/classification/output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/params.yaml deleted file mode 100644 index ae7272a6..00000000 --- a/examples/security/classification/output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/adv_predictions.json - adv_probabilities_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/5265a581a909fde2ff025bd9e8f51767.pkl - params_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/params.yaml - predictions_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/predictions.json - probabilities_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/probabilities.json - score_dict_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/score_dict.json - test_labels_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/test_labels.json - train_labels_file: output/reports/train/5ffa7c6202feaf0e3bf6fa838006fcf7/train_labels.json - model_dir: models - name: 5ffa7c6202feaf0e3bf6fa838006fcf7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c3d7b25aa96348841ce07ffb8d5caf92 - trainer: - kwargs: {} -name: 5ffa7c6202feaf0e3bf6fa838006fcf7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/params.yaml b/examples/security/classification/output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/params.yaml deleted file mode 100644 index 95341b3d..00000000 --- a/examples/security/classification/output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/adv_predictions.json - adv_probabilities_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/86f7f9779a32e3921683cbc0c054e6f0.pkl - params_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/params.yaml - predictions_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/predictions.json - probabilities_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/probabilities.json - score_dict_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/score_dict.json - test_labels_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/test_labels.json - train_labels_file: output/reports/train/5ffd5e6a599f89e5df349d92cdb5a52d/train_labels.json - model_dir: models - name: 5ffd5e6a599f89e5df349d92cdb5a52d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8b81882eacbe584cfae30af24d5168f2 - trainer: - kwargs: {} -name: 5ffd5e6a599f89e5df349d92cdb5a52d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/params.yaml b/examples/security/classification/output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/params.yaml deleted file mode 100644 index eb728327..00000000 --- a/examples/security/classification/output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/adv_predictions.json - adv_probabilities_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/8a6824d8d42ca1c739aed57ade9d9d00.pkl - params_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/params.yaml - predictions_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/predictions.json - probabilities_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/probabilities.json - score_dict_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/score_dict.json - test_labels_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/test_labels.json - train_labels_file: output/reports/train/6006925ee4ff5afbfb74e9a34e585f52/train_labels.json - model_dir: models - name: 6006925ee4ff5afbfb74e9a34e585f52 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f174a795fc383fad168e634c4edda51b - trainer: - kwargs: {} -name: 6006925ee4ff5afbfb74e9a34e585f52 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6064f20d09e29a664a00b8862a1ffa97/params.yaml b/examples/security/classification/output/reports/train/6064f20d09e29a664a00b8862a1ffa97/params.yaml deleted file mode 100644 index eb02c51a..00000000 --- a/examples/security/classification/output/reports/train/6064f20d09e29a664a00b8862a1ffa97/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/adv_predictions.json - adv_probabilities_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/d6a88f2bd50c18443457e975867f1233.pkl - params_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/params.yaml - predictions_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/predictions.json - probabilities_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/probabilities.json - score_dict_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/score_dict.json - test_labels_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/test_labels.json - train_labels_file: output/reports/train/6064f20d09e29a664a00b8862a1ffa97/train_labels.json - model_dir: models - name: 6064f20d09e29a664a00b8862a1ffa97 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fae8ad838f4d77a42862930011d06115 - trainer: - kwargs: {} -name: 6064f20d09e29a664a00b8862a1ffa97 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6069225b36f1fc2d7434d27643c4a073/params.yaml b/examples/security/classification/output/reports/train/6069225b36f1fc2d7434d27643c4a073/params.yaml deleted file mode 100644 index 1fbd4be8..00000000 --- a/examples/security/classification/output/reports/train/6069225b36f1fc2d7434d27643c4a073/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/adv_predictions.json - adv_probabilities_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/47d92edaea5bb9e5f743a88476078129.pkl - params_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/params.yaml - predictions_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/predictions.json - probabilities_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/probabilities.json - score_dict_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/score_dict.json - test_labels_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/test_labels.json - train_labels_file: output/reports/train/6069225b36f1fc2d7434d27643c4a073/train_labels.json - model_dir: models - name: 6069225b36f1fc2d7434d27643c4a073 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 531f005e3fa06e14e71807b059f89fd6 - trainer: - kwargs: {} -name: 6069225b36f1fc2d7434d27643c4a073 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/607442709380a81928ae56a35d23b9c9/params.yaml b/examples/security/classification/output/reports/train/607442709380a81928ae56a35d23b9c9/params.yaml deleted file mode 100644 index 51d0d127..00000000 --- a/examples/security/classification/output/reports/train/607442709380a81928ae56a35d23b9c9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/607442709380a81928ae56a35d23b9c9/adv_predictions.json - adv_probabilities_file: output/reports/train/607442709380a81928ae56a35d23b9c9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/47f95db3633aeb6dda4da66ac63901a7.pkl - params_file: output/reports/train/607442709380a81928ae56a35d23b9c9/params.yaml - predictions_file: output/reports/train/607442709380a81928ae56a35d23b9c9/predictions.json - probabilities_file: output/reports/train/607442709380a81928ae56a35d23b9c9/probabilities.json - score_dict_file: output/reports/train/607442709380a81928ae56a35d23b9c9/score_dict.json - test_labels_file: output/reports/train/607442709380a81928ae56a35d23b9c9/test_labels.json - train_labels_file: output/reports/train/607442709380a81928ae56a35d23b9c9/train_labels.json - model_dir: models - name: 607442709380a81928ae56a35d23b9c9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0508e88b02572d3d0e4168943e28ab36 - trainer: - kwargs: {} -name: 607442709380a81928ae56a35d23b9c9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/params.yaml b/examples/security/classification/output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/params.yaml deleted file mode 100644 index 98f8d8dd..00000000 --- a/examples/security/classification/output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/adv_predictions.json - adv_probabilities_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/825f6e2eb47382471953c1c473bc3ac2.pkl - params_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/params.yaml - predictions_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/predictions.json - probabilities_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/probabilities.json - score_dict_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/score_dict.json - test_labels_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/test_labels.json - train_labels_file: output/reports/train/607b7becfc4ceebca60a0cad5a17e62a/train_labels.json - model_dir: models - name: 607b7becfc4ceebca60a0cad5a17e62a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - degree: 2 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 562c64bdbcae37d4784ec50a60c81c21 - trainer: - kwargs: {} -name: 607b7becfc4ceebca60a0cad5a17e62a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/608ba0fd0599b25289a5089964fb586e/params.yaml b/examples/security/classification/output/reports/train/608ba0fd0599b25289a5089964fb586e/params.yaml deleted file mode 100644 index 96a968c2..00000000 --- a/examples/security/classification/output/reports/train/608ba0fd0599b25289a5089964fb586e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/adv_predictions.json - adv_probabilities_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/af650905465f60d95bcbb973f9865157.pkl - params_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/params.yaml - predictions_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/predictions.json - probabilities_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/probabilities.json - score_dict_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/score_dict.json - test_labels_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/test_labels.json - train_labels_file: output/reports/train/608ba0fd0599b25289a5089964fb586e/train_labels.json - model_dir: models - name: 608ba0fd0599b25289a5089964fb586e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 830ca75baa5170a2040dc11811d76171 - trainer: - kwargs: {} -name: 608ba0fd0599b25289a5089964fb586e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/608f097ce9fd306252d87e85564e7e6c/params.yaml b/examples/security/classification/output/reports/train/608f097ce9fd306252d87e85564e7e6c/params.yaml deleted file mode 100644 index f0857037..00000000 --- a/examples/security/classification/output/reports/train/608f097ce9fd306252d87e85564e7e6c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/adv_predictions.json - adv_probabilities_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/9a3108f20c6e06554ab4b08cf3eb3909.pkl - params_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/params.yaml - predictions_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/predictions.json - probabilities_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/probabilities.json - score_dict_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/score_dict.json - test_labels_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/test_labels.json - train_labels_file: output/reports/train/608f097ce9fd306252d87e85564e7e6c/train_labels.json - model_dir: models - name: 608f097ce9fd306252d87e85564e7e6c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 409a3324c476eebcf2727e7d9b708933 - trainer: - kwargs: {} -name: 608f097ce9fd306252d87e85564e7e6c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/60a092d859a107021e19a07fa5e26c04/params.yaml b/examples/security/classification/output/reports/train/60a092d859a107021e19a07fa5e26c04/params.yaml deleted file mode 100644 index 34484944..00000000 --- a/examples/security/classification/output/reports/train/60a092d859a107021e19a07fa5e26c04/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/adv_predictions.json - adv_probabilities_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/a4d98f20e3c50c6f7ece9b4b1aee1897.pkl - params_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/params.yaml - predictions_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/predictions.json - probabilities_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/probabilities.json - score_dict_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/score_dict.json - test_labels_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/test_labels.json - train_labels_file: output/reports/train/60a092d859a107021e19a07fa5e26c04/train_labels.json - model_dir: models - name: 60a092d859a107021e19a07fa5e26c04 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2200bcab90b3a751a9bbb2e0087e6a9b - trainer: - kwargs: {} -name: 60a092d859a107021e19a07fa5e26c04 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/60cb32222c720368439fcfed0556666a/params.yaml b/examples/security/classification/output/reports/train/60cb32222c720368439fcfed0556666a/params.yaml deleted file mode 100644 index 3c9e37f7..00000000 --- a/examples/security/classification/output/reports/train/60cb32222c720368439fcfed0556666a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/60cb32222c720368439fcfed0556666a/adv_predictions.json - adv_probabilities_file: output/reports/train/60cb32222c720368439fcfed0556666a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/bf8706cf60ffb2334f7374ec626b334b.pkl - params_file: output/reports/train/60cb32222c720368439fcfed0556666a/params.yaml - predictions_file: output/reports/train/60cb32222c720368439fcfed0556666a/predictions.json - probabilities_file: output/reports/train/60cb32222c720368439fcfed0556666a/probabilities.json - score_dict_file: output/reports/train/60cb32222c720368439fcfed0556666a/score_dict.json - test_labels_file: output/reports/train/60cb32222c720368439fcfed0556666a/test_labels.json - train_labels_file: output/reports/train/60cb32222c720368439fcfed0556666a/train_labels.json - model_dir: models - name: 60cb32222c720368439fcfed0556666a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fc2bf33b74df4e8c7e59750052c81838 - trainer: - kwargs: {} -name: 60cb32222c720368439fcfed0556666a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/60d173003e57e486ad364991f65928cf/params.yaml b/examples/security/classification/output/reports/train/60d173003e57e486ad364991f65928cf/params.yaml deleted file mode 100644 index 7dffac70..00000000 --- a/examples/security/classification/output/reports/train/60d173003e57e486ad364991f65928cf/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/60d173003e57e486ad364991f65928cf/adv_predictions.json - adv_probabilities_file: output/reports/train/60d173003e57e486ad364991f65928cf/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/37d5d18a7a51c18536043461fded5d9c.pkl - params_file: output/reports/train/60d173003e57e486ad364991f65928cf/params.yaml - predictions_file: output/reports/train/60d173003e57e486ad364991f65928cf/predictions.json - probabilities_file: output/reports/train/60d173003e57e486ad364991f65928cf/probabilities.json - score_dict_file: output/reports/train/60d173003e57e486ad364991f65928cf/score_dict.json - test_labels_file: output/reports/train/60d173003e57e486ad364991f65928cf/test_labels.json - train_labels_file: output/reports/train/60d173003e57e486ad364991f65928cf/train_labels.json - model_dir: models - name: 60d173003e57e486ad364991f65928cf - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 435e6aa6c223e523f537aa336132ccbe - trainer: - kwargs: {} -name: 60d173003e57e486ad364991f65928cf -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/params.yaml b/examples/security/classification/output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/params.yaml deleted file mode 100644 index 8cf6ef00..00000000 --- a/examples/security/classification/output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/adv_predictions.json - adv_probabilities_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/86f61368ac2a62ae4e2d9c8831dfa186.pkl - params_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/params.yaml - predictions_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/predictions.json - probabilities_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/probabilities.json - score_dict_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/score_dict.json - test_labels_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/test_labels.json - train_labels_file: output/reports/train/60db2b7f05fbc15b6e4e8a389bfc2e24/train_labels.json - model_dir: models - name: 60db2b7f05fbc15b6e4e8a389bfc2e24 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 362b9c82bef0e86e8654d63581a6c01e - trainer: - kwargs: {} -name: 60db2b7f05fbc15b6e4e8a389bfc2e24 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/60ecb155830fbe33531beea9e0923f34/params.yaml b/examples/security/classification/output/reports/train/60ecb155830fbe33531beea9e0923f34/params.yaml deleted file mode 100644 index 072daf6b..00000000 --- a/examples/security/classification/output/reports/train/60ecb155830fbe33531beea9e0923f34/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/adv_predictions.json - adv_probabilities_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/662449142d0b2f7896828722c6cf0587.pkl - params_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/params.yaml - predictions_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/predictions.json - probabilities_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/probabilities.json - score_dict_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/score_dict.json - test_labels_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/test_labels.json - train_labels_file: output/reports/train/60ecb155830fbe33531beea9e0923f34/train_labels.json - model_dir: models - name: 60ecb155830fbe33531beea9e0923f34 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c5ae8f4898118198816695ca222f95b7 - trainer: - kwargs: {} -name: 60ecb155830fbe33531beea9e0923f34 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/60f7927d0412e3b8dace04d766aec9fa/params.yaml b/examples/security/classification/output/reports/train/60f7927d0412e3b8dace04d766aec9fa/params.yaml deleted file mode 100644 index 9f82df5e..00000000 --- a/examples/security/classification/output/reports/train/60f7927d0412e3b8dace04d766aec9fa/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/adv_predictions.json - adv_probabilities_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/8fe310a63727033e5870e8478cbda67d.pkl - params_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/params.yaml - predictions_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/predictions.json - probabilities_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/probabilities.json - score_dict_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/score_dict.json - test_labels_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/test_labels.json - train_labels_file: output/reports/train/60f7927d0412e3b8dace04d766aec9fa/train_labels.json - model_dir: models - name: 60f7927d0412e3b8dace04d766aec9fa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f3be266621758b31eae737638988a7be - trainer: - kwargs: {} -name: 60f7927d0412e3b8dace04d766aec9fa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/61254b1beac95cb05ad11bff429cc1c9/params.yaml b/examples/security/classification/output/reports/train/61254b1beac95cb05ad11bff429cc1c9/params.yaml deleted file mode 100644 index d48d5512..00000000 --- a/examples/security/classification/output/reports/train/61254b1beac95cb05ad11bff429cc1c9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/adv_predictions.json - adv_probabilities_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/e4eb7fefae1d5c3782e19c0841574149.pkl - params_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/params.yaml - predictions_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/predictions.json - probabilities_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/probabilities.json - score_dict_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/score_dict.json - test_labels_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/test_labels.json - train_labels_file: output/reports/train/61254b1beac95cb05ad11bff429cc1c9/train_labels.json - model_dir: models - name: 61254b1beac95cb05ad11bff429cc1c9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3e128f862e741d92525ad0f95e48ec3d - trainer: - kwargs: {} -name: 61254b1beac95cb05ad11bff429cc1c9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/params.yaml b/examples/security/classification/output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/params.yaml deleted file mode 100644 index deac5274..00000000 --- a/examples/security/classification/output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/adv_predictions.json - adv_probabilities_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/5432227db192f36cd77c927dadd64a00.pkl - params_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/params.yaml - predictions_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/predictions.json - probabilities_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/probabilities.json - score_dict_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/score_dict.json - test_labels_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/test_labels.json - train_labels_file: output/reports/train/615a6dc4390247768e2d0d43a1b7fe14/train_labels.json - model_dir: models - name: 615a6dc4390247768e2d0d43a1b7fe14 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: da26e75c86b75ab7345500e0f0e75388 - trainer: - kwargs: {} -name: 615a6dc4390247768e2d0d43a1b7fe14 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/params.yaml b/examples/security/classification/output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/params.yaml deleted file mode 100644 index 234a7f89..00000000 --- a/examples/security/classification/output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/adv_predictions.json - adv_probabilities_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/4b21d041fdba68d6677b3a6de4b467d6.pkl - params_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/params.yaml - predictions_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/predictions.json - probabilities_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/probabilities.json - score_dict_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/score_dict.json - test_labels_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/test_labels.json - train_labels_file: output/reports/train/615d4c8a091ae4a84c3c5e5a29312d89/train_labels.json - model_dir: models - name: 615d4c8a091ae4a84c3c5e5a29312d89 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 576200abd5b89fd4b4db59fba9afc66c - trainer: - kwargs: {} -name: 615d4c8a091ae4a84c3c5e5a29312d89 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/617ab7417ec483782157f7583e879960/params.yaml b/examples/security/classification/output/reports/train/617ab7417ec483782157f7583e879960/params.yaml deleted file mode 100644 index db3e9476..00000000 --- a/examples/security/classification/output/reports/train/617ab7417ec483782157f7583e879960/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/617ab7417ec483782157f7583e879960/adv_predictions.json - adv_probabilities_file: output/reports/train/617ab7417ec483782157f7583e879960/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/9eb1d2c49a1cde0411d6de5f0a9ef8cc.pkl - params_file: output/reports/train/617ab7417ec483782157f7583e879960/params.yaml - predictions_file: output/reports/train/617ab7417ec483782157f7583e879960/predictions.json - probabilities_file: output/reports/train/617ab7417ec483782157f7583e879960/probabilities.json - score_dict_file: output/reports/train/617ab7417ec483782157f7583e879960/score_dict.json - test_labels_file: output/reports/train/617ab7417ec483782157f7583e879960/test_labels.json - train_labels_file: output/reports/train/617ab7417ec483782157f7583e879960/train_labels.json - model_dir: models - name: 617ab7417ec483782157f7583e879960 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5937c88b70053a337f4ca8e0fa08cc64 - trainer: - kwargs: {} -name: 617ab7417ec483782157f7583e879960 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/617bc9f664c6c64a0357b9bf6339e144/params.yaml b/examples/security/classification/output/reports/train/617bc9f664c6c64a0357b9bf6339e144/params.yaml deleted file mode 100644 index e1c02283..00000000 --- a/examples/security/classification/output/reports/train/617bc9f664c6c64a0357b9bf6339e144/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/adv_predictions.json - adv_probabilities_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/dc837b09cf7be0d77b14b76681207d6f.pkl - params_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/params.yaml - predictions_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/predictions.json - probabilities_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/probabilities.json - score_dict_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/score_dict.json - test_labels_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/test_labels.json - train_labels_file: output/reports/train/617bc9f664c6c64a0357b9bf6339e144/train_labels.json - model_dir: models - name: 617bc9f664c6c64a0357b9bf6339e144 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c5994b51075e62317e6633a32b697aa6 - trainer: - kwargs: {} -name: 617bc9f664c6c64a0357b9bf6339e144 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/params.yaml b/examples/security/classification/output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/params.yaml deleted file mode 100644 index d4afc065..00000000 --- a/examples/security/classification/output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/adv_predictions.json - adv_probabilities_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/7482e32a6f3dca2cf931d2502eced844.pkl - params_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/params.yaml - predictions_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/predictions.json - probabilities_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/probabilities.json - score_dict_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/score_dict.json - test_labels_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/test_labels.json - train_labels_file: output/reports/train/6182f973ee0934b9e569845f4d7d2cb4/train_labels.json - model_dir: models - name: 6182f973ee0934b9e569845f4d7d2cb4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3641764628f3acb1da8d58b4b050e55d - trainer: - kwargs: {} -name: 6182f973ee0934b9e569845f4d7d2cb4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/61ac2e73f93e0a14ba34c214864e6827/params.yaml b/examples/security/classification/output/reports/train/61ac2e73f93e0a14ba34c214864e6827/params.yaml deleted file mode 100644 index 41c02470..00000000 --- a/examples/security/classification/output/reports/train/61ac2e73f93e0a14ba34c214864e6827/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/adv_predictions.json - adv_probabilities_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/64705b8c3c6c6e73129daa66cca6e529.pkl - params_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/params.yaml - predictions_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/predictions.json - probabilities_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/probabilities.json - score_dict_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/score_dict.json - test_labels_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/test_labels.json - train_labels_file: output/reports/train/61ac2e73f93e0a14ba34c214864e6827/train_labels.json - model_dir: models - name: 61ac2e73f93e0a14ba34c214864e6827 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 50dfb42923d9d8219b7ecf8f541c7d2c - trainer: - kwargs: {} -name: 61ac2e73f93e0a14ba34c214864e6827 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/61e6334e12152199dd439ed4432e987e/params.yaml b/examples/security/classification/output/reports/train/61e6334e12152199dd439ed4432e987e/params.yaml deleted file mode 100644 index 89858af0..00000000 --- a/examples/security/classification/output/reports/train/61e6334e12152199dd439ed4432e987e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/61e6334e12152199dd439ed4432e987e/adv_predictions.json - adv_probabilities_file: output/reports/train/61e6334e12152199dd439ed4432e987e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/53d1da128b9d507fa2c19b84f5f3549b.pkl - params_file: output/reports/train/61e6334e12152199dd439ed4432e987e/params.yaml - predictions_file: output/reports/train/61e6334e12152199dd439ed4432e987e/predictions.json - probabilities_file: output/reports/train/61e6334e12152199dd439ed4432e987e/probabilities.json - score_dict_file: output/reports/train/61e6334e12152199dd439ed4432e987e/score_dict.json - test_labels_file: output/reports/train/61e6334e12152199dd439ed4432e987e/test_labels.json - train_labels_file: output/reports/train/61e6334e12152199dd439ed4432e987e/train_labels.json - model_dir: models - name: 61e6334e12152199dd439ed4432e987e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e48de9a11f2f523f27a3bc4c2b8a988a - trainer: - kwargs: {} -name: 61e6334e12152199dd439ed4432e987e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6228187ca82dd5394ac44bd4f7414080/params.yaml b/examples/security/classification/output/reports/train/6228187ca82dd5394ac44bd4f7414080/params.yaml deleted file mode 100644 index 18b6a828..00000000 --- a/examples/security/classification/output/reports/train/6228187ca82dd5394ac44bd4f7414080/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/adv_predictions.json - adv_probabilities_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/17954edde5adced4259b0d257394973c.pkl - params_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/params.yaml - predictions_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/predictions.json - probabilities_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/probabilities.json - score_dict_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/score_dict.json - test_labels_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/test_labels.json - train_labels_file: output/reports/train/6228187ca82dd5394ac44bd4f7414080/train_labels.json - model_dir: models - name: 6228187ca82dd5394ac44bd4f7414080 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8ce185c6cda7a7263d7f1fe36e912c7c - trainer: - kwargs: {} -name: 6228187ca82dd5394ac44bd4f7414080 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/params.yaml b/examples/security/classification/output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/params.yaml deleted file mode 100644 index 739eaf79..00000000 --- a/examples/security/classification/output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/adv_predictions.json - adv_probabilities_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/a2ddf64c0a24cb16a9cd8b094990335f.pkl - params_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/params.yaml - predictions_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/predictions.json - probabilities_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/probabilities.json - score_dict_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/score_dict.json - test_labels_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/test_labels.json - train_labels_file: output/reports/train/6230e241d5e0c7929dab34e4cb0efcb5/train_labels.json - model_dir: models - name: 6230e241d5e0c7929dab34e4cb0efcb5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bf0ea9df137cfc6a8e5be857c485b113 - trainer: - kwargs: {} -name: 6230e241d5e0c7929dab34e4cb0efcb5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/625577e9c13f002b0a37a28272b24576/params.yaml b/examples/security/classification/output/reports/train/625577e9c13f002b0a37a28272b24576/params.yaml deleted file mode 100644 index c0fe6df0..00000000 --- a/examples/security/classification/output/reports/train/625577e9c13f002b0a37a28272b24576/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/625577e9c13f002b0a37a28272b24576/adv_predictions.json - adv_probabilities_file: output/reports/train/625577e9c13f002b0a37a28272b24576/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/3434bd55f04bbdfa75f89fe3194cd63f.pkl - params_file: output/reports/train/625577e9c13f002b0a37a28272b24576/params.yaml - predictions_file: output/reports/train/625577e9c13f002b0a37a28272b24576/predictions.json - probabilities_file: output/reports/train/625577e9c13f002b0a37a28272b24576/probabilities.json - score_dict_file: output/reports/train/625577e9c13f002b0a37a28272b24576/score_dict.json - test_labels_file: output/reports/train/625577e9c13f002b0a37a28272b24576/test_labels.json - train_labels_file: output/reports/train/625577e9c13f002b0a37a28272b24576/train_labels.json - model_dir: models - name: 625577e9c13f002b0a37a28272b24576 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 16008022c917c85db08813ffbd92f2cf - trainer: - kwargs: {} -name: 625577e9c13f002b0a37a28272b24576 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/params.yaml b/examples/security/classification/output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/params.yaml deleted file mode 100644 index a682c24c..00000000 --- a/examples/security/classification/output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/adv_predictions.json - adv_probabilities_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/4afbfea8d41eeaa6c31baeed69dabd36.pkl - params_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/params.yaml - predictions_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/predictions.json - probabilities_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/probabilities.json - score_dict_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/score_dict.json - test_labels_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/test_labels.json - train_labels_file: output/reports/train/62c7eb5e23d33b38f0d84ee02541280e/train_labels.json - model_dir: models - name: 62c7eb5e23d33b38f0d84ee02541280e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3efd5c69e1d430aa0a662aed94019e24 - trainer: - kwargs: {} -name: 62c7eb5e23d33b38f0d84ee02541280e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/params.yaml b/examples/security/classification/output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/params.yaml deleted file mode 100644 index dd52d327..00000000 --- a/examples/security/classification/output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/adv_predictions.json - adv_probabilities_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/7e92dbf8e2bd28f681d3634297527d76.pkl - params_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/params.yaml - predictions_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/predictions.json - probabilities_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/probabilities.json - score_dict_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/score_dict.json - test_labels_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/test_labels.json - train_labels_file: output/reports/train/62c9f4e051c8652d0ab9af36b530bfef/train_labels.json - model_dir: models - name: 62c9f4e051c8652d0ab9af36b530bfef - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f04d2c7e34be1c42f88a5c40e9b4060a - trainer: - kwargs: {} -name: 62c9f4e051c8652d0ab9af36b530bfef -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/62d3569241d28a4211c6caed0a0f77ee/params.yaml b/examples/security/classification/output/reports/train/62d3569241d28a4211c6caed0a0f77ee/params.yaml deleted file mode 100644 index 71e30793..00000000 --- a/examples/security/classification/output/reports/train/62d3569241d28a4211c6caed0a0f77ee/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/adv_predictions.json - adv_probabilities_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/8cbb1231b881d325d61598967c392590.pkl - params_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/params.yaml - predictions_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/predictions.json - probabilities_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/probabilities.json - score_dict_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/score_dict.json - test_labels_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/test_labels.json - train_labels_file: output/reports/train/62d3569241d28a4211c6caed0a0f77ee/train_labels.json - model_dir: models - name: 62d3569241d28a4211c6caed0a0f77ee - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4558bb6b5255d259ab2df3d05b7fa818 - trainer: - kwargs: {} -name: 62d3569241d28a4211c6caed0a0f77ee -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/62ef184c456bdd6f910eaa180442a5c6/params.yaml b/examples/security/classification/output/reports/train/62ef184c456bdd6f910eaa180442a5c6/params.yaml deleted file mode 100644 index f67a0bff..00000000 --- a/examples/security/classification/output/reports/train/62ef184c456bdd6f910eaa180442a5c6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/adv_predictions.json - adv_probabilities_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/fb249987afdec6f4677539a0e5e29588.pkl - params_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/params.yaml - predictions_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/predictions.json - probabilities_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/probabilities.json - score_dict_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/score_dict.json - test_labels_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/test_labels.json - train_labels_file: output/reports/train/62ef184c456bdd6f910eaa180442a5c6/train_labels.json - model_dir: models - name: 62ef184c456bdd6f910eaa180442a5c6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b61b45fb388195c560ea03893ce69e22 - trainer: - kwargs: {} -name: 62ef184c456bdd6f910eaa180442a5c6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/62fcf51b134d1065176ed0538cf325a9/params.yaml b/examples/security/classification/output/reports/train/62fcf51b134d1065176ed0538cf325a9/params.yaml deleted file mode 100644 index 2ee5cf75..00000000 --- a/examples/security/classification/output/reports/train/62fcf51b134d1065176ed0538cf325a9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/adv_predictions.json - adv_probabilities_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/1898cf251ed92497f56c2eeb5dccbb07.pkl - params_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/params.yaml - predictions_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/predictions.json - probabilities_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/probabilities.json - score_dict_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/score_dict.json - test_labels_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/test_labels.json - train_labels_file: output/reports/train/62fcf51b134d1065176ed0538cf325a9/train_labels.json - model_dir: models - name: 62fcf51b134d1065176ed0538cf325a9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a211b43101994f8f1d97819111ab5774 - trainer: - kwargs: {} -name: 62fcf51b134d1065176ed0538cf325a9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/params.yaml b/examples/security/classification/output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/params.yaml deleted file mode 100644 index 4fe25203..00000000 --- a/examples/security/classification/output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/adv_predictions.json - adv_probabilities_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/7aed35ee29e98d7fe6a9d89e4156ee83.pkl - params_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/params.yaml - predictions_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/predictions.json - probabilities_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/probabilities.json - score_dict_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/score_dict.json - test_labels_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/test_labels.json - train_labels_file: output/reports/train/631fdead3acc05ea41c89c5a187c3bd6/train_labels.json - model_dir: models - name: 631fdead3acc05ea41c89c5a187c3bd6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 79ed7b40f257c606e1d92a0f9ea6f989 - trainer: - kwargs: {} -name: 631fdead3acc05ea41c89c5a187c3bd6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/params.yaml b/examples/security/classification/output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/params.yaml deleted file mode 100644 index 09c67df0..00000000 --- a/examples/security/classification/output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/adv_predictions.json - adv_probabilities_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/157ca98ab902d50a1ee7ba5d0e270c42.pkl - params_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/params.yaml - predictions_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/predictions.json - probabilities_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/probabilities.json - score_dict_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/score_dict.json - test_labels_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/test_labels.json - train_labels_file: output/reports/train/632e9e5c7dc748fc1a8072ad2248ca1d/train_labels.json - model_dir: models - name: 632e9e5c7dc748fc1a8072ad2248ca1d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6b0f09c3f7602e025d9ffddc3b9618b9 - trainer: - kwargs: {} -name: 632e9e5c7dc748fc1a8072ad2248ca1d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/63447d821e9d864dd2da27ca96cd3f23/params.yaml b/examples/security/classification/output/reports/train/63447d821e9d864dd2da27ca96cd3f23/params.yaml deleted file mode 100644 index 89417951..00000000 --- a/examples/security/classification/output/reports/train/63447d821e9d864dd2da27ca96cd3f23/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/adv_predictions.json - adv_probabilities_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/885b32405446c22bc1991e2a7480f881.pkl - params_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/params.yaml - predictions_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/predictions.json - probabilities_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/probabilities.json - score_dict_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/score_dict.json - test_labels_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/test_labels.json - train_labels_file: output/reports/train/63447d821e9d864dd2da27ca96cd3f23/train_labels.json - model_dir: models - name: 63447d821e9d864dd2da27ca96cd3f23 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2ca979d355d77f0e9e90dc03dfebce98 - trainer: - kwargs: {} -name: 63447d821e9d864dd2da27ca96cd3f23 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/637a69d7faab053f4bfb56725c99f149/params.yaml b/examples/security/classification/output/reports/train/637a69d7faab053f4bfb56725c99f149/params.yaml deleted file mode 100644 index 097200b9..00000000 --- a/examples/security/classification/output/reports/train/637a69d7faab053f4bfb56725c99f149/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/adv_predictions.json - adv_probabilities_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/019892bfa5cab7350db559558952b371.pkl - params_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/params.yaml - predictions_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/predictions.json - probabilities_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/probabilities.json - score_dict_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/score_dict.json - test_labels_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/test_labels.json - train_labels_file: output/reports/train/637a69d7faab053f4bfb56725c99f149/train_labels.json - model_dir: models - name: 637a69d7faab053f4bfb56725c99f149 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0510b82a73961c39c45ea749da2e12c3 - trainer: - kwargs: {} -name: 637a69d7faab053f4bfb56725c99f149 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/637ce5ed1e5cb4a91c009609c206a078/params.yaml b/examples/security/classification/output/reports/train/637ce5ed1e5cb4a91c009609c206a078/params.yaml deleted file mode 100644 index 5efd4d3b..00000000 --- a/examples/security/classification/output/reports/train/637ce5ed1e5cb4a91c009609c206a078/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/adv_predictions.json - adv_probabilities_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/f2ebc207a8e9fa3872583b77c4391bed.pkl - params_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/params.yaml - predictions_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/predictions.json - probabilities_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/probabilities.json - score_dict_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/score_dict.json - test_labels_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/test_labels.json - train_labels_file: output/reports/train/637ce5ed1e5cb4a91c009609c206a078/train_labels.json - model_dir: models - name: 637ce5ed1e5cb4a91c009609c206a078 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 91a2f7979f8f8bc8d4f538c1bef3bd49 - trainer: - kwargs: {} -name: 637ce5ed1e5cb4a91c009609c206a078 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6380d387aca64368fa85380e502c6d22/params.yaml b/examples/security/classification/output/reports/train/6380d387aca64368fa85380e502c6d22/params.yaml deleted file mode 100644 index 163c373b..00000000 --- a/examples/security/classification/output/reports/train/6380d387aca64368fa85380e502c6d22/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6380d387aca64368fa85380e502c6d22/adv_predictions.json - adv_probabilities_file: output/reports/train/6380d387aca64368fa85380e502c6d22/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/71782928f48f6eb5523712befce353d0.pkl - params_file: output/reports/train/6380d387aca64368fa85380e502c6d22/params.yaml - predictions_file: output/reports/train/6380d387aca64368fa85380e502c6d22/predictions.json - probabilities_file: output/reports/train/6380d387aca64368fa85380e502c6d22/probabilities.json - score_dict_file: output/reports/train/6380d387aca64368fa85380e502c6d22/score_dict.json - test_labels_file: output/reports/train/6380d387aca64368fa85380e502c6d22/test_labels.json - train_labels_file: output/reports/train/6380d387aca64368fa85380e502c6d22/train_labels.json - model_dir: models - name: 6380d387aca64368fa85380e502c6d22 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3cd22f1f775d0e02d7e990dd4b768473 - trainer: - kwargs: {} -name: 6380d387aca64368fa85380e502c6d22 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/params.yaml b/examples/security/classification/output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/params.yaml deleted file mode 100644 index cdab6eef..00000000 --- a/examples/security/classification/output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/adv_predictions.json - adv_probabilities_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/5c1b945cab49bf220a1532737cd5c813.pkl - params_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/params.yaml - predictions_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/predictions.json - probabilities_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/probabilities.json - score_dict_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/score_dict.json - test_labels_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/test_labels.json - train_labels_file: output/reports/train/638ed39a63fa3bc5b8e771caf5f39eb7/train_labels.json - model_dir: models - name: 638ed39a63fa3bc5b8e771caf5f39eb7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7861095494d79dbfe63a65d8b5551e24 - trainer: - kwargs: {} -name: 638ed39a63fa3bc5b8e771caf5f39eb7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/params.yaml b/examples/security/classification/output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/params.yaml deleted file mode 100644 index 120fb680..00000000 --- a/examples/security/classification/output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/adv_predictions.json - adv_probabilities_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/bcee52397d6b8d4e81c2b325c9624c7e.pkl - params_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/params.yaml - predictions_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/predictions.json - probabilities_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/probabilities.json - score_dict_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/score_dict.json - test_labels_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/test_labels.json - train_labels_file: output/reports/train/63b5dc650d4643fa8f9a4ed87eeab9e4/train_labels.json - model_dir: models - name: 63b5dc650d4643fa8f9a4ed87eeab9e4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a64917922f075cc5f047e2dc781ca33e - trainer: - kwargs: {} -name: 63b5dc650d4643fa8f9a4ed87eeab9e4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6406651174b056d17c7a2aa6ab176e9e/params.yaml b/examples/security/classification/output/reports/train/6406651174b056d17c7a2aa6ab176e9e/params.yaml deleted file mode 100644 index d68e93ff..00000000 --- a/examples/security/classification/output/reports/train/6406651174b056d17c7a2aa6ab176e9e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/adv_predictions.json - adv_probabilities_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/8715ae92df4f535d816065853b9b6ce7.pkl - params_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/params.yaml - predictions_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/predictions.json - probabilities_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/probabilities.json - score_dict_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/score_dict.json - test_labels_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/test_labels.json - train_labels_file: output/reports/train/6406651174b056d17c7a2aa6ab176e9e/train_labels.json - model_dir: models - name: 6406651174b056d17c7a2aa6ab176e9e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ba1215bb493535f133672ce9793f4c75 - trainer: - kwargs: {} -name: 6406651174b056d17c7a2aa6ab176e9e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/params.yaml b/examples/security/classification/output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/params.yaml deleted file mode 100644 index 24212033..00000000 --- a/examples/security/classification/output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/adv_predictions.json - adv_probabilities_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/9bcfd8052c610101f455d9edd80c2735.pkl - params_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/params.yaml - predictions_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/predictions.json - probabilities_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/probabilities.json - score_dict_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/score_dict.json - test_labels_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/test_labels.json - train_labels_file: output/reports/train/642679f669fb6d4dd25c78eb2d60ff04/train_labels.json - model_dir: models - name: 642679f669fb6d4dd25c78eb2d60ff04 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: baba484ec59c034366dc507e3d53d46e - trainer: - kwargs: {} -name: 642679f669fb6d4dd25c78eb2d60ff04 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/642cb8b2f97061cae17f98f65cab2de6/params.yaml b/examples/security/classification/output/reports/train/642cb8b2f97061cae17f98f65cab2de6/params.yaml deleted file mode 100644 index 7415b17d..00000000 --- a/examples/security/classification/output/reports/train/642cb8b2f97061cae17f98f65cab2de6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/adv_predictions.json - adv_probabilities_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/2570a8017fad689c60e3d52a16fcdeaa.pkl - params_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/params.yaml - predictions_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/predictions.json - probabilities_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/probabilities.json - score_dict_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/score_dict.json - test_labels_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/test_labels.json - train_labels_file: output/reports/train/642cb8b2f97061cae17f98f65cab2de6/train_labels.json - model_dir: models - name: 642cb8b2f97061cae17f98f65cab2de6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 56f3f7c4f3682452b8ff717def55996b - trainer: - kwargs: {} -name: 642cb8b2f97061cae17f98f65cab2de6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/params.yaml b/examples/security/classification/output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/params.yaml deleted file mode 100644 index 933c7536..00000000 --- a/examples/security/classification/output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/adv_predictions.json - adv_probabilities_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/8309c527e1b22db4b24c658dbfb72ff7.pkl - params_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/params.yaml - predictions_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/predictions.json - probabilities_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/probabilities.json - score_dict_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/score_dict.json - test_labels_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/test_labels.json - train_labels_file: output/reports/train/643dfbdcc1bb5331ded3c5dbf63f6902/train_labels.json - model_dir: models - name: 643dfbdcc1bb5331ded3c5dbf63f6902 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: be6bbbe46ea957959dd9038f00763ca9 - trainer: - kwargs: {} -name: 643dfbdcc1bb5331ded3c5dbf63f6902 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6459a4450c1911d5f06cea16d48c891f/params.yaml b/examples/security/classification/output/reports/train/6459a4450c1911d5f06cea16d48c891f/params.yaml deleted file mode 100644 index a3dc9045..00000000 --- a/examples/security/classification/output/reports/train/6459a4450c1911d5f06cea16d48c891f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/adv_predictions.json - adv_probabilities_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/013f4dd415b76f8f665696140ee2296b.pkl - params_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/params.yaml - predictions_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/predictions.json - probabilities_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/probabilities.json - score_dict_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/score_dict.json - test_labels_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/test_labels.json - train_labels_file: output/reports/train/6459a4450c1911d5f06cea16d48c891f/train_labels.json - model_dir: models - name: 6459a4450c1911d5f06cea16d48c891f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5c690a481123888543a8e10a70d1d0ed - trainer: - kwargs: {} -name: 6459a4450c1911d5f06cea16d48c891f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/params.yaml b/examples/security/classification/output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/params.yaml deleted file mode 100644 index b0fa74e1..00000000 --- a/examples/security/classification/output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/adv_predictions.json - adv_probabilities_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/983a4a72680889c3ce07862426625af9.pkl - params_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/params.yaml - predictions_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/predictions.json - probabilities_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/probabilities.json - score_dict_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/score_dict.json - test_labels_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/test_labels.json - train_labels_file: output/reports/train/648742c5a5dbb69ae76d7fc2976f0176/train_labels.json - model_dir: models - name: 648742c5a5dbb69ae76d7fc2976f0176 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbd4c43d7e93d9db38379b169663ac4c - trainer: - kwargs: {} -name: 648742c5a5dbb69ae76d7fc2976f0176 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/params.yaml b/examples/security/classification/output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/params.yaml deleted file mode 100644 index ca7367c5..00000000 --- a/examples/security/classification/output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/adv_predictions.json - adv_probabilities_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/29d5a14b61e41b86aff7ca71de355686.pkl - params_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/params.yaml - predictions_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/predictions.json - probabilities_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/probabilities.json - score_dict_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/score_dict.json - test_labels_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/test_labels.json - train_labels_file: output/reports/train/648ec8ed6be2cc0cdf2eaf55121e8cc7/train_labels.json - model_dir: models - name: 648ec8ed6be2cc0cdf2eaf55121e8cc7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0898132239fe41c91504e317fcebeb80 - trainer: - kwargs: {} -name: 648ec8ed6be2cc0cdf2eaf55121e8cc7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/64947d256a325a372489f75d1ba7db32/params.yaml b/examples/security/classification/output/reports/train/64947d256a325a372489f75d1ba7db32/params.yaml deleted file mode 100644 index f2510024..00000000 --- a/examples/security/classification/output/reports/train/64947d256a325a372489f75d1ba7db32/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/64947d256a325a372489f75d1ba7db32/adv_predictions.json - adv_probabilities_file: output/reports/train/64947d256a325a372489f75d1ba7db32/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/f18b20b6229a9613f4a30cd52d1bf6ee.pkl - params_file: output/reports/train/64947d256a325a372489f75d1ba7db32/params.yaml - predictions_file: output/reports/train/64947d256a325a372489f75d1ba7db32/predictions.json - probabilities_file: output/reports/train/64947d256a325a372489f75d1ba7db32/probabilities.json - score_dict_file: output/reports/train/64947d256a325a372489f75d1ba7db32/score_dict.json - test_labels_file: output/reports/train/64947d256a325a372489f75d1ba7db32/test_labels.json - train_labels_file: output/reports/train/64947d256a325a372489f75d1ba7db32/train_labels.json - model_dir: models - name: 64947d256a325a372489f75d1ba7db32 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 16cdd7d57cfcb3991c738654d3f605fb - trainer: - kwargs: {} -name: 64947d256a325a372489f75d1ba7db32 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/64977073273b87a949bb4d2c7ba4a158/params.yaml b/examples/security/classification/output/reports/train/64977073273b87a949bb4d2c7ba4a158/params.yaml deleted file mode 100644 index a37de1dc..00000000 --- a/examples/security/classification/output/reports/train/64977073273b87a949bb4d2c7ba4a158/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/adv_predictions.json - adv_probabilities_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/50aeecabfa1b699fb8f506a6b39e9e58.pkl - params_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/params.yaml - predictions_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/predictions.json - probabilities_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/probabilities.json - score_dict_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/score_dict.json - test_labels_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/test_labels.json - train_labels_file: output/reports/train/64977073273b87a949bb4d2c7ba4a158/train_labels.json - model_dir: models - name: 64977073273b87a949bb4d2c7ba4a158 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d6ee2c2c59d9500601d74e1caf1f854b - trainer: - kwargs: {} -name: 64977073273b87a949bb4d2c7ba4a158 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/64b064890ffa101114af628fff6598f6/params.yaml b/examples/security/classification/output/reports/train/64b064890ffa101114af628fff6598f6/params.yaml deleted file mode 100644 index ed5fc83e..00000000 --- a/examples/security/classification/output/reports/train/64b064890ffa101114af628fff6598f6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/64b064890ffa101114af628fff6598f6/adv_predictions.json - adv_probabilities_file: output/reports/train/64b064890ffa101114af628fff6598f6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/41bc27b99402c76c0c27a333af17039b.pkl - params_file: output/reports/train/64b064890ffa101114af628fff6598f6/params.yaml - predictions_file: output/reports/train/64b064890ffa101114af628fff6598f6/predictions.json - probabilities_file: output/reports/train/64b064890ffa101114af628fff6598f6/probabilities.json - score_dict_file: output/reports/train/64b064890ffa101114af628fff6598f6/score_dict.json - test_labels_file: output/reports/train/64b064890ffa101114af628fff6598f6/test_labels.json - train_labels_file: output/reports/train/64b064890ffa101114af628fff6598f6/train_labels.json - model_dir: models - name: 64b064890ffa101114af628fff6598f6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d431cb53ea5668b20c736e6a7ecd8a52 - trainer: - kwargs: {} -name: 64b064890ffa101114af628fff6598f6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/653601fda34c27f463473547b0b1211d/params.yaml b/examples/security/classification/output/reports/train/653601fda34c27f463473547b0b1211d/params.yaml deleted file mode 100644 index 8a146086..00000000 --- a/examples/security/classification/output/reports/train/653601fda34c27f463473547b0b1211d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/653601fda34c27f463473547b0b1211d/adv_predictions.json - adv_probabilities_file: output/reports/train/653601fda34c27f463473547b0b1211d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/2196dfc3ec98c3be5976f47f0ba48e65.pkl - params_file: output/reports/train/653601fda34c27f463473547b0b1211d/params.yaml - predictions_file: output/reports/train/653601fda34c27f463473547b0b1211d/predictions.json - probabilities_file: output/reports/train/653601fda34c27f463473547b0b1211d/probabilities.json - score_dict_file: output/reports/train/653601fda34c27f463473547b0b1211d/score_dict.json - test_labels_file: output/reports/train/653601fda34c27f463473547b0b1211d/test_labels.json - train_labels_file: output/reports/train/653601fda34c27f463473547b0b1211d/train_labels.json - model_dir: models - name: 653601fda34c27f463473547b0b1211d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3e34bdf44bfc359adeb963dbb8682e62 - trainer: - kwargs: {} -name: 653601fda34c27f463473547b0b1211d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/65376b31f93dd6eee6dca534fca03f7d/params.yaml b/examples/security/classification/output/reports/train/65376b31f93dd6eee6dca534fca03f7d/params.yaml deleted file mode 100644 index f88c3514..00000000 --- a/examples/security/classification/output/reports/train/65376b31f93dd6eee6dca534fca03f7d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/adv_predictions.json - adv_probabilities_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/4316db0ad1269550b0abf559d18c042c.pkl - params_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/params.yaml - predictions_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/predictions.json - probabilities_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/probabilities.json - score_dict_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/score_dict.json - test_labels_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/test_labels.json - train_labels_file: output/reports/train/65376b31f93dd6eee6dca534fca03f7d/train_labels.json - model_dir: models - name: 65376b31f93dd6eee6dca534fca03f7d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 644fae3ad242b61773896edf874c4545 - trainer: - kwargs: {} -name: 65376b31f93dd6eee6dca534fca03f7d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/params.yaml b/examples/security/classification/output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/params.yaml deleted file mode 100644 index 4f827885..00000000 --- a/examples/security/classification/output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/adv_predictions.json - adv_probabilities_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/2fd604e30cced4b5d15b916c89d3bae5.pkl - params_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/params.yaml - predictions_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/predictions.json - probabilities_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/probabilities.json - score_dict_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/score_dict.json - test_labels_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/test_labels.json - train_labels_file: output/reports/train/654cb6f98cffbfdf8e8522d43bfdf69b/train_labels.json - model_dir: models - name: 654cb6f98cffbfdf8e8522d43bfdf69b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4ad5af367140055ba35b87af85708bc0 - trainer: - kwargs: {} -name: 654cb6f98cffbfdf8e8522d43bfdf69b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6562352b7c068f984b672c25c480d7c6/params.yaml b/examples/security/classification/output/reports/train/6562352b7c068f984b672c25c480d7c6/params.yaml deleted file mode 100644 index d8471bb0..00000000 --- a/examples/security/classification/output/reports/train/6562352b7c068f984b672c25c480d7c6/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/adv_predictions.json - adv_probabilities_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/7d1a0e42090b33b15a1983af61e4e04d.pkl - params_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/params.yaml - predictions_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/predictions.json - probabilities_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/probabilities.json - score_dict_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/score_dict.json - test_labels_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/test_labels.json - train_labels_file: output/reports/train/6562352b7c068f984b672c25c480d7c6/train_labels.json - model_dir: models - name: 6562352b7c068f984b672c25c480d7c6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cdd31b998738835d623079305381b278 - trainer: - kwargs: {} -name: 6562352b7c068f984b672c25c480d7c6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/65bf03ae120e6ef11d759374bb313769/params.yaml b/examples/security/classification/output/reports/train/65bf03ae120e6ef11d759374bb313769/params.yaml deleted file mode 100644 index fa17ec5a..00000000 --- a/examples/security/classification/output/reports/train/65bf03ae120e6ef11d759374bb313769/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/adv_predictions.json - adv_probabilities_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/7b7ff0650294dcd8dbd9466e4ba896b9.pkl - params_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/params.yaml - predictions_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/predictions.json - probabilities_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/probabilities.json - score_dict_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/score_dict.json - test_labels_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/test_labels.json - train_labels_file: output/reports/train/65bf03ae120e6ef11d759374bb313769/train_labels.json - model_dir: models - name: 65bf03ae120e6ef11d759374bb313769 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 59ceeb8f75c859410f3b2a85b38a8eb8 - trainer: - kwargs: {} -name: 65bf03ae120e6ef11d759374bb313769 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/params.yaml b/examples/security/classification/output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/params.yaml deleted file mode 100644 index 318e22aa..00000000 --- a/examples/security/classification/output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/adv_predictions.json - adv_probabilities_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/1a3617eb065587cd4f64672bff18a738.pkl - params_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/params.yaml - predictions_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/predictions.json - probabilities_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/probabilities.json - score_dict_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/score_dict.json - test_labels_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/test_labels.json - train_labels_file: output/reports/train/65ee1016f26bc759f08d7c9d20c5c11c/train_labels.json - model_dir: models - name: 65ee1016f26bc759f08d7c9d20c5c11c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 512cfb3818ed4edea11386a7f76ca3ea - trainer: - kwargs: {} -name: 65ee1016f26bc759f08d7c9d20c5c11c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/660be89fefe6a78eddc794df695d6053/params.yaml b/examples/security/classification/output/reports/train/660be89fefe6a78eddc794df695d6053/params.yaml deleted file mode 100644 index b93b3f6a..00000000 --- a/examples/security/classification/output/reports/train/660be89fefe6a78eddc794df695d6053/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/660be89fefe6a78eddc794df695d6053/adv_predictions.json - adv_probabilities_file: output/reports/train/660be89fefe6a78eddc794df695d6053/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/3904fce38b437fa53cf99975c52f1504.pkl - params_file: output/reports/train/660be89fefe6a78eddc794df695d6053/params.yaml - predictions_file: output/reports/train/660be89fefe6a78eddc794df695d6053/predictions.json - probabilities_file: output/reports/train/660be89fefe6a78eddc794df695d6053/probabilities.json - score_dict_file: output/reports/train/660be89fefe6a78eddc794df695d6053/score_dict.json - test_labels_file: output/reports/train/660be89fefe6a78eddc794df695d6053/test_labels.json - train_labels_file: output/reports/train/660be89fefe6a78eddc794df695d6053/train_labels.json - model_dir: models - name: 660be89fefe6a78eddc794df695d6053 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8e13cc024636500f816529140e666180 - trainer: - kwargs: {} -name: 660be89fefe6a78eddc794df695d6053 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/660f7e30fba92967335df80ca8e4ae43/params.yaml b/examples/security/classification/output/reports/train/660f7e30fba92967335df80ca8e4ae43/params.yaml deleted file mode 100644 index 70d75895..00000000 --- a/examples/security/classification/output/reports/train/660f7e30fba92967335df80ca8e4ae43/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/adv_predictions.json - adv_probabilities_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/64ea8fe310e8a1def2a261b1cfaf0aa0.pkl - params_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/params.yaml - predictions_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/predictions.json - probabilities_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/probabilities.json - score_dict_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/score_dict.json - test_labels_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/test_labels.json - train_labels_file: output/reports/train/660f7e30fba92967335df80ca8e4ae43/train_labels.json - model_dir: models - name: 660f7e30fba92967335df80ca8e4ae43 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 81f81d6bb26da9afc0af8e9ceab8e926 - trainer: - kwargs: {} -name: 660f7e30fba92967335df80ca8e4ae43 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/params.yaml b/examples/security/classification/output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/params.yaml deleted file mode 100644 index a330f3de..00000000 --- a/examples/security/classification/output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/adv_predictions.json - adv_probabilities_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e031f496feae9877636560ac22a3cd99.pkl - params_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/params.yaml - predictions_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/predictions.json - probabilities_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/probabilities.json - score_dict_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/score_dict.json - test_labels_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/test_labels.json - train_labels_file: output/reports/train/66297a2107356eeb9c6e71d3aa3e4271/train_labels.json - model_dir: models - name: 66297a2107356eeb9c6e71d3aa3e4271 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ddde247bccb71c28bc33e549f2b4a249 - trainer: - kwargs: {} -name: 66297a2107356eeb9c6e71d3aa3e4271 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/662a15e30f4dbe268b72f21debaee4bd/params.yaml b/examples/security/classification/output/reports/train/662a15e30f4dbe268b72f21debaee4bd/params.yaml deleted file mode 100644 index cf0f0fd7..00000000 --- a/examples/security/classification/output/reports/train/662a15e30f4dbe268b72f21debaee4bd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/adv_predictions.json - adv_probabilities_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/3509db9f58b675f2c0bb91ec20071561.pkl - params_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/params.yaml - predictions_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/predictions.json - probabilities_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/probabilities.json - score_dict_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/score_dict.json - test_labels_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/test_labels.json - train_labels_file: output/reports/train/662a15e30f4dbe268b72f21debaee4bd/train_labels.json - model_dir: models - name: 662a15e30f4dbe268b72f21debaee4bd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4d2766fe39752164440e3df982d8260c - trainer: - kwargs: {} -name: 662a15e30f4dbe268b72f21debaee4bd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/662d499ff1527b584384dd4475824aaf/params.yaml b/examples/security/classification/output/reports/train/662d499ff1527b584384dd4475824aaf/params.yaml deleted file mode 100644 index 2db67612..00000000 --- a/examples/security/classification/output/reports/train/662d499ff1527b584384dd4475824aaf/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/662d499ff1527b584384dd4475824aaf/adv_predictions.json - adv_probabilities_file: output/reports/train/662d499ff1527b584384dd4475824aaf/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/552833eabe7e078a02433d36ac270113.pkl - params_file: output/reports/train/662d499ff1527b584384dd4475824aaf/params.yaml - predictions_file: output/reports/train/662d499ff1527b584384dd4475824aaf/predictions.json - probabilities_file: output/reports/train/662d499ff1527b584384dd4475824aaf/probabilities.json - score_dict_file: output/reports/train/662d499ff1527b584384dd4475824aaf/score_dict.json - test_labels_file: output/reports/train/662d499ff1527b584384dd4475824aaf/test_labels.json - train_labels_file: output/reports/train/662d499ff1527b584384dd4475824aaf/train_labels.json - model_dir: models - name: 662d499ff1527b584384dd4475824aaf - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f3ef190a7bfbeb813d58c30e5cdae62c - trainer: - kwargs: {} -name: 662d499ff1527b584384dd4475824aaf -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/params.yaml b/examples/security/classification/output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/params.yaml deleted file mode 100644 index f9b0fe66..00000000 --- a/examples/security/classification/output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/adv_predictions.json - adv_probabilities_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/2b20e147c723fafce058c3cd6ac9dbd9.pkl - params_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/params.yaml - predictions_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/predictions.json - probabilities_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/probabilities.json - score_dict_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/score_dict.json - test_labels_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/test_labels.json - train_labels_file: output/reports/train/663a1aaa4cfda00b26468cbc425ee13c/train_labels.json - model_dir: models - name: 663a1aaa4cfda00b26468cbc425ee13c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e2665d67746d49341961b5c7be831ae6 - trainer: - kwargs: {} -name: 663a1aaa4cfda00b26468cbc425ee13c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/params.yaml b/examples/security/classification/output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/params.yaml deleted file mode 100644 index 3b788d97..00000000 --- a/examples/security/classification/output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/adv_predictions.json - adv_probabilities_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/6a54a8f3e306831e760c83605ef4b251.pkl - params_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/params.yaml - predictions_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/predictions.json - probabilities_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/probabilities.json - score_dict_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/score_dict.json - test_labels_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/test_labels.json - train_labels_file: output/reports/train/66562f10769e30aa6436a8a5ed9ac7d0/train_labels.json - model_dir: models - name: 66562f10769e30aa6436a8a5ed9ac7d0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 33e7d835aec9f50e6de1bbf3cd57f584 - trainer: - kwargs: {} -name: 66562f10769e30aa6436a8a5ed9ac7d0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/667c4efaecfad617b1475850d323af69/params.yaml b/examples/security/classification/output/reports/train/667c4efaecfad617b1475850d323af69/params.yaml deleted file mode 100644 index 0ca19fbf..00000000 --- a/examples/security/classification/output/reports/train/667c4efaecfad617b1475850d323af69/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/667c4efaecfad617b1475850d323af69/adv_predictions.json - adv_probabilities_file: output/reports/train/667c4efaecfad617b1475850d323af69/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/650c9d9ae51513d68891e768a94f4895.pkl - params_file: output/reports/train/667c4efaecfad617b1475850d323af69/params.yaml - predictions_file: output/reports/train/667c4efaecfad617b1475850d323af69/predictions.json - probabilities_file: output/reports/train/667c4efaecfad617b1475850d323af69/probabilities.json - score_dict_file: output/reports/train/667c4efaecfad617b1475850d323af69/score_dict.json - test_labels_file: output/reports/train/667c4efaecfad617b1475850d323af69/test_labels.json - train_labels_file: output/reports/train/667c4efaecfad617b1475850d323af69/train_labels.json - model_dir: models - name: 667c4efaecfad617b1475850d323af69 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: edce649b1298417e6724a171b1ea129a - trainer: - kwargs: {} -name: 667c4efaecfad617b1475850d323af69 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6689011d3eb3a647e98295f7bf339393/params.yaml b/examples/security/classification/output/reports/train/6689011d3eb3a647e98295f7bf339393/params.yaml deleted file mode 100644 index 98defda1..00000000 --- a/examples/security/classification/output/reports/train/6689011d3eb3a647e98295f7bf339393/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/adv_predictions.json - adv_probabilities_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/1d395c77a051b8621053956dee40cfa1.pkl - params_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/params.yaml - predictions_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/predictions.json - probabilities_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/probabilities.json - score_dict_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/score_dict.json - test_labels_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/test_labels.json - train_labels_file: output/reports/train/6689011d3eb3a647e98295f7bf339393/train_labels.json - model_dir: models - name: 6689011d3eb3a647e98295f7bf339393 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fc4dd62cbf0c1c567de06e6312f9ca8e - trainer: - kwargs: {} -name: 6689011d3eb3a647e98295f7bf339393 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/66a58622dccd6846f2340e9c2f084b49/params.yaml b/examples/security/classification/output/reports/train/66a58622dccd6846f2340e9c2f084b49/params.yaml deleted file mode 100644 index 8c10b9b5..00000000 --- a/examples/security/classification/output/reports/train/66a58622dccd6846f2340e9c2f084b49/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/adv_predictions.json - adv_probabilities_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/973182f6bfc36c458baf6a0cf5d95c49.pkl - params_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/params.yaml - predictions_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/predictions.json - probabilities_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/probabilities.json - score_dict_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/score_dict.json - test_labels_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/test_labels.json - train_labels_file: output/reports/train/66a58622dccd6846f2340e9c2f084b49/train_labels.json - model_dir: models - name: 66a58622dccd6846f2340e9c2f084b49 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 19060661415a3836f6addcd862908f88 - trainer: - kwargs: {} -name: 66a58622dccd6846f2340e9c2f084b49 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/66f9160834be921cca8c3b218d46f033/params.yaml b/examples/security/classification/output/reports/train/66f9160834be921cca8c3b218d46f033/params.yaml deleted file mode 100644 index 1cb0c1cf..00000000 --- a/examples/security/classification/output/reports/train/66f9160834be921cca8c3b218d46f033/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/66f9160834be921cca8c3b218d46f033/adv_predictions.json - adv_probabilities_file: output/reports/train/66f9160834be921cca8c3b218d46f033/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/aea56dd96a9fea1acdba4e184f78433d.pkl - params_file: output/reports/train/66f9160834be921cca8c3b218d46f033/params.yaml - predictions_file: output/reports/train/66f9160834be921cca8c3b218d46f033/predictions.json - probabilities_file: output/reports/train/66f9160834be921cca8c3b218d46f033/probabilities.json - score_dict_file: output/reports/train/66f9160834be921cca8c3b218d46f033/score_dict.json - test_labels_file: output/reports/train/66f9160834be921cca8c3b218d46f033/test_labels.json - train_labels_file: output/reports/train/66f9160834be921cca8c3b218d46f033/train_labels.json - model_dir: models - name: 66f9160834be921cca8c3b218d46f033 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 427b93e3616b74d9d4ad5354a0c30cf6 - trainer: - kwargs: {} -name: 66f9160834be921cca8c3b218d46f033 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/66fd64466e40586700b67bfdb471d14c/params.yaml b/examples/security/classification/output/reports/train/66fd64466e40586700b67bfdb471d14c/params.yaml deleted file mode 100644 index a3fdd962..00000000 --- a/examples/security/classification/output/reports/train/66fd64466e40586700b67bfdb471d14c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/adv_predictions.json - adv_probabilities_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/2ffba9babe5747d7abdf2a051f7252bf.pkl - params_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/params.yaml - predictions_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/predictions.json - probabilities_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/probabilities.json - score_dict_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/score_dict.json - test_labels_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/test_labels.json - train_labels_file: output/reports/train/66fd64466e40586700b67bfdb471d14c/train_labels.json - model_dir: models - name: 66fd64466e40586700b67bfdb471d14c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 51ce2e4b21c5a65f8bb774e83b3addfa - trainer: - kwargs: {} -name: 66fd64466e40586700b67bfdb471d14c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/673c1fe5864b54eaca3097558d8e9b58/params.yaml b/examples/security/classification/output/reports/train/673c1fe5864b54eaca3097558d8e9b58/params.yaml deleted file mode 100644 index 3cd846ac..00000000 --- a/examples/security/classification/output/reports/train/673c1fe5864b54eaca3097558d8e9b58/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/adv_predictions.json - adv_probabilities_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/2c886e519d071a3e56dc69ad78e94ed0.pkl - params_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/params.yaml - predictions_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/predictions.json - probabilities_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/probabilities.json - score_dict_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/score_dict.json - test_labels_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/test_labels.json - train_labels_file: output/reports/train/673c1fe5864b54eaca3097558d8e9b58/train_labels.json - model_dir: models - name: 673c1fe5864b54eaca3097558d8e9b58 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 956d409bbdf235414103d0c9a1ca2a1a - trainer: - kwargs: {} -name: 673c1fe5864b54eaca3097558d8e9b58 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/67474eedc61be7ccd7169bdfc446016e/params.yaml b/examples/security/classification/output/reports/train/67474eedc61be7ccd7169bdfc446016e/params.yaml deleted file mode 100644 index fc46495a..00000000 --- a/examples/security/classification/output/reports/train/67474eedc61be7ccd7169bdfc446016e/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/adv_predictions.json - adv_probabilities_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/415626977807315bb082e942b950bdc8.pkl - params_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/params.yaml - predictions_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/predictions.json - probabilities_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/probabilities.json - score_dict_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/score_dict.json - test_labels_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/test_labels.json - train_labels_file: output/reports/train/67474eedc61be7ccd7169bdfc446016e/train_labels.json - model_dir: models - name: 67474eedc61be7ccd7169bdfc446016e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1ba44010b4472761dba9fa87f6ba3f6f - trainer: - kwargs: {} -name: 67474eedc61be7ccd7169bdfc446016e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6780043369fd79f77c607db5d7c3d364/params.yaml b/examples/security/classification/output/reports/train/6780043369fd79f77c607db5d7c3d364/params.yaml deleted file mode 100644 index 39ce77cf..00000000 --- a/examples/security/classification/output/reports/train/6780043369fd79f77c607db5d7c3d364/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/adv_predictions.json - adv_probabilities_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/d222453183ab615a612c1751093c3bd3.pkl - params_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/params.yaml - predictions_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/predictions.json - probabilities_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/probabilities.json - score_dict_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/score_dict.json - test_labels_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/test_labels.json - train_labels_file: output/reports/train/6780043369fd79f77c607db5d7c3d364/train_labels.json - model_dir: models - name: 6780043369fd79f77c607db5d7c3d364 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fc07a7198b81117b61254de7b99dd082 - trainer: - kwargs: {} -name: 6780043369fd79f77c607db5d7c3d364 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/679ec9b960183913a7f0f76709427729/params.yaml b/examples/security/classification/output/reports/train/679ec9b960183913a7f0f76709427729/params.yaml deleted file mode 100644 index 282f3056..00000000 --- a/examples/security/classification/output/reports/train/679ec9b960183913a7f0f76709427729/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/679ec9b960183913a7f0f76709427729/adv_predictions.json - adv_probabilities_file: output/reports/train/679ec9b960183913a7f0f76709427729/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/24be310b727403bd49f9d0abc44083a3.pkl - params_file: output/reports/train/679ec9b960183913a7f0f76709427729/params.yaml - predictions_file: output/reports/train/679ec9b960183913a7f0f76709427729/predictions.json - probabilities_file: output/reports/train/679ec9b960183913a7f0f76709427729/probabilities.json - score_dict_file: output/reports/train/679ec9b960183913a7f0f76709427729/score_dict.json - test_labels_file: output/reports/train/679ec9b960183913a7f0f76709427729/test_labels.json - train_labels_file: output/reports/train/679ec9b960183913a7f0f76709427729/train_labels.json - model_dir: models - name: 679ec9b960183913a7f0f76709427729 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 86c59abb1313574781433287ca215ee0 - trainer: - kwargs: {} -name: 679ec9b960183913a7f0f76709427729 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/67ab356365411d98ad7a41d3001d459f/params.yaml b/examples/security/classification/output/reports/train/67ab356365411d98ad7a41d3001d459f/params.yaml deleted file mode 100644 index cbfe2e43..00000000 --- a/examples/security/classification/output/reports/train/67ab356365411d98ad7a41d3001d459f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/adv_predictions.json - adv_probabilities_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/9bd75937f6504438710d50cc971b8b52.pkl - params_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/params.yaml - predictions_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/predictions.json - probabilities_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/probabilities.json - score_dict_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/score_dict.json - test_labels_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/test_labels.json - train_labels_file: output/reports/train/67ab356365411d98ad7a41d3001d459f/train_labels.json - model_dir: models - name: 67ab356365411d98ad7a41d3001d459f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 47a2ca1c4f0159971938213413869f3c - trainer: - kwargs: {} -name: 67ab356365411d98ad7a41d3001d459f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/params.yaml b/examples/security/classification/output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/params.yaml deleted file mode 100644 index 05e3655c..00000000 --- a/examples/security/classification/output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/adv_predictions.json - adv_probabilities_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/49cb831b3fdc7b6f26b02ba3528aa4e9.pkl - params_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/params.yaml - predictions_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/predictions.json - probabilities_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/probabilities.json - score_dict_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/score_dict.json - test_labels_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/test_labels.json - train_labels_file: output/reports/train/681700b94ff8fcdc3b065f256c04d5a4/train_labels.json - model_dir: models - name: 681700b94ff8fcdc3b065f256c04d5a4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 87a6cd65a4fa88c93a4f783c544849ae - trainer: - kwargs: {} -name: 681700b94ff8fcdc3b065f256c04d5a4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/params.yaml b/examples/security/classification/output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/params.yaml deleted file mode 100644 index bf856633..00000000 --- a/examples/security/classification/output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/adv_predictions.json - adv_probabilities_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/c2bb2b8ab3497b652a9991d2ce71706d.pkl - params_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/params.yaml - predictions_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/predictions.json - probabilities_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/probabilities.json - score_dict_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/score_dict.json - test_labels_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/test_labels.json - train_labels_file: output/reports/train/6831f8bc1abf072bab6b5e9d871e7667/train_labels.json - model_dir: models - name: 6831f8bc1abf072bab6b5e9d871e7667 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a567fef92fd5727a463712eedf7d4158 - trainer: - kwargs: {} -name: 6831f8bc1abf072bab6b5e9d871e7667 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/685bc14371c8978a854f2bc1e7aae457/params.yaml b/examples/security/classification/output/reports/train/685bc14371c8978a854f2bc1e7aae457/params.yaml deleted file mode 100644 index f795ac75..00000000 --- a/examples/security/classification/output/reports/train/685bc14371c8978a854f2bc1e7aae457/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/adv_predictions.json - adv_probabilities_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/d804c92c7b793b8e83dbca609a829551.pkl - params_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/params.yaml - predictions_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/predictions.json - probabilities_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/probabilities.json - score_dict_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/score_dict.json - test_labels_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/test_labels.json - train_labels_file: output/reports/train/685bc14371c8978a854f2bc1e7aae457/train_labels.json - model_dir: models - name: 685bc14371c8978a854f2bc1e7aae457 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 56d7631bed4ce869e8fd1f23fb588e2c - trainer: - kwargs: {} -name: 685bc14371c8978a854f2bc1e7aae457 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/687c18366d6fabc3befca22fef4e136a/params.yaml b/examples/security/classification/output/reports/train/687c18366d6fabc3befca22fef4e136a/params.yaml deleted file mode 100644 index b92f6759..00000000 --- a/examples/security/classification/output/reports/train/687c18366d6fabc3befca22fef4e136a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/adv_predictions.json - adv_probabilities_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/4074cdbf84e17b094e63a521dfbc2bd5.pkl - params_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/params.yaml - predictions_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/predictions.json - probabilities_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/probabilities.json - score_dict_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/score_dict.json - test_labels_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/test_labels.json - train_labels_file: output/reports/train/687c18366d6fabc3befca22fef4e136a/train_labels.json - model_dir: models - name: 687c18366d6fabc3befca22fef4e136a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e386f417828da37f9fb3421ffd92cbec - trainer: - kwargs: {} -name: 687c18366d6fabc3befca22fef4e136a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/68bd05a8f808cd8e6f664a188ac64801/params.yaml b/examples/security/classification/output/reports/train/68bd05a8f808cd8e6f664a188ac64801/params.yaml deleted file mode 100644 index 7bf97398..00000000 --- a/examples/security/classification/output/reports/train/68bd05a8f808cd8e6f664a188ac64801/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/adv_predictions.json - adv_probabilities_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/83ff7095a6db197c33af2eb4b2480b72.pkl - params_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/params.yaml - predictions_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/predictions.json - probabilities_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/probabilities.json - score_dict_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/score_dict.json - test_labels_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/test_labels.json - train_labels_file: output/reports/train/68bd05a8f808cd8e6f664a188ac64801/train_labels.json - model_dir: models - name: 68bd05a8f808cd8e6f664a188ac64801 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a4dbc435eb5c304dac481a0801f1533c - trainer: - kwargs: {} -name: 68bd05a8f808cd8e6f664a188ac64801 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/params.yaml b/examples/security/classification/output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/params.yaml deleted file mode 100644 index 15a464ed..00000000 --- a/examples/security/classification/output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/adv_predictions.json - adv_probabilities_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/8184620e9626b46f8c3ca105dd66efe4.pkl - params_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/params.yaml - predictions_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/predictions.json - probabilities_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/probabilities.json - score_dict_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/score_dict.json - test_labels_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/test_labels.json - train_labels_file: output/reports/train/68c2bf9adc79dc40beb5034fa5a6ad23/train_labels.json - model_dir: models - name: 68c2bf9adc79dc40beb5034fa5a6ad23 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d5f9eee3cf6b56319c8f9153498f900f - trainer: - kwargs: {} -name: 68c2bf9adc79dc40beb5034fa5a6ad23 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/params.yaml b/examples/security/classification/output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/params.yaml deleted file mode 100644 index 9f3644c6..00000000 --- a/examples/security/classification/output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/adv_predictions.json - adv_probabilities_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/5fdc57cf386b8f0132fd2bfe01b43694.pkl - params_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/params.yaml - predictions_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/predictions.json - probabilities_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/probabilities.json - score_dict_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/score_dict.json - test_labels_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/test_labels.json - train_labels_file: output/reports/train/6927a37b10e9e38b1e9201f3ebf18c2e/train_labels.json - model_dir: models - name: 6927a37b10e9e38b1e9201f3ebf18c2e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e89261075b6e093b5030efae3a1c7ef1 - trainer: - kwargs: {} -name: 6927a37b10e9e38b1e9201f3ebf18c2e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/params.yaml b/examples/security/classification/output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/params.yaml deleted file mode 100644 index 0291a005..00000000 --- a/examples/security/classification/output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/adv_predictions.json - adv_probabilities_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/b3c7cf7e91d56af5bb2bd6d5d3853eca.pkl - params_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/params.yaml - predictions_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/predictions.json - probabilities_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/probabilities.json - score_dict_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/score_dict.json - test_labels_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/test_labels.json - train_labels_file: output/reports/train/69330a9d873dc65a8fb9ffffe53d5694/train_labels.json - model_dir: models - name: 69330a9d873dc65a8fb9ffffe53d5694 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a1acebce094fe724c53aa27205290645 - trainer: - kwargs: {} -name: 69330a9d873dc65a8fb9ffffe53d5694 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/693a8509a94b2977402ee6d52af9ec07/params.yaml b/examples/security/classification/output/reports/train/693a8509a94b2977402ee6d52af9ec07/params.yaml deleted file mode 100644 index 7fbc4fd1..00000000 --- a/examples/security/classification/output/reports/train/693a8509a94b2977402ee6d52af9ec07/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/adv_predictions.json - adv_probabilities_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/a1782b475556bc4740563ba9074f666b.pkl - params_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/params.yaml - predictions_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/predictions.json - probabilities_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/probabilities.json - score_dict_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/score_dict.json - test_labels_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/test_labels.json - train_labels_file: output/reports/train/693a8509a94b2977402ee6d52af9ec07/train_labels.json - model_dir: models - name: 693a8509a94b2977402ee6d52af9ec07 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e59bf1428575073832d8dc2fd23da12e - trainer: - kwargs: {} -name: 693a8509a94b2977402ee6d52af9ec07 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6953a01b774a592e46d917a2134ac753/params.yaml b/examples/security/classification/output/reports/train/6953a01b774a592e46d917a2134ac753/params.yaml deleted file mode 100644 index db7cb6fd..00000000 --- a/examples/security/classification/output/reports/train/6953a01b774a592e46d917a2134ac753/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6953a01b774a592e46d917a2134ac753/adv_predictions.json - adv_probabilities_file: output/reports/train/6953a01b774a592e46d917a2134ac753/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/90b03c06e4cf060916a5b9a54e6e7a28.pkl - params_file: output/reports/train/6953a01b774a592e46d917a2134ac753/params.yaml - predictions_file: output/reports/train/6953a01b774a592e46d917a2134ac753/predictions.json - probabilities_file: output/reports/train/6953a01b774a592e46d917a2134ac753/probabilities.json - score_dict_file: output/reports/train/6953a01b774a592e46d917a2134ac753/score_dict.json - test_labels_file: output/reports/train/6953a01b774a592e46d917a2134ac753/test_labels.json - train_labels_file: output/reports/train/6953a01b774a592e46d917a2134ac753/train_labels.json - model_dir: models - name: 6953a01b774a592e46d917a2134ac753 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dae3e299a893a501d9d9c2a4e1499dff - trainer: - kwargs: {} -name: 6953a01b774a592e46d917a2134ac753 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/69642cb7febdb4e005a2aa592051c1b6/params.yaml b/examples/security/classification/output/reports/train/69642cb7febdb4e005a2aa592051c1b6/params.yaml deleted file mode 100644 index 39013e9f..00000000 --- a/examples/security/classification/output/reports/train/69642cb7febdb4e005a2aa592051c1b6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/adv_predictions.json - adv_probabilities_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/066d838bc76651adf450925b500c3afb.pkl - params_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/params.yaml - predictions_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/predictions.json - probabilities_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/probabilities.json - score_dict_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/score_dict.json - test_labels_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/test_labels.json - train_labels_file: output/reports/train/69642cb7febdb4e005a2aa592051c1b6/train_labels.json - model_dir: models - name: 69642cb7febdb4e005a2aa592051c1b6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b5d846e4050ed7d3011897e032210d65 - trainer: - kwargs: {} -name: 69642cb7febdb4e005a2aa592051c1b6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/params.yaml b/examples/security/classification/output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/params.yaml deleted file mode 100644 index 5bc0f453..00000000 --- a/examples/security/classification/output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/adv_predictions.json - adv_probabilities_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/1e36b8c61e8bc91558487a8935d71009.pkl - params_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/params.yaml - predictions_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/predictions.json - probabilities_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/probabilities.json - score_dict_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/score_dict.json - test_labels_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/test_labels.json - train_labels_file: output/reports/train/69746a4d0db0a4093dd48730e5ce8fcd/train_labels.json - model_dir: models - name: 69746a4d0db0a4093dd48730e5ce8fcd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 75a8a36509357c5f531a7add9722c52d - trainer: - kwargs: {} -name: 69746a4d0db0a4093dd48730e5ce8fcd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/params.yaml b/examples/security/classification/output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/params.yaml deleted file mode 100644 index 005d3bfe..00000000 --- a/examples/security/classification/output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/adv_predictions.json - adv_probabilities_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/72bd4bb17dad23fae5c8515c80f47ab7.pkl - params_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/params.yaml - predictions_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/predictions.json - probabilities_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/probabilities.json - score_dict_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/score_dict.json - test_labels_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/test_labels.json - train_labels_file: output/reports/train/697d28d4a7fe581cb1343dbf6fd321a5/train_labels.json - model_dir: models - name: 697d28d4a7fe581cb1343dbf6fd321a5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d7a0b33bae5825f037783cc2e6b9c013 - trainer: - kwargs: {} -name: 697d28d4a7fe581cb1343dbf6fd321a5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6981edfe00a28176c32ac1885c279321/params.yaml b/examples/security/classification/output/reports/train/6981edfe00a28176c32ac1885c279321/params.yaml deleted file mode 100644 index dc6551fb..00000000 --- a/examples/security/classification/output/reports/train/6981edfe00a28176c32ac1885c279321/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6981edfe00a28176c32ac1885c279321/adv_predictions.json - adv_probabilities_file: output/reports/train/6981edfe00a28176c32ac1885c279321/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/83d851bbde2edac9bcd9cd204c3b5f2f.pkl - params_file: output/reports/train/6981edfe00a28176c32ac1885c279321/params.yaml - predictions_file: output/reports/train/6981edfe00a28176c32ac1885c279321/predictions.json - probabilities_file: output/reports/train/6981edfe00a28176c32ac1885c279321/probabilities.json - score_dict_file: output/reports/train/6981edfe00a28176c32ac1885c279321/score_dict.json - test_labels_file: output/reports/train/6981edfe00a28176c32ac1885c279321/test_labels.json - train_labels_file: output/reports/train/6981edfe00a28176c32ac1885c279321/train_labels.json - model_dir: models - name: 6981edfe00a28176c32ac1885c279321 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d3e7f863c189a14ee0ed6c498bb8c1db - trainer: - kwargs: {} -name: 6981edfe00a28176c32ac1885c279321 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/69898ef2f92571389a14ccaa6ba341dc/params.yaml b/examples/security/classification/output/reports/train/69898ef2f92571389a14ccaa6ba341dc/params.yaml deleted file mode 100644 index 23456463..00000000 --- a/examples/security/classification/output/reports/train/69898ef2f92571389a14ccaa6ba341dc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/adv_predictions.json - adv_probabilities_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/9d67630db6792c789137cb20476fb09c.pkl - params_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/params.yaml - predictions_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/predictions.json - probabilities_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/probabilities.json - score_dict_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/score_dict.json - test_labels_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/test_labels.json - train_labels_file: output/reports/train/69898ef2f92571389a14ccaa6ba341dc/train_labels.json - model_dir: models - name: 69898ef2f92571389a14ccaa6ba341dc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e3cfc6482a4808b6e41329280e1e3c10 - trainer: - kwargs: {} -name: 69898ef2f92571389a14ccaa6ba341dc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/69a1c15b525bb1b8845b42b9b571365a/params.yaml b/examples/security/classification/output/reports/train/69a1c15b525bb1b8845b42b9b571365a/params.yaml deleted file mode 100644 index 458e8eed..00000000 --- a/examples/security/classification/output/reports/train/69a1c15b525bb1b8845b42b9b571365a/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/adv_predictions.json - adv_probabilities_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/5c90b05b97266342461d4dcc71fe7921.pkl - params_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/params.yaml - predictions_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/predictions.json - probabilities_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/probabilities.json - score_dict_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/score_dict.json - test_labels_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/test_labels.json - train_labels_file: output/reports/train/69a1c15b525bb1b8845b42b9b571365a/train_labels.json - model_dir: models - name: 69a1c15b525bb1b8845b42b9b571365a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ffc02fd19e24e4fc43c3225e1a33b319 - trainer: - kwargs: {} -name: 69a1c15b525bb1b8845b42b9b571365a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/69c279618f13faed659f00c17dfb142a/params.yaml b/examples/security/classification/output/reports/train/69c279618f13faed659f00c17dfb142a/params.yaml deleted file mode 100644 index b4a8f387..00000000 --- a/examples/security/classification/output/reports/train/69c279618f13faed659f00c17dfb142a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/69c279618f13faed659f00c17dfb142a/adv_predictions.json - adv_probabilities_file: output/reports/train/69c279618f13faed659f00c17dfb142a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/cc83c90e27dc89a84ea78ea1df1ed334.pkl - params_file: output/reports/train/69c279618f13faed659f00c17dfb142a/params.yaml - predictions_file: output/reports/train/69c279618f13faed659f00c17dfb142a/predictions.json - probabilities_file: output/reports/train/69c279618f13faed659f00c17dfb142a/probabilities.json - score_dict_file: output/reports/train/69c279618f13faed659f00c17dfb142a/score_dict.json - test_labels_file: output/reports/train/69c279618f13faed659f00c17dfb142a/test_labels.json - train_labels_file: output/reports/train/69c279618f13faed659f00c17dfb142a/train_labels.json - model_dir: models - name: 69c279618f13faed659f00c17dfb142a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4a0b6d0858abf88c2db2f14138bbbac0 - trainer: - kwargs: {} -name: 69c279618f13faed659f00c17dfb142a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/69d2f0003a82cb91c077c0ccf763a853/params.yaml b/examples/security/classification/output/reports/train/69d2f0003a82cb91c077c0ccf763a853/params.yaml deleted file mode 100644 index 9c45f79a..00000000 --- a/examples/security/classification/output/reports/train/69d2f0003a82cb91c077c0ccf763a853/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/adv_predictions.json - adv_probabilities_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/3368e5cb7a59dcb39f045527dbb4f558.pkl - params_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/params.yaml - predictions_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/predictions.json - probabilities_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/probabilities.json - score_dict_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/score_dict.json - test_labels_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/test_labels.json - train_labels_file: output/reports/train/69d2f0003a82cb91c077c0ccf763a853/train_labels.json - model_dir: models - name: 69d2f0003a82cb91c077c0ccf763a853 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 61fb398d0639262b8ede1c200916c013 - trainer: - kwargs: {} -name: 69d2f0003a82cb91c077c0ccf763a853 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/69ecb7186f579836de7726033bf5e830/params.yaml b/examples/security/classification/output/reports/train/69ecb7186f579836de7726033bf5e830/params.yaml deleted file mode 100644 index bd653a1b..00000000 --- a/examples/security/classification/output/reports/train/69ecb7186f579836de7726033bf5e830/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/69ecb7186f579836de7726033bf5e830/adv_predictions.json - adv_probabilities_file: output/reports/train/69ecb7186f579836de7726033bf5e830/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/86c7726adf3cd974fa584a617f92fadd.pkl - params_file: output/reports/train/69ecb7186f579836de7726033bf5e830/params.yaml - predictions_file: output/reports/train/69ecb7186f579836de7726033bf5e830/predictions.json - probabilities_file: output/reports/train/69ecb7186f579836de7726033bf5e830/probabilities.json - score_dict_file: output/reports/train/69ecb7186f579836de7726033bf5e830/score_dict.json - test_labels_file: output/reports/train/69ecb7186f579836de7726033bf5e830/test_labels.json - train_labels_file: output/reports/train/69ecb7186f579836de7726033bf5e830/train_labels.json - model_dir: models - name: 69ecb7186f579836de7726033bf5e830 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e8e6c784305f84cfe743089eb1f520c5 - trainer: - kwargs: {} -name: 69ecb7186f579836de7726033bf5e830 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/params.yaml b/examples/security/classification/output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/params.yaml deleted file mode 100644 index e9cf757a..00000000 --- a/examples/security/classification/output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/adv_predictions.json - adv_probabilities_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/e9fb5a698d257a2b39ffd3df16cfb5ed.pkl - params_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/params.yaml - predictions_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/predictions.json - probabilities_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/probabilities.json - score_dict_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/score_dict.json - test_labels_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/test_labels.json - train_labels_file: output/reports/train/69f4cc2cb0c972db22c296613cc9d09f/train_labels.json - model_dir: models - name: 69f4cc2cb0c972db22c296613cc9d09f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 805f03eab8997e319e1814db73bc3e9d - trainer: - kwargs: {} -name: 69f4cc2cb0c972db22c296613cc9d09f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/params.yaml b/examples/security/classification/output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/params.yaml deleted file mode 100644 index 859eb7db..00000000 --- a/examples/security/classification/output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/adv_predictions.json - adv_probabilities_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/d3b5c5d76ecfe07665eda0efcce65555.pkl - params_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/params.yaml - predictions_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/predictions.json - probabilities_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/probabilities.json - score_dict_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/score_dict.json - test_labels_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/test_labels.json - train_labels_file: output/reports/train/69fbd82eebdc0f3a7de6ce115b36a69e/train_labels.json - model_dir: models - name: 69fbd82eebdc0f3a7de6ce115b36a69e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 48a3f51c2f1350bf1356b90192e29dd4 - trainer: - kwargs: {} -name: 69fbd82eebdc0f3a7de6ce115b36a69e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/params.yaml b/examples/security/classification/output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/params.yaml deleted file mode 100644 index c577b8b1..00000000 --- a/examples/security/classification/output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/adv_predictions.json - adv_probabilities_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/3920c9b0c22c48fd6a178b5db392f442.pkl - params_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/params.yaml - predictions_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/predictions.json - probabilities_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/probabilities.json - score_dict_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/score_dict.json - test_labels_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/test_labels.json - train_labels_file: output/reports/train/6a3cb9f6081ec877879124d1ccc86f36/train_labels.json - model_dir: models - name: 6a3cb9f6081ec877879124d1ccc86f36 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e5c344b529ee006681b6993988576134 - trainer: - kwargs: {} -name: 6a3cb9f6081ec877879124d1ccc86f36 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6a45d861b6bed7a079ff804f005f09a4/params.yaml b/examples/security/classification/output/reports/train/6a45d861b6bed7a079ff804f005f09a4/params.yaml deleted file mode 100644 index 7d6133b5..00000000 --- a/examples/security/classification/output/reports/train/6a45d861b6bed7a079ff804f005f09a4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/adv_predictions.json - adv_probabilities_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/0fbdcd4f1daf8f6ba2086aad795d0820.pkl - params_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/params.yaml - predictions_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/predictions.json - probabilities_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/probabilities.json - score_dict_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/score_dict.json - test_labels_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/test_labels.json - train_labels_file: output/reports/train/6a45d861b6bed7a079ff804f005f09a4/train_labels.json - model_dir: models - name: 6a45d861b6bed7a079ff804f005f09a4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6a667ff4960adc5dbe602047fd23cc76 - trainer: - kwargs: {} -name: 6a45d861b6bed7a079ff804f005f09a4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/params.yaml b/examples/security/classification/output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/params.yaml deleted file mode 100644 index 329b0352..00000000 --- a/examples/security/classification/output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/adv_predictions.json - adv_probabilities_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/6d4619c67d92af4accc88a39d23f7030.pkl - params_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/params.yaml - predictions_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/predictions.json - probabilities_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/probabilities.json - score_dict_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/score_dict.json - test_labels_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/test_labels.json - train_labels_file: output/reports/train/6a5ae1951cdd50482c803e39c2ba3a67/train_labels.json - model_dir: models - name: 6a5ae1951cdd50482c803e39c2ba3a67 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3030dc0bc6711bc001b20aef5046feab - trainer: - kwargs: {} -name: 6a5ae1951cdd50482c803e39c2ba3a67 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/params.yaml b/examples/security/classification/output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/params.yaml deleted file mode 100644 index 39c025d3..00000000 --- a/examples/security/classification/output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/adv_predictions.json - adv_probabilities_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/ba8786b6048b1838587811d805d70df4.pkl - params_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/params.yaml - predictions_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/predictions.json - probabilities_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/probabilities.json - score_dict_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/score_dict.json - test_labels_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/test_labels.json - train_labels_file: output/reports/train/6a5b746f4b5b29ed7c875b6c964c0094/train_labels.json - model_dir: models - name: 6a5b746f4b5b29ed7c875b6c964c0094 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 60a6292b7e75d94d56c976f34625c8dd - trainer: - kwargs: {} -name: 6a5b746f4b5b29ed7c875b6c964c0094 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6a61498ef02bdcf7119d61301979d33b/params.yaml b/examples/security/classification/output/reports/train/6a61498ef02bdcf7119d61301979d33b/params.yaml deleted file mode 100644 index 60ec05fb..00000000 --- a/examples/security/classification/output/reports/train/6a61498ef02bdcf7119d61301979d33b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/adv_predictions.json - adv_probabilities_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/61255bc2ebcd29e0919acbe438e1c86a.pkl - params_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/params.yaml - predictions_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/predictions.json - probabilities_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/probabilities.json - score_dict_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/score_dict.json - test_labels_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/test_labels.json - train_labels_file: output/reports/train/6a61498ef02bdcf7119d61301979d33b/train_labels.json - model_dir: models - name: 6a61498ef02bdcf7119d61301979d33b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4bfa0d83bedca03492b5f7385fc74aea - trainer: - kwargs: {} -name: 6a61498ef02bdcf7119d61301979d33b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6a7d572d377dcd451790887ebf72753b/params.yaml b/examples/security/classification/output/reports/train/6a7d572d377dcd451790887ebf72753b/params.yaml deleted file mode 100644 index 23032aac..00000000 --- a/examples/security/classification/output/reports/train/6a7d572d377dcd451790887ebf72753b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/adv_predictions.json - adv_probabilities_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/eabeef6a1c39f8220b648bb666595a50.pkl - params_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/params.yaml - predictions_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/predictions.json - probabilities_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/probabilities.json - score_dict_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/score_dict.json - test_labels_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/test_labels.json - train_labels_file: output/reports/train/6a7d572d377dcd451790887ebf72753b/train_labels.json - model_dir: models - name: 6a7d572d377dcd451790887ebf72753b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 00e3cd0f7d1277abf1ed25e9d5b9efe6 - trainer: - kwargs: {} -name: 6a7d572d377dcd451790887ebf72753b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/params.yaml b/examples/security/classification/output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/params.yaml deleted file mode 100644 index 76b06bcb..00000000 --- a/examples/security/classification/output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/adv_predictions.json - adv_probabilities_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/656435913548910947013f89710cfd55.pkl - params_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/params.yaml - predictions_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/predictions.json - probabilities_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/probabilities.json - score_dict_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/score_dict.json - test_labels_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/test_labels.json - train_labels_file: output/reports/train/6aa9a4bbac1680f66407bf6bce43342f/train_labels.json - model_dir: models - name: 6aa9a4bbac1680f66407bf6bce43342f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5cf9a47f88f8b3b4b2d5b07eeb353ef4 - trainer: - kwargs: {} -name: 6aa9a4bbac1680f66407bf6bce43342f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6ab4308823cd60c03937aea7d18310a5/params.yaml b/examples/security/classification/output/reports/train/6ab4308823cd60c03937aea7d18310a5/params.yaml deleted file mode 100644 index f55bee16..00000000 --- a/examples/security/classification/output/reports/train/6ab4308823cd60c03937aea7d18310a5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/adv_predictions.json - adv_probabilities_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/193383eba4cbfa6a7840a8822e3fff74.pkl - params_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/params.yaml - predictions_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/predictions.json - probabilities_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/probabilities.json - score_dict_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/score_dict.json - test_labels_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/test_labels.json - train_labels_file: output/reports/train/6ab4308823cd60c03937aea7d18310a5/train_labels.json - model_dir: models - name: 6ab4308823cd60c03937aea7d18310a5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f14a6f84845a6a5234f1ae65eaefdba7 - trainer: - kwargs: {} -name: 6ab4308823cd60c03937aea7d18310a5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6afa543b7fc6d9605ada46847e14736f/params.yaml b/examples/security/classification/output/reports/train/6afa543b7fc6d9605ada46847e14736f/params.yaml deleted file mode 100644 index 27315ec2..00000000 --- a/examples/security/classification/output/reports/train/6afa543b7fc6d9605ada46847e14736f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/adv_predictions.json - adv_probabilities_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/68b7a4e5a006c029185a42d41fa8ad5f.pkl - params_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/params.yaml - predictions_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/predictions.json - probabilities_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/probabilities.json - score_dict_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/score_dict.json - test_labels_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/test_labels.json - train_labels_file: output/reports/train/6afa543b7fc6d9605ada46847e14736f/train_labels.json - model_dir: models - name: 6afa543b7fc6d9605ada46847e14736f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0ee15abab5e2befb17a6281ec0f1539c - trainer: - kwargs: {} -name: 6afa543b7fc6d9605ada46847e14736f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/params.yaml b/examples/security/classification/output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/params.yaml deleted file mode 100644 index c755a342..00000000 --- a/examples/security/classification/output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/adv_predictions.json - adv_probabilities_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/fbd557d366cc76da8557fb200535e225.pkl - params_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/params.yaml - predictions_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/predictions.json - probabilities_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/probabilities.json - score_dict_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/score_dict.json - test_labels_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/test_labels.json - train_labels_file: output/reports/train/6b951a762fa679ab1d80d0bd9c94e00c/train_labels.json - model_dir: models - name: 6b951a762fa679ab1d80d0bd9c94e00c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 45258da373506f7c491abc6f487213fd - trainer: - kwargs: {} -name: 6b951a762fa679ab1d80d0bd9c94e00c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/params.yaml b/examples/security/classification/output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/params.yaml deleted file mode 100644 index 1a37a7b6..00000000 --- a/examples/security/classification/output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/adv_predictions.json - adv_probabilities_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/872711afe7688c4b9f0a2513d42a3dd7.pkl - params_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/params.yaml - predictions_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/predictions.json - probabilities_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/probabilities.json - score_dict_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/score_dict.json - test_labels_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/test_labels.json - train_labels_file: output/reports/train/6ba7fb2da7818bf7569680c3694eb34d/train_labels.json - model_dir: models - name: 6ba7fb2da7818bf7569680c3694eb34d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5e7e77679258530ee2d93480400c5582 - trainer: - kwargs: {} -name: 6ba7fb2da7818bf7569680c3694eb34d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6baed18071eec33f49c7025a7b611571/params.yaml b/examples/security/classification/output/reports/train/6baed18071eec33f49c7025a7b611571/params.yaml deleted file mode 100644 index b3b85469..00000000 --- a/examples/security/classification/output/reports/train/6baed18071eec33f49c7025a7b611571/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6baed18071eec33f49c7025a7b611571/adv_predictions.json - adv_probabilities_file: output/reports/train/6baed18071eec33f49c7025a7b611571/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/992648b3721fe5cfdef2c1113727905f.pkl - params_file: output/reports/train/6baed18071eec33f49c7025a7b611571/params.yaml - predictions_file: output/reports/train/6baed18071eec33f49c7025a7b611571/predictions.json - probabilities_file: output/reports/train/6baed18071eec33f49c7025a7b611571/probabilities.json - score_dict_file: output/reports/train/6baed18071eec33f49c7025a7b611571/score_dict.json - test_labels_file: output/reports/train/6baed18071eec33f49c7025a7b611571/test_labels.json - train_labels_file: output/reports/train/6baed18071eec33f49c7025a7b611571/train_labels.json - model_dir: models - name: 6baed18071eec33f49c7025a7b611571 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7c0f46f5f9363968471810649d52af90 - trainer: - kwargs: {} -name: 6baed18071eec33f49c7025a7b611571 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6bb908b20365d7623e23fe29326834ee/params.yaml b/examples/security/classification/output/reports/train/6bb908b20365d7623e23fe29326834ee/params.yaml deleted file mode 100644 index aa188cc4..00000000 --- a/examples/security/classification/output/reports/train/6bb908b20365d7623e23fe29326834ee/params.yaml +++ /dev/null @@ -1,197 +0,0 @@ -attack: - attack_size: 10 - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 2000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 2000.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 70f4fa555cd8abb19e95d7b947f3bd17 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 2000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 13f19bb1e523e45d4339097324c905ea - trainer: - kwargs: {} - name: 4f4137502a34816ab46e7f436400aec9 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 2000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/adv_predictions.json - adv_probabilities_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/adv_probabilities.json - attack_file: output/attacks/5ea6e3bb4ca138d6670200aa2bd0d46c.pkl - data_file: output/data/d48637d71e55ddbb9a1fd3101e086a47.pkl - model_file: output/models/44c599c2f6f5a1e8bcd9a14a2807724d.pkl - params_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/params.yaml - predictions_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/predictions.json - probabilities_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/probabilities.json - score_dict_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/score_dict.json - test_labels_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/test_labels.json - train_labels_file: output/reports/train/6bb908b20365d7623e23fe29326834ee/train_labels.json - model_dir: models - name: 6bb908b20365d7623e23fe29326834ee - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 2000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 950578ad7710c6828f11b3c267acdfb9 - trainer: - kwargs: {} -name: 6bb908b20365d7623e23fe29326834ee -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/params.yaml b/examples/security/classification/output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/params.yaml deleted file mode 100644 index efa404bf..00000000 --- a/examples/security/classification/output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/adv_predictions.json - adv_probabilities_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/ebbeede5946fb51f71e1288e14f67c9c.pkl - params_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/params.yaml - predictions_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/predictions.json - probabilities_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/probabilities.json - score_dict_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/score_dict.json - test_labels_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/test_labels.json - train_labels_file: output/reports/train/6bbf4da79de16ae44ee6d9a1b84ae410/train_labels.json - model_dir: models - name: 6bbf4da79de16ae44ee6d9a1b84ae410 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8f1410a68269749d60703830774c81aa - trainer: - kwargs: {} -name: 6bbf4da79de16ae44ee6d9a1b84ae410 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/params.yaml b/examples/security/classification/output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/params.yaml deleted file mode 100644 index aeb7c714..00000000 --- a/examples/security/classification/output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/adv_predictions.json - adv_probabilities_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/b2439d998a2aa745f8ed7d45edc330c0.pkl - params_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/params.yaml - predictions_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/predictions.json - probabilities_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/probabilities.json - score_dict_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/score_dict.json - test_labels_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/test_labels.json - train_labels_file: output/reports/train/6bf4bcd452a168cbdb4a9c7f838f5367/train_labels.json - model_dir: models - name: 6bf4bcd452a168cbdb4a9c7f838f5367 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1a1d8038093d97f5425f3c2d6a6df1b4 - trainer: - kwargs: {} -name: 6bf4bcd452a168cbdb4a9c7f838f5367 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6c6b954587dc38d6d4364780d613ee3f/params.yaml b/examples/security/classification/output/reports/train/6c6b954587dc38d6d4364780d613ee3f/params.yaml deleted file mode 100644 index 7c00f731..00000000 --- a/examples/security/classification/output/reports/train/6c6b954587dc38d6d4364780d613ee3f/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/adv_predictions.json - adv_probabilities_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/33640f36d962099f93330b2a84ce76d5.pkl - params_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/params.yaml - predictions_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/predictions.json - probabilities_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/probabilities.json - score_dict_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/score_dict.json - test_labels_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/test_labels.json - train_labels_file: output/reports/train/6c6b954587dc38d6d4364780d613ee3f/train_labels.json - model_dir: models - name: 6c6b954587dc38d6d4364780d613ee3f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d694015e780dca53701b62731a815cc6 - trainer: - kwargs: {} -name: 6c6b954587dc38d6d4364780d613ee3f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/params.yaml b/examples/security/classification/output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/params.yaml deleted file mode 100644 index 310423fb..00000000 --- a/examples/security/classification/output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/adv_predictions.json - adv_probabilities_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/739075319b7141b65149a021a5dfdc72.pkl - params_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/params.yaml - predictions_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/predictions.json - probabilities_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/probabilities.json - score_dict_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/score_dict.json - test_labels_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/test_labels.json - train_labels_file: output/reports/train/6c6e5a2b263601c9b95ded6bf13e6938/train_labels.json - model_dir: models - name: 6c6e5a2b263601c9b95ded6bf13e6938 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fb14bcf642b06e0d7d217e993a064c94 - trainer: - kwargs: {} -name: 6c6e5a2b263601c9b95ded6bf13e6938 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6c761ceeefd59238b08976262bd1b8a7/params.yaml b/examples/security/classification/output/reports/train/6c761ceeefd59238b08976262bd1b8a7/params.yaml deleted file mode 100644 index da8dd1a8..00000000 --- a/examples/security/classification/output/reports/train/6c761ceeefd59238b08976262bd1b8a7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/adv_predictions.json - adv_probabilities_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/df04755723f24e459b08aeda602d4c12.pkl - params_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/params.yaml - predictions_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/predictions.json - probabilities_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/probabilities.json - score_dict_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/score_dict.json - test_labels_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/test_labels.json - train_labels_file: output/reports/train/6c761ceeefd59238b08976262bd1b8a7/train_labels.json - model_dir: models - name: 6c761ceeefd59238b08976262bd1b8a7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bfa74bb272fafd34697364fc9dc91a23 - trainer: - kwargs: {} -name: 6c761ceeefd59238b08976262bd1b8a7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6cbc82336640e789764d20ff13618c21/params.yaml b/examples/security/classification/output/reports/train/6cbc82336640e789764d20ff13618c21/params.yaml deleted file mode 100644 index e26e7746..00000000 --- a/examples/security/classification/output/reports/train/6cbc82336640e789764d20ff13618c21/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6cbc82336640e789764d20ff13618c21/adv_predictions.json - adv_probabilities_file: output/reports/train/6cbc82336640e789764d20ff13618c21/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/8d4cc85acfee286880f3788d17e9c0b4.pkl - params_file: output/reports/train/6cbc82336640e789764d20ff13618c21/params.yaml - predictions_file: output/reports/train/6cbc82336640e789764d20ff13618c21/predictions.json - probabilities_file: output/reports/train/6cbc82336640e789764d20ff13618c21/probabilities.json - score_dict_file: output/reports/train/6cbc82336640e789764d20ff13618c21/score_dict.json - test_labels_file: output/reports/train/6cbc82336640e789764d20ff13618c21/test_labels.json - train_labels_file: output/reports/train/6cbc82336640e789764d20ff13618c21/train_labels.json - model_dir: models - name: 6cbc82336640e789764d20ff13618c21 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e761d767e5ce81d0d1ce9d7f7985808d - trainer: - kwargs: {} -name: 6cbc82336640e789764d20ff13618c21 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/params.yaml b/examples/security/classification/output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/params.yaml deleted file mode 100644 index 88786979..00000000 --- a/examples/security/classification/output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/adv_predictions.json - adv_probabilities_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/b95efb8d770e4c9ff1b41a2d1a5e29b5.pkl - params_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/params.yaml - predictions_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/predictions.json - probabilities_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/probabilities.json - score_dict_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/score_dict.json - test_labels_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/test_labels.json - train_labels_file: output/reports/train/6cc9fcef1537ea6b227822bcbbd601e3/train_labels.json - model_dir: models - name: 6cc9fcef1537ea6b227822bcbbd601e3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 160a4d2d4d1048680eec953027f3a40b - trainer: - kwargs: {} -name: 6cc9fcef1537ea6b227822bcbbd601e3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/params.yaml b/examples/security/classification/output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/params.yaml deleted file mode 100644 index 89d0e38a..00000000 --- a/examples/security/classification/output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/adv_predictions.json - adv_probabilities_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/decb2daab882916de6b04d119faf6743.pkl - params_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/params.yaml - predictions_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/predictions.json - probabilities_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/probabilities.json - score_dict_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/score_dict.json - test_labels_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/test_labels.json - train_labels_file: output/reports/train/6cd9b29cfaf9d37a99e653b9a3d1b6e7/train_labels.json - model_dir: models - name: 6cd9b29cfaf9d37a99e653b9a3d1b6e7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fb661ce8f2ed6d0e87c829746d6572cf - trainer: - kwargs: {} -name: 6cd9b29cfaf9d37a99e653b9a3d1b6e7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/params.yaml b/examples/security/classification/output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/params.yaml deleted file mode 100644 index 45e94910..00000000 --- a/examples/security/classification/output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/adv_predictions.json - adv_probabilities_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/90c78d4254d6d25260c21c413d6141c1.pkl - params_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/params.yaml - predictions_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/predictions.json - probabilities_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/probabilities.json - score_dict_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/score_dict.json - test_labels_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/test_labels.json - train_labels_file: output/reports/train/6cdcc24c1162c714d8a2054aa2da8f7d/train_labels.json - model_dir: models - name: 6cdcc24c1162c714d8a2054aa2da8f7d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 78884464eb571f3dc32b01d6c3de5893 - trainer: - kwargs: {} -name: 6cdcc24c1162c714d8a2054aa2da8f7d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/params.yaml b/examples/security/classification/output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/params.yaml deleted file mode 100644 index 3dba9e82..00000000 --- a/examples/security/classification/output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/adv_predictions.json - adv_probabilities_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/64d4cfa942f989139ac14e4d0c77a499.pkl - params_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/params.yaml - predictions_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/predictions.json - probabilities_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/probabilities.json - score_dict_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/score_dict.json - test_labels_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/test_labels.json - train_labels_file: output/reports/train/6ce2bea1ed0f929c9e58c05642987c67/train_labels.json - model_dir: models - name: 6ce2bea1ed0f929c9e58c05642987c67 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4e0f2922a84a8c46074f8477a66682ea - trainer: - kwargs: {} -name: 6ce2bea1ed0f929c9e58c05642987c67 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/params.yaml b/examples/security/classification/output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/params.yaml deleted file mode 100644 index 431b08b5..00000000 --- a/examples/security/classification/output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/adv_predictions.json - adv_probabilities_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/811bc450dfa513773af496a77d973f7c.pkl - params_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/params.yaml - predictions_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/predictions.json - probabilities_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/probabilities.json - score_dict_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/score_dict.json - test_labels_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/test_labels.json - train_labels_file: output/reports/train/6d0fb0e50ef0839e80ecb948e29a343d/train_labels.json - model_dir: models - name: 6d0fb0e50ef0839e80ecb948e29a343d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 81ab089556507e46a248be6096a4ee58 - trainer: - kwargs: {} -name: 6d0fb0e50ef0839e80ecb948e29a343d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/params.yaml b/examples/security/classification/output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/params.yaml deleted file mode 100644 index 384660eb..00000000 --- a/examples/security/classification/output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/adv_predictions.json - adv_probabilities_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/c6fb8f2c5b46f1ca2d4d1a8248261c88.pkl - params_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/params.yaml - predictions_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/predictions.json - probabilities_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/probabilities.json - score_dict_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/score_dict.json - test_labels_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/test_labels.json - train_labels_file: output/reports/train/6d2783ea121f59db31181f98c1e9f4a2/train_labels.json - model_dir: models - name: 6d2783ea121f59db31181f98c1e9f4a2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 222bda93b2cb8684c1f54fcfeb3f2a2c - trainer: - kwargs: {} -name: 6d2783ea121f59db31181f98c1e9f4a2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/params.yaml b/examples/security/classification/output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/params.yaml deleted file mode 100644 index 6c9b644a..00000000 --- a/examples/security/classification/output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/adv_predictions.json - adv_probabilities_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/850d946ccf84d45e598961c552e578c4.pkl - params_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/params.yaml - predictions_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/predictions.json - probabilities_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/probabilities.json - score_dict_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/score_dict.json - test_labels_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/test_labels.json - train_labels_file: output/reports/train/6d56e234ff345aea0f2eb42f7078f42f/train_labels.json - model_dir: models - name: 6d56e234ff345aea0f2eb42f7078f42f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c75a02295d2ed61e754e6936ca7e79f9 - trainer: - kwargs: {} -name: 6d56e234ff345aea0f2eb42f7078f42f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/params.yaml b/examples/security/classification/output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/params.yaml deleted file mode 100644 index 1807302e..00000000 --- a/examples/security/classification/output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/adv_predictions.json - adv_probabilities_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/58d20bde07f65b16b8d9ef0e0211f31b.pkl - params_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/params.yaml - predictions_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/predictions.json - probabilities_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/probabilities.json - score_dict_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/score_dict.json - test_labels_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/test_labels.json - train_labels_file: output/reports/train/6d774dcc92b649fe1ed5b933c6631d7f/train_labels.json - model_dir: models - name: 6d774dcc92b649fe1ed5b933c6631d7f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 268a605ec3dea619bc4c9e13f25d3b05 - trainer: - kwargs: {} -name: 6d774dcc92b649fe1ed5b933c6631d7f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6d7843bb99a9a02affb9b28740f55638/params.yaml b/examples/security/classification/output/reports/train/6d7843bb99a9a02affb9b28740f55638/params.yaml deleted file mode 100644 index 4b10d86b..00000000 --- a/examples/security/classification/output/reports/train/6d7843bb99a9a02affb9b28740f55638/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/adv_predictions.json - adv_probabilities_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/7bb6071198cde2d9e33da36239921f43.pkl - params_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/params.yaml - predictions_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/predictions.json - probabilities_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/probabilities.json - score_dict_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/score_dict.json - test_labels_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/test_labels.json - train_labels_file: output/reports/train/6d7843bb99a9a02affb9b28740f55638/train_labels.json - model_dir: models - name: 6d7843bb99a9a02affb9b28740f55638 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ca61ca4fad257e2f9d2b775b79756ab9 - trainer: - kwargs: {} -name: 6d7843bb99a9a02affb9b28740f55638 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/params.yaml b/examples/security/classification/output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/params.yaml deleted file mode 100644 index 2d44683f..00000000 --- a/examples/security/classification/output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/adv_predictions.json - adv_probabilities_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/82d6bfda5bc9c3a6318d24a3d026b0aa.pkl - params_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/params.yaml - predictions_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/predictions.json - probabilities_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/probabilities.json - score_dict_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/score_dict.json - test_labels_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/test_labels.json - train_labels_file: output/reports/train/6d9218c0f1dd611cb029c5409fd1d7bc/train_labels.json - model_dir: models - name: 6d9218c0f1dd611cb029c5409fd1d7bc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f89dc2bf7c79b607780ea0ee08534bb9 - trainer: - kwargs: {} -name: 6d9218c0f1dd611cb029c5409fd1d7bc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/params.yaml b/examples/security/classification/output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/params.yaml deleted file mode 100644 index d4d5d8e4..00000000 --- a/examples/security/classification/output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/adv_predictions.json - adv_probabilities_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/17851692d3bc90e88b2e089972a532ad.pkl - params_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/params.yaml - predictions_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/predictions.json - probabilities_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/probabilities.json - score_dict_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/score_dict.json - test_labels_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/test_labels.json - train_labels_file: output/reports/train/6d94a0bb4c29d8107af6f5083bdcf49f/train_labels.json - model_dir: models - name: 6d94a0bb4c29d8107af6f5083bdcf49f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b51ac2939ae2101fb054c54e4ca872b9 - trainer: - kwargs: {} -name: 6d94a0bb4c29d8107af6f5083bdcf49f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/params.yaml b/examples/security/classification/output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/params.yaml deleted file mode 100644 index 76f2dd1d..00000000 --- a/examples/security/classification/output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/adv_predictions.json - adv_probabilities_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/bf08179ef455f52149df85d2da803077.pkl - params_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/params.yaml - predictions_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/predictions.json - probabilities_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/probabilities.json - score_dict_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/score_dict.json - test_labels_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/test_labels.json - train_labels_file: output/reports/train/6dae64ca9889f2a09a12d1b12d04809c/train_labels.json - model_dir: models - name: 6dae64ca9889f2a09a12d1b12d04809c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ca0696260a2b69a1ead20c832a1dd399 - trainer: - kwargs: {} -name: 6dae64ca9889f2a09a12d1b12d04809c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/params.yaml b/examples/security/classification/output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/params.yaml deleted file mode 100644 index cf5e3d75..00000000 --- a/examples/security/classification/output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/adv_predictions.json - adv_probabilities_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/6ebddc11bd903ffd6654a41152117a4c.pkl - params_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/params.yaml - predictions_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/predictions.json - probabilities_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/probabilities.json - score_dict_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/score_dict.json - test_labels_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/test_labels.json - train_labels_file: output/reports/train/6db206f304e1e16cc3bc7c319c8b73e5/train_labels.json - model_dir: models - name: 6db206f304e1e16cc3bc7c319c8b73e5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 99a055cc0054a3ad7c8706d4bda61832 - trainer: - kwargs: {} -name: 6db206f304e1e16cc3bc7c319c8b73e5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/params.yaml b/examples/security/classification/output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/params.yaml deleted file mode 100644 index a9d68d7a..00000000 --- a/examples/security/classification/output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/adv_predictions.json - adv_probabilities_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/e0b436adf3746a9262921fbb5d6654e1.pkl - params_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/params.yaml - predictions_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/predictions.json - probabilities_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/probabilities.json - score_dict_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/score_dict.json - test_labels_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/test_labels.json - train_labels_file: output/reports/train/6dbb4f43377fcc9f79b04caf7d81f01d/train_labels.json - model_dir: models - name: 6dbb4f43377fcc9f79b04caf7d81f01d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8c10ceb650ffc6945b4ccf1d3d917f55 - trainer: - kwargs: {} -name: 6dbb4f43377fcc9f79b04caf7d81f01d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/params.yaml b/examples/security/classification/output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/params.yaml deleted file mode 100644 index 703c88be..00000000 --- a/examples/security/classification/output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/adv_predictions.json - adv_probabilities_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/52cddb53287f0e2689f81e79c7d604de.pkl - params_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/params.yaml - predictions_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/predictions.json - probabilities_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/probabilities.json - score_dict_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/score_dict.json - test_labels_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/test_labels.json - train_labels_file: output/reports/train/6dbeeca3e9a8db1c520b38395abc6905/train_labels.json - model_dir: models - name: 6dbeeca3e9a8db1c520b38395abc6905 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0f46e4b58cbc110c5773cde2b18fd000 - trainer: - kwargs: {} -name: 6dbeeca3e9a8db1c520b38395abc6905 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6dc109a8b3f22b68ccd83108307cb378/params.yaml b/examples/security/classification/output/reports/train/6dc109a8b3f22b68ccd83108307cb378/params.yaml deleted file mode 100644 index 7d3b0f19..00000000 --- a/examples/security/classification/output/reports/train/6dc109a8b3f22b68ccd83108307cb378/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/adv_predictions.json - adv_probabilities_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/d25531adc2eeb38a08178263eb65c90a.pkl - params_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/params.yaml - predictions_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/predictions.json - probabilities_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/probabilities.json - score_dict_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/score_dict.json - test_labels_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/test_labels.json - train_labels_file: output/reports/train/6dc109a8b3f22b68ccd83108307cb378/train_labels.json - model_dir: models - name: 6dc109a8b3f22b68ccd83108307cb378 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: de84deaf9ff302f1dca4d4135030e560 - trainer: - kwargs: {} -name: 6dc109a8b3f22b68ccd83108307cb378 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6dc6241f817594e36f4f08b5a133afa3/params.yaml b/examples/security/classification/output/reports/train/6dc6241f817594e36f4f08b5a133afa3/params.yaml deleted file mode 100644 index f7c48645..00000000 --- a/examples/security/classification/output/reports/train/6dc6241f817594e36f4f08b5a133afa3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/adv_predictions.json - adv_probabilities_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/d14ffc4ba3506be47ae6fc7c06ad5f83.pkl - params_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/params.yaml - predictions_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/predictions.json - probabilities_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/probabilities.json - score_dict_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/score_dict.json - test_labels_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/test_labels.json - train_labels_file: output/reports/train/6dc6241f817594e36f4f08b5a133afa3/train_labels.json - model_dir: models - name: 6dc6241f817594e36f4f08b5a133afa3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4b401d8179fd1af53eaae6a5ecd0c174 - trainer: - kwargs: {} -name: 6dc6241f817594e36f4f08b5a133afa3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/params.yaml b/examples/security/classification/output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/params.yaml deleted file mode 100644 index b8ae3c40..00000000 --- a/examples/security/classification/output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/adv_predictions.json - adv_probabilities_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/2e10d51bc92e77a2a9ab41c2aeafcf11.pkl - params_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/params.yaml - predictions_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/predictions.json - probabilities_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/probabilities.json - score_dict_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/score_dict.json - test_labels_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/test_labels.json - train_labels_file: output/reports/train/6df9c611c1cd11a76be7fa0c6b2c30d3/train_labels.json - model_dir: models - name: 6df9c611c1cd11a76be7fa0c6b2c30d3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d7ada450afc2ca5dc9cbe8f2c6494097 - trainer: - kwargs: {} -name: 6df9c611c1cd11a76be7fa0c6b2c30d3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/params.yaml b/examples/security/classification/output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/params.yaml deleted file mode 100644 index a8dee60d..00000000 --- a/examples/security/classification/output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/adv_predictions.json - adv_probabilities_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/36b86a8cb7b5dd993c8a19688eda52eb.pkl - params_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/params.yaml - predictions_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/predictions.json - probabilities_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/probabilities.json - score_dict_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/score_dict.json - test_labels_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/test_labels.json - train_labels_file: output/reports/train/6e4f4d4ce68e9d5bf58f94708c63c249/train_labels.json - model_dir: models - name: 6e4f4d4ce68e9d5bf58f94708c63c249 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9ed9ab28c100046d8892bfb563798658 - trainer: - kwargs: {} -name: 6e4f4d4ce68e9d5bf58f94708c63c249 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6e73ce03f629192d785a94172a8016c0/params.yaml b/examples/security/classification/output/reports/train/6e73ce03f629192d785a94172a8016c0/params.yaml deleted file mode 100644 index 6a4569b0..00000000 --- a/examples/security/classification/output/reports/train/6e73ce03f629192d785a94172a8016c0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/adv_predictions.json - adv_probabilities_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/35092eb1cac5735460db8c436e915308.pkl - params_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/params.yaml - predictions_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/predictions.json - probabilities_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/probabilities.json - score_dict_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/score_dict.json - test_labels_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/test_labels.json - train_labels_file: output/reports/train/6e73ce03f629192d785a94172a8016c0/train_labels.json - model_dir: models - name: 6e73ce03f629192d785a94172a8016c0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f830281b9f67e554b1b186f2c718359c - trainer: - kwargs: {} -name: 6e73ce03f629192d785a94172a8016c0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/params.yaml b/examples/security/classification/output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/params.yaml deleted file mode 100644 index 1d43ebff..00000000 --- a/examples/security/classification/output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/adv_predictions.json - adv_probabilities_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/b0388ddd87fc8ff9f745c92db639e040.pkl - params_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/params.yaml - predictions_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/predictions.json - probabilities_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/probabilities.json - score_dict_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/score_dict.json - test_labels_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/test_labels.json - train_labels_file: output/reports/train/6e943ee8f5701a90a8850e3ef7f1ac1e/train_labels.json - model_dir: models - name: 6e943ee8f5701a90a8850e3ef7f1ac1e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ee71638bca125acd6592618a6aaa1fdf - trainer: - kwargs: {} -name: 6e943ee8f5701a90a8850e3ef7f1ac1e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/params.yaml b/examples/security/classification/output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/params.yaml deleted file mode 100644 index ca7abc98..00000000 --- a/examples/security/classification/output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/adv_predictions.json - adv_probabilities_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/2fac07deb9c0f8a3cb9149a532cc56f0.pkl - params_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/params.yaml - predictions_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/predictions.json - probabilities_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/probabilities.json - score_dict_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/score_dict.json - test_labels_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/test_labels.json - train_labels_file: output/reports/train/6eb5bd43b8af53f849bc1b06573ad34b/train_labels.json - model_dir: models - name: 6eb5bd43b8af53f849bc1b06573ad34b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbac90fd6c7f091023cf203809e27c90 - trainer: - kwargs: {} -name: 6eb5bd43b8af53f849bc1b06573ad34b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/params.yaml b/examples/security/classification/output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/params.yaml deleted file mode 100644 index 7341df55..00000000 --- a/examples/security/classification/output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/adv_predictions.json - adv_probabilities_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/0f43986be594ec409677271894882846.pkl - params_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/params.yaml - predictions_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/predictions.json - probabilities_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/probabilities.json - score_dict_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/score_dict.json - test_labels_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/test_labels.json - train_labels_file: output/reports/train/6f27a59dde5f5e2b15dc80f1daeb8705/train_labels.json - model_dir: models - name: 6f27a59dde5f5e2b15dc80f1daeb8705 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ceabdcbe106d946abc7dd2fb6de6a116 - trainer: - kwargs: {} -name: 6f27a59dde5f5e2b15dc80f1daeb8705 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/params.yaml b/examples/security/classification/output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/params.yaml deleted file mode 100644 index 0cf540de..00000000 --- a/examples/security/classification/output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/adv_predictions.json - adv_probabilities_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/aa9ef4b1a1b4d00b2f84a2e71d6f2d1f.pkl - params_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/params.yaml - predictions_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/predictions.json - probabilities_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/probabilities.json - score_dict_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/score_dict.json - test_labels_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/test_labels.json - train_labels_file: output/reports/train/6f3720cc11c82b58af37ddc54c2499ff/train_labels.json - model_dir: models - name: 6f3720cc11c82b58af37ddc54c2499ff - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4c1f579d913b1cbaf2cbe74db664f0a1 - trainer: - kwargs: {} -name: 6f3720cc11c82b58af37ddc54c2499ff -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/params.yaml b/examples/security/classification/output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/params.yaml deleted file mode 100644 index c5f50447..00000000 --- a/examples/security/classification/output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/adv_predictions.json - adv_probabilities_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/2b1e51b1a88f2b4b90cc759bb4ac64b0.pkl - params_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/params.yaml - predictions_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/predictions.json - probabilities_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/probabilities.json - score_dict_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/score_dict.json - test_labels_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/test_labels.json - train_labels_file: output/reports/train/6f3ccdfde1d26dae907d99962a93c5ab/train_labels.json - model_dir: models - name: 6f3ccdfde1d26dae907d99962a93c5ab - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a7dce3d36a3b43f035d153d5ec91c628 - trainer: - kwargs: {} -name: 6f3ccdfde1d26dae907d99962a93c5ab -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/params.yaml b/examples/security/classification/output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/params.yaml deleted file mode 100644 index dc2b4c9e..00000000 --- a/examples/security/classification/output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/adv_predictions.json - adv_probabilities_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/bb7640ac7a587eb2960a1b6282d8406c.pkl - params_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/params.yaml - predictions_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/predictions.json - probabilities_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/probabilities.json - score_dict_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/score_dict.json - test_labels_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/test_labels.json - train_labels_file: output/reports/train/6f7299a2f7a89059d14ce1f1ce2fb840/train_labels.json - model_dir: models - name: 6f7299a2f7a89059d14ce1f1ce2fb840 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9988f0ef3b0be88f09fba53eb7ba92db - trainer: - kwargs: {} -name: 6f7299a2f7a89059d14ce1f1ce2fb840 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/params.yaml b/examples/security/classification/output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/params.yaml deleted file mode 100644 index a2e51629..00000000 --- a/examples/security/classification/output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/adv_predictions.json - adv_probabilities_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/3fa846dcf62bb72cf86ba9e71fcf38d2.pkl - params_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/params.yaml - predictions_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/predictions.json - probabilities_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/probabilities.json - score_dict_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/score_dict.json - test_labels_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/test_labels.json - train_labels_file: output/reports/train/6fa4a47e0fefb4c7211e344ea044b642/train_labels.json - model_dir: models - name: 6fa4a47e0fefb4c7211e344ea044b642 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7dc39211017fb61a18a8b93ee4cb6798 - trainer: - kwargs: {} -name: 6fa4a47e0fefb4c7211e344ea044b642 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/params.yaml b/examples/security/classification/output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/params.yaml deleted file mode 100644 index e47c92fd..00000000 --- a/examples/security/classification/output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/adv_predictions.json - adv_probabilities_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/77745dae9fde26eda07bd9dbd54fb748.pkl - params_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/params.yaml - predictions_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/predictions.json - probabilities_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/probabilities.json - score_dict_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/score_dict.json - test_labels_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/test_labels.json - train_labels_file: output/reports/train/6fc8506ca4dde6a9c791324c311d92d2/train_labels.json - model_dir: models - name: 6fc8506ca4dde6a9c791324c311d92d2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1e32ddba5627d14f06618f8195d6c3f1 - trainer: - kwargs: {} -name: 6fc8506ca4dde6a9c791324c311d92d2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6fd62a74ae36b92122894def5cae7c76/params.yaml b/examples/security/classification/output/reports/train/6fd62a74ae36b92122894def5cae7c76/params.yaml deleted file mode 100644 index 64b5ea94..00000000 --- a/examples/security/classification/output/reports/train/6fd62a74ae36b92122894def5cae7c76/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/adv_predictions.json - adv_probabilities_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/528b960a0f2769b83407ddc17a4efcbd.pkl - params_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/params.yaml - predictions_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/predictions.json - probabilities_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/probabilities.json - score_dict_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/score_dict.json - test_labels_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/test_labels.json - train_labels_file: output/reports/train/6fd62a74ae36b92122894def5cae7c76/train_labels.json - model_dir: models - name: 6fd62a74ae36b92122894def5cae7c76 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e063dc83fbf6c23ba102d2634c890b49 - trainer: - kwargs: {} -name: 6fd62a74ae36b92122894def5cae7c76 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6fd64214566b1ce33f475bf133cb18e4/params.yaml b/examples/security/classification/output/reports/train/6fd64214566b1ce33f475bf133cb18e4/params.yaml deleted file mode 100644 index 9daac7c4..00000000 --- a/examples/security/classification/output/reports/train/6fd64214566b1ce33f475bf133cb18e4/params.yaml +++ /dev/null @@ -1,104 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/adv_predictions.json - adv_probabilities_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/656765a1a1a57a1a6fcaed8a6dfce17e.pkl - params_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/params.yaml - predictions_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/predictions.json - probabilities_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/probabilities.json - score_dict_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/score_dict.json - test_labels_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/test_labels.json - train_labels_file: output/reports/train/6fd64214566b1ce33f475bf133cb18e4/train_labels.json - model_dir: models - name: 6fd64214566b1ce33f475bf133cb18e4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: - - linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 93acd32c2f54af6150b1e310609dcdd0 - trainer: - kwargs: {} -name: 6fd64214566b1ce33f475bf133cb18e4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6fe46144087100252df62153fc322ddb/params.yaml b/examples/security/classification/output/reports/train/6fe46144087100252df62153fc322ddb/params.yaml deleted file mode 100644 index d01aab2d..00000000 --- a/examples/security/classification/output/reports/train/6fe46144087100252df62153fc322ddb/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6fe46144087100252df62153fc322ddb/adv_predictions.json - adv_probabilities_file: output/reports/train/6fe46144087100252df62153fc322ddb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/a0462197adeaebda45016935b45e7f36.pkl - params_file: output/reports/train/6fe46144087100252df62153fc322ddb/params.yaml - predictions_file: output/reports/train/6fe46144087100252df62153fc322ddb/predictions.json - probabilities_file: output/reports/train/6fe46144087100252df62153fc322ddb/probabilities.json - score_dict_file: output/reports/train/6fe46144087100252df62153fc322ddb/score_dict.json - test_labels_file: output/reports/train/6fe46144087100252df62153fc322ddb/test_labels.json - train_labels_file: output/reports/train/6fe46144087100252df62153fc322ddb/train_labels.json - model_dir: models - name: 6fe46144087100252df62153fc322ddb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: babd9913ed9dba19687d2ddfd57021fb - trainer: - kwargs: {} -name: 6fe46144087100252df62153fc322ddb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/params.yaml b/examples/security/classification/output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/params.yaml deleted file mode 100644 index 05587faf..00000000 --- a/examples/security/classification/output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/adv_predictions.json - adv_probabilities_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/30888f6a728bc1287c783dc8ed9eb0fa.pkl - params_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/params.yaml - predictions_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/predictions.json - probabilities_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/probabilities.json - score_dict_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/score_dict.json - test_labels_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/test_labels.json - train_labels_file: output/reports/train/6ffec5c2a73866b61a0747b6114d05f2/train_labels.json - model_dir: models - name: 6ffec5c2a73866b61a0747b6114d05f2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a1ccea44241d84b8067c65dbe4eae1e8 - trainer: - kwargs: {} -name: 6ffec5c2a73866b61a0747b6114d05f2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/params.yaml b/examples/security/classification/output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/params.yaml deleted file mode 100644 index ca0b1f00..00000000 --- a/examples/security/classification/output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/adv_predictions.json - adv_probabilities_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/d371c8c623f43f49b44529f54d7cebee.pkl - params_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/params.yaml - predictions_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/predictions.json - probabilities_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/probabilities.json - score_dict_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/score_dict.json - test_labels_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/test_labels.json - train_labels_file: output/reports/train/70035a1e0b7f1d4b6ef0d20278e8a45f/train_labels.json - model_dir: models - name: 70035a1e0b7f1d4b6ef0d20278e8a45f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ccbabe1b7bb01f77df8011622d385aca - trainer: - kwargs: {} -name: 70035a1e0b7f1d4b6ef0d20278e8a45f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7007d6fa536ce83029b9852c81fa24cd/params.yaml b/examples/security/classification/output/reports/train/7007d6fa536ce83029b9852c81fa24cd/params.yaml deleted file mode 100644 index f61a408f..00000000 --- a/examples/security/classification/output/reports/train/7007d6fa536ce83029b9852c81fa24cd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/adv_predictions.json - adv_probabilities_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/21cef85e5ea09b1b86aef977a7326be2.pkl - params_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/params.yaml - predictions_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/predictions.json - probabilities_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/probabilities.json - score_dict_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/score_dict.json - test_labels_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/test_labels.json - train_labels_file: output/reports/train/7007d6fa536ce83029b9852c81fa24cd/train_labels.json - model_dir: models - name: 7007d6fa536ce83029b9852c81fa24cd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c6a5e5bdaaf847ce5a3f22146a58c02e - trainer: - kwargs: {} -name: 7007d6fa536ce83029b9852c81fa24cd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/params.yaml b/examples/security/classification/output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/params.yaml deleted file mode 100644 index dec90cac..00000000 --- a/examples/security/classification/output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/adv_predictions.json - adv_probabilities_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/738b115176a7f941683ea375a1b569b7.pkl - params_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/params.yaml - predictions_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/predictions.json - probabilities_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/probabilities.json - score_dict_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/score_dict.json - test_labels_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/test_labels.json - train_labels_file: output/reports/train/70257a6ae3d26fe45efaa549d9c03f91/train_labels.json - model_dir: models - name: 70257a6ae3d26fe45efaa549d9c03f91 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d2b4770247be95eff64fda3c90f91d60 - trainer: - kwargs: {} -name: 70257a6ae3d26fe45efaa549d9c03f91 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/703023ea0762d60943628bc17e587d25/params.yaml b/examples/security/classification/output/reports/train/703023ea0762d60943628bc17e587d25/params.yaml deleted file mode 100644 index 0c83d8ca..00000000 --- a/examples/security/classification/output/reports/train/703023ea0762d60943628bc17e587d25/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/703023ea0762d60943628bc17e587d25/adv_predictions.json - adv_probabilities_file: output/reports/train/703023ea0762d60943628bc17e587d25/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/bf743c8d2c3b937442d28317a987f2e5.pkl - params_file: output/reports/train/703023ea0762d60943628bc17e587d25/params.yaml - predictions_file: output/reports/train/703023ea0762d60943628bc17e587d25/predictions.json - probabilities_file: output/reports/train/703023ea0762d60943628bc17e587d25/probabilities.json - score_dict_file: output/reports/train/703023ea0762d60943628bc17e587d25/score_dict.json - test_labels_file: output/reports/train/703023ea0762d60943628bc17e587d25/test_labels.json - train_labels_file: output/reports/train/703023ea0762d60943628bc17e587d25/train_labels.json - model_dir: models - name: 703023ea0762d60943628bc17e587d25 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 52041ea50facdc16f4223d8f6dedb73a - trainer: - kwargs: {} -name: 703023ea0762d60943628bc17e587d25 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/params.yaml b/examples/security/classification/output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/params.yaml deleted file mode 100644 index ac91d2d2..00000000 --- a/examples/security/classification/output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/adv_predictions.json - adv_probabilities_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/3bee6a7b567140dcd459ae906ef0e438.pkl - params_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/params.yaml - predictions_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/predictions.json - probabilities_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/probabilities.json - score_dict_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/score_dict.json - test_labels_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/test_labels.json - train_labels_file: output/reports/train/703de213b0337f3796c0d2ecebc3f2c4/train_labels.json - model_dir: models - name: 703de213b0337f3796c0d2ecebc3f2c4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7fe640d4aa8f58a5d43770c58552f042 - trainer: - kwargs: {} -name: 703de213b0337f3796c0d2ecebc3f2c4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/70642247962dddcfcb2cb118ce68d621/params.yaml b/examples/security/classification/output/reports/train/70642247962dddcfcb2cb118ce68d621/params.yaml deleted file mode 100644 index 06689dda..00000000 --- a/examples/security/classification/output/reports/train/70642247962dddcfcb2cb118ce68d621/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/adv_predictions.json - adv_probabilities_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/19ba4432773060ae6220d13bb52959e7.pkl - params_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/params.yaml - predictions_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/predictions.json - probabilities_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/probabilities.json - score_dict_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/score_dict.json - test_labels_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/test_labels.json - train_labels_file: output/reports/train/70642247962dddcfcb2cb118ce68d621/train_labels.json - model_dir: models - name: 70642247962dddcfcb2cb118ce68d621 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3ef400280926feb7696b036843face12 - trainer: - kwargs: {} -name: 70642247962dddcfcb2cb118ce68d621 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/params.yaml b/examples/security/classification/output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/params.yaml deleted file mode 100644 index a6ec205b..00000000 --- a/examples/security/classification/output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/adv_predictions.json - adv_probabilities_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/2db1e3d9945937a5215c63c95bdb6ddf.pkl - params_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/params.yaml - predictions_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/predictions.json - probabilities_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/probabilities.json - score_dict_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/score_dict.json - test_labels_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/test_labels.json - train_labels_file: output/reports/train/70aa79b126ff0acbd4d96eaa777767e3/train_labels.json - model_dir: models - name: 70aa79b126ff0acbd4d96eaa777767e3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7434efcba24c9d9a3e2c1c1ca753c758 - trainer: - kwargs: {} -name: 70aa79b126ff0acbd4d96eaa777767e3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/params.yaml b/examples/security/classification/output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/params.yaml deleted file mode 100644 index d3c99de4..00000000 --- a/examples/security/classification/output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/adv_predictions.json - adv_probabilities_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/93fb9cfed30de5c061c399973f5c3cfe.pkl - params_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/params.yaml - predictions_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/predictions.json - probabilities_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/probabilities.json - score_dict_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/score_dict.json - test_labels_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/test_labels.json - train_labels_file: output/reports/train/70b30fff7d37bc3528efaa2d97a5c2ea/train_labels.json - model_dir: models - name: 70b30fff7d37bc3528efaa2d97a5c2ea - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 75c77cc069f2e60cc52c723b98928dc3 - trainer: - kwargs: {} -name: 70b30fff7d37bc3528efaa2d97a5c2ea -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/70bb9717adb6c1333a84cb02dff6f219/params.yaml b/examples/security/classification/output/reports/train/70bb9717adb6c1333a84cb02dff6f219/params.yaml deleted file mode 100644 index 15ef4e32..00000000 --- a/examples/security/classification/output/reports/train/70bb9717adb6c1333a84cb02dff6f219/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/adv_predictions.json - adv_probabilities_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/5f2adaa5b38b80b82ad03ee18ddd6bc9.pkl - params_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/params.yaml - predictions_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/predictions.json - probabilities_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/probabilities.json - score_dict_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/score_dict.json - test_labels_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/test_labels.json - train_labels_file: output/reports/train/70bb9717adb6c1333a84cb02dff6f219/train_labels.json - model_dir: models - name: 70bb9717adb6c1333a84cb02dff6f219 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 25e9e6ab2e9d76795746cf09629b5e56 - trainer: - kwargs: {} -name: 70bb9717adb6c1333a84cb02dff6f219 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/70bfde1b161030506ea7452db6110d0d/params.yaml b/examples/security/classification/output/reports/train/70bfde1b161030506ea7452db6110d0d/params.yaml deleted file mode 100644 index b7a4261f..00000000 --- a/examples/security/classification/output/reports/train/70bfde1b161030506ea7452db6110d0d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/adv_predictions.json - adv_probabilities_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/4dbe5962c554342b5efb25c8d6852ccb.pkl - params_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/params.yaml - predictions_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/predictions.json - probabilities_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/probabilities.json - score_dict_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/score_dict.json - test_labels_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/test_labels.json - train_labels_file: output/reports/train/70bfde1b161030506ea7452db6110d0d/train_labels.json - model_dir: models - name: 70bfde1b161030506ea7452db6110d0d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 484a7ba82bd6b5f3d5cfadb26301da9e - trainer: - kwargs: {} -name: 70bfde1b161030506ea7452db6110d0d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/params.yaml b/examples/security/classification/output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/params.yaml deleted file mode 100644 index 86c77f6c..00000000 --- a/examples/security/classification/output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/adv_predictions.json - adv_probabilities_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/a746d0ad04240597ff690440ed3fa886.pkl - params_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/params.yaml - predictions_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/predictions.json - probabilities_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/probabilities.json - score_dict_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/score_dict.json - test_labels_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/test_labels.json - train_labels_file: output/reports/train/70c8e01b4b3f3f320242220e2ba85cce/train_labels.json - model_dir: models - name: 70c8e01b4b3f3f320242220e2ba85cce - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cf7c092cfc69acb3a153be504d48ae23 - trainer: - kwargs: {} -name: 70c8e01b4b3f3f320242220e2ba85cce -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/params.yaml b/examples/security/classification/output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/params.yaml deleted file mode 100644 index 0698b96a..00000000 --- a/examples/security/classification/output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/adv_predictions.json - adv_probabilities_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/7e5d641531a8cfd0149969f1ceb7305d.pkl - params_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/params.yaml - predictions_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/predictions.json - probabilities_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/probabilities.json - score_dict_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/score_dict.json - test_labels_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/test_labels.json - train_labels_file: output/reports/train/70f70056796bf9e26ffdde0a8f04dc86/train_labels.json - model_dir: models - name: 70f70056796bf9e26ffdde0a8f04dc86 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 92a8ca46be8527d3814d6ab14579bb36 - trainer: - kwargs: {} -name: 70f70056796bf9e26ffdde0a8f04dc86 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/710b44e5f413e77a1809c907c3bea871/params.yaml b/examples/security/classification/output/reports/train/710b44e5f413e77a1809c907c3bea871/params.yaml deleted file mode 100644 index 528ae032..00000000 --- a/examples/security/classification/output/reports/train/710b44e5f413e77a1809c907c3bea871/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/adv_predictions.json - adv_probabilities_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/19df21f9e539800098b57a0c11c79d0e.pkl - params_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/params.yaml - predictions_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/predictions.json - probabilities_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/probabilities.json - score_dict_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/score_dict.json - test_labels_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/test_labels.json - train_labels_file: output/reports/train/710b44e5f413e77a1809c907c3bea871/train_labels.json - model_dir: models - name: 710b44e5f413e77a1809c907c3bea871 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ea9ed192784d83669ec6f3a84edf6148 - trainer: - kwargs: {} -name: 710b44e5f413e77a1809c907c3bea871 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/711c7611c6bd4974065d224a60effb00/params.yaml b/examples/security/classification/output/reports/train/711c7611c6bd4974065d224a60effb00/params.yaml deleted file mode 100644 index 6e0ca23b..00000000 --- a/examples/security/classification/output/reports/train/711c7611c6bd4974065d224a60effb00/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/711c7611c6bd4974065d224a60effb00/adv_predictions.json - adv_probabilities_file: output/reports/train/711c7611c6bd4974065d224a60effb00/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/307ad291801e36581c05128ba5f52fe1.pkl - params_file: output/reports/train/711c7611c6bd4974065d224a60effb00/params.yaml - predictions_file: output/reports/train/711c7611c6bd4974065d224a60effb00/predictions.json - probabilities_file: output/reports/train/711c7611c6bd4974065d224a60effb00/probabilities.json - score_dict_file: output/reports/train/711c7611c6bd4974065d224a60effb00/score_dict.json - test_labels_file: output/reports/train/711c7611c6bd4974065d224a60effb00/test_labels.json - train_labels_file: output/reports/train/711c7611c6bd4974065d224a60effb00/train_labels.json - model_dir: models - name: 711c7611c6bd4974065d224a60effb00 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 53cacc2e4ed7f1ea816fb0b907b54136 - trainer: - kwargs: {} -name: 711c7611c6bd4974065d224a60effb00 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/711d9be1aa806223099d34a250671f0f/params.yaml b/examples/security/classification/output/reports/train/711d9be1aa806223099d34a250671f0f/params.yaml deleted file mode 100644 index 51baf92d..00000000 --- a/examples/security/classification/output/reports/train/711d9be1aa806223099d34a250671f0f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/711d9be1aa806223099d34a250671f0f/adv_predictions.json - adv_probabilities_file: output/reports/train/711d9be1aa806223099d34a250671f0f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/b6204df3c37834726603ce02ec72fc28.pkl - params_file: output/reports/train/711d9be1aa806223099d34a250671f0f/params.yaml - predictions_file: output/reports/train/711d9be1aa806223099d34a250671f0f/predictions.json - probabilities_file: output/reports/train/711d9be1aa806223099d34a250671f0f/probabilities.json - score_dict_file: output/reports/train/711d9be1aa806223099d34a250671f0f/score_dict.json - test_labels_file: output/reports/train/711d9be1aa806223099d34a250671f0f/test_labels.json - train_labels_file: output/reports/train/711d9be1aa806223099d34a250671f0f/train_labels.json - model_dir: models - name: 711d9be1aa806223099d34a250671f0f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4dd08f123a388645fa6db4995397cd8d - trainer: - kwargs: {} -name: 711d9be1aa806223099d34a250671f0f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/71374f5a0451241cd0932959be89d48e/params.yaml b/examples/security/classification/output/reports/train/71374f5a0451241cd0932959be89d48e/params.yaml deleted file mode 100644 index 4c6bb7a9..00000000 --- a/examples/security/classification/output/reports/train/71374f5a0451241cd0932959be89d48e/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/71374f5a0451241cd0932959be89d48e/adv_predictions.json - adv_probabilities_file: output/reports/train/71374f5a0451241cd0932959be89d48e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/41cb502237b975f3a4ad30528ec054a2.pkl - params_file: output/reports/train/71374f5a0451241cd0932959be89d48e/params.yaml - predictions_file: output/reports/train/71374f5a0451241cd0932959be89d48e/predictions.json - probabilities_file: output/reports/train/71374f5a0451241cd0932959be89d48e/probabilities.json - score_dict_file: output/reports/train/71374f5a0451241cd0932959be89d48e/score_dict.json - test_labels_file: output/reports/train/71374f5a0451241cd0932959be89d48e/test_labels.json - train_labels_file: output/reports/train/71374f5a0451241cd0932959be89d48e/train_labels.json - model_dir: models - name: 71374f5a0451241cd0932959be89d48e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 68feb414235f6cfd4ab7de844241ca5c - trainer: - kwargs: {} -name: 71374f5a0451241cd0932959be89d48e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/716d1517cd69c849833847e81570d2f6/params.yaml b/examples/security/classification/output/reports/train/716d1517cd69c849833847e81570d2f6/params.yaml deleted file mode 100644 index d248ebc6..00000000 --- a/examples/security/classification/output/reports/train/716d1517cd69c849833847e81570d2f6/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/716d1517cd69c849833847e81570d2f6/adv_predictions.json - adv_probabilities_file: output/reports/train/716d1517cd69c849833847e81570d2f6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/03d1766ae671ff3dd5f6024da6ea9bf7.pkl - params_file: output/reports/train/716d1517cd69c849833847e81570d2f6/params.yaml - predictions_file: output/reports/train/716d1517cd69c849833847e81570d2f6/predictions.json - probabilities_file: output/reports/train/716d1517cd69c849833847e81570d2f6/probabilities.json - score_dict_file: output/reports/train/716d1517cd69c849833847e81570d2f6/score_dict.json - test_labels_file: output/reports/train/716d1517cd69c849833847e81570d2f6/test_labels.json - train_labels_file: output/reports/train/716d1517cd69c849833847e81570d2f6/train_labels.json - model_dir: models - name: 716d1517cd69c849833847e81570d2f6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e6a9bc10f9699e84bb8a6889fae65277 - trainer: - kwargs: {} -name: 716d1517cd69c849833847e81570d2f6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7187d523031594631738ef1e2a75d35a/params.yaml b/examples/security/classification/output/reports/train/7187d523031594631738ef1e2a75d35a/params.yaml deleted file mode 100644 index 144654eb..00000000 --- a/examples/security/classification/output/reports/train/7187d523031594631738ef1e2a75d35a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7187d523031594631738ef1e2a75d35a/adv_predictions.json - adv_probabilities_file: output/reports/train/7187d523031594631738ef1e2a75d35a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/d47944bcd357716add06a6d30a4c6a94.pkl - params_file: output/reports/train/7187d523031594631738ef1e2a75d35a/params.yaml - predictions_file: output/reports/train/7187d523031594631738ef1e2a75d35a/predictions.json - probabilities_file: output/reports/train/7187d523031594631738ef1e2a75d35a/probabilities.json - score_dict_file: output/reports/train/7187d523031594631738ef1e2a75d35a/score_dict.json - test_labels_file: output/reports/train/7187d523031594631738ef1e2a75d35a/test_labels.json - train_labels_file: output/reports/train/7187d523031594631738ef1e2a75d35a/train_labels.json - model_dir: models - name: 7187d523031594631738ef1e2a75d35a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6c8d24fc1565f0b345911377b99a730e - trainer: - kwargs: {} -name: 7187d523031594631738ef1e2a75d35a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/params.yaml b/examples/security/classification/output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/params.yaml deleted file mode 100644 index 0a254a29..00000000 --- a/examples/security/classification/output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/adv_predictions.json - adv_probabilities_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/c078852816b20c8acec5b1f03ab78acb.pkl - params_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/params.yaml - predictions_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/predictions.json - probabilities_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/probabilities.json - score_dict_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/score_dict.json - test_labels_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/test_labels.json - train_labels_file: output/reports/train/71ae134d171a52bb1c52fe9be1ae799c/train_labels.json - model_dir: models - name: 71ae134d171a52bb1c52fe9be1ae799c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9ebffc85f13f38827bf7dba37fba0e52 - trainer: - kwargs: {} -name: 71ae134d171a52bb1c52fe9be1ae799c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/71dce034a9e750311561b0024eeb977c/params.yaml b/examples/security/classification/output/reports/train/71dce034a9e750311561b0024eeb977c/params.yaml deleted file mode 100644 index 4466531c..00000000 --- a/examples/security/classification/output/reports/train/71dce034a9e750311561b0024eeb977c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/71dce034a9e750311561b0024eeb977c/adv_predictions.json - adv_probabilities_file: output/reports/train/71dce034a9e750311561b0024eeb977c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/936c0275d23e5a95d14c64f6f63006a2.pkl - params_file: output/reports/train/71dce034a9e750311561b0024eeb977c/params.yaml - predictions_file: output/reports/train/71dce034a9e750311561b0024eeb977c/predictions.json - probabilities_file: output/reports/train/71dce034a9e750311561b0024eeb977c/probabilities.json - score_dict_file: output/reports/train/71dce034a9e750311561b0024eeb977c/score_dict.json - test_labels_file: output/reports/train/71dce034a9e750311561b0024eeb977c/test_labels.json - train_labels_file: output/reports/train/71dce034a9e750311561b0024eeb977c/train_labels.json - model_dir: models - name: 71dce034a9e750311561b0024eeb977c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9690cb76ceaf48d0fa79c30a16d300a7 - trainer: - kwargs: {} -name: 71dce034a9e750311561b0024eeb977c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/71f1bca7ba0edb940d24066115a7733f/params.yaml b/examples/security/classification/output/reports/train/71f1bca7ba0edb940d24066115a7733f/params.yaml deleted file mode 100644 index 4313a7ca..00000000 --- a/examples/security/classification/output/reports/train/71f1bca7ba0edb940d24066115a7733f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/adv_predictions.json - adv_probabilities_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/58e601a76ae0fc3fcf72ce471914b744.pkl - params_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/params.yaml - predictions_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/predictions.json - probabilities_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/probabilities.json - score_dict_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/score_dict.json - test_labels_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/test_labels.json - train_labels_file: output/reports/train/71f1bca7ba0edb940d24066115a7733f/train_labels.json - model_dir: models - name: 71f1bca7ba0edb940d24066115a7733f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 626b9482cb430f4d01ac7902608fc86d - trainer: - kwargs: {} -name: 71f1bca7ba0edb940d24066115a7733f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/71fc7919e08a23ea190bbcc563053a6d/params.yaml b/examples/security/classification/output/reports/train/71fc7919e08a23ea190bbcc563053a6d/params.yaml deleted file mode 100644 index b4b08c1e..00000000 --- a/examples/security/classification/output/reports/train/71fc7919e08a23ea190bbcc563053a6d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/adv_predictions.json - adv_probabilities_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/43eb13676ca2b3f8ef2c2a79cc6945e0.pkl - params_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/params.yaml - predictions_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/predictions.json - probabilities_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/probabilities.json - score_dict_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/score_dict.json - test_labels_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/test_labels.json - train_labels_file: output/reports/train/71fc7919e08a23ea190bbcc563053a6d/train_labels.json - model_dir: models - name: 71fc7919e08a23ea190bbcc563053a6d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bd8a717d0ae7c82095b8f731567cee59 - trainer: - kwargs: {} -name: 71fc7919e08a23ea190bbcc563053a6d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7208366df358307a415e62a26c0112cb/params.yaml b/examples/security/classification/output/reports/train/7208366df358307a415e62a26c0112cb/params.yaml deleted file mode 100644 index e27e49b8..00000000 --- a/examples/security/classification/output/reports/train/7208366df358307a415e62a26c0112cb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7208366df358307a415e62a26c0112cb/adv_predictions.json - adv_probabilities_file: output/reports/train/7208366df358307a415e62a26c0112cb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/eb5b23d5499ec28a9ac4bb390db6f7b7.pkl - params_file: output/reports/train/7208366df358307a415e62a26c0112cb/params.yaml - predictions_file: output/reports/train/7208366df358307a415e62a26c0112cb/predictions.json - probabilities_file: output/reports/train/7208366df358307a415e62a26c0112cb/probabilities.json - score_dict_file: output/reports/train/7208366df358307a415e62a26c0112cb/score_dict.json - test_labels_file: output/reports/train/7208366df358307a415e62a26c0112cb/test_labels.json - train_labels_file: output/reports/train/7208366df358307a415e62a26c0112cb/train_labels.json - model_dir: models - name: 7208366df358307a415e62a26c0112cb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 43364d6c62a94ad9c9f52795d4a44280 - trainer: - kwargs: {} -name: 7208366df358307a415e62a26c0112cb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/72140775c00a51cba3238e2a0f77ec6a/params.yaml b/examples/security/classification/output/reports/train/72140775c00a51cba3238e2a0f77ec6a/params.yaml deleted file mode 100644 index 962fb4ed..00000000 --- a/examples/security/classification/output/reports/train/72140775c00a51cba3238e2a0f77ec6a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/adv_predictions.json - adv_probabilities_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/b37a451cf661aeb8a76fb27d93242553.pkl - params_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/params.yaml - predictions_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/predictions.json - probabilities_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/probabilities.json - score_dict_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/score_dict.json - test_labels_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/test_labels.json - train_labels_file: output/reports/train/72140775c00a51cba3238e2a0f77ec6a/train_labels.json - model_dir: models - name: 72140775c00a51cba3238e2a0f77ec6a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1a1af3125e43b1b320e964b8d33c6bf1 - trainer: - kwargs: {} -name: 72140775c00a51cba3238e2a0f77ec6a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/params.yaml b/examples/security/classification/output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/params.yaml deleted file mode 100644 index fd7afe3f..00000000 --- a/examples/security/classification/output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/adv_predictions.json - adv_probabilities_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/513de5dc06ac54e8769a54ccf9ede64a.pkl - params_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/params.yaml - predictions_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/predictions.json - probabilities_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/probabilities.json - score_dict_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/score_dict.json - test_labels_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/test_labels.json - train_labels_file: output/reports/train/7222d85a71ea4756ae7e3c1c56db42b9/train_labels.json - model_dir: models - name: 7222d85a71ea4756ae7e3c1c56db42b9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d0c1e136ec590e8c51d910b29588728f - trainer: - kwargs: {} -name: 7222d85a71ea4756ae7e3c1c56db42b9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/params.yaml b/examples/security/classification/output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/params.yaml deleted file mode 100644 index c8faf3d8..00000000 --- a/examples/security/classification/output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/adv_predictions.json - adv_probabilities_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/d5bdbb1895998f26c20585cb85e5cfd7.pkl - params_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/params.yaml - predictions_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/predictions.json - probabilities_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/probabilities.json - score_dict_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/score_dict.json - test_labels_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/test_labels.json - train_labels_file: output/reports/train/723f5015060c0a90ee5c7b9e8d3900b4/train_labels.json - model_dir: models - name: 723f5015060c0a90ee5c7b9e8d3900b4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4455208a6c103650a6318bfd4f4c2602 - trainer: - kwargs: {} -name: 723f5015060c0a90ee5c7b9e8d3900b4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/724153c7ab286ef87ef34721feb154e7/params.yaml b/examples/security/classification/output/reports/train/724153c7ab286ef87ef34721feb154e7/params.yaml deleted file mode 100644 index a9a8b04d..00000000 --- a/examples/security/classification/output/reports/train/724153c7ab286ef87ef34721feb154e7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/adv_predictions.json - adv_probabilities_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/de4fd574ea4692c745b2972b37f65262.pkl - params_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/params.yaml - predictions_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/predictions.json - probabilities_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/probabilities.json - score_dict_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/score_dict.json - test_labels_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/test_labels.json - train_labels_file: output/reports/train/724153c7ab286ef87ef34721feb154e7/train_labels.json - model_dir: models - name: 724153c7ab286ef87ef34721feb154e7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 699e7a48c29c00c43f68a4fa9ffd7dae - trainer: - kwargs: {} -name: 724153c7ab286ef87ef34721feb154e7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/724d0841112116dd3e261064c8d2e72d/params.yaml b/examples/security/classification/output/reports/train/724d0841112116dd3e261064c8d2e72d/params.yaml deleted file mode 100644 index fcc46df1..00000000 --- a/examples/security/classification/output/reports/train/724d0841112116dd3e261064c8d2e72d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/adv_predictions.json - adv_probabilities_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/b4f212d5e1f9a2be12dcb6f543453d76.pkl - params_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/params.yaml - predictions_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/predictions.json - probabilities_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/probabilities.json - score_dict_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/score_dict.json - test_labels_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/test_labels.json - train_labels_file: output/reports/train/724d0841112116dd3e261064c8d2e72d/train_labels.json - model_dir: models - name: 724d0841112116dd3e261064c8d2e72d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f9a96fb5a4cd17992e5593ad414766c5 - trainer: - kwargs: {} -name: 724d0841112116dd3e261064c8d2e72d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/params.yaml b/examples/security/classification/output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/params.yaml deleted file mode 100644 index dcacf36e..00000000 --- a/examples/security/classification/output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/adv_predictions.json - adv_probabilities_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/178898dec863e96b0043b4ff1ea162ce.pkl - params_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/params.yaml - predictions_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/predictions.json - probabilities_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/probabilities.json - score_dict_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/score_dict.json - test_labels_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/test_labels.json - train_labels_file: output/reports/train/7295f28f097a7c39335f6a2a4f15a86b/train_labels.json - model_dir: models - name: 7295f28f097a7c39335f6a2a4f15a86b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1aa52d5c6b23f14bbf27ece6cbd48849 - trainer: - kwargs: {} -name: 7295f28f097a7c39335f6a2a4f15a86b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/72ebaff61ba085cb4f2a816e48734370/params.yaml b/examples/security/classification/output/reports/train/72ebaff61ba085cb4f2a816e48734370/params.yaml deleted file mode 100644 index 07a72563..00000000 --- a/examples/security/classification/output/reports/train/72ebaff61ba085cb4f2a816e48734370/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/adv_predictions.json - adv_probabilities_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/762efe38c0e716ed7dbd42b32b07baea.pkl - params_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/params.yaml - predictions_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/predictions.json - probabilities_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/probabilities.json - score_dict_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/score_dict.json - test_labels_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/test_labels.json - train_labels_file: output/reports/train/72ebaff61ba085cb4f2a816e48734370/train_labels.json - model_dir: models - name: 72ebaff61ba085cb4f2a816e48734370 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 20edc57b965f625a27a9070e0face4dd - trainer: - kwargs: {} -name: 72ebaff61ba085cb4f2a816e48734370 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/72f920ea8182b65ee301ff5a68a28327/params.yaml b/examples/security/classification/output/reports/train/72f920ea8182b65ee301ff5a68a28327/params.yaml deleted file mode 100644 index cf116142..00000000 --- a/examples/security/classification/output/reports/train/72f920ea8182b65ee301ff5a68a28327/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/adv_predictions.json - adv_probabilities_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/4902a9bfcc27868bb81a05bf282f9899.pkl - params_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/params.yaml - predictions_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/predictions.json - probabilities_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/probabilities.json - score_dict_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/score_dict.json - test_labels_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/test_labels.json - train_labels_file: output/reports/train/72f920ea8182b65ee301ff5a68a28327/train_labels.json - model_dir: models - name: 72f920ea8182b65ee301ff5a68a28327 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 61ce5fc69bf98a5e353a163fc285e34c - trainer: - kwargs: {} -name: 72f920ea8182b65ee301ff5a68a28327 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/72fddc997412eecd0a2e67f347d3fe67/params.yaml b/examples/security/classification/output/reports/train/72fddc997412eecd0a2e67f347d3fe67/params.yaml deleted file mode 100644 index 1aea8549..00000000 --- a/examples/security/classification/output/reports/train/72fddc997412eecd0a2e67f347d3fe67/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/adv_predictions.json - adv_probabilities_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/5bc8ed02ffe4e8ff9d331220421a2414.pkl - params_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/params.yaml - predictions_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/predictions.json - probabilities_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/probabilities.json - score_dict_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/score_dict.json - test_labels_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/test_labels.json - train_labels_file: output/reports/train/72fddc997412eecd0a2e67f347d3fe67/train_labels.json - model_dir: models - name: 72fddc997412eecd0a2e67f347d3fe67 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bc2da2e36e8aeb68a39c38c8e70d300e - trainer: - kwargs: {} -name: 72fddc997412eecd0a2e67f347d3fe67 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7329fe57d5a328d98931c4aab388aab1/params.yaml b/examples/security/classification/output/reports/train/7329fe57d5a328d98931c4aab388aab1/params.yaml deleted file mode 100644 index 920e0ead..00000000 --- a/examples/security/classification/output/reports/train/7329fe57d5a328d98931c4aab388aab1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/adv_predictions.json - adv_probabilities_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/90033f07b64bce72bc37c8326c339006.pkl - params_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/params.yaml - predictions_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/predictions.json - probabilities_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/probabilities.json - score_dict_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/score_dict.json - test_labels_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/test_labels.json - train_labels_file: output/reports/train/7329fe57d5a328d98931c4aab388aab1/train_labels.json - model_dir: models - name: 7329fe57d5a328d98931c4aab388aab1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1ed07c40e54412d0694152aac2da9cac - trainer: - kwargs: {} -name: 7329fe57d5a328d98931c4aab388aab1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7345cb2266426f89686081d32b359da1/params.yaml b/examples/security/classification/output/reports/train/7345cb2266426f89686081d32b359da1/params.yaml deleted file mode 100644 index d5be402b..00000000 --- a/examples/security/classification/output/reports/train/7345cb2266426f89686081d32b359da1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7345cb2266426f89686081d32b359da1/adv_predictions.json - adv_probabilities_file: output/reports/train/7345cb2266426f89686081d32b359da1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/843ae707782fd79fbe5f7d1cc058809b.pkl - params_file: output/reports/train/7345cb2266426f89686081d32b359da1/params.yaml - predictions_file: output/reports/train/7345cb2266426f89686081d32b359da1/predictions.json - probabilities_file: output/reports/train/7345cb2266426f89686081d32b359da1/probabilities.json - score_dict_file: output/reports/train/7345cb2266426f89686081d32b359da1/score_dict.json - test_labels_file: output/reports/train/7345cb2266426f89686081d32b359da1/test_labels.json - train_labels_file: output/reports/train/7345cb2266426f89686081d32b359da1/train_labels.json - model_dir: models - name: 7345cb2266426f89686081d32b359da1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6775af5ca2db2ef5e607f53d791f5260 - trainer: - kwargs: {} -name: 7345cb2266426f89686081d32b359da1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/737e088b5c25453cfed5c28b48b03b2a/params.yaml b/examples/security/classification/output/reports/train/737e088b5c25453cfed5c28b48b03b2a/params.yaml deleted file mode 100644 index 5ef26ae8..00000000 --- a/examples/security/classification/output/reports/train/737e088b5c25453cfed5c28b48b03b2a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/adv_predictions.json - adv_probabilities_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/8134b6ca80df59b57eb4593a1ffdbccc.pkl - params_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/params.yaml - predictions_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/predictions.json - probabilities_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/probabilities.json - score_dict_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/score_dict.json - test_labels_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/test_labels.json - train_labels_file: output/reports/train/737e088b5c25453cfed5c28b48b03b2a/train_labels.json - model_dir: models - name: 737e088b5c25453cfed5c28b48b03b2a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9d9b3145163949bc108b8645841a19ce - trainer: - kwargs: {} -name: 737e088b5c25453cfed5c28b48b03b2a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/739ab8e381252234ae338e6c86f9eec5/params.yaml b/examples/security/classification/output/reports/train/739ab8e381252234ae338e6c86f9eec5/params.yaml deleted file mode 100644 index 331f5d3f..00000000 --- a/examples/security/classification/output/reports/train/739ab8e381252234ae338e6c86f9eec5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/adv_predictions.json - adv_probabilities_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/a85c314ae8c82bdc82d12ab871cd0aee.pkl - params_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/params.yaml - predictions_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/predictions.json - probabilities_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/probabilities.json - score_dict_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/score_dict.json - test_labels_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/test_labels.json - train_labels_file: output/reports/train/739ab8e381252234ae338e6c86f9eec5/train_labels.json - model_dir: models - name: 739ab8e381252234ae338e6c86f9eec5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9fdfaada8e130932c6f8bc701390ea52 - trainer: - kwargs: {} -name: 739ab8e381252234ae338e6c86f9eec5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/73acc7128a9a36598c4eca5abaa04754/params.yaml b/examples/security/classification/output/reports/train/73acc7128a9a36598c4eca5abaa04754/params.yaml deleted file mode 100644 index 9372bcc3..00000000 --- a/examples/security/classification/output/reports/train/73acc7128a9a36598c4eca5abaa04754/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/adv_predictions.json - adv_probabilities_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/9ae572a6cebfdaaa267c82a6653dd2a0.pkl - params_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/params.yaml - predictions_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/predictions.json - probabilities_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/probabilities.json - score_dict_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/score_dict.json - test_labels_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/test_labels.json - train_labels_file: output/reports/train/73acc7128a9a36598c4eca5abaa04754/train_labels.json - model_dir: models - name: 73acc7128a9a36598c4eca5abaa04754 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 94e17e49226423f394cd1f0e0f3f6e53 - trainer: - kwargs: {} -name: 73acc7128a9a36598c4eca5abaa04754 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/73c90714880dea7d0052fa48b3619c0b/params.yaml b/examples/security/classification/output/reports/train/73c90714880dea7d0052fa48b3619c0b/params.yaml deleted file mode 100644 index 485347be..00000000 --- a/examples/security/classification/output/reports/train/73c90714880dea7d0052fa48b3619c0b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/adv_predictions.json - adv_probabilities_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/96fd69a8bd3e57ad68763dd3d053c16d.pkl - params_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/params.yaml - predictions_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/predictions.json - probabilities_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/probabilities.json - score_dict_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/score_dict.json - test_labels_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/test_labels.json - train_labels_file: output/reports/train/73c90714880dea7d0052fa48b3619c0b/train_labels.json - model_dir: models - name: 73c90714880dea7d0052fa48b3619c0b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7d249247ae22571cfc52e13978c78b8d - trainer: - kwargs: {} -name: 73c90714880dea7d0052fa48b3619c0b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/73e7d220c3131e6bae03132759fa9cce/params.yaml b/examples/security/classification/output/reports/train/73e7d220c3131e6bae03132759fa9cce/params.yaml deleted file mode 100644 index bae1b8ef..00000000 --- a/examples/security/classification/output/reports/train/73e7d220c3131e6bae03132759fa9cce/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/adv_predictions.json - adv_probabilities_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/ca60960a37cfd3e20623a8acec5ed3d1.pkl - params_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/params.yaml - predictions_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/predictions.json - probabilities_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/probabilities.json - score_dict_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/score_dict.json - test_labels_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/test_labels.json - train_labels_file: output/reports/train/73e7d220c3131e6bae03132759fa9cce/train_labels.json - model_dir: models - name: 73e7d220c3131e6bae03132759fa9cce - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e445f48196e724332f5f448e9fd6e7ef - trainer: - kwargs: {} -name: 73e7d220c3131e6bae03132759fa9cce -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/73f3fb4c880ab72e740e39904ed1454d/params.yaml b/examples/security/classification/output/reports/train/73f3fb4c880ab72e740e39904ed1454d/params.yaml deleted file mode 100644 index c7f92d38..00000000 --- a/examples/security/classification/output/reports/train/73f3fb4c880ab72e740e39904ed1454d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/adv_predictions.json - adv_probabilities_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/ffed06a756cb841fe09de3d34eedee34.pkl - params_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/params.yaml - predictions_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/predictions.json - probabilities_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/probabilities.json - score_dict_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/score_dict.json - test_labels_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/test_labels.json - train_labels_file: output/reports/train/73f3fb4c880ab72e740e39904ed1454d/train_labels.json - model_dir: models - name: 73f3fb4c880ab72e740e39904ed1454d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a330afc35242e05dff2ecdc6f6bc1cce - trainer: - kwargs: {} -name: 73f3fb4c880ab72e740e39904ed1454d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/73f7792d55663207d9af8dff9acaaf88/params.yaml b/examples/security/classification/output/reports/train/73f7792d55663207d9af8dff9acaaf88/params.yaml deleted file mode 100644 index 24949fa2..00000000 --- a/examples/security/classification/output/reports/train/73f7792d55663207d9af8dff9acaaf88/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/adv_predictions.json - adv_probabilities_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/db1ae00f7fc9326f38988e0d96a29392.pkl - params_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/params.yaml - predictions_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/predictions.json - probabilities_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/probabilities.json - score_dict_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/score_dict.json - test_labels_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/test_labels.json - train_labels_file: output/reports/train/73f7792d55663207d9af8dff9acaaf88/train_labels.json - model_dir: models - name: 73f7792d55663207d9af8dff9acaaf88 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ad6785ceb328843b1fa7c973b4ae9059 - trainer: - kwargs: {} -name: 73f7792d55663207d9af8dff9acaaf88 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/params.yaml b/examples/security/classification/output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/params.yaml deleted file mode 100644 index 2b9f6afb..00000000 --- a/examples/security/classification/output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/adv_predictions.json - adv_probabilities_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/3d5ee4641ef0821ffc77ef8704c91ea1.pkl - params_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/params.yaml - predictions_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/predictions.json - probabilities_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/probabilities.json - score_dict_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/score_dict.json - test_labels_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/test_labels.json - train_labels_file: output/reports/train/741f2b909a6f148c21dcd51ca6879ec1/train_labels.json - model_dir: models - name: 741f2b909a6f148c21dcd51ca6879ec1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c2fc0ef2e8c62bfdf8a98ef78c38cdc6 - trainer: - kwargs: {} -name: 741f2b909a6f148c21dcd51ca6879ec1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7434b97b339f912c26d9887b1bb45f92/params.yaml b/examples/security/classification/output/reports/train/7434b97b339f912c26d9887b1bb45f92/params.yaml deleted file mode 100644 index c41b58d7..00000000 --- a/examples/security/classification/output/reports/train/7434b97b339f912c26d9887b1bb45f92/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/adv_predictions.json - adv_probabilities_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/ed556fc48c87351d1240d5f68cb2781c.pkl - params_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/params.yaml - predictions_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/predictions.json - probabilities_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/probabilities.json - score_dict_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/score_dict.json - test_labels_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/test_labels.json - train_labels_file: output/reports/train/7434b97b339f912c26d9887b1bb45f92/train_labels.json - model_dir: models - name: 7434b97b339f912c26d9887b1bb45f92 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a1dd1b9c196a57e61ddc8a7689e0ee20 - trainer: - kwargs: {} -name: 7434b97b339f912c26d9887b1bb45f92 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7443674207c288482312dadb6724ec94/params.yaml b/examples/security/classification/output/reports/train/7443674207c288482312dadb6724ec94/params.yaml deleted file mode 100644 index d859e61e..00000000 --- a/examples/security/classification/output/reports/train/7443674207c288482312dadb6724ec94/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7443674207c288482312dadb6724ec94/adv_predictions.json - adv_probabilities_file: output/reports/train/7443674207c288482312dadb6724ec94/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/07504f693ee961aa145b6e6302e54f6e.pkl - params_file: output/reports/train/7443674207c288482312dadb6724ec94/params.yaml - predictions_file: output/reports/train/7443674207c288482312dadb6724ec94/predictions.json - probabilities_file: output/reports/train/7443674207c288482312dadb6724ec94/probabilities.json - score_dict_file: output/reports/train/7443674207c288482312dadb6724ec94/score_dict.json - test_labels_file: output/reports/train/7443674207c288482312dadb6724ec94/test_labels.json - train_labels_file: output/reports/train/7443674207c288482312dadb6724ec94/train_labels.json - model_dir: models - name: 7443674207c288482312dadb6724ec94 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4aa5140409c8724784017b502a0ee473 - trainer: - kwargs: {} -name: 7443674207c288482312dadb6724ec94 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/params.yaml b/examples/security/classification/output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/params.yaml deleted file mode 100644 index b42bdfce..00000000 --- a/examples/security/classification/output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/adv_predictions.json - adv_probabilities_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/4d35048a5954b4c0e7e8e74b7bd08de7.pkl - params_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/params.yaml - predictions_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/predictions.json - probabilities_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/probabilities.json - score_dict_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/score_dict.json - test_labels_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/test_labels.json - train_labels_file: output/reports/train/7455a0a8ff17b72b55117216d9f9bf1e/train_labels.json - model_dir: models - name: 7455a0a8ff17b72b55117216d9f9bf1e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c8fd5700ff59b70c9f168d02bb2039fb - trainer: - kwargs: {} -name: 7455a0a8ff17b72b55117216d9f9bf1e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/params.yaml b/examples/security/classification/output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/params.yaml deleted file mode 100644 index a9cba725..00000000 --- a/examples/security/classification/output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/adv_predictions.json - adv_probabilities_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/148736f24e042b932404186e1b40230c.pkl - params_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/params.yaml - predictions_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/predictions.json - probabilities_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/probabilities.json - score_dict_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/score_dict.json - test_labels_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/test_labels.json - train_labels_file: output/reports/train/7499d7e10aaca7aad452304b43d6ebfd/train_labels.json - model_dir: models - name: 7499d7e10aaca7aad452304b43d6ebfd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ce823bce87fa90c811f62c989ffc5f27 - trainer: - kwargs: {} -name: 7499d7e10aaca7aad452304b43d6ebfd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/params.yaml b/examples/security/classification/output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/params.yaml deleted file mode 100644 index 862be2f8..00000000 --- a/examples/security/classification/output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/adv_predictions.json - adv_probabilities_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/6bf45389c6c0ee1c64c1d9742a0e7df8.pkl - params_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/params.yaml - predictions_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/predictions.json - probabilities_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/probabilities.json - score_dict_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/score_dict.json - test_labels_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/test_labels.json - train_labels_file: output/reports/train/749cb5ae985f7ed5670d53e0cdde2a6e/train_labels.json - model_dir: models - name: 749cb5ae985f7ed5670d53e0cdde2a6e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 83073b85840145a6d30ed51b7c456811 - trainer: - kwargs: {} -name: 749cb5ae985f7ed5670d53e0cdde2a6e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/74a0443a293985b6f13316d2237c93af/params.yaml b/examples/security/classification/output/reports/train/74a0443a293985b6f13316d2237c93af/params.yaml deleted file mode 100644 index e76985e5..00000000 --- a/examples/security/classification/output/reports/train/74a0443a293985b6f13316d2237c93af/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/74a0443a293985b6f13316d2237c93af/adv_predictions.json - adv_probabilities_file: output/reports/train/74a0443a293985b6f13316d2237c93af/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/4f12e858060f149b9664670b1d0cf594.pkl - params_file: output/reports/train/74a0443a293985b6f13316d2237c93af/params.yaml - predictions_file: output/reports/train/74a0443a293985b6f13316d2237c93af/predictions.json - probabilities_file: output/reports/train/74a0443a293985b6f13316d2237c93af/probabilities.json - score_dict_file: output/reports/train/74a0443a293985b6f13316d2237c93af/score_dict.json - test_labels_file: output/reports/train/74a0443a293985b6f13316d2237c93af/test_labels.json - train_labels_file: output/reports/train/74a0443a293985b6f13316d2237c93af/train_labels.json - model_dir: models - name: 74a0443a293985b6f13316d2237c93af - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8941771a003e3ce2aa27cad6aa959fe2 - trainer: - kwargs: {} -name: 74a0443a293985b6f13316d2237c93af -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/params.yaml b/examples/security/classification/output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/params.yaml deleted file mode 100644 index 595a764b..00000000 --- a/examples/security/classification/output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/adv_predictions.json - adv_probabilities_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/1fb5bf53edea8157c1a041e79340aa70.pkl - params_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/params.yaml - predictions_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/predictions.json - probabilities_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/probabilities.json - score_dict_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/score_dict.json - test_labels_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/test_labels.json - train_labels_file: output/reports/train/74bcd789523b22553dba7d29cbe6b4c0/train_labels.json - model_dir: models - name: 74bcd789523b22553dba7d29cbe6b4c0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fff76779eacb404730cae97e230e360a - trainer: - kwargs: {} -name: 74bcd789523b22553dba7d29cbe6b4c0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/750872c8c5fb8dcebef92d49172f6408/params.yaml b/examples/security/classification/output/reports/train/750872c8c5fb8dcebef92d49172f6408/params.yaml deleted file mode 100644 index c55114e5..00000000 --- a/examples/security/classification/output/reports/train/750872c8c5fb8dcebef92d49172f6408/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/adv_predictions.json - adv_probabilities_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/ab6cb94596dfda8e66f254f2f36defd2.pkl - params_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/params.yaml - predictions_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/predictions.json - probabilities_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/probabilities.json - score_dict_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/score_dict.json - test_labels_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/test_labels.json - train_labels_file: output/reports/train/750872c8c5fb8dcebef92d49172f6408/train_labels.json - model_dir: models - name: 750872c8c5fb8dcebef92d49172f6408 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 090673c969b3a29ef273fdddef147957 - trainer: - kwargs: {} -name: 750872c8c5fb8dcebef92d49172f6408 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/751198a067bdf99bfb13fde03c2c23e9/params.yaml b/examples/security/classification/output/reports/train/751198a067bdf99bfb13fde03c2c23e9/params.yaml deleted file mode 100644 index da199145..00000000 --- a/examples/security/classification/output/reports/train/751198a067bdf99bfb13fde03c2c23e9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/adv_predictions.json - adv_probabilities_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/647deebd6b4340dd1427d1a2ac3c082f.pkl - params_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/params.yaml - predictions_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/predictions.json - probabilities_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/probabilities.json - score_dict_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/score_dict.json - test_labels_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/test_labels.json - train_labels_file: output/reports/train/751198a067bdf99bfb13fde03c2c23e9/train_labels.json - model_dir: models - name: 751198a067bdf99bfb13fde03c2c23e9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2e3b014a9279803632122cd90d92682f - trainer: - kwargs: {} -name: 751198a067bdf99bfb13fde03c2c23e9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/756812dca04cfd760c7d59b64e0fdb25/params.yaml b/examples/security/classification/output/reports/train/756812dca04cfd760c7d59b64e0fdb25/params.yaml deleted file mode 100644 index df14f529..00000000 --- a/examples/security/classification/output/reports/train/756812dca04cfd760c7d59b64e0fdb25/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/adv_predictions.json - adv_probabilities_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/a1e872bbc85e57a3abbc5ad7c62f775d.pkl - params_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/params.yaml - predictions_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/predictions.json - probabilities_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/probabilities.json - score_dict_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/score_dict.json - test_labels_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/test_labels.json - train_labels_file: output/reports/train/756812dca04cfd760c7d59b64e0fdb25/train_labels.json - model_dir: models - name: 756812dca04cfd760c7d59b64e0fdb25 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 60804dfb0c4d906164ef7912523848ce - trainer: - kwargs: {} -name: 756812dca04cfd760c7d59b64e0fdb25 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7574aef98a3c70629678194d3d656a9c/params.yaml b/examples/security/classification/output/reports/train/7574aef98a3c70629678194d3d656a9c/params.yaml deleted file mode 100644 index 7643db1f..00000000 --- a/examples/security/classification/output/reports/train/7574aef98a3c70629678194d3d656a9c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/adv_predictions.json - adv_probabilities_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/973076ea86d779cf2ad4ab369ded2459.pkl - params_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/params.yaml - predictions_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/predictions.json - probabilities_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/probabilities.json - score_dict_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/score_dict.json - test_labels_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/test_labels.json - train_labels_file: output/reports/train/7574aef98a3c70629678194d3d656a9c/train_labels.json - model_dir: models - name: 7574aef98a3c70629678194d3d656a9c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 88d88456158d51e276708a7fc35de46b - trainer: - kwargs: {} -name: 7574aef98a3c70629678194d3d656a9c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/759827f61234097b09f7bf3673cac3f1/params.yaml b/examples/security/classification/output/reports/train/759827f61234097b09f7bf3673cac3f1/params.yaml deleted file mode 100644 index 3af8abc5..00000000 --- a/examples/security/classification/output/reports/train/759827f61234097b09f7bf3673cac3f1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/adv_predictions.json - adv_probabilities_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/b686c27d203097c892d9e0b055b1e357.pkl - params_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/params.yaml - predictions_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/predictions.json - probabilities_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/probabilities.json - score_dict_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/score_dict.json - test_labels_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/test_labels.json - train_labels_file: output/reports/train/759827f61234097b09f7bf3673cac3f1/train_labels.json - model_dir: models - name: 759827f61234097b09f7bf3673cac3f1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 214193a840aeeb0adc7f4744566a14f1 - trainer: - kwargs: {} -name: 759827f61234097b09f7bf3673cac3f1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/75beeac725705420a59c0be987812d02/params.yaml b/examples/security/classification/output/reports/train/75beeac725705420a59c0be987812d02/params.yaml deleted file mode 100644 index 0de33533..00000000 --- a/examples/security/classification/output/reports/train/75beeac725705420a59c0be987812d02/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/75beeac725705420a59c0be987812d02/adv_predictions.json - adv_probabilities_file: output/reports/train/75beeac725705420a59c0be987812d02/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/c6f0c788e91d0e5fc529ccab72e6b361.pkl - params_file: output/reports/train/75beeac725705420a59c0be987812d02/params.yaml - predictions_file: output/reports/train/75beeac725705420a59c0be987812d02/predictions.json - probabilities_file: output/reports/train/75beeac725705420a59c0be987812d02/probabilities.json - score_dict_file: output/reports/train/75beeac725705420a59c0be987812d02/score_dict.json - test_labels_file: output/reports/train/75beeac725705420a59c0be987812d02/test_labels.json - train_labels_file: output/reports/train/75beeac725705420a59c0be987812d02/train_labels.json - model_dir: models - name: 75beeac725705420a59c0be987812d02 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2ff843c4d128b45b50129cb98171fb6a - trainer: - kwargs: {} -name: 75beeac725705420a59c0be987812d02 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/75c14cdae535aeb6c246db59faeea8da/params.yaml b/examples/security/classification/output/reports/train/75c14cdae535aeb6c246db59faeea8da/params.yaml deleted file mode 100644 index e8ff762c..00000000 --- a/examples/security/classification/output/reports/train/75c14cdae535aeb6c246db59faeea8da/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/adv_predictions.json - adv_probabilities_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/dacb7c03825889c9fb24dfd8f7db7c52.pkl - params_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/params.yaml - predictions_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/predictions.json - probabilities_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/probabilities.json - score_dict_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/score_dict.json - test_labels_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/test_labels.json - train_labels_file: output/reports/train/75c14cdae535aeb6c246db59faeea8da/train_labels.json - model_dir: models - name: 75c14cdae535aeb6c246db59faeea8da - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3b0953f98286e7645030ad19c576366e - trainer: - kwargs: {} -name: 75c14cdae535aeb6c246db59faeea8da -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/75c86e7df52a72bd549ba759c0db03b8/params.yaml b/examples/security/classification/output/reports/train/75c86e7df52a72bd549ba759c0db03b8/params.yaml deleted file mode 100644 index 02d62006..00000000 --- a/examples/security/classification/output/reports/train/75c86e7df52a72bd549ba759c0db03b8/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/adv_predictions.json - adv_probabilities_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/e333cdb3816aa06baacfafda8eee7964.pkl - params_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/params.yaml - predictions_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/predictions.json - probabilities_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/probabilities.json - score_dict_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/score_dict.json - test_labels_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/test_labels.json - train_labels_file: output/reports/train/75c86e7df52a72bd549ba759c0db03b8/train_labels.json - model_dir: models - name: 75c86e7df52a72bd549ba759c0db03b8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d1188a216eaddca2d57d4bb6793e9450 - trainer: - kwargs: {} -name: 75c86e7df52a72bd549ba759c0db03b8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/params.yaml b/examples/security/classification/output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/params.yaml deleted file mode 100644 index d190acf2..00000000 --- a/examples/security/classification/output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/adv_predictions.json - adv_probabilities_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/b173dfeaf20bbed8b06ecfcac31aa2fe.pkl - params_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/params.yaml - predictions_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/predictions.json - probabilities_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/probabilities.json - score_dict_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/score_dict.json - test_labels_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/test_labels.json - train_labels_file: output/reports/train/75fd25dc7b13a1670a852e02448bf9ee/train_labels.json - model_dir: models - name: 75fd25dc7b13a1670a852e02448bf9ee - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4faaade7b37e946f9370c81cd93e8575 - trainer: - kwargs: {} -name: 75fd25dc7b13a1670a852e02448bf9ee -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/75fd5f39eff9874699c642b0ac1f607c/params.yaml b/examples/security/classification/output/reports/train/75fd5f39eff9874699c642b0ac1f607c/params.yaml deleted file mode 100644 index 6ef1ea29..00000000 --- a/examples/security/classification/output/reports/train/75fd5f39eff9874699c642b0ac1f607c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/adv_predictions.json - adv_probabilities_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/dcdb16f471e84dd573ae27bace177497.pkl - params_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/params.yaml - predictions_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/predictions.json - probabilities_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/probabilities.json - score_dict_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/score_dict.json - test_labels_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/test_labels.json - train_labels_file: output/reports/train/75fd5f39eff9874699c642b0ac1f607c/train_labels.json - model_dir: models - name: 75fd5f39eff9874699c642b0ac1f607c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cd78518717bbaa1d3c65d99d7224ed3e - trainer: - kwargs: {} -name: 75fd5f39eff9874699c642b0ac1f607c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7629738353463cb686fa87f794e3e17c/params.yaml b/examples/security/classification/output/reports/train/7629738353463cb686fa87f794e3e17c/params.yaml deleted file mode 100644 index 6f426239..00000000 --- a/examples/security/classification/output/reports/train/7629738353463cb686fa87f794e3e17c/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7629738353463cb686fa87f794e3e17c/adv_predictions.json - adv_probabilities_file: output/reports/train/7629738353463cb686fa87f794e3e17c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/5b8baa19d97d4ee568fb5945ab6da400.pkl - params_file: output/reports/train/7629738353463cb686fa87f794e3e17c/params.yaml - predictions_file: output/reports/train/7629738353463cb686fa87f794e3e17c/predictions.json - probabilities_file: output/reports/train/7629738353463cb686fa87f794e3e17c/probabilities.json - score_dict_file: output/reports/train/7629738353463cb686fa87f794e3e17c/score_dict.json - test_labels_file: output/reports/train/7629738353463cb686fa87f794e3e17c/test_labels.json - train_labels_file: output/reports/train/7629738353463cb686fa87f794e3e17c/train_labels.json - model_dir: models - name: 7629738353463cb686fa87f794e3e17c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e3e1ef325b00a683810a37d9252173af - trainer: - kwargs: {} -name: 7629738353463cb686fa87f794e3e17c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/763356da55ee97986ab5eb652f4ae737/params.yaml b/examples/security/classification/output/reports/train/763356da55ee97986ab5eb652f4ae737/params.yaml deleted file mode 100644 index d133483f..00000000 --- a/examples/security/classification/output/reports/train/763356da55ee97986ab5eb652f4ae737/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/adv_predictions.json - adv_probabilities_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/60cac9da028cd0af7b2446496ddc9c4b.pkl - params_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/params.yaml - predictions_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/predictions.json - probabilities_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/probabilities.json - score_dict_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/score_dict.json - test_labels_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/test_labels.json - train_labels_file: output/reports/train/763356da55ee97986ab5eb652f4ae737/train_labels.json - model_dir: models - name: 763356da55ee97986ab5eb652f4ae737 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b6175765acbbdac2d8fe5ad9d5bbb6de - trainer: - kwargs: {} -name: 763356da55ee97986ab5eb652f4ae737 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/params.yaml b/examples/security/classification/output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/params.yaml deleted file mode 100644 index 336756cc..00000000 --- a/examples/security/classification/output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/adv_predictions.json - adv_probabilities_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/a476fbccb4f706c72e1c4b0506b2ee15.pkl - params_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/params.yaml - predictions_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/predictions.json - probabilities_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/probabilities.json - score_dict_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/score_dict.json - test_labels_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/test_labels.json - train_labels_file: output/reports/train/764efbf3e462d4aa8c17ce21ffc0ca77/train_labels.json - model_dir: models - name: 764efbf3e462d4aa8c17ce21ffc0ca77 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 71d626d4b29ad7724cd9af2a95e29ef3 - trainer: - kwargs: {} -name: 764efbf3e462d4aa8c17ce21ffc0ca77 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/params.yaml b/examples/security/classification/output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/params.yaml deleted file mode 100644 index 83ff6cb9..00000000 --- a/examples/security/classification/output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/adv_predictions.json - adv_probabilities_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/227d7091e3c68752ec30711a93962b3d.pkl - params_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/params.yaml - predictions_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/predictions.json - probabilities_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/probabilities.json - score_dict_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/score_dict.json - test_labels_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/test_labels.json - train_labels_file: output/reports/train/76563d36e9ae26ab86ccf2dacebd4bb0/train_labels.json - model_dir: models - name: 76563d36e9ae26ab86ccf2dacebd4bb0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7282f28c212bda4bf205c480e82ee7d1 - trainer: - kwargs: {} -name: 76563d36e9ae26ab86ccf2dacebd4bb0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7656b2c596027084ab4b649303d26417/params.yaml b/examples/security/classification/output/reports/train/7656b2c596027084ab4b649303d26417/params.yaml deleted file mode 100644 index 87d84fde..00000000 --- a/examples/security/classification/output/reports/train/7656b2c596027084ab4b649303d26417/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7656b2c596027084ab4b649303d26417/adv_predictions.json - adv_probabilities_file: output/reports/train/7656b2c596027084ab4b649303d26417/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/a97aa5d4e40b091c41af0d57e813cddc.pkl - params_file: output/reports/train/7656b2c596027084ab4b649303d26417/params.yaml - predictions_file: output/reports/train/7656b2c596027084ab4b649303d26417/predictions.json - probabilities_file: output/reports/train/7656b2c596027084ab4b649303d26417/probabilities.json - score_dict_file: output/reports/train/7656b2c596027084ab4b649303d26417/score_dict.json - test_labels_file: output/reports/train/7656b2c596027084ab4b649303d26417/test_labels.json - train_labels_file: output/reports/train/7656b2c596027084ab4b649303d26417/train_labels.json - model_dir: models - name: 7656b2c596027084ab4b649303d26417 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 625fb6aa8710478e5d85bf7fddc3c452 - trainer: - kwargs: {} -name: 7656b2c596027084ab4b649303d26417 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7663d4b30d84bb89a40faf0d53accf97/params.yaml b/examples/security/classification/output/reports/train/7663d4b30d84bb89a40faf0d53accf97/params.yaml deleted file mode 100644 index 79259df4..00000000 --- a/examples/security/classification/output/reports/train/7663d4b30d84bb89a40faf0d53accf97/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/adv_predictions.json - adv_probabilities_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/d5ef01dbeeb9fd574be1629e29bc4fd0.pkl - params_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/params.yaml - predictions_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/predictions.json - probabilities_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/probabilities.json - score_dict_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/score_dict.json - test_labels_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/test_labels.json - train_labels_file: output/reports/train/7663d4b30d84bb89a40faf0d53accf97/train_labels.json - model_dir: models - name: 7663d4b30d84bb89a40faf0d53accf97 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 68dbea5cabdc51f1f640176cb0d93c15 - trainer: - kwargs: {} -name: 7663d4b30d84bb89a40faf0d53accf97 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/76673ad0bfd7e6751106c8bd8993c801/params.yaml b/examples/security/classification/output/reports/train/76673ad0bfd7e6751106c8bd8993c801/params.yaml deleted file mode 100644 index ee8db7a6..00000000 --- a/examples/security/classification/output/reports/train/76673ad0bfd7e6751106c8bd8993c801/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/adv_predictions.json - adv_probabilities_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/8e1909aa1d7eba1fdb9391230c8aba3b.pkl - params_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/params.yaml - predictions_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/predictions.json - probabilities_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/probabilities.json - score_dict_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/score_dict.json - test_labels_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/test_labels.json - train_labels_file: output/reports/train/76673ad0bfd7e6751106c8bd8993c801/train_labels.json - model_dir: models - name: 76673ad0bfd7e6751106c8bd8993c801 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: af8d883dc18a5627b45f8ab2839e743d - trainer: - kwargs: {} -name: 76673ad0bfd7e6751106c8bd8993c801 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/params.yaml b/examples/security/classification/output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/params.yaml deleted file mode 100644 index e551358b..00000000 --- a/examples/security/classification/output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/adv_predictions.json - adv_probabilities_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/8f957595a30fa9fdfdb7ddd8eac61879.pkl - params_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/params.yaml - predictions_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/predictions.json - probabilities_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/probabilities.json - score_dict_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/score_dict.json - test_labels_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/test_labels.json - train_labels_file: output/reports/train/7683a7ff95655d4269a6ded582f3fc2d/train_labels.json - model_dir: models - name: 7683a7ff95655d4269a6ded582f3fc2d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6dc80ad0bb5600f1ad367844d3c21f41 - trainer: - kwargs: {} -name: 7683a7ff95655d4269a6ded582f3fc2d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/params.yaml b/examples/security/classification/output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/params.yaml deleted file mode 100644 index 735ba2d5..00000000 --- a/examples/security/classification/output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/adv_predictions.json - adv_probabilities_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/19944b6e7d8a0ff50eed59fbb9f45784.pkl - params_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/params.yaml - predictions_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/predictions.json - probabilities_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/probabilities.json - score_dict_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/score_dict.json - test_labels_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/test_labels.json - train_labels_file: output/reports/train/768bd3edd67c73e359d8e9bd9e4a94fd/train_labels.json - model_dir: models - name: 768bd3edd67c73e359d8e9bd9e4a94fd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f1c1b7237b5c7104459a35288bbe57ab - trainer: - kwargs: {} -name: 768bd3edd67c73e359d8e9bd9e4a94fd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/769044657f47e894b39e955f1d3d5a17/params.yaml b/examples/security/classification/output/reports/train/769044657f47e894b39e955f1d3d5a17/params.yaml deleted file mode 100644 index da34fc2b..00000000 --- a/examples/security/classification/output/reports/train/769044657f47e894b39e955f1d3d5a17/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/adv_predictions.json - adv_probabilities_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/ef98cc6b285af785d582d5cc4fe63793.pkl - params_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/params.yaml - predictions_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/predictions.json - probabilities_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/probabilities.json - score_dict_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/score_dict.json - test_labels_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/test_labels.json - train_labels_file: output/reports/train/769044657f47e894b39e955f1d3d5a17/train_labels.json - model_dir: models - name: 769044657f47e894b39e955f1d3d5a17 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4b7379b4b8906fabd6cc73a6079f6322 - trainer: - kwargs: {} -name: 769044657f47e894b39e955f1d3d5a17 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/params.yaml b/examples/security/classification/output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/params.yaml deleted file mode 100644 index b0fde7a0..00000000 --- a/examples/security/classification/output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/adv_predictions.json - adv_probabilities_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/2fa5efcccb2423ded1375d166a3f3251.pkl - params_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/params.yaml - predictions_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/predictions.json - probabilities_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/probabilities.json - score_dict_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/score_dict.json - test_labels_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/test_labels.json - train_labels_file: output/reports/train/7693b7797936bc5b0e7a003cb5dcd7c2/train_labels.json - model_dir: models - name: 7693b7797936bc5b0e7a003cb5dcd7c2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 741dae64203557250521bd0af43fdf2f - trainer: - kwargs: {} -name: 7693b7797936bc5b0e7a003cb5dcd7c2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/params.yaml b/examples/security/classification/output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/params.yaml deleted file mode 100644 index af604946..00000000 --- a/examples/security/classification/output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/adv_predictions.json - adv_probabilities_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/a74eb260bfbd23fe803c7d7feda1f38d.pkl - params_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/params.yaml - predictions_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/predictions.json - probabilities_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/probabilities.json - score_dict_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/score_dict.json - test_labels_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/test_labels.json - train_labels_file: output/reports/train/7696371b977c1ef1e1c9ea19a21768a5/train_labels.json - model_dir: models - name: 7696371b977c1ef1e1c9ea19a21768a5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 53fac683ca748693a37b0908822250fb - trainer: - kwargs: {} -name: 7696371b977c1ef1e1c9ea19a21768a5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/769a6e379d7d19d530d2e53ba19ea229/params.yaml b/examples/security/classification/output/reports/train/769a6e379d7d19d530d2e53ba19ea229/params.yaml deleted file mode 100644 index 59a737fd..00000000 --- a/examples/security/classification/output/reports/train/769a6e379d7d19d530d2e53ba19ea229/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/adv_predictions.json - adv_probabilities_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/f6e4979fade1f0d510a12232963cec57.pkl - params_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/params.yaml - predictions_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/predictions.json - probabilities_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/probabilities.json - score_dict_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/score_dict.json - test_labels_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/test_labels.json - train_labels_file: output/reports/train/769a6e379d7d19d530d2e53ba19ea229/train_labels.json - model_dir: models - name: 769a6e379d7d19d530d2e53ba19ea229 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 573ef69775932b6c25a9c79fc0fbf43d - trainer: - kwargs: {} -name: 769a6e379d7d19d530d2e53ba19ea229 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/76acafa3b1642199303c83f2c8963a59/params.yaml b/examples/security/classification/output/reports/train/76acafa3b1642199303c83f2c8963a59/params.yaml deleted file mode 100644 index 7b9d77b4..00000000 --- a/examples/security/classification/output/reports/train/76acafa3b1642199303c83f2c8963a59/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/adv_predictions.json - adv_probabilities_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/940a1fff91d167c4c6543e378aceb629.pkl - params_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/params.yaml - predictions_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/predictions.json - probabilities_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/probabilities.json - score_dict_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/score_dict.json - test_labels_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/test_labels.json - train_labels_file: output/reports/train/76acafa3b1642199303c83f2c8963a59/train_labels.json - model_dir: models - name: 76acafa3b1642199303c83f2c8963a59 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7235968dd92dd48a0a18ad6523a44c7c - trainer: - kwargs: {} -name: 76acafa3b1642199303c83f2c8963a59 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/params.yaml b/examples/security/classification/output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/params.yaml deleted file mode 100644 index 0bedc922..00000000 --- a/examples/security/classification/output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/adv_predictions.json - adv_probabilities_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/d8005ce9cc53ed5c9659e46e704db9d8.pkl - params_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/params.yaml - predictions_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/predictions.json - probabilities_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/probabilities.json - score_dict_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/score_dict.json - test_labels_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/test_labels.json - train_labels_file: output/reports/train/76b85a4ad1229bc26e5acc3eebc7d879/train_labels.json - model_dir: models - name: 76b85a4ad1229bc26e5acc3eebc7d879 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7c5facc7ad2d187c08723f9055afa86b - trainer: - kwargs: {} -name: 76b85a4ad1229bc26e5acc3eebc7d879 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/76bfd82137c976850ea4a264b329ab49/params.yaml b/examples/security/classification/output/reports/train/76bfd82137c976850ea4a264b329ab49/params.yaml deleted file mode 100644 index e0307295..00000000 --- a/examples/security/classification/output/reports/train/76bfd82137c976850ea4a264b329ab49/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/adv_predictions.json - adv_probabilities_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/a2b68db9ce77deeddbb6c66d3eca50c4.pkl - params_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/params.yaml - predictions_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/predictions.json - probabilities_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/probabilities.json - score_dict_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/score_dict.json - test_labels_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/test_labels.json - train_labels_file: output/reports/train/76bfd82137c976850ea4a264b329ab49/train_labels.json - model_dir: models - name: 76bfd82137c976850ea4a264b329ab49 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 01ee17cb3122042c90c5d8610c665ffa - trainer: - kwargs: {} -name: 76bfd82137c976850ea4a264b329ab49 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/params.yaml b/examples/security/classification/output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/params.yaml deleted file mode 100644 index c38b002b..00000000 --- a/examples/security/classification/output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/adv_predictions.json - adv_probabilities_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/42881817a38e0217b6921e029f7cc7f3.pkl - params_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/params.yaml - predictions_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/predictions.json - probabilities_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/probabilities.json - score_dict_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/score_dict.json - test_labels_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/test_labels.json - train_labels_file: output/reports/train/76d2805b1e63e51860b5a79b6e4fb4bd/train_labels.json - model_dir: models - name: 76d2805b1e63e51860b5a79b6e4fb4bd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cf8963ee9afb9ad68d61cb8e87e72e05 - trainer: - kwargs: {} -name: 76d2805b1e63e51860b5a79b6e4fb4bd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/params.yaml b/examples/security/classification/output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/params.yaml deleted file mode 100644 index c972c065..00000000 --- a/examples/security/classification/output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/params.yaml +++ /dev/null @@ -1,206 +0,0 @@ -attack: - attack_size: 10 - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 10.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f630ddce89f7884608f3844d9f9b2d06 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7caa19a44958b110a96fead78147243c - trainer: - kwargs: {} - name: 7c393bc52e8492a69de323b1005be838 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/adv_predictions.json - adv_probabilities_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/adv_probabilities.json - attack_file: output/attacks/d0ff171710284fafe13204a5a20a5c9f.pkl - data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl - model_file: output/models/e438a4b6a2cb134cb3278a89147f66c8.pkl - params_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/params.yaml - predictions_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/predictions.json - probabilities_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/probabilities.json - score_dict_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/score_dict.json - test_labels_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/test_labels.json - train_labels_file: output/reports/train/76e8cdd2a6b20ba65a2dd48f254c2ca3/train_labels.json - model_dir: models - name: 76e8cdd2a6b20ba65a2dd48f254c2ca3 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 59eb21345a29edb531b22f94b764fcd7 - trainer: - kwargs: {} -name: 76e8cdd2a6b20ba65a2dd48f254c2ca3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/76faecca5dcb787b722ca53eabfae08a/params.yaml b/examples/security/classification/output/reports/train/76faecca5dcb787b722ca53eabfae08a/params.yaml deleted file mode 100644 index 3c084353..00000000 --- a/examples/security/classification/output/reports/train/76faecca5dcb787b722ca53eabfae08a/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/adv_predictions.json - adv_probabilities_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/366e414a7cede5e47b129841cc8ac05b.pkl - params_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/params.yaml - predictions_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/predictions.json - probabilities_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/probabilities.json - score_dict_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/score_dict.json - test_labels_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/test_labels.json - train_labels_file: output/reports/train/76faecca5dcb787b722ca53eabfae08a/train_labels.json - model_dir: models - name: 76faecca5dcb787b722ca53eabfae08a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fc6498baf6765a4da53f209515b5c21d - trainer: - kwargs: {} -name: 76faecca5dcb787b722ca53eabfae08a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7708413db3c8e4f168d1807d8426f962/params.yaml b/examples/security/classification/output/reports/train/7708413db3c8e4f168d1807d8426f962/params.yaml deleted file mode 100644 index c5e1a2e3..00000000 --- a/examples/security/classification/output/reports/train/7708413db3c8e4f168d1807d8426f962/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/adv_predictions.json - adv_probabilities_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/a8c52948852476c751f06b3409f3bd0c.pkl - params_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/params.yaml - predictions_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/predictions.json - probabilities_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/probabilities.json - score_dict_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/score_dict.json - test_labels_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/test_labels.json - train_labels_file: output/reports/train/7708413db3c8e4f168d1807d8426f962/train_labels.json - model_dir: models - name: 7708413db3c8e4f168d1807d8426f962 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e9f863bb70dd534cb7372b04dac857ce - trainer: - kwargs: {} -name: 7708413db3c8e4f168d1807d8426f962 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/params.yaml b/examples/security/classification/output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/params.yaml deleted file mode 100644 index 8deb59be..00000000 --- a/examples/security/classification/output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/adv_predictions.json - adv_probabilities_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/b2e9dd6c4bcdf7c759c99c11f39d1da5.pkl - params_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/params.yaml - predictions_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/predictions.json - probabilities_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/probabilities.json - score_dict_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/score_dict.json - test_labels_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/test_labels.json - train_labels_file: output/reports/train/7749f7c5a5dd14f4b17240734e4af6ac/train_labels.json - model_dir: models - name: 7749f7c5a5dd14f4b17240734e4af6ac - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ef915bedccf61929fe41a7f729478a54 - trainer: - kwargs: {} -name: 7749f7c5a5dd14f4b17240734e4af6ac -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/774b075fe5ad36a85b1f74dcacac6818/params.yaml b/examples/security/classification/output/reports/train/774b075fe5ad36a85b1f74dcacac6818/params.yaml deleted file mode 100644 index 2c5fedd7..00000000 --- a/examples/security/classification/output/reports/train/774b075fe5ad36a85b1f74dcacac6818/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/adv_predictions.json - adv_probabilities_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/07121884f326774e89e55413f98ebeec.pkl - params_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/params.yaml - predictions_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/predictions.json - probabilities_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/probabilities.json - score_dict_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/score_dict.json - test_labels_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/test_labels.json - train_labels_file: output/reports/train/774b075fe5ad36a85b1f74dcacac6818/train_labels.json - model_dir: models - name: 774b075fe5ad36a85b1f74dcacac6818 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbef541ae19f0239e3d4341ffc2f5a72 - trainer: - kwargs: {} -name: 774b075fe5ad36a85b1f74dcacac6818 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/774e3dc54cd53a1979f6678defad8225/params.yaml b/examples/security/classification/output/reports/train/774e3dc54cd53a1979f6678defad8225/params.yaml deleted file mode 100644 index 069199d5..00000000 --- a/examples/security/classification/output/reports/train/774e3dc54cd53a1979f6678defad8225/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/adv_predictions.json - adv_probabilities_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/414b17879eb10674d36dc9fbf98df84a.pkl - params_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/params.yaml - predictions_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/predictions.json - probabilities_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/probabilities.json - score_dict_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/score_dict.json - test_labels_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/test_labels.json - train_labels_file: output/reports/train/774e3dc54cd53a1979f6678defad8225/train_labels.json - model_dir: models - name: 774e3dc54cd53a1979f6678defad8225 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4caf64b63270b32a518fd3ef0081908e - trainer: - kwargs: {} -name: 774e3dc54cd53a1979f6678defad8225 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7760a164ebcaeca288e6c986c81942e7/params.yaml b/examples/security/classification/output/reports/train/7760a164ebcaeca288e6c986c81942e7/params.yaml deleted file mode 100644 index 7b251d78..00000000 --- a/examples/security/classification/output/reports/train/7760a164ebcaeca288e6c986c81942e7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/adv_predictions.json - adv_probabilities_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/51d0257495c453def02a6d27fb55cd9f.pkl - params_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/params.yaml - predictions_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/predictions.json - probabilities_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/probabilities.json - score_dict_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/score_dict.json - test_labels_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/test_labels.json - train_labels_file: output/reports/train/7760a164ebcaeca288e6c986c81942e7/train_labels.json - model_dir: models - name: 7760a164ebcaeca288e6c986c81942e7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 46d8b44354ec0174c597f239905f7144 - trainer: - kwargs: {} -name: 7760a164ebcaeca288e6c986c81942e7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/777111e8a77eed89facf2744c32dab34/params.yaml b/examples/security/classification/output/reports/train/777111e8a77eed89facf2744c32dab34/params.yaml deleted file mode 100644 index 77e761d1..00000000 --- a/examples/security/classification/output/reports/train/777111e8a77eed89facf2744c32dab34/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/777111e8a77eed89facf2744c32dab34/adv_predictions.json - adv_probabilities_file: output/reports/train/777111e8a77eed89facf2744c32dab34/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/dbc948692a516c06ce5004d2bd4800cd.pkl - params_file: output/reports/train/777111e8a77eed89facf2744c32dab34/params.yaml - predictions_file: output/reports/train/777111e8a77eed89facf2744c32dab34/predictions.json - probabilities_file: output/reports/train/777111e8a77eed89facf2744c32dab34/probabilities.json - score_dict_file: output/reports/train/777111e8a77eed89facf2744c32dab34/score_dict.json - test_labels_file: output/reports/train/777111e8a77eed89facf2744c32dab34/test_labels.json - train_labels_file: output/reports/train/777111e8a77eed89facf2744c32dab34/train_labels.json - model_dir: models - name: 777111e8a77eed89facf2744c32dab34 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3d89dc73bbf593b0c333ae25675b7f68 - trainer: - kwargs: {} -name: 777111e8a77eed89facf2744c32dab34 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/778eebbb853d8bbac0e515b043984c0e/params.yaml b/examples/security/classification/output/reports/train/778eebbb853d8bbac0e515b043984c0e/params.yaml deleted file mode 100644 index 30dc2a07..00000000 --- a/examples/security/classification/output/reports/train/778eebbb853d8bbac0e515b043984c0e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/adv_predictions.json - adv_probabilities_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/32c7ce10eba1c580ede59421d9f79734.pkl - params_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/params.yaml - predictions_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/predictions.json - probabilities_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/probabilities.json - score_dict_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/score_dict.json - test_labels_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/test_labels.json - train_labels_file: output/reports/train/778eebbb853d8bbac0e515b043984c0e/train_labels.json - model_dir: models - name: 778eebbb853d8bbac0e515b043984c0e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f70352e0e2a8f7b9e6e95ab12beadfb8 - trainer: - kwargs: {} -name: 778eebbb853d8bbac0e515b043984c0e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/779758228b9d3518db63c2ec78122479/params.yaml b/examples/security/classification/output/reports/train/779758228b9d3518db63c2ec78122479/params.yaml deleted file mode 100644 index 43f65bfc..00000000 --- a/examples/security/classification/output/reports/train/779758228b9d3518db63c2ec78122479/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/779758228b9d3518db63c2ec78122479/adv_predictions.json - adv_probabilities_file: output/reports/train/779758228b9d3518db63c2ec78122479/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/291c4908e1dd9ce4e48435df4c6e2571.pkl - params_file: output/reports/train/779758228b9d3518db63c2ec78122479/params.yaml - predictions_file: output/reports/train/779758228b9d3518db63c2ec78122479/predictions.json - probabilities_file: output/reports/train/779758228b9d3518db63c2ec78122479/probabilities.json - score_dict_file: output/reports/train/779758228b9d3518db63c2ec78122479/score_dict.json - test_labels_file: output/reports/train/779758228b9d3518db63c2ec78122479/test_labels.json - train_labels_file: output/reports/train/779758228b9d3518db63c2ec78122479/train_labels.json - model_dir: models - name: 779758228b9d3518db63c2ec78122479 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 925dc6d3d3d7a8f64a6553b1a34d10a2 - trainer: - kwargs: {} -name: 779758228b9d3518db63c2ec78122479 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/params.yaml b/examples/security/classification/output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/params.yaml deleted file mode 100644 index 36e72c19..00000000 --- a/examples/security/classification/output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/adv_predictions.json - adv_probabilities_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/15df9a3c9072093ed91b227f1962a56a.pkl - params_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/params.yaml - predictions_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/predictions.json - probabilities_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/probabilities.json - score_dict_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/score_dict.json - test_labels_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/test_labels.json - train_labels_file: output/reports/train/779c08e3bf814cc39c8abb0d87f42f48/train_labels.json - model_dir: models - name: 779c08e3bf814cc39c8abb0d87f42f48 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c1283c0cd7fb3ae78e6664b24e0cd2b3 - trainer: - kwargs: {} -name: 779c08e3bf814cc39c8abb0d87f42f48 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/77d61212f874fda5bd98609df10720b2/params.yaml b/examples/security/classification/output/reports/train/77d61212f874fda5bd98609df10720b2/params.yaml deleted file mode 100644 index 0b8d8dc9..00000000 --- a/examples/security/classification/output/reports/train/77d61212f874fda5bd98609df10720b2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/77d61212f874fda5bd98609df10720b2/adv_predictions.json - adv_probabilities_file: output/reports/train/77d61212f874fda5bd98609df10720b2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/1969f358d54678ec1b50135100764397.pkl - params_file: output/reports/train/77d61212f874fda5bd98609df10720b2/params.yaml - predictions_file: output/reports/train/77d61212f874fda5bd98609df10720b2/predictions.json - probabilities_file: output/reports/train/77d61212f874fda5bd98609df10720b2/probabilities.json - score_dict_file: output/reports/train/77d61212f874fda5bd98609df10720b2/score_dict.json - test_labels_file: output/reports/train/77d61212f874fda5bd98609df10720b2/test_labels.json - train_labels_file: output/reports/train/77d61212f874fda5bd98609df10720b2/train_labels.json - model_dir: models - name: 77d61212f874fda5bd98609df10720b2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: df69c5ed82b841825133c936f1a5c50a - trainer: - kwargs: {} -name: 77d61212f874fda5bd98609df10720b2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/params.yaml b/examples/security/classification/output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/params.yaml deleted file mode 100644 index 37e804dc..00000000 --- a/examples/security/classification/output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/adv_predictions.json - adv_probabilities_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/ad0b1a6b94f9ff5ac35b2c1e1f0a0b42.pkl - params_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/params.yaml - predictions_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/predictions.json - probabilities_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/probabilities.json - score_dict_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/score_dict.json - test_labels_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/test_labels.json - train_labels_file: output/reports/train/77dd0ca460430ca027bc912f2c95c3e4/train_labels.json - model_dir: models - name: 77dd0ca460430ca027bc912f2c95c3e4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6459aee1317df38f95b2876ecabf725e - trainer: - kwargs: {} -name: 77dd0ca460430ca027bc912f2c95c3e4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/77ee84a703db9a0edefcf4b35d81928d/params.yaml b/examples/security/classification/output/reports/train/77ee84a703db9a0edefcf4b35d81928d/params.yaml deleted file mode 100644 index 448a920c..00000000 --- a/examples/security/classification/output/reports/train/77ee84a703db9a0edefcf4b35d81928d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/adv_predictions.json - adv_probabilities_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/189d6756b19d1980710c0718d7e9ad65.pkl - params_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/params.yaml - predictions_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/predictions.json - probabilities_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/probabilities.json - score_dict_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/score_dict.json - test_labels_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/test_labels.json - train_labels_file: output/reports/train/77ee84a703db9a0edefcf4b35d81928d/train_labels.json - model_dir: models - name: 77ee84a703db9a0edefcf4b35d81928d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: de7b0298e5ecc623a1a5f9885cae05c3 - trainer: - kwargs: {} -name: 77ee84a703db9a0edefcf4b35d81928d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/params.yaml b/examples/security/classification/output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/params.yaml deleted file mode 100644 index 1047c319..00000000 --- a/examples/security/classification/output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/adv_predictions.json - adv_probabilities_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/211793c986f3d2c79fd61068c6a542ee.pkl - params_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/params.yaml - predictions_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/predictions.json - probabilities_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/probabilities.json - score_dict_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/score_dict.json - test_labels_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/test_labels.json - train_labels_file: output/reports/train/7803d6407ba3d9a9be46a4582cc6b8d5/train_labels.json - model_dir: models - name: 7803d6407ba3d9a9be46a4582cc6b8d5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7c5b93eb80eb7432d1e4336e50e1c53f - trainer: - kwargs: {} -name: 7803d6407ba3d9a9be46a4582cc6b8d5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7833f12805f90e2a178c1c152f14a627/params.yaml b/examples/security/classification/output/reports/train/7833f12805f90e2a178c1c152f14a627/params.yaml deleted file mode 100644 index c18d33f9..00000000 --- a/examples/security/classification/output/reports/train/7833f12805f90e2a178c1c152f14a627/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/adv_predictions.json - adv_probabilities_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/7847c5d43b8be94af995819acb79b064.pkl - params_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/params.yaml - predictions_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/predictions.json - probabilities_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/probabilities.json - score_dict_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/score_dict.json - test_labels_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/test_labels.json - train_labels_file: output/reports/train/7833f12805f90e2a178c1c152f14a627/train_labels.json - model_dir: models - name: 7833f12805f90e2a178c1c152f14a627 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 411adb037574961369d899a8d573ffd9 - trainer: - kwargs: {} -name: 7833f12805f90e2a178c1c152f14a627 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/784c603d7b3170ac97e017ee241004ad/params.yaml b/examples/security/classification/output/reports/train/784c603d7b3170ac97e017ee241004ad/params.yaml deleted file mode 100644 index 458775c9..00000000 --- a/examples/security/classification/output/reports/train/784c603d7b3170ac97e017ee241004ad/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/adv_predictions.json - adv_probabilities_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/4a89c888e453b8ab6b9b725197e5518a.pkl - params_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/params.yaml - predictions_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/predictions.json - probabilities_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/probabilities.json - score_dict_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/score_dict.json - test_labels_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/test_labels.json - train_labels_file: output/reports/train/784c603d7b3170ac97e017ee241004ad/train_labels.json - model_dir: models - name: 784c603d7b3170ac97e017ee241004ad - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f7b0451ca8ff36551409ca3d147f220 - trainer: - kwargs: {} -name: 784c603d7b3170ac97e017ee241004ad -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/785dd8f22b06c9cc798c37664c62b722/params.yaml b/examples/security/classification/output/reports/train/785dd8f22b06c9cc798c37664c62b722/params.yaml deleted file mode 100644 index d9649edb..00000000 --- a/examples/security/classification/output/reports/train/785dd8f22b06c9cc798c37664c62b722/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/adv_predictions.json - adv_probabilities_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/6f399d8fbdb443e3cba133bfc1448073.pkl - params_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/params.yaml - predictions_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/predictions.json - probabilities_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/probabilities.json - score_dict_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/score_dict.json - test_labels_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/test_labels.json - train_labels_file: output/reports/train/785dd8f22b06c9cc798c37664c62b722/train_labels.json - model_dir: models - name: 785dd8f22b06c9cc798c37664c62b722 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 505d6f86ae99b45b01960bff18b89296 - trainer: - kwargs: {} -name: 785dd8f22b06c9cc798c37664c62b722 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/78996e2f500db1fb7b6ba8274d11678f/params.yaml b/examples/security/classification/output/reports/train/78996e2f500db1fb7b6ba8274d11678f/params.yaml deleted file mode 100644 index 8e35bf68..00000000 --- a/examples/security/classification/output/reports/train/78996e2f500db1fb7b6ba8274d11678f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/adv_predictions.json - adv_probabilities_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/e8a82dbe7e389c58336fdc92a993a7d8.pkl - params_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/params.yaml - predictions_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/predictions.json - probabilities_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/probabilities.json - score_dict_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/score_dict.json - test_labels_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/test_labels.json - train_labels_file: output/reports/train/78996e2f500db1fb7b6ba8274d11678f/train_labels.json - model_dir: models - name: 78996e2f500db1fb7b6ba8274d11678f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 19e0cbe36a99755829fb24583a16b4a6 - trainer: - kwargs: {} -name: 78996e2f500db1fb7b6ba8274d11678f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/789dde02275a7ae1b006ebe6cda7b698/params.yaml b/examples/security/classification/output/reports/train/789dde02275a7ae1b006ebe6cda7b698/params.yaml deleted file mode 100644 index 53d888ac..00000000 --- a/examples/security/classification/output/reports/train/789dde02275a7ae1b006ebe6cda7b698/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/adv_predictions.json - adv_probabilities_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/7fc62b8b35a63e91d236341bdf362ff3.pkl - params_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/params.yaml - predictions_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/predictions.json - probabilities_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/probabilities.json - score_dict_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/score_dict.json - test_labels_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/test_labels.json - train_labels_file: output/reports/train/789dde02275a7ae1b006ebe6cda7b698/train_labels.json - model_dir: models - name: 789dde02275a7ae1b006ebe6cda7b698 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cef9441ab62d2dd9be6b0e3fe04229d2 - trainer: - kwargs: {} -name: 789dde02275a7ae1b006ebe6cda7b698 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/params.yaml b/examples/security/classification/output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/params.yaml deleted file mode 100644 index 6f5e3395..00000000 --- a/examples/security/classification/output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/adv_predictions.json - adv_probabilities_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/dfae723e0c7040d28d0981439c28b2cc.pkl - params_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/params.yaml - predictions_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/predictions.json - probabilities_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/probabilities.json - score_dict_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/score_dict.json - test_labels_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/test_labels.json - train_labels_file: output/reports/train/78b2eea060e5613c0fa1c39420ad5c2b/train_labels.json - model_dir: models - name: 78b2eea060e5613c0fa1c39420ad5c2b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7a640964a9e2840fef1fa8e73b823b4a - trainer: - kwargs: {} -name: 78b2eea060e5613c0fa1c39420ad5c2b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/78b623c22869b757bd1145496daa5a1c/params.yaml b/examples/security/classification/output/reports/train/78b623c22869b757bd1145496daa5a1c/params.yaml deleted file mode 100644 index 5b0521e3..00000000 --- a/examples/security/classification/output/reports/train/78b623c22869b757bd1145496daa5a1c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/adv_predictions.json - adv_probabilities_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/36123b1915ae6053b0707c0fe112e5d3.pkl - params_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/params.yaml - predictions_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/predictions.json - probabilities_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/probabilities.json - score_dict_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/score_dict.json - test_labels_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/test_labels.json - train_labels_file: output/reports/train/78b623c22869b757bd1145496daa5a1c/train_labels.json - model_dir: models - name: 78b623c22869b757bd1145496daa5a1c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f6120598d57bde5ac77983d08990c40a - trainer: - kwargs: {} -name: 78b623c22869b757bd1145496daa5a1c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/params.yaml b/examples/security/classification/output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/params.yaml deleted file mode 100644 index f7875b59..00000000 --- a/examples/security/classification/output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/adv_predictions.json - adv_probabilities_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/20b76a7b03605c6a52fad102bcabe1a9.pkl - params_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/params.yaml - predictions_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/predictions.json - probabilities_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/probabilities.json - score_dict_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/score_dict.json - test_labels_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/test_labels.json - train_labels_file: output/reports/train/78bf21e2d7558f0c4854dd2d1d2c6b84/train_labels.json - model_dir: models - name: 78bf21e2d7558f0c4854dd2d1d2c6b84 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a29b5ee001811192052a3ede43c927e4 - trainer: - kwargs: {} -name: 78bf21e2d7558f0c4854dd2d1d2c6b84 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/78c690412f619ecbb7e534ab284ccd15/params.yaml b/examples/security/classification/output/reports/train/78c690412f619ecbb7e534ab284ccd15/params.yaml deleted file mode 100644 index ff5c53c4..00000000 --- a/examples/security/classification/output/reports/train/78c690412f619ecbb7e534ab284ccd15/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/adv_predictions.json - adv_probabilities_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/ae86a055dfde16069c5feb4d2643809e.pkl - params_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/params.yaml - predictions_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/predictions.json - probabilities_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/probabilities.json - score_dict_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/score_dict.json - test_labels_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/test_labels.json - train_labels_file: output/reports/train/78c690412f619ecbb7e534ab284ccd15/train_labels.json - model_dir: models - name: 78c690412f619ecbb7e534ab284ccd15 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 667b9f4a2ad9ecac3ab01b60d77ba3e9 - trainer: - kwargs: {} -name: 78c690412f619ecbb7e534ab284ccd15 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/78d5f2c2301b1b344accce5bf493efd6/params.yaml b/examples/security/classification/output/reports/train/78d5f2c2301b1b344accce5bf493efd6/params.yaml deleted file mode 100644 index cc4c4920..00000000 --- a/examples/security/classification/output/reports/train/78d5f2c2301b1b344accce5bf493efd6/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/adv_predictions.json - adv_probabilities_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/aad7e13ab66ac61a8e46389b9a724aa5.pkl - params_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/params.yaml - predictions_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/predictions.json - probabilities_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/probabilities.json - score_dict_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/score_dict.json - test_labels_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/test_labels.json - train_labels_file: output/reports/train/78d5f2c2301b1b344accce5bf493efd6/train_labels.json - model_dir: models - name: 78d5f2c2301b1b344accce5bf493efd6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6e18b5d06124a5f51823f5ce6a466f42 - trainer: - kwargs: {} -name: 78d5f2c2301b1b344accce5bf493efd6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/params.yaml b/examples/security/classification/output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/params.yaml deleted file mode 100644 index 30a64a5f..00000000 --- a/examples/security/classification/output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/adv_predictions.json - adv_probabilities_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/a86919ba5e8404499a0debff407fc534.pkl - params_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/params.yaml - predictions_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/predictions.json - probabilities_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/probabilities.json - score_dict_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/score_dict.json - test_labels_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/test_labels.json - train_labels_file: output/reports/train/7919ec08aa045c0959ea7aa4b8c1f1a1/train_labels.json - model_dir: models - name: 7919ec08aa045c0959ea7aa4b8c1f1a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d46a7b5ceeaba536ecb439e075b6b676 - trainer: - kwargs: {} -name: 7919ec08aa045c0959ea7aa4b8c1f1a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7920fca06ffeb482b0786132659e8415/params.yaml b/examples/security/classification/output/reports/train/7920fca06ffeb482b0786132659e8415/params.yaml deleted file mode 100644 index 03d1e729..00000000 --- a/examples/security/classification/output/reports/train/7920fca06ffeb482b0786132659e8415/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7920fca06ffeb482b0786132659e8415/adv_predictions.json - adv_probabilities_file: output/reports/train/7920fca06ffeb482b0786132659e8415/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/ba801da8907f81b7a08c3ee4d15c410f.pkl - params_file: output/reports/train/7920fca06ffeb482b0786132659e8415/params.yaml - predictions_file: output/reports/train/7920fca06ffeb482b0786132659e8415/predictions.json - probabilities_file: output/reports/train/7920fca06ffeb482b0786132659e8415/probabilities.json - score_dict_file: output/reports/train/7920fca06ffeb482b0786132659e8415/score_dict.json - test_labels_file: output/reports/train/7920fca06ffeb482b0786132659e8415/test_labels.json - train_labels_file: output/reports/train/7920fca06ffeb482b0786132659e8415/train_labels.json - model_dir: models - name: 7920fca06ffeb482b0786132659e8415 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3ca50155fd9cfecb2b87b6fc911cde92 - trainer: - kwargs: {} -name: 7920fca06ffeb482b0786132659e8415 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7922248f1319b69207980ab363a81153/params.yaml b/examples/security/classification/output/reports/train/7922248f1319b69207980ab363a81153/params.yaml deleted file mode 100644 index 83b18960..00000000 --- a/examples/security/classification/output/reports/train/7922248f1319b69207980ab363a81153/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7922248f1319b69207980ab363a81153/adv_predictions.json - adv_probabilities_file: output/reports/train/7922248f1319b69207980ab363a81153/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/08f7a63c3817ab14b0998d047a0d465a.pkl - params_file: output/reports/train/7922248f1319b69207980ab363a81153/params.yaml - predictions_file: output/reports/train/7922248f1319b69207980ab363a81153/predictions.json - probabilities_file: output/reports/train/7922248f1319b69207980ab363a81153/probabilities.json - score_dict_file: output/reports/train/7922248f1319b69207980ab363a81153/score_dict.json - test_labels_file: output/reports/train/7922248f1319b69207980ab363a81153/test_labels.json - train_labels_file: output/reports/train/7922248f1319b69207980ab363a81153/train_labels.json - model_dir: models - name: 7922248f1319b69207980ab363a81153 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 18e716781ad6f3527bdf37726da27250 - trainer: - kwargs: {} -name: 7922248f1319b69207980ab363a81153 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/79300bfc3bad0baa8d53159f61c76dde/params.yaml b/examples/security/classification/output/reports/train/79300bfc3bad0baa8d53159f61c76dde/params.yaml deleted file mode 100644 index 1baebcc9..00000000 --- a/examples/security/classification/output/reports/train/79300bfc3bad0baa8d53159f61c76dde/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/adv_predictions.json - adv_probabilities_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/284d8f7aefdd460c9750ee784f83208c.pkl - params_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/params.yaml - predictions_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/predictions.json - probabilities_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/probabilities.json - score_dict_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/score_dict.json - test_labels_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/test_labels.json - train_labels_file: output/reports/train/79300bfc3bad0baa8d53159f61c76dde/train_labels.json - model_dir: models - name: 79300bfc3bad0baa8d53159f61c76dde - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8c251eccf13f9f7fc2c14239946dd35e - trainer: - kwargs: {} -name: 79300bfc3bad0baa8d53159f61c76dde -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/793b06ba9e3221f2940ec698f58c21ff/params.yaml b/examples/security/classification/output/reports/train/793b06ba9e3221f2940ec698f58c21ff/params.yaml deleted file mode 100644 index 0e79f9d7..00000000 --- a/examples/security/classification/output/reports/train/793b06ba9e3221f2940ec698f58c21ff/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/adv_predictions.json - adv_probabilities_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/ce35f2f577cf65a3891feea16845007e.pkl - params_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/params.yaml - predictions_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/predictions.json - probabilities_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/probabilities.json - score_dict_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/score_dict.json - test_labels_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/test_labels.json - train_labels_file: output/reports/train/793b06ba9e3221f2940ec698f58c21ff/train_labels.json - model_dir: models - name: 793b06ba9e3221f2940ec698f58c21ff - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 804e35c8472972ea36092b4922461401 - trainer: - kwargs: {} -name: 793b06ba9e3221f2940ec698f58c21ff -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/params.yaml b/examples/security/classification/output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/params.yaml deleted file mode 100644 index 09a7c23d..00000000 --- a/examples/security/classification/output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/adv_predictions.json - adv_probabilities_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/c576225555b9e72c42bd61e0889d1000.pkl - params_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/params.yaml - predictions_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/predictions.json - probabilities_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/probabilities.json - score_dict_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/score_dict.json - test_labels_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/test_labels.json - train_labels_file: output/reports/train/796b3f8d3ebd7175bf587bfb13aa54e2/train_labels.json - model_dir: models - name: 796b3f8d3ebd7175bf587bfb13aa54e2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 662b1ba404cad48de6607d9d2f576374 - trainer: - kwargs: {} -name: 796b3f8d3ebd7175bf587bfb13aa54e2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/799012eb13dd8b49d369757292c35376/params.yaml b/examples/security/classification/output/reports/train/799012eb13dd8b49d369757292c35376/params.yaml deleted file mode 100644 index 438867cc..00000000 --- a/examples/security/classification/output/reports/train/799012eb13dd8b49d369757292c35376/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/799012eb13dd8b49d369757292c35376/adv_predictions.json - adv_probabilities_file: output/reports/train/799012eb13dd8b49d369757292c35376/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/b5539bbc827ceef6fb88f133caa0f22d.pkl - params_file: output/reports/train/799012eb13dd8b49d369757292c35376/params.yaml - predictions_file: output/reports/train/799012eb13dd8b49d369757292c35376/predictions.json - probabilities_file: output/reports/train/799012eb13dd8b49d369757292c35376/probabilities.json - score_dict_file: output/reports/train/799012eb13dd8b49d369757292c35376/score_dict.json - test_labels_file: output/reports/train/799012eb13dd8b49d369757292c35376/test_labels.json - train_labels_file: output/reports/train/799012eb13dd8b49d369757292c35376/train_labels.json - model_dir: models - name: 799012eb13dd8b49d369757292c35376 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f6245a572b4ae26d5ac678daff1367b2 - trainer: - kwargs: {} -name: 799012eb13dd8b49d369757292c35376 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/79eff8b7fa97f52afc69913f4581611e/params.yaml b/examples/security/classification/output/reports/train/79eff8b7fa97f52afc69913f4581611e/params.yaml deleted file mode 100644 index f44bd181..00000000 --- a/examples/security/classification/output/reports/train/79eff8b7fa97f52afc69913f4581611e/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/adv_predictions.json - adv_probabilities_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/b877a6e40702b2ccd2c26b58c61a8209.pkl - params_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/params.yaml - predictions_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/predictions.json - probabilities_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/probabilities.json - score_dict_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/score_dict.json - test_labels_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/test_labels.json - train_labels_file: output/reports/train/79eff8b7fa97f52afc69913f4581611e/train_labels.json - model_dir: models - name: 79eff8b7fa97f52afc69913f4581611e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f2c47171748cb6a7427ea3014979b556 - trainer: - kwargs: {} -name: 79eff8b7fa97f52afc69913f4581611e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/params.yaml b/examples/security/classification/output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/params.yaml deleted file mode 100644 index 69ab3e71..00000000 --- a/examples/security/classification/output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/adv_predictions.json - adv_probabilities_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/d58ff03519ab47342f82c4b2e3832545.pkl - params_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/params.yaml - predictions_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/predictions.json - probabilities_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/probabilities.json - score_dict_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/score_dict.json - test_labels_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/test_labels.json - train_labels_file: output/reports/train/7a06a82e1a2e328955353d51ef6c4f75/train_labels.json - model_dir: models - name: 7a06a82e1a2e328955353d51ef6c4f75 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2a1377e480b5d372c246c4223abd02ff - trainer: - kwargs: {} -name: 7a06a82e1a2e328955353d51ef6c4f75 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/params.yaml b/examples/security/classification/output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/params.yaml deleted file mode 100644 index ee65f6e5..00000000 --- a/examples/security/classification/output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/adv_predictions.json - adv_probabilities_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/a8b2d075fe5a172cc11576a3cfacbd4c.pkl - params_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/params.yaml - predictions_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/predictions.json - probabilities_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/probabilities.json - score_dict_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/score_dict.json - test_labels_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/test_labels.json - train_labels_file: output/reports/train/7a19ae69bad53407afbb0cfa83dcca59/train_labels.json - model_dir: models - name: 7a19ae69bad53407afbb0cfa83dcca59 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 970c7364cb5f279d7adc717b3a094536 - trainer: - kwargs: {} -name: 7a19ae69bad53407afbb0cfa83dcca59 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/params.yaml b/examples/security/classification/output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/params.yaml deleted file mode 100644 index 512f243b..00000000 --- a/examples/security/classification/output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/adv_predictions.json - adv_probabilities_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/8604e2c738c6f43ca8baa793b7cc255d.pkl - params_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/params.yaml - predictions_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/predictions.json - probabilities_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/probabilities.json - score_dict_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/score_dict.json - test_labels_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/test_labels.json - train_labels_file: output/reports/train/7a2aec9e13b69f66eefa972b73d42e41/train_labels.json - model_dir: models - name: 7a2aec9e13b69f66eefa972b73d42e41 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 94234c6bcf37ca5b6a1993acd5cd94cb - trainer: - kwargs: {} -name: 7a2aec9e13b69f66eefa972b73d42e41 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7a371010714827d8ca15a4bd91204d61/params.yaml b/examples/security/classification/output/reports/train/7a371010714827d8ca15a4bd91204d61/params.yaml deleted file mode 100644 index 4f9b9cfe..00000000 --- a/examples/security/classification/output/reports/train/7a371010714827d8ca15a4bd91204d61/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/adv_predictions.json - adv_probabilities_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/b414997b2e7c9ac98b8851564acfe5b2.pkl - params_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/params.yaml - predictions_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/predictions.json - probabilities_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/probabilities.json - score_dict_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/score_dict.json - test_labels_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/test_labels.json - train_labels_file: output/reports/train/7a371010714827d8ca15a4bd91204d61/train_labels.json - model_dir: models - name: 7a371010714827d8ca15a4bd91204d61 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e66e54d8174cad8c31c1d161da471ff2 - trainer: - kwargs: {} -name: 7a371010714827d8ca15a4bd91204d61 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/params.yaml b/examples/security/classification/output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/params.yaml deleted file mode 100644 index 1747b1d0..00000000 --- a/examples/security/classification/output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/adv_predictions.json - adv_probabilities_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/1c76ff175608a423449d51cc6af22f23.pkl - params_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/params.yaml - predictions_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/predictions.json - probabilities_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/probabilities.json - score_dict_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/score_dict.json - test_labels_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/test_labels.json - train_labels_file: output/reports/train/7a4a9fd3951355e1cde31393fe772e6e/train_labels.json - model_dir: models - name: 7a4a9fd3951355e1cde31393fe772e6e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f6e734a68afdad0fea66d4720d504c05 - trainer: - kwargs: {} -name: 7a4a9fd3951355e1cde31393fe772e6e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7a4e20b4aca9092cc993abd964f48a11/params.yaml b/examples/security/classification/output/reports/train/7a4e20b4aca9092cc993abd964f48a11/params.yaml deleted file mode 100644 index f0891f3c..00000000 --- a/examples/security/classification/output/reports/train/7a4e20b4aca9092cc993abd964f48a11/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/adv_predictions.json - adv_probabilities_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/eea8f14762f8800b4b9babfc75a17b0b.pkl - params_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/params.yaml - predictions_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/predictions.json - probabilities_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/probabilities.json - score_dict_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/score_dict.json - test_labels_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/test_labels.json - train_labels_file: output/reports/train/7a4e20b4aca9092cc993abd964f48a11/train_labels.json - model_dir: models - name: 7a4e20b4aca9092cc993abd964f48a11 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 35140c724bfee3f71389f0c332ef4e3c - trainer: - kwargs: {} -name: 7a4e20b4aca9092cc993abd964f48a11 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7a5a5a519779312f31d325e3efa12a69/params.yaml b/examples/security/classification/output/reports/train/7a5a5a519779312f31d325e3efa12a69/params.yaml deleted file mode 100644 index 443a902b..00000000 --- a/examples/security/classification/output/reports/train/7a5a5a519779312f31d325e3efa12a69/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/adv_predictions.json - adv_probabilities_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/a0390e617862f7f319547596ce256117.pkl - params_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/params.yaml - predictions_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/predictions.json - probabilities_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/probabilities.json - score_dict_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/score_dict.json - test_labels_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/test_labels.json - train_labels_file: output/reports/train/7a5a5a519779312f31d325e3efa12a69/train_labels.json - model_dir: models - name: 7a5a5a519779312f31d325e3efa12a69 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2b2705df651b517cc9f77f0d189a3027 - trainer: - kwargs: {} -name: 7a5a5a519779312f31d325e3efa12a69 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/params.yaml b/examples/security/classification/output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/params.yaml deleted file mode 100644 index b3dc7165..00000000 --- a/examples/security/classification/output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/adv_predictions.json - adv_probabilities_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/629c546c9a29b18814b5afcd539367ef.pkl - params_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/params.yaml - predictions_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/predictions.json - probabilities_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/probabilities.json - score_dict_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/score_dict.json - test_labels_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/test_labels.json - train_labels_file: output/reports/train/7a6d22aac3bc31a091ca7b7da8f6bb35/train_labels.json - model_dir: models - name: 7a6d22aac3bc31a091ca7b7da8f6bb35 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8360663ed9d55690d8b77579d76e0589 - trainer: - kwargs: {} -name: 7a6d22aac3bc31a091ca7b7da8f6bb35 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7a7130bdedfc550af08d9630125939b3/params.yaml b/examples/security/classification/output/reports/train/7a7130bdedfc550af08d9630125939b3/params.yaml deleted file mode 100644 index 7c4b50fc..00000000 --- a/examples/security/classification/output/reports/train/7a7130bdedfc550af08d9630125939b3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/adv_predictions.json - adv_probabilities_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/148735e8e52c05b8d00862b37fdc2702.pkl - params_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/params.yaml - predictions_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/predictions.json - probabilities_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/probabilities.json - score_dict_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/score_dict.json - test_labels_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/test_labels.json - train_labels_file: output/reports/train/7a7130bdedfc550af08d9630125939b3/train_labels.json - model_dir: models - name: 7a7130bdedfc550af08d9630125939b3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6a6e2bb8b0a51a74dc88b08f3764fdd4 - trainer: - kwargs: {} -name: 7a7130bdedfc550af08d9630125939b3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7aa078a1af3025b03c9c543d53c50938/params.yaml b/examples/security/classification/output/reports/train/7aa078a1af3025b03c9c543d53c50938/params.yaml deleted file mode 100644 index 47a680f7..00000000 --- a/examples/security/classification/output/reports/train/7aa078a1af3025b03c9c543d53c50938/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/adv_predictions.json - adv_probabilities_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/2e929489e3e60e9799d4f0cb535e0351.pkl - params_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/params.yaml - predictions_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/predictions.json - probabilities_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/probabilities.json - score_dict_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/score_dict.json - test_labels_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/test_labels.json - train_labels_file: output/reports/train/7aa078a1af3025b03c9c543d53c50938/train_labels.json - model_dir: models - name: 7aa078a1af3025b03c9c543d53c50938 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 28562e48e4f301d02254a8e5e9aff3b6 - trainer: - kwargs: {} -name: 7aa078a1af3025b03c9c543d53c50938 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/params.yaml b/examples/security/classification/output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/params.yaml deleted file mode 100644 index c0f671dd..00000000 --- a/examples/security/classification/output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/adv_predictions.json - adv_probabilities_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/4823e096e5496cda832407a95598f3ba.pkl - params_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/params.yaml - predictions_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/predictions.json - probabilities_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/probabilities.json - score_dict_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/score_dict.json - test_labels_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/test_labels.json - train_labels_file: output/reports/train/7ae7a5dceba8a3f7970644fd1650f26f/train_labels.json - model_dir: models - name: 7ae7a5dceba8a3f7970644fd1650f26f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 334b2eebf29985c96436ace16bba385c - trainer: - kwargs: {} -name: 7ae7a5dceba8a3f7970644fd1650f26f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/params.yaml b/examples/security/classification/output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/params.yaml deleted file mode 100644 index 1250be5c..00000000 --- a/examples/security/classification/output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/adv_predictions.json - adv_probabilities_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/04badb9749cb9a168662f4bc24c50dbd.pkl - params_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/params.yaml - predictions_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/predictions.json - probabilities_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/probabilities.json - score_dict_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/score_dict.json - test_labels_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/test_labels.json - train_labels_file: output/reports/train/7b1ccd68a1373d215e7d8a08424bd275/train_labels.json - model_dir: models - name: 7b1ccd68a1373d215e7d8a08424bd275 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c5b2fb893c1d95b3b05fe1916f57b9aa - trainer: - kwargs: {} -name: 7b1ccd68a1373d215e7d8a08424bd275 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7b47104a0cfe51901771971493f05e52/params.yaml b/examples/security/classification/output/reports/train/7b47104a0cfe51901771971493f05e52/params.yaml deleted file mode 100644 index 9c24ef4c..00000000 --- a/examples/security/classification/output/reports/train/7b47104a0cfe51901771971493f05e52/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7b47104a0cfe51901771971493f05e52/adv_predictions.json - adv_probabilities_file: output/reports/train/7b47104a0cfe51901771971493f05e52/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/07858d1ccb89ba82804ee125b7f2bf16.pkl - params_file: output/reports/train/7b47104a0cfe51901771971493f05e52/params.yaml - predictions_file: output/reports/train/7b47104a0cfe51901771971493f05e52/predictions.json - probabilities_file: output/reports/train/7b47104a0cfe51901771971493f05e52/probabilities.json - score_dict_file: output/reports/train/7b47104a0cfe51901771971493f05e52/score_dict.json - test_labels_file: output/reports/train/7b47104a0cfe51901771971493f05e52/test_labels.json - train_labels_file: output/reports/train/7b47104a0cfe51901771971493f05e52/train_labels.json - model_dir: models - name: 7b47104a0cfe51901771971493f05e52 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 15e7a226950b8544c40d8216dc6922d8 - trainer: - kwargs: {} -name: 7b47104a0cfe51901771971493f05e52 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7bab3bd1642fb706891efc6603988cd3/params.yaml b/examples/security/classification/output/reports/train/7bab3bd1642fb706891efc6603988cd3/params.yaml deleted file mode 100644 index c77fd657..00000000 --- a/examples/security/classification/output/reports/train/7bab3bd1642fb706891efc6603988cd3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/adv_predictions.json - adv_probabilities_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/5185016dfdc83e345234881a30b3f6e1.pkl - params_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/params.yaml - predictions_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/predictions.json - probabilities_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/probabilities.json - score_dict_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/score_dict.json - test_labels_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/test_labels.json - train_labels_file: output/reports/train/7bab3bd1642fb706891efc6603988cd3/train_labels.json - model_dir: models - name: 7bab3bd1642fb706891efc6603988cd3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5623721dceba36b96197428cfeeb22a7 - trainer: - kwargs: {} -name: 7bab3bd1642fb706891efc6603988cd3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7be24ce873cf11cc518525f72466bb67/params.yaml b/examples/security/classification/output/reports/train/7be24ce873cf11cc518525f72466bb67/params.yaml deleted file mode 100644 index 26587771..00000000 --- a/examples/security/classification/output/reports/train/7be24ce873cf11cc518525f72466bb67/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/adv_predictions.json - adv_probabilities_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/f59110595978a9649e8f52a26a7f852b.pkl - params_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/params.yaml - predictions_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/predictions.json - probabilities_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/probabilities.json - score_dict_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/score_dict.json - test_labels_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/test_labels.json - train_labels_file: output/reports/train/7be24ce873cf11cc518525f72466bb67/train_labels.json - model_dir: models - name: 7be24ce873cf11cc518525f72466bb67 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2f596f93e3ce0f0e86743ae437752dfb - trainer: - kwargs: {} -name: 7be24ce873cf11cc518525f72466bb67 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/params.yaml b/examples/security/classification/output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/params.yaml deleted file mode 100644 index d7e4c5ad..00000000 --- a/examples/security/classification/output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/adv_predictions.json - adv_probabilities_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/cca55dfc14e5c0f91865bf6545d01620.pkl - params_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/params.yaml - predictions_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/predictions.json - probabilities_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/probabilities.json - score_dict_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/score_dict.json - test_labels_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/test_labels.json - train_labels_file: output/reports/train/7bf466bc7db1c6f7b6d316bf90573a41/train_labels.json - model_dir: models - name: 7bf466bc7db1c6f7b6d316bf90573a41 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 69321176e61dc5ce65ffbff314aa0117 - trainer: - kwargs: {} -name: 7bf466bc7db1c6f7b6d316bf90573a41 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7c04bab5c761a44b42ded839be5369a5/params.yaml b/examples/security/classification/output/reports/train/7c04bab5c761a44b42ded839be5369a5/params.yaml deleted file mode 100644 index a534b36e..00000000 --- a/examples/security/classification/output/reports/train/7c04bab5c761a44b42ded839be5369a5/params.yaml +++ /dev/null @@ -1,206 +0,0 @@ -attack: - attack_size: 10 - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 10.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 388924becad0b51fffc74334a2566529 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bf574b9f84947d7fcceb05ce699fc30b - trainer: - kwargs: {} - name: 8f7f2ed1518e547f80392f673bf717df -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/adv_predictions.json - adv_probabilities_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/adv_probabilities.json - attack_file: output/attacks/96cc7582aa77498b90419ee40b8aaf7a.pkl - data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl - model_file: output/models/5d5f89bdef3cc6202af58867139353d4.pkl - params_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/params.yaml - predictions_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/predictions.json - probabilities_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/probabilities.json - score_dict_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/score_dict.json - test_labels_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/test_labels.json - train_labels_file: output/reports/train/7c04bab5c761a44b42ded839be5369a5/train_labels.json - model_dir: models - name: 7c04bab5c761a44b42ded839be5369a5 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7014473b2a2deb1fb79e8381cb573c9c - trainer: - kwargs: {} -name: 7c04bab5c761a44b42ded839be5369a5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/params.yaml b/examples/security/classification/output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/params.yaml deleted file mode 100644 index df7aac93..00000000 --- a/examples/security/classification/output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/adv_predictions.json - adv_probabilities_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/27f911590d1fda62c4fc5afdab11e71c.pkl - params_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/params.yaml - predictions_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/predictions.json - probabilities_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/probabilities.json - score_dict_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/score_dict.json - test_labels_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/test_labels.json - train_labels_file: output/reports/train/7c38e5f7e0aea8bbf7d47d8636f6128d/train_labels.json - model_dir: models - name: 7c38e5f7e0aea8bbf7d47d8636f6128d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fc7e19738ad46d3d036ef0fb3f93ae6e - trainer: - kwargs: {} -name: 7c38e5f7e0aea8bbf7d47d8636f6128d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/params.yaml b/examples/security/classification/output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/params.yaml deleted file mode 100644 index 8cf4694f..00000000 --- a/examples/security/classification/output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/adv_predictions.json - adv_probabilities_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/e27fa8c864bf3a72d9a6d57146c52865.pkl - params_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/params.yaml - predictions_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/predictions.json - probabilities_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/probabilities.json - score_dict_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/score_dict.json - test_labels_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/test_labels.json - train_labels_file: output/reports/train/7c60a8bf486ff9846cd6d46116b21dbe/train_labels.json - model_dir: models - name: 7c60a8bf486ff9846cd6d46116b21dbe - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3605ff060bd79757ea57852f771f14f7 - trainer: - kwargs: {} -name: 7c60a8bf486ff9846cd6d46116b21dbe -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/params.yaml b/examples/security/classification/output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/params.yaml deleted file mode 100644 index 9b3cea81..00000000 --- a/examples/security/classification/output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/adv_predictions.json - adv_probabilities_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/3303431a107e9f4dc3679e27857ae8bb.pkl - params_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/params.yaml - predictions_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/predictions.json - probabilities_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/probabilities.json - score_dict_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/score_dict.json - test_labels_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/test_labels.json - train_labels_file: output/reports/train/7cc31a0cc25b3a9044b4af06797a9857/train_labels.json - model_dir: models - name: 7cc31a0cc25b3a9044b4af06797a9857 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 18442a3636578b31ca2547c83e63f351 - trainer: - kwargs: {} -name: 7cc31a0cc25b3a9044b4af06797a9857 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/params.yaml b/examples/security/classification/output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/params.yaml deleted file mode 100644 index f6bf141f..00000000 --- a/examples/security/classification/output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/adv_predictions.json - adv_probabilities_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/dbdaa87fbfbc0dbf810e9a916a326283.pkl - params_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/params.yaml - predictions_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/predictions.json - probabilities_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/probabilities.json - score_dict_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/score_dict.json - test_labels_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/test_labels.json - train_labels_file: output/reports/train/7d1563fcd0611d3ca2bd018ba8fb67ee/train_labels.json - model_dir: models - name: 7d1563fcd0611d3ca2bd018ba8fb67ee - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1e2e394c1fc2a0b23a3e59f18505d83f - trainer: - kwargs: {} -name: 7d1563fcd0611d3ca2bd018ba8fb67ee -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/params.yaml b/examples/security/classification/output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/params.yaml deleted file mode 100644 index 45c67074..00000000 --- a/examples/security/classification/output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/adv_predictions.json - adv_probabilities_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/1e85017a140efeb5fe3fc881bf7675f5.pkl - params_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/params.yaml - predictions_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/predictions.json - probabilities_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/probabilities.json - score_dict_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/score_dict.json - test_labels_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/test_labels.json - train_labels_file: output/reports/train/7d2bdddd12d4714226e2b57a456a4e50/train_labels.json - model_dir: models - name: 7d2bdddd12d4714226e2b57a456a4e50 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b6c93f6d3d88983570f6249b4385c544 - trainer: - kwargs: {} -name: 7d2bdddd12d4714226e2b57a456a4e50 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7d2cff9eb1840bff9f3411a953131483/params.yaml b/examples/security/classification/output/reports/train/7d2cff9eb1840bff9f3411a953131483/params.yaml deleted file mode 100644 index 15323770..00000000 --- a/examples/security/classification/output/reports/train/7d2cff9eb1840bff9f3411a953131483/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/adv_predictions.json - adv_probabilities_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/abd3556c13f0fb08ce6645d8fda8aeed.pkl - params_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/params.yaml - predictions_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/predictions.json - probabilities_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/probabilities.json - score_dict_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/score_dict.json - test_labels_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/test_labels.json - train_labels_file: output/reports/train/7d2cff9eb1840bff9f3411a953131483/train_labels.json - model_dir: models - name: 7d2cff9eb1840bff9f3411a953131483 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5c534174828106203017dc2338d0e416 - trainer: - kwargs: {} -name: 7d2cff9eb1840bff9f3411a953131483 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/params.yaml b/examples/security/classification/output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/params.yaml deleted file mode 100644 index 73bf016c..00000000 --- a/examples/security/classification/output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/adv_predictions.json - adv_probabilities_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/7f785b132c8d0b148ed431a6684df151.pkl - params_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/params.yaml - predictions_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/predictions.json - probabilities_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/probabilities.json - score_dict_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/score_dict.json - test_labels_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/test_labels.json - train_labels_file: output/reports/train/7d40cc9308263ff7cb2802a69bf5aa01/train_labels.json - model_dir: models - name: 7d40cc9308263ff7cb2802a69bf5aa01 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d525c9db56dfb878adc732478c6d840c - trainer: - kwargs: {} -name: 7d40cc9308263ff7cb2802a69bf5aa01 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/params.yaml b/examples/security/classification/output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/params.yaml deleted file mode 100644 index ef81adda..00000000 --- a/examples/security/classification/output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/adv_predictions.json - adv_probabilities_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/2d557f5dcd248f83001489566b329640.pkl - params_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/params.yaml - predictions_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/predictions.json - probabilities_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/probabilities.json - score_dict_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/score_dict.json - test_labels_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/test_labels.json - train_labels_file: output/reports/train/7d61ef9c05b2afc0683c248dbad638d9/train_labels.json - model_dir: models - name: 7d61ef9c05b2afc0683c248dbad638d9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b79cdf14725253e7d5a7a556a4523a6a - trainer: - kwargs: {} -name: 7d61ef9c05b2afc0683c248dbad638d9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/params.yaml b/examples/security/classification/output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/params.yaml deleted file mode 100644 index ebb5d1a6..00000000 --- a/examples/security/classification/output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/params.yaml +++ /dev/null @@ -1,115 +0,0 @@ -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/adv_predictions.json - adv_probabilities_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl - model_file: output/models/d5f0276d20b7198eccf2a7d6fd9b4fb3.pkl - params_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/params.yaml - predictions_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/predictions.json - probabilities_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/probabilities.json - score_dict_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/score_dict.json - test_labels_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/test_labels.json - train_labels_file: output/reports/train/7d64a57e89ac17c6592fe94d6f9c61ef/train_labels.json - model_dir: models - name: 7d64a57e89ac17c6592fe94d6f9c61ef - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0e-05 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1977e506992eacbfd95b3e224d590292 - trainer: - kwargs: {} -name: 7d64a57e89ac17c6592fe94d6f9c61ef -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/params.yaml b/examples/security/classification/output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/params.yaml deleted file mode 100644 index ad8ceded..00000000 --- a/examples/security/classification/output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/adv_predictions.json - adv_probabilities_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/95f6548299a2ba427e44c908c3edffd6.pkl - params_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/params.yaml - predictions_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/predictions.json - probabilities_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/probabilities.json - score_dict_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/score_dict.json - test_labels_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/test_labels.json - train_labels_file: output/reports/train/7d84ffb388f90d465c9e7ab88e3311c0/train_labels.json - model_dir: models - name: 7d84ffb388f90d465c9e7ab88e3311c0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 693c9bf5aa6ef7aa568c4f8977471ced - trainer: - kwargs: {} -name: 7d84ffb388f90d465c9e7ab88e3311c0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/params.yaml b/examples/security/classification/output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/params.yaml deleted file mode 100644 index e536a2a4..00000000 --- a/examples/security/classification/output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/adv_predictions.json - adv_probabilities_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/bc881cc8d39ccb553269bf98ad5332a7.pkl - params_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/params.yaml - predictions_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/predictions.json - probabilities_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/probabilities.json - score_dict_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/score_dict.json - test_labels_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/test_labels.json - train_labels_file: output/reports/train/7d87ea29263f3afbac3e2e1adbc8cb6a/train_labels.json - model_dir: models - name: 7d87ea29263f3afbac3e2e1adbc8cb6a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 57cd0fea10c6783e9d70b7a375443fd8 - trainer: - kwargs: {} -name: 7d87ea29263f3afbac3e2e1adbc8cb6a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7db8297af502cd031abe3d1d4d8ed025/params.yaml b/examples/security/classification/output/reports/train/7db8297af502cd031abe3d1d4d8ed025/params.yaml deleted file mode 100644 index ee035d91..00000000 --- a/examples/security/classification/output/reports/train/7db8297af502cd031abe3d1d4d8ed025/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/adv_predictions.json - adv_probabilities_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/1aee8c9620a5a88046818b12847c3edb.pkl - params_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/params.yaml - predictions_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/predictions.json - probabilities_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/probabilities.json - score_dict_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/score_dict.json - test_labels_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/test_labels.json - train_labels_file: output/reports/train/7db8297af502cd031abe3d1d4d8ed025/train_labels.json - model_dir: models - name: 7db8297af502cd031abe3d1d4d8ed025 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 29dba220dda29b160560177fa6ba192d - trainer: - kwargs: {} -name: 7db8297af502cd031abe3d1d4d8ed025 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/params.yaml b/examples/security/classification/output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/params.yaml deleted file mode 100644 index 39d8327b..00000000 --- a/examples/security/classification/output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/adv_predictions.json - adv_probabilities_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/c0970ceeba1ce624081de0bbc6867411.pkl - params_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/params.yaml - predictions_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/predictions.json - probabilities_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/probabilities.json - score_dict_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/score_dict.json - test_labels_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/test_labels.json - train_labels_file: output/reports/train/7dce1cb98fee6f4379e2fd5aa948a736/train_labels.json - model_dir: models - name: 7dce1cb98fee6f4379e2fd5aa948a736 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a2b5974b7831fb1a4a254214b0ee65c2 - trainer: - kwargs: {} -name: 7dce1cb98fee6f4379e2fd5aa948a736 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/params.yaml b/examples/security/classification/output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/params.yaml deleted file mode 100644 index 430b5518..00000000 --- a/examples/security/classification/output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/adv_predictions.json - adv_probabilities_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/2bebf82f76a195df47f01be05d8d43f5.pkl - params_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/params.yaml - predictions_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/predictions.json - probabilities_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/probabilities.json - score_dict_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/score_dict.json - test_labels_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/test_labels.json - train_labels_file: output/reports/train/7dce78c8dd1ecc705f985d40a433f58e/train_labels.json - model_dir: models - name: 7dce78c8dd1ecc705f985d40a433f58e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d23a0ae721555c0c3c3a4c60ff15d504 - trainer: - kwargs: {} -name: 7dce78c8dd1ecc705f985d40a433f58e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7de39089880ee6867517c24f9030a258/params.yaml b/examples/security/classification/output/reports/train/7de39089880ee6867517c24f9030a258/params.yaml deleted file mode 100644 index 9c91a27e..00000000 --- a/examples/security/classification/output/reports/train/7de39089880ee6867517c24f9030a258/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7de39089880ee6867517c24f9030a258/adv_predictions.json - adv_probabilities_file: output/reports/train/7de39089880ee6867517c24f9030a258/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/1a68543bf3606e1dae18197617aa0e0c.pkl - params_file: output/reports/train/7de39089880ee6867517c24f9030a258/params.yaml - predictions_file: output/reports/train/7de39089880ee6867517c24f9030a258/predictions.json - probabilities_file: output/reports/train/7de39089880ee6867517c24f9030a258/probabilities.json - score_dict_file: output/reports/train/7de39089880ee6867517c24f9030a258/score_dict.json - test_labels_file: output/reports/train/7de39089880ee6867517c24f9030a258/test_labels.json - train_labels_file: output/reports/train/7de39089880ee6867517c24f9030a258/train_labels.json - model_dir: models - name: 7de39089880ee6867517c24f9030a258 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 37dcfc79b50ee0decfe1b0f3b2c33740 - trainer: - kwargs: {} -name: 7de39089880ee6867517c24f9030a258 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/params.yaml b/examples/security/classification/output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/params.yaml deleted file mode 100644 index e85c563b..00000000 --- a/examples/security/classification/output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/adv_predictions.json - adv_probabilities_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/c41556cff8f7cb535a6be17b7b6dd835.pkl - params_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/params.yaml - predictions_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/predictions.json - probabilities_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/probabilities.json - score_dict_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/score_dict.json - test_labels_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/test_labels.json - train_labels_file: output/reports/train/7ded7bba5a4cb4eb70af000e45af2e16/train_labels.json - model_dir: models - name: 7ded7bba5a4cb4eb70af000e45af2e16 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 856d8b90c2435b9face4cb7a151eb291 - trainer: - kwargs: {} -name: 7ded7bba5a4cb4eb70af000e45af2e16 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/params.yaml b/examples/security/classification/output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/params.yaml deleted file mode 100644 index 09da6eaf..00000000 --- a/examples/security/classification/output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/params.yaml +++ /dev/null @@ -1,115 +0,0 @@ -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/adv_predictions.json - adv_probabilities_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl - model_file: output/models/679755da19d5691b7f0ca7582166f7e8.pkl - params_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/params.yaml - predictions_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/predictions.json - probabilities_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/probabilities.json - score_dict_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/score_dict.json - test_labels_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/test_labels.json - train_labels_file: output/reports/train/7dfb31f8759dfa2f30f55166b074c67e/train_labels.json - model_dir: models - name: 7dfb31f8759dfa2f30f55166b074c67e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 80522c9ba43b39cb53be5b293cef121d - trainer: - kwargs: {} -name: 7dfb31f8759dfa2f30f55166b074c67e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7dfd50006717472825478c09b5c42970/params.yaml b/examples/security/classification/output/reports/train/7dfd50006717472825478c09b5c42970/params.yaml deleted file mode 100644 index 945e7c2b..00000000 --- a/examples/security/classification/output/reports/train/7dfd50006717472825478c09b5c42970/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7dfd50006717472825478c09b5c42970/adv_predictions.json - adv_probabilities_file: output/reports/train/7dfd50006717472825478c09b5c42970/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/4a1cefb2bb7b696f9cbfce5e20e92ef3.pkl - params_file: output/reports/train/7dfd50006717472825478c09b5c42970/params.yaml - predictions_file: output/reports/train/7dfd50006717472825478c09b5c42970/predictions.json - probabilities_file: output/reports/train/7dfd50006717472825478c09b5c42970/probabilities.json - score_dict_file: output/reports/train/7dfd50006717472825478c09b5c42970/score_dict.json - test_labels_file: output/reports/train/7dfd50006717472825478c09b5c42970/test_labels.json - train_labels_file: output/reports/train/7dfd50006717472825478c09b5c42970/train_labels.json - model_dir: models - name: 7dfd50006717472825478c09b5c42970 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0a47ee3b5f4697545454c13197f201bd - trainer: - kwargs: {} -name: 7dfd50006717472825478c09b5c42970 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7e11aff007778f24d3437491087b4af8/params.yaml b/examples/security/classification/output/reports/train/7e11aff007778f24d3437491087b4af8/params.yaml deleted file mode 100644 index 6015ad5a..00000000 --- a/examples/security/classification/output/reports/train/7e11aff007778f24d3437491087b4af8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7e11aff007778f24d3437491087b4af8/adv_predictions.json - adv_probabilities_file: output/reports/train/7e11aff007778f24d3437491087b4af8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/f8dee826f98dfd0a197196dcafeaa576.pkl - params_file: output/reports/train/7e11aff007778f24d3437491087b4af8/params.yaml - predictions_file: output/reports/train/7e11aff007778f24d3437491087b4af8/predictions.json - probabilities_file: output/reports/train/7e11aff007778f24d3437491087b4af8/probabilities.json - score_dict_file: output/reports/train/7e11aff007778f24d3437491087b4af8/score_dict.json - test_labels_file: output/reports/train/7e11aff007778f24d3437491087b4af8/test_labels.json - train_labels_file: output/reports/train/7e11aff007778f24d3437491087b4af8/train_labels.json - model_dir: models - name: 7e11aff007778f24d3437491087b4af8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f12fe051be42369fcf4cd7a80291578 - trainer: - kwargs: {} -name: 7e11aff007778f24d3437491087b4af8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/params.yaml b/examples/security/classification/output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/params.yaml deleted file mode 100644 index d2f8d2ba..00000000 --- a/examples/security/classification/output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/adv_predictions.json - adv_probabilities_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/ce219ad49a4ea5b6e179a3f9365945ad.pkl - params_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/params.yaml - predictions_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/predictions.json - probabilities_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/probabilities.json - score_dict_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/score_dict.json - test_labels_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/test_labels.json - train_labels_file: output/reports/train/7e3e1e4599f1ccc4452d145a502650d4/train_labels.json - model_dir: models - name: 7e3e1e4599f1ccc4452d145a502650d4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ffa30f88de3480aeb1cacc2b486ee76c - trainer: - kwargs: {} -name: 7e3e1e4599f1ccc4452d145a502650d4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7e470e9710a948c8aca6019e73aacb3c/params.yaml b/examples/security/classification/output/reports/train/7e470e9710a948c8aca6019e73aacb3c/params.yaml deleted file mode 100644 index 44d180ab..00000000 --- a/examples/security/classification/output/reports/train/7e470e9710a948c8aca6019e73aacb3c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/adv_predictions.json - adv_probabilities_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/f59fa4faa6fae488102f75644650eae7.pkl - params_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/params.yaml - predictions_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/predictions.json - probabilities_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/probabilities.json - score_dict_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/score_dict.json - test_labels_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/test_labels.json - train_labels_file: output/reports/train/7e470e9710a948c8aca6019e73aacb3c/train_labels.json - model_dir: models - name: 7e470e9710a948c8aca6019e73aacb3c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9776f2277ceaea5346599337f1f3bfe6 - trainer: - kwargs: {} -name: 7e470e9710a948c8aca6019e73aacb3c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/params.yaml b/examples/security/classification/output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/params.yaml deleted file mode 100644 index 4b803062..00000000 --- a/examples/security/classification/output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/adv_predictions.json - adv_probabilities_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/5bb363e0e980bef71179d31310cb34b2.pkl - params_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/params.yaml - predictions_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/predictions.json - probabilities_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/probabilities.json - score_dict_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/score_dict.json - test_labels_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/test_labels.json - train_labels_file: output/reports/train/7e6b11e43b10cfda0a606ec60bc9f539/train_labels.json - model_dir: models - name: 7e6b11e43b10cfda0a606ec60bc9f539 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b15bd97d97dc2753848e699eb1750167 - trainer: - kwargs: {} -name: 7e6b11e43b10cfda0a606ec60bc9f539 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/params.yaml b/examples/security/classification/output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/params.yaml deleted file mode 100644 index d74f8b71..00000000 --- a/examples/security/classification/output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/adv_predictions.json - adv_probabilities_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/d09d29578dfe3157a6ba247d538bbe7c.pkl - params_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/params.yaml - predictions_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/predictions.json - probabilities_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/probabilities.json - score_dict_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/score_dict.json - test_labels_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/test_labels.json - train_labels_file: output/reports/train/7e7a0e7fb57f14424d5d69b94a1fff8c/train_labels.json - model_dir: models - name: 7e7a0e7fb57f14424d5d69b94a1fff8c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 58f300179958776f7d79ead729e5760d - trainer: - kwargs: {} -name: 7e7a0e7fb57f14424d5d69b94a1fff8c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/params.yaml b/examples/security/classification/output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/params.yaml deleted file mode 100644 index 075c64ea..00000000 --- a/examples/security/classification/output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/adv_predictions.json - adv_probabilities_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/6ced7f6ac3d38a0185f14ae94ee98158.pkl - params_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/params.yaml - predictions_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/predictions.json - probabilities_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/probabilities.json - score_dict_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/score_dict.json - test_labels_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/test_labels.json - train_labels_file: output/reports/train/7ec344c1cd366e9b916f7b60cf61849f/train_labels.json - model_dir: models - name: 7ec344c1cd366e9b916f7b60cf61849f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 526a2d3d10d4c22ea66a9c8fb5c4dc29 - trainer: - kwargs: {} -name: 7ec344c1cd366e9b916f7b60cf61849f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/params.yaml b/examples/security/classification/output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/params.yaml deleted file mode 100644 index bb9e62fd..00000000 --- a/examples/security/classification/output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/adv_predictions.json - adv_probabilities_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/0d01f2e42cf3323a065223d3cbb29f64.pkl - params_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/params.yaml - predictions_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/predictions.json - probabilities_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/probabilities.json - score_dict_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/score_dict.json - test_labels_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/test_labels.json - train_labels_file: output/reports/train/7ee7eb8b80f270ef4828d04feb2ce6c4/train_labels.json - model_dir: models - name: 7ee7eb8b80f270ef4828d04feb2ce6c4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 31b71330b1ff41dc9b7eab0381fcf07f - trainer: - kwargs: {} -name: 7ee7eb8b80f270ef4828d04feb2ce6c4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/params.yaml b/examples/security/classification/output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/params.yaml deleted file mode 100644 index 6ee0c179..00000000 --- a/examples/security/classification/output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/adv_predictions.json - adv_probabilities_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/e2afb65f8779e535e60d2f024ec3ea52.pkl - params_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/params.yaml - predictions_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/predictions.json - probabilities_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/probabilities.json - score_dict_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/score_dict.json - test_labels_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/test_labels.json - train_labels_file: output/reports/train/7ef0d54c0484264eecdf2bacf9f44b10/train_labels.json - model_dir: models - name: 7ef0d54c0484264eecdf2bacf9f44b10 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 438cf02c2d90d304f6333d9d3192da80 - trainer: - kwargs: {} -name: 7ef0d54c0484264eecdf2bacf9f44b10 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/params.yaml b/examples/security/classification/output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/params.yaml deleted file mode 100644 index 2fa91106..00000000 --- a/examples/security/classification/output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/adv_predictions.json - adv_probabilities_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/382ef1685e502f3998c99074f7ccf479.pkl - params_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/params.yaml - predictions_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/predictions.json - probabilities_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/probabilities.json - score_dict_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/score_dict.json - test_labels_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/test_labels.json - train_labels_file: output/reports/train/7f2ddf3d3283f75e5eb11b388670169e/train_labels.json - model_dir: models - name: 7f2ddf3d3283f75e5eb11b388670169e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 61b5618cb786dca09f85e52e31412f15 - trainer: - kwargs: {} -name: 7f2ddf3d3283f75e5eb11b388670169e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/params.yaml b/examples/security/classification/output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/params.yaml deleted file mode 100644 index ee69ae2a..00000000 --- a/examples/security/classification/output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/adv_predictions.json - adv_probabilities_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/8108d0f5c9a0568c4541d0c1a961a488.pkl - params_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/params.yaml - predictions_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/predictions.json - probabilities_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/probabilities.json - score_dict_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/score_dict.json - test_labels_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/test_labels.json - train_labels_file: output/reports/train/7f4b80774ed2290f38c422d0b12acf1f/train_labels.json - model_dir: models - name: 7f4b80774ed2290f38c422d0b12acf1f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 93a477ebafda62533b3b905dc08cc940 - trainer: - kwargs: {} -name: 7f4b80774ed2290f38c422d0b12acf1f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7f6768417633a0436cea9a0e98fa744b/params.yaml b/examples/security/classification/output/reports/train/7f6768417633a0436cea9a0e98fa744b/params.yaml deleted file mode 100644 index 27f46804..00000000 --- a/examples/security/classification/output/reports/train/7f6768417633a0436cea9a0e98fa744b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/adv_predictions.json - adv_probabilities_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/dccf2757a517f0f40d271b898b36513d.pkl - params_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/params.yaml - predictions_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/predictions.json - probabilities_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/probabilities.json - score_dict_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/score_dict.json - test_labels_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/test_labels.json - train_labels_file: output/reports/train/7f6768417633a0436cea9a0e98fa744b/train_labels.json - model_dir: models - name: 7f6768417633a0436cea9a0e98fa744b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 16f5587263a86434dedea181c31cbe6d - trainer: - kwargs: {} -name: 7f6768417633a0436cea9a0e98fa744b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7faa91537a1f200580ae4d1798562a47/params.yaml b/examples/security/classification/output/reports/train/7faa91537a1f200580ae4d1798562a47/params.yaml deleted file mode 100644 index d9bc0132..00000000 --- a/examples/security/classification/output/reports/train/7faa91537a1f200580ae4d1798562a47/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/adv_predictions.json - adv_probabilities_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/f6ed1a9670445ef3e0bbb09bd0edc6c0.pkl - params_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/params.yaml - predictions_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/predictions.json - probabilities_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/probabilities.json - score_dict_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/score_dict.json - test_labels_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/test_labels.json - train_labels_file: output/reports/train/7faa91537a1f200580ae4d1798562a47/train_labels.json - model_dir: models - name: 7faa91537a1f200580ae4d1798562a47 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ec3f2d62cc7ff0b903923a04baee4aac - trainer: - kwargs: {} -name: 7faa91537a1f200580ae4d1798562a47 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/params.yaml b/examples/security/classification/output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/params.yaml deleted file mode 100644 index 84e388be..00000000 --- a/examples/security/classification/output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/adv_predictions.json - adv_probabilities_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/f954273c52dadb1a11e5970b381ab2ad.pkl - params_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/params.yaml - predictions_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/predictions.json - probabilities_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/probabilities.json - score_dict_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/score_dict.json - test_labels_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/test_labels.json - train_labels_file: output/reports/train/7fb6dfd4c528b9679362735e9dc2a2dc/train_labels.json - model_dir: models - name: 7fb6dfd4c528b9679362735e9dc2a2dc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6e698a32bd992a2cc08e6bd3a9478d0e - trainer: - kwargs: {} -name: 7fb6dfd4c528b9679362735e9dc2a2dc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/params.yaml b/examples/security/classification/output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/params.yaml deleted file mode 100644 index 4fc7566a..00000000 --- a/examples/security/classification/output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/adv_predictions.json - adv_probabilities_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/d9eee7c12f406ebf2e091e2f50f67aab.pkl - params_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/params.yaml - predictions_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/predictions.json - probabilities_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/probabilities.json - score_dict_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/score_dict.json - test_labels_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/test_labels.json - train_labels_file: output/reports/train/7fd5324183b1f76108d9c84f6d1cf6bd/train_labels.json - model_dir: models - name: 7fd5324183b1f76108d9c84f6d1cf6bd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 01b835fe4edcbc5bae415c61c35e2686 - trainer: - kwargs: {} -name: 7fd5324183b1f76108d9c84f6d1cf6bd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/800a4eca7fb319b6cae02805309f2345/params.yaml b/examples/security/classification/output/reports/train/800a4eca7fb319b6cae02805309f2345/params.yaml deleted file mode 100644 index 6a124bd4..00000000 --- a/examples/security/classification/output/reports/train/800a4eca7fb319b6cae02805309f2345/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/adv_predictions.json - adv_probabilities_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/91e144aec3c8018fe46d0a6d39b250f4.pkl - params_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/params.yaml - predictions_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/predictions.json - probabilities_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/probabilities.json - score_dict_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/score_dict.json - test_labels_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/test_labels.json - train_labels_file: output/reports/train/800a4eca7fb319b6cae02805309f2345/train_labels.json - model_dir: models - name: 800a4eca7fb319b6cae02805309f2345 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 57dcabc5cb64b7b3e9595d01fdefb58d - trainer: - kwargs: {} -name: 800a4eca7fb319b6cae02805309f2345 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/80201e16867e9c2061beab76aa02c56e/params.yaml b/examples/security/classification/output/reports/train/80201e16867e9c2061beab76aa02c56e/params.yaml deleted file mode 100644 index f900bc97..00000000 --- a/examples/security/classification/output/reports/train/80201e16867e9c2061beab76aa02c56e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/adv_predictions.json - adv_probabilities_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/4a2f63eb9b79dab18ad069b0f25a064d.pkl - params_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/params.yaml - predictions_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/predictions.json - probabilities_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/probabilities.json - score_dict_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/score_dict.json - test_labels_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/test_labels.json - train_labels_file: output/reports/train/80201e16867e9c2061beab76aa02c56e/train_labels.json - model_dir: models - name: 80201e16867e9c2061beab76aa02c56e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c72cf71beaf993df452964c8f49693e1 - trainer: - kwargs: {} -name: 80201e16867e9c2061beab76aa02c56e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/80474afa5f435f7e922fece05f649a43/params.yaml b/examples/security/classification/output/reports/train/80474afa5f435f7e922fece05f649a43/params.yaml deleted file mode 100644 index 995e0f77..00000000 --- a/examples/security/classification/output/reports/train/80474afa5f435f7e922fece05f649a43/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/80474afa5f435f7e922fece05f649a43/adv_predictions.json - adv_probabilities_file: output/reports/train/80474afa5f435f7e922fece05f649a43/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/e8ff50621d9a5260b11a7110403428bc.pkl - params_file: output/reports/train/80474afa5f435f7e922fece05f649a43/params.yaml - predictions_file: output/reports/train/80474afa5f435f7e922fece05f649a43/predictions.json - probabilities_file: output/reports/train/80474afa5f435f7e922fece05f649a43/probabilities.json - score_dict_file: output/reports/train/80474afa5f435f7e922fece05f649a43/score_dict.json - test_labels_file: output/reports/train/80474afa5f435f7e922fece05f649a43/test_labels.json - train_labels_file: output/reports/train/80474afa5f435f7e922fece05f649a43/train_labels.json - model_dir: models - name: 80474afa5f435f7e922fece05f649a43 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9c79e6d740542f1b284eb93039825d96 - trainer: - kwargs: {} -name: 80474afa5f435f7e922fece05f649a43 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/params.yaml b/examples/security/classification/output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/params.yaml deleted file mode 100644 index 0801f2ac..00000000 --- a/examples/security/classification/output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/adv_predictions.json - adv_probabilities_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/b0bec543e4b5a6c78dc361fb1b04b2d9.pkl - params_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/params.yaml - predictions_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/predictions.json - probabilities_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/probabilities.json - score_dict_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/score_dict.json - test_labels_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/test_labels.json - train_labels_file: output/reports/train/80729b5d6baed5b6c40402de9b7eb7f9/train_labels.json - model_dir: models - name: 80729b5d6baed5b6c40402de9b7eb7f9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 452d5ac3244ef488f9ca9381493e56b4 - trainer: - kwargs: {} -name: 80729b5d6baed5b6c40402de9b7eb7f9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/params.yaml b/examples/security/classification/output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/params.yaml deleted file mode 100644 index 9d3c7845..00000000 --- a/examples/security/classification/output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/adv_predictions.json - adv_probabilities_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/3c7ca6f814826b46d20ea5b07137e954.pkl - params_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/params.yaml - predictions_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/predictions.json - probabilities_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/probabilities.json - score_dict_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/score_dict.json - test_labels_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/test_labels.json - train_labels_file: output/reports/train/80756d09ef3519bc71aaf5b8251ce5e4/train_labels.json - model_dir: models - name: 80756d09ef3519bc71aaf5b8251ce5e4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5d04c787dbb7dc2c6d3bf7e90b42ae3b - trainer: - kwargs: {} -name: 80756d09ef3519bc71aaf5b8251ce5e4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/80aaa16ec930a784e4640eb553268591/params.yaml b/examples/security/classification/output/reports/train/80aaa16ec930a784e4640eb553268591/params.yaml deleted file mode 100644 index 1910b2be..00000000 --- a/examples/security/classification/output/reports/train/80aaa16ec930a784e4640eb553268591/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/80aaa16ec930a784e4640eb553268591/adv_predictions.json - adv_probabilities_file: output/reports/train/80aaa16ec930a784e4640eb553268591/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/33763598c6254935a823b1067c1245ed.pkl - params_file: output/reports/train/80aaa16ec930a784e4640eb553268591/params.yaml - predictions_file: output/reports/train/80aaa16ec930a784e4640eb553268591/predictions.json - probabilities_file: output/reports/train/80aaa16ec930a784e4640eb553268591/probabilities.json - score_dict_file: output/reports/train/80aaa16ec930a784e4640eb553268591/score_dict.json - test_labels_file: output/reports/train/80aaa16ec930a784e4640eb553268591/test_labels.json - train_labels_file: output/reports/train/80aaa16ec930a784e4640eb553268591/train_labels.json - model_dir: models - name: 80aaa16ec930a784e4640eb553268591 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d45a40c754bd2ee8645e92a81a1da71e - trainer: - kwargs: {} -name: 80aaa16ec930a784e4640eb553268591 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/params.yaml b/examples/security/classification/output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/params.yaml deleted file mode 100644 index 76d1a503..00000000 --- a/examples/security/classification/output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/adv_predictions.json - adv_probabilities_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/8e55f473630362bb4fa781502022bbae.pkl - params_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/params.yaml - predictions_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/predictions.json - probabilities_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/probabilities.json - score_dict_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/score_dict.json - test_labels_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/test_labels.json - train_labels_file: output/reports/train/80b85ee81048241d2194b9f0ff8e15dd/train_labels.json - model_dir: models - name: 80b85ee81048241d2194b9f0ff8e15dd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 83af83f63cd019d6e9554c23a3ce4a83 - trainer: - kwargs: {} -name: 80b85ee81048241d2194b9f0ff8e15dd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/params.yaml b/examples/security/classification/output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/params.yaml deleted file mode 100644 index e6102147..00000000 --- a/examples/security/classification/output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/adv_predictions.json - adv_probabilities_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/d18df932c9a3ee81356d962aa00e00b3.pkl - params_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/params.yaml - predictions_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/predictions.json - probabilities_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/probabilities.json - score_dict_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/score_dict.json - test_labels_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/test_labels.json - train_labels_file: output/reports/train/80bdc3da1507e8ca6f8d7d5b2641f094/train_labels.json - model_dir: models - name: 80bdc3da1507e8ca6f8d7d5b2641f094 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 589834c586b5903c89686563229a7714 - trainer: - kwargs: {} -name: 80bdc3da1507e8ca6f8d7d5b2641f094 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/80e01be0f2592d17eeb8e3754354a57d/params.yaml b/examples/security/classification/output/reports/train/80e01be0f2592d17eeb8e3754354a57d/params.yaml deleted file mode 100644 index 398e19aa..00000000 --- a/examples/security/classification/output/reports/train/80e01be0f2592d17eeb8e3754354a57d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/adv_predictions.json - adv_probabilities_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/e02e0388d4a14091f1ad6fb92eb02f1d.pkl - params_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/params.yaml - predictions_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/predictions.json - probabilities_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/probabilities.json - score_dict_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/score_dict.json - test_labels_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/test_labels.json - train_labels_file: output/reports/train/80e01be0f2592d17eeb8e3754354a57d/train_labels.json - model_dir: models - name: 80e01be0f2592d17eeb8e3754354a57d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c0ba7ff5a8cdd5c7dfe5be13b8363c7f - trainer: - kwargs: {} -name: 80e01be0f2592d17eeb8e3754354a57d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/params.yaml b/examples/security/classification/output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/params.yaml deleted file mode 100644 index 97d0acff..00000000 --- a/examples/security/classification/output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/adv_predictions.json - adv_probabilities_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/f52fd93b0344c0fbdaf3deb43cec12fe.pkl - params_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/params.yaml - predictions_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/predictions.json - probabilities_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/probabilities.json - score_dict_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/score_dict.json - test_labels_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/test_labels.json - train_labels_file: output/reports/train/80f7af5f20cd8185b1299ed3d98689cb/train_labels.json - model_dir: models - name: 80f7af5f20cd8185b1299ed3d98689cb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4d88e4b7cadbec656b33a5d915e50203 - trainer: - kwargs: {} -name: 80f7af5f20cd8185b1299ed3d98689cb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/810e6946c484a430a593fd1b562479af/params.yaml b/examples/security/classification/output/reports/train/810e6946c484a430a593fd1b562479af/params.yaml deleted file mode 100644 index aef1a96c..00000000 --- a/examples/security/classification/output/reports/train/810e6946c484a430a593fd1b562479af/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/810e6946c484a430a593fd1b562479af/adv_predictions.json - adv_probabilities_file: output/reports/train/810e6946c484a430a593fd1b562479af/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/bad907f3a3f5125177e2feb3d1bc9dad.pkl - params_file: output/reports/train/810e6946c484a430a593fd1b562479af/params.yaml - predictions_file: output/reports/train/810e6946c484a430a593fd1b562479af/predictions.json - probabilities_file: output/reports/train/810e6946c484a430a593fd1b562479af/probabilities.json - score_dict_file: output/reports/train/810e6946c484a430a593fd1b562479af/score_dict.json - test_labels_file: output/reports/train/810e6946c484a430a593fd1b562479af/test_labels.json - train_labels_file: output/reports/train/810e6946c484a430a593fd1b562479af/train_labels.json - model_dir: models - name: 810e6946c484a430a593fd1b562479af - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0168c3e43426fe1ba8fa3efe85c65245 - trainer: - kwargs: {} -name: 810e6946c484a430a593fd1b562479af -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/81190866ea7daee492b6d8035bf4e3c8/params.yaml b/examples/security/classification/output/reports/train/81190866ea7daee492b6d8035bf4e3c8/params.yaml deleted file mode 100644 index f8d82829..00000000 --- a/examples/security/classification/output/reports/train/81190866ea7daee492b6d8035bf4e3c8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/adv_predictions.json - adv_probabilities_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/484e66053e66095e11792d91fdc089db.pkl - params_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/params.yaml - predictions_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/predictions.json - probabilities_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/probabilities.json - score_dict_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/score_dict.json - test_labels_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/test_labels.json - train_labels_file: output/reports/train/81190866ea7daee492b6d8035bf4e3c8/train_labels.json - model_dir: models - name: 81190866ea7daee492b6d8035bf4e3c8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b6ef5f5bebfbc040ebca9c00b3453cb3 - trainer: - kwargs: {} -name: 81190866ea7daee492b6d8035bf4e3c8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/812aa38a4907f33dae8f2a81c24628f3/params.yaml b/examples/security/classification/output/reports/train/812aa38a4907f33dae8f2a81c24628f3/params.yaml deleted file mode 100644 index f4dbe1a8..00000000 --- a/examples/security/classification/output/reports/train/812aa38a4907f33dae8f2a81c24628f3/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/adv_predictions.json - adv_probabilities_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/dfebf35bb63fffe5efc82504fa582417.pkl - params_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/params.yaml - predictions_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/predictions.json - probabilities_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/probabilities.json - score_dict_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/score_dict.json - test_labels_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/test_labels.json - train_labels_file: output/reports/train/812aa38a4907f33dae8f2a81c24628f3/train_labels.json - model_dir: models - name: 812aa38a4907f33dae8f2a81c24628f3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 78c8c0b7d33166bf14a04492848c8382 - trainer: - kwargs: {} -name: 812aa38a4907f33dae8f2a81c24628f3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/81782cf2ac3d394adebb9385360416d8/params.yaml b/examples/security/classification/output/reports/train/81782cf2ac3d394adebb9385360416d8/params.yaml deleted file mode 100644 index a7698845..00000000 --- a/examples/security/classification/output/reports/train/81782cf2ac3d394adebb9385360416d8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/adv_predictions.json - adv_probabilities_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/e938389d2c7d2528f59d953d5b44ac4c.pkl - params_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/params.yaml - predictions_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/predictions.json - probabilities_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/probabilities.json - score_dict_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/score_dict.json - test_labels_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/test_labels.json - train_labels_file: output/reports/train/81782cf2ac3d394adebb9385360416d8/train_labels.json - model_dir: models - name: 81782cf2ac3d394adebb9385360416d8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cdf9240180eb9031f8bb89fbb6ea73bc - trainer: - kwargs: {} -name: 81782cf2ac3d394adebb9385360416d8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/819f3e2f9f190233a32b99b354295053/params.yaml b/examples/security/classification/output/reports/train/819f3e2f9f190233a32b99b354295053/params.yaml deleted file mode 100644 index ab6595cd..00000000 --- a/examples/security/classification/output/reports/train/819f3e2f9f190233a32b99b354295053/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/819f3e2f9f190233a32b99b354295053/adv_predictions.json - adv_probabilities_file: output/reports/train/819f3e2f9f190233a32b99b354295053/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/ee105039f299bbeda68806b7d06f00e6.pkl - params_file: output/reports/train/819f3e2f9f190233a32b99b354295053/params.yaml - predictions_file: output/reports/train/819f3e2f9f190233a32b99b354295053/predictions.json - probabilities_file: output/reports/train/819f3e2f9f190233a32b99b354295053/probabilities.json - score_dict_file: output/reports/train/819f3e2f9f190233a32b99b354295053/score_dict.json - test_labels_file: output/reports/train/819f3e2f9f190233a32b99b354295053/test_labels.json - train_labels_file: output/reports/train/819f3e2f9f190233a32b99b354295053/train_labels.json - model_dir: models - name: 819f3e2f9f190233a32b99b354295053 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 87d57e3f5860de93234cd7f3175d0152 - trainer: - kwargs: {} -name: 819f3e2f9f190233a32b99b354295053 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/params.yaml b/examples/security/classification/output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/params.yaml deleted file mode 100644 index 57c38214..00000000 --- a/examples/security/classification/output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/adv_predictions.json - adv_probabilities_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/529404233f86ca122cff94f1762fc28e.pkl - params_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/params.yaml - predictions_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/predictions.json - probabilities_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/probabilities.json - score_dict_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/score_dict.json - test_labels_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/test_labels.json - train_labels_file: output/reports/train/81e19a6bb8593f9a4b71f5669722cf54/train_labels.json - model_dir: models - name: 81e19a6bb8593f9a4b71f5669722cf54 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: addeb9129f601069a39556a0baf9fc3e - trainer: - kwargs: {} -name: 81e19a6bb8593f9a4b71f5669722cf54 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/82008bc15714db1e622a8459b79bddef/params.yaml b/examples/security/classification/output/reports/train/82008bc15714db1e622a8459b79bddef/params.yaml deleted file mode 100644 index 0e007d6f..00000000 --- a/examples/security/classification/output/reports/train/82008bc15714db1e622a8459b79bddef/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/82008bc15714db1e622a8459b79bddef/adv_predictions.json - adv_probabilities_file: output/reports/train/82008bc15714db1e622a8459b79bddef/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/1ad1b35261c0da50306ea96c134a4340.pkl - params_file: output/reports/train/82008bc15714db1e622a8459b79bddef/params.yaml - predictions_file: output/reports/train/82008bc15714db1e622a8459b79bddef/predictions.json - probabilities_file: output/reports/train/82008bc15714db1e622a8459b79bddef/probabilities.json - score_dict_file: output/reports/train/82008bc15714db1e622a8459b79bddef/score_dict.json - test_labels_file: output/reports/train/82008bc15714db1e622a8459b79bddef/test_labels.json - train_labels_file: output/reports/train/82008bc15714db1e622a8459b79bddef/train_labels.json - model_dir: models - name: 82008bc15714db1e622a8459b79bddef - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 31a95ec6c1100352271fce002321d6cb - trainer: - kwargs: {} -name: 82008bc15714db1e622a8459b79bddef -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8221641e09a02edb00bd3172b78b3fbe/params.yaml b/examples/security/classification/output/reports/train/8221641e09a02edb00bd3172b78b3fbe/params.yaml deleted file mode 100644 index 12325204..00000000 --- a/examples/security/classification/output/reports/train/8221641e09a02edb00bd3172b78b3fbe/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/adv_predictions.json - adv_probabilities_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/6732bd59bbf8a838c280f70ba80d9dd7.pkl - params_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/params.yaml - predictions_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/predictions.json - probabilities_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/probabilities.json - score_dict_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/score_dict.json - test_labels_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/test_labels.json - train_labels_file: output/reports/train/8221641e09a02edb00bd3172b78b3fbe/train_labels.json - model_dir: models - name: 8221641e09a02edb00bd3172b78b3fbe - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 14e75645bbda09ade207e6a407d1fd6b - trainer: - kwargs: {} -name: 8221641e09a02edb00bd3172b78b3fbe -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/params.yaml b/examples/security/classification/output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/params.yaml deleted file mode 100644 index f4920fd3..00000000 --- a/examples/security/classification/output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/adv_predictions.json - adv_probabilities_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/169e06ea9dd5a1322d7cb8588df08535.pkl - params_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/params.yaml - predictions_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/predictions.json - probabilities_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/probabilities.json - score_dict_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/score_dict.json - test_labels_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/test_labels.json - train_labels_file: output/reports/train/8223c79d2370ba2612bfd88d2e1eadc2/train_labels.json - model_dir: models - name: 8223c79d2370ba2612bfd88d2e1eadc2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3681912bc7ed2431c407f2635cc80df9 - trainer: - kwargs: {} -name: 8223c79d2370ba2612bfd88d2e1eadc2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/params.yaml b/examples/security/classification/output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/params.yaml deleted file mode 100644 index f73f3edc..00000000 --- a/examples/security/classification/output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/adv_predictions.json - adv_probabilities_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/f4f3016e8d1467382d7b8e96017c050d.pkl - params_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/params.yaml - predictions_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/predictions.json - probabilities_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/probabilities.json - score_dict_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/score_dict.json - test_labels_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/test_labels.json - train_labels_file: output/reports/train/822f1af8fdfc45ee55db6dc2dc78f990/train_labels.json - model_dir: models - name: 822f1af8fdfc45ee55db6dc2dc78f990 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.001 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 65b65b07172c8e6b0543c3bab49fc3f7 - trainer: - kwargs: {} -name: 822f1af8fdfc45ee55db6dc2dc78f990 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/82742e42c9b02ec181be1e8481d185f8/params.yaml b/examples/security/classification/output/reports/train/82742e42c9b02ec181be1e8481d185f8/params.yaml deleted file mode 100644 index fb819c55..00000000 --- a/examples/security/classification/output/reports/train/82742e42c9b02ec181be1e8481d185f8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/adv_predictions.json - adv_probabilities_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/461f5859e93ab37766f0cd935dc51939.pkl - params_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/params.yaml - predictions_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/predictions.json - probabilities_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/probabilities.json - score_dict_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/score_dict.json - test_labels_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/test_labels.json - train_labels_file: output/reports/train/82742e42c9b02ec181be1e8481d185f8/train_labels.json - model_dir: models - name: 82742e42c9b02ec181be1e8481d185f8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 42679a70b6c757c84492cd023d7eb23e - trainer: - kwargs: {} -name: 82742e42c9b02ec181be1e8481d185f8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/82787ce49222cfcc416d98979a1aab51/params.yaml b/examples/security/classification/output/reports/train/82787ce49222cfcc416d98979a1aab51/params.yaml deleted file mode 100644 index d10dbad4..00000000 --- a/examples/security/classification/output/reports/train/82787ce49222cfcc416d98979a1aab51/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/adv_predictions.json - adv_probabilities_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/14a0b0991566981cb3f29241d1dc8fe0.pkl - params_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/params.yaml - predictions_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/predictions.json - probabilities_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/probabilities.json - score_dict_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/score_dict.json - test_labels_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/test_labels.json - train_labels_file: output/reports/train/82787ce49222cfcc416d98979a1aab51/train_labels.json - model_dir: models - name: 82787ce49222cfcc416d98979a1aab51 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cabb2d8bd13ca2811abab0af9ae70a48 - trainer: - kwargs: {} -name: 82787ce49222cfcc416d98979a1aab51 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/828360be6317b9f0965ed65cb969ed34/params.yaml b/examples/security/classification/output/reports/train/828360be6317b9f0965ed65cb969ed34/params.yaml deleted file mode 100644 index 7bb0d847..00000000 --- a/examples/security/classification/output/reports/train/828360be6317b9f0965ed65cb969ed34/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/adv_predictions.json - adv_probabilities_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/376a3b4ee6f1976e56fde29e1df337d9.pkl - params_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/params.yaml - predictions_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/predictions.json - probabilities_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/probabilities.json - score_dict_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/score_dict.json - test_labels_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/test_labels.json - train_labels_file: output/reports/train/828360be6317b9f0965ed65cb969ed34/train_labels.json - model_dir: models - name: 828360be6317b9f0965ed65cb969ed34 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 90dd4041c07e66272fd16f1718d574ec - trainer: - kwargs: {} -name: 828360be6317b9f0965ed65cb969ed34 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8294f89363afa252f1e5a597a175f877/params.yaml b/examples/security/classification/output/reports/train/8294f89363afa252f1e5a597a175f877/params.yaml deleted file mode 100644 index f8c80368..00000000 --- a/examples/security/classification/output/reports/train/8294f89363afa252f1e5a597a175f877/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8294f89363afa252f1e5a597a175f877/adv_predictions.json - adv_probabilities_file: output/reports/train/8294f89363afa252f1e5a597a175f877/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/a3c7ec497d9898094421b79bb38545e4.pkl - params_file: output/reports/train/8294f89363afa252f1e5a597a175f877/params.yaml - predictions_file: output/reports/train/8294f89363afa252f1e5a597a175f877/predictions.json - probabilities_file: output/reports/train/8294f89363afa252f1e5a597a175f877/probabilities.json - score_dict_file: output/reports/train/8294f89363afa252f1e5a597a175f877/score_dict.json - test_labels_file: output/reports/train/8294f89363afa252f1e5a597a175f877/test_labels.json - train_labels_file: output/reports/train/8294f89363afa252f1e5a597a175f877/train_labels.json - model_dir: models - name: 8294f89363afa252f1e5a597a175f877 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d579306e508ebf11cdee33c23085d5ba - trainer: - kwargs: {} -name: 8294f89363afa252f1e5a597a175f877 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/829c6d97bc7dc632864c1c979e2bedae/params.yaml b/examples/security/classification/output/reports/train/829c6d97bc7dc632864c1c979e2bedae/params.yaml deleted file mode 100644 index 61218223..00000000 --- a/examples/security/classification/output/reports/train/829c6d97bc7dc632864c1c979e2bedae/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/adv_predictions.json - adv_probabilities_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/795d2cd38ae2a252a0ee05afbaf318a8.pkl - params_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/params.yaml - predictions_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/predictions.json - probabilities_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/probabilities.json - score_dict_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/score_dict.json - test_labels_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/test_labels.json - train_labels_file: output/reports/train/829c6d97bc7dc632864c1c979e2bedae/train_labels.json - model_dir: models - name: 829c6d97bc7dc632864c1c979e2bedae - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8588cb415642ba871c453de7757834e5 - trainer: - kwargs: {} -name: 829c6d97bc7dc632864c1c979e2bedae -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/params.yaml b/examples/security/classification/output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/params.yaml deleted file mode 100644 index ad1f51b1..00000000 --- a/examples/security/classification/output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/adv_predictions.json - adv_probabilities_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/cb7869432921616ce841a09e1ca77110.pkl - params_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/params.yaml - predictions_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/predictions.json - probabilities_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/probabilities.json - score_dict_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/score_dict.json - test_labels_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/test_labels.json - train_labels_file: output/reports/train/829e8042ca0b53ee1bd1e8c08c4b139e/train_labels.json - model_dir: models - name: 829e8042ca0b53ee1bd1e8c08c4b139e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d8d877de3b1e07e5d4220d0fd6c1e1b0 - trainer: - kwargs: {} -name: 829e8042ca0b53ee1bd1e8c08c4b139e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/params.yaml b/examples/security/classification/output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/params.yaml deleted file mode 100644 index 9b8f176e..00000000 --- a/examples/security/classification/output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/adv_predictions.json - adv_probabilities_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/f4661589799be5977b6c02f1716ac122.pkl - params_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/params.yaml - predictions_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/predictions.json - probabilities_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/probabilities.json - score_dict_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/score_dict.json - test_labels_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/test_labels.json - train_labels_file: output/reports/train/82aaed7adc5f7a3ec5badd12547b9550/train_labels.json - model_dir: models - name: 82aaed7adc5f7a3ec5badd12547b9550 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a87cd28a2d58abda9418a58b53011257 - trainer: - kwargs: {} -name: 82aaed7adc5f7a3ec5badd12547b9550 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/82c00e921dabed33298b27a571a9e718/params.yaml b/examples/security/classification/output/reports/train/82c00e921dabed33298b27a571a9e718/params.yaml deleted file mode 100644 index ef5d34b4..00000000 --- a/examples/security/classification/output/reports/train/82c00e921dabed33298b27a571a9e718/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/82c00e921dabed33298b27a571a9e718/adv_predictions.json - adv_probabilities_file: output/reports/train/82c00e921dabed33298b27a571a9e718/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/8bbee02f651a75581c324be8f0ff8c81.pkl - params_file: output/reports/train/82c00e921dabed33298b27a571a9e718/params.yaml - predictions_file: output/reports/train/82c00e921dabed33298b27a571a9e718/predictions.json - probabilities_file: output/reports/train/82c00e921dabed33298b27a571a9e718/probabilities.json - score_dict_file: output/reports/train/82c00e921dabed33298b27a571a9e718/score_dict.json - test_labels_file: output/reports/train/82c00e921dabed33298b27a571a9e718/test_labels.json - train_labels_file: output/reports/train/82c00e921dabed33298b27a571a9e718/train_labels.json - model_dir: models - name: 82c00e921dabed33298b27a571a9e718 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 67e03e0c440cd941756e52dea87ac32b - trainer: - kwargs: {} -name: 82c00e921dabed33298b27a571a9e718 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/834ec47b0659293267a615f62c5735a6/params.yaml b/examples/security/classification/output/reports/train/834ec47b0659293267a615f62c5735a6/params.yaml deleted file mode 100644 index 22515cbe..00000000 --- a/examples/security/classification/output/reports/train/834ec47b0659293267a615f62c5735a6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/834ec47b0659293267a615f62c5735a6/adv_predictions.json - adv_probabilities_file: output/reports/train/834ec47b0659293267a615f62c5735a6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/22e93dc4f7db522126af5f94f693939c.pkl - params_file: output/reports/train/834ec47b0659293267a615f62c5735a6/params.yaml - predictions_file: output/reports/train/834ec47b0659293267a615f62c5735a6/predictions.json - probabilities_file: output/reports/train/834ec47b0659293267a615f62c5735a6/probabilities.json - score_dict_file: output/reports/train/834ec47b0659293267a615f62c5735a6/score_dict.json - test_labels_file: output/reports/train/834ec47b0659293267a615f62c5735a6/test_labels.json - train_labels_file: output/reports/train/834ec47b0659293267a615f62c5735a6/train_labels.json - model_dir: models - name: 834ec47b0659293267a615f62c5735a6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e03f70a73495979ac12c511488ca9191 - trainer: - kwargs: {} -name: 834ec47b0659293267a615f62c5735a6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/836e158923a7ba76e05f5ebefca67ed3/params.yaml b/examples/security/classification/output/reports/train/836e158923a7ba76e05f5ebefca67ed3/params.yaml deleted file mode 100644 index df9c5234..00000000 --- a/examples/security/classification/output/reports/train/836e158923a7ba76e05f5ebefca67ed3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/adv_predictions.json - adv_probabilities_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/18412478b9f2c6297e3ab0b2878d341e.pkl - params_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/params.yaml - predictions_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/predictions.json - probabilities_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/probabilities.json - score_dict_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/score_dict.json - test_labels_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/test_labels.json - train_labels_file: output/reports/train/836e158923a7ba76e05f5ebefca67ed3/train_labels.json - model_dir: models - name: 836e158923a7ba76e05f5ebefca67ed3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b2d044384bde8b176d31e58229910401 - trainer: - kwargs: {} -name: 836e158923a7ba76e05f5ebefca67ed3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/params.yaml b/examples/security/classification/output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/params.yaml deleted file mode 100644 index f6b85156..00000000 --- a/examples/security/classification/output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/adv_predictions.json - adv_probabilities_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/735ad1e47f32849185fb92d7fefa5a56.pkl - params_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/params.yaml - predictions_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/predictions.json - probabilities_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/probabilities.json - score_dict_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/score_dict.json - test_labels_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/test_labels.json - train_labels_file: output/reports/train/83a067a6dbe1f4b4f2f8b893c95dd46d/train_labels.json - model_dir: models - name: 83a067a6dbe1f4b4f2f8b893c95dd46d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cbc0925401f4fed3bb6290f1d0eb7d00 - trainer: - kwargs: {} -name: 83a067a6dbe1f4b4f2f8b893c95dd46d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/83e2dbad6271d074374836bbec4abea3/params.yaml b/examples/security/classification/output/reports/train/83e2dbad6271d074374836bbec4abea3/params.yaml deleted file mode 100644 index ab1969f9..00000000 --- a/examples/security/classification/output/reports/train/83e2dbad6271d074374836bbec4abea3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/adv_predictions.json - adv_probabilities_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/740ed5426b4e4629f4112363514d61e4.pkl - params_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/params.yaml - predictions_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/predictions.json - probabilities_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/probabilities.json - score_dict_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/score_dict.json - test_labels_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/test_labels.json - train_labels_file: output/reports/train/83e2dbad6271d074374836bbec4abea3/train_labels.json - model_dir: models - name: 83e2dbad6271d074374836bbec4abea3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 50df3d41dd86ad6dcfbbd1e48e59c95b - trainer: - kwargs: {} -name: 83e2dbad6271d074374836bbec4abea3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/83faa2a56109e0ba252b7cda3ab37703/params.yaml b/examples/security/classification/output/reports/train/83faa2a56109e0ba252b7cda3ab37703/params.yaml deleted file mode 100644 index a87d4779..00000000 --- a/examples/security/classification/output/reports/train/83faa2a56109e0ba252b7cda3ab37703/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/adv_predictions.json - adv_probabilities_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/9cfc54677c627650b47e67042eec7c74.pkl - params_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/params.yaml - predictions_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/predictions.json - probabilities_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/probabilities.json - score_dict_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/score_dict.json - test_labels_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/test_labels.json - train_labels_file: output/reports/train/83faa2a56109e0ba252b7cda3ab37703/train_labels.json - model_dir: models - name: 83faa2a56109e0ba252b7cda3ab37703 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 313ae4de650f32beee4f1d24feff98d3 - trainer: - kwargs: {} -name: 83faa2a56109e0ba252b7cda3ab37703 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/params.yaml b/examples/security/classification/output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/params.yaml deleted file mode 100644 index d547df13..00000000 --- a/examples/security/classification/output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/adv_predictions.json - adv_probabilities_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/02a89a41d4bf1d02fcde6cc214147391.pkl - params_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/params.yaml - predictions_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/predictions.json - probabilities_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/probabilities.json - score_dict_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/score_dict.json - test_labels_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/test_labels.json - train_labels_file: output/reports/train/83fb5e6510768e0a4f5ff9a0e3a0b622/train_labels.json - model_dir: models - name: 83fb5e6510768e0a4f5ff9a0e3a0b622 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2a9aa36408f6c92373f528a39ff69816 - trainer: - kwargs: {} -name: 83fb5e6510768e0a4f5ff9a0e3a0b622 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/840622f460065f0af78fbc39bc52345d/params.yaml b/examples/security/classification/output/reports/train/840622f460065f0af78fbc39bc52345d/params.yaml deleted file mode 100644 index 2e3a4c21..00000000 --- a/examples/security/classification/output/reports/train/840622f460065f0af78fbc39bc52345d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/840622f460065f0af78fbc39bc52345d/adv_predictions.json - adv_probabilities_file: output/reports/train/840622f460065f0af78fbc39bc52345d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/fb1970d07d33f925407faa9555409abc.pkl - params_file: output/reports/train/840622f460065f0af78fbc39bc52345d/params.yaml - predictions_file: output/reports/train/840622f460065f0af78fbc39bc52345d/predictions.json - probabilities_file: output/reports/train/840622f460065f0af78fbc39bc52345d/probabilities.json - score_dict_file: output/reports/train/840622f460065f0af78fbc39bc52345d/score_dict.json - test_labels_file: output/reports/train/840622f460065f0af78fbc39bc52345d/test_labels.json - train_labels_file: output/reports/train/840622f460065f0af78fbc39bc52345d/train_labels.json - model_dir: models - name: 840622f460065f0af78fbc39bc52345d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b3565f785968706dfb013f35308495ee - trainer: - kwargs: {} -name: 840622f460065f0af78fbc39bc52345d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/84384eed5b73153c580b8d5ca535d0a1/params.yaml b/examples/security/classification/output/reports/train/84384eed5b73153c580b8d5ca535d0a1/params.yaml deleted file mode 100644 index 42aa2084..00000000 --- a/examples/security/classification/output/reports/train/84384eed5b73153c580b8d5ca535d0a1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/adv_predictions.json - adv_probabilities_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/a960d1c170c5f26706d612b75186aef5.pkl - params_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/params.yaml - predictions_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/predictions.json - probabilities_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/probabilities.json - score_dict_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/score_dict.json - test_labels_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/test_labels.json - train_labels_file: output/reports/train/84384eed5b73153c580b8d5ca535d0a1/train_labels.json - model_dir: models - name: 84384eed5b73153c580b8d5ca535d0a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1001bd20895a7a9f906370e2938e145b - trainer: - kwargs: {} -name: 84384eed5b73153c580b8d5ca535d0a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/params.yaml b/examples/security/classification/output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/params.yaml deleted file mode 100644 index 44374fb0..00000000 --- a/examples/security/classification/output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/adv_predictions.json - adv_probabilities_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/95b96e01c5edf9378a85492594690e32.pkl - params_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/params.yaml - predictions_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/predictions.json - probabilities_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/probabilities.json - score_dict_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/score_dict.json - test_labels_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/test_labels.json - train_labels_file: output/reports/train/845ad3fba9192e5943bca27b3c6bfc50/train_labels.json - model_dir: models - name: 845ad3fba9192e5943bca27b3c6bfc50 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e2974ce9860cacd1a9ee7c916b8da65a - trainer: - kwargs: {} -name: 845ad3fba9192e5943bca27b3c6bfc50 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/params.yaml b/examples/security/classification/output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/params.yaml deleted file mode 100644 index d3f09cd3..00000000 --- a/examples/security/classification/output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/params.yaml +++ /dev/null @@ -1,115 +0,0 @@ -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/adv_predictions.json - adv_probabilities_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl - model_file: output/models/a952e8bc9502a06c097e5c453a2c77bd.pkl - params_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/params.yaml - predictions_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/predictions.json - probabilities_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/probabilities.json - score_dict_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/score_dict.json - test_labels_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/test_labels.json - train_labels_file: output/reports/train/8462f601caf74e4fa3f819a5c0ee5db8/train_labels.json - model_dir: models - name: 8462f601caf74e4fa3f819a5c0ee5db8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 0.0001 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cb5ba0e2593feb3526e3b3f00318eabb - trainer: - kwargs: {} -name: 8462f601caf74e4fa3f819a5c0ee5db8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8477725447642bc3075860a072ecdb8c/params.yaml b/examples/security/classification/output/reports/train/8477725447642bc3075860a072ecdb8c/params.yaml deleted file mode 100644 index f1e8c42d..00000000 --- a/examples/security/classification/output/reports/train/8477725447642bc3075860a072ecdb8c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8477725447642bc3075860a072ecdb8c/adv_predictions.json - adv_probabilities_file: output/reports/train/8477725447642bc3075860a072ecdb8c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/584b1ba70f1ffc6e78c5158d937d7a46.pkl - params_file: output/reports/train/8477725447642bc3075860a072ecdb8c/params.yaml - predictions_file: output/reports/train/8477725447642bc3075860a072ecdb8c/predictions.json - probabilities_file: output/reports/train/8477725447642bc3075860a072ecdb8c/probabilities.json - score_dict_file: output/reports/train/8477725447642bc3075860a072ecdb8c/score_dict.json - test_labels_file: output/reports/train/8477725447642bc3075860a072ecdb8c/test_labels.json - train_labels_file: output/reports/train/8477725447642bc3075860a072ecdb8c/train_labels.json - model_dir: models - name: 8477725447642bc3075860a072ecdb8c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e3f34059a170d1ece3b54bae1a294a1e - trainer: - kwargs: {} -name: 8477725447642bc3075860a072ecdb8c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/848501996217976afa734e788c347dd7/params.yaml b/examples/security/classification/output/reports/train/848501996217976afa734e788c347dd7/params.yaml deleted file mode 100644 index 0b598851..00000000 --- a/examples/security/classification/output/reports/train/848501996217976afa734e788c347dd7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/848501996217976afa734e788c347dd7/adv_predictions.json - adv_probabilities_file: output/reports/train/848501996217976afa734e788c347dd7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/5570eed979ad6b3dbeae0527a6b5a710.pkl - params_file: output/reports/train/848501996217976afa734e788c347dd7/params.yaml - predictions_file: output/reports/train/848501996217976afa734e788c347dd7/predictions.json - probabilities_file: output/reports/train/848501996217976afa734e788c347dd7/probabilities.json - score_dict_file: output/reports/train/848501996217976afa734e788c347dd7/score_dict.json - test_labels_file: output/reports/train/848501996217976afa734e788c347dd7/test_labels.json - train_labels_file: output/reports/train/848501996217976afa734e788c347dd7/train_labels.json - model_dir: models - name: 848501996217976afa734e788c347dd7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 32cb314672d45efd24459e724df21791 - trainer: - kwargs: {} -name: 848501996217976afa734e788c347dd7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/84866bce100fd5e96c84256ead3e7749/params.yaml b/examples/security/classification/output/reports/train/84866bce100fd5e96c84256ead3e7749/params.yaml deleted file mode 100644 index 419e5fa7..00000000 --- a/examples/security/classification/output/reports/train/84866bce100fd5e96c84256ead3e7749/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/adv_predictions.json - adv_probabilities_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/a2c399fe1604908247af0733decc740f.pkl - params_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/params.yaml - predictions_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/predictions.json - probabilities_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/probabilities.json - score_dict_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/score_dict.json - test_labels_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/test_labels.json - train_labels_file: output/reports/train/84866bce100fd5e96c84256ead3e7749/train_labels.json - model_dir: models - name: 84866bce100fd5e96c84256ead3e7749 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7179ef7084413e0127a34db49e1709dc - trainer: - kwargs: {} -name: 84866bce100fd5e96c84256ead3e7749 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/params.yaml b/examples/security/classification/output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/params.yaml deleted file mode 100644 index af27c0c4..00000000 --- a/examples/security/classification/output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/params.yaml +++ /dev/null @@ -1,206 +0,0 @@ -attack: - attack_size: 10 - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 10.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 0.01 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0c012452aa7fe44fd025bb038da6b505 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 62e9935b8f9919fa5c8899f0c05f3f2a - trainer: - kwargs: {} - name: ed0c95ed9402534ff4c99a3247dd4302 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/adv_predictions.json - adv_probabilities_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/adv_probabilities.json - attack_file: output/attacks/0a0ab0f67a44e8b63fca04701af4011f.pkl - data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl - model_file: output/models/dc6e3c15bb6116053e786b748bac8835.pkl - params_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/params.yaml - predictions_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/predictions.json - probabilities_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/probabilities.json - score_dict_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/score_dict.json - test_labels_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/test_labels.json - train_labels_file: output/reports/train/84a4fb778a1cfb2c300ca4f10631cc1b/train_labels.json - model_dir: models - name: 84a4fb778a1cfb2c300ca4f10631cc1b - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 333762617eb08ba38207efe77f644c01 - trainer: - kwargs: {} -name: 84a4fb778a1cfb2c300ca4f10631cc1b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8514049ea118230ce7f1f99cdca7b126/params.yaml b/examples/security/classification/output/reports/train/8514049ea118230ce7f1f99cdca7b126/params.yaml deleted file mode 100644 index f70eaa2c..00000000 --- a/examples/security/classification/output/reports/train/8514049ea118230ce7f1f99cdca7b126/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/adv_predictions.json - adv_probabilities_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/eaef5c8594766723a49cac08315ae76a.pkl - params_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/params.yaml - predictions_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/predictions.json - probabilities_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/probabilities.json - score_dict_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/score_dict.json - test_labels_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/test_labels.json - train_labels_file: output/reports/train/8514049ea118230ce7f1f99cdca7b126/train_labels.json - model_dir: models - name: 8514049ea118230ce7f1f99cdca7b126 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2b878c7cf4af359208381a296bebe2fa - trainer: - kwargs: {} -name: 8514049ea118230ce7f1f99cdca7b126 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/params.yaml b/examples/security/classification/output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/params.yaml deleted file mode 100644 index 00f50c96..00000000 --- a/examples/security/classification/output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/adv_predictions.json - adv_probabilities_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/d15025c9aac7d8bf72fb3b26411d4355.pkl - params_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/params.yaml - predictions_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/predictions.json - probabilities_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/probabilities.json - score_dict_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/score_dict.json - test_labels_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/test_labels.json - train_labels_file: output/reports/train/8519d6c43dca33a8de9a16fbf01ef6fa/train_labels.json - model_dir: models - name: 8519d6c43dca33a8de9a16fbf01ef6fa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bae409a1b36c23573d78505b92a2a159 - trainer: - kwargs: {} -name: 8519d6c43dca33a8de9a16fbf01ef6fa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/params.yaml b/examples/security/classification/output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/params.yaml deleted file mode 100644 index 047b9bb0..00000000 --- a/examples/security/classification/output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/adv_predictions.json - adv_probabilities_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/dd464dffe1d8ca7d2fe27f8366183662.pkl - params_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/params.yaml - predictions_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/predictions.json - probabilities_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/probabilities.json - score_dict_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/score_dict.json - test_labels_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/test_labels.json - train_labels_file: output/reports/train/85439d044fd4224c456c7d1dc8cb9d4a/train_labels.json - model_dir: models - name: 85439d044fd4224c456c7d1dc8cb9d4a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1a73d5f51d89689dec4308aabb5f11bf - trainer: - kwargs: {} -name: 85439d044fd4224c456c7d1dc8cb9d4a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/857249d776ec69932704ee22616c6adc/params.yaml b/examples/security/classification/output/reports/train/857249d776ec69932704ee22616c6adc/params.yaml deleted file mode 100644 index ac880d90..00000000 --- a/examples/security/classification/output/reports/train/857249d776ec69932704ee22616c6adc/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/857249d776ec69932704ee22616c6adc/adv_predictions.json - adv_probabilities_file: output/reports/train/857249d776ec69932704ee22616c6adc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/10c2ac1f7e254e05ebcc2bf23852056b.pkl - params_file: output/reports/train/857249d776ec69932704ee22616c6adc/params.yaml - predictions_file: output/reports/train/857249d776ec69932704ee22616c6adc/predictions.json - probabilities_file: output/reports/train/857249d776ec69932704ee22616c6adc/probabilities.json - score_dict_file: output/reports/train/857249d776ec69932704ee22616c6adc/score_dict.json - test_labels_file: output/reports/train/857249d776ec69932704ee22616c6adc/test_labels.json - train_labels_file: output/reports/train/857249d776ec69932704ee22616c6adc/train_labels.json - model_dir: models - name: 857249d776ec69932704ee22616c6adc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3551abc243224d844ff22df51428e954 - trainer: - kwargs: {} -name: 857249d776ec69932704ee22616c6adc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/params.yaml b/examples/security/classification/output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/params.yaml deleted file mode 100644 index 65ac65bf..00000000 --- a/examples/security/classification/output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/adv_predictions.json - adv_probabilities_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/2735b6f6dab6cb94a0c0749f352c6112.pkl - params_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/params.yaml - predictions_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/predictions.json - probabilities_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/probabilities.json - score_dict_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/score_dict.json - test_labels_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/test_labels.json - train_labels_file: output/reports/train/85c85b116574dd5bc640ef6e9a21f9f5/train_labels.json - model_dir: models - name: 85c85b116574dd5bc640ef6e9a21f9f5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4ecc0aefce0f14385599868cd2a48f4c - trainer: - kwargs: {} -name: 85c85b116574dd5bc640ef6e9a21f9f5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/85d18c8130c621eaac00f1d1013296b8/dvc.yaml b/examples/security/classification/output/reports/train/85d18c8130c621eaac00f1d1013296b8/dvc.yaml deleted file mode 100644 index f914f5af..00000000 --- a/examples/security/classification/output/reports/train/85d18c8130c621eaac00f1d1013296b8/dvc.yaml +++ /dev/null @@ -1,14 +0,0 @@ -stages: - train: - cmd: python -m deckard.layers.experiment train - metrics: - - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} - - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} - - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} - outs: - - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} - params: - - data - - model - - files - - scorers diff --git a/examples/security/classification/output/reports/train/85d18c8130c621eaac00f1d1013296b8/params.yaml b/examples/security/classification/output/reports/train/85d18c8130c621eaac00f1d1013296b8/params.yaml deleted file mode 100644 index 81715afd..00000000 --- a/examples/security/classification/output/reports/train/85d18c8130c621eaac00f1d1013296b8/params.yaml +++ /dev/null @@ -1,215 +0,0 @@ -attack: - attack_size: 10 - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 100.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5cae49bbcb6dff3c0e2f0b3a53d5f104 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9b1ae58b464df54ee8918089f870a3ad - trainer: - kwargs: {} - name: 4aece7a6fa679e2415af83d26b3d61bd -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/adv_predictions.json - adv_probabilities_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/adv_probabilities.json - attack_file: output/attacks/ce690e1d2af793d75449f34a1c13a601.pkl - data_file: output/data/2db9ede9d723385cf4052c8e5300be74.pkl - model_file: output/models/e187c2eacc99734fa834a681c34ec11f.pkl - params_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/params.yaml - predictions_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/predictions.json - probabilities_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/probabilities.json - score_dict_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/score_dict.json - test_labels_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/test_labels.json - train_labels_file: output/reports/train/85d18c8130c621eaac00f1d1013296b8/train_labels.json - model_dir: models - name: 85d18c8130c621eaac00f1d1013296b8 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9b1ae58b464df54ee8918089f870a3ad - trainer: - kwargs: {} -name: 85d18c8130c621eaac00f1d1013296b8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/params.yaml b/examples/security/classification/output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/params.yaml deleted file mode 100644 index a55c35db..00000000 --- a/examples/security/classification/output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/adv_predictions.json - adv_probabilities_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/ac492a48780d83f42de46656c93c9683.pkl - params_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/params.yaml - predictions_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/predictions.json - probabilities_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/probabilities.json - score_dict_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/score_dict.json - test_labels_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/test_labels.json - train_labels_file: output/reports/train/85ead37a5255ce5e3c3f0d2cb1568afb/train_labels.json - model_dir: models - name: 85ead37a5255ce5e3c3f0d2cb1568afb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9c39c032a09e5231a22df6bd38e4dc40 - trainer: - kwargs: {} -name: 85ead37a5255ce5e3c3f0d2cb1568afb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/params.yaml b/examples/security/classification/output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/params.yaml deleted file mode 100644 index 845fe62d..00000000 --- a/examples/security/classification/output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/adv_predictions.json - adv_probabilities_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/25e3d1db602c304f76472fdbfb8ca060.pkl - params_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/params.yaml - predictions_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/predictions.json - probabilities_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/probabilities.json - score_dict_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/score_dict.json - test_labels_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/test_labels.json - train_labels_file: output/reports/train/85efdb113a16b0a70fc484f9b4f67bdd/train_labels.json - model_dir: models - name: 85efdb113a16b0a70fc484f9b4f67bdd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 192a5875bbc8c4824a5b5729ead37583 - trainer: - kwargs: {} -name: 85efdb113a16b0a70fc484f9b4f67bdd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/params.yaml b/examples/security/classification/output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/params.yaml deleted file mode 100644 index 7881a58f..00000000 --- a/examples/security/classification/output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/adv_predictions.json - adv_probabilities_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/fd12347bf6dfc44fe645a4a67109e64d.pkl - params_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/params.yaml - predictions_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/predictions.json - probabilities_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/probabilities.json - score_dict_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/score_dict.json - test_labels_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/test_labels.json - train_labels_file: output/reports/train/85f6a4d39b1ec69825c02ecda2744d54/train_labels.json - model_dir: models - name: 85f6a4d39b1ec69825c02ecda2744d54 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 057225ea83d3f424c3b887d55fdb97bf - trainer: - kwargs: {} -name: 85f6a4d39b1ec69825c02ecda2744d54 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/86074efaab70b55093c901add38d1c07/params.yaml b/examples/security/classification/output/reports/train/86074efaab70b55093c901add38d1c07/params.yaml deleted file mode 100644 index 85936550..00000000 --- a/examples/security/classification/output/reports/train/86074efaab70b55093c901add38d1c07/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/86074efaab70b55093c901add38d1c07/adv_predictions.json - adv_probabilities_file: output/reports/train/86074efaab70b55093c901add38d1c07/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/86ed43d665a550454f6b1d4d657b407c.pkl - params_file: output/reports/train/86074efaab70b55093c901add38d1c07/params.yaml - predictions_file: output/reports/train/86074efaab70b55093c901add38d1c07/predictions.json - probabilities_file: output/reports/train/86074efaab70b55093c901add38d1c07/probabilities.json - score_dict_file: output/reports/train/86074efaab70b55093c901add38d1c07/score_dict.json - test_labels_file: output/reports/train/86074efaab70b55093c901add38d1c07/test_labels.json - train_labels_file: output/reports/train/86074efaab70b55093c901add38d1c07/train_labels.json - model_dir: models - name: 86074efaab70b55093c901add38d1c07 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d3063adaf311bf1069bbbf66e388a6a4 - trainer: - kwargs: {} -name: 86074efaab70b55093c901add38d1c07 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/86181fba20afc49ee51965329c55f882/params.yaml b/examples/security/classification/output/reports/train/86181fba20afc49ee51965329c55f882/params.yaml deleted file mode 100644 index ca078551..00000000 --- a/examples/security/classification/output/reports/train/86181fba20afc49ee51965329c55f882/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/86181fba20afc49ee51965329c55f882/adv_predictions.json - adv_probabilities_file: output/reports/train/86181fba20afc49ee51965329c55f882/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/692bc71168bd2ab6c83f544bb8d6ba78.pkl - params_file: output/reports/train/86181fba20afc49ee51965329c55f882/params.yaml - predictions_file: output/reports/train/86181fba20afc49ee51965329c55f882/predictions.json - probabilities_file: output/reports/train/86181fba20afc49ee51965329c55f882/probabilities.json - score_dict_file: output/reports/train/86181fba20afc49ee51965329c55f882/score_dict.json - test_labels_file: output/reports/train/86181fba20afc49ee51965329c55f882/test_labels.json - train_labels_file: output/reports/train/86181fba20afc49ee51965329c55f882/train_labels.json - model_dir: models - name: 86181fba20afc49ee51965329c55f882 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 52ee2035127fb152e89eacd92d75c36c - trainer: - kwargs: {} -name: 86181fba20afc49ee51965329c55f882 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/params.yaml b/examples/security/classification/output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/params.yaml deleted file mode 100644 index ffe82fec..00000000 --- a/examples/security/classification/output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/adv_predictions.json - adv_probabilities_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/812f84d37a78fc1801f9ac2f5d597fce.pkl - params_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/params.yaml - predictions_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/predictions.json - probabilities_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/probabilities.json - score_dict_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/score_dict.json - test_labels_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/test_labels.json - train_labels_file: output/reports/train/86472a76b5a034ed8da7f0d4c5255b28/train_labels.json - model_dir: models - name: 86472a76b5a034ed8da7f0d4c5255b28 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a94ea942e2ee81bf1a2eeac73f2228e3 - trainer: - kwargs: {} -name: 86472a76b5a034ed8da7f0d4c5255b28 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/86510ba48c0b27fcd41abf596033aa1d/params.yaml b/examples/security/classification/output/reports/train/86510ba48c0b27fcd41abf596033aa1d/params.yaml deleted file mode 100644 index 75e00075..00000000 --- a/examples/security/classification/output/reports/train/86510ba48c0b27fcd41abf596033aa1d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/adv_predictions.json - adv_probabilities_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/e9de405677ed41f7cf7cdad0f5be1519.pkl - params_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/params.yaml - predictions_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/predictions.json - probabilities_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/probabilities.json - score_dict_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/score_dict.json - test_labels_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/test_labels.json - train_labels_file: output/reports/train/86510ba48c0b27fcd41abf596033aa1d/train_labels.json - model_dir: models - name: 86510ba48c0b27fcd41abf596033aa1d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 568382fa0cc71e6ce822372cb9f7cff6 - trainer: - kwargs: {} -name: 86510ba48c0b27fcd41abf596033aa1d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/params.yaml b/examples/security/classification/output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/params.yaml deleted file mode 100644 index d5414bd8..00000000 --- a/examples/security/classification/output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/adv_predictions.json - adv_probabilities_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/04edbf8c1876e7cb3ebcb90212a2506d.pkl - params_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/params.yaml - predictions_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/predictions.json - probabilities_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/probabilities.json - score_dict_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/score_dict.json - test_labels_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/test_labels.json - train_labels_file: output/reports/train/866e524b6b4ded3f8e1a028f88002ef7/train_labels.json - model_dir: models - name: 866e524b6b4ded3f8e1a028f88002ef7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: de35725fa80e326b24d14684098dd785 - trainer: - kwargs: {} -name: 866e524b6b4ded3f8e1a028f88002ef7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/86724816bcab6ebece3b259ebaaa69e2/params.yaml b/examples/security/classification/output/reports/train/86724816bcab6ebece3b259ebaaa69e2/params.yaml deleted file mode 100644 index 69ad6462..00000000 --- a/examples/security/classification/output/reports/train/86724816bcab6ebece3b259ebaaa69e2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/adv_predictions.json - adv_probabilities_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/440ca43365a54269eea687fc0e80501f.pkl - params_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/params.yaml - predictions_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/predictions.json - probabilities_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/probabilities.json - score_dict_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/score_dict.json - test_labels_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/test_labels.json - train_labels_file: output/reports/train/86724816bcab6ebece3b259ebaaa69e2/train_labels.json - model_dir: models - name: 86724816bcab6ebece3b259ebaaa69e2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2b5dae09623fbc7fb626aed79677f4d0 - trainer: - kwargs: {} -name: 86724816bcab6ebece3b259ebaaa69e2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/86cd1c783e061937029ef34091416e2b/params.yaml b/examples/security/classification/output/reports/train/86cd1c783e061937029ef34091416e2b/params.yaml deleted file mode 100644 index f37b1d66..00000000 --- a/examples/security/classification/output/reports/train/86cd1c783e061937029ef34091416e2b/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/86cd1c783e061937029ef34091416e2b/adv_predictions.json - adv_probabilities_file: output/reports/train/86cd1c783e061937029ef34091416e2b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/e52b21bae47d41b5127c652b3b36045a.pkl - params_file: output/reports/train/86cd1c783e061937029ef34091416e2b/params.yaml - predictions_file: output/reports/train/86cd1c783e061937029ef34091416e2b/predictions.json - probabilities_file: output/reports/train/86cd1c783e061937029ef34091416e2b/probabilities.json - score_dict_file: output/reports/train/86cd1c783e061937029ef34091416e2b/score_dict.json - test_labels_file: output/reports/train/86cd1c783e061937029ef34091416e2b/test_labels.json - train_labels_file: output/reports/train/86cd1c783e061937029ef34091416e2b/train_labels.json - model_dir: models - name: 86cd1c783e061937029ef34091416e2b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7b045b79cab849e16e52e7a084255e90 - trainer: - kwargs: {} -name: 86cd1c783e061937029ef34091416e2b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/params.yaml b/examples/security/classification/output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/params.yaml deleted file mode 100644 index 25331127..00000000 --- a/examples/security/classification/output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/adv_predictions.json - adv_probabilities_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/f4f6f94bce97734852576855cb6bfa74.pkl - params_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/params.yaml - predictions_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/predictions.json - probabilities_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/probabilities.json - score_dict_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/score_dict.json - test_labels_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/test_labels.json - train_labels_file: output/reports/train/86cf6eea4a59c25e9c8ffa8c4a595c4b/train_labels.json - model_dir: models - name: 86cf6eea4a59c25e9c8ffa8c4a595c4b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 16084e1b89880e2bb5d869a38a3e947f - trainer: - kwargs: {} -name: 86cf6eea4a59c25e9c8ffa8c4a595c4b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/87126b5b9d02b829fe323ff0fe39af68/params.yaml b/examples/security/classification/output/reports/train/87126b5b9d02b829fe323ff0fe39af68/params.yaml deleted file mode 100644 index f8fa535c..00000000 --- a/examples/security/classification/output/reports/train/87126b5b9d02b829fe323ff0fe39af68/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/adv_predictions.json - adv_probabilities_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/6be53aa8c0a7c57ccb7f94056d9a53af.pkl - params_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/params.yaml - predictions_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/predictions.json - probabilities_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/probabilities.json - score_dict_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/score_dict.json - test_labels_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/test_labels.json - train_labels_file: output/reports/train/87126b5b9d02b829fe323ff0fe39af68/train_labels.json - model_dir: models - name: 87126b5b9d02b829fe323ff0fe39af68 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a4b64139966c42e29c6a5c0f10576025 - trainer: - kwargs: {} -name: 87126b5b9d02b829fe323ff0fe39af68 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/871975f05f5771c104b0485db958a2f1/params.yaml b/examples/security/classification/output/reports/train/871975f05f5771c104b0485db958a2f1/params.yaml deleted file mode 100644 index 04bcb8cc..00000000 --- a/examples/security/classification/output/reports/train/871975f05f5771c104b0485db958a2f1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/871975f05f5771c104b0485db958a2f1/adv_predictions.json - adv_probabilities_file: output/reports/train/871975f05f5771c104b0485db958a2f1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/219ee8034f8d546458523b769b8d67f2.pkl - params_file: output/reports/train/871975f05f5771c104b0485db958a2f1/params.yaml - predictions_file: output/reports/train/871975f05f5771c104b0485db958a2f1/predictions.json - probabilities_file: output/reports/train/871975f05f5771c104b0485db958a2f1/probabilities.json - score_dict_file: output/reports/train/871975f05f5771c104b0485db958a2f1/score_dict.json - test_labels_file: output/reports/train/871975f05f5771c104b0485db958a2f1/test_labels.json - train_labels_file: output/reports/train/871975f05f5771c104b0485db958a2f1/train_labels.json - model_dir: models - name: 871975f05f5771c104b0485db958a2f1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fbc15a1e4d678eb2402d76b15f1428a6 - trainer: - kwargs: {} -name: 871975f05f5771c104b0485db958a2f1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/87214e96634ed36203ba574e3d65c1bf/params.yaml b/examples/security/classification/output/reports/train/87214e96634ed36203ba574e3d65c1bf/params.yaml deleted file mode 100644 index 5e434af9..00000000 --- a/examples/security/classification/output/reports/train/87214e96634ed36203ba574e3d65c1bf/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/adv_predictions.json - adv_probabilities_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/d5da6121aad1d325cedb38b788ee649c.pkl - params_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/params.yaml - predictions_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/predictions.json - probabilities_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/probabilities.json - score_dict_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/score_dict.json - test_labels_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/test_labels.json - train_labels_file: output/reports/train/87214e96634ed36203ba574e3d65c1bf/train_labels.json - model_dir: models - name: 87214e96634ed36203ba574e3d65c1bf - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b2b8352333f373c763505b1576ce85c5 - trainer: - kwargs: {} -name: 87214e96634ed36203ba574e3d65c1bf -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8764a82b0016391e7272f8b04fd69465/params.yaml b/examples/security/classification/output/reports/train/8764a82b0016391e7272f8b04fd69465/params.yaml deleted file mode 100644 index 2602fc0d..00000000 --- a/examples/security/classification/output/reports/train/8764a82b0016391e7272f8b04fd69465/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/adv_predictions.json - adv_probabilities_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/2cd1e67e5acbc727bd6d47adceb72162.pkl - params_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/params.yaml - predictions_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/predictions.json - probabilities_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/probabilities.json - score_dict_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/score_dict.json - test_labels_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/test_labels.json - train_labels_file: output/reports/train/8764a82b0016391e7272f8b04fd69465/train_labels.json - model_dir: models - name: 8764a82b0016391e7272f8b04fd69465 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8fbad9d7b1b5d41126d1e90bbd735f49 - trainer: - kwargs: {} -name: 8764a82b0016391e7272f8b04fd69465 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8773385ce29212115799528db12f2b6e/params.yaml b/examples/security/classification/output/reports/train/8773385ce29212115799528db12f2b6e/params.yaml deleted file mode 100644 index 41ea4b80..00000000 --- a/examples/security/classification/output/reports/train/8773385ce29212115799528db12f2b6e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8773385ce29212115799528db12f2b6e/adv_predictions.json - adv_probabilities_file: output/reports/train/8773385ce29212115799528db12f2b6e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/a5a1c76bd2642769ab11def5f46b862c.pkl - params_file: output/reports/train/8773385ce29212115799528db12f2b6e/params.yaml - predictions_file: output/reports/train/8773385ce29212115799528db12f2b6e/predictions.json - probabilities_file: output/reports/train/8773385ce29212115799528db12f2b6e/probabilities.json - score_dict_file: output/reports/train/8773385ce29212115799528db12f2b6e/score_dict.json - test_labels_file: output/reports/train/8773385ce29212115799528db12f2b6e/test_labels.json - train_labels_file: output/reports/train/8773385ce29212115799528db12f2b6e/train_labels.json - model_dir: models - name: 8773385ce29212115799528db12f2b6e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 33c1595063a82496ff3410651bca8530 - trainer: - kwargs: {} -name: 8773385ce29212115799528db12f2b6e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8776805c36455b1db7a793323296cd85/params.yaml b/examples/security/classification/output/reports/train/8776805c36455b1db7a793323296cd85/params.yaml deleted file mode 100644 index 58a4ea69..00000000 --- a/examples/security/classification/output/reports/train/8776805c36455b1db7a793323296cd85/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8776805c36455b1db7a793323296cd85/adv_predictions.json - adv_probabilities_file: output/reports/train/8776805c36455b1db7a793323296cd85/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/208bfd3621c4684d27d80e1b291aefb8.pkl - params_file: output/reports/train/8776805c36455b1db7a793323296cd85/params.yaml - predictions_file: output/reports/train/8776805c36455b1db7a793323296cd85/predictions.json - probabilities_file: output/reports/train/8776805c36455b1db7a793323296cd85/probabilities.json - score_dict_file: output/reports/train/8776805c36455b1db7a793323296cd85/score_dict.json - test_labels_file: output/reports/train/8776805c36455b1db7a793323296cd85/test_labels.json - train_labels_file: output/reports/train/8776805c36455b1db7a793323296cd85/train_labels.json - model_dir: models - name: 8776805c36455b1db7a793323296cd85 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 84484981489db2b895f6ba4712e00e74 - trainer: - kwargs: {} -name: 8776805c36455b1db7a793323296cd85 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/params.yaml b/examples/security/classification/output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/params.yaml deleted file mode 100644 index c482cb4c..00000000 --- a/examples/security/classification/output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/adv_predictions.json - adv_probabilities_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/653029789218a2ea424e5812c5c2ac19.pkl - params_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/params.yaml - predictions_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/predictions.json - probabilities_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/probabilities.json - score_dict_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/score_dict.json - test_labels_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/test_labels.json - train_labels_file: output/reports/train/8787d1a18b053ec5b6b05a63c9c4b8c8/train_labels.json - model_dir: models - name: 8787d1a18b053ec5b6b05a63c9c4b8c8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9249db76ad11b892854736c8a2fddd30 - trainer: - kwargs: {} -name: 8787d1a18b053ec5b6b05a63c9c4b8c8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/87cefca48a809d8e79d1571608bc788f/params.yaml b/examples/security/classification/output/reports/train/87cefca48a809d8e79d1571608bc788f/params.yaml deleted file mode 100644 index 274ccae3..00000000 --- a/examples/security/classification/output/reports/train/87cefca48a809d8e79d1571608bc788f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/adv_predictions.json - adv_probabilities_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/664c9d48433afadf1b2e795f7ee50843.pkl - params_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/params.yaml - predictions_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/predictions.json - probabilities_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/probabilities.json - score_dict_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/score_dict.json - test_labels_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/test_labels.json - train_labels_file: output/reports/train/87cefca48a809d8e79d1571608bc788f/train_labels.json - model_dir: models - name: 87cefca48a809d8e79d1571608bc788f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fbe51bfb5ada085f8cb0f8e1d9bc0478 - trainer: - kwargs: {} -name: 87cefca48a809d8e79d1571608bc788f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/87f6068448648d1beabf5486e7f7c3c9/params.yaml b/examples/security/classification/output/reports/train/87f6068448648d1beabf5486e7f7c3c9/params.yaml deleted file mode 100644 index d2654767..00000000 --- a/examples/security/classification/output/reports/train/87f6068448648d1beabf5486e7f7c3c9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/adv_predictions.json - adv_probabilities_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/c6685ee96739de0b3a439ab61a199d35.pkl - params_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/params.yaml - predictions_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/predictions.json - probabilities_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/probabilities.json - score_dict_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/score_dict.json - test_labels_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/test_labels.json - train_labels_file: output/reports/train/87f6068448648d1beabf5486e7f7c3c9/train_labels.json - model_dir: models - name: 87f6068448648d1beabf5486e7f7c3c9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8dd16e0b9eb30276f89fe6930d6772ab - trainer: - kwargs: {} -name: 87f6068448648d1beabf5486e7f7c3c9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/params.yaml b/examples/security/classification/output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/params.yaml deleted file mode 100644 index 30bf0ee5..00000000 --- a/examples/security/classification/output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/adv_predictions.json - adv_probabilities_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/fa6b52922baedca82c7fe9ffd685832c.pkl - params_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/params.yaml - predictions_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/predictions.json - probabilities_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/probabilities.json - score_dict_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/score_dict.json - test_labels_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/test_labels.json - train_labels_file: output/reports/train/88188c5c91b2ccbe1b6d9e3a3244823f/train_labels.json - model_dir: models - name: 88188c5c91b2ccbe1b6d9e3a3244823f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c4099549e655e1e833b221f689502c90 - trainer: - kwargs: {} -name: 88188c5c91b2ccbe1b6d9e3a3244823f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/params.yaml b/examples/security/classification/output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/params.yaml deleted file mode 100644 index 7c05cd2f..00000000 --- a/examples/security/classification/output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/adv_predictions.json - adv_probabilities_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/d690a16bd06a9a36c6b0a52dad1d3bf1.pkl - params_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/params.yaml - predictions_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/predictions.json - probabilities_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/probabilities.json - score_dict_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/score_dict.json - test_labels_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/test_labels.json - train_labels_file: output/reports/train/883e1c8e8b277dd2c3d35cbff3d5c729/train_labels.json - model_dir: models - name: 883e1c8e8b277dd2c3d35cbff3d5c729 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b5930e29ed19b29a5e027fdebee704bf - trainer: - kwargs: {} -name: 883e1c8e8b277dd2c3d35cbff3d5c729 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/params.yaml b/examples/security/classification/output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/params.yaml deleted file mode 100644 index 00e96ff5..00000000 --- a/examples/security/classification/output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/adv_predictions.json - adv_probabilities_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/20127c08294dca7e4b51a386856a0505.pkl - params_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/params.yaml - predictions_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/predictions.json - probabilities_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/probabilities.json - score_dict_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/score_dict.json - test_labels_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/test_labels.json - train_labels_file: output/reports/train/884f7e8c69b90ccecf2d31b5bd77ee83/train_labels.json - model_dir: models - name: 884f7e8c69b90ccecf2d31b5bd77ee83 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: da5fb2974554249c40448054a38710f8 - trainer: - kwargs: {} -name: 884f7e8c69b90ccecf2d31b5bd77ee83 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/886c32944577836facbeadeb88c0ea0e/params.yaml b/examples/security/classification/output/reports/train/886c32944577836facbeadeb88c0ea0e/params.yaml deleted file mode 100644 index 5556de4c..00000000 --- a/examples/security/classification/output/reports/train/886c32944577836facbeadeb88c0ea0e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/adv_predictions.json - adv_probabilities_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/1e7a1922697eba491e1d7bda703fa994.pkl - params_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/params.yaml - predictions_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/predictions.json - probabilities_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/probabilities.json - score_dict_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/score_dict.json - test_labels_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/test_labels.json - train_labels_file: output/reports/train/886c32944577836facbeadeb88c0ea0e/train_labels.json - model_dir: models - name: 886c32944577836facbeadeb88c0ea0e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0182782508fd381c3463ef6fca44b9d1 - trainer: - kwargs: {} -name: 886c32944577836facbeadeb88c0ea0e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/params.yaml b/examples/security/classification/output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/params.yaml deleted file mode 100644 index 3db153b9..00000000 --- a/examples/security/classification/output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/adv_predictions.json - adv_probabilities_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/5edd5b46912be67daa2146b33dfba7a9.pkl - params_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/params.yaml - predictions_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/predictions.json - probabilities_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/probabilities.json - score_dict_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/score_dict.json - test_labels_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/test_labels.json - train_labels_file: output/reports/train/888f39cf347c8d31ecf13f965a0a5a97/train_labels.json - model_dir: models - name: 888f39cf347c8d31ecf13f965a0a5a97 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8b9fbe1d0939a3e0f5262846d4dd15cb - trainer: - kwargs: {} -name: 888f39cf347c8d31ecf13f965a0a5a97 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8945e12c2f1d7757f666aff48468d735/params.yaml b/examples/security/classification/output/reports/train/8945e12c2f1d7757f666aff48468d735/params.yaml deleted file mode 100644 index 23e7353b..00000000 --- a/examples/security/classification/output/reports/train/8945e12c2f1d7757f666aff48468d735/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/adv_predictions.json - adv_probabilities_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/270bc03c895960f8acf40b15ad0c7bbb.pkl - params_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/params.yaml - predictions_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/predictions.json - probabilities_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/probabilities.json - score_dict_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/score_dict.json - test_labels_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/test_labels.json - train_labels_file: output/reports/train/8945e12c2f1d7757f666aff48468d735/train_labels.json - model_dir: models - name: 8945e12c2f1d7757f666aff48468d735 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dc248f2ffd355b3f4ca5e32c6ddf9a8e - trainer: - kwargs: {} -name: 8945e12c2f1d7757f666aff48468d735 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/89c71f60bf27969bcae44b4bcab5a945/params.yaml b/examples/security/classification/output/reports/train/89c71f60bf27969bcae44b4bcab5a945/params.yaml deleted file mode 100644 index 28bb583d..00000000 --- a/examples/security/classification/output/reports/train/89c71f60bf27969bcae44b4bcab5a945/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/adv_predictions.json - adv_probabilities_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/6757be524f626ab6f25fce892a961f24.pkl - params_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/params.yaml - predictions_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/predictions.json - probabilities_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/probabilities.json - score_dict_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/score_dict.json - test_labels_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/test_labels.json - train_labels_file: output/reports/train/89c71f60bf27969bcae44b4bcab5a945/train_labels.json - model_dir: models - name: 89c71f60bf27969bcae44b4bcab5a945 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ec0a9d26ac61c4cd462b10049092c865 - trainer: - kwargs: {} -name: 89c71f60bf27969bcae44b4bcab5a945 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/89cc805cfe06028825fbd0e6172249a3/params.yaml b/examples/security/classification/output/reports/train/89cc805cfe06028825fbd0e6172249a3/params.yaml deleted file mode 100644 index a13a3610..00000000 --- a/examples/security/classification/output/reports/train/89cc805cfe06028825fbd0e6172249a3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/adv_predictions.json - adv_probabilities_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/7d39744d4599864d42712486edaf58d7.pkl - params_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/params.yaml - predictions_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/predictions.json - probabilities_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/probabilities.json - score_dict_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/score_dict.json - test_labels_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/test_labels.json - train_labels_file: output/reports/train/89cc805cfe06028825fbd0e6172249a3/train_labels.json - model_dir: models - name: 89cc805cfe06028825fbd0e6172249a3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e96f10d7ec9211b6b834f30e3a5201cf - trainer: - kwargs: {} -name: 89cc805cfe06028825fbd0e6172249a3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/89f3e9b25dee47906b196e73947c3bae/params.yaml b/examples/security/classification/output/reports/train/89f3e9b25dee47906b196e73947c3bae/params.yaml deleted file mode 100644 index 8ce781d4..00000000 --- a/examples/security/classification/output/reports/train/89f3e9b25dee47906b196e73947c3bae/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/adv_predictions.json - adv_probabilities_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/4a8c3712492c3b3bef1a3aa622692dee.pkl - params_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/params.yaml - predictions_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/predictions.json - probabilities_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/probabilities.json - score_dict_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/score_dict.json - test_labels_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/test_labels.json - train_labels_file: output/reports/train/89f3e9b25dee47906b196e73947c3bae/train_labels.json - model_dir: models - name: 89f3e9b25dee47906b196e73947c3bae - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 57e300ed0d3f2be550ecfedb128f8840 - trainer: - kwargs: {} -name: 89f3e9b25dee47906b196e73947c3bae -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/params.yaml b/examples/security/classification/output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/params.yaml deleted file mode 100644 index f93526de..00000000 --- a/examples/security/classification/output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/adv_predictions.json - adv_probabilities_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/5a54029f115ecb7db21b103a2372964a.pkl - params_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/params.yaml - predictions_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/predictions.json - probabilities_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/probabilities.json - score_dict_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/score_dict.json - test_labels_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/test_labels.json - train_labels_file: output/reports/train/8a0d73dd7b208e98a6618d8734754f4e/train_labels.json - model_dir: models - name: 8a0d73dd7b208e98a6618d8734754f4e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fc3e7d08e15723dea51ed82ef3174a47 - trainer: - kwargs: {} -name: 8a0d73dd7b208e98a6618d8734754f4e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/params.yaml b/examples/security/classification/output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/params.yaml deleted file mode 100644 index 7729253a..00000000 --- a/examples/security/classification/output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/adv_predictions.json - adv_probabilities_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/38bcd9c1d5f6f44218b4cf2719a1495e.pkl - params_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/params.yaml - predictions_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/predictions.json - probabilities_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/probabilities.json - score_dict_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/score_dict.json - test_labels_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/test_labels.json - train_labels_file: output/reports/train/8a3e0658f4676c051eb9f19396f1ef06/train_labels.json - model_dir: models - name: 8a3e0658f4676c051eb9f19396f1ef06 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6f4579b3a9f3241f324f78e6c3c8fa93 - trainer: - kwargs: {} -name: 8a3e0658f4676c051eb9f19396f1ef06 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8a5f7f562e6fec536bcc9633b7944097/params.yaml b/examples/security/classification/output/reports/train/8a5f7f562e6fec536bcc9633b7944097/params.yaml deleted file mode 100644 index dc8c0c8a..00000000 --- a/examples/security/classification/output/reports/train/8a5f7f562e6fec536bcc9633b7944097/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/adv_predictions.json - adv_probabilities_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/b452524fed285bf12d12619a142e1e16.pkl - params_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/params.yaml - predictions_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/predictions.json - probabilities_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/probabilities.json - score_dict_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/score_dict.json - test_labels_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/test_labels.json - train_labels_file: output/reports/train/8a5f7f562e6fec536bcc9633b7944097/train_labels.json - model_dir: models - name: 8a5f7f562e6fec536bcc9633b7944097 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dfb865e576deea8231f6917d0b6b7af8 - trainer: - kwargs: {} -name: 8a5f7f562e6fec536bcc9633b7944097 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/params.yaml b/examples/security/classification/output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/params.yaml deleted file mode 100644 index 9eb1ca76..00000000 --- a/examples/security/classification/output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/adv_predictions.json - adv_probabilities_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/c4d1f30c094e26388799cacd671b4c8a.pkl - params_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/params.yaml - predictions_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/predictions.json - probabilities_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/probabilities.json - score_dict_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/score_dict.json - test_labels_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/test_labels.json - train_labels_file: output/reports/train/8a64f63313dde08bf8af1f5eaf4ee993/train_labels.json - model_dir: models - name: 8a64f63313dde08bf8af1f5eaf4ee993 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 91f67a03dc443e813d00bdd76427cf09 - trainer: - kwargs: {} -name: 8a64f63313dde08bf8af1f5eaf4ee993 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8a7d3e68628fa474e8a52f8280401494/params.yaml b/examples/security/classification/output/reports/train/8a7d3e68628fa474e8a52f8280401494/params.yaml deleted file mode 100644 index e827d78d..00000000 --- a/examples/security/classification/output/reports/train/8a7d3e68628fa474e8a52f8280401494/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/adv_predictions.json - adv_probabilities_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/28db062bc8721d298fa89a5946f58d18.pkl - params_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/params.yaml - predictions_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/predictions.json - probabilities_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/probabilities.json - score_dict_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/score_dict.json - test_labels_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/test_labels.json - train_labels_file: output/reports/train/8a7d3e68628fa474e8a52f8280401494/train_labels.json - model_dir: models - name: 8a7d3e68628fa474e8a52f8280401494 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0cdece709c75ec0a27c4ffa3ddf1ff46 - trainer: - kwargs: {} -name: 8a7d3e68628fa474e8a52f8280401494 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/params.yaml b/examples/security/classification/output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/params.yaml deleted file mode 100644 index 3d83cf10..00000000 --- a/examples/security/classification/output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/adv_predictions.json - adv_probabilities_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/53df7cc7ede3ce8bdf75b447ff44c3ef.pkl - params_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/params.yaml - predictions_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/predictions.json - probabilities_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/probabilities.json - score_dict_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/score_dict.json - test_labels_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/test_labels.json - train_labels_file: output/reports/train/8a988aca328b57ca8ebfa8de4e7583ba/train_labels.json - model_dir: models - name: 8a988aca328b57ca8ebfa8de4e7583ba - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 725da9a0a22d6f3ab35bc1c5b8bc41ec - trainer: - kwargs: {} -name: 8a988aca328b57ca8ebfa8de4e7583ba -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/params.yaml b/examples/security/classification/output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/params.yaml deleted file mode 100644 index b6652e41..00000000 --- a/examples/security/classification/output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/adv_predictions.json - adv_probabilities_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/155f4aa444d28e966cc23b7e7dca3187.pkl - params_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/params.yaml - predictions_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/predictions.json - probabilities_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/probabilities.json - score_dict_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/score_dict.json - test_labels_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/test_labels.json - train_labels_file: output/reports/train/8ac7bca10ff0af2c4bd55a51c25913d3/train_labels.json - model_dir: models - name: 8ac7bca10ff0af2c4bd55a51c25913d3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 25565b58c2f7f3f0e2c0eb46e437662b - trainer: - kwargs: {} -name: 8ac7bca10ff0af2c4bd55a51c25913d3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/params.yaml b/examples/security/classification/output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/params.yaml deleted file mode 100644 index 082403f9..00000000 --- a/examples/security/classification/output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/adv_predictions.json - adv_probabilities_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/ead9321d355a0800173393f29abfce31.pkl - params_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/params.yaml - predictions_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/predictions.json - probabilities_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/probabilities.json - score_dict_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/score_dict.json - test_labels_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/test_labels.json - train_labels_file: output/reports/train/8ae6cf3da1317cf6e4c6dbfcb62a553c/train_labels.json - model_dir: models - name: 8ae6cf3da1317cf6e4c6dbfcb62a553c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 78ef26c25e4812540dcf54d5ba431267 - trainer: - kwargs: {} -name: 8ae6cf3da1317cf6e4c6dbfcb62a553c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8b22c2875553fe165c6a3d6958e68f48/params.yaml b/examples/security/classification/output/reports/train/8b22c2875553fe165c6a3d6958e68f48/params.yaml deleted file mode 100644 index 686307d5..00000000 --- a/examples/security/classification/output/reports/train/8b22c2875553fe165c6a3d6958e68f48/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/adv_predictions.json - adv_probabilities_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/d47f51101a47bd8ebff62cbe53256d5f.pkl - params_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/params.yaml - predictions_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/predictions.json - probabilities_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/probabilities.json - score_dict_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/score_dict.json - test_labels_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/test_labels.json - train_labels_file: output/reports/train/8b22c2875553fe165c6a3d6958e68f48/train_labels.json - model_dir: models - name: 8b22c2875553fe165c6a3d6958e68f48 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 36f97f6a081ce2e66ba1fde97c768157 - trainer: - kwargs: {} -name: 8b22c2875553fe165c6a3d6958e68f48 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/params.yaml b/examples/security/classification/output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/params.yaml deleted file mode 100644 index de23e47d..00000000 --- a/examples/security/classification/output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/adv_predictions.json - adv_probabilities_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/df31d03f1c87c1e36aacbd89a45d0ece.pkl - params_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/params.yaml - predictions_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/predictions.json - probabilities_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/probabilities.json - score_dict_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/score_dict.json - test_labels_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/test_labels.json - train_labels_file: output/reports/train/8b4487c5fee63748c4591eb71c1afaa3/train_labels.json - model_dir: models - name: 8b4487c5fee63748c4591eb71c1afaa3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d833222f5c1f5ad8e83d005a98b7fb84 - trainer: - kwargs: {} -name: 8b4487c5fee63748c4591eb71c1afaa3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/params.yaml b/examples/security/classification/output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/params.yaml deleted file mode 100644 index add8584e..00000000 --- a/examples/security/classification/output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/adv_predictions.json - adv_probabilities_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/74c9ff49debc8bd1d2940194b937c2f3.pkl - params_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/params.yaml - predictions_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/predictions.json - probabilities_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/probabilities.json - score_dict_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/score_dict.json - test_labels_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/test_labels.json - train_labels_file: output/reports/train/8b81129e4fd27e62d97b0c7cc6972899/train_labels.json - model_dir: models - name: 8b81129e4fd27e62d97b0c7cc6972899 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 243e650e91e3b545edd8d8c73b3c855b - trainer: - kwargs: {} -name: 8b81129e4fd27e62d97b0c7cc6972899 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8b96973bdabff522e2a58cff36ad1f53/params.yaml b/examples/security/classification/output/reports/train/8b96973bdabff522e2a58cff36ad1f53/params.yaml deleted file mode 100644 index 4d74fb20..00000000 --- a/examples/security/classification/output/reports/train/8b96973bdabff522e2a58cff36ad1f53/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/adv_predictions.json - adv_probabilities_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/8b20e21837d54ac94c1f40064d95b326.pkl - params_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/params.yaml - predictions_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/predictions.json - probabilities_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/probabilities.json - score_dict_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/score_dict.json - test_labels_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/test_labels.json - train_labels_file: output/reports/train/8b96973bdabff522e2a58cff36ad1f53/train_labels.json - model_dir: models - name: 8b96973bdabff522e2a58cff36ad1f53 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2102179994717547217e8e9f92899bc5 - trainer: - kwargs: {} -name: 8b96973bdabff522e2a58cff36ad1f53 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8ba90caf325df6ea1a4661594ca0b093/params.yaml b/examples/security/classification/output/reports/train/8ba90caf325df6ea1a4661594ca0b093/params.yaml deleted file mode 100644 index 39c0245f..00000000 --- a/examples/security/classification/output/reports/train/8ba90caf325df6ea1a4661594ca0b093/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/adv_predictions.json - adv_probabilities_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/ed63b540868110117313132d44ab566a.pkl - params_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/params.yaml - predictions_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/predictions.json - probabilities_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/probabilities.json - score_dict_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/score_dict.json - test_labels_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/test_labels.json - train_labels_file: output/reports/train/8ba90caf325df6ea1a4661594ca0b093/train_labels.json - model_dir: models - name: 8ba90caf325df6ea1a4661594ca0b093 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 76eee1be6faae12d7c7fa21d6455c418 - trainer: - kwargs: {} -name: 8ba90caf325df6ea1a4661594ca0b093 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/params.yaml b/examples/security/classification/output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/params.yaml deleted file mode 100644 index a9b665c0..00000000 --- a/examples/security/classification/output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/adv_predictions.json - adv_probabilities_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/1c705362aea77395a79d1e01a8eac113.pkl - params_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/params.yaml - predictions_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/predictions.json - probabilities_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/probabilities.json - score_dict_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/score_dict.json - test_labels_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/test_labels.json - train_labels_file: output/reports/train/8bdda91ace925b08248a0d2c0181d3c0/train_labels.json - model_dir: models - name: 8bdda91ace925b08248a0d2c0181d3c0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5bda220d43f7919fd44e3f677c13afdc - trainer: - kwargs: {} -name: 8bdda91ace925b08248a0d2c0181d3c0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8bde41971ba2d57dbd95d33e2178e038/params.yaml b/examples/security/classification/output/reports/train/8bde41971ba2d57dbd95d33e2178e038/params.yaml deleted file mode 100644 index c47efcef..00000000 --- a/examples/security/classification/output/reports/train/8bde41971ba2d57dbd95d33e2178e038/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/adv_predictions.json - adv_probabilities_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/11a8c962bee7e19bb0d2d942499e91a5.pkl - params_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/params.yaml - predictions_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/predictions.json - probabilities_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/probabilities.json - score_dict_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/score_dict.json - test_labels_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/test_labels.json - train_labels_file: output/reports/train/8bde41971ba2d57dbd95d33e2178e038/train_labels.json - model_dir: models - name: 8bde41971ba2d57dbd95d33e2178e038 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9500b827501fd5b894dc7efb3b55b49c - trainer: - kwargs: {} -name: 8bde41971ba2d57dbd95d33e2178e038 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/params.yaml b/examples/security/classification/output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/params.yaml deleted file mode 100644 index 2f465978..00000000 --- a/examples/security/classification/output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/adv_predictions.json - adv_probabilities_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/1fd8359b8de467f1ad71fa20beb4b168.pkl - params_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/params.yaml - predictions_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/predictions.json - probabilities_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/probabilities.json - score_dict_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/score_dict.json - test_labels_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/test_labels.json - train_labels_file: output/reports/train/8befd2d9092e8ad33eead25c6ead38fe/train_labels.json - model_dir: models - name: 8befd2d9092e8ad33eead25c6ead38fe - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: afb5295fdf33e8c052c259ac2cb069be - trainer: - kwargs: {} -name: 8befd2d9092e8ad33eead25c6ead38fe -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8c185b51a35cdda7afd4f366c50d8494/params.yaml b/examples/security/classification/output/reports/train/8c185b51a35cdda7afd4f366c50d8494/params.yaml deleted file mode 100644 index bef1b404..00000000 --- a/examples/security/classification/output/reports/train/8c185b51a35cdda7afd4f366c50d8494/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/adv_predictions.json - adv_probabilities_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/0ff90ee60420426f465db45b4caf22f3.pkl - params_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/params.yaml - predictions_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/predictions.json - probabilities_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/probabilities.json - score_dict_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/score_dict.json - test_labels_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/test_labels.json - train_labels_file: output/reports/train/8c185b51a35cdda7afd4f366c50d8494/train_labels.json - model_dir: models - name: 8c185b51a35cdda7afd4f366c50d8494 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a3b877a249de326555480675c6f15451 - trainer: - kwargs: {} -name: 8c185b51a35cdda7afd4f366c50d8494 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8c2085700da92c43bbb7594d995bcba5/params.yaml b/examples/security/classification/output/reports/train/8c2085700da92c43bbb7594d995bcba5/params.yaml deleted file mode 100644 index 33c9691e..00000000 --- a/examples/security/classification/output/reports/train/8c2085700da92c43bbb7594d995bcba5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/adv_predictions.json - adv_probabilities_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/2aa2021b4bfc4faaaf732408975d9e54.pkl - params_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/params.yaml - predictions_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/predictions.json - probabilities_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/probabilities.json - score_dict_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/score_dict.json - test_labels_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/test_labels.json - train_labels_file: output/reports/train/8c2085700da92c43bbb7594d995bcba5/train_labels.json - model_dir: models - name: 8c2085700da92c43bbb7594d995bcba5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d194f97ec72fee3c1e75e65c3c04422c - trainer: - kwargs: {} -name: 8c2085700da92c43bbb7594d995bcba5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8c24bee058ef2d610e27488e4e312dff/params.yaml b/examples/security/classification/output/reports/train/8c24bee058ef2d610e27488e4e312dff/params.yaml deleted file mode 100644 index 7ff1a648..00000000 --- a/examples/security/classification/output/reports/train/8c24bee058ef2d610e27488e4e312dff/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/adv_predictions.json - adv_probabilities_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/552c5d8c4e8f38695428030204c82ed6.pkl - params_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/params.yaml - predictions_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/predictions.json - probabilities_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/probabilities.json - score_dict_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/score_dict.json - test_labels_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/test_labels.json - train_labels_file: output/reports/train/8c24bee058ef2d610e27488e4e312dff/train_labels.json - model_dir: models - name: 8c24bee058ef2d610e27488e4e312dff - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c34fb9779e849848746f79ce730e0213 - trainer: - kwargs: {} -name: 8c24bee058ef2d610e27488e4e312dff -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8c37023f3177aef03a4aef1913c81344/params.yaml b/examples/security/classification/output/reports/train/8c37023f3177aef03a4aef1913c81344/params.yaml deleted file mode 100644 index e4e51295..00000000 --- a/examples/security/classification/output/reports/train/8c37023f3177aef03a4aef1913c81344/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/adv_predictions.json - adv_probabilities_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/e9b365afe3c4bf47de26cba54708d200.pkl - params_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/params.yaml - predictions_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/predictions.json - probabilities_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/probabilities.json - score_dict_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/score_dict.json - test_labels_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/test_labels.json - train_labels_file: output/reports/train/8c37023f3177aef03a4aef1913c81344/train_labels.json - model_dir: models - name: 8c37023f3177aef03a4aef1913c81344 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cd72189069bc90567bd4aaf352888ae9 - trainer: - kwargs: {} -name: 8c37023f3177aef03a4aef1913c81344 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8c5b7638ca557ca16431d12b87991686/params.yaml b/examples/security/classification/output/reports/train/8c5b7638ca557ca16431d12b87991686/params.yaml deleted file mode 100644 index c1761ae3..00000000 --- a/examples/security/classification/output/reports/train/8c5b7638ca557ca16431d12b87991686/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/adv_predictions.json - adv_probabilities_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/1bf6061f11025d3bad6ca164816913a3.pkl - params_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/params.yaml - predictions_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/predictions.json - probabilities_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/probabilities.json - score_dict_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/score_dict.json - test_labels_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/test_labels.json - train_labels_file: output/reports/train/8c5b7638ca557ca16431d12b87991686/train_labels.json - model_dir: models - name: 8c5b7638ca557ca16431d12b87991686 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 127a16b8ea95480242262fcf9d6a12fc - trainer: - kwargs: {} -name: 8c5b7638ca557ca16431d12b87991686 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8c855a0903e679dc936ea07b3087e9c2/params.yaml b/examples/security/classification/output/reports/train/8c855a0903e679dc936ea07b3087e9c2/params.yaml deleted file mode 100644 index 683ad2ab..00000000 --- a/examples/security/classification/output/reports/train/8c855a0903e679dc936ea07b3087e9c2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/adv_predictions.json - adv_probabilities_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/7874ef4ce7b87bdbfd416a5e6b297e37.pkl - params_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/params.yaml - predictions_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/predictions.json - probabilities_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/probabilities.json - score_dict_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/score_dict.json - test_labels_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/test_labels.json - train_labels_file: output/reports/train/8c855a0903e679dc936ea07b3087e9c2/train_labels.json - model_dir: models - name: 8c855a0903e679dc936ea07b3087e9c2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b6f162a52722b4a7b23fef3a8d565a37 - trainer: - kwargs: {} -name: 8c855a0903e679dc936ea07b3087e9c2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8c933f637883c2f82549d1ce6b9f7301/params.yaml b/examples/security/classification/output/reports/train/8c933f637883c2f82549d1ce6b9f7301/params.yaml deleted file mode 100644 index 15033f0f..00000000 --- a/examples/security/classification/output/reports/train/8c933f637883c2f82549d1ce6b9f7301/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/adv_predictions.json - adv_probabilities_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/aac97a6efeaeddf548c3473fb103f892.pkl - params_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/params.yaml - predictions_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/predictions.json - probabilities_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/probabilities.json - score_dict_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/score_dict.json - test_labels_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/test_labels.json - train_labels_file: output/reports/train/8c933f637883c2f82549d1ce6b9f7301/train_labels.json - model_dir: models - name: 8c933f637883c2f82549d1ce6b9f7301 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ec04c4a81e69d3c54c6bc0b37758edba - trainer: - kwargs: {} -name: 8c933f637883c2f82549d1ce6b9f7301 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/params.yaml b/examples/security/classification/output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/params.yaml deleted file mode 100644 index d3d7a8e9..00000000 --- a/examples/security/classification/output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/adv_predictions.json - adv_probabilities_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/521d15ad9aa534e20ec8115627705d53.pkl - params_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/params.yaml - predictions_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/predictions.json - probabilities_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/probabilities.json - score_dict_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/score_dict.json - test_labels_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/test_labels.json - train_labels_file: output/reports/train/8c94eb8d0f28fd44340ab2d9ed522eb0/train_labels.json - model_dir: models - name: 8c94eb8d0f28fd44340ab2d9ed522eb0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a22b65a8a9a0c4393a10a9c6802b37b1 - trainer: - kwargs: {} -name: 8c94eb8d0f28fd44340ab2d9ed522eb0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/params.yaml b/examples/security/classification/output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/params.yaml deleted file mode 100644 index ed8f59b5..00000000 --- a/examples/security/classification/output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/adv_predictions.json - adv_probabilities_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/0b9a67406dd30f2bdc574fb20a709740.pkl - params_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/params.yaml - predictions_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/predictions.json - probabilities_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/probabilities.json - score_dict_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/score_dict.json - test_labels_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/test_labels.json - train_labels_file: output/reports/train/8caafd9d8ae4175a6400d69d8d038ec3/train_labels.json - model_dir: models - name: 8caafd9d8ae4175a6400d69d8d038ec3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0765535c43014ec6182d85e806efaf1b - trainer: - kwargs: {} -name: 8caafd9d8ae4175a6400d69d8d038ec3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/params.yaml b/examples/security/classification/output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/params.yaml deleted file mode 100644 index 1da508ab..00000000 --- a/examples/security/classification/output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/adv_predictions.json - adv_probabilities_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/37fac2c70a5946329e76212c19c0a724.pkl - params_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/params.yaml - predictions_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/predictions.json - probabilities_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/probabilities.json - score_dict_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/score_dict.json - test_labels_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/test_labels.json - train_labels_file: output/reports/train/8caffe8d3c901510c9bb1382796c4bd4/train_labels.json - model_dir: models - name: 8caffe8d3c901510c9bb1382796c4bd4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 57cac9a21f78293c9cc7fc3259581895 - trainer: - kwargs: {} -name: 8caffe8d3c901510c9bb1382796c4bd4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/params.yaml b/examples/security/classification/output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/params.yaml deleted file mode 100644 index e9a04ebf..00000000 --- a/examples/security/classification/output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/adv_predictions.json - adv_probabilities_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/d5801f226deacf42c0f6de49c940bf82.pkl - params_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/params.yaml - predictions_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/predictions.json - probabilities_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/probabilities.json - score_dict_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/score_dict.json - test_labels_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/test_labels.json - train_labels_file: output/reports/train/8cca7e5e85abc881070ed5f37a7f332b/train_labels.json - model_dir: models - name: 8cca7e5e85abc881070ed5f37a7f332b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d869f4a84824864c3a7be0b3aeb7d232 - trainer: - kwargs: {} -name: 8cca7e5e85abc881070ed5f37a7f332b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8d31bb8a4a09884d889b85f36faf8375/params.yaml b/examples/security/classification/output/reports/train/8d31bb8a4a09884d889b85f36faf8375/params.yaml deleted file mode 100644 index 4ce8cb02..00000000 --- a/examples/security/classification/output/reports/train/8d31bb8a4a09884d889b85f36faf8375/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/adv_predictions.json - adv_probabilities_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/051eaf0dad8df18ceacd5ad2b9765220.pkl - params_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/params.yaml - predictions_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/predictions.json - probabilities_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/probabilities.json - score_dict_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/score_dict.json - test_labels_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/test_labels.json - train_labels_file: output/reports/train/8d31bb8a4a09884d889b85f36faf8375/train_labels.json - model_dir: models - name: 8d31bb8a4a09884d889b85f36faf8375 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3bf9ae1c7fc927e606b328aee8ff76b0 - trainer: - kwargs: {} -name: 8d31bb8a4a09884d889b85f36faf8375 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8d5199b5c97c961931385a6fed44effa/params.yaml b/examples/security/classification/output/reports/train/8d5199b5c97c961931385a6fed44effa/params.yaml deleted file mode 100644 index c256401c..00000000 --- a/examples/security/classification/output/reports/train/8d5199b5c97c961931385a6fed44effa/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/adv_predictions.json - adv_probabilities_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/cbed577a105bc44fcb45ca628e3d435a.pkl - params_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/params.yaml - predictions_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/predictions.json - probabilities_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/probabilities.json - score_dict_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/score_dict.json - test_labels_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/test_labels.json - train_labels_file: output/reports/train/8d5199b5c97c961931385a6fed44effa/train_labels.json - model_dir: models - name: 8d5199b5c97c961931385a6fed44effa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c62615ac59167887be56c65c9bf608b7 - trainer: - kwargs: {} -name: 8d5199b5c97c961931385a6fed44effa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/params.yaml b/examples/security/classification/output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/params.yaml deleted file mode 100644 index 06ba483c..00000000 --- a/examples/security/classification/output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/adv_predictions.json - adv_probabilities_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/f8f9224ac7c10f398ce5693499a6da1b.pkl - params_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/params.yaml - predictions_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/predictions.json - probabilities_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/probabilities.json - score_dict_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/score_dict.json - test_labels_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/test_labels.json - train_labels_file: output/reports/train/8d87db1aeaebd77aaee7a22d59fae315/train_labels.json - model_dir: models - name: 8d87db1aeaebd77aaee7a22d59fae315 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0b94b0c815d331ca2d25954ced67be1c - trainer: - kwargs: {} -name: 8d87db1aeaebd77aaee7a22d59fae315 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8db03852ca019f67daad11bd13741630/params.yaml b/examples/security/classification/output/reports/train/8db03852ca019f67daad11bd13741630/params.yaml deleted file mode 100644 index b83c6837..00000000 --- a/examples/security/classification/output/reports/train/8db03852ca019f67daad11bd13741630/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8db03852ca019f67daad11bd13741630/adv_predictions.json - adv_probabilities_file: output/reports/train/8db03852ca019f67daad11bd13741630/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/b32603d420a657ee07e18083c140d7d8.pkl - params_file: output/reports/train/8db03852ca019f67daad11bd13741630/params.yaml - predictions_file: output/reports/train/8db03852ca019f67daad11bd13741630/predictions.json - probabilities_file: output/reports/train/8db03852ca019f67daad11bd13741630/probabilities.json - score_dict_file: output/reports/train/8db03852ca019f67daad11bd13741630/score_dict.json - test_labels_file: output/reports/train/8db03852ca019f67daad11bd13741630/test_labels.json - train_labels_file: output/reports/train/8db03852ca019f67daad11bd13741630/train_labels.json - model_dir: models - name: 8db03852ca019f67daad11bd13741630 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eff55ee2fdad0ab1f654a8a0e2f1f697 - trainer: - kwargs: {} -name: 8db03852ca019f67daad11bd13741630 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/params.yaml b/examples/security/classification/output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/params.yaml deleted file mode 100644 index 47fda7fe..00000000 --- a/examples/security/classification/output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/adv_predictions.json - adv_probabilities_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/f88f57f574cb4b38f865730e5dcce99c.pkl - params_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/params.yaml - predictions_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/predictions.json - probabilities_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/probabilities.json - score_dict_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/score_dict.json - test_labels_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/test_labels.json - train_labels_file: output/reports/train/8db67c05afcac2d515c9b3c6b6938e20/train_labels.json - model_dir: models - name: 8db67c05afcac2d515c9b3c6b6938e20 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f9357dabe6962eb74829d24f5b765627 - trainer: - kwargs: {} -name: 8db67c05afcac2d515c9b3c6b6938e20 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8e0e2842d78e20788dfe619498079235/params.yaml b/examples/security/classification/output/reports/train/8e0e2842d78e20788dfe619498079235/params.yaml deleted file mode 100644 index 7fb69069..00000000 --- a/examples/security/classification/output/reports/train/8e0e2842d78e20788dfe619498079235/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8e0e2842d78e20788dfe619498079235/adv_predictions.json - adv_probabilities_file: output/reports/train/8e0e2842d78e20788dfe619498079235/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/dad278776bff4e61508046b8e596c09b.pkl - params_file: output/reports/train/8e0e2842d78e20788dfe619498079235/params.yaml - predictions_file: output/reports/train/8e0e2842d78e20788dfe619498079235/predictions.json - probabilities_file: output/reports/train/8e0e2842d78e20788dfe619498079235/probabilities.json - score_dict_file: output/reports/train/8e0e2842d78e20788dfe619498079235/score_dict.json - test_labels_file: output/reports/train/8e0e2842d78e20788dfe619498079235/test_labels.json - train_labels_file: output/reports/train/8e0e2842d78e20788dfe619498079235/train_labels.json - model_dir: models - name: 8e0e2842d78e20788dfe619498079235 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 72b9fe7708fe8fa54cae458fc89fca21 - trainer: - kwargs: {} -name: 8e0e2842d78e20788dfe619498079235 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8e312be2b3bcc9acd94e478c21890a64/params.yaml b/examples/security/classification/output/reports/train/8e312be2b3bcc9acd94e478c21890a64/params.yaml deleted file mode 100644 index bf78e560..00000000 --- a/examples/security/classification/output/reports/train/8e312be2b3bcc9acd94e478c21890a64/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/adv_predictions.json - adv_probabilities_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/ee62c372070b74f0b3b2b98430855334.pkl - params_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/params.yaml - predictions_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/predictions.json - probabilities_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/probabilities.json - score_dict_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/score_dict.json - test_labels_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/test_labels.json - train_labels_file: output/reports/train/8e312be2b3bcc9acd94e478c21890a64/train_labels.json - model_dir: models - name: 8e312be2b3bcc9acd94e478c21890a64 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e190e57b585004b2a2ef9a55e1bceef5 - trainer: - kwargs: {} -name: 8e312be2b3bcc9acd94e478c21890a64 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/params.yaml b/examples/security/classification/output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/params.yaml deleted file mode 100644 index 5ecf424f..00000000 --- a/examples/security/classification/output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/adv_predictions.json - adv_probabilities_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/e15978b1ee3db1836a2a6fb4896591db.pkl - params_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/params.yaml - predictions_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/predictions.json - probabilities_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/probabilities.json - score_dict_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/score_dict.json - test_labels_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/test_labels.json - train_labels_file: output/reports/train/8e42dc1f350b6dde70235b673ebf7b10/train_labels.json - model_dir: models - name: 8e42dc1f350b6dde70235b673ebf7b10 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4e2c42bc70612c02a32cf9e449d31a3e - trainer: - kwargs: {} -name: 8e42dc1f350b6dde70235b673ebf7b10 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/params.yaml b/examples/security/classification/output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/params.yaml deleted file mode 100644 index 8ec72293..00000000 --- a/examples/security/classification/output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/adv_predictions.json - adv_probabilities_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/129431cabb119a284c8df078716fed74.pkl - params_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/params.yaml - predictions_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/predictions.json - probabilities_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/probabilities.json - score_dict_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/score_dict.json - test_labels_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/test_labels.json - train_labels_file: output/reports/train/8e5db43ee09c3757fb9bb36884e97ad8/train_labels.json - model_dir: models - name: 8e5db43ee09c3757fb9bb36884e97ad8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9dae46f99a50a005a71db1de2ff480f8 - trainer: - kwargs: {} -name: 8e5db43ee09c3757fb9bb36884e97ad8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8e64ccf01262acf65b80fe81b813276b/params.yaml b/examples/security/classification/output/reports/train/8e64ccf01262acf65b80fe81b813276b/params.yaml deleted file mode 100644 index 65409cd4..00000000 --- a/examples/security/classification/output/reports/train/8e64ccf01262acf65b80fe81b813276b/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/adv_predictions.json - adv_probabilities_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/de52313c5a424b51087d762ce462582b.pkl - params_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/params.yaml - predictions_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/predictions.json - probabilities_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/probabilities.json - score_dict_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/score_dict.json - test_labels_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/test_labels.json - train_labels_file: output/reports/train/8e64ccf01262acf65b80fe81b813276b/train_labels.json - model_dir: models - name: 8e64ccf01262acf65b80fe81b813276b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 13153fd13c95ca4a4070a90e03722688 - trainer: - kwargs: {} -name: 8e64ccf01262acf65b80fe81b813276b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/params.yaml b/examples/security/classification/output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/params.yaml deleted file mode 100644 index fd95d491..00000000 --- a/examples/security/classification/output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/adv_predictions.json - adv_probabilities_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/bbb1984239839c76e9eca1ab1661b32c.pkl - params_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/params.yaml - predictions_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/predictions.json - probabilities_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/probabilities.json - score_dict_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/score_dict.json - test_labels_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/test_labels.json - train_labels_file: output/reports/train/8e7dacaf1228cb5fdd1ca646472686f0/train_labels.json - model_dir: models - name: 8e7dacaf1228cb5fdd1ca646472686f0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bc27a137d6f3dca8b89109886042e692 - trainer: - kwargs: {} -name: 8e7dacaf1228cb5fdd1ca646472686f0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/params.yaml b/examples/security/classification/output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/params.yaml deleted file mode 100644 index f7be8bbe..00000000 --- a/examples/security/classification/output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/adv_predictions.json - adv_probabilities_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/caf6e368219452e8d9df16743ae7f160.pkl - params_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/params.yaml - predictions_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/predictions.json - probabilities_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/probabilities.json - score_dict_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/score_dict.json - test_labels_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/test_labels.json - train_labels_file: output/reports/train/8e9be1f3dcf2443bfe78e59f13352268/train_labels.json - model_dir: models - name: 8e9be1f3dcf2443bfe78e59f13352268 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 56f6f2f3eef8110b32d639a1e9a8dc63 - trainer: - kwargs: {} -name: 8e9be1f3dcf2443bfe78e59f13352268 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/params.yaml b/examples/security/classification/output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/params.yaml deleted file mode 100644 index 6f0dd81f..00000000 --- a/examples/security/classification/output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/adv_predictions.json - adv_probabilities_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/c19e932b9d81e594f06ffdb0f1fca641.pkl - params_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/params.yaml - predictions_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/predictions.json - probabilities_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/probabilities.json - score_dict_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/score_dict.json - test_labels_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/test_labels.json - train_labels_file: output/reports/train/8e9d7fe474fb2748815078dcb9403b7f/train_labels.json - model_dir: models - name: 8e9d7fe474fb2748815078dcb9403b7f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4e84e2a02c99980dbcc97e83807fab80 - trainer: - kwargs: {} -name: 8e9d7fe474fb2748815078dcb9403b7f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/params.yaml b/examples/security/classification/output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/params.yaml deleted file mode 100644 index 7a1ce7b1..00000000 --- a/examples/security/classification/output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/adv_predictions.json - adv_probabilities_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/f64c0a07a2227c8c06ec9e3056eb74c2.pkl - params_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/params.yaml - predictions_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/predictions.json - probabilities_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/probabilities.json - score_dict_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/score_dict.json - test_labels_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/test_labels.json - train_labels_file: output/reports/train/8edc2cf1eb82ee94c79a36d7a47928dc/train_labels.json - model_dir: models - name: 8edc2cf1eb82ee94c79a36d7a47928dc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4b9e0358d96fc725629a41ed4bbe1c65 - trainer: - kwargs: {} -name: 8edc2cf1eb82ee94c79a36d7a47928dc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/params.yaml b/examples/security/classification/output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/params.yaml deleted file mode 100644 index 3f967d83..00000000 --- a/examples/security/classification/output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/adv_predictions.json - adv_probabilities_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/265e29395c62f3ab03183e29648b6a28.pkl - params_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/params.yaml - predictions_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/predictions.json - probabilities_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/probabilities.json - score_dict_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/score_dict.json - test_labels_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/test_labels.json - train_labels_file: output/reports/train/8efd99aaab1921cdc9bef9d80e0ac9b6/train_labels.json - model_dir: models - name: 8efd99aaab1921cdc9bef9d80e0ac9b6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3484a2f4c1ebba05b3fb15a5461410f2 - trainer: - kwargs: {} -name: 8efd99aaab1921cdc9bef9d80e0ac9b6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8f024e116370bbed47afee11f6ab3386/params.yaml b/examples/security/classification/output/reports/train/8f024e116370bbed47afee11f6ab3386/params.yaml deleted file mode 100644 index 6648101a..00000000 --- a/examples/security/classification/output/reports/train/8f024e116370bbed47afee11f6ab3386/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/adv_predictions.json - adv_probabilities_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/a1fc3e1fa80481b5e2ae27baa89e2715.pkl - params_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/params.yaml - predictions_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/predictions.json - probabilities_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/probabilities.json - score_dict_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/score_dict.json - test_labels_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/test_labels.json - train_labels_file: output/reports/train/8f024e116370bbed47afee11f6ab3386/train_labels.json - model_dir: models - name: 8f024e116370bbed47afee11f6ab3386 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 13e42a680655f2a54f563ce7f1005b13 - trainer: - kwargs: {} -name: 8f024e116370bbed47afee11f6ab3386 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/params.yaml b/examples/security/classification/output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/params.yaml deleted file mode 100644 index dd8b4b26..00000000 --- a/examples/security/classification/output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/adv_predictions.json - adv_probabilities_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/bef0f15b3e001f0aa75a7fc1bf5adb32.pkl - params_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/params.yaml - predictions_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/predictions.json - probabilities_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/probabilities.json - score_dict_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/score_dict.json - test_labels_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/test_labels.json - train_labels_file: output/reports/train/8f2e4f6ca2c1c526d2b121cbbb1b13a8/train_labels.json - model_dir: models - name: 8f2e4f6ca2c1c526d2b121cbbb1b13a8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0e9a7ffd5f6a68dc233835201ff1259a - trainer: - kwargs: {} -name: 8f2e4f6ca2c1c526d2b121cbbb1b13a8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/params.yaml b/examples/security/classification/output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/params.yaml deleted file mode 100644 index d7d93a6e..00000000 --- a/examples/security/classification/output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/adv_predictions.json - adv_probabilities_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/ca72baa7b57efe7694a03745baa6d661.pkl - params_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/params.yaml - predictions_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/predictions.json - probabilities_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/probabilities.json - score_dict_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/score_dict.json - test_labels_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/test_labels.json - train_labels_file: output/reports/train/8f4ad92f4c39620b3c97f90b4410856c/train_labels.json - model_dir: models - name: 8f4ad92f4c39620b3c97f90b4410856c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 699cbc0782ce944c16414d4e69da035d - trainer: - kwargs: {} -name: 8f4ad92f4c39620b3c97f90b4410856c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/params.yaml b/examples/security/classification/output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/params.yaml deleted file mode 100644 index 37c33260..00000000 --- a/examples/security/classification/output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/adv_predictions.json - adv_probabilities_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/06513a2c00a6fa586fb3897cd39d9fd2.pkl - params_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/params.yaml - predictions_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/predictions.json - probabilities_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/probabilities.json - score_dict_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/score_dict.json - test_labels_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/test_labels.json - train_labels_file: output/reports/train/8f637709ff4f325bb2c871eefc6baf5b/train_labels.json - model_dir: models - name: 8f637709ff4f325bb2c871eefc6baf5b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0993b371594437885bee372329c5fe02 - trainer: - kwargs: {} -name: 8f637709ff4f325bb2c871eefc6baf5b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8f99af0e2373b660b7ffecdff13059cd/params.yaml b/examples/security/classification/output/reports/train/8f99af0e2373b660b7ffecdff13059cd/params.yaml deleted file mode 100644 index 8ab10da6..00000000 --- a/examples/security/classification/output/reports/train/8f99af0e2373b660b7ffecdff13059cd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/adv_predictions.json - adv_probabilities_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/ff1692a6b58b671ba3872f9a8fe1c51e.pkl - params_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/params.yaml - predictions_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/predictions.json - probabilities_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/probabilities.json - score_dict_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/score_dict.json - test_labels_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/test_labels.json - train_labels_file: output/reports/train/8f99af0e2373b660b7ffecdff13059cd/train_labels.json - model_dir: models - name: 8f99af0e2373b660b7ffecdff13059cd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6d3201a82fe2c4772af3b3716d4b81d7 - trainer: - kwargs: {} -name: 8f99af0e2373b660b7ffecdff13059cd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8fa7667d59626f1422aeb46f5b93c083/params.yaml b/examples/security/classification/output/reports/train/8fa7667d59626f1422aeb46f5b93c083/params.yaml deleted file mode 100644 index afab5055..00000000 --- a/examples/security/classification/output/reports/train/8fa7667d59626f1422aeb46f5b93c083/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/adv_predictions.json - adv_probabilities_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/4da0cab01d2c2f16504fa4bc568a33d9.pkl - params_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/params.yaml - predictions_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/predictions.json - probabilities_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/probabilities.json - score_dict_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/score_dict.json - test_labels_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/test_labels.json - train_labels_file: output/reports/train/8fa7667d59626f1422aeb46f5b93c083/train_labels.json - model_dir: models - name: 8fa7667d59626f1422aeb46f5b93c083 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f59217cf6f66149214dbae7116c67bf7 - trainer: - kwargs: {} -name: 8fa7667d59626f1422aeb46f5b93c083 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/params.yaml b/examples/security/classification/output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/params.yaml deleted file mode 100644 index 0fb43781..00000000 --- a/examples/security/classification/output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/adv_predictions.json - adv_probabilities_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/96f88c44883a09419334572270eb3291.pkl - params_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/params.yaml - predictions_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/predictions.json - probabilities_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/probabilities.json - score_dict_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/score_dict.json - test_labels_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/test_labels.json - train_labels_file: output/reports/train/8fcb3d1c16524ecd8a50290aad5b333f/train_labels.json - model_dir: models - name: 8fcb3d1c16524ecd8a50290aad5b333f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 387684d269c3fa23bdf8205a13c43b43 - trainer: - kwargs: {} -name: 8fcb3d1c16524ecd8a50290aad5b333f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/params.yaml b/examples/security/classification/output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/params.yaml deleted file mode 100644 index 151deb24..00000000 --- a/examples/security/classification/output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/adv_predictions.json - adv_probabilities_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/ac69d5d0bef3e2f6607a84e77cebcf18.pkl - params_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/params.yaml - predictions_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/predictions.json - probabilities_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/probabilities.json - score_dict_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/score_dict.json - test_labels_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/test_labels.json - train_labels_file: output/reports/train/8fd1ee98a7ffa0ee9b379d67989c7bb9/train_labels.json - model_dir: models - name: 8fd1ee98a7ffa0ee9b379d67989c7bb9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 08c1aa4d39ecfb42e94065018ff33f42 - trainer: - kwargs: {} -name: 8fd1ee98a7ffa0ee9b379d67989c7bb9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/900a983b6857c519df3dd1bc302fcff3/params.yaml b/examples/security/classification/output/reports/train/900a983b6857c519df3dd1bc302fcff3/params.yaml deleted file mode 100644 index 3b2a9769..00000000 --- a/examples/security/classification/output/reports/train/900a983b6857c519df3dd1bc302fcff3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/adv_predictions.json - adv_probabilities_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/2ab0ce6c93a6eac2a4db8dff6e32a855.pkl - params_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/params.yaml - predictions_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/predictions.json - probabilities_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/probabilities.json - score_dict_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/score_dict.json - test_labels_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/test_labels.json - train_labels_file: output/reports/train/900a983b6857c519df3dd1bc302fcff3/train_labels.json - model_dir: models - name: 900a983b6857c519df3dd1bc302fcff3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5dd3d7bbe224f239d32784e95db04406 - trainer: - kwargs: {} -name: 900a983b6857c519df3dd1bc302fcff3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/90178dd718a39d852e723419279dabb1/params.yaml b/examples/security/classification/output/reports/train/90178dd718a39d852e723419279dabb1/params.yaml deleted file mode 100644 index ac4c3630..00000000 --- a/examples/security/classification/output/reports/train/90178dd718a39d852e723419279dabb1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/90178dd718a39d852e723419279dabb1/adv_predictions.json - adv_probabilities_file: output/reports/train/90178dd718a39d852e723419279dabb1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/5ca185b1f2dd6ea26f406686e6ef3dba.pkl - params_file: output/reports/train/90178dd718a39d852e723419279dabb1/params.yaml - predictions_file: output/reports/train/90178dd718a39d852e723419279dabb1/predictions.json - probabilities_file: output/reports/train/90178dd718a39d852e723419279dabb1/probabilities.json - score_dict_file: output/reports/train/90178dd718a39d852e723419279dabb1/score_dict.json - test_labels_file: output/reports/train/90178dd718a39d852e723419279dabb1/test_labels.json - train_labels_file: output/reports/train/90178dd718a39d852e723419279dabb1/train_labels.json - model_dir: models - name: 90178dd718a39d852e723419279dabb1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 33bdcd58167c726358320c1c0ecf79d5 - trainer: - kwargs: {} -name: 90178dd718a39d852e723419279dabb1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/90251253925daddfc1b6e740f7ff7ccc/params.yaml b/examples/security/classification/output/reports/train/90251253925daddfc1b6e740f7ff7ccc/params.yaml deleted file mode 100644 index 352bdd95..00000000 --- a/examples/security/classification/output/reports/train/90251253925daddfc1b6e740f7ff7ccc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/adv_predictions.json - adv_probabilities_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/170aaad169118a88d926fde7a55d79d5.pkl - params_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/params.yaml - predictions_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/predictions.json - probabilities_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/probabilities.json - score_dict_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/score_dict.json - test_labels_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/test_labels.json - train_labels_file: output/reports/train/90251253925daddfc1b6e740f7ff7ccc/train_labels.json - model_dir: models - name: 90251253925daddfc1b6e740f7ff7ccc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b2a08c8077b23d0743ec3f2ac073d3df - trainer: - kwargs: {} -name: 90251253925daddfc1b6e740f7ff7ccc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/90338c9b8ddf30392668ca086f9d77f3/params.yaml b/examples/security/classification/output/reports/train/90338c9b8ddf30392668ca086f9d77f3/params.yaml deleted file mode 100644 index d356e75c..00000000 --- a/examples/security/classification/output/reports/train/90338c9b8ddf30392668ca086f9d77f3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/adv_predictions.json - adv_probabilities_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/8048482d83c0edb9431d13c4f26200c2.pkl - params_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/params.yaml - predictions_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/predictions.json - probabilities_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/probabilities.json - score_dict_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/score_dict.json - test_labels_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/test_labels.json - train_labels_file: output/reports/train/90338c9b8ddf30392668ca086f9d77f3/train_labels.json - model_dir: models - name: 90338c9b8ddf30392668ca086f9d77f3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c687f793c13533cbef4df3e37193235f - trainer: - kwargs: {} -name: 90338c9b8ddf30392668ca086f9d77f3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/params.yaml b/examples/security/classification/output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/params.yaml deleted file mode 100644 index a23c863c..00000000 --- a/examples/security/classification/output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/adv_predictions.json - adv_probabilities_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/f480e3c6a9d31817ce1cbb228adce231.pkl - params_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/params.yaml - predictions_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/predictions.json - probabilities_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/probabilities.json - score_dict_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/score_dict.json - test_labels_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/test_labels.json - train_labels_file: output/reports/train/909140e71fce1b3b6ae6d9ff17d8cabb/train_labels.json - model_dir: models - name: 909140e71fce1b3b6ae6d9ff17d8cabb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7c08677d8b621fc3a0272cadd96677ea - trainer: - kwargs: {} -name: 909140e71fce1b3b6ae6d9ff17d8cabb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/params.yaml b/examples/security/classification/output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/params.yaml deleted file mode 100644 index a9c929c9..00000000 --- a/examples/security/classification/output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/adv_predictions.json - adv_probabilities_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/9faafc1dcd858b02433f3596a77ae239.pkl - params_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/params.yaml - predictions_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/predictions.json - probabilities_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/probabilities.json - score_dict_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/score_dict.json - test_labels_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/test_labels.json - train_labels_file: output/reports/train/90bbfd1883d4a9872294ce3216ca8ba5/train_labels.json - model_dir: models - name: 90bbfd1883d4a9872294ce3216ca8ba5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 38b2e5e0aec2a27f5c4e7adf1d689cf4 - trainer: - kwargs: {} -name: 90bbfd1883d4a9872294ce3216ca8ba5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/90cc624073daad38c0f2ee45443e399f/params.yaml b/examples/security/classification/output/reports/train/90cc624073daad38c0f2ee45443e399f/params.yaml deleted file mode 100644 index 6b0164a4..00000000 --- a/examples/security/classification/output/reports/train/90cc624073daad38c0f2ee45443e399f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/adv_predictions.json - adv_probabilities_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/c9cbfd8ab0bccff4c0e2da20facd3720.pkl - params_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/params.yaml - predictions_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/predictions.json - probabilities_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/probabilities.json - score_dict_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/score_dict.json - test_labels_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/test_labels.json - train_labels_file: output/reports/train/90cc624073daad38c0f2ee45443e399f/train_labels.json - model_dir: models - name: 90cc624073daad38c0f2ee45443e399f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2af4d8410274dca2a6c9fc12bb1d278e - trainer: - kwargs: {} -name: 90cc624073daad38c0f2ee45443e399f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/params.yaml b/examples/security/classification/output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/params.yaml deleted file mode 100644 index 6a37036b..00000000 --- a/examples/security/classification/output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/adv_predictions.json - adv_probabilities_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/313ecf4a9f31aa526cc9e914efd55363.pkl - params_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/params.yaml - predictions_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/predictions.json - probabilities_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/probabilities.json - score_dict_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/score_dict.json - test_labels_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/test_labels.json - train_labels_file: output/reports/train/90e090e7cc51dd81a5204f805cd8c8f8/train_labels.json - model_dir: models - name: 90e090e7cc51dd81a5204f805cd8c8f8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fed52635a6ba2d58634b77e11036593c - trainer: - kwargs: {} -name: 90e090e7cc51dd81a5204f805cd8c8f8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/90e23cef329532186544dcd004700152/params.yaml b/examples/security/classification/output/reports/train/90e23cef329532186544dcd004700152/params.yaml deleted file mode 100644 index 1e560dd9..00000000 --- a/examples/security/classification/output/reports/train/90e23cef329532186544dcd004700152/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/90e23cef329532186544dcd004700152/adv_predictions.json - adv_probabilities_file: output/reports/train/90e23cef329532186544dcd004700152/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/6b381c898de23cf5afa03190280c1778.pkl - params_file: output/reports/train/90e23cef329532186544dcd004700152/params.yaml - predictions_file: output/reports/train/90e23cef329532186544dcd004700152/predictions.json - probabilities_file: output/reports/train/90e23cef329532186544dcd004700152/probabilities.json - score_dict_file: output/reports/train/90e23cef329532186544dcd004700152/score_dict.json - test_labels_file: output/reports/train/90e23cef329532186544dcd004700152/test_labels.json - train_labels_file: output/reports/train/90e23cef329532186544dcd004700152/train_labels.json - model_dir: models - name: 90e23cef329532186544dcd004700152 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 067cec326fe685c11e0e5e686caf3685 - trainer: - kwargs: {} -name: 90e23cef329532186544dcd004700152 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9101ff79c72f4a697800a5a63280cda0/params.yaml b/examples/security/classification/output/reports/train/9101ff79c72f4a697800a5a63280cda0/params.yaml deleted file mode 100644 index c7ad1fa0..00000000 --- a/examples/security/classification/output/reports/train/9101ff79c72f4a697800a5a63280cda0/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/adv_predictions.json - adv_probabilities_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/3fff19b7d7f11edb2ea60ee03af5379e.pkl - params_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/params.yaml - predictions_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/predictions.json - probabilities_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/probabilities.json - score_dict_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/score_dict.json - test_labels_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/test_labels.json - train_labels_file: output/reports/train/9101ff79c72f4a697800a5a63280cda0/train_labels.json - model_dir: models - name: 9101ff79c72f4a697800a5a63280cda0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 94b9010d803c55069a70d9b7ab7ff449 - trainer: - kwargs: {} -name: 9101ff79c72f4a697800a5a63280cda0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/911c83e80158f500ec35df3c8aa8813e/params.yaml b/examples/security/classification/output/reports/train/911c83e80158f500ec35df3c8aa8813e/params.yaml deleted file mode 100644 index b67e30f8..00000000 --- a/examples/security/classification/output/reports/train/911c83e80158f500ec35df3c8aa8813e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/adv_predictions.json - adv_probabilities_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/c4f0c9940866c99542c10225ff3c32fc.pkl - params_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/params.yaml - predictions_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/predictions.json - probabilities_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/probabilities.json - score_dict_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/score_dict.json - test_labels_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/test_labels.json - train_labels_file: output/reports/train/911c83e80158f500ec35df3c8aa8813e/train_labels.json - model_dir: models - name: 911c83e80158f500ec35df3c8aa8813e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8516bba5fb0c2b6628e6ac44d60c6dcb - trainer: - kwargs: {} -name: 911c83e80158f500ec35df3c8aa8813e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9132afba265736635b94846de8012165/params.yaml b/examples/security/classification/output/reports/train/9132afba265736635b94846de8012165/params.yaml deleted file mode 100644 index ced21055..00000000 --- a/examples/security/classification/output/reports/train/9132afba265736635b94846de8012165/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9132afba265736635b94846de8012165/adv_predictions.json - adv_probabilities_file: output/reports/train/9132afba265736635b94846de8012165/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/ca9501c2deb65d8f685f950d4a9c1d45.pkl - params_file: output/reports/train/9132afba265736635b94846de8012165/params.yaml - predictions_file: output/reports/train/9132afba265736635b94846de8012165/predictions.json - probabilities_file: output/reports/train/9132afba265736635b94846de8012165/probabilities.json - score_dict_file: output/reports/train/9132afba265736635b94846de8012165/score_dict.json - test_labels_file: output/reports/train/9132afba265736635b94846de8012165/test_labels.json - train_labels_file: output/reports/train/9132afba265736635b94846de8012165/train_labels.json - model_dir: models - name: 9132afba265736635b94846de8012165 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f39d067994799861b45bec4c502d56ea - trainer: - kwargs: {} -name: 9132afba265736635b94846de8012165 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9153f097023b55f880804709379c0ece/params.yaml b/examples/security/classification/output/reports/train/9153f097023b55f880804709379c0ece/params.yaml deleted file mode 100644 index d85118b3..00000000 --- a/examples/security/classification/output/reports/train/9153f097023b55f880804709379c0ece/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9153f097023b55f880804709379c0ece/adv_predictions.json - adv_probabilities_file: output/reports/train/9153f097023b55f880804709379c0ece/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/aa9661945898a2d621d675a52ce41328.pkl - params_file: output/reports/train/9153f097023b55f880804709379c0ece/params.yaml - predictions_file: output/reports/train/9153f097023b55f880804709379c0ece/predictions.json - probabilities_file: output/reports/train/9153f097023b55f880804709379c0ece/probabilities.json - score_dict_file: output/reports/train/9153f097023b55f880804709379c0ece/score_dict.json - test_labels_file: output/reports/train/9153f097023b55f880804709379c0ece/test_labels.json - train_labels_file: output/reports/train/9153f097023b55f880804709379c0ece/train_labels.json - model_dir: models - name: 9153f097023b55f880804709379c0ece - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2a77dc5903589eddec6176881f7451c1 - trainer: - kwargs: {} -name: 9153f097023b55f880804709379c0ece -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/916c2ff29bd5d47eeb773124be6c814f/params.yaml b/examples/security/classification/output/reports/train/916c2ff29bd5d47eeb773124be6c814f/params.yaml deleted file mode 100644 index 72746bc8..00000000 --- a/examples/security/classification/output/reports/train/916c2ff29bd5d47eeb773124be6c814f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/adv_predictions.json - adv_probabilities_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/51b1d81ca5beb3af7a48db89f9dcafd2.pkl - params_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/params.yaml - predictions_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/predictions.json - probabilities_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/probabilities.json - score_dict_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/score_dict.json - test_labels_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/test_labels.json - train_labels_file: output/reports/train/916c2ff29bd5d47eeb773124be6c814f/train_labels.json - model_dir: models - name: 916c2ff29bd5d47eeb773124be6c814f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 250a81021c1d69e6eccfd2288b2ea377 - trainer: - kwargs: {} -name: 916c2ff29bd5d47eeb773124be6c814f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/91732fd30a30873dea38a72e305f11dc/params.yaml b/examples/security/classification/output/reports/train/91732fd30a30873dea38a72e305f11dc/params.yaml deleted file mode 100644 index e340ceae..00000000 --- a/examples/security/classification/output/reports/train/91732fd30a30873dea38a72e305f11dc/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/adv_predictions.json - adv_probabilities_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/dbfe710a8a09cf1bdc1c95424b63b7c6.pkl - params_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/params.yaml - predictions_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/predictions.json - probabilities_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/probabilities.json - score_dict_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/score_dict.json - test_labels_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/test_labels.json - train_labels_file: output/reports/train/91732fd30a30873dea38a72e305f11dc/train_labels.json - model_dir: models - name: 91732fd30a30873dea38a72e305f11dc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bd97cefdc147c0db630ddb4c6c682879 - trainer: - kwargs: {} -name: 91732fd30a30873dea38a72e305f11dc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/params.yaml b/examples/security/classification/output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/params.yaml deleted file mode 100644 index 6d2f7c71..00000000 --- a/examples/security/classification/output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/adv_predictions.json - adv_probabilities_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/fdef4ae4e18b0f9d4dc30987129c9c1d.pkl - params_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/params.yaml - predictions_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/predictions.json - probabilities_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/probabilities.json - score_dict_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/score_dict.json - test_labels_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/test_labels.json - train_labels_file: output/reports/train/91834b22b7e26bc67bc9928ca1e8936e/train_labels.json - model_dir: models - name: 91834b22b7e26bc67bc9928ca1e8936e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 410b6e7b7895767b4b6fd4dd9d01df43 - trainer: - kwargs: {} -name: 91834b22b7e26bc67bc9928ca1e8936e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/91984a9b30818ba47d0523418f83dd59/params.yaml b/examples/security/classification/output/reports/train/91984a9b30818ba47d0523418f83dd59/params.yaml deleted file mode 100644 index 8338370b..00000000 --- a/examples/security/classification/output/reports/train/91984a9b30818ba47d0523418f83dd59/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/adv_predictions.json - adv_probabilities_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/f5483170a52beaade69fa42463dd10db.pkl - params_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/params.yaml - predictions_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/predictions.json - probabilities_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/probabilities.json - score_dict_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/score_dict.json - test_labels_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/test_labels.json - train_labels_file: output/reports/train/91984a9b30818ba47d0523418f83dd59/train_labels.json - model_dir: models - name: 91984a9b30818ba47d0523418f83dd59 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f8714f357a3c306b519b52d42dc68b12 - trainer: - kwargs: {} -name: 91984a9b30818ba47d0523418f83dd59 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/91a77a2474baa9eba2e727a379e79e11/params.yaml b/examples/security/classification/output/reports/train/91a77a2474baa9eba2e727a379e79e11/params.yaml deleted file mode 100644 index 6fd40754..00000000 --- a/examples/security/classification/output/reports/train/91a77a2474baa9eba2e727a379e79e11/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/adv_predictions.json - adv_probabilities_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/9b2f48aa61d53a6ca38a70802845bed3.pkl - params_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/params.yaml - predictions_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/predictions.json - probabilities_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/probabilities.json - score_dict_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/score_dict.json - test_labels_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/test_labels.json - train_labels_file: output/reports/train/91a77a2474baa9eba2e727a379e79e11/train_labels.json - model_dir: models - name: 91a77a2474baa9eba2e727a379e79e11 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d0f46bf839f2a750b20715d4b83dbbac - trainer: - kwargs: {} -name: 91a77a2474baa9eba2e727a379e79e11 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/91d483132757e15a5ed166872535c237/params.yaml b/examples/security/classification/output/reports/train/91d483132757e15a5ed166872535c237/params.yaml deleted file mode 100644 index 4c6614ba..00000000 --- a/examples/security/classification/output/reports/train/91d483132757e15a5ed166872535c237/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/91d483132757e15a5ed166872535c237/adv_predictions.json - adv_probabilities_file: output/reports/train/91d483132757e15a5ed166872535c237/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/da5b82af660970ad747b64a9e4d10efa.pkl - params_file: output/reports/train/91d483132757e15a5ed166872535c237/params.yaml - predictions_file: output/reports/train/91d483132757e15a5ed166872535c237/predictions.json - probabilities_file: output/reports/train/91d483132757e15a5ed166872535c237/probabilities.json - score_dict_file: output/reports/train/91d483132757e15a5ed166872535c237/score_dict.json - test_labels_file: output/reports/train/91d483132757e15a5ed166872535c237/test_labels.json - train_labels_file: output/reports/train/91d483132757e15a5ed166872535c237/train_labels.json - model_dir: models - name: 91d483132757e15a5ed166872535c237 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a0e3bab3f4f11932d131ab1c02e0026b - trainer: - kwargs: {} -name: 91d483132757e15a5ed166872535c237 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/params.yaml b/examples/security/classification/output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/params.yaml deleted file mode 100644 index 93894917..00000000 --- a/examples/security/classification/output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/adv_predictions.json - adv_probabilities_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/de6dff8b4bcb3104b102c3525770faef.pkl - params_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/params.yaml - predictions_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/predictions.json - probabilities_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/probabilities.json - score_dict_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/score_dict.json - test_labels_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/test_labels.json - train_labels_file: output/reports/train/91f9e04fa18e2e9bd52aac7ad22c345c/train_labels.json - model_dir: models - name: 91f9e04fa18e2e9bd52aac7ad22c345c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0d69bd4f1c57de80ff3b5363854a3bc0 - trainer: - kwargs: {} -name: 91f9e04fa18e2e9bd52aac7ad22c345c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/923f5c519cfa1431603a73d3fe3451ab/params.yaml b/examples/security/classification/output/reports/train/923f5c519cfa1431603a73d3fe3451ab/params.yaml deleted file mode 100644 index 7ea3c8cd..00000000 --- a/examples/security/classification/output/reports/train/923f5c519cfa1431603a73d3fe3451ab/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/adv_predictions.json - adv_probabilities_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/bf83ab69d36317ff678edcfe0773867a.pkl - params_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/params.yaml - predictions_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/predictions.json - probabilities_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/probabilities.json - score_dict_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/score_dict.json - test_labels_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/test_labels.json - train_labels_file: output/reports/train/923f5c519cfa1431603a73d3fe3451ab/train_labels.json - model_dir: models - name: 923f5c519cfa1431603a73d3fe3451ab - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c5cac6677ebd338c7419232b92bc21ea - trainer: - kwargs: {} -name: 923f5c519cfa1431603a73d3fe3451ab -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/params.yaml b/examples/security/classification/output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/params.yaml deleted file mode 100644 index 8f373a15..00000000 --- a/examples/security/classification/output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/adv_predictions.json - adv_probabilities_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/a481692f6d066d75c300aedb1edeac73.pkl - params_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/params.yaml - predictions_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/predictions.json - probabilities_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/probabilities.json - score_dict_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/score_dict.json - test_labels_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/test_labels.json - train_labels_file: output/reports/train/926c428cdcd6bca8c31bca51b4a32b28/train_labels.json - model_dir: models - name: 926c428cdcd6bca8c31bca51b4a32b28 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 827700bdef4d4c0e9c40ee5f648c9a76 - trainer: - kwargs: {} -name: 926c428cdcd6bca8c31bca51b4a32b28 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/params.yaml b/examples/security/classification/output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/params.yaml deleted file mode 100644 index 8390a4f7..00000000 --- a/examples/security/classification/output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/adv_predictions.json - adv_probabilities_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/680897e0eaed49f6c1fcca20523ea9cf.pkl - params_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/params.yaml - predictions_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/predictions.json - probabilities_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/probabilities.json - score_dict_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/score_dict.json - test_labels_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/test_labels.json - train_labels_file: output/reports/train/9274492a2fa76aceae9dc57aeb8c0e6d/train_labels.json - model_dir: models - name: 9274492a2fa76aceae9dc57aeb8c0e6d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 73b67d1fa6e5e535dc99756b874d46ea - trainer: - kwargs: {} -name: 9274492a2fa76aceae9dc57aeb8c0e6d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/params.yaml b/examples/security/classification/output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/params.yaml deleted file mode 100644 index b109e051..00000000 --- a/examples/security/classification/output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/adv_predictions.json - adv_probabilities_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/38c1cd654865dd76aead2e931e6542b1.pkl - params_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/params.yaml - predictions_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/predictions.json - probabilities_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/probabilities.json - score_dict_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/score_dict.json - test_labels_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/test_labels.json - train_labels_file: output/reports/train/92b999fbda0d5630738108ea9b3d6d3f/train_labels.json - model_dir: models - name: 92b999fbda0d5630738108ea9b3d6d3f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1320e341004e74107c128cab6729037a - trainer: - kwargs: {} -name: 92b999fbda0d5630738108ea9b3d6d3f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/params.yaml b/examples/security/classification/output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/params.yaml deleted file mode 100644 index b626b500..00000000 --- a/examples/security/classification/output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/adv_predictions.json - adv_probabilities_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/3ea9841ca12090a0c18c242f153ef992.pkl - params_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/params.yaml - predictions_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/predictions.json - probabilities_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/probabilities.json - score_dict_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/score_dict.json - test_labels_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/test_labels.json - train_labels_file: output/reports/train/92d2f79adbfe33d37c73be647b28e7c1/train_labels.json - model_dir: models - name: 92d2f79adbfe33d37c73be647b28e7c1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 13ed0deb36246601fc4d54af6dc0f6d7 - trainer: - kwargs: {} -name: 92d2f79adbfe33d37c73be647b28e7c1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/931f9ba3a65708a2cb63563c611d42f8/params.yaml b/examples/security/classification/output/reports/train/931f9ba3a65708a2cb63563c611d42f8/params.yaml deleted file mode 100644 index faaec31c..00000000 --- a/examples/security/classification/output/reports/train/931f9ba3a65708a2cb63563c611d42f8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/adv_predictions.json - adv_probabilities_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/130630ca31401dc7dcf36cff977aa7fd.pkl - params_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/params.yaml - predictions_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/predictions.json - probabilities_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/probabilities.json - score_dict_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/score_dict.json - test_labels_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/test_labels.json - train_labels_file: output/reports/train/931f9ba3a65708a2cb63563c611d42f8/train_labels.json - model_dir: models - name: 931f9ba3a65708a2cb63563c611d42f8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1ae89ccfba152a3930717bdbee4e2d1a - trainer: - kwargs: {} -name: 931f9ba3a65708a2cb63563c611d42f8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9343beedde54458e718e875d764af2de/params.yaml b/examples/security/classification/output/reports/train/9343beedde54458e718e875d764af2de/params.yaml deleted file mode 100644 index 68669bf1..00000000 --- a/examples/security/classification/output/reports/train/9343beedde54458e718e875d764af2de/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9343beedde54458e718e875d764af2de/adv_predictions.json - adv_probabilities_file: output/reports/train/9343beedde54458e718e875d764af2de/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/5ec09d5a82fbbb08dc0e2c75b70900b0.pkl - params_file: output/reports/train/9343beedde54458e718e875d764af2de/params.yaml - predictions_file: output/reports/train/9343beedde54458e718e875d764af2de/predictions.json - probabilities_file: output/reports/train/9343beedde54458e718e875d764af2de/probabilities.json - score_dict_file: output/reports/train/9343beedde54458e718e875d764af2de/score_dict.json - test_labels_file: output/reports/train/9343beedde54458e718e875d764af2de/test_labels.json - train_labels_file: output/reports/train/9343beedde54458e718e875d764af2de/train_labels.json - model_dir: models - name: 9343beedde54458e718e875d764af2de - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dcd6a26dc4866f78125be5a356aa23f6 - trainer: - kwargs: {} -name: 9343beedde54458e718e875d764af2de -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/934abf0d92bd01e993315de6cfb0d78f/params.yaml b/examples/security/classification/output/reports/train/934abf0d92bd01e993315de6cfb0d78f/params.yaml deleted file mode 100644 index e06f7e6a..00000000 --- a/examples/security/classification/output/reports/train/934abf0d92bd01e993315de6cfb0d78f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/adv_predictions.json - adv_probabilities_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/5534c3fa00e05ebc189ce85b9bc87842.pkl - params_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/params.yaml - predictions_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/predictions.json - probabilities_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/probabilities.json - score_dict_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/score_dict.json - test_labels_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/test_labels.json - train_labels_file: output/reports/train/934abf0d92bd01e993315de6cfb0d78f/train_labels.json - model_dir: models - name: 934abf0d92bd01e993315de6cfb0d78f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6a20b63e45c292487acd6e66d98d63ea - trainer: - kwargs: {} -name: 934abf0d92bd01e993315de6cfb0d78f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9351dc19a7247fa4e29ba765e189ae04/params.yaml b/examples/security/classification/output/reports/train/9351dc19a7247fa4e29ba765e189ae04/params.yaml deleted file mode 100644 index d4c73087..00000000 --- a/examples/security/classification/output/reports/train/9351dc19a7247fa4e29ba765e189ae04/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/adv_predictions.json - adv_probabilities_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/f31272c34bfe775d0c9d3b272b17091d.pkl - params_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/params.yaml - predictions_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/predictions.json - probabilities_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/probabilities.json - score_dict_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/score_dict.json - test_labels_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/test_labels.json - train_labels_file: output/reports/train/9351dc19a7247fa4e29ba765e189ae04/train_labels.json - model_dir: models - name: 9351dc19a7247fa4e29ba765e189ae04 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 746848bdd2189e586aa885e1a82aa87e - trainer: - kwargs: {} -name: 9351dc19a7247fa4e29ba765e189ae04 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/params.yaml b/examples/security/classification/output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/params.yaml deleted file mode 100644 index a8d28fc2..00000000 --- a/examples/security/classification/output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/adv_predictions.json - adv_probabilities_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/4d78cd7b615efdba369bd1cec9216d7e.pkl - params_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/params.yaml - predictions_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/predictions.json - probabilities_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/probabilities.json - score_dict_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/score_dict.json - test_labels_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/test_labels.json - train_labels_file: output/reports/train/9376989c55002fbd3bb4ad2cdbcbf8c3/train_labels.json - model_dir: models - name: 9376989c55002fbd3bb4ad2cdbcbf8c3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6104d74ff9a78e6f80dc2a9b4f7bdee1 - trainer: - kwargs: {} -name: 9376989c55002fbd3bb4ad2cdbcbf8c3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/93b550b76eb9367b9a9788ac35f5714a/params.yaml b/examples/security/classification/output/reports/train/93b550b76eb9367b9a9788ac35f5714a/params.yaml deleted file mode 100644 index 9504aa9a..00000000 --- a/examples/security/classification/output/reports/train/93b550b76eb9367b9a9788ac35f5714a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/adv_predictions.json - adv_probabilities_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/5edfa2412f5dd196be89ab7d6215a280.pkl - params_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/params.yaml - predictions_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/predictions.json - probabilities_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/probabilities.json - score_dict_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/score_dict.json - test_labels_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/test_labels.json - train_labels_file: output/reports/train/93b550b76eb9367b9a9788ac35f5714a/train_labels.json - model_dir: models - name: 93b550b76eb9367b9a9788ac35f5714a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8714d972fc540483cb4483cb4231ccad - trainer: - kwargs: {} -name: 93b550b76eb9367b9a9788ac35f5714a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/params.yaml b/examples/security/classification/output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/params.yaml deleted file mode 100644 index 0806272d..00000000 --- a/examples/security/classification/output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/adv_predictions.json - adv_probabilities_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/0ab516daea81c68ba4b767744d924ce2.pkl - params_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/params.yaml - predictions_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/predictions.json - probabilities_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/probabilities.json - score_dict_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/score_dict.json - test_labels_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/test_labels.json - train_labels_file: output/reports/train/93b806a0badbb8ffa2fc988ecf054d2b/train_labels.json - model_dir: models - name: 93b806a0badbb8ffa2fc988ecf054d2b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b8f1752c536ca85a224dc2f32c6799f3 - trainer: - kwargs: {} -name: 93b806a0badbb8ffa2fc988ecf054d2b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/params.yaml b/examples/security/classification/output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/params.yaml deleted file mode 100644 index e981112d..00000000 --- a/examples/security/classification/output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/adv_predictions.json - adv_probabilities_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/6ed6c0842351c635d70811d876063ab3.pkl - params_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/params.yaml - predictions_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/predictions.json - probabilities_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/probabilities.json - score_dict_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/score_dict.json - test_labels_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/test_labels.json - train_labels_file: output/reports/train/93bd69da59aebbbb8a9c9a0a12c6e78c/train_labels.json - model_dir: models - name: 93bd69da59aebbbb8a9c9a0a12c6e78c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e5c0d8a71019e57e70ab047418ce9497 - trainer: - kwargs: {} -name: 93bd69da59aebbbb8a9c9a0a12c6e78c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/params.yaml b/examples/security/classification/output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/params.yaml deleted file mode 100644 index 806ec02f..00000000 --- a/examples/security/classification/output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/adv_predictions.json - adv_probabilities_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/790b6acebf2d621d013076cf16412320.pkl - params_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/params.yaml - predictions_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/predictions.json - probabilities_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/probabilities.json - score_dict_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/score_dict.json - test_labels_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/test_labels.json - train_labels_file: output/reports/train/93c51e724f7eba15cb63a8a9d2c85b19/train_labels.json - model_dir: models - name: 93c51e724f7eba15cb63a8a9d2c85b19 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ae5e2a56747248a450c44f767128a171 - trainer: - kwargs: {} -name: 93c51e724f7eba15cb63a8a9d2c85b19 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/params.yaml b/examples/security/classification/output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/params.yaml deleted file mode 100644 index 352231b5..00000000 --- a/examples/security/classification/output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/adv_predictions.json - adv_probabilities_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/44d03061e46ace847faa096895037359.pkl - params_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/params.yaml - predictions_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/predictions.json - probabilities_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/probabilities.json - score_dict_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/score_dict.json - test_labels_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/test_labels.json - train_labels_file: output/reports/train/93cc459b4efa826b54ac2bcd956d7c82/train_labels.json - model_dir: models - name: 93cc459b4efa826b54ac2bcd956d7c82 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: de6979c105993dfc02e5de778213b683 - trainer: - kwargs: {} -name: 93cc459b4efa826b54ac2bcd956d7c82 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/941ebbf64dd77173d7a147f695289541/params.yaml b/examples/security/classification/output/reports/train/941ebbf64dd77173d7a147f695289541/params.yaml deleted file mode 100644 index 6c0b637f..00000000 --- a/examples/security/classification/output/reports/train/941ebbf64dd77173d7a147f695289541/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/941ebbf64dd77173d7a147f695289541/adv_predictions.json - adv_probabilities_file: output/reports/train/941ebbf64dd77173d7a147f695289541/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/dba1034b36fb84bb7524247246d97937.pkl - params_file: output/reports/train/941ebbf64dd77173d7a147f695289541/params.yaml - predictions_file: output/reports/train/941ebbf64dd77173d7a147f695289541/predictions.json - probabilities_file: output/reports/train/941ebbf64dd77173d7a147f695289541/probabilities.json - score_dict_file: output/reports/train/941ebbf64dd77173d7a147f695289541/score_dict.json - test_labels_file: output/reports/train/941ebbf64dd77173d7a147f695289541/test_labels.json - train_labels_file: output/reports/train/941ebbf64dd77173d7a147f695289541/train_labels.json - model_dir: models - name: 941ebbf64dd77173d7a147f695289541 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 055c19bc153e3a0fec209e58c24f62b6 - trainer: - kwargs: {} -name: 941ebbf64dd77173d7a147f695289541 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/params.yaml b/examples/security/classification/output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/params.yaml deleted file mode 100644 index 47b710f1..00000000 --- a/examples/security/classification/output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/adv_predictions.json - adv_probabilities_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/7c4c67bb3b48394b8e84da74577782e3.pkl - params_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/params.yaml - predictions_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/predictions.json - probabilities_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/probabilities.json - score_dict_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/score_dict.json - test_labels_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/test_labels.json - train_labels_file: output/reports/train/9428f73c64ca35b3acb71b5e289a1d18/train_labels.json - model_dir: models - name: 9428f73c64ca35b3acb71b5e289a1d18 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1b0971c19bab22ec571dbbd2c5c74d32 - trainer: - kwargs: {} -name: 9428f73c64ca35b3acb71b5e289a1d18 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/94643560d092400898b154226fbe2b25/params.yaml b/examples/security/classification/output/reports/train/94643560d092400898b154226fbe2b25/params.yaml deleted file mode 100644 index 4b35b02f..00000000 --- a/examples/security/classification/output/reports/train/94643560d092400898b154226fbe2b25/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/94643560d092400898b154226fbe2b25/adv_predictions.json - adv_probabilities_file: output/reports/train/94643560d092400898b154226fbe2b25/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/cd190ff1ffda1bc6ce19c092bffa23f3.pkl - params_file: output/reports/train/94643560d092400898b154226fbe2b25/params.yaml - predictions_file: output/reports/train/94643560d092400898b154226fbe2b25/predictions.json - probabilities_file: output/reports/train/94643560d092400898b154226fbe2b25/probabilities.json - score_dict_file: output/reports/train/94643560d092400898b154226fbe2b25/score_dict.json - test_labels_file: output/reports/train/94643560d092400898b154226fbe2b25/test_labels.json - train_labels_file: output/reports/train/94643560d092400898b154226fbe2b25/train_labels.json - model_dir: models - name: 94643560d092400898b154226fbe2b25 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ad27c578da5942130f204309e119e816 - trainer: - kwargs: {} -name: 94643560d092400898b154226fbe2b25 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/949936167a624c64aa13df8777920dce/params.yaml b/examples/security/classification/output/reports/train/949936167a624c64aa13df8777920dce/params.yaml deleted file mode 100644 index 5268418f..00000000 --- a/examples/security/classification/output/reports/train/949936167a624c64aa13df8777920dce/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/949936167a624c64aa13df8777920dce/adv_predictions.json - adv_probabilities_file: output/reports/train/949936167a624c64aa13df8777920dce/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/ddade0b7a735bab5dbe527a0f7cfb43e.pkl - params_file: output/reports/train/949936167a624c64aa13df8777920dce/params.yaml - predictions_file: output/reports/train/949936167a624c64aa13df8777920dce/predictions.json - probabilities_file: output/reports/train/949936167a624c64aa13df8777920dce/probabilities.json - score_dict_file: output/reports/train/949936167a624c64aa13df8777920dce/score_dict.json - test_labels_file: output/reports/train/949936167a624c64aa13df8777920dce/test_labels.json - train_labels_file: output/reports/train/949936167a624c64aa13df8777920dce/train_labels.json - model_dir: models - name: 949936167a624c64aa13df8777920dce - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b8016631850a4866e8163b3c37b0e493 - trainer: - kwargs: {} -name: 949936167a624c64aa13df8777920dce -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/94aa230c2531c5455bc8c1464bb49915/params.yaml b/examples/security/classification/output/reports/train/94aa230c2531c5455bc8c1464bb49915/params.yaml deleted file mode 100644 index 794fffe3..00000000 --- a/examples/security/classification/output/reports/train/94aa230c2531c5455bc8c1464bb49915/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/adv_predictions.json - adv_probabilities_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/11377c386a39c27457843519ef38a4c7.pkl - params_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/params.yaml - predictions_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/predictions.json - probabilities_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/probabilities.json - score_dict_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/score_dict.json - test_labels_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/test_labels.json - train_labels_file: output/reports/train/94aa230c2531c5455bc8c1464bb49915/train_labels.json - model_dir: models - name: 94aa230c2531c5455bc8c1464bb49915 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 67ac052e2d5bc9531599774e9e970d74 - trainer: - kwargs: {} -name: 94aa230c2531c5455bc8c1464bb49915 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/94b39db6f255f1e311f14c0f8a775a80/params.yaml b/examples/security/classification/output/reports/train/94b39db6f255f1e311f14c0f8a775a80/params.yaml deleted file mode 100644 index 67252191..00000000 --- a/examples/security/classification/output/reports/train/94b39db6f255f1e311f14c0f8a775a80/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/adv_predictions.json - adv_probabilities_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/5bff4d0205bccb3c0c9ec110b4ecde4d.pkl - params_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/params.yaml - predictions_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/predictions.json - probabilities_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/probabilities.json - score_dict_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/score_dict.json - test_labels_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/test_labels.json - train_labels_file: output/reports/train/94b39db6f255f1e311f14c0f8a775a80/train_labels.json - model_dir: models - name: 94b39db6f255f1e311f14c0f8a775a80 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a79ae8e51a799d4a68fe9a97698fd024 - trainer: - kwargs: {} -name: 94b39db6f255f1e311f14c0f8a775a80 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/94bee7c3df202d2aa865858727ff116d/params.yaml b/examples/security/classification/output/reports/train/94bee7c3df202d2aa865858727ff116d/params.yaml deleted file mode 100644 index d7acf59b..00000000 --- a/examples/security/classification/output/reports/train/94bee7c3df202d2aa865858727ff116d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/adv_predictions.json - adv_probabilities_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/3b3bcf4ffc8f4806de1ba23522f5f589.pkl - params_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/params.yaml - predictions_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/predictions.json - probabilities_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/probabilities.json - score_dict_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/score_dict.json - test_labels_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/test_labels.json - train_labels_file: output/reports/train/94bee7c3df202d2aa865858727ff116d/train_labels.json - model_dir: models - name: 94bee7c3df202d2aa865858727ff116d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 41fa9faa6290b097077571770f27b74c - trainer: - kwargs: {} -name: 94bee7c3df202d2aa865858727ff116d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/params.yaml b/examples/security/classification/output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/params.yaml deleted file mode 100644 index d74a58cb..00000000 --- a/examples/security/classification/output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/adv_predictions.json - adv_probabilities_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/b63e74103dc67a4a8e31354bce1663e8.pkl - params_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/params.yaml - predictions_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/predictions.json - probabilities_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/probabilities.json - score_dict_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/score_dict.json - test_labels_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/test_labels.json - train_labels_file: output/reports/train/94e4cb28f06d3ed47d6ab401683699f0/train_labels.json - model_dir: models - name: 94e4cb28f06d3ed47d6ab401683699f0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cb6567095bcae656e6ad94060a31c854 - trainer: - kwargs: {} -name: 94e4cb28f06d3ed47d6ab401683699f0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/params.yaml b/examples/security/classification/output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/params.yaml deleted file mode 100644 index 1da41547..00000000 --- a/examples/security/classification/output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/adv_predictions.json - adv_probabilities_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/72af0340a1c1d21e5b17aec0162fb6f3.pkl - params_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/params.yaml - predictions_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/predictions.json - probabilities_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/probabilities.json - score_dict_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/score_dict.json - test_labels_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/test_labels.json - train_labels_file: output/reports/train/94ef5194dbb5bfd35e7aceb61fadb0bb/train_labels.json - model_dir: models - name: 94ef5194dbb5bfd35e7aceb61fadb0bb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2bb1b45d3f4fc960d57abc7dcdfe1194 - trainer: - kwargs: {} -name: 94ef5194dbb5bfd35e7aceb61fadb0bb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/params.yaml b/examples/security/classification/output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/params.yaml deleted file mode 100644 index d1ebc08a..00000000 --- a/examples/security/classification/output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/adv_predictions.json - adv_probabilities_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/222a13831826917b64a90afb7e578c31.pkl - params_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/params.yaml - predictions_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/predictions.json - probabilities_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/probabilities.json - score_dict_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/score_dict.json - test_labels_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/test_labels.json - train_labels_file: output/reports/train/9534c6ce4eebd7d6e9f6b29853953957/train_labels.json - model_dir: models - name: 9534c6ce4eebd7d6e9f6b29853953957 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 923e0f8c7a0b67007daaac9001eac16a - trainer: - kwargs: {} -name: 9534c6ce4eebd7d6e9f6b29853953957 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/953eb8e6a313d912f3fffc5d374919ee/params.yaml b/examples/security/classification/output/reports/train/953eb8e6a313d912f3fffc5d374919ee/params.yaml deleted file mode 100644 index 60aa34f0..00000000 --- a/examples/security/classification/output/reports/train/953eb8e6a313d912f3fffc5d374919ee/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/adv_predictions.json - adv_probabilities_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/3dd8796080b66d5586c8806c43d48e14.pkl - params_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/params.yaml - predictions_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/predictions.json - probabilities_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/probabilities.json - score_dict_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/score_dict.json - test_labels_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/test_labels.json - train_labels_file: output/reports/train/953eb8e6a313d912f3fffc5d374919ee/train_labels.json - model_dir: models - name: 953eb8e6a313d912f3fffc5d374919ee - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 99187509dd1d1d057a9171e229805040 - trainer: - kwargs: {} -name: 953eb8e6a313d912f3fffc5d374919ee -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/955bc923dd984559b937b6de28a27179/params.yaml b/examples/security/classification/output/reports/train/955bc923dd984559b937b6de28a27179/params.yaml deleted file mode 100644 index 357fd97c..00000000 --- a/examples/security/classification/output/reports/train/955bc923dd984559b937b6de28a27179/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/955bc923dd984559b937b6de28a27179/adv_predictions.json - adv_probabilities_file: output/reports/train/955bc923dd984559b937b6de28a27179/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/79b72b0dd6f8930566dc086d401cf096.pkl - params_file: output/reports/train/955bc923dd984559b937b6de28a27179/params.yaml - predictions_file: output/reports/train/955bc923dd984559b937b6de28a27179/predictions.json - probabilities_file: output/reports/train/955bc923dd984559b937b6de28a27179/probabilities.json - score_dict_file: output/reports/train/955bc923dd984559b937b6de28a27179/score_dict.json - test_labels_file: output/reports/train/955bc923dd984559b937b6de28a27179/test_labels.json - train_labels_file: output/reports/train/955bc923dd984559b937b6de28a27179/train_labels.json - model_dir: models - name: 955bc923dd984559b937b6de28a27179 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e4e75e34e1da9acdff876204557bc69e - trainer: - kwargs: {} -name: 955bc923dd984559b937b6de28a27179 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/955e2ab947fde293d8d991a71d786c0a/params.yaml b/examples/security/classification/output/reports/train/955e2ab947fde293d8d991a71d786c0a/params.yaml deleted file mode 100644 index b91bf334..00000000 --- a/examples/security/classification/output/reports/train/955e2ab947fde293d8d991a71d786c0a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/adv_predictions.json - adv_probabilities_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/7a125b96cb67af89b7511b5d34cc0688.pkl - params_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/params.yaml - predictions_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/predictions.json - probabilities_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/probabilities.json - score_dict_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/score_dict.json - test_labels_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/test_labels.json - train_labels_file: output/reports/train/955e2ab947fde293d8d991a71d786c0a/train_labels.json - model_dir: models - name: 955e2ab947fde293d8d991a71d786c0a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b52731b4974c7c19cc9b487d83931b7a - trainer: - kwargs: {} -name: 955e2ab947fde293d8d991a71d786c0a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/957c10bc3a082318126caa107e75f0a0/params.yaml b/examples/security/classification/output/reports/train/957c10bc3a082318126caa107e75f0a0/params.yaml deleted file mode 100644 index ca93aae2..00000000 --- a/examples/security/classification/output/reports/train/957c10bc3a082318126caa107e75f0a0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/adv_predictions.json - adv_probabilities_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/c93476b1d15c12b36535ab07b0ee1f1a.pkl - params_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/params.yaml - predictions_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/predictions.json - probabilities_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/probabilities.json - score_dict_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/score_dict.json - test_labels_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/test_labels.json - train_labels_file: output/reports/train/957c10bc3a082318126caa107e75f0a0/train_labels.json - model_dir: models - name: 957c10bc3a082318126caa107e75f0a0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4453d2e304102c37d1bce38f2e26c9e8 - trainer: - kwargs: {} -name: 957c10bc3a082318126caa107e75f0a0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/959c41710839016512e17122b9b1a917/params.yaml b/examples/security/classification/output/reports/train/959c41710839016512e17122b9b1a917/params.yaml deleted file mode 100644 index 650b606c..00000000 --- a/examples/security/classification/output/reports/train/959c41710839016512e17122b9b1a917/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/959c41710839016512e17122b9b1a917/adv_predictions.json - adv_probabilities_file: output/reports/train/959c41710839016512e17122b9b1a917/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/47d0641cb382e3f24b376b5dcbf9ee5e.pkl - params_file: output/reports/train/959c41710839016512e17122b9b1a917/params.yaml - predictions_file: output/reports/train/959c41710839016512e17122b9b1a917/predictions.json - probabilities_file: output/reports/train/959c41710839016512e17122b9b1a917/probabilities.json - score_dict_file: output/reports/train/959c41710839016512e17122b9b1a917/score_dict.json - test_labels_file: output/reports/train/959c41710839016512e17122b9b1a917/test_labels.json - train_labels_file: output/reports/train/959c41710839016512e17122b9b1a917/train_labels.json - model_dir: models - name: 959c41710839016512e17122b9b1a917 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: add83a53520b01202c3a86b958fab818 - trainer: - kwargs: {} -name: 959c41710839016512e17122b9b1a917 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/95f874a1e6f7398a014d892bbf94153a/params.yaml b/examples/security/classification/output/reports/train/95f874a1e6f7398a014d892bbf94153a/params.yaml deleted file mode 100644 index 9d9cfaf5..00000000 --- a/examples/security/classification/output/reports/train/95f874a1e6f7398a014d892bbf94153a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/adv_predictions.json - adv_probabilities_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/c137f77e4c771c5321ba61fa09a76e85.pkl - params_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/params.yaml - predictions_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/predictions.json - probabilities_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/probabilities.json - score_dict_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/score_dict.json - test_labels_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/test_labels.json - train_labels_file: output/reports/train/95f874a1e6f7398a014d892bbf94153a/train_labels.json - model_dir: models - name: 95f874a1e6f7398a014d892bbf94153a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 507515cae8714af773c90c987e82da6f - trainer: - kwargs: {} -name: 95f874a1e6f7398a014d892bbf94153a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/96345dc957184419cb60e8c3c9d64898/params.yaml b/examples/security/classification/output/reports/train/96345dc957184419cb60e8c3c9d64898/params.yaml deleted file mode 100644 index ad2c4ab9..00000000 --- a/examples/security/classification/output/reports/train/96345dc957184419cb60e8c3c9d64898/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/adv_predictions.json - adv_probabilities_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/a55927e8c68f7a24f5a2f8716ed61a07.pkl - params_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/params.yaml - predictions_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/predictions.json - probabilities_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/probabilities.json - score_dict_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/score_dict.json - test_labels_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/test_labels.json - train_labels_file: output/reports/train/96345dc957184419cb60e8c3c9d64898/train_labels.json - model_dir: models - name: 96345dc957184419cb60e8c3c9d64898 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9c10220a06a7b60ac3c8a2f0214deec1 - trainer: - kwargs: {} -name: 96345dc957184419cb60e8c3c9d64898 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/965908f2652389f26014fc7c84f25e78/params.yaml b/examples/security/classification/output/reports/train/965908f2652389f26014fc7c84f25e78/params.yaml deleted file mode 100644 index 60f8007a..00000000 --- a/examples/security/classification/output/reports/train/965908f2652389f26014fc7c84f25e78/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/965908f2652389f26014fc7c84f25e78/adv_predictions.json - adv_probabilities_file: output/reports/train/965908f2652389f26014fc7c84f25e78/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/98621ed0aa67a762a95c58361b0334d5.pkl - params_file: output/reports/train/965908f2652389f26014fc7c84f25e78/params.yaml - predictions_file: output/reports/train/965908f2652389f26014fc7c84f25e78/predictions.json - probabilities_file: output/reports/train/965908f2652389f26014fc7c84f25e78/probabilities.json - score_dict_file: output/reports/train/965908f2652389f26014fc7c84f25e78/score_dict.json - test_labels_file: output/reports/train/965908f2652389f26014fc7c84f25e78/test_labels.json - train_labels_file: output/reports/train/965908f2652389f26014fc7c84f25e78/train_labels.json - model_dir: models - name: 965908f2652389f26014fc7c84f25e78 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8619cac6c5cdc6dc587d21a1d6adfc63 - trainer: - kwargs: {} -name: 965908f2652389f26014fc7c84f25e78 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/96a486b63a11301345c6fe4339dacc63/params.yaml b/examples/security/classification/output/reports/train/96a486b63a11301345c6fe4339dacc63/params.yaml deleted file mode 100644 index c99982e5..00000000 --- a/examples/security/classification/output/reports/train/96a486b63a11301345c6fe4339dacc63/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/adv_predictions.json - adv_probabilities_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/164ede0313c9472ad160df1e7e0f3fab.pkl - params_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/params.yaml - predictions_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/predictions.json - probabilities_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/probabilities.json - score_dict_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/score_dict.json - test_labels_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/test_labels.json - train_labels_file: output/reports/train/96a486b63a11301345c6fe4339dacc63/train_labels.json - model_dir: models - name: 96a486b63a11301345c6fe4339dacc63 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f27f5847bc4842f27f14c47b70c768cb - trainer: - kwargs: {} -name: 96a486b63a11301345c6fe4339dacc63 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/96b4331edcb507e783b12e801aa88c50/params.yaml b/examples/security/classification/output/reports/train/96b4331edcb507e783b12e801aa88c50/params.yaml deleted file mode 100644 index 60fa0c50..00000000 --- a/examples/security/classification/output/reports/train/96b4331edcb507e783b12e801aa88c50/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/adv_predictions.json - adv_probabilities_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/acf98d0d626cb51585966fc7b921478a.pkl - params_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/params.yaml - predictions_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/predictions.json - probabilities_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/probabilities.json - score_dict_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/score_dict.json - test_labels_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/test_labels.json - train_labels_file: output/reports/train/96b4331edcb507e783b12e801aa88c50/train_labels.json - model_dir: models - name: 96b4331edcb507e783b12e801aa88c50 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 99409fef7f6d19426654bc805fd9bcf8 - trainer: - kwargs: {} -name: 96b4331edcb507e783b12e801aa88c50 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/96b572df833549dad96780ce04fef589/params.yaml b/examples/security/classification/output/reports/train/96b572df833549dad96780ce04fef589/params.yaml deleted file mode 100644 index 42fe65da..00000000 --- a/examples/security/classification/output/reports/train/96b572df833549dad96780ce04fef589/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/96b572df833549dad96780ce04fef589/adv_predictions.json - adv_probabilities_file: output/reports/train/96b572df833549dad96780ce04fef589/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/f636ff7cfaa68f6043ebbf212a0a12e2.pkl - params_file: output/reports/train/96b572df833549dad96780ce04fef589/params.yaml - predictions_file: output/reports/train/96b572df833549dad96780ce04fef589/predictions.json - probabilities_file: output/reports/train/96b572df833549dad96780ce04fef589/probabilities.json - score_dict_file: output/reports/train/96b572df833549dad96780ce04fef589/score_dict.json - test_labels_file: output/reports/train/96b572df833549dad96780ce04fef589/test_labels.json - train_labels_file: output/reports/train/96b572df833549dad96780ce04fef589/train_labels.json - model_dir: models - name: 96b572df833549dad96780ce04fef589 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 338799ee8599faed4e8493e13f8d0937 - trainer: - kwargs: {} -name: 96b572df833549dad96780ce04fef589 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/params.yaml b/examples/security/classification/output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/params.yaml deleted file mode 100644 index 3772319e..00000000 --- a/examples/security/classification/output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/adv_predictions.json - adv_probabilities_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/d0ba3539f6bd369a3a2c6c7ee048cbd8.pkl - params_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/params.yaml - predictions_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/predictions.json - probabilities_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/probabilities.json - score_dict_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/score_dict.json - test_labels_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/test_labels.json - train_labels_file: output/reports/train/96bdeacfcabdcd894fec214c7aab2dd7/train_labels.json - model_dir: models - name: 96bdeacfcabdcd894fec214c7aab2dd7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1b95288ddb9525bfc50189bec88ff212 - trainer: - kwargs: {} -name: 96bdeacfcabdcd894fec214c7aab2dd7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/params.yaml b/examples/security/classification/output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/params.yaml deleted file mode 100644 index 902c3abd..00000000 --- a/examples/security/classification/output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/adv_predictions.json - adv_probabilities_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/1ab4a63db4f82475412898fab4070394.pkl - params_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/params.yaml - predictions_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/predictions.json - probabilities_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/probabilities.json - score_dict_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/score_dict.json - test_labels_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/test_labels.json - train_labels_file: output/reports/train/96e2cedb80a2bc4614495bd06d3e41e7/train_labels.json - model_dir: models - name: 96e2cedb80a2bc4614495bd06d3e41e7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 320502698241383a5549ac7f5652c13f - trainer: - kwargs: {} -name: 96e2cedb80a2bc4614495bd06d3e41e7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/96eebd73552f97c4d920153fb747ea06/params.yaml b/examples/security/classification/output/reports/train/96eebd73552f97c4d920153fb747ea06/params.yaml deleted file mode 100644 index 801d79e6..00000000 --- a/examples/security/classification/output/reports/train/96eebd73552f97c4d920153fb747ea06/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/adv_predictions.json - adv_probabilities_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/d68b96fdcabeb39d6529894f48a18862.pkl - params_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/params.yaml - predictions_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/predictions.json - probabilities_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/probabilities.json - score_dict_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/score_dict.json - test_labels_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/test_labels.json - train_labels_file: output/reports/train/96eebd73552f97c4d920153fb747ea06/train_labels.json - model_dir: models - name: 96eebd73552f97c4d920153fb747ea06 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c7cd3faca43db3dd2ccbcabe0653afe6 - trainer: - kwargs: {} -name: 96eebd73552f97c4d920153fb747ea06 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/96fa8f276ce55d865193469b63e71c25/params.yaml b/examples/security/classification/output/reports/train/96fa8f276ce55d865193469b63e71c25/params.yaml deleted file mode 100644 index 9a909cc9..00000000 --- a/examples/security/classification/output/reports/train/96fa8f276ce55d865193469b63e71c25/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/adv_predictions.json - adv_probabilities_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/c7243766f1d1ee8c011065b9183e1a42.pkl - params_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/params.yaml - predictions_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/predictions.json - probabilities_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/probabilities.json - score_dict_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/score_dict.json - test_labels_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/test_labels.json - train_labels_file: output/reports/train/96fa8f276ce55d865193469b63e71c25/train_labels.json - model_dir: models - name: 96fa8f276ce55d865193469b63e71c25 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b00c9355e6c4d8a7ea7ed64e3b3f3a13 - trainer: - kwargs: {} -name: 96fa8f276ce55d865193469b63e71c25 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9707a6d0e9d7911f7d5404b76229717e/params.yaml b/examples/security/classification/output/reports/train/9707a6d0e9d7911f7d5404b76229717e/params.yaml deleted file mode 100644 index 55aad2e8..00000000 --- a/examples/security/classification/output/reports/train/9707a6d0e9d7911f7d5404b76229717e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/adv_predictions.json - adv_probabilities_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/37a6c1a477202dc8693291648319adcf.pkl - params_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/params.yaml - predictions_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/predictions.json - probabilities_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/probabilities.json - score_dict_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/score_dict.json - test_labels_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/test_labels.json - train_labels_file: output/reports/train/9707a6d0e9d7911f7d5404b76229717e/train_labels.json - model_dir: models - name: 9707a6d0e9d7911f7d5404b76229717e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0947520ea7338aa5279692e3b059dc54 - trainer: - kwargs: {} -name: 9707a6d0e9d7911f7d5404b76229717e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/params.yaml b/examples/security/classification/output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/params.yaml deleted file mode 100644 index d5f147cf..00000000 --- a/examples/security/classification/output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/adv_predictions.json - adv_probabilities_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/e163c13078176ad887b3dc6eb689e2c5.pkl - params_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/params.yaml - predictions_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/predictions.json - probabilities_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/probabilities.json - score_dict_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/score_dict.json - test_labels_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/test_labels.json - train_labels_file: output/reports/train/97821d1fd3ca494db39aca78fc0ddefd/train_labels.json - model_dir: models - name: 97821d1fd3ca494db39aca78fc0ddefd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4e11bd59c8437e4b91f69d919aa6100d - trainer: - kwargs: {} -name: 97821d1fd3ca494db39aca78fc0ddefd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/params.yaml b/examples/security/classification/output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/params.yaml deleted file mode 100644 index ff4be689..00000000 --- a/examples/security/classification/output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/adv_predictions.json - adv_probabilities_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/e47d66a49988946cb21d63767d0594f0.pkl - params_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/params.yaml - predictions_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/predictions.json - probabilities_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/probabilities.json - score_dict_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/score_dict.json - test_labels_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/test_labels.json - train_labels_file: output/reports/train/97a84fe8529e6e8130ec441ce3b4f5e1/train_labels.json - model_dir: models - name: 97a84fe8529e6e8130ec441ce3b4f5e1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 44c4d1b60fda07f99adb75130aaee593 - trainer: - kwargs: {} -name: 97a84fe8529e6e8130ec441ce3b4f5e1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/97b470c188fe9048fa883ba02f1662fa/params.yaml b/examples/security/classification/output/reports/train/97b470c188fe9048fa883ba02f1662fa/params.yaml deleted file mode 100644 index af62d75c..00000000 --- a/examples/security/classification/output/reports/train/97b470c188fe9048fa883ba02f1662fa/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/adv_predictions.json - adv_probabilities_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/787421d4c0374649aee132438852a6d7.pkl - params_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/params.yaml - predictions_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/predictions.json - probabilities_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/probabilities.json - score_dict_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/score_dict.json - test_labels_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/test_labels.json - train_labels_file: output/reports/train/97b470c188fe9048fa883ba02f1662fa/train_labels.json - model_dir: models - name: 97b470c188fe9048fa883ba02f1662fa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 288589c63753df094fe9f8899cfc1f6b - trainer: - kwargs: {} -name: 97b470c188fe9048fa883ba02f1662fa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/97ba47162a3bef38fdbebf02cead31cd/params.yaml b/examples/security/classification/output/reports/train/97ba47162a3bef38fdbebf02cead31cd/params.yaml deleted file mode 100644 index 34ca96d1..00000000 --- a/examples/security/classification/output/reports/train/97ba47162a3bef38fdbebf02cead31cd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/adv_predictions.json - adv_probabilities_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/c8362e4385c7e74b9873f1125879c7fc.pkl - params_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/params.yaml - predictions_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/predictions.json - probabilities_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/probabilities.json - score_dict_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/score_dict.json - test_labels_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/test_labels.json - train_labels_file: output/reports/train/97ba47162a3bef38fdbebf02cead31cd/train_labels.json - model_dir: models - name: 97ba47162a3bef38fdbebf02cead31cd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5d7302b02eaca8bae4d51ba04c977005 - trainer: - kwargs: {} -name: 97ba47162a3bef38fdbebf02cead31cd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/params.yaml b/examples/security/classification/output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/params.yaml deleted file mode 100644 index ffe625fc..00000000 --- a/examples/security/classification/output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/adv_predictions.json - adv_probabilities_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/8216e0d8e5585972e636a00aee14f8bb.pkl - params_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/params.yaml - predictions_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/predictions.json - probabilities_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/probabilities.json - score_dict_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/score_dict.json - test_labels_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/test_labels.json - train_labels_file: output/reports/train/97d8471de1ce0cb9dd9fa5e09a98a16b/train_labels.json - model_dir: models - name: 97d8471de1ce0cb9dd9fa5e09a98a16b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d1c51986d424aeafac1fa34ea51415bd - trainer: - kwargs: {} -name: 97d8471de1ce0cb9dd9fa5e09a98a16b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/97da87a7e603985bb99537e4a018fbec/params.yaml b/examples/security/classification/output/reports/train/97da87a7e603985bb99537e4a018fbec/params.yaml deleted file mode 100644 index 99caeeff..00000000 --- a/examples/security/classification/output/reports/train/97da87a7e603985bb99537e4a018fbec/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/adv_predictions.json - adv_probabilities_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/d07336a75f7597f0485320d37e56f8c9.pkl - params_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/params.yaml - predictions_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/predictions.json - probabilities_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/probabilities.json - score_dict_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/score_dict.json - test_labels_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/test_labels.json - train_labels_file: output/reports/train/97da87a7e603985bb99537e4a018fbec/train_labels.json - model_dir: models - name: 97da87a7e603985bb99537e4a018fbec - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7a73d79c2b7a276cf47880b3869ed2ae - trainer: - kwargs: {} -name: 97da87a7e603985bb99537e4a018fbec -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/97f70738e3506072d35b557daaab6dd3/params.yaml b/examples/security/classification/output/reports/train/97f70738e3506072d35b557daaab6dd3/params.yaml deleted file mode 100644 index d4f874bc..00000000 --- a/examples/security/classification/output/reports/train/97f70738e3506072d35b557daaab6dd3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/adv_predictions.json - adv_probabilities_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/1def6ef35760885c2dc6c9369ebc936d.pkl - params_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/params.yaml - predictions_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/predictions.json - probabilities_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/probabilities.json - score_dict_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/score_dict.json - test_labels_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/test_labels.json - train_labels_file: output/reports/train/97f70738e3506072d35b557daaab6dd3/train_labels.json - model_dir: models - name: 97f70738e3506072d35b557daaab6dd3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 170289bf874d2b1541e76380cc15e37b - trainer: - kwargs: {} -name: 97f70738e3506072d35b557daaab6dd3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/params.yaml b/examples/security/classification/output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/params.yaml deleted file mode 100644 index 02effbc9..00000000 --- a/examples/security/classification/output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/adv_predictions.json - adv_probabilities_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/6a403281598e3bafc0f8016eaa66777e.pkl - params_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/params.yaml - predictions_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/predictions.json - probabilities_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/probabilities.json - score_dict_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/score_dict.json - test_labels_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/test_labels.json - train_labels_file: output/reports/train/987ddbb8664d3d1a33cb143b9ab0194d/train_labels.json - model_dir: models - name: 987ddbb8664d3d1a33cb143b9ab0194d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5d3fcde783e90ddacd45a4b2320673bc - trainer: - kwargs: {} -name: 987ddbb8664d3d1a33cb143b9ab0194d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/988758a7fcb530b1aee478c5cd622cf0/params.yaml b/examples/security/classification/output/reports/train/988758a7fcb530b1aee478c5cd622cf0/params.yaml deleted file mode 100644 index b1f90be4..00000000 --- a/examples/security/classification/output/reports/train/988758a7fcb530b1aee478c5cd622cf0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/adv_predictions.json - adv_probabilities_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/7b0958d0cfc8e52364ce14ba30736c61.pkl - params_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/params.yaml - predictions_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/predictions.json - probabilities_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/probabilities.json - score_dict_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/score_dict.json - test_labels_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/test_labels.json - train_labels_file: output/reports/train/988758a7fcb530b1aee478c5cd622cf0/train_labels.json - model_dir: models - name: 988758a7fcb530b1aee478c5cd622cf0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7a25480b094e7a47f41410be541bae97 - trainer: - kwargs: {} -name: 988758a7fcb530b1aee478c5cd622cf0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/98b14467c2442f11634390bb39c493ad/params.yaml b/examples/security/classification/output/reports/train/98b14467c2442f11634390bb39c493ad/params.yaml deleted file mode 100644 index b87db22f..00000000 --- a/examples/security/classification/output/reports/train/98b14467c2442f11634390bb39c493ad/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/98b14467c2442f11634390bb39c493ad/adv_predictions.json - adv_probabilities_file: output/reports/train/98b14467c2442f11634390bb39c493ad/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/14d5799e3130d455574d877dd0e0b40d.pkl - params_file: output/reports/train/98b14467c2442f11634390bb39c493ad/params.yaml - predictions_file: output/reports/train/98b14467c2442f11634390bb39c493ad/predictions.json - probabilities_file: output/reports/train/98b14467c2442f11634390bb39c493ad/probabilities.json - score_dict_file: output/reports/train/98b14467c2442f11634390bb39c493ad/score_dict.json - test_labels_file: output/reports/train/98b14467c2442f11634390bb39c493ad/test_labels.json - train_labels_file: output/reports/train/98b14467c2442f11634390bb39c493ad/train_labels.json - model_dir: models - name: 98b14467c2442f11634390bb39c493ad - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1ff429983bc93f34cf04858bcd712650 - trainer: - kwargs: {} -name: 98b14467c2442f11634390bb39c493ad -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/params.yaml b/examples/security/classification/output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/params.yaml deleted file mode 100644 index 27194568..00000000 --- a/examples/security/classification/output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/adv_predictions.json - adv_probabilities_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e9189e1c48ae5c282a956478d85dcb82.pkl - params_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/params.yaml - predictions_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/predictions.json - probabilities_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/probabilities.json - score_dict_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/score_dict.json - test_labels_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/test_labels.json - train_labels_file: output/reports/train/98d53f8d8837eae269e4cdd3df09ce77/train_labels.json - model_dir: models - name: 98d53f8d8837eae269e4cdd3df09ce77 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2fe49ca3fbaefbe63ee989d3067642ea - trainer: - kwargs: {} -name: 98d53f8d8837eae269e4cdd3df09ce77 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/98dca71229abf6d9914c405064085a2c/params.yaml b/examples/security/classification/output/reports/train/98dca71229abf6d9914c405064085a2c/params.yaml deleted file mode 100644 index de7a0612..00000000 --- a/examples/security/classification/output/reports/train/98dca71229abf6d9914c405064085a2c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/98dca71229abf6d9914c405064085a2c/adv_predictions.json - adv_probabilities_file: output/reports/train/98dca71229abf6d9914c405064085a2c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/7c091d4d0db1d500193db54fdbb8d2d5.pkl - params_file: output/reports/train/98dca71229abf6d9914c405064085a2c/params.yaml - predictions_file: output/reports/train/98dca71229abf6d9914c405064085a2c/predictions.json - probabilities_file: output/reports/train/98dca71229abf6d9914c405064085a2c/probabilities.json - score_dict_file: output/reports/train/98dca71229abf6d9914c405064085a2c/score_dict.json - test_labels_file: output/reports/train/98dca71229abf6d9914c405064085a2c/test_labels.json - train_labels_file: output/reports/train/98dca71229abf6d9914c405064085a2c/train_labels.json - model_dir: models - name: 98dca71229abf6d9914c405064085a2c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dc0f742bc8822b0a61ff7216fc540163 - trainer: - kwargs: {} -name: 98dca71229abf6d9914c405064085a2c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/98f7a4da41c51e63aaeea15480a69f91/params.yaml b/examples/security/classification/output/reports/train/98f7a4da41c51e63aaeea15480a69f91/params.yaml deleted file mode 100644 index 38f88e7b..00000000 --- a/examples/security/classification/output/reports/train/98f7a4da41c51e63aaeea15480a69f91/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/adv_predictions.json - adv_probabilities_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/40cc391464ce9bc3d04be0e87982be51.pkl - params_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/params.yaml - predictions_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/predictions.json - probabilities_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/probabilities.json - score_dict_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/score_dict.json - test_labels_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/test_labels.json - train_labels_file: output/reports/train/98f7a4da41c51e63aaeea15480a69f91/train_labels.json - model_dir: models - name: 98f7a4da41c51e63aaeea15480a69f91 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 567a12c1c9efd27c8a92abc770bc3cc8 - trainer: - kwargs: {} -name: 98f7a4da41c51e63aaeea15480a69f91 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/params.yaml b/examples/security/classification/output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/params.yaml deleted file mode 100644 index e6df8eb4..00000000 --- a/examples/security/classification/output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/adv_predictions.json - adv_probabilities_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/51f7249d0fac2e483f1df2ef64cddb6d.pkl - params_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/params.yaml - predictions_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/predictions.json - probabilities_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/probabilities.json - score_dict_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/score_dict.json - test_labels_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/test_labels.json - train_labels_file: output/reports/train/98ffa61d8c97dece8a221c3e387e04c8/train_labels.json - model_dir: models - name: 98ffa61d8c97dece8a221c3e387e04c8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 004fd22a6886c642872e258c948f67af - trainer: - kwargs: {} -name: 98ffa61d8c97dece8a221c3e387e04c8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/991138fb41884473a9925b2d6eb3c7fc/params.yaml b/examples/security/classification/output/reports/train/991138fb41884473a9925b2d6eb3c7fc/params.yaml deleted file mode 100644 index bbadec23..00000000 --- a/examples/security/classification/output/reports/train/991138fb41884473a9925b2d6eb3c7fc/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/adv_predictions.json - adv_probabilities_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/420192ee99b19fcf973747f969b7baa0.pkl - params_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/params.yaml - predictions_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/predictions.json - probabilities_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/probabilities.json - score_dict_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/score_dict.json - test_labels_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/test_labels.json - train_labels_file: output/reports/train/991138fb41884473a9925b2d6eb3c7fc/train_labels.json - model_dir: models - name: 991138fb41884473a9925b2d6eb3c7fc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 37e49563c31adc0eecce712131c06049 - trainer: - kwargs: {} -name: 991138fb41884473a9925b2d6eb3c7fc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9911d26b0a446a35e64d097bd2ae455a/params.yaml b/examples/security/classification/output/reports/train/9911d26b0a446a35e64d097bd2ae455a/params.yaml deleted file mode 100644 index 95047f17..00000000 --- a/examples/security/classification/output/reports/train/9911d26b0a446a35e64d097bd2ae455a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/adv_predictions.json - adv_probabilities_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/3e2d90c9e95411617294c7ab167d5193.pkl - params_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/params.yaml - predictions_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/predictions.json - probabilities_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/probabilities.json - score_dict_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/score_dict.json - test_labels_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/test_labels.json - train_labels_file: output/reports/train/9911d26b0a446a35e64d097bd2ae455a/train_labels.json - model_dir: models - name: 9911d26b0a446a35e64d097bd2ae455a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 88d761d2f32082d6807323a4c18cd2ee - trainer: - kwargs: {} -name: 9911d26b0a446a35e64d097bd2ae455a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9929d1ee5a304f555d5a5d877df72724/params.yaml b/examples/security/classification/output/reports/train/9929d1ee5a304f555d5a5d877df72724/params.yaml deleted file mode 100644 index 5e278dc9..00000000 --- a/examples/security/classification/output/reports/train/9929d1ee5a304f555d5a5d877df72724/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/adv_predictions.json - adv_probabilities_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/0c90694cf5338093ff976550b405fb1f.pkl - params_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/params.yaml - predictions_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/predictions.json - probabilities_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/probabilities.json - score_dict_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/score_dict.json - test_labels_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/test_labels.json - train_labels_file: output/reports/train/9929d1ee5a304f555d5a5d877df72724/train_labels.json - model_dir: models - name: 9929d1ee5a304f555d5a5d877df72724 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f819ae0d1e6bd2c36c9f18c2d7e54564 - trainer: - kwargs: {} -name: 9929d1ee5a304f555d5a5d877df72724 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/994cbc972114e7041e27a6377b2a293a/params.yaml b/examples/security/classification/output/reports/train/994cbc972114e7041e27a6377b2a293a/params.yaml deleted file mode 100644 index 54e204b1..00000000 --- a/examples/security/classification/output/reports/train/994cbc972114e7041e27a6377b2a293a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/adv_predictions.json - adv_probabilities_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/25ed70f34dae92bc2a3c8c35e87dcf17.pkl - params_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/params.yaml - predictions_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/predictions.json - probabilities_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/probabilities.json - score_dict_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/score_dict.json - test_labels_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/test_labels.json - train_labels_file: output/reports/train/994cbc972114e7041e27a6377b2a293a/train_labels.json - model_dir: models - name: 994cbc972114e7041e27a6377b2a293a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 21ad0c80b8bd1ea1e9154346ee1a6634 - trainer: - kwargs: {} -name: 994cbc972114e7041e27a6377b2a293a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/995d4945cd20e40ab7c14d05439d0597/params.yaml b/examples/security/classification/output/reports/train/995d4945cd20e40ab7c14d05439d0597/params.yaml deleted file mode 100644 index db4d3488..00000000 --- a/examples/security/classification/output/reports/train/995d4945cd20e40ab7c14d05439d0597/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/adv_predictions.json - adv_probabilities_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/06a055e17840dc970d464fe22dc2e4ad.pkl - params_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/params.yaml - predictions_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/predictions.json - probabilities_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/probabilities.json - score_dict_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/score_dict.json - test_labels_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/test_labels.json - train_labels_file: output/reports/train/995d4945cd20e40ab7c14d05439d0597/train_labels.json - model_dir: models - name: 995d4945cd20e40ab7c14d05439d0597 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ac0df191c7b3f6c2ce28e28ccadd70fe - trainer: - kwargs: {} -name: 995d4945cd20e40ab7c14d05439d0597 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/params.yaml b/examples/security/classification/output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/params.yaml deleted file mode 100644 index ae9678bc..00000000 --- a/examples/security/classification/output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/adv_predictions.json - adv_probabilities_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/53c565df3037862325413431bf529580.pkl - params_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/params.yaml - predictions_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/predictions.json - probabilities_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/probabilities.json - score_dict_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/score_dict.json - test_labels_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/test_labels.json - train_labels_file: output/reports/train/9975dd952460c9e6ed213e16fbfa0bf8/train_labels.json - model_dir: models - name: 9975dd952460c9e6ed213e16fbfa0bf8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4c9052894a3ffc213b3a852342981e16 - trainer: - kwargs: {} -name: 9975dd952460c9e6ed213e16fbfa0bf8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/99859d802f75f6e5ed27b172287e030d/params.yaml b/examples/security/classification/output/reports/train/99859d802f75f6e5ed27b172287e030d/params.yaml deleted file mode 100644 index ce309e31..00000000 --- a/examples/security/classification/output/reports/train/99859d802f75f6e5ed27b172287e030d/params.yaml +++ /dev/null @@ -1,211 +0,0 @@ -attack: - attack_size: 10 - data: - generate: - kwargs: - target: label - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: ad395d4f67bd301a0eaa8ea0e3d6240d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: - target: label - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: ad395d4f67bd301a0eaa8ea0e3d6240d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 10000.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d4aa8ac0a3a533383a96f260f3fe8351 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - target: label - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: ad395d4f67bd301a0eaa8ea0e3d6240d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5a02bbcdb25527687d0cb6ab1895391e - trainer: - kwargs: {} - name: 17481bcdfcbe588173d11745dd7a8457 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - target: label - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: fc4f9d93882b1b65c2cfc17d3d14b2e6 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/adv_predictions.json - adv_probabilities_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/adv_probabilities.json - attack_file: output/attacks/88a2d5998fb35f0f56b42c0bf39f9a4a.pkl - data_file: output/data/f6b10a7e3f0a1b0db33d73e886f4297c.pkl - model_file: output/models/9564a9eb9babd5fd1c93f1dc2fa40183.pkl - params_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/params.yaml - predictions_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/predictions.json - probabilities_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/probabilities.json - score_dict_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/score_dict.json - test_labels_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/test_labels.json - train_labels_file: output/reports/train/99859d802f75f6e5ed27b172287e030d/train_labels.json - model_dir: models - name: 99859d802f75f6e5ed27b172287e030d - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - target: label - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: fc4f9d93882b1b65c2cfc17d3d14b2e6 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1839e3f58ae8e974ba78387cb3aa4d45 - trainer: - kwargs: {} -name: 99859d802f75f6e5ed27b172287e030d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/99b147d7df6a5fbf6908893231042623/params.yaml b/examples/security/classification/output/reports/train/99b147d7df6a5fbf6908893231042623/params.yaml deleted file mode 100644 index 7b2d930b..00000000 --- a/examples/security/classification/output/reports/train/99b147d7df6a5fbf6908893231042623/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/99b147d7df6a5fbf6908893231042623/adv_predictions.json - adv_probabilities_file: output/reports/train/99b147d7df6a5fbf6908893231042623/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/4afadb0088a86587be2a729200d4ccf1.pkl - params_file: output/reports/train/99b147d7df6a5fbf6908893231042623/params.yaml - predictions_file: output/reports/train/99b147d7df6a5fbf6908893231042623/predictions.json - probabilities_file: output/reports/train/99b147d7df6a5fbf6908893231042623/probabilities.json - score_dict_file: output/reports/train/99b147d7df6a5fbf6908893231042623/score_dict.json - test_labels_file: output/reports/train/99b147d7df6a5fbf6908893231042623/test_labels.json - train_labels_file: output/reports/train/99b147d7df6a5fbf6908893231042623/train_labels.json - model_dir: models - name: 99b147d7df6a5fbf6908893231042623 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d0f7dc8c68e60e241486f291a2b16d34 - trainer: - kwargs: {} -name: 99b147d7df6a5fbf6908893231042623 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/params.yaml b/examples/security/classification/output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/params.yaml deleted file mode 100644 index 9439fa22..00000000 --- a/examples/security/classification/output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/adv_predictions.json - adv_probabilities_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/eccfe44331efa3bef90c3e92cb313390.pkl - params_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/params.yaml - predictions_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/predictions.json - probabilities_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/probabilities.json - score_dict_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/score_dict.json - test_labels_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/test_labels.json - train_labels_file: output/reports/train/99f4cacc101b36f602914c51bf2fcfe3/train_labels.json - model_dir: models - name: 99f4cacc101b36f602914c51bf2fcfe3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 46fb60194154d635c8e265c05b2268e2 - trainer: - kwargs: {} -name: 99f4cacc101b36f602914c51bf2fcfe3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/params.yaml b/examples/security/classification/output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/params.yaml deleted file mode 100644 index d4dd6506..00000000 --- a/examples/security/classification/output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/adv_predictions.json - adv_probabilities_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/ed155707b49a8d7aacc97c97512b2f5e.pkl - params_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/params.yaml - predictions_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/predictions.json - probabilities_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/probabilities.json - score_dict_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/score_dict.json - test_labels_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/test_labels.json - train_labels_file: output/reports/train/9a1e0a32ee438b3000e4a2b88b71629c/train_labels.json - model_dir: models - name: 9a1e0a32ee438b3000e4a2b88b71629c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a7635f226cdf455b0d55460293ba1c44 - trainer: - kwargs: {} -name: 9a1e0a32ee438b3000e4a2b88b71629c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/params.yaml b/examples/security/classification/output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/params.yaml deleted file mode 100644 index 4049103c..00000000 --- a/examples/security/classification/output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/adv_predictions.json - adv_probabilities_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/83357bd38f357cca6adaab4e1ce1f112.pkl - params_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/params.yaml - predictions_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/predictions.json - probabilities_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/probabilities.json - score_dict_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/score_dict.json - test_labels_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/test_labels.json - train_labels_file: output/reports/train/9a2a97f40f0ffb84adae7072337eb09d/train_labels.json - model_dir: models - name: 9a2a97f40f0ffb84adae7072337eb09d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0c76e203007c8af55d22a5c009ef407b - trainer: - kwargs: {} -name: 9a2a97f40f0ffb84adae7072337eb09d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/params.yaml b/examples/security/classification/output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/params.yaml deleted file mode 100644 index c4dca2e9..00000000 --- a/examples/security/classification/output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/adv_predictions.json - adv_probabilities_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/3dc1f0c7487bf86bec2766ad74014684.pkl - params_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/params.yaml - predictions_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/predictions.json - probabilities_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/probabilities.json - score_dict_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/score_dict.json - test_labels_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/test_labels.json - train_labels_file: output/reports/train/9a2ddf3a5ddd7e37b42f5d6ba58b11a1/train_labels.json - model_dir: models - name: 9a2ddf3a5ddd7e37b42f5d6ba58b11a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9d9f2d280a09205de9a5cc671cc4386b - trainer: - kwargs: {} -name: 9a2ddf3a5ddd7e37b42f5d6ba58b11a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9a32614a4863bba51feb12173341dcdb/params.yaml b/examples/security/classification/output/reports/train/9a32614a4863bba51feb12173341dcdb/params.yaml deleted file mode 100644 index 6a70f213..00000000 --- a/examples/security/classification/output/reports/train/9a32614a4863bba51feb12173341dcdb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/adv_predictions.json - adv_probabilities_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/1ff150ccc43429be8dd17ac9b790ab93.pkl - params_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/params.yaml - predictions_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/predictions.json - probabilities_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/probabilities.json - score_dict_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/score_dict.json - test_labels_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/test_labels.json - train_labels_file: output/reports/train/9a32614a4863bba51feb12173341dcdb/train_labels.json - model_dir: models - name: 9a32614a4863bba51feb12173341dcdb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a5ec47f52e60bec7f725ec8dcd7f4f67 - trainer: - kwargs: {} -name: 9a32614a4863bba51feb12173341dcdb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9a326369abceb3c9649aeab1c2108d26/params.yaml b/examples/security/classification/output/reports/train/9a326369abceb3c9649aeab1c2108d26/params.yaml deleted file mode 100644 index f9b19309..00000000 --- a/examples/security/classification/output/reports/train/9a326369abceb3c9649aeab1c2108d26/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/adv_predictions.json - adv_probabilities_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/085fb9a2cffdfe461fc0d7cd3eeb7f61.pkl - params_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/params.yaml - predictions_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/predictions.json - probabilities_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/probabilities.json - score_dict_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/score_dict.json - test_labels_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/test_labels.json - train_labels_file: output/reports/train/9a326369abceb3c9649aeab1c2108d26/train_labels.json - model_dir: models - name: 9a326369abceb3c9649aeab1c2108d26 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 844b2efbbb268c5c27c700136108feed - trainer: - kwargs: {} -name: 9a326369abceb3c9649aeab1c2108d26 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/params.yaml b/examples/security/classification/output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/params.yaml deleted file mode 100644 index 3eb5cd0c..00000000 --- a/examples/security/classification/output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/adv_predictions.json - adv_probabilities_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/11403891de7d10821e356d2117729913.pkl - params_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/params.yaml - predictions_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/predictions.json - probabilities_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/probabilities.json - score_dict_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/score_dict.json - test_labels_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/test_labels.json - train_labels_file: output/reports/train/9a367b6ab5526a5c5c39a1c552386bc3/train_labels.json - model_dir: models - name: 9a367b6ab5526a5c5c39a1c552386bc3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9de70e37f396cac9918abce379e1e7ab - trainer: - kwargs: {} -name: 9a367b6ab5526a5c5c39a1c552386bc3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/params.yaml b/examples/security/classification/output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/params.yaml deleted file mode 100644 index 741438b8..00000000 --- a/examples/security/classification/output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/adv_predictions.json - adv_probabilities_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/dcf11ed41c0c782ff4081d05ae50613e.pkl - params_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/params.yaml - predictions_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/predictions.json - probabilities_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/probabilities.json - score_dict_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/score_dict.json - test_labels_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/test_labels.json - train_labels_file: output/reports/train/9a84b3c7a1f01b393fec7bceffe25a1b/train_labels.json - model_dir: models - name: 9a84b3c7a1f01b393fec7bceffe25a1b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4e342be0b89fc5875f4e18590536877e - trainer: - kwargs: {} -name: 9a84b3c7a1f01b393fec7bceffe25a1b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/params.yaml b/examples/security/classification/output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/params.yaml deleted file mode 100644 index 7007e140..00000000 --- a/examples/security/classification/output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/adv_predictions.json - adv_probabilities_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/639a46dd3e5f4ea564bf715f2d59522e.pkl - params_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/params.yaml - predictions_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/predictions.json - probabilities_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/probabilities.json - score_dict_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/score_dict.json - test_labels_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/test_labels.json - train_labels_file: output/reports/train/9aa48053ea6a4c393bbef81fdbe0dc7b/train_labels.json - model_dir: models - name: 9aa48053ea6a4c393bbef81fdbe0dc7b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8e1d8c536f528ad03c794e2672e750c9 - trainer: - kwargs: {} -name: 9aa48053ea6a4c393bbef81fdbe0dc7b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9ac67edf8850addd3cc50b645eb4a027/params.yaml b/examples/security/classification/output/reports/train/9ac67edf8850addd3cc50b645eb4a027/params.yaml deleted file mode 100644 index 236a7b7d..00000000 --- a/examples/security/classification/output/reports/train/9ac67edf8850addd3cc50b645eb4a027/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/adv_predictions.json - adv_probabilities_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/e2506240e8a1449a9518c72bce239313.pkl - params_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/params.yaml - predictions_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/predictions.json - probabilities_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/probabilities.json - score_dict_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/score_dict.json - test_labels_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/test_labels.json - train_labels_file: output/reports/train/9ac67edf8850addd3cc50b645eb4a027/train_labels.json - model_dir: models - name: 9ac67edf8850addd3cc50b645eb4a027 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6e749ea0d2da6dee4a2c217acc533891 - trainer: - kwargs: {} -name: 9ac67edf8850addd3cc50b645eb4a027 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/params.yaml b/examples/security/classification/output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/params.yaml deleted file mode 100644 index 6fab22e9..00000000 --- a/examples/security/classification/output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/adv_predictions.json - adv_probabilities_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/6142a5364938d0bc5c4d011048e325d7.pkl - params_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/params.yaml - predictions_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/predictions.json - probabilities_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/probabilities.json - score_dict_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/score_dict.json - test_labels_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/test_labels.json - train_labels_file: output/reports/train/9aced515cf80c6b9f3b075dc0e49a2a6/train_labels.json - model_dir: models - name: 9aced515cf80c6b9f3b075dc0e49a2a6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 90459d17e836ce3e349ba307c466b355 - trainer: - kwargs: {} -name: 9aced515cf80c6b9f3b075dc0e49a2a6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9b43e561d8a06a41880caac37b238cc7/params.yaml b/examples/security/classification/output/reports/train/9b43e561d8a06a41880caac37b238cc7/params.yaml deleted file mode 100644 index 8a030b06..00000000 --- a/examples/security/classification/output/reports/train/9b43e561d8a06a41880caac37b238cc7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/adv_predictions.json - adv_probabilities_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/a527fab58ad1a573434d2a512342987c.pkl - params_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/params.yaml - predictions_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/predictions.json - probabilities_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/probabilities.json - score_dict_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/score_dict.json - test_labels_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/test_labels.json - train_labels_file: output/reports/train/9b43e561d8a06a41880caac37b238cc7/train_labels.json - model_dir: models - name: 9b43e561d8a06a41880caac37b238cc7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a21c7010abf6e59068fec27a1e9621fc - trainer: - kwargs: {} -name: 9b43e561d8a06a41880caac37b238cc7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9b6194044f68e270ffe5eedab9c16e01/params.yaml b/examples/security/classification/output/reports/train/9b6194044f68e270ffe5eedab9c16e01/params.yaml deleted file mode 100644 index 13af756b..00000000 --- a/examples/security/classification/output/reports/train/9b6194044f68e270ffe5eedab9c16e01/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/adv_predictions.json - adv_probabilities_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/da05c4d2d72502304a51c1a5364b64b6.pkl - params_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/params.yaml - predictions_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/predictions.json - probabilities_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/probabilities.json - score_dict_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/score_dict.json - test_labels_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/test_labels.json - train_labels_file: output/reports/train/9b6194044f68e270ffe5eedab9c16e01/train_labels.json - model_dir: models - name: 9b6194044f68e270ffe5eedab9c16e01 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7c5b6b7f84b14e931d37bb39a1b94278 - trainer: - kwargs: {} -name: 9b6194044f68e270ffe5eedab9c16e01 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9b975b9de8160b665b659ec38c6f0323/params.yaml b/examples/security/classification/output/reports/train/9b975b9de8160b665b659ec38c6f0323/params.yaml deleted file mode 100644 index 076db4cb..00000000 --- a/examples/security/classification/output/reports/train/9b975b9de8160b665b659ec38c6f0323/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/adv_predictions.json - adv_probabilities_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/01c81d8c55822a07dbd6eff24f31fbf9.pkl - params_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/params.yaml - predictions_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/predictions.json - probabilities_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/probabilities.json - score_dict_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/score_dict.json - test_labels_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/test_labels.json - train_labels_file: output/reports/train/9b975b9de8160b665b659ec38c6f0323/train_labels.json - model_dir: models - name: 9b975b9de8160b665b659ec38c6f0323 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 77f1efab7230fc9bc59ffc2a58008e46 - trainer: - kwargs: {} -name: 9b975b9de8160b665b659ec38c6f0323 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/params.yaml b/examples/security/classification/output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/params.yaml deleted file mode 100644 index 736a9003..00000000 --- a/examples/security/classification/output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/adv_predictions.json - adv_probabilities_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/f819a48a741e1c87b52a1c82d1182371.pkl - params_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/params.yaml - predictions_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/predictions.json - probabilities_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/probabilities.json - score_dict_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/score_dict.json - test_labels_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/test_labels.json - train_labels_file: output/reports/train/9baf0c43d93fc60bc9433b7b6ca119af/train_labels.json - model_dir: models - name: 9baf0c43d93fc60bc9433b7b6ca119af - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ca0a1f53426e616a3eb27717c417f23c - trainer: - kwargs: {} -name: 9baf0c43d93fc60bc9433b7b6ca119af -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9c1c9021c244634c95149349b13e9dd7/params.yaml b/examples/security/classification/output/reports/train/9c1c9021c244634c95149349b13e9dd7/params.yaml deleted file mode 100644 index ac2300be..00000000 --- a/examples/security/classification/output/reports/train/9c1c9021c244634c95149349b13e9dd7/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/adv_predictions.json - adv_probabilities_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/d32e36039e3eaddb49ed5a6bac26e7bf.pkl - params_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/params.yaml - predictions_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/predictions.json - probabilities_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/probabilities.json - score_dict_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/score_dict.json - test_labels_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/test_labels.json - train_labels_file: output/reports/train/9c1c9021c244634c95149349b13e9dd7/train_labels.json - model_dir: models - name: 9c1c9021c244634c95149349b13e9dd7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d899a138013fad7c3707e9a73fcef440 - trainer: - kwargs: {} -name: 9c1c9021c244634c95149349b13e9dd7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/params.yaml b/examples/security/classification/output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/params.yaml deleted file mode 100644 index c74f75dd..00000000 --- a/examples/security/classification/output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/adv_predictions.json - adv_probabilities_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/b3f0e4989392d3aed252983fbe052919.pkl - params_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/params.yaml - predictions_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/predictions.json - probabilities_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/probabilities.json - score_dict_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/score_dict.json - test_labels_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/test_labels.json - train_labels_file: output/reports/train/9c23df3ae9b5c9684dc83dc41e5849c9/train_labels.json - model_dir: models - name: 9c23df3ae9b5c9684dc83dc41e5849c9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dcc3acfef81ca497977470a3abd2644e - trainer: - kwargs: {} -name: 9c23df3ae9b5c9684dc83dc41e5849c9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/params.yaml b/examples/security/classification/output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/params.yaml deleted file mode 100644 index 8f3c40d2..00000000 --- a/examples/security/classification/output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/adv_predictions.json - adv_probabilities_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/de47b3cdf955b3e15c5810cdc53a6d18.pkl - params_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/params.yaml - predictions_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/predictions.json - probabilities_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/probabilities.json - score_dict_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/score_dict.json - test_labels_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/test_labels.json - train_labels_file: output/reports/train/9c37ae4dffd6e2cbe6d193243630ccb7/train_labels.json - model_dir: models - name: 9c37ae4dffd6e2cbe6d193243630ccb7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8edfccb4772d15cf4a025d657cba6a6a - trainer: - kwargs: {} -name: 9c37ae4dffd6e2cbe6d193243630ccb7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/params.yaml b/examples/security/classification/output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/params.yaml deleted file mode 100644 index 4bfd89b5..00000000 --- a/examples/security/classification/output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/adv_predictions.json - adv_probabilities_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/2073fb1e629b80e9ed0f6c21a15a8d99.pkl - params_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/params.yaml - predictions_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/predictions.json - probabilities_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/probabilities.json - score_dict_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/score_dict.json - test_labels_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/test_labels.json - train_labels_file: output/reports/train/9c3cfa7dedf51cdc8a14ad2e921795ff/train_labels.json - model_dir: models - name: 9c3cfa7dedf51cdc8a14ad2e921795ff - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0843735fc47f92c441eba2d951b405bc - trainer: - kwargs: {} -name: 9c3cfa7dedf51cdc8a14ad2e921795ff -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/params.yaml b/examples/security/classification/output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/params.yaml deleted file mode 100644 index f2385097..00000000 --- a/examples/security/classification/output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/adv_predictions.json - adv_probabilities_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/07e9e93aed04067c046a75861ce74192.pkl - params_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/params.yaml - predictions_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/predictions.json - probabilities_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/probabilities.json - score_dict_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/score_dict.json - test_labels_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/test_labels.json - train_labels_file: output/reports/train/9c3f94ba138b0d4ac17e87fa223225ec/train_labels.json - model_dir: models - name: 9c3f94ba138b0d4ac17e87fa223225ec - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c6e3b9b9602d6240fc7d4f98eb875280 - trainer: - kwargs: {} -name: 9c3f94ba138b0d4ac17e87fa223225ec -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9c49cb6a617bc9300ec9c0156e221867/params.yaml b/examples/security/classification/output/reports/train/9c49cb6a617bc9300ec9c0156e221867/params.yaml deleted file mode 100644 index adf63a9e..00000000 --- a/examples/security/classification/output/reports/train/9c49cb6a617bc9300ec9c0156e221867/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/adv_predictions.json - adv_probabilities_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/3e1a3a918cfa6caabe99e2c71f602ecc.pkl - params_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/params.yaml - predictions_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/predictions.json - probabilities_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/probabilities.json - score_dict_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/score_dict.json - test_labels_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/test_labels.json - train_labels_file: output/reports/train/9c49cb6a617bc9300ec9c0156e221867/train_labels.json - model_dir: models - name: 9c49cb6a617bc9300ec9c0156e221867 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 403d26680782e177402093576cd710d2 - trainer: - kwargs: {} -name: 9c49cb6a617bc9300ec9c0156e221867 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9c63778222cb2a86a11b1f7653bc4664/params.yaml b/examples/security/classification/output/reports/train/9c63778222cb2a86a11b1f7653bc4664/params.yaml deleted file mode 100644 index d120b2cc..00000000 --- a/examples/security/classification/output/reports/train/9c63778222cb2a86a11b1f7653bc4664/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/adv_predictions.json - adv_probabilities_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/3fa97e1023dd902e5888a03bdb086067.pkl - params_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/params.yaml - predictions_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/predictions.json - probabilities_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/probabilities.json - score_dict_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/score_dict.json - test_labels_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/test_labels.json - train_labels_file: output/reports/train/9c63778222cb2a86a11b1f7653bc4664/train_labels.json - model_dir: models - name: 9c63778222cb2a86a11b1f7653bc4664 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c5cd05ba41f8c4fb0c18f4b002628098 - trainer: - kwargs: {} -name: 9c63778222cb2a86a11b1f7653bc4664 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/params.yaml b/examples/security/classification/output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/params.yaml deleted file mode 100644 index 73cca965..00000000 --- a/examples/security/classification/output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/adv_predictions.json - adv_probabilities_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/d3bf9b9775197682f0a5b1e124f35819.pkl - params_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/params.yaml - predictions_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/predictions.json - probabilities_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/probabilities.json - score_dict_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/score_dict.json - test_labels_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/test_labels.json - train_labels_file: output/reports/train/9caa9ceba878f9a0ee762ea6ed4b72e4/train_labels.json - model_dir: models - name: 9caa9ceba878f9a0ee762ea6ed4b72e4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4275e5757a4be32149b6986aeac2e2aa - trainer: - kwargs: {} -name: 9caa9ceba878f9a0ee762ea6ed4b72e4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/params.yaml b/examples/security/classification/output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/params.yaml deleted file mode 100644 index 569c43ee..00000000 --- a/examples/security/classification/output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/adv_predictions.json - adv_probabilities_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/b52fb049f5d0e8fa8780dc6c3efd31cb.pkl - params_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/params.yaml - predictions_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/predictions.json - probabilities_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/probabilities.json - score_dict_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/score_dict.json - test_labels_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/test_labels.json - train_labels_file: output/reports/train/9cbd9bd78cb28ce186fc50bfb1c22bae/train_labels.json - model_dir: models - name: 9cbd9bd78cb28ce186fc50bfb1c22bae - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b50327731e54fcbe9f6ea473b6fbd3c8 - trainer: - kwargs: {} -name: 9cbd9bd78cb28ce186fc50bfb1c22bae -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9ccae745b589b789294b1ca060063878/params.yaml b/examples/security/classification/output/reports/train/9ccae745b589b789294b1ca060063878/params.yaml deleted file mode 100644 index 834e1ba8..00000000 --- a/examples/security/classification/output/reports/train/9ccae745b589b789294b1ca060063878/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9ccae745b589b789294b1ca060063878/adv_predictions.json - adv_probabilities_file: output/reports/train/9ccae745b589b789294b1ca060063878/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/238351ee4f115925b030d293b3632445.pkl - params_file: output/reports/train/9ccae745b589b789294b1ca060063878/params.yaml - predictions_file: output/reports/train/9ccae745b589b789294b1ca060063878/predictions.json - probabilities_file: output/reports/train/9ccae745b589b789294b1ca060063878/probabilities.json - score_dict_file: output/reports/train/9ccae745b589b789294b1ca060063878/score_dict.json - test_labels_file: output/reports/train/9ccae745b589b789294b1ca060063878/test_labels.json - train_labels_file: output/reports/train/9ccae745b589b789294b1ca060063878/train_labels.json - model_dir: models - name: 9ccae745b589b789294b1ca060063878 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 443dfdb0b7964ccf02f391fb51f98b1f - trainer: - kwargs: {} -name: 9ccae745b589b789294b1ca060063878 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/params.yaml b/examples/security/classification/output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/params.yaml deleted file mode 100644 index 2504a058..00000000 --- a/examples/security/classification/output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/adv_predictions.json - adv_probabilities_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/19f2f9ba9d762dc256ab40380609e39e.pkl - params_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/params.yaml - predictions_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/predictions.json - probabilities_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/probabilities.json - score_dict_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/score_dict.json - test_labels_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/test_labels.json - train_labels_file: output/reports/train/9ccfc5e835af2f6390bced4701b9d42e/train_labels.json - model_dir: models - name: 9ccfc5e835af2f6390bced4701b9d42e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4df01e903c6593c43c9f34f7339fe555 - trainer: - kwargs: {} -name: 9ccfc5e835af2f6390bced4701b9d42e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9ce7ab7e4118179be709e22579ba0607/params.yaml b/examples/security/classification/output/reports/train/9ce7ab7e4118179be709e22579ba0607/params.yaml deleted file mode 100644 index f53aa837..00000000 --- a/examples/security/classification/output/reports/train/9ce7ab7e4118179be709e22579ba0607/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/adv_predictions.json - adv_probabilities_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/4df77092eb1f501233e9a6dd3a2a24c1.pkl - params_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/params.yaml - predictions_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/predictions.json - probabilities_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/probabilities.json - score_dict_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/score_dict.json - test_labels_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/test_labels.json - train_labels_file: output/reports/train/9ce7ab7e4118179be709e22579ba0607/train_labels.json - model_dir: models - name: 9ce7ab7e4118179be709e22579ba0607 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 71ba1c0d64fac6b055a8865036b5ee14 - trainer: - kwargs: {} -name: 9ce7ab7e4118179be709e22579ba0607 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9cf61300da9878858004be857e1c11e3/params.yaml b/examples/security/classification/output/reports/train/9cf61300da9878858004be857e1c11e3/params.yaml deleted file mode 100644 index 821f528f..00000000 --- a/examples/security/classification/output/reports/train/9cf61300da9878858004be857e1c11e3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9cf61300da9878858004be857e1c11e3/adv_predictions.json - adv_probabilities_file: output/reports/train/9cf61300da9878858004be857e1c11e3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/d0810912df3a9bb086366d4034c75f27.pkl - params_file: output/reports/train/9cf61300da9878858004be857e1c11e3/params.yaml - predictions_file: output/reports/train/9cf61300da9878858004be857e1c11e3/predictions.json - probabilities_file: output/reports/train/9cf61300da9878858004be857e1c11e3/probabilities.json - score_dict_file: output/reports/train/9cf61300da9878858004be857e1c11e3/score_dict.json - test_labels_file: output/reports/train/9cf61300da9878858004be857e1c11e3/test_labels.json - train_labels_file: output/reports/train/9cf61300da9878858004be857e1c11e3/train_labels.json - model_dir: models - name: 9cf61300da9878858004be857e1c11e3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9eee77696a67b12ff0232550c5077009 - trainer: - kwargs: {} -name: 9cf61300da9878858004be857e1c11e3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/params.yaml b/examples/security/classification/output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/params.yaml deleted file mode 100644 index 17dd217f..00000000 --- a/examples/security/classification/output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/adv_predictions.json - adv_probabilities_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/64765dc9072b1da4ec6cab4fc723ae6c.pkl - params_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/params.yaml - predictions_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/predictions.json - probabilities_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/probabilities.json - score_dict_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/score_dict.json - test_labels_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/test_labels.json - train_labels_file: output/reports/train/9d1d23c6a94a8108d0133e7aac8b3c5d/train_labels.json - model_dir: models - name: 9d1d23c6a94a8108d0133e7aac8b3c5d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b519b7cc7c1dfaabc4452fc06aa3c5a6 - trainer: - kwargs: {} -name: 9d1d23c6a94a8108d0133e7aac8b3c5d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9d244f5d37e5234bde6012dfd910c507/params.yaml b/examples/security/classification/output/reports/train/9d244f5d37e5234bde6012dfd910c507/params.yaml deleted file mode 100644 index c714a8fd..00000000 --- a/examples/security/classification/output/reports/train/9d244f5d37e5234bde6012dfd910c507/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/adv_predictions.json - adv_probabilities_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/68340ee79786c9627dc8d5c6627f4185.pkl - params_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/params.yaml - predictions_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/predictions.json - probabilities_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/probabilities.json - score_dict_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/score_dict.json - test_labels_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/test_labels.json - train_labels_file: output/reports/train/9d244f5d37e5234bde6012dfd910c507/train_labels.json - model_dir: models - name: 9d244f5d37e5234bde6012dfd910c507 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 10ebdafaf0ce659ff408132f301f32c0 - trainer: - kwargs: {} -name: 9d244f5d37e5234bde6012dfd910c507 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/params.yaml b/examples/security/classification/output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/params.yaml deleted file mode 100644 index cc119920..00000000 --- a/examples/security/classification/output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/adv_predictions.json - adv_probabilities_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/755cc7f452e2a081a139a291be01f19e.pkl - params_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/params.yaml - predictions_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/predictions.json - probabilities_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/probabilities.json - score_dict_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/score_dict.json - test_labels_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/test_labels.json - train_labels_file: output/reports/train/9d4d1cd55dff13de76cd8aad7654326a/train_labels.json - model_dir: models - name: 9d4d1cd55dff13de76cd8aad7654326a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 43af58a792e88fed64b7141d0ff2613b - trainer: - kwargs: {} -name: 9d4d1cd55dff13de76cd8aad7654326a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9d747b760f721835fb44ad87e852bb90/params.yaml b/examples/security/classification/output/reports/train/9d747b760f721835fb44ad87e852bb90/params.yaml deleted file mode 100644 index 0368ba1d..00000000 --- a/examples/security/classification/output/reports/train/9d747b760f721835fb44ad87e852bb90/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/adv_predictions.json - adv_probabilities_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/88863e3541df1e8926accf8adfea98bb.pkl - params_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/params.yaml - predictions_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/predictions.json - probabilities_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/probabilities.json - score_dict_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/score_dict.json - test_labels_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/test_labels.json - train_labels_file: output/reports/train/9d747b760f721835fb44ad87e852bb90/train_labels.json - model_dir: models - name: 9d747b760f721835fb44ad87e852bb90 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 160d633b83cf68389d0cb0431f39a796 - trainer: - kwargs: {} -name: 9d747b760f721835fb44ad87e852bb90 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9d82827878a4e0d23d27fd522251aa4b/params.yaml b/examples/security/classification/output/reports/train/9d82827878a4e0d23d27fd522251aa4b/params.yaml deleted file mode 100644 index 3f366f2e..00000000 --- a/examples/security/classification/output/reports/train/9d82827878a4e0d23d27fd522251aa4b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/adv_predictions.json - adv_probabilities_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/bbf1e66ce1dfb02b69977386af102175.pkl - params_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/params.yaml - predictions_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/predictions.json - probabilities_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/probabilities.json - score_dict_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/score_dict.json - test_labels_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/test_labels.json - train_labels_file: output/reports/train/9d82827878a4e0d23d27fd522251aa4b/train_labels.json - model_dir: models - name: 9d82827878a4e0d23d27fd522251aa4b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4b8cd16e450707a72c4f83a476c85ca4 - trainer: - kwargs: {} -name: 9d82827878a4e0d23d27fd522251aa4b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/params.yaml b/examples/security/classification/output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/params.yaml deleted file mode 100644 index e0442471..00000000 --- a/examples/security/classification/output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/adv_predictions.json - adv_probabilities_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/7377c5cf90796f03def78e8c3d5fad18.pkl - params_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/params.yaml - predictions_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/predictions.json - probabilities_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/probabilities.json - score_dict_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/score_dict.json - test_labels_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/test_labels.json - train_labels_file: output/reports/train/9d9a5d6717de24d02b14ae9da9b4f1c4/train_labels.json - model_dir: models - name: 9d9a5d6717de24d02b14ae9da9b4f1c4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 185a9eab89bfde7ab0846dd1020fccb3 - trainer: - kwargs: {} -name: 9d9a5d6717de24d02b14ae9da9b4f1c4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/params.yaml b/examples/security/classification/output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/params.yaml deleted file mode 100644 index b54843b2..00000000 --- a/examples/security/classification/output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/adv_predictions.json - adv_probabilities_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/d9df668b5bba107e43fee2eee1866e54.pkl - params_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/params.yaml - predictions_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/predictions.json - probabilities_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/probabilities.json - score_dict_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/score_dict.json - test_labels_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/test_labels.json - train_labels_file: output/reports/train/9dbd90791e66d3645b4ba5b3525ed072/train_labels.json - model_dir: models - name: 9dbd90791e66d3645b4ba5b3525ed072 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ef047c8838f1cd94a32cd6341e9827ed - trainer: - kwargs: {} -name: 9dbd90791e66d3645b4ba5b3525ed072 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9decd6e2e664340695681c856074af17/params.yaml b/examples/security/classification/output/reports/train/9decd6e2e664340695681c856074af17/params.yaml deleted file mode 100644 index 528fe2d7..00000000 --- a/examples/security/classification/output/reports/train/9decd6e2e664340695681c856074af17/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9decd6e2e664340695681c856074af17/adv_predictions.json - adv_probabilities_file: output/reports/train/9decd6e2e664340695681c856074af17/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/7d5fd50667334996fa6a9adf2496d792.pkl - params_file: output/reports/train/9decd6e2e664340695681c856074af17/params.yaml - predictions_file: output/reports/train/9decd6e2e664340695681c856074af17/predictions.json - probabilities_file: output/reports/train/9decd6e2e664340695681c856074af17/probabilities.json - score_dict_file: output/reports/train/9decd6e2e664340695681c856074af17/score_dict.json - test_labels_file: output/reports/train/9decd6e2e664340695681c856074af17/test_labels.json - train_labels_file: output/reports/train/9decd6e2e664340695681c856074af17/train_labels.json - model_dir: models - name: 9decd6e2e664340695681c856074af17 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 90e9a746cdb38ed97b8637cef338aecf - trainer: - kwargs: {} -name: 9decd6e2e664340695681c856074af17 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9e4122225b62ebbdf06d58d603a786a1/params.yaml b/examples/security/classification/output/reports/train/9e4122225b62ebbdf06d58d603a786a1/params.yaml deleted file mode 100644 index 4dd37b5d..00000000 --- a/examples/security/classification/output/reports/train/9e4122225b62ebbdf06d58d603a786a1/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/adv_predictions.json - adv_probabilities_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/08eec954a7a0ad32a7b5a2ebb41d99c4.pkl - params_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/params.yaml - predictions_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/predictions.json - probabilities_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/probabilities.json - score_dict_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/score_dict.json - test_labels_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/test_labels.json - train_labels_file: output/reports/train/9e4122225b62ebbdf06d58d603a786a1/train_labels.json - model_dir: models - name: 9e4122225b62ebbdf06d58d603a786a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0a05d12e73962cfffaf9ec1974f51eba - trainer: - kwargs: {} -name: 9e4122225b62ebbdf06d58d603a786a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9e741fe262a805b95fd4aa180cb68861/params.yaml b/examples/security/classification/output/reports/train/9e741fe262a805b95fd4aa180cb68861/params.yaml deleted file mode 100644 index bf163591..00000000 --- a/examples/security/classification/output/reports/train/9e741fe262a805b95fd4aa180cb68861/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/adv_predictions.json - adv_probabilities_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/400e5b8af83c070bc2a8b56a5f049b94.pkl - params_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/params.yaml - predictions_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/predictions.json - probabilities_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/probabilities.json - score_dict_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/score_dict.json - test_labels_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/test_labels.json - train_labels_file: output/reports/train/9e741fe262a805b95fd4aa180cb68861/train_labels.json - model_dir: models - name: 9e741fe262a805b95fd4aa180cb68861 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e13f0d132c392e115c25110a577b4349 - trainer: - kwargs: {} -name: 9e741fe262a805b95fd4aa180cb68861 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9e8987520dd27eb8b2187c47dc678027/params.yaml b/examples/security/classification/output/reports/train/9e8987520dd27eb8b2187c47dc678027/params.yaml deleted file mode 100644 index e1c67acf..00000000 --- a/examples/security/classification/output/reports/train/9e8987520dd27eb8b2187c47dc678027/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/adv_predictions.json - adv_probabilities_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/2775fe9c21780d2ba8f9b3121d054d35.pkl - params_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/params.yaml - predictions_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/predictions.json - probabilities_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/probabilities.json - score_dict_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/score_dict.json - test_labels_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/test_labels.json - train_labels_file: output/reports/train/9e8987520dd27eb8b2187c47dc678027/train_labels.json - model_dir: models - name: 9e8987520dd27eb8b2187c47dc678027 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 11770d4d7cc3bbbd2a47dc30ce6d5163 - trainer: - kwargs: {} -name: 9e8987520dd27eb8b2187c47dc678027 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9f5be87069fb07821d847d822d3edd8e/params.yaml b/examples/security/classification/output/reports/train/9f5be87069fb07821d847d822d3edd8e/params.yaml deleted file mode 100644 index 05a383eb..00000000 --- a/examples/security/classification/output/reports/train/9f5be87069fb07821d847d822d3edd8e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/adv_predictions.json - adv_probabilities_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/fc63522fb9b119dc216474cd1dcd2aff.pkl - params_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/params.yaml - predictions_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/predictions.json - probabilities_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/probabilities.json - score_dict_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/score_dict.json - test_labels_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/test_labels.json - train_labels_file: output/reports/train/9f5be87069fb07821d847d822d3edd8e/train_labels.json - model_dir: models - name: 9f5be87069fb07821d847d822d3edd8e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a2fd552adf69afc2b573ae3275684eba - trainer: - kwargs: {} -name: 9f5be87069fb07821d847d822d3edd8e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/params.yaml b/examples/security/classification/output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/params.yaml deleted file mode 100644 index cb907a15..00000000 --- a/examples/security/classification/output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/adv_predictions.json - adv_probabilities_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/6323394ec28dcec86d9c1c1a9c675d8b.pkl - params_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/params.yaml - predictions_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/predictions.json - probabilities_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/probabilities.json - score_dict_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/score_dict.json - test_labels_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/test_labels.json - train_labels_file: output/reports/train/9f8b2f3876cbfd24936071c55c3b1228/train_labels.json - model_dir: models - name: 9f8b2f3876cbfd24936071c55c3b1228 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c86756555a535032166f9abe344ed699 - trainer: - kwargs: {} -name: 9f8b2f3876cbfd24936071c55c3b1228 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/params.yaml b/examples/security/classification/output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/params.yaml deleted file mode 100644 index 8aa08f95..00000000 --- a/examples/security/classification/output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/adv_predictions.json - adv_probabilities_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/5deb89883fa6c7edac14ff565b3db60a.pkl - params_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/params.yaml - predictions_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/predictions.json - probabilities_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/probabilities.json - score_dict_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/score_dict.json - test_labels_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/test_labels.json - train_labels_file: output/reports/train/9fc08ddc5331daec329e55e0209d7c1d/train_labels.json - model_dir: models - name: 9fc08ddc5331daec329e55e0209d7c1d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b2a6cc4f888e5cef6950e1ecfef59ef1 - trainer: - kwargs: {} -name: 9fc08ddc5331daec329e55e0209d7c1d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/9fdf681154346b46bef7c85d8e32c714/params.yaml b/examples/security/classification/output/reports/train/9fdf681154346b46bef7c85d8e32c714/params.yaml deleted file mode 100644 index 414780d1..00000000 --- a/examples/security/classification/output/reports/train/9fdf681154346b46bef7c85d8e32c714/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/adv_predictions.json - adv_probabilities_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/220358e05a9848cb54758f1cc85a20dc.pkl - params_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/params.yaml - predictions_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/predictions.json - probabilities_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/probabilities.json - score_dict_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/score_dict.json - test_labels_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/test_labels.json - train_labels_file: output/reports/train/9fdf681154346b46bef7c85d8e32c714/train_labels.json - model_dir: models - name: 9fdf681154346b46bef7c85d8e32c714 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0f05db395f94a2570daadb7fcb036e5f - trainer: - kwargs: {} -name: 9fdf681154346b46bef7c85d8e32c714 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a00025dafd87d76e60595b77ccb21bd4/params.yaml b/examples/security/classification/output/reports/train/a00025dafd87d76e60595b77ccb21bd4/params.yaml deleted file mode 100644 index df7459ba..00000000 --- a/examples/security/classification/output/reports/train/a00025dafd87d76e60595b77ccb21bd4/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/adv_predictions.json - adv_probabilities_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/c65a2b597f50cffc4fa37bbd2f9201fb.pkl - params_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/params.yaml - predictions_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/predictions.json - probabilities_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/probabilities.json - score_dict_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/score_dict.json - test_labels_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/test_labels.json - train_labels_file: output/reports/train/a00025dafd87d76e60595b77ccb21bd4/train_labels.json - model_dir: models - name: a00025dafd87d76e60595b77ccb21bd4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6cb58ebb5a7537683299b14882fed2f5 - trainer: - kwargs: {} -name: a00025dafd87d76e60595b77ccb21bd4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/params.yaml b/examples/security/classification/output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/params.yaml deleted file mode 100644 index bd3347a1..00000000 --- a/examples/security/classification/output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/adv_predictions.json - adv_probabilities_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/09de99034b4c834b7e8ea166514d678a.pkl - params_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/params.yaml - predictions_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/predictions.json - probabilities_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/probabilities.json - score_dict_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/score_dict.json - test_labels_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/test_labels.json - train_labels_file: output/reports/train/a059e8ea1cbbaf8f26caa8d9baac9e2a/train_labels.json - model_dir: models - name: a059e8ea1cbbaf8f26caa8d9baac9e2a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9019e4ebbf98cda48bf85dfa25fa9cc5 - trainer: - kwargs: {} -name: a059e8ea1cbbaf8f26caa8d9baac9e2a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/params.yaml b/examples/security/classification/output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/params.yaml deleted file mode 100644 index 8fc636a3..00000000 --- a/examples/security/classification/output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/adv_predictions.json - adv_probabilities_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/2b31d9d8ba0eb6fb09a4f7f7cf8906a8.pkl - params_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/params.yaml - predictions_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/predictions.json - probabilities_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/probabilities.json - score_dict_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/score_dict.json - test_labels_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/test_labels.json - train_labels_file: output/reports/train/a0674b16939b9be7c3a082156d2ddbd3/train_labels.json - model_dir: models - name: a0674b16939b9be7c3a082156d2ddbd3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 707d4c626306327b001974454e34f13c - trainer: - kwargs: {} -name: a0674b16939b9be7c3a082156d2ddbd3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a115c05d28e80a9c81a93792426ddf4e/params.yaml b/examples/security/classification/output/reports/train/a115c05d28e80a9c81a93792426ddf4e/params.yaml deleted file mode 100644 index 6d515eac..00000000 --- a/examples/security/classification/output/reports/train/a115c05d28e80a9c81a93792426ddf4e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/adv_predictions.json - adv_probabilities_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/5ac698adb1bafbd7120361e8b0fef2d1.pkl - params_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/params.yaml - predictions_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/predictions.json - probabilities_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/probabilities.json - score_dict_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/score_dict.json - test_labels_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/test_labels.json - train_labels_file: output/reports/train/a115c05d28e80a9c81a93792426ddf4e/train_labels.json - model_dir: models - name: a115c05d28e80a9c81a93792426ddf4e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f750b500a21b1f042fccc3bfb74cc926 - trainer: - kwargs: {} -name: a115c05d28e80a9c81a93792426ddf4e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a11da6a15b260a11982f474d0c4540ac/params.yaml b/examples/security/classification/output/reports/train/a11da6a15b260a11982f474d0c4540ac/params.yaml deleted file mode 100644 index 1699009f..00000000 --- a/examples/security/classification/output/reports/train/a11da6a15b260a11982f474d0c4540ac/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/adv_predictions.json - adv_probabilities_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/b5efdd8242c114950fc315711caf6675.pkl - params_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/params.yaml - predictions_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/predictions.json - probabilities_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/probabilities.json - score_dict_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/score_dict.json - test_labels_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/test_labels.json - train_labels_file: output/reports/train/a11da6a15b260a11982f474d0c4540ac/train_labels.json - model_dir: models - name: a11da6a15b260a11982f474d0c4540ac - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e7fc975600cd24dd17ae7bd92228b127 - trainer: - kwargs: {} -name: a11da6a15b260a11982f474d0c4540ac -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/params.yaml b/examples/security/classification/output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/params.yaml deleted file mode 100644 index 8ceb719b..00000000 --- a/examples/security/classification/output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/adv_predictions.json - adv_probabilities_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/d44fc83a3e815a3759504522c0078779.pkl - params_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/params.yaml - predictions_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/predictions.json - probabilities_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/probabilities.json - score_dict_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/score_dict.json - test_labels_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/test_labels.json - train_labels_file: output/reports/train/a12b0adb069ff9c8ac5cacc6fbff324d/train_labels.json - model_dir: models - name: a12b0adb069ff9c8ac5cacc6fbff324d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 413ca859f76d56b78663ea9aa9a2b120 - trainer: - kwargs: {} -name: a12b0adb069ff9c8ac5cacc6fbff324d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a131fb219be89309abb9b75b730a6175/params.yaml b/examples/security/classification/output/reports/train/a131fb219be89309abb9b75b730a6175/params.yaml deleted file mode 100644 index 67aefd4f..00000000 --- a/examples/security/classification/output/reports/train/a131fb219be89309abb9b75b730a6175/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a131fb219be89309abb9b75b730a6175/adv_predictions.json - adv_probabilities_file: output/reports/train/a131fb219be89309abb9b75b730a6175/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/ea05caa4867632c0f1d63756141da148.pkl - params_file: output/reports/train/a131fb219be89309abb9b75b730a6175/params.yaml - predictions_file: output/reports/train/a131fb219be89309abb9b75b730a6175/predictions.json - probabilities_file: output/reports/train/a131fb219be89309abb9b75b730a6175/probabilities.json - score_dict_file: output/reports/train/a131fb219be89309abb9b75b730a6175/score_dict.json - test_labels_file: output/reports/train/a131fb219be89309abb9b75b730a6175/test_labels.json - train_labels_file: output/reports/train/a131fb219be89309abb9b75b730a6175/train_labels.json - model_dir: models - name: a131fb219be89309abb9b75b730a6175 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 443fc7edda079bc3bdd67b8629de0e13 - trainer: - kwargs: {} -name: a131fb219be89309abb9b75b730a6175 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a13c6be370cec34097177c48b65b4a1d/params.yaml b/examples/security/classification/output/reports/train/a13c6be370cec34097177c48b65b4a1d/params.yaml deleted file mode 100644 index c62a1c18..00000000 --- a/examples/security/classification/output/reports/train/a13c6be370cec34097177c48b65b4a1d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/adv_predictions.json - adv_probabilities_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/9b0cd14ccf4a5d587fceea78cb6a0aa1.pkl - params_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/params.yaml - predictions_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/predictions.json - probabilities_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/probabilities.json - score_dict_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/score_dict.json - test_labels_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/test_labels.json - train_labels_file: output/reports/train/a13c6be370cec34097177c48b65b4a1d/train_labels.json - model_dir: models - name: a13c6be370cec34097177c48b65b4a1d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ce6fc413d7bce17d7774ed9f3e32541e - trainer: - kwargs: {} -name: a13c6be370cec34097177c48b65b4a1d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a1523110e8031fa5720540b15da9e41a/params.yaml b/examples/security/classification/output/reports/train/a1523110e8031fa5720540b15da9e41a/params.yaml deleted file mode 100644 index b3dc9a25..00000000 --- a/examples/security/classification/output/reports/train/a1523110e8031fa5720540b15da9e41a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/adv_predictions.json - adv_probabilities_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/f9805f4681f332b23e3590b6e721eea7.pkl - params_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/params.yaml - predictions_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/predictions.json - probabilities_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/probabilities.json - score_dict_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/score_dict.json - test_labels_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/test_labels.json - train_labels_file: output/reports/train/a1523110e8031fa5720540b15da9e41a/train_labels.json - model_dir: models - name: a1523110e8031fa5720540b15da9e41a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ee8d17b3edd77e8cda93a2accaa4f1f8 - trainer: - kwargs: {} -name: a1523110e8031fa5720540b15da9e41a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a153058599e383406804c081a093c24b/params.yaml b/examples/security/classification/output/reports/train/a153058599e383406804c081a093c24b/params.yaml deleted file mode 100644 index 437c5ddc..00000000 --- a/examples/security/classification/output/reports/train/a153058599e383406804c081a093c24b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a153058599e383406804c081a093c24b/adv_predictions.json - adv_probabilities_file: output/reports/train/a153058599e383406804c081a093c24b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/1df53a4bfdc9eda6e4ad7b3c3805cc92.pkl - params_file: output/reports/train/a153058599e383406804c081a093c24b/params.yaml - predictions_file: output/reports/train/a153058599e383406804c081a093c24b/predictions.json - probabilities_file: output/reports/train/a153058599e383406804c081a093c24b/probabilities.json - score_dict_file: output/reports/train/a153058599e383406804c081a093c24b/score_dict.json - test_labels_file: output/reports/train/a153058599e383406804c081a093c24b/test_labels.json - train_labels_file: output/reports/train/a153058599e383406804c081a093c24b/train_labels.json - model_dir: models - name: a153058599e383406804c081a093c24b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 85eb0a4eb74c18decbb95677314d5c04 - trainer: - kwargs: {} -name: a153058599e383406804c081a093c24b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a18076accc756f0c0afa648c2245b4d3/params.yaml b/examples/security/classification/output/reports/train/a18076accc756f0c0afa648c2245b4d3/params.yaml deleted file mode 100644 index 00f6f80d..00000000 --- a/examples/security/classification/output/reports/train/a18076accc756f0c0afa648c2245b4d3/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/adv_predictions.json - adv_probabilities_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/70bda7e5149c819bfd28fe9807013f57.pkl - params_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/params.yaml - predictions_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/predictions.json - probabilities_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/probabilities.json - score_dict_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/score_dict.json - test_labels_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/test_labels.json - train_labels_file: output/reports/train/a18076accc756f0c0afa648c2245b4d3/train_labels.json - model_dir: models - name: a18076accc756f0c0afa648c2245b4d3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 102948e067e01df7395a3b3fe548ea6b - trainer: - kwargs: {} -name: a18076accc756f0c0afa648c2245b4d3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/params.yaml b/examples/security/classification/output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/params.yaml deleted file mode 100644 index 364eb635..00000000 --- a/examples/security/classification/output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/adv_predictions.json - adv_probabilities_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/256fe7d91d6b73b82238a062a547f56e.pkl - params_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/params.yaml - predictions_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/predictions.json - probabilities_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/probabilities.json - score_dict_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/score_dict.json - test_labels_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/test_labels.json - train_labels_file: output/reports/train/a19d396d14f45e75ed90c21a4f83d35c/train_labels.json - model_dir: models - name: a19d396d14f45e75ed90c21a4f83d35c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f81d138ca3097b94b87a2d753f3887f5 - trainer: - kwargs: {} -name: a19d396d14f45e75ed90c21a4f83d35c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a19ddbf9b31e353d3313932893c235ce/params.yaml b/examples/security/classification/output/reports/train/a19ddbf9b31e353d3313932893c235ce/params.yaml deleted file mode 100644 index 3de668cb..00000000 --- a/examples/security/classification/output/reports/train/a19ddbf9b31e353d3313932893c235ce/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/adv_predictions.json - adv_probabilities_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/a63046270844a1621f5abea0856d556a.pkl - params_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/params.yaml - predictions_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/predictions.json - probabilities_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/probabilities.json - score_dict_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/score_dict.json - test_labels_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/test_labels.json - train_labels_file: output/reports/train/a19ddbf9b31e353d3313932893c235ce/train_labels.json - model_dir: models - name: a19ddbf9b31e353d3313932893c235ce - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1067029f87720e28d6e46e5691a175f1 - trainer: - kwargs: {} -name: a19ddbf9b31e353d3313932893c235ce -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/params.yaml b/examples/security/classification/output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/params.yaml deleted file mode 100644 index fd122f10..00000000 --- a/examples/security/classification/output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/adv_predictions.json - adv_probabilities_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/fdd2c76d629706c7b3d50402b9a96c65.pkl - params_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/params.yaml - predictions_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/predictions.json - probabilities_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/probabilities.json - score_dict_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/score_dict.json - test_labels_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/test_labels.json - train_labels_file: output/reports/train/a1cd9d6ca714960ad24f809e14d323bf/train_labels.json - model_dir: models - name: a1cd9d6ca714960ad24f809e14d323bf - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3445a35fef609d07ba1aa89df1c7a0a7 - trainer: - kwargs: {} -name: a1cd9d6ca714960ad24f809e14d323bf -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a201ffa05f1237d86b649db5821d7100/params.yaml b/examples/security/classification/output/reports/train/a201ffa05f1237d86b649db5821d7100/params.yaml deleted file mode 100644 index 7a7769b6..00000000 --- a/examples/security/classification/output/reports/train/a201ffa05f1237d86b649db5821d7100/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/adv_predictions.json - adv_probabilities_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/acb0aebaeb1ebe924f7c59c07db679c8.pkl - params_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/params.yaml - predictions_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/predictions.json - probabilities_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/probabilities.json - score_dict_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/score_dict.json - test_labels_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/test_labels.json - train_labels_file: output/reports/train/a201ffa05f1237d86b649db5821d7100/train_labels.json - model_dir: models - name: a201ffa05f1237d86b649db5821d7100 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d78b16b145c5d17856a183f52dcc28e3 - trainer: - kwargs: {} -name: a201ffa05f1237d86b649db5821d7100 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a2086ba52f947f963a39b3b384da85c8/params.yaml b/examples/security/classification/output/reports/train/a2086ba52f947f963a39b3b384da85c8/params.yaml deleted file mode 100644 index b510ab87..00000000 --- a/examples/security/classification/output/reports/train/a2086ba52f947f963a39b3b384da85c8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/adv_predictions.json - adv_probabilities_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/c877e7b719fef1324a692d47d9e32b36.pkl - params_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/params.yaml - predictions_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/predictions.json - probabilities_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/probabilities.json - score_dict_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/score_dict.json - test_labels_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/test_labels.json - train_labels_file: output/reports/train/a2086ba52f947f963a39b3b384da85c8/train_labels.json - model_dir: models - name: a2086ba52f947f963a39b3b384da85c8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0b5f26fb58dcd33981909a95293b402b - trainer: - kwargs: {} -name: a2086ba52f947f963a39b3b384da85c8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a222794142146d1c86ef64b4540d1112/params.yaml b/examples/security/classification/output/reports/train/a222794142146d1c86ef64b4540d1112/params.yaml deleted file mode 100644 index 65080bfd..00000000 --- a/examples/security/classification/output/reports/train/a222794142146d1c86ef64b4540d1112/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a222794142146d1c86ef64b4540d1112/adv_predictions.json - adv_probabilities_file: output/reports/train/a222794142146d1c86ef64b4540d1112/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/73a0baba27735a6bc48446dd7946b99e.pkl - params_file: output/reports/train/a222794142146d1c86ef64b4540d1112/params.yaml - predictions_file: output/reports/train/a222794142146d1c86ef64b4540d1112/predictions.json - probabilities_file: output/reports/train/a222794142146d1c86ef64b4540d1112/probabilities.json - score_dict_file: output/reports/train/a222794142146d1c86ef64b4540d1112/score_dict.json - test_labels_file: output/reports/train/a222794142146d1c86ef64b4540d1112/test_labels.json - train_labels_file: output/reports/train/a222794142146d1c86ef64b4540d1112/train_labels.json - model_dir: models - name: a222794142146d1c86ef64b4540d1112 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5f16e54be7ccd2d1fd4b971858c83a27 - trainer: - kwargs: {} -name: a222794142146d1c86ef64b4540d1112 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/params.yaml b/examples/security/classification/output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/params.yaml deleted file mode 100644 index 9ef32186..00000000 --- a/examples/security/classification/output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/adv_predictions.json - adv_probabilities_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/f592d0999dbb3d47bf0023fa14c883d9.pkl - params_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/params.yaml - predictions_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/predictions.json - probabilities_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/probabilities.json - score_dict_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/score_dict.json - test_labels_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/test_labels.json - train_labels_file: output/reports/train/a24d3400e42f6d80682bf96ce6fcd2aa/train_labels.json - model_dir: models - name: a24d3400e42f6d80682bf96ce6fcd2aa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ecd818faeeab4777bb6e994cf81bb3ec - trainer: - kwargs: {} -name: a24d3400e42f6d80682bf96ce6fcd2aa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a256db550d24ea8edb0c651433eac3be/params.yaml b/examples/security/classification/output/reports/train/a256db550d24ea8edb0c651433eac3be/params.yaml deleted file mode 100644 index 6d021c55..00000000 --- a/examples/security/classification/output/reports/train/a256db550d24ea8edb0c651433eac3be/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/adv_predictions.json - adv_probabilities_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/ed433a148ad42b0da11ad6b5d550a098.pkl - params_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/params.yaml - predictions_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/predictions.json - probabilities_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/probabilities.json - score_dict_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/score_dict.json - test_labels_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/test_labels.json - train_labels_file: output/reports/train/a256db550d24ea8edb0c651433eac3be/train_labels.json - model_dir: models - name: a256db550d24ea8edb0c651433eac3be - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a1177b363abbdb9b4df40674d53fadf0 - trainer: - kwargs: {} -name: a256db550d24ea8edb0c651433eac3be -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a25a54c1c0b9a3258e68e37a22998525/params.yaml b/examples/security/classification/output/reports/train/a25a54c1c0b9a3258e68e37a22998525/params.yaml deleted file mode 100644 index 6c018394..00000000 --- a/examples/security/classification/output/reports/train/a25a54c1c0b9a3258e68e37a22998525/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/adv_predictions.json - adv_probabilities_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/2893f52305ec2f6db447faa511dde915.pkl - params_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/params.yaml - predictions_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/predictions.json - probabilities_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/probabilities.json - score_dict_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/score_dict.json - test_labels_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/test_labels.json - train_labels_file: output/reports/train/a25a54c1c0b9a3258e68e37a22998525/train_labels.json - model_dir: models - name: a25a54c1c0b9a3258e68e37a22998525 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4cf0ab8f6ca95a5056cf624f5bf29003 - trainer: - kwargs: {} -name: a25a54c1c0b9a3258e68e37a22998525 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/params.yaml b/examples/security/classification/output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/params.yaml deleted file mode 100644 index 3fa4ce94..00000000 --- a/examples/security/classification/output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/adv_predictions.json - adv_probabilities_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/4ccd977096f72731781e01ac3c9db514.pkl - params_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/params.yaml - predictions_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/predictions.json - probabilities_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/probabilities.json - score_dict_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/score_dict.json - test_labels_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/test_labels.json - train_labels_file: output/reports/train/a26f27bdbe4106e0399f3ec015c7672b/train_labels.json - model_dir: models - name: a26f27bdbe4106e0399f3ec015c7672b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d67cf99c52fb78abf0de8a3b4597a252 - trainer: - kwargs: {} -name: a26f27bdbe4106e0399f3ec015c7672b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a2b8aa20917bd043a089865ee4d809fc/params.yaml b/examples/security/classification/output/reports/train/a2b8aa20917bd043a089865ee4d809fc/params.yaml deleted file mode 100644 index 8e1c892f..00000000 --- a/examples/security/classification/output/reports/train/a2b8aa20917bd043a089865ee4d809fc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/adv_predictions.json - adv_probabilities_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/4e8c838db680d8f5c1456f97cf1ccaea.pkl - params_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/params.yaml - predictions_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/predictions.json - probabilities_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/probabilities.json - score_dict_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/score_dict.json - test_labels_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/test_labels.json - train_labels_file: output/reports/train/a2b8aa20917bd043a089865ee4d809fc/train_labels.json - model_dir: models - name: a2b8aa20917bd043a089865ee4d809fc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3eb5d065013ed9b93f9cce9b5f96d9cc - trainer: - kwargs: {} -name: a2b8aa20917bd043a089865ee4d809fc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/params.yaml b/examples/security/classification/output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/params.yaml deleted file mode 100644 index e0f74387..00000000 --- a/examples/security/classification/output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/adv_predictions.json - adv_probabilities_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/da9523d62fdfe47040de5a5852555681.pkl - params_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/params.yaml - predictions_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/predictions.json - probabilities_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/probabilities.json - score_dict_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/score_dict.json - test_labels_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/test_labels.json - train_labels_file: output/reports/train/a2c0e27c6d1f98b109018d7cc178b806/train_labels.json - model_dir: models - name: a2c0e27c6d1f98b109018d7cc178b806 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ff6372096509e2a0e45c49e0ced61d8b - trainer: - kwargs: {} -name: a2c0e27c6d1f98b109018d7cc178b806 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/params.yaml b/examples/security/classification/output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/params.yaml deleted file mode 100644 index 55849e69..00000000 --- a/examples/security/classification/output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/adv_predictions.json - adv_probabilities_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/7391b6f6969596b8bc1b5c911f42ef3d.pkl - params_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/params.yaml - predictions_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/predictions.json - probabilities_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/probabilities.json - score_dict_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/score_dict.json - test_labels_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/test_labels.json - train_labels_file: output/reports/train/a2f6bfb1b851055e1d9f27e3e72d38c3/train_labels.json - model_dir: models - name: a2f6bfb1b851055e1d9f27e3e72d38c3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fabdcfde252523b0fc0e03226977a3ac - trainer: - kwargs: {} -name: a2f6bfb1b851055e1d9f27e3e72d38c3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/params.yaml b/examples/security/classification/output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/params.yaml deleted file mode 100644 index c2961e5e..00000000 --- a/examples/security/classification/output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/adv_predictions.json - adv_probabilities_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/925348a69c80547ed58ae97323083a82.pkl - params_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/params.yaml - predictions_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/predictions.json - probabilities_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/probabilities.json - score_dict_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/score_dict.json - test_labels_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/test_labels.json - train_labels_file: output/reports/train/a302d253f1e2b7e06db79149ce65ad7f/train_labels.json - model_dir: models - name: a302d253f1e2b7e06db79149ce65ad7f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 50da4439d4ce9ff4cbb92ded2cc05b97 - trainer: - kwargs: {} -name: a302d253f1e2b7e06db79149ce65ad7f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a327f6e43c0f4ccf31e861155496307b/params.yaml b/examples/security/classification/output/reports/train/a327f6e43c0f4ccf31e861155496307b/params.yaml deleted file mode 100644 index 96eef54f..00000000 --- a/examples/security/classification/output/reports/train/a327f6e43c0f4ccf31e861155496307b/params.yaml +++ /dev/null @@ -1,104 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/adv_predictions.json - adv_probabilities_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/ed05a3455871d532f0918e6f2f15ec96.pkl - params_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/params.yaml - predictions_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/predictions.json - probabilities_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/probabilities.json - score_dict_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/score_dict.json - test_labels_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/test_labels.json - train_labels_file: output/reports/train/a327f6e43c0f4ccf31e861155496307b/train_labels.json - model_dir: models - name: a327f6e43c0f4ccf31e861155496307b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: - - linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 951cedde8157a1d38083d3f155303db4 - trainer: - kwargs: {} -name: a327f6e43c0f4ccf31e861155496307b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/params.yaml b/examples/security/classification/output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/params.yaml deleted file mode 100644 index 889d3671..00000000 --- a/examples/security/classification/output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/adv_predictions.json - adv_probabilities_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/a5779a5604e93aca48d56e8ec586eb15.pkl - params_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/params.yaml - predictions_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/predictions.json - probabilities_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/probabilities.json - score_dict_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/score_dict.json - test_labels_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/test_labels.json - train_labels_file: output/reports/train/a34095aa51e2ecff74cf0b9e4c5f6949/train_labels.json - model_dir: models - name: a34095aa51e2ecff74cf0b9e4c5f6949 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1d9f0041fce6e0a5bfa14376b0b518e0 - trainer: - kwargs: {} -name: a34095aa51e2ecff74cf0b9e4c5f6949 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a340d3528f81cada226f7a6b7941509e/params.yaml b/examples/security/classification/output/reports/train/a340d3528f81cada226f7a6b7941509e/params.yaml deleted file mode 100644 index 55342ad2..00000000 --- a/examples/security/classification/output/reports/train/a340d3528f81cada226f7a6b7941509e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/adv_predictions.json - adv_probabilities_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/0ead9614d60a01face1c47904a909633.pkl - params_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/params.yaml - predictions_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/predictions.json - probabilities_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/probabilities.json - score_dict_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/score_dict.json - test_labels_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/test_labels.json - train_labels_file: output/reports/train/a340d3528f81cada226f7a6b7941509e/train_labels.json - model_dir: models - name: a340d3528f81cada226f7a6b7941509e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8ed5aaccf2f20fa51a641e60ed6ebeb7 - trainer: - kwargs: {} -name: a340d3528f81cada226f7a6b7941509e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a3553091d0fe90edf17ff73110d96eff/params.yaml b/examples/security/classification/output/reports/train/a3553091d0fe90edf17ff73110d96eff/params.yaml deleted file mode 100644 index 9c661f9c..00000000 --- a/examples/security/classification/output/reports/train/a3553091d0fe90edf17ff73110d96eff/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/adv_predictions.json - adv_probabilities_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/ffe183ee9b4da3bd6c216ae837e38a85.pkl - params_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/params.yaml - predictions_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/predictions.json - probabilities_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/probabilities.json - score_dict_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/score_dict.json - test_labels_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/test_labels.json - train_labels_file: output/reports/train/a3553091d0fe90edf17ff73110d96eff/train_labels.json - model_dir: models - name: a3553091d0fe90edf17ff73110d96eff - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 08acb41df95fa7bb575f1182b7fe26d4 - trainer: - kwargs: {} -name: a3553091d0fe90edf17ff73110d96eff -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/params.yaml b/examples/security/classification/output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/params.yaml deleted file mode 100644 index 70a0fe2b..00000000 --- a/examples/security/classification/output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/adv_predictions.json - adv_probabilities_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/94cc6d62be8453e29bf70c4ef896e739.pkl - params_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/params.yaml - predictions_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/predictions.json - probabilities_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/probabilities.json - score_dict_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/score_dict.json - test_labels_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/test_labels.json - train_labels_file: output/reports/train/a35bd0d7fa78b1b8b89458ae45c9977d/train_labels.json - model_dir: models - name: a35bd0d7fa78b1b8b89458ae45c9977d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f1022172fe3d7fd3a0dfe479d98248b4 - trainer: - kwargs: {} -name: a35bd0d7fa78b1b8b89458ae45c9977d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a367434727f2771d22f737f5fecd1af5/params.yaml b/examples/security/classification/output/reports/train/a367434727f2771d22f737f5fecd1af5/params.yaml deleted file mode 100644 index c866fcdb..00000000 --- a/examples/security/classification/output/reports/train/a367434727f2771d22f737f5fecd1af5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/adv_predictions.json - adv_probabilities_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/eab360aa3177b15f60e15a7343771648.pkl - params_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/params.yaml - predictions_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/predictions.json - probabilities_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/probabilities.json - score_dict_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/score_dict.json - test_labels_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/test_labels.json - train_labels_file: output/reports/train/a367434727f2771d22f737f5fecd1af5/train_labels.json - model_dir: models - name: a367434727f2771d22f737f5fecd1af5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 092123bae25316579bcdd50fcb4be105 - trainer: - kwargs: {} -name: a367434727f2771d22f737f5fecd1af5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/params.yaml b/examples/security/classification/output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/params.yaml deleted file mode 100644 index 53ac1b06..00000000 --- a/examples/security/classification/output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/params.yaml +++ /dev/null @@ -1,227 +0,0 @@ -attack: - attack_size: 10 - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3eeeae4f5fc7322ec26e3067eea6ac63 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a69fe21d7cc8b85a2791967c48b96352 - trainer: - kwargs: {} - name: ace9393f8458a8f5c55ab4c835676ac0 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/adv_predictions.json - adv_probabilities_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/adv_probabilities.json - attack_file: output/attacks/8430e3b494eb9233138cd46931395ede.pkl - data_file: output/data/4281f4ac2b8c4bfb618284c4afa4857c.pkl - model_file: output/models/1c62baf594b27c80b7716bea3348f279.pkl - params_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/params.yaml - predictions_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/predictions.json - probabilities_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/probabilities.json - score_dict_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/score_dict.json - test_labels_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/test_labels.json - train_labels_file: output/reports/train/a39faaa1e6cdc3e9d0e24cfba24d9a06/train_labels.json - model_dir: models - name: a39faaa1e6cdc3e9d0e24cfba24d9a06 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/11cc654b9263d958d08ca5901a37b7056f5623dd/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8798e0a8d1c5c1910f93a39d684af46f - trainer: - kwargs: {} -name: a39faaa1e6cdc3e9d0e24cfba24d9a06 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/params.yaml b/examples/security/classification/output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/params.yaml deleted file mode 100644 index 97ada161..00000000 --- a/examples/security/classification/output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/adv_predictions.json - adv_probabilities_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/cbc5819cef35e9f592fcdef5447930ae.pkl - params_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/params.yaml - predictions_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/predictions.json - probabilities_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/probabilities.json - score_dict_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/score_dict.json - test_labels_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/test_labels.json - train_labels_file: output/reports/train/a3a3cfaf325c806ae0ef0f062ecb732f/train_labels.json - model_dir: models - name: a3a3cfaf325c806ae0ef0f062ecb732f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f5e5ef4a3bc94b313378e21abe04f509 - trainer: - kwargs: {} -name: a3a3cfaf325c806ae0ef0f062ecb732f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/params.yaml b/examples/security/classification/output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/params.yaml deleted file mode 100644 index e07d7b3b..00000000 --- a/examples/security/classification/output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/adv_predictions.json - adv_probabilities_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/3432c8a4516eac96385d732649fbf6d5.pkl - params_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/params.yaml - predictions_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/predictions.json - probabilities_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/probabilities.json - score_dict_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/score_dict.json - test_labels_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/test_labels.json - train_labels_file: output/reports/train/a3b45a725e6605c4acb91d4c4d8073db/train_labels.json - model_dir: models - name: a3b45a725e6605c4acb91d4c4d8073db - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4acaef160fbd047e48a994b3d453b635 - trainer: - kwargs: {} -name: a3b45a725e6605c4acb91d4c4d8073db -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a3c05ef1bb8242338a28165480ec06bd/params.yaml b/examples/security/classification/output/reports/train/a3c05ef1bb8242338a28165480ec06bd/params.yaml deleted file mode 100644 index aded74d2..00000000 --- a/examples/security/classification/output/reports/train/a3c05ef1bb8242338a28165480ec06bd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/adv_predictions.json - adv_probabilities_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/0e4f0b19e664ab6bf5d18e93278fa649.pkl - params_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/params.yaml - predictions_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/predictions.json - probabilities_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/probabilities.json - score_dict_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/score_dict.json - test_labels_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/test_labels.json - train_labels_file: output/reports/train/a3c05ef1bb8242338a28165480ec06bd/train_labels.json - model_dir: models - name: a3c05ef1bb8242338a28165480ec06bd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 74fc16aafe77610355cefd5db5abbbe4 - trainer: - kwargs: {} -name: a3c05ef1bb8242338a28165480ec06bd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/params.yaml b/examples/security/classification/output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/params.yaml deleted file mode 100644 index a192fb7f..00000000 --- a/examples/security/classification/output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/adv_predictions.json - adv_probabilities_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/136993d858875e71d6b8317fafb05357.pkl - params_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/params.yaml - predictions_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/predictions.json - probabilities_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/probabilities.json - score_dict_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/score_dict.json - test_labels_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/test_labels.json - train_labels_file: output/reports/train/a3d0da6b0acd8f1c2b8da89d1d4eaf26/train_labels.json - model_dir: models - name: a3d0da6b0acd8f1c2b8da89d1d4eaf26 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5a43c0e47baa49eaa7dae413fa9049fd - trainer: - kwargs: {} -name: a3d0da6b0acd8f1c2b8da89d1d4eaf26 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/params.yaml b/examples/security/classification/output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/params.yaml deleted file mode 100644 index ac65a3b5..00000000 --- a/examples/security/classification/output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/adv_predictions.json - adv_probabilities_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/7d06fb50ee4d5d0c06f34c873a7c5b63.pkl - params_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/params.yaml - predictions_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/predictions.json - probabilities_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/probabilities.json - score_dict_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/score_dict.json - test_labels_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/test_labels.json - train_labels_file: output/reports/train/a3f701da8abc78ebda57788ba4cd3ce8/train_labels.json - model_dir: models - name: a3f701da8abc78ebda57788ba4cd3ce8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cbdc91e8a5ee3e35172323fe01a34e84 - trainer: - kwargs: {} -name: a3f701da8abc78ebda57788ba4cd3ce8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a40209a639c8f1b9802b339bd4aee976/params.yaml b/examples/security/classification/output/reports/train/a40209a639c8f1b9802b339bd4aee976/params.yaml deleted file mode 100644 index ed719248..00000000 --- a/examples/security/classification/output/reports/train/a40209a639c8f1b9802b339bd4aee976/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/adv_predictions.json - adv_probabilities_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/151dec8b8e6dfeaa970d593719a61d62.pkl - params_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/params.yaml - predictions_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/predictions.json - probabilities_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/probabilities.json - score_dict_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/score_dict.json - test_labels_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/test_labels.json - train_labels_file: output/reports/train/a40209a639c8f1b9802b339bd4aee976/train_labels.json - model_dir: models - name: a40209a639c8f1b9802b339bd4aee976 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9b043ae5b7a3bcc9d6f9373840c217df - trainer: - kwargs: {} -name: a40209a639c8f1b9802b339bd4aee976 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a4399e9abf80267200a6cb4c70b9015e/params.yaml b/examples/security/classification/output/reports/train/a4399e9abf80267200a6cb4c70b9015e/params.yaml deleted file mode 100644 index 5cd565a5..00000000 --- a/examples/security/classification/output/reports/train/a4399e9abf80267200a6cb4c70b9015e/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/adv_predictions.json - adv_probabilities_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/ca71410d6032e1a943220e389c88f3cc.pkl - params_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/params.yaml - predictions_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/predictions.json - probabilities_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/probabilities.json - score_dict_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/score_dict.json - test_labels_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/test_labels.json - train_labels_file: output/reports/train/a4399e9abf80267200a6cb4c70b9015e/train_labels.json - model_dir: models - name: a4399e9abf80267200a6cb4c70b9015e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8b916f385679c37eaba4eab2f7b1a15e - trainer: - kwargs: {} -name: a4399e9abf80267200a6cb4c70b9015e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a43a3246f7e2292ff2d7ee530312e507/params.yaml b/examples/security/classification/output/reports/train/a43a3246f7e2292ff2d7ee530312e507/params.yaml deleted file mode 100644 index 644f9409..00000000 --- a/examples/security/classification/output/reports/train/a43a3246f7e2292ff2d7ee530312e507/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/adv_predictions.json - adv_probabilities_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/524e7ba04781956148b3de1a1180952e.pkl - params_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/params.yaml - predictions_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/predictions.json - probabilities_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/probabilities.json - score_dict_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/score_dict.json - test_labels_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/test_labels.json - train_labels_file: output/reports/train/a43a3246f7e2292ff2d7ee530312e507/train_labels.json - model_dir: models - name: a43a3246f7e2292ff2d7ee530312e507 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 257f89c70c95f9d619297ddb7c6fbd6c - trainer: - kwargs: {} -name: a43a3246f7e2292ff2d7ee530312e507 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a446a0f192a2f8468721e718b1dad0a9/params.yaml b/examples/security/classification/output/reports/train/a446a0f192a2f8468721e718b1dad0a9/params.yaml deleted file mode 100644 index ed359ee4..00000000 --- a/examples/security/classification/output/reports/train/a446a0f192a2f8468721e718b1dad0a9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/adv_predictions.json - adv_probabilities_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/6a92f6ddc766c392d5006fba35129f66.pkl - params_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/params.yaml - predictions_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/predictions.json - probabilities_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/probabilities.json - score_dict_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/score_dict.json - test_labels_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/test_labels.json - train_labels_file: output/reports/train/a446a0f192a2f8468721e718b1dad0a9/train_labels.json - model_dir: models - name: a446a0f192a2f8468721e718b1dad0a9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 36a1e48deb3a85b7bcd795d629364807 - trainer: - kwargs: {} -name: a446a0f192a2f8468721e718b1dad0a9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/params.yaml b/examples/security/classification/output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/params.yaml deleted file mode 100644 index 6a01fa2f..00000000 --- a/examples/security/classification/output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/adv_predictions.json - adv_probabilities_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/9cb3f7464c73407390ecb644771a80f1.pkl - params_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/params.yaml - predictions_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/predictions.json - probabilities_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/probabilities.json - score_dict_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/score_dict.json - test_labels_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/test_labels.json - train_labels_file: output/reports/train/a464c0c3cfb5fb9e3f0a0e65b516409c/train_labels.json - model_dir: models - name: a464c0c3cfb5fb9e3f0a0e65b516409c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e3cd86c2ddeb0d6aa51d0691567f3fa2 - trainer: - kwargs: {} -name: a464c0c3cfb5fb9e3f0a0e65b516409c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a47b148dfdfa93407771b5665e46c361/params.yaml b/examples/security/classification/output/reports/train/a47b148dfdfa93407771b5665e46c361/params.yaml deleted file mode 100644 index eea566b0..00000000 --- a/examples/security/classification/output/reports/train/a47b148dfdfa93407771b5665e46c361/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/adv_predictions.json - adv_probabilities_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/0141751b4587a9b64962b4a12c5ff7de.pkl - params_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/params.yaml - predictions_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/predictions.json - probabilities_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/probabilities.json - score_dict_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/score_dict.json - test_labels_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/test_labels.json - train_labels_file: output/reports/train/a47b148dfdfa93407771b5665e46c361/train_labels.json - model_dir: models - name: a47b148dfdfa93407771b5665e46c361 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ccf3eb197dff2bc9ed4f3850c4b69546 - trainer: - kwargs: {} -name: a47b148dfdfa93407771b5665e46c361 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a48be9862a024a19e56c397ed67123a6/params.yaml b/examples/security/classification/output/reports/train/a48be9862a024a19e56c397ed67123a6/params.yaml deleted file mode 100644 index 9e6a3d4e..00000000 --- a/examples/security/classification/output/reports/train/a48be9862a024a19e56c397ed67123a6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/adv_predictions.json - adv_probabilities_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/af4ae425d89ec508b08e3605e07c0a79.pkl - params_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/params.yaml - predictions_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/predictions.json - probabilities_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/probabilities.json - score_dict_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/score_dict.json - test_labels_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/test_labels.json - train_labels_file: output/reports/train/a48be9862a024a19e56c397ed67123a6/train_labels.json - model_dir: models - name: a48be9862a024a19e56c397ed67123a6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b115083e0c5a9f7466e4d2bce9f12a8b - trainer: - kwargs: {} -name: a48be9862a024a19e56c397ed67123a6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/params.yaml b/examples/security/classification/output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/params.yaml deleted file mode 100644 index 670d42c2..00000000 --- a/examples/security/classification/output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/adv_predictions.json - adv_probabilities_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/aa3cf523688cc49aadf9cd0c6e198a96.pkl - params_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/params.yaml - predictions_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/predictions.json - probabilities_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/probabilities.json - score_dict_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/score_dict.json - test_labels_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/test_labels.json - train_labels_file: output/reports/train/a4a3679e567a52a60eacf24e06ca11f9/train_labels.json - model_dir: models - name: a4a3679e567a52a60eacf24e06ca11f9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cfca503d60295e6028fdcfd164fc236d - trainer: - kwargs: {} -name: a4a3679e567a52a60eacf24e06ca11f9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a4af362d9370232375e913bbc33eaff7/params.yaml b/examples/security/classification/output/reports/train/a4af362d9370232375e913bbc33eaff7/params.yaml deleted file mode 100644 index 9452be51..00000000 --- a/examples/security/classification/output/reports/train/a4af362d9370232375e913bbc33eaff7/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/adv_predictions.json - adv_probabilities_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/759c62a9b3e11b483d575b8286364ac9.pkl - params_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/params.yaml - predictions_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/predictions.json - probabilities_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/probabilities.json - score_dict_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/score_dict.json - test_labels_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/test_labels.json - train_labels_file: output/reports/train/a4af362d9370232375e913bbc33eaff7/train_labels.json - model_dir: models - name: a4af362d9370232375e913bbc33eaff7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6cc68e50193c3bac08d756a94ba6eafb - trainer: - kwargs: {} -name: a4af362d9370232375e913bbc33eaff7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a50603e45461cb16ffea69e5997cef65/params.yaml b/examples/security/classification/output/reports/train/a50603e45461cb16ffea69e5997cef65/params.yaml deleted file mode 100644 index 79db3fc7..00000000 --- a/examples/security/classification/output/reports/train/a50603e45461cb16ffea69e5997cef65/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/adv_predictions.json - adv_probabilities_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/5ca040b6cd0e67143a4c14ba70eb0162.pkl - params_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/params.yaml - predictions_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/predictions.json - probabilities_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/probabilities.json - score_dict_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/score_dict.json - test_labels_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/test_labels.json - train_labels_file: output/reports/train/a50603e45461cb16ffea69e5997cef65/train_labels.json - model_dir: models - name: a50603e45461cb16ffea69e5997cef65 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4339bbbe124cdd7d798dafa9ac3fdf85 - trainer: - kwargs: {} -name: a50603e45461cb16ffea69e5997cef65 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/params.yaml b/examples/security/classification/output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/params.yaml deleted file mode 100644 index 9e3dce4d..00000000 --- a/examples/security/classification/output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/adv_predictions.json - adv_probabilities_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/092d3da2d867f3fdba6d8bec0c0af86f.pkl - params_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/params.yaml - predictions_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/predictions.json - probabilities_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/probabilities.json - score_dict_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/score_dict.json - test_labels_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/test_labels.json - train_labels_file: output/reports/train/a50b41cda1c78b76114ffbc1458e9cc2/train_labels.json - model_dir: models - name: a50b41cda1c78b76114ffbc1458e9cc2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 84126a9bb30d27f3d6048fd0ea9b744c - trainer: - kwargs: {} -name: a50b41cda1c78b76114ffbc1458e9cc2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/params.yaml b/examples/security/classification/output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/params.yaml deleted file mode 100644 index b90caf06..00000000 --- a/examples/security/classification/output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/adv_predictions.json - adv_probabilities_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/720add5effc700cd5c5a9a60df0916fa.pkl - params_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/params.yaml - predictions_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/predictions.json - probabilities_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/probabilities.json - score_dict_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/score_dict.json - test_labels_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/test_labels.json - train_labels_file: output/reports/train/a5454f961ccda6dad2f1006d9fa7dfca/train_labels.json - model_dir: models - name: a5454f961ccda6dad2f1006d9fa7dfca - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d21f6ffa4830dfe52728b88e653e74c8 - trainer: - kwargs: {} -name: a5454f961ccda6dad2f1006d9fa7dfca -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/params.yaml b/examples/security/classification/output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/params.yaml deleted file mode 100644 index efc1f9ed..00000000 --- a/examples/security/classification/output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/adv_predictions.json - adv_probabilities_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/d9daf8569341dd3a8bb21f3ec744ee92.pkl - params_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/params.yaml - predictions_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/predictions.json - probabilities_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/probabilities.json - score_dict_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/score_dict.json - test_labels_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/test_labels.json - train_labels_file: output/reports/train/a57cc3f1d7d1b449e7085f11ad758388/train_labels.json - model_dir: models - name: a57cc3f1d7d1b449e7085f11ad758388 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 985b12fd395b9ecdf1f1c2136a124b01 - trainer: - kwargs: {} -name: a57cc3f1d7d1b449e7085f11ad758388 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a58d7415045358e7190c0cdebd9a7a03/params.yaml b/examples/security/classification/output/reports/train/a58d7415045358e7190c0cdebd9a7a03/params.yaml deleted file mode 100644 index cefbe162..00000000 --- a/examples/security/classification/output/reports/train/a58d7415045358e7190c0cdebd9a7a03/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/adv_predictions.json - adv_probabilities_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/cee0227242b313ecfe614991eeda705d.pkl - params_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/params.yaml - predictions_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/predictions.json - probabilities_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/probabilities.json - score_dict_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/score_dict.json - test_labels_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/test_labels.json - train_labels_file: output/reports/train/a58d7415045358e7190c0cdebd9a7a03/train_labels.json - model_dir: models - name: a58d7415045358e7190c0cdebd9a7a03 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dca1c7aa71af2599178915fba22bf2ec - trainer: - kwargs: {} -name: a58d7415045358e7190c0cdebd9a7a03 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a590a282d42d532d14187fc29996b60e/params.yaml b/examples/security/classification/output/reports/train/a590a282d42d532d14187fc29996b60e/params.yaml deleted file mode 100644 index 26187ad5..00000000 --- a/examples/security/classification/output/reports/train/a590a282d42d532d14187fc29996b60e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a590a282d42d532d14187fc29996b60e/adv_predictions.json - adv_probabilities_file: output/reports/train/a590a282d42d532d14187fc29996b60e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/ee362c02a35dcf5c8401a8f0d29fcf3e.pkl - params_file: output/reports/train/a590a282d42d532d14187fc29996b60e/params.yaml - predictions_file: output/reports/train/a590a282d42d532d14187fc29996b60e/predictions.json - probabilities_file: output/reports/train/a590a282d42d532d14187fc29996b60e/probabilities.json - score_dict_file: output/reports/train/a590a282d42d532d14187fc29996b60e/score_dict.json - test_labels_file: output/reports/train/a590a282d42d532d14187fc29996b60e/test_labels.json - train_labels_file: output/reports/train/a590a282d42d532d14187fc29996b60e/train_labels.json - model_dir: models - name: a590a282d42d532d14187fc29996b60e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 84155e03ece1858457c60b1ab5eb5147 - trainer: - kwargs: {} -name: a590a282d42d532d14187fc29996b60e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a59a0b126477bb9dc19a74eac3c17770/params.yaml b/examples/security/classification/output/reports/train/a59a0b126477bb9dc19a74eac3c17770/params.yaml deleted file mode 100644 index ed2df8bc..00000000 --- a/examples/security/classification/output/reports/train/a59a0b126477bb9dc19a74eac3c17770/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/adv_predictions.json - adv_probabilities_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/a1350091b7cce3e0165c89d840b97d42.pkl - params_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/params.yaml - predictions_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/predictions.json - probabilities_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/probabilities.json - score_dict_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/score_dict.json - test_labels_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/test_labels.json - train_labels_file: output/reports/train/a59a0b126477bb9dc19a74eac3c17770/train_labels.json - model_dir: models - name: a59a0b126477bb9dc19a74eac3c17770 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: df06b1354b2ab4dfddf020ccf524e346 - trainer: - kwargs: {} -name: a59a0b126477bb9dc19a74eac3c17770 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/params.yaml b/examples/security/classification/output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/params.yaml deleted file mode 100644 index 22d34c09..00000000 --- a/examples/security/classification/output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/adv_predictions.json - adv_probabilities_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/35613382351401f2c26144d7853ae450.pkl - params_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/params.yaml - predictions_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/predictions.json - probabilities_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/probabilities.json - score_dict_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/score_dict.json - test_labels_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/test_labels.json - train_labels_file: output/reports/train/a5a43ac8a2e3829fc4a1c9b2f66b803e/train_labels.json - model_dir: models - name: a5a43ac8a2e3829fc4a1c9b2f66b803e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 63a7391f584952bad4ad45a4db6ab7ac - trainer: - kwargs: {} -name: a5a43ac8a2e3829fc4a1c9b2f66b803e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a5a559791364c3698f084d345e7553d1/params.yaml b/examples/security/classification/output/reports/train/a5a559791364c3698f084d345e7553d1/params.yaml deleted file mode 100644 index 8abcb8b6..00000000 --- a/examples/security/classification/output/reports/train/a5a559791364c3698f084d345e7553d1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a5a559791364c3698f084d345e7553d1/adv_predictions.json - adv_probabilities_file: output/reports/train/a5a559791364c3698f084d345e7553d1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/110f67521597f34bc230fdb2908ef26b.pkl - params_file: output/reports/train/a5a559791364c3698f084d345e7553d1/params.yaml - predictions_file: output/reports/train/a5a559791364c3698f084d345e7553d1/predictions.json - probabilities_file: output/reports/train/a5a559791364c3698f084d345e7553d1/probabilities.json - score_dict_file: output/reports/train/a5a559791364c3698f084d345e7553d1/score_dict.json - test_labels_file: output/reports/train/a5a559791364c3698f084d345e7553d1/test_labels.json - train_labels_file: output/reports/train/a5a559791364c3698f084d345e7553d1/train_labels.json - model_dir: models - name: a5a559791364c3698f084d345e7553d1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0cbda44821e2cc029426f325fba74c3a - trainer: - kwargs: {} -name: a5a559791364c3698f084d345e7553d1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/params.yaml b/examples/security/classification/output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/params.yaml deleted file mode 100644 index d30a35ad..00000000 --- a/examples/security/classification/output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/adv_predictions.json - adv_probabilities_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/67e2bd0454ca0ec954d0cc412cbddf49.pkl - params_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/params.yaml - predictions_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/predictions.json - probabilities_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/probabilities.json - score_dict_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/score_dict.json - test_labels_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/test_labels.json - train_labels_file: output/reports/train/a5b2b557d0ecc7f7d43d87a036743989/train_labels.json - model_dir: models - name: a5b2b557d0ecc7f7d43d87a036743989 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 67f7552d549ee2f7224e24895a3a4d76 - trainer: - kwargs: {} -name: a5b2b557d0ecc7f7d43d87a036743989 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/params.yaml b/examples/security/classification/output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/params.yaml deleted file mode 100644 index a7034d62..00000000 --- a/examples/security/classification/output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/adv_predictions.json - adv_probabilities_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/48e2b378c50b4adc38fe0c032c086790.pkl - params_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/params.yaml - predictions_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/predictions.json - probabilities_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/probabilities.json - score_dict_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/score_dict.json - test_labels_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/test_labels.json - train_labels_file: output/reports/train/a5ce95b9c844c40cd05b94af46fd4a45/train_labels.json - model_dir: models - name: a5ce95b9c844c40cd05b94af46fd4a45 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8c38771c2672bc8e0a23c64ed455f1bf - trainer: - kwargs: {} -name: a5ce95b9c844c40cd05b94af46fd4a45 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a61870c04d57efcdbc23558d37c319c9/params.yaml b/examples/security/classification/output/reports/train/a61870c04d57efcdbc23558d37c319c9/params.yaml deleted file mode 100644 index 95d53af8..00000000 --- a/examples/security/classification/output/reports/train/a61870c04d57efcdbc23558d37c319c9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/adv_predictions.json - adv_probabilities_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/2205789959f1f7cc3130ac6413ddba1b.pkl - params_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/params.yaml - predictions_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/predictions.json - probabilities_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/probabilities.json - score_dict_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/score_dict.json - test_labels_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/test_labels.json - train_labels_file: output/reports/train/a61870c04d57efcdbc23558d37c319c9/train_labels.json - model_dir: models - name: a61870c04d57efcdbc23558d37c319c9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2a57c3767cd8b6f208318b9ee63a053f - trainer: - kwargs: {} -name: a61870c04d57efcdbc23558d37c319c9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a66f073278b10e184d5e492effb45266/params.yaml b/examples/security/classification/output/reports/train/a66f073278b10e184d5e492effb45266/params.yaml deleted file mode 100644 index fe2e1858..00000000 --- a/examples/security/classification/output/reports/train/a66f073278b10e184d5e492effb45266/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a66f073278b10e184d5e492effb45266/adv_predictions.json - adv_probabilities_file: output/reports/train/a66f073278b10e184d5e492effb45266/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/f48602affc7f92289a0a0081da354481.pkl - params_file: output/reports/train/a66f073278b10e184d5e492effb45266/params.yaml - predictions_file: output/reports/train/a66f073278b10e184d5e492effb45266/predictions.json - probabilities_file: output/reports/train/a66f073278b10e184d5e492effb45266/probabilities.json - score_dict_file: output/reports/train/a66f073278b10e184d5e492effb45266/score_dict.json - test_labels_file: output/reports/train/a66f073278b10e184d5e492effb45266/test_labels.json - train_labels_file: output/reports/train/a66f073278b10e184d5e492effb45266/train_labels.json - model_dir: models - name: a66f073278b10e184d5e492effb45266 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f2e1349ef54b2f4dad4d080bc5ac406 - trainer: - kwargs: {} -name: a66f073278b10e184d5e492effb45266 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/params.yaml b/examples/security/classification/output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/params.yaml deleted file mode 100644 index cd5a98eb..00000000 --- a/examples/security/classification/output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/adv_predictions.json - adv_probabilities_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/3309adebcce2315332f7eb777d472cdd.pkl - params_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/params.yaml - predictions_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/predictions.json - probabilities_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/probabilities.json - score_dict_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/score_dict.json - test_labels_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/test_labels.json - train_labels_file: output/reports/train/a67b9eeccf5f7b56ffb454a8c2fbb264/train_labels.json - model_dir: models - name: a67b9eeccf5f7b56ffb454a8c2fbb264 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b99ebb38bafc697d665b010af1bf3dc2 - trainer: - kwargs: {} -name: a67b9eeccf5f7b56ffb454a8c2fbb264 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a687227e8c474a8f0a26be341c559140/params.yaml b/examples/security/classification/output/reports/train/a687227e8c474a8f0a26be341c559140/params.yaml deleted file mode 100644 index 6d8fcb96..00000000 --- a/examples/security/classification/output/reports/train/a687227e8c474a8f0a26be341c559140/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a687227e8c474a8f0a26be341c559140/adv_predictions.json - adv_probabilities_file: output/reports/train/a687227e8c474a8f0a26be341c559140/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/c6063ee0f3d4059e0026543c92fc88f8.pkl - params_file: output/reports/train/a687227e8c474a8f0a26be341c559140/params.yaml - predictions_file: output/reports/train/a687227e8c474a8f0a26be341c559140/predictions.json - probabilities_file: output/reports/train/a687227e8c474a8f0a26be341c559140/probabilities.json - score_dict_file: output/reports/train/a687227e8c474a8f0a26be341c559140/score_dict.json - test_labels_file: output/reports/train/a687227e8c474a8f0a26be341c559140/test_labels.json - train_labels_file: output/reports/train/a687227e8c474a8f0a26be341c559140/train_labels.json - model_dir: models - name: a687227e8c474a8f0a26be341c559140 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 09c19827dbd5fab2781ee9be302987d4 - trainer: - kwargs: {} -name: a687227e8c474a8f0a26be341c559140 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a6b406419d3aad5f462c0af9e4faf132/params.yaml b/examples/security/classification/output/reports/train/a6b406419d3aad5f462c0af9e4faf132/params.yaml deleted file mode 100644 index 9a93453f..00000000 --- a/examples/security/classification/output/reports/train/a6b406419d3aad5f462c0af9e4faf132/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/adv_predictions.json - adv_probabilities_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/c5516a3931a4a555a8399684a5c9f2c5.pkl - params_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/params.yaml - predictions_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/predictions.json - probabilities_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/probabilities.json - score_dict_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/score_dict.json - test_labels_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/test_labels.json - train_labels_file: output/reports/train/a6b406419d3aad5f462c0af9e4faf132/train_labels.json - model_dir: models - name: a6b406419d3aad5f462c0af9e4faf132 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 21c15b6e58f66f81e27e8d0a8bfb7ec8 - trainer: - kwargs: {} -name: a6b406419d3aad5f462c0af9e4faf132 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a6c99e07d24a1762aada188e838734e9/params.yaml b/examples/security/classification/output/reports/train/a6c99e07d24a1762aada188e838734e9/params.yaml deleted file mode 100644 index 1c7744f1..00000000 --- a/examples/security/classification/output/reports/train/a6c99e07d24a1762aada188e838734e9/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/adv_predictions.json - adv_probabilities_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/9287295517711c9a033c2c1723f23b2b.pkl - params_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/params.yaml - predictions_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/predictions.json - probabilities_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/probabilities.json - score_dict_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/score_dict.json - test_labels_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/test_labels.json - train_labels_file: output/reports/train/a6c99e07d24a1762aada188e838734e9/train_labels.json - model_dir: models - name: a6c99e07d24a1762aada188e838734e9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8fb98cacbc283dedec469261149c319f - trainer: - kwargs: {} -name: a6c99e07d24a1762aada188e838734e9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/params.yaml b/examples/security/classification/output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/params.yaml deleted file mode 100644 index d48ce5cb..00000000 --- a/examples/security/classification/output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/adv_predictions.json - adv_probabilities_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e990daeb2ca22f098f9df7c420790721.pkl - params_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/params.yaml - predictions_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/predictions.json - probabilities_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/probabilities.json - score_dict_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/score_dict.json - test_labels_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/test_labels.json - train_labels_file: output/reports/train/a709b5b3e2ca20ac12a6bc30d12fbf7a/train_labels.json - model_dir: models - name: a709b5b3e2ca20ac12a6bc30d12fbf7a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 1 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: def533b60f4a54e38b79728d65e2af84 - trainer: - kwargs: {} -name: a709b5b3e2ca20ac12a6bc30d12fbf7a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a71451127e480314bcfc1e31b40c7985/params.yaml b/examples/security/classification/output/reports/train/a71451127e480314bcfc1e31b40c7985/params.yaml deleted file mode 100644 index 579a270f..00000000 --- a/examples/security/classification/output/reports/train/a71451127e480314bcfc1e31b40c7985/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/adv_predictions.json - adv_probabilities_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/19498bfed45301d54bb07f252235e2c2.pkl - params_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/params.yaml - predictions_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/predictions.json - probabilities_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/probabilities.json - score_dict_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/score_dict.json - test_labels_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/test_labels.json - train_labels_file: output/reports/train/a71451127e480314bcfc1e31b40c7985/train_labels.json - model_dir: models - name: a71451127e480314bcfc1e31b40c7985 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c4007bb6b0d6d16b3e8a1a6d02603421 - trainer: - kwargs: {} -name: a71451127e480314bcfc1e31b40c7985 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/params.yaml b/examples/security/classification/output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/params.yaml deleted file mode 100644 index 87542f7b..00000000 --- a/examples/security/classification/output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/adv_predictions.json - adv_probabilities_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/40bf6d973462a9f1579b97074a209afa.pkl - params_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/params.yaml - predictions_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/predictions.json - probabilities_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/probabilities.json - score_dict_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/score_dict.json - test_labels_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/test_labels.json - train_labels_file: output/reports/train/a754c9b68bce4b7ab00ec8ba4f1d3f88/train_labels.json - model_dir: models - name: a754c9b68bce4b7ab00ec8ba4f1d3f88 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0ba4b9217dc923d089011b8bc1651608 - trainer: - kwargs: {} -name: a754c9b68bce4b7ab00ec8ba4f1d3f88 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a76addfb000e1970b56aaeb6532e1932/params.yaml b/examples/security/classification/output/reports/train/a76addfb000e1970b56aaeb6532e1932/params.yaml deleted file mode 100644 index c038c395..00000000 --- a/examples/security/classification/output/reports/train/a76addfb000e1970b56aaeb6532e1932/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/adv_predictions.json - adv_probabilities_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/205c188ace7fe6514d92b37a2a9e0387.pkl - params_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/params.yaml - predictions_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/predictions.json - probabilities_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/probabilities.json - score_dict_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/score_dict.json - test_labels_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/test_labels.json - train_labels_file: output/reports/train/a76addfb000e1970b56aaeb6532e1932/train_labels.json - model_dir: models - name: a76addfb000e1970b56aaeb6532e1932 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bd17164bfedbba2ad4289ced8b516d4a - trainer: - kwargs: {} -name: a76addfb000e1970b56aaeb6532e1932 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/params.yaml b/examples/security/classification/output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/params.yaml deleted file mode 100644 index 80b45c04..00000000 --- a/examples/security/classification/output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/adv_predictions.json - adv_probabilities_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/f639b089f87671a118eb7dca2742b6d2.pkl - params_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/params.yaml - predictions_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/predictions.json - probabilities_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/probabilities.json - score_dict_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/score_dict.json - test_labels_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/test_labels.json - train_labels_file: output/reports/train/a78f2cdbef28bc4a4d9b4999ab6c57b7/train_labels.json - model_dir: models - name: a78f2cdbef28bc4a4d9b4999ab6c57b7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 237526123672c99c4c3c6a522ad18400 - trainer: - kwargs: {} -name: a78f2cdbef28bc4a4d9b4999ab6c57b7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/params.yaml b/examples/security/classification/output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/params.yaml deleted file mode 100644 index 6b4dd69b..00000000 --- a/examples/security/classification/output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/adv_predictions.json - adv_probabilities_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/5c6e1ad97e9d3cd04e00fd54c572cbe6.pkl - params_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/params.yaml - predictions_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/predictions.json - probabilities_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/probabilities.json - score_dict_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/score_dict.json - test_labels_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/test_labels.json - train_labels_file: output/reports/train/a79cf7f082b3175dcd3e6dcd266e035e/train_labels.json - model_dir: models - name: a79cf7f082b3175dcd3e6dcd266e035e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c641d78c08711e6bb2de7bd459736ed1 - trainer: - kwargs: {} -name: a79cf7f082b3175dcd3e6dcd266e035e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/params.yaml b/examples/security/classification/output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/params.yaml deleted file mode 100644 index e38ace92..00000000 --- a/examples/security/classification/output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/adv_predictions.json - adv_probabilities_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/977ca69c164a0c9262882270e9a6436e.pkl - params_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/params.yaml - predictions_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/predictions.json - probabilities_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/probabilities.json - score_dict_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/score_dict.json - test_labels_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/test_labels.json - train_labels_file: output/reports/train/a7b0d5b9853ae2bd067f63a373b24307/train_labels.json - model_dir: models - name: a7b0d5b9853ae2bd067f63a373b24307 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ed9b2e0a03cb3c6574df42e1ba97e18f - trainer: - kwargs: {} -name: a7b0d5b9853ae2bd067f63a373b24307 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/params.yaml b/examples/security/classification/output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/params.yaml deleted file mode 100644 index 1b5a265d..00000000 --- a/examples/security/classification/output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/adv_predictions.json - adv_probabilities_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/081b363802691c825bc6152d1ab1193f.pkl - params_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/params.yaml - predictions_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/predictions.json - probabilities_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/probabilities.json - score_dict_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/score_dict.json - test_labels_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/test_labels.json - train_labels_file: output/reports/train/a7e1e32b8f77f6c7a6c0dee7d7ea9db0/train_labels.json - model_dir: models - name: a7e1e32b8f77f6c7a6c0dee7d7ea9db0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 30b62abf7513b7dce42ce5fdcd85b9a5 - trainer: - kwargs: {} -name: a7e1e32b8f77f6c7a6c0dee7d7ea9db0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a7e7245d271d19722067ee3fb4d8f706/params.yaml b/examples/security/classification/output/reports/train/a7e7245d271d19722067ee3fb4d8f706/params.yaml deleted file mode 100644 index a48cd58c..00000000 --- a/examples/security/classification/output/reports/train/a7e7245d271d19722067ee3fb4d8f706/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/adv_predictions.json - adv_probabilities_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/28b510e46ec28f052d6559f9cb554979.pkl - params_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/params.yaml - predictions_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/predictions.json - probabilities_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/probabilities.json - score_dict_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/score_dict.json - test_labels_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/test_labels.json - train_labels_file: output/reports/train/a7e7245d271d19722067ee3fb4d8f706/train_labels.json - model_dir: models - name: a7e7245d271d19722067ee3fb4d8f706 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1cae82d95e29b3b8bcc8a3df201d149d - trainer: - kwargs: {} -name: a7e7245d271d19722067ee3fb4d8f706 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/params.yaml b/examples/security/classification/output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/params.yaml deleted file mode 100644 index ae89799a..00000000 --- a/examples/security/classification/output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/adv_predictions.json - adv_probabilities_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/127c3f328f2637dc4c38e9377d38dc3e.pkl - params_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/params.yaml - predictions_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/predictions.json - probabilities_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/probabilities.json - score_dict_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/score_dict.json - test_labels_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/test_labels.json - train_labels_file: output/reports/train/a7f3fde7da75225e24e3c66b87cdfd82/train_labels.json - model_dir: models - name: a7f3fde7da75225e24e3c66b87cdfd82 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 374e578e7556b6429dc80cf8ea26564c - trainer: - kwargs: {} -name: a7f3fde7da75225e24e3c66b87cdfd82 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a7f864155a304f14b3a2c62520ad8887/params.yaml b/examples/security/classification/output/reports/train/a7f864155a304f14b3a2c62520ad8887/params.yaml deleted file mode 100644 index 383c8ad7..00000000 --- a/examples/security/classification/output/reports/train/a7f864155a304f14b3a2c62520ad8887/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/adv_predictions.json - adv_probabilities_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/7d05a1687fe0392d6f468a26b8b6ae22.pkl - params_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/params.yaml - predictions_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/predictions.json - probabilities_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/probabilities.json - score_dict_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/score_dict.json - test_labels_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/test_labels.json - train_labels_file: output/reports/train/a7f864155a304f14b3a2c62520ad8887/train_labels.json - model_dir: models - name: a7f864155a304f14b3a2c62520ad8887 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bb009f71fae8a2c82e3bb8eb8a858940 - trainer: - kwargs: {} -name: a7f864155a304f14b3a2c62520ad8887 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a8135ac60cdee920ced5264287d35249/params.yaml b/examples/security/classification/output/reports/train/a8135ac60cdee920ced5264287d35249/params.yaml deleted file mode 100644 index 8e1a274c..00000000 --- a/examples/security/classification/output/reports/train/a8135ac60cdee920ced5264287d35249/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a8135ac60cdee920ced5264287d35249/adv_predictions.json - adv_probabilities_file: output/reports/train/a8135ac60cdee920ced5264287d35249/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/897326bc85a00a55220da92fe57a4882.pkl - params_file: output/reports/train/a8135ac60cdee920ced5264287d35249/params.yaml - predictions_file: output/reports/train/a8135ac60cdee920ced5264287d35249/predictions.json - probabilities_file: output/reports/train/a8135ac60cdee920ced5264287d35249/probabilities.json - score_dict_file: output/reports/train/a8135ac60cdee920ced5264287d35249/score_dict.json - test_labels_file: output/reports/train/a8135ac60cdee920ced5264287d35249/test_labels.json - train_labels_file: output/reports/train/a8135ac60cdee920ced5264287d35249/train_labels.json - model_dir: models - name: a8135ac60cdee920ced5264287d35249 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a2e1784426d6358e1c58b7529f01cdc9 - trainer: - kwargs: {} -name: a8135ac60cdee920ced5264287d35249 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a82848ffc54e0712b820e7101bed15ab/params.yaml b/examples/security/classification/output/reports/train/a82848ffc54e0712b820e7101bed15ab/params.yaml deleted file mode 100644 index 7790f360..00000000 --- a/examples/security/classification/output/reports/train/a82848ffc54e0712b820e7101bed15ab/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/adv_predictions.json - adv_probabilities_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/49fe9399ba265913c97271ad4d615033.pkl - params_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/params.yaml - predictions_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/predictions.json - probabilities_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/probabilities.json - score_dict_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/score_dict.json - test_labels_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/test_labels.json - train_labels_file: output/reports/train/a82848ffc54e0712b820e7101bed15ab/train_labels.json - model_dir: models - name: a82848ffc54e0712b820e7101bed15ab - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27695bd284d2872b4de9df7a2aed6655 - trainer: - kwargs: {} -name: a82848ffc54e0712b820e7101bed15ab -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a8340246d95d8c455359613540d7cbe1/params.yaml b/examples/security/classification/output/reports/train/a8340246d95d8c455359613540d7cbe1/params.yaml deleted file mode 100644 index 7fc2c156..00000000 --- a/examples/security/classification/output/reports/train/a8340246d95d8c455359613540d7cbe1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/adv_predictions.json - adv_probabilities_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/60d0595a497c4a4784d1b6fa8b47d087.pkl - params_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/params.yaml - predictions_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/predictions.json - probabilities_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/probabilities.json - score_dict_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/score_dict.json - test_labels_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/test_labels.json - train_labels_file: output/reports/train/a8340246d95d8c455359613540d7cbe1/train_labels.json - model_dir: models - name: a8340246d95d8c455359613540d7cbe1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f1bf804fda6696f058690047a8be18e7 - trainer: - kwargs: {} -name: a8340246d95d8c455359613540d7cbe1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/params.yaml b/examples/security/classification/output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/params.yaml deleted file mode 100644 index cca09d60..00000000 --- a/examples/security/classification/output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/adv_predictions.json - adv_probabilities_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/36ffea9eff1d9fe19d437365b868fb45.pkl - params_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/params.yaml - predictions_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/predictions.json - probabilities_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/probabilities.json - score_dict_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/score_dict.json - test_labels_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/test_labels.json - train_labels_file: output/reports/train/a83432dafd16c5d5748a9ba6e430ef97/train_labels.json - model_dir: models - name: a83432dafd16c5d5748a9ba6e430ef97 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 35d701b92fa9622cb2061b9a30eda505 - trainer: - kwargs: {} -name: a83432dafd16c5d5748a9ba6e430ef97 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/params.yaml b/examples/security/classification/output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/params.yaml deleted file mode 100644 index 4b5d3926..00000000 --- a/examples/security/classification/output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/adv_predictions.json - adv_probabilities_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/726afa7369f2e1965393be774c1c3e71.pkl - params_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/params.yaml - predictions_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/predictions.json - probabilities_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/probabilities.json - score_dict_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/score_dict.json - test_labels_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/test_labels.json - train_labels_file: output/reports/train/a83e4aeb62e9a279fb17c97b5f45a729/train_labels.json - model_dir: models - name: a83e4aeb62e9a279fb17c97b5f45a729 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 89545b55803624ac03482af0a3f879ac - trainer: - kwargs: {} -name: a83e4aeb62e9a279fb17c97b5f45a729 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/params.yaml b/examples/security/classification/output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/params.yaml deleted file mode 100644 index 176df04b..00000000 --- a/examples/security/classification/output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/adv_predictions.json - adv_probabilities_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/8ca4a636aaed54aa7cd8511754aa782c.pkl - params_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/params.yaml - predictions_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/predictions.json - probabilities_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/probabilities.json - score_dict_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/score_dict.json - test_labels_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/test_labels.json - train_labels_file: output/reports/train/a8415e20f3e08faebc112ea2eb8c4eb7/train_labels.json - model_dir: models - name: a8415e20f3e08faebc112ea2eb8c4eb7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7e317557d0d8c48aba60e71a0065f268 - trainer: - kwargs: {} -name: a8415e20f3e08faebc112ea2eb8c4eb7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a87578840849005d5ab95107f28e06ca/params.yaml b/examples/security/classification/output/reports/train/a87578840849005d5ab95107f28e06ca/params.yaml deleted file mode 100644 index 28ac9f21..00000000 --- a/examples/security/classification/output/reports/train/a87578840849005d5ab95107f28e06ca/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a87578840849005d5ab95107f28e06ca/adv_predictions.json - adv_probabilities_file: output/reports/train/a87578840849005d5ab95107f28e06ca/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/5b14ca21470b5616d3edb67714bd9bf1.pkl - params_file: output/reports/train/a87578840849005d5ab95107f28e06ca/params.yaml - predictions_file: output/reports/train/a87578840849005d5ab95107f28e06ca/predictions.json - probabilities_file: output/reports/train/a87578840849005d5ab95107f28e06ca/probabilities.json - score_dict_file: output/reports/train/a87578840849005d5ab95107f28e06ca/score_dict.json - test_labels_file: output/reports/train/a87578840849005d5ab95107f28e06ca/test_labels.json - train_labels_file: output/reports/train/a87578840849005d5ab95107f28e06ca/train_labels.json - model_dir: models - name: a87578840849005d5ab95107f28e06ca - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5b8daac61c5a8f2828435656c8815256 - trainer: - kwargs: {} -name: a87578840849005d5ab95107f28e06ca -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a87febee068e946a95a5d7369cc3f567/params.yaml b/examples/security/classification/output/reports/train/a87febee068e946a95a5d7369cc3f567/params.yaml deleted file mode 100644 index d30565ae..00000000 --- a/examples/security/classification/output/reports/train/a87febee068e946a95a5d7369cc3f567/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/adv_predictions.json - adv_probabilities_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/2838fece93db69d57963329595856633.pkl - params_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/params.yaml - predictions_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/predictions.json - probabilities_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/probabilities.json - score_dict_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/score_dict.json - test_labels_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/test_labels.json - train_labels_file: output/reports/train/a87febee068e946a95a5d7369cc3f567/train_labels.json - model_dir: models - name: a87febee068e946a95a5d7369cc3f567 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7f7bb8b87a903284282c8a5266d9b3b6 - trainer: - kwargs: {} -name: a87febee068e946a95a5d7369cc3f567 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/params.yaml b/examples/security/classification/output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/params.yaml deleted file mode 100644 index 4cd221d3..00000000 --- a/examples/security/classification/output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/adv_predictions.json - adv_probabilities_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/1cbb7d102aa8ecfe8e87eaa0b476e66d.pkl - params_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/params.yaml - predictions_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/predictions.json - probabilities_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/probabilities.json - score_dict_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/score_dict.json - test_labels_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/test_labels.json - train_labels_file: output/reports/train/a8853ec30b06b00961832ad6e72d1f0b/train_labels.json - model_dir: models - name: a8853ec30b06b00961832ad6e72d1f0b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b1835f90f4b5c1fcb020a6af4700c5e7 - trainer: - kwargs: {} -name: a8853ec30b06b00961832ad6e72d1f0b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/params.yaml b/examples/security/classification/output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/params.yaml deleted file mode 100644 index 37f1817c..00000000 --- a/examples/security/classification/output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/adv_predictions.json - adv_probabilities_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/0856c9a8e21e8594bcb4d0339790a1d7.pkl - params_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/params.yaml - predictions_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/predictions.json - probabilities_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/probabilities.json - score_dict_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/score_dict.json - test_labels_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/test_labels.json - train_labels_file: output/reports/train/a885d0ba60c14271e0ed7fd38c2c2745/train_labels.json - model_dir: models - name: a885d0ba60c14271e0ed7fd38c2c2745 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bd29212c04c661dbff36b2ae0fd2308f - trainer: - kwargs: {} -name: a885d0ba60c14271e0ed7fd38c2c2745 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a8d046b3571456df5e345180ca4990bf/params.yaml b/examples/security/classification/output/reports/train/a8d046b3571456df5e345180ca4990bf/params.yaml deleted file mode 100644 index 79af84a6..00000000 --- a/examples/security/classification/output/reports/train/a8d046b3571456df5e345180ca4990bf/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/adv_predictions.json - adv_probabilities_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/f0a480633aeec853fccd58e45c36f4bd.pkl - params_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/params.yaml - predictions_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/predictions.json - probabilities_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/probabilities.json - score_dict_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/score_dict.json - test_labels_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/test_labels.json - train_labels_file: output/reports/train/a8d046b3571456df5e345180ca4990bf/train_labels.json - model_dir: models - name: a8d046b3571456df5e345180ca4990bf - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2290a461c0d9572238030312bf86a983 - trainer: - kwargs: {} -name: a8d046b3571456df5e345180ca4990bf -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/params.yaml b/examples/security/classification/output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/params.yaml deleted file mode 100644 index a0a23e8c..00000000 --- a/examples/security/classification/output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/adv_predictions.json - adv_probabilities_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/632ae35801b4cc51ae09088e499760aa.pkl - params_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/params.yaml - predictions_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/predictions.json - probabilities_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/probabilities.json - score_dict_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/score_dict.json - test_labels_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/test_labels.json - train_labels_file: output/reports/train/a8f654a9b24219bdaafa96bb53ab82da/train_labels.json - model_dir: models - name: a8f654a9b24219bdaafa96bb53ab82da - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 60414b14a7d4049f8a0b18d95ee99639 - trainer: - kwargs: {} -name: a8f654a9b24219bdaafa96bb53ab82da -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/params.yaml b/examples/security/classification/output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/params.yaml deleted file mode 100644 index d75e2f4d..00000000 --- a/examples/security/classification/output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/adv_predictions.json - adv_probabilities_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/adb0348b424fb1e27424a7d25c8fe225.pkl - params_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/params.yaml - predictions_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/predictions.json - probabilities_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/probabilities.json - score_dict_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/score_dict.json - test_labels_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/test_labels.json - train_labels_file: output/reports/train/a8fb7f6707ce69fa331b0c01703349f9/train_labels.json - model_dir: models - name: a8fb7f6707ce69fa331b0c01703349f9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4d2ec846a3bdca669a8f4e770c006e9b - trainer: - kwargs: {} -name: a8fb7f6707ce69fa331b0c01703349f9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/params.yaml b/examples/security/classification/output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/params.yaml deleted file mode 100644 index b3c8cb92..00000000 --- a/examples/security/classification/output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/adv_predictions.json - adv_probabilities_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/cfabd68eaba3e90dd51e870ceeafedb1.pkl - params_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/params.yaml - predictions_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/predictions.json - probabilities_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/probabilities.json - score_dict_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/score_dict.json - test_labels_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/test_labels.json - train_labels_file: output/reports/train/a8fe4625a81e981f1f4fadd0339eeb59/train_labels.json - model_dir: models - name: a8fe4625a81e981f1f4fadd0339eeb59 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ad2dc5fb862dc591e088e14bea8df925 - trainer: - kwargs: {} -name: a8fe4625a81e981f1f4fadd0339eeb59 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/params.yaml b/examples/security/classification/output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/params.yaml deleted file mode 100644 index e3c11de7..00000000 --- a/examples/security/classification/output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/adv_predictions.json - adv_probabilities_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/4e7f487f221c91bb31b011b02606faf6.pkl - params_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/params.yaml - predictions_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/predictions.json - probabilities_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/probabilities.json - score_dict_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/score_dict.json - test_labels_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/test_labels.json - train_labels_file: output/reports/train/a92c60c57c88f7512065d16b3d0a0ef1/train_labels.json - model_dir: models - name: a92c60c57c88f7512065d16b3d0a0ef1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3b935c1fdaf28f4645d9c98c458e1bfc - trainer: - kwargs: {} -name: a92c60c57c88f7512065d16b3d0a0ef1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a9886a74a6030b31a40715b7715df0aa/params.yaml b/examples/security/classification/output/reports/train/a9886a74a6030b31a40715b7715df0aa/params.yaml deleted file mode 100644 index 03214ee2..00000000 --- a/examples/security/classification/output/reports/train/a9886a74a6030b31a40715b7715df0aa/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/adv_predictions.json - adv_probabilities_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/dd2c0902510caba68a8a6b1f8d38840d.pkl - params_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/params.yaml - predictions_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/predictions.json - probabilities_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/probabilities.json - score_dict_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/score_dict.json - test_labels_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/test_labels.json - train_labels_file: output/reports/train/a9886a74a6030b31a40715b7715df0aa/train_labels.json - model_dir: models - name: a9886a74a6030b31a40715b7715df0aa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e13e5d89365bf1f42aa022049d9c1752 - trainer: - kwargs: {} -name: a9886a74a6030b31a40715b7715df0aa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/params.yaml b/examples/security/classification/output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/params.yaml deleted file mode 100644 index 893628f8..00000000 --- a/examples/security/classification/output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/adv_predictions.json - adv_probabilities_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/5455b28ad32a818e2f662c6c293014e5.pkl - params_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/params.yaml - predictions_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/predictions.json - probabilities_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/probabilities.json - score_dict_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/score_dict.json - test_labels_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/test_labels.json - train_labels_file: output/reports/train/a98babb8e52c5fca8a9a56d45d42aeb4/train_labels.json - model_dir: models - name: a98babb8e52c5fca8a9a56d45d42aeb4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 428eeb07069b984257a60cb3086674b9 - trainer: - kwargs: {} -name: a98babb8e52c5fca8a9a56d45d42aeb4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a9be778efdd15774415920e4168b241e/params.yaml b/examples/security/classification/output/reports/train/a9be778efdd15774415920e4168b241e/params.yaml deleted file mode 100644 index b53e989f..00000000 --- a/examples/security/classification/output/reports/train/a9be778efdd15774415920e4168b241e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a9be778efdd15774415920e4168b241e/adv_predictions.json - adv_probabilities_file: output/reports/train/a9be778efdd15774415920e4168b241e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/5d1e75a00af023fefe3f945427cf36a5.pkl - params_file: output/reports/train/a9be778efdd15774415920e4168b241e/params.yaml - predictions_file: output/reports/train/a9be778efdd15774415920e4168b241e/predictions.json - probabilities_file: output/reports/train/a9be778efdd15774415920e4168b241e/probabilities.json - score_dict_file: output/reports/train/a9be778efdd15774415920e4168b241e/score_dict.json - test_labels_file: output/reports/train/a9be778efdd15774415920e4168b241e/test_labels.json - train_labels_file: output/reports/train/a9be778efdd15774415920e4168b241e/train_labels.json - model_dir: models - name: a9be778efdd15774415920e4168b241e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 29a26838f325f4cdddbb23d7c4a34310 - trainer: - kwargs: {} -name: a9be778efdd15774415920e4168b241e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a9cc2132d446a4121484c89e3a96f163/params.yaml b/examples/security/classification/output/reports/train/a9cc2132d446a4121484c89e3a96f163/params.yaml deleted file mode 100644 index d03ad2d7..00000000 --- a/examples/security/classification/output/reports/train/a9cc2132d446a4121484c89e3a96f163/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/adv_predictions.json - adv_probabilities_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/cf8d77f1fb901d51ee193d6c65993a77.pkl - params_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/params.yaml - predictions_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/predictions.json - probabilities_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/probabilities.json - score_dict_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/score_dict.json - test_labels_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/test_labels.json - train_labels_file: output/reports/train/a9cc2132d446a4121484c89e3a96f163/train_labels.json - model_dir: models - name: a9cc2132d446a4121484c89e3a96f163 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a46f02b4a26832382714a4ccbde89436 - trainer: - kwargs: {} -name: a9cc2132d446a4121484c89e3a96f163 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a9dc535921401e87dee2be776ec5454e/params.yaml b/examples/security/classification/output/reports/train/a9dc535921401e87dee2be776ec5454e/params.yaml deleted file mode 100644 index a68e628a..00000000 --- a/examples/security/classification/output/reports/train/a9dc535921401e87dee2be776ec5454e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/adv_predictions.json - adv_probabilities_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/cc6801a594ab3362a30bc9401af04968.pkl - params_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/params.yaml - predictions_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/predictions.json - probabilities_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/probabilities.json - score_dict_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/score_dict.json - test_labels_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/test_labels.json - train_labels_file: output/reports/train/a9dc535921401e87dee2be776ec5454e/train_labels.json - model_dir: models - name: a9dc535921401e87dee2be776ec5454e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 35ea584f91f36cbf826a21fbaae5f5dc - trainer: - kwargs: {} -name: a9dc535921401e87dee2be776ec5454e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/a9df98da4626f217911b54e16423e132/params.yaml b/examples/security/classification/output/reports/train/a9df98da4626f217911b54e16423e132/params.yaml deleted file mode 100644 index 760e50a2..00000000 --- a/examples/security/classification/output/reports/train/a9df98da4626f217911b54e16423e132/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/a9df98da4626f217911b54e16423e132/adv_predictions.json - adv_probabilities_file: output/reports/train/a9df98da4626f217911b54e16423e132/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/af6247d3c7beab493dd197140a5a547c.pkl - params_file: output/reports/train/a9df98da4626f217911b54e16423e132/params.yaml - predictions_file: output/reports/train/a9df98da4626f217911b54e16423e132/predictions.json - probabilities_file: output/reports/train/a9df98da4626f217911b54e16423e132/probabilities.json - score_dict_file: output/reports/train/a9df98da4626f217911b54e16423e132/score_dict.json - test_labels_file: output/reports/train/a9df98da4626f217911b54e16423e132/test_labels.json - train_labels_file: output/reports/train/a9df98da4626f217911b54e16423e132/train_labels.json - model_dir: models - name: a9df98da4626f217911b54e16423e132 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e6b36042d6cc65599e47c1860c7d44c5 - trainer: - kwargs: {} -name: a9df98da4626f217911b54e16423e132 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aa130eb20801e3a88b60800ba4660356/params.yaml b/examples/security/classification/output/reports/train/aa130eb20801e3a88b60800ba4660356/params.yaml deleted file mode 100644 index 5f4507e0..00000000 --- a/examples/security/classification/output/reports/train/aa130eb20801e3a88b60800ba4660356/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/adv_predictions.json - adv_probabilities_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/eea5a78cb8ca4bd72203cd0414f4f627.pkl - params_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/params.yaml - predictions_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/predictions.json - probabilities_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/probabilities.json - score_dict_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/score_dict.json - test_labels_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/test_labels.json - train_labels_file: output/reports/train/aa130eb20801e3a88b60800ba4660356/train_labels.json - model_dir: models - name: aa130eb20801e3a88b60800ba4660356 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fa3f159301843d361231fd9b5bf0015d - trainer: - kwargs: {} -name: aa130eb20801e3a88b60800ba4660356 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aa2173618540d5b98dd8d276219ecb59/params.yaml b/examples/security/classification/output/reports/train/aa2173618540d5b98dd8d276219ecb59/params.yaml deleted file mode 100644 index 4ad65588..00000000 --- a/examples/security/classification/output/reports/train/aa2173618540d5b98dd8d276219ecb59/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/adv_predictions.json - adv_probabilities_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/2f61e2f7f05e04dde8ff3f794106386c.pkl - params_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/params.yaml - predictions_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/predictions.json - probabilities_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/probabilities.json - score_dict_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/score_dict.json - test_labels_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/test_labels.json - train_labels_file: output/reports/train/aa2173618540d5b98dd8d276219ecb59/train_labels.json - model_dir: models - name: aa2173618540d5b98dd8d276219ecb59 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ffeed597cd6f3b64212ec9bb9059d669 - trainer: - kwargs: {} -name: aa2173618540d5b98dd8d276219ecb59 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aa280882f1b730f453fd924a3cf2270f/params.yaml b/examples/security/classification/output/reports/train/aa280882f1b730f453fd924a3cf2270f/params.yaml deleted file mode 100644 index ad485097..00000000 --- a/examples/security/classification/output/reports/train/aa280882f1b730f453fd924a3cf2270f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/adv_predictions.json - adv_probabilities_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/600d75cb5c82e00e53f1cf4f37c57cfe.pkl - params_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/params.yaml - predictions_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/predictions.json - probabilities_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/probabilities.json - score_dict_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/score_dict.json - test_labels_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/test_labels.json - train_labels_file: output/reports/train/aa280882f1b730f453fd924a3cf2270f/train_labels.json - model_dir: models - name: aa280882f1b730f453fd924a3cf2270f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 774b0ba78c1475043e8d0467a2c11ad4 - trainer: - kwargs: {} -name: aa280882f1b730f453fd924a3cf2270f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aa466f7ee59221aa63f4f85898760d59/params.yaml b/examples/security/classification/output/reports/train/aa466f7ee59221aa63f4f85898760d59/params.yaml deleted file mode 100644 index 3e3de1e0..00000000 --- a/examples/security/classification/output/reports/train/aa466f7ee59221aa63f4f85898760d59/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/adv_predictions.json - adv_probabilities_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/1e35c51a511c3b8d51153f491af33fe8.pkl - params_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/params.yaml - predictions_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/predictions.json - probabilities_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/probabilities.json - score_dict_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/score_dict.json - test_labels_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/test_labels.json - train_labels_file: output/reports/train/aa466f7ee59221aa63f4f85898760d59/train_labels.json - model_dir: models - name: aa466f7ee59221aa63f4f85898760d59 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5d4338003d3b2ce66af4f49339b472e9 - trainer: - kwargs: {} -name: aa466f7ee59221aa63f4f85898760d59 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aa4fcf3ef6083366358708be012530a9/params.yaml b/examples/security/classification/output/reports/train/aa4fcf3ef6083366358708be012530a9/params.yaml deleted file mode 100644 index f5032369..00000000 --- a/examples/security/classification/output/reports/train/aa4fcf3ef6083366358708be012530a9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/adv_predictions.json - adv_probabilities_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/0d7fa8a191286da7b612406fb3b6a479.pkl - params_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/params.yaml - predictions_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/predictions.json - probabilities_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/probabilities.json - score_dict_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/score_dict.json - test_labels_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/test_labels.json - train_labels_file: output/reports/train/aa4fcf3ef6083366358708be012530a9/train_labels.json - model_dir: models - name: aa4fcf3ef6083366358708be012530a9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f6ad56bf404b62db02d476e2dc3447a0 - trainer: - kwargs: {} -name: aa4fcf3ef6083366358708be012530a9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aa575571d93d998807f15b752cf96081/params.yaml b/examples/security/classification/output/reports/train/aa575571d93d998807f15b752cf96081/params.yaml deleted file mode 100644 index 1aee6387..00000000 --- a/examples/security/classification/output/reports/train/aa575571d93d998807f15b752cf96081/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aa575571d93d998807f15b752cf96081/adv_predictions.json - adv_probabilities_file: output/reports/train/aa575571d93d998807f15b752cf96081/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/5e1b2f5a8f5a1c429f6632fde220c0e0.pkl - params_file: output/reports/train/aa575571d93d998807f15b752cf96081/params.yaml - predictions_file: output/reports/train/aa575571d93d998807f15b752cf96081/predictions.json - probabilities_file: output/reports/train/aa575571d93d998807f15b752cf96081/probabilities.json - score_dict_file: output/reports/train/aa575571d93d998807f15b752cf96081/score_dict.json - test_labels_file: output/reports/train/aa575571d93d998807f15b752cf96081/test_labels.json - train_labels_file: output/reports/train/aa575571d93d998807f15b752cf96081/train_labels.json - model_dir: models - name: aa575571d93d998807f15b752cf96081 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: abdd3e9dd6b412b59dacb96df3f92be5 - trainer: - kwargs: {} -name: aa575571d93d998807f15b752cf96081 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/params.yaml b/examples/security/classification/output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/params.yaml deleted file mode 100644 index f331333a..00000000 --- a/examples/security/classification/output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/adv_predictions.json - adv_probabilities_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/48230b12f3ed967409ea041e1f92929e.pkl - params_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/params.yaml - predictions_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/predictions.json - probabilities_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/probabilities.json - score_dict_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/score_dict.json - test_labels_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/test_labels.json - train_labels_file: output/reports/train/aa5d3521a3ba8eddf220ec835ccd030d/train_labels.json - model_dir: models - name: aa5d3521a3ba8eddf220ec835ccd030d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8442938d2778bd4a71fa2ae437114ea2 - trainer: - kwargs: {} -name: aa5d3521a3ba8eddf220ec835ccd030d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/params.yaml b/examples/security/classification/output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/params.yaml deleted file mode 100644 index 8698d8e5..00000000 --- a/examples/security/classification/output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/adv_predictions.json - adv_probabilities_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/9db7bef06f7f667eeae472787c4f66ec.pkl - params_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/params.yaml - predictions_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/predictions.json - probabilities_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/probabilities.json - score_dict_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/score_dict.json - test_labels_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/test_labels.json - train_labels_file: output/reports/train/aa6ab6f65068f8b3e01898e47a87cbbd/train_labels.json - model_dir: models - name: aa6ab6f65068f8b3e01898e47a87cbbd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 63c64b76905cb65790be376d70bc9295 - trainer: - kwargs: {} -name: aa6ab6f65068f8b3e01898e47a87cbbd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/params.yaml b/examples/security/classification/output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/params.yaml deleted file mode 100644 index c363f76f..00000000 --- a/examples/security/classification/output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/adv_predictions.json - adv_probabilities_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/d1691433a9c6a1b12c6d8a10a58f2c2c.pkl - params_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/params.yaml - predictions_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/predictions.json - probabilities_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/probabilities.json - score_dict_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/score_dict.json - test_labels_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/test_labels.json - train_labels_file: output/reports/train/aa749f1b8b5f235b6dbbe172b58768eb/train_labels.json - model_dir: models - name: aa749f1b8b5f235b6dbbe172b58768eb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 69f3aa7a0f86adcd50ce6d972ca1f535 - trainer: - kwargs: {} -name: aa749f1b8b5f235b6dbbe172b58768eb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/params.yaml b/examples/security/classification/output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/params.yaml deleted file mode 100644 index b64c68e8..00000000 --- a/examples/security/classification/output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/adv_predictions.json - adv_probabilities_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/73a03929e768e1eaed59bda15cc73c3a.pkl - params_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/params.yaml - predictions_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/predictions.json - probabilities_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/probabilities.json - score_dict_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/score_dict.json - test_labels_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/test_labels.json - train_labels_file: output/reports/train/aa987ac3366c1c5f0b929717e7f47b0e/train_labels.json - model_dir: models - name: aa987ac3366c1c5f0b929717e7f47b0e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 929c3b265e8a5a82066c0ecebe5ca470 - trainer: - kwargs: {} -name: aa987ac3366c1c5f0b929717e7f47b0e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/params.yaml b/examples/security/classification/output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/params.yaml deleted file mode 100644 index 075be943..00000000 --- a/examples/security/classification/output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/adv_predictions.json - adv_probabilities_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/6c1eb98dc0ba3126b44854bd852f5e45.pkl - params_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/params.yaml - predictions_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/predictions.json - probabilities_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/probabilities.json - score_dict_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/score_dict.json - test_labels_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/test_labels.json - train_labels_file: output/reports/train/aab377c977c33ff8cd18fa31d1cfb6e3/train_labels.json - model_dir: models - name: aab377c977c33ff8cd18fa31d1cfb6e3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e319c20fa881d4578cf172116c58b887 - trainer: - kwargs: {} -name: aab377c977c33ff8cd18fa31d1cfb6e3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/params.yaml b/examples/security/classification/output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/params.yaml deleted file mode 100644 index ef4790af..00000000 --- a/examples/security/classification/output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/adv_predictions.json - adv_probabilities_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/2e98ff30074152ece783283ad9f2c7ee.pkl - params_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/params.yaml - predictions_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/predictions.json - probabilities_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/probabilities.json - score_dict_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/score_dict.json - test_labels_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/test_labels.json - train_labels_file: output/reports/train/aacf4fedf0fc579a3507a5ca7bc1fe66/train_labels.json - model_dir: models - name: aacf4fedf0fc579a3507a5ca7bc1fe66 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c157822bade1de528e2c06d4bdd7adfc - trainer: - kwargs: {} -name: aacf4fedf0fc579a3507a5ca7bc1fe66 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aad82667408d50fcf907185a5925226e/params.yaml b/examples/security/classification/output/reports/train/aad82667408d50fcf907185a5925226e/params.yaml deleted file mode 100644 index 87220b82..00000000 --- a/examples/security/classification/output/reports/train/aad82667408d50fcf907185a5925226e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aad82667408d50fcf907185a5925226e/adv_predictions.json - adv_probabilities_file: output/reports/train/aad82667408d50fcf907185a5925226e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/8abcfe4541c78656edc4ee28fe19df68.pkl - params_file: output/reports/train/aad82667408d50fcf907185a5925226e/params.yaml - predictions_file: output/reports/train/aad82667408d50fcf907185a5925226e/predictions.json - probabilities_file: output/reports/train/aad82667408d50fcf907185a5925226e/probabilities.json - score_dict_file: output/reports/train/aad82667408d50fcf907185a5925226e/score_dict.json - test_labels_file: output/reports/train/aad82667408d50fcf907185a5925226e/test_labels.json - train_labels_file: output/reports/train/aad82667408d50fcf907185a5925226e/train_labels.json - model_dir: models - name: aad82667408d50fcf907185a5925226e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e58fa80c0d99bd1c79947322539445b4 - trainer: - kwargs: {} -name: aad82667408d50fcf907185a5925226e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aafba71548e417f607383515ad034942/params.yaml b/examples/security/classification/output/reports/train/aafba71548e417f607383515ad034942/params.yaml deleted file mode 100644 index f2d6429c..00000000 --- a/examples/security/classification/output/reports/train/aafba71548e417f607383515ad034942/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aafba71548e417f607383515ad034942/adv_predictions.json - adv_probabilities_file: output/reports/train/aafba71548e417f607383515ad034942/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/dd51ceadfe260b770ca5052c72e720ee.pkl - params_file: output/reports/train/aafba71548e417f607383515ad034942/params.yaml - predictions_file: output/reports/train/aafba71548e417f607383515ad034942/predictions.json - probabilities_file: output/reports/train/aafba71548e417f607383515ad034942/probabilities.json - score_dict_file: output/reports/train/aafba71548e417f607383515ad034942/score_dict.json - test_labels_file: output/reports/train/aafba71548e417f607383515ad034942/test_labels.json - train_labels_file: output/reports/train/aafba71548e417f607383515ad034942/train_labels.json - model_dir: models - name: aafba71548e417f607383515ad034942 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 401859c7947fdd41083a932a5331b3df - trainer: - kwargs: {} -name: aafba71548e417f607383515ad034942 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/params.yaml b/examples/security/classification/output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/params.yaml deleted file mode 100644 index 2178e6ea..00000000 --- a/examples/security/classification/output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/adv_predictions.json - adv_probabilities_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/673890981671c43e203574954eb03637.pkl - params_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/params.yaml - predictions_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/predictions.json - probabilities_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/probabilities.json - score_dict_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/score_dict.json - test_labels_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/test_labels.json - train_labels_file: output/reports/train/ab134dda8ae47bfaf4cd4ca1cd19068c/train_labels.json - model_dir: models - name: ab134dda8ae47bfaf4cd4ca1cd19068c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 554cd39bf200be827b531f342ed6a565 - trainer: - kwargs: {} -name: ab134dda8ae47bfaf4cd4ca1cd19068c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/params.yaml b/examples/security/classification/output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/params.yaml deleted file mode 100644 index eadeeeb2..00000000 --- a/examples/security/classification/output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/adv_predictions.json - adv_probabilities_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/2ff323a299d39647e93ced563da0adf3.pkl - params_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/params.yaml - predictions_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/predictions.json - probabilities_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/probabilities.json - score_dict_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/score_dict.json - test_labels_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/test_labels.json - train_labels_file: output/reports/train/ab14bf9f41e3d3af8fe9b2bc708e32d8/train_labels.json - model_dir: models - name: ab14bf9f41e3d3af8fe9b2bc708e32d8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c9cc3900a49b3cc9da6f3c547426cc98 - trainer: - kwargs: {} -name: ab14bf9f41e3d3af8fe9b2bc708e32d8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ab4cd05856a2cb8f38a82073842182d7/params.yaml b/examples/security/classification/output/reports/train/ab4cd05856a2cb8f38a82073842182d7/params.yaml deleted file mode 100644 index cd067ba9..00000000 --- a/examples/security/classification/output/reports/train/ab4cd05856a2cb8f38a82073842182d7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/adv_predictions.json - adv_probabilities_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/123f11c149cf3beec9489247d79bd677.pkl - params_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/params.yaml - predictions_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/predictions.json - probabilities_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/probabilities.json - score_dict_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/score_dict.json - test_labels_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/test_labels.json - train_labels_file: output/reports/train/ab4cd05856a2cb8f38a82073842182d7/train_labels.json - model_dir: models - name: ab4cd05856a2cb8f38a82073842182d7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a9de782559bd9adf7d5a324e11722c53 - trainer: - kwargs: {} -name: ab4cd05856a2cb8f38a82073842182d7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ab62f505590371b23a0ebcc6a3878f24/params.yaml b/examples/security/classification/output/reports/train/ab62f505590371b23a0ebcc6a3878f24/params.yaml deleted file mode 100644 index 92caf260..00000000 --- a/examples/security/classification/output/reports/train/ab62f505590371b23a0ebcc6a3878f24/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/adv_predictions.json - adv_probabilities_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/6d9508f49d5f764a111d962d6c2091ff.pkl - params_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/params.yaml - predictions_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/predictions.json - probabilities_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/probabilities.json - score_dict_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/score_dict.json - test_labels_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/test_labels.json - train_labels_file: output/reports/train/ab62f505590371b23a0ebcc6a3878f24/train_labels.json - model_dir: models - name: ab62f505590371b23a0ebcc6a3878f24 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e12dca8a8d994da3d24952aafed52c94 - trainer: - kwargs: {} -name: ab62f505590371b23a0ebcc6a3878f24 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/params.yaml b/examples/security/classification/output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/params.yaml deleted file mode 100644 index f1a86375..00000000 --- a/examples/security/classification/output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/adv_predictions.json - adv_probabilities_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/f30384affe19bf306a52770be6beb1e2.pkl - params_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/params.yaml - predictions_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/predictions.json - probabilities_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/probabilities.json - score_dict_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/score_dict.json - test_labels_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/test_labels.json - train_labels_file: output/reports/train/ab8e10347e4d73b1ff6d316dbf41dac1/train_labels.json - model_dir: models - name: ab8e10347e4d73b1ff6d316dbf41dac1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ad97b92634f6504548b47a8851014ede - trainer: - kwargs: {} -name: ab8e10347e4d73b1ff6d316dbf41dac1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ab9434881d7c83ff823656c11ee3795e/params.yaml b/examples/security/classification/output/reports/train/ab9434881d7c83ff823656c11ee3795e/params.yaml deleted file mode 100644 index f656ca2b..00000000 --- a/examples/security/classification/output/reports/train/ab9434881d7c83ff823656c11ee3795e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/adv_predictions.json - adv_probabilities_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/93a19674a9bf1d13e31b2813e0dea186.pkl - params_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/params.yaml - predictions_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/predictions.json - probabilities_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/probabilities.json - score_dict_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/score_dict.json - test_labels_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/test_labels.json - train_labels_file: output/reports/train/ab9434881d7c83ff823656c11ee3795e/train_labels.json - model_dir: models - name: ab9434881d7c83ff823656c11ee3795e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9307148306cb8ece61c5269439332d75 - trainer: - kwargs: {} -name: ab9434881d7c83ff823656c11ee3795e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/abb35929794bb5c403acb7448eb6b2bc/params.yaml b/examples/security/classification/output/reports/train/abb35929794bb5c403acb7448eb6b2bc/params.yaml deleted file mode 100644 index ba738b87..00000000 --- a/examples/security/classification/output/reports/train/abb35929794bb5c403acb7448eb6b2bc/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/adv_predictions.json - adv_probabilities_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/f8ec654aef2ae419f4e561e454f5ca82.pkl - params_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/params.yaml - predictions_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/predictions.json - probabilities_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/probabilities.json - score_dict_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/score_dict.json - test_labels_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/test_labels.json - train_labels_file: output/reports/train/abb35929794bb5c403acb7448eb6b2bc/train_labels.json - model_dir: models - name: abb35929794bb5c403acb7448eb6b2bc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1fd8583de9c6219a7765a69b7726c4d3 - trainer: - kwargs: {} -name: abb35929794bb5c403acb7448eb6b2bc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/abb6b5a56053a57979f58acfad688cc1/params.yaml b/examples/security/classification/output/reports/train/abb6b5a56053a57979f58acfad688cc1/params.yaml deleted file mode 100644 index d7df03ae..00000000 --- a/examples/security/classification/output/reports/train/abb6b5a56053a57979f58acfad688cc1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/adv_predictions.json - adv_probabilities_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/e58fd1564f32934e31b0b01b49eb9538.pkl - params_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/params.yaml - predictions_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/predictions.json - probabilities_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/probabilities.json - score_dict_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/score_dict.json - test_labels_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/test_labels.json - train_labels_file: output/reports/train/abb6b5a56053a57979f58acfad688cc1/train_labels.json - model_dir: models - name: abb6b5a56053a57979f58acfad688cc1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 46d46794b95acad6f667da72c980fc59 - trainer: - kwargs: {} -name: abb6b5a56053a57979f58acfad688cc1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/abca74170f7be90488c55722c0ba00a4/params.yaml b/examples/security/classification/output/reports/train/abca74170f7be90488c55722c0ba00a4/params.yaml deleted file mode 100644 index 873959e0..00000000 --- a/examples/security/classification/output/reports/train/abca74170f7be90488c55722c0ba00a4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/adv_predictions.json - adv_probabilities_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/0389404b8ff5834a2387c965f89143fc.pkl - params_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/params.yaml - predictions_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/predictions.json - probabilities_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/probabilities.json - score_dict_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/score_dict.json - test_labels_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/test_labels.json - train_labels_file: output/reports/train/abca74170f7be90488c55722c0ba00a4/train_labels.json - model_dir: models - name: abca74170f7be90488c55722c0ba00a4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f3aedae8a4d77bbb33360e50ed74e7ec - trainer: - kwargs: {} -name: abca74170f7be90488c55722c0ba00a4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/params.yaml b/examples/security/classification/output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/params.yaml deleted file mode 100644 index fc743f09..00000000 --- a/examples/security/classification/output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/adv_predictions.json - adv_probabilities_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/e28a8dfb37e9e1486c5029e68e311892.pkl - params_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/params.yaml - predictions_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/predictions.json - probabilities_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/probabilities.json - score_dict_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/score_dict.json - test_labels_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/test_labels.json - train_labels_file: output/reports/train/abee2a355ab8ea1bceb5c06cc6c8ab7f/train_labels.json - model_dir: models - name: abee2a355ab8ea1bceb5c06cc6c8ab7f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0c28b93e1012ade88c6e34ef302a6986 - trainer: - kwargs: {} -name: abee2a355ab8ea1bceb5c06cc6c8ab7f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/params.yaml b/examples/security/classification/output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/params.yaml deleted file mode 100644 index 73d1fcbe..00000000 --- a/examples/security/classification/output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/adv_predictions.json - adv_probabilities_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/f2201365034d4da1ea7c79124a88c75c.pkl - params_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/params.yaml - predictions_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/predictions.json - probabilities_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/probabilities.json - score_dict_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/score_dict.json - test_labels_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/test_labels.json - train_labels_file: output/reports/train/ac612b9eddc48d5cfee08a86c1e1b5c9/train_labels.json - model_dir: models - name: ac612b9eddc48d5cfee08a86c1e1b5c9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 468fef5afd65c8861872616c02a3fdc5 - trainer: - kwargs: {} -name: ac612b9eddc48d5cfee08a86c1e1b5c9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ac8f813ea117c627bcfa93189ec5959d/params.yaml b/examples/security/classification/output/reports/train/ac8f813ea117c627bcfa93189ec5959d/params.yaml deleted file mode 100644 index d7aa55e1..00000000 --- a/examples/security/classification/output/reports/train/ac8f813ea117c627bcfa93189ec5959d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/adv_predictions.json - adv_probabilities_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/c4f2a7509551d8619ea27e8ae1d09237.pkl - params_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/params.yaml - predictions_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/predictions.json - probabilities_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/probabilities.json - score_dict_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/score_dict.json - test_labels_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/test_labels.json - train_labels_file: output/reports/train/ac8f813ea117c627bcfa93189ec5959d/train_labels.json - model_dir: models - name: ac8f813ea117c627bcfa93189ec5959d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6d7d9d2b5f8086389b19ebbc1b87541e - trainer: - kwargs: {} -name: ac8f813ea117c627bcfa93189ec5959d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ac96494b0461749b2241d24c962f9893/params.yaml b/examples/security/classification/output/reports/train/ac96494b0461749b2241d24c962f9893/params.yaml deleted file mode 100644 index 66c423f6..00000000 --- a/examples/security/classification/output/reports/train/ac96494b0461749b2241d24c962f9893/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ac96494b0461749b2241d24c962f9893/adv_predictions.json - adv_probabilities_file: output/reports/train/ac96494b0461749b2241d24c962f9893/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/7c889216c08000188f800b760d9814f1.pkl - params_file: output/reports/train/ac96494b0461749b2241d24c962f9893/params.yaml - predictions_file: output/reports/train/ac96494b0461749b2241d24c962f9893/predictions.json - probabilities_file: output/reports/train/ac96494b0461749b2241d24c962f9893/probabilities.json - score_dict_file: output/reports/train/ac96494b0461749b2241d24c962f9893/score_dict.json - test_labels_file: output/reports/train/ac96494b0461749b2241d24c962f9893/test_labels.json - train_labels_file: output/reports/train/ac96494b0461749b2241d24c962f9893/train_labels.json - model_dir: models - name: ac96494b0461749b2241d24c962f9893 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f6c01c63b88e061a1f550867a6e3b0c5 - trainer: - kwargs: {} -name: ac96494b0461749b2241d24c962f9893 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/params.yaml b/examples/security/classification/output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/params.yaml deleted file mode 100644 index e5a54193..00000000 --- a/examples/security/classification/output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/adv_predictions.json - adv_probabilities_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/c18b935fa9bc6a5a8a80dd43dcda3bec.pkl - params_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/params.yaml - predictions_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/predictions.json - probabilities_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/probabilities.json - score_dict_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/score_dict.json - test_labels_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/test_labels.json - train_labels_file: output/reports/train/acaf47f171b713ec54e5582ff7fa5ad7/train_labels.json - model_dir: models - name: acaf47f171b713ec54e5582ff7fa5ad7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a2f6df54d0d85116f83d9194c9d925ca - trainer: - kwargs: {} -name: acaf47f171b713ec54e5582ff7fa5ad7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/acd47903f172fe9830fbc88589d11e11/params.yaml b/examples/security/classification/output/reports/train/acd47903f172fe9830fbc88589d11e11/params.yaml deleted file mode 100644 index 3bf2eade..00000000 --- a/examples/security/classification/output/reports/train/acd47903f172fe9830fbc88589d11e11/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/adv_predictions.json - adv_probabilities_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/8a7a4b808102a0a6bae5cd93df8bda48.pkl - params_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/params.yaml - predictions_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/predictions.json - probabilities_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/probabilities.json - score_dict_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/score_dict.json - test_labels_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/test_labels.json - train_labels_file: output/reports/train/acd47903f172fe9830fbc88589d11e11/train_labels.json - model_dir: models - name: acd47903f172fe9830fbc88589d11e11 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8a81424fd850589c96b3c08e10eb6af5 - trainer: - kwargs: {} -name: acd47903f172fe9830fbc88589d11e11 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/params.yaml b/examples/security/classification/output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/params.yaml deleted file mode 100644 index 9bfaa6e8..00000000 --- a/examples/security/classification/output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/adv_predictions.json - adv_probabilities_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/6376dca2a4f28e397ef1cb74f898fd8b.pkl - params_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/params.yaml - predictions_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/predictions.json - probabilities_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/probabilities.json - score_dict_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/score_dict.json - test_labels_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/test_labels.json - train_labels_file: output/reports/train/acff3dc338f14b3a9d4d94949b64aa64/train_labels.json - model_dir: models - name: acff3dc338f14b3a9d4d94949b64aa64 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 061ad697b2a6440ef726d712fef704e7 - trainer: - kwargs: {} -name: acff3dc338f14b3a9d4d94949b64aa64 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/params.yaml b/examples/security/classification/output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/params.yaml deleted file mode 100644 index c1440adb..00000000 --- a/examples/security/classification/output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/adv_predictions.json - adv_probabilities_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/3875f4666585c2a7e73cf19a41c02f6a.pkl - params_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/params.yaml - predictions_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/predictions.json - probabilities_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/probabilities.json - score_dict_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/score_dict.json - test_labels_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/test_labels.json - train_labels_file: output/reports/train/ad1f3f40fde39f8c8ccf546c4738b33a/train_labels.json - model_dir: models - name: ad1f3f40fde39f8c8ccf546c4738b33a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 878f7d1060054df2fb0d0c4e49f6abb6 - trainer: - kwargs: {} -name: ad1f3f40fde39f8c8ccf546c4738b33a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ad3c993644f25e35f252cb79c7425e42/params.yaml b/examples/security/classification/output/reports/train/ad3c993644f25e35f252cb79c7425e42/params.yaml deleted file mode 100644 index 869284d3..00000000 --- a/examples/security/classification/output/reports/train/ad3c993644f25e35f252cb79c7425e42/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/adv_predictions.json - adv_probabilities_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/4963520e300bb124882ce186dd8315ae.pkl - params_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/params.yaml - predictions_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/predictions.json - probabilities_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/probabilities.json - score_dict_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/score_dict.json - test_labels_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/test_labels.json - train_labels_file: output/reports/train/ad3c993644f25e35f252cb79c7425e42/train_labels.json - model_dir: models - name: ad3c993644f25e35f252cb79c7425e42 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0d3c2f4d07681963c4892cd1e0e716fe - trainer: - kwargs: {} -name: ad3c993644f25e35f252cb79c7425e42 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/params.yaml b/examples/security/classification/output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/params.yaml deleted file mode 100644 index cc4677b5..00000000 --- a/examples/security/classification/output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/adv_predictions.json - adv_probabilities_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/c8c365e8e5db52db26886a35931eeaa1.pkl - params_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/params.yaml - predictions_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/predictions.json - probabilities_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/probabilities.json - score_dict_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/score_dict.json - test_labels_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/test_labels.json - train_labels_file: output/reports/train/ad44ba010f9faee4e38cc9d167412c5c/train_labels.json - model_dir: models - name: ad44ba010f9faee4e38cc9d167412c5c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e7cd9e7e4ee0d4f494f766ff5c40587c - trainer: - kwargs: {} -name: ad44ba010f9faee4e38cc9d167412c5c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ad5889ec619085a52cb2d5f545702c80/params.yaml b/examples/security/classification/output/reports/train/ad5889ec619085a52cb2d5f545702c80/params.yaml deleted file mode 100644 index ae635330..00000000 --- a/examples/security/classification/output/reports/train/ad5889ec619085a52cb2d5f545702c80/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/adv_predictions.json - adv_probabilities_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/dd4be87438bc7b9fef2bc3de59f58c48.pkl - params_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/params.yaml - predictions_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/predictions.json - probabilities_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/probabilities.json - score_dict_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/score_dict.json - test_labels_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/test_labels.json - train_labels_file: output/reports/train/ad5889ec619085a52cb2d5f545702c80/train_labels.json - model_dir: models - name: ad5889ec619085a52cb2d5f545702c80 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c81fe956b7a0fecec43c4a8294a2ef44 - trainer: - kwargs: {} -name: ad5889ec619085a52cb2d5f545702c80 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ad8735a67a823a4015bf907ba89e6e11/params.yaml b/examples/security/classification/output/reports/train/ad8735a67a823a4015bf907ba89e6e11/params.yaml deleted file mode 100644 index 9daabe90..00000000 --- a/examples/security/classification/output/reports/train/ad8735a67a823a4015bf907ba89e6e11/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/adv_predictions.json - adv_probabilities_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/65278cb975224d562bf9ef182db40dd7.pkl - params_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/params.yaml - predictions_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/predictions.json - probabilities_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/probabilities.json - score_dict_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/score_dict.json - test_labels_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/test_labels.json - train_labels_file: output/reports/train/ad8735a67a823a4015bf907ba89e6e11/train_labels.json - model_dir: models - name: ad8735a67a823a4015bf907ba89e6e11 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6fddf8f0b8c92f3ffb986570b335f3ff - trainer: - kwargs: {} -name: ad8735a67a823a4015bf907ba89e6e11 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/params.yaml b/examples/security/classification/output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/params.yaml deleted file mode 100644 index dcc1c9d2..00000000 --- a/examples/security/classification/output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/adv_predictions.json - adv_probabilities_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/c23b54c7f16d354265a1bee7368dbfa4.pkl - params_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/params.yaml - predictions_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/predictions.json - probabilities_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/probabilities.json - score_dict_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/score_dict.json - test_labels_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/test_labels.json - train_labels_file: output/reports/train/ad9c0c04f2e99d101a2b47611e5af9b7/train_labels.json - model_dir: models - name: ad9c0c04f2e99d101a2b47611e5af9b7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: aa84843f7899b9f28bcc31c877c64a35 - trainer: - kwargs: {} -name: ad9c0c04f2e99d101a2b47611e5af9b7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ada667b0682fe3f962e7eac4728d839c/params.yaml b/examples/security/classification/output/reports/train/ada667b0682fe3f962e7eac4728d839c/params.yaml deleted file mode 100644 index f548b57c..00000000 --- a/examples/security/classification/output/reports/train/ada667b0682fe3f962e7eac4728d839c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/adv_predictions.json - adv_probabilities_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/5b11b198bc0d70d3d6db2c4285832d63.pkl - params_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/params.yaml - predictions_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/predictions.json - probabilities_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/probabilities.json - score_dict_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/score_dict.json - test_labels_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/test_labels.json - train_labels_file: output/reports/train/ada667b0682fe3f962e7eac4728d839c/train_labels.json - model_dir: models - name: ada667b0682fe3f962e7eac4728d839c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 762a24119d83327bc235473f4a73bb57 - trainer: - kwargs: {} -name: ada667b0682fe3f962e7eac4728d839c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/params.yaml b/examples/security/classification/output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/params.yaml deleted file mode 100644 index aaa93a48..00000000 --- a/examples/security/classification/output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/adv_predictions.json - adv_probabilities_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/ade32c1df57c36e2a33415652440ee48.pkl - params_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/params.yaml - predictions_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/predictions.json - probabilities_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/probabilities.json - score_dict_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/score_dict.json - test_labels_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/test_labels.json - train_labels_file: output/reports/train/ada9150d53d6388e0d1c926dbe6ef747/train_labels.json - model_dir: models - name: ada9150d53d6388e0d1c926dbe6ef747 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6b555705bc328b22769d29bfe61aeaff - trainer: - kwargs: {} -name: ada9150d53d6388e0d1c926dbe6ef747 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/addfe639db7a07c0cb96f5772c6aa867/params.yaml b/examples/security/classification/output/reports/train/addfe639db7a07c0cb96f5772c6aa867/params.yaml deleted file mode 100644 index 16542c14..00000000 --- a/examples/security/classification/output/reports/train/addfe639db7a07c0cb96f5772c6aa867/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/adv_predictions.json - adv_probabilities_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/c4ef3e1bbf9245ff3abea6adb26428ff.pkl - params_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/params.yaml - predictions_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/predictions.json - probabilities_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/probabilities.json - score_dict_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/score_dict.json - test_labels_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/test_labels.json - train_labels_file: output/reports/train/addfe639db7a07c0cb96f5772c6aa867/train_labels.json - model_dir: models - name: addfe639db7a07c0cb96f5772c6aa867 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 23a6357c324c89eae6d070f6837db13c - trainer: - kwargs: {} -name: addfe639db7a07c0cb96f5772c6aa867 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/adff01e0049fa7109f80d43a36a01612/params.yaml b/examples/security/classification/output/reports/train/adff01e0049fa7109f80d43a36a01612/params.yaml deleted file mode 100644 index f7aa4f7f..00000000 --- a/examples/security/classification/output/reports/train/adff01e0049fa7109f80d43a36a01612/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/adv_predictions.json - adv_probabilities_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/d85757435ea684df4c61a63bbac3b21c.pkl - params_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/params.yaml - predictions_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/predictions.json - probabilities_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/probabilities.json - score_dict_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/score_dict.json - test_labels_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/test_labels.json - train_labels_file: output/reports/train/adff01e0049fa7109f80d43a36a01612/train_labels.json - model_dir: models - name: adff01e0049fa7109f80d43a36a01612 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b33d1efb56db392a968a0e4dc5393240 - trainer: - kwargs: {} -name: adff01e0049fa7109f80d43a36a01612 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ae2087d8d98dd1805586e3f90b75b483/params.yaml b/examples/security/classification/output/reports/train/ae2087d8d98dd1805586e3f90b75b483/params.yaml deleted file mode 100644 index 628fb624..00000000 --- a/examples/security/classification/output/reports/train/ae2087d8d98dd1805586e3f90b75b483/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/adv_predictions.json - adv_probabilities_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/f91a93437154f47cc018f6b805a5900e.pkl - params_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/params.yaml - predictions_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/predictions.json - probabilities_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/probabilities.json - score_dict_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/score_dict.json - test_labels_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/test_labels.json - train_labels_file: output/reports/train/ae2087d8d98dd1805586e3f90b75b483/train_labels.json - model_dir: models - name: ae2087d8d98dd1805586e3f90b75b483 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c3033fb023c33cc41fe3fc757098c3e9 - trainer: - kwargs: {} -name: ae2087d8d98dd1805586e3f90b75b483 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ae31d530dabcb6f6125622d5be610074/params.yaml b/examples/security/classification/output/reports/train/ae31d530dabcb6f6125622d5be610074/params.yaml deleted file mode 100644 index 77b4673d..00000000 --- a/examples/security/classification/output/reports/train/ae31d530dabcb6f6125622d5be610074/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/adv_predictions.json - adv_probabilities_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/2bec783c1cb770dcde0c788f97ca6a2d.pkl - params_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/params.yaml - predictions_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/predictions.json - probabilities_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/probabilities.json - score_dict_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/score_dict.json - test_labels_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/test_labels.json - train_labels_file: output/reports/train/ae31d530dabcb6f6125622d5be610074/train_labels.json - model_dir: models - name: ae31d530dabcb6f6125622d5be610074 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f1692b82a78b6b049f1f3d4f6b63990c - trainer: - kwargs: {} -name: ae31d530dabcb6f6125622d5be610074 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ae4434cb92786bde76f2a68017616e4d/params.yaml b/examples/security/classification/output/reports/train/ae4434cb92786bde76f2a68017616e4d/params.yaml deleted file mode 100644 index ad7df2fe..00000000 --- a/examples/security/classification/output/reports/train/ae4434cb92786bde76f2a68017616e4d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/adv_predictions.json - adv_probabilities_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/896443a56c72471a5753a25c1a0aedcf.pkl - params_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/params.yaml - predictions_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/predictions.json - probabilities_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/probabilities.json - score_dict_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/score_dict.json - test_labels_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/test_labels.json - train_labels_file: output/reports/train/ae4434cb92786bde76f2a68017616e4d/train_labels.json - model_dir: models - name: ae4434cb92786bde76f2a68017616e4d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 07f703c9ae6eef6288829293bef82d77 - trainer: - kwargs: {} -name: ae4434cb92786bde76f2a68017616e4d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ae642c4e75604fee38a7c7a975e90617/params.yaml b/examples/security/classification/output/reports/train/ae642c4e75604fee38a7c7a975e90617/params.yaml deleted file mode 100644 index d091d66b..00000000 --- a/examples/security/classification/output/reports/train/ae642c4e75604fee38a7c7a975e90617/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/adv_predictions.json - adv_probabilities_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/a333f1ac2eb827b6bd52d918cf9e1340.pkl - params_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/params.yaml - predictions_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/predictions.json - probabilities_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/probabilities.json - score_dict_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/score_dict.json - test_labels_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/test_labels.json - train_labels_file: output/reports/train/ae642c4e75604fee38a7c7a975e90617/train_labels.json - model_dir: models - name: ae642c4e75604fee38a7c7a975e90617 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ac5570ea97a9b566bd66eb0c691e1f78 - trainer: - kwargs: {} -name: ae642c4e75604fee38a7c7a975e90617 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aec637019225e04a01ba708a69f20138/params.yaml b/examples/security/classification/output/reports/train/aec637019225e04a01ba708a69f20138/params.yaml deleted file mode 100644 index 3055e137..00000000 --- a/examples/security/classification/output/reports/train/aec637019225e04a01ba708a69f20138/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aec637019225e04a01ba708a69f20138/adv_predictions.json - adv_probabilities_file: output/reports/train/aec637019225e04a01ba708a69f20138/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/8e77fc78fcc653b01bdb114c45c7ad4f.pkl - params_file: output/reports/train/aec637019225e04a01ba708a69f20138/params.yaml - predictions_file: output/reports/train/aec637019225e04a01ba708a69f20138/predictions.json - probabilities_file: output/reports/train/aec637019225e04a01ba708a69f20138/probabilities.json - score_dict_file: output/reports/train/aec637019225e04a01ba708a69f20138/score_dict.json - test_labels_file: output/reports/train/aec637019225e04a01ba708a69f20138/test_labels.json - train_labels_file: output/reports/train/aec637019225e04a01ba708a69f20138/train_labels.json - model_dir: models - name: aec637019225e04a01ba708a69f20138 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7c6a2278d378ae458a7e349116c9bead - trainer: - kwargs: {} -name: aec637019225e04a01ba708a69f20138 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aec89520903fd09599c9545e6ddc4d82/params.yaml b/examples/security/classification/output/reports/train/aec89520903fd09599c9545e6ddc4d82/params.yaml deleted file mode 100644 index b7182dfe..00000000 --- a/examples/security/classification/output/reports/train/aec89520903fd09599c9545e6ddc4d82/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/adv_predictions.json - adv_probabilities_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/d59e9f7f5282de64a02bb7dadfc4c680.pkl - params_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/params.yaml - predictions_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/predictions.json - probabilities_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/probabilities.json - score_dict_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/score_dict.json - test_labels_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/test_labels.json - train_labels_file: output/reports/train/aec89520903fd09599c9545e6ddc4d82/train_labels.json - model_dir: models - name: aec89520903fd09599c9545e6ddc4d82 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c631922bf1119e97bf6ecb4c067f1a35 - trainer: - kwargs: {} -name: aec89520903fd09599c9545e6ddc4d82 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/aed59d4c35c0be56408eb0ef8e455655/params.yaml b/examples/security/classification/output/reports/train/aed59d4c35c0be56408eb0ef8e455655/params.yaml deleted file mode 100644 index 28e5e351..00000000 --- a/examples/security/classification/output/reports/train/aed59d4c35c0be56408eb0ef8e455655/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/adv_predictions.json - adv_probabilities_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/35a6a20376e0f2df7c463ca197b1a05f.pkl - params_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/params.yaml - predictions_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/predictions.json - probabilities_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/probabilities.json - score_dict_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/score_dict.json - test_labels_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/test_labels.json - train_labels_file: output/reports/train/aed59d4c35c0be56408eb0ef8e455655/train_labels.json - model_dir: models - name: aed59d4c35c0be56408eb0ef8e455655 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0e55cf911237a85dc6754fcc9c4f53c0 - trainer: - kwargs: {} -name: aed59d4c35c0be56408eb0ef8e455655 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/af0f13738fc124aa24175581d9ec315b/params.yaml b/examples/security/classification/output/reports/train/af0f13738fc124aa24175581d9ec315b/params.yaml deleted file mode 100644 index 4b8b524a..00000000 --- a/examples/security/classification/output/reports/train/af0f13738fc124aa24175581d9ec315b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/adv_predictions.json - adv_probabilities_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/fa8db612f350da087535b418e0b5a9d4.pkl - params_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/params.yaml - predictions_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/predictions.json - probabilities_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/probabilities.json - score_dict_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/score_dict.json - test_labels_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/test_labels.json - train_labels_file: output/reports/train/af0f13738fc124aa24175581d9ec315b/train_labels.json - model_dir: models - name: af0f13738fc124aa24175581d9ec315b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8772faddca9cf3b3f20cf34768521acf - trainer: - kwargs: {} -name: af0f13738fc124aa24175581d9ec315b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/af253b097e87a1be907b9577817a2e4b/params.yaml b/examples/security/classification/output/reports/train/af253b097e87a1be907b9577817a2e4b/params.yaml deleted file mode 100644 index 7b30d7d1..00000000 --- a/examples/security/classification/output/reports/train/af253b097e87a1be907b9577817a2e4b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/adv_predictions.json - adv_probabilities_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/6c11b6fd8fcb732bbd4f7d0885eccbf7.pkl - params_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/params.yaml - predictions_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/predictions.json - probabilities_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/probabilities.json - score_dict_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/score_dict.json - test_labels_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/test_labels.json - train_labels_file: output/reports/train/af253b097e87a1be907b9577817a2e4b/train_labels.json - model_dir: models - name: af253b097e87a1be907b9577817a2e4b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4949c855e2eee9b24acd6651fa5f23f4 - trainer: - kwargs: {} -name: af253b097e87a1be907b9577817a2e4b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/params.yaml b/examples/security/classification/output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/params.yaml deleted file mode 100644 index 37bc5854..00000000 --- a/examples/security/classification/output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/adv_predictions.json - adv_probabilities_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/2bbddea058ddcafa9e0c5e386cd2fcf6.pkl - params_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/params.yaml - predictions_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/predictions.json - probabilities_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/probabilities.json - score_dict_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/score_dict.json - test_labels_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/test_labels.json - train_labels_file: output/reports/train/af4a35ea6f52f4cb96f4b55204404c91/train_labels.json - model_dir: models - name: af4a35ea6f52f4cb96f4b55204404c91 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6d6388462ca3f0b158a09674fcb3eef8 - trainer: - kwargs: {} -name: af4a35ea6f52f4cb96f4b55204404c91 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/params.yaml b/examples/security/classification/output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/params.yaml deleted file mode 100644 index 2396d8e0..00000000 --- a/examples/security/classification/output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/adv_predictions.json - adv_probabilities_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/95a61b99cdbf0611c223b7943d2e4521.pkl - params_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/params.yaml - predictions_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/predictions.json - probabilities_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/probabilities.json - score_dict_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/score_dict.json - test_labels_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/test_labels.json - train_labels_file: output/reports/train/af4e544fdbc53ac677c67493a3afcbc8/train_labels.json - model_dir: models - name: af4e544fdbc53ac677c67493a3afcbc8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cfdc9c2c7e5fb2e7ce3eaeb55c16af3d - trainer: - kwargs: {} -name: af4e544fdbc53ac677c67493a3afcbc8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/af62c23ced4ff1e076e55bfb856413c7/params.yaml b/examples/security/classification/output/reports/train/af62c23ced4ff1e076e55bfb856413c7/params.yaml deleted file mode 100644 index dae3e9af..00000000 --- a/examples/security/classification/output/reports/train/af62c23ced4ff1e076e55bfb856413c7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/adv_predictions.json - adv_probabilities_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/8df751f4ab8b80799e2fc9393db47f73.pkl - params_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/params.yaml - predictions_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/predictions.json - probabilities_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/probabilities.json - score_dict_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/score_dict.json - test_labels_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/test_labels.json - train_labels_file: output/reports/train/af62c23ced4ff1e076e55bfb856413c7/train_labels.json - model_dir: models - name: af62c23ced4ff1e076e55bfb856413c7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 894d19689148ac429adb72f47cc091cc - trainer: - kwargs: {} -name: af62c23ced4ff1e076e55bfb856413c7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/af68184484e8e9169640b6b207abee2f/params.yaml b/examples/security/classification/output/reports/train/af68184484e8e9169640b6b207abee2f/params.yaml deleted file mode 100644 index 084c4a8b..00000000 --- a/examples/security/classification/output/reports/train/af68184484e8e9169640b6b207abee2f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/af68184484e8e9169640b6b207abee2f/adv_predictions.json - adv_probabilities_file: output/reports/train/af68184484e8e9169640b6b207abee2f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/eb85a10ab84eb2efb2fe0fff537039eb.pkl - params_file: output/reports/train/af68184484e8e9169640b6b207abee2f/params.yaml - predictions_file: output/reports/train/af68184484e8e9169640b6b207abee2f/predictions.json - probabilities_file: output/reports/train/af68184484e8e9169640b6b207abee2f/probabilities.json - score_dict_file: output/reports/train/af68184484e8e9169640b6b207abee2f/score_dict.json - test_labels_file: output/reports/train/af68184484e8e9169640b6b207abee2f/test_labels.json - train_labels_file: output/reports/train/af68184484e8e9169640b6b207abee2f/train_labels.json - model_dir: models - name: af68184484e8e9169640b6b207abee2f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 73f10e4f09af6a93a2a5618176164759 - trainer: - kwargs: {} -name: af68184484e8e9169640b6b207abee2f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/params.yaml b/examples/security/classification/output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/params.yaml deleted file mode 100644 index 55d48961..00000000 --- a/examples/security/classification/output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/adv_predictions.json - adv_probabilities_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/ad40ff65648c7a1c19a62740347ddb05.pkl - params_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/params.yaml - predictions_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/predictions.json - probabilities_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/probabilities.json - score_dict_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/score_dict.json - test_labels_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/test_labels.json - train_labels_file: output/reports/train/af70b3d25ab02fe8b90da3a3b2a641c7/train_labels.json - model_dir: models - name: af70b3d25ab02fe8b90da3a3b2a641c7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c5ebb719f318f14b950ea0b29bb69378 - trainer: - kwargs: {} -name: af70b3d25ab02fe8b90da3a3b2a641c7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/params.yaml b/examples/security/classification/output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/params.yaml deleted file mode 100644 index 7ec4e1a4..00000000 --- a/examples/security/classification/output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/adv_predictions.json - adv_probabilities_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/133cabe67294b6e94d8ccb1e79d3b576.pkl - params_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/params.yaml - predictions_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/predictions.json - probabilities_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/probabilities.json - score_dict_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/score_dict.json - test_labels_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/test_labels.json - train_labels_file: output/reports/train/af82aae4bba3bb6ce5ee88e3e2845731/train_labels.json - model_dir: models - name: af82aae4bba3bb6ce5ee88e3e2845731 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 43ebb2ce5f71f3c066d917aaded080ec - trainer: - kwargs: {} -name: af82aae4bba3bb6ce5ee88e3e2845731 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/params.yaml b/examples/security/classification/output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/params.yaml deleted file mode 100644 index adeb5af4..00000000 --- a/examples/security/classification/output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/adv_predictions.json - adv_probabilities_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/af810d9c15ee3a32c225a4d640a5a40a.pkl - params_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/params.yaml - predictions_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/predictions.json - probabilities_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/probabilities.json - score_dict_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/score_dict.json - test_labels_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/test_labels.json - train_labels_file: output/reports/train/af85d1d5c07f3a9d614bc61579e2ce00/train_labels.json - model_dir: models - name: af85d1d5c07f3a9d614bc61579e2ce00 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3a48201b98fc70b27eab3d672d060ab1 - trainer: - kwargs: {} -name: af85d1d5c07f3a9d614bc61579e2ce00 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/afd0f0c00117d1871b49caee8a0dd177/params.yaml b/examples/security/classification/output/reports/train/afd0f0c00117d1871b49caee8a0dd177/params.yaml deleted file mode 100644 index a5cd0548..00000000 --- a/examples/security/classification/output/reports/train/afd0f0c00117d1871b49caee8a0dd177/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/adv_predictions.json - adv_probabilities_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/a6d6e6a3079ada8f2c86204e28448e86.pkl - params_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/params.yaml - predictions_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/predictions.json - probabilities_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/probabilities.json - score_dict_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/score_dict.json - test_labels_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/test_labels.json - train_labels_file: output/reports/train/afd0f0c00117d1871b49caee8a0dd177/train_labels.json - model_dir: models - name: afd0f0c00117d1871b49caee8a0dd177 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 88b2e6933cc0ec59a4d24f0210a33c62 - trainer: - kwargs: {} -name: afd0f0c00117d1871b49caee8a0dd177 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/afe9bf594b563997e4db920cef3664b6/params.yaml b/examples/security/classification/output/reports/train/afe9bf594b563997e4db920cef3664b6/params.yaml deleted file mode 100644 index 0ae37277..00000000 --- a/examples/security/classification/output/reports/train/afe9bf594b563997e4db920cef3664b6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/adv_predictions.json - adv_probabilities_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/f99dc1b6e1cc22ed0a68e5142a8200b8.pkl - params_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/params.yaml - predictions_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/predictions.json - probabilities_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/probabilities.json - score_dict_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/score_dict.json - test_labels_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/test_labels.json - train_labels_file: output/reports/train/afe9bf594b563997e4db920cef3664b6/train_labels.json - model_dir: models - name: afe9bf594b563997e4db920cef3664b6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 12d8f380bdc7b4aa1eaa0d784393215b - trainer: - kwargs: {} -name: afe9bf594b563997e4db920cef3664b6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/params.yaml b/examples/security/classification/output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/params.yaml deleted file mode 100644 index bc4a5245..00000000 --- a/examples/security/classification/output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/adv_predictions.json - adv_probabilities_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/ac045f6bd3c27ac90498422e2e234379.pkl - params_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/params.yaml - predictions_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/predictions.json - probabilities_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/probabilities.json - score_dict_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/score_dict.json - test_labels_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/test_labels.json - train_labels_file: output/reports/train/b012880bcb540d8ce4a0e191ef742b0e/train_labels.json - model_dir: models - name: b012880bcb540d8ce4a0e191ef742b0e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a6eb1d90acef172238b9ef76690c8d18 - trainer: - kwargs: {} -name: b012880bcb540d8ce4a0e191ef742b0e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b01653933b09c45899349536ca39922a/params.yaml b/examples/security/classification/output/reports/train/b01653933b09c45899349536ca39922a/params.yaml deleted file mode 100644 index b105c587..00000000 --- a/examples/security/classification/output/reports/train/b01653933b09c45899349536ca39922a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b01653933b09c45899349536ca39922a/adv_predictions.json - adv_probabilities_file: output/reports/train/b01653933b09c45899349536ca39922a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/08931484c492e5e99f35a594d5df25d0.pkl - params_file: output/reports/train/b01653933b09c45899349536ca39922a/params.yaml - predictions_file: output/reports/train/b01653933b09c45899349536ca39922a/predictions.json - probabilities_file: output/reports/train/b01653933b09c45899349536ca39922a/probabilities.json - score_dict_file: output/reports/train/b01653933b09c45899349536ca39922a/score_dict.json - test_labels_file: output/reports/train/b01653933b09c45899349536ca39922a/test_labels.json - train_labels_file: output/reports/train/b01653933b09c45899349536ca39922a/train_labels.json - model_dir: models - name: b01653933b09c45899349536ca39922a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bd7a6ea450361e1c0ad26d4964c12016 - trainer: - kwargs: {} -name: b01653933b09c45899349536ca39922a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b055507b9ccdd0127e578dca170e7550/params.yaml b/examples/security/classification/output/reports/train/b055507b9ccdd0127e578dca170e7550/params.yaml deleted file mode 100644 index 2db78917..00000000 --- a/examples/security/classification/output/reports/train/b055507b9ccdd0127e578dca170e7550/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/adv_predictions.json - adv_probabilities_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/74959b6634576e12bd4455ec83a4e3cb.pkl - params_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/params.yaml - predictions_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/predictions.json - probabilities_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/probabilities.json - score_dict_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/score_dict.json - test_labels_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/test_labels.json - train_labels_file: output/reports/train/b055507b9ccdd0127e578dca170e7550/train_labels.json - model_dir: models - name: b055507b9ccdd0127e578dca170e7550 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 97ca2227b89c496380d4ba72a329c561 - trainer: - kwargs: {} -name: b055507b9ccdd0127e578dca170e7550 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/params.yaml b/examples/security/classification/output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/params.yaml deleted file mode 100644 index 5d4dab98..00000000 --- a/examples/security/classification/output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/adv_predictions.json - adv_probabilities_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/cb0dcc18f715241285ffdebf98929ec2.pkl - params_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/params.yaml - predictions_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/predictions.json - probabilities_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/probabilities.json - score_dict_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/score_dict.json - test_labels_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/test_labels.json - train_labels_file: output/reports/train/b05bdc3d7501c589a622ff00f5dd3567/train_labels.json - model_dir: models - name: b05bdc3d7501c589a622ff00f5dd3567 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fe4aceb373cb9ab8d48b67b3369622e6 - trainer: - kwargs: {} -name: b05bdc3d7501c589a622ff00f5dd3567 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b073bf9c0864dbe478be45834fba4bfd/params.yaml b/examples/security/classification/output/reports/train/b073bf9c0864dbe478be45834fba4bfd/params.yaml deleted file mode 100644 index 0de5e0d9..00000000 --- a/examples/security/classification/output/reports/train/b073bf9c0864dbe478be45834fba4bfd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/adv_predictions.json - adv_probabilities_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/777d313f8a44e94391f9626a2a8e7af2.pkl - params_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/params.yaml - predictions_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/predictions.json - probabilities_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/probabilities.json - score_dict_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/score_dict.json - test_labels_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/test_labels.json - train_labels_file: output/reports/train/b073bf9c0864dbe478be45834fba4bfd/train_labels.json - model_dir: models - name: b073bf9c0864dbe478be45834fba4bfd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a7ebf0e2abe70c0f965bb8a633e7112b - trainer: - kwargs: {} -name: b073bf9c0864dbe478be45834fba4bfd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/params.yaml b/examples/security/classification/output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/params.yaml deleted file mode 100644 index fcce30bc..00000000 --- a/examples/security/classification/output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/adv_predictions.json - adv_probabilities_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/ac48774d8f6903ffb615ead5280f6df1.pkl - params_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/params.yaml - predictions_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/predictions.json - probabilities_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/probabilities.json - score_dict_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/score_dict.json - test_labels_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/test_labels.json - train_labels_file: output/reports/train/b08d67f463bc07fbcfca0800f88a21fd/train_labels.json - model_dir: models - name: b08d67f463bc07fbcfca0800f88a21fd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 86cbf7c1e4a09aacde3b4c36d135a369 - trainer: - kwargs: {} -name: b08d67f463bc07fbcfca0800f88a21fd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/params.yaml b/examples/security/classification/output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/params.yaml deleted file mode 100644 index 3d761b7e..00000000 --- a/examples/security/classification/output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/adv_predictions.json - adv_probabilities_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/ced3974dabdc9c9c5baea73e49f90827.pkl - params_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/params.yaml - predictions_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/predictions.json - probabilities_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/probabilities.json - score_dict_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/score_dict.json - test_labels_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/test_labels.json - train_labels_file: output/reports/train/b09715b1878c5e9d7a9c0e110be095d1/train_labels.json - model_dir: models - name: b09715b1878c5e9d7a9c0e110be095d1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 689fa324962cab081d8ca2d1f4bfd079 - trainer: - kwargs: {} -name: b09715b1878c5e9d7a9c0e110be095d1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/params.yaml b/examples/security/classification/output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/params.yaml deleted file mode 100644 index 0571ec76..00000000 --- a/examples/security/classification/output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/adv_predictions.json - adv_probabilities_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/f542cadbfd376493fb073801d0d5772a.pkl - params_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/params.yaml - predictions_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/predictions.json - probabilities_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/probabilities.json - score_dict_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/score_dict.json - test_labels_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/test_labels.json - train_labels_file: output/reports/train/b09b4524732cec7c943f61d7ddbac3a0/train_labels.json - model_dir: models - name: b09b4524732cec7c943f61d7ddbac3a0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b2e067155093df9a02364b0abdda1bd1 - trainer: - kwargs: {} -name: b09b4524732cec7c943f61d7ddbac3a0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/params.yaml b/examples/security/classification/output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/params.yaml deleted file mode 100644 index 75cbf0a0..00000000 --- a/examples/security/classification/output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/adv_predictions.json - adv_probabilities_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/fa830f178d58c7def8678fce834e77fa.pkl - params_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/params.yaml - predictions_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/predictions.json - probabilities_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/probabilities.json - score_dict_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/score_dict.json - test_labels_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/test_labels.json - train_labels_file: output/reports/train/b0ac28588ddf68cf92ad4469b09a81da/train_labels.json - model_dir: models - name: b0ac28588ddf68cf92ad4469b09a81da - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d25fbed063bc68511a9b0c01e19ef6c2 - trainer: - kwargs: {} -name: b0ac28588ddf68cf92ad4469b09a81da -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/params.yaml b/examples/security/classification/output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/params.yaml deleted file mode 100644 index 0c7d7aff..00000000 --- a/examples/security/classification/output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/adv_predictions.json - adv_probabilities_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/4a7b68e0326bf9fde7b0217a900cd3b3.pkl - params_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/params.yaml - predictions_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/predictions.json - probabilities_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/probabilities.json - score_dict_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/score_dict.json - test_labels_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/test_labels.json - train_labels_file: output/reports/train/b0b1fc0503a7aada52c030973c05f3d9/train_labels.json - model_dir: models - name: b0b1fc0503a7aada52c030973c05f3d9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 747af60beddb02af93dba6452928c02a - trainer: - kwargs: {} -name: b0b1fc0503a7aada52c030973c05f3d9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/params.yaml b/examples/security/classification/output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/params.yaml deleted file mode 100644 index e1b38216..00000000 --- a/examples/security/classification/output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/adv_predictions.json - adv_probabilities_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/c38b59fffec724e8c1304d4e9dc16def.pkl - params_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/params.yaml - predictions_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/predictions.json - probabilities_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/probabilities.json - score_dict_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/score_dict.json - test_labels_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/test_labels.json - train_labels_file: output/reports/train/b0ca43c78c36d5ab6d9480764ecc3f41/train_labels.json - model_dir: models - name: b0ca43c78c36d5ab6d9480764ecc3f41 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3405c92a11bfb2342ebea7b064c145e0 - trainer: - kwargs: {} -name: b0ca43c78c36d5ab6d9480764ecc3f41 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b0e618d6948e659d5b10de208c5e28d2/params.yaml b/examples/security/classification/output/reports/train/b0e618d6948e659d5b10de208c5e28d2/params.yaml deleted file mode 100644 index be3fe09f..00000000 --- a/examples/security/classification/output/reports/train/b0e618d6948e659d5b10de208c5e28d2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/adv_predictions.json - adv_probabilities_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/45b9ec01ca85badd1efcc4aeaae9f987.pkl - params_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/params.yaml - predictions_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/predictions.json - probabilities_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/probabilities.json - score_dict_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/score_dict.json - test_labels_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/test_labels.json - train_labels_file: output/reports/train/b0e618d6948e659d5b10de208c5e28d2/train_labels.json - model_dir: models - name: b0e618d6948e659d5b10de208c5e28d2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e989eb6de85667fba02ed752f87a3f47 - trainer: - kwargs: {} -name: b0e618d6948e659d5b10de208c5e28d2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b101166f2f63d4c89032296e7aba1d14/params.yaml b/examples/security/classification/output/reports/train/b101166f2f63d4c89032296e7aba1d14/params.yaml deleted file mode 100644 index c57f6ca9..00000000 --- a/examples/security/classification/output/reports/train/b101166f2f63d4c89032296e7aba1d14/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/adv_predictions.json - adv_probabilities_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/f81d6c0f9e739d1ada849b37b4322cfb.pkl - params_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/params.yaml - predictions_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/predictions.json - probabilities_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/probabilities.json - score_dict_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/score_dict.json - test_labels_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/test_labels.json - train_labels_file: output/reports/train/b101166f2f63d4c89032296e7aba1d14/train_labels.json - model_dir: models - name: b101166f2f63d4c89032296e7aba1d14 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fa101929735b869cd658de351f437508 - trainer: - kwargs: {} -name: b101166f2f63d4c89032296e7aba1d14 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b115bb5836f022a6622b76f60de5c187/params.yaml b/examples/security/classification/output/reports/train/b115bb5836f022a6622b76f60de5c187/params.yaml deleted file mode 100644 index b0752e68..00000000 --- a/examples/security/classification/output/reports/train/b115bb5836f022a6622b76f60de5c187/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/adv_predictions.json - adv_probabilities_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/579a3da495777545951539e21b6815ed.pkl - params_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/params.yaml - predictions_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/predictions.json - probabilities_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/probabilities.json - score_dict_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/score_dict.json - test_labels_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/test_labels.json - train_labels_file: output/reports/train/b115bb5836f022a6622b76f60de5c187/train_labels.json - model_dir: models - name: b115bb5836f022a6622b76f60de5c187 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 171e786f958decc759f5492a983bd739 - trainer: - kwargs: {} -name: b115bb5836f022a6622b76f60de5c187 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b144ce87e635b5009f96954d62b457a9/params.yaml b/examples/security/classification/output/reports/train/b144ce87e635b5009f96954d62b457a9/params.yaml deleted file mode 100644 index f5cb98c9..00000000 --- a/examples/security/classification/output/reports/train/b144ce87e635b5009f96954d62b457a9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/adv_predictions.json - adv_probabilities_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/c616caf3545995c371048faf19eefc5f.pkl - params_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/params.yaml - predictions_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/predictions.json - probabilities_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/probabilities.json - score_dict_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/score_dict.json - test_labels_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/test_labels.json - train_labels_file: output/reports/train/b144ce87e635b5009f96954d62b457a9/train_labels.json - model_dir: models - name: b144ce87e635b5009f96954d62b457a9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9985f1b0132b35000387e0123ee02662 - trainer: - kwargs: {} -name: b144ce87e635b5009f96954d62b457a9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b16411c8f872185d6a5955debbac01af/params.yaml b/examples/security/classification/output/reports/train/b16411c8f872185d6a5955debbac01af/params.yaml deleted file mode 100644 index 67cb0820..00000000 --- a/examples/security/classification/output/reports/train/b16411c8f872185d6a5955debbac01af/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b16411c8f872185d6a5955debbac01af/adv_predictions.json - adv_probabilities_file: output/reports/train/b16411c8f872185d6a5955debbac01af/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/5b44463d43a412981bacd6a4609540a2.pkl - params_file: output/reports/train/b16411c8f872185d6a5955debbac01af/params.yaml - predictions_file: output/reports/train/b16411c8f872185d6a5955debbac01af/predictions.json - probabilities_file: output/reports/train/b16411c8f872185d6a5955debbac01af/probabilities.json - score_dict_file: output/reports/train/b16411c8f872185d6a5955debbac01af/score_dict.json - test_labels_file: output/reports/train/b16411c8f872185d6a5955debbac01af/test_labels.json - train_labels_file: output/reports/train/b16411c8f872185d6a5955debbac01af/train_labels.json - model_dir: models - name: b16411c8f872185d6a5955debbac01af - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ef56d8cb480d48bd4a1dea4f75919232 - trainer: - kwargs: {} -name: b16411c8f872185d6a5955debbac01af -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b16ee196d18e3476d7524c03a462cb9c/params.yaml b/examples/security/classification/output/reports/train/b16ee196d18e3476d7524c03a462cb9c/params.yaml deleted file mode 100644 index e372d673..00000000 --- a/examples/security/classification/output/reports/train/b16ee196d18e3476d7524c03a462cb9c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/adv_predictions.json - adv_probabilities_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/76a6adaead1a24255f3884113a825421.pkl - params_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/params.yaml - predictions_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/predictions.json - probabilities_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/probabilities.json - score_dict_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/score_dict.json - test_labels_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/test_labels.json - train_labels_file: output/reports/train/b16ee196d18e3476d7524c03a462cb9c/train_labels.json - model_dir: models - name: b16ee196d18e3476d7524c03a462cb9c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8e99047255178d8e2a0b0f00a2b1ff04 - trainer: - kwargs: {} -name: b16ee196d18e3476d7524c03a462cb9c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b186a561bfb6d991711ea844aa49c184/params.yaml b/examples/security/classification/output/reports/train/b186a561bfb6d991711ea844aa49c184/params.yaml deleted file mode 100644 index 29640511..00000000 --- a/examples/security/classification/output/reports/train/b186a561bfb6d991711ea844aa49c184/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/adv_predictions.json - adv_probabilities_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/930ea8546e1e3526ddf742770c1804e6.pkl - params_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/params.yaml - predictions_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/predictions.json - probabilities_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/probabilities.json - score_dict_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/score_dict.json - test_labels_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/test_labels.json - train_labels_file: output/reports/train/b186a561bfb6d991711ea844aa49c184/train_labels.json - model_dir: models - name: b186a561bfb6d991711ea844aa49c184 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7a30672c4cd58c8aaa150c4008cd2a3b - trainer: - kwargs: {} -name: b186a561bfb6d991711ea844aa49c184 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b18e1705cd48728742ac0b744699ca62/params.yaml b/examples/security/classification/output/reports/train/b18e1705cd48728742ac0b744699ca62/params.yaml deleted file mode 100644 index eebf1d87..00000000 --- a/examples/security/classification/output/reports/train/b18e1705cd48728742ac0b744699ca62/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/adv_predictions.json - adv_probabilities_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/f028cdff38d532c53568acb1276a70bd.pkl - params_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/params.yaml - predictions_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/predictions.json - probabilities_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/probabilities.json - score_dict_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/score_dict.json - test_labels_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/test_labels.json - train_labels_file: output/reports/train/b18e1705cd48728742ac0b744699ca62/train_labels.json - model_dir: models - name: b18e1705cd48728742ac0b744699ca62 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 65afaf613d4d085757f8ac5ae4e66386 - trainer: - kwargs: {} -name: b18e1705cd48728742ac0b744699ca62 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/params.yaml b/examples/security/classification/output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/params.yaml deleted file mode 100644 index 8fc2125a..00000000 --- a/examples/security/classification/output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/adv_predictions.json - adv_probabilities_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/eef12e64836a22f728a4fd66c4ef2f77.pkl - params_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/params.yaml - predictions_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/predictions.json - probabilities_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/probabilities.json - score_dict_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/score_dict.json - test_labels_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/test_labels.json - train_labels_file: output/reports/train/b19e9c4cf442acef23a5ffc29400ee16/train_labels.json - model_dir: models - name: b19e9c4cf442acef23a5ffc29400ee16 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d9fe3b1790831349872dafb1bea10011 - trainer: - kwargs: {} -name: b19e9c4cf442acef23a5ffc29400ee16 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/params.yaml b/examples/security/classification/output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/params.yaml deleted file mode 100644 index 20cb5e37..00000000 --- a/examples/security/classification/output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/adv_predictions.json - adv_probabilities_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/3f7400b13338204448e8bbf1fb57bf62.pkl - params_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/params.yaml - predictions_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/predictions.json - probabilities_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/probabilities.json - score_dict_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/score_dict.json - test_labels_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/test_labels.json - train_labels_file: output/reports/train/b1bdc9c2e9a23eecd373a174cee69d9c/train_labels.json - model_dir: models - name: b1bdc9c2e9a23eecd373a174cee69d9c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3a7fd503710857efdb7df6249b193ad9 - trainer: - kwargs: {} -name: b1bdc9c2e9a23eecd373a174cee69d9c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/params.yaml b/examples/security/classification/output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/params.yaml deleted file mode 100644 index 3e83f82f..00000000 --- a/examples/security/classification/output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/adv_predictions.json - adv_probabilities_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/f5a0fec54c3becb6156cef8f2c680257.pkl - params_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/params.yaml - predictions_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/predictions.json - probabilities_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/probabilities.json - score_dict_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/score_dict.json - test_labels_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/test_labels.json - train_labels_file: output/reports/train/b1c79fd6256a6c94ab9e424b18f97e06/train_labels.json - model_dir: models - name: b1c79fd6256a6c94ab9e424b18f97e06 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9ccdbcfed7ddbe974f16200612fa4750 - trainer: - kwargs: {} -name: b1c79fd6256a6c94ab9e424b18f97e06 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/params.yaml b/examples/security/classification/output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/params.yaml deleted file mode 100644 index 38d98fb5..00000000 --- a/examples/security/classification/output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/adv_predictions.json - adv_probabilities_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/7bb8ef06e4d54b0a46e5d8ac498b717e.pkl - params_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/params.yaml - predictions_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/predictions.json - probabilities_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/probabilities.json - score_dict_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/score_dict.json - test_labels_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/test_labels.json - train_labels_file: output/reports/train/b1cb6017da805a6e56df3ef6de028ecb/train_labels.json - model_dir: models - name: b1cb6017da805a6e56df3ef6de028ecb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 212371939eced90881e29348c637e712 - trainer: - kwargs: {} -name: b1cb6017da805a6e56df3ef6de028ecb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b1d354ebffed26f6b367770a746d44c2/params.yaml b/examples/security/classification/output/reports/train/b1d354ebffed26f6b367770a746d44c2/params.yaml deleted file mode 100644 index f2c1d28b..00000000 --- a/examples/security/classification/output/reports/train/b1d354ebffed26f6b367770a746d44c2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/adv_predictions.json - adv_probabilities_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/62c240c5b8a978c04835baa76580112c.pkl - params_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/params.yaml - predictions_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/predictions.json - probabilities_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/probabilities.json - score_dict_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/score_dict.json - test_labels_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/test_labels.json - train_labels_file: output/reports/train/b1d354ebffed26f6b367770a746d44c2/train_labels.json - model_dir: models - name: b1d354ebffed26f6b367770a746d44c2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b16a67f5b43fc267bdf68636054caef6 - trainer: - kwargs: {} -name: b1d354ebffed26f6b367770a746d44c2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/params.yaml b/examples/security/classification/output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/params.yaml deleted file mode 100644 index eefcf414..00000000 --- a/examples/security/classification/output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/adv_predictions.json - adv_probabilities_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/9a2abb54c915d0cf947e9a4d5ee27448.pkl - params_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/params.yaml - predictions_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/predictions.json - probabilities_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/probabilities.json - score_dict_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/score_dict.json - test_labels_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/test_labels.json - train_labels_file: output/reports/train/b2374371b21a0cf5b9106cedb3e8d277/train_labels.json - model_dir: models - name: b2374371b21a0cf5b9106cedb3e8d277 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5b081fe0c3f1cd26bfc2ee001e84057f - trainer: - kwargs: {} -name: b2374371b21a0cf5b9106cedb3e8d277 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b23e641fa13500b02712fc234f9b5ec0/params.yaml b/examples/security/classification/output/reports/train/b23e641fa13500b02712fc234f9b5ec0/params.yaml deleted file mode 100644 index 1a4d2c82..00000000 --- a/examples/security/classification/output/reports/train/b23e641fa13500b02712fc234f9b5ec0/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/adv_predictions.json - adv_probabilities_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/a55f8e267f8ece1a566c89e1759d38ba.pkl - params_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/params.yaml - predictions_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/predictions.json - probabilities_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/probabilities.json - score_dict_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/score_dict.json - test_labels_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/test_labels.json - train_labels_file: output/reports/train/b23e641fa13500b02712fc234f9b5ec0/train_labels.json - model_dir: models - name: b23e641fa13500b02712fc234f9b5ec0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e89cc737f5ae323f26f0f63f5af6d7d9 - trainer: - kwargs: {} -name: b23e641fa13500b02712fc234f9b5ec0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b25b350d11ba86480ba563458a999744/params.yaml b/examples/security/classification/output/reports/train/b25b350d11ba86480ba563458a999744/params.yaml deleted file mode 100644 index 27dff65c..00000000 --- a/examples/security/classification/output/reports/train/b25b350d11ba86480ba563458a999744/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b25b350d11ba86480ba563458a999744/adv_predictions.json - adv_probabilities_file: output/reports/train/b25b350d11ba86480ba563458a999744/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/e79b283d6662cbe71bd905d42bde0050.pkl - params_file: output/reports/train/b25b350d11ba86480ba563458a999744/params.yaml - predictions_file: output/reports/train/b25b350d11ba86480ba563458a999744/predictions.json - probabilities_file: output/reports/train/b25b350d11ba86480ba563458a999744/probabilities.json - score_dict_file: output/reports/train/b25b350d11ba86480ba563458a999744/score_dict.json - test_labels_file: output/reports/train/b25b350d11ba86480ba563458a999744/test_labels.json - train_labels_file: output/reports/train/b25b350d11ba86480ba563458a999744/train_labels.json - model_dir: models - name: b25b350d11ba86480ba563458a999744 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6c902c54d032e1d9826eca81620fb2bc - trainer: - kwargs: {} -name: b25b350d11ba86480ba563458a999744 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/params.yaml b/examples/security/classification/output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/params.yaml deleted file mode 100644 index 4dfd455d..00000000 --- a/examples/security/classification/output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/adv_predictions.json - adv_probabilities_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/567cf7514cfdfb7cb759729dc5ff89b1.pkl - params_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/params.yaml - predictions_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/predictions.json - probabilities_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/probabilities.json - score_dict_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/score_dict.json - test_labels_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/test_labels.json - train_labels_file: output/reports/train/b266005d2f02fb7cc293c8c67a357fb0/train_labels.json - model_dir: models - name: b266005d2f02fb7cc293c8c67a357fb0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d6d8ace2daaf241fbd0e7637c0f60b2c - trainer: - kwargs: {} -name: b266005d2f02fb7cc293c8c67a357fb0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b28303e8ba27bfbbb77ae721e264104f/params.yaml b/examples/security/classification/output/reports/train/b28303e8ba27bfbbb77ae721e264104f/params.yaml deleted file mode 100644 index 54fccdc6..00000000 --- a/examples/security/classification/output/reports/train/b28303e8ba27bfbbb77ae721e264104f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/adv_predictions.json - adv_probabilities_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/56f7ff378a9be56451d0fd46e34296f2.pkl - params_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/params.yaml - predictions_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/predictions.json - probabilities_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/probabilities.json - score_dict_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/score_dict.json - test_labels_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/test_labels.json - train_labels_file: output/reports/train/b28303e8ba27bfbbb77ae721e264104f/train_labels.json - model_dir: models - name: b28303e8ba27bfbbb77ae721e264104f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b1b5612679a08224ef94b8c0e8914426 - trainer: - kwargs: {} -name: b28303e8ba27bfbbb77ae721e264104f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b2a349c668a2fe304d32917d7334e428/params.yaml b/examples/security/classification/output/reports/train/b2a349c668a2fe304d32917d7334e428/params.yaml deleted file mode 100644 index e6bbc1a2..00000000 --- a/examples/security/classification/output/reports/train/b2a349c668a2fe304d32917d7334e428/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/adv_predictions.json - adv_probabilities_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/0c25133068ff19e36fcc2b8f00b1e89f.pkl - params_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/params.yaml - predictions_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/predictions.json - probabilities_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/probabilities.json - score_dict_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/score_dict.json - test_labels_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/test_labels.json - train_labels_file: output/reports/train/b2a349c668a2fe304d32917d7334e428/train_labels.json - model_dir: models - name: b2a349c668a2fe304d32917d7334e428 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7ccc0c563096a679e7ce970a33256c27 - trainer: - kwargs: {} -name: b2a349c668a2fe304d32917d7334e428 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b2e4880b7012abdccf7d65bcd027afde/params.yaml b/examples/security/classification/output/reports/train/b2e4880b7012abdccf7d65bcd027afde/params.yaml deleted file mode 100644 index 96db6a59..00000000 --- a/examples/security/classification/output/reports/train/b2e4880b7012abdccf7d65bcd027afde/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/adv_predictions.json - adv_probabilities_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/9dd1c6554bab881d09c5ee9385a6f146.pkl - params_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/params.yaml - predictions_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/predictions.json - probabilities_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/probabilities.json - score_dict_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/score_dict.json - test_labels_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/test_labels.json - train_labels_file: output/reports/train/b2e4880b7012abdccf7d65bcd027afde/train_labels.json - model_dir: models - name: b2e4880b7012abdccf7d65bcd027afde - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 336dbedf8d94377ef7723ddb83954cb3 - trainer: - kwargs: {} -name: b2e4880b7012abdccf7d65bcd027afde -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b3119f0403cdf4582a1626bad0d3e288/params.yaml b/examples/security/classification/output/reports/train/b3119f0403cdf4582a1626bad0d3e288/params.yaml deleted file mode 100644 index dd1a5939..00000000 --- a/examples/security/classification/output/reports/train/b3119f0403cdf4582a1626bad0d3e288/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/adv_predictions.json - adv_probabilities_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/f6b9e651e088ebcf8422012cd7ab3acc.pkl - params_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/params.yaml - predictions_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/predictions.json - probabilities_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/probabilities.json - score_dict_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/score_dict.json - test_labels_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/test_labels.json - train_labels_file: output/reports/train/b3119f0403cdf4582a1626bad0d3e288/train_labels.json - model_dir: models - name: b3119f0403cdf4582a1626bad0d3e288 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d9847db4ee215b0e92c10a8db973712a - trainer: - kwargs: {} -name: b3119f0403cdf4582a1626bad0d3e288 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b31d529f0333ee008669c642de75d1c0/params.yaml b/examples/security/classification/output/reports/train/b31d529f0333ee008669c642de75d1c0/params.yaml deleted file mode 100644 index 6d4d620a..00000000 --- a/examples/security/classification/output/reports/train/b31d529f0333ee008669c642de75d1c0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/adv_predictions.json - adv_probabilities_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/c7920214a7e7c1c5df964ee62b5e8ff1.pkl - params_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/params.yaml - predictions_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/predictions.json - probabilities_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/probabilities.json - score_dict_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/score_dict.json - test_labels_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/test_labels.json - train_labels_file: output/reports/train/b31d529f0333ee008669c642de75d1c0/train_labels.json - model_dir: models - name: b31d529f0333ee008669c642de75d1c0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ee777e014e7846da3c20f262c342647d - trainer: - kwargs: {} -name: b31d529f0333ee008669c642de75d1c0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b331b411b3fb0bd9082b366763587fe1/params.yaml b/examples/security/classification/output/reports/train/b331b411b3fb0bd9082b366763587fe1/params.yaml deleted file mode 100644 index 845f89ad..00000000 --- a/examples/security/classification/output/reports/train/b331b411b3fb0bd9082b366763587fe1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/adv_predictions.json - adv_probabilities_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/d5e3b77f42401e60180ee8df64dea98a.pkl - params_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/params.yaml - predictions_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/predictions.json - probabilities_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/probabilities.json - score_dict_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/score_dict.json - test_labels_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/test_labels.json - train_labels_file: output/reports/train/b331b411b3fb0bd9082b366763587fe1/train_labels.json - model_dir: models - name: b331b411b3fb0bd9082b366763587fe1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 59acd8432a806802cd41deb69bc79999 - trainer: - kwargs: {} -name: b331b411b3fb0bd9082b366763587fe1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/params.yaml b/examples/security/classification/output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/params.yaml deleted file mode 100644 index c25d7a43..00000000 --- a/examples/security/classification/output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/adv_predictions.json - adv_probabilities_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/79207429287df6312476579489f6e840.pkl - params_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/params.yaml - predictions_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/predictions.json - probabilities_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/probabilities.json - score_dict_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/score_dict.json - test_labels_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/test_labels.json - train_labels_file: output/reports/train/b3442be7ae85249e5be15dc5912c5d6d/train_labels.json - model_dir: models - name: b3442be7ae85249e5be15dc5912c5d6d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 310b45acd29dc0a5be693e1b661369b8 - trainer: - kwargs: {} -name: b3442be7ae85249e5be15dc5912c5d6d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b3450c14390ce6087e1a0bb29d887909/params.yaml b/examples/security/classification/output/reports/train/b3450c14390ce6087e1a0bb29d887909/params.yaml deleted file mode 100644 index 9c05763b..00000000 --- a/examples/security/classification/output/reports/train/b3450c14390ce6087e1a0bb29d887909/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/adv_predictions.json - adv_probabilities_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/ff944458294e23bbd01c4c675f86f3c7.pkl - params_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/params.yaml - predictions_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/predictions.json - probabilities_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/probabilities.json - score_dict_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/score_dict.json - test_labels_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/test_labels.json - train_labels_file: output/reports/train/b3450c14390ce6087e1a0bb29d887909/train_labels.json - model_dir: models - name: b3450c14390ce6087e1a0bb29d887909 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9433972d7d3d88fb71285376e0a11981 - trainer: - kwargs: {} -name: b3450c14390ce6087e1a0bb29d887909 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b35a6ca7a6955989b0f18ade83048016/params.yaml b/examples/security/classification/output/reports/train/b35a6ca7a6955989b0f18ade83048016/params.yaml deleted file mode 100644 index 2533e350..00000000 --- a/examples/security/classification/output/reports/train/b35a6ca7a6955989b0f18ade83048016/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/adv_predictions.json - adv_probabilities_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/ccb9f1f844a43726b7608df1010cb39c.pkl - params_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/params.yaml - predictions_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/predictions.json - probabilities_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/probabilities.json - score_dict_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/score_dict.json - test_labels_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/test_labels.json - train_labels_file: output/reports/train/b35a6ca7a6955989b0f18ade83048016/train_labels.json - model_dir: models - name: b35a6ca7a6955989b0f18ade83048016 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a84f47c604fc9574dea66c21862b8173 - trainer: - kwargs: {} -name: b35a6ca7a6955989b0f18ade83048016 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b36bd99af799282c0b3d31e75527d319/params.yaml b/examples/security/classification/output/reports/train/b36bd99af799282c0b3d31e75527d319/params.yaml deleted file mode 100644 index 70933af6..00000000 --- a/examples/security/classification/output/reports/train/b36bd99af799282c0b3d31e75527d319/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/adv_predictions.json - adv_probabilities_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/04199156f6584f50eb04a78d18418c05.pkl - params_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/params.yaml - predictions_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/predictions.json - probabilities_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/probabilities.json - score_dict_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/score_dict.json - test_labels_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/test_labels.json - train_labels_file: output/reports/train/b36bd99af799282c0b3d31e75527d319/train_labels.json - model_dir: models - name: b36bd99af799282c0b3d31e75527d319 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 409621bf18548125a05201cb4e17575d - trainer: - kwargs: {} -name: b36bd99af799282c0b3d31e75527d319 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b36e66880452690a16d0fc1c39d7801c/params.yaml b/examples/security/classification/output/reports/train/b36e66880452690a16d0fc1c39d7801c/params.yaml deleted file mode 100644 index 979d54b5..00000000 --- a/examples/security/classification/output/reports/train/b36e66880452690a16d0fc1c39d7801c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/adv_predictions.json - adv_probabilities_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/47fd50a26513ef12c1acd59da30c2eb1.pkl - params_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/params.yaml - predictions_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/predictions.json - probabilities_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/probabilities.json - score_dict_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/score_dict.json - test_labels_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/test_labels.json - train_labels_file: output/reports/train/b36e66880452690a16d0fc1c39d7801c/train_labels.json - model_dir: models - name: b36e66880452690a16d0fc1c39d7801c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c1a0c66c34e26375605bea55b2dec246 - trainer: - kwargs: {} -name: b36e66880452690a16d0fc1c39d7801c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/params.yaml b/examples/security/classification/output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/params.yaml deleted file mode 100644 index 4d8b1668..00000000 --- a/examples/security/classification/output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/adv_predictions.json - adv_probabilities_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/cfc034174f2315cd2bcdbd7313714bfd.pkl - params_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/params.yaml - predictions_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/predictions.json - probabilities_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/probabilities.json - score_dict_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/score_dict.json - test_labels_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/test_labels.json - train_labels_file: output/reports/train/b3ef240e0f55a5c309b43d4bc35ea062/train_labels.json - model_dir: models - name: b3ef240e0f55a5c309b43d4bc35ea062 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e44cd7b38e4965055ed9acc22a40c700 - trainer: - kwargs: {} -name: b3ef240e0f55a5c309b43d4bc35ea062 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/params.yaml b/examples/security/classification/output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/params.yaml deleted file mode 100644 index 60fe65fa..00000000 --- a/examples/security/classification/output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/adv_predictions.json - adv_probabilities_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/e674e63afdbcbf7639968233c9884e8a.pkl - params_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/params.yaml - predictions_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/predictions.json - probabilities_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/probabilities.json - score_dict_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/score_dict.json - test_labels_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/test_labels.json - train_labels_file: output/reports/train/b3f7ae5c0c4e50adc6f90612151ba2aa/train_labels.json - model_dir: models - name: b3f7ae5c0c4e50adc6f90612151ba2aa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 50b81f12ab9c457d1f157048a83671d8 - trainer: - kwargs: {} -name: b3f7ae5c0c4e50adc6f90612151ba2aa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/params.yaml b/examples/security/classification/output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/params.yaml deleted file mode 100644 index e2fea91d..00000000 --- a/examples/security/classification/output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/adv_predictions.json - adv_probabilities_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/d9d5d8c027c19c97ae521d36137f8867.pkl - params_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/params.yaml - predictions_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/predictions.json - probabilities_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/probabilities.json - score_dict_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/score_dict.json - test_labels_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/test_labels.json - train_labels_file: output/reports/train/b3fd461471eb35c2aeef84d33f51e57f/train_labels.json - model_dir: models - name: b3fd461471eb35c2aeef84d33f51e57f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d98fa551231cd2ffeb0aff82c46b74eb - trainer: - kwargs: {} -name: b3fd461471eb35c2aeef84d33f51e57f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b4347aba65f776f8be3ec133765739c6/params.yaml b/examples/security/classification/output/reports/train/b4347aba65f776f8be3ec133765739c6/params.yaml deleted file mode 100644 index a9882dc7..00000000 --- a/examples/security/classification/output/reports/train/b4347aba65f776f8be3ec133765739c6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/adv_predictions.json - adv_probabilities_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/7963eab8a274989cb2933b065f2611dd.pkl - params_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/params.yaml - predictions_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/predictions.json - probabilities_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/probabilities.json - score_dict_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/score_dict.json - test_labels_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/test_labels.json - train_labels_file: output/reports/train/b4347aba65f776f8be3ec133765739c6/train_labels.json - model_dir: models - name: b4347aba65f776f8be3ec133765739c6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3e6a86fc428d6d2fa1a6e9b307e64b66 - trainer: - kwargs: {} -name: b4347aba65f776f8be3ec133765739c6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/params.yaml b/examples/security/classification/output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/params.yaml deleted file mode 100644 index cc45a55f..00000000 --- a/examples/security/classification/output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/adv_predictions.json - adv_probabilities_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/1d7c8dfdfbd5e108cca52ecfb5548753.pkl - params_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/params.yaml - predictions_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/predictions.json - probabilities_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/probabilities.json - score_dict_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/score_dict.json - test_labels_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/test_labels.json - train_labels_file: output/reports/train/b439a0efccf158906cd2c5f4f8c3bbff/train_labels.json - model_dir: models - name: b439a0efccf158906cd2c5f4f8c3bbff - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 776fae8f95eb8bf900a7ccb02e3957f6 - trainer: - kwargs: {} -name: b439a0efccf158906cd2c5f4f8c3bbff -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b448cd347129df31e3592b793da3c022/params.yaml b/examples/security/classification/output/reports/train/b448cd347129df31e3592b793da3c022/params.yaml deleted file mode 100644 index 019c1b2f..00000000 --- a/examples/security/classification/output/reports/train/b448cd347129df31e3592b793da3c022/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b448cd347129df31e3592b793da3c022/adv_predictions.json - adv_probabilities_file: output/reports/train/b448cd347129df31e3592b793da3c022/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/d2cc9ca096a3de0fd30ad6255fc62367.pkl - params_file: output/reports/train/b448cd347129df31e3592b793da3c022/params.yaml - predictions_file: output/reports/train/b448cd347129df31e3592b793da3c022/predictions.json - probabilities_file: output/reports/train/b448cd347129df31e3592b793da3c022/probabilities.json - score_dict_file: output/reports/train/b448cd347129df31e3592b793da3c022/score_dict.json - test_labels_file: output/reports/train/b448cd347129df31e3592b793da3c022/test_labels.json - train_labels_file: output/reports/train/b448cd347129df31e3592b793da3c022/train_labels.json - model_dir: models - name: b448cd347129df31e3592b793da3c022 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 012c2279d3343472102505dc6cc46ced - trainer: - kwargs: {} -name: b448cd347129df31e3592b793da3c022 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/params.yaml b/examples/security/classification/output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/params.yaml deleted file mode 100644 index e848f972..00000000 --- a/examples/security/classification/output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/adv_predictions.json - adv_probabilities_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/facc60c8ce987094127df32f360309ea.pkl - params_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/params.yaml - predictions_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/predictions.json - probabilities_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/probabilities.json - score_dict_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/score_dict.json - test_labels_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/test_labels.json - train_labels_file: output/reports/train/b4826a6dc881263fa5ae966e3df6bb07/train_labels.json - model_dir: models - name: b4826a6dc881263fa5ae966e3df6bb07 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 919e15e89f29d2f1a322acbb5038598c - trainer: - kwargs: {} -name: b4826a6dc881263fa5ae966e3df6bb07 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/params.yaml b/examples/security/classification/output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/params.yaml deleted file mode 100644 index 9b584e3c..00000000 --- a/examples/security/classification/output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/adv_predictions.json - adv_probabilities_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/cca6fceaec58a47e11385bea56925b77.pkl - params_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/params.yaml - predictions_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/predictions.json - probabilities_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/probabilities.json - score_dict_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/score_dict.json - test_labels_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/test_labels.json - train_labels_file: output/reports/train/b48bfeb80d5c92230840aa55ba2b95b4/train_labels.json - model_dir: models - name: b48bfeb80d5c92230840aa55ba2b95b4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 017e0cf88e8e833d55872d706262aca6 - trainer: - kwargs: {} -name: b48bfeb80d5c92230840aa55ba2b95b4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b53df724cd32d17761578a10935d939b/params.yaml b/examples/security/classification/output/reports/train/b53df724cd32d17761578a10935d939b/params.yaml deleted file mode 100644 index d12da70d..00000000 --- a/examples/security/classification/output/reports/train/b53df724cd32d17761578a10935d939b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b53df724cd32d17761578a10935d939b/adv_predictions.json - adv_probabilities_file: output/reports/train/b53df724cd32d17761578a10935d939b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/04d29a7bb3550e5f905c54f957564063.pkl - params_file: output/reports/train/b53df724cd32d17761578a10935d939b/params.yaml - predictions_file: output/reports/train/b53df724cd32d17761578a10935d939b/predictions.json - probabilities_file: output/reports/train/b53df724cd32d17761578a10935d939b/probabilities.json - score_dict_file: output/reports/train/b53df724cd32d17761578a10935d939b/score_dict.json - test_labels_file: output/reports/train/b53df724cd32d17761578a10935d939b/test_labels.json - train_labels_file: output/reports/train/b53df724cd32d17761578a10935d939b/train_labels.json - model_dir: models - name: b53df724cd32d17761578a10935d939b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1aaf9aa63405270ec321f119fc4901bb - trainer: - kwargs: {} -name: b53df724cd32d17761578a10935d939b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/params.yaml b/examples/security/classification/output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/params.yaml deleted file mode 100644 index 1d7e722d..00000000 --- a/examples/security/classification/output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/adv_predictions.json - adv_probabilities_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/fc7d95da492211c442e4b1b3d7d70884.pkl - params_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/params.yaml - predictions_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/predictions.json - probabilities_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/probabilities.json - score_dict_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/score_dict.json - test_labels_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/test_labels.json - train_labels_file: output/reports/train/b54424cf5b86b9ff8d67bbdefea78412/train_labels.json - model_dir: models - name: b54424cf5b86b9ff8d67bbdefea78412 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3155eef2acd41775cce077e2e2816915 - trainer: - kwargs: {} -name: b54424cf5b86b9ff8d67bbdefea78412 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b548ae1bd6c36238751519fcff86c2c6/params.yaml b/examples/security/classification/output/reports/train/b548ae1bd6c36238751519fcff86c2c6/params.yaml deleted file mode 100644 index 73c40472..00000000 --- a/examples/security/classification/output/reports/train/b548ae1bd6c36238751519fcff86c2c6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/adv_predictions.json - adv_probabilities_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/799d12ddd24c2ead1bc98a5c7e4d9ef0.pkl - params_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/params.yaml - predictions_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/predictions.json - probabilities_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/probabilities.json - score_dict_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/score_dict.json - test_labels_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/test_labels.json - train_labels_file: output/reports/train/b548ae1bd6c36238751519fcff86c2c6/train_labels.json - model_dir: models - name: b548ae1bd6c36238751519fcff86c2c6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2fe61643c9618450d5b2310ad4042edf - trainer: - kwargs: {} -name: b548ae1bd6c36238751519fcff86c2c6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/params.yaml b/examples/security/classification/output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/params.yaml deleted file mode 100644 index 7f550285..00000000 --- a/examples/security/classification/output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/adv_predictions.json - adv_probabilities_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/054679bda69ac91cc265ce84a038c0bb.pkl - params_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/params.yaml - predictions_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/predictions.json - probabilities_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/probabilities.json - score_dict_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/score_dict.json - test_labels_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/test_labels.json - train_labels_file: output/reports/train/b55a22b0c88b0c6613fa1a4ae68b8bbc/train_labels.json - model_dir: models - name: b55a22b0c88b0c6613fa1a4ae68b8bbc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1dd760efc13540bed9a0e8393add768c - trainer: - kwargs: {} -name: b55a22b0c88b0c6613fa1a4ae68b8bbc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b55b5efe408ba9665223dcefa2706bd4/params.yaml b/examples/security/classification/output/reports/train/b55b5efe408ba9665223dcefa2706bd4/params.yaml deleted file mode 100644 index 4d58d5d2..00000000 --- a/examples/security/classification/output/reports/train/b55b5efe408ba9665223dcefa2706bd4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/adv_predictions.json - adv_probabilities_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e8948f3857b7a66a7dcd16f763e8ad4d.pkl - params_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/params.yaml - predictions_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/predictions.json - probabilities_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/probabilities.json - score_dict_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/score_dict.json - test_labels_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/test_labels.json - train_labels_file: output/reports/train/b55b5efe408ba9665223dcefa2706bd4/train_labels.json - model_dir: models - name: b55b5efe408ba9665223dcefa2706bd4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bc4e3aee2468b3aaa5621d97f4bce51b - trainer: - kwargs: {} -name: b55b5efe408ba9665223dcefa2706bd4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/params.yaml b/examples/security/classification/output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/params.yaml deleted file mode 100644 index 6d9824af..00000000 --- a/examples/security/classification/output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/adv_predictions.json - adv_probabilities_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/02a1c7bceefd3497782f329e5924148f.pkl - params_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/params.yaml - predictions_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/predictions.json - probabilities_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/probabilities.json - score_dict_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/score_dict.json - test_labels_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/test_labels.json - train_labels_file: output/reports/train/b5bf5356d2fc64acd27d65d89e2f0d84/train_labels.json - model_dir: models - name: b5bf5356d2fc64acd27d65d89e2f0d84 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e9063a27c282c6497072f9e7f27c2e31 - trainer: - kwargs: {} -name: b5bf5356d2fc64acd27d65d89e2f0d84 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b60204be05c88754b96dcba7ffcc6b79/params.yaml b/examples/security/classification/output/reports/train/b60204be05c88754b96dcba7ffcc6b79/params.yaml deleted file mode 100644 index c48a2ce6..00000000 --- a/examples/security/classification/output/reports/train/b60204be05c88754b96dcba7ffcc6b79/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/adv_predictions.json - adv_probabilities_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/bafc9e81137a6cd0bbeb5d948f7b3c79.pkl - params_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/params.yaml - predictions_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/predictions.json - probabilities_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/probabilities.json - score_dict_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/score_dict.json - test_labels_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/test_labels.json - train_labels_file: output/reports/train/b60204be05c88754b96dcba7ffcc6b79/train_labels.json - model_dir: models - name: b60204be05c88754b96dcba7ffcc6b79 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 38ab3ca758ae98b928efa6c70e43077b - trainer: - kwargs: {} -name: b60204be05c88754b96dcba7ffcc6b79 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b60a62c0592f813af995d3401e7caa1a/params.yaml b/examples/security/classification/output/reports/train/b60a62c0592f813af995d3401e7caa1a/params.yaml deleted file mode 100644 index 517e5724..00000000 --- a/examples/security/classification/output/reports/train/b60a62c0592f813af995d3401e7caa1a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/adv_predictions.json - adv_probabilities_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/d05d96bdc1faaae35570ec8677308216.pkl - params_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/params.yaml - predictions_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/predictions.json - probabilities_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/probabilities.json - score_dict_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/score_dict.json - test_labels_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/test_labels.json - train_labels_file: output/reports/train/b60a62c0592f813af995d3401e7caa1a/train_labels.json - model_dir: models - name: b60a62c0592f813af995d3401e7caa1a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e6eb147dbd55dfb3cdeb695a95a02fa5 - trainer: - kwargs: {} -name: b60a62c0592f813af995d3401e7caa1a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b63762c712d097e444a81bc97bfe889a/params.yaml b/examples/security/classification/output/reports/train/b63762c712d097e444a81bc97bfe889a/params.yaml deleted file mode 100644 index bf16090e..00000000 --- a/examples/security/classification/output/reports/train/b63762c712d097e444a81bc97bfe889a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/adv_predictions.json - adv_probabilities_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/d84b9b980e577bf1e28edf162c1e06f1.pkl - params_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/params.yaml - predictions_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/predictions.json - probabilities_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/probabilities.json - score_dict_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/score_dict.json - test_labels_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/test_labels.json - train_labels_file: output/reports/train/b63762c712d097e444a81bc97bfe889a/train_labels.json - model_dir: models - name: b63762c712d097e444a81bc97bfe889a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 02411dec9e111e10f905a54cc827ece5 - trainer: - kwargs: {} -name: b63762c712d097e444a81bc97bfe889a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b6613c3c565c506c80c939afbb2135b9/params.yaml b/examples/security/classification/output/reports/train/b6613c3c565c506c80c939afbb2135b9/params.yaml deleted file mode 100644 index a3682e16..00000000 --- a/examples/security/classification/output/reports/train/b6613c3c565c506c80c939afbb2135b9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/adv_predictions.json - adv_probabilities_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/a66e326f26e363f76fdaeb14f278c9db.pkl - params_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/params.yaml - predictions_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/predictions.json - probabilities_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/probabilities.json - score_dict_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/score_dict.json - test_labels_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/test_labels.json - train_labels_file: output/reports/train/b6613c3c565c506c80c939afbb2135b9/train_labels.json - model_dir: models - name: b6613c3c565c506c80c939afbb2135b9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 45490b4a66c5978ce212a81914aef517 - trainer: - kwargs: {} -name: b6613c3c565c506c80c939afbb2135b9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b668f51d862309cf70b8a342024b42d2/params.yaml b/examples/security/classification/output/reports/train/b668f51d862309cf70b8a342024b42d2/params.yaml deleted file mode 100644 index 3c61978a..00000000 --- a/examples/security/classification/output/reports/train/b668f51d862309cf70b8a342024b42d2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/adv_predictions.json - adv_probabilities_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/06369cf98874d64b42014fe922fd69bc.pkl - params_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/params.yaml - predictions_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/predictions.json - probabilities_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/probabilities.json - score_dict_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/score_dict.json - test_labels_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/test_labels.json - train_labels_file: output/reports/train/b668f51d862309cf70b8a342024b42d2/train_labels.json - model_dir: models - name: b668f51d862309cf70b8a342024b42d2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1ab65fc24f7413374b87f6905a6bc602 - trainer: - kwargs: {} -name: b668f51d862309cf70b8a342024b42d2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/params.yaml b/examples/security/classification/output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/params.yaml deleted file mode 100644 index 9bec8241..00000000 --- a/examples/security/classification/output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/adv_predictions.json - adv_probabilities_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/08c73f998ed8427041c2a3e51d49479f.pkl - params_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/params.yaml - predictions_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/predictions.json - probabilities_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/probabilities.json - score_dict_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/score_dict.json - test_labels_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/test_labels.json - train_labels_file: output/reports/train/b66ac776b9e72e8dc83a0526e77afb5d/train_labels.json - model_dir: models - name: b66ac776b9e72e8dc83a0526e77afb5d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c3f4c04cc8017b44d19b872e74074b8d - trainer: - kwargs: {} -name: b66ac776b9e72e8dc83a0526e77afb5d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/params.yaml b/examples/security/classification/output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/params.yaml deleted file mode 100644 index b803949f..00000000 --- a/examples/security/classification/output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/adv_predictions.json - adv_probabilities_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/7ff492bd94cd1181d84401e93d3738e5.pkl - params_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/params.yaml - predictions_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/predictions.json - probabilities_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/probabilities.json - score_dict_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/score_dict.json - test_labels_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/test_labels.json - train_labels_file: output/reports/train/b673f6c4d60fe62c6bd3df79fa3adc45/train_labels.json - model_dir: models - name: b673f6c4d60fe62c6bd3df79fa3adc45 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e812b0dd4e6345081856ce109b0bd335 - trainer: - kwargs: {} -name: b673f6c4d60fe62c6bd3df79fa3adc45 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/params.yaml b/examples/security/classification/output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/params.yaml deleted file mode 100644 index a33a54c2..00000000 --- a/examples/security/classification/output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/adv_predictions.json - adv_probabilities_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/bdf6a2dc627b642d3786f23dd4502270.pkl - params_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/params.yaml - predictions_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/predictions.json - probabilities_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/probabilities.json - score_dict_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/score_dict.json - test_labels_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/test_labels.json - train_labels_file: output/reports/train/b68cbb13eb5e030b7c7f0b7cf74c1cbf/train_labels.json - model_dir: models - name: b68cbb13eb5e030b7c7f0b7cf74c1cbf - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f22256fba1a9f840cd089cbeeeff6be4 - trainer: - kwargs: {} -name: b68cbb13eb5e030b7c7f0b7cf74c1cbf -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b69fefe12e1a9b3660490e9b8691a493/params.yaml b/examples/security/classification/output/reports/train/b69fefe12e1a9b3660490e9b8691a493/params.yaml deleted file mode 100644 index 92f3278a..00000000 --- a/examples/security/classification/output/reports/train/b69fefe12e1a9b3660490e9b8691a493/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/adv_predictions.json - adv_probabilities_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/30d4990134ba39931d81f08c107d225c.pkl - params_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/params.yaml - predictions_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/predictions.json - probabilities_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/probabilities.json - score_dict_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/score_dict.json - test_labels_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/test_labels.json - train_labels_file: output/reports/train/b69fefe12e1a9b3660490e9b8691a493/train_labels.json - model_dir: models - name: b69fefe12e1a9b3660490e9b8691a493 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5c49313d65bbec400fb73784160aacb9 - trainer: - kwargs: {} -name: b69fefe12e1a9b3660490e9b8691a493 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/params.yaml b/examples/security/classification/output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/params.yaml deleted file mode 100644 index 5ee6f06a..00000000 --- a/examples/security/classification/output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/adv_predictions.json - adv_probabilities_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/e8f6b6025a34a602146bcae789d8be0c.pkl - params_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/params.yaml - predictions_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/predictions.json - probabilities_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/probabilities.json - score_dict_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/score_dict.json - test_labels_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/test_labels.json - train_labels_file: output/reports/train/b6aa456baf8c7416560fbcec8ca91d19/train_labels.json - model_dir: models - name: b6aa456baf8c7416560fbcec8ca91d19 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 88e2b00b889f1270dabfbbc4c89a256b - trainer: - kwargs: {} -name: b6aa456baf8c7416560fbcec8ca91d19 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/params.yaml b/examples/security/classification/output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/params.yaml deleted file mode 100644 index 79f6df49..00000000 --- a/examples/security/classification/output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/adv_predictions.json - adv_probabilities_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/d0054cbb9861b08dfedb06deaf52928e.pkl - params_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/params.yaml - predictions_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/predictions.json - probabilities_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/probabilities.json - score_dict_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/score_dict.json - test_labels_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/test_labels.json - train_labels_file: output/reports/train/b6bd47bfef20aa2e7af28184fd29e3d6/train_labels.json - model_dir: models - name: b6bd47bfef20aa2e7af28184fd29e3d6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dd9322cf2ef93990ab69684fd34b6901 - trainer: - kwargs: {} -name: b6bd47bfef20aa2e7af28184fd29e3d6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b6c4bdda2d755c51203f0901f460de3a/params.yaml b/examples/security/classification/output/reports/train/b6c4bdda2d755c51203f0901f460de3a/params.yaml deleted file mode 100644 index 9de21ea0..00000000 --- a/examples/security/classification/output/reports/train/b6c4bdda2d755c51203f0901f460de3a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/adv_predictions.json - adv_probabilities_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/dbc7506699a570c20489b2b7c91f7d6c.pkl - params_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/params.yaml - predictions_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/predictions.json - probabilities_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/probabilities.json - score_dict_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/score_dict.json - test_labels_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/test_labels.json - train_labels_file: output/reports/train/b6c4bdda2d755c51203f0901f460de3a/train_labels.json - model_dir: models - name: b6c4bdda2d755c51203f0901f460de3a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b6524aa2d6e78411207acd1281f2be28 - trainer: - kwargs: {} -name: b6c4bdda2d755c51203f0901f460de3a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/params.yaml b/examples/security/classification/output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/params.yaml deleted file mode 100644 index f2310975..00000000 --- a/examples/security/classification/output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/adv_predictions.json - adv_probabilities_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/64a682e96d6e1fbe26f7b9adb68143b0.pkl - params_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/params.yaml - predictions_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/predictions.json - probabilities_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/probabilities.json - score_dict_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/score_dict.json - test_labels_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/test_labels.json - train_labels_file: output/reports/train/b6d0c987a1a14591d7865f5b4ddce6b2/train_labels.json - model_dir: models - name: b6d0c987a1a14591d7865f5b4ddce6b2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8acd494d6c32381606d5dc33317e464d - trainer: - kwargs: {} -name: b6d0c987a1a14591d7865f5b4ddce6b2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b6edc9d763e5d16e7131cb556a699408/params.yaml b/examples/security/classification/output/reports/train/b6edc9d763e5d16e7131cb556a699408/params.yaml deleted file mode 100644 index 589b702f..00000000 --- a/examples/security/classification/output/reports/train/b6edc9d763e5d16e7131cb556a699408/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/adv_predictions.json - adv_probabilities_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/a50d8bf7e008d896cf4c36ef25a83ec6.pkl - params_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/params.yaml - predictions_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/predictions.json - probabilities_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/probabilities.json - score_dict_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/score_dict.json - test_labels_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/test_labels.json - train_labels_file: output/reports/train/b6edc9d763e5d16e7131cb556a699408/train_labels.json - model_dir: models - name: b6edc9d763e5d16e7131cb556a699408 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f6543bcbda12c802eee109bbd764cd16 - trainer: - kwargs: {} -name: b6edc9d763e5d16e7131cb556a699408 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/params.yaml b/examples/security/classification/output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/params.yaml deleted file mode 100644 index e332b20c..00000000 --- a/examples/security/classification/output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/adv_predictions.json - adv_probabilities_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/7dd5aa4a423abc77218b920c215a203f.pkl - params_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/params.yaml - predictions_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/predictions.json - probabilities_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/probabilities.json - score_dict_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/score_dict.json - test_labels_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/test_labels.json - train_labels_file: output/reports/train/b6f1e3373b763f0d52ecd631d78624ab/train_labels.json - model_dir: models - name: b6f1e3373b763f0d52ecd631d78624ab - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c14e8914a01964e262bd0904a698d892 - trainer: - kwargs: {} -name: b6f1e3373b763f0d52ecd631d78624ab -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/params.yaml b/examples/security/classification/output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/params.yaml deleted file mode 100644 index e38a001e..00000000 --- a/examples/security/classification/output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/adv_predictions.json - adv_probabilities_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/9b813e6ae0876e440c55501ef28b2433.pkl - params_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/params.yaml - predictions_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/predictions.json - probabilities_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/probabilities.json - score_dict_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/score_dict.json - test_labels_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/test_labels.json - train_labels_file: output/reports/train/b6f2f1c932258f4c2487648e0c9c620f/train_labels.json - model_dir: models - name: b6f2f1c932258f4c2487648e0c9c620f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1fa0537985acd167e03115c8305bd15a - trainer: - kwargs: {} -name: b6f2f1c932258f4c2487648e0c9c620f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b72727ff278a85a00e80535dbdfc921e/params.yaml b/examples/security/classification/output/reports/train/b72727ff278a85a00e80535dbdfc921e/params.yaml deleted file mode 100644 index 0d3b30b1..00000000 --- a/examples/security/classification/output/reports/train/b72727ff278a85a00e80535dbdfc921e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/adv_predictions.json - adv_probabilities_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/4d22807ef8fea96b41c9de12270fb797.pkl - params_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/params.yaml - predictions_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/predictions.json - probabilities_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/probabilities.json - score_dict_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/score_dict.json - test_labels_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/test_labels.json - train_labels_file: output/reports/train/b72727ff278a85a00e80535dbdfc921e/train_labels.json - model_dir: models - name: b72727ff278a85a00e80535dbdfc921e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 30efd902b0023b614d64fc3c9be780e4 - trainer: - kwargs: {} -name: b72727ff278a85a00e80535dbdfc921e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/params.yaml b/examples/security/classification/output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/params.yaml deleted file mode 100644 index d1484016..00000000 --- a/examples/security/classification/output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/adv_predictions.json - adv_probabilities_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/3eddc4d781985a51ec6b414ed39f7e84.pkl - params_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/params.yaml - predictions_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/predictions.json - probabilities_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/probabilities.json - score_dict_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/score_dict.json - test_labels_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/test_labels.json - train_labels_file: output/reports/train/b72edca2a6cfcf1d1c2d9d5dc292ce39/train_labels.json - model_dir: models - name: b72edca2a6cfcf1d1c2d9d5dc292ce39 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8bfbe7c626ad91830d6bc94f42ce9c8f - trainer: - kwargs: {} -name: b72edca2a6cfcf1d1c2d9d5dc292ce39 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b77425388d3d58f6e3a0a148ca15455b/params.yaml b/examples/security/classification/output/reports/train/b77425388d3d58f6e3a0a148ca15455b/params.yaml deleted file mode 100644 index 75e3f667..00000000 --- a/examples/security/classification/output/reports/train/b77425388d3d58f6e3a0a148ca15455b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/adv_predictions.json - adv_probabilities_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/c9cdbb6db0c1a61140343d2eadb92488.pkl - params_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/params.yaml - predictions_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/predictions.json - probabilities_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/probabilities.json - score_dict_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/score_dict.json - test_labels_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/test_labels.json - train_labels_file: output/reports/train/b77425388d3d58f6e3a0a148ca15455b/train_labels.json - model_dir: models - name: b77425388d3d58f6e3a0a148ca15455b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 84d1bfc33d553749180fea2a1c6abc0e - trainer: - kwargs: {} -name: b77425388d3d58f6e3a0a148ca15455b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/params.yaml b/examples/security/classification/output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/params.yaml deleted file mode 100644 index 892373d5..00000000 --- a/examples/security/classification/output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/adv_predictions.json - adv_probabilities_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/3edfb90a132d1f5ed26bd971a31bc485.pkl - params_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/params.yaml - predictions_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/predictions.json - probabilities_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/probabilities.json - score_dict_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/score_dict.json - test_labels_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/test_labels.json - train_labels_file: output/reports/train/b79f1dd92c7e2c7375a2ddca446ae993/train_labels.json - model_dir: models - name: b79f1dd92c7e2c7375a2ddca446ae993 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8b316de0adc33804432b5170d84dc87c - trainer: - kwargs: {} -name: b79f1dd92c7e2c7375a2ddca446ae993 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/params.yaml b/examples/security/classification/output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/params.yaml deleted file mode 100644 index 8301035b..00000000 --- a/examples/security/classification/output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/adv_predictions.json - adv_probabilities_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/ac94b186131b44b3d38faccdd547ab1b.pkl - params_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/params.yaml - predictions_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/predictions.json - probabilities_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/probabilities.json - score_dict_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/score_dict.json - test_labels_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/test_labels.json - train_labels_file: output/reports/train/b7a0d5a9655477b8989ce8256959bd5f/train_labels.json - model_dir: models - name: b7a0d5a9655477b8989ce8256959bd5f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 20312982c46c86615a30a20f68e08fee - trainer: - kwargs: {} -name: b7a0d5a9655477b8989ce8256959bd5f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b7a2cc0496252e67314a67831a48348a/params.yaml b/examples/security/classification/output/reports/train/b7a2cc0496252e67314a67831a48348a/params.yaml deleted file mode 100644 index 76640f16..00000000 --- a/examples/security/classification/output/reports/train/b7a2cc0496252e67314a67831a48348a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/adv_predictions.json - adv_probabilities_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/8cb3394ec6a45639fde4513b1f6ea86a.pkl - params_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/params.yaml - predictions_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/predictions.json - probabilities_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/probabilities.json - score_dict_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/score_dict.json - test_labels_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/test_labels.json - train_labels_file: output/reports/train/b7a2cc0496252e67314a67831a48348a/train_labels.json - model_dir: models - name: b7a2cc0496252e67314a67831a48348a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3b077aa4b381bc49b53475ad26ae59ea - trainer: - kwargs: {} -name: b7a2cc0496252e67314a67831a48348a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/params.yaml b/examples/security/classification/output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/params.yaml deleted file mode 100644 index ace27c29..00000000 --- a/examples/security/classification/output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/adv_predictions.json - adv_probabilities_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/ce750dc4f2c6699e2d58946f042eb413.pkl - params_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/params.yaml - predictions_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/predictions.json - probabilities_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/probabilities.json - score_dict_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/score_dict.json - test_labels_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/test_labels.json - train_labels_file: output/reports/train/b7bd890755f7f2055bdf550fff85c7f3/train_labels.json - model_dir: models - name: b7bd890755f7f2055bdf550fff85c7f3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 389ce40b4db4fda1ec66e54101b3f666 - trainer: - kwargs: {} -name: b7bd890755f7f2055bdf550fff85c7f3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b7e45486b414833805ee708a5833d9a7/params.yaml b/examples/security/classification/output/reports/train/b7e45486b414833805ee708a5833d9a7/params.yaml deleted file mode 100644 index 722501d2..00000000 --- a/examples/security/classification/output/reports/train/b7e45486b414833805ee708a5833d9a7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/adv_predictions.json - adv_probabilities_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/941de10cd2828f717d7be4ebbafe0d99.pkl - params_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/params.yaml - predictions_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/predictions.json - probabilities_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/probabilities.json - score_dict_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/score_dict.json - test_labels_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/test_labels.json - train_labels_file: output/reports/train/b7e45486b414833805ee708a5833d9a7/train_labels.json - model_dir: models - name: b7e45486b414833805ee708a5833d9a7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 385aac28511cc6a91201ddd90107eee8 - trainer: - kwargs: {} -name: b7e45486b414833805ee708a5833d9a7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/params.yaml b/examples/security/classification/output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/params.yaml deleted file mode 100644 index fdcc9511..00000000 --- a/examples/security/classification/output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/adv_predictions.json - adv_probabilities_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/f1269dbcc187d32da86a3eebcb2b53b5.pkl - params_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/params.yaml - predictions_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/predictions.json - probabilities_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/probabilities.json - score_dict_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/score_dict.json - test_labels_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/test_labels.json - train_labels_file: output/reports/train/b7e8cdbbd45dc1da7fc48597d71d39c4/train_labels.json - model_dir: models - name: b7e8cdbbd45dc1da7fc48597d71d39c4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 004465f2716e12fba6c8783d8d7f7c5f - trainer: - kwargs: {} -name: b7e8cdbbd45dc1da7fc48597d71d39c4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b7ef33477f98939d7388d7e91c5963cd/params.yaml b/examples/security/classification/output/reports/train/b7ef33477f98939d7388d7e91c5963cd/params.yaml deleted file mode 100644 index 01ea6e79..00000000 --- a/examples/security/classification/output/reports/train/b7ef33477f98939d7388d7e91c5963cd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/adv_predictions.json - adv_probabilities_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/06d4dee21e8a465b0db2f62f9a2aedbc.pkl - params_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/params.yaml - predictions_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/predictions.json - probabilities_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/probabilities.json - score_dict_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/score_dict.json - test_labels_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/test_labels.json - train_labels_file: output/reports/train/b7ef33477f98939d7388d7e91c5963cd/train_labels.json - model_dir: models - name: b7ef33477f98939d7388d7e91c5963cd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ebd46a65621492908e632cf1e69eec8f - trainer: - kwargs: {} -name: b7ef33477f98939d7388d7e91c5963cd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b7f496e4c721159abb82a217a048dd4e/params.yaml b/examples/security/classification/output/reports/train/b7f496e4c721159abb82a217a048dd4e/params.yaml deleted file mode 100644 index 03f125ac..00000000 --- a/examples/security/classification/output/reports/train/b7f496e4c721159abb82a217a048dd4e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/adv_predictions.json - adv_probabilities_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/fb53bc23f20bf903de5d8ac32a9f98b4.pkl - params_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/params.yaml - predictions_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/predictions.json - probabilities_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/probabilities.json - score_dict_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/score_dict.json - test_labels_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/test_labels.json - train_labels_file: output/reports/train/b7f496e4c721159abb82a217a048dd4e/train_labels.json - model_dir: models - name: b7f496e4c721159abb82a217a048dd4e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4ab5e0636374119e2d2c51fb92e422d3 - trainer: - kwargs: {} -name: b7f496e4c721159abb82a217a048dd4e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/params.yaml b/examples/security/classification/output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/params.yaml deleted file mode 100644 index c73ef7c1..00000000 --- a/examples/security/classification/output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/adv_predictions.json - adv_probabilities_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/2f73357245d13a550d3eed2f2b10ac67.pkl - params_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/params.yaml - predictions_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/predictions.json - probabilities_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/probabilities.json - score_dict_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/score_dict.json - test_labels_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/test_labels.json - train_labels_file: output/reports/train/b7f506104ccf6071fa8ebc1a6ff058c2/train_labels.json - model_dir: models - name: b7f506104ccf6071fa8ebc1a6ff058c2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 523ccd2145e027c1c9074f9177b5524d - trainer: - kwargs: {} -name: b7f506104ccf6071fa8ebc1a6ff058c2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/params.yaml b/examples/security/classification/output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/params.yaml deleted file mode 100644 index 645db07f..00000000 --- a/examples/security/classification/output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/params.yaml +++ /dev/null @@ -1,115 +0,0 @@ -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/adv_predictions.json - adv_probabilities_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl - model_file: output/models/22c84401c75484ca223cbcbabc8772dd.pkl - params_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/params.yaml - predictions_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/predictions.json - probabilities_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/probabilities.json - score_dict_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/score_dict.json - test_labels_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/test_labels.json - train_labels_file: output/reports/train/b7fb9e3a5ca1d84093a6fec5940a0480/train_labels.json - model_dir: models - name: b7fb9e3a5ca1d84093a6fec5940a0480 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1000 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e4bf40bf5f9a6bdf1f2ec21f474afcfd - trainer: - kwargs: {} -name: b7fb9e3a5ca1d84093a6fec5940a0480 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b7ffdd786addd5c6da587744db58f57c/params.yaml b/examples/security/classification/output/reports/train/b7ffdd786addd5c6da587744db58f57c/params.yaml deleted file mode 100644 index 0578d6c6..00000000 --- a/examples/security/classification/output/reports/train/b7ffdd786addd5c6da587744db58f57c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/adv_predictions.json - adv_probabilities_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/b119ddd1de872a17bdcaf3310e8189b1.pkl - params_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/params.yaml - predictions_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/predictions.json - probabilities_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/probabilities.json - score_dict_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/score_dict.json - test_labels_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/test_labels.json - train_labels_file: output/reports/train/b7ffdd786addd5c6da587744db58f57c/train_labels.json - model_dir: models - name: b7ffdd786addd5c6da587744db58f57c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9754a01500d1d143d56bc485054dcbf2 - trainer: - kwargs: {} -name: b7ffdd786addd5c6da587744db58f57c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b839a0dc0ec4f634732086e79b47f90b/params.yaml b/examples/security/classification/output/reports/train/b839a0dc0ec4f634732086e79b47f90b/params.yaml deleted file mode 100644 index d46a1ec6..00000000 --- a/examples/security/classification/output/reports/train/b839a0dc0ec4f634732086e79b47f90b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/adv_predictions.json - adv_probabilities_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/65920f962bcb3149e90efdd29ddebc7f.pkl - params_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/params.yaml - predictions_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/predictions.json - probabilities_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/probabilities.json - score_dict_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/score_dict.json - test_labels_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/test_labels.json - train_labels_file: output/reports/train/b839a0dc0ec4f634732086e79b47f90b/train_labels.json - model_dir: models - name: b839a0dc0ec4f634732086e79b47f90b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 419e0905ec7f126d1b1aead7b16adbb6 - trainer: - kwargs: {} -name: b839a0dc0ec4f634732086e79b47f90b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/params.yaml b/examples/security/classification/output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/params.yaml deleted file mode 100644 index c3704dba..00000000 --- a/examples/security/classification/output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/adv_predictions.json - adv_probabilities_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/4d67f432bfaa214cc63f41b6e2e6b56b.pkl - params_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/params.yaml - predictions_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/predictions.json - probabilities_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/probabilities.json - score_dict_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/score_dict.json - test_labels_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/test_labels.json - train_labels_file: output/reports/train/b8537e6d4abc1261acfc16121f0ac2d5/train_labels.json - model_dir: models - name: b8537e6d4abc1261acfc16121f0ac2d5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 87979735992d9254c11a020fc7c23a23 - trainer: - kwargs: {} -name: b8537e6d4abc1261acfc16121f0ac2d5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/params.yaml b/examples/security/classification/output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/params.yaml deleted file mode 100644 index f895359e..00000000 --- a/examples/security/classification/output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/adv_predictions.json - adv_probabilities_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/125e519f4cd7dc31e024673acfece592.pkl - params_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/params.yaml - predictions_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/predictions.json - probabilities_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/probabilities.json - score_dict_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/score_dict.json - test_labels_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/test_labels.json - train_labels_file: output/reports/train/b85d5757b0b2b87316daeaefb6a607ee/train_labels.json - model_dir: models - name: b85d5757b0b2b87316daeaefb6a607ee - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 96593ded75bfa60df0fa97afd4736ad6 - trainer: - kwargs: {} -name: b85d5757b0b2b87316daeaefb6a607ee -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b895db61daa9dec530d10aafc79da873/params.yaml b/examples/security/classification/output/reports/train/b895db61daa9dec530d10aafc79da873/params.yaml deleted file mode 100644 index 14843b75..00000000 --- a/examples/security/classification/output/reports/train/b895db61daa9dec530d10aafc79da873/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b895db61daa9dec530d10aafc79da873/adv_predictions.json - adv_probabilities_file: output/reports/train/b895db61daa9dec530d10aafc79da873/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/5f4a05647e2353edc70442f752cb60cd.pkl - params_file: output/reports/train/b895db61daa9dec530d10aafc79da873/params.yaml - predictions_file: output/reports/train/b895db61daa9dec530d10aafc79da873/predictions.json - probabilities_file: output/reports/train/b895db61daa9dec530d10aafc79da873/probabilities.json - score_dict_file: output/reports/train/b895db61daa9dec530d10aafc79da873/score_dict.json - test_labels_file: output/reports/train/b895db61daa9dec530d10aafc79da873/test_labels.json - train_labels_file: output/reports/train/b895db61daa9dec530d10aafc79da873/train_labels.json - model_dir: models - name: b895db61daa9dec530d10aafc79da873 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 5 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c87fa47da866dfc907b939f03216b4f4 - trainer: - kwargs: {} -name: b895db61daa9dec530d10aafc79da873 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b8fa2b00b52830b919a8ba8733434d81/params.yaml b/examples/security/classification/output/reports/train/b8fa2b00b52830b919a8ba8733434d81/params.yaml deleted file mode 100644 index 884b7c37..00000000 --- a/examples/security/classification/output/reports/train/b8fa2b00b52830b919a8ba8733434d81/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/adv_predictions.json - adv_probabilities_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/cc84d37f984f1f585db7a750ac228c25.pkl - params_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/params.yaml - predictions_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/predictions.json - probabilities_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/probabilities.json - score_dict_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/score_dict.json - test_labels_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/test_labels.json - train_labels_file: output/reports/train/b8fa2b00b52830b919a8ba8733434d81/train_labels.json - model_dir: models - name: b8fa2b00b52830b919a8ba8733434d81 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ab9c58dd4030fa07b62f62e9acbf3011 - trainer: - kwargs: {} -name: b8fa2b00b52830b919a8ba8733434d81 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/params.yaml b/examples/security/classification/output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/params.yaml deleted file mode 100644 index 94b94e71..00000000 --- a/examples/security/classification/output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/adv_predictions.json - adv_probabilities_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/ee46323197f594de7565075712a8fc49.pkl - params_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/params.yaml - predictions_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/predictions.json - probabilities_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/probabilities.json - score_dict_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/score_dict.json - test_labels_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/test_labels.json - train_labels_file: output/reports/train/b921c13bdcc42f9cdbb3fd8657142ac6/train_labels.json - model_dir: models - name: b921c13bdcc42f9cdbb3fd8657142ac6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0fd78cbc2ce93343862d8a72ae3c9a75 - trainer: - kwargs: {} -name: b921c13bdcc42f9cdbb3fd8657142ac6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b93e85675f1cfffaa262828a20778065/params.yaml b/examples/security/classification/output/reports/train/b93e85675f1cfffaa262828a20778065/params.yaml deleted file mode 100644 index 402fc98d..00000000 --- a/examples/security/classification/output/reports/train/b93e85675f1cfffaa262828a20778065/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b93e85675f1cfffaa262828a20778065/adv_predictions.json - adv_probabilities_file: output/reports/train/b93e85675f1cfffaa262828a20778065/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/98b99d884520e46a2b5ad84fb40b27bd.pkl - params_file: output/reports/train/b93e85675f1cfffaa262828a20778065/params.yaml - predictions_file: output/reports/train/b93e85675f1cfffaa262828a20778065/predictions.json - probabilities_file: output/reports/train/b93e85675f1cfffaa262828a20778065/probabilities.json - score_dict_file: output/reports/train/b93e85675f1cfffaa262828a20778065/score_dict.json - test_labels_file: output/reports/train/b93e85675f1cfffaa262828a20778065/test_labels.json - train_labels_file: output/reports/train/b93e85675f1cfffaa262828a20778065/train_labels.json - model_dir: models - name: b93e85675f1cfffaa262828a20778065 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 729127645e1552dd0dffecc146cba14f - trainer: - kwargs: {} -name: b93e85675f1cfffaa262828a20778065 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/params.yaml b/examples/security/classification/output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/params.yaml deleted file mode 100644 index 6c785c1f..00000000 --- a/examples/security/classification/output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/adv_predictions.json - adv_probabilities_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/bd133b7b1487e95eab0e08b5b1c25fb5.pkl - params_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/params.yaml - predictions_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/predictions.json - probabilities_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/probabilities.json - score_dict_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/score_dict.json - test_labels_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/test_labels.json - train_labels_file: output/reports/train/b958a8a870f0c51e4cbfd585f0f4d4ef/train_labels.json - model_dir: models - name: b958a8a870f0c51e4cbfd585f0f4d4ef - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b52e3f4869f67e1c1f06760a1370a8f5 - trainer: - kwargs: {} -name: b958a8a870f0c51e4cbfd585f0f4d4ef -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b96e6a9d841a682d65b690cd35842cb8/params.yaml b/examples/security/classification/output/reports/train/b96e6a9d841a682d65b690cd35842cb8/params.yaml deleted file mode 100644 index 7daf4174..00000000 --- a/examples/security/classification/output/reports/train/b96e6a9d841a682d65b690cd35842cb8/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/adv_predictions.json - adv_probabilities_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/877d722e658459d6fa0b012f2fd0c196.pkl - params_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/params.yaml - predictions_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/predictions.json - probabilities_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/probabilities.json - score_dict_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/score_dict.json - test_labels_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/test_labels.json - train_labels_file: output/reports/train/b96e6a9d841a682d65b690cd35842cb8/train_labels.json - model_dir: models - name: b96e6a9d841a682d65b690cd35842cb8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f1db7a51590d45aa303f982b366d75a1 - trainer: - kwargs: {} -name: b96e6a9d841a682d65b690cd35842cb8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b97ce643de0c881f07936bb55ecbcf96/params.yaml b/examples/security/classification/output/reports/train/b97ce643de0c881f07936bb55ecbcf96/params.yaml deleted file mode 100644 index 7d3025ea..00000000 --- a/examples/security/classification/output/reports/train/b97ce643de0c881f07936bb55ecbcf96/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/adv_predictions.json - adv_probabilities_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/8550f76c8c11932d43568e124430a8e2.pkl - params_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/params.yaml - predictions_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/predictions.json - probabilities_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/probabilities.json - score_dict_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/score_dict.json - test_labels_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/test_labels.json - train_labels_file: output/reports/train/b97ce643de0c881f07936bb55ecbcf96/train_labels.json - model_dir: models - name: b97ce643de0c881f07936bb55ecbcf96 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8194557cc872f0ce81529aca85187ec6 - trainer: - kwargs: {} -name: b97ce643de0c881f07936bb55ecbcf96 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/params.yaml b/examples/security/classification/output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/params.yaml deleted file mode 100644 index 3401d31a..00000000 --- a/examples/security/classification/output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/adv_predictions.json - adv_probabilities_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/5a3f273a3039ff7a902bac18862795ac.pkl - params_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/params.yaml - predictions_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/predictions.json - probabilities_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/probabilities.json - score_dict_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/score_dict.json - test_labels_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/test_labels.json - train_labels_file: output/reports/train/b98a33f558befb4fd90d9bdb1b7f9aca/train_labels.json - model_dir: models - name: b98a33f558befb4fd90d9bdb1b7f9aca - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e2c2e8dfe41e60e06fd632465359bb1f - trainer: - kwargs: {} -name: b98a33f558befb4fd90d9bdb1b7f9aca -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/params.yaml b/examples/security/classification/output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/params.yaml deleted file mode 100644 index e270ae71..00000000 --- a/examples/security/classification/output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/adv_predictions.json - adv_probabilities_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/4fc61eae59bc68d9ec210163ad20fa91.pkl - params_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/params.yaml - predictions_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/predictions.json - probabilities_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/probabilities.json - score_dict_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/score_dict.json - test_labels_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/test_labels.json - train_labels_file: output/reports/train/b9b0f943a7c110b1839a0e58da3a508e/train_labels.json - model_dir: models - name: b9b0f943a7c110b1839a0e58da3a508e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: efe5ea171fc1f9d5af3db3b548a18716 - trainer: - kwargs: {} -name: b9b0f943a7c110b1839a0e58da3a508e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/params.yaml b/examples/security/classification/output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/params.yaml deleted file mode 100644 index 526db5dd..00000000 --- a/examples/security/classification/output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/adv_predictions.json - adv_probabilities_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/76ff275e2819bd23c2ac1e038d95fd60.pkl - params_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/params.yaml - predictions_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/predictions.json - probabilities_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/probabilities.json - score_dict_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/score_dict.json - test_labels_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/test_labels.json - train_labels_file: output/reports/train/b9c07bbb5d6eaf0e8ef52e036d770731/train_labels.json - model_dir: models - name: b9c07bbb5d6eaf0e8ef52e036d770731 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c6103f9a27a543cd269af2626e7859f9 - trainer: - kwargs: {} -name: b9c07bbb5d6eaf0e8ef52e036d770731 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/params.yaml b/examples/security/classification/output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/params.yaml deleted file mode 100644 index e5a8d221..00000000 --- a/examples/security/classification/output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/adv_predictions.json - adv_probabilities_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/ecc8b130f49e96bbcaaca2f3ca70d471.pkl - params_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/params.yaml - predictions_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/predictions.json - probabilities_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/probabilities.json - score_dict_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/score_dict.json - test_labels_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/test_labels.json - train_labels_file: output/reports/train/b9eea72f450a4feddb1b95bf7b6df9a9/train_labels.json - model_dir: models - name: b9eea72f450a4feddb1b95bf7b6df9a9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7cafec5922da90d24ad3e5a865653ec4 - trainer: - kwargs: {} -name: b9eea72f450a4feddb1b95bf7b6df9a9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/params.yaml b/examples/security/classification/output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/params.yaml deleted file mode 100644 index 2c0845ab..00000000 --- a/examples/security/classification/output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/adv_predictions.json - adv_probabilities_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/996a266dacf71b1400b3827fa84fc656.pkl - params_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/params.yaml - predictions_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/predictions.json - probabilities_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/probabilities.json - score_dict_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/score_dict.json - test_labels_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/test_labels.json - train_labels_file: output/reports/train/ba21c03ad3a72d8e8feeec7c0c04a74a/train_labels.json - model_dir: models - name: ba21c03ad3a72d8e8feeec7c0c04a74a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7f51b54d02e236c1084bde16e7b97f14 - trainer: - kwargs: {} -name: ba21c03ad3a72d8e8feeec7c0c04a74a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/params.yaml b/examples/security/classification/output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/params.yaml deleted file mode 100644 index 06d606ac..00000000 --- a/examples/security/classification/output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/adv_predictions.json - adv_probabilities_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/577667a8d39b46f682c4919745637c13.pkl - params_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/params.yaml - predictions_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/predictions.json - probabilities_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/probabilities.json - score_dict_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/score_dict.json - test_labels_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/test_labels.json - train_labels_file: output/reports/train/ba2cecf45154cf30e6ef02a27ec76b6e/train_labels.json - model_dir: models - name: ba2cecf45154cf30e6ef02a27ec76b6e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 52d47b8a28d1eebb5917d4d143f2d72b - trainer: - kwargs: {} -name: ba2cecf45154cf30e6ef02a27ec76b6e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ba6073e354ed2f48ed572869ac753ed5/params.yaml b/examples/security/classification/output/reports/train/ba6073e354ed2f48ed572869ac753ed5/params.yaml deleted file mode 100644 index 6266c1a1..00000000 --- a/examples/security/classification/output/reports/train/ba6073e354ed2f48ed572869ac753ed5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/adv_predictions.json - adv_probabilities_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/2b82380abeafb95939a9bfd67ef1b410.pkl - params_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/params.yaml - predictions_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/predictions.json - probabilities_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/probabilities.json - score_dict_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/score_dict.json - test_labels_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/test_labels.json - train_labels_file: output/reports/train/ba6073e354ed2f48ed572869ac753ed5/train_labels.json - model_dir: models - name: ba6073e354ed2f48ed572869ac753ed5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 29c5ef33cc497502f0d3c1fcba4042b4 - trainer: - kwargs: {} -name: ba6073e354ed2f48ed572869ac753ed5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ba78f110a71b8670193d39d64510b377/params.yaml b/examples/security/classification/output/reports/train/ba78f110a71b8670193d39d64510b377/params.yaml deleted file mode 100644 index 26a6e8c7..00000000 --- a/examples/security/classification/output/reports/train/ba78f110a71b8670193d39d64510b377/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ba78f110a71b8670193d39d64510b377/adv_predictions.json - adv_probabilities_file: output/reports/train/ba78f110a71b8670193d39d64510b377/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/842266516d1e8af5f026a10f77c4024d.pkl - params_file: output/reports/train/ba78f110a71b8670193d39d64510b377/params.yaml - predictions_file: output/reports/train/ba78f110a71b8670193d39d64510b377/predictions.json - probabilities_file: output/reports/train/ba78f110a71b8670193d39d64510b377/probabilities.json - score_dict_file: output/reports/train/ba78f110a71b8670193d39d64510b377/score_dict.json - test_labels_file: output/reports/train/ba78f110a71b8670193d39d64510b377/test_labels.json - train_labels_file: output/reports/train/ba78f110a71b8670193d39d64510b377/train_labels.json - model_dir: models - name: ba78f110a71b8670193d39d64510b377 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: de8382222f196f0491bbc4401626236f - trainer: - kwargs: {} -name: ba78f110a71b8670193d39d64510b377 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/params.yaml b/examples/security/classification/output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/params.yaml deleted file mode 100644 index 72f6b967..00000000 --- a/examples/security/classification/output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/adv_predictions.json - adv_probabilities_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/4c1ab493f796eeaa78ad2855cba5ecee.pkl - params_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/params.yaml - predictions_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/predictions.json - probabilities_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/probabilities.json - score_dict_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/score_dict.json - test_labels_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/test_labels.json - train_labels_file: output/reports/train/ba8c9e2ab93345ecccb1cfa6746392aa/train_labels.json - model_dir: models - name: ba8c9e2ab93345ecccb1cfa6746392aa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bad4ab78e89b6746d64f06ee8ae32378 - trainer: - kwargs: {} -name: ba8c9e2ab93345ecccb1cfa6746392aa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/baa0b6682949e7b6970c9548d0463501/params.yaml b/examples/security/classification/output/reports/train/baa0b6682949e7b6970c9548d0463501/params.yaml deleted file mode 100644 index e3808083..00000000 --- a/examples/security/classification/output/reports/train/baa0b6682949e7b6970c9548d0463501/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/adv_predictions.json - adv_probabilities_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/984b0bf62b49e55b6ab0f7e4783991af.pkl - params_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/params.yaml - predictions_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/predictions.json - probabilities_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/probabilities.json - score_dict_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/score_dict.json - test_labels_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/test_labels.json - train_labels_file: output/reports/train/baa0b6682949e7b6970c9548d0463501/train_labels.json - model_dir: models - name: baa0b6682949e7b6970c9548d0463501 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 29e302354d5a9a0714b3598a391dc705 - trainer: - kwargs: {} -name: baa0b6682949e7b6970c9548d0463501 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/baaa89eddfb69f4b3f313d1090835968/params.yaml b/examples/security/classification/output/reports/train/baaa89eddfb69f4b3f313d1090835968/params.yaml deleted file mode 100644 index 41aaee88..00000000 --- a/examples/security/classification/output/reports/train/baaa89eddfb69f4b3f313d1090835968/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/adv_predictions.json - adv_probabilities_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/c1b412f4c93bc7e9681c4603a931aba8.pkl - params_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/params.yaml - predictions_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/predictions.json - probabilities_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/probabilities.json - score_dict_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/score_dict.json - test_labels_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/test_labels.json - train_labels_file: output/reports/train/baaa89eddfb69f4b3f313d1090835968/train_labels.json - model_dir: models - name: baaa89eddfb69f4b3f313d1090835968 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 78e7c925a701fed87c695cca6b9d8b90 - trainer: - kwargs: {} -name: baaa89eddfb69f4b3f313d1090835968 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/params.yaml b/examples/security/classification/output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/params.yaml deleted file mode 100644 index c4806442..00000000 --- a/examples/security/classification/output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/adv_predictions.json - adv_probabilities_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/d95cd17f40d44b953c96cbc9eae1bdd1.pkl - params_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/params.yaml - predictions_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/predictions.json - probabilities_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/probabilities.json - score_dict_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/score_dict.json - test_labels_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/test_labels.json - train_labels_file: output/reports/train/bace2da3bd6de72753a6cc7760eb7a02/train_labels.json - model_dir: models - name: bace2da3bd6de72753a6cc7760eb7a02 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c34800e371c9714bfe385727a045ab85 - trainer: - kwargs: {} -name: bace2da3bd6de72753a6cc7760eb7a02 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bad689b33de238b7022e3f678397b0c0/params.yaml b/examples/security/classification/output/reports/train/bad689b33de238b7022e3f678397b0c0/params.yaml deleted file mode 100644 index 87cb0371..00000000 --- a/examples/security/classification/output/reports/train/bad689b33de238b7022e3f678397b0c0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/adv_predictions.json - adv_probabilities_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/dfa5220d1129d3ce990c27c0c707dd0f.pkl - params_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/params.yaml - predictions_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/predictions.json - probabilities_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/probabilities.json - score_dict_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/score_dict.json - test_labels_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/test_labels.json - train_labels_file: output/reports/train/bad689b33de238b7022e3f678397b0c0/train_labels.json - model_dir: models - name: bad689b33de238b7022e3f678397b0c0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1e3467c5b200c4fee28633500a66ed6c - trainer: - kwargs: {} -name: bad689b33de238b7022e3f678397b0c0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bb2c522f81b45527334f57c19ca86172/params.yaml b/examples/security/classification/output/reports/train/bb2c522f81b45527334f57c19ca86172/params.yaml deleted file mode 100644 index f380ccfa..00000000 --- a/examples/security/classification/output/reports/train/bb2c522f81b45527334f57c19ca86172/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/adv_predictions.json - adv_probabilities_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/c91f582f4010271f4f4ea89710d763da.pkl - params_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/params.yaml - predictions_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/predictions.json - probabilities_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/probabilities.json - score_dict_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/score_dict.json - test_labels_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/test_labels.json - train_labels_file: output/reports/train/bb2c522f81b45527334f57c19ca86172/train_labels.json - model_dir: models - name: bb2c522f81b45527334f57c19ca86172 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4904c5c5db2c4618292bb6d92a2f0a27 - trainer: - kwargs: {} -name: bb2c522f81b45527334f57c19ca86172 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/params.yaml b/examples/security/classification/output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/params.yaml deleted file mode 100644 index 60492fb7..00000000 --- a/examples/security/classification/output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/adv_predictions.json - adv_probabilities_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/6588bf9c77fa870efdb66c940b805a47.pkl - params_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/params.yaml - predictions_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/predictions.json - probabilities_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/probabilities.json - score_dict_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/score_dict.json - test_labels_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/test_labels.json - train_labels_file: output/reports/train/bb2e0d02101e90eeb01618be7e9bd817/train_labels.json - model_dir: models - name: bb2e0d02101e90eeb01618be7e9bd817 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fe72b1755249e905fbc7f6eb467d10a2 - trainer: - kwargs: {} -name: bb2e0d02101e90eeb01618be7e9bd817 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bb3a72b508620d6e2082f0afffd01252/params.yaml b/examples/security/classification/output/reports/train/bb3a72b508620d6e2082f0afffd01252/params.yaml deleted file mode 100644 index 9a76bcec..00000000 --- a/examples/security/classification/output/reports/train/bb3a72b508620d6e2082f0afffd01252/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/adv_predictions.json - adv_probabilities_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/cc88283a98ec88894fd7cc0b2f8d7eaf.pkl - params_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/params.yaml - predictions_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/predictions.json - probabilities_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/probabilities.json - score_dict_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/score_dict.json - test_labels_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/test_labels.json - train_labels_file: output/reports/train/bb3a72b508620d6e2082f0afffd01252/train_labels.json - model_dir: models - name: bb3a72b508620d6e2082f0afffd01252 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 079856428833d0ead8902ad7c2d01cdc - trainer: - kwargs: {} -name: bb3a72b508620d6e2082f0afffd01252 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bb43f2459493583ae8dd03d307d37e7b/params.yaml b/examples/security/classification/output/reports/train/bb43f2459493583ae8dd03d307d37e7b/params.yaml deleted file mode 100644 index 5a8276ec..00000000 --- a/examples/security/classification/output/reports/train/bb43f2459493583ae8dd03d307d37e7b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/adv_predictions.json - adv_probabilities_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/687f219e74fef8762f1d82871e3e4154.pkl - params_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/params.yaml - predictions_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/predictions.json - probabilities_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/probabilities.json - score_dict_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/score_dict.json - test_labels_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/test_labels.json - train_labels_file: output/reports/train/bb43f2459493583ae8dd03d307d37e7b/train_labels.json - model_dir: models - name: bb43f2459493583ae8dd03d307d37e7b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0c50558aad5f59e928599fa4226cd400 - trainer: - kwargs: {} -name: bb43f2459493583ae8dd03d307d37e7b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bb56d13631809f9e120f45154373b045/params.yaml b/examples/security/classification/output/reports/train/bb56d13631809f9e120f45154373b045/params.yaml deleted file mode 100644 index 4aa42292..00000000 --- a/examples/security/classification/output/reports/train/bb56d13631809f9e120f45154373b045/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bb56d13631809f9e120f45154373b045/adv_predictions.json - adv_probabilities_file: output/reports/train/bb56d13631809f9e120f45154373b045/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/f329056c25085f2825f0bbbc9fdd3498.pkl - params_file: output/reports/train/bb56d13631809f9e120f45154373b045/params.yaml - predictions_file: output/reports/train/bb56d13631809f9e120f45154373b045/predictions.json - probabilities_file: output/reports/train/bb56d13631809f9e120f45154373b045/probabilities.json - score_dict_file: output/reports/train/bb56d13631809f9e120f45154373b045/score_dict.json - test_labels_file: output/reports/train/bb56d13631809f9e120f45154373b045/test_labels.json - train_labels_file: output/reports/train/bb56d13631809f9e120f45154373b045/train_labels.json - model_dir: models - name: bb56d13631809f9e120f45154373b045 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b9a7419d77756fb317834f42af7caab2 - trainer: - kwargs: {} -name: bb56d13631809f9e120f45154373b045 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bb79ea4778714a472a52c5892287a62f/params.yaml b/examples/security/classification/output/reports/train/bb79ea4778714a472a52c5892287a62f/params.yaml deleted file mode 100644 index 84558981..00000000 --- a/examples/security/classification/output/reports/train/bb79ea4778714a472a52c5892287a62f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/adv_predictions.json - adv_probabilities_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/a632ba64ce28b0212aecc321a84fd177.pkl - params_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/params.yaml - predictions_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/predictions.json - probabilities_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/probabilities.json - score_dict_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/score_dict.json - test_labels_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/test_labels.json - train_labels_file: output/reports/train/bb79ea4778714a472a52c5892287a62f/train_labels.json - model_dir: models - name: bb79ea4778714a472a52c5892287a62f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9d784eb3e919c8e1491e642ae0c0945a - trainer: - kwargs: {} -name: bb79ea4778714a472a52c5892287a62f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bb7b780eb55b79747a369868951088bd/params.yaml b/examples/security/classification/output/reports/train/bb7b780eb55b79747a369868951088bd/params.yaml deleted file mode 100644 index 7aaf49d6..00000000 --- a/examples/security/classification/output/reports/train/bb7b780eb55b79747a369868951088bd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bb7b780eb55b79747a369868951088bd/adv_predictions.json - adv_probabilities_file: output/reports/train/bb7b780eb55b79747a369868951088bd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/8dca07814d12e18226a279902a9262a6.pkl - params_file: output/reports/train/bb7b780eb55b79747a369868951088bd/params.yaml - predictions_file: output/reports/train/bb7b780eb55b79747a369868951088bd/predictions.json - probabilities_file: output/reports/train/bb7b780eb55b79747a369868951088bd/probabilities.json - score_dict_file: output/reports/train/bb7b780eb55b79747a369868951088bd/score_dict.json - test_labels_file: output/reports/train/bb7b780eb55b79747a369868951088bd/test_labels.json - train_labels_file: output/reports/train/bb7b780eb55b79747a369868951088bd/train_labels.json - model_dir: models - name: bb7b780eb55b79747a369868951088bd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 932d0bd0c6b3cc188b9353b1b9993c86 - trainer: - kwargs: {} -name: bb7b780eb55b79747a369868951088bd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/params.yaml b/examples/security/classification/output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/params.yaml deleted file mode 100644 index 0b8ccf64..00000000 --- a/examples/security/classification/output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/adv_predictions.json - adv_probabilities_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/719afc57747a3d0e2070add47c5aad9d.pkl - params_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/params.yaml - predictions_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/predictions.json - probabilities_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/probabilities.json - score_dict_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/score_dict.json - test_labels_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/test_labels.json - train_labels_file: output/reports/train/bb8cf5000f713b5c51df0505850dd5bd/train_labels.json - model_dir: models - name: bb8cf5000f713b5c51df0505850dd5bd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e9110baf32f53c92204ff8967c01ac29 - trainer: - kwargs: {} -name: bb8cf5000f713b5c51df0505850dd5bd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bbbcc6abc200a898c6504add3aa79d52/params.yaml b/examples/security/classification/output/reports/train/bbbcc6abc200a898c6504add3aa79d52/params.yaml deleted file mode 100644 index d9f54e7a..00000000 --- a/examples/security/classification/output/reports/train/bbbcc6abc200a898c6504add3aa79d52/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/adv_predictions.json - adv_probabilities_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/bce1a4934e5c587a49cd1beef50d2281.pkl - params_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/params.yaml - predictions_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/predictions.json - probabilities_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/probabilities.json - score_dict_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/score_dict.json - test_labels_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/test_labels.json - train_labels_file: output/reports/train/bbbcc6abc200a898c6504add3aa79d52/train_labels.json - model_dir: models - name: bbbcc6abc200a898c6504add3aa79d52 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b241b1d32bbd3c34e297d9ccf8b495da - trainer: - kwargs: {} -name: bbbcc6abc200a898c6504add3aa79d52 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/params.yaml b/examples/security/classification/output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/params.yaml deleted file mode 100644 index 1c1c31ff..00000000 --- a/examples/security/classification/output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/adv_predictions.json - adv_probabilities_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/f59632aebd32d9cf81f74016ada1b7a9.pkl - params_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/params.yaml - predictions_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/predictions.json - probabilities_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/probabilities.json - score_dict_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/score_dict.json - test_labels_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/test_labels.json - train_labels_file: output/reports/train/bbc81384d3f2ca32e25b844e290a68f2/train_labels.json - model_dir: models - name: bbc81384d3f2ca32e25b844e290a68f2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5d6018cbf06a77a9b8aa5b10a06f537e - trainer: - kwargs: {} -name: bbc81384d3f2ca32e25b844e290a68f2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bbddea1525acc855a2c83670090f8dc7/params.yaml b/examples/security/classification/output/reports/train/bbddea1525acc855a2c83670090f8dc7/params.yaml deleted file mode 100644 index 5df3b179..00000000 --- a/examples/security/classification/output/reports/train/bbddea1525acc855a2c83670090f8dc7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/adv_predictions.json - adv_probabilities_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/bce67ddd94c42073b8329276c01b3eb2.pkl - params_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/params.yaml - predictions_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/predictions.json - probabilities_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/probabilities.json - score_dict_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/score_dict.json - test_labels_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/test_labels.json - train_labels_file: output/reports/train/bbddea1525acc855a2c83670090f8dc7/train_labels.json - model_dir: models - name: bbddea1525acc855a2c83670090f8dc7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6280e8d82a6ef6acd7bfcc266513bd1b - trainer: - kwargs: {} -name: bbddea1525acc855a2c83670090f8dc7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bbe6c7acb81c2a624b875baa044198a4/params.yaml b/examples/security/classification/output/reports/train/bbe6c7acb81c2a624b875baa044198a4/params.yaml deleted file mode 100644 index 8b8e14fa..00000000 --- a/examples/security/classification/output/reports/train/bbe6c7acb81c2a624b875baa044198a4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/adv_predictions.json - adv_probabilities_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/634d7eb9fc728b31fcfe9ac583086ede.pkl - params_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/params.yaml - predictions_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/predictions.json - probabilities_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/probabilities.json - score_dict_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/score_dict.json - test_labels_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/test_labels.json - train_labels_file: output/reports/train/bbe6c7acb81c2a624b875baa044198a4/train_labels.json - model_dir: models - name: bbe6c7acb81c2a624b875baa044198a4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 851acbcef81da278a10d7d23c989d0a7 - trainer: - kwargs: {} -name: bbe6c7acb81c2a624b875baa044198a4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bbf96174af361a073ad5676da30fba41/params.yaml b/examples/security/classification/output/reports/train/bbf96174af361a073ad5676da30fba41/params.yaml deleted file mode 100644 index 4c65c2cd..00000000 --- a/examples/security/classification/output/reports/train/bbf96174af361a073ad5676da30fba41/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bbf96174af361a073ad5676da30fba41/adv_predictions.json - adv_probabilities_file: output/reports/train/bbf96174af361a073ad5676da30fba41/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/d2c2ba3c50c83d547c559ea1724b6974.pkl - params_file: output/reports/train/bbf96174af361a073ad5676da30fba41/params.yaml - predictions_file: output/reports/train/bbf96174af361a073ad5676da30fba41/predictions.json - probabilities_file: output/reports/train/bbf96174af361a073ad5676da30fba41/probabilities.json - score_dict_file: output/reports/train/bbf96174af361a073ad5676da30fba41/score_dict.json - test_labels_file: output/reports/train/bbf96174af361a073ad5676da30fba41/test_labels.json - train_labels_file: output/reports/train/bbf96174af361a073ad5676da30fba41/train_labels.json - model_dir: models - name: bbf96174af361a073ad5676da30fba41 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c105e358864001c53ced91ff670ceee3 - trainer: - kwargs: {} -name: bbf96174af361a073ad5676da30fba41 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/params.yaml b/examples/security/classification/output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/params.yaml deleted file mode 100644 index d9f30435..00000000 --- a/examples/security/classification/output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/adv_predictions.json - adv_probabilities_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/87f806dfc24b33ebfc25b089eb1317c6.pkl - params_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/params.yaml - predictions_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/predictions.json - probabilities_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/probabilities.json - score_dict_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/score_dict.json - test_labels_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/test_labels.json - train_labels_file: output/reports/train/bc09d4c37d7427f960bdf4fe7e4eccdf/train_labels.json - model_dir: models - name: bc09d4c37d7427f960bdf4fe7e4eccdf - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fc3b4da535448930ec17f4792e4b7546 - trainer: - kwargs: {} -name: bc09d4c37d7427f960bdf4fe7e4eccdf -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bc0f936807b548353a0b789cfed9c6a2/params.yaml b/examples/security/classification/output/reports/train/bc0f936807b548353a0b789cfed9c6a2/params.yaml deleted file mode 100644 index 22470834..00000000 --- a/examples/security/classification/output/reports/train/bc0f936807b548353a0b789cfed9c6a2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/adv_predictions.json - adv_probabilities_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/e67544c2ac948d2a30bf2b00ffac1623.pkl - params_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/params.yaml - predictions_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/predictions.json - probabilities_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/probabilities.json - score_dict_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/score_dict.json - test_labels_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/test_labels.json - train_labels_file: output/reports/train/bc0f936807b548353a0b789cfed9c6a2/train_labels.json - model_dir: models - name: bc0f936807b548353a0b789cfed9c6a2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fdca564b21cb003aa07b267bcc589697 - trainer: - kwargs: {} -name: bc0f936807b548353a0b789cfed9c6a2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/params.yaml b/examples/security/classification/output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/params.yaml deleted file mode 100644 index 3d1c7172..00000000 --- a/examples/security/classification/output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/adv_predictions.json - adv_probabilities_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/41d70375ed4c9a9822339e583a7d046c.pkl - params_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/params.yaml - predictions_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/predictions.json - probabilities_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/probabilities.json - score_dict_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/score_dict.json - test_labels_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/test_labels.json - train_labels_file: output/reports/train/bc16dab9ebe43214670a56fe28a89d2b/train_labels.json - model_dir: models - name: bc16dab9ebe43214670a56fe28a89d2b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9aca1db0b4dfcea6c010ab5b80ec7d7e - trainer: - kwargs: {} -name: bc16dab9ebe43214670a56fe28a89d2b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/params.yaml b/examples/security/classification/output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/params.yaml deleted file mode 100644 index 69944f1d..00000000 --- a/examples/security/classification/output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/adv_predictions.json - adv_probabilities_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/65b3237c4f0706bd629c41472715a3e8.pkl - params_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/params.yaml - predictions_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/predictions.json - probabilities_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/probabilities.json - score_dict_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/score_dict.json - test_labels_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/test_labels.json - train_labels_file: output/reports/train/bc4da34bb10e20bf565ebe9cc782a895/train_labels.json - model_dir: models - name: bc4da34bb10e20bf565ebe9cc782a895 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 64fd2baca596376c4d6ca65bcba58214 - trainer: - kwargs: {} -name: bc4da34bb10e20bf565ebe9cc782a895 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bc56356cec4a9e45aea76d99587c6f47/params.yaml b/examples/security/classification/output/reports/train/bc56356cec4a9e45aea76d99587c6f47/params.yaml deleted file mode 100644 index bfe1115a..00000000 --- a/examples/security/classification/output/reports/train/bc56356cec4a9e45aea76d99587c6f47/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/adv_predictions.json - adv_probabilities_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/9db49605c25fc5399421213bdfe42995.pkl - params_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/params.yaml - predictions_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/predictions.json - probabilities_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/probabilities.json - score_dict_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/score_dict.json - test_labels_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/test_labels.json - train_labels_file: output/reports/train/bc56356cec4a9e45aea76d99587c6f47/train_labels.json - model_dir: models - name: bc56356cec4a9e45aea76d99587c6f47 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27e33fb7ed33522e0d1dfee8da641c65 - trainer: - kwargs: {} -name: bc56356cec4a9e45aea76d99587c6f47 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bc8f586ec3f08ca3269b650699e0719b/params.yaml b/examples/security/classification/output/reports/train/bc8f586ec3f08ca3269b650699e0719b/params.yaml deleted file mode 100644 index 262146fe..00000000 --- a/examples/security/classification/output/reports/train/bc8f586ec3f08ca3269b650699e0719b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/adv_predictions.json - adv_probabilities_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/30d74d9616ed0e40c1e1a5b13a4cd95b.pkl - params_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/params.yaml - predictions_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/predictions.json - probabilities_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/probabilities.json - score_dict_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/score_dict.json - test_labels_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/test_labels.json - train_labels_file: output/reports/train/bc8f586ec3f08ca3269b650699e0719b/train_labels.json - model_dir: models - name: bc8f586ec3f08ca3269b650699e0719b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3820a4b251dc00266a3d569fd174f165 - trainer: - kwargs: {} -name: bc8f586ec3f08ca3269b650699e0719b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/params.yaml b/examples/security/classification/output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/params.yaml deleted file mode 100644 index c600c298..00000000 --- a/examples/security/classification/output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/adv_predictions.json - adv_probabilities_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/0a27bda0cc746a535a9b7743573095ba.pkl - params_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/params.yaml - predictions_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/predictions.json - probabilities_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/probabilities.json - score_dict_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/score_dict.json - test_labels_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/test_labels.json - train_labels_file: output/reports/train/bc8fff020b44aa7fa1346a0b9f9e468b/train_labels.json - model_dir: models - name: bc8fff020b44aa7fa1346a0b9f9e468b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 727bef4145283447969be6df2a01b179 - trainer: - kwargs: {} -name: bc8fff020b44aa7fa1346a0b9f9e468b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/params.yaml b/examples/security/classification/output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/params.yaml deleted file mode 100644 index 109745d9..00000000 --- a/examples/security/classification/output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/adv_predictions.json - adv_probabilities_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/367e550b04cd72d9aabe6c0bb4c2150a.pkl - params_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/params.yaml - predictions_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/predictions.json - probabilities_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/probabilities.json - score_dict_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/score_dict.json - test_labels_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/test_labels.json - train_labels_file: output/reports/train/bca1fca06cfdfc46953adc729fdca8ab/train_labels.json - model_dir: models - name: bca1fca06cfdfc46953adc729fdca8ab - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5c039446d6dbd8a565bb850ae9456767 - trainer: - kwargs: {} -name: bca1fca06cfdfc46953adc729fdca8ab -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/params.yaml b/examples/security/classification/output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/params.yaml deleted file mode 100644 index 016ec12c..00000000 --- a/examples/security/classification/output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/adv_predictions.json - adv_probabilities_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/3366d6b02f9cb6efb5f3059e2ae40698.pkl - params_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/params.yaml - predictions_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/predictions.json - probabilities_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/probabilities.json - score_dict_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/score_dict.json - test_labels_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/test_labels.json - train_labels_file: output/reports/train/bcd7ec9da436ca1eb750f1895197bf63/train_labels.json - model_dir: models - name: bcd7ec9da436ca1eb750f1895197bf63 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3221c10069dd49e20c113431365ffefe - trainer: - kwargs: {} -name: bcd7ec9da436ca1eb750f1895197bf63 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bcdaea33e4844c0396eae9474f14ea73/params.yaml b/examples/security/classification/output/reports/train/bcdaea33e4844c0396eae9474f14ea73/params.yaml deleted file mode 100644 index 567b3654..00000000 --- a/examples/security/classification/output/reports/train/bcdaea33e4844c0396eae9474f14ea73/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/adv_predictions.json - adv_probabilities_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/3b044c2f0c354f34af7f8b9d53d3e6f7.pkl - params_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/params.yaml - predictions_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/predictions.json - probabilities_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/probabilities.json - score_dict_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/score_dict.json - test_labels_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/test_labels.json - train_labels_file: output/reports/train/bcdaea33e4844c0396eae9474f14ea73/train_labels.json - model_dir: models - name: bcdaea33e4844c0396eae9474f14ea73 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ded4a94ddf112effb47e2af242a142f7 - trainer: - kwargs: {} -name: bcdaea33e4844c0396eae9474f14ea73 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/params.yaml b/examples/security/classification/output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/params.yaml deleted file mode 100644 index 368d3931..00000000 --- a/examples/security/classification/output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/adv_predictions.json - adv_probabilities_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/734835d117e6111b2e35625e82ca3753.pkl - params_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/params.yaml - predictions_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/predictions.json - probabilities_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/probabilities.json - score_dict_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/score_dict.json - test_labels_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/test_labels.json - train_labels_file: output/reports/train/bcfcabe23df2e4e1d7d54c15f5bd7911/train_labels.json - model_dir: models - name: bcfcabe23df2e4e1d7d54c15f5bd7911 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cbfbadf63d00a0a59a59d39e79242520 - trainer: - kwargs: {} -name: bcfcabe23df2e4e1d7d54c15f5bd7911 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/params.yaml b/examples/security/classification/output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/params.yaml deleted file mode 100644 index 58d6f03a..00000000 --- a/examples/security/classification/output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/adv_predictions.json - adv_probabilities_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/6a9fa6b7cec0613fb43b47e152d53626.pkl - params_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/params.yaml - predictions_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/predictions.json - probabilities_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/probabilities.json - score_dict_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/score_dict.json - test_labels_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/test_labels.json - train_labels_file: output/reports/train/bd27351c6c3470ba7d52ae0303fbffce/train_labels.json - model_dir: models - name: bd27351c6c3470ba7d52ae0303fbffce - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cd63ed3a9bc27696e7d92a4347a9ba17 - trainer: - kwargs: {} -name: bd27351c6c3470ba7d52ae0303fbffce -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/params.yaml b/examples/security/classification/output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/params.yaml deleted file mode 100644 index 9093144e..00000000 --- a/examples/security/classification/output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/adv_predictions.json - adv_probabilities_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/06a4af1179393e3a84307b43d6aefb4a.pkl - params_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/params.yaml - predictions_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/predictions.json - probabilities_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/probabilities.json - score_dict_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/score_dict.json - test_labels_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/test_labels.json - train_labels_file: output/reports/train/bd3f9dbea7550b0de658f0cd36e27786/train_labels.json - model_dir: models - name: bd3f9dbea7550b0de658f0cd36e27786 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 173db435713546e585db91f58e674fe2 - trainer: - kwargs: {} -name: bd3f9dbea7550b0de658f0cd36e27786 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bd42e549da46e1b115e910b922808e48/params.yaml b/examples/security/classification/output/reports/train/bd42e549da46e1b115e910b922808e48/params.yaml deleted file mode 100644 index e6fae29b..00000000 --- a/examples/security/classification/output/reports/train/bd42e549da46e1b115e910b922808e48/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bd42e549da46e1b115e910b922808e48/adv_predictions.json - adv_probabilities_file: output/reports/train/bd42e549da46e1b115e910b922808e48/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/f87235a52b363dbc70ddab990042d777.pkl - params_file: output/reports/train/bd42e549da46e1b115e910b922808e48/params.yaml - predictions_file: output/reports/train/bd42e549da46e1b115e910b922808e48/predictions.json - probabilities_file: output/reports/train/bd42e549da46e1b115e910b922808e48/probabilities.json - score_dict_file: output/reports/train/bd42e549da46e1b115e910b922808e48/score_dict.json - test_labels_file: output/reports/train/bd42e549da46e1b115e910b922808e48/test_labels.json - train_labels_file: output/reports/train/bd42e549da46e1b115e910b922808e48/train_labels.json - model_dir: models - name: bd42e549da46e1b115e910b922808e48 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 20885c90506f1e129cc614c3f592b178 - trainer: - kwargs: {} -name: bd42e549da46e1b115e910b922808e48 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bd75d6e06b94e3011b70637056647dac/params.yaml b/examples/security/classification/output/reports/train/bd75d6e06b94e3011b70637056647dac/params.yaml deleted file mode 100644 index cde1e379..00000000 --- a/examples/security/classification/output/reports/train/bd75d6e06b94e3011b70637056647dac/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/adv_predictions.json - adv_probabilities_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/70cb371f3c03d790d91eaccec198cb9e.pkl - params_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/params.yaml - predictions_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/predictions.json - probabilities_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/probabilities.json - score_dict_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/score_dict.json - test_labels_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/test_labels.json - train_labels_file: output/reports/train/bd75d6e06b94e3011b70637056647dac/train_labels.json - model_dir: models - name: bd75d6e06b94e3011b70637056647dac - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 52d6f23ec25e5a472efce1ef27d83c43 - trainer: - kwargs: {} -name: bd75d6e06b94e3011b70637056647dac -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bd7708a1e74405b4f51332d8f29122a1/params.yaml b/examples/security/classification/output/reports/train/bd7708a1e74405b4f51332d8f29122a1/params.yaml deleted file mode 100644 index b721a6bb..00000000 --- a/examples/security/classification/output/reports/train/bd7708a1e74405b4f51332d8f29122a1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/adv_predictions.json - adv_probabilities_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/0a11b1a69ec66affda1f7f3be0b7b2de.pkl - params_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/params.yaml - predictions_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/predictions.json - probabilities_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/probabilities.json - score_dict_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/score_dict.json - test_labels_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/test_labels.json - train_labels_file: output/reports/train/bd7708a1e74405b4f51332d8f29122a1/train_labels.json - model_dir: models - name: bd7708a1e74405b4f51332d8f29122a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2534c77838d0cc538b6b2b591e71f863 - trainer: - kwargs: {} -name: bd7708a1e74405b4f51332d8f29122a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/params.yaml b/examples/security/classification/output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/params.yaml deleted file mode 100644 index 9f210eba..00000000 --- a/examples/security/classification/output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/adv_predictions.json - adv_probabilities_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/d919253ce869404989a1c0c040e7cdee.pkl - params_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/params.yaml - predictions_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/predictions.json - probabilities_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/probabilities.json - score_dict_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/score_dict.json - test_labels_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/test_labels.json - train_labels_file: output/reports/train/bd8d446a22bb0b79122c37f5f65e3813/train_labels.json - model_dir: models - name: bd8d446a22bb0b79122c37f5f65e3813 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6659442b997079bc4278d2da6a9df57c - trainer: - kwargs: {} -name: bd8d446a22bb0b79122c37f5f65e3813 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bda59a87b059030c2971ceb484d164ae/params.yaml b/examples/security/classification/output/reports/train/bda59a87b059030c2971ceb484d164ae/params.yaml deleted file mode 100644 index b5a90d7c..00000000 --- a/examples/security/classification/output/reports/train/bda59a87b059030c2971ceb484d164ae/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/adv_predictions.json - adv_probabilities_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/829b1f6729da6ef7bae719a04e8d6c11.pkl - params_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/params.yaml - predictions_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/predictions.json - probabilities_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/probabilities.json - score_dict_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/score_dict.json - test_labels_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/test_labels.json - train_labels_file: output/reports/train/bda59a87b059030c2971ceb484d164ae/train_labels.json - model_dir: models - name: bda59a87b059030c2971ceb484d164ae - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e3b30cd3628c7573a9deb0c2ad71c4d0 - trainer: - kwargs: {} -name: bda59a87b059030c2971ceb484d164ae -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/params.yaml b/examples/security/classification/output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/params.yaml deleted file mode 100644 index 91feecd9..00000000 --- a/examples/security/classification/output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/adv_predictions.json - adv_probabilities_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/173c60c1cb97e774d15efc82cc37ba3d.pkl - params_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/params.yaml - predictions_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/predictions.json - probabilities_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/probabilities.json - score_dict_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/score_dict.json - test_labels_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/test_labels.json - train_labels_file: output/reports/train/bdc55e96315eae96bcca9bb6ab5863ad/train_labels.json - model_dir: models - name: bdc55e96315eae96bcca9bb6ab5863ad - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f2d5f09bf8af30ed21f859ca1e6ad4cd - trainer: - kwargs: {} -name: bdc55e96315eae96bcca9bb6ab5863ad -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bdcefcec280844587000667c4fbc07f3/params.yaml b/examples/security/classification/output/reports/train/bdcefcec280844587000667c4fbc07f3/params.yaml deleted file mode 100644 index 6791ea57..00000000 --- a/examples/security/classification/output/reports/train/bdcefcec280844587000667c4fbc07f3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/adv_predictions.json - adv_probabilities_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/09d849283da4740e17e7d6e81bb2d6af.pkl - params_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/params.yaml - predictions_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/predictions.json - probabilities_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/probabilities.json - score_dict_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/score_dict.json - test_labels_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/test_labels.json - train_labels_file: output/reports/train/bdcefcec280844587000667c4fbc07f3/train_labels.json - model_dir: models - name: bdcefcec280844587000667c4fbc07f3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 650c50bc67d82991eac08ed73dee3a99 - trainer: - kwargs: {} -name: bdcefcec280844587000667c4fbc07f3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bdd09d520e9760fc41806b28065e831c/params.yaml b/examples/security/classification/output/reports/train/bdd09d520e9760fc41806b28065e831c/params.yaml deleted file mode 100644 index c177948c..00000000 --- a/examples/security/classification/output/reports/train/bdd09d520e9760fc41806b28065e831c/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/adv_predictions.json - adv_probabilities_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/3d76ca554ffb8b15048807ced168440b.pkl - params_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/params.yaml - predictions_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/predictions.json - probabilities_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/probabilities.json - score_dict_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/score_dict.json - test_labels_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/test_labels.json - train_labels_file: output/reports/train/bdd09d520e9760fc41806b28065e831c/train_labels.json - model_dir: models - name: bdd09d520e9760fc41806b28065e831c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 81d9b68353daa78ec51f570cc14c38f8 - trainer: - kwargs: {} -name: bdd09d520e9760fc41806b28065e831c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bde7294ddca01175962e97ceeff2d420/params.yaml b/examples/security/classification/output/reports/train/bde7294ddca01175962e97ceeff2d420/params.yaml deleted file mode 100644 index d7657774..00000000 --- a/examples/security/classification/output/reports/train/bde7294ddca01175962e97ceeff2d420/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/adv_predictions.json - adv_probabilities_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/6868ab7c7a76926899bfd2484320b1ac.pkl - params_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/params.yaml - predictions_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/predictions.json - probabilities_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/probabilities.json - score_dict_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/score_dict.json - test_labels_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/test_labels.json - train_labels_file: output/reports/train/bde7294ddca01175962e97ceeff2d420/train_labels.json - model_dir: models - name: bde7294ddca01175962e97ceeff2d420 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8d39342fb0f4b9221dac714ea6598bbc - trainer: - kwargs: {} -name: bde7294ddca01175962e97ceeff2d420 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bdfc768b20609d71981f7622fa66bad2/params.yaml b/examples/security/classification/output/reports/train/bdfc768b20609d71981f7622fa66bad2/params.yaml deleted file mode 100644 index 51ffd7a3..00000000 --- a/examples/security/classification/output/reports/train/bdfc768b20609d71981f7622fa66bad2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/adv_predictions.json - adv_probabilities_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/ae31e34ec69bc499dd11811eec9cf424.pkl - params_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/params.yaml - predictions_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/predictions.json - probabilities_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/probabilities.json - score_dict_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/score_dict.json - test_labels_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/test_labels.json - train_labels_file: output/reports/train/bdfc768b20609d71981f7622fa66bad2/train_labels.json - model_dir: models - name: bdfc768b20609d71981f7622fa66bad2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f99cd785e9402ade5fcf16d091c95eff - trainer: - kwargs: {} -name: bdfc768b20609d71981f7622fa66bad2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/be1d77333efb16467e2dbd028d89da75/params.yaml b/examples/security/classification/output/reports/train/be1d77333efb16467e2dbd028d89da75/params.yaml deleted file mode 100644 index b93faa7f..00000000 --- a/examples/security/classification/output/reports/train/be1d77333efb16467e2dbd028d89da75/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/adv_predictions.json - adv_probabilities_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/5dc2228d6729b4720aa87a7f5e6d96cd.pkl - params_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/params.yaml - predictions_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/predictions.json - probabilities_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/probabilities.json - score_dict_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/score_dict.json - test_labels_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/test_labels.json - train_labels_file: output/reports/train/be1d77333efb16467e2dbd028d89da75/train_labels.json - model_dir: models - name: be1d77333efb16467e2dbd028d89da75 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7ea9d6c30d8f50701d87cde7a4bff02d - trainer: - kwargs: {} -name: be1d77333efb16467e2dbd028d89da75 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/be2008863ef4822daf8362a3c773386e/params.yaml b/examples/security/classification/output/reports/train/be2008863ef4822daf8362a3c773386e/params.yaml deleted file mode 100644 index 589ed82a..00000000 --- a/examples/security/classification/output/reports/train/be2008863ef4822daf8362a3c773386e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/be2008863ef4822daf8362a3c773386e/adv_predictions.json - adv_probabilities_file: output/reports/train/be2008863ef4822daf8362a3c773386e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/500e396a4239ebb3deb074f70dd2fe78.pkl - params_file: output/reports/train/be2008863ef4822daf8362a3c773386e/params.yaml - predictions_file: output/reports/train/be2008863ef4822daf8362a3c773386e/predictions.json - probabilities_file: output/reports/train/be2008863ef4822daf8362a3c773386e/probabilities.json - score_dict_file: output/reports/train/be2008863ef4822daf8362a3c773386e/score_dict.json - test_labels_file: output/reports/train/be2008863ef4822daf8362a3c773386e/test_labels.json - train_labels_file: output/reports/train/be2008863ef4822daf8362a3c773386e/train_labels.json - model_dir: models - name: be2008863ef4822daf8362a3c773386e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2d6cd00a2262f05577230a96c0b03038 - trainer: - kwargs: {} -name: be2008863ef4822daf8362a3c773386e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/be38edb90aaff5020c085a7dc1f7f315/params.yaml b/examples/security/classification/output/reports/train/be38edb90aaff5020c085a7dc1f7f315/params.yaml deleted file mode 100644 index 62a3ab3d..00000000 --- a/examples/security/classification/output/reports/train/be38edb90aaff5020c085a7dc1f7f315/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/adv_predictions.json - adv_probabilities_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/3509d9cde2567831c57638d9996f2936.pkl - params_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/params.yaml - predictions_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/predictions.json - probabilities_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/probabilities.json - score_dict_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/score_dict.json - test_labels_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/test_labels.json - train_labels_file: output/reports/train/be38edb90aaff5020c085a7dc1f7f315/train_labels.json - model_dir: models - name: be38edb90aaff5020c085a7dc1f7f315 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d2bb130c4857f65d950a9e8e3ad30065 - trainer: - kwargs: {} -name: be38edb90aaff5020c085a7dc1f7f315 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/be5982f17348d5706916166095db1676/params.yaml b/examples/security/classification/output/reports/train/be5982f17348d5706916166095db1676/params.yaml deleted file mode 100644 index db43f489..00000000 --- a/examples/security/classification/output/reports/train/be5982f17348d5706916166095db1676/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/be5982f17348d5706916166095db1676/adv_predictions.json - adv_probabilities_file: output/reports/train/be5982f17348d5706916166095db1676/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/286140ccc810715d6245dd3c53ac38fb.pkl - params_file: output/reports/train/be5982f17348d5706916166095db1676/params.yaml - predictions_file: output/reports/train/be5982f17348d5706916166095db1676/predictions.json - probabilities_file: output/reports/train/be5982f17348d5706916166095db1676/probabilities.json - score_dict_file: output/reports/train/be5982f17348d5706916166095db1676/score_dict.json - test_labels_file: output/reports/train/be5982f17348d5706916166095db1676/test_labels.json - train_labels_file: output/reports/train/be5982f17348d5706916166095db1676/train_labels.json - model_dir: models - name: be5982f17348d5706916166095db1676 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4ff66d01eb44cfafa0ab143a3885ac6a - trainer: - kwargs: {} -name: be5982f17348d5706916166095db1676 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/be5d76b5f971a05637ff5372b0d289af/params.yaml b/examples/security/classification/output/reports/train/be5d76b5f971a05637ff5372b0d289af/params.yaml deleted file mode 100644 index 51391cd1..00000000 --- a/examples/security/classification/output/reports/train/be5d76b5f971a05637ff5372b0d289af/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/adv_predictions.json - adv_probabilities_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/038cd4ef2d7ae25698832d2413e96245.pkl - params_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/params.yaml - predictions_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/predictions.json - probabilities_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/probabilities.json - score_dict_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/score_dict.json - test_labels_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/test_labels.json - train_labels_file: output/reports/train/be5d76b5f971a05637ff5372b0d289af/train_labels.json - model_dir: models - name: be5d76b5f971a05637ff5372b0d289af - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a2cc2ff3e200e07ef65581d26986da93 - trainer: - kwargs: {} -name: be5d76b5f971a05637ff5372b0d289af -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/be6a61a43da3a3d25648e8683c037e9f/params.yaml b/examples/security/classification/output/reports/train/be6a61a43da3a3d25648e8683c037e9f/params.yaml deleted file mode 100644 index 4e851119..00000000 --- a/examples/security/classification/output/reports/train/be6a61a43da3a3d25648e8683c037e9f/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/adv_predictions.json - adv_probabilities_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/a383f27acc6ddb9b36e0b1b1e5ddbcf3.pkl - params_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/params.yaml - predictions_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/predictions.json - probabilities_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/probabilities.json - score_dict_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/score_dict.json - test_labels_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/test_labels.json - train_labels_file: output/reports/train/be6a61a43da3a3d25648e8683c037e9f/train_labels.json - model_dir: models - name: be6a61a43da3a3d25648e8683c037e9f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 42dd40e9c1d183e01697ef5198bd68aa - trainer: - kwargs: {} -name: be6a61a43da3a3d25648e8683c037e9f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/params.yaml b/examples/security/classification/output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/params.yaml deleted file mode 100644 index aae000d1..00000000 --- a/examples/security/classification/output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/adv_predictions.json - adv_probabilities_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/f72ccd22ffc242757a1cd6046829cd14.pkl - params_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/params.yaml - predictions_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/predictions.json - probabilities_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/probabilities.json - score_dict_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/score_dict.json - test_labels_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/test_labels.json - train_labels_file: output/reports/train/be809044142ea80b2ba2fb5fc2a3edba/train_labels.json - model_dir: models - name: be809044142ea80b2ba2fb5fc2a3edba - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 098fd811423c5ef90c5a10abd5f072df - trainer: - kwargs: {} -name: be809044142ea80b2ba2fb5fc2a3edba -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/params.yaml b/examples/security/classification/output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/params.yaml deleted file mode 100644 index 8509d60c..00000000 --- a/examples/security/classification/output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/adv_predictions.json - adv_probabilities_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/ae77ccc77a0d9d867853bb1500a8175c.pkl - params_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/params.yaml - predictions_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/predictions.json - probabilities_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/probabilities.json - score_dict_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/score_dict.json - test_labels_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/test_labels.json - train_labels_file: output/reports/train/be923c6ddcc8cc5f8954e4ba53b5ab72/train_labels.json - model_dir: models - name: be923c6ddcc8cc5f8954e4ba53b5ab72 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0dbe6845697538e47bbd165f28dd52da - trainer: - kwargs: {} -name: be923c6ddcc8cc5f8954e4ba53b5ab72 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/be92986a16358d3380f835400e95068c/params.yaml b/examples/security/classification/output/reports/train/be92986a16358d3380f835400e95068c/params.yaml deleted file mode 100644 index 5b1dd220..00000000 --- a/examples/security/classification/output/reports/train/be92986a16358d3380f835400e95068c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/be92986a16358d3380f835400e95068c/adv_predictions.json - adv_probabilities_file: output/reports/train/be92986a16358d3380f835400e95068c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/cd7d2f5ca6dd38c4656c98d4645099cd.pkl - params_file: output/reports/train/be92986a16358d3380f835400e95068c/params.yaml - predictions_file: output/reports/train/be92986a16358d3380f835400e95068c/predictions.json - probabilities_file: output/reports/train/be92986a16358d3380f835400e95068c/probabilities.json - score_dict_file: output/reports/train/be92986a16358d3380f835400e95068c/score_dict.json - test_labels_file: output/reports/train/be92986a16358d3380f835400e95068c/test_labels.json - train_labels_file: output/reports/train/be92986a16358d3380f835400e95068c/train_labels.json - model_dir: models - name: be92986a16358d3380f835400e95068c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d996353069c18e674d6fd803e4f560b0 - trainer: - kwargs: {} -name: be92986a16358d3380f835400e95068c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/be94fe0e390955218284455e5970f181/params.yaml b/examples/security/classification/output/reports/train/be94fe0e390955218284455e5970f181/params.yaml deleted file mode 100644 index 049ccdb9..00000000 --- a/examples/security/classification/output/reports/train/be94fe0e390955218284455e5970f181/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/be94fe0e390955218284455e5970f181/adv_predictions.json - adv_probabilities_file: output/reports/train/be94fe0e390955218284455e5970f181/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/ad3428c38c42be2447f17ddd3edf521e.pkl - params_file: output/reports/train/be94fe0e390955218284455e5970f181/params.yaml - predictions_file: output/reports/train/be94fe0e390955218284455e5970f181/predictions.json - probabilities_file: output/reports/train/be94fe0e390955218284455e5970f181/probabilities.json - score_dict_file: output/reports/train/be94fe0e390955218284455e5970f181/score_dict.json - test_labels_file: output/reports/train/be94fe0e390955218284455e5970f181/test_labels.json - train_labels_file: output/reports/train/be94fe0e390955218284455e5970f181/train_labels.json - model_dir: models - name: be94fe0e390955218284455e5970f181 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a86b86fd8ba677dacd6885097f6b2595 - trainer: - kwargs: {} -name: be94fe0e390955218284455e5970f181 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/params.yaml b/examples/security/classification/output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/params.yaml deleted file mode 100644 index 9106045f..00000000 --- a/examples/security/classification/output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/adv_predictions.json - adv_probabilities_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/7c8e1ef7552a6106832e8b75ac62ca3e.pkl - params_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/params.yaml - predictions_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/predictions.json - probabilities_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/probabilities.json - score_dict_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/score_dict.json - test_labels_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/test_labels.json - train_labels_file: output/reports/train/beb5c6fa43c5ccf2145e6eeb0d3e5680/train_labels.json - model_dir: models - name: beb5c6fa43c5ccf2145e6eeb0d3e5680 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dd6b8e03743122afa3017784b2190c28 - trainer: - kwargs: {} -name: beb5c6fa43c5ccf2145e6eeb0d3e5680 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/params.yaml b/examples/security/classification/output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/params.yaml deleted file mode 100644 index b5f72248..00000000 --- a/examples/security/classification/output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/adv_predictions.json - adv_probabilities_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/59424b3adf2400fb531c32905597b202.pkl - params_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/params.yaml - predictions_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/predictions.json - probabilities_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/probabilities.json - score_dict_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/score_dict.json - test_labels_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/test_labels.json - train_labels_file: output/reports/train/bebc916cdfa9996dcdd94a75c376bc9a/train_labels.json - model_dir: models - name: bebc916cdfa9996dcdd94a75c376bc9a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e432578d3bb1100fda61159e6e9bcbae - trainer: - kwargs: {} -name: bebc916cdfa9996dcdd94a75c376bc9a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bebe212558bc34a45a31eb8087af3b84/params.yaml b/examples/security/classification/output/reports/train/bebe212558bc34a45a31eb8087af3b84/params.yaml deleted file mode 100644 index b3b0624d..00000000 --- a/examples/security/classification/output/reports/train/bebe212558bc34a45a31eb8087af3b84/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/adv_predictions.json - adv_probabilities_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/d9cdcd9abe36ad017a7f6d3cd30d6539.pkl - params_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/params.yaml - predictions_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/predictions.json - probabilities_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/probabilities.json - score_dict_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/score_dict.json - test_labels_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/test_labels.json - train_labels_file: output/reports/train/bebe212558bc34a45a31eb8087af3b84/train_labels.json - model_dir: models - name: bebe212558bc34a45a31eb8087af3b84 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9a43d0e937f6cc6fceb26d42dd5cbe4d - trainer: - kwargs: {} -name: bebe212558bc34a45a31eb8087af3b84 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bee32160854e5414f0c2f074fd388c56/params.yaml b/examples/security/classification/output/reports/train/bee32160854e5414f0c2f074fd388c56/params.yaml deleted file mode 100644 index d81d5980..00000000 --- a/examples/security/classification/output/reports/train/bee32160854e5414f0c2f074fd388c56/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/adv_predictions.json - adv_probabilities_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/d3b3357867e72786780422ecaa9339c8.pkl - params_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/params.yaml - predictions_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/predictions.json - probabilities_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/probabilities.json - score_dict_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/score_dict.json - test_labels_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/test_labels.json - train_labels_file: output/reports/train/bee32160854e5414f0c2f074fd388c56/train_labels.json - model_dir: models - name: bee32160854e5414f0c2f074fd388c56 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ffda291eab9cd2fbbf83b4dd3c1751ad - trainer: - kwargs: {} -name: bee32160854e5414f0c2f074fd388c56 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/beeafa37b43439b81f70afc9df29fc6d/params.yaml b/examples/security/classification/output/reports/train/beeafa37b43439b81f70afc9df29fc6d/params.yaml deleted file mode 100644 index 4ab9dd95..00000000 --- a/examples/security/classification/output/reports/train/beeafa37b43439b81f70afc9df29fc6d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/adv_predictions.json - adv_probabilities_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/e1ee2e819e4f2af76db10ef608a2d248.pkl - params_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/params.yaml - predictions_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/predictions.json - probabilities_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/probabilities.json - score_dict_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/score_dict.json - test_labels_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/test_labels.json - train_labels_file: output/reports/train/beeafa37b43439b81f70afc9df29fc6d/train_labels.json - model_dir: models - name: beeafa37b43439b81f70afc9df29fc6d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2a4ec4b55c8c3a83b6a7c1d85b5242ca - trainer: - kwargs: {} -name: beeafa37b43439b81f70afc9df29fc6d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/params.yaml b/examples/security/classification/output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/params.yaml deleted file mode 100644 index 3e924136..00000000 --- a/examples/security/classification/output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/adv_predictions.json - adv_probabilities_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/1d2275b82100da2ee32b57c9d2159268.pkl - params_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/params.yaml - predictions_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/predictions.json - probabilities_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/probabilities.json - score_dict_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/score_dict.json - test_labels_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/test_labels.json - train_labels_file: output/reports/train/bf284b12ee0213c1d53ce0fa751039fd/train_labels.json - model_dir: models - name: bf284b12ee0213c1d53ce0fa751039fd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dd495c6e19c66d4a6e3de4875b18c137 - trainer: - kwargs: {} -name: bf284b12ee0213c1d53ce0fa751039fd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bf66e0f4f437d25d612573c8d1737226/params.yaml b/examples/security/classification/output/reports/train/bf66e0f4f437d25d612573c8d1737226/params.yaml deleted file mode 100644 index bfd60b6d..00000000 --- a/examples/security/classification/output/reports/train/bf66e0f4f437d25d612573c8d1737226/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/adv_predictions.json - adv_probabilities_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/959d68b2f523c7f231772e2c653cc1ae.pkl - params_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/params.yaml - predictions_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/predictions.json - probabilities_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/probabilities.json - score_dict_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/score_dict.json - test_labels_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/test_labels.json - train_labels_file: output/reports/train/bf66e0f4f437d25d612573c8d1737226/train_labels.json - model_dir: models - name: bf66e0f4f437d25d612573c8d1737226 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3c4570f51612018f11ca4e97d369bb0a - trainer: - kwargs: {} -name: bf66e0f4f437d25d612573c8d1737226 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bf71718b6b6867da41cb4d606c651180/params.yaml b/examples/security/classification/output/reports/train/bf71718b6b6867da41cb4d606c651180/params.yaml deleted file mode 100644 index 22b7bb3b..00000000 --- a/examples/security/classification/output/reports/train/bf71718b6b6867da41cb4d606c651180/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/adv_predictions.json - adv_probabilities_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/bbc337887c63f19a716c7adeda1f7918.pkl - params_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/params.yaml - predictions_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/predictions.json - probabilities_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/probabilities.json - score_dict_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/score_dict.json - test_labels_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/test_labels.json - train_labels_file: output/reports/train/bf71718b6b6867da41cb4d606c651180/train_labels.json - model_dir: models - name: bf71718b6b6867da41cb4d606c651180 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 79b056853dc06aa353b9d0936263c320 - trainer: - kwargs: {} -name: bf71718b6b6867da41cb4d606c651180 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bf7984d7ef1761dec055e9aab5465247/params.yaml b/examples/security/classification/output/reports/train/bf7984d7ef1761dec055e9aab5465247/params.yaml deleted file mode 100644 index f42d6004..00000000 --- a/examples/security/classification/output/reports/train/bf7984d7ef1761dec055e9aab5465247/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/adv_predictions.json - adv_probabilities_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/0d0de4ac26d9249533c5a1eb0cd2938e.pkl - params_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/params.yaml - predictions_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/predictions.json - probabilities_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/probabilities.json - score_dict_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/score_dict.json - test_labels_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/test_labels.json - train_labels_file: output/reports/train/bf7984d7ef1761dec055e9aab5465247/train_labels.json - model_dir: models - name: bf7984d7ef1761dec055e9aab5465247 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3d95912de644fd9b20caaa226dc9a0f2 - trainer: - kwargs: {} -name: bf7984d7ef1761dec055e9aab5465247 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bf901849185724d14b61afd0a0d702e7/params.yaml b/examples/security/classification/output/reports/train/bf901849185724d14b61afd0a0d702e7/params.yaml deleted file mode 100644 index 189970d9..00000000 --- a/examples/security/classification/output/reports/train/bf901849185724d14b61afd0a0d702e7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/adv_predictions.json - adv_probabilities_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/e91f1ba238731e1596389e112c65b667.pkl - params_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/params.yaml - predictions_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/predictions.json - probabilities_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/probabilities.json - score_dict_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/score_dict.json - test_labels_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/test_labels.json - train_labels_file: output/reports/train/bf901849185724d14b61afd0a0d702e7/train_labels.json - model_dir: models - name: bf901849185724d14b61afd0a0d702e7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e5ded2ccec88fe51a41894f8cb6cf36a - trainer: - kwargs: {} -name: bf901849185724d14b61afd0a0d702e7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bf949a0986ad1c193b66115774a6125c/params.yaml b/examples/security/classification/output/reports/train/bf949a0986ad1c193b66115774a6125c/params.yaml deleted file mode 100644 index e41769bc..00000000 --- a/examples/security/classification/output/reports/train/bf949a0986ad1c193b66115774a6125c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/adv_predictions.json - adv_probabilities_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/42bd2c6ecaba92322f7636b097383107.pkl - params_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/params.yaml - predictions_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/predictions.json - probabilities_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/probabilities.json - score_dict_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/score_dict.json - test_labels_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/test_labels.json - train_labels_file: output/reports/train/bf949a0986ad1c193b66115774a6125c/train_labels.json - model_dir: models - name: bf949a0986ad1c193b66115774a6125c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 60d940b61070097aee9d40e175e45a2d - trainer: - kwargs: {} -name: bf949a0986ad1c193b66115774a6125c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/params.yaml b/examples/security/classification/output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/params.yaml deleted file mode 100644 index a7487f76..00000000 --- a/examples/security/classification/output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/adv_predictions.json - adv_probabilities_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/19e179b7fe6d3e12a239178c20e5924f.pkl - params_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/params.yaml - predictions_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/predictions.json - probabilities_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/probabilities.json - score_dict_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/score_dict.json - test_labels_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/test_labels.json - train_labels_file: output/reports/train/bf98bc28d7040201ab7a1914f86f0f1d/train_labels.json - model_dir: models - name: bf98bc28d7040201ab7a1914f86f0f1d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 40a62d82e51bbfc64c597992fd856e1d - trainer: - kwargs: {} -name: bf98bc28d7040201ab7a1914f86f0f1d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/params.yaml b/examples/security/classification/output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/params.yaml deleted file mode 100644 index d5ffcebe..00000000 --- a/examples/security/classification/output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/adv_predictions.json - adv_probabilities_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/bf87c13441dd7df782ac648d343ac26b.pkl - params_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/params.yaml - predictions_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/predictions.json - probabilities_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/probabilities.json - score_dict_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/score_dict.json - test_labels_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/test_labels.json - train_labels_file: output/reports/train/bfcf7d3a6edb3f1f277982d6b646b248/train_labels.json - model_dir: models - name: bfcf7d3a6edb3f1f277982d6b646b248 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a95267c0f4b33802a8fb35a6cf2602b1 - trainer: - kwargs: {} -name: bfcf7d3a6edb3f1f277982d6b646b248 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/params.yaml b/examples/security/classification/output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/params.yaml deleted file mode 100644 index 48dcf5ce..00000000 --- a/examples/security/classification/output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/adv_predictions.json - adv_probabilities_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/6cb9fd008348860121e52abc2e31038f.pkl - params_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/params.yaml - predictions_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/predictions.json - probabilities_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/probabilities.json - score_dict_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/score_dict.json - test_labels_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/test_labels.json - train_labels_file: output/reports/train/bfe6f88bebec7e22c6bd2113662e57b1/train_labels.json - model_dir: models - name: bfe6f88bebec7e22c6bd2113662e57b1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cb29e7011eb1ce44d3d512ae0366b0b7 - trainer: - kwargs: {} -name: bfe6f88bebec7e22c6bd2113662e57b1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/params.yaml b/examples/security/classification/output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/params.yaml deleted file mode 100644 index 3bada70e..00000000 --- a/examples/security/classification/output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/adv_predictions.json - adv_probabilities_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/68c790c69c67ae0f739969a1cb9b7855.pkl - params_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/params.yaml - predictions_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/predictions.json - probabilities_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/probabilities.json - score_dict_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/score_dict.json - test_labels_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/test_labels.json - train_labels_file: output/reports/train/c014e22dbb8c08fd668925ea068c0a9b/train_labels.json - model_dir: models - name: c014e22dbb8c08fd668925ea068c0a9b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 615032e76e4e4fea6c60e3b8358b5fdc - trainer: - kwargs: {} -name: c014e22dbb8c08fd668925ea068c0a9b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/params.yaml b/examples/security/classification/output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/params.yaml deleted file mode 100644 index a43dfb0d..00000000 --- a/examples/security/classification/output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/adv_predictions.json - adv_probabilities_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/6931453db5c2bba442635a1c31fcacc9.pkl - params_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/params.yaml - predictions_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/predictions.json - probabilities_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/probabilities.json - score_dict_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/score_dict.json - test_labels_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/test_labels.json - train_labels_file: output/reports/train/c0b7cd22f88dfada5b40871f52ba99ad/train_labels.json - model_dir: models - name: c0b7cd22f88dfada5b40871f52ba99ad - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 92e4c40fff13582dea45383596a72317 - trainer: - kwargs: {} -name: c0b7cd22f88dfada5b40871f52ba99ad -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c108ca96df8bf0e8e06074e22914beed/params.yaml b/examples/security/classification/output/reports/train/c108ca96df8bf0e8e06074e22914beed/params.yaml deleted file mode 100644 index 7245d5ea..00000000 --- a/examples/security/classification/output/reports/train/c108ca96df8bf0e8e06074e22914beed/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/adv_predictions.json - adv_probabilities_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/29f92b839f64c5f8c7d34d340b7e8586.pkl - params_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/params.yaml - predictions_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/predictions.json - probabilities_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/probabilities.json - score_dict_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/score_dict.json - test_labels_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/test_labels.json - train_labels_file: output/reports/train/c108ca96df8bf0e8e06074e22914beed/train_labels.json - model_dir: models - name: c108ca96df8bf0e8e06074e22914beed - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2d418e865469630af2c4f0ec6a40e3ad - trainer: - kwargs: {} -name: c108ca96df8bf0e8e06074e22914beed -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/params.yaml b/examples/security/classification/output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/params.yaml deleted file mode 100644 index 93400be0..00000000 --- a/examples/security/classification/output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/adv_predictions.json - adv_probabilities_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/e76daf18e0394e14fe4dcb322ae073e2.pkl - params_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/params.yaml - predictions_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/predictions.json - probabilities_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/probabilities.json - score_dict_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/score_dict.json - test_labels_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/test_labels.json - train_labels_file: output/reports/train/c11545eeba33d984fd40d6e03f0dac0e/train_labels.json - model_dir: models - name: c11545eeba33d984fd40d6e03f0dac0e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0f7ee024531cbbb1594c7128fe032f21 - trainer: - kwargs: {} -name: c11545eeba33d984fd40d6e03f0dac0e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c11cec1fa09084e0a182db0f6671a24f/params.yaml b/examples/security/classification/output/reports/train/c11cec1fa09084e0a182db0f6671a24f/params.yaml deleted file mode 100644 index 0d5aa801..00000000 --- a/examples/security/classification/output/reports/train/c11cec1fa09084e0a182db0f6671a24f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/adv_predictions.json - adv_probabilities_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/db79aa93f63026fbd94d9dc3399cd0d3.pkl - params_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/params.yaml - predictions_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/predictions.json - probabilities_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/probabilities.json - score_dict_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/score_dict.json - test_labels_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/test_labels.json - train_labels_file: output/reports/train/c11cec1fa09084e0a182db0f6671a24f/train_labels.json - model_dir: models - name: c11cec1fa09084e0a182db0f6671a24f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bf6d36e9a4da7cf90451bd67d7723348 - trainer: - kwargs: {} -name: c11cec1fa09084e0a182db0f6671a24f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/params.yaml b/examples/security/classification/output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/params.yaml deleted file mode 100644 index e73bc1cd..00000000 --- a/examples/security/classification/output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/adv_predictions.json - adv_probabilities_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/58e03f0fd960c96e58a140933e416a2d.pkl - params_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/params.yaml - predictions_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/predictions.json - probabilities_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/probabilities.json - score_dict_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/score_dict.json - test_labels_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/test_labels.json - train_labels_file: output/reports/train/c12fe34ba8589bd9f98b0a07ab694f47/train_labels.json - model_dir: models - name: c12fe34ba8589bd9f98b0a07ab694f47 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4871632995e732cd8b38c2defbf877b3 - trainer: - kwargs: {} -name: c12fe34ba8589bd9f98b0a07ab694f47 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/params.yaml b/examples/security/classification/output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/params.yaml deleted file mode 100644 index 4d194ea5..00000000 --- a/examples/security/classification/output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/adv_predictions.json - adv_probabilities_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/69cedd8b61ea140052e859cad128d338.pkl - params_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/params.yaml - predictions_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/predictions.json - probabilities_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/probabilities.json - score_dict_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/score_dict.json - test_labels_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/test_labels.json - train_labels_file: output/reports/train/c16094c874c4bebdaf00a4c9aa228ab0/train_labels.json - model_dir: models - name: c16094c874c4bebdaf00a4c9aa228ab0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9a76065fb6857c534f3a7cd32be7ae4d - trainer: - kwargs: {} -name: c16094c874c4bebdaf00a4c9aa228ab0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c16e951595963a3646a99a33712726b6/params.yaml b/examples/security/classification/output/reports/train/c16e951595963a3646a99a33712726b6/params.yaml deleted file mode 100644 index d109bed7..00000000 --- a/examples/security/classification/output/reports/train/c16e951595963a3646a99a33712726b6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c16e951595963a3646a99a33712726b6/adv_predictions.json - adv_probabilities_file: output/reports/train/c16e951595963a3646a99a33712726b6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/6cc0e474a471f2eb1c1beaa705010589.pkl - params_file: output/reports/train/c16e951595963a3646a99a33712726b6/params.yaml - predictions_file: output/reports/train/c16e951595963a3646a99a33712726b6/predictions.json - probabilities_file: output/reports/train/c16e951595963a3646a99a33712726b6/probabilities.json - score_dict_file: output/reports/train/c16e951595963a3646a99a33712726b6/score_dict.json - test_labels_file: output/reports/train/c16e951595963a3646a99a33712726b6/test_labels.json - train_labels_file: output/reports/train/c16e951595963a3646a99a33712726b6/train_labels.json - model_dir: models - name: c16e951595963a3646a99a33712726b6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5dbbbbfb6e23e1773ac874eb40c2b68a - trainer: - kwargs: {} -name: c16e951595963a3646a99a33712726b6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/params.yaml b/examples/security/classification/output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/params.yaml deleted file mode 100644 index 69527df3..00000000 --- a/examples/security/classification/output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/adv_predictions.json - adv_probabilities_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/6c4cab2cf05cfa04619149d7c22d9a13.pkl - params_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/params.yaml - predictions_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/predictions.json - probabilities_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/probabilities.json - score_dict_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/score_dict.json - test_labels_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/test_labels.json - train_labels_file: output/reports/train/c1add3431b048ccd1483cbcfb6b4d453/train_labels.json - model_dir: models - name: c1add3431b048ccd1483cbcfb6b4d453 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4bda9a40cc92dd1b7e981f8d2920d70d - trainer: - kwargs: {} -name: c1add3431b048ccd1483cbcfb6b4d453 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c1bea749d2575de05360adb4a72e778d/params.yaml b/examples/security/classification/output/reports/train/c1bea749d2575de05360adb4a72e778d/params.yaml deleted file mode 100644 index 64c7d295..00000000 --- a/examples/security/classification/output/reports/train/c1bea749d2575de05360adb4a72e778d/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/adv_predictions.json - adv_probabilities_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/13d3f33a12c808bae18199e7145be4d8.pkl - params_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/params.yaml - predictions_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/predictions.json - probabilities_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/probabilities.json - score_dict_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/score_dict.json - test_labels_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/test_labels.json - train_labels_file: output/reports/train/c1bea749d2575de05360adb4a72e778d/train_labels.json - model_dir: models - name: c1bea749d2575de05360adb4a72e778d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 632c93da21fe6abd127a7e7f80c25ce5 - trainer: - kwargs: {} -name: c1bea749d2575de05360adb4a72e778d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/params.yaml b/examples/security/classification/output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/params.yaml deleted file mode 100644 index 13ec0f20..00000000 --- a/examples/security/classification/output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/adv_predictions.json - adv_probabilities_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/dbc8f37807c2fae1bf3baca3e87d2ae7.pkl - params_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/params.yaml - predictions_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/predictions.json - probabilities_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/probabilities.json - score_dict_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/score_dict.json - test_labels_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/test_labels.json - train_labels_file: output/reports/train/c20abc3f690bd3772f20faafa14cd7b5/train_labels.json - model_dir: models - name: c20abc3f690bd3772f20faafa14cd7b5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eefdb05f76eccb962630d6fd381f7b27 - trainer: - kwargs: {} -name: c20abc3f690bd3772f20faafa14cd7b5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c22d1e859e6c559be0721de999ed2c7e/params.yaml b/examples/security/classification/output/reports/train/c22d1e859e6c559be0721de999ed2c7e/params.yaml deleted file mode 100644 index ca61ea60..00000000 --- a/examples/security/classification/output/reports/train/c22d1e859e6c559be0721de999ed2c7e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/adv_predictions.json - adv_probabilities_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/6df9527527f46524144fdc53ae13e41d.pkl - params_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/params.yaml - predictions_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/predictions.json - probabilities_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/probabilities.json - score_dict_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/score_dict.json - test_labels_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/test_labels.json - train_labels_file: output/reports/train/c22d1e859e6c559be0721de999ed2c7e/train_labels.json - model_dir: models - name: c22d1e859e6c559be0721de999ed2c7e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 661245fe5d603057b78ac72d41d338f5 - trainer: - kwargs: {} -name: c22d1e859e6c559be0721de999ed2c7e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/params.yaml b/examples/security/classification/output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/params.yaml deleted file mode 100644 index 3e3d051c..00000000 --- a/examples/security/classification/output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/adv_predictions.json - adv_probabilities_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/6ce82b49bcd66b2f6ee99044db6c38ac.pkl - params_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/params.yaml - predictions_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/predictions.json - probabilities_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/probabilities.json - score_dict_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/score_dict.json - test_labels_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/test_labels.json - train_labels_file: output/reports/train/c22f22d34c1a32e2f634b15f8f7e9b5c/train_labels.json - model_dir: models - name: c22f22d34c1a32e2f634b15f8f7e9b5c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e855a87927a9f7500a550a93da4d5291 - trainer: - kwargs: {} -name: c22f22d34c1a32e2f634b15f8f7e9b5c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c23fa55c7757f3ed87fabae473bfd197/params.yaml b/examples/security/classification/output/reports/train/c23fa55c7757f3ed87fabae473bfd197/params.yaml deleted file mode 100644 index a8adf634..00000000 --- a/examples/security/classification/output/reports/train/c23fa55c7757f3ed87fabae473bfd197/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/adv_predictions.json - adv_probabilities_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/a0fe58404124751e88a81edb765abdda.pkl - params_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/params.yaml - predictions_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/predictions.json - probabilities_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/probabilities.json - score_dict_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/score_dict.json - test_labels_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/test_labels.json - train_labels_file: output/reports/train/c23fa55c7757f3ed87fabae473bfd197/train_labels.json - model_dir: models - name: c23fa55c7757f3ed87fabae473bfd197 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cb6f31ac89e389455beb4134c76a479c - trainer: - kwargs: {} -name: c23fa55c7757f3ed87fabae473bfd197 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/params.yaml b/examples/security/classification/output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/params.yaml deleted file mode 100644 index f9698f88..00000000 --- a/examples/security/classification/output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/adv_predictions.json - adv_probabilities_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/ec638b6c810a3f4aebbc8367d8cca7df.pkl - params_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/params.yaml - predictions_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/predictions.json - probabilities_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/probabilities.json - score_dict_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/score_dict.json - test_labels_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/test_labels.json - train_labels_file: output/reports/train/c24a3455cb4412dc688eab1f6fae13d8/train_labels.json - model_dir: models - name: c24a3455cb4412dc688eab1f6fae13d8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e3421f620dbc31cf16de2d68700a3282 - trainer: - kwargs: {} -name: c24a3455cb4412dc688eab1f6fae13d8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c261da321db0a069a2b4556b76b38593/params.yaml b/examples/security/classification/output/reports/train/c261da321db0a069a2b4556b76b38593/params.yaml deleted file mode 100644 index 0953bea4..00000000 --- a/examples/security/classification/output/reports/train/c261da321db0a069a2b4556b76b38593/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c261da321db0a069a2b4556b76b38593/adv_predictions.json - adv_probabilities_file: output/reports/train/c261da321db0a069a2b4556b76b38593/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/e4cf34ef5b2a28c7ac2b1c11322fca10.pkl - params_file: output/reports/train/c261da321db0a069a2b4556b76b38593/params.yaml - predictions_file: output/reports/train/c261da321db0a069a2b4556b76b38593/predictions.json - probabilities_file: output/reports/train/c261da321db0a069a2b4556b76b38593/probabilities.json - score_dict_file: output/reports/train/c261da321db0a069a2b4556b76b38593/score_dict.json - test_labels_file: output/reports/train/c261da321db0a069a2b4556b76b38593/test_labels.json - train_labels_file: output/reports/train/c261da321db0a069a2b4556b76b38593/train_labels.json - model_dir: models - name: c261da321db0a069a2b4556b76b38593 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d0e08cd00819310e162aad1e59c01b7a - trainer: - kwargs: {} -name: c261da321db0a069a2b4556b76b38593 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c2716b625367d8781cb6de061e6302ff/params.yaml b/examples/security/classification/output/reports/train/c2716b625367d8781cb6de061e6302ff/params.yaml deleted file mode 100644 index 8a29ed17..00000000 --- a/examples/security/classification/output/reports/train/c2716b625367d8781cb6de061e6302ff/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/adv_predictions.json - adv_probabilities_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/11595f060afae4cc9a311a2e15907ab3.pkl - params_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/params.yaml - predictions_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/predictions.json - probabilities_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/probabilities.json - score_dict_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/score_dict.json - test_labels_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/test_labels.json - train_labels_file: output/reports/train/c2716b625367d8781cb6de061e6302ff/train_labels.json - model_dir: models - name: c2716b625367d8781cb6de061e6302ff - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f175740e1df6012b83c23210a4b3c559 - trainer: - kwargs: {} -name: c2716b625367d8781cb6de061e6302ff -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c2718a003fc5a5908d54890e1213567a/params.yaml b/examples/security/classification/output/reports/train/c2718a003fc5a5908d54890e1213567a/params.yaml deleted file mode 100644 index eb432c92..00000000 --- a/examples/security/classification/output/reports/train/c2718a003fc5a5908d54890e1213567a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/adv_predictions.json - adv_probabilities_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/82e206b999d973c4a027697a432cad67.pkl - params_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/params.yaml - predictions_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/predictions.json - probabilities_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/probabilities.json - score_dict_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/score_dict.json - test_labels_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/test_labels.json - train_labels_file: output/reports/train/c2718a003fc5a5908d54890e1213567a/train_labels.json - model_dir: models - name: c2718a003fc5a5908d54890e1213567a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 81814deb4cfda1084f00c8861e1bf6ff - trainer: - kwargs: {} -name: c2718a003fc5a5908d54890e1213567a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c29e9baadd22fb113afb364e6d3f4815/params.yaml b/examples/security/classification/output/reports/train/c29e9baadd22fb113afb364e6d3f4815/params.yaml deleted file mode 100644 index 60b13d66..00000000 --- a/examples/security/classification/output/reports/train/c29e9baadd22fb113afb364e6d3f4815/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/adv_predictions.json - adv_probabilities_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/2a14f3f6ef3241b52dd66f35d5e15d20.pkl - params_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/params.yaml - predictions_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/predictions.json - probabilities_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/probabilities.json - score_dict_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/score_dict.json - test_labels_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/test_labels.json - train_labels_file: output/reports/train/c29e9baadd22fb113afb364e6d3f4815/train_labels.json - model_dir: models - name: c29e9baadd22fb113afb364e6d3f4815 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6e47d249d154003a034afb1e40b61d67 - trainer: - kwargs: {} -name: c29e9baadd22fb113afb364e6d3f4815 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/params.yaml b/examples/security/classification/output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/params.yaml deleted file mode 100644 index 045ceb47..00000000 --- a/examples/security/classification/output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/adv_predictions.json - adv_probabilities_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/6c8e05be41ece7c09ba199e0a5c9c74a.pkl - params_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/params.yaml - predictions_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/predictions.json - probabilities_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/probabilities.json - score_dict_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/score_dict.json - test_labels_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/test_labels.json - train_labels_file: output/reports/train/c2b9e268390c7a272d200a7e5bb145dc/train_labels.json - model_dir: models - name: c2b9e268390c7a272d200a7e5bb145dc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1c56e5a9dd178d1e1168dc0d3001c14d - trainer: - kwargs: {} -name: c2b9e268390c7a272d200a7e5bb145dc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c2dfb170b77d277a6703017db23932a6/params.yaml b/examples/security/classification/output/reports/train/c2dfb170b77d277a6703017db23932a6/params.yaml deleted file mode 100644 index 5efb35d9..00000000 --- a/examples/security/classification/output/reports/train/c2dfb170b77d277a6703017db23932a6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/adv_predictions.json - adv_probabilities_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/6d8d4edd6517386c74f29a737a0b9801.pkl - params_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/params.yaml - predictions_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/predictions.json - probabilities_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/probabilities.json - score_dict_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/score_dict.json - test_labels_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/test_labels.json - train_labels_file: output/reports/train/c2dfb170b77d277a6703017db23932a6/train_labels.json - model_dir: models - name: c2dfb170b77d277a6703017db23932a6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 443b71764ca8a74e4b45b0f80e65391e - trainer: - kwargs: {} -name: c2dfb170b77d277a6703017db23932a6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/params.yaml b/examples/security/classification/output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/params.yaml deleted file mode 100644 index 97ad2d8f..00000000 --- a/examples/security/classification/output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/adv_predictions.json - adv_probabilities_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/277a3bf6c128e47a3bcb1f91a9ad9952.pkl - params_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/params.yaml - predictions_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/predictions.json - probabilities_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/probabilities.json - score_dict_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/score_dict.json - test_labels_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/test_labels.json - train_labels_file: output/reports/train/c2dfe8265a4637ba7ea38b7d8e9e4e06/train_labels.json - model_dir: models - name: c2dfe8265a4637ba7ea38b7d8e9e4e06 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4d163acc7cb2be488d4f89211f6a4560 - trainer: - kwargs: {} -name: c2dfe8265a4637ba7ea38b7d8e9e4e06 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/params.yaml b/examples/security/classification/output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/params.yaml deleted file mode 100644 index 88191d10..00000000 --- a/examples/security/classification/output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/adv_predictions.json - adv_probabilities_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/e10cee68e694554f49b09b00caa9ec8a.pkl - params_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/params.yaml - predictions_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/predictions.json - probabilities_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/probabilities.json - score_dict_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/score_dict.json - test_labels_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/test_labels.json - train_labels_file: output/reports/train/c30c0242e1bc367dd8acbfbf27456f41/train_labels.json - model_dir: models - name: c30c0242e1bc367dd8acbfbf27456f41 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 87bc4ef74d0aba086c52f3c2752bc856 - trainer: - kwargs: {} -name: c30c0242e1bc367dd8acbfbf27456f41 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c3179ff58af733b4e2ab50d349e94110/params.yaml b/examples/security/classification/output/reports/train/c3179ff58af733b4e2ab50d349e94110/params.yaml deleted file mode 100644 index 664836a2..00000000 --- a/examples/security/classification/output/reports/train/c3179ff58af733b4e2ab50d349e94110/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/adv_predictions.json - adv_probabilities_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/622d45879296f953a85b4a57a13cb636.pkl - params_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/params.yaml - predictions_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/predictions.json - probabilities_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/probabilities.json - score_dict_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/score_dict.json - test_labels_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/test_labels.json - train_labels_file: output/reports/train/c3179ff58af733b4e2ab50d349e94110/train_labels.json - model_dir: models - name: c3179ff58af733b4e2ab50d349e94110 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6e2e9eaefa6c6d6f312cb88e63d3fe12 - trainer: - kwargs: {} -name: c3179ff58af733b4e2ab50d349e94110 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c3880dd98badcd9deaf84cc7e597b066/params.yaml b/examples/security/classification/output/reports/train/c3880dd98badcd9deaf84cc7e597b066/params.yaml deleted file mode 100644 index 26891791..00000000 --- a/examples/security/classification/output/reports/train/c3880dd98badcd9deaf84cc7e597b066/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/adv_predictions.json - adv_probabilities_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/aa2cb68b688add76946be16a4a19ac99.pkl - params_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/params.yaml - predictions_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/predictions.json - probabilities_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/probabilities.json - score_dict_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/score_dict.json - test_labels_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/test_labels.json - train_labels_file: output/reports/train/c3880dd98badcd9deaf84cc7e597b066/train_labels.json - model_dir: models - name: c3880dd98badcd9deaf84cc7e597b066 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3978278fe37259f3d72b94ac5f4d2d36 - trainer: - kwargs: {} -name: c3880dd98badcd9deaf84cc7e597b066 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c3cc276410121eb872e477e447a6c243/params.yaml b/examples/security/classification/output/reports/train/c3cc276410121eb872e477e447a6c243/params.yaml deleted file mode 100644 index d84096ea..00000000 --- a/examples/security/classification/output/reports/train/c3cc276410121eb872e477e447a6c243/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c3cc276410121eb872e477e447a6c243/adv_predictions.json - adv_probabilities_file: output/reports/train/c3cc276410121eb872e477e447a6c243/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/4b7484f492c7a19cd8ab284160529d92.pkl - params_file: output/reports/train/c3cc276410121eb872e477e447a6c243/params.yaml - predictions_file: output/reports/train/c3cc276410121eb872e477e447a6c243/predictions.json - probabilities_file: output/reports/train/c3cc276410121eb872e477e447a6c243/probabilities.json - score_dict_file: output/reports/train/c3cc276410121eb872e477e447a6c243/score_dict.json - test_labels_file: output/reports/train/c3cc276410121eb872e477e447a6c243/test_labels.json - train_labels_file: output/reports/train/c3cc276410121eb872e477e447a6c243/train_labels.json - model_dir: models - name: c3cc276410121eb872e477e447a6c243 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8e0af9b9d883754a0db13bb56d7b996e - trainer: - kwargs: {} -name: c3cc276410121eb872e477e447a6c243 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/params.yaml b/examples/security/classification/output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/params.yaml deleted file mode 100644 index 6ae49dee..00000000 --- a/examples/security/classification/output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/adv_predictions.json - adv_probabilities_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/bf1bbef087e6145c2753d053564dd388.pkl - params_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/params.yaml - predictions_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/predictions.json - probabilities_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/probabilities.json - score_dict_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/score_dict.json - test_labels_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/test_labels.json - train_labels_file: output/reports/train/c40ef8aae5bd9e70669324b48d3e55d8/train_labels.json - model_dir: models - name: c40ef8aae5bd9e70669324b48d3e55d8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dc26927794f9adb8c921c790a563327f - trainer: - kwargs: {} -name: c40ef8aae5bd9e70669324b48d3e55d8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/params.yaml b/examples/security/classification/output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/params.yaml deleted file mode 100644 index b18be7f9..00000000 --- a/examples/security/classification/output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/adv_predictions.json - adv_probabilities_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/18dd322e4c2a8c4f6cca435f504c359d.pkl - params_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/params.yaml - predictions_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/predictions.json - probabilities_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/probabilities.json - score_dict_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/score_dict.json - test_labels_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/test_labels.json - train_labels_file: output/reports/train/c41d54b8f4a993e3dbfcc012ec85026e/train_labels.json - model_dir: models - name: c41d54b8f4a993e3dbfcc012ec85026e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 783771c66e7b04fd6a38c64edc67f23c - trainer: - kwargs: {} -name: c41d54b8f4a993e3dbfcc012ec85026e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/params.yaml b/examples/security/classification/output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/params.yaml deleted file mode 100644 index f413ac9a..00000000 --- a/examples/security/classification/output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/adv_predictions.json - adv_probabilities_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/e4723c688d7dde03e8823934b6d848c5.pkl - params_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/params.yaml - predictions_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/predictions.json - probabilities_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/probabilities.json - score_dict_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/score_dict.json - test_labels_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/test_labels.json - train_labels_file: output/reports/train/c425af38d9f73cec9f61fe49cf8f624d/train_labels.json - model_dir: models - name: c425af38d9f73cec9f61fe49cf8f624d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f4bc53c39aae6e2210434ffcd80fee2b - trainer: - kwargs: {} -name: c425af38d9f73cec9f61fe49cf8f624d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/params.yaml b/examples/security/classification/output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/params.yaml deleted file mode 100644 index 9cf93e1d..00000000 --- a/examples/security/classification/output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/params.yaml +++ /dev/null @@ -1,104 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/adv_predictions.json - adv_probabilities_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e682d61ad144d6317beb5c1a8b984489.pkl - params_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/params.yaml - predictions_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/predictions.json - probabilities_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/probabilities.json - score_dict_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/score_dict.json - test_labels_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/test_labels.json - train_labels_file: output/reports/train/c44a91cabfaa291cca29708b64ef7bb8/train_labels.json - model_dir: models - name: c44a91cabfaa291cca29708b64ef7bb8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: - - linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 333b37302fd2cc71e052d3cec78aabb0 - trainer: - kwargs: {} -name: c44a91cabfaa291cca29708b64ef7bb8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c4b7ff307d0c1823d6677845862db8f5/params.yaml b/examples/security/classification/output/reports/train/c4b7ff307d0c1823d6677845862db8f5/params.yaml deleted file mode 100644 index 1e7abb09..00000000 --- a/examples/security/classification/output/reports/train/c4b7ff307d0c1823d6677845862db8f5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/adv_predictions.json - adv_probabilities_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/dea31999ad373f637cd5e3aae90630d9.pkl - params_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/params.yaml - predictions_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/predictions.json - probabilities_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/probabilities.json - score_dict_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/score_dict.json - test_labels_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/test_labels.json - train_labels_file: output/reports/train/c4b7ff307d0c1823d6677845862db8f5/train_labels.json - model_dir: models - name: c4b7ff307d0c1823d6677845862db8f5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 60a3cec71f570dabaa9cdb2df09dd852 - trainer: - kwargs: {} -name: c4b7ff307d0c1823d6677845862db8f5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/params.yaml b/examples/security/classification/output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/params.yaml deleted file mode 100644 index 36f7c2ec..00000000 --- a/examples/security/classification/output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/adv_predictions.json - adv_probabilities_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/b4e9efa7c3fa1664974f9c4e65b7eeb9.pkl - params_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/params.yaml - predictions_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/predictions.json - probabilities_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/probabilities.json - score_dict_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/score_dict.json - test_labels_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/test_labels.json - train_labels_file: output/reports/train/c4bb673e05bb64bb50724e4be269b4dd/train_labels.json - model_dir: models - name: c4bb673e05bb64bb50724e4be269b4dd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dc026a4c82fe5a96a66fcea2dbad6fbb - trainer: - kwargs: {} -name: c4bb673e05bb64bb50724e4be269b4dd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c4be380e69c798e644e389918fd95506/params.yaml b/examples/security/classification/output/reports/train/c4be380e69c798e644e389918fd95506/params.yaml deleted file mode 100644 index 0d426f1d..00000000 --- a/examples/security/classification/output/reports/train/c4be380e69c798e644e389918fd95506/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c4be380e69c798e644e389918fd95506/adv_predictions.json - adv_probabilities_file: output/reports/train/c4be380e69c798e644e389918fd95506/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/23421d4c0df994a7becd5ff990e976ba.pkl - params_file: output/reports/train/c4be380e69c798e644e389918fd95506/params.yaml - predictions_file: output/reports/train/c4be380e69c798e644e389918fd95506/predictions.json - probabilities_file: output/reports/train/c4be380e69c798e644e389918fd95506/probabilities.json - score_dict_file: output/reports/train/c4be380e69c798e644e389918fd95506/score_dict.json - test_labels_file: output/reports/train/c4be380e69c798e644e389918fd95506/test_labels.json - train_labels_file: output/reports/train/c4be380e69c798e644e389918fd95506/train_labels.json - model_dir: models - name: c4be380e69c798e644e389918fd95506 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e647cfb6f4eb5205f20fc3b704d1deff - trainer: - kwargs: {} -name: c4be380e69c798e644e389918fd95506 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c4d8612f4ed7c63403c85942582923c1/params.yaml b/examples/security/classification/output/reports/train/c4d8612f4ed7c63403c85942582923c1/params.yaml deleted file mode 100644 index aa09be53..00000000 --- a/examples/security/classification/output/reports/train/c4d8612f4ed7c63403c85942582923c1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/adv_predictions.json - adv_probabilities_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/6f37f0c467ff75f77156dd5c96634633.pkl - params_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/params.yaml - predictions_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/predictions.json - probabilities_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/probabilities.json - score_dict_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/score_dict.json - test_labels_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/test_labels.json - train_labels_file: output/reports/train/c4d8612f4ed7c63403c85942582923c1/train_labels.json - model_dir: models - name: c4d8612f4ed7c63403c85942582923c1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a255ad841ccf1d8edb944106de6786ac - trainer: - kwargs: {} -name: c4d8612f4ed7c63403c85942582923c1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c4dc93add515096cf5ab0fd530111246/params.yaml b/examples/security/classification/output/reports/train/c4dc93add515096cf5ab0fd530111246/params.yaml deleted file mode 100644 index 61cfdd69..00000000 --- a/examples/security/classification/output/reports/train/c4dc93add515096cf5ab0fd530111246/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/adv_predictions.json - adv_probabilities_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/3f42cfc8ca58293f7a3048fb2bac4771.pkl - params_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/params.yaml - predictions_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/predictions.json - probabilities_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/probabilities.json - score_dict_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/score_dict.json - test_labels_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/test_labels.json - train_labels_file: output/reports/train/c4dc93add515096cf5ab0fd530111246/train_labels.json - model_dir: models - name: c4dc93add515096cf5ab0fd530111246 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 53f24f2808e78155d7698dd0c2b0716d - trainer: - kwargs: {} -name: c4dc93add515096cf5ab0fd530111246 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/params.yaml b/examples/security/classification/output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/params.yaml deleted file mode 100644 index 5e7c0374..00000000 --- a/examples/security/classification/output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/adv_predictions.json - adv_probabilities_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/17788ae0c943cb2a9179c0ac32cbe8ed.pkl - params_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/params.yaml - predictions_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/predictions.json - probabilities_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/probabilities.json - score_dict_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/score_dict.json - test_labels_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/test_labels.json - train_labels_file: output/reports/train/c4f2f7aa19e382606bdcb0984bd2525b/train_labels.json - model_dir: models - name: c4f2f7aa19e382606bdcb0984bd2525b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 41bffc67bf912f4faa5d9b34c7b7c006 - trainer: - kwargs: {} -name: c4f2f7aa19e382606bdcb0984bd2525b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/params.yaml b/examples/security/classification/output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/params.yaml deleted file mode 100644 index 27a7fbcb..00000000 --- a/examples/security/classification/output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/adv_predictions.json - adv_probabilities_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/19b5f14c9af31ad44e17d0da00e3a6d5.pkl - params_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/params.yaml - predictions_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/predictions.json - probabilities_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/probabilities.json - score_dict_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/score_dict.json - test_labels_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/test_labels.json - train_labels_file: output/reports/train/c52fc173ca8dfedd0cd1745ed734e126/train_labels.json - model_dir: models - name: c52fc173ca8dfedd0cd1745ed734e126 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0bae8a2782f593ce6c104936b63b821d - trainer: - kwargs: {} -name: c52fc173ca8dfedd0cd1745ed734e126 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/params.yaml b/examples/security/classification/output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/params.yaml deleted file mode 100644 index 748ccdac..00000000 --- a/examples/security/classification/output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/adv_predictions.json - adv_probabilities_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/c6ef3c87ca472ea11ae291d31ef96d5d.pkl - params_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/params.yaml - predictions_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/predictions.json - probabilities_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/probabilities.json - score_dict_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/score_dict.json - test_labels_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/test_labels.json - train_labels_file: output/reports/train/c56c81f86d8bc58a094acb23dd8fe89d/train_labels.json - model_dir: models - name: c56c81f86d8bc58a094acb23dd8fe89d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6a607494632c3d04565d4ee023fa5094 - trainer: - kwargs: {} -name: c56c81f86d8bc58a094acb23dd8fe89d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c572b6af94535d9ced166214dac58c34/params.yaml b/examples/security/classification/output/reports/train/c572b6af94535d9ced166214dac58c34/params.yaml deleted file mode 100644 index c4e3047c..00000000 --- a/examples/security/classification/output/reports/train/c572b6af94535d9ced166214dac58c34/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c572b6af94535d9ced166214dac58c34/adv_predictions.json - adv_probabilities_file: output/reports/train/c572b6af94535d9ced166214dac58c34/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/b2d2e578404ce841911154b0a2ec8579.pkl - params_file: output/reports/train/c572b6af94535d9ced166214dac58c34/params.yaml - predictions_file: output/reports/train/c572b6af94535d9ced166214dac58c34/predictions.json - probabilities_file: output/reports/train/c572b6af94535d9ced166214dac58c34/probabilities.json - score_dict_file: output/reports/train/c572b6af94535d9ced166214dac58c34/score_dict.json - test_labels_file: output/reports/train/c572b6af94535d9ced166214dac58c34/test_labels.json - train_labels_file: output/reports/train/c572b6af94535d9ced166214dac58c34/train_labels.json - model_dir: models - name: c572b6af94535d9ced166214dac58c34 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 404cbc8c24b978d8b9668a7204855a6f - trainer: - kwargs: {} -name: c572b6af94535d9ced166214dac58c34 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/params.yaml b/examples/security/classification/output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/params.yaml deleted file mode 100644 index 5b17a5c0..00000000 --- a/examples/security/classification/output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/adv_predictions.json - adv_probabilities_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/0f8f78ea5bee88612dd86c033909c00a.pkl - params_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/params.yaml - predictions_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/predictions.json - probabilities_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/probabilities.json - score_dict_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/score_dict.json - test_labels_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/test_labels.json - train_labels_file: output/reports/train/c5b4fad4cedf45a5ec25248aec2e600b/train_labels.json - model_dir: models - name: c5b4fad4cedf45a5ec25248aec2e600b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6bfa60dd2e7a420c2cbe0e53d984ba20 - trainer: - kwargs: {} -name: c5b4fad4cedf45a5ec25248aec2e600b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c5d5942a08e68e66945ba6593152aba9/params.yaml b/examples/security/classification/output/reports/train/c5d5942a08e68e66945ba6593152aba9/params.yaml deleted file mode 100644 index b7ffe910..00000000 --- a/examples/security/classification/output/reports/train/c5d5942a08e68e66945ba6593152aba9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/adv_predictions.json - adv_probabilities_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/1bc47ccd2ffe267c41c45170971b9922.pkl - params_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/params.yaml - predictions_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/predictions.json - probabilities_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/probabilities.json - score_dict_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/score_dict.json - test_labels_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/test_labels.json - train_labels_file: output/reports/train/c5d5942a08e68e66945ba6593152aba9/train_labels.json - model_dir: models - name: c5d5942a08e68e66945ba6593152aba9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 19dca0679221c2e9bca14c5488e7d720 - trainer: - kwargs: {} -name: c5d5942a08e68e66945ba6593152aba9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/params.yaml b/examples/security/classification/output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/params.yaml deleted file mode 100644 index b8ebf29d..00000000 --- a/examples/security/classification/output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/adv_predictions.json - adv_probabilities_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/affb16ac144694fbb1673065eef35e1e.pkl - params_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/params.yaml - predictions_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/predictions.json - probabilities_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/probabilities.json - score_dict_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/score_dict.json - test_labels_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/test_labels.json - train_labels_file: output/reports/train/c5eb4d7bcdbbe2136d870baa9ce28b42/train_labels.json - model_dir: models - name: c5eb4d7bcdbbe2136d870baa9ce28b42 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 15d9b713fed18f5eeeaa4400d8703f84 - trainer: - kwargs: {} -name: c5eb4d7bcdbbe2136d870baa9ce28b42 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/params.yaml b/examples/security/classification/output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/params.yaml deleted file mode 100644 index cd4c46b5..00000000 --- a/examples/security/classification/output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/adv_predictions.json - adv_probabilities_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/44bf6c89c039bed4eeac83e29348b397.pkl - params_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/params.yaml - predictions_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/predictions.json - probabilities_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/probabilities.json - score_dict_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/score_dict.json - test_labels_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/test_labels.json - train_labels_file: output/reports/train/c619946dc0b3a0ba8b82c829ee766d32/train_labels.json - model_dir: models - name: c619946dc0b3a0ba8b82c829ee766d32 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e13af8eecc55455cbe9aa420ac043633 - trainer: - kwargs: {} -name: c619946dc0b3a0ba8b82c829ee766d32 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/params.yaml b/examples/security/classification/output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/params.yaml deleted file mode 100644 index 5f777932..00000000 --- a/examples/security/classification/output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/adv_predictions.json - adv_probabilities_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/f1d7fd832d0f611873bed274a588c1b4.pkl - params_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/params.yaml - predictions_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/predictions.json - probabilities_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/probabilities.json - score_dict_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/score_dict.json - test_labels_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/test_labels.json - train_labels_file: output/reports/train/c663f5d9e1f96bf18d72361ccd30fdb9/train_labels.json - model_dir: models - name: c663f5d9e1f96bf18d72361ccd30fdb9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e2a61680d599004323f1514e9f3e38e9 - trainer: - kwargs: {} -name: c663f5d9e1f96bf18d72361ccd30fdb9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c68325a7327591458a8cf1080454e390/params.yaml b/examples/security/classification/output/reports/train/c68325a7327591458a8cf1080454e390/params.yaml deleted file mode 100644 index 0f9d926b..00000000 --- a/examples/security/classification/output/reports/train/c68325a7327591458a8cf1080454e390/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c68325a7327591458a8cf1080454e390/adv_predictions.json - adv_probabilities_file: output/reports/train/c68325a7327591458a8cf1080454e390/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/f5cea59a861186eacd56d5b05f20260a.pkl - params_file: output/reports/train/c68325a7327591458a8cf1080454e390/params.yaml - predictions_file: output/reports/train/c68325a7327591458a8cf1080454e390/predictions.json - probabilities_file: output/reports/train/c68325a7327591458a8cf1080454e390/probabilities.json - score_dict_file: output/reports/train/c68325a7327591458a8cf1080454e390/score_dict.json - test_labels_file: output/reports/train/c68325a7327591458a8cf1080454e390/test_labels.json - train_labels_file: output/reports/train/c68325a7327591458a8cf1080454e390/train_labels.json - model_dir: models - name: c68325a7327591458a8cf1080454e390 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e8a88cd6418f4489cfca5df51aa3739a - trainer: - kwargs: {} -name: c68325a7327591458a8cf1080454e390 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c6aee9652a95a8aef27322801bb1da08/params.yaml b/examples/security/classification/output/reports/train/c6aee9652a95a8aef27322801bb1da08/params.yaml deleted file mode 100644 index beba563f..00000000 --- a/examples/security/classification/output/reports/train/c6aee9652a95a8aef27322801bb1da08/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/adv_predictions.json - adv_probabilities_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/8d502efa598346e4304a0620d52459d0.pkl - params_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/params.yaml - predictions_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/predictions.json - probabilities_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/probabilities.json - score_dict_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/score_dict.json - test_labels_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/test_labels.json - train_labels_file: output/reports/train/c6aee9652a95a8aef27322801bb1da08/train_labels.json - model_dir: models - name: c6aee9652a95a8aef27322801bb1da08 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ffc0b17085652548f337af104d12d86a - trainer: - kwargs: {} -name: c6aee9652a95a8aef27322801bb1da08 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c713642e21dfd1a7d1812c34550dcb58/params.yaml b/examples/security/classification/output/reports/train/c713642e21dfd1a7d1812c34550dcb58/params.yaml deleted file mode 100644 index f8d4c909..00000000 --- a/examples/security/classification/output/reports/train/c713642e21dfd1a7d1812c34550dcb58/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/adv_predictions.json - adv_probabilities_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/2ef71d80b43ba6c69ee5b8a40382e5d4.pkl - params_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/params.yaml - predictions_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/predictions.json - probabilities_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/probabilities.json - score_dict_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/score_dict.json - test_labels_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/test_labels.json - train_labels_file: output/reports/train/c713642e21dfd1a7d1812c34550dcb58/train_labels.json - model_dir: models - name: c713642e21dfd1a7d1812c34550dcb58 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e7635ee802a655152081179539a36aaf - trainer: - kwargs: {} -name: c713642e21dfd1a7d1812c34550dcb58 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/params.yaml b/examples/security/classification/output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/params.yaml deleted file mode 100644 index f2b212ae..00000000 --- a/examples/security/classification/output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/adv_predictions.json - adv_probabilities_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/1931e7f238515f5397f9507797fcc2af.pkl - params_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/params.yaml - predictions_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/predictions.json - probabilities_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/probabilities.json - score_dict_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/score_dict.json - test_labels_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/test_labels.json - train_labels_file: output/reports/train/c754c7a27b024436e136c54e5a1bbd6f/train_labels.json - model_dir: models - name: c754c7a27b024436e136c54e5a1bbd6f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6f88c004e25301a81fa07dc15455db30 - trainer: - kwargs: {} -name: c754c7a27b024436e136c54e5a1bbd6f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c76347883264ae9bfd25d2aea6625673/params.yaml b/examples/security/classification/output/reports/train/c76347883264ae9bfd25d2aea6625673/params.yaml deleted file mode 100644 index 857f4618..00000000 --- a/examples/security/classification/output/reports/train/c76347883264ae9bfd25d2aea6625673/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/adv_predictions.json - adv_probabilities_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/1ad67a0f5512e1948ec3025b46fea351.pkl - params_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/params.yaml - predictions_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/predictions.json - probabilities_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/probabilities.json - score_dict_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/score_dict.json - test_labels_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/test_labels.json - train_labels_file: output/reports/train/c76347883264ae9bfd25d2aea6625673/train_labels.json - model_dir: models - name: c76347883264ae9bfd25d2aea6625673 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d74edef8766dafdfa9a0297c40f8e9a3 - trainer: - kwargs: {} -name: c76347883264ae9bfd25d2aea6625673 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c7735d453acc38058508af0fd2bf6d29/params.yaml b/examples/security/classification/output/reports/train/c7735d453acc38058508af0fd2bf6d29/params.yaml deleted file mode 100644 index 1cd91154..00000000 --- a/examples/security/classification/output/reports/train/c7735d453acc38058508af0fd2bf6d29/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/adv_predictions.json - adv_probabilities_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/d796da9df961faa58fbf3e5cf39e949d.pkl - params_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/params.yaml - predictions_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/predictions.json - probabilities_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/probabilities.json - score_dict_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/score_dict.json - test_labels_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/test_labels.json - train_labels_file: output/reports/train/c7735d453acc38058508af0fd2bf6d29/train_labels.json - model_dir: models - name: c7735d453acc38058508af0fd2bf6d29 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 017f23ff5ca8fe41fe4f0c387c5c6ff6 - trainer: - kwargs: {} -name: c7735d453acc38058508af0fd2bf6d29 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/params.yaml b/examples/security/classification/output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/params.yaml deleted file mode 100644 index 03802c06..00000000 --- a/examples/security/classification/output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/adv_predictions.json - adv_probabilities_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/dec4742504a1dea730128e31af0e9f58.pkl - params_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/params.yaml - predictions_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/predictions.json - probabilities_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/probabilities.json - score_dict_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/score_dict.json - test_labels_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/test_labels.json - train_labels_file: output/reports/train/c77cc291dc994c517e3f25d72c5e0d64/train_labels.json - model_dir: models - name: c77cc291dc994c517e3f25d72c5e0d64 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b00a7a6c7fff36cd909ff44aaa9afbbe - trainer: - kwargs: {} -name: c77cc291dc994c517e3f25d72c5e0d64 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/params.yaml b/examples/security/classification/output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/params.yaml deleted file mode 100644 index 2e923aa8..00000000 --- a/examples/security/classification/output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/adv_predictions.json - adv_probabilities_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/36108956b67fe945c5d8b538e51e9b3c.pkl - params_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/params.yaml - predictions_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/predictions.json - probabilities_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/probabilities.json - score_dict_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/score_dict.json - test_labels_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/test_labels.json - train_labels_file: output/reports/train/c7baa82fc45e2ffef8186733f9c155cc/train_labels.json - model_dir: models - name: c7baa82fc45e2ffef8186733f9c155cc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4d023ea6ee31bfb1fa37350b07b2ae37 - trainer: - kwargs: {} -name: c7baa82fc45e2ffef8186733f9c155cc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/params.yaml b/examples/security/classification/output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/params.yaml deleted file mode 100644 index 4f389d98..00000000 --- a/examples/security/classification/output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/adv_predictions.json - adv_probabilities_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/c3024472384de101f48455a5f7ef7b8a.pkl - params_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/params.yaml - predictions_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/predictions.json - probabilities_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/probabilities.json - score_dict_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/score_dict.json - test_labels_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/test_labels.json - train_labels_file: output/reports/train/c7c579a3f7c457a8f310cdc662e839a1/train_labels.json - model_dir: models - name: c7c579a3f7c457a8f310cdc662e839a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 710825fca037c09076feb4f2cc413839 - trainer: - kwargs: {} -name: c7c579a3f7c457a8f310cdc662e839a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/params.yaml b/examples/security/classification/output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/params.yaml deleted file mode 100644 index 2ddde5a0..00000000 --- a/examples/security/classification/output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/adv_predictions.json - adv_probabilities_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/8746db937c44a621056b765dd9157ceb.pkl - params_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/params.yaml - predictions_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/predictions.json - probabilities_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/probabilities.json - score_dict_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/score_dict.json - test_labels_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/test_labels.json - train_labels_file: output/reports/train/c7e26acb47d0706ae19cb2329f8a9c84/train_labels.json - model_dir: models - name: c7e26acb47d0706ae19cb2329f8a9c84 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0b4925bf7218544c4078d5639bb2913d - trainer: - kwargs: {} -name: c7e26acb47d0706ae19cb2329f8a9c84 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c7f1fd00947e0274a7ad733e71c09628/params.yaml b/examples/security/classification/output/reports/train/c7f1fd00947e0274a7ad733e71c09628/params.yaml deleted file mode 100644 index 4f582712..00000000 --- a/examples/security/classification/output/reports/train/c7f1fd00947e0274a7ad733e71c09628/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/adv_predictions.json - adv_probabilities_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/a916f88df302a97fe8d21ca0ddd4bd2a.pkl - params_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/params.yaml - predictions_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/predictions.json - probabilities_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/probabilities.json - score_dict_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/score_dict.json - test_labels_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/test_labels.json - train_labels_file: output/reports/train/c7f1fd00947e0274a7ad733e71c09628/train_labels.json - model_dir: models - name: c7f1fd00947e0274a7ad733e71c09628 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6e9cb650c18467d35e13c06470e5882c - trainer: - kwargs: {} -name: c7f1fd00947e0274a7ad733e71c09628 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c800b271cb0c9371607bfab0cfecd358/params.yaml b/examples/security/classification/output/reports/train/c800b271cb0c9371607bfab0cfecd358/params.yaml deleted file mode 100644 index 8eea8901..00000000 --- a/examples/security/classification/output/reports/train/c800b271cb0c9371607bfab0cfecd358/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/adv_predictions.json - adv_probabilities_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/a5f1f712222a43b94f365885b5d56470.pkl - params_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/params.yaml - predictions_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/predictions.json - probabilities_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/probabilities.json - score_dict_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/score_dict.json - test_labels_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/test_labels.json - train_labels_file: output/reports/train/c800b271cb0c9371607bfab0cfecd358/train_labels.json - model_dir: models - name: c800b271cb0c9371607bfab0cfecd358 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4e370e6b4c4ec30b5e4538b7064d5e1f - trainer: - kwargs: {} -name: c800b271cb0c9371607bfab0cfecd358 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c841770fcb0303ed13250bd10f45d682/params.yaml b/examples/security/classification/output/reports/train/c841770fcb0303ed13250bd10f45d682/params.yaml deleted file mode 100644 index d33c4431..00000000 --- a/examples/security/classification/output/reports/train/c841770fcb0303ed13250bd10f45d682/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/adv_predictions.json - adv_probabilities_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/18ffa24c6a59f70804fae1ec19c38308.pkl - params_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/params.yaml - predictions_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/predictions.json - probabilities_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/probabilities.json - score_dict_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/score_dict.json - test_labels_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/test_labels.json - train_labels_file: output/reports/train/c841770fcb0303ed13250bd10f45d682/train_labels.json - model_dir: models - name: c841770fcb0303ed13250bd10f45d682 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b2bd15095a180942e9398ccf73e87b81 - trainer: - kwargs: {} -name: c841770fcb0303ed13250bd10f45d682 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/params.yaml b/examples/security/classification/output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/params.yaml deleted file mode 100644 index 50c2ed22..00000000 --- a/examples/security/classification/output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/adv_predictions.json - adv_probabilities_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/053c453d46905170dfe459a3d7bef7f4.pkl - params_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/params.yaml - predictions_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/predictions.json - probabilities_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/probabilities.json - score_dict_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/score_dict.json - test_labels_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/test_labels.json - train_labels_file: output/reports/train/c8528c4d1aa1968bcbdeb086c6bf162d/train_labels.json - model_dir: models - name: c8528c4d1aa1968bcbdeb086c6bf162d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.01 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 176dde26aee2d3a238115de69df528ad - trainer: - kwargs: {} -name: c8528c4d1aa1968bcbdeb086c6bf162d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/params.yaml b/examples/security/classification/output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/params.yaml deleted file mode 100644 index 0af3fee1..00000000 --- a/examples/security/classification/output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/adv_predictions.json - adv_probabilities_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/a2bb63d7b8a2f0534917ded4c456c274.pkl - params_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/params.yaml - predictions_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/predictions.json - probabilities_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/probabilities.json - score_dict_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/score_dict.json - test_labels_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/test_labels.json - train_labels_file: output/reports/train/c8730d0c6e842f06dfffc0a7220bdef6/train_labels.json - model_dir: models - name: c8730d0c6e842f06dfffc0a7220bdef6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8b180c0d034af8aa0cb495c5bed00572 - trainer: - kwargs: {} -name: c8730d0c6e842f06dfffc0a7220bdef6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c88a5df09481a2441a693b48d168feb0/params.yaml b/examples/security/classification/output/reports/train/c88a5df09481a2441a693b48d168feb0/params.yaml deleted file mode 100644 index 74d1c057..00000000 --- a/examples/security/classification/output/reports/train/c88a5df09481a2441a693b48d168feb0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/adv_predictions.json - adv_probabilities_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/d7f48a1e7c80dd94e2064680cc4a6f7c.pkl - params_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/params.yaml - predictions_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/predictions.json - probabilities_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/probabilities.json - score_dict_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/score_dict.json - test_labels_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/test_labels.json - train_labels_file: output/reports/train/c88a5df09481a2441a693b48d168feb0/train_labels.json - model_dir: models - name: c88a5df09481a2441a693b48d168feb0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a64b10c3f9e6d162379dddd797712900 - trainer: - kwargs: {} -name: c88a5df09481a2441a693b48d168feb0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c9215f08fe121a6dab292775db734ba6/params.yaml b/examples/security/classification/output/reports/train/c9215f08fe121a6dab292775db734ba6/params.yaml deleted file mode 100644 index 22924e5d..00000000 --- a/examples/security/classification/output/reports/train/c9215f08fe121a6dab292775db734ba6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/adv_predictions.json - adv_probabilities_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/32b261cd6a6ee453b5a108217fb8cd94.pkl - params_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/params.yaml - predictions_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/predictions.json - probabilities_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/probabilities.json - score_dict_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/score_dict.json - test_labels_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/test_labels.json - train_labels_file: output/reports/train/c9215f08fe121a6dab292775db734ba6/train_labels.json - model_dir: models - name: c9215f08fe121a6dab292775db734ba6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3348e8b5aaa937e6076b755271ac8aa3 - trainer: - kwargs: {} -name: c9215f08fe121a6dab292775db734ba6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c94623bc02d36f5738ea88df13863105/params.yaml b/examples/security/classification/output/reports/train/c94623bc02d36f5738ea88df13863105/params.yaml deleted file mode 100644 index 4d904b92..00000000 --- a/examples/security/classification/output/reports/train/c94623bc02d36f5738ea88df13863105/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c94623bc02d36f5738ea88df13863105/adv_predictions.json - adv_probabilities_file: output/reports/train/c94623bc02d36f5738ea88df13863105/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/c549905fc1262fec0a2c92d8015a04e2.pkl - params_file: output/reports/train/c94623bc02d36f5738ea88df13863105/params.yaml - predictions_file: output/reports/train/c94623bc02d36f5738ea88df13863105/predictions.json - probabilities_file: output/reports/train/c94623bc02d36f5738ea88df13863105/probabilities.json - score_dict_file: output/reports/train/c94623bc02d36f5738ea88df13863105/score_dict.json - test_labels_file: output/reports/train/c94623bc02d36f5738ea88df13863105/test_labels.json - train_labels_file: output/reports/train/c94623bc02d36f5738ea88df13863105/train_labels.json - model_dir: models - name: c94623bc02d36f5738ea88df13863105 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 37b47e578d8143f2a17f578134b3f3e1 - trainer: - kwargs: {} -name: c94623bc02d36f5738ea88df13863105 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c9663463051eb5f3c70e82dcba17284d/params.yaml b/examples/security/classification/output/reports/train/c9663463051eb5f3c70e82dcba17284d/params.yaml deleted file mode 100644 index 569541cd..00000000 --- a/examples/security/classification/output/reports/train/c9663463051eb5f3c70e82dcba17284d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/adv_predictions.json - adv_probabilities_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/77c254338da712342e843889f3cd2bef.pkl - params_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/params.yaml - predictions_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/predictions.json - probabilities_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/probabilities.json - score_dict_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/score_dict.json - test_labels_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/test_labels.json - train_labels_file: output/reports/train/c9663463051eb5f3c70e82dcba17284d/train_labels.json - model_dir: models - name: c9663463051eb5f3c70e82dcba17284d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c6e63dc0a4673e6a45ab9fab85c88387 - trainer: - kwargs: {} -name: c9663463051eb5f3c70e82dcba17284d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c98c713595a08626eac065d0e99806db/params.yaml b/examples/security/classification/output/reports/train/c98c713595a08626eac065d0e99806db/params.yaml deleted file mode 100644 index d348109f..00000000 --- a/examples/security/classification/output/reports/train/c98c713595a08626eac065d0e99806db/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c98c713595a08626eac065d0e99806db/adv_predictions.json - adv_probabilities_file: output/reports/train/c98c713595a08626eac065d0e99806db/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/9dfa4f60bfe73ff961fea13d7bc468d5.pkl - params_file: output/reports/train/c98c713595a08626eac065d0e99806db/params.yaml - predictions_file: output/reports/train/c98c713595a08626eac065d0e99806db/predictions.json - probabilities_file: output/reports/train/c98c713595a08626eac065d0e99806db/probabilities.json - score_dict_file: output/reports/train/c98c713595a08626eac065d0e99806db/score_dict.json - test_labels_file: output/reports/train/c98c713595a08626eac065d0e99806db/test_labels.json - train_labels_file: output/reports/train/c98c713595a08626eac065d0e99806db/train_labels.json - model_dir: models - name: c98c713595a08626eac065d0e99806db - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2f72c2254c85a0187212c11a2bf1aef0 - trainer: - kwargs: {} -name: c98c713595a08626eac065d0e99806db -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/params.yaml b/examples/security/classification/output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/params.yaml deleted file mode 100644 index f4d79a67..00000000 --- a/examples/security/classification/output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/adv_predictions.json - adv_probabilities_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/34077a43379db203885b94ed0449b350.pkl - params_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/params.yaml - predictions_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/predictions.json - probabilities_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/probabilities.json - score_dict_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/score_dict.json - test_labels_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/test_labels.json - train_labels_file: output/reports/train/c99707732dde1c8c8be93e3e835ec2e5/train_labels.json - model_dir: models - name: c99707732dde1c8c8be93e3e835ec2e5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a340ba61df09dd52e3e4c49cfac5aff6 - trainer: - kwargs: {} -name: c99707732dde1c8c8be93e3e835ec2e5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c99d6e04d0ea11639873d85628b34c97/params.yaml b/examples/security/classification/output/reports/train/c99d6e04d0ea11639873d85628b34c97/params.yaml deleted file mode 100644 index 09fdd194..00000000 --- a/examples/security/classification/output/reports/train/c99d6e04d0ea11639873d85628b34c97/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/adv_predictions.json - adv_probabilities_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/7db0e4dccf6fd82738ad3d074b0b9667.pkl - params_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/params.yaml - predictions_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/predictions.json - probabilities_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/probabilities.json - score_dict_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/score_dict.json - test_labels_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/test_labels.json - train_labels_file: output/reports/train/c99d6e04d0ea11639873d85628b34c97/train_labels.json - model_dir: models - name: c99d6e04d0ea11639873d85628b34c97 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6faabe5539e309a03d6b529365d2eeb5 - trainer: - kwargs: {} -name: c99d6e04d0ea11639873d85628b34c97 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/params.yaml b/examples/security/classification/output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/params.yaml deleted file mode 100644 index 02465d93..00000000 --- a/examples/security/classification/output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/adv_predictions.json - adv_probabilities_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/0e885121abf16a962e106c3b50c47284.pkl - params_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/params.yaml - predictions_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/predictions.json - probabilities_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/probabilities.json - score_dict_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/score_dict.json - test_labels_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/test_labels.json - train_labels_file: output/reports/train/c9af6bee16dcb8fcbd93050e219484f3/train_labels.json - model_dir: models - name: c9af6bee16dcb8fcbd93050e219484f3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: da764bbe4c7444a0613f1d2ba9f62264 - trainer: - kwargs: {} -name: c9af6bee16dcb8fcbd93050e219484f3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c9b0277deb574aaade6538d229f7016f/params.yaml b/examples/security/classification/output/reports/train/c9b0277deb574aaade6538d229f7016f/params.yaml deleted file mode 100644 index dba19048..00000000 --- a/examples/security/classification/output/reports/train/c9b0277deb574aaade6538d229f7016f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/adv_predictions.json - adv_probabilities_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/bda23311ba0722d23458ddcd6a37331c.pkl - params_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/params.yaml - predictions_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/predictions.json - probabilities_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/probabilities.json - score_dict_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/score_dict.json - test_labels_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/test_labels.json - train_labels_file: output/reports/train/c9b0277deb574aaade6538d229f7016f/train_labels.json - model_dir: models - name: c9b0277deb574aaade6538d229f7016f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b09bb73348411b2f99e9550e5bfe5313 - trainer: - kwargs: {} -name: c9b0277deb574aaade6538d229f7016f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c9c335593ca172f41879092bd01a1616/params.yaml b/examples/security/classification/output/reports/train/c9c335593ca172f41879092bd01a1616/params.yaml deleted file mode 100644 index 07d8b430..00000000 --- a/examples/security/classification/output/reports/train/c9c335593ca172f41879092bd01a1616/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c9c335593ca172f41879092bd01a1616/adv_predictions.json - adv_probabilities_file: output/reports/train/c9c335593ca172f41879092bd01a1616/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/0a4fbea364c5b3e4feeed749b3087c4c.pkl - params_file: output/reports/train/c9c335593ca172f41879092bd01a1616/params.yaml - predictions_file: output/reports/train/c9c335593ca172f41879092bd01a1616/predictions.json - probabilities_file: output/reports/train/c9c335593ca172f41879092bd01a1616/probabilities.json - score_dict_file: output/reports/train/c9c335593ca172f41879092bd01a1616/score_dict.json - test_labels_file: output/reports/train/c9c335593ca172f41879092bd01a1616/test_labels.json - train_labels_file: output/reports/train/c9c335593ca172f41879092bd01a1616/train_labels.json - model_dir: models - name: c9c335593ca172f41879092bd01a1616 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 02acb9420babacf247e44acfa3408304 - trainer: - kwargs: {} -name: c9c335593ca172f41879092bd01a1616 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/params.yaml b/examples/security/classification/output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/params.yaml deleted file mode 100644 index 984789d3..00000000 --- a/examples/security/classification/output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/adv_predictions.json - adv_probabilities_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/072b58d1b6c56560e96e18df32e77de9.pkl - params_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/params.yaml - predictions_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/predictions.json - probabilities_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/probabilities.json - score_dict_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/score_dict.json - test_labels_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/test_labels.json - train_labels_file: output/reports/train/c9f5d6c0d4bcdadba73ca2905d2a9306/train_labels.json - model_dir: models - name: c9f5d6c0d4bcdadba73ca2905d2a9306 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 864ebc09ff89843f4f831db1e333f568 - trainer: - kwargs: {} -name: c9f5d6c0d4bcdadba73ca2905d2a9306 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/params.yaml b/examples/security/classification/output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/params.yaml deleted file mode 100644 index 44cca5ec..00000000 --- a/examples/security/classification/output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/adv_predictions.json - adv_probabilities_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/54877cef1f5a0987237c75080b43acfb.pkl - params_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/params.yaml - predictions_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/predictions.json - probabilities_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/probabilities.json - score_dict_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/score_dict.json - test_labels_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/test_labels.json - train_labels_file: output/reports/train/ca1f05f66c4ee40ccaa57002a30c0650/train_labels.json - model_dir: models - name: ca1f05f66c4ee40ccaa57002a30c0650 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8b82017f47580a78f3818ae6a04dc1ab - trainer: - kwargs: {} -name: ca1f05f66c4ee40ccaa57002a30c0650 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ca28301efac63cd826688d978b2ff397/params.yaml b/examples/security/classification/output/reports/train/ca28301efac63cd826688d978b2ff397/params.yaml deleted file mode 100644 index fd813d31..00000000 --- a/examples/security/classification/output/reports/train/ca28301efac63cd826688d978b2ff397/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ca28301efac63cd826688d978b2ff397/adv_predictions.json - adv_probabilities_file: output/reports/train/ca28301efac63cd826688d978b2ff397/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/097db94a754fef177ff8da74fb0ea5f7.pkl - params_file: output/reports/train/ca28301efac63cd826688d978b2ff397/params.yaml - predictions_file: output/reports/train/ca28301efac63cd826688d978b2ff397/predictions.json - probabilities_file: output/reports/train/ca28301efac63cd826688d978b2ff397/probabilities.json - score_dict_file: output/reports/train/ca28301efac63cd826688d978b2ff397/score_dict.json - test_labels_file: output/reports/train/ca28301efac63cd826688d978b2ff397/test_labels.json - train_labels_file: output/reports/train/ca28301efac63cd826688d978b2ff397/train_labels.json - model_dir: models - name: ca28301efac63cd826688d978b2ff397 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: adbc05436ded75471a35beffafde17f6 - trainer: - kwargs: {} -name: ca28301efac63cd826688d978b2ff397 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/params.yaml b/examples/security/classification/output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/params.yaml deleted file mode 100644 index f18f9557..00000000 --- a/examples/security/classification/output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/adv_predictions.json - adv_probabilities_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/f8d89e93fcdba36931c785190e9a0220.pkl - params_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/params.yaml - predictions_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/predictions.json - probabilities_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/probabilities.json - score_dict_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/score_dict.json - test_labels_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/test_labels.json - train_labels_file: output/reports/train/ca5e7c06cd2a6c84ee902f737fa29880/train_labels.json - model_dir: models - name: ca5e7c06cd2a6c84ee902f737fa29880 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 44b4a26e9d91311d64651b2b39a81bf9 - trainer: - kwargs: {} -name: ca5e7c06cd2a6c84ee902f737fa29880 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ca770e528a441faf0764007a10280041/params.yaml b/examples/security/classification/output/reports/train/ca770e528a441faf0764007a10280041/params.yaml deleted file mode 100644 index 92b626b9..00000000 --- a/examples/security/classification/output/reports/train/ca770e528a441faf0764007a10280041/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ca770e528a441faf0764007a10280041/adv_predictions.json - adv_probabilities_file: output/reports/train/ca770e528a441faf0764007a10280041/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/9a51ab2b5239caade33cfdfe1fcdb12f.pkl - params_file: output/reports/train/ca770e528a441faf0764007a10280041/params.yaml - predictions_file: output/reports/train/ca770e528a441faf0764007a10280041/predictions.json - probabilities_file: output/reports/train/ca770e528a441faf0764007a10280041/probabilities.json - score_dict_file: output/reports/train/ca770e528a441faf0764007a10280041/score_dict.json - test_labels_file: output/reports/train/ca770e528a441faf0764007a10280041/test_labels.json - train_labels_file: output/reports/train/ca770e528a441faf0764007a10280041/train_labels.json - model_dir: models - name: ca770e528a441faf0764007a10280041 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 070eb7472a3986bc561a2ac8079d3c6a - trainer: - kwargs: {} -name: ca770e528a441faf0764007a10280041 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/params.yaml b/examples/security/classification/output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/params.yaml deleted file mode 100644 index 476ae7df..00000000 --- a/examples/security/classification/output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/adv_predictions.json - adv_probabilities_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/0e845d310bf2057926b58069198c9c77.pkl - params_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/params.yaml - predictions_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/predictions.json - probabilities_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/probabilities.json - score_dict_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/score_dict.json - test_labels_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/test_labels.json - train_labels_file: output/reports/train/ca9aaa92914b0933f6d12dd2f5ee7609/train_labels.json - model_dir: models - name: ca9aaa92914b0933f6d12dd2f5ee7609 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5c8ff4883352e485ca1670650c89f5e1 - trainer: - kwargs: {} -name: ca9aaa92914b0933f6d12dd2f5ee7609 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/params.yaml b/examples/security/classification/output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/params.yaml deleted file mode 100644 index 0f45988a..00000000 --- a/examples/security/classification/output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/adv_predictions.json - adv_probabilities_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/96b0e8d1813f9d1f61bc3cf1ea81fc9e.pkl - params_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/params.yaml - predictions_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/predictions.json - probabilities_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/probabilities.json - score_dict_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/score_dict.json - test_labels_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/test_labels.json - train_labels_file: output/reports/train/cad5efe8f1645d02c831966c1ca5fe8b/train_labels.json - model_dir: models - name: cad5efe8f1645d02c831966c1ca5fe8b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bf0ebe8bb2c5e74d4357d87a75448016 - trainer: - kwargs: {} -name: cad5efe8f1645d02c831966c1ca5fe8b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cb29628695165a10ec7327a7ec07ac8f/params.yaml b/examples/security/classification/output/reports/train/cb29628695165a10ec7327a7ec07ac8f/params.yaml deleted file mode 100644 index 097750ac..00000000 --- a/examples/security/classification/output/reports/train/cb29628695165a10ec7327a7ec07ac8f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/adv_predictions.json - adv_probabilities_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/a270677414bb56e53b318f4edf7ef661.pkl - params_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/params.yaml - predictions_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/predictions.json - probabilities_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/probabilities.json - score_dict_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/score_dict.json - test_labels_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/test_labels.json - train_labels_file: output/reports/train/cb29628695165a10ec7327a7ec07ac8f/train_labels.json - model_dir: models - name: cb29628695165a10ec7327a7ec07ac8f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c1f29c0ec091a0d9a3994e93f9dd9f06 - trainer: - kwargs: {} -name: cb29628695165a10ec7327a7ec07ac8f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cb30f6369bd206d04297d6984107d67d/params.yaml b/examples/security/classification/output/reports/train/cb30f6369bd206d04297d6984107d67d/params.yaml deleted file mode 100644 index cc471d57..00000000 --- a/examples/security/classification/output/reports/train/cb30f6369bd206d04297d6984107d67d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/adv_predictions.json - adv_probabilities_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e0a8354599f3b6d147822b1a43f2db89.pkl - params_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/params.yaml - predictions_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/predictions.json - probabilities_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/probabilities.json - score_dict_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/score_dict.json - test_labels_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/test_labels.json - train_labels_file: output/reports/train/cb30f6369bd206d04297d6984107d67d/train_labels.json - model_dir: models - name: cb30f6369bd206d04297d6984107d67d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 00dd09111603a9b86a347b8e504bfa68 - trainer: - kwargs: {} -name: cb30f6369bd206d04297d6984107d67d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cb386c53bb93b598b3470f8d62db89eb/params.yaml b/examples/security/classification/output/reports/train/cb386c53bb93b598b3470f8d62db89eb/params.yaml deleted file mode 100644 index 153badc9..00000000 --- a/examples/security/classification/output/reports/train/cb386c53bb93b598b3470f8d62db89eb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/adv_predictions.json - adv_probabilities_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/c8c96df1b1928a4e3ef64fd8edb45d69.pkl - params_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/params.yaml - predictions_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/predictions.json - probabilities_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/probabilities.json - score_dict_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/score_dict.json - test_labels_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/test_labels.json - train_labels_file: output/reports/train/cb386c53bb93b598b3470f8d62db89eb/train_labels.json - model_dir: models - name: cb386c53bb93b598b3470f8d62db89eb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 231e05a541a003d0803f4d4c3baaab13 - trainer: - kwargs: {} -name: cb386c53bb93b598b3470f8d62db89eb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cb5636c1e422beafedfe8fab2dea9805/params.yaml b/examples/security/classification/output/reports/train/cb5636c1e422beafedfe8fab2dea9805/params.yaml deleted file mode 100644 index ad0fd90f..00000000 --- a/examples/security/classification/output/reports/train/cb5636c1e422beafedfe8fab2dea9805/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/adv_predictions.json - adv_probabilities_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/df12bfa20bd02cb3e5ce42d954fca16f.pkl - params_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/params.yaml - predictions_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/predictions.json - probabilities_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/probabilities.json - score_dict_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/score_dict.json - test_labels_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/test_labels.json - train_labels_file: output/reports/train/cb5636c1e422beafedfe8fab2dea9805/train_labels.json - model_dir: models - name: cb5636c1e422beafedfe8fab2dea9805 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3b9063f63206571668f14c0102b24d5d - trainer: - kwargs: {} -name: cb5636c1e422beafedfe8fab2dea9805 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/params.yaml b/examples/security/classification/output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/params.yaml deleted file mode 100644 index 2fc56616..00000000 --- a/examples/security/classification/output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/adv_predictions.json - adv_probabilities_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/1625bd437b27b22f1c809984b92ff4a9.pkl - params_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/params.yaml - predictions_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/predictions.json - probabilities_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/probabilities.json - score_dict_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/score_dict.json - test_labels_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/test_labels.json - train_labels_file: output/reports/train/cb57bffd3eb4bb3913454f9e30b3a00e/train_labels.json - model_dir: models - name: cb57bffd3eb4bb3913454f9e30b3a00e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 34b11d88803b73f93fa8384453c4d8a4 - trainer: - kwargs: {} -name: cb57bffd3eb4bb3913454f9e30b3a00e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cb5d836bb6e80f8f1df86907fab57874/params.yaml b/examples/security/classification/output/reports/train/cb5d836bb6e80f8f1df86907fab57874/params.yaml deleted file mode 100644 index 1187ab20..00000000 --- a/examples/security/classification/output/reports/train/cb5d836bb6e80f8f1df86907fab57874/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/adv_predictions.json - adv_probabilities_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/a6ce6af2432002e6b83f205d76d0cdb9.pkl - params_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/params.yaml - predictions_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/predictions.json - probabilities_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/probabilities.json - score_dict_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/score_dict.json - test_labels_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/test_labels.json - train_labels_file: output/reports/train/cb5d836bb6e80f8f1df86907fab57874/train_labels.json - model_dir: models - name: cb5d836bb6e80f8f1df86907fab57874 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6f5ad5ef79bd95e955401b72f29e020e - trainer: - kwargs: {} -name: cb5d836bb6e80f8f1df86907fab57874 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cb92f4da4330687c48af183808aa2cef/params.yaml b/examples/security/classification/output/reports/train/cb92f4da4330687c48af183808aa2cef/params.yaml deleted file mode 100644 index 1a49c9ac..00000000 --- a/examples/security/classification/output/reports/train/cb92f4da4330687c48af183808aa2cef/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/adv_predictions.json - adv_probabilities_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/65ee766a1b39aea866f217ffb90ce67d.pkl - params_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/params.yaml - predictions_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/predictions.json - probabilities_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/probabilities.json - score_dict_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/score_dict.json - test_labels_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/test_labels.json - train_labels_file: output/reports/train/cb92f4da4330687c48af183808aa2cef/train_labels.json - model_dir: models - name: cb92f4da4330687c48af183808aa2cef - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a7b2da5a801394815ca594e1d1581e48 - trainer: - kwargs: {} -name: cb92f4da4330687c48af183808aa2cef -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cba0e2b80b4f54a65a675207efc035d3/params.yaml b/examples/security/classification/output/reports/train/cba0e2b80b4f54a65a675207efc035d3/params.yaml deleted file mode 100644 index 140b1977..00000000 --- a/examples/security/classification/output/reports/train/cba0e2b80b4f54a65a675207efc035d3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/adv_predictions.json - adv_probabilities_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/8f9cd00c3531c0df85fc85bdd6a9104d.pkl - params_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/params.yaml - predictions_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/predictions.json - probabilities_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/probabilities.json - score_dict_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/score_dict.json - test_labels_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/test_labels.json - train_labels_file: output/reports/train/cba0e2b80b4f54a65a675207efc035d3/train_labels.json - model_dir: models - name: cba0e2b80b4f54a65a675207efc035d3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4606f2e414909c23149148a15bdfa0cd - trainer: - kwargs: {} -name: cba0e2b80b4f54a65a675207efc035d3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/params.yaml b/examples/security/classification/output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/params.yaml deleted file mode 100644 index 9db442b8..00000000 --- a/examples/security/classification/output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/adv_predictions.json - adv_probabilities_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/cc37ba2baf29dc49f02bcb73458d6e6e.pkl - params_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/params.yaml - predictions_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/predictions.json - probabilities_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/probabilities.json - score_dict_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/score_dict.json - test_labels_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/test_labels.json - train_labels_file: output/reports/train/cbbb252ee3db0f8d1b7802f4b99d7c7e/train_labels.json - model_dir: models - name: cbbb252ee3db0f8d1b7802f4b99d7c7e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ba46eca4135a5b0291c725738a9749be - trainer: - kwargs: {} -name: cbbb252ee3db0f8d1b7802f4b99d7c7e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/params.yaml b/examples/security/classification/output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/params.yaml deleted file mode 100644 index 809bde5c..00000000 --- a/examples/security/classification/output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/adv_predictions.json - adv_probabilities_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/90643d962f64762ec28bff23fba24b12.pkl - params_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/params.yaml - predictions_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/predictions.json - probabilities_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/probabilities.json - score_dict_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/score_dict.json - test_labels_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/test_labels.json - train_labels_file: output/reports/train/cbe9c552582e901bf2e912e7f2e7d3e3/train_labels.json - model_dir: models - name: cbe9c552582e901bf2e912e7f2e7d3e3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c533d658861f10f5234b293bbdf0265c - trainer: - kwargs: {} -name: cbe9c552582e901bf2e912e7f2e7d3e3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cbfcec6c464f0758b41ad24c0152af31/params.yaml b/examples/security/classification/output/reports/train/cbfcec6c464f0758b41ad24c0152af31/params.yaml deleted file mode 100644 index 39084d45..00000000 --- a/examples/security/classification/output/reports/train/cbfcec6c464f0758b41ad24c0152af31/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/adv_predictions.json - adv_probabilities_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/780723799a16700e492c8394a6664944.pkl - params_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/params.yaml - predictions_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/predictions.json - probabilities_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/probabilities.json - score_dict_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/score_dict.json - test_labels_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/test_labels.json - train_labels_file: output/reports/train/cbfcec6c464f0758b41ad24c0152af31/train_labels.json - model_dir: models - name: cbfcec6c464f0758b41ad24c0152af31 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a40428894e8f676759c60b13330c5700 - trainer: - kwargs: {} -name: cbfcec6c464f0758b41ad24c0152af31 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cc094bb166feccb3efe178fefb405edd/params.yaml b/examples/security/classification/output/reports/train/cc094bb166feccb3efe178fefb405edd/params.yaml deleted file mode 100644 index b2535108..00000000 --- a/examples/security/classification/output/reports/train/cc094bb166feccb3efe178fefb405edd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/adv_predictions.json - adv_probabilities_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/e09abe40aa75cc2397d7392fece9e021.pkl - params_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/params.yaml - predictions_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/predictions.json - probabilities_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/probabilities.json - score_dict_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/score_dict.json - test_labels_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/test_labels.json - train_labels_file: output/reports/train/cc094bb166feccb3efe178fefb405edd/train_labels.json - model_dir: models - name: cc094bb166feccb3efe178fefb405edd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6c8bd8d1545a96c84d9f0e4ff330d90a - trainer: - kwargs: {} -name: cc094bb166feccb3efe178fefb405edd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cc13cf0b31326c94ef410fd16459d282/params.yaml b/examples/security/classification/output/reports/train/cc13cf0b31326c94ef410fd16459d282/params.yaml deleted file mode 100644 index 0532fe5f..00000000 --- a/examples/security/classification/output/reports/train/cc13cf0b31326c94ef410fd16459d282/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/adv_predictions.json - adv_probabilities_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/48fdd4e50c9c699a8f15bc1ed3e2753c.pkl - params_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/params.yaml - predictions_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/predictions.json - probabilities_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/probabilities.json - score_dict_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/score_dict.json - test_labels_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/test_labels.json - train_labels_file: output/reports/train/cc13cf0b31326c94ef410fd16459d282/train_labels.json - model_dir: models - name: cc13cf0b31326c94ef410fd16459d282 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ca992686f446024c06b59dcea7334f84 - trainer: - kwargs: {} -name: cc13cf0b31326c94ef410fd16459d282 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cc34592ab360c6ec53426d2cf89411ce/params.yaml b/examples/security/classification/output/reports/train/cc34592ab360c6ec53426d2cf89411ce/params.yaml deleted file mode 100644 index e7049565..00000000 --- a/examples/security/classification/output/reports/train/cc34592ab360c6ec53426d2cf89411ce/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/adv_predictions.json - adv_probabilities_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/a6b6a731989804a3cb393fc2e11e3abf.pkl - params_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/params.yaml - predictions_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/predictions.json - probabilities_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/probabilities.json - score_dict_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/score_dict.json - test_labels_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/test_labels.json - train_labels_file: output/reports/train/cc34592ab360c6ec53426d2cf89411ce/train_labels.json - model_dir: models - name: cc34592ab360c6ec53426d2cf89411ce - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1d8e8613525bffbe10da768b7fd0b125 - trainer: - kwargs: {} -name: cc34592ab360c6ec53426d2cf89411ce -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/params.yaml b/examples/security/classification/output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/params.yaml deleted file mode 100644 index c9d64688..00000000 --- a/examples/security/classification/output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/adv_predictions.json - adv_probabilities_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/2656c3525aa721a4db542065ca6f2221.pkl - params_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/params.yaml - predictions_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/predictions.json - probabilities_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/probabilities.json - score_dict_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/score_dict.json - test_labels_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/test_labels.json - train_labels_file: output/reports/train/cc4def629ba2c5aa9e6593c2ff495d2e/train_labels.json - model_dir: models - name: cc4def629ba2c5aa9e6593c2ff495d2e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d27a84b20eb68d5f3e2ea0e04ebc0987 - trainer: - kwargs: {} -name: cc4def629ba2c5aa9e6593c2ff495d2e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/params.yaml b/examples/security/classification/output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/params.yaml deleted file mode 100644 index 7f8f639e..00000000 --- a/examples/security/classification/output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/adv_predictions.json - adv_probabilities_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/8467ec3b138dce9f74c694c45031af14.pkl - params_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/params.yaml - predictions_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/predictions.json - probabilities_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/probabilities.json - score_dict_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/score_dict.json - test_labels_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/test_labels.json - train_labels_file: output/reports/train/ccb41d7e06653c094c8ad184c83cadfb/train_labels.json - model_dir: models - name: ccb41d7e06653c094c8ad184c83cadfb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b703c07122e75dd4ecf2dcc4bac9cb35 - trainer: - kwargs: {} -name: ccb41d7e06653c094c8ad184c83cadfb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/params.yaml b/examples/security/classification/output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/params.yaml deleted file mode 100644 index 4434e235..00000000 --- a/examples/security/classification/output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/adv_predictions.json - adv_probabilities_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/2ec265057c462680dfb6597f43931d4a.pkl - params_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/params.yaml - predictions_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/predictions.json - probabilities_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/probabilities.json - score_dict_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/score_dict.json - test_labels_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/test_labels.json - train_labels_file: output/reports/train/ccfb1763ed25eb8fc94c49a232c65109/train_labels.json - model_dir: models - name: ccfb1763ed25eb8fc94c49a232c65109 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c3e1d0f771b6167d283cfb373ab0c9e4 - trainer: - kwargs: {} -name: ccfb1763ed25eb8fc94c49a232c65109 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/params.yaml b/examples/security/classification/output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/params.yaml deleted file mode 100644 index 2a91b546..00000000 --- a/examples/security/classification/output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/adv_predictions.json - adv_probabilities_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/78bcf020d78127e082a778d0f3668c42.pkl - params_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/params.yaml - predictions_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/predictions.json - probabilities_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/probabilities.json - score_dict_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/score_dict.json - test_labels_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/test_labels.json - train_labels_file: output/reports/train/cd052978fbaaeed25c90e6cfb52ce466/train_labels.json - model_dir: models - name: cd052978fbaaeed25c90e6cfb52ce466 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e54d6bfe97b9dc3bc834b441781adc66 - trainer: - kwargs: {} -name: cd052978fbaaeed25c90e6cfb52ce466 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/params.yaml b/examples/security/classification/output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/params.yaml deleted file mode 100644 index d084b055..00000000 --- a/examples/security/classification/output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/adv_predictions.json - adv_probabilities_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/8d4fdb426e97535530112d1a0e223bf6.pkl - params_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/params.yaml - predictions_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/predictions.json - probabilities_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/probabilities.json - score_dict_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/score_dict.json - test_labels_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/test_labels.json - train_labels_file: output/reports/train/cd4dda188304eb9d197523d5fd90e2bb/train_labels.json - model_dir: models - name: cd4dda188304eb9d197523d5fd90e2bb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 64c7d739e2632ba9da49892616ee4a8c - trainer: - kwargs: {} -name: cd4dda188304eb9d197523d5fd90e2bb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/params.yaml b/examples/security/classification/output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/params.yaml deleted file mode 100644 index 4f0ee3ef..00000000 --- a/examples/security/classification/output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/adv_predictions.json - adv_probabilities_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/9019088a022775cedb75d0d40a8e48ee.pkl - params_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/params.yaml - predictions_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/predictions.json - probabilities_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/probabilities.json - score_dict_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/score_dict.json - test_labels_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/test_labels.json - train_labels_file: output/reports/train/cd58fa2cd6c3fa7c502b6e9c77b4f5c3/train_labels.json - model_dir: models - name: cd58fa2cd6c3fa7c502b6e9c77b4f5c3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9eda1f549547f593990e396b34527b2f - trainer: - kwargs: {} -name: cd58fa2cd6c3fa7c502b6e9c77b4f5c3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/params.yaml b/examples/security/classification/output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/params.yaml deleted file mode 100644 index 3f25eb80..00000000 --- a/examples/security/classification/output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/adv_predictions.json - adv_probabilities_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/8534f1fbd6b5438b169f483bf23f71d3.pkl - params_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/params.yaml - predictions_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/predictions.json - probabilities_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/probabilities.json - score_dict_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/score_dict.json - test_labels_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/test_labels.json - train_labels_file: output/reports/train/cd62701c3f51aa7a9c2dc8d5141dadd7/train_labels.json - model_dir: models - name: cd62701c3f51aa7a9c2dc8d5141dadd7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 705280b0ca1ac8bcac43a75760282e4a - trainer: - kwargs: {} -name: cd62701c3f51aa7a9c2dc8d5141dadd7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/params.yaml b/examples/security/classification/output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/params.yaml deleted file mode 100644 index d41ef396..00000000 --- a/examples/security/classification/output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/adv_predictions.json - adv_probabilities_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/d86ffcb60f72bfc79f200087920f701c.pkl - params_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/params.yaml - predictions_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/predictions.json - probabilities_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/probabilities.json - score_dict_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/score_dict.json - test_labels_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/test_labels.json - train_labels_file: output/reports/train/cd7f8cac43c9bcd36c5058a567c5d6db/train_labels.json - model_dir: models - name: cd7f8cac43c9bcd36c5058a567c5d6db - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1d4a1311ce2b986aac55f092552e3f40 - trainer: - kwargs: {} -name: cd7f8cac43c9bcd36c5058a567c5d6db -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cdba26940c0ca51dcc485a2284204cc3/params.yaml b/examples/security/classification/output/reports/train/cdba26940c0ca51dcc485a2284204cc3/params.yaml deleted file mode 100644 index b31f8012..00000000 --- a/examples/security/classification/output/reports/train/cdba26940c0ca51dcc485a2284204cc3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/adv_predictions.json - adv_probabilities_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/6c69455eac38ff2f6f054b15f32fd240.pkl - params_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/params.yaml - predictions_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/predictions.json - probabilities_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/probabilities.json - score_dict_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/score_dict.json - test_labels_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/test_labels.json - train_labels_file: output/reports/train/cdba26940c0ca51dcc485a2284204cc3/train_labels.json - model_dir: models - name: cdba26940c0ca51dcc485a2284204cc3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 637adf86fd3f9d6e412eed25bd5ced67 - trainer: - kwargs: {} -name: cdba26940c0ca51dcc485a2284204cc3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/params.yaml b/examples/security/classification/output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/params.yaml deleted file mode 100644 index 01aae082..00000000 --- a/examples/security/classification/output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/adv_predictions.json - adv_probabilities_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/ee5903f9f4a677172dbbd46a8b403517.pkl - params_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/params.yaml - predictions_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/predictions.json - probabilities_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/probabilities.json - score_dict_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/score_dict.json - test_labels_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/test_labels.json - train_labels_file: output/reports/train/cdd3f787e4d498bc944d581ed85a17d2/train_labels.json - model_dir: models - name: cdd3f787e4d498bc944d581ed85a17d2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7beb952f7808906b14d4589db41557b4 - trainer: - kwargs: {} -name: cdd3f787e4d498bc944d581ed85a17d2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cde9d43c88023b8eda074d297076c3c8/params.yaml b/examples/security/classification/output/reports/train/cde9d43c88023b8eda074d297076c3c8/params.yaml deleted file mode 100644 index 9e169c35..00000000 --- a/examples/security/classification/output/reports/train/cde9d43c88023b8eda074d297076c3c8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/adv_predictions.json - adv_probabilities_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/5ed31148a44e5c1633c196c2cd586509.pkl - params_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/params.yaml - predictions_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/predictions.json - probabilities_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/probabilities.json - score_dict_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/score_dict.json - test_labels_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/test_labels.json - train_labels_file: output/reports/train/cde9d43c88023b8eda074d297076c3c8/train_labels.json - model_dir: models - name: cde9d43c88023b8eda074d297076c3c8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7fb6d571b195c05139745c4b784d93ee - trainer: - kwargs: {} -name: cde9d43c88023b8eda074d297076c3c8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ce1ac0e27372fedb201faff974a1b458/params.yaml b/examples/security/classification/output/reports/train/ce1ac0e27372fedb201faff974a1b458/params.yaml deleted file mode 100644 index c1f53764..00000000 --- a/examples/security/classification/output/reports/train/ce1ac0e27372fedb201faff974a1b458/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/adv_predictions.json - adv_probabilities_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/1bb5c81d6d394795e0af0f36173c9e6f.pkl - params_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/params.yaml - predictions_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/predictions.json - probabilities_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/probabilities.json - score_dict_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/score_dict.json - test_labels_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/test_labels.json - train_labels_file: output/reports/train/ce1ac0e27372fedb201faff974a1b458/train_labels.json - model_dir: models - name: ce1ac0e27372fedb201faff974a1b458 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 06bd44cecc7f853994c96f0c17cfe7ee - trainer: - kwargs: {} -name: ce1ac0e27372fedb201faff974a1b458 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ce2a3cca517437573bad138e7f6d401a/params.yaml b/examples/security/classification/output/reports/train/ce2a3cca517437573bad138e7f6d401a/params.yaml deleted file mode 100644 index 9a4ea6c9..00000000 --- a/examples/security/classification/output/reports/train/ce2a3cca517437573bad138e7f6d401a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/adv_predictions.json - adv_probabilities_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/d0d67a9eec2d3c2c98855a5aec8364c0.pkl - params_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/params.yaml - predictions_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/predictions.json - probabilities_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/probabilities.json - score_dict_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/score_dict.json - test_labels_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/test_labels.json - train_labels_file: output/reports/train/ce2a3cca517437573bad138e7f6d401a/train_labels.json - model_dir: models - name: ce2a3cca517437573bad138e7f6d401a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 42ebfa80943d06071871f3d473c54ceb - trainer: - kwargs: {} -name: ce2a3cca517437573bad138e7f6d401a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ce745df6eb5c97da2b947a0438358a0a/params.yaml b/examples/security/classification/output/reports/train/ce745df6eb5c97da2b947a0438358a0a/params.yaml deleted file mode 100644 index 5c3b2bd5..00000000 --- a/examples/security/classification/output/reports/train/ce745df6eb5c97da2b947a0438358a0a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/adv_predictions.json - adv_probabilities_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/80c11180aa645bcfa56ce800c2305645.pkl - params_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/params.yaml - predictions_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/predictions.json - probabilities_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/probabilities.json - score_dict_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/score_dict.json - test_labels_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/test_labels.json - train_labels_file: output/reports/train/ce745df6eb5c97da2b947a0438358a0a/train_labels.json - model_dir: models - name: ce745df6eb5c97da2b947a0438358a0a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 49b00b24e75ae9ffdce0e9ad711f2244 - trainer: - kwargs: {} -name: ce745df6eb5c97da2b947a0438358a0a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/params.yaml b/examples/security/classification/output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/params.yaml deleted file mode 100644 index 7ea71e07..00000000 --- a/examples/security/classification/output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/adv_predictions.json - adv_probabilities_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/1d3f2cba6d233d181d51a27fde4a7851.pkl - params_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/params.yaml - predictions_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/predictions.json - probabilities_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/probabilities.json - score_dict_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/score_dict.json - test_labels_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/test_labels.json - train_labels_file: output/reports/train/ce8a83a9516b08ad9f8d0f0840aa67c4/train_labels.json - model_dir: models - name: ce8a83a9516b08ad9f8d0f0840aa67c4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 12c130258ae536684b7d4814585fc637 - trainer: - kwargs: {} -name: ce8a83a9516b08ad9f8d0f0840aa67c4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/params.yaml b/examples/security/classification/output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/params.yaml deleted file mode 100644 index 1354a159..00000000 --- a/examples/security/classification/output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/adv_predictions.json - adv_probabilities_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/9fcf5ebb3490f961fe8747d4830dffda.pkl - params_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/params.yaml - predictions_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/predictions.json - probabilities_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/probabilities.json - score_dict_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/score_dict.json - test_labels_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/test_labels.json - train_labels_file: output/reports/train/ce91622ba883a97dea577fa17ed3ae2f/train_labels.json - model_dir: models - name: ce91622ba883a97dea577fa17ed3ae2f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 234a01488e192ac2de4f9985336134ad - trainer: - kwargs: {} -name: ce91622ba883a97dea577fa17ed3ae2f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ceae30d896d3152596480727b5fa4d8e/params.yaml b/examples/security/classification/output/reports/train/ceae30d896d3152596480727b5fa4d8e/params.yaml deleted file mode 100644 index 75028768..00000000 --- a/examples/security/classification/output/reports/train/ceae30d896d3152596480727b5fa4d8e/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/adv_predictions.json - adv_probabilities_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/88523dee6495c67f59da022559e99035.pkl - params_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/params.yaml - predictions_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/predictions.json - probabilities_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/probabilities.json - score_dict_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/score_dict.json - test_labels_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/test_labels.json - train_labels_file: output/reports/train/ceae30d896d3152596480727b5fa4d8e/train_labels.json - model_dir: models - name: ceae30d896d3152596480727b5fa4d8e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - degree: 3 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: baf9fb7eedabed12dffda9289125ae86 - trainer: - kwargs: {} -name: ceae30d896d3152596480727b5fa4d8e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/params.yaml b/examples/security/classification/output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/params.yaml deleted file mode 100644 index 147023f2..00000000 --- a/examples/security/classification/output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/adv_predictions.json - adv_probabilities_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/054d011bf5e1c701ac9dc22eeabc4280.pkl - params_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/params.yaml - predictions_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/predictions.json - probabilities_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/probabilities.json - score_dict_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/score_dict.json - test_labels_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/test_labels.json - train_labels_file: output/reports/train/ceb8139cfd7cc7b06ab01c01ba242a01/train_labels.json - model_dir: models - name: ceb8139cfd7cc7b06ab01c01ba242a01 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ab30c7b78af954208bfca8e2abae6568 - trainer: - kwargs: {} -name: ceb8139cfd7cc7b06ab01c01ba242a01 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cec3cd4d92465d84167b1600ce96eecb/params.yaml b/examples/security/classification/output/reports/train/cec3cd4d92465d84167b1600ce96eecb/params.yaml deleted file mode 100644 index fe9e78a7..00000000 --- a/examples/security/classification/output/reports/train/cec3cd4d92465d84167b1600ce96eecb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/adv_predictions.json - adv_probabilities_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/f8ccd34a698c10337840195251db35b6.pkl - params_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/params.yaml - predictions_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/predictions.json - probabilities_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/probabilities.json - score_dict_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/score_dict.json - test_labels_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/test_labels.json - train_labels_file: output/reports/train/cec3cd4d92465d84167b1600ce96eecb/train_labels.json - model_dir: models - name: cec3cd4d92465d84167b1600ce96eecb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bc56c8d910d6ba2d6882a2e779c8856a - trainer: - kwargs: {} -name: cec3cd4d92465d84167b1600ce96eecb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/params.yaml b/examples/security/classification/output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/params.yaml deleted file mode 100644 index b27ace6f..00000000 --- a/examples/security/classification/output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/params.yaml +++ /dev/null @@ -1,206 +0,0 @@ -attack: - attack_size: 10 - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 10.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ef9fcd079cf9bd3744561e6b95d1d3a5 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 03996dd853313e6b73c0088d70c20607 - trainer: - kwargs: {} - name: f6bc509de4b6b6d40203d8d656edc7b4 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/adv_predictions.json - adv_probabilities_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/adv_probabilities.json - attack_file: output/attacks/1a9a1aef11d6fd8596351f69acbd1118.pkl - data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl - model_file: output/models/8990a2dbb057543bbff10dc45b80f44a.pkl - params_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/params.yaml - predictions_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/predictions.json - probabilities_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/probabilities.json - score_dict_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/score_dict.json - test_labels_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/test_labels.json - train_labels_file: output/reports/train/cee0dd1700b6f00fb1d54a2a6eb95a4f/train_labels.json - model_dir: models - name: cee0dd1700b6f00fb1d54a2a6eb95a4f - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3be40bc56e6b67f6d9ed5cbcbecca960 - trainer: - kwargs: {} -name: cee0dd1700b6f00fb1d54a2a6eb95a4f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cf21278ba69ff282bf1823598b0a42ba/params.yaml b/examples/security/classification/output/reports/train/cf21278ba69ff282bf1823598b0a42ba/params.yaml deleted file mode 100644 index 21498697..00000000 --- a/examples/security/classification/output/reports/train/cf21278ba69ff282bf1823598b0a42ba/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/adv_predictions.json - adv_probabilities_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/91936c6291d83c9c9e31dd01dae24b54.pkl - params_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/params.yaml - predictions_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/predictions.json - probabilities_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/probabilities.json - score_dict_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/score_dict.json - test_labels_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/test_labels.json - train_labels_file: output/reports/train/cf21278ba69ff282bf1823598b0a42ba/train_labels.json - model_dir: models - name: cf21278ba69ff282bf1823598b0a42ba - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66d038de73edfd05b81aaffa145d12cc - trainer: - kwargs: {} -name: cf21278ba69ff282bf1823598b0a42ba -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/params.yaml b/examples/security/classification/output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/params.yaml deleted file mode 100644 index 11066e1e..00000000 --- a/examples/security/classification/output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/adv_predictions.json - adv_probabilities_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/e7d2035c3bed5c47f26813815d4ed2b3.pkl - params_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/params.yaml - predictions_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/predictions.json - probabilities_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/probabilities.json - score_dict_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/score_dict.json - test_labels_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/test_labels.json - train_labels_file: output/reports/train/cf49a841c5efd84a8e0f86a7e58df726/train_labels.json - model_dir: models - name: cf49a841c5efd84a8e0f86a7e58df726 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 95212dc33d6f2dd00981aa92039ac6b8 - trainer: - kwargs: {} -name: cf49a841c5efd84a8e0f86a7e58df726 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/params.yaml b/examples/security/classification/output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/params.yaml deleted file mode 100644 index f7caf182..00000000 --- a/examples/security/classification/output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/adv_predictions.json - adv_probabilities_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/88cf13df54a0a2182c517a11647a6265.pkl - params_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/params.yaml - predictions_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/predictions.json - probabilities_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/probabilities.json - score_dict_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/score_dict.json - test_labels_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/test_labels.json - train_labels_file: output/reports/train/cfbb7067a11d6fcb16de293a0d1777a3/train_labels.json - model_dir: models - name: cfbb7067a11d6fcb16de293a0d1777a3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b3381dc7273130758bc934a618983bf0 - trainer: - kwargs: {} -name: cfbb7067a11d6fcb16de293a0d1777a3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/params.yaml b/examples/security/classification/output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/params.yaml deleted file mode 100644 index 8983c4fa..00000000 --- a/examples/security/classification/output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/adv_predictions.json - adv_probabilities_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/48686e897b52d4f2e3c280ed89c88530.pkl - params_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/params.yaml - predictions_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/predictions.json - probabilities_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/probabilities.json - score_dict_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/score_dict.json - test_labels_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/test_labels.json - train_labels_file: output/reports/train/d01ca3c032a155e187c9629e13c9c1e1/train_labels.json - model_dir: models - name: d01ca3c032a155e187c9629e13c9c1e1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 81eb1a5367f791141fed2e882331768b - trainer: - kwargs: {} -name: d01ca3c032a155e187c9629e13c9c1e1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d032d9ac0191463f3e76d966b76e70b7/params.yaml b/examples/security/classification/output/reports/train/d032d9ac0191463f3e76d966b76e70b7/params.yaml deleted file mode 100644 index c541fa2d..00000000 --- a/examples/security/classification/output/reports/train/d032d9ac0191463f3e76d966b76e70b7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/adv_predictions.json - adv_probabilities_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/0af3963ff796091daf4e657dac7f5296.pkl - params_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/params.yaml - predictions_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/predictions.json - probabilities_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/probabilities.json - score_dict_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/score_dict.json - test_labels_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/test_labels.json - train_labels_file: output/reports/train/d032d9ac0191463f3e76d966b76e70b7/train_labels.json - model_dir: models - name: d032d9ac0191463f3e76d966b76e70b7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2e74f08f2d00459d544eca06f5c747e1 - trainer: - kwargs: {} -name: d032d9ac0191463f3e76d966b76e70b7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d038580b363fcf3e497a008821c88028/params.yaml b/examples/security/classification/output/reports/train/d038580b363fcf3e497a008821c88028/params.yaml deleted file mode 100644 index 6e6a560e..00000000 --- a/examples/security/classification/output/reports/train/d038580b363fcf3e497a008821c88028/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d038580b363fcf3e497a008821c88028/adv_predictions.json - adv_probabilities_file: output/reports/train/d038580b363fcf3e497a008821c88028/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/d71bb252ceba88c8ea2b47a1c20e17e6.pkl - params_file: output/reports/train/d038580b363fcf3e497a008821c88028/params.yaml - predictions_file: output/reports/train/d038580b363fcf3e497a008821c88028/predictions.json - probabilities_file: output/reports/train/d038580b363fcf3e497a008821c88028/probabilities.json - score_dict_file: output/reports/train/d038580b363fcf3e497a008821c88028/score_dict.json - test_labels_file: output/reports/train/d038580b363fcf3e497a008821c88028/test_labels.json - train_labels_file: output/reports/train/d038580b363fcf3e497a008821c88028/train_labels.json - model_dir: models - name: d038580b363fcf3e497a008821c88028 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9172bcbad8046d097dd9a9cb044f8f90 - trainer: - kwargs: {} -name: d038580b363fcf3e497a008821c88028 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d076822808110ee8d09f170d953614bb/params.yaml b/examples/security/classification/output/reports/train/d076822808110ee8d09f170d953614bb/params.yaml deleted file mode 100644 index d495cdc2..00000000 --- a/examples/security/classification/output/reports/train/d076822808110ee8d09f170d953614bb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d076822808110ee8d09f170d953614bb/adv_predictions.json - adv_probabilities_file: output/reports/train/d076822808110ee8d09f170d953614bb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/8f47dce04bf92222b821083963089a9a.pkl - params_file: output/reports/train/d076822808110ee8d09f170d953614bb/params.yaml - predictions_file: output/reports/train/d076822808110ee8d09f170d953614bb/predictions.json - probabilities_file: output/reports/train/d076822808110ee8d09f170d953614bb/probabilities.json - score_dict_file: output/reports/train/d076822808110ee8d09f170d953614bb/score_dict.json - test_labels_file: output/reports/train/d076822808110ee8d09f170d953614bb/test_labels.json - train_labels_file: output/reports/train/d076822808110ee8d09f170d953614bb/train_labels.json - model_dir: models - name: d076822808110ee8d09f170d953614bb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9f6eb5a1981512cd60059054a262381c - trainer: - kwargs: {} -name: d076822808110ee8d09f170d953614bb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/params.yaml b/examples/security/classification/output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/params.yaml deleted file mode 100644 index 4683b461..00000000 --- a/examples/security/classification/output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/adv_predictions.json - adv_probabilities_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/a37828cfe7ffb64d6de58f92f22ac218.pkl - params_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/params.yaml - predictions_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/predictions.json - probabilities_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/probabilities.json - score_dict_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/score_dict.json - test_labels_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/test_labels.json - train_labels_file: output/reports/train/d090c2896ed5218dd8d5fb8f223a11da/train_labels.json - model_dir: models - name: d090c2896ed5218dd8d5fb8f223a11da - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7226d1d80404878e178f0f2d580a11b8 - trainer: - kwargs: {} -name: d090c2896ed5218dd8d5fb8f223a11da -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/params.yaml b/examples/security/classification/output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/params.yaml deleted file mode 100644 index 3f6c56d9..00000000 --- a/examples/security/classification/output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/adv_predictions.json - adv_probabilities_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/2f0a86313defa3cb4a73dceed086e543.pkl - params_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/params.yaml - predictions_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/predictions.json - probabilities_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/probabilities.json - score_dict_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/score_dict.json - test_labels_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/test_labels.json - train_labels_file: output/reports/train/d0a71182dc3ccc0d7511f109c07d59eb/train_labels.json - model_dir: models - name: d0a71182dc3ccc0d7511f109c07d59eb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ac532d2f6b3f887484a8cb0a26b10902 - trainer: - kwargs: {} -name: d0a71182dc3ccc0d7511f109c07d59eb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/params.yaml b/examples/security/classification/output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/params.yaml deleted file mode 100644 index 29730047..00000000 --- a/examples/security/classification/output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/adv_predictions.json - adv_probabilities_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/a94cdd1140e2847dc7a68647e00335fd.pkl - params_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/params.yaml - predictions_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/predictions.json - probabilities_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/probabilities.json - score_dict_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/score_dict.json - test_labels_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/test_labels.json - train_labels_file: output/reports/train/d0eeda5c03651a0da8bf1ac50974d966/train_labels.json - model_dir: models - name: d0eeda5c03651a0da8bf1ac50974d966 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ac75d8464c6a9da7683d388aa994323c - trainer: - kwargs: {} -name: d0eeda5c03651a0da8bf1ac50974d966 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d10608ac1d220a86ea60a8041c93966c/params.yaml b/examples/security/classification/output/reports/train/d10608ac1d220a86ea60a8041c93966c/params.yaml deleted file mode 100644 index cc28e03a..00000000 --- a/examples/security/classification/output/reports/train/d10608ac1d220a86ea60a8041c93966c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/adv_predictions.json - adv_probabilities_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/1e478f82dc41606b4aa5825e1a27f992.pkl - params_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/params.yaml - predictions_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/predictions.json - probabilities_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/probabilities.json - score_dict_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/score_dict.json - test_labels_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/test_labels.json - train_labels_file: output/reports/train/d10608ac1d220a86ea60a8041c93966c/train_labels.json - model_dir: models - name: d10608ac1d220a86ea60a8041c93966c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1ebc8f9aba5af6f33809a71696f87d81 - trainer: - kwargs: {} -name: d10608ac1d220a86ea60a8041c93966c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d113b9f279cef527b1c6f72d96429c0f/params.yaml b/examples/security/classification/output/reports/train/d113b9f279cef527b1c6f72d96429c0f/params.yaml deleted file mode 100644 index 2ad3570f..00000000 --- a/examples/security/classification/output/reports/train/d113b9f279cef527b1c6f72d96429c0f/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/adv_predictions.json - adv_probabilities_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/1dd50524bc8f5a9555467f1f7a7cdc16.pkl - params_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/params.yaml - predictions_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/predictions.json - probabilities_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/probabilities.json - score_dict_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/score_dict.json - test_labels_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/test_labels.json - train_labels_file: output/reports/train/d113b9f279cef527b1c6f72d96429c0f/train_labels.json - model_dir: models - name: d113b9f279cef527b1c6f72d96429c0f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2fbcf48ede5142cc9568efd285d22d6c - trainer: - kwargs: {} -name: d113b9f279cef527b1c6f72d96429c0f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/params.yaml b/examples/security/classification/output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/params.yaml deleted file mode 100644 index 2a27df9d..00000000 --- a/examples/security/classification/output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/adv_predictions.json - adv_probabilities_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/3fc84a2e77d0b26b71edb42410500e02.pkl - params_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/params.yaml - predictions_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/predictions.json - probabilities_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/probabilities.json - score_dict_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/score_dict.json - test_labels_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/test_labels.json - train_labels_file: output/reports/train/d1425c9ef3662e6cd6306ebb0999a253/train_labels.json - model_dir: models - name: d1425c9ef3662e6cd6306ebb0999a253 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 52da7ce6a3dea25467e7cd4f3055f5c7 - trainer: - kwargs: {} -name: d1425c9ef3662e6cd6306ebb0999a253 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/params.yaml b/examples/security/classification/output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/params.yaml deleted file mode 100644 index 8213a681..00000000 --- a/examples/security/classification/output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/adv_predictions.json - adv_probabilities_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/49e7c70f4e213e81c127e6216bb8a6f1.pkl - params_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/params.yaml - predictions_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/predictions.json - probabilities_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/probabilities.json - score_dict_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/score_dict.json - test_labels_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/test_labels.json - train_labels_file: output/reports/train/d16d6cbd4bcc97b48c73a0dab68012cd/train_labels.json - model_dir: models - name: d16d6cbd4bcc97b48c73a0dab68012cd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 01f9c40b88e93bf33e78b87cbbd39952 - trainer: - kwargs: {} -name: d16d6cbd4bcc97b48c73a0dab68012cd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d182456a01a94832b884fd7aa2dd74ed/params.yaml b/examples/security/classification/output/reports/train/d182456a01a94832b884fd7aa2dd74ed/params.yaml deleted file mode 100644 index c5e9835b..00000000 --- a/examples/security/classification/output/reports/train/d182456a01a94832b884fd7aa2dd74ed/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/adv_predictions.json - adv_probabilities_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/9e38d18c89925a87a45a2abf26f54442.pkl - params_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/params.yaml - predictions_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/predictions.json - probabilities_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/probabilities.json - score_dict_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/score_dict.json - test_labels_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/test_labels.json - train_labels_file: output/reports/train/d182456a01a94832b884fd7aa2dd74ed/train_labels.json - model_dir: models - name: d182456a01a94832b884fd7aa2dd74ed - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5217ee1a25bb54eac85ce0dfac18f1b6 - trainer: - kwargs: {} -name: d182456a01a94832b884fd7aa2dd74ed -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d192dba1610d64cb83cee0f69a99d58c/params.yaml b/examples/security/classification/output/reports/train/d192dba1610d64cb83cee0f69a99d58c/params.yaml deleted file mode 100644 index 11388992..00000000 --- a/examples/security/classification/output/reports/train/d192dba1610d64cb83cee0f69a99d58c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/adv_predictions.json - adv_probabilities_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/aee30cc2731267dfece42ec8c0d465fa.pkl - params_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/params.yaml - predictions_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/predictions.json - probabilities_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/probabilities.json - score_dict_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/score_dict.json - test_labels_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/test_labels.json - train_labels_file: output/reports/train/d192dba1610d64cb83cee0f69a99d58c/train_labels.json - model_dir: models - name: d192dba1610d64cb83cee0f69a99d58c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ccb5450b05fd824d19832b24b4a8e635 - trainer: - kwargs: {} -name: d192dba1610d64cb83cee0f69a99d58c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/params.yaml b/examples/security/classification/output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/params.yaml deleted file mode 100644 index eb6b9ba4..00000000 --- a/examples/security/classification/output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/adv_predictions.json - adv_probabilities_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/a899d1b1b1ccff618af6b91dc21a0270.pkl - params_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/params.yaml - predictions_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/predictions.json - probabilities_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/probabilities.json - score_dict_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/score_dict.json - test_labels_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/test_labels.json - train_labels_file: output/reports/train/d1ab10ac751ae58cd6338fbcc2e92db8/train_labels.json - model_dir: models - name: d1ab10ac751ae58cd6338fbcc2e92db8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 10805c7fa2e2db5b430795dfc629ac76 - trainer: - kwargs: {} -name: d1ab10ac751ae58cd6338fbcc2e92db8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d1bb97eacd379c65128c9607d04593ad/params.yaml b/examples/security/classification/output/reports/train/d1bb97eacd379c65128c9607d04593ad/params.yaml deleted file mode 100644 index d53e6185..00000000 --- a/examples/security/classification/output/reports/train/d1bb97eacd379c65128c9607d04593ad/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/adv_predictions.json - adv_probabilities_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/649c45313b02db08a2e3a71b4b36dc09.pkl - params_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/params.yaml - predictions_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/predictions.json - probabilities_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/probabilities.json - score_dict_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/score_dict.json - test_labels_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/test_labels.json - train_labels_file: output/reports/train/d1bb97eacd379c65128c9607d04593ad/train_labels.json - model_dir: models - name: d1bb97eacd379c65128c9607d04593ad - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 86b14458a8b07ddbed345b034a5fa516 - trainer: - kwargs: {} -name: d1bb97eacd379c65128c9607d04593ad -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/params.yaml b/examples/security/classification/output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/params.yaml deleted file mode 100644 index d7e685df..00000000 --- a/examples/security/classification/output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/adv_predictions.json - adv_probabilities_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/16fe4280fb662db726bb8f14bba94e3d.pkl - params_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/params.yaml - predictions_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/predictions.json - probabilities_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/probabilities.json - score_dict_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/score_dict.json - test_labels_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/test_labels.json - train_labels_file: output/reports/train/d1bd1e58499df06e68a7f00a1ebc6098/train_labels.json - model_dir: models - name: d1bd1e58499df06e68a7f00a1ebc6098 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d491374b86b27bf4bb65ef54ed9644ce - trainer: - kwargs: {} -name: d1bd1e58499df06e68a7f00a1ebc6098 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d1c6ee44152546dd537a953b4a4840dd/params.yaml b/examples/security/classification/output/reports/train/d1c6ee44152546dd537a953b4a4840dd/params.yaml deleted file mode 100644 index e847a211..00000000 --- a/examples/security/classification/output/reports/train/d1c6ee44152546dd537a953b4a4840dd/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/adv_predictions.json - adv_probabilities_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/2595b097302e5d253166130a9cbada03.pkl - params_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/params.yaml - predictions_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/predictions.json - probabilities_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/probabilities.json - score_dict_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/score_dict.json - test_labels_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/test_labels.json - train_labels_file: output/reports/train/d1c6ee44152546dd537a953b4a4840dd/train_labels.json - model_dir: models - name: d1c6ee44152546dd537a953b4a4840dd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 11a5e199fab50fc1b2e6a15c49c1f76d - trainer: - kwargs: {} -name: d1c6ee44152546dd537a953b4a4840dd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/params.yaml b/examples/security/classification/output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/params.yaml deleted file mode 100644 index 3db2b330..00000000 --- a/examples/security/classification/output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/adv_predictions.json - adv_probabilities_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/55589676601b8cb7920b2707999d98eb.pkl - params_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/params.yaml - predictions_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/predictions.json - probabilities_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/probabilities.json - score_dict_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/score_dict.json - test_labels_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/test_labels.json - train_labels_file: output/reports/train/d1f4fff9d2b98de31bbdc2e3bb54418d/train_labels.json - model_dir: models - name: d1f4fff9d2b98de31bbdc2e3bb54418d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1c846e6d32a60c2e6fc2c2abc65d3273 - trainer: - kwargs: {} -name: d1f4fff9d2b98de31bbdc2e3bb54418d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/params.yaml b/examples/security/classification/output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/params.yaml deleted file mode 100644 index b785e4b1..00000000 --- a/examples/security/classification/output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/adv_predictions.json - adv_probabilities_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/94e31afbeeded1085ff82050d62cda5e.pkl - params_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/params.yaml - predictions_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/predictions.json - probabilities_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/probabilities.json - score_dict_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/score_dict.json - test_labels_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/test_labels.json - train_labels_file: output/reports/train/d2200183564b6ab2a1ccd2cfb99d6059/train_labels.json - model_dir: models - name: d2200183564b6ab2a1ccd2cfb99d6059 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dc5ba638a59403b47e2a59eaaa73e737 - trainer: - kwargs: {} -name: d2200183564b6ab2a1ccd2cfb99d6059 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d22895293f5459285485749a2cf341ea/params.yaml b/examples/security/classification/output/reports/train/d22895293f5459285485749a2cf341ea/params.yaml deleted file mode 100644 index c1c8a06c..00000000 --- a/examples/security/classification/output/reports/train/d22895293f5459285485749a2cf341ea/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d22895293f5459285485749a2cf341ea/adv_predictions.json - adv_probabilities_file: output/reports/train/d22895293f5459285485749a2cf341ea/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/952e4a8ad672f7689fafa8dfc1511dce.pkl - params_file: output/reports/train/d22895293f5459285485749a2cf341ea/params.yaml - predictions_file: output/reports/train/d22895293f5459285485749a2cf341ea/predictions.json - probabilities_file: output/reports/train/d22895293f5459285485749a2cf341ea/probabilities.json - score_dict_file: output/reports/train/d22895293f5459285485749a2cf341ea/score_dict.json - test_labels_file: output/reports/train/d22895293f5459285485749a2cf341ea/test_labels.json - train_labels_file: output/reports/train/d22895293f5459285485749a2cf341ea/train_labels.json - model_dir: models - name: d22895293f5459285485749a2cf341ea - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a263da5ede41a28bbc152b505dcb642b - trainer: - kwargs: {} -name: d22895293f5459285485749a2cf341ea -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d23df2c9d62a9efbd6794b48833db818/params.yaml b/examples/security/classification/output/reports/train/d23df2c9d62a9efbd6794b48833db818/params.yaml deleted file mode 100644 index 321321f8..00000000 --- a/examples/security/classification/output/reports/train/d23df2c9d62a9efbd6794b48833db818/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/adv_predictions.json - adv_probabilities_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/f98a5e6058a1647de758fe9bc2a684cb.pkl - params_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/params.yaml - predictions_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/predictions.json - probabilities_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/probabilities.json - score_dict_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/score_dict.json - test_labels_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/test_labels.json - train_labels_file: output/reports/train/d23df2c9d62a9efbd6794b48833db818/train_labels.json - model_dir: models - name: d23df2c9d62a9efbd6794b48833db818 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e6472a4f91a179eefff44b53f644cacd - trainer: - kwargs: {} -name: d23df2c9d62a9efbd6794b48833db818 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d24b08265baed7f690c8edf2e8e94674/params.yaml b/examples/security/classification/output/reports/train/d24b08265baed7f690c8edf2e8e94674/params.yaml deleted file mode 100644 index c2a00f3a..00000000 --- a/examples/security/classification/output/reports/train/d24b08265baed7f690c8edf2e8e94674/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/adv_predictions.json - adv_probabilities_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/55dc37d1618e140e204eb8f4290e5af0.pkl - params_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/params.yaml - predictions_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/predictions.json - probabilities_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/probabilities.json - score_dict_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/score_dict.json - test_labels_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/test_labels.json - train_labels_file: output/reports/train/d24b08265baed7f690c8edf2e8e94674/train_labels.json - model_dir: models - name: d24b08265baed7f690c8edf2e8e94674 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 47227629c713c84ca5edf3b6f619825a - trainer: - kwargs: {} -name: d24b08265baed7f690c8edf2e8e94674 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d289eab3d89d08e928e3f6dde343ba34/params.yaml b/examples/security/classification/output/reports/train/d289eab3d89d08e928e3f6dde343ba34/params.yaml deleted file mode 100644 index a737d5ad..00000000 --- a/examples/security/classification/output/reports/train/d289eab3d89d08e928e3f6dde343ba34/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/adv_predictions.json - adv_probabilities_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/42e213c5826b69a18d5ec9a074fedb42.pkl - params_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/params.yaml - predictions_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/predictions.json - probabilities_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/probabilities.json - score_dict_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/score_dict.json - test_labels_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/test_labels.json - train_labels_file: output/reports/train/d289eab3d89d08e928e3f6dde343ba34/train_labels.json - model_dir: models - name: d289eab3d89d08e928e3f6dde343ba34 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5fe1f375edb67b742c50951dc432a5b6 - trainer: - kwargs: {} -name: d289eab3d89d08e928e3f6dde343ba34 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d3004412a6db49f768427cf19487524c/params.yaml b/examples/security/classification/output/reports/train/d3004412a6db49f768427cf19487524c/params.yaml deleted file mode 100644 index 736b9b37..00000000 --- a/examples/security/classification/output/reports/train/d3004412a6db49f768427cf19487524c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d3004412a6db49f768427cf19487524c/adv_predictions.json - adv_probabilities_file: output/reports/train/d3004412a6db49f768427cf19487524c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/88d4159fbcae76094f28efa179af6864.pkl - params_file: output/reports/train/d3004412a6db49f768427cf19487524c/params.yaml - predictions_file: output/reports/train/d3004412a6db49f768427cf19487524c/predictions.json - probabilities_file: output/reports/train/d3004412a6db49f768427cf19487524c/probabilities.json - score_dict_file: output/reports/train/d3004412a6db49f768427cf19487524c/score_dict.json - test_labels_file: output/reports/train/d3004412a6db49f768427cf19487524c/test_labels.json - train_labels_file: output/reports/train/d3004412a6db49f768427cf19487524c/train_labels.json - model_dir: models - name: d3004412a6db49f768427cf19487524c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 59348541a71bef452b63ec86949edda3 - trainer: - kwargs: {} -name: d3004412a6db49f768427cf19487524c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d30329a4183bb3b7970d530f6c0390d1/params.yaml b/examples/security/classification/output/reports/train/d30329a4183bb3b7970d530f6c0390d1/params.yaml deleted file mode 100644 index 109b5184..00000000 --- a/examples/security/classification/output/reports/train/d30329a4183bb3b7970d530f6c0390d1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/adv_predictions.json - adv_probabilities_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/bbf3d57d17aca51a88368e72ab7fd8cd.pkl - params_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/params.yaml - predictions_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/predictions.json - probabilities_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/probabilities.json - score_dict_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/score_dict.json - test_labels_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/test_labels.json - train_labels_file: output/reports/train/d30329a4183bb3b7970d530f6c0390d1/train_labels.json - model_dir: models - name: d30329a4183bb3b7970d530f6c0390d1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 49810619ab1a1ab998b68f47642bd8af - trainer: - kwargs: {} -name: d30329a4183bb3b7970d530f6c0390d1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/params.yaml b/examples/security/classification/output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/params.yaml deleted file mode 100644 index 7436c8c1..00000000 --- a/examples/security/classification/output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/adv_predictions.json - adv_probabilities_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/d3748063ae0fc210d48d55780886eb99.pkl - params_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/params.yaml - predictions_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/predictions.json - probabilities_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/probabilities.json - score_dict_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/score_dict.json - test_labels_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/test_labels.json - train_labels_file: output/reports/train/d315b51ce2d5633eb8fd0439df451e1a/train_labels.json - model_dir: models - name: d315b51ce2d5633eb8fd0439df451e1a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 74db705a8d739b030b480900aea5fbd1 - trainer: - kwargs: {} -name: d315b51ce2d5633eb8fd0439df451e1a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d3407ebbe49029496324b7b5acc007f8/params.yaml b/examples/security/classification/output/reports/train/d3407ebbe49029496324b7b5acc007f8/params.yaml deleted file mode 100644 index ea15f1e0..00000000 --- a/examples/security/classification/output/reports/train/d3407ebbe49029496324b7b5acc007f8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/adv_predictions.json - adv_probabilities_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/78513babe8a6d060d6268ecf813b8e03.pkl - params_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/params.yaml - predictions_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/predictions.json - probabilities_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/probabilities.json - score_dict_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/score_dict.json - test_labels_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/test_labels.json - train_labels_file: output/reports/train/d3407ebbe49029496324b7b5acc007f8/train_labels.json - model_dir: models - name: d3407ebbe49029496324b7b5acc007f8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f63b2fd011612fef5472161034c7f163 - trainer: - kwargs: {} -name: d3407ebbe49029496324b7b5acc007f8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d39407340c5f9158c58e09c103a464a6/params.yaml b/examples/security/classification/output/reports/train/d39407340c5f9158c58e09c103a464a6/params.yaml deleted file mode 100644 index 5eea90d2..00000000 --- a/examples/security/classification/output/reports/train/d39407340c5f9158c58e09c103a464a6/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/adv_predictions.json - adv_probabilities_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/0b5b1b328ff600901be8ef2caa146fc3.pkl - params_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/params.yaml - predictions_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/predictions.json - probabilities_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/probabilities.json - score_dict_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/score_dict.json - test_labels_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/test_labels.json - train_labels_file: output/reports/train/d39407340c5f9158c58e09c103a464a6/train_labels.json - model_dir: models - name: d39407340c5f9158c58e09c103a464a6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d25fa7cf386bd3f8f72c7aa73f1586f8 - trainer: - kwargs: {} -name: d39407340c5f9158c58e09c103a464a6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/params.yaml b/examples/security/classification/output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/params.yaml deleted file mode 100644 index dc8a94d5..00000000 --- a/examples/security/classification/output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/adv_predictions.json - adv_probabilities_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/414af31323c218d42e34f9b7a8ae54d5.pkl - params_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/params.yaml - predictions_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/predictions.json - probabilities_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/probabilities.json - score_dict_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/score_dict.json - test_labels_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/test_labels.json - train_labels_file: output/reports/train/d3a34f7ec4a2a8d72a43bb5836451c77/train_labels.json - model_dir: models - name: d3a34f7ec4a2a8d72a43bb5836451c77 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 264fa35fda6d3558921f776990d281c3 - trainer: - kwargs: {} -name: d3a34f7ec4a2a8d72a43bb5836451c77 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d3a735927356d831ec25c1bdb4322236/params.yaml b/examples/security/classification/output/reports/train/d3a735927356d831ec25c1bdb4322236/params.yaml deleted file mode 100644 index 1e3edf2c..00000000 --- a/examples/security/classification/output/reports/train/d3a735927356d831ec25c1bdb4322236/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/adv_predictions.json - adv_probabilities_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/ca855344a8614c14970c1535086fdf93.pkl - params_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/params.yaml - predictions_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/predictions.json - probabilities_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/probabilities.json - score_dict_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/score_dict.json - test_labels_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/test_labels.json - train_labels_file: output/reports/train/d3a735927356d831ec25c1bdb4322236/train_labels.json - model_dir: models - name: d3a735927356d831ec25c1bdb4322236 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8cdc9482054e4fb0270d776b5fa47da8 - trainer: - kwargs: {} -name: d3a735927356d831ec25c1bdb4322236 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/params.yaml b/examples/security/classification/output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/params.yaml deleted file mode 100644 index 54520afb..00000000 --- a/examples/security/classification/output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/adv_predictions.json - adv_probabilities_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/1581d6fa25427e9a786e490f3309b18a.pkl - params_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/params.yaml - predictions_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/predictions.json - probabilities_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/probabilities.json - score_dict_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/score_dict.json - test_labels_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/test_labels.json - train_labels_file: output/reports/train/d3b8674a56eb6fa2a19b601d33f5b085/train_labels.json - model_dir: models - name: d3b8674a56eb6fa2a19b601d33f5b085 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 94d23de9a61ee30518e249d7f18f146d - trainer: - kwargs: {} -name: d3b8674a56eb6fa2a19b601d33f5b085 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d3c4f2eedaa0282319828cad55ad8326/params.yaml b/examples/security/classification/output/reports/train/d3c4f2eedaa0282319828cad55ad8326/params.yaml deleted file mode 100644 index d83610fb..00000000 --- a/examples/security/classification/output/reports/train/d3c4f2eedaa0282319828cad55ad8326/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/adv_predictions.json - adv_probabilities_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/21a6c72b039bd382f7435bfd02e05294.pkl - params_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/params.yaml - predictions_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/predictions.json - probabilities_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/probabilities.json - score_dict_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/score_dict.json - test_labels_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/test_labels.json - train_labels_file: output/reports/train/d3c4f2eedaa0282319828cad55ad8326/train_labels.json - model_dir: models - name: d3c4f2eedaa0282319828cad55ad8326 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4d37c3e75fa62ffa7a14b23f87c775b1 - trainer: - kwargs: {} -name: d3c4f2eedaa0282319828cad55ad8326 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/params.yaml b/examples/security/classification/output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/params.yaml deleted file mode 100644 index 4ff63702..00000000 --- a/examples/security/classification/output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/adv_predictions.json - adv_probabilities_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/da05ecb7287abb7a83bf29fa43949db3.pkl - params_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/params.yaml - predictions_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/predictions.json - probabilities_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/probabilities.json - score_dict_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/score_dict.json - test_labels_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/test_labels.json - train_labels_file: output/reports/train/d3caa5a1c75941a1b4fbccb312781f42/train_labels.json - model_dir: models - name: d3caa5a1c75941a1b4fbccb312781f42 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 93d75b58f67a181e5dfea06d32566e83 - trainer: - kwargs: {} -name: d3caa5a1c75941a1b4fbccb312781f42 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d3f8127c0841abcf24db6a0dca23f177/params.yaml b/examples/security/classification/output/reports/train/d3f8127c0841abcf24db6a0dca23f177/params.yaml deleted file mode 100644 index af706da8..00000000 --- a/examples/security/classification/output/reports/train/d3f8127c0841abcf24db6a0dca23f177/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/adv_predictions.json - adv_probabilities_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/ba6916f534db0f2242a7b5dd7e8181c8.pkl - params_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/params.yaml - predictions_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/predictions.json - probabilities_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/probabilities.json - score_dict_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/score_dict.json - test_labels_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/test_labels.json - train_labels_file: output/reports/train/d3f8127c0841abcf24db6a0dca23f177/train_labels.json - model_dir: models - name: d3f8127c0841abcf24db6a0dca23f177 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: aa0c1a90af1a57fa58458b255e077e63 - trainer: - kwargs: {} -name: d3f8127c0841abcf24db6a0dca23f177 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d4552f9038939953684977c4506dba9e/params.yaml b/examples/security/classification/output/reports/train/d4552f9038939953684977c4506dba9e/params.yaml deleted file mode 100644 index eb571482..00000000 --- a/examples/security/classification/output/reports/train/d4552f9038939953684977c4506dba9e/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d4552f9038939953684977c4506dba9e/adv_predictions.json - adv_probabilities_file: output/reports/train/d4552f9038939953684977c4506dba9e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/46807b9aefbdc2cd0e9628c5eeb9bef8.pkl - params_file: output/reports/train/d4552f9038939953684977c4506dba9e/params.yaml - predictions_file: output/reports/train/d4552f9038939953684977c4506dba9e/predictions.json - probabilities_file: output/reports/train/d4552f9038939953684977c4506dba9e/probabilities.json - score_dict_file: output/reports/train/d4552f9038939953684977c4506dba9e/score_dict.json - test_labels_file: output/reports/train/d4552f9038939953684977c4506dba9e/test_labels.json - train_labels_file: output/reports/train/d4552f9038939953684977c4506dba9e/train_labels.json - model_dir: models - name: d4552f9038939953684977c4506dba9e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 33cfcb8ef0f1b70956296e9056b27dbe - trainer: - kwargs: {} -name: d4552f9038939953684977c4506dba9e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/params.yaml b/examples/security/classification/output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/params.yaml deleted file mode 100644 index 87cb775d..00000000 --- a/examples/security/classification/output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/adv_predictions.json - adv_probabilities_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/6a78013331e11b7b142e6204fcce7a0f.pkl - params_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/params.yaml - predictions_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/predictions.json - probabilities_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/probabilities.json - score_dict_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/score_dict.json - test_labels_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/test_labels.json - train_labels_file: output/reports/train/d47405f3d834bbae0eec99e0c92bb7c8/train_labels.json - model_dir: models - name: d47405f3d834bbae0eec99e0c92bb7c8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 088f0f8cec605c9efa72614802d7a632 - trainer: - kwargs: {} -name: d47405f3d834bbae0eec99e0c92bb7c8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/params.yaml b/examples/security/classification/output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/params.yaml deleted file mode 100644 index 5b836a86..00000000 --- a/examples/security/classification/output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/adv_predictions.json - adv_probabilities_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/d0e2ab5cd56df8e2e81aa6fbeb755b59.pkl - params_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/params.yaml - predictions_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/predictions.json - probabilities_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/probabilities.json - score_dict_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/score_dict.json - test_labels_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/test_labels.json - train_labels_file: output/reports/train/d4766acf4f9fc80c1bb105dcb3fed799/train_labels.json - model_dir: models - name: d4766acf4f9fc80c1bb105dcb3fed799 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8f7d8785a44b3905fff83f10cad7e7f3 - trainer: - kwargs: {} -name: d4766acf4f9fc80c1bb105dcb3fed799 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/params.yaml b/examples/security/classification/output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/params.yaml deleted file mode 100644 index 66c128c5..00000000 --- a/examples/security/classification/output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/adv_predictions.json - adv_probabilities_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/9e79ee75e9be1fcfc53b9a57f940be25.pkl - params_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/params.yaml - predictions_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/predictions.json - probabilities_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/probabilities.json - score_dict_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/score_dict.json - test_labels_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/test_labels.json - train_labels_file: output/reports/train/d4b1d5a940c1b45d766da5e728c8b125/train_labels.json - model_dir: models - name: d4b1d5a940c1b45d766da5e728c8b125 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 663c0a7e4b1d8e2e2a92dce1da1da636 - trainer: - kwargs: {} -name: d4b1d5a940c1b45d766da5e728c8b125 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d4c23768942a93175f0425246b12fec3/params.yaml b/examples/security/classification/output/reports/train/d4c23768942a93175f0425246b12fec3/params.yaml deleted file mode 100644 index c7abb256..00000000 --- a/examples/security/classification/output/reports/train/d4c23768942a93175f0425246b12fec3/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d4c23768942a93175f0425246b12fec3/adv_predictions.json - adv_probabilities_file: output/reports/train/d4c23768942a93175f0425246b12fec3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/bf77d20bf0fc85cc09fecb4a8d6deb91.pkl - params_file: output/reports/train/d4c23768942a93175f0425246b12fec3/params.yaml - predictions_file: output/reports/train/d4c23768942a93175f0425246b12fec3/predictions.json - probabilities_file: output/reports/train/d4c23768942a93175f0425246b12fec3/probabilities.json - score_dict_file: output/reports/train/d4c23768942a93175f0425246b12fec3/score_dict.json - test_labels_file: output/reports/train/d4c23768942a93175f0425246b12fec3/test_labels.json - train_labels_file: output/reports/train/d4c23768942a93175f0425246b12fec3/train_labels.json - model_dir: models - name: d4c23768942a93175f0425246b12fec3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ed704141c027ca70a10625214cb1c235 - trainer: - kwargs: {} -name: d4c23768942a93175f0425246b12fec3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d5aeca69561c75b7a258b9b64cc64497/params.yaml b/examples/security/classification/output/reports/train/d5aeca69561c75b7a258b9b64cc64497/params.yaml deleted file mode 100644 index 7c84e7bc..00000000 --- a/examples/security/classification/output/reports/train/d5aeca69561c75b7a258b9b64cc64497/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/adv_predictions.json - adv_probabilities_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/9dbc392b4e18175e2f3841da25652298.pkl - params_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/params.yaml - predictions_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/predictions.json - probabilities_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/probabilities.json - score_dict_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/score_dict.json - test_labels_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/test_labels.json - train_labels_file: output/reports/train/d5aeca69561c75b7a258b9b64cc64497/train_labels.json - model_dir: models - name: d5aeca69561c75b7a258b9b64cc64497 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bb32eaf48da27ded87a5280c77ac831d - trainer: - kwargs: {} -name: d5aeca69561c75b7a258b9b64cc64497 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/params.yaml b/examples/security/classification/output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/params.yaml deleted file mode 100644 index 47ca4284..00000000 --- a/examples/security/classification/output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/adv_predictions.json - adv_probabilities_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/272e5ab006102931df9cbf1e6e25bca8.pkl - params_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/params.yaml - predictions_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/predictions.json - probabilities_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/probabilities.json - score_dict_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/score_dict.json - test_labels_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/test_labels.json - train_labels_file: output/reports/train/d5bf055ff08dc82a70bf5fa13a7fa89a/train_labels.json - model_dir: models - name: d5bf055ff08dc82a70bf5fa13a7fa89a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b42b1815eb9b0d9a8987797cd5834bd1 - trainer: - kwargs: {} -name: d5bf055ff08dc82a70bf5fa13a7fa89a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d5dd96727584c85f39041280c7bfbf49/params.yaml b/examples/security/classification/output/reports/train/d5dd96727584c85f39041280c7bfbf49/params.yaml deleted file mode 100644 index 7477b746..00000000 --- a/examples/security/classification/output/reports/train/d5dd96727584c85f39041280c7bfbf49/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/adv_predictions.json - adv_probabilities_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/f995c4eb8d54b602a023ffd6e73dd977.pkl - params_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/params.yaml - predictions_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/predictions.json - probabilities_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/probabilities.json - score_dict_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/score_dict.json - test_labels_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/test_labels.json - train_labels_file: output/reports/train/d5dd96727584c85f39041280c7bfbf49/train_labels.json - model_dir: models - name: d5dd96727584c85f39041280c7bfbf49 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9cbdbbfea50641bacdfc102d0f91582e - trainer: - kwargs: {} -name: d5dd96727584c85f39041280c7bfbf49 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/params.yaml b/examples/security/classification/output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/params.yaml deleted file mode 100644 index 7f49d197..00000000 --- a/examples/security/classification/output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/adv_predictions.json - adv_probabilities_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/d772826dc47b2c9e7de27f0181bba076.pkl - params_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/params.yaml - predictions_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/predictions.json - probabilities_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/probabilities.json - score_dict_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/score_dict.json - test_labels_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/test_labels.json - train_labels_file: output/reports/train/d60feadd9c521dcdcd72b88731d66f2c/train_labels.json - model_dir: models - name: d60feadd9c521dcdcd72b88731d66f2c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8e3e5c9688c1b98b21a724d733eb1aec - trainer: - kwargs: {} -name: d60feadd9c521dcdcd72b88731d66f2c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d614cdf8a8126b71eda180882f7e73b2/params.yaml b/examples/security/classification/output/reports/train/d614cdf8a8126b71eda180882f7e73b2/params.yaml deleted file mode 100644 index f83a4812..00000000 --- a/examples/security/classification/output/reports/train/d614cdf8a8126b71eda180882f7e73b2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/adv_predictions.json - adv_probabilities_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/8d65488a44c9dcdf9ed11ec75238cc77.pkl - params_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/params.yaml - predictions_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/predictions.json - probabilities_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/probabilities.json - score_dict_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/score_dict.json - test_labels_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/test_labels.json - train_labels_file: output/reports/train/d614cdf8a8126b71eda180882f7e73b2/train_labels.json - model_dir: models - name: d614cdf8a8126b71eda180882f7e73b2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0676a34db2c26f44dab02d9b468dfd25 - trainer: - kwargs: {} -name: d614cdf8a8126b71eda180882f7e73b2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/params.yaml b/examples/security/classification/output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/params.yaml deleted file mode 100644 index a4f91643..00000000 --- a/examples/security/classification/output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/adv_predictions.json - adv_probabilities_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/97d05be4cc891e9123941cf888114dcf.pkl - params_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/params.yaml - predictions_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/predictions.json - probabilities_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/probabilities.json - score_dict_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/score_dict.json - test_labels_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/test_labels.json - train_labels_file: output/reports/train/d62a5024104ddad2f3b82ebd4c441b1a/train_labels.json - model_dir: models - name: d62a5024104ddad2f3b82ebd4c441b1a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c2909709dc950593e6fdd713c0717c03 - trainer: - kwargs: {} -name: d62a5024104ddad2f3b82ebd4c441b1a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d6607a8a2f967005647615d2278114e1/params.yaml b/examples/security/classification/output/reports/train/d6607a8a2f967005647615d2278114e1/params.yaml deleted file mode 100644 index 20cfa59e..00000000 --- a/examples/security/classification/output/reports/train/d6607a8a2f967005647615d2278114e1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d6607a8a2f967005647615d2278114e1/adv_predictions.json - adv_probabilities_file: output/reports/train/d6607a8a2f967005647615d2278114e1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/a96257c158ba4838c08dc44fd5f70d5b.pkl - params_file: output/reports/train/d6607a8a2f967005647615d2278114e1/params.yaml - predictions_file: output/reports/train/d6607a8a2f967005647615d2278114e1/predictions.json - probabilities_file: output/reports/train/d6607a8a2f967005647615d2278114e1/probabilities.json - score_dict_file: output/reports/train/d6607a8a2f967005647615d2278114e1/score_dict.json - test_labels_file: output/reports/train/d6607a8a2f967005647615d2278114e1/test_labels.json - train_labels_file: output/reports/train/d6607a8a2f967005647615d2278114e1/train_labels.json - model_dir: models - name: d6607a8a2f967005647615d2278114e1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6095ac2ca8618f4d10238ed783f15e8c - trainer: - kwargs: {} -name: d6607a8a2f967005647615d2278114e1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d69626eef8ef2e524ad475863e6538d8/params.yaml b/examples/security/classification/output/reports/train/d69626eef8ef2e524ad475863e6538d8/params.yaml deleted file mode 100644 index ec7a82bf..00000000 --- a/examples/security/classification/output/reports/train/d69626eef8ef2e524ad475863e6538d8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/adv_predictions.json - adv_probabilities_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/07741404f47ba4122b4ac9d460fa428a.pkl - params_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/params.yaml - predictions_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/predictions.json - probabilities_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/probabilities.json - score_dict_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/score_dict.json - test_labels_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/test_labels.json - train_labels_file: output/reports/train/d69626eef8ef2e524ad475863e6538d8/train_labels.json - model_dir: models - name: d69626eef8ef2e524ad475863e6538d8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 52424da337470766b8661db59492f68c - trainer: - kwargs: {} -name: d69626eef8ef2e524ad475863e6538d8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/params.yaml b/examples/security/classification/output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/params.yaml deleted file mode 100644 index d009c768..00000000 --- a/examples/security/classification/output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/adv_predictions.json - adv_probabilities_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/3676ac980c136d658ada00c4a1cab58e.pkl - params_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/params.yaml - predictions_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/predictions.json - probabilities_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/probabilities.json - score_dict_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/score_dict.json - test_labels_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/test_labels.json - train_labels_file: output/reports/train/d6aa81b04aff72ae7ed0d2fd90b15693/train_labels.json - model_dir: models - name: d6aa81b04aff72ae7ed0d2fd90b15693 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ef9be58a97d05f0d6b17cd01f74b638b - trainer: - kwargs: {} -name: d6aa81b04aff72ae7ed0d2fd90b15693 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/params.yaml b/examples/security/classification/output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/params.yaml deleted file mode 100644 index ee44ac9a..00000000 --- a/examples/security/classification/output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/adv_predictions.json - adv_probabilities_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/978e847300732de34f9daacefb977122.pkl - params_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/params.yaml - predictions_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/predictions.json - probabilities_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/probabilities.json - score_dict_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/score_dict.json - test_labels_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/test_labels.json - train_labels_file: output/reports/train/d6d5b9675f00563bfa30a0d8f646708a/train_labels.json - model_dir: models - name: d6d5b9675f00563bfa30a0d8f646708a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 85422a74a18a83f4673a5e22a545edf7 - trainer: - kwargs: {} -name: d6d5b9675f00563bfa30a0d8f646708a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/params.yaml b/examples/security/classification/output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/params.yaml deleted file mode 100644 index 90aa1557..00000000 --- a/examples/security/classification/output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/adv_predictions.json - adv_probabilities_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/19b7609f748d217eda2a94a49d3e4441.pkl - params_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/params.yaml - predictions_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/predictions.json - probabilities_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/probabilities.json - score_dict_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/score_dict.json - test_labels_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/test_labels.json - train_labels_file: output/reports/train/d6d9bd2cddc4eee6729c88fcd47ceea8/train_labels.json - model_dir: models - name: d6d9bd2cddc4eee6729c88fcd47ceea8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a0ddbbe113e527ca0a2e9f9967bec2ac - trainer: - kwargs: {} -name: d6d9bd2cddc4eee6729c88fcd47ceea8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d6e019b9de15227df801aad189331c14/params.yaml b/examples/security/classification/output/reports/train/d6e019b9de15227df801aad189331c14/params.yaml deleted file mode 100644 index a58f6a0e..00000000 --- a/examples/security/classification/output/reports/train/d6e019b9de15227df801aad189331c14/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d6e019b9de15227df801aad189331c14/adv_predictions.json - adv_probabilities_file: output/reports/train/d6e019b9de15227df801aad189331c14/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/9906607c6ee58bc262690ea776ce5821.pkl - params_file: output/reports/train/d6e019b9de15227df801aad189331c14/params.yaml - predictions_file: output/reports/train/d6e019b9de15227df801aad189331c14/predictions.json - probabilities_file: output/reports/train/d6e019b9de15227df801aad189331c14/probabilities.json - score_dict_file: output/reports/train/d6e019b9de15227df801aad189331c14/score_dict.json - test_labels_file: output/reports/train/d6e019b9de15227df801aad189331c14/test_labels.json - train_labels_file: output/reports/train/d6e019b9de15227df801aad189331c14/train_labels.json - model_dir: models - name: d6e019b9de15227df801aad189331c14 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4a838644b48b2e193576850e74e1f6c2 - trainer: - kwargs: {} -name: d6e019b9de15227df801aad189331c14 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d6e0575f806620984df51600b5fd0338/params.yaml b/examples/security/classification/output/reports/train/d6e0575f806620984df51600b5fd0338/params.yaml deleted file mode 100644 index dafb2faa..00000000 --- a/examples/security/classification/output/reports/train/d6e0575f806620984df51600b5fd0338/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d6e0575f806620984df51600b5fd0338/adv_predictions.json - adv_probabilities_file: output/reports/train/d6e0575f806620984df51600b5fd0338/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/5f2e20b2deed520b3b17bb1ebe6a3e11.pkl - params_file: output/reports/train/d6e0575f806620984df51600b5fd0338/params.yaml - predictions_file: output/reports/train/d6e0575f806620984df51600b5fd0338/predictions.json - probabilities_file: output/reports/train/d6e0575f806620984df51600b5fd0338/probabilities.json - score_dict_file: output/reports/train/d6e0575f806620984df51600b5fd0338/score_dict.json - test_labels_file: output/reports/train/d6e0575f806620984df51600b5fd0338/test_labels.json - train_labels_file: output/reports/train/d6e0575f806620984df51600b5fd0338/train_labels.json - model_dir: models - name: d6e0575f806620984df51600b5fd0338 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8676672310471ec319075c90b50ca982 - trainer: - kwargs: {} -name: d6e0575f806620984df51600b5fd0338 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/params.yaml b/examples/security/classification/output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/params.yaml deleted file mode 100644 index b2e330fd..00000000 --- a/examples/security/classification/output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/adv_predictions.json - adv_probabilities_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/34e40f9c4e35ea802f4c890210ce903a.pkl - params_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/params.yaml - predictions_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/predictions.json - probabilities_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/probabilities.json - score_dict_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/score_dict.json - test_labels_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/test_labels.json - train_labels_file: output/reports/train/d6e4d512d1897529e2cd7d7f8f7af8b5/train_labels.json - model_dir: models - name: d6e4d512d1897529e2cd7d7f8f7af8b5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 26db99af1048dde4aafdc9b9ac0c5e04 - trainer: - kwargs: {} -name: d6e4d512d1897529e2cd7d7f8f7af8b5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/params.yaml b/examples/security/classification/output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/params.yaml deleted file mode 100644 index 8d5df82d..00000000 --- a/examples/security/classification/output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/adv_predictions.json - adv_probabilities_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/86f64b77db9b41382ab919d5aa364bb3.pkl - params_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/params.yaml - predictions_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/predictions.json - probabilities_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/probabilities.json - score_dict_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/score_dict.json - test_labels_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/test_labels.json - train_labels_file: output/reports/train/d6f3102b4147cfe502fcdc9a7f583b3d/train_labels.json - model_dir: models - name: d6f3102b4147cfe502fcdc9a7f583b3d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c76120f25a3e8572408d7f46c4508584 - trainer: - kwargs: {} -name: d6f3102b4147cfe502fcdc9a7f583b3d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d70e7698c153704e65aad1b0c19c0508/params.yaml b/examples/security/classification/output/reports/train/d70e7698c153704e65aad1b0c19c0508/params.yaml deleted file mode 100644 index 92ba3a2c..00000000 --- a/examples/security/classification/output/reports/train/d70e7698c153704e65aad1b0c19c0508/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/adv_predictions.json - adv_probabilities_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/62c378bff4241805dcec5758226f50db.pkl - params_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/params.yaml - predictions_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/predictions.json - probabilities_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/probabilities.json - score_dict_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/score_dict.json - test_labels_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/test_labels.json - train_labels_file: output/reports/train/d70e7698c153704e65aad1b0c19c0508/train_labels.json - model_dir: models - name: d70e7698c153704e65aad1b0c19c0508 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 872b42178b6fec57ba5cb2f2451c0903 - trainer: - kwargs: {} -name: d70e7698c153704e65aad1b0c19c0508 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d73cd95cb190e1afda7630e04ef18888/params.yaml b/examples/security/classification/output/reports/train/d73cd95cb190e1afda7630e04ef18888/params.yaml deleted file mode 100644 index 50f1f6f4..00000000 --- a/examples/security/classification/output/reports/train/d73cd95cb190e1afda7630e04ef18888/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/adv_predictions.json - adv_probabilities_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/9d44b85e2c0ff9581c024c56229cb456.pkl - params_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/params.yaml - predictions_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/predictions.json - probabilities_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/probabilities.json - score_dict_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/score_dict.json - test_labels_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/test_labels.json - train_labels_file: output/reports/train/d73cd95cb190e1afda7630e04ef18888/train_labels.json - model_dir: models - name: d73cd95cb190e1afda7630e04ef18888 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4a5b04af74e4e541784d14ab4afaf604 - trainer: - kwargs: {} -name: d73cd95cb190e1afda7630e04ef18888 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d74992721b32fad604cf654f92f32f6c/params.yaml b/examples/security/classification/output/reports/train/d74992721b32fad604cf654f92f32f6c/params.yaml deleted file mode 100644 index cdd8efc2..00000000 --- a/examples/security/classification/output/reports/train/d74992721b32fad604cf654f92f32f6c/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/adv_predictions.json - adv_probabilities_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/bdfe7c5fc60798e5a76182ffcc609b57.pkl - params_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/params.yaml - predictions_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/predictions.json - probabilities_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/probabilities.json - score_dict_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/score_dict.json - test_labels_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/test_labels.json - train_labels_file: output/reports/train/d74992721b32fad604cf654f92f32f6c/train_labels.json - model_dir: models - name: d74992721b32fad604cf654f92f32f6c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2b81b3d95fb629740c71c0fc1fa8f3f5 - trainer: - kwargs: {} -name: d74992721b32fad604cf654f92f32f6c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d78d2329e135c09b475863a510d5bc6c/params.yaml b/examples/security/classification/output/reports/train/d78d2329e135c09b475863a510d5bc6c/params.yaml deleted file mode 100644 index bd5f4219..00000000 --- a/examples/security/classification/output/reports/train/d78d2329e135c09b475863a510d5bc6c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/adv_predictions.json - adv_probabilities_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/3f25c2eb710ae9434035b17b63571b65.pkl - params_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/params.yaml - predictions_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/predictions.json - probabilities_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/probabilities.json - score_dict_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/score_dict.json - test_labels_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/test_labels.json - train_labels_file: output/reports/train/d78d2329e135c09b475863a510d5bc6c/train_labels.json - model_dir: models - name: d78d2329e135c09b475863a510d5bc6c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b8dfaf592bd1d4e7f3c897f048ca92ed - trainer: - kwargs: {} -name: d78d2329e135c09b475863a510d5bc6c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/params.yaml b/examples/security/classification/output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/params.yaml deleted file mode 100644 index f0866131..00000000 --- a/examples/security/classification/output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/adv_predictions.json - adv_probabilities_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/85096c093381b4e133dc1d7814830020.pkl - params_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/params.yaml - predictions_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/predictions.json - probabilities_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/probabilities.json - score_dict_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/score_dict.json - test_labels_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/test_labels.json - train_labels_file: output/reports/train/d7c2e60cd7e1cffc5661bed77ac8e5f6/train_labels.json - model_dir: models - name: d7c2e60cd7e1cffc5661bed77ac8e5f6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3b8e7dbfeeedfe144c2e7aeb165cde99 - trainer: - kwargs: {} -name: d7c2e60cd7e1cffc5661bed77ac8e5f6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d81de434fa8a13193d782cac4a03feba/params.yaml b/examples/security/classification/output/reports/train/d81de434fa8a13193d782cac4a03feba/params.yaml deleted file mode 100644 index be2a578e..00000000 --- a/examples/security/classification/output/reports/train/d81de434fa8a13193d782cac4a03feba/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/adv_predictions.json - adv_probabilities_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/38c1d4371d4326e1b47be7d1e86da82f.pkl - params_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/params.yaml - predictions_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/predictions.json - probabilities_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/probabilities.json - score_dict_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/score_dict.json - test_labels_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/test_labels.json - train_labels_file: output/reports/train/d81de434fa8a13193d782cac4a03feba/train_labels.json - model_dir: models - name: d81de434fa8a13193d782cac4a03feba - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cd95725847a5e370ef2628fe150eaacc - trainer: - kwargs: {} -name: d81de434fa8a13193d782cac4a03feba -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d82bce39029e5045373f7fe5766f322a/params.yaml b/examples/security/classification/output/reports/train/d82bce39029e5045373f7fe5766f322a/params.yaml deleted file mode 100644 index 7cb6c896..00000000 --- a/examples/security/classification/output/reports/train/d82bce39029e5045373f7fe5766f322a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/adv_predictions.json - adv_probabilities_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/d73b76aa6c6a704ff38d41f8903fd323.pkl - params_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/params.yaml - predictions_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/predictions.json - probabilities_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/probabilities.json - score_dict_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/score_dict.json - test_labels_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/test_labels.json - train_labels_file: output/reports/train/d82bce39029e5045373f7fe5766f322a/train_labels.json - model_dir: models - name: d82bce39029e5045373f7fe5766f322a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 21c7ee4d330f3d21e37f5a5c7d68d661 - trainer: - kwargs: {} -name: d82bce39029e5045373f7fe5766f322a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d83f76beaeb279c6fb2aba648e120881/params.yaml b/examples/security/classification/output/reports/train/d83f76beaeb279c6fb2aba648e120881/params.yaml deleted file mode 100644 index 504b758f..00000000 --- a/examples/security/classification/output/reports/train/d83f76beaeb279c6fb2aba648e120881/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/adv_predictions.json - adv_probabilities_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/72c3e13ee13b9754b15c4db656cb63ba.pkl - params_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/params.yaml - predictions_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/predictions.json - probabilities_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/probabilities.json - score_dict_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/score_dict.json - test_labels_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/test_labels.json - train_labels_file: output/reports/train/d83f76beaeb279c6fb2aba648e120881/train_labels.json - model_dir: models - name: d83f76beaeb279c6fb2aba648e120881 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4d5be2f4156018f5e72554978f5d6980 - trainer: - kwargs: {} -name: d83f76beaeb279c6fb2aba648e120881 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d86943eb6e94f4f786b35065cec4be64/params.yaml b/examples/security/classification/output/reports/train/d86943eb6e94f4f786b35065cec4be64/params.yaml deleted file mode 100644 index d3eae7d0..00000000 --- a/examples/security/classification/output/reports/train/d86943eb6e94f4f786b35065cec4be64/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/adv_predictions.json - adv_probabilities_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/43702e2abc3572612290af8b4f0ebc7c.pkl - params_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/params.yaml - predictions_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/predictions.json - probabilities_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/probabilities.json - score_dict_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/score_dict.json - test_labels_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/test_labels.json - train_labels_file: output/reports/train/d86943eb6e94f4f786b35065cec4be64/train_labels.json - model_dir: models - name: d86943eb6e94f4f786b35065cec4be64 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 463298c235e6bc83af10cb66283d4ebb - trainer: - kwargs: {} -name: d86943eb6e94f4f786b35065cec4be64 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/params.yaml b/examples/security/classification/output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/params.yaml deleted file mode 100644 index 1ac621f0..00000000 --- a/examples/security/classification/output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/adv_predictions.json - adv_probabilities_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/f3522c7920cb614d156428798eff113d.pkl - params_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/params.yaml - predictions_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/predictions.json - probabilities_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/probabilities.json - score_dict_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/score_dict.json - test_labels_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/test_labels.json - train_labels_file: output/reports/train/d86db3b066a7aaf88d9a48cec787e05f/train_labels.json - model_dir: models - name: d86db3b066a7aaf88d9a48cec787e05f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 799a9d79c8fdc251df41976e0a6065fe - trainer: - kwargs: {} -name: d86db3b066a7aaf88d9a48cec787e05f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d880ead035306efaedd479008c24a797/params.yaml b/examples/security/classification/output/reports/train/d880ead035306efaedd479008c24a797/params.yaml deleted file mode 100644 index 901a3707..00000000 --- a/examples/security/classification/output/reports/train/d880ead035306efaedd479008c24a797/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d880ead035306efaedd479008c24a797/adv_predictions.json - adv_probabilities_file: output/reports/train/d880ead035306efaedd479008c24a797/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e41a764287f114894d85d65c7e66eeea.pkl - params_file: output/reports/train/d880ead035306efaedd479008c24a797/params.yaml - predictions_file: output/reports/train/d880ead035306efaedd479008c24a797/predictions.json - probabilities_file: output/reports/train/d880ead035306efaedd479008c24a797/probabilities.json - score_dict_file: output/reports/train/d880ead035306efaedd479008c24a797/score_dict.json - test_labels_file: output/reports/train/d880ead035306efaedd479008c24a797/test_labels.json - train_labels_file: output/reports/train/d880ead035306efaedd479008c24a797/train_labels.json - model_dir: models - name: d880ead035306efaedd479008c24a797 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0a95c9a07071ec3c927d4c80e0d0bf28 - trainer: - kwargs: {} -name: d880ead035306efaedd479008c24a797 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d88c5872c96eb2d8b11002cf2817b611/params.yaml b/examples/security/classification/output/reports/train/d88c5872c96eb2d8b11002cf2817b611/params.yaml deleted file mode 100644 index cb7fe3fd..00000000 --- a/examples/security/classification/output/reports/train/d88c5872c96eb2d8b11002cf2817b611/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/adv_predictions.json - adv_probabilities_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/8d283e3fdd03bb8921b293224c4c28a8.pkl - params_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/params.yaml - predictions_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/predictions.json - probabilities_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/probabilities.json - score_dict_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/score_dict.json - test_labels_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/test_labels.json - train_labels_file: output/reports/train/d88c5872c96eb2d8b11002cf2817b611/train_labels.json - model_dir: models - name: d88c5872c96eb2d8b11002cf2817b611 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 683bd3e59ae453df627dd0de42a604df - trainer: - kwargs: {} -name: d88c5872c96eb2d8b11002cf2817b611 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d8bed13ff35520656c812f8ccc49aaea/params.yaml b/examples/security/classification/output/reports/train/d8bed13ff35520656c812f8ccc49aaea/params.yaml deleted file mode 100644 index 36c6c44c..00000000 --- a/examples/security/classification/output/reports/train/d8bed13ff35520656c812f8ccc49aaea/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/adv_predictions.json - adv_probabilities_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/2d106d8fbd0e91cdfa325c85d253c6e9.pkl - params_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/params.yaml - predictions_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/predictions.json - probabilities_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/probabilities.json - score_dict_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/score_dict.json - test_labels_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/test_labels.json - train_labels_file: output/reports/train/d8bed13ff35520656c812f8ccc49aaea/train_labels.json - model_dir: models - name: d8bed13ff35520656c812f8ccc49aaea - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3b89157c4a9164a4f85255ebffd5fa83 - trainer: - kwargs: {} -name: d8bed13ff35520656c812f8ccc49aaea -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/params.yaml b/examples/security/classification/output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/params.yaml deleted file mode 100644 index 167b77cb..00000000 --- a/examples/security/classification/output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/adv_predictions.json - adv_probabilities_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/f0ef527c9441be9f1d285734188b303d.pkl - params_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/params.yaml - predictions_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/predictions.json - probabilities_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/probabilities.json - score_dict_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/score_dict.json - test_labels_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/test_labels.json - train_labels_file: output/reports/train/d8bee46cc1451ff84b988cf76e97f5db/train_labels.json - model_dir: models - name: d8bee46cc1451ff84b988cf76e97f5db - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c6b945fb2a2a1ed0c58679cda47c5c41 - trainer: - kwargs: {} -name: d8bee46cc1451ff84b988cf76e97f5db -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/params.yaml b/examples/security/classification/output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/params.yaml deleted file mode 100644 index 04e3ff86..00000000 --- a/examples/security/classification/output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/adv_predictions.json - adv_probabilities_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/d3a455e91a81267a2d01bb76579efda2.pkl - params_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/params.yaml - predictions_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/predictions.json - probabilities_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/probabilities.json - score_dict_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/score_dict.json - test_labels_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/test_labels.json - train_labels_file: output/reports/train/d8d5ae64626ab04c3dcdbcb5c1d83f03/train_labels.json - model_dir: models - name: d8d5ae64626ab04c3dcdbcb5c1d83f03 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3cc484e3e00cc15d88b604e00d0e2a90 - trainer: - kwargs: {} -name: d8d5ae64626ab04c3dcdbcb5c1d83f03 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d90fabd56fa41732bff7a6819d288ba7/params.yaml b/examples/security/classification/output/reports/train/d90fabd56fa41732bff7a6819d288ba7/params.yaml deleted file mode 100644 index b504e885..00000000 --- a/examples/security/classification/output/reports/train/d90fabd56fa41732bff7a6819d288ba7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/adv_predictions.json - adv_probabilities_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/8ad2b16827ac2b7c6df0193daedb7883.pkl - params_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/params.yaml - predictions_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/predictions.json - probabilities_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/probabilities.json - score_dict_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/score_dict.json - test_labels_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/test_labels.json - train_labels_file: output/reports/train/d90fabd56fa41732bff7a6819d288ba7/train_labels.json - model_dir: models - name: d90fabd56fa41732bff7a6819d288ba7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 25eccab226bc95115f0af0c3ad2e3701 - trainer: - kwargs: {} -name: d90fabd56fa41732bff7a6819d288ba7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/params.yaml b/examples/security/classification/output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/params.yaml deleted file mode 100644 index d05e6ba8..00000000 --- a/examples/security/classification/output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/adv_predictions.json - adv_probabilities_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/639a315945d707e5ee8bcc78450e6420.pkl - params_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/params.yaml - predictions_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/predictions.json - probabilities_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/probabilities.json - score_dict_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/score_dict.json - test_labels_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/test_labels.json - train_labels_file: output/reports/train/d91c29c8132ce5cc7679eba094cebdc8/train_labels.json - model_dir: models - name: d91c29c8132ce5cc7679eba094cebdc8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ef2cb43772e5eaa09c31d4209c89d3b0 - trainer: - kwargs: {} -name: d91c29c8132ce5cc7679eba094cebdc8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d9206212559d0e2bccd51a49b510bd72/params.yaml b/examples/security/classification/output/reports/train/d9206212559d0e2bccd51a49b510bd72/params.yaml deleted file mode 100644 index 9adcdfab..00000000 --- a/examples/security/classification/output/reports/train/d9206212559d0e2bccd51a49b510bd72/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/adv_predictions.json - adv_probabilities_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/950ce953b0c3ac5f46281f5879452e2b.pkl - params_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/params.yaml - predictions_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/predictions.json - probabilities_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/probabilities.json - score_dict_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/score_dict.json - test_labels_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/test_labels.json - train_labels_file: output/reports/train/d9206212559d0e2bccd51a49b510bd72/train_labels.json - model_dir: models - name: d9206212559d0e2bccd51a49b510bd72 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e65c510acf69a3afe531818f10911c91 - trainer: - kwargs: {} -name: d9206212559d0e2bccd51a49b510bd72 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d9650d811e30357b1d5966445c4b3f48/params.yaml b/examples/security/classification/output/reports/train/d9650d811e30357b1d5966445c4b3f48/params.yaml deleted file mode 100644 index 91a26446..00000000 --- a/examples/security/classification/output/reports/train/d9650d811e30357b1d5966445c4b3f48/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/adv_predictions.json - adv_probabilities_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/870044f83dc40376c4a933683c81c0e3.pkl - params_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/params.yaml - predictions_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/predictions.json - probabilities_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/probabilities.json - score_dict_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/score_dict.json - test_labels_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/test_labels.json - train_labels_file: output/reports/train/d9650d811e30357b1d5966445c4b3f48/train_labels.json - model_dir: models - name: d9650d811e30357b1d5966445c4b3f48 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1d39ca817ee5d9629e69d693b121fa80 - trainer: - kwargs: {} -name: d9650d811e30357b1d5966445c4b3f48 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d966569100da275b398e3d13cb4c3d11/params.yaml b/examples/security/classification/output/reports/train/d966569100da275b398e3d13cb4c3d11/params.yaml deleted file mode 100644 index 1d1eb7aa..00000000 --- a/examples/security/classification/output/reports/train/d966569100da275b398e3d13cb4c3d11/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/adv_predictions.json - adv_probabilities_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/e24e9b7f2e21df7bc8f9d29e7582f98a.pkl - params_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/params.yaml - predictions_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/predictions.json - probabilities_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/probabilities.json - score_dict_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/score_dict.json - test_labels_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/test_labels.json - train_labels_file: output/reports/train/d966569100da275b398e3d13cb4c3d11/train_labels.json - model_dir: models - name: d966569100da275b398e3d13cb4c3d11 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 94dd7e9b24959c4c735d1e02ab8da679 - trainer: - kwargs: {} -name: d966569100da275b398e3d13cb4c3d11 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d97cf03457f0116b24bf98ce6a938393/params.yaml b/examples/security/classification/output/reports/train/d97cf03457f0116b24bf98ce6a938393/params.yaml deleted file mode 100644 index 45a75f73..00000000 --- a/examples/security/classification/output/reports/train/d97cf03457f0116b24bf98ce6a938393/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/adv_predictions.json - adv_probabilities_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/a9eab5a909ac95b5c58dcbd752d97d8d.pkl - params_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/params.yaml - predictions_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/predictions.json - probabilities_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/probabilities.json - score_dict_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/score_dict.json - test_labels_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/test_labels.json - train_labels_file: output/reports/train/d97cf03457f0116b24bf98ce6a938393/train_labels.json - model_dir: models - name: d97cf03457f0116b24bf98ce6a938393 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dccb776c194d3c8e020c502012b99e7b - trainer: - kwargs: {} -name: d97cf03457f0116b24bf98ce6a938393 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/params.yaml b/examples/security/classification/output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/params.yaml deleted file mode 100644 index 960188a9..00000000 --- a/examples/security/classification/output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/adv_predictions.json - adv_probabilities_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/618c226b510456460d4fed36f298c313.pkl - params_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/params.yaml - predictions_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/predictions.json - probabilities_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/probabilities.json - score_dict_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/score_dict.json - test_labels_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/test_labels.json - train_labels_file: output/reports/train/d993aa4a7b84070d5b7a1ed4775e12ee/train_labels.json - model_dir: models - name: d993aa4a7b84070d5b7a1ed4775e12ee - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4dd101cfc8692f943b8b9a913bb72c91 - trainer: - kwargs: {} -name: d993aa4a7b84070d5b7a1ed4775e12ee -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/params.yaml b/examples/security/classification/output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/params.yaml deleted file mode 100644 index e246e2b4..00000000 --- a/examples/security/classification/output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/adv_predictions.json - adv_probabilities_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/71296dc8add7db59fd6fe61216b88df4.pkl - params_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/params.yaml - predictions_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/predictions.json - probabilities_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/probabilities.json - score_dict_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/score_dict.json - test_labels_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/test_labels.json - train_labels_file: output/reports/train/d99f69e0ddc6d30aad42f99313c5ac0d/train_labels.json - model_dir: models - name: d99f69e0ddc6d30aad42f99313c5ac0d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 646cbcdb7df1750b3a4164fbef09d370 - trainer: - kwargs: {} -name: d99f69e0ddc6d30aad42f99313c5ac0d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/params.yaml b/examples/security/classification/output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/params.yaml deleted file mode 100644 index 0a93c5cd..00000000 --- a/examples/security/classification/output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/adv_predictions.json - adv_probabilities_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/746277262c2e4a9a52a964049d12abeb.pkl - params_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/params.yaml - predictions_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/predictions.json - probabilities_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/probabilities.json - score_dict_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/score_dict.json - test_labels_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/test_labels.json - train_labels_file: output/reports/train/d9bfb70d3d7910a5abc08e8c614e3a9b/train_labels.json - model_dir: models - name: d9bfb70d3d7910a5abc08e8c614e3a9b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2130594750a3cf6cf1a045fc89c9ef5a - trainer: - kwargs: {} -name: d9bfb70d3d7910a5abc08e8c614e3a9b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/params.yaml b/examples/security/classification/output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/params.yaml deleted file mode 100644 index 37f5b8a4..00000000 --- a/examples/security/classification/output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/adv_predictions.json - adv_probabilities_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/4dc88d0a3fd38f298d046d5ed4cadd03.pkl - params_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/params.yaml - predictions_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/predictions.json - probabilities_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/probabilities.json - score_dict_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/score_dict.json - test_labels_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/test_labels.json - train_labels_file: output/reports/train/d9cb771ea2e6d7b0343810e9ecf533d2/train_labels.json - model_dir: models - name: d9cb771ea2e6d7b0343810e9ecf533d2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6f64f323705976b3665d680292422a4a - trainer: - kwargs: {} -name: d9cb771ea2e6d7b0343810e9ecf533d2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d9cdd499b40a11ee477c598f31f5265c/params.yaml b/examples/security/classification/output/reports/train/d9cdd499b40a11ee477c598f31f5265c/params.yaml deleted file mode 100644 index 1a7765fa..00000000 --- a/examples/security/classification/output/reports/train/d9cdd499b40a11ee477c598f31f5265c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/adv_predictions.json - adv_probabilities_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/8805a7367c3989e5197a49b1e74a3edf.pkl - params_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/params.yaml - predictions_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/predictions.json - probabilities_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/probabilities.json - score_dict_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/score_dict.json - test_labels_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/test_labels.json - train_labels_file: output/reports/train/d9cdd499b40a11ee477c598f31f5265c/train_labels.json - model_dir: models - name: d9cdd499b40a11ee477c598f31f5265c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 58298f82ff6e75dc59993853e6001e4e - trainer: - kwargs: {} -name: d9cdd499b40a11ee477c598f31f5265c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/params.yaml b/examples/security/classification/output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/params.yaml deleted file mode 100644 index 0e2f2484..00000000 --- a/examples/security/classification/output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/adv_predictions.json - adv_probabilities_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/6f50fda6410d6451be3a599b112b8948.pkl - params_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/params.yaml - predictions_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/predictions.json - probabilities_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/probabilities.json - score_dict_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/score_dict.json - test_labels_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/test_labels.json - train_labels_file: output/reports/train/d9d717e9ce9d942cb464b7ff0c2c5c4f/train_labels.json - model_dir: models - name: d9d717e9ce9d942cb464b7ff0c2c5c4f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7f066213fdb47fe571d20b05c642f2e3 - trainer: - kwargs: {} -name: d9d717e9ce9d942cb464b7ff0c2c5c4f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/params.yaml b/examples/security/classification/output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/params.yaml deleted file mode 100644 index d825b2cb..00000000 --- a/examples/security/classification/output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/adv_predictions.json - adv_probabilities_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/024ac25f9e6a8004a64cb1e29c343df4.pkl - params_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/params.yaml - predictions_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/predictions.json - probabilities_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/probabilities.json - score_dict_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/score_dict.json - test_labels_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/test_labels.json - train_labels_file: output/reports/train/d9dc0878aa7e697149784a5976e2d6a1/train_labels.json - model_dir: models - name: d9dc0878aa7e697149784a5976e2d6a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3e1124673cd69bc751a24a7401e66fd5 - trainer: - kwargs: {} -name: d9dc0878aa7e697149784a5976e2d6a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/params.yaml b/examples/security/classification/output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/params.yaml deleted file mode 100644 index 53a2b8f9..00000000 --- a/examples/security/classification/output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/adv_predictions.json - adv_probabilities_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/06cbc82db5cf652dd462a20a72492546.pkl - params_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/params.yaml - predictions_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/predictions.json - probabilities_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/probabilities.json - score_dict_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/score_dict.json - test_labels_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/test_labels.json - train_labels_file: output/reports/train/d9e6daa16b5fd44d1287f3b765ec64e7/train_labels.json - model_dir: models - name: d9e6daa16b5fd44d1287f3b765ec64e7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6e8a4319ab952fc991d8ba21213ee806 - trainer: - kwargs: {} -name: d9e6daa16b5fd44d1287f3b765ec64e7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/da26ab0f41802765917aa728a48c644b/params.yaml b/examples/security/classification/output/reports/train/da26ab0f41802765917aa728a48c644b/params.yaml deleted file mode 100644 index e283525a..00000000 --- a/examples/security/classification/output/reports/train/da26ab0f41802765917aa728a48c644b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/da26ab0f41802765917aa728a48c644b/adv_predictions.json - adv_probabilities_file: output/reports/train/da26ab0f41802765917aa728a48c644b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/0fbb9bac008ea2975ab5bb1e2452b21a.pkl - params_file: output/reports/train/da26ab0f41802765917aa728a48c644b/params.yaml - predictions_file: output/reports/train/da26ab0f41802765917aa728a48c644b/predictions.json - probabilities_file: output/reports/train/da26ab0f41802765917aa728a48c644b/probabilities.json - score_dict_file: output/reports/train/da26ab0f41802765917aa728a48c644b/score_dict.json - test_labels_file: output/reports/train/da26ab0f41802765917aa728a48c644b/test_labels.json - train_labels_file: output/reports/train/da26ab0f41802765917aa728a48c644b/train_labels.json - model_dir: models - name: da26ab0f41802765917aa728a48c644b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 28b40c998acb14a852a21f989e6e0b02 - trainer: - kwargs: {} -name: da26ab0f41802765917aa728a48c644b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/da37f3877f5d8396df95a668372dedaa/params.yaml b/examples/security/classification/output/reports/train/da37f3877f5d8396df95a668372dedaa/params.yaml deleted file mode 100644 index 46415ce0..00000000 --- a/examples/security/classification/output/reports/train/da37f3877f5d8396df95a668372dedaa/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/adv_predictions.json - adv_probabilities_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/f49d30c2a42bf73a0bdc61ae4754456c.pkl - params_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/params.yaml - predictions_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/predictions.json - probabilities_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/probabilities.json - score_dict_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/score_dict.json - test_labels_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/test_labels.json - train_labels_file: output/reports/train/da37f3877f5d8396df95a668372dedaa/train_labels.json - model_dir: models - name: da37f3877f5d8396df95a668372dedaa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: abe4d11aacc1819d744cefe54f4f582c - trainer: - kwargs: {} -name: da37f3877f5d8396df95a668372dedaa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/da58f2b53d9cb07759b7e54edfa16354/params.yaml b/examples/security/classification/output/reports/train/da58f2b53d9cb07759b7e54edfa16354/params.yaml deleted file mode 100644 index 184c5433..00000000 --- a/examples/security/classification/output/reports/train/da58f2b53d9cb07759b7e54edfa16354/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/adv_predictions.json - adv_probabilities_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/4d79e47fec549b7d3a5a43babce1b758.pkl - params_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/params.yaml - predictions_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/predictions.json - probabilities_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/probabilities.json - score_dict_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/score_dict.json - test_labels_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/test_labels.json - train_labels_file: output/reports/train/da58f2b53d9cb07759b7e54edfa16354/train_labels.json - model_dir: models - name: da58f2b53d9cb07759b7e54edfa16354 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0b7714d55df9f96701fa06349d8accf7 - trainer: - kwargs: {} -name: da58f2b53d9cb07759b7e54edfa16354 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/da712422226f466e4d8e5d19c408ee29/params.yaml b/examples/security/classification/output/reports/train/da712422226f466e4d8e5d19c408ee29/params.yaml deleted file mode 100644 index 0f180fca..00000000 --- a/examples/security/classification/output/reports/train/da712422226f466e4d8e5d19c408ee29/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/adv_predictions.json - adv_probabilities_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/801304ea563e631a94624d3055f93b43.pkl - params_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/params.yaml - predictions_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/predictions.json - probabilities_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/probabilities.json - score_dict_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/score_dict.json - test_labels_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/test_labels.json - train_labels_file: output/reports/train/da712422226f466e4d8e5d19c408ee29/train_labels.json - model_dir: models - name: da712422226f466e4d8e5d19c408ee29 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 535c08409eef86412cf68b1383514428 - trainer: - kwargs: {} -name: da712422226f466e4d8e5d19c408ee29 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/params.yaml b/examples/security/classification/output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/params.yaml deleted file mode 100644 index fc3e5808..00000000 --- a/examples/security/classification/output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/adv_predictions.json - adv_probabilities_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/dfc45dd7cb1dd51f941856b1ba566200.pkl - params_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/params.yaml - predictions_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/predictions.json - probabilities_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/probabilities.json - score_dict_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/score_dict.json - test_labels_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/test_labels.json - train_labels_file: output/reports/train/da71de7ea9d71b8fdf18f99b2c87985e/train_labels.json - model_dir: models - name: da71de7ea9d71b8fdf18f99b2c87985e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4c0f063208dac22499417231a9ff9397 - trainer: - kwargs: {} -name: da71de7ea9d71b8fdf18f99b2c87985e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/params.yaml b/examples/security/classification/output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/params.yaml deleted file mode 100644 index bf5504dd..00000000 --- a/examples/security/classification/output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/adv_predictions.json - adv_probabilities_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/23b03e51b54f01b0af53a46cee3e4c9a.pkl - params_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/params.yaml - predictions_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/predictions.json - probabilities_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/probabilities.json - score_dict_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/score_dict.json - test_labels_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/test_labels.json - train_labels_file: output/reports/train/da75ff85eb626ca35fe29d39e9f648b1/train_labels.json - model_dir: models - name: da75ff85eb626ca35fe29d39e9f648b1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c68b53fa590c319b763dae18f1fbc6b0 - trainer: - kwargs: {} -name: da75ff85eb626ca35fe29d39e9f648b1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/da7b150d51903d6cafba98057b5b76e8/params.yaml b/examples/security/classification/output/reports/train/da7b150d51903d6cafba98057b5b76e8/params.yaml deleted file mode 100644 index 88c5e6f4..00000000 --- a/examples/security/classification/output/reports/train/da7b150d51903d6cafba98057b5b76e8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/adv_predictions.json - adv_probabilities_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/bdacb7efc36b9b93310769e3ae058205.pkl - params_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/params.yaml - predictions_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/predictions.json - probabilities_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/probabilities.json - score_dict_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/score_dict.json - test_labels_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/test_labels.json - train_labels_file: output/reports/train/da7b150d51903d6cafba98057b5b76e8/train_labels.json - model_dir: models - name: da7b150d51903d6cafba98057b5b76e8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8fb1bafb1fa57eb892070e39b114062f - trainer: - kwargs: {} -name: da7b150d51903d6cafba98057b5b76e8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/params.yaml b/examples/security/classification/output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/params.yaml deleted file mode 100644 index 983b9004..00000000 --- a/examples/security/classification/output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/adv_predictions.json - adv_probabilities_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/866d845a5b3758a084fe0ac35214fdcf.pkl - params_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/params.yaml - predictions_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/predictions.json - probabilities_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/probabilities.json - score_dict_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/score_dict.json - test_labels_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/test_labels.json - train_labels_file: output/reports/train/da8f8c2d5ae4d40553438db9189fa7e5/train_labels.json - model_dir: models - name: da8f8c2d5ae4d40553438db9189fa7e5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 49a0a632eac76d05eac2a28139f01418 - trainer: - kwargs: {} -name: da8f8c2d5ae4d40553438db9189fa7e5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/params.yaml b/examples/security/classification/output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/params.yaml deleted file mode 100644 index a55d08e8..00000000 --- a/examples/security/classification/output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/adv_predictions.json - adv_probabilities_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/81bc127b1d9579bb013b5a5494bc18cb.pkl - params_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/params.yaml - predictions_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/predictions.json - probabilities_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/probabilities.json - score_dict_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/score_dict.json - test_labels_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/test_labels.json - train_labels_file: output/reports/train/da93939c6d1bf7572cd6b1b0036148b5/train_labels.json - model_dir: models - name: da93939c6d1bf7572cd6b1b0036148b5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ba8e6e16ac8c46ecc82b98611e220ce7 - trainer: - kwargs: {} -name: da93939c6d1bf7572cd6b1b0036148b5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/params.yaml b/examples/security/classification/output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/params.yaml deleted file mode 100644 index d2fe481c..00000000 --- a/examples/security/classification/output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/adv_predictions.json - adv_probabilities_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/35d3b1288e18cb8fa4eb2c3c229eb40f.pkl - params_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/params.yaml - predictions_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/predictions.json - probabilities_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/probabilities.json - score_dict_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/score_dict.json - test_labels_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/test_labels.json - train_labels_file: output/reports/train/dab880c4fc1af5ae082b398b21aa9bbb/train_labels.json - model_dir: models - name: dab880c4fc1af5ae082b398b21aa9bbb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3c231a86565a12bed4236f0f9d7c8860 - trainer: - kwargs: {} -name: dab880c4fc1af5ae082b398b21aa9bbb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/params.yaml b/examples/security/classification/output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/params.yaml deleted file mode 100644 index 52f2e4fa..00000000 --- a/examples/security/classification/output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/adv_predictions.json - adv_probabilities_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/1168eb21aad2493348a43760824ea08a.pkl - params_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/params.yaml - predictions_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/predictions.json - probabilities_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/probabilities.json - score_dict_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/score_dict.json - test_labels_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/test_labels.json - train_labels_file: output/reports/train/dac4f856b23682a3c5cc734ff30ffcaa/train_labels.json - model_dir: models - name: dac4f856b23682a3c5cc734ff30ffcaa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0865fffececfff42c434cd687d9ca622 - trainer: - kwargs: {} -name: dac4f856b23682a3c5cc734ff30ffcaa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dadd4a1f979db30baa31fbbac14642e4/params.yaml b/examples/security/classification/output/reports/train/dadd4a1f979db30baa31fbbac14642e4/params.yaml deleted file mode 100644 index 15285a5c..00000000 --- a/examples/security/classification/output/reports/train/dadd4a1f979db30baa31fbbac14642e4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/adv_predictions.json - adv_probabilities_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/1c41dda98d91dbc23c0f87be7407d881.pkl - params_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/params.yaml - predictions_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/predictions.json - probabilities_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/probabilities.json - score_dict_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/score_dict.json - test_labels_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/test_labels.json - train_labels_file: output/reports/train/dadd4a1f979db30baa31fbbac14642e4/train_labels.json - model_dir: models - name: dadd4a1f979db30baa31fbbac14642e4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: abcc84e8f23ef6c04cd1bafa42b8c5c2 - trainer: - kwargs: {} -name: dadd4a1f979db30baa31fbbac14642e4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/params.yaml b/examples/security/classification/output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/params.yaml deleted file mode 100644 index ec6a2368..00000000 --- a/examples/security/classification/output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/adv_predictions.json - adv_probabilities_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/a885be38b9cb42864abf32ca17d2562f.pkl - params_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/params.yaml - predictions_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/predictions.json - probabilities_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/probabilities.json - score_dict_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/score_dict.json - test_labels_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/test_labels.json - train_labels_file: output/reports/train/dae5b3f7961f23aa814e3639e3f7630b/train_labels.json - model_dir: models - name: dae5b3f7961f23aa814e3639e3f7630b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1d7a1d3422ad6d92c6000ed0e9e9e0a0 - trainer: - kwargs: {} -name: dae5b3f7961f23aa814e3639e3f7630b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dae9c3187f05766d51d40df5260f1339/params.yaml b/examples/security/classification/output/reports/train/dae9c3187f05766d51d40df5260f1339/params.yaml deleted file mode 100644 index 8ac61de4..00000000 --- a/examples/security/classification/output/reports/train/dae9c3187f05766d51d40df5260f1339/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/adv_predictions.json - adv_probabilities_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/a4fb3fea56d88c3448603bd792e1f1d1.pkl - params_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/params.yaml - predictions_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/predictions.json - probabilities_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/probabilities.json - score_dict_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/score_dict.json - test_labels_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/test_labels.json - train_labels_file: output/reports/train/dae9c3187f05766d51d40df5260f1339/train_labels.json - model_dir: models - name: dae9c3187f05766d51d40df5260f1339 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3e7d81e7ec623e4211d42f68d138daa3 - trainer: - kwargs: {} -name: dae9c3187f05766d51d40df5260f1339 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/daf3e2d05958a0eff858cff4581359e5/params.yaml b/examples/security/classification/output/reports/train/daf3e2d05958a0eff858cff4581359e5/params.yaml deleted file mode 100644 index 00cf410e..00000000 --- a/examples/security/classification/output/reports/train/daf3e2d05958a0eff858cff4581359e5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/adv_predictions.json - adv_probabilities_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/f8cb163c62d41c9effbb8fa359cb49ec.pkl - params_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/params.yaml - predictions_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/predictions.json - probabilities_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/probabilities.json - score_dict_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/score_dict.json - test_labels_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/test_labels.json - train_labels_file: output/reports/train/daf3e2d05958a0eff858cff4581359e5/train_labels.json - model_dir: models - name: daf3e2d05958a0eff858cff4581359e5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a8fc222b9a5dfd1aa8d50794f846749a - trainer: - kwargs: {} -name: daf3e2d05958a0eff858cff4581359e5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/db338fee14e162ca34ace199264f33b6/params.yaml b/examples/security/classification/output/reports/train/db338fee14e162ca34ace199264f33b6/params.yaml deleted file mode 100644 index 95210c95..00000000 --- a/examples/security/classification/output/reports/train/db338fee14e162ca34ace199264f33b6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/db338fee14e162ca34ace199264f33b6/adv_predictions.json - adv_probabilities_file: output/reports/train/db338fee14e162ca34ace199264f33b6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/3052d459d1057a5b4bd159cf285c8061.pkl - params_file: output/reports/train/db338fee14e162ca34ace199264f33b6/params.yaml - predictions_file: output/reports/train/db338fee14e162ca34ace199264f33b6/predictions.json - probabilities_file: output/reports/train/db338fee14e162ca34ace199264f33b6/probabilities.json - score_dict_file: output/reports/train/db338fee14e162ca34ace199264f33b6/score_dict.json - test_labels_file: output/reports/train/db338fee14e162ca34ace199264f33b6/test_labels.json - train_labels_file: output/reports/train/db338fee14e162ca34ace199264f33b6/train_labels.json - model_dir: models - name: db338fee14e162ca34ace199264f33b6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c1c553f0a99d424a21c6e72290b065d2 - trainer: - kwargs: {} -name: db338fee14e162ca34ace199264f33b6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/params.yaml b/examples/security/classification/output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/params.yaml deleted file mode 100644 index 4a151b7d..00000000 --- a/examples/security/classification/output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/adv_predictions.json - adv_probabilities_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/a5495da8b36989883b553fa977b80ee3.pkl - params_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/params.yaml - predictions_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/predictions.json - probabilities_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/probabilities.json - score_dict_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/score_dict.json - test_labels_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/test_labels.json - train_labels_file: output/reports/train/db3e6fc14b575b9a25337dc8000ac9fa/train_labels.json - model_dir: models - name: db3e6fc14b575b9a25337dc8000ac9fa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1ae9af6d635624af7ec48f9713554f3f - trainer: - kwargs: {} -name: db3e6fc14b575b9a25337dc8000ac9fa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/params.yaml b/examples/security/classification/output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/params.yaml deleted file mode 100644 index 04e2e711..00000000 --- a/examples/security/classification/output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/adv_predictions.json - adv_probabilities_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/e125b2f8d2f420c706c1cf316a8ffa00.pkl - params_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/params.yaml - predictions_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/predictions.json - probabilities_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/probabilities.json - score_dict_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/score_dict.json - test_labels_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/test_labels.json - train_labels_file: output/reports/train/db8d243ff9ed2b3f32c2c6a1ff18e4b7/train_labels.json - model_dir: models - name: db8d243ff9ed2b3f32c2c6a1ff18e4b7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7886c06d76eb1814d5d461ec5f797240 - trainer: - kwargs: {} -name: db8d243ff9ed2b3f32c2c6a1ff18e4b7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/db9ca5350d494393a63c29d718392b38/params.yaml b/examples/security/classification/output/reports/train/db9ca5350d494393a63c29d718392b38/params.yaml deleted file mode 100644 index 76c59073..00000000 --- a/examples/security/classification/output/reports/train/db9ca5350d494393a63c29d718392b38/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/db9ca5350d494393a63c29d718392b38/adv_predictions.json - adv_probabilities_file: output/reports/train/db9ca5350d494393a63c29d718392b38/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/b7dce59a9c65f5580f917388b0a8726d.pkl - params_file: output/reports/train/db9ca5350d494393a63c29d718392b38/params.yaml - predictions_file: output/reports/train/db9ca5350d494393a63c29d718392b38/predictions.json - probabilities_file: output/reports/train/db9ca5350d494393a63c29d718392b38/probabilities.json - score_dict_file: output/reports/train/db9ca5350d494393a63c29d718392b38/score_dict.json - test_labels_file: output/reports/train/db9ca5350d494393a63c29d718392b38/test_labels.json - train_labels_file: output/reports/train/db9ca5350d494393a63c29d718392b38/train_labels.json - model_dir: models - name: db9ca5350d494393a63c29d718392b38 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a31f85f9f607bde1cf7a0f993982499d - trainer: - kwargs: {} -name: db9ca5350d494393a63c29d718392b38 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/params.yaml b/examples/security/classification/output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/params.yaml deleted file mode 100644 index 73e354ad..00000000 --- a/examples/security/classification/output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/adv_predictions.json - adv_probabilities_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/7778c1132cbcacdc2616d71d9f2a2f6d.pkl - params_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/params.yaml - predictions_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/predictions.json - probabilities_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/probabilities.json - score_dict_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/score_dict.json - test_labels_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/test_labels.json - train_labels_file: output/reports/train/dbda5ff49db9e9b8e34f62384bb4e7f3/train_labels.json - model_dir: models - name: dbda5ff49db9e9b8e34f62384bb4e7f3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 28e6ada77decb3c97dafa00d997ff593 - trainer: - kwargs: {} -name: dbda5ff49db9e9b8e34f62384bb4e7f3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/params.yaml b/examples/security/classification/output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/params.yaml deleted file mode 100644 index f4c5ab6a..00000000 --- a/examples/security/classification/output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/adv_predictions.json - adv_probabilities_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/cb15408096e11f766358992835291409.pkl - params_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/params.yaml - predictions_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/predictions.json - probabilities_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/probabilities.json - score_dict_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/score_dict.json - test_labels_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/test_labels.json - train_labels_file: output/reports/train/dbdb6dfeb7f88437260a3a373a7ac612/train_labels.json - model_dir: models - name: dbdb6dfeb7f88437260a3a373a7ac612 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2fe415441df5a45ed4ba302f163b1c03 - trainer: - kwargs: {} -name: dbdb6dfeb7f88437260a3a373a7ac612 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dc104245a02d6785a9eab221b57a79fe/params.yaml b/examples/security/classification/output/reports/train/dc104245a02d6785a9eab221b57a79fe/params.yaml deleted file mode 100644 index 17d24337..00000000 --- a/examples/security/classification/output/reports/train/dc104245a02d6785a9eab221b57a79fe/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/adv_predictions.json - adv_probabilities_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/3d3b9a094dab4aa5ae562e1ca8a29304.pkl - params_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/params.yaml - predictions_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/predictions.json - probabilities_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/probabilities.json - score_dict_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/score_dict.json - test_labels_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/test_labels.json - train_labels_file: output/reports/train/dc104245a02d6785a9eab221b57a79fe/train_labels.json - model_dir: models - name: dc104245a02d6785a9eab221b57a79fe - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 321554c439f6a40c20cb298d5b303edd - trainer: - kwargs: {} -name: dc104245a02d6785a9eab221b57a79fe -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dc2d062910de6f10a723fd731d70675a/params.yaml b/examples/security/classification/output/reports/train/dc2d062910de6f10a723fd731d70675a/params.yaml deleted file mode 100644 index 543f74b5..00000000 --- a/examples/security/classification/output/reports/train/dc2d062910de6f10a723fd731d70675a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/adv_predictions.json - adv_probabilities_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/53b4f132afcd36c648a04be83c3ba2dc.pkl - params_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/params.yaml - predictions_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/predictions.json - probabilities_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/probabilities.json - score_dict_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/score_dict.json - test_labels_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/test_labels.json - train_labels_file: output/reports/train/dc2d062910de6f10a723fd731d70675a/train_labels.json - model_dir: models - name: dc2d062910de6f10a723fd731d70675a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 998b739f545785adbcebc688e892b74f - trainer: - kwargs: {} -name: dc2d062910de6f10a723fd731d70675a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/params.yaml b/examples/security/classification/output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/params.yaml deleted file mode 100644 index 8f40eff8..00000000 --- a/examples/security/classification/output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/adv_predictions.json - adv_probabilities_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/c2768741371def9e57cf6f81a67ada73.pkl - params_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/params.yaml - predictions_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/predictions.json - probabilities_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/probabilities.json - score_dict_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/score_dict.json - test_labels_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/test_labels.json - train_labels_file: output/reports/train/dc7dbdc4075c8743f477d541fa0cdeb4/train_labels.json - model_dir: models - name: dc7dbdc4075c8743f477d541fa0cdeb4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2702f4eff9c6a31881a01308e4e3f7ca - trainer: - kwargs: {} -name: dc7dbdc4075c8743f477d541fa0cdeb4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/params.yaml b/examples/security/classification/output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/params.yaml deleted file mode 100644 index cb980fe9..00000000 --- a/examples/security/classification/output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/adv_predictions.json - adv_probabilities_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/49b86ed9fc03b036b1707d35c1478992.pkl - params_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/params.yaml - predictions_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/predictions.json - probabilities_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/probabilities.json - score_dict_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/score_dict.json - test_labels_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/test_labels.json - train_labels_file: output/reports/train/dc9ebdeaafade24bf142efaf02fa350f/train_labels.json - model_dir: models - name: dc9ebdeaafade24bf142efaf02fa350f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6dce779c777f879dea873a46aee1895a - trainer: - kwargs: {} -name: dc9ebdeaafade24bf142efaf02fa350f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/params.yaml b/examples/security/classification/output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/params.yaml deleted file mode 100644 index b41ef485..00000000 --- a/examples/security/classification/output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/adv_predictions.json - adv_probabilities_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/dcd7349c768d71e83fe0aebcf92c59a2.pkl - params_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/params.yaml - predictions_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/predictions.json - probabilities_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/probabilities.json - score_dict_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/score_dict.json - test_labels_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/test_labels.json - train_labels_file: output/reports/train/dcc0ba6c062ec551d53c748e77901b6e/train_labels.json - model_dir: models - name: dcc0ba6c062ec551d53c748e77901b6e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a8e04de76c82189b2dc1dcaf175c9e71 - trainer: - kwargs: {} -name: dcc0ba6c062ec551d53c748e77901b6e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dd01b2dcb10e40974fcebee053ad031e/params.yaml b/examples/security/classification/output/reports/train/dd01b2dcb10e40974fcebee053ad031e/params.yaml deleted file mode 100644 index b4d6a9d3..00000000 --- a/examples/security/classification/output/reports/train/dd01b2dcb10e40974fcebee053ad031e/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/adv_predictions.json - adv_probabilities_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/bc9037250671b397efc20a3383a86cb4.pkl - params_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/params.yaml - predictions_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/predictions.json - probabilities_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/probabilities.json - score_dict_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/score_dict.json - test_labels_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/test_labels.json - train_labels_file: output/reports/train/dd01b2dcb10e40974fcebee053ad031e/train_labels.json - model_dir: models - name: dd01b2dcb10e40974fcebee053ad031e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5a4b02f9abd1669dae68a782ef7168ed - trainer: - kwargs: {} -name: dd01b2dcb10e40974fcebee053ad031e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/params.yaml b/examples/security/classification/output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/params.yaml deleted file mode 100644 index e77011d2..00000000 --- a/examples/security/classification/output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/adv_predictions.json - adv_probabilities_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/ee32d8e4949989cffccce27a03c9abca.pkl - params_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/params.yaml - predictions_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/predictions.json - probabilities_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/probabilities.json - score_dict_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/score_dict.json - test_labels_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/test_labels.json - train_labels_file: output/reports/train/dd0f6db3c04fe3dbefa23463406062dd/train_labels.json - model_dir: models - name: dd0f6db3c04fe3dbefa23463406062dd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c3e7db7be025d4475a74a81cc9a6ebab - trainer: - kwargs: {} -name: dd0f6db3c04fe3dbefa23463406062dd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dd20d2da499acc55b5e113862170f8f9/params.yaml b/examples/security/classification/output/reports/train/dd20d2da499acc55b5e113862170f8f9/params.yaml deleted file mode 100644 index 44550ab5..00000000 --- a/examples/security/classification/output/reports/train/dd20d2da499acc55b5e113862170f8f9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/adv_predictions.json - adv_probabilities_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/12ce2b674ecb9ef40a31d5b6ecda336f.pkl - params_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/params.yaml - predictions_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/predictions.json - probabilities_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/probabilities.json - score_dict_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/score_dict.json - test_labels_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/test_labels.json - train_labels_file: output/reports/train/dd20d2da499acc55b5e113862170f8f9/train_labels.json - model_dir: models - name: dd20d2da499acc55b5e113862170f8f9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ac816587f961ed904d16a094d19fba7c - trainer: - kwargs: {} -name: dd20d2da499acc55b5e113862170f8f9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dd57b0a13c85d92345c35d76db603716/params.yaml b/examples/security/classification/output/reports/train/dd57b0a13c85d92345c35d76db603716/params.yaml deleted file mode 100644 index e655d4cd..00000000 --- a/examples/security/classification/output/reports/train/dd57b0a13c85d92345c35d76db603716/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/adv_predictions.json - adv_probabilities_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/1c092aa3a926b766bcefaa56a1a91c1c.pkl - params_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/params.yaml - predictions_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/predictions.json - probabilities_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/probabilities.json - score_dict_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/score_dict.json - test_labels_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/test_labels.json - train_labels_file: output/reports/train/dd57b0a13c85d92345c35d76db603716/train_labels.json - model_dir: models - name: dd57b0a13c85d92345c35d76db603716 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fb3bd9f446810001f5939e03202de2ff - trainer: - kwargs: {} -name: dd57b0a13c85d92345c35d76db603716 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/params.yaml b/examples/security/classification/output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/params.yaml deleted file mode 100644 index 92bd6978..00000000 --- a/examples/security/classification/output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/adv_predictions.json - adv_probabilities_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/06cb203f98529e70c209c4f6efc792c9.pkl - params_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/params.yaml - predictions_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/predictions.json - probabilities_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/probabilities.json - score_dict_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/score_dict.json - test_labels_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/test_labels.json - train_labels_file: output/reports/train/dd6929921b671b8ba5c5cf27dec3cfeb/train_labels.json - model_dir: models - name: dd6929921b671b8ba5c5cf27dec3cfeb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b0b668798fed57652342f0383ee6b6f7 - trainer: - kwargs: {} -name: dd6929921b671b8ba5c5cf27dec3cfeb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ddcabac2a19736914917edcf860fd285/params.yaml b/examples/security/classification/output/reports/train/ddcabac2a19736914917edcf860fd285/params.yaml deleted file mode 100644 index 60feedbe..00000000 --- a/examples/security/classification/output/reports/train/ddcabac2a19736914917edcf860fd285/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ddcabac2a19736914917edcf860fd285/adv_predictions.json - adv_probabilities_file: output/reports/train/ddcabac2a19736914917edcf860fd285/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/ad5db8bed2ebf76089c08c0d5c0d68ef.pkl - params_file: output/reports/train/ddcabac2a19736914917edcf860fd285/params.yaml - predictions_file: output/reports/train/ddcabac2a19736914917edcf860fd285/predictions.json - probabilities_file: output/reports/train/ddcabac2a19736914917edcf860fd285/probabilities.json - score_dict_file: output/reports/train/ddcabac2a19736914917edcf860fd285/score_dict.json - test_labels_file: output/reports/train/ddcabac2a19736914917edcf860fd285/test_labels.json - train_labels_file: output/reports/train/ddcabac2a19736914917edcf860fd285/train_labels.json - model_dir: models - name: ddcabac2a19736914917edcf860fd285 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 58e7d8163d20c914e05fbc8ec67dce73 - trainer: - kwargs: {} -name: ddcabac2a19736914917edcf860fd285 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ddd06578673f15e7ee26f76969ea5016/params.yaml b/examples/security/classification/output/reports/train/ddd06578673f15e7ee26f76969ea5016/params.yaml deleted file mode 100644 index 7cc0d0c4..00000000 --- a/examples/security/classification/output/reports/train/ddd06578673f15e7ee26f76969ea5016/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/adv_predictions.json - adv_probabilities_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/83bb61c7df2decf1ae194c58fee42762.pkl - params_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/params.yaml - predictions_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/predictions.json - probabilities_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/probabilities.json - score_dict_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/score_dict.json - test_labels_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/test_labels.json - train_labels_file: output/reports/train/ddd06578673f15e7ee26f76969ea5016/train_labels.json - model_dir: models - name: ddd06578673f15e7ee26f76969ea5016 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eb263e8454a6299d8c84e0186f791f0a - trainer: - kwargs: {} -name: ddd06578673f15e7ee26f76969ea5016 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/params.yaml b/examples/security/classification/output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/params.yaml deleted file mode 100644 index 8e3677eb..00000000 --- a/examples/security/classification/output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/adv_predictions.json - adv_probabilities_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/00c2316f4bab581ae9a4e5a6d1bec592.pkl - params_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/params.yaml - predictions_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/predictions.json - probabilities_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/probabilities.json - score_dict_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/score_dict.json - test_labels_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/test_labels.json - train_labels_file: output/reports/train/ddd44b7d67383b9d6cdbe0b2d6d0214c/train_labels.json - model_dir: models - name: ddd44b7d67383b9d6cdbe0b2d6d0214c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 24b94eb724ac3571c6128cbcf180705e - trainer: - kwargs: {} -name: ddd44b7d67383b9d6cdbe0b2d6d0214c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dde0c2540a69515776afa11b1400245f/params.yaml b/examples/security/classification/output/reports/train/dde0c2540a69515776afa11b1400245f/params.yaml deleted file mode 100644 index 274b9571..00000000 --- a/examples/security/classification/output/reports/train/dde0c2540a69515776afa11b1400245f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dde0c2540a69515776afa11b1400245f/adv_predictions.json - adv_probabilities_file: output/reports/train/dde0c2540a69515776afa11b1400245f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/09e6c87c1ca87a086f975d01f6215486.pkl - params_file: output/reports/train/dde0c2540a69515776afa11b1400245f/params.yaml - predictions_file: output/reports/train/dde0c2540a69515776afa11b1400245f/predictions.json - probabilities_file: output/reports/train/dde0c2540a69515776afa11b1400245f/probabilities.json - score_dict_file: output/reports/train/dde0c2540a69515776afa11b1400245f/score_dict.json - test_labels_file: output/reports/train/dde0c2540a69515776afa11b1400245f/test_labels.json - train_labels_file: output/reports/train/dde0c2540a69515776afa11b1400245f/train_labels.json - model_dir: models - name: dde0c2540a69515776afa11b1400245f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 66cb134bc8aee58d81a1fc725873efd3 - trainer: - kwargs: {} -name: dde0c2540a69515776afa11b1400245f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/params.yaml b/examples/security/classification/output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/params.yaml deleted file mode 100644 index 7ccadab8..00000000 --- a/examples/security/classification/output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/adv_predictions.json - adv_probabilities_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/3cb792cdd7f4e58a3127af3ed944edf3.pkl - params_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/params.yaml - predictions_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/predictions.json - probabilities_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/probabilities.json - score_dict_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/score_dict.json - test_labels_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/test_labels.json - train_labels_file: output/reports/train/dde4c164a8eb06ac815e69bb45cf703d/train_labels.json - model_dir: models - name: dde4c164a8eb06ac815e69bb45cf703d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1aa766c86439bf925027b0e5e678c460 - trainer: - kwargs: {} -name: dde4c164a8eb06ac815e69bb45cf703d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/params.yaml b/examples/security/classification/output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/params.yaml deleted file mode 100644 index 4b06f1ad..00000000 --- a/examples/security/classification/output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/adv_predictions.json - adv_probabilities_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/a0ea855ab85edcbabb7ce0a38abb11ae.pkl - params_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/params.yaml - predictions_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/predictions.json - probabilities_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/probabilities.json - score_dict_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/score_dict.json - test_labels_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/test_labels.json - train_labels_file: output/reports/train/ddecfdc6997ed6e7c45a32be0e795fb1/train_labels.json - model_dir: models - name: ddecfdc6997ed6e7c45a32be0e795fb1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e16a2fba93bc3db4ea02429ec2dc35a2 - trainer: - kwargs: {} -name: ddecfdc6997ed6e7c45a32be0e795fb1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/de1100610852082d0df0721803615270/params.yaml b/examples/security/classification/output/reports/train/de1100610852082d0df0721803615270/params.yaml deleted file mode 100644 index b2531b44..00000000 --- a/examples/security/classification/output/reports/train/de1100610852082d0df0721803615270/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/de1100610852082d0df0721803615270/adv_predictions.json - adv_probabilities_file: output/reports/train/de1100610852082d0df0721803615270/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/a1273d1de33cedf92f9e8e3ae04b9f85.pkl - params_file: output/reports/train/de1100610852082d0df0721803615270/params.yaml - predictions_file: output/reports/train/de1100610852082d0df0721803615270/predictions.json - probabilities_file: output/reports/train/de1100610852082d0df0721803615270/probabilities.json - score_dict_file: output/reports/train/de1100610852082d0df0721803615270/score_dict.json - test_labels_file: output/reports/train/de1100610852082d0df0721803615270/test_labels.json - train_labels_file: output/reports/train/de1100610852082d0df0721803615270/train_labels.json - model_dir: models - name: de1100610852082d0df0721803615270 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d4ce71bde4ca1ab16354077df4749d02 - trainer: - kwargs: {} -name: de1100610852082d0df0721803615270 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/de43c54bf61a5700264fb3fbb6490268/params.yaml b/examples/security/classification/output/reports/train/de43c54bf61a5700264fb3fbb6490268/params.yaml deleted file mode 100644 index debea064..00000000 --- a/examples/security/classification/output/reports/train/de43c54bf61a5700264fb3fbb6490268/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/adv_predictions.json - adv_probabilities_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/7832d1ace5d9465833248c65715b317c.pkl - params_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/params.yaml - predictions_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/predictions.json - probabilities_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/probabilities.json - score_dict_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/score_dict.json - test_labels_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/test_labels.json - train_labels_file: output/reports/train/de43c54bf61a5700264fb3fbb6490268/train_labels.json - model_dir: models - name: de43c54bf61a5700264fb3fbb6490268 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 48f3671ce64e9adc85b6ca5504929b73 - trainer: - kwargs: {} -name: de43c54bf61a5700264fb3fbb6490268 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/de484e6957196344e2f4cb86f700cf4a/params.yaml b/examples/security/classification/output/reports/train/de484e6957196344e2f4cb86f700cf4a/params.yaml deleted file mode 100644 index 704b5799..00000000 --- a/examples/security/classification/output/reports/train/de484e6957196344e2f4cb86f700cf4a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/adv_predictions.json - adv_probabilities_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/a6acacc58e9689f3af131b1f202dae94.pkl - params_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/params.yaml - predictions_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/predictions.json - probabilities_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/probabilities.json - score_dict_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/score_dict.json - test_labels_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/test_labels.json - train_labels_file: output/reports/train/de484e6957196344e2f4cb86f700cf4a/train_labels.json - model_dir: models - name: de484e6957196344e2f4cb86f700cf4a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 48910f55c9b32d9acd752334044130a7 - trainer: - kwargs: {} -name: de484e6957196344e2f4cb86f700cf4a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/params.yaml b/examples/security/classification/output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/params.yaml deleted file mode 100644 index c7c74c0a..00000000 --- a/examples/security/classification/output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/adv_predictions.json - adv_probabilities_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/066ee66bab2a0c1517f13d145e64a507.pkl - params_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/params.yaml - predictions_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/predictions.json - probabilities_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/probabilities.json - score_dict_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/score_dict.json - test_labels_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/test_labels.json - train_labels_file: output/reports/train/debf8d0db23b7d8342c25531b1fd18cb/train_labels.json - model_dir: models - name: debf8d0db23b7d8342c25531b1fd18cb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1798a865a607b47f59a83aad8acc7071 - trainer: - kwargs: {} -name: debf8d0db23b7d8342c25531b1fd18cb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/decd1896bc50d5023affaefff2db6e8d/params.yaml b/examples/security/classification/output/reports/train/decd1896bc50d5023affaefff2db6e8d/params.yaml deleted file mode 100644 index da314f5a..00000000 --- a/examples/security/classification/output/reports/train/decd1896bc50d5023affaefff2db6e8d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/adv_predictions.json - adv_probabilities_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/d785197a32bde00575b9cb4563f16e62.pkl - params_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/params.yaml - predictions_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/predictions.json - probabilities_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/probabilities.json - score_dict_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/score_dict.json - test_labels_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/test_labels.json - train_labels_file: output/reports/train/decd1896bc50d5023affaefff2db6e8d/train_labels.json - model_dir: models - name: decd1896bc50d5023affaefff2db6e8d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c95ffa41e1bae651d2ba5a4e9f0d24b4 - trainer: - kwargs: {} -name: decd1896bc50d5023affaefff2db6e8d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ded68a9aabaae66fda1123b098c786a1/params.yaml b/examples/security/classification/output/reports/train/ded68a9aabaae66fda1123b098c786a1/params.yaml deleted file mode 100644 index fc3e2e85..00000000 --- a/examples/security/classification/output/reports/train/ded68a9aabaae66fda1123b098c786a1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/adv_predictions.json - adv_probabilities_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/0a1980c72e084f34e0327142a5c04a31.pkl - params_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/params.yaml - predictions_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/predictions.json - probabilities_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/probabilities.json - score_dict_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/score_dict.json - test_labels_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/test_labels.json - train_labels_file: output/reports/train/ded68a9aabaae66fda1123b098c786a1/train_labels.json - model_dir: models - name: ded68a9aabaae66fda1123b098c786a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ca0cb3d62c454d8d36fc161a7605d260 - trainer: - kwargs: {} -name: ded68a9aabaae66fda1123b098c786a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/params.yaml b/examples/security/classification/output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/params.yaml deleted file mode 100644 index ea02211c..00000000 --- a/examples/security/classification/output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/adv_predictions.json - adv_probabilities_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/813387f70a83ef559fa06806ded0ec44.pkl - params_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/params.yaml - predictions_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/predictions.json - probabilities_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/probabilities.json - score_dict_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/score_dict.json - test_labels_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/test_labels.json - train_labels_file: output/reports/train/def04b6f2c5c5a5a9b7ccde5002c24f3/train_labels.json - model_dir: models - name: def04b6f2c5c5a5a9b7ccde5002c24f3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c4364980571aceecdd41860f89918f10 - trainer: - kwargs: {} -name: def04b6f2c5c5a5a9b7ccde5002c24f3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/params.yaml b/examples/security/classification/output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/params.yaml deleted file mode 100644 index 1485a615..00000000 --- a/examples/security/classification/output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/adv_predictions.json - adv_probabilities_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/92717b0f6984d1aba5826c1b4941a672.pkl - params_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/params.yaml - predictions_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/predictions.json - probabilities_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/probabilities.json - score_dict_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/score_dict.json - test_labels_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/test_labels.json - train_labels_file: output/reports/train/df3068568b0d05a18a9eefc89e0f4bc0/train_labels.json - model_dir: models - name: df3068568b0d05a18a9eefc89e0f4bc0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8aabde0852af66ae8bee8d96c84406dc - trainer: - kwargs: {} -name: df3068568b0d05a18a9eefc89e0f4bc0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/params.yaml b/examples/security/classification/output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/params.yaml deleted file mode 100644 index 8be317f3..00000000 --- a/examples/security/classification/output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/adv_predictions.json - adv_probabilities_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/53467b6a29cc0ad87ef8de2825e7992a.pkl - params_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/params.yaml - predictions_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/predictions.json - probabilities_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/probabilities.json - score_dict_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/score_dict.json - test_labels_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/test_labels.json - train_labels_file: output/reports/train/df3b2ef1fdd263f71e4aa07e88d7411e/train_labels.json - model_dir: models - name: df3b2ef1fdd263f71e4aa07e88d7411e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: abd31692cfcba9a52f0109243c83b539 - trainer: - kwargs: {} -name: df3b2ef1fdd263f71e4aa07e88d7411e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/df60994cafb11874e063ffde6999fb31/params.yaml b/examples/security/classification/output/reports/train/df60994cafb11874e063ffde6999fb31/params.yaml deleted file mode 100644 index ff14f1e6..00000000 --- a/examples/security/classification/output/reports/train/df60994cafb11874e063ffde6999fb31/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/df60994cafb11874e063ffde6999fb31/adv_predictions.json - adv_probabilities_file: output/reports/train/df60994cafb11874e063ffde6999fb31/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/fdff2fd1dcc15955a5037f4cbd0b29bb.pkl - params_file: output/reports/train/df60994cafb11874e063ffde6999fb31/params.yaml - predictions_file: output/reports/train/df60994cafb11874e063ffde6999fb31/predictions.json - probabilities_file: output/reports/train/df60994cafb11874e063ffde6999fb31/probabilities.json - score_dict_file: output/reports/train/df60994cafb11874e063ffde6999fb31/score_dict.json - test_labels_file: output/reports/train/df60994cafb11874e063ffde6999fb31/test_labels.json - train_labels_file: output/reports/train/df60994cafb11874e063ffde6999fb31/train_labels.json - model_dir: models - name: df60994cafb11874e063ffde6999fb31 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bb161ebd2a3de24b64bd6264d7c06371 - trainer: - kwargs: {} -name: df60994cafb11874e063ffde6999fb31 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/params.yaml b/examples/security/classification/output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/params.yaml deleted file mode 100644 index 46fb5e3c..00000000 --- a/examples/security/classification/output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/adv_predictions.json - adv_probabilities_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/02cbd07a2c853da9ea50df22f8a5a0db.pkl - params_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/params.yaml - predictions_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/predictions.json - probabilities_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/probabilities.json - score_dict_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/score_dict.json - test_labels_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/test_labels.json - train_labels_file: output/reports/train/df8f31b59b1ca9a5a0cc642f17ab3eb7/train_labels.json - model_dir: models - name: df8f31b59b1ca9a5a0cc642f17ab3eb7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 62419bc6bcb85abb4000fa0e776f18f4 - trainer: - kwargs: {} -name: df8f31b59b1ca9a5a0cc642f17ab3eb7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/params.yaml b/examples/security/classification/output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/params.yaml deleted file mode 100644 index 74643886..00000000 --- a/examples/security/classification/output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/adv_predictions.json - adv_probabilities_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/3fc61385443097e1b2d187df4246e6f3.pkl - params_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/params.yaml - predictions_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/predictions.json - probabilities_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/probabilities.json - score_dict_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/score_dict.json - test_labels_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/test_labels.json - train_labels_file: output/reports/train/df99cd044742a4a8fbb6b7499a7c562c/train_labels.json - model_dir: models - name: df99cd044742a4a8fbb6b7499a7c562c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a06dc2c994aff70e6c11d28ba3c7fcd1 - trainer: - kwargs: {} -name: df99cd044742a4a8fbb6b7499a7c562c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dfa00dd84be2703d7716b591c491fb5e/params.yaml b/examples/security/classification/output/reports/train/dfa00dd84be2703d7716b591c491fb5e/params.yaml deleted file mode 100644 index cb5681b3..00000000 --- a/examples/security/classification/output/reports/train/dfa00dd84be2703d7716b591c491fb5e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/adv_predictions.json - adv_probabilities_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/9944e829d483de1ecddf7ac127472dab.pkl - params_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/params.yaml - predictions_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/predictions.json - probabilities_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/probabilities.json - score_dict_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/score_dict.json - test_labels_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/test_labels.json - train_labels_file: output/reports/train/dfa00dd84be2703d7716b591c491fb5e/train_labels.json - model_dir: models - name: dfa00dd84be2703d7716b591c491fb5e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fe08ef9fc78ecb0522466d5ba30b49fe - trainer: - kwargs: {} -name: dfa00dd84be2703d7716b591c491fb5e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/params.yaml b/examples/security/classification/output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/params.yaml deleted file mode 100644 index 66c546cb..00000000 --- a/examples/security/classification/output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/adv_predictions.json - adv_probabilities_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/51fec476dc3bb51ccc75f0836d6a213a.pkl - params_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/params.yaml - predictions_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/predictions.json - probabilities_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/probabilities.json - score_dict_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/score_dict.json - test_labels_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/test_labels.json - train_labels_file: output/reports/train/dfa81153ba3c091ccce22f6c4f1cb282/train_labels.json - model_dir: models - name: dfa81153ba3c091ccce22f6c4f1cb282 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 716f00ac5fdc0f4cd329e5a77442657a - trainer: - kwargs: {} -name: dfa81153ba3c091ccce22f6c4f1cb282 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/params.yaml b/examples/security/classification/output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/params.yaml deleted file mode 100644 index 9edccc4d..00000000 --- a/examples/security/classification/output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/adv_predictions.json - adv_probabilities_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/1ed2a475a43de3bed6e1fa4c0108c8ca.pkl - params_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/params.yaml - predictions_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/predictions.json - probabilities_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/probabilities.json - score_dict_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/score_dict.json - test_labels_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/test_labels.json - train_labels_file: output/reports/train/dfe9722d30be9c127e8d8c8e9a53bce3/train_labels.json - model_dir: models - name: dfe9722d30be9c127e8d8c8e9a53bce3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b742b7fe7b17ed434dbcd324ebd1110f - trainer: - kwargs: {} -name: dfe9722d30be9c127e8d8c8e9a53bce3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e001aa2c549d03d559173d888862a2e9/params.yaml b/examples/security/classification/output/reports/train/e001aa2c549d03d559173d888862a2e9/params.yaml deleted file mode 100644 index c02a3847..00000000 --- a/examples/security/classification/output/reports/train/e001aa2c549d03d559173d888862a2e9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/adv_predictions.json - adv_probabilities_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/7278125a3d54afdb20aa168310e2f8d5.pkl - params_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/params.yaml - predictions_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/predictions.json - probabilities_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/probabilities.json - score_dict_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/score_dict.json - test_labels_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/test_labels.json - train_labels_file: output/reports/train/e001aa2c549d03d559173d888862a2e9/train_labels.json - model_dir: models - name: e001aa2c549d03d559173d888862a2e9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1379c17111c3de3ac6187d9644696a41 - trainer: - kwargs: {} -name: e001aa2c549d03d559173d888862a2e9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e00974b438bc9617c3ded020fa5c5f98/params.yaml b/examples/security/classification/output/reports/train/e00974b438bc9617c3ded020fa5c5f98/params.yaml deleted file mode 100644 index 0431d466..00000000 --- a/examples/security/classification/output/reports/train/e00974b438bc9617c3ded020fa5c5f98/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/adv_predictions.json - adv_probabilities_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/612b79a545c4eac10037916c4203202a.pkl - params_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/params.yaml - predictions_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/predictions.json - probabilities_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/probabilities.json - score_dict_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/score_dict.json - test_labels_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/test_labels.json - train_labels_file: output/reports/train/e00974b438bc9617c3ded020fa5c5f98/train_labels.json - model_dir: models - name: e00974b438bc9617c3ded020fa5c5f98 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 995a90036d2e0ca4b1690bae1e7af13e - trainer: - kwargs: {} -name: e00974b438bc9617c3ded020fa5c5f98 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/params.yaml b/examples/security/classification/output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/params.yaml deleted file mode 100644 index 02ddbac1..00000000 --- a/examples/security/classification/output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/adv_predictions.json - adv_probabilities_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/d3922dddd8d7dbe1e704af7d6930011b.pkl - params_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/params.yaml - predictions_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/predictions.json - probabilities_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/probabilities.json - score_dict_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/score_dict.json - test_labels_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/test_labels.json - train_labels_file: output/reports/train/e00c86bdd5b732b1baf4750ae9a4fec9/train_labels.json - model_dir: models - name: e00c86bdd5b732b1baf4750ae9a4fec9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ad249a7d95abfb82c81747ba7d907a7b - trainer: - kwargs: {} -name: e00c86bdd5b732b1baf4750ae9a4fec9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e03b0173a597b67adc552ec7277a0106/params.yaml b/examples/security/classification/output/reports/train/e03b0173a597b67adc552ec7277a0106/params.yaml deleted file mode 100644 index 8e378bdb..00000000 --- a/examples/security/classification/output/reports/train/e03b0173a597b67adc552ec7277a0106/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/adv_predictions.json - adv_probabilities_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/a3a19eb73b9c225558ce2bed09f16a4b.pkl - params_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/params.yaml - predictions_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/predictions.json - probabilities_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/probabilities.json - score_dict_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/score_dict.json - test_labels_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/test_labels.json - train_labels_file: output/reports/train/e03b0173a597b67adc552ec7277a0106/train_labels.json - model_dir: models - name: e03b0173a597b67adc552ec7277a0106 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a594b7a25b6a0900030aacb0413a3505 - trainer: - kwargs: {} -name: e03b0173a597b67adc552ec7277a0106 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/params.yaml b/examples/security/classification/output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/params.yaml deleted file mode 100644 index 1f85ffe7..00000000 --- a/examples/security/classification/output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/adv_predictions.json - adv_probabilities_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/57d7e5ae55ba15356ac1d8a89faf581f.pkl - params_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/params.yaml - predictions_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/predictions.json - probabilities_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/probabilities.json - score_dict_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/score_dict.json - test_labels_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/test_labels.json - train_labels_file: output/reports/train/e0517937f383ef37cf1edcb8b6b2c1c8/train_labels.json - model_dir: models - name: e0517937f383ef37cf1edcb8b6b2c1c8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a0908824ebb6c549df125cc601962459 - trainer: - kwargs: {} -name: e0517937f383ef37cf1edcb8b6b2c1c8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e060805f292ad9afeefab59125a47036/params.yaml b/examples/security/classification/output/reports/train/e060805f292ad9afeefab59125a47036/params.yaml deleted file mode 100644 index 6760b85e..00000000 --- a/examples/security/classification/output/reports/train/e060805f292ad9afeefab59125a47036/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e060805f292ad9afeefab59125a47036/adv_predictions.json - adv_probabilities_file: output/reports/train/e060805f292ad9afeefab59125a47036/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/c9dd367dc79e805b2982720843e92f8b.pkl - params_file: output/reports/train/e060805f292ad9afeefab59125a47036/params.yaml - predictions_file: output/reports/train/e060805f292ad9afeefab59125a47036/predictions.json - probabilities_file: output/reports/train/e060805f292ad9afeefab59125a47036/probabilities.json - score_dict_file: output/reports/train/e060805f292ad9afeefab59125a47036/score_dict.json - test_labels_file: output/reports/train/e060805f292ad9afeefab59125a47036/test_labels.json - train_labels_file: output/reports/train/e060805f292ad9afeefab59125a47036/train_labels.json - model_dir: models - name: e060805f292ad9afeefab59125a47036 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8365feafee4d9d425d31d733d7005c19 - trainer: - kwargs: {} -name: e060805f292ad9afeefab59125a47036 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/params.yaml b/examples/security/classification/output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/params.yaml deleted file mode 100644 index 93508a3b..00000000 --- a/examples/security/classification/output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/adv_predictions.json - adv_probabilities_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/08088bbf4bcf9cc3fc0b825c3d3c28cf.pkl - params_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/params.yaml - predictions_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/predictions.json - probabilities_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/probabilities.json - score_dict_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/score_dict.json - test_labels_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/test_labels.json - train_labels_file: output/reports/train/e06ce94205c432b4e4de10bd06d6cbb9/train_labels.json - model_dir: models - name: e06ce94205c432b4e4de10bd06d6cbb9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1707a07740ae6924b2b7bb2342a73a8c - trainer: - kwargs: {} -name: e06ce94205c432b4e4de10bd06d6cbb9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e07e3048621d157de2f8511723b2c6c8/params.yaml b/examples/security/classification/output/reports/train/e07e3048621d157de2f8511723b2c6c8/params.yaml deleted file mode 100644 index 24821b65..00000000 --- a/examples/security/classification/output/reports/train/e07e3048621d157de2f8511723b2c6c8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/adv_predictions.json - adv_probabilities_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/2747b484cd4fb00c3407306506aa008c.pkl - params_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/params.yaml - predictions_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/predictions.json - probabilities_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/probabilities.json - score_dict_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/score_dict.json - test_labels_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/test_labels.json - train_labels_file: output/reports/train/e07e3048621d157de2f8511723b2c6c8/train_labels.json - model_dir: models - name: e07e3048621d157de2f8511723b2c6c8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ce212026cdac76b07d9da9509b4ad769 - trainer: - kwargs: {} -name: e07e3048621d157de2f8511723b2c6c8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e09f70f192f2a44501b505911c862c16/params.yaml b/examples/security/classification/output/reports/train/e09f70f192f2a44501b505911c862c16/params.yaml deleted file mode 100644 index 4345a0e3..00000000 --- a/examples/security/classification/output/reports/train/e09f70f192f2a44501b505911c862c16/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e09f70f192f2a44501b505911c862c16/adv_predictions.json - adv_probabilities_file: output/reports/train/e09f70f192f2a44501b505911c862c16/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/42d97f0cdc4020f802c2ab442a717a7a.pkl - params_file: output/reports/train/e09f70f192f2a44501b505911c862c16/params.yaml - predictions_file: output/reports/train/e09f70f192f2a44501b505911c862c16/predictions.json - probabilities_file: output/reports/train/e09f70f192f2a44501b505911c862c16/probabilities.json - score_dict_file: output/reports/train/e09f70f192f2a44501b505911c862c16/score_dict.json - test_labels_file: output/reports/train/e09f70f192f2a44501b505911c862c16/test_labels.json - train_labels_file: output/reports/train/e09f70f192f2a44501b505911c862c16/train_labels.json - model_dir: models - name: e09f70f192f2a44501b505911c862c16 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a6e3d7d4bda78e161da1a997a03a5285 - trainer: - kwargs: {} -name: e09f70f192f2a44501b505911c862c16 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/params.yaml b/examples/security/classification/output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/params.yaml deleted file mode 100644 index b02f7f19..00000000 --- a/examples/security/classification/output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/adv_predictions.json - adv_probabilities_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/7987fb8a7980411b777aa941b1c3fcf2.pkl - params_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/params.yaml - predictions_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/predictions.json - probabilities_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/probabilities.json - score_dict_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/score_dict.json - test_labels_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/test_labels.json - train_labels_file: output/reports/train/e0ad25b14c165d6d7eb670652a8fbd79/train_labels.json - model_dir: models - name: e0ad25b14c165d6d7eb670652a8fbd79 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 502753766d2841e5e0a96ec746dc698d - trainer: - kwargs: {} -name: e0ad25b14c165d6d7eb670652a8fbd79 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/params.yaml b/examples/security/classification/output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/params.yaml deleted file mode 100644 index 1c95c998..00000000 --- a/examples/security/classification/output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/adv_predictions.json - adv_probabilities_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/5fc9d28314344e2386ba7c7db820fb08.pkl - params_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/params.yaml - predictions_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/predictions.json - probabilities_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/probabilities.json - score_dict_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/score_dict.json - test_labels_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/test_labels.json - train_labels_file: output/reports/train/e0b7b95df2710fe1b60b3c6601c295de/train_labels.json - model_dir: models - name: e0b7b95df2710fe1b60b3c6601c295de - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fdaff09fe7c58ed7119d24bca6ccfa58 - trainer: - kwargs: {} -name: e0b7b95df2710fe1b60b3c6601c295de -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e0c019162949138f503d5a22e5a115b2/params.yaml b/examples/security/classification/output/reports/train/e0c019162949138f503d5a22e5a115b2/params.yaml deleted file mode 100644 index 78613570..00000000 --- a/examples/security/classification/output/reports/train/e0c019162949138f503d5a22e5a115b2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/adv_predictions.json - adv_probabilities_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/df76883ca2db883bcffcfabff372eb15.pkl - params_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/params.yaml - predictions_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/predictions.json - probabilities_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/probabilities.json - score_dict_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/score_dict.json - test_labels_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/test_labels.json - train_labels_file: output/reports/train/e0c019162949138f503d5a22e5a115b2/train_labels.json - model_dir: models - name: e0c019162949138f503d5a22e5a115b2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 65e4f5c0a74576885523e74edf029690 - trainer: - kwargs: {} -name: e0c019162949138f503d5a22e5a115b2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/params.yaml b/examples/security/classification/output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/params.yaml deleted file mode 100644 index acf5ec22..00000000 --- a/examples/security/classification/output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/adv_predictions.json - adv_probabilities_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/cc475a4e1ba686598cff99614ea0dc5f.pkl - params_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/params.yaml - predictions_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/predictions.json - probabilities_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/probabilities.json - score_dict_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/score_dict.json - test_labels_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/test_labels.json - train_labels_file: output/reports/train/e0d5331c4cf57fa7f543d231a21dfb7a/train_labels.json - model_dir: models - name: e0d5331c4cf57fa7f543d231a21dfb7a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 29101bbf24243a7ad68d7df18f3eede3 - trainer: - kwargs: {} -name: e0d5331c4cf57fa7f543d231a21dfb7a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/params.yaml b/examples/security/classification/output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/params.yaml deleted file mode 100644 index 03beea6e..00000000 --- a/examples/security/classification/output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/adv_predictions.json - adv_probabilities_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/1f649871db8015bdf2a4df3431da2118.pkl - params_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/params.yaml - predictions_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/predictions.json - probabilities_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/probabilities.json - score_dict_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/score_dict.json - test_labels_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/test_labels.json - train_labels_file: output/reports/train/e0e4044e5c6d3c9eb37e881e1fb622d5/train_labels.json - model_dir: models - name: e0e4044e5c6d3c9eb37e881e1fb622d5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a4a91bd2ade5aa1a31f82caf52c7ae42 - trainer: - kwargs: {} -name: e0e4044e5c6d3c9eb37e881e1fb622d5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/params.yaml b/examples/security/classification/output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/params.yaml deleted file mode 100644 index aa495da3..00000000 --- a/examples/security/classification/output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/adv_predictions.json - adv_probabilities_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/0f967f896d920d89210810b7f005c8f2.pkl - params_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/params.yaml - predictions_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/predictions.json - probabilities_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/probabilities.json - score_dict_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/score_dict.json - test_labels_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/test_labels.json - train_labels_file: output/reports/train/e0e75f5dad6440a9f5c948b763cc2e7b/train_labels.json - model_dir: models - name: e0e75f5dad6440a9f5c948b763cc2e7b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bc898444f9e49905b737d77d5a996c69 - trainer: - kwargs: {} -name: e0e75f5dad6440a9f5c948b763cc2e7b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/params.yaml b/examples/security/classification/output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/params.yaml deleted file mode 100644 index daf15c3f..00000000 --- a/examples/security/classification/output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/adv_predictions.json - adv_probabilities_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/df35a1a4bf384dd82c1a25d761756e97.pkl - params_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/params.yaml - predictions_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/predictions.json - probabilities_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/probabilities.json - score_dict_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/score_dict.json - test_labels_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/test_labels.json - train_labels_file: output/reports/train/e10bf0b476f3bc4beef5bed7e6bccd25/train_labels.json - model_dir: models - name: e10bf0b476f3bc4beef5bed7e6bccd25 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: efa37f734ae9bb88c5222c3938427a75 - trainer: - kwargs: {} -name: e10bf0b476f3bc4beef5bed7e6bccd25 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/params.yaml b/examples/security/classification/output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/params.yaml deleted file mode 100644 index 16bd6f21..00000000 --- a/examples/security/classification/output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/adv_predictions.json - adv_probabilities_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/4768316560553131fe7db2aae3722eb0.pkl - params_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/params.yaml - predictions_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/predictions.json - probabilities_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/probabilities.json - score_dict_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/score_dict.json - test_labels_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/test_labels.json - train_labels_file: output/reports/train/e10c8abfdc237a6f8fc22a9c1e15d26b/train_labels.json - model_dir: models - name: e10c8abfdc237a6f8fc22a9c1e15d26b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b5ec859e8a5caca5ea0a9a0140493a32 - trainer: - kwargs: {} -name: e10c8abfdc237a6f8fc22a9c1e15d26b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/params.yaml b/examples/security/classification/output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/params.yaml deleted file mode 100644 index 8905f0d5..00000000 --- a/examples/security/classification/output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/adv_predictions.json - adv_probabilities_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/96ed37ffdd0dd6069993a71aa4a0e66f.pkl - params_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/params.yaml - predictions_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/predictions.json - probabilities_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/probabilities.json - score_dict_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/score_dict.json - test_labels_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/test_labels.json - train_labels_file: output/reports/train/e179ab77b82a6cb576d3c1cfbce39356/train_labels.json - model_dir: models - name: e179ab77b82a6cb576d3c1cfbce39356 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 32221b768f297c17b44429e21972b401 - trainer: - kwargs: {} -name: e179ab77b82a6cb576d3c1cfbce39356 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e1818ced567f2a2897e3c09e058f9ece/params.yaml b/examples/security/classification/output/reports/train/e1818ced567f2a2897e3c09e058f9ece/params.yaml deleted file mode 100644 index 12c3e14c..00000000 --- a/examples/security/classification/output/reports/train/e1818ced567f2a2897e3c09e058f9ece/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/adv_predictions.json - adv_probabilities_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/eeca950c325622eb6bfc0fb4a28ca040.pkl - params_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/params.yaml - predictions_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/predictions.json - probabilities_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/probabilities.json - score_dict_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/score_dict.json - test_labels_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/test_labels.json - train_labels_file: output/reports/train/e1818ced567f2a2897e3c09e058f9ece/train_labels.json - model_dir: models - name: e1818ced567f2a2897e3c09e058f9ece - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2ba2ee92c3e0d2c74a233c4ff9188533 - trainer: - kwargs: {} -name: e1818ced567f2a2897e3c09e058f9ece -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e1a5849afe730c490f0f11da540e1eca/params.yaml b/examples/security/classification/output/reports/train/e1a5849afe730c490f0f11da540e1eca/params.yaml deleted file mode 100644 index a617065d..00000000 --- a/examples/security/classification/output/reports/train/e1a5849afe730c490f0f11da540e1eca/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/adv_predictions.json - adv_probabilities_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/aa4aa365924d82d730ffa5d3999163be.pkl - params_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/params.yaml - predictions_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/predictions.json - probabilities_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/probabilities.json - score_dict_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/score_dict.json - test_labels_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/test_labels.json - train_labels_file: output/reports/train/e1a5849afe730c490f0f11da540e1eca/train_labels.json - model_dir: models - name: e1a5849afe730c490f0f11da540e1eca - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 322d0dd265b517056fdd555afe312f8d - trainer: - kwargs: {} -name: e1a5849afe730c490f0f11da540e1eca -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e1acb27a552a4bf1428566a9752b2525/params.yaml b/examples/security/classification/output/reports/train/e1acb27a552a4bf1428566a9752b2525/params.yaml deleted file mode 100644 index 079640d7..00000000 --- a/examples/security/classification/output/reports/train/e1acb27a552a4bf1428566a9752b2525/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/adv_predictions.json - adv_probabilities_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/fdcb655b5d9b52120926eba61f38b4bd.pkl - params_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/params.yaml - predictions_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/predictions.json - probabilities_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/probabilities.json - score_dict_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/score_dict.json - test_labels_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/test_labels.json - train_labels_file: output/reports/train/e1acb27a552a4bf1428566a9752b2525/train_labels.json - model_dir: models - name: e1acb27a552a4bf1428566a9752b2525 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3e5f5f366ea7f55a1b812d687764b87d - trainer: - kwargs: {} -name: e1acb27a552a4bf1428566a9752b2525 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/params.yaml b/examples/security/classification/output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/params.yaml deleted file mode 100644 index 6a77dca8..00000000 --- a/examples/security/classification/output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/adv_predictions.json - adv_probabilities_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/79dc5842f73399b6968e59d006cd459f.pkl - params_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/params.yaml - predictions_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/predictions.json - probabilities_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/probabilities.json - score_dict_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/score_dict.json - test_labels_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/test_labels.json - train_labels_file: output/reports/train/e23e5b431a29d08dfe0f434ae34da1a2/train_labels.json - model_dir: models - name: e23e5b431a29d08dfe0f434ae34da1a2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 098d535b68cf2e6fba2ce8d3c1894879 - trainer: - kwargs: {} -name: e23e5b431a29d08dfe0f434ae34da1a2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e288cf0b630b13d95c76e60da4c36a83/params.yaml b/examples/security/classification/output/reports/train/e288cf0b630b13d95c76e60da4c36a83/params.yaml deleted file mode 100644 index 480260c3..00000000 --- a/examples/security/classification/output/reports/train/e288cf0b630b13d95c76e60da4c36a83/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/adv_predictions.json - adv_probabilities_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/f61a5b07a978b94aab3ddaf572664086.pkl - params_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/params.yaml - predictions_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/predictions.json - probabilities_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/probabilities.json - score_dict_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/score_dict.json - test_labels_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/test_labels.json - train_labels_file: output/reports/train/e288cf0b630b13d95c76e60da4c36a83/train_labels.json - model_dir: models - name: e288cf0b630b13d95c76e60da4c36a83 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 853e796a3be15032df0567f7ae1caf72 - trainer: - kwargs: {} -name: e288cf0b630b13d95c76e60da4c36a83 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e28f304300b14a528eec6cf97c07cfbb/params.yaml b/examples/security/classification/output/reports/train/e28f304300b14a528eec6cf97c07cfbb/params.yaml deleted file mode 100644 index 1391e298..00000000 --- a/examples/security/classification/output/reports/train/e28f304300b14a528eec6cf97c07cfbb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/adv_predictions.json - adv_probabilities_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/daa854bf9c285efa229f6da1cd7f12af.pkl - params_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/params.yaml - predictions_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/predictions.json - probabilities_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/probabilities.json - score_dict_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/score_dict.json - test_labels_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/test_labels.json - train_labels_file: output/reports/train/e28f304300b14a528eec6cf97c07cfbb/train_labels.json - model_dir: models - name: e28f304300b14a528eec6cf97c07cfbb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: df7e1001f0fab813a87113c873b54cdd - trainer: - kwargs: {} -name: e28f304300b14a528eec6cf97c07cfbb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e2aade5727f0b370bb23e5414259f55e/params.yaml b/examples/security/classification/output/reports/train/e2aade5727f0b370bb23e5414259f55e/params.yaml deleted file mode 100644 index e12e18ce..00000000 --- a/examples/security/classification/output/reports/train/e2aade5727f0b370bb23e5414259f55e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/adv_predictions.json - adv_probabilities_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/1db202422ff4fe1a855098ac052004ee.pkl - params_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/params.yaml - predictions_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/predictions.json - probabilities_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/probabilities.json - score_dict_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/score_dict.json - test_labels_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/test_labels.json - train_labels_file: output/reports/train/e2aade5727f0b370bb23e5414259f55e/train_labels.json - model_dir: models - name: e2aade5727f0b370bb23e5414259f55e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1971f7b7a5cee97a0c4075f9b1953cdb - trainer: - kwargs: {} -name: e2aade5727f0b370bb23e5414259f55e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e300d41ffd94b5a15cff9207676657ab/params.yaml b/examples/security/classification/output/reports/train/e300d41ffd94b5a15cff9207676657ab/params.yaml deleted file mode 100644 index 37a0228d..00000000 --- a/examples/security/classification/output/reports/train/e300d41ffd94b5a15cff9207676657ab/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/adv_predictions.json - adv_probabilities_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/cc1a5b795bae005da8ce3889c5d4e069.pkl - params_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/params.yaml - predictions_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/predictions.json - probabilities_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/probabilities.json - score_dict_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/score_dict.json - test_labels_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/test_labels.json - train_labels_file: output/reports/train/e300d41ffd94b5a15cff9207676657ab/train_labels.json - model_dir: models - name: e300d41ffd94b5a15cff9207676657ab - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 33e85f232cc125c66b02a53a17205e99 - trainer: - kwargs: {} -name: e300d41ffd94b5a15cff9207676657ab -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e3677153c39e6a0d24227435b5c82d78/params.yaml b/examples/security/classification/output/reports/train/e3677153c39e6a0d24227435b5c82d78/params.yaml deleted file mode 100644 index ce90e5e2..00000000 --- a/examples/security/classification/output/reports/train/e3677153c39e6a0d24227435b5c82d78/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/adv_predictions.json - adv_probabilities_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/6b2d66fb11b5a4b611db73744506de7d.pkl - params_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/params.yaml - predictions_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/predictions.json - probabilities_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/probabilities.json - score_dict_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/score_dict.json - test_labels_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/test_labels.json - train_labels_file: output/reports/train/e3677153c39e6a0d24227435b5c82d78/train_labels.json - model_dir: models - name: e3677153c39e6a0d24227435b5c82d78 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7b5bb7cb60db56de1cefe3427a72c450 - trainer: - kwargs: {} -name: e3677153c39e6a0d24227435b5c82d78 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e3691d832eeaeb10574a10582c4ca433/params.yaml b/examples/security/classification/output/reports/train/e3691d832eeaeb10574a10582c4ca433/params.yaml deleted file mode 100644 index 22dab848..00000000 --- a/examples/security/classification/output/reports/train/e3691d832eeaeb10574a10582c4ca433/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/adv_predictions.json - adv_probabilities_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/932af5a4d668905ce66495161d986857.pkl - params_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/params.yaml - predictions_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/predictions.json - probabilities_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/probabilities.json - score_dict_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/score_dict.json - test_labels_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/test_labels.json - train_labels_file: output/reports/train/e3691d832eeaeb10574a10582c4ca433/train_labels.json - model_dir: models - name: e3691d832eeaeb10574a10582c4ca433 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 11aceb3f10f39fce40e1a62f367ef6ea - trainer: - kwargs: {} -name: e3691d832eeaeb10574a10582c4ca433 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/params.yaml b/examples/security/classification/output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/params.yaml deleted file mode 100644 index 7d40e057..00000000 --- a/examples/security/classification/output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/adv_predictions.json - adv_probabilities_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/a5d185460a0c32576ebf50998b8f41e8.pkl - params_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/params.yaml - predictions_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/predictions.json - probabilities_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/probabilities.json - score_dict_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/score_dict.json - test_labels_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/test_labels.json - train_labels_file: output/reports/train/e36db73b3632876ebe8018f1ad55bc8d/train_labels.json - model_dir: models - name: e36db73b3632876ebe8018f1ad55bc8d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6ac747d41502fb01b193b88166c80e50 - trainer: - kwargs: {} -name: e36db73b3632876ebe8018f1ad55bc8d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e3701567224e033f74082ae439c250e0/params.yaml b/examples/security/classification/output/reports/train/e3701567224e033f74082ae439c250e0/params.yaml deleted file mode 100644 index 304c7e65..00000000 --- a/examples/security/classification/output/reports/train/e3701567224e033f74082ae439c250e0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e3701567224e033f74082ae439c250e0/adv_predictions.json - adv_probabilities_file: output/reports/train/e3701567224e033f74082ae439c250e0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/85eb02a64113a85f5363848dc933e8da.pkl - params_file: output/reports/train/e3701567224e033f74082ae439c250e0/params.yaml - predictions_file: output/reports/train/e3701567224e033f74082ae439c250e0/predictions.json - probabilities_file: output/reports/train/e3701567224e033f74082ae439c250e0/probabilities.json - score_dict_file: output/reports/train/e3701567224e033f74082ae439c250e0/score_dict.json - test_labels_file: output/reports/train/e3701567224e033f74082ae439c250e0/test_labels.json - train_labels_file: output/reports/train/e3701567224e033f74082ae439c250e0/train_labels.json - model_dir: models - name: e3701567224e033f74082ae439c250e0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5d0c36d016e5e8eb9176f0e77021e26e - trainer: - kwargs: {} -name: e3701567224e033f74082ae439c250e0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e3dd0686331226c1f789f25d882756f1/params.yaml b/examples/security/classification/output/reports/train/e3dd0686331226c1f789f25d882756f1/params.yaml deleted file mode 100644 index 49731537..00000000 --- a/examples/security/classification/output/reports/train/e3dd0686331226c1f789f25d882756f1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/adv_predictions.json - adv_probabilities_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/6a8eb39f8c16fb423451ea8036f63c0b.pkl - params_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/params.yaml - predictions_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/predictions.json - probabilities_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/probabilities.json - score_dict_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/score_dict.json - test_labels_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/test_labels.json - train_labels_file: output/reports/train/e3dd0686331226c1f789f25d882756f1/train_labels.json - model_dir: models - name: e3dd0686331226c1f789f25d882756f1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3df2f23e6547d42c95dd9202a7e9cd18 - trainer: - kwargs: {} -name: e3dd0686331226c1f789f25d882756f1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e4570785a5c863e12886affb36b1ae5f/params.yaml b/examples/security/classification/output/reports/train/e4570785a5c863e12886affb36b1ae5f/params.yaml deleted file mode 100644 index b2eee22a..00000000 --- a/examples/security/classification/output/reports/train/e4570785a5c863e12886affb36b1ae5f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/adv_predictions.json - adv_probabilities_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/fef4659bdbebf0ac8031c0a0f1080fdf.pkl - params_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/params.yaml - predictions_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/predictions.json - probabilities_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/probabilities.json - score_dict_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/score_dict.json - test_labels_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/test_labels.json - train_labels_file: output/reports/train/e4570785a5c863e12886affb36b1ae5f/train_labels.json - model_dir: models - name: e4570785a5c863e12886affb36b1ae5f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f70d66d17091e479cecf8bbb8887baac - trainer: - kwargs: {} -name: e4570785a5c863e12886affb36b1ae5f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e472d9728f57a8c9cded66997912c756/params.yaml b/examples/security/classification/output/reports/train/e472d9728f57a8c9cded66997912c756/params.yaml deleted file mode 100644 index c81081ef..00000000 --- a/examples/security/classification/output/reports/train/e472d9728f57a8c9cded66997912c756/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e472d9728f57a8c9cded66997912c756/adv_predictions.json - adv_probabilities_file: output/reports/train/e472d9728f57a8c9cded66997912c756/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/6628b0bb149ce8a848e00a0fda61397e.pkl - params_file: output/reports/train/e472d9728f57a8c9cded66997912c756/params.yaml - predictions_file: output/reports/train/e472d9728f57a8c9cded66997912c756/predictions.json - probabilities_file: output/reports/train/e472d9728f57a8c9cded66997912c756/probabilities.json - score_dict_file: output/reports/train/e472d9728f57a8c9cded66997912c756/score_dict.json - test_labels_file: output/reports/train/e472d9728f57a8c9cded66997912c756/test_labels.json - train_labels_file: output/reports/train/e472d9728f57a8c9cded66997912c756/train_labels.json - model_dir: models - name: e472d9728f57a8c9cded66997912c756 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cdb170ac54b988d68f15708b037ed4e9 - trainer: - kwargs: {} -name: e472d9728f57a8c9cded66997912c756 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e4bb678c3419902d93bdccbab14a98a9/params.yaml b/examples/security/classification/output/reports/train/e4bb678c3419902d93bdccbab14a98a9/params.yaml deleted file mode 100644 index 566a78a5..00000000 --- a/examples/security/classification/output/reports/train/e4bb678c3419902d93bdccbab14a98a9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/adv_predictions.json - adv_probabilities_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/dfef41a9c8b679f9f9bcc3f56d4a36de.pkl - params_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/params.yaml - predictions_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/predictions.json - probabilities_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/probabilities.json - score_dict_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/score_dict.json - test_labels_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/test_labels.json - train_labels_file: output/reports/train/e4bb678c3419902d93bdccbab14a98a9/train_labels.json - model_dir: models - name: e4bb678c3419902d93bdccbab14a98a9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6c980a25e7ac97aa3d4fd33a7fba910c - trainer: - kwargs: {} -name: e4bb678c3419902d93bdccbab14a98a9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/params.yaml b/examples/security/classification/output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/params.yaml deleted file mode 100644 index 4579fa79..00000000 --- a/examples/security/classification/output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/adv_predictions.json - adv_probabilities_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/f8ae3d6da92dc6aacfc3553d51f1c4a7.pkl - params_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/params.yaml - predictions_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/predictions.json - probabilities_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/probabilities.json - score_dict_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/score_dict.json - test_labels_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/test_labels.json - train_labels_file: output/reports/train/e4bf17243244dbe0e8771cb85f8966fc/train_labels.json - model_dir: models - name: e4bf17243244dbe0e8771cb85f8966fc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a94ee2181b2ed8ba1b8f5a5ca441ec4c - trainer: - kwargs: {} -name: e4bf17243244dbe0e8771cb85f8966fc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/params.yaml b/examples/security/classification/output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/params.yaml deleted file mode 100644 index 3f0e2e3e..00000000 --- a/examples/security/classification/output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/adv_predictions.json - adv_probabilities_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/52253601f9a98bd03c89c8c4f5c00109.pkl - params_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/params.yaml - predictions_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/predictions.json - probabilities_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/probabilities.json - score_dict_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/score_dict.json - test_labels_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/test_labels.json - train_labels_file: output/reports/train/e4c7fab3ce132a7b02f2693d26b9b58c/train_labels.json - model_dir: models - name: e4c7fab3ce132a7b02f2693d26b9b58c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0791fb0df9971a5ef0b0f59ef62c525c - trainer: - kwargs: {} -name: e4c7fab3ce132a7b02f2693d26b9b58c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e503f5a0549fa365e8c1c4a064836062/params.yaml b/examples/security/classification/output/reports/train/e503f5a0549fa365e8c1c4a064836062/params.yaml deleted file mode 100644 index 923ca5b4..00000000 --- a/examples/security/classification/output/reports/train/e503f5a0549fa365e8c1c4a064836062/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/adv_predictions.json - adv_probabilities_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/15a0c36475ab2bc5fb6a1616e8049ecc.pkl - params_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/params.yaml - predictions_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/predictions.json - probabilities_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/probabilities.json - score_dict_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/score_dict.json - test_labels_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/test_labels.json - train_labels_file: output/reports/train/e503f5a0549fa365e8c1c4a064836062/train_labels.json - model_dir: models - name: e503f5a0549fa365e8c1c4a064836062 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6fa24993eaaee72bdaaed29701b70ffc - trainer: - kwargs: {} -name: e503f5a0549fa365e8c1c4a064836062 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e50479c2a7d5961d4a7738bc94786d66/params.yaml b/examples/security/classification/output/reports/train/e50479c2a7d5961d4a7738bc94786d66/params.yaml deleted file mode 100644 index 3ef3724f..00000000 --- a/examples/security/classification/output/reports/train/e50479c2a7d5961d4a7738bc94786d66/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/adv_predictions.json - adv_probabilities_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/464ec1a3cfd7b41e53d67acd9de96ba3.pkl - params_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/params.yaml - predictions_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/predictions.json - probabilities_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/probabilities.json - score_dict_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/score_dict.json - test_labels_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/test_labels.json - train_labels_file: output/reports/train/e50479c2a7d5961d4a7738bc94786d66/train_labels.json - model_dir: models - name: e50479c2a7d5961d4a7738bc94786d66 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6a382ad5610a7d25de45281f9b8337e2 - trainer: - kwargs: {} -name: e50479c2a7d5961d4a7738bc94786d66 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/params.yaml b/examples/security/classification/output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/params.yaml deleted file mode 100644 index cfb96728..00000000 --- a/examples/security/classification/output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/adv_predictions.json - adv_probabilities_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/2844f5180915083e8f5fa908bff48aa7.pkl - params_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/params.yaml - predictions_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/predictions.json - probabilities_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/probabilities.json - score_dict_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/score_dict.json - test_labels_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/test_labels.json - train_labels_file: output/reports/train/e50d3d3482f54ae01ff02c99bb0f6276/train_labels.json - model_dir: models - name: e50d3d3482f54ae01ff02c99bb0f6276 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 72a151d8b68cfe2f6f1fb0286fd3ffb5 - trainer: - kwargs: {} -name: e50d3d3482f54ae01ff02c99bb0f6276 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e51330ff22301d3535956e21ca511d83/params.yaml b/examples/security/classification/output/reports/train/e51330ff22301d3535956e21ca511d83/params.yaml deleted file mode 100644 index 00973622..00000000 --- a/examples/security/classification/output/reports/train/e51330ff22301d3535956e21ca511d83/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e51330ff22301d3535956e21ca511d83/adv_predictions.json - adv_probabilities_file: output/reports/train/e51330ff22301d3535956e21ca511d83/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/762ee1ffe7e32fdc158c4c30257e46a3.pkl - params_file: output/reports/train/e51330ff22301d3535956e21ca511d83/params.yaml - predictions_file: output/reports/train/e51330ff22301d3535956e21ca511d83/predictions.json - probabilities_file: output/reports/train/e51330ff22301d3535956e21ca511d83/probabilities.json - score_dict_file: output/reports/train/e51330ff22301d3535956e21ca511d83/score_dict.json - test_labels_file: output/reports/train/e51330ff22301d3535956e21ca511d83/test_labels.json - train_labels_file: output/reports/train/e51330ff22301d3535956e21ca511d83/train_labels.json - model_dir: models - name: e51330ff22301d3535956e21ca511d83 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 19ca6a8fb399249b66c79842ee20034e - trainer: - kwargs: {} -name: e51330ff22301d3535956e21ca511d83 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/params.yaml b/examples/security/classification/output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/params.yaml deleted file mode 100644 index f061b9d5..00000000 --- a/examples/security/classification/output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/adv_predictions.json - adv_probabilities_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/8c7ac66f282cdb9af5d7dc8db9dc3d9f.pkl - params_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/params.yaml - predictions_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/predictions.json - probabilities_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/probabilities.json - score_dict_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/score_dict.json - test_labels_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/test_labels.json - train_labels_file: output/reports/train/e5445e6edc2f2cd889dfa033ee82bf7d/train_labels.json - model_dir: models - name: e5445e6edc2f2cd889dfa033ee82bf7d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 534fa1ed1a0b3a86331e9a4a3a555f04 - trainer: - kwargs: {} -name: e5445e6edc2f2cd889dfa033ee82bf7d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e547043a9deda6bb57337dac0f54ec52/params.yaml b/examples/security/classification/output/reports/train/e547043a9deda6bb57337dac0f54ec52/params.yaml deleted file mode 100644 index 75942e92..00000000 --- a/examples/security/classification/output/reports/train/e547043a9deda6bb57337dac0f54ec52/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/adv_predictions.json - adv_probabilities_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/9d6b2a3ef9145d2e8870676ca7b2ada7.pkl - params_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/params.yaml - predictions_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/predictions.json - probabilities_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/probabilities.json - score_dict_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/score_dict.json - test_labels_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/test_labels.json - train_labels_file: output/reports/train/e547043a9deda6bb57337dac0f54ec52/train_labels.json - model_dir: models - name: e547043a9deda6bb57337dac0f54ec52 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8d9a2157c37ff7577cef3bda9a3fa44b - trainer: - kwargs: {} -name: e547043a9deda6bb57337dac0f54ec52 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e555e957861ac0efa1e4077f429d9622/params.yaml b/examples/security/classification/output/reports/train/e555e957861ac0efa1e4077f429d9622/params.yaml deleted file mode 100644 index 7d29c334..00000000 --- a/examples/security/classification/output/reports/train/e555e957861ac0efa1e4077f429d9622/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/adv_predictions.json - adv_probabilities_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/85fdf1feaf4a37b631a99c29857f9d78.pkl - params_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/params.yaml - predictions_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/predictions.json - probabilities_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/probabilities.json - score_dict_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/score_dict.json - test_labels_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/test_labels.json - train_labels_file: output/reports/train/e555e957861ac0efa1e4077f429d9622/train_labels.json - model_dir: models - name: e555e957861ac0efa1e4077f429d9622 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 978dc4327f3d4d892f5a838ff5b64ad5 - trainer: - kwargs: {} -name: e555e957861ac0efa1e4077f429d9622 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e5960384348f6cb1706c59020a31a5f5/params.yaml b/examples/security/classification/output/reports/train/e5960384348f6cb1706c59020a31a5f5/params.yaml deleted file mode 100644 index a06a1510..00000000 --- a/examples/security/classification/output/reports/train/e5960384348f6cb1706c59020a31a5f5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/adv_predictions.json - adv_probabilities_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/8ca356fb8cfb59a55e986ed191c748ae.pkl - params_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/params.yaml - predictions_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/predictions.json - probabilities_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/probabilities.json - score_dict_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/score_dict.json - test_labels_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/test_labels.json - train_labels_file: output/reports/train/e5960384348f6cb1706c59020a31a5f5/train_labels.json - model_dir: models - name: e5960384348f6cb1706c59020a31a5f5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 22825b403e9b7e978357b38f7b65ceee - trainer: - kwargs: {} -name: e5960384348f6cb1706c59020a31a5f5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e5adf83a9939c19778e6061b7641432f/params.yaml b/examples/security/classification/output/reports/train/e5adf83a9939c19778e6061b7641432f/params.yaml deleted file mode 100644 index 05046cd9..00000000 --- a/examples/security/classification/output/reports/train/e5adf83a9939c19778e6061b7641432f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/adv_predictions.json - adv_probabilities_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/01b66947285c28a8eeb31436886514a7.pkl - params_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/params.yaml - predictions_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/predictions.json - probabilities_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/probabilities.json - score_dict_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/score_dict.json - test_labels_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/test_labels.json - train_labels_file: output/reports/train/e5adf83a9939c19778e6061b7641432f/train_labels.json - model_dir: models - name: e5adf83a9939c19778e6061b7641432f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 475285d4f81f8a9ad9ee63c405721a85 - trainer: - kwargs: {} -name: e5adf83a9939c19778e6061b7641432f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/params.yaml b/examples/security/classification/output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/params.yaml deleted file mode 100644 index e78d02b7..00000000 --- a/examples/security/classification/output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/adv_predictions.json - adv_probabilities_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/68f8e842a08a752c8eedae6dba02c068.pkl - params_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/params.yaml - predictions_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/predictions.json - probabilities_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/probabilities.json - score_dict_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/score_dict.json - test_labels_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/test_labels.json - train_labels_file: output/reports/train/e5ca904fd99fe56b92e8968e40b67c00/train_labels.json - model_dir: models - name: e5ca904fd99fe56b92e8968e40b67c00 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f76e94656920f72fe1c0a207c5a3e55c - trainer: - kwargs: {} -name: e5ca904fd99fe56b92e8968e40b67c00 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/params.yaml b/examples/security/classification/output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/params.yaml deleted file mode 100644 index 979b31fe..00000000 --- a/examples/security/classification/output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/adv_predictions.json - adv_probabilities_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/82e00c389d9514b3a4ba4c9055fd4152.pkl - params_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/params.yaml - predictions_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/predictions.json - probabilities_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/probabilities.json - score_dict_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/score_dict.json - test_labels_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/test_labels.json - train_labels_file: output/reports/train/e5e3370e2bd21aeb248ed2c28b0e7e77/train_labels.json - model_dir: models - name: e5e3370e2bd21aeb248ed2c28b0e7e77 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 32d4aaca70b6bfba272b048d6a75d297 - trainer: - kwargs: {} -name: e5e3370e2bd21aeb248ed2c28b0e7e77 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/params.yaml b/examples/security/classification/output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/params.yaml deleted file mode 100644 index cb0831e1..00000000 --- a/examples/security/classification/output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/adv_predictions.json - adv_probabilities_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/6ea318e136e4da4a5fc685a041b9d349.pkl - params_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/params.yaml - predictions_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/predictions.json - probabilities_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/probabilities.json - score_dict_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/score_dict.json - test_labels_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/test_labels.json - train_labels_file: output/reports/train/e5f1c170bfc8465c8a58c34521a91c08/train_labels.json - model_dir: models - name: e5f1c170bfc8465c8a58c34521a91c08 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ee0c8313d01ac9f326d19c9ab2e9fa8a - trainer: - kwargs: {} -name: e5f1c170bfc8465c8a58c34521a91c08 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e6368532f01acfe507037de593e49b0d/params.yaml b/examples/security/classification/output/reports/train/e6368532f01acfe507037de593e49b0d/params.yaml deleted file mode 100644 index 8662b956..00000000 --- a/examples/security/classification/output/reports/train/e6368532f01acfe507037de593e49b0d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e6368532f01acfe507037de593e49b0d/adv_predictions.json - adv_probabilities_file: output/reports/train/e6368532f01acfe507037de593e49b0d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/b70d617cf6ff28a51e8fe6e2111ce302.pkl - params_file: output/reports/train/e6368532f01acfe507037de593e49b0d/params.yaml - predictions_file: output/reports/train/e6368532f01acfe507037de593e49b0d/predictions.json - probabilities_file: output/reports/train/e6368532f01acfe507037de593e49b0d/probabilities.json - score_dict_file: output/reports/train/e6368532f01acfe507037de593e49b0d/score_dict.json - test_labels_file: output/reports/train/e6368532f01acfe507037de593e49b0d/test_labels.json - train_labels_file: output/reports/train/e6368532f01acfe507037de593e49b0d/train_labels.json - model_dir: models - name: e6368532f01acfe507037de593e49b0d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a699c4fa8376178c431975e29e93287d - trainer: - kwargs: {} -name: e6368532f01acfe507037de593e49b0d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/params.yaml b/examples/security/classification/output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/params.yaml deleted file mode 100644 index 3ae3233d..00000000 --- a/examples/security/classification/output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/adv_predictions.json - adv_probabilities_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/7daf258920d483dc1d8562131cb4450d.pkl - params_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/params.yaml - predictions_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/predictions.json - probabilities_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/probabilities.json - score_dict_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/score_dict.json - test_labels_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/test_labels.json - train_labels_file: output/reports/train/e6760775ee6c0b6d8495522112d3a2e1/train_labels.json - model_dir: models - name: e6760775ee6c0b6d8495522112d3a2e1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8ef8c4936cfd64262c7957fc686a2850 - trainer: - kwargs: {} -name: e6760775ee6c0b6d8495522112d3a2e1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/params.yaml b/examples/security/classification/output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/params.yaml deleted file mode 100644 index 2ffd9dd2..00000000 --- a/examples/security/classification/output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/adv_predictions.json - adv_probabilities_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/cdebebfc5c3c98ff8e1b503fc42959b4.pkl - params_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/params.yaml - predictions_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/predictions.json - probabilities_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/probabilities.json - score_dict_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/score_dict.json - test_labels_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/test_labels.json - train_labels_file: output/reports/train/e67d39c9c4b0c0e4369650c48de5a29d/train_labels.json - model_dir: models - name: e67d39c9c4b0c0e4369650c48de5a29d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 027e7778fdfcfb63335a6081a02acb49 - trainer: - kwargs: {} -name: e67d39c9c4b0c0e4369650c48de5a29d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/params.yaml b/examples/security/classification/output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/params.yaml deleted file mode 100644 index d6578184..00000000 --- a/examples/security/classification/output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/adv_predictions.json - adv_probabilities_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/a167f3ce89e24fba92f122d49a65fdc9.pkl - params_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/params.yaml - predictions_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/predictions.json - probabilities_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/probabilities.json - score_dict_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/score_dict.json - test_labels_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/test_labels.json - train_labels_file: output/reports/train/e69338e55c6e18eda0500def6ee3e7d9/train_labels.json - model_dir: models - name: e69338e55c6e18eda0500def6ee3e7d9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 782abee3f9858f3706140220e131e599 - trainer: - kwargs: {} -name: e69338e55c6e18eda0500def6ee3e7d9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e69a32fd3c1ef53974cb116290305997/params.yaml b/examples/security/classification/output/reports/train/e69a32fd3c1ef53974cb116290305997/params.yaml deleted file mode 100644 index 493143d2..00000000 --- a/examples/security/classification/output/reports/train/e69a32fd3c1ef53974cb116290305997/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/adv_predictions.json - adv_probabilities_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/09bca0bb136efdb41b3459b71055b90a.pkl - params_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/params.yaml - predictions_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/predictions.json - probabilities_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/probabilities.json - score_dict_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/score_dict.json - test_labels_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/test_labels.json - train_labels_file: output/reports/train/e69a32fd3c1ef53974cb116290305997/train_labels.json - model_dir: models - name: e69a32fd3c1ef53974cb116290305997 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f5f39235648d90d051a80756d6d02dc0 - trainer: - kwargs: {} -name: e69a32fd3c1ef53974cb116290305997 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e69ced39ae1e694d111d39441696eb14/params.yaml b/examples/security/classification/output/reports/train/e69ced39ae1e694d111d39441696eb14/params.yaml deleted file mode 100644 index df7b16c1..00000000 --- a/examples/security/classification/output/reports/train/e69ced39ae1e694d111d39441696eb14/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/adv_predictions.json - adv_probabilities_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/a2f28b8606c2da8e030c3218fba25cc4.pkl - params_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/params.yaml - predictions_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/predictions.json - probabilities_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/probabilities.json - score_dict_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/score_dict.json - test_labels_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/test_labels.json - train_labels_file: output/reports/train/e69ced39ae1e694d111d39441696eb14/train_labels.json - model_dir: models - name: e69ced39ae1e694d111d39441696eb14 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b3f62e70e30fce3aa2281420fa4311ae - trainer: - kwargs: {} -name: e69ced39ae1e694d111d39441696eb14 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/params.yaml b/examples/security/classification/output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/params.yaml deleted file mode 100644 index 190e6b8c..00000000 --- a/examples/security/classification/output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/adv_predictions.json - adv_probabilities_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/74c88875112cc54de1f95719ba3c6f85.pkl - params_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/params.yaml - predictions_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/predictions.json - probabilities_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/probabilities.json - score_dict_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/score_dict.json - test_labels_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/test_labels.json - train_labels_file: output/reports/train/e6c2e24cac4eb73808fe0fc0719ae8da/train_labels.json - model_dir: models - name: e6c2e24cac4eb73808fe0fc0719ae8da - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4feb2ca07d3da872d00bfb1d927bbaaf - trainer: - kwargs: {} -name: e6c2e24cac4eb73808fe0fc0719ae8da -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/params.yaml b/examples/security/classification/output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/params.yaml deleted file mode 100644 index 37426895..00000000 --- a/examples/security/classification/output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/adv_predictions.json - adv_probabilities_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/89f319e75ea8fc549d73c60d834d57b2.pkl - params_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/params.yaml - predictions_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/predictions.json - probabilities_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/probabilities.json - score_dict_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/score_dict.json - test_labels_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/test_labels.json - train_labels_file: output/reports/train/e6e00b6a7971d0ca7ad1daa6f49bcbd4/train_labels.json - model_dir: models - name: e6e00b6a7971d0ca7ad1daa6f49bcbd4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6e07216c5cde3a8d7d541ca5b3f591b6 - trainer: - kwargs: {} -name: e6e00b6a7971d0ca7ad1daa6f49bcbd4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/params.yaml b/examples/security/classification/output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/params.yaml deleted file mode 100644 index f88ee2c5..00000000 --- a/examples/security/classification/output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/adv_predictions.json - adv_probabilities_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/a9ebbefb18758565bd7a4bf289745f9f.pkl - params_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/params.yaml - predictions_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/predictions.json - probabilities_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/probabilities.json - score_dict_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/score_dict.json - test_labels_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/test_labels.json - train_labels_file: output/reports/train/e72c709618dfcc5c67a8cccc967c62e7/train_labels.json - model_dir: models - name: e72c709618dfcc5c67a8cccc967c62e7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 767850b8cd8512df0ccaca645b071962 - trainer: - kwargs: {} -name: e72c709618dfcc5c67a8cccc967c62e7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e7307e33c4f6792eb839dbf6d356340c/params.yaml b/examples/security/classification/output/reports/train/e7307e33c4f6792eb839dbf6d356340c/params.yaml deleted file mode 100644 index d1b80364..00000000 --- a/examples/security/classification/output/reports/train/e7307e33c4f6792eb839dbf6d356340c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/adv_predictions.json - adv_probabilities_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/a15daf11989bce81353e1bd5225dfa6e.pkl - params_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/params.yaml - predictions_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/predictions.json - probabilities_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/probabilities.json - score_dict_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/score_dict.json - test_labels_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/test_labels.json - train_labels_file: output/reports/train/e7307e33c4f6792eb839dbf6d356340c/train_labels.json - model_dir: models - name: e7307e33c4f6792eb839dbf6d356340c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e57245ec84f577e1683c248dc6d30dcc - trainer: - kwargs: {} -name: e7307e33c4f6792eb839dbf6d356340c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/params.yaml b/examples/security/classification/output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/params.yaml deleted file mode 100644 index 859e0407..00000000 --- a/examples/security/classification/output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/adv_predictions.json - adv_probabilities_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/e2d69ade391f88872085e1b4824df742.pkl - params_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/params.yaml - predictions_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/predictions.json - probabilities_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/probabilities.json - score_dict_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/score_dict.json - test_labels_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/test_labels.json - train_labels_file: output/reports/train/e74b3d32aabfa89daf98d7fe6618076f/train_labels.json - model_dir: models - name: e74b3d32aabfa89daf98d7fe6618076f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9b79c88a27bc5a321d4e7935beb3911f - trainer: - kwargs: {} -name: e74b3d32aabfa89daf98d7fe6618076f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e74d7f380c82daea3f3c938e71677d6b/params.yaml b/examples/security/classification/output/reports/train/e74d7f380c82daea3f3c938e71677d6b/params.yaml deleted file mode 100644 index f282b415..00000000 --- a/examples/security/classification/output/reports/train/e74d7f380c82daea3f3c938e71677d6b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/adv_predictions.json - adv_probabilities_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/48f7e695f1b80de92ade394ee4751c39.pkl - params_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/params.yaml - predictions_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/predictions.json - probabilities_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/probabilities.json - score_dict_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/score_dict.json - test_labels_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/test_labels.json - train_labels_file: output/reports/train/e74d7f380c82daea3f3c938e71677d6b/train_labels.json - model_dir: models - name: e74d7f380c82daea3f3c938e71677d6b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 843eddbda83bf0999848ff60b09a19ec - trainer: - kwargs: {} -name: e74d7f380c82daea3f3c938e71677d6b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e7c5e291845b14ee05e1de88c676c0af/params.yaml b/examples/security/classification/output/reports/train/e7c5e291845b14ee05e1de88c676c0af/params.yaml deleted file mode 100644 index 1538efe7..00000000 --- a/examples/security/classification/output/reports/train/e7c5e291845b14ee05e1de88c676c0af/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/adv_predictions.json - adv_probabilities_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/738d79050c9f035e572a11408c1bcba4.pkl - params_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/params.yaml - predictions_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/predictions.json - probabilities_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/probabilities.json - score_dict_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/score_dict.json - test_labels_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/test_labels.json - train_labels_file: output/reports/train/e7c5e291845b14ee05e1de88c676c0af/train_labels.json - model_dir: models - name: e7c5e291845b14ee05e1de88c676c0af - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 21185b2b1f38b12f02a53988359a0983 - trainer: - kwargs: {} -name: e7c5e291845b14ee05e1de88c676c0af -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/params.yaml b/examples/security/classification/output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/params.yaml deleted file mode 100644 index 219b62ea..00000000 --- a/examples/security/classification/output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/adv_predictions.json - adv_probabilities_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/c1515849d71b42f22875280eba966d1c.pkl - params_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/params.yaml - predictions_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/predictions.json - probabilities_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/probabilities.json - score_dict_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/score_dict.json - test_labels_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/test_labels.json - train_labels_file: output/reports/train/e818bcdb4ed3e4b6f669d9a8ea1ebf10/train_labels.json - model_dir: models - name: e818bcdb4ed3e4b6f669d9a8ea1ebf10 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 31d9601311030ee58391a702e11b2bdb - trainer: - kwargs: {} -name: e818bcdb4ed3e4b6f669d9a8ea1ebf10 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e81bf0d976c5e716cb16d63e205cf344/params.yaml b/examples/security/classification/output/reports/train/e81bf0d976c5e716cb16d63e205cf344/params.yaml deleted file mode 100644 index ce7c2662..00000000 --- a/examples/security/classification/output/reports/train/e81bf0d976c5e716cb16d63e205cf344/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/adv_predictions.json - adv_probabilities_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/30bce144aca3da9b3c2fa5dea989f559.pkl - params_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/params.yaml - predictions_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/predictions.json - probabilities_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/probabilities.json - score_dict_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/score_dict.json - test_labels_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/test_labels.json - train_labels_file: output/reports/train/e81bf0d976c5e716cb16d63e205cf344/train_labels.json - model_dir: models - name: e81bf0d976c5e716cb16d63e205cf344 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7064e3fd57dcc2e76b833915dc29a72f - trainer: - kwargs: {} -name: e81bf0d976c5e716cb16d63e205cf344 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/params.yaml b/examples/security/classification/output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/params.yaml deleted file mode 100644 index bde289fe..00000000 --- a/examples/security/classification/output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/adv_predictions.json - adv_probabilities_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/e146b7a92ad7539f0b645320150e0a9c.pkl - params_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/params.yaml - predictions_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/predictions.json - probabilities_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/probabilities.json - score_dict_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/score_dict.json - test_labels_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/test_labels.json - train_labels_file: output/reports/train/e825df223c6fc9d2403b2e9b1ae5dc78/train_labels.json - model_dir: models - name: e825df223c6fc9d2403b2e9b1ae5dc78 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 97c029aaddf43603c7dba7757f6a3d19 - trainer: - kwargs: {} -name: e825df223c6fc9d2403b2e9b1ae5dc78 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e846aa4f707855adb801c7f279d67bda/params.yaml b/examples/security/classification/output/reports/train/e846aa4f707855adb801c7f279d67bda/params.yaml deleted file mode 100644 index 896e02f2..00000000 --- a/examples/security/classification/output/reports/train/e846aa4f707855adb801c7f279d67bda/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/adv_predictions.json - adv_probabilities_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/3dd88d6f3f6b7897156d2060843d8383.pkl - params_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/params.yaml - predictions_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/predictions.json - probabilities_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/probabilities.json - score_dict_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/score_dict.json - test_labels_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/test_labels.json - train_labels_file: output/reports/train/e846aa4f707855adb801c7f279d67bda/train_labels.json - model_dir: models - name: e846aa4f707855adb801c7f279d67bda - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cfda25196afacc423519b0584cf89b34 - trainer: - kwargs: {} -name: e846aa4f707855adb801c7f279d67bda -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/params.yaml b/examples/security/classification/output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/params.yaml deleted file mode 100644 index 484994fe..00000000 --- a/examples/security/classification/output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/adv_predictions.json - adv_probabilities_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/1d6cadc58e5d38e196f73e3499dd765f.pkl - params_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/params.yaml - predictions_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/predictions.json - probabilities_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/probabilities.json - score_dict_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/score_dict.json - test_labels_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/test_labels.json - train_labels_file: output/reports/train/e8665e7e3392fa1e10dc4185f291ca6a/train_labels.json - model_dir: models - name: e8665e7e3392fa1e10dc4185f291ca6a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b06e3d8c90800de747eff86e6f581f49 - trainer: - kwargs: {} -name: e8665e7e3392fa1e10dc4185f291ca6a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e87ef61295a5b27373cdb1319ada9db9/params.yaml b/examples/security/classification/output/reports/train/e87ef61295a5b27373cdb1319ada9db9/params.yaml deleted file mode 100644 index fd6a9f11..00000000 --- a/examples/security/classification/output/reports/train/e87ef61295a5b27373cdb1319ada9db9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/adv_predictions.json - adv_probabilities_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/e6f3c587a45af8bc7773e92101830d9c.pkl - params_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/params.yaml - predictions_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/predictions.json - probabilities_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/probabilities.json - score_dict_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/score_dict.json - test_labels_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/test_labels.json - train_labels_file: output/reports/train/e87ef61295a5b27373cdb1319ada9db9/train_labels.json - model_dir: models - name: e87ef61295a5b27373cdb1319ada9db9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 58050d2f22f7fbeee1e61ed950b39e40 - trainer: - kwargs: {} -name: e87ef61295a5b27373cdb1319ada9db9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e899358679c801f6e43a49370a0d7acd/params.yaml b/examples/security/classification/output/reports/train/e899358679c801f6e43a49370a0d7acd/params.yaml deleted file mode 100644 index 9fa11fdd..00000000 --- a/examples/security/classification/output/reports/train/e899358679c801f6e43a49370a0d7acd/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/adv_predictions.json - adv_probabilities_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/c4ed5d492da8a2ccb0607d25e58e11f5.pkl - params_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/params.yaml - predictions_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/predictions.json - probabilities_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/probabilities.json - score_dict_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/score_dict.json - test_labels_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/test_labels.json - train_labels_file: output/reports/train/e899358679c801f6e43a49370a0d7acd/train_labels.json - model_dir: models - name: e899358679c801f6e43a49370a0d7acd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1af2b8036db4feb1473d8049812d57b2 - trainer: - kwargs: {} -name: e899358679c801f6e43a49370a0d7acd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/params.yaml b/examples/security/classification/output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/params.yaml deleted file mode 100644 index a2b035e3..00000000 --- a/examples/security/classification/output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/adv_predictions.json - adv_probabilities_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/ddc9ec8a76c5db57f1f80f3860a73b46.pkl - params_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/params.yaml - predictions_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/predictions.json - probabilities_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/probabilities.json - score_dict_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/score_dict.json - test_labels_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/test_labels.json - train_labels_file: output/reports/train/e8b19e7e62a5e0ae2888cc140e5d3266/train_labels.json - model_dir: models - name: e8b19e7e62a5e0ae2888cc140e5d3266 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 07bee317568afac49ce8603f2cdda145 - trainer: - kwargs: {} -name: e8b19e7e62a5e0ae2888cc140e5d3266 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e8c117a2334a052205fd5f472aaa3518/params.yaml b/examples/security/classification/output/reports/train/e8c117a2334a052205fd5f472aaa3518/params.yaml deleted file mode 100644 index a107f52e..00000000 --- a/examples/security/classification/output/reports/train/e8c117a2334a052205fd5f472aaa3518/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/adv_predictions.json - adv_probabilities_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/e90dd22e591f680e0d29929bdb086d2d.pkl - params_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/params.yaml - predictions_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/predictions.json - probabilities_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/probabilities.json - score_dict_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/score_dict.json - test_labels_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/test_labels.json - train_labels_file: output/reports/train/e8c117a2334a052205fd5f472aaa3518/train_labels.json - model_dir: models - name: e8c117a2334a052205fd5f472aaa3518 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6b2011e44f8db3b21c8484b96db5f2ec - trainer: - kwargs: {} -name: e8c117a2334a052205fd5f472aaa3518 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e8c27be43209312dd147cb4264e59891/params.yaml b/examples/security/classification/output/reports/train/e8c27be43209312dd147cb4264e59891/params.yaml deleted file mode 100644 index 49fac461..00000000 --- a/examples/security/classification/output/reports/train/e8c27be43209312dd147cb4264e59891/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e8c27be43209312dd147cb4264e59891/adv_predictions.json - adv_probabilities_file: output/reports/train/e8c27be43209312dd147cb4264e59891/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/0cc6ad04670a7d5821ae061eeaa0acd5.pkl - params_file: output/reports/train/e8c27be43209312dd147cb4264e59891/params.yaml - predictions_file: output/reports/train/e8c27be43209312dd147cb4264e59891/predictions.json - probabilities_file: output/reports/train/e8c27be43209312dd147cb4264e59891/probabilities.json - score_dict_file: output/reports/train/e8c27be43209312dd147cb4264e59891/score_dict.json - test_labels_file: output/reports/train/e8c27be43209312dd147cb4264e59891/test_labels.json - train_labels_file: output/reports/train/e8c27be43209312dd147cb4264e59891/train_labels.json - model_dir: models - name: e8c27be43209312dd147cb4264e59891 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 816578005ca7119282c6c63d195a5acc - trainer: - kwargs: {} -name: e8c27be43209312dd147cb4264e59891 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/params.yaml b/examples/security/classification/output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/params.yaml deleted file mode 100644 index 9843fd20..00000000 --- a/examples/security/classification/output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/adv_predictions.json - adv_probabilities_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/97ab13a97393ee9fbf29460b8fd1e689.pkl - params_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/params.yaml - predictions_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/predictions.json - probabilities_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/probabilities.json - score_dict_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/score_dict.json - test_labels_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/test_labels.json - train_labels_file: output/reports/train/e8eeb704f17e2acce4c27aa12a6e9d58/train_labels.json - model_dir: models - name: e8eeb704f17e2acce4c27aa12a6e9d58 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 27a9df117275f6e8bba7da6f77316e96 - trainer: - kwargs: {} -name: e8eeb704f17e2acce4c27aa12a6e9d58 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/params.yaml b/examples/security/classification/output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/params.yaml deleted file mode 100644 index d1e7686c..00000000 --- a/examples/security/classification/output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/adv_predictions.json - adv_probabilities_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/155293e1bafe1e04e64003f53c57d0ee.pkl - params_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/params.yaml - predictions_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/predictions.json - probabilities_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/probabilities.json - score_dict_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/score_dict.json - test_labels_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/test_labels.json - train_labels_file: output/reports/train/e9057ae801790ac5b84e40d8e92b3e3c/train_labels.json - model_dir: models - name: e9057ae801790ac5b84e40d8e92b3e3c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e7d18d1adca8a9638fe4725dbdeb8fa1 - trainer: - kwargs: {} -name: e9057ae801790ac5b84e40d8e92b3e3c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/params.yaml b/examples/security/classification/output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/params.yaml deleted file mode 100644 index b4ebfbe7..00000000 --- a/examples/security/classification/output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/adv_predictions.json - adv_probabilities_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/1b833cee6f013f71840c7b973bd31668.pkl - params_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/params.yaml - predictions_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/predictions.json - probabilities_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/probabilities.json - score_dict_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/score_dict.json - test_labels_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/test_labels.json - train_labels_file: output/reports/train/e911d15b5231f4cb8016cc5bf724a8a5/train_labels.json - model_dir: models - name: e911d15b5231f4cb8016cc5bf724a8a5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 981ada45c89feb7e164c8430b28caa16 - trainer: - kwargs: {} -name: e911d15b5231f4cb8016cc5bf724a8a5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e945cd7d151563162b4a0ed15af65335/params.yaml b/examples/security/classification/output/reports/train/e945cd7d151563162b4a0ed15af65335/params.yaml deleted file mode 100644 index 92f19b4e..00000000 --- a/examples/security/classification/output/reports/train/e945cd7d151563162b4a0ed15af65335/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/adv_predictions.json - adv_probabilities_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/ce50e1a25d7e9ac6ba74f66f4d43df35.pkl - params_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/params.yaml - predictions_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/predictions.json - probabilities_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/probabilities.json - score_dict_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/score_dict.json - test_labels_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/test_labels.json - train_labels_file: output/reports/train/e945cd7d151563162b4a0ed15af65335/train_labels.json - model_dir: models - name: e945cd7d151563162b4a0ed15af65335 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2e136962733461d33ddfe9a1a89b830d - trainer: - kwargs: {} -name: e945cd7d151563162b4a0ed15af65335 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/params.yaml b/examples/security/classification/output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/params.yaml deleted file mode 100644 index 3396be48..00000000 --- a/examples/security/classification/output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/adv_predictions.json - adv_probabilities_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/d64f8853edd371dc112f9ba2ab596cc2.pkl - params_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/params.yaml - predictions_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/predictions.json - probabilities_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/probabilities.json - score_dict_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/score_dict.json - test_labels_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/test_labels.json - train_labels_file: output/reports/train/e97e1ef7703bf336edeba5e89e11bfaf/train_labels.json - model_dir: models - name: e97e1ef7703bf336edeba5e89e11bfaf - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cf63e39b76e8090165b811c80777458a - trainer: - kwargs: {} -name: e97e1ef7703bf336edeba5e89e11bfaf -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e980b6b22cf8b2e6904673f6e536d967/params.yaml b/examples/security/classification/output/reports/train/e980b6b22cf8b2e6904673f6e536d967/params.yaml deleted file mode 100644 index 963e0c90..00000000 --- a/examples/security/classification/output/reports/train/e980b6b22cf8b2e6904673f6e536d967/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/adv_predictions.json - adv_probabilities_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/0f6ebb1be2d5f6f35d833441784c8c6c.pkl - params_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/params.yaml - predictions_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/predictions.json - probabilities_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/probabilities.json - score_dict_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/score_dict.json - test_labels_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/test_labels.json - train_labels_file: output/reports/train/e980b6b22cf8b2e6904673f6e536d967/train_labels.json - model_dir: models - name: e980b6b22cf8b2e6904673f6e536d967 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 77a45de33fe348e531fed4766428b040 - trainer: - kwargs: {} -name: e980b6b22cf8b2e6904673f6e536d967 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/params.yaml b/examples/security/classification/output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/params.yaml deleted file mode 100644 index 17663903..00000000 --- a/examples/security/classification/output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/adv_predictions.json - adv_probabilities_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/b3c69e04754a59f1bc3b7ab66ebea7d0.pkl - params_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/params.yaml - predictions_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/predictions.json - probabilities_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/probabilities.json - score_dict_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/score_dict.json - test_labels_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/test_labels.json - train_labels_file: output/reports/train/e9bc99d6d38f0f0fb467af2af4f83b38/train_labels.json - model_dir: models - name: e9bc99d6d38f0f0fb467af2af4f83b38 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 26571b7a2b0035a87d8fce5e1c3228f0 - trainer: - kwargs: {} -name: e9bc99d6d38f0f0fb467af2af4f83b38 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e9c7eed98101ac4a0441a7048598653f/params.yaml b/examples/security/classification/output/reports/train/e9c7eed98101ac4a0441a7048598653f/params.yaml deleted file mode 100644 index e0c74ebe..00000000 --- a/examples/security/classification/output/reports/train/e9c7eed98101ac4a0441a7048598653f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/adv_predictions.json - adv_probabilities_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/00ef7b4695bc287d961b7ebae9985534.pkl - params_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/params.yaml - predictions_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/predictions.json - probabilities_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/probabilities.json - score_dict_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/score_dict.json - test_labels_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/test_labels.json - train_labels_file: output/reports/train/e9c7eed98101ac4a0441a7048598653f/train_labels.json - model_dir: models - name: e9c7eed98101ac4a0441a7048598653f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6559ac03fd4b46f0089776d62ca9b590 - trainer: - kwargs: {} -name: e9c7eed98101ac4a0441a7048598653f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/params.yaml b/examples/security/classification/output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/params.yaml deleted file mode 100644 index ec037814..00000000 --- a/examples/security/classification/output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/adv_predictions.json - adv_probabilities_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/b425dae9b39cb9159610e00efb8a9087.pkl - params_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/params.yaml - predictions_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/predictions.json - probabilities_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/probabilities.json - score_dict_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/score_dict.json - test_labels_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/test_labels.json - train_labels_file: output/reports/train/e9e1fdf3a1e29c6952158cbcc84960a7/train_labels.json - model_dir: models - name: e9e1fdf3a1e29c6952158cbcc84960a7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dcb4335bfd8c0c83200fd86441dd1cd6 - trainer: - kwargs: {} -name: e9e1fdf3a1e29c6952158cbcc84960a7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/params.yaml b/examples/security/classification/output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/params.yaml deleted file mode 100644 index f48c6db0..00000000 --- a/examples/security/classification/output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/adv_predictions.json - adv_probabilities_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/cff2c811af31d09ada796aa2cce7f688.pkl - params_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/params.yaml - predictions_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/predictions.json - probabilities_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/probabilities.json - score_dict_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/score_dict.json - test_labels_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/test_labels.json - train_labels_file: output/reports/train/e9f9e8a186608a3180226a93afdb2cfe/train_labels.json - model_dir: models - name: e9f9e8a186608a3180226a93afdb2cfe - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d3acb166223257fd354caa358ec7cf65 - trainer: - kwargs: {} -name: e9f9e8a186608a3180226a93afdb2cfe -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ea145a9601bf11b9454253497cbdfd9f/params.yaml b/examples/security/classification/output/reports/train/ea145a9601bf11b9454253497cbdfd9f/params.yaml deleted file mode 100644 index ee451751..00000000 --- a/examples/security/classification/output/reports/train/ea145a9601bf11b9454253497cbdfd9f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/adv_predictions.json - adv_probabilities_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/7b9e4edf3ad1fbf591c31ee44c0f3b78.pkl - params_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/params.yaml - predictions_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/predictions.json - probabilities_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/probabilities.json - score_dict_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/score_dict.json - test_labels_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/test_labels.json - train_labels_file: output/reports/train/ea145a9601bf11b9454253497cbdfd9f/train_labels.json - model_dir: models - name: ea145a9601bf11b9454253497cbdfd9f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a87befcf20bc49bc6443ea965c0ee173 - trainer: - kwargs: {} -name: ea145a9601bf11b9454253497cbdfd9f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ea378eed920b312df869e722cb22f52a/params.yaml b/examples/security/classification/output/reports/train/ea378eed920b312df869e722cb22f52a/params.yaml deleted file mode 100644 index 1f7b9388..00000000 --- a/examples/security/classification/output/reports/train/ea378eed920b312df869e722cb22f52a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ea378eed920b312df869e722cb22f52a/adv_predictions.json - adv_probabilities_file: output/reports/train/ea378eed920b312df869e722cb22f52a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/cb2ee7b045d671f85c520e12310068c0.pkl - params_file: output/reports/train/ea378eed920b312df869e722cb22f52a/params.yaml - predictions_file: output/reports/train/ea378eed920b312df869e722cb22f52a/predictions.json - probabilities_file: output/reports/train/ea378eed920b312df869e722cb22f52a/probabilities.json - score_dict_file: output/reports/train/ea378eed920b312df869e722cb22f52a/score_dict.json - test_labels_file: output/reports/train/ea378eed920b312df869e722cb22f52a/test_labels.json - train_labels_file: output/reports/train/ea378eed920b312df869e722cb22f52a/train_labels.json - model_dir: models - name: ea378eed920b312df869e722cb22f52a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cea19640e6b4a19ea977321c3b925459 - trainer: - kwargs: {} -name: ea378eed920b312df869e722cb22f52a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/params.yaml b/examples/security/classification/output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/params.yaml deleted file mode 100644 index 47cf3d41..00000000 --- a/examples/security/classification/output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/adv_predictions.json - adv_probabilities_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/09d537f1b554212a531b4a4c949215aa.pkl - params_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/params.yaml - predictions_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/predictions.json - probabilities_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/probabilities.json - score_dict_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/score_dict.json - test_labels_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/test_labels.json - train_labels_file: output/reports/train/ea7db42fd62cf90ee4bae2cbb561468e/train_labels.json - model_dir: models - name: ea7db42fd62cf90ee4bae2cbb561468e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 149a489a42f095f2369d2950676aa3af - trainer: - kwargs: {} -name: ea7db42fd62cf90ee4bae2cbb561468e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/params.yaml b/examples/security/classification/output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/params.yaml deleted file mode 100644 index 9ce4d7e3..00000000 --- a/examples/security/classification/output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/adv_predictions.json - adv_probabilities_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/5255e49f6827262ae609199a2b80125f.pkl - params_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/params.yaml - predictions_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/predictions.json - probabilities_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/probabilities.json - score_dict_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/score_dict.json - test_labels_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/test_labels.json - train_labels_file: output/reports/train/eacde15c490a4776a80e8ff0f05db4bc/train_labels.json - model_dir: models - name: eacde15c490a4776a80e8ff0f05db4bc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 86cd8e861b44dc28c8778951ba30a98d - trainer: - kwargs: {} -name: eacde15c490a4776a80e8ff0f05db4bc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/params.yaml b/examples/security/classification/output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/params.yaml deleted file mode 100644 index ce170fc2..00000000 --- a/examples/security/classification/output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/adv_predictions.json - adv_probabilities_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/e8cf5fd1f5d3a18771da77166622b2bd.pkl - params_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/params.yaml - predictions_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/predictions.json - probabilities_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/probabilities.json - score_dict_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/score_dict.json - test_labels_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/test_labels.json - train_labels_file: output/reports/train/ead0b6385d8a4b5a288c9eb906c33e15/train_labels.json - model_dir: models - name: ead0b6385d8a4b5a288c9eb906c33e15 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b6aa9f6c9f4460f0334e23405dd4b7fe - trainer: - kwargs: {} -name: ead0b6385d8a4b5a288c9eb906c33e15 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/eb40ff16f16a4c348441badffcc94251/params.yaml b/examples/security/classification/output/reports/train/eb40ff16f16a4c348441badffcc94251/params.yaml deleted file mode 100644 index 33b27bb7..00000000 --- a/examples/security/classification/output/reports/train/eb40ff16f16a4c348441badffcc94251/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/adv_predictions.json - adv_probabilities_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/83e8820199c4ba4c1cf78b96a7abf02e.pkl - params_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/params.yaml - predictions_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/predictions.json - probabilities_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/probabilities.json - score_dict_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/score_dict.json - test_labels_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/test_labels.json - train_labels_file: output/reports/train/eb40ff16f16a4c348441badffcc94251/train_labels.json - model_dir: models - name: eb40ff16f16a4c348441badffcc94251 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4993d77a8adea02b9c86920985164777 - trainer: - kwargs: {} -name: eb40ff16f16a4c348441badffcc94251 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/eb4a93f1a67703e587ea895032a7003a/params.yaml b/examples/security/classification/output/reports/train/eb4a93f1a67703e587ea895032a7003a/params.yaml deleted file mode 100644 index 2d4a1fa6..00000000 --- a/examples/security/classification/output/reports/train/eb4a93f1a67703e587ea895032a7003a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/adv_predictions.json - adv_probabilities_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/138ab5acb8edbdad4b835db9ad3a0206.pkl - params_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/params.yaml - predictions_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/predictions.json - probabilities_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/probabilities.json - score_dict_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/score_dict.json - test_labels_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/test_labels.json - train_labels_file: output/reports/train/eb4a93f1a67703e587ea895032a7003a/train_labels.json - model_dir: models - name: eb4a93f1a67703e587ea895032a7003a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a8a3ffbd25cc1c77e3be273385e71a4d - trainer: - kwargs: {} -name: eb4a93f1a67703e587ea895032a7003a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/eb68f7a15f8799590ca0fdde540c5640/params.yaml b/examples/security/classification/output/reports/train/eb68f7a15f8799590ca0fdde540c5640/params.yaml deleted file mode 100644 index 7a972456..00000000 --- a/examples/security/classification/output/reports/train/eb68f7a15f8799590ca0fdde540c5640/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/adv_predictions.json - adv_probabilities_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/a11546a1c7ca364c3db0d0af8992c45f.pkl - params_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/params.yaml - predictions_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/predictions.json - probabilities_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/probabilities.json - score_dict_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/score_dict.json - test_labels_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/test_labels.json - train_labels_file: output/reports/train/eb68f7a15f8799590ca0fdde540c5640/train_labels.json - model_dir: models - name: eb68f7a15f8799590ca0fdde540c5640 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5beef159fe944dc8bc490ab5857ad6e2 - trainer: - kwargs: {} -name: eb68f7a15f8799590ca0fdde540c5640 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/params.yaml b/examples/security/classification/output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/params.yaml deleted file mode 100644 index df542e57..00000000 --- a/examples/security/classification/output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/adv_predictions.json - adv_probabilities_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/620a7d8b8085d2fbb953987ab84dada2.pkl - params_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/params.yaml - predictions_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/predictions.json - probabilities_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/probabilities.json - score_dict_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/score_dict.json - test_labels_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/test_labels.json - train_labels_file: output/reports/train/eb86ebe2a20746bcd80ee3523f1e2886/train_labels.json - model_dir: models - name: eb86ebe2a20746bcd80ee3523f1e2886 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e3f8ea926e214e7bb4fc69479b2282f4 - trainer: - kwargs: {} -name: eb86ebe2a20746bcd80ee3523f1e2886 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/params.yaml b/examples/security/classification/output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/params.yaml deleted file mode 100644 index bbfc1bbb..00000000 --- a/examples/security/classification/output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/adv_predictions.json - adv_probabilities_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/d1b7fb50ee0e1a72db4158a93ebd534f.pkl - params_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/params.yaml - predictions_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/predictions.json - probabilities_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/probabilities.json - score_dict_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/score_dict.json - test_labels_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/test_labels.json - train_labels_file: output/reports/train/eb9835c40bb7edbc11bd27aa7591037a/train_labels.json - model_dir: models - name: eb9835c40bb7edbc11bd27aa7591037a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c54a3353cadb3ebfa57d663da3ca23bb - trainer: - kwargs: {} -name: eb9835c40bb7edbc11bd27aa7591037a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/params.yaml b/examples/security/classification/output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/params.yaml deleted file mode 100644 index 01dfc437..00000000 --- a/examples/security/classification/output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/adv_predictions.json - adv_probabilities_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/f6461b87ab9ad3e446d9bb0220f523ae.pkl - params_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/params.yaml - predictions_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/predictions.json - probabilities_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/probabilities.json - score_dict_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/score_dict.json - test_labels_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/test_labels.json - train_labels_file: output/reports/train/ebb0f191822e30c3fba8fe8e4a4d798d/train_labels.json - model_dir: models - name: ebb0f191822e30c3fba8fe8e4a4d798d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9aeecfd6c8da3f6535f3055361b75fd1 - trainer: - kwargs: {} -name: ebb0f191822e30c3fba8fe8e4a4d798d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ebed79e91a7b458890045c8039133ca9/params.yaml b/examples/security/classification/output/reports/train/ebed79e91a7b458890045c8039133ca9/params.yaml deleted file mode 100644 index 38f4de99..00000000 --- a/examples/security/classification/output/reports/train/ebed79e91a7b458890045c8039133ca9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/adv_predictions.json - adv_probabilities_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/f61a0bf1162bf2ab9fd355d072a94c08.pkl - params_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/params.yaml - predictions_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/predictions.json - probabilities_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/probabilities.json - score_dict_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/score_dict.json - test_labels_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/test_labels.json - train_labels_file: output/reports/train/ebed79e91a7b458890045c8039133ca9/train_labels.json - model_dir: models - name: ebed79e91a7b458890045c8039133ca9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0299eb6f28967305366f33b1d78f9daa - trainer: - kwargs: {} -name: ebed79e91a7b458890045c8039133ca9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ebf48577409cfdaf8fa1d317fde02664/params.yaml b/examples/security/classification/output/reports/train/ebf48577409cfdaf8fa1d317fde02664/params.yaml deleted file mode 100644 index 4fbfac44..00000000 --- a/examples/security/classification/output/reports/train/ebf48577409cfdaf8fa1d317fde02664/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/adv_predictions.json - adv_probabilities_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/3e5372c0782be74c613b613f92cd779b.pkl - params_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/params.yaml - predictions_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/predictions.json - probabilities_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/probabilities.json - score_dict_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/score_dict.json - test_labels_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/test_labels.json - train_labels_file: output/reports/train/ebf48577409cfdaf8fa1d317fde02664/train_labels.json - model_dir: models - name: ebf48577409cfdaf8fa1d317fde02664 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 009dc769d25c3ffc447d3c098376087b - trainer: - kwargs: {} -name: ebf48577409cfdaf8fa1d317fde02664 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/params.yaml b/examples/security/classification/output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/params.yaml deleted file mode 100644 index 04efa776..00000000 --- a/examples/security/classification/output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/adv_predictions.json - adv_probabilities_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/64e83305feab8a9bb66d278bda023347.pkl - params_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/params.yaml - predictions_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/predictions.json - probabilities_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/probabilities.json - score_dict_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/score_dict.json - test_labels_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/test_labels.json - train_labels_file: output/reports/train/ebffc486a1707d02aff2c7dfc61a4b50/train_labels.json - model_dir: models - name: ebffc486a1707d02aff2c7dfc61a4b50 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: acbf4338bb00066312fdbefc718d439e - trainer: - kwargs: {} -name: ebffc486a1707d02aff2c7dfc61a4b50 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ec103ed79e24da73d0fa4284d28054c6/params.yaml b/examples/security/classification/output/reports/train/ec103ed79e24da73d0fa4284d28054c6/params.yaml deleted file mode 100644 index 0119dbcd..00000000 --- a/examples/security/classification/output/reports/train/ec103ed79e24da73d0fa4284d28054c6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/adv_predictions.json - adv_probabilities_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/7953a074cb8272be6486d72942d0f5af.pkl - params_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/params.yaml - predictions_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/predictions.json - probabilities_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/probabilities.json - score_dict_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/score_dict.json - test_labels_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/test_labels.json - train_labels_file: output/reports/train/ec103ed79e24da73d0fa4284d28054c6/train_labels.json - model_dir: models - name: ec103ed79e24da73d0fa4284d28054c6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eb43ba23847684df408bf77a5c7aae54 - trainer: - kwargs: {} -name: ec103ed79e24da73d0fa4284d28054c6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ec130594a417a85d8f4c082270fbc165/params.yaml b/examples/security/classification/output/reports/train/ec130594a417a85d8f4c082270fbc165/params.yaml deleted file mode 100644 index e42963d2..00000000 --- a/examples/security/classification/output/reports/train/ec130594a417a85d8f4c082270fbc165/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/adv_predictions.json - adv_probabilities_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/93ac07df0663de7c7bce162fdde8f8e9.pkl - params_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/params.yaml - predictions_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/predictions.json - probabilities_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/probabilities.json - score_dict_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/score_dict.json - test_labels_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/test_labels.json - train_labels_file: output/reports/train/ec130594a417a85d8f4c082270fbc165/train_labels.json - model_dir: models - name: ec130594a417a85d8f4c082270fbc165 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ac8a6d4edbc8d812e139f1365812291b - trainer: - kwargs: {} -name: ec130594a417a85d8f4c082270fbc165 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ec1bbba6d3f703c803790644d8a87d52/params.yaml b/examples/security/classification/output/reports/train/ec1bbba6d3f703c803790644d8a87d52/params.yaml deleted file mode 100644 index 4133065a..00000000 --- a/examples/security/classification/output/reports/train/ec1bbba6d3f703c803790644d8a87d52/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/adv_predictions.json - adv_probabilities_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/24355d4540e42fea4478b3401c45e396.pkl - params_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/params.yaml - predictions_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/predictions.json - probabilities_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/probabilities.json - score_dict_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/score_dict.json - test_labels_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/test_labels.json - train_labels_file: output/reports/train/ec1bbba6d3f703c803790644d8a87d52/train_labels.json - model_dir: models - name: ec1bbba6d3f703c803790644d8a87d52 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 39829a54873b0bff9cbaa0c8380f4fbb - trainer: - kwargs: {} -name: ec1bbba6d3f703c803790644d8a87d52 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/params.yaml b/examples/security/classification/output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/params.yaml deleted file mode 100644 index 23ead2f2..00000000 --- a/examples/security/classification/output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/adv_predictions.json - adv_probabilities_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/4c5423c17eafc6d7bf6bf9c273c748c5.pkl - params_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/params.yaml - predictions_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/predictions.json - probabilities_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/probabilities.json - score_dict_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/score_dict.json - test_labels_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/test_labels.json - train_labels_file: output/reports/train/ec213f520ac4ef81e9c8da6a22afdfd9/train_labels.json - model_dir: models - name: ec213f520ac4ef81e9c8da6a22afdfd9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eee930038d736854721ae1046c91039d - trainer: - kwargs: {} -name: ec213f520ac4ef81e9c8da6a22afdfd9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ec2404a0434fd5af43c0a3b77b668003/params.yaml b/examples/security/classification/output/reports/train/ec2404a0434fd5af43c0a3b77b668003/params.yaml deleted file mode 100644 index 27f40b7c..00000000 --- a/examples/security/classification/output/reports/train/ec2404a0434fd5af43c0a3b77b668003/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/adv_predictions.json - adv_probabilities_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/b38efade392b8e04b6c9d5c4c10e4c23.pkl - params_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/params.yaml - predictions_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/predictions.json - probabilities_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/probabilities.json - score_dict_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/score_dict.json - test_labels_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/test_labels.json - train_labels_file: output/reports/train/ec2404a0434fd5af43c0a3b77b668003/train_labels.json - model_dir: models - name: ec2404a0434fd5af43c0a3b77b668003 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 34478b6c293f404754e4776bf082db8a - trainer: - kwargs: {} -name: ec2404a0434fd5af43c0a3b77b668003 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ec33273879697620f55a0a83a9a3c6f3/params.yaml b/examples/security/classification/output/reports/train/ec33273879697620f55a0a83a9a3c6f3/params.yaml deleted file mode 100644 index 7bdafa0e..00000000 --- a/examples/security/classification/output/reports/train/ec33273879697620f55a0a83a9a3c6f3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/adv_predictions.json - adv_probabilities_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/63b095517368dc3e5cfc812e3096d332.pkl - params_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/params.yaml - predictions_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/predictions.json - probabilities_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/probabilities.json - score_dict_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/score_dict.json - test_labels_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/test_labels.json - train_labels_file: output/reports/train/ec33273879697620f55a0a83a9a3c6f3/train_labels.json - model_dir: models - name: ec33273879697620f55a0a83a9a3c6f3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0769f0daeeb4214e943614301fa8cefc - trainer: - kwargs: {} -name: ec33273879697620f55a0a83a9a3c6f3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ec7690e362ffe357e03688dc91e073de/params.yaml b/examples/security/classification/output/reports/train/ec7690e362ffe357e03688dc91e073de/params.yaml deleted file mode 100644 index 2dad436f..00000000 --- a/examples/security/classification/output/reports/train/ec7690e362ffe357e03688dc91e073de/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/adv_predictions.json - adv_probabilities_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/7eebd27111282e42b7ed577c958b0aba.pkl - params_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/params.yaml - predictions_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/predictions.json - probabilities_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/probabilities.json - score_dict_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/score_dict.json - test_labels_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/test_labels.json - train_labels_file: output/reports/train/ec7690e362ffe357e03688dc91e073de/train_labels.json - model_dir: models - name: ec7690e362ffe357e03688dc91e073de - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0091aed8b516908a93141cbac7a62aa5 - trainer: - kwargs: {} -name: ec7690e362ffe357e03688dc91e073de -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/params.yaml b/examples/security/classification/output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/params.yaml deleted file mode 100644 index 84bc81a1..00000000 --- a/examples/security/classification/output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/adv_predictions.json - adv_probabilities_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/ba348845fc220c8fabad81f830c1aa69.pkl - params_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/params.yaml - predictions_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/predictions.json - probabilities_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/probabilities.json - score_dict_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/score_dict.json - test_labels_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/test_labels.json - train_labels_file: output/reports/train/ec7939361cc53dbca55d18c7be6ae31a/train_labels.json - model_dir: models - name: ec7939361cc53dbca55d18c7be6ae31a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bd4d52461b6506003e337379c5fe1d81 - trainer: - kwargs: {} -name: ec7939361cc53dbca55d18c7be6ae31a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/params.yaml b/examples/security/classification/output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/params.yaml deleted file mode 100644 index d9fc6161..00000000 --- a/examples/security/classification/output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/adv_predictions.json - adv_probabilities_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/d9106a2d5ba78e76c8fccbae57d41be8.pkl - params_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/params.yaml - predictions_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/predictions.json - probabilities_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/probabilities.json - score_dict_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/score_dict.json - test_labels_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/test_labels.json - train_labels_file: output/reports/train/ec9283d7a922c9eb8b2556a2c42a0ab4/train_labels.json - model_dir: models - name: ec9283d7a922c9eb8b2556a2c42a0ab4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5339f103b4bc2e5064e90e4a57fbc78f - trainer: - kwargs: {} -name: ec9283d7a922c9eb8b2556a2c42a0ab4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/params.yaml b/examples/security/classification/output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/params.yaml deleted file mode 100644 index 051f8cf5..00000000 --- a/examples/security/classification/output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/adv_predictions.json - adv_probabilities_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/9570e9ed0b80974ae3e83bec98a05c63.pkl - params_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/params.yaml - predictions_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/predictions.json - probabilities_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/probabilities.json - score_dict_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/score_dict.json - test_labels_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/test_labels.json - train_labels_file: output/reports/train/ec937bd4e7ddceb9624b7ebb75afef35/train_labels.json - model_dir: models - name: ec937bd4e7ddceb9624b7ebb75afef35 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e15fe99437c3e24c6884f2a63f903f65 - trainer: - kwargs: {} -name: ec937bd4e7ddceb9624b7ebb75afef35 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/params.yaml b/examples/security/classification/output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/params.yaml deleted file mode 100644 index 8ed44971..00000000 --- a/examples/security/classification/output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/adv_predictions.json - adv_probabilities_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/5164952bcb5a52a590bde98034177cab.pkl - params_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/params.yaml - predictions_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/predictions.json - probabilities_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/probabilities.json - score_dict_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/score_dict.json - test_labels_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/test_labels.json - train_labels_file: output/reports/train/ec9abc2e1bfe08fe410eeebe865e752b/train_labels.json - model_dir: models - name: ec9abc2e1bfe08fe410eeebe865e752b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d110376096062211be6e8ddb879bf36a - trainer: - kwargs: {} -name: ec9abc2e1bfe08fe410eeebe865e752b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/params.yaml b/examples/security/classification/output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/params.yaml deleted file mode 100644 index 5f704c03..00000000 --- a/examples/security/classification/output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/adv_predictions.json - adv_probabilities_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/6c1d43e3f88348e257140d738e693f74.pkl - params_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/params.yaml - predictions_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/predictions.json - probabilities_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/probabilities.json - score_dict_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/score_dict.json - test_labels_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/test_labels.json - train_labels_file: output/reports/train/ecaeb4bebb2631d11ce8290e79f2e168/train_labels.json - model_dir: models - name: ecaeb4bebb2631d11ce8290e79f2e168 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1fccb5a5e5d3f6307f70d0d49dd40aa1 - trainer: - kwargs: {} -name: ecaeb4bebb2631d11ce8290e79f2e168 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/params.yaml b/examples/security/classification/output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/params.yaml deleted file mode 100644 index 01917910..00000000 --- a/examples/security/classification/output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/adv_predictions.json - adv_probabilities_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/45ec1f76fe4d8d49232219d5ed58bdda.pkl - params_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/params.yaml - predictions_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/predictions.json - probabilities_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/probabilities.json - score_dict_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/score_dict.json - test_labels_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/test_labels.json - train_labels_file: output/reports/train/ecc586d351e7e3d1f69d372c8274cfe5/train_labels.json - model_dir: models - name: ecc586d351e7e3d1f69d372c8274cfe5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c8713eed03b82c79cf96fdb80ebe3e13 - trainer: - kwargs: {} -name: ecc586d351e7e3d1f69d372c8274cfe5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/params.yaml b/examples/security/classification/output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/params.yaml deleted file mode 100644 index beb585d3..00000000 --- a/examples/security/classification/output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/adv_predictions.json - adv_probabilities_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/38bb6eb7643db9ecb5805c9f4e16798b.pkl - params_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/params.yaml - predictions_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/predictions.json - probabilities_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/probabilities.json - score_dict_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/score_dict.json - test_labels_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/test_labels.json - train_labels_file: output/reports/train/ecd4bbed9e3584dab0fd8feb694791f7/train_labels.json - model_dir: models - name: ecd4bbed9e3584dab0fd8feb694791f7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 51398844aca4858f7dc72521173463d8 - trainer: - kwargs: {} -name: ecd4bbed9e3584dab0fd8feb694791f7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ecea211e44cc272689fefa340db04d79/params.yaml b/examples/security/classification/output/reports/train/ecea211e44cc272689fefa340db04d79/params.yaml deleted file mode 100644 index e36b8f7c..00000000 --- a/examples/security/classification/output/reports/train/ecea211e44cc272689fefa340db04d79/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ecea211e44cc272689fefa340db04d79/adv_predictions.json - adv_probabilities_file: output/reports/train/ecea211e44cc272689fefa340db04d79/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/57714f7d42b0d18885baa6f2abe13131.pkl - params_file: output/reports/train/ecea211e44cc272689fefa340db04d79/params.yaml - predictions_file: output/reports/train/ecea211e44cc272689fefa340db04d79/predictions.json - probabilities_file: output/reports/train/ecea211e44cc272689fefa340db04d79/probabilities.json - score_dict_file: output/reports/train/ecea211e44cc272689fefa340db04d79/score_dict.json - test_labels_file: output/reports/train/ecea211e44cc272689fefa340db04d79/test_labels.json - train_labels_file: output/reports/train/ecea211e44cc272689fefa340db04d79/train_labels.json - model_dir: models - name: ecea211e44cc272689fefa340db04d79 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cb96203b414db5dd4f809a6a5b50dc6d - trainer: - kwargs: {} -name: ecea211e44cc272689fefa340db04d79 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/eceeb3632296ca1181e6af1da4b8d236/params.yaml b/examples/security/classification/output/reports/train/eceeb3632296ca1181e6af1da4b8d236/params.yaml deleted file mode 100644 index 9bd3c698..00000000 --- a/examples/security/classification/output/reports/train/eceeb3632296ca1181e6af1da4b8d236/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/adv_predictions.json - adv_probabilities_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/702ec808ffdf13723d0abe124e9c6a1f.pkl - params_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/params.yaml - predictions_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/predictions.json - probabilities_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/probabilities.json - score_dict_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/score_dict.json - test_labels_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/test_labels.json - train_labels_file: output/reports/train/eceeb3632296ca1181e6af1da4b8d236/train_labels.json - model_dir: models - name: eceeb3632296ca1181e6af1da4b8d236 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fe586ebfad866c217c6975571c098ed3 - trainer: - kwargs: {} -name: eceeb3632296ca1181e6af1da4b8d236 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/params.yaml b/examples/security/classification/output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/params.yaml deleted file mode 100644 index e3fbf68e..00000000 --- a/examples/security/classification/output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/adv_predictions.json - adv_probabilities_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/2c49ff29c5dd958059d8477e79254f13.pkl - params_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/params.yaml - predictions_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/predictions.json - probabilities_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/probabilities.json - score_dict_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/score_dict.json - test_labels_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/test_labels.json - train_labels_file: output/reports/train/ed7e0c762ffda376ef1fa17f933d0203/train_labels.json - model_dir: models - name: ed7e0c762ffda376ef1fa17f933d0203 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1c779f8b41c714a3b1c4443029849947 - trainer: - kwargs: {} -name: ed7e0c762ffda376ef1fa17f933d0203 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/params.yaml b/examples/security/classification/output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/params.yaml deleted file mode 100644 index 4ac7a5c8..00000000 --- a/examples/security/classification/output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/adv_predictions.json - adv_probabilities_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/3e11aba446d7bf611ec9efab3572d48e.pkl - params_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/params.yaml - predictions_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/predictions.json - probabilities_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/probabilities.json - score_dict_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/score_dict.json - test_labels_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/test_labels.json - train_labels_file: output/reports/train/ed8299b68b5945337e7f1f91d1e6641d/train_labels.json - model_dir: models - name: ed8299b68b5945337e7f1f91d1e6641d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b71609cd89e6f095b3d79dd720fc02b1 - trainer: - kwargs: {} -name: ed8299b68b5945337e7f1f91d1e6641d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/params.yaml b/examples/security/classification/output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/params.yaml deleted file mode 100644 index 74728812..00000000 --- a/examples/security/classification/output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/adv_predictions.json - adv_probabilities_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/dbc0adde52a7df74796c9b349b96d7a1.pkl - params_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/params.yaml - predictions_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/predictions.json - probabilities_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/probabilities.json - score_dict_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/score_dict.json - test_labels_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/test_labels.json - train_labels_file: output/reports/train/ed83ac495e7c5159f8a5402ec64ed1f4/train_labels.json - model_dir: models - name: ed83ac495e7c5159f8a5402ec64ed1f4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d4362692c172d6393dbbe35fb561af95 - trainer: - kwargs: {} -name: ed83ac495e7c5159f8a5402ec64ed1f4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/eda189c060b9856696f99b67af2c80b2/params.yaml b/examples/security/classification/output/reports/train/eda189c060b9856696f99b67af2c80b2/params.yaml deleted file mode 100644 index b4aecec2..00000000 --- a/examples/security/classification/output/reports/train/eda189c060b9856696f99b67af2c80b2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/adv_predictions.json - adv_probabilities_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/439110f50d4913862250feced8378e22.pkl - params_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/params.yaml - predictions_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/predictions.json - probabilities_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/probabilities.json - score_dict_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/score_dict.json - test_labels_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/test_labels.json - train_labels_file: output/reports/train/eda189c060b9856696f99b67af2c80b2/train_labels.json - model_dir: models - name: eda189c060b9856696f99b67af2c80b2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ed7148696f299881cc04d24fc04c007a - trainer: - kwargs: {} -name: eda189c060b9856696f99b67af2c80b2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/params.yaml b/examples/security/classification/output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/params.yaml deleted file mode 100644 index 40dda27c..00000000 --- a/examples/security/classification/output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/adv_predictions.json - adv_probabilities_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/1509dfb4f54093dc69ca302039b8df86.pkl - params_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/params.yaml - predictions_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/predictions.json - probabilities_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/probabilities.json - score_dict_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/score_dict.json - test_labels_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/test_labels.json - train_labels_file: output/reports/train/edb74b9125de21a83b6b01f7ad5b1639/train_labels.json - model_dir: models - name: edb74b9125de21a83b6b01f7ad5b1639 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8cad8738d57bee1e02b27614eca621bd - trainer: - kwargs: {} -name: edb74b9125de21a83b6b01f7ad5b1639 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/params.yaml b/examples/security/classification/output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/params.yaml deleted file mode 100644 index 67d1ef6d..00000000 --- a/examples/security/classification/output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/adv_predictions.json - adv_probabilities_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/c73d1ad91169d917adad5177f82a49cb.pkl - params_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/params.yaml - predictions_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/predictions.json - probabilities_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/probabilities.json - score_dict_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/score_dict.json - test_labels_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/test_labels.json - train_labels_file: output/reports/train/edbee5e2358800a90199efc1c3c7bc6f/train_labels.json - model_dir: models - name: edbee5e2358800a90199efc1c3c7bc6f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 91605fb80ca11b9ab84830a67afbd00a - trainer: - kwargs: {} -name: edbee5e2358800a90199efc1c3c7bc6f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/params.yaml b/examples/security/classification/output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/params.yaml deleted file mode 100644 index 16ee249b..00000000 --- a/examples/security/classification/output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/adv_predictions.json - adv_probabilities_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/1084f2d4dc4ebeef1a9e3f6dfc800af3.pkl - params_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/params.yaml - predictions_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/predictions.json - probabilities_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/probabilities.json - score_dict_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/score_dict.json - test_labels_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/test_labels.json - train_labels_file: output/reports/train/ee246c4414eadc0dfd4eadb6645a1771/train_labels.json - model_dir: models - name: ee246c4414eadc0dfd4eadb6645a1771 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b6afc84971094b3cac950d5116c85c3f - trainer: - kwargs: {} -name: ee246c4414eadc0dfd4eadb6645a1771 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/params.yaml b/examples/security/classification/output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/params.yaml deleted file mode 100644 index 5f2b1bb7..00000000 --- a/examples/security/classification/output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/adv_predictions.json - adv_probabilities_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/59ab59e7b00565434cba08bb4557e6c9.pkl - model_file: output/models/e44125a6bcf2f980e0da3609458b1970.pkl - params_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/params.yaml - predictions_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/predictions.json - probabilities_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/probabilities.json - score_dict_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/score_dict.json - test_labels_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/test_labels.json - train_labels_file: output/reports/train/eec0a40d026c7c2724f535a63bf1d7b8/train_labels.json - model_dir: models - name: eec0a40d026c7c2724f535a63bf1d7b8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 1010000 - random_state: 0 - name: classification - name: 4bec4681e85b26448e6272780b649e5d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2fecae8cb1395aef26c5dbb482ed45e7 - trainer: - kwargs: {} -name: eec0a40d026c7c2724f535a63bf1d7b8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/eed8957284b5864041a990a59f4947c9/params.yaml b/examples/security/classification/output/reports/train/eed8957284b5864041a990a59f4947c9/params.yaml deleted file mode 100644 index db2ff03c..00000000 --- a/examples/security/classification/output/reports/train/eed8957284b5864041a990a59f4947c9/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/eed8957284b5864041a990a59f4947c9/adv_predictions.json - adv_probabilities_file: output/reports/train/eed8957284b5864041a990a59f4947c9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/b053ac793ee5166579b0bf753daf4bb8.pkl - params_file: output/reports/train/eed8957284b5864041a990a59f4947c9/params.yaml - predictions_file: output/reports/train/eed8957284b5864041a990a59f4947c9/predictions.json - probabilities_file: output/reports/train/eed8957284b5864041a990a59f4947c9/probabilities.json - score_dict_file: output/reports/train/eed8957284b5864041a990a59f4947c9/score_dict.json - test_labels_file: output/reports/train/eed8957284b5864041a990a59f4947c9/test_labels.json - train_labels_file: output/reports/train/eed8957284b5864041a990a59f4947c9/train_labels.json - model_dir: models - name: eed8957284b5864041a990a59f4947c9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4f7afff0e9ad18c0c9bc2e35bf72954d - trainer: - kwargs: {} -name: eed8957284b5864041a990a59f4947c9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/eee21f6d983316f8da2d2d924969ee36/params.yaml b/examples/security/classification/output/reports/train/eee21f6d983316f8da2d2d924969ee36/params.yaml deleted file mode 100644 index 90008663..00000000 --- a/examples/security/classification/output/reports/train/eee21f6d983316f8da2d2d924969ee36/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/adv_predictions.json - adv_probabilities_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/caed8d268b3d279bff66051b588c00f4.pkl - params_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/params.yaml - predictions_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/predictions.json - probabilities_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/probabilities.json - score_dict_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/score_dict.json - test_labels_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/test_labels.json - train_labels_file: output/reports/train/eee21f6d983316f8da2d2d924969ee36/train_labels.json - model_dir: models - name: eee21f6d983316f8da2d2d924969ee36 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9138a000ba3186489249c158e1783710 - trainer: - kwargs: {} -name: eee21f6d983316f8da2d2d924969ee36 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/eee295d6504db8f55b9b892f50c387af/params.yaml b/examples/security/classification/output/reports/train/eee295d6504db8f55b9b892f50c387af/params.yaml deleted file mode 100644 index f6b6bedc..00000000 --- a/examples/security/classification/output/reports/train/eee295d6504db8f55b9b892f50c387af/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/adv_predictions.json - adv_probabilities_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/7068e71e3ed8dc965e30e71177e1c8f8.pkl - params_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/params.yaml - predictions_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/predictions.json - probabilities_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/probabilities.json - score_dict_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/score_dict.json - test_labels_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/test_labels.json - train_labels_file: output/reports/train/eee295d6504db8f55b9b892f50c387af/train_labels.json - model_dir: models - name: eee295d6504db8f55b9b892f50c387af - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6dbb596ed6807c3f61b8f9754064e604 - trainer: - kwargs: {} -name: eee295d6504db8f55b9b892f50c387af -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/params.yaml b/examples/security/classification/output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/params.yaml deleted file mode 100644 index a7f88533..00000000 --- a/examples/security/classification/output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/adv_predictions.json - adv_probabilities_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/c7dc4ab15d699757cde44611b20028e4.pkl - params_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/params.yaml - predictions_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/predictions.json - probabilities_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/probabilities.json - score_dict_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/score_dict.json - test_labels_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/test_labels.json - train_labels_file: output/reports/train/ef14a6d45facf3676d1bfe0d180cf3ed/train_labels.json - model_dir: models - name: ef14a6d45facf3676d1bfe0d180cf3ed - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7a4dd4ccb5a79da84765d552ee7cb62b - trainer: - kwargs: {} -name: ef14a6d45facf3676d1bfe0d180cf3ed -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/params.yaml b/examples/security/classification/output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/params.yaml deleted file mode 100644 index e5d9d9a3..00000000 --- a/examples/security/classification/output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/adv_predictions.json - adv_probabilities_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/159d350d1a36a8020dce9bfbc2e31aa8.pkl - params_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/params.yaml - predictions_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/predictions.json - probabilities_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/probabilities.json - score_dict_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/score_dict.json - test_labels_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/test_labels.json - train_labels_file: output/reports/train/ef1c3fe772fe3f60f2427cd24e409eed/train_labels.json - model_dir: models - name: ef1c3fe772fe3f60f2427cd24e409eed - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ab652966d4f57ded7ec983c7aed9d2e7 - trainer: - kwargs: {} -name: ef1c3fe772fe3f60f2427cd24e409eed -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ef36b1f736cf76d5166331989f2a7190/params.yaml b/examples/security/classification/output/reports/train/ef36b1f736cf76d5166331989f2a7190/params.yaml deleted file mode 100644 index 8792067e..00000000 --- a/examples/security/classification/output/reports/train/ef36b1f736cf76d5166331989f2a7190/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/adv_predictions.json - adv_probabilities_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/480a85413f71b4d08b46f29842fb7ab3.pkl - params_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/params.yaml - predictions_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/predictions.json - probabilities_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/probabilities.json - score_dict_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/score_dict.json - test_labels_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/test_labels.json - train_labels_file: output/reports/train/ef36b1f736cf76d5166331989f2a7190/train_labels.json - model_dir: models - name: ef36b1f736cf76d5166331989f2a7190 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9388eb06643c614e16cbc4a6a7e7cc7d - trainer: - kwargs: {} -name: ef36b1f736cf76d5166331989f2a7190 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ef462af0f8351e0b47ec657379f3f224/params.yaml b/examples/security/classification/output/reports/train/ef462af0f8351e0b47ec657379f3f224/params.yaml deleted file mode 100644 index 6b88d29b..00000000 --- a/examples/security/classification/output/reports/train/ef462af0f8351e0b47ec657379f3f224/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/adv_predictions.json - adv_probabilities_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1514a3e1d86ce0ee46b688851c00b076.pkl - model_file: output/models/e887bcbacb5a14a42a3830bdee5e9946.pkl - params_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/params.yaml - predictions_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/predictions.json - probabilities_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/probabilities.json - score_dict_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/score_dict.json - test_labels_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/test_labels.json - train_labels_file: output/reports/train/ef462af0f8351e0b47ec657379f3f224/train_labels.json - model_dir: models - name: ef462af0f8351e0b47ec657379f3f224 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 072fce735f22f879258b106dcee0c0d7 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7a2a2a6992d4557d843667c30b82c962 - trainer: - kwargs: {} -name: ef462af0f8351e0b47ec657379f3f224 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ef511110eca1792d64254fce51bf07b4/params.yaml b/examples/security/classification/output/reports/train/ef511110eca1792d64254fce51bf07b4/params.yaml deleted file mode 100644 index b4bd8422..00000000 --- a/examples/security/classification/output/reports/train/ef511110eca1792d64254fce51bf07b4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/adv_predictions.json - adv_probabilities_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/31ed65856fe8510a3a88ce438f276cf5.pkl - params_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/params.yaml - predictions_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/predictions.json - probabilities_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/probabilities.json - score_dict_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/score_dict.json - test_labels_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/test_labels.json - train_labels_file: output/reports/train/ef511110eca1792d64254fce51bf07b4/train_labels.json - model_dir: models - name: ef511110eca1792d64254fce51bf07b4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9117ed527a5a58d2e21bfc296ff189a0 - trainer: - kwargs: {} -name: ef511110eca1792d64254fce51bf07b4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/params.yaml b/examples/security/classification/output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/params.yaml deleted file mode 100644 index 86edf5ff..00000000 --- a/examples/security/classification/output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/params.yaml +++ /dev/null @@ -1,115 +0,0 @@ -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/adv_predictions.json - adv_probabilities_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl - model_file: output/models/4bfc23ca7b797d6c10d341b670cfcafc.pkl - params_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/params.yaml - predictions_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/predictions.json - probabilities_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/probabilities.json - score_dict_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/score_dict.json - test_labels_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/test_labels.json - train_labels_file: output/reports/train/ef5698cfbb0d5391ce0b82e6b505966f/train_labels.json - model_dir: models - name: ef5698cfbb0d5391ce0b82e6b505966f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 0.1 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7566e9d12d80f8d5b6aea1651b9ee47d - trainer: - kwargs: {} -name: ef5698cfbb0d5391ce0b82e6b505966f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ef60180c5a265fe09902bb2e92d6d969/params.yaml b/examples/security/classification/output/reports/train/ef60180c5a265fe09902bb2e92d6d969/params.yaml deleted file mode 100644 index 82a42e08..00000000 --- a/examples/security/classification/output/reports/train/ef60180c5a265fe09902bb2e92d6d969/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/adv_predictions.json - adv_probabilities_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/6d37fef5c040b50207a6a31e37f1af21.pkl - params_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/params.yaml - predictions_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/predictions.json - probabilities_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/probabilities.json - score_dict_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/score_dict.json - test_labels_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/test_labels.json - train_labels_file: output/reports/train/ef60180c5a265fe09902bb2e92d6d969/train_labels.json - model_dir: models - name: ef60180c5a265fe09902bb2e92d6d969 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 57d9efbc95fb4604d1113861233adf55 - trainer: - kwargs: {} -name: ef60180c5a265fe09902bb2e92d6d969 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/params.yaml b/examples/security/classification/output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/params.yaml deleted file mode 100644 index 5464039a..00000000 --- a/examples/security/classification/output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/adv_predictions.json - adv_probabilities_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/0ce779092f0c55b6ac82ed97e32a7a90.pkl - params_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/params.yaml - predictions_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/predictions.json - probabilities_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/probabilities.json - score_dict_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/score_dict.json - test_labels_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/test_labels.json - train_labels_file: output/reports/train/ef60a9945f2b7aebf5f596acca17b2b2/train_labels.json - model_dir: models - name: ef60a9945f2b7aebf5f596acca17b2b2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5f8e74d13e20dde6061af1afb3d91db1 - trainer: - kwargs: {} -name: ef60a9945f2b7aebf5f596acca17b2b2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/params.yaml b/examples/security/classification/output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/params.yaml deleted file mode 100644 index 27b796cb..00000000 --- a/examples/security/classification/output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/adv_predictions.json - adv_probabilities_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/8110a6e4635a3e6603b1d7912d8b7871.pkl - params_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/params.yaml - predictions_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/predictions.json - probabilities_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/probabilities.json - score_dict_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/score_dict.json - test_labels_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/test_labels.json - train_labels_file: output/reports/train/ef6f79f2653f2ca602f1d8233e76d485/train_labels.json - model_dir: models - name: ef6f79f2653f2ca602f1d8233e76d485 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3a71b8d32b57a1f484a14961347301a0 - trainer: - kwargs: {} -name: ef6f79f2653f2ca602f1d8233e76d485 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ef72ade688a4aa02a5e15c198038cc46/params.yaml b/examples/security/classification/output/reports/train/ef72ade688a4aa02a5e15c198038cc46/params.yaml deleted file mode 100644 index c3429804..00000000 --- a/examples/security/classification/output/reports/train/ef72ade688a4aa02a5e15c198038cc46/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/adv_predictions.json - adv_probabilities_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/1febc2d516461b330c5fb33de08f3a6d.pkl - params_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/params.yaml - predictions_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/predictions.json - probabilities_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/probabilities.json - score_dict_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/score_dict.json - test_labels_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/test_labels.json - train_labels_file: output/reports/train/ef72ade688a4aa02a5e15c198038cc46/train_labels.json - model_dir: models - name: ef72ade688a4aa02a5e15c198038cc46 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7659c7ee759ed84525724367d1774ba7 - trainer: - kwargs: {} -name: ef72ade688a4aa02a5e15c198038cc46 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/efbaaf8f93921ac2f381e18778727004/params.yaml b/examples/security/classification/output/reports/train/efbaaf8f93921ac2f381e18778727004/params.yaml deleted file mode 100644 index b11c3357..00000000 --- a/examples/security/classification/output/reports/train/efbaaf8f93921ac2f381e18778727004/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/adv_predictions.json - adv_probabilities_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/99de6398bee7e82acc15c19af1950cb2.pkl - params_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/params.yaml - predictions_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/predictions.json - probabilities_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/probabilities.json - score_dict_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/score_dict.json - test_labels_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/test_labels.json - train_labels_file: output/reports/train/efbaaf8f93921ac2f381e18778727004/train_labels.json - model_dir: models - name: efbaaf8f93921ac2f381e18778727004 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 40b521ecc2e0322fbb8563a6bf2b0631 - trainer: - kwargs: {} -name: efbaaf8f93921ac2f381e18778727004 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/params.yaml b/examples/security/classification/output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/params.yaml deleted file mode 100644 index a08e429c..00000000 --- a/examples/security/classification/output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/adv_predictions.json - adv_probabilities_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/79683100f6c87bcb3f3014d2c65e6956.pkl - params_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/params.yaml - predictions_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/predictions.json - probabilities_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/probabilities.json - score_dict_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/score_dict.json - test_labels_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/test_labels.json - train_labels_file: output/reports/train/efbb8be9c28c74b75dff24ea08dfcf94/train_labels.json - model_dir: models - name: efbb8be9c28c74b75dff24ea08dfcf94 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 1000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3dd6bc837a549ee428cc94e5edcd4462 - trainer: - kwargs: {} -name: efbb8be9c28c74b75dff24ea08dfcf94 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/params.yaml b/examples/security/classification/output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/params.yaml deleted file mode 100644 index 96a7d643..00000000 --- a/examples/security/classification/output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/adv_predictions.json - adv_probabilities_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/a385afd9c6eb27ea6d623488874fc49b.pkl - params_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/params.yaml - predictions_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/predictions.json - probabilities_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/probabilities.json - score_dict_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/score_dict.json - test_labels_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/test_labels.json - train_labels_file: output/reports/train/efd6d4f4a693c0a3a4bbb44eb401245a/train_labels.json - model_dir: models - name: efd6d4f4a693c0a3a4bbb44eb401245a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c5cd5ccdfbaf4ced6d1e008c16ebabe5 - trainer: - kwargs: {} -name: efd6d4f4a693c0a3a4bbb44eb401245a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/params.yaml b/examples/security/classification/output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/params.yaml deleted file mode 100644 index f446601a..00000000 --- a/examples/security/classification/output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/adv_predictions.json - adv_probabilities_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/14ba88f14bfd9fba05af72eb697dcf8c.pkl - params_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/params.yaml - predictions_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/predictions.json - probabilities_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/probabilities.json - score_dict_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/score_dict.json - test_labels_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/test_labels.json - train_labels_file: output/reports/train/f0040de2f9a1f4a69b22f9aa7f60cd19/train_labels.json - model_dir: models - name: f0040de2f9a1f4a69b22f9aa7f60cd19 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 43b74be48a4d1fadfd1ab81f71f01291 - trainer: - kwargs: {} -name: f0040de2f9a1f4a69b22f9aa7f60cd19 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f034f340114688f10f7e94dcf2d23152/params.yaml b/examples/security/classification/output/reports/train/f034f340114688f10f7e94dcf2d23152/params.yaml deleted file mode 100644 index 0f06c4aa..00000000 --- a/examples/security/classification/output/reports/train/f034f340114688f10f7e94dcf2d23152/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/adv_predictions.json - adv_probabilities_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/c865ccd3668046112f52cc5006ae1d6c.pkl - params_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/params.yaml - predictions_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/predictions.json - probabilities_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/probabilities.json - score_dict_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/score_dict.json - test_labels_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/test_labels.json - train_labels_file: output/reports/train/f034f340114688f10f7e94dcf2d23152/train_labels.json - model_dir: models - name: f034f340114688f10f7e94dcf2d23152 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cc31038a670e7c0e82bf3d9a13e14603 - trainer: - kwargs: {} -name: f034f340114688f10f7e94dcf2d23152 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f077397458c52493586d2b466f4ee959/params.yaml b/examples/security/classification/output/reports/train/f077397458c52493586d2b466f4ee959/params.yaml deleted file mode 100644 index 3424805a..00000000 --- a/examples/security/classification/output/reports/train/f077397458c52493586d2b466f4ee959/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f077397458c52493586d2b466f4ee959/adv_predictions.json - adv_probabilities_file: output/reports/train/f077397458c52493586d2b466f4ee959/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/7929283f1c067353e61165eede2a2047.pkl - params_file: output/reports/train/f077397458c52493586d2b466f4ee959/params.yaml - predictions_file: output/reports/train/f077397458c52493586d2b466f4ee959/predictions.json - probabilities_file: output/reports/train/f077397458c52493586d2b466f4ee959/probabilities.json - score_dict_file: output/reports/train/f077397458c52493586d2b466f4ee959/score_dict.json - test_labels_file: output/reports/train/f077397458c52493586d2b466f4ee959/test_labels.json - train_labels_file: output/reports/train/f077397458c52493586d2b466f4ee959/train_labels.json - model_dir: models - name: f077397458c52493586d2b466f4ee959 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 393ad91975474bf147e5782b75b3fd3b - trainer: - kwargs: {} -name: f077397458c52493586d2b466f4ee959 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f08744cea1675c1c019ad82513e22aa7/params.yaml b/examples/security/classification/output/reports/train/f08744cea1675c1c019ad82513e22aa7/params.yaml deleted file mode 100644 index bb9331ab..00000000 --- a/examples/security/classification/output/reports/train/f08744cea1675c1c019ad82513e22aa7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/adv_predictions.json - adv_probabilities_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/dcf9f7f238fee5e649e9f4229e9adb77.pkl - params_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/params.yaml - predictions_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/predictions.json - probabilities_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/probabilities.json - score_dict_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/score_dict.json - test_labels_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/test_labels.json - train_labels_file: output/reports/train/f08744cea1675c1c019ad82513e22aa7/train_labels.json - model_dir: models - name: f08744cea1675c1c019ad82513e22aa7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: adf0851c561bfd2defcea6fe96f32a10 - trainer: - kwargs: {} -name: f08744cea1675c1c019ad82513e22aa7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f0cba93a472a43c59665957a206ea9f0/params.yaml b/examples/security/classification/output/reports/train/f0cba93a472a43c59665957a206ea9f0/params.yaml deleted file mode 100644 index 3343b4a2..00000000 --- a/examples/security/classification/output/reports/train/f0cba93a472a43c59665957a206ea9f0/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/adv_predictions.json - adv_probabilities_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/4efa479ce2d4f2c2824f45e27cd6ea51.pkl - params_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/params.yaml - predictions_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/predictions.json - probabilities_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/probabilities.json - score_dict_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/score_dict.json - test_labels_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/test_labels.json - train_labels_file: output/reports/train/f0cba93a472a43c59665957a206ea9f0/train_labels.json - model_dir: models - name: f0cba93a472a43c59665957a206ea9f0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9463646f56c403846b25ebbea0cf79d0 - trainer: - kwargs: {} -name: f0cba93a472a43c59665957a206ea9f0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f104107b17e79b83761d6faf79e14733/params.yaml b/examples/security/classification/output/reports/train/f104107b17e79b83761d6faf79e14733/params.yaml deleted file mode 100644 index 323ec105..00000000 --- a/examples/security/classification/output/reports/train/f104107b17e79b83761d6faf79e14733/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f104107b17e79b83761d6faf79e14733/adv_predictions.json - adv_probabilities_file: output/reports/train/f104107b17e79b83761d6faf79e14733/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/95cc9b55d2b447f56ccd9fa50221e988.pkl - params_file: output/reports/train/f104107b17e79b83761d6faf79e14733/params.yaml - predictions_file: output/reports/train/f104107b17e79b83761d6faf79e14733/predictions.json - probabilities_file: output/reports/train/f104107b17e79b83761d6faf79e14733/probabilities.json - score_dict_file: output/reports/train/f104107b17e79b83761d6faf79e14733/score_dict.json - test_labels_file: output/reports/train/f104107b17e79b83761d6faf79e14733/test_labels.json - train_labels_file: output/reports/train/f104107b17e79b83761d6faf79e14733/train_labels.json - model_dir: models - name: f104107b17e79b83761d6faf79e14733 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 441fc3b9cd0c30830c6e043ef080732a - trainer: - kwargs: {} -name: f104107b17e79b83761d6faf79e14733 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f11b476501a4d27a0efadb8134297028/params.yaml b/examples/security/classification/output/reports/train/f11b476501a4d27a0efadb8134297028/params.yaml deleted file mode 100644 index 2437d8cc..00000000 --- a/examples/security/classification/output/reports/train/f11b476501a4d27a0efadb8134297028/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f11b476501a4d27a0efadb8134297028/adv_predictions.json - adv_probabilities_file: output/reports/train/f11b476501a4d27a0efadb8134297028/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/29deab736eb75ddedf1515a1ebe14e6b.pkl - params_file: output/reports/train/f11b476501a4d27a0efadb8134297028/params.yaml - predictions_file: output/reports/train/f11b476501a4d27a0efadb8134297028/predictions.json - probabilities_file: output/reports/train/f11b476501a4d27a0efadb8134297028/probabilities.json - score_dict_file: output/reports/train/f11b476501a4d27a0efadb8134297028/score_dict.json - test_labels_file: output/reports/train/f11b476501a4d27a0efadb8134297028/test_labels.json - train_labels_file: output/reports/train/f11b476501a4d27a0efadb8134297028/train_labels.json - model_dir: models - name: f11b476501a4d27a0efadb8134297028 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 100 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3d0540c2ef4a962c77a72a853b60ac57 - trainer: - kwargs: {} -name: f11b476501a4d27a0efadb8134297028 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f14e1de498be925efa9a2162a1504847/params.yaml b/examples/security/classification/output/reports/train/f14e1de498be925efa9a2162a1504847/params.yaml deleted file mode 100644 index 8b6dc519..00000000 --- a/examples/security/classification/output/reports/train/f14e1de498be925efa9a2162a1504847/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f14e1de498be925efa9a2162a1504847/adv_predictions.json - adv_probabilities_file: output/reports/train/f14e1de498be925efa9a2162a1504847/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/94946013b94e0f4365b10b8ccf7ab0b2.pkl - params_file: output/reports/train/f14e1de498be925efa9a2162a1504847/params.yaml - predictions_file: output/reports/train/f14e1de498be925efa9a2162a1504847/predictions.json - probabilities_file: output/reports/train/f14e1de498be925efa9a2162a1504847/probabilities.json - score_dict_file: output/reports/train/f14e1de498be925efa9a2162a1504847/score_dict.json - test_labels_file: output/reports/train/f14e1de498be925efa9a2162a1504847/test_labels.json - train_labels_file: output/reports/train/f14e1de498be925efa9a2162a1504847/train_labels.json - model_dir: models - name: f14e1de498be925efa9a2162a1504847 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b124272d2e28de266313740bb79126b0 - trainer: - kwargs: {} -name: f14e1de498be925efa9a2162a1504847 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/params.yaml b/examples/security/classification/output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/params.yaml deleted file mode 100644 index 76982cb1..00000000 --- a/examples/security/classification/output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/adv_predictions.json - adv_probabilities_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/320dd42a88d3036ac25b12e2d29d9a94.pkl - params_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/params.yaml - predictions_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/predictions.json - probabilities_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/probabilities.json - score_dict_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/score_dict.json - test_labels_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/test_labels.json - train_labels_file: output/reports/train/f1670a7b0d13c3cdb3023e07c122fd56/train_labels.json - model_dir: models - name: f1670a7b0d13c3cdb3023e07c122fd56 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 55577acc7cc139bd420e7f1eaad6b436 - trainer: - kwargs: {} -name: f1670a7b0d13c3cdb3023e07c122fd56 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/params.yaml b/examples/security/classification/output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/params.yaml deleted file mode 100644 index 67622dce..00000000 --- a/examples/security/classification/output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/adv_predictions.json - adv_probabilities_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/4c20af50acccc61f0e781fa85949fb12.pkl - params_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/params.yaml - predictions_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/predictions.json - probabilities_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/probabilities.json - score_dict_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/score_dict.json - test_labels_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/test_labels.json - train_labels_file: output/reports/train/f1815572b4f29ff4bb2c11875f705c4d/train_labels.json - model_dir: models - name: f1815572b4f29ff4bb2c11875f705c4d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 310a822840f506f70b8dfe89769d8b51 - trainer: - kwargs: {} -name: f1815572b4f29ff4bb2c11875f705c4d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/params.yaml b/examples/security/classification/output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/params.yaml deleted file mode 100644 index 2aabed41..00000000 --- a/examples/security/classification/output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/adv_predictions.json - adv_probabilities_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/c1adfbb3acfac935077cb09f742e1ee7.pkl - params_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/params.yaml - predictions_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/predictions.json - probabilities_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/probabilities.json - score_dict_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/score_dict.json - test_labels_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/test_labels.json - train_labels_file: output/reports/train/f196c0f1c310309a5eb8d9b034ec636f/train_labels.json - model_dir: models - name: f196c0f1c310309a5eb8d9b034ec636f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 851bb2f4251273a7647f7e08256f5582 - trainer: - kwargs: {} -name: f196c0f1c310309a5eb8d9b034ec636f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/params.yaml b/examples/security/classification/output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/params.yaml deleted file mode 100644 index cefe7cb4..00000000 --- a/examples/security/classification/output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/adv_predictions.json - adv_probabilities_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/ebeeeacefbe6f16ea6eb1e30049102de.pkl - params_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/params.yaml - predictions_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/predictions.json - probabilities_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/probabilities.json - score_dict_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/score_dict.json - test_labels_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/test_labels.json - train_labels_file: output/reports/train/f1c1e6bb842b1cabfb61cdf9145ff340/train_labels.json - model_dir: models - name: f1c1e6bb842b1cabfb61cdf9145ff340 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1001d9f34a4241000584692b8db96607 - trainer: - kwargs: {} -name: f1c1e6bb842b1cabfb61cdf9145ff340 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/params.yaml b/examples/security/classification/output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/params.yaml deleted file mode 100644 index ca091fe8..00000000 --- a/examples/security/classification/output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/adv_predictions.json - adv_probabilities_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/2fd71691533306ee788e0ffc87821e9e.pkl - params_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/params.yaml - predictions_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/predictions.json - probabilities_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/probabilities.json - score_dict_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/score_dict.json - test_labels_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/test_labels.json - train_labels_file: output/reports/train/f1dbe93146259b17a8d8dbc3f5b43e28/train_labels.json - model_dir: models - name: f1dbe93146259b17a8d8dbc3f5b43e28 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0a173e26ed0b597f859bceeb7953266e - trainer: - kwargs: {} -name: f1dbe93146259b17a8d8dbc3f5b43e28 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/params.yaml b/examples/security/classification/output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/params.yaml deleted file mode 100644 index aa17c109..00000000 --- a/examples/security/classification/output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/adv_predictions.json - adv_probabilities_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/25d277181032020328157fe64d2b5a17.pkl - params_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/params.yaml - predictions_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/predictions.json - probabilities_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/probabilities.json - score_dict_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/score_dict.json - test_labels_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/test_labels.json - train_labels_file: output/reports/train/f1ed42e533e3ed2bc4c684cd3658daeb/train_labels.json - model_dir: models - name: f1ed42e533e3ed2bc4c684cd3658daeb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 328068c9978553ad64ad4295a18dd9af - trainer: - kwargs: {} -name: f1ed42e533e3ed2bc4c684cd3658daeb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f22f679839fe36b27d910acc6afaff73/params.yaml b/examples/security/classification/output/reports/train/f22f679839fe36b27d910acc6afaff73/params.yaml deleted file mode 100644 index 7f5cc6ae..00000000 --- a/examples/security/classification/output/reports/train/f22f679839fe36b27d910acc6afaff73/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/adv_predictions.json - adv_probabilities_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/7f5aef4493f8a5953a6c5895810eaff2.pkl - params_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/params.yaml - predictions_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/predictions.json - probabilities_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/probabilities.json - score_dict_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/score_dict.json - test_labels_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/test_labels.json - train_labels_file: output/reports/train/f22f679839fe36b27d910acc6afaff73/train_labels.json - model_dir: models - name: f22f679839fe36b27d910acc6afaff73 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d18bf770af24e1b79d1637087d38bc06 - trainer: - kwargs: {} -name: f22f679839fe36b27d910acc6afaff73 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f25721df84630ddc84706c2d82e37ec1/params.yaml b/examples/security/classification/output/reports/train/f25721df84630ddc84706c2d82e37ec1/params.yaml deleted file mode 100644 index 487fd070..00000000 --- a/examples/security/classification/output/reports/train/f25721df84630ddc84706c2d82e37ec1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/adv_predictions.json - adv_probabilities_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/b959918fa1e6d48d780e6e8d277165b0.pkl - params_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/params.yaml - predictions_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/predictions.json - probabilities_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/probabilities.json - score_dict_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/score_dict.json - test_labels_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/test_labels.json - train_labels_file: output/reports/train/f25721df84630ddc84706c2d82e37ec1/train_labels.json - model_dir: models - name: f25721df84630ddc84706c2d82e37ec1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 166b5a44818480f55221c527c6f32344 - trainer: - kwargs: {} -name: f25721df84630ddc84706c2d82e37ec1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/params.yaml b/examples/security/classification/output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/params.yaml deleted file mode 100644 index 5aef74ee..00000000 --- a/examples/security/classification/output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/adv_predictions.json - adv_probabilities_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/83fdd1e8e56a3b002a9cc3583145e862.pkl - params_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/params.yaml - predictions_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/predictions.json - probabilities_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/probabilities.json - score_dict_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/score_dict.json - test_labels_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/test_labels.json - train_labels_file: output/reports/train/f2647d13bc344b402d42e75d6dc9c9a9/train_labels.json - model_dir: models - name: f2647d13bc344b402d42e75d6dc9c9a9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bbda112b0b6b992b4361bc2f6e66d3df - trainer: - kwargs: {} -name: f2647d13bc344b402d42e75d6dc9c9a9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f27e63e2fc69334f22edc827da4d9b75/params.yaml b/examples/security/classification/output/reports/train/f27e63e2fc69334f22edc827da4d9b75/params.yaml deleted file mode 100644 index 730eedbc..00000000 --- a/examples/security/classification/output/reports/train/f27e63e2fc69334f22edc827da4d9b75/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/adv_predictions.json - adv_probabilities_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/4facd08cb9565d931b4a0d1d6f13b5dc.pkl - params_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/params.yaml - predictions_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/predictions.json - probabilities_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/probabilities.json - score_dict_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/score_dict.json - test_labels_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/test_labels.json - train_labels_file: output/reports/train/f27e63e2fc69334f22edc827da4d9b75/train_labels.json - model_dir: models - name: f27e63e2fc69334f22edc827da4d9b75 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.001 - degree: 2 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 73a439f9be242bc9ab65a71d21741851 - trainer: - kwargs: {} -name: f27e63e2fc69334f22edc827da4d9b75 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f27ff349d0e303f877034df0861950c3/params.yaml b/examples/security/classification/output/reports/train/f27ff349d0e303f877034df0861950c3/params.yaml deleted file mode 100644 index 1375b934..00000000 --- a/examples/security/classification/output/reports/train/f27ff349d0e303f877034df0861950c3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f27ff349d0e303f877034df0861950c3/adv_predictions.json - adv_probabilities_file: output/reports/train/f27ff349d0e303f877034df0861950c3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/520945f2ca5adfdfcd79d3f17faee7be.pkl - params_file: output/reports/train/f27ff349d0e303f877034df0861950c3/params.yaml - predictions_file: output/reports/train/f27ff349d0e303f877034df0861950c3/predictions.json - probabilities_file: output/reports/train/f27ff349d0e303f877034df0861950c3/probabilities.json - score_dict_file: output/reports/train/f27ff349d0e303f877034df0861950c3/score_dict.json - test_labels_file: output/reports/train/f27ff349d0e303f877034df0861950c3/test_labels.json - train_labels_file: output/reports/train/f27ff349d0e303f877034df0861950c3/train_labels.json - model_dir: models - name: f27ff349d0e303f877034df0861950c3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 10000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2dbaab0f5ed52605a9378e7f2582debf - trainer: - kwargs: {} -name: f27ff349d0e303f877034df0861950c3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f282fc490c80de418fa3b00f30d0a076/params.yaml b/examples/security/classification/output/reports/train/f282fc490c80de418fa3b00f30d0a076/params.yaml deleted file mode 100644 index 156aed6d..00000000 --- a/examples/security/classification/output/reports/train/f282fc490c80de418fa3b00f30d0a076/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/adv_predictions.json - adv_probabilities_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/3c0a13e86ce4f2d1fe9ccfe05df27b89.pkl - params_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/params.yaml - predictions_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/predictions.json - probabilities_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/probabilities.json - score_dict_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/score_dict.json - test_labels_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/test_labels.json - train_labels_file: output/reports/train/f282fc490c80de418fa3b00f30d0a076/train_labels.json - model_dir: models - name: f282fc490c80de418fa3b00f30d0a076 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 063bd7dbd5bf440830494b019d90d526 - trainer: - kwargs: {} -name: f282fc490c80de418fa3b00f30d0a076 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/params.yaml b/examples/security/classification/output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/params.yaml deleted file mode 100644 index 25f55242..00000000 --- a/examples/security/classification/output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/adv_predictions.json - adv_probabilities_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/f9c8b4eea9db5be42f0ecdc5f04aeda3.pkl - params_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/params.yaml - predictions_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/predictions.json - probabilities_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/probabilities.json - score_dict_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/score_dict.json - test_labels_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/test_labels.json - train_labels_file: output/reports/train/f2aeb82e29cdd0a6a94908724faef20f/train_labels.json - model_dir: models - name: f2aeb82e29cdd0a6a94908724faef20f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c291991d224e173a97b05e0989062ad1 - trainer: - kwargs: {} -name: f2aeb82e29cdd0a6a94908724faef20f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f2be86387711078e61ccdc900fc21a4c/params.yaml b/examples/security/classification/output/reports/train/f2be86387711078e61ccdc900fc21a4c/params.yaml deleted file mode 100644 index 15d833a3..00000000 --- a/examples/security/classification/output/reports/train/f2be86387711078e61ccdc900fc21a4c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/adv_predictions.json - adv_probabilities_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/cf8d769a1c478a476cf794c344839964.pkl - params_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/params.yaml - predictions_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/predictions.json - probabilities_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/probabilities.json - score_dict_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/score_dict.json - test_labels_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/test_labels.json - train_labels_file: output/reports/train/f2be86387711078e61ccdc900fc21a4c/train_labels.json - model_dir: models - name: f2be86387711078e61ccdc900fc21a4c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cdd9b8f39284797665b14bce133726b1 - trainer: - kwargs: {} -name: f2be86387711078e61ccdc900fc21a4c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f2c14882610c65baceb0d9ba22f7c324/params.yaml b/examples/security/classification/output/reports/train/f2c14882610c65baceb0d9ba22f7c324/params.yaml deleted file mode 100644 index 6bd7049a..00000000 --- a/examples/security/classification/output/reports/train/f2c14882610c65baceb0d9ba22f7c324/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/adv_predictions.json - adv_probabilities_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/2810f0fef4973685865e937a9ca6570a.pkl - params_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/params.yaml - predictions_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/predictions.json - probabilities_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/probabilities.json - score_dict_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/score_dict.json - test_labels_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/test_labels.json - train_labels_file: output/reports/train/f2c14882610c65baceb0d9ba22f7c324/train_labels.json - model_dir: models - name: f2c14882610c65baceb0d9ba22f7c324 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4bed6ae71a3c5490acfd28e8e9b2363b - trainer: - kwargs: {} -name: f2c14882610c65baceb0d9ba22f7c324 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/params.yaml b/examples/security/classification/output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/params.yaml deleted file mode 100644 index c5a7e474..00000000 --- a/examples/security/classification/output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/adv_predictions.json - adv_probabilities_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/b4fbc3d12f9b3e921323343960bbec75.pkl - params_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/params.yaml - predictions_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/predictions.json - probabilities_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/probabilities.json - score_dict_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/score_dict.json - test_labels_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/test_labels.json - train_labels_file: output/reports/train/f2c5d5f79d79818cea7a1dd7c9c23292/train_labels.json - model_dir: models - name: f2c5d5f79d79818cea7a1dd7c9c23292 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c40e7e8465ef51377677bb04d6e95dc9 - trainer: - kwargs: {} -name: f2c5d5f79d79818cea7a1dd7c9c23292 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/params.yaml b/examples/security/classification/output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/params.yaml deleted file mode 100644 index e3968444..00000000 --- a/examples/security/classification/output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/adv_predictions.json - adv_probabilities_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/77de9335bee959edfddfe9cd4352d32b.pkl - params_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/params.yaml - predictions_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/predictions.json - probabilities_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/probabilities.json - score_dict_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/score_dict.json - test_labels_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/test_labels.json - train_labels_file: output/reports/train/f2ca5b9dbdd1b111b5ec81165efdb594/train_labels.json - model_dir: models - name: f2ca5b9dbdd1b111b5ec81165efdb594 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: fe797ff575349fa4a18af69cc11f36f6 - trainer: - kwargs: {} -name: f2ca5b9dbdd1b111b5ec81165efdb594 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/params.yaml b/examples/security/classification/output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/params.yaml deleted file mode 100644 index d24ea751..00000000 --- a/examples/security/classification/output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/adv_predictions.json - adv_probabilities_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/f7b673670b83179e7220af6383cf4328.pkl - params_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/params.yaml - predictions_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/predictions.json - probabilities_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/probabilities.json - score_dict_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/score_dict.json - test_labels_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/test_labels.json - train_labels_file: output/reports/train/f2db86e99b3fa72c892868db4dbdf6fb/train_labels.json - model_dir: models - name: f2db86e99b3fa72c892868db4dbdf6fb - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9008f160188e8eee5023d4cf8e732dae - trainer: - kwargs: {} -name: f2db86e99b3fa72c892868db4dbdf6fb -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/params.yaml b/examples/security/classification/output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/params.yaml deleted file mode 100644 index 2f54c72f..00000000 --- a/examples/security/classification/output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/adv_predictions.json - adv_probabilities_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/ac9024f94c34fd771c9fd9e7d6b54ee2.pkl - params_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/params.yaml - predictions_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/predictions.json - probabilities_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/probabilities.json - score_dict_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/score_dict.json - test_labels_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/test_labels.json - train_labels_file: output/reports/train/f353ba32c7c78daa4c84e31c15a018ae/train_labels.json - model_dir: models - name: f353ba32c7c78daa4c84e31c15a018ae - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 265f176aa0979c10198201ead903ca4d - trainer: - kwargs: {} -name: f353ba32c7c78daa4c84e31c15a018ae -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f3609e6c3d330413160f865f74edc8e2/params.yaml b/examples/security/classification/output/reports/train/f3609e6c3d330413160f865f74edc8e2/params.yaml deleted file mode 100644 index da208c59..00000000 --- a/examples/security/classification/output/reports/train/f3609e6c3d330413160f865f74edc8e2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/adv_predictions.json - adv_probabilities_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/3e5f4356c2799ca8d072a5c6efab7b82.pkl - params_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/params.yaml - predictions_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/predictions.json - probabilities_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/probabilities.json - score_dict_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/score_dict.json - test_labels_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/test_labels.json - train_labels_file: output/reports/train/f3609e6c3d330413160f865f74edc8e2/train_labels.json - model_dir: models - name: f3609e6c3d330413160f865f74edc8e2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 221ea58af2b7e20f52ab89a077fd33e0 - trainer: - kwargs: {} -name: f3609e6c3d330413160f865f74edc8e2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f3816018f11ffa98afac0839588cb1f8/params.yaml b/examples/security/classification/output/reports/train/f3816018f11ffa98afac0839588cb1f8/params.yaml deleted file mode 100644 index 612766e1..00000000 --- a/examples/security/classification/output/reports/train/f3816018f11ffa98afac0839588cb1f8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/adv_predictions.json - adv_probabilities_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/6c7dd399fefe8d33380bd49cd54efe8a.pkl - params_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/params.yaml - predictions_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/predictions.json - probabilities_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/probabilities.json - score_dict_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/score_dict.json - test_labels_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/test_labels.json - train_labels_file: output/reports/train/f3816018f11ffa98afac0839588cb1f8/train_labels.json - model_dir: models - name: f3816018f11ffa98afac0839588cb1f8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 630366fd91184e976247be9b3b257e74 - trainer: - kwargs: {} -name: f3816018f11ffa98afac0839588cb1f8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/params.yaml b/examples/security/classification/output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/params.yaml deleted file mode 100644 index e0ea0090..00000000 --- a/examples/security/classification/output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/adv_predictions.json - adv_probabilities_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/60c4965686b803cb0453f175e86438ff.pkl - params_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/params.yaml - predictions_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/predictions.json - probabilities_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/probabilities.json - score_dict_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/score_dict.json - test_labels_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/test_labels.json - train_labels_file: output/reports/train/f3cb891c478ca1873ebaa0db9c0f8288/train_labels.json - model_dir: models - name: f3cb891c478ca1873ebaa0db9c0f8288 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 135c81a1cb3bb278bb15ac332e80784d - trainer: - kwargs: {} -name: f3cb891c478ca1873ebaa0db9c0f8288 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f40244315167904d4ab5bedcdb4c7750/params.yaml b/examples/security/classification/output/reports/train/f40244315167904d4ab5bedcdb4c7750/params.yaml deleted file mode 100644 index 26fa769a..00000000 --- a/examples/security/classification/output/reports/train/f40244315167904d4ab5bedcdb4c7750/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/adv_predictions.json - adv_probabilities_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/e077e8fae71b4ea3d0283ddbb14af9a9.pkl - params_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/params.yaml - predictions_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/predictions.json - probabilities_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/probabilities.json - score_dict_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/score_dict.json - test_labels_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/test_labels.json - train_labels_file: output/reports/train/f40244315167904d4ab5bedcdb4c7750/train_labels.json - model_dir: models - name: f40244315167904d4ab5bedcdb4c7750 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 170064bdadaf278c436a4e4f183c2b8b - trainer: - kwargs: {} -name: f40244315167904d4ab5bedcdb4c7750 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/params.yaml b/examples/security/classification/output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/params.yaml deleted file mode 100644 index 8e45de79..00000000 --- a/examples/security/classification/output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/adv_predictions.json - adv_probabilities_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/71644f905c2abe8b3f78a404c0c5701f.pkl - params_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/params.yaml - predictions_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/predictions.json - probabilities_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/probabilities.json - score_dict_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/score_dict.json - test_labels_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/test_labels.json - train_labels_file: output/reports/train/f42021c77a41ba9d3d40c499142b8d3c/train_labels.json - model_dir: models - name: f42021c77a41ba9d3d40c499142b8d3c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 38375080c3d9ac0962ff9f2c930703bf - trainer: - kwargs: {} -name: f42021c77a41ba9d3d40c499142b8d3c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f42ed6406db5252352fc0f8e112661c5/params.yaml b/examples/security/classification/output/reports/train/f42ed6406db5252352fc0f8e112661c5/params.yaml deleted file mode 100644 index cb71a93f..00000000 --- a/examples/security/classification/output/reports/train/f42ed6406db5252352fc0f8e112661c5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/adv_predictions.json - adv_probabilities_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/ea34509e1b59adea4dc868ecc13755e6.pkl - params_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/params.yaml - predictions_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/predictions.json - probabilities_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/probabilities.json - score_dict_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/score_dict.json - test_labels_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/test_labels.json - train_labels_file: output/reports/train/f42ed6406db5252352fc0f8e112661c5/train_labels.json - model_dir: models - name: f42ed6406db5252352fc0f8e112661c5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dd6d9dbfe77b63c6ca104bb356844738 - trainer: - kwargs: {} -name: f42ed6406db5252352fc0f8e112661c5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/params.yaml b/examples/security/classification/output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/params.yaml deleted file mode 100644 index c5057a3e..00000000 --- a/examples/security/classification/output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/adv_predictions.json - adv_probabilities_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/bbc013def9362d2c1b2a42f8f859bd9c.pkl - params_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/params.yaml - predictions_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/predictions.json - probabilities_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/probabilities.json - score_dict_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/score_dict.json - test_labels_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/test_labels.json - train_labels_file: output/reports/train/f43aaa1eb79495ee17b38b640f716ba3/train_labels.json - model_dir: models - name: f43aaa1eb79495ee17b38b640f716ba3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 93c4c54bb7641bb58b3ab2e0df27de7c - trainer: - kwargs: {} -name: f43aaa1eb79495ee17b38b640f716ba3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f44642425b9437278df4a76ff706c4b6/params.yaml b/examples/security/classification/output/reports/train/f44642425b9437278df4a76ff706c4b6/params.yaml deleted file mode 100644 index 1ff3d7dc..00000000 --- a/examples/security/classification/output/reports/train/f44642425b9437278df4a76ff706c4b6/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/adv_predictions.json - adv_probabilities_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/ed993f2fcdf2e3419766df7be0b02f2d.pkl - params_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/params.yaml - predictions_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/predictions.json - probabilities_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/probabilities.json - score_dict_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/score_dict.json - test_labels_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/test_labels.json - train_labels_file: output/reports/train/f44642425b9437278df4a76ff706c4b6/train_labels.json - model_dir: models - name: f44642425b9437278df4a76ff706c4b6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b53e13b9e2677e8c717e61919665939a - trainer: - kwargs: {} -name: f44642425b9437278df4a76ff706c4b6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/params.yaml b/examples/security/classification/output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/params.yaml deleted file mode 100644 index 65b16d55..00000000 --- a/examples/security/classification/output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/adv_predictions.json - adv_probabilities_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/1cf6da722782fd15baa813b2cb399b60.pkl - params_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/params.yaml - predictions_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/predictions.json - probabilities_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/probabilities.json - score_dict_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/score_dict.json - test_labels_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/test_labels.json - train_labels_file: output/reports/train/f44bea96c537cc71b1dabc3d7ae498a3/train_labels.json - model_dir: models - name: f44bea96c537cc71b1dabc3d7ae498a3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 509cc8278549c8cdba32c23ca84d945f - trainer: - kwargs: {} -name: f44bea96c537cc71b1dabc3d7ae498a3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/params.yaml b/examples/security/classification/output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/params.yaml deleted file mode 100644 index 4b1af025..00000000 --- a/examples/security/classification/output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/adv_predictions.json - adv_probabilities_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/babdd08a0b917a156120c9afd88cf82b.pkl - params_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/params.yaml - predictions_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/predictions.json - probabilities_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/probabilities.json - score_dict_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/score_dict.json - test_labels_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/test_labels.json - train_labels_file: output/reports/train/f456ff1740cba6dfaabead9a82f7a6e9/train_labels.json - model_dir: models - name: f456ff1740cba6dfaabead9a82f7a6e9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: eb424481d348e4f5e8e196883824ad4d - trainer: - kwargs: {} -name: f456ff1740cba6dfaabead9a82f7a6e9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/params.yaml b/examples/security/classification/output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/params.yaml deleted file mode 100644 index 967971e6..00000000 --- a/examples/security/classification/output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/adv_predictions.json - adv_probabilities_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/dc922c5392a071e8cba7ce060a116236.pkl - params_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/params.yaml - predictions_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/predictions.json - probabilities_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/probabilities.json - score_dict_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/score_dict.json - test_labels_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/test_labels.json - train_labels_file: output/reports/train/f473fe326cdfbc7f675f1a6d409180ed/train_labels.json - model_dir: models - name: f473fe326cdfbc7f675f1a6d409180ed - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e4c5b29f997d4551f210333ad0e3069c - trainer: - kwargs: {} -name: f473fe326cdfbc7f675f1a6d409180ed -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/params.yaml b/examples/security/classification/output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/params.yaml deleted file mode 100644 index 47543bf8..00000000 --- a/examples/security/classification/output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/adv_predictions.json - adv_probabilities_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/683fb77c9fd6ed3f40607d0957dfd933.pkl - params_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/params.yaml - predictions_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/predictions.json - probabilities_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/probabilities.json - score_dict_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/score_dict.json - test_labels_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/test_labels.json - train_labels_file: output/reports/train/f49a386efc5ffdfb48e2685792cc4b9e/train_labels.json - model_dir: models - name: f49a386efc5ffdfb48e2685792cc4b9e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: df9b7f0d34b24cbb8be022a9368bc779 - trainer: - kwargs: {} -name: f49a386efc5ffdfb48e2685792cc4b9e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/params.yaml b/examples/security/classification/output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/params.yaml deleted file mode 100644 index a150cd17..00000000 --- a/examples/security/classification/output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/adv_predictions.json - adv_probabilities_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/29563ab911b07c98660f34ae322a043b.pkl - params_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/params.yaml - predictions_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/predictions.json - probabilities_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/probabilities.json - score_dict_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/score_dict.json - test_labels_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/test_labels.json - train_labels_file: output/reports/train/f4b6e2fa405c9eaa7215bc7dc7f48cb4/train_labels.json - model_dir: models - name: f4b6e2fa405c9eaa7215bc7dc7f48cb4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 19f329b59529e51ddb435dadb776ac7d - trainer: - kwargs: {} -name: f4b6e2fa405c9eaa7215bc7dc7f48cb4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f4c8206cd02321c5153020fb929580f7/params.yaml b/examples/security/classification/output/reports/train/f4c8206cd02321c5153020fb929580f7/params.yaml deleted file mode 100644 index 6c75d08f..00000000 --- a/examples/security/classification/output/reports/train/f4c8206cd02321c5153020fb929580f7/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/adv_predictions.json - adv_probabilities_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/e4c1b6794aa27d625e9c50d7692e0947.pkl - params_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/params.yaml - predictions_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/predictions.json - probabilities_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/probabilities.json - score_dict_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/score_dict.json - test_labels_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/test_labels.json - train_labels_file: output/reports/train/f4c8206cd02321c5153020fb929580f7/train_labels.json - model_dir: models - name: f4c8206cd02321c5153020fb929580f7 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2584758b070261af9c2886310690e017 - trainer: - kwargs: {} -name: f4c8206cd02321c5153020fb929580f7 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/params.yaml b/examples/security/classification/output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/params.yaml deleted file mode 100644 index 317b4a15..00000000 --- a/examples/security/classification/output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/adv_predictions.json - adv_probabilities_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/11631499cbbf7ed3cb78a15fb5624a5b.pkl - params_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/params.yaml - predictions_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/predictions.json - probabilities_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/probabilities.json - score_dict_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/score_dict.json - test_labels_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/test_labels.json - train_labels_file: output/reports/train/f4c8d5033ea577bdc68c3ecc6e67a4c4/train_labels.json - model_dir: models - name: f4c8d5033ea577bdc68c3ecc6e67a4c4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 67498ce7d7801a205df2fac1092d872f - trainer: - kwargs: {} -name: f4c8d5033ea577bdc68c3ecc6e67a4c4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/params.yaml b/examples/security/classification/output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/params.yaml deleted file mode 100644 index 0d562912..00000000 --- a/examples/security/classification/output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/adv_predictions.json - adv_probabilities_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/d02036960717fe8e6df983822f1b1bef.pkl - params_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/params.yaml - predictions_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/predictions.json - probabilities_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/probabilities.json - score_dict_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/score_dict.json - test_labels_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/test_labels.json - train_labels_file: output/reports/train/f4e7e9abe73c2ee6927e81da477ea935/train_labels.json - model_dir: models - name: f4e7e9abe73c2ee6927e81da477ea935 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d026c2a67efbed8996e6a03029454d18 - trainer: - kwargs: {} -name: f4e7e9abe73c2ee6927e81da477ea935 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f507bacadd76b963cbe4677061bf4f3d/params.yaml b/examples/security/classification/output/reports/train/f507bacadd76b963cbe4677061bf4f3d/params.yaml deleted file mode 100644 index 8d49aaa8..00000000 --- a/examples/security/classification/output/reports/train/f507bacadd76b963cbe4677061bf4f3d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/adv_predictions.json - adv_probabilities_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/218821fd2a5679d7217ae68f6f5d5e8b.pkl - params_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/params.yaml - predictions_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/predictions.json - probabilities_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/probabilities.json - score_dict_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/score_dict.json - test_labels_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/test_labels.json - train_labels_file: output/reports/train/f507bacadd76b963cbe4677061bf4f3d/train_labels.json - model_dir: models - name: f507bacadd76b963cbe4677061bf4f3d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d684b2da461b397df704e0342ebf204e - trainer: - kwargs: {} -name: f507bacadd76b963cbe4677061bf4f3d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f53e5dc0431f6532e0db23b2718e841a/params.yaml b/examples/security/classification/output/reports/train/f53e5dc0431f6532e0db23b2718e841a/params.yaml deleted file mode 100644 index b13f0760..00000000 --- a/examples/security/classification/output/reports/train/f53e5dc0431f6532e0db23b2718e841a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/adv_predictions.json - adv_probabilities_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/272a004d61aa3028c866927e11c0d8c8.pkl - params_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/params.yaml - predictions_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/predictions.json - probabilities_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/probabilities.json - score_dict_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/score_dict.json - test_labels_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/test_labels.json - train_labels_file: output/reports/train/f53e5dc0431f6532e0db23b2718e841a/train_labels.json - model_dir: models - name: f53e5dc0431f6532e0db23b2718e841a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0551cf895a301de8a0faf9f406fd9dd4 - trainer: - kwargs: {} -name: f53e5dc0431f6532e0db23b2718e841a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/params.yaml b/examples/security/classification/output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/params.yaml deleted file mode 100644 index 9ac635dc..00000000 --- a/examples/security/classification/output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/adv_predictions.json - adv_probabilities_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/45e6aa9614385e64b92b59611e80414a.pkl - params_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/params.yaml - predictions_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/predictions.json - probabilities_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/probabilities.json - score_dict_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/score_dict.json - test_labels_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/test_labels.json - train_labels_file: output/reports/train/f5543a7ce23c9ab0cffc2faefa0f45a5/train_labels.json - model_dir: models - name: f5543a7ce23c9ab0cffc2faefa0f45a5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 4c528844b500281d12667bc8eccd5a6f - trainer: - kwargs: {} -name: f5543a7ce23c9ab0cffc2faefa0f45a5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/params.yaml b/examples/security/classification/output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/params.yaml deleted file mode 100644 index 687e603b..00000000 --- a/examples/security/classification/output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/adv_predictions.json - adv_probabilities_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/1a6d03a1de9c26f07fee97b188f459d9.pkl - params_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/params.yaml - predictions_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/predictions.json - probabilities_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/probabilities.json - score_dict_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/score_dict.json - test_labels_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/test_labels.json - train_labels_file: output/reports/train/f58f48c3ffe4e69ed9c2be87a9559106/train_labels.json - model_dir: models - name: f58f48c3ffe4e69ed9c2be87a9559106 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2127f1f61b5dfd8ce47d5564a47d84dc - trainer: - kwargs: {} -name: f58f48c3ffe4e69ed9c2be87a9559106 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/params.yaml b/examples/security/classification/output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/params.yaml deleted file mode 100644 index eefd0519..00000000 --- a/examples/security/classification/output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/adv_predictions.json - adv_probabilities_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/c079b4a980997516527b0c7dc1378384.pkl - params_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/params.yaml - predictions_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/predictions.json - probabilities_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/probabilities.json - score_dict_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/score_dict.json - test_labels_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/test_labels.json - train_labels_file: output/reports/train/f5ce28091f1c745fbcdb0eeaefee846d/train_labels.json - model_dir: models - name: f5ce28091f1c745fbcdb0eeaefee846d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d09f9157ca5a5d60f2baf92417fc574a - trainer: - kwargs: {} -name: f5ce28091f1c745fbcdb0eeaefee846d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/params.yaml b/examples/security/classification/output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/params.yaml deleted file mode 100644 index f95b4391..00000000 --- a/examples/security/classification/output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/adv_predictions.json - adv_probabilities_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/0082af42aa5ae8d4000c88292c9cfa1f.pkl - params_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/params.yaml - predictions_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/predictions.json - probabilities_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/probabilities.json - score_dict_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/score_dict.json - test_labels_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/test_labels.json - train_labels_file: output/reports/train/f5f547b2e47eeff36eceeff2a9f395c1/train_labels.json - model_dir: models - name: f5f547b2e47eeff36eceeff2a9f395c1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 193be15007b8245f5ef8d26dfeba537f - trainer: - kwargs: {} -name: f5f547b2e47eeff36eceeff2a9f395c1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/params.yaml b/examples/security/classification/output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/params.yaml deleted file mode 100644 index 8e8bc21e..00000000 --- a/examples/security/classification/output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/adv_predictions.json - adv_probabilities_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/cc2d1c7f0a61fd19fde2b5e6ade4af6d.pkl - params_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/params.yaml - predictions_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/predictions.json - probabilities_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/probabilities.json - score_dict_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/score_dict.json - test_labels_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/test_labels.json - train_labels_file: output/reports/train/f61711fb2c5bbb39dd8e17626ac71c5a/train_labels.json - model_dir: models - name: f61711fb2c5bbb39dd8e17626ac71c5a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 140618c85fd4279e6a667ffd793ce483 - trainer: - kwargs: {} -name: f61711fb2c5bbb39dd8e17626ac71c5a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/params.yaml b/examples/security/classification/output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/params.yaml deleted file mode 100644 index 808c45a8..00000000 --- a/examples/security/classification/output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/adv_predictions.json - adv_probabilities_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/2c5fc459436c9ec41c501f4b1e177eb5.pkl - params_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/params.yaml - predictions_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/predictions.json - probabilities_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/probabilities.json - score_dict_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/score_dict.json - test_labels_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/test_labels.json - train_labels_file: output/reports/train/f6672e7f1fd944ab3da9459c0d79f589/train_labels.json - model_dir: models - name: f6672e7f1fd944ab3da9459c0d79f589 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 34ba4c7772c583eb4cbabbece841361a - trainer: - kwargs: {} -name: f6672e7f1fd944ab3da9459c0d79f589 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/params.yaml b/examples/security/classification/output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/params.yaml deleted file mode 100644 index 3750e3a5..00000000 --- a/examples/security/classification/output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/adv_predictions.json - adv_probabilities_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/8ca3c15a207b54882a70e2c0e2a9c22a.pkl - params_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/params.yaml - predictions_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/predictions.json - probabilities_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/probabilities.json - score_dict_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/score_dict.json - test_labels_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/test_labels.json - train_labels_file: output/reports/train/f6951d7c42c2bada3342bad9e9f9fc08/train_labels.json - model_dir: models - name: f6951d7c42c2bada3342bad9e9f9fc08 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 055c65e1b23eb1a53c9bd3692e26099d - trainer: - kwargs: {} -name: f6951d7c42c2bada3342bad9e9f9fc08 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/params.yaml b/examples/security/classification/output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/params.yaml deleted file mode 100644 index 7853a54f..00000000 --- a/examples/security/classification/output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/adv_predictions.json - adv_probabilities_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/f89e7654843df6652a752cc8b292bb0c.pkl - params_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/params.yaml - predictions_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/predictions.json - probabilities_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/probabilities.json - score_dict_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/score_dict.json - test_labels_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/test_labels.json - train_labels_file: output/reports/train/f69e3e32a09ba64d4227203032ddfa2a/train_labels.json - model_dir: models - name: f69e3e32a09ba64d4227203032ddfa2a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d5d168aab130e43e095165b9dc825b3f - trainer: - kwargs: {} -name: f69e3e32a09ba64d4227203032ddfa2a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/params.yaml b/examples/security/classification/output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/params.yaml deleted file mode 100644 index 9e08854f..00000000 --- a/examples/security/classification/output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/adv_predictions.json - adv_probabilities_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/cad2be7faa8e5ba99eabba08b77fad30.pkl - params_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/params.yaml - predictions_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/predictions.json - probabilities_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/probabilities.json - score_dict_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/score_dict.json - test_labels_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/test_labels.json - train_labels_file: output/reports/train/f6a108569171e8f1dc6c420d3d0f3a58/train_labels.json - model_dir: models - name: f6a108569171e8f1dc6c420d3d0f3a58 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 871257c85d9bd1a35a5c56b61036b311 - trainer: - kwargs: {} -name: f6a108569171e8f1dc6c420d3d0f3a58 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f6b641491e817c7a16d531945d70d1a1/params.yaml b/examples/security/classification/output/reports/train/f6b641491e817c7a16d531945d70d1a1/params.yaml deleted file mode 100644 index 4ba05c8a..00000000 --- a/examples/security/classification/output/reports/train/f6b641491e817c7a16d531945d70d1a1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/adv_predictions.json - adv_probabilities_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/88d4135ab38836dd0e43fc72a72af33f.pkl - params_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/params.yaml - predictions_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/predictions.json - probabilities_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/probabilities.json - score_dict_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/score_dict.json - test_labels_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/test_labels.json - train_labels_file: output/reports/train/f6b641491e817c7a16d531945d70d1a1/train_labels.json - model_dir: models - name: f6b641491e817c7a16d531945d70d1a1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2628ac8169c5dd027b37ba48547d949a - trainer: - kwargs: {} -name: f6b641491e817c7a16d531945d70d1a1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/params.yaml b/examples/security/classification/output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/params.yaml deleted file mode 100644 index 3b52ebca..00000000 --- a/examples/security/classification/output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/adv_predictions.json - adv_probabilities_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/65196e1b3c9fc49ff0db2cadeee23483.pkl - params_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/params.yaml - predictions_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/predictions.json - probabilities_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/probabilities.json - score_dict_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/score_dict.json - test_labels_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/test_labels.json - train_labels_file: output/reports/train/f6c4d684820168b9c4f714ec20de0c9c/train_labels.json - model_dir: models - name: f6c4d684820168b9c4f714ec20de0c9c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9e74bbbaf261ab6859194175fa2f3582 - trainer: - kwargs: {} -name: f6c4d684820168b9c4f714ec20de0c9c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/params.yaml b/examples/security/classification/output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/params.yaml deleted file mode 100644 index d6cb5351..00000000 --- a/examples/security/classification/output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/adv_predictions.json - adv_probabilities_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/9d327ba86411e28960509b163981171c.pkl - params_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/params.yaml - predictions_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/predictions.json - probabilities_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/probabilities.json - score_dict_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/score_dict.json - test_labels_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/test_labels.json - train_labels_file: output/reports/train/f6dccee630cc8d1c8ef418f245699b3d/train_labels.json - model_dir: models - name: f6dccee630cc8d1c8ef418f245699b3d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 100 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 87d648fba3547dd06e254d13d3d8a849 - trainer: - kwargs: {} -name: f6dccee630cc8d1c8ef418f245699b3d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f720b75135dfaf2d02b112622d0128f4/params.yaml b/examples/security/classification/output/reports/train/f720b75135dfaf2d02b112622d0128f4/params.yaml deleted file mode 100644 index a9b5503a..00000000 --- a/examples/security/classification/output/reports/train/f720b75135dfaf2d02b112622d0128f4/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/adv_predictions.json - adv_probabilities_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/daf405a67081a1a4add9f1f86a958316.pkl - params_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/params.yaml - predictions_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/predictions.json - probabilities_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/probabilities.json - score_dict_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/score_dict.json - test_labels_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/test_labels.json - train_labels_file: output/reports/train/f720b75135dfaf2d02b112622d0128f4/train_labels.json - model_dir: models - name: f720b75135dfaf2d02b112622d0128f4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 50f8d3fe2bd4d2baffab1ce662e26b8f - trainer: - kwargs: {} -name: f720b75135dfaf2d02b112622d0128f4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f72531988b39b77c3e8ec59d40537de9/params.yaml b/examples/security/classification/output/reports/train/f72531988b39b77c3e8ec59d40537de9/params.yaml deleted file mode 100644 index 318d9f51..00000000 --- a/examples/security/classification/output/reports/train/f72531988b39b77c3e8ec59d40537de9/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/adv_predictions.json - adv_probabilities_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/6c03fa9bc14a06f8b5dbb17c7c6882a0.pkl - params_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/params.yaml - predictions_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/predictions.json - probabilities_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/probabilities.json - score_dict_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/score_dict.json - test_labels_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/test_labels.json - train_labels_file: output/reports/train/f72531988b39b77c3e8ec59d40537de9/train_labels.json - model_dir: models - name: f72531988b39b77c3e8ec59d40537de9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f3e8a58180c3f9696d7251ac3ce44ef5 - trainer: - kwargs: {} -name: f72531988b39b77c3e8ec59d40537de9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/params.yaml b/examples/security/classification/output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/params.yaml deleted file mode 100644 index 3e48bfa1..00000000 --- a/examples/security/classification/output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/adv_predictions.json - adv_probabilities_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/7bd66a3687debb4c7f916b40319db721.pkl - params_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/params.yaml - predictions_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/predictions.json - probabilities_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/probabilities.json - score_dict_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/score_dict.json - test_labels_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/test_labels.json - train_labels_file: output/reports/train/f7323b696d94bb46e4869bfd92a6ca32/train_labels.json - model_dir: models - name: f7323b696d94bb46e4869bfd92a6ca32 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6bfbaafb18a795f7c0725e0fe25b4a07 - trainer: - kwargs: {} -name: f7323b696d94bb46e4869bfd92a6ca32 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/params.yaml b/examples/security/classification/output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/params.yaml deleted file mode 100644 index 43ddf202..00000000 --- a/examples/security/classification/output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/adv_predictions.json - adv_probabilities_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/50fa78d0c4f616841bd7eb55b89a83e5.pkl - params_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/params.yaml - predictions_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/predictions.json - probabilities_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/probabilities.json - score_dict_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/score_dict.json - test_labels_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/test_labels.json - train_labels_file: output/reports/train/f75ec5623e07bdf5b9b00efe4adc79b8/train_labels.json - model_dir: models - name: f75ec5623e07bdf5b9b00efe4adc79b8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d671ea3ccbef0c3cba7d1596fca906d7 - trainer: - kwargs: {} -name: f75ec5623e07bdf5b9b00efe4adc79b8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f766e79660c502433ae4d65d022fd9ce/params.yaml b/examples/security/classification/output/reports/train/f766e79660c502433ae4d65d022fd9ce/params.yaml deleted file mode 100644 index e638f700..00000000 --- a/examples/security/classification/output/reports/train/f766e79660c502433ae4d65d022fd9ce/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/adv_predictions.json - adv_probabilities_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/b14f32d1d382842de638c03599a0c83f.pkl - params_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/params.yaml - predictions_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/predictions.json - probabilities_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/probabilities.json - score_dict_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/score_dict.json - test_labels_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/test_labels.json - train_labels_file: output/reports/train/f766e79660c502433ae4d65d022fd9ce/train_labels.json - model_dir: models - name: f766e79660c502433ae4d65d022fd9ce - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 10 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1004340d6b4006268764c2bd2ecc7942 - trainer: - kwargs: {} -name: f766e79660c502433ae4d65d022fd9ce -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f783e108743c07edaffdc258e0583e2d/params.yaml b/examples/security/classification/output/reports/train/f783e108743c07edaffdc258e0583e2d/params.yaml deleted file mode 100644 index 0e2a3d77..00000000 --- a/examples/security/classification/output/reports/train/f783e108743c07edaffdc258e0583e2d/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/adv_predictions.json - adv_probabilities_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/98753945e8b565b33956a56ec7651b74.pkl - params_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/params.yaml - predictions_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/predictions.json - probabilities_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/probabilities.json - score_dict_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/score_dict.json - test_labels_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/test_labels.json - train_labels_file: output/reports/train/f783e108743c07edaffdc258e0583e2d/train_labels.json - model_dir: models - name: f783e108743c07edaffdc258e0583e2d - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: abdb5501a699b4243d8c62d20af59880 - trainer: - kwargs: {} -name: f783e108743c07edaffdc258e0583e2d -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f7ad4a0617981515a197e94af180ea36/params.yaml b/examples/security/classification/output/reports/train/f7ad4a0617981515a197e94af180ea36/params.yaml deleted file mode 100644 index d417f382..00000000 --- a/examples/security/classification/output/reports/train/f7ad4a0617981515a197e94af180ea36/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/adv_predictions.json - adv_probabilities_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/349529b56dc89a08ca197b9206049e10.pkl - params_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/params.yaml - predictions_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/predictions.json - probabilities_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/probabilities.json - score_dict_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/score_dict.json - test_labels_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/test_labels.json - train_labels_file: output/reports/train/f7ad4a0617981515a197e94af180ea36/train_labels.json - model_dir: models - name: f7ad4a0617981515a197e94af180ea36 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 10a30e3c674337c05e9580f7808de5a7 - trainer: - kwargs: {} -name: f7ad4a0617981515a197e94af180ea36 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/params.yaml b/examples/security/classification/output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/params.yaml deleted file mode 100644 index 8b2ba613..00000000 --- a/examples/security/classification/output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/adv_predictions.json - adv_probabilities_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/e73cfa84d8cbe40f837147098d05d0d5.pkl - model_file: output/models/7b11c28e946be22ef18d2b7f493e41cf.pkl - params_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/params.yaml - predictions_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/predictions.json - probabilities_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/probabilities.json - score_dict_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/score_dict.json - test_labels_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/test_labels.json - train_labels_file: output/reports/train/f7b66b5d3f04f67312dd22a5bdda3262/train_labels.json - model_dir: models - name: f7b66b5d3f04f67312dd22a5bdda3262 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 558b40e4a5de01a1816e029b48e75a52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 457612e89e16acbf257358f473459f59 - trainer: - kwargs: {} -name: f7b66b5d3f04f67312dd22a5bdda3262 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f811fe78599038f6ad928729a677d376/params.yaml b/examples/security/classification/output/reports/train/f811fe78599038f6ad928729a677d376/params.yaml deleted file mode 100644 index 1813a5c4..00000000 --- a/examples/security/classification/output/reports/train/f811fe78599038f6ad928729a677d376/params.yaml +++ /dev/null @@ -1,102 +0,0 @@ -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f811fe78599038f6ad928729a677d376/adv_predictions.json - adv_probabilities_file: output/reports/train/f811fe78599038f6ad928729a677d376/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/2db9ede9d723385cf4052c8e5300be74.pkl - model_file: output/models/ffa17bcdff44b3d99961b5b9ea0db4c8.pkl - params_file: output/reports/train/f811fe78599038f6ad928729a677d376/params.yaml - predictions_file: output/reports/train/f811fe78599038f6ad928729a677d376/predictions.json - probabilities_file: output/reports/train/f811fe78599038f6ad928729a677d376/probabilities.json - score_dict_file: output/reports/train/f811fe78599038f6ad928729a677d376/score_dict.json - test_labels_file: output/reports/train/f811fe78599038f6ad928729a677d376/test_labels.json - train_labels_file: output/reports/train/f811fe78599038f6ad928729a677d376/train_labels.json - model_dir: models - name: f811fe78599038f6ad928729a677d376 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: 0b50a84d7472bc7095f9199da9fa02bc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9b1ae58b464df54ee8918089f870a3ad - trainer: - kwargs: {} -name: f811fe78599038f6ad928729a677d376 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/params.yaml b/examples/security/classification/output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/params.yaml deleted file mode 100644 index bfe58ee9..00000000 --- a/examples/security/classification/output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/adv_predictions.json - adv_probabilities_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/340fbc12d8ebd7ebc2649f184fb1b6c8.pkl - params_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/params.yaml - predictions_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/predictions.json - probabilities_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/probabilities.json - score_dict_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/score_dict.json - test_labels_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/test_labels.json - train_labels_file: output/reports/train/f81cb8acc6909a41d0c17b836c0f1cf8/train_labels.json - model_dir: models - name: f81cb8acc6909a41d0c17b836c0f1cf8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: dfc9731dc44b0da582b2133ccaa4e320 - trainer: - kwargs: {} -name: f81cb8acc6909a41d0c17b836c0f1cf8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/params.yaml b/examples/security/classification/output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/params.yaml deleted file mode 100644 index 642ac0d1..00000000 --- a/examples/security/classification/output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/adv_predictions.json - adv_probabilities_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/7645f001fa544aac7433e3a1ad76b9a7.pkl - params_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/params.yaml - predictions_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/predictions.json - probabilities_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/probabilities.json - score_dict_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/score_dict.json - test_labels_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/test_labels.json - train_labels_file: output/reports/train/f825b6ff31d02f49aec0b03dfdbfbee8/train_labels.json - model_dir: models - name: f825b6ff31d02f49aec0b03dfdbfbee8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2ddcf810e7e9b714608c657a7893c71a - trainer: - kwargs: {} -name: f825b6ff31d02f49aec0b03dfdbfbee8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f82ba4c202c55c371dbb65594b172c8b/params.yaml b/examples/security/classification/output/reports/train/f82ba4c202c55c371dbb65594b172c8b/params.yaml deleted file mode 100644 index bb5f01eb..00000000 --- a/examples/security/classification/output/reports/train/f82ba4c202c55c371dbb65594b172c8b/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/adv_predictions.json - adv_probabilities_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/06c09c7df9bfc16693763f1df73edb39.pkl - params_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/params.yaml - predictions_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/predictions.json - probabilities_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/probabilities.json - score_dict_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/score_dict.json - test_labels_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/test_labels.json - train_labels_file: output/reports/train/f82ba4c202c55c371dbb65594b172c8b/train_labels.json - model_dir: models - name: f82ba4c202c55c371dbb65594b172c8b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e7302a8d8c2056b007716b4fc50cdafc - trainer: - kwargs: {} -name: f82ba4c202c55c371dbb65594b172c8b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/params.yaml b/examples/security/classification/output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/params.yaml deleted file mode 100644 index 4f966c2a..00000000 --- a/examples/security/classification/output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/adv_predictions.json - adv_probabilities_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/ef26a6953bd1d217f5cf7a0ed7a11a28.pkl - params_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/params.yaml - predictions_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/predictions.json - probabilities_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/probabilities.json - score_dict_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/score_dict.json - test_labels_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/test_labels.json - train_labels_file: output/reports/train/f8445d9a9eaf421d069945f99c8e08c2/train_labels.json - model_dir: models - name: f8445d9a9eaf421d069945f99c8e08c2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 8a8dcba8d751d568c3450a04eb59f9fa - trainer: - kwargs: {} -name: f8445d9a9eaf421d069945f99c8e08c2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/params.yaml b/examples/security/classification/output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/params.yaml deleted file mode 100644 index ae6981c4..00000000 --- a/examples/security/classification/output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/adv_predictions.json - adv_probabilities_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/261a8258ab643f86fbf8af2dd5ba18d7.pkl - params_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/params.yaml - predictions_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/predictions.json - probabilities_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/probabilities.json - score_dict_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/score_dict.json - test_labels_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/test_labels.json - train_labels_file: output/reports/train/f8b2f650a4a2bcafa1ef9e116cd49b5e/train_labels.json - model_dir: models - name: f8b2f650a4a2bcafa1ef9e116cd49b5e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b8939f11dc1e4baf59cc778365645b0f - trainer: - kwargs: {} -name: f8b2f650a4a2bcafa1ef9e116cd49b5e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f8db4f1e4eec6c381669e227670cf491/params.yaml b/examples/security/classification/output/reports/train/f8db4f1e4eec6c381669e227670cf491/params.yaml deleted file mode 100644 index 9453f248..00000000 --- a/examples/security/classification/output/reports/train/f8db4f1e4eec6c381669e227670cf491/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/adv_predictions.json - adv_probabilities_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/38cf29096c94c002970b8cff61144089.pkl - params_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/params.yaml - predictions_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/predictions.json - probabilities_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/probabilities.json - score_dict_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/score_dict.json - test_labels_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/test_labels.json - train_labels_file: output/reports/train/f8db4f1e4eec6c381669e227670cf491/train_labels.json - model_dir: models - name: f8db4f1e4eec6c381669e227670cf491 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 386b2aa451fd6de145d069ffef80116e - trainer: - kwargs: {} -name: f8db4f1e4eec6c381669e227670cf491 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f8ec690640736b198c9d88be43f0ca9a/params.yaml b/examples/security/classification/output/reports/train/f8ec690640736b198c9d88be43f0ca9a/params.yaml deleted file mode 100644 index a64bd26d..00000000 --- a/examples/security/classification/output/reports/train/f8ec690640736b198c9d88be43f0ca9a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/adv_predictions.json - adv_probabilities_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/018f585ab93d590716ced72c8f3dcd09.pkl - params_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/params.yaml - predictions_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/predictions.json - probabilities_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/probabilities.json - score_dict_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/score_dict.json - test_labels_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/test_labels.json - train_labels_file: output/reports/train/f8ec690640736b198c9d88be43f0ca9a/train_labels.json - model_dir: models - name: f8ec690640736b198c9d88be43f0ca9a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3e05aa234c039b32f482343e29ce5d1d - trainer: - kwargs: {} -name: f8ec690640736b198c9d88be43f0ca9a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/params.yaml b/examples/security/classification/output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/params.yaml deleted file mode 100644 index 9ef505d5..00000000 --- a/examples/security/classification/output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/adv_predictions.json - adv_probabilities_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/82c79849aa7730daadee6516ab94fe83.pkl - params_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/params.yaml - predictions_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/predictions.json - probabilities_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/probabilities.json - score_dict_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/score_dict.json - test_labels_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/test_labels.json - train_labels_file: output/reports/train/f8ecd1ecd39272b540bbef2511acd55f/train_labels.json - model_dir: models - name: f8ecd1ecd39272b540bbef2511acd55f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 63f45129a1a1d02820be042a425fded3 - trainer: - kwargs: {} -name: f8ecd1ecd39272b540bbef2511acd55f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f902d021581a35b1a9906b1ddffb61e5/params.yaml b/examples/security/classification/output/reports/train/f902d021581a35b1a9906b1ddffb61e5/params.yaml deleted file mode 100644 index 0309f20a..00000000 --- a/examples/security/classification/output/reports/train/f902d021581a35b1a9906b1ddffb61e5/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/adv_predictions.json - adv_probabilities_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/10a5aa4f7641729ec91b1f01ffece2a2.pkl - params_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/params.yaml - predictions_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/predictions.json - probabilities_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/probabilities.json - score_dict_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/score_dict.json - test_labels_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/test_labels.json - train_labels_file: output/reports/train/f902d021581a35b1a9906b1ddffb61e5/train_labels.json - model_dir: models - name: f902d021581a35b1a9906b1ddffb61e5 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 04e354b63777df1aa50d0b48d15f1354 - trainer: - kwargs: {} -name: f902d021581a35b1a9906b1ddffb61e5 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/params.yaml b/examples/security/classification/output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/params.yaml deleted file mode 100644 index 6e262422..00000000 --- a/examples/security/classification/output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/adv_predictions.json - adv_probabilities_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/0c26909810420725670bdeae6ee1e0ac.pkl - params_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/params.yaml - predictions_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/predictions.json - probabilities_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/probabilities.json - score_dict_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/score_dict.json - test_labels_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/test_labels.json - train_labels_file: output/reports/train/f906b74ab32bfc34f03d43f3d3672d10/train_labels.json - model_dir: models - name: f906b74ab32bfc34f03d43f3d3672d10 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3deeabfc2e1900a86ebfbfd45881a872 - trainer: - kwargs: {} -name: f906b74ab32bfc34f03d43f3d3672d10 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/params.yaml b/examples/security/classification/output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/params.yaml deleted file mode 100644 index 1f9a66fe..00000000 --- a/examples/security/classification/output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/adv_predictions.json - adv_probabilities_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/6afad5956da9aa59313a25b1ea2063e5.pkl - params_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/params.yaml - predictions_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/predictions.json - probabilities_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/probabilities.json - score_dict_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/score_dict.json - test_labels_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/test_labels.json - train_labels_file: output/reports/train/f90a0df48a4e6eab8792a789a2c766b1/train_labels.json - model_dir: models - name: f90a0df48a4e6eab8792a789a2c766b1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: caa5a95b6ae5cf502ab3c935331fef3e - trainer: - kwargs: {} -name: f90a0df48a4e6eab8792a789a2c766b1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f95064d252686f686ee7160faf806443/params.yaml b/examples/security/classification/output/reports/train/f95064d252686f686ee7160faf806443/params.yaml deleted file mode 100644 index ee3b88cc..00000000 --- a/examples/security/classification/output/reports/train/f95064d252686f686ee7160faf806443/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f95064d252686f686ee7160faf806443/adv_predictions.json - adv_probabilities_file: output/reports/train/f95064d252686f686ee7160faf806443/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/2850a15da759103fe79db723fc4b33de.pkl - params_file: output/reports/train/f95064d252686f686ee7160faf806443/params.yaml - predictions_file: output/reports/train/f95064d252686f686ee7160faf806443/predictions.json - probabilities_file: output/reports/train/f95064d252686f686ee7160faf806443/probabilities.json - score_dict_file: output/reports/train/f95064d252686f686ee7160faf806443/score_dict.json - test_labels_file: output/reports/train/f95064d252686f686ee7160faf806443/test_labels.json - train_labels_file: output/reports/train/f95064d252686f686ee7160faf806443/train_labels.json - model_dir: models - name: f95064d252686f686ee7160faf806443 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a8709dc27d8955fb3fa90d5cb1458306 - trainer: - kwargs: {} -name: f95064d252686f686ee7160faf806443 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/params.yaml b/examples/security/classification/output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/params.yaml deleted file mode 100644 index 1c354e53..00000000 --- a/examples/security/classification/output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/adv_predictions.json - adv_probabilities_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/abd71b41dc7059012a299d0351f176ac.pkl - params_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/params.yaml - predictions_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/predictions.json - probabilities_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/probabilities.json - score_dict_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/score_dict.json - test_labels_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/test_labels.json - train_labels_file: output/reports/train/f9882e5a34cc7f107d9fc4eb8b604825/train_labels.json - model_dir: models - name: f9882e5a34cc7f107d9fc4eb8b604825 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e7b2765ad085176fb193850db76ac43c - trainer: - kwargs: {} -name: f9882e5a34cc7f107d9fc4eb8b604825 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f997096429d1f3d07026c235f61771fa/params.yaml b/examples/security/classification/output/reports/train/f997096429d1f3d07026c235f61771fa/params.yaml deleted file mode 100644 index 00d2ade1..00000000 --- a/examples/security/classification/output/reports/train/f997096429d1f3d07026c235f61771fa/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f997096429d1f3d07026c235f61771fa/adv_predictions.json - adv_probabilities_file: output/reports/train/f997096429d1f3d07026c235f61771fa/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/4af95142a2005618103ec75abcc1cb03.pkl - params_file: output/reports/train/f997096429d1f3d07026c235f61771fa/params.yaml - predictions_file: output/reports/train/f997096429d1f3d07026c235f61771fa/predictions.json - probabilities_file: output/reports/train/f997096429d1f3d07026c235f61771fa/probabilities.json - score_dict_file: output/reports/train/f997096429d1f3d07026c235f61771fa/score_dict.json - test_labels_file: output/reports/train/f997096429d1f3d07026c235f61771fa/test_labels.json - train_labels_file: output/reports/train/f997096429d1f3d07026c235f61771fa/train_labels.json - model_dir: models - name: f997096429d1f3d07026c235f61771fa - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e8b20fedc129c7fc38da2ca824434428 - trainer: - kwargs: {} -name: f997096429d1f3d07026c235f61771fa -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/params.yaml b/examples/security/classification/output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/params.yaml deleted file mode 100644 index 2b4fbde2..00000000 --- a/examples/security/classification/output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/adv_predictions.json - adv_probabilities_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/2f9d25217cd5152a49e9b600065b6988.pkl - params_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/params.yaml - predictions_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/predictions.json - probabilities_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/probabilities.json - score_dict_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/score_dict.json - test_labels_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/test_labels.json - train_labels_file: output/reports/train/f99f13f7c5329652d8f029ebaa6f10b1/train_labels.json - model_dir: models - name: f99f13f7c5329652d8f029ebaa6f10b1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b1f86e3caa985e6d603c00e48d52f302 - trainer: - kwargs: {} -name: f99f13f7c5329652d8f029ebaa6f10b1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f9a9e6f313f76c597464dee05a03944f/params.yaml b/examples/security/classification/output/reports/train/f9a9e6f313f76c597464dee05a03944f/params.yaml deleted file mode 100644 index 912eaff4..00000000 --- a/examples/security/classification/output/reports/train/f9a9e6f313f76c597464dee05a03944f/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/adv_predictions.json - adv_probabilities_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/2a8c6baf3e52e8e59b81932712566521.pkl - params_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/params.yaml - predictions_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/predictions.json - probabilities_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/probabilities.json - score_dict_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/score_dict.json - test_labels_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/test_labels.json - train_labels_file: output/reports/train/f9a9e6f313f76c597464dee05a03944f/train_labels.json - model_dir: models - name: f9a9e6f313f76c597464dee05a03944f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f68a8c89c834e2c458c2e749b0a05e12 - trainer: - kwargs: {} -name: f9a9e6f313f76c597464dee05a03944f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/params.yaml b/examples/security/classification/output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/params.yaml deleted file mode 100644 index 179c9515..00000000 --- a/examples/security/classification/output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/adv_predictions.json - adv_probabilities_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3b2cbdd93cfb212717a2a798859a65c5.pkl - model_file: output/models/04834be10162a484eb0cca74ef0573bd.pkl - params_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/params.yaml - predictions_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/predictions.json - probabilities_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/probabilities.json - score_dict_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/score_dict.json - test_labels_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/test_labels.json - train_labels_file: output/reports/train/f9adf91a581503c76ac3c2b4ee0ee951/train_labels.json - model_dir: models - name: f9adf91a581503c76ac3c2b4ee0ee951 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 7d24ace0c72b0d2db1a9a5e41567083c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.0001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1cb581c846242874891fb468479f2720 - trainer: - kwargs: {} -name: f9adf91a581503c76ac3c2b4ee0ee951 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f9b41a391d248a16e134009ba1694e9c/params.yaml b/examples/security/classification/output/reports/train/f9b41a391d248a16e134009ba1694e9c/params.yaml deleted file mode 100644 index 55494e5e..00000000 --- a/examples/security/classification/output/reports/train/f9b41a391d248a16e134009ba1694e9c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/adv_predictions.json - adv_probabilities_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/c40f0b20272414fcbe10e2e07680c6fa.pkl - params_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/params.yaml - predictions_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/predictions.json - probabilities_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/probabilities.json - score_dict_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/score_dict.json - test_labels_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/test_labels.json - train_labels_file: output/reports/train/f9b41a391d248a16e134009ba1694e9c/train_labels.json - model_dir: models - name: f9b41a391d248a16e134009ba1694e9c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ffac1e04c194ee6e067b119eb472fb47 - trainer: - kwargs: {} -name: f9b41a391d248a16e134009ba1694e9c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/params.yaml b/examples/security/classification/output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/params.yaml deleted file mode 100644 index 6a27e595..00000000 --- a/examples/security/classification/output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/adv_predictions.json - adv_probabilities_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/2c71ee06a16b0c42e977a0402d904d38.pkl - params_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/params.yaml - predictions_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/predictions.json - probabilities_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/probabilities.json - score_dict_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/score_dict.json - test_labels_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/test_labels.json - train_labels_file: output/reports/train/f9d6eb9cb4222a4ccf000f4239b829c2/train_labels.json - model_dir: models - name: f9d6eb9cb4222a4ccf000f4239b829c2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f9fda14b9cfcfd15807d2459adf91cce - trainer: - kwargs: {} -name: f9d6eb9cb4222a4ccf000f4239b829c2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/params.yaml b/examples/security/classification/output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/params.yaml deleted file mode 100644 index 4927f00f..00000000 --- a/examples/security/classification/output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/adv_predictions.json - adv_probabilities_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/3a3befe8e2b8f875851cd741cf42136c.pkl - model_file: output/models/014a3f1e0e04c8a6bbe3a7093125ef58.pkl - params_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/params.yaml - predictions_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/predictions.json - probabilities_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/probabilities.json - score_dict_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/score_dict.json - test_labels_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/test_labels.json - train_labels_file: output/reports/train/f9f35e4452e13f09e7670252d24b7dd1/train_labels.json - model_dir: models - name: f9f35e4452e13f09e7670252d24b7dd1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: adad2c2f1660f3b976f025cc3901addc - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.0001 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1be8bf5e13d0311b8d1e631fc09d8376 - trainer: - kwargs: {} -name: f9f35e4452e13f09e7670252d24b7dd1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f9f3caa439887279484339dbe2d05cf2/params.yaml b/examples/security/classification/output/reports/train/f9f3caa439887279484339dbe2d05cf2/params.yaml deleted file mode 100644 index 491171a7..00000000 --- a/examples/security/classification/output/reports/train/f9f3caa439887279484339dbe2d05cf2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/adv_predictions.json - adv_probabilities_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/021b1f7df6fa918344484cbaa29accf7.pkl - params_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/params.yaml - predictions_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/predictions.json - probabilities_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/probabilities.json - score_dict_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/score_dict.json - test_labels_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/test_labels.json - train_labels_file: output/reports/train/f9f3caa439887279484339dbe2d05cf2/train_labels.json - model_dir: models - name: f9f3caa439887279484339dbe2d05cf2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 36b6ddea0ead7ac79e68b090873f95d7 - trainer: - kwargs: {} -name: f9f3caa439887279484339dbe2d05cf2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/params.yaml b/examples/security/classification/output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/params.yaml deleted file mode 100644 index 276091a9..00000000 --- a/examples/security/classification/output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/params.yaml +++ /dev/null @@ -1,107 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/adv_predictions.json - adv_probabilities_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/0e805b59ed829e9f5ea6cc4a7fcefc5e.pkl - params_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/params.yaml - predictions_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/predictions.json - probabilities_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/probabilities.json - score_dict_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/score_dict.json - test_labels_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/test_labels.json - train_labels_file: output/reports/train/f9f685a89cdbe694f02acc840bd14ecd/train_labels.json - model_dir: models - name: f9f685a89cdbe694f02acc840bd14ecd - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1000 - degree: 5 - gamma: scale - kernel: - - poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c84cc16ceea3d325d4026a8e7d6acb6c - trainer: - kwargs: {} -name: f9f685a89cdbe694f02acc840bd14ecd -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/params.yaml b/examples/security/classification/output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/params.yaml deleted file mode 100644 index 371fda26..00000000 --- a/examples/security/classification/output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/adv_predictions.json - adv_probabilities_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/09e4d24d205c842eb40197568c4f0927.pkl - params_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/params.yaml - predictions_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/predictions.json - probabilities_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/probabilities.json - score_dict_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/score_dict.json - test_labels_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/test_labels.json - train_labels_file: output/reports/train/fa0b9ed5cf79088577de74ba70398f9b/train_labels.json - model_dir: models - name: fa0b9ed5cf79088577de74ba70398f9b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 09c863a354d2ecedf53ba4c62df11bb5 - trainer: - kwargs: {} -name: fa0b9ed5cf79088577de74ba70398f9b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/params.yaml b/examples/security/classification/output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/params.yaml deleted file mode 100644 index 4243b7f5..00000000 --- a/examples/security/classification/output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/adv_predictions.json - adv_probabilities_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/bc1c9abc322e684f09f30581e4f6d674.pkl - params_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/params.yaml - predictions_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/predictions.json - probabilities_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/probabilities.json - score_dict_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/score_dict.json - test_labels_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/test_labels.json - train_labels_file: output/reports/train/fa20b52eb5056d1a8ad6d486d23a1f6c/train_labels.json - model_dir: models - name: fa20b52eb5056d1a8ad6d486d23a1f6c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2f5c96adacc55cd0556ddaf7751b5b45 - trainer: - kwargs: {} -name: fa20b52eb5056d1a8ad6d486d23a1f6c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/params.yaml b/examples/security/classification/output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/params.yaml deleted file mode 100644 index 1fb123f5..00000000 --- a/examples/security/classification/output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/adv_predictions.json - adv_probabilities_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/1b88f024917d23e1dc7db82b98982f37.pkl - params_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/params.yaml - predictions_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/predictions.json - probabilities_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/probabilities.json - score_dict_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/score_dict.json - test_labels_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/test_labels.json - train_labels_file: output/reports/train/fa2f50ea72ba5a7e20c2838475586c45/train_labels.json - model_dir: models - name: fa2f50ea72ba5a7e20c2838475586c45 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 77b3a3698e280778a7193395d324ef13 - trainer: - kwargs: {} -name: fa2f50ea72ba5a7e20c2838475586c45 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fa53d45bdb490b035034f11d11176825/params.yaml b/examples/security/classification/output/reports/train/fa53d45bdb490b035034f11d11176825/params.yaml deleted file mode 100644 index 3726e820..00000000 --- a/examples/security/classification/output/reports/train/fa53d45bdb490b035034f11d11176825/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fa53d45bdb490b035034f11d11176825/adv_predictions.json - adv_probabilities_file: output/reports/train/fa53d45bdb490b035034f11d11176825/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/045ae7b0228f2b4bedec55bf6586f9fc.pkl - params_file: output/reports/train/fa53d45bdb490b035034f11d11176825/params.yaml - predictions_file: output/reports/train/fa53d45bdb490b035034f11d11176825/predictions.json - probabilities_file: output/reports/train/fa53d45bdb490b035034f11d11176825/probabilities.json - score_dict_file: output/reports/train/fa53d45bdb490b035034f11d11176825/score_dict.json - test_labels_file: output/reports/train/fa53d45bdb490b035034f11d11176825/test_labels.json - train_labels_file: output/reports/train/fa53d45bdb490b035034f11d11176825/train_labels.json - model_dir: models - name: fa53d45bdb490b035034f11d11176825 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0eb125ddfe465b774ab25c2fa597bf81 - trainer: - kwargs: {} -name: fa53d45bdb490b035034f11d11176825 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/params.yaml b/examples/security/classification/output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/params.yaml deleted file mode 100644 index b88bc16b..00000000 --- a/examples/security/classification/output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/adv_predictions.json - adv_probabilities_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/cfa54b061caa70f76fa83bc7f27c7566.pkl - model_file: output/models/1bf049137a86510e4fd1ad30f9c1848d.pkl - params_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/params.yaml - predictions_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/predictions.json - probabilities_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/probabilities.json - score_dict_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/score_dict.json - test_labels_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/test_labels.json - train_labels_file: output/reports/train/fa92c0b651467be12fa65ee0f0cb8689/train_labels.json - model_dir: models - name: fa92c0b651467be12fa65ee0f0cb8689 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: c19017431413b39638d3aec6a3342a81 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 1000 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 45020341d0b49ad56c6730400435aead - trainer: - kwargs: {} -name: fa92c0b651467be12fa65ee0f0cb8689 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/params.yaml b/examples/security/classification/output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/params.yaml deleted file mode 100644 index 4e38afa0..00000000 --- a/examples/security/classification/output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/adv_predictions.json - adv_probabilities_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/fe3f725e2791b3f1243f53d01bf84a61.pkl - params_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/params.yaml - predictions_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/predictions.json - probabilities_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/probabilities.json - score_dict_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/score_dict.json - test_labels_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/test_labels.json - train_labels_file: output/reports/train/faa9c91d771afc95c4ea027fb5e684e4/train_labels.json - model_dir: models - name: faa9c91d771afc95c4ea027fb5e684e4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0e9c33b6bcd29f5421db12b16010f28e - trainer: - kwargs: {} -name: faa9c91d771afc95c4ea027fb5e684e4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fac17f44332a6dd8c3ea807774b6c247/params.yaml b/examples/security/classification/output/reports/train/fac17f44332a6dd8c3ea807774b6c247/params.yaml deleted file mode 100644 index 5f6abc01..00000000 --- a/examples/security/classification/output/reports/train/fac17f44332a6dd8c3ea807774b6c247/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/adv_predictions.json - adv_probabilities_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/0a413dc3a4cf30a6c3aa6c2812dc76e0.pkl - params_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/params.yaml - predictions_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/predictions.json - probabilities_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/probabilities.json - score_dict_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/score_dict.json - test_labels_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/test_labels.json - train_labels_file: output/reports/train/fac17f44332a6dd8c3ea807774b6c247/train_labels.json - model_dir: models - name: fac17f44332a6dd8c3ea807774b6c247 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1fd99c93fbe2940545bd86b6eb41aeae - trainer: - kwargs: {} -name: fac17f44332a6dd8c3ea807774b6c247 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fadc650bab3c7e833094acfba16068b9/params.yaml b/examples/security/classification/output/reports/train/fadc650bab3c7e833094acfba16068b9/params.yaml deleted file mode 100644 index 4e57007a..00000000 --- a/examples/security/classification/output/reports/train/fadc650bab3c7e833094acfba16068b9/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/adv_predictions.json - adv_probabilities_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/645ddc632c726160ad52a12bb1e9f674.pkl - params_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/params.yaml - predictions_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/predictions.json - probabilities_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/probabilities.json - score_dict_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/score_dict.json - test_labels_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/test_labels.json - train_labels_file: output/reports/train/fadc650bab3c7e833094acfba16068b9/train_labels.json - model_dir: models - name: fadc650bab3c7e833094acfba16068b9 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bf79b52003937c4d4fba59a95a14ce2e - trainer: - kwargs: {} -name: fadc650bab3c7e833094acfba16068b9 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fadf57ffb82b49ad128097e830d4dddc/params.yaml b/examples/security/classification/output/reports/train/fadf57ffb82b49ad128097e830d4dddc/params.yaml deleted file mode 100644 index 354dacd7..00000000 --- a/examples/security/classification/output/reports/train/fadf57ffb82b49ad128097e830d4dddc/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/adv_predictions.json - adv_probabilities_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/30740a89c60c07f6676d137493e3620d.pkl - params_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/params.yaml - predictions_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/predictions.json - probabilities_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/probabilities.json - score_dict_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/score_dict.json - test_labels_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/test_labels.json - train_labels_file: output/reports/train/fadf57ffb82b49ad128097e830d4dddc/train_labels.json - model_dir: models - name: fadf57ffb82b49ad128097e830d4dddc - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bde64ea3f0b55e4b4c57737be33e90f6 - trainer: - kwargs: {} -name: fadf57ffb82b49ad128097e830d4dddc -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fb01aec510bdf73ce0a211ab751636ca/params.yaml b/examples/security/classification/output/reports/train/fb01aec510bdf73ce0a211ab751636ca/params.yaml deleted file mode 100644 index a990c153..00000000 --- a/examples/security/classification/output/reports/train/fb01aec510bdf73ce0a211ab751636ca/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/adv_predictions.json - adv_probabilities_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/14ce1b0ce5c9dbd8ba4cb8b6b78817bc.pkl - params_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/params.yaml - predictions_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/predictions.json - probabilities_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/probabilities.json - score_dict_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/score_dict.json - test_labels_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/test_labels.json - train_labels_file: output/reports/train/fb01aec510bdf73ce0a211ab751636ca/train_labels.json - model_dir: models - name: fb01aec510bdf73ce0a211ab751636ca - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: a793a9edd9c3ecedb7fa998779ddc5ea - trainer: - kwargs: {} -name: fb01aec510bdf73ce0a211ab751636ca -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fb08b1ac8d579c576eae21463d2e9883/params.yaml b/examples/security/classification/output/reports/train/fb08b1ac8d579c576eae21463d2e9883/params.yaml deleted file mode 100644 index a1ebad11..00000000 --- a/examples/security/classification/output/reports/train/fb08b1ac8d579c576eae21463d2e9883/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/adv_predictions.json - adv_probabilities_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/19cb90556e27f7856d79094a7cdbd5d5.pkl - params_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/params.yaml - predictions_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/predictions.json - probabilities_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/probabilities.json - score_dict_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/score_dict.json - test_labels_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/test_labels.json - train_labels_file: output/reports/train/fb08b1ac8d579c576eae21463d2e9883/train_labels.json - model_dir: models - name: fb08b1ac8d579c576eae21463d2e9883 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ec7ca04cdef37316c9de4d834038eb4c - trainer: - kwargs: {} -name: fb08b1ac8d579c576eae21463d2e9883 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/params.yaml b/examples/security/classification/output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/params.yaml deleted file mode 100644 index d9217bec..00000000 --- a/examples/security/classification/output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/adv_predictions.json - adv_probabilities_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/089fdc6f4a1fd7b09abe2f61d5df8bef.pkl - params_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/params.yaml - predictions_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/predictions.json - probabilities_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/probabilities.json - score_dict_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/score_dict.json - test_labels_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/test_labels.json - train_labels_file: output/reports/train/fb14765f2377c73cbfcf8ba8e9ac04a6/train_labels.json - model_dir: models - name: fb14765f2377c73cbfcf8ba8e9ac04a6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d69d5bd83f887172897b541c0efab437 - trainer: - kwargs: {} -name: fb14765f2377c73cbfcf8ba8e9ac04a6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/params.yaml b/examples/security/classification/output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/params.yaml deleted file mode 100644 index 158753e6..00000000 --- a/examples/security/classification/output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/adv_predictions.json - adv_probabilities_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/2577b7551395febb04ae8bd76fddc3c9.pkl - params_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/params.yaml - predictions_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/predictions.json - probabilities_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/probabilities.json - score_dict_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/score_dict.json - test_labels_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/test_labels.json - train_labels_file: output/reports/train/fb179f9342c16a9c9123ed76e3a7bb00/train_labels.json - model_dir: models - name: fb179f9342c16a9c9123ed76e3a7bb00 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 73e22735885e3d35005d776101d06b1a - trainer: - kwargs: {} -name: fb179f9342c16a9c9123ed76e3a7bb00 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fb23461e811a7530ab87cbc51427cce8/params.yaml b/examples/security/classification/output/reports/train/fb23461e811a7530ab87cbc51427cce8/params.yaml deleted file mode 100644 index 0ce0427c..00000000 --- a/examples/security/classification/output/reports/train/fb23461e811a7530ab87cbc51427cce8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/adv_predictions.json - adv_probabilities_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c3773084b43214d68623c23705a68aeb.pkl - model_file: output/models/62887a6b3b058afa2aa26df7d36d0227.pkl - params_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/params.yaml - predictions_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/predictions.json - probabilities_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/probabilities.json - score_dict_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/score_dict.json - test_labels_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/test_labels.json - train_labels_file: output/reports/train/fb23461e811a7530ab87cbc51427cce8/train_labels.json - model_dir: models - name: fb23461e811a7530ab87cbc51427cce8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 0416443d5d0c2adf85c100e28a421762 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.0001 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: f27a2551e9bfea27bf365e29490d72ca - trainer: - kwargs: {} -name: fb23461e811a7530ab87cbc51427cce8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/params.yaml b/examples/security/classification/output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/params.yaml deleted file mode 100644 index efd7fb94..00000000 --- a/examples/security/classification/output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/adv_predictions.json - adv_probabilities_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/6ae3b5812e657d27256da536a4aaf1ee.pkl - params_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/params.yaml - predictions_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/predictions.json - probabilities_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/probabilities.json - score_dict_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/score_dict.json - test_labels_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/test_labels.json - train_labels_file: output/reports/train/fb4413494a3630a31e0dacf79aaaf33e/train_labels.json - model_dir: models - name: fb4413494a3630a31e0dacf79aaaf33e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: cc732dffa5e9e5803a3db4a39a299287 - trainer: - kwargs: {} -name: fb4413494a3630a31e0dacf79aaaf33e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fb4755fa4fe2a11a007381d048cc3142/params.yaml b/examples/security/classification/output/reports/train/fb4755fa4fe2a11a007381d048cc3142/params.yaml deleted file mode 100644 index 32353a74..00000000 --- a/examples/security/classification/output/reports/train/fb4755fa4fe2a11a007381d048cc3142/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/adv_predictions.json - adv_probabilities_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/cda5ef19a3cc4fbbea82dc700924e5ef.pkl - params_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/params.yaml - predictions_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/predictions.json - probabilities_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/probabilities.json - score_dict_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/score_dict.json - test_labels_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/test_labels.json - train_labels_file: output/reports/train/fb4755fa4fe2a11a007381d048cc3142/train_labels.json - model_dir: models - name: fb4755fa4fe2a11a007381d048cc3142 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 10000 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 32e5b406cb9b33a029812d46436e2aa8 - trainer: - kwargs: {} -name: fb4755fa4fe2a11a007381d048cc3142 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fb513682740458bd659bea08757c66c0/params.yaml b/examples/security/classification/output/reports/train/fb513682740458bd659bea08757c66c0/params.yaml deleted file mode 100644 index 584c6eb1..00000000 --- a/examples/security/classification/output/reports/train/fb513682740458bd659bea08757c66c0/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fb513682740458bd659bea08757c66c0/adv_predictions.json - adv_probabilities_file: output/reports/train/fb513682740458bd659bea08757c66c0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/bb27e68d68f198637925f23c1561a40c.pkl - params_file: output/reports/train/fb513682740458bd659bea08757c66c0/params.yaml - predictions_file: output/reports/train/fb513682740458bd659bea08757c66c0/predictions.json - probabilities_file: output/reports/train/fb513682740458bd659bea08757c66c0/probabilities.json - score_dict_file: output/reports/train/fb513682740458bd659bea08757c66c0/score_dict.json - test_labels_file: output/reports/train/fb513682740458bd659bea08757c66c0/test_labels.json - train_labels_file: output/reports/train/fb513682740458bd659bea08757c66c0/train_labels.json - model_dir: models - name: fb513682740458bd659bea08757c66c0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: bfe59ae11397776036e6083c1343f625 - trainer: - kwargs: {} -name: fb513682740458bd659bea08757c66c0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/params.yaml b/examples/security/classification/output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/params.yaml deleted file mode 100644 index 968fd977..00000000 --- a/examples/security/classification/output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/adv_predictions.json - adv_probabilities_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/2a077604b53d4a4ec9242a953e9dfd5d.pkl - params_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/params.yaml - predictions_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/predictions.json - probabilities_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/probabilities.json - score_dict_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/score_dict.json - test_labels_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/test_labels.json - train_labels_file: output/reports/train/fb70727062b0c2b7d20d049d42ec06f6/train_labels.json - model_dir: models - name: fb70727062b0c2b7d20d049d42ec06f6 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1000 - gamma: scale - kernel: - - rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 920d773ccdc73dd608822667ab08bf51 - trainer: - kwargs: {} -name: fb70727062b0c2b7d20d049d42ec06f6 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/params.yaml b/examples/security/classification/output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/params.yaml deleted file mode 100644 index 28b50c34..00000000 --- a/examples/security/classification/output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/adv_predictions.json - adv_probabilities_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/67bafdfbdd424ba32cb3a59195bb01df.pkl - params_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/params.yaml - predictions_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/predictions.json - probabilities_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/probabilities.json - score_dict_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/score_dict.json - test_labels_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/test_labels.json - train_labels_file: output/reports/train/fc397d0184822d72e181f49aa2e7a3ed/train_labels.json - model_dir: models - name: fc397d0184822d72e181f49aa2e7a3ed - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7afc461b580b167ac9aa374a52e6599f - trainer: - kwargs: {} -name: fc397d0184822d72e181f49aa2e7a3ed -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fc54944e029839b6ec676791c41f0eec/params.yaml b/examples/security/classification/output/reports/train/fc54944e029839b6ec676791c41f0eec/params.yaml deleted file mode 100644 index 6132f83a..00000000 --- a/examples/security/classification/output/reports/train/fc54944e029839b6ec676791c41f0eec/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/adv_predictions.json - adv_probabilities_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/6c7bad768ceccd0f82cf401c7832620e.pkl - params_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/params.yaml - predictions_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/predictions.json - probabilities_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/probabilities.json - score_dict_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/score_dict.json - test_labels_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/test_labels.json - train_labels_file: output/reports/train/fc54944e029839b6ec676791c41f0eec/train_labels.json - model_dir: models - name: fc54944e029839b6ec676791c41f0eec - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6c627fcd72bef2cb69d6cc1e1ef40bad - trainer: - kwargs: {} -name: fc54944e029839b6ec676791c41f0eec -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fc5dd786b88040fb926d1efbd0da8935/params.yaml b/examples/security/classification/output/reports/train/fc5dd786b88040fb926d1efbd0da8935/params.yaml deleted file mode 100644 index b2fe4da1..00000000 --- a/examples/security/classification/output/reports/train/fc5dd786b88040fb926d1efbd0da8935/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/adv_predictions.json - adv_probabilities_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/31aa461949cca3435137356ddfd1540c.pkl - params_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/params.yaml - predictions_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/predictions.json - probabilities_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/probabilities.json - score_dict_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/score_dict.json - test_labels_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/test_labels.json - train_labels_file: output/reports/train/fc5dd786b88040fb926d1efbd0da8935/train_labels.json - model_dir: models - name: fc5dd786b88040fb926d1efbd0da8935 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c33dd02cab635ca7969877b47e862f72 - trainer: - kwargs: {} -name: fc5dd786b88040fb926d1efbd0da8935 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/params.yaml b/examples/security/classification/output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/params.yaml deleted file mode 100644 index c13f5eaf..00000000 --- a/examples/security/classification/output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/adv_predictions.json - adv_probabilities_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/062103ebabc958cdd3feb4664fe800dc.pkl - model_file: output/models/f624e57ef711de2a97a4d0342b87cbe2.pkl - params_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/params.yaml - predictions_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/predictions.json - probabilities_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/probabilities.json - score_dict_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/score_dict.json - test_labels_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/test_labels.json - train_labels_file: output/reports/train/fc6c7262d8f25ad77eda12b8325502d0/train_labels.json - model_dir: models - name: fc6c7262d8f25ad77eda12b8325502d0 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 5d7ff5375ad08af05930926f2c0aeb78 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9fa54271a7c33523b5637d57ff44746b - trainer: - kwargs: {} -name: fc6c7262d8f25ad77eda12b8325502d0 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/params.yaml b/examples/security/classification/output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/params.yaml deleted file mode 100644 index 54d786df..00000000 --- a/examples/security/classification/output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/adv_predictions.json - adv_probabilities_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/7be8dc369f9ae46c1bec66236b3d76cd.pkl - model_file: output/models/db0ef1e5f3feae5e3dea46f55bf3f5a3.pkl - params_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/params.yaml - predictions_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/predictions.json - probabilities_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/probabilities.json - score_dict_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/score_dict.json - test_labels_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/test_labels.json - train_labels_file: output/reports/train/fc6dbf2af2c90df889c98aee11bf8276/train_labels.json - model_dir: models - name: fc6dbf2af2c90df889c98aee11bf8276 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: e25866e5c6b40fe7dad2dddab07f52ff - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.001 - coef0: 0.001 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1395033c8a58ac6d3a1b97ae94131c2c - trainer: - kwargs: {} -name: fc6dbf2af2c90df889c98aee11bf8276 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/params.yaml b/examples/security/classification/output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/params.yaml deleted file mode 100644 index 7fb61980..00000000 --- a/examples/security/classification/output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/adv_predictions.json - adv_probabilities_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/deb223244689141c484f46d6350b734c.pkl - params_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/params.yaml - predictions_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/predictions.json - probabilities_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/probabilities.json - score_dict_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/score_dict.json - test_labels_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/test_labels.json - train_labels_file: output/reports/train/fca7de23aa97b9225a2bbd6f8b11ee96/train_labels.json - model_dir: models - name: fca7de23aa97b9225a2bbd6f8b11ee96 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d7cb0d36725d9cb5e0178be17f125adc - trainer: - kwargs: {} -name: fca7de23aa97b9225a2bbd6f8b11ee96 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fcc9639c47002b9bb83739ab67336ac2/params.yaml b/examples/security/classification/output/reports/train/fcc9639c47002b9bb83739ab67336ac2/params.yaml deleted file mode 100644 index 6c198fe3..00000000 --- a/examples/security/classification/output/reports/train/fcc9639c47002b9bb83739ab67336ac2/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/adv_predictions.json - adv_probabilities_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/4992f1177927ae1f26ceb3d13719b260.pkl - params_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/params.yaml - predictions_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/predictions.json - probabilities_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/probabilities.json - score_dict_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/score_dict.json - test_labels_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/test_labels.json - train_labels_file: output/reports/train/fcc9639c47002b9bb83739ab67336ac2/train_labels.json - model_dir: models - name: fcc9639c47002b9bb83739ab67336ac2 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.001 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 6bfdc9e274c20663f8b44e4642b26540 - trainer: - kwargs: {} -name: fcc9639c47002b9bb83739ab67336ac2 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/params.yaml b/examples/security/classification/output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/params.yaml deleted file mode 100644 index 8872a171..00000000 --- a/examples/security/classification/output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/adv_predictions.json - adv_probabilities_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/9a3064d0570b1d1f9926ba08abf64de6.pkl - params_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/params.yaml - predictions_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/predictions.json - probabilities_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/probabilities.json - score_dict_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/score_dict.json - test_labels_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/test_labels.json - train_labels_file: output/reports/train/fcfa80f7eaa34c8a42310702e2e1d64e/train_labels.json - model_dir: models - name: fcfa80f7eaa34c8a42310702e2e1d64e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 0.1 - degree: 1 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 2c21a82c4cb1a1acb0ea08d433b70390 - trainer: - kwargs: {} -name: fcfa80f7eaa34c8a42310702e2e1d64e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/params.yaml b/examples/security/classification/output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/params.yaml deleted file mode 100644 index 4e86bd20..00000000 --- a/examples/security/classification/output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/adv_predictions.json - adv_probabilities_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/2d3c3f4e0bd32f21f8121b38b5337316.pkl - params_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/params.yaml - predictions_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/predictions.json - probabilities_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/probabilities.json - score_dict_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/score_dict.json - test_labels_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/test_labels.json - train_labels_file: output/reports/train/fcfcb64fc3d3c8d6eb5ee3a0c7d396a4/train_labels.json - model_dir: models - name: fcfcb64fc3d3c8d6eb5ee3a0c7d396a4 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - degree: 3 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1d435f2fd8685d0eadf1169897119876 - trainer: - kwargs: {} -name: fcfcb64fc3d3c8d6eb5ee3a0c7d396a4 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/params.yaml b/examples/security/classification/output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/params.yaml deleted file mode 100644 index 6ba35d3b..00000000 --- a/examples/security/classification/output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/adv_predictions.json - adv_probabilities_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/41813519062308b1c2e1d69b39243ce7.pkl - model_file: output/models/9bbd235b3b785c2e51f810a9b9a5e1f6.pkl - params_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/params.yaml - predictions_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/predictions.json - probabilities_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/probabilities.json - score_dict_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/score_dict.json - test_labels_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/test_labels.json - train_labels_file: output/reports/train/fd1e87b8a402c2d98ead0b7caca67c6a/train_labels.json - model_dir: models - name: fd1e87b8a402c2d98ead0b7caca67c6a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: df8212985b44252bf2d873135313ad43 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: acd9259363ebdfa5c2feb3c79ac0b1ce - trainer: - kwargs: {} -name: fd1e87b8a402c2d98ead0b7caca67c6a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/params.yaml b/examples/security/classification/output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/params.yaml deleted file mode 100644 index 94775c4e..00000000 --- a/examples/security/classification/output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/adv_predictions.json - adv_probabilities_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/52ad5350efc0a98c3c42addcf267a58e.pkl - model_file: output/models/880487d23814bb7ce5b15f05d2914dd8.pkl - params_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/params.yaml - predictions_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/predictions.json - probabilities_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/probabilities.json - score_dict_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/score_dict.json - test_labels_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/test_labels.json - train_labels_file: output/reports/train/fd1f6cff410e8d3f807f3d8db97a9ad3/train_labels.json - model_dir: models - name: fd1f6cff410e8d3f807f3d8db97a9ad3 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: db374a98e9c571bca6dcbbf091eec40b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 10000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 28ad861af92ac15f5d0041a6852635cf - trainer: - kwargs: {} -name: fd1f6cff410e8d3f807f3d8db97a9ad3 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/params.yaml b/examples/security/classification/output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/params.yaml deleted file mode 100644 index 06e5322d..00000000 --- a/examples/security/classification/output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/adv_predictions.json - adv_probabilities_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c2f00fae93b0c94ea799886a59a93358.pkl - model_file: output/models/a096a075643b558cbe40adc0d4c03f70.pkl - params_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/params.yaml - predictions_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/predictions.json - probabilities_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/probabilities.json - score_dict_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/score_dict.json - test_labels_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/test_labels.json - train_labels_file: output/reports/train/fd375c576de5cfea71cdd9607a94fa7a/train_labels.json - model_dir: models - name: fd375c576de5cfea71cdd9607a94fa7a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 206841f63cbde0cb85b56364545456c8 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 1 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: b3291b618ec1328414c741591254b96c - trainer: - kwargs: {} -name: fd375c576de5cfea71cdd9607a94fa7a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fd6435f2e206379615b4b4007d31e167/params.yaml b/examples/security/classification/output/reports/train/fd6435f2e206379615b4b4007d31e167/params.yaml deleted file mode 100644 index fe794ca4..00000000 --- a/examples/security/classification/output/reports/train/fd6435f2e206379615b4b4007d31e167/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/adv_predictions.json - adv_probabilities_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/47d941b1d7c60bd2b4d268e48442c6bf.pkl - params_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/params.yaml - predictions_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/predictions.json - probabilities_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/probabilities.json - score_dict_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/score_dict.json - test_labels_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/test_labels.json - train_labels_file: output/reports/train/fd6435f2e206379615b4b4007d31e167/train_labels.json - model_dir: models - name: fd6435f2e206379615b4b4007d31e167 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10000 - degree: 3 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 14ba5b7a1d786a0d9c55849d31e3d240 - trainer: - kwargs: {} -name: fd6435f2e206379615b4b4007d31e167 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/params.yaml b/examples/security/classification/output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/params.yaml deleted file mode 100644 index 8fbab9ac..00000000 --- a/examples/security/classification/output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/adv_predictions.json - adv_probabilities_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/9921825c42078f57702c88471cdd1537.pkl - model_file: output/models/b8a5c074b6838ea657b79ede55b71fb7.pkl - params_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/params.yaml - predictions_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/predictions.json - probabilities_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/probabilities.json - score_dict_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/score_dict.json - test_labels_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/test_labels.json - train_labels_file: output/reports/train/fd65f6a0f6463364ef40c056cc5501b1/train_labels.json - model_dir: models - name: fd65f6a0f6463364ef40c056cc5501b1 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10000 - n_samples: 110000 - random_state: 0 - name: classification - name: 3036dd9056c364ce4717b01d38bd62c4 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: caf8026e7801d023d74dcf86c912273e - trainer: - kwargs: {} -name: fd65f6a0f6463364ef40c056cc5501b1 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/params.yaml b/examples/security/classification/output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/params.yaml deleted file mode 100644 index 2d477f2a..00000000 --- a/examples/security/classification/output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/adv_predictions.json - adv_probabilities_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ef9518a2308a68cddc93348c7e58fad4.pkl - model_file: output/models/e0add251e98d8373cbf0b61e67470fd1.pkl - params_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/params.yaml - predictions_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/predictions.json - probabilities_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/probabilities.json - score_dict_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/score_dict.json - test_labels_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/test_labels.json - train_labels_file: output/reports/train/fd6d3610f17b3118e0ed4493e34f5ff8/train_labels.json - model_dir: models - name: fd6d3610f17b3118e0ed4493e34f5ff8 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 0dc7ad7c144259e984975957950b2c52 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.01 - coef0: 1000 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 41dcfa133335d798276eb68f8c66b134 - trainer: - kwargs: {} -name: fd6d3610f17b3118e0ed4493e34f5ff8 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/params.yaml b/examples/security/classification/output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/params.yaml deleted file mode 100644 index 751aae7b..00000000 --- a/examples/security/classification/output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/adv_predictions.json - adv_probabilities_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/7a08d3514a007f4986b7f3ba4171e5c2.pkl - params_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/params.yaml - predictions_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/predictions.json - probabilities_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/probabilities.json - score_dict_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/score_dict.json - test_labels_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/test_labels.json - train_labels_file: output/reports/train/fdb0ee7bf33dfd0fc28bd970705cb208/train_labels.json - model_dir: models - name: fdb0ee7bf33dfd0fc28bd970705cb208 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1000 - coef0: 0.001 - degree: 5 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: aa0095918ae079390b05ff5a49be1d26 - trainer: - kwargs: {} -name: fdb0ee7bf33dfd0fc28bd970705cb208 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/params.yaml b/examples/security/classification/output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/params.yaml deleted file mode 100644 index 68b28867..00000000 --- a/examples/security/classification/output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/adv_predictions.json - adv_probabilities_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/517012bdc8320af7cb28021220c382e8.pkl - params_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/params.yaml - predictions_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/predictions.json - probabilities_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/probabilities.json - score_dict_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/score_dict.json - test_labels_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/test_labels.json - train_labels_file: output/reports/train/fe13fda00ac7e07fc883a8a5c27dc4be/train_labels.json - model_dir: models - name: fe13fda00ac7e07fc883a8a5c27dc4be - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 53c98e3f226f76194505a8b98a3f5369 - trainer: - kwargs: {} -name: fe13fda00ac7e07fc883a8a5c27dc4be -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/params.yaml b/examples/security/classification/output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/params.yaml deleted file mode 100644 index c11a1127..00000000 --- a/examples/security/classification/output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/adv_predictions.json - adv_probabilities_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/6196f64d5e95240f11840048f9b0c26d.pkl - model_file: output/models/8b5dc9823d5b3acd47dff6eae429665c.pkl - params_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/params.yaml - predictions_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/predictions.json - probabilities_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/probabilities.json - score_dict_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/score_dict.json - test_labels_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/test_labels.json - train_labels_file: output/reports/train/fe8ddc0ecbaed829983aabb3f92c249c/train_labels.json - model_dir: models - name: fe8ddc0ecbaed829983aabb3f92c249c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 5e7f8fb4d9da05ff8d6a1e6284494318 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 0.01 - degree: 4 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 7311993522a051bc1381bf68ee626e39 - trainer: - kwargs: {} -name: fe8ddc0ecbaed829983aabb3f92c249c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/params.yaml b/examples/security/classification/output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/params.yaml deleted file mode 100644 index a5ff6bee..00000000 --- a/examples/security/classification/output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/adv_predictions.json - adv_probabilities_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/665124469fbfe512ff8949468dae0a84.pkl - model_file: output/models/a237ec48d72ab8b02675cf53941e09ed.pkl - params_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/params.yaml - predictions_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/predictions.json - probabilities_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/probabilities.json - score_dict_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/score_dict.json - test_labels_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/test_labels.json - train_labels_file: output/reports/train/fe9266ea06e93c3c8540bcf631c2564e/train_labels.json - model_dir: models - name: fe9266ea06e93c3c8540bcf631c2564e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 955aa47fcd397e98dc2d7eeb13f7a251 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - coef0: 1 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: ef53942ad863739f812a0b3e91d48924 - trainer: - kwargs: {} -name: fe9266ea06e93c3c8540bcf631c2564e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fec6b66375c3ffa0ac639de1eb589356/params.yaml b/examples/security/classification/output/reports/train/fec6b66375c3ffa0ac639de1eb589356/params.yaml deleted file mode 100644 index b782a312..00000000 --- a/examples/security/classification/output/reports/train/fec6b66375c3ffa0ac639de1eb589356/params.yaml +++ /dev/null @@ -1,206 +0,0 @@ -attack: - attack_size: 10 - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: {} - model: - art: - library: sklearn-svc - name: 37e40fba19cda476dad12a95c79baf44 - pipeline: {} - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000.0 - time_series: false - train_size: 10.0 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - kwargs: - C: 10000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d3de07647c3505f0dd7f58327724e3a7 - trainer: - kwargs: - kwargs: {} - name: art.attacks.evasion.HopSkipJump - kwargs: {} - method: evasion - model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: {} - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 21499fc66c748249dbd6219e76a7fe4f - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: d1db5d4ce6d952ed89b5b87e0a2b8eed - trainer: - kwargs: {} - name: f767fdf82eff5d5decbf7a0425adf459 -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/adv_predictions.json - adv_probabilities_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/adv_probabilities.json - attack_file: output/attacks/6820d87929e14a603509d91b807b4dae.pkl - data_file: output/data/113b217ec1fa4fd19aa03303a60a10f0.pkl - model_file: output/models/2e2546e74bbb0e4570a91ce5f2b0db12.pkl - params_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/params.yaml - predictions_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/predictions.json - probabilities_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/probabilities.json - score_dict_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/score_dict.json - test_labels_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/test_labels.json - train_labels_file: output/reports/train/fec6b66375c3ffa0ac639de1eb589356/train_labels.json - model_dir: models - name: fec6b66375c3ffa0ac639de1eb589356 - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d578b45003f99832c2f61d21c6c231dd67df5ff5/kdd_nsl.csv - name: 5724fd0f78a918663d5d84d131787520 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10000 - kernel: linear - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 9d65f4c5a39650c61655adfe988b464a - trainer: - kwargs: {} -name: fec6b66375c3ffa0ac639de1eb589356 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/params.yaml b/examples/security/classification/output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/params.yaml deleted file mode 100644 index 7dfe698c..00000000 --- a/examples/security/classification/output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/adv_predictions.json - adv_probabilities_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/985b7d67f5f8dbb5309c133a31e41110.pkl - model_file: output/models/e94f04f4924c88c4218c0cffeb38be8c.pkl - params_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/params.yaml - predictions_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/predictions.json - probabilities_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/probabilities.json - score_dict_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/score_dict.json - test_labels_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/test_labels.json - train_labels_file: output/reports/train/fed3e0a9c687d6b0ecb6a59cc34c6aea/train_labels.json - model_dir: models - name: fed3e0a9c687d6b0ecb6a59cc34c6aea - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 3c658331bf9aa200e4bb240d23c93537 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.1 - coef0: 0.01 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 5ef906bc3a3c1ae6925cad5c82b19078 - trainer: - kwargs: {} -name: fed3e0a9c687d6b0ecb6a59cc34c6aea -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fed48412a902a1acf55afbd4f5c05e90/params.yaml b/examples/security/classification/output/reports/train/fed48412a902a1acf55afbd4f5c05e90/params.yaml deleted file mode 100644 index 17993033..00000000 --- a/examples/security/classification/output/reports/train/fed48412a902a1acf55afbd4f5c05e90/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/adv_predictions.json - adv_probabilities_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/8f0214d99fc0fe3490630e9e13ff5b6c.pkl - model_file: output/models/d2cdc597b6cb85e8d0f76d6d508df9d8.pkl - params_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/params.yaml - predictions_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/predictions.json - probabilities_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/probabilities.json - score_dict_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/score_dict.json - test_labels_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/test_labels.json - train_labels_file: output/reports/train/fed48412a902a1acf55afbd4f5c05e90/train_labels.json - model_dir: models - name: fed48412a902a1acf55afbd4f5c05e90 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 764d34f2d7b2544174db30270e130e83 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - gamma: scale - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 0d50a1e8330178f028856ad987a10317 - trainer: - kwargs: {} -name: fed48412a902a1acf55afbd4f5c05e90 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/fefee03d9029aa8de6187d838fd95d7c/params.yaml b/examples/security/classification/output/reports/train/fefee03d9029aa8de6187d838fd95d7c/params.yaml deleted file mode 100644 index ba6f8ff8..00000000 --- a/examples/security/classification/output/reports/train/fefee03d9029aa8de6187d838fd95d7c/params.yaml +++ /dev/null @@ -1,103 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/adv_predictions.json - adv_probabilities_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/a1f6a8157f1050b2a6520ead369d2fb1.pkl - model_file: output/models/324ae9df16d0db7e1e3b4534803da896.pkl - params_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/params.yaml - predictions_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/predictions.json - probabilities_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/probabilities.json - score_dict_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/score_dict.json - test_labels_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/test_labels.json - train_labels_file: output/reports/train/fefee03d9029aa8de6187d838fd95d7c/train_labels.json - model_dir: models - name: fefee03d9029aa8de6187d838fd95d7c - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: 12cc1a19978b591ed330f9a91ea5b2a0 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 0.0001 - kernel: linear - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 99e4cab232281b81ca46cdc23a1d3c72 - trainer: - kwargs: {} -name: fefee03d9029aa8de6187d838fd95d7c -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ff0a2a54859b72a53699b2a15d91a818/params.yaml b/examples/security/classification/output/reports/train/ff0a2a54859b72a53699b2a15d91a818/params.yaml deleted file mode 100644 index f38dd830..00000000 --- a/examples/security/classification/output/reports/train/ff0a2a54859b72a53699b2a15d91a818/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/adv_predictions.json - adv_probabilities_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/1c9bd1d3a96dbaae9bc842e53cee0a35.pkl - model_file: output/models/f4016278e77b7303bbe9e6ba61df5254.pkl - params_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/params.yaml - predictions_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/predictions.json - probabilities_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/probabilities.json - score_dict_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/score_dict.json - test_labels_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/test_labels.json - train_labels_file: output/reports/train/ff0a2a54859b72a53699b2a15d91a818/train_labels.json - model_dir: models - name: ff0a2a54859b72a53699b2a15d91a818 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: 2572635b7a115744874d878d0ffcd47b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 0.01 - degree: 1 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 91f40ed1f12d2b922664b759a23708ba - trainer: - kwargs: {} -name: ff0a2a54859b72a53699b2a15d91a818 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ff435217cfd79669e2bfa36cf473a30a/params.yaml b/examples/security/classification/output/reports/train/ff435217cfd79669e2bfa36cf473a30a/params.yaml deleted file mode 100644 index 5a184820..00000000 --- a/examples/security/classification/output/reports/train/ff435217cfd79669e2bfa36cf473a30a/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/adv_predictions.json - adv_probabilities_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/4943e578bde3617e86866619d243dee2.pkl - model_file: output/models/8d979d8b4022f03e3a4346f43a761c13.pkl - params_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/params.yaml - predictions_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/predictions.json - probabilities_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/probabilities.json - score_dict_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/score_dict.json - test_labels_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/test_labels.json - train_labels_file: output/reports/train/ff435217cfd79669e2bfa36cf473a30a/train_labels.json - model_dir: models - name: ff435217cfd79669e2bfa36cf473a30a - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 81fa69e4e139f9c390fa6d5bf216069b - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 100000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - degree: 4 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 39789529ef38c1568d3cb8ee5aa25d29 - trainer: - kwargs: {} -name: ff435217cfd79669e2bfa36cf473a30a -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/params.yaml b/examples/security/classification/output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/params.yaml deleted file mode 100644 index 4a4ac4b4..00000000 --- a/examples/security/classification/output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/adv_predictions.json - adv_probabilities_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/034661ef7296964a6193c3d6f76fdc53.pkl - model_file: output/models/6ea0c83e303c880adf69b5c3f73e0dc2.pkl - params_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/params.yaml - predictions_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/predictions.json - probabilities_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/probabilities.json - score_dict_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/score_dict.json - test_labels_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/test_labels.json - train_labels_file: output/reports/train/ff49a54d68d8e432eeb292fd1230bf4f/train_labels.json - model_dir: models - name: ff49a54d68d8e432eeb292fd1230bf4f - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 1010000 - random_state: 0 - name: classification - name: 6a684319b5187070a58128a5b600489c - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 10 - degree: 5 - gamma: scale - kernel: poly - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 1e660d6fb6ea45dbbba76808d8e89549 - trainer: - kwargs: {} -name: ff49a54d68d8e432eeb292fd1230bf4f -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/params.yaml b/examples/security/classification/output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/params.yaml deleted file mode 100644 index 75489dcc..00000000 --- a/examples/security/classification/output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/params.yaml +++ /dev/null @@ -1,115 +0,0 @@ -data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/adv_predictions.json - adv_probabilities_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/5f3dc8b47eaaffa8b6d5d3f8296d2de8.pkl - model_file: output/models/ade8ed57191158b3cdb3d18179ba5c70.pkl - params_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/params.yaml - predictions_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/predictions.json - probabilities_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/probabilities.json - score_dict_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/score_dict.json - test_labels_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/test_labels.json - train_labels_file: output/reports/train/ff61521aa5cfeb2dff3957f3c0e6006b/train_labels.json - model_dir: models - name: ff61521aa5cfeb2dff3957f3c0e6006b - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 20 - n_samples: 1100 - random_state: 0 - name: classification - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 0.2 - time_series: false - train_size: 0.8 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: ignore - sparse: false - name: sklearn.preprocessing.OneHotEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 0.001 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c60bca6e3aecbdfee321f2aa473be68b - trainer: - kwargs: {} -name: ff61521aa5cfeb2dff3957f3c0e6006b -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ffab8a13ab042999ffae702acd280cdf/params.yaml b/examples/security/classification/output/reports/train/ffab8a13ab042999ffae702acd280cdf/params.yaml deleted file mode 100644 index 6a93a450..00000000 --- a/examples/security/classification/output/reports/train/ffab8a13ab042999ffae702acd280cdf/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/adv_predictions.json - adv_probabilities_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/c05bb91a1c81aef1b99d92e7466ba73c.pkl - model_file: output/models/eaa4843929b5743158570eae7935416a.pkl - params_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/params.yaml - predictions_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/predictions.json - probabilities_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/probabilities.json - score_dict_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/score_dict.json - test_labels_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/test_labels.json - train_labels_file: output/reports/train/ffab8a13ab042999ffae702acd280cdf/train_labels.json - model_dir: models - name: ffab8a13ab042999ffae702acd280cdf - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 1000 - n_samples: 110000 - random_state: 0 - name: classification - name: e2537bfc84785ae872f4a2df5b87b0c9 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 1000 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 45d2c4bc3e260a372648dd964d0f5f06 - trainer: - kwargs: {} -name: ffab8a13ab042999ffae702acd280cdf -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/params.yaml b/examples/security/classification/output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/params.yaml deleted file mode 100644 index 07c4e33e..00000000 --- a/examples/security/classification/output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/adv_predictions.json - adv_probabilities_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/28c8893047d09b9157cc0f9d6642edf0.pkl - model_file: output/models/7588f507246b15fcb0249ec6a389ef1f.pkl - params_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/params.yaml - predictions_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/predictions.json - probabilities_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/probabilities.json - score_dict_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/score_dict.json - test_labels_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/test_labels.json - train_labels_file: output/reports/train/ffc6cb1a40072c62411c2c86e4693d61/train_labels.json - model_dir: models - name: ffc6cb1a40072c62411c2c86e4693d61 - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 100 - n_samples: 110000 - random_state: 0 - name: classification - name: 6c007e9fe4704d82fb7915f82f581d1d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 10000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 100 - coef0: 10 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: c0d431ba776e252cd655f083cf5931ca - trainer: - kwargs: {} -name: ffc6cb1a40072c62411c2c86e4693d61 -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/params.yaml b/examples/security/classification/output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/params.yaml deleted file mode 100644 index f6d77b68..00000000 --- a/examples/security/classification/output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/params.yaml +++ /dev/null @@ -1,106 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/adv_predictions.json - adv_probabilities_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/ba08176020c6e3b14c3f2e24ac54337c.pkl - model_file: output/models/1baf2f21a5a38f23fcf3910e9d8de355.pkl - params_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/params.yaml - predictions_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/predictions.json - probabilities_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/probabilities.json - score_dict_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/score_dict.json - test_labels_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/test_labels.json - train_labels_file: output/reports/train/ffc844a02a3e59d94f5ddd6b1c43089e/train_labels.json - model_dir: models - name: ffc844a02a3e59d94f5ddd6b1c43089e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 1010000 - random_state: 0 - name: classification - name: 1f7903275c72068a778612a686c61d3d - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 10 - coef0: 0.01 - degree: 2 - gamma: scale - kernel: poly - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: abd9e19303adfe819d6d4f07c8053cc7 - trainer: - kwargs: {} -name: ffc844a02a3e59d94f5ddd6b1c43089e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/params.yaml b/examples/security/classification/output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/params.yaml deleted file mode 100644 index 1255dbe2..00000000 --- a/examples/security/classification/output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/params.yaml +++ /dev/null @@ -1,105 +0,0 @@ -data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler -files: - data_dir: data - files: - adv_predictions_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/adv_predictions.json - adv_probabilities_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/adv_probabilities.json - attack_file: output/attacks/attack.pkl - data_file: output/data/dd96b619d78f6fefd9a3e43e8b764b20.pkl - model_file: output/models/d4894dacdc5de6cb562a8bfbbcb1e07c.pkl - params_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/params.yaml - predictions_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/predictions.json - probabilities_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/probabilities.json - score_dict_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/score_dict.json - test_labels_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/test_labels.json - train_labels_file: output/reports/train/ffd7416df4e7ba8f91c2d1ef03ed195e/train_labels.json - model_dir: models - name: ffd7416df4e7ba8f91c2d1ef03ed195e - reports: reports - stage: train -kwargs: - direction: maximize -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - generate: - kwargs: - n_features: 10 - n_samples: 110000 - random_state: 0 - name: classification - name: a9530d121484629b59a0240897e7b455 - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 1000000 - sklearn_pipeline: - pipeline: - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - init: - kwargs: - C: 1 - coef0: 100 - gamma: scale - kernel: rbf - max_iter: 100 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: 3b7d2e19708501cda689b54f97a4e328 - trainer: - kwargs: {} -name: ffd7416df4e7ba8f91c2d1ef03ed195e -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} diff --git a/examples/security/classification/plots.py b/examples/security/classification/plots.py index a381f887..3e515da7 100644 --- a/examples/security/classification/plots.py +++ b/examples/security/classification/plots.py @@ -58,7 +58,7 @@ graph1.set_xscale("log") graph1.get_figure().tight_layout() graph1.set(xlim=(10, 1e6)) -graph1.get_figure().savefig("plots/accuracy_vs_samples.pdf") +graph1.get_figure().savefig("plots/accuracy_vs_samples.eps") plt.gcf().clear() graph2 = sns.lineplot( @@ -73,7 +73,7 @@ graph2.set_xscale("log") graph2.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") graph2.get_figure().tight_layout() -graph2.get_figure().savefig("plots/accuracy_vs_features.pdf") +graph2.get_figure().savefig("plots/accuracy_vs_features.eps") plt.gcf().clear() @@ -89,7 +89,7 @@ graph3.set(yscale="log", xscale="log") graph3.legend(title="Kernel") graph3.get_figure().tight_layout() -graph3.get_figure().savefig("plots/train_time_vs_features.pdf") +graph3.get_figure().savefig("plots/train_time_vs_features.eps") plt.gcf().clear() graph4 = sns.lineplot( @@ -104,7 +104,7 @@ graph4.set(yscale="log", xscale="log", xlim=(10, 1e6)) graph4.legend(title="Kernel") graph4.get_figure().tight_layout() -graph4.get_figure().savefig("plots/train_time_vs_samples.pdf") +graph4.get_figure().savefig("plots/train_time_vs_samples.eps") plt.gcf().clear() fig, ax = plt.subplots(2, 2) @@ -153,7 +153,7 @@ graph8.set(xscale="log", xlabel="Batch Size", ylabel="Accuracy") graph6.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") fig.tight_layout() -fig.savefig("plots/accuracy_vs_attack_parameters.pdf") +fig.savefig("plots/accuracy_vs_attack_parameters.eps") plt.gcf().clear() fig, ax = plt.subplots(2, 2) @@ -202,7 +202,7 @@ graph12.set(xscale="log", xlabel="Batch Size", ylabel="Attack Time") graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") fig.tight_layout(h_pad=0.5) -fig.savefig("plots/train_time_vs_attack_parameters.pdf") +fig.savefig("plots/train_time_vs_attack_parameters.eps") plt.gcf().clear() retrain_df = pd.DataFrame() @@ -239,7 +239,7 @@ retrain.set_xlabel("Retraining Epochs") retrain.set_ylabel("Accuracy") retrain.get_figure().tight_layout() -retrain.get_figure().savefig("plots/retrain_accuracy.pdf") +retrain.get_figure().savefig("plots/retrain_accuracy.eps") plt.gcf().clear() retrain_df["ben_time"] = retrain_df["ben_time"] * retrain_df["train_size"] * 10 @@ -265,7 +265,7 @@ retrain.set_ylabel("Time") retrain.set_yscale("log") retrain.get_figure().tight_layout() -retrain.get_figure().savefig("plots/retrain_time.pdf") +retrain.get_figure().savefig("plots/retrain_time.eps") plt.gcf().clear() confidence_df = pd.read_csv("plots/before_retrain_confidence.csv") @@ -315,7 +315,7 @@ graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") fig.tight_layout(h_pad=0.5) -fig.savefig("plots/confidence_vs_attack_parameters.pdf") +fig.savefig("plots/confidence_vs_attack_parameters.eps") plt.gcf().clear() confdence_df = pd.read_csv("plots/after_retrain_confidence.csv") @@ -366,5 +366,5 @@ graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") fig.tight_layout(h_pad=0.5) -fig.savefig("plots/retrain_confidence_vs_attack_parameters.pdf") +fig.savefig("plots/retrain_confidence_vs_attack_parameters.eps") plt.gcf().clear() diff --git a/examples/security/classification/plots/.gitignore b/examples/security/classification/plots/.gitignore deleted file mode 100644 index 2617ec05..00000000 --- a/examples/security/classification/plots/.gitignore +++ /dev/null @@ -1,20 +0,0 @@ -/accuracy_vs_attack_parameters.pdf -/accuracy_vs_features.pdf -/accuracy_vs_samples.pdf -/confidence_vs_attack_parameters.pdf -/train_time_vs_attack_parameters.pdf -/train_time_vs_features.pdf -/train_time_vs_samples.pdf -/retrain_accuracy.pdf -/retrain_confidence_vs_attack_parameters.pdf -/retrain_time.pdf -/accuracy_vs_attack_parameters.eps -/accuracy_vs_features.eps -/accuracy_vs_samples.eps -/confidence_vs_attack_parameters.eps -/train_time_vs_attack_parameters.eps -/train_time_vs_features.eps -/train_time_vs_samples.eps -/retrain_accuracy.eps -/retrain_confidence_vs_attack_parameters.eps -/retrain_time.eps From b75ab1bea01751eb6a7a2716372d99cbfefeb368 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Wed, 1 Nov 2023 21:21:21 +0100 Subject: [PATCH 116/148] stop tracking params.yaml --- examples/security/classification/dvc.lock | 10 +- examples/security/classification/models.sh | 61 +++++++++++- .../output/reports/train/default/params.yaml | 95 ------------------- 3 files changed, 63 insertions(+), 103 deletions(-) delete mode 100644 examples/security/kdd-nsl/output/reports/train/default/params.yaml diff --git a/examples/security/classification/dvc.lock b/examples/security/classification/dvc.lock index d87303ca..6d6e7536 100644 --- a/examples/security/classification/dvc.lock +++ b/examples/security/classification/dvc.lock @@ -100,8 +100,8 @@ stages: size: 42667 - path: output/reports/train/default/score_dict.json hash: md5 - md5: 5df33da7e8d1e12c45432edcba0a010f - size: 355 + md5: b5002e336c23ad2c890f50dcb4ae88a5 + size: 353 attack: cmd: python -m deckard.layers.experiment attack deps: @@ -119,8 +119,8 @@ stages: size: 42667 - path: output/reports/train/default/score_dict.json hash: md5 - md5: 5df33da7e8d1e12c45432edcba0a010f - size: 355 + md5: b5002e336c23ad2c890f50dcb4ae88a5 + size: 353 params: params.yaml: attack: @@ -318,7 +318,7 @@ stages: size: 42667 - path: output/reports/attack/default/score_dict.json hash: md5 - md5: 474014d6f0eae42d3b65e5840cea2dfe + md5: 10fee78b3899c113c799a056cf9a20ee size: 577 models: cmd: bash models.sh +stage=train --config-name=model.yaml diff --git a/examples/security/classification/models.sh b/examples/security/classification/models.sh index 2d5a7b8a..6940825e 100644 --- a/examples/security/classification/models.sh +++ b/examples/security/classification/models.sh @@ -1,6 +1,6 @@ -# N_FEATURES=( 10 100 1000 10000 10000 100000 1000000) -# TRAIN_SIZES=( 10 100 1000 ) -TRAIN_SIZES=( 1000 ) +N_FEATURES=( 10 100 1000 10000 ) +TRAIN_SIZES=( 10 100 1000 10000 100000 ) +# TRAIN_SIZES=( 1000 10000 100000 1000000 ) N_FEATURES=( 100 ) N_SAMPLES=( 1010000 ) TOTAL=$(( ${#N_FEATURES[@]} * ${#N_SAMPLES[@]} * ${#TRAIN_SIZES[@]} )) @@ -54,3 +54,58 @@ for train_size in ${TRAIN_SIZES[@]}; do done; done; done; + +N_FEATURES=( 10 100 1000 ) +TRAIN_SIZES=( 1000000 ) +N_SAMPLES=( 1010000 ) +TOTAL=$(( ${#N_FEATURES[@]} * ${#N_SAMPLES[@]} * ${#TRAIN_SIZES[@]} )) +i=$(( 0 )) +mkdir -p logs/models +for train_size in ${TRAIN_SIZES[@]}; do + for n_samples in ${N_SAMPLES[@]}; do + for n_features in ${N_FEATURES[@]}; do + i=$(( i + 1 )) + echo "Running experiment ${i} of ${TOTAL}" + # Keeps a meta log of the experiments + echo "Running experiment ${i} of ${TOTAL}" >> log.txt + echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" >> log.txt + # Runs the linear kernel, tries to find the best C value + HYDRA_FULL_ERROR=1; python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + ++model.init.kernel=linear \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=linear_${n_features}_${train_size} "$@" --multirun \ + # Keeps a log of the output for each experiment + >| logs/models/linear_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Linear Kernel Done" >> log.txt + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + ++model.init.kernel=rbf \ + +model.init.gamma=scale \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=rbf_${n_features}_${train_size} "$@" --multirun \ + >| logs/models/rbf_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "RBF Kernel Done" >> log.txt + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + ++model.init.kernel=poly \ + +model.init.degree=1,2,3,4,5 \ + +model.init.gamma=scale \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=poly_${n_features}_${train_size} "$@" --multirun \ + >| logs/models/poly_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Poly Kernel Done" >> log.txt + echo "Successfully completed experiment ${i} of ${TOTAL}" >> log.txt + done; + done; +done; \ No newline at end of file diff --git a/examples/security/kdd-nsl/output/reports/train/default/params.yaml b/examples/security/kdd-nsl/output/reports/train/default/params.yaml deleted file mode 100644 index 144fc869..00000000 --- a/examples/security/kdd-nsl/output/reports/train/default/params.yaml +++ /dev/null @@ -1,95 +0,0 @@ -data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 5000 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: use_encoded_value - unknown_value: -1 - name: sklearn.preprocessing.OrdinalEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - params_file: output/reports/train/default/params.yaml - predictions_file: output/reports/train/default/predictions.json - probabilities_file: output/reports/train/default/probabilities.json - score_dict_file: output/reports/train/default/score_dict.json - model_dir: models - name: default - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 5000 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: use_encoded_value - unknown_value: -1 - name: sklearn.preprocessing.OrdinalEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 - trainer: - kwargs: {} -name: default -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} From 068e110b7a1d55b2abafcd4f89f64ab64cda1594 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Wed, 1 Nov 2023 21:22:03 +0100 Subject: [PATCH 117/148] stop tracking params.yaml --- .../output/reports/train/default/params.yaml | 95 ------------------- 1 file changed, 95 deletions(-) delete mode 100644 examples/security/truthseeker/output/reports/train/default/params.yaml diff --git a/examples/security/truthseeker/output/reports/train/default/params.yaml b/examples/security/truthseeker/output/reports/train/default/params.yaml deleted file mode 100644 index 144fc869..00000000 --- a/examples/security/truthseeker/output/reports/train/default/params.yaml +++ /dev/null @@ -1,95 +0,0 @@ -data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 5000 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: use_encoded_value - unknown_value: -1 - name: sklearn.preprocessing.OrdinalEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label -files: - data_dir: data - files: - params_file: output/reports/train/default/params.yaml - predictions_file: output/reports/train/default/predictions.json - probabilities_file: output/reports/train/default/probabilities.json - score_dict_file: output/reports/train/default/score_dict.json - model_dir: models - name: default - reports: reports - stage: train -kwargs: {} -model: - art: - library: sklearn-svc - name: 9b86c9b0cfb8d5e872a0126c0611580a - pipeline: - initialize: - kwargs: {} - name: initialize - data: - name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv - sample: - random_state: 0 - shuffle: true - stratify: true - test_size: 1000 - time_series: false - train_size: 5000 - sklearn_pipeline: - pipeline: - encoder: - kwargs: - handle_unknown: use_encoded_value - unknown_value: -1 - name: sklearn.preprocessing.OrdinalEncoder - preprocessor: - kwargs: - with_mean: true - with_std: true - name: sklearn.preprocessing.StandardScaler - target: label - init: - kwargs: - C: 1.0 - kernel: rbf - max_iter: 10 - probability: true - random_state: 0 - name: sklearn.svm.SVC - library: sklearn-svc - name: e3caa32f5c2703ab4c6ef3a4d2b3ca80 - trainer: - kwargs: {} -name: default -scorers: - scorers: - accuracy: - alias: accuracy_score - args: - - y_true - - y_pred - direction: maximize - name: sklearn.metrics.accuracy_score - params: {} - log_loss: - alias: log_loss - args: - - y_true - - y_pred - direction: minimize - name: sklearn.metrics.log_loss - params: {} From b7758c2003609af45551bec20a62c040dbd8a5fd Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 14 Nov 2023 13:17:54 +0000 Subject: [PATCH 118/148] bug fixes and config cleanup --- .gitignore | 16 +- deckard/base/attack/attack.py | 18 +- deckard/base/data/data.py | 37 +- deckard/base/data/generator.py | 46 +- deckard/base/experiment/experiment.py | 11 +- deckard/base/files/files.py | 12 +- deckard/base/model/model.py | 116 ++--- deckard/base/model/sklearn_pipeline.py | 8 +- deckard/base/model/torch_models.py | 35 +- deckard/layers/afr.py | 112 +++-- deckard/layers/compile.py | 15 +- deckard/layers/experiment.py | 29 +- deckard/layers/optimise.py | 107 +++-- deckard/layers/plots.py | 39 +- examples/pytorch/.gitignore | 2 + examples/pytorch/attacks.sh | 31 +- examples/pytorch/conf/compile.yaml | 29 +- .../conf/files/mnist.yaml} | 2 +- examples/pytorch/conf/hydra/default.yaml | 38 -- .../hydra/sweeper/params/attack/cw_0.yaml | 4 - .../hydra/sweeper/params/attack/cw_2.yaml | 5 - .../hydra/sweeper/params/attack/cw_inf.yaml | 4 - .../hydra/sweeper/params/attack/deepfool.yaml | 5 - .../conf/hydra/sweeper/params/attack/fgm.yaml | 4 - .../conf/hydra/sweeper/params/attack/hsj.yaml | 6 - .../hydra/sweeper/params/attack/patch.yaml | 5 - .../conf/hydra/sweeper/params/attack/pgd.yaml | 6 - .../hydra/sweeper/params/attack/pixel.yaml | 4 - .../sweeper/params/attack/threshold.yaml | 4 - .../conf/hydra/sweeper/params/default.yaml | 11 - .../sweeper/params/model/confidence.yaml | 5 - .../hydra/sweeper/params/model/default.yaml | 1 - .../conf/hydra/sweeper/params/model/fsq.yaml | 4 - .../hydra/sweeper/params/model/gauss-in.yaml | 5 - .../hydra/sweeper/params/model/gauss-out.yaml | 3 - .../pytorch/conf/{default.yaml => mnist.yaml} | 25 +- .../conf/model/art/initialize/default.yaml | 2 +- examples/pytorch/conf/model/torch_cifar.yaml | 2 +- examples/pytorch/conf/model/torch_mnist.yaml | 2 +- examples/pytorch/conf/plots.yaml | 96 ++-- examples/pytorch/dvc.lock | 416 +++++++++++------- examples/pytorch/dvc.yaml | 58 ++- examples/pytorch/models.sh | 14 +- examples/pytorch/params.yaml | 22 +- examples/pytorch_cifar/.gitignore | 3 + examples/pytorch_cifar/attacks.sh | 25 +- .../conf/{default.yaml => cifar10.yaml} | 27 +- examples/pytorch_cifar/conf/compile.yaml | 29 +- .../conf/files/mnist.yaml} | 2 +- .../pytorch_cifar/conf/hydra/default.yaml | 38 -- .../hydra/sweeper/params/attack/cw_0.yaml | 4 - .../hydra/sweeper/params/attack/cw_2.yaml | 5 - .../hydra/sweeper/params/attack/cw_inf.yaml | 4 - .../hydra/sweeper/params/attack/deepfool.yaml | 5 - .../conf/hydra/sweeper/params/attack/fgm.yaml | 4 - .../conf/hydra/sweeper/params/attack/hsj.yaml | 6 - .../hydra/sweeper/params/attack/patch.yaml | 5 - .../conf/hydra/sweeper/params/attack/pgd.yaml | 6 - .../hydra/sweeper/params/attack/pixel.yaml | 4 - .../sweeper/params/attack/threshold.yaml | 4 - .../conf/hydra/sweeper/params/default.yaml | 11 - .../sweeper/params/model/confidence.yaml | 5 - .../hydra/sweeper/params/model/default.yaml | 1 - .../conf/hydra/sweeper/params/model/fsq.yaml | 4 - .../hydra/sweeper/params/model/gauss-in.yaml | 5 - .../hydra/sweeper/params/model/gauss-out.yaml | 3 - .../conf/model/art/initialize/default.yaml | 2 +- .../pytorch_cifar/conf/model/torch_cifar.yaml | 2 +- examples/pytorch_cifar/conf/plots.yaml | 340 ++------------ examples/pytorch_cifar/dvc.lock | 80 ++-- examples/pytorch_cifar/dvc.yaml | 118 +++-- examples/pytorch_cifar/models.sh | 37 +- examples/pytorch_cifar/params.yaml | 14 +- examples/pytorch_cifar_100/.dvc/.gitignore | 3 + examples/pytorch_cifar_100/.dvc/config | 0 examples/pytorch_cifar_100/.dvcignore | 3 + .../conf/attack/default.yaml | 9 + examples/pytorch_cifar_100/conf/cifar100.yaml | 44 ++ examples/pytorch_cifar_100/conf/compile.yaml | 32 ++ .../conf/deploy/default.yaml} | 6 +- .../pytorch_cifar_100/conf/deploy/pod.yaml | 30 ++ .../pytorch_cifar_100/conf/deploy/pvc.yaml | 11 + .../pytorch_cifar_100/conf/deploy/sclass.yaml | 10 + .../conf/files/cifar100.yaml | 21 + .../pytorch_cifar_100/conf/files/mnist.yaml | 21 + .../conf/model/art/default.yaml | 7 + .../conf/model/art/initialize/default.yaml | 7 + .../model/art/postprocessor/confidence.yaml | 3 + .../model/art/postprocessor/gauss-out.yaml | 2 + .../conf/model/art/preprocessor/default.yaml | 0 .../conf/model/art/preprocessor/fsq.yaml | 3 + .../conf/model/art/preprocessor/gauss-in.yaml | 4 + .../conf/model/torch_cifar100.yaml | 13 + .../conf/model/torch_mnist.yaml | 13 + examples/pytorch_cifar_100/conf/plots.yaml | 250 +++++++++++ .../conf/scorers/default.yaml | 9 + 96 files changed, 1462 insertions(+), 1248 deletions(-) create mode 100644 examples/pytorch/.gitignore rename examples/{pytorch_cifar/conf/files/default.yaml => pytorch/conf/files/mnist.yaml} (96%) delete mode 100644 examples/pytorch/conf/hydra/default.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/attack/cw_0.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/attack/cw_2.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/attack/cw_inf.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/attack/deepfool.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/attack/fgm.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/attack/hsj.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/attack/patch.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/attack/pgd.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/attack/pixel.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/attack/threshold.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/default.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/model/confidence.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/model/default.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/model/fsq.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/model/gauss-in.yaml delete mode 100644 examples/pytorch/conf/hydra/sweeper/params/model/gauss-out.yaml rename examples/pytorch/conf/{default.yaml => mnist.yaml} (54%) create mode 100644 examples/pytorch_cifar/.gitignore rename examples/pytorch_cifar/conf/{default.yaml => cifar10.yaml} (53%) rename examples/{pytorch/conf/files/default.yaml => pytorch_cifar/conf/files/mnist.yaml} (96%) delete mode 100644 examples/pytorch_cifar/conf/hydra/default.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_0.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_2.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_inf.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/deepfool.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/fgm.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/hsj.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/patch.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pgd.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pixel.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/attack/threshold.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/default.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/model/confidence.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/model/default.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/model/fsq.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-in.yaml delete mode 100644 examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-out.yaml create mode 100644 examples/pytorch_cifar_100/.dvc/.gitignore create mode 100644 examples/pytorch_cifar_100/.dvc/config create mode 100644 examples/pytorch_cifar_100/.dvcignore create mode 100644 examples/pytorch_cifar_100/conf/attack/default.yaml create mode 100644 examples/pytorch_cifar_100/conf/cifar100.yaml create mode 100644 examples/pytorch_cifar_100/conf/compile.yaml rename examples/{pytorch_cifar/conf/deploy.yaml => pytorch_cifar_100/conf/deploy/default.yaml} (69%) create mode 100644 examples/pytorch_cifar_100/conf/deploy/pod.yaml create mode 100644 examples/pytorch_cifar_100/conf/deploy/pvc.yaml create mode 100644 examples/pytorch_cifar_100/conf/deploy/sclass.yaml create mode 100644 examples/pytorch_cifar_100/conf/files/cifar100.yaml create mode 100644 examples/pytorch_cifar_100/conf/files/mnist.yaml create mode 100644 examples/pytorch_cifar_100/conf/model/art/default.yaml create mode 100644 examples/pytorch_cifar_100/conf/model/art/initialize/default.yaml create mode 100644 examples/pytorch_cifar_100/conf/model/art/postprocessor/confidence.yaml create mode 100644 examples/pytorch_cifar_100/conf/model/art/postprocessor/gauss-out.yaml create mode 100644 examples/pytorch_cifar_100/conf/model/art/preprocessor/default.yaml create mode 100644 examples/pytorch_cifar_100/conf/model/art/preprocessor/fsq.yaml create mode 100644 examples/pytorch_cifar_100/conf/model/art/preprocessor/gauss-in.yaml create mode 100644 examples/pytorch_cifar_100/conf/model/torch_cifar100.yaml create mode 100644 examples/pytorch_cifar_100/conf/model/torch_mnist.yaml create mode 100644 examples/pytorch_cifar_100/conf/plots.yaml create mode 100644 examples/pytorch_cifar_100/conf/scorers/default.yaml diff --git a/.gitignore b/.gitignore index f4837ea5..949d8926 100644 --- a/.gitignore +++ b/.gitignore @@ -94,7 +94,6 @@ ipython_config.py *.json *.npz *.npy -examples/pytorch/output/models/model.optimizer.pt # PyTorch Model Checkpoints @@ -117,15 +116,24 @@ examples/pytorch/output/models/model.optimizer.pt # env env/ examples/*/multirun/* -examples/sklearn/example-study.db -examples/sklearn/output/.hydra/config.yaml +# examples/*/output/.hydra/config.yaml +# examples/*/*/reports/*/*/dvc.yaml +# examples/*/*/reports/*/*/params.yaml +# examples/*/*/plots/ *.hydra # Example output -examples/*/output/* +examples/*/*/data +examples/*/*/models +examples/*/*/attacks +examples/*/*/reports # Python egg metadata **/*.egg-info/* **/*.egg-info deckard/deckard.egg-info/* + + + +examples/*/20_epochs/reports/attack/000632dec5ae20d1b5b1e5ec8542c504/params.yaml diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index 52157d90..149e85e6 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -8,6 +8,7 @@ from omegaconf import DictConfig, OmegaConf from hydra.utils import instantiate from art.utils import to_categorical, compute_success +from random import randint from ..data import Data from ..model import Model from ..utils import my_hash @@ -329,16 +330,25 @@ def __call__( if "expected scalar type Long" in str(e): # if hasattr(y_train, "type"): import torch - - device = torch.device( - "cuda" if torch.cuda.is_available() else "cpu", - ) + if torch.cuda.is_available(): + number_of_devices = torch.cuda.device_count() + device_number = randint(0, number_of_devices - 1) + device = f"cuda:{device_number}" + else: + device = torch.device("cpu") + if hasattr(model, "to"): + model.to(self.device) + elif hasattr(model, "model") and hasattr(model.model, "to"): + model.model.to(self.device) + else: + raise RuntimeError("Cannot move model to device.") y_train = torch.tensor(y_train, device=device) y_trigger = torch.tensor(y_trigger, device=device) x_train = torch.tensor(x_train, device=device) x_trigger = torch.tensor(x_trigger, device=device) y_trigger = y_trigger.to(torch.long) y_trigger = y_trigger.to(torch.long) + atk = self.init(model=model, data=data, attack_size=self.attack_size) start = process_time_ns() samples, _ = atk.poison( x_trigger=x_trigger, diff --git a/deckard/base/data/data.py b/deckard/base/data/data.py index e22c69a2..664fc945 100644 --- a/deckard/base/data/data.py +++ b/deckard/base/data/data.py @@ -85,26 +85,29 @@ def __hash__(self): """Get the hash of the data object.""" return int(my_hash(self), 16) - def initialize(self): + def initialize(self, filename=None): """Initialize the data object. If the data is generated, then generate the data and sample it. If the data is loaded, then load the data and sample it. :return: X_train, X_test, y_train, y_test """ - if self.generate is not None: - result = self.generate() - if len(result) == 2: - result = self.sample(*result) - else: - assert len(result) == 4, f"Data is not generated: {self.name}" + if filename is not None and Path(filename).exists(): + result = self.load(filename) else: - result = self.load(self.name) - if len(result) == 1: - assert self.target is not None, "Target is not specified" - y = result[self.target] - X = result.drop(self.target, axis=1) - result = self.sample(X, y) - if len(result) == 2: - result = self.sample(*result) - assert len(result) == 4 + if self.generate is not None: + result = self.generate() + if len(result) == 2: + result = self.sample(*result) + else: + assert len(result) == 4, f"Data is not generated: {self.name}" + else: + result = self.load(self.name) + if len(result) == 1: + assert self.target is not None, "Target is not specified" + y = result[self.target] + X = result.drop(self.target, axis=1) + result = self.sample(X, y) + if len(result) == 2: + result = self.sample(*result) + assert len(result) == 4 return result def load(self, filename) -> DataFrame: @@ -171,7 +174,7 @@ def __call__( data = self.load(data_file) assert len(data) == 4, f"Some data is missing: {self.name}" else: - data = self.initialize() + data = self.initialize(filename=data_file) assert len(data) == 4, f"Some data is missing: {self.name}" data_file = self.save(data, data_file) result_dict["data"] = data diff --git a/deckard/base/data/generator.py b/deckard/base/data/generator.py index 6d78fd02..8ce30e7c 100644 --- a/deckard/base/data/generator.py +++ b/deckard/base/data/generator.py @@ -1,4 +1,9 @@ import logging + +from typing import Literal +from dataclasses import dataclass, field +from pathlib import Path +import numpy as np from sklearn.datasets import ( make_classification, make_regression, @@ -7,11 +12,8 @@ make_circles, make_biclusters, ) -from typing import Literal -from dataclasses import dataclass, field +from art.utils import load_mnist, load_cifar10, load_diabetes, to_categorical from ..utils import my_hash -import numpy as np -from art.utils import load_mnist, load_cifar10, load_diabetes __all__ = [ "SklearnDataGenerator", @@ -71,12 +73,12 @@ def __hash__(self): return int(my_hash(self), 16) -TORCH_DATASETS = ["torch_mnist", "torch_cifar10", "torch_diabetes"] +TORCH_DATASETS = ["torch_mnist", "torch_cifar10", "torch_diabetes", "torch_cifar100"] @dataclass class TorchDataGenerator: - name: Literal["torch_mnist", "torch_cifar10", "torch_diabetes"] = "torch_mnist" + name: Literal["torch_mnist", "torch_cifar10", "torch_diabetes", "torch_cifar100"] = "torch_mnist" path = None kwargs: dict = field(default_factory=dict) @@ -107,6 +109,36 @@ def __call__(self): (X_train, y_train), (X_test, y_test), _, _ = load_diabetes() X = np.concatenate((X_train, X_test)) y = np.concatenate((y_train, y_test)) + elif self.name == "torch_cifar100": + try: + from torchvision.datasets import CIFAR100 + from torchvision import transforms + except: + raise ImportError("Please install torchvision to use CIFAR100") + if self.path is None: + raise ValueError(f"path attribute must be specified for dataset: {self.name}.") + original_filename = Path(self.path, self.name, f"{self.name}.npz") + Path(original_filename.parent).mkdir(parents=True, exist_ok=True) + if not original_filename.exists(): + train_set = CIFAR100(Path(self.path, self.name), train=True, download=True, transform=transforms.ToTensor()) + test_set = CIFAR100(Path(self.path, self.name), train=False, download=True, transform=transforms.ToTensor()) + # lambda function to turn each image, label into an np.array + X_ = lambda x: np.array(x[0]) + y_ = lambda x: np.array(x[1]) + X_train = np.array(list(map(X_, train_set))) + y_train = np.array(list(map(y_, train_set))) + X_test = np.array(list(map(X_, test_set))) + y_test = np.array(list(map(y_, test_set))) + y_train = to_categorical(y_train, 100) + y_test = to_categorical(y_test, 100) + X = np.concatenate((X_train, X_test)) + y = np.concatenate((y_train, y_test)) + np.savez(file=original_filename.as_posix(), X=X, y=y) + else: + dict_ = np.load(original_filename.as_posix()) + X = dict_["X"] + y = dict_["y"] + else: raise ValueError(f"Unknown dataset name {self.name}") return [X, y] @@ -171,4 +203,4 @@ def __call__(self): raise ValueError(f"Invalid name {self.name}. Please choose from ") def __hash__(self): - return int(my_hash(self), 16) + return int(my_hash(self), 16) \ No newline at end of file diff --git a/deckard/base/experiment/experiment.py b/deckard/base/experiment/experiment.py index c360c80d..e49ec935 100644 --- a/deckard/base/experiment/experiment.py +++ b/deckard/base/experiment/experiment.py @@ -126,9 +126,7 @@ def __call__(self): old_hash = my_hash(self) # Setup files, data, and model files = deepcopy(self.files).get_filenames() - # Save params file - if "params_file" in files and files["params_file"] is not None: - self.save_params_file() + # Check status of files assert ( "score_dict_file" in files @@ -307,10 +305,3 @@ def _set_name(self): def get_name(self): return self.name - def save_params_file(self): - files = self.files.get_filenames() - params_file = files["params_file"] - Path(params_file).parent.mkdir(parents=True, exist_ok=True) - params = to_dict(self) - with open(params_file, "w") as f: - yaml.dump(params, f) diff --git a/deckard/base/files/files.py b/deckard/base/files/files.py index 8b4f9448..f80e0f33 100644 --- a/deckard/base/files/files.py +++ b/deckard/base/files/files.py @@ -5,6 +5,7 @@ from pathlib import Path from typing import Any, Dict, List from copy import deepcopy +from omegaconf import DictConfig import numpy as np import pandas as pd import yaml @@ -69,7 +70,7 @@ def __init__( self.directory = directory self.name = name self.stage = stage - self.files = self._set_filenames(**files) + self.files = files logger.debug(f"FileConfig init: {self.files}") def __call__(self): @@ -78,11 +79,14 @@ def __call__(self): def get_filenames(self): files = deepcopy(self.files) + files = self._set_filenames(**files) return files def _set_filenames(self, **kwargs): name = kwargs.pop("name", self.name) stage = kwargs.pop("stage", self.stage) + if isinstance(stage, DictConfig): + stage = list(stage.keys())[0] if hasattr(self, "files"): kwargs.update(self.files) files = dict(kwargs) @@ -270,12 +274,6 @@ def save(self, **kwargs) -> List[str]: pass elif isinstance(contents, dict): contents = to_dict(contents) - # elif isinstance(contents, (torch.Tensor, torch.nn.Module)): - # raise NotImplementedError(f"Saving {type(contents)} to json is not supported.") - # elif isinstance(contents, (tf.keras.Model, tf.keras.layers.Layer)): - # raise NotImplementedError(f"Saving {type(contents)} to json is not supported.") - # elif isinstance(contents, (tf.Tensor, tf.Variable)): - # raise NotImplementedError(f"Saving {type(contents)} to json is not supported.") else: raise ValueError( f"Contents of type {type(contents)} cannot be saved to json.", diff --git a/deckard/base/model/model.py b/deckard/base/model/model.py index 5f6fff1a..2d2b0a53 100644 --- a/deckard/base/model/model.py +++ b/deckard/base/model/model.py @@ -14,6 +14,7 @@ from .art_pipeline import ArtPipeline from .sklearn_pipeline import SklearnModelPipeline +from random import randint __all__ = ["Model"] logger = logging.getLogger(__name__) @@ -118,14 +119,8 @@ def __call__(self, data: list, model: object, library=None): if library == "sklearn" or library is None: pass elif library in ["torch", "pytorch"]: - import torch - - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - X_train, X_test, y_train, y_test = data - X_train = torch.FloatTensor(X_train).to(device) - X_test = torch.FloatTensor(X_test).to(device) - y_train = torch.LongTensor(y_train).to(device) - y_test = torch.LongTensor(y_test).to(device) + pass + elif library in ["tensorflow", "tf"]: import tensorflow as tf @@ -277,9 +272,9 @@ def __call__( ): result_dict = {} if isinstance(data, Data): - data = data.initialize() + data = data.initialize(data_file) elif isinstance(data, type(None)): - data = self.data.initialize() + data = self.data.initialize(data_file) elif isinstance(data, (str, Path)): data = self.load(data) assert isinstance( @@ -334,7 +329,7 @@ def __call__( time_dict.update(**fit_time_dict) result_dict["model"] = model result_dict["data"] = data - result_dict["time_dict"].update(**fit_time_dict) + result_dict["time_dict"].update(**time_dict) elif Path(model_file).exists(): model = self.load(model_file) result_dict["model"] = model @@ -417,9 +412,9 @@ def __call__( time_dict = old_time_dict self.data.save(time_dict, time_dict_file) result_dict["time_dict"] = time_dict - if data_file is not None: + if data_file is not None and not Path(data_file).exists(): self.data.save(data, data_file) - if model_file is not None: + if model_file is not None and not Path(model_file).exists(): self.save(model, model_file) return result_dict @@ -434,14 +429,14 @@ def initialize(self, data=None, model=None): tuple: The data and model as Data and Model objects. """ if isinstance(data, Data): - data = data.initialize() + data = data.initialize(data) elif isinstance(data, (str, Path)): data = self.data(data) elif isinstance(data, type(None)): - data = self.data.initialize() + data = self.data.initialize(data) assert isinstance( data, - (type(None), list), + (list), ), f"Data {data} is not a list. It is of type {type(data)}." if isinstance(model, (str, Path)) and Path(model).exists(): model = self.load(model) @@ -463,10 +458,10 @@ def initialize(self, data=None, model=None): model = self.init() else: raise e - if self.art is not None: - model = self.art(model=model, data=data) - else: - pass + if self.art is not None: + model = self.art(model=model, data=data) + else: + pass assert hasattr(model, "fit"), f"Model {model} does not have a fit method." return data, model @@ -476,20 +471,19 @@ def fit(self, data, model, model_file=None): :type data: tuple :return: The fitted model and the average time per sample. """ - if isinstance(data, Data): - data = data.initialize() - elif isinstance(data, (str, Path)): - data = self.data(data) - assert isinstance(data, (type(None), list)), f"Data {data} is not a tuple." - if isinstance(model, Model): - data, model = model.initialize(data) - elif isinstance(model, (str, Path)): - model = self.load(model) - assert hasattr(model, "fit"), f"Model {model} does not have a fit method." - model, time_dict = self.trainer(data, model) + assert isinstance(data, list), f"Data {data} is not a list." + assert len(data) == 4, "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." + assert len(data[0]) == len(data[2]), "X_train and y_train must have the same length." + assert len(data[1]) == len(data[3]), "X_test and y_test must have the same length." assert hasattr(model, "fit"), f"Model {model} does not have a fit method." - if model_file is not None: - self.save(model, model_file) + if Path(model_file).exists(): + model = self.load(model_file) + time_dict = {} + else: + assert hasattr(model, "fit"), f"Model {model} does not have a fit method." + model, time_dict = self.trainer(data, model, library=self.library) + if model_file is not None: + self.save(model, model_file) return model, time_dict def predict(self, data=None, model=None, predictions_file=None): @@ -498,23 +492,12 @@ def predict(self, data=None, model=None, predictions_file=None): :type model: object :param data: The data to predict on. """ - if isinstance(data, Data): - data = data.initialize() - elif isinstance(data, (str, Path)): - data = self.data(data) - elif isinstance(data, type(None)): - data = self.data.initialize() - assert isinstance(data, (type(None), list)), f"Data {data} is not a tuple." - if isinstance(model, (Model)): - data, model = model.initialize(data) - elif isinstance(model, (str, Path)): - model = self.load(model) - elif isinstance(model, type(None)): - data, model = self.initialize(data) - assert hasattr( - model, - "predict", - ), f"Model {model} does not have a predict method." + assert isinstance(data, list), f"Data {data} is not a list." + assert len(data) == 4, "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." + assert len(data[0]) == len(data[2]), "X_train and y_train must have the same length." + assert len(data[1]) == len(data[3]), "X_test and y_test must have the same length." + assert hasattr(model, "fit"), f"Model {model} does not have a fit method." + assert hasattr(model, "predict"), f"Model {model} does not have a predict method." try: start = process_time_ns() predictions = model.predict(data[1]) @@ -553,18 +536,11 @@ def predict_proba(self, data=None, model=None, probabilities_file=None): :type data: tuple :return: The predictions and the average time per sample. """ - if isinstance(data, Data): - data = data.initialize() - elif isinstance(data, type(None)): - data = self.data.initialize() - elif isinstance(data, (str, Path)): - data = self.data(data) - else: - assert isinstance(data, list), f"Data {data} is not a list." - if isinstance(model, Model): - data, model = model.initialize(data) - elif isinstance(model, (str, Path)): - model = self.load(model) + assert isinstance(data, list), f"Data {data} is not a list." + assert len(data) == 4, "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." + assert len(data[0]) == len(data[2]), "X_train and y_train must have the same length." + assert len(data[1]) == len(data[3]), "X_test and y_test must have the same length." + assert hasattr(model, "fit"), f"Model {model} does not have a fit method." if ( str("art") in str(type(model)) and "sklearn" in str(type(model)) @@ -600,15 +576,11 @@ def predict_log_loss(self, data, model, loss_file=None): :type data: tuple :return: The predictions and the average time per sample. """ - if isinstance(data, Data): - data = data.initialize() - elif isinstance(data, (str, Path)): - data = self.data(data) - assert isinstance(data, (type(None), list)), f"Data {data} is not a tuple." - if isinstance(model, Model): - data, model = model.initialize(data) - elif isinstance(model, (str, Path)): - model = self.load(model) + assert isinstance(data, list), f"Data {data} is not a list." + assert len(data) == 4, "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." + assert len(data[0]) == len(data[2]), "X_train and y_train must have the same length." + assert len(data[1]) == len(data[3]), "X_test and y_test must have the same length." + assert hasattr(model, "fit"), f"Model {model} does not have a fit method." if str("art") in str(type(model)) and ( hasattr(model.model, "predict_log_proba") or hasattr(model.model, "predict_proba") @@ -711,4 +683,4 @@ def save(self, model, filename): f"Saving model to {suffix} is not implemented. You can add support for your model by adding a new method to the class {self.__class__.__name__} in {__file__}", ) else: - logger.warning(f"File {filename} already exists. Skipping saving.") + logger.warning(f"File {filename} already exists. Will not overwrite.") diff --git a/deckard/base/model/sklearn_pipeline.py b/deckard/base/model/sklearn_pipeline.py index 775f734d..631b39ed 100644 --- a/deckard/base/model/sklearn_pipeline.py +++ b/deckard/base/model/sklearn_pipeline.py @@ -62,7 +62,7 @@ class SklearnModelPipelineStage: kwargs: dict = field(default_factory=dict) def __init__(self, name, stage_name, **kwargs): - logger.info( + logger.debug( f"Instantiating {self.__class__.__name__} with name={name} and kwargs={kwargs}", ) self.name = name @@ -73,7 +73,7 @@ def __hash__(self): return int(my_hash(self), 16) def __call__(self, model): - logger.info( + logger.debug( f"Calling SklearnModelPipelineStage with name={self.name} and kwargs={self.kwargs}", ) name = self.name @@ -107,7 +107,7 @@ class SklearnModelPipeline: pipeline: Dict[str, SklearnModelPipelineStage] = field(default_factory=dict) def __init__(self, **kwargs): - logger.info(f"Instantiating {self.__class__.__name__} with kwargs={kwargs}") + logger.debug(f"Instantiating {self.__class__.__name__} with kwargs={kwargs}") pipe = {} while "kwargs" in kwargs: pipe.update(**kwargs.pop("kwargs")) @@ -232,7 +232,7 @@ def __init__(self, data, model=None, library="sklearn", pipeline={}, **kwargs): self.pipeline = None def __call__(self): - logger.info(f"Initializing model {self.model} with kwargs {self.kwargs}") + logger.debug(f"Initializing model {self.model} with kwargs {self.kwargs}") data = self.data model = self.model library = self.library diff --git a/deckard/base/model/torch_models.py b/deckard/base/model/torch_models.py index 81803699..4edce11c 100644 --- a/deckard/base/model/torch_models.py +++ b/deckard/base/model/torch_models.py @@ -2,7 +2,7 @@ from copy import deepcopy from dataclasses import dataclass, field from typing import Union - +from random import randint import numpy as np from art.estimators.classification import PyTorchClassifier from art.estimators.regression import PyTorchRegressor @@ -25,9 +25,8 @@ supported_models = list(torch_dict.keys()) __all__ = ["TorchInitializer", "TorchCriterion", "TorchOptimizer"] -dataclass - +@dataclass class TorchCriterion: name: str kwargs: Union[dict, None] = field(default_factory=dict) @@ -92,15 +91,9 @@ class TorchInitializer: kwargs: Union[dict, None] = field(default_factory=dict) def __init__(self, data, model, library, **kwargs): - import torch - self.data = data self.model = model self.library = library - self.device = kwargs.pop( - "device", - "cuda" if torch.cuda.is_available() else "cpu", - ) while "kwargs" in kwargs: new_kwargs = kwargs.pop("kwargs", {}) kwargs.update(**new_kwargs) @@ -113,8 +106,9 @@ def __call__(self): kwargs.update(**kwargs.pop("kwargs", {})) data = self.data import torch - - if "art" in str(type(model)) and hasattr(model, "model"): + device = "cuda" if torch.cuda.is_available() else "cpu" + devices = range(torch.cuda.device_count()) + if str(type(model)).startswith("art.") and hasattr(model, "model"): model = model.model if "optimizer" in kwargs: optimizer = TorchOptimizer(**kwargs.pop("optimizer"))(model) @@ -135,10 +129,21 @@ def __call__(self): kwargs.update({"nb_classes": len(np.unique(data[2]))}) else: kwargs.update({"nb_classes": data[2].shape[1]}) - if hasattr(model, "to"): - model.to(self.device) - elif hasattr(model, "model") and hasattr(model.model, "to"): - model.model.to(self.device) + try: + if hasattr(model, "to"): + model.to(device) + elif hasattr(model, "model") and hasattr(model.model, "to"): + model.model.to(device) + except Exception as e: + if "CUDA out of memory" in str(e) and len(devices) > 0: + device_number = devices[randint(0, len(devices) - 1)] + device = f"cuda:{device_number}" + logger.info(f"Out of memory error. Trying device {device}") + model.to(device) + for datum in data: + datum.to(device) + else: + raise e if library in torch_dict: kwargs.pop("library", None) model = torch_dict[library](model, **kwargs) diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index 3c8393da..6f452357 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -26,6 +26,8 @@ afr_parser.add_argument("--target", type=str, default="adv_failures") afr_parser.add_argument("--duration_col", type=str, default="adv_fit_time") afr_parser.add_argument("--dataset", type=str, default="mnist") + afr_parser.add_argument("--plots_folder", type=str, default="mnist/plots") + afr_parser.add_argument("--file", type=str, default="mnist/plots/data.csv") afr_args = afr_parser.parse_args() target = afr_args.target duration_col = afr_args.duration_col @@ -38,10 +40,8 @@ } matplotlib.rc("font", **font) - - FOLDER = Path("output/plots/") - csv_file = FOLDER / "data.csv" - data = pd.read_csv(csv_file, index_col=0) + FOLDER = Path(afr_args.plots_folder) + data = pd.read_csv(afr_args.file, index_col=0) data.columns = data.columns.str.strip() data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x) data.def_value.replace("", 0, inplace=True) @@ -51,13 +51,8 @@ data = min_max_scaling(data) data.dropna(axis=0, subset=["atk_value", "atk_param"], inplace=True) data.dropna(axis=0, subset=["def_value", "def_param"], inplace=True) - data.loc[:, "adv_failures"] = (1 - data.loc[:, "adv_accuracy"]) * data.loc[ - :, "attack.attack_size" - ] - data.loc[:, "ben_failures"] = (1 - data.loc[:, "accuracy"]) * data.loc[ - :, "attack.attack_size" - ] - + data.loc[:, "adv_failures"] = (1 - data.loc[:, "adv_success"]) + data.loc[:, "ben_failures"] = (1 - data.loc[:, "accuracy"]) def plot_aft( df, file, @@ -206,6 +201,7 @@ def clean_data_for_aft( cleaned = cleaned[cleaned["adv_accuracy"] != -1e10] cleaned.dropna(inplace=True, how="any", axis=0) y = cleaned[target] + del cleaned[target] assert ( target in cleaned ), f"Target {target} not in dataframe with columns {cleaned.columns}" @@ -244,7 +240,7 @@ def split_data_for_aft( "adv_failure_rate", "model_layers", "adv_fit_time", - "model.art.pipeline.initialize.kwargs.optimizer.lr", + "model.trainer.nb_epoch", ] X_train, X_test, y_train, y_test = split_data_for_aft( @@ -308,51 +304,51 @@ def split_data_for_aft( ) wft_scores = score_model(wft, X_train, X_test) - cox_replacement_dict = { - "adv_failure_rate": "$h_{adv}(t,;\\theta)$", - "def_value": "Defence Strength", - "data.sample.random_state": "Random State", - "train_time": "$t_{train}$", - "model_layers": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", - "adv_accuracy": "$\lambda_{adv.}$", # noqa W605 - "adv_fit_time": "$t_{attack}$", - "adv_log_loss": "Adv. Log Loss", - "predict_time": "$t_{predict}$", - "accuracy": "$\lambda_{ben.}$", # noqa W605 - "failure_rate": "$h_{ben.}(t,;\\theta)$", - "atk_value": "Attack Strength", - } - cox_partial_dict = { - "file": "cox_partial_effects.pdf", - "covariate_array": "model_layers", - "values_array": [18, 34, 50, 101, 152], - "replacement_dict": cox_replacement_dict, - "title": "$S(t)$ for Cox AFR", - "ylabel": "Expectation of $S(t)$", - "xlabel": "Time $T$ (seconds)", - "legend_kwargs": { - "title": "No. of Layers", - "labels": ["18", "34", "50", "101", "152"], - }, - } - cox_plot_dict = { - "file": "cox_aft.pdf", - "duration_col": duration_col, - "title": "Cox AFR Model", - "mtype": "cox", - "replacement_dict": cox_replacement_dict, - } - cox_afr, cft = plot_aft( - df=X_train, - event_col=target, - **cox_plot_dict, - ) - cox_scores = score_model(cft, X_train, X_test) - cox_partial = plot_partial_effects( - aft=cft, - **cox_partial_dict, - ) + # cox_replacement_dict = { + # "adv_failure_rate": "$h_{adv}(t,;\\theta)$", + # "def_value": "Defence Strength", + # "data.sample.random_state": "Random State", + # "train_time": "$t_{train}$", + # "model_layers": "No. of Layers", + # "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", + # "adv_accuracy": "$\lambda_{adv.}$", # noqa W605 + # "adv_fit_time": "$t_{attack}$", + # "adv_log_loss": "Adv. Log Loss", + # "predict_time": "$t_{predict}$", + # "accuracy": "$\lambda_{ben.}$", # noqa W605 + # "failure_rate": "$h_{ben.}(t,;\\theta)$", + # "atk_value": "Attack Strength", + # } + # cox_partial_dict = { + # "file": "cox_partial_effects.pdf", + # "covariate_array": "model_layers", + # "values_array": [18, 34, 50, 101, 152], + # "replacement_dict": cox_replacement_dict, + # "title": "$S(t)$ for Cox AFR", + # "ylabel": "Expectation of $S(t)$", + # "xlabel": "Time $T$ (seconds)", + # "legend_kwargs": { + # "title": "No. of Layers", + # "labels": ["18", "34", "50", "101", "152"], + # }, + # } + # cox_plot_dict = { + # "file": "cox_aft.pdf", + # "duration_col": duration_col, + # "title": "Cox AFR Model", + # "mtype": "cox", + # "replacement_dict": cox_replacement_dict, + # } + # cox_afr, cft = plot_aft( + # df=X_train, + # event_col=target, + # **cox_plot_dict, + # ) + # cox_scores = score_model(cft, X_train, X_test) + # cox_partial = plot_partial_effects( + # aft=cft, + # **cox_partial_dict, + # ) log_normal_dict = { "Intercept: sigma_": "$\sigma$", # noqa W605 @@ -409,7 +405,7 @@ def split_data_for_aft( "accuracy: alpha_": "$\lambda_{ben.}$", # noqa W605 "adv_fit_time: alpha_": "$t_{attack}$", "model_layers: alpha_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", + "model.art.pipeline.initialize.kwargs.optimizer.lr:": "Learning Rate", "adv_failure_rate: alpha_": "$h_{adv.}(t,\\theta)$", # noqa W605 "alpha_": "", } diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index e5c20645..6d60cb28 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -50,7 +50,11 @@ def parse_folder(folder, files=["params.yaml", "score_dict.json"]) -> pd.DataFra results[folder] = {} if suffix == ".json": with open(file, "r") as f: - dict_ = json.load(f) + try: + dict_ = json.load(f) + except json.decoder.JSONDecodeError as e: + raise e + elif suffix == ".yaml": with open(file, "r") as f: dict_ = yaml.safe_load(f) @@ -184,7 +188,6 @@ def clean_data_for_plotting( atk_gen_dict, control_dict, file, - folder, ): logger.info("Replacing attack and defence names with short names...") if hasattr(data, "def_gen"): @@ -199,13 +202,12 @@ def clean_data_for_plotting( logger.info("Shortening model names...") data["model_name"] = data["model.init.name"].str.split(".").str[-1] data["model_layers"] = data["model_name"].str.split("Net").str[-1] - # Rename columns that end in '.1' by removing the '.1' - data.columns.rename(lambda x: x[:-2] if x.endswith(".1") else x, inplace=True) + data = data.loc[:, ~data.columns.str.endswith(".1")] logger.info("Replacing data.sample.random_state with random_state...") data["data.sample.random_state"].rename("random_state", inplace=True) data = format_control_parameter(data, control_dict) - logger.info(f"Saving data to {Path(folder) / file}") - data.to_csv(Path(folder) / "data.csv") + logger.info(f"Saving data to {file}") + data.to_csv( file) return data @@ -264,7 +266,6 @@ def save_results(results, results_file) -> str: atk_gen_dict, control_dict, results_file, - report_folder, ) report_file = save_results(results, results_file) assert Path( diff --git a/deckard/layers/experiment.py b/deckard/layers/experiment.py index 99ff9a91..ae518ef7 100644 --- a/deckard/layers/experiment.py +++ b/deckard/layers/experiment.py @@ -27,17 +27,18 @@ def get_dvc_stage_params( directory=".", name=None, ): + logger.info( f"Getting params for stage {stage} from {params_file} and {pipeline_file} in {directory}.", ) - params = dvc.api.params_show(params_file, stages=stage, repo=directory) - if "_target_" not in params: - params.update({"_target_": "deckard.base.experiment.Experiment"}) + params = dvc.api.params_show(stages=stage) + params.update({"_target_": "deckard.base.experiment.Experiment"}) files = dvc.api.params_show(pipeline_file, stages=stage, repo=directory) unflattened_files = unflatten_dict(files) params["files"] = dict(unflattened_files.get("files", unflattened_files)) params["files"]["_target_"] = "deckard.base.files.FileConfig" params["files"]["stage"] = stage + params['stage']=stage if name is not None: params["files"]["name"] = name return params @@ -61,12 +62,10 @@ def run_stage( exp = instantiate(params) id_ = exp.name files = deepcopy(exp.files()) - new_params_file = files.pop("score_dict_file", None) - if new_params_file is not None: - new_params_file = Path(new_params_file).with_name(params_file).as_posix() - Path(new_params_file).parent.mkdir(parents=True, exist_ok=True) - with open(new_params_file, "w") as f: - yaml.dump(params, f) + params_file = Path(files['score_dict_file']).with_name("params.yaml").as_posix() + Path(params_file).parent.mkdir(exist_ok=True, parents=True) + with Path(params_file).open("w") as f: + yaml.dump(params, f) score = exp() return id_, score @@ -118,12 +117,12 @@ def run_stages(stages, pipeline_file="dvc.yaml", params_file="params.yaml", repo dvc_parser.add_argument("--config_file", type=str, default="default") dvc_parser.add_argument("--workdir", type=str, default=".") args = dvc_parser.parse_args() - config_dir = Path(Path(), args.config_dir).resolve().as_posix() - save_params_file( - config_dir=config_dir, - config_file=args.config_file, - params_file=args.params_file, - ) + config_dir = Path(args.workdir, args.config_dir).resolve().as_posix() + # save_params_file( + # config_dir=config_dir, + # config_file=args.config_file, + # params_file=args.params_file, + # ) logging.basicConfig( level=args.verbosity, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index 68964a6c..417122f8 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -85,12 +85,9 @@ def merge_params(default, params) -> dict: for key, value in params.items(): if key in default and isinstance(value, dict) and value is not None: default[key] = merge_params(default[key], value) - elif ( - isinstance(value, (list, tuple, int, float, str, bool, dict)) - and value is not None - ): + elif isinstance(value, (list, tuple, int, float, str, bool)) and value is not None: default[key] = value - elif value is None: + elif key in default and value is None: continue else: logger.warning(f"Key {key} not found in default params. Skipping.") @@ -153,9 +150,37 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: else: assert isinstance(stage, list), f"args.stage is of type {type(stage)}" stages = stage - if params is None: - with open(Path(path, "params.yaml"), "r") as f: - default_params = yaml.load(f, Loader=yaml.FullLoader) + # if params is None: + # with open(Path(path, "params.yaml"), "r") as f: + # default_params = yaml.load(f, Loader=yaml.FullLoader) + # key_list = [] + # for stage in stages: + # with open(Path(path, "dvc.yaml"), "r") as f: + # new_keys = yaml.load(f, Loader=yaml.FullLoader)["stages"][stage][ + # "params" + # ] + # key_list.extend(new_keys) + # params = read_subset_of_params(key_list, params) + # params = merge_params(default_params, params) + # elif isinstance(params, str) and Path(params).is_file() and Path(params).exists(): + # with open(Path(params), "r") as f: + # params = yaml.load(f, Loader=yaml.FullLoader) + # assert isinstance( + # params, + # dict, + # ), f"Params in file {params} must be a dict. It is a {type(params)}." + # key_list = [] + # for stage in stages: + # with open(Path(path, "dvc.yaml"), "r") as f: + # new_keys = yaml.load(f, Loader=yaml.FullLoader)["stages"][stage][ + # "params" + # ] + # key_list.extend(new_keys) + # with open(Path(path, "params.yaml"), "r") as f: + # all_params = yaml.load(f, Loader=yaml.FullLoader) + # default_params = read_subset_of_params(key_list, all_params) + # params = merge_params(default_params, params) + if isinstance(params, dict): key_list = [] for stage in stages: with open(Path(path, "dvc.yaml"), "r") as f: @@ -163,63 +188,33 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: "params" ] key_list.extend(new_keys) - params = read_subset_of_params(key_list, params) - params = merge_params(default_params, params) - elif isinstance(params, str) and Path(params).is_file() and Path(params).exists(): - with open(Path(params), "r") as f: - params = yaml.load(f, Loader=yaml.FullLoader) - assert isinstance( - params, - dict, - ), f"Params in file {params} must be a dict. It is a {type(params)}." - key_list = [] - for stage in stages: - with open(Path(path, "dvc.yaml"), "r") as f: - new_keys = yaml.load(f, Loader=yaml.FullLoader)["stages"][stage][ - "params" - ] - key_list.extend(new_keys) - with open(Path(path, "params.yaml"), "r") as f: - all_params = yaml.load(f, Loader=yaml.FullLoader) - default_params = read_subset_of_params(key_list, all_params) - params = merge_params(default_params, params) - elif isinstance(params, dict): - key_list = [] - for stage in stages: - with open(Path(path, "dvc.yaml"), "r") as f: - new_keys = yaml.load(f, Loader=yaml.FullLoader)["stages"][stage][ - "params" - ] - key_list.extend(new_keys) - with open(Path(path, "params.yaml"), "r") as f: - all_params = yaml.load(f, Loader=yaml.FullLoader) - default_params = read_subset_of_params(key_list, all_params) - params = merge_params(default_params, params) else: raise TypeError(f"Expected str or dict, got {type(params)}") - assert isinstance( - params, - dict, - ), f"Params must be a dict. It is type {type(params)}." + params = read_subset_of_params(key_list, params) # Load files from dvc with open(Path(path, "dvc.yaml"), "r") as f: - key_list = [] + file_list = [] for stage in stages: pipe = yaml.load(f, Loader=yaml.FullLoader)["stages"][stage] if "deps" in pipe: - key_list.extend(pipe["deps"]) + dep_list =[x.split(":")[0] for x in pipe["deps"]] + file_list.extend(dep_list) if "outs" in pipe: - key_list.extend(pipe["outs"]) + out_list = [x.split(":")[0] for x in pipe['outs']] + file_list.extend(out_list) if "metrics" in pipe: - key_list.extend(pipe["metrics"]) - with open(Path(path, "params.yaml"), "r") as f: - all_params = yaml.load(f, Loader=yaml.FullLoader) - files = {} - for filename, file in all_params["files"].items(): - if filename in str(key_list): - files[filename] = file - files["_target_"] = "deckard.base.files.FileConfig" - params = get_files(params, stage=stages[-1]) + metric_list = [x.split(":")[0] for x in pipe['metrics']] + file_list.extend(metric_list) + file_string = str(file_list) + files = params['files'] + file_list = list(files.keys()) + for key in file_list: + template_string = "${files."+ key + "}" + if template_string in file_string: + pass + else: + params['files'].pop(key) + params = get_files(params, stage) return params diff --git a/deckard/layers/plots.py b/deckard/layers/plots.py index f18261f7..ddd2c6f7 100644 --- a/deckard/layers/plots.py +++ b/deckard/layers/plots.py @@ -125,7 +125,7 @@ def scatter_plot( def drop_frames_without_results( data, - subset=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time"], + subset=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time", "adv_success"], ): logger.info(f"Dropping frames without results for {subset}") data.dropna(axis=0, subset=subset, inplace=True) @@ -147,12 +147,12 @@ def calculate_failure_rate(data): / data.loc[:, "predict_time"] ) data.loc[:, "adv_failure_rate"] = ( - (1 - data.loc[:, "adv_accuracy"]) + (1 - data.loc[:, "adv_success"]) * data.loc[:, "attack.attack_size"] / data.loc[:, "adv_fit_time"] ) data.loc[:, "adv_success_rate"] = ( - data.loc[:, "adv_accuracy"] + data.loc[:, "adv_success"] * data.loc[:, "attack.attack_size"] / data.loc[:, "adv_fit_time"] ) @@ -169,8 +169,14 @@ def calculate_failure_rate(data): def pareto_set(data, sense_dict): - subset = data.loc[:, sense_dict.keys()] - these = paretoset(subset, sense=sense_dict.values()) + new_sense_dict = {} + for k,v in sense_dict.items(): + if k in data.columns: + new_sense_dict[k] = v + else: + pass + subset = data.loc[:, new_sense_dict.keys()] + these = paretoset(subset, sense=new_sense_dict.values()) return data.iloc[these, :] @@ -247,13 +253,12 @@ def min_max_scaling(data, **kwargs): assert Path( args.file, ).exists(), f"File {args.file} does not exist. Please specify a valid file using the -f flag." - csv_file = args.file - data = pd.read_csv(csv_file) + data = pd.read_csv(args.file) data = drop_frames_without_results( data, subset=[ "accuracy", - "adv_accuracy", + "adv_success", "train_time", "adv_fit_time", "predict_time", @@ -261,11 +266,11 @@ def min_max_scaling(data, **kwargs): ) sense_dict = { "accuracy": "max", - "adv_accuracy": "min", - "data.sample.random_state": "diff", + "adv_accuracy": "max", + "adv_success": "min", "model_layers": "diff", - "atk_param": "diff", - "def_param": "diff", + # "atk_param": "diff", + # "def_param": "diff", "atk_gen": "diff", "def_gen": "diff", "data.sample.random_state": "diff", @@ -275,13 +280,15 @@ def min_max_scaling(data, **kwargs): data = min_max_scaling(data) if "Unnamed: 0" in data.columns: data.drop("Unnamed: 0", axis=1, inplace=True) - if Path(args.path).absolute().exists(): + if Path(args.path).absolute() == Path(args.path): logger.info("Absolute path specified") FOLDER = Path(args.path).absolute() else: logger.info("Relative path specified") FOLDER = Path(Path(), args.path) + logger.info(f"Creating folder {FOLDER}") FOLDER.mkdir(parents=True, exist_ok=True) + logger.info(f"Saving data to {FOLDER / args.output}") data.to_csv(FOLDER / args.output) IMAGE_FILETYPE = ( args.plotfiletype @@ -302,15 +309,15 @@ def min_max_scaling(data, **kwargs): for dict_ in cat_plot_list: i += 1 logger.info(f"Rendering graph {i}") - locals()[f"graph{i}"] = cat_plot(data, **dict_, folder=FOLDER) + cat_plot(data, **dict_, folder=FOLDER) line_plot_list = big_dict["line_plot"] for dict_ in line_plot_list: i += 1 logger.info(f"Rendering graph {i}") - locals()[f"graph{i}"] = line_plot(data, **dict_, folder=FOLDER) + line_plot(data, **dict_, folder=FOLDER) scatter_plot_list = big_dict["scatter_plot"] for dict_ in scatter_plot_list: i += 1 logger.info(f"Rendering graph {i}") - locals()[f"graph{i}"] = scatter_plot(data, **dict_, folder=FOLDER) + scatter_plot(data, **dict_, folder=FOLDER) diff --git a/examples/pytorch/.gitignore b/examples/pytorch/.gitignore new file mode 100644 index 00000000..a129798e --- /dev/null +++ b/examples/pytorch/.gitignore @@ -0,0 +1,2 @@ +mnist/ +20_epochs/ \ No newline at end of file diff --git a/examples/pytorch/attacks.sh b/examples/pytorch/attacks.sh index 6627fc7f..8ec1f079 100644 --- a/examples/pytorch/attacks.sh +++ b/examples/pytorch/attacks.sh @@ -2,37 +2,36 @@ # # This script is used to generate the attacks for the example. -# # Fast Gradient Method -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm ++hydra.sweeper.direction=minimize $@ +# Fast Gradient Method +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 stage=attack ++hydra.sweeper.study_name=fgm ++direction=maximize $@ -# # Projected Gradient Descent -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd ++hydra.sweeper.direction=minimize $@ +# Projected Gradient Descent +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 stage=attack ++hydra.sweeper.study_name=pgd ++direction=maximize $@ -# # DeepFool -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep ++hydra.sweeper.direction=minimize $@ +# DeepFool +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=1,3,5,10 stage=attack ++hydra.sweeper.study_name=deep ++direction=maximize $@ - -# # HopSkipJump -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 ++stage=attack ++hydra.sweeper.study_name=hsj ++hydra.sweeper.direction=minimize $@ +# HopSkipJump +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 stage=attack ++hydra.sweeper.study_name=hsj ++direction=maximize $@ # ##################################################### # PixelAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=pixel ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=pixel ++direction=maximize $@ # ThresholdAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=thresh ++hydra.sweeper.direction=minimize $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=thresh ++direction=maximize $@ # # AdversarialPatch -# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 ++stage=patch ++hydra.sweeper.study_name=attack ++hydra.sweeper.direction=minimize ++attack.init.patch_shape=[1,28,28] $@ +# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 stage=patch ++hydra.sweeper.study_name=attack ++direction=maximize ++attack.init.patch_shape=[1,28,28] $@ ##################################################### # # Carlini L0 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw0 ++hydra.sweeper.study_name=cw0 ++hydra.sweeper.direction=minimize $@ +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw0 ++hydra.sweeper.study_name=cw0 ++direction=maximize $@ -# # # Carlini L2 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw2 ++hydra.sweeper.study_name=cw2 ++hydra.sweeper.direction=minimize $@ +# # Carlini L2 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw2 ++hydra.sweeper.study_name=cw2 ++direction=maximize $@ # # Carlini LInf Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=cwinf ++hydra.sweeper.direction=minimize $@ +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=attack ++hydra.sweeper.study_name=cwinf ++direction=maximize $@ rm -rf output/models/* diff --git a/examples/pytorch/conf/compile.yaml b/examples/pytorch/conf/compile.yaml index 0d721a44..43418520 100644 --- a/examples/pytorch/conf/compile.yaml +++ b/examples/pytorch/conf/compile.yaml @@ -15,17 +15,18 @@ defences: GaussianNoise: Gauss-out HighConfidence: Conf params: - # art.attacks.evasion.CarliniL0Method: attack.init.kwargs.confidence - # art.attacks.evasion.CarliniL2Method: attack.init.kwargs.confidence - # art.attacks.evasion.CarliniLInfMethod: attack.init.kwargs.confidence - Deep: attack.init.kwargs.nb_grads - FGM: attack.init.kwargs.eps - HSJ: attack.init.kwargs.max_iter - Pixel: attack.init.kwargs.th - PGD: attack.init.kwargs.eps - Thresh: attack.init.kwargs.th - Gauss-out: model.art.pipeline.postprocessor.kwargs.scale - Conf: model.art.pipeline.postprocessor.kwargs.cutoff - FSQ: model.art.pipeline.preprocessor.kwargs.bit_depth - Gauss-in: model.art.pipeline.preprocessor.kwargs.sigma - Control: model_layers + # art.attacks.evasion.CarliniL0Method: attack.init.confidence + # art.attacks.evasion.CarliniL2Method: attack.init.confidence + # art.attacks.evasion.CarliniLInfMethod: attack.init.confidence + Deep: attack.init.nb_grads + FGM: attack.init.eps + HSJ: attack.init.max_iter + Pixel: attack.init.th + PGD: attack.init.eps + Thresh: attack.init.th + Gauss-out: model.art.pipeline.postprocessor.scale + Conf: model.art.pipeline.postprocessor.cutoff + FSQ: model.art.pipeline.preprocessor.bit_depth + Gauss-in: model.art.pipeline.preprocessor.sigma + Depth: model_layers + Epochs: model.train.nb_epoch diff --git a/examples/pytorch_cifar/conf/files/default.yaml b/examples/pytorch/conf/files/mnist.yaml similarity index 96% rename from examples/pytorch_cifar/conf/files/default.yaml rename to examples/pytorch/conf/files/mnist.yaml index bcaf75ce..efdb1c42 100644 --- a/examples/pytorch_cifar/conf/files/default.yaml +++ b/examples/pytorch/conf/files/mnist.yaml @@ -3,7 +3,7 @@ reports: reports data_dir: data model_dir: models attack_dir: attacks -directory: output +directory: mnist score_dict_file: score_dict.json data_file : data model_file : model diff --git a/examples/pytorch/conf/hydra/default.yaml b/examples/pytorch/conf/hydra/default.yaml deleted file mode 100644 index 53e5bd5d..00000000 --- a/examples/pytorch/conf/hydra/default.yaml +++ /dev/null @@ -1,38 +0,0 @@ -defaults: - - params : default - - override hydra/sweeper : optuna - - override hydra/sweeper/sampler : random - - override hydra/launcher : joblib - - override hydra/sweeper/params : default.yaml -run: - dir: output/${hydra.job.override_dirname}/seed=${data.sample.random_state} -job: - config: - override_dirname: - exclude_keys: - - seed - kv_sep: ":" - item_sep: "_" -sweep: - dir: multirun - subdir: ${hydra.job.override_dirname} -sweeper: - sampler: - _target_: optuna.samplers.RandomSampler - direction: maximize - study_name: model - storage: sqlite:///model.db - n_jobs: 4 - n_trials: 1 - -launcher: - _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 8 - prefer : processes - verbose: 1 - timeout: null - pre_dispatch: n_jobs - batch_size: auto - temp_folder: /tmp/deckard - max_nbytes: 100000 - mmap_mode: r diff --git a/examples/pytorch/conf/hydra/sweeper/params/attack/cw_0.yaml b/examples/pytorch/conf/hydra/sweeper/params/attack/cw_0.yaml deleted file mode 100644 index d28a31d4..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/attack/cw_0.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.CarliniL0Method -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.confidence : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/pytorch/conf/hydra/sweeper/params/attack/cw_2.yaml b/examples/pytorch/conf/hydra/sweeper/params/attack/cw_2.yaml deleted file mode 100644 index ec2ed44c..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/attack/cw_2.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.model : ${model} -init.name : art.attacks.evasion.CarliniL0Method -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.confidence : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/pytorch/conf/hydra/sweeper/params/attack/cw_inf.yaml b/examples/pytorch/conf/hydra/sweeper/params/attack/cw_inf.yaml deleted file mode 100644 index 99b16e45..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/attack/cw_inf.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.CarliniLInfMethod -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.confidence : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/pytorch/conf/hydra/sweeper/params/attack/deepfool.yaml b/examples/pytorch/conf/hydra/sweeper/params/attack/deepfool.yaml deleted file mode 100644 index 8b56a8d9..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/attack/deepfool.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.name : art.attacks.evasion.DeepFool -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.nb_grads : choice(10,100,1000) -init.eps: choice(0.01, 0.03, 0.3, 0.1, 1.0) diff --git a/examples/pytorch/conf/hydra/sweeper/params/attack/fgm.yaml b/examples/pytorch/conf/hydra/sweeper/params/attack/fgm.yaml deleted file mode 100644 index c46ee4df..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/attack/fgm.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.FastGradientMethod -init.eps: choice(0.01, 0.03, 0.3, 0.1) -init.norm: choice(inf, 1, 2) -init.eps_step: choice(0.001, 0.003, 0.01) diff --git a/examples/pytorch/conf/hydra/sweeper/params/attack/hsj.yaml b/examples/pytorch/conf/hydra/sweeper/params/attack/hsj.yaml deleted file mode 100644 index 45b3af5d..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/attack/hsj.yaml +++ /dev/null @@ -1,6 +0,0 @@ -attack: - init.name : art.attacks.evasion.HopSkipJump - init.batch_size : choice(1, 4, 16, 65, 128) - init.max_iter : choice(1, 10, 100, 1000) - init.max_eval : choice(1, 10, 100, 1000) - init.init_eval : choice(1, 10, 100, 1000) diff --git a/examples/pytorch/conf/hydra/sweeper/params/attack/patch.yaml b/examples/pytorch/conf/hydra/sweeper/params/attack/patch.yaml deleted file mode 100644 index c9409de4..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/attack/patch.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.name : art.attacks.evasion.ThresholdAttack -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.scale_min : choice(0.01,) -init.scale_max : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/pytorch/conf/hydra/sweeper/params/attack/pgd.yaml b/examples/pytorch/conf/hydra/sweeper/params/attack/pgd.yaml deleted file mode 100644 index 42bc9514..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/attack/pgd.yaml +++ /dev/null @@ -1,6 +0,0 @@ -init.name : art.attacks.evasion.ProjectedGradientDescent -init.eps : choice(0.01, 0.03, 0.3, 0.1) -init.norm : choice(inf, 1, 2) -init.eps_step : choice(0.001, 0.003, 0.01) -init.batch_size : choice(1024) -init.max_iter : choice(10) diff --git a/examples/pytorch/conf/hydra/sweeper/params/attack/pixel.yaml b/examples/pytorch/conf/hydra/sweeper/params/attack/pixel.yaml deleted file mode 100644 index 84450f38..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/attack/pixel.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.ProjectedGradientDescent -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.th : choice(1,4,16,64,256) diff --git a/examples/pytorch/conf/hydra/sweeper/params/attack/threshold.yaml b/examples/pytorch/conf/hydra/sweeper/params/attack/threshold.yaml deleted file mode 100644 index d6079457..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/attack/threshold.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.ThresholdAttack -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.th: choice(1,4,16,64,256) diff --git a/examples/pytorch/conf/hydra/sweeper/params/default.yaml b/examples/pytorch/conf/hydra/sweeper/params/default.yaml deleted file mode 100644 index 49b1de65..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/default.yaml +++ /dev/null @@ -1,11 +0,0 @@ -defaults: - - _self_ - - data: torch_mnist - - model: torch_mnist - - files: default - - scorers: default - - stage : null - - params/model : null - - params/attack : null -++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) -++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001) diff --git a/examples/pytorch/conf/hydra/sweeper/params/model/confidence.yaml b/examples/pytorch/conf/hydra/sweeper/params/model/confidence.yaml deleted file mode 100644 index cfda12d2..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/model/confidence.yaml +++ /dev/null @@ -1,5 +0,0 @@ - -defence: - art.postprocessor.name : [art.defences.postprocessor.HighConfidence] - art.postprocessor.cutoff : [.1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99] - art.postprocessor.apply_fit : [True, False] diff --git a/examples/pytorch/conf/hydra/sweeper/params/model/default.yaml b/examples/pytorch/conf/hydra/sweeper/params/model/default.yaml deleted file mode 100644 index 8163e568..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/model/default.yaml +++ /dev/null @@ -1 +0,0 @@ -defence: diff --git a/examples/pytorch/conf/hydra/sweeper/params/model/fsq.yaml b/examples/pytorch/conf/hydra/sweeper/params/model/fsq.yaml deleted file mode 100644 index bb508203..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/model/fsq.yaml +++ /dev/null @@ -1,4 +0,0 @@ -defence: - art.preprocessor.name : [art.defences.preprocessor.FeatureSqueezing] - art.preprocessor.clip_values : [[0, 1]] - art.preprocessor.bit_depth : [1, 2, 4, 8, 16, 32, 64] diff --git a/examples/pytorch/conf/hydra/sweeper/params/model/gauss-in.yaml b/examples/pytorch/conf/hydra/sweeper/params/model/gauss-in.yaml deleted file mode 100644 index fd45ea84..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/model/gauss-in.yaml +++ /dev/null @@ -1,5 +0,0 @@ -defence: - art.preprocessor.name : art.defences.preprocessor.GaussianAugmentation - art.preprocessor.clip_values : ${art.initialize.clip_values} - art.preprocessor.sigma : [.01, .1, .2, .3, .5, 1] - art.preprocessor.ratio : [.1, .5, 1] diff --git a/examples/pytorch/conf/hydra/sweeper/params/model/gauss-out.yaml b/examples/pytorch/conf/hydra/sweeper/params/model/gauss-out.yaml deleted file mode 100644 index 8abd9ee1..00000000 --- a/examples/pytorch/conf/hydra/sweeper/params/model/gauss-out.yaml +++ /dev/null @@ -1,3 +0,0 @@ -defence: - art.postprocessor.name : art.defences.postprocessor.GaussianNoise - art.postprocessor.scale : [.01, .1, .2, .3, .5, 1, 2, 3, 5, 10] diff --git a/examples/pytorch/conf/default.yaml b/examples/pytorch/conf/mnist.yaml similarity index 54% rename from examples/pytorch/conf/default.yaml rename to examples/pytorch/conf/mnist.yaml index 4ae0442a..aa05956f 100644 --- a/examples/pytorch/conf/default.yaml +++ b/examples/pytorch/conf/mnist.yaml @@ -3,30 +3,39 @@ defaults: - data: torch_mnist - model: torch_mnist - attack: default - - files: default + - files: mnist - scorers: default - - stage : null - override hydra/sweeper : optuna - override hydra/sweeper/sampler : grid - override hydra/launcher : joblib +stage : '???' +direction : "maximize" _target_ : deckard.base.experiment.Experiment optimizers : accuracy hydra: + run: + dir: ${files.directory}/${files.reports}/${stage}/logs + sweep: + dir: ${files.directory}/${stage}/${model.init.name} + subdir : ${hydra.sweeper.study_name}/${hydra.job.num} sweeper: sampler: _target_: optuna.samplers.GridSampler - direction: maximize - study_name: model + direction: ${direction} + study_name: control storage: sqlite:///model.db - n_jobs: 1 + n_jobs: ${hydra.launcher.n_jobs} + n_trials : 32 params: ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) - ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) + ++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, .0001, .00001, 0.000001) + ++model.trainer.nb_epoch: choice(1, 10, 30, 50, 100) + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 10 + n_jobs: 8 prefer : processes - verbose: 1 + verbose: 10 timeout: null pre_dispatch: n_jobs batch_size: auto diff --git a/examples/pytorch/conf/model/art/initialize/default.yaml b/examples/pytorch/conf/model/art/initialize/default.yaml index 6c5edde5..b694473b 100644 --- a/examples/pytorch/conf/model/art/initialize/default.yaml +++ b/examples/pytorch/conf/model/art/initialize/default.yaml @@ -4,4 +4,4 @@ optimizer: name : torch.optim.SGD lr : 0.01 momentum : 0.9 -clip_values : [0.0, 1.0] +clip_values : [0, 255] diff --git a/examples/pytorch/conf/model/torch_cifar.yaml b/examples/pytorch/conf/model/torch_cifar.yaml index 2562064a..5ca2e24e 100644 --- a/examples/pytorch/conf/model/torch_cifar.yaml +++ b/examples/pytorch/conf/model/torch_cifar.yaml @@ -8,6 +8,6 @@ init: num_classes: 10 _target_: deckard.base.model.Model trainer: - nb_epoch: 20 + nb_epoch: 100 batch_size: 1024 library : pytorch diff --git a/examples/pytorch/conf/model/torch_mnist.yaml b/examples/pytorch/conf/model/torch_mnist.yaml index 762addc1..9c18c54b 100644 --- a/examples/pytorch/conf/model/torch_mnist.yaml +++ b/examples/pytorch/conf/model/torch_mnist.yaml @@ -8,6 +8,6 @@ init: name : torch_example.ResNet18 _target_: deckard.base.model.Model trainer: - nb_epoch: 20 + nb_epoch: 100 batch_size: 1024 library : pytorch diff --git a/examples/pytorch/conf/plots.yaml b/examples/pytorch/conf/plots.yaml index 97a523bc..accab9df 100644 --- a/examples/pytorch/conf/plots.yaml +++ b/examples/pytorch/conf/plots.yaml @@ -150,54 +150,54 @@ cat_plot: - ResNet101 - ResNet152 line_plot: -- file: def_param_vs_accuracy.pdf - hue: def_gen - legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} - title: $\lambda_{ben.}$ vs Defence Strength - x: def_value - x_scale: linear - xlabel: Defence Control Parameter - y: accuracy - y_scale: - ylabel: $\lambda_{ben.}$ - hue_order: - - Control - - Gauss-in - - Gauss-out - - Conf - - FSQ -- file: def_param_vs_adv_accuracy.pdf - hue: def_gen - legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} - title: $\lambda_{adv.}$ vs Defence Strength - x: def_value - x_scale: linear - xlabel: Defence Control Parameter - y: adv_accuracy - y_scale: - ylabel: $\lambda_{adv.}$ - hue_order: - - Control - - Gauss-in - - Gauss-out - - Conf - - FSQ -- file: def_param_vs_adv_failure_rate.pdf - hue: def_gen - legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} - title: $h_{adv}$ vs Defence Strength - x: def_value - x_scale: linear - xlabel: Defence Control Parameter - y: adv_failure_rate - y_scale: log - ylabel: $h_{adv.}$ - hue_order: - - Control - - Gauss-in - - Gauss-out - - Conf - - FSQ +# - file: def_param_vs_accuracy.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $\lambda_{ben.}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: accuracy +# y_scale: +# ylabel: $\lambda_{ben.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +# - file: def_param_vs_adv_accuracy.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $\lambda_{adv.}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: adv_accuracy +# y_scale: +# ylabel: $\lambda_{adv.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +# - file: def_param_vs_adv_failure_rate.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $h_{adv}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: adv_failure_rate +# y_scale: log +# ylabel: $h_{adv.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ - file: atk_param_vs_accuracy.pdf hue: atk_gen legend: {bbox_to_anchor: [1.05, 1]} diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index 2603c83b..589dc9b8 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -1,7 +1,7 @@ schema: '2.0' stages: train: - cmd: python -m deckard.layers.experiment train + cmd: python -m deckard.layers.experiment train --config_file mnist.yaml params: params.yaml: data: @@ -28,7 +28,7 @@ stages: data_dir: data data_file: data data_type: .pkl - directory: output + directory: mnist model_dir: models model_file: model model_type: .pt @@ -58,8 +58,8 @@ stages: with_std: true initialize: clip_values: - - 0.0 - - 1.0 + - 0 + - 255 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -89,7 +89,7 @@ stages: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 scorers: _target_: deckard.base.scorer.ScorerDict accuracy: @@ -101,33 +101,35 @@ stages: direction: minimize name: sklearn.metrics.log_loss outs: - - path: output/data/data.pkl + - path: mnist/data/data.pkl md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - - path: output/models/model.optimizer.pt - md5: d7e9bcabd3955eb8aacfe089f7ddde3c + - path: mnist/models/model.optimizer.pt + hash: md5 + md5: 1316143c68da44c95cc0294482ba8a50 size: 44780845 - - path: output/models/model.pt - md5: 07befc67780200ae3d4c2d628d320b45 + - path: mnist/models/model.pt + hash: md5 + md5: 0c750c2da34962313084b05ceb424aa2 size: 44785941 - - path: output/reports/train/default/params.yaml - md5: eedabd38cbbfce21b879b647497174da - size: 2148 - - path: output/reports/train/default/predictions.json - md5: fe686ff742c5141e69b9b3e71b8c5c08 - size: 2880504 - - path: output/reports/train/default/score_dict.json - md5: a25e1709e4e4a47709fdb89d707ee5c8 - size: 409 + - path: mnist/reports/train/default/predictions.json + hash: md5 + md5: 1396b6032cbd1e76ace82789dcf54861 + size: 2883588 + - path: mnist/reports/train/default/score_dict.json + hash: md5 + md5: 45631341a862aa88bfffbde78d099c6d + size: 411 attack: - cmd: python -m deckard.layers.experiment attack + cmd: python -m deckard.layers.experiment attack --config_file mnist.yaml deps: - - path: output/data/data.pkl + - path: mnist/data/data.pkl md5: de934a5f5157970e5f30b8f3f1856a68 size: 222320311 - - path: output/models/model.pt - md5: 1c754810b53db7311b2d1185f67d3f00 - size: 44786389 + - path: mnist/models/model.pt + hash: md5 + md5: 0c750c2da34962313084b05ceb424aa2 + size: 44785941 params: params.yaml: attack: @@ -171,8 +173,8 @@ stages: with_std: true initialize: clip_values: - - 0.0 - - 1.0 + - 0 + - 255 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -202,7 +204,7 @@ stages: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 name: art.attacks.evasion.HopSkipJump method: evasion model: @@ -226,8 +228,8 @@ stages: with_std: true initialize: clip_values: - - 0.0 - - 1.0 + - 0 + - 255 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -257,7 +259,7 @@ stages: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 data: _target_: deckard.base.data.Data generate: @@ -282,7 +284,7 @@ stages: data_dir: data data_file: data data_type: .pkl - directory: output + directory: mnist model_dir: models model_file: model model_type: .pt @@ -312,8 +314,8 @@ stages: with_std: true initialize: clip_values: - - 0.0 - - 1.0 + - 0 + - 255 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -343,7 +345,7 @@ stages: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 scorers: _target_: deckard.base.scorer.ScorerDict accuracy: @@ -355,18 +357,18 @@ stages: direction: minimize name: sklearn.metrics.log_loss outs: - - path: output/attacks/attack.pkl - md5: f6933d3d01f97b4f400414bfaf4b72c2 + - path: mnist/attacks/attack.pkl + hash: md5 + md5: e1047df78f629f7267cfa7ef869f19f5 size: 31517 - - path: output/reports/attack/default/adv_predictions.json - md5: 7142652340b38ec663fd7769eb2baa61 - size: 2169 - - path: output/reports/attack/default/params.yaml - md5: a15accd2ecf7bcbb5ed47d4b6bf7ed97 - size: 5093 - - path: output/reports/attack/default/score_dict.json - md5: b92be5957cc92fc91e76d42e654151f9 - size: 533 + - path: mnist/reports/attack/default/adv_predictions.json + hash: md5 + md5: 2ab54c7713532e6e1303e5653c9e14f3 + size: 2194 + - path: mnist/reports/attack/default/score_dict.json + hash: md5 + md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 + size: 472 models: cmd: bash models.sh ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 @@ -447,28 +449,25 @@ stages: output/reports/attack.csv deps: - path: ResNet101.db - md5: 744f187243001db70f63c8161d7f913f - size: 1314816 - - path: ResNet152.db - md5: e0fcb3876ec636b12d7dc9e71b9d1e1c - size: 1314816 + md5: c47414423a51a4b866be6ce8312cb97e + size: 561152 - path: ResNet18.db - md5: aa69b1818219c139f8e33ac205771ec1 - size: 110592 + md5: 2ae52c7b342b46ac01c79f06bfdc6c38 + size: 2768896 - path: ResNet34.db - md5: e4d151b9800a1255a0143368057cb146 - size: 1339392 + md5: 6a5795cbb9d977cdaadf365a6ac76a0a + size: 983040 - path: ResNet50.db - md5: 9c9bc0aab00251ca5e9bd210366bc055 - size: 1339392 + md5: 7e215d670119b0703c3d97d86e28e117 + size: 561152 - path: output/reports/attack/ - md5: 639f99ca4725ce7b72afe921ec04c56d.dir - size: 21892372967 - nfiles: 52819 + md5: a76eb881be1813685bf988cf8dbe7a52.dir + size: 5109780242 + nfiles: 10514 outs: - path: output/reports/attack.csv - md5: dd2d965986d4b3cc1583730d92caf151 - size: 34597178 + md5: 69c87ac633b8abae15d65852c79ea89c + size: 6309975 compile@train: cmd: python -m deckard.layers.compile --report_folder output/reports/train --results_file output/reports/train.csv @@ -498,142 +497,265 @@ stages: size: 411488 attacks@ResNet152: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet152 - ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet152.db --config-name - default.yaml + stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet152.db + --config-name mnist.yaml deps: - path: attacks.sh - md5: 4c7b931c1ebbd717c18aea456b49342d - size: 3092 + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: mnist/reports/attack/default/score_dict.json + hash: md5 + md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 + size: 472 - path: models.sh - md5: 6ef2a88669f50dcdc0329e777dd7f9d6 - size: 1235 - - path: output/reports/attack/default/score_dict.json - md5: b92be5957cc92fc91e76d42e654151f9 - size: 533 + hash: md5 + md5: 1937e58bedac027034aea7d4a5712407 + size: 1380 outs: - - path: ResNet152.db - md5: e0fcb3876ec636b12d7dc9e71b9d1e1c - size: 1314816 + - path: mnist/reports/attack/ResNet152.db + hash: md5 + md5: afb6f3d2616446226012b3ff1143086a + size: 462848 plot: cmd: python -m deckard.layers.plots --path output/plots/ --file output/reports/attack.csv deps: - path: ResNet101.db - md5: 744f187243001db70f63c8161d7f913f - size: 1314816 + md5: c47414423a51a4b866be6ce8312cb97e + size: 561152 - path: ResNet152.db - md5: e0fcb3876ec636b12d7dc9e71b9d1e1c - size: 1314816 + md5: 690f931bf696f8e5f1e044fa7b335411 + size: 425984 - path: ResNet18.db - md5: aa69b1818219c139f8e33ac205771ec1 - size: 110592 + md5: 2ae52c7b342b46ac01c79f06bfdc6c38 + size: 2768896 - path: ResNet34.db - md5: e4d151b9800a1255a0143368057cb146 - size: 1339392 + md5: 6a5795cbb9d977cdaadf365a6ac76a0a + size: 983040 - path: ResNet50.db - md5: 9c9bc0aab00251ca5e9bd210366bc055 - size: 1339392 + md5: 7e215d670119b0703c3d97d86e28e117 + size: 561152 - path: output/reports/attack.csv - md5: dd2d965986d4b3cc1583730d92caf151 - size: 34597178 + md5: 69c87ac633b8abae15d65852c79ea89c + size: 6309975 outs: - path: output/plots/data.csv - md5: 1c7613836c312443c60d082beedad07a - size: 5271552 + md5: 8283c78e358db5d4ce19e5d44b6e735e + size: 2185730 attacks@ResNet101: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet101 - ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet101.db --config-name - default.yaml + stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet101.db + --config-name mnist.yaml deps: - path: attacks.sh - md5: 4c7b931c1ebbd717c18aea456b49342d - size: 3092 + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: mnist/reports/attack/default/score_dict.json + hash: md5 + md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 + size: 472 - path: models.sh - md5: 6ef2a88669f50dcdc0329e777dd7f9d6 - size: 1235 - - path: output/reports/attack/default/score_dict.json - md5: b92be5957cc92fc91e76d42e654151f9 - size: 533 + hash: md5 + md5: 1937e58bedac027034aea7d4a5712407 + size: 1380 outs: - - path: ResNet101.db - md5: 744f187243001db70f63c8161d7f913f - size: 1314816 + - path: mnist/reports/attack/ResNet101.db + hash: md5 + md5: 600452804d96c8b8483c3f8da01130c4 + size: 462848 attacks@ResNet34: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 - ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet34.db --config-name default.yaml + stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet34.db + --config-name mnist.yaml deps: - path: attacks.sh - md5: 4c7b931c1ebbd717c18aea456b49342d - size: 3092 + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: mnist/reports/attack/default/score_dict.json + hash: md5 + md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 + size: 472 - path: models.sh - md5: 6e85b74c043a1411e8322a2901557bfa - size: 1445 - - path: output/reports/attack/default/score_dict.json - md5: bed91bb1f645feb61a922c7a81825f95 - size: 538 + hash: md5 + md5: 1937e58bedac027034aea7d4a5712407 + size: 1380 outs: - - path: ResNet34.db - md5: e4d151b9800a1255a0143368057cb146 - size: 1339392 + - path: mnist/reports/attack/ResNet34.db + hash: md5 + md5: 9244c6545aa2d39680b601311b446b7d + size: 1593344 attacks@ResNet50: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 - ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet50.db --config-name default.yaml + stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet50.db + --config-name mnist.yaml deps: - path: attacks.sh - md5: 4c7b931c1ebbd717c18aea456b49342d - size: 3092 + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: mnist/reports/attack/default/score_dict.json + hash: md5 + md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 + size: 472 - path: models.sh - md5: 6e85b74c043a1411e8322a2901557bfa - size: 1445 - - path: output/reports/attack/default/score_dict.json - md5: bed91bb1f645feb61a922c7a81825f95 - size: 538 + hash: md5 + md5: 1937e58bedac027034aea7d4a5712407 + size: 1380 outs: - - path: ResNet50.db - md5: 9c9bc0aab00251ca5e9bd210366bc055 - size: 1339392 + - path: mnist/reports/attack/ResNet50.db + hash: md5 + md5: d9ee221b942b56d9bb720e022e05bf4b + size: 462848 afr: cmd: python -m deckard.layers.afr --dataset mnist deps: - path: output/plots/data.csv - md5: 1c7613836c312443c60d082beedad07a - size: 5271552 + md5: 8283c78e358db5d4ce19e5d44b6e735e + size: 2185730 outs: - path: output/plots/aft_comparison.csv - md5: a6f73c9391f954e99589a821212ad21c - size: 188 + md5: a72d5fbf5c21b5055e6bc20a0e48c6aa + size: 178 - path: output/plots/aft_comparison.tex - md5: cce0d126a79add14d7ff788533686bde - size: 413 + md5: e53edcb94e353f2e03324d1c02673332 + size: 401 - path: output/plots/cox_aft.pdf - md5: b76444e45979116a99351d5a7ba8f077 - size: 31142 + md5: 6b5fd449d724cf097eea5d8569dda3f0 + size: 30712 - path: output/plots/cox_partial_effects.pdf - md5: 19b5cf8862a73ec6d61b906422ffd4d7 - size: 42272 + md5: a434a352ea874a5c188f76ef8456dbdf + size: 28132 - path: output/plots/log_logistic_aft.pdf - md5: d942647f45fcd634c3c83111930feb2e - size: 34244 + md5: 6b1e524a7b7c2da913e4c9346115167c + size: 33524 - path: output/plots/log_logistic_partial_effects.pdf - md5: 4824409000ca32f6b98dd74725162fbc - size: 30378 + md5: f3c5ccbbb5129cbffe6b38f70d655245 + size: 29147 - path: output/plots/log_normal_aft.pdf - md5: 3637064ee8589436adbf0c8cfaf319e2 - size: 34412 + md5: c0b1f808746c28b5b352ca425c6d2d33 + size: 34017 - path: output/plots/log_normal_partial_effects.pdf - md5: d81620b4263ce48f527b37acfa24d7f3 - size: 31085 + md5: 1c67a22e51019ceeb39573ea21b4a7ba + size: 29844 - path: output/plots/weibull_aft.pdf - md5: a5faa8ed7d552e661044082591932ece - size: 32250 + md5: 4b8119d87bd201bea0fa9955e3f3481d + size: 32175 - path: output/plots/weibull_partial_effects.pdf - md5: 65e320a974b0d727696ec5d22b31ed2f - size: 30796 + md5: 4b1433467ad94132bde9003c9faaed10 + size: 29359 copy_results: cmd: cp -r output/plots/* ~/ml_afr/mnist/ deps: - path: output/plots/aft_comparison.csv - md5: a6f73c9391f954e99589a821212ad21c - size: 188 + md5: a72d5fbf5c21b5055e6bc20a0e48c6aa + size: 178 - path: output/plots/data.csv - md5: 1c7613836c312443c60d082beedad07a - size: 5271552 + md5: 8283c78e358db5d4ce19e5d44b6e735e + size: 2185730 + attacks@ResNet18: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 + stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet18.db + --config-name mnist.yaml + deps: + - path: attacks.sh + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: mnist/reports/attack/default/score_dict.json + hash: md5 + md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 + size: 472 + - path: models.sh + hash: md5 + md5: 1937e58bedac027034aea7d4a5712407 + size: 1380 + outs: + - path: mnist/reports/attack/ResNet18.db + hash: md5 + md5: 2b4e2a2ac1cdd015cf469496a9861088 + size: 999424 + models@ResNet50: + cmd: bash models.sh ++model.init.name=torch_example.ResNet50 stage=train ++hydra.sweeper.storage=sqlite:///mnist/reports/train/ResNet50.db + --config-name mnist.yaml + deps: + - path: mnist/models/model.optimizer.pt + hash: md5 + md5: cc914518d5c8fdab1e7ec43c7db63a3b + size: 44780845 + - path: mnist/models/model.pt + hash: md5 + md5: 0508fa68d600b39cd8ce1dad0152fba3 + size: 44785941 + - path: models.sh + md5: e5079ede85d4f9b286984e507a157af4 + size: 1371 + outs: + - path: mnist/reports/train/ResNet50.db + hash: md5 + md5: 957e39666f06c9e8e207a9f98bc569b5 + size: 913408 + models@ResNet18: + cmd: bash models.sh ++model.init.name=torch_example.ResNet18 stage=train ++hydra.sweeper.storage=sqlite:///mnist/reports/train/ResNet18.db + --config-name mnist.yaml + deps: + - path: mnist/models/model.optimizer.pt + hash: md5 + md5: f4e28adc9c0d30180eca422da2dd00e3 + size: 44780845 + - path: mnist/models/model.pt + hash: md5 + md5: 53a2d89abb350e2601f35f36b95f6095 + size: 44785941 + - path: models.sh + hash: md5 + md5: cd5b2760310df71a36a2ea26021477b4 + size: 1366 + outs: + - path: mnist/reports/train/ResNet18.db + hash: md5 + md5: 225273e0494668fa42bc6f69fb29d392 + size: 901120 + models@ResNet34: + cmd: bash models.sh ++model.init.name=torch_example.ResNet34 stage=train ++hydra.sweeper.storage=sqlite:///mnist/reports/train/ResNet34.db + --config-name mnist.yaml + deps: + - path: mnist/models/model.optimizer.pt + hash: md5 + md5: cc914518d5c8fdab1e7ec43c7db63a3b + size: 44780845 + - path: mnist/models/model.pt + hash: md5 + md5: 0508fa68d600b39cd8ce1dad0152fba3 + size: 44785941 + - path: models.sh + hash: md5 + md5: e5079ede85d4f9b286984e507a157af4 + size: 1371 + outs: + - path: mnist/reports/train/ResNet34.db + hash: md5 + md5: af60ce478f27cda303ebd34c63cf05d3 + size: 913408 + models@ResNet101: + cmd: bash models.sh ++model.init.name=torch_example.ResNet101 stage=train ++hydra.sweeper.storage=sqlite:///mnist/reports/train/ResNet101.db + --config-name mnist.yaml + deps: + - path: mnist/models/model.optimizer.pt + hash: md5 + md5: cc914518d5c8fdab1e7ec43c7db63a3b + size: 44780845 + - path: mnist/models/model.pt + hash: md5 + md5: 0508fa68d600b39cd8ce1dad0152fba3 + size: 44785941 + - path: models.sh + hash: md5 + md5: cd5b2760310df71a36a2ea26021477b4 + size: 1366 + outs: + - path: mnist/reports/train/ResNet101.db + hash: md5 + md5: a8e178cba49addf77bac3b27e974ce8c + size: 917504 diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index 1489b04b..21d8c792 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -1,8 +1,6 @@ stages: - install: - cmd: python -m pip install ../deckard train: - cmd: python -m deckard.layers.experiment train + cmd: python -m deckard.layers.experiment train --config_file mnist.yaml params: - data - model @@ -12,14 +10,14 @@ stages: - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} - - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} # Omit to save space - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} # logit outputs for our model # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} # Omit to save space metrics: - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} attack: - cmd: python -m deckard.layers.experiment attack + cmd: python -m deckard.layers.experiment attack --config_file mnist.yaml params: - data - model @@ -29,7 +27,7 @@ stages: outs: - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} - - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + # - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} deps: - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} @@ -37,6 +35,23 @@ stages: - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} ############################################################################## + # models: # This is a loop over the ResNet models + # foreach: + # - ResNet18 + # # - ResNet34 + # # - ResNet50 + # # - ResNet101 + # # - ResNet152 + # do: # This script configures eazch defence + # cmd: bash models.sh ++model.init.name=torch_example.${item} stage=train ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/train/${item}.db --config-name mnist.yaml + # deps: + # - models.sh + # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + # - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} + # outs: + # - ${files.directory}/${files.reports}/train/${item}.db: # This outputs a database file for each model + # cache: True + # persist: True attacks: foreach: # This is a loop over the ResNet models - ResNet18 @@ -45,13 +60,16 @@ stages: - ResNet101 - ResNet152 do: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name default.yaml + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/attack/${item}.db --config-name mnist.yaml deps: - models.sh # This script configures each defence - attacks.sh # This script configures each attack - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} # This is here just to ensure it runs after the attack stage + # - ${files.directory}/${files.reports}/train/${item}.db outs: - - ${item}.db # This outputs a database file for each model + - ${files.directory}/${files.reports}/attack/${item}.db: # This outputs a database file for each model + cache: True + persist: True compile: foreach: # iterates through each stage # - train @@ -60,26 +78,26 @@ stages: cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv deps: - ${files.directory}/${files.reports}/${item}/ - - ResNet18.db - - ResNet34.db - - ResNet50.db - - ResNet101.db - - ResNet152.db + - ${files.directory}/${files.reports}/${item}/ResNet18.db + - ${files.directory}/${files.reports}/${item}/ResNet34.db + - ${files.directory}/${files.reports}/${item}/ResNet50.db + - ${files.directory}/${files.reports}/${item}/ResNet101.db + # - ${files.directory}/${files.reports}/${item}/ResNet152.db outs: - ${files.directory}/${files.reports}/${item}.csv plot: - cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv + cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv -o data.csv deps: - ${files.directory}/${files.reports}/attack.csv - - ResNet18.db - - ResNet34.db - - ResNet50.db - - ResNet101.db - - ResNet152.db + - ${files.directory}/${files.reports}/attack/ResNet18.db + - ${files.directory}/${files.reports}/attack/ResNet34.db + - ${files.directory}/${files.reports}/attack/ResNet50.db + - ${files.directory}/${files.reports}/attack/ResNet101.db + - ${files.directory}/${files.reports}/attack/ResNet152.db outs: - ${files.directory}/plots/data.csv afr: - cmd: python -m deckard.layers.afr --dataset mnist + cmd: python -m deckard.layers.afr --dataset ${files.directory} --file ${files.directory}/plots/data.csv deps: - ${files.directory}/plots/data.csv plots: diff --git a/examples/pytorch/models.sh b/examples/pytorch/models.sh index dc3ae853..2978d445 100644 --- a/examples/pytorch/models.sh +++ b/examples/pytorch/models.sh @@ -7,13 +7,13 @@ echo "python -m deckard.layers.optimise " $@ "--multirun" python -m deckard.layers.optimise $@ --multirun # # This line generates the model and adds the FeatureSqueezing preprocessing defence. -python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] $@ --multirun +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,255] ++hydra.sweeper.study_name=FSQ $@ --multirun -# # Gaussian Augmentation (Input) -python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False $@ --multirun +# # # Gaussian Augmentation (Input) +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.5 +model.art.preprocessor.params.augmentation=False ++hydra.sweeper.study_name=gauss-in $@ --multirun -# # # Gaussian Noise (Output) -python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 $@ --multirun +# # # # Gaussian Noise (Output) +# python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 ++hydra.sweeper.study_name=gauss-out $@ --multirun -# # # High Confidence -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 $@ --multirun +# # # # High Confidence +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 ++hydra.sweeper.study_name=conf $@ --multirun diff --git a/examples/pytorch/params.yaml b/examples/pytorch/params.yaml index 26648398..7a9ccdc6 100644 --- a/examples/pytorch/params.yaml +++ b/examples/pytorch/params.yaml @@ -40,8 +40,8 @@ attack: with_std: true initialize: clip_values: - - 0.0 - - 1.0 + - 0 + - 255 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -71,7 +71,7 @@ attack: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 name: art.attacks.evasion.HopSkipJump method: evasion model: @@ -95,8 +95,8 @@ attack: with_std: true initialize: clip_values: - - 0.0 - - 1.0 + - 0 + - 255 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -126,7 +126,7 @@ attack: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 data: _target_: deckard.base.data.Data generate: @@ -142,6 +142,7 @@ data: name: sklearn.preprocessing.StandardScaler with_mean: true with_std: true +direction: maximize files: _target_: deckard.base.files.FileConfig adv_predictions_file: adv_predictions.json @@ -151,7 +152,7 @@ files: data_dir: data data_file: data data_type: .pkl - directory: output + directory: mnist model_dir: models model_file: model model_type: .pt @@ -181,8 +182,8 @@ model: with_std: true initialize: clip_values: - - 0.0 - - 1.0 + - 0 + - 255 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -212,7 +213,7 @@ model: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 optimizers: accuracy scorers: _target_: deckard.base.scorer.ScorerDict @@ -224,3 +225,4 @@ scorers: _target_: deckard.base.scorer.ScorerConfig direction: minimize name: sklearn.metrics.log_loss +stage: ??? diff --git a/examples/pytorch_cifar/.gitignore b/examples/pytorch_cifar/.gitignore new file mode 100644 index 00000000..af0b54a3 --- /dev/null +++ b/examples/pytorch_cifar/.gitignore @@ -0,0 +1,3 @@ +20_epochs/ +multirun/ +cifar/ \ No newline at end of file diff --git a/examples/pytorch_cifar/attacks.sh b/examples/pytorch_cifar/attacks.sh index 4ece8b3d..8ec1f079 100644 --- a/examples/pytorch_cifar/attacks.sh +++ b/examples/pytorch_cifar/attacks.sh @@ -3,36 +3,35 @@ # # This script is used to generate the attacks for the example. # Fast Gradient Method -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.03,.1,.2,.3,.5,.8,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++stage=attack ++hydra.sweeper.study_name=fgm $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 stage=attack ++hydra.sweeper.study_name=fgm ++direction=maximize $@ # Projected Gradient Descent -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.01,.03,.3,.1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++stage=attack ++hydra.sweeper.study_name=pgd $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 stage=attack ++hydra.sweeper.study_name=pgd ++direction=maximize $@ # DeepFool -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=10,100,1000 ++stage=attack ++hydra.sweeper.study_name=deep $@ - +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=1,3,5,10 stage=attack ++hydra.sweeper.study_name=deep ++direction=maximize $@ # HopSkipJump -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 ++stage=attack ++hydra.sweeper.study_name=hsj $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 stage=attack ++hydra.sweeper.study_name=hsj ++direction=maximize $@ -#################################################### +# ##################################################### # PixelAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=pixel $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=pixel ++direction=maximize $@ # ThresholdAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 ++stage=attack ++hydra.sweeper.study_name=thresh $@ +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=thresh ++direction=maximize $@ # # AdversarialPatch -# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 ++stage=patch ++hydra.sweeper.study_name=attack ++attack.init.patch_shape=[1,28,28] $@ +# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 stage=patch ++hydra.sweeper.study_name=attack ++direction=maximize ++attack.init.patch_shape=[1,28,28] $@ ##################################################### # # Carlini L0 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw0 ++hydra.sweeper.study_name=cw0 $@ +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw0 ++hydra.sweeper.study_name=cw0 ++direction=maximize $@ -# # # Carlini L2 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=cw2 ++hydra.sweeper.study_name=cw2 $@ +# # Carlini L2 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw2 ++hydra.sweeper.study_name=cw2 ++direction=maximize $@ # # Carlini LInf Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 ++stage=attack ++hydra.sweeper.study_name=cwinf $@ +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=attack ++hydra.sweeper.study_name=cwinf ++direction=maximize $@ rm -rf output/models/* diff --git a/examples/pytorch_cifar/conf/default.yaml b/examples/pytorch_cifar/conf/cifar10.yaml similarity index 53% rename from examples/pytorch_cifar/conf/default.yaml rename to examples/pytorch_cifar/conf/cifar10.yaml index 7b6d8f7a..a0140d00 100644 --- a/examples/pytorch_cifar/conf/default.yaml +++ b/examples/pytorch_cifar/conf/cifar10.yaml @@ -3,33 +3,40 @@ defaults: - data: torch_cifar - model: torch_cifar - attack: default - - files: default + - files: cifar - scorers: default - override hydra/sweeper : optuna - override hydra/sweeper/sampler : grid - override hydra/launcher : joblib +stage : '???' +direction : "maximize" _target_ : deckard.base.experiment.Experiment optimizers : accuracy -stage : null hydra: run: - dir : ./${files.directory} + dir: ${files.directory}/${files.reports}/${stage}/logs + sweep: + dir: ${files.directory}/${stage}/${model.init.name} + subdir : ${hydra.sweeper.study_name}/${hydra.job.num} sweeper: sampler: _target_: optuna.samplers.GridSampler - direction: maximize - study_name: model + direction: ${direction} + study_name: control storage: sqlite:///model.db - n_jobs: 1 + n_jobs: ${hydra.launcher.n_jobs} + n_trials : 32 params: ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) - ++model.art.initialize.optimizer.lr: choice(10000, 100, 10, 1, 0.1, 0.01, 0.001, 0.000001) + ++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, .0001, .00001, 0.000001) + ++model.trainer.nb_epoch: choice(1, 10, 30, 50, 100) + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 32 + n_jobs: 8 prefer : processes - verbose: 1 - timeout: 3600 + verbose: 10 + timeout: null pre_dispatch: n_jobs batch_size: auto temp_folder: /tmp/deckard diff --git a/examples/pytorch_cifar/conf/compile.yaml b/examples/pytorch_cifar/conf/compile.yaml index 0d721a44..52106b1c 100644 --- a/examples/pytorch_cifar/conf/compile.yaml +++ b/examples/pytorch_cifar/conf/compile.yaml @@ -15,17 +15,18 @@ defences: GaussianNoise: Gauss-out HighConfidence: Conf params: - # art.attacks.evasion.CarliniL0Method: attack.init.kwargs.confidence - # art.attacks.evasion.CarliniL2Method: attack.init.kwargs.confidence - # art.attacks.evasion.CarliniLInfMethod: attack.init.kwargs.confidence - Deep: attack.init.kwargs.nb_grads - FGM: attack.init.kwargs.eps - HSJ: attack.init.kwargs.max_iter - Pixel: attack.init.kwargs.th - PGD: attack.init.kwargs.eps - Thresh: attack.init.kwargs.th - Gauss-out: model.art.pipeline.postprocessor.kwargs.scale - Conf: model.art.pipeline.postprocessor.kwargs.cutoff - FSQ: model.art.pipeline.preprocessor.kwargs.bit_depth - Gauss-in: model.art.pipeline.preprocessor.kwargs.sigma - Control: model_layers + # art.attacks.evasion.CarliniL0Method: attack.init.confidence + # art.attacks.evasion.CarliniL2Method: attack.init.confidence + # art.attacks.evasion.CarliniLInfMethod: attack.init.confidence + Deep: attack.init.nb_grads + FGM: attack.init.eps + HSJ: attack.init.max_iter + Pixel: attack.init.th + PGD: attack.init.eps + Thresh: attack.init.th + Gauss-out: model.art.pipeline.postprocessor.scale + Conf: model.art.pipeline.postprocessor.cutoff + FSQ: model.art.pipeline.preprocessor.bit_depth + Gauss-in: model.art.pipeline.preprocessor.sigma + Depth: model_layers + Epochs: model.train.nb_epoch \ No newline at end of file diff --git a/examples/pytorch/conf/files/default.yaml b/examples/pytorch_cifar/conf/files/mnist.yaml similarity index 96% rename from examples/pytorch/conf/files/default.yaml rename to examples/pytorch_cifar/conf/files/mnist.yaml index bcaf75ce..efdb1c42 100644 --- a/examples/pytorch/conf/files/default.yaml +++ b/examples/pytorch_cifar/conf/files/mnist.yaml @@ -3,7 +3,7 @@ reports: reports data_dir: data model_dir: models attack_dir: attacks -directory: output +directory: mnist score_dict_file: score_dict.json data_file : data model_file : model diff --git a/examples/pytorch_cifar/conf/hydra/default.yaml b/examples/pytorch_cifar/conf/hydra/default.yaml deleted file mode 100644 index 53e5bd5d..00000000 --- a/examples/pytorch_cifar/conf/hydra/default.yaml +++ /dev/null @@ -1,38 +0,0 @@ -defaults: - - params : default - - override hydra/sweeper : optuna - - override hydra/sweeper/sampler : random - - override hydra/launcher : joblib - - override hydra/sweeper/params : default.yaml -run: - dir: output/${hydra.job.override_dirname}/seed=${data.sample.random_state} -job: - config: - override_dirname: - exclude_keys: - - seed - kv_sep: ":" - item_sep: "_" -sweep: - dir: multirun - subdir: ${hydra.job.override_dirname} -sweeper: - sampler: - _target_: optuna.samplers.RandomSampler - direction: maximize - study_name: model - storage: sqlite:///model.db - n_jobs: 4 - n_trials: 1 - -launcher: - _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 8 - prefer : processes - verbose: 1 - timeout: null - pre_dispatch: n_jobs - batch_size: auto - temp_folder: /tmp/deckard - max_nbytes: 100000 - mmap_mode: r diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_0.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_0.yaml deleted file mode 100644 index d28a31d4..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_0.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.CarliniL0Method -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.confidence : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_2.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_2.yaml deleted file mode 100644 index ec2ed44c..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_2.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.model : ${model} -init.name : art.attacks.evasion.CarliniL0Method -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.confidence : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_inf.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_inf.yaml deleted file mode 100644 index 99b16e45..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/cw_inf.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.CarliniLInfMethod -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.confidence : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/deepfool.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/deepfool.yaml deleted file mode 100644 index 8b56a8d9..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/deepfool.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.name : art.attacks.evasion.DeepFool -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.nb_grads : choice(10,100,1000) -init.eps: choice(0.01, 0.03, 0.3, 0.1, 1.0) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/fgm.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/fgm.yaml deleted file mode 100644 index c46ee4df..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/fgm.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.FastGradientMethod -init.eps: choice(0.01, 0.03, 0.3, 0.1) -init.norm: choice(inf, 1, 2) -init.eps_step: choice(0.001, 0.003, 0.01) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/hsj.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/hsj.yaml deleted file mode 100644 index 45b3af5d..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/hsj.yaml +++ /dev/null @@ -1,6 +0,0 @@ -attack: - init.name : art.attacks.evasion.HopSkipJump - init.batch_size : choice(1, 4, 16, 65, 128) - init.max_iter : choice(1, 10, 100, 1000) - init.max_eval : choice(1, 10, 100, 1000) - init.init_eval : choice(1, 10, 100, 1000) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/patch.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/patch.yaml deleted file mode 100644 index c9409de4..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/patch.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.name : art.attacks.evasion.ThresholdAttack -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.scale_min : choice(0.01,) -init.scale_max : choice(.01, .1, .3, .5, .9, .99, 1) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pgd.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pgd.yaml deleted file mode 100644 index 42bc9514..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pgd.yaml +++ /dev/null @@ -1,6 +0,0 @@ -init.name : art.attacks.evasion.ProjectedGradientDescent -init.eps : choice(0.01, 0.03, 0.3, 0.1) -init.norm : choice(inf, 1, 2) -init.eps_step : choice(0.001, 0.003, 0.01) -init.batch_size : choice(1024) -init.max_iter : choice(10) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pixel.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pixel.yaml deleted file mode 100644 index 84450f38..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/pixel.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.ProjectedGradientDescent -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.th : choice(1,4,16,64,256) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/threshold.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/threshold.yaml deleted file mode 100644 index d6079457..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/attack/threshold.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.ThresholdAttack -init.max_iter : choice(10) -init.batch_size : choice(1024) -init.th: choice(1,4,16,64,256) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/default.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/default.yaml deleted file mode 100644 index 49b1de65..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/default.yaml +++ /dev/null @@ -1,11 +0,0 @@ -defaults: - - _self_ - - data: torch_mnist - - model: torch_mnist - - files: default - - scorers: default - - stage : null - - params/model : null - - params/attack : null -++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) -++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, 0.0001, 0.00001, 0.000001) diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/confidence.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/confidence.yaml deleted file mode 100644 index cfda12d2..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/confidence.yaml +++ /dev/null @@ -1,5 +0,0 @@ - -defence: - art.postprocessor.name : [art.defences.postprocessor.HighConfidence] - art.postprocessor.cutoff : [.1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99] - art.postprocessor.apply_fit : [True, False] diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/default.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/default.yaml deleted file mode 100644 index 8163e568..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/default.yaml +++ /dev/null @@ -1 +0,0 @@ -defence: diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/fsq.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/fsq.yaml deleted file mode 100644 index bb508203..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/fsq.yaml +++ /dev/null @@ -1,4 +0,0 @@ -defence: - art.preprocessor.name : [art.defences.preprocessor.FeatureSqueezing] - art.preprocessor.clip_values : [[0, 1]] - art.preprocessor.bit_depth : [1, 2, 4, 8, 16, 32, 64] diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-in.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-in.yaml deleted file mode 100644 index fd45ea84..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-in.yaml +++ /dev/null @@ -1,5 +0,0 @@ -defence: - art.preprocessor.name : art.defences.preprocessor.GaussianAugmentation - art.preprocessor.clip_values : ${art.initialize.clip_values} - art.preprocessor.sigma : [.01, .1, .2, .3, .5, 1] - art.preprocessor.ratio : [.1, .5, 1] diff --git a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-out.yaml b/examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-out.yaml deleted file mode 100644 index 8abd9ee1..00000000 --- a/examples/pytorch_cifar/conf/hydra/sweeper/params/model/gauss-out.yaml +++ /dev/null @@ -1,3 +0,0 @@ -defence: - art.postprocessor.name : art.defences.postprocessor.GaussianNoise - art.postprocessor.scale : [.01, .1, .2, .3, .5, 1, 2, 3, 5, 10] diff --git a/examples/pytorch_cifar/conf/model/art/initialize/default.yaml b/examples/pytorch_cifar/conf/model/art/initialize/default.yaml index 6c5edde5..40e7983e 100644 --- a/examples/pytorch_cifar/conf/model/art/initialize/default.yaml +++ b/examples/pytorch_cifar/conf/model/art/initialize/default.yaml @@ -4,4 +4,4 @@ optimizer: name : torch.optim.SGD lr : 0.01 momentum : 0.9 -clip_values : [0.0, 1.0] +clip_values : [0.0, 255.0] diff --git a/examples/pytorch_cifar/conf/model/torch_cifar.yaml b/examples/pytorch_cifar/conf/model/torch_cifar.yaml index 2562064a..5ca2e24e 100644 --- a/examples/pytorch_cifar/conf/model/torch_cifar.yaml +++ b/examples/pytorch_cifar/conf/model/torch_cifar.yaml @@ -8,6 +8,6 @@ init: num_classes: 10 _target_: deckard.base.model.Model trainer: - nb_epoch: 20 + nb_epoch: 100 batch_size: 1024 library : pytorch diff --git a/examples/pytorch_cifar/conf/plots.yaml b/examples/pytorch_cifar/conf/plots.yaml index 586f3725..accab9df 100644 --- a/examples/pytorch_cifar/conf/plots.yaml +++ b/examples/pytorch_cifar/conf/plots.yaml @@ -150,304 +150,54 @@ cat_plot: - ResNet101 - ResNet152 line_plot: -- file: def_param_vs_accuracy.pdf - hue: def_gen - legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} - title: $\lambda_{ben.}$ vs Defence Strength - x: def_value - x_scale: linear - xlabel: Defence Control Parameter - y: accuracy - y_scale: - ylabel: $\lambda_{ben.}$ - hue_order: - - Control - - Gauss-in - - Gauss-out - - Conf - - FSQ -- file: def_param_vs_adv_accuracy.pdf - hue: def_gen - legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} - title: $\lambda_{adv.}$ vs Defence Strength - x: def_value - x_scale: linear - xlabel: Defence Control Parameter - y: adv_accuracy - y_scale: - ylabel: $\lambda_{adv.}$ - hue_order: - - Control - - Gauss-in - - Gauss-out - - Conf - - FSQ -- file: def_param_vs_adv_failure_rate.pdf - hue: def_gen - legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} - title: $h_{adv}$ vs Defence Strength - x: def_value - x_scale: linear - xlabel: Defence Control Parameter - y: adv_failure_rate - y_scale: log - ylabel: $h_{adv.}$ - hue_order: - - Control - - Gauss-in - - Gauss-out - - Conf - - FSQ -- file: atk_param_vs_accuracy.pdf - hue: atk_gen - legend: {bbox_to_anchor: [1.05, 1]} - title: $\lambda_{adv.}$ vs Attack Strength - x: atk_value - x_scale: linear - xlabel: Attack Control Parameter - y: adv_accuracy - y_scale: - ylabel: $\lambda_{adv.}$ - hue_order: - - FGM - - PGD - - Deep - - HSJ - - Pixel - - Thresh - -scatter_plot: -- x: train_time_per_sample - y: adv_failure_rate - hue: model_name - xlabel: $t_{train}$ - ylabel: $h_{adv}$ - title: $h_{adv}$ vs $t_{train}$ - file: adv_failure_rate_vs_train_time.pdf - y_scale: log - x_scale: log - legend: - title: Model Name - bbox_to_anchor: [1.05, 1] - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 - # style : atk_gen -# - x: train_time_per_sample +# - file: def_param_vs_accuracy.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $\lambda_{ben.}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: accuracy +# y_scale: +# ylabel: $\lambda_{ben.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +# - file: def_param_vs_adv_accuracy.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $\lambda_{adv.}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: adv_accuracy +# y_scale: +# ylabel: $\lambda_{adv.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +# - file: def_param_vs_adv_failure_rate.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $h_{adv}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter # y: adv_failure_rate -# hue: model_name -# xlabel: $t_{train}$ -# ylabel: $h_{adv}$ -# title: $h_{adv}$ vs $t_{train}$ for each Defence -# file: adv_failure_rate_vs_train_time_def.pdf # y_scale: log -# x_scale: linear -# legend: -# title: Model Name -# # style : def_gen -cat_plot: -- file: adv_accuracy_vs_defence_type.pdf - hue: model_name - kind: boxen - set: - yscale: linear - titles: $\lambda_{adv.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: adv_accuracy - ylabels: $\lambda_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: ben_accuracy_vs_defence_type.pdf - hue: model_name - kind: boxen - titles: $\lambda_{ben.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: accuracy - ylabels: $\lambda_{ben.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: ben_failures_per_train_time_vs_defence_type.pdf - hue: model_name - kind: boxen - set: - yscale: log - titles: $\bar{C}_{ben.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: training_time_per_failure - ylabels: $\bar{C}_{ben.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: adv_failures_per_train_time_vs_defence_type.pdf - hue: model_name - kind: boxen - set: - yscale: log - titles: $\bar{C}_{adv.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: training_time_per_adv_failure - ylabels: $\bar{C}_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: adv_failures_per_train_time_vs_attack_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - set: - yscale: log - titles: $\bar{C}_{adv.}$ vs Attack Type - x: atk_gen - xlabels: Attack Type - y: training_time_per_adv_failure - ylabels: $\bar{C}_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: adv_failures_per_test_time_vs_defence_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - titles: $h_{adv}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: adv_failure_rate - ylabels: $h_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -# - file: adv_accuracy_vs_defence_type.pdf -# hue: model_name -# kind: boxen -# legend_title: Model Name -# titles: $\lambda_{adv.}$ vs Defence Type -# x: def_gen -# xlabels: Defence Type -# y: adv_accuracy -# ylabels: Adv. $\lambda_{ben.}$ -# rotation : 90 +# ylabel: $h_{adv.}$ # hue_order: -# - ResNet18 -# - ResNet34 -# - ResNet50 -# - ResNet101 -# - ResNet152 -- file: adv_accuracy_vs_attack_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - titles: $\lambda_{adv.}$ vs Attack Type - x: atk_gen - xlabels: Attack Type - y: adv_accuracy - ylabels: $\lambda_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: ben_failure_rate_vs_defence_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - set: - yscale: log - titles: $h_{ben}(t; \theta)$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: failure_rate - ylabels: $h_{ben}(t; \theta)$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -line_plot: -- file: def_param_vs_accuracy.pdf - hue: def_gen - legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} - title: $\lambda_{ben.}$ vs Defence Strength - x: def_value - x_scale: linear - xlabel: Defence Control Parameter - y: accuracy - y_scale: - ylabel: $\lambda_{ben.}$ - hue_order: - - Control - - Gauss-in - - Gauss-out - - Conf - - FSQ -- file: def_param_vs_adv_accuracy.pdf - hue: def_gen - legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} - title: $\lambda_{adv.}$ vs Defence Strength - x: def_value - x_scale: linear - xlabel: Defence Control Parameter - y: adv_accuracy - y_scale: - ylabel: $\lambda_{adv.}$ - hue_order: - - Control - - Gauss-in - - Gauss-out - - Conf - - FSQ -- file: def_param_vs_adv_failure_rate.pdf - hue: def_gen - legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} - title: $h_{adv}$ vs Defence Strength - x: def_value - x_scale: linear - xlabel: Defence Control Parameter - y: adv_failure_rate - y_scale: log - ylabel: $h_{adv.}$ - hue_order: - - Control - - Gauss-in - - Gauss-out - - Conf - - FSQ +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ - file: atk_param_vs_accuracy.pdf hue: atk_gen legend: {bbox_to_anchor: [1.05, 1]} diff --git a/examples/pytorch_cifar/dvc.lock b/examples/pytorch_cifar/dvc.lock index 451f49a5..5f3f4149 100644 --- a/examples/pytorch_cifar/dvc.lock +++ b/examples/pytorch_cifar/dvc.lock @@ -1,7 +1,7 @@ schema: '2.0' stages: train: - cmd: python -m deckard.layers.experiment train + cmd: python -m deckard.layers.experiment train --config_file cifar params: params.yaml: data: @@ -25,7 +25,7 @@ stages: data_dir: data data_file: data data_type: .pkl - directory: output + directory: cifar model_dir: models model_file: model model_type: .pt @@ -53,7 +53,7 @@ stages: initialize: clip_values: - 0.0 - - 1.0 + - 255.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -81,7 +81,7 @@ stages: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 scorers: _target_: deckard.base.scorer.ScorerDict accuracy: @@ -93,32 +93,32 @@ stages: direction: minimize name: sklearn.metrics.log_loss outs: - - path: output/data/data.pkl + - path: cifar/data/data.pkl md5: 6503fed5d4e6cc1163898c0ab6a863dd size: 739680311 - - path: output/models/model.optimizer.pt - md5: f5b6422f339473318808616c7fb926c4 + - path: cifar/models/model.optimizer.pt + md5: 86884b532b711a567d5a544d3ad693d3 size: 44805933 - - path: output/models/model.pt - md5: c7a6c14dc3dc96868cb9f3b93d7d31ba + - path: cifar/models/model.pt + md5: 5c3af8f99e01afbc493b3f57dbf09c1d size: 44811029 - - path: output/reports/train/default/params.yaml - md5: 320abf8eddbae67cd6873f8d1e3bba81 - size: 2174 - - path: output/reports/train/default/predictions.json - md5: ed5c6a014bf8fc8ee4aadacc3c5cb07c - size: 2437716 - - path: output/reports/train/default/score_dict.json - md5: 368af71ad6cf7bf06b64ffeb109a2da9 - size: 412 + - path: cifar/reports/train/default/params.yaml + md5: f18b8df67e934151c2d59117666c2ee3 + size: 2172 + - path: cifar/reports/train/default/predictions.json + md5: 16cb8eef7a76e00b58036b252c84e6e7 + size: 2439927 + - path: cifar/reports/train/default/score_dict.json + md5: bc21cb437fafee94057df96be84566d7 + size: 405 attack: - cmd: python -m deckard.layers.experiment attack + cmd: python -m deckard.layers.experiment attack --config_file cifar deps: - - path: output/data/data.pkl + - path: cifar/data/data.pkl md5: 6503fed5d4e6cc1163898c0ab6a863dd size: 739680311 - - path: output/models/model.pt - md5: c7a6c14dc3dc96868cb9f3b93d7d31ba + - path: cifar/models/model.pt + md5: 5c3af8f99e01afbc493b3f57dbf09c1d size: 44811029 params: params.yaml: @@ -158,7 +158,7 @@ stages: initialize: clip_values: - 0.0 - - 1.0 + - 255.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -186,7 +186,7 @@ stages: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 name: art.attacks.evasion.HopSkipJump method: evasion model: @@ -208,7 +208,7 @@ stages: initialize: clip_values: - 0.0 - - 1.0 + - 255.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -236,7 +236,7 @@ stages: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 data: _target_: deckard.base.data.Data generate: @@ -258,7 +258,7 @@ stages: data_dir: data data_file: data data_type: .pkl - directory: output + directory: cifar model_dir: models model_file: model model_type: .pt @@ -286,7 +286,7 @@ stages: initialize: clip_values: - 0.0 - - 1.0 + - 255.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -314,7 +314,7 @@ stages: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 scorers: _target_: deckard.base.scorer.ScorerDict accuracy: @@ -326,18 +326,18 @@ stages: direction: minimize name: sklearn.metrics.log_loss outs: - - path: output/attacks/attack.pkl - md5: e57b871f32a1a8b7de497f6e5de4ebf1 + - path: cifar/attacks/attack.pkl + md5: 1763dcf2dd4b821ea2a9309e1df63805 size: 123046 - - path: output/reports/attack/default/adv_predictions.json - md5: 0a097842037186d905fb2fd7ec4072d9 - size: 2125 - - path: output/reports/attack/default/params.yaml - md5: 5177666d7f2e1e55ccad8ab4b735ffc7 - size: 5175 - - path: output/reports/attack/default/score_dict.json - md5: f5cc403e9e3fafd09cd985d86b3c80a5 - size: 549 + - path: cifar/reports/attack/default/adv_predictions.json + md5: 5add137290da41324a28055f45ce97b7 + size: 2160 + - path: cifar/reports/attack/default/params.yaml + md5: 835365f5be5d0a922541f050fc78ac79 + size: 5178 + - path: cifar/reports/attack/default/score_dict.json + md5: 904a964ee783155dbef6416c197695e0 + size: 557 attacks@ResNet18: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet18.db --config-name default.yaml diff --git a/examples/pytorch_cifar/dvc.yaml b/examples/pytorch_cifar/dvc.yaml index fc77b4fb..30efbe5b 100644 --- a/examples/pytorch_cifar/dvc.yaml +++ b/examples/pytorch_cifar/dvc.yaml @@ -1,6 +1,6 @@ stages: - train: # The Experiment layers are prototypes for the Optimizer layers - cmd: python -m deckard.layers.experiment train + train: + cmd: python -m deckard.layers.experiment train --config_file cifar10.yaml params: - data - model @@ -10,14 +10,14 @@ stages: - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} - - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} - # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} - - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} - # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} # Omit to save space + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} # logit outputs for our model + # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} # Omit to save space metrics: - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} - attack: # The Experiment layers are prototypes for the Optimizer layers - cmd: python -m deckard.layers.experiment attack + attack: + cmd: python -m deckard.layers.experiment attack --config_file cifar10.yaml params: - data - model @@ -27,79 +27,77 @@ stages: outs: - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} - - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + # - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} deps: - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} metrics: - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + ############################################################################## - models: # This is a loop over the ResNet models - foreach: + # models: # This is a loop over the ResNet models + # foreach: + # - ResNet18 + # # - ResNet34 + # # - ResNet50 + # # - ResNet101 + # # - ResNet152 + # do: # This script configures eazch defence + # cmd: bash models.sh ++model.init.name=torch_example.${item} stage=train ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/train/${item}.db --config-name cifar10.yaml + # deps: + # - models.sh + # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + # - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} + # outs: + # - ${files.directory}/${files.reports}/train/${item}.db: # This outputs a database file for each model + # cache: True + # persist: True + attacks: + foreach: # This is a loop over the ResNet models - ResNet18 - ResNet34 - ResNet50 - ResNet101 - ResNet152 - do: # This script configures each defence - cmd: bash models.sh ++model.init.name=torch_example.${item} ++stage=train ++hydra.sweeper.study=${item} --config-name default.yaml - deps: - - models.sh - - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} - attacks: # This is a loop over the ResNet models - foreach: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 - do: # This script configures each attack and calls models.sh which configures each defence - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} ++stage=attack ++hydra.sweeper.storage=sqlite:///${item}.db --config-name default.yaml + do: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/attack/${item}.db --config-name cifar10.yaml deps: - - models.sh - - attacks.sh - - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} - - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} - - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + - models.sh # This script configures each defence + - attacks.sh # This script configures each attack + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} # This is here just to ensure it runs after the attack stage + # - ${files.directory}/${files.reports}/train/${item}.db outs: - - ${item}.db - compile: # - foreach: + - ${files.directory}/${files.reports}/attack/${item}.db: # This outputs a database file for each model + cache: True + persist: True + compile: + foreach: # iterates through each stage + # - train - attack do: cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv deps: - ${files.directory}/${files.reports}/${item}/ - - ResNet18.db - - ResNet34.db - - ResNet50.db - - ResNet101.db - - ResNet152.db + - ${files.directory}/${files.reports}/${item}/ResNet18.db + - ${files.directory}/${files.reports}/${item}/ResNet34.db + - ${files.directory}/${files.reports}/${item}/ResNet50.db + - ${files.directory}/${files.reports}/${item}/ResNet101.db + # - ${files.directory}/${files.reports}/${item}/ResNet152.db outs: - ${files.directory}/${files.reports}/${item}.csv plot: - cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv + cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv -o data.csv deps: - ${files.directory}/${files.reports}/attack.csv - metrics: + - ${files.directory}/${files.reports}/attack/ResNet18.db + # - ${files.directory}/${files.reports}/attack/ResNet34.db + # - ${files.directory}/${files.reports}/attack/ResNet50.db + # - ${files.directory}/${files.reports}/attack/ResNet101.db + # - ${files.directory}/${files.reports}/attack/ResNet152.db + outs: - ${files.directory}/plots/data.csv - plots: - - ${files.directory}/plots/adv_accuracy_vs_attack_type.pdf - - ${files.directory}/plots/adv_accuracy_vs_defence_type.pdf - - ${files.directory}/plots/adv_failure_rate_vs_train_time.pdf - - ${files.directory}/plots/adv_failures_per_test_time_vs_defence_type.pdf - - ${files.directory}/plots/adv_failures_per_train_time_vs_attack_type.pdf - - ${files.directory}/plots/adv_failures_per_train_time_vs_defence_type.pdf - - ${files.directory}/plots/atk_param_vs_accuracy.pdf - - ${files.directory}/plots/ben_accuracy_vs_defence_type.pdf - - ${files.directory}/plots/ben_failure_rate_vs_defence_type.pdf - - ${files.directory}/plots/ben_failures_per_train_time_vs_defence_type.pdf - - ${files.directory}/plots/def_param_vs_accuracy.pdf - - ${files.directory}/plots/def_param_vs_adv_accuracy.pdf - - ${files.directory}/plots/def_param_vs_adv_failure_rate.pdf afr: - cmd: python -m deckard.layers.afr --dataset cifar + cmd: python -m deckard.layers.afr --dataset ${files.directory} --file ${files.directory}/plots/data.csv deps: - ${files.directory}/plots/data.csv plots: @@ -116,7 +114,7 @@ stages: outs: - ${files.directory}/plots/aft_comparison.tex copy_results: - cmd: cp -r ${files.directory}/plots/* ~/ml_afr/cifar/ - deps: - - ${files.directory}/plots/data.csv - - ${files.directory}/plots/aft_comparison.csv + cmd: cp -r ${files.directory}/plots/* ~/ml_afr/cifar10/ + deps: + - ${files.directory}/plots/data.csv + - ${files.directory}/plots/aft_comparison.csv diff --git a/examples/pytorch_cifar/models.sh b/examples/pytorch_cifar/models.sh index 57d368c8..8d64d588 100644 --- a/examples/pytorch_cifar/models.sh +++ b/examples/pytorch_cifar/models.sh @@ -2,18 +2,37 @@ # This script is used to generate the models for the sklearn example. -# Default model +# # Default model echo "python -m deckard.layers.optimise " $@ "--multirun" python -m deckard.layers.optimise $@ --multirun -# This line generates the model and adds the FeatureSqueezing preprocessing defence. -python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,1] $@ --multirun +# # This line generates the model and adds the FeatureSqueezing preprocessing defence. +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,255] ++hydra.sweeper.study_name=FSQ $@ --multirun -# Gaussian Augmentation (Input) -python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.1,.5,1 +model.art.preprocessor.params.augmentation=False $@ --multirun +# # # Gaussian Augmentation (Input) +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.5 +model.art.preprocessor.params.augmentation=False ++hydra.sweeper.study_name=gauss-in $@ --multirun -# Gaussian Noise (Output) -python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 $@ --multirun +# # # # Gaussian Noise (Output) +# python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 ++hydra.sweeper.study_name=gauss-out $@ --multirun -# High Confidence -python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 $@ --multirun +# # # # High Confidence +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 ++hydra.sweeper.study_name=conf $@ --multirun +#!/bin/bash + +# This script is used to generate the models for the sklearn example. + +# # Default model +echo "python -m deckard.layers.optimise " $@ "--multirun" +python -m deckard.layers.optimise $@ --multirun + +# # This line generates the model and adds the FeatureSqueezing preprocessing defence. +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,255] ++hydra.sweeper.study_name=FSQ $@ --multirun + +# # # Gaussian Augmentation (Input) +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.5 +model.art.preprocessor.params.augmentation=False ++hydra.sweeper.study_name=gauss-in $@ --multirun + +# # # # Gaussian Noise (Output) +# python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 ++hydra.sweeper.study_name=gauss-out $@ --multirun + +# # # # High Confidence +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 ++hydra.sweeper.study_name=conf $@ --multirun diff --git a/examples/pytorch_cifar/params.yaml b/examples/pytorch_cifar/params.yaml index 31a81451..5d94e208 100644 --- a/examples/pytorch_cifar/params.yaml +++ b/examples/pytorch_cifar/params.yaml @@ -35,7 +35,7 @@ attack: initialize: clip_values: - 0.0 - - 1.0 + - 255.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -63,7 +63,7 @@ attack: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 name: art.attacks.evasion.HopSkipJump method: evasion model: @@ -85,7 +85,7 @@ attack: initialize: clip_values: - 0.0 - - 1.0 + - 255.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -113,7 +113,7 @@ attack: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 data: _target_: deckard.base.data.Data generate: @@ -135,7 +135,7 @@ files: data_dir: data data_file: data data_type: .pkl - directory: output + directory: cifar model_dir: models model_file: model model_type: .pt @@ -163,7 +163,7 @@ model: initialize: clip_values: - 0.0 - - 1.0 + - 255.0 criterion: name: torch.nn.CrossEntropyLoss optimizer: @@ -191,7 +191,7 @@ model: library: pytorch trainer: batch_size: 1024 - nb_epoch: 20 + nb_epoch: 100 optimizers: accuracy scorers: _target_: deckard.base.scorer.ScorerDict diff --git a/examples/pytorch_cifar_100/.dvc/.gitignore b/examples/pytorch_cifar_100/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/pytorch_cifar_100/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/pytorch_cifar_100/.dvc/config b/examples/pytorch_cifar_100/.dvc/config new file mode 100644 index 00000000..e69de29b diff --git a/examples/pytorch_cifar_100/.dvcignore b/examples/pytorch_cifar_100/.dvcignore new file mode 100644 index 00000000..51973055 --- /dev/null +++ b/examples/pytorch_cifar_100/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/examples/pytorch_cifar_100/conf/attack/default.yaml b/examples/pytorch_cifar_100/conf/attack/default.yaml new file mode 100644 index 00000000..76b4d62d --- /dev/null +++ b/examples/pytorch_cifar_100/conf/attack/default.yaml @@ -0,0 +1,9 @@ +data: ${data} +model: ${model} +_target_ : deckard.base.attack.Attack +init: + model: ${model} + _target_: deckard.base.attack.AttackInitializer + name: art.attacks.evasion.HopSkipJump +attack_size : 10 +method : evasion diff --git a/examples/pytorch_cifar_100/conf/cifar100.yaml b/examples/pytorch_cifar_100/conf/cifar100.yaml new file mode 100644 index 00000000..760e2feb --- /dev/null +++ b/examples/pytorch_cifar_100/conf/cifar100.yaml @@ -0,0 +1,44 @@ +defaults: + - _self_ + - data: torch_cifar100 + - model: torch_cifar100 + - attack: default + - files: cifar100 + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +stage : '???' +direction : "maximize" +_target_ : deckard.base.experiment.Experiment +optimizers : accuracy +hydra: + run: + dir: ${files.directory}/${files.reports}/${stage}/logs + sweep: + dir: ${files.directory}/${stage}/${model.init.name} + subdir : ${hydra.sweeper.study_name}/${hydra.job.num} + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + direction: ${direction} + study_name: control + storage: sqlite:///model.db + n_jobs: ${hydra.launcher.n_jobs} + n_trials : 32 + params: + ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) + ++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, .0001, .00001, 0.000001) + ++model.trainer.nb_epoch: choice(1, 10, 30, 50, 100) + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 8 + prefer : processes + verbose: 10 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/pytorch_cifar_100/conf/compile.yaml b/examples/pytorch_cifar_100/conf/compile.yaml new file mode 100644 index 00000000..43418520 --- /dev/null +++ b/examples/pytorch_cifar_100/conf/compile.yaml @@ -0,0 +1,32 @@ +attacks: + # CarliniL0Method: CW_0 + # CarliniL2Method: CW_2 + # CarliniLInfMethod: CW_inf + DeepFool: Deep + FastGradientMethod: FGM + HopSkipJump: HSJ + PixelAttack: Pixel + ProjectedGradientDescent: PGD + ThresholdAttack: Thresh +defences: + Control: Control + FeatureSqueezing: FSQ + GaussianAugmentation: Gauss-in + GaussianNoise: Gauss-out + HighConfidence: Conf +params: + # art.attacks.evasion.CarliniL0Method: attack.init.confidence + # art.attacks.evasion.CarliniL2Method: attack.init.confidence + # art.attacks.evasion.CarliniLInfMethod: attack.init.confidence + Deep: attack.init.nb_grads + FGM: attack.init.eps + HSJ: attack.init.max_iter + Pixel: attack.init.th + PGD: attack.init.eps + Thresh: attack.init.th + Gauss-out: model.art.pipeline.postprocessor.scale + Conf: model.art.pipeline.postprocessor.cutoff + FSQ: model.art.pipeline.preprocessor.bit_depth + Gauss-in: model.art.pipeline.preprocessor.sigma + Depth: model_layers + Epochs: model.train.nb_epoch diff --git a/examples/pytorch_cifar/conf/deploy.yaml b/examples/pytorch_cifar_100/conf/deploy/default.yaml similarity index 69% rename from examples/pytorch_cifar/conf/deploy.yaml rename to examples/pytorch_cifar_100/conf/deploy/default.yaml index c64d7b87..134da65f 100644 --- a/examples/pytorch_cifar/conf/deploy.yaml +++ b/examples/pytorch_cifar_100/conf/deploy/default.yaml @@ -6,9 +6,9 @@ gpu_driver_version: default machine_type: n1-standard-2 min_nodes: 1 max_nodes: 1 -storage_config: sclass.yaml -persistent_volume_claim: pvc.yaml -pod : pod.yaml +storage_config: conf/deploy/sclass.yaml +persistent_volume_claim: conf/deploy/pvc.yaml +pod : conf/deploy/pod.yaml image_project: ubuntu-os-cloud image_family: ubuntu-2204-lts mount_directory: /mnt/filestore diff --git a/examples/pytorch_cifar_100/conf/deploy/pod.yaml b/examples/pytorch_cifar_100/conf/deploy/pod.yaml new file mode 100644 index 00000000..fc68306a --- /dev/null +++ b/examples/pytorch_cifar_100/conf/deploy/pod.yaml @@ -0,0 +1,30 @@ +apiVersion: v1 +kind: Pod +metadata: + name: deckard +spec: + containers: + - name: deckard + image: ghcr.io/simplymathematics/deckard:main + imagePullPolicy: Always + workingDir: /deckard/examples/pytorch + args: ["python", "-m", "dvc", "repro"] + env: + - name: REDIS_HOST + value: "redis" + - name: REDIS_PORT + value: "6379" + - name: REDIS_DB + value: "0" + - name: REDIS_PASSWORD + value: "" + resources: + limits: + nvidia.com/gpu: 1 + volumeMounts: + - mountPath: /data + name: mypvc + volumes: + - name: mypvc + persistentVolumeClaim: + claimName: podpvc diff --git a/examples/pytorch_cifar_100/conf/deploy/pvc.yaml b/examples/pytorch_cifar_100/conf/deploy/pvc.yaml new file mode 100644 index 00000000..a7deee4d --- /dev/null +++ b/examples/pytorch_cifar_100/conf/deploy/pvc.yaml @@ -0,0 +1,11 @@ +kind: PersistentVolumeClaim +apiVersion: v1 +metadata: + name: podpvc +spec: + accessModes: + - ReadWriteMany + storageClassName: filestore-sc + resources: + requests: + storage: 256Gi diff --git a/examples/pytorch_cifar_100/conf/deploy/sclass.yaml b/examples/pytorch_cifar_100/conf/deploy/sclass.yaml new file mode 100644 index 00000000..d566656c --- /dev/null +++ b/examples/pytorch_cifar_100/conf/deploy/sclass.yaml @@ -0,0 +1,10 @@ +apiVersion: storage.k8s.io/v1 +kind: StorageClass +metadata: + name: filestore-sc +provisioner: filestore.csi.storage.gke.io +volumeBindingMode: Immediate +allowVolumeExpansion: true +parameters: + tier: standard + network: default diff --git a/examples/pytorch_cifar_100/conf/files/cifar100.yaml b/examples/pytorch_cifar_100/conf/files/cifar100.yaml new file mode 100644 index 00000000..969635c2 --- /dev/null +++ b/examples/pytorch_cifar_100/conf/files/cifar100.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: cifar100 +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pt +params_file : params.yaml +# train_labels_file : train_labels.json +# test_labels_file : test_labels.json +# probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +# adv_probabilities_file: adv_probabilities.json +name: default diff --git a/examples/pytorch_cifar_100/conf/files/mnist.yaml b/examples/pytorch_cifar_100/conf/files/mnist.yaml new file mode 100644 index 00000000..efdb1c42 --- /dev/null +++ b/examples/pytorch_cifar_100/conf/files/mnist.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: mnist +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pt +params_file : params.yaml +# train_labels_file : train_labels.json +# test_labels_file : test_labels.json +# probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +# adv_probabilities_file: adv_probabilities.json +name: default diff --git a/examples/pytorch_cifar_100/conf/model/art/default.yaml b/examples/pytorch_cifar_100/conf/model/art/default.yaml new file mode 100644 index 00000000..4251627d --- /dev/null +++ b/examples/pytorch_cifar_100/conf/model/art/default.yaml @@ -0,0 +1,7 @@ +defaults: + - initialize : default + # - preprocessor: fsq + # - postprocessor: confidence +data : ${..data} +library : ${..library} +_target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/pytorch_cifar_100/conf/model/art/initialize/default.yaml b/examples/pytorch_cifar_100/conf/model/art/initialize/default.yaml new file mode 100644 index 00000000..b694473b --- /dev/null +++ b/examples/pytorch_cifar_100/conf/model/art/initialize/default.yaml @@ -0,0 +1,7 @@ +criterion: + name : torch.nn.CrossEntropyLoss +optimizer: + name : torch.optim.SGD + lr : 0.01 + momentum : 0.9 +clip_values : [0, 255] diff --git a/examples/pytorch_cifar_100/conf/model/art/postprocessor/confidence.yaml b/examples/pytorch_cifar_100/conf/model/art/postprocessor/confidence.yaml new file mode 100644 index 00000000..6fcdde95 --- /dev/null +++ b/examples/pytorch_cifar_100/conf/model/art/postprocessor/confidence.yaml @@ -0,0 +1,3 @@ + +name : art.defences.postprocessor.HighConfidence +cutoff : .1 diff --git a/examples/pytorch_cifar_100/conf/model/art/postprocessor/gauss-out.yaml b/examples/pytorch_cifar_100/conf/model/art/postprocessor/gauss-out.yaml new file mode 100644 index 00000000..c912ebd9 --- /dev/null +++ b/examples/pytorch_cifar_100/conf/model/art/postprocessor/gauss-out.yaml @@ -0,0 +1,2 @@ +name : art.defences.postprocessor.GaussianNoise +scale : 1 diff --git a/examples/pytorch_cifar_100/conf/model/art/preprocessor/default.yaml b/examples/pytorch_cifar_100/conf/model/art/preprocessor/default.yaml new file mode 100644 index 00000000..e69de29b diff --git a/examples/pytorch_cifar_100/conf/model/art/preprocessor/fsq.yaml b/examples/pytorch_cifar_100/conf/model/art/preprocessor/fsq.yaml new file mode 100644 index 00000000..27ddf58b --- /dev/null +++ b/examples/pytorch_cifar_100/conf/model/art/preprocessor/fsq.yaml @@ -0,0 +1,3 @@ +name : art.defences.preprocessor.FeatureSqueezing +clip_values : ${model.art.initialize.clip_values} +bit_depth : 64 diff --git a/examples/pytorch_cifar_100/conf/model/art/preprocessor/gauss-in.yaml b/examples/pytorch_cifar_100/conf/model/art/preprocessor/gauss-in.yaml new file mode 100644 index 00000000..c438e8f5 --- /dev/null +++ b/examples/pytorch_cifar_100/conf/model/art/preprocessor/gauss-in.yaml @@ -0,0 +1,4 @@ +name : art.defences.preprocessor.GaussianAugmentation +clip_values : ${model.art.initialize.clip_values} +sigma : 1 +ratio : .5 diff --git a/examples/pytorch_cifar_100/conf/model/torch_cifar100.yaml b/examples/pytorch_cifar_100/conf/model/torch_cifar100.yaml new file mode 100644 index 00000000..ffc0db53 --- /dev/null +++ b/examples/pytorch_cifar_100/conf/model/torch_cifar100.yaml @@ -0,0 +1,13 @@ +defaults: + - art : default +data: ${data} +init: + _target_: deckard.base.model.ModelInitializer + name : torch_example.ResNet18 + num_channels: 3 + num_classes: 100 +_target_: deckard.base.model.Model +trainer: + nb_epoch: 100 + batch_size: 1024 +library : pytorch diff --git a/examples/pytorch_cifar_100/conf/model/torch_mnist.yaml b/examples/pytorch_cifar_100/conf/model/torch_mnist.yaml new file mode 100644 index 00000000..9c18c54b --- /dev/null +++ b/examples/pytorch_cifar_100/conf/model/torch_mnist.yaml @@ -0,0 +1,13 @@ + +defaults: + - art : default +data: ${data} +init: + _target_: deckard.base.model.ModelInitializer + num_channels : 1 + name : torch_example.ResNet18 +_target_: deckard.base.model.Model +trainer: + nb_epoch: 100 + batch_size: 1024 +library : pytorch diff --git a/examples/pytorch_cifar_100/conf/plots.yaml b/examples/pytorch_cifar_100/conf/plots.yaml new file mode 100644 index 00000000..accab9df --- /dev/null +++ b/examples/pytorch_cifar_100/conf/plots.yaml @@ -0,0 +1,250 @@ +cat_plot: +- file: adv_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: linear + titles: $\lambda_{adv.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_accuracy + ylabels: $\lambda_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + titles: $\lambda_{ben.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: accuracy + ylabels: $\lambda_{ben.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: log + titles: $\bar{C}_{ben.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_failure + ylabels: $\bar{C}_{ben.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: log + titles: $\bar{C}_{adv.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_adv_failure + ylabels: $\bar{C}_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_train_time_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: $\bar{C}_{adv.}$ vs Attack Type + x: atk_gen + xlabels: Attack Type + y: training_time_per_adv_failure + ylabels: $\bar{C}_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_test_time_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: $h_{adv}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_failure_rate + ylabels: $h_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +# - file: adv_accuracy_vs_defence_type.pdf +# hue: model_name +# kind: boxen +# legend_title: Model Name +# titles: $\lambda_{adv.}$ vs Defence Type +# x: def_gen +# xlabels: Defence Type +# y: adv_accuracy +# ylabels: Adv. $\lambda_{ben.}$ +# rotation : 90 +# hue_order: +# - ResNet18 +# - ResNet34 +# - ResNet50 +# - ResNet101 +# - ResNet152 +- file: adv_accuracy_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: $\lambda_{adv.}$ vs Attack Type + x: atk_gen + xlabels: Attack Type + y: adv_accuracy + ylabels: $\lambda_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_failure_rate_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: $h_{ben}(t; \theta)$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: failure_rate + ylabels: $h_{ben}(t; \theta)$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +line_plot: +# - file: def_param_vs_accuracy.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $\lambda_{ben.}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: accuracy +# y_scale: +# ylabel: $\lambda_{ben.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +# - file: def_param_vs_adv_accuracy.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $\lambda_{adv.}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: adv_accuracy +# y_scale: +# ylabel: $\lambda_{adv.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +# - file: def_param_vs_adv_failure_rate.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $h_{adv}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: adv_failure_rate +# y_scale: log +# ylabel: $h_{adv.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +- file: atk_param_vs_accuracy.pdf + hue: atk_gen + legend: {bbox_to_anchor: [1.05, 1]} + title: $\lambda_{adv.}$ vs Attack Strength + x: atk_value + x_scale: linear + xlabel: Attack Control Parameter + y: adv_accuracy + y_scale: + ylabel: $\lambda_{adv.}$ + hue_order: + - FGM + - PGD + - Deep + - HSJ + - Pixel + - Thresh + +scatter_plot: +- x: train_time_per_sample + y: adv_failure_rate + hue: model_name + xlabel: $t_{train}$ + ylabel: $h_{adv}$ + title: $h_{adv}$ vs $t_{train}$ + file: adv_failure_rate_vs_train_time.pdf + y_scale: log + x_scale: log + legend: + title: Model Name + bbox_to_anchor: [1.05, 1] + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 + # style : atk_gen +# - x: train_time_per_sample +# y: adv_failure_rate +# hue: model_name +# xlabel: $t_{train}$ +# ylabel: $h_{adv}$ +# title: $h_{adv}$ vs $t_{train}$ for each Defence +# file: adv_failure_rate_vs_train_time_def.pdf +# y_scale: log +# x_scale: linear +# legend: +# title: Model Name +# # style : def_gen diff --git a/examples/pytorch_cifar_100/conf/scorers/default.yaml b/examples/pytorch_cifar_100/conf/scorers/default.yaml new file mode 100644 index 00000000..4503c5bf --- /dev/null +++ b/examples/pytorch_cifar_100/conf/scorers/default.yaml @@ -0,0 +1,9 @@ +_target_: deckard.base.scorer.ScorerDict +accuracy: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.accuracy_score + direction: maximize +log_loss: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.log_loss + direction: minimize From de101131f74169353bace2f8c200f6f3a37a8169 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 14 Nov 2023 13:18:58 +0000 Subject: [PATCH 119/148] add pytorch cifar100 --- .gitignore | 5 +- examples/pytorch/dvc.yaml | 8 +- examples/pytorch_cifar/dvc.lock | 141 ++++---- examples/pytorch_cifar/params.yaml | 3 +- examples/pytorch_cifar_100/.gitignore | 3 + examples/pytorch_cifar_100/attacks.sh | 37 ++ examples/pytorch_cifar_100/dvc.lock | 377 ++++++++++++++++++++ examples/pytorch_cifar_100/dvc.yaml | 120 +++++++ examples/pytorch_cifar_100/models.sh | 19 + examples/pytorch_cifar_100/params.yaml | 215 +++++++++++ examples/pytorch_cifar_100/torch_example.py | 79 ++++ 11 files changed, 928 insertions(+), 79 deletions(-) create mode 100644 examples/pytorch_cifar_100/.gitignore create mode 100644 examples/pytorch_cifar_100/attacks.sh create mode 100644 examples/pytorch_cifar_100/dvc.lock create mode 100644 examples/pytorch_cifar_100/dvc.yaml create mode 100644 examples/pytorch_cifar_100/models.sh create mode 100644 examples/pytorch_cifar_100/params.yaml create mode 100644 examples/pytorch_cifar_100/torch_example.py diff --git a/.gitignore b/.gitignore index 949d8926..80a442bf 100644 --- a/.gitignore +++ b/.gitignore @@ -134,6 +134,5 @@ examples/*/*/reports **/*.egg-info deckard/deckard.egg-info/* - - -examples/*/20_epochs/reports/attack/000632dec5ae20d1b5b1e5ec8542c504/params.yaml +# Compressed Data Files +*.tar.gz diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index 21d8c792..0cbb2c6f 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -55,10 +55,10 @@ stages: attacks: foreach: # This is a loop over the ResNet models - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 + #- ResNet34 + #- ResNet50 + #- ResNet101 + #- ResNet152 do: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/attack/${item}.db --config-name mnist.yaml deps: diff --git a/examples/pytorch_cifar/dvc.lock b/examples/pytorch_cifar/dvc.lock index 5f3f4149..d90598f9 100644 --- a/examples/pytorch_cifar/dvc.lock +++ b/examples/pytorch_cifar/dvc.lock @@ -1,7 +1,7 @@ schema: '2.0' stages: train: - cmd: python -m deckard.layers.experiment train --config_file cifar + cmd: python -m deckard.layers.experiment train --config_file cifar10.yaml params: params.yaml: data: @@ -97,28 +97,30 @@ stages: md5: 6503fed5d4e6cc1163898c0ab6a863dd size: 739680311 - path: cifar/models/model.optimizer.pt - md5: 86884b532b711a567d5a544d3ad693d3 + hash: md5 + md5: 09f978b389dbce742b1a8731b81913b0 size: 44805933 - path: cifar/models/model.pt - md5: 5c3af8f99e01afbc493b3f57dbf09c1d + hash: md5 + md5: 7b6a070a946d4f5fcc79ac17de88296e size: 44811029 - - path: cifar/reports/train/default/params.yaml - md5: f18b8df67e934151c2d59117666c2ee3 - size: 2172 - path: cifar/reports/train/default/predictions.json - md5: 16cb8eef7a76e00b58036b252c84e6e7 - size: 2439927 + hash: md5 + md5: c0a3cdf07b36af7ef9084ab0e2030fcc + size: 2435001 - path: cifar/reports/train/default/score_dict.json - md5: bc21cb437fafee94057df96be84566d7 - size: 405 + hash: md5 + md5: 665f7680184855a05db884355132024c + size: 397 attack: - cmd: python -m deckard.layers.experiment attack --config_file cifar + cmd: python -m deckard.layers.experiment attack --config_file cifar10.yaml deps: - path: cifar/data/data.pkl md5: 6503fed5d4e6cc1163898c0ab6a863dd size: 739680311 - path: cifar/models/model.pt - md5: 5c3af8f99e01afbc493b3f57dbf09c1d + hash: md5 + md5: 7b6a070a946d4f5fcc79ac17de88296e size: 44811029 params: params.yaml: @@ -327,86 +329,83 @@ stages: name: sklearn.metrics.log_loss outs: - path: cifar/attacks/attack.pkl - md5: 1763dcf2dd4b821ea2a9309e1df63805 + hash: md5 + md5: 7af56fed692b7c502bf7431adc68a3d0 size: 123046 - path: cifar/reports/attack/default/adv_predictions.json - md5: 5add137290da41324a28055f45ce97b7 - size: 2160 - - path: cifar/reports/attack/default/params.yaml - md5: 835365f5be5d0a922541f050fc78ac79 - size: 5178 + hash: md5 + md5: 7860bb5575e86ce8df3240e01abc930d + size: 2099 - path: cifar/reports/attack/default/score_dict.json - md5: 904a964ee783155dbef6416c197695e0 - size: 557 + hash: md5 + md5: d3213b13fda99e50a582440a9752f827 + size: 558 attacks@ResNet18: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 - ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet18.db --config-name default.yaml + stage=attack ++hydra.sweeper.storage=sqlite:///cifar/reports/attack/ResNet18.db + --config-name cifar10.yaml deps: - path: attacks.sh - md5: 6b79f7071f855be0ea09ad5602215c35 - size: 2746 + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: cifar/reports/attack/default/score_dict.json + hash: md5 + md5: d3213b13fda99e50a582440a9752f827 + size: 558 - path: models.sh - md5: 9386478ad9795b80ec8ebc4abf1dbc81 - size: 1221 - - path: output/attacks/attack.pkl - md5: 8d7c09d7bf7c7d759648942a99880c3f - size: 123046 - - path: output/reports/attack/default/adv_predictions.json - md5: fb5dee98575e0d67e0ab051cc0182ecf - size: 2145 - - path: output/reports/attack/default/params.yaml - md5: 5177666d7f2e1e55ccad8ab4b735ffc7 - size: 5175 + hash: md5 + md5: 02a4961b4afe7ba84c41e9ad49c30c83 + size: 2760 outs: - - path: ResNet18.db - md5: 5de89603f1f440df03669e79e0a7b414 - size: 360448 + - path: cifar/reports/attack/ResNet18.db + hash: md5 + md5: cb2a1edd44e08358b52d5ab5b6232aa5 + size: 1212416 attacks@ResNet34: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 - ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet34.db --config-name default.yaml + stage=attack ++hydra.sweeper.storage=sqlite:///cifar/reports/attack/ResNet34.db + --config-name cifar10.yaml deps: - path: attacks.sh - md5: 18b363f1e2d0622c372e07116bed4fd8 - size: 2731 + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: cifar/reports/attack/default/score_dict.json + hash: md5 + md5: d3213b13fda99e50a582440a9752f827 + size: 558 - path: models.sh - md5: 9386478ad9795b80ec8ebc4abf1dbc81 - size: 1221 - - path: output/attacks/attack.pkl - md5: e57b871f32a1a8b7de497f6e5de4ebf1 - size: 123046 - - path: output/reports/attack/default/adv_predictions.json - md5: 0a097842037186d905fb2fd7ec4072d9 - size: 2125 - - path: output/reports/attack/default/params.yaml - md5: 5177666d7f2e1e55ccad8ab4b735ffc7 - size: 5175 + hash: md5 + md5: 02a4961b4afe7ba84c41e9ad49c30c83 + size: 2760 outs: - - path: ResNet34.db - md5: 946c9a1a88d7caaa6af3679a343cc770 - size: 1290240 + - path: cifar/reports/attack/ResNet34.db + hash: md5 + md5: c19b84776f8a6f99705c5f6bbd2a28c6 + size: 819200 attacks@ResNet50: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 - ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet50.db --config-name default.yaml + stage=attack ++hydra.sweeper.storage=sqlite:///cifar/reports/attack/ResNet50.db + --config-name cifar10.yaml deps: - path: attacks.sh - md5: 6dd27be960da7b3e34c0281143ca30ba - size: 2742 + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: cifar/reports/attack/default/score_dict.json + hash: md5 + md5: d3213b13fda99e50a582440a9752f827 + size: 558 - path: models.sh - md5: 9386478ad9795b80ec8ebc4abf1dbc81 - size: 1221 - - path: output/attacks/attack.pkl - md5: 6b7117801434d91c71e870a3922eea7e - size: 123046 - - path: output/reports/attack/default/adv_predictions.json - md5: 39344f99a210e0ecbcaa16e54cec9d4b - size: 2147 - - path: output/reports/attack/default/params.yaml - md5: 5177666d7f2e1e55ccad8ab4b735ffc7 - size: 5175 + hash: md5 + md5: 02a4961b4afe7ba84c41e9ad49c30c83 + size: 2760 outs: - - path: ResNet50.db - md5: 1e3b638560b7ee5e73c674d0e1eb0421 - size: 446464 + - path: cifar/reports/attack/ResNet50.db + hash: md5 + md5: e44f2b22e7d876571148078c180842ea + size: 819200 attacks@ResNet101: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet101 ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet101.db --config-name diff --git a/examples/pytorch_cifar/params.yaml b/examples/pytorch_cifar/params.yaml index 5d94e208..d8ad2b2e 100644 --- a/examples/pytorch_cifar/params.yaml +++ b/examples/pytorch_cifar/params.yaml @@ -126,6 +126,7 @@ data: name: sklearn.preprocessing.StandardScaler with_mean: true with_std: true +direction: maximize files: _target_: deckard.base.files.FileConfig adv_predictions_file: adv_predictions.json @@ -203,4 +204,4 @@ scorers: _target_: deckard.base.scorer.ScorerConfig direction: minimize name: sklearn.metrics.log_loss -stage: null +stage: ??? diff --git a/examples/pytorch_cifar_100/.gitignore b/examples/pytorch_cifar_100/.gitignore new file mode 100644 index 00000000..de9b09c5 --- /dev/null +++ b/examples/pytorch_cifar_100/.gitignore @@ -0,0 +1,3 @@ +cifar100/ +20_epochs/ +cifar-100-python/ \ No newline at end of file diff --git a/examples/pytorch_cifar_100/attacks.sh b/examples/pytorch_cifar_100/attacks.sh new file mode 100644 index 00000000..8ec1f079 --- /dev/null +++ b/examples/pytorch_cifar_100/attacks.sh @@ -0,0 +1,37 @@ +# #!/bin/bash + +# # This script is used to generate the attacks for the example. + +# Fast Gradient Method +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 stage=attack ++hydra.sweeper.study_name=fgm ++direction=maximize $@ + +# Projected Gradient Descent +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 stage=attack ++hydra.sweeper.study_name=pgd ++direction=maximize $@ + +# DeepFool +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=1,3,5,10 stage=attack ++hydra.sweeper.study_name=deep ++direction=maximize $@ + +# HopSkipJump +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 stage=attack ++hydra.sweeper.study_name=hsj ++direction=maximize $@ + +# ##################################################### +# PixelAttack +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=pixel ++direction=maximize $@ + +# ThresholdAttack +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=thresh ++direction=maximize $@ + +# # AdversarialPatch +# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 stage=patch ++hydra.sweeper.study_name=attack ++direction=maximize ++attack.init.patch_shape=[1,28,28] $@ +##################################################### + +# # Carlini L0 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw0 ++hydra.sweeper.study_name=cw0 ++direction=maximize $@ + +# # Carlini L2 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw2 ++hydra.sweeper.study_name=cw2 ++direction=maximize $@ + +# # Carlini LInf Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=attack ++hydra.sweeper.study_name=cwinf ++direction=maximize $@ + +rm -rf output/models/* diff --git a/examples/pytorch_cifar_100/dvc.lock b/examples/pytorch_cifar_100/dvc.lock new file mode 100644 index 00000000..de9319c7 --- /dev/null +++ b/examples/pytorch_cifar_100/dvc.lock @@ -0,0 +1,377 @@ +schema: '2.0' +stages: + train: + cmd: python -m deckard.layers.experiment train --config_file cifar100.yaml + params: + params.yaml: + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: cifar100 + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 100 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: cifar100/data/data.pkl + hash: md5 + md5: 1070854e6c00fc787bc0fdfc82792fd6 + size: 761280311 + - path: cifar100/models/model.optimizer.pt + hash: md5 + md5: 8b65d9bf40085b3f4573ed859d3b0696 + size: 44990637 + - path: cifar100/models/model.pt + hash: md5 + md5: 4321db4b035a2016cabb9eb9d758ace6 + size: 44995733 + - path: cifar100/reports/train/default/predictions.json + hash: md5 + md5: bdffc46adff64ccce60dcfb5960add7c + size: 24402965 + - path: cifar100/reports/train/default/score_dict.json + hash: md5 + md5: 60904225857c24ad2822fb92e170a26b + size: 387 + attack: + cmd: python -m deckard.layers.experiment attack --config_file cifar100.yaml + deps: + - path: cifar100/data/data.pkl + hash: md5 + md5: 1070854e6c00fc787bc0fdfc82792fd6 + size: 761280311 + - path: cifar100/models/model.pt + hash: md5 + md5: 4321db4b035a2016cabb9eb9d758ace6 + size: 44995733 + params: + params.yaml: + attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.attack.AttackInitializer + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 100 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 100 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: cifar100 + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 100 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: cifar100/attacks/attack.pkl + hash: md5 + md5: 53b5fbaa742a6e711248fd5bef50e333 + size: 123046 + - path: cifar100/reports/attack/default/adv_predictions.json + hash: md5 + md5: c113079d397ba3f2714280524524a127 + size: 21335 + - path: cifar100/reports/attack/default/score_dict.json + hash: md5 + md5: df6268a9e688028be341b32b9cbc7f65 + size: 540 + attacks@ResNet18: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 + stage=attack ++hydra.sweeper.storage=sqlite:///cifar100/reports/attack/ResNet18.db + --config-name cifar100.yaml + deps: + - path: attacks.sh + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: cifar100/reports/attack/default/score_dict.json + hash: md5 + md5: df6268a9e688028be341b32b9cbc7f65 + size: 540 + - path: models.sh + hash: md5 + md5: 1937e58bedac027034aea7d4a5712407 + size: 1380 + outs: + - path: cifar100/reports/attack/ResNet18.db + hash: md5 + md5: 5542bfcf3caf0cccc60d1cdf43a0a35a + size: 462848 diff --git a/examples/pytorch_cifar_100/dvc.yaml b/examples/pytorch_cifar_100/dvc.yaml new file mode 100644 index 00000000..254a637a --- /dev/null +++ b/examples/pytorch_cifar_100/dvc.yaml @@ -0,0 +1,120 @@ +stages: + train: + cmd: python -m deckard.layers.experiment train --config_file cifar100.yaml + params: + - data + - model + - scorers + - files + outs: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} # Omit to save space + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} # logit outputs for our model + # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} # Omit to save space + metrics: + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + attack: + cmd: python -m deckard.layers.experiment attack --config_file cifar100.yaml + params: + - data + - model + - attack + - scorers + - files + outs: + - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + # - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + deps: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + metrics: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + + ############################################################################## + # models: # This is a loop over the ResNet models + # foreach: + # - ResNet18 + # # - ResNet34 + # # - ResNet50 + # # - ResNet101 + # # - ResNet152 + # do: # This script configures eazch defence + # cmd: bash models.sh ++model.init.name=torch_example.${item} stage=train ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/train/${item}.db --config-name cifar100.yaml + # deps: + # - models.sh + # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + # - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} + # outs: + # - ${files.directory}/${files.reports}/train/${item}.db: # This outputs a database file for each model + # cache: True + # persist: True + attacks: + foreach: # This is a loop over the ResNet models + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 + do: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/attack/${item}.db --config-name cifar100.yaml + deps: + - models.sh # This script configures each defence + - attacks.sh # This script configures each attack + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} # This is here just to ensure it runs after the attack stage + # - ${files.directory}/${files.reports}/train/${item}.db + outs: + - ${files.directory}/${files.reports}/attack/${item}.db: # This outputs a database file for each model + cache: True + persist: True + compile: + foreach: # iterates through each stage + # - train + - attack + do: + cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv + deps: + - ${files.directory}/${files.reports}/${item}/ + - ${files.directory}/${files.reports}/${item}/ResNet18.db + - ${files.directory}/${files.reports}/${item}/ResNet34.db + - ${files.directory}/${files.reports}/${item}/ResNet50.db + - ${files.directory}/${files.reports}/${item}/ResNet101.db + # - ${files.directory}/${files.reports}/${item}/ResNet152.db + outs: + - ${files.directory}/${files.reports}/${item}.csv + plot: + cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv -o data.csv + deps: + - ${files.directory}/${files.reports}/attack.csv + - ${files.directory}/${files.reports}/attack/ResNet18.db + # - ${files.directory}/${files.reports}/attack/ResNet34.db + # - ${files.directory}/${files.reports}/attack/ResNet50.db + # - ${files.directory}/${files.reports}/attack/ResNet101.db + # - ${files.directory}/${files.reports}/attack/ResNet152.db + outs: + - ${files.directory}/plots/data.csv + afr: + cmd: python -m deckard.layers.afr --dataset ${files.directory} --file ${files.directory}/plots/data.csv + deps: + - ${files.directory}/plots/data.csv + plots: + - ${files.directory}/plots/weibull_aft.pdf + - ${files.directory}/plots/weibull_partial_effects.pdf + - ${files.directory}/plots/cox_partial_effects.pdf + - ${files.directory}/plots/cox_aft.pdf + - ${files.directory}/plots/log_logistic_aft.pdf + - ${files.directory}/plots/log_logistic_partial_effects.pdf + - ${files.directory}/plots/log_normal_aft.pdf + - ${files.directory}/plots/log_normal_partial_effects.pdf + metrics: + - ${files.directory}/plots/aft_comparison.csv + outs: + - ${files.directory}/plots/aft_comparison.tex + copy_results: + cmd: cp -r ${files.directory}/plots/* ~/ml_afr/cifar100/ + deps: + - ${files.directory}/plots/data.csv + - ${files.directory}/plots/aft_comparison.csv diff --git a/examples/pytorch_cifar_100/models.sh b/examples/pytorch_cifar_100/models.sh new file mode 100644 index 00000000..2978d445 --- /dev/null +++ b/examples/pytorch_cifar_100/models.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +# This script is used to generate the models for the sklearn example. + +# # Default model +echo "python -m deckard.layers.optimise " $@ "--multirun" +python -m deckard.layers.optimise $@ --multirun + +# # This line generates the model and adds the FeatureSqueezing preprocessing defence. +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,255] ++hydra.sweeper.study_name=FSQ $@ --multirun + +# # # Gaussian Augmentation (Input) +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.5 +model.art.preprocessor.params.augmentation=False ++hydra.sweeper.study_name=gauss-in $@ --multirun + +# # # # Gaussian Noise (Output) +# python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 ++hydra.sweeper.study_name=gauss-out $@ --multirun + +# # # # High Confidence +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 ++hydra.sweeper.study_name=conf $@ --multirun diff --git a/examples/pytorch_cifar_100/params.yaml b/examples/pytorch_cifar_100/params.yaml new file mode 100644 index 00000000..ef99ba24 --- /dev/null +++ b/examples/pytorch_cifar_100/params.yaml @@ -0,0 +1,215 @@ +_target_: deckard.base.experiment.Experiment +attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.attack.AttackInitializer + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 100 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 100 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 +data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true +direction: maximize +files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: cifar100 + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json +model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 100 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 +optimizers: accuracy +scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss +stage: ??? diff --git a/examples/pytorch_cifar_100/torch_example.py b/examples/pytorch_cifar_100/torch_example.py new file mode 100644 index 00000000..2007d9d0 --- /dev/null +++ b/examples/pytorch_cifar_100/torch_example.py @@ -0,0 +1,79 @@ +import torch.nn as nn +from torchvision import models + +__all__ = [ + "ResNet18", + "ResNet50", + "ResNet101", + "ResNet152", +] + + +def ResNet18(num_channels=1, num_classes=10): + model = models.resnet18() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(512, num_classes) + return model + + +def ResNet34(num_channels=1, num_classes=10): + model = models.resnet34() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(512, num_classes) + return model + + +def ResNet50(num_channels=1, num_classes=10): + model = models.resnet50() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model + + +def ResNet101(num_channels=1, num_classes=10): + model = models.resnet101() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model + + +def ResNet152(num_channels=1, num_classes=10): + model = models.resnet152() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model From 0d1410f218badaa2d59591b0557e47864b36b876 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 14 Nov 2023 16:03:09 +0000 Subject: [PATCH 120/148] update .gitignores --- examples/.dvc/.gitignore | 3 +++ examples/classification/conf/attack/.gitignore | 1 - examples/classification/conf/model/.gitignore | 1 - examples/security/classification/.dvc/.gitignore | 3 +++ examples/security/classification/.gitignore | 4 ++++ .../security/classification/conf/attack/.gitignore | 4 ++++ .../security/classification/conf/model/.gitignore | 4 ++++ examples/security/kdd-nsl/.dvc/.gitignore | 3 +++ examples/security/kdd-nsl/.gitignore | 4 ++++ examples/security/kdd-nsl/conf/attack/.gitignore | 4 ++++ examples/security/kdd-nsl/conf/model/.gitignore | 4 ++++ examples/security/kdd-nsl/plots/.gitignore | 12 ++++++++++++ examples/security/truthseeker/.dvc/.gitignore | 3 +++ examples/security/truthseeker/.gitignore | 4 ++++ examples/security/truthseeker/conf/attack/.gitignore | 4 ++++ examples/security/truthseeker/conf/model/.gitignore | 4 ++++ examples/security/truthseeker/plots/.gitignore | 6 ++++++ 17 files changed, 66 insertions(+), 2 deletions(-) create mode 100644 examples/.dvc/.gitignore delete mode 100644 examples/classification/conf/attack/.gitignore delete mode 100644 examples/classification/conf/model/.gitignore create mode 100644 examples/security/classification/.dvc/.gitignore create mode 100644 examples/security/classification/.gitignore create mode 100644 examples/security/classification/conf/attack/.gitignore create mode 100644 examples/security/classification/conf/model/.gitignore create mode 100644 examples/security/kdd-nsl/.dvc/.gitignore create mode 100644 examples/security/kdd-nsl/.gitignore create mode 100644 examples/security/kdd-nsl/conf/attack/.gitignore create mode 100644 examples/security/kdd-nsl/conf/model/.gitignore create mode 100644 examples/security/kdd-nsl/plots/.gitignore create mode 100644 examples/security/truthseeker/.dvc/.gitignore create mode 100644 examples/security/truthseeker/.gitignore create mode 100644 examples/security/truthseeker/conf/attack/.gitignore create mode 100644 examples/security/truthseeker/conf/model/.gitignore create mode 100644 examples/security/truthseeker/plots/.gitignore diff --git a/examples/.dvc/.gitignore b/examples/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/classification/conf/attack/.gitignore b/examples/classification/conf/attack/.gitignore deleted file mode 100644 index 41ee9b5a..00000000 --- a/examples/classification/conf/attack/.gitignore +++ /dev/null @@ -1 +0,0 @@ -/best.yaml diff --git a/examples/classification/conf/model/.gitignore b/examples/classification/conf/model/.gitignore deleted file mode 100644 index 41ee9b5a..00000000 --- a/examples/classification/conf/model/.gitignore +++ /dev/null @@ -1 +0,0 @@ -/best.yaml diff --git a/examples/security/classification/.dvc/.gitignore b/examples/security/classification/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/security/classification/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/security/classification/.gitignore b/examples/security/classification/.gitignore new file mode 100644 index 00000000..96901c72 --- /dev/null +++ b/examples/security/classification/.gitignore @@ -0,0 +1,4 @@ +/kdd_nsl +/retrain +/multirun +/logs \ No newline at end of file diff --git a/examples/security/classification/conf/attack/.gitignore b/examples/security/classification/conf/attack/.gitignore new file mode 100644 index 00000000..29ec330a --- /dev/null +++ b/examples/security/classification/conf/attack/.gitignore @@ -0,0 +1,4 @@ +/best.yaml +/best_linear.yaml +/best_rbf.yaml +/best_poly.yaml diff --git a/examples/security/classification/conf/model/.gitignore b/examples/security/classification/conf/model/.gitignore new file mode 100644 index 00000000..9f645db6 --- /dev/null +++ b/examples/security/classification/conf/model/.gitignore @@ -0,0 +1,4 @@ +/best.yaml +/best_rbf.yaml +/best_linear.yaml +/best_poly.yaml diff --git a/examples/security/kdd-nsl/.dvc/.gitignore b/examples/security/kdd-nsl/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/security/kdd-nsl/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/security/kdd-nsl/.gitignore b/examples/security/kdd-nsl/.gitignore new file mode 100644 index 00000000..60a7ed4b --- /dev/null +++ b/examples/security/kdd-nsl/.gitignore @@ -0,0 +1,4 @@ +/output +/retrain +/multirun +/logs \ No newline at end of file diff --git a/examples/security/kdd-nsl/conf/attack/.gitignore b/examples/security/kdd-nsl/conf/attack/.gitignore new file mode 100644 index 00000000..29ec330a --- /dev/null +++ b/examples/security/kdd-nsl/conf/attack/.gitignore @@ -0,0 +1,4 @@ +/best.yaml +/best_linear.yaml +/best_rbf.yaml +/best_poly.yaml diff --git a/examples/security/kdd-nsl/conf/model/.gitignore b/examples/security/kdd-nsl/conf/model/.gitignore new file mode 100644 index 00000000..9f645db6 --- /dev/null +++ b/examples/security/kdd-nsl/conf/model/.gitignore @@ -0,0 +1,4 @@ +/best.yaml +/best_rbf.yaml +/best_linear.yaml +/best_poly.yaml diff --git a/examples/security/kdd-nsl/plots/.gitignore b/examples/security/kdd-nsl/plots/.gitignore new file mode 100644 index 00000000..642f14d4 --- /dev/null +++ b/examples/security/kdd-nsl/plots/.gitignore @@ -0,0 +1,12 @@ +/accuracy_vs_attack_parameters.eps +/confidence_vs_attack_parameters.eps +/train_time_vs_attack_parameters.eps +/retrain_accuracy.eps +/retrain_confidence_vs_attack_parameters.eps +/retrain_time.eps +/accuracy_vs_attack_parameters.pdf +/confidence_vs_attack_parameters.pdf +/train_time_vs_attack_parameters.pdf +/retrain_accuracy.pdf +/retrain_confidence_vs_attack_parameters.pdf +/retrain_time.pdf diff --git a/examples/security/truthseeker/.dvc/.gitignore b/examples/security/truthseeker/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/security/truthseeker/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/security/truthseeker/.gitignore b/examples/security/truthseeker/.gitignore new file mode 100644 index 00000000..60a7ed4b --- /dev/null +++ b/examples/security/truthseeker/.gitignore @@ -0,0 +1,4 @@ +/output +/retrain +/multirun +/logs \ No newline at end of file diff --git a/examples/security/truthseeker/conf/attack/.gitignore b/examples/security/truthseeker/conf/attack/.gitignore new file mode 100644 index 00000000..29ec330a --- /dev/null +++ b/examples/security/truthseeker/conf/attack/.gitignore @@ -0,0 +1,4 @@ +/best.yaml +/best_linear.yaml +/best_rbf.yaml +/best_poly.yaml diff --git a/examples/security/truthseeker/conf/model/.gitignore b/examples/security/truthseeker/conf/model/.gitignore new file mode 100644 index 00000000..9f645db6 --- /dev/null +++ b/examples/security/truthseeker/conf/model/.gitignore @@ -0,0 +1,4 @@ +/best.yaml +/best_rbf.yaml +/best_linear.yaml +/best_poly.yaml diff --git a/examples/security/truthseeker/plots/.gitignore b/examples/security/truthseeker/plots/.gitignore new file mode 100644 index 00000000..dd345776 --- /dev/null +++ b/examples/security/truthseeker/plots/.gitignore @@ -0,0 +1,6 @@ +/accuracy_vs_attack_parameters.pdf +/confidence_vs_attack_parameters.pdf +/train_time_vs_attack_parameters.pdf +/retrain_accuracy.pdf +/retrain_confidence_vs_attack_parameters.pdf +/retrain_time.pdf From 336cb3c85c32dda514a9dafb579b8d92cf0a9afc Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 14 Nov 2023 16:05:03 +0000 Subject: [PATCH 121/148] cleanup old example --- .gitignore | 6 + Dockerfile | 3 +- examples/classification/README.md | 32 -- examples/classification/attacks.sh | 28 -- examples/classification/conf/RQ.md | 1 - examples/classification/conf/attack.yaml | 36 -- .../conf/attack/attack_grid.yaml | 49 --- .../classification/conf/attack/default.yaml | 2 - examples/classification/conf/attack/hsj.yaml | 8 - .../classification/conf/data/default.yaml | 22 - examples/classification/conf/data/large.yaml | 20 - examples/classification/conf/data/medium.yaml | 20 - examples/classification/conf/data/small.yaml | 20 - .../classification/conf/data/very_large.yaml | 20 - examples/classification/conf/default.yaml | 41 -- .../classification/conf/files/default.yaml | 21 - .../classification/conf/hydra/default.yaml | 7 - .../conf/hydra/launcher/default.yaml | 10 - .../conf/hydra/run/default.yaml | 1 - .../conf/hydra/sweeper/default.yaml | 8 - .../conf/hydra/sweeper/sampler/default.yaml | 7 - .../hydra/sweeper/sampler/params/default.yaml | 1 - examples/classification/conf/model.yaml | 34 -- .../conf/model/art/defense_grid.yaml | 44 -- .../conf/model/art/postprocessor.yaml | 6 - .../art/postprocessor/high_confidence.yaml | 6 - .../conf/model/art/postprocessor/labels.yaml | 6 - .../conf/model/art/preprocessor.yaml | 6 - .../art/preprocessor/feature_squeezing.yaml | 6 - .../classification/conf/model/default.yaml | 2 - .../classification/conf/model/linear.yaml | 15 - examples/classification/conf/model/poly.yaml | 16 - examples/classification/conf/model/rbf.yaml | 15 - examples/classification/conf/rq.yaml | 51 --- .../classification/conf/scorers/default.yaml | 10 - examples/classification/dvc.yaml | 125 ------ examples/classification/models.sh | 53 --- examples/classification/params.yaml | 168 -------- examples/classification/plots.py | 381 ------------------ 39 files changed, 8 insertions(+), 1299 deletions(-) delete mode 100644 examples/classification/README.md delete mode 100644 examples/classification/attacks.sh delete mode 100644 examples/classification/conf/RQ.md delete mode 100644 examples/classification/conf/attack.yaml delete mode 100644 examples/classification/conf/attack/attack_grid.yaml delete mode 100644 examples/classification/conf/attack/default.yaml delete mode 100644 examples/classification/conf/attack/hsj.yaml delete mode 100644 examples/classification/conf/data/default.yaml delete mode 100644 examples/classification/conf/data/large.yaml delete mode 100644 examples/classification/conf/data/medium.yaml delete mode 100644 examples/classification/conf/data/small.yaml delete mode 100644 examples/classification/conf/data/very_large.yaml delete mode 100644 examples/classification/conf/default.yaml delete mode 100644 examples/classification/conf/files/default.yaml delete mode 100644 examples/classification/conf/hydra/default.yaml delete mode 100644 examples/classification/conf/hydra/launcher/default.yaml delete mode 100644 examples/classification/conf/hydra/run/default.yaml delete mode 100644 examples/classification/conf/hydra/sweeper/default.yaml delete mode 100644 examples/classification/conf/hydra/sweeper/sampler/default.yaml delete mode 100644 examples/classification/conf/hydra/sweeper/sampler/params/default.yaml delete mode 100644 examples/classification/conf/model.yaml delete mode 100644 examples/classification/conf/model/art/defense_grid.yaml delete mode 100644 examples/classification/conf/model/art/postprocessor.yaml delete mode 100644 examples/classification/conf/model/art/postprocessor/high_confidence.yaml delete mode 100644 examples/classification/conf/model/art/postprocessor/labels.yaml delete mode 100644 examples/classification/conf/model/art/preprocessor.yaml delete mode 100644 examples/classification/conf/model/art/preprocessor/feature_squeezing.yaml delete mode 100644 examples/classification/conf/model/default.yaml delete mode 100644 examples/classification/conf/model/linear.yaml delete mode 100644 examples/classification/conf/model/poly.yaml delete mode 100644 examples/classification/conf/model/rbf.yaml delete mode 100644 examples/classification/conf/rq.yaml delete mode 100644 examples/classification/conf/scorers/default.yaml delete mode 100644 examples/classification/dvc.yaml delete mode 100644 examples/classification/models.sh delete mode 100644 examples/classification/params.yaml delete mode 100644 examples/classification/plots.py diff --git a/.gitignore b/.gitignore index 80a442bf..4fac6a80 100644 --- a/.gitignore +++ b/.gitignore @@ -136,3 +136,9 @@ deckard/deckard.egg-info/* # Compressed Data Files *.tar.gz + + +# Example output files +examples/**/params.yaml +examples/**/multirun/* +*log.txt diff --git a/Dockerfile b/Dockerfile index 64d129ae..d8f8401f 100644 --- a/Dockerfile +++ b/Dockerfile @@ -5,7 +5,8 @@ RUN apt-get install -y python3 python3-distutils python3-pip ffmpeg libavcodec-e RUN python3 -m pip install nvidia-pyindex nvidia-cuda-runtime-cu11 RUN git clone https://github.com/simplymathematics/deckard.git WORKDIR /deckard -RUN python3 -m pip install --editable .[pytorch_image] +RUN python3 -m pip install --editable .[torch,torchvision,tensorflow] +RUN python3 -m pip install pytest RUN git clone https://github.com/Trusted-AI/adversarial-robustness-toolbox.git RUN cd adversarial-robustness-toolbox && python3 -m pip install . RUN apt install python-is-python3 diff --git a/examples/classification/README.md b/examples/classification/README.md deleted file mode 100644 index c3c227e6..00000000 --- a/examples/classification/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# Classification Experiments - -## Directory Contents - -├── conf: contains the configuration files. -│ ├── attack -│ ├── config.yaml : contains the default configuration -│ ├── data -│ ├── files -│ ├── model -│ ├── plots -│ └── scorers -├── dag.md: contains the [dvc](https://dvc.org/doc/start/data-management/data-pipelines) pipeline, visualized as a graph -├── dvc.lock: contains the git trackable information for all the data and model binaries -├── dvc.yaml: specifies the pipeline visualized in `dag.md` -├── experiments.sh: specifies the grid search parameters and executes the pipeline for each set of parameters -├── multirun: contains the hydra configuration information -├── params.yaml: contains the parsed default configuration file -├── plots.ipynb: is a jupyter notebook for visualizing the data -├── queue: contains all of the configurations for all experiments -├── reports: contains the results of all experiments, with a folder for each layer (e.g. `reports/train/`), containing scores, plots, predictions, probabilities, and ground_truth json files. -├── train: contains the `data/` and `models/` for each model -│ ├── data -│ ├── models - -## Execution instructions - -To check basic code functionality, run -```dvc repro --force``` -which will execute the [dvc pipeline](https://dvc.org/doc/start/data-management/data-pipelines) that makes all of the inputs and outputs git trackable. This will execute all of the code on the default configurations first (stages `generate`, `train`, and `attack` on the `dvc.yaml`), followed by grid search on all of the models in stage `model-queue` and all of the attacks in stage `attack-queue`. After finding the best configuration for each kernel (stage `compile`) - -which overrides the default configurations, using [hydra](https://hydra.cc/docs/patterns/configuring_experiments/) and its [optuna plugin](https://hydra.cc/docs/plugins/optuna_sweeper/) to specify a search space for each set of parameters specified in the bash script. Sets of parameters that apply to all experiments are set in the `config.yaml`. Parameters particular to a experiment are specified using command line overrides within the bash script. diff --git a/examples/classification/attacks.sh b/examples/classification/attacks.sh deleted file mode 100644 index 169de6ee..00000000 --- a/examples/classification/attacks.sh +++ /dev/null @@ -1,28 +0,0 @@ - -# break -# PGD -bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.norm=1,2,inf ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=100 ++attack.init.eps=.001,.01,.1,.3,.5,1 $@ - -# # Carlini L0 -# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ - -# # Carlini L2 -# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ - -# # Carlini LInf -# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ - -# # DeepFool -# bash models.sh ++attack.init.nb_grads=1,3,5,10 ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.batch_size=100 $@ - -# #Threshold Attack -# bash models.sh ++attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ - -# #Pixel Attack -# bash models.sh ++attack.init.name=art.attacks.evasion.PixelAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ - -# #Adversarial Patch -# bash models.sh ++attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.scale_max=.1,.2,.3,.5,.8,.9,.99 ++attack.init.batch_size=100 $@ - -# #Hop Skip Jump -# bash models.sh ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.batch_size=100 $@ diff --git a/examples/classification/conf/RQ.md b/examples/classification/conf/RQ.md deleted file mode 100644 index c1e745ce..00000000 --- a/examples/classification/conf/RQ.md +++ /dev/null @@ -1 +0,0 @@ -sudo apt install lsb-release curl gpg diff --git a/examples/classification/conf/attack.yaml b/examples/classification/conf/attack.yaml deleted file mode 100644 index 7a029490..00000000 --- a/examples/classification/conf/attack.yaml +++ /dev/null @@ -1,36 +0,0 @@ -defaults: - # - _target_ : deckard.base.experiment.Experiment - - _self_ - - data: default - - model: default - - attack : hsj - - files: default - - scorers: default - - override hydra/sweeper : optuna - - override hydra/sweeper/sampler : grid - - override hydra/launcher : joblib -hydra: - run: - dir : "./${files.directory}" - sweeper: - sampler: - _target_: optuna.samplers.GridSampler - _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - direction: minimize - study_name: attack - storage: sqlite:///attack.db - n_trials: 1 - n_jobs: 1 - params: - # model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) - launcher: - _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 32 - prefer : processes - verbose: 1 - timeout: null - pre_dispatch: n_jobs - batch_size: auto - temp_folder: /tmp/deckard - max_nbytes: 100000 - mmap_mode: r diff --git a/examples/classification/conf/attack/attack_grid.yaml b/examples/classification/conf/attack/attack_grid.yaml deleted file mode 100644 index d7dc3050..00000000 --- a/examples/classification/conf/attack/attack_grid.yaml +++ /dev/null @@ -1,49 +0,0 @@ -- attack.init.name: [art.attacks.evasion.FastGradientMethod] - attack.init.eps : [.01, .03, .3, .1] - attack.init.norm : ['inf', 1, 2] - attack.init.eps_step : [.001, .003, .01] - attack.init.batch_size : [100] - -- attack.init.name: [art.attacks.evasion.ProjectedGradientDescent] - attack.init.eps : [.01, .03, .3, .1] - attack.init.norm : ['inf', 1, 2] - attack.init.eps_step : [.001, .003, .01] - attack.init.batch_size : [100] - attack.init.max_iter : [10] - -- attack.init.name: [art.attacks.evasion.CarliniL0Method] - attack.init.batch_size : [100] - attack.init.max_iter : [10] - attack.init.confidence : [.1, .9, .99] - -- attack.init.name: [art.attacks.evasion.CarliniL2Method] - attack.init.batch_size : [100] - attack.init.max_iter : [10] - attack.init.confidence : [.1, .9, .99] - -- attack.init.name: [art.attacks.evasion.CarliniLInfMethod] - attack.init.max_iter : [10] - attack.init.confidence: [.1, .9, .99] - -- attack.init.name: [art.attacks.evasion.DeepFool] - attack.init.max_iter : [10] - attack.init.batch_size : [100] - attack.init.nb_grads : [10, 100, 1000] - -- attack.init.name: [art.attacks.evasion.HopSkipJump] - attack.init.max_iter : [10] - attack.init.max_eval : [10] - attack.init.init_eval : [10] - attack.init.norm : ['inf', 2] - -- attack.init.name: [art.attacks.evasion.PixelAttack] - attack.init.th : [.5, .9, .99] - attack.init.max_iter : [10] - -- attack.init.name: [art.attacks.evasion.ThresholdAttack] - attack.init.th : [.5, .9, .99] - attack.init.max_iter : [10] - -- attack.init.name: [art.attacks.evasion.AdversarialPatch] - attack.init.max_iter : [10] - attack.init.learning_rate : [.5, 5.0, 50.0] diff --git a/examples/classification/conf/attack/default.yaml b/examples/classification/conf/attack/default.yaml deleted file mode 100644 index e0967ff7..00000000 --- a/examples/classification/conf/attack/default.yaml +++ /dev/null @@ -1,2 +0,0 @@ -defaults: - - hsj diff --git a/examples/classification/conf/attack/hsj.yaml b/examples/classification/conf/attack/hsj.yaml deleted file mode 100644 index cb2399a3..00000000 --- a/examples/classification/conf/attack/hsj.yaml +++ /dev/null @@ -1,8 +0,0 @@ -data: ${data} -model: ${model} -_target_ : deckard.base.attack.Attack -init: - name: art.attacks.evasion.HopSkipJump - model: ${model} -attack_size : 10 -method : evasion diff --git a/examples/classification/conf/data/default.yaml b/examples/classification/conf/data/default.yaml deleted file mode 100644 index d5aa9091..00000000 --- a/examples/classification/conf/data/default.yaml +++ /dev/null @@ -1,22 +0,0 @@ -# _target_: deckard.base.data.Data -# generate: -# # _target_: deckard.base.data.generator.DataGenerator -# name: classification -# random_state : 0 -# n_samples : 1100 -# n_features : 20 -# sample: -# # _target_: deckard.base.data.sampler.SklearnDataSampler -# random_state : 0 -# stratify: True -# train_size : 100 -# test_size : 1000 -# sklearn_pipeline: -# # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline -# preprocessor: -# # -# name: sklearn.preprocessing.StandardScaler -# with_mean: True -# with_std: True -defaults: - - small diff --git a/examples/classification/conf/data/large.yaml b/examples/classification/conf/data/large.yaml deleted file mode 100644 index ea6c3120..00000000 --- a/examples/classification/conf/data/large.yaml +++ /dev/null @@ -1,20 +0,0 @@ -_target_: deckard.base.data.Data -generate: - # _target_: deckard.base.data.generator.DataGenerator - name: classification - random_state : 0 - n_samples : 101000 - n_features : 20 -sample: - # _target_: deckard.base.data.sampler.SklearnDataSampler - random_state : 0 - stratify: True - train_size : 100000 - test_size : 1000 -sklearn_pipeline: - # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - # - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/examples/classification/conf/data/medium.yaml b/examples/classification/conf/data/medium.yaml deleted file mode 100644 index 079b9aea..00000000 --- a/examples/classification/conf/data/medium.yaml +++ /dev/null @@ -1,20 +0,0 @@ -_target_: deckard.base.data.Data -generate: - # _target_: deckard.base.data.generator.DataGenerator - name: classification - random_state : 0 - n_samples : 11000 - n_features : 20 -sample: - # _target_: deckard.base.data.sampler.SklearnDataSampler - random_state : 0 - stratify: True - train_size : 10000 - test_size : 1000 -sklearn_pipeline: - # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - # - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/examples/classification/conf/data/small.yaml b/examples/classification/conf/data/small.yaml deleted file mode 100644 index 2207d717..00000000 --- a/examples/classification/conf/data/small.yaml +++ /dev/null @@ -1,20 +0,0 @@ -_target_: deckard.base.data.Data -generate: - # _target_: deckard.base.data.generator.DataGenerator - name: classification - random_state : 0 - n_samples : 1100 - n_features : 20 -sample: - # _target_: deckard.base.data.sampler.SklearnDataSampler - random_state : 0 - stratify: True - train_size : 100 - test_size : 1000 -sklearn_pipeline: - # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - # - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/examples/classification/conf/data/very_large.yaml b/examples/classification/conf/data/very_large.yaml deleted file mode 100644 index ff552d03..00000000 --- a/examples/classification/conf/data/very_large.yaml +++ /dev/null @@ -1,20 +0,0 @@ -_target_: deckard.base.data.Data -generate: - # _target_: deckard.base.data.generator.DataGenerator - name: classification - random_state : 0 - n_samples : 1001000 - n_features : 20 -sample: - # _target_: deckard.base.data.sampler.SklearnDataSampler - random_state : 0 - stratify: True - train_size : 100000 - test_size : 1000 -sklearn_pipeline: - # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - # - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/examples/classification/conf/default.yaml b/examples/classification/conf/default.yaml deleted file mode 100644 index b0340b69..00000000 --- a/examples/classification/conf/default.yaml +++ /dev/null @@ -1,41 +0,0 @@ -defaults: - - _self_ - - data: default - - model: default - - attack: hsj - - files: default - - scorers: default - - override hydra/sweeper : optuna - - override hydra/launcher : joblib -hydra: - run: - dir : "./${files.directory}" - sweeper: - sampler: - _target_: optuna.samplers.TPESampler - # seed: 123 - consider_prior: true - prior_weight: 1.0 - consider_magic_clip: true - consider_endpoints: false - n_startup_trials: 10 - n_ei_candidates: 24 - multivariate: false - warn_independent_sampling: true - _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - direction: maximize - study_name: model - storage: sqlite:///model.db - n_trials: 100 - n_jobs: 1 - launcher: - _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 6 - prefer : processes - verbose: 1 - timeout: null - pre_dispatch: n_jobs - batch_size: auto - temp_folder: /tmp/deckard - max_nbytes: 100000 - mmap_mode: r diff --git a/examples/classification/conf/files/default.yaml b/examples/classification/conf/files/default.yaml deleted file mode 100644 index e2247d7e..00000000 --- a/examples/classification/conf/files/default.yaml +++ /dev/null @@ -1,21 +0,0 @@ -_target_: deckard.base.files.FileConfig -reports: reports -data_dir: data -model_dir: models -attack_dir: attacks -directory: output -score_dict_file: score_dict.json -data_file : data -model_file : model -attack_file : attack -attack_type : .pkl -data_type : .pkl -model_type : .pkl -params_file : params.yaml -train_labels_file : train_labels.json -test_labels_file : test_labels.json -probabilities_file: probabilities.json -predictions_file : predictions.json -adv_predictions_file : adv_predictions.json -adv_probabilities_file: adv_probabilities.json -name : default diff --git a/examples/classification/conf/hydra/default.yaml b/examples/classification/conf/hydra/default.yaml deleted file mode 100644 index 8dceecaf..00000000 --- a/examples/classification/conf/hydra/default.yaml +++ /dev/null @@ -1,7 +0,0 @@ -defaults: -- run : default -- launcher : default -- sweeper : default -- override sweeper : optuna -- override sweeper/sampler : grid -- override launcher : joblib diff --git a/examples/classification/conf/hydra/launcher/default.yaml b/examples/classification/conf/hydra/launcher/default.yaml deleted file mode 100644 index 8b8d3f41..00000000 --- a/examples/classification/conf/hydra/launcher/default.yaml +++ /dev/null @@ -1,10 +0,0 @@ -_target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher -n_jobs: 6 -prefer : processes -verbose: 1 -timeout: null -pre_dispatch: n_jobs -batch_size: auto -temp_folder: /tmp/deckard -max_nbytes: 100000 -mmap_mode: r diff --git a/examples/classification/conf/hydra/run/default.yaml b/examples/classification/conf/hydra/run/default.yaml deleted file mode 100644 index ec38cd67..00000000 --- a/examples/classification/conf/hydra/run/default.yaml +++ /dev/null @@ -1 +0,0 @@ -dir : "./${files.directory}" diff --git a/examples/classification/conf/hydra/sweeper/default.yaml b/examples/classification/conf/hydra/sweeper/default.yaml deleted file mode 100644 index 402132d0..00000000 --- a/examples/classification/conf/hydra/sweeper/default.yaml +++ /dev/null @@ -1,8 +0,0 @@ -defaults: -- sampler: default -_target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper -direction: maximize -study_name: model -storage: sqlite:///model.db -n_trials: 100 -n_jobs: 1 diff --git a/examples/classification/conf/hydra/sweeper/sampler/default.yaml b/examples/classification/conf/hydra/sweeper/sampler/default.yaml deleted file mode 100644 index 5401b736..00000000 --- a/examples/classification/conf/hydra/sweeper/sampler/default.yaml +++ /dev/null @@ -1,7 +0,0 @@ -defaults: -- params : default -_target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper -direction: minimize -study_name: attack -storage: sqlite:///attack.db -n_jobs: 1 diff --git a/examples/classification/conf/hydra/sweeper/sampler/params/default.yaml b/examples/classification/conf/hydra/sweeper/sampler/params/default.yaml deleted file mode 100644 index 33f9dfa6..00000000 --- a/examples/classification/conf/hydra/sweeper/sampler/params/default.yaml +++ /dev/null @@ -1 +0,0 @@ -model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) diff --git a/examples/classification/conf/model.yaml b/examples/classification/conf/model.yaml deleted file mode 100644 index d50b2407..00000000 --- a/examples/classification/conf/model.yaml +++ /dev/null @@ -1,34 +0,0 @@ -defaults: - # - _target_ : deckard.base.experiment.Experiment - - _self_ - - data: default - - model: default - - files: default - - scorers: default - - override hydra/sweeper : optuna - - override hydra/sweeper/sampler : grid - - override hydra/launcher : joblib -hydra: - run: - dir : "./${files.directory}" - sweeper: - sampler: - _target_: optuna.samplers.GridSampler - _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - direction: maximize - study_name: model - storage: sqlite:///model.db - n_jobs: 1 - params: - model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) - launcher: - _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 32 - prefer : processes - verbose: 1 - timeout: null - pre_dispatch: n_jobs - batch_size: auto - temp_folder: /tmp/deckard - max_nbytes: 100000 - mmap_mode: r diff --git a/examples/classification/conf/model/art/defense_grid.yaml b/examples/classification/conf/model/art/defense_grid.yaml deleted file mode 100644 index a3fe3719..00000000 --- a/examples/classification/conf/model/art/defense_grid.yaml +++ /dev/null @@ -1,44 +0,0 @@ -- model.art.preprocessor.name: art.defences.preprocessor.FeatureSqueezing - model.art.preprocessor.params: - clip_values : - - [0,255] - bit_depth : [4, 8, 16, 32, 64] - -- model.art.preprocessor.name: art.defences.preprocessor.GaussianAugmentation - model.art.preprocessor.params: - clip_values : - - [0,255] - sigma : [.1, .3, 1] - ratio : [.1, .5, 1] - -- model.art.preprocessor.name: art.defences.preprocessor.SpatialSmoothing - model.art.preprocessor.params: - clip_values : - - [0,255] - window_size : [2,3,4] - -- model.art.preprocessor.name: art.defences.preprocessor.TotalVarMin - model.art.preprocessor.params: - clip_values : - - [0,255] - prob : [.001, .01, .1] - norm : [1, 2, 3] - lamb : [.05, .5, .95] - max_iter : [100] - -- model.art.postprocessor.name : art.defences.postprocessor.GaussianNoise - model.art.postprocessor.params: - clip_values : - - [0,255] - scale: [.1, .9, .999] - -- model.art.postprocessor.name : art.defences.postprocessor.HighConfidence - model.art.postprocessor.params: - cutoff : [.1, .5, .9, .99] - - -- model.art.postprocessor.name : art.defences.postprocessor.Rounded - model.art.preprocessor.params: - clip_values : - - [0,255] - decimals : [1, 2, 4, 8] diff --git a/examples/classification/conf/model/art/postprocessor.yaml b/examples/classification/conf/model/art/postprocessor.yaml deleted file mode 100644 index 91f3c00a..00000000 --- a/examples/classification/conf/model/art/postprocessor.yaml +++ /dev/null @@ -1,6 +0,0 @@ -_target_ : deckard.base.model.art_pipeline.ArtPipeline -library : sklearn-svc -postprocessor: - name: art.defences.postprocessor.HighConfidence - threshold: 0.0 -initialize: diff --git a/examples/classification/conf/model/art/postprocessor/high_confidence.yaml b/examples/classification/conf/model/art/postprocessor/high_confidence.yaml deleted file mode 100644 index 16ae0e50..00000000 --- a/examples/classification/conf/model/art/postprocessor/high_confidence.yaml +++ /dev/null @@ -1,6 +0,0 @@ -_target_ : deckard.base.model.art_pipeline.ArtPipeline -library : ${model.library} -postprocessor: - name: art.defences.postprocessor.HighConfidence - threshold: 0.0 -initialize: diff --git a/examples/classification/conf/model/art/postprocessor/labels.yaml b/examples/classification/conf/model/art/postprocessor/labels.yaml deleted file mode 100644 index 16ae0e50..00000000 --- a/examples/classification/conf/model/art/postprocessor/labels.yaml +++ /dev/null @@ -1,6 +0,0 @@ -_target_ : deckard.base.model.art_pipeline.ArtPipeline -library : ${model.library} -postprocessor: - name: art.defences.postprocessor.HighConfidence - threshold: 0.0 -initialize: diff --git a/examples/classification/conf/model/art/preprocessor.yaml b/examples/classification/conf/model/art/preprocessor.yaml deleted file mode 100644 index ebf46909..00000000 --- a/examples/classification/conf/model/art/preprocessor.yaml +++ /dev/null @@ -1,6 +0,0 @@ -_target_ : deckard.base.model.art_pipeline.ArtPipeline -library : sklearn-svc -preprocessor: - name: art.defences.preprocessor.FeatureSqueezing - bit_depth: 32 -initialize: diff --git a/examples/classification/conf/model/art/preprocessor/feature_squeezing.yaml b/examples/classification/conf/model/art/preprocessor/feature_squeezing.yaml deleted file mode 100644 index 1c48b3e7..00000000 --- a/examples/classification/conf/model/art/preprocessor/feature_squeezing.yaml +++ /dev/null @@ -1,6 +0,0 @@ -_target_ : deckard.base.model.art_pipeline.ArtPipeline -library : ${model.library} -preprocessor: - name: art.defences.preprocessor.FeatureSqueezing - bit_depth: 32 -initialize: diff --git a/examples/classification/conf/model/default.yaml b/examples/classification/conf/model/default.yaml deleted file mode 100644 index 5f8bd822..00000000 --- a/examples/classification/conf/model/default.yaml +++ /dev/null @@ -1,2 +0,0 @@ -defaults: - - rbf diff --git a/examples/classification/conf/model/linear.yaml b/examples/classification/conf/model/linear.yaml deleted file mode 100644 index b900f88a..00000000 --- a/examples/classification/conf/model/linear.yaml +++ /dev/null @@ -1,15 +0,0 @@ -data: ${data} -library : sklearn-svc -init: - _target_: deckard.base.model.ModelInitializer - name : sklearn.svm.SVC - C : 1.0 - kernel : linear - probability : true - random_state : 0 - max_iter : 100 -_target_: deckard.base.model.Model -art: - _target_ : deckard.base.model.art_pipeline.ArtPipeline - library : sklearn-svc - initialize: diff --git a/examples/classification/conf/model/poly.yaml b/examples/classification/conf/model/poly.yaml deleted file mode 100644 index 7a5be797..00000000 --- a/examples/classification/conf/model/poly.yaml +++ /dev/null @@ -1,16 +0,0 @@ -data: ${data} -library : sklearn-svc -init: - _target_: deckard.base.model.ModelInitializer - name : sklearn.svm.SVC - C : 1.0 - coef0 : 0.0 - kernel : poly - probability : true - random_state : 0 - max_iter : 100 -_target_: deckard.base.model.Model -art: - _target_ : deckard.base.model.art_pipeline.ArtPipeline - library : sklearn-svc - initialize: diff --git a/examples/classification/conf/model/rbf.yaml b/examples/classification/conf/model/rbf.yaml deleted file mode 100644 index 42ca4c07..00000000 --- a/examples/classification/conf/model/rbf.yaml +++ /dev/null @@ -1,15 +0,0 @@ -data: ${data} -library : sklearn-svc -init: - _target_: deckard.base.model.ModelInitializer - name : sklearn.svm.SVC - C : 1.0 - kernel : rbf - probability : true - random_state : 0 - max_iter : 100 -_target_: deckard.base.model.Model -art: - _target_ : deckard.base.model.art_pipeline.ArtPipeline - library : sklearn-svc - initialize: diff --git a/examples/classification/conf/rq.yaml b/examples/classification/conf/rq.yaml deleted file mode 100644 index 25ad380b..00000000 --- a/examples/classification/conf/rq.yaml +++ /dev/null @@ -1,51 +0,0 @@ -defaults: - - _self_ - - data: default - - model: default - - attack: hsj - - files: default - - scorers: default - - override hydra/sweeper : optuna - - override hydra/launcher : joblib -hydra: - run: - dir : "./${files.directory}" - sweeper: - sampler: - _target_: optuna.samplers.TPESampler - # seed: 123 - consider_prior: true - prior_weight: 1.0 - consider_magic_clip: true - consider_endpoints: false - n_startup_trials: 10 - n_ei_candidates: 24 - multivariate: false - warn_independent_sampling: true - _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - direction: maximize - study_name: model - storage: sqlite:///model.db - n_trials: 100 - n_jobs: 1 - launcher: - _target_: hydra_plugins.hydra_rq_launcher.rq_launcher.RQLauncher - enqueue: - job_timeout: null - ttl: null - result_ttl: null - failure_ttl: null - at_front: false - job_id: null - description: null - queue: default - redis: - host: ${oc.env:REDIS_HOST,localhost} - port: ${oc.env:REDIS_PORT,6379} - db: ${oc.env:REDIS_DB,0} - password: ${oc.env:REDIS_PASSWORD,null} - ssl: ${oc.env:REDIS_SSL,False} - ssl_ca_certs: ${oc.env:REDIS_SSL_CA_CERTS,null - mock: ${oc.env:REDIS_MOCK,False} - stop_after_enqueue: false - wait_polling: 1.0 diff --git a/examples/classification/conf/scorers/default.yaml b/examples/classification/conf/scorers/default.yaml deleted file mode 100644 index 108c1520..00000000 --- a/examples/classification/conf/scorers/default.yaml +++ /dev/null @@ -1,10 +0,0 @@ -_target_: deckard.base.scorer.ScorerDict -accuracy: - _target_: deckard.base.scorer.ScorerConfig - name: sklearn.metrics.accuracy_score - direction: maximize - -log_loss: - _target_: deckard.base.scorer.ScorerConfig - name: sklearn.metrics.log_loss - direction: minimize diff --git a/examples/classification/dvc.yaml b/examples/classification/dvc.yaml deleted file mode 100644 index 8c3d0f3c..00000000 --- a/examples/classification/dvc.yaml +++ /dev/null @@ -1,125 +0,0 @@ -stages: - attack: - cmd: python -m deckard.layers.experiment --stage attack - deps: - - ${files.data_file} - - ${files.reports}/train/${files.path}/${files.params_file} - - ${files.reports}/train/${files.path}/${files.predictions_file} - metrics: - - ${files.reports}/attack/${files.path}/${files.score_dict_file} - - ${files.reports}/attack/${files.path}/${files.time_dict_file} - - ${files.reports}/attack/${files.path}/${files.attack_score_dict_file} - - ${files.reports}/attack/${files.path}/${files.attack_time_dict_file} - - ${files.reports}/attack/${files.path}/${files.attack_predictions_file} - - ${files.reports}/attack/${files.path}/${files.attack_probabilities_file} - outs: - - ${files.reports}/attack/${files.path}/${files.params_file} - - ${files.reports}/attack/${files.path}/${files.attack_samples_file} - - ${files.reports}/attack/${files.path}/${files.predictions_file} - - ${files.reports}/attack/${files.path}/${files.probabilities_file} - params: - - data - - model.init - - files - - scorers - - attack - generate_data: - cmd: python -m deckard.layers.experiment --stage generate_data - deps: - - params.yaml - metrics: - - ${files.reports}/generate_data/${files.path}/${files.ground_truth_file} - outs: - - ${files.data_file} - params: - - data - - files - train: - cmd: python -m deckard.layers.experiment --stage train - deps: - - ${files.data_file} - metrics: - - ${files.reports}/train/${files.path}/${files.score_dict_file} - - ${files.reports}/train/${files.path}/${files.time_dict_file} - - ${files.reports}/train/${files.path}/${files.predictions_file} - - ${files.reports}/train/${files.path}/${files.probabilities_file} - outs: - - ${files.reports}/train/${files.path}/${files.params_file} - params: - - data - - model.init - - files - - scorers - models: - cmd: bash experiments.sh - outs: - - logs/models/ - - ${files.reports}/models/ - params: - - data - - model.init - - files - - scorers - deps: - - ${files.reports}/train/ - compile_results: - cmd: - python find_best.py --output_folder best_models --kwargs "data.sample.train_size=1000" "data.generate.n_features=100" --report_folder "reports/model_queue" --results_file "plots/model_results.csv" --scorer_minimize False --control_for "model.init.kernel" --exclude sigmoid --stage models - outs: - - best_models/ - metrics: - - plots/model_results.csv - deps: - - ${files.reports}/models/ - attacks: - cmd: bash attacks.sh - outs: - - logs/attacks/ - - ${files.reports}/attacks/ - deps: - - best_models/ - params: - - data - - model.init - - files - - attack - - scorers - compile_attacks: - cmd: - python find_best.py --output_folder best_attacks --kwargs "attack.init.max_iter=10" --report_folder "reports/attack" --results_file "plots/attack_results.csv" --scorer_minimize True --exclude sigmoid --stage attacks - metrics: - - plots/attack_results.csv - deps: - - ${files.reports}/attacks/ - outs: - - best_attacks/ - retrain: - cmd : python retrain.py - deps: - - ${files.reports}/models/ - - ${files.reports}/attacks/ - - best_models/ - - best_attacks/ - outs: - - retrain/ - metrics: - - plots/before_retrain_confidence.csv - - plots/after_retrain_confidence.csv - plots: - cmd : python plots.py - deps : - - plots/after_retrain_confidence.csv - - plots/attack_results.csv - - plots/before_retrain_confidence.csv - - plots/model_results.csv - plots : - - plots/accuracy_vs_attack_parameters.pdf - - plots/accuracy_vs_features.pdf - - plots/accuracy_vs_samples.pdf - - plots/confidence_vs_attack_parameters.pdf - - plots/fit_time_vs_attack_parameters.pdf - - plots/fit_time_vs_features.pdf - - plots/fit_time_vs_samples.pdf - - plots/retrain_accuracy.pdf - - plots/retrain_confidence_vs_attack_parameters.pdf - - plots/retrain_time.pdf diff --git a/examples/classification/models.sh b/examples/classification/models.sh deleted file mode 100644 index f3b3ff33..00000000 --- a/examples/classification/models.sh +++ /dev/null @@ -1,53 +0,0 @@ -N_FEATURES=( 10 100 1000 10000) -TRAIN_SIZES=( 100 1000 10000 100000 1000000 ) -# TRAIN_SIZES=( 2000 ) -# N_FEATURES=( 100 ) -N_SAMPLES=( 110000 ) -TOTAL=$(( ${#N_FEATURES[@]} * ${#N_SAMPLES[@]} * ${#TRAIN_SIZES[@]} )) -i=$(( 0 )) -mkdir -p logs/models -for train_size in ${TRAIN_SIZES[@]}; do - for n_samples in ${N_SAMPLES[@]}; do - for n_features in ${N_FEATURES[@]}; do - i=$(( i + 1 )) - n_informative=$(( $n_features-1 )) - echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" - echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" >| log.txt - HYDRA_FULL_ERROR=1; python ../../deckard/layers/optimize.py \ - data.generate.n_features=$n_features \ - data.generate.n_samples=$n_samples \ - data.sample.train_size=$train_size \ - model.init.kernel=linear \ - model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - +stage=models "$@" --multirun \ - >| logs/models/linear_features-${n_features}_samples-${n_samples}_train-${train_size}.log - echo "Linear Kernel Done" >> log.txt - i=$(( i + 1 )) - python ../../deckard/layers/optimize.py \ - data.generate.n_features=$n_features \ - data.generate.n_samples=$n_samples \ - data.sample.train_size=$train_size \ - model.init.kernel=rbf \ - +model.init.gamma=scale \ - model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - +stage=models "$@" --multirun \ - >| logs/models/rbf_features-${n_features}_samples-${n_samples}_train-${train_size}.log - echo "RBF Kernel Done" >> log.txt - i=$(( i + 1 )) - python ../../deckard/layers/optimize.py \ - data.generate.n_features=$n_features \ - data.generate.n_samples=$n_samples \ - data.sample.train_size=$train_size \ - model.init.kernel=poly \ - model.init.degree=1,2,3,4,5 \ - +model.init.gamma=scale \ - +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ - +stage=models "$@" --multirun \ - >| logs/models/poly_features-${n_features}_samples-${n_samples}_train-${train_size}.log - echo "Poly Kernel Done" >> log.txt - echo "Successfully completed experiment ${i} of ${TOTAL}" >> log.txt - done - done -done diff --git a/examples/classification/params.yaml b/examples/classification/params.yaml deleted file mode 100644 index b6dd6a11..00000000 --- a/examples/classification/params.yaml +++ /dev/null @@ -1,168 +0,0 @@ -attack: - _target_: deckard.base.attack.Attack - attack_size: 10 - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - initialize: null - library: sklearn-svc - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - C: 1.0 - _target_: deckard.base.model.ModelInitializer - kernel: rbf - max_iter: 100 - name: sklearn.svm.SVC - probability: true - random_state: 0 - library: sklearn-svc - name: art.attacks.evasion.HopSkipJump - method: evasion - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - initialize: null - library: sklearn-svc - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - C: 1.0 - _target_: deckard.base.model.ModelInitializer - kernel: rbf - max_iter: 100 - name: sklearn.svm.SVC - probability: true - random_state: 0 - library: sklearn-svc -data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true -files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - adv_probabilities_file: adv_probabilities.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: output - model_dir: models - model_file: model - model_type: .pkl - name: default - params_file: params.yaml - predictions_file: predictions.json - probabilities_file: probabilities.json - reports: reports - score_dict_file: score_dict.json - test_labels_file: test_labels.json - train_labels_file: train_labels.json -model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - initialize: null - library: sklearn-svc - data: - _target_: deckard.base.data.Data - generate: - n_features: 20 - n_samples: 1100 - name: classification - random_state: 0 - sample: - random_state: 0 - stratify: true - test_size: 1000 - train_size: 100 - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - C: 1.0 - _target_: deckard.base.model.ModelInitializer - kernel: rbf - max_iter: 100 - name: sklearn.svm.SVC - probability: true - random_state: 0 - library: sklearn-svc -scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss diff --git a/examples/classification/plots.py b/examples/classification/plots.py deleted file mode 100644 index 1420e713..00000000 --- a/examples/classification/plots.py +++ /dev/null @@ -1,381 +0,0 @@ -import pandas as pd -import seaborn as sns -from pathlib import Path -import matplotlib.pyplot as plt - -from deckard.layers.compile import parse_results -import logging - -sns.set_style("whitegrid") -sns.set_context("paper", font_scale=1.5) -sns.set_style({"font.family": "serif", "font.serif": "Times New Roman"}) -Path("plots").mkdir(parents=True, exist_ok=True) - - -logger = logging.getLogger(__name__) - - -if Path("plots", "model_results.csv").exists(): - results = pd.read_csv("plots/model_results.csv") -else: - results = parse_results("reports/model_queue/") -input_size = results["data.generate.n_samples"] * results["data.generate.n_features"] -results["Kernel"] = results["model.init.kernel"].copy() -results["Features"] = results["data.generate.n_features"].copy() -results["Samples"] = results["data.sample.train_size"].copy() -results["input_size"] = input_size -sample_list = results["data.generate.n_samples"].unique() -feature_list = results["data.generate.n_features"].unique() -kernel_list = results["model.init.kernel"].unique() -if "Unnamed: 0" in results.columns: - del results["Unnamed: 0"] -for col in results.columns: - if col == "data.name" and isinstance(results[col][0], list): - results[col] = results[col].apply(lambda x: x[0]) -results = results[results["model.init.kernel"] != "sigmoid"] - - -if Path("plots", "attack_results.csv").exists(): - attack_results = pd.read_csv("plots/attack_results.csv") -else: - attack_results = parse_results("reports/attack/") -attack_results["Kernel"] = attack_results["model.init.kernel"].copy() -attack_results["Features"] = attack_results["data.generate.n_features"].copy() -attack_results["Samples"] = attack_results["data.sample.train_size"].copy() -sample_list = attack_results["data.generate.n_samples"].unique() -feature_list = attack_results["data.generate.n_features"].unique() -kernel_list = attack_results["model.init.kernel"].unique() -if "Unnamed: 0" in attack_results.columns: - del attack_results["Unnamed: 0"] -for col in attack_results.columns: - if col == "data.name" and isinstance(attack_results[col][0], list): - attack_results[col] = attack_results[col].apply(lambda x: x[0]) - - -graph1 = sns.lineplot( - x="data.sample.train_size", - y="accuracy", - data=results, - style="Kernel", - style_order=["rbf", "poly", "linear"], -) -graph1.legend(labels=["Linear", "RBF", "Poly"]) -graph1.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") -graph1.set_xlabel("Number of Samples") -graph1.set_ylabel("Accuracy") -graph1.set_xscale("log") -graph1.get_figure().tight_layout() -graph1.get_figure().savefig("plots/accuracy_vs_samples.pdf") - - -graph2 = sns.lineplot( - x="data.generate.n_features", - y="accuracy", - data=results, - style="Kernel", - style_order=["rbf", "poly", "linear"], -) -graph2.set_xlabel("Number of Features") -graph2.set_ylabel("Accuracy") -graph2.set_xscale("log") -graph2.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") -graph2.get_figure().tight_layout() -graph2.get_figure().savefig("plots/accuracy_vs_features.pdf") - - -results["fit_time"] = ( - results["fit_time"] - * results["data.sample.train_size"] - * results["data.generate.n_samples"] -) -graph3 = sns.lineplot( - x="data.generate.n_features", - y="fit_time", - data=results, - style="Kernel", - style_order=["rbf", "poly", "linear"], -) -graph3.set_xlabel("Number of Features") -graph3.set_ylabel("Training Time") -graph3.set(yscale="log", xscale="log") -graph3.legend(title="Kernel") -graph3.get_figure().tight_layout() -graph3.get_figure().savefig("plots/fit_time_vs_features.pdf") - - -graph4 = sns.lineplot( - x="data.sample.train_size", - y="fit_time", - data=results, - style="Kernel", - style_order=["rbf", "poly", "linear"], -) -graph4.set_xlabel("Number of Samples") -graph4.set_ylabel("Training Time") -graph4.set(yscale="log", xscale="log") -graph4.legend(title="Kernel") -graph4.get_figure().tight_layout() -graph4.get_figure().savefig("plots/fit_time_vs_samples.pdf") - - -fig, ax = plt.subplots(2, 2) -graph5 = sns.lineplot( - x="attack.init.eps", - y="accuracy", - data=attack_results, - style="Kernel", - ax=ax[0, 0], - legend=False, - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph5.set(xscale="log", xlabel="Perturbation Distance", ylabel="Accuracy") -graph6 = sns.lineplot( - x="attack.init.eps_step", - y="accuracy", - data=attack_results, - style="Kernel", - ax=ax[0, 1], - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph6.set(xscale="log", xlabel="Perturbation Step", ylabel="Accuracy") -graph7 = sns.lineplot( - x="attack.init.max_iter", - y="accuracy", - data=attack_results, - style="Kernel", - ax=ax[1, 0], - legend=False, - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph7.set(xscale="log", xlabel="Maximum Iterations", ylabel="Accuracy") -graph8 = sns.lineplot( - x="attack.init.batch_size", - y="accuracy", - data=attack_results, - style="Kernel", - ax=ax[1, 1], - legend=False, - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph8.set(xscale="log", xlabel="Batch Size", ylabel="Accuracy") -graph6.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") -fig.tight_layout() -fig.savefig("plots/accuracy_vs_attack_parameters.pdf") - - -fig, ax = plt.subplots(2, 2) -graph9 = sns.lineplot( - x="attack.init.eps", - y="fit_time", - data=attack_results, - style="Kernel", - ax=ax[0, 0], - legend=False, - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="Attack Time") -graph10 = sns.lineplot( - x="attack.init.eps_step", - y="fit_time", - data=attack_results, - style="Kernel", - ax=ax[0, 1], - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="Attack Time") -graph11 = sns.lineplot( - x="attack.init.max_iter", - y="fit_time", - data=attack_results, - style="Kernel", - ax=ax[1, 0], - legend=False, - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="Attack Time") -graph12 = sns.lineplot( - x="attack.init.batch_size", - y="fit_time", - data=attack_results, - style="Kernel", - ax=ax[1, 1], - legend=False, - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph12.set(xscale="log", xlabel="Batch Size", ylabel="Attack Time") -graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") -fig.tight_layout(h_pad=0.5) -fig.savefig("plots/fit_time_vs_attack_parameters.pdf") - - -retrain_df = pd.DataFrame() -for kernel in ["rbf", "linear", "poly"]: - try: - tmp = pd.read_csv(f"retrain/{kernel}/results.csv") - except FileNotFoundError: - break - tmp["Kernel"] = kernel - tmp["Epochs"] = tmp.index - retrain_df = retrain_df.append(tmp) - retrain_df = retrain_df.reset_index(drop=True) - if "Unnamed: 0" in retrain_df.columns: - del retrain_df["Unnamed: 0"] -# retrain_df['train_size'] = retrain_df['train_size'] * 100 - - -retrain = sns.lineplot( - x="Epochs", - y="ben_score", - data=retrain_df, - style="Kernel", - style_order=["rbf", "poly", "linear"], -) -retrain = sns.lineplot( - x="Epochs", - y="adv_score", - data=retrain_df, - style="Kernel", - color="darkred", - legend=False, - style_order=["rbf", "poly", "linear"], -) -retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") -retrain.set_xlabel("Retraining Epochs") -retrain.set_ylabel("Accuracy") -retrain.get_figure().tight_layout() -retrain.get_figure().savefig("plots/retrain_accuracy.pdf") - - -retrain_df["ben_time"] = retrain_df["ben_time"] * retrain_df["train_size"] * 10 -retrain_df["adv_time"] = retrain_df["adv_time"] * retrain_df["attack_size"] -retrain = sns.lineplot( - x="Epochs", - y="ben_time", - data=retrain_df, - style="Kernel", - style_order=["rbf", "poly", "linear"], -) -retrain = sns.lineplot( - x="Epochs", - y="adv_time", - data=retrain_df, - style="Kernel", - color="darkred", - legend=False, - style_order=["rbf", "poly", "linear"], -) -retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") -retrain.set_xlabel("Retraining Epochs") -retrain.set_ylabel("Time") -retrain.set_yscale("log") -retrain.get_figure().tight_layout() -retrain.get_figure().savefig("plots/retrain_time.pdf") - - -confidence_df = pd.read_csv("plots/before_retrain_confidence.csv") -fig, ax = plt.subplots(2, 2) -graph9 = sns.lineplot( - x="eps", - y="Average False Confidence", - data=confidence_df, - style="Kernel", - ax=ax[0, 0], - legend=False, - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") -graph10 = sns.lineplot( - x="eps_step", - y="Average False Confidence", - data=confidence_df, - style="Kernel", - ax=ax[0, 1], - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") -graph11 = sns.lineplot( - x="max_iter", - y="Average False Confidence", - data=confidence_df, - style="Kernel", - ax=ax[1, 0], - legend=False, - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") -graph12 = sns.lineplot( - x="batch_size", - y="Average False Confidence", - data=confidence_df, - style="Kernel", - ax=ax[1, 1], - legend=False, - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") -graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") -fig.tight_layout(h_pad=0.5) -fig.savefig("plots/confidence_vs_attack_parameters.pdf") - - -confdence_df = pd.read_csv("plots/after_retrain_confidence.csv") -confidence_df.columns -fig, ax = plt.subplots(2, 2) -graph9 = sns.lineplot( - x="eps", - y="Average False Confidence", - data=confidence_df, - style="Kernel", - ax=ax[0, 0], - legend=False, - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") -graph10 = sns.lineplot( - x="eps_step", - y="Average False Confidence", - data=confidence_df, - style="Kernel", - ax=ax[0, 1], - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") -graph11 = sns.lineplot( - x="max_iter", - y="Average False Confidence", - data=confidence_df, - style="Kernel", - ax=ax[1, 0], - legend=False, - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") -graph12 = sns.lineplot( - x="batch_size", - y="Average False Confidence", - data=confidence_df, - style="Kernel", - ax=ax[1, 1], - legend=False, - color="darkred", - style_order=["rbf", "poly", "linear"], -) -graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") -graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") -fig.tight_layout(h_pad=0.5) -fig.savefig("plots/retrain_confidence_vs_attack_parameters.pdf") From 7f2df3488aceaea50e68e7c5389b9444ea483367 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 14 Nov 2023 16:05:37 +0000 Subject: [PATCH 122/148] update configs for security example --- examples/security/classification/conf/RQ.md | 1 + .../security/classification/conf/attack.yaml | 34 +++++++++++++ .../conf/attack/attack_grid.yaml | 49 ++++++++++++++++++ .../classification/conf/attack/default.yaml | 2 + .../classification/conf/attack/hsj.yaml | 8 +++ .../security/classification/conf/compile.yaml | 8 +++ .../classification/conf/data/attack.yaml | 23 +++++++++ .../classification/conf/data/default.yaml | 22 ++++++++ .../classification/conf/data/kdd_nsl.yaml | 17 +++++++ .../classification/conf/data/small.yaml | 20 ++++++++ .../classification/conf/data/truthseeker.yaml | 18 +++++++ .../security/classification/conf/default.yaml | 41 +++++++++++++++ .../classification/conf/files/default.yaml | 21 ++++++++ .../security/classification/conf/model.yaml | 34 +++++++++++++ .../conf/model/art/defense_grid.yaml | 44 ++++++++++++++++ .../conf/model/art/postprocessor.yaml | 6 +++ .../art/postprocessor/high_confidence.yaml | 6 +++ .../conf/model/art/postprocessor/labels.yaml | 6 +++ .../conf/model/art/preprocessor.yaml | 6 +++ .../art/preprocessor/feature_squeezing.yaml | 6 +++ .../classification/conf/model/default.yaml | 2 + .../classification/conf/model/linear.yaml | 15 ++++++ .../classification/conf/model/poly.yaml | 16 ++++++ .../classification/conf/model/rbf.yaml | 15 ++++++ examples/security/classification/conf/rq.yaml | 51 +++++++++++++++++++ .../classification/conf/scorers/default.yaml | 10 ++++ examples/security/kdd-nsl/conf/RQ.md | 1 + examples/security/kdd-nsl/conf/attack.yaml | 36 +++++++++++++ .../kdd-nsl/conf/attack/attack_grid.yaml | 49 ++++++++++++++++++ .../security/kdd-nsl/conf/attack/default.yaml | 2 + .../security/kdd-nsl/conf/attack/hsj.yaml | 8 +++ examples/security/kdd-nsl/conf/compile.yaml | 8 +++ .../security/kdd-nsl/conf/data/attack.yaml | 23 +++++++++ .../security/kdd-nsl/conf/data/default.yaml | 2 + .../security/kdd-nsl/conf/data/kdd_nsl.yaml | 18 +++++++ .../security/kdd-nsl/conf/data/small.yaml | 20 ++++++++ .../kdd-nsl/conf/data/truthseeker.yaml | 18 +++++++ examples/security/kdd-nsl/conf/default.yaml | 39 ++++++++++++++ .../security/kdd-nsl/conf/files/default.yaml | 21 ++++++++ examples/security/kdd-nsl/conf/model.yaml | 34 +++++++++++++ .../kdd-nsl/conf/model/art/defense_grid.yaml | 44 ++++++++++++++++ .../kdd-nsl/conf/model/art/postprocessor.yaml | 6 +++ .../art/postprocessor/high_confidence.yaml | 6 +++ .../conf/model/art/postprocessor/labels.yaml | 6 +++ .../kdd-nsl/conf/model/art/preprocessor.yaml | 6 +++ .../art/preprocessor/feature_squeezing.yaml | 6 +++ .../security/kdd-nsl/conf/model/default.yaml | 2 + .../security/kdd-nsl/conf/model/linear.yaml | 15 ++++++ .../security/kdd-nsl/conf/model/poly.yaml | 16 ++++++ examples/security/kdd-nsl/conf/model/rbf.yaml | 15 ++++++ examples/security/kdd-nsl/conf/rq.yaml | 51 +++++++++++++++++++ .../kdd-nsl/conf/scorers/default.yaml | 10 ++++ .../security/truthseeker/conf/attack.yaml | 36 +++++++++++++ .../truthseeker/conf/attack/attack_grid.yaml | 49 ++++++++++++++++++ .../truthseeker/conf/attack/default.yaml | 2 + .../security/truthseeker/conf/attack/hsj.yaml | 8 +++ .../security/truthseeker/conf/compile.yaml | 8 +++ .../truthseeker/conf/data/attack.yaml | 23 +++++++++ .../truthseeker/conf/data/default.yaml | 2 + .../truthseeker/conf/data/kdd_nsl.yaml | 18 +++++++ .../security/truthseeker/conf/data/small.yaml | 20 ++++++++ .../truthseeker/conf/data/truthseeker.yaml | 20 ++++++++ .../security/truthseeker/conf/default.yaml | 41 +++++++++++++++ .../truthseeker/conf/files/default.yaml | 21 ++++++++ examples/security/truthseeker/conf/model.yaml | 36 +++++++++++++ .../conf/model/art/defense_grid.yaml | 44 ++++++++++++++++ .../conf/model/art/postprocessor.yaml | 6 +++ .../art/postprocessor/high_confidence.yaml | 6 +++ .../conf/model/art/postprocessor/labels.yaml | 6 +++ .../conf/model/art/preprocessor.yaml | 6 +++ .../art/preprocessor/feature_squeezing.yaml | 6 +++ .../truthseeker/conf/model/default.yaml | 2 + .../truthseeker/conf/model/linear.yaml | 15 ++++++ .../security/truthseeker/conf/model/poly.yaml | 16 ++++++ .../security/truthseeker/conf/model/rbf.yaml | 15 ++++++ .../truthseeker/conf/scorers/default.yaml | 10 ++++ 76 files changed, 1359 insertions(+) create mode 100644 examples/security/classification/conf/RQ.md create mode 100644 examples/security/classification/conf/attack.yaml create mode 100644 examples/security/classification/conf/attack/attack_grid.yaml create mode 100644 examples/security/classification/conf/attack/default.yaml create mode 100644 examples/security/classification/conf/attack/hsj.yaml create mode 100644 examples/security/classification/conf/compile.yaml create mode 100644 examples/security/classification/conf/data/attack.yaml create mode 100644 examples/security/classification/conf/data/default.yaml create mode 100644 examples/security/classification/conf/data/kdd_nsl.yaml create mode 100644 examples/security/classification/conf/data/small.yaml create mode 100644 examples/security/classification/conf/data/truthseeker.yaml create mode 100644 examples/security/classification/conf/default.yaml create mode 100644 examples/security/classification/conf/files/default.yaml create mode 100644 examples/security/classification/conf/model.yaml create mode 100644 examples/security/classification/conf/model/art/defense_grid.yaml create mode 100644 examples/security/classification/conf/model/art/postprocessor.yaml create mode 100644 examples/security/classification/conf/model/art/postprocessor/high_confidence.yaml create mode 100644 examples/security/classification/conf/model/art/postprocessor/labels.yaml create mode 100644 examples/security/classification/conf/model/art/preprocessor.yaml create mode 100644 examples/security/classification/conf/model/art/preprocessor/feature_squeezing.yaml create mode 100644 examples/security/classification/conf/model/default.yaml create mode 100644 examples/security/classification/conf/model/linear.yaml create mode 100644 examples/security/classification/conf/model/poly.yaml create mode 100644 examples/security/classification/conf/model/rbf.yaml create mode 100644 examples/security/classification/conf/rq.yaml create mode 100644 examples/security/classification/conf/scorers/default.yaml create mode 100644 examples/security/kdd-nsl/conf/RQ.md create mode 100644 examples/security/kdd-nsl/conf/attack.yaml create mode 100644 examples/security/kdd-nsl/conf/attack/attack_grid.yaml create mode 100644 examples/security/kdd-nsl/conf/attack/default.yaml create mode 100644 examples/security/kdd-nsl/conf/attack/hsj.yaml create mode 100644 examples/security/kdd-nsl/conf/compile.yaml create mode 100644 examples/security/kdd-nsl/conf/data/attack.yaml create mode 100644 examples/security/kdd-nsl/conf/data/default.yaml create mode 100644 examples/security/kdd-nsl/conf/data/kdd_nsl.yaml create mode 100644 examples/security/kdd-nsl/conf/data/small.yaml create mode 100644 examples/security/kdd-nsl/conf/data/truthseeker.yaml create mode 100644 examples/security/kdd-nsl/conf/default.yaml create mode 100644 examples/security/kdd-nsl/conf/files/default.yaml create mode 100644 examples/security/kdd-nsl/conf/model.yaml create mode 100644 examples/security/kdd-nsl/conf/model/art/defense_grid.yaml create mode 100644 examples/security/kdd-nsl/conf/model/art/postprocessor.yaml create mode 100644 examples/security/kdd-nsl/conf/model/art/postprocessor/high_confidence.yaml create mode 100644 examples/security/kdd-nsl/conf/model/art/postprocessor/labels.yaml create mode 100644 examples/security/kdd-nsl/conf/model/art/preprocessor.yaml create mode 100644 examples/security/kdd-nsl/conf/model/art/preprocessor/feature_squeezing.yaml create mode 100644 examples/security/kdd-nsl/conf/model/default.yaml create mode 100644 examples/security/kdd-nsl/conf/model/linear.yaml create mode 100644 examples/security/kdd-nsl/conf/model/poly.yaml create mode 100644 examples/security/kdd-nsl/conf/model/rbf.yaml create mode 100644 examples/security/kdd-nsl/conf/rq.yaml create mode 100644 examples/security/kdd-nsl/conf/scorers/default.yaml create mode 100644 examples/security/truthseeker/conf/attack.yaml create mode 100644 examples/security/truthseeker/conf/attack/attack_grid.yaml create mode 100644 examples/security/truthseeker/conf/attack/default.yaml create mode 100644 examples/security/truthseeker/conf/attack/hsj.yaml create mode 100644 examples/security/truthseeker/conf/compile.yaml create mode 100644 examples/security/truthseeker/conf/data/attack.yaml create mode 100644 examples/security/truthseeker/conf/data/default.yaml create mode 100644 examples/security/truthseeker/conf/data/kdd_nsl.yaml create mode 100644 examples/security/truthseeker/conf/data/small.yaml create mode 100644 examples/security/truthseeker/conf/data/truthseeker.yaml create mode 100644 examples/security/truthseeker/conf/default.yaml create mode 100644 examples/security/truthseeker/conf/files/default.yaml create mode 100644 examples/security/truthseeker/conf/model.yaml create mode 100644 examples/security/truthseeker/conf/model/art/defense_grid.yaml create mode 100644 examples/security/truthseeker/conf/model/art/postprocessor.yaml create mode 100644 examples/security/truthseeker/conf/model/art/postprocessor/high_confidence.yaml create mode 100644 examples/security/truthseeker/conf/model/art/postprocessor/labels.yaml create mode 100644 examples/security/truthseeker/conf/model/art/preprocessor.yaml create mode 100644 examples/security/truthseeker/conf/model/art/preprocessor/feature_squeezing.yaml create mode 100644 examples/security/truthseeker/conf/model/default.yaml create mode 100644 examples/security/truthseeker/conf/model/linear.yaml create mode 100644 examples/security/truthseeker/conf/model/poly.yaml create mode 100644 examples/security/truthseeker/conf/model/rbf.yaml create mode 100644 examples/security/truthseeker/conf/scorers/default.yaml diff --git a/examples/security/classification/conf/RQ.md b/examples/security/classification/conf/RQ.md new file mode 100644 index 00000000..c1e745ce --- /dev/null +++ b/examples/security/classification/conf/RQ.md @@ -0,0 +1 @@ +sudo apt install lsb-release curl gpg diff --git a/examples/security/classification/conf/attack.yaml b/examples/security/classification/conf/attack.yaml new file mode 100644 index 00000000..da237ab0 --- /dev/null +++ b/examples/security/classification/conf/attack.yaml @@ -0,0 +1,34 @@ +defaults: + # - _target_ : deckard.base.experiment.Experiment + - _self_ + - data: attack + - model: default + - attack : hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +optimizers: adv_accuracy +direction : minimize +hydra: + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: ${direction} + study_name: attack + storage: sqlite:///attack.db + n_jobs: 1 + params: + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 32 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/security/classification/conf/attack/attack_grid.yaml b/examples/security/classification/conf/attack/attack_grid.yaml new file mode 100644 index 00000000..d7dc3050 --- /dev/null +++ b/examples/security/classification/conf/attack/attack_grid.yaml @@ -0,0 +1,49 @@ +- attack.init.name: [art.attacks.evasion.FastGradientMethod] + attack.init.eps : [.01, .03, .3, .1] + attack.init.norm : ['inf', 1, 2] + attack.init.eps_step : [.001, .003, .01] + attack.init.batch_size : [100] + +- attack.init.name: [art.attacks.evasion.ProjectedGradientDescent] + attack.init.eps : [.01, .03, .3, .1] + attack.init.norm : ['inf', 1, 2] + attack.init.eps_step : [.001, .003, .01] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.CarliniL0Method] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + attack.init.confidence : [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.CarliniL2Method] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + attack.init.confidence : [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.CarliniLInfMethod] + attack.init.max_iter : [10] + attack.init.confidence: [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.DeepFool] + attack.init.max_iter : [10] + attack.init.batch_size : [100] + attack.init.nb_grads : [10, 100, 1000] + +- attack.init.name: [art.attacks.evasion.HopSkipJump] + attack.init.max_iter : [10] + attack.init.max_eval : [10] + attack.init.init_eval : [10] + attack.init.norm : ['inf', 2] + +- attack.init.name: [art.attacks.evasion.PixelAttack] + attack.init.th : [.5, .9, .99] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.ThresholdAttack] + attack.init.th : [.5, .9, .99] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.AdversarialPatch] + attack.init.max_iter : [10] + attack.init.learning_rate : [.5, 5.0, 50.0] diff --git a/examples/security/classification/conf/attack/default.yaml b/examples/security/classification/conf/attack/default.yaml new file mode 100644 index 00000000..e0967ff7 --- /dev/null +++ b/examples/security/classification/conf/attack/default.yaml @@ -0,0 +1,2 @@ +defaults: + - hsj diff --git a/examples/security/classification/conf/attack/hsj.yaml b/examples/security/classification/conf/attack/hsj.yaml new file mode 100644 index 00000000..8dbce941 --- /dev/null +++ b/examples/security/classification/conf/attack/hsj.yaml @@ -0,0 +1,8 @@ +data: ${data} +model: ${model} +_target_ : deckard.base.attack.Attack +init: + name: art.attacks.evasion.ProjectedGradientDescent + model: ${model} +attack_size : 10 +method : evasion diff --git a/examples/security/classification/conf/compile.yaml b/examples/security/classification/conf/compile.yaml new file mode 100644 index 00000000..817d2b4e --- /dev/null +++ b/examples/security/classification/conf/compile.yaml @@ -0,0 +1,8 @@ +attacks: + ProjectedGradientDescent: PGD + null : null +defences: + Control: Control +params: + Control: model.init.kwargs.kernel + PGD: attack.init.kwargs.eps diff --git a/examples/security/classification/conf/data/attack.yaml b/examples/security/classification/conf/data/attack.yaml new file mode 100644 index 00000000..3d77ffb4 --- /dev/null +++ b/examples/security/classification/conf/data/attack.yaml @@ -0,0 +1,23 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 11100 + n_features : 100 + n_informative : 99 + n_redundant : 0 + n_repeated : 0 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 10000 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/security/classification/conf/data/default.yaml b/examples/security/classification/conf/data/default.yaml new file mode 100644 index 00000000..d5aa9091 --- /dev/null +++ b/examples/security/classification/conf/data/default.yaml @@ -0,0 +1,22 @@ +# _target_: deckard.base.data.Data +# generate: +# # _target_: deckard.base.data.generator.DataGenerator +# name: classification +# random_state : 0 +# n_samples : 1100 +# n_features : 20 +# sample: +# # _target_: deckard.base.data.sampler.SklearnDataSampler +# random_state : 0 +# stratify: True +# train_size : 100 +# test_size : 1000 +# sklearn_pipeline: +# # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline +# preprocessor: +# # +# name: sklearn.preprocessing.StandardScaler +# with_mean: True +# with_std: True +defaults: + - small diff --git a/examples/security/classification/conf/data/kdd_nsl.yaml b/examples/security/classification/conf/data/kdd_nsl.yaml new file mode 100644 index 00000000..ceae4f30 --- /dev/null +++ b/examples/security/classification/conf/data/kdd_nsl.yaml @@ -0,0 +1,17 @@ +_target_: deckard.base.data.Data +name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv +target : label +sample: + random_state : 0 + stratify: True + train_size : null + test_size : 1000 +sklearn_pipeline: + encoder: + name : sklearn.preprocessing.OneHotEncoder + handle_unknown: ignore + sparse : false + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/security/classification/conf/data/small.yaml b/examples/security/classification/conf/data/small.yaml new file mode 100644 index 00000000..2207d717 --- /dev/null +++ b/examples/security/classification/conf/data/small.yaml @@ -0,0 +1,20 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 1100 + n_features : 20 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 100 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/security/classification/conf/data/truthseeker.yaml b/examples/security/classification/conf/data/truthseeker.yaml new file mode 100644 index 00000000..9e0acbe3 --- /dev/null +++ b/examples/security/classification/conf/data/truthseeker.yaml @@ -0,0 +1,18 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/379b679bdea30724e9fa188931f0109ff422cce0/kdd_nsl.csv + target : -2 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 10000 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/security/classification/conf/default.yaml b/examples/security/classification/conf/default.yaml new file mode 100644 index 00000000..271ce8c2 --- /dev/null +++ b/examples/security/classification/conf/default.yaml @@ -0,0 +1,41 @@ +defaults: + - _self_ + - data: default + - model: default + - attack: hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +direction: maximize +optimizers : accuracy +hydra: + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + # seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 100 + n_jobs: 1 + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 6 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/security/classification/conf/files/default.yaml b/examples/security/classification/conf/files/default.yaml new file mode 100644 index 00000000..e2247d7e --- /dev/null +++ b/examples/security/classification/conf/files/default.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: output +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pkl +params_file : params.yaml +train_labels_file : train_labels.json +test_labels_file : test_labels.json +probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +adv_probabilities_file: adv_probabilities.json +name : default diff --git a/examples/security/classification/conf/model.yaml b/examples/security/classification/conf/model.yaml new file mode 100644 index 00000000..6380abc4 --- /dev/null +++ b/examples/security/classification/conf/model.yaml @@ -0,0 +1,34 @@ +defaults: + # - _target_ : deckard.base.experiment.Experiment + - _self_ + - data: default + - model: default + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +optimizers : accuracy +direction : maximize +hydra: + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: ${direction} + study_name: model + storage: sqlite:///model.db + n_jobs: 1 + params: + model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 32 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/security/classification/conf/model/art/defense_grid.yaml b/examples/security/classification/conf/model/art/defense_grid.yaml new file mode 100644 index 00000000..a3fe3719 --- /dev/null +++ b/examples/security/classification/conf/model/art/defense_grid.yaml @@ -0,0 +1,44 @@ +- model.art.preprocessor.name: art.defences.preprocessor.FeatureSqueezing + model.art.preprocessor.params: + clip_values : + - [0,255] + bit_depth : [4, 8, 16, 32, 64] + +- model.art.preprocessor.name: art.defences.preprocessor.GaussianAugmentation + model.art.preprocessor.params: + clip_values : + - [0,255] + sigma : [.1, .3, 1] + ratio : [.1, .5, 1] + +- model.art.preprocessor.name: art.defences.preprocessor.SpatialSmoothing + model.art.preprocessor.params: + clip_values : + - [0,255] + window_size : [2,3,4] + +- model.art.preprocessor.name: art.defences.preprocessor.TotalVarMin + model.art.preprocessor.params: + clip_values : + - [0,255] + prob : [.001, .01, .1] + norm : [1, 2, 3] + lamb : [.05, .5, .95] + max_iter : [100] + +- model.art.postprocessor.name : art.defences.postprocessor.GaussianNoise + model.art.postprocessor.params: + clip_values : + - [0,255] + scale: [.1, .9, .999] + +- model.art.postprocessor.name : art.defences.postprocessor.HighConfidence + model.art.postprocessor.params: + cutoff : [.1, .5, .9, .99] + + +- model.art.postprocessor.name : art.defences.postprocessor.Rounded + model.art.preprocessor.params: + clip_values : + - [0,255] + decimals : [1, 2, 4, 8] diff --git a/examples/security/classification/conf/model/art/postprocessor.yaml b/examples/security/classification/conf/model/art/postprocessor.yaml new file mode 100644 index 00000000..91f3c00a --- /dev/null +++ b/examples/security/classification/conf/model/art/postprocessor.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : sklearn-svc +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/security/classification/conf/model/art/postprocessor/high_confidence.yaml b/examples/security/classification/conf/model/art/postprocessor/high_confidence.yaml new file mode 100644 index 00000000..16ae0e50 --- /dev/null +++ b/examples/security/classification/conf/model/art/postprocessor/high_confidence.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/security/classification/conf/model/art/postprocessor/labels.yaml b/examples/security/classification/conf/model/art/postprocessor/labels.yaml new file mode 100644 index 00000000..16ae0e50 --- /dev/null +++ b/examples/security/classification/conf/model/art/postprocessor/labels.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/security/classification/conf/model/art/preprocessor.yaml b/examples/security/classification/conf/model/art/preprocessor.yaml new file mode 100644 index 00000000..ebf46909 --- /dev/null +++ b/examples/security/classification/conf/model/art/preprocessor.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : sklearn-svc +preprocessor: + name: art.defences.preprocessor.FeatureSqueezing + bit_depth: 32 +initialize: diff --git a/examples/security/classification/conf/model/art/preprocessor/feature_squeezing.yaml b/examples/security/classification/conf/model/art/preprocessor/feature_squeezing.yaml new file mode 100644 index 00000000..1c48b3e7 --- /dev/null +++ b/examples/security/classification/conf/model/art/preprocessor/feature_squeezing.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +preprocessor: + name: art.defences.preprocessor.FeatureSqueezing + bit_depth: 32 +initialize: diff --git a/examples/security/classification/conf/model/default.yaml b/examples/security/classification/conf/model/default.yaml new file mode 100644 index 00000000..5f8bd822 --- /dev/null +++ b/examples/security/classification/conf/model/default.yaml @@ -0,0 +1,2 @@ +defaults: + - rbf diff --git a/examples/security/classification/conf/model/linear.yaml b/examples/security/classification/conf/model/linear.yaml new file mode 100644 index 00000000..337909e1 --- /dev/null +++ b/examples/security/classification/conf/model/linear.yaml @@ -0,0 +1,15 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + kernel : linear + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/security/classification/conf/model/poly.yaml b/examples/security/classification/conf/model/poly.yaml new file mode 100644 index 00000000..8bdae408 --- /dev/null +++ b/examples/security/classification/conf/model/poly.yaml @@ -0,0 +1,16 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + coef0 : 0.0 + kernel : poly + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/security/classification/conf/model/rbf.yaml b/examples/security/classification/conf/model/rbf.yaml new file mode 100644 index 00000000..8b2d61af --- /dev/null +++ b/examples/security/classification/conf/model/rbf.yaml @@ -0,0 +1,15 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + kernel : rbf + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/security/classification/conf/rq.yaml b/examples/security/classification/conf/rq.yaml new file mode 100644 index 00000000..25ad380b --- /dev/null +++ b/examples/security/classification/conf/rq.yaml @@ -0,0 +1,51 @@ +defaults: + - _self_ + - data: default + - model: default + - attack: hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + # seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 100 + n_jobs: 1 + launcher: + _target_: hydra_plugins.hydra_rq_launcher.rq_launcher.RQLauncher + enqueue: + job_timeout: null + ttl: null + result_ttl: null + failure_ttl: null + at_front: false + job_id: null + description: null + queue: default + redis: + host: ${oc.env:REDIS_HOST,localhost} + port: ${oc.env:REDIS_PORT,6379} + db: ${oc.env:REDIS_DB,0} + password: ${oc.env:REDIS_PASSWORD,null} + ssl: ${oc.env:REDIS_SSL,False} + ssl_ca_certs: ${oc.env:REDIS_SSL_CA_CERTS,null + mock: ${oc.env:REDIS_MOCK,False} + stop_after_enqueue: false + wait_polling: 1.0 diff --git a/examples/security/classification/conf/scorers/default.yaml b/examples/security/classification/conf/scorers/default.yaml new file mode 100644 index 00000000..108c1520 --- /dev/null +++ b/examples/security/classification/conf/scorers/default.yaml @@ -0,0 +1,10 @@ +_target_: deckard.base.scorer.ScorerDict +accuracy: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.accuracy_score + direction: maximize + +log_loss: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.log_loss + direction: minimize diff --git a/examples/security/kdd-nsl/conf/RQ.md b/examples/security/kdd-nsl/conf/RQ.md new file mode 100644 index 00000000..c1e745ce --- /dev/null +++ b/examples/security/kdd-nsl/conf/RQ.md @@ -0,0 +1 @@ +sudo apt install lsb-release curl gpg diff --git a/examples/security/kdd-nsl/conf/attack.yaml b/examples/security/kdd-nsl/conf/attack.yaml new file mode 100644 index 00000000..4b7c0838 --- /dev/null +++ b/examples/security/kdd-nsl/conf/attack.yaml @@ -0,0 +1,36 @@ +defaults: + # - _target_ : deckard.base.experiment.Experiment + - _self_ + - data: kdd_nsl + - model: default + - attack : hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +optimizers: adv_accuracy +direction : minimize +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: ${direction} + study_name: attack + storage: sqlite:///attack.db + n_jobs: 1 + params: + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 32 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/security/kdd-nsl/conf/attack/attack_grid.yaml b/examples/security/kdd-nsl/conf/attack/attack_grid.yaml new file mode 100644 index 00000000..d7dc3050 --- /dev/null +++ b/examples/security/kdd-nsl/conf/attack/attack_grid.yaml @@ -0,0 +1,49 @@ +- attack.init.name: [art.attacks.evasion.FastGradientMethod] + attack.init.eps : [.01, .03, .3, .1] + attack.init.norm : ['inf', 1, 2] + attack.init.eps_step : [.001, .003, .01] + attack.init.batch_size : [100] + +- attack.init.name: [art.attacks.evasion.ProjectedGradientDescent] + attack.init.eps : [.01, .03, .3, .1] + attack.init.norm : ['inf', 1, 2] + attack.init.eps_step : [.001, .003, .01] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.CarliniL0Method] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + attack.init.confidence : [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.CarliniL2Method] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + attack.init.confidence : [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.CarliniLInfMethod] + attack.init.max_iter : [10] + attack.init.confidence: [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.DeepFool] + attack.init.max_iter : [10] + attack.init.batch_size : [100] + attack.init.nb_grads : [10, 100, 1000] + +- attack.init.name: [art.attacks.evasion.HopSkipJump] + attack.init.max_iter : [10] + attack.init.max_eval : [10] + attack.init.init_eval : [10] + attack.init.norm : ['inf', 2] + +- attack.init.name: [art.attacks.evasion.PixelAttack] + attack.init.th : [.5, .9, .99] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.ThresholdAttack] + attack.init.th : [.5, .9, .99] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.AdversarialPatch] + attack.init.max_iter : [10] + attack.init.learning_rate : [.5, 5.0, 50.0] diff --git a/examples/security/kdd-nsl/conf/attack/default.yaml b/examples/security/kdd-nsl/conf/attack/default.yaml new file mode 100644 index 00000000..e0967ff7 --- /dev/null +++ b/examples/security/kdd-nsl/conf/attack/default.yaml @@ -0,0 +1,2 @@ +defaults: + - hsj diff --git a/examples/security/kdd-nsl/conf/attack/hsj.yaml b/examples/security/kdd-nsl/conf/attack/hsj.yaml new file mode 100644 index 00000000..cb2399a3 --- /dev/null +++ b/examples/security/kdd-nsl/conf/attack/hsj.yaml @@ -0,0 +1,8 @@ +data: ${data} +model: ${model} +_target_ : deckard.base.attack.Attack +init: + name: art.attacks.evasion.HopSkipJump + model: ${model} +attack_size : 10 +method : evasion diff --git a/examples/security/kdd-nsl/conf/compile.yaml b/examples/security/kdd-nsl/conf/compile.yaml new file mode 100644 index 00000000..817d2b4e --- /dev/null +++ b/examples/security/kdd-nsl/conf/compile.yaml @@ -0,0 +1,8 @@ +attacks: + ProjectedGradientDescent: PGD + null : null +defences: + Control: Control +params: + Control: model.init.kwargs.kernel + PGD: attack.init.kwargs.eps diff --git a/examples/security/kdd-nsl/conf/data/attack.yaml b/examples/security/kdd-nsl/conf/data/attack.yaml new file mode 100644 index 00000000..3d77ffb4 --- /dev/null +++ b/examples/security/kdd-nsl/conf/data/attack.yaml @@ -0,0 +1,23 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 11100 + n_features : 100 + n_informative : 99 + n_redundant : 0 + n_repeated : 0 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 10000 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/security/kdd-nsl/conf/data/default.yaml b/examples/security/kdd-nsl/conf/data/default.yaml new file mode 100644 index 00000000..b3d67001 --- /dev/null +++ b/examples/security/kdd-nsl/conf/data/default.yaml @@ -0,0 +1,2 @@ +defaults: + - kdd_nsl diff --git a/examples/security/kdd-nsl/conf/data/kdd_nsl.yaml b/examples/security/kdd-nsl/conf/data/kdd_nsl.yaml new file mode 100644 index 00000000..e55be441 --- /dev/null +++ b/examples/security/kdd-nsl/conf/data/kdd_nsl.yaml @@ -0,0 +1,18 @@ +_target_: deckard.base.data.Data +name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv +target : label +sample: + _target_: deckard.base.data.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 5000 + test_size : 1000 +sklearn_pipeline: + encoder: + name : sklearn.preprocessing.OrdinalEncoder + handle_unknown : use_encoded_value + unknown_value : -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/security/kdd-nsl/conf/data/small.yaml b/examples/security/kdd-nsl/conf/data/small.yaml new file mode 100644 index 00000000..2207d717 --- /dev/null +++ b/examples/security/kdd-nsl/conf/data/small.yaml @@ -0,0 +1,20 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 1100 + n_features : 20 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 100 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/security/kdd-nsl/conf/data/truthseeker.yaml b/examples/security/kdd-nsl/conf/data/truthseeker.yaml new file mode 100644 index 00000000..9e0acbe3 --- /dev/null +++ b/examples/security/kdd-nsl/conf/data/truthseeker.yaml @@ -0,0 +1,18 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/379b679bdea30724e9fa188931f0109ff422cce0/kdd_nsl.csv + target : -2 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 10000 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/security/kdd-nsl/conf/default.yaml b/examples/security/kdd-nsl/conf/default.yaml new file mode 100644 index 00000000..e28d87bb --- /dev/null +++ b/examples/security/kdd-nsl/conf/default.yaml @@ -0,0 +1,39 @@ +defaults: + - _self_ + - data: default + - model: default + - attack: hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +hydra: + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + # seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 100 + n_jobs: 1 + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 6 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/security/kdd-nsl/conf/files/default.yaml b/examples/security/kdd-nsl/conf/files/default.yaml new file mode 100644 index 00000000..e2247d7e --- /dev/null +++ b/examples/security/kdd-nsl/conf/files/default.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: output +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pkl +params_file : params.yaml +train_labels_file : train_labels.json +test_labels_file : test_labels.json +probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +adv_probabilities_file: adv_probabilities.json +name : default diff --git a/examples/security/kdd-nsl/conf/model.yaml b/examples/security/kdd-nsl/conf/model.yaml new file mode 100644 index 00000000..7d52b51f --- /dev/null +++ b/examples/security/kdd-nsl/conf/model.yaml @@ -0,0 +1,34 @@ +defaults: + # - _target_ : deckard.base.experiment.Experiment + - _self_ + - data: kdd_nsl + - model: default + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +optimizers : accuracy +direction : maximize +hydra: + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: ${direction} + study_name: model + storage: sqlite:///model.db + n_jobs: 1 + params: + model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 32 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/security/kdd-nsl/conf/model/art/defense_grid.yaml b/examples/security/kdd-nsl/conf/model/art/defense_grid.yaml new file mode 100644 index 00000000..a3fe3719 --- /dev/null +++ b/examples/security/kdd-nsl/conf/model/art/defense_grid.yaml @@ -0,0 +1,44 @@ +- model.art.preprocessor.name: art.defences.preprocessor.FeatureSqueezing + model.art.preprocessor.params: + clip_values : + - [0,255] + bit_depth : [4, 8, 16, 32, 64] + +- model.art.preprocessor.name: art.defences.preprocessor.GaussianAugmentation + model.art.preprocessor.params: + clip_values : + - [0,255] + sigma : [.1, .3, 1] + ratio : [.1, .5, 1] + +- model.art.preprocessor.name: art.defences.preprocessor.SpatialSmoothing + model.art.preprocessor.params: + clip_values : + - [0,255] + window_size : [2,3,4] + +- model.art.preprocessor.name: art.defences.preprocessor.TotalVarMin + model.art.preprocessor.params: + clip_values : + - [0,255] + prob : [.001, .01, .1] + norm : [1, 2, 3] + lamb : [.05, .5, .95] + max_iter : [100] + +- model.art.postprocessor.name : art.defences.postprocessor.GaussianNoise + model.art.postprocessor.params: + clip_values : + - [0,255] + scale: [.1, .9, .999] + +- model.art.postprocessor.name : art.defences.postprocessor.HighConfidence + model.art.postprocessor.params: + cutoff : [.1, .5, .9, .99] + + +- model.art.postprocessor.name : art.defences.postprocessor.Rounded + model.art.preprocessor.params: + clip_values : + - [0,255] + decimals : [1, 2, 4, 8] diff --git a/examples/security/kdd-nsl/conf/model/art/postprocessor.yaml b/examples/security/kdd-nsl/conf/model/art/postprocessor.yaml new file mode 100644 index 00000000..91f3c00a --- /dev/null +++ b/examples/security/kdd-nsl/conf/model/art/postprocessor.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : sklearn-svc +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/security/kdd-nsl/conf/model/art/postprocessor/high_confidence.yaml b/examples/security/kdd-nsl/conf/model/art/postprocessor/high_confidence.yaml new file mode 100644 index 00000000..16ae0e50 --- /dev/null +++ b/examples/security/kdd-nsl/conf/model/art/postprocessor/high_confidence.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/security/kdd-nsl/conf/model/art/postprocessor/labels.yaml b/examples/security/kdd-nsl/conf/model/art/postprocessor/labels.yaml new file mode 100644 index 00000000..16ae0e50 --- /dev/null +++ b/examples/security/kdd-nsl/conf/model/art/postprocessor/labels.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/security/kdd-nsl/conf/model/art/preprocessor.yaml b/examples/security/kdd-nsl/conf/model/art/preprocessor.yaml new file mode 100644 index 00000000..ebf46909 --- /dev/null +++ b/examples/security/kdd-nsl/conf/model/art/preprocessor.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : sklearn-svc +preprocessor: + name: art.defences.preprocessor.FeatureSqueezing + bit_depth: 32 +initialize: diff --git a/examples/security/kdd-nsl/conf/model/art/preprocessor/feature_squeezing.yaml b/examples/security/kdd-nsl/conf/model/art/preprocessor/feature_squeezing.yaml new file mode 100644 index 00000000..1c48b3e7 --- /dev/null +++ b/examples/security/kdd-nsl/conf/model/art/preprocessor/feature_squeezing.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +preprocessor: + name: art.defences.preprocessor.FeatureSqueezing + bit_depth: 32 +initialize: diff --git a/examples/security/kdd-nsl/conf/model/default.yaml b/examples/security/kdd-nsl/conf/model/default.yaml new file mode 100644 index 00000000..5f8bd822 --- /dev/null +++ b/examples/security/kdd-nsl/conf/model/default.yaml @@ -0,0 +1,2 @@ +defaults: + - rbf diff --git a/examples/security/kdd-nsl/conf/model/linear.yaml b/examples/security/kdd-nsl/conf/model/linear.yaml new file mode 100644 index 00000000..337909e1 --- /dev/null +++ b/examples/security/kdd-nsl/conf/model/linear.yaml @@ -0,0 +1,15 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + kernel : linear + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/security/kdd-nsl/conf/model/poly.yaml b/examples/security/kdd-nsl/conf/model/poly.yaml new file mode 100644 index 00000000..8bdae408 --- /dev/null +++ b/examples/security/kdd-nsl/conf/model/poly.yaml @@ -0,0 +1,16 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + coef0 : 0.0 + kernel : poly + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/security/kdd-nsl/conf/model/rbf.yaml b/examples/security/kdd-nsl/conf/model/rbf.yaml new file mode 100644 index 00000000..8b2d61af --- /dev/null +++ b/examples/security/kdd-nsl/conf/model/rbf.yaml @@ -0,0 +1,15 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + kernel : rbf + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/security/kdd-nsl/conf/rq.yaml b/examples/security/kdd-nsl/conf/rq.yaml new file mode 100644 index 00000000..25ad380b --- /dev/null +++ b/examples/security/kdd-nsl/conf/rq.yaml @@ -0,0 +1,51 @@ +defaults: + - _self_ + - data: default + - model: default + - attack: hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + # seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 100 + n_jobs: 1 + launcher: + _target_: hydra_plugins.hydra_rq_launcher.rq_launcher.RQLauncher + enqueue: + job_timeout: null + ttl: null + result_ttl: null + failure_ttl: null + at_front: false + job_id: null + description: null + queue: default + redis: + host: ${oc.env:REDIS_HOST,localhost} + port: ${oc.env:REDIS_PORT,6379} + db: ${oc.env:REDIS_DB,0} + password: ${oc.env:REDIS_PASSWORD,null} + ssl: ${oc.env:REDIS_SSL,False} + ssl_ca_certs: ${oc.env:REDIS_SSL_CA_CERTS,null + mock: ${oc.env:REDIS_MOCK,False} + stop_after_enqueue: false + wait_polling: 1.0 diff --git a/examples/security/kdd-nsl/conf/scorers/default.yaml b/examples/security/kdd-nsl/conf/scorers/default.yaml new file mode 100644 index 00000000..108c1520 --- /dev/null +++ b/examples/security/kdd-nsl/conf/scorers/default.yaml @@ -0,0 +1,10 @@ +_target_: deckard.base.scorer.ScorerDict +accuracy: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.accuracy_score + direction: maximize + +log_loss: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.log_loss + direction: minimize diff --git a/examples/security/truthseeker/conf/attack.yaml b/examples/security/truthseeker/conf/attack.yaml new file mode 100644 index 00000000..4b7c0838 --- /dev/null +++ b/examples/security/truthseeker/conf/attack.yaml @@ -0,0 +1,36 @@ +defaults: + # - _target_ : deckard.base.experiment.Experiment + - _self_ + - data: kdd_nsl + - model: default + - attack : hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +optimizers: adv_accuracy +direction : minimize +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: ${direction} + study_name: attack + storage: sqlite:///attack.db + n_jobs: 1 + params: + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 32 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/security/truthseeker/conf/attack/attack_grid.yaml b/examples/security/truthseeker/conf/attack/attack_grid.yaml new file mode 100644 index 00000000..d7dc3050 --- /dev/null +++ b/examples/security/truthseeker/conf/attack/attack_grid.yaml @@ -0,0 +1,49 @@ +- attack.init.name: [art.attacks.evasion.FastGradientMethod] + attack.init.eps : [.01, .03, .3, .1] + attack.init.norm : ['inf', 1, 2] + attack.init.eps_step : [.001, .003, .01] + attack.init.batch_size : [100] + +- attack.init.name: [art.attacks.evasion.ProjectedGradientDescent] + attack.init.eps : [.01, .03, .3, .1] + attack.init.norm : ['inf', 1, 2] + attack.init.eps_step : [.001, .003, .01] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.CarliniL0Method] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + attack.init.confidence : [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.CarliniL2Method] + attack.init.batch_size : [100] + attack.init.max_iter : [10] + attack.init.confidence : [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.CarliniLInfMethod] + attack.init.max_iter : [10] + attack.init.confidence: [.1, .9, .99] + +- attack.init.name: [art.attacks.evasion.DeepFool] + attack.init.max_iter : [10] + attack.init.batch_size : [100] + attack.init.nb_grads : [10, 100, 1000] + +- attack.init.name: [art.attacks.evasion.HopSkipJump] + attack.init.max_iter : [10] + attack.init.max_eval : [10] + attack.init.init_eval : [10] + attack.init.norm : ['inf', 2] + +- attack.init.name: [art.attacks.evasion.PixelAttack] + attack.init.th : [.5, .9, .99] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.ThresholdAttack] + attack.init.th : [.5, .9, .99] + attack.init.max_iter : [10] + +- attack.init.name: [art.attacks.evasion.AdversarialPatch] + attack.init.max_iter : [10] + attack.init.learning_rate : [.5, 5.0, 50.0] diff --git a/examples/security/truthseeker/conf/attack/default.yaml b/examples/security/truthseeker/conf/attack/default.yaml new file mode 100644 index 00000000..e0967ff7 --- /dev/null +++ b/examples/security/truthseeker/conf/attack/default.yaml @@ -0,0 +1,2 @@ +defaults: + - hsj diff --git a/examples/security/truthseeker/conf/attack/hsj.yaml b/examples/security/truthseeker/conf/attack/hsj.yaml new file mode 100644 index 00000000..cb2399a3 --- /dev/null +++ b/examples/security/truthseeker/conf/attack/hsj.yaml @@ -0,0 +1,8 @@ +data: ${data} +model: ${model} +_target_ : deckard.base.attack.Attack +init: + name: art.attacks.evasion.HopSkipJump + model: ${model} +attack_size : 10 +method : evasion diff --git a/examples/security/truthseeker/conf/compile.yaml b/examples/security/truthseeker/conf/compile.yaml new file mode 100644 index 00000000..817d2b4e --- /dev/null +++ b/examples/security/truthseeker/conf/compile.yaml @@ -0,0 +1,8 @@ +attacks: + ProjectedGradientDescent: PGD + null : null +defences: + Control: Control +params: + Control: model.init.kwargs.kernel + PGD: attack.init.kwargs.eps diff --git a/examples/security/truthseeker/conf/data/attack.yaml b/examples/security/truthseeker/conf/data/attack.yaml new file mode 100644 index 00000000..3d77ffb4 --- /dev/null +++ b/examples/security/truthseeker/conf/data/attack.yaml @@ -0,0 +1,23 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 11100 + n_features : 100 + n_informative : 99 + n_redundant : 0 + n_repeated : 0 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 10000 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/security/truthseeker/conf/data/default.yaml b/examples/security/truthseeker/conf/data/default.yaml new file mode 100644 index 00000000..b3d67001 --- /dev/null +++ b/examples/security/truthseeker/conf/data/default.yaml @@ -0,0 +1,2 @@ +defaults: + - kdd_nsl diff --git a/examples/security/truthseeker/conf/data/kdd_nsl.yaml b/examples/security/truthseeker/conf/data/kdd_nsl.yaml new file mode 100644 index 00000000..e55be441 --- /dev/null +++ b/examples/security/truthseeker/conf/data/kdd_nsl.yaml @@ -0,0 +1,18 @@ +_target_: deckard.base.data.Data +name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv +target : label +sample: + _target_: deckard.base.data.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 5000 + test_size : 1000 +sklearn_pipeline: + encoder: + name : sklearn.preprocessing.OrdinalEncoder + handle_unknown : use_encoded_value + unknown_value : -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/security/truthseeker/conf/data/small.yaml b/examples/security/truthseeker/conf/data/small.yaml new file mode 100644 index 00000000..2207d717 --- /dev/null +++ b/examples/security/truthseeker/conf/data/small.yaml @@ -0,0 +1,20 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: classification + random_state : 0 + n_samples : 1100 + n_features : 20 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 100 + test_size : 1000 +sklearn_pipeline: + # _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + # + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/security/truthseeker/conf/data/truthseeker.yaml b/examples/security/truthseeker/conf/data/truthseeker.yaml new file mode 100644 index 00000000..7fdb84a9 --- /dev/null +++ b/examples/security/truthseeker/conf/data/truthseeker.yaml @@ -0,0 +1,20 @@ +_target_: deckard.base.data.Data +generate: + # _target_: deckard.base.data.generator.DataGenerator + name: https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/379b679bdea30724e9fa188931f0109ff422cce0/kdd_nsl.csv + target : -2 +sample: + # _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True + train_size : 10000 + test_size : 1000 +ssklearn_pipeline: + encoder: + name : sklearn.preprocessing.OrdinalEncoder + handle_unknown : use_encoded_value + unknown_value : -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/security/truthseeker/conf/default.yaml b/examples/security/truthseeker/conf/default.yaml new file mode 100644 index 00000000..b0340b69 --- /dev/null +++ b/examples/security/truthseeker/conf/default.yaml @@ -0,0 +1,41 @@ +defaults: + - _self_ + - data: default + - model: default + - attack: hsj + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/launcher : joblib +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.TPESampler + # seed: 123 + consider_prior: true + prior_weight: 1.0 + consider_magic_clip: true + consider_endpoints: false + n_startup_trials: 10 + n_ei_candidates: 24 + multivariate: false + warn_independent_sampling: true + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: maximize + study_name: model + storage: sqlite:///model.db + n_trials: 100 + n_jobs: 1 + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 6 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/security/truthseeker/conf/files/default.yaml b/examples/security/truthseeker/conf/files/default.yaml new file mode 100644 index 00000000..e2247d7e --- /dev/null +++ b/examples/security/truthseeker/conf/files/default.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: output +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pkl +params_file : params.yaml +train_labels_file : train_labels.json +test_labels_file : test_labels.json +probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +adv_probabilities_file: adv_probabilities.json +name : default diff --git a/examples/security/truthseeker/conf/model.yaml b/examples/security/truthseeker/conf/model.yaml new file mode 100644 index 00000000..d1c2344a --- /dev/null +++ b/examples/security/truthseeker/conf/model.yaml @@ -0,0 +1,36 @@ +defaults: + # - _target_ : deckard.base.experiment.Experiment + - _self_ + - data: kdd_nsl + - model: default + - files: default + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +optimizers : accuracy +direction : maximize +hydra: + run: + dir : "./${files.directory}" + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + direction: ${direction} + study_name: model + storage: sqlite:///model.db + n_jobs: 1 + params: + model.init.C : choice(.00001, .0001, .001, .01, .1, 1, 10, 100, 1000, 10000) + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 32 + prefer : processes + verbose: 1 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/security/truthseeker/conf/model/art/defense_grid.yaml b/examples/security/truthseeker/conf/model/art/defense_grid.yaml new file mode 100644 index 00000000..a3fe3719 --- /dev/null +++ b/examples/security/truthseeker/conf/model/art/defense_grid.yaml @@ -0,0 +1,44 @@ +- model.art.preprocessor.name: art.defences.preprocessor.FeatureSqueezing + model.art.preprocessor.params: + clip_values : + - [0,255] + bit_depth : [4, 8, 16, 32, 64] + +- model.art.preprocessor.name: art.defences.preprocessor.GaussianAugmentation + model.art.preprocessor.params: + clip_values : + - [0,255] + sigma : [.1, .3, 1] + ratio : [.1, .5, 1] + +- model.art.preprocessor.name: art.defences.preprocessor.SpatialSmoothing + model.art.preprocessor.params: + clip_values : + - [0,255] + window_size : [2,3,4] + +- model.art.preprocessor.name: art.defences.preprocessor.TotalVarMin + model.art.preprocessor.params: + clip_values : + - [0,255] + prob : [.001, .01, .1] + norm : [1, 2, 3] + lamb : [.05, .5, .95] + max_iter : [100] + +- model.art.postprocessor.name : art.defences.postprocessor.GaussianNoise + model.art.postprocessor.params: + clip_values : + - [0,255] + scale: [.1, .9, .999] + +- model.art.postprocessor.name : art.defences.postprocessor.HighConfidence + model.art.postprocessor.params: + cutoff : [.1, .5, .9, .99] + + +- model.art.postprocessor.name : art.defences.postprocessor.Rounded + model.art.preprocessor.params: + clip_values : + - [0,255] + decimals : [1, 2, 4, 8] diff --git a/examples/security/truthseeker/conf/model/art/postprocessor.yaml b/examples/security/truthseeker/conf/model/art/postprocessor.yaml new file mode 100644 index 00000000..91f3c00a --- /dev/null +++ b/examples/security/truthseeker/conf/model/art/postprocessor.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : sklearn-svc +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/security/truthseeker/conf/model/art/postprocessor/high_confidence.yaml b/examples/security/truthseeker/conf/model/art/postprocessor/high_confidence.yaml new file mode 100644 index 00000000..16ae0e50 --- /dev/null +++ b/examples/security/truthseeker/conf/model/art/postprocessor/high_confidence.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/security/truthseeker/conf/model/art/postprocessor/labels.yaml b/examples/security/truthseeker/conf/model/art/postprocessor/labels.yaml new file mode 100644 index 00000000..16ae0e50 --- /dev/null +++ b/examples/security/truthseeker/conf/model/art/postprocessor/labels.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +postprocessor: + name: art.defences.postprocessor.HighConfidence + threshold: 0.0 +initialize: diff --git a/examples/security/truthseeker/conf/model/art/preprocessor.yaml b/examples/security/truthseeker/conf/model/art/preprocessor.yaml new file mode 100644 index 00000000..ebf46909 --- /dev/null +++ b/examples/security/truthseeker/conf/model/art/preprocessor.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : sklearn-svc +preprocessor: + name: art.defences.preprocessor.FeatureSqueezing + bit_depth: 32 +initialize: diff --git a/examples/security/truthseeker/conf/model/art/preprocessor/feature_squeezing.yaml b/examples/security/truthseeker/conf/model/art/preprocessor/feature_squeezing.yaml new file mode 100644 index 00000000..1c48b3e7 --- /dev/null +++ b/examples/security/truthseeker/conf/model/art/preprocessor/feature_squeezing.yaml @@ -0,0 +1,6 @@ +_target_ : deckard.base.model.art_pipeline.ArtPipeline +library : ${model.library} +preprocessor: + name: art.defences.preprocessor.FeatureSqueezing + bit_depth: 32 +initialize: diff --git a/examples/security/truthseeker/conf/model/default.yaml b/examples/security/truthseeker/conf/model/default.yaml new file mode 100644 index 00000000..5f8bd822 --- /dev/null +++ b/examples/security/truthseeker/conf/model/default.yaml @@ -0,0 +1,2 @@ +defaults: + - rbf diff --git a/examples/security/truthseeker/conf/model/linear.yaml b/examples/security/truthseeker/conf/model/linear.yaml new file mode 100644 index 00000000..337909e1 --- /dev/null +++ b/examples/security/truthseeker/conf/model/linear.yaml @@ -0,0 +1,15 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + kernel : linear + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/security/truthseeker/conf/model/poly.yaml b/examples/security/truthseeker/conf/model/poly.yaml new file mode 100644 index 00000000..8bdae408 --- /dev/null +++ b/examples/security/truthseeker/conf/model/poly.yaml @@ -0,0 +1,16 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + coef0 : 0.0 + kernel : poly + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/security/truthseeker/conf/model/rbf.yaml b/examples/security/truthseeker/conf/model/rbf.yaml new file mode 100644 index 00000000..8b2d61af --- /dev/null +++ b/examples/security/truthseeker/conf/model/rbf.yaml @@ -0,0 +1,15 @@ +data: ${data} +library : sklearn-svc +init: + _target_: deckard.base.model.ModelInitializer + name : sklearn.svm.SVC + C : 1.0 + kernel : rbf + probability : true + random_state : 0 + max_iter : 10 +_target_: deckard.base.model.Model +art: + _target_ : deckard.base.model.art_pipeline.ArtPipeline + library : sklearn-svc + initialize: diff --git a/examples/security/truthseeker/conf/scorers/default.yaml b/examples/security/truthseeker/conf/scorers/default.yaml new file mode 100644 index 00000000..108c1520 --- /dev/null +++ b/examples/security/truthseeker/conf/scorers/default.yaml @@ -0,0 +1,10 @@ +_target_: deckard.base.scorer.ScorerDict +accuracy: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.accuracy_score + direction: maximize + +log_loss: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.log_loss + direction: minimize From 255221dfb8aa6b523610e9ebf645dca492cec5bb Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 14 Nov 2023 16:06:14 +0000 Subject: [PATCH 123/148] update scripts for security example --- examples/security/classification/.dvc/config | 0 examples/security/classification/.dvcignore | 3 + examples/security/classification/README.md | 32 + examples/security/classification/attacks.sh | 54 ++ examples/security/classification/dvc.lock | 791 ++++++++++++++++++ examples/security/classification/dvc.yaml | 155 ++++ examples/security/classification/models.sh | 111 +++ examples/security/classification/plots.py | 370 +++++++++ examples/security/classification/retrain.py | 446 +++++++++++ examples/security/kdd-nsl/.dvc/config | 0 examples/security/kdd-nsl/.dvcignore | 3 + examples/security/kdd-nsl/README.md | 32 + examples/security/kdd-nsl/attacks.sh | 54 ++ examples/security/kdd-nsl/dvc.lock | 798 +++++++++++++++++++ examples/security/kdd-nsl/dvc.yaml | 151 ++++ examples/security/kdd-nsl/other_data.sh | 35 + examples/security/kdd-nsl/plots.py | 376 +++++++++ examples/security/kdd-nsl/retrain.py | 443 ++++++++++ examples/security/truthseeker/.dvc/config | 0 examples/security/truthseeker/.dvcignore | 3 + examples/security/truthseeker/README.md | 32 + examples/security/truthseeker/attacks.sh | 54 ++ examples/security/truthseeker/dvc.lock | 798 +++++++++++++++++++ examples/security/truthseeker/dvc.yaml | 154 ++++ examples/security/truthseeker/models.sh | 56 ++ examples/security/truthseeker/other_data.sh | 35 + examples/security/truthseeker/plots.py | 377 +++++++++ examples/security/truthseeker/retrain.py | 443 ++++++++++ 28 files changed, 5806 insertions(+) create mode 100644 examples/security/classification/.dvc/config create mode 100644 examples/security/classification/.dvcignore create mode 100644 examples/security/classification/README.md create mode 100644 examples/security/classification/attacks.sh create mode 100644 examples/security/classification/dvc.lock create mode 100644 examples/security/classification/dvc.yaml create mode 100644 examples/security/classification/models.sh create mode 100644 examples/security/classification/plots.py create mode 100644 examples/security/classification/retrain.py create mode 100644 examples/security/kdd-nsl/.dvc/config create mode 100644 examples/security/kdd-nsl/.dvcignore create mode 100644 examples/security/kdd-nsl/README.md create mode 100644 examples/security/kdd-nsl/attacks.sh create mode 100644 examples/security/kdd-nsl/dvc.lock create mode 100644 examples/security/kdd-nsl/dvc.yaml create mode 100644 examples/security/kdd-nsl/other_data.sh create mode 100644 examples/security/kdd-nsl/plots.py create mode 100644 examples/security/kdd-nsl/retrain.py create mode 100644 examples/security/truthseeker/.dvc/config create mode 100644 examples/security/truthseeker/.dvcignore create mode 100644 examples/security/truthseeker/README.md create mode 100644 examples/security/truthseeker/attacks.sh create mode 100644 examples/security/truthseeker/dvc.lock create mode 100644 examples/security/truthseeker/dvc.yaml create mode 100644 examples/security/truthseeker/models.sh create mode 100644 examples/security/truthseeker/other_data.sh create mode 100644 examples/security/truthseeker/plots.py create mode 100644 examples/security/truthseeker/retrain.py diff --git a/examples/security/classification/.dvc/config b/examples/security/classification/.dvc/config new file mode 100644 index 00000000..e69de29b diff --git a/examples/security/classification/.dvcignore b/examples/security/classification/.dvcignore new file mode 100644 index 00000000..51973055 --- /dev/null +++ b/examples/security/classification/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/examples/security/classification/README.md b/examples/security/classification/README.md new file mode 100644 index 00000000..c3c227e6 --- /dev/null +++ b/examples/security/classification/README.md @@ -0,0 +1,32 @@ +# Classification Experiments + +## Directory Contents + +├── conf: contains the configuration files. +│ ├── attack +│ ├── config.yaml : contains the default configuration +│ ├── data +│ ├── files +│ ├── model +│ ├── plots +│ └── scorers +├── dag.md: contains the [dvc](https://dvc.org/doc/start/data-management/data-pipelines) pipeline, visualized as a graph +├── dvc.lock: contains the git trackable information for all the data and model binaries +├── dvc.yaml: specifies the pipeline visualized in `dag.md` +├── experiments.sh: specifies the grid search parameters and executes the pipeline for each set of parameters +├── multirun: contains the hydra configuration information +├── params.yaml: contains the parsed default configuration file +├── plots.ipynb: is a jupyter notebook for visualizing the data +├── queue: contains all of the configurations for all experiments +├── reports: contains the results of all experiments, with a folder for each layer (e.g. `reports/train/`), containing scores, plots, predictions, probabilities, and ground_truth json files. +├── train: contains the `data/` and `models/` for each model +│ ├── data +│ ├── models + +## Execution instructions + +To check basic code functionality, run +```dvc repro --force``` +which will execute the [dvc pipeline](https://dvc.org/doc/start/data-management/data-pipelines) that makes all of the inputs and outputs git trackable. This will execute all of the code on the default configurations first (stages `generate`, `train`, and `attack` on the `dvc.yaml`), followed by grid search on all of the models in stage `model-queue` and all of the attacks in stage `attack-queue`. After finding the best configuration for each kernel (stage `compile`) + +which overrides the default configurations, using [hydra](https://hydra.cc/docs/patterns/configuring_experiments/) and its [optuna plugin](https://hydra.cc/docs/plugins/optuna_sweeper/) to specify a search space for each set of parameters specified in the bash script. Sets of parameters that apply to all experiments are set in the `config.yaml`. Parameters particular to a experiment are specified using command line overrides within the bash script. diff --git a/examples/security/classification/attacks.sh b/examples/security/classification/attacks.sh new file mode 100644 index 00000000..d9e3f1e0 --- /dev/null +++ b/examples/security/classification/attacks.sh @@ -0,0 +1,54 @@ +#!/bin/bash +MODEL_CONFIGS=$(ls conf/model/best_*.yaml) +CONFIG_NAMES=$(ls conf/model/best_*.yaml | cut -d'/' -f3 | cut -d'.' -f1) +TOTAL=$(( ${#CONFIG_NAMES[@]} )) +i=$(( 0 )) +mkdir -p logs/attacks/ +for model_config in $CONFIG_NAMES; do + kernel_name=$(echo $model_config | cut -d'_' -f2) + i=$(( i + 1 )) + if [ $model_config == "default" ]; then + continue + fi + HYDRA_FULL_ERROR=1 python -m deckard.layers.optimise \ + ++model.init.kernel=$kernel_name \ + ++stage=attack \ + ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent \ + ++attack.init.norm=1,2,inf \ + ++attack.init.eps_step=.001,.01,.1,.3,.5,1 \ + ++attack.init.batch_size=1,10,50,100 \ + ++attack.init.eps=.001,.01,.1,.3,.5,1 \ + ++attack.init.max_iter=1,10,100,1000 \ + ++hydra.sweeper.study_name=$model_config \ + ++attack.attack_size=100 \ + model=$model_config $@ --multirun >> logs/attacks/$model_config.log + echo "Successfully completed model $model_config" >> attack_log.txt +done + +# Other attacks listed below +# PGD +# bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.norm=1,2,inf ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=1,10,50,100 ++attack.init.eps=.001,.01,.1,.3,.5,1 $@ + +# # Carlini L0 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # Carlini L2 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # Carlini LInf +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # DeepFool +# bash models.sh ++attack.init.nb_grads=1,3,5,10 ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.batch_size=100 $@ + +# #Threshold Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ + +# #Pixel Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.PixelAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ + +# #Adversarial Patch +# bash models.sh ++attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.scale_max=.1,.2,.3,.5,.8,.9,.99 ++attack.init.batch_size=100 $@ + +# #Hop Skip Jump +# bash models.sh ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.batch_size=100 $@ diff --git a/examples/security/classification/dvc.lock b/examples/security/classification/dvc.lock new file mode 100644 index 00000000..6d6e7536 --- /dev/null +++ b/examples/security/classification/dvc.lock @@ -0,0 +1,791 @@ +schema: '2.0' +stages: + train: + cmd: python -m deckard.layers.experiment train + params: + params.yaml: + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/reports/train/default/params.yaml + hash: md5 + md5: d4e0a34b2b15765ca71fa5ecaf7e3826 + size: 2100 + - path: output/reports/train/default/predictions.json + hash: md5 + md5: 1bfeaf1fa0cb4e90604169e61dc95892 + size: 42667 + - path: output/reports/train/default/probabilities.json + hash: md5 + md5: 1bfeaf1fa0cb4e90604169e61dc95892 + size: 42667 + - path: output/reports/train/default/score_dict.json + hash: md5 + md5: b5002e336c23ad2c890f50dcb4ae88a5 + size: 353 + attack: + cmd: python -m deckard.layers.experiment attack + deps: + - path: output/reports/train/default/params.yaml + hash: md5 + md5: d4e0a34b2b15765ca71fa5ecaf7e3826 + size: 2100 + - path: output/reports/train/default/predictions.json + hash: md5 + md5: 1bfeaf1fa0cb4e90604169e61dc95892 + size: 42667 + - path: output/reports/train/default/probabilities.json + hash: md5 + md5: 1bfeaf1fa0cb4e90604169e61dc95892 + size: 42667 + - path: output/reports/train/default/score_dict.json + hash: md5 + md5: b5002e336c23ad2c890f50dcb4ae88a5 + size: 353 + params: + params.yaml: + attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + name: art.attacks.evasion.ProjectedGradientDescent + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/attacks/attack.pkl + hash: md5 + md5: bbce7d8f6ca5653f7cb6a6dd5f974582 + size: 952 + - path: output/reports/attack/default/adv_predictions.json + hash: md5 + md5: 2ad26f915f08b13757e052dda7146c7d + size: 427 + - path: output/reports/attack/default/adv_probabilities.json + hash: md5 + md5: 2ad26f915f08b13757e052dda7146c7d + size: 427 + - path: output/reports/attack/default/params.yaml + hash: md5 + md5: 5be65828d59c309890bae9649e491dba + size: 5010 + - path: output/reports/attack/default/predictions.json + hash: md5 + md5: 1bfeaf1fa0cb4e90604169e61dc95892 + size: 42667 + - path: output/reports/attack/default/probabilities.json + hash: md5 + md5: 1bfeaf1fa0cb4e90604169e61dc95892 + size: 42667 + - path: output/reports/attack/default/score_dict.json + hash: md5 + md5: 10fee78b3899c113c799a056cf9a20ee + size: 577 + models: + cmd: bash models.sh +stage=train --config-name=model.yaml + deps: + - path: conf/model.yaml + hash: md5 + md5: d2fdcee453cdf1eb749eb28931e8ebbf + size: 950 + - path: models.sh + hash: md5 + md5: 034c94712f10de83e562ffa58ba204f9 + size: 2974 + - path: output/reports/train/default/params.yaml + hash: md5 + md5: d4e0a34b2b15765ca71fa5ecaf7e3826 + size: 2100 + - path: params.yaml + hash: md5 + md5: 4b1c07b7f8c3a67f5c257b7c5fa72c85 + size: 4223 + params: + params.yaml: + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + generate: + n_features: 20 + n_samples: 1100 + name: classification + random_state: 0 + sample: + random_state: 0 + stratify: true + test_size: 1000 + train_size: 100 + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: logs/models/ + hash: md5 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 + nfiles: 60 + - path: model.db + hash: md5 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 + compile_models: + cmd: python -m deckard.layers.compile --report_folder output/reports/train/ --results_file + output/train.csv + deps: + - path: logs/models/ + hash: md5 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 + nfiles: 60 + - path: model.db + hash: md5 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 + - path: output/reports/train/ + hash: md5 + md5: 58131db53b5c1975bc969ee083a14602.dir + size: 87802 + nfiles: 5 + outs: + - path: output/train.csv + hash: md5 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 + find_best_model@rbf: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_rbf --study_name=rbf_100_10000 --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 + nfiles: 60 + - path: model.db + hash: md5 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 + - path: output/train.csv + hash: md5 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 + outs: + - path: conf/model/best_rbf.yaml + hash: md5 + md5: 0a90767d020934a3cd6d0c42a6f21606 + size: 357 + find_best_model@linear: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_linear --study_name=linear_100_10000 --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 + nfiles: 60 + - path: model.db + hash: md5 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 + - path: output/train.csv + hash: md5 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 + outs: + - path: conf/model/best_linear.yaml + hash: md5 + md5: 23a7c49f5a8ddf63a7ac89fb61c0034d + size: 332 + find_best_model@poly: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_poly --study_name=poly_100_10000 --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 + nfiles: 60 + - path: model.db + hash: md5 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 + - path: output/train.csv + hash: md5 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 + outs: + - path: conf/model/best_poly.yaml + hash: md5 + md5: a9d600cc46e9f49c3a0cca90f7c7d876 + size: 370 + attacks: + cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml + deps: + - path: conf/model/best_linear.yaml + hash: md5 + md5: 23a7c49f5a8ddf63a7ac89fb61c0034d + size: 332 + - path: conf/model/best_poly.yaml + hash: md5 + md5: a9d600cc46e9f49c3a0cca90f7c7d876 + size: 370 + - path: conf/model/best_rbf.yaml + hash: md5 + md5: 0a90767d020934a3cd6d0c42a6f21606 + size: 357 + - path: logs/models/ + hash: md5 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 + nfiles: 60 + - path: model.db + hash: md5 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 + - path: output/train.csv + hash: md5 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 + outs: + - path: attack.db + hash: md5 + md5: c67caa2e3ab1e72e19f441f93472cab8 + size: 2236416 + - path: logs/attacks/ + hash: md5 + md5: 8012d759b75d0670b7f4db74037aa13a.dir + size: 19145346 + nfiles: 3 + compile_attacks: + cmd: python -m deckard.layers.compile --report_folder output/reports/attack/ --results_file + output/attack.csv + deps: + - path: attack.db + hash: md5 + md5: c67caa2e3ab1e72e19f441f93472cab8 + size: 2236416 + - path: logs/attacks/ + hash: md5 + md5: 8012d759b75d0670b7f4db74037aa13a.dir + size: 19145346 + nfiles: 3 + - path: output/reports/attack/ + hash: md5 + md5: 1130569bbd62ca77a62f12313fb38aa0.dir + size: 91788 + nfiles: 7 + outs: + - path: output/attack.csv + hash: md5 + md5: b8ea071d580c07f2ea03c74a02947c6e + size: 7276 + find_best_attack@linear: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_linear --study_name=best_linear --default_config attack.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 + nfiles: 60 + - path: model.db + hash: md5 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 + - path: output/train.csv + hash: md5 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 + outs: + - path: conf/attack/best_linear.yaml + hash: md5 + md5: 4bb6215963ae7f0025f72ec31e26f29d + size: 244 + find_best_attack@rbf: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_rbf --study_name=best_rbf --default_config attack.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 + nfiles: 60 + - path: model.db + hash: md5 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 + - path: output/train.csv + hash: md5 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 + outs: + - path: conf/attack/best_rbf.yaml + hash: md5 + md5: eca3091f7c0eb0b8958bc6becf43191d + size: 244 + find_best_attack@poly: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_poly --study_name=best_poly --default_config attack.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 24d1439cc3939aca8c9df30d84731c58.dir + size: 4940033 + nfiles: 60 + - path: model.db + hash: md5 + md5: fc090255a8e51f228ef1df5af0109396 + size: 1949696 + - path: output/train.csv + hash: md5 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 + outs: + - path: conf/attack/best_poly.yaml + hash: md5 + md5: b5f8f874e44dbc8bdb0ababc67295174 + size: 246 + other_data_train@kdd_nsl: + cmd: DATASET_NAME=kdd_nsl bash other_data.sh data=kdd_nsl +stage=train --config-name=model.yaml + deps: + - path: conf/model.yaml + hash: md5 + md5: daaa0663d05972a5b8645c35d364da88 + size: 990 + - path: other_data.sh + hash: md5 + md5: 6ebecf100cc02847ad31901bebb2ee5a + size: 2759 + - path: output/reports/train/default/params.yaml + hash: md5 + md5: d4e0a34b2b15765ca71fa5ecaf7e3826 + size: 2100 + outs: + - path: kdd_nsl.db + hash: md5 + md5: 06933f8fc0a1feca0944c131b6a3854b + size: 348160 + - path: kdd_nsl/ + hash: md5 + md5: 9076c4e55fd1058e7446588d99930d58.dir + size: 39137423 + nfiles: 1072 + - path: logs/kdd_nsl/ + hash: md5 + md5: e7c227947468122b62f891c0d54e0c54.dir + size: 1314288 + nfiles: 12 + retrain: + cmd: python retrain.py + deps: + - path: conf/attack/best_linear.yaml + hash: md5 + md5: 4bb6215963ae7f0025f72ec31e26f29d + size: 244 + - path: conf/attack/best_poly.yaml + hash: md5 + md5: b5f8f874e44dbc8bdb0ababc67295174 + size: 246 + - path: conf/attack/best_rbf.yaml + hash: md5 + md5: eca3091f7c0eb0b8958bc6becf43191d + size: 244 + - path: conf/model/best_linear.yaml + hash: md5 + md5: 23a7c49f5a8ddf63a7ac89fb61c0034d + size: 332 + - path: conf/model/best_poly.yaml + hash: md5 + md5: a9d600cc46e9f49c3a0cca90f7c7d876 + size: 370 + - path: conf/model/best_rbf.yaml + hash: md5 + md5: 0a90767d020934a3cd6d0c42a6f21606 + size: 357 + - path: output/attacks/ + hash: md5 + md5: ac2eb814d85404eb185c98af5f129ccf.dir + size: 2410072 + nfiles: 61 + - path: output/models/ + hash: md5 + md5: c98656661f89d049ad99de3948c07c9e.dir + size: 54772 + nfiles: 3 + outs: + - path: plots/after_retrain_confidence.csv + hash: md5 + md5: 85b6c20f3db9f521dc19697f1804b1de + size: 18008 + - path: plots/before_retrain_confidence.csv + hash: md5 + md5: 4cf5f55f22c167803b9e8965d28d13c5 + size: 17991 + - path: retrain/ + hash: md5 + md5: 4d533cad670b054775f1ad0ddb241370.dir + size: 176885 + nfiles: 12 + plots: + cmd: python plots.py + deps: + - path: output/attack.csv + hash: md5 + md5: feef3638a92cb9ec2fe9c361f0ac8aee + size: 204088 + - path: output/train.csv + hash: md5 + md5: 7c371b2f23b54ae5917d60d68b1c53ba + size: 3238 + - path: plots/after_retrain_confidence.csv + hash: md5 + md5: 85b6c20f3db9f521dc19697f1804b1de + size: 18008 + - path: plots/before_retrain_confidence.csv + hash: md5 + md5: 4cf5f55f22c167803b9e8965d28d13c5 + size: 17991 + outs: + - path: plots/accuracy_vs_attack_parameters.eps + hash: md5 + md5: 3d2dd3aac0d156e1fd9c33e36af4ba8d + size: 41230 + - path: plots/accuracy_vs_features.eps + hash: md5 + md5: fd4589a083f49c1867c8266f65079042 + size: 21386 + - path: plots/accuracy_vs_samples.eps + hash: md5 + md5: 68cac57cb36da240f16c59de0cd6fab8 + size: 23933 + - path: plots/confidence_vs_attack_parameters.eps + hash: md5 + md5: 4bed1ae75648072e60b1de618c3a458d + size: 41285 + - path: plots/retrain_accuracy.eps + hash: md5 + md5: 8f31fb8e6bfde27e322e94d3ca403e2d + size: 23419 + - path: plots/retrain_confidence_vs_attack_parameters.eps + hash: md5 + md5: af27dbbce36fe7118d2ab36e5f5e51d2 + size: 41304 + - path: plots/retrain_time.eps + hash: md5 + md5: 173c20ed56ff30f749652814fb6e6b3a + size: 20953 + - path: plots/train_time_vs_attack_parameters.eps + hash: md5 + md5: f65d32d2dccabac8b992a2f7fe1a029f + size: 38831 + - path: plots/train_time_vs_features.eps + hash: md5 + md5: f093fffdb45713b11a9a302b7f6765b8 + size: 17543 + - path: plots/train_time_vs_samples.eps + hash: md5 + md5: c359b1e7c36783d590a57df80ed0dc0a + size: 20679 diff --git a/examples/security/classification/dvc.yaml b/examples/security/classification/dvc.yaml new file mode 100644 index 00000000..e44f6357 --- /dev/null +++ b/examples/security/classification/dvc.yaml @@ -0,0 +1,155 @@ +stages: + attack: # Prototype stage + cmd: python -m deckard.layers.experiment attack + deps: + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} #optionally, store the data + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + metrics: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + outs: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.probabilities_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} + params: + - data + - model + - files + - scorers + - attack + train: # Prototype stage + cmd: python -m deckard.layers.experiment train + metrics: + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + outs: + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + params: + - data + - model + - files + - scorers + models: # Grid search stage + cmd: bash models.sh +stage=train --config-name=model.yaml # specifies the prototype stage, file to be used + outs: + - model.db: # This allows to run this section piecewise, adding features or samples inside models.sh which are then stored in the .db + cache: true + persist: true + - logs/models/: # Each feature/sample/train size combination log is stored in a separate file + cache: true + persist: true + # - ${files.directory}/${files.reports}/train/: # Each feature/sample/train size combination experiment is stored in a separate folder + # cache: true + # persist: true + params: # These are the parameters that are passed to the prototype stage + - data + - model + - files + - scorers + deps: + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + - params.yaml + - conf/model.yaml + - models.sh + # - conf/model/ + compile_models: + cmd: + python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/train/ --results_file ${files.directory}/train.csv + outs: + - ${files.directory}/train.csv + deps: + - ${files.directory}/${files.reports}/train/ # Each feature/sample/train size combination experiment is stored in a separate folder + - model.db + - logs/models/ + find_best_model: + foreach: + - linear + - rbf + - poly + do: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model --params_file best_${item} --study_name=${item}_100_10000 --default_config model.yaml + outs: + - conf/model/best_${item}.yaml + deps: + - ${files.directory}/train.csv + - model.db + - logs/models/ + attacks: + cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml + outs: + - logs/attacks/: + cache: true + persist: true + - attack.db: + cache: true + persist: true + deps: + - ${files.directory}/train.csv + - model.db + - logs/models/ + - conf/model/best_linear.yaml + - conf/model/best_rbf.yaml + - conf/model/best_poly.yaml + compile_attacks: + cmd: + python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/attack/ --results_file ${files.directory}/attack.csv + outs: + - ${files.directory}/attack.csv + deps: + - ${files.directory}/${files.reports}/attack/ # Each feature/sample/train size combination experiment is stored in a separate folder + - attack.db + - logs/attacks/ + find_best_attack: + foreach: + - linear + - rbf + - poly + do: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack --params_file best_${item} --study_name=best_${item} --default_config attack.yaml + outs: + - conf/attack/best_${item}.yaml + deps: + - ${files.directory}/train.csv + - model.db + - logs/models/ + retrain: + cmd : python retrain.py + deps: + - ${files.directory}/models/ + - ${files.directory}/attacks/ + - conf/attack/best_linear.yaml + - conf/attack/best_rbf.yaml + - conf/attack/best_poly.yaml + - conf/model/best_linear.yaml + - conf/model/best_rbf.yaml + - conf/model/best_poly.yaml + outs: + - retrain/ + metrics: + - plots/before_retrain_confidence.csv + - plots/after_retrain_confidence.csv + plots: + cmd : python plots.py + deps : + - plots/after_retrain_confidence.csv + - output/train.csv + - plots/before_retrain_confidence.csv + - output/attack.csv + plots : + - plots/accuracy_vs_attack_parameters.eps + - plots/accuracy_vs_features.eps + - plots/accuracy_vs_samples.eps + - plots/confidence_vs_attack_parameters.eps + - plots/train_time_vs_attack_parameters.eps + - plots/train_time_vs_features.eps + - plots/train_time_vs_samples.eps + - plots/retrain_accuracy.eps + - plots/retrain_confidence_vs_attack_parameters.eps + - plots/retrain_time.eps diff --git a/examples/security/classification/models.sh b/examples/security/classification/models.sh new file mode 100644 index 00000000..6940825e --- /dev/null +++ b/examples/security/classification/models.sh @@ -0,0 +1,111 @@ +N_FEATURES=( 10 100 1000 10000 ) +TRAIN_SIZES=( 10 100 1000 10000 100000 ) +# TRAIN_SIZES=( 1000 10000 100000 1000000 ) +N_FEATURES=( 100 ) +N_SAMPLES=( 1010000 ) +TOTAL=$(( ${#N_FEATURES[@]} * ${#N_SAMPLES[@]} * ${#TRAIN_SIZES[@]} )) +i=$(( 0 )) +mkdir -p logs/models +for train_size in ${TRAIN_SIZES[@]}; do + for n_samples in ${N_SAMPLES[@]}; do + for n_features in ${N_FEATURES[@]}; do + i=$(( i + 1 )) + echo "Running experiment ${i} of ${TOTAL}" + # Keeps a meta log of the experiments + echo "Running experiment ${i} of ${TOTAL}" >> log.txt + echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" >> log.txt + # Runs the linear kernel, tries to find the best C value + HYDRA_FULL_ERROR=1; python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + ++model.init.kernel=linear \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=linear_${n_features}_${train_size} "$@" --multirun \ + # Keeps a log of the output for each experiment + >| logs/models/linear_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Linear Kernel Done" >> log.txt + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + ++model.init.kernel=rbf \ + +model.init.gamma=scale \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=rbf_${n_features}_${train_size} "$@" --multirun \ + >| logs/models/rbf_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "RBF Kernel Done" >> log.txt + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + ++model.init.kernel=poly \ + +model.init.degree=1,2,3,4,5 \ + +model.init.gamma=scale \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=poly_${n_features}_${train_size} "$@" --multirun \ + >| logs/models/poly_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Poly Kernel Done" >> log.txt + echo "Successfully completed experiment ${i} of ${TOTAL}" >> log.txt + done; + done; +done; + +N_FEATURES=( 10 100 1000 ) +TRAIN_SIZES=( 1000000 ) +N_SAMPLES=( 1010000 ) +TOTAL=$(( ${#N_FEATURES[@]} * ${#N_SAMPLES[@]} * ${#TRAIN_SIZES[@]} )) +i=$(( 0 )) +mkdir -p logs/models +for train_size in ${TRAIN_SIZES[@]}; do + for n_samples in ${N_SAMPLES[@]}; do + for n_features in ${N_FEATURES[@]}; do + i=$(( i + 1 )) + echo "Running experiment ${i} of ${TOTAL}" + # Keeps a meta log of the experiments + echo "Running experiment ${i} of ${TOTAL}" >> log.txt + echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" >> log.txt + # Runs the linear kernel, tries to find the best C value + HYDRA_FULL_ERROR=1; python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + ++model.init.kernel=linear \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=linear_${n_features}_${train_size} "$@" --multirun \ + # Keeps a log of the output for each experiment + >| logs/models/linear_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Linear Kernel Done" >> log.txt + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + ++model.init.kernel=rbf \ + +model.init.gamma=scale \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=rbf_${n_features}_${train_size} "$@" --multirun \ + >| logs/models/rbf_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "RBF Kernel Done" >> log.txt + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + ++model.init.kernel=poly \ + +model.init.degree=1,2,3,4,5 \ + +model.init.gamma=scale \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=poly_${n_features}_${train_size} "$@" --multirun \ + >| logs/models/poly_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Poly Kernel Done" >> log.txt + echo "Successfully completed experiment ${i} of ${TOTAL}" >> log.txt + done; + done; +done; \ No newline at end of file diff --git a/examples/security/classification/plots.py b/examples/security/classification/plots.py new file mode 100644 index 00000000..3e515da7 --- /dev/null +++ b/examples/security/classification/plots.py @@ -0,0 +1,370 @@ +import pandas as pd +import seaborn as sns +from pathlib import Path +import matplotlib.pyplot as plt +import logging + +sns.set_style("whitegrid") +sns.set_context("paper", font_scale=1.5) +sns.set_style({"font.family": "serif", "font.serif": "Times New Roman"}) +Path("plots").mkdir(parents=True, exist_ok=True) + + +logger = logging.getLogger(__name__) + + +# if Path("plots", "model_results.csv").exists(): +# results = pd.read_csv("plots/model_results.csv") +# else: +# results = parse_results("reports/model_queue/") +results = pd.read_csv("output/train.csv") +input_size = ( + results["data.generate.kwargs.n_samples"] + * results["data.generate.kwargs.n_features"] +) +results["Kernel"] = results["model.init.kwargs.kernel"].copy() +results["Features"] = results["data.generate.kwargs.n_features"].copy() +results["Samples"] = results["data.sample.train_size"].copy() +results["input_size"] = input_size +if "Unnamed: 0" in results.columns: + del results["Unnamed: 0"] +for col in results.columns: + if col == "data.name" and isinstance(results[col][0], list): + results[col] = results[col].apply(lambda x: x[0]) +results = results[results["model.init.kwargs.kernel"] != "sigmoid"] + +attack_results = pd.read_csv("output/attack.csv") +attack_results["Kernel"] = attack_results["model.init.kwargs.kernel"].copy() +attack_results["Features"] = attack_results["data.generate.kwargs.n_features"].copy() +attack_results["Samples"] = attack_results["data.sample.train_size"].copy() +if "Unnamed: 0" in attack_results.columns: + del attack_results["Unnamed: 0"] +for col in attack_results.columns: + if col == "data.name" and isinstance(attack_results[col][0], list): + attack_results[col] = attack_results[col].apply(lambda x: x[0]) + + +graph1 = sns.lineplot( + x="data.sample.train_size", + y="accuracy", + data=results, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +graph1.legend(labels=["Linear", "RBF", "Poly"]) +graph1.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +graph1.set_xlabel("Number of Samples") +graph1.set_ylabel("Accuracy") +graph1.set_xscale("log") +graph1.get_figure().tight_layout() +graph1.set(xlim=(10, 1e6)) +graph1.get_figure().savefig("plots/accuracy_vs_samples.eps") +plt.gcf().clear() + +graph2 = sns.lineplot( + x="data.generate.kwargs.n_features", + y="accuracy", + data=results, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +graph2.set_xlabel("Number of Features") +graph2.set_ylabel("Accuracy") +graph2.set_xscale("log") +graph2.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +graph2.get_figure().tight_layout() +graph2.get_figure().savefig("plots/accuracy_vs_features.eps") +plt.gcf().clear() + + +graph3 = sns.lineplot( + x="data.generate.kwargs.n_features", + y="train_time", + data=results, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +graph3.set_xlabel("Number of Features") +graph3.set_ylabel("Training Time") +graph3.set(yscale="log", xscale="log") +graph3.legend(title="Kernel") +graph3.get_figure().tight_layout() +graph3.get_figure().savefig("plots/train_time_vs_features.eps") +plt.gcf().clear() + +graph4 = sns.lineplot( + x="data.sample.train_size", + y="train_time", + data=results, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +graph4.set_xlabel("Number of Samples") +graph4.set_ylabel("Training Time") +graph4.set(yscale="log", xscale="log", xlim=(10, 1e6)) +graph4.legend(title="Kernel") +graph4.get_figure().tight_layout() +graph4.get_figure().savefig("plots/train_time_vs_samples.eps") +plt.gcf().clear() + +fig, ax = plt.subplots(2, 2) +graph5 = sns.lineplot( + x="attack.init.kwargs.eps", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph5.set(xscale="log", xlabel="Perturbation Distance", ylabel="Accuracy") +graph6 = sns.lineplot( + x="attack.init.kwargs.eps_step", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph6.set(xscale="log", xlabel="Perturbation Step", ylabel="Accuracy") +graph7 = sns.lineplot( + x="attack.init.kwargs.max_iter", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph7.set(xscale="log", xlabel="Maximum Iterations", ylabel="Accuracy") +graph8 = sns.lineplot( + x="attack.init.kwargs.batch_size", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph8.set(xscale="log", xlabel="Batch Size", ylabel="Accuracy") +graph6.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout() +fig.savefig("plots/accuracy_vs_attack_parameters.eps") +plt.gcf().clear() + +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="attack.init.kwargs.eps", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="Attack Time") +graph10 = sns.lineplot( + x="attack.init.kwargs.eps_step", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="Attack Time") +graph11 = sns.lineplot( + x="attack.init.kwargs.max_iter", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="Attack Time") +graph12 = sns.lineplot( + x="attack.init.kwargs.batch_size", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="Attack Time") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/train_time_vs_attack_parameters.eps") +plt.gcf().clear() + +retrain_df = pd.DataFrame() +for kernel in ["rbf", "linear", "poly"]: + try: + tmp = pd.read_csv(f"retrain/{kernel}/results.csv") + except FileNotFoundError: + break + tmp["Kernel"] = kernel + tmp["Epochs"] = tmp.index + retrain_df = pd.concat([retrain_df, tmp], axis=0) + retrain_df = retrain_df.reset_index(drop=True) + if "Unnamed: 0" in retrain_df.columns: + del retrain_df["Unnamed: 0"] + + +retrain = sns.lineplot( + x="Epochs", + y="ben_score", + data=retrain_df, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +retrain = sns.lineplot( + x="Epochs", + y="adv_score", + data=retrain_df, + style="Kernel", + color="darkred", + legend=False, + style_order=["rbf", "poly", "linear"], +) +retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +retrain.set_xlabel("Retraining Epochs") +retrain.set_ylabel("Accuracy") +retrain.get_figure().tight_layout() +retrain.get_figure().savefig("plots/retrain_accuracy.eps") +plt.gcf().clear() + +retrain_df["ben_time"] = retrain_df["ben_time"] * retrain_df["train_size"] * 10 +retrain_df["adv_time"] = retrain_df["adv_time"] * retrain_df["attack_size"] +retrain = sns.lineplot( + x="Epochs", + y="ben_time", + data=retrain_df, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +retrain = sns.lineplot( + x="Epochs", + y="adv_time", + data=retrain_df, + style="Kernel", + color="darkred", + legend=False, + style_order=["rbf", "poly", "linear"], +) +retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +retrain.set_xlabel("Retraining Epochs") +retrain.set_ylabel("Time") +retrain.set_yscale("log") +retrain.get_figure().tight_layout() +retrain.get_figure().savefig("plots/retrain_time.eps") +plt.gcf().clear() + +confidence_df = pd.read_csv("plots/before_retrain_confidence.csv") +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="eps", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") +graph10 = sns.lineplot( + x="eps_step", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") +graph11 = sns.lineplot( + x="max_iter", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") +graph12 = sns.lineplot( + x="batch_size", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/confidence_vs_attack_parameters.eps") +plt.gcf().clear() + +confdence_df = pd.read_csv("plots/after_retrain_confidence.csv") +confidence_df.columns +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="eps", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") +graph10 = sns.lineplot( + x="eps_step", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") +graph11 = sns.lineplot( + x="max_iter", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") +graph12 = sns.lineplot( + x="batch_size", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/retrain_confidence_vs_attack_parameters.eps") +plt.gcf().clear() diff --git a/examples/security/classification/retrain.py b/examples/security/classification/retrain.py new file mode 100644 index 00000000..9623e19d --- /dev/null +++ b/examples/security/classification/retrain.py @@ -0,0 +1,446 @@ +import logging +import os +import pickle +from pathlib import Path +from time import process_time +import json +from typing import List +import numpy as np +import pandas as pd +import yaml +from art.attacks.evasion import ProjectedGradientDescent +from art.estimators.classification.scikitlearn import ScikitlearnSVC +from art.utils import to_categorical +from pandas import DataFrame +from sklearn.svm import SVC +from tqdm import tqdm +from hydra.utils import instantiate + + +logger = logging.getLogger(__name__) + +logging.basicConfig(level=logging.INFO) + + +def parse_folder( + folder, + exclude=[ + "probabilities", + "predictions", + "plots", + "ground_truth", + "attack_predictions", + "attack_probabilities", + "samples", + ], +) -> pd.DataFrame: + """ + Parse a folder containing json files and return a dataframe with the results, excluding the files in the exclude list. + :param folder: Path to folder containing json files + :param exclude: List of files to exclude. Default: ['probabilities', 'predictions', 'plots', 'ground_truth']. + :return: Pandas dataframe with the results + """ + folder = Path(folder) + results = {} + results[folder] = {} + logger.debug(f"Parsing folder {folder}...") + for file in folder.glob("*.json"): + if Path(file).stem in exclude: + continue + else: + with open(file, "r") as f: + results[folder][Path(file).stem] = json.load(f) + return pd.DataFrame(results).T + + +def flatten_results(results): + """ + Flatten a dataframe containing json files. So that each json dict entry becomes a column with dot notation (e.g. "key1.subkey1") + :param results: Pandas dataframe containing json files + """ + new_results = pd.DataFrame() + logger.debug("Flattening results...") + for col in results.columns: + tmp = pd.json_normalize(results[col]) + new_results = pd.concat([new_results, tmp], axis=1) + return new_results + + +def parse_results(result_dir, flatten=True): + """ + Recursively parse a directory containing json files and return a dataframe with the results. + :param result_dir: Path to directory containing json files + :param regex: Regex to match folders to parse. Default: "*/*" + :param flatten: Whether to flatten the results. Default: True + :return: Pandas dataframe with the results + """ + result_dir = Path(result_dir) + assert result_dir.is_dir(), f"Result directory {result_dir} does not exist." + results = pd.DataFrame() + logger.debug("Parsing results...") + total = len(list(Path(result_dir).iterdir())) + logger.info(f"Parsing {total} folders...") + for folder in Path(result_dir).iterdir(): + tmp = parse_folder(folder) + if flatten is True: + tmp = flatten_results(tmp) + tmp = tmp.loc[:, ~tmp.columns.duplicated()] + results = pd.concat([results, tmp]) + return results + + +def set_for_keys(my_dict, key_arr, val) -> dict: + """ + Set val at path in my_dict defined by the string (or serializable object) array key_arr. + :param my_dict: Dictionary to set value in + :param key_arr: Array of keys to set value at + :param val: Value to set + :return: Dictionary with value set + """ + current = my_dict + for i in range(len(key_arr)): + key = key_arr[i] + if key not in current: + if i == len(key_arr) - 1: + current[key] = val + else: + current[key] = {} + else: + if type(current[key]) is not dict: + logger.info( + "Given dictionary is not compatible with key structure requested", + ) + raise ValueError("Dictionary key already occupied") + current = current[key] + return my_dict + + +def unflatten_results(df, sep=".") -> List[dict]: + """ + Unflatten a dataframe with dot notation columns (e.g. "key1.subkey1") into a list of dictionaries. + :param df: Pandas dataframe with dot notation columns + :param sep: Separator to use. Default: "." + :return: List of dictionaries + """ + logger.debug("Unflattening results...") + result = [] + for _, row in df.iterrows(): + parsed_row = {} + for idx, val in row.iteritems(): + if val == val: + keys = idx.split(sep) + parsed_row = set_for_keys(parsed_row, keys, val) + result.append(parsed_row) + return result + + +def retrain_loop( + clf, + X_train, + y_train, + X_test, + y_test, + atk, + attack_size, + epochs, +) -> tuple: + i = 0 + results = [] + for epochs in tqdm(range(epochs), desc="Epochs"): + # Benign Experiment + logger.info("Epoch: {} - Benign Training".format(i)) + try: + y_train = to_categorical(y_train) + except: # noqa E722 + pass + try: + y_test = to_categorical(y_test) + except: # noqa E722 + pass + start = process_time() + clf.fit(X_train, y_train) + ben_time = (process_time() - start) / len(X_train) + ben_predictions = clf.predict(X_test) + ben_score = np.mean( + np.argmax(ben_predictions, axis=1) == np.argmax(y_test, axis=1), + ) + ben_loss = np.mean(ben_predictions[:, 0] - y_test[:, 0]) + # Adversarial Experiment + logger.info("Epoch: {} - Adversarial Training".format(i)) + start = process_time() + adv = atk.generate(X_test[:attack_size]) + adv_time = (process_time() - start) / attack_size + adv_predictions = clf.predict(adv) + adv_score = np.mean( + np.argmax(adv_predictions, axis=1) + == np.argmax(y_test[:attack_size], axis=1), + ) + adv_loss = np.mean(adv_predictions[:, 0] - y_test[:attack_size, 0]) + # Append Adversarial Examples to Training Set + X_train = np.concatenate((X_train, adv), axis=0) + adv_labels = to_categorical(y_test[:attack_size, 0]) + y_train = np.concatenate((y_train, adv_labels), axis=0) + i += 1 + # Save Results + results.append( + { + "ben_time": ben_time, + "ben_score": ben_score, + "adv_time": adv_time, + "adv_score": adv_score, + "ben_loss": ben_loss, + "adv_loss": adv_loss, + "attack_size": attack_size, + "train_size": len(X_train), + "test_size": attack_size, + }, + ) + outputs = { + "ben_predictions": DataFrame(ben_predictions), + "adv_predictions": DataFrame(adv_predictions), + } + # Some Logging + print( + "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( + i, + ben_time, + ben_score, + adv_time, + adv_score, + ), + ) + logger.info( + "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( + i, + ben_time, + ben_score, + adv_time, + adv_score, + ), + ) + results = pd.DataFrame(results) + return results, outputs + + +def save_results_and_outputs(results, outputs, path="retrain") -> list: + Path(path).mkdir(parents=True, exist_ok=True) + for output in outputs: + pd.DataFrame(outputs[output]).to_csv(f"{path}/{output}.csv") + assert Path(f"{path}/{output}.csv").exists(), f"Problem saving {path}/{output}" + pd.DataFrame(results).to_csv(f"{path}/results.csv") + assert Path( + f"{path}/results.csv", + ).exists(), f"Problem saving results to {path}/results.csv" + + +# Parse Model Results +results = pd.read_csv("output/train.csv") +# Some convenient variable names +input_size = ( + results["data.generate.kwargs.n_samples"] + * results["data.generate.kwargs.n_features"] +) +results["Kernel"] = results["model.init.kwargs.kernel"].copy() +results["Features"] = results["data.generate.kwargs.n_features"].copy() +results["Samples"] = results["data.sample.train_size"].copy() +results["input_size"] = input_size +# Clean up results +if "Unnamed: 0" in results.columns: + del results["Unnamed: 0"] +for col in results.columns: + if col == "data.name" and isinstance(results[col][0], list): + results[col] = results[col].apply(lambda x: x[0]) +# Subset results +subset = results[results["data.sample.train_size"] == 10000] +subset = subset[subset["data.generate.kwargs.n_features"] == 100] +with open("conf/model/best_rbf.yaml", "r") as f: + best_rbf = yaml.safe_load(f) +best_rbf["init"].pop("_target_", None) +best_rbf["init"].pop("name", None) +with open("conf/model/best_poly.yaml", "r") as f: + best_poly = yaml.safe_load(f) +best_poly["init"].pop("_target_", None) +best_poly["init"].pop("name", None) +with open("conf/model/best_linear.yaml", "r") as f: + best_lin = yaml.safe_load(f) +best_lin["init"].pop("_target_", None) +best_lin["init"].pop("name", None) +rbf_model = SVC(**best_rbf["init"]) +# Poly +poly_model = SVC(**best_poly["init"]) +# Linear +linear_model = SVC(**best_lin["init"]) +# Load Data +with open("conf/data/attack.yaml", "r") as f: + data = yaml.safe_load(f) +data = instantiate(data) +X_train, X_test, y_train, y_test = data() +# Fit Models +rbf_model.fit(X_train, y_train) +poly_model.fit(X_train, y_train) +linear_model.fit(X_train, y_train) +models = [rbf_model, poly_model, linear_model] +model_names = ["rbf", "poly", "linear"] +# ART Models +art_models = [ + ScikitlearnSVC(model=rbf_model), + ScikitlearnSVC(model=poly_model), + ScikitlearnSVC(model=linear_model), +] +i = 1 +epochs = 20 +df1 = pd.DataFrame() +df2 = pd.DataFrame() +for model in art_models: + # Define model name + name = model_names[i - 1] + # Define attack + atk = ProjectedGradientDescent( + estimator=model, + eps=1, + eps_step=0.1, + max_iter=10, + targeted=False, + num_random_init=0, + batch_size=10, + ) + confidence_df = pd.DataFrame() + for folder in tqdm(os.listdir("output/reports/attack")): + confidence_ser = pd.Series() + if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): + with open( + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", + ) as f: + probs = json.load(f) + probs = np.array(probs) + false_confidence = y_test[: len(probs)] - probs[:, 1] + avg_prob = np.mean(false_confidence) + with open( + Path("output/reports/attack", folder, "score_dict.json"), + "r", + ) as f: + try: + scores = json.load(f) + except: # noqa E722 + scores = {} + if "False Confidence" in scores: + del scores["False Confidence"] + scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob + with open( + Path("output/reports/attack", folder, "score_dict.json"), + "w", + ) as f: + json.dump(scores, f) + yaml_file = Path("output/reports/attack", folder, "params.yaml") + json_file = Path("output/reports/attack", folder, "params.json") + if yaml_file.exists(): + params_file = yaml_file + with open(params_file, "r") as f: + params = yaml.safe_load(f) + elif json_file.exists(): + params_file = json_file + with open(params_file, "r") as f: + params = json.load(f) + else: + raise ValueError(f"No params file found for {folder}") + attack_params = params["attack"]["init"]["kwargs"] + attack_params.update({"name": params["attack"]["init"]["name"]}) + confidence_ser["Kernel"] = name + confidence_ser["Average False Confidence"] = avg_prob + # print(f"Shape of confidence ser: {confidence_ser.shape}") + attack_ser = pd.Series(attack_params) + confidence_ser = confidence_ser._append(attack_ser) + # print(f"Shape of confidence ser: {confidence_ser.shape}") + if "Unnamed: 0" in confidence_df.columns: + del confidence_df["Unnamed: 0"] + confidence_df = confidence_df._append(confidence_ser, ignore_index=True) + # print(f"Shape of confidence df: {confidence_df.shape}") + # print(confidence_df.head()) + # input("Press Enter to continue...") + else: + pass + df1 = pd.concat([df1, confidence_df], ignore_index=True) + Path("plots").mkdir(parents=True, exist_ok=True) + df1.to_csv(Path("plots", "before_retrain_confidence.csv")) + # Train model on attack samples + print(f"Training Model {i} of {len(models)}") + if not Path("retrain", name, f"{epochs}.pkl").exists(): + results, outputs = retrain_loop( + model, + X_train, + y_train, + X_test, + y_test, + atk, + epochs=epochs, + attack_size=50, + ) + save_results_and_outputs(results, outputs, path=f"retrain/{name}/") + Path("retrain", name).mkdir(parents=True, exist_ok=True) + with Path("retrain", name, f"{epochs}.pkl").open("wb") as f: + pickle.dump(model, f) + else: + with open(Path("retrain", name, f"{epochs}.pkl"), "rb") as f: + model = pickle.load(f) + print(f"Evaluating Model {i} of {len(models)}") + # Get false confidence scores after retraining + confidence_df = pd.DataFrame() + for folder in tqdm(os.listdir("output/reports/attack")): + confidence_ser = pd.Series() + if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): + with open( + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", + ) as f: + probs = json.load(f) + probs = np.array(probs) + false_confidence = y_test[: len(probs)] - probs[:, 1] + avg_prob = np.mean(false_confidence) + pd.DataFrame(probs).to_csv( + Path( + "output/reports/attack", + folder, + f"probs_after_retraining_{name}.csv", + ), + ) + with open( + Path("output/reports/attack", folder, "score_dict.json"), + "r", + ) as f: + scores = json.load(f) + if "False Confidence" in scores: + del scores["False Confidence"] + scores[f"False Confidence {name.capitalize()}"] = avg_prob + with open( + Path("output/reports/attack", folder, "score_dict.json"), + "w", + ) as f: + json.dump(scores, f) + if Path("output/reports/attack", folder, "params.yaml").exists(): + with open( + Path("output/reports/attack", folder, "params.yaml"), + "r", + ) as f: + params = yaml.safe_load(f) + elif Path("output/reports/attack", folder, "params.json").exists(): + with open( + Path("output/reports/attack", folder, "params.json"), + "r", + ) as f: + params = json.load(f) + else: + logger.warning(f"No params file found for {folder}") + continue + attack_params = params["attack"]["init"]["kwargs"] + attack_params.update({"name": params["attack"]["init"]["name"]}) + confidence_ser["Kernel"] = name + confidence_ser["Average False Confidence After Retraining"] = avg_prob + attack_ser = pd.Series(attack_params) + confidence_ser = confidence_ser._append(attack_ser) + if "Unnamed: 0" in confidence_df.columns: + del confidence_df["Unnamed: 0"] + confidence_df = confidence_df._append(confidence_ser, ignore_index=True) + df2 = pd.concat([df2, confidence_df], ignore_index=True) + df2.to_csv(Path("plots", "after_retrain_confidence.csv")) + i += 1 diff --git a/examples/security/kdd-nsl/.dvc/config b/examples/security/kdd-nsl/.dvc/config new file mode 100644 index 00000000..e69de29b diff --git a/examples/security/kdd-nsl/.dvcignore b/examples/security/kdd-nsl/.dvcignore new file mode 100644 index 00000000..51973055 --- /dev/null +++ b/examples/security/kdd-nsl/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/examples/security/kdd-nsl/README.md b/examples/security/kdd-nsl/README.md new file mode 100644 index 00000000..c3c227e6 --- /dev/null +++ b/examples/security/kdd-nsl/README.md @@ -0,0 +1,32 @@ +# Classification Experiments + +## Directory Contents + +├── conf: contains the configuration files. +│ ├── attack +│ ├── config.yaml : contains the default configuration +│ ├── data +│ ├── files +│ ├── model +│ ├── plots +│ └── scorers +├── dag.md: contains the [dvc](https://dvc.org/doc/start/data-management/data-pipelines) pipeline, visualized as a graph +├── dvc.lock: contains the git trackable information for all the data and model binaries +├── dvc.yaml: specifies the pipeline visualized in `dag.md` +├── experiments.sh: specifies the grid search parameters and executes the pipeline for each set of parameters +├── multirun: contains the hydra configuration information +├── params.yaml: contains the parsed default configuration file +├── plots.ipynb: is a jupyter notebook for visualizing the data +├── queue: contains all of the configurations for all experiments +├── reports: contains the results of all experiments, with a folder for each layer (e.g. `reports/train/`), containing scores, plots, predictions, probabilities, and ground_truth json files. +├── train: contains the `data/` and `models/` for each model +│ ├── data +│ ├── models + +## Execution instructions + +To check basic code functionality, run +```dvc repro --force``` +which will execute the [dvc pipeline](https://dvc.org/doc/start/data-management/data-pipelines) that makes all of the inputs and outputs git trackable. This will execute all of the code on the default configurations first (stages `generate`, `train`, and `attack` on the `dvc.yaml`), followed by grid search on all of the models in stage `model-queue` and all of the attacks in stage `attack-queue`. After finding the best configuration for each kernel (stage `compile`) + +which overrides the default configurations, using [hydra](https://hydra.cc/docs/patterns/configuring_experiments/) and its [optuna plugin](https://hydra.cc/docs/plugins/optuna_sweeper/) to specify a search space for each set of parameters specified in the bash script. Sets of parameters that apply to all experiments are set in the `config.yaml`. Parameters particular to a experiment are specified using command line overrides within the bash script. diff --git a/examples/security/kdd-nsl/attacks.sh b/examples/security/kdd-nsl/attacks.sh new file mode 100644 index 00000000..76ed02bc --- /dev/null +++ b/examples/security/kdd-nsl/attacks.sh @@ -0,0 +1,54 @@ +#!/bin/bash +MODEL_CONFIGS=$(ls conf/model/best_*.yaml) +CONFIG_NAMES=$(ls conf/model/best_*.yaml | cut -d'/' -f3 | cut -d'.' -f1) +TOTAL=$(( ${#CONFIG_NAMES[@]} )) +i=$(( 0 )) +mkdir -p logs/attacks/ +for model_config in $CONFIG_NAMES; do + kernel_name=$(echo $model_config | cut -d'_' -f2) + i=$(( i + 1 )) + if [ $model_config == "default" ]; then + continue + fi + HYDRA_FULL_ERROR=1 python -m deckard.layers.optimise \ + ++model.init.kernel=kernel_name \ + ++stage=attack \ + ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent \ + ++attack.init.norm=1,2,inf \ + ++attack.init.eps_step=.001,.01,.1,.3,.5,1 \ + ++attack.init.batch_size=1,10,50,100 \ + ++attack.init.eps=.001,.01,.1,.3,.5,1 \ + ++attack.init.max_iter=1,10,100,1000 \ + ++hydra.sweeper.study_name=$model_config \ + ++attack.attack_size=100 \ + model=$model_config $@ --multirun >> logs/attacks/$model_config.log + echo "Successfully completed model $model_config" >> attack_log.txt +done + +# Other attacks listed below +# PGD +# bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.norm=1,2,inf ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=1,10,50,100 ++attack.init.eps=.001,.01,.1,.3,.5,1 $@ + +# # Carlini L0 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # Carlini L2 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # Carlini LInf +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # DeepFool +# bash models.sh ++attack.init.nb_grads=1,3,5,10 ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.batch_size=100 $@ + +# #Threshold Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ + +# #Pixel Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.PixelAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ + +# #Adversarial Patch +# bash models.sh ++attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.scale_max=.1,.2,.3,.5,.8,.9,.99 ++attack.init.batch_size=100 $@ + +# #Hop Skip Jump +# bash models.sh ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.batch_size=100 $@ diff --git a/examples/security/kdd-nsl/dvc.lock b/examples/security/kdd-nsl/dvc.lock new file mode 100644 index 00000000..287cb3d2 --- /dev/null +++ b/examples/security/kdd-nsl/dvc.lock @@ -0,0 +1,798 @@ +schema: '2.0' +stages: + train: + cmd: python -m deckard.layers.experiment train + params: + params.yaml: + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/reports/train/default/params.yaml + hash: md5 + md5: 7234aab7d5edae504afa2090d96e4c3f + size: 2434 + - path: output/reports/train/default/predictions.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/probabilities.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/score_dict.json + hash: md5 + md5: e82308d212b314ee0e82e2d8e0bbdca0 + size: 358 + attack: + cmd: python -m deckard.layers.experiment attack + deps: + - path: output/reports/train/default/params.yaml + hash: md5 + md5: 7234aab7d5edae504afa2090d96e4c3f + size: 2434 + - path: output/reports/train/default/predictions.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/probabilities.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/score_dict.json + hash: md5 + md5: e82308d212b314ee0e82e2d8e0bbdca0 + size: 358 + params: + params.yaml: + attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/attacks/attack.pkl + hash: md5 + md5: 13ab6d4b68ddf03df92475f5f6881590 + size: 1832 + - path: output/reports/attack/default/adv_predictions.json + hash: md5 + md5: e8af38b6680c2109b29779ecc9aab259 + size: 439 + - path: output/reports/attack/default/adv_probabilities.json + hash: md5 + md5: e8af38b6680c2109b29779ecc9aab259 + size: 439 + - path: output/reports/attack/default/params.yaml + hash: md5 + md5: b300c684dc58fc23684ccefbb9f83265 + size: 5832 + - path: output/reports/attack/default/predictions.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/attack/default/probabilities.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/attack/default/score_dict.json + hash: md5 + md5: dc2399a3d8aa37830553f4a19b1ed07e + size: 582 + models: + cmd: bash other_data.sh +stage=train --config-name=model.yaml + deps: + - path: conf/model.yaml + hash: md5 + md5: 7895842addd2cae0477e8bd3d1c3a075 + size: 950 + - path: other_data.sh + hash: md5 + md5: 4360b9dfaf6373d673f2f940b7a645e8 + size: 1313 + - path: params.yaml + hash: md5 + md5: c7e85851f691450d5050508ebe39b823 + size: 5442 + params: + params.yaml: + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: logs/models/ + hash: md5 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 + nfiles: 3 + - path: model.db + hash: md5 + md5: 4afc0f97fb9b487396c761d8d4f43aa3 + size: 679936 + compile_models: + cmd: python -m deckard.layers.compile --report_folder output/reports/train/ --results_file + output/train.csv + deps: + - path: logs/models/ + hash: md5 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 + nfiles: 3 + - path: model.db + hash: md5 + md5: 4afc0f97fb9b487396c761d8d4f43aa3 + size: 679936 + - path: output/reports/train/ + hash: md5 + md5: 26644ddebe405806dafb68b1c541b7a9.dir + size: 39353128 + nfiles: 1552 + outs: + - path: output/train.csv + hash: md5 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 + find_best_model@rbf: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_rbf --study_name=rbf --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 + nfiles: 3 + - path: model.db + hash: md5 + md5: 4afc0f97fb9b487396c761d8d4f43aa3 + size: 679936 + - path: output/train.csv + hash: md5 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 + outs: + - path: conf/model/best_rbf.yaml + hash: md5 + md5: 3092c0288833989d2e77d849993a2a40 + size: 360 + find_best_model@linear: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_linear --study_name=linear --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 + nfiles: 3 + - path: model.db + hash: md5 + md5: 4afc0f97fb9b487396c761d8d4f43aa3 + size: 679936 + - path: output/train.csv + hash: md5 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 + outs: + - path: conf/model/best_linear.yaml + hash: md5 + md5: e4ae7059114d8724d4947e952145d4fe + size: 330 + find_best_model@poly: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_poly --study_name=poly --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 + nfiles: 3 + - path: model.db + hash: md5 + md5: 4afc0f97fb9b487396c761d8d4f43aa3 + size: 679936 + - path: output/train.csv + hash: md5 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 + outs: + - path: conf/model/best_poly.yaml + hash: md5 + md5: 12f892f3ba4ef8bab095b36bd7558d3e + size: 372 + attacks: + cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml + deps: + - path: conf/model/best_linear.yaml + hash: md5 + md5: e4ae7059114d8724d4947e952145d4fe + size: 330 + - path: conf/model/best_poly.yaml + hash: md5 + md5: 12f892f3ba4ef8bab095b36bd7558d3e + size: 372 + - path: conf/model/best_rbf.yaml + hash: md5 + md5: 3092c0288833989d2e77d849993a2a40 + size: 360 + - path: logs/models/ + hash: md5 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 + nfiles: 3 + - path: model.db + hash: md5 + md5: 4afc0f97fb9b487396c761d8d4f43aa3 + size: 679936 + - path: output/train.csv + hash: md5 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 + outs: + - path: attack.db + hash: md5 + md5: a234b8e39120ae2369ef7012ea8a1c2c + size: 700416 + - path: logs/attacks/ + hash: md5 + md5: b703e5852611cc780892d5c508371061.dir + size: 7097696 + nfiles: 3 + compile_attacks: + cmd: python -m deckard.layers.compile --report_folder output/reports/attack/ --results_file + output/attack.csv + deps: + - path: attack.db + hash: md5 + md5: a234b8e39120ae2369ef7012ea8a1c2c + size: 700416 + - path: logs/attacks/ + hash: md5 + md5: b703e5852611cc780892d5c508371061.dir + size: 7097696 + nfiles: 3 + - path: output/reports/attack/ + hash: md5 + md5: 28cff7ce7b900ec67d9a55be7a1b3665.dir + size: 64940924 + nfiles: 4355 + outs: + - path: output/attack.csv + hash: md5 + md5: e346372d8713fe75bb16c02f12924027 + size: 1545938 + find_best_attack@linear: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_linear --study_name=best_linear --default_config attack.yaml + deps: + - path: attack.db + hash: md5 + md5: a234b8e39120ae2369ef7012ea8a1c2c + size: 700416 + - path: logs/models/ + hash: md5 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 + nfiles: 3 + - path: output/train.csv + hash: md5 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 + outs: + - path: conf/attack/best_linear.yaml + hash: md5 + md5: f048059aaa0e383f9c5ae9c085927588 + size: 231 + find_best_attack@rbf: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_rbf --study_name=best_rbf --default_config attack.yaml + deps: + - path: attack.db + hash: md5 + md5: a234b8e39120ae2369ef7012ea8a1c2c + size: 700416 + - path: logs/models/ + hash: md5 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 + nfiles: 3 + - path: output/train.csv + hash: md5 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 + outs: + - path: conf/attack/best_rbf.yaml + hash: md5 + md5: 936f60710cd2fba6d1b3584accc94943 + size: 246 + find_best_attack@poly: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_poly --study_name=best_poly --default_config attack.yaml + deps: + - path: attack.db + hash: md5 + md5: a234b8e39120ae2369ef7012ea8a1c2c + size: 700416 + - path: logs/models/ + hash: md5 + md5: 1a02fcfbe25af3d2f3012066ef4bfcc2.dir + size: 360396 + nfiles: 3 + - path: output/train.csv + hash: md5 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 + outs: + - path: conf/attack/best_poly.yaml + hash: md5 + md5: 26b55aad33b06e46b07904b00c5cb236 + size: 228 + other_data_train@kdd_nsl: + cmd: DATASET_NAME=kdd_nsl bash other_data.sh data=kdd_nsl +stage=train --config-name=model.yaml + deps: + - path: conf/model.yaml + hash: md5 + md5: daaa0663d05972a5b8645c35d364da88 + size: 990 + - path: other_data.sh + hash: md5 + md5: 6ebecf100cc02847ad31901bebb2ee5a + size: 2759 + - path: output/reports/train/default/params.yaml + hash: md5 + md5: d4e0a34b2b15765ca71fa5ecaf7e3826 + size: 2100 + outs: + - path: kdd_nsl.db + hash: md5 + md5: 06933f8fc0a1feca0944c131b6a3854b + size: 348160 + - path: kdd_nsl/ + hash: md5 + md5: 9076c4e55fd1058e7446588d99930d58.dir + size: 39137423 + nfiles: 1072 + - path: logs/kdd_nsl/ + hash: md5 + md5: e7c227947468122b62f891c0d54e0c54.dir + size: 1314288 + nfiles: 12 + retrain: + cmd: python retrain.py + deps: + - path: conf/attack/best_linear.yaml + hash: md5 + md5: f048059aaa0e383f9c5ae9c085927588 + size: 231 + - path: conf/attack/best_poly.yaml + hash: md5 + md5: 26b55aad33b06e46b07904b00c5cb236 + size: 228 + - path: conf/attack/best_rbf.yaml + hash: md5 + md5: 936f60710cd2fba6d1b3584accc94943 + size: 246 + - path: conf/model/best_linear.yaml + hash: md5 + md5: e4ae7059114d8724d4947e952145d4fe + size: 330 + - path: conf/model/best_poly.yaml + hash: md5 + md5: 12f892f3ba4ef8bab095b36bd7558d3e + size: 372 + - path: conf/model/best_rbf.yaml + hash: md5 + md5: 3092c0288833989d2e77d849993a2a40 + size: 360 + - path: output/attacks/ + hash: md5 + md5: 5dde4d7ab3e40708071184d4694123ee.dir + size: 725472 + nfiles: 396 + - path: output/models/ + hash: md5 + md5: d4a08c418e8ed1b0187f7f25a7299f0b.dir + size: 2228914 + nfiles: 260 + outs: + - path: plots/after_retrain_confidence.csv + hash: md5 + md5: cc14c3955a972163e623e0446766809a + size: 114012 + - path: plots/before_retrain_confidence.csv + hash: md5 + md5: 97809e651e09f8b326b8d30baa7ebeef + size: 113995 + - path: retrain/ + hash: md5 + md5: 2a50215eab128a532d22ed3c0d2cba50.dir + size: 174457 + nfiles: 12 + plots: + cmd: python plots.py + deps: + - path: output/attack.csv + hash: md5 + md5: e346372d8713fe75bb16c02f12924027 + size: 1545938 + - path: output/train.csv + hash: md5 + md5: 305667e30ef303edf0e6dd12e89ef37c + size: 568223 + - path: plots/after_retrain_confidence.csv + hash: md5 + md5: cc14c3955a972163e623e0446766809a + size: 114012 + - path: plots/before_retrain_confidence.csv + hash: md5 + md5: 97809e651e09f8b326b8d30baa7ebeef + size: 113995 + outs: + - path: plots/accuracy_vs_attack_parameters.pdf + hash: md5 + md5: a82ad408178362b8f82239d18f90e5e0 + size: 16739 + - path: plots/confidence_vs_attack_parameters.pdf + hash: md5 + md5: d0632672e8312f99ee76c962f26144ae + size: 18215 + - path: plots/retrain_accuracy.pdf + hash: md5 + md5: 0027d350b615182d5b38cd86eb308dd5 + size: 13913 + - path: plots/retrain_confidence_vs_attack_parameters.pdf + hash: md5 + md5: 8c0e54e8402cc482ccd0dff257abd98c + size: 18186 + - path: plots/retrain_time.pdf + hash: md5 + md5: e03052e9715b9ed42016cc418ff9f7dc + size: 12900 + - path: plots/train_time_vs_attack_parameters.pdf + hash: md5 + md5: ef7acbcbb4ffa4eda0ff8e0107565eef + size: 17026 diff --git a/examples/security/kdd-nsl/dvc.yaml b/examples/security/kdd-nsl/dvc.yaml new file mode 100644 index 00000000..04164939 --- /dev/null +++ b/examples/security/kdd-nsl/dvc.yaml @@ -0,0 +1,151 @@ +stages: + attack: # Prototype stage + cmd: python -m deckard.layers.experiment attack + deps: + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} #optionally, store the data + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + metrics: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + outs: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.probabilities_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} + params: + - data + - model + - files + - scorers + - attack + train: # Prototype stage + cmd: python -m deckard.layers.experiment train + metrics: + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + outs: + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + params: + - data + - model + - files + - scorers + models: # Grid search stage + cmd: bash other_data.sh +stage=train --config-name=model.yaml # specifies the prototype stage, file to be used + outs: + - model.db: # This allows to run this section piecewise, adding features or samples inside models.sh which are then stored in the .db + cache: true + persist: true + - logs/models/: # Each feature/sample/train size combination log is stored in a separate file + cache: true + persist: true + params: # These are the parameters that are passed to the prototype stage + - data + - model + - files + - scorers + deps: + - params.yaml + - conf/model.yaml + - other_data.sh + # - conf/model/ + compile_models: + cmd: + python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/train/ --results_file ${files.directory}/train.csv + outs: + - ${files.directory}/train.csv + deps: + - ${files.directory}/${files.reports}/train/ # Each feature/sample/train size combination experiment is stored in a separate folder + - model.db + - logs/models/ + find_best_model: + foreach: + - linear + - rbf + - poly + do: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model --params_file best_${item} --study_name=${item} --default_config model.yaml + outs: + - conf/model/best_${item}.yaml + deps: + - ${files.directory}/train.csv + - model.db + - logs/models/ + attacks: + cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml + outs: + - logs/attacks/: + cache: true + persist: true + - attack.db: + cache: true + persist: true + deps: + - ${files.directory}/train.csv + - model.db + - logs/models/ + - conf/model/best_linear.yaml + - conf/model/best_rbf.yaml + - conf/model/best_poly.yaml + compile_attacks: + cmd: + python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/attack/ --results_file ${files.directory}/attack.csv + outs: + - ${files.directory}/attack.csv + deps: + - ${files.directory}/${files.reports}/attack/ # Each feature/sample/train size combination experiment is stored in a separate folder + - attack.db + - logs/attacks/ + find_best_attack: + foreach: + - linear + - rbf + - poly + do: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack --params_file best_${item} --study_name=best_${item} --default_config attack.yaml + outs: + - conf/attack/best_${item}.yaml + deps: + - ${files.directory}/train.csv + - attack.db + - logs/models/ + retrain: + cmd : python retrain.py + deps: + - ${files.directory}/models/ + - ${files.directory}/attacks/ + - conf/attack/best_linear.yaml + - conf/attack/best_rbf.yaml + - conf/attack/best_poly.yaml + - conf/model/best_linear.yaml + - conf/model/best_rbf.yaml + - conf/model/best_poly.yaml + outs: + - retrain/ + metrics: + - plots/before_retrain_confidence.csv + - plots/after_retrain_confidence.csv + plots: + cmd : python plots.py + deps : + - plots/after_retrain_confidence.csv + - output/attack.csv + - plots/before_retrain_confidence.csv + - output/train.csv + plots : + - plots/accuracy_vs_attack_parameters.pdf + # - plots/accuracy_vs_features.pdf + # - plots/accuracy_vs_samples.pdf + - plots/confidence_vs_attack_parameters.pdf + - plots/train_time_vs_attack_parameters.pdf + # - plots/train_time_vs_features.pdf + # - plots/train_time_vs_samples.pdf + - plots/retrain_accuracy.pdf + - plots/retrain_confidence_vs_attack_parameters.pdf + - plots/retrain_time.pdf diff --git a/examples/security/kdd-nsl/other_data.sh b/examples/security/kdd-nsl/other_data.sh new file mode 100644 index 00000000..88f33d4c --- /dev/null +++ b/examples/security/kdd-nsl/other_data.sh @@ -0,0 +1,35 @@ + + +mkdir -p logs/models +HYDRA_FULL_ERROR=1; python -m deckard.layers.optimise \ +++data.sample.test_size=1000 \ +model.init.kernel=linear \ +model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +++hydra.sweeper.storage=sqlite:///model.db \ +++hydra.sweeper.study_name=linear "$@" --multirun \ +>| logs/models/linear.log +echo "Linear Kernel Done" >> model_log.txt +# Runs the poly kernel +python -m deckard.layers.optimise \ +++data.sample.test_size=1000 \ +model.init.kernel=rbf \ ++model.init.gamma=scale \ +model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ ++model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +++hydra.sweeper.storage=sqlite:///model.db \ +++hydra.sweeper.study_name=rbf "$@" --multirun \ +>| logs/models/rbf.log +echo "RBF Kernel Done" >> model_log.txt +# Runs the poly kernel +python -m deckard.layers.optimise \ +++data.sample.test_size=1000 \ +model.init.kernel=poly \ ++model.init.degree=1,2,3,4,5 \ ++model.init.gamma=scale \ ++model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +++hydra.sweeper.storage=sqlite:///model.db \ +++hydra.sweeper.study_name=poly "$@" --multirun \ +>| logs/models/poly.log +echo "Poly Kernel Done" >> model_log.txt +echo "Successfully completed experiment ${i} of ${TOTAL}" >> model_log.txt diff --git a/examples/security/kdd-nsl/plots.py b/examples/security/kdd-nsl/plots.py new file mode 100644 index 00000000..06375d98 --- /dev/null +++ b/examples/security/kdd-nsl/plots.py @@ -0,0 +1,376 @@ +import pandas as pd +import seaborn as sns +from pathlib import Path +import matplotlib.pyplot as plt +import logging + +sns.set_style("whitegrid") +sns.set_context("paper", font_scale=1.5) +sns.set_style({"font.family": "serif", "font.serif": "Times New Roman"}) +Path("plots").mkdir(parents=True, exist_ok=True) + + +logger = logging.getLogger(__name__) + + +# if Path("plots", "model_results.csv").exists(): +# results = pd.read_csv("plots/model_results.csv") +# else: +# results = parse_results("reports/model_queue/") +results = pd.read_csv("output/train.csv") +# input_size = results["data.generate.kwargs.n_samples"] * results["data.generate.kwargs.n_features"] +results["Kernel"] = results["model.init.kwargs.kernel"].copy() +# results["Features"] = results["data.generate.kwargs.n_features"].copy() +results["Samples"] = results["data.sample.train_size"].copy() +# results["input_size"] = input_size +# sample_list = results["data.generate.kwargs.n_samples"].unique() +# feature_list = results["data.generate.kwargs.n_features"].unique() +kernel_list = results["model.init.kwargs.kernel"].unique() +if "Unnamed: 0" in results.columns: + del results["Unnamed: 0"] +for col in results.columns: + if col == "data.name" and isinstance(results[col][0], list): + results[col] = results[col].apply(lambda x: x[0]) +results = results[results["model.init.kwargs.kernel"] != "sigmoid"] + +attack_results = pd.read_csv("output/attack.csv") +attack_results["Kernel"] = attack_results["model.init.kwargs.kernel"].copy() +# attack_results["Features"] = attack_results["data.generate.kwargs.n_features"].copy() +# attack_results["Samples"] = attack_results["data.sample.train_size"].copy() +# sample_list = attack_results["data.generate.kwargs.n_samples"].unique() +# feature_list = attack_results["data.generate.kwargs.n_features"].unique() +kernel_list = attack_results["model.init.kwargs.kernel"].unique() +if "Unnamed: 0" in attack_results.columns: + del attack_results["Unnamed: 0"] +for col in attack_results.columns: + if col == "data.name" and isinstance(attack_results[col][0], list): + attack_results[col] = attack_results[col].apply(lambda x: x[0]) + + +# graph1 = sns.lineplot( +# x="data.sample.train_size", +# y="accuracy", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph1.legend(labels=["Linear", "RBF", "Poly"]) +# graph1.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +# graph1.set_xlabel("Number of Samples") +# graph1.set_ylabel("Accuracy") +# graph1.set_xscale("log") +# graph1.get_figure().tight_layout() +# graph1.get_figure().savefig("plots/accuracy_vs_samples.pdf") +# plt.gcf().clear() + +# graph2 = sns.lineplot( +# x="data.generate.kwargs.n_features", +# y="accuracy", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph2.set_xlabel("Number of Features") +# graph2.set_ylabel("Accuracy") +# graph2.set_xscale("log") +# graph2.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +# graph2.get_figure().tight_layout() +# graph2.get_figure().savefig("plots/accuracy_vs_features.pdf") +# plt.gcf().clear() + +# results["train_time"] = ( +# results["train_time"] +# * results["data.sample.train_size"] +# * results["data.generate.kwargs.n_samples"] +# ) +# graph3 = sns.lineplot( +# x="data.generate.kwargs.n_features", +# y="train_time", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph3.set_xlabel("Number of Features") +# graph3.set_ylabel("Training Time") +# graph3.set(yscale="log", xscale="log") +# graph3.legend(title="Kernel") +# graph3.get_figure().tight_layout() +# graph3.get_figure().savefig("plots/train_time_vs_features.pdf") +# plt.gcf().clear() + +# graph4 = sns.lineplot( +# x="data.sample.train_size", +# y="train_time", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph4.set_xlabel("Number of Samples") +# graph4.set_ylabel("Training Time") +# graph4.set(yscale="log", xscale="log") +# graph4.legend(title="Kernel") +# graph4.get_figure().tight_layout() +# graph4.get_figure().savefig("plots/train_time_vs_samples.pdf") +# plt.gcf().clear() + +fig, ax = plt.subplots(2, 2) +graph5 = sns.lineplot( + x="attack.init.kwargs.eps", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph5.set(xscale="log", xlabel="Perturbation Distance", ylabel="Accuracy") +graph6 = sns.lineplot( + x="attack.init.kwargs.eps_step", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph6.set(xscale="log", xlabel="Perturbation Step", ylabel="Accuracy") +graph7 = sns.lineplot( + x="attack.init.kwargs.max_iter", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph7.set(xscale="log", xlabel="Maximum Iterations", ylabel="Accuracy") +graph8 = sns.lineplot( + x="attack.init.kwargs.batch_size", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph8.set(xscale="log", xlabel="Batch Size", ylabel="Accuracy") +graph6.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout() +fig.savefig("plots/accuracy_vs_attack_parameters.pdf") +plt.gcf().clear() + +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="attack.init.kwargs.eps", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="Attack Time") +graph10 = sns.lineplot( + x="attack.init.kwargs.eps_step", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="Attack Time") +graph11 = sns.lineplot( + x="attack.init.kwargs.max_iter", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="Attack Time") +graph12 = sns.lineplot( + x="attack.init.kwargs.batch_size", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="Attack Time") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/train_time_vs_attack_parameters.pdf") +plt.gcf().clear() + +retrain_df = pd.DataFrame() +for kernel in ["rbf", "linear", "poly"]: + try: + tmp = pd.read_csv(f"retrain/{kernel}/results.csv") + except FileNotFoundError: + break + tmp["Kernel"] = kernel + tmp["Epochs"] = tmp.index + retrain_df = pd.concat([retrain_df, tmp], axis=0) + retrain_df = retrain_df.reset_index(drop=True) + if "Unnamed: 0" in retrain_df.columns: + del retrain_df["Unnamed: 0"] + + +retrain = sns.lineplot( + x="Epochs", + y="ben_score", + data=retrain_df, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +retrain = sns.lineplot( + x="Epochs", + y="adv_score", + data=retrain_df, + style="Kernel", + color="darkred", + legend=False, + style_order=["rbf", "poly", "linear"], +) +retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +retrain.set_xlabel("Retraining Epochs") +retrain.set_ylabel("Accuracy") +retrain.get_figure().tight_layout() +retrain.get_figure().savefig("plots/retrain_accuracy.pdf") +plt.gcf().clear() + +retrain_df["ben_time"] = retrain_df["ben_time"] * retrain_df["train_size"] * 10 +retrain_df["adv_time"] = retrain_df["adv_time"] * retrain_df["attack_size"] +retrain = sns.lineplot( + x="Epochs", + y="ben_time", + data=retrain_df, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +retrain = sns.lineplot( + x="Epochs", + y="adv_time", + data=retrain_df, + style="Kernel", + color="darkred", + legend=False, + style_order=["rbf", "poly", "linear"], +) +retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +retrain.set_xlabel("Retraining Epochs") +retrain.set_ylabel("Time") +retrain.set_yscale("log") +retrain.get_figure().tight_layout() +retrain.get_figure().savefig("plots/retrain_time.pdf") +plt.gcf().clear() + +confidence_df = pd.read_csv("plots/before_retrain_confidence.csv") +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="eps", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") +graph10 = sns.lineplot( + x="eps_step", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") +graph11 = sns.lineplot( + x="max_iter", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") +graph12 = sns.lineplot( + x="batch_size", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/confidence_vs_attack_parameters.pdf") +plt.gcf().clear() + +confdence_df = pd.read_csv("plots/after_retrain_confidence.csv") +confidence_df.columns +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="eps", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") +graph10 = sns.lineplot( + x="eps_step", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") +graph11 = sns.lineplot( + x="max_iter", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") +graph12 = sns.lineplot( + x="batch_size", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/retrain_confidence_vs_attack_parameters.pdf") +plt.gcf().clear() diff --git a/examples/security/kdd-nsl/retrain.py b/examples/security/kdd-nsl/retrain.py new file mode 100644 index 00000000..a7dbac4f --- /dev/null +++ b/examples/security/kdd-nsl/retrain.py @@ -0,0 +1,443 @@ +import logging +import os +import pickle +from pathlib import Path +from time import process_time +import json +from typing import List +import numpy as np +import pandas as pd +import yaml +from art.attacks.evasion import ProjectedGradientDescent +from art.estimators.classification.scikitlearn import ScikitlearnSVC +from art.utils import to_categorical +from pandas import DataFrame +from sklearn.svm import SVC +from tqdm import tqdm +from hydra.utils import instantiate + + +logger = logging.getLogger(__name__) + +logging.basicConfig(level=logging.INFO) + + +def parse_folder( + folder, + exclude=[ + "probabilities", + "predictions", + "plots", + "ground_truth", + "attack_predictions", + "attack_probabilities", + "samples", + ], +) -> pd.DataFrame: + """ + Parse a folder containing json files and return a dataframe with the results, excluding the files in the exclude list. + :param folder: Path to folder containing json files + :param exclude: List of files to exclude. Default: ['probabilities', 'predictions', 'plots', 'ground_truth']. + :return: Pandas dataframe with the results + """ + folder = Path(folder) + results = {} + results[folder] = {} + logger.debug(f"Parsing folder {folder}...") + for file in folder.glob("*.json"): + if Path(file).stem in exclude: + continue + else: + with open(file, "r") as f: + results[folder][Path(file).stem] = json.load(f) + return pd.DataFrame(results).T + + +def flatten_results(results): + """ + Flatten a dataframe containing json files. So that each json dict entry becomes a column with dot notation (e.g. "key1.subkey1") + :param results: Pandas dataframe containing json files + """ + new_results = pd.DataFrame() + logger.debug("Flattening results...") + for col in results.columns: + tmp = pd.json_normalize(results[col]) + new_results = pd.concat([new_results, tmp], axis=1) + return new_results + + +def parse_results(result_dir, flatten=True): + """ + Recursively parse a directory containing json files and return a dataframe with the results. + :param result_dir: Path to directory containing json files + :param regex: Regex to match folders to parse. Default: "*/*" + :param flatten: Whether to flatten the results. Default: True + :return: Pandas dataframe with the results + """ + result_dir = Path(result_dir) + assert result_dir.is_dir(), f"Result directory {result_dir} does not exist." + results = pd.DataFrame() + logger.debug("Parsing results...") + total = len(list(Path(result_dir).iterdir())) + logger.info(f"Parsing {total} folders...") + for folder in Path(result_dir).iterdir(): + tmp = parse_folder(folder) + if flatten is True: + tmp = flatten_results(tmp) + tmp = tmp.loc[:, ~tmp.columns.duplicated()] + results = pd.concat([results, tmp]) + return results + + +def set_for_keys(my_dict, key_arr, val) -> dict: + """ + Set val at path in my_dict defined by the string (or serializable object) array key_arr. + :param my_dict: Dictionary to set value in + :param key_arr: Array of keys to set value at + :param val: Value to set + :return: Dictionary with value set + """ + current = my_dict + for i in range(len(key_arr)): + key = key_arr[i] + if key not in current: + if i == len(key_arr) - 1: + current[key] = val + else: + current[key] = {} + else: + if type(current[key]) is not dict: + logger.info( + "Given dictionary is not compatible with key structure requested", + ) + raise ValueError("Dictionary key already occupied") + current = current[key] + return my_dict + + +def unflatten_results(df, sep=".") -> List[dict]: + """ + Unflatten a dataframe with dot notation columns (e.g. "key1.subkey1") into a list of dictionaries. + :param df: Pandas dataframe with dot notation columns + :param sep: Separator to use. Default: "." + :return: List of dictionaries + """ + logger.debug("Unflattening results...") + result = [] + for _, row in df.iterrows(): + parsed_row = {} + for idx, val in row.iteritems(): + if val == val: + keys = idx.split(sep) + parsed_row = set_for_keys(parsed_row, keys, val) + result.append(parsed_row) + return result + + +def retrain_loop( + clf, + X_train, + y_train, + X_test, + y_test, + atk, + attack_size, + epochs, +) -> tuple: + i = 0 + results = [] + for epochs in tqdm(range(epochs), desc="Epochs"): + # Benign Experiment + logger.info("Epoch: {} - Benign Training".format(i)) + try: + y_train = to_categorical(y_train) + except: # noqa E722 + pass + try: + y_test = to_categorical(y_test) + except: # noqa E722 + pass + start = process_time() + clf.fit(X_train, y_train) + ben_time = (process_time() - start) / len(X_train) + ben_predictions = clf.predict(X_test) + ben_score = np.mean( + np.argmax(ben_predictions, axis=1) == np.argmax(y_test, axis=1), + ) + ben_loss = np.mean(ben_predictions[:, 0] - y_test[:, 0]) + # Adversarial Experiment + logger.info("Epoch: {} - Adversarial Training".format(i)) + start = process_time() + adv = atk.generate(X_test[:attack_size]) + adv_time = (process_time() - start) / attack_size + adv_predictions = clf.predict(adv) + adv_score = np.mean( + np.argmax(adv_predictions, axis=1) + == np.argmax(y_test[:attack_size], axis=1), + ) + adv_loss = np.mean(adv_predictions[:, 0] - y_test[:attack_size, 0]) + # Append Adversarial Examples to Training Set + X_train = np.concatenate((X_train, adv), axis=0) + adv_labels = to_categorical(y_test[:attack_size, 0]) + y_train = np.concatenate((y_train, adv_labels), axis=0) + i += 1 + # Save Results + results.append( + { + "ben_time": ben_time, + "ben_score": ben_score, + "adv_time": adv_time, + "adv_score": adv_score, + "ben_loss": ben_loss, + "adv_loss": adv_loss, + "attack_size": attack_size, + "train_size": len(X_train), + "test_size": attack_size, + }, + ) + outputs = { + "ben_predictions": DataFrame(ben_predictions), + "adv_predictions": DataFrame(adv_predictions), + } + # Some Logging + print( + "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( + i, + ben_time, + ben_score, + adv_time, + adv_score, + ), + ) + logger.info( + "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( + i, + ben_time, + ben_score, + adv_time, + adv_score, + ), + ) + results = pd.DataFrame(results) + return results, outputs + + +def save_results_and_outputs(results, outputs, path="retrain") -> list: + Path(path).mkdir(parents=True, exist_ok=True) + for output in outputs: + pd.DataFrame(outputs[output]).to_csv(f"{path}/{output}.csv") + assert Path(f"{path}/{output}.csv").exists(), f"Problem saving {path}/{output}" + pd.DataFrame(results).to_csv(f"{path}/results.csv") + assert Path( + f"{path}/results.csv", + ).exists(), f"Problem saving results to {path}/results.csv" + + +# Parse Model Results +results = pd.read_csv("output/train.csv") +# Some convenient variable names +# input_size = results["data.generate.kwargs.n_samples"] * results["data.generate.kwargs.n_features"] +results["Kernel"] = results["model.init.kwargs.kernel"].copy() +# results["Features"] = results["data.generate.kwargs.n_features"].copy() +# results["Samples"] = results["data.sample.train_size"].copy() +# results["input_size"] = input_size +# Clean up results +if "Unnamed: 0" in results.columns: + del results["Unnamed: 0"] +for col in results.columns: + if col == "data.name" and isinstance(results[col][0], list): + results[col] = results[col].apply(lambda x: x[0]) +# Subset results +# subset = results[results["data.sample.train_size"] == 10000] +# subset = subset[subset["data.generate.kwargs.n_features"] == 100] +with open("conf/model/best_rbf.yaml", "r") as f: + best_rbf = yaml.safe_load(f) +best_rbf["init"].pop("_target_", None) +best_rbf["init"].pop("name", None) +with open("conf/model/best_poly.yaml", "r") as f: + best_poly = yaml.safe_load(f) +best_poly["init"].pop("_target_", None) +best_poly["init"].pop("name", None) +with open("conf/model/best_linear.yaml", "r") as f: + best_lin = yaml.safe_load(f) +best_lin["init"].pop("_target_", None) +best_lin["init"].pop("name", None) +rbf_model = SVC(**best_rbf["init"]) +# Poly +poly_model = SVC(**best_poly["init"]) +# Linear +linear_model = SVC(**best_lin["init"]) +# Load Data +with open("conf/data/attack.yaml", "r") as f: + data = yaml.safe_load(f) +data = instantiate(data) +X_train, X_test, y_train, y_test = data() +# Fit Models +rbf_model.fit(X_train, y_train) +poly_model.fit(X_train, y_train) +linear_model.fit(X_train, y_train) +models = [rbf_model, poly_model, linear_model] +model_names = ["rbf", "poly", "linear"] +# ART Models +art_models = [ + ScikitlearnSVC(model=rbf_model), + ScikitlearnSVC(model=poly_model), + ScikitlearnSVC(model=linear_model), +] +i = 1 +epochs = 20 +df1 = pd.DataFrame() +df2 = pd.DataFrame() +for model in art_models: + # Define model name + name = model_names[i - 1] + # Define attack + atk = ProjectedGradientDescent( + estimator=model, + eps=1, + eps_step=0.1, + max_iter=10, + targeted=False, + num_random_init=0, + batch_size=10, + ) + confidence_df = pd.DataFrame() + for folder in tqdm(os.listdir("output/reports/attack")): + confidence_ser = pd.Series() + if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): + with open( + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", + ) as f: + probs = json.load(f) + probs = np.array(probs) + false_confidence = y_test[: len(probs)] - probs[:, 1] + avg_prob = np.mean(false_confidence) + with open( + Path("output/reports/attack", folder, "score_dict.json"), + "r", + ) as f: + try: + scores = json.load(f) + except: # noqa E722 + scores = {} + if "False Confidence" in scores: + del scores["False Confidence"] + scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob + with open( + Path("output/reports/attack", folder, "score_dict.json"), + "w", + ) as f: + json.dump(scores, f) + yaml_file = Path("output/reports/attack", folder, "params.yaml") + json_file = Path("output/reports/attack", folder, "params.json") + if yaml_file.exists(): + params_file = yaml_file + with open(params_file, "r") as f: + params = yaml.safe_load(f) + elif json_file.exists(): + params_file = json_file + with open(params_file, "r") as f: + params = json.load(f) + else: + raise ValueError(f"No params file found for {folder}") + attack_params = params["attack"]["init"]["kwargs"] + attack_params.update({"name": params["attack"]["init"]["name"]}) + confidence_ser["Kernel"] = name + confidence_ser["Average False Confidence"] = avg_prob + # print(f"Shape of confidence ser: {confidence_ser.shape}") + attack_ser = pd.Series(attack_params) + confidence_ser = confidence_ser._append(attack_ser) + # print(f"Shape of confidence ser: {confidence_ser.shape}") + if "Unnamed: 0" in confidence_df.columns: + del confidence_df["Unnamed: 0"] + confidence_df = confidence_df._append(confidence_ser, ignore_index=True) + # print(f"Shape of confidence df: {confidence_df.shape}") + # print(confidence_df.head()) + # input("Press Enter to continue...") + else: + pass + df1 = pd.concat([df1, confidence_df], ignore_index=True) + Path("plots").mkdir(parents=True, exist_ok=True) + df1.to_csv(Path("plots", "before_retrain_confidence.csv")) + # Train model on attack samples + print(f"Training Model {i} of {len(models)}") + if not Path("retrain", name, f"{epochs}.pkl").exists(): + results, outputs = retrain_loop( + model, + X_train, + y_train, + X_test, + y_test, + atk, + epochs=epochs, + attack_size=50, + ) + save_results_and_outputs(results, outputs, path=f"retrain/{name}/") + Path("retrain", name).mkdir(parents=True, exist_ok=True) + with Path("retrain", name, f"{epochs}.pkl").open("wb") as f: + pickle.dump(model, f) + else: + with open(Path("retrain", name, f"{epochs}.pkl"), "rb") as f: + model = pickle.load(f) + print(f"Evaluating Model {i} of {len(models)}") + # Get false confidence scores after retraining + confidence_df = pd.DataFrame() + for folder in tqdm(os.listdir("output/reports/attack")): + confidence_ser = pd.Series() + if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): + with open( + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", + ) as f: + probs = json.load(f) + probs = np.array(probs) + false_confidence = y_test[: len(probs)] - probs[:, 1] + avg_prob = np.mean(false_confidence) + pd.DataFrame(probs).to_csv( + Path( + "output/reports/attack", + folder, + f"probs_after_retraining_{name}.csv", + ), + ) + with open( + Path("output/reports/attack", folder, "score_dict.json"), + "r", + ) as f: + scores = json.load(f) + if "False Confidence" in scores: + del scores["False Confidence"] + scores[f"False Confidence {name.capitalize()}"] = avg_prob + with open( + Path("output/reports/attack", folder, "score_dict.json"), + "w", + ) as f: + json.dump(scores, f) + if Path("output/reports/attack", folder, "params.yaml").exists(): + with open( + Path("output/reports/attack", folder, "params.yaml"), + "r", + ) as f: + params = yaml.safe_load(f) + elif Path("output/reports/attack", folder, "params.json").exists(): + with open( + Path("output/reports/attack", folder, "params.json"), + "r", + ) as f: + params = json.load(f) + else: + logger.warning(f"No params file found for {folder}") + continue + attack_params = params["attack"]["init"]["kwargs"] + attack_params.update({"name": params["attack"]["init"]["name"]}) + confidence_ser["Kernel"] = name + confidence_ser["Average False Confidence After Retraining"] = avg_prob + attack_ser = pd.Series(attack_params) + confidence_ser = confidence_ser._append(attack_ser) + if "Unnamed: 0" in confidence_df.columns: + del confidence_df["Unnamed: 0"] + confidence_df = confidence_df._append(confidence_ser, ignore_index=True) + df2 = pd.concat([df2, confidence_df], ignore_index=True) + df2.to_csv(Path("plots", "after_retrain_confidence.csv")) + i += 1 diff --git a/examples/security/truthseeker/.dvc/config b/examples/security/truthseeker/.dvc/config new file mode 100644 index 00000000..e69de29b diff --git a/examples/security/truthseeker/.dvcignore b/examples/security/truthseeker/.dvcignore new file mode 100644 index 00000000..51973055 --- /dev/null +++ b/examples/security/truthseeker/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/examples/security/truthseeker/README.md b/examples/security/truthseeker/README.md new file mode 100644 index 00000000..c3c227e6 --- /dev/null +++ b/examples/security/truthseeker/README.md @@ -0,0 +1,32 @@ +# Classification Experiments + +## Directory Contents + +├── conf: contains the configuration files. +│ ├── attack +│ ├── config.yaml : contains the default configuration +│ ├── data +│ ├── files +│ ├── model +│ ├── plots +│ └── scorers +├── dag.md: contains the [dvc](https://dvc.org/doc/start/data-management/data-pipelines) pipeline, visualized as a graph +├── dvc.lock: contains the git trackable information for all the data and model binaries +├── dvc.yaml: specifies the pipeline visualized in `dag.md` +├── experiments.sh: specifies the grid search parameters and executes the pipeline for each set of parameters +├── multirun: contains the hydra configuration information +├── params.yaml: contains the parsed default configuration file +├── plots.ipynb: is a jupyter notebook for visualizing the data +├── queue: contains all of the configurations for all experiments +├── reports: contains the results of all experiments, with a folder for each layer (e.g. `reports/train/`), containing scores, plots, predictions, probabilities, and ground_truth json files. +├── train: contains the `data/` and `models/` for each model +│ ├── data +│ ├── models + +## Execution instructions + +To check basic code functionality, run +```dvc repro --force``` +which will execute the [dvc pipeline](https://dvc.org/doc/start/data-management/data-pipelines) that makes all of the inputs and outputs git trackable. This will execute all of the code on the default configurations first (stages `generate`, `train`, and `attack` on the `dvc.yaml`), followed by grid search on all of the models in stage `model-queue` and all of the attacks in stage `attack-queue`. After finding the best configuration for each kernel (stage `compile`) + +which overrides the default configurations, using [hydra](https://hydra.cc/docs/patterns/configuring_experiments/) and its [optuna plugin](https://hydra.cc/docs/plugins/optuna_sweeper/) to specify a search space for each set of parameters specified in the bash script. Sets of parameters that apply to all experiments are set in the `config.yaml`. Parameters particular to a experiment are specified using command line overrides within the bash script. diff --git a/examples/security/truthseeker/attacks.sh b/examples/security/truthseeker/attacks.sh new file mode 100644 index 00000000..76ed02bc --- /dev/null +++ b/examples/security/truthseeker/attacks.sh @@ -0,0 +1,54 @@ +#!/bin/bash +MODEL_CONFIGS=$(ls conf/model/best_*.yaml) +CONFIG_NAMES=$(ls conf/model/best_*.yaml | cut -d'/' -f3 | cut -d'.' -f1) +TOTAL=$(( ${#CONFIG_NAMES[@]} )) +i=$(( 0 )) +mkdir -p logs/attacks/ +for model_config in $CONFIG_NAMES; do + kernel_name=$(echo $model_config | cut -d'_' -f2) + i=$(( i + 1 )) + if [ $model_config == "default" ]; then + continue + fi + HYDRA_FULL_ERROR=1 python -m deckard.layers.optimise \ + ++model.init.kernel=kernel_name \ + ++stage=attack \ + ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent \ + ++attack.init.norm=1,2,inf \ + ++attack.init.eps_step=.001,.01,.1,.3,.5,1 \ + ++attack.init.batch_size=1,10,50,100 \ + ++attack.init.eps=.001,.01,.1,.3,.5,1 \ + ++attack.init.max_iter=1,10,100,1000 \ + ++hydra.sweeper.study_name=$model_config \ + ++attack.attack_size=100 \ + model=$model_config $@ --multirun >> logs/attacks/$model_config.log + echo "Successfully completed model $model_config" >> attack_log.txt +done + +# Other attacks listed below +# PGD +# bash models.sh ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.norm=1,2,inf ++attack.init.eps_step=.001,.01,.1,.3,.5,1 ++attack.init.batch_size=1,10,50,100 ++attack.init.eps=.001,.01,.1,.3,.5,1 $@ + +# # Carlini L0 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # Carlini L2 +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # Carlini LInf +# bash models.sh ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.confidence=1,4,16,64,256 ++attack.init.confidence=1,4,16,64,256 ++attack.init.batch_size=100 $@ + +# # DeepFool +# bash models.sh ++attack.init.nb_grads=1,3,5,10 ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.batch_size=100 $@ + +# #Threshold Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.ThresholdAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ + +# #Pixel Attack +# bash models.sh ++attack.init.name=art.attacks.evasion.PixelAttack +attack.init.th=1,4,16,64,255 ++attack.init.batch_size=100 $@ + +# #Adversarial Patch +# bash models.sh ++attack.init.name=art.attacks.evasion.AdversarialPatch +attack.init.scale_max=.1,.2,.3,.5,.8,.9,.99 ++attack.init.batch_size=100 $@ + +# #Hop Skip Jump +# bash models.sh ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.batch_size=100 $@ diff --git a/examples/security/truthseeker/dvc.lock b/examples/security/truthseeker/dvc.lock new file mode 100644 index 00000000..09e87c52 --- /dev/null +++ b/examples/security/truthseeker/dvc.lock @@ -0,0 +1,798 @@ +schema: '2.0' +stages: + train: + cmd: python -m deckard.layers.experiment train + params: + params.yaml: + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/reports/train/default/params.yaml + hash: md5 + md5: 7234aab7d5edae504afa2090d96e4c3f + size: 2434 + - path: output/reports/train/default/predictions.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/probabilities.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/score_dict.json + hash: md5 + md5: 6d3cfc684f415652e388c55793d59191 + size: 358 + attack: + cmd: python -m deckard.layers.experiment attack + deps: + - path: output/reports/train/default/params.yaml + hash: md5 + md5: 7234aab7d5edae504afa2090d96e4c3f + size: 2434 + - path: output/reports/train/default/predictions.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/probabilities.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/train/default/score_dict.json + hash: md5 + md5: 6d3cfc684f415652e388c55793d59191 + size: 358 + params: + params.yaml: + attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: output/attacks/attack.pkl + hash: md5 + md5: 2e7cda9fbf135dd3204e7185e8b7bc3c + size: 1832 + - path: output/reports/attack/default/adv_predictions.json + hash: md5 + md5: d7dd22f45004b84bb9a3e27d3b28525e + size: 438 + - path: output/reports/attack/default/adv_probabilities.json + hash: md5 + md5: d7dd22f45004b84bb9a3e27d3b28525e + size: 438 + - path: output/reports/attack/default/params.yaml + hash: md5 + md5: b300c684dc58fc23684ccefbb9f83265 + size: 5832 + - path: output/reports/attack/default/predictions.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/attack/default/probabilities.json + hash: md5 + md5: 7e3dec7b2d06af151bf81addc33fba5a + size: 44061 + - path: output/reports/attack/default/score_dict.json + hash: md5 + md5: 4777b08cf979362a082cd08a942de150 + size: 582 + models: + cmd: bash other_data.sh +stage=train --config-name=model.yaml + deps: + - path: conf/model.yaml + hash: md5 + md5: bfdd4743dda1272364c4bdf8c569972c + size: 990 + - path: models.sh + hash: md5 + md5: e622d712564afec3b5826e4e91044f90 + size: 2970 + - path: params.yaml + hash: md5 + md5: c7e85851f691450d5050508ebe39b823 + size: 5442 + params: + params.yaml: + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + adv_probabilities_file: adv_probabilities.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: output + model_dir: models + model_file: model + model_type: .pkl + name: default + params_file: params.yaml + predictions_file: predictions.json + probabilities_file: probabilities.json + reports: reports + score_dict_file: score_dict.json + test_labels_file: test_labels.json + train_labels_file: train_labels.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + initialize: + library: sklearn-svc + data: + _target_: deckard.base.data.Data + name: + https://gist.githubusercontent.com/simplymathematics/8c6c04bd151950d5ea9e62825db97fdd/raw/d6a22cdb42a1db624c89f0298cb4f654d3812703/kdd_nsl.csv + sample: + _target_: deckard.base.data.SklearnDataSampler + random_state: 0 + stratify: true + test_size: 1000 + train_size: 5000 + sklearn_pipeline: + encoder: + handle_unknown: use_encoded_value + name: sklearn.preprocessing.OrdinalEncoder + unknown_value: -1 + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + target: label + init: + C: 1.0 + _target_: deckard.base.model.ModelInitializer + kernel: rbf + max_iter: 10 + name: sklearn.svm.SVC + probability: true + random_state: 0 + library: sklearn-svc + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: model.db + hash: md5 + md5: dadeb40d9dd9eca4387a25275f38816e + size: 724992 + compile_models: + cmd: python -m deckard.layers.compile --report_folder output/reports/train/ --results_file + output/train.csv + deps: + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: model.db + hash: md5 + md5: dadeb40d9dd9eca4387a25275f38816e + size: 724992 + - path: output/reports/train/ + hash: md5 + md5: 1bb6c991ff80349437e71e756b14fced.dir + size: 39603815 + nfiles: 1541 + outs: + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + find_best_model@rbf: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_rbf --study_name=rbf --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: model.db + hash: md5 + md5: dadeb40d9dd9eca4387a25275f38816e + size: 724992 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: conf/model/best_rbf.yaml + hash: md5 + md5: 4932ceac75d6256ce2a7864aa4a5ea3c + size: 359 + find_best_model@linear: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_linear --study_name=linear --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: model.db + hash: md5 + md5: dadeb40d9dd9eca4387a25275f38816e + size: 724992 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: conf/model/best_linear.yaml + hash: md5 + md5: e4ae7059114d8724d4947e952145d4fe + size: 330 + find_best_model@poly: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model + --params_file best_poly --study_name=poly --default_config model.yaml + deps: + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: model.db + hash: md5 + md5: dadeb40d9dd9eca4387a25275f38816e + size: 724992 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: conf/model/best_poly.yaml + hash: md5 + md5: 12f892f3ba4ef8bab095b36bd7558d3e + size: 372 + attacks: + cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml + deps: + - path: conf/model/best_linear.yaml + hash: md5 + md5: e4ae7059114d8724d4947e952145d4fe + size: 330 + - path: conf/model/best_poly.yaml + hash: md5 + md5: 12f892f3ba4ef8bab095b36bd7558d3e + size: 372 + - path: conf/model/best_rbf.yaml + hash: md5 + md5: 4932ceac75d6256ce2a7864aa4a5ea3c + size: 359 + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: model.db + hash: md5 + md5: dadeb40d9dd9eca4387a25275f38816e + size: 724992 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: attack.db + hash: md5 + md5: bd26152de960368cdf78a53ca553e3cd + size: 389120 + - path: logs/attacks/ + hash: md5 + md5: 7c9359bc7817966313dbacae78a34ae8.dir + size: 3179344 + nfiles: 3 + compile_attacks: + cmd: python -m deckard.layers.compile --report_folder output/reports/attack/ --results_file + output/attack.csv + deps: + - path: attack.db + hash: md5 + md5: bd26152de960368cdf78a53ca553e3cd + size: 389120 + - path: logs/attacks/ + hash: md5 + md5: 7c9359bc7817966313dbacae78a34ae8.dir + size: 3179344 + nfiles: 3 + - path: output/reports/attack/ + hash: md5 + md5: 59384232efdbd374769f678c18db8999.dir + size: 29299858 + nfiles: 1968 + outs: + - path: output/attack.csv + hash: md5 + md5: efae37cc4b0186950ea8b638bed64e59 + size: 703047 + find_best_attack@linear: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_linear --study_name=best_linear --default_config attack.yaml + deps: + - path: attack.db + hash: md5 + md5: bd26152de960368cdf78a53ca553e3cd + size: 389120 + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: conf/attack/best_linear.yaml + hash: md5 + md5: df65ae18996a57abebd38df98db37edb + size: 245 + find_best_attack@rbf: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_rbf --study_name=best_rbf --default_config attack.yaml + deps: + - path: attack.db + hash: md5 + md5: bd26152de960368cdf78a53ca553e3cd + size: 389120 + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: conf/attack/best_rbf.yaml + hash: md5 + md5: 9871a9d8d50ef211c7f0ae884bb39fe4 + size: 247 + find_best_attack@poly: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack + --params_file best_poly --study_name=best_poly --default_config attack.yaml + deps: + - path: attack.db + hash: md5 + md5: bd26152de960368cdf78a53ca553e3cd + size: 389120 + - path: logs/models/ + hash: md5 + md5: 796a4382a4e728b573dd7d4f55fe8b3d.dir + size: 360258 + nfiles: 3 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + outs: + - path: conf/attack/best_poly.yaml + hash: md5 + md5: d4c4945873617b0652018e6f27e52b89 + size: 247 + other_data_train@kdd_nsl: + cmd: DATASET_NAME=kdd_nsl bash other_data.sh data=kdd_nsl +stage=train --config-name=model.yaml + deps: + - path: conf/model.yaml + hash: md5 + md5: daaa0663d05972a5b8645c35d364da88 + size: 990 + - path: other_data.sh + hash: md5 + md5: 6ebecf100cc02847ad31901bebb2ee5a + size: 2759 + - path: output/reports/train/default/params.yaml + hash: md5 + md5: d4e0a34b2b15765ca71fa5ecaf7e3826 + size: 2100 + outs: + - path: kdd_nsl.db + hash: md5 + md5: 06933f8fc0a1feca0944c131b6a3854b + size: 348160 + - path: kdd_nsl/ + hash: md5 + md5: 9076c4e55fd1058e7446588d99930d58.dir + size: 39137423 + nfiles: 1072 + - path: logs/kdd_nsl/ + hash: md5 + md5: e7c227947468122b62f891c0d54e0c54.dir + size: 1314288 + nfiles: 12 + retrain: + cmd: python retrain.py + deps: + - path: conf/attack/best_linear.yaml + hash: md5 + md5: df65ae18996a57abebd38df98db37edb + size: 245 + - path: conf/attack/best_poly.yaml + hash: md5 + md5: d4c4945873617b0652018e6f27e52b89 + size: 247 + - path: conf/attack/best_rbf.yaml + hash: md5 + md5: 9871a9d8d50ef211c7f0ae884bb39fe4 + size: 247 + - path: conf/model/best_linear.yaml + hash: md5 + md5: e4ae7059114d8724d4947e952145d4fe + size: 330 + - path: conf/model/best_poly.yaml + hash: md5 + md5: 12f892f3ba4ef8bab095b36bd7558d3e + size: 372 + - path: conf/model/best_rbf.yaml + hash: md5 + md5: 4932ceac75d6256ce2a7864aa4a5ea3c + size: 359 + - path: output/attacks/ + hash: md5 + md5: 73abb55647e64c6606b4b85647cdec8f.dir + size: 327928 + nfiles: 179 + - path: output/models/ + hash: md5 + md5: eb0714fe755dd05dcb5825bd01a33bc2.dir + size: 2189662 + nfiles: 256 + outs: + - path: plots/after_retrain_confidence.csv + hash: md5 + md5: 3eb2c3fdaecb8dc4c392770f334dd053 + size: 52143 + - path: plots/before_retrain_confidence.csv + hash: md5 + md5: 47e8f88d3d987a1b830857c7ab11a3e6 + size: 52126 + - path: retrain/ + hash: md5 + md5: 5289901f2296645822cfcf4e05a5e7d8.dir + size: 174462 + nfiles: 12 + plots: + cmd: python plots.py + deps: + - path: output/attack.csv + hash: md5 + md5: efae37cc4b0186950ea8b638bed64e59 + size: 703047 + - path: output/train.csv + hash: md5 + md5: 31f596095c272f4967c9e7be0e9c12e2 + size: 563726 + - path: plots/after_retrain_confidence.csv + hash: md5 + md5: 3eb2c3fdaecb8dc4c392770f334dd053 + size: 52143 + - path: plots/before_retrain_confidence.csv + hash: md5 + md5: 47e8f88d3d987a1b830857c7ab11a3e6 + size: 52126 + outs: + - path: plots/accuracy_vs_attack_parameters.pdf + hash: md5 + md5: f0a257ff3ab0e6f2aec55d5ee5a2e2c3 + size: 15792 + - path: plots/confidence_vs_attack_parameters.pdf + hash: md5 + md5: d8cd02f26aa27ad611cf94ec2a47836d + size: 17496 + - path: plots/retrain_accuracy.pdf + hash: md5 + md5: ce6a6f8cdd5dc36f8f2050afe6ba7360 + size: 13913 + - path: plots/retrain_confidence_vs_attack_parameters.pdf + hash: md5 + md5: 005663dc89f0b9b3e7b409692307170c + size: 17506 + - path: plots/retrain_time.pdf + hash: md5 + md5: 8bd841840d88462fb0865fce1e8adf30 + size: 12907 + - path: plots/train_time_vs_attack_parameters.pdf + hash: md5 + md5: e91efb84487a1e9b54e0e057145a8d61 + size: 17150 diff --git a/examples/security/truthseeker/dvc.yaml b/examples/security/truthseeker/dvc.yaml new file mode 100644 index 00000000..6b6c8962 --- /dev/null +++ b/examples/security/truthseeker/dvc.yaml @@ -0,0 +1,154 @@ +stages: + attack: # Prototype stage + cmd: python -m deckard.layers.experiment attack + deps: + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} #optionally, store the data + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + metrics: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + outs: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.probabilities_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_probabilities_file} + params: + - data + - model + - files + - scorers + - attack + train: # Prototype stage + cmd: python -m deckard.layers.experiment train + metrics: + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} + - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} + outs: + - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + params: + - data + - model + - files + - scorers + models: # Grid search stage + cmd: bash other_data.sh +stage=train --config-name=model.yaml # specifies the prototype stage, file to be used + outs: + - model.db: # This allows to run this section piecewise, adding features or samples inside models.sh which are then stored in the .db + cache: true + persist: true + - logs/models/: # Each feature/sample/train size combination log is stored in a separate file + cache: true + persist: true + # - ${files.directory}/${files.reports}/train/: # Each feature/sample/train size combination experiment is stored in a separate folder + # cache: true + # persist: true + params: # These are the parameters that are passed to the prototype stage + - data + - model + - files + - scorers + deps: + - params.yaml + - conf/model.yaml + - models.sh + # - conf/model/ + compile_models: + cmd: + python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/train/ --results_file ${files.directory}/train.csv + outs: + - ${files.directory}/train.csv + deps: + - ${files.directory}/${files.reports}/train/ # Each feature/sample/train size combination experiment is stored in a separate folder + - model.db + - logs/models/ + find_best_model: + foreach: + - linear + - rbf + - poly + do: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir model --params_file best_${item} --study_name=${item} --default_config model.yaml + outs: + - conf/model/best_${item}.yaml + deps: + - ${files.directory}/train.csv + - model.db + - logs/models/ + attacks: + cmd: bash attacks.sh ++stage=attack --config-name=attack.yaml + outs: + - logs/attacks/: + cache: true + persist: true + - attack.db: + cache: true + persist: true + deps: + - ${files.directory}/train.csv + - model.db + - logs/models/ + - conf/model/best_linear.yaml + - conf/model/best_rbf.yaml + - conf/model/best_poly.yaml + compile_attacks: + cmd: + python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/attack/ --results_file ${files.directory}/attack.csv + outs: + - ${files.directory}/attack.csv + deps: + - ${files.directory}/${files.reports}/attack/ # Each feature/sample/train size combination experiment is stored in a separate folder + - attack.db + - logs/attacks/ + find_best_attack: + foreach: + - linear + - rbf + - poly + do: + cmd: python -m deckard.layers.find_best --config_folder conf --config_subdir attack --params_file best_${item} --study_name=best_${item} --default_config attack.yaml + outs: + - conf/attack/best_${item}.yaml + deps: + - ${files.directory}/train.csv + - attack.db + - logs/models/ + retrain: + cmd : python retrain.py + deps: + - ${files.directory}/models/ + - ${files.directory}/attacks/ + - conf/attack/best_linear.yaml + - conf/attack/best_rbf.yaml + - conf/attack/best_poly.yaml + - conf/model/best_linear.yaml + - conf/model/best_rbf.yaml + - conf/model/best_poly.yaml + outs: + - retrain/ + metrics: + - plots/before_retrain_confidence.csv + - plots/after_retrain_confidence.csv + plots: + cmd : python plots.py + deps : + - plots/after_retrain_confidence.csv + - output/attack.csv + - plots/before_retrain_confidence.csv + - output/train.csv + plots : + - plots/accuracy_vs_attack_parameters.pdf + # - plots/accuracy_vs_features.pdf + # - plots/accuracy_vs_samples.pdf + - plots/confidence_vs_attack_parameters.pdf + - plots/train_time_vs_attack_parameters.pdf + # - plots/train_time_vs_features.pdf + # - plots/train_time_vs_samples.pdf + - plots/retrain_accuracy.pdf + - plots/retrain_confidence_vs_attack_parameters.pdf + - plots/retrain_time.pdf diff --git a/examples/security/truthseeker/models.sh b/examples/security/truthseeker/models.sh new file mode 100644 index 00000000..fb8c7cad --- /dev/null +++ b/examples/security/truthseeker/models.sh @@ -0,0 +1,56 @@ +# N_FEATURES=( 10 100 1000 10000 10000 100000 ) +# TRAIN_SIZES=( 10 100 1000 ) +TRAIN_SIZES=( 1000000 ) +N_FEATURES=( 10 100 1000 ) +N_SAMPLES=( 1010000 ) +TOTAL=$(( ${#N_FEATURES[@]} * ${#N_SAMPLES[@]} * ${#TRAIN_SIZES[@]} )) +i=$(( 0 )) +mkdir -p logs/models +for train_size in ${TRAIN_SIZES[@]}; do + for n_samples in ${N_SAMPLES[@]}; do + for n_features in ${N_FEATURES[@]}; do + i=$(( i + 1 )) + echo "Running experiment ${i} of ${TOTAL}" + # Keeps a meta log of the experiments + echo "Running experiment ${i} of ${TOTAL}" >> log.txt + echo "Running experiment with n_features=${n_features} and n_samples=${n_samples} and a train size of ${train_size}" >> log.txt + # Runs the linear kernel, tries to find the best C value + HYDRA_FULL_ERROR=1; python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + model.init.kernel=linear \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=linear_${n_features}_${train_size} "$@" --multirun \ + # Keeps a log of the output for each experiment + >| logs/models/linear_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Linear Kernel Done" >> log.txt + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + model.init.kernel=rbf \ + +model.init.gamma=scale \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=rbf_${n_features}_${train_size} "$@" --multirun \ + >| logs/models/rbf_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "RBF Kernel Done" >> log.txt + # Runs the poly kernel + python -m deckard.layers.optimise \ + ++data.generate.n_features=$n_features \ + ++data.generate.n_samples=$n_samples \ + ++data.sample.train_size=$train_size \ + model.init.kernel=poly \ + +model.init.degree=1,2,3,4,5 \ + +model.init.gamma=scale \ + +model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ + ++hydra.sweeper.study_name=poly_${n_features}_${train_size} "$@" --multirun \ + >| logs/models/poly_features-${n_features}_samples-${n_samples}_train-${train_size}.log + echo "Poly Kernel Done" >> log.txt + echo "Successfully completed experiment ${i} of ${TOTAL}" >> log.txt + done; + done; +done; diff --git a/examples/security/truthseeker/other_data.sh b/examples/security/truthseeker/other_data.sh new file mode 100644 index 00000000..88f33d4c --- /dev/null +++ b/examples/security/truthseeker/other_data.sh @@ -0,0 +1,35 @@ + + +mkdir -p logs/models +HYDRA_FULL_ERROR=1; python -m deckard.layers.optimise \ +++data.sample.test_size=1000 \ +model.init.kernel=linear \ +model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +++hydra.sweeper.storage=sqlite:///model.db \ +++hydra.sweeper.study_name=linear "$@" --multirun \ +>| logs/models/linear.log +echo "Linear Kernel Done" >> model_log.txt +# Runs the poly kernel +python -m deckard.layers.optimise \ +++data.sample.test_size=1000 \ +model.init.kernel=rbf \ ++model.init.gamma=scale \ +model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ ++model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +++hydra.sweeper.storage=sqlite:///model.db \ +++hydra.sweeper.study_name=rbf "$@" --multirun \ +>| logs/models/rbf.log +echo "RBF Kernel Done" >> model_log.txt +# Runs the poly kernel +python -m deckard.layers.optimise \ +++data.sample.test_size=1000 \ +model.init.kernel=poly \ ++model.init.degree=1,2,3,4,5 \ ++model.init.gamma=scale \ ++model.init.coef0=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +model.init.C=.0001,.001,.01,.1,1,10,100,1000,10000,10000 \ +++hydra.sweeper.storage=sqlite:///model.db \ +++hydra.sweeper.study_name=poly "$@" --multirun \ +>| logs/models/poly.log +echo "Poly Kernel Done" >> model_log.txt +echo "Successfully completed experiment ${i} of ${TOTAL}" >> model_log.txt diff --git a/examples/security/truthseeker/plots.py b/examples/security/truthseeker/plots.py new file mode 100644 index 00000000..c5ae8ac3 --- /dev/null +++ b/examples/security/truthseeker/plots.py @@ -0,0 +1,377 @@ +import pandas as pd +import seaborn as sns +from pathlib import Path +import matplotlib.pyplot as plt + +import logging + +sns.set_style("whitegrid") +sns.set_context("paper", font_scale=1.5) +sns.set_style({"font.family": "serif", "font.serif": "Times New Roman"}) +Path("plots").mkdir(parents=True, exist_ok=True) + + +logger = logging.getLogger(__name__) + + +# if Path("plots", "model_results.csv").exists(): +# results = pd.read_csv("plots/model_results.csv") +# else: +# results = parse_results("reports/model_queue/") +results = pd.read_csv("output/train.csv") +# input_size = results["data.generate.kwargs.n_samples"] * results["data.generate.kwargs.n_features"] +results["Kernel"] = results["model.init.kwargs.kernel"].copy() +# results["Features"] = results["data.generate.kwargs.n_features"].copy() +results["Samples"] = results["data.sample.train_size"].copy() +# results["input_size"] = input_size +# sample_list = results["data.generate.kwargs.n_samples"].unique() +# feature_list = results["data.generate.kwargs.n_features"].unique() +kernel_list = results["model.init.kwargs.kernel"].unique() +if "Unnamed: 0" in results.columns: + del results["Unnamed: 0"] +for col in results.columns: + if col == "data.name" and isinstance(results[col][0], list): + results[col] = results[col].apply(lambda x: x[0]) +results = results[results["model.init.kwargs.kernel"] != "sigmoid"] + +attack_results = pd.read_csv("output/attack.csv") +attack_results["Kernel"] = attack_results["model.init.kwargs.kernel"].copy() +# attack_results["Features"] = attack_results["data.generate.kwargs.n_features"].copy() +# attack_results["Samples"] = attack_results["data.sample.train_size"].copy() +# sample_list = attack_results["data.generate.kwargs.n_samples"].unique() +# feature_list = attack_results["data.generate.kwargs.n_features"].unique() +kernel_list = attack_results["model.init.kwargs.kernel"].unique() +if "Unnamed: 0" in attack_results.columns: + del attack_results["Unnamed: 0"] +for col in attack_results.columns: + if col == "data.name" and isinstance(attack_results[col][0], list): + attack_results[col] = attack_results[col].apply(lambda x: x[0]) + + +# graph1 = sns.lineplot( +# x="data.sample.train_size", +# y="accuracy", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph1.legend(labels=["Linear", "RBF", "Poly"]) +# graph1.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +# graph1.set_xlabel("Number of Samples") +# graph1.set_ylabel("Accuracy") +# graph1.set_xscale("log") +# graph1.get_figure().tight_layout() +# graph1.get_figure().savefig("plots/accuracy_vs_samples.pdf") +# plt.gcf().clear() + +# graph2 = sns.lineplot( +# x="data.generate.kwargs.n_features", +# y="accuracy", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph2.set_xlabel("Number of Features") +# graph2.set_ylabel("Accuracy") +# graph2.set_xscale("log") +# graph2.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +# graph2.get_figure().tight_layout() +# graph2.get_figure().savefig("plots/accuracy_vs_features.pdf") +# plt.gcf().clear() + +# results["train_time"] = ( +# results["train_time"] +# * results["data.sample.train_size"] +# * results["data.generate.kwargs.n_samples"] +# ) +# graph3 = sns.lineplot( +# x="data.generate.kwargs.n_features", +# y="train_time", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph3.set_xlabel("Number of Features") +# graph3.set_ylabel("Training Time") +# graph3.set(yscale="log", xscale="log") +# graph3.legend(title="Kernel") +# graph3.get_figure().tight_layout() +# graph3.get_figure().savefig("plots/train_time_vs_features.pdf") +# plt.gcf().clear() + +# graph4 = sns.lineplot( +# x="data.sample.train_size", +# y="train_time", +# data=results, +# style="Kernel", +# style_order=["rbf", "poly", "linear"], +# ) +# graph4.set_xlabel("Number of Samples") +# graph4.set_ylabel("Training Time") +# graph4.set(yscale="log", xscale="log") +# graph4.legend(title="Kernel") +# graph4.get_figure().tight_layout() +# graph4.get_figure().savefig("plots/train_time_vs_samples.eps") +# plt.gcf().clear() + +fig, ax = plt.subplots(2, 2) +graph5 = sns.lineplot( + x="attack.init.kwargs.eps", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph5.set(xscale="log", xlabel="Perturbation Distance", ylabel="Accuracy") +graph6 = sns.lineplot( + x="attack.init.kwargs.eps_step", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph6.set(xscale="log", xlabel="Perturbation Step", ylabel="Accuracy") +graph7 = sns.lineplot( + x="attack.init.kwargs.max_iter", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph7.set(xscale="log", xlabel="Maximum Iterations", ylabel="Accuracy") +graph8 = sns.lineplot( + x="attack.init.kwargs.batch_size", + y="accuracy", + data=attack_results, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph8.set(xscale="log", xlabel="Batch Size", ylabel="Accuracy") +graph6.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout() +fig.savefig("plots/accuracy_vs_attack_parameters.pdf") +plt.gcf().clear() + +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="attack.init.kwargs.eps", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="Attack Time") +graph10 = sns.lineplot( + x="attack.init.kwargs.eps_step", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="Attack Time") +graph11 = sns.lineplot( + x="attack.init.kwargs.max_iter", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="Attack Time") +graph12 = sns.lineplot( + x="attack.init.kwargs.batch_size", + y="adv_fit_time", + data=attack_results, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="Attack Time") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/train_time_vs_attack_parameters.pdf") +plt.gcf().clear() + +retrain_df = pd.DataFrame() +for kernel in ["rbf", "linear", "poly"]: + try: + tmp = pd.read_csv(f"retrain/{kernel}/results.csv") + except FileNotFoundError: + break + tmp["Kernel"] = kernel + tmp["Epochs"] = tmp.index + retrain_df = pd.concat([retrain_df, tmp], axis=0) + retrain_df = retrain_df.reset_index(drop=True) + if "Unnamed: 0" in retrain_df.columns: + del retrain_df["Unnamed: 0"] + + +retrain = sns.lineplot( + x="Epochs", + y="ben_score", + data=retrain_df, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +retrain = sns.lineplot( + x="Epochs", + y="adv_score", + data=retrain_df, + style="Kernel", + color="darkred", + legend=False, + style_order=["rbf", "poly", "linear"], +) +retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +retrain.set_xlabel("Retraining Epochs") +retrain.set_ylabel("Accuracy") +retrain.get_figure().tight_layout() +retrain.get_figure().savefig("plots/retrain_accuracy.pdf") +plt.gcf().clear() + +retrain_df["ben_time"] = retrain_df["ben_time"] * retrain_df["train_size"] * 10 +retrain_df["adv_time"] = retrain_df["adv_time"] * retrain_df["attack_size"] +retrain = sns.lineplot( + x="Epochs", + y="ben_time", + data=retrain_df, + style="Kernel", + style_order=["rbf", "poly", "linear"], +) +retrain = sns.lineplot( + x="Epochs", + y="adv_time", + data=retrain_df, + style="Kernel", + color="darkred", + legend=False, + style_order=["rbf", "poly", "linear"], +) +retrain.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +retrain.set_xlabel("Retraining Epochs") +retrain.set_ylabel("Time") +retrain.set_yscale("log") +retrain.get_figure().tight_layout() +retrain.get_figure().savefig("plots/retrain_time.pdf") +plt.gcf().clear() + +confidence_df = pd.read_csv("plots/before_retrain_confidence.csv") +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="eps", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") +graph10 = sns.lineplot( + x="eps_step", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") +graph11 = sns.lineplot( + x="max_iter", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") +graph12 = sns.lineplot( + x="batch_size", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/confidence_vs_attack_parameters.pdf") +plt.gcf().clear() + +confdence_df = pd.read_csv("plots/after_retrain_confidence.csv") +confidence_df.columns +fig, ax = plt.subplots(2, 2) +graph9 = sns.lineplot( + x="eps", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph9.set(xscale="log", xlabel="Perturbation Distance", ylabel="False Confidence") +graph10 = sns.lineplot( + x="eps_step", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[0, 1], + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph10.set(xscale="log", xlabel="Perturbation Step", ylabel="False Confidence") +graph11 = sns.lineplot( + x="max_iter", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 0], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph11.set(xscale="log", xlabel="Maximum Iterations", ylabel="False Confidence") +graph12 = sns.lineplot( + x="batch_size", + y="Average False Confidence", + data=confidence_df, + style="Kernel", + ax=ax[1, 1], + legend=False, + color="darkred", + style_order=["rbf", "poly", "linear"], +) +graph12.set(xscale="log", xlabel="Batch Size", ylabel="False Confidence") +graph10.legend(loc="center left", bbox_to_anchor=(1, 0.5), ncol=1, title="Kernel") +fig.tight_layout(h_pad=0.5) +fig.savefig("plots/retrain_confidence_vs_attack_parameters.pdf") +plt.gcf().clear() diff --git a/examples/security/truthseeker/retrain.py b/examples/security/truthseeker/retrain.py new file mode 100644 index 00000000..6b91b13c --- /dev/null +++ b/examples/security/truthseeker/retrain.py @@ -0,0 +1,443 @@ +import json +import logging +import os +import pickle +from pathlib import Path +from time import process_time +from typing import List +import numpy as np +import pandas as pd +import yaml +from art.attacks.evasion import ProjectedGradientDescent +from art.estimators.classification.scikitlearn import ScikitlearnSVC +from art.utils import to_categorical +from pandas import DataFrame +from sklearn.svm import SVC +from tqdm import tqdm +from hydra.utils import instantiate + + +logger = logging.getLogger(__name__) + +logging.basicConfig(level=logging.INFO) + + +def parse_folder( + folder, + exclude=[ + "probabilities", + "predictions", + "plots", + "ground_truth", + "attack_predictions", + "attack_probabilities", + "samples", + ], +) -> pd.DataFrame: + """ + Parse a folder containing json files and return a dataframe with the results, excluding the files in the exclude list. + :param folder: Path to folder containing json files + :param exclude: List of files to exclude. Default: ['probabilities', 'predictions', 'plots', 'ground_truth']. + :return: Pandas dataframe with the results + """ + folder = Path(folder) + results = {} + results[folder] = {} + logger.debug(f"Parsing folder {folder}...") + for file in folder.glob("*.json"): + if Path(file).stem in exclude: + continue + else: + with open(file, "r") as f: + results[folder][Path(file).stem] = json.load(f) + return pd.DataFrame(results).T + + +def flatten_results(results): + """ + Flatten a dataframe containing json files. So that each json dict entry becomes a column with dot notation (e.g. "key1.subkey1") + :param results: Pandas dataframe containing json files + """ + new_results = pd.DataFrame() + logger.debug("Flattening results...") + for col in results.columns: + tmp = pd.json_normalize(results[col]) + new_results = pd.concat([new_results, tmp], axis=1) + return new_results + + +def parse_results(result_dir, flatten=True): + """ + Recursively parse a directory containing json files and return a dataframe with the results. + :param result_dir: Path to directory containing json files + :param regex: Regex to match folders to parse. Default: "*/*" + :param flatten: Whether to flatten the results. Default: True + :return: Pandas dataframe with the results + """ + result_dir = Path(result_dir) + assert result_dir.is_dir(), f"Result directory {result_dir} does not exist." + results = pd.DataFrame() + logger.debug("Parsing results...") + total = len(list(Path(result_dir).iterdir())) + logger.info(f"Parsing {total} folders...") + for folder in Path(result_dir).iterdir(): + tmp = parse_folder(folder) + if flatten is True: + tmp = flatten_results(tmp) + tmp = tmp.loc[:, ~tmp.columns.duplicated()] + results = pd.concat([results, tmp]) + return results + + +def set_for_keys(my_dict, key_arr, val) -> dict: + """ + Set val at path in my_dict defined by the string (or serializable object) array key_arr. + :param my_dict: Dictionary to set value in + :param key_arr: Array of keys to set value at + :param val: Value to set + :return: Dictionary with value set + """ + current = my_dict + for i in range(len(key_arr)): + key = key_arr[i] + if key not in current: + if i == len(key_arr) - 1: + current[key] = val + else: + current[key] = {} + else: + if type(current[key]) is not dict: + logger.info( + "Given dictionary is not compatible with key structure requested", + ) + raise ValueError("Dictionary key already occupied") + current = current[key] + return my_dict + + +def unflatten_results(df, sep=".") -> List[dict]: + """ + Unflatten a dataframe with dot notation columns (e.g. "key1.subkey1") into a list of dictionaries. + :param df: Pandas dataframe with dot notation columns + :param sep: Separator to use. Default: "." + :return: List of dictionaries + """ + logger.debug("Unflattening results...") + result = [] + for _, row in df.iterrows(): + parsed_row = {} + for idx, val in row.iteritems(): + if val == val: + keys = idx.split(sep) + parsed_row = set_for_keys(parsed_row, keys, val) + result.append(parsed_row) + return result + + +def retrain_loop( + clf, + X_train, + y_train, + X_test, + y_test, + atk, + attack_size, + epochs, +) -> tuple: + i = 0 + results = [] + for epochs in tqdm(range(epochs), desc="Epochs"): + # Benign Experiment + logger.info("Epoch: {} - Benign Training".format(i)) + try: + y_train = to_categorical(y_train) + except: # noqa E722 + pass + try: + y_test = to_categorical(y_test) + except: # noqa E722 + pass + start = process_time() + clf.fit(X_train, y_train) + ben_time = (process_time() - start) / len(X_train) + ben_predictions = clf.predict(X_test) + ben_score = np.mean( + np.argmax(ben_predictions, axis=1) == np.argmax(y_test, axis=1), + ) + ben_loss = np.mean(ben_predictions[:, 0] - y_test[:, 0]) + # Adversarial Experiment + logger.info("Epoch: {} - Adversarial Training".format(i)) + start = process_time() + adv = atk.generate(X_test[:attack_size]) + adv_time = (process_time() - start) / attack_size + adv_predictions = clf.predict(adv) + adv_score = np.mean( + np.argmax(adv_predictions, axis=1) + == np.argmax(y_test[:attack_size], axis=1), + ) + adv_loss = np.mean(adv_predictions[:, 0] - y_test[:attack_size, 0]) + # Append Adversarial Examples to Training Set + X_train = np.concatenate((X_train, adv), axis=0) + adv_labels = to_categorical(y_test[:attack_size, 0]) + y_train = np.concatenate((y_train, adv_labels), axis=0) + i += 1 + # Save Results + results.append( + { + "ben_time": ben_time, + "ben_score": ben_score, + "adv_time": adv_time, + "adv_score": adv_score, + "ben_loss": ben_loss, + "adv_loss": adv_loss, + "attack_size": attack_size, + "train_size": len(X_train), + "test_size": attack_size, + }, + ) + outputs = { + "ben_predictions": DataFrame(ben_predictions), + "adv_predictions": DataFrame(adv_predictions), + } + # Some Logging + print( + "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( + i, + ben_time, + ben_score, + adv_time, + adv_score, + ), + ) + logger.info( + "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( + i, + ben_time, + ben_score, + adv_time, + adv_score, + ), + ) + results = pd.DataFrame(results) + return results, outputs + + +def save_results_and_outputs(results, outputs, path="retrain") -> list: + Path(path).mkdir(parents=True, exist_ok=True) + for output in outputs: + pd.DataFrame(outputs[output]).to_csv(f"{path}/{output}.csv") + assert Path(f"{path}/{output}.csv").exists(), f"Problem saving {path}/{output}" + pd.DataFrame(results).to_csv(f"{path}/results.csv") + assert Path( + f"{path}/results.csv", + ).exists(), f"Problem saving results to {path}/results.csv" + + +# Parse Model Results +results = pd.read_csv("output/train.csv") +# Some convenient variable names +# input_size = results["data.generate.kwargs.n_samples"] * results["data.generate.kwargs.n_features"] +results["Kernel"] = results["model.init.kwargs.kernel"].copy() +# results["Features"] = results["data.generate.kwargs.n_features"].copy() +# results["Samples"] = results["data.sample.train_size"].copy() +# results["input_size"] = input_size +# Clean up results +if "Unnamed: 0" in results.columns: + del results["Unnamed: 0"] +for col in results.columns: + if col == "data.name" and isinstance(results[col][0], list): + results[col] = results[col].apply(lambda x: x[0]) +# Subset results +# subset = results[results["data.sample.train_size"] == 10000] +# subset = subset[subset["data.generate.kwargs.n_features"] == 100] +with open("conf/model/best_rbf.yaml", "r") as f: + best_rbf = yaml.safe_load(f) +best_rbf["init"].pop("_target_", None) +best_rbf["init"].pop("name", None) +with open("conf/model/best_poly.yaml", "r") as f: + best_poly = yaml.safe_load(f) +best_poly["init"].pop("_target_", None) +best_poly["init"].pop("name", None) +with open("conf/model/best_linear.yaml", "r") as f: + best_lin = yaml.safe_load(f) +best_lin["init"].pop("_target_", None) +best_lin["init"].pop("name", None) +rbf_model = SVC(**best_rbf["init"]) +# Poly +poly_model = SVC(**best_poly["init"]) +# Linear +linear_model = SVC(**best_lin["init"]) +# Load Data +with open("conf/data/attack.yaml", "r") as f: + data = yaml.safe_load(f) +data = instantiate(data) +X_train, X_test, y_train, y_test = data() +# Fit Models +rbf_model.fit(X_train, y_train) +poly_model.fit(X_train, y_train) +linear_model.fit(X_train, y_train) +models = [rbf_model, poly_model, linear_model] +model_names = ["rbf", "poly", "linear"] +# ART Models +art_models = [ + ScikitlearnSVC(model=rbf_model), + ScikitlearnSVC(model=poly_model), + ScikitlearnSVC(model=linear_model), +] +i = 1 +epochs = 20 +df1 = pd.DataFrame() +df2 = pd.DataFrame() +for model in art_models: + # Define model name + name = model_names[i - 1] + # Define attack + atk = ProjectedGradientDescent( + estimator=model, + eps=1, + eps_step=0.1, + max_iter=10, + targeted=False, + num_random_init=0, + batch_size=10, + ) + confidence_df = pd.DataFrame() + for folder in tqdm(os.listdir("output/reports/attack")): + confidence_ser = pd.Series() + if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): + with open( + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", + ) as f: + probs = json.load(f) + probs = np.array(probs) + false_confidence = y_test[: len(probs)] - probs[:, 1] + avg_prob = np.mean(false_confidence) + with open( + Path("output/reports/attack", folder, "score_dict.json"), + "r", + ) as f: + try: + scores = json.load(f) + except: # noqa E722 + scores = {} + if "False Confidence" in scores: + del scores["False Confidence"] + scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob + with open( + Path("output/reports/attack", folder, "score_dict.json"), + "w", + ) as f: + json.dump(scores, f) + yaml_file = Path("output/reports/attack", folder, "params.yaml") + json_file = Path("output/reports/attack", folder, "params.json") + if yaml_file.exists(): + params_file = yaml_file + with open(params_file, "r") as f: + params = yaml.safe_load(f) + elif json_file.exists(): + params_file = json_file + with open(params_file, "r") as f: + params = json.load(f) + else: + raise ValueError(f"No params file found for {folder}") + attack_params = params["attack"]["init"]["kwargs"] + attack_params.update({"name": params["attack"]["init"]["name"]}) + confidence_ser["Kernel"] = name + confidence_ser["Average False Confidence"] = avg_prob + # print(f"Shape of confidence ser: {confidence_ser.shape}") + attack_ser = pd.Series(attack_params) + confidence_ser = confidence_ser._append(attack_ser) + # print(f"Shape of confidence ser: {confidence_ser.shape}") + if "Unnamed: 0" in confidence_df.columns: + del confidence_df["Unnamed: 0"] + confidence_df = confidence_df._append(confidence_ser, ignore_index=True) + # print(f"Shape of confidence df: {confidence_df.shape}") + # print(confidence_df.head()) + # input("Press Enter to continue...") + else: + pass + df1 = pd.concat([df1, confidence_df], ignore_index=True) + Path("plots").mkdir(parents=True, exist_ok=True) + df1.to_csv(Path("plots", "before_retrain_confidence.csv")) + # Train model on attack samples + print(f"Training Model {i} of {len(models)}") + if not Path("retrain", name, f"{epochs}.pkl").exists(): + results, outputs = retrain_loop( + model, + X_train, + y_train, + X_test, + y_test, + atk, + epochs=epochs, + attack_size=50, + ) + save_results_and_outputs(results, outputs, path=f"retrain/{name}/") + Path("retrain", name).mkdir(parents=True, exist_ok=True) + with Path("retrain", name, f"{epochs}.pkl").open("wb") as f: + pickle.dump(model, f) + else: + with open(Path("retrain", name, f"{epochs}.pkl"), "rb") as f: + model = pickle.load(f) + print(f"Evaluating Model {i} of {len(models)}") + # Get false confidence scores after retraining + confidence_df = pd.DataFrame() + for folder in tqdm(os.listdir("output/reports/attack")): + confidence_ser = pd.Series() + if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): + with open( + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", + ) as f: + probs = json.load(f) + probs = np.array(probs) + false_confidence = y_test[: len(probs)] - probs[:, 1] + avg_prob = np.mean(false_confidence) + pd.DataFrame(probs).to_csv( + Path( + "output/reports/attack", + folder, + f"probs_after_retraining_{name}.csv", + ), + ) + with open( + Path("output/reports/attack", folder, "score_dict.json"), + "r", + ) as f: + scores = json.load(f) + if "False Confidence" in scores: + del scores["False Confidence"] + scores[f"False Confidence {name.capitalize()}"] = avg_prob + with open( + Path("output/reports/attack", folder, "score_dict.json"), + "w", + ) as f: + json.dump(scores, f) + if Path("output/reports/attack", folder, "params.yaml").exists(): + with open( + Path("output/reports/attack", folder, "params.yaml"), + "r", + ) as f: + params = yaml.safe_load(f) + elif Path("output/reports/attack", folder, "params.json").exists(): + with open( + Path("output/reports/attack", folder, "params.json"), + "r", + ) as f: + params = json.load(f) + else: + logger.warning(f"No params file found for {folder}") + continue + attack_params = params["attack"]["init"]["kwargs"] + attack_params.update({"name": params["attack"]["init"]["name"]}) + confidence_ser["Kernel"] = name + confidence_ser["Average False Confidence After Retraining"] = avg_prob + attack_ser = pd.Series(attack_params) + confidence_ser = confidence_ser._append(attack_ser) + if "Unnamed: 0" in confidence_df.columns: + del confidence_df["Unnamed: 0"] + confidence_df = confidence_df._append(confidence_ser, ignore_index=True) + df2 = pd.concat([df2, confidence_df], ignore_index=True) + df2.to_csv(Path("plots", "after_retrain_confidence.csv")) + i += 1 From f5fbaade49676217dc91a4aba8162c223c536712 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 14 Nov 2023 16:07:10 +0000 Subject: [PATCH 124/148] update test configs --- examples/.dvc/.gitignore | 3 --- test/conf/data/keras_cifar.yaml | 7 ------- test/conf/data/keras_mnist.yaml | 7 ------- test/conf/data/tensorflow_cifar.yaml | 7 ------- test/conf/data/tensorflow_mnist.yaml | 6 ------ test/conf/data/torch_cifar.yaml | 7 ------- test/conf/data/torch_mnist.yaml | 6 ------ test/conf/model/keras_mnist.yaml | 7 ------- test/conf/model/tf_mnist.yaml | 7 ------- test/conf/model/torch_mnist.yaml | 7 ------- 10 files changed, 64 deletions(-) delete mode 100644 examples/.dvc/.gitignore diff --git a/examples/.dvc/.gitignore b/examples/.dvc/.gitignore deleted file mode 100644 index 528f30c7..00000000 --- a/examples/.dvc/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -/config.local -/tmp -/cache diff --git a/test/conf/data/keras_cifar.yaml b/test/conf/data/keras_cifar.yaml index ce5cd4c9..7b3cfb0d 100644 --- a/test/conf/data/keras_cifar.yaml +++ b/test/conf/data/keras_cifar.yaml @@ -6,10 +6,3 @@ sample: _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - # - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/test/conf/data/keras_mnist.yaml b/test/conf/data/keras_mnist.yaml index ad09362e..3fd63fef 100644 --- a/test/conf/data/keras_mnist.yaml +++ b/test/conf/data/keras_mnist.yaml @@ -6,10 +6,3 @@ sample: _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/test/conf/data/tensorflow_cifar.yaml b/test/conf/data/tensorflow_cifar.yaml index c4674928..6567f19f 100644 --- a/test/conf/data/tensorflow_cifar.yaml +++ b/test/conf/data/tensorflow_cifar.yaml @@ -6,10 +6,3 @@ sample: _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/test/conf/data/tensorflow_mnist.yaml b/test/conf/data/tensorflow_mnist.yaml index 42ba394e..09dfde89 100644 --- a/test/conf/data/tensorflow_mnist.yaml +++ b/test/conf/data/tensorflow_mnist.yaml @@ -6,9 +6,3 @@ sample: _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/test/conf/data/torch_cifar.yaml b/test/conf/data/torch_cifar.yaml index 09d489e4..36f29c69 100644 --- a/test/conf/data/torch_cifar.yaml +++ b/test/conf/data/torch_cifar.yaml @@ -6,10 +6,3 @@ sample: _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/test/conf/data/torch_mnist.yaml b/test/conf/data/torch_mnist.yaml index 56e9c38b..3b77487f 100644 --- a/test/conf/data/torch_mnist.yaml +++ b/test/conf/data/torch_mnist.yaml @@ -6,9 +6,3 @@ sample: _target_: deckard.base.data.sampler.SklearnDataSampler random_state : 0 stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/test/conf/model/keras_mnist.yaml b/test/conf/model/keras_mnist.yaml index 40c87042..d4eee24a 100644 --- a/test/conf/model/keras_mnist.yaml +++ b/test/conf/model/keras_mnist.yaml @@ -9,13 +9,6 @@ data: stratify: True train_size : .01 test_size : .01 - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True init: name : keras_example.MNISTNet loss: categorical_crossentropy diff --git a/test/conf/model/tf_mnist.yaml b/test/conf/model/tf_mnist.yaml index 71152f57..0fcbe654 100644 --- a/test/conf/model/tf_mnist.yaml +++ b/test/conf/model/tf_mnist.yaml @@ -9,13 +9,6 @@ data: stratify: True train_size : .01 test_size : .01 - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True init: _target_: deckard.base.model.ModelInitializer name : tfv2_example.TFNet diff --git a/test/conf/model/torch_mnist.yaml b/test/conf/model/torch_mnist.yaml index f00031b4..62869f35 100644 --- a/test/conf/model/torch_mnist.yaml +++ b/test/conf/model/torch_mnist.yaml @@ -9,13 +9,6 @@ data: stratify: True train_size : .01 test_size : .01 - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True init: _target_: deckard.base.model.ModelInitializer name: torch_example.MNISTNet From 7824f08a102833cf0a3809a7b447f0f7c2b8eaec Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 18:08:57 +0000 Subject: [PATCH 125/148] cleaned deckard base code, updated conf --- deckard/base/attack/attack.py | 2 +- deckard/layers/afr.py | 370 ++++++++++++++--------------- deckard/layers/compile.py | 25 +- examples/pytorch/conf/compile.yaml | 2 +- examples/pytorch/dvc.lock | 80 ++++--- examples/pytorch/dvc.yaml | 8 +- examples/pytorch_cifar/dvc.lock | 38 +-- 7 files changed, 267 insertions(+), 258 deletions(-) diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index 5b89fade..0b2a8d21 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -175,7 +175,7 @@ def __call__( x_clean=ben_samples, labels=data[3][: self.attack_size], x_adv=samples, - targeted=self.kwargs.pop("targeted", False), + targeted=targeted, ) except TypeError as e: logger.error(f"Failed to compute success rate. Error: {e}") diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index a19e887d..eee8da54 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -20,6 +20,188 @@ logger = logging.getLogger(__name__) + + +def plot_aft( + df, + file, + event_col, + duration_col, + title, + mtype, + xlabel=None, + ylabel=None, + replacement_dict={}, + **kwargs, +): + if mtype == "weibull": + aft = WeibullAFTFitter(**kwargs) + elif mtype == "log_normal": + aft = LogNormalAFTFitter(**kwargs) + elif mtype == "log_logistic": + aft = LogLogisticAFTFitter(**kwargs) + elif mtype == "cox": + aft = CoxPHFitter(**kwargs) + assert ( + duration_col in df.columns + ), f"Column {duration_col} not in dataframe with columns {df.columns}" + if event_col is not None: + assert ( + event_col in df.columns + ), f"Column {event_col} not in dataframe with columns {df.columns}" + plt.gcf().clear() + aft.fit(df, duration_col=duration_col, event_col=event_col) + ax = aft.plot() + labels = ax.get_yticklabels() + labels = [label.get_text() for label in labels] + for k, v in replacement_dict.items(): + labels = [label.replace(k, v) for label in labels] + ax.set_yticklabels(labels) + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_title(title) + ax.get_figure().tight_layout() + ax.get_figure().savefig(FOLDER / file) + logger.info(f"Saved graph to {FOLDER / file}") + plt.show() + plt.gcf().clear() + return ax, aft + +def plot_partial_effects( + aft, + covariate_array, + values_array, + title=None, + file="partial_effects.pdf", + xlabel="Covariate", + ylabel="Failure rate", + legend_kwargs={"loc": "upper left"}, + replacement_dict={}, + cmap="coolwarm", + **kwargs, +): + plt.gcf().clear() + # kwargs.pop("replacement_dict") + pareto = aft.plot_partial_effects_on_outcome( + covariate_array, values_array, cmap=cmap, **kwargs + ) + labels = pareto.get_yticklabels() + labels = [label.get_text() for label in labels] + for k, v in replacement_dict.items(): + labels = [label.replace(k, v) for label in labels] + pareto.set_yticklabels(labels) + pareto.legend(**legend_kwargs) + pareto.set_ylabel(ylabel) + pareto.set_xlabel(xlabel) + pareto.set_title(title) + pareto.get_figure().tight_layout() + pareto.get_figure().savefig(FOLDER / file) + logger.info(f"Saved graph to {FOLDER / file}") + plt.gcf().clear() + return pareto + +def score_model(aft, train, test): + train_score = aft.score(train) + test_score = aft.score(test) + scores = {"train_score": train_score, "test_score": test_score} + plt.show() + return scores + +def make_afr_table(score_list, aft_dict, dataset): + assert len(score_list) == len( + aft_dict, + ), "Length of score list and aft dict must be equal" + aft_data = pd.DataFrame() + aft_data.index.name = "Model" + aft_data.index = aft_dict.keys() + aft_data["AIC"] = [ + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan + for x in aft_dict.values() + ] + aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] + aft_data["BIC"] = [ + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan + for x in aft_dict.values() + ] + # aft_data["Train LL"] = [x["train_score"] for x in score_list] + # aft_data["Test LL"] = [x["test_score"] for x in score_list] + aft_data["Mean $S(t;\\theta)$"] = [ + x.predict_expectation(X_train).mean() for x in aft_dict.values() + ] + aft_data["Median $S(t;\\theta)$"] = [ + x.predict_median(X_train).median() for x in aft_dict.values() + ] + aft_data = aft_data.round(2) + aft_data.to_csv(FOLDER / "aft_comparison.csv") + logger.info(f"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}") + aft_data = aft_data.round(2) + aft_data.fillna("--", inplace=True) + aft_data.to_latex( + FOLDER / "aft_comparison.tex", + float_format="%.2f", + label=f"tab:{dataset}", + caption=f"Comparison of AFR Models on the {dataset.upper()} dataset.", + ) + + print(yaml.dump(aft_data.to_dict(), default_flow_style=False, indent=4)) + return aft_data + +def clean_data_for_aft( + data, + kwarg_list, + target="adv_failure_rate", +): + subset = data.copy() + assert ( + target in subset + ), f"Target {target} not in dataframe with columns {subset.columns}" + + cleaned = pd.DataFrame() + kwarg_list.append(target) + for kwarg in kwarg_list: + cleaned = pd.concat([cleaned, subset[kwarg]], axis=1) + cols = cleaned.columns + cleaned = pd.DataFrame(subset, columns=cols) + if "accuracy" in cleaned.columns: + cleaned = cleaned[cleaned["accuracy"] != 1e10] + cleaned = cleaned[cleaned["accuracy"] != -1e10] + if "adv_accuracy" in cleaned.columns: + cleaned = cleaned[cleaned["adv_accuracy"] != 1e10] + cleaned = cleaned[cleaned["adv_accuracy"] != -1e10] + cleaned.dropna(inplace=True, how="any", axis=0) + y = cleaned[target] + del cleaned[target] + assert ( + target in cleaned + ), f"Target {target} not in dataframe with columns {cleaned.columns}" + return cleaned, y, data + +def split_data_for_aft( + data, + target, + duration_col, + kwarg_list, + test_size=0.2, + random_state=42, +): + cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) + X_train, X_test, y_train, y_test = train_test_split( + cleaned, + y, + test_size=test_size, + random_state=random_state, + ) + assert ( + target in cleaned + ), f"Target {target} not in dataframe with columns {cleaned.columns}" + assert ( + duration_col in cleaned + ), f"Duration {duration_col} not in dataframe with columns {cleaned.columns}" + return X_train, X_test, y_train, y_test + + + + if "__main__" == __name__: afr_parser = argparse.ArgumentParser() afr_parser.add_argument("--target", type=str, default="adv_failures") @@ -32,13 +214,7 @@ duration_col = afr_args.duration_col dataset = afr_args.dataset - font = { - "family": "Times New Roman", - "weight": "bold", - "size": 22, - } - - matplotlib.rc("font", **font) + FOLDER = Path(afr_args.plots_folder) data = pd.read_csv(afr_args.file, index_col=0) data.columns = data.columns.str.strip() @@ -58,184 +234,7 @@ :, "attack.attack_size", ] - - def plot_aft( - df, - file, - event_col, - duration_col, - title, - mtype, - xlabel=None, - ylabel=None, - replacement_dict={}, - **kwargs, - ): - if mtype == "weibull": - aft = WeibullAFTFitter(**kwargs) - elif mtype == "log_normal": - aft = LogNormalAFTFitter(**kwargs) - elif mtype == "log_logistic": - aft = LogLogisticAFTFitter(**kwargs) - elif mtype == "cox": - aft = CoxPHFitter(**kwargs) - assert ( - duration_col in df.columns - ), f"Column {duration_col} not in dataframe with columns {df.columns}" - if event_col is not None: - assert ( - event_col in df.columns - ), f"Column {event_col} not in dataframe with columns {df.columns}" - plt.gcf().clear() - aft.fit(df, duration_col=duration_col, event_col=event_col) - ax = aft.plot() - labels = ax.get_yticklabels() - labels = [label.get_text() for label in labels] - for k, v in replacement_dict.items(): - labels = [label.replace(k, v) for label in labels] - ax.set_yticklabels(labels) - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel) - ax.set_title(title) - ax.get_figure().tight_layout() - ax.get_figure().savefig(FOLDER / file) - logger.info(f"Saved graph to {FOLDER / file}") - plt.show() - plt.gcf().clear() - return ax, aft - - def plot_partial_effects( - aft, - covariate_array, - values_array, - title=None, - file="partial_effects.pdf", - xlabel="Covariate", - ylabel="Failure rate", - legend_kwargs={"loc": "upper left"}, - replacement_dict={}, - cmap="coolwarm", - **kwargs, - ): - plt.gcf().clear() - # kwargs.pop("replacement_dict") - pareto = aft.plot_partial_effects_on_outcome( - covariate_array, values_array, cmap=cmap, **kwargs - ) - labels = pareto.get_yticklabels() - labels = [label.get_text() for label in labels] - for k, v in replacement_dict.items(): - labels = [label.replace(k, v) for label in labels] - pareto.set_yticklabels(labels) - pareto.legend(**legend_kwargs) - pareto.set_ylabel(ylabel) - pareto.set_xlabel(xlabel) - pareto.set_title(title) - pareto.get_figure().tight_layout() - pareto.get_figure().savefig(FOLDER / file) - logger.info(f"Saved graph to {FOLDER / file}") - plt.gcf().clear() - return pareto - - def score_model(aft, train, test): - train_score = aft.score(train) - test_score = aft.score(test) - scores = {"train_score": train_score, "test_score": test_score} - plt.show() - return scores - - def make_afr_table(score_list, aft_dict, dataset): - assert len(score_list) == len( - aft_dict, - ), "Length of score list and aft dict must be equal" - aft_data = pd.DataFrame() - aft_data.index.name = "Model" - aft_data.index = aft_dict.keys() - aft_data["AIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan - for x in aft_dict.values() - ] - aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] - aft_data["BIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan - for x in aft_dict.values() - ] - # aft_data["Train LL"] = [x["train_score"] for x in score_list] - # aft_data["Test LL"] = [x["test_score"] for x in score_list] - aft_data["Mean $S(t;\\theta)$"] = [ - x.predict_expectation(X_train).mean() for x in aft_dict.values() - ] - aft_data["Median $S(t;\\theta)$"] = [ - x.predict_median(X_train).median() for x in aft_dict.values() - ] - aft_data = aft_data.round(2) - aft_data.to_csv(FOLDER / "aft_comparison.csv") - logger.info(f"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}") - aft_data = aft_data.round(2) - aft_data.fillna("--", inplace=True) - aft_data.to_latex( - FOLDER / "aft_comparison.tex", - float_format="%.2f", - label=f"tab:{dataset}", - caption=f"Comparison of AFR Models on the {dataset.upper()} dataset.", - ) - - print(yaml.dump(aft_data.to_dict(), default_flow_style=False, indent=4)) - return aft_data - - def clean_data_for_aft( - data, - kwarg_list, - target="adv_failure_rate", - ): - subset = data.copy() - assert ( - target in subset - ), f"Target {target} not in dataframe with columns {subset.columns}" - - cleaned = pd.DataFrame() - kwarg_list.append(target) - for kwarg in kwarg_list: - cleaned = pd.concat([cleaned, subset[kwarg]], axis=1) - cols = cleaned.columns - cleaned = pd.DataFrame(subset, columns=cols) - if "accuracy" in cleaned.columns: - cleaned = cleaned[cleaned["accuracy"] != 1e10] - cleaned = cleaned[cleaned["accuracy"] != -1e10] - if "adv_accuracy" in cleaned.columns: - cleaned = cleaned[cleaned["adv_accuracy"] != 1e10] - cleaned = cleaned[cleaned["adv_accuracy"] != -1e10] - cleaned.dropna(inplace=True, how="any", axis=0) - y = cleaned[target] - del cleaned[target] - assert ( - target in cleaned - ), f"Target {target} not in dataframe with columns {cleaned.columns}" - return cleaned, y, data - - def split_data_for_aft( - data, - target, - duration_col, - kwarg_list, - test_size=0.2, - random_state=42, - ): - cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) - X_train, X_test, y_train, y_test = train_test_split( - cleaned, - y, - test_size=test_size, - random_state=random_state, - ) - assert ( - target in cleaned - ), f"Target {target} not in dataframe with columns {cleaned.columns}" - assert ( - duration_col in cleaned - ), f"Duration {duration_col} not in dataframe with columns {cleaned.columns}" - return X_train, X_test, y_train, y_test - + kwarg_list = [ "accuracy", "train_time", @@ -258,6 +257,7 @@ def split_data_for_aft( random_state=42, ) + weibull_dict = { "Intercept: rho_": "$\\rho$", "Intercept: lambda_": "$\lambda$", # noqa W605 diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 8ee86b3c..4ec7fb92 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -62,17 +62,17 @@ def parse_folder(folder, files=["params.yaml", "score_dict.json"]) -> pd.DataFra raise ValueError(f"File type {suffix} not supported.") results[folder]["stage"] = stage results[folder].update(dict_) - all_files = Path(folder).glob("**/*") - for file in all_files: - if file not in path_gen: - if file.parent.name not in results: - results[file.parent.name] = {} - results[file.parent.name][file.stem] = file + all_files = Path(folder).glob("**/*") + for file in all_files: + if file not in path_gen: + if file.parent.name not in results: + results[file.parent.name] = {} + results[file.parent.name][file.stem] = file df = pd.DataFrame(results).T return df -def merge_defences(results: pd.DataFrame): +def merge_defences(results: pd.DataFrame, default_epochs=20): defences = [] def_gens = [] for _, entry in results.iterrows(): @@ -97,6 +97,8 @@ def merge_defences(results: pd.DataFrame): and entry["model.art.pipeline.trainer.name"] not in nones ): defence.append(entry["model.art.pipeline.trainer.name"]) + if ("model.init.nb_epoch" in entry and entry["model.init.nb_epoch"] != default_epochs): + defence.append(entry["model.init.nb_epoch"]) ############################################################################################################ if len(defence) > 1: def_gen = [str(x).split(".")[-1] for x in defence] @@ -133,10 +135,10 @@ def merge_attacks(results: pd.DataFrame): return results -def parse_results(folder, files=["score_dict.json", "params.yaml"]): +def parse_results(folder, files=["score_dict.json", "params.yaml"], default_epochs=20): df = parse_folder(folder, files=files) df = flatten_results(df) - df = merge_defences(df) + df = merge_defences(df, default_epochs=default_epochs) df = merge_attacks(df) return df @@ -211,7 +213,7 @@ def clean_data_for_plotting( data["data.sample.random_state"].rename("random_state", inplace=True) data = format_control_parameter(data, control_dict) logger.info(f"Saving data to {file}") - data.to_csv( file) + # data.to_csv( file) return data @@ -248,6 +250,7 @@ def save_results(results, results_file) -> str: parser.add_argument("--config", type=str, default="conf/compile.yaml") parser.add_argument("--exclude", type=list, default=None, nargs="*") parser.add_argument("--verbose", type=str, default="INFO") + parser.add_argument("--default_epochs", type=int, default=20) parser.add_argument( "--kwargs", type=list, @@ -258,7 +261,7 @@ def save_results(results, results_file) -> str: logging.basicConfig(level=args.verbose) report_folder = args.report_folder results_file = args.results_file - results = parse_results(report_folder) + results = parse_results(report_folder, default_epochs=args.default_epochs) with open(Path(Path(), args.config), "r") as f: big_dict = yaml.load(f, Loader=yaml.FullLoader) def_gen_dict = big_dict["defences"] diff --git a/examples/pytorch/conf/compile.yaml b/examples/pytorch/conf/compile.yaml index 43418520..f3b729e0 100644 --- a/examples/pytorch/conf/compile.yaml +++ b/examples/pytorch/conf/compile.yaml @@ -28,5 +28,5 @@ params: Conf: model.art.pipeline.postprocessor.cutoff FSQ: model.art.pipeline.preprocessor.bit_depth Gauss-in: model.art.pipeline.preprocessor.sigma - Depth: model_layers + Control: model_layers Epochs: model.train.nb_epoch diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock index 589dc9b8..e4dd2fa2 100644 --- a/examples/pytorch/dvc.lock +++ b/examples/pytorch/dvc.lock @@ -106,20 +106,20 @@ stages: size: 222320311 - path: mnist/models/model.optimizer.pt hash: md5 - md5: 1316143c68da44c95cc0294482ba8a50 + md5: 1e527d70896a4a05a2d6ac103382cd50 size: 44780845 - path: mnist/models/model.pt hash: md5 - md5: 0c750c2da34962313084b05ceb424aa2 + md5: f01e051c7b7dfa20eca3fe1caab0b25e size: 44785941 - path: mnist/reports/train/default/predictions.json hash: md5 - md5: 1396b6032cbd1e76ace82789dcf54861 - size: 2883588 + md5: 1e2cf0100bb5f0a42182021e12b00dd9 + size: 2882749 - path: mnist/reports/train/default/score_dict.json hash: md5 - md5: 45631341a862aa88bfffbde78d099c6d - size: 411 + md5: ebe552d99842320709ca466da6d3092c + size: 410 attack: cmd: python -m deckard.layers.experiment attack --config_file mnist.yaml deps: @@ -128,7 +128,7 @@ stages: size: 222320311 - path: mnist/models/model.pt hash: md5 - md5: 0c750c2da34962313084b05ceb424aa2 + md5: f01e051c7b7dfa20eca3fe1caab0b25e size: 44785941 params: params.yaml: @@ -359,16 +359,16 @@ stages: outs: - path: mnist/attacks/attack.pkl hash: md5 - md5: e1047df78f629f7267cfa7ef869f19f5 + md5: dfae95e05b35dc8c6bdabd2b5c13a54f size: 31517 - path: mnist/reports/attack/default/adv_predictions.json hash: md5 - md5: 2ab54c7713532e6e1303e5653c9e14f3 - size: 2194 + md5: 8ee95327d4f44692f4753e183090d669 + size: 2162 - path: mnist/reports/attack/default/score_dict.json hash: md5 - md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 - size: 472 + md5: b11f759014274f38b082f200820c5544 + size: 474 models: cmd: bash models.sh ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 @@ -445,29 +445,35 @@ stages: md5: 36ffafc8cb80eb6fbed190be9d420ef7 size: 3674355 compile@attack: - cmd: python -m deckard.layers.compile --report_folder output/reports/attack --results_file - output/reports/attack.csv + cmd: python -m deckard.layers.compile --report_folder mnist/reports/attack --results_file + mnist/reports/attack.csv deps: - - path: ResNet101.db - md5: c47414423a51a4b866be6ce8312cb97e - size: 561152 - - path: ResNet18.db - md5: 2ae52c7b342b46ac01c79f06bfdc6c38 - size: 2768896 - - path: ResNet34.db - md5: 6a5795cbb9d977cdaadf365a6ac76a0a - size: 983040 - - path: ResNet50.db - md5: 7e215d670119b0703c3d97d86e28e117 - size: 561152 - - path: output/reports/attack/ - md5: a76eb881be1813685bf988cf8dbe7a52.dir - size: 5109780242 - nfiles: 10514 + - path: mnist/reports/attack/ + hash: md5 + md5: 72220a80b6e0a4c6f9ca707c985761c5.dir + size: 63191244 + nfiles: 8010 + - path: mnist/reports/attack/ResNet101.db + hash: md5 + md5: 600452804d96c8b8483c3f8da01130c4 + size: 462848 + - path: mnist/reports/attack/ResNet18.db + hash: md5 + md5: 920b0ed178ec504c0d7990777862283f + size: 1363968 + - path: mnist/reports/attack/ResNet34.db + hash: md5 + md5: 3f56dd2ea0783a56a2a9e3eaaad88c21 + size: 1945600 + - path: mnist/reports/attack/ResNet50.db + hash: md5 + md5: d9ee221b942b56d9bb720e022e05bf4b + size: 462848 outs: - - path: output/reports/attack.csv - md5: 69c87ac633b8abae15d65852c79ea89c - size: 6309975 + - path: mnist/reports/attack.csv + hash: md5 + md5: 95476c59ee7966ee3458a873c0a89b7d + size: 8506622 compile@train: cmd: python -m deckard.layers.compile --report_folder output/reports/train --results_file output/reports/train.csv @@ -665,8 +671,8 @@ stages: size: 2907 - path: mnist/reports/attack/default/score_dict.json hash: md5 - md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 - size: 472 + md5: b11f759014274f38b082f200820c5544 + size: 474 - path: models.sh hash: md5 md5: 1937e58bedac027034aea7d4a5712407 @@ -674,8 +680,8 @@ stages: outs: - path: mnist/reports/attack/ResNet18.db hash: md5 - md5: 2b4e2a2ac1cdd015cf469496a9861088 - size: 999424 + md5: 920b0ed178ec504c0d7990777862283f + size: 1363968 models@ResNet50: cmd: bash models.sh ++model.init.name=torch_example.ResNet50 stage=train ++hydra.sweeper.storage=sqlite:///mnist/reports/train/ResNet50.db --config-name mnist.yaml diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml index 0cbb2c6f..21d8c792 100644 --- a/examples/pytorch/dvc.yaml +++ b/examples/pytorch/dvc.yaml @@ -55,10 +55,10 @@ stages: attacks: foreach: # This is a loop over the ResNet models - ResNet18 - #- ResNet34 - #- ResNet50 - #- ResNet101 - #- ResNet152 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 do: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/attack/${item}.db --config-name mnist.yaml deps: diff --git a/examples/pytorch_cifar/dvc.lock b/examples/pytorch_cifar/dvc.lock index d90598f9..e27f04eb 100644 --- a/examples/pytorch_cifar/dvc.lock +++ b/examples/pytorch_cifar/dvc.lock @@ -98,20 +98,20 @@ stages: size: 739680311 - path: cifar/models/model.optimizer.pt hash: md5 - md5: 09f978b389dbce742b1a8731b81913b0 + md5: e7f811b9879af3e82c339327ebbda4e3 size: 44805933 - path: cifar/models/model.pt hash: md5 - md5: 7b6a070a946d4f5fcc79ac17de88296e + md5: 451eaf13b09e9c9df7eddedfd7597c91 size: 44811029 - path: cifar/reports/train/default/predictions.json hash: md5 - md5: c0a3cdf07b36af7ef9084ab0e2030fcc - size: 2435001 + md5: d260d0626de24848c45e2b4ccc091e28 + size: 2439901 - path: cifar/reports/train/default/score_dict.json hash: md5 - md5: 665f7680184855a05db884355132024c - size: 397 + md5: a7d27eb11ba150347b705d90fe2c52d4 + size: 408 attack: cmd: python -m deckard.layers.experiment attack --config_file cifar10.yaml deps: @@ -120,7 +120,7 @@ stages: size: 739680311 - path: cifar/models/model.pt hash: md5 - md5: 7b6a070a946d4f5fcc79ac17de88296e + md5: 451eaf13b09e9c9df7eddedfd7597c91 size: 44811029 params: params.yaml: @@ -330,16 +330,16 @@ stages: outs: - path: cifar/attacks/attack.pkl hash: md5 - md5: 7af56fed692b7c502bf7431adc68a3d0 + md5: 166ad9c95ddf728a437514ab201b7818 size: 123046 - path: cifar/reports/attack/default/adv_predictions.json hash: md5 - md5: 7860bb5575e86ce8df3240e01abc930d - size: 2099 + md5: 7b935916e2afee6a7c6e3647de882c59 + size: 2126 - path: cifar/reports/attack/default/score_dict.json hash: md5 - md5: d3213b13fda99e50a582440a9752f827 - size: 558 + md5: 372c2d735838be9121b207cbe8739f41 + size: 468 attacks@ResNet18: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 stage=attack ++hydra.sweeper.storage=sqlite:///cifar/reports/attack/ResNet18.db @@ -351,8 +351,8 @@ stages: size: 2907 - path: cifar/reports/attack/default/score_dict.json hash: md5 - md5: d3213b13fda99e50a582440a9752f827 - size: 558 + md5: 372c2d735838be9121b207cbe8739f41 + size: 468 - path: models.sh hash: md5 md5: 02a4961b4afe7ba84c41e9ad49c30c83 @@ -360,8 +360,8 @@ stages: outs: - path: cifar/reports/attack/ResNet18.db hash: md5 - md5: cb2a1edd44e08358b52d5ab5b6232aa5 - size: 1212416 + md5: 8e9fc2e0821e82193a8d50cab6fd2dd9 + size: 819200 attacks@ResNet34: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 stage=attack ++hydra.sweeper.storage=sqlite:///cifar/reports/attack/ResNet34.db @@ -373,8 +373,8 @@ stages: size: 2907 - path: cifar/reports/attack/default/score_dict.json hash: md5 - md5: d3213b13fda99e50a582440a9752f827 - size: 558 + md5: 372c2d735838be9121b207cbe8739f41 + size: 468 - path: models.sh hash: md5 md5: 02a4961b4afe7ba84c41e9ad49c30c83 @@ -382,7 +382,7 @@ stages: outs: - path: cifar/reports/attack/ResNet34.db hash: md5 - md5: c19b84776f8a6f99705c5f6bbd2a28c6 + md5: 8da0c0cc1f20f5a492e730ffa54b573a size: 819200 attacks@ResNet50: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 From 2f574899ce2d05768131daa653c008c0d0718f7a Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 18:09:27 +0000 Subject: [PATCH 126/148] moved pytorch mnist folder --- examples/pytorch/mnist/.dvc/.gitignore | 3 + examples/pytorch/mnist/.dvc/config | 0 examples/pytorch/mnist/.dvcignore | 3 + examples/pytorch/mnist/.gitignore | 2 + examples/pytorch/mnist/attacks.sh | 37 + .../mnist/average_across_random_states.ipynb | 802 +++++++ .../pytorch/mnist/conf/attack/default.yaml | 9 + examples/pytorch/mnist/conf/compile.yaml | 33 + examples/pytorch/mnist/conf/data/default.yaml | 2 + .../pytorch/mnist/conf/data/torch_cifar.yaml | 11 + .../pytorch/mnist/conf/data/torch_mnist.yaml | 15 + .../pytorch/mnist/conf/deploy/default.yaml | 14 + examples/pytorch/mnist/conf/deploy/pod.yaml | 30 + examples/pytorch/mnist/conf/deploy/pvc.yaml | 11 + .../pytorch/mnist/conf/deploy/sclass.yaml | 10 + examples/pytorch/mnist/conf/files/cifar.yaml | 21 + examples/pytorch/mnist/conf/files/mnist.yaml | 21 + .../pytorch/mnist/conf/grid/attack/cw_0.yaml | 4 + .../pytorch/mnist/conf/grid/attack/cw_2.yaml | 5 + .../mnist/conf/grid/attack/cw_inf.yaml | 4 + .../mnist/conf/grid/attack/deepfool.yaml | 5 + .../pytorch/mnist/conf/grid/attack/fgm.yaml | 4 + .../pytorch/mnist/conf/grid/attack/hsj.yaml | 5 + .../pytorch/mnist/conf/grid/attack/patch.yaml | 5 + .../pytorch/mnist/conf/grid/attack/pgd.yaml | 6 + .../pytorch/mnist/conf/grid/attack/pixel.yaml | 4 + .../mnist/conf/grid/attack/threshold.yaml | 4 + .../mnist/conf/grid/model/confidence.yaml | 4 + .../mnist/conf/grid/model/default.yaml | 0 .../pytorch/mnist/conf/grid/model/fsq.yaml | 3 + .../mnist/conf/grid/model/gauss-in.yaml | 4 + .../mnist/conf/grid/model/gauss-out.yaml | 2 + examples/pytorch/mnist/conf/mnist.yaml | 44 + .../pytorch/mnist/conf/model/art/default.yaml | 7 + .../conf/model/art/initialize/default.yaml | 7 + .../model/art/postprocessor/confidence.yaml | 3 + .../model/art/postprocessor/gauss-out.yaml | 2 + .../conf/model/art/preprocessor/default.yaml | 0 .../conf/model/art/preprocessor/fsq.yaml | 3 + .../conf/model/art/preprocessor/gauss-in.yaml | 4 + .../pytorch/mnist/conf/model/torch_cifar.yaml | 13 + .../pytorch/mnist/conf/model/torch_mnist.yaml | 13 + examples/pytorch/mnist/conf/plots.yaml | 250 ++ .../pytorch/mnist/conf/scorers/default.yaml | 9 + examples/pytorch/mnist/dvc.lock | 363 +++ examples/pytorch/mnist/dvc.yaml | 120 + examples/pytorch/mnist/models.sh | 19 + examples/pytorch/mnist/torch_example.py | 79 + examples/pytorch/mnist/weibull.ipynb | 2062 +++++++++++++++++ 49 files changed, 4081 insertions(+) create mode 100644 examples/pytorch/mnist/.dvc/.gitignore create mode 100644 examples/pytorch/mnist/.dvc/config create mode 100644 examples/pytorch/mnist/.dvcignore create mode 100644 examples/pytorch/mnist/.gitignore create mode 100644 examples/pytorch/mnist/attacks.sh create mode 100644 examples/pytorch/mnist/average_across_random_states.ipynb create mode 100644 examples/pytorch/mnist/conf/attack/default.yaml create mode 100644 examples/pytorch/mnist/conf/compile.yaml create mode 100644 examples/pytorch/mnist/conf/data/default.yaml create mode 100644 examples/pytorch/mnist/conf/data/torch_cifar.yaml create mode 100644 examples/pytorch/mnist/conf/data/torch_mnist.yaml create mode 100644 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a/examples/pytorch/mnist/.dvcignore b/examples/pytorch/mnist/.dvcignore new file mode 100644 index 00000000..51973055 --- /dev/null +++ b/examples/pytorch/mnist/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/examples/pytorch/mnist/.gitignore b/examples/pytorch/mnist/.gitignore new file mode 100644 index 00000000..a129798e --- /dev/null +++ b/examples/pytorch/mnist/.gitignore @@ -0,0 +1,2 @@ +mnist/ +20_epochs/ \ No newline at end of file diff --git a/examples/pytorch/mnist/attacks.sh b/examples/pytorch/mnist/attacks.sh new file mode 100644 index 00000000..8ec1f079 --- /dev/null +++ b/examples/pytorch/mnist/attacks.sh @@ -0,0 +1,37 @@ +# #!/bin/bash + +# # This script is used to generate the attacks for the example. + +# Fast Gradient Method +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 stage=attack ++hydra.sweeper.study_name=fgm ++direction=maximize $@ + +# Projected Gradient Descent +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 stage=attack ++hydra.sweeper.study_name=pgd ++direction=maximize $@ + +# DeepFool +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=1,3,5,10 stage=attack ++hydra.sweeper.study_name=deep ++direction=maximize $@ + +# HopSkipJump +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 stage=attack ++hydra.sweeper.study_name=hsj ++direction=maximize $@ + +# ##################################################### +# PixelAttack +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=pixel ++direction=maximize $@ + +# ThresholdAttack +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=thresh ++direction=maximize $@ + +# # AdversarialPatch +# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 stage=patch ++hydra.sweeper.study_name=attack ++direction=maximize ++attack.init.patch_shape=[1,28,28] $@ +##################################################### + +# # Carlini L0 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw0 ++hydra.sweeper.study_name=cw0 ++direction=maximize $@ + +# # Carlini L2 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw2 ++hydra.sweeper.study_name=cw2 ++direction=maximize $@ + +# # Carlini LInf Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=attack ++hydra.sweeper.study_name=cwinf ++direction=maximize $@ + +rm -rf output/models/* diff --git a/examples/pytorch/mnist/average_across_random_states.ipynb b/examples/pytorch/mnist/average_across_random_states.ipynb new file mode 100644 index 00000000..1bf51a59 --- /dev/null +++ b/examples/pytorch/mnist/average_across_random_states.ipynb @@ -0,0 +1,802 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from pathlib import Path\n", + "from paretoset import paretoset\n", + "\n", + "# Compiled data file\n", + "data_file = \"mnist/reports/attack.csv\"\n", + "\n", + "# Read data\n", + "df = pd.read_csv(data_file)\n", + "df.head()\n", + "sense_dict = {\n", + " \"model_layers\": \"diff\",\n", + " \"def_gen\": \"diff\",\n", + " \"def_param\": \"diff\",\n", + " \"train_time\": \"min\",\n", + " \"predict_time\": \"min\",\n", + " \"accuracy\": \"max\",\n", + " \"data.sample.random_state\" : \"diff\",\n", + " \"model.trainer.nb_epoch\": \"diff\",\n", + " \"atk_gen\": \"diff\",\n", + " \"atk_param\" : \"diff\",\n", + " \"adv_accuracy\": \"max\",\n", + " \"adv_fit_time\": \"min\",\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of models: 5\n", + "Layers: [ 18 34 50 101 152]\n", + "Number of epochs: 5\n", + "Epochs: [ 1 10 30 50 100]\n", + "Number of attacks: 6\n", + "Attacks: ['Deep' 'FGM' 'HSJ' 'PGD' 'Pixel' 'Thresh']\n", + "Number of defenses: 1\n", + "Defenses: ['Control']\n", + "\n" + ] + } + ], + "source": [ + "layers = df.model_layers.unique()\n", + "layers.sort()\n", + "epochs = df['model.trainer.nb_epoch'].unique()\n", + "epochs.sort()\n", + "attacks = df.atk_gen.unique()\n", + "attacks.sort()\n", + "defenses = df.def_gen.unique()\n", + "defenses.sort()\n", + "\n", + "print(\n", + " f\"Number of models: {len(layers)}\\n\"\n", + " f\"Layers: {layers}\\n\"\n", + " f\"Number of epochs: {len(epochs)}\\n\"\n", + " f\"Epochs: {epochs}\\n\"\n", + " f\"Number of attacks: {len(attacks)}\\n\"\n", + " f\"Attacks: {attacks}\\n\"\n", + " f\"Number of defenses: {len(defenses)}\\n\"\n", + " f\"Defenses: {defenses}\\n\"\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Grouping by ['model_layers', 'model.trainer.nb_epoch', 'atk_gen', 'def_gen', 'attack.attack_size'] for accuracy\n", + "Grouping by ['model_layers', 'model.trainer.nb_epoch', 'atk_gen', 'def_gen', 'attack.attack_size'] for adv_fit_time\n", + "Grouping by ['model_layers', 'model.trainer.nb_epoch', 'atk_gen', 'def_gen', 'attack.attack_size'] for adv_accuracy\n", + "Grouping by ['model_layers', 'model.trainer.nb_epoch', 'atk_gen', 'def_gen', 'attack.attack_size'] for predict_time\n", + "Grouping by ['model_layers', 'model.trainer.nb_epoch', 'atk_gen', 'def_gen', 'attack.attack_size'] for train_time\n" + ] + }, + { + "data": { + "text/html": [ + "
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2171 rows × 202 columns

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" + ], + "text/plain": [ + " Unnamed: 0 stage \\\n", + "0 00175a623388caceb6fa35784ab33620 attack \n", + "1 00345bccad4a3b4c9580a84c905273a9 attack \n", + "2 00364b815dfeb4eec8959ff206821ec6 attack \n", + "3 00afb3206b473526516167a7c6023c30 attack \n", + "4 0105b26b34f312bfc99989600bf15cbb attack \n", + "... ... ... \n", + "2166 ff15b7d3369b63c99a52e96aed22e6f9 attack \n", + "2167 ff1cbae69eb7f49a5629b48d942dfca1 attack \n", + "2168 ff24625e3c442e965b5e8be4631fe7b7 attack \n", + "2169 ff3df4c627576b21e736467bb164bb5b attack \n", + "2170 ffa0197ac2d21ef824d03ff81c6cebb2 attack \n", + "\n", + " _target_ attack \\\n", + "0 deckard.base.experiment.Experiment 00175a623388caceb6fa35784ab33620 \n", + "1 deckard.base.experiment.Experiment 00345bccad4a3b4c9580a84c905273a9 \n", + "2 deckard.base.experiment.Experiment 00364b815dfeb4eec8959ff206821ec6 \n", + "3 deckard.base.experiment.Experiment 00afb3206b473526516167a7c6023c30 \n", + "4 deckard.base.experiment.Experiment 0105b26b34f312bfc99989600bf15cbb \n", + "... ... ... \n", + "2166 deckard.base.experiment.Experiment ff15b7d3369b63c99a52e96aed22e6f9 \n", + "2167 deckard.base.experiment.Experiment ff1cbae69eb7f49a5629b48d942dfca1 \n", + "2168 deckard.base.experiment.Experiment ff24625e3c442e965b5e8be4631fe7b7 \n", + "2169 deckard.base.experiment.Experiment ff3df4c627576b21e736467bb164bb5b \n", + "2170 deckard.base.experiment.Experiment ffa0197ac2d21ef824d03ff81c6cebb2 \n", + "\n", + " data files \\\n", + "0 00175a623388caceb6fa35784ab33620 00175a623388caceb6fa35784ab33620 \n", + "1 00345bccad4a3b4c9580a84c905273a9 00345bccad4a3b4c9580a84c905273a9 \n", + "2 00364b815dfeb4eec8959ff206821ec6 00364b815dfeb4eec8959ff206821ec6 \n", + "3 00afb3206b473526516167a7c6023c30 00afb3206b473526516167a7c6023c30 \n", + "4 0105b26b34f312bfc99989600bf15cbb 0105b26b34f312bfc99989600bf15cbb \n", + "... ... ... \n", + "2166 ff15b7d3369b63c99a52e96aed22e6f9 ff15b7d3369b63c99a52e96aed22e6f9 \n", + "2167 ff1cbae69eb7f49a5629b48d942dfca1 ff1cbae69eb7f49a5629b48d942dfca1 \n", + "2168 ff24625e3c442e965b5e8be4631fe7b7 ff24625e3c442e965b5e8be4631fe7b7 \n", + "2169 ff3df4c627576b21e736467bb164bb5b ff3df4c627576b21e736467bb164bb5b \n", + "2170 ffa0197ac2d21ef824d03ff81c6cebb2 ffa0197ac2d21ef824d03ff81c6cebb2 \n", + "\n", + " model name \\\n", + "0 00175a623388caceb6fa35784ab33620 00175a623388caceb6fa35784ab33620 \n", + "1 00345bccad4a3b4c9580a84c905273a9 00345bccad4a3b4c9580a84c905273a9 \n", + "2 00364b815dfeb4eec8959ff206821ec6 00364b815dfeb4eec8959ff206821ec6 \n", + "3 00afb3206b473526516167a7c6023c30 00afb3206b473526516167a7c6023c30 \n", + "4 0105b26b34f312bfc99989600bf15cbb 0105b26b34f312bfc99989600bf15cbb \n", + "... ... ... \n", + "2166 ff15b7d3369b63c99a52e96aed22e6f9 ff15b7d3369b63c99a52e96aed22e6f9 \n", + "2167 ff1cbae69eb7f49a5629b48d942dfca1 ff1cbae69eb7f49a5629b48d942dfca1 \n", + "2168 ff24625e3c442e965b5e8be4631fe7b7 ff24625e3c442e965b5e8be4631fe7b7 \n", + "2169 ff3df4c627576b21e736467bb164bb5b ff3df4c627576b21e736467bb164bb5b \n", + "2170 ffa0197ac2d21ef824d03ff81c6cebb2 ffa0197ac2d21ef824d03ff81c6cebb2 \n", + "\n", + " scorers predict_time ... def_param \\\n", + "0 00175a623388caceb6fa35784ab33620 4.726294 ... model_layers \n", + "1 00345bccad4a3b4c9580a84c905273a9 6.605143 ... model_layers \n", + "2 00364b815dfeb4eec8959ff206821ec6 6.140715 ... model_layers \n", + "3 00afb3206b473526516167a7c6023c30 1.534690 ... model_layers \n", + "4 0105b26b34f312bfc99989600bf15cbb 3.562745 ... model_layers \n", + "... ... ... ... ... \n", + "2166 ff15b7d3369b63c99a52e96aed22e6f9 2.928936 ... model_layers \n", + "2167 ff1cbae69eb7f49a5629b48d942dfca1 2.278838 ... model_layers \n", + "2168 ff24625e3c442e965b5e8be4631fe7b7 5.990600 ... model_layers \n", + "2169 ff3df4c627576b21e736467bb164bb5b 5.604070 ... model_layers \n", + "2170 ffa0197ac2d21ef824d03ff81c6cebb2 12.892382 ... model_layers \n", + "\n", + " def_value atk_param atk_value mean_accuracy \\\n", + "0 18 max_iter NaN 0.679269 \n", + "1 34 max_iter NaN 0.478656 \n", + "2 34 eps NaN 0.639914 \n", + "3 18 eps NaN 0.625216 \n", + "4 34 max_iter NaN 0.649362 \n", + "... ... ... ... ... \n", + "2166 34 th NaN 0.632347 \n", + "2167 18 eps NaN 0.654034 \n", + "2168 34 eps NaN 0.577663 \n", + "2169 34 eps NaN 0.539134 \n", + "2170 18 eps NaN 0.625216 \n", + "\n", + " mean_model.trainer.nb_epoch mean_adv_fit_time mean_adv_accuracy \\\n", + "0 31.625000 104.097943 0.106923 \n", + "1 40.864583 317.707078 0.098750 \n", + "2 37.406250 0.132487 0.099750 \n", + "3 37.152074 15.955465 0.099036 \n", + "4 40.864583 245.041809 0.107037 \n", + "... ... ... ... \n", + "2166 37.781250 318.647072 0.094878 \n", + "2167 37.152074 18.702322 0.109503 \n", + "2168 37.406250 0.146108 0.100937 \n", + "2169 37.406250 0.106205 0.113750 \n", + "2170 37.152074 15.955465 0.099036 \n", + "\n", + " mean_predict_time mean_train_time \n", + "0 3.097091 94.782790 \n", + "1 5.919636 264.578600 \n", + "2 4.967563 304.453833 \n", + "3 2.202140 113.288236 \n", + "4 4.813240 245.121432 \n", + "... ... ... \n", + "2166 6.364891 256.425714 \n", + "2167 2.038393 114.272417 \n", + "2168 4.530041 284.475388 \n", + "2169 5.053017 301.237607 \n", + "2170 2.202140 113.288236 \n", + "\n", + "[2171 rows x 202 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sense_dict = {\n", + " \"model_layers\": \"diff\",\n", + " \"accuracy\": \"max\",\n", + " \"data.sample.random_state\" : \"diff\",\n", + " \"model.trainer.nb_epoch\": \"diff\",\n", + " \"model_layers\" : \"diff\",\n", + " \"atk_gen\": \"diff\",\n", + " \"def_gen\": \"diff\",\n", + " \"adv_fit_time\": \"min\",\n", + " \"adv_accuracy\": \"min\",\n", + " \"predict_time\": \"min\",\n", + " \"train_time\" : \"min\",\n", + " \"attack.attack_size\" : \"diff\",\n", + "}\n", + "\n", + "# Average across random states\n", + "scorer = \"accuracy\"\n", + "\n", + "def average_across_random_states(df, scorer, sense_dict):\n", + " sense_dict.pop(\"data.sample.random_state\", None)\n", + " group_list = [k for k,v in sense_dict.items() if v == 'diff']\n", + " group_list_wo_random_state = group_list.copy()\n", + " print(f\"Grouping by {group_list_wo_random_state} for {scorer}\")\n", + " df[f'mean_{scorer}'] = df.groupby(group_list_wo_random_state)[scorer].transform('mean')\n", + " return df\n", + "\n", + "\n", + "def drop_poorly_merged_columns(df):\n", + " cols = df.columns\n", + " for col in cols:\n", + " if col.endswith(\".1\") and col[:-2] in cols:\n", + " df = df.drop(col, axis=1)\n", + " return df\n", + "\n", + "def find_pareto_set_for_graph(df, sense_dict):\n", + " scorers = [k for k,v in sense_dict.items() if v in [\"max\", \"min\"]]\n", + " group_list = [k for k,v in sense_dict.items() if v == 'diff']\n", + " group_list_wo_attack = group_list.copy()\n", + " for group in group_list:\n", + " if group in [\"atk_gen\", \"atk_value\", \"atk_param\"]:\n", + " group_list_wo_attack.remove(group)\n", + " elif group.startswith(\"attack_\") or group.startswith(\"attack_\"):\n", + " group_list_wo_attack.remove(group)\n", + " elif group.startswith(\"adv.\") or group.startswith(\"adv_\"):\n", + " group_list_wo_attack.remove(group)\n", + " else:\n", + " continue\n", + " for scorer in scorers:\n", + " scores = df[scorer].fillna(df.groupby(group_list_wo_attack)[scorer].transform('mean'))\n", + " df[scorer] = scores.fillna(scores.mean())\n", + " df = average_across_random_states(df, scorer, sense_dict)\n", + " value = sense_dict.get(scorer)\n", + " sense_dict.update({f\"mean_{scorer}\": value})\n", + " del sense_dict[scorer]\n", + " # sub_df = df[[*sense_dict.keys()]]\n", + " # bools = paretoset(sub_df, list(sense_dict.values()))\n", + " # df = df[bools]\n", + " return df\n", + "\n", + "df = find_pareto_set_for_graph(df, sense_dict)\n", + "\n", + "def drop_col_if_no_variance(df):\n", + " drop_these = []\n", + " for col in df.columns:\n", + " if df[col].nunique() == 1:\n", + " drop_these.append(col)\n", + " tmp = df.drop(drop_these, axis=1)\n", + " return tmp\n", + "\n", + "df = drop_poorly_merged_columns(df)\n", + "\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lineplot(data=df, y=\"adv_log_loss\", x=\"model.trainer.nb_epoch\", hue=\"model_layers\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting cox model\n" + ] + }, + { + "ename": "KeyError", + "evalue": "\"['atk_gen', 'def_gen'] not found in axis\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/cmeyers/deckard/examples/pytorch/average_across_random_states.ipynb Cell 6\u001b[0m line \u001b[0;36m5\n\u001b[1;32m 53\u001b[0m \u001b[39mfor\u001b[39;00m model_name \u001b[39min\u001b[39;00m model_dict:\n\u001b[1;32m 54\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mFitting \u001b[39m\u001b[39m{\u001b[39;00mmodel_name\u001b[39m}\u001b[39;00m\u001b[39m model\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m---> 55\u001b[0m model, plot, score \u001b[39m=\u001b[39m fit_aft_model(aft_df, new_sense_dict, model_name)\n\u001b[1;32m 56\u001b[0m models\u001b[39m.\u001b[39mupdate({model_name : model})\n\u001b[1;32m 57\u001b[0m scores\u001b[39m.\u001b[39mupdate({model_name : score})\n", + "\u001b[1;32m/home/cmeyers/deckard/examples/pytorch/average_across_random_states.ipynb Cell 6\u001b[0m line \u001b[0;36m1\n\u001b[1;32m 17\u001b[0m stratify \u001b[39m=\u001b[39m [\u001b[39m'\u001b[39m\u001b[39matk_gen\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mdef_gen\u001b[39m\u001b[39m'\u001b[39m,]\n\u001b[1;32m 18\u001b[0m subset_df \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39mcopy()\n\u001b[0;32m---> 19\u001b[0m subset_df \u001b[39m=\u001b[39m subset_df\u001b[39m.\u001b[39;49mdrop(stratify, axis\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m)\n\u001b[1;32m 20\u001b[0m model \u001b[39m=\u001b[39m model_dict[model_name]()\n\u001b[1;32m 21\u001b[0m model\u001b[39m.\u001b[39mfit(df, duration_col \u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mmean_adv_fit_time\u001b[39m\u001b[39m'\u001b[39m, event_col\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39madv_failures\u001b[39m\u001b[39m'\u001b[39m)\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/core/frame.py:5258\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 5110\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdrop\u001b[39m(\n\u001b[1;32m 5111\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[1;32m 5112\u001b[0m labels: IndexLabel \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 5119\u001b[0m errors: IgnoreRaise \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mraise\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 5120\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m DataFrame \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 5121\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 5122\u001b[0m \u001b[39m Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[1;32m 5123\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 5256\u001b[0m \u001b[39m weight 1.0 0.8\u001b[39;00m\n\u001b[1;32m 5257\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 5258\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mdrop(\n\u001b[1;32m 5259\u001b[0m labels\u001b[39m=\u001b[39;49mlabels,\n\u001b[1;32m 5260\u001b[0m axis\u001b[39m=\u001b[39;49maxis,\n\u001b[1;32m 5261\u001b[0m index\u001b[39m=\u001b[39;49mindex,\n\u001b[1;32m 5262\u001b[0m columns\u001b[39m=\u001b[39;49mcolumns,\n\u001b[1;32m 5263\u001b[0m level\u001b[39m=\u001b[39;49mlevel,\n\u001b[1;32m 5264\u001b[0m inplace\u001b[39m=\u001b[39;49minplace,\n\u001b[1;32m 5265\u001b[0m errors\u001b[39m=\u001b[39;49merrors,\n\u001b[1;32m 5266\u001b[0m )\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/core/generic.py:4549\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m 4547\u001b[0m \u001b[39mfor\u001b[39;00m axis, labels \u001b[39min\u001b[39;00m axes\u001b[39m.\u001b[39mitems():\n\u001b[1;32m 4548\u001b[0m \u001b[39mif\u001b[39;00m labels \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 4549\u001b[0m obj \u001b[39m=\u001b[39m obj\u001b[39m.\u001b[39;49m_drop_axis(labels, axis, level\u001b[39m=\u001b[39;49mlevel, errors\u001b[39m=\u001b[39;49merrors)\n\u001b[1;32m 4551\u001b[0m \u001b[39mif\u001b[39;00m inplace:\n\u001b[1;32m 4552\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_update_inplace(obj)\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/core/generic.py:4591\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m 4589\u001b[0m new_axis \u001b[39m=\u001b[39m axis\u001b[39m.\u001b[39mdrop(labels, level\u001b[39m=\u001b[39mlevel, errors\u001b[39m=\u001b[39merrors)\n\u001b[1;32m 4590\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 4591\u001b[0m new_axis \u001b[39m=\u001b[39m axis\u001b[39m.\u001b[39;49mdrop(labels, errors\u001b[39m=\u001b[39;49merrors)\n\u001b[1;32m 4592\u001b[0m indexer \u001b[39m=\u001b[39m axis\u001b[39m.\u001b[39mget_indexer(new_axis)\n\u001b[1;32m 4594\u001b[0m \u001b[39m# Case for non-unique axis\u001b[39;00m\n\u001b[1;32m 4595\u001b[0m \u001b[39melse\u001b[39;00m:\n", + "File \u001b[0;32m~/deckard/env/lib/python3.8/site-packages/pandas/core/indexes/base.py:6696\u001b[0m, in \u001b[0;36mIndex.drop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m 6694\u001b[0m \u001b[39mif\u001b[39;00m mask\u001b[39m.\u001b[39many():\n\u001b[1;32m 6695\u001b[0m \u001b[39mif\u001b[39;00m errors \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m-> 6696\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlist\u001b[39m(labels[mask])\u001b[39m}\u001b[39;00m\u001b[39m not found in axis\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 6697\u001b[0m indexer \u001b[39m=\u001b[39m indexer[\u001b[39m~\u001b[39mmask]\n\u001b[1;32m 6698\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdelete(indexer)\n", + "\u001b[0;31mKeyError\u001b[0m: \"['atk_gen', 'def_gen'] not found in axis\"" + ] + } + ], + "source": [ + "from lifelines import CoxPHFitter, KaplanMeierFitter, NelsonAalenFitter, AalenAdditiveFitter, WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter, PiecewiseExponentialRegressionFitter\n", + "\n", + "\n", + "model_dict = {\n", + " \"cox\" : CoxPHFitter,\n", + " # \"kaplan_meier\" : KaplanMeierFitter,\n", + " # \"nelson_aalen\" : NelsonAalenFitter,\n", + " # \"aalen_additive\" : AalenAdditiveFitter,\n", + " \"weibull\" : WeibullAFTFitter,\n", + " \"log_normal\" : LogNormalAFTFitter,\n", + " \"log_logistic\" : LogLogisticAFTFitter,\n", + " # \"piecewise_exponential\" : PiecewiseExponentialRegressionFitter,\n", + "}\n", + "\n", + "def fit_aft_model(df, sense_dict, model_name):\n", + " \n", + " stratify = ['atk_gen', 'def_gen',]\n", + " subset_df = df.copy()\n", + " subset_df = subset_df.drop(stratify, axis=1)\n", + " model = model_dict[model_name]()\n", + " model.fit(df, duration_col ='mean_adv_fit_time', event_col='adv_failures')\n", + " model.print_summary()\n", + " plot = model.plot()\n", + " concordance = model.score(df, scoring_method='concordance_index')\n", + " print(f\"Concordance index: {concordance}\")\n", + " measured_median = np.median(df.mean_adv_fit_time / df['attack.attack_size'] * ((1 - df.adv_failures)/100))\n", + " print(\"Measured median attack time:\", measured_median)\n", + " modelled_median = np.median(model.predict_median(df, ancillary=df))\n", + " print(\"Predicted median attack time:\", modelled_median)\n", + " score = model.score(df, scoring_method='log_likelihood')\n", + " score_dict = {\n", + " \"model\" : model_name,\n", + " \"concordance\" : concordance,\n", + " \"measured_median\" : measured_median,\n", + " \"modelled_median\" : modelled_median,\n", + " \"log_likelihood\" : score,\n", + " }\n", + " return model, plot, score\n", + "\n", + "models = {}\n", + "scores = {}\n", + "plots = {}\n", + "stratify = ['atk_gen', 'def_gen']\n", + "subset_cols = [k for k in sense_dict if k not in stratify]\n", + "aft_df = df[subset_cols].copy()\n", + "aft_df['adv_failures'] = (1 - df['mean_adv_accuracy']) * df['attack.attack_size']\n", + "del aft_df['mean_adv_accuracy']\n", + "new_sense_dict = sense_dict.copy()\n", + "new_sense_dict.update({\"adv_failures\" : sense_dict['mean_adv_accuracy']})\n", + "new_sense_dict.pop(\"mean_adv_accuracy\", None)\n", + "new_sense_dict\n", + "\n", + "for model_name in model_dict:\n", + " print(f\"Fitting {model_name} model\")\n", + " model, plot, score = fit_aft_model(aft_df, new_sense_dict, model_name)\n", + " models.update({model_name : model})\n", + " scores.update({model_name : score})\n", + " plots.update({model_name : plot})\n", + " plt.xscale('linear')\n", + " plt.show()\n", + " plt.gcf().clear()\n", + " \n", + "# scores = pd.DataFrame.from_dict(scores, orient='index', columns=['score'])\n", + "\n", + "# covariates = [k for k,v in sense_dict.items() if v == 'diff']\n", + "# values = [np.array(df[k].unique()) for k in covariates]\n", + "# print(f\"Number of covariates: {len(covariates)}\")\n", + "# print(f\"Number of values: {len(values)}\")\n", + "# print(f\"Values: \\n{[value.tolist() for value in values]}\")\n", + "# for i in range(len(covariates)):\n", + "# if covariates[i] in stratify:\n", + "# continue\n", + "# else:\n", + "# print(f\"Plotting {covariates[i]} with values {values[i]}\")\n", + "# graph = model.plot_partial_effects_on_outcome(covariates = covariates[i], values =values[i], cmap='coolwarm', figsize=(10, 10))\n", + "# print(type(graph))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = models['weibull']\n", + "expectations = model.predict_expectation(df, ancillary=df)\n", + "survival_function = model.predict_survival_function(df, ancillary=df)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "scores.T" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/pytorch/mnist/conf/attack/default.yaml b/examples/pytorch/mnist/conf/attack/default.yaml new file mode 100644 index 00000000..76b4d62d --- /dev/null +++ b/examples/pytorch/mnist/conf/attack/default.yaml @@ -0,0 +1,9 @@ +data: ${data} +model: ${model} +_target_ : deckard.base.attack.Attack +init: + model: ${model} + _target_: deckard.base.attack.AttackInitializer + name: art.attacks.evasion.HopSkipJump +attack_size : 10 +method : evasion diff --git a/examples/pytorch/mnist/conf/compile.yaml b/examples/pytorch/mnist/conf/compile.yaml new file mode 100644 index 00000000..1314aceb --- /dev/null +++ b/examples/pytorch/mnist/conf/compile.yaml @@ -0,0 +1,33 @@ +attacks: + # CarliniL0Method: CW_0 + # CarliniL2Method: CW_2 + # CarliniLInfMethod: CW_inf + DeepFool: Deep + FastGradientMethod: FGM + HopSkipJump: HSJ + PixelAttack: Pixel + ProjectedGradientDescent: PGD + ThresholdAttack: Thresh +defences: + Control: Control + FeatureSqueezing: FSQ + GaussianAugmentation: Gauss-in + GaussianNoise: Gauss-out + HighConfidence: Conf + Epochs: Epochs +params: + # art.attacks.evasion.CarliniL0Method: attack.init.confidence + # art.attacks.evasion.CarliniL2Method: attack.init.confidence + # art.attacks.evasion.CarliniLInfMethod: attack.init.confidence + Deep: attack.init.nb_grads + FGM: attack.init.eps + HSJ: attack.init.max_iter + Pixel: attack.init.th + PGD: attack.init.eps + Thresh: attack.init.th + Gauss-out: model.art.pipeline.postprocessor.scale + Conf: model.art.pipeline.postprocessor.cutoff + FSQ: model.art.pipeline.preprocessor.bit_depth + Gauss-in: model.art.pipeline.preprocessor.sigma + Control: model_layers + Epochs: model.trainer.nb_epoch diff --git a/examples/pytorch/mnist/conf/data/default.yaml b/examples/pytorch/mnist/conf/data/default.yaml new file mode 100644 index 00000000..5eb78278 --- /dev/null +++ b/examples/pytorch/mnist/conf/data/default.yaml @@ -0,0 +1,2 @@ +defaults: + - torch_mnist diff --git a/examples/pytorch/mnist/conf/data/torch_cifar.yaml b/examples/pytorch/mnist/conf/data/torch_cifar.yaml new file mode 100644 index 00000000..b7603125 --- /dev/null +++ b/examples/pytorch/mnist/conf/data/torch_cifar.yaml @@ -0,0 +1,11 @@ +_target_: deckard.base.data.Data +generate: + name: torch_cifar10 +sample: + random_state : 0 + stratify: True +sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/pytorch/mnist/conf/data/torch_mnist.yaml b/examples/pytorch/mnist/conf/data/torch_mnist.yaml new file mode 100644 index 00000000..9d79c036 --- /dev/null +++ b/examples/pytorch/mnist/conf/data/torch_mnist.yaml @@ -0,0 +1,15 @@ +_target_: deckard.base.data.Data +generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist +sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True +sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/pytorch/mnist/conf/deploy/default.yaml b/examples/pytorch/mnist/conf/deploy/default.yaml new file mode 100644 index 00000000..134da65f --- /dev/null +++ b/examples/pytorch/mnist/conf/deploy/default.yaml @@ -0,0 +1,14 @@ +num_nodes: 1 +cluster_name: k8s-cluster +gpu_type: nvidia-tesla-v100 +gpu_count: 1 +gpu_driver_version: default +machine_type: n1-standard-2 +min_nodes: 1 +max_nodes: 1 +storage_config: conf/deploy/sclass.yaml +persistent_volume_claim: conf/deploy/pvc.yaml +pod : conf/deploy/pod.yaml +image_project: ubuntu-os-cloud +image_family: ubuntu-2204-lts +mount_directory: /mnt/filestore diff --git a/examples/pytorch/mnist/conf/deploy/pod.yaml b/examples/pytorch/mnist/conf/deploy/pod.yaml new file mode 100644 index 00000000..fc68306a --- /dev/null +++ b/examples/pytorch/mnist/conf/deploy/pod.yaml @@ -0,0 +1,30 @@ +apiVersion: v1 +kind: Pod +metadata: + name: deckard +spec: + containers: + - name: deckard + image: ghcr.io/simplymathematics/deckard:main + imagePullPolicy: Always + workingDir: /deckard/examples/pytorch + args: ["python", "-m", "dvc", "repro"] + env: + - name: REDIS_HOST + value: "redis" + - name: REDIS_PORT + value: "6379" + - name: REDIS_DB + value: "0" + - name: REDIS_PASSWORD + value: "" + resources: + limits: + nvidia.com/gpu: 1 + volumeMounts: + - mountPath: /data + name: mypvc + volumes: + - name: mypvc + persistentVolumeClaim: + claimName: podpvc diff --git a/examples/pytorch/mnist/conf/deploy/pvc.yaml b/examples/pytorch/mnist/conf/deploy/pvc.yaml new file mode 100644 index 00000000..a7deee4d --- /dev/null +++ b/examples/pytorch/mnist/conf/deploy/pvc.yaml @@ -0,0 +1,11 @@ +kind: PersistentVolumeClaim +apiVersion: v1 +metadata: + name: podpvc +spec: + accessModes: + - ReadWriteMany + storageClassName: filestore-sc + resources: + requests: + storage: 256Gi diff --git a/examples/pytorch/mnist/conf/deploy/sclass.yaml b/examples/pytorch/mnist/conf/deploy/sclass.yaml new file mode 100644 index 00000000..d566656c --- /dev/null +++ b/examples/pytorch/mnist/conf/deploy/sclass.yaml @@ -0,0 +1,10 @@ +apiVersion: storage.k8s.io/v1 +kind: StorageClass +metadata: + name: filestore-sc +provisioner: filestore.csi.storage.gke.io +volumeBindingMode: Immediate +allowVolumeExpansion: true +parameters: + tier: standard + network: default diff --git a/examples/pytorch/mnist/conf/files/cifar.yaml b/examples/pytorch/mnist/conf/files/cifar.yaml new file mode 100644 index 00000000..e43cab72 --- /dev/null +++ b/examples/pytorch/mnist/conf/files/cifar.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: cifar +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pt +params_file : params.yaml +# train_labels_file : train_labels.json +# test_labels_file : test_labels.json +# probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +# adv_probabilities_file: adv_probabilities.json +name: default diff --git a/examples/pytorch/mnist/conf/files/mnist.yaml b/examples/pytorch/mnist/conf/files/mnist.yaml new file mode 100644 index 00000000..efdb1c42 --- /dev/null +++ b/examples/pytorch/mnist/conf/files/mnist.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: mnist +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pt +params_file : params.yaml +# train_labels_file : train_labels.json +# test_labels_file : test_labels.json +# probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +# adv_probabilities_file: adv_probabilities.json +name: default diff --git a/examples/pytorch/mnist/conf/grid/attack/cw_0.yaml b/examples/pytorch/mnist/conf/grid/attack/cw_0.yaml new file mode 100644 index 00000000..20573ea2 --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/attack/cw_0.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.CarliniL0Method +init.max_iter : [10] +init.batch_size : [1024] +init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/mnist/conf/grid/attack/cw_2.yaml b/examples/pytorch/mnist/conf/grid/attack/cw_2.yaml new file mode 100644 index 00000000..14965baa --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/attack/cw_2.yaml @@ -0,0 +1,5 @@ +init.model : ${model} +init.name : art.attacks.evasion.CarliniL0Method +init.max_iter : [10] +init.batch_size : [1024] +init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/mnist/conf/grid/attack/cw_inf.yaml b/examples/pytorch/mnist/conf/grid/attack/cw_inf.yaml new file mode 100644 index 00000000..9e694da5 --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/attack/cw_inf.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.CarliniLInfMethod +init.max_iter : [10] +init.batch_size : [1024] +init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/mnist/conf/grid/attack/deepfool.yaml b/examples/pytorch/mnist/conf/grid/attack/deepfool.yaml new file mode 100644 index 00000000..1fc9055a --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/attack/deepfool.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.DeepFool +init.max_iter : [10] +init.batch_size : [1024] +init.nb_grads : [10,100,1000] +init.eps: [0.01, 0.03, 0.3, 0.1, 1.0] diff --git a/examples/pytorch/mnist/conf/grid/attack/fgm.yaml b/examples/pytorch/mnist/conf/grid/attack/fgm.yaml new file mode 100644 index 00000000..41032d3e --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/attack/fgm.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.FastGradientMethod +init.eps: [0.01, 0.03, 0.3, 0.1] +init.norm: [inf, 1, 2] +init.eps_step: [0.001, 0.003, 0.01] diff --git a/examples/pytorch/mnist/conf/grid/attack/hsj.yaml b/examples/pytorch/mnist/conf/grid/attack/hsj.yaml new file mode 100644 index 00000000..1fba407d --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/attack/hsj.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.HopSkipJump +init.batch_size : [1, 4, 16, 65, 128] +init.max_iter : [1, 10, 100, 1000] +init.max_eval : [1, 10, 100, 1000] +init.init_eval : [1, 10, 100, 1000] diff --git a/examples/pytorch/mnist/conf/grid/attack/patch.yaml b/examples/pytorch/mnist/conf/grid/attack/patch.yaml new file mode 100644 index 00000000..e7285674 --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/attack/patch.yaml @@ -0,0 +1,5 @@ +init.name : art.attacks.evasion.ThresholdAttack +init.max_iter : [10] +init.batch_size : [1024] +init.scale_min : [0.01,] +init.scale_max : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/mnist/conf/grid/attack/pgd.yaml b/examples/pytorch/mnist/conf/grid/attack/pgd.yaml new file mode 100644 index 00000000..49819d5f --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/attack/pgd.yaml @@ -0,0 +1,6 @@ +init.name : art.attacks.evasion.ProjectedGradientDescent +init.eps : [0.01, 0.03, 0.3, 0.1] +init.norm : [inf, 1, 2] +init.eps_step : [0.001, 0.003, 0.01] +init.batch_size : [1024] +init.max_iter : [10] diff --git a/examples/pytorch/mnist/conf/grid/attack/pixel.yaml b/examples/pytorch/mnist/conf/grid/attack/pixel.yaml new file mode 100644 index 00000000..9e0060a7 --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/attack/pixel.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.ProjectedGradientDescent +init.max_iter : [10] +init.batch_size : [1024] +init.th : [1,4,16,64,256] diff --git a/examples/pytorch/mnist/conf/grid/attack/threshold.yaml b/examples/pytorch/mnist/conf/grid/attack/threshold.yaml new file mode 100644 index 00000000..07437c71 --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/attack/threshold.yaml @@ -0,0 +1,4 @@ +init.name : art.attacks.evasion.ThresholdAttack +init.max_iter : [10] +init.batch_size : [1024] +init.th: [1,4,16,64,256] diff --git a/examples/pytorch/mnist/conf/grid/model/confidence.yaml b/examples/pytorch/mnist/conf/grid/model/confidence.yaml new file mode 100644 index 00000000..d07598f8 --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/model/confidence.yaml @@ -0,0 +1,4 @@ + +art.postprocessor.name : [art.defences.postprocessor.HighConfidence] +art.postprocessor.cutoff : [.1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99] +art.postprocessor.apply_fit : [True, False] diff --git a/examples/pytorch/mnist/conf/grid/model/default.yaml b/examples/pytorch/mnist/conf/grid/model/default.yaml new file mode 100644 index 00000000..e69de29b diff --git a/examples/pytorch/mnist/conf/grid/model/fsq.yaml b/examples/pytorch/mnist/conf/grid/model/fsq.yaml new file mode 100644 index 00000000..9740b4b4 --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/model/fsq.yaml @@ -0,0 +1,3 @@ +art.preprocessor.name : [art.defences.preprocessor.FeatureSqueezing] +art.preprocessor.clip_values : [[0, 1]] +art.preprocessor.bit_depth : [1, 2, 4, 8, 16, 32, 64] diff --git a/examples/pytorch/mnist/conf/grid/model/gauss-in.yaml b/examples/pytorch/mnist/conf/grid/model/gauss-in.yaml new file mode 100644 index 00000000..21392a82 --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/model/gauss-in.yaml @@ -0,0 +1,4 @@ +art.preprocessor.name : art.defences.preprocessor.GaussianAugmentation +art.preprocessor.clip_values : ${art.initialize.clip_values} +art.preprocessor.sigma : [.01, .1, .2, .3, .5, 1] +art.preprocessor.ratio : [.1, .5, 1] diff --git a/examples/pytorch/mnist/conf/grid/model/gauss-out.yaml b/examples/pytorch/mnist/conf/grid/model/gauss-out.yaml new file mode 100644 index 00000000..377bbcc9 --- /dev/null +++ b/examples/pytorch/mnist/conf/grid/model/gauss-out.yaml @@ -0,0 +1,2 @@ +art.postprocessor.name : art.defences.postprocessor.GaussianNoise +art.postprocessor.scale : [.01, .1, .2, .3, .5, 1, 2, 3, 5, 10] diff --git a/examples/pytorch/mnist/conf/mnist.yaml b/examples/pytorch/mnist/conf/mnist.yaml new file mode 100644 index 00000000..aa05956f --- /dev/null +++ b/examples/pytorch/mnist/conf/mnist.yaml @@ -0,0 +1,44 @@ +defaults: + - _self_ + - data: torch_mnist + - model: torch_mnist + - attack: default + - files: mnist + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +stage : '???' +direction : "maximize" +_target_ : deckard.base.experiment.Experiment +optimizers : accuracy +hydra: + run: + dir: ${files.directory}/${files.reports}/${stage}/logs + sweep: + dir: ${files.directory}/${stage}/${model.init.name} + subdir : ${hydra.sweeper.study_name}/${hydra.job.num} + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + direction: ${direction} + study_name: control + storage: sqlite:///model.db + n_jobs: ${hydra.launcher.n_jobs} + n_trials : 32 + params: + ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) + ++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, .0001, .00001, 0.000001) + ++model.trainer.nb_epoch: choice(1, 10, 30, 50, 100) + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 8 + prefer : processes + verbose: 10 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/pytorch/mnist/conf/model/art/default.yaml b/examples/pytorch/mnist/conf/model/art/default.yaml new file mode 100644 index 00000000..4251627d --- /dev/null +++ b/examples/pytorch/mnist/conf/model/art/default.yaml @@ -0,0 +1,7 @@ +defaults: + - initialize : default + # - preprocessor: fsq + # - postprocessor: confidence +data : ${..data} +library : ${..library} +_target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/pytorch/mnist/conf/model/art/initialize/default.yaml b/examples/pytorch/mnist/conf/model/art/initialize/default.yaml new file mode 100644 index 00000000..b694473b --- /dev/null +++ b/examples/pytorch/mnist/conf/model/art/initialize/default.yaml @@ -0,0 +1,7 @@ +criterion: + name : torch.nn.CrossEntropyLoss +optimizer: + name : torch.optim.SGD + lr : 0.01 + momentum : 0.9 +clip_values : [0, 255] diff --git a/examples/pytorch/mnist/conf/model/art/postprocessor/confidence.yaml b/examples/pytorch/mnist/conf/model/art/postprocessor/confidence.yaml new file mode 100644 index 00000000..6fcdde95 --- /dev/null +++ b/examples/pytorch/mnist/conf/model/art/postprocessor/confidence.yaml @@ -0,0 +1,3 @@ + +name : art.defences.postprocessor.HighConfidence +cutoff : .1 diff --git a/examples/pytorch/mnist/conf/model/art/postprocessor/gauss-out.yaml b/examples/pytorch/mnist/conf/model/art/postprocessor/gauss-out.yaml new file mode 100644 index 00000000..c912ebd9 --- /dev/null +++ b/examples/pytorch/mnist/conf/model/art/postprocessor/gauss-out.yaml @@ -0,0 +1,2 @@ +name : art.defences.postprocessor.GaussianNoise +scale : 1 diff --git a/examples/pytorch/mnist/conf/model/art/preprocessor/default.yaml b/examples/pytorch/mnist/conf/model/art/preprocessor/default.yaml new file mode 100644 index 00000000..e69de29b diff --git a/examples/pytorch/mnist/conf/model/art/preprocessor/fsq.yaml b/examples/pytorch/mnist/conf/model/art/preprocessor/fsq.yaml new file mode 100644 index 00000000..27ddf58b --- /dev/null +++ b/examples/pytorch/mnist/conf/model/art/preprocessor/fsq.yaml @@ -0,0 +1,3 @@ +name : art.defences.preprocessor.FeatureSqueezing +clip_values : ${model.art.initialize.clip_values} +bit_depth : 64 diff --git a/examples/pytorch/mnist/conf/model/art/preprocessor/gauss-in.yaml b/examples/pytorch/mnist/conf/model/art/preprocessor/gauss-in.yaml new file mode 100644 index 00000000..c438e8f5 --- /dev/null +++ b/examples/pytorch/mnist/conf/model/art/preprocessor/gauss-in.yaml @@ -0,0 +1,4 @@ +name : art.defences.preprocessor.GaussianAugmentation +clip_values : ${model.art.initialize.clip_values} +sigma : 1 +ratio : .5 diff --git a/examples/pytorch/mnist/conf/model/torch_cifar.yaml b/examples/pytorch/mnist/conf/model/torch_cifar.yaml new file mode 100644 index 00000000..5ca2e24e --- /dev/null +++ b/examples/pytorch/mnist/conf/model/torch_cifar.yaml @@ -0,0 +1,13 @@ +defaults: + - art : default +data: ${data} +init: + _target_: deckard.base.model.ModelInitializer + name : torch_example.ResNet18 + num_channels: 3 + num_classes: 10 +_target_: deckard.base.model.Model +trainer: + nb_epoch: 100 + batch_size: 1024 +library : pytorch diff --git a/examples/pytorch/mnist/conf/model/torch_mnist.yaml b/examples/pytorch/mnist/conf/model/torch_mnist.yaml new file mode 100644 index 00000000..9c18c54b --- /dev/null +++ b/examples/pytorch/mnist/conf/model/torch_mnist.yaml @@ -0,0 +1,13 @@ + +defaults: + - art : default +data: ${data} +init: + _target_: deckard.base.model.ModelInitializer + num_channels : 1 + name : torch_example.ResNet18 +_target_: deckard.base.model.Model +trainer: + nb_epoch: 100 + batch_size: 1024 +library : pytorch diff --git a/examples/pytorch/mnist/conf/plots.yaml b/examples/pytorch/mnist/conf/plots.yaml new file mode 100644 index 00000000..accab9df --- /dev/null +++ b/examples/pytorch/mnist/conf/plots.yaml @@ -0,0 +1,250 @@ +cat_plot: +- file: adv_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: linear + titles: $\lambda_{adv.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_accuracy + ylabels: $\lambda_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + titles: $\lambda_{ben.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: accuracy + ylabels: $\lambda_{ben.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: log + titles: $\bar{C}_{ben.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_failure + ylabels: $\bar{C}_{ben.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: log + titles: $\bar{C}_{adv.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_adv_failure + ylabels: $\bar{C}_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_train_time_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: $\bar{C}_{adv.}$ vs Attack Type + x: atk_gen + xlabels: Attack Type + y: training_time_per_adv_failure + ylabels: $\bar{C}_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_test_time_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: $h_{adv}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_failure_rate + ylabels: $h_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +# - file: adv_accuracy_vs_defence_type.pdf +# hue: model_name +# kind: boxen +# legend_title: Model Name +# titles: $\lambda_{adv.}$ vs Defence Type +# x: def_gen +# xlabels: Defence Type +# y: adv_accuracy +# ylabels: Adv. $\lambda_{ben.}$ +# rotation : 90 +# hue_order: +# - ResNet18 +# - ResNet34 +# - ResNet50 +# - ResNet101 +# - ResNet152 +- file: adv_accuracy_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: $\lambda_{adv.}$ vs Attack Type + x: atk_gen + xlabels: Attack Type + y: adv_accuracy + ylabels: $\lambda_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_failure_rate_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: $h_{ben}(t; \theta)$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: failure_rate + ylabels: $h_{ben}(t; \theta)$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +line_plot: +# - file: def_param_vs_accuracy.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $\lambda_{ben.}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: accuracy +# y_scale: +# ylabel: $\lambda_{ben.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +# - file: def_param_vs_adv_accuracy.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $\lambda_{adv.}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: adv_accuracy +# y_scale: +# ylabel: $\lambda_{adv.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +# - file: def_param_vs_adv_failure_rate.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $h_{adv}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: adv_failure_rate +# y_scale: log +# ylabel: $h_{adv.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +- file: atk_param_vs_accuracy.pdf + hue: atk_gen + legend: {bbox_to_anchor: [1.05, 1]} + title: $\lambda_{adv.}$ vs Attack Strength + x: atk_value + x_scale: linear + xlabel: Attack Control Parameter + y: adv_accuracy + y_scale: + ylabel: $\lambda_{adv.}$ + hue_order: + - FGM + - PGD + - Deep + - HSJ + - Pixel + - Thresh + +scatter_plot: +- x: train_time_per_sample + y: adv_failure_rate + hue: model_name + xlabel: $t_{train}$ + ylabel: $h_{adv}$ + title: $h_{adv}$ vs $t_{train}$ + file: adv_failure_rate_vs_train_time.pdf + y_scale: log + x_scale: log + legend: + title: Model Name + bbox_to_anchor: [1.05, 1] + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 + # style : atk_gen +# - x: train_time_per_sample +# y: adv_failure_rate +# hue: model_name +# xlabel: $t_{train}$ +# ylabel: $h_{adv}$ +# title: $h_{adv}$ vs $t_{train}$ for each Defence +# file: adv_failure_rate_vs_train_time_def.pdf +# y_scale: log +# x_scale: linear +# legend: +# title: Model Name +# # style : def_gen diff --git a/examples/pytorch/mnist/conf/scorers/default.yaml b/examples/pytorch/mnist/conf/scorers/default.yaml new file mode 100644 index 00000000..4503c5bf --- /dev/null +++ b/examples/pytorch/mnist/conf/scorers/default.yaml @@ -0,0 +1,9 @@ +_target_: deckard.base.scorer.ScorerDict +accuracy: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.accuracy_score + direction: maximize +log_loss: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.log_loss + direction: minimize diff --git a/examples/pytorch/mnist/dvc.lock b/examples/pytorch/mnist/dvc.lock new file mode 100644 index 00000000..3202a3a4 --- /dev/null +++ b/examples/pytorch/mnist/dvc.lock @@ -0,0 +1,363 @@ +schema: '2.0' +stages: + train: + cmd: python -m deckard.layers.experiment train --config_file mnist.yaml + params: + params.yaml: + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: mnist + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 1 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: mnist/data/data.pkl + md5: de934a5f5157970e5f30b8f3f1856a68 + size: 222320311 + - path: mnist/models/model.optimizer.pt + md5: 7cc094a1d1c432411977b26f03b17086 + size: 44780845 + - path: mnist/models/model.pt + md5: 8e3eb9a35ce142da8a2c1b70163a5dcf + size: 44785941 + - path: mnist/reports/train/default/predictions.json + md5: df8bbdb790d3af78ca4214a8558620ae + size: 2883117 + - path: mnist/reports/train/default/score_dict.json + md5: a9efba8e9e862c7fe28fa1c4d2b1819f + size: 404 + attack: + cmd: python -m deckard.layers.experiment attack --config_file mnist.yaml + deps: + - path: mnist/data/data.pkl + md5: de934a5f5157970e5f30b8f3f1856a68 + size: 222320311 + - path: mnist/models/model.pt + md5: 8e3eb9a35ce142da8a2c1b70163a5dcf + size: 44785941 + params: + params.yaml: + attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.attack.AttackInitializer + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 1 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 1 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: mnist + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist + sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state: 0 + stratify: true + sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 1 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: mnist/attacks/attack.pkl + md5: 1a18d2941654a34e7f45f129826065e0 + size: 31517 + - path: mnist/reports/attack/default/adv_predictions.json + md5: 5b369c0249f7e0e6209fc3d502b51bca + size: 2167 + - path: mnist/reports/attack/default/score_dict.json + md5: fe4ddf5680ad4e6856132f01b8123bd8 + size: 544 diff --git a/examples/pytorch/mnist/dvc.yaml b/examples/pytorch/mnist/dvc.yaml new file mode 100644 index 00000000..21d8c792 --- /dev/null +++ b/examples/pytorch/mnist/dvc.yaml @@ -0,0 +1,120 @@ +stages: + train: + cmd: python -m deckard.layers.experiment train --config_file mnist.yaml + params: + - data + - model + - scorers + - files + outs: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} # Omit to save space + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} # logit outputs for our model + # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} # Omit to save space + metrics: + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + attack: + cmd: python -m deckard.layers.experiment attack --config_file mnist.yaml + params: + - data + - model + - attack + - scorers + - files + outs: + - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + # - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + deps: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + metrics: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + + ############################################################################## + # models: # This is a loop over the ResNet models + # foreach: + # - ResNet18 + # # - ResNet34 + # # - ResNet50 + # # - ResNet101 + # # - ResNet152 + # do: # This script configures eazch defence + # cmd: bash models.sh ++model.init.name=torch_example.${item} stage=train ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/train/${item}.db --config-name mnist.yaml + # deps: + # - models.sh + # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + # - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} + # outs: + # - ${files.directory}/${files.reports}/train/${item}.db: # This outputs a database file for each model + # cache: True + # persist: True + attacks: + foreach: # This is a loop over the ResNet models + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 + do: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/attack/${item}.db --config-name mnist.yaml + deps: + - models.sh # This script configures each defence + - attacks.sh # This script configures each attack + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} # This is here just to ensure it runs after the attack stage + # - ${files.directory}/${files.reports}/train/${item}.db + outs: + - ${files.directory}/${files.reports}/attack/${item}.db: # This outputs a database file for each model + cache: True + persist: True + compile: + foreach: # iterates through each stage + # - train + - attack + do: + cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv + deps: + - ${files.directory}/${files.reports}/${item}/ + - ${files.directory}/${files.reports}/${item}/ResNet18.db + - ${files.directory}/${files.reports}/${item}/ResNet34.db + - ${files.directory}/${files.reports}/${item}/ResNet50.db + - ${files.directory}/${files.reports}/${item}/ResNet101.db + # - ${files.directory}/${files.reports}/${item}/ResNet152.db + outs: + - ${files.directory}/${files.reports}/${item}.csv + plot: + cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv -o data.csv + deps: + - ${files.directory}/${files.reports}/attack.csv + - ${files.directory}/${files.reports}/attack/ResNet18.db + - ${files.directory}/${files.reports}/attack/ResNet34.db + - ${files.directory}/${files.reports}/attack/ResNet50.db + - ${files.directory}/${files.reports}/attack/ResNet101.db + - ${files.directory}/${files.reports}/attack/ResNet152.db + outs: + - ${files.directory}/plots/data.csv + afr: + cmd: python -m deckard.layers.afr --dataset ${files.directory} --file ${files.directory}/plots/data.csv + deps: + - ${files.directory}/plots/data.csv + plots: + - ${files.directory}/plots/weibull_aft.pdf + - ${files.directory}/plots/weibull_partial_effects.pdf + - ${files.directory}/plots/cox_partial_effects.pdf + - ${files.directory}/plots/cox_aft.pdf + - ${files.directory}/plots/log_logistic_aft.pdf + - ${files.directory}/plots/log_logistic_partial_effects.pdf + - ${files.directory}/plots/log_normal_aft.pdf + - ${files.directory}/plots/log_normal_partial_effects.pdf + metrics: + - ${files.directory}/plots/aft_comparison.csv + outs: + - ${files.directory}/plots/aft_comparison.tex + copy_results: + cmd: cp -r ${files.directory}/plots/* ~/ml_afr/mnist/ + deps: + - ${files.directory}/plots/data.csv + - ${files.directory}/plots/aft_comparison.csv diff --git a/examples/pytorch/mnist/models.sh b/examples/pytorch/mnist/models.sh new file mode 100644 index 00000000..2978d445 --- /dev/null +++ b/examples/pytorch/mnist/models.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +# This script is used to generate the models for the sklearn example. + +# # Default model +echo "python -m deckard.layers.optimise " $@ "--multirun" +python -m deckard.layers.optimise $@ --multirun + +# # This line generates the model and adds the FeatureSqueezing preprocessing defence. +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,255] ++hydra.sweeper.study_name=FSQ $@ --multirun + +# # # Gaussian Augmentation (Input) +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.5 +model.art.preprocessor.params.augmentation=False ++hydra.sweeper.study_name=gauss-in $@ --multirun + +# # # # Gaussian Noise (Output) +# python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 ++hydra.sweeper.study_name=gauss-out $@ --multirun + +# # # # High Confidence +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 ++hydra.sweeper.study_name=conf $@ --multirun diff --git a/examples/pytorch/mnist/torch_example.py b/examples/pytorch/mnist/torch_example.py new file mode 100644 index 00000000..2007d9d0 --- /dev/null +++ b/examples/pytorch/mnist/torch_example.py @@ -0,0 +1,79 @@ +import torch.nn as nn +from torchvision import models + +__all__ = [ + "ResNet18", + "ResNet50", + "ResNet101", + "ResNet152", +] + + +def ResNet18(num_channels=1, num_classes=10): + model = models.resnet18() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(512, num_classes) + return model + + +def ResNet34(num_channels=1, num_classes=10): + model = models.resnet34() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(512, num_classes) + return model + + +def ResNet50(num_channels=1, num_classes=10): + model = models.resnet50() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model + + +def ResNet101(num_channels=1, num_classes=10): + model = models.resnet101() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model + + +def ResNet152(num_channels=1, num_classes=10): + model = models.resnet152() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model diff --git a/examples/pytorch/mnist/weibull.ipynb b/examples/pytorch/mnist/weibull.ipynb new file mode 100644 index 00000000..def6e2d1 --- /dev/null +++ b/examples/pytorch/mnist/weibull.ipynb @@ -0,0 +1,2062 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from pathlib import Path\n", + "import seaborn as sns\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "\n", + "from paretoset import paretoset\n", + "from lifelines import (\n", + " WeibullAFTFitter,\n", + " LogNormalAFTFitter,\n", + " LogLogisticAFTFitter,\n", + " CoxPHFitter,\n", + ")\n", + "from plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling\n", + "import matplotlib\n", + "import argparse\n", + "from pathlib import Path\n", + "import logging\n", + "\n", + "logger = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "font = {\"family\": \"Times New Roman\", \"weight\": \"bold\", \"size\": 22}\n", + "\n", + "matplotlib.rc(\"font\", **font)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adversarial Accuracy: \n", + " ResNet152: 0.08431102362204726 \n", + " Resnet101: 0.08595785440613028 \n", + " Resnet50: 0.09093333333333335 \n", + " Resnet34: 0.08867549668874172 \n", + " Resnet18: 0.07971698113207548 \n", + "\n" + ] + } + ], + "source": [ + "FOLDER = Path(\"output/plots/\")\n", + "csv_file = FOLDER / \"data.csv\"\n", + "data = pd.read_csv(csv_file, index_col=0)\n", + "data.columns = data.columns.str.strip()\n", + "data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n", + "data.def_value.replace(\"\", 0, inplace=True)\n", + "data.atk_value.replace(\"\", 0, inplace=True)\n", + "data = drop_frames_without_results(data)\n", + "data = calculate_failure_rate(data)\n", + "data = min_max_scaling(data)\n", + "data.dropna(axis=0, subset=[\"atk_value\", \"atk_param\"], inplace=True)\n", + "data.dropna(axis=0, subset=[\"def_value\", \"def_param\"], inplace=True)\n", + "# data=data[data['def_gen'] == 'Gauss-in']\n", + "# data=data[data['atk_gen'] == 'HSJ']\n", + "\n", + "print(\n", + " \"Adversarial Accuracy:\",\n", + " \"\\n\",\n", + " \"ResNet152:\",\n", + " data[data[\"model_layers\"] == 152].adv_accuracy.mean(skipna=True),\n", + " \"\\n\",\n", + " \"Resnet101:\",\n", + " data[data[\"model_layers\"] == 101].adv_accuracy.mean(skipna=True),\n", + " \"\\n\",\n", + " \"Resnet50:\",\n", + " data[data[\"model_layers\"] == 50].adv_accuracy.mean(skipna=True),\n", + " \"\\n\",\n", + " \"Resnet34:\",\n", + " data[data[\"model_layers\"] == 34].adv_accuracy.mean(skipna=True),\n", + " \"\\n\",\n", + " \"Resnet18:\",\n", + " data[data[\"model_layers\"] == 18].adv_accuracy.mean(skipna=True),\n", + " \"\\n\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_aft(\n", + " df,\n", + " file,\n", + " event_col,\n", + " duration_col,\n", + " title,\n", + " mtype,\n", + " xlabel=None,\n", + " ylabel=None,\n", + " replacement_dict={},\n", + " **kwargs,\n", + "):\n", + " if mtype == \"weibull\":\n", + " aft = WeibullAFTFitter(**kwargs)\n", + " elif mtype == \"log_normal\":\n", + " aft = LogNormalAFTFitter(**kwargs)\n", + " elif mtype == \"log_logistic\":\n", + " aft = LogLogisticAFTFitter(**kwargs)\n", + " elif mtype == \"cox\":\n", + " aft = CoxPHFitter(**kwargs)\n", + " assert (\n", + " duration_col in df.columns\n", + " ), f\"Column {duration_col} not in dataframe with columns {df.columns}\"\n", + " if event_col is not None:\n", + " assert (\n", + " event_col in df.columns\n", + " ), f\"Column {event_col} not in dataframe with columns {df.columns}\"\n", + " aft.fit(df, duration_col=duration_col, event_col=event_col)\n", + " ax = aft.plot()\n", + " labels = ax.get_yticklabels()\n", + " labels = [label.get_text() for label in labels]\n", + " for k, v in replacement_dict.items():\n", + " labels = [label.replace(k, v) for label in labels]\n", + " ax.set_yticklabels(labels)\n", + " ax.set_xlabel(xlabel)\n", + " ax.set_ylabel(ylabel)\n", + " ax.set_title(title)\n", + " ax.get_figure().tight_layout()\n", + " ax.get_figure().savefig(FOLDER / file)\n", + " logger.info(f\"Saved graph to {FOLDER / file}\")\n", + " plt.show()\n", + " return ax, aft\n", + "\n", + "\n", + "def plot_partial_effects(\n", + " file,\n", + " aft,\n", + " covariate_array,\n", + " values_array,\n", + " title,\n", + " xlabel=\"Covariate\",\n", + " ylabel=\"Failure rate\",\n", + " legend_kwargs={\"loc\": \"upper left\"},\n", + " replacement_dict={},\n", + " cmap=\"coolwarm\",\n", + " **kwargs,\n", + "):\n", + " plt.gcf().clear()\n", + " # kwargs.pop(\"replacement_dict\")\n", + " pareto = aft.plot_partial_effects_on_outcome(\n", + " covariate_array, values_array, cmap=cmap, **kwargs\n", + " )\n", + " labels = pareto.get_yticklabels()\n", + " labels = [label.get_text() for label in labels]\n", + " for k, v in replacement_dict.items():\n", + " labels = [label.replace(k, v) for label in labels]\n", + " pareto.set_yticklabels(labels)\n", + " pareto.legend(**legend_kwargs)\n", + " pareto.set_ylabel(ylabel)\n", + " pareto.set_xlabel(xlabel)\n", + " pareto.set_title(title)\n", + " pareto.get_figure().tight_layout()\n", + " pareto.get_figure().savefig(FOLDER / file)\n", + " logger.info(f\"Saved graph to {FOLDER / file}\")\n", + " return pareto\n", + "\n", + "\n", + "def score_model(aft, train, test):\n", + " train_score = aft.score(train)\n", + " test_score = aft.score(test)\n", + " scores = {\"train_score\": train_score, \"test_score\": test_score}\n", + " plt.show()\n", + " return scores\n", + "\n", + "\n", + "def clean_data_for_aft(data, kwarg_list, target=\"adv_failure_rate\"):\n", + " subset = data.copy()\n", + " assert (\n", + " target in subset\n", + " ), f\"Target {target} not in dataframe with columns {subset.columns}\"\n", + "\n", + " cleaned = pd.DataFrame()\n", + " kwarg_list.append(target)\n", + " for kwarg in kwarg_list:\n", + " cleaned = pd.concat([cleaned, subset[kwarg]], axis=1)\n", + " cols = cleaned.columns\n", + " cleaned = pd.DataFrame(subset, columns=cols)\n", + "\n", + " # if \"accuracy\" in cleaned.columns:\n", + " # cleaned = cleaned[~cleaned[cleaned['accuracy'] != 1e10]]\n", + " # cleaned = cleaned[~cleaned[cleaned['accuracy'] != -1e10]]\n", + " # if \"adv_accuracy\" in cleaned.columns:\n", + " # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != 1e10]]\n", + " # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != -1e10]]\n", + " cleaned.dropna(inplace=True, how=\"any\", axis=0)\n", + " y = cleaned[target]\n", + " assert (\n", + " target in cleaned\n", + " ), f\"Target {target} not in dataframe with columns {cleaned.columns}\"\n", + " return cleaned, y, data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "kwarg_list = [\n", + " # \"accuracy\",\n", + " \"train_time\",\n", + " \"predict_time\",\n", + " \"atk_value\",\n", + " \"def_value\",\n", + " \"data.sample.random_state\",\n", + " \"adv_failure_rate\",\n", + " # \"failure_rate\",\n", + " \"model_layers\",\n", + " \"adv_fit_time\",\n", + " # \"atk_param\",\n", + " # \"def_param\",\n", + " \"model.art.pipeline.initialize.kwargs.optimizer.lr\",\n", + " # \"def_gen\",\n", + " # \"atk_gen\",\n", + " # \"adv_log_loss\",\n", + " # \"adv_accuracy\",\n", + " # \"adv_accuracy\",\n", + "]\n", + "\n", + "\n", + "# cleaned['accuracy'] = y" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "data.loc[:, \"adv_failures\"] = 1 - data.loc[:, \"adv_accuracy\"]\n", + "data.loc[:, \"ben_failures\"] = 1 - data.loc[:, \"accuracy\"]\n", + "target = \"adv_failures\"\n", + "duration_col = \"adv_fit_time\"\n", + "cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target)\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " cleaned, y, test_size=0.2, random_state=42\n", + ")\n", + "assert (\n", + " target in cleaned\n", + "), f\"Target {target} not in dataframe with columns {cleaned.columns}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# from sklearn.preprocessing import PowerTransformer\n", + "# pt = PowerTransformer(method='yeo-johnson', standardize=False)\n", + "# del X_train[target]\n", + "# del X_test[target]\n", + "# X_train_cols = X_train.columns\n", + "# X_train = pt.fit(X_train).transform(X_train)\n", + "# X_test = pt.transform(X_test)\n", + "# X_train = pd.DataFrame(X_train, columns=X_train_cols)\n", + "# X_test = pd.DataFrame(X_test, columns=X_train_cols)\n", + "# X_train[target] = y_train\n", + "# y_train = X_train[target]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# sense_dict ={\n", + "# \"accuracy\" : \"max\",\n", + "# \"train_time\" : \"min\",\n", + "# \"predict_time\" : \"min\",\n", + "# # \"atk_value\" : \"diff\",\n", + "# # \"def_value\" : \"diff\",\n", + "# \"data.sample.random_state\" : \"diff\",\n", + "# \"adv_accuracy\" : \"min\",\n", + "# \"model_layers\" : \"diff\",\n", + "# # \"adv_fit_time\" : \"min\",\n", + "# # \"atk_param\" : \"diff\",\n", + "# # \"def_param\" : \"diff\",\n", + "# \"model.art.pipeline.initialize.kwargs.optimizer.lr\" : \"diff\",\n", + "# # \"adv_failure_rate\" : \"maximize\",\n", + "# }\n", + "# subset = X_train.loc[:, sense_dict.keys()]\n", + "# senses = sense_dict.values()\n", + "# these = paretoset(subset)\n", + "# X_train = X_train.iloc[these, :]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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r+cIX7sL7nkq+6OT5uI6q5ubmhrZt2+LOnTusbjjhH+FHoMhj0LmmpqbYQ6il8fvvvzOzHKOiorB48eIqm7o/ZswY5hEpr1+/hr+/P7KysnDu3DmMHDmyzJYlUcLLKAgv2SBKT0+PeV3yHhKesVrR1dtLZuLl5eXh8+fPFTqWSEaBFCGk2mjVqhUzVsbX1xdubm5o3749mjZtyspnY2PDvL5y5Qpu3LgBKysriWWWfPHdu3evzHMnJCTg27dvlai9ZFlZWQDAugZFrHItrZIFOB0cHMpsqTA3N0ePHj0AAMHBwazxO9/bihUr0LZtWwDF75s9e/ZUyXm0tLRYA7SdnZ1x7tw55OTksMZQScPY2Jh5LTo2T5jwWKySblbh7sOwsLAKnVf4WXn3798vM29wcDBrHTQiGQVShJBqpWTQecmDX4WDphLdu3dnZo5du3YN2dnZTCuVqA4dOgAofnRMYGBgqed1d3eX+dlspYmNjUV2dja4XC769u3LpAuPJyptinqJ770w4vnz56Gvr88Mci6LcFeWvBforAh1dXXs2bOHGc9z5MiRcoMEWU2aNInpWn7y5AmOHDkCS0tLNGjQoELltG/fngmIfH19S11Dq+T5k+rq6swq68Iz/B48eFBusCP8OJ+GDRsyszDd3NzK7Ardu3cv8xBxUjoKpAgh1YqVlRXz4a2mpoZhw4aJ5eFwOMwyBwKBAIMGDSp1yYCSliqBQIA///xT4srpb9++hb+/P+vZaCXHCP9bUXfu3AEAjB8/HoaGhky6vr4+0yolPPOwhPCg67y8PLH9wvWRVLfy9peGx+PB3d0d48aNk+oLtF+/fszA8bt37yIqKkpiPuEvclnvZXkBZf369Zl1pAQCAf7+++8Kt9aInk9SXWvWrIkxY8Yw2xkZGZg+fXq5dRYtS11dHWPHjgVQ3BpaWotpyfpRVlZWTKBoZmbGLDaalJSE3bt3Szy25JzC4/M4HA4zljApKQlOTk4Sx++dPn0aDRs2FBsjVdn/E8qIAilCSLViaGiIPn36ACj+ohbuihAmvFyCpEfClPjtt9+Y5Qni4+MxatQoHDx4EK9fv8aLFy+wf/9+TJkyBYsXLxY7tuTRMSXdc6WRFEDExcXh0KFDaN++PRYtWsTap6mpiUaNGgEoXuAxODiYWRNpyZIlrJazZ8+egcfjseog3HJWsmq6MOG8kvaXxtXVFYmJicyaXuVRVVVFr169ABSPvSntC71kwD8ApKWlyfQlXDJIGvi/VhpRgwYNYgaEZ2dnY9q0aWV2m5WmqKgIqamppa6R5eDgwLQq9urVi+lWFCV8vKSy5s6dy8wk3bp1q1iLKJ/Px8WLF2FoaIi///6bte/vv/9mVvo/fvw4Nm/ezLxfMzIysHbtWma1/idPnuDt27fMSvSzZ89mAvuAgADY2Njg4sWLePfuHZ48eYLly5fj4MGDmDNnjlidS+pYkfeVsqNAihBS7ZQESZK69Uo0bNgQv/zyC+rUqcNaRFCUiooK9uzZg3bt2gEoDjL27NmDsWPHYsKECdi/fz8WLlzIjPcBir/A3rx5wzzn7sOHD7hz5w6rdcjAwAC9e/cGAKxevRpbt25FREQEcnJy4Ovri4kTJ6Jdu3Y4duyYxNaykjE1UVFRGD16NNq2bYthw4bB2NiYFdS5uLhg1qxZ+PbtG4qKihAfH89a1f3o0aOsL+CMjAzWY0zOnDmDhISEMlt0srKycOnSJeZZd9euXSv3GW98Ph8RERGsFbdv3LiBnTt3IiYmBkVFRSgsLERMTAx27NjB5MnJycG///6LpKQkqbot8/PzERISwlpl+8mTJ/D29kZOTo5YULZkyRJ07twZQHHwNXbsWGzevBkvX74s98u/qKgIiYmJ2LFjB/Ly8nD58mW8fv0aPB6Plc/ExISZYSr6cOKSIMzNzY01mPvw4cP4+vUr67Erurq6OH78OMzMzBAdHY1Jkybh2bNnyM7ORnh4OGbPno28vDy4u7uzFkUFgC5dumD9+vVMMOXm5oaePXvCwsICvXr1grGxMfOswY8fP2Lv3r1MN3KdOnVw+PBh1KpVC0DxDMJVq1Zh5MiRmDp1Ku7du4d9+/Yx+4HiltFr164x77U7d+4gPDy83K7pnwFHQO1zhJBqJj8/H1ZWVrhx40aZq0RfvHgRHz9+xPLly6Uq8/Tp07h27Ro+f/4MLpeLjh07YsaMGcwaVSWWLFkicfXq2rVr48mTJ6y0Fy9e4NKlSwgICEBCQgJ0dHTQrl07jBkzBpaWlmUOLD9z5gxcXFyQmJiI5s2bY/r06RgyZAiioqJgZWWFESNGYPLkyWjWrBmA4gBHtGWixI0bN6Cvr49+/fpJ3L969WqJK1kDxd1Gklpu1qxZg4kTJ0o8Zv369cyCkpK4u7tj7dq1Zc4M6927N44fP17q/ri4uFKvp8SWLVvE1g+LjY2FjY2N2PgfKysrVlAnas6cOcyyG6IuXbrEPFwbKF4HatmyZbh48SIrX3n3RU9PT2ysXnZ2Nk6cOIE7d+7g27dv4HK5aNCgAYYPH47Ro0eXuTZVcHAwDh06hMDAQOTn56Nt27aYOXMm+vbti0GDBqFdu3aYNWuWxAVPk5OT4ezsjPv37yM+Ph41atRA7969MW/ePLE11wYPHoyvX7+KlWFpaVnpx/v86CiQIoQQQgiREXXtEUIIIYTIiAIpQgghhBAZUSBFCCGEECIjCqQIIYQQQmREgRQhhBBCiIwokCKEEEIIkRE9RIeQaigoKAgCgYD1TDZCCCHfB5/PB4fDYRZ3LQu1SBFSDQkEAnqWlZwIBALk5+fT/SRyR+8t5VWRz2BqkSKkGippiRJeSZnIJjIyEnv37oWTkxPzfDtC5CEnJwehoaFo1qxZqQ/NJj+m4OBgqfNSixQhRKnxeDzExsaKPS+NEELkgQIpQgghhBAZUSBFCCGEECIjCqQIIYQQQmREgRQhRKnVqlULw4YNQ61atRRdFUKIEqJAihCi1HR0dNCmTRvo6OgouiqEECVEgRQhRKllZmYiKCgImZmZiq4KIUQJ0TpShMjg69ev8PDwwMuXL/Hx40ekpKRAIBDA0NAQnTp1wuTJk9GtWzdFV5MASEtLw4MHD9CnTx/UrVtX0dUhhCgZCqQIqYCcnBz8+++/OHPmDAoLC8X2JyYm4t69e7h37x4cHR2xePFiBdSSEELI90KBFCFS+vbtG2bPno0PHz5Ild/Z2RmNGjWCra1tFdeMzdbWFjExMWXmMTU1xeXLl79TjQghRHnRGClCpBAbGws7OzuxIKpDhw4YN24c+vbtCw6HI3bc4cOHv1cVGTExMYiOji51f3R0dLmBFiGEEOlQixQhUlixYgUr+FBRUcHmzZsxcuRIJu3atWv4+++/Wcd9/foVKSkp333qvZmZGfz8/CTu69Gjx3eti6JpamqiUaNG0NTUVHRVCCFKiFqkCCnHf//9h6dPn7LSHBwcWEEUAIwYMQK6urpix8fHx1dp/UjZ6tSpg9GjR6NOnTqKrgohRAlRixQh5Th//jxrm8vlYvr06WL5OBwODA0NkZWVJZZfFgKBADk5ORU+rqioCCoqZf+NVFRUJFPZP6Ls7GzweDxkZ2cruipEyeTm5rL+JcpDIBBIHK4hCQVShJShoKAAT548YaV17ty51K66pKQksTRTU1OZzs3n8xEaGirTcRoaGlVS9o8oLi4Op06dwqRJk2BsbKzo6hAlFBkZqegqkCqgrq4uVT4KpAgpw7t378Rabpo3by4xb3R0tFirR5MmTaCtrS3TublcLpo1aybTcdLkad26tSzV+uGoqRV/zNWrV6/U3x0hssjNzUVkZCQaNWoELS0tRVeHyNHHjx+lzkuBFCFlCAkJEUsrrTXK29tbLK1nz54yn5vD4cgUhJXXrVeSR9YA70dTMshcU1Pzp7lm8n1paWnRe0vJSNutB1AgRUiZJHV/paWliaXxeDycOHFCLF10QPr3Eh0dXersvOjoaJiZmX3nGhFCiHKiWXuElOH9+/diabdv30ZycjKznZeXh7///hvfvn1j5evZsyfatWtX5XUUZWpqWmagZGZmJvO4LUIIIWzUIkVIKYqKihAeHi6WnpSUhOHDh6N///4AAF9fXyQkJLDyaGtrY/Xq1d+jmmJoxXI2U1NTzJkzh4JHQkiVoECKkFJ8/vxZbFpz06ZNERERgZSUFFy5ckXicWpqavjf//6HJk2afI9qknKoqqpCW1sbqqqqiq4KIUQJUdceIaWQ1K03ZcoUTJ48udRjDAwMcOTIEQwaNKgqq0YqICkpCVevXpW4NAUhhFQWtUgRUgpJA81btmyJsWPHon379jh9+jQzRbZRo0YYOHAg7OzsoKen972rSsqQm5uLiIgIWjSREFIlKJAipBSigRSHw2HWIbK2toa1tbUiqkUIIaQaoa49Qkoh2rVXv359WiuGEEIICwVShEiQmJgoNqamRYsWCqoNIYSQ6ooCKUIkKG18FPnx1KxZE/3790fNmjUVXRVCiBKiQIoQCSTN2KMWqR+Tnp4eunTpQpMACCFVggIpQiSgFinlkZOTg7CwMLGHTxNCiDzQrD1CJNi1axd27dql6GoQOUhOTsaNGzdgbm6O2rVrK7o6hBAlQy1ShBBCCCEyokCKEEIIIURGFEgRQgghhMiIAilCiFLjcrkwMjICl8tVdFUIIUqIAilCiFIzNjaGnZ0djI2NFV0VQogSokCKEEIIIURGFEgRQpRaVFQUdu3ahaioKEVXhRCihCiQIqSCzp8/j5YtW6Jly5Zo3bo1unXrhqlTp8LHx0fRVSMSCAQCFBYWQiAQKLoqhBAlRIEUIRX04cMH5nVRURHS0tLw5MkTODo64tatWwqsGSGEkO+NAilCKig8PLzUfZs3bwafz/+OtSGEEKJIFEgRUkFdu3bF5MmT8dtvv4lNqU9MTMTTp08VVDNCCCHfGz1rj5AKmjdvHvP65MmT2LhxI2v/8+fP0a9fv+9dLVKKunXrYsqUKahbt66iq0IIUULUIkVIJVhYWIilhYaGKqAmpDTq6uqoXbs21NXVFV0VQogSokCKkEowMzODrq4uK62sMVTk+0tJSYGXlxdSUlIUXRVCiBKirj1CKqlhw4YICQlhthMSEpCeno4aNWoosFakRHZ2Nt6+fYvs7GxFV4WQn4qtrS1iYmLKzGNqaorLly9/pxpVDWqRIqQS3r9/j/fv34ulCy+RQAgh1VWPHj3Qo0ePKik7JiYG0dHRpe6Pjo4uN9CSVVVelyhqkSKkEtavX4/CwkKx9PDwcHTp0kUBNSKEkOrDzMwMfn5+Evd9r0CnqlGLFCEy8vDwwIsXLyTuoxYpQgj5OVCLFCEyyMrKwo4dO0rdL49ASiAQICcnp9Ll/Oy4XC66du0KLpdL95PIVW5uLuvfH1FRURFiY2PRrVs3uZcdGxsLMzOzMvNER0dX2blNTExk/j8vEAjA4XCkykuBFCEy2L9/PxITE0vdL49Ais/n01IKctK3b1+kpqYiNTVV0VUhSigyMlLRVZBZyZMYFPlEhqo6d2U/Q6VdMoUCKUIq6OPHjzh58iQrTVVVlTVWKi0tDQkJCTAyMpL5PFwuF82aNZP5eFIsLS0NAQEB6NatG2rWrKno6hAlkpubi8jISDRq1AhaWlqKro5MuFwuTExM4O3tLfeyBwwYUG6eqj5369atZTr+48ePUuelQIqQCtqwYQMKCgqY7YYNG8Lc3BxXrlxh5fvw4UOlAikOhwNtbW2ZjyfFvn37hgsXLqBNmzYwNTVVdHWIEtLS0vph/6+qqBQPla6K+peUXV6eqjy3rGVL260HUCBFSIXcunUL/v7+rLSlS5ciLi5OYiDVq1ev71k9QgipVqKjo0udnRcdHV3uGKofAQVShEgpJycH27ZtY6X17NkTAwcORGBgoFh+WuGcEFLdlbY0gTyU1wJsZmZWZa3EVXldoiiQIkRKhw4dQlxcHLOtqqqKZcuWAQBatmwplp+WQCCE/Mx+9BXLpUXrSBEihcjISLi6urLSxowZwwRQenp6Yn9Zffz4EQKB4LvVkUimpqYGXV1dqKnR342EEPmjQIoQKWzcuJE1RVdPTw8LFixg5RFtlcrJyUFUVNR3qR8pnYmJCWbNmgUTExNFV4UQooQokCKkHPfv38d///3HSpszZw5q1arFSqPuPUII+flQIEVIGXg8HrZs2cJKa9iwISZNmiSWlwKp6ik2NhaHDx9GbGysoqtCCFFCNGiAkDJoaGjgwYMHUuUdNmwYhg0bVsU1IhVVUFCArKws1tpfhBAiL9QiRQghhBAiIwqkCCGEEEJkRIEUIYQQQoiMKJAihCg1IyMjjB07tlLPPSSEkNJQIEUIUWoaGhpo0KABNDQ0FF0VQogSokCKEKLU0tLS8PjxY6SlpSm6KoQQJUSBFCFEqWVmZuLZs2fIzMxUdFUIIUqIAilCCCGEEBlRIEUIIYQQIiMKpAghhBBCZESBFCFEqeno6KBdu3bQ0dFRdFUIIUqIAilCiFKrVasWhgwZglq1aim6KoQQJUSBFCFEqeXn5yMpKQn5+fmKrgohRAlRIEUIUWrx8fE4ceIE4uPjFV0VQogSokCKEEIIIURG1TqQSklJQVxcnKKrQQghhBAiUbUOpFxdXeHs7Czz8Q8fPpRLnqqQnZ2NZcuWoWXLlqwfeeLxeHB1dcXo0aPxyy+/oHv37vj999+xceNGfPz4EQCwcuVKFBQUlFpGWFgYoqKi5FqvH4Gk6/by8oKFhQXr97Vv3z4F1ZAQQkh1UG0DqaysLJw9exZXrlxBampqhY/39PSEq6trmXlevHiBzZs3y1rFStHR0cHWrVvRrFmzKik/KSkJY8eOxfbt2zF06FD4+PjA398fzs7O0NTUhLW1NQYMGIBLly6VWkZ+fj6WLFmC6OjoKqljdVXadQ8ZMgRLlixRUK2IrDgcDlRVVcHhcBRdFUKIEqq2gdS5c+eQmZmJ3NxcnDlzpkLHhoeHY+3atWXmiY+Px59//omioqLKVLPSDAwM5F6mQCDA/Pnz8f79e4wfPx7Tpk2Dnp4eAMDMzAxLliyBs7MzkpKSyixn3bp1CA8Pl3v9qruyrpum0P946tWrh0WLFqFevXqKrgohRAmpKboCkuTn58PNzY3ZPnPmDGbMmAF1dfVyj42MjMTMmTORlZVVap7k5GQ4OjoiLi4OZmZmcqmzrKrir+Rnz57h5cuXAIDGjRtLzNOrVy8sW7YM69evl7j/33//xeXLl+Vet+quvOumVg3lYmtri5iYmDLzmJqa/pT/Fwgh0qmWLVLXr19HQkICs52UlAQPD49yjwsMDMSECRPK/GD8+PEjxo8fj/fv38ujqtVScHAw87qkZU+SP/74AyYmJqw0Ho+HFStW4MiRI1Vax+rmZ73uH1GPHj3Qo0cPqfPHxcXB3d1d4sSVmJiYMruuo6Ojyw20KqKidSeEVH/VrkVKIBDg+PHj0NDQAI/HY9JdXFwwZsyYUlsEPD09sWHDBqSlpTFpL168QJcuXQAA5ubmcHBwwF9//YXExEQmT0xMDJPHxMQEN27cYPYlJyfj7Nmz8PX1RVRUFNLT01G3bl0MGDAAs2bNgqGhocS6ZGVl4ejRo7h37x7i4uKgra2N5s2bw9HRUeoP0U+fPmH48OEoLCxkpf/zzz/4448/yjxWuOXu48ePGDNmDDZs2IBff/2VlU9VVRUWFhbMdk5ODhwdHfH69WtWvlmzZkFVVRUAcPjwYSQkJGDjxo1ITk5m8owcORJbt27Ft2/fsGXLFvj7+6Nv377YvXs3k6ewsBAXLlzApUuXEBkZCTU1NfTs2RMLFy5Ew4YNmXyurq7Yu3cvcnJymLQtW7agdevWOHToEAICAlBQUIBu3bph1apVMDU1FbsHaWlpcHFxwYMHD/Dt2zcUFhayBtVramqCy+WiWbNmcHFxkeq6S94norKysrBv3z7cvHkTOTk56Nq1K1auXIn69etLzE++Lz6fj4SEBPD5fIn7zczM4OfnJ3EfBT2EkPJUuxapBw8eICMjA8uWLWOlf/78ucwZdsOHD0dAQAArzdzcHIGBgQgMDMSRI0fQvXt3/Pfff6wvXlNTUyaPcBDl4+ODwYMHIygoCIcOHYKPjw/mzJmDb9++wd3dHWPHjpU4xigqKgojRozA4cOHMXv2bDx9+hQ1atTA06dPMWXKFJw+fVqq+9CkSRM8ffqU6Xq0srLCf//9V24QBQBdu3ZlbX/+/BmTJk2Cg4MDnj17xtq3Zs0aqKkVx9Pa2to4deoUHB0dWXkOHz7M3KMuXbpg2LBh2Llzp9h54+PjMWHCBDx48ADZ2dm4ffs20/KXnZ2N6dOnY926dejUqRP8/f2xfPly3Lp1C6NHj8bbt2+ZchwcHDBjxgxW2devX8eSJUtgZmYGNTU1ZGVl4cGDB5g+fbpYsPnp0yeMGDECR44cQVxcHE6dOoWXL19i8ODBTJ7mzZvD398f586dk/q6JYmNjcXo0aPh5eWFpKQkZGdnw9vbG5MnT0Z2drbEYwghhCiPatcidezYMUyYMAG2trbYu3cva8aei4sLBg4cWOV1SExMxKJFi5CdnY2MjAzo6+tDVVUVdnZ2TAtLVFQUjh49iuXLlzPH5efnY+bMmYiOjka7du1gZWUFoHg8UslyA//++y/Gjx8PFZXyY1gOh4PMzEzMnz8f8+bNk7r+rVq1wvDhw+Hp6clKf/r0KZ4+fYpff/0VCxcuLDU4kIbowF2BQIAlS5agdu3aSExMhEAgAADmOlesWIGnT59CR0cHCxcuBJfLhY2NDZydnREREYHFixfj1q1bTAuQkZERq/zk5GScP38eurq6aNq0KVauXAkAiIiIgK+vL/r16weguNXLycmJWcV6+PDh6NChAwDA0dER9+7dA1Dc/enl5YXhw4fLfA8AwMPDAzt27MCwYcNw8eJFrFq1CkBxgHX79m2MHj1a5rIFAgGrVY4UKyoqQmxsLLp16yZVfj6fj/T0dIwfPx5cLpe1LzY2ttxxktHR0VKfqzyxsbEwMTGh36uSyM3NZf1LlIdAIJB6TGy1CqQCAwPx7t07HDx4EBoaGhg7dixrzEpgYCDevHnDfDFWlVevXjGtCW/evIGPjw8sLCzEnh7/+fNn1vb58+eZgKljx45MeteuXZnB84WFhSgqKio3kMrKysKsWbMwf/582NnZVfgaNm7ciMzMTPj4+Ijte/78OSZOnAhra2usWbMGurq6FS5f9A3m7e2NqVOnYtasWbh48SL+97//oXv37mjRogVevHgBLy8vAECbNm2YGYRAcctbREQEIiMj8fTpU/Tp0wcAxO6PnZ0dU0/RrrzIyEgmkHr58iU+fPjA7GvatCnrXMKCgoIqHUiNGTMGw4YNA1DcAiosIiKiUmXz+XyEhoZWqgxlVNJFV1pXnaiSFkvRlktZzikP9HtVPpGRkYquAqkC0kxwA6pZIHX06FFYW1szU8wnTJiA48ePs8a2HD9+HHv27KnSenTs2BFGRkZISEiAnp4eWrRoAQBiSyXk5eWxtq9evcq81tfXZ14PGjQIixcvRnBwMEaNGsV0pZUmLS0N06ZNQ9euXWUKogBAS0sLhw8fhru7O/bv3y9xwPm1a9cQGhoKd3f3Si/DwOPx4ODgAKA4uBgzZgyzT7hlrG7duqzjtLW1mdevXr1iAilRJS1Voq8BsP66Fx7/Jlq+8GsA5f4epFG7dm3mteh/utIG+UurZAwXYeNyuTAxMYG3t7dU+ZOTk/Hff/+hT58+YuMaBwwYUO7xFTlXeUrO17p1a7mURxQrNzcXkZGRaNSoEbS0tBRdHSJHJY0i0qg2gVR4eDgeP37MGqdkbGyMwYMH4/bt20zavXv38O3btyodyGtkZAQvLy+EhoaiSZMm0NfXx+XLl1lLMgBguq+A4kBC+K9M0eUXRMfflCYxMRFTp05FeHg4EhIS4OjoKHOQo6KigilTpsDGxgaurq44deqUWL3Cw8OxYcMG/PvvvzKdo0S7du2goaEhcV9ISAjz2svLi9VKVlBQwAQg6enpMp1buKWhUaNGrH3CLQnCkxeA4taxqlSZFhCguNVPNPgj/9daWZF707JlSxgaGoodI00Xu4qKitx+D7LUnVR/Wlpa9DtVMhVZ6qbaDDY/duwYevXqJfYX+OTJk1nbhYWFOHHiRJXXR0dHB126dEFAQACGDRsGNzc3iQOsS6SlpbFarGSdMj1p0iRmMciEhASsXr26wmXs378fsbGxzHbNmjWxaNEieHt7Y+7cuWJ/Od2+fRspKSky1beE6JgmYcIBUtu2bZkB3IGBgXj16hWCg4MRHBzMjC+qKOGAtk2bNqzZicJN7sJdsfXr18fQoUNlOp8s9SKKk5mZicDAwFJbCKOjo5llCUR/frZV/QkhFVctAqnY2FjcunWLWa5A+GfmzJliXTlXrlxhLXNQFVJSUuDo6IiFCxdCR0cHp06dQvPmzUvNr6mpydoODAyUadX0v//+G+3bt2e27927h4sXL1aoDIFAIHFslL6+PpycnHD9+nXWQp1FRUX48uVLhesqrLTWKACsAb7yXJOnNPv372cGB3t4eODt27dIT0/Hrl27ABRPdz9y5IjU/d+kevHz8yt1uQJJ0tLS8OjRI4mfGaampmUONjczM5O4vIasKlp3Qkj1Vy269lxdXdG5c2ecPHlS4n4fHx9W11hOTg7Onj2L2bNnV0l98vLyYG9vz7QMrV+/njXmSZIaNWqgTp06zBid9PR03L9/H7/99luFzj1w4EA0a9YMNjY2zNifzZs3o2vXrqy1lspz9uxZjBs3TmLzZIMGDbB//34MHz6caTURvj55r97doEEDZgB4YmIiQkJC0LZtW7F8FZklUZaaNWvC3d0dzs7O2LlzJ8aPHw81NTXUq1cPCxcuhJ2dndjEAYBWLf8Z0YrlhJDKUniLVEpKCi5evFhmUNSvXz+xL153d3eJU05FpzdLUl6eixcvsp61VtpjVkQJr1MEALt27RLrTggLCyt3EFvDhg1Z3Vw5OTn466+/WIPuy/P+/XucO3eu1P3NmjVDjRo1ABT/1S08o02ae1gRvXv3Zm3v3LlTrLXu+fPn5T5kuiJ27dqFM2fO4MGDBwgODkZQUBBu3LiB2bNnSwyiAPlfNyGEEOWn8EBq9+7dUFNTK3cFYdEFGlNSUiSOlRKeRSU80Fg4eCkvj2igs27dOjx48ABOTk6sdB6Ph6KiImYQ84wZM1hf0p8+fYK9vT0ePnyI8PBwnD59GitWrGB1FYhOqy7ZtrW1ZbVmvX79Gnv37hW73rJs2rSp1EVMQ0NDkZaWBg6HgxUrVrBaY0RnNpUEcLGxscyyEKIDt8saD2RtbY06deow20+ePIGTkxPCw8ORkpICDw8PbNy4EePGjWPyiAZawtuig7hFz3327FkcPnwY/fr1q9CDaqW5btFzCddLdB+NkSKEEOWnsEAqOzsbBw4cwPnz55GVlYXHjx+XuVZLSeuJsEOHDuHRo0esL6wJEyYwryMiIpCUlIRbt27h06dPEvMkJyfj48ePeP78ObMyumjr140bN7B48WJYWFiwWqeCg4Mxfvx4fPv2DUDxeIudO3eyWjZCQkIwe/ZsWFlZ4fjx49i6dSszu+Pbt2+segHA48ePmdeiSx84OzvjypUrUo+9UldXx9y5c7F27VpmTSMej4eHDx9izpw5UFdXx4YNGzBo0CDWcb/99hsr8AkICEBGRgaOHTvGjCsSHecREhJS6oOidXR0sHfvXtaslnv37sHKygo9evTAli1bsHXrVlYQKrpqvPCzF0WfmSaa98yZMwCK15QSHnRfHmmuW3RQvvBjcoTrKKleRDG0tLTQtGlTmp5OCKkSHIGC/mz+448/EBQUxEpTUVHB8ePH0bNnTyYtMDAQ06ZNE1uzSZiDgwPzSBmBQICjR4/izJkzSE1NRatWreDo6Ci2Ivrly5eZR4g0btwYkydPZlahLiwsxObNm3H9+nVwuVwMGDAAc+bMgZmZGZ4/f441a9YgOjoaLVu2xKpVq1iLbwLAhw8fcOjQIfj7+yMzMxOmpqYYMmQI7O3tmTWy4uPj0bdvX4nXs23bNtjY2ODXX39FRkaG2P65c+eKtY4J279/Pxo1aoThw4cjODgY165dg5+fH5KSksDj8WBsbIxevXrBzs6u1HFXERER2LhxI169egVtbW389ttvWLRoEfT19bF//37s27dP7BhVVVW4urqWugr0169fcfDgQTx9+hSpqakwMjJCv3794OjoCGNjYyafm5sbdu/ezVofSk9PDytWrIChoSGWLVvGCmg0NDTg4OCARYsWASheq0fSoHYOhwN1dXXUqlULLVu2xPjx49G/f3+pr/vWrVvYunUrs2o6UBysLliwAF27dsXixYvx9etX1v0YOXIkNm3aJPF+lKXkwdPCEw+IbHJychAaGorWrVvTFHUiV/TeUl4V+QxWWCBFSFXZvXs3Dh06JFXe7du3w9rauoprVHEUSMlPZmYmXr16hU6dOrFW1SeksiiQUl4V+QxW+BgpQuTNyckJlpaWUuU9depUFdeGKFpMTAwOHjz4XZbeIIT8fKrF8geEyEtOTg5mzpyJZ8+eYfPmzbC0tISOjg4EAgEKCgqQl5eHL1++MN13lV19nBBCyM+NWqSIUnny5AmePXsGbW1t2NjYQFdXFxwOByoqKlBXV4e+vj7at2/PLFUhOtCeEEIIqQgKpIhSMTc3h5mZGXJycrBkyRJ8+vSJmeVYVFSE6OhouLu74/Dhw7C0tMT06dMVXGNCCCE/MuraI0qlVq1auH79Oi5fvgxfX19Mnz4d6enpUFNTA5fLRe3atWFubo59+/aVu3YZIYQQUh4KpIjS0dXVhb29Pezt7RVdFVINmJmZYf78+WU+U48QQmRFXXuEEKWmoqICDQ0NqKjQxx0hRP7ok4UQotQSExNx6dIl5oHihBAiTxRIEUKUWl5eHiIjI8t8OgIhhMiKAilCCCGEEBlRIEUIIYQQIiMKpAghhBBCZESBFCFEqdWsWRMDBw5EzZo1FV0VQogSokCKEKLU9PT00LlzZ+jp6Sm6KoQQJUSBFCFEqWVnZ+Pdu3fIzs5WdFUIIUpIqVY29/X1hbe3N+7fv4+4uDjWPhUVFXA4HAAAl8uFnp4eTExM0KZNG4wbNw5t2rRRRJUrZNOmTTh58iQEAgErPSwsTEE1kp/IyEi4uLjgyZMnSElJgb6+PurVq4ehQ4fCxsYGqqqq2LZtG9atW1dqGQ8fPoSFhUWV1K8qyyZVKyUlBbdu3ULXrl1Rp04dRVeHEKJklKpFqnfv3li9ejVcXFzE9p04cQLv3r3Dixcv4OLignbt2uHNmzc4d+4cRo4cic2bN4sFKNXNypUr4e3trXRjPe7evQsbGxv4+/tj06ZNeP78OR4+fIiFCxfCy8sLvXr1wsCBA5GVlVVqGS9evMDmzZurpH6enp5wdXWtkrIJIYT82JQqkCrRsGHDUvdpaWnB3Nwchw4dQteuXZl0Nze3H+LL0sTEBE2aNFF0NeTmw4cP+PPPP5Gbm4udO3eie/fuUFNTg6qqKn799Ve4ublhxIgRSE5OLrWM+Ph4/PnnnygqKpJ7/cLDw7F27Vq5l0sIIUQ5KGUgpaZWfo8lh8PBxIkTWWmHDx8Gn8+vqmrJjTTX96M4fvw4c88bN24stl9VVRX//PMPOnXqJPH45ORkODo6inXlykNkZCRmzpxZZksYIYSQn5vyfCPLoFmzZqzt9PR0fP36FU2bNlVQjX4+wcHBzOujR49i0aJFYnlUVFTg5OSEq1evstI/fvyIOXPm4MuXL3KvV2BgIJycnMpsCSPVm62tLWJiYlBQUID09HRMmjRJ7I8QU1NTXL58WUE1JIQog586kJI0Jqq0mT3h4eE4f/48AgMDERsbCz6fj3r16mHUqFGYNGkSuFwuk/e///7DunXrEBUVxaSNHDkSS5YsweHDh3Hv3j2kpaWhRYsWWLp0Kbp06VLqOQ8fPoxnz54hJycHTZo0wdSpU6W6tvz8fJw6dQqenp748uULNDQ00LBhQ4waNQo2Njas+mZlZeGvv/7Cw4cPWWW8e/cO58+fx6VLl/Dx40doamqiR48eWL58OYyNjfHp0yccPnwYT548QVZWFpo2bYrFixejV69eUtURANTV1ZnXhw8fRnx8PP766y8YGhqy8nXv3h0PHjxgtv39/fHXX3+xHkQbExPD3EsTExPcuHGD2ZecnIyzZ8/C19cXUVFRSE9PR926dTFgwADMmjWLdT5PT09s2LABaWlpTNqLFy+Yss3NzXHkyBFmH4/Hg5ubG27cuIGoqChoa2vDwsICTk5ONLhZgWJiYhAdHQ0zMzOx9xMAREdHK6BWhBBlo5Rde9L69OkTa1tDQ0Nia9T+/fsxYsQI6Orq4uLFi3j48CG6d++O8PBwbN26FfPmzWMFZX369MG2bdtYZYSHh2PixIngcDgwMDBAXl4e3rx5g+nTpyMyMlLsnD4+Phg7dixu3ryJVq1awd/fH/v378f58+cRFBRU5nVlZmZi0qRJ2LZtG/Lz83H37l3cvXsXfD4fq1atgoODA1JTU5n8urq6OHToEBo1asQqZ9q0aQgODsaIESOgrq6O9PR0eHl5wc7ODufOncOCBQvQpk0btGjRAnl5eQgJCcHMmTMRERFRZv2E/frrr6ztq1evYuDAgdi2bRsSEhKYdFVVVaxZs4bZ7t69O/777z+YmpoyaaampggMDERgYCAriPLx8cHgwYMRFBSEQ4cOwcfHB3PmzMG3b9/g7u6OsWPHIikpick/fPhwBAQEsOplbm7OlC0cRCUmJmLcuHHYuXMnrK2t8ezZM0ycOBEXLlzA6NGjWcE0+f7MzMzg5+cn8cfMzEzR1SOEKIGfNpAqLCzEyZMnWWkTJ06Ejo4OK+3Ro0fYt28fBAIBeDwe1NXVoauri7Fjx7LyiLbm1K1bl7X9+fNn7N27FytXrsS+ffuY9NzcXFy8eJGVNyUlBX/99Rdyc3MBAAsXLoS6ujqMjY2xf//+cmftrVq1Cq9fvwYATJ48GYaGhtDV1cWsWbMAAM+fP8eqVavEjqtduzZru3v37tiyZQumTJkCOzs7Jv3Lly84f/48zpw5gylTpmDTpk3MPj6fX6GukmnTpsHAwICVlpubCxcXFwwaNAgbN25ESkqK1OWJSkxMxKJFi5CdnY2MjAzo6+tDVVWVdT1RUVE4evRohcsuLCzE/PnzERoainr16sHBwQFcLhczZsyArq4u4uLisHLlSpnrTgghpPr76br2srKy8P79exw7dgzPnz9n0keNGoXFixeL5ff19WVeu7m5Ye7cudDT04O2tjYr3+fPn1nbJWtWlRg0aBBatmwJoLjbSZhoi5SbmxvS09MBANra2mjbti2zT09PD40bN2Z1aQl79eoVvLy8mO2ScwJA8+bNmdf379/H8+fPWS1CKirsuLok8JJU58mTJzMrRYt2X0lqYStN3bp14ezsjJkzZ4oFTDweDydPnsS1a9ewfPlyjBo1SupyS7x69Yrprn3z5g18fHxgYWEhFjCL/v6kcfPmTaZ1sEuXLlBVVQVQvE5ZgwYN8O7dO/j7++PTp08yzbQUCATIycmp8HGkWFFRkdh7WlIeusdEViV/7Jb8S5SHQCAQ+x4vzU8TSM2aNQt8Pl9sVl7JGJnSZoUNGDAA58+fR35+Plq1asV8AYtOtc/Lyyvz/CVfsoD4rDvRD/J79+4xr42MjKT+ZQLAtWvXWNvCY0NEH5Fx8+ZNsa610gjXX5TweCug9HFmpenQoQM8PDywZcsW3L59W2x/RkYGli9fjs+fP0sMdsvSsWNHGBkZISEhAXp6emjRogWAiv/+JPH09GReGxsbs/YJB9qvXr2SKZDi8/kIDQ2t8HGkGJ/Ph4aGRrl56B6TyqrIH4/kxyE8hrcsP00gdfjwYdSsWRNjxowBj8dj0r9+/cp8uUrSq1cvPHjwAF+/fkX79u2RmZmJkydP4vz586x8lVnMs6CggPVaeOxWeV8Eot6+fcva1tLSYl6LBkNVtSK6LOs51a1bF7t378bUqVNx4MABPHr0SCyPs7Mzunbtij59+khdrpGREby8vBAaGoomTZpAX18fly9fhpubGyufLL+/kJAQ5vXx48dx+vRpZpvP5zP/CYUHrVcEl8sVm1lKpCca4JeWp3Xr1t+hNkQZ5ebmIjIyEo0aNWJ91pIf38ePH6XO+9MEUkBxN9fy5ctZjxmJiIjA6tWrsXPnzlKPMzIyQs2aNeHq6oqjR4+iZ8+eWL16NebPny/3OqakpLC+1Cv6BV/SJVhC+MtENMCpzNgjeVmxYgVrRfIOHTrgyJEjePfuHXbv3g0fHx9W/jNnzlQokAIAHR0ddOnSBbdv38auXbugqamJnTt3Yvjw4ZWqu/C9Hjx4MHbt2lWp8kRxOByxLmQivfK69Ury0D0mlaWlpUXvIyVTkZ6gnyqQAoDx48fDz88Pd+7cYdI8PT3xyy+/iC3QWSI0NBQLFy5EZGQkbG1tsWnTJjx79qxK6ifa7VfRbjJdXd1S9xUWFrK2RccJKUJQUBAyMzPFuh3btGkDZ2dnXL16FStXrmTqLjrTUhopKSlYtmwZfHx80KZNG7i5uUFfX7/SdedyuUxXcUxMTKXLI/IXHR2NHj16lLqPZu4RQirrp5y1t3HjRrEP0C1btuDNmzdieT9+/IiJEyciMjISBgYGWLt2bYUi1YoyMDBgBRUJCQmsrr/yiD58OSMjg3ktOiCyrC7N74XH45U5y2/kyJFwcHBgtmvUqFGh8vPy8mBvb8+0bK1fv14uQRQANGjQgHkdEhLCWkJBWHV/hqOyMjU1hZmZGYqKisDj8cRaZM3MzFjLZxBCiCx+ykBKX18f//77L6v1h8/nY8GCBaz1lQBgz549TKuQiYlJhccsVRSHw2EtaMnn81mzC/Pz88UWEhT+gvj9999Z+4S7n0S7/SwtLeVS58o6ePAga80oUcILlop265U3DubixYsIDw9ntiU9hqY05ZXdu3dv5jWfz5fYtXfjxg2JA+hJ1bt8+TL8/Pxw6dIl2NjY4NKlS2JrSdGq5oSQylLKQErSdGbR1phOnTphwYIFrLSYmBgsWbKE1QUmPODs3bt3cHZ2xs2bN1njeoDilhXh84q2QggHO6ItTKJ5p02bxmr12r17N3g8HvLy8rB8+XKxoEO4u6tHjx6sYEP48SnCC2V26dIF/fv3Z5UjOqNReCab6D7hAfui97aizytMT0+Ho6NjqS06T58+BVDcgiDcOgWw174SPm/J7010wOC6devw4MEDODk5sdJLWiyEr6u8sidNmsQaYHrp0iWsWbMGkZGRSEhIgJubG86dO1dtAtafVU5ODkJDQ2mZA0JIlVC6QCo/Px9nz54VS79x44bYw2dnzJjBalUAiteNWrVqFZNXtKts586d2LlzJxYsWMAazHr+/Hk4OTkxwYfoOk/CwY/oA3ZF83bo0IEV5L169Qp9+vRh6tq5c2dWfnt7e9aYr+3btzPddkePHkVKSgrS0tKYmWrNmzfH3r17WcFafHy82FpKT548AVAcUP3333+sfQEBAUzAKboY6efPnyv0jDoul4uIiAjY2Njg/PnzTHdkcnIy9uzZg5MnT6JJkyZwdXUVGwM2YcIE5nVycjI+fvyI58+fMyuTC6/BBRS/DxYvXgwLCwtW61RwcDDGjx+Pb9++SSw7IiICSUlJuHXrFhO4mpiYYOvWrayWzfPnz8PS0hJ9+vSBm5sbduzYUebSEYQQQn5sHIESDeDYu3cvDh06VOr0ew6Hg06dOuHcuXNMWnJyMqytrcWCGRUVFfj5+YHP52PFihV4/vw56tSpA2tra0ydOhXa2to4ffo0Dh06hOzsbPTq1Qvr1q1D7dq18eTJE6xdu5b1pczhcDBp0iTY29tjzpw5rO4moHjBzm3btrECBS8vL7i4uCA8PBw6OjqwtbWFk5MTFixYgLy8PHTu3BmdOnVCp06dxAKMvLw8uLm54fbt20w96tevj2HDhsHe3p7VRZmQkID+/fuLDUYHigPHa9eu4fHjx2L7OnbsiMmTJ2PJkiVi+1RVVfHo0SMYGRlJ/F2UmDx5Mnbu3Al9fX08fPgQnp6eePfuHTIzM6GiooIWLVpgyJAhGDNmDDQ1NSWWcfnyZRw5cgRxcXFo3LgxJk+ejNGjRwMoHmC/efNmXL9+HVwuFwMGDMCcOXNgZmaG58+fY82aNYiOjkbLli2xatUqdOzYkSlXIBDg6NGjOHPmDFJTU9GqVSs4Ojpi4MCBrPOHhITgyJEjCAwMRFZWFkxNTWFpaQkHB4dyV6EvTcnDnNu3by/T8eT/hIWFYd26dVi3bh1rgVpCKquktbN169Y0a0/JVOQzWKkCKUKUBQVS8kOBFKkqFEgpr4p8Bitd1x4hhAirUaMGevbsWeEZn4QQIg0KpAghSk1fXx89e/aU27IXhBAijAIpQohSy8vLw+fPn2V6niIhhJSHAilCiFJLTEzE5cuXxSaUEEKIPFAgRQghhBAiIwqkCCGEEEJkRIEUIYQQQoiMKJAihCg1NTU11KxZk7UCPSGEyAsFUoQQpWZiYoLp06fDxMRE0VUhhCghCqQIIYQQQmREgRQhRKnFxMTgwIEDiImJUXRVCCFKiAIpQohSKywsRG5ursSHchNCSGVRIEUIIYQQIiMKpAghhBBCZESBFCGEEEKIjCiQIoQoNSMjI0yYMAFGRkaKrgohRAlRIEUIUWoaGhowNTWFhoaGoqtCCFFCP91Sv9u3b8fx48fF0jkcDvbv349BgwaJ7ZsyZQr8/PzE0k+cOIEePXpUST2rUnx8PFxcXODr64u4uDjw+XyYmJjA1tYWM2bMAIfDYeUPCwvDH3/8gZycnFLL1NTUxKxZszB79uyqrj4hFZKWlgZvb2+YmJhAW1tb0dUhhCiZn65F6u+//4aPjw9++eUXVrpAIMBff/2FDx8+iB3j6uqKmzdvomPHjgCAqVOnws/P74cMol69eoXff/8dJ06cgIGBAfz9/VG3bl1ERkZi586duH37ttgxLVu2RFBQEK5du4ZatWqx9qmrq+P06dN4/fo1BVGkWsrMzMSLFy+QmZmp6KoQQpTQTxdIAYCxsTGcnZ1Ro0YNVnpOTg7mzJmD9PR0VjqHw0GzZs3w559/QkNDA3/++adYQPEjEAgEWLp0KfOFUq9ePXC5XHTs2BFqampo3rw5OnToUOrxrVq1QteuXcXSunTpUqX1JoQQQqqrnzKQAgA9PT3o6upCXV2dlf7161csWrRI4uJ9pqamMDAwAJfL/V7VlKukpCRERkaKpe/YsQMhISHw9PREvXr1yixDS0uLta2pqSnPKhJCCCE/lJ9ujJSoDRs2YNWqVeDz+UzakydP8L///Q/Lli1j5VVRUYGqqur3rqLc8Hg8RVeBkO/C1taWeSQMn89HamoqJk6cyPojyNTUFJcvX1ZUFQkhSuKnbZEq0aVLF2zevFks3dXVFR4eHhUqKzk5GZs3b8bgwYPxyy+/oG/fvpg9ezZ8fX3lVFs2f39/zJo1Cz169ECXLl1gaWmJbdu2SXymmJWVFUaMGMFK8/T0RJcuXeDs7Fwl9SvP48ePsXjxYgwZMgQdO3ZEt27dMGnSJNy/f5+V78KFC2jZsqXYT6tWrRAYGAgAuHnzJmvf8OHDWWUUFhbi7NmzsLW1hbm5Obp164ZFixbhy5cvrHzu7u4wNzdnlbVv3z4AwPv372FnZ4fOnTtj69atzDEFBQXYvXs3LCws0KFDB9axBw8erIpbR8oRExOD6OhoAACXy4WRkREriIqOjqZn7xFC5OKnD6QAYMSIEZg/f75Y+po1axAcHCxVGWFhYRg+fDjc3NzQsWNH+Pv7w9XVFYGBgZg2bRo2bNiAoqIiudXZ2dkZ9vb2ePToEbZs2YLAwEDY2trCxcUF1tbWePLkCSv/jRs3cP36dVba8OHDERgYCEdHR7nVSxq5ubmYNWsWZsyYASsrK9y+fRuenp7Q09PD8+fPMXfuXBw7dozJP3r0aMyaNUusnMuXLzPjs37//Xfcv38fGhoaMDc3x7lz55h82dnZmD59OtatW4dOnTrB398fy5cvx61btzB69Gi8ffuWyWtnZ4fly5eLnev9+/eYMGECAgICkJOTA1dXV2RkZAAANm3ahEOHDqF9+/Z48eIFvL29MWTIELndLyIbMzMz+Pn5SfwxMzNTdPUIIUqCAqn/b968ebCxsWGl8Xg8zJs3D4mJiWUeWzJIPSUlBQAwd+5cqKuro2nTpkyZp06dgru7u1zq6uPjg507dwIAOnXqhP79+wMonk1Ys2ZNZGRkYP78+YiNjZXL+eRt79698Pb2BgAUFRWBw+Ggfv36GDx4MJNn9+7dzP1UUVHBggUL0KpVK1Y5orOw6tevDw0NDfz999/Q1dVl0lesWIGnT59CR0cHCxcuBJfLhY2NDZo2bYqMjAwsXryYNSZO9EuWx+NhyZIlaNCgAZPG4XCgoqKCqKgoJmhr2rQpuFwuTE1NsXv3bvTp06cyt4kQQsgP4KcfIyVs48aNiImJwbNnz5i0uLg4zJ8/v8wg6NSpU4iKigJQPPi6UaNGzL4WLVowr/fs2YNx48aJDdiuCIFAwOpWEi5fTU0NTZo0wcuXL5GdnY29e/diy5YtMp+rqgi3lu3fvx8WFhYAwFrjh8/nIyoqipkdqaKigpkzZ2LRokVMnitXrqB79+7MdmhoKOrXr49OnToxaS9evICXlxcAoE2bNtDT02P2NWnSBBEREYiMjMTTp0+ZwEdFhf33xYULF7B69WpYWVnhwIEDcHFxga2tLXR1deHn58e0NB45cgSGhoaYOHEiOBwO1q5dK3E5CWkJBIIy1+4ipSsqKhL7PUrKQ/eXVEZubi7rX6I8BAKB2JqKpaFASgiXy8X+/fvxxx9/4NOnT0x6UFAQNmzYgJkzZ0o87tq1a8xrAwMD1s0XbhnJycmBt7c3hg0bJnMdg4ODWXUzNDRk7RcOFO7du4d//vlHbGaiov32228ICwsDAJibmzPpol2fooPjLS0tYWJiwrS03b59G0uXLmXuwc2bNzF69GjWMZ6enszrunXrsvYJB26vXr0qtQVJT08PVlZWAIpbG+fOncvsMzAwYF4XFBRg/fr1ePLkCTZs2ID69etXqtuUz+cjNDRU5uN/Znw+v9yVzOn+EnmRNBua/Pik/e6kQEpEjRo14OzsjHHjxiE5OZlJv3DhAisoKpGbm4uPHz8y26KtTWpq7FscFhZWqUBKeDyPpPMJzyrMzMxEbGwsGjZsKPP5qsK8efMwdOhQZGdno0OHDvj8+TOcnZ1x584dVj7RwEpVVRUTJkxgujXz8/Nx7tw5zJ07F4WFhbhz547YLKyQkBDmtZeXF3x8fJjtgoIC5j+K6NphwoSDPVGdOnVC8+bNWQu5PnjwAK9evcK///7LajGrKC6Xi2bNmsl8/M9MmiVKuFwuWrdu/R1qQ5RVbm4uIiMj0ahRo0r1NJDqR/h7vTwUSElQv359HDx4EHZ2dqxWERcXF7HxMyUDjkuIfoCLBgMl435kJfqFLxoxCwQC1nZycnK1CKTS09NZC6A2bdoUiYmJWLlyJa5du4Y5c+bAzs4Ohw4dKrOcMWPG4MCBA8jLywMAnD17Fo6Ojnjy5Ak6dOgAfX19sfOWaNu2LS5cuFDhupf1sFs1NTXs2LED06ZNQ1JSEpOenJyM6dOn48CBA+jXr1+FzwkUj8OiR5rIprxuvZI8dH+JPGhpadF7SclI260H0GDzUnXq1Anbt28v92bq6OiUuV90Yc/y8pdHUquYsIKCArmeTx7i4+Oxd+9eVpqnpyeGDh2KS5cuYcOGDZgzZ45Ua3QZGBgw3WwAkJiYiNu3b+PKlSti3XoAO7CVdbp7eV1ErVq1wuXLl8UeO8Tn87FmzRrWGmXk+4mOjkaPHj0k/pQsjUAIIZVFgVQZhgwZgiVLlpSZR1dXl9XiIzqTTHQQovDg8JcvX8LS0hI9evTA2bNnpapTmzZtWNuiLWIlLTVAcRDRuHFjqcqtSteuXWONT/Lw8MDixYuRmZmJ/v37Y+TIkRUqb/LkyaxtZ2dnhIWFSexGE55pl5iYyOrqEybakietqKgonD17FsbGxjh16hQWLFjA6s6Ni4tDRESETGUT2ZmamjKtx0VFReDxeKzWYTMzM5iamiqqeoQQJfJTB1KFhYXlru00ffp0jBs3rsw8wmOe0tLSWPuEu5bU1dUxYMAAAMVf3H/++SciIyORkpKCDRs24PPnz+XWuVOnTqzuRdGuPuFtCwsLhQ80z83NxcmTJ5lAqrCwENu3b2f2C89wlFbLli1Zz/z78OEDhg8fLrH1sHfv3qztnTt3iv3Onz9/DldX1wrXo8TZs2chEAigqqqKOXPm4NixY6yWMGm6mYh8Xb58mVkzytvbG0ePHoW3tzdrLSla1ZwQIg8/7Sd8fn4+kpKS8O3bt3LzrlmzRuwLWZiDgwPq1KkDoHhmXkJCArNPuDVi2rRpzCyv1NRU1jpPhYWFzEy2sqiqqmLx4sXMtvBskfz8fOZ6uFwunJycWMeKdvsJt15JSzQIyc/PLzP/2rVrkZCQABMTEwDF1y08iN/DwwOenp44deoUzpw5wzqWx+OVOj3d3t6eec3hcDBq1CiJ+aytrZnfDVC89IKTkxPCw8ORkpICDw8PbNy4kRUsi96X8lqrwsLC4Obmxmz36NGDGRdVt25dNG3atMzjSdWKi4uDq6sr4uLiFF0VQogS+ikDqbS0NGzatAkFBQXYuXNnuS1Bampq2LNnD1q2bClxf40aNXDgwAFm6YFdu3aBz+fj/fv3zBpGv//+O2v1dAMDAxgbGzPbqqqqpZYv6vfff2em1fv5+eHx48cQCARwdnZGbm4u1NXVsWvXLrEZX48ePWJtBwUFSRVIChNd5DMiIoJZQ6tEQUEBXr16BUdHR2ZpiJJAytDQkNXNl5aWhsWLF+Pu3buYM2cOq5x//vkH//77r8R6WFhYMC1z3bt3L3Wlah0dHezdu5c1EPTevXuwsrJCjx49sGXLFmzdupU1lszf359VRlBQULkB47Zt23DkyBHweDzEx8fj3bt34HA4WL58+Q/9fEZlwOfzkZycTGPVCCFVgiOQdXDID2rbtm1wcXERS69fv77YM95ExcXFYeHChazHjwiLj4/HkSNH8PjxYyQnJ4PL5aJVq1YYN24cfv/9d7H8gYGBWLlyJdLT07FgwQKMHz++Qtfy+PFjnDp1Cm/evAGPx0ONGjXQo0cPzJgxA02aNGHlHTJkSKkB44IFC8SCGGGpqam4desWgoODcfXqVYl5tLW1oaqqCoFAgOzsbFYrDofDwZs3b5huxlevXmHt2rWIjIxEs2bNMGnSJNjY2KCoqAirV6+Gl5cX1NXVMWrUKCxatKjUqezHjx/H9u3bsWPHDtYAdEm+fv2KgwcP4unTp0hNTYWRkRH69esHR0dHVkC7fPlyXLlyRex4LpeL27dvo379+qz0qKgoDBw4kJWv5Pc+a9YsmWfslTyaqH379jIdT/5PWFgY1q1bh3Xr1kn9xwoh0sjJyUFoaChat25Ns/aUTEU+g3+6QIooj/j4eAwfPhy+vr7lzqz70VAgJT8USJGqQoGU8qrIZ/BP2bVHlENAQACsra2VLogihBDy46BAivwQ7ty5g65du2Ly5MnMWJcrV65UuDuU/Hxq164NGxsb1K5dW9FVIYQoIVrZnPwQDh48iPT0dDx79gyhoaFQUVGBpqYmzYgj5dLS0kKzZs3oER6EkCpBgRT5IRgbG+P9+/cAgJMnTyI4OBi7d+9WbKXIDyEjIwP+/v4wMzOjcSyEELmjrj3yQ1i9ejW6d+8OTU1NBAcHY/ny5WjVqpWiq0V+AOnp6fD19S3zwdSEECIrapEiP4R69eqxFr0khBBCqgNqkSKEEEIIkREFUoQQQgghMqJAihCi1LS1tdGiRQsaaE4IqRIUSBFClJqhoSFGjBgBQ0NDRVeFEKKEKJAihCi1goICZGZmoqCgQNFVIYQoIQqkCCFKLTY2FkeOHEFsbKyiq0IIUUIUSBFCCCGEyIgCKUIIIYQQGVEgRQghhBAiIwqkCCGEEEJkpDSPiGnZsiXzmsPhQEtLC6qqqsjMzGTl09TUBJfLRV5eHvh8PpO+ZcsWjBo16rvUNSQkBH///TcSEhIwa9YsTJs2TW5l+/r6Ys2aNcjLy8OyZcswYsQIuZUtTxYWFoiOjma2tbW1oaqqiqysLAgEAiZdXV0dGhoa4PF4yM/PZ9LnzZuH+fPnAwA8PDywfft2aGpqYuPGjejZs+f3uxBS7dWrVw8LFy5EvXr1FF0VQogSUqoWKU1NTWzatAmBgYEICgpCYGCgWJ61a9ciMDAQb9++xcWLF9G6devvXs9Nmzbh48ePyMjIwP/+9z98/fpVbmWvXLkS0dHRSE5OxsqVK5GXlye3suVNRUUFf/31F/z9/Znfl6mpKSuPo6MjAgMDERwcjFu3bqF79+6s/bm5uVi1ahWSk5MRHR2NFStWfM9LID8ADocDNTU1cDgcRVeFEKKElCqQWrVqFUaPHg1dXV2p8nfo0AGurq4wMDCo4pqxCbe4CAQC1nZ1LlveHB0dMX36dKnvf9OmTeHs7IymTZuWmqc6Xy9RjISEBJw7dw4JCQmKrgohRAkpTSBlZmaGMWPGVPg4AwMDTJo0qQpqVLoVK1agSZMm0NfXx+LFi9GwYUO5lb1hwwaYmJjA0NAQGzduhJaWltzKlidNTU3MmjWrwsdpaGhgxowZzLaWlhbWr1+PWrVqwdTUFJs2bZJnNYkS4PF4iIqKAo/HU3RVCCFKSGnGSNnb28t87LBhwxAfHy/H2pStffv2uH37dpWU3a9fPzx69KhKypancePGyRzkWVhY4L///mO2R40a9d3Gt5Hqz9bWFjExMcw2n89HamoqJk6cCC6XCwAwNTXF5cuXFVVFQogSoUAKQJMmTeDr64t58+YhKyuLSS8Z0Pz+/Xts3rwZwcHBGDduHJYtW8bkKSwsxJ07d3Dr1i2EhYUhLi4O+vr6aN26NWbOnIlff/2VyRsWFgZra2ux7qcHDx6gXr16iIqKwpIlSxAUFMTsMzMzg5eXF06ePImrV6/i69evqFu3LqZPn45x48Yx+by9vSW28ISFhQEA3r59i+XLlyM8PJzZ17VrVxw6dAjHjh3DzZs3ER8fj4YNG8LJyQmDBw+WeK9u3ryJCxcu4N27d8jNzWUN2FdVVWUeDHvy5Mkyx59V5vdVo0YNDB8+HKdOncKGDRtY+8zMzPDw4UMAxYP6V69ejZCQENY1HzhwAM7OzvDy8kJcXBxq1aqF33//HYsWLYK6ujp8fX3h6uqK169fQyAQoEuXLli5ciUaNGggsT6fP3/G4cOH8eTJE2RlZaF+/fr4448/MGHCBBqXowAxMTGIjo6GmZkZAIDL5cLIyIjZLzzJgRBCKktpuvYqy87ODsuXLxdLf//+PSZMmICAgADk5OTA1dUVGRkZAICUlBRMmDABK1euxIwZM3Dv3j1cuHABfD4f//33H+zt7eHp6cmU1bJlSzx79gz169eXWId69erh5MmT0NDQYNKys7MxadIkvH//Hg0bNgSPx8PXr1+xZs0a3Lhxg8k3YMAAPH78GDo6OhLLbteuHY4ePcpKi4+Px/jx45GamgpjY2PweDyEh4djwYIFYgP1CwsLsWjRIvz555/w9/fHpEmT8OrVK+zatYvJo6KiAldXVwQGBn6XQfyTJk2Ch4cHVFQkv43btm2LQ4cOsdJiY2Pxxx9/QE1NDUOHDgWfz0d8fDxcXFywbNky/PPPP3BxcUGfPn1gaGiIrKwsPHr0CFOnTmUFjSXu378Pa2treHt7w83NDd7e3uByuVi/fj3+/vvvKrluUj4zMzP4+flJ/CkJsAghRB4okBIi+gHL4/GwZMkSVksEh8NhvrjXrVuHV69eIT8/H2pqxY17rVu3ZmaWFRYWYuPGjSgqKmKO19fXR/v27UutA5fLRa1atZjttLQ0jBs3Dv/73/+wb98+Vh3d3d1Zx9atWxfNmjUrtWzhv8oB4Nu3b1i+fDn++ecfODs7MwFcYWEhTp06xcp7/Phx3Lp1CwCgo6ODuXPnQk1NDcOGDWMGf/P5fOzevbvU81eF1q1bs+6XqDp16rC24+LisHbtWixcuBCLFy9mtRjevHkT+fn5OH78OKZMmYLZs2cz+759+4anT5+yynr37h0WLVoEHo+HSZMmoWnTpjAwMMD06dMBANevX4eHh4ccrpIQQkh1pTRde/Ig2rJx4cIFrF69GlZWVjhw4ABcXFxga2vLzAp88uQJgOKnyx8+fBj79u0DAKZ7CwBSU1ORlpbG+rIXbnEqrx7GxsawtbVl0uvWrct0TURGRoodW1bZotfXuXNnZs0lLS0t1KxZkxkrJlr2+fPnmdcNGzZkAkeguGs0IiICAPDy5csyr60qVPSau3XrxmwbGxuz9s+ePZvpjhMNwj5//ox+/fox2//73/+Yta26du3KpDdp0oR5ffbsWdjY2Eh5JWwCgQA5OTkyHfszKyoqKrWVUjgP3VtSWbm5uax/ifIQCARSD82gQKoMenp6sLKyAgDMnTsXc+fOZe3/7bffcOXKFQCAubk5ky7cAgWgUms5qaqqsraFA5jKfhFUpOzExETmtXCgKLpdMpj3RyF8zeXty87OZl6npKSwWqiEAzLh7tWQkBDw+XyZ7gufz0doaGiFj/vZ8fn8cv9YoXtL5EnSH7Xkx6euri5VPgqkyiAcHEmyZcsWTJ48GWpqamjRogXevn2LI0eO4PHjx6x8ooGVvBQUFFRJuZLKbtSoETNwXXSskPC08jZt2lRZnRRN+Pf49u1b1r6RI0cygalAIGD9B8zIyIChoWGFz8flcsvsqiWSSRO0crlchSzGS5RLbm4uIiMj0ahRo2q71AyRzcePH6XOS4FUGUTHFEnSpk0bfPnyBfPnz8d///2H5cuXQ0dHB1evXv0ONfx+7O3tmVXDv3z5wmr2/Pz5M4Di8WPCazwps/T0dNb2nj170LdvX7meg8PhiLX+kfKV161XkofuLZEXLS0tej8pmYrMuKZAqgzldQ8AwIkTJ/Dvv/+iqKgIR44cQa9evVjLFygLW1tbpKSkYM+ePUhLS8Phw4cxdepUXL9+HWFhYVBTU8OyZcvQq1cvRVf1uxBt9RBet4goXnR0NHr06FHqPpq5RwiRF5q1VwkHDx7Eli1bwOPxMHbsWKUPImbMmIErV67AwMAABw4cQNeuXbFv3z7Y2Njg8uXLmDx5sqKr+N2IrkZf2iKo9Mia78/U1JQVKPH5fCQkJDBd0mZmZmLPdCSEEFlRi5SMUlNTcfDgQWa7UaNGiqvMdxIUFIS5c+di8eLFGD169E+92GSLFi1Qp04dZhC+j48Pnj9/zlpOoaioCH/99Rc2bdoETU1NRVX1pyO6YnlkZCQOHDiAuXPn/hT/Twkh35dSt0hJmi1X1jRV0fxltSZ8/fqVNej6xIkTuHv3Lg4fPow7d+6w8vJ4PNYsONFnfoluCw9qFh2oLjoIXLSOZZUtWlZZZYuWm5SUhOnTpyM7OxtjxoypsiBK9HcgzcxE4WsUvf6S5QlKK7+sgfOieYXvj6qqKqZNm8ZsFxUVYc6cOfDw8EBKSgrCw8Mxb948/PLLLxREKZiRkRH++OMPqcY8EkJIRSltIFVYWIhz586Jpd+6dQspKSkSj/H392dtBwUFiX0Rl2jUqBFrcGF0dDTmz5+PsLAwscefzJs3j1ngMjk5GcHBwaz9z549Y14XFBQgLS2N2U5NTWV92Ys+E1B4WYJv374x6zmVCAgIkJgXABISEpjXPB4PqamprPMWFhYy29evX0dWVhby8vLw8OFDuc8YFAgE8PLyQnJyMiv90aNHZY4/evPmDev3mZKSwpptIfo7/fjxI3MPk5KSxMazlSxpIBAI4O3tzdpXsvhqCXt7e/z222/MdkZGBpYuXYoePXrAysoKBgYGmDhxYpnXTaqeQCBAQUEBdbMSQqoER6CEny5btmzB6dOnJT7SAygejV+7dm34+voyacuXL2fWhBLG5XJx+/ZtiY918fb2xtatWxEfH4927drBwcEBAwcORHZ2NpYsWQI/Pz/o6+tj8uTJmD59OsLDwyU+aw8Apk6dikmTJuGvv/7CixcvWPu6deuGvXv34u+//4aPjw9rX7t27bBhwwbEx8dLfNYeAKxevRqdO3fGihUr8P79e9a+QYMGYdOmTZg9e7bYYprdu3fH5s2bYWZmhr179+LAgQMSy+dyudDR0UH9+vXRv39/TJ8+vUKtMCdOnMDOnTtLDVqB4rWZHj58iJo1azJpkp61V+Lw4cPIy8vDwoULxfapqqri4cOHsLS0lNhqOWPGDKSnp+PChQti+5o1a4abN28y2wKBABcuXMClS5fw8eNHqKmpoXnz5pg8eTKGDh1axlWXrSTYLmsVfCKdsLAwrFu3DuvWrUPLli0VXR2iRHJychAaGorWrVvTrD0lU5HPYKUMpIj8ffjwAaNGjSoz2CnRs2dPuLq6fodaKS8KpOSHAilSVSiQUl4V+QxW2q49Il/NmzfHzp07y1wJvMTTp0/FuhgJIYQQZUSz9ohUTpw4gR07dqBfv35Ys2YNateuDRUVFRQVFSE/Px8pKSm4dOkSDh06BKBqV10nhBBCqgtqkSJS2bdvH/h8PoYPHw5jY2OoqalBRUUFampq0NbWRr169WBnZwcAaNy4MT3ahBBCyE+BAikilREjRgAAduzYgf/++481SDsrKwuPHj3CnDlzULduXezZs0fsgciEKIqJiQlmzpwJExMTRVeFEKKEqGuPSGXt2rXo378/bt++je3btyMuLg4CgYCZsdemTRuMHDkSI0aMoId3kmpFTU0Nenp6Uo3vI4SQiqJPFiK1fv36oV+/foquBiEVkpycjOvXr8PIyIhmVhFC5I669gghSi0nJwfh4eFSrZJPCCEVRYEUIYQQQoiMKJAihBBCCJERBVKEEEIIITKiQIoQotRq1KiB3r17o0aNGoquCiFECVEgRQhRavr6+ujevTv09fUVXRVCiBKiQIoQotRyc3Px8eNH5ObmKroqhBAlRIEUIUSpJSUlwcPDA0lJSYquCiFECVEgRQghhBAiIwqkCCGEEEJk9MM/IubMmTPYsmUL8vPzS82jo6ODw4cPo2vXrt+xZtXLxo0bceXKFTRr1gy7du2CmZmZoqsk5suXLzh37hwCAgIQEhLC2sfhcKCiogKBQAA1NTXo6uqidu3aaNGiBYYOHYqBAweCw+FUWd0ePnwICwuLKiufEELIj+mHb5GaMGEC3rx5g127donta9KkCR49eoSXL1/+1EGUn58fTp48iezsbLx+/Rp79uxRdJUkatiwIZYuXYoLFy6gbt26rH1z587Fu3fvEBwcDA8PD9jY2ODjx4/w9PTE3LlzMXnyZGRlZVVJvTw9PeHq6lolZZOqx+VyYWhoCC6Xq+iqEEKU0A8fSAHFrRXDhg1DrVq1WOkWFhYwMTFRUK2qD4FAUOZ2daOmplZqi5mamhqaNm2KpUuXYs6cOUz68+fPsWTJErnXJTw8HGvXrpV7ueT7MTY2hoODA4yNjRVdFUKIElKKQKqElpYWa1tTU1NBNaleevbsiYkTJ0JbWxsdOnTAggULFF2lcqmpld/rPHHiRNa2t7c3goOD5VaHyMhIzJw5s8paugghhPz4fvgxUkQ6a9aswZo1axRdDbmqVasWatWqhZSUFCYtODgY7du3r3TZgYGBcHJyQnJycqXLIt+Xra0tYmJimG0+n4/U1FQYGBgw3Xumpqa4fPmyoqpICFEiFEiV4smTJ3BxcUFISAh4PB7atWuH2bNno2fPnhLzP378GNeuXUNISAhiY2OhqamJ5s2bY8qUKRg0aBArr7u7O/bs2cNq6Zg3bx7mz5+P9+/fY/PmzQgODsa4ceOwbNkyXLt2Ddu2bWN9qc+bNw82NjY4ePAgHj9+jJycHLRv3x6rVq1CixYtmHxOTk64c+cO6/wjR47E1q1bAQCurq7Yu3cvcnJymP1btmxB69atcejQIQQEBKCgoADdunXDqlWrYGpqKnbtaWlpcHFxwYMHD/Dt2zcUFhaioKCA2a+pqQkul4tmzZrh3Llz0tx+qYl2Uwpfh7Dk5GScPXsWvr6+iIqKQnp6OurWrYsBAwZg1qxZMDQ0ZPJ6enpiw4YNSEtLY9JevHiBLl26AADMzc1x5MgRZh+Px4Obmxtu3LiBqKgoaGtrw8LCAk5OTqhTp44cr5ZIIyYmBtHR0Uz3MJfLhZGREbM/OjpaUVUjhCghperak5cdO3Zg6tSpyMzMxL1793D27Fm8e/cOU6dOxYULF1h5c3NzMWvWLMyYMQNWVla4ffs2PD09oaenh+fPn2Pu3Lk4duwY6xg7OzssX75c7Lzv37/HhAkTEBAQgJycHLi6uiIjIwPW1tZYunQpK6+fnx+mT5+OmjVrQltbGzk5OQgICMCUKVOQnp7O5Nu7dy9WrVpV6rU6ODhgxowZrLTr169jyZIlMDMzg5qaGrKysvDgwQNMnz4dhYWFrLyfPn3CiBEjcOTIEcTFxeHUqVN4+fIlBg8ezORp3rw5/P395R5EpaSkIDU1lZXWtm1bsXw+Pj4YPHgwgoKCcOjQIfj4+GDOnDn49u0b3N3dMXbsWNZijcOHD0dAQACrDHNzcwQGBiIwMJAVRCUmJmLcuHHYuXMnrK2t8ezZM0ycOBEXLlzA6NGjERUVJddrJtIxMzODn5+fxJ/qOGOVEPLjokBKxOnTp3H06FEAwJ9//gk9PT20atUKVlZWEAgEWL9+Pb5+/crk37t3L7y9vQEARUVF4HA4qF+/PiuQ2L17N6v7CYDYhzmPx8OSJUvQoEEDJq1kyj8A1l/UAPD161e4u7tj6dKlWL9+PZOenJyMmzdvsvKW1opWQrTs5ORknD9/HkuXLsWiRYuY9IiICPj6+jLbhYWFcHJyQnx8PIDiAKRDhw7Q0NCAo6Mjky84OBheXl5l1kEWbm5urO327duje/furLTExEQsWrQI2dnZyMjIgL6+PlRVVWFnZ8fkiYqKYn7nFVFYWIj58+cjNDQU9erVg4ODA7hcLmbMmAFdXV3ExcVh5cqVsl0cIYSQHwJ17QnJzs5mlgZQVVVlunKA4qUUgOLxFhcvXsTixYsBFHcBlti/fz+z1pC2tjaTzufzERUVxZpVWBIglbhw4QJWr14NKysrHDhwAC4uLrC1tYWurq7E/KNHj2aWCBDtbouMjGRta2holHndomXb2dkx55VUdr9+/QAAL1++xIcPH5h9TZs2ZV6X3K8SQUFBGD58eJn1kAaPx0NkZCSuXbsGFxcXJr1jx47Yt2+f2FpSr169QnZ2NgDgzZs38PHxgYWFBXR0dFj5Pn/+XOG63Lx5E0FBQQCALl26QFVVFUBxV1KDBg3w7t07+Pv749OnT2L3QxoCgaDUrkpSuqKiIrH3tKQ8dG9JZZU8v5Ge46h8BAKB1GsTUiAl5PHjx0y3mKGhIWvmmHBg9OrVK+b1b7/9hrCwMADF3T8lioqKWGXzeLwyz62npwcrKysAxWsmzZ07t8z8JV/aoq+B0scJSUvashMTE1n7hO+R8GtAull4ZXF2dsbx48fFPrCaNWuGhQsXwsLCQqyuQHGAZWRkhISEBOjp6THjx0R/P3l5eRWuk6enJ/NadGq96PtFlkCKz+cjNDS0wsf97Ph8frl/PNC9JfIk+scrUQ7q6upS5aNASojwatoJCQmsFqnCwkLmpmZkZDDp8+bNw9ChQ5GdnY0OHTrg8+fPcHZ2FhvgLfrFLUo4CKss0XFM8iRcdqNGjVj7+Hw+81o0cGzTpk2lzuvo6Ihp06Zh1KhRrNaj6OhoNGzYUGIQBRR3W3p5eSE0NBRNmjSBvr4+Ll++LNYtKMvaWsLvl+PHj+P06dPMNp/PZ94vwoPWK6JkgD6pGGkW3uRyuWjduvV3qA1RZrm5uYiMjESjRo3Elt8hP7aPHz9KnfenD6T4fD7y8/Oho6PDGqQNFHfblfeXLVDcpZWYmIiVK1fi2rVrmDNnDuzs7HDo0CGp6yE6TqkyqnLBTeGy27Rpg19//RXPnz8HwP6rTDjYqV+/PoYOHVrpc2tra2P37t0YM2YM80ig3NxczJ8/H5cvX2a6I0Xp6OigS5cuuH37Nnbt2gVNTU3s3Lmz0l2Nwu+XwYMHS1xdvzI4HI5Yyx4pX3ndeiV56N4SedHS0qL3k5KpyCPHfvrB5nfv3sXjx48BiP8lK+00aU9PTwwdOhSXLl3Chg0bMGfOnFJbSEojTcBWHe3fvx/dunUDAHh4eODt27dIT09nggozMzMcOXJE6ibS8rRq1UpsBmNkZGSZg7pTUlLg6OiIhQsXQkdHB6dOnULz5s0rXRfh94vwukVE8aKjo9GjRw+JP7T8ASFEnn76QOrq1avMY2SEZ8wBxdPmJRFulfHw8MDixYuRmZmJ/v37Y+TIkVVX2WqoZs2acHd3x+LFi5GWlobx48ejf//+iIuLw8KFC3Hjxg3WIHR5mDRpktjaXF5eXjhx4oRY3ry8PNjb2zO/y/Xr10NfX18u9RB+v4SEhLCWUBBW3R/Jo2xMTU1Zs2L5fD4SEhKYrmczMzOJ66ERQogsfupA6t27d/D19WUGCvfp04e1//jx42LLFqSkpGD16tUAiscLbd++ndknOmboZ7Fr1y6cOXMGDx48QHBwMIKCgnDjxg3Mnj1bbHacvGzevFnsy3DHjh3MLLoSFy9eRHh4OLPduHFjqc9R3lib3r17M6/5fL7Err0bN27g9u3bUp+TVN7ly5dZ60adPn0aXbt2xenTp5k0WtWcECIvShVIiQ7oLhlHI0lWVhb+/vtvqKioMKtPN23aFAMGDGDyJCYmwt7eHn5+fkhNTYW/vz8cHByYZ7ylpqayVhv38PCAp6cnTp06hTNnzrDOx+PxWDPeRGeJlddqIXptwtuig8tFyxId+C26XZmyz549i8OHD6Nfv36oV69eWZdQIaIzD0W3a9SogR07drBmA/L5fCxYsIA1m1B0wOC6devw4MEDODk5sdJ5PB6KiopY96Z27dqsskXLnDRpEmuA6aVLl7BmzRpERkYiISEBbm5uOHfuHCwtLaW+bkIIIT8WpQmkeDye2CrXz58/F/sCzs3Nxf379zF69Gh8+PABRkZGrPFMmzdvZrUshYeHY8qUKejevTumTp0Ke3t7ZraPoaEhs5YTUDw7a/Hixbh79y7mzJnDOu8///yDf//9l9n29/dn7Q8KCioz8BPtNkpISGBex8XFlZpXIBAwY8BKvHv3jvV4GlnLBsAEjC9fvkRsbGyp9ZeWQCBAQEAAqxUJKF6aQnRsi7m5OebNm8dKi4+Px8yZM5kVxUVXOr9x4wYWL14MCwsLVutUcHAwxo8fj2/fvjFpEyZMYF5HREQgKSkJt27dwqdPnwAAJiYm2Lp1KyuYO3/+PCwtLdGnTx+4ublhx44dFR4vR+RLT08P5ubm0NPTU3RVCCFKiCP4wQdwhIWFwc/PD/fv32dmjwlTUVGBlpYWVFRUUFhYKBZYde7cWezRJVlZWTh69Ci8vLwQGxsLfX19dOrUCY6OjujQoQMr76tXr7B27VpERkaiWbNmmDRpEmxsbFBUVITVq1fDy8sL6urqGDVqFBYtWgQul4vly5fjypUrYnXlcrm4ffs26tevz0r39PTEli1bWEEMl8uFk5MTunfvjoULF7KCDBUVFVhbW2Pr1q0Sn7UHFK8PFRAQgCtXrmD37t2s+6Knp4cVK1bA0NAQy5YtY3VvamhowMHBgVnxfMCAARIHWnM4HKirq6NWrVpo2bIlM3aqLH5+fpgxYwar9UeUpqYmfH19mS/FoqIiTJ06FX5+fmJ5Dxw4gAEDBmDz5s24fv06uFwuBgwYgDlz5sDMzAzPnz/HmjVrEB0djZYtW2LVqlXo2LEjc7xAIMDRo0dx5swZpKamolWrVnB0dMTAgQNZ5wkJCcGRI0cQGBiIrKwsmJqawtLSEg4ODqhZs2aZ11ya4OBgAJDLA5h/djk5OQgNDUXr1q1pZhWRK3pvKa+KfAb/8IEUUazdu3dLvczD9u3bYW1tXcU1Ug4USMlPamoqfH190bt3bxgYGCi6OkSJUCClvCryGaw0XXtEMZycnKQeA3Tq1Kkqrg0h4hISEnDmzBlWlzUhhMjLT78gJ5FdTk4OZs6ciWfPnmHz5s2wtLSEjo4OBAIBCgoKkJeXhy9fvmDjxo149epVla64TgghhCgCtUgRmT158gTPnj2DtrY2bGxsoKurCw6HAxUVFairq0NfXx/t27fH4MGDAUBs7SdCCCHkR0eBFJGZubk5zMzMkJOTgyVLluDTp0/M0glFRUWIjo6Gu7s7Dh8+DEtLS0yfPl3BNSaEEELki7r2iMxq1aqF69ev4/Lly/D19cX06dORnp4ONTU1cLlc1K5dG+bm5ti3bx969Oih6OqSn5Sqqiq0tLRoGQpCSJWgQIpUiq6uLuzt7WFvb6/oqhAikampKebOnUuPhSGEVAnq2iOEEEIIkREFUoQQpRYbG4tjx47JZeV9QggRRYEUIUSpFRQUIC0tDQUFBYquCiFECVEgRQghhBAiIwqkCCGEEEJkRIEUIYQQQoiMKJAihCi1OnXqwNbWFnXq1FF0VQghSogCKUKIUtPU1ETjxo2hqamp6KoQQpQQBVKEEKWWkZGBp0+fIiMjQ9FVIYQoIbkGUikpKYiLi5NnkYQQUinp6el4+vQp0tPTFV0VQogSkusjYlxdXZGdnY01a9ZUqpxNmzbh5MmTEAgErPSwsLBKlVtRiYmJOHXqFIKCghAQEFDh4z08PNC6detK1WHjxo24cuUKmjVrhl27dsHMzAwA0LZtW7F1cdzd3dGtW7dKnU+R3rx5A09PTzx+/BifP39m7VNRUQGHw4FAIACXy4Wenh7q1q2Lli1bwsbGpkqvu7CwEL6+vujXr1+VnYMQQsiPSW4tUllZWTh79iyuXLmC1NTUSpW1cuVKeHt7o2bNmvKpnIzq1KmDRYsWwd3dHR07dmTtMzc3x8uXL/Hy5Us8f/4cPj4+OHHiBMaPHw81NfnEp35+fjh58iSys7Px+vVr7Nmzh9n38uVLdOnSRS7nqS46dOiAFStW4OLFi+Byuax9mzZtwrt37/D69WucPXsWffv2RUhICK5cuQI7OzssWLAA+fn5VVKvY8eO4fbt21VSNiGEkB+b3AKpc+fOITMzE7m5uThz5kylyzMxMUGTJk3kUDP5qF+/PmtbVVUVOjo60NHRgb6+PoyNjdGjRw+sW7cOx48fh4pK5W+taIuc8LaGhgZ++eWXSp+jOtLT00OtWrUk7lNXV0fbtm2xZcsWjBw5kkn38vLCtm3b5F4XPz8/7Nu3T+7lEkIIUQ5yCaTy8/Ph5ubGbJ85c0YurQPyatmRB9EWkrJ0794dv/32W6XP2bNnT0ycOBHa2tro0KEDFixYwNqvrq5e6XNUV9L87idOnMjaPn/+PBISEuRWh1evXsHJyQl8Pl9uZZLvT1tbG61bt4a2traiq0IIUUJyiVSuX7/O+gJLSkqCh4cHxo4dK4/ifxibNm3CypUrAQC2traoUaNGpctcs2ZNpcecKatmzZqxtvl8PkJDQ2FkZFTpsu/du4e///4bOTk5lS6LfF+2traIiYlhtouKisDn83Hz5k2mpdjU1BSXL19WVBUJIUqk0oGUQCDA8ePHoaGhAR6Px6S7uLhgzJgx4HA45ZYRHh6Ow4cP49mzZ8jJyUGTJk0wderUUvOvXr0aFy5cEEtXVVVFYGAgtLW14ezsjJ07dzL7xo4diw0bNlTw6qQXHx/P+vDu27evWJ6oqCicPXsW/v7+iImJQU5ODkxMTDBs2DBMmzYNOjo6TF4nJyfcuXOHdfzIkSOxdevWcuuSlZWFP//8Ez4+Pqz0ksH6CxcuhJeXF6ur8MGDB6hXrx6A4kHre/bsQVZWFrN/3rx5mD9/Pt6/f4/NmzcjODgY48aNw7Jly5g8PB4Pbm5uuHHjBqKioqCtrQ0LCws4OTlVyWKIol2fAEoNfCpy711cXHDgwAFWWZ6enrh//z4AYPjw4Vi3bh2zLzMzE87Ozrh79y4SEhJQo0YNDB06FHPnzoWurq6crpZIKyYmBtHR0czEDBUVFWhoaDD7o6OjFVU1QogSqnTX3oMHD5CRkcH6QgWAz58/4+HDh+Ue7+Pjg7Fjx+LmzZto1aoV/P39sX//fpw/fx5BQUESj1m1ahWGDBnCStPW1oavry/TfO/o6IhDhw4BAMaNG4fVq1fLcnlSO3r0qMQv9hKXLl3CkCFDkJycDHd3d/j4+MDGxgafP3/GgQMHMHnyZOTl5TH59+7di1WrVslUF11dXTg7O6Nhw4YS9+/evRvdu3cv9Xg7OzssX75cLP39+/eYMGECAgICkJOTA1dXV2ZtnsTERIwbNw47d+6EtbU1nj17hokTJ+LChQsYPXo0oqKiZLqWsnz69EksrU2bNmJpFb33U6dOxbVr11hlDB8+HIGBgQgMDGQFUZ8+fYK1tTWcnZ2xYMECBAQEoG/fvnBxccH48eNp7SIFMTMzg5+fn8SfkgCLEELkodKB1LFjxzBhwgTY2trCwMCAtc/FxaXMY1NSUvDXX38hNzcXQHFLibq6OoyNjbF///5SZ+1paGhg7dq1rDEPfD5fbMyQrq4uDAwMsHTp0ioZTyQQCBAbG4udO3fi5MmTpeZ7//491qxZAz6fj+zsbOjo6EBdXZ01xickJESsla1nz56Vql9ZrUDldX+JftnweDwsWbIEDRo0YNI4HA5UVFRQWFiI+fPnIzQ0FPXq1YODgwO4XC5mzJgBXV1dxMXFMV2e8iQ8Lg8ALC0txYJHWe+9NLKysjBz5kxER0fj119/xbBhw6Curg4nJycAxS2t0rQgEkII+XFVqmsvMDAQ7969w8GDB6GhoYGxY8fiyJEjrP1v3rxBhw4dJB7v5ubGLJKnra2Ntm3bMvv09PTQuHFjJCYmSjy2Vq1a+OOPP5hgjc/n48aNGxg/fjyT5+HDh5g4cSKr20ZeXrx4gfbt20s1ENnf3x+FhYUAgLt37yIsLAwtW7YUG/wqunaScHeELMqaOVjerELR/RcuXMDq1athZWWFAwcOwMXFBba2ttDV1cX169eZ1sMuXbpAVVUVQPEA/QYNGuDdu3fw9/fHp0+fKj0TMzc3Fx8/fsSZM2dw/fp1Jr1v377YvHmzWH5Z7700Tp48ia9fvwIAunbtyqTXrl0bNWrUQHp6Oq5fv44VK1bI1MUnEAhojJYMioqKyn1/FxUV0b0llVbSCFDyL1EeAoFAqqFJQCUDqaNHj8La2pqZqj5hwgQcP36ctVDk8ePHWesfCbt37x7z2sjISOpKl7Czs4O7uztzPnd3d/zxxx/gcDjg8/m4e/euTC0N0jA3N4eLiwsiIyNx8uRJnD9/vtS8PXv2hK6uLrKysmBqasq09oh2BQp3L1U3enp6sLKyAgDMnTsXc+fOZfZ5enoyr42NjVnHCQcsr169kjmQ+ueff7Bu3TrWODwA+OWXXzBv3jz06tVL4nFVee/Lu+709HTw+XyEhITItGBoyeB5UjF8Pr/cP0Lo3hJ5ioyMVHQVSBWQtidL5kAqPDwcjx8/xo0bN5g0Y2NjDB48mLV44b179/Dt2zexdZgKCgpYY1xkaX0xMTHBkCFDmC+0T58+wcfHB/3798edO3fQtWtX1K5du8LlSovL5aJ58+ZYv349atSogYiICIn5WrRogfv37yMiIgKtWrWCqqoqTpw4gdOnT7PylTXGStHMzc1L3RcSEsK8Pn78OOu6hLtc09LSZD7/2rVr0atXL9jY2CAlJYVJ//r1K5o3b17qcVV17/Py8li/740bN2L79u3Mdn5+PnPdsi5Qy+VyxWYmkvJJs1QJl8ut9FMHCMnNzUVkZCQaNWoELS0tRVeHyNHHjx+lzitzIHXs2DH06tVL7IN+8uTJrECqsLAQJ06cEBvsnZKSwvrykjWIcHBwYLUMuLi4oH///jh9+vR3XTbA3t6eNQhZlIGBAX755RdcuHAB+/fvR+PGjbFlyxaxtZCqq7LGVAk/w2zw4MHYtWtXldShbt262Lp1K2bOnMm8X5KSkrBo0SK4ubmVuvZUVdz79PR01nvWzs4OS5Yskbk8STgcDq19JANpFsNVUVGhe0vkRktLi95PSqYiPWQyBVKxsbG4desWuFyuxMeUqKqqMuNSAODKlSuYP38+a/C46Jdedna2LFVBu3bt0LVrVzx79gwAEBAQgCtXrkBNTe27/sVZu3ZtZvkASaKiorBo0SK8efMGvXv3xqFDh+S6eGRVK6vFkMvlMmPFhJeAqAr9+vWDg4MDayJDYGAgdu7ciaVLl0o8piruvWirR1VfN6mY6Oho9OjRo9R9NHOPECIvMs3ac3V1RefOnREUFMRMCRf+KVl2oEROTg7Onj3LSjMwMICenh6znZCQIPYQXmk5ODiwttesWQN7e3uxfGfPnkWPHj1gaWmJly9fynSusoguAVEiKSkJ48ePx5s3b6Cqqopt27Z9l1XJ5fGYGmkIz+QLCQlBUlKSxHzy6rr8888/0b59e1aai4sLs86TMFnvfXl/jdSqVQv6+vrM9tOnTyWu5l+du2uVlfBYOKB4GEFycjLz+WJmZgZTU1NFVY8QomQq/E2bkpKCixcvYvbs2aXm6devH2sGHlA8EFx4ZgOHw2ENEObz+Xj+/DmznZ+fL7ZwXlFRkcTzDRgwAI0bN2a2jY2NYWFhwcrz+fNnrF+/HikpKYiMjMSff/5ZqS+5ihx77NgxpgVEV1e3SsdtCROdKSbcSihr0CpJ7969mdd8Pl9i196NGzfk9uBfLpeLXbt2iV3fsmXLmFl0JWS999KMsxF+/6ampuLYsWNieY4cOYLXr19LdU4iH5cvX2atG3Xq1CmYm5vj1KlTTBqtak4IkZcKB1K7d++Gmppaqc3mJWbMmMHaTklJwYkTJ1hp06ZNY/3lv3v3bvB4POTl5WH58uVi3S+SFmAEioOyKVOmMNuTJk0Sa415//49KxCLjY2t0CBg0anSFZk6LTxoLT09HVu3bsXdu3fFFr3k8Xjg8XhMPUVnqIlui7aAiG4LB5cAEBcXBwB4+fIlvLy8WPvKm8VWVuA4adIk1kDLS5cuYc2aNYiMjERCQgLc3Nxw7tw5WFpallqGKNH7Kzq9uH79+mIr1WdmZmLevHmsY2W99zVr1mQFU8LLXJQMMhd9/+7duxd79uxBTEwMYmNjsWvXLoSGhqJjx45SXzchhJAfi9SBVHZ2Ng4cOIDz588jKysLjx8/LnMNJUnPmTt06BAePXrEfCmLPoj31atX6NOnD9PC0blzZ9bx9vb2Yo9NKWFjYwMDAwPo6Ohg9OjRYvtbtmzJCq5MTEyYZRvKExERgcDAQFbahw8fWC1oZRFtnXN1dcXq1asxdepU1hpXDx48wLRp05iB+I8fP2Yd9+7dO+axLbm5uWLdkwEBAaztcePGsQZAOjk5YePGjVi2bJnY+DHR9br8/f1Z20FBQaU+iNrExARbt25ljXs7f/48LC0t0adPH7i5uWHHjh3M+lJlKSwsxK1bt8SC3Dt37rBm6wHAsGHDMGbMGFZaWFgYnJycmLyy3HugeNrrqFGjmP3BwcHIycmBm5sbMjMzAQDt27dndecKBAIcPHgQAwYMQP/+/fHff/9h48aN5V4zIYSQHxdHIGUf1R9//CH2yBYVFRUcP36ctQJ3YGAgpk2bVua6PA4ODqwvIC8vL7i4uCA8PBw6OjqwtbWFk5MTFixYgLy8PHTu3BmdOnVCp06dylzYcO/evcjMzCx1Fe3Tp09j3759qFGjBrZs2YJffvmlzGsOCwvDH3/8UWbrk6amJvbs2YP+/fuXmic7Oxtr1qyBt7c39PT0MGTIEMycORO1atWCl5cXtm/fjuTkZHTq1Anr1q1D48aNJT5rDygeyB8QEIBu3bqxuupKdO3albXK+uvXr7Flyxa8e/cONWvWhIWFBebNm4cdO3bg6tWrTL5GjRphzZo16NWrF5YvX44rV66Ilc3lcnH79m2xpSxKhISE4MiRIwgMDGTWbbK0tISDg0Opq9QLu3TpEtauXVtmt2OdOnXg6+vLbOfl5WH06NH48OEDKx+Hw8GVK1fQsGHDCt/7Evn5+di1axeuX7+OnJwcdOjQAfPnzxebYOHn5wcXFxe8efMGeXl5aNCgAaytrTFx4kSZp0QHBwcDgNhYMFJxYWFhWLduHdatW4eWLVsqujpEieTk5CA0NBStW7emWXtKpiKfwVIHUoSQ74cCKflJTEyEl5cXhgwZUiUPzyY/LwqklFdFPoO/z7QuQghREB0dHbRp06ZKHhVFCCEUSBFClFpmZiaCgoKYsW2EECJPFEgRQpRaWloaHjx4UKlHFBFCSGkokCKEEEIIkREFUoQQQgghMqJAihBCCCFERhRIEUKUmqamJho1agRNTU1FV4UQooQokCKEKLU6depg9OjRtIYUIaRKUCBFCFFqRUVFrOcoEkKIPFEgRQhRatHR0di3bx+io6MVXRVCiBKiQIoQQgghREYUSBFCCCGEyIgCKUIIIYQQGVEgRQghhBAiIwqkCCFKzdTUFHPmzIGpqamiq0IIUUIUSBFClJqqqiq0tbWhqqqq6KoQQpSQmqIrUJ4zZ85g69at4PF4pebR0dHBw4cPUbNmTbmd982bN3B3d8fLly+RkpICVVVVNGnSBDNnzsSgQYPkdp6f1eTJk/Hs2TNmW1NTE1wuFzk5OSgsLGTSuVwuNDU1wePxkJ+fz6SPHDkSW7duBQD4+vpizZo1yMvLw7JlyzBixIjvdyGk2ktKSsLVq1dRp04dNGjQQNHVIYQomWrfIjVhwgS8fv0ae/fuFdvXpEkTPHr0CC9fvpRrEHX69GmMHTsWN27cwIgRI3Dz5k1kZWXhzZs3mD9/PhITE+V2rp/d1KlT8fjxY7x+/RqBgYEwNzdn7R8+fDgCAwMRHByMR48eYejQoWJlrFy5EtHR0UhOTsbKlSuRl5f3vapPfgC5ubmIiIhAbm6uoqtCCFFC1b5FCgA4HA4sLS1hYGCA1NRUJr1///4wMTGR67mio6OxefNmCAQCAECDBg2gr6+PFi1a4NOnT+jTp49cg7af2fDhw7F06VKp85uYmGDXrl1IS0tjpZf8rkpeC28TQgghVanat0gJ09bWLnNbHl69eoWCggJWmp6eHm7cuIGQkBAcPnwYXC5X7uf9GS1cuLDCx3A4HMyZM4eVtmHDBpiYmMDQ0BAbN26ElpaWnGpICCGElO2HaJH6nsoai0XkZ9CgQahfv75Mx3bp0gURERHMdr9+/fDo0SM51YxUR7a2toiJiSkzj6mpKS5fvvydakQIIcV+qBap8rx9+xZWVlZo2bIl8zN58mRkZWVh9+7dGDx4MDp06AArKyvcu3ePdWxMTAy6dOmCf/75h5X+zz//oEuXLggMDGSlFxYW4uzZs7C1tYW5uTm6deuGRYsW4cuXL6x87u7uMDc3Z9Vp3759AID379/Dzs4OnTt3ZgZOl+DxeHB2doaVlRU6d+6MXr16YfXq1WLjs7Zt24Z27dqxyg8ICMCzZ88wbdo0pm5Lly5Fenp6qfcuODgYixYtQr9+/dCxY0dYWlpi3bp1SEhIkJj/8+fPWLp0KXr37o1OnTrBysoKp0+flrpbzd7eXqp8kqioqGD8+PHw9vZmXXfJT4no6GhMnjyZtc/CwgI8Hg9HjhzBsGHD0L59e/Ts2ROrVq1CRkYGgOKJBvPmzUO3bt3QuXNnTJo0CW/fvi21PgkJCVi/fj0GDBiATp064bfffsOhQ4dYg+NJ5cTExJT5rLzo6OhSA62aNWuif//+1CVPCKkSShVItWvXDkePHmWlxcfHY/z48UhNTYWxsTF4PB7Cw8OxYMECVnBkamqKwMBArF27lnX82rVrERgYiC5dujBp2dnZmD59OtatW4dOnTrB398fy5cvx61btzB69GjWl66dnR2WL18uVtf3799jwoQJCAgIQE5ODlxdXZkv8sTERIwbNw47d+6EtbU1nj17hokTJ+LChQsYPXo0oqKimHKWLl0Ka2trVtnOzs7Ytm0bmjZtiqKiIqSlpcHDwwOLFi2SeN8uXLiAcePG4c2bN7hw4QKOHj2KyMhInD17FjY2NqzzAcD9+/dhbW0Nb29vuLm5wdvbG1wuF+vXr8fff/8t8RxVYcCAAXj8+DF0dHQk7jczM4Orqys0NTWZtOzsbIwfPx5JSUmwsbFBUVERkpOTcfHiRcyePRuHDh3Chg0b8Ouvv6JevXrIycnB8+fP4eDgwBqfV+Lly5ewsrLChQsX8L///Q9PnjxB48aNsXv3bkyfPh18Pr/Krv9nY2ZmBj8/P4k/ZmZmpR6np6eHLl26QE9P7zvWlhDys1CqQAoAjIyMWNvfvn3D8uXL8c8//8DZ2RkaGhoAiluUTp06JdM5VqxYgadPn0JHRwcLFy4El8uFjY0NmjZtioyMDCxevJg1hV/0Q57H42HJkiWsqdgcDgcqKiooLCzE/PnzERoainr16sHBwQFcLhczZsyArq4u4uLisHLlyjKvubCwEOfOncOKFSswZcoUJv3JkyesLjEAePbsGdauXYvCwkJMnToVdevWRdeuXVGjRg0AQHJyMlxdXZn87969w6JFi8Dj8TBp0iQ0bdoUBgYGmD59OgDg+vXr8PDwqPhNlVHdunXRrFmzUverqanBwMCA2U5LS8OUKVOwcuVKODo6Yvjw4cy+wMBAvHjxAqdPn4a9vT2WLVvG7MvIyMCtW7dYZcfGxmL27NlIS0vD77//ji5dukBHRwdz584FAAQEBMDZ2Vlel0pklJOTg7CwMOTk5Ci6KoQQJaR0Y6RUVNixYefOndGzZ08AgJaWFmrWrIn4+HgAQGRkZIXLf/HiBby8vAAAbdq0Yf2V26RJE0RERCAyMhJPnz5Fnz59JNbpwoULWL16NaysrHDgwAG4uLjA1tYWurq6uH79OoKCggAUjwUqWUSQy+WiQYMGePfuHfz9/fHp0yc0adJEYvkzZ85kBsSLrub8+fNnNG3alNneunUrioqKAAAdOnRg0n/99Vfcv38fAFitKv/73/+YLquuXbuyrr1ESUvW91ISHJdG+P6YmZmx1pkyNjZm5Z0xYwbU1dUBAHXq1GHtE32/HDhwgJlBWNa9KAmsKkogENCX//9XVFQk9j6XlEfS/YqOjsaNGzfQtm3bKpmgQn5eJUtq0NIaykcgEIDD4UiVV+kCKVGiqxmrqf3fJcvyJeXp6cm8rlu3Lmuf8If0q1evmEBKlJ6eHqysrAAAc+fOZX3RCpcv+iUvWr7wF7Yw4S8c0esXvuawsDCEhIQw2yWtUACwatUqZrukfikpKXj69KnE+gl3r4WEhIDP5/8QsxuF3w/l7cvOzmZeFxYW4vbt28y28L0Q/j0lJiYiKioK9erVq3Dd+Hw+QkNDK3ycMuLz+eUGzKXdr7i4OABAVFSU2IxcQuRBlj/KSfVX8kd1eZQ+kCqLLB+qwoGHl5cXfHx8WOWV3PiyBnaLLjpZWvnHjx/H6dOnmW0+n8+UL7qWkrSEuxxfv37N2peVlcW8NjExwebNm1n7RQdcjxw5kgnUBAIB602XkZEBQ0NDmepYXZW03AHFH5zC92vu3LmswEv4XqSkpMgUSHG53DK7LX8m0gTlXC4XrVu3Fksv+b3Uq1cPzZs3l3vdyM8rNzcXkZGRaNSoES27omQ+fvwodd6fOpCShXCA1LZtW1y4cKHCZYiOaSqt/MGDB2PXrl0VLr8swrPqUlJSWPtiYmLQpk0bqeoGAHv27EHfvn3lWr8fhei9WLp0KcaPHy/Xc3A4HOqK+v/K69YrySPpfpVMNtDU1KT7SaqElpYWvbeUjLTdegAFUhUm/JdxeevalKasLgoul8uMSZK1fGkJz2YDigdHl/UcQdFWgaquX3VG9+L7i46ORo8ePUrdV9rMPS6XCyMjox+iq5kQ8uNRull7VU14pl1iYiKrK06YrI8pES4/JCQESUlJci1fmPCgcwC4detWmc+pa9iwIWu7tEUwf4ZHtIg+/Fa4i1fYz3AvvgdTU9MylzgwMzMTm1hRwtjYGHZ2dmJjDgkhRB4okKqg3r17s7Z37tzJGjsDAM+fP2ctGSBr+Xw+X2LX3o0bN1gDnWX166+/shYpTEpKwoEDB8Ty3bp1CwKBAC1atGDNZPPx8cHz589ZeYuKirBkyRKlf3BwjRo1WLMcw8LCcO3aNbF869evZ2aJEtldvny51DWkSn5oVXNCiCL8UIGU6BRTSVNORYMa0W3hAeaSWgtEB6CLBgTW1tasYOLJk//X3r3H1ZTufwD/7K6UXEIJg5qZTDOMw3H/oRknmdDIRBhzwmgMuYzL5NK4JA6hcMzJaWJQM3VGrzhGTA5TGCrNxOSajJxcEqmdqKTd1u8Pr9bZa1+67HbtbJ/36+U161nr2Ws9z6rZ69tzW0mYP38+rl+/DqlUioMHD2LdunWYOHGixnNU10rxySefiAYtxsbGYtWqVcjOzkZeXh4iIiLwww8/YOTIkbWqs+LgcuVrN2vWDLNnzxYdDw8PR2BgIC5cuICLFy9i+fLlyMjIgEQigbGxMWbMmCG6jq+vLw4ePAipVIrr169j7ty56NOnj0q3YW0p36vazKxUfq2PclpxhXHl8ysvmKn42ZryfvbZZ6L0ihUrEBERgYcPH+LWrVtYuXIlmjVrpjK7kxrX3bt3sXXrVpWFZYmIdOGlCaR+/vlnlcHRJ0+eVBmbovwKFcVXnDx79ky0OnVhYaEo0JDJZDhz5ozo8wkJCaLZWZaWlti+fbtoYOHx48fh7u6OQYMGYcOGDQgKChItB3D27FnROX///XeNrw+xs7NDUFCQaAbYvn37MHLkSAwdOhQREREIDg4WLWug3P2nWOeqqd+a8np7e4vWVQKAqKgoeHl5YcKECSgvL8e8efOEY1OnToWrq6uQfvz4MZYuXYpBgwbB3d0dbdq0wZQpU9TWrSZpaWm4du2ayr7r169r/MydO3dUFhlNTU0Vtm/cuIGCggIhXVBQIFyjuLhY5WeTkpIiBJuJiYmiYxkZGaLfBVdXV9GrbsrLy7F+/XoMGTIErq6uyM3NxaJFi6qtMzW8yspKyOVydrMSUYOQVDbxb5fo6GgEBQVV+zJhS0tLJCYmIicnB/7+/ioPYxcXF/ztb3/D7Nmzcf78edGxgQMHYv369ZBIJHB1ddX4So+oqCjRa2Ju376NHTt2IDk5GYWFhbCxsYGzszNmzpwpGouxfPlyHDhwQOV8pqamiI+P1/ji3itXruCbb75BWloaiouL0bFjR4wcORLTp08XdccFBwdj7969onLb2Nhg7dq1kEqlCAwMFLXctWzZEvPmzYO3t7ewr7KyEgcPHsS+ffuQmZkJExMTODo6YvLkyaKVvxXzx8TEIDY2Fjdu3ICJiQnefPNN/PWvf4Wbm5va+lTn2LFj8PPzq7Y70MLCAtHR0aLp7SdOnMCsWbPU5l+5ciV69uyJiRMnqjxAJRIJoqOjsWLFCpUgDABGjRqF7t27q+1Wbd68OdLT00X74uPjER0djYyMDMjlctjb22PixInw9PSsdp2q6ly6dAkA0LNnT60+T/+TmZmJgIAABAQEiN7FSFRfpaWlyMjIgJOTE2ftGZi6fAc3+UCK6FXEQEp3GEhRQ2EgZbjq8h380nTtERERETU1DKSIyKDZ2tpi2rRpHPRPRA2CgRQRGTQzMzO0a9eu1u/NIiKqCwZSRGTQpFIpjh49qjLrl4hIFxhIEZFBKykpweXLl1FSUqLvohCRAWIgRURERKQlBlJEREREWmIgRURERKQlBlJEZNCsrKzQv39/WFlZ6bsoRGSAGEgRkUFr3bo1hg0bJnq1EhGRrjCQIiKD9uzZM9y+fbva93USEWmLgRQRGbS8vDzExMQgLy9P30UhIgPEQIqIiIhISwykiIiIiLTEQIqIiIhISwykiMigmZiYoEWLFjAxMdF3UYjIAOnkm+XIkSM4d+4cDh8+jKKiIpXjEokE5ubmaNOmDV577TX06dMHbm5ueOutt3RxeRUXL15EZGQkzp8/D6lUCmNjYzg4OODzzz+Hi4tLg1yzqcrLy8Pu3btx8uRJ3L9/H1ZWVujQoQNGjBgBT09PWFtbw9/fHxs2bNB4jsTERAwfPrwRS900qKv3P/7xD+zcuRNlZWXCvsjISAwYMKCxi0e1ZGdnh1mzZsHOzk7fRSEiA6STFqnRo0dj1apV2Lx5s8qxnTt3Ijk5GdHR0Rg/fjwuX76MsLAwjB07Fr6+vjqfSRMVFQUvLy/ExcXhww8/xJEjR1BcXIyLFy9i3rx5ePjwoU6v15SdO3cO7u7uOHToEL788kv8+uuv+OWXX7B69WpcuHABzs7OGDZsGLKzszWe4/bt21i0aFHjFbqJ0FTvuXPnwsPDo/ELRERETZJOu/a6deumss/c3BzW1tZ45513MHfuXERGRsLCwgIAkJCQAA8PD/zxxx86uX5OTg7Wr1+PyspKAECXLl3QsmVLODo6wsTEBM7Ozq/MonwFBQWYPXs2Hj16hFWrVsHFxQVmZmaQSCTo0aMHQkNDawxkS0pKMHfuXDx9+rQRS65/NdW7bdu2jVwiqo/c3FyEhYUhNzdX30UhIgOk00DK2Ni4xjw9e/bEvHnzhHRBQQFmzpyJJ0+e1Pv66enpqKioEO2zsrJCXFwcrly5grCwMJiamtb7Oi+D6OhooZvV3t5ebR5fX1988MEHao+VlpZi7ty5yMzMbLAyNkW1qbdEImnEElF9VVRUoLi4WOW7gYhIF/Qy2HzSpElo0aKFkL537x5CQ0PrfV6uXPw/ly5dErZ37doltNIpW7BggUpgcP/+fUydOhXJyckNWsam5lWtd1Pj6emJQYMGVfvP09NT38UkIgKgo8HmdWVhYYHBgwfj2LFjwr59+/bhiy++QPPmzUV58/LyEBYWhhMnTqCwsBA2NjYYN24cZsyYATMzMwAvArEPP/wQMplM9Nk1a9Zg/fr1CAsLQ9++fYX9crkcMTExiI2NRXZ2NkxMTDB48GAsWLAAXbt2FfLt2bMH27dvR2lpqbBvw4YNcHJywj//+U+kpqaioqICAwYMwIoVK9CxY0e19c3OzsbOnTuRkpKCwsJCWFtbo3fv3pg7d67a7tDa1LkmivkOHTqEoqIirFy5Eq+99poon729Pd544w0hff36dcyZMwc5OTmifIr3Ly0tDSEhIdi7dy/Ky8tF9+ajjz5CamoqNm/ejJs3b2L+/PmYNm2akOfJkycIDw/HsWPHkJeXh1atWsHNzQ1z5swRBdeLFy/GkSNHRAFgQkICrl69ioiICFy9ehUWFhYYNWoU/Pz81N6XrKws7Nq1C2fPnkV+fj5kMpnofJaWljAyMoKXlxc8PDxqVW9Nbt26hZCQECQlJcHMzAxubm7w8/NT+X02RIMGDQIApKSk6OR89+7dQ05ODjp16qT2uPLPqL50XX4ierXobfmDnj17itKlpaUqLQHnz5+Hu7s7YmJisHnzZiQlJcHe3h7btm2Dj4+PEDh17NgRaWlpWL16tejzq1evRlpamuhhWFJSAh8fHwQEBOBPf/oTzp49i+XLl+Onn34SBsNXmT59Oj777DPROasGbnfq1AkmJiYoLi5GQkICfHx8IJfLVep58uRJjB07FseOHUN4eDgOHjyIBw8eIC4uDuPGjcPFixe1qnNN+vXrJ0qfOnUKbm5u+Oqrr3Dr1i3RscDAQGHb0dERx48fx5///GdRnrS0NOEf8CLQ8fHxUbnumTNn8Omnn+LSpUsoKSkRtTTevHkTY8eORXh4OL744gukpqZi2LBh2L17NyZPnozHjx8LeUNCQjBw4EDRuQMCAvD999+je/fuePbsGfLz8xEZGYm1a9eqlOM///kPxo0bhwMHDqBVq1Y4efIkTp06hS5dugh5JkyYgLS0NCxZsqTW9VYnJSUF48ePx/nz51FcXAypVIqoqCj4+flp/AxVr1OnTkhJSVH7T1OARUSkD3oLpDp37qyy7+rVq8J2bm6uMFh69OjR6Nu3LywtLTFnzhwAQGpqKsLDw+t8XX9/fyQnJ8PS0hILFiyAqakpPDw88Prrr+Px48dYvHixKCCysbERfb6goAD79u3D0qVLsXDhQmF/VlYWzpw5I8r73//+FwsWLEBZWRnGjx+PN954A127dhVagEpLS7Fjx44GqfP48eNVWrtkMhliY2Ph5uaGJUuW4O7du7U6lybKD7SCggKsXLlSdF0joxe/YsXFxfj888+Rk5ODfv36YdSoUTAzM8P8+fMBvGgJCwoKEp1P+d63a9cOkZGRWLVqFcaMGSPs//e//43i4mIhfffuXfj5+QldvTNnzkTbtm1ha2sLLy8vIV9kZCTu379fjzvwQmxsLKKionDmzBm4u7sL+48fP47bt2/X+/xUPzY2NvDy8lL5fSIi0gW9rVCn2I1TJT8/X9gODQ3Fo0ePAAD9+/cX9js4OAjb//rXv4QgozbOnTuHo0ePAgDefvttWFlZic6blZWF7OxsJCcnY+jQoQD+FwhU8fb2Fsqu3JWXnZ0NZ2dnIb1lyxZh5levXr2E/f3790dGRgYAiFqYdFlnS0tL7Ny5EzNmzFB5mMvlcvz44484evQo5s2bBx8fH60GUCvfm127diE0NBR9+vTBmjVrEBcXh1mzZgEAvvvuO6EcinVr164dWrVqhaKiIhw6dAj+/v7C/VU+v6+vr7CtuCaQTCbD3bt3hXXJDh48KBovp3j/FLefP3+OCxcuoEOHDnWuuyJ/f384OjoCeNEVGBcXJxy7efOmqBWsLiorK0Xdyk3V8+fPkZubq7O1tHJzc2tsdcrJyan19SorK1FRUYENGzao/T3Pzc2FnZ3dS3GvqWmp+n5/1WY2vwoqKytr/VzUWyClbpXhqgenXC5HfHy8sF/xQVe1dAIAPHz4EHfv3lXbuqXO4cOHhW1bW1vRMcXzpqenC4GUMsWZicqzFBW/iIuKipCQkCCkW7VqJWzPmTMHcrkcjx49EoKDhqhzly5dcODAAYSEhCAmJkal6/HZs2cIDg7GtWvXEBwcXO/ZaI6OjkI36po1a7BmzRrhmOK9Vw5cLCwsUFRUBJlMhitXrmh8QCoGVsq/PyUlJcK28lphlpaWomsp0sVq14rLISiP1VLsrqwrmUwmBNxNWdUfA7Xtdtb1dWvy/PlzlJeXw8zMTCU4VzzXy3CvqWmqbi0+ennVdkyy3gIpxQdflaoHUnZ2tqirZs6cOaIHnmLlpFJprQOpK1euCNtHjx7FqVOnhHRFRYVwXnWrs9eGYqBy+fJlUVqxPq1atcLKlStFn22oOltZWSEgIADe3t4IDQ1FfHy8SkB1+PBh9OvXD5MmTarVOTVRHIumqKysDFlZWUJ63bp12LRpk5CuesgBQGFhoVbXVqyT8nIPigPiFbeNjIwabHV9deWqK1NTU9FEgKbK1NQUdnZ2OHHihE7O9/7779eYpy7X++OPPxAUFIRly5bhzTff1Hg9JyenuhWUXnlPnz5FdnY2unXr9kpMLHmV3Lhxo9Z59RZIFRQUqOyrGoCuHMgsXboUkydPrvc1Fc/7zjvvICYmpt7nVKQ4I0wqlYqO1TTTSNd19vf3x/r164W0g4MDQkJCMH/+fHz99dc4fPiwqLzR0dH1DqQ0jUEpKioSXcvb2xtffvllva6lTPH8Hh4e2LFjh3BPs7Oz8frrrwN4MW6tyujRoxt84LKmZSdqQyKRqLSgNUVVrTy6KqumViPlPLW9XrNmzYT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modellifelines.WeibullAFTFitter
duration col'adv_fit_time'
event col'adv_failures'
number of observations1500
number of events observed1500
log-likelihood-5531.26
time fit was run2023-09-29 11:13:25 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
lambda_adv_failure_rate-0.001.000.00-0.00-0.001.001.000.00-39.62<0.005inf
atk_value0.151.160.16-0.160.460.851.580.000.950.341.56
data.sample.random_state0.031.030.02-0.010.070.991.070.001.310.192.39
def_value-0.210.810.16-0.530.100.591.110.00-1.310.192.41
model.art.pipeline.initialize.kwargs.optimizer.lr-0.001.000.00-0.000.001.001.000.00-0.530.600.74
model_layers0.011.010.000.010.011.011.010.007.04<0.00538.88
predict_time-0.150.860.01-0.17-0.120.840.880.00-12.17<0.005110.86
train_time0.001.000.000.000.001.001.000.0010.81<0.00588.14
Intercept3.0020.180.182.653.3614.1428.790.0016.56<0.005202.20
rho_Intercept-0.840.430.02-0.88-0.800.410.450.00-43.75<0.005inf

\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Concordance0.84
AIC11082.52
log-likelihood ratio test800.84 on 8 df
-log2(p) of ll-ratio test554.32
\n", + "
" + ], + "text/latex": [ + "\\begin{tabular}{llrrrrrrrrrrr}\n", + " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", + "param & covariate & & & & & & & & & & & \\\\\n", + "\\multirow[c]{9}{*}{lambda_} & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -39.62 & 0.00 & inf \\\\\n", + " & atk_value & 0.15 & 1.16 & 0.16 & -0.16 & 0.46 & 0.85 & 1.58 & 0.00 & 0.95 & 0.34 & 1.56 \\\\\n", + " & data.sample.random_state & 0.03 & 1.03 & 0.02 & -0.01 & 0.07 & 0.99 & 1.07 & 0.00 & 1.31 & 0.19 & 2.39 \\\\\n", + " & def_value & -0.21 & 0.81 & 0.16 & -0.53 & 0.10 & 0.59 & 1.11 & 0.00 & -1.31 & 0.19 & 2.41 \\\\\n", + " & model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.53 & 0.60 & 0.74 \\\\\n", + " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 7.04 & 0.00 & 38.88 \\\\\n", + " & predict_time & -0.15 & 0.86 & 0.01 & -0.17 & -0.12 & 0.84 & 0.88 & 0.00 & -12.17 & 0.00 & 110.86 \\\\\n", + " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 10.81 & 0.00 & 88.14 \\\\\n", + " & Intercept & 3.00 & 20.18 & 0.18 & 2.65 & 3.36 & 14.14 & 28.79 & 0.00 & 16.56 & 0.00 & 202.20 \\\\\n", + "rho_ & Intercept & -0.84 & 0.43 & 0.02 & -0.88 & -0.80 & 0.41 & 0.45 & 0.00 & -43.75 & 0.00 & inf \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\n", + " duration col = 'adv_fit_time'\n", + " event col = 'adv_failures'\n", + " number of observations = 1500\n", + "number of events observed = 1500\n", + " log-likelihood = -5531.26\n", + " time fit was run = 2023-09-29 11:13:25 UTC\n", + "\n", + "---\n", + " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", + "param covariate \n", + "lambda_ adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + " atk_value 0.15 1.16 0.16 -0.16 0.46 0.85 1.58\n", + " data.sample.random_state 0.03 1.03 0.02 -0.01 0.07 0.99 1.07\n", + " def_value -0.21 0.81 0.16 -0.53 0.10 0.59 1.11\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + " model_layers 0.01 1.01 0.00 0.01 0.01 1.01 1.01\n", + " predict_time -0.15 0.86 0.01 -0.17 -0.12 0.84 0.88\n", + " train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", + " Intercept 3.00 20.18 0.18 2.65 3.36 14.14 28.79\n", + "rho_ Intercept -0.84 0.43 0.02 -0.88 -0.80 0.41 0.45\n", + "\n", + " cmp to z p -log2(p)\n", + "param covariate \n", + "lambda_ adv_failure_rate 0.00 -39.62 <0.005 inf\n", + " atk_value 0.00 0.95 0.34 1.56\n", + " data.sample.random_state 0.00 1.31 0.19 2.39\n", + " def_value 0.00 -1.31 0.19 2.41\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.53 0.60 0.74\n", + " model_layers 0.00 7.04 <0.005 38.88\n", + " predict_time 0.00 -12.17 <0.005 110.86\n", + " train_time 0.00 10.81 <0.005 88.14\n", + " Intercept 0.00 16.56 <0.005 202.20\n", + "rho_ Intercept 0.00 -43.75 <0.005 inf\n", + "---\n", + "Concordance = 0.84\n", + "AIC = 11082.52\n", + "log-likelihood ratio test = 800.84 on 8 df\n", + "-log2(p) of ll-ratio test = 554.32" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "weibull_dict = {\n", + " \"Intercept: rho_\": \"$\\\\rho$\",\n", + " \"Intercept: lambda_\": \"$\\lambda$\",\n", + " \"data.sample.random_state: lambda_\": \"Random State\",\n", + " \"def_value: lambda_\": \"Defence Strength\",\n", + " \"atk_value: lambda_\": \"Attack Strength\",\n", + " \"train_time: lambda_\": \"Training Time\",\n", + " \"predict_time: lambda_\": \"Inference Time\",\n", + " \"adv_accuracy: lambda_\": \"Adv. Accuracy\",\n", + " \"accuracy: lambda_\": \"Ben. Accuracy\",\n", + " \"adv_fit_time: lambda_\": \"Adv. Fit Time\",\n", + " \"adv_log_loss: lambda_\": \"Adv. Log Loss\",\n", + " \"adv_failure_rate: lambda_\": \"Adv. Failure Rate\",\n", + " \"failure_rate: lambda_\": \"Ben. Failure Rate\",\n", + " \"model_layers: lambda_\": \"No. of Layers\",\n", + " \"model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_\": \"Learning Rate\",\n", + " \"def_gen\": \"Defence\",\n", + "}\n", + "\n", + "weibull_afr, wft = plot_aft(\n", + " X_train,\n", + " file=\"weibull_aft.pdf\",\n", + " event_col=target,\n", + " duration_col=duration_col,\n", + " title=\"Weibull AFR Model\",\n", + " mtype=\"weibull\",\n", + " replacement_dict=weibull_dict,\n", + ")\n", + "wft.print_summary()\n", + "wft_scores = score_model(wft, X_train, X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_773113/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " pareto.set_yticklabels(labels)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pareto_dict = {\n", + " \"model_layers=18\": \"18\",\n", + " \"model_layers=34\": \"34\",\n", + " \"model_layers=50\": \"50\",\n", + " \"model_layers=101\": \"101\",\n", + " \"model_layers=152\": \"152\",\n", + "}\n", + "pareto_weibull = plot_partial_effects(\n", + " file=\"weibull_partial_effects.pdf\",\n", + " aft=wft,\n", + " covariate_array=\"model_layers\",\n", + " values_array=[18, 34, 50, 101, 152],\n", + " title=\"Partial Effects of No. of Layers on Failure Rate for Weibull AFR\",\n", + " replacement_dict=pareto_dict,\n", + " ylabel=\"% Chance of Survival\",\n", + " xlabel=\"Time $T$ (seconds)\",\n", + " legend_kwargs={\n", + " \"title\": \"No. of Layers\",\n", + " \"labels\": [\"18\", \"34\", \"50\", \"101\", \"152\"],\n", + " },\n", + ")\n", + "\n", + "# weibull_accuracy = plot_partial_effects(\n", + "# file = \"weibull_partial_effect_accuracy.pdf\",\n", + "# aft = wft,\n", + "# covariate_array = \"accuracy\",\n", + "# values_array = [.9, .99, .999, .9999],\n", + "# replacement_dict=weibull_dict,\n", + "# title=\"Partial Effects of Benign Accuracy on Failure Rate\",\n", + "# ylabel=\"% Chance of Survival\",\n", + "# xlabel=\"Time $T$ (seconds)\",\n", + "# legend = {\"title\" : \"Benign Accuracy\"},\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cmeyers/deckard/env/lib/python3.8/site-packages/lifelines/fitters/coxph_fitter.py:1614: ConvergenceWarning: Newton-Raphson failed to converge sufficiently. Please see the following tips in the lifelines documentation: https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-model\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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modellifelines.CoxPHFitter
duration col'adv_fit_time'
event col'adv_failures'
baseline estimationbreslow
number of observations1500
number of events observed1500
partial log-likelihood-7421.70
time fit was run2023-09-29 11:13:28 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
train_time-0.001.000.00-0.00-0.001.001.000.00-4.25<0.00515.52
predict_time0.031.030.010.020.041.021.040.004.21<0.00515.26
atk_value-0.090.910.07-0.230.050.801.050.00-1.310.192.40
def_value0.041.050.07-0.090.180.911.200.000.630.530.92
data.sample.random_state-0.010.990.01-0.030.000.971.000.00-1.570.123.10
adv_failure_rate0.011.010.000.010.011.011.010.0028.70<0.005599.53
model_layers-0.001.000.00-0.00-0.001.001.000.00-5.20<0.00522.23
model.art.pipeline.initialize.kwargs.optimizer.lr-0.001.000.00-0.000.001.001.000.00-0.130.900.15

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Concordance0.92
Partial AIC14859.40
log-likelihood ratio test4105.41 on 8 df
-log2(p) of ll-ratio testinf
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" + ], + "text/latex": [ + "\\begin{tabular}{lrrrrrrrrrrr}\n", + " & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", + "covariate & & & & & & & & & & & \\\\\n", + "train_time & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -4.25 & 0.00 & 15.52 \\\\\n", + "predict_time & 0.03 & 1.03 & 0.01 & 0.02 & 0.04 & 1.02 & 1.04 & 0.00 & 4.21 & 0.00 & 15.26 \\\\\n", + "atk_value & -0.09 & 0.91 & 0.07 & -0.23 & 0.05 & 0.80 & 1.05 & 0.00 & -1.31 & 0.19 & 2.40 \\\\\n", + "def_value & 0.04 & 1.05 & 0.07 & -0.09 & 0.18 & 0.91 & 1.20 & 0.00 & 0.63 & 0.53 & 0.92 \\\\\n", + "data.sample.random_state & -0.01 & 0.99 & 0.01 & -0.03 & 0.00 & 0.97 & 1.00 & 0.00 & -1.57 & 0.12 & 3.10 \\\\\n", + "adv_failure_rate & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 28.70 & 0.00 & 599.53 \\\\\n", + "model_layers & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -5.20 & 0.00 & 22.23 \\\\\n", + "model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.13 & 0.90 & 0.15 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\n", + " duration col = 'adv_fit_time'\n", + " event col = 'adv_failures'\n", + " baseline estimation = breslow\n", + " number of observations = 1500\n", + "number of events observed = 1500\n", + " partial log-likelihood = -7421.70\n", + " time fit was run = 2023-09-29 11:13:28 UTC\n", + "\n", + "---\n", + " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", + "covariate \n", + "train_time -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + "predict_time 0.03 1.03 0.01 0.02 0.04 1.02 1.04\n", + "atk_value -0.09 0.91 0.07 -0.23 0.05 0.80 1.05\n", + "def_value 0.04 1.05 0.07 -0.09 0.18 0.91 1.20\n", + "data.sample.random_state -0.01 0.99 0.01 -0.03 0.00 0.97 1.00\n", + "adv_failure_rate 0.01 1.01 0.00 0.01 0.01 1.01 1.01\n", + "model_layers -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + "model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + "\n", + " cmp to z p -log2(p)\n", + "covariate \n", + "train_time 0.00 -4.25 <0.005 15.52\n", + "predict_time 0.00 4.21 <0.005 15.26\n", + "atk_value 0.00 -1.31 0.19 2.40\n", + "def_value 0.00 0.63 0.53 0.92\n", + "data.sample.random_state 0.00 -1.57 0.12 3.10\n", + "adv_failure_rate 0.00 28.70 <0.005 599.53\n", + "model_layers 0.00 -5.20 <0.005 22.23\n", + "model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.13 0.90 0.15\n", + "---\n", + "Concordance = 0.92\n", + "Partial AIC = 14859.40\n", + "log-likelihood ratio test = 4105.41 on 8 df\n", + "-log2(p) of ll-ratio test = inf" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_773113/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " pareto.set_yticklabels(labels)\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cox_dict = {\n", + " \"adv_failure_rate\": \"Adv. Failure Rate\",\n", + " \"def_value\": \"Defence Strength\",\n", + " \"data.sample.random_state\": \"Random State\",\n", + " \"train_time\": \"Training Time\",\n", + " \"model_layers\": \"No. of Layers\",\n", + " \"model.art.pipeline.initialize.kwargs.optimizer.lr\": \"Learning Rate\",\n", + " \"adv_accuracy\": \"Adv. Accuracy\",\n", + " \"adv_fit_time\": \"Adv. Fit Time\",\n", + " \"adv_log_loss\": \"Adv. Log Loss\",\n", + " \"predict_time\": \"Inference Time\",\n", + " \"accuracy\": \"Ben. Accuracy\",\n", + " \"failure_rate\": \"Ben. Failure Rate\",\n", + " \"atk_value\": \"Attack Strength\",\n", + "}\n", + "\n", + "cox_afr, cft = plot_aft(\n", + " X_train,\n", + " file=\"cox_aft.pdf\",\n", + " event_col=target,\n", + " duration_col=duration_col,\n", + " title=\"Cox AFR Model\",\n", + " mtype=\"cox\",\n", + " replacement_dict=cox_dict,\n", + ")\n", + "cox_scores = score_model(cft, X_train, X_test)\n", + "cft.print_summary()\n", + "cox_partial = plot_partial_effects(\n", + " file=\"cox_partial_effects.pdf\",\n", + " aft=cft,\n", + " covariate_array=\"model_layers\",\n", + " values_array=[18, 34, 50, 101, 152],\n", + " replacement_dict=cox_dict,\n", + " title=\"Survival Time for Cox AFR\",\n", + " ylabel=\"% Chance of Survival\",\n", + " xlabel=\"Time $T$ (seconds)\",\n", + " legend_kwargs={\n", + " \"title\": \"No. of Layers\",\n", + " \"labels\": [\"18\", \"34\", \"50\", \"101\", \"152\"],\n", + " },\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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modellifelines.LogNormalAFTFitter
duration col'adv_fit_time'
event col'adv_failures'
number of observations1500
number of events observed1500
log-likelihood-5374.54
time fit was run2023-09-29 11:13:31 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
mu_adv_failure_rate-0.001.000.00-0.00-0.001.001.000.00-31.84<0.005736.80
atk_value0.081.090.16-0.220.390.801.480.000.520.600.74
data.sample.random_state0.021.020.02-0.020.060.981.060.000.830.411.30
def_value-0.160.850.16-0.480.160.621.170.00-0.980.321.62
model.art.pipeline.initialize.kwargs.optimizer.lr-0.001.000.00-0.000.001.001.000.00-0.060.950.07
model_layers0.011.010.000.010.011.011.010.007.13<0.00539.81
predict_time-0.210.810.02-0.26-0.160.770.850.00-8.37<0.00553.94
train_time0.001.000.000.000.001.001.000.007.13<0.00539.89
Intercept2.027.540.171.682.365.3710.610.0011.63<0.005101.36
sigma_Intercept0.842.310.020.800.872.232.390.0045.86<0.005inf

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Concordance0.84
AIC10769.08
log-likelihood ratio test925.72 on 8 df
-log2(p) of ll-ratio test643.78
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" + ], + "text/latex": [ + "\\begin{tabular}{llrrrrrrrrrrr}\n", + " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", + "param & covariate & & & & & & & & & & & \\\\\n", + "\\multirow[c]{9}{*}{mu_} & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -31.84 & 0.00 & 736.80 \\\\\n", + " & atk_value & 0.08 & 1.09 & 0.16 & -0.22 & 0.39 & 0.80 & 1.48 & 0.00 & 0.52 & 0.60 & 0.74 \\\\\n", + " & data.sample.random_state & 0.02 & 1.02 & 0.02 & -0.02 & 0.06 & 0.98 & 1.06 & 0.00 & 0.83 & 0.41 & 1.30 \\\\\n", + " & def_value & -0.16 & 0.85 & 0.16 & -0.48 & 0.16 & 0.62 & 1.17 & 0.00 & -0.98 & 0.32 & 1.62 \\\\\n", + " & model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.06 & 0.95 & 0.07 \\\\\n", + " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 7.13 & 0.00 & 39.81 \\\\\n", + " & predict_time & -0.21 & 0.81 & 0.02 & -0.26 & -0.16 & 0.77 & 0.85 & 0.00 & -8.37 & 0.00 & 53.94 \\\\\n", + " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 7.13 & 0.00 & 39.89 \\\\\n", + " & Intercept & 2.02 & 7.54 & 0.17 & 1.68 & 2.36 & 5.37 & 10.61 & 0.00 & 11.63 & 0.00 & 101.36 \\\\\n", + "sigma_ & Intercept & 0.84 & 2.31 & 0.02 & 0.80 & 0.87 & 2.23 & 2.39 & 0.00 & 45.86 & 0.00 & inf \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\n", + " duration col = 'adv_fit_time'\n", + " event col = 'adv_failures'\n", + " number of observations = 1500\n", + "number of events observed = 1500\n", + " log-likelihood = -5374.54\n", + " time fit was run = 2023-09-29 11:13:31 UTC\n", + "\n", + "---\n", + " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", + "param covariate \n", + "mu_ adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + " atk_value 0.08 1.09 0.16 -0.22 0.39 0.80 1.48\n", + " data.sample.random_state 0.02 1.02 0.02 -0.02 0.06 0.98 1.06\n", + " def_value -0.16 0.85 0.16 -0.48 0.16 0.62 1.17\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + " model_layers 0.01 1.01 0.00 0.01 0.01 1.01 1.01\n", + " predict_time -0.21 0.81 0.02 -0.26 -0.16 0.77 0.85\n", + " train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", + " Intercept 2.02 7.54 0.17 1.68 2.36 5.37 10.61\n", + "sigma_ Intercept 0.84 2.31 0.02 0.80 0.87 2.23 2.39\n", + "\n", + " cmp to z p -log2(p)\n", + "param covariate \n", + "mu_ adv_failure_rate 0.00 -31.84 <0.005 736.80\n", + " atk_value 0.00 0.52 0.60 0.74\n", + " data.sample.random_state 0.00 0.83 0.41 1.30\n", + " def_value 0.00 -0.98 0.32 1.62\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.06 0.95 0.07\n", + " model_layers 0.00 7.13 <0.005 39.81\n", + " predict_time 0.00 -8.37 <0.005 53.94\n", + " train_time 0.00 7.13 <0.005 39.89\n", + " Intercept 0.00 11.63 <0.005 101.36\n", + "sigma_ Intercept 0.00 45.86 <0.005 inf\n", + "---\n", + "Concordance = 0.84\n", + "AIC = 10769.08\n", + "log-likelihood ratio test = 925.72 on 8 df\n", + "-log2(p) of ll-ratio test = 643.78" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_773113/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " pareto.set_yticklabels(labels)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "log_normal_dict = {\n", + " \"Intercept: sigma_\": \"$\\sigma$\",\n", + " \"Intercept: mu_\": \"$\\mu$\",\n", + " \"def_value: mu_\": \"Defence Strength\",\n", + " \"atk_value: mu_\": \"Attack Strength\",\n", + " \"train_time: mu_\": \"Training Time\",\n", + " \"predict_time: mu_\": \"Inference Time\",\n", + " \"adv_fit_time: mu_\": \"Adv. Fit Time\",\n", + " \"model_layers: mu_\": \"No. of Layers\",\n", + " \"model.art.pipeline.initialize.kwargs.optimizer.lr: mu_\": \"Learning Rate\",\n", + " \"data.sample.random_state: mu_\": \"Random State\",\n", + " \"adv_log_loss: mu_\": \"Adv. Log Loss\",\n", + " \"adv_accuracy: mu_\": \"Adv. Accuracy\",\n", + " \"accuracy: mu_\": \"Ben. Accuracy\",\n", + " \"adv_failure_rate: mu_\": \"Adv. Failure Rate\",\n", + " \"def_gen\": \"Defence\",\n", + " \"learning_rate: mu_\": \"Learning Rate\",\n", + "}\n", + "\n", + "log_normal_graph, lnt = plot_aft(\n", + " X_train,\n", + " \"log_normal_aft.pdf\",\n", + " target,\n", + " duration_col,\n", + " \"Log Normal AFR Model\",\n", + " \"log_normal\",\n", + " replacement_dict=log_normal_dict,\n", + ")\n", + "lnt_scores = score_model(lnt, X_train, X_test)\n", + "lnt.print_summary()\n", + "lnt_partial = plot_partial_effects(\n", + " file=\"log_normal_partial_effects.pdf\",\n", + " aft=lnt,\n", + " covariate_array=\"model_layers\",\n", + " values_array=[18, 34, 50, 101, 152],\n", + " replacement_dict=log_normal_dict,\n", + " title=\"Survival Time for Log-Normal AFR\",\n", + " ylabel=\"% Chance of Survival\",\n", + " xlabel=\"Time $T$ (seconds)\",\n", + " legend_kwargs={\n", + " \"title\": \"No. of Layers\",\n", + " \"labels\": [\"18\", \"34\", \"50\", \"101\", \"152\"],\n", + " },\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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modellifelines.LogLogisticAFTFitter
duration col'adv_fit_time'
event col'adv_failures'
number of observations1500
number of events observed1500
log-likelihood-5426.84
time fit was run2023-09-29 11:13:33 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
alpha_adv_failure_rate-0.001.000.00-0.00-0.001.001.000.00-26.62<0.005516.20
atk_value0.071.070.17-0.260.400.771.490.000.420.670.58
data.sample.random_state0.021.020.02-0.020.060.981.070.000.910.361.47
def_value-0.190.820.18-0.540.150.581.160.00-1.110.271.90
model.art.pipeline.initialize.kwargs.optimizer.lr0.001.000.00-0.000.001.001.000.000.030.970.04
model_layers0.011.010.000.010.021.011.020.007.88<0.00548.13
predict_time-0.310.740.04-0.38-0.240.690.790.00-8.73<0.00558.49
train_time0.001.000.000.000.001.001.000.006.12<0.00530.02
Intercept1.886.540.191.512.254.529.460.009.97<0.00575.31
beta_Intercept-0.320.720.02-0.36-0.280.690.750.00-15.46<0.005176.59

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Concordance0.82
AIC10873.68
log-likelihood ratio test961.93 on 8 df
-log2(p) of ll-ratio test669.73
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" + ], + "text/latex": [ + "\\begin{tabular}{llrrrrrrrrrrr}\n", + " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", + "param & covariate & & & & & & & & & & & \\\\\n", + "\\multirow[c]{9}{*}{alpha_} & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -26.62 & 0.00 & 516.20 \\\\\n", + " & atk_value & 0.07 & 1.07 & 0.17 & -0.26 & 0.40 & 0.77 & 1.49 & 0.00 & 0.42 & 0.67 & 0.58 \\\\\n", + " & data.sample.random_state & 0.02 & 1.02 & 0.02 & -0.02 & 0.06 & 0.98 & 1.07 & 0.00 & 0.91 & 0.36 & 1.47 \\\\\n", + " & def_value & -0.19 & 0.82 & 0.18 & -0.54 & 0.15 & 0.58 & 1.16 & 0.00 & -1.11 & 0.27 & 1.90 \\\\\n", + " & model.art.pipeline.initialize.kwargs.optimizer.lr & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 0.03 & 0.97 & 0.04 \\\\\n", + " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.02 & 1.01 & 1.02 & 0.00 & 7.88 & 0.00 & 48.13 \\\\\n", + " & predict_time & -0.31 & 0.74 & 0.04 & -0.38 & -0.24 & 0.69 & 0.79 & 0.00 & -8.73 & 0.00 & 58.49 \\\\\n", + " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 6.12 & 0.00 & 30.02 \\\\\n", + " & Intercept & 1.88 & 6.54 & 0.19 & 1.51 & 2.25 & 4.52 & 9.46 & 0.00 & 9.97 & 0.00 & 75.31 \\\\\n", + "beta_ & Intercept & -0.32 & 0.72 & 0.02 & -0.36 & -0.28 & 0.69 & 0.75 & 0.00 & -15.46 & 0.00 & 176.59 \\\\\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "\n", + " duration col = 'adv_fit_time'\n", + " event col = 'adv_failures'\n", + " number of observations = 1500\n", + "number of events observed = 1500\n", + " log-likelihood = -5426.84\n", + " time fit was run = 2023-09-29 11:13:33 UTC\n", + "\n", + "---\n", + " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", + "param covariate \n", + "alpha_ adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", + " atk_value 0.07 1.07 0.17 -0.26 0.40 0.77 1.49\n", + " data.sample.random_state 0.02 1.02 0.02 -0.02 0.06 0.98 1.07\n", + " def_value -0.19 0.82 0.18 -0.54 0.15 0.58 1.16\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", + " model_layers 0.01 1.01 0.00 0.01 0.02 1.01 1.02\n", + " predict_time -0.31 0.74 0.04 -0.38 -0.24 0.69 0.79\n", + " train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", + " Intercept 1.88 6.54 0.19 1.51 2.25 4.52 9.46\n", + "beta_ Intercept -0.32 0.72 0.02 -0.36 -0.28 0.69 0.75\n", + "\n", + " cmp to z p -log2(p)\n", + "param covariate \n", + "alpha_ adv_failure_rate 0.00 -26.62 <0.005 516.20\n", + " atk_value 0.00 0.42 0.67 0.58\n", + " data.sample.random_state 0.00 0.91 0.36 1.47\n", + " def_value 0.00 -1.11 0.27 1.90\n", + " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 0.03 0.97 0.04\n", + " model_layers 0.00 7.88 <0.005 48.13\n", + " predict_time 0.00 -8.73 <0.005 58.49\n", + " train_time 0.00 6.12 <0.005 30.02\n", + " Intercept 0.00 9.97 <0.005 75.31\n", + "beta_ Intercept 0.00 -15.46 <0.005 176.59\n", + "---\n", + "Concordance = 0.82\n", + "AIC = 10873.68\n", + "log-likelihood ratio test = 961.93 on 8 df\n", + "-log2(p) of ll-ratio test = 669.73" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'train_score': -3.617893432558881, 'test_score': -3.971188942813805}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_773113/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " pareto.set_yticklabels(labels)\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "log_logistic_dict = {\n", + " \"Intercept: beta_\": \"$\\\\beta$\",\n", + " \"Intercept: alpha_\": \"$\\\\alpha$\",\n", + " \"data.sample.random_state: alpha_\": \"Random State\",\n", + " \"def_value: alpha_\": \"Defence Strength\",\n", + " \"atk_value: alpha_\": \"Attack Strength\",\n", + " \"train_time: alpha_\": \"Training Time\",\n", + " \"predict_time: alpha_\": \"Inference Time\",\n", + " \"adv_accuracy: alpha_\": \"Adv. Accuracy\",\n", + " \"accuracy: alpha_\": \"Ben. Accuracy\",\n", + " \"adv_fit_time: alpha_\": \"Adv. Fit Time\",\n", + " \"model_layers: alpha_\": \"No. of Layers\",\n", + " \"model.art.pipeline.initialize.kwargs.optimizer.lr\": \"Learning Rate\",\n", + " \"adv_failure_rate: alpha_\": \"Adv. Failure Rate\",\n", + " \"alpha_\": \"\",\n", + "}\n", + "\n", + "log_logistic_graph, llt = plot_aft(\n", + " X_train,\n", + " \"log_logistic_aft.pdf\",\n", + " target,\n", + " duration_col,\n", + " \"Log Logistic AFR Model\",\n", + " \"log_logistic\",\n", + " replacement_dict=log_logistic_dict,\n", + ")\n", + "llt.print_summary()\n", + "llt_scores = score_model(llt, X_train, X_test)\n", + "print(llt_scores)\n", + "llt_partial = plot_partial_effects(\n", + " file=\"log_logistic_partial_effects.pdf\",\n", + " aft=llt,\n", + " covariate_array=\"model_layers\",\n", + " values_array=[18, 34, 50, 101, 152],\n", + " replacement_dict=log_logistic_dict,\n", + " title=\"Survival Time for Log-Logistic AFR\",\n", + " ylabel=\"% Chance of Survival\",\n", + " xlabel=\"Time $T$ (seconds)\",\n", + " legend_kwargs={\n", + " \"title\": \"No. of Layers\",\n", + " \"labels\": [\"18\", \"34\", \"50\", \"101\", \"152\"],\n", + " },\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8.62393284274078" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(llt.predict_median(X_train))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AICConcordanceBICTrain LLTest LLMean STMedian ST
Weibull11082.520.8411082.52-3.69-4.0071.5310.23
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LogLogistic10873.680.8210873.68-3.62-3.97NaN6.62
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" + ], + "text/plain": [ + " AIC Concordance BIC Train LL Test LL Mean ST \\\n", + "Weibull 11082.52 0.84 11082.52 -3.69 -4.00 71.53 \n", + "LogNormal 10769.08 0.84 10769.08 -3.58 -3.92 122.81 \n", + "LogLogistic 10873.68 0.82 10873.68 -3.62 -3.97 NaN \n", + "\n", + " Median ST \n", + "Weibull 10.23 \n", + "LogNormal 7.79 \n", + "LogLogistic 6.62 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aft_dict = {\n", + " \"Weibull\": wft,\n", + " \"LogNormal\": lnt,\n", + " \"LogLogistic\": llt,\n", + " # \"Cox\": cft,\n", + "}\n", + "\n", + "score_list = [\n", + " wft_scores,\n", + " lnt_scores,\n", + " llt_scores,\n", + " # cft_scores,\n", + "]\n", + "aft_data = pd.DataFrame()\n", + "aft_data.index.name = \"Model\"\n", + "aft_data.index = aft_dict.keys()\n", + "aft_data[\"AIC\"] = [\n", + " x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()\n", + "]\n", + "aft_data[\"Concordance\"] = [x.concordance_index_ for x in aft_dict.values()]\n", + "aft_data[\"BIC\"] = [\n", + " x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()\n", + "]\n", + "aft_data[\"Train LL\"] = [x[\"train_score\"] for x in score_list]\n", + "aft_data[\"Test LL\"] = [x[\"test_score\"] for x in score_list]\n", + "aft_data[\"Mean ST\"] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()]\n", + "aft_data[\"Median ST\"] = [x.predict_median(X_train).median() for x in aft_dict.values()]\n", + "aft_data = aft_data.round(2)\n", + "aft_data.to_csv(FOLDER / \"aft_comparison.csv\")\n", + "logger.info(f\"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}\")\n", + "aft_data = aft_data.round(\n", + " 2,\n", + ")\n", + "aft_data.to_latex(\n", + " FOLDER / \"aft_comparison.tex\",\n", + " float_format=\"%.2f\",\n", + " label=\"tab:mnist\",\n", + " caption=\"Comparison of AFR Models on the MNIST dataset.\",\n", + ")\n", + "aft_data" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From ed6f757a1df04cf8da5e8a0bdf66851cbbac16c7 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 18:10:25 +0000 Subject: [PATCH 127/148] moved pytorch cifar10 folder --- examples/pytorch/cifar10/.dvc/.gitignore | 3 + examples/pytorch/cifar10/.dvc/config | 0 examples/pytorch/cifar10/.dvcignore | 3 + examples/pytorch/cifar10/.gitignore | 3 + examples/pytorch/cifar10/attacks.sh | 37 ++ .../pytorch/cifar10/conf/attack/default.yaml | 9 + examples/pytorch/cifar10/conf/cifar10.yaml | 44 ++ examples/pytorch/cifar10/conf/compile.yaml | 32 ++ .../pytorch/cifar10/conf/data/default.yaml | 2 + .../cifar10/conf/data/torch_cifar.yaml | 11 + .../cifar10/conf/data/torch_mnist.yaml | 15 + .../pytorch/cifar10/conf/files/cifar.yaml | 21 + .../pytorch/cifar10/conf/files/mnist.yaml | 21 + .../cifar10/conf/model/art/default.yaml | 7 + .../conf/model/art/initialize/default.yaml | 7 + .../model/art/postprocessor/confidence.yaml | 3 + .../model/art/postprocessor/gauss-out.yaml | 2 + .../conf/model/art/preprocessor/default.yaml | 0 .../conf/model/art/preprocessor/fsq.yaml | 3 + .../conf/model/art/preprocessor/gauss-in.yaml | 4 + .../cifar10/conf/model/torch_cifar.yaml | 13 + .../cifar10/conf/model/torch_mnist.yaml | 13 + examples/pytorch/cifar10/conf/plots.yaml | 250 ++++++++++ .../pytorch/cifar10/conf/scorers/default.yaml | 9 + examples/pytorch/cifar10/dvc.lock | 453 ++++++++++++++++++ examples/pytorch/cifar10/dvc.yaml | 120 +++++ examples/pytorch/cifar10/models.sh | 38 ++ examples/pytorch/cifar10/torch_example.py | 79 +++ 28 files changed, 1202 insertions(+) create mode 100644 examples/pytorch/cifar10/.dvc/.gitignore create mode 100644 examples/pytorch/cifar10/.dvc/config create mode 100644 examples/pytorch/cifar10/.dvcignore create mode 100644 examples/pytorch/cifar10/.gitignore create mode 100644 examples/pytorch/cifar10/attacks.sh create mode 100644 examples/pytorch/cifar10/conf/attack/default.yaml create mode 100644 examples/pytorch/cifar10/conf/cifar10.yaml create mode 100644 examples/pytorch/cifar10/conf/compile.yaml create mode 100644 examples/pytorch/cifar10/conf/data/default.yaml create mode 100644 examples/pytorch/cifar10/conf/data/torch_cifar.yaml create mode 100644 examples/pytorch/cifar10/conf/data/torch_mnist.yaml create mode 100644 examples/pytorch/cifar10/conf/files/cifar.yaml create mode 100644 examples/pytorch/cifar10/conf/files/mnist.yaml create mode 100644 examples/pytorch/cifar10/conf/model/art/default.yaml create mode 100644 examples/pytorch/cifar10/conf/model/art/initialize/default.yaml create mode 100644 examples/pytorch/cifar10/conf/model/art/postprocessor/confidence.yaml create mode 100644 examples/pytorch/cifar10/conf/model/art/postprocessor/gauss-out.yaml create mode 100644 examples/pytorch/cifar10/conf/model/art/preprocessor/default.yaml create mode 100644 examples/pytorch/cifar10/conf/model/art/preprocessor/fsq.yaml create mode 100644 examples/pytorch/cifar10/conf/model/art/preprocessor/gauss-in.yaml create mode 100644 examples/pytorch/cifar10/conf/model/torch_cifar.yaml create mode 100644 examples/pytorch/cifar10/conf/model/torch_mnist.yaml create mode 100644 examples/pytorch/cifar10/conf/plots.yaml create mode 100644 examples/pytorch/cifar10/conf/scorers/default.yaml create mode 100644 examples/pytorch/cifar10/dvc.lock create mode 100644 examples/pytorch/cifar10/dvc.yaml create mode 100644 examples/pytorch/cifar10/models.sh create mode 100644 examples/pytorch/cifar10/torch_example.py diff --git a/examples/pytorch/cifar10/.dvc/.gitignore b/examples/pytorch/cifar10/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/pytorch/cifar10/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/pytorch/cifar10/.dvc/config b/examples/pytorch/cifar10/.dvc/config new file mode 100644 index 00000000..e69de29b diff --git a/examples/pytorch/cifar10/.dvcignore b/examples/pytorch/cifar10/.dvcignore new file mode 100644 index 00000000..51973055 --- /dev/null +++ b/examples/pytorch/cifar10/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/examples/pytorch/cifar10/.gitignore b/examples/pytorch/cifar10/.gitignore new file mode 100644 index 00000000..af0b54a3 --- /dev/null +++ b/examples/pytorch/cifar10/.gitignore @@ -0,0 +1,3 @@ +20_epochs/ +multirun/ +cifar/ \ No newline at end of file diff --git a/examples/pytorch/cifar10/attacks.sh b/examples/pytorch/cifar10/attacks.sh new file mode 100644 index 00000000..8ec1f079 --- /dev/null +++ b/examples/pytorch/cifar10/attacks.sh @@ -0,0 +1,37 @@ +# #!/bin/bash + +# # This script is used to generate the attacks for the example. + +# Fast Gradient Method +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 stage=attack ++hydra.sweeper.study_name=fgm ++direction=maximize $@ + +# Projected Gradient Descent +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 stage=attack ++hydra.sweeper.study_name=pgd ++direction=maximize $@ + +# DeepFool +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=1,3,5,10 stage=attack ++hydra.sweeper.study_name=deep ++direction=maximize $@ + +# HopSkipJump +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 stage=attack ++hydra.sweeper.study_name=hsj ++direction=maximize $@ + +# ##################################################### +# PixelAttack +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=pixel ++direction=maximize $@ + +# ThresholdAttack +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=thresh ++direction=maximize $@ + +# # AdversarialPatch +# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 stage=patch ++hydra.sweeper.study_name=attack ++direction=maximize ++attack.init.patch_shape=[1,28,28] $@ +##################################################### + +# # Carlini L0 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw0 ++hydra.sweeper.study_name=cw0 ++direction=maximize $@ + +# # Carlini L2 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw2 ++hydra.sweeper.study_name=cw2 ++direction=maximize $@ + +# # Carlini LInf Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=attack ++hydra.sweeper.study_name=cwinf ++direction=maximize $@ + +rm -rf output/models/* diff --git a/examples/pytorch/cifar10/conf/attack/default.yaml b/examples/pytorch/cifar10/conf/attack/default.yaml new file mode 100644 index 00000000..76b4d62d --- /dev/null +++ b/examples/pytorch/cifar10/conf/attack/default.yaml @@ -0,0 +1,9 @@ +data: ${data} +model: ${model} +_target_ : deckard.base.attack.Attack +init: + model: ${model} + _target_: deckard.base.attack.AttackInitializer + name: art.attacks.evasion.HopSkipJump +attack_size : 10 +method : evasion diff --git a/examples/pytorch/cifar10/conf/cifar10.yaml b/examples/pytorch/cifar10/conf/cifar10.yaml new file mode 100644 index 00000000..474fdfc6 --- /dev/null +++ b/examples/pytorch/cifar10/conf/cifar10.yaml @@ -0,0 +1,44 @@ +defaults: + - _self_ + - data: torch_cifar + - model: torch_cifar + - attack: default + - files: cifar + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +stage : '???' +direction : "maximize" +_target_ : deckard.base.experiment.Experiment +optimizers : accuracy +hydra: + run: + dir: ${files.directory}/${files.reports}/${stage}/logs + sweep: + dir: ${files.directory}/${stage}/${model.init.name} + subdir : ${hydra.sweeper.study_name}/${hydra.job.num} + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + direction: ${direction} + study_name: control + storage: sqlite:///model.db + n_jobs: ${hydra.launcher.n_jobs} + n_trials: 32 + params: + ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) + ++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, .0001, .00001, 0.000001) + ++model.trainer.nb_epoch: choice(1, 10, 30, 50, 100) + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 8 + prefer : processes + verbose: 10 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/pytorch/cifar10/conf/compile.yaml b/examples/pytorch/cifar10/conf/compile.yaml new file mode 100644 index 00000000..52106b1c --- /dev/null +++ b/examples/pytorch/cifar10/conf/compile.yaml @@ -0,0 +1,32 @@ +attacks: + # CarliniL0Method: CW_0 + # CarliniL2Method: CW_2 + # CarliniLInfMethod: CW_inf + DeepFool: Deep + FastGradientMethod: FGM + HopSkipJump: HSJ + PixelAttack: Pixel + ProjectedGradientDescent: PGD + ThresholdAttack: Thresh +defences: + Control: Control + FeatureSqueezing: FSQ + GaussianAugmentation: Gauss-in + GaussianNoise: Gauss-out + HighConfidence: Conf +params: + # art.attacks.evasion.CarliniL0Method: attack.init.confidence + # art.attacks.evasion.CarliniL2Method: attack.init.confidence + # art.attacks.evasion.CarliniLInfMethod: attack.init.confidence + Deep: attack.init.nb_grads + FGM: attack.init.eps + HSJ: attack.init.max_iter + Pixel: attack.init.th + PGD: attack.init.eps + Thresh: attack.init.th + Gauss-out: model.art.pipeline.postprocessor.scale + Conf: model.art.pipeline.postprocessor.cutoff + FSQ: model.art.pipeline.preprocessor.bit_depth + Gauss-in: model.art.pipeline.preprocessor.sigma + Depth: model_layers + Epochs: model.train.nb_epoch \ No newline at end of file diff --git a/examples/pytorch/cifar10/conf/data/default.yaml b/examples/pytorch/cifar10/conf/data/default.yaml new file mode 100644 index 00000000..5eb78278 --- /dev/null +++ b/examples/pytorch/cifar10/conf/data/default.yaml @@ -0,0 +1,2 @@ +defaults: + - torch_mnist diff --git a/examples/pytorch/cifar10/conf/data/torch_cifar.yaml b/examples/pytorch/cifar10/conf/data/torch_cifar.yaml new file mode 100644 index 00000000..b7603125 --- /dev/null +++ b/examples/pytorch/cifar10/conf/data/torch_cifar.yaml @@ -0,0 +1,11 @@ +_target_: deckard.base.data.Data +generate: + name: torch_cifar10 +sample: + random_state : 0 + stratify: True +sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/pytorch/cifar10/conf/data/torch_mnist.yaml b/examples/pytorch/cifar10/conf/data/torch_mnist.yaml new file mode 100644 index 00000000..9d79c036 --- /dev/null +++ b/examples/pytorch/cifar10/conf/data/torch_mnist.yaml @@ -0,0 +1,15 @@ +_target_: deckard.base.data.Data +generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist +sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True +sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/pytorch/cifar10/conf/files/cifar.yaml b/examples/pytorch/cifar10/conf/files/cifar.yaml new file mode 100644 index 00000000..e43cab72 --- /dev/null +++ b/examples/pytorch/cifar10/conf/files/cifar.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: cifar +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pt +params_file : params.yaml +# train_labels_file : train_labels.json +# test_labels_file : test_labels.json +# probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +# adv_probabilities_file: adv_probabilities.json +name: default diff --git a/examples/pytorch/cifar10/conf/files/mnist.yaml b/examples/pytorch/cifar10/conf/files/mnist.yaml new file mode 100644 index 00000000..efdb1c42 --- /dev/null +++ b/examples/pytorch/cifar10/conf/files/mnist.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: mnist +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pt +params_file : params.yaml +# train_labels_file : train_labels.json +# test_labels_file : test_labels.json +# probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +# adv_probabilities_file: adv_probabilities.json +name: default diff --git a/examples/pytorch/cifar10/conf/model/art/default.yaml b/examples/pytorch/cifar10/conf/model/art/default.yaml new file mode 100644 index 00000000..4251627d --- /dev/null +++ b/examples/pytorch/cifar10/conf/model/art/default.yaml @@ -0,0 +1,7 @@ +defaults: + - initialize : default + # - preprocessor: fsq + # - postprocessor: confidence +data : ${..data} +library : ${..library} +_target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/pytorch/cifar10/conf/model/art/initialize/default.yaml b/examples/pytorch/cifar10/conf/model/art/initialize/default.yaml new file mode 100644 index 00000000..40e7983e --- /dev/null +++ b/examples/pytorch/cifar10/conf/model/art/initialize/default.yaml @@ -0,0 +1,7 @@ +criterion: + name : torch.nn.CrossEntropyLoss +optimizer: + name : torch.optim.SGD + lr : 0.01 + momentum : 0.9 +clip_values : [0.0, 255.0] diff --git a/examples/pytorch/cifar10/conf/model/art/postprocessor/confidence.yaml b/examples/pytorch/cifar10/conf/model/art/postprocessor/confidence.yaml new file mode 100644 index 00000000..6fcdde95 --- /dev/null +++ b/examples/pytorch/cifar10/conf/model/art/postprocessor/confidence.yaml @@ -0,0 +1,3 @@ + +name : art.defences.postprocessor.HighConfidence +cutoff : .1 diff --git a/examples/pytorch/cifar10/conf/model/art/postprocessor/gauss-out.yaml b/examples/pytorch/cifar10/conf/model/art/postprocessor/gauss-out.yaml new file mode 100644 index 00000000..c912ebd9 --- /dev/null +++ b/examples/pytorch/cifar10/conf/model/art/postprocessor/gauss-out.yaml @@ -0,0 +1,2 @@ +name : art.defences.postprocessor.GaussianNoise +scale : 1 diff --git a/examples/pytorch/cifar10/conf/model/art/preprocessor/default.yaml b/examples/pytorch/cifar10/conf/model/art/preprocessor/default.yaml new file mode 100644 index 00000000..e69de29b diff --git a/examples/pytorch/cifar10/conf/model/art/preprocessor/fsq.yaml b/examples/pytorch/cifar10/conf/model/art/preprocessor/fsq.yaml new file mode 100644 index 00000000..27ddf58b --- /dev/null +++ b/examples/pytorch/cifar10/conf/model/art/preprocessor/fsq.yaml @@ -0,0 +1,3 @@ +name : art.defences.preprocessor.FeatureSqueezing +clip_values : ${model.art.initialize.clip_values} +bit_depth : 64 diff --git a/examples/pytorch/cifar10/conf/model/art/preprocessor/gauss-in.yaml b/examples/pytorch/cifar10/conf/model/art/preprocessor/gauss-in.yaml new file mode 100644 index 00000000..c438e8f5 --- /dev/null +++ b/examples/pytorch/cifar10/conf/model/art/preprocessor/gauss-in.yaml @@ -0,0 +1,4 @@ +name : art.defences.preprocessor.GaussianAugmentation +clip_values : ${model.art.initialize.clip_values} +sigma : 1 +ratio : .5 diff --git a/examples/pytorch/cifar10/conf/model/torch_cifar.yaml b/examples/pytorch/cifar10/conf/model/torch_cifar.yaml new file mode 100644 index 00000000..5ca2e24e --- /dev/null +++ b/examples/pytorch/cifar10/conf/model/torch_cifar.yaml @@ -0,0 +1,13 @@ +defaults: + - art : default +data: ${data} +init: + _target_: deckard.base.model.ModelInitializer + name : torch_example.ResNet18 + num_channels: 3 + num_classes: 10 +_target_: deckard.base.model.Model +trainer: + nb_epoch: 100 + batch_size: 1024 +library : pytorch diff --git a/examples/pytorch/cifar10/conf/model/torch_mnist.yaml b/examples/pytorch/cifar10/conf/model/torch_mnist.yaml new file mode 100644 index 00000000..762addc1 --- /dev/null +++ b/examples/pytorch/cifar10/conf/model/torch_mnist.yaml @@ -0,0 +1,13 @@ + +defaults: + - art : default +data: ${data} +init: + _target_: deckard.base.model.ModelInitializer + num_channels : 1 + name : torch_example.ResNet18 +_target_: deckard.base.model.Model +trainer: + nb_epoch: 20 + batch_size: 1024 +library : pytorch diff --git a/examples/pytorch/cifar10/conf/plots.yaml b/examples/pytorch/cifar10/conf/plots.yaml new file mode 100644 index 00000000..accab9df --- /dev/null +++ b/examples/pytorch/cifar10/conf/plots.yaml @@ -0,0 +1,250 @@ +cat_plot: +- file: adv_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: linear + titles: $\lambda_{adv.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_accuracy + ylabels: $\lambda_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + titles: $\lambda_{ben.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: accuracy + ylabels: $\lambda_{ben.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: log + titles: $\bar{C}_{ben.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_failure + ylabels: $\bar{C}_{ben.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: log + titles: $\bar{C}_{adv.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_adv_failure + ylabels: $\bar{C}_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_train_time_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: $\bar{C}_{adv.}$ vs Attack Type + x: atk_gen + xlabels: Attack Type + y: training_time_per_adv_failure + ylabels: $\bar{C}_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_test_time_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: $h_{adv}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_failure_rate + ylabels: $h_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +# - file: adv_accuracy_vs_defence_type.pdf +# hue: model_name +# kind: boxen +# legend_title: Model Name +# titles: $\lambda_{adv.}$ vs Defence Type +# x: def_gen +# xlabels: Defence Type +# y: adv_accuracy +# ylabels: Adv. $\lambda_{ben.}$ +# rotation : 90 +# hue_order: +# - ResNet18 +# - ResNet34 +# - ResNet50 +# - ResNet101 +# - ResNet152 +- file: adv_accuracy_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: $\lambda_{adv.}$ vs Attack Type + x: atk_gen + xlabels: Attack Type + y: adv_accuracy + ylabels: $\lambda_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_failure_rate_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: $h_{ben}(t; \theta)$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: failure_rate + ylabels: $h_{ben}(t; \theta)$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +line_plot: +# - file: def_param_vs_accuracy.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $\lambda_{ben.}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: accuracy +# y_scale: +# ylabel: $\lambda_{ben.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +# - file: def_param_vs_adv_accuracy.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $\lambda_{adv.}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: adv_accuracy +# y_scale: +# ylabel: $\lambda_{adv.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +# - file: def_param_vs_adv_failure_rate.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $h_{adv}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: adv_failure_rate +# y_scale: log +# ylabel: $h_{adv.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +- file: atk_param_vs_accuracy.pdf + hue: atk_gen + legend: {bbox_to_anchor: [1.05, 1]} + title: $\lambda_{adv.}$ vs Attack Strength + x: atk_value + x_scale: linear + xlabel: Attack Control Parameter + y: adv_accuracy + y_scale: + ylabel: $\lambda_{adv.}$ + hue_order: + - FGM + - PGD + - Deep + - HSJ + - Pixel + - Thresh + +scatter_plot: +- x: train_time_per_sample + y: adv_failure_rate + hue: model_name + xlabel: $t_{train}$ + ylabel: $h_{adv}$ + title: $h_{adv}$ vs $t_{train}$ + file: adv_failure_rate_vs_train_time.pdf + y_scale: log + x_scale: log + legend: + title: Model Name + bbox_to_anchor: [1.05, 1] + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 + # style : atk_gen +# - x: train_time_per_sample +# y: adv_failure_rate +# hue: model_name +# xlabel: $t_{train}$ +# ylabel: $h_{adv}$ +# title: $h_{adv}$ vs $t_{train}$ for each Defence +# file: adv_failure_rate_vs_train_time_def.pdf +# y_scale: log +# x_scale: linear +# legend: +# title: Model Name +# # style : def_gen diff --git a/examples/pytorch/cifar10/conf/scorers/default.yaml b/examples/pytorch/cifar10/conf/scorers/default.yaml new file mode 100644 index 00000000..4503c5bf --- /dev/null +++ b/examples/pytorch/cifar10/conf/scorers/default.yaml @@ -0,0 +1,9 @@ +_target_: deckard.base.scorer.ScorerDict +accuracy: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.accuracy_score + direction: maximize +log_loss: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.log_loss + direction: minimize diff --git a/examples/pytorch/cifar10/dvc.lock b/examples/pytorch/cifar10/dvc.lock new file mode 100644 index 00000000..95038e8d --- /dev/null +++ b/examples/pytorch/cifar10/dvc.lock @@ -0,0 +1,453 @@ +schema: '2.0' +stages: + train: + cmd: python -m deckard.layers.experiment train --config_file cifar10.yaml + params: + params.yaml: + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: cifar + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 255.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 10 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: cifar/data/data.pkl + md5: 6503fed5d4e6cc1163898c0ab6a863dd + size: 739680311 + - path: cifar/models/model.optimizer.pt + hash: md5 + md5: d46598fb7feec074c02bd0ed081184da + size: 44805933 + - path: cifar/models/model.pt + hash: md5 + md5: f5d11f93160ad27b8468efc0d71eb695 + size: 44811029 + - path: cifar/reports/train/default/predictions.json + hash: md5 + md5: 37f581850d9f6d491cb0d9025e620bf9 + size: 2439094 + - path: cifar/reports/train/default/score_dict.json + hash: md5 + md5: 055f95d856bc09b533eccb57314db0c4 + size: 397 + attack: + cmd: python -m deckard.layers.experiment attack --config_file cifar10.yaml + deps: + - path: cifar/data/data.pkl + hash: md5 + md5: 6503fed5d4e6cc1163898c0ab6a863dd + size: 739680311 + - path: cifar/models/model.pt + hash: md5 + md5: f5d11f93160ad27b8468efc0d71eb695 + size: 44811029 + params: + params.yaml: + attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.attack.AttackInitializer + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 255.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 10 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 255.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 10 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: cifar + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0.0 + - 255.0 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar10 + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 10 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 100 + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: cifar/attacks/attack.pkl + hash: md5 + md5: 4c6d7b56c319a2a3a8f4288873141a44 + size: 123046 + - path: cifar/reports/attack/default/adv_predictions.json + hash: md5 + md5: 0e905b6a95defafe1472cd1d329ed124 + size: 2136 + - path: cifar/reports/attack/default/score_dict.json + hash: md5 + md5: f40b5d8125bf8b6370a94fe65d43cffa + size: 458 + attacks@ResNet101: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet101 + stage=attack ++hydra.sweeper.storage=sqlite:///cifar/reports/attack/ResNet101.db + --config-name cifar10.yaml + deps: + - path: attacks.sh + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: cifar/reports/attack/default/score_dict.json + hash: md5 + md5: f40b5d8125bf8b6370a94fe65d43cffa + size: 458 + - path: models.sh + hash: md5 + md5: 02a4961b4afe7ba84c41e9ad49c30c83 + size: 2760 + outs: + - path: cifar/reports/attack/ResNet101.db + hash: md5 + md5: 268500e55100c8e2c0de628e8b66b612 + size: 819200 + attacks@ResNet152: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet152 + stage=attack ++hydra.sweeper.storage=sqlite:///cifar/reports/attack/ResNet152.db + --config-name cifar10.yaml + deps: + - path: attacks.sh + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: cifar/reports/attack/default/score_dict.json + hash: md5 + md5: f40b5d8125bf8b6370a94fe65d43cffa + size: 458 + - path: models.sh + hash: md5 + md5: 02a4961b4afe7ba84c41e9ad49c30c83 + size: 2760 + outs: + - path: cifar/reports/attack/ResNet152.db + hash: md5 + md5: 47684cf7d10b05f6343f58579fd05af3 + size: 249856 + attacks@ResNet18: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 + stage=attack ++hydra.sweeper.storage=sqlite:///cifar/reports/attack/ResNet18.db + --config-name cifar10.yaml + deps: + - path: attacks.sh + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: cifar/reports/attack/default/score_dict.json + hash: md5 + md5: f40b5d8125bf8b6370a94fe65d43cffa + size: 458 + - path: models.sh + hash: md5 + md5: 02a4961b4afe7ba84c41e9ad49c30c83 + size: 2760 + outs: + - path: cifar/reports/attack/ResNet18.db + hash: md5 + md5: bf2b93a31c49e96b219c23095504a7f1 + size: 819200 + attacks@ResNet34: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 + stage=attack ++hydra.sweeper.storage=sqlite:///cifar/reports/attack/ResNet34.db + --config-name cifar10.yaml + deps: + - path: attacks.sh + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: cifar/reports/attack/default/score_dict.json + hash: md5 + md5: f40b5d8125bf8b6370a94fe65d43cffa + size: 458 + - path: models.sh + hash: md5 + md5: 02a4961b4afe7ba84c41e9ad49c30c83 + size: 2760 + outs: + - path: cifar/reports/attack/ResNet34.db + hash: md5 + md5: 8de8f4dfcda52bb40f206cf3c4977dd5 + size: 819200 + attacks@ResNet50: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 + stage=attack ++hydra.sweeper.storage=sqlite:///cifar/reports/attack/ResNet50.db + --config-name cifar10.yaml + deps: + - path: attacks.sh + hash: md5 + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: cifar/reports/attack/default/score_dict.json + hash: md5 + md5: f40b5d8125bf8b6370a94fe65d43cffa + size: 458 + - path: models.sh + hash: md5 + md5: 02a4961b4afe7ba84c41e9ad49c30c83 + size: 2760 + outs: + - path: cifar/reports/attack/ResNet50.db + hash: md5 + md5: 8adabcf8a15b13fc20ea31f58ae7388b + size: 1069056 diff --git a/examples/pytorch/cifar10/dvc.yaml b/examples/pytorch/cifar10/dvc.yaml new file mode 100644 index 00000000..54d84654 --- /dev/null +++ b/examples/pytorch/cifar10/dvc.yaml @@ -0,0 +1,120 @@ +stages: + train: + cmd: python -m deckard.layers.experiment train --config_file cifar10.yaml + params: + - data + - model + - scorers + - files + outs: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} # Omit to save space + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} # logit outputs for our model + # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} # Omit to save space + metrics: + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + attack: + cmd: python -m deckard.layers.experiment attack --config_file cifar10.yaml + params: + - data + - model + - attack + - scorers + - files + outs: + - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + # - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + deps: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + metrics: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + + ############################################################################## + # models: # This is a loop over the ResNet models + # foreach: + # - ResNet18 + # # - ResNet34 + # # - ResNet50 + # # - ResNet101 + # # - ResNet152 + # do: # This script configures eazch defence + # cmd: bash models.sh ++model.init.name=torch_example.${item} stage=train ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/train/${item}.db --config-name cifar10.yaml + # deps: + # - models.sh + # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + # - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} + # outs: + # - ${files.directory}/${files.reports}/train/${item}.db: # This outputs a database file for each model + # cache: True + # persist: True + attacks: + foreach: # This is a loop over the ResNet models + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 + do: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/attack/${item}.db --config-name cifar10.yaml + deps: + - models.sh # This script configures each defence + - attacks.sh # This script configures each attack + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} # This is here just to ensure it runs after the attack stage + # - ${files.directory}/${files.reports}/train/${item}.db + outs: + - ${files.directory}/${files.reports}/attack/${item}.db: # This outputs a database file for each model + cache: True + persist: True + compile: + foreach: # iterates through each stage + # - train + - attack + do: + cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv + deps: + - ${files.directory}/${files.reports}/${item}/ + - ${files.directory}/${files.reports}/${item}/ResNet18.db + - ${files.directory}/${files.reports}/${item}/ResNet34.db + - ${files.directory}/${files.reports}/${item}/ResNet50.db + - ${files.directory}/${files.reports}/${item}/ResNet101.db + # - ${files.directory}/${files.reports}/${item}/ResNet152.db + outs: + - ${files.directory}/${files.reports}/${item}.csv + plot: + cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv -o data.csv + deps: + - ${files.directory}/${files.reports}/attack.csv + - ${files.directory}/${files.reports}/attack/ResNet18.db + # - ${files.directory}/${files.reports}/attack/ResNet34.db + # - ${files.directory}/${files.reports}/attack/ResNet50.db + # - ${files.directory}/${files.reports}/attack/ResNet101.db + # - ${files.directory}/${files.reports}/attack/ResNet152.db + outs: + - ${files.directory}/plots/data.csv + afr: + cmd: python -m deckard.layers.afr --dataset ${files.directory} --file ${files.directory}/plots/data.csv + deps: + - ${files.directory}/plots/data.csv + plots: + - ${files.directory}/plots/weibull_aft.pdf + - ${files.directory}/plots/weibull_partial_effects.pdf + - ${files.directory}/plots/cox_partial_effects.pdf + - ${files.directory}/plots/cox_aft.pdf + - ${files.directory}/plots/log_logistic_aft.pdf + - ${files.directory}/plots/log_logistic_partial_effects.pdf + - ${files.directory}/plots/log_normal_aft.pdf + - ${files.directory}/plots/log_normal_partial_effects.pdf + metrics: + - ${files.directory}/plots/aft_comparison.csv + outs: + - ${files.directory}/plots/aft_comparison.tex + copy_results: + cmd: cp -r ${files.directory}/plots/* ~/ml_afr/cifar10/ + deps: + - ${files.directory}/plots/data.csv + - ${files.directory}/plots/aft_comparison.csv diff --git a/examples/pytorch/cifar10/models.sh b/examples/pytorch/cifar10/models.sh new file mode 100644 index 00000000..8d64d588 --- /dev/null +++ b/examples/pytorch/cifar10/models.sh @@ -0,0 +1,38 @@ +#!/bin/bash + +# This script is used to generate the models for the sklearn example. + +# # Default model +echo "python -m deckard.layers.optimise " $@ "--multirun" +python -m deckard.layers.optimise $@ --multirun + +# # This line generates the model and adds the FeatureSqueezing preprocessing defence. +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,255] ++hydra.sweeper.study_name=FSQ $@ --multirun + +# # # Gaussian Augmentation (Input) +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.5 +model.art.preprocessor.params.augmentation=False ++hydra.sweeper.study_name=gauss-in $@ --multirun + +# # # # Gaussian Noise (Output) +# python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 ++hydra.sweeper.study_name=gauss-out $@ --multirun + +# # # # High Confidence +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 ++hydra.sweeper.study_name=conf $@ --multirun +#!/bin/bash + +# This script is used to generate the models for the sklearn example. + +# # Default model +echo "python -m deckard.layers.optimise " $@ "--multirun" +python -m deckard.layers.optimise $@ --multirun + +# # This line generates the model and adds the FeatureSqueezing preprocessing defence. +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,255] ++hydra.sweeper.study_name=FSQ $@ --multirun + +# # # Gaussian Augmentation (Input) +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.5 +model.art.preprocessor.params.augmentation=False ++hydra.sweeper.study_name=gauss-in $@ --multirun + +# # # # Gaussian Noise (Output) +# python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 ++hydra.sweeper.study_name=gauss-out $@ --multirun + +# # # # High Confidence +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 ++hydra.sweeper.study_name=conf $@ --multirun diff --git a/examples/pytorch/cifar10/torch_example.py b/examples/pytorch/cifar10/torch_example.py new file mode 100644 index 00000000..32d333bd --- /dev/null +++ b/examples/pytorch/cifar10/torch_example.py @@ -0,0 +1,79 @@ +import torch.nn as nn +from torchvision import models + +__all__ = [ + "ResNet18", + "ResNet50", + "ResNet101", + "ResNet152", +] + + +def ResNet18(num_channels=1, num_classes=10): + model = models.resnet18(pretrained=True) + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(512, num_classes) + return model + + +def ResNet34(num_channels=1, num_classes=10): + model = models.resnet34(pretrained=True) + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(512, num_classes) + return model + + +def ResNet50(num_channels=1, num_classes=10): + model = models.resnet50(pretrained=True) + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model + + +def ResNet101(num_channels=1, num_classes=10): + model = models.resnet101(pretrained=True) + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model + + +def ResNet152(num_channels=1, num_classes=10): + model = models.resnet152(pretrained=True) + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model From 129a6e0d1d067d04adfba05fe5a6a805b33aa315 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 18:11:05 +0000 Subject: [PATCH 128/148] moved pytorch cifar100 folder --- examples/pytorch/cifar100/.dvc/.gitignore | 3 + examples/pytorch/cifar100/.dvc/config | 0 examples/pytorch/cifar100/.dvcignore | 3 + examples/pytorch/cifar100/.gitignore | 3 + examples/pytorch/cifar100/attacks.sh | 37 ++ .../pytorch/cifar100/conf/attack/default.yaml | 9 + examples/pytorch/cifar100/conf/cifar100.yaml | 44 ++ examples/pytorch/cifar100/conf/compile.yaml | 32 ++ .../pytorch/cifar100/conf/data/default.yaml | 2 + .../cifar100/conf/data/torch_cifar100.yaml | 12 + .../cifar100/conf/data/torch_mnist.yaml | 15 + .../pytorch/cifar100/conf/deploy/default.yaml | 14 + .../pytorch/cifar100/conf/deploy/pod.yaml | 30 ++ .../pytorch/cifar100/conf/deploy/pvc.yaml | 11 + .../pytorch/cifar100/conf/deploy/sclass.yaml | 10 + .../pytorch/cifar100/conf/files/cifar100.yaml | 21 + .../pytorch/cifar100/conf/files/mnist.yaml | 21 + .../cifar100/conf/model/art/default.yaml | 7 + .../conf/model/art/initialize/default.yaml | 7 + .../model/art/postprocessor/confidence.yaml | 3 + .../model/art/postprocessor/gauss-out.yaml | 2 + .../conf/model/art/preprocessor/default.yaml | 0 .../conf/model/art/preprocessor/fsq.yaml | 3 + .../conf/model/art/preprocessor/gauss-in.yaml | 4 + .../cifar100/conf/model/torch_cifar100.yaml | 13 + .../cifar100/conf/model/torch_mnist.yaml | 13 + examples/pytorch/cifar100/conf/plots.yaml | 250 +++++++++++ .../cifar100/conf/scorers/default.yaml | 9 + examples/pytorch/cifar100/dvc.lock | 417 ++++++++++++++++++ examples/pytorch/cifar100/dvc.yaml | 120 +++++ examples/pytorch/cifar100/models.sh | 19 + examples/pytorch/cifar100/torch_example.py | 79 ++++ 32 files changed, 1213 insertions(+) create mode 100644 examples/pytorch/cifar100/.dvc/.gitignore create mode 100644 examples/pytorch/cifar100/.dvc/config create mode 100644 examples/pytorch/cifar100/.dvcignore create mode 100644 examples/pytorch/cifar100/.gitignore create mode 100644 examples/pytorch/cifar100/attacks.sh create mode 100644 examples/pytorch/cifar100/conf/attack/default.yaml create mode 100644 examples/pytorch/cifar100/conf/cifar100.yaml create mode 100644 examples/pytorch/cifar100/conf/compile.yaml create mode 100644 examples/pytorch/cifar100/conf/data/default.yaml create mode 100644 examples/pytorch/cifar100/conf/data/torch_cifar100.yaml create mode 100644 examples/pytorch/cifar100/conf/data/torch_mnist.yaml create mode 100644 examples/pytorch/cifar100/conf/deploy/default.yaml create mode 100644 examples/pytorch/cifar100/conf/deploy/pod.yaml create mode 100644 examples/pytorch/cifar100/conf/deploy/pvc.yaml create mode 100644 examples/pytorch/cifar100/conf/deploy/sclass.yaml create mode 100644 examples/pytorch/cifar100/conf/files/cifar100.yaml create mode 100644 examples/pytorch/cifar100/conf/files/mnist.yaml create mode 100644 examples/pytorch/cifar100/conf/model/art/default.yaml create mode 100644 examples/pytorch/cifar100/conf/model/art/initialize/default.yaml create mode 100644 examples/pytorch/cifar100/conf/model/art/postprocessor/confidence.yaml create mode 100644 examples/pytorch/cifar100/conf/model/art/postprocessor/gauss-out.yaml create mode 100644 examples/pytorch/cifar100/conf/model/art/preprocessor/default.yaml create mode 100644 examples/pytorch/cifar100/conf/model/art/preprocessor/fsq.yaml create mode 100644 examples/pytorch/cifar100/conf/model/art/preprocessor/gauss-in.yaml create mode 100644 examples/pytorch/cifar100/conf/model/torch_cifar100.yaml create mode 100644 examples/pytorch/cifar100/conf/model/torch_mnist.yaml create mode 100644 examples/pytorch/cifar100/conf/plots.yaml create mode 100644 examples/pytorch/cifar100/conf/scorers/default.yaml create mode 100644 examples/pytorch/cifar100/dvc.lock create mode 100644 examples/pytorch/cifar100/dvc.yaml create mode 100644 examples/pytorch/cifar100/models.sh create mode 100644 examples/pytorch/cifar100/torch_example.py diff --git a/examples/pytorch/cifar100/.dvc/.gitignore b/examples/pytorch/cifar100/.dvc/.gitignore new file mode 100644 index 00000000..528f30c7 --- /dev/null +++ b/examples/pytorch/cifar100/.dvc/.gitignore @@ -0,0 +1,3 @@ +/config.local +/tmp +/cache diff --git a/examples/pytorch/cifar100/.dvc/config b/examples/pytorch/cifar100/.dvc/config new file mode 100644 index 00000000..e69de29b diff --git a/examples/pytorch/cifar100/.dvcignore b/examples/pytorch/cifar100/.dvcignore new file mode 100644 index 00000000..51973055 --- /dev/null +++ b/examples/pytorch/cifar100/.dvcignore @@ -0,0 +1,3 @@ +# Add patterns of files dvc should ignore, which could improve +# the performance. Learn more at +# https://dvc.org/doc/user-guide/dvcignore diff --git a/examples/pytorch/cifar100/.gitignore b/examples/pytorch/cifar100/.gitignore new file mode 100644 index 00000000..de9b09c5 --- /dev/null +++ b/examples/pytorch/cifar100/.gitignore @@ -0,0 +1,3 @@ +cifar100/ +20_epochs/ +cifar-100-python/ \ No newline at end of file diff --git a/examples/pytorch/cifar100/attacks.sh b/examples/pytorch/cifar100/attacks.sh new file mode 100644 index 00000000..8ec1f079 --- /dev/null +++ b/examples/pytorch/cifar100/attacks.sh @@ -0,0 +1,37 @@ +# #!/bin/bash + +# # This script is used to generate the attacks for the example. + +# Fast Gradient Method +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 stage=attack ++hydra.sweeper.study_name=fgm ++direction=maximize $@ + +# Projected Gradient Descent +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 stage=attack ++hydra.sweeper.study_name=pgd ++direction=maximize $@ + +# DeepFool +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=1,3,5,10 stage=attack ++hydra.sweeper.study_name=deep ++direction=maximize $@ + +# HopSkipJump +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 stage=attack ++hydra.sweeper.study_name=hsj ++direction=maximize $@ + +# ##################################################### +# PixelAttack +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=pixel ++direction=maximize $@ + +# ThresholdAttack +bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=thresh ++direction=maximize $@ + +# # AdversarialPatch +# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 stage=patch ++hydra.sweeper.study_name=attack ++direction=maximize ++attack.init.patch_shape=[1,28,28] $@ +##################################################### + +# # Carlini L0 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw0 ++hydra.sweeper.study_name=cw0 ++direction=maximize $@ + +# # Carlini L2 Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw2 ++hydra.sweeper.study_name=cw2 ++direction=maximize $@ + +# # Carlini LInf Method +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=attack ++hydra.sweeper.study_name=cwinf ++direction=maximize $@ + +rm -rf output/models/* diff --git a/examples/pytorch/cifar100/conf/attack/default.yaml b/examples/pytorch/cifar100/conf/attack/default.yaml new file mode 100644 index 00000000..76b4d62d --- /dev/null +++ b/examples/pytorch/cifar100/conf/attack/default.yaml @@ -0,0 +1,9 @@ +data: ${data} +model: ${model} +_target_ : deckard.base.attack.Attack +init: + model: ${model} + _target_: deckard.base.attack.AttackInitializer + name: art.attacks.evasion.HopSkipJump +attack_size : 10 +method : evasion diff --git a/examples/pytorch/cifar100/conf/cifar100.yaml b/examples/pytorch/cifar100/conf/cifar100.yaml new file mode 100644 index 00000000..541b92dd --- /dev/null +++ b/examples/pytorch/cifar100/conf/cifar100.yaml @@ -0,0 +1,44 @@ +defaults: + - _self_ + - data: torch_cifar100 + - model: torch_cifar100 + - attack: default + - files: cifar100 + - scorers: default + - override hydra/sweeper : optuna + - override hydra/sweeper/sampler : grid + - override hydra/launcher : joblib +stage : '???' +direction : "maximize" +_target_ : deckard.base.experiment.Experiment +optimizers : accuracy +hydra: + run: + dir: ${files.directory}/${files.reports}/${stage}/logs + sweep: + dir: ${files.directory}/${stage}/${model.init.name} + subdir : ${hydra.sweeper.study_name}/${hydra.job.num} + sweeper: + sampler: + _target_: optuna.samplers.GridSampler + direction: ${direction} + study_name: control + storage: sqlite:///model.db + n_jobs: ${hydra.launcher.n_jobs} + n_trials : 32 + params: + ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) + ++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, .0001, .00001, 0.000001) + ++model.trainer.nb_epoch: choice(1, 10, 30, 50, 100) + _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper + launcher: + _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + n_jobs: 6 + prefer : processes + verbose: 10 + timeout: null + pre_dispatch: n_jobs + batch_size: auto + temp_folder: /tmp/deckard + max_nbytes: 100000 + mmap_mode: r diff --git a/examples/pytorch/cifar100/conf/compile.yaml b/examples/pytorch/cifar100/conf/compile.yaml new file mode 100644 index 00000000..43418520 --- /dev/null +++ b/examples/pytorch/cifar100/conf/compile.yaml @@ -0,0 +1,32 @@ +attacks: + # CarliniL0Method: CW_0 + # CarliniL2Method: CW_2 + # CarliniLInfMethod: CW_inf + DeepFool: Deep + FastGradientMethod: FGM + HopSkipJump: HSJ + PixelAttack: Pixel + ProjectedGradientDescent: PGD + ThresholdAttack: Thresh +defences: + Control: Control + FeatureSqueezing: FSQ + GaussianAugmentation: Gauss-in + GaussianNoise: Gauss-out + HighConfidence: Conf +params: + # art.attacks.evasion.CarliniL0Method: attack.init.confidence + # art.attacks.evasion.CarliniL2Method: attack.init.confidence + # art.attacks.evasion.CarliniLInfMethod: attack.init.confidence + Deep: attack.init.nb_grads + FGM: attack.init.eps + HSJ: attack.init.max_iter + Pixel: attack.init.th + PGD: attack.init.eps + Thresh: attack.init.th + Gauss-out: model.art.pipeline.postprocessor.scale + Conf: model.art.pipeline.postprocessor.cutoff + FSQ: model.art.pipeline.preprocessor.bit_depth + Gauss-in: model.art.pipeline.preprocessor.sigma + Depth: model_layers + Epochs: model.train.nb_epoch diff --git a/examples/pytorch/cifar100/conf/data/default.yaml b/examples/pytorch/cifar100/conf/data/default.yaml new file mode 100644 index 00000000..5eb78278 --- /dev/null +++ b/examples/pytorch/cifar100/conf/data/default.yaml @@ -0,0 +1,2 @@ +defaults: + - torch_mnist diff --git a/examples/pytorch/cifar100/conf/data/torch_cifar100.yaml b/examples/pytorch/cifar100/conf/data/torch_cifar100.yaml new file mode 100644 index 00000000..756bedca --- /dev/null +++ b/examples/pytorch/cifar100/conf/data/torch_cifar100.yaml @@ -0,0 +1,12 @@ +_target_: deckard.base.data.Data +generate: + name: torch_cifar100 + path: original_data +sample: + random_state : 0 + stratify: True +sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/pytorch/cifar100/conf/data/torch_mnist.yaml b/examples/pytorch/cifar100/conf/data/torch_mnist.yaml new file mode 100644 index 00000000..9d79c036 --- /dev/null +++ b/examples/pytorch/cifar100/conf/data/torch_mnist.yaml @@ -0,0 +1,15 @@ +_target_: deckard.base.data.Data +generate: + _target_: deckard.base.data.generator.DataGenerator + name: torch_mnist +sample: + _target_: deckard.base.data.sampler.SklearnDataSampler + random_state : 0 + stratify: True +sklearn_pipeline: + _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline + preprocessor: + + name: sklearn.preprocessing.StandardScaler + with_mean: True + with_std: True diff --git a/examples/pytorch/cifar100/conf/deploy/default.yaml b/examples/pytorch/cifar100/conf/deploy/default.yaml new file mode 100644 index 00000000..134da65f --- /dev/null +++ b/examples/pytorch/cifar100/conf/deploy/default.yaml @@ -0,0 +1,14 @@ +num_nodes: 1 +cluster_name: k8s-cluster +gpu_type: nvidia-tesla-v100 +gpu_count: 1 +gpu_driver_version: default +machine_type: n1-standard-2 +min_nodes: 1 +max_nodes: 1 +storage_config: conf/deploy/sclass.yaml +persistent_volume_claim: conf/deploy/pvc.yaml +pod : conf/deploy/pod.yaml +image_project: ubuntu-os-cloud +image_family: ubuntu-2204-lts +mount_directory: /mnt/filestore diff --git a/examples/pytorch/cifar100/conf/deploy/pod.yaml b/examples/pytorch/cifar100/conf/deploy/pod.yaml new file mode 100644 index 00000000..fc68306a --- /dev/null +++ b/examples/pytorch/cifar100/conf/deploy/pod.yaml @@ -0,0 +1,30 @@ +apiVersion: v1 +kind: Pod +metadata: + name: deckard +spec: + containers: + - name: deckard + image: ghcr.io/simplymathematics/deckard:main + imagePullPolicy: Always + workingDir: /deckard/examples/pytorch + args: ["python", "-m", "dvc", "repro"] + env: + - name: REDIS_HOST + value: "redis" + - name: REDIS_PORT + value: "6379" + - name: REDIS_DB + value: "0" + - name: REDIS_PASSWORD + value: "" + resources: + limits: + nvidia.com/gpu: 1 + volumeMounts: + - mountPath: /data + name: mypvc + volumes: + - name: mypvc + persistentVolumeClaim: + claimName: podpvc diff --git a/examples/pytorch/cifar100/conf/deploy/pvc.yaml b/examples/pytorch/cifar100/conf/deploy/pvc.yaml new file mode 100644 index 00000000..a7deee4d --- /dev/null +++ b/examples/pytorch/cifar100/conf/deploy/pvc.yaml @@ -0,0 +1,11 @@ +kind: PersistentVolumeClaim +apiVersion: v1 +metadata: + name: podpvc +spec: + accessModes: + - ReadWriteMany + storageClassName: filestore-sc + resources: + requests: + storage: 256Gi diff --git a/examples/pytorch/cifar100/conf/deploy/sclass.yaml b/examples/pytorch/cifar100/conf/deploy/sclass.yaml new file mode 100644 index 00000000..d566656c --- /dev/null +++ b/examples/pytorch/cifar100/conf/deploy/sclass.yaml @@ -0,0 +1,10 @@ +apiVersion: storage.k8s.io/v1 +kind: StorageClass +metadata: + name: filestore-sc +provisioner: filestore.csi.storage.gke.io +volumeBindingMode: Immediate +allowVolumeExpansion: true +parameters: + tier: standard + network: default diff --git a/examples/pytorch/cifar100/conf/files/cifar100.yaml b/examples/pytorch/cifar100/conf/files/cifar100.yaml new file mode 100644 index 00000000..969635c2 --- /dev/null +++ b/examples/pytorch/cifar100/conf/files/cifar100.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: cifar100 +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pt +params_file : params.yaml +# train_labels_file : train_labels.json +# test_labels_file : test_labels.json +# probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +# adv_probabilities_file: adv_probabilities.json +name: default diff --git a/examples/pytorch/cifar100/conf/files/mnist.yaml b/examples/pytorch/cifar100/conf/files/mnist.yaml new file mode 100644 index 00000000..efdb1c42 --- /dev/null +++ b/examples/pytorch/cifar100/conf/files/mnist.yaml @@ -0,0 +1,21 @@ +_target_: deckard.base.files.FileConfig +reports: reports +data_dir: data +model_dir: models +attack_dir: attacks +directory: mnist +score_dict_file: score_dict.json +data_file : data +model_file : model +attack_file : attack +attack_type : .pkl +data_type : .pkl +model_type : .pt +params_file : params.yaml +# train_labels_file : train_labels.json +# test_labels_file : test_labels.json +# probabilities_file: probabilities.json +predictions_file : predictions.json +adv_predictions_file : adv_predictions.json +# adv_probabilities_file: adv_probabilities.json +name: default diff --git a/examples/pytorch/cifar100/conf/model/art/default.yaml b/examples/pytorch/cifar100/conf/model/art/default.yaml new file mode 100644 index 00000000..4251627d --- /dev/null +++ b/examples/pytorch/cifar100/conf/model/art/default.yaml @@ -0,0 +1,7 @@ +defaults: + - initialize : default + # - preprocessor: fsq + # - postprocessor: confidence +data : ${..data} +library : ${..library} +_target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/pytorch/cifar100/conf/model/art/initialize/default.yaml b/examples/pytorch/cifar100/conf/model/art/initialize/default.yaml new file mode 100644 index 00000000..b694473b --- /dev/null +++ b/examples/pytorch/cifar100/conf/model/art/initialize/default.yaml @@ -0,0 +1,7 @@ +criterion: + name : torch.nn.CrossEntropyLoss +optimizer: + name : torch.optim.SGD + lr : 0.01 + momentum : 0.9 +clip_values : [0, 255] diff --git a/examples/pytorch/cifar100/conf/model/art/postprocessor/confidence.yaml b/examples/pytorch/cifar100/conf/model/art/postprocessor/confidence.yaml new file mode 100644 index 00000000..6fcdde95 --- /dev/null +++ b/examples/pytorch/cifar100/conf/model/art/postprocessor/confidence.yaml @@ -0,0 +1,3 @@ + +name : art.defences.postprocessor.HighConfidence +cutoff : .1 diff --git a/examples/pytorch/cifar100/conf/model/art/postprocessor/gauss-out.yaml b/examples/pytorch/cifar100/conf/model/art/postprocessor/gauss-out.yaml new file mode 100644 index 00000000..c912ebd9 --- /dev/null +++ b/examples/pytorch/cifar100/conf/model/art/postprocessor/gauss-out.yaml @@ -0,0 +1,2 @@ +name : art.defences.postprocessor.GaussianNoise +scale : 1 diff --git a/examples/pytorch/cifar100/conf/model/art/preprocessor/default.yaml b/examples/pytorch/cifar100/conf/model/art/preprocessor/default.yaml new file mode 100644 index 00000000..e69de29b diff --git a/examples/pytorch/cifar100/conf/model/art/preprocessor/fsq.yaml b/examples/pytorch/cifar100/conf/model/art/preprocessor/fsq.yaml new file mode 100644 index 00000000..27ddf58b --- /dev/null +++ b/examples/pytorch/cifar100/conf/model/art/preprocessor/fsq.yaml @@ -0,0 +1,3 @@ +name : art.defences.preprocessor.FeatureSqueezing +clip_values : ${model.art.initialize.clip_values} +bit_depth : 64 diff --git a/examples/pytorch/cifar100/conf/model/art/preprocessor/gauss-in.yaml b/examples/pytorch/cifar100/conf/model/art/preprocessor/gauss-in.yaml new file mode 100644 index 00000000..c438e8f5 --- /dev/null +++ b/examples/pytorch/cifar100/conf/model/art/preprocessor/gauss-in.yaml @@ -0,0 +1,4 @@ +name : art.defences.preprocessor.GaussianAugmentation +clip_values : ${model.art.initialize.clip_values} +sigma : 1 +ratio : .5 diff --git a/examples/pytorch/cifar100/conf/model/torch_cifar100.yaml b/examples/pytorch/cifar100/conf/model/torch_cifar100.yaml new file mode 100644 index 00000000..ea5628ae --- /dev/null +++ b/examples/pytorch/cifar100/conf/model/torch_cifar100.yaml @@ -0,0 +1,13 @@ +defaults: + - art : default +data: ${data} +init: + _target_: deckard.base.model.ModelInitializer + name : torch_example.ResNet18 + num_channels: 3 + num_classes: 100 +_target_: deckard.base.model.Model +trainer: + nb_epoch: 10 + batch_size: 1024 +library : pytorch diff --git a/examples/pytorch/cifar100/conf/model/torch_mnist.yaml b/examples/pytorch/cifar100/conf/model/torch_mnist.yaml new file mode 100644 index 00000000..9c18c54b --- /dev/null +++ b/examples/pytorch/cifar100/conf/model/torch_mnist.yaml @@ -0,0 +1,13 @@ + +defaults: + - art : default +data: ${data} +init: + _target_: deckard.base.model.ModelInitializer + num_channels : 1 + name : torch_example.ResNet18 +_target_: deckard.base.model.Model +trainer: + nb_epoch: 100 + batch_size: 1024 +library : pytorch diff --git a/examples/pytorch/cifar100/conf/plots.yaml b/examples/pytorch/cifar100/conf/plots.yaml new file mode 100644 index 00000000..accab9df --- /dev/null +++ b/examples/pytorch/cifar100/conf/plots.yaml @@ -0,0 +1,250 @@ +cat_plot: +- file: adv_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: linear + titles: $\lambda_{adv.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_accuracy + ylabels: $\lambda_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_accuracy_vs_defence_type.pdf + hue: model_name + kind: boxen + titles: $\lambda_{ben.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: accuracy + ylabels: $\lambda_{ben.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: log + titles: $\bar{C}_{ben.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_failure + ylabels: $\bar{C}_{ben.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_train_time_vs_defence_type.pdf + hue: model_name + kind: boxen + set: + yscale: log + titles: $\bar{C}_{adv.}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: training_time_per_adv_failure + ylabels: $\bar{C}_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_train_time_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: $\bar{C}_{adv.}$ vs Attack Type + x: atk_gen + xlabels: Attack Type + y: training_time_per_adv_failure + ylabels: $\bar{C}_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: adv_failures_per_test_time_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: $h_{adv}$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: adv_failure_rate + ylabels: $h_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +# - file: adv_accuracy_vs_defence_type.pdf +# hue: model_name +# kind: boxen +# legend_title: Model Name +# titles: $\lambda_{adv.}$ vs Defence Type +# x: def_gen +# xlabels: Defence Type +# y: adv_accuracy +# ylabels: Adv. $\lambda_{ben.}$ +# rotation : 90 +# hue_order: +# - ResNet18 +# - ResNet34 +# - ResNet50 +# - ResNet101 +# - ResNet152 +- file: adv_accuracy_vs_attack_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + titles: $\lambda_{adv.}$ vs Attack Type + x: atk_gen + xlabels: Attack Type + y: adv_accuracy + ylabels: $\lambda_{adv.}$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +- file: ben_failure_rate_vs_defence_type.pdf + hue: model_name + kind: boxen + legend_title: Model Name + set: + yscale: log + titles: $h_{ben}(t; \theta)$ vs Defence Type + x: def_gen + xlabels: Defence Type + y: failure_rate + ylabels: $h_{ben}(t; \theta)$ + rotation : 90 + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 +line_plot: +# - file: def_param_vs_accuracy.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $\lambda_{ben.}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: accuracy +# y_scale: +# ylabel: $\lambda_{ben.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +# - file: def_param_vs_adv_accuracy.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $\lambda_{adv.}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: adv_accuracy +# y_scale: +# ylabel: $\lambda_{adv.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +# - file: def_param_vs_adv_failure_rate.pdf +# hue: def_gen +# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} +# title: $h_{adv}$ vs Defence Strength +# x: def_value +# x_scale: linear +# xlabel: Defence Control Parameter +# y: adv_failure_rate +# y_scale: log +# ylabel: $h_{adv.}$ +# hue_order: +# - Control +# - Gauss-in +# - Gauss-out +# - Conf +# - FSQ +- file: atk_param_vs_accuracy.pdf + hue: atk_gen + legend: {bbox_to_anchor: [1.05, 1]} + title: $\lambda_{adv.}$ vs Attack Strength + x: atk_value + x_scale: linear + xlabel: Attack Control Parameter + y: adv_accuracy + y_scale: + ylabel: $\lambda_{adv.}$ + hue_order: + - FGM + - PGD + - Deep + - HSJ + - Pixel + - Thresh + +scatter_plot: +- x: train_time_per_sample + y: adv_failure_rate + hue: model_name + xlabel: $t_{train}$ + ylabel: $h_{adv}$ + title: $h_{adv}$ vs $t_{train}$ + file: adv_failure_rate_vs_train_time.pdf + y_scale: log + x_scale: log + legend: + title: Model Name + bbox_to_anchor: [1.05, 1] + hue_order: + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 + # style : atk_gen +# - x: train_time_per_sample +# y: adv_failure_rate +# hue: model_name +# xlabel: $t_{train}$ +# ylabel: $h_{adv}$ +# title: $h_{adv}$ vs $t_{train}$ for each Defence +# file: adv_failure_rate_vs_train_time_def.pdf +# y_scale: log +# x_scale: linear +# legend: +# title: Model Name +# # style : def_gen diff --git a/examples/pytorch/cifar100/conf/scorers/default.yaml b/examples/pytorch/cifar100/conf/scorers/default.yaml new file mode 100644 index 00000000..4503c5bf --- /dev/null +++ b/examples/pytorch/cifar100/conf/scorers/default.yaml @@ -0,0 +1,9 @@ +_target_: deckard.base.scorer.ScorerDict +accuracy: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.accuracy_score + direction: maximize +log_loss: + _target_: deckard.base.scorer.ScorerConfig + name: sklearn.metrics.log_loss + direction: minimize diff --git a/examples/pytorch/cifar100/dvc.lock b/examples/pytorch/cifar100/dvc.lock new file mode 100644 index 00000000..d9f081aa --- /dev/null +++ b/examples/pytorch/cifar100/dvc.lock @@ -0,0 +1,417 @@ +schema: '2.0' +stages: + train: + cmd: python -m deckard.layers.experiment train --config_file cifar100.yaml + params: + params.yaml: + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: cifar100 + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 100 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 10 + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: cifar100/data/data.pkl + md5: 1070854e6c00fc787bc0fdfc82792fd6 + size: 761280311 + - path: cifar100/models/model.optimizer.pt + md5: dead7ac200960668972932049a2695cb + size: 44989261 + - path: cifar100/models/model.pt + md5: ac30436350a0765a65ab535299e93103 + size: 44998157 + - path: cifar100/reports/train/default/predictions.json + md5: 960c0bcc9c969d9218ae3a5f4256d337 + size: 24436522 + - path: cifar100/reports/train/default/score_dict.json + md5: d5d237be531584dab39c38b994c9ed1f + size: 411 + attack: + cmd: python -m deckard.layers.experiment attack --config_file cifar100.yaml + deps: + - path: cifar100/data/data.pkl + md5: 1070854e6c00fc787bc0fdfc82792fd6 + size: 761280311 + - path: cifar100/models/model.pt + md5: ac30436350a0765a65ab535299e93103 + size: 44998157 + params: + params.yaml: + attack: + _target_: deckard.base.attack.Attack + attack_size: 10 + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.attack.AttackInitializer + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 100 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 10 + name: art.attacks.evasion.HopSkipJump + method: evasion + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 100 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 10 + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + files: + _target_: deckard.base.files.FileConfig + adv_predictions_file: adv_predictions.json + attack_dir: attacks + attack_file: attack + attack_type: .pkl + data_dir: data + data_file: data + data_type: .pkl + directory: cifar100 + model_dir: models + model_file: model + model_type: .pt + name: default + params_file: params.yaml + predictions_file: predictions.json + reports: reports + score_dict_file: score_dict.json + model: + _target_: deckard.base.model.Model + art: + _target_: deckard.base.model.art_pipeline.ArtPipeline + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + initialize: + clip_values: + - 0 + - 255 + criterion: + name: torch.nn.CrossEntropyLoss + optimizer: + lr: 0.01 + momentum: 0.9 + name: torch.optim.SGD + library: pytorch + data: + _target_: deckard.base.data.Data + generate: + name: torch_cifar100 + path: original_data + sample: + random_state: 0 + stratify: true + sklearn_pipeline: + preprocessor: + name: sklearn.preprocessing.StandardScaler + with_mean: true + with_std: true + init: + _target_: deckard.base.model.ModelInitializer + name: torch_example.ResNet18 + num_channels: 3 + num_classes: 100 + library: pytorch + trainer: + batch_size: 1024 + nb_epoch: 10 + scorers: + _target_: deckard.base.scorer.ScorerDict + accuracy: + _target_: deckard.base.scorer.ScorerConfig + direction: maximize + name: sklearn.metrics.accuracy_score + log_loss: + _target_: deckard.base.scorer.ScorerConfig + direction: minimize + name: sklearn.metrics.log_loss + outs: + - path: cifar100/attacks/attack.pkl + md5: 18be96acfa0eae64fac63405bd53cac1 + size: 123046 + - path: cifar100/reports/attack/default/adv_predictions.json + md5: 98ceb6604311d2273da8b11e49e4150d + size: 21402 + - path: cifar100/reports/attack/default/score_dict.json + md5: f21ab891918c857171941e84dcc1b09a + size: 561 + attacks@ResNet18: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 + stage=attack ++hydra.sweeper.storage=sqlite:///cifar100/reports/attack/ResNet18.db + --config-name cifar100.yaml + deps: + - path: attacks.sh + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: cifar100/reports/attack/default/score_dict.json + md5: f21ab891918c857171941e84dcc1b09a + size: 561 + - path: models.sh + md5: 1937e58bedac027034aea7d4a5712407 + size: 1380 + outs: + - path: cifar100/reports/attack/ResNet18.db + md5: 89fd1d229465cb1c49d1fd99cacbad33 + size: 475136 + attacks@ResNet152: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet152 + stage=attack ++hydra.sweeper.storage=sqlite:///cifar100/reports/attack/ResNet152.db + --config-name cifar100.yaml + deps: + - path: attacks.sh + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: cifar100/reports/attack/default/score_dict.json + md5: f21ab891918c857171941e84dcc1b09a + size: 561 + - path: models.sh + md5: 1937e58bedac027034aea7d4a5712407 + size: 1380 + outs: + - path: cifar100/reports/attack/ResNet152.db + md5: 5561d1c232b35787d315ebc17c28386d + size: 249856 + attacks@ResNet34: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 + stage=attack ++hydra.sweeper.storage=sqlite:///cifar100/reports/attack/ResNet34.db + --config-name cifar100.yaml + deps: + - path: attacks.sh + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: cifar100/reports/attack/default/score_dict.json + md5: f21ab891918c857171941e84dcc1b09a + size: 561 + - path: models.sh + md5: 1937e58bedac027034aea7d4a5712407 + size: 1380 + outs: + - path: cifar100/reports/attack/ResNet34.db + md5: 5cd8e5a0e50fe7ef759c6c04fc430863 + size: 475136 + attacks@ResNet50: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 + stage=attack ++hydra.sweeper.storage=sqlite:///cifar100/reports/attack/ResNet50.db + --config-name cifar100.yaml + deps: + - path: attacks.sh + md5: 963c858a322d7a4990a92a25d5684c57 + size: 2907 + - path: cifar100/reports/attack/default/score_dict.json + md5: f21ab891918c857171941e84dcc1b09a + size: 561 + - path: models.sh + md5: 1937e58bedac027034aea7d4a5712407 + size: 1380 + outs: + - path: cifar100/reports/attack/ResNet50.db + md5: 5556f78590f52e7879cb434450ab78c8 + size: 745472 diff --git a/examples/pytorch/cifar100/dvc.yaml b/examples/pytorch/cifar100/dvc.yaml new file mode 100644 index 00000000..254a637a --- /dev/null +++ b/examples/pytorch/cifar100/dvc.yaml @@ -0,0 +1,120 @@ +stages: + train: + cmd: python -m deckard.layers.experiment train --config_file cifar100.yaml + params: + - data + - model + - scorers + - files + outs: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} + # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} # Omit to save space + - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} # logit outputs for our model + # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} # Omit to save space + metrics: + - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} + attack: + cmd: python -m deckard.layers.experiment attack --config_file cifar100.yaml + params: + - data + - model + - attack + - scorers + - files + outs: + - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} + - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} + # - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} + deps: + - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} + - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + metrics: + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} + + ############################################################################## + # models: # This is a loop over the ResNet models + # foreach: + # - ResNet18 + # # - ResNet34 + # # - ResNet50 + # # - ResNet101 + # # - ResNet152 + # do: # This script configures eazch defence + # cmd: bash models.sh ++model.init.name=torch_example.${item} stage=train ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/train/${item}.db --config-name cifar100.yaml + # deps: + # - models.sh + # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} + # - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} + # outs: + # - ${files.directory}/${files.reports}/train/${item}.db: # This outputs a database file for each model + # cache: True + # persist: True + attacks: + foreach: # This is a loop over the ResNet models + - ResNet18 + - ResNet34 + - ResNet50 + - ResNet101 + - ResNet152 + do: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/attack/${item}.db --config-name cifar100.yaml + deps: + - models.sh # This script configures each defence + - attacks.sh # This script configures each attack + - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} # This is here just to ensure it runs after the attack stage + # - ${files.directory}/${files.reports}/train/${item}.db + outs: + - ${files.directory}/${files.reports}/attack/${item}.db: # This outputs a database file for each model + cache: True + persist: True + compile: + foreach: # iterates through each stage + # - train + - attack + do: + cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv + deps: + - ${files.directory}/${files.reports}/${item}/ + - ${files.directory}/${files.reports}/${item}/ResNet18.db + - ${files.directory}/${files.reports}/${item}/ResNet34.db + - ${files.directory}/${files.reports}/${item}/ResNet50.db + - ${files.directory}/${files.reports}/${item}/ResNet101.db + # - ${files.directory}/${files.reports}/${item}/ResNet152.db + outs: + - ${files.directory}/${files.reports}/${item}.csv + plot: + cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv -o data.csv + deps: + - ${files.directory}/${files.reports}/attack.csv + - ${files.directory}/${files.reports}/attack/ResNet18.db + # - ${files.directory}/${files.reports}/attack/ResNet34.db + # - ${files.directory}/${files.reports}/attack/ResNet50.db + # - ${files.directory}/${files.reports}/attack/ResNet101.db + # - ${files.directory}/${files.reports}/attack/ResNet152.db + outs: + - ${files.directory}/plots/data.csv + afr: + cmd: python -m deckard.layers.afr --dataset ${files.directory} --file ${files.directory}/plots/data.csv + deps: + - ${files.directory}/plots/data.csv + plots: + - ${files.directory}/plots/weibull_aft.pdf + - ${files.directory}/plots/weibull_partial_effects.pdf + - ${files.directory}/plots/cox_partial_effects.pdf + - ${files.directory}/plots/cox_aft.pdf + - ${files.directory}/plots/log_logistic_aft.pdf + - ${files.directory}/plots/log_logistic_partial_effects.pdf + - ${files.directory}/plots/log_normal_aft.pdf + - ${files.directory}/plots/log_normal_partial_effects.pdf + metrics: + - ${files.directory}/plots/aft_comparison.csv + outs: + - ${files.directory}/plots/aft_comparison.tex + copy_results: + cmd: cp -r ${files.directory}/plots/* ~/ml_afr/cifar100/ + deps: + - ${files.directory}/plots/data.csv + - ${files.directory}/plots/aft_comparison.csv diff --git a/examples/pytorch/cifar100/models.sh b/examples/pytorch/cifar100/models.sh new file mode 100644 index 00000000..2978d445 --- /dev/null +++ b/examples/pytorch/cifar100/models.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +# This script is used to generate the models for the sklearn example. + +# # Default model +echo "python -m deckard.layers.optimise " $@ "--multirun" +python -m deckard.layers.optimise $@ --multirun + +# # This line generates the model and adds the FeatureSqueezing preprocessing defence. +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,255] ++hydra.sweeper.study_name=FSQ $@ --multirun + +# # # Gaussian Augmentation (Input) +# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.5 +model.art.preprocessor.params.augmentation=False ++hydra.sweeper.study_name=gauss-in $@ --multirun + +# # # # Gaussian Noise (Output) +# python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 ++hydra.sweeper.study_name=gauss-out $@ --multirun + +# # # # High Confidence +# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 ++hydra.sweeper.study_name=conf $@ --multirun diff --git a/examples/pytorch/cifar100/torch_example.py b/examples/pytorch/cifar100/torch_example.py new file mode 100644 index 00000000..2007d9d0 --- /dev/null +++ b/examples/pytorch/cifar100/torch_example.py @@ -0,0 +1,79 @@ +import torch.nn as nn +from torchvision import models + +__all__ = [ + "ResNet18", + "ResNet50", + "ResNet101", + "ResNet152", +] + + +def ResNet18(num_channels=1, num_classes=10): + model = models.resnet18() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(512, num_classes) + return model + + +def ResNet34(num_channels=1, num_classes=10): + model = models.resnet34() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(512, num_classes) + return model + + +def ResNet50(num_channels=1, num_classes=10): + model = models.resnet50() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model + + +def ResNet101(num_channels=1, num_classes=10): + model = models.resnet101() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model + + +def ResNet152(num_channels=1, num_classes=10): + model = models.resnet152() + model.conv1 = nn.Conv2d( + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ) + model.fc = nn.Linear(2048, num_classes) + return model From 2c8fa8680b65afe859eec86805a9b4b14a902d49 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 18:11:51 +0000 Subject: [PATCH 129/148] deleted old pytorch examples --- examples/pytorch/.dvc/.gitignore | 3 - examples/pytorch/.dvc/config | 0 examples/pytorch/.dvcignore | 3 - examples/pytorch/.gitignore | 2 - examples/pytorch/attacks.sh | 37 - examples/pytorch/conf/attack/default.yaml | 9 - examples/pytorch/conf/compile.yaml | 32 - examples/pytorch/conf/data/default.yaml | 2 - examples/pytorch/conf/data/torch_cifar.yaml | 11 - examples/pytorch/conf/data/torch_mnist.yaml | 15 - examples/pytorch/conf/deploy/default.yaml | 14 - examples/pytorch/conf/deploy/pod.yaml | 30 - 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b/examples/pytorch/.dvc/config deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/pytorch/.dvcignore b/examples/pytorch/.dvcignore deleted file mode 100644 index 51973055..00000000 --- a/examples/pytorch/.dvcignore +++ /dev/null @@ -1,3 +0,0 @@ -# Add patterns of files dvc should ignore, which could improve -# the performance. Learn more at -# https://dvc.org/doc/user-guide/dvcignore diff --git a/examples/pytorch/.gitignore b/examples/pytorch/.gitignore deleted file mode 100644 index a129798e..00000000 --- a/examples/pytorch/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -mnist/ -20_epochs/ \ No newline at end of file diff --git a/examples/pytorch/attacks.sh b/examples/pytorch/attacks.sh deleted file mode 100644 index 8ec1f079..00000000 --- a/examples/pytorch/attacks.sh +++ /dev/null @@ -1,37 +0,0 @@ -# #!/bin/bash - -# # This script is used to generate the attacks for the example. - -# Fast Gradient Method -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 stage=attack ++hydra.sweeper.study_name=fgm ++direction=maximize $@ - -# Projected Gradient Descent -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 stage=attack ++hydra.sweeper.study_name=pgd ++direction=maximize $@ - -# DeepFool -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=1,3,5,10 stage=attack ++hydra.sweeper.study_name=deep ++direction=maximize $@ - -# HopSkipJump -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 stage=attack ++hydra.sweeper.study_name=hsj ++direction=maximize $@ - -# ##################################################### -# PixelAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=pixel ++direction=maximize $@ - -# ThresholdAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=thresh ++direction=maximize $@ - -# # AdversarialPatch -# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 stage=patch ++hydra.sweeper.study_name=attack ++direction=maximize ++attack.init.patch_shape=[1,28,28] $@ -##################################################### - -# # Carlini L0 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw0 ++hydra.sweeper.study_name=cw0 ++direction=maximize $@ - -# # Carlini L2 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw2 ++hydra.sweeper.study_name=cw2 ++direction=maximize $@ - -# # Carlini LInf Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=attack ++hydra.sweeper.study_name=cwinf ++direction=maximize $@ - -rm -rf output/models/* diff --git a/examples/pytorch/conf/attack/default.yaml b/examples/pytorch/conf/attack/default.yaml deleted file mode 100644 index 76b4d62d..00000000 --- a/examples/pytorch/conf/attack/default.yaml +++ /dev/null @@ -1,9 +0,0 @@ -data: ${data} -model: ${model} -_target_ : deckard.base.attack.Attack -init: - model: ${model} - _target_: deckard.base.attack.AttackInitializer - name: art.attacks.evasion.HopSkipJump -attack_size : 10 -method : evasion diff --git a/examples/pytorch/conf/compile.yaml b/examples/pytorch/conf/compile.yaml deleted file mode 100644 index f3b729e0..00000000 --- a/examples/pytorch/conf/compile.yaml +++ /dev/null @@ -1,32 +0,0 @@ -attacks: - # CarliniL0Method: CW_0 - # CarliniL2Method: CW_2 - # CarliniLInfMethod: CW_inf - DeepFool: Deep - FastGradientMethod: FGM - HopSkipJump: HSJ - PixelAttack: Pixel - ProjectedGradientDescent: PGD - ThresholdAttack: Thresh -defences: - Control: Control - FeatureSqueezing: FSQ - GaussianAugmentation: Gauss-in - GaussianNoise: Gauss-out - HighConfidence: Conf -params: - # art.attacks.evasion.CarliniL0Method: attack.init.confidence - # art.attacks.evasion.CarliniL2Method: attack.init.confidence - # art.attacks.evasion.CarliniLInfMethod: attack.init.confidence - Deep: attack.init.nb_grads - FGM: attack.init.eps - HSJ: attack.init.max_iter - Pixel: attack.init.th - PGD: attack.init.eps - Thresh: attack.init.th - Gauss-out: model.art.pipeline.postprocessor.scale - Conf: model.art.pipeline.postprocessor.cutoff - FSQ: model.art.pipeline.preprocessor.bit_depth - Gauss-in: model.art.pipeline.preprocessor.sigma - Control: model_layers - Epochs: model.train.nb_epoch diff --git a/examples/pytorch/conf/data/default.yaml b/examples/pytorch/conf/data/default.yaml deleted file mode 100644 index 5eb78278..00000000 --- a/examples/pytorch/conf/data/default.yaml +++ /dev/null @@ -1,2 +0,0 @@ -defaults: - - torch_mnist diff --git a/examples/pytorch/conf/data/torch_cifar.yaml b/examples/pytorch/conf/data/torch_cifar.yaml deleted file mode 100644 index b7603125..00000000 --- a/examples/pytorch/conf/data/torch_cifar.yaml +++ /dev/null @@ -1,11 +0,0 @@ -_target_: deckard.base.data.Data -generate: - name: torch_cifar10 -sample: - random_state : 0 - stratify: True -sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/examples/pytorch/conf/data/torch_mnist.yaml b/examples/pytorch/conf/data/torch_mnist.yaml deleted file mode 100644 index 9d79c036..00000000 --- a/examples/pytorch/conf/data/torch_mnist.yaml +++ /dev/null @@ -1,15 +0,0 @@ -_target_: deckard.base.data.Data -generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist -sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state : 0 - stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/examples/pytorch/conf/deploy/default.yaml b/examples/pytorch/conf/deploy/default.yaml deleted file mode 100644 index 134da65f..00000000 --- a/examples/pytorch/conf/deploy/default.yaml +++ /dev/null @@ -1,14 +0,0 @@ -num_nodes: 1 -cluster_name: k8s-cluster -gpu_type: nvidia-tesla-v100 -gpu_count: 1 -gpu_driver_version: default -machine_type: n1-standard-2 -min_nodes: 1 -max_nodes: 1 -storage_config: conf/deploy/sclass.yaml -persistent_volume_claim: conf/deploy/pvc.yaml -pod : conf/deploy/pod.yaml -image_project: ubuntu-os-cloud -image_family: ubuntu-2204-lts -mount_directory: /mnt/filestore diff --git a/examples/pytorch/conf/deploy/pod.yaml b/examples/pytorch/conf/deploy/pod.yaml deleted file mode 100644 index fc68306a..00000000 --- a/examples/pytorch/conf/deploy/pod.yaml +++ /dev/null @@ -1,30 +0,0 @@ -apiVersion: v1 -kind: Pod -metadata: - name: deckard -spec: - containers: - - name: deckard - image: ghcr.io/simplymathematics/deckard:main - imagePullPolicy: Always - workingDir: /deckard/examples/pytorch - args: ["python", "-m", "dvc", "repro"] - env: - - name: REDIS_HOST - value: "redis" - - name: REDIS_PORT - value: "6379" - - name: REDIS_DB - value: "0" - - name: REDIS_PASSWORD - value: "" - resources: - limits: - nvidia.com/gpu: 1 - volumeMounts: - - mountPath: /data - name: mypvc - volumes: - - name: mypvc - persistentVolumeClaim: - claimName: podpvc diff --git a/examples/pytorch/conf/deploy/pvc.yaml b/examples/pytorch/conf/deploy/pvc.yaml deleted file mode 100644 index a7deee4d..00000000 --- a/examples/pytorch/conf/deploy/pvc.yaml +++ /dev/null @@ -1,11 +0,0 @@ -kind: PersistentVolumeClaim -apiVersion: v1 -metadata: - name: podpvc -spec: - accessModes: - - ReadWriteMany - storageClassName: filestore-sc - resources: - requests: - storage: 256Gi diff --git a/examples/pytorch/conf/deploy/sclass.yaml b/examples/pytorch/conf/deploy/sclass.yaml deleted file mode 100644 index d566656c..00000000 --- a/examples/pytorch/conf/deploy/sclass.yaml +++ /dev/null @@ -1,10 +0,0 @@ -apiVersion: storage.k8s.io/v1 -kind: StorageClass -metadata: - name: filestore-sc -provisioner: filestore.csi.storage.gke.io -volumeBindingMode: Immediate -allowVolumeExpansion: true -parameters: - tier: standard - network: default diff --git a/examples/pytorch/conf/files/cifar.yaml b/examples/pytorch/conf/files/cifar.yaml deleted file mode 100644 index e43cab72..00000000 --- a/examples/pytorch/conf/files/cifar.yaml +++ /dev/null @@ -1,21 +0,0 @@ -_target_: deckard.base.files.FileConfig -reports: reports -data_dir: data -model_dir: models -attack_dir: attacks -directory: cifar -score_dict_file: score_dict.json -data_file : data -model_file : model -attack_file : attack -attack_type : .pkl -data_type : .pkl -model_type : .pt -params_file : params.yaml -# train_labels_file : train_labels.json -# test_labels_file : test_labels.json -# probabilities_file: probabilities.json -predictions_file : predictions.json -adv_predictions_file : adv_predictions.json -# adv_probabilities_file: adv_probabilities.json -name: default diff --git a/examples/pytorch/conf/files/mnist.yaml b/examples/pytorch/conf/files/mnist.yaml deleted file mode 100644 index efdb1c42..00000000 --- a/examples/pytorch/conf/files/mnist.yaml +++ /dev/null @@ -1,21 +0,0 @@ -_target_: deckard.base.files.FileConfig -reports: reports -data_dir: data -model_dir: models -attack_dir: attacks -directory: mnist -score_dict_file: score_dict.json -data_file : data -model_file : model -attack_file : attack -attack_type : .pkl -data_type : .pkl -model_type : .pt -params_file : params.yaml -# train_labels_file : train_labels.json -# test_labels_file : test_labels.json -# probabilities_file: probabilities.json -predictions_file : predictions.json -adv_predictions_file : adv_predictions.json -# adv_probabilities_file: adv_probabilities.json -name: default diff --git a/examples/pytorch/conf/grid/attack/cw_0.yaml b/examples/pytorch/conf/grid/attack/cw_0.yaml deleted file mode 100644 index 20573ea2..00000000 --- a/examples/pytorch/conf/grid/attack/cw_0.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.CarliniL0Method -init.max_iter : [10] -init.batch_size : [1024] -init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/cw_2.yaml b/examples/pytorch/conf/grid/attack/cw_2.yaml deleted file mode 100644 index 14965baa..00000000 --- a/examples/pytorch/conf/grid/attack/cw_2.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.model : ${model} -init.name : art.attacks.evasion.CarliniL0Method -init.max_iter : [10] -init.batch_size : [1024] -init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/cw_inf.yaml b/examples/pytorch/conf/grid/attack/cw_inf.yaml deleted file mode 100644 index 9e694da5..00000000 --- a/examples/pytorch/conf/grid/attack/cw_inf.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.CarliniLInfMethod -init.max_iter : [10] -init.batch_size : [1024] -init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/deepfool.yaml b/examples/pytorch/conf/grid/attack/deepfool.yaml deleted file mode 100644 index 1fc9055a..00000000 --- a/examples/pytorch/conf/grid/attack/deepfool.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.name : art.attacks.evasion.DeepFool -init.max_iter : [10] -init.batch_size : [1024] -init.nb_grads : [10,100,1000] -init.eps: [0.01, 0.03, 0.3, 0.1, 1.0] diff --git a/examples/pytorch/conf/grid/attack/fgm.yaml b/examples/pytorch/conf/grid/attack/fgm.yaml deleted file mode 100644 index 41032d3e..00000000 --- a/examples/pytorch/conf/grid/attack/fgm.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.FastGradientMethod -init.eps: [0.01, 0.03, 0.3, 0.1] -init.norm: [inf, 1, 2] -init.eps_step: [0.001, 0.003, 0.01] diff --git a/examples/pytorch/conf/grid/attack/hsj.yaml b/examples/pytorch/conf/grid/attack/hsj.yaml deleted file mode 100644 index 1fba407d..00000000 --- a/examples/pytorch/conf/grid/attack/hsj.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.name : art.attacks.evasion.HopSkipJump -init.batch_size : [1, 4, 16, 65, 128] -init.max_iter : [1, 10, 100, 1000] -init.max_eval : [1, 10, 100, 1000] -init.init_eval : [1, 10, 100, 1000] diff --git a/examples/pytorch/conf/grid/attack/patch.yaml b/examples/pytorch/conf/grid/attack/patch.yaml deleted file mode 100644 index e7285674..00000000 --- a/examples/pytorch/conf/grid/attack/patch.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.name : art.attacks.evasion.ThresholdAttack -init.max_iter : [10] -init.batch_size : [1024] -init.scale_min : [0.01,] -init.scale_max : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/pgd.yaml b/examples/pytorch/conf/grid/attack/pgd.yaml deleted file mode 100644 index 49819d5f..00000000 --- a/examples/pytorch/conf/grid/attack/pgd.yaml +++ /dev/null @@ -1,6 +0,0 @@ -init.name : art.attacks.evasion.ProjectedGradientDescent -init.eps : [0.01, 0.03, 0.3, 0.1] -init.norm : [inf, 1, 2] -init.eps_step : [0.001, 0.003, 0.01] -init.batch_size : [1024] -init.max_iter : [10] diff --git a/examples/pytorch/conf/grid/attack/pixel.yaml b/examples/pytorch/conf/grid/attack/pixel.yaml deleted file mode 100644 index 9e0060a7..00000000 --- a/examples/pytorch/conf/grid/attack/pixel.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.ProjectedGradientDescent -init.max_iter : [10] -init.batch_size : [1024] -init.th : [1,4,16,64,256] diff --git a/examples/pytorch/conf/grid/attack/threshold.yaml b/examples/pytorch/conf/grid/attack/threshold.yaml deleted file mode 100644 index 07437c71..00000000 --- a/examples/pytorch/conf/grid/attack/threshold.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.ThresholdAttack -init.max_iter : [10] -init.batch_size : [1024] -init.th: [1,4,16,64,256] diff --git a/examples/pytorch/conf/grid/model/confidence.yaml b/examples/pytorch/conf/grid/model/confidence.yaml deleted file mode 100644 index d07598f8..00000000 --- a/examples/pytorch/conf/grid/model/confidence.yaml +++ /dev/null @@ -1,4 +0,0 @@ - -art.postprocessor.name : [art.defences.postprocessor.HighConfidence] -art.postprocessor.cutoff : [.1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99] -art.postprocessor.apply_fit : [True, False] diff --git a/examples/pytorch/conf/grid/model/default.yaml b/examples/pytorch/conf/grid/model/default.yaml deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/pytorch/conf/grid/model/fsq.yaml b/examples/pytorch/conf/grid/model/fsq.yaml deleted file mode 100644 index 9740b4b4..00000000 --- a/examples/pytorch/conf/grid/model/fsq.yaml +++ /dev/null @@ -1,3 +0,0 @@ -art.preprocessor.name : [art.defences.preprocessor.FeatureSqueezing] -art.preprocessor.clip_values : [[0, 1]] -art.preprocessor.bit_depth : [1, 2, 4, 8, 16, 32, 64] diff --git a/examples/pytorch/conf/grid/model/gauss-in.yaml b/examples/pytorch/conf/grid/model/gauss-in.yaml deleted file mode 100644 index 21392a82..00000000 --- a/examples/pytorch/conf/grid/model/gauss-in.yaml +++ /dev/null @@ -1,4 +0,0 @@ -art.preprocessor.name : art.defences.preprocessor.GaussianAugmentation -art.preprocessor.clip_values : ${art.initialize.clip_values} -art.preprocessor.sigma : [.01, .1, .2, .3, .5, 1] -art.preprocessor.ratio : [.1, .5, 1] diff --git a/examples/pytorch/conf/grid/model/gauss-out.yaml b/examples/pytorch/conf/grid/model/gauss-out.yaml deleted file mode 100644 index 377bbcc9..00000000 --- a/examples/pytorch/conf/grid/model/gauss-out.yaml +++ /dev/null @@ -1,2 +0,0 @@ -art.postprocessor.name : art.defences.postprocessor.GaussianNoise -art.postprocessor.scale : [.01, .1, .2, .3, .5, 1, 2, 3, 5, 10] diff --git a/examples/pytorch/conf/mnist.yaml b/examples/pytorch/conf/mnist.yaml deleted file mode 100644 index aa05956f..00000000 --- a/examples/pytorch/conf/mnist.yaml +++ /dev/null @@ -1,44 +0,0 @@ -defaults: - - _self_ - - data: torch_mnist - - model: torch_mnist - - attack: default - - files: mnist - - scorers: default - - override hydra/sweeper : optuna - - override hydra/sweeper/sampler : grid - - override hydra/launcher : joblib -stage : '???' -direction : "maximize" -_target_ : deckard.base.experiment.Experiment -optimizers : accuracy -hydra: - run: - dir: ${files.directory}/${files.reports}/${stage}/logs - sweep: - dir: ${files.directory}/${stage}/${model.init.name} - subdir : ${hydra.sweeper.study_name}/${hydra.job.num} - sweeper: - sampler: - _target_: optuna.samplers.GridSampler - direction: ${direction} - study_name: control - storage: sqlite:///model.db - n_jobs: ${hydra.launcher.n_jobs} - n_trials : 32 - params: - ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) - ++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, .0001, .00001, 0.000001) - ++model.trainer.nb_epoch: choice(1, 10, 30, 50, 100) - _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - launcher: - _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 8 - prefer : processes - verbose: 10 - timeout: null - pre_dispatch: n_jobs - batch_size: auto - temp_folder: /tmp/deckard - max_nbytes: 100000 - mmap_mode: r diff --git a/examples/pytorch/conf/model/art/default.yaml b/examples/pytorch/conf/model/art/default.yaml deleted file mode 100644 index 4251627d..00000000 --- a/examples/pytorch/conf/model/art/default.yaml +++ /dev/null @@ -1,7 +0,0 @@ -defaults: - - initialize : default - # - preprocessor: fsq - # - postprocessor: confidence -data : ${..data} -library : ${..library} -_target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/pytorch/conf/model/art/initialize/default.yaml b/examples/pytorch/conf/model/art/initialize/default.yaml deleted file mode 100644 index b694473b..00000000 --- a/examples/pytorch/conf/model/art/initialize/default.yaml +++ /dev/null @@ -1,7 +0,0 @@ -criterion: - name : torch.nn.CrossEntropyLoss -optimizer: - name : torch.optim.SGD - lr : 0.01 - momentum : 0.9 -clip_values : [0, 255] diff --git a/examples/pytorch/conf/model/art/postprocessor/confidence.yaml b/examples/pytorch/conf/model/art/postprocessor/confidence.yaml deleted file mode 100644 index 6fcdde95..00000000 --- a/examples/pytorch/conf/model/art/postprocessor/confidence.yaml +++ /dev/null @@ -1,3 +0,0 @@ - -name : art.defences.postprocessor.HighConfidence -cutoff : .1 diff --git a/examples/pytorch/conf/model/art/postprocessor/gauss-out.yaml b/examples/pytorch/conf/model/art/postprocessor/gauss-out.yaml deleted file mode 100644 index c912ebd9..00000000 --- a/examples/pytorch/conf/model/art/postprocessor/gauss-out.yaml +++ /dev/null @@ -1,2 +0,0 @@ -name : art.defences.postprocessor.GaussianNoise -scale : 1 diff --git a/examples/pytorch/conf/model/art/preprocessor/default.yaml b/examples/pytorch/conf/model/art/preprocessor/default.yaml deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/pytorch/conf/model/art/preprocessor/fsq.yaml b/examples/pytorch/conf/model/art/preprocessor/fsq.yaml deleted file mode 100644 index 27ddf58b..00000000 --- a/examples/pytorch/conf/model/art/preprocessor/fsq.yaml +++ /dev/null @@ -1,3 +0,0 @@ -name : art.defences.preprocessor.FeatureSqueezing -clip_values : ${model.art.initialize.clip_values} -bit_depth : 64 diff --git a/examples/pytorch/conf/model/art/preprocessor/gauss-in.yaml b/examples/pytorch/conf/model/art/preprocessor/gauss-in.yaml deleted file mode 100644 index c438e8f5..00000000 --- a/examples/pytorch/conf/model/art/preprocessor/gauss-in.yaml +++ /dev/null @@ -1,4 +0,0 @@ -name : art.defences.preprocessor.GaussianAugmentation -clip_values : ${model.art.initialize.clip_values} -sigma : 1 -ratio : .5 diff --git a/examples/pytorch/conf/model/torch_cifar.yaml b/examples/pytorch/conf/model/torch_cifar.yaml deleted file mode 100644 index 5ca2e24e..00000000 --- a/examples/pytorch/conf/model/torch_cifar.yaml +++ /dev/null @@ -1,13 +0,0 @@ -defaults: - - art : default -data: ${data} -init: - _target_: deckard.base.model.ModelInitializer - name : torch_example.ResNet18 - num_channels: 3 - num_classes: 10 -_target_: deckard.base.model.Model -trainer: - nb_epoch: 100 - batch_size: 1024 -library : pytorch diff --git a/examples/pytorch/conf/model/torch_mnist.yaml b/examples/pytorch/conf/model/torch_mnist.yaml deleted file mode 100644 index 9c18c54b..00000000 --- a/examples/pytorch/conf/model/torch_mnist.yaml +++ /dev/null @@ -1,13 +0,0 @@ - -defaults: - - art : default -data: ${data} -init: - _target_: deckard.base.model.ModelInitializer - num_channels : 1 - name : torch_example.ResNet18 -_target_: deckard.base.model.Model -trainer: - nb_epoch: 100 - batch_size: 1024 -library : pytorch diff --git a/examples/pytorch/conf/plots.yaml b/examples/pytorch/conf/plots.yaml deleted file mode 100644 index accab9df..00000000 --- a/examples/pytorch/conf/plots.yaml +++ /dev/null @@ -1,250 +0,0 @@ -cat_plot: -- file: adv_accuracy_vs_defence_type.pdf - hue: model_name - kind: boxen - set: - yscale: linear - titles: $\lambda_{adv.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: adv_accuracy - ylabels: $\lambda_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: ben_accuracy_vs_defence_type.pdf - hue: model_name - kind: boxen - titles: $\lambda_{ben.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: accuracy - ylabels: $\lambda_{ben.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: ben_failures_per_train_time_vs_defence_type.pdf - hue: model_name - kind: boxen - set: - yscale: log - titles: $\bar{C}_{ben.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: training_time_per_failure - ylabels: $\bar{C}_{ben.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: adv_failures_per_train_time_vs_defence_type.pdf - hue: model_name - kind: boxen - set: - yscale: log - titles: $\bar{C}_{adv.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: training_time_per_adv_failure - ylabels: $\bar{C}_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: adv_failures_per_train_time_vs_attack_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - set: - yscale: log - titles: $\bar{C}_{adv.}$ vs Attack Type - x: atk_gen - xlabels: Attack Type - y: training_time_per_adv_failure - ylabels: $\bar{C}_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: adv_failures_per_test_time_vs_defence_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - titles: $h_{adv}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: adv_failure_rate - ylabels: $h_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -# - file: adv_accuracy_vs_defence_type.pdf -# hue: model_name -# kind: boxen -# legend_title: Model Name -# titles: $\lambda_{adv.}$ vs Defence Type -# x: def_gen -# xlabels: Defence Type -# y: adv_accuracy -# ylabels: Adv. $\lambda_{ben.}$ -# rotation : 90 -# hue_order: -# - ResNet18 -# - ResNet34 -# - ResNet50 -# - ResNet101 -# - ResNet152 -- file: adv_accuracy_vs_attack_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - titles: $\lambda_{adv.}$ vs Attack Type - x: atk_gen - xlabels: Attack Type - y: adv_accuracy - ylabels: $\lambda_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: ben_failure_rate_vs_defence_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - set: - yscale: log - titles: $h_{ben}(t; \theta)$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: failure_rate - ylabels: $h_{ben}(t; \theta)$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -line_plot: -# - file: def_param_vs_accuracy.pdf -# hue: def_gen -# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} -# title: $\lambda_{ben.}$ vs Defence Strength -# x: def_value -# x_scale: linear -# xlabel: Defence Control Parameter -# y: accuracy -# y_scale: -# ylabel: $\lambda_{ben.}$ -# hue_order: -# - Control -# - Gauss-in -# - Gauss-out -# - Conf -# - FSQ -# - file: def_param_vs_adv_accuracy.pdf -# hue: def_gen -# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} -# title: $\lambda_{adv.}$ vs Defence Strength -# x: def_value -# x_scale: linear -# xlabel: Defence Control Parameter -# y: adv_accuracy -# y_scale: -# ylabel: $\lambda_{adv.}$ -# hue_order: -# - Control -# - Gauss-in -# - Gauss-out -# - Conf -# - FSQ -# - file: def_param_vs_adv_failure_rate.pdf -# hue: def_gen -# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} -# title: $h_{adv}$ vs Defence Strength -# x: def_value -# x_scale: linear -# xlabel: Defence Control Parameter -# y: adv_failure_rate -# y_scale: log -# ylabel: $h_{adv.}$ -# hue_order: -# - Control -# - Gauss-in -# - Gauss-out -# - Conf -# - FSQ -- file: atk_param_vs_accuracy.pdf - hue: atk_gen - legend: {bbox_to_anchor: [1.05, 1]} - title: $\lambda_{adv.}$ vs Attack Strength - x: atk_value - x_scale: linear - xlabel: Attack Control Parameter - y: adv_accuracy - y_scale: - ylabel: $\lambda_{adv.}$ - hue_order: - - FGM - - PGD - - Deep - - HSJ - - Pixel - - Thresh - -scatter_plot: -- x: train_time_per_sample - y: adv_failure_rate - hue: model_name - xlabel: $t_{train}$ - ylabel: $h_{adv}$ - title: $h_{adv}$ vs $t_{train}$ - file: adv_failure_rate_vs_train_time.pdf - y_scale: log - x_scale: log - legend: - title: Model Name - bbox_to_anchor: [1.05, 1] - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 - # style : atk_gen -# - x: train_time_per_sample -# y: adv_failure_rate -# hue: model_name -# xlabel: $t_{train}$ -# ylabel: $h_{adv}$ -# title: $h_{adv}$ vs $t_{train}$ for each Defence -# file: adv_failure_rate_vs_train_time_def.pdf -# y_scale: log -# x_scale: linear -# legend: -# title: Model Name -# # style : def_gen diff --git a/examples/pytorch/conf/scorers/default.yaml b/examples/pytorch/conf/scorers/default.yaml deleted file mode 100644 index 4503c5bf..00000000 --- a/examples/pytorch/conf/scorers/default.yaml +++ /dev/null @@ -1,9 +0,0 @@ -_target_: deckard.base.scorer.ScorerDict -accuracy: - _target_: deckard.base.scorer.ScorerConfig - name: sklearn.metrics.accuracy_score - direction: maximize -log_loss: - _target_: deckard.base.scorer.ScorerConfig - name: sklearn.metrics.log_loss - direction: minimize diff --git a/examples/pytorch/dvc.lock b/examples/pytorch/dvc.lock deleted file mode 100644 index e4dd2fa2..00000000 --- a/examples/pytorch/dvc.lock +++ /dev/null @@ -1,767 +0,0 @@ -schema: '2.0' -stages: - train: - cmd: python -m deckard.layers.experiment train --config_file mnist.yaml - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: mnist - model_dir: models - model_file: model - model_type: .pt - name: default - params_file: params.yaml - predictions_file: predictions.json - reports: reports - score_dict_file: score_dict.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss - outs: - - path: mnist/data/data.pkl - md5: de934a5f5157970e5f30b8f3f1856a68 - size: 222320311 - - path: mnist/models/model.optimizer.pt - hash: md5 - md5: 1e527d70896a4a05a2d6ac103382cd50 - size: 44780845 - - path: mnist/models/model.pt - hash: md5 - md5: f01e051c7b7dfa20eca3fe1caab0b25e - size: 44785941 - - path: mnist/reports/train/default/predictions.json - hash: md5 - md5: 1e2cf0100bb5f0a42182021e12b00dd9 - size: 2882749 - - path: mnist/reports/train/default/score_dict.json - hash: md5 - md5: ebe552d99842320709ca466da6d3092c - size: 410 - attack: - cmd: python -m deckard.layers.experiment attack --config_file mnist.yaml - deps: - - path: mnist/data/data.pkl - md5: de934a5f5157970e5f30b8f3f1856a68 - size: 222320311 - - path: mnist/models/model.pt - hash: md5 - md5: f01e051c7b7dfa20eca3fe1caab0b25e - size: 44785941 - params: - params.yaml: - attack: - _target_: deckard.base.attack.Attack - attack_size: 10 - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.attack.AttackInitializer - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - name: art.attacks.evasion.HopSkipJump - method: evasion - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: mnist - model_dir: models - model_file: model - model_type: .pt - name: default - params_file: params.yaml - predictions_file: predictions.json - reports: reports - score_dict_file: score_dict.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss - outs: - - path: mnist/attacks/attack.pkl - hash: md5 - md5: dfae95e05b35dc8c6bdabd2b5c13a54f - size: 31517 - - path: mnist/reports/attack/default/adv_predictions.json - hash: md5 - md5: 8ee95327d4f44692f4753e183090d669 - size: 2162 - - path: mnist/reports/attack/default/score_dict.json - hash: md5 - md5: b11f759014274f38b082f200820c5544 - size: 474 - models: - cmd: bash models.sh - ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 - --config-name grid.yaml - deps: - - path: output/data/data.pkl - md5: de934a5f5157970e5f30b8f3f1856a68 - size: 222320311 - - path: output/models/model.pt - md5: 38451da384fb8f787707a2b39b8418de - size: 44786389 - outs: - - path: model.db - md5: d1eac324650402da6e3de1aebe0e3b3c - size: 237568 - attacks: - cmd: bash attacks.sh - ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 - ++stage=attack --config-name grid.yaml - deps: - - path: model.db - md5: d1eac324650402da6e3de1aebe0e3b3c - size: 237568 - - path: output/data/data.pkl - md5: de934a5f5157970e5f30b8f3f1856a68 - size: 222320311 - - path: output/models/model.pt - md5: 38451da384fb8f787707a2b39b8418de - size: 44786389 - outs: - - path: attack.db - md5: c9f920c7233802e9c46e4051c2da78e6 - size: 307200 - compile_train: - cmd: python -m deckard.layers.compile --report_folder output/reports/train/ --results_file - output/reports/train.csv - deps: - - path: model.db - md5: e5dc2d529f4841baf9cccedd7b417988 - size: 110592 - - path: output/data/ - md5: 0078b738d3ac5d26c4c487d9c43903da.dir - size: 1111601555 - nfiles: 5 - - path: output/models/ - md5: 2dc57f423c263fa18830ef6d532f592f.dir - size: 1074846 - nfiles: 14 - outs: - - path: output/reports/train.csv - md5: 54707302f1ee42d25231d73ee2c03cf3 - size: 12177 - compile_attack: - cmd: python -m deckard.layers.compile --report_folder output/reports/attack/ --results_file - output/reports/attack.csv - deps: - - path: attack.db - md5: 576e07b1a496218659b7a425a812412b - size: 319488 - - path: output/attacks/ - md5: f6967d943832917c2b1e0db449d514f7.dir - size: 336979704 - nfiles: 1044 - - path: output/data/ - md5: 837a1c3acf188d7955e48419b38d8135.dir - size: 2445523421 - nfiles: 11 - - path: output/models/ - md5: 33fa241d9672dfc7f7f27927869d4948.dir - size: 160466396 - nfiles: 2088 - outs: - - path: output/reports/attack.csv - md5: 36ffafc8cb80eb6fbed190be9d420ef7 - size: 3674355 - compile@attack: - cmd: python -m deckard.layers.compile --report_folder mnist/reports/attack --results_file - mnist/reports/attack.csv - deps: - - path: mnist/reports/attack/ - hash: md5 - md5: 72220a80b6e0a4c6f9ca707c985761c5.dir - size: 63191244 - nfiles: 8010 - - path: mnist/reports/attack/ResNet101.db - hash: md5 - md5: 600452804d96c8b8483c3f8da01130c4 - size: 462848 - - path: mnist/reports/attack/ResNet18.db - hash: md5 - md5: 920b0ed178ec504c0d7990777862283f - size: 1363968 - - path: mnist/reports/attack/ResNet34.db - hash: md5 - md5: 3f56dd2ea0783a56a2a9e3eaaad88c21 - size: 1945600 - - path: mnist/reports/attack/ResNet50.db - hash: md5 - md5: d9ee221b942b56d9bb720e022e05bf4b - size: 462848 - outs: - - path: mnist/reports/attack.csv - hash: md5 - md5: 95476c59ee7966ee3458a873c0a89b7d - size: 8506622 - compile@train: - cmd: python -m deckard.layers.compile --report_folder output/reports/train --results_file - output/reports/train.csv - deps: - - path: ResNet101.db - md5: 744f187243001db70f63c8161d7f913f - size: 1314816 - - path: ResNet152.db - md5: e0fcb3876ec636b12d7dc9e71b9d1e1c - size: 1314816 - - path: ResNet18.db - md5: aa69b1818219c139f8e33ac205771ec1 - size: 110592 - - path: ResNet34.db - md5: e4d151b9800a1255a0143368057cb146 - size: 1339392 - - path: ResNet50.db - md5: 9c9bc0aab00251ca5e9bd210366bc055 - size: 1339392 - - path: output/reports/train/ - md5: 098781f04bdba3498c00a157e3e45826.dir - size: 403646689 - nfiles: 640 - outs: - - path: output/reports/train.csv - md5: 152c61d1ae1a58e89c03c1351d5bf406 - size: 411488 - attacks@ResNet152: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet152 - stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet152.db - --config-name mnist.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: mnist/reports/attack/default/score_dict.json - hash: md5 - md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 - size: 472 - - path: models.sh - hash: md5 - md5: 1937e58bedac027034aea7d4a5712407 - size: 1380 - outs: - - path: mnist/reports/attack/ResNet152.db - hash: md5 - md5: afb6f3d2616446226012b3ff1143086a - size: 462848 - plot: - cmd: python -m deckard.layers.plots --path output/plots/ --file output/reports/attack.csv - deps: - - path: ResNet101.db - md5: c47414423a51a4b866be6ce8312cb97e - size: 561152 - - path: ResNet152.db - md5: 690f931bf696f8e5f1e044fa7b335411 - size: 425984 - - path: ResNet18.db - md5: 2ae52c7b342b46ac01c79f06bfdc6c38 - size: 2768896 - - path: ResNet34.db - md5: 6a5795cbb9d977cdaadf365a6ac76a0a - size: 983040 - - path: ResNet50.db - md5: 7e215d670119b0703c3d97d86e28e117 - size: 561152 - - path: output/reports/attack.csv - md5: 69c87ac633b8abae15d65852c79ea89c - size: 6309975 - outs: - - path: output/plots/data.csv - md5: 8283c78e358db5d4ce19e5d44b6e735e - size: 2185730 - attacks@ResNet101: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet101 - stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet101.db - --config-name mnist.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: mnist/reports/attack/default/score_dict.json - hash: md5 - md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 - size: 472 - - path: models.sh - hash: md5 - md5: 1937e58bedac027034aea7d4a5712407 - size: 1380 - outs: - - path: mnist/reports/attack/ResNet101.db - hash: md5 - md5: 600452804d96c8b8483c3f8da01130c4 - size: 462848 - attacks@ResNet34: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 - stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet34.db - --config-name mnist.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: mnist/reports/attack/default/score_dict.json - hash: md5 - md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 - size: 472 - - path: models.sh - hash: md5 - md5: 1937e58bedac027034aea7d4a5712407 - size: 1380 - outs: - - path: mnist/reports/attack/ResNet34.db - hash: md5 - md5: 9244c6545aa2d39680b601311b446b7d - size: 1593344 - attacks@ResNet50: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 - stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet50.db - --config-name mnist.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: mnist/reports/attack/default/score_dict.json - hash: md5 - md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 - size: 472 - - path: models.sh - hash: md5 - md5: 1937e58bedac027034aea7d4a5712407 - size: 1380 - outs: - - path: mnist/reports/attack/ResNet50.db - hash: md5 - md5: d9ee221b942b56d9bb720e022e05bf4b - size: 462848 - afr: - cmd: python -m deckard.layers.afr --dataset mnist - deps: - - path: output/plots/data.csv - md5: 8283c78e358db5d4ce19e5d44b6e735e - size: 2185730 - outs: - - path: output/plots/aft_comparison.csv - md5: a72d5fbf5c21b5055e6bc20a0e48c6aa - size: 178 - - path: output/plots/aft_comparison.tex - md5: e53edcb94e353f2e03324d1c02673332 - size: 401 - - path: output/plots/cox_aft.pdf - md5: 6b5fd449d724cf097eea5d8569dda3f0 - size: 30712 - - path: output/plots/cox_partial_effects.pdf - md5: a434a352ea874a5c188f76ef8456dbdf - size: 28132 - - path: output/plots/log_logistic_aft.pdf - md5: 6b1e524a7b7c2da913e4c9346115167c - size: 33524 - - path: output/plots/log_logistic_partial_effects.pdf - md5: f3c5ccbbb5129cbffe6b38f70d655245 - size: 29147 - - path: output/plots/log_normal_aft.pdf - md5: c0b1f808746c28b5b352ca425c6d2d33 - size: 34017 - - path: output/plots/log_normal_partial_effects.pdf - md5: 1c67a22e51019ceeb39573ea21b4a7ba - size: 29844 - - path: output/plots/weibull_aft.pdf - md5: 4b8119d87bd201bea0fa9955e3f3481d - size: 32175 - - path: output/plots/weibull_partial_effects.pdf - md5: 4b1433467ad94132bde9003c9faaed10 - size: 29359 - copy_results: - cmd: cp -r output/plots/* ~/ml_afr/mnist/ - deps: - - path: output/plots/aft_comparison.csv - md5: a72d5fbf5c21b5055e6bc20a0e48c6aa - size: 178 - - path: output/plots/data.csv - md5: 8283c78e358db5d4ce19e5d44b6e735e - size: 2185730 - attacks@ResNet18: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 - stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet18.db - --config-name mnist.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: mnist/reports/attack/default/score_dict.json - hash: md5 - md5: b11f759014274f38b082f200820c5544 - size: 474 - - path: models.sh - hash: md5 - md5: 1937e58bedac027034aea7d4a5712407 - size: 1380 - outs: - - path: mnist/reports/attack/ResNet18.db - hash: md5 - md5: 920b0ed178ec504c0d7990777862283f - size: 1363968 - models@ResNet50: - cmd: bash models.sh ++model.init.name=torch_example.ResNet50 stage=train ++hydra.sweeper.storage=sqlite:///mnist/reports/train/ResNet50.db - --config-name mnist.yaml - deps: - - path: mnist/models/model.optimizer.pt - hash: md5 - md5: cc914518d5c8fdab1e7ec43c7db63a3b - size: 44780845 - - path: mnist/models/model.pt - hash: md5 - md5: 0508fa68d600b39cd8ce1dad0152fba3 - size: 44785941 - - path: models.sh - md5: e5079ede85d4f9b286984e507a157af4 - size: 1371 - outs: - - path: mnist/reports/train/ResNet50.db - hash: md5 - md5: 957e39666f06c9e8e207a9f98bc569b5 - size: 913408 - models@ResNet18: - cmd: bash models.sh ++model.init.name=torch_example.ResNet18 stage=train ++hydra.sweeper.storage=sqlite:///mnist/reports/train/ResNet18.db - --config-name mnist.yaml - deps: - - path: mnist/models/model.optimizer.pt - hash: md5 - md5: f4e28adc9c0d30180eca422da2dd00e3 - size: 44780845 - - path: mnist/models/model.pt - hash: md5 - md5: 53a2d89abb350e2601f35f36b95f6095 - size: 44785941 - - path: models.sh - hash: md5 - md5: cd5b2760310df71a36a2ea26021477b4 - size: 1366 - outs: - - path: mnist/reports/train/ResNet18.db - hash: md5 - md5: 225273e0494668fa42bc6f69fb29d392 - size: 901120 - models@ResNet34: - cmd: bash models.sh ++model.init.name=torch_example.ResNet34 stage=train ++hydra.sweeper.storage=sqlite:///mnist/reports/train/ResNet34.db - --config-name mnist.yaml - deps: - - path: mnist/models/model.optimizer.pt - hash: md5 - md5: cc914518d5c8fdab1e7ec43c7db63a3b - size: 44780845 - - path: mnist/models/model.pt - hash: md5 - md5: 0508fa68d600b39cd8ce1dad0152fba3 - size: 44785941 - - path: models.sh - hash: md5 - md5: e5079ede85d4f9b286984e507a157af4 - size: 1371 - outs: - - path: mnist/reports/train/ResNet34.db - hash: md5 - md5: af60ce478f27cda303ebd34c63cf05d3 - size: 913408 - models@ResNet101: - cmd: bash models.sh ++model.init.name=torch_example.ResNet101 stage=train ++hydra.sweeper.storage=sqlite:///mnist/reports/train/ResNet101.db - --config-name mnist.yaml - deps: - - path: mnist/models/model.optimizer.pt - hash: md5 - md5: cc914518d5c8fdab1e7ec43c7db63a3b - size: 44780845 - - path: mnist/models/model.pt - hash: md5 - md5: 0508fa68d600b39cd8ce1dad0152fba3 - size: 44785941 - - path: models.sh - hash: md5 - md5: cd5b2760310df71a36a2ea26021477b4 - size: 1366 - outs: - - path: mnist/reports/train/ResNet101.db - hash: md5 - md5: a8e178cba49addf77bac3b27e974ce8c - size: 917504 diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml deleted file mode 100644 index 21d8c792..00000000 --- a/examples/pytorch/dvc.yaml +++ /dev/null @@ -1,120 +0,0 @@ -stages: - train: - cmd: python -m deckard.layers.experiment train --config_file mnist.yaml - params: - - data - - model - - scorers - - files - outs: - - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} - # - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} - # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} # Omit to save space - - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} # logit outputs for our model - # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} # Omit to save space - metrics: - - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} - attack: - cmd: python -m deckard.layers.experiment attack --config_file mnist.yaml - params: - - data - - model - - attack - - scorers - - files - outs: - - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} - - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} - # - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} - deps: - - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - metrics: - - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} - - ############################################################################## - # models: # This is a loop over the ResNet models - # foreach: - # - ResNet18 - # # - ResNet34 - # # - ResNet50 - # # - ResNet101 - # # - ResNet152 - # do: # This script configures eazch defence - # cmd: bash models.sh ++model.init.name=torch_example.${item} stage=train ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/train/${item}.db --config-name mnist.yaml - # deps: - # - models.sh - # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - # - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} - # outs: - # - ${files.directory}/${files.reports}/train/${item}.db: # This outputs a database file for each model - # cache: True - # persist: True - attacks: - foreach: # This is a loop over the ResNet models - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 - do: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/attack/${item}.db --config-name mnist.yaml - deps: - - models.sh # This script configures each defence - - attacks.sh # This script configures each attack - - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} # This is here just to ensure it runs after the attack stage - # - ${files.directory}/${files.reports}/train/${item}.db - outs: - - ${files.directory}/${files.reports}/attack/${item}.db: # This outputs a database file for each model - cache: True - persist: True - compile: - foreach: # iterates through each stage - # - train - - attack - do: - cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv - deps: - - ${files.directory}/${files.reports}/${item}/ - - ${files.directory}/${files.reports}/${item}/ResNet18.db - - ${files.directory}/${files.reports}/${item}/ResNet34.db - - ${files.directory}/${files.reports}/${item}/ResNet50.db - - ${files.directory}/${files.reports}/${item}/ResNet101.db - # - ${files.directory}/${files.reports}/${item}/ResNet152.db - outs: - - ${files.directory}/${files.reports}/${item}.csv - plot: - cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv -o data.csv - deps: - - ${files.directory}/${files.reports}/attack.csv - - ${files.directory}/${files.reports}/attack/ResNet18.db - - ${files.directory}/${files.reports}/attack/ResNet34.db - - ${files.directory}/${files.reports}/attack/ResNet50.db - - ${files.directory}/${files.reports}/attack/ResNet101.db - - ${files.directory}/${files.reports}/attack/ResNet152.db - outs: - - ${files.directory}/plots/data.csv - afr: - cmd: python -m deckard.layers.afr --dataset ${files.directory} --file ${files.directory}/plots/data.csv - deps: - - ${files.directory}/plots/data.csv - plots: - - ${files.directory}/plots/weibull_aft.pdf - - ${files.directory}/plots/weibull_partial_effects.pdf - - ${files.directory}/plots/cox_partial_effects.pdf - - ${files.directory}/plots/cox_aft.pdf - - ${files.directory}/plots/log_logistic_aft.pdf - - ${files.directory}/plots/log_logistic_partial_effects.pdf - - ${files.directory}/plots/log_normal_aft.pdf - - ${files.directory}/plots/log_normal_partial_effects.pdf - metrics: - - ${files.directory}/plots/aft_comparison.csv - outs: - - ${files.directory}/plots/aft_comparison.tex - copy_results: - cmd: cp -r ${files.directory}/plots/* ~/ml_afr/mnist/ - deps: - - ${files.directory}/plots/data.csv - - ${files.directory}/plots/aft_comparison.csv diff --git a/examples/pytorch/models.sh b/examples/pytorch/models.sh deleted file mode 100644 index 2978d445..00000000 --- a/examples/pytorch/models.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash - -# This script is used to generate the models for the sklearn example. - -# # Default model -echo "python -m deckard.layers.optimise " $@ "--multirun" -python -m deckard.layers.optimise $@ --multirun - -# # This line generates the model and adds the FeatureSqueezing preprocessing defence. -# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,255] ++hydra.sweeper.study_name=FSQ $@ --multirun - -# # # Gaussian Augmentation (Input) -# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.5 +model.art.preprocessor.params.augmentation=False ++hydra.sweeper.study_name=gauss-in $@ --multirun - -# # # # Gaussian Noise (Output) -# python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 ++hydra.sweeper.study_name=gauss-out $@ --multirun - -# # # # High Confidence -# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 ++hydra.sweeper.study_name=conf $@ --multirun diff --git a/examples/pytorch/params.yaml b/examples/pytorch/params.yaml deleted file mode 100644 index 7a9ccdc6..00000000 --- a/examples/pytorch/params.yaml +++ /dev/null @@ -1,228 +0,0 @@ -_target_: deckard.base.experiment.Experiment -attack: - _target_: deckard.base.attack.Attack - attack_size: 10 - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.attack.AttackInitializer - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - name: art.attacks.evasion.HopSkipJump - method: evasion - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 -data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true -direction: maximize -files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: mnist - model_dir: models - model_file: model - model_type: .pt - name: default - params_file: params.yaml - predictions_file: predictions.json - reports: reports - score_dict_file: score_dict.json -model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 -optimizers: accuracy -scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss -stage: ??? diff --git a/examples/pytorch/torch_example.py b/examples/pytorch/torch_example.py deleted file mode 100644 index 2007d9d0..00000000 --- a/examples/pytorch/torch_example.py +++ /dev/null @@ -1,79 +0,0 @@ -import torch.nn as nn -from torchvision import models - -__all__ = [ - "ResNet18", - "ResNet50", - "ResNet101", - "ResNet152", -] - - -def ResNet18(num_channels=1, num_classes=10): - model = models.resnet18() - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(512, num_classes) - return model - - -def ResNet34(num_channels=1, num_classes=10): - model = models.resnet34() - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(512, num_classes) - return model - - -def ResNet50(num_channels=1, num_classes=10): - model = models.resnet50() - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(2048, num_classes) - return model - - -def ResNet101(num_channels=1, num_classes=10): - model = models.resnet101() - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(2048, num_classes) - return model - - -def ResNet152(num_channels=1, num_classes=10): - model = models.resnet152() - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(2048, num_classes) - return model diff --git a/examples/pytorch/weibull.ipynb b/examples/pytorch/weibull.ipynb deleted file mode 100644 index def6e2d1..00000000 --- a/examples/pytorch/weibull.ipynb +++ /dev/null @@ -1,2062 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from pathlib import Path\n", - "import seaborn as sns\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "\n", - "from paretoset import paretoset\n", - "from lifelines import (\n", - " WeibullAFTFitter,\n", - " LogNormalAFTFitter,\n", - " LogLogisticAFTFitter,\n", - " CoxPHFitter,\n", - ")\n", - "from plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling\n", - "import matplotlib\n", - "import argparse\n", - "from pathlib import Path\n", - "import logging\n", - "\n", - "logger = logging.getLogger(__name__)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "font = {\"family\": \"Times New Roman\", \"weight\": \"bold\", \"size\": 22}\n", - "\n", - "matplotlib.rc(\"font\", **font)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Adversarial Accuracy: \n", - " ResNet152: 0.08431102362204726 \n", - " Resnet101: 0.08595785440613028 \n", - " Resnet50: 0.09093333333333335 \n", - " Resnet34: 0.08867549668874172 \n", - " Resnet18: 0.07971698113207548 \n", - "\n" - ] - } - ], - "source": [ - "FOLDER = Path(\"output/plots/\")\n", - "csv_file = FOLDER / \"data.csv\"\n", - "data = pd.read_csv(csv_file, index_col=0)\n", - "data.columns = data.columns.str.strip()\n", - "data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n", - "data.def_value.replace(\"\", 0, inplace=True)\n", - "data.atk_value.replace(\"\", 0, inplace=True)\n", - "data = drop_frames_without_results(data)\n", - "data = calculate_failure_rate(data)\n", - "data = min_max_scaling(data)\n", - "data.dropna(axis=0, subset=[\"atk_value\", \"atk_param\"], inplace=True)\n", - "data.dropna(axis=0, subset=[\"def_value\", \"def_param\"], inplace=True)\n", - "# data=data[data['def_gen'] == 'Gauss-in']\n", - "# data=data[data['atk_gen'] == 'HSJ']\n", - "\n", - "print(\n", - " \"Adversarial Accuracy:\",\n", - " \"\\n\",\n", - " \"ResNet152:\",\n", - " data[data[\"model_layers\"] == 152].adv_accuracy.mean(skipna=True),\n", - " \"\\n\",\n", - " \"Resnet101:\",\n", - " data[data[\"model_layers\"] == 101].adv_accuracy.mean(skipna=True),\n", - " \"\\n\",\n", - " \"Resnet50:\",\n", - " data[data[\"model_layers\"] == 50].adv_accuracy.mean(skipna=True),\n", - " \"\\n\",\n", - " \"Resnet34:\",\n", - " data[data[\"model_layers\"] == 34].adv_accuracy.mean(skipna=True),\n", - " \"\\n\",\n", - " \"Resnet18:\",\n", - " data[data[\"model_layers\"] == 18].adv_accuracy.mean(skipna=True),\n", - " \"\\n\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_aft(\n", - " df,\n", - " file,\n", - " event_col,\n", - " duration_col,\n", - " title,\n", - " mtype,\n", - " xlabel=None,\n", - " ylabel=None,\n", - " replacement_dict={},\n", - " **kwargs,\n", - "):\n", - " if mtype == \"weibull\":\n", - " aft = WeibullAFTFitter(**kwargs)\n", - " elif mtype == \"log_normal\":\n", - " aft = LogNormalAFTFitter(**kwargs)\n", - " elif mtype == \"log_logistic\":\n", - " aft = LogLogisticAFTFitter(**kwargs)\n", - " elif mtype == \"cox\":\n", - " aft = CoxPHFitter(**kwargs)\n", - " assert (\n", - " duration_col in df.columns\n", - " ), f\"Column {duration_col} not in dataframe with columns {df.columns}\"\n", - " if event_col is not None:\n", - " assert (\n", - " event_col in df.columns\n", - " ), f\"Column {event_col} not in dataframe with columns {df.columns}\"\n", - " aft.fit(df, duration_col=duration_col, event_col=event_col)\n", - " ax = aft.plot()\n", - " labels = ax.get_yticklabels()\n", - " labels = [label.get_text() for label in labels]\n", - " for k, v in replacement_dict.items():\n", - " labels = [label.replace(k, v) for label in labels]\n", - " ax.set_yticklabels(labels)\n", - " ax.set_xlabel(xlabel)\n", - " ax.set_ylabel(ylabel)\n", - " ax.set_title(title)\n", - " ax.get_figure().tight_layout()\n", - " ax.get_figure().savefig(FOLDER / file)\n", - " logger.info(f\"Saved graph to {FOLDER / file}\")\n", - " plt.show()\n", - " return ax, aft\n", - "\n", - "\n", - "def plot_partial_effects(\n", - " file,\n", - " aft,\n", - " covariate_array,\n", - " values_array,\n", - " title,\n", - " xlabel=\"Covariate\",\n", - " ylabel=\"Failure rate\",\n", - " legend_kwargs={\"loc\": \"upper left\"},\n", - " replacement_dict={},\n", - " cmap=\"coolwarm\",\n", - " **kwargs,\n", - "):\n", - " plt.gcf().clear()\n", - " # kwargs.pop(\"replacement_dict\")\n", - " pareto = aft.plot_partial_effects_on_outcome(\n", - " covariate_array, values_array, cmap=cmap, **kwargs\n", - " )\n", - " labels = pareto.get_yticklabels()\n", - " labels = [label.get_text() for label in labels]\n", - " for k, v in replacement_dict.items():\n", - " labels = [label.replace(k, v) for label in labels]\n", - " pareto.set_yticklabels(labels)\n", - " pareto.legend(**legend_kwargs)\n", - " pareto.set_ylabel(ylabel)\n", - " pareto.set_xlabel(xlabel)\n", - " pareto.set_title(title)\n", - " pareto.get_figure().tight_layout()\n", - " pareto.get_figure().savefig(FOLDER / file)\n", - " logger.info(f\"Saved graph to {FOLDER / file}\")\n", - " return pareto\n", - "\n", - "\n", - "def score_model(aft, train, test):\n", - " train_score = aft.score(train)\n", - " test_score = aft.score(test)\n", - " scores = {\"train_score\": train_score, \"test_score\": test_score}\n", - " plt.show()\n", - " return scores\n", - "\n", - "\n", - "def clean_data_for_aft(data, kwarg_list, target=\"adv_failure_rate\"):\n", - " subset = data.copy()\n", - " assert (\n", - " target in subset\n", - " ), f\"Target {target} not in dataframe with columns {subset.columns}\"\n", - "\n", - " cleaned = pd.DataFrame()\n", - " kwarg_list.append(target)\n", - " for kwarg in kwarg_list:\n", - " cleaned = pd.concat([cleaned, subset[kwarg]], axis=1)\n", - " cols = cleaned.columns\n", - " cleaned = pd.DataFrame(subset, columns=cols)\n", - "\n", - " # if \"accuracy\" in cleaned.columns:\n", - " # cleaned = cleaned[~cleaned[cleaned['accuracy'] != 1e10]]\n", - " # cleaned = cleaned[~cleaned[cleaned['accuracy'] != -1e10]]\n", - " # if \"adv_accuracy\" in cleaned.columns:\n", - " # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != 1e10]]\n", - " # cleaned = cleaned[cleaned[cleaned['adv_accuracy'] != -1e10]]\n", - " cleaned.dropna(inplace=True, how=\"any\", axis=0)\n", - " y = cleaned[target]\n", - " assert (\n", - " target in cleaned\n", - " ), f\"Target {target} not in dataframe with columns {cleaned.columns}\"\n", - " return cleaned, y, data" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "kwarg_list = [\n", - " # \"accuracy\",\n", - " \"train_time\",\n", - " \"predict_time\",\n", - " \"atk_value\",\n", - " \"def_value\",\n", - " \"data.sample.random_state\",\n", - " \"adv_failure_rate\",\n", - " # \"failure_rate\",\n", - " \"model_layers\",\n", - " \"adv_fit_time\",\n", - " # \"atk_param\",\n", - " # \"def_param\",\n", - " \"model.art.pipeline.initialize.kwargs.optimizer.lr\",\n", - " # \"def_gen\",\n", - " # \"atk_gen\",\n", - " # \"adv_log_loss\",\n", - " # \"adv_accuracy\",\n", - " # \"adv_accuracy\",\n", - "]\n", - "\n", - "\n", - "# cleaned['accuracy'] = y" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "data.loc[:, \"adv_failures\"] = 1 - data.loc[:, \"adv_accuracy\"]\n", - "data.loc[:, \"ben_failures\"] = 1 - data.loc[:, \"accuracy\"]\n", - "target = \"adv_failures\"\n", - "duration_col = \"adv_fit_time\"\n", - "cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target)\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " cleaned, y, test_size=0.2, random_state=42\n", - ")\n", - "assert (\n", - " target in cleaned\n", - "), f\"Target {target} not in dataframe with columns {cleaned.columns}\"" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# from sklearn.preprocessing import PowerTransformer\n", - "# pt = PowerTransformer(method='yeo-johnson', standardize=False)\n", - "# del X_train[target]\n", - "# del X_test[target]\n", - "# X_train_cols = X_train.columns\n", - "# X_train = pt.fit(X_train).transform(X_train)\n", - "# X_test = pt.transform(X_test)\n", - "# X_train = pd.DataFrame(X_train, columns=X_train_cols)\n", - "# X_test = pd.DataFrame(X_test, columns=X_train_cols)\n", - "# X_train[target] = y_train\n", - "# y_train = X_train[target]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# sense_dict ={\n", - "# \"accuracy\" : \"max\",\n", - "# \"train_time\" : \"min\",\n", - "# \"predict_time\" : \"min\",\n", - "# # \"atk_value\" : \"diff\",\n", - "# # \"def_value\" : \"diff\",\n", - "# \"data.sample.random_state\" : \"diff\",\n", - "# \"adv_accuracy\" : \"min\",\n", - "# \"model_layers\" : \"diff\",\n", - "# # \"adv_fit_time\" : \"min\",\n", - "# # \"atk_param\" : \"diff\",\n", - "# # \"def_param\" : \"diff\",\n", - "# \"model.art.pipeline.initialize.kwargs.optimizer.lr\" : \"diff\",\n", - "# # \"adv_failure_rate\" : \"maximize\",\n", - "# }\n", - "# subset = X_train.loc[:, sense_dict.keys()]\n", - "# senses = sense_dict.values()\n", - "# these = paretoset(subset)\n", - "# X_train = X_train.iloc[these, :]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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r+cIX7sL7nkq+6OT5uI6q5ubmhrZt2+LOnTusbjjhH+FHoMhj0LmmpqbYQ6il8fvvvzOzHKOiorB48eIqm7o/ZswY5hEpr1+/hr+/P7KysnDu3DmMHDmyzJYlUcLLKAgv2SBKT0+PeV3yHhKesVrR1dtLZuLl5eXh8+fPFTqWSEaBFCGk2mjVqhUzVsbX1xdubm5o3749mjZtyspnY2PDvL5y5Qpu3LgBKysriWWWfPHdu3evzHMnJCTg27dvlai9ZFlZWQDAugZFrHItrZIFOB0cHMpsqTA3N0ePHj0AAMHBwazxO9/bihUr0LZtWwDF75s9e/ZUyXm0tLRYA7SdnZ1x7tw55OTksMZQScPY2Jh5LTo2T5jwWKySblbh7sOwsLAKnVf4WXn3798vM29wcDBrHTQiGQVShJBqpWTQecmDX4WDphLdu3dnZo5du3YN2dnZTCuVqA4dOgAofnRMYGBgqed1d3eX+dlspYmNjUV2dja4XC769u3LpAuPJyptinqJ770w4vnz56Gvr88Mci6LcFeWvBforAh1dXXs2bOHGc9z5MiRcoMEWU2aNInpWn7y5AmOHDkCS0tLNGjQoELltG/fngmIfH19S11Dq+T5k+rq6swq68Iz/B48eFBusCP8OJ+GDRsyszDd3NzK7Ardu3cv8xBxUjoKpAgh1YqVlRXz4a2mpoZhw4aJ5eFwOMwyBwKBAIMGDSp1yYCSliqBQIA///xT4srpb9++hb+/P+vZaCXHCP9bUXfu3AEAjB8/HoaGhky6vr4+0yolPPOwhPCg67y8PLH9wvWRVLfy9peGx+PB3d0d48aNk+oLtF+/fszA8bt37yIqKkpiPuEvclnvZXkBZf369Zl1pAQCAf7+++8Kt9aInk9SXWvWrIkxY8Yw2xkZGZg+fXq5dRYtS11dHWPHjgVQ3BpaWotpyfpRVlZWTKBoZmbGLDaalJSE3bt3Szy25JzC4/M4HA4zljApKQlOTk4Sx++dPn0aDRs2FBsjVdn/E8qIAilCSLViaGiIPn36ACj+ohbuihAmvFyCpEfClPjtt9+Y5Qni4+MxatQoHDx4EK9fv8aLFy+wf/9+TJkyBYsXLxY7tuTRMSXdc6WRFEDExcXh0KFDaN++PRYtWsTap6mpiUaNGgEoXuAxODiYWRNpyZIlrJazZ8+egcfjseog3HJWsmq6MOG8kvaXxtXVFYmJicyaXuVRVVVFr169ABSPvSntC71kwD8ApKWlyfQlXDJIGvi/VhpRgwYNYgaEZ2dnY9q0aWV2m5WmqKgIqamppa6R5eDgwLQq9urVi+lWFCV8vKSy5s6dy8wk3bp1q1iLKJ/Px8WLF2FoaIi///6bte/vv/9mVvo/fvw4Nm/ezLxfMzIysHbtWma1/idPnuDt27fMSvSzZ89mAvuAgADY2Njg4sWLePfuHZ48eYLly5fj4MGDmDNnjlidS+pYkfeVsqNAihBS7ZQESZK69Uo0bNgQv/zyC+rUqcNaRFCUiooK9uzZg3bt2gEoDjL27NmDsWPHYsKECdi/fz8WLlzIjPcBir/A3rx5wzzn7sOHD7hz5w6rdcjAwAC9e/cGAKxevRpbt25FREQEcnJy4Ovri4kTJ6Jdu3Y4duyYxNaykjE1UVFRGD16NNq2bYthw4bB2NiYFdS5uLhg1qxZ+PbtG4qKihAfH89a1f3o0aOsL+CMjAzWY0zOnDmDhISEMlt0srKycOnSJeZZd9euXSv3GW98Ph8RERGsFbdv3LiBnTt3IiYmBkVFRSgsLERMTAx27NjB5MnJycG///6LpKQkqbot8/PzERISwlpl+8mTJ/D29kZOTo5YULZkyRJ07twZQHHwNXbsWGzevBkvX74s98u/qKgIiYmJ2LFjB/Ly8nD58mW8fv0aPB6Plc/ExISZYSr6cOKSIMzNzY01mPvw4cP4+vUr67Erurq6OH78OMzMzBAdHY1Jkybh2bNnyM7ORnh4OGbPno28vDy4u7uzFkUFgC5dumD9+vVMMOXm5oaePXvCwsICvXr1grGxMfOswY8fP2Lv3r1MN3KdOnVw+PBh1KpVC0DxDMJVq1Zh5MiRmDp1Ku7du4d9+/Yx+4HiltFr164x77U7d+4gPDy83K7pnwFHQO1zhJBqJj8/H1ZWVrhx40aZq0RfvHgRHz9+xPLly6Uq8/Tp07h27Ro+f/4MLpeLjh07YsaMGcwaVSWWLFkicfXq2rVr48mTJ6y0Fy9e4NKlSwgICEBCQgJ0dHTQrl07jBkzBpaWlmUOLD9z5gxcXFyQmJiI5s2bY/r06RgyZAiioqJgZWWFESNGYPLkyWjWrBmA4gBHtGWixI0bN6Cvr49+/fpJ3L969WqJK1kDxd1Gklpu1qxZg4kTJ0o8Zv369cyCkpK4u7tj7dq1Zc4M6927N44fP17q/ri4uFKvp8SWLVvE1g+LjY2FjY2N2PgfKysrVlAnas6cOcyyG6IuXbrEPFwbKF4HatmyZbh48SIrX3n3RU9PT2ysXnZ2Nk6cOIE7d+7g27dv4HK5aNCgAYYPH47Ro0eXuTZVcHAwDh06hMDAQOTn56Nt27aYOXMm+vbti0GDBqFdu3aYNWuWxAVPk5OT4ezsjPv37yM+Ph41atRA7969MW/ePLE11wYPHoyvX7+KlWFpaVnpx/v86CiQIoQQQgiREXXtEUIIIYTIiAIpQgghhBAZUSBFCCGEECIjCqQIIYQQQmREgRQhhBBCiIwokCKEEEIIkRE9RIeQaigoKAgCgYD1TDZCCCHfB5/PB4fDYRZ3LQu1SBFSDQkEAnqWlZwIBALk5+fT/SRyR+8t5VWRz2BqkSKkGippiRJeSZnIJjIyEnv37oWTkxPzfDtC5CEnJwehoaFo1qxZqQ/NJj+m4OBgqfNSixQhRKnxeDzExsaKPS+NEELkgQIpQgghhBAZUSBFCCGEECIjCqQIIYQQQmREgRQhRKnVqlULw4YNQ61atRRdFUKIEqJAihCi1HR0dNCmTRvo6OgouiqEECVEgRQhRKllZmYiKCgImZmZiq4KIUQJ0TpShMjg69ev8PDwwMuXL/Hx40ekpKRAIBDA0NAQnTp1wuTJk9GtWzdFV5MASEtLw4MHD9CnTx/UrVtX0dUhhCgZCqQIqYCcnBz8+++/OHPmDAoLC8X2JyYm4t69e7h37x4cHR2xePFiBdSSEELI90KBFCFS+vbtG2bPno0PHz5Ild/Z2RmNGjWCra1tFdeMzdbWFjExMWXmMTU1xeXLl79TjQghRHnRGClCpBAbGws7OzuxIKpDhw4YN24c+vbtCw6HI3bc4cOHv1cVGTExMYiOji51f3R0dLmBFiGEEOlQixQhUlixYgUr+FBRUcHmzZsxcuRIJu3atWv4+++/Wcd9/foVKSkp333qvZmZGfz8/CTu69Gjx3eti6JpamqiUaNG0NTUVHRVCCFKiFqkCCnHf//9h6dPn7LSHBwcWEEUAIwYMQK6urpix8fHx1dp/UjZ6tSpg9GjR6NOnTqKrgohRAlRixQh5Th//jxrm8vlYvr06WL5OBwODA0NkZWVJZZfFgKBADk5ORU+rqioCCoqZf+NVFRUJFPZP6Ls7GzweDxkZ2cruipEyeTm5rL+JcpDIBBIHK4hCQVShJShoKAAT548YaV17ty51K66pKQksTRTU1OZzs3n8xEaGirTcRoaGlVS9o8oLi4Op06dwqRJk2BsbKzo6hAlFBkZqegqkCqgrq4uVT4KpAgpw7t378Rabpo3by4xb3R0tFirR5MmTaCtrS3TublcLpo1aybTcdLkad26tSzV+uGoqRV/zNWrV6/U3x0hssjNzUVkZCQaNWoELS0tRVeHyNHHjx+lzkuBFCFlCAkJEUsrrTXK29tbLK1nz54yn5vD4cgUhJXXrVeSR9YA70dTMshcU1Pzp7lm8n1paWnRe0vJSNutB1AgRUiZJHV/paWliaXxeDycOHFCLF10QPr3Eh0dXersvOjoaJiZmX3nGhFCiHKiWXuElOH9+/diabdv30ZycjKznZeXh7///hvfvn1j5evZsyfatWtX5XUUZWpqWmagZGZmJvO4LUIIIWzUIkVIKYqKihAeHi6WnpSUhOHDh6N///4AAF9fXyQkJLDyaGtrY/Xq1d+jmmJoxXI2U1NTzJkzh4JHQkiVoECKkFJ8/vxZbFpz06ZNERERgZSUFFy5ckXicWpqavjf//6HJk2afI9qknKoqqpCW1sbqqqqiq4KIUQJUdceIaWQ1K03ZcoUTJ48udRjDAwMcOTIEQwaNKgqq0YqICkpCVevXpW4NAUhhFQWtUgRUgpJA81btmyJsWPHon379jh9+jQzRbZRo0YYOHAg7OzsoKen972rSsqQm5uLiIgIWjSREFIlKJAipBSigRSHw2HWIbK2toa1tbUiqkUIIaQaoa49Qkoh2rVXv359WiuGEEIICwVShEiQmJgoNqamRYsWCqoNIYSQ6ooCKUIkKG18FPnx1KxZE/3790fNmjUVXRVCiBKiQIoQCSTN2KMWqR+Tnp4eunTpQpMACCFVggIpQiSgFinlkZOTg7CwMLGHTxNCiDzQrD1CJNi1axd27dql6GoQOUhOTsaNGzdgbm6O2rVrK7o6hBAlQy1ShBBCCCEyokCKEEIIIURGFEgRQgghhMiIAilCiFLjcrkwMjICl8tVdFUIIUqIAilCiFIzNjaGnZ0djI2NFV0VQogSokCKEEIIIURGFEgRQpRaVFQUdu3ahaioKEVXhRCihCiQIqSCzp8/j5YtW6Jly5Zo3bo1unXrhqlTp8LHx0fRVSMSCAQCFBYWQiAQKLoqhBAlRIEUIRX04cMH5nVRURHS0tLw5MkTODo64tatWwqsGSGEkO+NAilCKig8PLzUfZs3bwafz/+OtSGEEKJIFEgRUkFdu3bF5MmT8dtvv4lNqU9MTMTTp08VVDNCCCHfGz1rj5AKmjdvHvP65MmT2LhxI2v/8+fP0a9fv+9dLVKKunXrYsqUKahbt66iq0IIUULUIkVIJVhYWIilhYaGKqAmpDTq6uqoXbs21NXVFV0VQogSokCKkEowMzODrq4uK62sMVTk+0tJSYGXlxdSUlIUXRVCiBKirj1CKqlhw4YICQlhthMSEpCeno4aNWoosFakRHZ2Nt6+fYvs7GxFV4WQn4qtrS1iYmLKzGNqaorLly9/pxpVDWqRIqQS3r9/j/fv34ulCy+RQAgh1VWPHj3Qo0ePKik7JiYG0dHRpe6Pjo4uN9CSVVVelyhqkSKkEtavX4/CwkKx9PDwcHTp0kUBNSKEkOrDzMwMfn5+Evd9r0CnqlGLFCEy8vDwwIsXLyTuoxYpQgj5OVCLFCEyyMrKwo4dO0rdL49ASiAQICcnp9Ll/Oy4XC66du0KLpdL95PIVW5uLuvfH1FRURFiY2PRrVs3uZcdGxsLMzOzMvNER0dX2blNTExk/j8vEAjA4XCkykuBFCEy2L9/PxITE0vdL49Ais/n01IKctK3b1+kpqYiNTVV0VUhSigyMlLRVZBZyZMYFPlEhqo6d2U/Q6VdMoUCKUIq6OPHjzh58iQrTVVVlTVWKi0tDQkJCTAyMpL5PFwuF82aNZP5eFIsLS0NAQEB6NatG2rWrKno6hAlkpubi8jISDRq1AhaWlqKro5MuFwuTExM4O3tLfeyBwwYUG6eqj5369atZTr+48ePUuelQIqQCtqwYQMKCgqY7YYNG8Lc3BxXrlxh5fvw4UOlAikOhwNtbW2ZjyfFvn37hgsXLqBNmzYwNTVVdHWIEtLS0vph/6+qqBQPla6K+peUXV6eqjy3rGVL260HUCBFSIXcunUL/v7+rLSlS5ciLi5OYiDVq1ev71k9QgipVqKjo0udnRcdHV3uGKofAQVShEgpJycH27ZtY6X17NkTAwcORGBgoFh+WuGcEFLdlbY0gTyU1wJsZmZWZa3EVXldoiiQIkRKhw4dQlxcHLOtqqqKZcuWAQBatmwplp+WQCCE/Mx+9BXLpUXrSBEihcjISLi6urLSxowZwwRQenp6Yn9Zffz4EQKB4LvVkUimpqYGXV1dqKnR342EEPmjQIoQKWzcuJE1RVdPTw8LFixg5RFtlcrJyUFUVNR3qR8pnYmJCWbNmgUTExNFV4UQooQokCKkHPfv38d///3HSpszZw5q1arFSqPuPUII+flQIEVIGXg8HrZs2cJKa9iwISZNmiSWlwKp6ik2NhaHDx9GbGysoqtCCFFCNGiAkDJoaGjgwYMHUuUdNmwYhg0bVsU1IhVVUFCArKws1tpfhBAiL9QiRQghhBAiIwqkCCGEEEJkRIEUIYQQQoiMKJAihCg1IyMjjB07tlLPPSSEkNJQIEUIUWoaGhpo0KABNDQ0FF0VQogSokCKEKLU0tLS8PjxY6SlpSm6KoQQJUSBFCFEqWVmZuLZs2fIzMxUdFUIIUqIAilCCCGEEBlRIEUIIYQQIiMKpAghhBBCZESBFCFEqeno6KBdu3bQ0dFRdFUIIUqIAilCiFKrVasWhgwZglq1aim6KoQQJUSBFCFEqeXn5yMpKQn5+fmKrgohRAlRIEUIUWrx8fE4ceIE4uPjFV0VQogSokCKEEIIIURG1TqQSklJQVxcnKKrQQghhBAiUbUOpFxdXeHs7Czz8Q8fPpRLnqqQnZ2NZcuWoWXLlqwfeeLxeHB1dcXo0aPxyy+/oHv37vj999+xceNGfPz4EQCwcuVKFBQUlFpGWFgYoqKi5FqvH4Gk6/by8oKFhQXr97Vv3z4F1ZAQQkh1UG0DqaysLJw9exZXrlxBampqhY/39PSEq6trmXlevHiBzZs3y1rFStHR0cHWrVvRrFmzKik/KSkJY8eOxfbt2zF06FD4+PjA398fzs7O0NTUhLW1NQYMGIBLly6VWkZ+fj6WLFmC6OjoKqljdVXadQ8ZMgRLlixRUK2IrDgcDlRVVcHhcBRdFUKIEqq2gdS5c+eQmZmJ3NxcnDlzpkLHhoeHY+3atWXmiY+Px59//omioqLKVLPSDAwM5F6mQCDA/Pnz8f79e4wfPx7Tpk2Dnp4eAMDMzAxLliyBs7MzkpKSyixn3bp1CA8Pl3v9qruyrpum0P946tWrh0WLFqFevXqKrgohRAmpKboCkuTn58PNzY3ZPnPmDGbMmAF1dfVyj42MjMTMmTORlZVVap7k5GQ4OjoiLi4OZmZmcqmzrKrir+Rnz57h5cuXAIDGjRtLzNOrVy8sW7YM69evl7j/33//xeXLl+Vet+quvOumVg3lYmtri5iYmDLzmJqa/pT/Fwgh0qmWLVLXr19HQkICs52UlAQPD49yjwsMDMSECRPK/GD8+PEjxo8fj/fv38ujqtVScHAw87qkZU+SP/74AyYmJqw0Ho+HFStW4MiRI1Vax+rmZ73uH1GPHj3Qo0cPqfPHxcXB3d1d4sSVmJiYMruuo6Ojyw20KqKidSeEVH/VrkVKIBDg+PHj0NDQAI/HY9JdXFwwZsyYUlsEPD09sWHDBqSlpTFpL168QJcuXQAA5ubmcHBwwF9//YXExEQmT0xMDJPHxMQEN27cYPYlJyfj7Nmz8PX1RVRUFNLT01G3bl0MGDAAs2bNgqGhocS6ZGVl4ejRo7h37x7i4uKgra2N5s2bw9HRUeoP0U+fPmH48OEoLCxkpf/zzz/4448/yjxWuOXu48ePGDNmDDZs2IBff/2VlU9VVRUWFhbMdk5ODhwdHfH69WtWvlmzZkFVVRUAcPjwYSQkJGDjxo1ITk5m8owcORJbt27Ft2/fsGXLFvj7+6Nv377YvXs3k6ewsBAXLlzApUuXEBkZCTU1NfTs2RMLFy5Ew4YNmXyurq7Yu3cvcnJymLQtW7agdevWOHToEAICAlBQUIBu3bph1apVMDU1FbsHaWlpcHFxwYMHD/Dt2zcUFhayBtVramqCy+WiWbNmcHFxkeq6S94norKysrBv3z7cvHkTOTk56Nq1K1auXIn69etLzE++Lz6fj4SEBPD5fIn7zczM4OfnJ3EfBT2EkPJUuxapBw8eICMjA8uWLWOlf/78ucwZdsOHD0dAQAArzdzcHIGBgQgMDMSRI0fQvXt3/Pfff6wvXlNTUyaPcBDl4+ODwYMHIygoCIcOHYKPjw/mzJmDb9++wd3dHWPHjpU4xigqKgojRozA4cOHMXv2bDx9+hQ1atTA06dPMWXKFJw+fVqq+9CkSRM8ffqU6Xq0srLCf//9V24QBQBdu3ZlbX/+/BmTJk2Cg4MDnj17xtq3Zs0aqKkVx9Pa2to4deoUHB0dWXkOHz7M3KMuXbpg2LBh2Llzp9h54+PjMWHCBDx48ADZ2dm4ffs20/KXnZ2N6dOnY926dejUqRP8/f2xfPly3Lp1C6NHj8bbt2+ZchwcHDBjxgxW2devX8eSJUtgZmYGNTU1ZGVl4cGDB5g+fbpYsPnp0yeMGDECR44cQVxcHE6dOoWXL19i8ODBTJ7mzZvD398f586dk/q6JYmNjcXo0aPh5eWFpKQkZGdnw9vbG5MnT0Z2drbEYwghhCiPatcidezYMUyYMAG2trbYu3cva8aei4sLBg4cWOV1SExMxKJFi5CdnY2MjAzo6+tDVVUVdnZ2TAtLVFQUjh49iuXLlzPH5efnY+bMmYiOjka7du1gZWUFoHg8UslyA//++y/Gjx8PFZXyY1gOh4PMzEzMnz8f8+bNk7r+rVq1wvDhw+Hp6clKf/r0KZ4+fYpff/0VCxcuLDU4kIbowF2BQIAlS5agdu3aSExMhEAgAADmOlesWIGnT59CR0cHCxcuBJfLhY2NDZydnREREYHFixfj1q1bTAuQkZERq/zk5GScP38eurq6aNq0KVauXAkAiIiIgK+vL/r16weguNXLycmJWcV6+PDh6NChAwDA0dER9+7dA1Dc/enl5YXhw4fLfA8AwMPDAzt27MCwYcNw8eJFrFq1CkBxgHX79m2MHj1a5rIFAgGrVY4UKyoqQmxsLLp16yZVfj6fj/T0dIwfPx5cLpe1LzY2ttxxktHR0VKfqzyxsbEwMTGh36uSyM3NZf1LlIdAIJB6TGy1CqQCAwPx7t07HDx4EBoaGhg7dixrzEpgYCDevHnDfDFWlVevXjGtCW/evIGPjw8sLCzEnh7/+fNn1vb58+eZgKljx45MeteuXZnB84WFhSgqKio3kMrKysKsWbMwf/582NnZVfgaNm7ciMzMTPj4+Ijte/78OSZOnAhra2usWbMGurq6FS5f9A3m7e2NqVOnYtasWbh48SL+97//oXv37mjRogVevHgBLy8vAECbNm2YGYRAcctbREQEIiMj8fTpU/Tp0wcAxO6PnZ0dU0/RrrzIyEgmkHr58iU+fPjA7GvatCnrXMKCgoIqHUiNGTMGw4YNA1DcAiosIiKiUmXz+XyEhoZWqgxlVNJFV1pXnaiSFkvRlktZzikP9HtVPpGRkYquAqkC0kxwA6pZIHX06FFYW1szU8wnTJiA48ePs8a2HD9+HHv27KnSenTs2BFGRkZISEiAnp4eWrRoAQBiSyXk5eWxtq9evcq81tfXZ14PGjQIixcvRnBwMEaNGsV0pZUmLS0N06ZNQ9euXWUKogBAS0sLhw8fhru7O/bv3y9xwPm1a9cQGhoKd3f3Si/DwOPx4ODgAKA4uBgzZgyzT7hlrG7duqzjtLW1mdevXr1iAilRJS1Voq8BsP66Fx7/Jlq+8GsA5f4epFG7dm3mteh/utIG+UurZAwXYeNyuTAxMYG3t7dU+ZOTk/Hff/+hT58+YuMaBwwYUO7xFTlXeUrO17p1a7mURxQrNzcXkZGRaNSoEbS0tBRdHSJHJY0i0qg2gVR4eDgeP37MGqdkbGyMwYMH4/bt20zavXv38O3btyodyGtkZAQvLy+EhoaiSZMm0NfXx+XLl1lLMgBguq+A4kBC+K9M0eUXRMfflCYxMRFTp05FeHg4EhIS4OjoKHOQo6KigilTpsDGxgaurq44deqUWL3Cw8OxYcMG/PvvvzKdo0S7du2goaEhcV9ISAjz2svLi9VKVlBQwAQg6enpMp1buKWhUaNGrH3CLQnCkxeA4taxqlSZFhCguNVPNPgj/9daWZF707JlSxgaGoodI00Xu4qKitx+D7LUnVR/Wlpa9DtVMhVZ6qbaDDY/duwYevXqJfYX+OTJk1nbhYWFOHHiRJXXR0dHB126dEFAQACGDRsGNzc3iQOsS6SlpbFarGSdMj1p0iRmMciEhASsXr26wmXs378fsbGxzHbNmjWxaNEieHt7Y+7cuWJ/Od2+fRspKSky1beE6JgmYcIBUtu2bZkB3IGBgXj16hWCg4MRHBzMjC+qKOGAtk2bNqzZicJN7sJdsfXr18fQoUNlOp8s9SKKk5mZicDAwFJbCKOjo5llCUR/frZV/QkhFVctAqnY2FjcunWLWa5A+GfmzJliXTlXrlxhLXNQFVJSUuDo6IiFCxdCR0cHp06dQvPmzUvNr6mpydoODAyUadX0v//+G+3bt2e27927h4sXL1aoDIFAIHFslL6+PpycnHD9+nXWQp1FRUX48uVLhesqrLTWKACsAb7yXJOnNPv372cGB3t4eODt27dIT0/Hrl27ABRPdz9y5IjU/d+kevHz8yt1uQJJ0tLS8OjRI4mfGaampmUONjczM5O4vIasKlp3Qkj1Vy269lxdXdG5c2ecPHlS4n4fHx9W11hOTg7Onj2L2bNnV0l98vLyYG9vz7QMrV+/njXmSZIaNWqgTp06zBid9PR03L9/H7/99luFzj1w4EA0a9YMNjY2zNifzZs3o2vXrqy1lspz9uxZjBs3TmLzZIMGDbB//34MHz6caTURvj55r97doEEDZgB4YmIiQkJC0LZtW7F8FZklUZaaNWvC3d0dzs7O2LlzJ8aPHw81NTXUq1cPCxcuhJ2dndjEAYBWLf8Z0YrlhJDKUniLVEpKCi5evFhmUNSvXz+xL153d3eJU05FpzdLUl6eixcvsp61VtpjVkQJr1MEALt27RLrTggLCyt3EFvDhg1Z3Vw5OTn466+/WIPuy/P+/XucO3eu1P3NmjVDjRo1ABT/1S08o02ae1gRvXv3Zm3v3LlTrLXu+fPn5T5kuiJ27dqFM2fO4MGDBwgODkZQUBBu3LiB2bNnSwyiAPlfNyGEEOWn8EBq9+7dUFNTK3cFYdEFGlNSUiSOlRKeRSU80Fg4eCkvj2igs27dOjx48ABOTk6sdB6Ph6KiImYQ84wZM1hf0p8+fYK9vT0ePnyI8PBwnD59GitWrGB1FYhOqy7ZtrW1ZbVmvX79Gnv37hW73rJs2rSp1EVMQ0NDkZaWBg6HgxUrVrBaY0RnNpUEcLGxscyyEKIDt8saD2RtbY06deow20+ePIGTkxPCw8ORkpICDw8PbNy4EePGjWPyiAZawtuig7hFz3327FkcPnwY/fr1q9CDaqW5btFzCddLdB+NkSKEEOWnsEAqOzsbBw4cwPnz55GVlYXHjx+XuVZLSeuJsEOHDuHRo0esL6wJEyYwryMiIpCUlIRbt27h06dPEvMkJyfj48ePeP78ObMyumjr140bN7B48WJYWFiwWqeCg4Mxfvx4fPv2DUDxeIudO3eyWjZCQkIwe/ZsWFlZ4fjx49i6dSszu+Pbt2+segHA48ePmdeiSx84OzvjypUrUo+9UldXx9y5c7F27VpmTSMej4eHDx9izpw5UFdXx4YNGzBo0CDWcb/99hsr8AkICEBGRgaOHTvGjCsSHecREhJS6oOidXR0sHfvXtaslnv37sHKygo9evTAli1bsHXrVlYQKrpqvPCzF0WfmSaa98yZMwCK15QSHnRfHmmuW3RQvvBjcoTrKKleRDG0tLTQtGlTmp5OCKkSHIGC/mz+448/EBQUxEpTUVHB8ePH0bNnTyYtMDAQ06ZNE1uzSZiDgwPzSBmBQICjR4/izJkzSE1NRatWreDo6Ci2Ivrly5eZR4g0btwYkydPZlahLiwsxObNm3H9+nVwuVwMGDAAc+bMgZmZGZ4/f441a9YgOjoaLVu2xKpVq1iLbwLAhw8fcOjQIfj7+yMzMxOmpqYYMmQI7O3tmTWy4uPj0bdvX4nXs23bNtjY2ODXX39FRkaG2P65c+eKtY4J279/Pxo1aoThw4cjODgY165dg5+fH5KSksDj8WBsbIxevXrBzs6u1HFXERER2LhxI169egVtbW389ttvWLRoEfT19bF//37s27dP7BhVVVW4urqWugr0169fcfDgQTx9+hSpqakwMjJCv3794OjoCGNjYyafm5sbdu/ezVofSk9PDytWrIChoSGWLVvGCmg0NDTg4OCARYsWASheq0fSoHYOhwN1dXXUqlULLVu2xPjx49G/f3+pr/vWrVvYunUrs2o6UBysLliwAF27dsXixYvx9etX1v0YOXIkNm3aJPF+lKXkwdPCEw+IbHJychAaGorWrVvTFHUiV/TeUl4V+QxWWCBFSFXZvXs3Dh06JFXe7du3w9rauoprVHEUSMlPZmYmXr16hU6dOrFW1SeksiiQUl4V+QxW+BgpQuTNyckJlpaWUuU9depUFdeGKFpMTAwOHjz4XZbeIIT8fKrF8geEyEtOTg5mzpyJZ8+eYfPmzbC0tISOjg4EAgEKCgqQl5eHL1++MN13lV19nBBCyM+NWqSIUnny5AmePXsGbW1t2NjYQFdXFxwOByoqKlBXV4e+vj7at2/PLFUhOtCeEEIIqQgKpIhSMTc3h5mZGXJycrBkyRJ8+vSJmeVYVFSE6OhouLu74/Dhw7C0tMT06dMVXGNCCCE/MuraI0qlVq1auH79Oi5fvgxfX19Mnz4d6enpUFNTA5fLRe3atWFubo59+/aVu3YZIYQQUh4KpIjS0dXVhb29Pezt7RVdFVINmJmZYf78+WU+U48QQmRFXXuEEKWmoqICDQ0NqKjQxx0hRP7ok4UQotQSExNx6dIl5oHihBAiTxRIEUKUWl5eHiIjI8t8OgIhhMiKAilCCCGEEBlRIEUIIYQQIiMKpAghhBBCZESBFCFEqdWsWRMDBw5EzZo1FV0VQogSokCKEKLU9PT00LlzZ+jp6Sm6KoQQJUSBFCFEqWVnZ+Pdu3fIzs5WdFUIIUpIqVY29/X1hbe3N+7fv4+4uDjWPhUVFXA4HAAAl8uFnp4eTExM0KZNG4wbNw5t2rRRRJUrZNOmTTh58iQEAgErPSwsTEE1kp/IyEi4uLjgyZMnSElJgb6+PurVq4ehQ4fCxsYGqqqq2LZtG9atW1dqGQ8fPoSFhUWV1K8qyyZVKyUlBbdu3ULXrl1Rp04dRVeHEKJklKpFqnfv3li9ejVcXFzE9p04cQLv3r3Dixcv4OLignbt2uHNmzc4d+4cRo4cic2bN4sFKNXNypUr4e3trXRjPe7evQsbGxv4+/tj06ZNeP78OR4+fIiFCxfCy8sLvXr1wsCBA5GVlVVqGS9evMDmzZurpH6enp5wdXWtkrIJIYT82JQqkCrRsGHDUvdpaWnB3Nwchw4dQteuXZl0Nze3H+LL0sTEBE2aNFF0NeTmw4cP+PPPP5Gbm4udO3eie/fuUFNTg6qqKn799Ve4ublhxIgRSE5OLrWM+Ph4/PnnnygqKpJ7/cLDw7F27Vq5l0sIIUQ5KGUgpaZWfo8lh8PBxIkTWWmHDx8Gn8+vqmrJjTTX96M4fvw4c88bN24stl9VVRX//PMPOnXqJPH45ORkODo6inXlykNkZCRmzpxZZksYIYSQn5vyfCPLoFmzZqzt9PR0fP36FU2bNlVQjX4+wcHBzOujR49i0aJFYnlUVFTg5OSEq1evstI/fvyIOXPm4MuXL3KvV2BgIJycnMpsCSPVm62tLWJiYlBQUID09HRMmjRJ7I8QU1NTXL58WUE1JIQog586kJI0Jqq0mT3h4eE4f/48AgMDERsbCz6fj3r16mHUqFGYNGkSuFwuk/e///7DunXrEBUVxaSNHDkSS5YsweHDh3Hv3j2kpaWhRYsWWLp0Kbp06VLqOQ8fPoxnz54hJycHTZo0wdSpU6W6tvz8fJw6dQqenp748uULNDQ00LBhQ4waNQo2Njas+mZlZeGvv/7Cw4cPWWW8e/cO58+fx6VLl/Dx40doamqiR48eWL58OYyNjfHp0yccPnwYT548QVZWFpo2bYrFixejV69eUtURANTV1ZnXhw8fRnx8PP766y8YGhqy8nXv3h0PHjxgtv39/fHXX3+xHkQbExPD3EsTExPcuHGD2ZecnIyzZ8/C19cXUVFRSE9PR926dTFgwADMmjWLdT5PT09s2LABaWlpTNqLFy+Yss3NzXHkyBFmH4/Hg5ubG27cuIGoqChoa2vDwsICTk5ONLhZgWJiYhAdHQ0zMzOx9xMAREdHK6BWhBBlo5Rde9L69OkTa1tDQ0Nia9T+/fsxYsQI6Orq4uLFi3j48CG6d++O8PBwbN26FfPmzWMFZX369MG2bdtYZYSHh2PixIngcDgwMDBAXl4e3rx5g+nTpyMyMlLsnD4+Phg7dixu3ryJVq1awd/fH/v378f58+cRFBRU5nVlZmZi0qRJ2LZtG/Lz83H37l3cvXsXfD4fq1atgoODA1JTU5n8urq6OHToEBo1asQqZ9q0aQgODsaIESOgrq6O9PR0eHl5wc7ODufOncOCBQvQpk0btGjRAnl5eQgJCcHMmTMRERFRZv2E/frrr6ztq1evYuDAgdi2bRsSEhKYdFVVVaxZs4bZ7t69O/777z+YmpoyaaampggMDERgYCAriPLx8cHgwYMRFBSEQ4cOwcfHB3PmzMG3b9/g7u6OsWPHIikpick/fPhwBAQEsOplbm7OlC0cRCUmJmLcuHHYuXMnrK2t8ezZM0ycOBEXLlzA6NGjWcE0+f7MzMzg5+cn8cfMzEzR1SOEKIGfNpAqLCzEyZMnWWkTJ06Ejo4OK+3Ro0fYt28fBAIBeDwe1NXVoauri7Fjx7LyiLbm1K1bl7X9+fNn7N27FytXrsS+ffuY9NzcXFy8eJGVNyUlBX/99Rdyc3MBAAsXLoS6ujqMjY2xf//+cmftrVq1Cq9fvwYATJ48GYaGhtDV1cWsWbMAAM+fP8eqVavEjqtduzZru3v37tiyZQumTJkCOzs7Jv3Lly84f/48zpw5gylTpmDTpk3MPj6fX6GukmnTpsHAwICVlpubCxcXFwwaNAgbN25ESkqK1OWJSkxMxKJFi5CdnY2MjAzo6+tDVVWVdT1RUVE4evRohcsuLCzE/PnzERoainr16sHBwQFcLhczZsyArq4u4uLisHLlSpnrTgghpPr76br2srKy8P79exw7dgzPnz9n0keNGoXFixeL5ff19WVeu7m5Ye7cudDT04O2tjYr3+fPn1nbJWtWlRg0aBBatmwJoLjbSZhoi5SbmxvS09MBANra2mjbti2zT09PD40bN2Z1aQl79eoVvLy8mO2ScwJA8+bNmdf379/H8+fPWS1CKirsuLok8JJU58mTJzMrRYt2X0lqYStN3bp14ezsjJkzZ4oFTDweDydPnsS1a9ewfPlyjBo1SupyS7x69Yrprn3z5g18fHxgYWEhFjCL/v6kcfPmTaZ1sEuXLlBVVQVQvE5ZgwYN8O7dO/j7++PTp08yzbQUCATIycmp8HGkWFFRkdh7WlIeusdEViV/7Jb8S5SHQCAQ+x4vzU8TSM2aNQt8Pl9sVl7JGJnSZoUNGDAA58+fR35+Plq1asV8AYtOtc/Lyyvz/CVfsoD4rDvRD/J79+4xr42MjKT+ZQLAtWvXWNvCY0NEH5Fx8+ZNsa610gjXX5TweCug9HFmpenQoQM8PDywZcsW3L59W2x/RkYGli9fjs+fP0sMdsvSsWNHGBkZISEhAXp6emjRogWAiv/+JPH09GReGxsbs/YJB9qvXr2SKZDi8/kIDQ2t8HGkGJ/Ph4aGRrl56B6TyqrIH4/kxyE8hrcsP00gdfjwYdSsWRNjxowBj8dj0r9+/cp8uUrSq1cvPHjwAF+/fkX79u2RmZmJkydP4vz586x8lVnMs6CggPVaeOxWeV8Eot6+fcva1tLSYl6LBkNVtSK6LOs51a1bF7t378bUqVNx4MABPHr0SCyPs7Mzunbtij59+khdrpGREby8vBAaGoomTZpAX18fly9fhpubGyufLL+/kJAQ5vXx48dx+vRpZpvP5zP/CYUHrVcEl8sVm1lKpCca4JeWp3Xr1t+hNkQZ5ebmIjIyEo0aNWJ91pIf38ePH6XO+9MEUkBxN9fy5ctZjxmJiIjA6tWrsXPnzlKPMzIyQs2aNeHq6oqjR4+iZ8+eWL16NebPny/3OqakpLC+1Cv6BV/SJVhC+MtENMCpzNgjeVmxYgVrRfIOHTrgyJEjePfuHXbv3g0fHx9W/jNnzlQokAIAHR0ddOnSBbdv38auXbugqamJnTt3Yvjw4ZWqu/C9Hjx4MHbt2lWp8kRxOByxLmQivfK69Ury0D0mlaWlpUXvIyVTkZ6gnyqQAoDx48fDz88Pd+7cYdI8PT3xyy+/iC3QWSI0NBQLFy5EZGQkbG1tsWnTJjx79qxK6ifa7VfRbjJdXd1S9xUWFrK2RccJKUJQUBAyMzPFuh3btGkDZ2dnXL16FStXrmTqLjrTUhopKSlYtmwZfHx80KZNG7i5uUFfX7/SdedyuUxXcUxMTKXLI/IXHR2NHj16lLqPZu4RQirrp5y1t3HjRrEP0C1btuDNmzdieT9+/IiJEyciMjISBgYGWLt2bYUi1YoyMDBgBRUJCQmsrr/yiD58OSMjg3ktOiCyrC7N74XH45U5y2/kyJFwcHBgtmvUqFGh8vPy8mBvb8+0bK1fv14uQRQANGjQgHkdEhLCWkJBWHV/hqOyMjU1hZmZGYqKisDj8cRaZM3MzFjLZxBCiCx+ykBKX18f//77L6v1h8/nY8GCBaz1lQBgz549TKuQiYlJhccsVRSHw2EtaMnn81mzC/Pz88UWEhT+gvj9999Z+4S7n0S7/SwtLeVS58o6ePAga80oUcILlop265U3DubixYsIDw9ntiU9hqY05ZXdu3dv5jWfz5fYtXfjxg2JA+hJ1bt8+TL8/Pxw6dIl2NjY4NKlS2JrSdGq5oSQylLKQErSdGbR1phOnTphwYIFrLSYmBgsWbKE1QUmPODs3bt3cHZ2xs2bN1njeoDilhXh84q2QggHO6ItTKJ5p02bxmr12r17N3g8HvLy8rB8+XKxoEO4u6tHjx6sYEP48SnCC2V26dIF/fv3Z5UjOqNReCab6D7hAfui97aizytMT0+Ho6NjqS06T58+BVDcgiDcOgWw174SPm/J7010wOC6devw4MEDODk5sdJLWiyEr6u8sidNmsQaYHrp0iWsWbMGkZGRSEhIgJubG86dO1dtAtafVU5ODkJDQ2mZA0JIlVC6QCo/Px9nz54VS79x44bYw2dnzJjBalUAiteNWrVqFZNXtKts586d2LlzJxYsWMAazHr+/Hk4OTkxwYfoOk/CwY/oA3ZF83bo0IEV5L169Qp9+vRh6tq5c2dWfnt7e9aYr+3btzPddkePHkVKSgrS0tKYmWrNmzfH3r17WcFafHy82FpKT548AVAcUP3333+sfQEBAUzAKboY6efPnyv0jDoul4uIiAjY2Njg/PnzTHdkcnIy9uzZg5MnT6JJkyZwdXUVGwM2YcIE5nVycjI+fvyI58+fMyuTC6/BBRS/DxYvXgwLCwtW61RwcDDGjx+Pb9++SSw7IiICSUlJuHXrFhO4mpiYYOvWrayWzfPnz8PS0hJ9+vSBm5sbduzYUebSEYQQQn5sHIESDeDYu3cvDh06VOr0ew6Hg06dOuHcuXNMWnJyMqytrcWCGRUVFfj5+YHP52PFihV4/vw56tSpA2tra0ydOhXa2to4ffo0Dh06hOzsbPTq1Qvr1q1D7dq18eTJE6xdu5b1pczhcDBp0iTY29tjzpw5rO4moHjBzm3btrECBS8vL7i4uCA8PBw6OjqwtbWFk5MTFixYgLy8PHTu3BmdOnVCp06dxAKMvLw8uLm54fbt20w96tevj2HDhsHe3p7VRZmQkID+/fuLDUYHigPHa9eu4fHjx2L7OnbsiMmTJ2PJkiVi+1RVVfHo0SMYGRlJ/F2UmDx5Mnbu3Al9fX08fPgQnp6eePfuHTIzM6GiooIWLVpgyJAhGDNmDDQ1NSWWcfnyZRw5cgRxcXFo3LgxJk+ejNGjRwMoHmC/efNmXL9+HVwuFwMGDMCcOXNgZmaG58+fY82aNYiOjkbLli2xatUqdOzYkSlXIBDg6NGjOHPmDFJTU9GqVSs4Ojpi4MCBrPOHhITgyJEjCAwMRFZWFkxNTWFpaQkHB4dyV6EvTcnDnNu3by/T8eT/hIWFYd26dVi3bh1rgVpCKquktbN169Y0a0/JVOQzWKkCKUKUBQVS8kOBFKkqFEgpr4p8Bitd1x4hhAirUaMGevbsWeEZn4QQIg0KpAghSk1fXx89e/aU27IXhBAijAIpQohSy8vLw+fPn2V6niIhhJSHAilCiFJLTEzE5cuXxSaUEEKIPFAgRQghhBAiIwqkCCGEEEJkRIEUIYQQQoiMKJAihCg1NTU11KxZk7UCPSGEyAsFUoQQpWZiYoLp06fDxMRE0VUhhCghCqQIIYQQQmREgRQhRKnFxMTgwIEDiImJUXRVCCFKiAIpQohSKywsRG5ursSHchNCSGVRIEUIIYQQIiMKpAghhBBCZESBFCGEEEKIjCiQIoQoNSMjI0yYMAFGRkaKrgohRAlRIEUIUWoaGhowNTWFhoaGoqtCCFFCP91Sv9u3b8fx48fF0jkcDvbv349BgwaJ7ZsyZQr8/PzE0k+cOIEePXpUST2rUnx8PFxcXODr64u4uDjw+XyYmJjA1tYWM2bMAIfDYeUPCwvDH3/8gZycnFLL1NTUxKxZszB79uyqrj4hFZKWlgZvb2+YmJhAW1tb0dUhhCiZn65F6u+//4aPjw9++eUXVrpAIMBff/2FDx8+iB3j6uqKmzdvomPHjgCAqVOnws/P74cMol69eoXff/8dJ06cgIGBAfz9/VG3bl1ERkZi586duH37ttgxLVu2RFBQEK5du4ZatWqx9qmrq+P06dN4/fo1BVGkWsrMzMSLFy+QmZmp6KoQQpTQTxdIAYCxsTGcnZ1Ro0YNVnpOTg7mzJmD9PR0VjqHw0GzZs3w559/QkNDA3/++adYQPEjEAgEWLp0KfOFUq9ePXC5XHTs2BFqampo3rw5OnToUOrxrVq1QteuXcXSunTpUqX1JoQQQqqrnzKQAgA9PT3o6upCXV2dlf7161csWrRI4uJ9pqamMDAwAJfL/V7VlKukpCRERkaKpe/YsQMhISHw9PREvXr1yixDS0uLta2pqSnPKhJCCCE/lJ9ujJSoDRs2YNWqVeDz+UzakydP8L///Q/Lli1j5VVRUYGqqur3rqLc8Hg8RVeBkO/C1taWeSQMn89HamoqJk6cyPojyNTUFJcvX1ZUFQkhSuKnbZEq0aVLF2zevFks3dXVFR4eHhUqKzk5GZs3b8bgwYPxyy+/oG/fvpg9ezZ8fX3lVFs2f39/zJo1Cz169ECXLl1gaWmJbdu2SXymmJWVFUaMGMFK8/T0RJcuXeDs7Fwl9SvP48ePsXjxYgwZMgQdO3ZEt27dMGnSJNy/f5+V78KFC2jZsqXYT6tWrRAYGAgAuHnzJmvf8OHDWWUUFhbi7NmzsLW1hbm5Obp164ZFixbhy5cvrHzu7u4wNzdnlbVv3z4AwPv372FnZ4fOnTtj69atzDEFBQXYvXs3LCws0KFDB9axBw8erIpbR8oRExOD6OhoAACXy4WRkREriIqOjqZn7xFC5OKnD6QAYMSIEZg/f75Y+po1axAcHCxVGWFhYRg+fDjc3NzQsWNH+Pv7w9XVFYGBgZg2bRo2bNiAoqIiudXZ2dkZ9vb2ePToEbZs2YLAwEDY2trCxcUF1tbWePLkCSv/jRs3cP36dVba8OHDERgYCEdHR7nVSxq5ubmYNWsWZsyYASsrK9y+fRuenp7Q09PD8+fPMXfuXBw7dozJP3r0aMyaNUusnMuXLzPjs37//Xfcv38fGhoaMDc3x7lz55h82dnZmD59OtatW4dOnTrB398fy5cvx61btzB69Gi8ffuWyWtnZ4fly5eLnev9+/eYMGECAgICkJOTA1dXV2RkZAAANm3ahEOHDqF9+/Z48eIFvL29MWTIELndLyIbMzMz+Pn5SfwxMzNTdPUIIUqCAqn/b968ebCxsWGl8Xg8zJs3D4mJiWUeWzJIPSUlBQAwd+5cqKuro2nTpkyZp06dgru7u1zq6uPjg507dwIAOnXqhP79+wMonk1Ys2ZNZGRkYP78+YiNjZXL+eRt79698Pb2BgAUFRWBw+Ggfv36GDx4MJNn9+7dzP1UUVHBggUL0KpVK1Y5orOw6tevDw0NDfz999/Q1dVl0lesWIGnT59CR0cHCxcuBJfLhY2NDZo2bYqMjAwsXryYNSZO9EuWx+NhyZIlaNCgAZPG4XCgoqKCqKgoJmhr2rQpuFwuTE1NsXv3bvTp06cyt4kQQsgP4KcfIyVs48aNiImJwbNnz5i0uLg4zJ8/v8wg6NSpU4iKigJQPPi6UaNGzL4WLVowr/fs2YNx48aJDdiuCIFAwOpWEi5fTU0NTZo0wcuXL5GdnY29e/diy5YtMp+rqgi3lu3fvx8WFhYAwFrjh8/nIyoqipkdqaKigpkzZ2LRokVMnitXrqB79+7MdmhoKOrXr49OnToxaS9evICXlxcAoE2bNtDT02P2NWnSBBEREYiMjMTTp0+ZwEdFhf33xYULF7B69WpYWVnhwIEDcHFxga2tLXR1deHn58e0NB45cgSGhoaYOHEiOBwO1q5dK3E5CWkJBIIy1+4ipSsqKhL7PUrKQ/eXVEZubi7rX6I8BAKB2JqKpaFASgiXy8X+/fvxxx9/4NOnT0x6UFAQNmzYgJkzZ0o87tq1a8xrAwMD1s0XbhnJycmBt7c3hg0bJnMdg4ODWXUzNDRk7RcOFO7du4d//vlHbGaiov32228ICwsDAJibmzPpol2fooPjLS0tYWJiwrS03b59G0uXLmXuwc2bNzF69GjWMZ6enszrunXrsvYJB26vXr0qtQVJT08PVlZWAIpbG+fOncvsMzAwYF4XFBRg/fr1ePLkCTZs2ID69etXqtuUz+cjNDRU5uN/Znw+v9yVzOn+EnmRNBua/Pik/e6kQEpEjRo14OzsjHHjxiE5OZlJv3DhAisoKpGbm4uPHz8y26KtTWpq7FscFhZWqUBKeDyPpPMJzyrMzMxEbGwsGjZsKPP5qsK8efMwdOhQZGdno0OHDvj8+TOcnZ1x584dVj7RwEpVVRUTJkxgujXz8/Nx7tw5zJ07F4WFhbhz547YLKyQkBDmtZeXF3x8fJjtgoIC5j+K6NphwoSDPVGdOnVC8+bNWQu5PnjwAK9evcK///7LajGrKC6Xi2bNmsl8/M9MmiVKuFwuWrdu/R1qQ5RVbm4uIiMj0ahRo0r1NJDqR/h7vTwUSElQv359HDx4EHZ2dqxWERcXF7HxMyUDjkuIfoCLBgMl435kJfqFLxoxCwQC1nZycnK1CKTS09NZC6A2bdoUiYmJWLlyJa5du4Y5c+bAzs4Ohw4dKrOcMWPG4MCBA8jLywMAnD17Fo6Ojnjy5Ak6dOgAfX19sfOWaNu2LS5cuFDhupf1sFs1NTXs2LED06ZNQ1JSEpOenJyM6dOn48CBA+jXr1+FzwkUj8OiR5rIprxuvZI8dH+JPGhpadF7SclI260H0GDzUnXq1Anbt28v92bq6OiUuV90Yc/y8pdHUquYsIKCArmeTx7i4+Oxd+9eVpqnpyeGDh2KS5cuYcOGDZgzZ45Ua3QZGBgw3WwAkJiYiNu3b+PKlSti3XoAO7CVdbp7eV1ErVq1wuXLl8UeO8Tn87FmzRrWGmXk+4mOjkaPHj0k/pQsjUAIIZVFgVQZhgwZgiVLlpSZR1dXl9XiIzqTTHQQovDg8JcvX8LS0hI9evTA2bNnpapTmzZtWNuiLWIlLTVAcRDRuHFjqcqtSteuXWONT/Lw8MDixYuRmZmJ/v37Y+TIkRUqb/LkyaxtZ2dnhIWFSexGE55pl5iYyOrqEybakietqKgonD17FsbGxjh16hQWLFjA6s6Ni4tDRESETGUT2ZmamjKtx0VFReDxeKzWYTMzM5iamiqqeoQQJfJTB1KFhYXlru00ffp0jBs3rsw8wmOe0tLSWPuEu5bU1dUxYMAAAMVf3H/++SciIyORkpKCDRs24PPnz+XWuVOnTqzuRdGuPuFtCwsLhQ80z83NxcmTJ5lAqrCwENu3b2f2C89wlFbLli1Zz/z78OEDhg8fLrH1sHfv3qztnTt3iv3Onz9/DldX1wrXo8TZs2chEAigqqqKOXPm4NixY6yWMGm6mYh8Xb58mVkzytvbG0ePHoW3tzdrLSla1ZwQIg8/7Sd8fn4+kpKS8O3bt3LzrlmzRuwLWZiDgwPq1KkDoHhmXkJCArNPuDVi2rRpzCyv1NRU1jpPhYWFzEy2sqiqqmLx4sXMtvBskfz8fOZ6uFwunJycWMeKdvsJt15JSzQIyc/PLzP/2rVrkZCQABMTEwDF1y08iN/DwwOenp44deoUzpw5wzqWx+OVOj3d3t6eec3hcDBq1CiJ+aytrZnfDVC89IKTkxPCw8ORkpICDw8PbNy4kRUsi96X8lqrwsLC4Obmxmz36NGDGRdVt25dNG3atMzjSdWKi4uDq6sr4uLiFF0VQogS+ikDqbS0NGzatAkFBQXYuXNnuS1Bampq2LNnD1q2bClxf40aNXDgwAFm6YFdu3aBz+fj/fv3zBpGv//+O2v1dAMDAxgbGzPbqqqqpZYv6vfff2em1fv5+eHx48cQCARwdnZGbm4u1NXVsWvXLrEZX48ePWJtBwUFSRVIChNd5DMiIoJZQ6tEQUEBXr16BUdHR2ZpiJJAytDQkNXNl5aWhsWLF+Pu3buYM2cOq5x//vkH//77r8R6WFhYMC1z3bt3L3Wlah0dHezdu5c1EPTevXuwsrJCjx49sGXLFmzdupU1lszf359VRlBQULkB47Zt23DkyBHweDzEx8fj3bt34HA4WL58+Q/9fEZlwOfzkZycTGPVCCFVgiOQdXDID2rbtm1wcXERS69fv77YM95ExcXFYeHChazHjwiLj4/HkSNH8PjxYyQnJ4PL5aJVq1YYN24cfv/9d7H8gYGBWLlyJdLT07FgwQKMHz++Qtfy+PFjnDp1Cm/evAGPx0ONGjXQo0cPzJgxA02aNGHlHTJkSKkB44IFC8SCGGGpqam4desWgoODcfXqVYl5tLW1oaqqCoFAgOzsbFYrDofDwZs3b5huxlevXmHt2rWIjIxEs2bNMGnSJNjY2KCoqAirV6+Gl5cX1NXVMWrUKCxatKjUqezHjx/H9u3bsWPHDtYAdEm+fv2KgwcP4unTp0hNTYWRkRH69esHR0dHVkC7fPlyXLlyRex4LpeL27dvo379+qz0qKgoDBw4kJWv5Pc+a9YsmWfslTyaqH379jIdT/5PWFgY1q1bh3Xr1kn9xwoh0sjJyUFoaChat25Ns/aUTEU+g3+6QIooj/j4eAwfPhy+vr7lzqz70VAgJT8USJGqQoGU8qrIZ/BP2bVHlENAQACsra2VLogihBDy46BAivwQ7ty5g65du2Ly5MnMWJcrV65UuDuU/Hxq164NGxsb1K5dW9FVIYQoIVrZnPwQDh48iPT0dDx79gyhoaFQUVGBpqYmzYgj5dLS0kKzZs3oER6EkCpBgRT5IRgbG+P9+/cAgJMnTyI4OBi7d+9WbKXIDyEjIwP+/v4wMzOjcSyEELmjrj3yQ1i9ejW6d+8OTU1NBAcHY/ny5WjVqpWiq0V+AOnp6fD19S3zwdSEECIrapEiP4R69eqxFr0khBBCqgNqkSKEEEIIkREFUoQQQgghMqJAihCi1LS1tdGiRQsaaE4IqRIUSBFClJqhoSFGjBgBQ0NDRVeFEKKEKJAihCi1goICZGZmoqCgQNFVIYQoIQqkCCFKLTY2FkeOHEFsbKyiq0IIUUIUSBFCCCGEyIgCKUIIIYQQGVEgRQghhBAiIwqkCCGEEEJkpDSPiGnZsiXzmsPhQEtLC6qqqsjMzGTl09TUBJfLRV5eHvh8PpO+ZcsWjBo16rvUNSQkBH///TcSEhIwa9YsTJs2TW5l+/r6Ys2aNcjLy8OyZcswYsQIuZUtTxYWFoiOjma2tbW1oaqqiqysLAgEAiZdXV0dGhoa4PF4yM/PZ9LnzZuH+fPnAwA8PDywfft2aGpqYuPGjejZs+f3uxBS7dWrVw8LFy5EvXr1FF0VQogSUqoWKU1NTWzatAmBgYEICgpCYGCgWJ61a9ciMDAQb9++xcWLF9G6devvXs9Nmzbh48ePyMjIwP/+9z98/fpVbmWvXLkS0dHRSE5OxsqVK5GXlye3suVNRUUFf/31F/z9/Znfl6mpKSuPo6MjAgMDERwcjFu3bqF79+6s/bm5uVi1ahWSk5MRHR2NFStWfM9LID8ADocDNTU1cDgcRVeFEKKElCqQWrVqFUaPHg1dXV2p8nfo0AGurq4wMDCo4pqxCbe4CAQC1nZ1LlveHB0dMX36dKnvf9OmTeHs7IymTZuWmqc6Xy9RjISEBJw7dw4JCQmKrgohRAkpTSBlZmaGMWPGVPg4AwMDTJo0qQpqVLoVK1agSZMm0NfXx+LFi9GwYUO5lb1hwwaYmJjA0NAQGzduhJaWltzKlidNTU3MmjWrwsdpaGhgxowZzLaWlhbWr1+PWrVqwdTUFJs2bZJnNYkS4PF4iIqKAo/HU3RVCCFKSGnGSNnb28t87LBhwxAfHy/H2pStffv2uH37dpWU3a9fPzx69KhKypancePGyRzkWVhY4L///mO2R40a9d3Gt5Hqz9bWFjExMcw2n89HamoqJk6cCC6XCwAwNTXF5cuXFVVFQogSoUAKQJMmTeDr64t58+YhKyuLSS8Z0Pz+/Xts3rwZwcHBGDduHJYtW8bkKSwsxJ07d3Dr1i2EhYUhLi4O+vr6aN26NWbOnIlff/2VyRsWFgZra2ux7qcHDx6gXr16iIqKwpIlSxAUFMTsMzMzg5eXF06ePImrV6/i69evqFu3LqZPn45x48Yx+by9vSW28ISFhQEA3r59i+XLlyM8PJzZ17VrVxw6dAjHjh3DzZs3ER8fj4YNG8LJyQmDBw+WeK9u3ryJCxcu4N27d8jNzWUN2FdVVWUeDHvy5Mkyx59V5vdVo0YNDB8+HKdOncKGDRtY+8zMzPDw4UMAxYP6V69ejZCQENY1HzhwAM7OzvDy8kJcXBxq1aqF33//HYsWLYK6ujp8fX3h6uqK169fQyAQoEuXLli5ciUaNGggsT6fP3/G4cOH8eTJE2RlZaF+/fr4448/MGHCBBqXowAxMTGIjo6GmZkZAIDL5cLIyIjZLzzJgRBCKktpuvYqy87ODsuXLxdLf//+PSZMmICAgADk5OTA1dUVGRkZAICUlBRMmDABK1euxIwZM3Dv3j1cuHABfD4f//33H+zt7eHp6cmU1bJlSzx79gz169eXWId69erh5MmT0NDQYNKys7MxadIkvH//Hg0bNgSPx8PXr1+xZs0a3Lhxg8k3YMAAPH78GDo6OhLLbteuHY4ePcpKi4+Px/jx45GamgpjY2PweDyEh4djwYIFYgP1CwsLsWjRIvz555/w9/fHpEmT8OrVK+zatYvJo6KiAldXVwQGBn6XQfyTJk2Ch4cHVFQkv43btm2LQ4cOsdJiY2Pxxx9/QE1NDUOHDgWfz0d8fDxcXFywbNky/PPPP3BxcUGfPn1gaGiIrKwsPHr0CFOnTmUFjSXu378Pa2treHt7w83NDd7e3uByuVi/fj3+/vvvKrluUj4zMzP4+flJ/CkJsAghRB4okBIi+gHL4/GwZMkSVksEh8NhvrjXrVuHV69eIT8/H2pqxY17rVu3ZmaWFRYWYuPGjSgqKmKO19fXR/v27UutA5fLRa1atZjttLQ0jBs3Dv/73/+wb98+Vh3d3d1Zx9atWxfNmjUrtWzhv8oB4Nu3b1i+fDn++ecfODs7MwFcYWEhTp06xcp7/Phx3Lp1CwCgo6ODuXPnQk1NDcOGDWMGf/P5fOzevbvU81eF1q1bs+6XqDp16rC24+LisHbtWixcuBCLFy9mtRjevHkT+fn5OH78OKZMmYLZs2cz+759+4anT5+yynr37h0WLVoEHo+HSZMmoWnTpjAwMMD06dMBANevX4eHh4ccrpIQQkh1pTRde/Ig2rJx4cIFrF69GlZWVjhw4ABcXFxga2vLzAp88uQJgOKnyx8+fBj79u0DAKZ7CwBSU1ORlpbG+rIXbnEqrx7GxsawtbVl0uvWrct0TURGRoodW1bZotfXuXNnZs0lLS0t1KxZkxkrJlr2+fPnmdcNGzZkAkeguGs0IiICAPDy5csyr60qVPSau3XrxmwbGxuz9s+ePZvpjhMNwj5//ox+/fox2//73/+Yta26du3KpDdp0oR5ffbsWdjY2Eh5JWwCgQA5OTkyHfszKyoqKrWVUjgP3VtSWbm5uax/ifIQCARSD82gQKoMenp6sLKyAgDMnTsXc+fOZe3/7bffcOXKFQCAubk5ky7cAgWgUms5qaqqsraFA5jKfhFUpOzExETmtXCgKLpdMpj3RyF8zeXty87OZl6npKSwWqiEAzLh7tWQkBDw+XyZ7gufz0doaGiFj/vZ8fn8cv9YoXtL5EnSH7Xkx6euri5VPgqkyiAcHEmyZcsWTJ48GWpqamjRogXevn2LI0eO4PHjx6x8ooGVvBQUFFRJuZLKbtSoETNwXXSskPC08jZt2lRZnRRN+Pf49u1b1r6RI0cygalAIGD9B8zIyIChoWGFz8flcsvsqiWSSRO0crlchSzGS5RLbm4uIiMj0ahRo2q71AyRzcePH6XOS4FUGUTHFEnSpk0bfPnyBfPnz8d///2H5cuXQ0dHB1evXv0ONfx+7O3tmVXDv3z5wmr2/Pz5M4Di8WPCazwps/T0dNb2nj170LdvX7meg8PhiLX+kfKV161XkofuLZEXLS0tej8pmYrMuKZAqgzldQ8AwIkTJ/Dvv/+iqKgIR44cQa9evVjLFygLW1tbpKSkYM+ePUhLS8Phw4cxdepUXL9+HWFhYVBTU8OyZcvQq1cvRVf1uxBt9RBet4goXnR0NHr06FHqPpq5RwiRF5q1VwkHDx7Eli1bwOPxMHbsWKUPImbMmIErV67AwMAABw4cQNeuXbFv3z7Y2Njg8uXLmDx5sqKr+N2IrkZf2iKo9Mia78/U1JQVKPH5fCQkJDBd0mZmZmLPdCSEEFlRi5SMUlNTcfDgQWa7UaNGiqvMdxIUFIS5c+di8eLFGD169E+92GSLFi1Qp04dZhC+j48Pnj9/zlpOoaioCH/99Rc2bdoETU1NRVX1pyO6YnlkZCQOHDiAuXPn/hT/Twkh35dSt0hJmi1X1jRV0fxltSZ8/fqVNej6xIkTuHv3Lg4fPow7d+6w8vJ4PNYsONFnfoluCw9qFh2oLjoIXLSOZZUtWlZZZYuWm5SUhOnTpyM7OxtjxoypsiBK9HcgzcxE4WsUvf6S5QlKK7+sgfOieYXvj6qqKqZNm8ZsFxUVYc6cOfDw8EBKSgrCw8Mxb948/PLLLxREKZiRkRH++OMPqcY8EkJIRSltIFVYWIhz586Jpd+6dQspKSkSj/H392dtBwUFiX0Rl2jUqBFrcGF0dDTmz5+PsLAwscefzJs3j1ngMjk5GcHBwaz9z549Y14XFBQgLS2N2U5NTWV92Ys+E1B4WYJv374x6zmVCAgIkJgXABISEpjXPB4PqamprPMWFhYy29evX0dWVhby8vLw8OFDuc8YFAgE8PLyQnJyMiv90aNHZY4/evPmDev3mZKSwpptIfo7/fjxI3MPk5KSxMazlSxpIBAI4O3tzdpXsvhqCXt7e/z222/MdkZGBpYuXYoePXrAysoKBgYGmDhxYpnXTaqeQCBAQUEBdbMSQqoER6CEny5btmzB6dOnJT7SAygejV+7dm34+voyacuXL2fWhBLG5XJx+/ZtiY918fb2xtatWxEfH4927drBwcEBAwcORHZ2NpYsWQI/Pz/o6+tj8uTJmD59OsLDwyU+aw8Apk6dikmTJuGvv/7CixcvWPu6deuGvXv34u+//4aPjw9rX7t27bBhwwbEx8dLfNYeAKxevRqdO3fGihUr8P79e9a+QYMGYdOmTZg9e7bYYprdu3fH5s2bYWZmhr179+LAgQMSy+dyudDR0UH9+vXRv39/TJ8+vUKtMCdOnMDOnTtLDVqB4rWZHj58iJo1azJpkp61V+Lw4cPIy8vDwoULxfapqqri4cOHsLS0lNhqOWPGDKSnp+PChQti+5o1a4abN28y2wKBABcuXMClS5fw8eNHqKmpoXnz5pg8eTKGDh1axlWXrSTYLmsVfCKdsLAwrFu3DuvWrUPLli0VXR2iRHJychAaGorWrVvTrD0lU5HPYKUMpIj8ffjwAaNGjSoz2CnRs2dPuLq6fodaKS8KpOSHAilSVSiQUl4V+QxW2q49Il/NmzfHzp07y1wJvMTTp0/FuhgJIYQQZUSz9ohUTpw4gR07dqBfv35Ys2YNateuDRUVFRQVFSE/Px8pKSm4dOkSDh06BKBqV10nhBBCqgtqkSJS2bdvH/h8PoYPHw5jY2OoqalBRUUFampq0NbWRr169WBnZwcAaNy4MT3ahBBCyE+BAikilREjRgAAduzYgf/++481SDsrKwuPHj3CnDlzULduXezZs0fsgciEKIqJiQlmzpwJExMTRVeFEKKEqGuPSGXt2rXo378/bt++je3btyMuLg4CgYCZsdemTRuMHDkSI0aMoId3kmpFTU0Nenp6Uo3vI4SQiqJPFiK1fv36oV+/foquBiEVkpycjOvXr8PIyIhmVhFC5I669gghSi0nJwfh4eFSrZJPCCEVRYEUIYQQQoiMKJAihBBCCJERBVKEEEIIITKiQIoQotRq1KiB3r17o0aNGoquCiFECVEgRQhRavr6+ujevTv09fUVXRVCiBKiQIoQotRyc3Px8eNH5ObmKroqhBAlRIEUIUSpJSUlwcPDA0lJSYquCiFECVEgRQghhBAiIwqkCCGEEEJk9MM/IubMmTPYsmUL8vPzS82jo6ODw4cPo2vXrt+xZtXLxo0bceXKFTRr1gy7du2CmZmZoqsk5suXLzh37hwCAgIQEhLC2sfhcKCiogKBQAA1NTXo6uqidu3aaNGiBYYOHYqBAweCw+FUWd0ePnwICwuLKiufEELIj+mHb5GaMGEC3rx5g127donta9KkCR49eoSXL1/+1EGUn58fTp48iezsbLx+/Rp79uxRdJUkatiwIZYuXYoLFy6gbt26rH1z587Fu3fvEBwcDA8PD9jY2ODjx4/w9PTE3LlzMXnyZGRlZVVJvTw9PeHq6lolZZOqx+VyYWhoCC6Xq+iqEEKU0A8fSAHFrRXDhg1DrVq1WOkWFhYwMTFRUK2qD4FAUOZ2daOmplZqi5mamhqaNm2KpUuXYs6cOUz68+fPsWTJErnXJTw8HGvXrpV7ueT7MTY2hoODA4yNjRVdFUKIElKKQKqElpYWa1tTU1NBNaleevbsiYkTJ0JbWxsdOnTAggULFF2lcqmpld/rPHHiRNa2t7c3goOD5VaHyMhIzJw5s8paugghhPz4fvgxUkQ6a9aswZo1axRdDbmqVasWatWqhZSUFCYtODgY7du3r3TZgYGBcHJyQnJycqXLIt+Xra0tYmJimG0+n4/U1FQYGBgw3Xumpqa4fPmyoqpICFEiFEiV4smTJ3BxcUFISAh4PB7atWuH2bNno2fPnhLzP378GNeuXUNISAhiY2OhqamJ5s2bY8qUKRg0aBArr7u7O/bs2cNq6Zg3bx7mz5+P9+/fY/PmzQgODsa4ceOwbNkyXLt2Ddu2bWN9qc+bNw82NjY4ePAgHj9+jJycHLRv3x6rVq1CixYtmHxOTk64c+cO6/wjR47E1q1bAQCurq7Yu3cvcnJymP1btmxB69atcejQIQQEBKCgoADdunXDqlWrYGpqKnbtaWlpcHFxwYMHD/Dt2zcUFhaioKCA2a+pqQkul4tmzZrh3Llz0tx+qYl2Uwpfh7Dk5GScPXsWvr6+iIqKQnp6OurWrYsBAwZg1qxZMDQ0ZPJ6enpiw4YNSEtLY9JevHiBLl26AADMzc1x5MgRZh+Px4Obmxtu3LiBqKgoaGtrw8LCAk5OTqhTp44cr5ZIIyYmBtHR0Uz3MJfLhZGREbM/OjpaUVUjhCghperak5cdO3Zg6tSpyMzMxL1793D27Fm8e/cOU6dOxYULF1h5c3NzMWvWLMyYMQNWVla4ffs2PD09oaenh+fPn2Pu3Lk4duwY6xg7OzssX75c7Lzv37/HhAkTEBAQgJycHLi6uiIjIwPW1tZYunQpK6+fnx+mT5+OmjVrQltbGzk5OQgICMCUKVOQnp7O5Nu7dy9WrVpV6rU6ODhgxowZrLTr169jyZIlMDMzg5qaGrKysvDgwQNMnz4dhYWFrLyfPn3CiBEjcOTIEcTFxeHUqVN4+fIlBg8ezORp3rw5/P395R5EpaSkIDU1lZXWtm1bsXw+Pj4YPHgwgoKCcOjQIfj4+GDOnDn49u0b3N3dMXbsWNZijcOHD0dAQACrDHNzcwQGBiIwMJAVRCUmJmLcuHHYuXMnrK2t8ezZM0ycOBEXLlzA6NGjERUVJddrJtIxMzODn5+fxJ/qOGOVEPLjokBKxOnTp3H06FEAwJ9//gk9PT20atUKVlZWEAgEWL9+Pb5+/crk37t3L7y9vQEARUVF4HA4qF+/PiuQ2L17N6v7CYDYhzmPx8OSJUvQoEEDJq1kyj8A1l/UAPD161e4u7tj6dKlWL9+PZOenJyMmzdvsvKW1opWQrTs5ORknD9/HkuXLsWiRYuY9IiICPj6+jLbhYWFcHJyQnx8PIDiAKRDhw7Q0NCAo6Mjky84OBheXl5l1kEWbm5urO327duje/furLTExEQsWrQI2dnZyMjIgL6+PlRVVWFnZ8fkiYqKYn7nFVFYWIj58+cjNDQU9erVg4ODA7hcLmbMmAFdXV3ExcVh5cqVsl0cIYSQHwJ17QnJzs5mlgZQVVVlunKA4qUUgOLxFhcvXsTixYsBFHcBlti/fz+z1pC2tjaTzufzERUVxZpVWBIglbhw4QJWr14NKysrHDhwAC4uLrC1tYWurq7E/KNHj2aWCBDtbouMjGRta2holHndomXb2dkx55VUdr9+/QAAL1++xIcPH5h9TZs2ZV6X3K8SQUFBGD58eJn1kAaPx0NkZCSuXbsGFxcXJr1jx47Yt2+f2FpSr169QnZ2NgDgzZs38PHxgYWFBXR0dFj5Pn/+XOG63Lx5E0FBQQCALl26QFVVFUBxV1KDBg3w7t07+Pv749OnT2L3QxoCgaDUrkpSuqKiIrH3tKQ8dG9JZZU8v5Ge46h8BAKB1GsTUiAl5PHjx0y3mKGhIWvmmHBg9OrVK+b1b7/9hrCwMADF3T8lioqKWGXzeLwyz62npwcrKysAxWsmzZ07t8z8JV/aoq+B0scJSUvashMTE1n7hO+R8GtAull4ZXF2dsbx48fFPrCaNWuGhQsXwsLCQqyuQHGAZWRkhISEBOjp6THjx0R/P3l5eRWuk6enJ/NadGq96PtFlkCKz+cjNDS0wsf97Ph8frl/PNC9JfIk+scrUQ7q6upS5aNASojwatoJCQmsFqnCwkLmpmZkZDDp8+bNw9ChQ5GdnY0OHTrg8+fPcHZ2FhvgLfrFLUo4CKss0XFM8iRcdqNGjVj7+Hw+81o0cGzTpk2lzuvo6Ihp06Zh1KhRrNaj6OhoNGzYUGIQBRR3W3p5eSE0NBRNmjSBvr4+Ll++LNYtKMvaWsLvl+PHj+P06dPMNp/PZ94vwoPWK6JkgD6pGGkW3uRyuWjduvV3qA1RZrm5uYiMjESjRo3Elt8hP7aPHz9KnfenD6T4fD7y8/Oho6PDGqQNFHfblfeXLVDcpZWYmIiVK1fi2rVrmDNnDuzs7HDo0CGp6yE6TqkyqnLBTeGy27Rpg19//RXPnz8HwP6rTDjYqV+/PoYOHVrpc2tra2P37t0YM2YM80ig3NxczJ8/H5cvX2a6I0Xp6OigS5cuuH37Nnbt2gVNTU3s3Lmz0l2Nwu+XwYMHS1xdvzI4HI5Yyx4pX3ndeiV56N4SedHS0qL3k5KpyCPHfvrB5nfv3sXjx48BiP8lK+00aU9PTwwdOhSXLl3Chg0bMGfOnFJbSEojTcBWHe3fvx/dunUDAHh4eODt27dIT09nggozMzMcOXJE6ibS8rRq1UpsBmNkZGSZg7pTUlLg6OiIhQsXQkdHB6dOnULz5s0rXRfh94vwukVE8aKjo9GjRw+JP7T8ASFEnn76QOrq1avMY2SEZ8wBxdPmJRFulfHw8MDixYuRmZmJ/v37Y+TIkVVX2WqoZs2acHd3x+LFi5GWlobx48ejf//+iIuLw8KFC3Hjxg3WIHR5mDRpktjaXF5eXjhx4oRY3ry8PNjb2zO/y/Xr10NfX18u9RB+v4SEhLCWUBBW3R/Jo2xMTU1Zs2L5fD4SEhKYrmczMzOJ66ERQogsfupA6t27d/D19WUGCvfp04e1//jx42LLFqSkpGD16tUAiscLbd++ndknOmboZ7Fr1y6cOXMGDx48QHBwMIKCgnDjxg3Mnj1bbHacvGzevFnsy3DHjh3MLLoSFy9eRHh4OLPduHFjqc9R3lib3r17M6/5fL7Err0bN27g9u3bUp+TVN7ly5dZ60adPn0aXbt2xenTp5k0WtWcECIvShVIiQ7oLhlHI0lWVhb+/vtvqKioMKtPN23aFAMGDGDyJCYmwt7eHn5+fkhNTYW/vz8cHByYZ7ylpqayVhv38PCAp6cnTp06hTNnzrDOx+PxWDPeRGeJlddqIXptwtuig8tFyxId+C26XZmyz549i8OHD6Nfv36oV69eWZdQIaIzD0W3a9SogR07drBmA/L5fCxYsIA1m1B0wOC6devw4MEDODk5sdJ5PB6KiopY96Z27dqsskXLnDRpEmuA6aVLl7BmzRpERkYiISEBbm5uOHfuHCwtLaW+bkIIIT8WpQmkeDye2CrXz58/F/sCzs3Nxf379zF69Gh8+PABRkZGrPFMmzdvZrUshYeHY8qUKejevTumTp0Ke3t7ZraPoaEhs5YTUDw7a/Hixbh79y7mzJnDOu8///yDf//9l9n29/dn7Q8KCioz8BPtNkpISGBex8XFlZpXIBAwY8BKvHv3jvV4GlnLBsAEjC9fvkRsbGyp9ZeWQCBAQEAAqxUJKF6aQnRsi7m5OebNm8dKi4+Px8yZM5kVxUVXOr9x4wYWL14MCwsLVutUcHAwxo8fj2/fvjFpEyZMYF5HREQgKSkJt27dwqdPnwAAJiYm2Lp1KyuYO3/+PCwtLdGnTx+4ublhx44dFR4vR+RLT08P5ubm0NPTU3RVCCFKiCP4wQdwhIWFwc/PD/fv32dmjwlTUVGBlpYWVFRUUFhYKBZYde7cWezRJVlZWTh69Ci8vLwQGxsLfX19dOrUCY6OjujQoQMr76tXr7B27VpERkaiWbNmmDRpEmxsbFBUVITVq1fDy8sL6urqGDVqFBYtWgQul4vly5fjypUrYnXlcrm4ffs26tevz0r39PTEli1bWEEMl8uFk5MTunfvjoULF7KCDBUVFVhbW2Pr1q0Sn7UHFK8PFRAQgCtXrmD37t2s+6Knp4cVK1bA0NAQy5YtY3VvamhowMHBgVnxfMCAARIHWnM4HKirq6NWrVpo2bIlM3aqLH5+fpgxYwar9UeUpqYmfH19mS/FoqIiTJ06FX5+fmJ5Dxw4gAEDBmDz5s24fv06uFwuBgwYgDlz5sDMzAzPnz/HmjVrEB0djZYtW2LVqlXo2LEjc7xAIMDRo0dx5swZpKamolWrVnB0dMTAgQNZ5wkJCcGRI0cQGBiIrKwsmJqawtLSEg4ODqhZs2aZ11ya4OBgAJDLA5h/djk5OQgNDUXr1q1pZhWRK3pvKa+KfAb/8IEUUazdu3dLvczD9u3bYW1tXcU1Ug4USMlPamoqfH190bt3bxgYGCi6OkSJUCClvCryGaw0XXtEMZycnKQeA3Tq1Kkqrg0h4hISEnDmzBlWlzUhhMjLT78gJ5FdTk4OZs6ciWfPnmHz5s2wtLSEjo4OBAIBCgoKkJeXhy9fvmDjxo149epVla64TgghhCgCtUgRmT158gTPnj2DtrY2bGxsoKurCw6HAxUVFairq0NfXx/t27fH4MGDAUBs7SdCCCHkR0eBFJGZubk5zMzMkJOTgyVLluDTp0/M0glFRUWIjo6Gu7s7Dh8+DEtLS0yfPl3BNSaEEELki7r2iMxq1aqF69ev4/Lly/D19cX06dORnp4ONTU1cLlc1K5dG+bm5ti3bx969Oih6OqSn5Sqqiq0tLRoGQpCSJWgQIpUiq6uLuzt7WFvb6/oqhAikampKebOnUuPhSGEVAnq2iOEEEIIkREFUoQQpRYbG4tjx47JZeV9QggRRYEUIUSpFRQUIC0tDQUFBYquCiFECVEgRQghhBAiIwqkCCGEEEJkRIEUIYQQQoiMKJAihCi1OnXqwNbWFnXq1FF0VQghSogCKUKIUtPU1ETjxo2hqamp6KoQQpQQBVKEEKWWkZGBp0+fIiMjQ9FVIYQoIbkGUikpKYiLi5NnkYQQUinp6el4+vQp0tPTFV0VQogSkusjYlxdXZGdnY01a9ZUqpxNmzbh5MmTEAgErPSwsLBKlVtRiYmJOHXqFIKCghAQEFDh4z08PNC6detK1WHjxo24cuUKmjVrhl27dsHMzAwA0LZtW7F1cdzd3dGtW7dKnU+R3rx5A09PTzx+/BifP39m7VNRUQGHw4FAIACXy4Wenh7q1q2Lli1bwsbGpkqvu7CwEL6+vujXr1+VnYMQQsiPSW4tUllZWTh79iyuXLmC1NTUSpW1cuVKeHt7o2bNmvKpnIzq1KmDRYsWwd3dHR07dmTtMzc3x8uXL/Hy5Us8f/4cPj4+OHHiBMaPHw81NfnEp35+fjh58iSys7Px+vVr7Nmzh9n38uVLdOnSRS7nqS46dOiAFStW4OLFi+Byuax9mzZtwrt37/D69WucPXsWffv2RUhICK5cuQI7OzssWLAA+fn5VVKvY8eO4fbt21VSNiGEkB+b3AKpc+fOITMzE7m5uThz5kylyzMxMUGTJk3kUDP5qF+/PmtbVVUVOjo60NHRgb6+PoyNjdGjRw+sW7cOx48fh4pK5W+taIuc8LaGhgZ++eWXSp+jOtLT00OtWrUk7lNXV0fbtm2xZcsWjBw5kkn38vLCtm3b5F4XPz8/7Nu3T+7lEkIIUQ5yCaTy8/Ph5ubGbJ85c0YurQPyatmRB9EWkrJ0794dv/32W6XP2bNnT0ycOBHa2tro0KEDFixYwNqvrq5e6XNUV9L87idOnMjaPn/+PBISEuRWh1evXsHJyQl8Pl9uZZLvT1tbG61bt4a2traiq0IIUUJyiVSuX7/O+gJLSkqCh4cHxo4dK4/ifxibNm3CypUrAQC2traoUaNGpctcs2ZNpcecKatmzZqxtvl8PkJDQ2FkZFTpsu/du4e///4bOTk5lS6LfF+2traIiYlhtouKisDn83Hz5k2mpdjU1BSXL19WVBUJIUqk0oGUQCDA8ePHoaGhAR6Px6S7uLhgzJgx4HA45ZYRHh6Ow4cP49mzZ8jJyUGTJk0wderUUvOvXr0aFy5cEEtXVVVFYGAgtLW14ezsjJ07dzL7xo4diw0bNlTw6qQXHx/P+vDu27evWJ6oqCicPXsW/v7+iImJQU5ODkxMTDBs2DBMmzYNOjo6TF4nJyfcuXOHdfzIkSOxdevWcuuSlZWFP//8Ez4+Pqz0ksH6CxcuhJeXF6ur8MGDB6hXrx6A4kHre/bsQVZWFrN/3rx5mD9/Pt6/f4/NmzcjODgY48aNw7Jly5g8PB4Pbm5uuHHjBqKioqCtrQ0LCws4OTlVyWKIol2fAEoNfCpy711cXHDgwAFWWZ6enrh//z4AYPjw4Vi3bh2zLzMzE87Ozrh79y4SEhJQo0YNDB06FHPnzoWurq6crpZIKyYmBtHR0czEDBUVFWhoaDD7o6OjFVU1QogSqnTX3oMHD5CRkcH6QgWAz58/4+HDh+Ue7+Pjg7Fjx+LmzZto1aoV/P39sX//fpw/fx5BQUESj1m1ahWGDBnCStPW1oavry/TfO/o6IhDhw4BAMaNG4fVq1fLcnlSO3r0qMQv9hKXLl3CkCFDkJycDHd3d/j4+MDGxgafP3/GgQMHMHnyZOTl5TH59+7di1WrVslUF11dXTg7O6Nhw4YS9+/evRvdu3cv9Xg7OzssX75cLP39+/eYMGECAgICkJOTA1dXV2ZtnsTERIwbNw47d+6EtbU1nj17hokTJ+LChQsYPXo0oqKiZLqWsnz69EksrU2bNmJpFb33U6dOxbVr11hlDB8+HIGBgQgMDGQFUZ8+fYK1tTWcnZ2xYMECBAQEoG/fvnBxccH48eNp7SIFMTMzg5+fn8SfkgCLEELkodKB1LFjxzBhwgTY2trCwMCAtc/FxaXMY1NSUvDXX38hNzcXQHFLibq6OoyNjbF///5SZ+1paGhg7dq1rDEPfD5fbMyQrq4uDAwMsHTp0ioZTyQQCBAbG4udO3fi5MmTpeZ7//491qxZAz6fj+zsbOjo6EBdXZ01xickJESsla1nz56Vql9ZrUDldX+JftnweDwsWbIEDRo0YNI4HA5UVFRQWFiI+fPnIzQ0FPXq1YODgwO4XC5mzJgBXV1dxMXFMV2e8iQ8Lg8ALC0txYJHWe+9NLKysjBz5kxER0fj119/xbBhw6Curg4nJycAxS2t0rQgEkII+XFVqmsvMDAQ7969w8GDB6GhoYGxY8fiyJEjrP1v3rxBhw4dJB7v5ubGLJKnra2Ntm3bMvv09PTQuHFjJCYmSjy2Vq1a+OOPP5hgjc/n48aNGxg/fjyT5+HDh5g4cSKr20ZeXrx4gfbt20s1ENnf3x+FhYUAgLt37yIsLAwtW7YUG/wqunaScHeELMqaOVjerELR/RcuXMDq1athZWWFAwcOwMXFBba2ttDV1cX169eZ1sMuXbpAVVUVQPEA/QYNGuDdu3fw9/fHp0+fKj0TMzc3Fx8/fsSZM2dw/fp1Jr1v377YvHmzWH5Z7700Tp48ia9fvwIAunbtyqTXrl0bNWrUQHp6Oq5fv44VK1bI1MUnEAhojJYMioqKyn1/FxUV0b0llVbSCFDyL1EeAoFAqqFJQCUDqaNHj8La2pqZqj5hwgQcP36ctVDk8ePHWesfCbt37x7z2sjISOpKl7Czs4O7uztzPnd3d/zxxx/gcDjg8/m4e/euTC0N0jA3N4eLiwsiIyNx8uRJnD9/vtS8PXv2hK6uLrKysmBqasq09oh2BQp3L1U3enp6sLKyAgDMnTsXc+fOZfZ5enoyr42NjVnHCQcsr169kjmQ+ueff7Bu3TrWODwA+OWXXzBv3jz06tVL4nFVee/Lu+709HTw+XyEhITItGBoyeB5UjF8Pr/cP0Lo3hJ5ioyMVHQVSBWQtidL5kAqPDwcjx8/xo0bN5g0Y2NjDB48mLV44b179/Dt2zexdZgKCgpYY1xkaX0xMTHBkCFDmC+0T58+wcfHB/3798edO3fQtWtX1K5du8LlSovL5aJ58+ZYv349atSogYiICIn5WrRogfv37yMiIgKtWrWCqqoqTpw4gdOnT7PylTXGStHMzc1L3RcSEsK8Pn78OOu6hLtc09LSZD7/2rVr0atXL9jY2CAlJYVJ//r1K5o3b17qcVV17/Py8li/740bN2L79u3Mdn5+PnPdsi5Qy+VyxWYmkvJJs1QJl8ut9FMHCMnNzUVkZCQaNWoELS0tRVeHyNHHjx+lzitzIHXs2DH06tVL7IN+8uTJrECqsLAQJ06cEBvsnZKSwvrykjWIcHBwYLUMuLi4oH///jh9+vR3XTbA3t6eNQhZlIGBAX755RdcuHAB+/fvR+PGjbFlyxaxtZCqq7LGVAk/w2zw4MHYtWtXldShbt262Lp1K2bOnMm8X5KSkrBo0SK4ubmVuvZUVdz79PR01nvWzs4OS5Yskbk8STgcDq19JANpFsNVUVGhe0vkRktLi95PSqYiPWQyBVKxsbG4desWuFyuxMeUqKqqMuNSAODKlSuYP38+a/C46Jdedna2LFVBu3bt0LVrVzx79gwAEBAQgCtXrkBNTe27/sVZu3ZtZvkASaKiorBo0SK8efMGvXv3xqFDh+S6eGRVK6vFkMvlMmPFhJeAqAr9+vWDg4MDayJDYGAgdu7ciaVLl0o8piruvWirR1VfN6mY6Oho9OjRo9R9NHOPECIvMs3ac3V1RefOnREUFMRMCRf+KVl2oEROTg7Onj3LSjMwMICenh6znZCQIPYQXmk5ODiwttesWQN7e3uxfGfPnkWPHj1gaWmJly9fynSusoguAVEiKSkJ48ePx5s3b6Cqqopt27Z9l1XJ5fGYGmkIz+QLCQlBUlKSxHzy6rr8888/0b59e1aai4sLs86TMFnvfXl/jdSqVQv6+vrM9tOnTyWu5l+du2uVlfBYOKB4GEFycjLz+WJmZgZTU1NFVY8QomQq/E2bkpKCixcvYvbs2aXm6devH2sGHlA8EFx4ZgOHw2ENEObz+Xj+/DmznZ+fL7ZwXlFRkcTzDRgwAI0bN2a2jY2NYWFhwcrz+fNnrF+/HikpKYiMjMSff/5ZqS+5ihx77NgxpgVEV1e3SsdtCROdKSbcSihr0CpJ7969mdd8Pl9i196NGzfk9uBfLpeLXbt2iV3fsmXLmFl0JWS999KMsxF+/6ampuLYsWNieY4cOYLXr19LdU4iH5cvX2atG3Xq1CmYm5vj1KlTTBqtak4IkZcKB1K7d++Gmppaqc3mJWbMmMHaTklJwYkTJ1hp06ZNY/3lv3v3bvB4POTl5WH58uVi3S+SFmAEioOyKVOmMNuTJk0Sa415//49KxCLjY2t0CBg0anSFZk6LTxoLT09HVu3bsXdu3fFFr3k8Xjg8XhMPUVnqIlui7aAiG4LB5cAEBcXBwB4+fIlvLy8WPvKm8VWVuA4adIk1kDLS5cuYc2aNYiMjERCQgLc3Nxw7tw5WFpallqGKNH7Kzq9uH79+mIr1WdmZmLevHmsY2W99zVr1mQFU8LLXJQMMhd9/+7duxd79uxBTEwMYmNjsWvXLoSGhqJjx45SXzchhJAfi9SBVHZ2Ng4cOIDz588jKysLjx8/LnMNJUnPmTt06BAePXrEfCmLPoj31atX6NOnD9PC0blzZ9bx9vb2Yo9NKWFjYwMDAwPo6Ohg9OjRYvtbtmzJCq5MTEyYZRvKExERgcDAQFbahw8fWC1oZRFtnXN1dcXq1asxdepU1hpXDx48wLRp05iB+I8fP2Yd9+7dO+axLbm5uWLdkwEBAaztcePGsQZAOjk5YePGjVi2bJnY+DHR9br8/f1Z20FBQaU+iNrExARbt25ljXs7f/48LC0t0adPH7i5uWHHjh3M+lJlKSwsxK1bt8SC3Dt37rBm6wHAsGHDMGbMGFZaWFgYnJycmLyy3HugeNrrqFGjmP3BwcHIycmBm5sbMjMzAQDt27dndecKBAIcPHgQAwYMQP/+/fHff/9h48aN5V4zIYSQHxdHIGUf1R9//CH2yBYVFRUcP36ctQJ3YGAgpk2bVua6PA4ODqwvIC8vL7i4uCA8PBw6OjqwtbWFk5MTFixYgLy8PHTu3BmdOnVCp06dylzYcO/evcjMzCx1Fe3Tp09j3759qFGjBrZs2YJffvmlzGsOCwvDH3/8UWbrk6amJvbs2YP+/fuXmic7Oxtr1qyBt7c39PT0MGTIEMycORO1atWCl5cXtm/fjuTkZHTq1Anr1q1D48aNJT5rDygeyB8QEIBu3bqxuupKdO3albXK+uvXr7Flyxa8e/cONWvWhIWFBebNm4cdO3bg6tWrTL5GjRphzZo16NWrF5YvX44rV66Ilc3lcnH79m2xpSxKhISE4MiRIwgMDGTWbbK0tISDg0Opq9QLu3TpEtauXVtmt2OdOnXg6+vLbOfl5WH06NH48OEDKx+Hw8GVK1fQsGHDCt/7Evn5+di1axeuX7+OnJwcdOjQAfPnzxebYOHn5wcXFxe8efMGeXl5aNCgAaytrTFx4kSZp0QHBwcDgNhYMFJxYWFhWLduHdatW4eWLVsqujpEieTk5CA0NBStW7emWXtKpiKfwVIHUoSQ74cCKflJTEyEl5cXhgwZUiUPzyY/LwqklFdFPoO/z7QuQghREB0dHbRp06ZKHhVFCCEUSBFClFpmZiaCgoKYsW2EECJPFEgRQpRaWloaHjx4UKlHFBFCSGkokCKEEEIIkREFUoQQQgghMqJAihBCCCFERhRIEUKUmqamJho1agRNTU1FV4UQooQokCKEKLU6depg9OjRtIYUIaRKUCBFCFFqRUVFrOcoEkKIPFEgRQhRatHR0di3bx+io6MVXRVCiBKiQIoQQgghREYUSBFCCCGEyIgCKUIIIYQQGVEgRQghhBAiIwqkCCFKzdTUFHPmzIGpqamiq0IIUUIUSBFClJqqqiq0tbWhqqqq6KoQQpSQmqIrUJ4zZ85g69at4PF4pebR0dHBw4cPUbNmTbmd982bN3B3d8fLly+RkpICVVVVNGnSBDNnzsSgQYPkdp6f1eTJk/Hs2TNmW1NTE1wuFzk5OSgsLGTSuVwuNDU1wePxkJ+fz6SPHDkSW7duBQD4+vpizZo1yMvLw7JlyzBixIjvdyGk2ktKSsLVq1dRp04dNGjQQNHVIYQomWrfIjVhwgS8fv0ae/fuFdvXpEkTPHr0CC9fvpRrEHX69GmMHTsWN27cwIgRI3Dz5k1kZWXhzZs3mD9/PhITE+V2rp/d1KlT8fjxY7x+/RqBgYEwNzdn7R8+fDgCAwMRHByMR48eYejQoWJlrFy5EtHR0UhOTsbKlSuRl5f3vapPfgC5ubmIiIhAbm6uoqtCCFFC1b5FCgA4HA4sLS1hYGCA1NRUJr1///4wMTGR67mio6OxefNmCAQCAECDBg2gr6+PFi1a4NOnT+jTp49cg7af2fDhw7F06VKp85uYmGDXrl1IS0tjpZf8rkpeC28TQgghVanat0gJ09bWLnNbHl69eoWCggJWmp6eHm7cuIGQkBAcPnwYXC5X7uf9GS1cuLDCx3A4HMyZM4eVtmHDBpiYmMDQ0BAbN26ElpaWnGpICCGElO2HaJH6nsoai0XkZ9CgQahfv75Mx3bp0gURERHMdr9+/fDo0SM51YxUR7a2toiJiSkzj6mpKS5fvvydakQIIcV+qBap8rx9+xZWVlZo2bIl8zN58mRkZWVh9+7dGDx4MDp06AArKyvcu3ePdWxMTAy6dOmCf/75h5X+zz//oEuXLggMDGSlFxYW4uzZs7C1tYW5uTm6deuGRYsW4cuXL6x87u7uMDc3Z9Vp3759AID379/Dzs4OnTt3ZgZOl+DxeHB2doaVlRU6d+6MXr16YfXq1WLjs7Zt24Z27dqxyg8ICMCzZ88wbdo0pm5Lly5Fenp6qfcuODgYixYtQr9+/dCxY0dYWlpi3bp1SEhIkJj/8+fPWLp0KXr37o1OnTrBysoKp0+flrpbzd7eXqp8kqioqGD8+PHw9vZmXXfJT4no6GhMnjyZtc/CwgI8Hg9HjhzBsGHD0L59e/Ts2ROrVq1CRkYGgOKJBvPmzUO3bt3QuXNnTJo0CW/fvi21PgkJCVi/fj0GDBiATp064bfffsOhQ4dYg+NJ5cTExJT5rLzo6OhSA62aNWuif//+1CVPCKkSShVItWvXDkePHmWlxcfHY/z48UhNTYWxsTF4PB7Cw8OxYMECVnBkamqKwMBArF27lnX82rVrERgYiC5dujBp2dnZmD59OtatW4dOnTrB398fy5cvx61btzB69GjWl66dnR2WL18uVtf3799jwoQJCAgIQE5ODlxdXZkv8sTERIwbNw47d+6EtbU1nj17hokTJ+LChQsYPXo0oqKimHKWLl0Ka2trVtnOzs7Ytm0bmjZtiqKiIqSlpcHDwwOLFi2SeN8uXLiAcePG4c2bN7hw4QKOHj2KyMhInD17FjY2NqzzAcD9+/dhbW0Nb29vuLm5wdvbG1wuF+vXr8fff/8t8RxVYcCAAXj8+DF0dHQk7jczM4Orqys0NTWZtOzsbIwfPx5JSUmwsbFBUVERkpOTcfHiRcyePRuHDh3Chg0b8Ouvv6JevXrIycnB8+fP4eDgwBqfV+Lly5ewsrLChQsX8L///Q9PnjxB48aNsXv3bkyfPh18Pr/Krv9nY2ZmBj8/P4k/ZmZmpR6np6eHLl26QE9P7zvWlhDys1CqQAoAjIyMWNvfvn3D8uXL8c8//8DZ2RkaGhoAiluUTp06JdM5VqxYgadPn0JHRwcLFy4El8uFjY0NmjZtioyMDCxevJg1hV/0Q57H42HJkiWsqdgcDgcqKiooLCzE/PnzERoainr16sHBwQFcLhczZsyArq4u4uLisHLlyjKvubCwEOfOncOKFSswZcoUJv3JkyesLjEAePbsGdauXYvCwkJMnToVdevWRdeuXVGjRg0AQHJyMlxdXZn87969w6JFi8Dj8TBp0iQ0bdoUBgYGmD59OgDg+vXr8PDwqPhNlVHdunXRrFmzUverqanBwMCA2U5LS8OUKVOwcuVKODo6Yvjw4cy+wMBAvHjxAqdPn4a9vT2WLVvG7MvIyMCtW7dYZcfGxmL27NlIS0vD77//ji5dukBHRwdz584FAAQEBMDZ2Vlel0pklJOTg7CwMOTk5Ci6KoQQJaR0Y6RUVNixYefOndGzZ08AgJaWFmrWrIn4+HgAQGRkZIXLf/HiBby8vAAAbdq0Yf2V26RJE0RERCAyMhJPnz5Fnz59JNbpwoULWL16NaysrHDgwAG4uLjA1tYWurq6uH79OoKCggAUjwUqWUSQy+WiQYMGePfuHfz9/fHp0yc0adJEYvkzZ85kBsSLrub8+fNnNG3alNneunUrioqKAAAdOnRg0n/99Vfcv38fAFitKv/73/+YLquuXbuyrr1ESUvW91ISHJdG+P6YmZmx1pkyNjZm5Z0xYwbU1dUBAHXq1GHtE32/HDhwgJlBWNa9KAmsKkogENCX//9XVFQk9j6XlEfS/YqOjsaNGzfQtm3bKpmgQn5eJUtq0NIaykcgEIDD4UiVV+kCKVGiqxmrqf3fJcvyJeXp6cm8rlu3Lmuf8If0q1evmEBKlJ6eHqysrAAAc+fOZX3RCpcv+iUvWr7wF7Yw4S8c0esXvuawsDCEhIQw2yWtUACwatUqZrukfikpKXj69KnE+gl3r4WEhIDP5/8QsxuF3w/l7cvOzmZeFxYW4vbt28y28L0Q/j0lJiYiKioK9erVq3Dd+Hw+QkNDK3ycMuLz+eUGzKXdr7i4OABAVFSU2IxcQuRBlj/KSfVX8kd1eZQ+kCqLLB+qwoGHl5cXfHx8WOWV3PiyBnaLLjpZWvnHjx/H6dOnmW0+n8+UL7qWkrSEuxxfv37N2peVlcW8NjExwebNm1n7RQdcjxw5kgnUBAIB602XkZEBQ0NDmepYXZW03AHFH5zC92vu3LmswEv4XqSkpMgUSHG53DK7LX8m0gTlXC4XrVu3Fksv+b3Uq1cPzZs3l3vdyM8rNzcXkZGRaNSoES27omQ+fvwodd6fOpCShXCA1LZtW1y4cKHCZYiOaSqt/MGDB2PXrl0VLr8swrPqUlJSWPtiYmLQpk0bqeoGAHv27EHfvn3lWr8fhei9WLp0KcaPHy/Xc3A4HOqK+v/K69YrySPpfpVMNtDU1KT7SaqElpYWvbeUjLTdegAFUhUm/JdxeevalKasLgoul8uMSZK1fGkJz2YDigdHl/UcQdFWgaquX3VG9+L7i46ORo8ePUrdV9rMPS6XCyMjox+iq5kQ8uNRull7VU14pl1iYiKrK06YrI8pES4/JCQESUlJci1fmPCgcwC4detWmc+pa9iwIWu7tEUwf4ZHtIg+/Fa4i1fYz3AvvgdTU9MylzgwMzMTm1hRwtjYGHZ2dmJjDgkhRB4okKqg3r17s7Z37tzJGjsDAM+fP2ctGSBr+Xw+X2LX3o0bN1gDnWX166+/shYpTEpKwoEDB8Ty3bp1CwKBAC1atGDNZPPx8cHz589ZeYuKirBkyRKlf3BwjRo1WLMcw8LCcO3aNbF869evZ2aJEtldvny51DWkSn5oVXNCiCL8UIGU6BRTSVNORYMa0W3hAeaSWgtEB6CLBgTW1tasYOLJk//X3r3H1ZTufwD/7K6UXEIJg5qZTDOMw3H/oRknmdDIRBhzwmgMuYzL5NK4JA6hcMzJaWJQM3VGrzhGTA5TGCrNxOSajJxcEqmdqKTd1u8Pr9bZa1+67HbtbJ/36+U161nr2Ws9z6rZ69tzW0mYP38+rl+/DqlUioMHD2LdunWYOHGixnNU10rxySefiAYtxsbGYtWqVcjOzkZeXh4iIiLwww8/YOTIkbWqs+LgcuVrN2vWDLNnzxYdDw8PR2BgIC5cuICLFy9i+fLlyMjIgEQigbGxMWbMmCG6jq+vLw4ePAipVIrr169j7ty56NOnj0q3YW0p36vazKxUfq2PclpxhXHl8ysvmKn42ZryfvbZZ6L0ihUrEBERgYcPH+LWrVtYuXIlmjVrpjK7kxrX3bt3sXXrVpWFZYmIdOGlCaR+/vlnlcHRJ0+eVBmbovwKFcVXnDx79ky0OnVhYaEo0JDJZDhz5ozo8wkJCaLZWZaWlti+fbtoYOHx48fh7u6OQYMGYcOGDQgKChItB3D27FnROX///XeNrw+xs7NDUFCQaAbYvn37MHLkSAwdOhQREREIDg4WLWug3P2nWOeqqd+a8np7e4vWVQKAqKgoeHl5YcKECSgvL8e8efOEY1OnToWrq6uQfvz4MZYuXYpBgwbB3d0dbdq0wZQpU9TWrSZpaWm4du2ayr7r169r/MydO3dUFhlNTU0Vtm/cuIGCggIhXVBQIFyjuLhY5WeTkpIiBJuJiYmiYxkZGaLfBVdXV9GrbsrLy7F+/XoMGTIErq6uyM3NxaJFi6qtMzW8yspKyOVydrMSUYOQVDbxb5fo6GgEBQVV+zJhS0tLJCYmIicnB/7+/ioPYxcXF/ztb3/D7Nmzcf78edGxgQMHYv369ZBIJHB1ddX4So+oqCjRa2Ju376NHTt2IDk5GYWFhbCxsYGzszNmzpwpGouxfPlyHDhwQOV8pqamiI+P1/ji3itXruCbb75BWloaiouL0bFjR4wcORLTp08XdccFBwdj7969onLb2Nhg7dq1kEqlCAwMFLXctWzZEvPmzYO3t7ewr7KyEgcPHsS+ffuQmZkJExMTODo6YvLkyaKVvxXzx8TEIDY2Fjdu3ICJiQnefPNN/PWvf4Wbm5va+lTn2LFj8PPzq7Y70MLCAtHR0aLp7SdOnMCsWbPU5l+5ciV69uyJiRMnqjxAJRIJoqOjsWLFCpUgDABGjRqF7t27q+1Wbd68OdLT00X74uPjER0djYyMDMjlctjb22PixInw9PSsdp2q6ly6dAkA0LNnT60+T/+TmZmJgIAABAQEiN7FSFRfpaWlyMjIgJOTE2ftGZi6fAc3+UCK6FXEQEp3GEhRQ2EgZbjq8h380nTtERERETU1DKSIyKDZ2tpi2rRpHPRPRA2CgRQRGTQzMzO0a9eu1u/NIiKqCwZSRGTQpFIpjh49qjLrl4hIFxhIEZFBKykpweXLl1FSUqLvohCRAWIgRURERKQlBlJEREREWmIgRURERKQlBlJEZNCsrKzQv39/WFlZ6bsoRGSAGEgRkUFr3bo1hg0bJnq1EhGRrjCQIiKD9uzZM9y+fbva93USEWmLgRQRGbS8vDzExMQgLy9P30UhIgPEQIqIiIhISwykiIiIiLTEQIqIiIhISwykiMigmZiYoEWLFjAxMdF3UYjIAOnkm+XIkSM4d+4cDh8+jKKiIpXjEokE5ubmaNOmDV577TX06dMHbm5ueOutt3RxeRUXL15EZGQkzp8/D6lUCmNjYzg4OODzzz+Hi4tLg1yzqcrLy8Pu3btx8uRJ3L9/H1ZWVujQoQNGjBgBT09PWFtbw9/fHxs2bNB4jsTERAwfPrwRS900qKv3P/7xD+zcuRNlZWXCvsjISAwYMKCxi0e1ZGdnh1mzZsHOzk7fRSEiA6STFqnRo0dj1apV2Lx5s8qxnTt3Ijk5GdHR0Rg/fjwuX76MsLAwjB07Fr6+vjqfSRMVFQUvLy/ExcXhww8/xJEjR1BcXIyLFy9i3rx5ePjwoU6v15SdO3cO7u7uOHToEL788kv8+uuv+OWXX7B69WpcuHABzs7OGDZsGLKzszWe4/bt21i0aFHjFbqJ0FTvuXPnwsPDo/ELRERETZJOu/a6deumss/c3BzW1tZ45513MHfuXERGRsLCwgIAkJCQAA8PD/zxxx86uX5OTg7Wr1+PyspKAECXLl3QsmVLODo6wsTEBM7Ozq/MonwFBQWYPXs2Hj16hFWrVsHFxQVmZmaQSCTo0aMHQkNDawxkS0pKMHfuXDx9+rQRS65/NdW7bdu2jVwiqo/c3FyEhYUhNzdX30UhIgOk00DK2Ni4xjw9e/bEvHnzhHRBQQFmzpyJJ0+e1Pv66enpqKioEO2zsrJCXFwcrly5grCwMJiamtb7Oi+D6OhooZvV3t5ebR5fX1988MEHao+VlpZi7ty5yMzMbLAyNkW1qbdEImnEElF9VVRUoLi4WOW7gYhIF/Qy2HzSpElo0aKFkL537x5CQ0PrfV6uXPw/ly5dErZ37doltNIpW7BggUpgcP/+fUydOhXJyckNWsam5lWtd1Pj6emJQYMGVfvP09NT38UkIgKgo8HmdWVhYYHBgwfj2LFjwr59+/bhiy++QPPmzUV58/LyEBYWhhMnTqCwsBA2NjYYN24cZsyYATMzMwAvArEPP/wQMplM9Nk1a9Zg/fr1CAsLQ9++fYX9crkcMTExiI2NRXZ2NkxMTDB48GAsWLAAXbt2FfLt2bMH27dvR2lpqbBvw4YNcHJywj//+U+kpqaioqICAwYMwIoVK9CxY0e19c3OzsbOnTuRkpKCwsJCWFtbo3fv3pg7d67a7tDa1LkmivkOHTqEoqIirFy5Eq+99poon729Pd544w0hff36dcyZMwc5OTmifIr3Ly0tDSEhIdi7dy/Ky8tF9+ajjz5CamoqNm/ejJs3b2L+/PmYNm2akOfJkycIDw/HsWPHkJeXh1atWsHNzQ1z5swRBdeLFy/GkSNHRAFgQkICrl69ioiICFy9ehUWFhYYNWoU/Pz81N6XrKws7Nq1C2fPnkV+fj5kMpnofJaWljAyMoKXlxc8PDxqVW9Nbt26hZCQECQlJcHMzAxubm7w8/NT+X02RIMGDQIApKSk6OR89+7dQ05ODjp16qT2uPLPqL50XX4ierXobfmDnj17itKlpaUqLQHnz5+Hu7s7YmJisHnzZiQlJcHe3h7btm2Dj4+PEDh17NgRaWlpWL16tejzq1evRlpamuhhWFJSAh8fHwQEBOBPf/oTzp49i+XLl+Onn34SBsNXmT59Oj777DPROasGbnfq1AkmJiYoLi5GQkICfHx8IJfLVep58uRJjB07FseOHUN4eDgOHjyIBw8eIC4uDuPGjcPFixe1qnNN+vXrJ0qfOnUKbm5u+Oqrr3Dr1i3RscDAQGHb0dERx48fx5///GdRnrS0NOEf8CLQ8fHxUbnumTNn8Omnn+LSpUsoKSkRtTTevHkTY8eORXh4OL744gukpqZi2LBh2L17NyZPnozHjx8LeUNCQjBw4EDRuQMCAvD999+je/fuePbsGfLz8xEZGYm1a9eqlOM///kPxo0bhwMHDqBVq1Y4efIkTp06hS5dugh5JkyYgLS0NCxZsqTW9VYnJSUF48ePx/nz51FcXAypVIqoqCj4+flp/AxVr1OnTkhJSVH7T1OARUSkD3oLpDp37qyy7+rVq8J2bm6uMFh69OjR6Nu3LywtLTFnzhwAQGpqKsLDw+t8XX9/fyQnJ8PS0hILFiyAqakpPDw88Prrr+Px48dYvHixKCCysbERfb6goAD79u3D0qVLsXDhQmF/VlYWzpw5I8r73//+FwsWLEBZWRnGjx+PN954A127dhVagEpLS7Fjx44GqfP48eNVWrtkMhliY2Ph5uaGJUuW4O7du7U6lybKD7SCggKsXLlSdF0joxe/YsXFxfj888+Rk5ODfv36YdSoUTAzM8P8+fMBvGgJCwoKEp1P+d63a9cOkZGRWLVqFcaMGSPs//e//43i4mIhfffuXfj5+QldvTNnzkTbtm1ha2sLLy8vIV9kZCTu379fjzvwQmxsLKKionDmzBm4u7sL+48fP47bt2/X+/xUPzY2NvDy8lL5fSIi0gW9rVCn2I1TJT8/X9gODQ3Fo0ePAAD9+/cX9js4OAjb//rXv4QgozbOnTuHo0ePAgDefvttWFlZic6blZWF7OxsJCcnY+jQoQD+FwhU8fb2Fsqu3JWXnZ0NZ2dnIb1lyxZh5levXr2E/f3790dGRgYAiFqYdFlnS0tL7Ny5EzNmzFB5mMvlcvz44484evQo5s2bBx8fH60GUCvfm127diE0NBR9+vTBmjVrEBcXh1mzZgEAvvvuO6EcinVr164dWrVqhaKiIhw6dAj+/v7C/VU+v6+vr7CtuCaQTCbD3bt3hXXJDh48KBovp3j/FLefP3+OCxcuoEOHDnWuuyJ/f384OjoCeNEVGBcXJxy7efOmqBWsLiorK0Xdyk3V8+fPkZubq7O1tHJzc2tsdcrJyan19SorK1FRUYENGzao/T3Pzc2FnZ3dS3GvqWmp+n5/1WY2vwoqKytr/VzUWyClbpXhqgenXC5HfHy8sF/xQVe1dAIAPHz4EHfv3lXbuqXO4cOHhW1bW1vRMcXzpqenC4GUMsWZicqzFBW/iIuKipCQkCCkW7VqJWzPmTMHcrkcjx49EoKDhqhzly5dcODAAYSEhCAmJkal6/HZs2cIDg7GtWvXEBwcXO/ZaI6OjkI36po1a7BmzRrhmOK9Vw5cLCwsUFRUBJlMhitXrmh8QCoGVsq/PyUlJcK28lphlpaWomsp0sVq14rLISiP1VLsrqwrmUwmBNxNWdUfA7Xtdtb1dWvy/PlzlJeXw8zMTCU4VzzXy3CvqWmqbi0+ennVdkyy3gIpxQdflaoHUnZ2tqirZs6cOaIHnmLlpFJprQOpK1euCNtHjx7FqVOnhHRFRYVwXnWrs9eGYqBy+fJlUVqxPq1atcLKlStFn22oOltZWSEgIADe3t4IDQ1FfHy8SkB1+PBh9OvXD5MmTarVOTVRHIumqKysDFlZWUJ63bp12LRpk5CuesgBQGFhoVbXVqyT8nIPigPiFbeNjIwabHV9deWqK1NTU9FEgKbK1NQUdnZ2OHHihE7O9/7779eYpy7X++OPPxAUFIRly5bhzTff1Hg9JyenuhWUXnlPnz5FdnY2unXr9kpMLHmV3Lhxo9Z59RZIFRQUqOyrGoCuHMgsXboUkydPrvc1Fc/7zjvvICYmpt7nVKQ4I0wqlYqO1TTTSNd19vf3x/r164W0g4MDQkJCMH/+fHz99dc4fPiwqLzR0dH1DqQ0jUEpKioSXcvb2xtffvllva6lTPH8Hh4e2LFjh3BPs7Oz8frrrwN4MW6tyujRoxt84LKmZSdqQyKRqLSgNUVVrTy6KqumViPlPLW9XrNmzYT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modellifelines.WeibullAFTFitter
duration col'adv_fit_time'
event col'adv_failures'
number of observations1500
number of events observed1500
log-likelihood-5531.26
time fit was run2023-09-29 11:13:25 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
lambda_adv_failure_rate-0.001.000.00-0.00-0.001.001.000.00-39.62<0.005inf
atk_value0.151.160.16-0.160.460.851.580.000.950.341.56
data.sample.random_state0.031.030.02-0.010.070.991.070.001.310.192.39
def_value-0.210.810.16-0.530.100.591.110.00-1.310.192.41
model.art.pipeline.initialize.kwargs.optimizer.lr-0.001.000.00-0.000.001.001.000.00-0.530.600.74
model_layers0.011.010.000.010.011.011.010.007.04<0.00538.88
predict_time-0.150.860.01-0.17-0.120.840.880.00-12.17<0.005110.86
train_time0.001.000.000.000.001.001.000.0010.81<0.00588.14
Intercept3.0020.180.182.653.3614.1428.790.0016.56<0.005202.20
rho_Intercept-0.840.430.02-0.88-0.800.410.450.00-43.75<0.005inf

\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Concordance0.84
AIC11082.52
log-likelihood ratio test800.84 on 8 df
-log2(p) of ll-ratio test554.32
\n", - "
" - ], - "text/latex": [ - "\\begin{tabular}{llrrrrrrrrrrr}\n", - " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", - "param & covariate & & & & & & & & & & & \\\\\n", - "\\multirow[c]{9}{*}{lambda_} & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -39.62 & 0.00 & inf \\\\\n", - " & atk_value & 0.15 & 1.16 & 0.16 & -0.16 & 0.46 & 0.85 & 1.58 & 0.00 & 0.95 & 0.34 & 1.56 \\\\\n", - " & data.sample.random_state & 0.03 & 1.03 & 0.02 & -0.01 & 0.07 & 0.99 & 1.07 & 0.00 & 1.31 & 0.19 & 2.39 \\\\\n", - " & def_value & -0.21 & 0.81 & 0.16 & -0.53 & 0.10 & 0.59 & 1.11 & 0.00 & -1.31 & 0.19 & 2.41 \\\\\n", - " & model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.53 & 0.60 & 0.74 \\\\\n", - " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 7.04 & 0.00 & 38.88 \\\\\n", - " & predict_time & -0.15 & 0.86 & 0.01 & -0.17 & -0.12 & 0.84 & 0.88 & 0.00 & -12.17 & 0.00 & 110.86 \\\\\n", - " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 10.81 & 0.00 & 88.14 \\\\\n", - " & Intercept & 3.00 & 20.18 & 0.18 & 2.65 & 3.36 & 14.14 & 28.79 & 0.00 & 16.56 & 0.00 & 202.20 \\\\\n", - "rho_ & Intercept & -0.84 & 0.43 & 0.02 & -0.88 & -0.80 & 0.41 & 0.45 & 0.00 & -43.75 & 0.00 & inf \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\n", - " duration col = 'adv_fit_time'\n", - " event col = 'adv_failures'\n", - " number of observations = 1500\n", - "number of events observed = 1500\n", - " log-likelihood = -5531.26\n", - " time fit was run = 2023-09-29 11:13:25 UTC\n", - "\n", - "---\n", - " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", - "param covariate \n", - "lambda_ adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", - " atk_value 0.15 1.16 0.16 -0.16 0.46 0.85 1.58\n", - " data.sample.random_state 0.03 1.03 0.02 -0.01 0.07 0.99 1.07\n", - " def_value -0.21 0.81 0.16 -0.53 0.10 0.59 1.11\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - " model_layers 0.01 1.01 0.00 0.01 0.01 1.01 1.01\n", - " predict_time -0.15 0.86 0.01 -0.17 -0.12 0.84 0.88\n", - " train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", - " Intercept 3.00 20.18 0.18 2.65 3.36 14.14 28.79\n", - "rho_ Intercept -0.84 0.43 0.02 -0.88 -0.80 0.41 0.45\n", - "\n", - " cmp to z p -log2(p)\n", - "param covariate \n", - "lambda_ adv_failure_rate 0.00 -39.62 <0.005 inf\n", - " atk_value 0.00 0.95 0.34 1.56\n", - " data.sample.random_state 0.00 1.31 0.19 2.39\n", - " def_value 0.00 -1.31 0.19 2.41\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.53 0.60 0.74\n", - " model_layers 0.00 7.04 <0.005 38.88\n", - " predict_time 0.00 -12.17 <0.005 110.86\n", - " train_time 0.00 10.81 <0.005 88.14\n", - " Intercept 0.00 16.56 <0.005 202.20\n", - "rho_ Intercept 0.00 -43.75 <0.005 inf\n", - "---\n", - "Concordance = 0.84\n", - "AIC = 11082.52\n", - "log-likelihood ratio test = 800.84 on 8 df\n", - "-log2(p) of ll-ratio test = 554.32" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "weibull_dict = {\n", - " \"Intercept: rho_\": \"$\\\\rho$\",\n", - " \"Intercept: lambda_\": \"$\\lambda$\",\n", - " \"data.sample.random_state: lambda_\": \"Random State\",\n", - " \"def_value: lambda_\": \"Defence Strength\",\n", - " \"atk_value: lambda_\": \"Attack Strength\",\n", - " \"train_time: lambda_\": \"Training Time\",\n", - " \"predict_time: lambda_\": \"Inference Time\",\n", - " \"adv_accuracy: lambda_\": \"Adv. Accuracy\",\n", - " \"accuracy: lambda_\": \"Ben. Accuracy\",\n", - " \"adv_fit_time: lambda_\": \"Adv. Fit Time\",\n", - " \"adv_log_loss: lambda_\": \"Adv. Log Loss\",\n", - " \"adv_failure_rate: lambda_\": \"Adv. Failure Rate\",\n", - " \"failure_rate: lambda_\": \"Ben. Failure Rate\",\n", - " \"model_layers: lambda_\": \"No. of Layers\",\n", - " \"model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_\": \"Learning Rate\",\n", - " \"def_gen\": \"Defence\",\n", - "}\n", - "\n", - "weibull_afr, wft = plot_aft(\n", - " X_train,\n", - " file=\"weibull_aft.pdf\",\n", - " event_col=target,\n", - " duration_col=duration_col,\n", - " title=\"Weibull AFR Model\",\n", - " mtype=\"weibull\",\n", - " replacement_dict=weibull_dict,\n", - ")\n", - "wft.print_summary()\n", - "wft_scores = score_model(wft, X_train, X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_773113/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " pareto.set_yticklabels(labels)\n" - ] - }, - { - "data": { - "image/png": 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64t27d7h58ya+fv2KEiVK6G1baZ37dKXrsZymzDT8SN4DgrZGKklJSWq9H1y9epWbFx0dzXbu3JktX7486+HhwcbExLAfP37keg+pU6eOWmNbHx8ftd6bWrZsyX758oVt0KCBWi8ML168YKVSKbt+/XpuOalUyv1jWZY9efIk+9tvv6mtKyWxWMwOGjSIdXV1ZdeuXctGRUWxoaGh7Ny5c1lXV1e2cuXKbGBgoNb9omrNn9nGROntV5VFixZpnS+Xy7nPq1qPl5cX95pMJmNZluWmU9tXyXs9Cg8PZ7t168bOmTOH/fDhAxsXF8deuXKFbdGiBevq6sr26NFDay9A27dvZ6tWrcru37+fjYyMZCMjI9klS5awrq6ubNWqVdm7d++yLKtskCSVStm7d+9yMX/58kUjlo8fP7K1a9dmZ8+ezYaGhrKRkZHsuXPnuIZxuja4VklISGAnTpzI1q5dmz158iQbExPDhoaGsmvWrGHLly/PVqlShfX29tZ4X0b2XXru3bvHurq6ctPJe7RRfXfazJo1K9XfmFQqZZcsWcJWrlyZ3bdvHxsZGcn++PGD3bVrF1u5cmW2QoUK7NatW3WOUVeZ3a5qv3l5eXGfO/n+lMvlOscQHBzMrcPNzS1TnyOjx39kZCS7dOlStd75Hj9+zB1rKnK5nGvU3rt3b41Gl6tXr2Y7d+7M3rp1i42JiWHfvXvHNQasXr061xNPZGQkO3XqVLZy5cpqjRuXLVum9tsZO3Ysy7LGP24kEgm7YsUKtQasGzduZB8+fMjWr1+fbdy4sdq1YvDgwRmKR2Xjxo3pNvbs27dvqu+fPn06d+4ODw9nw8PD2Q0bNrCurq5s+fLl2evXr6stv3PnTu4cmNbvTXWMp2w0HhcXxx46dEhtnyQXHR3NnjhxQuv81atXc408VTFfunSJrVChAvda8s4s4uLi2KFDh7Jjx45lX716xcbFxbH37t1jO3fuzLq6urKtWrViv337luq+SU3Lli1TbWiq2m7yOG/cuKF1uYwec6rrXFrXehVfX1+2adOm7LFjx9gfP36w379/Z9evX8/tq23btun8eXW9vqrcunWLrVOnDjthwgT23bt3bHx8POvn58fdg/Tr10+tg4xnz56xAwcOVDuOg4KCuN6FVP+0NVRPza9fv7hzRcuWLbVen/7880+2YsWK3PoPHjyosUxCQgJbo0YNrT2CeXh4sK1atWLPnz/PRkVFsV+/fmUXLFjAdYZy8uTJND+jts4Ukt9L+fr6qnVEpPpXo0YN9uHDh2ox+vr6cp835TUyLi6OvXnzZqrzVdI7j2T1Pk9l1apVbPv27bnv5NixY9y2ly5dqvN60rtvlMvlbL9+/dJch76O5fQYpErVnj17IBKJAADlypVD8+bNuXnbt2/Hu3fvULlyZbi5ucHGxgalSpXCmjVrYGpqitjYWLWnCE2aNMHRo0e5Jxwsy2LlypXYv38/NmzYACcnJ7i6uqJUqVIwMTFRqzNrYmLC/QOAjh074vz582k+0Tt27Bju378POzs7TJs2DXZ2dihSpAgWL16MwoULc09ijIFlWdy7dy/VBjs8Hk/t86Z8TfU0RTWd2r5SFRNLpVKMHz+eKz0qU6YMrKys0Lp1a67u7KtXr7B8+XK1OA4cOIC///4bs2fPxuDBg2Fvbw97e3vMmDEDAoEAYrGYGzSHYRiNWJLHrIpl8+bNAIClS5eiSJEisLe3R8eOHbFr165M1amcN28efH19sXbtWnTv3h02NjYoUqQIpk+fjgkTJkAikWDOnDm4fv262vt03XeZMXr0aLXSkUWLFuHRo0cZWseGDRvg4eGBOXPmYMiQIbC3t0fBggUxYsQILF68GAqFAuvXr8ehQ4cyHac+t5ve/szuOvAZPf7t7e0xd+5ctXqx5cqV0/hN8ng8rmHeokWL1Epx9+3bh1OnTmH//v1o2rQpbGxsUK5cOaxfvx5VqlRBYmIipk2bhri4OFhZWWHevHlqY7LcvXsXMpkMt27dQufOnSEUCtGqVSsAxj9uBAIB3N3dcfLkSe67fPnyJbZu3Yrdu3fjzp07CAgIQKdOnQAoS8hUT2T1RS6X49ixY3j69KnW+devX8eZM2cAAAsWLEChQoVQqFAhTJo0CbVq1QLLsjhw4IDaewYPHoxLly6l25FBaqysrDBgwIBUn1QXKFAAPXv21PqkefDgwTh27Bj3e1KVdt2+fRuTJ0+GhYUF2rZtyy0/a9YsMAyDLVu2oFKlSrCyskL9+vWxZ88emJmZISQkRO+DmEmlUsyZMwfR0dEAgHbt2qndBySX0WNOdX1I61oPKEvPp0+fjrVr16JPnz4oWLAgnJycMHnyZAwePBiAcnyJtMaySk7X6yugbFg7btw4lC5dGps2bUK5cuVgaWmJRo0awcPDAw4ODnjy5AnGjBnDVZ8tVaoUtm3bpjauxfbt2+Hm5oZTp06hWrVqKFCgAOrVq6dTvABga2vLnQtCQ0Px8OFDtfkymQznz5/H+PHjuWuXtmpVly9fhpWVFRo2bKj2+pUrV7B27Vrs3LkTHTp0gJ2dHYoXL44///wTbdu2hUwmw7x587ixIipUqAAPDw9MnjxZp/jfvHmD9evXY8mSJfD19YWHhweaNm0KQFlTZvLkydxvzMLCAu3atdNaag4oj7lmzZpl29geaVENIDh06FBuv3ft2pXrkMnb21utmnFmSSQSbNq0KUvNGjJyLKcny1fznz9/cgdMfHw8Nm3ahA0bNgBQ9hSzadMmtQPxw4cPAJQn1OSsrKy4k7eqQRagvKDb2tpy1YfCwsLQrVs3uLq64rfffsOtW7dw5swZWFpaphurUCgEwzCoUKFCqsukFh+Px+OK7JLHZygTJ05E48aN1f7VqFEDQ4YM4ZI5Qzt16hRevHjBnZyTq1ChAndw+Pj4cDFFRERgzZo1cHZ2VivSB5Q9T6gGWYyOjs5Q/dtXr14hMTGR+9Enj0N1QtXV9evXce7cOY1kWGXMmDEoVqwYFAoFFixYoLfR6HWxePFi7oIilUoxceJEhIWF6fTeN2/eYPfu3bC2ttbY94CyEWGtWrUAAKtXr8bPnz/1ErOxtmsImT3+58yZAx6Px1Vd0ebUqVOoWbOmWtF/bGwsNmzYgM6dO2v0/sfj8bjzXnR0NC5cuAChUAh7e3u13/yHDx8wZ84cODk5Ye3atXj+/Dk34GROOG4sLS3h6OjI3TxLpVJs376d2w98Ph8jR47k1pPVhOOvv/5SO29Wr14d8+fPT3V51XeuGugqOdWDrpTfuVAohEAgyPLgtmn1DgNA63WtUKFCcHFxQdmyZQEoq+L9+eefKFiwIMaPH4/Hjx9zVUXu3r2LK1euwM3NTSN5d3R05OL39/fXqYe89ERFReH8+fPo27cvfH19wePx0L9//zQHDTbUNXf58uUoV66cRvU0AGoPHT09PTO87vTMmTMHUqkUw4YN03gIVbBgQW4A0ydPnnDbt7a2hrW1tVrjXBsbG3Tu3BkVK1aEl5cX7t+/j0qVKmUolu7du3N/pzw33b59G2KxGMOGDeP207NnzzTae5w+fRqdO3dW+w3J5XIsW7YMTZo00Zp4q5ITqVSK48ePA/jvHqxixYo6x3/kyBF0794dJUuWRL169bBjxw6uofvPnz81vr/MHFPZ7cSJE+Dz+WrfjampKfr16wdAeS+tbaTz9KS8b6xZsya2bt2aqRgzcyynJ8ttOPbs2YO///4b8fHxiIuLg1wuR4kSJdCuXTuMGDFC4yLq5uaGmJgYDBgwQGNdqh+Cths8VZsQJycnjfqvGZXW6J+9evXCmzdv1H4IusSnb0uXLkXNmjW5aZZlER0djcuXL3NPLQ1NVdKkGgU0JdVNjFQqxadPn1C5cmUcP34cYrEYDRo00PoEddOmTbh06RIqVaqkVnc0PXZ2dvj06RMmTpyIv//+W63L2eRP83Shespeu3ZtrfOFQiF69OjBPRm4dOkSOnfunKFtZJZAIMCmTZvQt29ffPnyBZGRkRg3bhw8PT3TPZF6enpCLpejRo0aqfbS1K9fPzx+/BiJiYk4ceIExowZk+WYjbVdQ8js8V+mTBm0b98eFy5cwO7du9G3b1+133dSUhJOnjypcePr6+sLkUgELy8v7il7cskfLiQfTTt5G7mBAweqHWvJb3By0nGjirlmzZoaXUsmr6+c1W5OJ02ahI4dO3LTIpEIFy9exN9//611edWDK21PjtM752f3SNLJqfZh69at1epUJ//+VefwOXPmaD0fx8TEcH+/e/cuU92dfv36FY0bN4ZIJOJ+r3w+H+7u7ujWrVu6ddENcc39+vUrHj9+DFNTU63tNJI/7MroKPXpefbsGV6+fAlA2W5Amy5dumD58uUQiUTw8PBQK9VIfmwMGjRI7X2ZKUFv1qwZHBwcEBkZCV9fXyxYsIBrd3n69Gm0bdsWlpaW6NGjBx48eABAvb3Hjx8/4O/vjxkzZqit98GDBwgNDcWvX7+07uPkI3a/fftWbZ6ux02FChU02hfxeDwsXLgQ169fh1gsxs2bNzFu3Did1pcTKBQKHDhwQKM3VwAYMGAAdu3aBalUioMHD2LQoEEZ+s5T3jfGxsbiyJEjOvcUm9VjOT1ZTjhmzpyJ+vXrQyqVIj4+HpaWlmn2U9y8eXO1p2OJiYk4d+4cTp48yT3ZYllW432qzFof3V2mVU2jatWqOHLkCDctlUpx+fJleHt7c9VbtMWnbwUKFFDrFQBQPtlydXXVy5Oo9Mjlcu77OH78eLr7XfX0UlVkm9o4CI6Ojhg4cGCG4+nZsyceP36MgIAAtG/fHm5ubhgyZAgcHR3VetlIT0JCAvz8/AAonzSlpn79+ti0aRMA5Yk1uxIOQLkv//nnH/Tr1w8xMTF48+YNZs6ciU2bNqV58lF1gJDW50p+U/XgwQO93Pgba7uGkJXjf+zYsbh48SJCQ0Nx6tQprpQBAM6cOQMTExON/vlV1XzGjx+Prl27phlb8saayc9haVWNyknHTVpxWllZcX9n9YGOtbW1xrlzzJgxqVadKV68uFqDb4VCgdu3b+PEiRNcF5WpfefG7PZWl2ui6ve1detWFC9ePM31ZbbhOJ/Ph4+PD1iWxezZs3H79m3I5XJIJBKdblAMcc1VdcjQrFkzLFy4MN349enSpUsAlMlBamOWmZubo2rVqrh//z4+f/6MHz9+cL9ZXY9tXZmYmKBLly7cmCVXr15Fx44dER8fj2vXrnHVo9u3b48lS5ZAJBLh9OnTmDJlChiGwdmzZ1GmTBmNmiGqfdyrV690z+cp7wmzetzY29ujfv36uHXrFj5+/JildWW3a9eu4fv371rvgwoVKoQOHTrg9OnT+PLlC27cuIGWLVvqvO6U942Ojo6YO3euzveMWT2W06O3s6VAIICdnZ3Og6JERkZi9erV6Nq1K75+/YqVK1dyvarkBPHx8di+fTs6deqER48eYdasWWjfvr2xwwIArnqKIcXExHAXfhMTEzg6Oqb5T/U0V9WTgb67K+zTpw+mT58OgUCAxMRE7Ny5E61bt8ayZcsy9EQ0JCQECoUCQNpPi5IXESfvQSu7lCpVChs2bOD26+XLl7mqitrEx8dzA+el9bmKFCnC3bjq43MZa7uGlpnjP3k1pX/++QcymYyb5+npiT59+micH1XVy+RyebrHWPKbcl3lt+MmLenV3ZZIJDh06BA6deqE8+fPY/jw4Vqrk+Ymqt8Xj8dL9/eVlcHWChYsCEdHR6xatYp72LRz506tvUCmRp/XXNXnTkxMTPdzp5YUZJaqe3eGYdK8sc7OY6VHjx7c36o2oL6+vrC1teUGELa0tORqj4SFhXGD8Pr4+Gh9GKLaxxKJJN19nLK6nD6o9l92VTHXl71790KhUKBbt24a1eYbN26s1tNZyrZjmZWRdiv6OJZTY5THMydPnkS7du0QGRkJb29vTJs2Ld1u0bLTzZs38dtvv+HRo0c4ePAg5s+fn+W6uvrUv39/jS5x9S35zdKbN290fp/qSVRISIjeYxo9ejTOnTvH1SUVi8XYv38/unbtqnMGn5iYyP2dsl57ctbW1tzfhjhZ6qJhw4ZqT+e2bduG8+fPa102+ef69etXmutV3bjq43MZa7uGlJXjX1W0HxwcjNOnTwNQVrF48+YN/ve//2ksrzrOMnKMZVR+O25Sk7JKSHLPnz9Hly5dcOrUKWzduhUrV65EjRo1si84A1E9+DHk7ys5e3t7rFu3DiYmJmBZFrNmzUJQUFC679P3NVd1XKWsypMdVDfACoUizWMleYmSobolVqlQoQLXbsLPzw+RkZE4ffo0unTpopYUJU9MfHx88O7dO7x9+1ZrwmHMfQz8Vy0rp51n0vLy5Us8evQIHh4eOHXqlNZ/Fy9e5Nq63r17F+/fv8/ydtM696Ums8dyWrI94di3bx/++OMPNG7cGCtWrFC7QOUEvr6+GDNmDIoWLYpt27ZlepCU7KR68qhPtra23Ikovcw2KCiIO7E6ODgAAO7fv59mKUdsbGymLoIuLi5Yu3Ytzpw5w/UMFBYWhqlTp+pU7J68rrOudXddXFwyHKe+9OnTR60NzezZs7XGbWdnx52AdT1B6eNzGWu7+hQdHc1dPLN6/FetWpX7Xf7zzz+Qy+U4fPgwmjdvjiJFimgsr6qKePv2bbU6z9o8fvw4Q7Ekl9+Om4x4/Pgx3NzcIJVKsW/fPq3jiORWql6w0juHR0REcD0JZVWtWrUwbdo0AMqqeO7u7mn2uGOIa67qc//48SPV3slUMtoTYHqSHytpnRNVx52ZmVmqVZD1SdVGRiaTYdeuXXjw4IFaggEoq0SqBsX09fXFkSNHUK9ePbW2XyqqffzixQu1MRq00fc+Bv4bfDGzPcUZw969e9G4cWPUqlUrzRKh5CWruo7vYwgZPZbTk60JR3R0NNfCXVsDMWNTKBRYvHgxWJZF586d9dJexNAuXrzIPUnVJ6FQyHUv5+Pjgy9fvqS67NatW7mkRzXIT3R0dJpx7dmzR60UJT2qH71K2bJlsXv3bq53m8DAQJ3qcjo5OXE3FI8fP0714ImKiuL+zmonBVk1Y8YMbsDBpKQkvHr1SmMZExMTroFiSEhIqvtCLperdW+XVcbarj6tXbsWLMvq7fgfP348AODz58/w8PDAhQsXUm23pDpeYmNjsXfv3lTXeevWLTx//jzDseTn40ZXf/31F8RiMdq1a5ftPdioqkymVwU1sw+VVL8vPz+/NLt/PXDgQJYb6yc3YsQIrnrhx48f8ccff2hNbA11zU0+2NzGjRtTXS4qKkrv3dwn7zr21q1baW4bAFq1apUt9xpdunThtrN3715UqlQJZcqUUVuGYRiubZdIJMLhw4dTbeul6sFNLpdz7ba0CQoKgq+vrz4+ghpV72YpB+NVVVs11DGVWaqB/oYOHZrush06dODaYvj4+KRZUmZouh7LushUwpH8i8rIl/blyxeuXYCq3ndyqh+Iqpvd9LatjaqRVUJCAvcay7JqDRFVOyvlTouKiuLqJWrrujO9+FJbr64y+j6RSIQNGzZoHHDJP2ta+1L1eVK78e/SpQsA5U2uu7u72s2EyvXr1yESibh6sMmLXletWsXVZ00uICAAt2/f5ooNAfXGccnrZKqe+gYFBWm90Z46dSr3hD15tY+0qHr+kEql8PLy0rqMqpeRBg0aaJyUVe8FUt93ulD9ltP73nk8HtasWZNud4jJezRJ7SL6/v17SKVSlCxZMlMjTBtqu8kvDlnZpxn16tUrKBQKCASCLB//KrVr1+Yaya9cuRKFCxdOdUTj3377jbvp3LJlC65cuaKxTExMDFavXq3W81JyaZ0Tc9Jxo+u1IjPnz+TfSUbfr3oKre07V/0WM3vOT/66tmVUJVzaqrY9f/6cq6aYVulXWvtVdQ5nWRbTpk3T+uDo5cuXuHfvXoa7W1Xtk9Q++8qVK7mn5ZcvX8aWLVs0lsnKMZfWtb5ChQpcvXU/Pz+sWbNG4/0sy2LJkiUZbieS3vW1bdu2XInAiRMnUv3uVMelth47k8eoLw4ODtwYFizLpvrQN3mph6mpaaoPDpo0acJd9728vLSO7SSRSLBgwQKNKlnpHRfp+fnzJx49eoRChQpxXcmqqKpYpezaF1Deg6qONW3fiy5xZfY+b+/evShatKjamE2pEQqFXKKXlJSUZlKclXNfynVk5VjWRaYSjuTJgrbEITXJixq3bdvG/SDevHmDcePG4fXr1wCUmaBUKlU7Sah+HFFRUWn2YqL6sUVHR+PFixdgWRYbN25Uq2cYGxsLABpP6ezt7bmGc4cPH+aeKAYHB2P27Nlc12IRERFQKBTYvHmz2slO9YQo+WsZkfwASO/izLIs5syZg1KlSmlUS0t+4v7+/Xuq61A1VEttnw4YMIArrnz37h26du2K/fv348WLF7h//z6WLVuGGTNm4Pfff+feU6FCBfTq1QuA8jvo06cP9u3bh5cvX+Lu3bv466+/MHToULX3AFAb4Or27dsAgDt37qj1XjJ79myNmyPVgeLg4JDqgD8p/e9//+PqZ2/dulXrOBeenp4wMzPDn3/+qXUd6e07XaiOHV3GpbCwsMC2bds0et9JrkWLFtzF88iRI9zNX3KHDh0CwzBYunSpxlO1TZs2oWbNmhgzZkyGGv1ndbuAeqPJtH6zaUn+PehyDEZFReH333/nLqhZPf6TU5VyKBQK9O/fP9WG1k5OThg1ahSA/8ZdmT9/Pvz9/fHy5UscO3YMPXr0QPv27dWqmiQ/V6S3v3LCccOyLNcFa3pVxzJTZJ/8OpTRp4Gq69LFixdx8+ZNAMrf48qVK3Hw4EFunWKxGEePHlUrEUrtWqKS/HVtvxXVvr927RrOnz8PmUyGyMhIbN68GRs2bOBKXJ48eQKxWKzWTkq1H9P6/tu1a8clv+Hh4ejZsye2bt2KZ8+e4dGjR9i8eTOGDh2K6dOnp7WLNMjlci4WVQ+VKdnY2Kh1fLFp0ybs2bNHbZmsHHPpXetnz57NVQveuXMnhgwZgsuXL+P169e4dOkS3NzcEBkZmeGSuPSur0KhEIsWLQLDMIiMjMS6des0lnn8+DFev36N3r17a4wTkpFjO6NUA8sKBAJusM2USpQowXV93bp161Q7qzAzM1P73SxevBiTJk3CzZs3ERgYiNOnT6NXr14oUaKE2sNFQL3r67TO1WFhYVqTumXLloFhGPz9999qvfcB4HrTevXqFTw8PCCRSBAXFwcPDw/MmjWLu9d4+fIl4uPj1R6k6hJXZu7zgoOD4enpiebNm+vcza0qOQSUTRG0PfAFsnbuA/R3LOtC54SDZVnExcXh1KlTuH//Pvf6vn37uKeX6SlUqBDXxVdoaCg6dOiAevXqYeDAgejSpQvXXe6DBw/QoEEDVKlSBRKJBG/evOHqoIrFYvz999/4+fOn1mysfv363Emmf//+aNKkCUJDQ1G1alXI5XJ8/PiRW1dsbCwOHTrE/XB4PB7XlWVsbCz69OmDevXqoWPHjqhYsSL69+8PQPnjqV27NqysrGBpaQmJRAJ/f3+uiM/f3x8BAQEZuhFVKBRqSdGtW7cgEokgk8m4fxKJBNHR0QgICMCoUaNw4cIFtaeeCoUCP378UEvUjh8/jqdPn0IqlXL7SyKR4Pbt21wxp1Qqxbp16xAdHa12cJubm+Off/7hulP88eMHli1bht69e2Pw4ME4fPgwli9frvEkc8GCBdzgRdHR0Vi+fDl69eqFYcOG4fDhw5g3b57GiKWlS5fmngqtXr0azZs3x4oVK9S6Fn379i169+6Nq1evIioqCsHBwZgxYwY3AJGu43rw+Xxs27YNlSpVQmxsLNzc3HD9+nXEx8fj69evmDlzJl69eoXdu3dr9Euv675Li+o3vWvXLgDA+vXrER4enu77nZ2dsX37do0TbHKrVq1CkyZNIJVKMWLECJw+fRqxsbEIDw/HqlWrcObMGWzcuFHrYFh79uyBSCTCjRs3Mtw3fWa3K5VK8ezZMxw7dox7bd26dfjx40eGSzqSP9F6//49Hj58qHYMSaVSJCQk4MuXL/Dy8kLv3r3x8+dP7reY2eNfm4YNG6JmzZowNzdXGz1eG3d3dy5JVygU3Ai0vXr1wvz581G9enUugQGUjfNVvx1A+XTx0aNHEIvFWtdv7OMmMTERx44d40oufX198fHjR+6aoaq6oXL58mUEBQWl+/2zLIuEhATcuHGD64oUUCa9AQEBqe6PlFRPSKVSKUaPHo26deuiRYsWEAgEmDRpEgDlTWCDBg3w/v17lC5dGlKpFM+fP+euhe/fv4evry93s8iyLKKiorhxMABg//79iIqKUrtuubm5QSgUQiqVYurUqahWrRoaNWqEgIAAbNy4kase8uTJE/Ts2RP37t3jjlHVA7pHjx7Bx8dH640Cj8fDhg0buBu++Ph4bNiwAX379sWAAQOwefNmTJkyReN8nBqFQoHw8HAsX75cbf+uX79ebRBglapVq6qNYr5y5UoMGzYMFy9exPfv37N0zKV1rQeUg/stXryYKwm5d+8e3N3d0b17d0ycOBExMTFak4G0Prsu11dAWU1q0aJFMDExwb59+7B48WIEBwcjPj4ely5dgru7O3r06KHWKYhCoUBYWBiOHj3KvbZ582Z8/vxZb6W+LVq0gK2tLZo2bZpm71yqUo70us7u3bs3JkyYwE37+vpi9OjR6NGjB2bMmAFbW1ssWLCAm686LpJ/xj179mgcF23btoWZmRnu37+PgQMHws/PD/Hx8fjy5QumT58Of39/7NixQ+t1rGfPnlxSsXTpUlSvXh116tTB8ePHsXnzZm5eaGgounbtigsXLgBQJhDJBxH09vZGSEgIt+8zc5+nUCjw7NkzuLu7QywW48GDB3j16lWa98uqfXTjxg3utZiYGIwbNw7Pnj2DWCwGy7KIjY2Fj4+P2ujxu3btQmBgoE73nvo+lnXBsDqWwezYsSPdEQYfP36cbh3Y2NhYrFixAtevX4dUKkXTpk0xffp0FCtWDP7+/pgyZQrs7e3xxx9/oHnz5pg7dy43SmVKBw4c0Npb0/Hjx7FhwwZIpVL06NEDU6dOhVAoxLp167B9+3at63r+/DlMTU0hFouxfv16nD17FvHx8ahTpw6mT5+OChUq4N27dxg5ciT4fD4mTZrEHZRDhw7l+mtPrnLlyumOFvn06VPcunULN27c0Fr1IS1mZma4e/cut8/XrFmDnTt3prr85MmTMWLECLX6rSnx+XwEBgaqvRYfH489e/bgwoULCAkJgYWFBerWrYvx48enWgyvunE6duwYgoKCYGpqinr16mHMmDHcBSGlZ8+eYf78+QgODkaTJk0wb948Lgnp1q2bRiPzAgUKoEGDBhg/fnyao8enRiqV4vDhwzh79iw+fvwIlmVRvHhxtG3bFv379+cawKuIxeIM77uUYmNjtZ4kAaBcuXI4e/ZsunFfunQJ169fx/Lly7XOZ1kWp06dgre3N969ewexWIyiRYuiadOmcHNzS7VHuM2bN2P37t3cWAoZGZgxs9tt3LhxmiU8N2/eTLdB5a1bt/Dlyxds27YtQyWugPJ3tWrVKm46M8d/aqZOnQpLS0ssXbpUp1guX76MgwcP4uXLl5DJZChTpgz69euHPn36cDdW379/1zrKN6Ac6yRlA8OccNyk9h23b98eM2fO1KgSqjJ8+HDMmjUr1TjOnTun0UYlpaNHj6bb25RCocCuXbtw5MgRREZGokqVKpg8eTLq1auHiIgIDBkyBDExMRg+fDjX9uX333/XOlBjwYIFufYSqbXb2b59u1rf+vfv38fq1avx7t07ODk5oXfv3hgxYgRMTEzQpk0blC1bFoMGDeKq5bVt21ZrVVUg9R6DVF3++vj44NOnTxAIBKhevTpGjRrFdYuqi9SudSrafoMAMGXKFO7GLrlXr15BLpdn+phL7Vqf3Js3b7Bjxw7cu3cPsbGxKFy4MDp06IDRo0dnqKtpXa6vyR8MqLa9d+9e3L9/H5GRkXBwcOASqeQjigPKuvozZ87Uum53d3dMnDhR51jTsnjxYtSvXz/NqmTx8fHo2rUrLl26pFP7kgcPHmDPnj148uQJRCIRXFxc0K1bNwwZMkTt+7h79y6GDRumdR07d+5U2yc/fvzA0aNHcf36dXz8+BEKhYIbVHrw4MFp9k715s0bLFu2DM+fP0eBAgXQuXNnTJgwARYWFlzVUDc3N7Rp04ZLSCtVqqT1oZ/qPJSZ+7w9e/Zg5cqVGq9XqVIFJ06c0PqeFy9eoHfv3ql+th49eqB06dLp3o/fuXMnzRoRhjiW0/ut6JxwEEII0c2PHz/QsmVLHDt2LMN14wkhhJC8xnjDpBJCSB7l6emJKlWqULJBCCGEgBIOQgjRq/DwcHh4eHDVbwghhJD8LucPNEEIITnYggUL4OPjg3LlyqFhw4a4ePEiXF1d0aZNG2OHRgghhOQI1IYjD7p8+TL27t2Lt2/fgs/no169emk28E6Pn58fjh49imfPnnHdQBYpUgSNGzfG8OHDs2WUVEJyqtq1a6v1EFSyZEkcOnQIBQsWNGJUhBBCSM5BVarymLVr18Ld3R0KhQJ+fn7w8vKCn58f+vbty3UHrCuWZbFw4UIMHz4cvr6+GDp0KB48eICLFy/CysoK+/fvR+fOnfHs2TMDfRpCcr7Zs2fD3t4ejo6OGDRoEI4ePUrJBiGEEJIMlXDkIcePH8fcuXMBAMuXL+f6/580aRJ8fX0hEAhw4sQJnQf62r9/P5YtWwYAqFmzptogfM+fP+f6Ty9WrBguX77Mdd1JCCGEEEKICrXhyCMkEonacPMlSpTg/nZxcQHw3yB1qY1FkpJqlF0AGk9sq1atCqFQCIlEgpCQELx9+xYVK1bMcNxPnjwBy7IZHvOBEEIIIbqRSqVgGAY1a9Y0digkn6JH0nmEv78/wsLCuOnkAxolH3jn1q1biIuL02mdyUePfPz4MTeKLgAwDMON2Akg0wkDy7JaR4zPKpZlIZFIDLJuYlj03eVe9N3lXvTd5W7pfX+GutYSoisq4cgj7t27pzadWgIgl8tx7949tG3bNt11FitWDB8/fgQAREZGYtOmTZgxYwYAQCaTITo6GgBQpkwZlCpVKlNxq+JMbfTxzBKJRHj9+jXKli0LCwsLva6bGBZ9d7kXfXe5F313uVt639+LFy+MEBUh/6ESjjziyZMnatNpDTH/9OlTndapagOismvXLqxZswYKhQKPHj2CRCKBvb091q5dCz6fn+GYCSGEEEJI3kclHHlERESE2nRaDbgjIyN1WuewYcPw6NEjXL9+nXtt586dePLkCeRyOdq3b4+5c+fCyckpc0ETQgghhJA8jxKOPOLXr19q0wzDpLpsVFSUTus0MTHB5s2bsWzZMhw6dIh7PSAgAABQunRpxMfHZznhYFkWIpEoS+tIKTExUe1/knvQd5d70XeXe9F3l7ul9/2xLJvmfQEhhkYJRx4hlUp1XjYjDcdMTEzQrl07XL9+HU2bNoW3tze3rY8fP6J///7Yv39/pnqoUpFKpXj9+nWm35+Wz58/G2S9xPDou8u96LvLvei7y93S+v6SdyBDSHajhCOPsLGx0bmqlJ2dnU7LSaVSLFiwAN7e3ujUqRMWL16Mnj17YsyYMVyD8ZiYGEyYMAEXL17M9MlMIBCgbNmymXpvahITE/H582eULFkS5ubmel03MSz67nIv+u5yL/rucrf0vr8PHz4YISpC/kMJRx7h7OyslnCkVYrh6Oio0zpXrVoFb29vAEDLli0BADVq1MCOHTswePBgrpvc0NBQXLhwAd26dctU7AzDGKxXFHNzc+pxJZei7y73ou8u96LvLndL7fuj6lTE2KiXqjyiWrVqatNyuTzVZXUZ+Cc2Nhaenp7cdPHixbm/q1evjsGDB6stHxgYqGuohBBCCCEkH6GEI49o1KiR2nTyQfqS4/F4qFOnDjcdFBSEnj17ol69etiwYQP3+ufPn9XahSQf5A/Q7DI3rQSHEEIIIYTkX5Rw5BEtWrSAg4MDNx0TE8P9nTwZaN68uVryMH/+fLx69QoxMTHYunUr/P39AWi280jZPsTZ2Vltunr16ln+DIQQQgghJO+hhCOPEAqFmDp1KjedvKeK8PBwAMrG2VOmTFF7X8qqUKrp4sWLo27dutzrfn5+qb7P1dUVv/32W5biJ4QQQggheRMlHHlInz59MHToUADAiRMnIBKJEB4ejitXrkAgEGDVqlWoUKGC2ntSTleqVIn7e9WqVXBxcQEA7N27lxsA8OPHj1i0aBEAoFy5cvjnn38gEAgM9KkIIYQQQkhuRr1U5TGzZ89G9erVceDAATRv3hx8Ph8NGjTAhAkTNJILAFi6dClmzJiBkJAQuLm5oWHDhty8IkWK4OTJkzh06BCuXLmC33//HTKZDGZmZnB1dcX8+fPRp08fmJqaZudHTJc8MQmflm2DtEIJIAvjgxBCci6WZSGVSqFQKIwdSp4gFou5/3k8ehaZU/B4PAgEAupliuR6lHDkQR07dkTHjh11WrZs2bI4efJkqvMtLS0xevRojB49Wl/hGdzPq3fxeeUOCBtWB3p2MnY4hBA9EolEiImJQVxcHHVWoUcKhQImJiYICwujhCOH4fP5sLa2RoECBajLYpJrUcJB8hy5KBEAwPz7PyEkb4iLi0NISAgEAgFsbW1haWkJHo9HT3/1QC6XQywWw9TUFHw+39jhEChL8RQKBRISEhAbG4vo6GgUK1YM1tbWxg6NkAyjhIPkOUHvXuKChQSFY8LQKP3FCSG5gEgkQkhICGxsbFCkSBFKMvRMVVpkZmZGCUcOY2lpCUdHR4SFhSEkJAQuLi5U0kFyHSo3JXlO0PcwnLSS4Lo42tihEEL0JCYmBgKBgJINki8xDIMiRYpAIBCodXtPSG5BCQfJcwrb2aFRogmqCyyNHQohRA9YlkVcXBxsbGwo2SD5FsMwsLGxQVxcHFiWNXY4hGQIJRwkzyld0AlD48zQVWiX/sKEkBxPKpVCLpfD0pIeIpD8zcLCAnK5HFKp1NihEJIhlHCQPCdepHzyQ0+ACMkbVF3fUu9JJL9Tta+h7qBJbkNnb5LnJEr+/YPyDULyFKpORfI7OgZIbkW9VJE859HX91hXMB5lRXKcMnYwhBBCCCH5HJVwkDxHzrIQ8wAxqMiZEEIIIcTYKOHIQViWxaFDh4wdRq5XrXgZLI20wCRhIWOHQgghhBCS71GVqhzk58+fWLp0KQYOHGjsUHI1M1NTFJLzYAEavIqQ/GjlypXYs2eP1nklSpTAiRMnYGNjozFvwYIFOHr0qMbrfD4fgYGBeo/TGBQKBXx8fHD69Gm8fv0a8fHxKFCgAKpWrYqxY8eiatWqGu9JSEhArVq1dFr/2LFjMXXqVH2HTQjJ5SjhyKKHDx9meR0syyIhIYFKN/SEGtURkr9NmzYNPXv2xMKFC/Ho0SO1eV+/fsWMGTOwfft2jXPFnDlzMGDAAGzcuBFXr16FhYUF5s2bh0aNGmVn+AaTkJCAcePG4f79+xAIBDh58iSuX7+OtWvX4vr163j69Clu3LihMdK4paUlXr58ieDgYMyZMwdPnjxRm29vb49169ahRo0aEAqF2fmRCCG5BCUcWTRx4kS9jfrJsizdLOtBRFw0bppLUFAWj/rGDoYQku0EAgHKlSuHHTt2oG/fvggKClKbf+PGDWzatAmTJk1Se93MzAwVKlTAhg0b0KhRI/Ts2RO9evXKztANauXKlbh//z4AwNHREeXKlVNLyORyOeRyudb3CgQClC5dGiNGjIC7u7vavM6dO6NBgwaGC5wQkutRwpFFPXr0wN69e40dBkkm+NdPHLWWoJQ8FlOMHQwhxGisrKxQrVo1jYQDALZu3YoqVaqgVatWGvMEAgFKliyJUqVKZUeY2UIikcDHx4ebVj3c6tmzJ8RiMX79+oXWrVvDzMwszfVYW1vr9BohhCRHCUcW/e9//8OBAwewevVqlC9fHkKhMMODU7Esi/j4eBw9ehRHjhwxUKT5h51VAdRJMoGzRdoXTkJI/tCsWTPcv38fYrGYe41lWcycORPHjx9HyZIlNd5jZmaWp6oHRUZGIikpSeN1oVCIIUOGQC6Xa52fkrZSeCqZJ4SkhxKOLHJxcUHLli3RoUOHLJ90p06dCk9PTz1Fln+VLlQYo2PNYG5dwNihEEJygGrVqqFbt26YPn262utxcXFwd3fHsWPHYGFhYaToskdqVaUIISQ7UMKhB3PmzNHLemxsbHDz5k29rCtfU5Uw0UjjhJB/de7cGR8/fsSWLVvUXn///j3mzJmD9evXZ2h9EokEhw8fxvnz5/Hx40fI5XIUK1YMrVq1woABA+Dk5KTH6P+jUChw8uRJnDp1Cu/evUNSUhKKFCmCxo0bY+DAgRrVwEJCQtC6dWuN9YSGhqJ8+fIAgLdv3xokVl29fv0aJ06cQEBAAEJDQ5GUlAQnJyc0aNAAo0aNgouLC7fsy5cv02xX8/DhQ9jY2ODKlSuYMGGC2rx9+/ahYcOG3HRSUhL279+P8+fP48uXLxAKhahduzbGjRuHatWqcctFRkZi0qRJCAgIUFufu7s7Jk6ciB8/fuDvv//G1atXUbBgQWzdupUrNYuJicH69etx9epV/PjxAwrFf+NDVa5cGd7e3pnaZ4TkNjQOhx4UKVIky6Ubu3btwtevXw12kcpXGOXPmmUp4yCE/GfixIno2LGjxusXLlzArl27dF7Pz58/0atXLyxfvhzfvn3Dzp07cePGDVSqVAnbt29Hx44dcfXqVX2GDkDZy9TQoUMxZ84cvHz5EqtXr4afnx/atm0LDw8PdOnSRaNb32LFiuHVq1e4fPmy2utFixbFq1ev8OrVK73HqSupVIoFCxage/fu4PP52LFjB3x9fdG1a1cEBwfDy8sLPXv2xOPHj7n3VK5cGStXroRAIFBbV9GiRXHr1i2uu+NWrVrhwoULcHZ2hkAgwI4dO9QatgcHB6Nbt274+++/UbduXVy7dg1z5szBtWvX0L9/f5w8eZJb1sHBAYcOHUL16tU1PsO3b9/Qt29feHt7IyYmBkFBQVxNBbFYjCFDhuDw4cOoW7cu/P39ceTIEVSuXFmv+5GQ3IASjhyiR48eGDJkCL5+/WrsUHK9wJBPmF4wAcvlEcYOhRCSgzAMgxUrVqg9vVb5+++/4e/vn+46ZDIZxowZg3fv3gEAhgwZgpo1a6JAgQKYN28ezMzMEB8fj4kTJ+L58+d6jX/mzJlcL1Pdu3dHs2bNYGVlhcmTJ8PZ2Zm7gff19VV7n4mJida2hSYmJjAxMV5Fh40bN3IJkqurKwoVKgR7e3v8/vvv3DLx8fGYO3cuN80wDLp3744RI0aorUsmk6FQof8Ge+XxeChdujRKlSqFPn36oHnz5tyDwbi4OAwbNgyfP3+Go6MjZsyYAXt7e3Tv3h316tWDTCbD/Pnz8f79e7VtJC9pUW3T3d0ddnZ2avtR1a3w0aNH8fr1awBAu3btYGtri5o1a8LDwwNlypTJ9H4jJDeihCMbPH36FBcuXICPjw9OnTql8e/EiRM4cuQIwsPDsXDhQmOHm+vJFArE8VjEQZH+woSQfMXU1BRbt25F4cKF1V6Xy+WYNm0awsLC0nz/0aNH8fLlS246efJibW2NcuXKcetLfqOcVTdu3MCVK1e0bpfP56NGjRrc9KJFi9QayOdUx48f5/5evnw5F3OBAurt7z5+/Ii4uDi110aOHKnWO1Z4eDju3LmjtoxEIsGrV68wdOhQtde3bNmC4OBgAED9+vVhamrKzVNVM5NKpfDw8FB7X8rxSY4cOYK2bdvC29ub+025uLigf//+AIC7d+9yy65cuZLrLc3S0hLz58/XtksIybOoDYcBJSQkYMSIEXj27JlOy7Msq/OyJHXlirpgQaQ5rByoq0ZCiCZHR0ds374d/fv3h0gk4l6PiorCxIkTcfjw4VTfm3Keo6Oj2rS9vT3397t37/Dy5UtUqVIlyzFnZLtRUVG4ceMG2rdvn+XtGpKTkxOioqIAKNumqKrBaiuNEYlEagmGtbU1+vfvjx07dnCv7du3D02bNuWmb968iWrVqqmVTMhkMnh5eXHTRYsWVduOlZUV97eqNCk1LMti+PDhAIDmzZvjxo0bavOTdzEcGhqK3r17448//kC/fv3QsGFDraO6E5JXUQmHAe3cuRNPnz4Fy7I6/bOzs8PYsWONHXauZ2FmjmJyPpwonyaEpKJChQpYu3atxs3ty5cv8eeff2p9z8+fP/Hhwwe115LfoALQ6Er33r17WY5VoVDg4cOH2b5dQ/vnn38wZMgQdO/eHbt374aZmRliYmKwdetWjWVlMpnGa25ubmptOe7cuYM3b95w08ePH0ffvn3V3hMYGIj4+Hhu2sPDA40bN+b+7d+/n5sXHh6eZvy1atVKs+vklCPUi0QiLFiwAKNGjcLPnz9T/Z0RkhdRwmFAV69eRalSpbB3714EBAQgMDAQ5cuXR0BAAN68ecP9e/HiBapVq4b9+/dj9OjRxg4712MZVS9V1GicEJK6Vq1aqbUXUDlx4oTW9hfaqlslr46jzffv3zMf4L+io6PVSmKya7uG5uTkhDlz5mDlypUoXbo01q1bh1atWmm90dfWCYiTkxM6d+6s9tqePXsAKBtzBwYGagzs+O3bN7XpRo0aqVVxvnTpEu7cuYM7d+6oVWHTpnjx4mnO79Wrl9aG5rdu3UKPHj2M3jsYIdmJEg4D+vbtG5YtW4aGDRvCysoKPB4PHTp0wJkzZ9SWEwgEmDBhAiZPnqzTwEskbb8S4nDHTIqHbKKxQyGE5HAjRoxA7969NV7Xdi7WdtObsl5/8m5PAWVbgKxKuc7s2q6+nT9/XiORUygUOHjwINq0aYM9e/Zg9erVGXryP2zYMI1tfP/+HcePH0e3bt00erNK2bZFJBLB0dFR67+CBQumue30Rljn8/nYtWsXmjRpojEvIiICI0aMUCttISQvo4TDgMRiMVxdXdVe69Wrl9bB/Zo1a4aIiAiNPuJJxn379QMHbMQ4wcSlvzAhJN9btGgR6tWrl+5y2rotT3ljn/KGNnnPSZlla2urUXUnO7arb15eXjA3N+em4+PjMXz4cCxZsgSxsbFYtWqVRolEesqXL692Qy+VSrF37154e3trVKcClPsyucDAwEwPiqhLd/g2NjbYuXMnpk+frpH8/PjxAz4+PpnaNiG5DSUcBuTs7IwXL16ovebo6Ihy5crhwIEDaq8nJCRALBZrlH6QjLO2tEI1MR/l2dTr1hJCiIpAIMCmTZs0uj1NydnZGSVKlFB7LSYmRm06ZdWnWrVqcX9HR0dj/PjxqFWrFgYNGpRuj1gqJiYmqF27dqa3mxN8/foV9+7dU+sdbM6cOVxXxCVLlkSHDh0yte6UXeQeOHAALi4uGt8VAFSsWFFtOjo6GteuXdO63ocPH2ZpPKetW7ciODgYPB4Po0ePxvHjxzW6w03ZJoiQvIoSDgNq2LAhpkyZgtWrV2PHjh3cGBujR4/G6tWr4enpCZFIhODgYEybNg0ymUyj6z+ScSWcisI9xhxD5TbGDoUQYmSJiYlaGxynZGtri+3bt3MDx6WmX79+atPR0dFq06pelwDluA3169fnpletWoWrV68iISEBDx48yFC3uRnZrrW1NX777Ted150dNmzYAHNzc66xe1hYmNp4IXK5nKsWlpiYseqwjRo1QoUKFbhphUKhtXQDUD70q1OnjtprK1as0Ejgnj9/Dg8PD7VSjIwmH3K5nGtTAig7Kjh27JjaaPAODg4ZWichuRUlHAY0ZswYyGQy7NmzB+vWreMuLuXLl4ebmxv+/PNP1K5dG+3atcPt27fBMIxaX+okc/67PlCjcULyu/fv36v1XJSW0qVLY+PGjWkOhjd48GCULVuWm04+Jkd0dDRCQkIAKLt2XbhwoVovWCnbLzx9+lSnuADgt99+U+v1KPl2ZTIZN8AcAPzxxx8avVilbJOSskREV9qSt/TWdfDgQZw9exbOzs7ca5GRkWrLBAcHY/Lkydi8eTN69OihUV0pNjaWG8dCm+RtOezt7dGmTZtUl50yZYra9xISEoL+/fvD19cXgYGBOHToECZNmoSZM2eqvS8hIUFtWpeqWJ6enmqNz62srNCyZUsAyipZOb3rYkL0hRIOAypatCj27duHihUrwtTUVK2e6bRp09C6dWu1bnEdHR31OlBUvsVTNqZkFZRwEJIfSSQSfP36FcuXL0dQUBBu3ryJzZs3Izw8XGsD7OQaNmyY5qBsQqEQO3bs4Kpfbd++Hc+fP8evX7+wdOlSyGQyCIVCLF++HI0bN1Z7b+XKldWmMzI+B8MwWL9+PTfgn6enJ/z8/BAXF4e///4bUVFR4PF4mD59ukYj+JiYGLVB9gDg169fOHLkCH79+qVzDAC03vTfvHkTX758gUQigUwmg0wmQ1RUFB48eIDp06djyZIlAKCWcJQrV05jLJFLly7h+PHj+OuvvzR6dxoyZEia7R06derErb979+5pdldbt25dzJ07Vy3pCAoKwqRJk9CjRw+sWbMGf/31F4oVK6Y2P2WCeOPGDYSHh6dZ8sGyLKZMmYItW7YgPDwcQUFBXAIyevRobqBIQvI6hs1KBUWSJSzL4tatW3j37h2KFCmC5s2bazyVyutUbVz0OQDS4WXbsXLjUtiDj3MfX8PCwkJv6yaGJxKJ8Pr1a1SsWJG+u1zGUN9dUlISPn36hFKlSqkNppaalStXqlVlSW7UqFFau8JN6a+//kLFihXRs2dPrfMTExPh4eGBS5cu4fPnzxCLxXByckKjRo0wbNgwtWozKlFRUZg9ezYePHiAypUrY/ny5el2rZqSTCbDsWPHcO7cObx//x4ikQgFCxZE3bp1MXjwYI1zaUhICFq3bp3mOt++fQu5XI6kpCSYmZlp9IAllUpx6NAhfP36FSdOnMh0b4o9e/bE8uXLuennz59j8eLFeP/+PYoWLYouXbpgyJAhsLCwwLt37zB//ny8fv0ajo6OGDJkCAYPHpzm+nft2oXVq1fj4sWLWvd/Sk+ePMHevXvx+PFjxMTEwNnZGQ0bNsSoUaPUvpevX7+ibdu2qa5n+/btXKlFcps2bcLmzZvVXjM3N0e5cuUwaNAgdO3aNd0YU0rtWEjv2DPEtZaQjKCEw4DCwsJQpEgRY4eRoxniJOjx1xb8sXUZCip48A96SzetuQwlHLlXTkk4SMallXDkFg8ePMDmzZs1OmXJSyjhILkVVakyoNatW+PLly/GDiPfcSlcHH9EmWO8yNLYoRBCCDGAmJgYjepdPj4+GDhwoJEiIoSkhRIOA2JZFhMmTMC9e/eMHUq+YmZlhdIyPkrIcudTOkIIIal7/vw5WrRogY4dO2LlypUAlAnIs2fP0mwsTggxHko4DEwul2PmzJno3r07vLy8NAZnIvrH8FWNxtNuHEoIIST3OX36NNczlmoMjV27dmHAgAG5tjoYIXkdJRwG5unpiZs3b2Ly5Mm4dOkSmjdvjpUrV3JdJxL9ixMn4YGpFE/4EmOHQgghRM+SD54XEhKCGTNm4M6dO+jTp48RoyKEpIUSDgPau3cvbG1twTAMWrZsiZ07d+Lo0aMAgN69e2Ps2LHw8/MzcpR5z49fkdhVQIzDVmIq5SCEkDymX79+GDlyJAoWLAihUAixWIwdO3ZAIBAYOzRCSCoo4TCghg0barzm4uKCWbNm4ebNm2jdujXWrl2LDh064NChQxqDCpHMsbC0hquEj7JSPlgdBmYihBCSe/B4PMyYMQN+fn548uQJNm7cqDGmByEkZ6GEw0hMTU3RrVs3DBgwALGxsVi6dCmaNWuGxYsXGzu0XK9I0WL4Pdoco2PNwMqphIMQQgghxJhMjB1AfhQeHo7Dhw/j2LFjiI6OBqDs0crBwUGnwYpIOkz+K1ZnZVTCQQghhBBiTJRwGFCtWrVw7949CIVCAEBAQAA8PDxw9epVyOVyqMZcbNiwIQYPHowWLVqAYRhjhpw38CnhIIQQQgjJKSjhMCCRSIS//voLJUqUwNmzZ/HmzRsAytIMMzMzdO3aFYMGDUK5cuWMHGneEhEdiT/tRRCwwDm5zNjhEEIIIYTka5RwGNixY8cAgCvNKFy4MAYMGIC+ffuiQIECxgwtz2IZBqEmCghYANSGgxBCCCHEqCjhyAYsy6JWrVoYPHgw2rZtSwMTGZidrQOmxpqDJ6cqVYQQQgghxkYJh4EVLVoUa9euRfXq1Y0dSr4hNDNDZbkQCqkcrIyqVBFCCCGEGBN1i2tgK1asoGQjm7EMDwxf2fheQQkHIYQQQohRUcJhQKtWrUKtWrWMHUa+I5PJ8cxEiidCGeRiibHDIYQQQgjJ1yjhMKCuXbuCx9N9F8tkMkyfPt2AEeUPYqkYGy0SsM02CTJKOAghhBBCjIoSjhwkKCgI58+fN3YYuZ5AIERphQlKS3lQyKTGDocQQgghJF+jRuNZ5OnpCU9PTwwYMAD/+9//1OZt3rxZ5/XEx8fj4sWL+g4vXzI1s8JCmR3E0YnKrnEJIYQQQojRUMKRRWvWrIFIJMLq1as1Eo5z587h8+fPOq+LZVkaaVwfeDwwfGXhnUJKJRyEEEIIIcZEVaqyqFWrVmBZFm3atNGY16dPH27AvwIFCsDZ2RmFCxfW+s/Kyiq7Q8+zWB4Dnonyp82KxUaOhhBCCCEkf6MSjixavXo15syZAzs7O415PXr0wNatW3H27Fk4Ozunu65jx45h4cKFhggzX+GBwZ+IhNROhn8if6GosQMihBCik0+fPuHgwYO4evUqbty4kepyfn5+OHr0KJ49e4bY2FgAQJEiRdC4cWMMHz5cp2suIST7UAmHHmhLNlSvt23bFg4ODjqtp1u3bihUqJA+Q8uXGIbBB1aCzwIFxKIEY4dDCCEkDSzL4ubNmxg5ciQ6dOiAgwcPIj4+PtVlFy5ciOHDh8PX1xdDhw7FgwcPcPHiRVhZWWH//v3o3Lkznj17ls2fghCSFirhMLClS5eCz+frtKypqSlu3rxp4IjyPobPYLp5ISR9i4UVX2DscAghhGghFovh5eWFI0eO4P379zq958CBAzhy5AgAoGbNmhg2bBgAwMnJCXPnzkWfPn0QFxeHadOm4fLlyxnqmp4QYjh0JBrQ4MGDkZiYmO3bvXz5MgYMGIDatWujXr16cHd3R2BgoN7WHxMTA29vb0yfPh1Dhw7F0qVL4e/vr7f1ZxWfAeqZWaOaxAQCBXVTRQghORHDMKhbty7OnDmDdevW6fSegwcPcn8XLFhQbV7VqlUhFAoBACEhIXj79q3+giWEZAklHAb04MEDbN68GXK5PNu2uXbtWri7u0OhUMDPzw9eXl7w8/ND3759cfny5SytOz4+HitWrECLFi2wf/9+tGnTBrt27cK8efPQsGFDPX2CrGN4DHiCf3upokbjhBCSIwmFQpQvXx4Mw2jteEWb79+/c38/fvwYSUlJ3DTDMLC1teWmBQIq4SYkp6CEw8D27duH9u3bY9++fanWSdWX48ePY8eOHQCAvn37wszMDC4uLmjatCmkUimmTp2a6Sc+T548QceOHbF3717873//w/Hjx9GhQweYmOS8Wnl8HoPXcjECBTIkxMUZOxxCCCHpUJVMpKdYsWLc35GRkdi0aRM3LZPJEB0dDQAoU6YMSpUqpdcYCSGZl/PuFvOYHTt2gGEYeHl5YcuWLejYsSPc3NxQrlw5vW5HIpFgy5Yt3HSJEiW4v11cXAAAUqkU69atw/bt2zO07jt37mDcuHGQSCRwc3PDrFmz9BO0gfB4wMpfoYi3k6N6VJSxwyGE5FEsyyJJrDB2GHohl8shTpKDhRyqZodmprwcNzZUz549sWbNGm56165dYBgG06ZNw6NHjyCRSGBvb4+1a9fq3H6SEGJ4lHAYUJcuXdCkSRPweDw0bdoU4eHhOHr0KIYNG4bSpUtj0KBBaN26tV4atfn7+yMsLIybTj6uR/InR7du3UJcXBysra11Wu+nT58wceJESCQSODs74/fff89yrIbGZxiUEJohNi4RPFn2VWcjhOQfLMti/KynePE61tihGEzVijbYurJGjko6hg0bhkePHuH69evcazt37sSTJ08gl8vRvn17zJ07F05OTkaMkhCSElWpMqDVq1erJRNOTk6YNGkSbty4gX79+mHfvn1o3bo1duzYgV+/fmVpW/fu3VObTq3uqlwu11g2LXPmzIFIJAIA9O/fH+bm5pkPMpvw+AxWuJTDgl8WKGxqYexwCCGE6ImJiQk2b96MgQMHqr0eEBCAJ0+e4P379wavvkwIyTgq4TACExMTdOrUCZ06dUJAQAAmT57MVbcaMGAAqlatmuF1PnnyRGMbqXn69Cnatm2b7jpv3ryJx48fq22jc+fOiIiIgFAoRL169TBhwgSUKVMmw/EaEsMwYP79/AqJxMjREELyIoZhsHVljTxWpSoJpmZmXFWknFilClBe39q1a4fr16+jadOm8Pb2hlQqBQB8/PgR/fv3x/79+1GxYkUjR0oIUaGEw0hUo6meOnUKIpEILMvi5MmTePv2Lby9vTO8voiICLXptKppRUZG6rROLy8v7m9bW1tMnz4dRYoUwfr16+Hh4YFz587h+vXr2LlzJ+rUqZPhmA3FxIQBT6C8YMqTKOEghBgGwzAwN8sb7QTkcoABH2Zm/Bzd9kEqlWLBggXw9vZGp06dsHjxYvTs2RNjxozhGozHxMRgwoQJuHjxos6N0QkhhkUJhwEtXboU8+bNU3vt5s2b8PDwwN27d8GyLFiWBZ/PR+vWrTF48OBM37inrJKV1lOpKB0aUqu61VWxtLSEq6srAOCPP/7A5cuX8f37d4hEIvz++++4dOlSpk/sLMty1bb0QSoWY0PIJ3y2FWFcxHe46nHdxPBUY9cYYwwbkjWG+u7EYjEUCgXkcnm2djOen7Asy/2fU/axtjhWrlzJPZRr3rw55HI5qlatim3btmHYsGFcN7mhoaE4d+4cunbtmq0xG5pcLodCoUBiYiIUiv9K19I79liWzZGlVST/oITDgA4dOoTffvsNpUuXxtmzZ3Ho0CF8/foVgPLgt7GxQe/eveHm5oYiRYpkaVuq4mRdqC4safn69WuqSYCJiQmaN2+Oo0ePAgC+ffuG69evo3379jrHkJxUKsXr168z9V5touOAd6IEfBYq8ONXlF7XTbLP58+fjR0CySRDfHcmJiYQ07g6BpdT9jHLsmpjbABAXFwcPD09uelChQpxy5QvXx79+/fH3r17ufkvXrxAu3btsifgbCIWiyGTyfDx40et89M69qi0hxgTJRwGxLIsBg0apDYNKPsHd3NzQ/fu3fXWCNvGxkbnqlJ2dnbpLpOyxCT5kxQAXGmHyvPnzzOdcAgEApQtWzZT79UmMlqKEWXKIvjOG5SxLED1eHOZxMREfP78GSVLlswVnRSQ/xjquxOLxQgLC4OpqSnMzMz0tl7yH5ZlIRaLYWpqmiOehDMMo/Fdv3v3DjKZjJsuVKiQ2jJ9+vRRSzgA5Mnfi4mJCUqUKAFTU1PutfSOvQ8fPmRniIRooIQjG6iKMps3b47BgwejcePGet+Gs7OzWsKRVimGo6NjuutLeZJOWdqRssvB2NjMdw3JMAwsLPTXm5RYJkV9Jyc4iYNgy5jodd0k+5ibm9N3l0vp+7vj8Xjg8Xjg83N2+4LcTFV9iWEYo+zjlA+1VNWNkytYsKDadHR0tNoyKWsK1KxZM8/9Xvh8Png8HszNzbUmU6kdezkhiST5G3WLa2A8Hg/9+vXDhQsX8M8//xgk2QCAatWqqU2nVQe3Zs2a6a4vZUIRHx+vVm0r5YnO3t5elzCzBZ9hwPzbLbBCTI3GCSEkp0vZlW1SUpJGElK8eHHUrVuXm07ezhAAAgMDub9dXV3x22+/GSBSQkhmUMJhYNOnT8eff/6JkiVLGnQ7jRo1UptOWfdVhcfjqTVMDwoKQs+ePVGvXj1s2LCBe93e3h4VKlTgpuVyOUJCQrjplN3u6rNKVFbx+QxCxIn4IJAjKoH6YyeEkJwsKSlJoyqUTCbD/v371apQAcCqVavg4uICANi7dy83AODHjx+xaNEiAEC5cuXwzz//pDoeFSEk+1HCYUANGzZEnz59smVbLVq0gIODAzcdExPD/Z28tKN58+awtbXlpufPn49Xr14hJiYGW7duhb+/Pzeve/fuatt48+YN93fynjDMzc3RqlUrfXwMveDzgO2vA7HKLhGPY34aOxxCCCGpqFWrFmrUqIGtW7dqzFuxYgWqVavGJRKAstrUyZMnMX36dJQrVw6///47qlevjv79+8PW1hbz58/HiRMnstwRCyFEv6gNhwHVrl0b9erVQ4MGDTSe3uibUCjE1KlTuW54P3/+jPr16wMAwsPDASgbZ0+ZMkXtfcmLoFXTDRs2BAAMHDgQJ06cwPv37wEA169fR4cOHQCA620LACZOnAhLS0v9f6hM4vEZFLSyQiEZA4E0Z3TvSAghRFPywWV1ZWlpidGjR2P06NEGiIgQYghUwmFAe/fuBcuyGjf1htKnTx8MHToUAHDixAmIRCKEh4fjypUrEAgEWLVqlVo1KQAa05UqVeL+FgqF2LZtGzeS+Pnz5/Ho0SNERUXhyJEjAIB+/fph2LBhBvxUGcdjgJktm2FplCXqsdTLESGEEEKIMVEJhwEVL14cb9++xfTp03V+T1hYWJaKgmfPno3q1avjwIEDaN68Ofh8Pho0aIAJEyZoJBeAcnDCGTNmICQkBG5ublzpRvLPcOzYMRw7dgxnz57FqFGjYGFhgfLly2PWrFlo2bJlpmM1FB6PAc9c2V2gIjFn9ClPCCGEEJJfUcJhQPPmzcPIkSPRtGlTnZaPiYlB69atszxQXceOHdGxY0edli1btixOnjyZ5jJWVlYYPnw4hg8fnqW4sgvDAMy//ZNTL1WEEEIIIcZFVaoMqE6dOti/fz8WLlyoU7Uqb2/vbIgq72MYBqdeBmJjgUT4yeKMHQ4hhBBCSL5GJRwGNHToUCgUCojFYvTp0wc1atTQOggRy7L4/v27WrezJGs+/4rGS1M5Sogk3MCLhBBCCCEk+1HCYUAMw+DBgwdgGAYsy+LRo0c6vYdkXbu6NWHp+wzFZDwoJFLwTYXGDokQQgghJF+ihMOA+vfvD39/fzg7O6NGjRoQCoXg8bTXYktKSoK/v7/a+Bkk86pWcAWS/h1tXJRICQchhBBCiJFQwmFArVu3hpOTE06cOAF7e/t0l3/06BHc3NyyIbK8jzURgOEzYOUsZAmJENgVMHZIhBBCCCH5EiUcBsTn8zM0RkXVqlXRoEEDA0aUf/yKT0SoGSBMVEAuSkz/DYQQQgghxCAo4TAw1UB86fn16xcuXrxo8BHJ84tz/vex1TIODXgm6J5ACQchhBBCiLFQt7g5hFAoxK5du8CyrLFDyRMszC1hy/JgxjJUwkEIIYQQYkRUwmFAs2fPTncZlmWRmJiIV69eISwsDFevXkWbNm2yIbq8rWPzFqjpeQ0JP2IgT0wydjiEEEIIIfkWJRwGdPLkSZ27uVWVbHh4eFDCoQcKhg++UDnmiZyqVBFCCCGEGA0lHAbG5/NRvnx5WFhYpLrMly9fYGdnBxsbG6pSpSdy8ME3U3aLK4uNN3I0hBBCCCH5FyUcBrZ9+3Y0adIkzWW+fPmCWbNmYcOGDTp1n0vS9/ZLMI6KImBpKUZlSjgIIYQQQoyGGo0bkLW1NWrWrJnuci4uLujUqRNGjBiBpCRqb6APUfEJ8EuMQ6BQDmlsnLHDIYQQQgjJtyjhMKCHDx/C0tJSp2V79OiB169fY+vWrQaOKn8oWqQkxhYuho4iIVWpIoQQQggxIko4cgihUAgej4czZ84YO5Q8wbagM3oWK4I6YhPIYqiEgxBCCCHEWCjhyCGOHTsGhUKB6OhoY4eSJ8hh8l+j8bgEI0dDCCGEEJJ/UaNxA9JlHA6pVIrPnz/j1atXYBgG1atXz4bI8j6xHAhXyBDGV8CRSjgIIYQQQoyGEg4D0nUcDlVXuAUKFNApSSHp+xWfgCl+98HYA0ejY40dDiGEkFTI5XIcO3YMx48fx4cPH8Dn81GtWjWMGzcO9evX13k9Bw8exJIlS9CjRw+sWLHCgBETQjKKEg4D4/P5qFChAszNzTXmMQwDgUAAW1tblC9fHr169YKDg4MRosx7+AILmJvwwZfIIaZeqgghJEeKi4vDuHHj8PDhQ7XX/f39cf/+faxevRqdO3dOdz1PnjyhJIOQHIwSDgNbv349jRxuBAILW9we0AmP11wDa05tOAghJCeaOnUqAgIC4OzsjNjYWIhEIm6eQqHAn3/+iTZt2sDMzCzVdURGRmLy5MmQSqXZETIhJBOo0biBpTfoHzEMOXjgmwsB0EjjhBCSE506dQoymQyXL1/GzZs38ejRIyxYsAA83n+3JrGxsXj37l2q65DL5ZgyZQrCw8OzI2RCSCZRwmFAr169SvOpDDEclmXAszQFoOylStVOhhBCSM4QExODHTt2oHjx4gAAHo+HgQMHolevXmrL2draprqONWvWIDEx0ZBhEkL0gBIOA+Lz+anOe/36NS5cuIC7d+9CLBZnY1T5Awtgjd8z7LJJQiRkkMdTtSpCCMlJhgwZAqFQqPF6xYoVub9r166NEiVKaH3/xYsXce3aNSxdutRgMRJC9IPacOhBWk9XUjYWf/PmDebMmYPXr19zr1lZWeH3339Hv379DBZjfsMqgMtvPuO7mQxtRALIYhNgYm1l7LAIIXkIy7KQyY0dhX7I5YBUDvBlgOLfEmETPnTqaVHfnjx5AgAoUaIE1qxZo3WZoKAgLFmyBLt374aVFZ3bCcnpKOHQA3d3d9y9e5ebZhgGlSpVQuXKlfHnn39yr799+xaDBw9GXFycWhWfuLg4LFq0CLGxsRg1alS2xp5XKQCMblkX747eh60CkEbHwqyok7HDIoTkESzL4ugdICzK2JHoCw+AhdorReyBfk3YbE06rl27hnPnzgEARo0ahSJFimgsk5CQgIkTJ2LGjBmoUKECQkJCsi0+QkjmUMKhB2vWrEHDhg0BAL1798bYsWNRrFgxtWWkUimmTZuG2NhY7uTdqVMndOrUCQkJCdi7dy/Wr1+Ppk2bokKFCtn+GfIalgX6NaqJx6deIzEmHpKoaGOHRAghJBXnz5/H6dOncePGDe6B3Pz583Hnzh2sXbsWAoGAW3b27NmoW7cuunfvbqRoCSEZRQmHHrx58wYAMHfuXAwaNEjrMnv37kVQUBA3nXLZdu3aoXfv3jh06BCWLFli2IDzAZZlwPJNILAUIhGA5OcvY4dECMlDGIZBvyZ5qUqVAkniJJiZmoHPVzbvzM4qVQzDgM/ng2EYtRoAvr6+KFWqFKZOnQoA2L17N8LCwlKtakUIyZmo0bgeXLt2Dc2bN0812YiKisI///wDhmHAMAxat26tsaypqSkmTpyIe/fuZUfIeZ5CwSJWIsMvMx5EDAtpZLSxQyKE5DEMw0Bgklf+AQI+lP//+1p2VqXq0KEDtmzZghMnTqBkyZJq8w4dOgQAePDgAfbs2YONGzdqbWxOCMm5KOHQg3v37qWabADAtm3bkJCg7JpVIBBg1qxZWperVq0aIiIiDBVmvsKyDOZ7nse4yM94YCajKlWEEJILVKpUCQcPHkShQoW41+Li4hAVFQVvb2/8/PkTLVu2RPny5bl/rVu3VlvHyZMnUb58eXh7e2d3+ISQVFDCoQffv39H5cqVtc4LDg7GkSNHuNKN/v37c32Op2RlZaU24BHJPAULWJibQcgwUIClKlWEEJJLODo6ajyYS9njIyEkd6E2HHoglUohk8m0zlu9ejWkUikAwMbGBuPHj091PaGhobC3tzdIjPkNywJ/DuuHSYwZPp1+SVWqCCEkF2nXrh2EQiEkEgkqVqwIc3NzODo6olSpUhrLymQyBAcHc9NWVlZwdHSEtbV1doZMCEkDJRx6ULRoUQQEBKBDhw5qr1+9ehWXLl3i6sGOHz8eBQoUSHU9ly9fRtWqVQ0aa37BsgAjFEJgqaznK4miEg5CCMlJvn79ClNTUzg5aXZZLhQKYW1tjcjISAwZMgQAMH36dEyfPl1j2ZCQELVqVW3btsWKFSsMFzghJMOo/o4eNG/eHGvWrEF4eDj32qdPnzB37lwu2ShXrlya7TwiIiJw6NAhNG7c2ODx5gsswJoIILA0BQBIfkYbNx5CCCGcCxcuoF27dmjRogXWr18PhUKhNj80NBSRkZHo0KEDdX9LSB5AJRx6MGzYMJw4cQLdunVDu3btAABnz55FYmIiWJaFqakpli9fDj6fr/X9UVFRcHd3R3R0NBo1apSdoeddDPD4wxecC3wLU3MJulAJByGE5Bg/f/4Ey7JgWRbbtm3D3bt3MXPmTNSoUQPfvn3DjBkzMGjQIMyaNcsoo50TQvSLEg49cHR0xMaNGzFu3Dh4eXkBANePuJmZGdasWaO1UfmbN29w5coVeHp6IjIyEgzDUKNxfWGBL+GROPnhK6oI+WhPbTgIISTHGDhwIMRiMc6fP49Pnz7hxYsXGDt2LFxcXFCzZk0sWbIE5cqVM3aYhBA9oYRDT+rXr4+zZ89i586dePz4MViWRbVq1TB8+HCULl1aY/k//vgDIpEIAFC7dm3u9XXr1mHVqlXZFndexYBBBdcymNCwCqQX30MqjQUrl4NJpZSJEEJI9uHxeBg5ciRGjhyZ5XUVK1YMb9++1UNUhBBDoYRDj4oUKYKFCxfqtCw1aDMshgHKli6D2q1q4ZbPZwAKSH/FQFiQegEjhBBCCMlOVH+H5Ek8hgVrYgIenweBlbLhuDg80shREUIIIYTkP5RwkDzJhM9AzvARnShGrLUAACAO/2nkqAghhBBC8h9KOEieJBAwCPnxC/VWe2KmXNldsfj7DyNHRQghhBCS/1AbDpInmZowMBXaKCcYQAGWSjgIIYQQQoyASjhIniQUMLCwd8bLOYNwsnIN8MBQCQchhBBCiBFQwqEHJ06cwNSpU/HjB93Q5hRCAcAyfJiYmkJoYwYAEH+nEg5CCCGEkOxGCUcW3b17F3PnzsXFixfh6+tr7HDIv0yFDBQsD6yJ8L+Eg6pUEUIIIYRkO2rDkUU7duwAAJQoUQK//fabkaMhKqYCBjIFg/33XuHNu4+oxpfDmko4CCGEEEKyHZVwZNHr169Rt25deHl5oWDBgmrzTp06ZZygCExNeZApGFx+FYRTH4Px3YRF0rcIY4dFCCGEEJLvUMKRRQzDYPny5bCxsdGYN3v2bCgUCp3XpVAoUKVKFX2Gl28JTBjIZAw616uBCY2qorCMgTQqGnJRorFDI4QQQgjJVyjhyKJy5crBwsJC6zyWZTO0rri4OMhkMn2Ele8JhTxI5Qx6Nm+ASa1roYSp8jtKDPlu5MgIIYQQQvIXSjiyqF+/fli+fDl+/fqlMY9hGDAMo9N6ZDIZtm/frvPyJG0CEwYyOaAwEYJhGJgVsgUAJFHCQQghhBCSrajReBZ17twZFy5cQKNGjWBlZQULCwvw+XwucWjTpk2665DL5YiKioJUKjV0uPmGUMiDRMZAzhcgNkkMiZ0F8BlIDP5m7NAIIYQQQvIVSjj0YN26dVizZg0OHz6MuLg4tXmhoaEZWheVcOiHwISBVAZ4XruLtQe90MS2IAYDSAqhhIMQQgghJDtRwqEHQqEQc+bMgbu7O/z9/fHt2zfEx8djy5YtGDduHHi8tGuuSaVS/PjxA76+vhCJRNkUdd7G5zOQylhY/9uYX8pXJnKJwVSlihBCCCEkO1HCoUc2NjZo3749N71lyxa4u7unm3CodOjQAaNHjzZUePkKwzCQSIE2zZqit60EUa9j8eb1VSrhIIQQQgjJZtRo3IAy2ktV/fr1M/wekjqJlIWJhQWEJnyY2pgCoF6qCCGEEEKyGyUcBnT16lWdSzcAZdWs58+fZ3m7ly9fxoABA1C7dm3Uq1cP7u7uCAwMzPJ6kzt48CDKly+PP/74Q6/r1SeplIXCRJloCK2VhXlJ1GicEEIIISRbUcJhQEWLFs3we4RCYZa2uXbtWri7u0OhUMDPzw9eXl7w8/ND3759cfny5SytW+XJkydYsWKFXtZlSBIpi0Tw8Pe1x/jr1gPIwUIWGw/prxhjh0YIIYQQkm9QG45sIpVKcfnyZTx48AA/fvyAjY0NSpYsiXbt2qFUqVJ62cbx48exY8cOAEDfvn1hZmYGFxcXNG3aFL6+vpg6dSpOnDiB8uXLZ3obkZGRmDx5cq7owlcqY8E3s8A/d56DBdC0UDGYR0QjIegrbOtUNXZ4hBBCAIjFYjRq1Ajx8fGpLjNs2DC1EnWJRAIPDw+cOHEC3759g7W1NVq3bg13d3c4ODhkR9iEkAyghCMb3Lx5EwsWLEBERITGvPXr16Nhw4b4888/Ubx48UxvQyKRYMuWLdx0iRIluL9dXFwAKJOedevWYfv27Znahlwux5QpUxAeHp7pOLOTVKoA30SAEU1rwIrPwPoDH7KIaIg+BlPCQQghOcTVq1fTTDYEAgGGDh3KTSclJWH06NG4f/8+hg4ditmzZ+P8+fOYOnUqrly5ggMHDujtQR4hRD+oSpWBnTp1CuPHj0dERARYltX67+7du+jatSuePHmS6e34+/sjLCyMm7aysuL+Tl5N69atWxpjhehqzZo1SExMzHSM2U0uUwAApnRqgfHNqqOQSxEAgOhTsDHDIoQQkoyPj0+a8zt27AhnZ2duesGCBbh//z4AYNCgQQCUvTza2dkhIiICI0aMgFgsNlzAhJAMoxIOA/r8+TMWLFgAuVyOYsWKoUePHqhfvz5Kly4Na2trsCyLX79+4eXLlzh8+DAmTJiAs2fPwt7ePsPbunfvntq0QCDQupxcLse9e/fQtm3bDK3/4sWLuHbtGjZs2IBu3bplOD5jkMmVPX6xQnNAFA3zospidlHQV2OGRQgh5F+RkZF48OABAgICYG1tne7yHz58wJkzZwAAJiYmKFasGABlV+guLi749esXQkNDcejQIQwfPtygsRNCdEclHAa0d+9eSKVSuLu748KFC5gwYQLq1KkDe3t7CAQCCIVCODk5oXXr1ti9ezeaNm0KT0/PTG0rZemIiUnqueTTp08ztO6goCAsWbIEGzZsUCs5yenkcmUJh1xgiphEMWS2ZgCAhI+UcBBCSE5w7tw5NGzYUKdkAwC8vb2hUCjP7RYWFmrzkpfmnz17Vn9BEkKyjBIOA7p79y5Gjx4Nd3f3VEscknN3d8eVK1cyta2U7UPS6o43MjJS5/UmJCRg4sSJmDFjBipUqJCp2IxFLlVelFaevIS6qz3h9fYFAED0kapUEUJITuDj44Nr166hTp06aNWqFcaMGYOtW7ciOFj7eVpVlQpIvSQfAF6/fo1fv37pPV5CSOZQlSoDioiIQP/+/XVe3s7OLtWTbHpSnlgZhkl12aioKJ3XO3v2bNStWxfdu3fPVFy6YFkWIpFIr+tMTEwEy8qhYE1QoEABAICIlQEAkkK+I/5XNHimWeuCmBiGqp1QbmovRJQM9d2JxWIoFArI5XLI5XK9rjsrVO3w8gKWZcH+u49VJQgMw6R5Lcmqjx8/4uXLlwCAuLg4xMXFITQ0FDdu3MDGjRvRvn17zJ07l+t1SiwW4/Xr19z7TUxM1H4Pyb8LhUKBp0+folmzZgaL3xhU309iYiL3PQHpH3ssyxr0uyQkPZRwGJCNjU2GqiAFBARk+oSQkW5qdb1A7t69G2FhYVizZk2mYtKVVCpVu4joC59nCpmcB7euHeFesSCSHIriyfkPYEVJeHX9Nkz+bUROcqbPnz8bOwSSSYb47kxMTHJUQ2CWZfErKipXdBGeWQKBAHb29ga7UfX29k51HsuyuHjxIh4+fIgdO3agVKlSCAsLU0swGIZBUlISN538BhwAvn//rjY/LxCLxZDJZPj48aPW+Wkde1kd54uQrKCEw4AqVqyI69evo0uXLuku+/37dyxevBilS5fO1LZsbGx0riplZ2eX7jIPHjzAnj174OXlZfCTlEAgQNmyZfW6zsTERJjcDYVUwYO1VQEITfgQCk1gWcYF8S/eojBrgoIVK+p1m0Q/EhMT8fnzZ5QsWRLm5ubGDodkgKG+O7FYjLCwMJiamsLMzExv680KlmXTrLqaF/B4PJiZmRkk4VAlFOmJjIzEtGnT4OPjo/H0XhVf8unk4uLicszvRZ9MTExQokQJmJqacq+ld+x9+PAhO0MkRAMlHAbUt29fzJkzB3Z2dmjSpInWZRISEuDl5YVt27YhNjYWw4YNy9S2nJ2d1RKOtEoxHB0d012ft7c3fv78iZYtW6a53MmTJ3Hy5EksX74cPXv21D3gZBiG0Wj8pw8mfAWkch4UQuUFh5EkwqZyOcS/eAvppxCDbJPoj7m5OX1HuZS+vzsejwcejwc+nw8+n6+39WZVkaJF80yVKrlcDnFSEkzNzLh9bOgqVdevX4dEIkF8fDw+f/6MFy9ewNfXF48ePVJb7suXLzh//rzG2BoMw6j9HlLGmnJ+XsDn88Hj8WBubq41mUrt2KPqVMTYKOEwoDZt2uD06dMYNWoUypUrh9q1a8PBwQE8Hg+/fv3C+/fv8fjxY0ilUrAsi0qVKmHAgAGZ2la1atXw6tUrbjqtes41a9bM1DZyG8G/CUeclMX+a48RlSTBuPq9AADxgUFGjo4QktsZ+oY8O7EsC+bfxC47S26EQiHs7e1hb2+PWrVqYciQIXj8+DEWLlyId+/eccvdvXsXNWrUyNC6dSnNJ4RkD0o4DGzlypUwMTHB+fPn8f79e435qqdjNWrUwLZt2zL9NKZRo0ZqXeqmVm+Vx+OhTp063HRQUBBmzJiBkJAQDBw4EJMnTwagLAXRNlKrTCZTa9huZWUFR0dHnbs0zE5CvgIyBQ+shSW23XkOABjbS1m6E/eaipcJISQnqlWrFo4dO4ZRo0bh4cOHAICYmBg4OTmBYRjuuple6ZIupfmEkOxBCYeBmZub4++//0aXLl1w6NAh3L17V61hW6lSpeDm5ob+/ftn6alSixYt4ODgwFWriomJ4eYlL+1o3rw5bG1tuen58+dzJSNbt25FvXr10LBhQ0yfPh3Tp0/X2E5ISAhat27NTbdt2xYrVqzIdNyGJBTIIZXzYG5jg6GNqsLOVACr4soLUPybIOq1gxBCcihzc3OsXbsWrVu3hlQqRdGiRWFlZYXSpUsjKEhZQi2TyVJ9P4/Hy3CJCCHEcCjhyCYtW7ZEy5YtIRKJEBISgsTERDg7O8PJyUkv6xcKhZg6dSrmzZsHQNlTRf369QEA4eHhAJSNs6dMmaL2vsDAQI3phg0b6iUmYzMVABKZMqGY1aMd+HGREJZ0BmNiAnm8CEkh32FevLCRoySEEKKNk5MTateujXv37qFx48YAlKX5qoQjrR6oypcvz3WJTggxvrzdxUYOZGFhAVdXV1SvXl1vyYZKnz59MHToUADAiRMnIBKJEB4ejitXrkAgEGDVqlUag/elnK5UqZJeYzImoQBIkij/VphaKv+QiGDpWhIAEE/VqgghxGgkEgnevHmTZuJgY2ODUqVKoU2bNgCAXr16cfPi4+PVSvCT/92tWzcDREwIySxKOPKY2bNnY926deDxeGjevDm6deuGBg0a4Pjx4+jYsaPG8kuXLkWlSpVgY2OD8ePH55nSDQAQChku4ZAJLfBLlISfYSGwqlAGABBHDccJIcRoBg8ejG7duqFx48bYu3evxjgaIpEI7969w4YNG7gqxxUrVkTXrl0BKMfd+Pr1K7f89+/fAQAlSpRAv379sulTEEJ0QVWq8qCOHTtqTS60KVu2LE6ePKnzuosVK4a3b99mNrRsJRQAv8TKRoWbL9zETp8LcGsXhuEVGwAA4gM1G/ETQgjJHgkJCQCUJRUrVqzAxYsXMXPmTNSsWRMRERHYv38/Vq9ejfLly6u9b/HixQgNDcWjR49w6NAhzJkzB7dv30ZoaCgcHR2xdetW6lKbkByGEg6SZ5kKGSTGKBMOe3sHAEBcbBysK5cDAMS+yB2JEyGE5EX79u3D9u3bcefOHYSGhiIwMBAzZsxApUqV0KZNG0yZMkVtcDsVc3Nz7Nu3D3v27IGPjw/q1q0LKysrDBo0COPHj4e9vb0RPg0hJC2UcJA8y9yMgei7soj+tzZt4VYYMC/sAkVVZTuVuBdvoZBKwRMIjBkmIYTkSw4ODpg7d26m3isUCjF27FiMHTtWz1ERQgyB2nCQPMvMFIhLUJZwCK0LwNTEBGxCLCzKlICJtSUUYgni33w0cpSEEEIIIXkbJRwkz+LxGIgSlSUcrJm58n9RPMAqYFO9IgAg9ulro8VHCCGEEJIfUMJB8jSx+N+Ew9QM2/xe4veTNxH24S1saiqrVcU+DUzr7YQQQgghJIso4cgmX79+xZUrVyCRSLjXQkJC8ObNGyNGlfexChYyOQMwDE6//IjTLz7i/asXKFBDmXDEPKGEgxBCCCHEkCjhMLDw8HAMHz4c7du3x8SJExEXF8fNMzU1xblz59C3b188ePDAiFHmXXweC4mcDwAY0LIRpreqhWI25v+VcDx7DTZF3++EEEIIIUR/qJcqA4qLi4ObmxtCQkLAsiwYhlGb7+joiOnTp+Ply5cYNmwYpk6digEDBhgp2rxJwGchlvNgAWBQ5/bAu0cQWJvBpEJp8EyFkMXGQ/QxGJZlXYwdKiGEEEJInkQlHAa0a9cuBAcHw9TUFFWrVoWJifb8rkqVKhgyZAiWLl2Khw8fZnOUeZuQz0IiU5ZwKCxslP/H/QJPIOAajkc/fG60+AghhBBC8jpKOAzo0qVLKF++PK5evQovLy8UKFAg1WUbNWoEhUKBHTt2ZGOEeZ+p4L8qVXILa0QnivHhvXKEcbsGNQAAv/yfGCs8QgghhJA8jxIOAwoNDcW8efPg4OCQ7rKq0o9nz54ZOqx8xVzIQiJT/swDQyJQb7UnBqzbDwCwa1gTACUchBBCCCGGRAmHAZmbm6NSpUo6Levn5wcAkEqlhgwp37EwBcT/lnA4upQBAPAYIDEuFrYNlAlH3Iu3kMUnGC1GQgghhJC8jBIOAypfvjyCg4PTXe7r16/YvXs3GIZBqVKlsiGy/MPSgo9EsfJvMxs7PFswAn7T+sFUmgjzYs4wK14YrFyO6IAXxg2UEEIIISSPooTDgHr27IlNmzaluczjx48xePBgxMfHAwC6du2aHaHlG5YWfCQkKv+WyeSwLOgEAFDE/ATwX7Wq6HtPjREeIYQQQkieRwmHAXXr1g2JiYkYOXIkbty4AYVCgZCQEDx//hzHjh3DqFGj4ObmhvDwcADK3qrc3NyMHHXeYmnBR1wCCwBgFXIwto4AAEX0vwlH/RoAgKi7j40SHyGEEEJIXkfjcBgQwzDYtGkTZs6cibFjxwIA/ve//6ktw7LKm+F69eph/fr1qXadSzLHxsoEsZ/kAACWlePxt184dOo2Sr2KwO81m8OuUS0AwK+7j8HK5WD4fGOGSwghhBCS59DdrYFZWlpiy5YtuHv3Lk6dOoWnT5/ix48fkMlksLOzQ7Vq1dClSxe0a9dOY2BAknU21iaIjlM2xGfA4keSDKeeB6F6bBJ+B1CgZiWYFLCGLCYOMU8CYVunqnEDJoQQQgjJYyjhyCaNGjVCo0aNjB1GvmNjZYKEeBFkCgYmPBZV6zbA1Ja1UKGYk3L0dz4fDs3rIfz0VUTeuEcJByGEEEKInlEbDpKnWVubQJIkh/jf0cYLl3HFuGbV0bKUE9hEZUN9h+b1AQCR1+8ZLU5CCCGEkLyKEg4DUygUOH78OLZv346kpCS1eQEBAZg1axauXLlipOjyPqGAB1YuQ6JUWZgnBwPG2h4AwEb/AAA4tGwAAIi68wgKicQ4gRJCCCGE5FGUcBiQQqHAuHHjMH/+fGzYsAGnT59Wm1+nTh3Mnj0b3t7eGDx4MKKjo40TaB7H57FIkipLOGQyGRLNrPEy7Cc+vFSO6m5duRyEjvaQixIR/eC5MUMlhBBCCMlzKOEwoIMHD+LmzZtgWRYsy8LZ2VljGVtbW2zYsAFRUVEYMWIEJPSEXe+EPMV/JRwyGTZd8kfPXWdx4Jg3AIDh8VCwtbJ9TcTFW0aLkxBCCCEkL6KEw4BOnjyJggULYsKECdizZw+aNWumdTmBQICRI0fi1atXOHDgQDZHmfeZCVkkypQJh0wmQzlXV9iam0IgE3PLFOrQHAAQcf6GMUIkhBAC4NOnT1iyZAlatGiR5nISiQS7d+9Gx44dUbNmTTRr1gx//vknIiMjddqORCKBj48PevfujX379mU9cEJImqiXKgP6+PEjPDw8UK1atXSXdXV1BQCcOnUKI0eONHRo+YqlGcNVqZLKZOjTfwC6mceCMbdU9lTFMHBs3xTg8RD34i0Sg7/BvHhhI0dNCCH5A8uyuHXrFjw8PHDnzh2wLAtra+tUl09KSsLo0aNx//59DB06FLNnz8b58+cxdepUXLlyBQcOHECpUqW0vvfHjx84cuQIjhw5gp8/lQPAdu7c2SCfixDyHyrhMCATExMukUiP6qnM169fDRlSvmRjyUdCkvKnLpPKIShYWDnAX5IIbEIsAEDoYAe7+tUBABEXbhotVkIIyS/EYjEOHjyILl26YPTo0bh9+zY3GG5aFixYgPv37wMABg0aBADo0KED7OzsEBERgREjRkAsFqu9JzAwEDNnzkTbtm2xefNmLtkghGQPSjgMqEyZMvj06ZNOyx45cgQAYGNjY8iQ8qUC1ib4Fae8iCkUMoBvAsbWUTkd+Y1bzlFVrerCjWyPkRBC8huGYVC3bl2cOXMG69at0+k9Hz58wJkzZwAoH+oVK1aMW5eLiwsAIDQ0FIcOHVJ7n6mpKRYuXIg7d+6gevXqevwUhBBdUMJhQB07dsSyZcs0nrQkJ5fLsWLFCly9ehUMw6BJkybZGGH+YFtAiF/RUqgenMnlcpwMDEa/PeewY/dubrlCHVoAACKv3YM8KfXvjBBCSNYJhUKUL18eDMOgTZs2Or3H29sbCoUCAGBhYaGxPpWzZ8+qzStTpgwsLS1hZWWFBg0aZDFyQkhGUcJhQAMGDEBkZCS6dOkCLy8vhIaGQi6XQywW48OHD9i/fz86duyI/fv3AwDMzMwwYcIEI0ed99jZCpCYIEHSv4P/yWUyxCh4eBLyA4+fveSWs6leAWbFnCEXJeLnFT9jhUsIIflO8mQhLaqqVICyw5XUvH79Gr9+/dI6L633EUIMgxqNG5BQKMQ///yDYcOGYcGCBakux7IszM3NsX79ehQvXjwbI8wf7G2FSEyIQ6LUBOYCOaQyGdq27wCHqK+oWq4MtxzDMHDu0Q6fNx3AN68LcOrcyohRE0JyOpZlAZnU2GHoBSuXAzIJWCkPrEL5cAYmAjAMY9zAkhGLxXj9+jU3bWKS+i2MQqHA8+fP0bx58+wIjRCSDko4DKx48eI4efIkNmzYgBMnTiAxMVFtPp/PR6tWrTB16lSULl3aSFHmbcqEQwyRxAr2FmLIpFKUrVkXRV5cBsCCFSeBMTUDABTu3QGfNx1A+NlrkCeJwTczNW7whJAciWVZJPnsgCI8b3X0kbwyKc/ZBWZdR+WYpOPnz5+Qy+XcNI+XdiUNXbvIJYQYHiUc2cDa2hrz5s3DzJkz8fLlS4SHh4NlWTg4OKBKlSqwtLQ0doh5mr2dMuFIkNgBUHaNy5jbgbGyBRsfDcWPUPCLKUs67BrUgFlRJySFhuPn5Ttw6tLamKETQnKyHHIjnl+krCKVXsIRFRVlyHAIIRlACUc2EgqFqFWrVqrz5XI51q5di5kzZ2ZjVHmflSUf0iQJEiTKersyqbIKxE/TAvC/9xgO/HNoM3ISAOWo44V7/YZPG/fj2/GLlHAQQrRiGAZmXUflmSpVyvaFSTA1NQOfnzOrVEkkkgwtr0sXu4SQ7EGNxnOQz58/Y+/evcYOI89hGAamJiwSJMr8WvpvwnHh9RdM876F/Sd81JYv3LsDACD8zFXqrYoQkiqGYcAIhHnmH0xSvJaDkg0AKFCgQIaWt7OzM1AkhJCMohKObPDq1Su8ePECsbGxqT6hiY+Px6VLl7I5svzDxhKIFyt/7jKZDCzLom6T5qh6+iwqFrTiRhwHANv61WFWvDCSgr8h4tx1FO71mzFDJ4QQAsDJyQkMw3AlF+mVYDg6OmZHWIQQHVDCYUAxMTGYMmUK7t27p9PyyW96iX45OpgiOlYOuYIBn8dCJpOhZrNWODG6G6CQg42NAlPAAYCyWlXRAV0RtPIfBO/3poSDEEJyACsrK5QuXRpBQUEAlA+PUsPj8VCjRo1siowQkh6qUmVAixcvhr+/P1iW1ekfMZxCDqZITJCoVati+CbgORYFACjCg9WWLza4BwDgh+9tJIWFZ2+whBBCtGrUqBH3d1JSUqrLlS9fPsNVsAghhkMJhwHdvn0bDMOgU6dOOHnyJAICAvDmzZtU/6U1VgfJGseCpkiIS9JoOM4rVBwsyyLi/Su15a1cS8GuUS1AoUDoIR+N9RFCCNEf1ejhKqk9hOvVqxf3d3x8vFo3ucn/7tatm87bIoQYHiUcBiQQCGBhYYE1a9agYsWKsLKySnP5Hj16UEmHgRTiEg71huPPI0Vo/PdRDFiwSuM9xYb0BAAE7/em74UQQgwoPj5ebTopKUlrYlCxYkV07doVgDJx+Pr1v3FQvn//DgAoUaIE+vXrp/O2Uk4TQvSPEg4DatGiBSwsLHRul2Fubo6rV68aOKr8ydHBFAmxiVwJh/Tfur+la9XHz4QkBP/8hZiIb2rvKdKnA/gW5kh4+wlRdwKyPWZCCMkPkpKSNHpolMlk2L9/v9Z2GosXL0bt2rUBAIcOHYJCocDNmzcRGhoKR0dHbN26FRYWFlq39eHDB43r7Pnz5xEcHKx1eUKIflDCYUATJ06ETCbDmzdvdFpeJpNh1SrNJ+0k6wqlqFIl/be3MIeixeE9bQgCZg6AZdxPtfeYWFuh6EDlk7QvWw9mb8CEEJIP1KpVCzVq1MDWrVs15q1YsQLVqlXDokWL1F43NzfHvn37MHXqVPj5+aFu3bpYsGABBg0ahNOnT6NcuXIa69qxYweqVq2KTp06ITQ0VG1eUFAQ2rRpg5o1ayIsLEyvn48QokS9VBmQs7MzNm3ahNWrV+Off/6BiUnauzs4OJi6xjUQBzshRHFJiBMLASirVKl6BavRsDFkL+5CHhoEkzJV1d7nMm4gvu48iu8nLyMx5DvMizkbI3xCCMmTHj9+nKn3CYVCjB07FmPHjtVp+dGjR2P06NGZ2hYhJOso4TCgzZs3A1C25RgzZgxq1qyZ6rJisRi+vr7ZFVq+IxDwYC5gEZ/IQCpnIOCzkEqlEAqF4BcprUw4wj5pvM+mannYN6uHqFsP8HXnEZT/c0r2B08IIYQQkotRwmFAFy5cwMePH7npu3fvprk8jcNhWI4OpkiIFyNeLISdhRgSiQRCoRA8ZxfsuvsSdz+G4e+6nVC0jKva+0qOH6hMOHYdQ9nZ48A3MzXSJyCEEEIIyX2oDYcB9e/fn0si7Ozs4OTkhMKFC2v8c3Z2hpmZmbHDzfOKOJshITYRsWL1dhw8MwtcfBeKOx/DcOO0t8b7nLq2hlnxwpBERCLU41R2hkwIIYQQkutRCYcBde/eHf/88w9OnToFBweHdJc/evSoRuM4oj9FC5vhWXAS4sSWAADJv13jAsDQXl0RFfgEDZ1tNN7HEwhQesowBE5fhqA1O1FsWC/w0mmPQwghhBBClKiEw4CsrKzQpUuXVLvnS6lbt246JSYkc4o6myMhLhFxScqG45J/SzgAoPeQ4RhcvxKcEn+AlWt2w1h8RB8IHGwh+hiMb8cvZlvMhBBCCCG5HSUcBjZt2jSYm5unu9yuXbsQERGBO3fuZENU+VORwuaIj0lEzL8Jh1Qi4QaW4hUsAsbCCpBKoPj2WeO9JpYWKDVxMAAgaNUOGgiQEEIIIURHlHAYWHpd4ar06NEDQ4YMURs1lehXUWczxP4SIUnGh1jGB/BfKQfD8CBzLo1bH0Jw5uhhre8vOd4NfCsLxL14i4jzN7IrbEIIIYSQXI0qomeDp0+f4tu3b5BIJFqfjMvlcnz//h3h4eFYuHChxoirRD8cHUwhl0gglcjxK1EIZ+tESMRirsH+7bAYjD58BSUcHqLr1Hng8dTzcYFdAbiMHYCPa3bh3aKNKNShORge5eyEEEIIIWmhhMOAEhISMGLECDx79kyn5VmW1XlZknE8HoMizuaIixYhJtEUztaJEIvF3PzmXXuj8Io1aFKqMMQRoTB3Lq6xjjK/j8TXHUcQ+zQQ37wuoEi/Ttn5EQghhBBCch16PGtAO3fuxNOnT8GyrE7/7OzsdB41lWROkX+rVUUnKcfSSJ5wWNnZ4/bauVjUsQH4wW+1vl/oYIfS00cAAN4uXA9Fsp6uCCGEEEKIJko4DOjq1asoVaoU9u7di4CAAAQGBqJ8+fIICAjAmzdvuH8vXrxAtWrVsH//fowePdrYYedpRQubIzY6ATGJ//VUlbyam6BcdQCALOhFqg3DS00aAmEhB4iCviJ4z3HDB00IIYQQkotRwmFA3759w7Jly9CwYUNYWVmBx+OhQ4cOOHPmjNpyAoEAEyZMwOTJk5GUlGSkaPOHYoXNEfdLBJHUBFK58uefvHtcvktFgG+CL58/ISxQe/U2EytLlJszHgDwfslmSGPiDB84IYQQQkguRQmHAYnFYri6uqq91qtXL3h6emos26xZM0RERGDLli3ZFV6+VKKoOaIj4wEw+JWoWa2KEZpi7f0gtNl0Aru3bk59PaP6wtK1JMThP/F+8SZDh00IIYQQkmtRwmFAzs7OePHihdprjo6OKFeuHA4cOKD2ekJCAsRisUbpR2ZcvnwZAwYMQO3atVGvXj24u7sjMDAw0+t7/fo1pk6dikaNGqFq1apo06YNli9fjqioqCzHmt1KuVhCnChFYoIYv0TKhCMpMVFtmeoNGoPHMPgR8iXValU8oRCV180DAHzechBxL98ZNnBCCCGEkFyKEg4DatiwIaZMmYLVq1djx44d3Bgbo0ePxurVq+Hp6QmRSITg4GBMmzYNMpkMcXFZq56zdu1auLu7Q6FQwM/PD15eXvDz80Pfvn1x+fLlDK/v+PHj6N27N86fP4/IyEhIJBIEBwdj37596Ny5M4KCgrIUb3ZzsBPC2soEv37GIVKk7A43ZTW2dn3dcGemG1Z0rAfFt0+prsuxXVM4dW8LVi7Hy8lLaDBAQgghhBAtKOEwoDFjxkAmk2HPnj1Yt24d5s6dCwAoX7483Nzc8Oeff6J27dpo164dbt++DYZhUKNGjUxv7/jx49ixYwcAoG/fvjAzM4OLiwuaNm0KqVSKqVOn4u1b7b0vafP06VMsWLAAMplM6/zIyEhMmTIlV91oMwyDUiUsEP0zHlEiM7AsIJPJ1D6jubU1nGs0AABIXwekub5Ka2aDZ26GqFsPEHb0nEFjJ4QQQgjJjSjhMKCiRYti3759qFixIkxNTdGkSRNu3rRp09C6dWu1bnEdHR25pCSjJBKJWvuPEiVKcH+7uLgAAKRSKdatW6fzOtesWYPu3bvj/PnzePr0KTw9PfH/9u47PIqqbeDwb7ZveiEklNAJHelFBRQRFKRYUFFR7IpYsGJDX157fW1YP0SkWEBAFEFAKQpI701KgIT03rbP98cmS5YUUjYhgee+yLVTz5ydwybz7GmdOnXyOubQoUNs2VL+Q3ld06q5P5mpuThcGvJs7tGqzqzl0LXvBUD2gR3kZaSVmZZf8ya0mXI/APueeA1bav1rZiaEEEIIUZNk4r8a1rlzZ3766acS2/V6PR9//DFr167l0KFDNG7cmEGDBhEQEFCl62zYsIFTp0551ounYzAYPMtr164lJyeHwMDActNLT0+na9euPP30055tPXr04P/+7/8YPny4V/+NjIyMKuX5XGnZzJ/fVqcCkJxrIsBow2KxeN0zTUQTPtt6lM9XrOfpTB33PD+tzPRaPXEPp35YSu7ef9n72Ct0n/1ejb8HIYQQQoj6Qmo4ziFFURg0aBD33nsvI0aMICAggM2bN1cprY0bN3qt6/X6Uo9zOp0lji1NWFiYV7BRJDQ0lMsuu8xrW4sWLSqcz7qgqOO4tcBGWl5hP44zOo4rikJQ8zbk2Rz8/de6cpuNaY0GLvrqdRStllPf/0rCwt9rNP9CCCGEEPWJBBx1SHp6OrfffnuVzt2+fbvXuk5XduXVjh07qnSNIhEREZ7l1q1b07Zt22qlV9tiWrlrMtKSsz0dx202G06n0+u4sfdNYs6dI/hodH9cCbHlphnSqwutnrwHgD2TXsaaIk2rhBBCCCFAmlTVmri4ODIyMrBarSW+LVdVldzcXH744Ycqp5+cnOy1rtGUHUumpZXdJ6Ei4uPjPcv33HMPiqJUK73aFuCvI7qJmbSkbBo3b4DFoceks1NQUODVrCokIpL+Q4fj2L8Z++6/0TZuWW66bV+cRNIvf5C791923fMsvRZ+ilJOOQghhPB27NgxZs+ezapVq1i9enWZx23bto1x48aVm9ann37K4MGDvbatWLGC2bNns2fPHpxOJy1atGDMmDHceuutZbYMEEJUnwQcNeyrr77i66+/rtCcFaqqVvnh/cx+FOWlU535M1wuF//88w8AAwYM4LrrrqtyWkVUVSU/P7/a6RRXUNhEquCMplJF2rQws+NQFgCJ2WZahNnJyc4uEaipbXvA/s3Yju4l++ghAqKalnvdDl+8wtbB40leupqDb39Bs4erVmN1ITtb2Ym6q6bKzmq14nK5cDqdJWoihW8UfRGmqmqt32NVVVm3bh2zZ8/m77//RlVVAgMDy83HokWLyk2zVatWDBw40JOGqqq89NJLzJ8/3+u4/fv3s3//fpYuXcqMGTMwm83Vfj81yel04nK5KCgowOVyebaf7bNXnecLIXxBAo4a9Nlnn/HBBx/UyrCxdru9wsdWJz+rVq0iJSWFFi1a8M4771Q5neLsdjv79+/3SVpnio2NLXV7sL+NzDQ7LqeTU9l+tAjLJjcvj1MJCSWOPRqXw3sLf6fvukOMf+K58i+ohYBHbyP7rRkceelDMhqFYujUxgfv5MJTVtmJuq8myk6n02G1Wn2ervBWm/fYarWycOFCFixYUGJOJ1VVS4weWMRut7Ns2bJy077tttu83susWbNKBBvF7dy5kzfffJMpU6ZU4h3UPqvVisPh4OjRo6XuL++zV3wAGSFqmwQcNei7775DVVVatmzJbbfdRtOmTTGZTGV+y7Bw4cKzfmtTlqCgoAo3lQoNDa3SNWw2G++99x4NGzbkq6++IiQkpErpnEmv19OmjW8fygsKCoiNjaVFixalfmNlV3NY9PsBstNy0esCUVXQajS0bdu2RP+XtEM9OTFjAa5de/lPdBP0AUHlXlt9rj17D54gZfFK8qd9Tsc1c9GHln+OOO1sZSfqrpoqO6vVyqlTpzAajZhMJp+lK05TVRWr1YrRaKy1b8I1Gg0XX3wxd9xxB8uWLeOJJ57w7FMUpcyy/uuvv2jevDnfffddha6Tk5PDrFmzeOKJJxg1ahQmk4m//vqL1157zevv5qJFi3j++efrfNMqnU5Hs2bNMBqNnm1n++wdPny4NrMoRAkScNSgrKwsDAYDs2fPJjw8/KzHt23bloULF1bpWlFRUV6/OMurxSje6bsyPvzwQ88v7ujo6CqlURpFUfDz8/NZesWZzeZS0+7a0YCiwKmTGYQ0DCbfYcZfX4DL6cQvyDs4GHLTeF7f+jdXNw/BfGwnhr7Dznrd7l+9zrpdByg4FsfB+56n1+LP0ZTTkV+UVFbZibrP12Wn0WjQaDRotVq0Wq3P0hWnFTU9UhSl1u6x2WymQ4cOAAwdOrTE/rLysWTJEkaMGFHhfG7fvp2XX36ZYcNO/+6+5ppraNiwIePHj/dss9ncQ6TX5aBWq9Wi0Wgwm82l5rOsz540pxLnmvRorUFdunQhIiKiQsEGuGse/vvf/1bpWl27dvVaL6/ta/fu3Sud/tq1a1m6dCnffvstrVq18tq3bt26Ep3W6zo/Px3Nm/qRlpgNwMkM9y/ovLy8EsdqNBpumTQZP4Me+56NqAUljzmTPiSInj98jNbPTMrvf3HgWd80PxNCiPNRRZv7ZGZmsnr1at5++2369u3L1VdfzeOPP87cuXPJzs4u9ZzLL7/cK9go0qdPH5o2Pd0vLyQkxGc190IIbxJw1KCJEyeSnJxc4U7aqqpis9mqdK2LL77Ya72stq8ajYZevXp51o8cOcJ1111Hnz59+OCDD0o95+TJk0yfPp05c+bQsuXpkZpsNhubN2/mxRdfrJe/pLt0DCYtORtUF7Hp/sDp9rFn0jZvj6ZBY3DYiF/9S4XSD+7WgYv+73UAjv3va+JmVa32SghRN6mqiiMv/7z5ceYVeK3XRv/Dyvrtt9+w2+04HA4yMzM5evQov/76K//5z38YMGAAH3zwQaX+jhav8b/qqqtqIstCCKRJVY3q168fTz31FO+++y6vvvrqWY9PSUnhlVde4dZbb630tS677DLCw8M9zaqysrI8+4rXdgwaNMgrOHjxxRfZu3cvANOnT6dPnz7079/fsz8vL4+JEydy6NChEhP+FWnTpk297IzWrVMwS5YnkJuRC2FBOFQjOsVKfl4eQcHBXscqikJ+TB8e/nASW07M4e91/Yho2vys12h0w9W02XOIw69OZ9cDL2Js3JCIIZfU1FsSQtQSVVXZMGgcGRu2n/3geir04h70Xz23TjXHWbx4cZn7LBYL06dPZ+PGjXz11Vf4+/ufNb2iYd71ej0TJkzwVTaFEGeQgKOazjYzeIcOHdi2bRsffvih14P8mfLz8/npp5+qnA+DwcDkyZN54YUXAPdIFX379gUgKSkJcP9Cfeyxx7zO27dvX4n1ony6XC6eeOIJDh06VO6127VrV+V8n0sXdXIHFccOpdClXxBJuf40CbSSV0rAARDaqSfpNicWu4N1c/+P656eVqHrxEx9mLyDR0mYv4ytN0yi3+/fENKn69lPFELUbXXoQfxCcPLkyRKT3JZm27ZtTJs2jTfffLPc4w4fPuxpDvzII4941eALIXxLAo5qmjRpUpntRs/06aeflru/uuNkjx07lsOHDzNz5kwWLFjAyJEjycnJYeXKlej1et566y3at2/vdU779u29foF37NjRs/z666/z559/nvW69TXgiGpoIqqhkYST6XTp15qDSX40CUynoKAAp9NZokOiRqPhrddeg/WLaR2i4spIRhPa8KzXUTQaLpr5NvaMbFJXrWfTqHvp/+dcAju0rqm3JoSoYYqi0H/1XJz558d8MU6nE6vFitFk9Pzu0/qZ61TtRnR0NAcPHqSgoIDs7GwOHz7Mli1bWLp0aYnhYBctWsSkSZPKHeCkaJSrq6++mnvvvbcmsy7EBU8Cjmq69tprmTlz5rnOhsezzz7LRRddxKxZsxg0aBBarZZ+/frx0EMPlQg2AF555RWeeuop4uLiuO222zy1G4sWLWLWrFkVumZ9DTgALuoUwvI/k1BcDrItBlSNAcVlIy83t9Rajq6XD8ViS8YZux/bxt8wXX1Hha6jNRroOf9j/hk6gczNu9g0/C4uXjMPc7PGvn5LQohaoigKOv/zYyQ1xenEodWgM5nq/EhgZrMZs9lMZGQkl1xyCQ8//DA///wzr732mldz4g0bNpQZcBw7dozvv/+ePn368Oabb9apwEqI85EEHNU0btw4vvnmG15//XU6d+6M0WgsMVv12aiqSm5uLvPmzeOHH36odp6GDx/O8OHDK3RsmzZtSh2Kd8yYMYwZM6baeanruncOZvmfSaQnpBPapCGpeQFEmNPJyckpNeAAMPS9ioITB4nfs50Mgul19ZgKXUsX4E/vnz9nw+DbyN1/hH+G30X/P+dijAjz4TsSQogLi0ajYcyYMfTs2ZObbrrJ05cxMzOz1OMdDgfPPfccXbt25fPPP/eaz0IIUTNklKpqat68OZdffjmjR4+mTZs2REdH06RJk0r9NG3alPbt2/PII4/UyVFBzmd9ergf9ndvPwXArvgAwD1aVVkjnWhCGrBdG8HV0xcy8akp5GVlVPh6hgZh9Fk6A3OzxuQdPMbGK2/HkphSzXchhBAiOjra048R8BrytrgPPvgAo9HIF198UWLOigULFtRoHoW4UEnA4QNTpkwpd96LigoPD2fJkiU+yJGoqIYNjLRq7k9qUjY6xUmORQda9yytuTk5ZZ7X/frbCfE3E+lvJnntr5W6prlpFH1+m4GxcUNy9/7LxituoyAusVrvQwghhHsCQb1ej16v9xoCvsiKFSs4fvw4X3zxhdcoVvn5+fz4448sWrSoFnMrxIVDmlT5QLNmzap87vHjx2ne/PTwqm3btvVFlkQl9OsZytHjeRRkZqMPDiUhJ5BGfgXk5OQQGhZWatte/+AQfvi/zwnfvhxNwgGcKfFoI5pU+JoBMS3p/8cc/hl6B3mHYtkw+Fb6LZ+JX0vfzeAuhBD1hcvl8lovq7Y/MzOT1NRUWrVqVWrzZZ1Oh7+/P1deeSUNG3oP6nHo0CGefvpp8vPzWb58eanp33LLLVV8B0KI8kjA4QMPPfRQiYfS8PBwxo0bV2pH7eKWLl2Ky+XioYceqsksinL06xXO3J/i2L39FD0uC2V3nB9N2mtwOp3k5+eXOZZ7i76DsOQk4DyyG9uahZiufQBFW/GPlH/rZvT7Yzb/DJtA/pETrB84jt6LPye4RydfvTUhhKgXcnNzvdYtFgsul8srqEhNTWXEiBFkZmYSHR3Niy++yKBBg7zO27dvHxEREUyZMsVre1paGg8++CD5+fnl5qM+D4IiRF0mTap84I477mDr1q2sWrWKrVu3ctlllzF16tSzBhsADz74IC1atOCll16qhZyK0nTtEISfWcvxI2kYtC7ybRqc2kAAssrodFjEcPEIXHoTXy9dxdtPPlzpa/s1b0L/P2YT2KUd1sQUNgy+jeRla6ryNoQQol6yWCx8/fXXXtscDgfffPMNDofDs83pdGKxWAD3nBz33XcfTz/9NMePH0dVVXbt2sWiRYuYMWMGAQEBnvOsVisTJ04kLi7urHmRgEOImiEBhw/06dOHNm3a0KZNG37++WduuOGGSg0rOGLECNq3b1+nhte9kOh0Gvr3DkNVwZHrnlPlYJJ7hCqLxYLNai3zXI1fILtC2/Dq8k18+MPP7Pqz9Gr68pgaR9J/9VwaXHExzrx8tox5kBNfVX+0MiGEqOt69OhBt27dmD59eol9b7zxBl27duXll18GIDIyku+++44RI0bQqFEjdDodK1as4P7772fq1Knk5OTw3HPPlWhK9fLLL7Njx46z5kVRFGJiYnzxtoQQZ5AmVT5w8uRJ9u/fz5IlS4iIiKhSGuPGjeOuu+5i9OjRhIaG+jiH4mwGXxLBqrUpbNlwgh6DQziYoOOixv5YLXlkZWUR0bDsCf4uuXYcdyxZQkutldbxO1AtA1BMlRubXx8UQO+fP2fX/S8SP3sRux98kezdB+n4zhQ0en11354QQtRJ27Ztq9TxHTp04L333qvUOa+//jqvv/56pc4RQviW1HD4wMKFC7n55ptp1KhRtdIZOXKkDMl3jvTrGYbZpCH2aCbBJieqCsn5QYC7bXHxav3SvPr519x6xSWQl4111feoZ3SArAiNwcBFM94g5iV306zj02fzz7A7sSanVf4NCSGEEELUERJw+MD69esZOnRotdPp3r0769at80GORGUZjVou7hMOQG5KOgA7jpswGo2oqnrWvhyK3ojpyltAp6fg+EGWffpOlfKhKAptX5hEr5+mowv0J33dZv7qdz3p6yv3LaAQQgghRF0hAYcPHDt2jFatWlU7ncjISI4cOeKDHImquOJSd7OpNX8cQ6+FjDwFm8bdvC07OxvnWWo5NOFR0P8a7pi1nHte+4AlX39e5bxEjryCi//+Ef+YFlhOJrDh8ls5NO0jXGfJgxBCCCFEXSMBhw/k5eX5JB2Hw0HmWb5JFzWnf68wQoL1JKdYCDMWALAt1uyp5cjMyjprGv4de9Gt20UEGPUo+//BmXL2UVHKEtihNZesn0+TW0aBy8W///2YDZffRv7Rk1VOUwghhBCitknA4QMBAQGcPFn9h8Bjx45hNpt9kCNRFXq9hqsHRwKwd9tJFOB4ioLGWFjLkZV11r4cAC9++DlLpj7M5a0bYVk6C1dW1ftg6IMD6fbN23Sb9Q66oAAyN25nXa/RxH27qMyJsYQQQggh6hIJOHygZcuWPul7sWbNGho0aOCDHImquuZKd8f/v9cn0qyBE4CdJ82YTCZUVSU9Pf2saegNRtre8hCaBo3Akkf899NJPhFbrXw1GTeSAVsXE3pJTxw5eey86xm23/Y4tvTMaqUrhBBCCFHTJODwgT59+jB37lxsNluV08jNzeW7776TMcDPsebRfnTpEITTBRnxKQDsj1PQ+4UBkJuTg7WceTmKKAYTxqvv4IRV4cYP5zD+xuvJSa/eaFN+LZrSf9W3xEx7DEWrJeGHpazpMpz4736R2g4hhBBC1FkScPjA6NGjSUpKqvJs4S6Xi8mTJ5Oenu6T0a5E9Yy5ujEAS36JpXmEiqrClqMm/Atnrk1NSanQA77GLxDDoGvJsdrJzMklefHXqDZLtfKmaLW0ffZB+q+Zh3/7VtiS09gx/gk2X3OP9O0QQgghRJ0kAYcPtGrViuHDh7No0SIeeOABEhMTK3xucnIykyZNYt26dURGRnLFFVfUYE5FRVwxIIKIcANpGTbIcTeh2h8HGkMYiqJgtVrJyc6uUFqtL+rJ7P/7nLn3jibKloHllxmolvxq5zG070UM2LKYmJcfQWM0kPL7X6y5aASH3/gMp+XsNTBCCCGEELVFAg4fef755wkLC2PNmjUMHTqUJ598kuXLl5cafOTl5bF+/XpeeeUVRowYwZ9//omiKDz//POYTKZzkHtRnE6nYeyopgAsXHyMNlHu7esP6QgLd8/VkZ6eXqEO5ADdBg6hxa2PgskPV0o8f3/wMqeOHq52PrVGA22ff4gB234m/PJ+uCxWDr74Pmu6XE3C/N+kmZUQQggh6gTduc7A+SIsLIzPP/+cu+++m6ysLH799Vd+/fVXAPR6PUFBQeh0OrKysrBYTjerKXoofPjhh7nyyivPSd5FSaOGNWLmd8eJPZmPwZqBooRyJBG6tQzEaHT340hLTaVhZCSKopw1PW1EE8yj7mXzZ69z1xc/ETbnFxbMn0+TmI7VzmtATEv6Lp9J/NyfOfj8uxTExrNt3GOEXtKTjm9PIaR312pfQwghhBCiqqSGw4c6d+7MvHnz6NChA6qqen5sNhupqakkJiZSUFDgtc9oNPL888/z0EMPnevsi2IC/HWMHdUEgNnfHaFrc/f21XsUwsLdI4nl5eWRm5NT4TQ1oQ2JHH0nYYH+NAk04/fXT7jSKt78rjyKotD01tEM2ruMti9OQmM2kfH3Vv6+eCzbb3ucvMPHfXIdIc4lqbUTFzr5DIj6SgIOH2vVqhXz58/nv//9b7kjThkMBsaMGcPixYsZP358LeZQVNTNY6IJDNARezIfS2oKJgOk5cC+U0ZCw9yjVqWmpmKvxOhkLTp2ZeHin/n0/psw2AsoWPw5jtj9Psuzzt+PmKkPc9m+5TS5dTQAp77/lTVdhrPznmfJ+zfWZ9cSorZotVqACjdjFOJ8VfQZKPpMCFFfSJOqGqDVahk7dixjx47l5MmT7N69m8TEROx2O4GBgbRs2ZJu3brJJH91XGCAjluui+bzWceYOe8Yzz3bgD/3KPy9H1oOCsZkysdisZCUnEyTJk0q1LQKoFHLNqiNJmFZMQ/XqaN8Me05bJEteeTVd9BofPMdgLlpFN1mvkXLx+7k4NT3SfltDXHf/ETct4tofONw2kx5gMBObX1yLSFqmk6nw2g0kpWVRWBg4LnOjhDnTFZWFkajEZ1OHt9E/SL/Y2tYdHQ00dHR5zoboopuGNmEH3+OIyHJwoHd8URHNOVkKqzYqXBd34bEx8Vhs1pJTU2lQYMGFQ46FJMfpuET2PvDl7z++ze41E10bhjEFROfQTH6LhAN7taBPj9/QcbGHRx+4zOSf/2TU9/9wqnvfiHq2qG0mfIAwT06+ex6QtQERVEICQkhKSmJjIwMQkNDz3WWhKh1GRkZ5OTkEFnBvoNC1CUScAhRDrNJy33jW/LGR4f4eu5xPn+/IYkZBuLTYPcJHe2jGpKYmEhOdjYGg4Hg4OAKp61otXQe9wD/OZ7EjnV/0s9so2DBJxiH3IS2oW+D1NB+3ei96DOytu/j8BufkfjTchIX/k7iwt+JuGogLR+dQIMrLpY/YqLOCg0NxWazkZiYSHZ2NgEBAZhMJjQajfy/9QGn0+mZ1FSa69QNqqricrmwWCzk5uaSn59PaGioBNyiXpKAQ4izGD4kip9/T2DfwRy+mXuUMde354/dsG4fNAn3IywsjPT0dNJSUzHo9Zj9/CqV/l1TXsJx513YVn2PmpNB9oJPWZpr5qbHpqDT6336XoK7d6Tn9x+Ss/dfDr/5Oae+/5WUZWtJWbaWgA6taTHxNprcNhpdgL9PrytEdSmKQlRUFGazmezsbFJTU3G5XOc6W+cNl8uFw+FAp9P5rGmn8A2NRoOfnx+NGzeu1JdaQtQliipDHohzaPfu3QB06dLFp+nm5+ezf/9+OnTogF8lA4DSHDicw31PbMPlgtee60SGJpwjiRDsB7cMUsnJSCE3NxdFo6Fx48YYjcZKX0O1WrCuXch/pv8fszbt5+oeHfli9ndogsOrnf+y5B0+Tuwn3xL3zU84cvIA0AUHEn3nDTS/fxz+bZrX2LXL4uuyE7WnNsuu6AFZgg7fKCgo4OjRo7Rq1Ur6F9YhGo2mQkHg2T57NfW3VoiKkhoOISqgfZtAxl0bzZwFJ3ln+iG++l9vUrJ1ZOXD79sVrunVAIfDgcViITEhgcaNG6M3GCp1DcVowjjkZjrsPIr/jsOMiWlEwfyPMPQZiq5TP5Qa+NbRv01zOr3/AjH/eYy4WQs5Pn02ef/Gcux/X3Psf18TNqgP0XfeQKPrhqE1y6SUou7QaDQYKvkZE2UrCtyMRqNMQCuE8DmpNxWigu66pQUtov1Iz7Tz4ReHGNETtBo4kgh/H9AQFRWFwWDA6XSSkJCA3W6v9DUUReH2J59nw5o/ueLyy8Bhx7b+V/588xk2/LbY5++piD4ogJaTxjNoz2/0/vkLIq4aCIpC+ppN7JzwNCujL2X3pJfJ2rpHxoEXQgghRKVIwCFEBRkNGl6Y3B6dTmH1+lTW/RXPld3c+7Ychj0nNTRq1Ai9Xo/D4eBUfDy2SszRUVx4s5aYRtyJYcAorIqO5+Ys4YZ7JvLjq1Nw5Wb57k2dQdFoaHj1IPos+ZLBR/4k5uVHMLdogiMrhxOfz+Ovfteztts1/PvadPKOnKixfAghhBDi/CEBhxCV0L5tIA/d2QqAT2YcxZWXTf/27n2rdkFsipZGjRuj1+vdNR2nTmErHPmlshRFQd+xL9oxD3JJty5EBfkxKMhFwffvY9v2J6qj8jUolWGObkTb5x/i8oMr6bvsaxrfNAKN0UDuvsMceukDVre/kr8vHsuxD2ZiSUiu0bwIIYQQov6SgEOISrphZBMGXxqB06ny/Ov7aBFipVM0qCos2Qwn0rQ0btzY07wq/tQp8vPzq3y94IZRvDv7R/747VcCm7UBhx375pW8eNt1zP/0fziq0HSrMhSNhgZXXEz32e8xJO5vun75Gg2GXAIaDZmbd7HvyddZ1XwgG4aM59hHsyg4capG8yOEEEKI+kU6jdeyw4cPs2DBAnbu3El6ejpms5nWrVszfPhwBg8efK6zJypAURSmPBxD7Ml8jh7P46n/7ObjN7phd+o4dAqWbIJRfbQ0a9yYpMRET0fyBhERBAUFVfm6wS1iUJu3xXlkF+vmfsXX67ah/Ws7ndVM2gy9Dm3rzihKzX6HoA8JInrC9URPuB5rUiqnfvyNU9//SubG7aSv2UT6mk3se/xVgnt0InLMlUSNGkJAxzYyT4IQQghxAZOAoxZ98cUXfPjhhzidTq+Ot/v37+fXX3/lyiuv5L333kOnk2Kp6/z8dLz9Umfuf3I7sSfzeeH1vbz9cldcqsLhBFj8D1zVQ0O7Jo1JSU4mNzeX1JQUrBYL4Q0aVHmce0VR0LW5iN6PTOOJPC3pxw4RrbVjXfU9yrY/ORLcgvaXX4XeUPlheSvLGNmAlpPG03LSePJj40hctJKkxStI/3srWdv2krVtL4em/g9zy6ZEDLmEBkMuocHl/dCHyjjyQgghxIVE5uGoJStXrmTSpEk0atSIHj16EBkZiclkwmazkZyczLZt24iLi2PSpElMmjTpXGe31tSXeTjKcvhYLhOf2UF+gZP+vcL4zzOd+GO3wqHCVkWDOkOPVpCZmUlGejoAer2eyMIRrapLtVqw71mPfdffpGVkMPjDBYQH+PHTx+/Q+OIhKLUQeJzJmpxG0i9/kLR4Jakr/8ZlK9bkS6MhpHdXGgy5mIghlxDS9yI0Z0xuKPNw1F9SdvWXlF39JvNwiLpOvkr3gZycHAIDA8s95ptvvuGBBx7g0UcfLbN5yaeffsr3339/QQUc9V2blgG8/nwnnp62hw1b0nnx9T28+lxn/IwKO47Bmj2QmQuXdQnBZDKRnJSE3W4nPi6OBhERZ/1/czaK0YSh52D0nfvzz7dfYNLrCDHpCT7wN/nHtqHv0BtH2+74hUf66B2fnbFhOM3uGkuzu8biyM0jbe1mUlf+TerKv8ndf4TMf3aQ+c8ODr86HV2gP+GX9aXBFZfQ4MpL8G/botbyKYQQQojaIZ3GfWD06NFs27at3GOSkpIYMWJEuW3Zb7jhBrKyam7IU1Ezel4UyltTO2MyavhnWwbPTNtN71ZOLu3o3r8zFub/DS7FRJOmTTGbzaiqSkpyMkmJiTgcjmrnQTGaGXzPo2zavoOPX/8vmpAIsFko2L6GywcO5M6Rw0jYsQHV5az2tSpDF+BP5PDL6PTe8wzatZTBx9bQ9cvXaHzTCAwNQnHk5JG05A/2PvZf1nS6ij/bXsGBh6dRsGIDVhn5SgghhDgvSMDhAy+88AKPPPIIH3/8cZmTonXt2pXnnnuOrVu3lnjAVFWVPXv28Mwzz9C9e/fayLLwsZ4XhfLOy10wm7Vs3ZXJQ1N20DzEypi+YNBBfDrMXg0nUrVENWpEaGgoAHl5ecSdPElOdrZPJtTzCwik7ZWjMd/0KMZht7E9X0tcZi6bD/yLaf1iCua8jW3jMvJOHa/2tarC3DSK6AnXu0e8il/Ppf/8RLtXnyD88n5oDHoKjseTMGshWS9+xPr2w/gj5gp2THiaE19+T87ef1ELZ0MWQgghRP0hfTh8JCkpiWeeeQabzcZ7771HVFSU1/5jx45x4403kpubi0ajISQkBKPRiMPhICMjA4fDgclk4ttvv6Vz587n6F3Uvvreh+NMBw/n8PS0PaRl2AgPNfDyUx1o0TKYnzdBWo77mM7N3H07cNlISU72TA5oMpkIb9AAo9G3/S4ObNnAoXWruCJMAYt7eN6bv16KS6vn1Wee4KJho1CMZp9esyocefmkr9tMwrI1JP7+F44jJ+GMAEMfGkxo/+6E9u9OcK8uhPTsLJ3Q6xDpB1B/SdnVb9KHQ9R1EnD4kKqqfPnll8ycOZOpU6dy1VVXee3ft28fU6dOZc+ePSXObd26Nf/973/p0aNHbWW3TjjfAg6AxGQLT0/bw9HjeWg0cO9tLblpTDQbDsLWI+5jgswwrAc0DYeszEwyMjI8NRyBgYGEhoX5fLQy1enAeeIgp/5ZzYCnXselwtrHxtIwOBBt0zYkBUQS1O4iQhtGnT2xGlRUdm2bRGPdfYj0v7eS8fdWMjftwplfUOJ4vzbNCenVheCeXQjp1Zmg7h3R+csD07kgD631l5Rd/SYBh6jrJOCoAbt27eLJJ5+kT58+vPDCC5hMJq/9hw4dYseOHWRkZBAUFESnTp3o2rXrOcrtuXU+BhwABRYn70z/l+V/JgFwca8wnnusPbl2Hcu2Q3bhPIBdmsMlHcCgdZKelkZubi7gHv42KDiYkJAQtFqtz/OXfCKWDUvmc2VDA2q6O49PLVrHr3uOMnXcSG6/6250zTugmP19fu2zKavsXHY72Tv2k/73NjI37yRry27yj54smYBGQ2DHNgT37Exw904EXdSewK7t0QcF1OK7uDCd68+dqDopu/pNAg5R10nAUUPy8vJ46aWX2Lt3L++//z7t27c/11mqk87XgAPcNV6//J7I+5//i82uEhKs5+G7W3PZpQ1Zuxd2F3ajMOqgf3u4qCXYbVbSUlOxWq2AO/AIDg4mODgYbQ3Mz6KqKmpGMvYju7nx8efZcjSOuROuplezSFAUYlUzy4+mcOWoMXS+eFCtTOBXmbKzpWWQtXUPmVt2k7VlN5lbdmNNSCn1WL9W0QR2bU9Q1/YEXeR+NTdrjFLFOVFESXXhcyeqRsqufpOAQ9R1EnDUsEWLFvHGG2/wwAMPMGHChHOdnTrnfA44ivx7NJdp7+7n2Al3tUaPriE8+WBbtGYzf+6G5MKByYL9oX87aNdExVJQQEZ6uqd/B0BAYCDBwcE+7+Phldftm4l25qDG7sOVlsDnf+3i3T+2cXnbpnxx97Voo2PQRrclP7AhQQ1rZqjd6padJT7JHYBs3U32zgNk7zqAJS6x1GO1/n4EdGhNYIc2BHRqQ0CHNgR2aI25eRMJRKqgLn3uROVI2dVvEnCIuk7m4ahhY8aMoUePHjzxxBOsW7eOt99+m7CwsHOdLVGL2rYKYMb/evLdojhmfnecbbsyuePhLdx6fTS3XN+Mw0ka1h+ArDxYtg02HVLo396PNo3NWAryyczMxGq1kpuTQ25ODiaTieDgYPz8/X1e49C2e2/3Qq/BuHIz6aCbw5AMC0NaRKDm5+A4uJWs3Rvp8/Y82kQ2YN5rzxHatjPaRi1RjKbyE68lpiaRRDWJJGr0EM82W1oG2bsOkL3zIDm7DpC9cz85+4/gzMsnq7B2pDitn5mA9q0I6NiWgA6tCYhpiX/bFvi1aY7WWP0JG4UQQogLiQQctaBZs2bMmzeP999/n1GjRvH6668zYMCAc50tUYv0eg3jxzbjigERvPfZYTZuTWfm9ydYsiKRCTc15/bLo9hzUmHzYUjPhV+3QLCfQo/W/nRu5o/TYSUrK4u83FwsFgsWiwWdTkdgYCABgYHoz5it2xc0ASFcffdDXH33Q6hOB66EWBwnDrJ3zZ/YnS5y8vPxO7oD69EdoCjM2HmCJDvcdMMNdLrkMjT+QT7PU1UZwkNpcHl/Glze37PNZbeTf+QEOfuPkLvvX3L2HSZ332HyDh3DmV9A1ra9ZG3b652QRoO5eWNPAOLfpgV+raIxt2iKX4smaM11I+gSQggh6hJpUlXL1q9fz5QpUxg+fDhPPPFEjTwo1icXQpOqM6mqypr1qXwy4ygJyRYAGkWaGD+2GZddGsnekwrbj4GlsDWVSQ8do6Fzcwjxc5KdlUV2djauYkPGmkwmAgIDCfD3R1MDnczPlBJ3nBM7NtM5SIfz1DHUrFSunr6QI6lZTL9pMEPaNUMJDCVBF8if/8bTo19/ug+8AkV/9tqBc112LofDHYjsO0zuvn/JPXiMvEPHyPs3Fkd2brnnGqMiPMGHX8um7uWWTfFr0RRTdCM0NdAPpy4512Unqk7Krn6TJlWirpOAw4cyMjJYu3YtSUlJBAUF0bdvX1q2bFniuPT0dJ577jmSkpJ47733Sj3mQnEhBhxFbHYXS35P4JvvjpOeaQcgLETPDSObcM2wxpxI07H1CGTlnz4nKsQdeMQ0cmG35ZObk0NBwemhYhVFwWw24+/vj5+/f42McFUaZ04mC2fPZNM/G3lkUHdCbTmAyndbDzL11w1c3LIRM8dfhRLSAE2DxizZfYTIFq3pddkQ/EK8mxjW1bJTVRVbchq5h46RdyiWvENHyTtygoJjceTHxp01GFG0WkxNozC3aIJfYSDiDk7cy8aoiHrfb6Sulp04Oym7+k0CDlHXScDhI7NmzeL999/HYrF4timKwrhx43jxxRdLPefbb7/l448/5sknn2Ts2LG1ldU65UIOOIoUWJwsXnaKHxbHk5zqHp3KaNBwxcCGjBrWCHNIIHtPwNFEcBV+WnVaiGkM7ZtCoxAHBXm55OTkYLfbvdI2m834+fvjZzaj0+trZZQpANVmwZl0kmWLfuK7pcvp2Tic+/rEAOB0uejx5lwK7A5+mziGNi1bomnQmMN5ThJsKu269yElJ69elF0RVVWxZ2R5go/82Djyj8VRULhcEBuPy2orNw2NyYi5eWPM0Y0xNWqIsVEExkYNMTVu6F5v3BBjVESd7kNSnz53wpuUXf0mAYeo6yTg8IGffvqJ5557DgB/f38CAwMpKCggKysLRVF48sknufvuu0s998CBAzzxxBO0adOGV199lYCAC2uuAAk4TnM4XKxal8Lcn05yJDbPs71NS3+GXhZJ/z4RpFmM7Dnu7udRxKiHVpHQtpFKo1A7toI88vLyvEa4AtDpdPj5+WH288NsNqOp5W/TXfk5uFJPkRH7L1Pe/ZgjJ0/x870j0Bbm4+2VW/hy/R7G9WzHi2MuQxfeGG14JN/9vZ02HTvRd9AV6AKDay1o8iXV5cKamEL+saIAJK7YcjwFJxNKzKpeFkODUE8gYoyKcL82aoipceTpwCQyHM05aK5ZHz93wk3Krn6TgEPUdRJw+MCoUaNo1KgRzz77LC1atPBsT0hI4N133+Wff/5h3bp1ZZ5vsVh47bXX+Ouvv3j77bfp2bNnLeS6bpCAoyRVVdlzIJtFvyXw51/J2OynP6Kd2wdx+aURdO3akPhsPYcTIN96+ly9FppFQIuG0DTMjlbNJz8/H0tByRm6DQYDJrMZk8mEyWTy+czmFaFaC3ClJuBKPcX0md+yYM0G7urTjrHd3bUhidl5DPzfj2gVhZ3P3YbBLwBNaENWHU7gZE4BAwYMoGOP3iiBoSi6+tsfymW3YzmZSH5sHJb4JCwJyVhPJbtfE5I96y6b/eyJASgKxobhGKMiMBbWkJwOTBp6alD04aE+rTGpz5+7C52UXf0mAYeo6yTg8IGuXbuyevXqUoe7tdvt9O7dm3Xr1hEYGFhuOr///jsvv/wy69evr6ms1jkScJQvO8fOqnUprFqXzM69WRR9WhUFLuoYzMV9w2kT04A8l4nDCZBzRlwR4g/NI6BpuIsI/wJcjgLy8/NxOBwlrqXX6zGaTJhNJowmE/pabIJVXF5WJrE7NtMi1J/4w4eY9uUsbBYLX988GHDfgEk//MnvB47zwrA+3N63IwCpLh2Pfb+S1tFNeHXyRLTB4ShBYTjNQRgCyv/s1QeqqmJPz8RyqjAI8QQkKWcEKCmopZRvWbQBfhjCQzGEh6AvfDWEh6IPD8EQFoKhQcllrZ+51P8b58vn7kIkZVe/ScAh6rrze8iUWhIeHs6ePXsYOHBgiX3Hjx/H6XTi7+9/1nSGDh1K165dayKLop4KCtRz7fDGXDu8MalpVv78O4U//kph9/5sduzNYsfeLOAoDRsY6dsjlC5dIwgICyIhS0tCOmTmuX92xmoAf0L9/WnaABqHOggzW9ApFqwWCzabDbvdjt1uJzcnB3D3QTIajRiMRoyFP7URhCh6A5aAMJQ2HYjpejGzr5sAgOqw48pKRc1I4dJEF7qgnXTp0A4MRrBZOXYyjk3/xpKQmoZj8wqKHrkf+fFP1h9L4KUbhjHm8ktQAkLIUfT8te8Izdq0pVvf/ihmfxSlbnfYVhSlMDAIhS7tyjxOdbmwpWa4A5PEZK8ApXigYktKQ3U6cebmU5CbT8Hx+ArnRWM0uIOPsBB3gFK4rAQHkGe3ktjhCAGNIzGEFQYrDULRBQXUy+ZwQgghqk8CDh+48soreeyxxxg9ejRt2rTBbDZTUFDAkSNH+PXXX7n00ksr3F4+KiqqhnMr6qsG4UbGjmrK2FFNSUy2sHZDKhu3pbNjTxbJqVaW/J7Ikt/dM2q3bObHRZ1DadEmHFNQABkFOlKyICPP/bP7uA4IQK8NoGEINApxEhloJcBQAE53AKKqqmfOjyKKomAwGNAbDBiK/Wi12poPRHR6tOGNILwRd7/UlaJeUaqqgiWfDrGH+V/Tzjjzc9C17YArOx1XdhonMnLIttjws+fhPH4AgL0nknho5m80DQngj0duAK0OxS+Qd1du5kRGDhNGDqN3jx5o/IOwaPUkZucT1aIVASF1f9JORaNxN6dqGA50KPM41eXCkZWDLS0TW2oGtvRM7F7LGe59aRnu7YWvLpsdl9XmbvoVn1Rq2vtLy5dOhz4sGEODUAxhIe5AxFOzEoI+NBh9cCD6kCB0QQHu1+BAdMGBdbqjvBBCiLOTgMMHHnnkEdatW8e8efO8HrpUVSUsLIznn3/+HOZOnI+iGpq4cXRTbhzdFKvVyY69WfyzNZ1N2zOIPZnPsRPuH3B/a92wgZH2MUG0aN2AwLAAnFojGXka7E6IT4P4NC3gB/hh1EPDYJWoIDvh/lb89Va02LDbraiqitVqxWq1euVHo9Gg1+vRGwzuV70eg16PTq+v8c7piqKA2Z/IDhcxtsNFJfYvHPUAcYcPEulvxKDaUXMy0bq207N1NFH+Znf7NKcDNSeDv3bvZ8+pNEa2DMduTwZgy/FEbv1mGc3DAlnx+C0ofkEofoHMXLeNlDwL1w29gnbt26OY/bGiJTXPQoPGTfEPDqnR911dikbjfsgPDca/TfMKnaOqKs68fGyphUFIemGAkpaJPT2T/MQU0mJPYLa7cGbmYC/c78wvQHU4sCWnYUtOq3ReNSYj+uBAdCGB6IMC0YUEoQ8OQBdc+BoShC7AH13g6R9tgD+6AL/T64EBErgIIcQ5IgGHDwQEBDB//ny++OILli1bRkJCAmFhYQwaNIiHHnqIhg0bnussivOY0ailb48w+vZwf/uekWVj975sdu3LYue+LA4dySU51Upyagpr16d4zgsL0dOhYzjRLcIIDPXHqTWQXaDBalc4mapwMtUAGAB3/wd/k0qTEDsRgTYCjXZMOhsa1YbLacflcpUaiABotVp0Oh06vd79qtOhL3ytjYAkICSM9r36e227tO8wLn1wCgCq04man42al83j/i2JPXaMLt06ovXTo+bnkBufhb9RT2SgH9isqLYU1MwUFv25jj2n0uhutNMy0T0j+cZjCdz+7XJaNQhm2SM3opj8UUx+/O/3DcRn5HD7yKF069wZxexPrhP+jU+iYZOmNG/jbhpW15scKYrifrAP8IcWTUvsL6sdudNiLVZ7knFGTYp72Z6Vgz0zG0dmDvbsHByZ2Thy3KO1uSxWrBYr1qTU6uVfrz8dlAT4o/Us+3mvF+53ByrF1gP8vI45FyOBCSFEfSQBh4/4+/szefJkJk+efK6zwooVK/j66685ePAgWq2WPn36MHHiRDp27Fil9Gw2G99++y0LFiwgISGBwMBArrjiCiZNmkR4eLiPcy+qKzTYwMD+DRjYvwHgnufjwL85HDqSy6GjuRw6ksPxuHzSM+38vT4R1id6ztXqFFq1DqVZ8xBCGwRgCjDj1OixOjTkWRQOJRo4lOj9LbFOoxIV7A5Egkx2/PR2DBo7CnZQXTidTpxOZ6nBCLhrR3TFAhDV5UKv12O32XDUQnMtRatFCQyFwFCG3XZPif3XjIRrpn2MJScbg9OKmpeDmpfNzWkajsQep03PvmhD/FAteeQfT8Og0xLmZwKHHTU3EzU3k9Vbd7EnIY2rooOx558CYOuxBO74djmtGwTz28RrQaNFMfnxwuK1HE3J4LFrh9GvaycUg4mk3AJ+2bCVyMhIRl81FMVgBqOR1Ox8dCYzQQ0aojcaa+weVZfWZETbJBJTk8hKnac6nTiycwuDkRwc2YVBSVau+zU7B3tWrjs4yc3DkeP+cebkea27LO7/e6rdjj3dXRvjCxqjwR2U+JnR+pvRmk2nl/0K14uWvbb5ofUzlb7Nz4zO34zGz4zGcG4GbhBCCF+TgOM88+677/LFF1/QvXt3/v77b5KSkhgzZgyrV6/m/fff58orr6xUehaLhfvuu49//vmHCRMm8Oyzz7J06VImT57MypUrmTVr1gU9U3p9YDZp6d4lhO5dQjzbLBYnh2Pz+LcwADkcm8eJuHzy8p38ezCdfw+me6Wh02sJjwigSbNgGkT44x9kRmcy4lJ0OFwKcRkG4jLObK6iYtC6CDTZCfVzEGR2EGBwYNI5MGgdaBUHCi5cLhc2m81r3pAAf3/S0tJIS3M3v9Fqtad/dDp0xZaL79NoNDX2gGYKDHIvhEQAcOdz3UocM/J6GPGKC1teLgbVgVqQh2rJYyKRHD95go59uqELNKFa8lDTrDQNDaJxSOEIWi4nan4Oe4/HsedUGrknj+I0u4fBPXj0FK/O/p22ESFcpT3dJGnSt8tZfyyBd64dwKjuHVCMJv5Nz+WFH3+neWQE7zx4O4rBhGI08fvWvaTl5nFx7160bN0axWDCpkJSegaBwSGENYwCXd16wFW0Wk+zr+pwORyFQUj+6aCkWEDiKBageLbnFgYuJfbneyZxdFlt2Kw2IMMH77YkRav1BCHugMaMxmRAYzKiNZvQmAxoTUY0xtPrp/cZ0Rq91zUmQ7F93us21Ykr34KrEiOcCSFERUnAcR4patYFcOONN2IymWjevDkDBgxg+fLlTJ48mQULFtCuXdkj3Jxp6tSp/PPPPwCMHz8egKuvvppp06aRnJzM3XffzW+//YaxDn+7KkoymbR0bh9E5/ZBnm2qqpKeaed4XD4n4vI5HpfP8ZPu1+RUK0mnskg6lVUiLbO/kYBgMyGhfoRF+BEcYsbkb0Jn1GNDR1qelrS8EqcBoNO4MOsd+OkdBBgdBJvt+BnsGDRW/AygVZzuLhaFtSQVodFo3MGHVotWo/G8erYVBiZFr0VBiq8etDUazengJMjdzG30/e1LHHfl1XDlc+8AoNpt7iDEkse06N4kJiTQvX1rDAFmVKuFCP9/GXNJOhEBfmiimqPaLGC1UDRFS6DRAA4bqsNGyqkEth2NIycnF+fhnZ7rfTPHHZy8PWYAjbu2BmBXfCo3/N8vNAryZ81jY90H6vQ8u3gd/xw9xZMjB3FNn4tAZyAxO5/XFywjIiSYqXff4p73RG9g7c59xKWk06f7RTSPjsYvKwlrnJEjqWn4BwbTolUrFL0BdAaUWp5ssohGp0Pjg8CliMtm8wQvzpw8nPkWnAUF7te8fPdrfgGO/AJc+QU48go8207/WHDmFZS6rWhYY9Xp9AQ8tSUZd6DjDlSMhYGK0R3YFH8t2mc0ojEa0Bj0aPQ6FIO+2LIBjd69ruh17u2eZYP39hLHGUo5R49yjobrFkJUjwQc5wmbzcYnn3ziWW/WrJlnuXlzd4dQu93O+++/z2effVahNA8fPsySJUsA9yzVTZu622wrikLz5s3JyMggPj6eOXPmcNddd/nqrYhzRFEUwkMNhIca6FGsNgTAbneRlGIlIdlCYpKFhGQLCUkWEgtfU05lknIqs0SaGq0Gk58Bs58Bk58Bk58Rs7+BgCAT/gFGzH4GbEY9OVoDSbklTgdUjFonJr0To86JSVf06sDP4MRscGLUOjFonWg17pm6XS53rQn2Ck6SV6h4AKIpDFS81kv5URTFvawoKNWoXVH0BvdDeWAofYeX7BvRrc+VfHLbxBLbF932NHabFWxWtC4HqrWALokJfN6+LzpVxdCzC6q1ANVmoVe3RPyCQ2jeJgZNeASq1YJdl4VZr8PPUOxPgcNOYkY2cRnZ2LIzcSXHAZCSkMav/+wgMtCP5/qertWc++OfLN9/nKlX96V57w40A2I3/sZVn/xEgFHPtmdu9Rw7delGlu09xqPD+nPbwN4oOj2ZNgePz1xEgJ+Zjx+5yx2Y6A2s2bWfQ3EJ9LmoC906dUDR6rG5XKzbuhOjycwlfXu7H0q1etKzsymw2QkJDSMgOAS0OncTtRp6MNUYDBjCDBjCQmokfZfd7h28FAUmBRacFisuixVngQWXxeZetlpxFVhwFq579ltt7uM95xUebyl2boHFfU6xSSVVp9N97bz8Gnl/1aXoSwtuCpd1+tPLpQU6+pLBTInjdGVsLx4YGfTuQNZQ7Hp6fal5UnQ6CZLEBU8CjvPEhg0bOHXqlGc9ICDAs2wwnG7qsnbtWnJycs46CSHATz/95H5wgxITCRVP85dffpGA4zyn12to2thM08bmUvdbbS5S06ykpFlJSbORmm4tXLeRnmkjM8tOZnoBCcftFP6X8qLVFQUmxsLA5PSPwajHaNJjMuvRGw3o9KX/2lJQMWidGHQu79fCZXdg4sKgc2Is3KfTuCh6DqhMLUpZimpKigcjntdigUmJ1zOO96RTLJApemA588FFbzC65yIpFBnRhGu69CqRt2cuuabEtoHA4akf43I5UZwOsNtRHTbe6DuGtJQUmkU2xBjkDw4bTZKSmKoNx6BR0HcfiGq3g8PGRV1TcRr9aRnTHho0wZKXg8PkokGAHwFGPaBQNGFjVr6FzAIrTksBamYKKpCZns3aPYfwN+hwHNruydsvS/5m/vZ/mXx5DzqmuucnSssr4K53vwfg4It3eO7Fu8v+Ydam/TxwaVceH9wDgAK7g95vz8Oo0/LXs3fiZzaDTse3f+9k0Za9jOrTlQlXXoqi1aFqtDz51fcY9HpemHAjAQGBKFodWw4eYcuBw3Ru25oBvbqDVgsaLb+s/guNTs/gS/ph9vND0ehITEvnVHIKDSIa0LxZc9BoQaMhKS0dvd5ISFgoWr3BnYZSfnCq0evRBOvRB9fehJV5ubns37mbts1bYNRocFnOCFasNk9wUhS8uNctuGx2VLvDPWSyZ9l2etluP2Nf0XLhdvvp7aqt2LbC5TOpdjtOux3yC0p5J3WToj9bEKT3HBM2sDftXn70XGdZCJ+SgOM8sXHjRq91fRmjpzidTjZu3FihvhxFTanKSw9g//79ZGRkEBoaWsHcivON0aChSSMzTRqVHpAUcTpVcnIdZGYXBiFZdjKz7Z7ljGwb6elWklLSSLdpycp24HSqXmkoGgWDUYfRpMdg1GMw6TEYdRhM7sBEb9CiN+jQGXTo9Vr0RiN6gw69QYdWe2aTHhWdRkWvdaHXON2vxX80LvRap+cYncbledUV7tdpVE/Qoqoqqqp6AvWaoCgKFAUjZwQ4pf6Usq/oXIpvd99cFL2Zxu0607i9e7urcF/D5h25u+9gr8BHURQeGTjGsy0/P5/YwlGqdj7+uuee4HSA3cZrw+7kqdRUwgIDMAX6odptRGZm8rY5GtVpQ993INjdTcN6Jtpx+AXToWsXtM1iUJ0OlIxMujZrhNPlQhPSwD2csdOBotVi0Gox6rSe+2R1OLEV/ujtBahOd8fxuPhT7Dp+ij6Nw3DFHQbA5nSy6O8tADzdpwUOkzuAW7N6Ox+v3cktvdrT1xrnSfuxV77B4VJZ+9hYooLck7rOX7+Ht1ZuYUzX1rw1ZoDn2MFvzSXbYmP5Q9fSMtzdpGvelgO8unwzV3VqyTs3XYmi0YJWy83TfyQj38L0u66jTZNI0GhZe+AY/7dyPd1aRfP4tcNAowGNlte+W0J6Th6TrruKlo0bgaJh34l4Fq3bRMvGUdxy1eDCYzX8uGodWbl5XD3gYpo2igRFQ0JaOht27qVBWCiD+vYGu51ASxp7tpzE7nLRsV1bwsPCITyY3IICTiUkYg4PpGXzLu50FQ0Z2dmoqkJAYABGk3uIaZeqYnM40On06Az6ak+oqaqqeyQ5TyDiKFy2lQxgHEXLNq/tJY8rY3thoOMJhooHQZ5l93VdDocnT6UFUSXeR2GQ5KxA67is7Xtp+/xEGQVNnFck4DhPbN++3Wtdpyu7aHfs2HHWgMNqtbJ//+npu8pLz+VysWvXLgYNGlTB3IoLlVarEBKsJyRYD9GlH1N8aFWz2UxevpPcPAd5+Q5y85yFr47CbcX3WcnLzyM31UFm4fb8AgcFFpcnaNFoFXfwodehN+rQG7To9DoMRh1anRadXotOr0HnWdai02nRFlv2bNcXPdyqaBQVrUZ1ByIaFW3hq07j8mw/89VznOJ9vEajoi1Mryjd4lRVBVWlenUxvuXOoTvoCAwMJiEhqfBBUyn8VxTcmDBGRJOvKBQ4QdGaURqEMOjGligKpHkCIrh6Yh+GFy4XFAZHgcC8kXehAHZPkKQwZcwkni3Ki8sJLieBNhurL7sFa0E+umZNwOlAcbm4se2l9BsVR9PIBmiaNQWnA63VypS7nNisVvx6Xu5+nnY66ZCt5XrVSPcObdC07OTu2O900LddS2x2J/6Nm6MxGVBdTvyD42gaFkSD0GAw+UNhPtTC4tMV679ic7qwOZ24XE6wFlBUwrEp6aTlWXBkpuAyuPtxJMQe4e/9RzA6bThPHPSksXzjVuIycxnXriHRWe6h1w/tOcpXS9bSr0Ujxkaevt5XcxdxKDmTtpZkGrZqDMDOgyd5/PtVXNSkAf3udtd+NQUm/98v7IxP5dObBnNFO3fT3E1HTzFh9u/ENAzhlwfGeNK9b9ZyNsYm8N51A7mmcysAdsQlc+OMpUSHBrLq4evdByoaHvp+FWsOxzFt5KVc16M9KAqHUzK57avFRAT68cvkW0HRgKLwys+rWbM/lklD+zO6d2cURUNCVg4PfrUAf5OROY+5a7e0isKXK9azbt9hbry0FyP7dgNFIbvAwpSvF6DTavjooTtAo4CiYfH63azfd4ghPboy9FL3sVa7kzfnLUKr1fDMrdejNxhAUVi3cy9bDxymR4dODOxxESgKKgozFi9FUTTces1QTCYTKAp7Dsey78gxWjeLpmeXjoCC6nTx2+q/UF0ql3bvikmjx+V0EpeQxPH4BMIDA2ndMAqXw4nL7mRfbCxOh4NW4RFE9O8rwYY470jAcZ5ITk72Wi9vboOikX/Kk5qa6tW85GxzJVQkTSEqS1EUAvx1BPhX71eV3e6iwOKkwOLEYim2bHWSX+DEYnVRUHB6W0GBnQKrC6vViTXfhdXmIsfmxGpzYbW61202Fw6nilNVcKnuhxGtVoNWpykcQatoWYNWp0WjLb7u3nb6eJ3nPI1WQaPVoNGcftXrFHQ6Bb1ORa9V0OlwByOKWixAcXkClDP3lQhiFHdgo1Hc6SiKioZiy4p7X1nLJcoJKAo7dFoFcIFaWMuj4nmgVkueWqO0gcH4BQaTZHGB+x3i3zyGDs1jAEgoOtAPRkx4AIDiv8m6XteJrtfhfSzwxucDAchTIRdAhUHdrmHgw+7leEAtvCuLfrsD1eVCVV2cVEFRnVzcLY/vx2Wh1+uIDwxCUV0oqotXX+yA1WpB36olSUYDiuqiTVgXpkZ3pUFICKkx7VFU9729/ToXOXl5+HfpTUawO42GNOT2PD1NGoSR3aQLqCqK6uKS7rG0Sc8ksEUH8iIiUFQn5mwNF7dvTYuIcCwhjcHlwuV00LRhBAUuBb+QBjj8QkB1oZiyaBDgR4i/Hy6d0b1NdaEWlqi22N8Hp6swuC/+/0R1eWqbVKcD7O7aJltBHul5BegUUPOyPYcnpaYTm5pBdkYaakYyKpCflsWek4kEGPW4kk54jj105AjrDxzlkiahOBu7m/7m5uTz+7Y9aBUF59HdnmO3bN7M/C0HiFQtDA5010LkWazMXLoKgMldo9xN3oC1K7bw1YY93NWvE/2d7r+vdqeLV778FoBRwTY0ZndN2DJPTVg7ugw/PefPo6/Nwu5ysfaxsZiD/NECKwprwkZ3bc3bxWrCJn7mrglbNvFaInckoHZ8CkV7usZOiPpOAo7zREaG97CM5bUPTk9PL3NfWemdLeCoSJplUVWV/Hzfdk4sKCjwehX1R02VnU4Lgf7uH9AW/viOy6XicKjY7C7sdhW7w/1qs7uwO1Tsdhc2u/u1aN1udxbb5z7e4VRxOlUctmLLDhWb073ucLi3OV3gUMHlVFABpwtUVcGp4g6AVPdDrwugMCBCKXoQPqOplUZBoylqXuVutuZuqqV4LysKiga0GgWtTkGnUdDq3Ou6wletVnWvu7s7uLdp3M9xWncLH/eyAhqN4g6CirZrioIe96sG97JSFPQUrZd4dR9T+X1F6buX3Q/Jhdcvtq8sRelTeJ63M9c1nle9OYSgBiEl0ut0sfcElSrQuFFTGvfoDeBVq3X1hBjPctGA0s3bduXeIe7aiuKfnrs69fMsF43N0KobvHndBACKjz03pf8oz3JR8NXqElh4u3uOqZRix74z2B1MoaokKYCq0sjhYPn1D6C6VJL9/VBUFVB5qvMwHrFYCAwMINVsBlUl2FLAt92uRKtAWnThYAmqiwlR3RiTmUWTqIakh4WiqCqGggLeC2mDRoHMrl3dd0dVucbQlB6DkolpHk1Wk8agqmCxMOUuPagq2TH9UFBBdXHx5QGEtWxLt7atyG3VClQVq9XKXSOScblcWFp0wa4ooKp07OZkrCGATu1bk984BkVVsTsdjOhzkXtgisatsOjd12jSIouBafm0bNESa1gTwIWiqnRv1RS7w4kS2hC7v/s9B4WGERPVgKgG4Tj8gj3vIyokEP8CKzo/f3L9GuDKy0erq/jvqLP93lRVVTqui3NKUVW1tr90EjWgc+fO2IuNyrNq1SrPqFIfffQRH3/8sWffwIED+fLLL8tNb9u2bYwbN86z3qRJE/744w/P+vjx49m0aZNn/cknn+Tee++tdL53797tNf+CEKJ2uPubgMsFLvfzGK7Cdff2wv1e24rWzzjX63y1jPSKjlVLbit8dRamq7oUXO5wAJfL/aoWBlLuL9BP1yqpRcGUqrqbvajF61vcwRhF2xSlcIfi1QzMfXzxhzHv/d6BWGELncIfjaJ4XtG4m495jqUwYFGKpYHibrKlFNtHsTQU1XOsO221sEkaxYKjYstn/lB0TtE293rRdYoCOoreB8XzUNQfqfAcCvMEJfYVz4fnThVbLv5sW9YxFL03z3JZx1GBfarXtco/Vy2xr1iy3seUdd2yrle0r5y0lWLBqPd297nJ2XoMziR0Ot8GCAaDgS5duvg0TSEqSmo4zhNBQUEVbtZUkc7dwcGVG6++Oh3G9Xo9bdq0qfL5pSkoKCA2NpYWLVpgNpffkVnULVJ29ZeUXf1VvOxMJlNhDRmFX8CrnkfkwgoLVE4HrcUVXz+9rHqte169d1P0/afXtYrvx/u4ktvP2F9ie7FtZ+x0uby3e52jguuM91B0oKvY8Z6fMq7tvd29wXXmvQDaRhsICgynMs722Tt8+HCl0hPC1yTgOE9ERUV5BRzlVVxFREScNb3IyEgURTn9B+AsFWEVSbMsiqKUGHbXV8xmc42lLWqWlF39JWVXf0nZ1W9llZ80pxLn2rmZ9lX4XNeuXb3Wy5tPoHv37mdNLyAggFatWnnWHYUz35ZGo9HQrVu3s2dSCCGEEEJccCTgOE9cfPHFXusWi6XU4zQaDb16nZ4U7MiRI1x33XX06dOHDz74oMw0y0oPoF27dpVugiWEEEIIIS4MEnCcJy677DLCw0+3+czKOj3uSPHajkGDBhESEuJZf/HFF9m7dy9ZWVlMnz6dDRs2ePZdf/31nuXc3FyvdIovjx492mfvQwghhBBCnF8k4DhPGAwGJk+e7FmPjY31LCclJQHuztmPPfaY13n79u0rc71Dhw6MGuUeItHlcnHixOmxzxMTEwFo1qwZN910k0/egxBCCCGEOP9IwHEeGTt2LBMmTABgwYIF5Ofnk5SUxMqVK9Hr9bz11lu0b9/e65wz1zt27Oi1Pm3aNHr27AnAnDlzcLlcrFmzhvj4eCIiIpg+fbp0MBRCCCGEEGWSUarOM88++ywXXXQRs2bNYtCgQWi1Wvr168dDDz1UIrgAeOWVV3jqqaeIi4vjtttuo39/74mnzGYzM2fOZMaMGSxevJjevXsTEBDA+PHjmThxImFhYbX11oQQQgghRD0kAcd5aPjw4QwfPrxCx7Zp04aFCxeWe4zBYOCBBx7ggQce8EX2hBBCCCHEBUSaVAkhhBBCCCFqjAQcQgghhBBCiBojAYcQQgghhBCixiiqqqrnOhPiwrVt2zZUVcVgMPg0XVVVsdvt6PV6FEXxadqiZknZ1V9SdvWXlF39drbys9lsKIpCjx49zkHuhJBO4+Icq6k/bIqi+DyIEbVDyq7+krKrv6Ts6rezlZ+iKBJIinNKajiEEEIIIYQQNUb6cAghhBBCCCFqjAQcQgghhBBCiBojAYcQQgghhBCixkjAIYQQQgghhKgxEnAIIYQQQgghaowEHEIIIYQQQogaIwGHEEIIIYQQosZIwCGEEEIIIYSoMRJwCCGEEEIIIWqMBBxCCCGEEEKIGiMBhxBCCCGEEKLG6M51BoTwpRUrVvD1119z8OBBtFotffr0YeLEiXTs2PFcZ+2CYbVaufjii8nNzS3zmDvvvJMpU6Z41m02G99++y0LFiwgISGBwMBArrjiCiZNmkR4eHiZ6aSlpTF9+nRWrFhBTk4OUVFRXH/99dx+++0YDAafvq/z0bFjx5g9ezarVq1i9erVZR5X2+Wzf/9+pk+fzqZNm7Db7bRr144777yToUOHVuftnncqWn7btm1j3Lhx5ab16aefMnjwYK9tUn6+lZqayhdffMEff/xBYmIiISEh9O3bl3vuuYcOHTqUeo6qqsyfP5958+YRGxuL0WhkwIABPPzww0RHR5d5rby8PL744gt++eUX0tPTCQsLY8SIEdx3330EBASUed7Jkyf55JNPWLt2LQUFBbRo0YJbbrmFG264AUVRqn0PxAVMFeI88c4776gxMTHqTTfdpBYUFKixsbFqt27d1E6dOqm///77uc7eBePXX39VY2Jiyvzp1KmTmpCQ4Dm+oKBAHT9+vBoTE6O+9tprXmlceuml6tGjR0u9zokTJ9QBAwaoMTEx6ooVK1SXy6W+9NJLakxMjHrbbbepeXl5tfJ+6xuXy6WuXr1avfvuu9V27dqpMTExas+ePcs8vrbL548//lA7deqk9urVSz158qSam5urXnfddWpMTIz6xhtvVP8G1HOVLT9VVdWpU6eW+5m8+uqrVZfL5XWOlJ9v7dmzR+3Xr1+ZvxN/+eWXEuc4nU71iSeeUGNiYtSHH35Ytdvt6rZt29R27dqp3bt3V7dt21bqtdLT09VrrrlGjYmJUWfOnKmqqqp+/vnnakxMjDp8+HA1NTW11PN27typ9ujRQ+3YsaO6a9cu1Wazqffff78aExOjPvbYY6rD4fDdDREXHGlSJc4L8+fP54svvgDgxhtvxGQy0bx5cwYMGIDdbmfy5MkcPHjwHOfywrB48eJy9w8fPpyoqCjP+tSpU/nnn38AGD9+PABXX301oaGhJCcnc/fdd2O1Wr3SsNls3H333SQlJdGkSROGDBmCoijccsstAGzatInnn3/el2+r3rNarcyePZuRI0dy3333sW7dOlRVPet5tVk+R44c4ZFHHsFut3PFFVfQtGlT/P39ufbaawGYMWMG8+bNq9Z9qK+qWn42m41ly5aVe8xdd93l9e21lJ9vZWdn8+CDD5Kenl7qfrvdzrPPPktCQoLX9o8++oglS5YAcOutt6LT6ejevTsdO3YkLy+Pe+65h9TU1BLpPfzwwxw6dAiDwcDNN98MwC233IKiKBw+fJiJEyeWOCc9PZ17772X3NxcevToQZcuXdDr9dx0000ALF26lPfff79a90Fc2CTgEPWezWbjk08+8aw3a9bMs9y8eXPA/QtdflnWvLS0NDZt2sSWLVs4ePBgqT9vvfWW5/jDhw97/qDqdDqaNm0KgKIonrKLj49nzpw5XtdZsGABx48fB7zLu0WLFp7lpUuXsnv37hp5n/WRoij07t2bJUuWVPizUNvl8/HHH2Oz2UqcV3StomMKCgoqlP/zSVXKD2DNmjU0b968zM/jwYMHueGGG7zOkfLzrZkzZ9KkSRPmzJnDjh07+O2337jmmmu8jrFarSxYsMCznp6ezsyZMz3rxe9hUTnk5uby6aefeqWzdu1aNm/eDEBUVBRGoxGAgIAAGjRoAMCOHTtYvny513kzZswgMzMTKLvsZs2aRVJSUmXeuhAeEnCIem/Dhg2cOnXKs168fWrxdsZr164lJyenVvN2ofn111/p378/gYGBFTr+p59+wuVyAeDn5+e1r3jZ/fLLL177iv9h9vf3L/UccD8UCTeDwUC7du1QFIUhQ4ZU6JzaLJ/c3Fyvh6CyzktNTWXjxo0Vyv/5pCrlB+4ax+HDh1fqWlJ+vpWamso333xDr169MJvNtGrVinfffZe+fft6HVf0wA/w22+/kZ+f71kv634uXbrUq6arrLI787xff/3Va99PP/101mtZrVZWrFhR9hsVohwScIh678w/Xnq9vtTjnE7nBfeHrrYtXryYP/74g169ejF48GDuv/9+pk+fzsmTJ0s9vqipDpRdbuDuhJqRkQG4H2z27dtXofP+/vvvyr6FC0JFO9TXZvls2bIFp9NZ6fMuRBUtv8zMTFavXs3bb79N3759ufrqq3n88ceZO3cu2dnZpZ4j5ed706ZNK7XMrrvuOq/14jVIxT97UPb9TE9PZ//+/Z71TZs2nfUccP/dLPoy4d9//yUtLa1C511oZSd8RwIOUe9t377da12nK3vwtR07dtRwbi5cR44cYc+ePaiqSk5ODvHx8axevZoPPviAK6+8kscee8zrj5rVavX6Q1leublcLnbt2gXAzp07vR5syjvv0KFDF1zzDV+p7fI583Nc3kPPzp07y8+8ANzfktvtdhwOB5mZmRw9epRff/2V//znPwwYMIAPPvjA0wSqiJRf7Slq4gTu+1y85qoqf9diY2O9+omUd05WVhbHjh2r9LWk7ERVScAh6r3k5GSvdY2m7P/WxR94hW/9/PPPZe5TVZXffvuNUaNGceTIEcDdzKD4g0155Qany64y5a2qqpR5FdV2+Zx5XnlDcEqZVkx5AzhYLBamT5/OHXfcQV5enme7lF/tiY+P9yyPHDnSM5iGy+UqcY8q8netMmUHeDqcV+a8jIwMT82IEJUhAYeo94qachQp7w9dWaOEiOpRVdXTubg8qampTJw4EZvNVqLczvbHsajsqnqeqJzaLp/KnCdlenYnT54s8c11abZt28a0adM861J+tWf9+vUANGzYkGeeecazPSsryyvYh4rdz9ooO5fL5dXXRIiKkon/RL1nt9srfGxFhpEUlacoCn/88Qc2m43c3FxiY2PZvXs3y5cvZ+vWrV7HxsbGsmTJElq2bFmpaxSV3ZlNQETNqOx9rm75VOY8+RyfXXR0NAcPHqSgoIDs7GwOHz7Mli1bWLp0KbGxsV7HLlq0iEmTJhEdHS3lV0uSk5P5448/MJvNfPLJJ4SGhnr21dZnr7rnCVEZUsMh6r2goKAKH1v8l7rwPYPBQFhYGD169OCOO+5g7ty5zJs3j5iYGK/j1q9fT3BwcKXSLiq7ypR38fNE5dR2+cjnuGaYzWYiIyO55JJLePTRR/ntt9948803S5Tvhg0bACm/2vL++++jqirvvfceXbt29dpXlz97iqIQEhJSqesIARJwiPNA8UnkoPxvXyIiImo6O+IMPXr04IcffqB3796ebVlZWURGRno1fzvbt2ZFZdeoUSOv7eWdp9FoCA8Pr0q2L3i1XT6VOU8+x1Wn0WgYM2YMCxYs8PpsFDWTkfKreWvWrGHJkiW89957DB48uMR+k8lU4qG+IvezMmUA7qZclT0vLCwMrVZbbrpClEYCDlHvnfnt0JltX4vr3r17TWdHlMJsNvPuu+96Rq5p0qQJAQEBtGrVynOMw+Eo83yNRkO3bt2AkuVd3nnt2rUrMX+EqJjaLp8uXbp47ZPPcc2Kjo7mhRde8KwXTeoo5VezkpKSmDp1Kv/73/8YOnSo1774+HjP0O2VKYcePXoA0KZNG6/fd+WdExIS4vl8y99QURsk4BD13sUXX+y1brFYSj1Oo9HQq1ev2siSKEVkZCQ9e/YE4JJLLgG8y66scgP3g01RM4Pw8HCvJlrlndenT59q5flCV5vl07dvX6/hOMsbzljK1TeGDh2KXq9Hr9d7fjdK+dUcm83GlClTeOONN7yGwHU6ncTGxvL00097ahcq+nctJCTEU14ajcZrMsHyyq5Xr16eGswOHTp41ahI2YmaIAGHqPcuu+wyr6YBWVlZnuXi39QMGjRI2p7WIJvNxoEDB8r9IxcUFETLli09f2yvv/56z77c3Fyv8iq+PHr0aK90ip9XfAKzM7+ZO/M84XbmsJZlNaGozfIJDw9n0KBBpZ5XPL9hYWEMHDiw1PxeKCpafpmZmRw+fLjMYUx1Oh3+/v6MGTPG07wGpPxqyksvvcT69euZMGEC7dq18/x07NiRYcOGsWXLFtq1awfANddc4zVZYFl/10aOHOk1qtQNN9zgWT5zcseyPrN6vZ5Ro0aVel7xstPr9ZWetV6IIhJwiHrPYDAwefJkz3rxEViSkpIA9y/Kxx57rJZzdmG5/fbbGT16NJdccglff/11iYec/Px8Dh06xAcffOD5A9mhQwfPHzqXy8WJEyc8xycmJgLQrFkzbrrpJq+0xo0b55mVt3h5F50DMHz4cDp16uSz93c+yc3N9Vq3WCylPpTWdvlMnjwZo9FY7nmPPPJIhWfaPl9VpPxSU1MZNmwYI0aMYOjQoaxZs6ZEOvv27SMiIoIpU6Z4bZfy872vvvqKn376qdxjIiIiCAsL8yzffffdnn2l3c/g4GDuvfderzSuuOIKT21VUlKSp7bC4XB45uvo3r27Vw0LwP333+/pRF5W2U2YMOGC738jqk4CDnFeGDt2LBMmTABgwYIF5Ofnk5SUxMqVK9Hr9bz11lu0b9/+3GbyPFc0eVhubi5vvPEG48aNY+vWrbhcLhITE/noo494++23Pd/gFZk2bZqnqdWcOXNwuVysWbOG+Ph4IiIimD59eol+GEajkU8//ZSIiAiSk5NZtmwZAPPmzQOgZ8+e/Pe//63pt1wvWSwWvv76a69tDoeDb775ptQ237VZPm3btuWtt95Cr9ezZs0aTpw4gc1mY8GCBQCMHz+ecePGVf8m1GMVLT+n0+mpbTx58iT33XcfTz/9NMePH0dVVXbt2sWiRYuYMWMGAQEBXulJ+fnWihUreOedd8563Jm/Gx955BGGDRsGwHfffYfdbufgwYNs27YNf39/Pv74YyIjI73OURSFDz/8kFatWuFwODxl9sMPP2C322nVqpXXlz5FGjRowMcff0xAQAC7du1i586duFwuvv/+ewCGDRvGo48+WuV7IISiyoDK4jyydOlSZs2axZEjR9BqtfTu3ZuHHnpIgo1akJaWxmeffcZff/1FfHw8qqoSERFBx44dGTJkCFdffbXn288z2Ww2ZsyYweLFi0lOTiYgIIArr7ySiRMner7xK01KSgoff/wxf/75J3l5eURFRXH99ddz++23e7UnF249evQgPz+/zCY4Wq2WG2+8kZdfftlre22Xz+7du5k+fTrbt2/H5XLRpk0b7rrrrhLfyl5oKlt++/fv58svv2Tbtm2kpKRgMBiIjIykd+/eXHXVVZ6+VGWR8qu+I0eOcP3115fbL6LIXXfd5TUBILiby82bN48ffviBuLg4jEYjAwYMYNKkSZ6O/qXJzc3l008/ZdmyZWRkZBAWFsY111zDfffdV+5AGrGxsXz88cds2LABq9VKs2bNuOWWW7j++uvLnVRXiLORgEMIIYQQQghRY6RJlRBCCCGEEKLGSMAhhBBCCCGEqDEScAghhBBCCCFqjAQcQgghhBBCiBojAYcQQgghhBCixkjAIYQQQgghhKgxEnAIIYQQQgghaowEHEIIIYQQQogaIwGHEEIIIYQQosZIwCGEEEIIIYSoMRJwCCGEEEIIIWqMBBxCCCGEEEKIGiMBhxBCCCGEEKLG6M51BoQQQlTMNddcw7///uuz9D744AOuuuqqKp3rcrnQaOQ7q3NF7r8Qoj6R31ZCiPPaU089RY8ePZgzZ865zkq15OTkcPjwYZ+m2a1bt0qfExcXx3PPPceBAwd8mpcLicvlYs2aNTzwwAMMGTKkSmls27aNV155hczMTN9mTgghaoDUcAgh6pR27dpV6/xnn32WCRMmAJCens7PP/8MwHfffcett95a3eydMzt37kRVVZ+lFxkZSVRUVKXO+fPPP/nss8947bXXaN26tc/yciFZtGgRX375pSd4bNKkSZXS6dWrFxqNhltuuYVXX32V7t27+zKbQgjhUxJwCCHqnC5dujBlyhTatWuH2WwGYPPmzZ5AYsyYMbz66qsAOJ1OTp06xQ8//MDXX3/tlU5YWBijRo1i5cqV3HzzzbX6HnwtOTmZTp06lbovJyeHEydOlNjerFkzAgMDSz2nT58+lbr+zz//zFtvvcX3339f5YdkAVdddRUjR45kwoQJbNq0qVpp9ejRgzfeeIN7772X1157jSuuuMJHuRRCCN+SgEMIUacEBwfzf//3fwQHB3ttL95eXVEUdDr3ry+dTkfLli155plnsNlsJdJ7++23azbDteS6667juuuuK3XfrFmzPAFYce+++y5du3at9rXXr1/Ps88+y/Tp0yXYqCaTyQRA586dqx1wAHTt2pVHH32UJ598kh9++IG2bdtWO00hhPA16cMhhKhThg4dWiLYqKgbb7zRx7mpH/bv319im1arJSYmptppZ2Vl8cwzz9ClSxcGDRpU7fSEm9Fo9Fla48aNo1mzZkyaNInc3FyfpSuEEL4iAYcQok6pTtOn1q1b069fPx/mpn4oLeBo2bKl59v06njnnXdITk7mjjvuqHZa4jStVuuztBRF4c477yQ2NpYvvvjCZ+kKIYSvSMAhhKhTOnfuXOVzdTod7du392Fu6j673V7q6FW+uA8nT57kp59+QqfTMWDAgGqnJ2rOkCFD0Ov1fPvtt6Snp5/r7AghhBfpwyGEOG+lp6ezaNEifvjhB0aMGMHDDz/stX/Tpk18++23HDhwgBUrVqCqKvPmzWPu3LmcOHGC5s2b8+CDDzJ8+HDA3UF9zpw5/Pjjjxw/fpyoqCjuuuuucmtlVq5cyffff8/u3bvJzc0lMjKSQYMGcf/99xMZGVnt93jkyBHsdnuJ7R06dKh22t988w0Oh4PevXsTEBBQ5nE2m41PP/2Un3/+maSkJKKiorj00ktp27Yte/fu5bXXXiv1vKrcm40bNzJnzhy2bdtGVlYW4eHhXHrppUyaNIlGjRqVmcc9e/Ywe/ZsNm/eTHJyMsHBwXTv3p1x48Zx8cUXlzje6XTyxx9/MGfOHJxOJ99++y1Wq5WvvvqKhQsXkpqaSufOnXn++efLvddHjhxhxowZrF+/npSUFBo2bMjo0aNxOp0+vZ8BAQHExMSwd+9eZs6cyeOPP15m+kIIUdukhkMIcd5xOBxMmTKFK6+8kjfffJNjx4557V+5ciWjR49m/Pjx/P777zidTmw2Gw899BBvvfUWmZmZWK1WDh06xOOPP86qVauwWCzcd999vP322+Tk5GCz2Th+/DgvvfQSixcvLpEHu93OU089xYwZM3jggQdYuXIlc+fOpXHjxsyZM4fRo0f7ZC6L0ppTQfUDDqfTyW+//VahtB566CGWLl3Km2++ycaNG/nggw+Ii4tj2rRppXbkr8q9cTqdTJs2jUmTJnHFFVewbNkyVq1aRffu3Zk/fz5jxozh4MGDpebviy++4KabbiIqKoq5c+eybt06Hn30UTZs2MCdd97Jyy+/7DXk8Lx587jhhhuYNGkSGzZsACApKYmbb76ZGTNmkJeXR0FBgWfktKysrFKvu2TJEq699loSExP59NNP2bhxIy+88AKLFi0qMaJade5nkaLawcWLF/t0CGUhhKg2VQgh6oGNGzeqMTExakxMjPrMM8+c9Xir1arGx8er7dq1U2NiYtQPP/zQsy8xMVFNTU1Vx44dq8bExKiXXnqp+tRTT6lz5sxRLRaLqqqqumXLFrVHjx5qTEyMesMNN6gTJ05UP/vsMzUnJ0dVVVVNSEhQr7rqKjUmJkYdMWJEieu/8sor6jXXXKMWFBR4bc/Pz1cHDhyoxsTEqMOGDVMdDkd1bov66quveu5L8Z+0tLRqpbtt2zZPWnPmzCnzuL/++kuNiYlRly9f7rXd4XCoY8eOVZ944okS51Tl3rz22mtqTEyMumbNGq9zTpw44cnnzTffXOJa8+bNU2NiYtS33nqrxL7169d7/n+8+eabnu25ubmq0+lUR4wYocbExKgjR45UJ0yYoC5fvlx1Op2qqqrq7NmzPdf96quvSk27Q4cO6s0336za7XavfUePHlU7deqkxsTEqJdffrnXvqrczyJffvmlJ0979uwp8zghhKhtUsMhhDgvGQwGGjduXOqIV5GRkYSHh3smS8vPz+fhhx/mlltu8Ywe1LNnT0aPHg3Arl27uOeee7j//vs9TYuioqK4/fbbAfj3338pKCjwpH/s2DG+/fZbbrzxxhIdt81ms2eG72PHjrFx48Zqvc/SajgaNmxIWFhYtdLds2ePZ7lZs2ZlHrd3714AEhISvLZrtVruv//+EsdX5d4UNRPq3r07AwcO9DonOjra0/wqNTXVa19KSgpvvvkmiqJw1113lchL//79GTFiBABff/21p1bF398fjUbjmdwwMzOTN954g6FDh3qGZ7755psJCQkBYPfu3V7p2mw2nn/+eZxOJ08//bRnCOciLVu25LLLLiuRn6L3ChW/n8U1bdrUs7x169ZyjxVCiNokAYcQ4rxWNHFgefuCg4OJjo4usb9Vq1ae5dJmcm7cuLFnOTs727Nc1KTlo48+4pJLLinx8+eff3qOPXToUOXe0BlKa0bki/4bxfMVFBRU5nGhoaEAfPjhh6xevdpr34ABA/Dz8/PaVpV7M3fuXIBS+1oAzJ49mxdeeIFPPvnEa/vChQvJz8+nZcuWhIeHl3puUf8bl8vluU4Rg8EAQPPmzUv0KdFqtZ7yz8nJKfEe4+PjCQsLK3MG8LLmy6js/SwuIiLCs3z06NEyjxNCiNomncaFEOe14hMGnulsQ5OW93AHeH1DX7zj9o4dOwB4+eWX6d27d7lp+Pv7l7u/PPHx8aX2H/BFwJGRkeFZLu8+DBkyhLfeeovs7Gzuv/9+Lr30Uh588EF69eqFwWBg2rRpXsdX5d5s3rwZcNcqlaZZs2aMHz++xPYVK1YA0KBBgzKv0a1bNwwGAzabrcREfGf7/1FU23Vmv4rly5cD7kClLGX9v6zs/SyueHCdlJRUbt6FEKI2SQ2HEEL4WFHTHlVViYiIKPfnbEFNeWqqwzjgNYFceZPUhYaGMmPGDE+zq7/++otbb72VW2+9tURTI6javSl6eC5tNK7ynDhxAnDPU1EWvV7vqd1KSUmpVPpl2bdvH1B+7VpZKns/iyseABdv4ieEEOeaBBxCCOFjRQ/GvhiFqjw1GXAU73dgsVjKPbZLly788ssvPP30054mQVu2bOHGG28s0UypKvdGLRxxKS4ursLngLtvDnjX1pSmqMlYVWe4P1NRrVPxZnaVUZn7WVzxGhlfTPoohBC+IgGHEEL4WNFD4sqVK8s9zmKxeL4Nr4rSAg5/f/9yO3lXVPGH74p8W240Grn77rv5448/ePjhhzEYDLhcLqZNm+bpCA1VuzdF/S/+/vvvcs+Ji4vz6mxdNC9HbGxsucPJFgU05TWBqoyiplZHjx7F4XBUKY2K3s/i8vLyPMvl9bsRQojaJgGHEEL4WNeuXQH3A+fPP/9c5nELFy4kPj6+ytcprZagXbt25TYhqqjiD99ndooububMmezcudOz7ufnx6RJk5g3bx4mkwlVVT3zeUDV7k3ROQcPHix3VK+PP/7Yq/lXv379AHcfi3/++afM84pm5h46dGiZx1RGTEwM4K5hObPj95nOnACwsvezuOIBhy+CTiGE8BUJOIQQ9YLL5Sp1+WyKvr1WS5kIrbRtVVU8rZEjR3qW//Of/3g9QBaJj49n9uzZJYZ5rajs7OxSgxVfNKeC05PIAWcNipYsWVLq+aNGjQK8a0iqcm+K0gGYOnVqqU2kli5dSlZWltdwwLfccounc/acOXNKzXtmZibx8fGEhIR4ZpQvUtH/Z2f+PyqeTtFEkWWdU9Tsq7jK3M/ikpOTPcsdO3asQM6FEKJ2SMAhhKgXinfoTUtLq/B5RQ97xTtBFynaVvyb4eKsVqtnubQHw+LNdIqn0aVLF88cHrm5udx66628+eabbNmyhV27dvH1119zww03cM8995TbIbs8Ndl/A6BXr17o9XoATp48We6x8+bNKzHCE+BpTtS/f3/Ptqrcm8GDB3uGxD1+/DjXX389P/74I/v27WPNmjU888wzTJkyhSeeeMLr+u3bt2fChAkA/Pnnn6xatapEHr/77jucTifPPfdciT4cmZmZwNn7sJz5f+v666/31HLExsZy++23e5qHuVwuli5d6gmAsrOz+fXXX9m0aZMnwKnM/Szu2LFjgLsjfM+ePcvNsxBC1CYJOIQQdZrNZmPfvn18+eWXnm2bN29m2bJl5ObmlllLYbFY+OGHHzwBx7Jlyzhw4AB2ux2Hw8Hhw4f5/fffAfeD5fz58z1BhcPh4OTJkyxevNiT3rfffktmZiaqquJyuUhKSuL777/37J81a5bXN+//+c9/PN/Q2+12ZsyYwa233srYsWN54403GDNmDNdee22V70tZAUf79u2rnGZxQUFBXHrppYB7YsPyOBwO7rvvPj777DNiY2PJysrixx9/5Oeff2bkyJEMGTLE6/jK3htFUXj33Xfp1KkT4K4BeeGFF7j22mu57777WL58Of/73/9o06ZNibw99dRTnrQmT57M7NmzSU9PJz09na+++opPPvmEqVOneoKgovezY8cOz3C8Bw4cYO3atZ7Aw2azsW3bNs/kiP/++y+rV6/2BKAGg4Hp06d7Rr/at28f1157LQMHDqR///7MmDHDq9bmvffe4/Dhw57/y5W9n0WK5t649NJLfdYBXgghfEFRfdmmQAghfCg7O/usczW8/PLLjBs3rsT2gQMHljoXwfDhw2nSpIlXAFPc5s2b+eijj5g1a1ap+7///nt27NjB66+/Xur+33//3dP/weVy8dNPPzF//nzPBH0dOnTgjjvuYNiwYeW+r7OZMmUKCxcu9Nqm0+nYtm1blWtNzrRp0ybGjx9PaGhomX0nZs6cWeJeGAwG2rZty6233sp1111Xap+Sqtwbm83GzJkzWbx4MSdOnCAwMNAzT0XLli3LfS8rV67k+++/Z8+ePeTl5dGoUSP69OnD+PHjPbURRSZPnszSpUtLpBESEsI///zD4MGDS21m1qlTJ3766SfPek5ODp999hnLli0jKSmJRo0aMXr0aO677z4+//xzli9fzr333ss111zjGWGqqvdTVVUGDBhASkoKn3/+eZkzmQshxLkgAYcQQogy3X777fzzzz/8+OOPns7bou7Zs2cP119/PV26dGH+/PnnOjtCCOFFmlQJIYQo0+OPP45Wqy1RmyLqlp9//hmtVsvzzz9/rrMihBAlSMAhhBCiTN26dePJJ59k/vz5lZ54T9SOpKQkvvvuO+655x66d+9+rrMjhBAlSJMqIYQQZ/X444+TkpLCzJkzvWa0FueWqqpMmjQJrVbL//73P88wwEIIUZfIbyYhhBBn9dZbb9G8eXNeeOEFn85fIqrnrbfewmg08s4770iwIYSos6SGQwghRIX98MMPbN++neeff56AgIBznZ0LVlZWFq+99hqdO3dm/Pjx5zo7QghRLgk4hBBCVEp6ejr5+fk0bdr0XGflgnXs2DGCg4O9ZlYXQoi6SgIOIYQQQgghRI2RBp9CCCGEEEKIGiMBhxBCCCGEEKLGSMAhhBBCCCGEqDEScAghhBBCCCFqjAQcQgghhBBCiBojAYcQQgghhBCixkjAIYQQQgghhKgxEnAIIYQQQgghaowEHEIIIYQQQogaIwGHEEIIIYQQosb8P/E3bMlkSm3tAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pareto_dict = {\n", - " \"model_layers=18\": \"18\",\n", - " \"model_layers=34\": \"34\",\n", - " \"model_layers=50\": \"50\",\n", - " \"model_layers=101\": \"101\",\n", - " \"model_layers=152\": \"152\",\n", - "}\n", - "pareto_weibull = plot_partial_effects(\n", - " file=\"weibull_partial_effects.pdf\",\n", - " aft=wft,\n", - " covariate_array=\"model_layers\",\n", - " values_array=[18, 34, 50, 101, 152],\n", - " title=\"Partial Effects of No. of Layers on Failure Rate for Weibull AFR\",\n", - " replacement_dict=pareto_dict,\n", - " ylabel=\"% Chance of Survival\",\n", - " xlabel=\"Time $T$ (seconds)\",\n", - " legend_kwargs={\n", - " \"title\": \"No. of Layers\",\n", - " \"labels\": [\"18\", \"34\", \"50\", \"101\", \"152\"],\n", - " },\n", - ")\n", - "\n", - "# weibull_accuracy = plot_partial_effects(\n", - "# file = \"weibull_partial_effect_accuracy.pdf\",\n", - "# aft = wft,\n", - "# covariate_array = \"accuracy\",\n", - "# values_array = [.9, .99, .999, .9999],\n", - "# replacement_dict=weibull_dict,\n", - "# title=\"Partial Effects of Benign Accuracy on Failure Rate\",\n", - "# ylabel=\"% Chance of Survival\",\n", - "# xlabel=\"Time $T$ (seconds)\",\n", - "# legend = {\"title\" : \"Benign Accuracy\"},\n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/cmeyers/deckard/env/lib/python3.8/site-packages/lifelines/fitters/coxph_fitter.py:1614: ConvergenceWarning: Newton-Raphson failed to converge sufficiently. Please see the following tips in the lifelines documentation: https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-model\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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z1K024ueTVlfhOmBCaWlphd4qSLTO4mWpqqpiwoQJAArGQokuCitKuL6Uubk5EyyamZkx78OEhAQcOHBA6rHCcwq7lIGCMXrCJTcSEhJga2vL2i904cIF1K9fX2LMlLBMebu5ifwomCKEVElcLhenTp1ibmC7atWqQgMhobS0NKxYsQIWFhasFoUWLVowA5ofP34s9V53ly9fRl5eHtatWycxZsrBwQHJycnMbWSEJk6cyIzv2rdvX6nWb3J2dkZ8fDz69+9fovzKysro2bMngIKAsbAvddEux5SUFJm+iEUH4Atba8QNHDiQeU4zMjIwe/ZsmW6lk5+fj+Tk5EInGsycOZNZELRnz56Fzp4UPV5aWYsWLWImGOzevVti/BOfz8c///wDAwMD/Pnnn6x9f/75J7NEx5kzZ7Bz507mXoRpaWnYvHkzYmNjARS0oL1//55ZsX7hwoXMchz+/v6wsLDAP//8g5CQEDx79gx2dnY4duwYbGxsJOosrGNGRobUayYVh4IpQkiVVbNmTZw/fx7W1tYQCATYunUrJk6ciOvXr+Pr16/Izs5GUlISgoKCsH//fixZsgTz5s1Dnz59JMpavXo1xowZA6Dgnnvnz59HUlISkpKScPr0aRw9ehSbNm1iraidnp6OgwcP4vTp09DX15doVcjIyGDWF8rJyYGNjQ18fX2L7ApKT0/H1atXmXvj3bhxo9h7wvH5fHz69Im1MreHhwccHBwQHR2N/Px85OXlITo6Gvv27WPyZGZm4u+//0ZCQkKJBorn5OQgODiY1a367NkzPH78GJmZmRKB2apVq9ChQwcABQHYhAkTsHPnTrx+/brYACA/Px/x8fHYt28fsrOzce3aNbx9+xY8Ho+Vz8TEBCNHjgQAiRsaCwMxFxcX1gDvEydO4Nu3b6xbtGhpaeHMmTMwMzNDVFQUpk6dihcvXiAjIwPh4eFYuHAhsrOz4erqKrFQbOfOnbF161YmoHJxcUGPHj0wYMAA9OzZE8bGxsy4vI8fP+LQoUPMDMzatWvjxIkT0NfXB1AwgWHDhg0YM2YMZs2ahfv37+Pw4cPMfqBgwPqNGzeYYOru3bsIDw8v1bIYpGxxBNQ+SAipBr59+4Y7d+7A29sb0dHRSExMBIfDgZGREdq0aYOhQ4di0KBBxU4vf/DgAa5cuYL3798jIyMDJiYm6NKlC6ZNmyaxZMDOnTtZ3XctWrTAjRs3mO1p06ZJXZPKysoKW7dulXp+c3NzqS04mzZtwpQpU6Qes3Xr1iJb5VxdXbF58+YiZ4z16tULZ86cKXR/bGws+vbtW+h+oOBehWPHjmWlxcTEwMLCQmI8kLm5OSuwE2djYyO1hRAArl69irZt2zLbnz59wtq1a/HPP/+w8hX3vGhrayMgIICVlpGRgbNnz+Lu3bv4/v07uFwu6tWrh5EjR2LcuHFFrl0VFBSE48ePIyAgADk5OWjdujXmz5+PPn36YODAgWjTpg0WLFggdVHUxMREODo64sGDB/jx4wdq1qyJXr16YfHixUyLmdCgQYOY5TxEDRkyRO5bARHZUDBFCCGEECIH6uYjhBBCCJEDBVOEEEIIIXKgYIoQQgghRA4UTBFCCCGEyIGCKUIIIYQQOVAwRQghhBAiB7rRDyGVUGBgIAQCAbOyMyGEkNLj8/ngcDjM4rHlhVqmCKmEBAJBpbzflkAgQE5OTqWsG6ma6D1FyoPo+6oi3lvUMkVIJSRskRJd5bkyyMzMRGhoKJo0aQINDQ1FV4dUAxERETh06BBsbW3RoEEDRVeHVBPCzyoulwsOh1Pu56OWKUIIIQrD4/EQExMjcc89QqoSCqYIIYQQQuRAwRQhhBBCiBwomCKEEEIIkQMFU4QQQhRGX18fw4cPh76+vqKrQojMymQ2361bt/Dq1St4enoiNTVVYj+Hw4Gamhr09PRQt25ddOzYEcOGDUOLFi3K4vQS3r17B1dXV7x+/RpJSUlQVlZGo0aNMH/+fAwcOLBczllZxcXFwcnJCU+ePEFsbCy0tbVhbGyMQYMGwdLSEvr6+li3bh127dpVaBmPHj3CgAEDKrDWlYO06z5y5AhOnTqF7OxsJs3V1RVdu3at6OoRUi1oamqiVatW0NTUVHRVCJFZmbRMjRgxAps2bcJff/0lse/UqVN4/vw5Ll68iHHjxuH9+/c4ceIERo8eDRsbG8TFxZVFFRgXLlzAhAkT4OHhgVGjRuHWrVtIT0/Hu3fvsGTJEsTHx5fp+SqzV69ewdzcHDdv3sSqVavw4sULPH36FJs3b8bbt2/Rt29f9OnTBxEREYWW8e3bN6xYsaLiKl1JFHbdixcvhoWFRcVXiJBq6ufPnwgMDMTPnz8VXRVCZFam3XzS1ghRU1ODvr4+WrdujcWLF8PV1ZVZn+bhw4ewsLDA//73vzI5f1RUFHbu3Mks0FWvXj3o6OigWbNmUFFRQd++faGrq1sm56rsEhMTsXDhQqSkpGDTpk0YOHAgVFVVweFw0KZNGxw9erTYYDYjIwOLFy9GVlZWBdZc8Yq7bgMDgwquESHVV0pKCh4+fIiUlBRFV4UQmZVpMKWsrFxsnrZt22LJkiXMdmJiIubNm1cmv0revHmD3NxcVpq2tjY8PDwQHByMEydO/DK357h48SLT5dqwYUOpeWxsbDB06FCp+zIzM7F48WKEhYWVWx0ro5Jcd0UsAEcIIaTqUMgA9IkTJ0JLS4vZjo6OxtGjR+UulxZ9+z9BQUHM49OnTxe6nP6yZcskgoPY2FhYW1vj+fPn5VrHyuZXvW5CfjWWlpbo3r17kX+WlpaKriapQhRyOxkNDQ306NED9+7dY9KuXLmCpUuXokaNGqy8cXFxOHHiBB4/fozk5GQYGhpizJgxmD17NlRVVQEUBGOjRo0Cn89nHbtlyxbs3LkTJ06cQOfOnZn0vLw8uLm54erVq4iIiICKigp69OiBZcuWoX79+kw+Z2dnHDp0CJmZmUzarl270LJlSxw/fhz+/v7Izc1F165dsWHDBpiamkq93oiICJw6dQq+vr5ITk6Gvr4+OnTogMWLF0vtGi3JNRdHNN/NmzeRmpqKjRs3om7duqx8DRs2RJMmTZjt8PBwLFq0CFFRUax8os9fQEAAHBwccPbsWeTk5LCem7Fjx8Lf3x9//fUXPn/+DFtbW8yYMYPJ8/PnTzg6OuLevXuIi4tDzZo1MWzYMCxatIgVYK9cuRK3bt1iBYEPHz5ESEgIXFxcEBISAg0NDQwfPhyrV6+W+rx8+vQJp0+fhp+fHxISEsDn81nlaWpqQklJCRMmTICFhUWJrrswX79+hYODA549ewZVVVUMGzYMq1evlng/k19X9+7dAQC+vr4KrgmJjo5GVFQUzMzMpO4X/xwgilUV/u8obGkE8XuOZWZmSrQIvH79Gubm5nBzc8Nff/2FZ8+eoWHDhjhw4ADmzJnDBE+mpqYICAjA5s2bWcdv3rwZAQEBrC/EjIwMzJkzB/b29mjfvj38/PxgZ2eH27dvMwPkhWbOnIm5c+eyyhQO5jYzM4OKigrS09Px8OFDzJkzB3l5eRLX+eTJE4wePRr37t2Do6Mj3N3d8ePHD3h4eGDMmDF49+6dTNdcnN9//5217e3tjWHDhmH9+vX4+vUra9/WrVuZx82aNcP9+/fRqVMnVp6AgADmDygIdubMmSNxXh8fH8yaNQtBQUHIyMhgtTh+/vwZo0ePhqOjI5YuXQp/f3/06dMHTk5OmDRpEtLS0pi8Dg4O6NatG6tse3t7nD9/Hs2bNwePx0NCQgJcXV2xbds2iXrcvXsXY8aMwfXr11GzZk08efIE3t7eqFevHpNn/PjxCAgIwJ9//lni65bG19cX48aNw+vXr5Geno6kpCRcuHABq1evLvQYQkgBdXV1NGjQAOrq6hV6XjMzM/j6+kr9KyzIIqQwCgum6tSpI5EWEhLCPI6JiWEGUI8YMQKdO3eGpqYmFi1aBADw9/eHo6Njqc+7bt06PH/+HJqamli2bBm4XC4sLCzQuHFjpKWlYeXKlaygyNDQkHV8YmIirly5gjVr1mD58uVM+qdPn+Dj48PK++XLFyxbtgzZ2dkYN24cmjRpgvr16zMtQZmZmTh27Fi5XPO4ceMkWr34fD6uXr2KYcOG4c8//0RkZGSJyiqM+AdOYmIiNm7cyDqvklLBWyw9PR3z589HVFQUfv/9dwwfPhyqqqqwtbUFUNAitnv3blZ54s99rVq14Orqik2bNmHkyJFM+r///ov09HRmOzIyEqtXr2a6fefNmwcDAwMYGRlhwoQJTD5XV1fExsbK8QwUuHr1Ki5cuAAfHx+Ym5sz6ffv38e3b9/kLp+Q6qx27doYN24cateureiqECIzhXTzAWB16QglJCQwj48ePcrM7ujSpQuT3qhRI+bxpUuXmECjJF69egUvLy8AQKtWraCtrc0q99OnT4iIiMDz58/Ru3dvAP8XDAhNnz6dqbt4t15ERAT69u3LbP/999/MjLDffvuNSe/SpQtCQ0MBgNXSVJbXrKmpiVOnTmH27NkSX+h5eXm4ceMGvLy8sGTJEsyZM0emQdXiz83p06dx9OhRdOzYEVu2bIGHhwcWLFgAADh37hxTD9Frq1WrFmrWrInU1FTcvHkT69atY55f8fJtbGyYxyYmJsxjPp+PyMhIZt0yd3d31vg50edP9HF+fj7evn0LY2PjUl+7qHXr1qFZs2YACroFPTw8mH2fP39mtYaVhkAgYHUxVwbC9/OvNsOzLOTn5yMmJobWJBOTn5+P3NxcqKioSPyfLy8xMTHFtj5FRUXRa1VJxMTEwMTEpFSfh8LPKIFAUCGThhQWTKmoSJ5a+B8pLy8Pd+7cYdJFv+yEyyoAQHx8PCIjI6W2cknj6enJPDYyMmLtEy33zZs3TDAlTnTGovjsRdEXOjU1FQ8fPmS2a9asyTxetGgR8vLykJKSwgQI5XHN9erVw/Xr1+Hg4AA3NzeJbkgej4d9+/bhw4cP2Ldvn9xvuGbNmjFdqlu2bMGWLVuYfaLPvXjwoqGhgdTUVPD5fAQHBxf6ASb6QSv+/snIyGAei68lJroYoOhzKa0cWYgulSA+dku067K0+Hw+E3RXNkWtTUakE/5wKmlX/a8iLy8P6enp0NLSKtGM8IpEr1XlIevnYW5ubonHGstDYcGU6JefkPBLKSIigtVts2jRItaXnugTk5SUVOJgKjg4mHns5eUFb29vZlv0CZe2intJiAYr79+/Z22LXk/NmjWxceNG1rHldc3a2tqwt7fH9OnTcfToUdy5c0ciqPL09MTvv/+OiRMnlqjMwoiOTROVnZ2NT58+Mdvbt2/H3r17me2cnBzm+pKTk2U6t+g1iS8FITpIXvSxkpJSua3CL61epcXlclmTAyqDrKwsREREoEGDBjS4vpS4XC5MTEzw+PFjRVelUvnf//6H3bt3Y+3atWjatGmFnLN///7F5qHXqvIQvl4tW7Ys8THCz6qy+MFcEgoLphITEyXShIPSxYOZNWvWYNKkSXKfU7Tc1q1bw83NTe4yRYnOFEtKSmLtK252SFlf87p167Bz505mu1GjRnBwcICtrS0OHz4MT09PVn0vXrwodzAlPsZJKDU1lXWu6dOnY9WqVXKdS5xo+RYWFjh27BjznEZERKBx48YACsaxCY0YMaLcB5oWtiRFSXA4HImWtMqiRo0albZulZWwZZWeNzbhwHN1dfUKe25K0p2opKREr1UlIc//nYpaF1BhA9DFVz3X0NBgxtKIL6wZHR1dJucULbesyiyM+MyUFy9eFJm/rK/5+/fv+Pz5s0R6/fr1sW/fPpw6dYr1xpSWt7TU1NSkppfX61kYXV1duLi4MIHSiRMnkJCQgE+fPsHV1RUA0K1bN9YsRkLIryUqKqrQNaZoaQRSWgoLpp49e8banjRpEtNtID5gV7Q7TlRpf/WLlhsfH8/q9pOnXGmELSFCPj4+Rd66pTyu+eLFi4Xu6927N2vqvuiYrrKmr68PHR0dZvv58+es7jahsnjehVq2bAkvLy8MGDAA79+/xx9//IGJEyfC1NQUe/bswdmzZ+lXJ6lQwmn3RPFMTU2LbJU2MzMrdN1AUvGqwv8dhQRTAQEBrO6WOnXqMLO+gIIv9nbt2jHbYWFhuHHjhkQ5W7duxY8fP0p83l69erG2HRwckJ+fz0p7+fIlnJ2dS1xmYRo1asQa78Lj8bBnzx6JgOHRo0fIzs4ul2u+fPlykbdFER3jJD7gvqwH7PXs2ZN5nJycjNOnT0vkOXnyJN6+fVsm58vMzISNjQ1ycnLw6tUrvH37Fi9fvsT58+dhYWFRaNNvRQxUJIT8H1NTU9jY2FRo8HLt2rVC15gS/l27dq3C6kOqvjINpqS1LIgHK1lZWaxFFo2MjHDixAlWywUAicUyN2zYABcXF8THx+Pr16/YuHEj1NXVWbPyxO/Ll52dzdoePXo0ay2TZ8+ewdbWFuHh4UhKSoK7uzu2b98OKyurQusvui0+uFj8+pctW8ba9vT0xPLlyxEQEICQkBDs2bMHt27dYroEZbnmovD5fCxYsKDQmVfCRVJ1dHRY90sEJG/mK5zV8vHjRyZN/PktqmVp9uzZrADm0KFDOHjwIKKjoxETE4P9+/cjNDSUtYSE+HMvWr74ay1+7vXr1+O///6DpaVlqVqgSnLd4ucSraf4vrJsbSOkOlJWVoaGhkalm8lHSGmUaTAlbYHCN2/eACj48vP19cXUqVPx4cMHcDgcDBs2DFevXpU6g2Pw4MGwtrZmtnNycrBz50706tULgwcPRkxMDFasWMHs5/P5EotmPnz4kDVDTlNTE4cOHWJ9ud6/fx/m5ubo3r07du3ahd27d7Om0ouufQWA1VUnvuCjeN5BgwaxWtwA4M6dO5gyZQrGjBmD0NBQ2Nvby3zNxVFWVkZSUhLGjRuH06dPM/VLS0uDi4sL/v77bxgaGuLMmTMSTd5WVlasDzd/f398/PiRWacrLy9PYhxYQECARAAk1LZtW6xdu5bZFggEOHbsGPr3749+/frhv//+w/bt21nHiD+foi1y4q1zonmTkpJw+/ZtAMDjx49Z74HiFHfdwvJFiU6mEO/KlTbRghDyfxISEvDvv/9K/H8npCrhCMrgp/OjR48QFBSEK1euSP3yELa8aGtro1mzZvj9998xcuRIifvESXPnzh1cvHgRoaGhyMvLQ8OGDWFlZQVLS0tmymN0dDQGDx5c6JogFy5cYHVpffv2DceOHcPz58+Ze9/17dsX8+bNY62B5OLiggMHDrDWj9LW1sa6detgYGCAtWvXsr5Y1dTUMHPmTNbK6MLnx8XFBcHBwcjLy0OjRo1gaWmJCRMmSJ22WZJrLo6NjQ1WrlyJevXq4dmzZ/Dw8MCbN2+QlpaG/Px8NGzYEAMHDsTkyZMlWgWFHj9+jP379yMiIgImJiYYP348Zs2aBSUlJUyfPh3+/v4Sx6iqquLNmzeF/sr09fWFk5MT3r17h+zsbNSrVw+jR4/GlClTWFPtV69eDQ8PD1bLToMGDbB37174+/vjwIEDrJZBIyMjrFmzBiNGjMD3798xcOBAqedXUlKCuro6DA0N8dtvv2HevHkSyw8Udd0HDhzAmTNnWGO+jIyMsG3bNiQlJWHr1q2s94uuri5sbW0xZcoUqfUpjPBG1eK3XVK0zMxMhIaGomXLljTmjJSJsLAw2Nvbw97eHs2bN1d0dUg1Ifys4nK54HA45f5ZWibBFCGVzeTJk/Hq1ati82loaODq1asSEwYUjYIp8qugYIqUh4oOphQ2m4+Q8nT48GHWrWMKk5mZievXr1dAjQghhFRXClu0k5DyEh4ejgULFiArKwsXLlxA27ZtoaqqytwDLCMjA0FBQbCzs0NiYqJcq5QTQggh1DJFqh03NzdERUWhTZs26Ny5M9TU1MDhcKCsrAw1NTXo6+ujb9++aNOmDQAUOr6KEFL+dHV10a9fP+jq6iq6KoTIjIIpUu0MGTIEampq+O+//3DixAnWDDs+n48PHz5g9+7dePbsGVavXl3oPQUJIeVPW1sbnTt3hra2tqKrQojMqJuPVDu///47bt26hatXr+K///7DuXPnkJ2dDRUVFaipqaFOnTro0qULPDw8SjSuihBSfjIzMxEWFob69evTpAZSZVEwRaqlunXrSixRQQipfBITE+Hh4YFOnTqhVq1aiq4OITKhbj5CCCGEEDlQMEUIIYQQIgcKpgghhBBC5EDBFCGEEIXhcrkwNDQEl8tVdFUIkRkFU4QQQhTG2NgY06dPZ90XlZCqhoIpQgghhBA5UDBFCCFEYSIjI7F//35ERkYquiqEyIyCKUIIIQojEAiQl5cHgUCg6KoQIjMKpgghhBBC5EDBFCGEEEKIHCiYIoQQQgiRAwVThBBCFMbIyAgzZsyAkZGRoqtCiMwomCKEEKIwqqqqqFWrFlRVVRVdFUJkRsEUIYQQhUlKSoKXlxeSkpIUXRVCZEbBFCGEEIXJyMjA+/fvkZGRoeiqECIzCqYIIYQQQuRAwRQhhBBCiBwomCKEEEIIkQMFU4QQQhRGW1sbXbp0gba2tqKrQojMKJgihBCiMLq6uujTpw90dXUVXRVCZKai6AoU5+LFi9i9ezd4PF6heTQ1NfHo0aMy/c/47t07uLq64vXr10hKSoKysjIaNWqE+fPnY+DAgWV2nl/VtGnT8OLFC2ZbXV0dXC4XmZmZyMvLY9K5XC7U1dXB4/GQk5PDpI8ZMwa7d+8GAPj4+GDTpk3Izs7G2rVrMWrUqIq7EEKIXHg8Hr59+4ZGjRpBQ0ND0dUhRCaVvmVq8uTJePv2LQ4dOiSxr1GjRnjy5Alev35dpoHUhQsXMGHCBHh4eGDUqFG4desW0tPT8e7dOyxZsgTx8fFldq5f3axZs/D06VO8ffsWAQEB6NSpE2v/yJEjERAQgKCgIDx58gTDhg2TKGP9+vWIiopCYmIi1q9fj+zs7IqqPiFETnFxcXBzc0NcXJyiq0KIzCp9yxQAcDgcDBkyBHp6ekhOTmbS+/XrBxMTkzI9V1RUFHbu3AmBQAAAqFevHnR0dNCsWTN8/vwZvXv3puboMjJy5EisWbOmxPlNTEywf/9+pKSksNKFr5Xwseg2IYQQUt4qfcuUKPEm4PJoEn7z5g1yc3NZadra2vDw8EBwcDBOnDgBLpdb5uf9FS1btqzUx3A4HNjY2LDStm3bBhMTExgYGGD79u2oUaNGGdWQEEIIKV6VaJmqSEWNzSJlZ+DAgahbt65Mx3bu3BmfPn1itvv27YsnT56UUc0IIVWRpaUloqOji8xjamqKa9euVVCNyK+kSrVMFef9+/cwNzdH8+bNmb9p06YhPT0dBw4cwKBBg9CuXTuYm5vj/v37rGOjo6PRuXNnbNmyhZW+ZcsWdO7cGQEBAaz0vLw8XLp0CZaWlujUqRO6du2K5cuX4+vXr6x8rq6u6NSpE6tOhw8fBgB8+PAB06dPR4cOHZjB1EI8Hg+Ojo4wNzdHhw4d0LNnT2zcuFFivNaePXvQpk0bVvn+/v548eIFZs+ezdRtzZo1SE1NLfS5CwoKwvLly9G3b1/89ttvGDJkCOzt7Qsdx/DlyxesWbMGvXr1Qvv27WFubo4LFy6UuIvN2tq6RPmkUVJSwqRJk/D48WPWdQv/hKKiojBt2jTWvgEDBoDH4+HkyZMYPnw42rZtix49emDDhg1IS0sDUDD5YPHixejatSs6dOiAqVOn4v3794XWJy4uDlu3bkX//v3Rvn17DB48GMePH2cNmCeESKeiogItLS2oqJTst3337t3RvXt3ifTo6GhERUUVelxUVJTUYKuw8ggpjWoVTLVp0wanTp1ipf348QOTJk1CcnIyjI2NwePxEB4ejqVLl7ICJFNTUwQEBGDz5s2s4zdv3oyAgAB07tyZScvIyMCcOXNgb2+P9u3bw8/PD3Z2drh9+zbGjRvH+uKdPn067OzsJOr64cMHTJ48Gf7+/sjMzISzszPzZR4fHw8rKys4ODhg9OjRePHiBaZMmQI3NzeMGzcOkZGRTDlr1qzB6NGjWWU7Ojpiz549aNy4MfLz85GSkgJ3d3csX75c6vPm5uYGKysrvHv3Dm5ubjh16hQiIiJw6dIlWFhYsM4HAA8ePMDo0aPx+PFjuLi44PHjx+Byudi6dSv+/PNPqecoD/3798fTp0+hqakpdb+ZmRmcnZ2hrq7OpGVkZGDSpElISEiAhYUF8vPzkZiYiH/++QcLFy7E8ePHsW3bNvz++++oU6cOMjMz8fLlS8ycOZM1Xk/o9evXMDc3h5ubG/766y88e/YMDRs2xIEDBzBnzhzw+fxyu35CqgMTExMsWLCgTMa/mpmZwdfXV+qfmZlZGdSWEOmqVTAFAIaGhqzt79+/w87ODlu2bIGjoyPU1NQAFLQsnT9/XqZzrFu3Ds+fP4empiaWLVsGLpcLCwsLNG7cGGlpaVi5ciVrer/4f2Iej4dVq1ahXr16TBqHw4GSkhLy8vKwZMkShIaGok6dOpg5cya4XC7mzp0LLS0txMbGYv369UVec15eHi5fvox169ZhxowZTPqzZ89Y3WMA8OLFC2zevBl5eXmYNWsWjIyM0KVLF9SsWRMAkJiYCGdnZyZ/SEgIli9fDh6Ph6lTp6Jx48bQ09PDnDlzAAA3b96Eu7t76Z9UGRkZGaFJkyaF7ldRUYGenh6znZKSghkzZmD9+vWYN28eRo4cyewLCAjAq1evcOHCBVhbW2Pt2rXMvrS0NNy+fZtVdkxMDBYuXIiUlBSMGDECnTt3hqamJhYtWgQA8Pf3h6OjY1ldKiGEkEqq2o2ZUlJix4cdOnRAjx49AAA1atSArq4ufvz4AQCIiIgodfmvXr2Cl5cXAKBVq1asVXsbNWqET58+ISIiAs+fP0fv3r2l1snNzQ0bN26Eubk5jh49CicnJ1haWkJLSws3b95EYGAggIKxQcrKygAK1luqV68eQkJC4Ofnh8+fP6NRo0ZSy58/fz4zSN7U1JS178uXL2jcuDGzvXv3buTn5wMA2rVrx6T//vvvePDgAQCwWlf++usvpvuqS5curGsXErZoVRRhgFwY0efHzMyMtQ6VsbExK+/cuXOhqqoKAKhduzZrn/j75ejRo8zMwqKeC2FwVVoCgQCZmZkyHVtesrKyWP8SIq8vX77gxIkTWLZsGRo2bFhs/vz8fMTExKBr166s9JiYmGJbn6KioqQeZ2JiUun+rxH5CD+jBAIBOBxOuZ+v2gVT4oTBiJBov7ws/3k8PT2Zx0ZGRqx9orML37x5wwRT4rS1tWFubg4AWLRoEevLVrR88S968fJFv7RFiQYP4tcves1hYWEIDg5mtoWtUQCwYcMGZltYv6SkJDx//lxq/US72oKDg8Hn86vErMeixmmI78vIyGAe5+Xl4c6dO8y26HMh+jrFx8cjMjISderUKXXd+Hw+QkNDS31cRZDlhwgh0sTGxiI9PR0RERElWiNO+ONO1i50acdV5v9rRD65ubnMD+TyVO2DqaKIL4FQEqLBh5eXF7y9vVnlCV+0ogZ7iy9MWVj5Z86cwYULF5htPp/PlC++1lJJiXY/vn37lrUvPT2deWxiYoKdO3ey9osPwh4zZgwTrAkEAtYbNi0tDQYGBjLVsbIStuABBcGE6PO1aNEiVvAl+lwkJSXJFExxudwiuzAVISsrCxEREWjQoAEtQUHKhPD/TZ06ddC0adNi83O5XJiYmODx48es9P79+xd7bFHHtWzZsqRVJlWA8LOqpBMb5PVLB1OyEA2SWrduDTc3t1KXIT7GqbDyBw0ahP3795e6/KKIzrZLSkpi7YuOjkarVq1KVDcAOHjwIPr06VOm9asqxJ+LNWvWYNKkSWV6Dg6HU2lvr1GjRo1KWzdStQgniKirq5foPSVseRfPKz7cobBjCzuO3s/VU0V08QEUTJWaaNdVcWuaFKaoMT5cLpdphpa1/JISneUGFAyYLuq+g+LdduVdv8qMngtCKp+oqKhClzmIioqiGX2k3FS72XzlTXQGXnx8PKtbTpSstzQRLT84OBgJCQllWr4o0YHoAHD79u0ixyzUr1+ftV3YQpm/wu1cRF8nAKzuXlG/wnNBiDwMDQ0xYcKEIlvsRQmXOhBnampaZLBkZmYmMSGnqPIIKQ0KpkqpV69erG0HBwfWWBoAePnyJWs5AVnL5/P5Urv5PDw8WIOfZfX777+z7jOYkJCAo0ePSuS7ffs2BAIBmjVrxprh5u3tjZcvX7Ly5ufnY9WqVdX+ZsM1a9ZkzX4MCwvDjRs3JPJt3bqVmT1KCJGkpqaGevXqFTsrtzjXrl0rdI0p4R+tfk7KS5UKpsSnY0ubni0e2Ihviw46l9ZqID4oXTwoGD16NCugePbsGWxtbREeHo6kpCS4u7tj+/btsLKyKrSMolorpk6dyhrYe/XqVWzatAkRERGIi4uDi4sLLl++jCFDhpTomkUHnIufW11dHQsXLmTtd3R0xNatW/H27Vu8e/cOdnZ2CA0NBYfDgbKyMmbPns06j42NDdzd3ZGUlITw8HAsXrwYHTt2lOhCLCnx56okMy7FbwEkvi26Erl4+eIze0SPLS7v3LlzWdsbNmyAi4sL4uPj8fXrV2zcuBHq6uoSsz4JIf8nJSUFT58+lXlSDSGVQZUJph48eCAxYPrJkycSY1XEb7ciejsUHo/HWsU6OTmZFWzw+Xz4+Piwjn/48CFr1pampiYOHTrEGqx4//59mJubo3v37ti1axd2797NWirAz8+PVWZgYGChtxoxMTHB7t27WTMQrly5giFDhqB3795wcXHBvn37WEseiHcFil5zbGwsa5943unTp7PWXQKACxcuYMKECRg/fjxycnKwZMkSZp+1tTUGDx7MbKelpWHNmjXo3r07zM3NoaenhylTpki9tuIEBATgw4cPEmnh4eGFHvP9+3eJhUj9/f2Zxx8/fkRiYiKznZiYyJwjPT1d4rXx9fVlAs5Hjx6x9oWGhrLeC4MHD2bdFicnJwc7d+5Er169MHjwYMTExGDFihVFXjMhv7qfP3/ixYsX+Pnzp6KrQojMOIJKPqjj4sWL2L17d5E3INbU1MSjR48QFRWFdevWSXwhDxw4EDt27MDChQvx+vVr1r5u3bph586d4HA4GDx4cKFrl1y4cIF1S5lv377h2LFjeP78OZKTk2FoaIi+ffti3rx5rDWH7OzscP36dYnyuFwu7ty5U+jNfoODg3Hy5EkEBAQgPT0dpqamGDJkCGbOnMnqmtu3bx/Onj3LqrehoSG2bduGpKQkbN26ldWCp6OjgyVLlmD69OlMmkAggLu7O65cuYKwsDCoqKigWbNmmDRpEmuFcNH8bm5uuHr1Kj5+/AgVFRU0bdoU06ZNw7Bhw6ReT1Hu3buH1atXF9k1qKGhgYsXL7KmLz9+/BgLFiyQmn/jxo1o27YtrKysJFoCORwOLl68iA0bNkgEYgAwfPhwNG/eXGoXa40aNfDmzRtW2p07d3Dx4kWEhoYiLy8PDRs2hJWVFSwtLWWelhsUFAQAaNu2rUzHl5fMzEyEhoaiZcuWNPuJlImwsDDY29vD3t6edW9NQuQh/KzicrngcDjl/lla6YMpQn5FFEyRXwUFU6Q8VHQwVWW6+QghhBBCKiMKpgghhCiMpqYm2rRpwxpnSkhVQ8EUIYQQhdHX18fQoUOhr6+v6KoQIjMKpgghhChMTk4OEhISCp3hTEhVQMEUIYQQhfnx4wfOnj1Li9uSKo2CKUIIIYQQOVAwRQghhBAiBwqmCCGEEELkQMEUIYQQhRHe95PD4Si6KoTIjIIpQgghClOnTh0sX74cderUUXRVCJEZBVOEEEIIIXKgYIoQQojCxMbGwtXVFbGxsYquCiEyo2CKEEKIwvD5fMTFxYHP5yu6KoTIjIIpQgghhBA5UDBFCCGEECIHCqYIIYQQQuRAwRQhhBCFMTAwgLm5OQwMDBRdFUJkRsEUIYQQhdHQ0EDz5s2hoaGh6KoQIjMKpgghhCjMz58/ERAQgJ8/fyq6KoTIjIIpQgghCpOSkoInT54gJSVF0VUhRGYUTBFCCCGEyIGCKUIIIYQQOVAwRQghhBAiBwqmCCGEKEyNGjXQuHFj1KhRQ9FVIURmFEwRQghRmFq1amHMmDGoVauWoqtCiMzKNJhKSkqiO38TQggpsby8PGRmZiIvL0/RVSFEZiplWZizszMyMjKwadMmucrZsWMHzp07B4FAwEoPCwuTq9zSio+Px/nz5xEYGAh/f/9SH+/u7o6WLVvKVYft27fj+vXraNKkCfbv3w8zMzMAQOvWrZGbm8vK6+rqiq5du8p1PkV69+4dPD098fTpU3z58oW1T0lJCRwOBwKBAFwuF9ra2jAyMkLz5s1hYWFRrtedl5cHHx8f9O3bt9zOQcivKjo6GseOHYO9vT2aN2+u6OoQIpMya5lKT0/HpUuXcP36dSQnJ8tV1vr16/H48WPo6uqWTeVkVLt2bSxfvhyurq747bffWPs6deqE169f4/Xr13j58iW8vb1x9uxZTJo0CSoqZROj+vr64ty5c8jIyMDbt29x8OBBZt/r16/RuXPnMjlPZdGuXTusW7cO//zzD7hcLmvfjh07EBISgrdv3+LSpUvo06cPgoODcf36dUyfPh1Lly5FTk5OudTr9OnTuHPnTrmUTQghpOors2Dq8uXL+PnzJ7KysnDx4kW5yzMxMUGjRo3KoGZlo27duqxtZWVlaGpqQlNTEzo6OjA2Nkb37t1hb2+PM2fOQElJ/qdWvGVOdFtNTQ0dO3aU+xyVkba2NvT19aXuU1VVRevWrbFr1y6MGTOGSffy8sKePXvKvC6+vr44fPhwmZdLCCGk+iiTYConJwcuLi7M9sWLF8uklaCsWnjKgnhLSVG6deuGwYMHy33OHj16YMqUKdDQ0EC7du2wdOlS1n5VVVW5z1FZleS1nzJlCmv7ypUriIuLK7M6vHnzBra2tuDz+WVWJiGEkOqnTKKVmzdvsr7EEhIS4O7ujgkTJpRF8VXGjh07sH79egCApaUlatasKXeZmzZtknsMWnXVpEkT1jafz0doaCgMDQ3lLvv+/fv4888/kZmZKXdZhJCSs7S0RHR0dJF5TE1Nce3atQqqESHFkzuYEggEOHPmDNTU1MDj8Zh0JycnjB8/HhwOp9gywsPDceLECbx48QKZmZlo1KgRZs2aVWj+jRs3ws3NTSJdWVkZAQEB0NDQgKOjIxwcHJh9EyZMwLZt20p5dSX348cP1gdAnz59JPJERkbi0qVL8PPzQ3R0NDIzM2FiYoLhw4dj9uzZ0NTUZPLa2tri7t27rOPHjBmD3bt3F1uX9PR0rFixAt7e3qx04QD+ZcuWwcvLi9Vt+PDhQ9SpUwdAwUD2gwcPIj09ndm/ePFiLFmyBB8+fMDOnTsRFBQEKysrrF27lsnD4/Hg4uICDw8PREZGQkNDAwMGDICtrS1q165dbL1LS7wbFEChwU9pnnsnJyccPXqUVZanpycePHgAABg5ciTs7e2ZfT9//oSjoyPu3buHuLg41KxZE8OGDcOiRYugpaVVRldLSPVkZmaGJUuWMJNroqOjERUVxWyLi4qKqsjqEVIicnfzPXz4EGlpaawvVQD48uULHj16VOzx3t7emDBhAm7duoUWLVrAz88PR44cwZUrVxAYGCj1mA0bNmDo0KGsNA0NDfj4+EBDQwMAMG/ePBw/fhwAYGVlhY0bN8pyeSV26tQpqV/uQlevXsXQoUORmJgIV1dXeHt7w8LCAl++fMHRo0cxbdo0ZGdnM/kPHTqEDRs2yFQXLS0tODo6on79+lL3HzhwAN26dSv0+OnTp8POzk4i/cOHD5g8eTL8/f2RmZkJZ2dnpKWlASiY+WhlZQUHBweMHj0aL168wJQpU+Dm5oZx48YhMjJSpmspyufPnyXSWrVqJZFW2ud+1qxZuHHjBquMkSNHIiAgAAEBAaxA6vPnzxg9ejQcHR2xdOlS+Pv7o0+fPnBycsKkSZOY54cQIp2SkhLU1NRY40zNzMzg6+sr9a+wIIsQRZI7mDp9+jQmT54MS0tL6OnpsfY5OTkVeWxSUhJWr16NrKwsAAUtJqqqqjA2NsaRI0cKnc2npqaGzZs3M4ETUNDFIz6GSEtLC3p6elizZk25jC8SCASIiYmBg4MDzp07V2i+Dx8+YNOmTeDz+cjIyICmpiZUVVVZY36Cg4MlWtt69OghV/2Kag0qritM/AOLx+Nh1apVqFevHpPG4XCgpKSEvLw8LFmyBKGhoahTpw5mzpwJLpeLuXPnQktLC7GxsUz3Z1kSHacHAEOGDJEIIGV97ksiPT0d8+fPR1RUFH7//XcMHz4cqqqqsLW1BVDQ4lqSlkRCfmXx8fG4evUq4uPjFV0VQmQmVzdfQEAAQkJCcOzYMaipqWHChAk4efIka/+7d+/Qrl07qce7uLggNTUVQEHLUuvWrZl92traaNiwYaH/wfT19TFx4kQmYOPz+fDw8MCkSZOYPI8ePcKUKVNYXThl5dWrV2jbtm2JBif7+fkxC9Ldu3cPYWFhaN68OSsYBCCxtpKamppcdSxqRmFxsw3F97u5uWHjxo0wNzfH0aNH4eTkBEtLS2hpaeHmzZtMK2Lnzp2hrKwMoGDQfr169RASEgI/Pz98/vxZ7hmaWVlZ+PjxIy5evIibN28y6X369MHOnTsl8sv63JfEuXPn8O3bNwBAly5dmPRatWqhZs2aSE1Nxc2bN7Fu3TqZuvsEAkGlG7Ml/OEj/JcQeaWkpCAiIgIpKSmoXbs28vPzi/18ys/Pr3T/N0jlIvyMEggEJRpuJC+5gqlTp05h9OjRzDT2yZMn48yZM6zFJM+cOcNaH0nU/fv3mceGhoalvuDp06fD1dWVOZ+rqysmTpwIDocDPp+Pe/fuydTiUBKdOnWCk5MTIiIicO7cOVy5cqXQvD169ICWlhbS09NhamrKtPqIdwuKdjVVNtra2jA3NwcALFq0CIsWLWL2eXp6Mo+NjY1Zx4kGLW/evJE5mNqyZQvs7e1Z4/IAoGPHjli8eDF69uwp9bjyfO6Lu+7U1FTw+XwEBwfLtKiocEB9ZRQREaHoKpBqQnjXjMjISOTm5oLP5xf7Q7Iy/98glUtubm6FzHyXOZgKDw/H06dP4eHhwaQZGxtj0KBBrAUO79+/j+/fv0us05Sbm8sa8yJLK4yJiQmGDh3KfKl9/vwZ3t7e6NevH+7evYsuXbqU6/2euFwumjZtiq1bt6JmzZr49OmT1HzNmjXDgwcP8OnTJ7Ro0QLKyso4e/YsLly4wMpX1JgrRevUqVOh+4KDg5nHZ86cYV2XaPdrSkqKzOffvHkzevbsCQsLCyQlJTHp3759Q9OmTQs9rrye++zsbNbrvX37duzdu5fZzsnJYa5b1kVsuVyuxIxFRcvKykJERAQaNGhAN6YlZUK4DEqdOnXQtGnTEi1Dw+Vy5b67BKnehJ9VFbXEksxnOX36NHr27CnxYT9t2jRWMJWXl4ezZ89KDABPSkpifYHJGkjMnDmT1ULg5OSEfv364cKFCxW6pIC1tTVrYLI4PT09dOzYEW5ubjhy5AgaNmyIXbt2SayVVFkVNcZK2FULAIMGDcL+/fvLpQ5GRkbYvXs35s+fz7xfEhISsHz5cri4uBT6n6Y8nvvU1FTWe3b69OlYtWqVzOVJw+FwJLojK4saNWpU2rqRqkVdXZ35V0NDo0QLHispKdH7j5RIRXTxATIGUzExMbh9+za4XK7UW5ooKyuzblp5/fp1LFmyhDWgXPyLLyMjQ5aqoE2bNujSpQtevHgBAPD398f169ehoqJSob9catWqxSwtIE1kZCSWL1+Od+/eoVevXjh+/HiZLjBZ3opqOeRyuczYseLWh5FX3759MXPmTNbkhoCAADg4OGDNmjVSjymP517813N5Xzch1ZWuri7++OMP1vdDVFQUunfvLjV/UcsmEKIoMs3mc3Z2RocOHRAYGMhMFxf9Ey5JIJSZmYlLly6x0vT09KCtrc1sx8XFSdy4t6RmzpzJ2t60aROsra0l8l26dAndu3fHkCFD8Pr1a5nOVRTx5SGEEhISMGnSJLx79w7KysrYs2dPhfThlsUtbUpCdIZfcHAwEhISpOYrq27MFStWoG3btqw0JycnZh0oUbI+98X9mtHX14eOjg6z/fz5c6mr/lfmrltCKgNtbW106NCB+T4QHdsojZmZGUxNTSuqeoSUSKm/bZOSkvDPP/9g4cKFhebp27cva2YeUDA4XHQGEIfDYQ0a5vP5ePnyJbOdk5MjsThbfn6+1PP1798fDRs2ZLaNjY0xYMAAVp4vX75g69atSEpKQkREBFasWCHXF11pjj19+jTTEqKlpVWu47hEic8gE20tlDVwlaZXr17MYz6fL7Wbz8PDo8xuFszlcrF//36J61u7di0zu05I1ue+JOM2RN+/ycnJOH36tESekydP4u3btyU6JyG/ooyMDISEhDC9E9euXSt0jSnhH61+TiqbUgdTBw4cgIqKSqFNsEJz585lbSclJeHs2bOstNmzZ7NaAA4cOAAej4fs7GzY2dlJdMVIW6QRKAjMZsyYwWxPnTpVolXmw4cPrGAsJiamVAODxafhlmZa7sePH5nHqamp2L17N+7duyexMCaPxwOPx2PqKT5zTXxbvCVEfFs0wAT+b9bM69ev4eXlxdpX3Oy2ooLHqVOnsgYjX716FZs2bUJERATi4uLg4uKCy5cvY8iQIYWWIU78+RWfil+3bl2JFe1//vyJxYsXs46V9bnX1dVlBVSiS2AIB56Lv38PHTqEgwcPIjo6GjExMdi/fz9CQ0Px22+/lfi6CfnVJCUl4fbt26yJJYRUNSUOpjIyMnD06FFcuXIF6enpePr0aZFrLEm7L93x48fx5MkT5otZ/Oa9b968Qe/evZmWjg4dOrCOt7a2lrjFipCFhQX09PSgqamJcePGSexv3rw5K8AyMTFhlnQozqdPnxAQEMBK+9///sdqSSuKeCuds7MzNm7ciFmzZrHWwHr48CFmz57NDM5/+vQp67iQkBDmFi9ZWVkSXZX+/v6sbSsrK9YgTVtbW2zfvh1r166VGE8mvp6Xn58fazswMLDQm1ebmJhg9+7drHFwV65cwZAhQ9C7d2+4uLhg3759zPpTRcnLy8Pt27clAt27d+9KfNgOHz4c48ePZ6WFhYXB1taWySvLcw8U3ER67NixzP6goCBkZmbCxcUFP3/+BAC0bduW1bUrEAhw7Ngx9O/fH/369cN///2H7du3F3vNhBBCqjaOoIT9VRMnTpS4vYuSkhLOnDnDWqk7ICAAs2fPLnLdnpkzZ7K+hLy8vODk5ITw8HBoamrC0tIStra2WLp0KbKzs9GhQwe0b98e7du3L3Lxw0OHDuHnz5+FrrZ94cIFHD58GDVr1sSuXbvQsWPHIq85LCwMEydOLLIVSl1dHQcPHkS/fv0KzZORkYFNmzbh8ePH0NbWxtChQzF//nzo6+vDy8sLe/fuRWJiItq3bw97e3s0bNhQ6r35gILB/f7+/ujatSur206oS5curNXY3759i127diEkJAS6uroYMGAAFi9ejH379uHff/9l8jVo0ACbNm1Cz549YWdnh+vXr0uUzeVycefOHYllLoSCg4Nx8uRJBAQEMOs6DRkyBDNnzix0NXtRV69exebNm4vsgqxduzZ8fHyY7ezsbIwbNw7/+9//WPk4HA6uX7+O+vXrl/q5F8rJycH+/ftx8+ZNZGZmol27dliyZInEpAtfX184OTnh3bt3yM7ORr169TB69GhMmTJF5uUDgoKCAEBibJiiZWZmIjQ0FC1btqTZVKRMhIWFwd7eHvb29mjevLmiq0OqCeFnFZfLBYfDKffP0hIHU4SQikPBFPlVUDBFykNFB1MVM92LEEIIkUJNTQ0mJiZy3z6LEEWiYIoQQojCGBoaYsqUKcXefJ2QyoyCKUIIIYQQOVAwRQghRGG+f/+Offv24fv374quCiEyo2CKEEIIIUQOFEwRQgghhMiBgilCCCGEEDlQMEUIIYQQIgcKpgghhCiMsbExZs+eDWNjY0VXhRCZUTBFCCFEYbhcLvT09Fg3FiekqqFgihBCiMIkJibi1q1bSExMVHRVCJEZBVOEEEIURngPtaJuKE9IZUfBFCGEEEKIHCiYIoQQQgiRAwVThBBCCCFyoGCKEEKIwtSsWRM9evRAzZo1FV0VQmRGwRQhhBCF0dHRQY8ePaCjo6PoqhAiMwqmCCGEKEx2dja+fPmC7OxsRVeFEJlRMEUIIURh4uPjce3aNcTHxyu6KoTIjIIpQgghhBA5UDBFCCGEECIHCqYIIYQQQuRAwRQhhBCFUVFRga6uLlRUVBRdFUJkRsEUIYQQhTExMcGcOXNgYmKi6KoQIjMKpgghhBBC5EDBFCGEEIWJjo7G0aNHER0dreiqECIzCqYIIYQoTF5eHrKyspCXl6foqhAisyo/4u/ixYvYtWsXcnJyCs2jqamJEydOoEuXLhVYs8pl+/btuH79Opo0aYL9+/fDzMxM0VWS8PXrV1y+fBn+/v4IDg5m7eNwOFBSUoJAIICKigq0tLRQq1YtNGvWDMOGDcMff/wBDodTbnV79OgRBgwYUG7lE0IIqbqqfMvU5MmT8e7dO+zfv19iX6NGjfDkyRO8fv36lw6kfH19ce7cOWRkZODt27c4ePCgoqskVf369bFmzRq4ubnByMiItW/RokUICQlBUFAQ3N3dYWFhgY8fP8LT0xOLFi3CtGnTkJ6eXi718vT0hLOzc7mUTQghpOqr8sEUUNBqMXz4cOjr67PSBwwYQDNEAAgEgiK3KxsVFZVCW85UVFTQuHFjrFmzBjY2Nkz6y5cvsWrVqjKvS3h4ODZv3lzm5RJCCKk+qkUwJVSjRg3Wtrq6uoJqUrn06NEDU6ZMgYaGBtq1a4elS5cqukrFKsmaM1OmTGFtP378GEFBQWVWh4iICMyfP7/cWrwIIYChoSEmT54MQ0NDRVeFEJlV+TFTpGQ2bdqETZs2KboaZUpfXx/6+vpISkpi0oKCgtC2bVu5yw4ICICtrS0SExPlLosQ8n8sLS1ZM/fy8/PB5/PB5XKhpFTw+97U1BTXrl1TVBUJKTUKpgrx7NkzODk5ITg4GDweD23atMHChQvRo0cPqfmfPn2KGzduIDg4GDExMVBXV0fTpk0xY8YMDBw4kJXX1dUVBw8eZLV4LF68GEuWLMGHDx+wc+dOBAUFwcrKCmvXrsWNGzewZ88e1hf74sWLYWFhgWPHjuHp06fIzMxE27ZtsWHDBjRr1ozJZ2tri7t377LOP2bMGOzevRsA4OzsjEOHDiEzM5PZv2vXLrRs2RLHjx+Hv78/cnNz0bVrV2zYsAGmpqYS156SkgInJyc8fPgQ379/R15eHnJzc5n96urq4HK5aNKkCS5fvlySp7/ExLssRa9DVGJiIi5dugQfHx9ERkYiNTUVRkZG6N+/PxYsWAADAwMmr6enJ7Zt24aUlBQm7dWrV+jcuTMAoFOnTjh58iSzj8fjwcXFBR4eHoiMjISGhgYGDBgAW1tb1K5duwyvlpCqLzo6GlFRUUxXvpKSEtTU1Jj9UVFRiqoaITKrVt18ZWXfvn2YNWsWfv78ifv37+PSpUsICQnBrFmz4ObmxsqblZWFBQsWYO7cuTA3N8edO3fg6ekJbW1tvHz5EosWLcLp06dZx0yfPh12dnYS5/3w4QMmT54Mf39/ZGZmwtnZGWlpaRg9ejTWrFnDyuvr64s5c+ZAV1cXGhoayMzMhL+/P2bMmIHU1FQm36FDh7Bhw4ZCr3XmzJmYO3cuK+3mzZtYtWoVzMzMoKKigvT0dDx8+BBz5syRmL78+fNnjBo1CidPnkRsbCzOnz+P169fY9CgQUyepk2bws/Pr8wDqaSkJCQnJ7PSWrduLZHP29sbgwYNQmBgII4fPw5vb2/Y2Njg+/fvcHV1xYQJE5CQkMDkHzlyJPz9/VlldOrUCQEBAQgICGAFUvHx8bCysoKDgwNGjx6NFy9eYMqUKXBzc8O4ceMQGRlZptdMSHVgZmYGX19fqX+VcaYxIcWhYErMhQsXcOrUKQDAihUroK2tjRYtWsDc3BwCgQBbt27Ft2/fmPyHDh3C48ePARQ0V3M4HNStW5cVTBw4cIDVFQVA4gODx+Nh1apVqFevHpMmXA4AgMR4gm/fvsHV1RVr1qzB1q1bmfTExETcunWLlbew1jQh8bITExNx5coVrFmzBsuXL2fSP336BB8fH2Y7Ly8Ptra2+PHjB4CCIKRdu3ZQU1PDvHnzmHxBQUHw8vIqsg6ycHFxYW23bdsW3bp1Y6XFx8dj+fLlyMjIQFpaGnR0dKCsrIzp06czeSIjI5nXvDTy8vKwZMkShIaGok6dOpg5cya4XC7mzp0LLS0txMbGYv369bJdHCGEkCqDuvlEZGRkMMsGKCsrM906QMEyCwDA5/Pxzz//YOXKlQAKugOFjhw5wqxFpKGhwaTz+XxERkayZhsKgyQhNzc3bNy4Eebm5jh69CicnJxgaWkJLS0tqfnHjRvHLB8g3vUWERHB2hZtQpdGvOzp06cz55VWdt++fQEAr1+/xv/+9z9mX+PGjZnHwudLKDAwECNHjiyyHiXB4/EQERGBGzduwMnJiUn/7bffcPjwYYm1pt68eYOMjAwAwLt37+Dt7Y0BAwZAU1OTle/Lly+lrsutW7cQGBgIAOjcuTOUlZUBAFwuF/Xq1UNISAj8/Pzw+fNnieejJAQCQaHdloqSlZXF+peQ0srPz5f4zJGWp7K990nVIvyMEggE5boGoRAFUyKePn3KdJEZGBiwZpSJBkdv3rxhHg8ePBhhYWEACrqChPLz81ll83i8Is+tra0Nc3NzAAVrKi1atKjI/MIvbvHHQOHjhkqqpGXHx8ez9ok+R6KPgZLNziuKo6Mjzpw5I/El3qRJEyxbtgwDBgyQqCtQEGQZGhoiLi4O2trazHgy8dcnOzu71HXy9PRkHhsbG7P2ib9fZAmm+Hw+QkNDS31cRRAP2AkpKT6fX+wPvMr83idVS25uLlRVVcv9PBRMiRBddTsuLo7VMpWXl8e8IGlpaUz64sWLMWzYMGRkZKBdu3b48uULHB0dJQZ9i395ixMNxORVnrdlEC27QYMGrH18Pp95LB48tmrVSq7zzps3D7Nnz8bYsWNZrUhRUVGoX7++1EAKKOjC9PLyQmhoKBo1agQdHR1cu3ZNootQlrW3RN8vZ86cwYULF5htPp/PvF9EB7KXhnDQfmWSlZWFiIgINGjQQGIpEkJKgsvllihPy5YtK6A2pLoSflbJ+0O+pH75YIrP5yMnJweampqsgdtAQRdecb+ggILurfj4eKxfvx43btyAjY0Npk+fjuPHj5e4HmW5xkp5LsopWnarVq3w+++/4+XLlwDYrRWiAU/dunUxbNgwuc+toaGBAwcOYPz48cztg7KysrBkyRJcu3aN6ZoUp6mpic6dO+POnTvYv38/1NXV4eDgIHe3o+j7ZdCgQVJX4ZcHh8ORaOGrLGrUqFFp60Yqt+K6+IR56P1FykJFdPEBNAAd9+7dw9OnTwFI/mIq6RRdT09PDBs2DFevXsW2bdtgY2NTaEtJYUoStFVGR44cQdeuXQEA7u7ueP/+PVJTU5nAwszMDCdPniyzZtYWLVpIzGyMiIgocqB3UlIS5s2bh2XLlkFTUxPnz59H06ZN5a6L6PuF7nhPSMlFRUWhe/fuUv9oaQRSFf3ywdS///7L3HJGdCYdUDClXhrR1hl3d3esXLkSP3/+RL9+/TBmzJjyq2wlpKurC1dXV6xcuRIpKSmYNGkS+vXrh9jYWCxbtgweHh6sgellYerUqRJrd3l5eeHs2bMSebOzs2Ftbc28llu3boWOjk6Z1EP0/RIcHMxaXkFUZb99DyEVydTUlDWbmc/nIy4ujhkmYGZmJnU9O0Iqs186mAoJCYGPjw8zeLh3796s/WfOnJFY0iApKQkbN24EUDB+aO/evcw+8TFEv4r9+/fj4sWLePjwIYKCghAYGAgPDw8sXLhQYtZcWdm5c6fEB+6+ffuY2XVC//zzD8LDw5nthg0blvgcxY3t6NWrF/OYz+dL7ebz8PDAnTt3SnxOQqq7a9eusdaVunDhArp06YILFy4wabT6OalqqlUwJT7IWziuRpr09HT8+eefUFJSYlapbty4Mfr378/kiY+Ph7W1NXx9fZGcnAw/Pz/MnDmTuSdccnIya1Vyd3d3eHp64vz587h48SLrfDwejzUTTnz2WHGtF+LXJrotPuBcvCzxweDi2/KUfenSJZw4cQJ9+/ZFnTp1irqEUhGfkSi+XbNmTezbt481uJDP52Pp0qWsWYYfP35kHWdvb4+HDx/C1taWlc7j8ZCfn896bmrVqsUqW7zMqVOnsgZhX716FZs2bUJERATi4uLg4uKCy5cvY8iQISW+bkIIIVVPtQmmeDyexGrYL1++lPgSzsrKwoMHDzBu3Dj873//g6GhIWt8086dO1ktTOHh4ZgxYwa6deuGWbNmwdrampllYmBgwKz1BBTM2lq5ciXu3bsHGxsb1nm3bNmCv//+m9n28/Nj7Q8MDCwy+BPvQoqLi2Mex8bGFppXIBAwY8KEQkJCWLeykbVsAEzQ+Pr1a8TExBRa/5ISCATw9/dntSYBBctWiI+l6NSpExYvXsxK+/HjB+bPn8+sPC6+IrqHhwdWrlyJAQMGsFqpgoKCMGnSJHz//p1Jmzx5MvP406dPSEhIwO3bt/H582cAgImJCXbv3s0K6K5cuYIhQ4agd+/ecHFxwb59+0o9fo4QQkjVUuVn84WFhcHX1xcPHjyQaO0JDAxEp06dUKNGDSgpKSEvL08iuBJfH0hfXx/Xrl3DqVOn4OXlhZiYGOjo6KB9+/aYN28e2rVrx+TlcDg4dOgQNm/ejIiICDRp0gRTp06FhYUF8vPzERoaCi8vL6iqqmLIkCHMauJ2dna4fv0667zPnz9Hx44dcefOHdStW5e1z9PTk7mXntCNGzdQv359dOvWTeLWNM+fP8fatWuxe/duLF26VGKZhoiICHTp0gX+/v64fv066/YoAODk5ARjY2MYGBjgr7/+Yu1zd3eHnp4ecy3CoCw8PBz9+vVjPTeqqqrQ19dH8+bNmbFURfH19cXcuXNZrUBCHz9+xIABA6Curg4fHx9oa2sDAObPnw9/f3/4+voyeYODg/HHH3/g6NGjsLS0RFhYGG7evAkul4v+/fvDxsYGZmZmMDMzw6ZNmxAVFYXmzZtj3bp1rKUIhLfZuXjxIpKTk7Fo0SLMmzcPf/zxB5Nn6NChqFu3Lk6ePImAgACkp6fD1NQUQ4YMwcyZM6Grq1vkNRPyq1NSUmLd5JiQqogjoNGxRA4HDhwo8RIQe/fuxejRo8u5RtVDUFAQgIJb5FQmmZmZCA0NRcuWLWnqOikT9J4i5UH4vuJyueBwOOX+WUo/BYhcbG1tSzwm6Pz58+VcG0IIIaTiUTBFZJaZmQlra2vcvXsXO3fuxKtXr/DhwweEhoYiKCgIL1++xNWrV9G+fXsA5bsyOyGkaoqNjYWzs7PE+ExCqhIKpojMnj17hhcvXkBDQwMWFhbQ0tICh8OBkpISVFVVoaOjg7Zt22LQoEEAILE2FCGE8Pl8JCYmSh0rSUhVQcEUkVmnTp1gZmaGzMxMrFq1Cp8/f2aWVcjPz0dUVBRcXV1x4sQJDBkyBHPmzFFwjQkhhJCyV+Vn8xHF0dfXx82bN3Ht2jX4+Phgzpw5SE1NhYqKCrhcLmrVqoVOnTrh8OHD6N69u6KrSwghhJQLCqaIXLS0tGBtbQ1ra2tFV4UQQghRCOrmI4QQojC1atWChYUF644DhFQ1FEwRQghRmBo1aqBJkyasWzMRUtVQMEUIIURh0tLS4Ofnh7S0NEVXhRCZUTBFCCFEYVJTU+Hj44PU1FRFV4UQmVEwRQghhBAiBwqmCCGEEELkQMEUIYQQQogcKJgihBCiMBoaGmjWrBk0NDQUXRVCZEbBFCGEEIUxMDDAqFGjYGBgoOiqECIzCqYIIYQoTG5uLn7+/Inc3FxFV4UQmVEwRQghRGFiYmJw8uRJxMTEKLoqhMiMgilCCCGEEDlQMEUIIYQQIgcKpgghhBBC5EDBFCGEEEKIHCiYIoQQojB16tTBsmXLUKdOHUVXhRCZUTBFCCFEYTgcDlRUVMDhcBRdFUJkRsEUIYQQhYmLi8Ply5cRFxen6KoQIjMKpgghhCgMj8dDZGQkeDyeoqtCiMwomCKEEEIIkYOKoitQVpo3b8485nA4qFGjBpSVlfHz509WPnV1dXC5XGRnZ4PP5zPpu3btwtixYyukrsHBwfjzzz8RFxeHBQsWYPbs2WVWto+PDzZt2oTs7GysXbsWo0aNKrOyy9KAAQMQFRXFbGtoaEBZWRnp6ekQCARMuqqqKtTU1MDj8ZCTk8OkL168GEuWLAEAuLu7Y+/evVBXV8f27dvRo0ePirsQQgghv7xq1TKlrq6OHTt2ICAgAIGBgQgICJDIs3nzZgQEBOD9+/f4559/0LJlywqv544dO/Dx40ekpaXhr7/+wrdv38qs7PXr1yMqKgqJiYlYv349srOzy6zssqakpITVq1fDz8+Peb1MTU1ZeebNm4eAgAAEBQXh9u3b6NatG2t/VlYWNmzYgMTERERFRWHdunUVeQmEEEJI9QqmNmzYgHHjxkFLS6tE+du1awdnZ2fo6emVc83YRFteBAIBa7syl13W5s2bhzlz5pT4+W/cuDEcHR3RuHHjQvNU5uslhEjS09PD4MGDK/xzmJCyVG2CKTMzM4wfP77Ux+np6WHq1KnlUKPCrVu3Do0aNYKOjg5WrlyJ+vXrl1nZ27Ztg4mJCQwMDLB9+3bUqFGjzMouS+rq6liwYEGpj1NTU8PcuXOZ7Ro1amDr1q3Q19eHqakpduzYUZbVJISUMy0tLbRr167EP4IJqYyqzZgpa2trmY8dPnw4fvz4UYa1KVrbtm1x586dcim7b9++ePLkSbmUXZasrKxkDvQGDBiA//77j9keO3ZshY13I4TIx9LSEtHR0cx2Xl4eMjMzmXGTAGBqaopr164pqoqElBoFUwAaNWoEHx8fLF68GOnp6Uy6cJDzhw8fsHPnTgQFBcHKygpr165l8uTl5eHu3bu4ffs2wsLCEBsbCx0dHbRs2RLz58/H77//zuQNCwvD6NGjJbqiHj58iDp16iAyMhKrVq1CYGAgs8/MzAxeXl44d+4c/v33X3z79g1GRkaYM2cOrKysmHyPHz+W2tITFhYGAHj//j3s7OwQHh7O7OvSpQuOHz+O06dP49atW/jx4wfq168PW1tbDBo0SOpzdevWLbi5uSEkJARZWVmsQfzKysrQ0NAAAJw7d67I8WjyvF41a9bEyJEjcf78eWzbto21z8zMDI8ePQJQMNB/48aNCA4OZl3z0aNH4ejoCC8vL8TGxkJfXx8jRozA8uXLoaqqCh8fHzg7O+Pt27cQCATo3Lkz1q9fj3r16kmtz5cvX3DixAk8e/YM6enpqFu3LiZOnIjJkyfTQoSEiImOjkZUVBTMzMwAFHxuaGtrM/tFJ6YQUlVUm24+eU2fPh12dnYS6R8+fMDkyZPh7++PzMxMODs7Iy0tDQCQlJSEyZMnY/369Zg7dy7u378PNzc38Pl8/Pfff7C2toanpydTVvPmzfHixQvUrVtXah3q1KmDc+fOQU1NjUnLyMjA1KlT8eHDB9SvXx88Hg/fvn3Dpk2b4OHhweTr378/nj59Ck1NTallt2nTBqdOnWKl/fjxA5MmTUJycjKMjY3B4/EQHh6OpUuXSgzez8vLw/Lly7FixQr4+flh6tSpePPmDfbv38/kUVJSgrOzMwICAipkYP/UqVPh7u4OJSXpb+PWrVvj+PHjrLSYmBhMnDgRKioqGDZsGPh8Pn78+AEnJyesXbsWW7ZsgZOTE3r37g0DAwOkp6fjyZMnmDVrFitwFHrw4AFGjx6Nx48fw8XFBY8fPwaXy8XWrVvx559/lst1E1LVmZmZwdfXV+qfMMgipCqhYEqE+H9iHo+HVatWsVokOBwO8+Vtb2+PN2/eICcnByoqBY18LVu2ZGac5eXlYfv27cjPz2eO19HRQdu2bQutA5fLhb6+PrOdkpICKysr/PXXXzh8+DCrjq6urqxjjYyM0KRJk0LLNjQ0ZG1///4ddnZ22LJlCxwdHZkgLi8vD+fPn2flPXPmDG7fvg0A0NTUxKJFi6CiooLhw4czA8L5fD4OHDhQ6PnLQ8uWLVnPl7jatWuztmNjY7F582YsW7YMK1euZLUc3rp1Czk5OThz5gxmzJiBhQsXMvu+f/+O58+fs8oKCQnB8uXLwePxMHXqVDRu3Bh6enqYM2cOAODmzZtwd3cvg6skhBBSmVWbbr6yIN7C4ebmho0bN8Lc3BxHjx6Fk5MTLC0tmYGSz549AwDk5ubixIkTOHz4MAAwXV0AkJycjJSUFNYXvmjLU3H1MDY2hqWlJZNuZGTENINHRERIHFtU2eLX16FDB2ZNpho1akBXV5cZOyZe9pUrV5jH9evXZ4JHoKCb9NOnTwCA169fF3lt5aG019y1a1dm29jYmLV/4cKFTNeceCD25csX9O3bl9n+66+/mLWvunTpwqQ3atSIeXzp0iVYWFiU8ErYBAIBMjMzZTq2vGRlZbH+JaS08vPzC21NFs1T2d77pGoRfkYJBIIKGW5BwVQRtLW1YW5uDgBYtGgRFi1axNo/ePBgXL9+HQDQqVMnJl20JQqAXGs9CQdkCokGMfJ+2JSm7Pj4eOaxaLAovs3lcuWqU0UTvebi9mVkZDCPk5KSWC1VokGZaFdrcHAw+Hy+TM8Ln89HaGhoqY+rCNICeUJKgs/nF/uDsjK/90nVkpubC1VV1XI/DwVTRRANkKTZtWsXpk2bBhUVFTRr1gzv37/HyZMn8fTpU1Y+8eCqrOTm5pZLudLKbtCgATOYXXzskOg9tVq1alVudVI00dfx/fv3rH1jxoxhglOBQMD6z5uWlgYDA4NSn4/L5RbZbasIWVlZiIiIQIMGDSrtshukcivJDwsul6uQBZVJ9SH8rCrqB3NZomCqCOJjjKRp1aoVvn79iiVLluC///6DnZ0dNDU18e+//1ZADSuOtbU1s7r4169fWU2nX758AVAwnkx0DajqLDU1lbV98OBB9OnTp0zPweFwJFoBK4saNWpU2rqRyq24Lj5hHnp/kbJQUTOqKZgqQnFN0QBw9uxZ/P3338jPz8fJkyfRs2dP1tIG1YWlpSWSkpJw8OBBpKSk4MSJE5g1axZu3ryJsLAwqKioYO3atejZs6eiq1ohxH9di66bQwgpWlRUFLp3717oPprRR6oams0nh2PHjmHXrl3g8XiYMGFCtQ8k5s6di+vXr0NPTw9Hjx5Fly5dcPjwYVhYWODatWuYNm2aoqtYYcRXrS9soVS6vQ0hbKampqxgic/nIy4ujhk+YGZmJnGPTkIqO2qZklFycjKOHTvGbDdo0EBxlakggYGBWLRoEVauXIlx48b90gtSNmvWDLVr12YG5nt7e+Ply5espRby8/OxevVq7NixA+rq6oqqKiGVivjK5mFhYbC3t4e9vT2aN2+uoFoRIp9q3TIlbRZdUVO6xfMX1arw7ds31kDss2fP4t69ezhx4gTu3r3Lysvj8Viz40QHbEvbFh3oLD54XXxguHgdiypbvKyiyhYvNyEhAXPmzEFGRgbGjx9fboGU+GtQkhmLotcofv3CpQsKK7+owfTieUWfH2VlZcyePZvZzs/Ph42NDdzd3ZGUlITw8HAsXrwYHTt2pECKEEKquWobTOXl5eHy5csS6bdv30ZSUpLUY/z8/FjbgYGBEl/GQg0aNGANkIyKisKSJUsQFhYmcauUxYsXM4tgJiYmIigoiLX/xYsXzOPc3FykpKQw28nJyawvfPF7CIouWfD9+3dmvSchf39/qXkBIC4ujnnM4/GQnJzMOm9eXh6zffPmTaSnpyM7OxuPHj0q85mEAoEAXl5eSExMZKU/efKkyPFI7969Y72eSUlJ+PjxI7Mt/pp+/PiReQ4TEhIkxrcJlzsQCAR4/Pgxa59wgVYha2trDB48mNlOS0vDmjVr0L17d5ibm0NPTw9Tpkwp8roJIYRUfRxBNRzUsWvXLly4cEHq7T+AgtH9tWrVgo+PD5NmZ2fHrBklisvl4s6dO1JvAfP48WPs3r0bP378QJs2bTBz5kz88ccfyMjIwKpVq+Dr6wsdHR1MmzYNc+bMQXh4uNR78wHArFmzMHXqVKxevRqvXr1i7evatSsOHTqEP//8E97e3qx9bdq0wbZt2/Djxw+p9+YDgI0bN6JDhw5Yt24dPnz4wNo3cOBA7NixAwsXLpRYcLNbt27YuXMnzMzMcOjQIRw9elRq+VwuF5qamqhbty769euHOXPmlKo15uzZs3BwcCg0cAUK1m569OgRdHV1mTRp9+YTOnHiBLKzs7Fs2TKJfcrKynj06BGGDBkitfVy7ty5SE1NhZubm8S+Jk2a4NatW8y2QCCAm5sbrl69io8fP0JFRQVNmzbFtGnTMGzYsCKuumjCgLuo1fIVITMzE6GhoWjZsiXNtiJlIi0tDa9fv0bHjh2ho6Oj6OqQakL4WcXlcsHhcMr9s7RaBlOk7P3vf//D2LFjiwx4hHr06AFnZ+cKqFX1RcEU+VXQe4qUh4oOpqptNx8pW02bNoWDg0OJFkB7/vy5RHcjIYRIk5iYiJs3b0p08RNSldBsPlIiZ8+exb59+9C3b19s2rQJtWrVgpKSEvLz85GTk4OkpCRcvXoVx48fB1C+q7MTQqqPzMxMhIeH0734SJVGLVOkRA4fPgw+n4+RI0fC2NgYKioqUFJSgoqKCjQ0NFCnTh1Mnz4dANCwYcNKdxsUQgghpLxQMEVKZNSoUQCAffv24b///mMN3E5PT8eTJ09gY2MDIyMjHDx4UOImyoQQQkh1Rd18pEQ2b96Mfv364c6dO9i7dy9iY2MhEAiYmXytWrXCmDFjMGrUKLoBLiGEkF8KBVOkxPr27Yu+ffsquhqEkGqkZs2a6NWrF2rWrKnoqhAiM+rmI4QQojA6Ojro1q0brTFFqjQKpgghhChMVlYWPn78WOStvgip7CiYIoQQojAJCQlwd3dHQkKCoqtCiMwomCKEEEIIkQMFU4QQQgghcqBgihBCCCFEDhRMEUIIURgulwsDAwNwuVxFV4UQmVEwRQghRGGMjY0xc+ZMGBsbK7oqhMiMgilCCCGEEDlQMEUIIURhoqKicPDgQURFRSm6KoTIjIIpQgghCpOfnw8+n4/8/HxFV4UQmVEwRQghhBAiBwqmCCGEEELkQMEUIYQQQogcKJgihBCiMIaGhpg6dSoMDQ0VXRVCZEbBFCGEEIVRU1ODsbEx1NTUFF0VQmRGwRQhhBCFSUpKwoMHD5CUlKToqhAiMwqmCCGEKExGRgbevHmDjIwMRVeFEJlRMEUIIYQQIgcKpgghhBBC5KCi6ApUtL179+LMmTMS6RwOB0eOHMHAgQMl9s2YMQO+vr4S6WfPnkX37t3LpZ7l6cePH3BycoKPjw9iY2PB5/NhYmICS0tLzJ07FxwOh5U/LCwMEydORGZmZqFlqqurY8GCBVi4cGF5V58QQgipVH65lqk///wT3t7e6NixIytdIBBg9erV+N///idxjLOzM27duoXffvsNADBr1iz4+vpWyUDqzZs3GDFiBM6ePQs9PT34+fnByMgIERERcHBwwJ07dySOad68OQIDA3Hjxg3o6+uz9qmqquLChQt4+/YtBVKEkFLT1tZGp06doK2treiqECKzXy6YAgBjY2M4OjqiZs2arPTMzEzY2NggNTWVlc7hcNCkSROsWLECampqWLFihURQURUIBAKsWbMGP3/+BADUqVMHXC4Xv/32G1RUVNC0aVO0a9eu0ONbtGiBLl26SKR17ty5XOtNCKm+dHV10b9/f+jq6iq6KoTI7JcMpoCCX0NaWlpQVVVlpX/79g3Lly9HXl6exDGmpqbQ09MDl8utqGqWqYSEBEREREik79u3D8HBwfD09ESdOnWKLKNGjRqsbXV19bKsIiHkF8Pj8RAdHQ0ej6foqhAis19uzJS4bdu2YcOGDeDz+Uzas2fP8Ndff2Ht2rWsvEpKSlBWVq7oKpYZ+rAihCiKpaUloqOjJdL5fD6Sk5Ohp6eH+vXr49q1awqoHSHy+WVbpoQ6d+6MnTt3SqQ7OzvD3d29VGUlJiZi586dGDRoEDp27Ig+ffpg4cKF8PHxKaPasvn5+WHBggXo3r07OnfujCFDhmDPnj1SP7DMzc0xatQoVpqnpyc6d+4MR0fHcqlfcZ4+fYqVK1di6NCh+O2339C1a1dMnToVDx48YOVzc3ND8+bNJf5atGiBgIAAAMCtW7dY+0aOHMkqIy8vD5cuXYKlpSU6deqErl27Yvny5fj69Ssrn6urKzp16sQq6/DhwwCADx8+YPr06ejQoQN2797NHJObm4sDBw5gwIABaNeuHevYY8eOlcdTR0iVEx0djaioKIl0LpcLQ0NDxMXFSf3sIqQq+OWDKQAYNWoUlixZIpG+adMmBAUFlaiMsLAwjBw5Ei4uLvjtt9/g5+cHZ2dnBAQEYPbs2di2bRvy8/PLrM6Ojo6wtrbGkydPsGvXLgQEBMDS0hJOTk4YPXo0nj17xsrv4eGBmzdvstJGjhyJgIAAzJs3r8zqVRJZWVlYsGAB5s6dC3Nzc9y5cweenp7Q1tbGy5cvsWjRIpw+fZrJP27cOCxYsECinGvXrjHjtUaMGIEHDx5ATU0NnTp1wuXLl5l8GRkZmDNnDuzt7dG+fXv4+fnBzs4Ot2/fxrhx4/D+/Xsm7/Tp02FnZydxrg8fPmDy5Mnw9/dHZmYmnJ2dkZaWBgDYsWMHjh8/jrZt2+LVq1d4/Pgxhg4dWmbPFyHVhZmZGXx9faX+mZmZKbp6hMiMgqn/b/HixbCwsGCl8Xg8LF68GPHx8UUeKxy4LrwdwqJFi6CqqorGjRszZZ4/fx6urq5lUldvb284ODgAANq3b49+/foBKJhlqKuri7S0NCxZsgQxMTFlcr6ydujQITx+/BgAkJ+fDw6Hg7p162LQoEFMngMHDjDPp5KSEpYuXYoWLVqwyhEOpBeqW7cu1NTU8Oeff0JLS4tJX7duHZ4/fw5NTU0sW7YMXC4XFhYWaNy4MdLS0rBy5UrWGDnxD3Uej4dVq1ahXr16TBqHw4GSkhIiIyOZwK1x48bgcrkwNTXFgQMH0Lt3b3meJkIIIVXELz9mStT27dsRHR2NFy9eMGmxsbFYsmRJkYHQ+fPnERkZCaBgQHaDBg2Yfc2aNWMeHzx4EFZWVhKDuEtDIBCwuphEy1dRUUGjRo3w+vVrZGRk4NChQ9i1a5fM5yovoq1mR44cwYABAwAAGhoaTDqfz0dkZCQza1JJSQnz58/H8uXLmTzXr19Ht27dmO3Q0FDUrVsX7du3Z9JevXoFLy8vAECrVq1Y068bNWqET58+ISIiAs+fP2eCHyUl9m8MNzc3bNy4Eebm5jh69CicnJxgaWkJLS0t+Pr6Mi2OJ0+ehIGBAaZMmQIOh4PNmzdLXWqipAQCQZFreylCVlYW619CSio/P1/i/5a0PJXtPU+qJuFnlEAgkFg7sTxQMCWCy+XiyJEjmDhxIj5//sykBwYGYtu2bZg/f77U427cuME81tPTY71woi0kmZmZePz4MYYPHy5zHYOCglh1MzAwYO0XDRbu37+PLVu2SMxYVLTBgwcjLCwMANCpUycmXbwbVHzA/JAhQ2BiYsK0uN25cwdr1qxhnoNbt25h3LhxrGM8PT2Zx0ZGRqx9osHbmzdvCm1J0tbWhrm5OYCCVsdFixYx+/T09JjHubm52Lp1K549e4Zt27ahbt26cnWh8vl8hIaGynx8eZI2K5SQovD5fKipqRWbp7K+50nVlJubWyHfgRRMialZsyYcHR1hZWWFxMREJt3NzY0VGAllZWXh48ePzLZ4q5OKCvspDgsLkyuYEh3fI+18orMNf/78iZiYGNSvX1/m85WHxYsXY9iwYcjIyEC7du3w5csXODo64u7du6x84sGVsrIyJk+ezHRx5uTk4PLly1i0aBHy8vJw9+5diZlAwcHBzGMvLy94e3sz26L/ycTXFhMlGvCJa9++PZo2bcpa7PXhw4d48+YN/v77b1bLWWlxuVw0adJE5uPLQ1ZWFiIiItCgQQO5WljJr6ckS8pwuVy0bNmyAmpDqjvhZ5X4d3B5oWBKirp16+LYsWOYPn06q3XEyclJYjyNcBCykPgHhnhAIBwHJCvxL33xiFsgELC2ExMTK0UwlZqayloktXHjxoiPj8f69etx48YN2NjYYPr06Th+/HiR5YwfPx5Hjx5FdnY2AODSpUuYN28enj17hnbt2kFHR0fivEKtW7eGm5tbqetuaGhY6D4VFRXs27cPs2fPRkJCApOemJiIOXPm4OjRo+jbt2+pzwkUjMsSbT2rTGrUqFFp60Yqp+K6+IR56H1FylJFdPEBNAC9UO3bt8fevXuLfSE0NTWL3C+++Gdx+YsjrXVMVG5ubpmeryz8+PEDhw4dYqV5enpi2LBhuHr1KrZt2wYbG5sSreGlp6fHdLkBQHx8PO7cuYPr169LdPEB7OBW1mnXxXVNtGjRAteuXZO4RRGfz8emTZtYa5gR8iuLiopC9+7dpf5JWzaBkKqCgqkiDB06FKtWrSoyj5aWFqvlR3yGmfhAXdEB469fv8aQIUPQvXt3XLp0qUR1atWqFWtbvGVM2GIDFAQSDRs2LFG55enGjRus8Uru7u5YuXIlfv78iX79+mHMmDGlKm/atGmsbUdHR4SFhUntUhOdgRcfH8/q9hMl3qJXUpGRkbh06RKMjY1x/vx5LF26lNWsHBsbi0+fPslUNiHViampqdTlD/h8PuLi4mBoaAhTU1MF1IwQ+f3SwVReXl6xaz/NmTMHVlZWReYRHQOVkpLC2ifazaSqqor+/fsDKPjyXrFiBSIiIpCUlIRt27bhy5cvxda5ffv2rA8k8W4/0e0BAwYofPB5VlYWzp07xwRTeXl52Lt3L7NfdOZjSTVv3px1j8D//e9/GDlypNRWxF69erG2HRwcJF7zly9fwtnZudT1ELp06RIEAgGUlZVhY2OD06dPs1rEStK9QUh1d+3aNanrS124cAFdunTBhQsXaPVzUmX9sp/yOTk5SEhIwPfv34vNu2nTJokvZVEzZ85E7dq1ARTM2IuLi2P2ibZKzJ49m5n9lZyczFoHKi8vj5nhVhRlZWWsXLmS2RadVZWTk8NcD5fLha2tLetY8S5A0VaskhIPRHJycorMv3nzZsTFxcHExARAwXWLDux3d3eHp6cnzp8/j4sXL7KO5fF4hU6Ttra2Zh5zOByMHTtWar7Ro0czrw1QsCyDra0twsPDkZSUBHd3d2zfvp0VMIs/L8W1WoWFhcHFxYXZ7t69OzNOysjICI0bNy7yeEIIIVXbLxlMpaSkYMeOHcjNzYWDg0OxLUIqKio4ePAgmjdvLnV/zZo1cfToUWZZgv3794PP5+PDhw/MGkcjRoxgrbKup6cHY2NjZltZWbnQ8sWNGDGCmXLv6+uLp0+fQiAQwNHREVlZWVBVVcX+/fslZoI9efKEtR0YGFiiYFKU+EKgnz59YtbYEsrNzcWbN28wb948ZtkIYTBlYGDA6vJLSUnBypUrce/ePdjY2LDK2bJlC/7++2+p9RgwYADTQtetW7dCV0/W1NTEoUOHWINa79+/D3Nzc3Tv3h27du3C7t27WWPL/Pz8WGUEBgYWGzTu2bMHJ0+eBI/Hw48fPxASEgIOhwM7O7sqfT9HQspb7dq1YWlpyfrRQ0hVwxHIOlikitqzZw+cnJwk0uvWrStxTzhxsbGxWLZsGetWJaJ+/PiBkydP4unTp0hMTASXy0WLFi1gZWWFESNGSOQPCAjA+vXrkZqaiqVLl2LSpEmlupanT5/i/PnzePfuHXg8HmrWrInu3btj7ty5aNSoESvv0KFDCw0aly5dKhHIiEpOTsbt27cRFBSEf//9V2oeDQ0NKCsrQyAQICMjg9Waw+Fw8O7dO6bL8c2bN9i8eTMiIiLQpEkTTJ06FRYWFsjPz8fGjRvh5eUFVVVVjB07FsuXLy90SvWZM2ewd+9e7Nu3jzUoXZpv377h2LFjeP78OZKTk2FoaIi+ffti3rx5rKDWzs4O169flziey+Xizp07qFu3Lis9MjISf/zxByuf8HVfsGCBzDP5hLcxatu2rUzHl5fMzEyEhoaiZcuWNOuKlAl6T5HyIHxfcblccDiccv8s/eWCKVJ9/PjxAyNHjoSPj0+xM+6qGgqmyK8iNjYW//zzD8aPH8/6YUOIPCo6mPolu/lI9eDv74/Ro0dXu0CKkF9Jamoqnj9/XuTCuYRUdhRMkSrh7t276NKlC6ZNm8as23T9+vVSd40SQgghZY1WQCdVwrFjx5CamooXL14gNDQUSkpKUFdXp5lyhBBCFI6CKVIlGBsb48OHDwCAc+fOISgoCAcOHFBspQghhBBQMEWqiI0bNyI7Oxtv3rxBUFAQ7Ozs0KJFC0VXixAiJw0NDZrQQKo8CqZIlVCnTh3WwpiEkOrBwMAAI0aMgIGBgaKrQojMaAA6IYQQheHz+UhOTqYbgpMqjYIpQgghChMbG4szZ84gNjZW0VUhRGYUTBFCCCGEyIGCKUIIIYQQOVAwRQghhBAiBwqmCCGEEELkQMEUIYQQhalbty5WrVqFunXrKroqhMiMgilCCCGEEDlQMEUIIURh4uLicOHCBcTFxSm6KoTIjIIpQgghCsPj8RATEwMej6foqhAiMwqmCCGEEELkQMEUIYQQQogcKJgihBBCCJEDBVOEEEIURl9fH8OHD4e+vr6iq0KIzCiYIoQQojCamppo1aoVNDU1FV0VQmRGwRQhhBCF+fnzJwIDA/Hz509FV4UQmVEwRQghRGFSUlLw8OFDpKSkKLoqhMiMgilCCCGEEDlQMEUIIYQQIgcKpgghhBBC5KCi6AqUJR8fHzx+/BgPHjxAbGwsa5+SkhI4HA4AgMvlQltbGyYmJmjVqhWsrKzQqlUrRVS5VHbs2IFz585BIBCw0sPCwhRUo7ITEREBJycnPHv2DElJSdDR0UGdOnUwbNgwWFhYQFlZGXv27IG9vX2hZTx69AgDBgwol/qVZ9mE/MrU1dXRoEEDqKurK7oqhMisWrVM9erVCxs3boSTk5PEvrNnzyIkJASvXr2Ck5MT2rRpg3fv3uHy5csYM2YMdu7cKRGkVDbr16/H48ePoaurq+iqlKl79+7BwsICfn5+2LFjB16+fIlHjx5h2bJl8PLyQs+ePfHHH38gPT290DJevXqFnTt3lkv9PD094ezsXC5lE/Krq127NsaNG4fatWsruiqEyKxaBVNC9evXL3RfjRo10KlTJxw/fhxdunRh0l1cXKrEF6aJiQkaNWqk6GqUmf/9739YsWIFsrKy4ODggG7dukFFRQXKysr4/fff4eLiglGjRiExMbHQMn78+IEVK1YgPz+/zOsXHh6OzZs3l3m5hJAC+fn54PF45fL/l5CKUi2DKRWV4nsvORwOpkyZwko7ceIE+Hx+eVWrzJTk+qqKM2fOMM95w4YNJfYrKytjy5YtaN++vdTjExMTMW/ePIlu3bIQERGB+fPnF9kiRgiRT1RUFA4fPoyoqChFV4UQmVWfb2UZNGnShLWdmpqKb9++oXHjxgqq0a8nKCiIeXzq1CksX75cIo+SkhJsbW3x77//stI/fvwIGxsbfP36tczrFRAQAFtb2yJbxAghBSwtLREdHV1kHlNTU1y7dq2CakRIxfqlgylpY6QyMjKk5g0PD8eVK1cQEBCAmJgY8Pl81KlTB2PHjsXUqVPB5XKZvP/99x/s7e0RGRnJpI0ZMwarVq3CiRMncP/+faSkpKBZs2ZYs2YNOnfuXOg5T5w4gRcvXiAzMxONGjXCrFmzSnRtOTk5OH/+PDw9PfH161eoqamhfv36GDt2LCwsLFj1TU9Px+rVq/Ho0SNWGSEhIbhy5QquXr2Kjx8/Ql1dHd27d4ednR2MjY3x+fNnnDhxAs+ePUN6ejoaN26MlStXomfPniWqIwCoqqoyj0+cOIEfP35g9erVMDAwYOXr1q0bHj58yGz7+flh9erViI+PZ9Kio6OZ59LExAQeHh7MvsTERFy6dAk+Pj6IjIxEamoqjIyM0L9/fyxYsIB1Pk9PT2zbto21iOCrV6+Ysjt16oSTJ08y+3g8HlxcXODh4YHIyEhoaGhgwIABsLW1pXEgpMrr3r07AMDX17fQPNHR0YiKioKZmZnU/SVtdSrJuQipjKplN19Jff78mbWtpqYmtVXqyJEjGDVqFLS0tPDPP//g0aNH6NatG8LDw7F7924sXryYFZj17t0be/bsYZURHh6OKVOmgMPhQE9PD9nZ2Xj37h3mzJmDiIgIiXN6e3tjwoQJuHXrFlq0aAE/Pz8cOXIEV65cQWBgYJHX9fPnT0ydOhV79uxBTk4O7t27h3v37oHP52PDhg2YOXMmkpOTmfxaWlo4fvw4GjRowCpn9uzZCAoKwqhRo6CqqorU1FR4eXlh+vTpuHz5MpYuXYpWrVqhWbNmyM7ORnBwMObPn49Pnz4VWT9Rv//+O2v733//xR9//IE9e/YgLi6OSVdWVsamTZuY7W7duuG///6Dqakpk2ZqaoqAgAAEBASwAilvb28MGjQIgYGBOH78OLy9vWFjY4Pv37/D1dUVEyZMQEJCApN/5MiR8Pf3Z9WrU6dOTNmigVR8fDysrKzg4OCA0aNH48WLF5gyZQrc3Nwwbtw4VkBNSHVmZmYGX19fqX+FBVmEVBe/bDCVl5eHc+fOsdKmTJkicbPNJ0+e4PDhwxAIBODxeFBVVYWWlhYmTJjAyiPeqmNkZMTa/vLlCw4dOoT169fj8OHDTHpWVhb++ecfVt6kpCSsXr0aWVlZAIBly5ZBVVUVxsbGOHLkSLGz+TZs2IC3b98CAKZNmwYDAwNoaWlhwYIFAICXL19iw4YNEsfVqlWLtd2tWzfs2rULM2bMwPTp05n0r1+/4sqVK7h48SJmzJiBHTt2MPv4fH6pmvJnz54NPT09VlpWVhacnJwwcOBAbN++HUlJSSUuT1x8fDyWL1+OjIwMpKWlQUdHB8rKyqzriYyMxKlTp0pddl5eHpYsWYLQ0FDUqVMHM2fOBJfLxdy5c6GlpYXY2FisX79e5roTQgipGn65br709HR8+PABp0+fxsuXL5n0sWPHYuXKlRL5fXx8mMcuLi5YtGgRtLW1oaGhwcr35csX1rZwTSuhgQMHonnz5gAKuqBEibdMubi4IDU1FQCgoaGB1q1bM/u0tbXRsGFDVveWqDdv3sDLy4vZFp4TAJo2bco8fvDgAV6+fMlqGVJSYsfWwuBLWp2nTZsGbW1tAJDoypLW0lYYIyMjODo6Yv78+RJBE4/Hw7lz53Djxg3Y2dlh7NixJS5X6M2bN0zX7bt37+Dt7Y0BAwZIBM3ir19J3Lp1i2kl7Ny5M5SVlQEUrGNWr149hISEwM/PD58/f5ZpBqZAIEBmZmapjytPwgBf+C+p/vLz8xETE4OuXbsWmicmJqbY1qeoqCipZeTn54PP52Pq1Kn48eMHTExMKt37nlQ9ws8ogUAg8X1cHn6ZYGrBggXg8/kSs/WEY2YKmy3Wv39/XLlyBTk5OWjRogXzJSw+jTc7O7vI8wu/aAHJ2XjiHxz3799nHhsaGpbqjXDjxg3WtuhYIGHwI3Tr1i2JbrbCiNZfnOj4K6DwcWeFadeuHdzd3bFr1y7cuXNHYn9aWhrs7Ozw5csXqQFvUX777TcYGhoiLi4O2traaNasGYDSv37SeHp6Mo+NjY1Z+0SD7Tdv3sgUTPH5fISGhpb6uIpQmoCZVG3Cz8yymOlcWBlKSkrIy8tj8lTW9z2penJzc1ljc8vLLxNMnThxArq6uhg/fjx4PB6T/u3bN+YLVpqePXvi4cOH+PbtG9q2bYufP3/i3LlzuHLlCiufPAt+5ubmsh6LjuVSU1MrVVnv379nbdeoUYN5LB4QldfK6bKsF2NkZIQDBw5g1qxZOHr0KJ48eSKRx9HREV26dEHv3r1LXK6hoSG8vLwQGhqKRo0aQUdHB9euXYOLiwsrnyyvX3BwMPP4zJkzuHDhArPN5/OZ/8CiA9lLg8vlSsw4VbSsrCxERESgQYMGrPcWqb64XC5MTEzw+PHjQvP079+/2HIKKyMyMhKurq6YPn06pk2bBgBo2bKl7BUmBP/3WVVRSwn9MsEUUNDlZWdnx7olyadPn7Bx40Y4ODgUepyhoSF0dXXh7OyMU6dOoUePHti4cSOWLFlS5nVMSkpifbGX9kte2D0oJNpqJB7kyDMWqaysW7eOtXJ5u3btcPLkSYSEhODAgQPw9vZm5b948WKpgikA0NTUROfOnXHnzh3s378f6urqcHBwwMiRI+Wqu+hzPWjQIOzfv1+u8sRxOByJ7uTKokaNGpW2bqRsCbv/i3q9xYcIFJZHWhkCgQCfPn2CQCAo0bkIKY2K6OIDfrFgCgAmTZoEX19f3L17l0nz9PREx44dJRbxFAoNDcWyZcsQEREBS0tL7NixAy9evCiX+olH0aXtMtPS0ip0n7AZXUh83JAiBAYG4ufPnxJdkK1atYKjoyP+/fdfrF+/nqm7+AzMkkhKSsLatWvh7e2NVq1awcXFBTo6OnLXncvlMt0Wxa2xQ0h1FxUVxSxtIG0fzegj1dkvOZtv+/btEv+xd+3ahXfv3knk/fjxI6ZMmYKIiAjo6elh8+bN5Rrp6unpsQKLuLg4VjdgccRv2JyWlsY8Fh80XFT3ZkXh8XhFzv4bM2YMZs6cyWzXrFmzVOVnZ2fD2tqaaeHaunVrmQRSAFCvXj3mcXBwMGt5BVGV/Z6PhBRFuLxBUUxNTYsMlszMzFjLmMhzLkIqo18ymNLR0cHff//NagXi8/lYunQpa/0lADh48CDTOmRiYlLqMUylxeFwWIte8vl81qzDnJwciQXwRLvvRowYwdon2hUl3gU4ZMiQMqmzvI4dO8ZaU0qc6KKm4l184oPfxf3zzz8IDw9ntqXdsqYwxZXdq1cv5jGfz5fazefh4SF1UD0h1cm1a9cKXWNK+Eern5PqrFoGU9Km1Yq3yrRv3x5Lly5lpUVHR2PVqlWs7rCPHz8yj0NCQuDo6Ihbt26xxvkABS0soucVb40QDXjEW5rE886ePZvV+nXgwAHweDxkZ2fDzs5OIvAQ7frq3r07K+AQvdWK6GKanTt3Rr9+/VjliM+0EZ3hJr5PdBC/+HNb2lk/qampmDdvXqEtO8+fPwdQ8OtWtJUKYK+NJXpe4esm+voBgL29PR4+fAhbW1tWuvBGq6LXVVzZU6dOZQ3Cvnr1KjZt2oSIiAjExcXBxcUFly9frjRBKyGVka6uLvr161fs+nmEVGbVLpjKycnBpUuXJNI9PDwkblg7d+5cVusCULCu1IYNG5i84t1mDg4OcHBwwNKlS1mDLq9cuQJbW1smABFfB0o0ABK/Ka943nbt2rECvTdv3qB3795MXTt06MDKb21tzRoDtnfvXqYL79SpU0hKSkJKSgozg61p06Y4dOgQK2D78eOHxFpLz549A1AQVP3333+sff7+/kzQKb5g6ZcvX0p1Tzsul4tPnz7BwsICV65cYbomExMTcfDgQZw7dw6NGjWCs7OzxJiwyZMnM48TExPx8eNHvHz5klnBXHSNLqDgfbBy5UoMGDCA1UoVFBSESZMm4fv371LL/vTpExISEnD79m0meDUxMcHu3btZLZxXrlzBkCFD0Lt3b7i4uGDfvn1FLitByK9OW1sbnTt3lhg3SUhVwhFUowEdhw4dwvHjxwudms/hcNC+fXtcvnyZSUtMTMTo0aMlAholJSX4+vqCz+dj3bp1ePnyJWrXro3Ro0dj1qxZ0NDQwIULF3D8+HFkZGSgZ8+esLe3R61atfDs2TNs3ryZ9cXM4XAwdepUWFtbw8bGhtX1BBQs6rlnzx5WsODl5QUnJyeEh4dDU1MTlpaWsLW1xdKlS5GdnY0OHTqgffv2aN++vUSQkZ2dDRcXF9y5c4epR926dTF8+HBYW1uzuivj4uLQr18/iQHqQEHweOPGDTx9+lRi32+//YZp06Zh1apVEvuUlZXx5MkTGBoaSn0thKZNmwYHBwfo6Ojg0aNH8PT0REhICH7+/AklJSU0a9YMQ4cOxfjx46Guri61jGvXruHkyZOIjY1Fw4YNMW3aNIwbNw5AwaD7nTt34ubNm+Byuejfvz9sbGxgZmaGly9fYtOmTYiKikLz5s2xYcMG/Pbbb0y5AoEAp06dwsWLF5GcnIwWLVpg3rx5+OOPP1jnDw4OxsmTJxEQEID09HSYmppiyJAhmDlzpsy/toU3gG7btq1Mx5eXzMxMhIaGomXLljTjipSJhIQE3Lt3D4MHD5a4CwMhshJ+VnG5XHA4nHL/LK1WwRQh1QUFU+RXERYWBnt7e9jb27Pu2ECIPCo6mKp23XyEEEIIIRWJgilCCCGEEDlQMEUIIYQQIgcKpgghhCgMl8uFoaFhseu6EVKZUTBFCCFEYYyNjTF9+nQYGxsruiqEyIyCKUIIIYQQOVAwRQghRGEiIyOxf/9+REZGKroqhMiMgilCCCEKIxAIkJeXRzcEJ1UaBVOEEEIIIXKgYIoQQgghRA4UTBFCCCGEyIGCKUIIIQpjZGSEGTNmwMjISNFVIURmFEwRQghRGFVVVdSqVQuqqqqKrgohMqNgihBCiMIkJSXBy8sLSUlJiq4KITKjYIoQQojCZGRk4P3798jIyFB0VQiRGQVThBBCCCFyoGCKEEIIIUQOFEwRQgghhMiBgilCCCEKo62tjS5dukBbW1vRVSFEZhRMEUIIURhdXV306dMHurq6iq4KITKjYIoQQojC8Hg8fPv2DTweT9FVIURmFEwRQghRmLi4OLi5uSEuLk7RVSFEZhRMEUIIIYTIgYIpQgghhBA5UDBFCCGEECKHSh1MJSUlITY2VtHVIIQQUk5UVFSgpaUFFRUVRVeFEJlV6mDK2dkZjo6OMh//6NGjMslTHjIyMrB27Vo0b96c9VeWeDwenJ2dMW7cOHTs2BHdunXDiBEjsH37dnz8+BEAsH79euTm5hZaRlhYGCIjI8u0XlWBtOv28vLCgAEDWK/X4cOHFVRDQqoHExMTLFiwACYmJoquCiEyq7TBVHp6Oi5duoTr168jOTm51Md7enrC2dm5yDyvXr3Czp07Za2iXDQ1NbF79240adKkXMpPSEjAhAkTsHfvXgwbNgze3t7w8/ODo6Mj1NXVMXr0aPTv3x9Xr14ttIycnBysWrUKUVFR5VLHyqqw6x46dChWrVqloFoRQgiprCptMHX58mX8/PkTWVlZuHjxYqmODQ8Px+bNm4vM8+PHD6xYsQL5+fnyVFNuenp6ZV6mQCDAkiVL8OHDB0yaNAmzZ89mVhc2MzPDqlWr4OjoiISEhCLLsbe3R3h4eJnXr7Ir6rr19fUruDaEVG8xMTE4ceIEYmJiFF0VQmRWKYOpnJwcuLi4MNsXL15ETk5OiY6NiIjA/PnzkZ6eXmiexMREzJs3r1KMx+JwOGVe5osXL/D69WsAQMOGDaXm6dmzJ9auXVtoGX///TeuXbtW5nWr7Iq77vJ4vQj5leXm5iI9Pb3I4QaEVHaVcsTfzZs3WQu4JSQkwN3dHRMmTCjyuICAANja2iIxMbHQPB8/foSNjQ2+fv1aZvWtbIKCgpjHly9fhoWFhdT7Xk2cOBGnTp1ipfF4PGzZsuWXC6R+1esm5c/S0hLR0dFF5jE1NaX3HiFVWKULpgQCAc6cOQM1NTXW7QWcnJwwfvz4QlsGPD09sW3bNqSkpDBpr169QufOnQEAnTp1wsyZM7F69WrEx8czeaKjo5k8JiYm8PDwYPYlJibi0qVL8PHxQWRkJFJTU2FkZIT+/ftjwYIFMDAwkFqX9PT0/9fenYdFcaR/AP8OlwoIQhRRvIIKisZbDBrjfcSgEhU0LngiKq4IiyZZV9GoEeOKF4RDBQyCqIjrie4qYcGEQ1ghGoMSrogEQUQ55Zrp3x/8pp/pOWCGGWQk7+d5eOyarq6uHmt63qmursbJkydx+/ZtPH/+HLq6uhg8eDBcXFxgY2Mj1/uQm5sLW1tb8Pl8zutff/01li1b1uy2Ojo67HJ2djbs7e2xd+9ejB8/npNPU1MT06dPZ9M1NTVwcXHBzz//zMm3YcMGaGpqAgACAwNRUlKCffv2cYLWzz77DAcOHEBBQQG8vb2RnJyMjz/+GEePHmXz8Pl8XLhwARcvXkR+fj60tLQwceJEuLu7o3///my+0NBQHD9+HDU1Nexr3t7eGDp0KAICApCSkoLGxkZMmDABO3bsQO/evSXeg9evXyMkJASxsbEoKCgAn8/n/PLt3LkztLW1MWjQIISEhMh13MJ2Iq6qqgq+vr64ceMGampqYG1tjX/84x/o27ev1Pxvg7CdJSUltVsdSJM//vgDhYWFMDMzk7r+zzYmUV3RZ4YoQ+0u88XGxqKiokLiElReXl6zd97Z2toiJSWF89rYsWORlpaGtLQ0BAUF4cMPP8Tdu3c5X769e/dm84gGUvHx8Zg1axbS09MREBCA+Ph4uLq6oqCgAGFhYXBwcJA65ujZs2dYsGABAgMDsXHjRiQmJsLQ0BCJiYlYtWoVIiIi5HofzM3NkZiYyJ6A58+fj7t377YYSAGAtbU1J52XlwdHR0esXr0a9+7d46zz8vJib0nW1dVFeHg4XFxcOHkCAwPZ92jcuHGYN28efHx8JPZbXFyM5cuXIzY2FtXV1bh58yYeP34MoOnuRWdnZ+zevRujRo1CcnIy/v73vyMmJgZLlizBL7/8wpazevVqrFu3jlP21atXsXXrVpiZmUFLSwtVVVWIjY2Fs7OzRMCZm5uLBQsWICgoCM+fP0d4eDju37+PWbNmsXkGDx6M5ORknDt3Tu7jlqaoqAhLlizBrVu3UFpaiurqasTFxcHJyQnV1dVStyF/PmZmZkhKSpL6JyvIIoS8O9QumDp16hSWL1+OxYsXSwzODgkJeSt1ePHiBTw8PFBdXY2KigoYGBhAU1MTK1asYPM8e/ZM4hJZfX091q9fj8LCQgwfPhzz589H586dMWnSJDbP4cOH5R70zuPxUFlZic2bN+PQoUMwMTGRa7shQ4bA1tZW4vXExEQ4OTnB0dERaWlpcpUlS58+fThphmGwdetWdO/endN7qKHR1MS2b9+OxMRE6Onpwd3dHdra2rCzs8PAgQNRUVEBT09PTlAkfqwvX77E+fPn8eWXX8LDw4N9PScnBz/++COb5vP5cHNzQ3FxMYCmIHvEiBHo1KkTJ1h6+PAhbt26pdR7AACXL1+Gm5sb4uPjsXfvXvb1oqIi3Lx5U+nyCenoTExM4ODgIPf5jRB1pFaX+dLS0vDrr7/C398fnTp1goODA4KCgjjrHzx4gBEjRrRpPTIyMthehQcPHiA+Ph7Tp0+Hnp4eJ19eXh4nff78eXb+ppEjR7KvW1tbswPq+Xw+BAIBG2TIUlVVhQ0bNmDz5s2cIE5e+/btQ2VlJeLj4yXWpaam4i9/+QsWLlwILy8v6OvrK1y++OXWuLg4rFmzBhs2bEBUVBT++c9/4sMPP4SFhQX+97//sYGLlZUVZ/yWubk5cnJykJ+fj8TEREyePBkAJN6fFStWsPUUv6yXn5+PKVOmAADu37+P3377jV03cOBAzr5EpaenSw06FWFvb4958+YBaOoJFZWTk6NU2QzDcC51KkIgEKCoqAgTJkxQqg7S6tTY2AgtLS0ajC+noqKiFnufCgsLVf5/9a4Qtilvb+92bVNFRUXo1atXqz9zRL28efMGQFP7ehvtSq2CqZMnT2LhwoXs7efLly9HcHAwZ6xLcHAwjh071qb1GDlyJExMTFBSUoKuXbvCwsICACR6lGpraznpf/3rX+yygYEBuzxz5kx4enri4cOHWLRoUYsz/b5+/Rpr166FtbV1qwIpAOjSpQsCAwMRFhYGPz8/VFZWSuS5cuUKMjMzERYWpvQUDXV1dVi9ejWApgDD3t6eXXf9+nV2uWfPnpztdHV12eWMjAw2mBInHLskvgyAc/ITHQ8nXr7oMgCVzLjcvXt3dll0rBoAqe+5IhoaGpCZmdnqbUX/VTW680r12ur/St0JBALU19dDR0enxR+ZbU2ZzxxRT42NjRLn5ragNsFUVlYWEhISOOOWTE1NMWvWLM7lktu3b6OgoKBNB/eamJjg1q1byMzMhLm5OQwMDBAdHc2ZrgFoiniF6urqOB9C8akZxMfjyPLixQusWbMGWVlZKCkpgYuLS6sDHQ0NDaxatQp2dnYIDQ1FeHi4RL2ysrKwd+9eHD58uFX7EBo+fDg6deokdd2jR4/Y5Vu3bnF6y0Qbenl5eav2LXp5cMCAAZx1ol9Qojc0AE29ZG1JfCyXooQD5Fu7ba9evRAXF6dUHcS9efMG+fn5GDBgALp06aLSsjuqadOmtZinLf6v3hW//fYbDhw4gK+++gqDBw9ut3oI/5+GDh3abnUgqiM8V72txxSpTTB16tQpTJo0SeLLw8nJiRNM8fl8nD59Gjt37mzT+ujp6WHcuHG4efMmjhw5gs6dO8PHx0fmZaHXr19zeq5auhVaFkdHR+Tn5wMASkpKsHPnTvj5+SlUhp+fHxYvXsw+nqFbt27w8PDA2rVrcfr0aYSEhLBdoABw8+ZN7NixQ6kJKZsb7yAaJA0bNgwXLlxo9X6kEQ1qraysMH78eKSmpgIA+14C3Muyffv2xSeffKLSejRXr9bg8XgSvWnyEv7Cb+32LenSpUubld3RyNPboqGh8ad9Pzt37sz+257vQVt/Zkj7eFuXjtViAHpRURFiYmLYqQxE/9avXy9xWefSpUucKRDaQllZGVxcXODu7g49PT2Eh4c3+6tJeEIQSktLa9Xs6l988QU++OADNn379m1ERUUpVAbDMFLHShkYGMDNzQ1Xr17lTOYpEAiUnndLVq8U0NRLItTaIFMRfn5+7PiTy5cv45dffkF5eTmOHDkCoOnOqqCgoLfS9UsI0DQmysbGRuofTY1AyLtPLYKp0NBQjB49Gunp6eyt6KJ/AQEBnPw1NTWIjIxss/rU1tZi5cqVbECyZ88ezhgoaQwNDdGjRw82XV5ejjt37ii87xkzZsDHx4fz62j//v0KBzuRkZEye0b69esHPz8/TsQuenyqjuT79evHLr948YJz2U+Usj05Qt26dUNYWBg8PT3x+vVrfP7555g6dSqeP38Od3d3XLt2jTMwXagjDagW3nZP2l/v3r2bHYBuZmYmda408nbRZ4Yoo92DqbKyMkRFRWHjxo0y80yZMgXDhg3jvBYWFsa5VCUk2gsiS0t5oqKiOM9mk/VIFnGi8xgBwJEjRyQGIT958oS940+W/v37Y8eOHWy6pqYG27ZtU2jQ7+PHj3Hu3DmZ6wcNGgRDQ0MATSdz0Tvd5HkPFfHRRx9x0j4+PhK9dqmpqS0+mFoRR44cwdmzZxEbG4uHDx8iPT0d165dw8aNGyXuyhRS9XETAgDR0dEy55gS/v2ZZz/X09PD8OHDZX4uCXkXtHswdfToUWhpabU4M7j4JI5lZWU4ffq0RD7Ru6tEBx+LBjAt5REPdnbv3o3Y2Fi4ublxXq+rq4NAIGAHNq9bt45zQsjNzcXKlSvxww8/ICsrCxEREdi+fTvnV6j4HTzC9OLFizF79mz29Z9//hnHjx+XON7mfPPNNzInOs3MzMTr16/B4/Gwfft2Tq+M+MzuwiCuqKiInTJCfDB3c71KCxcu5PTa/fTTT3Bzc0NWVhbKyspw+fJl7Nu3D0uXLmXziAdbomnxgd3i+46MjERgYCCmTJkiMR9Wc+Q5bvF9idZLfJ2qetoI6ciMjY0xd+5ceog4eae1WzBVXV2N7777DufPn0dVVRUSEhKavTVY2IsiKiAgAP/97385X1rLly9nl3NyclBaWoqYmBjk5uZKzfPy5UtkZ2cjNTWVnUFdvBfs2rVr8PT0xPTp0zm9VA8fPsTnn3+OgoICAE3d+T4+PpwejkePHmHjxo2YP38+goODceDAAfYSXkFBAadeAJCQkMAui0+LcOLECVy6dEnusVg6OjrYtGkTdu3axc55VFdXhx9++AGurq7Q0dHB3r17MXPmTM52s2fP5gQ/KSkpqKiowKlTp9hxRuLd4Y8ePZL5cGk9PT0cP36cc+ny9u3bmD9/PmxsbODt7Y0DBw5wAlHx2eVFn9Uo/oBq8bxnz54F0DTnlCJPopfnuMvKyjjbiD5SR7SO0upFCJFUX1+P0tJSuR9mT4g64jHt9PN52bJlSE9P57ymoaGB4OBgTJw4kX0tLS0Na9eulZjTSdTq1avZx88wDIOTJ0/i7NmzePXqFYYMGQIXFxfMmDGDs010dDT7uJH3338fTk5OWLJkCYCmno/9+/fj6tWr0NbWxrRp0+Dq6gozMzOkpqbCy8sLhYWFsLS0xI4dOzgTdAJNt/oGBAQgOTkZlZWV6N27N+bOnYuVK1eyv76Ki4vx8ccfSz2eb7/9FnZ2dhg/fjwqKiok1m/atEmil0yUn58fBgwYAFtbWzx8+BBXrlxBUlISSktLUVdXB1NTU0yaNAkrVqzgPBNPVE5ODvbt24eMjAzo6upi9uzZ8PDwgIGBAfz8/ODr6yuxjaamJkJDQ2VOPvj06VP4+/sjMTERr169gomJCaZMmQIXFxeYmpqy+b7//nscPXqUM39U165dsX37drz33nv46quvOEFNp06dsHr1anZm9GnTpkkd6M7j8aCjowNjY2NYWlqyY6nkPe6YmBgcOHCAnV0daApYt2zZAmtra3h6euLp06ec9+Ozzz7DN998I/X9aI7wYdWiNyOog5qaGmRmZmLo0KF01xNRiSdPnmD37t3YvXs3LC0t27s6pIMQnqu0tbXB4/Ha/FzabsEUIW3l6NGjEjctyHLw4EEsXLiwjWukOAqmyJ8FBVOkLbztYKrdx0wRompubm6YM2eOXHnDw8PbuDaEEEI6OrWZtJMQVaipqcH69etx79497N+/H3PmzIGenh77/K/a2lr8/vvv7KU8ZWcpJ4QQQqhninQoP/30E+7duwddXV3Y2dlBX18fPB4PGhoa0NHRgYGBAT744AN2GgvxwfeEkLeLx+NBU1OzQ83zRv58KJgiHcrYsWNhZmaGmpoabN26Fbm5uezdjwKBAIWFhQgLC0NgYCDmzJkDZ2fndq4xIX9uffr0gYeHh0LTmBCibugyH+lQjI2NcfXqVURHR+PHH3+Es7MzysvLoaWlBW1tbXTv3h1jx46Fr69vi3ObEUIIIfKgu/kIUUP3798HwzBq9/xAhmHQ0NDA3iFDiLIaGxtRXl4OQ0NDaGnR73uiGsJzFdB0KXnMmDFtuj9quYSoIXUNVIRzdRGiKlpaWhJPHyBEWcJzVUNDw1s5n1LPFCGEEEKIEmgAOiGEEEKIEiiYIoQQQghRAgVThBBCCCFKoGCKEEIIIUQJFEwRQgghhCiBgilCCCGEECVQMEUIIYQQogQKpgghhBBClEDBFCGEEEKIEiiYIoQQQghRAgVThBBCCCFKoAcdE0IkVFdX48SJE7h+/TrKyspgbGyMTz/9FC4uLtDX11e4PIZhEB0djYsXL+Lx48fQ1NTE4MGDYW9vj0WLFqntg52J4m7fvo3Q0FA8efIEmpqasLa2hqurK6ysrFpVXn19Pc6cOYPo6GgUFRWha9eumDFjBv7617/SA5L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modellifelines.CoxPHFitter
duration col'adv_fit_time'
event col'adv_failures'
baseline estimationbreslow
number of observations1500
number of events observed1500
partial log-likelihood-7421.70
time fit was run2023-09-29 11:13:28 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
train_time-0.001.000.00-0.00-0.001.001.000.00-4.25<0.00515.52
predict_time0.031.030.010.020.041.021.040.004.21<0.00515.26
atk_value-0.090.910.07-0.230.050.801.050.00-1.310.192.40
def_value0.041.050.07-0.090.180.911.200.000.630.530.92
data.sample.random_state-0.010.990.01-0.030.000.971.000.00-1.570.123.10
adv_failure_rate0.011.010.000.010.011.011.010.0028.70<0.005599.53
model_layers-0.001.000.00-0.00-0.001.001.000.00-5.20<0.00522.23
model.art.pipeline.initialize.kwargs.optimizer.lr-0.001.000.00-0.000.001.001.000.00-0.130.900.15

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Concordance0.92
Partial AIC14859.40
log-likelihood ratio test4105.41 on 8 df
-log2(p) of ll-ratio testinf
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" - ], - "text/latex": [ - "\\begin{tabular}{lrrrrrrrrrrr}\n", - " & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", - "covariate & & & & & & & & & & & \\\\\n", - "train_time & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -4.25 & 0.00 & 15.52 \\\\\n", - "predict_time & 0.03 & 1.03 & 0.01 & 0.02 & 0.04 & 1.02 & 1.04 & 0.00 & 4.21 & 0.00 & 15.26 \\\\\n", - "atk_value & -0.09 & 0.91 & 0.07 & -0.23 & 0.05 & 0.80 & 1.05 & 0.00 & -1.31 & 0.19 & 2.40 \\\\\n", - "def_value & 0.04 & 1.05 & 0.07 & -0.09 & 0.18 & 0.91 & 1.20 & 0.00 & 0.63 & 0.53 & 0.92 \\\\\n", - "data.sample.random_state & -0.01 & 0.99 & 0.01 & -0.03 & 0.00 & 0.97 & 1.00 & 0.00 & -1.57 & 0.12 & 3.10 \\\\\n", - "adv_failure_rate & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 28.70 & 0.00 & 599.53 \\\\\n", - "model_layers & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -5.20 & 0.00 & 22.23 \\\\\n", - "model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.13 & 0.90 & 0.15 \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\n", - " duration col = 'adv_fit_time'\n", - " event col = 'adv_failures'\n", - " baseline estimation = breslow\n", - " number of observations = 1500\n", - "number of events observed = 1500\n", - " partial log-likelihood = -7421.70\n", - " time fit was run = 2023-09-29 11:13:28 UTC\n", - "\n", - "---\n", - " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", - "covariate \n", - "train_time -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", - "predict_time 0.03 1.03 0.01 0.02 0.04 1.02 1.04\n", - "atk_value -0.09 0.91 0.07 -0.23 0.05 0.80 1.05\n", - "def_value 0.04 1.05 0.07 -0.09 0.18 0.91 1.20\n", - "data.sample.random_state -0.01 0.99 0.01 -0.03 0.00 0.97 1.00\n", - "adv_failure_rate 0.01 1.01 0.00 0.01 0.01 1.01 1.01\n", - "model_layers -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", - "model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - "\n", - " cmp to z p -log2(p)\n", - "covariate \n", - "train_time 0.00 -4.25 <0.005 15.52\n", - "predict_time 0.00 4.21 <0.005 15.26\n", - "atk_value 0.00 -1.31 0.19 2.40\n", - "def_value 0.00 0.63 0.53 0.92\n", - "data.sample.random_state 0.00 -1.57 0.12 3.10\n", - "adv_failure_rate 0.00 28.70 <0.005 599.53\n", - "model_layers 0.00 -5.20 <0.005 22.23\n", - "model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.13 0.90 0.15\n", - "---\n", - "Concordance = 0.92\n", - "Partial AIC = 14859.40\n", - "log-likelihood ratio test = 4105.41 on 8 df\n", - "-log2(p) of ll-ratio test = inf" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_773113/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " pareto.set_yticklabels(labels)\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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7adSoERo1aoRr167lqo7BwcHw8fFB165d8enTJ+6PprQ/CEjBRIEUKVC6du2Kc+fOoU+fPiozyoKCgjBhwgSMHj1a5yZzTdKmovfo0UPlH+yMrSVdunThvnh27typVs7FixdRtmxZVKtWTe1YdlOe01pDAOV4m4yD7lNTU3HkyBH069dPZb+rqys6duyIDh06qC31kj6AzKxVz9Cyel3SB8ialrGpUKEC97hWrVpq71exYsW49yvjOKljx46BZVl07NhR5cuwSZMmKmOGwsPD1bpjdSEWi7lu2fT5hNJzcnLixiBdvHhRJfBJnzahf//+Kp8TbQeAp88DlZuZmxldunQJ4eHhMDExyXS5obRxT4ByTFJGTZo04VqILl68CF9fX0ybNg2LFy9W6RLNjaCgIFy6dEljJvb0rVKaPtPZOXbsGPdz4sQJHDx4MEezWo8fPw53d3e4ubmhTZs2WLx4MXg8HubOnYvr16+jb9++Oa4TyV8USJECx9raGgsWLMCJEyfQqlUrlWNXr17FgAEDNI6d0kVad0F209WNjY252T6nT5/mxu6k8fX1zTQXjzZfcgMHDgTDMEhMTFRL3nnq1ClUr15dJXgAAIFAgNWrV2Pt2rXcl6y/vz8WLFjAjR0CoPfBtPqSVVdNdt04mbU0ppc29ix98BAWFoawsDDY2NiofBGm/7l16xb3k7GlTxf37t3jgrqsxialBVIKhQIPHz7k9qd/TXIbBJmbm3OP9REkXrhwAQBgY2OT6e95mTJluIDo4cOHGidATJkyhVs2Z8GCBejSpYvavwG5sXPnTjg7O2sMcNq2bYsyZcoAUH7GMn6ms2Nra8v9lCxZEm5ubli+fLnWQW3NmjVx4sQJnDhxgksLEhUVxXU1k4KPAilSYFWqVAmbNm3Czp07VQajvn37lkvgaQhpSUPFYjEOHjzI7X/z5g0+f/6sNgA9JypUqICmTZsCUP7Vnv7LZs+ePdmuRXf16lX069cPixcvhru7u15mDhVFX79+BaCcsVeiRAmVL0NNP/pMuREYGMg9zqrc9AFzWn31JX0m76ioKJ3LS3tO2QUPac8pNTVV40xKoVCIlStXcukCPn36pHPd0hJwfvr0Sa3lsUmTJmjevDkiIyMBKFsL9+/fr/M9bW1ttU7xIRAIYGtri/Lly+Pvv/+GkZERFAoFpk6diqCgIJ3rQvIeBVKkQEg/1iWjhg0b4vDhw/Dw8OD2HT161GAtLPb29mjXrh0AYO/evdzsJ19fX/Tp04ebUp1baeMhPn78iFu3bgEAnj59iri4OLRs2VLjNRERERg+fDhmz56NMWPGYMeOHWjXrp1eum2KorT3LDU1Nd8zRqekpHCPsxq3lr41Qt8tEw0bNuQev3//PsvcbdpIy0eVXetW2vPg8/mZtiaGhIRwLcNXrlzBnj17dKrbgQMHYGZmhvPnz2fa+nj8+HEueNuzZ49elsBJP6heW/Xq1cPEiRMBKBMVe3l5qfy+kIKJAilSILx48SLLwEgoFGLJkiVc10BCQkKOm+D1KW023JcvX3Dx4kUkJCRwY7t01bx5c5QvXx4AuOUrfH190a9fP40tGNHR0ejfvz/u3LmDrVu3ar2a/Y8s/QyoS5cuZXnuixcv9Dq+LP3abG/fvs30vPStkelzp+lDw4YNuc+SRCLhlrTJrbQcR4mJiQgNDc30vLTnVLZsWY3d6MHBwZg5cya2bt2KNm3aAACWLl3KpSbJqbQEnAMGDOASyWr6cXJy4tKbREREZDmxJa8NHz6c68709/fHnDlzDFYXoh0KpEiBEBsbyyUxzIxIJOIGAfN4PJVxHgC4lqDs/prUR0tWnTp1uEG1u3btwtGjR9G0aVOULFlS57IZhuECtRs3buDx48e4fv26xjW7AODff/9FYGAgXFxcuPElJGvlypXjBqHv2LEjy5aUtWvX6nWpl4YNG3IBcVYBTNofCmkzS/WJx+Nh/Pjx3PaWLVty9LmQy+WYPXs217KXfuzRjRs3Mr0u7Tm1b99e7ZhEIsGECRO4ZKuLFi2Cra0tUlNTMXny5FwFsxcuXEBMTIzKQPfMpJ8Zl5tB5/rCMAyWLl3Kjds6efKk3lIzkLxBgRQpMDZs2JBtrqO0LohGjRqpLbuQ9sWoaVzF9evXucepqamZlp+TL5O0YOfRo0fYtGlTtuOX0rcwZJdp3NPTE+bm5mBZFl5eXmjfvr3GNAEA8O7dOwDKsS4Zy02fTFLTc8tJnbSR/v3T9rVMu6+m++szI3v6shiGQadOnQAo0yZMmDCB655Kz9fXF+XKlcvxGKm0YF5TMk87Ozuua/jp06d49eqVxjLS9v/vf//L9P66/FHg6enJBTQvXrzA1q1btb72zz//RMeOHbkAs2fPntwMyz179mh83+RyOfz8/CAUCjWmaFiwYAEcHR25z5WNjQ2X+sHf3x8rVqzI2ROEMkDs1KmTVikeXFxc4ObmBgB49uyZygB/Tc8lTW5+R7N73ywtLbF69Wruj8Nly5bhzp07Ob4PyR8USJEC48GDB5g7d26mf3kGBQXhzJkzEIlEGscfODs7AwAOHz6MO3fuQKFQIDQ0FH/++SdOnDjBnffo0SNIJBKVwa5pwVVa8kNtdOzYkcsBZGdnx+WjyUz6JT+ym3VYrFgxrgUqKioq05mAALi8SmFhYVi7di0UCgVSUlKwZ88eLrdP2vFnz56pLA2Svk66jpMBwA3aTau3NtLqoOk1SV8nTfVLS9wIINOxJGm/TxmvHzNmDDdr7v79+/D09MTBgwfx+vVr3L59GzNmzMDGjRtVsu5rK21wePq0BenNmDGD+3L/448/1FpR4+PjcerUKY2LCaf/QyAnv6+arFy5kpvcsGLFCvz9999ZfsmLxWLMmTMH1apVU8nxVbx4cW6c45s3b7gu6fTOnDmD2NhYjBkzRq2rcs+ePbh16xYWLVqksr958+ZcSpCdO3dm2w2b3smTJ/Hq1asczfpL3y2+atWqTAPC9GPbcpOfLf3kgcyGKLi5uWH69OkAlAH5uHHjVJaWIgUHBVKkQDl06BC6d++O/fv3IzAwEElJSfj48SN27dqF3r17w8jICOvXr0fVqlXVrh0yZAgYhkFcXByGDBmC6tWro1WrVmqZhs+cOYMBAwbg1atXSExMxKFDh7gv/QsXLuD69esaWycyEgqF+N///gcA2bZGxcfHq+TO8fX1RXh4eJZfWv379wePx0O9evWy7LL7+eefuRaLjRs3om7duqhfvz6uX7+usjTM8OHDsXjxYrRu3RoKhQLh4eEqQdW///6b6fpuWWFZFklJSbhy5YrK2oOHDx/Gs2fPVIKd9FJTU3HgwAHunufOnYO/vz8XVMTFxal0aezfvx+RkZFQKBRgWRZxcXHcUiKAcup6YGAgd31SUhKOHj3KfWldvHgRb9684VqJbG1tsWnTJm521efPnzF79mx0794dQ4cOxcWLF7Fu3TqtZ1+lvQ5bt27l1tR7/PgxTp48iZSUFJX3ulSpUvD29oa1tTWePXuGYcOG4cWLF0hOTsazZ88wdOhQ2NvbY9u2bSp5o2JiYrBlyxZu++DBg3j06FGmr3F2hEIh/vnnH0ycOBEikQibN29Gt27d4Ovri4CAACQnJyMuLg7+/v7w9vbGsGHD8NNPP6Fnz55qZfXv3x+jR48GwzBYvHgx1q1bh/DwcMTHx+PgwYOYO3cuRo0ahTFjxnDXSCQS+Pr6YtGiRbCyslJrLRaLxdwfKyzLYtq0aTh//nyWA7BTU1Nx8eJFLFiwAABw9uxZBAUFZdlyJJfLERwcrNIK9fDhQ8ycOROfP3+GXC6HQqFAREQEVq5cqfJ6L1++HCEhIVqtHCCVShEQEIBVq1Zx+/z9/XHs2DEkJiaq/XswaNAgbhZwcnIyhg4dipkzZ+Lu3bu0iHEBwrAFdTVT8kMZNmwYVq9ejaCgINy6dQt3797F27dvERcXB4FAAAcHB7Ro0QIDBw7MchzS+fPnsW7dOgQGBqJcuXLo378/F+xUr14dP/30EwYOHIiaNWsCAKpWrarxH8AyZcpotfZWVFQUPD09cfHiRZW18tL7/PmzxjEhADB//vwsE+6NGzcOnTp1UpmxqMnVq1exevVqfPz4EQ4ODhg4cCD69OkDhmEwevRoPHjwAB06dMDs2bNRrFgxHD9+nPtrN6OTJ0+qpJvIzqVLl1SWt9HkzJkzKhmlAWVrg6YWGw8PD3h5eWX6nGfMmIGGDRtyLRUZ9enTB1OnTuXWu8to9OjRmDRpErcdFRUFb29vLqmkpaUlmjZtCi8vLzg4OGT5vNK7ceOGWutReu7u7vDx8VHZFx0djS1btuDq1asIDQ2FqakpnJyc0L17d3Tt2lWl+zosLCzTdeYaNGigsRUoJ8LDw7ms44GBgYiKioJCoUCJEiXg6uqKtm3bcsvQZOW///7Drl278PjxY8TFxaFkyZKoVasWBgwYoDbWa8eOHVz3HaDMb5U+mPn999/V8qkBQNOmTTPtihw9erTGhY2HDh2K3377TeM1W7duVVl6J6PFixfj9OnT3CxaTZycnNQWMc8os39v0nh5eamMXQOULbU9evTA58+fVfbXqVMHe/fuzfJ+JH9QIEUIIYQQkkvUtUcIIYQQkksUSBFCCCGE5BIFUoQQQgghuUSBFCGEEEJILlEgRQghhBCSSxRIEUIIIYTkkv4WkCKZevLkCViW5dL9E0IIIUS/pFIpGIbR+9qU2aEWqXzAsqxe1wxLX65EIsmTskneoveu8KL3rnCj96/wyu69y6vv2uxQi1Q+SGuJqlGjhl7LTU5Ohp+fHypVqsQtGEoKB3rvCi967wo3ev8Kr+zeuxcvXhigVtQiRQghhBCSaxRIEUIIIYTkEgVShBBCCCG5VKjHSF28eBHbt2/HmzdvwOfz0aBBA4wdOxZVq1bNVXm3b9/G/v378ezZM8THxwMASpcujSZNmmDo0KGwt7fXZ/UJIYQQUsgV2haplStXwsvLCwqFArdv38bBgwdx+/Zt9O7dGxcvXsxRWSzLYt68eRg6dCjOnz+PX375BQ8ePMC5c+dgZmaGHTt2oHPnznj27FkePRtCCCGEFEaFMpA6dOgQvL29AQC9e/eGsbExypUrh2bNmkEqlWLSpEl48+aN1uXt3LkT+/btAwDUrl0bQ4YMgUAggJ2dHWbNmgUASEhIwOTJk6FQKPT/hAghhBBSKBW6QEoikWDDhg3ctqOjI/e4XLlyAJRJuVatWqV1mbt37+YelyhRQuVYjRo1IBQKAQDBwcE5CtAIIYQQUrQVukDq7t27CA0N5bbNzMy4x2kBDwDcuHEDCQkJWpUZFhbGPX78+DFSU1O5bYZhYGVlxW1TdnJCCCGEpCl0gdS9e/dUtjMLbORyudq5mSlbtiz3OCoqCuvWreO2ZTIZYmNjAQAVK1aEk5NTDmtMCCGEkKKq0AVST548Udk2Msp84uHTp0+1KrNHjx4q21u2bMGKFSugUCjw6NEjSCQS2NjYYOXKleDz+TmuMyGEEEKKpkKX/iAiIkJlm8fLPBaMiorSqswhQ4bg0aNHuHr1Krfv33//xZMnTyCXy9GhQwfMmjULdnZ2uas0IYQQQoqkQhdIxcTEqGwzDJPpudHR0VqVaWRkhPXr1+Ovv/6Cr68vt//hw4cAgAoVKiAxMVGnQIplWSQnJ+f6ek0eT10MaTFzpPxeXq/lkryXkpKi8n9SeOTne8eyLGQyGc0W1qO0MbBxcXEQi8UGrg1Jw+fzwefzs/xOz+6zx7JsltfnlUIXSEmlUq3Pzckq0EZGRmjfvj2uXr2KZs2a4ciRI9y9Pnz4gL59+2LHjh1wdXXNcZ0BZb39/Pxyda0m8WExOOC7G5+FCszt3gwCkTD7i0iB8+nTJ0NXgeRSfrx3PB4PDMMY5MuhKDMyMlLr3SCGk/ZdrVAotPrezuqzl37SWX4pdIGUhYWF1l121tbWWp0nlUoxd+5cHDlyBJ06dcKCBQvQo0cPjBo1ihtoHhcXh3HjxuHcuXO5eqMEAgEqVaqU4+sy4xf7HCfMJAAAh7JlYV2iuN7KJnkvJSUFnz59Qvny5WFiYmLo6pAcyOv3LikpCV+/foVAIIC5uTlMTU1pbKYesSwLiUQCoVBIAWoBIpVKkZCQgISEBJibm6N4cfXvtOw+e+/fv8+PqqopdIGUvb29SiCVVfRqa2urVZnLli3DkSNHAACtWrUCANSqVQve3t4YNGgQ1xQcEhKCs2fPolu3bjmuN8MwMDU1zfF1mbG0tkbnJOWMRWNjE72WTfKPiQm9d4VVXrx3ycnJiIyMhKWlJUqXLk1f9HlALpeDYRgYGxtTgFrAWFlZISYmBmFhYbCwsIClpaXG8zL77Bnq81LoZu25ubmpbMvl8kzPrV27drblxcfHY+/evdy2g4MD97hmzZoYNGiQyvmvX7/Wtqp5ysTEBF2TROiaJAJDwycIKRLi4uIgEAgoiCI/LGtra5iamnLr3RYGhS6Qaty4scp2+uSZ6fF4PNSrV4/bDggIQI8ePdCgQQOsWbOG2//p0yeVcVfpk28C6qkRsgrc8hPD+/6XFFtA6kQIyT2WZZGQkAALCwsKosgPzczMDMnJyYVmkkWhC6Ratmyp0ncaFxfHPU4f5LRo0UIlKJozZw5evXqFuLg4bNy4EXfv3gWgPo4q4/gre3t7le2aNWvq/Bz0geExSGZYJDEs5IXkl40QkjmpVAq5XI5ixYoZuiqEGJSxsTEUCgVkMpmhq6KVQhdICYVCTJo0idtOP3o/PDwcgHJg98SJE1Wuy9gll7bt4OCA+vXrc/tv376d6XVVqlTBTz/9pFP99SVFnIqJtkmYZJuEVJpCT0ihl/bXd1a58Qj5EaR9BqhFKg/16tULv/zyCwDg8OHDSE5ORnh4OC5dugSBQIBly5bBxcVF5ZqM21WrVuUeL1u2jFvwePv27Vxizg8fPmD+/PkAgMqVK2Pz5s0FZq299P/YKhTap3kghBRs1K1HfnSF7TNQ6GbtpZkxYwZq1qyJnTt3okWLFuDz+WjUqBHGjRunFjQBwKJFizBt2jQEBwdjwIABcHd3546VLl0aR48eha+vLy5duoSpU6dCJpPB2NgYVapUwZw5c9CrVy+IRKL8fIpZMjUrho0Ryi4AEb/Qvo2EEEJIoVaov4E9PDzg4eGh1bmVKlXC0aNHMz1erFgxjBw5EiNHjtRX9fKUEZ8PI6RF7YUreieEEEKKinzv2mNZVmUZFpJL6bv2aNYeIYQQYhD53iIVGRmJRYsWoX///vl96yJFzshxqJgYLANUocHmhPxwli5dim3btmk85ujoiMOHD8PCwkLt2Ny5c7F//361/Xw+v8DkydOVQqHA8ePHceLECfj5+SExMRGWlpZwc3PDhAkT4OjoqHZNUlIS6tSpo1X5o0ePVpn0RH5sWgVS//33n843YlkWSUlJ1BqlJwyAC8WU+a+mZ5JLixBSdE2ePBk9evTAvHnz8OjRI5VjgYGBmDZtGjZt2qQ2cHfmzJno168f1q5di8uXL8PU1BSzZ89Wy9FXWCUlJWHMmDG4f/8+BAIBjh49iqtXr2LlypW4cuUKnj59irNnz8LY2FjlumLFiuHly5cICgrCzJkz8eTJE5XjNjY2WLVqFWrVqmWQ9dxIwaVVIDV+/HiVfE26MNTqzEWNkUCA9t+WiDHiF4yZhISQ/CMQCFC5cmV4e3ujd+/eCAgIUDl+7do1rFu3DhMmTFDZb2xsDBcXF6xZswaNGzdGjx490LNnz/ysep5aunQp7t+/D0C5TFjlypVVAk2ZTJZpYmWBQIAKFSpg2LBh8PLyUjnWuXNnNGrUKO8qTgotrQKp7t27Y/v27XldF5IDRkIRfk5SziIUGVEgRciPyszMDG5ubmqBFABs3LgR1atXR+vWrdWOCQQClC9fHk5OTvlRzXwhkUhw/Phxbjvtj/YePXpALBYjNjYWHTt2VGuNysjc3FyrfYQAWgZS//vf/7Bz504sX74czs7OEAqFOU4ax7IsEhMTsX//fuzbty9XlSXfMelefxaFI2kZISTvNG/eHPfv34dYLOb2sSyL6dOn49ChQyhfvrzaNcbGxkWqmyoqKkrjsmFCoRCDBw8GoFwBI7OlxdJo6jWhnhSSGa0CqXLlyqFVq1bo2LGjzr9MkyZNUlkkmOQOwwNkDAuwgEJGs/YI+dG5ubmhW7dumDJlisr+hIQEeHl54cCBAzA1NTVQ7fJHQVkLlfxYtJ61N3PmTL3c0MLCAtevX9dLWT8yhmEw1jYJAHAiJsbAtSGEFASdO3fGhw8fsGHDBpX97969w8yZM7F69eoclSeRSLBnzx6cOXMGHz58gFwuR9myZdG6dWv069cPdnZ2eqz9dwqFAkePHsWxY8fw9u1bpKamonTp0mjSpAn69++v1h0ZHByMNm3aqJUTEhICZ2dnAMCbN2/ypK7a8vPzw+HDh/Hw4UOEhIQgNTUVdnZ2aNSoEUaMGMGtrgEAL1++zHLc2n///QcLCwtcunQJ48aNUznm4+OjknA6NTUVO3bswJkzZ/D582cIhULUrVsXY8aMgZubG3deVFQUJkyYgIcPH6qU5+XlhfHjx+Pr16/4+++/cfnyZZQoUQIbN27kWjnj4uKwevVqXL58GV+/flVZ2qVatWo4cuRIrl6zwkLr/rnSpUvr3Bq1ZcsWBAYG5tmH70dCS8QQQjQZP368xkTFZ8+exZYtW7QuJzIyEj179sTixYvx5csX/Pvvv7h27RqqVq2KTZs2wcPDA5cvX9Zn1QEoZ9398ssvmDlzJl6+fInly5fj9u3baNeuHXbt2oUuXbqopW8oW7YsXr16hYsXL6rsL1OmDF69eoVXr17pvZ7akkqlmDt3Ljw9PcHn8+Ht7Y3z58+ja9euCAoKwsGDB9GjRw88fvyYu6ZatWpYunSp2pJkZcqUwY0bN7i0Fq1bt8bZs2dhb28PgUAAb29vlQHxQUFB6NatG/7++2/Ur18fV65cwcyZM3HlyhX07dtXJUl18eLF4evri5o1a6o9hy9fvqB37944cuQI4uLiEBAQwPUsicViDB48GHv27EH9+vVx9+5d7Nu3D9WqVdPr61iQ5WtCzu7du2Pw4MEIDAzMz9sWSQwDrI42w+qvxWBpZmbo6hBCCgiGYbBkyRKV1oY0f//9N+7evZttGTKZDKNGjcLbt28BAIMHD0bt2rVhaWmJ2bNnw9jYGImJiRg/fjyeP3+u1/pPnz6dm3Xn6emJ5s2bw8zMDL/++ivs7e25wOT8+fMq1xkZGWkcu2tkZAQjI8Mt4rF27Vou8KtSpQpKliwJGxsbTJ06lTsnMTERs2bN4rYZhoGnpyeGDRumUpZMJkPJkiW5bR6PhwoVKsDJyQm9evVCixYtuAaPhIQEDBkyBJ8+fYKtrS2mTZsGGxsbeHp6okGDBpDJZJgzZw7evXunco/0LWNp9/Ty8oK1tbXK68jn8wEA+/fvh5+fHwCgffv2sLKyQu3atbFr1y5UrFgx169bYaK3QCotN8fx48dx7NgxtZ/Dhw9j3759CA8Px7x58/R12x8WA6AYeDBlGTBsoVx7mhCSR0QiETZu3IhSpUqp7JfL5Zg8eTJCQ0OzvH7//v14+fIlt50+KDM3N0flypW58tIHALq6du0aLl26pPG+fD4ftWrV4rbnz5+vMrC+oDp06BD3ePHixVydLS0tVc778OEDEhISVPYNHz5cZbZgeHg4bt26pXKORCLBq1ev8Msvv6js37BhA4KCggAADRs2VFkrNq27UyqVYteuXSrXpQVIafbt24d27drhyJEj3O9UuXLl0LdvXwDAnTt3uHOXLl3KzR4tVqwY5syZo+klKXJ0DtOTkpIwbNgwPHv2TKvzWZbV+lySOR6PAStT9kOzMpmBa0MIKWhsbW2xadMm9O3bF8nJydz+6OhojB8/Hnv27Mn02ozHbG1tVbZtbGy4x2/fvsXLly9RvXp1neuck/tGR0fj2rVr6NChg873zUt2dnaIjo4GoBz7xbLKoRiaWs+Sk5NVAidzc3P07dsX3t7e3D4fHx80a9aM275+/Trc3NxUWpJkMhkOHjzIbZcpU0blPmbpejHSWv8yw7Ishg4dCgBo0aIFrl27pnI8fSqJkJAQ/Pzzz/j999/Rp08fuLu7o0aNGlmWXxTo3JTx77//4unTp2BZVqsfa2trjB49Wh91/6HxeMApUwlOFBMjMTU5+wsIIT8cFxcXrFy5Uu1L++XLl/jjjz80XhMZGYn379+r7DPLMHwgY8qEe/fu6VxXhUKhtopGftw3r23evBmDBw+Gp6cntm7dCmNjY8TFxWHjxo1q58o0/FE8YMAAlbFSt27dgr+/P7d96NAh9O7dW+Wa169fIzExkdvetWsXmjRpwv3s2LGDOxYeHp5l/evUqZNlioyMGfGTk5Mxd+5cjBgxApGRkZn+nhUlOgdSly9fhpOTE7Zv346HDx/i9evXcHZ2xsOHD+Hv78/9vHjxAm5ubtixYwdGjhypj7r/0BiGwZliEpwqJkVSYpKhq0MIKaBat26tMh4nzeHDhzWOb9LU7Ze+W0iTsLCw3Ffwm9jYWJWWs/y6b16zs7PDzJkzsXTpUlSoUAGrVq1C69atNQYwaa1VGa/v3Lmzyr60NRa/fPmC169fqyVc/fLli8p248aNVYbaXLhwAbdu3cKtW7dUulI1cXBwyPJ4z549NQ5Qv3HjBrp3727w2ZL5QedA6suXL/jrr7/g7u4OMzMz8Hg8dOzYESdPnlQ5TyAQYNy4cfj111+zTYZGtNNcIkTLZAGMDTiQkhBS8A0bNgw///yz2n5N/xZr+jLPOG4m/fR2QDnWRlcZy8yv++rbmTNn1AJUhUKB3bt3o23btti2bRuWL1+eo5aaIUOGqN0jLCwMhw4dQrdu3dRm92UcO5acnAxbW1uNPyVKlMjy3tlldOfz+diyZQuaNm2qdiwiIgLDhg1TaR0rinQOpMRiMapUqaKyr2fPnhqTbjZv3hwRERFqOU5I7vRLMUG/RBHMzWjpAkJI1ubPn48GDRpke56m9DQZA5aMX9TpZ5LllpWVlVoXUn7cV98OHjwIExMTbjsxMRFDhw7FwoULER8fj2XLlmlcsicrzs7OKoGKVCrF9u3bceTIEbVuPUD5Wqb3+vXrXCcr1SbtkYWFBf79919MmTJFLaj7+vWryrI9RZHOgZS9vT1evHihsi9tocidO3eq7E9KSoJYLFZrrSK5k/YLzlIeKUJINgQCAdatW6c2vT0je3t7ODo6quzLuGh9xi64OnXqcI9jY2MxduxY1KlTBwMHDsx2hmAaIyMj1K1bN9f3LQgCAwNx7949ldmSM2fO5FJOlC9fHh07dsxV2RlTIezcuRPlypVTe68AwNXVVWU7NjYWV65c0Vjuf//9p7EVUlsbN25EUFAQeDweRo4ciUOHDqmlPcg45q6o0TmQcnd3x8SJE7F8+XJ4e3tzOaJGjhyJ5cuXY+/evUhOTkZQUBAmT54MmUymNsWT6IgCKUJ+aCkpKRoHKmdkZWWFTZs2cQkdM9OnTx+V7djYWJXttFlogDLvUMOGDbntZcuW4fLly0hKSsKDBw9ylB4hJ/c1NzfHTz/9pHXZ+WHNmjUwMTHhBsmHhoaq5LuSy+Vc92RKSkqOym7cuDFcXFy4bYVCobE1ClA2ZtSrV09l35IlS9QC0+fPn2PXrl0qrU45Darkcjk3ZgtQTnA4cOCASvb54sWL56jMwkbnQGrUqFGQyWTYtm0bVq1axX1onJ2dMWDAAPzxxx+oW7cu2rdvj5s3b4JhGJVcICT3vCxiMdo2ESFfIwxdFUKIAb17905lJldWKlSogLVr12aZpHLQoEGoVKkSt50+p1RsbCyCg4MBKKfwz5s3T2VWYMbxQU+fPtWqXgDw008/qcwCS39fmUzGJX4EgN9//11tVl/GMV8ZW7C0pSkoza6s3bt349SpU7C3t+f2RUVFqZwTFBSEX3/9FevXr0f37t3Vus3i4+O5PEyapB8rZWNjg7Zt22Z67sSJE1Xel+DgYPTt2xfnz5/H69ev4evriwkTJmD69Okq1yUlqU5e0qZLcO/evSqD1s3MzNCqVSsAyp6Tgp6iQlc6B1JlypSBj48PXF1dIRKJVPpxJ0+ejDZt2qikP7C1tdVrArcfmRyAggEA9UGahJCiTSKRIDAwEIsXL0ZAQACuX7+O9evXIzw8XOPA7fTc3d2zTJYoFArh7e3NdQNu2rQJz58/R0xMDBYtWgSZTAahUIjFixejSZMmKtdmXBokJ/mlGIbB6tWruUSce/fuxe3bt5GQkIC///4b0dHR4PF4mDJlitrg+bi4OJXklwAQExODffv2ISaH65FqCmauX7+Oz58/QyKRQCaTQSaTITo6Gg8ePMCUKVOwcOFCAFAJpCpXrqyWC+vChQs4dOgQ/vzzT7XZboMHD85yPFGnTp248j09PbNMS1C/fn3MmjVLJZgKCAjAhAkT0L17d6xYsQJ//vknypYtq3I8Y+B77do1hIeHZ9lSxbIsJk6ciA0bNiA8PBwBAQFcYDVy5EgugWtRxbC6dI5qgWVZ3LhxA2/fvkXp0qXRokULtb8iirq0MWT6Tky217Iq5KlSVN+3GbW652zwIjGs5ORk+Pn5wdXVFaampoauDsmBvHrvUlNT8fHjRzg5OakkOczM0qVLVbpU0hsxYoTGlAcZ/fnnn3B1dUWPHj00Hk9JScGuXbtw4cIFfPr0CWKxGHZ2dmjcuDGGDBmitngwoOx+mzFjBh48eIBq1aph8eLF2U6hz0gmk+HAgQM4ffo03r17h+TkZJQoUQL169fHoEGD1P4tzWzR4vTSpuHL5XKkpqbC2NhYZVagVCqFr68vAgMDcfjw4VzPLu/RowcWL17MbT9//hwLFizAu3fvUKZMGXTp0gWDBw+Gqakp3r59izlz5sDPzw+2trYYPHgwBg0alGX5W7ZswfLly3Hu3DmNr39GT548wfbt2/H48WPExcXB3t4e7u7uGDFihMr7EhgYiHbt2mVazqZNm7hWpvTWrVuH9evXq+wzMTFB5cqVMXDgQHTt2jXbOmaU2Wchu89eXn3XZkfnQCo0NBSlS5fWV32KpLx6c89bVIcsRYryh71RrWsLvZZN8hYFUoVXQQmkSO5kFkgVFg8ePMD69evVJnMVJYUtkNK5a69Nmzb4/PmzPupCcupb/7qCBpsTQkiRExcXp9bNePz4cfTv399ANSKa6BxIsSyLcePGFYpU/UXNRUEqzplKkJBIsyAJIaQoef78OVq2bAkPDw8sXboUgDKwevbsWZaDzEn+0zmQApRNpdOnT4enpycOHjxYKFbkLgqOCVNwxEyCuISinTWWEEJ+NCdOnOBmCqblgNqyZQv69etXKLskizK9rC2yd+9eWFpa4tq1a9izZw9WrlyJ7t27o3///iozAoh+NZIJkSqTw0SU+cwNQgghhU/6pJbBwcGYNm0a3r9/jwMHDhiwVkQTnQOp7du3c+noW7VqhVatWuHz58/Yt28ffv75Z9SqVQsDBw5UmyJLdDdIagZpohjWFpaGrgohhBA96tOnD4KDg3Hs2DEkJydDLBbD29tbbQkWYnh6yWyeUbly5fDbb7/h+vXraNOmDVauXImOHTvC19dXLdkX0UFaLjcabE4IIUUKj8fDtGnTcPv2bTx58gRr165Vy0lFCga9jJHKjEgkQrdu3dCvXz/Ex8dj0aJFaN68ORYsWJCXt/2BfFtrL29TgRFCCCEkE3oZI6VJeHg49uzZgwMHDnDrJbEsi+LFi2uVRIxkb7ooGgkiBdaEh6KWoStDCCGE/IB0DqTq1KmDe/fucanqHz58iF27duHy5cuQy+Vca4m7uzsGDRqEli1bqq0vRHInhWGRygByOS0RQwghhBiCzoFUcnIy/vzzTzg6OuLUqVPcwpksy8LY2Bhdu3bFwIEDi/xaO4YwV2INcWIqShcvYeiqEEIIIT8kvXTtpU3HTGt9KlWqFPr164fevXvD0pJmlOWVkgwfEjkPAn6e9dASQgghJAt6+wZmWRZ16tTBoEGD0K5dO0oYli+oi5QQQggxJL0EUmXKlMHKlStRs2ZNfRRHtHSTl4JkEwm6JMQbuiqEEELID0kvgdSSJUsoiDKAQ/wkxJkr4B4XY+iqEEIIIT8knQOpZcuWoU6dOvqoC8mhWqwQCWIpiomMDV0VQggh5Iekc0LOrl27gsfTvhiZTIYpU6boelsCYKjCEqPjTVDS0sbQVSGEEEJ+SHma2VyTgIAAnDlzJr9vWzSljTVnKY8UIYQQYghade3t3bsXe/fuRb9+/fC///1P5dj69eu1vlliYiLOnTuXsxqSLNASMYQQQoghaRVIrVixAsnJyVi+fLlaIHX69Gl8+vRJ6xuyLEuZzfVkHi8SMcXlWBgWirqGrgwhhBDyA9Kqa69169ZgWRZt27ZVO9arVy+uRcTS0hL29vYoVaqUxh8zMzP91v4HFwcF4vgsZDKZoatCCCGE/JC0apFavnw5Zs6cCWtra7Vj3bt3x8aNG3Hq1CnY29tnW9aBAwcwb968nNeUqJnKWiM1JhllzCh7PCGEFBYfP37E7t27cfnyZVy7di3T827fvo39+/fj2bNniI9X5gssXbo0mjRpgqFDh2r1nUvyntaDzTUFUWn727Vrh+LFi2tVTrdu3VCyZEltb0uyYBsphoOMD2Nq6SOEkAKNZVlcv34dw4cPR8eOHbF7924kJiZmeu68efMwdOhQnD9/Hr/88gsePHiAc+fOwczMDDt27EDnzp3x7NmzfH4WRBO9zNpbtGgRBAKBVueKRCJcv35dH7f94ZnYWygfKGjWHiGEFERisRi7d+9Gly5dMHLkSNy8eTPbCUI7d+7Evn37AAC1a9fGkCFDIBAIYGdnh1mzZgEAEhISMHnyZCjo33+D0zmQGjRoEFJSUvRRF5JDD5CK28ZSxCUmGLoqhBBCNGAYBvXr18fJkyexatUqra7ZvXs397hEiRIqx2rUqAGhUAgACA4Oxps3b/RXWZIrOgdSDx48wPr16yGXy/VRH5IDexVx2GEhRlhMlKGrQgghRAOhUAhnZ2cwDKNxwpYmYWFh3OPHjx8jNTWV22YYBlZWVty2tr1BJO/opWvPx8cHHTp0gI+PT6Z9vkT/qjIiVBfzUUwgNHRVCCGEZCOtJSk7ZcuW5R5HRUVh3bp13LZMJkNsbCwAoGLFinByctJrHUnO6WXRYm9vbzAMg4MHD2LDhg3w8PDAgAEDULlyZX0UTzIxUlAcyeExKGlja+iqEEKKMJZlkSouGmNx5HI5xKlysJCDz1fuMxbxClR+wx49emDFihXc9pYtW8AwDCZPnoxHjx5BIpHAxsYGK1euBD/tSRCD0TmQ6tKlC5o2bQoej4dmzZohPDwc+/fvx5AhQ1ChQgUMHDgQbdq0ydF6fEQ7aR98Vk6ZzQkheYNlWYz97Sle+MUbuip5poarBTYurVVggqkhQ4bg0aNHuHr1Krfv33//xZMnTyCXy9GhQwfMmjULdnZ2BqwlSaNzdLN8+XKVIMnOzg4TJkzAtWvX0KdPH/j4+KBNmzbw9vZGTEyMrrcjKpQBFCOXGrgehBBC9MXIyAjr169H//79VfY/fPgQT548wbt372gYTQGil649jQUbGaFTp07o1KkTHj58iF9//ZXr9uvXrx9q1KiRV7f+YSyWRCCsuAS/hYegvqErQwgpkhiGwcaltYpY114qRMbGXLdYQevaA5Tfoe3bt8fVq1fRrFkzHDlyBFKp8o/mDx8+oG/fvtixYwdcXV0NXFOSZ4EU8D1767Fjx5CcnAyWZXH06FG8efMGR44c0bn8ixcvYvv27Xjz5g34fD4aNGiAsWPHomrVqnqoPRAXF4fLly/j9u3biIqKQqVKldCmTRu4u7vrpXxdRUGOSD4LsVRi6KoQQoowhmFgYlw0xuLI5QADPoyN+QV2fJFUKsXcuXNx5MgRdOrUCQsWLECPHj0watQobqB5XFwcxo0bh3Pnzmk9iJ3kDZ0DqUWLFmH27Nkq+65fv45du3bhzp07YFkWLMuCz+ejTZs2GDRoEOrVq6frbbFy5Up4e3ujdu3auH37NsLDw+Hp6Ylr165h1apVaNeuXa7LTkxMxPr167F//344Ojpi9OjRaNeuHYyM8jTuzLHxxiURFxKD8sUpUzwhhBQVy5Yt4xobWrVqBQCoVasWvL29MWjQIC4dQkhICM6ePYtu3boZrK5ED2OkfH198fDhQ0RHR2Pnzp3o0KEDRo8ejdu3b0OhUMDc3BxDhw7FxYsXsXbtWr0EUYcOHYK3tzcAoHfv3jA2Nka5cuXQrFkzSKVSTJo0KddJyp48eQIPDw9s374d//vf/3Do0CF07NixwAVRAFCeb4yKMj5MjUSGrgohhBA9iI+Px969e7ltBwcH7nHNmjUxaNAglfNfv36db3UjmukcHbAsi4EDB6psA8r8FgMGDICnpydMTEx0vQ1HIpFgw4YN3LajoyP3uFy5cgCUzaKrVq3Cpk2bclT2rVu3MGbMGEgkEgwYMAC//fabfiqdRxjet1l7oFl7hBBSFHz69IkbCwVAJfkmoEyNkNaQAICSYRcAemtmYVkWDMOgRYsWGDRoEJo0aaKvolXcvXsXoaGh3LZZugV70/cT37hxAwkJCTA3N9eq3I8fP2L8+PGQSCSwt7fH1KlT9VfpPPJMloxIkRTuCUV3WjIhhBQVGdfF07TmnrW1tcp2VFQUypcvz23b29urHK9Zs6b+KkhyRS/JnXg8Hvr06YOzZ89i8+bNeRZEAcC9e/dUtjNLjy+Xy9XOzcrMmTORnJwMAOjbt69eW9Hyyp7UKGyxFONzZLihq0IIISQbGVMWpKamqgVXDg4OqF//+zzs27dvqxxP35VXpUoV/PTTT3lQU5ITegmkpkyZgj/++EMlas4rT548UdnOauzS06dPtSrz+vXrePz4sco9OnfujAYNGqBp06aYPHkyAgICclXfvFTRyBjOEj5MZdS0SwghBVlqaiq2b9+usk8mk2HHjh2QyWQq+5ctW8YNVdm+fTuXmPPDhw+YP38+AKBy5crYvHkzrbVXAOjctefu7o5evXrpoy5aiYiIUNnOKmN6VJR2i/kePHiQe2xlZYUpU6agdOnSWL16NXbt2oXTp0/j6tWr+Pfff/UyWF5fBsYIIEsyQfHSDtmfTAghxCDq1KnDpQDKaMmSJVi+fDl69+7NBUmlS5fG0aNH4evri0uXLmHq1KmQyWQwNjZGlSpVMGfOHPTq1QsiEU00Kgh0DqTq1q2LBg0aoFGjRmrRdl7ImB09qyRq0dHR2ZanUChUmk6LFSuGKlWqAAB+//13XLx4EWFhYUhOTsbUqVNx4cKFXOXsYFmW6zrUF1OH4oj3D4UiD8omeSslJUXl/6TwyKv3TiwWQ6FQQC6X0wDiPJQWzLAsm2+v83///afVeenrY2xsjGHDhmHYsGFanV+UyOVyKBQKpKSkqHR9ZvfZSxurnd90DqS2b98OlmXzbQpm+tkM2dEU/WcUGBiYaRBiZGSEFi1aYP/+/QCAL1++4OrVq+jQoYPWdUgjlUrh5+eX4+uywhgpk8mJU1L0XjbJH58+fTJ0FUgu5cV7Z2RkBLFYrPdyiTp6nQsusVgMmUyGDx8+aDye1WfPEMlJdQ6kHBwc8ObNG0yZMkXra0JDQ1G6dOlc3c/CwkLrLruMsx80ydjClXHgX1rrVJrnz5/nKpASCASoVKlSjq/LytD4UHywScbwqHAMoGUCCpWUlBR8+vQJ5cuXLxQTG8h3efXeicVihIaGQiQSwdjYWG/lElUsy0IsFkMkEhW4ZWHId0ZGRnB0dFTpvszus/f+/fv8rCJH50Bq9uzZGD58OJo1a6bV+XFxcWjTpk2uW1Ds7e1VAqmsWp1sbW2zLS/jP1gZW6cyrq4dH5+7VAMMw8DU1DRX12YmTC5BiJECqVKx3ssm+cPExITeu0JK3+8dj8cDj8cDn19wly4pCtK6wxiGode5gOLz+eDxeDAxMdH4R0Vmnz1DBcY6z9qrV68eduzYgXnz5mnVvafrGntubm4q21n1EdeuXTvb8jIGSomJiSrdhxnfRBsbG22qmS+GWJXCxBhjVLG2z/5kQgghhOidzi1Sv/zyCxQKBcRiMXr16oVatWppjPJZlkVYWBiCg4N1ul/jxo1V0uenrTmUEY/HU5lhFxAQgGnTpiE4OBj9+/fHr7/+CkAZGLm4uMDf3x+AMjALDg6Gk5MTAPX0CvruntNFZWMzlJAawVxAC1YSQgghhqBzIMUwDB48eACGYcCyLB49eqTVNbnVsmVLFC9enOvei4uL446lb51q0aKFSmr9OXPm4NWrVwCAjRs3okGDBnB3dwcAeHp6YsmSJdy5/v7+XCCVfnaAiYkJWrduneu6692315FhFdmcSAghhJC8oHMg1bdvX9y9exf29vaoVasWhEJhprmdUlNTcffuXZXgJ6eEQiEmTZqE2bNnA1CO3m/YsCEAIDxcmeFbIBBg4sSJKtdl7HZ8/fo1F0j1798fhw8fxrt37wAAV69eRceOHQEoZ/WlGT9+PIoVK5bruuvbW0kyQoUyuCUlZn8yIYQQQvRO50CqTZs2sLOzw+HDh7UaP/To0SMMGDBAp3v26tUL79+/h4+PDw4fPowuXbogISEBly5dgkAgwLJly+Di4qJyjYuLi0pW9KpVq3KPhUIh/vnnH4waNQoBAQE4c+YM+vTpAycnJ+zbtw8A0KdPHwwZMkSneuubb+wXvLZKxXC/Z+hs6MoQQgghPyCdAyk+n5+jAKNGjRpo1KiRrrfFjBkzULNmTezcuRMtWrQAn89Ho0aNMG7cOLUgCgAWLVrEjZEaMGAA1xqVxsHBAQcOHMCBAwdw6tQpjBgxAqampnB2dsZvv/2GVq1a6VxnfVOIpXCS8mBrW9LQVSGEEEJ+SDoHUoBywLk2YmJicO7cOb1lQPfw8ICHh4dW51aqVAlHjx7N8hwzMzMMHToUQ4cO1Uf18tz8ytURdcsfJUqUMXRVCCGEkB+SXhYt1pZQKMSWLVu0yjhOsifkG0EABqDB5oQQQohB6NwiNWPGjGzPYVkWKSkpePXqFUJDQ3H58mW0bdtW11sTbtYeBaaEEEKIIegcSB09elTrdAZpLVG7du2iQEoPdoR/xjPrZPQOD0YTQ1eGEEII+QHpZYwUn8+Hs7NzlsslfP78GdbW1rCwsKCuPT0JkaTik0CBWLF+V6EnhBBCiHb0Ekht2rQJTZs2zfKcz58/47fffsOaNWsK1DIrhdnPJR3QMCgFVa1p1h4hhBBiCDoPNjc3N9dqTbty5cqhU6dOGDZsWKbLupCcqVLMAjUlRighokVvCSGEEEPQOZD677//tM723b17d/j5+WHjxo263pYAQFoGeZq1RwghhBhEvqc/4PF4OHnyZH7etsgKTEnCa4EMkSlJhq4KIYQQ8kPK10DqwIEDUCgUiI2Nzc/bFlmHIgKx2joVp4P8DV0VQggh5IeUL3mkpFIpPn36hFevXoFhGNSsWVPX2xIAErkMJWUMyptZGboqhBBCyA8p3/JIpaU8sLS01Cr4Itmb4VwbEafvw6qEg6GrQgghJAtyuRwHDhzAoUOH8P79e/D5fLi5uWHMmDFo2LCh1uXs3r0bCxcuRPfu3bFkyZI8rDHRlt7ySLm4uMDExETtGMMwEAgEsLKygrOzM3r27InixYvr47Y/PBbMt/8oLxchhBRUCQkJGDNmDP777z+V/Xfv3sX9+/exfPlydO7cOdtynjx5QsFTAaSXQGr16tWUqdwQGOUQN2lUnIErQgghJDOTJk3Cw4cPYW9vj/j4eCQnJ3PHFAoF/vjjD7Rt2xbGxsaZlhEVFYVff/0VUqk0P6pMckAvg82zS8ZJ8sbRoAAss0rG9chgQ1eFEEKIBseOHYNMJsPFixdx/fp1PHr0CHPnzgWP9/3rNz4+Hm/fvs20DLlcjokTJyI8PDw/qkxySOdA6tWrV1lG0STvhLNSvBcqEElLxBBCSIEUFxcHb29vODgox7LyeDz0798fPXv2VDnPysoq0zJWrFiBlBT6d76g0jmQ4vP5mR7z8/PD2bNncefOHYjFYl1vRTLoWLEKRscZo25KvmaxIIQQoqXBgwdDKBSq7Xd1deUe161bF46OjhqvP3fuHK5cuYJFixblWR2JbrQeI5VVNJxxkLm/vz9mzpwJPz8/bp+ZmRmmTp2KPn365KKaRJPKJe1QTGwEkYW1oatCCCnCWJaFTG7oWuiHXA5I5QBfBii+zSY34kOr2ef69OTJEwCAo6MjVqxYofGcgIAALFy4EFu3boWZmVl+Vo/kgNaBlJeXF+7cucNtMwyDqlWrolq1avjjjz+4/W/evMGgQYOQkJDApTwAlLMW5s+fj/j4eIwYMUJP1f+x8Sy/fbCkMsNWhBBSZLEsi/23gNBoQ9dEX3gAVNcnLW0D9GnK5lswdeXKFZw+fRoAMGLECJQuXVrtnKSkJIwfPx7Tpk2Di4sLgoNpLGxBpXUgtWLFCri7uwMAfv75Z4wePRply5ZVOUcqlWLy5MmIj4/nfiE7deqETp06ISkpCdu3b8fq1avRrFkzuLi46PFp/JjCUpPxViCHzecgQ1eFEEJINs6cOYMTJ07g2rVrXEPDnDlzcOvWLaxcuRICgYA7d8aMGahfvz48PT0NVFuiLa0DKX9/5TIks2bNwsCBAzWes337dgQEBHDbGc9t3749fv75Z/j6+mLhwoW5rTP55tiLZzhhnYLOsMBgQ1eGEFIkMQyDPk2LUteeAqniVBiLjMHnK8eX5lfXHsMw4PP5YBhGpcfm/PnzcHJywqRJkwAAW7duRWhoaKZdfqRg0XqU8pUrV9CiRYtMg6jo6Ghs3rwZDMOAYRi0adNG7VyRSITx48fj3r17utWaAACszc1hJ2Ngnr9LJhJCfjAMw0BgVFR+AAEfyv9/25dfXXodO3bEhg0bcPjwYZQvX17lmK+vLwDgwYMH2LZtG9auXatxkDopeLT+Br53716mQRQA/PPPP0hKSgLLshAIBPjtt980nufm5oaIiIic15SoGeTeEAuji6EdS4MQCSGksKhatSp2796NkiVLcvsSEhIQHR2NI0eOIDIyEq1atYKzszP306ZNG5Uyjh49CmdnZxw5ciS/q08y0DqQCgsLQ7Vq1TQeCwoKwr59+7jWqL59+3I5MzIyMzNTSURGck/Ofks9oaAlYgghpDCxtbVVa3DQtMwaKfi0HiMllUohk2meHbZ8+XIubb2FhQXGjh2baTkhISGwsbHJYTWJJnJ8a45mKZAihJDCpn379hAKhZBIJHB1dYWJiQlsbW3h5OSkdq5MJkNQ0PeJRWZmZrC1tYW5uXl+VplooHUgVaZMGTx8+BAdO3ZU2X/58mVcuHCB62MeO3YsLC0tMy3n4sWLqFGjRi6rS9I7//oFLlqmoC746GDoyhBCCFETGBgIkUgEOzs7tWNCoRDm5uaIiorC4MHKKUNTpkzBlClT1M4NDg5W6d5r164dLWBcQGjdx9aiRQusWLFCZa2fjx8/YtasWVwQVbly5SzHUUVERMDX1xdNmjTRocokTXBMDF6J5HjIJGd/MiGEkHx19uxZtG/fHi1btsTq1auhUChUjoeEhCAqKgodO3akNAeFmNYtUkOGDMHhw4fRrVs3tG/fHgBw6tQppKSkgGVZiEQiLF68ONMlY6Kjo+Hl5YXY2Fg0btxYP7X/wbmWLIHhcSKUEZlmfzIhhJB8FRkZCZZlwbIs/vnnH9y5cwfTp09HrVq18OXLF0ybNg0DBw7Eb7/9lu+Z1Yn+aB1I2draYu3atRgzZgwOHjwIAFweDGNjY6xYsULjYHR/f39cunQJe/fuRVRUFBiGocHmelLfrRE++FyDuSDz9Q4JIYQYRv/+/SEWi3HmzBl8/PgRL168wOjRo1GuXDnUrl0bCxcuROXKlQ1dTaIjrQMpAGjYsCFOnTqFf//9F48fPwbLsnBzc8PQoUNRoUIFtfN///13JCcru53q1q3L7V+1ahWWLVumY9UJwwNMWYZm7RFCSAHE4/EwfPhwDB8+XOeyypYtizdv3uihVkTfchRIAUDp0qUxb948rc6lgXB562uqDE+FMpjyGPxk6MoQQgghPyDqYyvE3oWFY6NVKg4IkgxdFUIIIeSHlOMWKVJwmFtYoryUh1IyiocJIYQQQ6BAqhCrXNkFM2OUM/ZYuRxMJjMmCSGEEJI3qCmjEFMYf097IE8VG7AmhBBCyI+JAqnCTPR9ZXB5IiXlJIQQQvKb1oHU4cOHMWnSJHz9+jUv60NyIDjsC/60TsZayxQoxBJDV4cQQgj54Wg1RurOnTvcUjB169bFgAED8rpeRAsSiQSfBQpYyxkopJoXlCaEEEJI3tEqkPL29gYAODo64qefKGNRQWFXsjQmppiBnyoDK5UaujqEEELID0errj0/Pz/Ur18fBw8eRIkSJVSOHTt2LC/qRbRgaloMNSGCs9QICjEFUoQQQkh+0yqQYhgGixcvhoWFhdqxGTNmqK1onRWFQoHq1atrX0OSKRYMpAnK2XqyhEQD14YQQgj58WgVSFWuXBmmpqYaj6UtXKythIQEyGQ0nkcf5PJk+BVj4SeQAbQQNCGEEJLvtPr27dOnDxYvXoyYmBi1YwzDgGEYrW4mk8mwadMmrc8nWfsUGoNVxZLgbZkK5KBVkBBCCCH6odVg886dO+Ps2bNo3LgxzMzMYGpqCj6fzwVEbdu2zbYMuVyO6OhoSGlQtN6IBEKUgxFEMgVYCqQIIYSQfKf1EjGrVq3CihUrsGfPHiQkJKgcCwkJydFNqUVKPyytS+NPfkkkx8aDlcsNXR1CCCHkh6N1ICUUCjFz5kx4eXnh7t27+PLlCxITE7FhwwaMGTMGvGzG6EilUnz9+hXnz59HcjJl4dYHqYIPhqcMSlk5tUgRQggh+S3HixZbWFigQ4cO3PaGDRvg5eWVbSCVpmPHjhg5cmROb0s0UCjSte5R1x4hhBCS73IcSGWU01l7DRs2zPE1RLOE5ESsl0Yg1VqGI5TZnBBCCMl3OgdSly9f1ro1ClB2ET5//lzX2xIAUpjgDSsBBIAkJcXQ1SGEEEJ+ODoHUmXKlMnxNUKhUNfbEgB8gRCTRSUgDU8EI6cWKUIIISS/6RxIpSeVSnHx4kU8ePAAX79+hYWFBcqXL4/27dvDyclJn7ciAJJTGdTnmyJBkgp5RJShq0MIISQTYrEYjRs3RmJi5qtQDBkyBL///ju3LZFIsGvXLhw+fBhfvnyBubk52rRpAy8vLxQvXjw/qk20oLdA6vr165g7dy4iIiLUjq1evRru7u74448/4ODgoK9b/vBsi4uQEqFMRSGwKGbg2hBCCMnM5cuXswyiBAIBfvnlF247NTUVI0eOxP379/HLL79gxowZOHPmDCZNmoRLly5h586d1EBRQOhlXZFjx45h7NixiIiIAMuyGn/u3LmDrl274smTJ/q4JQDg4sWL6NevH+rWrYsGDRrAy8sLr1+/1lv5ALB79244Ozur/JVQUPCNGATam+CFUJblB5QQQohhHT9+PMvjHh4esLe357bnzp2L+/fvAwAGDhwIQDnr3draGhERERg2bBjEYnHeVZhoTecWqU+fPmHu3LmQy+UoW7YsunfvjoYNG6JChQowNzcHy7KIiYnBy5cvsWfPHowbNw6nTp2CjY2NTvdduXIlvL29Ubt2bdy+fRvh4eHw9PTEtWvXsGrVKrRr107Xp4YnT55gyZIlOpeTVxRyFiuTwpBgpUDj6Cg4G7pChBBC1ERFReHBgwd4+PAhzM3Nsz3//fv3OHnyJADAyMgIZcuWBaBMd1OuXDnExMQgJCQEvr6+GDp0aJ7WnWRP5xap7du3QyqVwsvLC2fPnsW4ceNQr1492NjYQCAQQCgUws7ODm3atMHWrVvRrFkz7N27V6d7Hjp0CN7e3gCA3r17w9jYGOXKlUOzZs0glUoxadIkvHnzRqd7REVF4ddffy3QS9pYWhjB0cgYjlIeeDTYnBBCCqTTp0/D3d1dqyAKAI4cOQLFt9yApqamKsfST9Y6deqU/ipJck3nQOrOnTsYOXIkvLy8IBAIsj3fy8sLly5dyvX9JBIJNmzYwG07Ojpyj8uVKwdAOeh91apVub6HXC7HxIkTER4enusy8gOfARbYlMHsGFOUSCi4AR8hhPzIjh8/jitXrqBevXpo3bo1Ro0ahY0bNyIoKEjj+WldegCy/F718/NDTEyM3utLckbnQCoiIgJ9+/bV+nxra+tMf3m0cffuXYSGhnLbZmZm3OP0kfqNGzfU1gTU1ooVK5BSCPIy8Y0YSFOUa+wlBgQauDaEkKKKZVkoFIoi88Nm3M7DJNEBAQF4+fIlWJZFQkICQkJCcO3aNaxZswbt2rXDxIkTERX1fda1WCyGn58ft21klPkIHIVCQXkZCwCdx0hZWFioBDPZefjwoU6LFt+7d09lO7NoXS6X4969ezkeK3Xu3DlcuXIFa9asQbdu3XJdz/ygULAwtTUH3kcgNUR9tiQhhOiKZVmEhoZCnJpq6KrkGZGxMUqXLq3Td1NmTpw4kekxlmVx9uxZ/Pfff9i5cycqVqyIyMhIyNMtQp9dwuv0QRgxDJ1bpFxdXXH16lWtzg0LC8OCBQtQoUKFXN8v46y/rKL1p0+f5qjsgIAALFy4EGvWrMlRcGgoIiEPaxPD8Jd1Mj6yNHuDEEIKEpZluUHjWYmMjMTYsWMhkUjUuuqyC6Sio6N1qiPRnc4tUr1798bMmTNhbW2Npk2bajwnKSkJBw8exD///IP4+HgMGTIk1/fLmKcqq1+ynETqSUlJGD9+PKZNmwYXFxcEBwfnuo75hWGAT6nJ+CRQIOL1e0NXhxBSBDEMg9KlSxeZNVLlcjnEqakQGRuDz+cDUD7HvGiNYhgGV65cgUQiQWJiIj59+oQXL17g/PnzePTokcq5nz59wsmTJ3OcG6qovC+Fmc6BVNu2bXHixAmMGDEClStXRt26dVG8eHHweDzExMTg3bt3ePz4MaRSKViWRdWqVdGvX79c3y9jtJ7VL39OIvUZM2agfv368PT0zG3VssSyLJKTk/VaplQmw6/1auHt4f9QsVQpvZdP8k7aGLzCMBaPqMqr904sFkOhUEAul6t07RD9YhgGDI8HhmG4ICSvgxE+nw9LS0vUrFkTNWvWxIABA/DkyRPMnz8f79694867ffs2atSooXIty7Iqvw8Z62ppaVnkfl/kcjkUCgVSUlK42YtA9p89lmXzJCDOjl4ymy9duhRGRkY4c+aMyi9FmrQ3vlatWvjnn3+4vwJyIyfpCLT9cGzduhWhoaFYsWJFbquVLalUqjKAUB9io4RoVKU8TCVPYJQq03v5JO99+vTJ0FUguZQX752RkRElWcwnhn6dXV1d4ePjg/Hjx+Px48cAlA0FlpaWKkGeQqFAarrxaekDCwCwsrJSOV4UiMViyGQyfPjwQePxrD57hljLVy+BlImJCf7++2906dIFvr6+uHPnjsqb7eTkhAEDBqBv377Z9vdmx8LCQusuO2tr62zPefDgAbZt24aDBw/m6RsgEAhQqVIlvZaZoogFz0gZlMoiYuDq6qrX8kneSUlJwadPn1C+fHmYmJgYujokB/LqvROLxQgNDYVIJIKxsbHeyiWqWJaFWCyGSCQySOtFesbGxli5ciXatWsHmUwGBwcHlChRAk5OTlwQoVAoVH4f0n+H8ng81KtXr0j+vhgZGcHR0REikYjbl91n7/17wwxx0euixa1atUKrVq2QnJyM4OBgpKSkwN7eHnZ2dnq7h729vUoglVWrk62tbbblHTlyBJGRkWjVqlWW5x09ehRHjx7F4sWL0aNHD+0r/A3DMGqJ1XQlECTjbVwc/AQylJXxYSISgdGhtY/kPxMTE73/XpD8oe/3jsfjgcfjgc/n69RqT7KW1g3GMEyBeJ1Lly6NevXq4d69e2jatCn4fD6aNGnCBVKpqakq9Uwf/Dk7O+u8SkhBxOfzwePxYGJiojFIzOyzZ6jAWC9r7WVkamqKKlWqoGbNmnoNogDAzc1NZTurvuHatWvr9d4FDsNg2dV7WGWdigCBHLIkGm9DCCEFiUQigb+/f5bdbxYWFnByckLbtm0BAD179uSOJSYmqnzPpX9c0FP0/CjyJJDKS40bN1bZzuyXM63JM01AQAB69OiBBg0aYM2aNdx+W1tbODk5qf04ODiolGdmZgYnJyetU/znB4YBHGytUFrGg4gFpJGU4ZYQQgqSQYMGoVu3bmjSpAm2b9+uNsYpOTkZb9++xZo1a7huO1dXV3Tt2hWAsmsvMPB7wuWwsDAAylU9+vTpk0/PgmSl0AVSLVu2RPHixbntuLg47nH6SL1FixawsrLitufMmYNXr14hLi4OGzduxN27dwEAU6ZMwblz59R+fHx8VO7brl07nDt3Ti+LIesLn8fDym5NMD/aFK5SI0TffpT9RYQQQvJNUlISAGXL0pIlS9C3b188evQICoUCYWFhWLduHZYvXw5nZ9Vl5xcsWIC6desCAHx9faFQKHD9+nWEhITA1tYWGzdupGEBBUShC6SEQiEmTZrEbacfvZ+2Np5AIMDEiRNVrnv9+nWW24URn2EhMbcFT6DsP5dEUmI2QggpSHx8fDBo0CBUqFABIpEIr1+/xrRp0zBhwgTcu3cPEydOVBuyAijHAfn4+GDSpEm4ffs26tevj7lz52LgwIE4ceIEKleubIBnQzTR62Dz/NKrVy+8f/8ePj4+OHz4MLp06YKEhARcunQJAoEAy5Ytg4uLi8o1Li4uKlnRq1atmt/V1jsjIwAMD7a1yyD8QSC+HDyLCpOGGrpahBBCvilevDhmzZqVq2uFQiFGjx6N0aNH67lWRJ8KXYtUmhkzZmDVqlXg8Xho0aIFunXrhkaNGuHQoUPw8PBQO3/RokWoWrUqLCwsMHbsWLi7uxug1vrFZ4C1Z65hRtAHvBDKYFrR0dBVIoQQQn4ohbJFKo2Hh4fGoEmTSpUq4ejRo1qXXbZsWbx58ya3VcsXfD7wNjQCr+WpqMcTgS1i2W0JIYSQgq5QB1I/Oj4P+KVtY7Rg+TC+EYio6w8MXSVCCCHkh6LXrr3AwEBcunQJEomE2xccHAx/f3993oZ8IxDwUMexNFpa28BWwYPItnj2FxFCCCFEb/QSSIWHh2Po0KHo0KEDxo8fj4SEBO6YSCTC6dOn0bt3bzx4QC0m+mQs4kEmMoNZWSsAQMKrt4atECGEEPKD0TmQSkhIwIABA3D37l2Ny7XY2tpiypQpmDt3LsaNG4c9e/boekvyjVDAQ6DcBGdDvuCeSPvFnAkhhBCiHzoHUlu2bEFQUBBEIhFq1KgBIyPNw66qV6+OwYMHY9GiRfjvv/90vS0BIBAwePkxEPNvPcFOC+VK5kkBgdlcRQghhBB90TmQunDhApydnXH58mUcPHgQlpaWmZ7buHFjKBQKeHt763pbAuUCjbZ2ZWBnboqmKQIAQMy9J9lcRQghhBB90TmQCgkJwezZs1WWbclMWmvVs2fPdL0t+aZCxSq4Oak3Rtsr1wYMO3LewDUihBBCfhw6B1ImJiZaZwm/ffs2AEAqpfE8+qJglG+hOEo5wD85IMiQ1SGEEEJ+KDoHUs7OzggKyv7LOzAwEFu3bgXDMHByctL1tuQbBU/ZyleynjKrOc3cI4QQQvKPzoFUjx49sG7duizPefz4MQYNGoTExEQAQNeuXXW9LfkmkRWg7lJfdH7+GCmMctakLDHJwLUihBBCfgw6Zzbv1q0bTpw4geHDh2PAgAFQKBQIDg5GSEgI/P39cfHiRdy+fZtLjVC9enUMGDBA54oTJZ7ABIliKVgASQwLE5bBees6+CnhOfjGIkNXjxBCCCnSdA6kGIbBunXrMH36dG6F6v/9738q56QFUQ0aNMDq1aszTZFAcs7aXIGVPZqjZWUHvFpzHymBYQCASw5N0eErpZkghBBC8pJeIppixYphw4YNuHPnDo4dO4anT5/i69evkMlksLa2hpubG7p06YL27duDYRh93JJ8E5fEh0f1CmBYFs1OLMX1jlMh/vIVsth4JH8Khmn5soauIiGEEFJk6bVpqHHjxmjcuLE+iyTZkCsYiEtVgvTjK3y5fwOt3l3BObMaAIAHnYej5ctzBq4hIYQQUnTpddFikv8ULHDkyi1MPXoTvneegS8Sfj8moTQThBBCSF7SSyClUChw6NAhbNq0CampqSrHHj58iN9++w2XLl3Sx61IBiwLRLICXHsXjJCoGABA7T2rAAApH4MNWTVCCCGkyNO5a0+hUGDMmDG4ceMGAMDGxga9e/fmjterVw+VKlXCzJkzsXPnTqxduxZWVla63pZ8w/D4aONeH5XZeJR2UGY3t6jpyh1nWZbGpRFCCCF5ROcWqd27d+P69etgWRYsy8Le3l7tHCsrK6xZswbR0dEYNmwYJBKJrrcl34iM5ChnZwuPak6oXbqEcl/J78v1JNMixoQQQkie0TmQOnr0KEqUKIFx48Zh27ZtaN68ucbzBAIBhg8fjlevXmHnzp263pZ8E5PIQG5iDplCgdCISACAkaU5d/zLYRpsTgghBcnHjx+xcOFCtGzZMsvzJBIJtm7dCg8PD9SuXRvNmzfHH3/8gaioKK3uI5FIcPz4cfz888/w8fHRveJEI5279j58+IBdu3bBzc0t23OrVKkCADh27BiGDx+u660JlOkPYgRi3PH7jFdfojB/kjK3l2lFRyQHBCLp/WdDV5EQQn54LMvixo0b2LVrF27dugWWZWFubp7p+ampqRg5ciTu37+PX375BTNmzMCZM2cwadIkXLp0CTt37sx0ubWvX79i37592LdvHyIjlX9gd+7cOU+eF9FDi5SRkREXIGUnLYoODKTuJn2JT+ZBLDTBxMPX8e+dl5CKlYP9S7RyBwAE+xw2ZPUIIeSHJhaLsXv3bnTp0gUjR47EzZs3uSTVWZk7dy7u378PABg4cCAAoGPHjrC2tkZERASGDRsGsViscs3r168xffp0tGvXDuvXr+eCKJK3dA6kKlasiI8fP2p17r59+wAAFhYWut6WfCOXszAvXhIAcHBoJ8T7PwUAFKtSnjvHf9ZKA9SMEEIIwzCoX78+Tp48iVWrVml1zfv373Hy5EkAysaKsmXLcmWVK1cOABASEgJfX1+V60QiEebNm4dbt26hZs2aenwWJCs6B1IeHh7466+/1CLj9ORyOZYsWYLLly+DYRg0bdpU19uSb1LlAhgJhVjXqxUcrM1hnhILAHCaMJg7J2CZt4FqRwghPzahUAhnZ2cwDIO2bdtqdc2RI0egUCgAAKampmrlpTl16pTKsYoVK6JYsWIwMzNDo0aNdKw50ZbOgVS/fv0QFRWFLl264ODBgwgJCYFcLodYLMb79++xY8cOeHh4YMeOHQAAY2NjjBs3TueKEyVWpky62aZ2dZiJBJBHhwMAGD4f7le//7UiiY41RPUIIYR8kz4Iykpalx6gnKiVGT8/P8TExGg8ltV1RL90HmwuFAqxefNmDBkyBHPnzs30PJZlYWJigtWrV8PhW74jojtxqjKVxAeFCPcevcHixbsRGKxsjbKsV4M77/WkP1Frx3KD1JEQUrixLAvIisZKCaxcDsgkYKU8sAq+cqeRoMDk2xOLxfDz8+O2jYwy/5pWKBR4/vw5WrRokR9VI5nQy1p7Dg4OOHr0KNasWYPDhw8jJSVF5Tifz0fr1q0xadIkVKhQQR+3JN/ERicBEOHcszdYf/4BHK3NoVAowOPxwDcWgV/MFPKkZITsOQGT8mXg/MdEQ1eZEFKIsCyL1OPeUIQXrUlC6Qej8OzLwbjriAIRTEVGRkIul3PbPF7WHUfapkIgeUdvixabm5tj9uzZmD59Ol6+fInw8HCwLIvixYujevXqKFasmL5uRdKRypT/7zFyPNYfPIndg3+C+M5ZmDTtBACoe2g9HnQcCgB4/9c/FEgRQnKuAAQYP4qMXXXZBVLR0dF5WR2iBb0FUmmEQiHq1KmT6XG5XI6VK1di+vTp+r71D0mcIgEggkBkjGMju8DKRIQv9y5BCCOUbdoBtm2boMWrc7he7ScAgCwpGUbFTLMulBBCvmEYBsZdRxSZrj3lGN5UiETG4PMLXtdeTlf+0CaVAslbelm0OCc+ffqE7du35/dtiyw+7/uHqMqAX/EiNBJ7/vNHwz5DkZqcBAAoVrk8d855q9pQyGT5XU1CSCHGMAwYgbDI/MAow74CEkQBgKWlZY7Ot7a2zqOaEG3prUXq1atXePHiBeLj4zONqBMTE3HhwgV93ZIAMDMFopNFsDEVQ2pRAlbt/od/dvwCALi/bxtaDB0PhmFgWaca4h6/AgDE3n8GmyZ1DVhrQgghmtjZ2YFhGK6lKbsWJ1tb2/yoFsmCzoFUXFwcJk6ciHv37ml1PsuyBSr6L+zKl2YhkSmbp8ViCWq1aocl3ZqiU7XyMEmX97Tp/SM4LXAGAASs+JcCKUIIKYDMzMxQoUIFBAQEAABkWfQg8Hg81KpVK59qRjKjc9feggULcPfuXbAsq9UP0TNWBrFc+TYy3/r7+wweBiGfD0VUmMqplnWqAQAiTl2FNCYuf+tJCCFEK40bN+Yep6amZnqes7NzjrsCif7pHEjdvHkTDMOgU6dOOHr0KB4+fAh/f/9Mf7LKNUVyztociE0RAQC4P1zsyyEsPhmB0fEq56bPI/XCa34+1ZAQQggALlt5mswaF3r27Mk9TkxMVEmHkP5xt27dtL4XyTs6B1ICgQCmpqZYsWIFXF1dYWZmluX53bt3p5YpPeLzAZZVdpVKJckAgAQjU7RYcxBt1x+BPF2zsJlLRfDNlDP2vhw4A2lsvHqBhBBC8kRiYqLKdmpqqsaAx9XVFV27dgWgDIgCA7/n8AoLU/Y0ODo6ok+fPlrfK+M20R+dA6mWLVvC1NRU63FPJiYmuHz5sq63Jd/w+d+zm8sUyiUBTC2tAAAz2tdH2KNbKufXP76Ze3zBtn7+VJIQQn5wqampajPWZTIZduzYoXEc1IIFC1C3rnIsq6+vLxQKBa5fv46QkBDY2tpi48aNauvwpXn//r3a9+yZM2cQFBSkp2dD0tM5kBo/fjxkMhn8/f21Ol8mk2HZsmW63pZ8Y2LMICRMmd9FwFMGVCZm5hjUwBUNy9nDlqf6AS3evAHsun1fOJPW4COEkLxVp04d1KpVCxs3blQ7tmTJEri5uWH+/Pkq+01MTODj44NJkybh9u3bqF+/PubOnYuBAwfixIkTqFy5slpZ3t7eqFGjBjp16oSQkBCVYwEBAWjbti1q166N0NBQvT6/H53Os/bs7e2xbt06LF++HJs3b85yXSAACAoKohQIemRejIFE9j0eTpsVOW/4AIgD30D28TWE9VVXHK93aAM3g08SGQOhjVV+VpkQQn4ojx8/ztV1QqEQo0ePxujRo7U6f+TIkRg5cmSu7kVyT+dAav369QCUY6VGjRqF2rVrZ3quWCzG+fPndb0lSYfPBz58SABaKIMpiUQCkUgEWFjDLywaPRftxPMm3VG8jOpC0cZl7ZEaHIbwU1dgNnmYIapOCCGEFHo6B1Jnz57Fhw8fuO07d+5keT7lkdIvhmEgFn8fvC8WiyESiZBgVwmj9l4CALRv2xqP/N6pXJcarByw+HHVdlSkQIoQQgjJFZ3HSPXt25cLjqytrWFnZ4dSpUqp/djb28PY2FgfdSYZmJjyEZmkfG2TkpTLwthWqYZ6FR0wu0MDXB7/s9pMSadJyoWMxWFfaRYlIYQQkks6t0h5enpi8+bNOHbsGIoXL57t+fv371cbVEd0U7YEAyFfmVtEnC5527+HTyJl1xIoWBbyoLcwcnTmjtl3bYOPq7YBAKKu3kOJ1u75W2lCCCGkCNC5RcrMzAxdunTJdBpmRt26ddMq4CLai4kVIyxB+fqnz0nCMzUHACy9+BDl3Fsj4PkT7phN03rc44Dl/+ZTTQkhhJCiRedACgAmT54MExOTbM/bsmULIiIicOvWrWzPJdqrU9UEgbHm3Hb6zLePUgU4+/ojGADLf5+kcp1Vw1oAgMhLt/OjmoQQQkiRo5dAKruUB2m6d++OwYMHq2RpJboT8oFEsZDb/vzpExdMNR47C93c62LbgPZY2a0JWPZ7i1XVlTO5xynBquvyEUIIISR7Oo+RSvP06VN8+fIFEolE4+BluVyOsLAwhIeHY968eWoZXknuiSUKREbHIbycCezMUwAogymnChVgJBBgzr+7kbxlHkLjEvFgwW/oP0+55p51w5pcGVecWqBj8kvwBAKDPAdCCCGkMNI5kEpKSsKwYcPw7Nkzrc5nWVbrc4l2KpYzRaBEhLufLdGsQhiKmyrX3EvLKcXwlW+zf1gMpu+/jNaDRqGUUyUAQJn+3RDiexwA8GzIb6i9+2/DPAlCCCGkENK5a+/ff//F06dPwbKsVj/W1tZaZ2kl2jEx4SHoQwQA4OYHO25/eNj37jqTvlMw65RyLNTVI/u4/bV8vi/XE7r/dF5XlRBCCClSdA6kLl++DCcnJ2zfvh0PHz7E69ev4ezsjIcPH8Lf35/7efHiBdzc3LBjxw5KYa9nZeyNEfop6tsWwyU8VZnBZ2GDmR5NcH5cd3iYJKmMlWr+9BT3OOr6/XypMyGEEFIU6BxIffnyBX/99Rfc3d1hZmYGHo+Hjh074uTJkyrnCQQCjBs3Dr/++itS0+U6IrozK8ZHzNcEbvve55IAVAMpAOg5dBScilsiNiUVKf7fUyGYVa30/dq2g/K4toQQQkjRoXMgJRaLUaVKFZV9PXv2xN69e9XObd68OSIiIrBhwwZdb0vSYRgGrpXNERuVCACITRFxx9IP/Be4NcH7r7G48jYYldt2hVwu4663796eOy/u0ct8qjkhhBBSuOkcSNnb2+PFixcq+2xtbVG5cmXs3LlTZX9SUhLEYrFaaxXRXWS0GFeOPkZqYgqk8u9vq1gsVjnvTKQcc04p10O8unAit7/2nlXc41uNekIhk+VthQkhhJAiQOdAyt3dHRMnTsTy5cvh7e3N5YgaOXIkli9fjr179yI5ORlBQUGYPHkyZDIZEhISsimV5FS7FsruvLP7/4NM8f1tTUlJUTlvxupNmNG+Pv6b1hfupSzBSiUAAJ6REWr8s5A770KJ+vlQa0IIIaRw0zmQGjVqFGQyGbZt24ZVq1Zh1qxZAABnZ2cMGDAAf/zxB+rWrYv27dvj5s2bYBgGtWrV0vW2AICLFy+iX79+qFu3Lho0aAAvLy+8fv061+X5+flh0qRJaNy4MWrUqIG2bdti8eLFiI6O1kt981KjujYAgLSevOhkZfdeSnKy2rnjthyCpYnyOJv8Pah1HN6beyxPSqYuPkIIISQbOgdSZcqUgY+PD1xdXSESidC0aVPu2OTJk9GmTRuV9Ae2trZcsKWLlStXwsvLCwqFArdv38bBgwdx+/Zt9O7dGxcvXsxxeYcOHcLPP/+MM2fOICoqChKJBEFBQfDx8UHnzp0REBCgc53zUu0aVtzje5dec+OkUlNT1RKkMnwjhManYNbJ23h+54bKsQ6x3weh32rUM+8qTAghhBQBelkipnr16jhy5AiePn2KUaNGcfsFAgHWr1+PzZs3Y8qUKVi5ciXOnDmDChUq6HS/Q4cOwdvbGwDQu3dvGBsbo1y5cmjWrBmkUikmTZqEN2/eaF3e06dPMXfuXMgyGRcUFRWFiRMnaszYXlAwDINyZZULF8d8TUBE4ve1Dz99/Kh2fsvV+3HwyTsMmjhNZb9RMVMYWZhx289Hzc6jGhNCCCGFn14CqawwDIMWLVpgxIgR6NSpE8zMzPDff//lujyJRKIy68/R0ZF7XK5cOQCAVCrFqlWr1K7NzIoVK+Dp6YkzZ87g6dOn2Lt3L6pVq6Zyztu3b/Hw4cNc1zs/+P5TH6XtjSGVyBCWUIzbrykAHNauCeo52uEPj0Zqx9p//f7+BG07iKuu7cFmSKVACCGEkHwIpDKKjo7GoEG5z1V09+5dhIaGcttmZt9bT4TC7wv33rhxQ6tB7dHR0XBzc8Nff/2FihUrwsTEBHXq1MHWrVthY2Ojcm5MTEyu651f9vxTHzKpcsHi82++B5mpGQadz134J3YM7ID2LuUgjwhSOcbweGgbfJvbTn7/GWfN3PKw1oQQQkjhpLdFiwEgODgYMTExEIvFaq0gLMsiMTERBw4c0Oke9+7dU9kWZLLIrlwux71799CuXbssy7OxscH06dPV9ltbW6Nly5Y4cuQIt698+fI5r3A+MzJSxsZR4fEobmfB7Q8NDYWjoyOMvr1eRg6VIeDz8CUuCTGHd6P+mBkq5YjsSqBD7BOct6oNAGClUqQEhsLEsXQ+PRNCCCmaPn78iN27d+Py5cu4du1apuc9fvwYffv2zbKsf/75B61bt1bZd/HiRezevRsvX76EXC5H+fLl4enpif79+2f6nUlyTy+B1JYtW7B9+3atZrexLMstYZIbT548Udk2Msr8KTx9+jTbQCortra23OOKFSuicuXKuS4rP/05oyoOXE9EcTsLRCYZo0QxZSb5kJAQlEsXDB54E4YT957gwedwhGQIpADleKlO0jc4LXAGAFyp2AqdpNqPPSOEEKLEsixu3LiBXbt24datW2BZFubm5llec/z48SyPV6xYEa1atVK5x5w5c3Dw4EGV8/z8/ODn54ezZ8/Cx8cHJiYmGYsiOtC5a2/Tpk1YuXIloqKitFq0WFcREREq2zxe5k8hKioq02PaCAkJ4R4PHz5cpwAwP7lUNseXz8rnfvtjKcSnKv8CkcvlKu+BS7uuePA5HCIjPpLCQzSWBQCiUt8DSrlYkke1JoSQokcsFmP37t3o0qULRo4ciZs3b2r1XSiRSHDu3Lkszxk6dKjK99K2bdvUgqj0nj59iuXLl2tfeaIVnVuk9u3bB5Zl4eTkhAEDBqBs2bIwNjbONOg4evQojh07luv7ZRynlFVwo0v+J4VCgfv3lQv4NmvWDD169Mh1WYDyL4VkDTmddJGWbDNj0k0jvhwRIcrXiQWDawFl0bWacuZeSHAwbIoXBwDUbNEOx0Z2gaudDXBsI5L6TgdjpN7s2+DBEdx0aAYAiLj/BJb1auj1efyIMnvvSMGXV++dWCyGQqGAXC6HXC7Xa9nku7QghmXZfHmdFQoF6tati//97384d+4cpkyZonI8szpcvXoVjo6OuHPnTpblp12fkJCALVu2YMqUKejatSuMjY1x69Yt/PXXXyqNCgcPHsT06dMLdBefXC6HQqFASkqKypqx2X32dO3xyi2dA6m4uDgIhULs3r0bxb99QWelcuXKOHr0aK7vJ5VKtT5Xlxawy5cv4+vXryhfvjxWrFiR63LSSKVS+Pn56VyOJp8+fdK4/4zvPXj0bwQF+/0XSyKR4M2bN9wvp71bAzARAYhLESN02wowzTyzvNe7U5dQrJheh9b90DJ770jBlxfvnZGRkdqyTiRv5Ofr7OjoCLFYrJJnEVB+R6Wmpmq85ujRo2jXrl2mxzO6e/cuZsyYgTZt2nD7WrVqBQsLC4wYMYLbJ5FIEB0dDUtLy1w8k/whFoshk8nw4cMHjcez+uyln3SWX3T+RqxRowZCQkK0CqIA5SDuhQsXZn9iJiwsLLTusrO2ts7VPSQSCf7++2+ULFkSW7ZsgZWVVa7KSU8gEKBSpUo6l5NeSkoKPn36hPLly6v1eddw9ccLv++zFk/7lUMn188AgFKlSsHC4ttAdFdXHJ8+FFOP3gQAvBn+m8bu0nA+H6xcDkuJHJVdXfX6PH5EWb13pGDLq/dOLBYjNDQUIpEIxsbGeiuXqGJZFmKxGCKRKN9bLzK+rwzDaHyvY2NjcevWLdy8eZObQe7i4oJ69erBw8Pj+7/f6bRv315tHwA0adIEZcuWRXBwMADAysoKdnZ2eng2ecvIyAiOjo4QiUTcvuw+e+/fv8/PKnJ0DqTGjh2LESNGIDo6Wi1dgCYsy0Iiyf04G3t7e5VAKqtWp/SDxXNi7dq1SEhIwM6dO+Hg4JCrMjJiGAampqZ6KSsjExMTtbIrVzDHC78EBH/4irIVbCGV8yGW8SAyUiA5KQklS5bkAqamE+YCR9uhrkNJsC/vwLSR+gfSukldRN94ACMeP8+ex49I03tHCgd9v3c8Hg88Hg98Ph98Pl9v5eoDy7KQJxeNbmi5XA55qhhyuYJ7nfmmJgYbA6vpvb5w4QLX+xIbG4vY2Fh8+PABZ86cwbJlyzB06FCMGTNG69YXW1tbLpD66aefCtzvV0Z8Ph88Hg8mJiYaA83MPnuGeg91DqQaNWqEadOmYeXKlfjzzz+zPf/r169YtGgR+vfvn6v7ubm54dWrV9x2Vn3ctWvXznH5N27cwJkzZ7Br1y44OTmpHLt58yacnZ1RsmTJHJeb3zq2tsOR06F4cMUPErEUFVxL42FwSTQpHwYAiIqMhO235+FQpSrezv1FeeGz64CGQMqyTjVE33gAhUT7rlVCSOHHsizutuiLmLtPsj+5kLJuXAfu1/YUmAlFWc3WS01NxcaNG3Hv3j1s2bIFxYoVy/TcNGkTpwQCAX755Rd9VZN8o1UglV0mcldXVzx+/Bhr166Fu7t7puclJyer5GXKjcaNG2Pv3r3cdmb9xzweD/Xq1eO2AwICMG3aNAQHB6N///749ddf1a4JCgrCxo0b4evri1KlSnH7JRIJnj17hjlz5uDChQs61T+/uFaxgJOjKT4GJuPpnfeo4FoaXxNNwbIAwygHJtrY2ID/LX2EsGVPSK4dxsPAcNwfNwTTN2xXKU9oq2xtjPvveb4/F0KIgRWQAONHEBQUpJbmR5PHjx9jwYIFWLp0aZbnvX//npvtPmHCBLUGAqI7rQIpLy8vxMfHa1XgP//8k+VxXUfVt2zZEsWLF+e69+Li4rhj6VunWrRooTK2ac6cOVxL1saNG9GgQQOVoC8pKQljx47F27dv0bJlS433rlSpkkEGsuXWhiW14NHvDpCu9/P2p1Jo6vQFAPD582c4VagAhmEgcK6DKZMn4fBTZR/z5D++wKjE92BSlpAEAIh/5p9/T4AQYnAMw8D92p4i1bUnThVDZCwqEF17GTk4OODNmzdISUlBfHw83r9/j4cPH+LMmTNqg6yPHTsGLy+vLIeg7Nu3DwDQsWNHlUHnRH+0yiPVvXt3rXJE5UceKaFQiEmTJnHb6X+xwsPDASibLydOnKhy3evXrzPdVigUmDJlCt6+fZvlvZ2dnXNZa8OwMP8+vfXIlhsAgMgkE0QkfU8Cp0gXfP6+bisA4OiILojdtxqs7Hs3npmLcqFplqZlE/LDYRhGuaB5EfnhFzNR2S4oQVR6JiYmsLOzQ5MmTfDrr7/i7NmzWLp0qdpsu7t372ZaxsePH7F//340aNAAS5cuLZDPsyjQqkWqb9++2LFjBxYvXozq1atDJBJlmQhTk7QlYvbu3avzMjG9evXC+/fv4ePjg8OHD6NLly5ISEjApUuXIBAIsGzZMri4uKhc4+LiotJcWrVqVe7x4sWLcfXq1WzvW9gCKQCYNLoSVm1StjLFRiXCqrgZ7ny0hWd15Yw+uUKBtGGHpStUwrHl81A64i3eRsQAG/9C4wnzAAAWNQrfcyeEkKKCx+PB09MTdevWRZ8+fbhemdjYWI3ny2QyzJw5E25ubti8ebPK7DeiX1pFQ+XKlUOrVq3QrVs3VKpUCQ4ODihTpkyOfsqWLQsXFxdMmDBBLy1TM2bMwKpVq8Dj8dCiRQt069YNjRo1wqFDh+Dh4aF2/qJFi1C1alVYWFhg7NixXLfesWPHsHPnTq3uWRgDqZ6dymDj0loAgHsXvw/SF8uU4dPXDJni6/cbiRSJDAGRcei11BthH94BAESlvg+w95+pe14tQgghOefg4IDZs2dz22XLltV43po1ayASieDt7a02w+3w4cN5Wscfjdaz9n7//XfI5fIs17bTRvHixXHy5Emdykjj4eGhMWjSpFKlShoTgXp6esLT01Mv9Smoargqc44kJ4qRnJAKU3NjiIyUXXSaktIZd+iP3z080a+eC0J2/40SA70gcqrGHf/87364/DU1fypPCCFERfv27bnM5OknVaW5ePEiPn/+DG9vb5VxvcnJyTh9+jROnDiBnj175lt9izqtoyJHR8dc3+Tz588oV64ct11YFv8tKhiGQYdWdjh/NRyXjz5Cl0FN8CSkBGqXiQSgbAJOHyCXrVkfC8YMQcLb53Cxs8Gljctx6Usifr/gg/vtf4EsNt5gqfgJIaSwSr/cCZB5HsTY2FhERkaiQoUKGofRGBkZoVixYmjXrp1aOp63b99i+vTpSE5Oxvnz5zWW369fv1w+A6KJ1oHUuHHj1L44ixcvjr59+6qNR8rozJkzUCgUGDduXO5qSXQ2Z7ILLl0Ph1Qix5Vjj9G2ey0ukIqJjuZySqUZNnsRpG+f4MPR7Xga8hV7rz/FnL++NyEnPH8Di5pZv++EEEK+S0xMVNlOTU2FQqFQCZYiIyPRqVMnxMbGwsHBAXPmzEGLFi1Urnv9+jVsbW3x+++/q+yPiorCmDFjsl3XtTAOUynItB4xPnjwYDx69AiXL1/Go0eP0LJlS8ydOzfbIAoAxowZg/Lly2PevHk6VZbopmMbewBAbGQiFCwPEpny7U9ISNB4vqBKbVSauAzrrj/F1DZ1kXjamzv2fOTMvK8wIYQUEampqdi+XTU/n0wmw44dOyCTybh9crmcy48YFBSEkSNHYvr06fj8+TNYlsXz589x7NgxbNu2DWZmZtx1YrEYY8eO5TKYZ4UCKf3SOpBq0KABKlWqhEqVKuHEiRP4+eefc5RmvlOnTnBxcYGPj09u6kn0YNq4KirbT0K/L6GT2WLQfJEx5nR0R80yJWDFU8CsqnK9wLjHr/DlaOFITkoIIYZUp04d1KpVCxs3blQ7tmTJEri5uWH+/PkAADs7O+zbtw+dOnVCqVKlYGRkhIsXL2LUqFGYO3cuEhISMHPmTLUuvfnz5+Pp06fZ1oVhGFSpUiXb84j2tO7aCwoKgp+fH06ePJnrNez69u2LoUOHolu3brleUJjkHp/PYHAfR+zYH4jIsDgA3xe+DAoMRHknJ4398aPW+iB512IAQJ1/5+BGk8EAgMe9x8ND4k9jpQghJAuPHz/O0fmurq74+++/c3TN4sWLsXjx4hxdQ/RD6xapo0eP4n//+5/K0im50aVLF5p6aUAjBjiBz2fw8r+PABgERKkGU5owpmY4/PQdRu29hJtP78NxVF/umPhLhMZrCCGEkB+B1oHUnTt30L69+mK2OVW7dm3cvHlT53JI7l072gzR4colf158KQHxt7FScrk801kka649xdV3wbhy6w5qrJ/P7b9apS0UEkme15kQQggpiLQOpD5+/IgKFSrofEM7OzsEBAToXA7JPYZhUNxGiOAPXwEAl95+X6cpOChI4zW/tG2CX1vWRu92zVX2K8QSnC1WA08HT8u7ChNCCCEFlNaBVFJSkl5uKJPJMk1pT/JPVLQEj28q1xaUKr5PGpBKpWq5TgBg1C8DMbxxNdSwNgEAtA25o3I8ZM8JnBY4I/pOzsYCEEIIIYWZ1oGUmZkZgjJprciJjx8/wsTEROdyiG5+86oCmfT7AsQX0rVKffr4Ub2LTyaFyMgIbIxyTJSoZHF0kr5Bm0DVbtq7LfqCEEII+VFoHUg5OTnpZWzT9evXUaJECZ3LIbrp0NoOAHB8+y0AQLJEgLD47+sxffzwQeX8RAVw430w9t58pLLfuFRJeEj8UbpPJ27faYEzLpZqhOtuHnpZV5EQQggpqHKUR2rPnj2Q6DCwODExEfv27aMcFgWAUMBDyRIiyOUKXD3+BABwL9Be5ZyE+Hju8YdEOYbvuYS5p1W79ADlmKtau1aq7JNExiDRLwCxD57nQe0JIYSQgkHrQKpbt24IDw/PdXZyhUKBSZMmITo6Wi+z/4ju/pxRFQAQ8/V7ZvPjr5y4x1+/fuXGSzk6u0DI56Fl5bKQvX2qVhbDMPBIfY1mj0+g2cPj3H5Kj0AIIaQo0zqQqlChAjw8PHDs2DGMHj0aYWFhWt8kIiICXl5euHnzJuzs7NCmTZtcVZbol2uV7zmk/rvqDwBgWQbvvlpy+z99/IjkpCTYOjjh5axB8O7bFuJbx9XKAgCGz4dFDWdY1HRBibZNAAAJr9/l4TMghBBCDEvrQAoAZs2aBRsbG1y/fh3t27fH1KlTcf78eY1BVVJSEu7cuYNFixahU6dOuHr1KhiGwaxZs2BsbKy3J0B0s/avmgCAoIDvLUevwosjOvX7Gk5hYWEI+/IFog4DAAB/nboFuVyGrMgSlLM8P2/eq+8qE0IIIQWG1kvEAICNjQ02b96MYcOGIS4uDqdPn8bp06cBAAKBABYWFjAyMkJcXBy36CIAbsDx+PHj0a5dOz1Wn+iqTg0rjB7shE07PuLIlhvoMVyZJ+rG+5L4ub4IspQoAEBKSgoUpRzw6ksUfO6/Rq9b11CrRdtMyzVzqYDY+08hDqWuPUIIIUVXjlqkAKB69erYu3cvXF1dwbIs9yORSBAZGYmwsDCkpKSoHBOJRJg1axbGjRuXF8+B6Kh/z++pD07t+j6Y/NB/lnAo933MVPCXcPx2/Bbau5TDh6M+YDXkm0pT3msQ9zji/A0915iQootmupIfXWH7DOQ4kAKU46UOHTqEhQsXZjkDTygUwtPTE8ePH8fAgQNzXUmStxiGwc9dygAAJGIZ4iK/z9Zbd5oBwxdy246lS8HdqRTauzgi9cS/mZZpUdOFe/xf5xGF7oNBSH7j85WJcWWyrLvNCSnq5HJljkMeL1chSr7LUddeenw+H7169UKvXr0QFBSEFy9eICwsDFKpFObm5nByckKtWrUo+WYhMXFkJRw6GQIAuHzsKbr2rw+jb+/d0Wdl4Fn9IwBgza79SNr5FxiGwe1799G681DwjARq5TEMA7uubRB+4jIA4IzQBe0jHkBgbal2LiEEMDIygkgkQlxcHMzNzQ1dHUIMJiEhAQKBAAKB+ndLQZTrQCo9BwcHODg4ZH8iKdCuHW2Glt2VSVdP+P6H8ZMbIiReBIDhzpEpFLAfMgMuNesgWSrDmpLL8PPkWRrLq7N3Nc4Wq8FtXyjZAC39L6JYRcc8fR6EFEYMw8DKygrh4eGIiYmBtbW1oatESL5LSUlBfHw8rKyswDBM9hcUAHoJpEjRYGTEw4md7ug66C4AYN3f93F6b1NsvcxDQqoA5sZSAADfsjiSpTJ0quYE87B3SNw0E5KarWDjrjqRgCcU4qeE5zhn7sbtu+bSDlUWTESl30cXmg8JIfnF2toaEokEYWFhiI+Ph5mZGYyNjcHj8ejzoidyuRxisRjA9+5UYlgsy0IulyMhIQHx8fEQiUSFagUUCqSIChtrIRrXs8Gdh9EAgE59b6H/yEaISDKBubEUKanKzPbbli3CnaP70LRiGUhkctT4+RcAwN1TR+BYuyFXHt9YhE7SN3jYYwzCT14BALyduxpv565GnQPrYO/Zjr4gCPmGYRjY29vDxMQE8fHxiIyM1LiIOMk9hUIBmUwGIyOjQjMG50chEAhgZWWFEiVKFKoglwIpombZvBqY+dcr3LgbCQDw9b6H2bOqAwAk3/6S69B/CNr/bxDkn17DZ9EcAMBw9+p4f2AzrOzLIC4+HmUqu3D/UNU78g8iL9/B/Z+GcPd53Hs8AEBYsjjsOrdG2YGeMHYoBQAQWJjReCryw7K0tISlpSX3pU/BlP6kpKTgw4cPcHR0pDG8BQiPx4NAICiUf1hTIEU0+mtmNZy7Eo5Fq5QZz8UyZUDE4Ps/6AyfD6OKNTBy8344b1yCSvJY2JqZYv/8SZhzSplG4cP7dxCZKBdDLtGmMTpJ3+D11MX4uMaHK0cSEYWgbQcRtO2gWj1KtGuKmtuWwNjeNq+eKiEFFo/Hg1AozP5EorW0oFQkElFyaKIX1K5JMvVTazvcPKFM0Pkx5Hv6grSpqWkEQhHaTpwH2xK2AI+P2GRlMtbf29VH0qc3auVWXTEDnaRv0ObzDVSaOQb2PTsAAHgmxuCZqP7DFnnxFi47NMX7pZv1+twIIYQQfaAWKZIlhmEwfEB5PHgbC9RT7gsNCYGDo/rMu2KDZwIAJo8Aeq+aAksTEYylyZmWbVzaDs5/TFTbL09JxedNexHkcwiJr98DAN7M/hsVpw4HU4j6zQkhhBR91CJFsvVLn3KQs3ykSJVBjFQqzfYam7LlEJGQjJCn93J8P76JMSpMGoIWz06jzr413P6Xvy7McVmEEEJIXqJAimglMTYZV9+X5bYlEkmW54uLWeNZSCSazvhbp/uW6vkT9zhw816cFjhDQZmfCSGEFBB5Eki9f/8eS5cuRb9+/fDTTz+he/fumDp1Kq5cuZIXtyP5oLw9HxL592614KCgLGcShVmWxbJLD8ECeH/5hE73rrVzhcr2WZNqOpVHCCGE6IveAylvb294enrCx8cHjx8/xqdPn+Dn54dTp05h3LhxmDBhAq0lVQhVcFDOHAqKNeP2ffr4MdPznd1bICgmAQs6uaPEm7tQJMbm+t5l+naBh8RfZd+zYTNyXR4hhBCiL3odbH7p0iX8/fffKFWqFOrUqQM7OzsYGxtDIpEgIiICjx8/xoULF7Bp0yZ4eXnp89Ykj/EZBrfOPgc61oCDVSK3Py2xXUY8Hg9vD29DzN0LkMrlmDOoFx6HROLs/Se5uj/DMOgkfYPTAmcAQPDOI7Dv3g52nVvn7gkRQggheqB1IJWQkJDtQpo7duzA6NGj8euvv2aaVOuff/7B/v37KZAqZNzr2WCL7ycADE68ckLXasrWqMDPn2FarBhsrK0hFIlUrinWqAP4MeGoOFzZemRlIkLU+t9g3X8KeNYlc1WP5s/P4IabBwDgYfcx8JD4F8oEboQQQooGrbv2unXrhsePH2d5Tnh4ODp16pTlF9vPP/+MuLg47WtICgTHssqkmnHRiVCwDOJSvicJTE5KQnBwMD4EBHCZz9MYdxyER3eUCyEv7toExgIjnPtzGsqUKYMvj++AZXOWsdnctSKqzBvPbZ8RuuDDqm25fVqEEEKITrQOpGbPno0JEyZg/fr1YFlW4zlubm6YOXMmHj16pDYOimVZvHz5Er/99htq166tW61JvjMx5qNNc1vcPvcSAHA1oCz+CyoJoUg1gWZwcDCSEhORmJjI/Q7Yl6uA4OAgdOjqiUSxFMsvPwQAnPdehWTvOUjaPAu92zTD9j9n4//t3Xd4VMXewPHv2ZbeKy0QhIQuoUoTL1XpgiBFEEFBBQsqglcvKiqvYLsqAqIXkKoISC+CSFG6ofcWEgKk92T7ef9YcpIlhWQTAoH5PA8Pe86ZmT17ZssvM3NmzJdPYb58CsvVC0UGWXXfs2/NPP32dI69+J/yfsmCIAiCcFsl7trr1KkTDRs2ZNKkSQwbNowvv/yS4OBguzTjxo1j0KBBPPPMM6hUKry9vXFycsJsNpOSkoLZbMbZ2ZlFixaV+wsR7rx3X69Hp/672bh0Hz2GPkJsmjvL/3GnVR0LIe7XMZttUyLExcUpeUJq1kSj0SBJKpweG0Bgx/6EbolEJUm0rml7/9xIz2LPmUucu3qdQf62IP1sXAojFm3GSaNm+3ef4vnYk0j5FhjtaTpL6oFj/N1uIAAx/1tOzP+WA+DTthkAWecuU3VIb1RaDcbkNEKefxqf1g/f+QslCIIgPDBKNdg8KCiI+fPn88MPP9C/f3+mTJnC44/nzfMTGhrKTz/9xJQpUzhx4gRJSUl2+R966CE++ugjGjVqVD5nL1QonVZFoL8T8YkGZFlWunAPXFBzgOq0rBFHiJ8ZtUpW5pnS6/W4u+fd6SdJEvM2bANANuoxXz6FOuoS9arupGntGqiCQrDGRWORraRkG3isbnUMJ/eTfT4St7Gf2J2Pd6smdPhnDbub97Xbn7Inrws66tuFyuOrC1ai8XDDv2t7ZKuV5su/FeOrBEEQhDKR5KL66W7j2LFjvPXWW7Rq1Yr33nuvwOKP586d48iRI6SkpODp6UnDhg1p0qRJuZx0ZXP8+HEAGjduXK7lZmdnc/r0aerXr4+rq2u5ll2U6KvZDH3pIABN29WhUdMqZBvsg5HuEeBkuooKI25ubgTd0nJZElmJcRz4cyvmQ3/QtnZV1hy7SLNBo6jXsVuBtLIsY4hLJOPYGcxZOQCkHzmFJVuPpFaRevA4ybsOFPo8PU0F1wKsCHej7oTyIequchP1V3ndru7u1G/t7TgcSAFkZWXx/vvvc/LkSb766ivq1atXnud237ifAimA2Bs5PP1CXmDSoX0wNRrVQW/O63prV+saAe62xYtrP/SQw88lyzL/eao75+JT+PvSNSInDSVw7FQkJ5dSlWNMTiV28RokrYaTr061O1Z/xiRCX3+uQlunxJd55SXqrnIT9Vd53auBVJkm5HRzc+Pzzz9n7NixjBw5kgULFpTTaQn3smrBLnh7aZXt3X/dYOmcv4g6ex03tZFqfnAxyUs5HnfjhsPPJUkSmsDq/H3pGv+qWx03nZb476eUuhydrzehrz5LrZeG0fWG/fp/p9+ezkZdPWSLxeHzFARBEB5M5TKzeb9+/Vi+fDkbNmxg9OjRJCcnl0exwj1s/eK2TH41jFYRPsq+yN3nWfT9Pk78dZqrCXlvraysLC5dvFiixY4L88HsecTExPDFwM7IwISVu6hWrRrZmRkOlafz86Gn6Szhn7xpt3+jcwO2VnmELf4tONBnDFkXo4u8Q1UQBEEQoByXiAkJCWHZsmXUq1ePPn36sHv37vIqWrhH9epahS+nNmHHbx14aWSosv/PvxNITMhhx4VqduljoqOJiY52aIkglUpF0Lhp7L18nZ0XrgLwyYj+ZTr/Om+PoXuq/UzrxsQUzGkZJGzayY56Xdmoq8dfrftjTE4t03MJgiAI96dyXSJGo9EwceJE2rVrx+TJk+nRowdvvvkmWq329pmFSkujUTFsQAj1wzw5fS4dSZKY/8s5gms8woZTNekWHoNWbZsTymQyEX3lCgCeXl54eHig0+lKPD6p2/T5PLa/NS+1b0JEjUD+mDyKzdez+OynXxw7dzdXeprOYkpJI/3YGZJ3H+Tch9/apUmLPMnWoNYAtN48H//ObR16LkEQBOH+U+pAKiUlhV27dhEXF4enpyetW7cmNDTULk3btm1ZvXo1//73vxk0aBBffvllgTTC/adZY2+aNfYGYPe+RABMVjUbTtdCp7bwrzpXcdHmjUNKT0sjPd8s92q1Wrn702g04urmhk6nw93d3S7QWvznPrLnvkdUUjrHryWy9I9/CB7em1cnTiJTVnMq+jpOTjoimjdH5eoJgN5sxaWYJY60Pl74dWyNX8fWyoSfCdv+5sATo+zS7X/8OaoO6U1At/bIJjM+bZvhHl67DFdNEARBqMxKFUgtXLiQr776Cr1er+yTJIkhQ4bwn//Yzyzt6+vLnDlzWLRoEYMHD+att95i4MCB5XPWwj2vwyP+zPpxF71HtEWr02C0qNlytiYgE1EtgRreWagk+/FHFouFrKwsZTstNRWAhPh4nF1cqFq1KmB7z7mN/YS6cVfp1qw1WrWK4a3qY9y7idNXExg0bwPVvd3Z/upTgO3Ov/CPfgLg4MKZBFSpiuTijia0YbGvIaBLO3qazmI1Gjnz7pdc/u98AK4tW8e1Zevs0ur8faj6dC8opmHNEJdI+Iev41a31u0unyAIglBJlDiQWrVqFdOmTQNsd+t5eHiQk5NDWloaS5cupWrVqowePbpAvuHDh9OyZUvefPNN/vrrLz755BO7CRqF+9OQJ6sza/4l1i3cgyRJPDm6w80jEodjAzkca9tSS1bcdBbqBuXg6izj5gRqWY9KNqCW8sZS6XNySEtNxdPLS2md0gVVJyYmhgOLvsPDzw1r7CWcff15KMiPYLe8BZR3XrA9Wf+H63Dxz4141QgkKimd2X8d5dv1fyKp1MW+FpVOR4PPJlN9eD9OjP8AjYcbqf+cwJSUqqQxJqYQ9d3tZ+y//usmHv7fp2gfqoHVkHXb9IIgCMK9rcTzSPXp04cqVarwzjvvUKtWLWX/9evX+eKLL9i/f3+xA8z1ej3Tpk3jr7/+4rPPPqN58+ZlPvnK4n6bR6qkomOzGfriQWVbrVER/nAI9SJCSlyGh5ORznWv2u3z8/fHy8uriBx5ctfqk60yk0YNJdTHnVE9OjP/lxV8vHk/AKvH9KZBsB+uI/+DdMu6gbcvXyb5r0MkbNkNkkRxw7wStuwmLfJkgf1BA5+g4fRJuNSoUqrnFu6ee/1zJxRP1F/lda/OI1XiQKpJkybs2LEDX1/fAsdMJhMtW7Zk9+7deBQzDgXg999/54MPPmDPnj2OnXEl9KAGUgBWq8zytVeZ+b9LBY55+brhX8ULP19ngqt74+KswtlJRYbZPqAJD0ihflCK3T6tVkuNkJIHZPklxcbQpNUjNAj2ZfWYPgC0nLGUmgE+/Pblx6jUaggORQ6sjpNL+V3XC9O/5+x7X+JcPRj9Vfu5tQIef5T60yehdnXGKTgAtbNTEaUId1tl+NwJRRP1V3ndq4FUibv2/Pz8OHHiBI8++miBY1euXMFiseDm5nbbcrp16/bALhXzIFKpJAb3q8HgfjU4cSaNz2ed58JlW5dWWnIWaclZXCwmf4MWtaBpCGcTfKjulUmLGvGALXi/dPEitUJDUalKN4uHX7UaxMbGIudkkb1wGv9Ex5GmNxKflkHKwe14Ojtx4toq+v+4HoBDH40n8IkhqKvUcuAK5KkzaSx1Jo0FIHrlJo4Pfl05lrB5FwmbdxXIE9SvK05B/lQb0hvfdg9OK64gCEJlUeJAqmvXrrz++uv07duXOnXq4OLiQk5ODhcvXmTDhg20b9++xD9owQ6svSZUfo3qebHgmxYApKWbOHA4hXMXMzBbZM5eyMDZ2TZW6UBkXuvTqUNRqNUq6jauztU0d5JznOgWFqMcj7p8GYAqVavi7OxcqmVeJBc33MZ+QuipYwyJTqeqvw9efoHIWWlsOxsNwIR/NcOYkoB+7Q8MXbCJQ9FxRF2+hFZXthYj/yc6ErxvKSGSjgOtbYPi1a4uWLJz7NLFrd4KQPT3y4C7tzagIAiCULgSB1Kvvvoqu3fvZtmyZXY/VrIs4+vry7vvvntHTlC4P3l5aunaMZCuHQOLTCPLMktWxjDnp0sc33+JKiF+tOnWkNUnQunX6LJd2uvXrimP1WpbQGaxWGxzVKlUWMxmzGYzQUFBSJKEWqPByckWDNVo0ITPFy23K2/ScwaqfjWd0Owb+Lu7EJ+RzaHoOAAWv/IMg1rUZ/+lq7zy6w5a1qrCnHdeQ12lJokpqWz+az9uLs482aWjUt6Og5Gcu3iZIS+/jm9wVWW/W72H7IIjq9lMzuWrxG/cQdbFaBK37yHrbN5r3aANx6dNBLXGDafq0z1LfL0FQRCEO6PEgZS7uzsrVqxg7ty5bN68mevXr+Pr60vHjh0ZN24cgYFF/yAKgiMkSeKZp0Lw93Xi46/OcD06idXzd9PvuQ6sPlEbkHm09jV8XQ12+Sz51swzGo12x+Li4uy2AwICcPfwKNCSpdE58dykvDX9amalof1mBVvGPUl1bw+wWth4MorUHAM6lYTlwlEsF45y5Voi7/24nqpebvR0ypsj67sFmzgYHYfh8mleHvIUOV7BHNrxN/Xr17d7XpVGg1vdWoS+NlLZZzWZ2OTaSNlO2XuYlL2HOfzMG0hqNbLFQsTSr/Bu2QTXWtVLdnEFQRCEclGqeaTc3NyYMGECEyZMuFPnUypbt25l/vz5nD17FrVaTatWrXj55Zdp0KCBQ+UZjUYWLVrEypUruX79Oh4eHnTu3Jnx48fj5+dXzmcvlNTjnYJ4vFMQZrOVxwf/zaofd9G0XR1q16/KrkvVABmtyopOY0Gjst07oVVb0aktyLKEVm2hWfVEMg0azFY13i55gVdCQgIJCQmo1Wr8/P1RSRIarRaNRmPXVa1y8yIqOgbZkINsNoIMr0QcJfnjTxj4SGPUtRqAJOHtmkj35lH4uLuhzj9PlbQZgDHtGmOJOUfs4QNMnbWaj2b9j3Ofv4HTvwZCQO48WSr759Zq6WE8Q8z8FcSv307cuu3KsdyFlg8PzftMhjz/NH6dHqFK/+5I6uKndhAEQRDKpsR37d1rvvjiC+bOnUtERAQLFiwgLi6Ofv36YTKZ+Oqrr+jatWupytPr9YwZM4b9+/czcuRI3nnnHTZu3MiECRMIDAxk4cKFDs/O/iDftXcnHDuVxsuTjgDg5KylUevaqFQSarUKldr2f0BVH3KyDGg0N/dp8v5mUEtWetaPoqRj1F1cXHBxccHVzQ2tVluqcVj55Vw8gXz4T1Q+QTw0whb4fNyrLYOahQHQa85qzsWnMrFzc0Z1bI7abGTeyevsPXuZcQP70HbUq8qcV8bkVEwp6Rwb+y5p/5zAkpld+JNKEjo/b/w7t8WSo8dqMFL95hqFkkrCp11znIIDHH5ND6IH9XN3vxD1V3lV+rv27iW5XYwAgwYNwtnZmZo1a9KhQwe2bNnChAkTWLlyJeHh4SUuc8qUKezfb5tbaPjw4QA88cQTTJ06lfj4eEaPHs2mTZuUcTXC3dOkgRd/revIpStZzJh5jn923n4Atkqtwj/YC61OTfWHAlkr25Z10aotNK2aiFplxUVrwcvZWCBvTk4OOTk5JCcnA+Dk5ISLiwuSJCFjW2NSo9GgUavJP5mUdMtjbc16qGs3RJIkzp/vxW//m8MjPlrIsp8KoVv9mmDUE5+l5/f9h4m8Gs9T9f8h+4cp3PB/iMi4NLoNfAavh0Josy1vEtALn87h+sotpB85lVeYLGNMTOHaLxuUXQlbCp/vza1ebVyqF5zPypiUis7PG7ew8lvmyXAjAd8OLcutPACsViSNBre6NQFba517eG1cQ2uU7/MIgiDkU+kCKaPRyHfffadsh+SbS6hmTdsXaG6r1Jw5c0pU5oULF1i3zrbkh0ajoXp12zgTSZKoWbMmKSkpxMbGsmTJEkaNGlVcUUIFql3TjTmfRQCQmWXGZLZiMcuYLTLZORaSUoxYrTIms5U9B5KxWmVyDBb+2GYLNFzdnakXEYLJEozVas3XnSbjqjXj6WzE11VPkHsOXi55AZbBYMBgMNx6OiXm5OSERqulaafu+FevjouHB5LVwqq+48hMS8XXVYcu5QYBmZlEnLxGcraexlVtXcvbNq1n6qb9VJs5mz9fG4jKrwrnrsTwj15H7Yce4tHf56LyCcCiN2C4Hk/85l3IZgtWg4Ez73yO32O2xZeNiSlknDhnd15ZZy6RdabgfF+5Erf97fBrLsyNVVvKtbziSOW6cLqMLMvESRLFrQkkm0y41KqGIS6JgG7tAcg6d5nAJzpya3OoMSEZj0bhaDyLmELGYkXl6ox3yyZoPEu+MoTO1wuVTlfi9IIglF6lC6T27t3LtXx3aOVfbkaX7wtj165dZGRk3HaCULAtf2O12mbBvrW5MH+Z69evF4HUPcrdreBbuXbNvB+l9q38lccfToQb8Xp+3xHHms1XiNxtCygklYSziw53Lxdc3JzwDfTAy9cN38BqSJKEp5OBKp7ZaFRWVJKMm5MJJ40FHxcjOSY1EiBJ8s3/AfIeq1V5Pei5gZiLszNJiYkkJSYiSRIajQYnH3+ygexg22LLb3z1CG9JEpqoE3B6P2fibFNDPNfGNvjcmnSdfafPM3XTfrrXr0nLtPMAxKRk8sIv2xnSvROPt21JSFgENfv8wqHTZ9F5evNwvXpoNVpMOTKZ56+QcyUWlVPBH1xTSjpZ56PQ+ng6Xjm3SPpzH85VAyl2KvhSkq1Wbqz6Hc8m9QDQx97AmJg3jYZsMpXbcyllliBNTpRteaK4NduUfZmni5s57c4IHtAd95stirIMlswsPBqG4dWiMW51a6JxE11cguCoShdI7du3z25bW8RfmhaLhX379pVorFRul15x5QGcPn2alJQUfHx8Sni2wr0qONCZEYNqMmKQrRUzJc2IwWAlPtHA9Tg96RkmTp9PJ+ZMHLs3ZGC1QpUQP/yCPVGpVahUEm6eLuh0GnwCPMjK0CNJed15kiTBzW0JUGlUeLpYqe2XhixL1PZLR29W46azrScoyzImkwlTUT/47sHQsi+vturHO25uWNKSMEtW1NcuUC1DQ9draTQNsP1RYTCbORR9g0vxyUT+c4hO3jLG66eRZZkBNxdv3vvm0/i5uQDw3y0HWLD/FK8/FsG4no8BEJOczsi5K/BwdmL1hOEgWQCJzzfsYuuJCzz/r1YMbN0YJInEjCyenf0LWrWaNW89d7ORRmL21j1sOnKGoe2aMbhdBEgSmXoDE36/wJWD+/ln5ofcvFgs2Po3+89eYkD7FnSJaAiSREpmFlMWrkKn1vDFi0OxXVD4decB9p4+T/eWD/N4C9vkvtkGIz8RgyRJ/N8LQ1Cr1MhWC+v+3MvpG8l0bNyAiDq27tz07GwWbtuJRq3mxZ7dlYBu57GTnIqOoVW9ujSvWweAHKOJxdt2APB8z25IkoTZbGbr/oPEpmbQtO5DtAyvCxKYLVaWbPsTgEFt2mCNTyLzWhqXszJIyMrENymb6r5+qJycMFqs3MjKQKNW4xGbhDXHgMbTjTSTEYPVgrtGi6va9l1kMhq58udePGTptutC2rn5xyHAjZW3b/3zjGhI1rnLuD0Ugn/XdiBJuIbWQOfrhfcjEbhUF/P/CUJhKl0gdfjwYbttjabol3DkyJHbBlIGg4HTp0+XqDyr1cqxY8fo2LFjkWmEysnHy9YaExzoTJMGBdfxk2WZy9HZHDqagsUiYzZbSU5NJjnZyOHDWQQFOCPLMrIM1pv/y7JtiRxkMJmtnIvKoUbtAJAkqtcOwGyyUCXEB3/XHHxcDVistvX6tGornk5GrLKEl7MBdyez3XlkZmaC2okcgOqNaVC9MVOeHAFAvMmAOjMFT/MuhnfT4KmWCaofgclqRrpxiZr+3pjMFrTafK23F22tJu7OOuQMWyuOMS2N6KQ0PJ11yOlJStobiUlcik8mLSkROTUBAFN6FueuJ6JVqbAm5433unb9BqeuxhF34zrWxCBbuTkGjl65hotWg/7SKbRqWxfX0RMn+P34JSL8nLF42/JnpmWy8cBRdGoVM7rmDR49fDiSNZHnqKm10MXLFixkZetZufsAAB89Go58s+ts+8GDrD1+CV9zKi1uTkeRnZrJd+s246RRM65JgFLu9r/2sDzyHK8/FkErXartfLP1fLlyDQBjG3jb5iADjh3/hwX7TzGmXWNaa23Xx2Iy839LfwVgUIgOD50Wj2owa/1efrlZbodmQYCF8/EpjJ6zBh9XJ/a/NUQ5hzdW7WT9ycu8060lzz1iu+vzamoGA0/YbiY4N2XkzcBP4tVft7P9XAzv92jLwBb1QJK4mJDKoLmr8XVz4ffXh5Fy9ApJFzL4NTmOZLOJXlVCcDkSTc71JOLUVs5pLbjJEk0NatIP29aCjD5xihvHT+MqF2wxlLRaZJMJ9wZ1kK1Wss5cosboQVhz9Hg1b4TVaETj7oZztSBcQqrhXCMYtauLWPJIuK9VukAqPj7ebru42dSTkpKKPJYrMTHRbt6h283OXpIyhfuPJEnUrulm111YWrIsk5RiJC3dxKUraZw4GU3aGQ/izRIWWY3ZAucvZ+Hv54zaSUtGphkvH1d8/L0JCfWkhlcmkiRTxdP2o2q2qgh0z8FslZRpH2StE2afYBr1GkSjXoMAUDq4mnRicbfnAcgBcmQZyWLmu6Y9ib10niqBgST7eIMMzgYDc2s2R6VSkRxeR+nHGhL0MN1TU6kWGEhygD8gIxuNzPQJQwJSmuTOdyXTx78BrZ9IonqVQFKq2FozTEYTE5+14ubqSk6jtuTcvGn48d4+NGgZR5M6tUmveXOMYo6et0aoUUsS6WGtbQ1dskzHrp5UrdeIh+vWJqNOKMhgNhp4ZVAOIJNVpwUq2YomM5lWLVviFlCF2o0akl39IVu53pkM7PgIGrWK7Gr1yH1xTR7OwuzqRe2G9cmuans9Vr2evm2bgQw5VerebHGUCW+QRS9ZS5164eQE28o1msz0aNEEGRnJyQ2Dly+SLBMYGEjD6mkEerhi0TiBJCFrnfByccLTxRnrzZYnZBmNWoOTRo1KpUaWVLbXY7AF0tW83JR0IGMyWzCaLVitFjDbWjItRgMZeiNalQrJZMC3QTC+DYLZO+cUZ+NTeLJTOK17/guAVUfOs2jt33SsEUznhmEEmCUSj8YyJuECAO84BxIabXuvrXEzcExnoUuOljYmLZmnLpCisjLbR0+jnxfRJ8uJ2KVrATjoZCJDJdPEoMHfavs+TVVZiavlj4ekIUyb240occmcjQErNdDhlJ6Df+dHyLyWgEebpqg1Wpx1WlCpkVQSZmSQJDRqDSqNGiQVkkqFpJJsi4arVKBSoQvwwa1uKBp3V3T+Pqh0WjENiHBHVbrpDyIiIsjOzrvV+48//lAGh3/77bfMnDlTOdaxY0fl7r6inDhxggEDBijb1apVY/v2vHl6hg8fzoEDB5TtiRMn8vzzz5fqnI8fP44sy9SpU6dU+W4nJyeHqKgoatWqhYuLS7mWLdxZJa07i1VGr7dw7FQGx89kIqlUnLtswM/PGYsM1xKsuDirUDm54O3jhLuTierVnHGW9EhaHVqtCtliQauW8XIxoTerUUkyGrWMr6uBTIPW1iWJrAxZUsZ5AZ7ORiyyhNGiKnJYdYH9N8sraVr7/bfJJxWy75a8EgXGcld6JoOenPQ0vD09AVtzZ2paGnqDHk93D1xdnJFkGaPRxI24OFQqiRqB/qgNWWgyU/ll204SU9Pp274lNQJ80aYl8vfFWD5Y/Bu9Iuox4Yn2aLJS0ZvNNJm2GICz/3lW6ap+49cdrD8dxYQWDRlcx9Ydvv7v03wUcxkf1MyvEYY5x0T6lWSm6lKJ0loZn+pME6Ptb/V/nMx876WnjlHF26l547E+8skmRmvl1VRnGt1Mu8fZxAJPA40Mal5Ny/tsvOebRbxG5u0UF+qY1Eq5cz31hJnUvJmal/ZTn2wuaa2MSHeivd4WqB7TmZnvZSDUomZCTt7Y2c9dMohRWRijd6OhxTa9yWm1idnOmVSzqpmUkzc+8BvnDC6ozTyndyPCYmvVvagy87VLBoFWFe/l5LVmz3HK5LTaxFCjK60ttha5q5KZz10y8LSqmKrPSztfl8VRjZEBRlc63Fy0PV6yMM05DRck/i8nbzjJEl0WBzUG+phc6XQzbSpWPnRJRS1LfK63pZUk+FWTxR6Nge5mFx432657NlbedU4F4DO9D5qbn6S1mmz+1Oj5l9mZPjfTmpF529n2p9gnem9cJNsHa7M6m981etpZnBhgzvvjcqIuGYsE7xt88OJmq7A6h/WabFpanBhizhvT/IVrBp/MW0SDdk0pqdt9b164cAFJksT0B7dT5BiSQpQkRrx15uvyKLMwJpPJrguxPEVFRd2RcoU7r6R15+0GHW6uWdw+AuDWuwZt3VYWi4zBCNk5MlYjWHNswZjBAhezZFQq29AZiwXMVomUdBXOzjdDkptdOfLNH0/bW10ClYbcICXv7Z+7Xcxda7c8yAtzbjleIA1IN9PlT3PrR0+WsYuwUlKteHpISiZJowOr5eZYNfs8qsJCvfzTVciA6mZR8s30BYK+m2ea+5SyrcUk/3Nl62U8XFW2Gw60WmSrBVmWba9PUoaT5b1m5SaFfMcBZyc3LFY35fltz+FpC3QSbXlUki2nSrJ1o56N0eHhZMJgBL8OrQhQwQUJ0n3VpGqsaIPgow7jqeJr5XCWCpUEKgmWrx2PKSuNiy4S7joTxmwTT3o+TMfkBEJ93TEHBaPGwkO1Qhl59Ax+Xh6EdOmAZDGhzUymy+/7iI5P5F+t61M3tDaZl+PIvhRD/cijhPl607BNS6VVLXTH30gp6dRuXIs6Af6kn4/Dy5AO8bE4+7gQXL+W7TtXBtXVc2A24RMeSIDO9iPqkZWGnHIdjbsOvxpVSLuUhEqr5rImEwB/S15tWYAsSSbHasWSk/c7ku1sJVMlYzKasdz8OTDqzGS6yGRbrZiz834jsnQWMjUyBqMZ882PoElrIdNVxlW2T5uttZChvZlWbzsPo8ZCuquMCivmrHzlqs2k56bNsRVsUltJd5UxW2XMWXmf92yVLW2O0YQ521auSWUlzVVGjYw5My9tlruZVK1MtsGMKVtvOwdJJtXF1iVuytDfvCUGstxMpGqtZBlNmLJsac3IpNxMa8w0oJHz0qZorWQazZgy9crzJQdYsUpgzMzBdLM1MsvVaEtrMmHKsK0lelxnZnimihsnziH5lr7bt7jvTd1duEu10gVSnp6eJe5eK8mgcC+vguNhylpmYbRarWiREhSi7iqvB6PubNNt5I77q96oAVZZxmq1jfuTZXi4cXsaD7IF5kZZxmqBNLOVoS1HKKUkIiO3gcZAbl+Bsiy3DP8ZpTzEDLgCjxsNdDYaUCGh0zmR2wI3OysTs8WMq4srarUGCZlHDXqaZ2agVqlw9/LGQ7YiI7E4MZ6slAQCvQNwUmuwZmThk5PFw6kpqCQJV50THh6eaDQapiTGYzSbCPDxw0Vr+1H3Neipl5yEVqulin+gEsRPTkrAYDLi7+WDq7MLMjI+RgMzkxLQatRUCQhWXs/ryYnoDXp8vXxwd7UFwT5GIzMT41GpVFQJyltzc1xSAtn6HHy9fPB0s7Xa+JtNfBt/A0lSERxc7eaFknk+NYnB2dn4eHjhffOudH+Lma/jrgMQXLWG8lfHs6nJ9M/OwtvdE28P22+dxWLhqzjbuMiqVWrY/mhAZnBaCk9kZeDl7omfpzdgGxf81fWrAIRUqY5aZetuHpCeSueMdDzc3Anw9lVe82exV0CGkKCqaG+ON+6dkUbb9FQ8XN0I9LHdPd3JZEHl70ODri1K9G7MVZIWqbuh0gVSwcHBdoFUcS1EAQEBRR7LlbuIbW45t2txKkmZhZEk6Y7Nouvi4iJm6K2kRN1VXqLu7mVFTx57v85s3voOpW1VirTlPMVukYr67N2tFRoq3UiCJk2a2G3nHyh+q4iIiNuW5+7uTu3atZVts9lcZFqVSkXTpk1vf5KCIAiCIDwQKl0g1bZtW7ttvV5faDqVSkWLFnnNhhcvXqR///60atWKr7/+usgyiyoPIDw8vNRdgYIgCIIg3L8qXSD12GOP4efnp2ynpaUpj/O3TnXs2BFvb29l+z//+Q8nT54kLS2NWbNmsXfvXuVY/rv2MjMz7crJ/7hv377l9joEQRAEQaj8Kl0gpdPpmDBhgrKdf/R+XFwcYBvY/frrr9vlO3XqVJHb9evXp0+fPoBtcF10dLRy7MYN2wSDISEhPP300+XyGgRBEARBuD9UukAKYODAgYwcORKAlStXkp2dTVxcHNu2bUOr1TJjxgzq1atnl+fW7QYNGthtT506lebNbfeXL1myBKvVys6dO4mNjSUgIIBZs2bdVwMTBUEQBEEou0p3116ud955h4cffpiFCxfSsWNH1Go1jzzyCOPGjSsQNAF8/PHHTJw4katXr/LMM8/Qpk0bu+MuLi4sWLCAefPmsWbNGlq2bIm7uzvDhw/n5ZdfxtfXt6JemiAIgiAIlUSlDaQAevToQY8ePUqUtk6dOvz222/FptHpdLz44ou8+OKL5XF6giAIgiDc5ypl154gCIIgCMK9QARSgiAIgiAIDhKBlCAIgiAIgoMk2dFVeIUSi4yMRJblcl9MUZZlTCYTWq32rk2NLzhG1F3lJequchP1V3ndru6MRiOSJNGsWbMKPa9KPdi8srhTH1ZJku7KStdC2Ym6q7xE3VVuov4qr9vVnSRJdyU4Fi1SgiAIgiAIDhJjpARBEARBEBwkAilBEARBEAQHiUBKEARBEATBQSKQEgRBEARBcJAIpARBEARBEBwkAilBEARBEAQHiUBKEARBEATBQSKQEgRBEARBcJAIpARBEARBEBwkAilBEARBEAQHiUBKEARBEATBQWLR4kpo69atzJ8/n7Nnz6JWq2nVqhUvv/wyDRo0uNun9sAwGAy0bduWzMzMItM899xzTJ48Wdk2Go0sWrSIlStXcv36dTw8POjcuTPjx4/Hz8+vyHKSkpKYNWsWW7duJSMjg+DgYAYMGMCIESPE4qsldPnyZRYvXswff/zBjh07ikxX0XV0+vRpZs2axYEDBzCZTISHh/Pcc8/RrVu3srzc+0pJ6y4yMpIhQ4YUW9bs2bPp1KmT3T5Rd+UrMTGRuXPnsn37dm7cuIG3tzetW7fm+eefp379+oXmkWWZFStWsGzZMqKionBycqJDhw688sor1KhRo8jnysrKYu7cuaxfv57k5GR8fX3p2bMnY8aMwd3dvch8MTExfPfdd+zatYucnBxq1arF0KFDeeqppxxb9FgWKpXPP/9cDgsLk59++mk5JydHjoqKkps2bSo3bNhQ/v333+/26T0wNmzYIIeFhRX5r2HDhvL169eV9Dk5OfLw4cPlsLAwedq0aXZltG/fXr506VKhzxMdHS136NBBDgsLk7du3SpbrVb5/fffl8PCwuRnnnlGzsrKqpDXWxlZrVZ5x44d8ujRo+Xw8HA5LCxMbt68eZHpK7qOtm/fLjds2FBu0aKFHBMTI2dmZsr9+/eXw8LC5E8//bTsF6ASK23dybIsT5kypdjP5BNPPCFbrVa7PKLuyteJEyfkRx55pMjvxPXr1xfIY7FY5DfffFMOCwuTX3nlFdlkMsmRkZFyeHi4HBERIUdGRhb6XMnJyXKvXr3ksLAwecGCBbIsy/L3338vh4WFyT169JATExMLzXf06FG5WbNmcoMGDeRjx47JRqNRHjt2rBwWFia//vrrstlsLvXrFl17lciKFSuYO3cuAIMGDcLZ2ZmaNWvSoUMHTCYTEyZM4OzZs3f5LB8Ma9asKfZ4jx49CA4OVranTJnC/v37ARg+fDgATzzxBD4+PsTHxzN69GgMBoNdGUajkdGjRxMXF0e1atXo0qULkiQxdOhQAA4cOMC7775bni/rvmAwGFi8eDG9e/dmzJgx7N69G1mWb5uvIuvo4sWLvPrqq5hMJjp37kz16tVxc3PjySefBGDevHksW7asTNehMnK07oxGI5s3by42zahRo+xaG0Tdla/09HReeuklkpOTCz1uMpl45513uH79ut3+b7/9lnXr1gEwbNgwNBoNERERNGjQgKysLJ5//nkSExMLlPfKK69w7tw5dDodgwcPBmDo0KFIksSFCxd4+eWXC+RJTk7mhRdeIDMzk2bNmtG4cWO0Wi1PP/00ABs3buSrr74q9WsXgVQlYTQa+e6775TtkJAQ5XHNmjUB2xvVkTeBUDpJSUkcOHCAQ4cOcfbs2UL/zZgxQ0l/4cIF5YtCo9FQvXp1ACRJUuouNjaWJUuW2D3PypUruXLlCmBf37Vq1VIeb9y4kePHj9+R11lZSZJEy5YtWbduXYk/DxVdRzNnzsRoNBbIl/tcuWlycnJKdP73C0fqDmDnzp3UrFmzyM/j2bNneeqpp+zyiLorXwsWLKBatWosWbKEI0eOsGnTJnr16mWXxmAwsHLlSmU7OTmZBQsWKNv5r2FuPWRmZjJ79my7cnbt2sXBgwcBCA4OxsnJCQB3d3f8/f0BOHLkCFu2bLHLN2/ePFJTU4Gi627hwoXExcWV5qWLQKqy2Lt3L9euXVO28/f/5u/H37VrFxkZGRV6bg+aDRs20KZNGzw8PEqUftWqVVitVgBcXV3tjuWvu/Xr19sdy/+F4+bmVmgesH3ZC3l0Oh3h4eFIkkSXLl1KlKci6ygzM9PuC76ofImJiezbt69E53+/cKTuwNZC3KNHj1I9l6i78pWYmMhPP/1EixYtcHFxoXbt2nzxxRe0bt3aLl1uIAOwadMmsrOzle2irufGjRvtWiaLqrtb823YsMHu2KpVq277XAaDga1btxb9QgshAqlK4tYPpVarLTSdxWJ54D7AFW3NmjVs376dFi1a0KlTJ8aOHcusWbOIiYkpNH1udxEUXW9gG7yakpIC2L6wT506VaJ8f//9d2lfwgOjpIPxK7KODh06hMViKXW+B01J6y41NZUdO3bw2Wef0bp1a5544gneeOMNli5dSnp6eqF5RN2Vv6lTpxZaZ/3797fbzt/il/9zB0Vfz+TkZE6fPq1sHzhw4LZ5wPa7mfsH0vnz50lKSipRvtLWnQikKonDhw/bbWs0Rd9weeTIkTt8Ng+uixcvcuLECWRZJiMjg9jYWHbs2MHXX39N165def311+0+rAaDwe4LoLh6s1qtHDt2DICjR4/afWEXl+/cuXMPXDdCearoOrr1s1zcF/rRo0eLP3mBTZs2YTKZMJvNpKamcunSJTZs2MCHH35Ihw4d+Prrr5WuuFyi7ipOblcb2K5z/pZGR37XoqKi7MZhFZcnLS2Ny5cvl/q5Slt3IpCqJOLj4+22Vaqiqy7/D7lQvtauXVvkMVmW2bRpE3369OHixYuArbk7/xd2cfUGeXVXmvqWZVnUeRlUdB3dmq+4261Fvd5ecTd+6PV6Zs2axbPPPktWVpayX9RdxYmNjVUe9+7dW7kJx2q1FrhGJfldK03dAcpA9dLkS0lJUVqySkIEUpVEbndCruI+wEXdNSGUjSzLyoDk4iQmJvLyyy9jNBoL1NvtPvS5dedoPqH0KrqOSpNP1GvxYmJiCrQ0FCYyMpKpU6cq26LuKs6ePXsACAwMZNKkScr+tLQ0uz9goGTXsyLqzmq12o3luh0xIWclYTKZSpy2JLcLC6UnSRLbt2/HaDSSmZlJVFQUx48fZ8uWLfzzzz92aaOioli3bh2hoaGleo7curu1K0K4c0p7rctaR6XJJz7LxatRowZnz54lJyeH9PR0Lly4wKFDh9i4cSNRUVF2aVevXs348eOpUaOGqLsKEh8fz/bt23FxceG7777Dx8dHOVZRn7uy5isJ0SJVSXh6epY4bf43q1D+dDodvr6+NGvWjGeffZalS5eybNkywsLC7NLt2bMHLy+vUpWdW3elqe/8+YTSq+g6Ep/l8ufi4kJQUBDt2rXjtddeY9OmTUyfPr1A3e7duxcQdVdRvvrqK2RZ5ssvv6RJkyZ2x+7lz50kSXh7e5c4vQikKon8kztC8dFyQEDAnT4d4RbNmjVj+fLltGzZUtmXlpZGUFCQXTfs7f7Kya27KlWq2O0vLp9KpSp2+RKheBVdR6XJJz7LjlGpVPTr14+VK1fafTZyu2tE3d15O3fuZN26dXz55ZcFluUBcHZ2LhCslOR6lqYOwNalWNp8vr6+qNXqYsvNTwRSlcSt0fytfcv5RURE3OnTEQrh4uLCF198odzJU61aNdzd3aldu7aSxmw2F5lfpVLRtGlToGB9F5cvPDy8wNxHQslVdB01btzY7pj4LN85NWrU4L333lO2cydaFXV3Z8XFxTFlyhT++9//Flh7MDY2VpmipzT10KxZMwDq1Klj931XXB5vb2/ls30nf0NFIFVJtG3b1m5br9cXmk6lUtGiRYuKOCWhEEFBQTRv3hyAdu3aAfZ1V1S9ge0LO7e528/Pz66rsLh8rVq1KtM5CxVbR61bt7a79bq4qStE3ZZdt27d0Gq1aLVa5btR1N2dYzQamTx5Mp9++qndVAcWi4WoqCjefvttpTWopL9r3t7eSn2pVCq7ST6Lq7sWLVoorc3169e3awErz7oTgVQl8dhjj9k1UaelpSmP80fWHTt2LFXfrlA6RqORM2fOFPvh9fT0JDQ0VPkSGTBggHIsMzPTrr7yP+7bt69dOfnz5Z9Y8Na/pG7NJ+S59RbmoprzK7KO/Pz86NixY6H58p+vr68vjz76aKHn+yAoad2lpqZy4cKFIm9X12g0uLm50a9fP6WbB0Td3Snvv/8+e/bsYeTIkYSHhyv/GjRoQPfu3Tl06BDh4eEA9OrVy24Sz6J+13r37m13l13+5X5unXS1qM+rVqulT58+hebLX3darbbUs+SLQKqS0Ol0TJgwQdnOf0dK7rpAWq2W119/vYLP7MEyYsQI+vbtS7t27Zg/f36BL+/s7GzOnTvH119/rXzw69evr3yArVYr0dHRSvobN24AtnWfchfOzDVkyBBlFuD89Z2bB2yLIzds2LDcXt/9JjMz025br9cX+oNb0XU0YcIEZX2wovK9+uqrJZ7d+35UkrpLTEyke/fu9OzZk27durFz584C5Zw6dYqAgAAmT55st1/UXfn78ccf7ZZhKUxAQAC+vr7K49GjRyvHCrueXl5evPDCC3ZldO7cWWldjIuLU1qXzGazMt9UREREgWWGxo4dqww+L6ruRo4cWerxbSKQqkQGDhzIyJEjAdtaQ9nZ2cTFxbFt2za0Wi0zZsygXr16d/ck73O5k/plZmby6aefMmTIEP755x+sVis3btzg22+/5bPPPlP+4so1depUpctvyZIlWK1Wdu7cSWxsLAEBAcyaNavAOCcnJydmz55NQEAA8fHxyur2uSvLN2/enI8++uhOv+RKS6/XM3/+fLt9ZrOZn376qdBxFRVZR3Xr1mXGjBlotVp27txJdHQ0RqNRWUNs+PDhDBkypOwXoZIqad1ZLBaldTgmJoYxY8bw9ttvc+XKFWRZ5tixY6xevZp58+bZrU8Kou7K29atW/n8889vm+7W78ZXX32V7t27A/Dzzz9jMpk4e/YskZGRuLm5MXPmTIKCguzySJLEN998Q+3atTGbzUqdLV++HJPJRO3ate3+mM3l7+/PzJkzcXd359ixYxw9ehSr1covv/wCQPfu3XnttddK/dolWUx2Uels3LiRhQsXcvHiRdRqNS1btmTcuHEiiKoASUlJzJkzh7/++ovY2FhkWSYgIIAGDRrQpUsXnnjiCeWv1VsZjUbmzZvHmjVriI+Px93dna5du/Lyyy8rf6EVJiEhgZkzZ/Lnn3+SlZVFcHAwAwYMYMSIEcUuc/Aga9asGdnZ2UV2B6nVagYNGsQHH3xgt7+i6+j48ePMmjWLw4cPY7VaqVOnDqNGjSrVgr33m9LW3enTp/nhhx+IjIwkISEBnU5HUFAQLVu25PHHH1fGKhZF1F3ZXbx4kQEDBpRoqapRo0bZTcwJtm7bZcuWsXz5cq5evYqTkxMdOnRg/Pjxyg0ChcnMzGT27Nls3ryZlJQUfH196dWrF2PGjCn2BpyoqChmzpzJ3r17MRgMhISEMHToUAYMGFDsZNdFEYGUIAiCIAiCg0TXniAIgiAIgoNEICUIgiAIguAgEUgJgiAIgiA4SARSgiAIgiAIDhKBlCAIgiAIgoNEICUIgiAIguAgEUgJgiAIgiA4SARSgiAIgiAIDhKBlCAIgiAIgoNEICUIgiAIguAgEUgJgiAIgiA4SARSgiAIgiAIDhKBlCAIgiAIgoM0d/sEBEEQ8uvVqxfnz58vt/K+/vprHn/8cYfyWq1WVCrx9+bdIq6/UBmId6gg3AcmTpxIs2bNWLJkyd0+lTLJyMjgwoUL5Vpm06ZNS53n6tWr/Pvf/+bMmTPlei4PEqvVys6dO3nxxRfp0qWLQ2VERkby8ccfk5qaWr4nJwjlSLRICcJdEB4eXqb877zzDiNHjgQgOTmZtWvXAvDzzz8zbNiwsp7eXXP06FFkWS638oKCgggODi5Vnj///JM5c+Ywbdo0HnrooXI7lwfJ6tWr+eGHH5SguFq1ag6V06JFC1QqFUOHDuWTTz4hIiKiPE9TEMqFCKQE4S5p3LgxkydPJjw8HBcXFwAOHjyoBEj9+vXjk08+AcBisXDt2jWWL1/O/Pnz7crx9fWlT58+bNu2jcGDB1foayhv8fHxNGzYsNBjGRkZREdHF9gfEhKCh4dHoXlatWpVqudfu3YtM2bM4JdffnH4x1+Axx9/nN69ezNy5EgOHDhQprKaNWvGp59+ygsvvMC0adPo3LlzOZ2lIJQPEUgJwl3g5eXF//73P7y8vOz25x8PIkkSGo3tI6rRaAgNDWXSpEkYjcYC5X322Wd39oQrSP/+/enfv3+hxxYuXKgElvl98cUXNGnSpMzPvWfPHt555x1mzZolgqgycnZ2BqBRo0ZlDqQAmjRpwmuvvcZbb73F8uXLqVu3bpnLFITyIsZICcJd0K1btwJBVEkNGjSonM+mcjh9+nSBfWq1mrCwsDKXnZaWxqRJk2jcuDEdO3Ysc3mCjZOTU7mVNWTIEEJCQhg/fjyZmZnlVq4glJUIpAThLihLF9xDDz3EI488Uo5nUzkUFkiFhoYqrR9l8fnnnxMfH8+zzz5b5rKEPGq1utzKkiSJ5557jqioKObOnVtu5QpCWYlAShDugkaNGjmcV6PRUK9evXI8m3ufyWQq9G6+8rgOMTExrFq1Co1GQ4cOHcpcnnDndOnSBa1Wy6JFi0hOTr7bpyMIgBgjJQiVXnJyMqtXr2b58uX07NmTV155xe74gQMHWLRoEWfOnGHr1q3IssyyZctYunQp0dHR1KxZk5deeokePXoAtoHtS5Ys4ddff+XKlSsEBwczatSoYlvRtm3bxi+//MLx48fJzMwkKCiIjh07MnbsWIKCgsr8Gi9evIjJZCqwv379+mUu+6effsJsNtOyZUvc3d2LTGc0Gpk9ezZr164lLi6O4OBg2rdvT926dTl58iTTpk0rNJ8j12bfvn0sWbKEyMhI0tLS8PPzo3379owfP54qVaoUeY4nTpxg8eLFHDx4kPj4eLy8vIiIiGDIkCG0bdu2QHqLxcL27dtZsmQJFouFRYsWYTAY+PHHH/ntt99ITEykUaNGvPvuu8Ve64sXLzJv3jz27NlDQkICgYGB9O3bF4vFUq7X093dnbCwME6ePMmCBQt44403iixfECqKaJEShErKbDYzefJkunbtyvTp07l8+bLd8W3bttG3b1+GDx/O77//jsViwWg0Mm7cOGbMmEFqaioGg4Fz587xxhtv8Mcff6DX6xkzZgyfffYZGRkZGI1Grly5wvvvv8+aNWsKnIPJZGLixInMmzePF198kW3btrF06VKqVq3KkiVL6Nu3b7nMxVRYtx6UPZCyWCxs2rSpRGWNGzeOjRs3Mn36dPbt28fXX3/N1atXmTp1aqE3ADhybSwWC1OnTmX8+PF07tyZzZs388cffxAREcGKFSvo168fZ8+eLfT85s6dy9NPP01wcDBLly5l9+7dvPbaa+zdu5fnnnuODz74wG5qiWXLlvHUU08xfvx49u7dC0BcXByDBw9m3rx5ZGVlkZOTo9xJmpaWVujzrlu3jieffJIbN24we/Zs9u3bx3vvvcfq1asL3GFaluuZK7c1d82aNeU6VYYgOEwWBOGesW/fPjksLEwOCwuTJ02adNv0BoNBjo2NlcPDw+WwsDD5m2++UY7duHFDTkxMlAcOHCiHhYXJ7du3lydOnCgvWbJE1uv1sizL8qFDh+RmzZrJYWFh8lNPPSW//PLL8pw5c+SMjAxZlmX5+vXr8uOPPy6HhYXJPXv2LPD8H3/8sdyrVy85JyfHbn92drb86KOPymFhYXL37t1ls9lclssif/LJJ8p1yf8vKSmpTOVGRkYqZS1ZsqTIdH/99ZccFhYmb9myxW6/2WyWBw4cKL/55psF8jhybaZNmyaHhYXJO3futMsTHR2tnOfgwYMLPNeyZcvksLAwecaMGQWO7dmzR3l/TJ8+XdmfmZkpWywWuWfPnnJYWJjcu3dveeTIkfKWLVtki8Uiy7IsL168WHneH3/8sdCy69evLw8ePFg2mUx2xy5duiQ3bNhQDgsLk//1r3/ZHXPkeub64YcflHM6ceJEkekEoaKIFilBqMR0Oh1Vq1Yt9A7AoKAg/Pz8lEkMs7OzeeWVVxg6dKhyN1Xz5s3p27cvAMeOHeP5559n7NixShdXcHAwI0aMAOD8+fPk5OQo5V++fJlFixYxaNCgAgO+XVxclBnFL1++zL59+8r0OgtrkQoMDMTX17dM5Z44cUJ5HBISUmS6kydPAnD9+nW7/Wq1mrFjxxZI78i1ye2uioiI4NFHH7XLU6NGDaUbMDEx0e5YQkIC06dPR5IkRo0aVeBc2rRpQ8+ePQGYP3++0grm5uaGSqVSJh1NTU3l008/pVu3bso0HIMHD8bb2xuA48eP25VrNBp59913sVgsvP3228pUHblCQ0N57LHHCpxP7muFkl/P/KpXr648/ueff4pNKwgVQQRSgnAfyJ3Qs7hjXl5e1KhRo8Dx2rVrK48Lmzm6atWqyuP09HTlcW7Xyrfffku7du0K/Pvzzz+VtOfOnSvdC7pFYd1Z5TE+Kv95eXp6FpnOx8cHgG+++YYdO3bYHevQoQOurq52+xy5NkuXLgUodCwTwOLFi3nvvff47rvv7Pb/9ttvZGdnExoaip+fX6F5c8e3Wa1W5Xly6XQ6AGrWrFlgzJZarVbqPyMjo8BrjI2NxdfXt8gZx4ua76m01zO/gIAA5fGlS5eKTCcIFUUMNheE+0BxC7ve7hb04n60ALsWlfwDvo8cOQLABx98QMuWLYstw83NrdjjxYmNjS10fE55BFIpKSnK4+KuQ5cuXZgxYwbp6emMHTuW9u3b89JLL9GiRQt0Oh1Tp061S+/ItTl48CBAkUvahISEMHz48AL7t27dCoC/v3+Rz9G0aVN0Oh1Go7HABJm3e3/ktk7eOm5py5YtgC0AK0pR78vSXs/88v/REBcXV+y5C0JFEC1SgiA4JLeLSZZlAgICiv13u2CtOHdqoDlgN7FjcZNH+vj4MG/ePKX776+//mLYsGEMGzasQJcXOHZtcoOCwu5OLE7usjmSJBWZRqvVKq2RCQkJpSq/KKdOnQKKbw0tSmmvZ375A/v8Xc2CcLeIQEoQBIfk/uCXx115xbmTgVT+cT16vb7YtI0bN2b9+vW8/fbbStfUoUOHGDRoUIHuMkeujXzzDrSrV6+WOA/Yxr6BfetaYXK7Lh2dUf9Wua2E+bt7S6M01zO//C1o5TEZqyCUlQikBEFwSO6P37Zt24pNp9frldYLRxQWSLm5uRU7OLyk8gcVJWndcHJyYvTo0Wzfvp1XXnkFnU6H1Wpl6tSpygBqcOza5I5v+vvvv4vNc/XqVbtB2rnzSkVFRRU7bUBuoFZcV1xp5Hb5Xbp0CbPZ7FAZJb2e+WVlZSmPixvXJggVRQRSgiA4JHeh4EuXLrF27doi0/3222/ExsY6/DyFteqEh4cX25VVUvmDilsHU+e3YMECjh49qmy7uroyfvx4li1bhrOzM7IsK/NRgWPXJjfP2bNni73LcebMmXbdkLnLBRmNRvbv319kvtyZwLt161ZkmtLIXeMwOzu7wIDxW906MWdpr2d++QOp8gimBaGsRCAlCPcQq9Va6OPbyW1tkAuZoLCwfY7KX1bv3r2Vxx9++KHdD2Ou2NhYFi9eXOB2/pJKT08vNAgrj249sF+q53bB3rp16wrN36dPH8C+RcuRa5NbDsCUKVMK7arbuHEjaWlpdtM+DB06VBnUvWTJkkLPPTU1ldjYWLy9vZUZ7HOV9H126/sofzm5E7gWlSe3+zG/0lzP/OLj45XHDRo0KMGZC8KdJQIpQbiH5B8InJSUVOJ8uT9i+QdP58rdl/8v+fwMBoPyuLAfvPzdRfnLaNy4sTIHVWZmJsOGDWP69OkcOnSIY8eOMX/+fJ566imef/75YgdyF+dOjo8CaNGiBVqtFrCtuVecZcuWFbjjDVC6tdq0aaPsc+TadOrUSZn64MqVKwwYMIBff/2VU6dOsXPnTiZNmsTkyZN588037Z6/Xr16jBw5EoA///yTP/74o8A5/vzzz1gsFv79738XGCOVmpoK3H6M2K3vrQEDBiitUlFRUYwYMULpprRarWzcuFEJ7NLT09mwYQMHDhxQArfSXM/8cmfw12q1NG/evNhzFoSKIAIpQbgHGI1GTp06xQ8//KDsO3jwIJs3byYzM7PIViW9Xs/y5cuVQGrz5s2cOXMGk8mE2WzmwoUL/P7774DtB3PFihVKsGQ2m4mJibFb+mXRokWkpqYiyzJWq5W4uDh++eUX5fjChQvtWko+/PBDpUXFZDIxb948hg0bxsCBA/n000/p168fTz75pMPXpahAqrwWbfb09KR9+/aAbcLR4pjNZsaMGcOcOXOIiooiLS2NX3/9lbVr19K7d2+6dOlil76010aSJL744gsaNmwI2Fqs3nvvPZ588knGjBnDli1b+O9//0udOnUKnNvEiROVsiZMmMDixYtJTk4mOTmZH3/8ke+++44pU6YowV3u6zly5Igy7cKZM2fYtWuXElAZjUYiIyOVSUvPnz/Pjh07lMBap9Mxa9Ys5W7AU6dO8eSTT/Loo4/Spk0b5s2bZ9fK9uWXX3LhwgXlvVza65krd+6o9u3bl9vAeUEoC0kuz3Z/QRBKLT09/bZzDX3wwQcMGTKkwP5HH3200Ll0evToQbVq1ewCs/wOHjzIt99+y8KFCws9/ssvv3DkyBH+7//+r9Djv//+uzK+yGq1smrVKlasWKFMnFm/fn2effZZunfvXuzrup3Jkyfz22+/2e3TaDRERkY63Mp1qwMHDjB8+HB8fHyKHJu0YMGCAtdCp9NRt25dhg0bRv/+/Qsds+XItTEajSxYsIA1a9YQHR2Nh4eHMs9SaGhosa8ld4HkEydOkJWVRZUqVWjVqhXDhw9XWo9yTZgwgY0bNxYow9vbm/3799OpU6dCuzsbNmzIqlWrlO2MjAzmzJnD5s2biYuLo0qVKvTt25cxY8bw/fffs2XLFl544QV69eql3HHn6PWUZZkOHTqQkJDA999/X+TM6YJQkUQgJQjCA2/EiBHs37+fX3/9VRn0Ldx7Tpw4wYABA2jcuDErVqy426cjCIDo2hMEQeCNN95ArVYXaP0S7i1r165FrVbz7rvv3u1TEQSFCKQEQXjgNW3alLfeeosVK1aUekJMoWLExcXx888/8/zzzxe5tp8g3A2ia08QBOGmN954g4SEBBYsWHDbNeiEiiPLMuPHj0etVvPf//632LUlBaGiiXejIAjCTTNmzKBmzZq899575Tr/llA2M2bMwMnJic8//1wEUcI9R7RICYIg3GL58uUcPnyYd999V1kKRah4aWlpTJs2jUaNGjF8+PC7fTqCUCgRSAmCIBQiOTmZ7OxsqlevfrdP5YF1+fJlvLy87GZyF4R7jQikBEEQBEEQHCQ6mwVBEARBEBwkAilBEARBEAQHiUBKEARBEATBQSKQEgRBEARBcJAIpARBEARBEBwkAilBEARBEAQHiUBKEARBEATBQSKQEgRBEARBcJAIpARBEARBEBwkAilBEARBEAQH/T8ttkXSgmfYfwAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cox_dict = {\n", - " \"adv_failure_rate\": \"Adv. Failure Rate\",\n", - " \"def_value\": \"Defence Strength\",\n", - " \"data.sample.random_state\": \"Random State\",\n", - " \"train_time\": \"Training Time\",\n", - " \"model_layers\": \"No. of Layers\",\n", - " \"model.art.pipeline.initialize.kwargs.optimizer.lr\": \"Learning Rate\",\n", - " \"adv_accuracy\": \"Adv. Accuracy\",\n", - " \"adv_fit_time\": \"Adv. Fit Time\",\n", - " \"adv_log_loss\": \"Adv. Log Loss\",\n", - " \"predict_time\": \"Inference Time\",\n", - " \"accuracy\": \"Ben. Accuracy\",\n", - " \"failure_rate\": \"Ben. Failure Rate\",\n", - " \"atk_value\": \"Attack Strength\",\n", - "}\n", - "\n", - "cox_afr, cft = plot_aft(\n", - " X_train,\n", - " file=\"cox_aft.pdf\",\n", - " event_col=target,\n", - " duration_col=duration_col,\n", - " title=\"Cox AFR Model\",\n", - " mtype=\"cox\",\n", - " replacement_dict=cox_dict,\n", - ")\n", - "cox_scores = score_model(cft, X_train, X_test)\n", - "cft.print_summary()\n", - "cox_partial = plot_partial_effects(\n", - " file=\"cox_partial_effects.pdf\",\n", - " aft=cft,\n", - " covariate_array=\"model_layers\",\n", - " values_array=[18, 34, 50, 101, 152],\n", - " replacement_dict=cox_dict,\n", - " title=\"Survival Time for Cox AFR\",\n", - " ylabel=\"% Chance of Survival\",\n", - " xlabel=\"Time $T$ (seconds)\",\n", - " legend_kwargs={\n", - " \"title\": \"No. of Layers\",\n", - " \"labels\": [\"18\", \"34\", \"50\", \"101\", \"152\"],\n", - " },\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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modellifelines.LogNormalAFTFitter
duration col'adv_fit_time'
event col'adv_failures'
number of observations1500
number of events observed1500
log-likelihood-5374.54
time fit was run2023-09-29 11:13:31 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
mu_adv_failure_rate-0.001.000.00-0.00-0.001.001.000.00-31.84<0.005736.80
atk_value0.081.090.16-0.220.390.801.480.000.520.600.74
data.sample.random_state0.021.020.02-0.020.060.981.060.000.830.411.30
def_value-0.160.850.16-0.480.160.621.170.00-0.980.321.62
model.art.pipeline.initialize.kwargs.optimizer.lr-0.001.000.00-0.000.001.001.000.00-0.060.950.07
model_layers0.011.010.000.010.011.011.010.007.13<0.00539.81
predict_time-0.210.810.02-0.26-0.160.770.850.00-8.37<0.00553.94
train_time0.001.000.000.000.001.001.000.007.13<0.00539.89
Intercept2.027.540.171.682.365.3710.610.0011.63<0.005101.36
sigma_Intercept0.842.310.020.800.872.232.390.0045.86<0.005inf

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Concordance0.84
AIC10769.08
log-likelihood ratio test925.72 on 8 df
-log2(p) of ll-ratio test643.78
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" - ], - "text/latex": [ - "\\begin{tabular}{llrrrrrrrrrrr}\n", - " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", - "param & covariate & & & & & & & & & & & \\\\\n", - "\\multirow[c]{9}{*}{mu_} & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -31.84 & 0.00 & 736.80 \\\\\n", - " & atk_value & 0.08 & 1.09 & 0.16 & -0.22 & 0.39 & 0.80 & 1.48 & 0.00 & 0.52 & 0.60 & 0.74 \\\\\n", - " & data.sample.random_state & 0.02 & 1.02 & 0.02 & -0.02 & 0.06 & 0.98 & 1.06 & 0.00 & 0.83 & 0.41 & 1.30 \\\\\n", - " & def_value & -0.16 & 0.85 & 0.16 & -0.48 & 0.16 & 0.62 & 1.17 & 0.00 & -0.98 & 0.32 & 1.62 \\\\\n", - " & model.art.pipeline.initialize.kwargs.optimizer.lr & -0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & -0.06 & 0.95 & 0.07 \\\\\n", - " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.01 & 1.01 & 1.01 & 0.00 & 7.13 & 0.00 & 39.81 \\\\\n", - " & predict_time & -0.21 & 0.81 & 0.02 & -0.26 & -0.16 & 0.77 & 0.85 & 0.00 & -8.37 & 0.00 & 53.94 \\\\\n", - " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 7.13 & 0.00 & 39.89 \\\\\n", - " & Intercept & 2.02 & 7.54 & 0.17 & 1.68 & 2.36 & 5.37 & 10.61 & 0.00 & 11.63 & 0.00 & 101.36 \\\\\n", - "sigma_ & Intercept & 0.84 & 2.31 & 0.02 & 0.80 & 0.87 & 2.23 & 2.39 & 0.00 & 45.86 & 0.00 & inf \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\n", - " duration col = 'adv_fit_time'\n", - " event col = 'adv_failures'\n", - " number of observations = 1500\n", - "number of events observed = 1500\n", - " log-likelihood = -5374.54\n", - " time fit was run = 2023-09-29 11:13:31 UTC\n", - "\n", - "---\n", - " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", - "param covariate \n", - "mu_ adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", - " atk_value 0.08 1.09 0.16 -0.22 0.39 0.80 1.48\n", - " data.sample.random_state 0.02 1.02 0.02 -0.02 0.06 0.98 1.06\n", - " def_value -0.16 0.85 0.16 -0.48 0.16 0.62 1.17\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr -0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - " model_layers 0.01 1.01 0.00 0.01 0.01 1.01 1.01\n", - " predict_time -0.21 0.81 0.02 -0.26 -0.16 0.77 0.85\n", - " train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", - " Intercept 2.02 7.54 0.17 1.68 2.36 5.37 10.61\n", - "sigma_ Intercept 0.84 2.31 0.02 0.80 0.87 2.23 2.39\n", - "\n", - " cmp to z p -log2(p)\n", - "param covariate \n", - "mu_ adv_failure_rate 0.00 -31.84 <0.005 736.80\n", - " atk_value 0.00 0.52 0.60 0.74\n", - " data.sample.random_state 0.00 0.83 0.41 1.30\n", - " def_value 0.00 -0.98 0.32 1.62\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 -0.06 0.95 0.07\n", - " model_layers 0.00 7.13 <0.005 39.81\n", - " predict_time 0.00 -8.37 <0.005 53.94\n", - " train_time 0.00 7.13 <0.005 39.89\n", - " Intercept 0.00 11.63 <0.005 101.36\n", - "sigma_ Intercept 0.00 45.86 <0.005 inf\n", - "---\n", - "Concordance = 0.84\n", - "AIC = 10769.08\n", - "log-likelihood ratio test = 925.72 on 8 df\n", - "-log2(p) of ll-ratio test = 643.78" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_773113/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " pareto.set_yticklabels(labels)\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "log_normal_dict = {\n", - " \"Intercept: sigma_\": \"$\\sigma$\",\n", - " \"Intercept: mu_\": \"$\\mu$\",\n", - " \"def_value: mu_\": \"Defence Strength\",\n", - " \"atk_value: mu_\": \"Attack Strength\",\n", - " \"train_time: mu_\": \"Training Time\",\n", - " \"predict_time: mu_\": \"Inference Time\",\n", - " \"adv_fit_time: mu_\": \"Adv. Fit Time\",\n", - " \"model_layers: mu_\": \"No. of Layers\",\n", - " \"model.art.pipeline.initialize.kwargs.optimizer.lr: mu_\": \"Learning Rate\",\n", - " \"data.sample.random_state: mu_\": \"Random State\",\n", - " \"adv_log_loss: mu_\": \"Adv. Log Loss\",\n", - " \"adv_accuracy: mu_\": \"Adv. Accuracy\",\n", - " \"accuracy: mu_\": \"Ben. Accuracy\",\n", - " \"adv_failure_rate: mu_\": \"Adv. Failure Rate\",\n", - " \"def_gen\": \"Defence\",\n", - " \"learning_rate: mu_\": \"Learning Rate\",\n", - "}\n", - "\n", - "log_normal_graph, lnt = plot_aft(\n", - " X_train,\n", - " \"log_normal_aft.pdf\",\n", - " target,\n", - " duration_col,\n", - " \"Log Normal AFR Model\",\n", - " \"log_normal\",\n", - " replacement_dict=log_normal_dict,\n", - ")\n", - "lnt_scores = score_model(lnt, X_train, X_test)\n", - "lnt.print_summary()\n", - "lnt_partial = plot_partial_effects(\n", - " file=\"log_normal_partial_effects.pdf\",\n", - " aft=lnt,\n", - " covariate_array=\"model_layers\",\n", - " values_array=[18, 34, 50, 101, 152],\n", - " replacement_dict=log_normal_dict,\n", - " title=\"Survival Time for Log-Normal AFR\",\n", - " ylabel=\"% Chance of Survival\",\n", - " xlabel=\"Time $T$ (seconds)\",\n", - " legend_kwargs={\n", - " \"title\": \"No. of Layers\",\n", - " \"labels\": [\"18\", \"34\", \"50\", \"101\", \"152\"],\n", - " },\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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modellifelines.LogLogisticAFTFitter
duration col'adv_fit_time'
event col'adv_failures'
number of observations1500
number of events observed1500
log-likelihood-5426.84
time fit was run2023-09-29 11:13:33 UTC
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coefexp(coef)se(coef)coef lower 95%coef upper 95%exp(coef) lower 95%exp(coef) upper 95%cmp tozp-log2(p)
alpha_adv_failure_rate-0.001.000.00-0.00-0.001.001.000.00-26.62<0.005516.20
atk_value0.071.070.17-0.260.400.771.490.000.420.670.58
data.sample.random_state0.021.020.02-0.020.060.981.070.000.910.361.47
def_value-0.190.820.18-0.540.150.581.160.00-1.110.271.90
model.art.pipeline.initialize.kwargs.optimizer.lr0.001.000.00-0.000.001.001.000.000.030.970.04
model_layers0.011.010.000.010.021.011.020.007.88<0.00548.13
predict_time-0.310.740.04-0.38-0.240.690.790.00-8.73<0.00558.49
train_time0.001.000.000.000.001.001.000.006.12<0.00530.02
Intercept1.886.540.191.512.254.529.460.009.97<0.00575.31
beta_Intercept-0.320.720.02-0.36-0.280.690.750.00-15.46<0.005176.59

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Concordance0.82
AIC10873.68
log-likelihood ratio test961.93 on 8 df
-log2(p) of ll-ratio test669.73
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" - ], - "text/latex": [ - "\\begin{tabular}{llrrrrrrrrrrr}\n", - " & & coef & exp(coef) & se(coef) & coef lower 95% & coef upper 95% & exp(coef) lower 95% & exp(coef) upper 95% & cmp to & z & p & -log2(p) \\\\\n", - "param & covariate & & & & & & & & & & & \\\\\n", - "\\multirow[c]{9}{*}{alpha_} & adv_failure_rate & -0.00 & 1.00 & 0.00 & -0.00 & -0.00 & 1.00 & 1.00 & 0.00 & -26.62 & 0.00 & 516.20 \\\\\n", - " & atk_value & 0.07 & 1.07 & 0.17 & -0.26 & 0.40 & 0.77 & 1.49 & 0.00 & 0.42 & 0.67 & 0.58 \\\\\n", - " & data.sample.random_state & 0.02 & 1.02 & 0.02 & -0.02 & 0.06 & 0.98 & 1.07 & 0.00 & 0.91 & 0.36 & 1.47 \\\\\n", - " & def_value & -0.19 & 0.82 & 0.18 & -0.54 & 0.15 & 0.58 & 1.16 & 0.00 & -1.11 & 0.27 & 1.90 \\\\\n", - " & model.art.pipeline.initialize.kwargs.optimizer.lr & 0.00 & 1.00 & 0.00 & -0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 0.03 & 0.97 & 0.04 \\\\\n", - " & model_layers & 0.01 & 1.01 & 0.00 & 0.01 & 0.02 & 1.01 & 1.02 & 0.00 & 7.88 & 0.00 & 48.13 \\\\\n", - " & predict_time & -0.31 & 0.74 & 0.04 & -0.38 & -0.24 & 0.69 & 0.79 & 0.00 & -8.73 & 0.00 & 58.49 \\\\\n", - " & train_time & 0.00 & 1.00 & 0.00 & 0.00 & 0.00 & 1.00 & 1.00 & 0.00 & 6.12 & 0.00 & 30.02 \\\\\n", - " & Intercept & 1.88 & 6.54 & 0.19 & 1.51 & 2.25 & 4.52 & 9.46 & 0.00 & 9.97 & 0.00 & 75.31 \\\\\n", - "beta_ & Intercept & -0.32 & 0.72 & 0.02 & -0.36 & -0.28 & 0.69 & 0.75 & 0.00 & -15.46 & 0.00 & 176.59 \\\\\n", - "\\end{tabular}\n" - ], - "text/plain": [ - "\n", - " duration col = 'adv_fit_time'\n", - " event col = 'adv_failures'\n", - " number of observations = 1500\n", - "number of events observed = 1500\n", - " log-likelihood = -5426.84\n", - " time fit was run = 2023-09-29 11:13:33 UTC\n", - "\n", - "---\n", - " coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%\n", - "param covariate \n", - "alpha_ adv_failure_rate -0.00 1.00 0.00 -0.00 -0.00 1.00 1.00\n", - " atk_value 0.07 1.07 0.17 -0.26 0.40 0.77 1.49\n", - " data.sample.random_state 0.02 1.02 0.02 -0.02 0.06 0.98 1.07\n", - " def_value -0.19 0.82 0.18 -0.54 0.15 0.58 1.16\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 1.00 0.00 -0.00 0.00 1.00 1.00\n", - " model_layers 0.01 1.01 0.00 0.01 0.02 1.01 1.02\n", - " predict_time -0.31 0.74 0.04 -0.38 -0.24 0.69 0.79\n", - " train_time 0.00 1.00 0.00 0.00 0.00 1.00 1.00\n", - " Intercept 1.88 6.54 0.19 1.51 2.25 4.52 9.46\n", - "beta_ Intercept -0.32 0.72 0.02 -0.36 -0.28 0.69 0.75\n", - "\n", - " cmp to z p -log2(p)\n", - "param covariate \n", - "alpha_ adv_failure_rate 0.00 -26.62 <0.005 516.20\n", - " atk_value 0.00 0.42 0.67 0.58\n", - " data.sample.random_state 0.00 0.91 0.36 1.47\n", - " def_value 0.00 -1.11 0.27 1.90\n", - " model.art.pipeline.initialize.kwargs.optimizer.lr 0.00 0.03 0.97 0.04\n", - " model_layers 0.00 7.88 <0.005 48.13\n", - " predict_time 0.00 -8.73 <0.005 58.49\n", - " train_time 0.00 6.12 <0.005 30.02\n", - " Intercept 0.00 9.97 <0.005 75.31\n", - "beta_ Intercept 0.00 -15.46 <0.005 176.59\n", - "---\n", - "Concordance = 0.82\n", - "AIC = 10873.68\n", - "log-likelihood ratio test = 961.93 on 8 df\n", - "-log2(p) of ll-ratio test = 669.73" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'train_score': -3.617893432558881, 'test_score': -3.971188942813805}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_773113/12050270.py:64: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " pareto.set_yticklabels(labels)\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "log_logistic_dict = {\n", - " \"Intercept: beta_\": \"$\\\\beta$\",\n", - " \"Intercept: alpha_\": \"$\\\\alpha$\",\n", - " \"data.sample.random_state: alpha_\": \"Random State\",\n", - " \"def_value: alpha_\": \"Defence Strength\",\n", - " \"atk_value: alpha_\": \"Attack Strength\",\n", - " \"train_time: alpha_\": \"Training Time\",\n", - " \"predict_time: alpha_\": \"Inference Time\",\n", - " \"adv_accuracy: alpha_\": \"Adv. Accuracy\",\n", - " \"accuracy: alpha_\": \"Ben. Accuracy\",\n", - " \"adv_fit_time: alpha_\": \"Adv. Fit Time\",\n", - " \"model_layers: alpha_\": \"No. of Layers\",\n", - " \"model.art.pipeline.initialize.kwargs.optimizer.lr\": \"Learning Rate\",\n", - " \"adv_failure_rate: alpha_\": \"Adv. Failure Rate\",\n", - " \"alpha_\": \"\",\n", - "}\n", - "\n", - "log_logistic_graph, llt = plot_aft(\n", - " X_train,\n", - " \"log_logistic_aft.pdf\",\n", - " target,\n", - " duration_col,\n", - " \"Log Logistic AFR Model\",\n", - " \"log_logistic\",\n", - " replacement_dict=log_logistic_dict,\n", - ")\n", - "llt.print_summary()\n", - "llt_scores = score_model(llt, X_train, X_test)\n", - "print(llt_scores)\n", - "llt_partial = plot_partial_effects(\n", - " file=\"log_logistic_partial_effects.pdf\",\n", - " aft=llt,\n", - " covariate_array=\"model_layers\",\n", - " values_array=[18, 34, 50, 101, 152],\n", - " replacement_dict=log_logistic_dict,\n", - " title=\"Survival Time for Log-Logistic AFR\",\n", - " ylabel=\"% Chance of Survival\",\n", - " xlabel=\"Time $T$ (seconds)\",\n", - " legend_kwargs={\n", - " \"title\": \"No. of Layers\",\n", - " \"labels\": [\"18\", \"34\", \"50\", \"101\", \"152\"],\n", - " },\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "8.62393284274078" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean(llt.predict_median(X_train))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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AICConcordanceBICTrain LLTest LLMean STMedian ST
Weibull11082.520.8411082.52-3.69-4.0071.5310.23
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" - ], - "text/plain": [ - " AIC Concordance BIC Train LL Test LL Mean ST \\\n", - "Weibull 11082.52 0.84 11082.52 -3.69 -4.00 71.53 \n", - "LogNormal 10769.08 0.84 10769.08 -3.58 -3.92 122.81 \n", - "LogLogistic 10873.68 0.82 10873.68 -3.62 -3.97 NaN \n", - "\n", - " Median ST \n", - "Weibull 10.23 \n", - "LogNormal 7.79 \n", - "LogLogistic 6.62 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "aft_dict = {\n", - " \"Weibull\": wft,\n", - " \"LogNormal\": lnt,\n", - " \"LogLogistic\": llt,\n", - " # \"Cox\": cft,\n", - "}\n", - "\n", - "score_list = [\n", - " wft_scores,\n", - " lnt_scores,\n", - " llt_scores,\n", - " # cft_scores,\n", - "]\n", - "aft_data = pd.DataFrame()\n", - "aft_data.index.name = \"Model\"\n", - "aft_data.index = aft_dict.keys()\n", - "aft_data[\"AIC\"] = [\n", - " x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()\n", - "]\n", - "aft_data[\"Concordance\"] = [x.concordance_index_ for x in aft_dict.values()]\n", - "aft_data[\"BIC\"] = [\n", - " x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values()\n", - "]\n", - "aft_data[\"Train LL\"] = [x[\"train_score\"] for x in score_list]\n", - "aft_data[\"Test LL\"] = [x[\"test_score\"] for x in score_list]\n", - "aft_data[\"Mean ST\"] = [x.predict_expectation(X_train).mean() for x in aft_dict.values()]\n", - "aft_data[\"Median ST\"] = [x.predict_median(X_train).median() for x in aft_dict.values()]\n", - "aft_data = aft_data.round(2)\n", - "aft_data.to_csv(FOLDER / \"aft_comparison.csv\")\n", - "logger.info(f\"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}\")\n", - "aft_data = aft_data.round(\n", - " 2,\n", - ")\n", - "aft_data.to_latex(\n", - " FOLDER / \"aft_comparison.tex\",\n", - " float_format=\"%.2f\",\n", - " label=\"tab:mnist\",\n", - " caption=\"Comparison of AFR Models on the MNIST dataset.\",\n", - ")\n", - "aft_data" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "env", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/pytorch_cifar/.dvc/.gitignore b/examples/pytorch_cifar/.dvc/.gitignore deleted file mode 100644 index 528f30c7..00000000 --- a/examples/pytorch_cifar/.dvc/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -/config.local -/tmp -/cache diff --git a/examples/pytorch_cifar/.dvc/config b/examples/pytorch_cifar/.dvc/config deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/pytorch_cifar/.dvcignore b/examples/pytorch_cifar/.dvcignore deleted file mode 100644 index 51973055..00000000 --- a/examples/pytorch_cifar/.dvcignore +++ /dev/null @@ -1,3 +0,0 @@ -# Add patterns of files dvc should ignore, which could improve -# the performance. Learn more at -# https://dvc.org/doc/user-guide/dvcignore diff --git a/examples/pytorch_cifar/.gitignore b/examples/pytorch_cifar/.gitignore deleted file mode 100644 index af0b54a3..00000000 --- a/examples/pytorch_cifar/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -20_epochs/ -multirun/ -cifar/ \ No newline at end of file diff --git a/examples/pytorch_cifar/attacks.sh b/examples/pytorch_cifar/attacks.sh deleted file mode 100644 index 8ec1f079..00000000 --- a/examples/pytorch_cifar/attacks.sh +++ /dev/null @@ -1,37 +0,0 @@ -# #!/bin/bash - -# # This script is used to generate the attacks for the example. - -# Fast Gradient Method -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 stage=attack ++hydra.sweeper.study_name=fgm ++direction=maximize $@ - -# Projected Gradient Descent -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 stage=attack ++hydra.sweeper.study_name=pgd ++direction=maximize $@ - -# DeepFool -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=1,3,5,10 stage=attack ++hydra.sweeper.study_name=deep ++direction=maximize $@ - -# HopSkipJump -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 stage=attack ++hydra.sweeper.study_name=hsj ++direction=maximize $@ - -# ##################################################### -# PixelAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=pixel ++direction=maximize $@ - -# ThresholdAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=thresh ++direction=maximize $@ - -# # AdversarialPatch -# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 stage=patch ++hydra.sweeper.study_name=attack ++direction=maximize ++attack.init.patch_shape=[1,28,28] $@ -##################################################### - -# # Carlini L0 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw0 ++hydra.sweeper.study_name=cw0 ++direction=maximize $@ - -# # Carlini L2 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw2 ++hydra.sweeper.study_name=cw2 ++direction=maximize $@ - -# # Carlini LInf Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=attack ++hydra.sweeper.study_name=cwinf ++direction=maximize $@ - -rm -rf output/models/* diff --git a/examples/pytorch_cifar/conf/attack/default.yaml b/examples/pytorch_cifar/conf/attack/default.yaml deleted file mode 100644 index 76b4d62d..00000000 --- a/examples/pytorch_cifar/conf/attack/default.yaml +++ /dev/null @@ -1,9 +0,0 @@ -data: ${data} -model: ${model} -_target_ : deckard.base.attack.Attack -init: - model: ${model} - _target_: deckard.base.attack.AttackInitializer - name: art.attacks.evasion.HopSkipJump -attack_size : 10 -method : evasion diff --git a/examples/pytorch_cifar/conf/cifar10.yaml b/examples/pytorch_cifar/conf/cifar10.yaml deleted file mode 100644 index a0140d00..00000000 --- a/examples/pytorch_cifar/conf/cifar10.yaml +++ /dev/null @@ -1,44 +0,0 @@ -defaults: - - _self_ - - data: torch_cifar - - model: torch_cifar - - attack: default - - files: cifar - - scorers: default - - override hydra/sweeper : optuna - - override hydra/sweeper/sampler : grid - - override hydra/launcher : joblib -stage : '???' -direction : "maximize" -_target_ : deckard.base.experiment.Experiment -optimizers : accuracy -hydra: - run: - dir: ${files.directory}/${files.reports}/${stage}/logs - sweep: - dir: ${files.directory}/${stage}/${model.init.name} - subdir : ${hydra.sweeper.study_name}/${hydra.job.num} - sweeper: - sampler: - _target_: optuna.samplers.GridSampler - direction: ${direction} - study_name: control - storage: sqlite:///model.db - n_jobs: ${hydra.launcher.n_jobs} - n_trials : 32 - params: - ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) - ++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, .0001, .00001, 0.000001) - ++model.trainer.nb_epoch: choice(1, 10, 30, 50, 100) - _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - launcher: - _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 8 - prefer : processes - verbose: 10 - timeout: null - pre_dispatch: n_jobs - batch_size: auto - temp_folder: /tmp/deckard - max_nbytes: 100000 - mmap_mode: r diff --git a/examples/pytorch_cifar/conf/compile.yaml b/examples/pytorch_cifar/conf/compile.yaml deleted file mode 100644 index 52106b1c..00000000 --- a/examples/pytorch_cifar/conf/compile.yaml +++ /dev/null @@ -1,32 +0,0 @@ -attacks: - # CarliniL0Method: CW_0 - # CarliniL2Method: CW_2 - # CarliniLInfMethod: CW_inf - DeepFool: Deep - FastGradientMethod: FGM - HopSkipJump: HSJ - PixelAttack: Pixel - ProjectedGradientDescent: PGD - ThresholdAttack: Thresh -defences: - Control: Control - FeatureSqueezing: FSQ - GaussianAugmentation: Gauss-in - GaussianNoise: Gauss-out - HighConfidence: Conf -params: - # art.attacks.evasion.CarliniL0Method: attack.init.confidence - # art.attacks.evasion.CarliniL2Method: attack.init.confidence - # art.attacks.evasion.CarliniLInfMethod: attack.init.confidence - Deep: attack.init.nb_grads - FGM: attack.init.eps - HSJ: attack.init.max_iter - Pixel: attack.init.th - PGD: attack.init.eps - Thresh: attack.init.th - Gauss-out: model.art.pipeline.postprocessor.scale - Conf: model.art.pipeline.postprocessor.cutoff - FSQ: model.art.pipeline.preprocessor.bit_depth - Gauss-in: model.art.pipeline.preprocessor.sigma - Depth: model_layers - Epochs: model.train.nb_epoch \ No newline at end of file diff --git a/examples/pytorch_cifar/conf/data/default.yaml b/examples/pytorch_cifar/conf/data/default.yaml deleted file mode 100644 index 5eb78278..00000000 --- a/examples/pytorch_cifar/conf/data/default.yaml +++ /dev/null @@ -1,2 +0,0 @@ -defaults: - - torch_mnist diff --git a/examples/pytorch_cifar/conf/data/torch_cifar.yaml b/examples/pytorch_cifar/conf/data/torch_cifar.yaml deleted file mode 100644 index b7603125..00000000 --- a/examples/pytorch_cifar/conf/data/torch_cifar.yaml +++ /dev/null @@ -1,11 +0,0 @@ -_target_: deckard.base.data.Data -generate: - name: torch_cifar10 -sample: - random_state : 0 - stratify: True -sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/examples/pytorch_cifar/conf/data/torch_mnist.yaml b/examples/pytorch_cifar/conf/data/torch_mnist.yaml deleted file mode 100644 index 9d79c036..00000000 --- a/examples/pytorch_cifar/conf/data/torch_mnist.yaml +++ /dev/null @@ -1,15 +0,0 @@ -_target_: deckard.base.data.Data -generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist -sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state : 0 - stratify: True -sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - - name: sklearn.preprocessing.StandardScaler - with_mean: True - with_std: True diff --git a/examples/pytorch_cifar/conf/files/cifar.yaml b/examples/pytorch_cifar/conf/files/cifar.yaml deleted file mode 100644 index e43cab72..00000000 --- a/examples/pytorch_cifar/conf/files/cifar.yaml +++ /dev/null @@ -1,21 +0,0 @@ -_target_: deckard.base.files.FileConfig -reports: reports -data_dir: data -model_dir: models -attack_dir: attacks -directory: cifar -score_dict_file: score_dict.json -data_file : data -model_file : model -attack_file : attack -attack_type : .pkl -data_type : .pkl -model_type : .pt -params_file : params.yaml -# train_labels_file : train_labels.json -# test_labels_file : test_labels.json -# probabilities_file: probabilities.json -predictions_file : predictions.json -adv_predictions_file : adv_predictions.json -# adv_probabilities_file: adv_probabilities.json -name: default diff --git a/examples/pytorch_cifar/conf/files/mnist.yaml b/examples/pytorch_cifar/conf/files/mnist.yaml deleted file mode 100644 index efdb1c42..00000000 --- a/examples/pytorch_cifar/conf/files/mnist.yaml +++ /dev/null @@ -1,21 +0,0 @@ -_target_: deckard.base.files.FileConfig -reports: reports -data_dir: data -model_dir: models -attack_dir: attacks -directory: mnist -score_dict_file: score_dict.json -data_file : data -model_file : model -attack_file : attack -attack_type : .pkl -data_type : .pkl -model_type : .pt -params_file : params.yaml -# train_labels_file : train_labels.json -# test_labels_file : test_labels.json -# probabilities_file: probabilities.json -predictions_file : predictions.json -adv_predictions_file : adv_predictions.json -# adv_probabilities_file: adv_probabilities.json -name: default diff --git a/examples/pytorch_cifar/conf/model/art/default.yaml b/examples/pytorch_cifar/conf/model/art/default.yaml deleted file mode 100644 index 4251627d..00000000 --- a/examples/pytorch_cifar/conf/model/art/default.yaml +++ /dev/null @@ -1,7 +0,0 @@ -defaults: - - initialize : default - # - preprocessor: fsq - # - postprocessor: confidence -data : ${..data} -library : ${..library} -_target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/pytorch_cifar/conf/model/art/initialize/default.yaml b/examples/pytorch_cifar/conf/model/art/initialize/default.yaml deleted file mode 100644 index 40e7983e..00000000 --- a/examples/pytorch_cifar/conf/model/art/initialize/default.yaml +++ /dev/null @@ -1,7 +0,0 @@ -criterion: - name : torch.nn.CrossEntropyLoss -optimizer: - name : torch.optim.SGD - lr : 0.01 - momentum : 0.9 -clip_values : [0.0, 255.0] diff --git a/examples/pytorch_cifar/conf/model/art/postprocessor/confidence.yaml b/examples/pytorch_cifar/conf/model/art/postprocessor/confidence.yaml deleted file mode 100644 index 6fcdde95..00000000 --- a/examples/pytorch_cifar/conf/model/art/postprocessor/confidence.yaml +++ /dev/null @@ -1,3 +0,0 @@ - -name : art.defences.postprocessor.HighConfidence -cutoff : .1 diff --git a/examples/pytorch_cifar/conf/model/art/postprocessor/gauss-out.yaml b/examples/pytorch_cifar/conf/model/art/postprocessor/gauss-out.yaml deleted file mode 100644 index c912ebd9..00000000 --- a/examples/pytorch_cifar/conf/model/art/postprocessor/gauss-out.yaml +++ /dev/null @@ -1,2 +0,0 @@ -name : art.defences.postprocessor.GaussianNoise -scale : 1 diff --git a/examples/pytorch_cifar/conf/model/art/preprocessor/default.yaml b/examples/pytorch_cifar/conf/model/art/preprocessor/default.yaml deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/pytorch_cifar/conf/model/art/preprocessor/fsq.yaml b/examples/pytorch_cifar/conf/model/art/preprocessor/fsq.yaml deleted file mode 100644 index 27ddf58b..00000000 --- a/examples/pytorch_cifar/conf/model/art/preprocessor/fsq.yaml +++ /dev/null @@ -1,3 +0,0 @@ -name : art.defences.preprocessor.FeatureSqueezing -clip_values : ${model.art.initialize.clip_values} -bit_depth : 64 diff --git a/examples/pytorch_cifar/conf/model/art/preprocessor/gauss-in.yaml b/examples/pytorch_cifar/conf/model/art/preprocessor/gauss-in.yaml deleted file mode 100644 index c438e8f5..00000000 --- a/examples/pytorch_cifar/conf/model/art/preprocessor/gauss-in.yaml +++ /dev/null @@ -1,4 +0,0 @@ -name : art.defences.preprocessor.GaussianAugmentation -clip_values : ${model.art.initialize.clip_values} -sigma : 1 -ratio : .5 diff --git a/examples/pytorch_cifar/conf/model/torch_cifar.yaml b/examples/pytorch_cifar/conf/model/torch_cifar.yaml deleted file mode 100644 index 5ca2e24e..00000000 --- a/examples/pytorch_cifar/conf/model/torch_cifar.yaml +++ /dev/null @@ -1,13 +0,0 @@ -defaults: - - art : default -data: ${data} -init: - _target_: deckard.base.model.ModelInitializer - name : torch_example.ResNet18 - num_channels: 3 - num_classes: 10 -_target_: deckard.base.model.Model -trainer: - nb_epoch: 100 - batch_size: 1024 -library : pytorch diff --git a/examples/pytorch_cifar/conf/model/torch_mnist.yaml b/examples/pytorch_cifar/conf/model/torch_mnist.yaml deleted file mode 100644 index 762addc1..00000000 --- a/examples/pytorch_cifar/conf/model/torch_mnist.yaml +++ /dev/null @@ -1,13 +0,0 @@ - -defaults: - - art : default -data: ${data} -init: - _target_: deckard.base.model.ModelInitializer - num_channels : 1 - name : torch_example.ResNet18 -_target_: deckard.base.model.Model -trainer: - nb_epoch: 20 - batch_size: 1024 -library : pytorch diff --git a/examples/pytorch_cifar/conf/plots.yaml b/examples/pytorch_cifar/conf/plots.yaml deleted file mode 100644 index accab9df..00000000 --- a/examples/pytorch_cifar/conf/plots.yaml +++ /dev/null @@ -1,250 +0,0 @@ -cat_plot: -- file: adv_accuracy_vs_defence_type.pdf - hue: model_name - kind: boxen - set: - yscale: linear - titles: $\lambda_{adv.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: adv_accuracy - ylabels: $\lambda_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: ben_accuracy_vs_defence_type.pdf - hue: model_name - kind: boxen - titles: $\lambda_{ben.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: accuracy - ylabels: $\lambda_{ben.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: ben_failures_per_train_time_vs_defence_type.pdf - hue: model_name - kind: boxen - set: - yscale: log - titles: $\bar{C}_{ben.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: training_time_per_failure - ylabels: $\bar{C}_{ben.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: adv_failures_per_train_time_vs_defence_type.pdf - hue: model_name - kind: boxen - set: - yscale: log - titles: $\bar{C}_{adv.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: training_time_per_adv_failure - ylabels: $\bar{C}_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: adv_failures_per_train_time_vs_attack_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - set: - yscale: log - titles: $\bar{C}_{adv.}$ vs Attack Type - x: atk_gen - xlabels: Attack Type - y: training_time_per_adv_failure - ylabels: $\bar{C}_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: adv_failures_per_test_time_vs_defence_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - titles: $h_{adv}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: adv_failure_rate - ylabels: $h_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -# - file: adv_accuracy_vs_defence_type.pdf -# hue: model_name -# kind: boxen -# legend_title: Model Name -# titles: $\lambda_{adv.}$ vs Defence Type -# x: def_gen -# xlabels: Defence Type -# y: adv_accuracy -# ylabels: Adv. $\lambda_{ben.}$ -# rotation : 90 -# hue_order: -# - ResNet18 -# - ResNet34 -# - ResNet50 -# - ResNet101 -# - ResNet152 -- file: adv_accuracy_vs_attack_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - titles: $\lambda_{adv.}$ vs Attack Type - x: atk_gen - xlabels: Attack Type - y: adv_accuracy - ylabels: $\lambda_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: ben_failure_rate_vs_defence_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - set: - yscale: log - titles: $h_{ben}(t; \theta)$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: failure_rate - ylabels: $h_{ben}(t; \theta)$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -line_plot: -# - file: def_param_vs_accuracy.pdf -# hue: def_gen -# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} -# title: $\lambda_{ben.}$ vs Defence Strength -# x: def_value -# x_scale: linear -# xlabel: Defence Control Parameter -# y: accuracy -# y_scale: -# ylabel: $\lambda_{ben.}$ -# hue_order: -# - Control -# - Gauss-in -# - Gauss-out -# - Conf -# - FSQ -# - file: def_param_vs_adv_accuracy.pdf -# hue: def_gen -# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} -# title: $\lambda_{adv.}$ vs Defence Strength -# x: def_value -# x_scale: linear -# xlabel: Defence Control Parameter -# y: adv_accuracy -# y_scale: -# ylabel: $\lambda_{adv.}$ -# hue_order: -# - Control -# - Gauss-in -# - Gauss-out -# - Conf -# - FSQ -# - file: def_param_vs_adv_failure_rate.pdf -# hue: def_gen -# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} -# title: $h_{adv}$ vs Defence Strength -# x: def_value -# x_scale: linear -# xlabel: Defence Control Parameter -# y: adv_failure_rate -# y_scale: log -# ylabel: $h_{adv.}$ -# hue_order: -# - Control -# - Gauss-in -# - Gauss-out -# - Conf -# - FSQ -- file: atk_param_vs_accuracy.pdf - hue: atk_gen - legend: {bbox_to_anchor: [1.05, 1]} - title: $\lambda_{adv.}$ vs Attack Strength - x: atk_value - x_scale: linear - xlabel: Attack Control Parameter - y: adv_accuracy - y_scale: - ylabel: $\lambda_{adv.}$ - hue_order: - - FGM - - PGD - - Deep - - HSJ - - Pixel - - Thresh - -scatter_plot: -- x: train_time_per_sample - y: adv_failure_rate - hue: model_name - xlabel: $t_{train}$ - ylabel: $h_{adv}$ - title: $h_{adv}$ vs $t_{train}$ - file: adv_failure_rate_vs_train_time.pdf - y_scale: log - x_scale: log - legend: - title: Model Name - bbox_to_anchor: [1.05, 1] - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 - # style : atk_gen -# - x: train_time_per_sample -# y: adv_failure_rate -# hue: model_name -# xlabel: $t_{train}$ -# ylabel: $h_{adv}$ -# title: $h_{adv}$ vs $t_{train}$ for each Defence -# file: adv_failure_rate_vs_train_time_def.pdf -# y_scale: log -# x_scale: linear -# legend: -# title: Model Name -# # style : def_gen diff --git a/examples/pytorch_cifar/conf/scorers/default.yaml b/examples/pytorch_cifar/conf/scorers/default.yaml deleted file mode 100644 index 4503c5bf..00000000 --- a/examples/pytorch_cifar/conf/scorers/default.yaml +++ /dev/null @@ -1,9 +0,0 @@ -_target_: deckard.base.scorer.ScorerDict -accuracy: - _target_: deckard.base.scorer.ScorerConfig - name: sklearn.metrics.accuracy_score - direction: maximize -log_loss: - _target_: deckard.base.scorer.ScorerConfig - name: sklearn.metrics.log_loss - direction: minimize diff --git a/examples/pytorch_cifar/dvc.lock b/examples/pytorch_cifar/dvc.lock deleted file mode 100644 index e27f04eb..00000000 --- a/examples/pytorch_cifar/dvc.lock +++ /dev/null @@ -1,578 +0,0 @@ -schema: '2.0' -stages: - train: - cmd: python -m deckard.layers.experiment train --config_file cifar10.yaml - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: cifar - model_dir: models - model_file: model - model_type: .pt - name: default - params_file: params.yaml - predictions_file: predictions.json - reports: reports - score_dict_file: score_dict.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0.0 - - 255.0 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 10 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss - outs: - - path: cifar/data/data.pkl - md5: 6503fed5d4e6cc1163898c0ab6a863dd - size: 739680311 - - path: cifar/models/model.optimizer.pt - hash: md5 - md5: e7f811b9879af3e82c339327ebbda4e3 - size: 44805933 - - path: cifar/models/model.pt - hash: md5 - md5: 451eaf13b09e9c9df7eddedfd7597c91 - size: 44811029 - - path: cifar/reports/train/default/predictions.json - hash: md5 - md5: d260d0626de24848c45e2b4ccc091e28 - size: 2439901 - - path: cifar/reports/train/default/score_dict.json - hash: md5 - md5: a7d27eb11ba150347b705d90fe2c52d4 - size: 408 - attack: - cmd: python -m deckard.layers.experiment attack --config_file cifar10.yaml - deps: - - path: cifar/data/data.pkl - md5: 6503fed5d4e6cc1163898c0ab6a863dd - size: 739680311 - - path: cifar/models/model.pt - hash: md5 - md5: 451eaf13b09e9c9df7eddedfd7597c91 - size: 44811029 - params: - params.yaml: - attack: - _target_: deckard.base.attack.Attack - attack_size: 10 - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.attack.AttackInitializer - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0.0 - - 255.0 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 10 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - name: art.attacks.evasion.HopSkipJump - method: evasion - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0.0 - - 255.0 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 10 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: cifar - model_dir: models - model_file: model - model_type: .pt - name: default - params_file: params.yaml - predictions_file: predictions.json - reports: reports - score_dict_file: score_dict.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0.0 - - 255.0 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 10 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss - outs: - - path: cifar/attacks/attack.pkl - hash: md5 - md5: 166ad9c95ddf728a437514ab201b7818 - size: 123046 - - path: cifar/reports/attack/default/adv_predictions.json - hash: md5 - md5: 7b935916e2afee6a7c6e3647de882c59 - size: 2126 - - path: cifar/reports/attack/default/score_dict.json - hash: md5 - md5: 372c2d735838be9121b207cbe8739f41 - size: 468 - attacks@ResNet18: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 - stage=attack ++hydra.sweeper.storage=sqlite:///cifar/reports/attack/ResNet18.db - --config-name cifar10.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: cifar/reports/attack/default/score_dict.json - hash: md5 - md5: 372c2d735838be9121b207cbe8739f41 - size: 468 - - path: models.sh - hash: md5 - md5: 02a4961b4afe7ba84c41e9ad49c30c83 - size: 2760 - outs: - - path: cifar/reports/attack/ResNet18.db - hash: md5 - md5: 8e9fc2e0821e82193a8d50cab6fd2dd9 - size: 819200 - attacks@ResNet34: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 - stage=attack ++hydra.sweeper.storage=sqlite:///cifar/reports/attack/ResNet34.db - --config-name cifar10.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: cifar/reports/attack/default/score_dict.json - hash: md5 - md5: 372c2d735838be9121b207cbe8739f41 - size: 468 - - path: models.sh - hash: md5 - md5: 02a4961b4afe7ba84c41e9ad49c30c83 - size: 2760 - outs: - - path: cifar/reports/attack/ResNet34.db - hash: md5 - md5: 8da0c0cc1f20f5a492e730ffa54b573a - size: 819200 - attacks@ResNet50: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 - stage=attack ++hydra.sweeper.storage=sqlite:///cifar/reports/attack/ResNet50.db - --config-name cifar10.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: cifar/reports/attack/default/score_dict.json - hash: md5 - md5: d3213b13fda99e50a582440a9752f827 - size: 558 - - path: models.sh - hash: md5 - md5: 02a4961b4afe7ba84c41e9ad49c30c83 - size: 2760 - outs: - - path: cifar/reports/attack/ResNet50.db - hash: md5 - md5: e44f2b22e7d876571148078c180842ea - size: 819200 - attacks@ResNet101: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet101 - ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet101.db --config-name - default.yaml - deps: - - path: attacks.sh - md5: 18b363f1e2d0622c372e07116bed4fd8 - size: 2731 - - path: models.sh - md5: 9386478ad9795b80ec8ebc4abf1dbc81 - size: 1221 - - path: output/attacks/attack.pkl - md5: e57b871f32a1a8b7de497f6e5de4ebf1 - size: 123046 - - path: output/reports/attack/default/adv_predictions.json - md5: 0a097842037186d905fb2fd7ec4072d9 - size: 2125 - - path: output/reports/attack/default/params.yaml - md5: 5177666d7f2e1e55ccad8ab4b735ffc7 - size: 5175 - outs: - - path: ResNet101.db - md5: 076f51ae8bc6f487920baee046c1a930 - size: 1282048 - attacks@ResNet152: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet152 - ++stage=attack ++hydra.sweeper.storage=sqlite:///ResNet152.db --config-name - default.yaml - deps: - - path: attacks.sh - md5: da51ec626ca4fa915be37e944ac5218b - size: 2745 - - path: models.sh - md5: 9386478ad9795b80ec8ebc4abf1dbc81 - size: 1221 - - path: output/attacks/attack.pkl - md5: aab66fe871d14945003859fa44125c75 - size: 123046 - - path: output/reports/attack/default/adv_predictions.json - md5: 61d687b977e8c7e9404e69a690b244dd - size: 2154 - - path: output/reports/attack/default/params.yaml - md5: 5177666d7f2e1e55ccad8ab4b735ffc7 - size: 5175 - outs: - - path: ResNet152.db - md5: 143435750b3f57d1a1280d412df74e0b - size: 638976 - compile@attack: - cmd: python -m deckard.layers.compile --report_folder output/reports/attack --results_file - output/reports/attack.csv - deps: - - path: ResNet101.db - md5: 076f51ae8bc6f487920baee046c1a930 - size: 1282048 - - path: ResNet152.db - md5: 143435750b3f57d1a1280d412df74e0b - size: 638976 - - path: ResNet18.db - md5: ff0eb78d052a31f3710336a21e81601e - size: 110592 - - path: ResNet34.db - md5: 946c9a1a88d7caaa6af3679a343cc770 - size: 1290240 - - path: ResNet50.db - md5: 1e3b638560b7ee5e73c674d0e1eb0421 - size: 446464 - - path: output/reports/attack/ - md5: e9c9c2d886498cd61644b8780106edf3.dir - size: 9928248484 - nfiles: 30111 - outs: - - path: output/reports/attack.csv - md5: 4ff19338677ca0ae0d766354a77fa4c8 - size: 18272588 - plot: - cmd: python -m deckard.layers.plots --path output/plots/ --file output/reports/attack.csv - deps: - - path: output/reports/attack.csv - md5: 4ff19338677ca0ae0d766354a77fa4c8 - size: 18272588 - outs: - - path: output/plots/adv_accuracy_vs_attack_type.pdf - md5: 8352f085dde6740b1c87a38bd2a87516 - size: 34926 - - path: output/plots/adv_accuracy_vs_defence_type.pdf - md5: db4aeb461e389047e05eeef7408c5266 - size: 28996 - - path: output/plots/adv_failure_rate_vs_train_time.pdf - md5: a71510ed101d113855d2f9d9ed000393 - size: 42293 - - path: output/plots/adv_failures_per_test_time_vs_defence_type.pdf - md5: 05872213e13a2b0d71f2b6876f769c45 - size: 35029 - - path: output/plots/adv_failures_per_train_time_vs_attack_type.pdf - md5: cfbd9e3d4ff3acf690409cc1af22cbee - size: 39001 - - path: output/plots/adv_failures_per_train_time_vs_defence_type.pdf - md5: b267b951ccc797ccd0bb88e1b359fb6c - size: 32071 - - path: output/plots/atk_param_vs_accuracy.pdf - md5: 8fc619749560fd1be8ac91d04af75203 - size: 24819 - - path: output/plots/ben_accuracy_vs_defence_type.pdf - md5: 2b8f3b735b4eec3572734048dfb859d4 - size: 30297 - - path: output/plots/ben_failure_rate_vs_defence_type.pdf - md5: d2110a2ffdbaa984fe4eb0470dea5c32 - size: 36994 - - path: output/plots/ben_failures_per_train_time_vs_defence_type.pdf - md5: 3aee46faf5a24cd7e0fe3a7053e0f3a2 - size: 31693 - - path: output/plots/data.csv - md5: 1a5ba962d3fc11adcde869712cae4c73 - size: 5018537 - - path: output/plots/def_param_vs_accuracy.pdf - md5: 8e1eea047988b58adafa0919d9674f1a - size: 22770 - - path: output/plots/def_param_vs_adv_accuracy.pdf - md5: aa888fef90099ec3b4866bf0cc22686e - size: 22765 - - path: output/plots/def_param_vs_adv_failure_rate.pdf - md5: 30a15ed503ec93e3b34e69d1af6473e5 - size: 21905 - afr: - cmd: python -m deckard.layers.afr --dataset cifar - deps: - - path: output/plots/data.csv - md5: 1a5ba962d3fc11adcde869712cae4c73 - size: 5018537 - outs: - - path: output/plots/aft_comparison.csv - md5: 7e07c42f2d7c3f3d6a4f49a7823ee62f - size: 191 - - path: output/plots/aft_comparison.tex - md5: c9bf05dced494a1e69aa23534540902e - size: 416 - - path: output/plots/cox_aft.pdf - md5: 08ba7bf48594f94cc66ceccba7b38939 - size: 31502 - - path: output/plots/cox_partial_effects.pdf - md5: 7f7a310fbde6ee46749632023f6b0015 - size: 46475 - - path: output/plots/log_logistic_aft.pdf - md5: e21d6c4176804a522022461b02ab7e20 - size: 34160 - - path: output/plots/log_logistic_partial_effects.pdf - md5: 800456e5361a2cab4a36533fbe149f97 - size: 30412 - - path: output/plots/log_normal_aft.pdf - md5: a6c08cd4cea95e707ed21b35c3d450e8 - size: 34314 - - path: output/plots/log_normal_partial_effects.pdf - md5: 7f8cf2863b9f4a959848675c6c1e8817 - size: 31071 - - path: output/plots/weibull_aft.pdf - md5: 3f3a04df96a120e3a878ece4365df8d6 - size: 32480 - - path: output/plots/weibull_partial_effects.pdf - md5: 45e3c5680ca070186224dda2141db4d8 - size: 30790 - copy_results: - cmd: cp -r output/plots/* ~/ml_afr/cifar/ - deps: - - path: output/plots/aft_comparison.csv - md5: 7e07c42f2d7c3f3d6a4f49a7823ee62f - size: 191 - - path: output/plots/data.csv - md5: 1a5ba962d3fc11adcde869712cae4c73 - size: 5018537 diff --git a/examples/pytorch_cifar/dvc.yaml b/examples/pytorch_cifar/dvc.yaml deleted file mode 100644 index 30efbe5b..00000000 --- a/examples/pytorch_cifar/dvc.yaml +++ /dev/null @@ -1,120 +0,0 @@ -stages: - train: - cmd: python -m deckard.layers.experiment train --config_file cifar10.yaml - params: - - data - - model - - scorers - - files - outs: - - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} - # - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} - # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} # Omit to save space - - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} # logit outputs for our model - # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} # Omit to save space - metrics: - - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} - attack: - cmd: python -m deckard.layers.experiment attack --config_file cifar10.yaml - params: - - data - - model - - attack - - scorers - - files - outs: - - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} - - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} - # - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} - deps: - - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - metrics: - - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} - - ############################################################################## - # models: # This is a loop over the ResNet models - # foreach: - # - ResNet18 - # # - ResNet34 - # # - ResNet50 - # # - ResNet101 - # # - ResNet152 - # do: # This script configures eazch defence - # cmd: bash models.sh ++model.init.name=torch_example.${item} stage=train ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/train/${item}.db --config-name cifar10.yaml - # deps: - # - models.sh - # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - # - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} - # outs: - # - ${files.directory}/${files.reports}/train/${item}.db: # This outputs a database file for each model - # cache: True - # persist: True - attacks: - foreach: # This is a loop over the ResNet models - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 - do: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/attack/${item}.db --config-name cifar10.yaml - deps: - - models.sh # This script configures each defence - - attacks.sh # This script configures each attack - - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} # This is here just to ensure it runs after the attack stage - # - ${files.directory}/${files.reports}/train/${item}.db - outs: - - ${files.directory}/${files.reports}/attack/${item}.db: # This outputs a database file for each model - cache: True - persist: True - compile: - foreach: # iterates through each stage - # - train - - attack - do: - cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv - deps: - - ${files.directory}/${files.reports}/${item}/ - - ${files.directory}/${files.reports}/${item}/ResNet18.db - - ${files.directory}/${files.reports}/${item}/ResNet34.db - - ${files.directory}/${files.reports}/${item}/ResNet50.db - - ${files.directory}/${files.reports}/${item}/ResNet101.db - # - ${files.directory}/${files.reports}/${item}/ResNet152.db - outs: - - ${files.directory}/${files.reports}/${item}.csv - plot: - cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv -o data.csv - deps: - - ${files.directory}/${files.reports}/attack.csv - - ${files.directory}/${files.reports}/attack/ResNet18.db - # - ${files.directory}/${files.reports}/attack/ResNet34.db - # - ${files.directory}/${files.reports}/attack/ResNet50.db - # - ${files.directory}/${files.reports}/attack/ResNet101.db - # - ${files.directory}/${files.reports}/attack/ResNet152.db - outs: - - ${files.directory}/plots/data.csv - afr: - cmd: python -m deckard.layers.afr --dataset ${files.directory} --file ${files.directory}/plots/data.csv - deps: - - ${files.directory}/plots/data.csv - plots: - - ${files.directory}/plots/weibull_aft.pdf - - ${files.directory}/plots/weibull_partial_effects.pdf - - ${files.directory}/plots/cox_partial_effects.pdf - - ${files.directory}/plots/cox_aft.pdf - - ${files.directory}/plots/log_logistic_aft.pdf - - ${files.directory}/plots/log_logistic_partial_effects.pdf - - ${files.directory}/plots/log_normal_aft.pdf - - ${files.directory}/plots/log_normal_partial_effects.pdf - metrics: - - ${files.directory}/plots/aft_comparison.csv - outs: - - ${files.directory}/plots/aft_comparison.tex - copy_results: - cmd: cp -r ${files.directory}/plots/* ~/ml_afr/cifar10/ - deps: - - ${files.directory}/plots/data.csv - - ${files.directory}/plots/aft_comparison.csv diff --git a/examples/pytorch_cifar/models.sh b/examples/pytorch_cifar/models.sh deleted file mode 100644 index 8d64d588..00000000 --- a/examples/pytorch_cifar/models.sh +++ /dev/null @@ -1,38 +0,0 @@ -#!/bin/bash - -# This script is used to generate the models for the sklearn example. - -# # Default model -echo "python -m deckard.layers.optimise " $@ "--multirun" -python -m deckard.layers.optimise $@ --multirun - -# # This line generates the model and adds the FeatureSqueezing preprocessing defence. -# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,255] ++hydra.sweeper.study_name=FSQ $@ --multirun - -# # # Gaussian Augmentation (Input) -# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.5 +model.art.preprocessor.params.augmentation=False ++hydra.sweeper.study_name=gauss-in $@ --multirun - -# # # # Gaussian Noise (Output) -# python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 ++hydra.sweeper.study_name=gauss-out $@ --multirun - -# # # # High Confidence -# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 ++hydra.sweeper.study_name=conf $@ --multirun -#!/bin/bash - -# This script is used to generate the models for the sklearn example. - -# # Default model -echo "python -m deckard.layers.optimise " $@ "--multirun" -python -m deckard.layers.optimise $@ --multirun - -# # This line generates the model and adds the FeatureSqueezing preprocessing defence. -# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,255] ++hydra.sweeper.study_name=FSQ $@ --multirun - -# # # Gaussian Augmentation (Input) -# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.5 +model.art.preprocessor.params.augmentation=False ++hydra.sweeper.study_name=gauss-in $@ --multirun - -# # # # Gaussian Noise (Output) -# python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 ++hydra.sweeper.study_name=gauss-out $@ --multirun - -# # # # High Confidence -# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 ++hydra.sweeper.study_name=conf $@ --multirun diff --git a/examples/pytorch_cifar/params.yaml b/examples/pytorch_cifar/params.yaml deleted file mode 100644 index d8ad2b2e..00000000 --- a/examples/pytorch_cifar/params.yaml +++ /dev/null @@ -1,207 +0,0 @@ -_target_: deckard.base.experiment.Experiment -attack: - _target_: deckard.base.attack.Attack - attack_size: 10 - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.attack.AttackInitializer - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0.0 - - 255.0 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 10 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - name: art.attacks.evasion.HopSkipJump - method: evasion - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0.0 - - 255.0 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 10 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 -data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true -direction: maximize -files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: cifar - model_dir: models - model_file: model - model_type: .pt - name: default - params_file: params.yaml - predictions_file: predictions.json - reports: reports - score_dict_file: score_dict.json -model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0.0 - - 255.0 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar10 - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 10 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 -optimizers: accuracy -scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss -stage: ??? diff --git a/examples/pytorch_cifar/torch_example.py b/examples/pytorch_cifar/torch_example.py deleted file mode 100644 index 32d333bd..00000000 --- a/examples/pytorch_cifar/torch_example.py +++ /dev/null @@ -1,79 +0,0 @@ -import torch.nn as nn -from torchvision import models - -__all__ = [ - "ResNet18", - "ResNet50", - "ResNet101", - "ResNet152", -] - - -def ResNet18(num_channels=1, num_classes=10): - model = models.resnet18(pretrained=True) - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(512, num_classes) - return model - - -def ResNet34(num_channels=1, num_classes=10): - model = models.resnet34(pretrained=True) - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(512, num_classes) - return model - - -def ResNet50(num_channels=1, num_classes=10): - model = models.resnet50(pretrained=True) - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(2048, num_classes) - return model - - -def ResNet101(num_channels=1, num_classes=10): - model = models.resnet101(pretrained=True) - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(2048, num_classes) - return model - - -def ResNet152(num_channels=1, num_classes=10): - model = models.resnet152(pretrained=True) - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(2048, num_classes) - return model diff --git a/examples/pytorch_cifar_100/.dvc/.gitignore b/examples/pytorch_cifar_100/.dvc/.gitignore deleted file mode 100644 index 528f30c7..00000000 --- a/examples/pytorch_cifar_100/.dvc/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -/config.local -/tmp -/cache diff --git a/examples/pytorch_cifar_100/.dvc/config b/examples/pytorch_cifar_100/.dvc/config deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/pytorch_cifar_100/.dvcignore b/examples/pytorch_cifar_100/.dvcignore deleted file mode 100644 index 51973055..00000000 --- a/examples/pytorch_cifar_100/.dvcignore +++ /dev/null @@ -1,3 +0,0 @@ -# Add patterns of files dvc should ignore, which could improve -# the performance. Learn more at -# https://dvc.org/doc/user-guide/dvcignore diff --git a/examples/pytorch_cifar_100/.gitignore b/examples/pytorch_cifar_100/.gitignore deleted file mode 100644 index de9b09c5..00000000 --- a/examples/pytorch_cifar_100/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -cifar100/ -20_epochs/ -cifar-100-python/ \ No newline at end of file diff --git a/examples/pytorch_cifar_100/attacks.sh b/examples/pytorch_cifar_100/attacks.sh deleted file mode 100644 index 8ec1f079..00000000 --- a/examples/pytorch_cifar_100/attacks.sh +++ /dev/null @@ -1,37 +0,0 @@ -# #!/bin/bash - -# # This script is used to generate the attacks for the example. - -# Fast Gradient Method -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 stage=attack ++hydra.sweeper.study_name=fgm ++direction=maximize $@ - -# Projected Gradient Descent -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 stage=attack ++hydra.sweeper.study_name=pgd ++direction=maximize $@ - -# DeepFool -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=1,3,5,10 stage=attack ++hydra.sweeper.study_name=deep ++direction=maximize $@ - -# HopSkipJump -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 stage=attack ++hydra.sweeper.study_name=hsj ++direction=maximize $@ - -# ##################################################### -# PixelAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=pixel ++direction=maximize $@ - -# ThresholdAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=thresh ++direction=maximize $@ - -# # AdversarialPatch -# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 stage=patch ++hydra.sweeper.study_name=attack ++direction=maximize ++attack.init.patch_shape=[1,28,28] $@ -##################################################### - -# # Carlini L0 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw0 ++hydra.sweeper.study_name=cw0 ++direction=maximize $@ - -# # Carlini L2 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw2 ++hydra.sweeper.study_name=cw2 ++direction=maximize $@ - -# # Carlini LInf Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=attack ++hydra.sweeper.study_name=cwinf ++direction=maximize $@ - -rm -rf output/models/* diff --git a/examples/pytorch_cifar_100/conf/attack/default.yaml b/examples/pytorch_cifar_100/conf/attack/default.yaml deleted file mode 100644 index 76b4d62d..00000000 --- a/examples/pytorch_cifar_100/conf/attack/default.yaml +++ /dev/null @@ -1,9 +0,0 @@ -data: ${data} -model: ${model} -_target_ : deckard.base.attack.Attack -init: - model: ${model} - _target_: deckard.base.attack.AttackInitializer - name: art.attacks.evasion.HopSkipJump -attack_size : 10 -method : evasion diff --git a/examples/pytorch_cifar_100/conf/cifar100.yaml b/examples/pytorch_cifar_100/conf/cifar100.yaml deleted file mode 100644 index 760e2feb..00000000 --- a/examples/pytorch_cifar_100/conf/cifar100.yaml +++ /dev/null @@ -1,44 +0,0 @@ -defaults: - - _self_ - - data: torch_cifar100 - - model: torch_cifar100 - - attack: default - - files: cifar100 - - scorers: default - - override hydra/sweeper : optuna - - override hydra/sweeper/sampler : grid - - override hydra/launcher : joblib -stage : '???' -direction : "maximize" -_target_ : deckard.base.experiment.Experiment -optimizers : accuracy -hydra: - run: - dir: ${files.directory}/${files.reports}/${stage}/logs - sweep: - dir: ${files.directory}/${stage}/${model.init.name} - subdir : ${hydra.sweeper.study_name}/${hydra.job.num} - sweeper: - sampler: - _target_: optuna.samplers.GridSampler - direction: ${direction} - study_name: control - storage: sqlite:///model.db - n_jobs: ${hydra.launcher.n_jobs} - n_trials : 32 - params: - ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) - ++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, .0001, .00001, 0.000001) - ++model.trainer.nb_epoch: choice(1, 10, 30, 50, 100) - _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - launcher: - _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 8 - prefer : processes - verbose: 10 - timeout: null - pre_dispatch: n_jobs - batch_size: auto - temp_folder: /tmp/deckard - max_nbytes: 100000 - mmap_mode: r diff --git a/examples/pytorch_cifar_100/conf/compile.yaml b/examples/pytorch_cifar_100/conf/compile.yaml deleted file mode 100644 index 43418520..00000000 --- a/examples/pytorch_cifar_100/conf/compile.yaml +++ /dev/null @@ -1,32 +0,0 @@ -attacks: - # CarliniL0Method: CW_0 - # CarliniL2Method: CW_2 - # CarliniLInfMethod: CW_inf - DeepFool: Deep - FastGradientMethod: FGM - HopSkipJump: HSJ - PixelAttack: Pixel - ProjectedGradientDescent: PGD - ThresholdAttack: Thresh -defences: - Control: Control - FeatureSqueezing: FSQ - GaussianAugmentation: Gauss-in - GaussianNoise: Gauss-out - HighConfidence: Conf -params: - # art.attacks.evasion.CarliniL0Method: attack.init.confidence - # art.attacks.evasion.CarliniL2Method: attack.init.confidence - # art.attacks.evasion.CarliniLInfMethod: attack.init.confidence - Deep: attack.init.nb_grads - FGM: attack.init.eps - HSJ: attack.init.max_iter - Pixel: attack.init.th - PGD: attack.init.eps - Thresh: attack.init.th - Gauss-out: model.art.pipeline.postprocessor.scale - Conf: model.art.pipeline.postprocessor.cutoff - FSQ: model.art.pipeline.preprocessor.bit_depth - Gauss-in: model.art.pipeline.preprocessor.sigma - Depth: model_layers - Epochs: model.train.nb_epoch diff --git a/examples/pytorch_cifar_100/conf/deploy/default.yaml b/examples/pytorch_cifar_100/conf/deploy/default.yaml deleted file mode 100644 index 134da65f..00000000 --- a/examples/pytorch_cifar_100/conf/deploy/default.yaml +++ /dev/null @@ -1,14 +0,0 @@ -num_nodes: 1 -cluster_name: k8s-cluster -gpu_type: nvidia-tesla-v100 -gpu_count: 1 -gpu_driver_version: default -machine_type: n1-standard-2 -min_nodes: 1 -max_nodes: 1 -storage_config: conf/deploy/sclass.yaml -persistent_volume_claim: conf/deploy/pvc.yaml -pod : conf/deploy/pod.yaml -image_project: ubuntu-os-cloud -image_family: ubuntu-2204-lts -mount_directory: /mnt/filestore diff --git a/examples/pytorch_cifar_100/conf/deploy/pod.yaml b/examples/pytorch_cifar_100/conf/deploy/pod.yaml deleted file mode 100644 index fc68306a..00000000 --- a/examples/pytorch_cifar_100/conf/deploy/pod.yaml +++ /dev/null @@ -1,30 +0,0 @@ -apiVersion: v1 -kind: Pod -metadata: - name: deckard -spec: - containers: - - name: deckard - image: ghcr.io/simplymathematics/deckard:main - imagePullPolicy: Always - workingDir: /deckard/examples/pytorch - args: ["python", "-m", "dvc", "repro"] - env: - - name: REDIS_HOST - value: "redis" - - name: REDIS_PORT - value: "6379" - - name: REDIS_DB - value: "0" - - name: REDIS_PASSWORD - value: "" - resources: - limits: - nvidia.com/gpu: 1 - volumeMounts: - - mountPath: /data - name: mypvc - volumes: - - name: mypvc - persistentVolumeClaim: - claimName: podpvc diff --git a/examples/pytorch_cifar_100/conf/deploy/pvc.yaml b/examples/pytorch_cifar_100/conf/deploy/pvc.yaml deleted file mode 100644 index a7deee4d..00000000 --- a/examples/pytorch_cifar_100/conf/deploy/pvc.yaml +++ /dev/null @@ -1,11 +0,0 @@ -kind: PersistentVolumeClaim -apiVersion: v1 -metadata: - name: podpvc -spec: - accessModes: - - ReadWriteMany - storageClassName: filestore-sc - resources: - requests: - storage: 256Gi diff --git a/examples/pytorch_cifar_100/conf/deploy/sclass.yaml b/examples/pytorch_cifar_100/conf/deploy/sclass.yaml deleted file mode 100644 index d566656c..00000000 --- a/examples/pytorch_cifar_100/conf/deploy/sclass.yaml +++ /dev/null @@ -1,10 +0,0 @@ -apiVersion: storage.k8s.io/v1 -kind: StorageClass -metadata: - name: filestore-sc -provisioner: filestore.csi.storage.gke.io -volumeBindingMode: Immediate -allowVolumeExpansion: true -parameters: - tier: standard - network: default diff --git a/examples/pytorch_cifar_100/conf/files/cifar100.yaml b/examples/pytorch_cifar_100/conf/files/cifar100.yaml deleted file mode 100644 index 969635c2..00000000 --- a/examples/pytorch_cifar_100/conf/files/cifar100.yaml +++ /dev/null @@ -1,21 +0,0 @@ -_target_: deckard.base.files.FileConfig -reports: reports -data_dir: data -model_dir: models -attack_dir: attacks -directory: cifar100 -score_dict_file: score_dict.json -data_file : data -model_file : model -attack_file : attack -attack_type : .pkl -data_type : .pkl -model_type : .pt -params_file : params.yaml -# train_labels_file : train_labels.json -# test_labels_file : test_labels.json -# probabilities_file: probabilities.json -predictions_file : predictions.json -adv_predictions_file : adv_predictions.json -# adv_probabilities_file: adv_probabilities.json -name: default diff --git a/examples/pytorch_cifar_100/conf/files/mnist.yaml b/examples/pytorch_cifar_100/conf/files/mnist.yaml deleted file mode 100644 index efdb1c42..00000000 --- a/examples/pytorch_cifar_100/conf/files/mnist.yaml +++ /dev/null @@ -1,21 +0,0 @@ -_target_: deckard.base.files.FileConfig -reports: reports -data_dir: data -model_dir: models -attack_dir: attacks -directory: mnist -score_dict_file: score_dict.json -data_file : data -model_file : model -attack_file : attack -attack_type : .pkl -data_type : .pkl -model_type : .pt -params_file : params.yaml -# train_labels_file : train_labels.json -# test_labels_file : test_labels.json -# probabilities_file: probabilities.json -predictions_file : predictions.json -adv_predictions_file : adv_predictions.json -# adv_probabilities_file: adv_probabilities.json -name: default diff --git a/examples/pytorch_cifar_100/conf/model/art/default.yaml b/examples/pytorch_cifar_100/conf/model/art/default.yaml deleted file mode 100644 index 4251627d..00000000 --- a/examples/pytorch_cifar_100/conf/model/art/default.yaml +++ /dev/null @@ -1,7 +0,0 @@ -defaults: - - initialize : default - # - preprocessor: fsq - # - postprocessor: confidence -data : ${..data} -library : ${..library} -_target_ : deckard.base.model.art_pipeline.ArtPipeline diff --git a/examples/pytorch_cifar_100/conf/model/art/initialize/default.yaml b/examples/pytorch_cifar_100/conf/model/art/initialize/default.yaml deleted file mode 100644 index b694473b..00000000 --- a/examples/pytorch_cifar_100/conf/model/art/initialize/default.yaml +++ /dev/null @@ -1,7 +0,0 @@ -criterion: - name : torch.nn.CrossEntropyLoss -optimizer: - name : torch.optim.SGD - lr : 0.01 - momentum : 0.9 -clip_values : [0, 255] diff --git a/examples/pytorch_cifar_100/conf/model/art/postprocessor/confidence.yaml b/examples/pytorch_cifar_100/conf/model/art/postprocessor/confidence.yaml deleted file mode 100644 index 6fcdde95..00000000 --- a/examples/pytorch_cifar_100/conf/model/art/postprocessor/confidence.yaml +++ /dev/null @@ -1,3 +0,0 @@ - -name : art.defences.postprocessor.HighConfidence -cutoff : .1 diff --git a/examples/pytorch_cifar_100/conf/model/art/postprocessor/gauss-out.yaml b/examples/pytorch_cifar_100/conf/model/art/postprocessor/gauss-out.yaml deleted file mode 100644 index c912ebd9..00000000 --- a/examples/pytorch_cifar_100/conf/model/art/postprocessor/gauss-out.yaml +++ /dev/null @@ -1,2 +0,0 @@ -name : art.defences.postprocessor.GaussianNoise -scale : 1 diff --git a/examples/pytorch_cifar_100/conf/model/art/preprocessor/default.yaml b/examples/pytorch_cifar_100/conf/model/art/preprocessor/default.yaml deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/pytorch_cifar_100/conf/model/art/preprocessor/fsq.yaml b/examples/pytorch_cifar_100/conf/model/art/preprocessor/fsq.yaml deleted file mode 100644 index 27ddf58b..00000000 --- a/examples/pytorch_cifar_100/conf/model/art/preprocessor/fsq.yaml +++ /dev/null @@ -1,3 +0,0 @@ -name : art.defences.preprocessor.FeatureSqueezing -clip_values : ${model.art.initialize.clip_values} -bit_depth : 64 diff --git a/examples/pytorch_cifar_100/conf/model/art/preprocessor/gauss-in.yaml b/examples/pytorch_cifar_100/conf/model/art/preprocessor/gauss-in.yaml deleted file mode 100644 index c438e8f5..00000000 --- a/examples/pytorch_cifar_100/conf/model/art/preprocessor/gauss-in.yaml +++ /dev/null @@ -1,4 +0,0 @@ -name : art.defences.preprocessor.GaussianAugmentation -clip_values : ${model.art.initialize.clip_values} -sigma : 1 -ratio : .5 diff --git a/examples/pytorch_cifar_100/conf/model/torch_cifar100.yaml b/examples/pytorch_cifar_100/conf/model/torch_cifar100.yaml deleted file mode 100644 index ffc0db53..00000000 --- a/examples/pytorch_cifar_100/conf/model/torch_cifar100.yaml +++ /dev/null @@ -1,13 +0,0 @@ -defaults: - - art : default -data: ${data} -init: - _target_: deckard.base.model.ModelInitializer - name : torch_example.ResNet18 - num_channels: 3 - num_classes: 100 -_target_: deckard.base.model.Model -trainer: - nb_epoch: 100 - batch_size: 1024 -library : pytorch diff --git a/examples/pytorch_cifar_100/conf/model/torch_mnist.yaml b/examples/pytorch_cifar_100/conf/model/torch_mnist.yaml deleted file mode 100644 index 9c18c54b..00000000 --- a/examples/pytorch_cifar_100/conf/model/torch_mnist.yaml +++ /dev/null @@ -1,13 +0,0 @@ - -defaults: - - art : default -data: ${data} -init: - _target_: deckard.base.model.ModelInitializer - num_channels : 1 - name : torch_example.ResNet18 -_target_: deckard.base.model.Model -trainer: - nb_epoch: 100 - batch_size: 1024 -library : pytorch diff --git a/examples/pytorch_cifar_100/conf/plots.yaml b/examples/pytorch_cifar_100/conf/plots.yaml deleted file mode 100644 index accab9df..00000000 --- a/examples/pytorch_cifar_100/conf/plots.yaml +++ /dev/null @@ -1,250 +0,0 @@ -cat_plot: -- file: adv_accuracy_vs_defence_type.pdf - hue: model_name - kind: boxen - set: - yscale: linear - titles: $\lambda_{adv.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: adv_accuracy - ylabels: $\lambda_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: ben_accuracy_vs_defence_type.pdf - hue: model_name - kind: boxen - titles: $\lambda_{ben.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: accuracy - ylabels: $\lambda_{ben.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: ben_failures_per_train_time_vs_defence_type.pdf - hue: model_name - kind: boxen - set: - yscale: log - titles: $\bar{C}_{ben.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: training_time_per_failure - ylabels: $\bar{C}_{ben.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: adv_failures_per_train_time_vs_defence_type.pdf - hue: model_name - kind: boxen - set: - yscale: log - titles: $\bar{C}_{adv.}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: training_time_per_adv_failure - ylabels: $\bar{C}_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: adv_failures_per_train_time_vs_attack_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - set: - yscale: log - titles: $\bar{C}_{adv.}$ vs Attack Type - x: atk_gen - xlabels: Attack Type - y: training_time_per_adv_failure - ylabels: $\bar{C}_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: adv_failures_per_test_time_vs_defence_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - titles: $h_{adv}$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: adv_failure_rate - ylabels: $h_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -# - file: adv_accuracy_vs_defence_type.pdf -# hue: model_name -# kind: boxen -# legend_title: Model Name -# titles: $\lambda_{adv.}$ vs Defence Type -# x: def_gen -# xlabels: Defence Type -# y: adv_accuracy -# ylabels: Adv. $\lambda_{ben.}$ -# rotation : 90 -# hue_order: -# - ResNet18 -# - ResNet34 -# - ResNet50 -# - ResNet101 -# - ResNet152 -- file: adv_accuracy_vs_attack_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - titles: $\lambda_{adv.}$ vs Attack Type - x: atk_gen - xlabels: Attack Type - y: adv_accuracy - ylabels: $\lambda_{adv.}$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -- file: ben_failure_rate_vs_defence_type.pdf - hue: model_name - kind: boxen - legend_title: Model Name - set: - yscale: log - titles: $h_{ben}(t; \theta)$ vs Defence Type - x: def_gen - xlabels: Defence Type - y: failure_rate - ylabels: $h_{ben}(t; \theta)$ - rotation : 90 - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 -line_plot: -# - file: def_param_vs_accuracy.pdf -# hue: def_gen -# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} -# title: $\lambda_{ben.}$ vs Defence Strength -# x: def_value -# x_scale: linear -# xlabel: Defence Control Parameter -# y: accuracy -# y_scale: -# ylabel: $\lambda_{ben.}$ -# hue_order: -# - Control -# - Gauss-in -# - Gauss-out -# - Conf -# - FSQ -# - file: def_param_vs_adv_accuracy.pdf -# hue: def_gen -# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} -# title: $\lambda_{adv.}$ vs Defence Strength -# x: def_value -# x_scale: linear -# xlabel: Defence Control Parameter -# y: adv_accuracy -# y_scale: -# ylabel: $\lambda_{adv.}$ -# hue_order: -# - Control -# - Gauss-in -# - Gauss-out -# - Conf -# - FSQ -# - file: def_param_vs_adv_failure_rate.pdf -# hue: def_gen -# legend: {"bbox_to_anchor": [1.05, 1], "title": "Defence"} -# title: $h_{adv}$ vs Defence Strength -# x: def_value -# x_scale: linear -# xlabel: Defence Control Parameter -# y: adv_failure_rate -# y_scale: log -# ylabel: $h_{adv.}$ -# hue_order: -# - Control -# - Gauss-in -# - Gauss-out -# - Conf -# - FSQ -- file: atk_param_vs_accuracy.pdf - hue: atk_gen - legend: {bbox_to_anchor: [1.05, 1]} - title: $\lambda_{adv.}$ vs Attack Strength - x: atk_value - x_scale: linear - xlabel: Attack Control Parameter - y: adv_accuracy - y_scale: - ylabel: $\lambda_{adv.}$ - hue_order: - - FGM - - PGD - - Deep - - HSJ - - Pixel - - Thresh - -scatter_plot: -- x: train_time_per_sample - y: adv_failure_rate - hue: model_name - xlabel: $t_{train}$ - ylabel: $h_{adv}$ - title: $h_{adv}$ vs $t_{train}$ - file: adv_failure_rate_vs_train_time.pdf - y_scale: log - x_scale: log - legend: - title: Model Name - bbox_to_anchor: [1.05, 1] - hue_order: - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 - # style : atk_gen -# - x: train_time_per_sample -# y: adv_failure_rate -# hue: model_name -# xlabel: $t_{train}$ -# ylabel: $h_{adv}$ -# title: $h_{adv}$ vs $t_{train}$ for each Defence -# file: adv_failure_rate_vs_train_time_def.pdf -# y_scale: log -# x_scale: linear -# legend: -# title: Model Name -# # style : def_gen diff --git a/examples/pytorch_cifar_100/conf/scorers/default.yaml b/examples/pytorch_cifar_100/conf/scorers/default.yaml deleted file mode 100644 index 4503c5bf..00000000 --- a/examples/pytorch_cifar_100/conf/scorers/default.yaml +++ /dev/null @@ -1,9 +0,0 @@ -_target_: deckard.base.scorer.ScorerDict -accuracy: - _target_: deckard.base.scorer.ScorerConfig - name: sklearn.metrics.accuracy_score - direction: maximize -log_loss: - _target_: deckard.base.scorer.ScorerConfig - name: sklearn.metrics.log_loss - direction: minimize diff --git a/examples/pytorch_cifar_100/dvc.lock b/examples/pytorch_cifar_100/dvc.lock deleted file mode 100644 index de9319c7..00000000 --- a/examples/pytorch_cifar_100/dvc.lock +++ /dev/null @@ -1,377 +0,0 @@ -schema: '2.0' -stages: - train: - cmd: python -m deckard.layers.experiment train --config_file cifar100.yaml - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: cifar100 - model_dir: models - model_file: model - model_type: .pt - name: default - params_file: params.yaml - predictions_file: predictions.json - reports: reports - score_dict_file: score_dict.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 100 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss - outs: - - path: cifar100/data/data.pkl - hash: md5 - md5: 1070854e6c00fc787bc0fdfc82792fd6 - size: 761280311 - - path: cifar100/models/model.optimizer.pt - hash: md5 - md5: 8b65d9bf40085b3f4573ed859d3b0696 - size: 44990637 - - path: cifar100/models/model.pt - hash: md5 - md5: 4321db4b035a2016cabb9eb9d758ace6 - size: 44995733 - - path: cifar100/reports/train/default/predictions.json - hash: md5 - md5: bdffc46adff64ccce60dcfb5960add7c - size: 24402965 - - path: cifar100/reports/train/default/score_dict.json - hash: md5 - md5: 60904225857c24ad2822fb92e170a26b - size: 387 - attack: - cmd: python -m deckard.layers.experiment attack --config_file cifar100.yaml - deps: - - path: cifar100/data/data.pkl - hash: md5 - md5: 1070854e6c00fc787bc0fdfc82792fd6 - size: 761280311 - - path: cifar100/models/model.pt - hash: md5 - md5: 4321db4b035a2016cabb9eb9d758ace6 - size: 44995733 - params: - params.yaml: - attack: - _target_: deckard.base.attack.Attack - attack_size: 10 - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.attack.AttackInitializer - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 100 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - name: art.attacks.evasion.HopSkipJump - method: evasion - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 100 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: cifar100 - model_dir: models - model_file: model - model_type: .pt - name: default - params_file: params.yaml - predictions_file: predictions.json - reports: reports - score_dict_file: score_dict.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 100 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss - outs: - - path: cifar100/attacks/attack.pkl - hash: md5 - md5: 53b5fbaa742a6e711248fd5bef50e333 - size: 123046 - - path: cifar100/reports/attack/default/adv_predictions.json - hash: md5 - md5: c113079d397ba3f2714280524524a127 - size: 21335 - - path: cifar100/reports/attack/default/score_dict.json - hash: md5 - md5: df6268a9e688028be341b32b9cbc7f65 - size: 540 - attacks@ResNet18: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 - stage=attack ++hydra.sweeper.storage=sqlite:///cifar100/reports/attack/ResNet18.db - --config-name cifar100.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: cifar100/reports/attack/default/score_dict.json - hash: md5 - md5: df6268a9e688028be341b32b9cbc7f65 - size: 540 - - path: models.sh - hash: md5 - md5: 1937e58bedac027034aea7d4a5712407 - size: 1380 - outs: - - path: cifar100/reports/attack/ResNet18.db - hash: md5 - md5: 5542bfcf3caf0cccc60d1cdf43a0a35a - size: 462848 diff --git a/examples/pytorch_cifar_100/dvc.yaml b/examples/pytorch_cifar_100/dvc.yaml deleted file mode 100644 index 254a637a..00000000 --- a/examples/pytorch_cifar_100/dvc.yaml +++ /dev/null @@ -1,120 +0,0 @@ -stages: - train: - cmd: python -m deckard.layers.experiment train --config_file cifar100.yaml - params: - - data - - model - - scorers - - files - outs: - - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} - # - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} - # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} # Omit to save space - - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} # logit outputs for our model - # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} # Omit to save space - metrics: - - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} - attack: - cmd: python -m deckard.layers.experiment attack --config_file cifar100.yaml - params: - - data - - model - - attack - - scorers - - files - outs: - - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} - - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} - # - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} - deps: - - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - metrics: - - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} - - ############################################################################## - # models: # This is a loop over the ResNet models - # foreach: - # - ResNet18 - # # - ResNet34 - # # - ResNet50 - # # - ResNet101 - # # - ResNet152 - # do: # This script configures eazch defence - # cmd: bash models.sh ++model.init.name=torch_example.${item} stage=train ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/train/${item}.db --config-name cifar100.yaml - # deps: - # - models.sh - # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - # - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} - # outs: - # - ${files.directory}/${files.reports}/train/${item}.db: # This outputs a database file for each model - # cache: True - # persist: True - attacks: - foreach: # This is a loop over the ResNet models - - ResNet18 - - ResNet34 - - ResNet50 - - ResNet101 - - ResNet152 - do: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/attack/${item}.db --config-name cifar100.yaml - deps: - - models.sh # This script configures each defence - - attacks.sh # This script configures each attack - - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} # This is here just to ensure it runs after the attack stage - # - ${files.directory}/${files.reports}/train/${item}.db - outs: - - ${files.directory}/${files.reports}/attack/${item}.db: # This outputs a database file for each model - cache: True - persist: True - compile: - foreach: # iterates through each stage - # - train - - attack - do: - cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv - deps: - - ${files.directory}/${files.reports}/${item}/ - - ${files.directory}/${files.reports}/${item}/ResNet18.db - - ${files.directory}/${files.reports}/${item}/ResNet34.db - - ${files.directory}/${files.reports}/${item}/ResNet50.db - - ${files.directory}/${files.reports}/${item}/ResNet101.db - # - ${files.directory}/${files.reports}/${item}/ResNet152.db - outs: - - ${files.directory}/${files.reports}/${item}.csv - plot: - cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv -o data.csv - deps: - - ${files.directory}/${files.reports}/attack.csv - - ${files.directory}/${files.reports}/attack/ResNet18.db - # - ${files.directory}/${files.reports}/attack/ResNet34.db - # - ${files.directory}/${files.reports}/attack/ResNet50.db - # - ${files.directory}/${files.reports}/attack/ResNet101.db - # - ${files.directory}/${files.reports}/attack/ResNet152.db - outs: - - ${files.directory}/plots/data.csv - afr: - cmd: python -m deckard.layers.afr --dataset ${files.directory} --file ${files.directory}/plots/data.csv - deps: - - ${files.directory}/plots/data.csv - plots: - - ${files.directory}/plots/weibull_aft.pdf - - ${files.directory}/plots/weibull_partial_effects.pdf - - ${files.directory}/plots/cox_partial_effects.pdf - - ${files.directory}/plots/cox_aft.pdf - - ${files.directory}/plots/log_logistic_aft.pdf - - ${files.directory}/plots/log_logistic_partial_effects.pdf - - ${files.directory}/plots/log_normal_aft.pdf - - ${files.directory}/plots/log_normal_partial_effects.pdf - metrics: - - ${files.directory}/plots/aft_comparison.csv - outs: - - ${files.directory}/plots/aft_comparison.tex - copy_results: - cmd: cp -r ${files.directory}/plots/* ~/ml_afr/cifar100/ - deps: - - ${files.directory}/plots/data.csv - - ${files.directory}/plots/aft_comparison.csv diff --git a/examples/pytorch_cifar_100/models.sh b/examples/pytorch_cifar_100/models.sh deleted file mode 100644 index 2978d445..00000000 --- a/examples/pytorch_cifar_100/models.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash - -# This script is used to generate the models for the sklearn example. - -# # Default model -echo "python -m deckard.layers.optimise " $@ "--multirun" -python -m deckard.layers.optimise $@ --multirun - -# # This line generates the model and adds the FeatureSqueezing preprocessing defence. -# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,255] ++hydra.sweeper.study_name=FSQ $@ --multirun - -# # # Gaussian Augmentation (Input) -# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.5 +model.art.preprocessor.params.augmentation=False ++hydra.sweeper.study_name=gauss-in $@ --multirun - -# # # # Gaussian Noise (Output) -# python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 ++hydra.sweeper.study_name=gauss-out $@ --multirun - -# # # # High Confidence -# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 ++hydra.sweeper.study_name=conf $@ --multirun diff --git a/examples/pytorch_cifar_100/params.yaml b/examples/pytorch_cifar_100/params.yaml deleted file mode 100644 index ef99ba24..00000000 --- a/examples/pytorch_cifar_100/params.yaml +++ /dev/null @@ -1,215 +0,0 @@ -_target_: deckard.base.experiment.Experiment -attack: - _target_: deckard.base.attack.Attack - attack_size: 10 - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.attack.AttackInitializer - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 100 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - name: art.attacks.evasion.HopSkipJump - method: evasion - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 100 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 -data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true -direction: maximize -files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: cifar100 - model_dir: models - model_file: model - model_type: .pt - name: default - params_file: params.yaml - predictions_file: predictions.json - reports: reports - score_dict_file: score_dict.json -model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - name: torch_cifar100 - path: original_data - sample: - random_state: 0 - stratify: true - sklearn_pipeline: - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 3 - num_classes: 100 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 -optimizers: accuracy -scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss -stage: ??? diff --git a/examples/pytorch_cifar_100/torch_example.py b/examples/pytorch_cifar_100/torch_example.py deleted file mode 100644 index 2007d9d0..00000000 --- a/examples/pytorch_cifar_100/torch_example.py +++ /dev/null @@ -1,79 +0,0 @@ -import torch.nn as nn -from torchvision import models - -__all__ = [ - "ResNet18", - "ResNet50", - "ResNet101", - "ResNet152", -] - - -def ResNet18(num_channels=1, num_classes=10): - model = models.resnet18() - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(512, num_classes) - return model - - -def ResNet34(num_channels=1, num_classes=10): - model = models.resnet34() - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(512, num_classes) - return model - - -def ResNet50(num_channels=1, num_classes=10): - model = models.resnet50() - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(2048, num_classes) - return model - - -def ResNet101(num_channels=1, num_classes=10): - model = models.resnet101() - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(2048, num_classes) - return model - - -def ResNet152(num_channels=1, num_classes=10): - model = models.resnet152() - model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, - ) - model.fc = nn.Linear(2048, num_classes) - return model From 3a0edd66897cfa180bfcbde772e17a59ce14a24f Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 18:15:12 +0000 Subject: [PATCH 130/148] linting --- deckard/base/attack/attack.py | 5 +- deckard/base/data/generator.py | 26 +- deckard/base/experiment/experiment.py | 1 - deckard/base/model/model.py | 55 +++-- deckard/base/model/torch_models.py | 2 + deckard/layers/afr.py | 18 +- deckard/layers/compile.py | 9 +- deckard/layers/experiment.py | 6 +- deckard/layers/optimise.py | 14 +- deckard/layers/plots.py | 11 +- examples/classification/plots.ipynb | 252 ++++++++++++++++++++ examples/pytorch/cifar10/.gitignore | 2 +- examples/pytorch/cifar10/conf/compile.yaml | 2 +- examples/pytorch/cifar100/.gitignore | 2 +- examples/pytorch/mnist/.gitignore | 2 +- examples/security/classification/.gitignore | 2 +- examples/security/classification/models.sh | 2 +- examples/security/kdd-nsl/.gitignore | 2 +- examples/security/truthseeker/.gitignore | 2 +- 19 files changed, 359 insertions(+), 56 deletions(-) create mode 100644 examples/classification/plots.ipynb diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index 0b2a8d21..9d61d98b 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -337,6 +337,7 @@ def __call__( if "expected scalar type Long" in str(e): # if hasattr(y_train, "type"): import torch + if torch.cuda.is_available(): number_of_devices = torch.cuda.device_count() device_number = randint(0, number_of_devices - 1) @@ -355,7 +356,9 @@ def __call__( x_trigger = torch.tensor(x_trigger, device=device) y_trigger = y_trigger.to(torch.long) y_trigger = y_trigger.to(torch.long) - atk = self.init(model=model, data=data, attack_size=self.attack_size) + atk = self.init( + model=model, data=data, attack_size=self.attack_size + ) start = process_time_ns() samples, _ = atk.poison( x_trigger=x_trigger, diff --git a/deckard/base/data/generator.py b/deckard/base/data/generator.py index 8ce30e7c..4ac428f6 100644 --- a/deckard/base/data/generator.py +++ b/deckard/base/data/generator.py @@ -78,7 +78,9 @@ def __hash__(self): @dataclass class TorchDataGenerator: - name: Literal["torch_mnist", "torch_cifar10", "torch_diabetes", "torch_cifar100"] = "torch_mnist" + name: Literal[ + "torch_mnist", "torch_cifar10", "torch_diabetes", "torch_cifar100" + ] = "torch_mnist" path = None kwargs: dict = field(default_factory=dict) @@ -116,12 +118,24 @@ def __call__(self): except: raise ImportError("Please install torchvision to use CIFAR100") if self.path is None: - raise ValueError(f"path attribute must be specified for dataset: {self.name}.") + raise ValueError( + f"path attribute must be specified for dataset: {self.name}." + ) original_filename = Path(self.path, self.name, f"{self.name}.npz") Path(original_filename.parent).mkdir(parents=True, exist_ok=True) if not original_filename.exists(): - train_set = CIFAR100(Path(self.path, self.name), train=True, download=True, transform=transforms.ToTensor()) - test_set = CIFAR100(Path(self.path, self.name), train=False, download=True, transform=transforms.ToTensor()) + train_set = CIFAR100( + Path(self.path, self.name), + train=True, + download=True, + transform=transforms.ToTensor(), + ) + test_set = CIFAR100( + Path(self.path, self.name), + train=False, + download=True, + transform=transforms.ToTensor(), + ) # lambda function to turn each image, label into an np.array X_ = lambda x: np.array(x[0]) y_ = lambda x: np.array(x[1]) @@ -138,7 +152,7 @@ def __call__(self): dict_ = np.load(original_filename.as_posix()) X = dict_["X"] y = dict_["y"] - + else: raise ValueError(f"Unknown dataset name {self.name}") return [X, y] @@ -203,4 +217,4 @@ def __call__(self): raise ValueError(f"Invalid name {self.name}. Please choose from ") def __hash__(self): - return int(my_hash(self), 16) \ No newline at end of file + return int(my_hash(self), 16) diff --git a/deckard/base/experiment/experiment.py b/deckard/base/experiment/experiment.py index e49ec935..45745b34 100644 --- a/deckard/base/experiment/experiment.py +++ b/deckard/base/experiment/experiment.py @@ -304,4 +304,3 @@ def _set_name(self): def get_name(self): return self.name - diff --git a/deckard/base/model/model.py b/deckard/base/model/model.py index 2d2b0a53..3298aec4 100644 --- a/deckard/base/model/model.py +++ b/deckard/base/model/model.py @@ -14,7 +14,6 @@ from .art_pipeline import ArtPipeline from .sklearn_pipeline import SklearnModelPipeline -from random import randint __all__ = ["Model"] logger = logging.getLogger(__name__) @@ -120,7 +119,7 @@ def __call__(self, data: list, model: object, library=None): pass elif library in ["torch", "pytorch"]: pass - + elif library in ["tensorflow", "tf"]: import tensorflow as tf @@ -472,9 +471,15 @@ def fit(self, data, model, model_file=None): :return: The fitted model and the average time per sample. """ assert isinstance(data, list), f"Data {data} is not a list." - assert len(data) == 4, "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." - assert len(data[0]) == len(data[2]), "X_train and y_train must have the same length." - assert len(data[1]) == len(data[3]), "X_test and y_test must have the same length." + assert ( + len(data) == 4 + ), "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." + assert len(data[0]) == len( + data[2] + ), "X_train and y_train must have the same length." + assert len(data[1]) == len( + data[3] + ), "X_test and y_test must have the same length." assert hasattr(model, "fit"), f"Model {model} does not have a fit method." if Path(model_file).exists(): model = self.load(model_file) @@ -493,11 +498,19 @@ def predict(self, data=None, model=None, predictions_file=None): :param data: The data to predict on. """ assert isinstance(data, list), f"Data {data} is not a list." - assert len(data) == 4, "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." - assert len(data[0]) == len(data[2]), "X_train and y_train must have the same length." - assert len(data[1]) == len(data[3]), "X_test and y_test must have the same length." + assert ( + len(data) == 4 + ), "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." + assert len(data[0]) == len( + data[2] + ), "X_train and y_train must have the same length." + assert len(data[1]) == len( + data[3] + ), "X_test and y_test must have the same length." assert hasattr(model, "fit"), f"Model {model} does not have a fit method." - assert hasattr(model, "predict"), f"Model {model} does not have a predict method." + assert hasattr( + model, "predict" + ), f"Model {model} does not have a predict method." try: start = process_time_ns() predictions = model.predict(data[1]) @@ -537,9 +550,15 @@ def predict_proba(self, data=None, model=None, probabilities_file=None): :return: The predictions and the average time per sample. """ assert isinstance(data, list), f"Data {data} is not a list." - assert len(data) == 4, "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." - assert len(data[0]) == len(data[2]), "X_train and y_train must have the same length." - assert len(data[1]) == len(data[3]), "X_test and y_test must have the same length." + assert ( + len(data) == 4 + ), "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." + assert len(data[0]) == len( + data[2] + ), "X_train and y_train must have the same length." + assert len(data[1]) == len( + data[3] + ), "X_test and y_test must have the same length." assert hasattr(model, "fit"), f"Model {model} does not have a fit method." if ( str("art") in str(type(model)) @@ -577,9 +596,15 @@ def predict_log_loss(self, data, model, loss_file=None): :return: The predictions and the average time per sample. """ assert isinstance(data, list), f"Data {data} is not a list." - assert len(data) == 4, "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." - assert len(data[0]) == len(data[2]), "X_train and y_train must have the same length." - assert len(data[1]) == len(data[3]), "X_test and y_test must have the same length." + assert ( + len(data) == 4 + ), "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." + assert len(data[0]) == len( + data[2] + ), "X_train and y_train must have the same length." + assert len(data[1]) == len( + data[3] + ), "X_test and y_test must have the same length." assert hasattr(model, "fit"), f"Model {model} does not have a fit method." if str("art") in str(type(model)) and ( hasattr(model.model, "predict_log_proba") diff --git a/deckard/base/model/torch_models.py b/deckard/base/model/torch_models.py index 4edce11c..552aabcc 100644 --- a/deckard/base/model/torch_models.py +++ b/deckard/base/model/torch_models.py @@ -26,6 +26,7 @@ __all__ = ["TorchInitializer", "TorchCriterion", "TorchOptimizer"] + @dataclass class TorchCriterion: name: str @@ -106,6 +107,7 @@ def __call__(self): kwargs.update(**kwargs.pop("kwargs", {})) data = self.data import torch + device = "cuda" if torch.cuda.is_available() else "cpu" devices = range(torch.cuda.device_count()) if str(type(model)).startswith("art.") and hasattr(model, "model"): diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index eee8da54..2c86b5d8 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -21,7 +21,6 @@ logger = logging.getLogger(__name__) - def plot_aft( df, file, @@ -67,6 +66,7 @@ def plot_aft( plt.gcf().clear() return ax, aft + def plot_partial_effects( aft, covariate_array, @@ -100,6 +100,7 @@ def plot_partial_effects( plt.gcf().clear() return pareto + def score_model(aft, train, test): train_score = aft.score(train) test_score = aft.score(test) @@ -107,6 +108,7 @@ def score_model(aft, train, test): plt.show() return scores + def make_afr_table(score_list, aft_dict, dataset): assert len(score_list) == len( aft_dict, @@ -115,13 +117,11 @@ def make_afr_table(score_list, aft_dict, dataset): aft_data.index.name = "Model" aft_data.index = aft_dict.keys() aft_data["AIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan - for x in aft_dict.values() + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() ] aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] aft_data["BIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan - for x in aft_dict.values() + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() ] # aft_data["Train LL"] = [x["train_score"] for x in score_list] # aft_data["Test LL"] = [x["test_score"] for x in score_list] @@ -146,6 +146,7 @@ def make_afr_table(score_list, aft_dict, dataset): print(yaml.dump(aft_data.to_dict(), default_flow_style=False, indent=4)) return aft_data + def clean_data_for_aft( data, kwarg_list, @@ -176,6 +177,7 @@ def clean_data_for_aft( ), f"Target {target} not in dataframe with columns {cleaned.columns}" return cleaned, y, data + def split_data_for_aft( data, target, @@ -199,8 +201,6 @@ def split_data_for_aft( ), f"Duration {duration_col} not in dataframe with columns {cleaned.columns}" return X_train, X_test, y_train, y_test - - if "__main__" == __name__: afr_parser = argparse.ArgumentParser() @@ -214,7 +214,6 @@ def split_data_for_aft( duration_col = afr_args.duration_col dataset = afr_args.dataset - FOLDER = Path(afr_args.plots_folder) data = pd.read_csv(afr_args.file, index_col=0) data.columns = data.columns.str.strip() @@ -234,7 +233,7 @@ def split_data_for_aft( :, "attack.attack_size", ] - + kwarg_list = [ "accuracy", "train_time", @@ -257,7 +256,6 @@ def split_data_for_aft( random_state=42, ) - weibull_dict = { "Intercept: rho_": "$\\rho$", "Intercept: lambda_": "$\lambda$", # noqa W605 diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 4ec7fb92..c5b9b6dd 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -53,8 +53,8 @@ def parse_folder(folder, files=["params.yaml", "score_dict.json"]) -> pd.DataFra try: dict_ = json.load(f) except json.decoder.JSONDecodeError as e: - raise e - + raise e + elif suffix == ".yaml": with open(file, "r") as f: dict_ = yaml.safe_load(f) @@ -97,7 +97,10 @@ def merge_defences(results: pd.DataFrame, default_epochs=20): and entry["model.art.pipeline.trainer.name"] not in nones ): defence.append(entry["model.art.pipeline.trainer.name"]) - if ("model.init.nb_epoch" in entry and entry["model.init.nb_epoch"] != default_epochs): + if ( + "model.init.nb_epoch" in entry + and entry["model.init.nb_epoch"] != default_epochs + ): defence.append(entry["model.init.nb_epoch"]) ############################################################################################################ if len(defence) > 1: diff --git a/deckard/layers/experiment.py b/deckard/layers/experiment.py index ae518ef7..c0144a29 100644 --- a/deckard/layers/experiment.py +++ b/deckard/layers/experiment.py @@ -27,7 +27,7 @@ def get_dvc_stage_params( directory=".", name=None, ): - + logger.info( f"Getting params for stage {stage} from {params_file} and {pipeline_file} in {directory}.", ) @@ -38,7 +38,7 @@ def get_dvc_stage_params( params["files"] = dict(unflattened_files.get("files", unflattened_files)) params["files"]["_target_"] = "deckard.base.files.FileConfig" params["files"]["stage"] = stage - params['stage']=stage + params["stage"] = stage if name is not None: params["files"]["name"] = name return params @@ -62,7 +62,7 @@ def run_stage( exp = instantiate(params) id_ = exp.name files = deepcopy(exp.files()) - params_file = Path(files['score_dict_file']).with_name("params.yaml").as_posix() + params_file = Path(files["score_dict_file"]).with_name("params.yaml").as_posix() Path(params_file).parent.mkdir(exist_ok=True, parents=True) with Path(params_file).open("w") as f: yaml.dump(params, f) diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index ac6bab6a..5d57183f 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -91,7 +91,7 @@ def merge_params(default, params) -> dict: ): default.update({key: value}) elif value is None: - default.update({key : {}}) + default.update({key: {}}) else: logger.warning(f"Key {key} not found in default params. Skipping.") return default @@ -200,23 +200,23 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: for stage in stages: pipe = yaml.load(f, Loader=yaml.FullLoader)["stages"][stage] if "deps" in pipe: - dep_list =[x.split(":")[0] for x in pipe["deps"]] + dep_list = [x.split(":")[0] for x in pipe["deps"]] file_list.extend(dep_list) if "outs" in pipe: - out_list = [x.split(":")[0] for x in pipe['outs']] + out_list = [x.split(":")[0] for x in pipe["outs"]] file_list.extend(out_list) if "metrics" in pipe: - metric_list = [x.split(":")[0] for x in pipe['metrics']] + metric_list = [x.split(":")[0] for x in pipe["metrics"]] file_list.extend(metric_list) file_string = str(file_list) - files = params['files'] + files = params["files"] file_list = list(files.keys()) for key in file_list: - template_string = "${files."+ key + "}" + template_string = "${files." + key + "}" if template_string in file_string: pass else: - params['files'].pop(key) + params["files"].pop(key) params = get_files(params, stage) return params diff --git a/deckard/layers/plots.py b/deckard/layers/plots.py index 03ef18c4..888c8527 100644 --- a/deckard/layers/plots.py +++ b/deckard/layers/plots.py @@ -124,7 +124,14 @@ def scatter_plot( def drop_frames_without_results( data, - subset=["accuracy", "adv_accuracy", "train_time", "adv_fit_time", "predict_time", "adv_success"], + subset=[ + "accuracy", + "adv_accuracy", + "train_time", + "adv_fit_time", + "predict_time", + "adv_success", + ], ): logger.info(f"Dropping frames without results for {subset}") data.dropna(axis=0, subset=subset, inplace=True) @@ -169,7 +176,7 @@ def calculate_failure_rate(data): def pareto_set(data, sense_dict): new_sense_dict = {} - for k,v in sense_dict.items(): + for k, v in sense_dict.items(): if k in data.columns: new_sense_dict[k] = v else: diff --git a/examples/classification/plots.ipynb b/examples/classification/plots.ipynb new file mode 100644 index 00000000..1ef9111e --- /dev/null +++ b/examples/classification/plots.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "\n", + "# Load data\n", + "df = pd.read_csv(\"output/attack.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['attacks', 'defences', 'params'])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_3723846/651469242.py:12: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " attack_results['Kernel'] = attack_results['model.init.kwargs.kernel']\n" + ] + } + ], + "source": [ + "from deckard.layers.compile import clean_data_for_plotting\n", + "import yaml\n", + "\n", + "with open(\"conf/compile.yaml\", \"r\") as f:\n", + " config = yaml.load(f, Loader=yaml.FullLoader)\n", + "print(config.keys())\n", + "def_gen_dict = config[\"defences\"]\n", + "atk_gen_dict = config[\"attacks\"]\n", + "control_dict = config[\"params\"]\n", + "\n", + "df = clean_data_for_plotting(df, def_gen_dict, atk_gen_dict, control_dict)\n", + "attack_results = df.dropna(subset=[\"accuracy\", \"adv_accuracy\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2, 2)\n", + "graph5 = sns.lineplot(\n", + " x=\"attack.init.kwargs.eps\",\n", + " y=\"accuracy\",\n", + " data=attack_results,\n", + " style=\"model.init.kwargs.kernel\",\n", + " ax=ax[0, 0],\n", + " legend=False,\n", + " color=\"darkred\",\n", + " style_order=[\"rbf\", \"poly\", \"linear\"],\n", + ")\n", + "graph5.set(xscale=\"log\", xlabel=\"Perturbation Distance\", ylabel=\"Accuracy\")\n", + "graph6 = sns.lineplot(\n", + " x=\"attack.init.kwargs.eps_step\",\n", + " y=\"accuracy\",\n", + " data=attack_results,\n", + " style=\"model.init.kwargs.kernel\",\n", + " ax=ax[0, 1],\n", + " color=\"darkred\",\n", + " style_order=[\"rbf\", \"poly\", \"linear\"],\n", + ")\n", + "graph6.set(xscale=\"log\", xlabel=\"Perturbation Step\", ylabel=\"Accuracy\")\n", + "graph7 = sns.lineplot(\n", + " x=\"attack.init.kwargs.max_iter\",\n", + " y=\"accuracy\",\n", + " data=attack_results,\n", + " style=\"Kernel\",\n", + " ax=ax[1, 0],\n", + " legend=False,\n", + " color=\"darkred\",\n", + " style_order=[\"rbf\", \"poly\", \"linear\"],\n", + ")\n", + "graph7.set(xscale=\"log\", xlabel=\"Maximum Iterations\", ylabel=\"Accuracy\")\n", + "graph8 = sns.lineplot(\n", + " x=\"attack.init.kwargs.batch_size\",\n", + " y=\"accuracy\",\n", + " data=attack_results,\n", + " style=\"Kernel\",\n", + " ax=ax[1, 1],\n", + " legend=False,\n", + " color=\"darkred\",\n", + " style_order=[\"rbf\", \"poly\", \"linear\"],\n", + ")\n", + "graph8.set(xscale=\"log\", xlabel=\"Batch Size\", ylabel=\"Accuracy\")\n", + "graph6.legend(loc=\"center left\", bbox_to_anchor=(1, 0.5), ncol=1, title=\"Kernel\")\n", + "fig.tight_layout()\n", + "fig.savefig(\"plots/accuracy_vs_attack_parameters.pdf\")\n", + "plt.gcf().clear()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lineplot(\n", + " data=df,\n", + " y=\"adv_fit_time\",\n", + " x=\"attack.init.kwargs.eps\",\n", + " hue=\"model.init.kwargs.kernel\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lineplot(\n", + " data=df,\n", + " y=\"adv_fit_time\",\n", + " x=\"attack.init.kwargs.eps_step\",\n", + " hue=\"model.init.kwargs.kernel\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.lineplot(\n", + " data=df,\n", + " y=\"adv_fit_time\",\n", + " x=\"attack.init.kwargs.batch_size\",\n", + " hue=\"model.init.kwargs.kernel\",\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/pytorch/cifar10/.gitignore b/examples/pytorch/cifar10/.gitignore index af0b54a3..ee2a3723 100644 --- a/examples/pytorch/cifar10/.gitignore +++ b/examples/pytorch/cifar10/.gitignore @@ -1,3 +1,3 @@ 20_epochs/ multirun/ -cifar/ \ No newline at end of file +cifar/ diff --git a/examples/pytorch/cifar10/conf/compile.yaml b/examples/pytorch/cifar10/conf/compile.yaml index 52106b1c..43418520 100644 --- a/examples/pytorch/cifar10/conf/compile.yaml +++ b/examples/pytorch/cifar10/conf/compile.yaml @@ -29,4 +29,4 @@ params: FSQ: model.art.pipeline.preprocessor.bit_depth Gauss-in: model.art.pipeline.preprocessor.sigma Depth: model_layers - Epochs: model.train.nb_epoch \ No newline at end of file + Epochs: model.train.nb_epoch diff --git a/examples/pytorch/cifar100/.gitignore b/examples/pytorch/cifar100/.gitignore index de9b09c5..027ee501 100644 --- a/examples/pytorch/cifar100/.gitignore +++ b/examples/pytorch/cifar100/.gitignore @@ -1,3 +1,3 @@ cifar100/ 20_epochs/ -cifar-100-python/ \ No newline at end of file +cifar-100-python/ diff --git a/examples/pytorch/mnist/.gitignore b/examples/pytorch/mnist/.gitignore index a129798e..6811d155 100644 --- a/examples/pytorch/mnist/.gitignore +++ b/examples/pytorch/mnist/.gitignore @@ -1,2 +1,2 @@ mnist/ -20_epochs/ \ No newline at end of file +20_epochs/ diff --git a/examples/security/classification/.gitignore b/examples/security/classification/.gitignore index 96901c72..98060d5a 100644 --- a/examples/security/classification/.gitignore +++ b/examples/security/classification/.gitignore @@ -1,4 +1,4 @@ /kdd_nsl /retrain /multirun -/logs \ No newline at end of file +/logs diff --git a/examples/security/classification/models.sh b/examples/security/classification/models.sh index 6940825e..44e7294f 100644 --- a/examples/security/classification/models.sh +++ b/examples/security/classification/models.sh @@ -108,4 +108,4 @@ for train_size in ${TRAIN_SIZES[@]}; do echo "Successfully completed experiment ${i} of ${TOTAL}" >> log.txt done; done; -done; \ No newline at end of file +done; diff --git a/examples/security/kdd-nsl/.gitignore b/examples/security/kdd-nsl/.gitignore index 60a7ed4b..c95c6c8b 100644 --- a/examples/security/kdd-nsl/.gitignore +++ b/examples/security/kdd-nsl/.gitignore @@ -1,4 +1,4 @@ /output /retrain /multirun -/logs \ No newline at end of file +/logs diff --git a/examples/security/truthseeker/.gitignore b/examples/security/truthseeker/.gitignore index 60a7ed4b..c95c6c8b 100644 --- a/examples/security/truthseeker/.gitignore +++ b/examples/security/truthseeker/.gitignore @@ -1,4 +1,4 @@ /output /retrain /multirun -/logs \ No newline at end of file +/logs From 34e2c5759f848a5072daa58ee75f473bf895f4ac Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 18:17:16 +0000 Subject: [PATCH 131/148] linting --- deckard/base/attack/attack.py | 4 +- deckard/base/data/generator.py | 13 +- deckard/base/experiment/experiment.py | 2 - deckard/base/model/model.py | 19 +-- deckard/layers/afr.py | 1 - deckard/layers/experiment.py | 1 - deckard/layers/optimise_redo.py | 208 -------------------------- 7 files changed, 21 insertions(+), 227 deletions(-) delete mode 100644 deckard/layers/optimise_redo.py diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index 9d61d98b..bff65475 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -357,7 +357,9 @@ def __call__( y_trigger = y_trigger.to(torch.long) y_trigger = y_trigger.to(torch.long) atk = self.init( - model=model, data=data, attack_size=self.attack_size + model=model, + data=data, + attack_size=self.attack_size, ) start = process_time_ns() samples, _ = atk.poison( diff --git a/deckard/base/data/generator.py b/deckard/base/data/generator.py index 4ac428f6..e0ec7c07 100644 --- a/deckard/base/data/generator.py +++ b/deckard/base/data/generator.py @@ -79,7 +79,10 @@ def __hash__(self): @dataclass class TorchDataGenerator: name: Literal[ - "torch_mnist", "torch_cifar10", "torch_diabetes", "torch_cifar100" + "torch_mnist", + "torch_cifar10", + "torch_diabetes", + "torch_cifar100", ] = "torch_mnist" path = None kwargs: dict = field(default_factory=dict) @@ -115,11 +118,11 @@ def __call__(self): try: from torchvision.datasets import CIFAR100 from torchvision import transforms - except: + except ImportError: raise ImportError("Please install torchvision to use CIFAR100") if self.path is None: raise ValueError( - f"path attribute must be specified for dataset: {self.name}." + f"path attribute must be specified for dataset: {self.name}.", ) original_filename = Path(self.path, self.name, f"{self.name}.npz") Path(original_filename.parent).mkdir(parents=True, exist_ok=True) @@ -137,8 +140,8 @@ def __call__(self): transform=transforms.ToTensor(), ) # lambda function to turn each image, label into an np.array - X_ = lambda x: np.array(x[0]) - y_ = lambda x: np.array(x[1]) + X_ = lambda x: np.array(x[0]) # noqa E731 + y_ = lambda x: np.array(x[1]) # noqa E731 X_train = np.array(list(map(X_, train_set))) y_train = np.array(list(map(y_, train_set))) X_test = np.array(list(map(X_, test_set))) diff --git a/deckard/base/experiment/experiment.py b/deckard/base/experiment/experiment.py index 45745b34..36e21a14 100644 --- a/deckard/base/experiment/experiment.py +++ b/deckard/base/experiment/experiment.py @@ -5,7 +5,6 @@ from typing import Union import numpy as np -import yaml from hydra.utils import instantiate from omegaconf import DictConfig, OmegaConf @@ -14,7 +13,6 @@ from ..files import FileConfig from ..model import Model from ..scorer import ScorerDict -from ..utils import to_dict from ..utils.hashing import my_hash __all__ = ["Experiment"] diff --git a/deckard/base/model/model.py b/deckard/base/model/model.py index 3298aec4..34c6f37f 100644 --- a/deckard/base/model/model.py +++ b/deckard/base/model/model.py @@ -475,10 +475,10 @@ def fit(self, data, model, model_file=None): len(data) == 4 ), "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." assert len(data[0]) == len( - data[2] + data[2], ), "X_train and y_train must have the same length." assert len(data[1]) == len( - data[3] + data[3], ), "X_test and y_test must have the same length." assert hasattr(model, "fit"), f"Model {model} does not have a fit method." if Path(model_file).exists(): @@ -502,14 +502,15 @@ def predict(self, data=None, model=None, predictions_file=None): len(data) == 4 ), "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." assert len(data[0]) == len( - data[2] + data[2], ), "X_train and y_train must have the same length." assert len(data[1]) == len( - data[3] + data[3], ), "X_test and y_test must have the same length." assert hasattr(model, "fit"), f"Model {model} does not have a fit method." assert hasattr( - model, "predict" + model, + "predict", ), f"Model {model} does not have a predict method." try: start = process_time_ns() @@ -554,10 +555,10 @@ def predict_proba(self, data=None, model=None, probabilities_file=None): len(data) == 4 ), "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." assert len(data[0]) == len( - data[2] + data[2], ), "X_train and y_train must have the same length." assert len(data[1]) == len( - data[3] + data[3], ), "X_test and y_test must have the same length." assert hasattr(model, "fit"), f"Model {model} does not have a fit method." if ( @@ -600,10 +601,10 @@ def predict_log_loss(self, data, model, loss_file=None): len(data) == 4 ), "Data must be a list containing X_train, X_test, y_train, y_test (i.e. 4 elements)." assert len(data[0]) == len( - data[2] + data[2], ), "X_train and y_train must have the same length." assert len(data[1]) == len( - data[3] + data[3], ), "X_test and y_test must have the same length." assert hasattr(model, "fit"), f"Model {model} does not have a fit method." if str("art") in str(type(model)) and ( diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index 2c86b5d8..7d99270c 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -10,7 +10,6 @@ LogLogisticAFTFitter, CoxPHFitter, ) -import matplotlib from pathlib import Path import logging import yaml diff --git a/deckard/layers/experiment.py b/deckard/layers/experiment.py index c0144a29..1e481821 100644 --- a/deckard/layers/experiment.py +++ b/deckard/layers/experiment.py @@ -8,7 +8,6 @@ import argparse from copy import deepcopy from ..base.utils import unflatten_dict -from .utils import save_params_file logger = logging.getLogger(__name__) diff --git a/deckard/layers/optimise_redo.py b/deckard/layers/optimise_redo.py deleted file mode 100644 index 36da18fe..00000000 --- a/deckard/layers/optimise_redo.py +++ /dev/null @@ -1,208 +0,0 @@ -import logging -import os -import traceback -from copy import deepcopy -from pathlib import Path -import time -import random -import yaml -from hydra.utils import instantiate -from hydra import compose, initialize -from omegaconf import DictConfig, OmegaConf, ListConfig -import hydra -from dvc.api import params_show -from deckard.base.experiment import Experiment -from dvc.lock import LockError -from ..base.utils import my_hash, unflatten_dict -from .utils import deckard_nones - -logger = logging.getLogger(__name__) - -__all__ = ["write_stage", "optimise", "parse_stage", "get_files"] - - -def get_files( - cfg, - stage, -): - """ - Gets the file names from - """ - if isinstance(cfg, dict): - pass - elif isinstance(cfg, list): - cfg = unflatten_dict(cfg) - else: - raise TypeError(f"Expected dict or list, got {type(cfg)}") - if "_target_" not in cfg: - cfg.update({"_target_": "deckard.base.experiment.Experiment"}) - if ( - "attack_file" in cfg["files"] - and cfg["files"]["attack_file"] is not None - and "attack" in cfg - ): - cfg["files"]["attack_file"] = str( - Path(cfg["files"]["attack_file"]) - .with_name(my_hash(cfg["attack"])) - .as_posix(), - ) - if ( - "model_file" in cfg["files"] - and cfg["files"]["model_file"] is not None - and "model" in cfg - ): - cfg["files"]["model_file"] = str( - Path(cfg["files"]["model_file"]) - .with_name(my_hash(cfg["model"])) - .as_posix(), - ) - if ( - "data_file" in cfg["files"] - and cfg["files"]["data_file"] is not None - and "data" in cfg - ): - cfg["files"]["data_file"] = str( - Path(cfg["files"]["data_file"]).with_name(my_hash(cfg["data"])).as_posix(), - ) - id_ = my_hash(cfg) - - id_ = my_hash(cfg) - cfg["name"] = id_ - cfg["files"]["name"] = id_ - cfg["files"]["stage"] = stage - cfg['stage'] = stage - return cfg - -def parse_stage(*args, repo, stages): - if not isinstance(stages, list): - stages = [stages] - base_delay = 10 - max_retries = 100 - args = list(args) - logger.info(f"Parsing {args[:]} from {repo} with stages {stages}") - retries = 0 - params = params_show(*args, repo=repo, stages=stages, deps=True) - # while retries < max_retries: - # try: - # params = params_show(*args, repo=repo, stages=stages, deps=True) - # except LockError: - # retries += 1 - # delay = base_delay ** 2 **retries - # logger.warning(f"LockError occured. Retrying in {delay:.2f} seconds. Retry {retries} of {max_retries}.") - # time.sleep(delay) - # raise LockError(f"LockError occured {max_retries} times. Aborting.") - return params - -def parse_stage(cfg, repo, stages): - if not isinstance(stages, list): - stages = [stages] - params_subset = [] - all_params = [] - with open(Path(repo, "dvc.yaml"), "r") as f: - for stage in stages: - params_dict = yaml.load(f, Loader=yaml.FullLoader)["stages"][stage]['params'] - if isinstance(params_dict, dict): - params_subset.extend(list(params_dict.keys())) - elif isinstance(params_dict, list): - params_subset.extend(params_dict) - - - - with open(Path(repo, "params.yaml"), "r") as f: - - - - - - -def write_stage(params: dict, stage: str, path:str, working_dir:str) -> None: - """ - Write params to dvc.yaml - :param params: Params to write to dvc.yaml - :param stage: Stage to write params to - """ - with open(Path(working_dir, "dvc.yaml"), "r") as f: - dvc = yaml.load(f, Loader=yaml.FullLoader) - name = Path(path).name - stage_params = {"stages": {}} - stage_params["stages"][f"{stage}"] = dvc["stages"][stage] - path.mkdir(exist_ok=True, parents=True) - with open(path / "dvc.yaml", "w") as f: - yaml.dump(stage_params, f, default_flow_style=False) - assert Path(path / "dvc.yaml").exists(), f"File {path/'dvc.yaml'} does not exist." - - with open(Path(path, "params.yaml"), "w") as f: - yaml.dump(params, f, default_flow_style=False) - assert Path( - path / "params.yaml", - ).exists(), f"File {path/'params.yaml'} does not exist." - return None - - -def optimise(cfg: DictConfig) -> None: - cfg = OmegaConf.to_container(OmegaConf.create(cfg), resolve=False) - stage = cfg.get("stage", None) - if isinstance(stage, ListConfig): - stage = OmegaConf.to_container(stage, resolve=True) - if isinstance(stage, list): - stage = stage[0] - assert stage is not None, f"Stage must be specified. Add stage= to command line." - assert isinstance(stage, str), f"Expected str, got {type(stage)}" - scorer = cfg.pop("optimizers", None) - working_dir = cfg.pop("working_dir", Path().resolve().as_posix()) - cfg = get_files(cfg, stage=stage) - direction = cfg.pop("direction", "minimize") - exp = instantiate(cfg) - files = deepcopy(exp.files)() - id_ = Path(files["score_dict_file"]).parent.name - old = id_ - folder = Path(files["score_dict_file"]).parent - assert write_stage(cfg, stage, path=folder, working_dir=working_dir) is None, f"Failed to write stage {stage} to {folder}" - targets = [] - cfg = parse_stage(*targets, stages=stage, repo=folder) - cfg['files']['name'] = old - cfg['_target_'] = 'deckard.base.Experiment' - cfg["files"]["_target_"] = "deckard.base.files.FileConfig" - cfg['files']['stage'] = stage - cfg['stage'] = stage - exp = instantiate(cfg) - assert isinstance(exp, Experiment), f"Expected Experiment, got {type(exp)}" - assert exp.name == old, f"Expected {old}, got {exp.name}" - - try: - scores = exp() - if isinstance(scorer, str): - score = scores[scorer] - elif isinstance(scorer, list): - score = [scores[s] for s in scorer] - elif scorer is None: - score = list(scores.values())[0] - else: - raise TypeError(f"Expected str or list, got {type(scorer)}") - except Exception as e: - logger.warning( - f"Exception {e} occured while running experiment {id_}. Setting score to default for specified direction (e.g. -/+ 1e10).", - ) - with open(Path(folder, "exception.log"), "w") as f: - f.write(str(e)) - f.write(traceback.format_exc()) - if direction == "minimize": - score = 1e10 - else: - score = -1e10 - return score - - -if __name__ == "__main__": - logger = logging.getLogger(__name__) - config_path = os.environ.get( - "DECKARD_CONFIG_PATH", - str(Path(Path(), "conf").absolute().as_posix()), - ) - config_name = os.environ.get("DECKARD_DEFAULT_CONFIG", "default.yaml") - - @hydra.main(config_path=config_path, config_name=config_name, version_base="1.3") - def hydra_optimise(cfg: DictConfig) -> float: - score = optimise(cfg) - return score - hydra_optimise() From a8ab32a6e9e798240d3d2cc807b902ff0b9d45fc Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 18:47:45 +0000 Subject: [PATCH 132/148] linting --- deckard/base/model/torch_models.py | 2 - deckard/layers/afr.py | 452 ----------------------------- deckard/layers/compile.py | 18 +- 3 files changed, 7 insertions(+), 465 deletions(-) diff --git a/deckard/base/model/torch_models.py b/deckard/base/model/torch_models.py index e746fd12..9e532c96 100644 --- a/deckard/base/model/torch_models.py +++ b/deckard/base/model/torch_models.py @@ -2,8 +2,6 @@ from copy import deepcopy from dataclasses import dataclass, field from typing import Union -from random import randint -import numpy as np from art.estimators.classification import PyTorchClassifier from art.estimators.regression import PyTorchRegressor from art.utils import to_categorical diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index 066444fe..a19e887d 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -416,458 +416,6 @@ def split_data_for_aft( "alpha_": "", } - log_logistic_graph, llt = plot_aft( - X_train, - "log_logistic_aft.pdf", - target, - duration_col, - "Log Logistic AFR Model", - "log_logistic", - replacement_dict=log_logistic_dict, - ) - llt_scores = score_model(llt, X_train, X_test) - llt_partial = plot_partial_effects( - file="log_logistic_partial_effects.pdf", - aft=llt, - covariate_array="model_layers", - values_array=[18, 34, 50, 101, 152], - replacement_dict=log_logistic_dict, - title="$S(t)$ for Log-Logistic AFR", - ylabel="Expectation of $S(t)$", - xlabel="Time $T$ (seconds)", - legend_kwargs={ - "title": "No. of Layers", - "labels": ["18", "34", "50", "101", "152"], - }, - ) - aft_dict = { - "Weibull": wft, - "LogNormal": lnt, - "LogLogistic": llt, - # "Cox": cft, - } - score_list = [ - wft_scores, - lnt_scores, - llt_scores, - # cft_scores, - ] - aft_data = make_afr_table(score_list, aft_dict, dataset) -import pandas as pd -import numpy as np - -import matplotlib.pyplot as plt - -from sklearn.model_selection import train_test_split -from lifelines import ( - WeibullAFTFitter, - LogNormalAFTFitter, - LogLogisticAFTFitter, - CoxPHFitter, -) -from pathlib import Path -import logging -import yaml -import argparse -from .plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling - - -logger = logging.getLogger(__name__) - - -def plot_aft( - df, - file, - event_col, - duration_col, - title, - mtype, - xlabel=None, - ylabel=None, - replacement_dict={}, - **kwargs, -): - if mtype == "weibull": - aft = WeibullAFTFitter(**kwargs) - elif mtype == "log_normal": - aft = LogNormalAFTFitter(**kwargs) - elif mtype == "log_logistic": - aft = LogLogisticAFTFitter(**kwargs) - elif mtype == "cox": - aft = CoxPHFitter(**kwargs) - assert ( - duration_col in df.columns - ), f"Column {duration_col} not in dataframe with columns {df.columns}" - if event_col is not None: - assert ( - event_col in df.columns - ), f"Column {event_col} not in dataframe with columns {df.columns}" - plt.gcf().clear() - aft.fit(df, duration_col=duration_col, event_col=event_col) - ax = aft.plot() - labels = ax.get_yticklabels() - labels = [label.get_text() for label in labels] - for k, v in replacement_dict.items(): - labels = [label.replace(k, v) for label in labels] - ax.set_yticklabels(labels) - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel) - ax.set_title(title) - ax.get_figure().tight_layout() - ax.get_figure().savefig(FOLDER / file) - logger.info(f"Saved graph to {FOLDER / file}") - plt.show() - plt.gcf().clear() - return ax, aft - - -def plot_partial_effects( - aft, - covariate_array, - values_array, - title=None, - file="partial_effects.pdf", - xlabel="Covariate", - ylabel="Failure rate", - legend_kwargs={"loc": "upper left"}, - replacement_dict={}, - cmap="coolwarm", - **kwargs, -): - plt.gcf().clear() - # kwargs.pop("replacement_dict") - pareto = aft.plot_partial_effects_on_outcome( - covariate_array, values_array, cmap=cmap, **kwargs - ) - labels = pareto.get_yticklabels() - labels = [label.get_text() for label in labels] - for k, v in replacement_dict.items(): - labels = [label.replace(k, v) for label in labels] - pareto.set_yticklabels(labels) - pareto.legend(**legend_kwargs) - pareto.set_ylabel(ylabel) - pareto.set_xlabel(xlabel) - pareto.set_title(title) - pareto.get_figure().tight_layout() - pareto.get_figure().savefig(FOLDER / file) - logger.info(f"Saved graph to {FOLDER / file}") - plt.gcf().clear() - return pareto - - -def score_model(aft, train, test): - train_score = aft.score(train) - test_score = aft.score(test) - scores = {"train_score": train_score, "test_score": test_score} - plt.show() - return scores - - -def make_afr_table(score_list, aft_dict, dataset): - assert len(score_list) == len( - aft_dict, - ), "Length of score list and aft dict must be equal" - aft_data = pd.DataFrame() - aft_data.index.name = "Model" - aft_data.index = aft_dict.keys() - aft_data["AIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() - ] - aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] - aft_data["BIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() - ] - # aft_data["Train LL"] = [x["train_score"] for x in score_list] - # aft_data["Test LL"] = [x["test_score"] for x in score_list] - aft_data["Mean $S(t;\\theta)$"] = [ - x.predict_expectation(X_train).mean() for x in aft_dict.values() - ] - aft_data["Median $S(t;\\theta)$"] = [ - x.predict_median(X_train).median() for x in aft_dict.values() - ] - aft_data = aft_data.round(2) - aft_data.to_csv(FOLDER / "aft_comparison.csv") - logger.info(f"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}") - aft_data = aft_data.round(2) - aft_data.fillna("--", inplace=True) - aft_data.to_latex( - FOLDER / "aft_comparison.tex", - float_format="%.2f", - label=f"tab:{dataset}", - caption=f"Comparison of AFR Models on the {dataset.upper()} dataset.", - ) - - print(yaml.dump(aft_data.to_dict(), default_flow_style=False, indent=4)) - return aft_data - - -def clean_data_for_aft( - data, - kwarg_list, - target="adv_failure_rate", -): - subset = data.copy() - assert ( - target in subset - ), f"Target {target} not in dataframe with columns {subset.columns}" - - cleaned = pd.DataFrame() - kwarg_list.append(target) - for kwarg in kwarg_list: - cleaned = pd.concat([cleaned, subset[kwarg]], axis=1) - cols = cleaned.columns - cleaned = pd.DataFrame(subset, columns=cols) - if "accuracy" in cleaned.columns: - cleaned = cleaned[cleaned["accuracy"] != 1e10] - cleaned = cleaned[cleaned["accuracy"] != -1e10] - if "adv_accuracy" in cleaned.columns: - cleaned = cleaned[cleaned["adv_accuracy"] != 1e10] - cleaned = cleaned[cleaned["adv_accuracy"] != -1e10] - cleaned.dropna(inplace=True, how="any", axis=0) - y = cleaned[target] - del cleaned[target] - assert ( - target in cleaned - ), f"Target {target} not in dataframe with columns {cleaned.columns}" - return cleaned, y, data - - -def split_data_for_aft( - data, - target, - duration_col, - kwarg_list, - test_size=0.2, - random_state=42, -): - cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) - X_train, X_test, y_train, y_test = train_test_split( - cleaned, - y, - test_size=test_size, - random_state=random_state, - ) - assert ( - target in cleaned - ), f"Target {target} not in dataframe with columns {cleaned.columns}" - assert ( - duration_col in cleaned - ), f"Duration {duration_col} not in dataframe with columns {cleaned.columns}" - return X_train, X_test, y_train, y_test - - -if "__main__" == __name__: - afr_parser = argparse.ArgumentParser() - afr_parser.add_argument("--target", type=str, default="adv_failures") - afr_parser.add_argument("--duration_col", type=str, default="adv_fit_time") - afr_parser.add_argument("--dataset", type=str, default="mnist") - afr_parser.add_argument("--plots_folder", type=str, default="mnist/plots") - afr_parser.add_argument("--file", type=str, default="mnist/plots/data.csv") - afr_args = afr_parser.parse_args() - target = afr_args.target - duration_col = afr_args.duration_col - dataset = afr_args.dataset - - FOLDER = Path(afr_args.plots_folder) - data = pd.read_csv(afr_args.file, index_col=0) - data.columns = data.columns.str.strip() - data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x) - data.def_value.replace("", 0, inplace=True) - data.atk_value.replace("", 0, inplace=True) - data = drop_frames_without_results(data) - data = calculate_failure_rate(data) - data = min_max_scaling(data) - data.dropna(axis=0, subset=["atk_value", "atk_param"], inplace=True) - data.dropna(axis=0, subset=["def_value", "def_param"], inplace=True) - data.loc[:, "adv_failures"] = (1 - data.loc[:, "adv_accuracy"]) * data.loc[ - :, - "attack.attack_size", - ] - data.loc[:, "ben_failures"] = (1 - data.loc[:, "accuracy"]) * data.loc[ - :, - "attack.attack_size", - ] - - kwarg_list = [ - "accuracy", - "train_time", - "predict_time", - "atk_value", - "def_value", - "data.sample.random_state", - "adv_failure_rate", - "model_layers", - "adv_fit_time", - "model.trainer.nb_epoch", - ] - - X_train, X_test, y_train, y_test = split_data_for_aft( - data, - target, - duration_col, - kwarg_list, - test_size=0.2, - random_state=42, - ) - - weibull_dict = { - "Intercept: rho_": "$\\rho$", - "Intercept: lambda_": "$\lambda$", # noqa W605 - "data.sample.random_state: lambda_": "Random State", - "def_value: lambda_": "Defence Strength", - "atk_value: lambda_": "Attack Strength", - "train_time: lambda_": "$t_{train}$", - "predict_time: lambda_": "$t_{predict}$", - "adv_accuracy: lambda_": "$\lambda_{adv.}$", # noqa W605 - "accuracy: lambda_": "$\lambda_{ben.}$", # noqa W605 - "adv_fit_time: lambda_": "$t_{attack}$", - "adv_log_loss: lambda_": "Adv. Log Loss", - "adv_failure_rate: lambda_": "$h_{adv.}(t,;\\theta)$", - "failure_rate: lambda_": "$h_{ben.}(t,;\\theta)$", - "model_layers: lambda_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_": "Learning Rate", - "def_gen": "Defence", - } - - weibull_plot_dict = { - "file": "weibull_aft.pdf", - "title": "Weibull AFR Model", - "mtype": "weibull", - } - - weibull_afr, wft = plot_aft( - X_train, - event_col=target, - duration_col=duration_col, - **weibull_plot_dict, - replacement_dict=weibull_dict, - ) - - weibull_partial_dict_layers = { - "file": "weibull_partial_effects.pdf", - "covariate_array": "model_layers", - "values_array": [18, 34, 50, 101, 152], - "title": "$S(t)$ for Weibull AFR", - "ylabel": "Expectation of $S(t)$", - "xlabel": "Time $T$ (seconds)", - "legend_kwargs": { - "title": "No. of Layers", - "labels": ["18", "34", "50", "101", "152"], - }, - } - - weibull_layers = plot_partial_effects( - aft=wft, - **weibull_partial_dict_layers, - ) - wft_scores = score_model(wft, X_train, X_test) - - # cox_replacement_dict = { - # "adv_failure_rate": "$h_{adv}(t,;\\theta)$", - # "def_value": "Defence Strength", - # "data.sample.random_state": "Random State", - # "train_time": "$t_{train}$", - # "model_layers": "No. of Layers", - # "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", - # "adv_accuracy": "$\lambda_{adv.}$", # noqa W605 - # "adv_fit_time": "$t_{attack}$", - # "adv_log_loss": "Adv. Log Loss", - # "predict_time": "$t_{predict}$", - # "accuracy": "$\lambda_{ben.}$", # noqa W605 - # "failure_rate": "$h_{ben.}(t,;\\theta)$", - # "atk_value": "Attack Strength", - # } - # cox_partial_dict = { - # "file": "cox_partial_effects.pdf", - # "covariate_array": "model_layers", - # "values_array": [18, 34, 50, 101, 152], - # "replacement_dict": cox_replacement_dict, - # "title": "$S(t)$ for Cox AFR", - # "ylabel": "Expectation of $S(t)$", - # "xlabel": "Time $T$ (seconds)", - # "legend_kwargs": { - # "title": "No. of Layers", - # "labels": ["18", "34", "50", "101", "152"], - # }, - # } - # cox_plot_dict = { - # "file": "cox_aft.pdf", - # "duration_col": duration_col, - # "title": "Cox AFR Model", - # "mtype": "cox", - # "replacement_dict": cox_replacement_dict, - # } - # cox_afr, cft = plot_aft( - # df=X_train, - # event_col=target, - # **cox_plot_dict, - # ) - # cox_scores = score_model(cft, X_train, X_test) - # cox_partial = plot_partial_effects( - # aft=cft, - # **cox_partial_dict, - # ) - - log_normal_dict = { - "Intercept: sigma_": "$\sigma$", # noqa W605 - "Intercept: mu_": "$\mu$", # noqa W605 - "def_value: mu_": "Defence Strength", - "atk_value: mu_": "Attack Strength", - "train_time: mu_": "$t_{train}$", - "predict_time: mu_": "$t_{predict}$", - "adv_fit_time: mu_": "$t_{attack}$", - "model_layers: mu_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_": "Learning Rate", - "data.sample.random_state: mu_": "Random State", - "adv_log_loss: mu_": "Adv. Log Loss", - "adv_accuracy: mu_": "$\lambda_{adv.}$", # noqa W605 - "accuracy: mu_": "$\lambda_{ben.}$", # noqa W605 - "adv_failure_rate: mu_": "$h_{adv}(t,;\\theta)$", - "def_gen": "Defence", - "learning_rate: mu_": "Learning Rate", - } - - log_normal_graph, lnt = plot_aft( - X_train, - "log_normal_aft.pdf", - target, - duration_col, - "Log Normal AFR Model", - "log_normal", - replacement_dict=log_normal_dict, - ) - lnt_scores = score_model(lnt, X_train, X_test) - lnt_partial = plot_partial_effects( - file="log_normal_partial_effects.pdf", - aft=lnt, - covariate_array="model_layers", - values_array=[18, 34, 50, 101, 152], - replacement_dict=log_normal_dict, - title="$S(t)$ for Log-Normal AFR", - ylabel="Expectation of $S(t)$", - xlabel="Time $T$ (seconds)", - legend_kwargs={ - "title": "No. of Layers", - "labels": ["18", "34", "50", "101", "152"], - }, - ) - log_logistic_dict = { - "Intercept: beta_": "$\\beta$", - "Intercept: alpha_": "$\\alpha$", - "data.sample.random_state: alpha_": "Random State", - "def_value: alpha_": "Defence Strength", - "atk_value: alpha_": "Attack Strength", - "train_time: alpha_": "$t_{train}$", - "predict_time: alpha_": "$t_{predict}$", - "adv_accuracy: alpha_": "$\lambda_{adv.}$", # noqa W605 - "accuracy: alpha_": "$\lambda_{ben.}$", # noqa W605 - "adv_fit_time: alpha_": "$t_{attack}$", - "model_layers: alpha_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr:": "Learning Rate", - "adv_failure_rate: alpha_": "$h_{adv.}(t,\\theta)$", # noqa W605 - "alpha_": "", - } - log_logistic_graph, llt = plot_aft( X_train, "log_logistic_aft.pdf", diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 2e83c8ed..3627c7a2 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -153,7 +153,6 @@ def parse_results(folder, files=["score_dict.json", "params.yaml"], default_epoc return df -def format_control_parameter(data, control_dict): def format_control_parameter(data, control_dict): logger.info("Formatting control parameters...") @@ -176,7 +175,6 @@ def format_control_parameter(data, control_dict): value = np.nan data.loc[data.def_gen == defence, "def_value"] = value control_dict.pop(defence) - value = np.nan data.loc[data.def_gen == defence, "def_value"] = value control_dict.pop(defence) else: @@ -222,6 +220,7 @@ def clean_data_for_plotting( atk_gen_dict, control_dict, file, + folder, ): logger.info("Replacing attack and defence names with short names...") if hasattr(data, "def_gen"): @@ -240,17 +239,11 @@ def clean_data_for_plotting( if data["model.init.name"].str.contains("Net").any(): data["model_layers"] = data["model_name"].str.split("Net").str[-1] data = data.loc[:, ~data.columns.str.endswith(".1")] - if data["model.init.name"].str.contains("Net").any(): - data["model_layers"] = data["model_name"].str.split("Net").str[-1] - data = data.loc[:, ~data.columns.str.endswith(".1")] logger.info("Replacing data.sample.random_state with random_state...") data["data.sample.random_state"].rename("random_state", inplace=True) - data = format_control_parameter(data, control_dict) - logger.info(f"Saving data to {file}") - # data.to_csv( file) - data = format_control_parameter(data, control_dict) - logger.info(f"Saving data to {file}") - data.to_csv(file) + data = format_control_parameter(data, control_dict, min_max=True) + logger.info(f"Saving data to {Path(folder) / file}") + data.to_csv(Path(folder) / "data.csv") return data @@ -284,6 +277,7 @@ def save_results(results, results_file) -> str: parser = argparse.ArgumentParser() parser.add_argument("--results_file", type=str, default="results.csv") parser.add_argument("--report_folder", type=str, default="reports", required=True) + parser.add_argument("--results_folder", type=str, default="results") parser.add_argument("--config", type=str, default="conf/compile.yaml") parser.add_argument("--exclude", type=list, default=None, nargs="*") parser.add_argument("--verbose", type=str, default="INFO") @@ -298,6 +292,7 @@ def save_results(results, results_file) -> str: logging.basicConfig(level=args.verbose) report_folder = args.report_folder results_file = args.results_file + results_folder = args.results_folder results = parse_results(report_folder, default_epochs=args.default_epochs) with open(Path(Path(), args.config), "r") as f: big_dict = yaml.load(f, Loader=yaml.FullLoader) @@ -310,6 +305,7 @@ def save_results(results, results_file) -> str: atk_gen_dict, control_dict, results_file, + results_folder, ) report_file = save_results(results, results_file) assert Path( From d73d8fc9eb95f6f43439b95e390c3f2ad6df0dbf Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 18:49:20 +0000 Subject: [PATCH 133/148] removed old pytorch example --- examples/pytorch/.gitignore | 2 - examples/pytorch/attacks.sh | 37 ------ examples/pytorch/conf/attack/default.yaml | 9 -- examples/pytorch/conf/compile.yaml | 32 ----- examples/pytorch/conf/files/cifar.yaml | 21 --- examples/pytorch/conf/files/mnist.yaml | 21 --- examples/pytorch/conf/grid/attack/cw_0.yaml | 4 - examples/pytorch/conf/grid/attack/cw_2.yaml | 5 - examples/pytorch/conf/grid/attack/cw_inf.yaml | 4 - .../pytorch/conf/grid/attack/deepfool.yaml | 5 - examples/pytorch/conf/grid/attack/fgm.yaml | 4 - examples/pytorch/conf/grid/attack/hsj.yaml | 5 - examples/pytorch/conf/grid/attack/patch.yaml | 5 - examples/pytorch/conf/grid/attack/pgd.yaml | 6 - examples/pytorch/conf/grid/attack/pixel.yaml | 4 - .../pytorch/conf/grid/attack/threshold.yaml | 4 - .../pytorch/conf/grid/model/confidence.yaml | 4 - examples/pytorch/conf/grid/model/default.yaml | 0 examples/pytorch/conf/grid/model/fsq.yaml | 3 - .../pytorch/conf/grid/model/gauss-in.yaml | 4 - .../pytorch/conf/grid/model/gauss-out.yaml | 2 - examples/pytorch/conf/mnist.yaml | 44 ------- examples/pytorch/dvc.yaml | 120 ------------------ examples/pytorch/models.sh | 19 --- hardcopy.1 | 24 ++++ 25 files changed, 24 insertions(+), 364 deletions(-) delete mode 100644 examples/pytorch/.gitignore delete mode 100644 examples/pytorch/attacks.sh delete mode 100644 examples/pytorch/conf/attack/default.yaml delete mode 100644 examples/pytorch/conf/compile.yaml delete mode 100644 examples/pytorch/conf/files/cifar.yaml delete mode 100644 examples/pytorch/conf/files/mnist.yaml delete mode 100644 examples/pytorch/conf/grid/attack/cw_0.yaml delete mode 100644 examples/pytorch/conf/grid/attack/cw_2.yaml delete mode 100644 examples/pytorch/conf/grid/attack/cw_inf.yaml delete mode 100644 examples/pytorch/conf/grid/attack/deepfool.yaml delete mode 100644 examples/pytorch/conf/grid/attack/fgm.yaml delete mode 100644 examples/pytorch/conf/grid/attack/hsj.yaml delete mode 100644 examples/pytorch/conf/grid/attack/patch.yaml delete mode 100644 examples/pytorch/conf/grid/attack/pgd.yaml delete mode 100644 examples/pytorch/conf/grid/attack/pixel.yaml delete mode 100644 examples/pytorch/conf/grid/attack/threshold.yaml delete mode 100644 examples/pytorch/conf/grid/model/confidence.yaml delete mode 100644 examples/pytorch/conf/grid/model/default.yaml delete mode 100644 examples/pytorch/conf/grid/model/fsq.yaml delete mode 100644 examples/pytorch/conf/grid/model/gauss-in.yaml delete mode 100644 examples/pytorch/conf/grid/model/gauss-out.yaml delete mode 100644 examples/pytorch/conf/mnist.yaml delete mode 100644 examples/pytorch/dvc.yaml delete mode 100644 examples/pytorch/models.sh create mode 100644 hardcopy.1 diff --git a/examples/pytorch/.gitignore b/examples/pytorch/.gitignore deleted file mode 100644 index 6811d155..00000000 --- a/examples/pytorch/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -mnist/ -20_epochs/ diff --git a/examples/pytorch/attacks.sh b/examples/pytorch/attacks.sh deleted file mode 100644 index 8ec1f079..00000000 --- a/examples/pytorch/attacks.sh +++ /dev/null @@ -1,37 +0,0 @@ -# #!/bin/bash - -# # This script is used to generate the attacks for the example. - -# Fast Gradient Method -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 stage=attack ++hydra.sweeper.study_name=fgm ++direction=maximize $@ - -# Projected Gradient Descent -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 stage=attack ++hydra.sweeper.study_name=pgd ++direction=maximize $@ - -# DeepFool -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=1,3,5,10 stage=attack ++hydra.sweeper.study_name=deep ++direction=maximize $@ - -# HopSkipJump -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 stage=attack ++hydra.sweeper.study_name=hsj ++direction=maximize $@ - -# ##################################################### -# PixelAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=pixel ++direction=maximize $@ - -# ThresholdAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=thresh ++direction=maximize $@ - -# # AdversarialPatch -# bash models.sh attack=default --attack.init.batch_size ++attack.init.name=art.attacks.evasion.AdversarialPatch ++attack.init.max_iter=10 ++attack.init.learning_rate=.5,5.0,50.0 stage=patch ++hydra.sweeper.study_name=attack ++direction=maximize ++attack.init.patch_shape=[1,28,28] $@ -##################################################### - -# # Carlini L0 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL0Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw0 ++hydra.sweeper.study_name=cw0 ++direction=maximize $@ - -# # Carlini L2 Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniL2Method ++attack.init.batch_size=1024 ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=cw2 ++hydra.sweeper.study_name=cw2 ++direction=maximize $@ - -# # Carlini LInf Method -# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.CarliniLInfMethod ++attack.init.max_iter=10 ++attack.init.confidence=.1,.9,.99 stage=attack ++hydra.sweeper.study_name=cwinf ++direction=maximize $@ - -rm -rf output/models/* diff --git a/examples/pytorch/conf/attack/default.yaml b/examples/pytorch/conf/attack/default.yaml deleted file mode 100644 index 76b4d62d..00000000 --- a/examples/pytorch/conf/attack/default.yaml +++ /dev/null @@ -1,9 +0,0 @@ -data: ${data} -model: ${model} -_target_ : deckard.base.attack.Attack -init: - model: ${model} - _target_: deckard.base.attack.AttackInitializer - name: art.attacks.evasion.HopSkipJump -attack_size : 10 -method : evasion diff --git a/examples/pytorch/conf/compile.yaml b/examples/pytorch/conf/compile.yaml deleted file mode 100644 index 43418520..00000000 --- a/examples/pytorch/conf/compile.yaml +++ /dev/null @@ -1,32 +0,0 @@ -attacks: - # CarliniL0Method: CW_0 - # CarliniL2Method: CW_2 - # CarliniLInfMethod: CW_inf - DeepFool: Deep - FastGradientMethod: FGM - HopSkipJump: HSJ - PixelAttack: Pixel - ProjectedGradientDescent: PGD - ThresholdAttack: Thresh -defences: - Control: Control - FeatureSqueezing: FSQ - GaussianAugmentation: Gauss-in - GaussianNoise: Gauss-out - HighConfidence: Conf -params: - # art.attacks.evasion.CarliniL0Method: attack.init.confidence - # art.attacks.evasion.CarliniL2Method: attack.init.confidence - # art.attacks.evasion.CarliniLInfMethod: attack.init.confidence - Deep: attack.init.nb_grads - FGM: attack.init.eps - HSJ: attack.init.max_iter - Pixel: attack.init.th - PGD: attack.init.eps - Thresh: attack.init.th - Gauss-out: model.art.pipeline.postprocessor.scale - Conf: model.art.pipeline.postprocessor.cutoff - FSQ: model.art.pipeline.preprocessor.bit_depth - Gauss-in: model.art.pipeline.preprocessor.sigma - Depth: model_layers - Epochs: model.train.nb_epoch diff --git a/examples/pytorch/conf/files/cifar.yaml b/examples/pytorch/conf/files/cifar.yaml deleted file mode 100644 index e43cab72..00000000 --- a/examples/pytorch/conf/files/cifar.yaml +++ /dev/null @@ -1,21 +0,0 @@ -_target_: deckard.base.files.FileConfig -reports: reports -data_dir: data -model_dir: models -attack_dir: attacks -directory: cifar -score_dict_file: score_dict.json -data_file : data -model_file : model -attack_file : attack -attack_type : .pkl -data_type : .pkl -model_type : .pt -params_file : params.yaml -# train_labels_file : train_labels.json -# test_labels_file : test_labels.json -# probabilities_file: probabilities.json -predictions_file : predictions.json -adv_predictions_file : adv_predictions.json -# adv_probabilities_file: adv_probabilities.json -name: default diff --git a/examples/pytorch/conf/files/mnist.yaml b/examples/pytorch/conf/files/mnist.yaml deleted file mode 100644 index efdb1c42..00000000 --- a/examples/pytorch/conf/files/mnist.yaml +++ /dev/null @@ -1,21 +0,0 @@ -_target_: deckard.base.files.FileConfig -reports: reports -data_dir: data -model_dir: models -attack_dir: attacks -directory: mnist -score_dict_file: score_dict.json -data_file : data -model_file : model -attack_file : attack -attack_type : .pkl -data_type : .pkl -model_type : .pt -params_file : params.yaml -# train_labels_file : train_labels.json -# test_labels_file : test_labels.json -# probabilities_file: probabilities.json -predictions_file : predictions.json -adv_predictions_file : adv_predictions.json -# adv_probabilities_file: adv_probabilities.json -name: default diff --git a/examples/pytorch/conf/grid/attack/cw_0.yaml b/examples/pytorch/conf/grid/attack/cw_0.yaml deleted file mode 100644 index 20573ea2..00000000 --- a/examples/pytorch/conf/grid/attack/cw_0.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.CarliniL0Method -init.max_iter : [10] -init.batch_size : [1024] -init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/cw_2.yaml b/examples/pytorch/conf/grid/attack/cw_2.yaml deleted file mode 100644 index 14965baa..00000000 --- a/examples/pytorch/conf/grid/attack/cw_2.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.model : ${model} -init.name : art.attacks.evasion.CarliniL0Method -init.max_iter : [10] -init.batch_size : [1024] -init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/cw_inf.yaml b/examples/pytorch/conf/grid/attack/cw_inf.yaml deleted file mode 100644 index 9e694da5..00000000 --- a/examples/pytorch/conf/grid/attack/cw_inf.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.CarliniLInfMethod -init.max_iter : [10] -init.batch_size : [1024] -init.confidence : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/deepfool.yaml b/examples/pytorch/conf/grid/attack/deepfool.yaml deleted file mode 100644 index 1fc9055a..00000000 --- a/examples/pytorch/conf/grid/attack/deepfool.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.name : art.attacks.evasion.DeepFool -init.max_iter : [10] -init.batch_size : [1024] -init.nb_grads : [10,100,1000] -init.eps: [0.01, 0.03, 0.3, 0.1, 1.0] diff --git a/examples/pytorch/conf/grid/attack/fgm.yaml b/examples/pytorch/conf/grid/attack/fgm.yaml deleted file mode 100644 index 41032d3e..00000000 --- a/examples/pytorch/conf/grid/attack/fgm.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.FastGradientMethod -init.eps: [0.01, 0.03, 0.3, 0.1] -init.norm: [inf, 1, 2] -init.eps_step: [0.001, 0.003, 0.01] diff --git a/examples/pytorch/conf/grid/attack/hsj.yaml b/examples/pytorch/conf/grid/attack/hsj.yaml deleted file mode 100644 index 1fba407d..00000000 --- a/examples/pytorch/conf/grid/attack/hsj.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.name : art.attacks.evasion.HopSkipJump -init.batch_size : [1, 4, 16, 65, 128] -init.max_iter : [1, 10, 100, 1000] -init.max_eval : [1, 10, 100, 1000] -init.init_eval : [1, 10, 100, 1000] diff --git a/examples/pytorch/conf/grid/attack/patch.yaml b/examples/pytorch/conf/grid/attack/patch.yaml deleted file mode 100644 index e7285674..00000000 --- a/examples/pytorch/conf/grid/attack/patch.yaml +++ /dev/null @@ -1,5 +0,0 @@ -init.name : art.attacks.evasion.ThresholdAttack -init.max_iter : [10] -init.batch_size : [1024] -init.scale_min : [0.01,] -init.scale_max : [.01, .1, .3, .5, .9, .99, 1] diff --git a/examples/pytorch/conf/grid/attack/pgd.yaml b/examples/pytorch/conf/grid/attack/pgd.yaml deleted file mode 100644 index 49819d5f..00000000 --- a/examples/pytorch/conf/grid/attack/pgd.yaml +++ /dev/null @@ -1,6 +0,0 @@ -init.name : art.attacks.evasion.ProjectedGradientDescent -init.eps : [0.01, 0.03, 0.3, 0.1] -init.norm : [inf, 1, 2] -init.eps_step : [0.001, 0.003, 0.01] -init.batch_size : [1024] -init.max_iter : [10] diff --git a/examples/pytorch/conf/grid/attack/pixel.yaml b/examples/pytorch/conf/grid/attack/pixel.yaml deleted file mode 100644 index 9e0060a7..00000000 --- a/examples/pytorch/conf/grid/attack/pixel.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.ProjectedGradientDescent -init.max_iter : [10] -init.batch_size : [1024] -init.th : [1,4,16,64,256] diff --git a/examples/pytorch/conf/grid/attack/threshold.yaml b/examples/pytorch/conf/grid/attack/threshold.yaml deleted file mode 100644 index 07437c71..00000000 --- a/examples/pytorch/conf/grid/attack/threshold.yaml +++ /dev/null @@ -1,4 +0,0 @@ -init.name : art.attacks.evasion.ThresholdAttack -init.max_iter : [10] -init.batch_size : [1024] -init.th: [1,4,16,64,256] diff --git a/examples/pytorch/conf/grid/model/confidence.yaml b/examples/pytorch/conf/grid/model/confidence.yaml deleted file mode 100644 index d07598f8..00000000 --- a/examples/pytorch/conf/grid/model/confidence.yaml +++ /dev/null @@ -1,4 +0,0 @@ - -art.postprocessor.name : [art.defences.postprocessor.HighConfidence] -art.postprocessor.cutoff : [.1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99] -art.postprocessor.apply_fit : [True, False] diff --git a/examples/pytorch/conf/grid/model/default.yaml b/examples/pytorch/conf/grid/model/default.yaml deleted file mode 100644 index e69de29b..00000000 diff --git a/examples/pytorch/conf/grid/model/fsq.yaml b/examples/pytorch/conf/grid/model/fsq.yaml deleted file mode 100644 index 9740b4b4..00000000 --- a/examples/pytorch/conf/grid/model/fsq.yaml +++ /dev/null @@ -1,3 +0,0 @@ -art.preprocessor.name : [art.defences.preprocessor.FeatureSqueezing] -art.preprocessor.clip_values : [[0, 1]] -art.preprocessor.bit_depth : [1, 2, 4, 8, 16, 32, 64] diff --git a/examples/pytorch/conf/grid/model/gauss-in.yaml b/examples/pytorch/conf/grid/model/gauss-in.yaml deleted file mode 100644 index 21392a82..00000000 --- a/examples/pytorch/conf/grid/model/gauss-in.yaml +++ /dev/null @@ -1,4 +0,0 @@ -art.preprocessor.name : art.defences.preprocessor.GaussianAugmentation -art.preprocessor.clip_values : ${art.initialize.clip_values} -art.preprocessor.sigma : [.01, .1, .2, .3, .5, 1] -art.preprocessor.ratio : [.1, .5, 1] diff --git a/examples/pytorch/conf/grid/model/gauss-out.yaml b/examples/pytorch/conf/grid/model/gauss-out.yaml deleted file mode 100644 index 377bbcc9..00000000 --- a/examples/pytorch/conf/grid/model/gauss-out.yaml +++ /dev/null @@ -1,2 +0,0 @@ -art.postprocessor.name : art.defences.postprocessor.GaussianNoise -art.postprocessor.scale : [.01, .1, .2, .3, .5, 1, 2, 3, 5, 10] diff --git a/examples/pytorch/conf/mnist.yaml b/examples/pytorch/conf/mnist.yaml deleted file mode 100644 index aa05956f..00000000 --- a/examples/pytorch/conf/mnist.yaml +++ /dev/null @@ -1,44 +0,0 @@ -defaults: - - _self_ - - data: torch_mnist - - model: torch_mnist - - attack: default - - files: mnist - - scorers: default - - override hydra/sweeper : optuna - - override hydra/sweeper/sampler : grid - - override hydra/launcher : joblib -stage : '???' -direction : "maximize" -_target_ : deckard.base.experiment.Experiment -optimizers : accuracy -hydra: - run: - dir: ${files.directory}/${files.reports}/${stage}/logs - sweep: - dir: ${files.directory}/${stage}/${model.init.name} - subdir : ${hydra.sweeper.study_name}/${hydra.job.num} - sweeper: - sampler: - _target_: optuna.samplers.GridSampler - direction: ${direction} - study_name: control - storage: sqlite:///model.db - n_jobs: ${hydra.launcher.n_jobs} - n_trials : 32 - params: - ++data.sample.random_state: choice(0, 1, 2, 3, 4, 5, 6, 7, 8, 9) - ++model.art.initialize.optimizer.lr: choice(10, 1, 0.1, 0.01, 0.001, .0001, .00001, 0.000001) - ++model.trainer.nb_epoch: choice(1, 10, 30, 50, 100) - _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper - launcher: - _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 8 - prefer : processes - verbose: 10 - timeout: null - pre_dispatch: n_jobs - batch_size: auto - temp_folder: /tmp/deckard - max_nbytes: 100000 - mmap_mode: r diff --git a/examples/pytorch/dvc.yaml b/examples/pytorch/dvc.yaml deleted file mode 100644 index 0cbb2c6f..00000000 --- a/examples/pytorch/dvc.yaml +++ /dev/null @@ -1,120 +0,0 @@ -stages: - train: - cmd: python -m deckard.layers.experiment train --config_file mnist.yaml - params: - - data - - model - - scorers - - files - outs: - - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} - # - ${files.directory}/${files.reports}/train/${files.name}/${files.params_file} - # - ${files.directory}/${files.reports}/train/${files.name}/${files.test_labels_file} # Omit to save space - - ${files.directory}/${files.reports}/train/${files.name}/${files.predictions_file} # logit outputs for our model - # - ${files.directory}/${files.reports}/train/${files.name}/${files.probabilities_file} # Omit to save space - metrics: - - ${files.directory}/${files.reports}/train/${files.name}/${files.score_dict_file} - attack: - cmd: python -m deckard.layers.experiment attack --config_file mnist.yaml - params: - - data - - model - - attack - - scorers - - files - outs: - - ${files.directory}/${files.attack_dir}/${files.attack_file}${files.attack_type} - - ${files.directory}/${files.reports}/attack/${files.name}/${files.adv_predictions_file} - # - ${files.directory}/${files.reports}/attack/${files.name}/${files.params_file} - deps: - - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type} - - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - metrics: - - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} - - ############################################################################## - # models: # This is a loop over the ResNet models - # foreach: - # - ResNet18 - # # - ResNet34 - # # - ResNet50 - # # - ResNet101 - # # - ResNet152 - # do: # This script configures eazch defence - # cmd: bash models.sh ++model.init.name=torch_example.${item} stage=train ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/train/${item}.db --config-name mnist.yaml - # deps: - # - models.sh - # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type} - # - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type} - # outs: - # - ${files.directory}/${files.reports}/train/${item}.db: # This outputs a database file for each model - # cache: True - # persist: True - attacks: - foreach: # This is a loop over the ResNet models - - ResNet18 - #- ResNet34 - #- ResNet50 - #- ResNet101 - #- ResNet152 - do: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.${item} stage=attack ++hydra.sweeper.storage=sqlite:///${files.directory}/${files.reports}/attack/${item}.db --config-name mnist.yaml - deps: - - models.sh # This script configures each defence - - attacks.sh # This script configures each attack - - ${files.directory}/${files.reports}/attack/${files.name}/${files.score_dict_file} # This is here just to ensure it runs after the attack stage - # - ${files.directory}/${files.reports}/train/${item}.db - outs: - - ${files.directory}/${files.reports}/attack/${item}.db: # This outputs a database file for each model - cache: True - persist: True - compile: - foreach: # iterates through each stage - # - train - - attack - do: - cmd: python -m deckard.layers.compile --report_folder ${files.directory}/${files.reports}/${item} --results_file ${files.directory}/${files.reports}/${item}.csv - deps: - - ${files.directory}/${files.reports}/${item}/ - - ${files.directory}/${files.reports}/${item}/ResNet18.db - - ${files.directory}/${files.reports}/${item}/ResNet34.db - - ${files.directory}/${files.reports}/${item}/ResNet50.db - - ${files.directory}/${files.reports}/${item}/ResNet101.db - # - ${files.directory}/${files.reports}/${item}/ResNet152.db - outs: - - ${files.directory}/${files.reports}/${item}.csv - plot: - cmd : python -m deckard.layers.plots --path ${files.directory}/plots/ --file ${files.directory}/${files.reports}/attack.csv -o data.csv - deps: - - ${files.directory}/${files.reports}/attack.csv - - ${files.directory}/${files.reports}/attack/ResNet18.db - - ${files.directory}/${files.reports}/attack/ResNet34.db - - ${files.directory}/${files.reports}/attack/ResNet50.db - - ${files.directory}/${files.reports}/attack/ResNet101.db - - ${files.directory}/${files.reports}/attack/ResNet152.db - outs: - - ${files.directory}/plots/data.csv - afr: - cmd: python -m deckard.layers.afr --dataset ${files.directory} --file ${files.directory}/plots/data.csv - deps: - - ${files.directory}/plots/data.csv - plots: - - ${files.directory}/plots/weibull_aft.pdf - - ${files.directory}/plots/weibull_partial_effects.pdf - - ${files.directory}/plots/cox_partial_effects.pdf - - ${files.directory}/plots/cox_aft.pdf - - ${files.directory}/plots/log_logistic_aft.pdf - - ${files.directory}/plots/log_logistic_partial_effects.pdf - - ${files.directory}/plots/log_normal_aft.pdf - - ${files.directory}/plots/log_normal_partial_effects.pdf - metrics: - - ${files.directory}/plots/aft_comparison.csv - outs: - - ${files.directory}/plots/aft_comparison.tex - copy_results: - cmd: cp -r ${files.directory}/plots/* ~/ml_afr/mnist/ - deps: - - ${files.directory}/plots/data.csv - - ${files.directory}/plots/aft_comparison.csv diff --git a/examples/pytorch/models.sh b/examples/pytorch/models.sh deleted file mode 100644 index 2978d445..00000000 --- a/examples/pytorch/models.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash - -# This script is used to generate the models for the sklearn example. - -# # Default model -echo "python -m deckard.layers.optimise " $@ "--multirun" -python -m deckard.layers.optimise $@ --multirun - -# # This line generates the model and adds the FeatureSqueezing preprocessing defence. -# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.FeatureSqueezing +model.art.preprocessor.params.bit_depth=4,8,16,32,64 +model.art.preprocessor.params.clip_values=[0,255] ++hydra.sweeper.study_name=FSQ $@ --multirun - -# # # Gaussian Augmentation (Input) -# python -m deckard.layers.optimise ++model.art.preprocessor.name=art.defences.preprocessor.GaussianAugmentation +model.art.preprocessor.params.sigma=.01,.1,.3,.5,1 +model.art.preprocessor.params.ratio=.5 +model.art.preprocessor.params.augmentation=False ++hydra.sweeper.study_name=gauss-in $@ --multirun - -# # # # Gaussian Noise (Output) -# python -m deckard.layers.optimise ++model.art.postprocessor.name=art.defences.postprocessor.GaussianNoise ++model.art.postprocessor.params.scale=.01,.1,.3,.5,1 ++hydra.sweeper.study_name=gauss-out $@ --multirun - -# # # # High Confidence -# python -m deckard.layers.optimise +model.art.postprocessor.name=art.defences.postprocessor.HighConfidence +model.art.postprocessor.params.cutoff=.1,.3,.5,.9,.99 ++hydra.sweeper.study_name=conf $@ --multirun diff --git a/hardcopy.1 b/hardcopy.1 new file mode 100644 index 00000000..4302b333 --- /dev/null +++ b/hardcopy.1 @@ -0,0 +1,24 @@ + model, time_dict = self.trainer(data, model, library=self.library) + File "/home/cmeyers/deckard/deckard/base/model/model.py", line 132, in __call_ +_ + model.fit(data[0], data[2], **trainer) + File "/home/cmeyers/anaconda3/lib/python3.8/site-packages/art/estimators/class +ification/classifier.py", line 73, in replacement_function + return fdict[func_name](self, *args, **kwargs) + File "/home/cmeyers/anaconda3/lib/python3.8/site-packages/art/estimators/class +ification/pytorch.py", line 456, in fit + loss.backward() + File "/home/cmeyers/anaconda3/lib/python3.8/site-packages/torch/_tensor.py", l +ine 363, in backward + torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=i +nputs) + File "/home/cmeyers/anaconda3/lib/python3.8/site-packages/torch/autograd/__ini +t__.py", line 173, in backward + Variable._execution_engine.run_backward( # Calls into the C++ engine to run + the backward pass +KeyboardInterrupt +ERROR: failed to reproduce 'attacks@ResNet34': failed to run: bash attacks.sh ++ +attack.attack_size=100 ++model.init.name=torch_example.ResNet34 stage=attack ++h +ydra.sweeper.storage=sqlite:///cifar100/reports/attack/ResNet34.db --config-name + cifar100.yaml, exited with -2 +(base) cmeyers@cmeyers:~/deckard/examples/pytorch_cifar_100$ From 5c3a4c3a5a51e0a7af1659c00d1e36063fbfca3e Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 18:52:56 +0000 Subject: [PATCH 134/148] linting --- deckard/__main__.py | 15 ++---- deckard/base/attack/attack.py | 26 +++------ deckard/base/data/data.py | 8 +-- deckard/base/data/generator.py | 12 +---- deckard/base/data/sklearn_pipeline.py | 5 +- deckard/base/model/art_pipeline.py | 15 ++---- deckard/base/model/model.py | 47 +++++----------- deckard/base/model/sklearn_pipeline.py | 36 +++++-------- deckard/base/model/tensorflow_models.py | 3 +- deckard/base/plots/plots.py | 54 ++++++------------- deckard/base/utils/factory.py | 9 ++-- deckard/base/utils/hashing.py | 4 +- deckard/iaac/gcp/deploy.py | 4 +- deckard/layers/afr.py | 34 +++--------- deckard/layers/compile.py | 19 ++----- deckard/layers/experiment.py | 5 +- deckard/layers/find_best.py | 11 ++-- deckard/layers/generate_grid.py | 9 +--- deckard/layers/optimise.py | 9 ++-- deckard/layers/plots.py | 41 +++----------- deckard/layers/prepare_queue.py | 18 ++----- deckard/layers/utils.py | 11 +--- deckard/layers/watcher.py | 35 +++--------- examples/keras/keras_example.py | 4 +- examples/power/torch_example.py | 35 ++---------- examples/pytorch/cifar10/torch_example.py | 35 ++---------- examples/pytorch/cifar100/torch_example.py | 35 ++---------- examples/pytorch/mnist/torch_example.py | 35 ++---------- examples/scratch/compute_attack_success.py | 15 ++---- examples/scratch/deckard_queue.py | 8 +-- examples/scratch/objective.py | 13 ++--- examples/scratch/torch_example.py | 35 ++---------- examples/security/classification/retrain.py | 54 +++++-------------- examples/security/kdd-nsl/retrain.py | 54 +++++-------------- examples/security/truthseeker/retrain.py | 54 +++++-------------- examples/tfv1/tfv1_example.py | 10 +--- examples/tfv2/tfv2_example.py | 5 +- test/base/test_attack/test_attack.py | 6 +-- test/base/test_attack/torch_example.py | 25 ++------- test/base/test_data/test_data.py | 6 +-- test/base/test_data/test_generator.py | 6 +-- test/base/test_data/test_sampler.py | 15 ++---- test/base/test_data/test_sklearn_pipeline.py | 22 +++----- test/base/test_experiment/test_experiment.py | 6 +-- test/base/test_experiment/torch_example.py | 25 ++------- test/base/test_files/test_files.py | 3 +- test/base/test_model/keras_example.py | 4 +- test/base/test_model/test_art_pipeline.py | 3 +- test/base/test_model/test_model.py | 3 +- .../test_model/test_sklearn_model_pipeline.py | 7 +-- test/base/test_model/tfv2_example.py | 5 +- test/base/test_model/torch_example.py | 25 ++------- test/base/test_scorer/test_scorer.py | 12 ++--- 53 files changed, 223 insertions(+), 772 deletions(-) diff --git a/deckard/__main__.py b/deckard/__main__.py index 03153d92..815f30e9 100644 --- a/deckard/__main__.py +++ b/deckard/__main__.py @@ -24,10 +24,7 @@ def run_submodule(submodule, args): cmd = f"python -m deckard.layers.{submodule} {args}" logger.info(f"Running {cmd}") with subprocess.Popen( - cmd, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - shell=True, + cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, ) as proc: for line in proc.stdout: print(line.rstrip().decode("utf-8")) @@ -55,11 +52,7 @@ def parse_and_repro(args, default_config="default.yaml", config_dir="conf"): else: cmd = f"python -m deckard.layers.parse {args} --config_file {default_config}" # error = f"error parsing command: {cmd} {args}" - with subprocess.Popen( - cmd, - stdout=subprocess.PIPE, - shell=True, - ) as proc: + with subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True,) as proc: for line in proc.stdout: print(line.rstrip().decode("utf-8")) if Path(Path(), "dvc.yaml").exists(): @@ -77,9 +70,7 @@ def parse_and_repro(args, default_config="default.yaml", config_dir="conf"): logging.basicConfig(level=logging.INFO) parser = argparse.ArgumentParser() parser.add_argument( - "--submodule", - type=str, - help=f"Submodule to run. Choices: {layer_list}", + "--submodule", type=str, help=f"Submodule to run. Choices: {layer_list}", ) parser.add_argument( "--config_file", diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index 237b21ff..85d2a230 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -156,9 +156,7 @@ def __call__( start = process_time_ns() patches, _ = atk.generate(ben_samples, **kwargs) samples = atk.apply_patch( - ben_samples, - scale=scale_max, - patch_external=patches, + ben_samples, scale=scale_max, patch_external=patches, ) else: start = process_time_ns() @@ -201,8 +199,7 @@ def __call__( results["adv_probabilities"] = np.array(adv_probabilities) else: if hasattr(self.model, "model") and hasattr( - self.model.model, - "predict_proba", + self.model.model, "predict_proba", ): start = process_time_ns() adv_probabilities = model.model.predict_proba(samples) @@ -235,8 +232,7 @@ def __call__( results["adv_losses"] = np.array(adv_loss) elif adv_losses_file is not None: assert hasattr( - model, - "compute_loss", + model, "compute_loss", ), "Model does not have compute_loss method." try: adv_loss = model.compute_loss(samples, data[3][: self.attack_size]) @@ -354,9 +350,7 @@ def __call__( y_trigger = y_trigger.to(torch.long) y_trigger = y_trigger.to(torch.long) atk = self.init( - model=model, - data=data, - attack_size=self.attack_size, + model=model, data=data, attack_size=self.attack_size, ) start = process_time_ns() samples, _ = atk.poison( @@ -393,8 +387,7 @@ def __call__( adv_probabilities = self.data.load(adv_probabilities_file) else: if hasattr(self.model, "model") and hasattr( - self.model.model, - "predict_proba", + self.model.model, "predict_proba", ): start = process_time_ns() adv_probabilities = model.model.predict_proba(samples) @@ -425,8 +418,7 @@ def __call__( adv_loss = self.data.load(adv_losses_file) elif adv_losses_file is not None: assert hasattr( - model, - "compute_loss", + model, "compute_loss", ), "Model does not have compute_loss method." adv_loss = model.compute_loss(samples, data[3][: self.attack_size]) adv_loss = np.array(adv_loss) @@ -494,8 +486,7 @@ def __call__( initial_image = np.random.uniform(0, 1, data_shape) elif self.initial_image == "average": initial_image = np.zeroes(data_shape) + np.mean( - data[1][: self.attack_size], - axis=0, + data[1][: self.attack_size], axis=0, ) elif self.initial_image is None: pass @@ -665,8 +656,7 @@ def __call__( else: if hasattr(attacked_model, "compute_loss"): loss = attacked_model.compute_loss( - data[1][: self.attack_size], - data[3][: self.attack_size], + data[1][: self.attack_size], data[3][: self.attack_size], ) else: from sklearn.metrics import log_loss diff --git a/deckard/base/data/data.py b/deckard/base/data/data.py index 690d27c0..47fbc6bb 100644 --- a/deckard/base/data/data.py +++ b/deckard/base/data/data.py @@ -158,8 +158,7 @@ def save(self, data, filename): json.dump(data, f) elif suffix in [".csv"]: assert isinstance( - data, - (Series, DataFrame, dict, np.ndarray), + data, (Series, DataFrame, dict, np.ndarray), ), f"Data must be a Series, DataFrame, or dict, not {type(data)} to save to {filename}" DataFrame(data).to_csv(filename, index=False) elif suffix in [".pkl", ".pickle"]: @@ -170,10 +169,7 @@ def save(self, data, filename): assert Path(filename).exists() def __call__( - self, - data_file=None, - train_labels_file=None, - test_labels_file=None, + self, data_file=None, train_labels_file=None, test_labels_file=None, ) -> list: """Loads data from file if it exists, otherwise generates data and saves it to file. Returns X_train, X_test, y_train, y_test as a list of arrays, typed according to the framework. :param filename: str diff --git a/deckard/base/data/generator.py b/deckard/base/data/generator.py index 4283ed22..40957823 100644 --- a/deckard/base/data/generator.py +++ b/deckard/base/data/generator.py @@ -34,12 +34,7 @@ @dataclass class SklearnDataGenerator: name: Literal[ - "classification", - "regression", - "blobs", - "moons", - "circles", - "biclusters", + "classification", "regression", "blobs", "moons", "circles", "biclusters", ] = "classification" kwargs: dict = field(default_factory=dict) @@ -75,10 +70,7 @@ def __hash__(self): @dataclass class TorchDataGenerator: name: Literal[ - "torch_mnist", - "torch_cifar10", - "torch_diabetes", - "torch_cifar100", + "torch_mnist", "torch_cifar10", "torch_diabetes", "torch_cifar100", ] = "torch_mnist" path = None kwargs: dict = field(default_factory=dict) diff --git a/deckard/base/data/sklearn_pipeline.py b/deckard/base/data/sklearn_pipeline.py index 0c725896..8a53ed8e 100644 --- a/deckard/base/data/sklearn_pipeline.py +++ b/deckard/base/data/sklearn_pipeline.py @@ -68,9 +68,6 @@ def __call__(self, X_train, X_test, y_train, y_test): for stage in pipeline: transformer = pipeline[stage] X_train, X_test, y_train, y_test = transformer( - X_train, - X_test, - y_train, - y_test, + X_train, X_test, y_train, y_test, ) return [X_train, X_test, y_train, y_test] diff --git a/deckard/base/model/art_pipeline.py b/deckard/base/model/art_pipeline.py index 679a089f..2c3ed3b1 100644 --- a/deckard/base/model/art_pipeline.py +++ b/deckard/base/model/art_pipeline.py @@ -60,15 +60,13 @@ def __call__(self): model = self.model kwargs = self.kwargs if "torch" in str(library) and not isinstance( - model, - tuple(torch_dict.values()), + model, tuple(torch_dict.values()), ): model = TorchInitializer( data=data, model=model, library=library, **kwargs )() elif "keras" in str(library) and not isinstance( - model, - tuple(keras_dict.values()), + model, tuple(keras_dict.values()), ): # pragma: no cover raise NotImplementedError("Keras not implemented yet") # try: @@ -106,16 +104,14 @@ def __call__(self): data=data, model=model, library=library, **kwargs )() elif library in ["tf1", "tensorflowv1", "tfv1"] and not isinstance( - model, - tuple(tensorflow_dict.values()), + model, tuple(tensorflow_dict.values()), ): # pragma: no cover raise NotImplementedError("Tensorflow V1 not implemented yet") # model = TensorflowV1Initializer( # data=data, model=model, library=library, **kwargs # )() elif library in supported_models and isinstance( - model, - tuple(all_models.values()), + model, tuple(all_models.values()), ): pass else: # pragma: no cover @@ -123,8 +119,7 @@ def __call__(self): f"library must be one of {supported_models}. Got {library}", ) assert hasattr( - model, - "fit", + model, "fit", ), f"model must have a fit method. Got type {type(model)}" return model diff --git a/deckard/base/model/model.py b/deckard/base/model/model.py index bd69bc4a..11f95553 100644 --- a/deckard/base/model/model.py +++ b/deckard/base/model/model.py @@ -67,8 +67,7 @@ def __call__(self): params["output_dim"] = params["output_dim"] elif isinstance(params["output_dim"], ListConfig): output_dim_list = OmegaConf.to_container( - params["output_dim"], - resolve=True, + params["output_dim"], resolve=True, ) if len(output_dim_list) == 1: params["output_dim"] = output_dim_list[0] @@ -310,8 +309,7 @@ def __call__( elif isinstance(data, (str, Path)): data = self.load(data) assert isinstance( - data, - (type(None), list, tuple), + data, (type(None), list, tuple), ), f"Data {data} is not a list. It is of type {type(data)}." assert len(data) == 4, f"Data {data} is not a tuple of length 4." result_dict["data"] = data @@ -324,8 +322,7 @@ def __call__( model = self.load(model) elif hasattr(model, ("fit", "fit_generator")): assert hasattr(model, "predict") or hasattr( - model, - "predict_proba", + model, "predict_proba", ), f"Model {model} does not have a predict or predict_proba method." else: # pragma: no cover raise ValueError(f"Model {model} is not a valid model.") @@ -354,9 +351,7 @@ def __call__( # Fitting if model_file is None: model, fit_time_dict = self.fit( - data=data, - model=model, - model_file=model_file, + data=data, model=model, model_file=model_file, ) time_dict.update(**fit_time_dict) result_dict["model"] = model @@ -367,9 +362,7 @@ def __call__( result_dict["model"] = model else: model, fit_time_dict = self.fit( - data=data, - model=model, - model_file=model_file, + data=data, model=model, model_file=model_file, ) result_dict["model"] = model result_dict["data"] = data @@ -377,9 +370,7 @@ def __call__( # Predicting if predictions_file is not None and not Path(predictions_file).exists(): preds, pred_time_dict = self.predict( - data=data, - model=model, - predictions_file=predictions_file, + data=data, model=model, predictions_file=predictions_file, ) result_dict["time_dict"].update(**pred_time_dict) result_dict["predictions"] = preds @@ -388,18 +379,14 @@ def __call__( result_dict["predictions"] = preds else: preds, pred_time_dict = self.predict( - data=data, - model=model, - predictions_file=predictions_file, + data=data, model=model, predictions_file=predictions_file, ) result_dict["time_dict"].update(**pred_time_dict) result_dict["predictions"] = preds # Predicting probabilities if probabilities_file is not None: probs, prob_time_dict = self.predict_proba( - data=data, - model=model, - probabilities_file=probabilities_file, + data=data, model=model, probabilities_file=probabilities_file, ) result_dict["probabilities"] = probs result_dict["time_dict"].update(**prob_time_dict) @@ -409,18 +396,14 @@ def __call__( result_dict["time_dict"].update(**prob_time_dict) else: probs, prob_time_dict = self.predict_proba( - data=data, - model=model, - probabilities_file=probabilities_file, + data=data, model=model, probabilities_file=probabilities_file, ) result_dict["probabilities"] = probs result_dict["time_dict"].update(**prob_time_dict) # Predicting loss if losses_file is not None: loss, loss_time_dict = self.predict_log_loss( - data=data, - model=model, - losses_file=losses_file, + data=data, model=model, losses_file=losses_file, ) time_dict.update(**loss_time_dict) result_dict["losses"] = loss @@ -430,9 +413,7 @@ def __call__( result_dict["losses"] = loss else: loss, loss_time_dict = self.predict_log_loss( - data=data, - model=model, - losses_file=losses_file, + data=data, model=model, losses_file=losses_file, ) time_dict.update(**loss_time_dict) result_dict["losses"] = loss @@ -468,8 +449,7 @@ def initialize(self, data=None, model=None): elif isinstance(data, type(None)): data = self.data.initialize(data) assert isinstance( - data, - (list), + data, (list), ), f"Data {data} is not a list. It is of type {type(data)}." if isinstance(model, (str, Path)) and Path(model).exists(): model = self.load(model) @@ -544,8 +524,7 @@ def predict(self, data=None, model=None, predictions_file=None): ), "X_test and y_test must have the same length." assert hasattr(model, "fit"), f"Model {model} does not have a fit method." assert hasattr( - model, - "predict", + model, "predict", ), f"Model {model} does not have a predict method." device = str(model.device) if hasattr(model, "device") else "cpu" try: diff --git a/deckard/base/model/sklearn_pipeline.py b/deckard/base/model/sklearn_pipeline.py index 631b39ed..ab83e1d7 100644 --- a/deckard/base/model/sklearn_pipeline.py +++ b/deckard/base/model/sklearn_pipeline.py @@ -83,21 +83,18 @@ def __call__(self, model): kwargs.update(**kwargs.pop("kwargs")) if "art." in str(type(model)): assert isinstance( - model.model, - BaseEstimator, + model.model, BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model.model)}" else: assert isinstance( - model, - BaseEstimator, + model, BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model)}" if not isinstance(model, Pipeline): model = Pipeline([("model", model)]) else: model.steps.insert(-2, [stage_name, model]) assert isinstance( - model, - Pipeline, + model, Pipeline, ), f"model must be a sklearn pipeline. Got {type(model)}" return model @@ -123,8 +120,7 @@ def __init__(self, **kwargs): pipe[stage] = asdict(pipe[stage]) else: assert hasattr( - pipe[stage], - "transform", + pipe[stage], "transform", ), f"Pipeline stage must be a SklearnModelPipelineStage, dict, or have a transform method. Got {type(pipe[stage])}" if isinstance(pipe[stage], dict): params = deepcopy(pipe[stage]) @@ -132,8 +128,7 @@ def __init__(self, **kwargs): pipe[stage] = SklearnModelPipelineStage(params, stage_name=stage_name) elif hasattr(pipe[stage], "transform"): assert hasattr( - pipe[stage], - "fit", + pipe[stage], "fit", ), f"Pipeline stage must have a fit method. Got {type(pipe[stage])}" else: raise ValueError( @@ -179,19 +174,16 @@ def __call__(self, model): elif hasattr(stage, "fit"): if "art." in str(type(model)): assert isinstance( - model.model, - BaseEstimator, + model.model, BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model.model)}" else: assert isinstance( - model, - BaseEstimator, + model, BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model)}" if not isinstance(model, Pipeline) and "art." not in str(type(model)): model = Pipeline([("model", model)]) elif "art." in str(type(model)) and not isinstance( - model.model, - Pipeline, + model.model, Pipeline, ): model.model = Pipeline([("model", model.model)]) elif "art." in str(type(model)) and isinstance(model.model, Pipeline): @@ -200,13 +192,11 @@ def __call__(self, model): model.steps.insert(-2, [stage, model]) if "art." not in str(type(model)): assert isinstance( - model, - Pipeline, + model, Pipeline, ), f"model must be a sklearn pipeline. Got {type(model)}" else: assert isinstance( - model.model, - Pipeline, + model.model, Pipeline, ), f"model must be a sklearn pipeline. Got {type(model)}" return model @@ -263,8 +253,7 @@ def __call__(self): if self.pipeline is not None: obj = self.pipeline(model) assert isinstance( - obj, - BaseEstimator, + obj, BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model)}" else: obj = model @@ -299,8 +288,7 @@ def __call__(self): else: raise ValueError(f"library must be one of {sklearn_models}. Got {library}") assert hasattr( - model, - "fit", + model, "fit", ), f"model must have a fit method. Got type {type(model)}" return model diff --git a/deckard/base/model/tensorflow_models.py b/deckard/base/model/tensorflow_models.py index d9796d43..4b5f27f9 100644 --- a/deckard/base/model/tensorflow_models.py +++ b/deckard/base/model/tensorflow_models.py @@ -131,8 +131,7 @@ def __call__(self): train_step = kwargs.pop("train_step") train_step = factory(name=train_step.pop("name"), **train_step) if library in tensorflow_dict and not isinstance( - model, - tuple(tensorflow_dict.values()), + model, tuple(tensorflow_dict.values()), ): est = tensorflow_dict[library] model = est(model, **kwargs, train_step=train_step) diff --git a/deckard/base/plots/plots.py b/deckard/base/plots/plots.py index 89393f49..31c0bb0a 100644 --- a/deckard/base/plots/plots.py +++ b/deckard/base/plots/plots.py @@ -146,10 +146,7 @@ class Yellowbrick_Visualiser: scorers: ScorerDict = field(default_factory=ScorerDict) def visualise_data( - self, - data: list, - classes: list = None, - features: list = None, + self, data: list, classes: list = None, features: list = None, ) -> List[Path]: """ Visualise classification results according to the configuration file. @@ -189,8 +186,7 @@ def visualise_data( ) paths["radviz"] = str( Path( - path, - plots["radviz"] + str(plots.pop("filetype", ".png")), + path, plots["radviz"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -202,8 +198,7 @@ def visualise_data( ) paths["rank1d"] = str( Path( - path, - plots["rank1d"] + str(plots.pop("filetype", ".png")), + path, plots["rank1d"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -215,8 +210,7 @@ def visualise_data( ) paths["rank2d"] = str( Path( - path, - plots["rank2d"] + str(plots.pop("filetype", ".png")), + path, plots["rank2d"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -228,8 +222,7 @@ def visualise_data( ) paths["balance"] = str( Path( - path, - plots["balance"] + str(plots.pop("filetype", ".png")), + path, plots["balance"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -238,14 +231,12 @@ def visualise_data( visualiser.fit(X_train, y_train) visualiser.show( Path( - path, - plots["correlation"] + str(plots.pop("filetype", ".png")), + path, plots["correlation"] + str(plots.pop("filetype", ".png")), ), ) paths["correlation"] = str( Path( - path, - plots["correlation"] + str(plots.pop("filetype", ".png")), + path, plots["correlation"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -257,8 +248,7 @@ def visualise_data( ) paths["pca"] = str( Path( - path, - plots["pca"] + str(plots.pop("filetype", ".png")), + path, plots["pca"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -270,8 +260,7 @@ def visualise_data( ) paths["manifold"] = str( Path( - path, - plots["manifold"] + str(plots.pop("filetype", ".png")), + path, plots["manifold"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -283,19 +272,14 @@ def visualise_data( ) paths["parallel"] = str( Path( - path, - plots["parallel"] + str(plots.pop("filetype", ".png")), + path, plots["parallel"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() return paths def visualise_classification( - self, - data: list, - model: Callable, - classes: list = None, - predictions_file=None, + self, data: list, model: Callable, classes: list = None, predictions_file=None, ) -> List[Path]: """ Visualise classification results according to the configuration file. @@ -330,8 +314,7 @@ def visualise_classification( data[3] = np.argmax(data[3], axis=1) if len(set(np.unique(data[2]))) > 2: viz = visualiser( - model, - classes=[int(y) for y in np.unique(data[2])], + model, classes=[int(y) for y in np.unique(data[2])], ) elif len(set(np.unique(data[2]))) == 2: try: @@ -345,8 +328,7 @@ def visualise_classification( f"Failed due to error {e}. Trying without binary", ) viz = visualiser( - model, - classes=[int(y) for y in np.unique(data[2])], + model, classes=[int(y) for y in np.unique(data[2])], ) else: viz = visualiser(model, classes=[0]) @@ -360,10 +342,7 @@ def visualise_classification( return paths def visualise_regression( - self, - data: list, - model: object, - predictions_file=None, + self, data: list, model: object, predictions_file=None, ) -> List[Path]: """ Visualise classification results according to the configuration file. @@ -414,10 +393,7 @@ def visualise_regression( return paths def visualise_clustering( - self, - data: list, - model: object, - predictions_file=None, + self, data: list, model: object, predictions_file=None, ) -> List[Path]: """ Visualise classification results according to the configuration file. diff --git a/deckard/base/utils/factory.py b/deckard/base/utils/factory.py index e5fffd07..7b82be5f 100644 --- a/deckard/base/utils/factory.py +++ b/deckard/base/utils/factory.py @@ -43,15 +43,13 @@ def factory(module_class_string, *args, super_cls: type = None, **kwargs) -> obj raise e module = import_module(module_name) assert hasattr(module, class_name), "class {} is not in {}".format( - class_name, - module_name, + class_name, module_name, ) logger.debug("reading class {} from module {}".format(class_name, module_name)) cls = getattr(module, class_name) if super_cls is not None: assert issubclass(cls, super_cls), "class {} should inherit from {}".format( - class_name, - super_cls.__name__, + class_name, super_cls.__name__, ) logger.debug("initialising {} with params {}".format(class_name, kwargs)) try: @@ -93,8 +91,7 @@ def make_grid(dictionary: list) -> list: big = [] if not isinstance(dictionary, list): assert isinstance( - dictionary, - dict, + dictionary, dict, ), f"dictionary must be a list or dict, not {type(dictionary)}" dict_list = [dictionary] else: diff --git a/deckard/base/utils/hashing.py b/deckard/base/utils/hashing.py index 4ee35d15..0176f4a7 100644 --- a/deckard/base/utils/hashing.py +++ b/deckard/base/utils/hashing.py @@ -20,9 +20,7 @@ def to_dict(obj: Union[dict, OrderedDict, NamedTuple]) -> dict: obj = dict( deepcopy( OmegaConf.to_container( - obj, - resolve=True, - structured_config_mode=SCMode.DICT, + obj, resolve=True, structured_config_mode=SCMode.DICT, ), ), ) diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index e6606921..425450e0 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -152,9 +152,7 @@ def prepare_access_values(self): logger.info(f"Running command: {command}") command = command.split(" ") output = subprocess.run( - command, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, + command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, ) logger.info(f"{output}") return output.stdout diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index a19e887d..c5462318 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -51,12 +51,10 @@ data.dropna(axis=0, subset=["atk_value", "atk_param"], inplace=True) data.dropna(axis=0, subset=["def_value", "def_param"], inplace=True) data.loc[:, "adv_failures"] = (1 - data.loc[:, "adv_accuracy"]) * data.loc[ - :, - "attack.attack_size", + :, "attack.attack_size", ] data.loc[:, "ben_failures"] = (1 - data.loc[:, "accuracy"]) * data.loc[ - :, - "attack.attack_size", + :, "attack.attack_size", ] def plot_aft( @@ -184,9 +182,7 @@ def make_afr_table(score_list, aft_dict, dataset): return aft_data def clean_data_for_aft( - data, - kwarg_list, - target="adv_failure_rate", + data, kwarg_list, target="adv_failure_rate", ): subset = data.copy() assert ( @@ -214,19 +210,11 @@ def clean_data_for_aft( return cleaned, y, data def split_data_for_aft( - data, - target, - duration_col, - kwarg_list, - test_size=0.2, - random_state=42, + data, target, duration_col, kwarg_list, test_size=0.2, random_state=42, ): cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) X_train, X_test, y_train, y_test = train_test_split( - cleaned, - y, - test_size=test_size, - random_state=random_state, + cleaned, y, test_size=test_size, random_state=random_state, ) assert ( target in cleaned @@ -250,12 +238,7 @@ def split_data_for_aft( ] X_train, X_test, y_train, y_test = split_data_for_aft( - data, - target, - duration_col, - kwarg_list, - test_size=0.2, - random_state=42, + data, target, duration_col, kwarg_list, test_size=0.2, random_state=42, ) weibull_dict = { @@ -304,10 +287,7 @@ def split_data_for_aft( }, } - weibull_layers = plot_partial_effects( - aft=wft, - **weibull_partial_dict_layers, - ) + weibull_layers = plot_partial_effects(aft=wft, **weibull_partial_dict_layers,) wft_scores = score_model(wft, X_train, X_test) # cox_replacement_dict = { diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 3627c7a2..e71f9fc6 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -215,12 +215,7 @@ def format_control_parameter(data, control_dict): def clean_data_for_plotting( - data, - def_gen_dict, - atk_gen_dict, - control_dict, - file, - folder, + data, def_gen_dict, atk_gen_dict, control_dict, file, folder, ): logger.info("Replacing attack and defence names with short names...") if hasattr(data, "def_gen"): @@ -283,10 +278,7 @@ def save_results(results, results_file) -> str: parser.add_argument("--verbose", type=str, default="INFO") parser.add_argument("--default_epochs", type=int, default=20) parser.add_argument( - "--kwargs", - type=list, - default=None, - nargs="*", + "--kwargs", type=list, default=None, nargs="*", ) args = parser.parse_args() logging.basicConfig(level=args.verbose) @@ -300,12 +292,7 @@ def save_results(results, results_file) -> str: atk_gen_dict = big_dict["attacks"] control_dict = big_dict["params"] results = clean_data_for_plotting( - results, - def_gen_dict, - atk_gen_dict, - control_dict, - results_file, - results_folder, + results, def_gen_dict, atk_gen_dict, control_dict, results_file, results_folder, ) report_file = save_results(results, results_file) assert Path( diff --git a/deckard/layers/experiment.py b/deckard/layers/experiment.py index 1e481821..84805375 100644 --- a/deckard/layers/experiment.py +++ b/deckard/layers/experiment.py @@ -44,10 +44,7 @@ def get_dvc_stage_params( def run_stage( - params_file="params.yaml", - pipeline_file="dvc.yaml", - directory=".", - stage=None, + params_file="params.yaml", pipeline_file="dvc.yaml", directory=".", stage=None, ): logger.info( f"Running stage {stage} with params_file: {params_file} and pipeline_file: {pipeline_file} in directory {directory}", diff --git a/deckard/layers/find_best.py b/deckard/layers/find_best.py index 0461f28e..fcc7504a 100644 --- a/deckard/layers/find_best.py +++ b/deckard/layers/find_best.py @@ -50,9 +50,7 @@ def find_optuna_best( if params_file is True: if config_subdir is not None: params_file = Path( - config_folder, - f"{config_subdir}", - f"{default_config}.yaml", + config_folder, f"{config_subdir}", f"{default_config}.yaml", ) params = cfg.get(config_subdir) else: @@ -61,9 +59,7 @@ def find_optuna_best( else: if config_subdir is not None: params_file = Path( - config_folder, - f"{config_subdir}", - f"{params_file}.yaml", + config_folder, f"{config_subdir}", f"{params_file}.yaml", ) params = cfg.get(config_subdir) else: @@ -97,8 +93,7 @@ def find_optuna_best( if args.study_type == "optuna": with open( - Path(args.config_folder, args.default_config).with_suffix(".yaml"), - "r", + Path(args.config_folder, args.default_config).with_suffix(".yaml"), "r", ) as f: default_params = yaml.load(f, Loader=yaml.FullLoader) if "hydra" in default_params: diff --git a/deckard/layers/generate_grid.py b/deckard/layers/generate_grid.py index 487ce801..3e5a16ae 100644 --- a/deckard/layers/generate_grid.py +++ b/deckard/layers/generate_grid.py @@ -67,19 +67,14 @@ def generate_grid_from_folders(conf_dir, regex): big_list = make_grid(big_dict) assert len(big_list) == reduce( - mul, - layers, + mul, layers, ), f"Grid size {len(big_list)} does not match product of layer sizes {reduce(mul, layers)}" logger.info(f"Generated grid with {len(big_list)} configs") return big_list def generate_queue( - conf_root, - grid_dir, - regex, - queue_folder="queue", - default_file="default.yaml", + conf_root, grid_dir, regex, queue_folder="queue", default_file="default.yaml", ): this_dir = os.getcwd() conf_dir = os.path.join(this_dir, conf_root, grid_dir) diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index a1877c59..aa81452d 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -17,8 +17,7 @@ __all__ = ["write_stage", "optimise", "parse_stage", "get_files"] config_path = os.environ.get( - "DECKARD_CONFIG_PATH", - str(Path(Path.cwd(), "conf").absolute().as_posix()), + "DECKARD_CONFIG_PATH", str(Path(Path.cwd(), "conf").absolute().as_posix()), ) assert Path( config_path, @@ -28,8 +27,7 @@ def get_files( - cfg, - stage, + cfg, stage, ): """ Gets the file names from @@ -199,8 +197,7 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: key_list = [] for stage in stages: assert Path( - path, - "dvc.yaml", + path, "dvc.yaml", ).exists(), f"{Path(path, 'dvc.yaml')} does not exist." with open(Path(path, "dvc.yaml"), "r") as f: new_keys = yaml.load(f, Loader=yaml.FullLoader)["stages"][stage][ diff --git a/deckard/layers/plots.py b/deckard/layers/plots.py index 888c8527..91690a9b 100644 --- a/deckard/layers/plots.py +++ b/deckard/layers/plots.py @@ -101,12 +101,7 @@ def scatter_plot( # plt.gcf().clear() data = data.sort_values(by=[hue, x, y]) graph = sns.scatterplot( - data=data, - x=x, - y=y, - hue=hue, - hue_order=hue_order, - **kwargs, + data=data, x=x, y=y, hue=hue, hue_order=hue_order, **kwargs, ) graph.set_yscale(y_scale) graph.set_xscale(x_scale) @@ -215,44 +210,22 @@ def min_max_scaling(data, **kwargs): if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( - "-p", - "--path", - type=str, - help="Path to the plot folder", - required=True, + "-p", "--path", type=str, help="Path to the plot folder", required=True, ) parser.add_argument( - "-f", - "--file", - type=str, - help="Data file to read from", - required=True, + "-f", "--file", type=str, help="Data file to read from", required=True, ) parser.add_argument( - "-o", - "--output", - type=str, - help="Output file name", - default="data.csv", + "-o", "--output", type=str, help="Output file name", default="data.csv", ) parser.add_argument( - "-t", - "--plotfiletype", - type=str, - help="Filetype of the plots", - default=".pdf", + "-t", "--plotfiletype", type=str, help="Filetype of the plots", default=".pdf", ) parser.add_argument( - "-v", - "--verbosity", - default="INFO", - help="Increase output verbosity", + "-v", "--verbosity", default="INFO", help="Increase output verbosity", ) parser.add_argument( - "-c", - "--config", - help="Path to the config file", - default="conf/plots.yaml", + "-c", "--config", help="Path to the config file", default="conf/plots.yaml", ) args = parser.parse_args() logging.basicConfig(level=args.verbosity) diff --git a/deckard/layers/prepare_queue.py b/deckard/layers/prepare_queue.py index 6c4aeb94..86bc58ee 100644 --- a/deckard/layers/prepare_queue.py +++ b/deckard/layers/prepare_queue.py @@ -16,8 +16,7 @@ def get_files( - cfg, - stage, + cfg, stage, ): """ Gets the file names from @@ -169,8 +168,7 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: with open(Path(params), "r") as f: params = yaml.load(f, Loader=yaml.FullLoader) assert isinstance( - params, - dict, + params, dict, ), f"Params in file {params} must be a dict. It is a {type(params)}." key_list = [] for stage in stages: @@ -198,8 +196,7 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: else: raise TypeError(f"Expected str or dict, got {type(params)}") assert isinstance( - params, - dict, + params, dict, ), f"Params must be a dict. It is type {type(params)}." # Load files from dvc with open(Path(path, "dvc.yaml"), "r") as f: @@ -224,11 +221,7 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: def write_stage( - params: dict, - stage: str, - id_: str, - path=None, - working_dir=None, + params: dict, stage: str, id_: str, path=None, working_dir=None, ) -> None: """ Write params to dvc.yaml @@ -276,8 +269,7 @@ def prepare_experiment_folder(cfg: DictConfig) -> None: if __name__ == "__main__": logger = logging.getLogger(__name__) config_path = os.environ.pop( - "DECKARD_CONFIG_PATH", - str(Path(Path(), "conf").absolute().as_posix()), + "DECKARD_CONFIG_PATH", str(Path(Path(), "conf").absolute().as_posix()), ) config_name = os.environ.pop("DECKARD_DEFAULT_CONFIG", "default.yaml") diff --git a/deckard/layers/utils.py b/deckard/layers/utils.py index 208c4006..1fb7c783 100644 --- a/deckard/layers/utils.py +++ b/deckard/layers/utils.py @@ -29,11 +29,7 @@ def find_conf_files( - config_name, - config_subdir, - config_dir, - config_regex=None, - default_file=None, + config_name, config_subdir, config_dir, config_regex=None, default_file=None, ): if config_name is not None: assert config_regex is None, "Cannot specify both config_name and config_regex" @@ -120,10 +116,7 @@ def compose_experiment(file, config_dir, overrides=None, default_file="default.y def save_params_file( - config_dir="conf", - config_file="default", - params_file="params.yaml", - overrides=[], + config_dir="conf", config_file="default", params_file="params.yaml", overrides=[], ): config_dir = str(Path(Path(), config_dir).absolute().as_posix()) with initialize_config_dir(config_dir=config_dir, version_base="1.3"): diff --git a/deckard/layers/watcher.py b/deckard/layers/watcher.py index 2668b972..6cb6752a 100644 --- a/deckard/layers/watcher.py +++ b/deckard/layers/watcher.py @@ -26,10 +26,7 @@ class JSONHandler(watchdog.events.PatternMatchingEventHandler): def __init__(self, servers, port, user, password, filename, destination, **kwargs): # Set the patterns for PatternMatchingEventHandler watchdog.events.PatternMatchingEventHandler.__init__( - self, - patterns=[REGEX], - ignore_directories=True, - case_sensitive=False, + self, patterns=[REGEX], ignore_directories=True, case_sensitive=False, ) self.ssh = createSSHClient(servers, port, user, password) logger.info("Initiated SSH client") @@ -38,8 +35,7 @@ def __init__(self, servers, port, user, password, filename, destination, **kwarg self.recurse = kwargs["recursive"] if "recurse" in kwargs else False logger.info( "Source file is {} and destination is {}".format( - self.filename, - self.destination, + self.filename, self.destination, ), ) logger.info("Regex is {}".format(REGEX)) @@ -116,11 +112,7 @@ def send_json_with_scp(self): ) parser.add_argument("--port", "-p", type=int, help="The port to send the files to.") parser.add_argument( - "--user", - "-u", - type=str, - required=True, - help="The user to send the files to.", + "--user", "-u", type=str, required=True, help="The user to send the files to.", ) parser.add_argument( "--password", @@ -132,18 +124,10 @@ def send_json_with_scp(self): parser.add_argument("--original", type=str, help="The original queue file.") parser.add_argument("--queue", type=str, help="The current queue file.") parser.add_argument( - "--regex", - "-e", - type=str, - required=True, - help="The regex to watch for.", + "--regex", "-e", type=str, required=True, help="The regex to watch for.", ) parser.add_argument( - "--recursive", - "-r", - type=bool, - default=True, - help="Whether to recurse or not.", + "--recursive", "-r", type=bool, default=True, help="Whether to recurse or not.", ) parser.add_argument( "--n_jobs", @@ -153,17 +137,12 @@ def send_json_with_scp(self): help="The number of jobs to run in parallel.", ) parser.add_argument( - "--log", - "-l", - type=int, - default=logging.INFO, - help="The log level.", + "--log", "-l", type=int, default=logging.INFO, help="The log level.", ) args = parser.parse_args() # Set up logging logging.basicConfig( - level=args.log, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + level=args.log, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", ) if args.regex is not None: REGEX = args.regex diff --git a/examples/keras/keras_example.py b/examples/keras/keras_example.py index 709754a0..3a3fba11 100644 --- a/examples/keras/keras_example.py +++ b/examples/keras/keras_example.py @@ -44,8 +44,6 @@ def __init__(self, optimizer, loss, metrics): def __call__(self): self.model.compile( - optimizer=self.optimizer, - loss=self.loss, - metrics=self.metrics, + optimizer=self.optimizer, loss=self.loss, metrics=self.metrics, ) return self.model diff --git a/examples/power/torch_example.py b/examples/power/torch_example.py index 580b7f80..47dd9eb1 100644 --- a/examples/power/torch_example.py +++ b/examples/power/torch_example.py @@ -27,12 +27,7 @@ def forward(self, x): def ResNet18(num_channels=1, num_classes=10): model = models.resnet18() model.conv1 = torch.nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = torch.nn.Linear(512, num_classes) return model @@ -41,12 +36,7 @@ def ResNet18(num_channels=1, num_classes=10): def ResNet34(num_channels=1, num_classes=10): model = models.resnet34() model.conv1 = torch.nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = torch.nn.Linear(512, num_classes) return model @@ -55,12 +45,7 @@ def ResNet34(num_channels=1, num_classes=10): def ResNet50(num_channels=1, num_classes=10): model = models.resnet50() model.conv1 = torch.nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = torch.nn.Linear(2048, num_classes) return model @@ -69,12 +54,7 @@ def ResNet50(num_channels=1, num_classes=10): def ResNet101(num_channels=1, num_classes=10): model = models.resnet101() model.conv1 = torch.nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = torch.nn.Linear(2048, num_classes) return model @@ -83,12 +63,7 @@ def ResNet101(num_channels=1, num_classes=10): def ResNet152(num_channels=1, num_classes=10): model = models.resnet152() model.conv1 = torch.nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = torch.nn.Linear(2048, num_classes) return model diff --git a/examples/pytorch/cifar10/torch_example.py b/examples/pytorch/cifar10/torch_example.py index 32d333bd..e4a067b2 100644 --- a/examples/pytorch/cifar10/torch_example.py +++ b/examples/pytorch/cifar10/torch_example.py @@ -12,12 +12,7 @@ def ResNet18(num_channels=1, num_classes=10): model = models.resnet18(pretrained=True) model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -26,12 +21,7 @@ def ResNet18(num_channels=1, num_classes=10): def ResNet34(num_channels=1, num_classes=10): model = models.resnet34(pretrained=True) model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -40,12 +30,7 @@ def ResNet34(num_channels=1, num_classes=10): def ResNet50(num_channels=1, num_classes=10): model = models.resnet50(pretrained=True) model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -54,12 +39,7 @@ def ResNet50(num_channels=1, num_classes=10): def ResNet101(num_channels=1, num_classes=10): model = models.resnet101(pretrained=True) model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -68,12 +48,7 @@ def ResNet101(num_channels=1, num_classes=10): def ResNet152(num_channels=1, num_classes=10): model = models.resnet152(pretrained=True) model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model diff --git a/examples/pytorch/cifar100/torch_example.py b/examples/pytorch/cifar100/torch_example.py index 2007d9d0..d1f8e038 100644 --- a/examples/pytorch/cifar100/torch_example.py +++ b/examples/pytorch/cifar100/torch_example.py @@ -12,12 +12,7 @@ def ResNet18(num_channels=1, num_classes=10): model = models.resnet18() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -26,12 +21,7 @@ def ResNet18(num_channels=1, num_classes=10): def ResNet34(num_channels=1, num_classes=10): model = models.resnet34() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -40,12 +30,7 @@ def ResNet34(num_channels=1, num_classes=10): def ResNet50(num_channels=1, num_classes=10): model = models.resnet50() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -54,12 +39,7 @@ def ResNet50(num_channels=1, num_classes=10): def ResNet101(num_channels=1, num_classes=10): model = models.resnet101() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -68,12 +48,7 @@ def ResNet101(num_channels=1, num_classes=10): def ResNet152(num_channels=1, num_classes=10): model = models.resnet152() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model diff --git a/examples/pytorch/mnist/torch_example.py b/examples/pytorch/mnist/torch_example.py index 2007d9d0..d1f8e038 100644 --- a/examples/pytorch/mnist/torch_example.py +++ b/examples/pytorch/mnist/torch_example.py @@ -12,12 +12,7 @@ def ResNet18(num_channels=1, num_classes=10): model = models.resnet18() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -26,12 +21,7 @@ def ResNet18(num_channels=1, num_classes=10): def ResNet34(num_channels=1, num_classes=10): model = models.resnet34() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -40,12 +30,7 @@ def ResNet34(num_channels=1, num_classes=10): def ResNet50(num_channels=1, num_classes=10): model = models.resnet50() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -54,12 +39,7 @@ def ResNet50(num_channels=1, num_classes=10): def ResNet101(num_channels=1, num_classes=10): model = models.resnet101() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -68,12 +48,7 @@ def ResNet101(num_channels=1, num_classes=10): def ResNet152(num_channels=1, num_classes=10): model = models.resnet152() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model diff --git a/examples/scratch/compute_attack_success.py b/examples/scratch/compute_attack_success.py index acb4f951..f22a9e02 100644 --- a/examples/scratch/compute_attack_success.py +++ b/examples/scratch/compute_attack_success.py @@ -67,22 +67,13 @@ def write_data_file(data, file: str): required=True, ) attack_success_parser.add_argument( - "-l", - "--labels_file", - help="Full path to the predictions file", - required=True, + "-l", "--labels_file", help="Full path to the predictions file", required=True, ) attack_success_parser.add_argument( - "-i", - "--input_score_file", - default=None, - required=True, + "-i", "--input_score_file", default=None, required=True, ) attack_success_parser.add_argument( - "-o", - "--output_score_file", - help="Full path to the labels file", - required=True, + "-o", "--output_score_file", help="Full path to the labels file", required=True, ) args = attack_success_parser.parse_args() diff --git a/examples/scratch/deckard_queue.py b/examples/scratch/deckard_queue.py index dc3dedac..ba3626c3 100644 --- a/examples/scratch/deckard_queue.py +++ b/examples/scratch/deckard_queue.py @@ -28,10 +28,7 @@ nargs="+", ) queue_parser.add_argument( - "--params_file", - "-p", - default=[], - nargs="+", + "--params_file", "-p", default=[], nargs="+", ) queue_parser.add_argument( "--output_file", @@ -93,8 +90,7 @@ def add_overrides_from_params_file(params_file, default_config): param_name = param_names[j] j += 1 new_config = add_overrides_from_params_file( - params_file=params_file, - default_config=default_config, + params_file=params_file, default_config=default_config, ) if conf_name == "default": study_name = f"{param_name}" diff --git a/examples/scratch/objective.py b/examples/scratch/objective.py index e1f4e34a..7bbf8c00 100644 --- a/examples/scratch/objective.py +++ b/examples/scratch/objective.py @@ -9,8 +9,7 @@ logger = logging.getLogger(__name__) config_path = os.environ.pop( - "DECKARD_CONFIG_PATH", - str(Path(Path(), "conf").absolute().as_posix()), + "DECKARD_CONFIG_PATH", str(Path(Path(), "conf").absolute().as_posix()), ) config_name = os.environ.pop("DECKARD_DEFAULT_CONFIG", "default.yaml") @@ -57,12 +56,10 @@ def configure(cfg: DictConfig, trial: Trial) -> None: if postprocessor is not None: if postprocessor.strip() == "art.defences.postprocessor.HighConfidence": _ = trial.suggest_int( - "model.art.pipeline.preprocessor.defences.HighConfidence.threshold", - 1, + "model.art.pipeline.preprocessor.defences.HighConfidence.threshold", 1, ) trial.suggest_categorical( - "model.art.pipeline.preprocessor.defences.HighConfidence.abs", - [True], + "model.art.pipeline.preprocessor.defences.HighConfidence.abs", [True], ) trial.suggest_categorical( "model.art.pipeline.preprocessor.defences.HighConfidence.clip_values", @@ -70,9 +67,7 @@ def configure(cfg: DictConfig, trial: Trial) -> None: ) elif postprocessor.strip() == "art.defences.postprocessor.GaussianNoise": _ = trial.suggest_loguniform( - "model.art.pipeline.preprocessor.defences.GaussianNoise.sigma", - 0.1, - 10, + "model.art.pipeline.preprocessor.defences.GaussianNoise.sigma", 0.1, 10, ) trial.suggest_categorical( "model.art.pipeline.preprocessor.defences.GaussianNoise.clip_values", diff --git a/examples/scratch/torch_example.py b/examples/scratch/torch_example.py index 2007d9d0..d1f8e038 100644 --- a/examples/scratch/torch_example.py +++ b/examples/scratch/torch_example.py @@ -12,12 +12,7 @@ def ResNet18(num_channels=1, num_classes=10): model = models.resnet18() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -26,12 +21,7 @@ def ResNet18(num_channels=1, num_classes=10): def ResNet34(num_channels=1, num_classes=10): model = models.resnet34() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -40,12 +30,7 @@ def ResNet34(num_channels=1, num_classes=10): def ResNet50(num_channels=1, num_classes=10): model = models.resnet50() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -54,12 +39,7 @@ def ResNet50(num_channels=1, num_classes=10): def ResNet101(num_channels=1, num_classes=10): model = models.resnet101() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -68,12 +48,7 @@ def ResNet101(num_channels=1, num_classes=10): def ResNet152(num_channels=1, num_classes=10): model = models.resnet152() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model diff --git a/examples/security/classification/retrain.py b/examples/security/classification/retrain.py index 9623e19d..4d83db60 100644 --- a/examples/security/classification/retrain.py +++ b/examples/security/classification/retrain.py @@ -135,14 +135,7 @@ def unflatten_results(df, sep=".") -> List[dict]: def retrain_loop( - clf, - X_train, - y_train, - X_test, - y_test, - atk, - attack_size, - epochs, + clf, X_train, y_train, X_test, y_test, atk, attack_size, epochs, ) -> tuple: i = 0 results = [] @@ -202,20 +195,12 @@ def retrain_loop( # Some Logging print( "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( - i, - ben_time, - ben_score, - adv_time, - adv_score, + i, ben_time, ben_score, adv_time, adv_score, ), ) logger.info( "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( - i, - ben_time, - ben_score, - adv_time, - adv_score, + i, ben_time, ben_score, adv_time, adv_score, ), ) results = pd.DataFrame(results) @@ -309,16 +294,14 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), - "r", + Path("output/reports/attack", folder, "adv_probabilities.json"), "r", ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) with open( - Path("output/reports/attack", folder, "score_dict.json"), - "r", + Path("output/reports/attack", folder, "score_dict.json"), "r", ) as f: try: scores = json.load(f) @@ -328,8 +311,7 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: del scores["False Confidence"] scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), - "w", + Path("output/reports/attack", folder, "score_dict.json"), "w", ) as f: json.dump(scores, f) yaml_file = Path("output/reports/attack", folder, "params.yaml") @@ -367,14 +349,7 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: print(f"Training Model {i} of {len(models)}") if not Path("retrain", name, f"{epochs}.pkl").exists(): results, outputs = retrain_loop( - model, - X_train, - y_train, - X_test, - y_test, - atk, - epochs=epochs, - attack_size=50, + model, X_train, y_train, X_test, y_test, atk, epochs=epochs, attack_size=50, ) save_results_and_outputs(results, outputs, path=f"retrain/{name}/") Path("retrain", name).mkdir(parents=True, exist_ok=True) @@ -390,8 +365,7 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), - "r", + Path("output/reports/attack", folder, "adv_probabilities.json"), "r", ) as f: probs = json.load(f) probs = np.array(probs) @@ -405,28 +379,24 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: ), ) with open( - Path("output/reports/attack", folder, "score_dict.json"), - "r", + Path("output/reports/attack", folder, "score_dict.json"), "r", ) as f: scores = json.load(f) if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), - "w", + Path("output/reports/attack", folder, "score_dict.json"), "w", ) as f: json.dump(scores, f) if Path("output/reports/attack", folder, "params.yaml").exists(): with open( - Path("output/reports/attack", folder, "params.yaml"), - "r", + Path("output/reports/attack", folder, "params.yaml"), "r", ) as f: params = yaml.safe_load(f) elif Path("output/reports/attack", folder, "params.json").exists(): with open( - Path("output/reports/attack", folder, "params.json"), - "r", + Path("output/reports/attack", folder, "params.json"), "r", ) as f: params = json.load(f) else: diff --git a/examples/security/kdd-nsl/retrain.py b/examples/security/kdd-nsl/retrain.py index a7dbac4f..dae75fe6 100644 --- a/examples/security/kdd-nsl/retrain.py +++ b/examples/security/kdd-nsl/retrain.py @@ -135,14 +135,7 @@ def unflatten_results(df, sep=".") -> List[dict]: def retrain_loop( - clf, - X_train, - y_train, - X_test, - y_test, - atk, - attack_size, - epochs, + clf, X_train, y_train, X_test, y_test, atk, attack_size, epochs, ) -> tuple: i = 0 results = [] @@ -202,20 +195,12 @@ def retrain_loop( # Some Logging print( "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( - i, - ben_time, - ben_score, - adv_time, - adv_score, + i, ben_time, ben_score, adv_time, adv_score, ), ) logger.info( "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( - i, - ben_time, - ben_score, - adv_time, - adv_score, + i, ben_time, ben_score, adv_time, adv_score, ), ) results = pd.DataFrame(results) @@ -306,16 +291,14 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), - "r", + Path("output/reports/attack", folder, "adv_probabilities.json"), "r", ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) with open( - Path("output/reports/attack", folder, "score_dict.json"), - "r", + Path("output/reports/attack", folder, "score_dict.json"), "r", ) as f: try: scores = json.load(f) @@ -325,8 +308,7 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: del scores["False Confidence"] scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), - "w", + Path("output/reports/attack", folder, "score_dict.json"), "w", ) as f: json.dump(scores, f) yaml_file = Path("output/reports/attack", folder, "params.yaml") @@ -364,14 +346,7 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: print(f"Training Model {i} of {len(models)}") if not Path("retrain", name, f"{epochs}.pkl").exists(): results, outputs = retrain_loop( - model, - X_train, - y_train, - X_test, - y_test, - atk, - epochs=epochs, - attack_size=50, + model, X_train, y_train, X_test, y_test, atk, epochs=epochs, attack_size=50, ) save_results_and_outputs(results, outputs, path=f"retrain/{name}/") Path("retrain", name).mkdir(parents=True, exist_ok=True) @@ -387,8 +362,7 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), - "r", + Path("output/reports/attack", folder, "adv_probabilities.json"), "r", ) as f: probs = json.load(f) probs = np.array(probs) @@ -402,28 +376,24 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: ), ) with open( - Path("output/reports/attack", folder, "score_dict.json"), - "r", + Path("output/reports/attack", folder, "score_dict.json"), "r", ) as f: scores = json.load(f) if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), - "w", + Path("output/reports/attack", folder, "score_dict.json"), "w", ) as f: json.dump(scores, f) if Path("output/reports/attack", folder, "params.yaml").exists(): with open( - Path("output/reports/attack", folder, "params.yaml"), - "r", + Path("output/reports/attack", folder, "params.yaml"), "r", ) as f: params = yaml.safe_load(f) elif Path("output/reports/attack", folder, "params.json").exists(): with open( - Path("output/reports/attack", folder, "params.json"), - "r", + Path("output/reports/attack", folder, "params.json"), "r", ) as f: params = json.load(f) else: diff --git a/examples/security/truthseeker/retrain.py b/examples/security/truthseeker/retrain.py index 6b91b13c..6251005d 100644 --- a/examples/security/truthseeker/retrain.py +++ b/examples/security/truthseeker/retrain.py @@ -135,14 +135,7 @@ def unflatten_results(df, sep=".") -> List[dict]: def retrain_loop( - clf, - X_train, - y_train, - X_test, - y_test, - atk, - attack_size, - epochs, + clf, X_train, y_train, X_test, y_test, atk, attack_size, epochs, ) -> tuple: i = 0 results = [] @@ -202,20 +195,12 @@ def retrain_loop( # Some Logging print( "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( - i, - ben_time, - ben_score, - adv_time, - adv_score, + i, ben_time, ben_score, adv_time, adv_score, ), ) logger.info( "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( - i, - ben_time, - ben_score, - adv_time, - adv_score, + i, ben_time, ben_score, adv_time, adv_score, ), ) results = pd.DataFrame(results) @@ -306,16 +291,14 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), - "r", + Path("output/reports/attack", folder, "adv_probabilities.json"), "r", ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) with open( - Path("output/reports/attack", folder, "score_dict.json"), - "r", + Path("output/reports/attack", folder, "score_dict.json"), "r", ) as f: try: scores = json.load(f) @@ -325,8 +308,7 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: del scores["False Confidence"] scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), - "w", + Path("output/reports/attack", folder, "score_dict.json"), "w", ) as f: json.dump(scores, f) yaml_file = Path("output/reports/attack", folder, "params.yaml") @@ -364,14 +346,7 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: print(f"Training Model {i} of {len(models)}") if not Path("retrain", name, f"{epochs}.pkl").exists(): results, outputs = retrain_loop( - model, - X_train, - y_train, - X_test, - y_test, - atk, - epochs=epochs, - attack_size=50, + model, X_train, y_train, X_test, y_test, atk, epochs=epochs, attack_size=50, ) save_results_and_outputs(results, outputs, path=f"retrain/{name}/") Path("retrain", name).mkdir(parents=True, exist_ok=True) @@ -387,8 +362,7 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), - "r", + Path("output/reports/attack", folder, "adv_probabilities.json"), "r", ) as f: probs = json.load(f) probs = np.array(probs) @@ -402,28 +376,24 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: ), ) with open( - Path("output/reports/attack", folder, "score_dict.json"), - "r", + Path("output/reports/attack", folder, "score_dict.json"), "r", ) as f: scores = json.load(f) if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), - "w", + Path("output/reports/attack", folder, "score_dict.json"), "w", ) as f: json.dump(scores, f) if Path("output/reports/attack", folder, "params.yaml").exists(): with open( - Path("output/reports/attack", folder, "params.yaml"), - "r", + Path("output/reports/attack", folder, "params.yaml"), "r", ) as f: params = yaml.safe_load(f) elif Path("output/reports/attack", folder, "params.json").exists(): with open( - Path("output/reports/attack", folder, "params.json"), - "r", + Path("output/reports/attack", folder, "params.json"), "r", ) as f: params = json.load(f) else: diff --git a/examples/tfv1/tfv1_example.py b/examples/tfv1/tfv1_example.py index d5a9f88a..529dfdd3 100644 --- a/examples/tfv1/tfv1_example.py +++ b/examples/tfv1/tfv1_example.py @@ -12,17 +12,11 @@ def __init__(self, input_shape=(28, 28, 1), num_classes=10): self.input_ph = tf.placeholder(tf.float32, shape=[None, *input_shape]) self.labels_ph = tf.placeholder(tf.float32, shape=[None, num_classes]) self.layer1 = tf.layers.conv2d( - self.input_ph, - filters=4, - kernel_size=5, - activation=tf.nn.relu, + self.input_ph, filters=4, kernel_size=5, activation=tf.nn.relu, ) self.layer2 = tf.layers.max_pooling2d(self.layer1, 2, 2) self.layer3 = tf.layers.conv2d( - self.layer2, - filters=10, - kernel_size=5, - activation=tf.nn.relu, + self.layer2, filters=10, kernel_size=5, activation=tf.nn.relu, ) self.layer4 = tf.layers.max_pooling2d(self.layer3, 2, 2) self.layer5 = tf.layers.flatten(self.layer4) diff --git a/examples/tfv2/tfv2_example.py b/examples/tfv2/tfv2_example.py index b3ff9ba3..ae871ee3 100644 --- a/examples/tfv2/tfv2_example.py +++ b/examples/tfv2/tfv2_example.py @@ -19,10 +19,7 @@ def __init__(self, optimizer: dict, loss_object: dict, output_shape=10): self.conv1 = Conv2D(filters=4, kernel_size=5, activation="relu") self.conv2 = Conv2D(filters=10, kernel_size=5, activation="relu") self.maxpool = MaxPool2D( - pool_size=(2, 2), - strides=(2, 2), - padding="valid", - data_format=None, + pool_size=(2, 2), strides=(2, 2), padding="valid", data_format=None, ) self.flatten = Flatten() self.dense1 = Dense(100, activation="relu") diff --git a/test/base/test_attack/test_attack.py b/test/base/test_attack/test_attack.py index 1805bbe6..df2f7825 100644 --- a/test/base/test_attack/test_attack.py +++ b/test/base/test_attack/test_attack.py @@ -19,8 +19,7 @@ class testAttackInitializer(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg @@ -71,8 +70,7 @@ class testAttack(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_attack/torch_example.py b/test/base/test_attack/torch_example.py index 59554442..8eaa3954 100644 --- a/test/base/test_attack/torch_example.py +++ b/test/base/test_attack/torch_example.py @@ -214,12 +214,7 @@ def __init__( # CIFAR10: kernel_size 7 -> 3, stride 2 -> 1, padding 3->1 self.conv1 = nn.Conv2d( - 3, - self.inplanes, - kernel_size=3, - stride=1, - padding=1, - bias=False, + 3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False, ) # END @@ -228,25 +223,13 @@ def __init__( self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer( - block, - 128, - layers[1], - stride=2, - dilate=replace_stride_with_dilation[0], + block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0], ) self.layer3 = self._make_layer( - block, - 256, - layers[2], - stride=2, - dilate=replace_stride_with_dilation[1], + block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1], ) self.layer4 = self._make_layer( - block, - 512, - layers[3], - stride=2, - dilate=replace_stride_with_dilation[2], + block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2], ) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) diff --git a/test/base/test_data/test_data.py b/test/base/test_data/test_data.py index 5f41cf1d..7a583819 100644 --- a/test/base/test_data/test_data.py +++ b/test/base/test_data/test_data.py @@ -21,8 +21,7 @@ class testSklearnData(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg @@ -78,8 +77,7 @@ def test_resave(self): train_labels_file = Path(data_file).with_suffix(".csv").as_posix() test_labels_file = Path(data_file).with_suffix(".json").as_posix() _ = self.data( - train_labels_file=train_labels_file, - test_labels_file=test_labels_file, + train_labels_file=train_labels_file, test_labels_file=test_labels_file, ) score_dict = {"test_score": 0.5} score_series = Series(score_dict) diff --git a/test/base/test_data/test_generator.py b/test/base/test_data/test_generator.py index 0dbab957..688add4b 100644 --- a/test/base/test_data/test_generator.py +++ b/test/base/test_data/test_generator.py @@ -25,8 +25,7 @@ class testDataGenerator(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg @@ -126,8 +125,7 @@ def test_call(self): for name in self.names: data = TorchDataGenerator(name=name, path=self.dir)() data = TorchDataGenerator( - name=name, - path=self.dir, + name=name, path=self.dir, )() # Test it again to make sure download only happens once self.assertIsInstance(data, list) self.assertIsInstance(data[0], np.ndarray) diff --git a/test/base/test_data/test_sampler.py b/test/base/test_data/test_sampler.py index e3b2f0cc..f32adb68 100644 --- a/test/base/test_data/test_sampler.py +++ b/test/base/test_data/test_sampler.py @@ -14,8 +14,7 @@ class testSklearnDataSampler(unittest.TestCase): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -52,8 +51,7 @@ def tearDown(self) -> None: class testTimeSeriesSklearnDataSampler(unittest.TestCase): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -90,8 +88,7 @@ def tearDown(self) -> None: class testTimeSeriesSklearnDataSampler2(testTimeSeriesSklearnDataSampler): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -104,8 +101,7 @@ def setUp(self, config_dir=config_dir, config_file=config_file): class testTimeSeriesSklearnDataSampler3(testTimeSeriesSklearnDataSampler): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -118,8 +114,7 @@ def setUp(self, config_dir=config_dir, config_file=config_file): class testTimeSeriesSklearnDataSampler4(testTimeSeriesSklearnDataSampler): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg diff --git a/test/base/test_data/test_sklearn_pipeline.py b/test/base/test_data/test_sklearn_pipeline.py index 09b40eb6..c70dfa8f 100644 --- a/test/base/test_data/test_sklearn_pipeline.py +++ b/test/base/test_data/test_sklearn_pipeline.py @@ -20,8 +20,7 @@ class testSklearnDataPipeline(unittest.TestCase): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -38,10 +37,7 @@ def test_call(self): X_train, X_test, y_train, y_test = self.data.sample(X, y) old_mean = np.mean(X_train) X_train, X_test, y_train, y_test = self.data.sklearn_pipeline( - X_train, - X_test, - y_train, - y_test, + X_train, X_test, y_train, y_test, ) new_mean = np.mean(X_train) self.assertNotEqual(old_mean, new_mean) @@ -71,8 +67,7 @@ def test_len(self): def test_getitem(self): for stage in self.data.sklearn_pipeline: self.assertIsInstance( - self.data.sklearn_pipeline[stage], - SklearnDataPipelineStage, + self.data.sklearn_pipeline[stage], SklearnDataPipelineStage, ) def tearDown(self) -> None: @@ -82,8 +77,7 @@ def tearDown(self) -> None: class testSklearnDataPipelineStage(unittest.TestCase): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -92,8 +86,7 @@ def setUp(self, config_dir=config_dir, config_file=config_file): def test_init(self): self.assertTrue( isinstance( - self.data.sklearn_pipeline["preprocessor"], - SklearnDataPipelineStage, + self.data.sklearn_pipeline["preprocessor"], SklearnDataPipelineStage, ) or hasattr(self.data.sklearn_pipeline["preprocessor"], "transform"), ) @@ -103,10 +96,7 @@ def test_call(self): X_train, X_test, y_train, y_test = self.data.sample(X, y) old_mean = np.mean(X_train) X_train, X_test, y_train, y_test = self.data.sklearn_pipeline["preprocessor"]( - X_train, - X_test, - y_train, - y_test, + X_train, X_test, y_train, y_test, ) new_mean = np.mean(X_train) self.assertNotEqual(old_mean, new_mean) diff --git a/test/base/test_experiment/test_experiment.py b/test/base/test_experiment/test_experiment.py index 260059b3..a705524c 100644 --- a/test/base/test_experiment/test_experiment.py +++ b/test/base/test_experiment/test_experiment.py @@ -19,8 +19,7 @@ class testExperiment(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg @@ -88,8 +87,7 @@ class testExperimentReRun(testExperiment): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=self.config_file) cfg = OmegaConf.to_container(cfg, resolve=True) diff --git a/test/base/test_experiment/torch_example.py b/test/base/test_experiment/torch_example.py index e7479cb8..33cfe035 100644 --- a/test/base/test_experiment/torch_example.py +++ b/test/base/test_experiment/torch_example.py @@ -214,12 +214,7 @@ def __init__( # CIFAR10: kernel_size 7 -> 3, stride 2 -> 1, padding 3->1 self.conv1 = nn.Conv2d( - 3, - self.inplanes, - kernel_size=3, - stride=1, - padding=1, - bias=False, + 3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False, ) # END @@ -228,25 +223,13 @@ def __init__( self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer( - block, - 128, - layers[1], - stride=2, - dilate=replace_stride_with_dilation[0], + block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0], ) self.layer3 = self._make_layer( - block, - 256, - layers[2], - stride=2, - dilate=replace_stride_with_dilation[1], + block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1], ) self.layer4 = self._make_layer( - block, - 512, - layers[3], - stride=2, - dilate=replace_stride_with_dilation[2], + block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2], ) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) diff --git a/test/base/test_files/test_files.py b/test/base/test_files/test_files.py index 0cca058b..5ce170a5 100644 --- a/test/base/test_files/test_files.py +++ b/test/base/test_files/test_files.py @@ -16,8 +16,7 @@ class testFiles(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_model/keras_example.py b/test/base/test_model/keras_example.py index c091de91..f0ee24ad 100644 --- a/test/base/test_model/keras_example.py +++ b/test/base/test_model/keras_example.py @@ -43,8 +43,6 @@ def __init__(self, optimizer, loss, metrics): def __call__(self): self.model.compile( - optimizer=self.optimizer, - loss=self.loss, - metrics=self.metrics, + optimizer=self.optimizer, loss=self.loss, metrics=self.metrics, ) return self.model diff --git a/test/base/test_model/test_art_pipeline.py b/test/base/test_model/test_art_pipeline.py index c96e03bb..caabc6d6 100644 --- a/test/base/test_model/test_art_pipeline.py +++ b/test/base/test_model/test_art_pipeline.py @@ -18,8 +18,7 @@ class testArtPipeline(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_model/test_model.py b/test/base/test_model/test_model.py index 9580afd9..dc9ca087 100644 --- a/test/base/test_model/test_model.py +++ b/test/base/test_model/test_model.py @@ -22,8 +22,7 @@ class testModel(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_model/test_sklearn_model_pipeline.py b/test/base/test_model/test_sklearn_model_pipeline.py index fd904655..d018f808 100644 --- a/test/base/test_model/test_sklearn_model_pipeline.py +++ b/test/base/test_model/test_sklearn_model_pipeline.py @@ -6,9 +6,7 @@ from hydra import initialize_config_dir, compose from hydra.utils import instantiate -from deckard.base.model.sklearn_pipeline import ( - SklearnModelPipeline, -) +from deckard.base.model.sklearn_pipeline import SklearnModelPipeline this_dir = Path(os.path.realpath(__file__)).parent.resolve().as_posix() @@ -20,8 +18,7 @@ class testSklearnModelPipeline(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_model/tfv2_example.py b/test/base/test_model/tfv2_example.py index 52303cff..d5d2164e 100644 --- a/test/base/test_model/tfv2_example.py +++ b/test/base/test_model/tfv2_example.py @@ -24,10 +24,7 @@ def __init__(self, optimizer: dict, loss_object: dict, output_shape=10): self.conv1 = Conv2D(filters=4, kernel_size=5, activation="relu") self.conv2 = Conv2D(filters=10, kernel_size=5, activation="relu") self.maxpool = MaxPool2D( - pool_size=(2, 2), - strides=(2, 2), - padding="valid", - data_format=None, + pool_size=(2, 2), strides=(2, 2), padding="valid", data_format=None, ) self.flatten = Flatten() self.dense1 = Dense(100, activation="relu") diff --git a/test/base/test_model/torch_example.py b/test/base/test_model/torch_example.py index e7479cb8..33cfe035 100644 --- a/test/base/test_model/torch_example.py +++ b/test/base/test_model/torch_example.py @@ -214,12 +214,7 @@ def __init__( # CIFAR10: kernel_size 7 -> 3, stride 2 -> 1, padding 3->1 self.conv1 = nn.Conv2d( - 3, - self.inplanes, - kernel_size=3, - stride=1, - padding=1, - bias=False, + 3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False, ) # END @@ -228,25 +223,13 @@ def __init__( self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer( - block, - 128, - layers[1], - stride=2, - dilate=replace_stride_with_dilation[0], + block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0], ) self.layer3 = self._make_layer( - block, - 256, - layers[2], - stride=2, - dilate=replace_stride_with_dilation[1], + block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1], ) self.layer4 = self._make_layer( - block, - 512, - layers[3], - stride=2, - dilate=replace_stride_with_dilation[2], + block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2], ) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) diff --git a/test/base/test_scorer/test_scorer.py b/test/base/test_scorer/test_scorer.py index 06d1a807..13238faa 100644 --- a/test/base/test_scorer/test_scorer.py +++ b/test/base/test_scorer/test_scorer.py @@ -23,16 +23,14 @@ class testScorerDict(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = OmegaConf.to_container(cfg, resolve=True) self.scorers = instantiate(config=self.cfg) self.directory = mkdtemp() self.file = Path( - self.directory, - self.score_dict_file + self.score_dict_type, + self.directory, self.score_dict_file + self.score_dict_type, ).as_posix() true_file = "true.pkl" preds_file = "preds.pkl" @@ -201,8 +199,7 @@ class testComputeAccuracy(testScorerConfig): model_config_dir = Path(this_dir, "../../conf/model").resolve().as_posix() model_config_file = "classification.yaml" with initialize_config_dir( - config_dir=Path(model_config_dir).resolve().as_posix(), - version_base="1.3", + config_dir=Path(model_config_dir).resolve().as_posix(), version_base="1.3", ): cfg = compose(config_name=model_config_file) model_cfg = cfg @@ -236,8 +233,7 @@ def setUp(self): self.scorers = ScorerDict(**self.cfg) self.directory = mkdtemp() self.file = Path( - self.directory, - self.score_dict_file + self.score_dict_type, + self.directory, self.score_dict_file + self.score_dict_type, ).as_posix() true_file = "true.pkl" preds_file = "preds.pkl" From 500db31b575ca52e20da47f01c262fcca61e1bc1 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 18:54:30 +0000 Subject: [PATCH 135/148] linting --- deckard/__main__.py | 11 ++-- deckard/base/attack/attack.py | 26 ++++++--- deckard/base/data/data.py | 8 ++- deckard/base/data/generator.py | 12 ++++- deckard/base/data/sklearn_pipeline.py | 5 +- deckard/base/model/art_pipeline.py | 15 ++++-- deckard/base/model/model.py | 47 +++++++++++----- deckard/base/model/sklearn_pipeline.py | 36 ++++++++----- deckard/base/model/tensorflow_models.py | 3 +- deckard/base/plots/plots.py | 54 +++++++++++++------ deckard/base/utils/factory.py | 9 ++-- deckard/base/utils/hashing.py | 4 +- deckard/iaac/gcp/deploy.py | 4 +- deckard/layers/afr.py | 31 ++++++++--- deckard/layers/compile.py | 19 +++++-- deckard/layers/experiment.py | 5 +- deckard/layers/find_best.py | 11 ++-- deckard/layers/generate_grid.py | 9 +++- deckard/layers/optimise.py | 9 ++-- deckard/layers/plots.py | 41 +++++++++++--- deckard/layers/prepare_queue.py | 18 +++++-- deckard/layers/utils.py | 11 +++- deckard/layers/watcher.py | 35 +++++++++--- examples/keras/keras_example.py | 4 +- examples/power/torch_example.py | 35 ++++++++++-- examples/pytorch/cifar10/torch_example.py | 35 ++++++++++-- examples/pytorch/cifar100/torch_example.py | 35 ++++++++++-- examples/pytorch/mnist/torch_example.py | 35 ++++++++++-- examples/scratch/compute_attack_success.py | 15 ++++-- examples/scratch/deckard_queue.py | 8 ++- examples/scratch/objective.py | 13 +++-- examples/scratch/torch_example.py | 35 ++++++++++-- examples/security/classification/retrain.py | 54 ++++++++++++++----- examples/security/kdd-nsl/retrain.py | 54 ++++++++++++++----- examples/security/truthseeker/retrain.py | 54 ++++++++++++++----- examples/tfv1/tfv1_example.py | 10 +++- examples/tfv2/tfv2_example.py | 5 +- test/base/test_attack/test_attack.py | 6 ++- test/base/test_attack/torch_example.py | 25 +++++++-- test/base/test_data/test_data.py | 6 ++- test/base/test_data/test_generator.py | 6 ++- test/base/test_data/test_sampler.py | 15 ++++-- test/base/test_data/test_sklearn_pipeline.py | 22 +++++--- test/base/test_experiment/test_experiment.py | 6 ++- test/base/test_experiment/torch_example.py | 25 +++++++-- test/base/test_files/test_files.py | 3 +- test/base/test_model/keras_example.py | 4 +- test/base/test_model/test_art_pipeline.py | 3 +- test/base/test_model/test_model.py | 3 +- .../test_model/test_sklearn_model_pipeline.py | 3 +- test/base/test_model/tfv2_example.py | 5 +- test/base/test_model/torch_example.py | 25 +++++++-- test/base/test_scorer/test_scorer.py | 12 +++-- 53 files changed, 762 insertions(+), 222 deletions(-) diff --git a/deckard/__main__.py b/deckard/__main__.py index 815f30e9..ce7b27e4 100644 --- a/deckard/__main__.py +++ b/deckard/__main__.py @@ -24,7 +24,10 @@ def run_submodule(submodule, args): cmd = f"python -m deckard.layers.{submodule} {args}" logger.info(f"Running {cmd}") with subprocess.Popen( - cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + shell=True, ) as proc: for line in proc.stdout: print(line.rstrip().decode("utf-8")) @@ -52,7 +55,7 @@ def parse_and_repro(args, default_config="default.yaml", config_dir="conf"): else: cmd = f"python -m deckard.layers.parse {args} --config_file {default_config}" # error = f"error parsing command: {cmd} {args}" - with subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True,) as proc: + with subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True) as proc: for line in proc.stdout: print(line.rstrip().decode("utf-8")) if Path(Path(), "dvc.yaml").exists(): @@ -70,7 +73,9 @@ def parse_and_repro(args, default_config="default.yaml", config_dir="conf"): logging.basicConfig(level=logging.INFO) parser = argparse.ArgumentParser() parser.add_argument( - "--submodule", type=str, help=f"Submodule to run. Choices: {layer_list}", + "--submodule", + type=str, + help=f"Submodule to run. Choices: {layer_list}", ) parser.add_argument( "--config_file", diff --git a/deckard/base/attack/attack.py b/deckard/base/attack/attack.py index 85d2a230..237b21ff 100644 --- a/deckard/base/attack/attack.py +++ b/deckard/base/attack/attack.py @@ -156,7 +156,9 @@ def __call__( start = process_time_ns() patches, _ = atk.generate(ben_samples, **kwargs) samples = atk.apply_patch( - ben_samples, scale=scale_max, patch_external=patches, + ben_samples, + scale=scale_max, + patch_external=patches, ) else: start = process_time_ns() @@ -199,7 +201,8 @@ def __call__( results["adv_probabilities"] = np.array(adv_probabilities) else: if hasattr(self.model, "model") and hasattr( - self.model.model, "predict_proba", + self.model.model, + "predict_proba", ): start = process_time_ns() adv_probabilities = model.model.predict_proba(samples) @@ -232,7 +235,8 @@ def __call__( results["adv_losses"] = np.array(adv_loss) elif adv_losses_file is not None: assert hasattr( - model, "compute_loss", + model, + "compute_loss", ), "Model does not have compute_loss method." try: adv_loss = model.compute_loss(samples, data[3][: self.attack_size]) @@ -350,7 +354,9 @@ def __call__( y_trigger = y_trigger.to(torch.long) y_trigger = y_trigger.to(torch.long) atk = self.init( - model=model, data=data, attack_size=self.attack_size, + model=model, + data=data, + attack_size=self.attack_size, ) start = process_time_ns() samples, _ = atk.poison( @@ -387,7 +393,8 @@ def __call__( adv_probabilities = self.data.load(adv_probabilities_file) else: if hasattr(self.model, "model") and hasattr( - self.model.model, "predict_proba", + self.model.model, + "predict_proba", ): start = process_time_ns() adv_probabilities = model.model.predict_proba(samples) @@ -418,7 +425,8 @@ def __call__( adv_loss = self.data.load(adv_losses_file) elif adv_losses_file is not None: assert hasattr( - model, "compute_loss", + model, + "compute_loss", ), "Model does not have compute_loss method." adv_loss = model.compute_loss(samples, data[3][: self.attack_size]) adv_loss = np.array(adv_loss) @@ -486,7 +494,8 @@ def __call__( initial_image = np.random.uniform(0, 1, data_shape) elif self.initial_image == "average": initial_image = np.zeroes(data_shape) + np.mean( - data[1][: self.attack_size], axis=0, + data[1][: self.attack_size], + axis=0, ) elif self.initial_image is None: pass @@ -656,7 +665,8 @@ def __call__( else: if hasattr(attacked_model, "compute_loss"): loss = attacked_model.compute_loss( - data[1][: self.attack_size], data[3][: self.attack_size], + data[1][: self.attack_size], + data[3][: self.attack_size], ) else: from sklearn.metrics import log_loss diff --git a/deckard/base/data/data.py b/deckard/base/data/data.py index 47fbc6bb..690d27c0 100644 --- a/deckard/base/data/data.py +++ b/deckard/base/data/data.py @@ -158,7 +158,8 @@ def save(self, data, filename): json.dump(data, f) elif suffix in [".csv"]: assert isinstance( - data, (Series, DataFrame, dict, np.ndarray), + data, + (Series, DataFrame, dict, np.ndarray), ), f"Data must be a Series, DataFrame, or dict, not {type(data)} to save to {filename}" DataFrame(data).to_csv(filename, index=False) elif suffix in [".pkl", ".pickle"]: @@ -169,7 +170,10 @@ def save(self, data, filename): assert Path(filename).exists() def __call__( - self, data_file=None, train_labels_file=None, test_labels_file=None, + self, + data_file=None, + train_labels_file=None, + test_labels_file=None, ) -> list: """Loads data from file if it exists, otherwise generates data and saves it to file. Returns X_train, X_test, y_train, y_test as a list of arrays, typed according to the framework. :param filename: str diff --git a/deckard/base/data/generator.py b/deckard/base/data/generator.py index 40957823..4283ed22 100644 --- a/deckard/base/data/generator.py +++ b/deckard/base/data/generator.py @@ -34,7 +34,12 @@ @dataclass class SklearnDataGenerator: name: Literal[ - "classification", "regression", "blobs", "moons", "circles", "biclusters", + "classification", + "regression", + "blobs", + "moons", + "circles", + "biclusters", ] = "classification" kwargs: dict = field(default_factory=dict) @@ -70,7 +75,10 @@ def __hash__(self): @dataclass class TorchDataGenerator: name: Literal[ - "torch_mnist", "torch_cifar10", "torch_diabetes", "torch_cifar100", + "torch_mnist", + "torch_cifar10", + "torch_diabetes", + "torch_cifar100", ] = "torch_mnist" path = None kwargs: dict = field(default_factory=dict) diff --git a/deckard/base/data/sklearn_pipeline.py b/deckard/base/data/sklearn_pipeline.py index 8a53ed8e..0c725896 100644 --- a/deckard/base/data/sklearn_pipeline.py +++ b/deckard/base/data/sklearn_pipeline.py @@ -68,6 +68,9 @@ def __call__(self, X_train, X_test, y_train, y_test): for stage in pipeline: transformer = pipeline[stage] X_train, X_test, y_train, y_test = transformer( - X_train, X_test, y_train, y_test, + X_train, + X_test, + y_train, + y_test, ) return [X_train, X_test, y_train, y_test] diff --git a/deckard/base/model/art_pipeline.py b/deckard/base/model/art_pipeline.py index 2c3ed3b1..679a089f 100644 --- a/deckard/base/model/art_pipeline.py +++ b/deckard/base/model/art_pipeline.py @@ -60,13 +60,15 @@ def __call__(self): model = self.model kwargs = self.kwargs if "torch" in str(library) and not isinstance( - model, tuple(torch_dict.values()), + model, + tuple(torch_dict.values()), ): model = TorchInitializer( data=data, model=model, library=library, **kwargs )() elif "keras" in str(library) and not isinstance( - model, tuple(keras_dict.values()), + model, + tuple(keras_dict.values()), ): # pragma: no cover raise NotImplementedError("Keras not implemented yet") # try: @@ -104,14 +106,16 @@ def __call__(self): data=data, model=model, library=library, **kwargs )() elif library in ["tf1", "tensorflowv1", "tfv1"] and not isinstance( - model, tuple(tensorflow_dict.values()), + model, + tuple(tensorflow_dict.values()), ): # pragma: no cover raise NotImplementedError("Tensorflow V1 not implemented yet") # model = TensorflowV1Initializer( # data=data, model=model, library=library, **kwargs # )() elif library in supported_models and isinstance( - model, tuple(all_models.values()), + model, + tuple(all_models.values()), ): pass else: # pragma: no cover @@ -119,7 +123,8 @@ def __call__(self): f"library must be one of {supported_models}. Got {library}", ) assert hasattr( - model, "fit", + model, + "fit", ), f"model must have a fit method. Got type {type(model)}" return model diff --git a/deckard/base/model/model.py b/deckard/base/model/model.py index 11f95553..bd69bc4a 100644 --- a/deckard/base/model/model.py +++ b/deckard/base/model/model.py @@ -67,7 +67,8 @@ def __call__(self): params["output_dim"] = params["output_dim"] elif isinstance(params["output_dim"], ListConfig): output_dim_list = OmegaConf.to_container( - params["output_dim"], resolve=True, + params["output_dim"], + resolve=True, ) if len(output_dim_list) == 1: params["output_dim"] = output_dim_list[0] @@ -309,7 +310,8 @@ def __call__( elif isinstance(data, (str, Path)): data = self.load(data) assert isinstance( - data, (type(None), list, tuple), + data, + (type(None), list, tuple), ), f"Data {data} is not a list. It is of type {type(data)}." assert len(data) == 4, f"Data {data} is not a tuple of length 4." result_dict["data"] = data @@ -322,7 +324,8 @@ def __call__( model = self.load(model) elif hasattr(model, ("fit", "fit_generator")): assert hasattr(model, "predict") or hasattr( - model, "predict_proba", + model, + "predict_proba", ), f"Model {model} does not have a predict or predict_proba method." else: # pragma: no cover raise ValueError(f"Model {model} is not a valid model.") @@ -351,7 +354,9 @@ def __call__( # Fitting if model_file is None: model, fit_time_dict = self.fit( - data=data, model=model, model_file=model_file, + data=data, + model=model, + model_file=model_file, ) time_dict.update(**fit_time_dict) result_dict["model"] = model @@ -362,7 +367,9 @@ def __call__( result_dict["model"] = model else: model, fit_time_dict = self.fit( - data=data, model=model, model_file=model_file, + data=data, + model=model, + model_file=model_file, ) result_dict["model"] = model result_dict["data"] = data @@ -370,7 +377,9 @@ def __call__( # Predicting if predictions_file is not None and not Path(predictions_file).exists(): preds, pred_time_dict = self.predict( - data=data, model=model, predictions_file=predictions_file, + data=data, + model=model, + predictions_file=predictions_file, ) result_dict["time_dict"].update(**pred_time_dict) result_dict["predictions"] = preds @@ -379,14 +388,18 @@ def __call__( result_dict["predictions"] = preds else: preds, pred_time_dict = self.predict( - data=data, model=model, predictions_file=predictions_file, + data=data, + model=model, + predictions_file=predictions_file, ) result_dict["time_dict"].update(**pred_time_dict) result_dict["predictions"] = preds # Predicting probabilities if probabilities_file is not None: probs, prob_time_dict = self.predict_proba( - data=data, model=model, probabilities_file=probabilities_file, + data=data, + model=model, + probabilities_file=probabilities_file, ) result_dict["probabilities"] = probs result_dict["time_dict"].update(**prob_time_dict) @@ -396,14 +409,18 @@ def __call__( result_dict["time_dict"].update(**prob_time_dict) else: probs, prob_time_dict = self.predict_proba( - data=data, model=model, probabilities_file=probabilities_file, + data=data, + model=model, + probabilities_file=probabilities_file, ) result_dict["probabilities"] = probs result_dict["time_dict"].update(**prob_time_dict) # Predicting loss if losses_file is not None: loss, loss_time_dict = self.predict_log_loss( - data=data, model=model, losses_file=losses_file, + data=data, + model=model, + losses_file=losses_file, ) time_dict.update(**loss_time_dict) result_dict["losses"] = loss @@ -413,7 +430,9 @@ def __call__( result_dict["losses"] = loss else: loss, loss_time_dict = self.predict_log_loss( - data=data, model=model, losses_file=losses_file, + data=data, + model=model, + losses_file=losses_file, ) time_dict.update(**loss_time_dict) result_dict["losses"] = loss @@ -449,7 +468,8 @@ def initialize(self, data=None, model=None): elif isinstance(data, type(None)): data = self.data.initialize(data) assert isinstance( - data, (list), + data, + (list), ), f"Data {data} is not a list. It is of type {type(data)}." if isinstance(model, (str, Path)) and Path(model).exists(): model = self.load(model) @@ -524,7 +544,8 @@ def predict(self, data=None, model=None, predictions_file=None): ), "X_test and y_test must have the same length." assert hasattr(model, "fit"), f"Model {model} does not have a fit method." assert hasattr( - model, "predict", + model, + "predict", ), f"Model {model} does not have a predict method." device = str(model.device) if hasattr(model, "device") else "cpu" try: diff --git a/deckard/base/model/sklearn_pipeline.py b/deckard/base/model/sklearn_pipeline.py index ab83e1d7..631b39ed 100644 --- a/deckard/base/model/sklearn_pipeline.py +++ b/deckard/base/model/sklearn_pipeline.py @@ -83,18 +83,21 @@ def __call__(self, model): kwargs.update(**kwargs.pop("kwargs")) if "art." in str(type(model)): assert isinstance( - model.model, BaseEstimator, + model.model, + BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model.model)}" else: assert isinstance( - model, BaseEstimator, + model, + BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model)}" if not isinstance(model, Pipeline): model = Pipeline([("model", model)]) else: model.steps.insert(-2, [stage_name, model]) assert isinstance( - model, Pipeline, + model, + Pipeline, ), f"model must be a sklearn pipeline. Got {type(model)}" return model @@ -120,7 +123,8 @@ def __init__(self, **kwargs): pipe[stage] = asdict(pipe[stage]) else: assert hasattr( - pipe[stage], "transform", + pipe[stage], + "transform", ), f"Pipeline stage must be a SklearnModelPipelineStage, dict, or have a transform method. Got {type(pipe[stage])}" if isinstance(pipe[stage], dict): params = deepcopy(pipe[stage]) @@ -128,7 +132,8 @@ def __init__(self, **kwargs): pipe[stage] = SklearnModelPipelineStage(params, stage_name=stage_name) elif hasattr(pipe[stage], "transform"): assert hasattr( - pipe[stage], "fit", + pipe[stage], + "fit", ), f"Pipeline stage must have a fit method. Got {type(pipe[stage])}" else: raise ValueError( @@ -174,16 +179,19 @@ def __call__(self, model): elif hasattr(stage, "fit"): if "art." in str(type(model)): assert isinstance( - model.model, BaseEstimator, + model.model, + BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model.model)}" else: assert isinstance( - model, BaseEstimator, + model, + BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model)}" if not isinstance(model, Pipeline) and "art." not in str(type(model)): model = Pipeline([("model", model)]) elif "art." in str(type(model)) and not isinstance( - model.model, Pipeline, + model.model, + Pipeline, ): model.model = Pipeline([("model", model.model)]) elif "art." in str(type(model)) and isinstance(model.model, Pipeline): @@ -192,11 +200,13 @@ def __call__(self, model): model.steps.insert(-2, [stage, model]) if "art." not in str(type(model)): assert isinstance( - model, Pipeline, + model, + Pipeline, ), f"model must be a sklearn pipeline. Got {type(model)}" else: assert isinstance( - model.model, Pipeline, + model.model, + Pipeline, ), f"model must be a sklearn pipeline. Got {type(model)}" return model @@ -253,7 +263,8 @@ def __call__(self): if self.pipeline is not None: obj = self.pipeline(model) assert isinstance( - obj, BaseEstimator, + obj, + BaseEstimator, ), f"model must be a sklearn estimator. Got {type(model)}" else: obj = model @@ -288,7 +299,8 @@ def __call__(self): else: raise ValueError(f"library must be one of {sklearn_models}. Got {library}") assert hasattr( - model, "fit", + model, + "fit", ), f"model must have a fit method. Got type {type(model)}" return model diff --git a/deckard/base/model/tensorflow_models.py b/deckard/base/model/tensorflow_models.py index 4b5f27f9..d9796d43 100644 --- a/deckard/base/model/tensorflow_models.py +++ b/deckard/base/model/tensorflow_models.py @@ -131,7 +131,8 @@ def __call__(self): train_step = kwargs.pop("train_step") train_step = factory(name=train_step.pop("name"), **train_step) if library in tensorflow_dict and not isinstance( - model, tuple(tensorflow_dict.values()), + model, + tuple(tensorflow_dict.values()), ): est = tensorflow_dict[library] model = est(model, **kwargs, train_step=train_step) diff --git a/deckard/base/plots/plots.py b/deckard/base/plots/plots.py index 31c0bb0a..89393f49 100644 --- a/deckard/base/plots/plots.py +++ b/deckard/base/plots/plots.py @@ -146,7 +146,10 @@ class Yellowbrick_Visualiser: scorers: ScorerDict = field(default_factory=ScorerDict) def visualise_data( - self, data: list, classes: list = None, features: list = None, + self, + data: list, + classes: list = None, + features: list = None, ) -> List[Path]: """ Visualise classification results according to the configuration file. @@ -186,7 +189,8 @@ def visualise_data( ) paths["radviz"] = str( Path( - path, plots["radviz"] + str(plots.pop("filetype", ".png")), + path, + plots["radviz"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -198,7 +202,8 @@ def visualise_data( ) paths["rank1d"] = str( Path( - path, plots["rank1d"] + str(plots.pop("filetype", ".png")), + path, + plots["rank1d"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -210,7 +215,8 @@ def visualise_data( ) paths["rank2d"] = str( Path( - path, plots["rank2d"] + str(plots.pop("filetype", ".png")), + path, + plots["rank2d"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -222,7 +228,8 @@ def visualise_data( ) paths["balance"] = str( Path( - path, plots["balance"] + str(plots.pop("filetype", ".png")), + path, + plots["balance"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -231,12 +238,14 @@ def visualise_data( visualiser.fit(X_train, y_train) visualiser.show( Path( - path, plots["correlation"] + str(plots.pop("filetype", ".png")), + path, + plots["correlation"] + str(plots.pop("filetype", ".png")), ), ) paths["correlation"] = str( Path( - path, plots["correlation"] + str(plots.pop("filetype", ".png")), + path, + plots["correlation"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -248,7 +257,8 @@ def visualise_data( ) paths["pca"] = str( Path( - path, plots["pca"] + str(plots.pop("filetype", ".png")), + path, + plots["pca"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -260,7 +270,8 @@ def visualise_data( ) paths["manifold"] = str( Path( - path, plots["manifold"] + str(plots.pop("filetype", ".png")), + path, + plots["manifold"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() @@ -272,14 +283,19 @@ def visualise_data( ) paths["parallel"] = str( Path( - path, plots["parallel"] + str(plots.pop("filetype", ".png")), + path, + plots["parallel"] + str(plots.pop("filetype", ".png")), ).as_posix(), ) plt.gcf().clear() return paths def visualise_classification( - self, data: list, model: Callable, classes: list = None, predictions_file=None, + self, + data: list, + model: Callable, + classes: list = None, + predictions_file=None, ) -> List[Path]: """ Visualise classification results according to the configuration file. @@ -314,7 +330,8 @@ def visualise_classification( data[3] = np.argmax(data[3], axis=1) if len(set(np.unique(data[2]))) > 2: viz = visualiser( - model, classes=[int(y) for y in np.unique(data[2])], + model, + classes=[int(y) for y in np.unique(data[2])], ) elif len(set(np.unique(data[2]))) == 2: try: @@ -328,7 +345,8 @@ def visualise_classification( f"Failed due to error {e}. Trying without binary", ) viz = visualiser( - model, classes=[int(y) for y in np.unique(data[2])], + model, + classes=[int(y) for y in np.unique(data[2])], ) else: viz = visualiser(model, classes=[0]) @@ -342,7 +360,10 @@ def visualise_classification( return paths def visualise_regression( - self, data: list, model: object, predictions_file=None, + self, + data: list, + model: object, + predictions_file=None, ) -> List[Path]: """ Visualise classification results according to the configuration file. @@ -393,7 +414,10 @@ def visualise_regression( return paths def visualise_clustering( - self, data: list, model: object, predictions_file=None, + self, + data: list, + model: object, + predictions_file=None, ) -> List[Path]: """ Visualise classification results according to the configuration file. diff --git a/deckard/base/utils/factory.py b/deckard/base/utils/factory.py index 7b82be5f..e5fffd07 100644 --- a/deckard/base/utils/factory.py +++ b/deckard/base/utils/factory.py @@ -43,13 +43,15 @@ def factory(module_class_string, *args, super_cls: type = None, **kwargs) -> obj raise e module = import_module(module_name) assert hasattr(module, class_name), "class {} is not in {}".format( - class_name, module_name, + class_name, + module_name, ) logger.debug("reading class {} from module {}".format(class_name, module_name)) cls = getattr(module, class_name) if super_cls is not None: assert issubclass(cls, super_cls), "class {} should inherit from {}".format( - class_name, super_cls.__name__, + class_name, + super_cls.__name__, ) logger.debug("initialising {} with params {}".format(class_name, kwargs)) try: @@ -91,7 +93,8 @@ def make_grid(dictionary: list) -> list: big = [] if not isinstance(dictionary, list): assert isinstance( - dictionary, dict, + dictionary, + dict, ), f"dictionary must be a list or dict, not {type(dictionary)}" dict_list = [dictionary] else: diff --git a/deckard/base/utils/hashing.py b/deckard/base/utils/hashing.py index 0176f4a7..4ee35d15 100644 --- a/deckard/base/utils/hashing.py +++ b/deckard/base/utils/hashing.py @@ -20,7 +20,9 @@ def to_dict(obj: Union[dict, OrderedDict, NamedTuple]) -> dict: obj = dict( deepcopy( OmegaConf.to_container( - obj, resolve=True, structured_config_mode=SCMode.DICT, + obj, + resolve=True, + structured_config_mode=SCMode.DICT, ), ), ) diff --git a/deckard/iaac/gcp/deploy.py b/deckard/iaac/gcp/deploy.py index 425450e0..e6606921 100644 --- a/deckard/iaac/gcp/deploy.py +++ b/deckard/iaac/gcp/deploy.py @@ -152,7 +152,9 @@ def prepare_access_values(self): logger.info(f"Running command: {command}") command = command.split(" ") output = subprocess.run( - command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, + command, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, ) logger.info(f"{output}") return output.stdout diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index c5462318..ad085550 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -51,10 +51,12 @@ data.dropna(axis=0, subset=["atk_value", "atk_param"], inplace=True) data.dropna(axis=0, subset=["def_value", "def_param"], inplace=True) data.loc[:, "adv_failures"] = (1 - data.loc[:, "adv_accuracy"]) * data.loc[ - :, "attack.attack_size", + :, + "attack.attack_size", ] data.loc[:, "ben_failures"] = (1 - data.loc[:, "accuracy"]) * data.loc[ - :, "attack.attack_size", + :, + "attack.attack_size", ] def plot_aft( @@ -182,7 +184,9 @@ def make_afr_table(score_list, aft_dict, dataset): return aft_data def clean_data_for_aft( - data, kwarg_list, target="adv_failure_rate", + data, + kwarg_list, + target="adv_failure_rate", ): subset = data.copy() assert ( @@ -210,11 +214,19 @@ def clean_data_for_aft( return cleaned, y, data def split_data_for_aft( - data, target, duration_col, kwarg_list, test_size=0.2, random_state=42, + data, + target, + duration_col, + kwarg_list, + test_size=0.2, + random_state=42, ): cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) X_train, X_test, y_train, y_test = train_test_split( - cleaned, y, test_size=test_size, random_state=random_state, + cleaned, + y, + test_size=test_size, + random_state=random_state, ) assert ( target in cleaned @@ -238,7 +250,12 @@ def split_data_for_aft( ] X_train, X_test, y_train, y_test = split_data_for_aft( - data, target, duration_col, kwarg_list, test_size=0.2, random_state=42, + data, + target, + duration_col, + kwarg_list, + test_size=0.2, + random_state=42, ) weibull_dict = { @@ -287,7 +304,7 @@ def split_data_for_aft( }, } - weibull_layers = plot_partial_effects(aft=wft, **weibull_partial_dict_layers,) + weibull_layers = plot_partial_effects(aft=wft, **weibull_partial_dict_layers) wft_scores = score_model(wft, X_train, X_test) # cox_replacement_dict = { diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index e71f9fc6..3627c7a2 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -215,7 +215,12 @@ def format_control_parameter(data, control_dict): def clean_data_for_plotting( - data, def_gen_dict, atk_gen_dict, control_dict, file, folder, + data, + def_gen_dict, + atk_gen_dict, + control_dict, + file, + folder, ): logger.info("Replacing attack and defence names with short names...") if hasattr(data, "def_gen"): @@ -278,7 +283,10 @@ def save_results(results, results_file) -> str: parser.add_argument("--verbose", type=str, default="INFO") parser.add_argument("--default_epochs", type=int, default=20) parser.add_argument( - "--kwargs", type=list, default=None, nargs="*", + "--kwargs", + type=list, + default=None, + nargs="*", ) args = parser.parse_args() logging.basicConfig(level=args.verbose) @@ -292,7 +300,12 @@ def save_results(results, results_file) -> str: atk_gen_dict = big_dict["attacks"] control_dict = big_dict["params"] results = clean_data_for_plotting( - results, def_gen_dict, atk_gen_dict, control_dict, results_file, results_folder, + results, + def_gen_dict, + atk_gen_dict, + control_dict, + results_file, + results_folder, ) report_file = save_results(results, results_file) assert Path( diff --git a/deckard/layers/experiment.py b/deckard/layers/experiment.py index 84805375..1e481821 100644 --- a/deckard/layers/experiment.py +++ b/deckard/layers/experiment.py @@ -44,7 +44,10 @@ def get_dvc_stage_params( def run_stage( - params_file="params.yaml", pipeline_file="dvc.yaml", directory=".", stage=None, + params_file="params.yaml", + pipeline_file="dvc.yaml", + directory=".", + stage=None, ): logger.info( f"Running stage {stage} with params_file: {params_file} and pipeline_file: {pipeline_file} in directory {directory}", diff --git a/deckard/layers/find_best.py b/deckard/layers/find_best.py index fcc7504a..0461f28e 100644 --- a/deckard/layers/find_best.py +++ b/deckard/layers/find_best.py @@ -50,7 +50,9 @@ def find_optuna_best( if params_file is True: if config_subdir is not None: params_file = Path( - config_folder, f"{config_subdir}", f"{default_config}.yaml", + config_folder, + f"{config_subdir}", + f"{default_config}.yaml", ) params = cfg.get(config_subdir) else: @@ -59,7 +61,9 @@ def find_optuna_best( else: if config_subdir is not None: params_file = Path( - config_folder, f"{config_subdir}", f"{params_file}.yaml", + config_folder, + f"{config_subdir}", + f"{params_file}.yaml", ) params = cfg.get(config_subdir) else: @@ -93,7 +97,8 @@ def find_optuna_best( if args.study_type == "optuna": with open( - Path(args.config_folder, args.default_config).with_suffix(".yaml"), "r", + Path(args.config_folder, args.default_config).with_suffix(".yaml"), + "r", ) as f: default_params = yaml.load(f, Loader=yaml.FullLoader) if "hydra" in default_params: diff --git a/deckard/layers/generate_grid.py b/deckard/layers/generate_grid.py index 3e5a16ae..487ce801 100644 --- a/deckard/layers/generate_grid.py +++ b/deckard/layers/generate_grid.py @@ -67,14 +67,19 @@ def generate_grid_from_folders(conf_dir, regex): big_list = make_grid(big_dict) assert len(big_list) == reduce( - mul, layers, + mul, + layers, ), f"Grid size {len(big_list)} does not match product of layer sizes {reduce(mul, layers)}" logger.info(f"Generated grid with {len(big_list)} configs") return big_list def generate_queue( - conf_root, grid_dir, regex, queue_folder="queue", default_file="default.yaml", + conf_root, + grid_dir, + regex, + queue_folder="queue", + default_file="default.yaml", ): this_dir = os.getcwd() conf_dir = os.path.join(this_dir, conf_root, grid_dir) diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index aa81452d..a1877c59 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -17,7 +17,8 @@ __all__ = ["write_stage", "optimise", "parse_stage", "get_files"] config_path = os.environ.get( - "DECKARD_CONFIG_PATH", str(Path(Path.cwd(), "conf").absolute().as_posix()), + "DECKARD_CONFIG_PATH", + str(Path(Path.cwd(), "conf").absolute().as_posix()), ) assert Path( config_path, @@ -27,7 +28,8 @@ def get_files( - cfg, stage, + cfg, + stage, ): """ Gets the file names from @@ -197,7 +199,8 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: key_list = [] for stage in stages: assert Path( - path, "dvc.yaml", + path, + "dvc.yaml", ).exists(), f"{Path(path, 'dvc.yaml')} does not exist." with open(Path(path, "dvc.yaml"), "r") as f: new_keys = yaml.load(f, Loader=yaml.FullLoader)["stages"][stage][ diff --git a/deckard/layers/plots.py b/deckard/layers/plots.py index 91690a9b..888c8527 100644 --- a/deckard/layers/plots.py +++ b/deckard/layers/plots.py @@ -101,7 +101,12 @@ def scatter_plot( # plt.gcf().clear() data = data.sort_values(by=[hue, x, y]) graph = sns.scatterplot( - data=data, x=x, y=y, hue=hue, hue_order=hue_order, **kwargs, + data=data, + x=x, + y=y, + hue=hue, + hue_order=hue_order, + **kwargs, ) graph.set_yscale(y_scale) graph.set_xscale(x_scale) @@ -210,22 +215,44 @@ def min_max_scaling(data, **kwargs): if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( - "-p", "--path", type=str, help="Path to the plot folder", required=True, + "-p", + "--path", + type=str, + help="Path to the plot folder", + required=True, ) parser.add_argument( - "-f", "--file", type=str, help="Data file to read from", required=True, + "-f", + "--file", + type=str, + help="Data file to read from", + required=True, ) parser.add_argument( - "-o", "--output", type=str, help="Output file name", default="data.csv", + "-o", + "--output", + type=str, + help="Output file name", + default="data.csv", ) parser.add_argument( - "-t", "--plotfiletype", type=str, help="Filetype of the plots", default=".pdf", + "-t", + "--plotfiletype", + type=str, + help="Filetype of the plots", + default=".pdf", ) parser.add_argument( - "-v", "--verbosity", default="INFO", help="Increase output verbosity", + "-v", + "--verbosity", + default="INFO", + help="Increase output verbosity", ) parser.add_argument( - "-c", "--config", help="Path to the config file", default="conf/plots.yaml", + "-c", + "--config", + help="Path to the config file", + default="conf/plots.yaml", ) args = parser.parse_args() logging.basicConfig(level=args.verbosity) diff --git a/deckard/layers/prepare_queue.py b/deckard/layers/prepare_queue.py index 86bc58ee..6c4aeb94 100644 --- a/deckard/layers/prepare_queue.py +++ b/deckard/layers/prepare_queue.py @@ -16,7 +16,8 @@ def get_files( - cfg, stage, + cfg, + stage, ): """ Gets the file names from @@ -168,7 +169,8 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: with open(Path(params), "r") as f: params = yaml.load(f, Loader=yaml.FullLoader) assert isinstance( - params, dict, + params, + dict, ), f"Params in file {params} must be a dict. It is a {type(params)}." key_list = [] for stage in stages: @@ -196,7 +198,8 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: else: raise TypeError(f"Expected str or dict, got {type(params)}") assert isinstance( - params, dict, + params, + dict, ), f"Params must be a dict. It is type {type(params)}." # Load files from dvc with open(Path(path, "dvc.yaml"), "r") as f: @@ -221,7 +224,11 @@ def parse_stage(stage: str = None, params: dict = None, path=None) -> dict: def write_stage( - params: dict, stage: str, id_: str, path=None, working_dir=None, + params: dict, + stage: str, + id_: str, + path=None, + working_dir=None, ) -> None: """ Write params to dvc.yaml @@ -269,7 +276,8 @@ def prepare_experiment_folder(cfg: DictConfig) -> None: if __name__ == "__main__": logger = logging.getLogger(__name__) config_path = os.environ.pop( - "DECKARD_CONFIG_PATH", str(Path(Path(), "conf").absolute().as_posix()), + "DECKARD_CONFIG_PATH", + str(Path(Path(), "conf").absolute().as_posix()), ) config_name = os.environ.pop("DECKARD_DEFAULT_CONFIG", "default.yaml") diff --git a/deckard/layers/utils.py b/deckard/layers/utils.py index 1fb7c783..208c4006 100644 --- a/deckard/layers/utils.py +++ b/deckard/layers/utils.py @@ -29,7 +29,11 @@ def find_conf_files( - config_name, config_subdir, config_dir, config_regex=None, default_file=None, + config_name, + config_subdir, + config_dir, + config_regex=None, + default_file=None, ): if config_name is not None: assert config_regex is None, "Cannot specify both config_name and config_regex" @@ -116,7 +120,10 @@ def compose_experiment(file, config_dir, overrides=None, default_file="default.y def save_params_file( - config_dir="conf", config_file="default", params_file="params.yaml", overrides=[], + config_dir="conf", + config_file="default", + params_file="params.yaml", + overrides=[], ): config_dir = str(Path(Path(), config_dir).absolute().as_posix()) with initialize_config_dir(config_dir=config_dir, version_base="1.3"): diff --git a/deckard/layers/watcher.py b/deckard/layers/watcher.py index 6cb6752a..2668b972 100644 --- a/deckard/layers/watcher.py +++ b/deckard/layers/watcher.py @@ -26,7 +26,10 @@ class JSONHandler(watchdog.events.PatternMatchingEventHandler): def __init__(self, servers, port, user, password, filename, destination, **kwargs): # Set the patterns for PatternMatchingEventHandler watchdog.events.PatternMatchingEventHandler.__init__( - self, patterns=[REGEX], ignore_directories=True, case_sensitive=False, + self, + patterns=[REGEX], + ignore_directories=True, + case_sensitive=False, ) self.ssh = createSSHClient(servers, port, user, password) logger.info("Initiated SSH client") @@ -35,7 +38,8 @@ def __init__(self, servers, port, user, password, filename, destination, **kwarg self.recurse = kwargs["recursive"] if "recurse" in kwargs else False logger.info( "Source file is {} and destination is {}".format( - self.filename, self.destination, + self.filename, + self.destination, ), ) logger.info("Regex is {}".format(REGEX)) @@ -112,7 +116,11 @@ def send_json_with_scp(self): ) parser.add_argument("--port", "-p", type=int, help="The port to send the files to.") parser.add_argument( - "--user", "-u", type=str, required=True, help="The user to send the files to.", + "--user", + "-u", + type=str, + required=True, + help="The user to send the files to.", ) parser.add_argument( "--password", @@ -124,10 +132,18 @@ def send_json_with_scp(self): parser.add_argument("--original", type=str, help="The original queue file.") parser.add_argument("--queue", type=str, help="The current queue file.") parser.add_argument( - "--regex", "-e", type=str, required=True, help="The regex to watch for.", + "--regex", + "-e", + type=str, + required=True, + help="The regex to watch for.", ) parser.add_argument( - "--recursive", "-r", type=bool, default=True, help="Whether to recurse or not.", + "--recursive", + "-r", + type=bool, + default=True, + help="Whether to recurse or not.", ) parser.add_argument( "--n_jobs", @@ -137,12 +153,17 @@ def send_json_with_scp(self): help="The number of jobs to run in parallel.", ) parser.add_argument( - "--log", "-l", type=int, default=logging.INFO, help="The log level.", + "--log", + "-l", + type=int, + default=logging.INFO, + help="The log level.", ) args = parser.parse_args() # Set up logging logging.basicConfig( - level=args.log, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + level=args.log, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", ) if args.regex is not None: REGEX = args.regex diff --git a/examples/keras/keras_example.py b/examples/keras/keras_example.py index 3a3fba11..709754a0 100644 --- a/examples/keras/keras_example.py +++ b/examples/keras/keras_example.py @@ -44,6 +44,8 @@ def __init__(self, optimizer, loss, metrics): def __call__(self): self.model.compile( - optimizer=self.optimizer, loss=self.loss, metrics=self.metrics, + optimizer=self.optimizer, + loss=self.loss, + metrics=self.metrics, ) return self.model diff --git a/examples/power/torch_example.py b/examples/power/torch_example.py index 47dd9eb1..580b7f80 100644 --- a/examples/power/torch_example.py +++ b/examples/power/torch_example.py @@ -27,7 +27,12 @@ def forward(self, x): def ResNet18(num_channels=1, num_classes=10): model = models.resnet18() model.conv1 = torch.nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = torch.nn.Linear(512, num_classes) return model @@ -36,7 +41,12 @@ def ResNet18(num_channels=1, num_classes=10): def ResNet34(num_channels=1, num_classes=10): model = models.resnet34() model.conv1 = torch.nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = torch.nn.Linear(512, num_classes) return model @@ -45,7 +55,12 @@ def ResNet34(num_channels=1, num_classes=10): def ResNet50(num_channels=1, num_classes=10): model = models.resnet50() model.conv1 = torch.nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = torch.nn.Linear(2048, num_classes) return model @@ -54,7 +69,12 @@ def ResNet50(num_channels=1, num_classes=10): def ResNet101(num_channels=1, num_classes=10): model = models.resnet101() model.conv1 = torch.nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = torch.nn.Linear(2048, num_classes) return model @@ -63,7 +83,12 @@ def ResNet101(num_channels=1, num_classes=10): def ResNet152(num_channels=1, num_classes=10): model = models.resnet152() model.conv1 = torch.nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = torch.nn.Linear(2048, num_classes) return model diff --git a/examples/pytorch/cifar10/torch_example.py b/examples/pytorch/cifar10/torch_example.py index e4a067b2..32d333bd 100644 --- a/examples/pytorch/cifar10/torch_example.py +++ b/examples/pytorch/cifar10/torch_example.py @@ -12,7 +12,12 @@ def ResNet18(num_channels=1, num_classes=10): model = models.resnet18(pretrained=True) model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -21,7 +26,12 @@ def ResNet18(num_channels=1, num_classes=10): def ResNet34(num_channels=1, num_classes=10): model = models.resnet34(pretrained=True) model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -30,7 +40,12 @@ def ResNet34(num_channels=1, num_classes=10): def ResNet50(num_channels=1, num_classes=10): model = models.resnet50(pretrained=True) model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -39,7 +54,12 @@ def ResNet50(num_channels=1, num_classes=10): def ResNet101(num_channels=1, num_classes=10): model = models.resnet101(pretrained=True) model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -48,7 +68,12 @@ def ResNet101(num_channels=1, num_classes=10): def ResNet152(num_channels=1, num_classes=10): model = models.resnet152(pretrained=True) model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model diff --git a/examples/pytorch/cifar100/torch_example.py b/examples/pytorch/cifar100/torch_example.py index d1f8e038..2007d9d0 100644 --- a/examples/pytorch/cifar100/torch_example.py +++ b/examples/pytorch/cifar100/torch_example.py @@ -12,7 +12,12 @@ def ResNet18(num_channels=1, num_classes=10): model = models.resnet18() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -21,7 +26,12 @@ def ResNet18(num_channels=1, num_classes=10): def ResNet34(num_channels=1, num_classes=10): model = models.resnet34() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -30,7 +40,12 @@ def ResNet34(num_channels=1, num_classes=10): def ResNet50(num_channels=1, num_classes=10): model = models.resnet50() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -39,7 +54,12 @@ def ResNet50(num_channels=1, num_classes=10): def ResNet101(num_channels=1, num_classes=10): model = models.resnet101() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -48,7 +68,12 @@ def ResNet101(num_channels=1, num_classes=10): def ResNet152(num_channels=1, num_classes=10): model = models.resnet152() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model diff --git a/examples/pytorch/mnist/torch_example.py b/examples/pytorch/mnist/torch_example.py index d1f8e038..2007d9d0 100644 --- a/examples/pytorch/mnist/torch_example.py +++ b/examples/pytorch/mnist/torch_example.py @@ -12,7 +12,12 @@ def ResNet18(num_channels=1, num_classes=10): model = models.resnet18() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -21,7 +26,12 @@ def ResNet18(num_channels=1, num_classes=10): def ResNet34(num_channels=1, num_classes=10): model = models.resnet34() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -30,7 +40,12 @@ def ResNet34(num_channels=1, num_classes=10): def ResNet50(num_channels=1, num_classes=10): model = models.resnet50() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -39,7 +54,12 @@ def ResNet50(num_channels=1, num_classes=10): def ResNet101(num_channels=1, num_classes=10): model = models.resnet101() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -48,7 +68,12 @@ def ResNet101(num_channels=1, num_classes=10): def ResNet152(num_channels=1, num_classes=10): model = models.resnet152() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model diff --git a/examples/scratch/compute_attack_success.py b/examples/scratch/compute_attack_success.py index f22a9e02..acb4f951 100644 --- a/examples/scratch/compute_attack_success.py +++ b/examples/scratch/compute_attack_success.py @@ -67,13 +67,22 @@ def write_data_file(data, file: str): required=True, ) attack_success_parser.add_argument( - "-l", "--labels_file", help="Full path to the predictions file", required=True, + "-l", + "--labels_file", + help="Full path to the predictions file", + required=True, ) attack_success_parser.add_argument( - "-i", "--input_score_file", default=None, required=True, + "-i", + "--input_score_file", + default=None, + required=True, ) attack_success_parser.add_argument( - "-o", "--output_score_file", help="Full path to the labels file", required=True, + "-o", + "--output_score_file", + help="Full path to the labels file", + required=True, ) args = attack_success_parser.parse_args() diff --git a/examples/scratch/deckard_queue.py b/examples/scratch/deckard_queue.py index ba3626c3..dc3dedac 100644 --- a/examples/scratch/deckard_queue.py +++ b/examples/scratch/deckard_queue.py @@ -28,7 +28,10 @@ nargs="+", ) queue_parser.add_argument( - "--params_file", "-p", default=[], nargs="+", + "--params_file", + "-p", + default=[], + nargs="+", ) queue_parser.add_argument( "--output_file", @@ -90,7 +93,8 @@ def add_overrides_from_params_file(params_file, default_config): param_name = param_names[j] j += 1 new_config = add_overrides_from_params_file( - params_file=params_file, default_config=default_config, + params_file=params_file, + default_config=default_config, ) if conf_name == "default": study_name = f"{param_name}" diff --git a/examples/scratch/objective.py b/examples/scratch/objective.py index 7bbf8c00..e1f4e34a 100644 --- a/examples/scratch/objective.py +++ b/examples/scratch/objective.py @@ -9,7 +9,8 @@ logger = logging.getLogger(__name__) config_path = os.environ.pop( - "DECKARD_CONFIG_PATH", str(Path(Path(), "conf").absolute().as_posix()), + "DECKARD_CONFIG_PATH", + str(Path(Path(), "conf").absolute().as_posix()), ) config_name = os.environ.pop("DECKARD_DEFAULT_CONFIG", "default.yaml") @@ -56,10 +57,12 @@ def configure(cfg: DictConfig, trial: Trial) -> None: if postprocessor is not None: if postprocessor.strip() == "art.defences.postprocessor.HighConfidence": _ = trial.suggest_int( - "model.art.pipeline.preprocessor.defences.HighConfidence.threshold", 1, + "model.art.pipeline.preprocessor.defences.HighConfidence.threshold", + 1, ) trial.suggest_categorical( - "model.art.pipeline.preprocessor.defences.HighConfidence.abs", [True], + "model.art.pipeline.preprocessor.defences.HighConfidence.abs", + [True], ) trial.suggest_categorical( "model.art.pipeline.preprocessor.defences.HighConfidence.clip_values", @@ -67,7 +70,9 @@ def configure(cfg: DictConfig, trial: Trial) -> None: ) elif postprocessor.strip() == "art.defences.postprocessor.GaussianNoise": _ = trial.suggest_loguniform( - "model.art.pipeline.preprocessor.defences.GaussianNoise.sigma", 0.1, 10, + "model.art.pipeline.preprocessor.defences.GaussianNoise.sigma", + 0.1, + 10, ) trial.suggest_categorical( "model.art.pipeline.preprocessor.defences.GaussianNoise.clip_values", diff --git a/examples/scratch/torch_example.py b/examples/scratch/torch_example.py index d1f8e038..2007d9d0 100644 --- a/examples/scratch/torch_example.py +++ b/examples/scratch/torch_example.py @@ -12,7 +12,12 @@ def ResNet18(num_channels=1, num_classes=10): model = models.resnet18() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -21,7 +26,12 @@ def ResNet18(num_channels=1, num_classes=10): def ResNet34(num_channels=1, num_classes=10): model = models.resnet34() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -30,7 +40,12 @@ def ResNet34(num_channels=1, num_classes=10): def ResNet50(num_channels=1, num_classes=10): model = models.resnet50() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -39,7 +54,12 @@ def ResNet50(num_channels=1, num_classes=10): def ResNet101(num_channels=1, num_classes=10): model = models.resnet101() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -48,7 +68,12 @@ def ResNet101(num_channels=1, num_classes=10): def ResNet152(num_channels=1, num_classes=10): model = models.resnet152() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model diff --git a/examples/security/classification/retrain.py b/examples/security/classification/retrain.py index 4d83db60..9623e19d 100644 --- a/examples/security/classification/retrain.py +++ b/examples/security/classification/retrain.py @@ -135,7 +135,14 @@ def unflatten_results(df, sep=".") -> List[dict]: def retrain_loop( - clf, X_train, y_train, X_test, y_test, atk, attack_size, epochs, + clf, + X_train, + y_train, + X_test, + y_test, + atk, + attack_size, + epochs, ) -> tuple: i = 0 results = [] @@ -195,12 +202,20 @@ def retrain_loop( # Some Logging print( "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( - i, ben_time, ben_score, adv_time, adv_score, + i, + ben_time, + ben_score, + adv_time, + adv_score, ), ) logger.info( "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( - i, ben_time, ben_score, adv_time, adv_score, + i, + ben_time, + ben_score, + adv_time, + adv_score, ), ) results = pd.DataFrame(results) @@ -294,14 +309,16 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), "r", + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) with open( - Path("output/reports/attack", folder, "score_dict.json"), "r", + Path("output/reports/attack", folder, "score_dict.json"), + "r", ) as f: try: scores = json.load(f) @@ -311,7 +328,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: del scores["False Confidence"] scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), "w", + Path("output/reports/attack", folder, "score_dict.json"), + "w", ) as f: json.dump(scores, f) yaml_file = Path("output/reports/attack", folder, "params.yaml") @@ -349,7 +367,14 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: print(f"Training Model {i} of {len(models)}") if not Path("retrain", name, f"{epochs}.pkl").exists(): results, outputs = retrain_loop( - model, X_train, y_train, X_test, y_test, atk, epochs=epochs, attack_size=50, + model, + X_train, + y_train, + X_test, + y_test, + atk, + epochs=epochs, + attack_size=50, ) save_results_and_outputs(results, outputs, path=f"retrain/{name}/") Path("retrain", name).mkdir(parents=True, exist_ok=True) @@ -365,7 +390,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), "r", + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", ) as f: probs = json.load(f) probs = np.array(probs) @@ -379,24 +405,28 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: ), ) with open( - Path("output/reports/attack", folder, "score_dict.json"), "r", + Path("output/reports/attack", folder, "score_dict.json"), + "r", ) as f: scores = json.load(f) if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), "w", + Path("output/reports/attack", folder, "score_dict.json"), + "w", ) as f: json.dump(scores, f) if Path("output/reports/attack", folder, "params.yaml").exists(): with open( - Path("output/reports/attack", folder, "params.yaml"), "r", + Path("output/reports/attack", folder, "params.yaml"), + "r", ) as f: params = yaml.safe_load(f) elif Path("output/reports/attack", folder, "params.json").exists(): with open( - Path("output/reports/attack", folder, "params.json"), "r", + Path("output/reports/attack", folder, "params.json"), + "r", ) as f: params = json.load(f) else: diff --git a/examples/security/kdd-nsl/retrain.py b/examples/security/kdd-nsl/retrain.py index dae75fe6..a7dbac4f 100644 --- a/examples/security/kdd-nsl/retrain.py +++ b/examples/security/kdd-nsl/retrain.py @@ -135,7 +135,14 @@ def unflatten_results(df, sep=".") -> List[dict]: def retrain_loop( - clf, X_train, y_train, X_test, y_test, atk, attack_size, epochs, + clf, + X_train, + y_train, + X_test, + y_test, + atk, + attack_size, + epochs, ) -> tuple: i = 0 results = [] @@ -195,12 +202,20 @@ def retrain_loop( # Some Logging print( "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( - i, ben_time, ben_score, adv_time, adv_score, + i, + ben_time, + ben_score, + adv_time, + adv_score, ), ) logger.info( "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( - i, ben_time, ben_score, adv_time, adv_score, + i, + ben_time, + ben_score, + adv_time, + adv_score, ), ) results = pd.DataFrame(results) @@ -291,14 +306,16 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), "r", + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) with open( - Path("output/reports/attack", folder, "score_dict.json"), "r", + Path("output/reports/attack", folder, "score_dict.json"), + "r", ) as f: try: scores = json.load(f) @@ -308,7 +325,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: del scores["False Confidence"] scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), "w", + Path("output/reports/attack", folder, "score_dict.json"), + "w", ) as f: json.dump(scores, f) yaml_file = Path("output/reports/attack", folder, "params.yaml") @@ -346,7 +364,14 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: print(f"Training Model {i} of {len(models)}") if not Path("retrain", name, f"{epochs}.pkl").exists(): results, outputs = retrain_loop( - model, X_train, y_train, X_test, y_test, atk, epochs=epochs, attack_size=50, + model, + X_train, + y_train, + X_test, + y_test, + atk, + epochs=epochs, + attack_size=50, ) save_results_and_outputs(results, outputs, path=f"retrain/{name}/") Path("retrain", name).mkdir(parents=True, exist_ok=True) @@ -362,7 +387,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), "r", + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", ) as f: probs = json.load(f) probs = np.array(probs) @@ -376,24 +402,28 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: ), ) with open( - Path("output/reports/attack", folder, "score_dict.json"), "r", + Path("output/reports/attack", folder, "score_dict.json"), + "r", ) as f: scores = json.load(f) if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), "w", + Path("output/reports/attack", folder, "score_dict.json"), + "w", ) as f: json.dump(scores, f) if Path("output/reports/attack", folder, "params.yaml").exists(): with open( - Path("output/reports/attack", folder, "params.yaml"), "r", + Path("output/reports/attack", folder, "params.yaml"), + "r", ) as f: params = yaml.safe_load(f) elif Path("output/reports/attack", folder, "params.json").exists(): with open( - Path("output/reports/attack", folder, "params.json"), "r", + Path("output/reports/attack", folder, "params.json"), + "r", ) as f: params = json.load(f) else: diff --git a/examples/security/truthseeker/retrain.py b/examples/security/truthseeker/retrain.py index 6251005d..6b91b13c 100644 --- a/examples/security/truthseeker/retrain.py +++ b/examples/security/truthseeker/retrain.py @@ -135,7 +135,14 @@ def unflatten_results(df, sep=".") -> List[dict]: def retrain_loop( - clf, X_train, y_train, X_test, y_test, atk, attack_size, epochs, + clf, + X_train, + y_train, + X_test, + y_test, + atk, + attack_size, + epochs, ) -> tuple: i = 0 results = [] @@ -195,12 +202,20 @@ def retrain_loop( # Some Logging print( "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( - i, ben_time, ben_score, adv_time, adv_score, + i, + ben_time, + ben_score, + adv_time, + adv_score, ), ) logger.info( "Epoch: {} - Benign Time: {} - Benign Score: {} - Adversarial Time: {} - Adversarial Score: {}".format( - i, ben_time, ben_score, adv_time, adv_score, + i, + ben_time, + ben_score, + adv_time, + adv_score, ), ) results = pd.DataFrame(results) @@ -291,14 +306,16 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), "r", + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", ) as f: probs = json.load(f) probs = np.array(probs) false_confidence = y_test[: len(probs)] - probs[:, 1] avg_prob = np.mean(false_confidence) with open( - Path("output/reports/attack", folder, "score_dict.json"), "r", + Path("output/reports/attack", folder, "score_dict.json"), + "r", ) as f: try: scores = json.load(f) @@ -308,7 +325,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: del scores["False Confidence"] scores[f"False Confidence before retraining {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), "w", + Path("output/reports/attack", folder, "score_dict.json"), + "w", ) as f: json.dump(scores, f) yaml_file = Path("output/reports/attack", folder, "params.yaml") @@ -346,7 +364,14 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: print(f"Training Model {i} of {len(models)}") if not Path("retrain", name, f"{epochs}.pkl").exists(): results, outputs = retrain_loop( - model, X_train, y_train, X_test, y_test, atk, epochs=epochs, attack_size=50, + model, + X_train, + y_train, + X_test, + y_test, + atk, + epochs=epochs, + attack_size=50, ) save_results_and_outputs(results, outputs, path=f"retrain/{name}/") Path("retrain", name).mkdir(parents=True, exist_ok=True) @@ -362,7 +387,8 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: confidence_ser = pd.Series() if Path("output/reports/attack", folder, "adv_probabilities.json").exists(): with open( - Path("output/reports/attack", folder, "adv_probabilities.json"), "r", + Path("output/reports/attack", folder, "adv_probabilities.json"), + "r", ) as f: probs = json.load(f) probs = np.array(probs) @@ -376,24 +402,28 @@ def save_results_and_outputs(results, outputs, path="retrain") -> list: ), ) with open( - Path("output/reports/attack", folder, "score_dict.json"), "r", + Path("output/reports/attack", folder, "score_dict.json"), + "r", ) as f: scores = json.load(f) if "False Confidence" in scores: del scores["False Confidence"] scores[f"False Confidence {name.capitalize()}"] = avg_prob with open( - Path("output/reports/attack", folder, "score_dict.json"), "w", + Path("output/reports/attack", folder, "score_dict.json"), + "w", ) as f: json.dump(scores, f) if Path("output/reports/attack", folder, "params.yaml").exists(): with open( - Path("output/reports/attack", folder, "params.yaml"), "r", + Path("output/reports/attack", folder, "params.yaml"), + "r", ) as f: params = yaml.safe_load(f) elif Path("output/reports/attack", folder, "params.json").exists(): with open( - Path("output/reports/attack", folder, "params.json"), "r", + Path("output/reports/attack", folder, "params.json"), + "r", ) as f: params = json.load(f) else: diff --git a/examples/tfv1/tfv1_example.py b/examples/tfv1/tfv1_example.py index 529dfdd3..d5a9f88a 100644 --- a/examples/tfv1/tfv1_example.py +++ b/examples/tfv1/tfv1_example.py @@ -12,11 +12,17 @@ def __init__(self, input_shape=(28, 28, 1), num_classes=10): self.input_ph = tf.placeholder(tf.float32, shape=[None, *input_shape]) self.labels_ph = tf.placeholder(tf.float32, shape=[None, num_classes]) self.layer1 = tf.layers.conv2d( - self.input_ph, filters=4, kernel_size=5, activation=tf.nn.relu, + self.input_ph, + filters=4, + kernel_size=5, + activation=tf.nn.relu, ) self.layer2 = tf.layers.max_pooling2d(self.layer1, 2, 2) self.layer3 = tf.layers.conv2d( - self.layer2, filters=10, kernel_size=5, activation=tf.nn.relu, + self.layer2, + filters=10, + kernel_size=5, + activation=tf.nn.relu, ) self.layer4 = tf.layers.max_pooling2d(self.layer3, 2, 2) self.layer5 = tf.layers.flatten(self.layer4) diff --git a/examples/tfv2/tfv2_example.py b/examples/tfv2/tfv2_example.py index ae871ee3..b3ff9ba3 100644 --- a/examples/tfv2/tfv2_example.py +++ b/examples/tfv2/tfv2_example.py @@ -19,7 +19,10 @@ def __init__(self, optimizer: dict, loss_object: dict, output_shape=10): self.conv1 = Conv2D(filters=4, kernel_size=5, activation="relu") self.conv2 = Conv2D(filters=10, kernel_size=5, activation="relu") self.maxpool = MaxPool2D( - pool_size=(2, 2), strides=(2, 2), padding="valid", data_format=None, + pool_size=(2, 2), + strides=(2, 2), + padding="valid", + data_format=None, ) self.flatten = Flatten() self.dense1 = Dense(100, activation="relu") diff --git a/test/base/test_attack/test_attack.py b/test/base/test_attack/test_attack.py index df2f7825..1805bbe6 100644 --- a/test/base/test_attack/test_attack.py +++ b/test/base/test_attack/test_attack.py @@ -19,7 +19,8 @@ class testAttackInitializer(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg @@ -70,7 +71,8 @@ class testAttack(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_attack/torch_example.py b/test/base/test_attack/torch_example.py index 8eaa3954..59554442 100644 --- a/test/base/test_attack/torch_example.py +++ b/test/base/test_attack/torch_example.py @@ -214,7 +214,12 @@ def __init__( # CIFAR10: kernel_size 7 -> 3, stride 2 -> 1, padding 3->1 self.conv1 = nn.Conv2d( - 3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False, + 3, + self.inplanes, + kernel_size=3, + stride=1, + padding=1, + bias=False, ) # END @@ -223,13 +228,25 @@ def __init__( self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer( - block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0], + block, + 128, + layers[1], + stride=2, + dilate=replace_stride_with_dilation[0], ) self.layer3 = self._make_layer( - block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1], + block, + 256, + layers[2], + stride=2, + dilate=replace_stride_with_dilation[1], ) self.layer4 = self._make_layer( - block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2], + block, + 512, + layers[3], + stride=2, + dilate=replace_stride_with_dilation[2], ) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) diff --git a/test/base/test_data/test_data.py b/test/base/test_data/test_data.py index 7a583819..5f41cf1d 100644 --- a/test/base/test_data/test_data.py +++ b/test/base/test_data/test_data.py @@ -21,7 +21,8 @@ class testSklearnData(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg @@ -77,7 +78,8 @@ def test_resave(self): train_labels_file = Path(data_file).with_suffix(".csv").as_posix() test_labels_file = Path(data_file).with_suffix(".json").as_posix() _ = self.data( - train_labels_file=train_labels_file, test_labels_file=test_labels_file, + train_labels_file=train_labels_file, + test_labels_file=test_labels_file, ) score_dict = {"test_score": 0.5} score_series = Series(score_dict) diff --git a/test/base/test_data/test_generator.py b/test/base/test_data/test_generator.py index 688add4b..0dbab957 100644 --- a/test/base/test_data/test_generator.py +++ b/test/base/test_data/test_generator.py @@ -25,7 +25,8 @@ class testDataGenerator(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg @@ -125,7 +126,8 @@ def test_call(self): for name in self.names: data = TorchDataGenerator(name=name, path=self.dir)() data = TorchDataGenerator( - name=name, path=self.dir, + name=name, + path=self.dir, )() # Test it again to make sure download only happens once self.assertIsInstance(data, list) self.assertIsInstance(data[0], np.ndarray) diff --git a/test/base/test_data/test_sampler.py b/test/base/test_data/test_sampler.py index f32adb68..e3b2f0cc 100644 --- a/test/base/test_data/test_sampler.py +++ b/test/base/test_data/test_sampler.py @@ -14,7 +14,8 @@ class testSklearnDataSampler(unittest.TestCase): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -51,7 +52,8 @@ def tearDown(self) -> None: class testTimeSeriesSklearnDataSampler(unittest.TestCase): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -88,7 +90,8 @@ def tearDown(self) -> None: class testTimeSeriesSklearnDataSampler2(testTimeSeriesSklearnDataSampler): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -101,7 +104,8 @@ def setUp(self, config_dir=config_dir, config_file=config_file): class testTimeSeriesSklearnDataSampler3(testTimeSeriesSklearnDataSampler): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -114,7 +118,8 @@ def setUp(self, config_dir=config_dir, config_file=config_file): class testTimeSeriesSklearnDataSampler4(testTimeSeriesSklearnDataSampler): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg diff --git a/test/base/test_data/test_sklearn_pipeline.py b/test/base/test_data/test_sklearn_pipeline.py index c70dfa8f..09b40eb6 100644 --- a/test/base/test_data/test_sklearn_pipeline.py +++ b/test/base/test_data/test_sklearn_pipeline.py @@ -20,7 +20,8 @@ class testSklearnDataPipeline(unittest.TestCase): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -37,7 +38,10 @@ def test_call(self): X_train, X_test, y_train, y_test = self.data.sample(X, y) old_mean = np.mean(X_train) X_train, X_test, y_train, y_test = self.data.sklearn_pipeline( - X_train, X_test, y_train, y_test, + X_train, + X_test, + y_train, + y_test, ) new_mean = np.mean(X_train) self.assertNotEqual(old_mean, new_mean) @@ -67,7 +71,8 @@ def test_len(self): def test_getitem(self): for stage in self.data.sklearn_pipeline: self.assertIsInstance( - self.data.sklearn_pipeline[stage], SklearnDataPipelineStage, + self.data.sklearn_pipeline[stage], + SklearnDataPipelineStage, ) def tearDown(self) -> None: @@ -77,7 +82,8 @@ def tearDown(self) -> None: class testSklearnDataPipelineStage(unittest.TestCase): def setUp(self, config_dir=config_dir, config_file=config_file): with initialize_config_dir( - config_dir=Path(config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=config_file) self.cfg = cfg @@ -86,7 +92,8 @@ def setUp(self, config_dir=config_dir, config_file=config_file): def test_init(self): self.assertTrue( isinstance( - self.data.sklearn_pipeline["preprocessor"], SklearnDataPipelineStage, + self.data.sklearn_pipeline["preprocessor"], + SklearnDataPipelineStage, ) or hasattr(self.data.sklearn_pipeline["preprocessor"], "transform"), ) @@ -96,7 +103,10 @@ def test_call(self): X_train, X_test, y_train, y_test = self.data.sample(X, y) old_mean = np.mean(X_train) X_train, X_test, y_train, y_test = self.data.sklearn_pipeline["preprocessor"]( - X_train, X_test, y_train, y_test, + X_train, + X_test, + y_train, + y_test, ) new_mean = np.mean(X_train) self.assertNotEqual(old_mean, new_mean) diff --git a/test/base/test_experiment/test_experiment.py b/test/base/test_experiment/test_experiment.py index a705524c..260059b3 100644 --- a/test/base/test_experiment/test_experiment.py +++ b/test/base/test_experiment/test_experiment.py @@ -19,7 +19,8 @@ class testExperiment(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg @@ -87,7 +88,8 @@ class testExperimentReRun(testExperiment): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) cfg = OmegaConf.to_container(cfg, resolve=True) diff --git a/test/base/test_experiment/torch_example.py b/test/base/test_experiment/torch_example.py index 33cfe035..e7479cb8 100644 --- a/test/base/test_experiment/torch_example.py +++ b/test/base/test_experiment/torch_example.py @@ -214,7 +214,12 @@ def __init__( # CIFAR10: kernel_size 7 -> 3, stride 2 -> 1, padding 3->1 self.conv1 = nn.Conv2d( - 3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False, + 3, + self.inplanes, + kernel_size=3, + stride=1, + padding=1, + bias=False, ) # END @@ -223,13 +228,25 @@ def __init__( self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer( - block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0], + block, + 128, + layers[1], + stride=2, + dilate=replace_stride_with_dilation[0], ) self.layer3 = self._make_layer( - block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1], + block, + 256, + layers[2], + stride=2, + dilate=replace_stride_with_dilation[1], ) self.layer4 = self._make_layer( - block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2], + block, + 512, + layers[3], + stride=2, + dilate=replace_stride_with_dilation[2], ) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) diff --git a/test/base/test_files/test_files.py b/test/base/test_files/test_files.py index 5ce170a5..0cca058b 100644 --- a/test/base/test_files/test_files.py +++ b/test/base/test_files/test_files.py @@ -16,7 +16,8 @@ class testFiles(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_model/keras_example.py b/test/base/test_model/keras_example.py index f0ee24ad..c091de91 100644 --- a/test/base/test_model/keras_example.py +++ b/test/base/test_model/keras_example.py @@ -43,6 +43,8 @@ def __init__(self, optimizer, loss, metrics): def __call__(self): self.model.compile( - optimizer=self.optimizer, loss=self.loss, metrics=self.metrics, + optimizer=self.optimizer, + loss=self.loss, + metrics=self.metrics, ) return self.model diff --git a/test/base/test_model/test_art_pipeline.py b/test/base/test_model/test_art_pipeline.py index caabc6d6..c96e03bb 100644 --- a/test/base/test_model/test_art_pipeline.py +++ b/test/base/test_model/test_art_pipeline.py @@ -18,7 +18,8 @@ class testArtPipeline(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_model/test_model.py b/test/base/test_model/test_model.py index dc9ca087..9580afd9 100644 --- a/test/base/test_model/test_model.py +++ b/test/base/test_model/test_model.py @@ -22,7 +22,8 @@ class testModel(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_model/test_sklearn_model_pipeline.py b/test/base/test_model/test_sklearn_model_pipeline.py index d018f808..1705af2a 100644 --- a/test/base/test_model/test_sklearn_model_pipeline.py +++ b/test/base/test_model/test_sklearn_model_pipeline.py @@ -18,7 +18,8 @@ class testSklearnModelPipeline(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = cfg diff --git a/test/base/test_model/tfv2_example.py b/test/base/test_model/tfv2_example.py index d5d2164e..52303cff 100644 --- a/test/base/test_model/tfv2_example.py +++ b/test/base/test_model/tfv2_example.py @@ -24,7 +24,10 @@ def __init__(self, optimizer: dict, loss_object: dict, output_shape=10): self.conv1 = Conv2D(filters=4, kernel_size=5, activation="relu") self.conv2 = Conv2D(filters=10, kernel_size=5, activation="relu") self.maxpool = MaxPool2D( - pool_size=(2, 2), strides=(2, 2), padding="valid", data_format=None, + pool_size=(2, 2), + strides=(2, 2), + padding="valid", + data_format=None, ) self.flatten = Flatten() self.dense1 = Dense(100, activation="relu") diff --git a/test/base/test_model/torch_example.py b/test/base/test_model/torch_example.py index 33cfe035..e7479cb8 100644 --- a/test/base/test_model/torch_example.py +++ b/test/base/test_model/torch_example.py @@ -214,7 +214,12 @@ def __init__( # CIFAR10: kernel_size 7 -> 3, stride 2 -> 1, padding 3->1 self.conv1 = nn.Conv2d( - 3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False, + 3, + self.inplanes, + kernel_size=3, + stride=1, + padding=1, + bias=False, ) # END @@ -223,13 +228,25 @@ def __init__( self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer( - block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0], + block, + 128, + layers[1], + stride=2, + dilate=replace_stride_with_dilation[0], ) self.layer3 = self._make_layer( - block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1], + block, + 256, + layers[2], + stride=2, + dilate=replace_stride_with_dilation[1], ) self.layer4 = self._make_layer( - block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2], + block, + 512, + layers[3], + stride=2, + dilate=replace_stride_with_dilation[2], ) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) diff --git a/test/base/test_scorer/test_scorer.py b/test/base/test_scorer/test_scorer.py index 13238faa..06d1a807 100644 --- a/test/base/test_scorer/test_scorer.py +++ b/test/base/test_scorer/test_scorer.py @@ -23,14 +23,16 @@ class testScorerDict(unittest.TestCase): def setUp(self): with initialize_config_dir( - config_dir=Path(self.config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(self.config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=self.config_file) self.cfg = OmegaConf.to_container(cfg, resolve=True) self.scorers = instantiate(config=self.cfg) self.directory = mkdtemp() self.file = Path( - self.directory, self.score_dict_file + self.score_dict_type, + self.directory, + self.score_dict_file + self.score_dict_type, ).as_posix() true_file = "true.pkl" preds_file = "preds.pkl" @@ -199,7 +201,8 @@ class testComputeAccuracy(testScorerConfig): model_config_dir = Path(this_dir, "../../conf/model").resolve().as_posix() model_config_file = "classification.yaml" with initialize_config_dir( - config_dir=Path(model_config_dir).resolve().as_posix(), version_base="1.3", + config_dir=Path(model_config_dir).resolve().as_posix(), + version_base="1.3", ): cfg = compose(config_name=model_config_file) model_cfg = cfg @@ -233,7 +236,8 @@ def setUp(self): self.scorers = ScorerDict(**self.cfg) self.directory = mkdtemp() self.file = Path( - self.directory, self.score_dict_file + self.score_dict_type, + self.directory, + self.score_dict_file + self.score_dict_type, ).as_posix() true_file = "true.pkl" preds_file = "preds.pkl" From 66aaa739669e9f36d0f834af314f0e9ac5c18e72 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 19:22:18 +0000 Subject: [PATCH 136/148] delete binary --- hardcopy.1 | 24 ------------------------ 1 file changed, 24 deletions(-) delete mode 100644 hardcopy.1 diff --git a/hardcopy.1 b/hardcopy.1 deleted file mode 100644 index 4302b333..00000000 --- a/hardcopy.1 +++ /dev/null @@ -1,24 +0,0 @@ - model, time_dict = self.trainer(data, model, library=self.library) - File "/home/cmeyers/deckard/deckard/base/model/model.py", line 132, in __call_ -_ - model.fit(data[0], data[2], **trainer) - File "/home/cmeyers/anaconda3/lib/python3.8/site-packages/art/estimators/class -ification/classifier.py", line 73, in replacement_function - return fdict[func_name](self, *args, **kwargs) - File "/home/cmeyers/anaconda3/lib/python3.8/site-packages/art/estimators/class -ification/pytorch.py", line 456, in fit - loss.backward() - File "/home/cmeyers/anaconda3/lib/python3.8/site-packages/torch/_tensor.py", l -ine 363, in backward - torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=i -nputs) - File "/home/cmeyers/anaconda3/lib/python3.8/site-packages/torch/autograd/__ini -t__.py", line 173, in backward - Variable._execution_engine.run_backward( # Calls into the C++ engine to run - the backward pass -KeyboardInterrupt -ERROR: failed to reproduce 'attacks@ResNet34': failed to run: bash attacks.sh ++ -attack.attack_size=100 ++model.init.name=torch_example.ResNet34 stage=attack ++h -ydra.sweeper.storage=sqlite:///cifar100/reports/attack/ResNet34.db --config-name - cifar100.yaml, exited with -2 -(base) cmeyers@cmeyers:~/deckard/examples/pytorch_cifar_100$ From 9aa79608e9f88d786102fc06ffe76b4cfe7ccf03 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 19:23:47 +0000 Subject: [PATCH 137/148] linting --- deckard/layers/experiment.py | 1 - .../mnist/average_across_random_states.ipynb | 110 +++++++++++------- 2 files changed, 67 insertions(+), 44 deletions(-) diff --git a/deckard/layers/experiment.py b/deckard/layers/experiment.py index 1e481821..14d9ba43 100644 --- a/deckard/layers/experiment.py +++ b/deckard/layers/experiment.py @@ -26,7 +26,6 @@ def get_dvc_stage_params( directory=".", name=None, ): - logger.info( f"Getting params for stage {stage} from {params_file} and {pipeline_file} in {directory}.", ) diff --git a/examples/pytorch/mnist/average_across_random_states.ipynb b/examples/pytorch/mnist/average_across_random_states.ipynb index 1bf51a59..26730596 100644 --- a/examples/pytorch/mnist/average_across_random_states.ipynb +++ b/examples/pytorch/mnist/average_across_random_states.ipynb @@ -26,13 +26,13 @@ " \"train_time\": \"min\",\n", " \"predict_time\": \"min\",\n", " \"accuracy\": \"max\",\n", - " \"data.sample.random_state\" : \"diff\",\n", + " \"data.sample.random_state\": \"diff\",\n", " \"model.trainer.nb_epoch\": \"diff\",\n", " \"atk_gen\": \"diff\",\n", - " \"atk_param\" : \"diff\",\n", + " \"atk_param\": \"diff\",\n", " \"adv_accuracy\": \"max\",\n", " \"adv_fit_time\": \"min\",\n", - "}\n" + "}" ] }, { @@ -59,7 +59,7 @@ "source": [ "layers = df.model_layers.unique()\n", "layers.sort()\n", - "epochs = df['model.trainer.nb_epoch'].unique()\n", + "epochs = df[\"model.trainer.nb_epoch\"].unique()\n", "epochs.sort()\n", "attacks = df.atk_gen.unique()\n", "attacks.sort()\n", @@ -75,7 +75,7 @@ " f\"Attacks: {attacks}\\n\"\n", " f\"Number of defenses: {len(defenses)}\\n\"\n", " f\"Defenses: {defenses}\\n\"\n", - ")\n" + ")" ] }, { @@ -525,27 +525,30 @@ "sense_dict = {\n", " \"model_layers\": \"diff\",\n", " \"accuracy\": \"max\",\n", - " \"data.sample.random_state\" : \"diff\",\n", + " \"data.sample.random_state\": \"diff\",\n", " \"model.trainer.nb_epoch\": \"diff\",\n", - " \"model_layers\" : \"diff\",\n", + " \"model_layers\": \"diff\",\n", " \"atk_gen\": \"diff\",\n", " \"def_gen\": \"diff\",\n", " \"adv_fit_time\": \"min\",\n", " \"adv_accuracy\": \"min\",\n", " \"predict_time\": \"min\",\n", - " \"train_time\" : \"min\",\n", - " \"attack.attack_size\" : \"diff\",\n", + " \"train_time\": \"min\",\n", + " \"attack.attack_size\": \"diff\",\n", "}\n", "\n", "# Average across random states\n", "scorer = \"accuracy\"\n", "\n", + "\n", "def average_across_random_states(df, scorer, sense_dict):\n", " sense_dict.pop(\"data.sample.random_state\", None)\n", - " group_list = [k for k,v in sense_dict.items() if v == 'diff']\n", + " group_list = [k for k, v in sense_dict.items() if v == \"diff\"]\n", " group_list_wo_random_state = group_list.copy()\n", " print(f\"Grouping by {group_list_wo_random_state} for {scorer}\")\n", - " df[f'mean_{scorer}'] = df.groupby(group_list_wo_random_state)[scorer].transform('mean')\n", + " df[f\"mean_{scorer}\"] = df.groupby(group_list_wo_random_state)[scorer].transform(\n", + " \"mean\"\n", + " )\n", " return df\n", "\n", "\n", @@ -556,9 +559,10 @@ " df = df.drop(col, axis=1)\n", " return df\n", "\n", + "\n", "def find_pareto_set_for_graph(df, sense_dict):\n", - " scorers = [k for k,v in sense_dict.items() if v in [\"max\", \"min\"]]\n", - " group_list = [k for k,v in sense_dict.items() if v == 'diff']\n", + " scorers = [k for k, v in sense_dict.items() if v in [\"max\", \"min\"]]\n", + " group_list = [k for k, v in sense_dict.items() if v == \"diff\"]\n", " group_list_wo_attack = group_list.copy()\n", " for group in group_list:\n", " if group in [\"atk_gen\", \"atk_value\", \"atk_param\"]:\n", @@ -570,7 +574,9 @@ " else:\n", " continue\n", " for scorer in scorers:\n", - " scores = df[scorer].fillna(df.groupby(group_list_wo_attack)[scorer].transform('mean'))\n", + " scores = df[scorer].fillna(\n", + " df.groupby(group_list_wo_attack)[scorer].transform(\"mean\")\n", + " )\n", " df[scorer] = scores.fillna(scores.mean())\n", " df = average_across_random_states(df, scorer, sense_dict)\n", " value = sense_dict.get(scorer)\n", @@ -581,8 +587,10 @@ " # df = df[bools]\n", " return df\n", "\n", + "\n", "df = find_pareto_set_for_graph(df, sense_dict)\n", "\n", + "\n", "def drop_col_if_no_variance(df):\n", " drop_these = []\n", " for col in df.columns:\n", @@ -591,6 +599,7 @@ " tmp = df.drop(drop_these, axis=1)\n", " return tmp\n", "\n", + "\n", "df = drop_poorly_merged_columns(df)\n", "\n", "df" @@ -623,7 +632,7 @@ } ], "source": [ - "sns.lineplot(data=df, y=\"adv_log_loss\", x=\"model.trainer.nb_epoch\", hue=\"model_layers\")\n" + "sns.lineplot(data=df, y=\"adv_log_loss\", x=\"model.trainer.nb_epoch\", hue=\"model_layers\")" ] }, { @@ -663,68 +672,83 @@ } ], "source": [ - "from lifelines import CoxPHFitter, KaplanMeierFitter, NelsonAalenFitter, AalenAdditiveFitter, WeibullAFTFitter, LogNormalAFTFitter, LogLogisticAFTFitter, PiecewiseExponentialRegressionFitter\n", + "from lifelines import (\n", + " CoxPHFitter,\n", + " KaplanMeierFitter,\n", + " NelsonAalenFitter,\n", + " AalenAdditiveFitter,\n", + " WeibullAFTFitter,\n", + " LogNormalAFTFitter,\n", + " LogLogisticAFTFitter,\n", + " PiecewiseExponentialRegressionFitter,\n", + ")\n", "\n", "\n", "model_dict = {\n", - " \"cox\" : CoxPHFitter,\n", + " \"cox\": CoxPHFitter,\n", " # \"kaplan_meier\" : KaplanMeierFitter,\n", " # \"nelson_aalen\" : NelsonAalenFitter,\n", " # \"aalen_additive\" : AalenAdditiveFitter,\n", - " \"weibull\" : WeibullAFTFitter,\n", - " \"log_normal\" : LogNormalAFTFitter,\n", - " \"log_logistic\" : LogLogisticAFTFitter,\n", + " \"weibull\": WeibullAFTFitter,\n", + " \"log_normal\": LogNormalAFTFitter,\n", + " \"log_logistic\": LogLogisticAFTFitter,\n", " # \"piecewise_exponential\" : PiecewiseExponentialRegressionFitter,\n", "}\n", "\n", + "\n", "def fit_aft_model(df, sense_dict, model_name):\n", - " \n", - " stratify = ['atk_gen', 'def_gen',]\n", - " subset_df = df.copy()\n", + " stratify = [\n", + " \"atk_gen\",\n", + " \"def_gen\",\n", + " ]\n", + " subset_df = df.copy()\n", " subset_df = subset_df.drop(stratify, axis=1)\n", " model = model_dict[model_name]()\n", - " model.fit(df, duration_col ='mean_adv_fit_time', event_col='adv_failures')\n", + " model.fit(df, duration_col=\"mean_adv_fit_time\", event_col=\"adv_failures\")\n", " model.print_summary()\n", " plot = model.plot()\n", - " concordance = model.score(df, scoring_method='concordance_index')\n", + " concordance = model.score(df, scoring_method=\"concordance_index\")\n", " print(f\"Concordance index: {concordance}\")\n", - " measured_median = np.median(df.mean_adv_fit_time / df['attack.attack_size'] * ((1 - df.adv_failures)/100))\n", + " measured_median = np.median(\n", + " df.mean_adv_fit_time / df[\"attack.attack_size\"] * ((1 - df.adv_failures) / 100)\n", + " )\n", " print(\"Measured median attack time:\", measured_median)\n", " modelled_median = np.median(model.predict_median(df, ancillary=df))\n", " print(\"Predicted median attack time:\", modelled_median)\n", - " score = model.score(df, scoring_method='log_likelihood')\n", + " score = model.score(df, scoring_method=\"log_likelihood\")\n", " score_dict = {\n", - " \"model\" : model_name,\n", - " \"concordance\" : concordance,\n", - " \"measured_median\" : measured_median,\n", - " \"modelled_median\" : modelled_median,\n", - " \"log_likelihood\" : score,\n", + " \"model\": model_name,\n", + " \"concordance\": concordance,\n", + " \"measured_median\": measured_median,\n", + " \"modelled_median\": modelled_median,\n", + " \"log_likelihood\": score,\n", " }\n", " return model, plot, score\n", "\n", + "\n", "models = {}\n", "scores = {}\n", "plots = {}\n", - "stratify = ['atk_gen', 'def_gen']\n", + "stratify = [\"atk_gen\", \"def_gen\"]\n", "subset_cols = [k for k in sense_dict if k not in stratify]\n", "aft_df = df[subset_cols].copy()\n", - "aft_df['adv_failures'] = (1 - df['mean_adv_accuracy']) * df['attack.attack_size']\n", - "del aft_df['mean_adv_accuracy']\n", + "aft_df[\"adv_failures\"] = (1 - df[\"mean_adv_accuracy\"]) * df[\"attack.attack_size\"]\n", + "del aft_df[\"mean_adv_accuracy\"]\n", "new_sense_dict = sense_dict.copy()\n", - "new_sense_dict.update({\"adv_failures\" : sense_dict['mean_adv_accuracy']})\n", + "new_sense_dict.update({\"adv_failures\": sense_dict[\"mean_adv_accuracy\"]})\n", "new_sense_dict.pop(\"mean_adv_accuracy\", None)\n", "new_sense_dict\n", "\n", "for model_name in model_dict:\n", " print(f\"Fitting {model_name} model\")\n", " model, plot, score = fit_aft_model(aft_df, new_sense_dict, model_name)\n", - " models.update({model_name : model})\n", - " scores.update({model_name : score})\n", - " plots.update({model_name : plot})\n", - " plt.xscale('linear')\n", + " models.update({model_name: model})\n", + " scores.update({model_name: score})\n", + " plots.update({model_name: plot})\n", + " plt.xscale(\"linear\")\n", " plt.show()\n", " plt.gcf().clear()\n", - " \n", + "\n", "# scores = pd.DataFrame.from_dict(scores, orient='index', columns=['score'])\n", "\n", "# covariates = [k for k,v in sense_dict.items() if v == 'diff']\n", @@ -747,9 +771,9 @@ "metadata": {}, "outputs": [], "source": [ - "model = models['weibull']\n", + "model = models[\"weibull\"]\n", "expectations = model.predict_expectation(df, ancillary=df)\n", - "survival_function = model.predict_survival_function(df, ancillary=df)\n" + "survival_function = model.predict_survival_function(df, ancillary=df)" ] }, { From 92739801e1049a6f4006ddc7504c1e5ed778a1af Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 22:29:28 +0000 Subject: [PATCH 138/148] fix bad merge --- deckard/base/experiment/experiment.py | 25 +- deckard/layers/compile.py | 161 ++-- deckard/layers/optimise.py | 29 +- .../deploy-nvidia-tesla-a100/default.yaml | 14 - .../deploy-nvidia-tesla-p100/default.yaml | 14 - .../deploy-nvidia-tesla-v100/default.yaml | 14 - examples/power/dvc.yaml | 8 +- examples/pytorch/cifar100/conf/cifar100.yaml | 2 +- .../cifar100/conf/data/torch_mnist.yaml | 1 - .../cifar100/conf/model/torch_mnist.yaml | 2 +- examples/pytorch/cifar100/dvc.lock | 46 +- examples/pytorch/cifar100/dvc.yaml | 6 +- examples/pytorch/mnist/dvc.lock | 790 ------------------ 13 files changed, 139 insertions(+), 973 deletions(-) delete mode 100644 examples/power/conf/deploy-nvidia-tesla-a100/default.yaml delete mode 100644 examples/power/conf/deploy-nvidia-tesla-p100/default.yaml delete mode 100644 examples/power/conf/deploy-nvidia-tesla-v100/default.yaml delete mode 100644 examples/pytorch/mnist/dvc.lock diff --git a/deckard/base/experiment/experiment.py b/deckard/base/experiment/experiment.py index 522ae9d3..c04df0b2 100644 --- a/deckard/base/experiment/experiment.py +++ b/deckard/base/experiment/experiment.py @@ -258,28 +258,7 @@ def __call__(self): self.data.save(score_dict, files["score_dict_file"]) else: # pragma: no cover raise ValueError("Scorer is None. Please specify a scorer.") - ######################################################################### - # Returns score if scorer is not None, otherwise returns status - ######################################################################### - if self.optimizers is not None and self.optimizers != []: - self.optimizers = ( - [self.optimizers] - if not isinstance(self.optimizers, (list, ListConfig)) - else self.optimizers - ) - scores = {} - for scorer in self.optimizers: - try: - score = score_dict[scorer] - except KeyError: # pragma: no cover - raise KeyError( - f"Scorer {scorer} not found in score_dict. Available self.optimizers: {score_dict.keys()}", - ) - scores[scorer] = score - logger.info(f"Score for id : {self.get_name()} : {scorer}: {score}") - else: - scores = score_dict - logger.info(f"Score for id : {self.get_name()}: {score_dict}") + logger.info(f"Score for id : {self.get_name()}: {score_dict}") logger.info("Finished running experiment with id: {}".format(self.get_name())) new_name = self.get_name() assert ( @@ -288,7 +267,7 @@ def __call__(self): logger.debug( f"Experiment deckard hash changed from {old_hash} to {my_hash(self)}.", ) - return scores + return score_dict def _set_name(self): if self.files.name is not None: diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 3627c7a2..21a1cbbc 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -11,7 +11,15 @@ def flatten_results(df: pd.DataFrame) -> pd.DataFrame: - for col in df.columns: + """ + Args: + df (pd.DataFrame): a dataframe with dictionaries as entries in some columns + + Returns: + pd.DataFrame: a dataframe with the dictionaries flattened into columns using pd.json_normalize + """ + df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x) + for col in tqdm(df.columns, desc="Flattening columns"): if isinstance(df[col][0], dict): tmp = pd.json_normalize(df[col]) tmp.columns = [f"{col}.{subcol}" for subcol in tmp.columns] @@ -34,53 +42,57 @@ def parse_folder(folder, files=["params.yaml", "score_dict.json"]) -> pd.DataFra :return: Pandas dataframe with the results """ folder = Path(folder) - results = {} + logger.debug(f"Parsing folder {folder}...") path_gen = [] for file in files: path_gen.extend(folder.glob(f"**/{file}")) path_gen.sort() - indices = [] - for file in tqdm(path_gen): - indices.append(file.parent.name) - suffix = file.suffix - folder = file.parent.name - stage = file.parent.parent.name - if folder not in results: - results[folder] = {} - if suffix == ".json": - with open(file, "r") as f: - try: - dict_ = json.load(f) - except json.decoder.JSONDecodeError as e: - raise e - - try: - dict_ = json.load(f) - except json.decoder.JSONDecodeError as e: - raise e - - elif suffix == ".yaml": - with open(file, "r") as f: - dict_ = yaml.safe_load(f) - else: - raise ValueError(f"File type {suffix} not supported.") - results[folder]["stage"] = stage - results[folder].update(dict_) - all_files = Path(folder).glob("**/*") - for file in all_files: - if file not in path_gen: - if file.parent.name not in results: - results[file.parent.name] = {} - results[file.parent.name][file.stem] = file + folder_gen = map(lambda x: x.parent, path_gen) + folder_gen = set(folder_gen) + results = {} + for file in tqdm(path_gen, desc="Parsing Specified files"): + results = read_file(file, results) + for folder in tqdm(folder_gen, desc="Adding other files to results"): + results = add_file(folder, path_gen, results) df = pd.DataFrame(results).T return df +def add_file(folder, path_gen, results): + all_files = Path(folder).glob("**/*") + for file in all_files: + if file not in path_gen: + if file.parent.name not in results: + results[file.parent.name] = {} + results[file.parent.name][file.stem] = file + return results + +def read_file(file, results): + suffix = file.suffix + folder = file.parent.name + stage = file.parent.parent.name + if folder not in results: + results[folder] = {} + if suffix == ".json": + with open(file, "r") as f: + try: + dict_ = json.load(f) + except json.decoder.JSONDecodeError as e: + raise e + elif suffix == ".yaml": + with open(file, "r") as f: + dict_ = yaml.safe_load(f) + else: + raise ValueError(f"File type {suffix} not supported.") + results[folder]["stage"] = stage + results[folder].update(dict_) + return results + def merge_defences(results: pd.DataFrame, default_epochs=20): defences = [] def_gens = [] - for _, entry in results.iterrows(): + for _, entry in tqdm(results.iterrows(), desc="Merging defences"): defence = [] if ( "model.art.pipeline.preprocessor.name" in entry @@ -106,7 +118,7 @@ def merge_defences(results: pd.DataFrame, default_epochs=20): "model.init.nb_epoch" in entry and entry["model.init.nb_epoch"] != default_epochs ): - defence.append(entry["model.init.nb_epoch"]) + defence.append("Epochs") ############################################################################################################ if len(defence) > 1: def_gen = [str(x).split(".")[-1] for x in defence] @@ -117,8 +129,6 @@ def merge_defences(results: pd.DataFrame, default_epochs=20): else: def_gen = "Control" defence = "Control" - def_gen = "Control" - defence = "Control" ############################################################################################################ if defence != []: defences.append(defence) @@ -133,7 +143,7 @@ def merge_defences(results: pd.DataFrame, default_epochs=20): def merge_attacks(results: pd.DataFrame): attacks = [] - for _, entry in results.iterrows(): + for _, entry in tqdm(results.iterrows(), desc="Merging attacks"): if "attack.init.name" in entry and entry["attack.init.name"] not in nones: attack = entry["attack.init.name"] else: @@ -148,14 +158,14 @@ def merge_attacks(results: pd.DataFrame): def parse_results(folder, files=["score_dict.json", "params.yaml"], default_epochs=20): df = parse_folder(folder, files=files) df = flatten_results(df) + df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x) df = merge_defences(df, default_epochs=default_epochs) df = merge_attacks(df) return df -def format_control_parameter(data, control_dict): +def format_control_parameter(data, control_dict, min_max = True): logger.info("Formatting control parameters...") - if hasattr(data, "def_gen"): defences = data.def_gen.unique() else: @@ -164,7 +174,8 @@ def format_control_parameter(data, control_dict): attacks = data.atk_gen.unique() else: attacks = [] - + if data["model.init.name"].str.contains("Net").any(): + data["model_layers"] = data["model_name"].str.split("Net").str[-1] for defence in defences: if defence in control_dict: param = control_dict[defence] @@ -175,24 +186,17 @@ def format_control_parameter(data, control_dict): value = np.nan data.loc[data.def_gen == defence, "def_value"] = value control_dict.pop(defence) - data.loc[data.def_gen == defence, "def_value"] = value - control_dict.pop(defence) + else: logger.warning(f"Defence {defence} not in control_dict. Deleting rows.") data = data[data.def_gen != defence] - - for attack in attacks: - if attack in control_dict: - param = control_dict[attack] - data.loc[data.atk_gen == attack, "atk_param"] = param.split(".")[-1] - if param in data.columns: - value = data[data.atk_gen == attack][param] - else: - value = np.nan - data.loc[data.atk_gen == attack, "atk_value"] = value - control_dict.pop(attack) - logger.warning(f"Defence {defence} not in control_dict. Deleting rows.") - data = data[data.def_gen != defence] + # if min_max is True: + # def_min = data[data.def_gen == defence].def_value.min() + # def_max = data[data.def_gen == defence].def_value.max() + # data.loc[data.def_gen == defence, "def_value"] = pd.to_numeric(data.loc[data.def_gen == defence, "def_value"], errors='coerce') + # data.loc[data.def_gen == defence, "def_value"] = ( + # data[data.def_gen == defence].def_value - def_min + # ) / (def_max - def_min) for attack in attacks: if attack in control_dict: @@ -207,10 +211,14 @@ def format_control_parameter(data, control_dict): else: logger.warning(f"Attack {attack} not in control_dict. Deleting rows.") data = data[data.atk_gen != attack] - - logger.warning(f"Attack {attack} not in control_dict. Deleting rows.") - data = data[data.atk_gen != attack] - + + # if min_max is True: + # atk_min = data[data.atk_gen == attack].atk_value.min() + # atk_max = data[data.atk_gen == attack].atk_value.max() + # data.loc[data.atk_gen == attack, "atk_value"] = pd.to_numeric(data.loc[data.atk_gen == attack, "atk_value"], errors='coerce') + # data.loc[data.atk_gen == attack, "atk_value"] = ( + # data[data.atk_gen == attack].atk_value - atk_min + # ) / (atk_max - atk_min) return data @@ -219,8 +227,6 @@ def clean_data_for_plotting( def_gen_dict, atk_gen_dict, control_dict, - file, - folder, ): logger.info("Replacing attack and defence names with short names...") if hasattr(data, "def_gen"): @@ -234,26 +240,24 @@ def clean_data_for_plotting( data.dropna(axis=1, how="all", inplace=True) logger.info("Shortening model names...") # Removes the path and to the model object and leaves the name of the model - # Removes the path and to the model object and leaves the name of the model - data["model_name"] = data["model.init.name"].str.split(".").str[-1] - if data["model.init.name"].str.contains("Net").any(): - data["model_layers"] = data["model_name"].str.split("Net").str[-1] + if hasattr(data, "model.init.name") and len(data["model.init.name"].unique()) > 1: + model_names = data["model.init.name"].str.split(".").str[-1] + data["model_name"] = model_names data = data.loc[:, ~data.columns.str.endswith(".1")] - logger.info("Replacing data.sample.random_state with random_state...") - data["data.sample.random_state"].rename("random_state", inplace=True) + if hasattr(data, "data.sample.random_state"): + logger.info("Replacing data.sample.random_state with random_state...") + data["data.sample.random_state"].rename("random_state", inplace=True) data = format_control_parameter(data, control_dict, min_max=True) - logger.info(f"Saving data to {Path(folder) / file}") - data.to_csv(Path(folder) / "data.csv") return data -def save_results(results, results_file) -> str: +def save_results(results, results_file, results_folder) -> str: """ Compile results from a folder of reports and save to a csv file; return the path to the csv file. It will optionally delete columns from the results. """ - logger.info("Compiling results...") - - suffix = Path(results_file).suffix + logger.info(f"Saving data to {Path(results_folder) / results_file}") + Path(results_folder).mkdir(exist_ok=True, parents=True) + suffix = Path(results_folder, results_file).suffix if suffix == ".csv": results.to_csv(results_file) elif suffix == ".xlsx": @@ -267,7 +271,6 @@ def save_results(results, results_file) -> str: assert Path( results_file, ).exists(), f"Results file {results_file} does not exist. Something went wrong." - logger.debug(f"Results saved to {results_file}") return results_file @@ -304,10 +307,8 @@ def save_results(results, results_file) -> str: def_gen_dict, atk_gen_dict, control_dict, - results_file, - results_folder, ) - report_file = save_results(results, results_file) + report_file = save_results(results, results_file, results_folder,) assert Path( report_file, ).exists(), f"Results file {report_file} does not exist. Something went wrong." diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index a1877c59..9594d0b6 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -266,7 +266,6 @@ def write_stage(params: dict, stage: str, path=None, working_dir=None) -> None: def optimise(cfg: DictConfig) -> None: cfg = OmegaConf.to_container(OmegaConf.create(cfg), resolve=True) raise_exception = cfg.pop("raise_exception", False) - scorer = cfg.pop("optimizers", None) working_dir = Path(config_path).parent stage = cfg.pop("stage", None) cfg = parse_stage(params=cfg, stage=stage, path=working_dir) @@ -278,16 +277,11 @@ def optimise(cfg: DictConfig) -> None: id_ = Path(files["score_dict_file"]).parent.name direction = cfg.get("direction", "minimize") direction = [direction] if not isinstance(direction, list) else direction + optimizers = cfg.get("optimizers") + optimizers = [optimizers] if not isinstance(optimizers, list) else optimizers try: scores = exp() - if isinstance(scorer, str): - score = scores[scorer] - elif isinstance(scorer, list): - score = [scores[s] for s in scorer] - elif scorer is None: - score = list(scores.values())[0] - else: - raise TypeError(f"Expected str or list, got {type(scorer)}") + score = [v for k,v in scores.items() if k in optimizers] logger.info(f"Score is : {score}") except Exception as e: logger.warning( @@ -296,14 +290,21 @@ def optimise(cfg: DictConfig) -> None: with open(Path(folder, "exception.log"), "w") as f: f.write(str(e)) f.write(traceback.format_exc()) - if direction == "minimize": - score = [1e10] * len(direction) - else: - score = [-1e10] * len(direction) + fake_scores = [] + for direction in direction: + if direction == "minimize": + fake_scores.append(1e10) + elif direction == "maximize": + fake_scores.append(-1e10) + else: + raise ValueError(f"Unknown direction {direction}") + score = fake_scores logger.info(f"Score: {score}") if raise_exception: raise e - return tuple(score) + if len(score) == 1: + score = score[0] + return score if __name__ == "__main__": diff --git a/examples/power/conf/deploy-nvidia-tesla-a100/default.yaml b/examples/power/conf/deploy-nvidia-tesla-a100/default.yaml deleted file mode 100644 index 134da65f..00000000 --- a/examples/power/conf/deploy-nvidia-tesla-a100/default.yaml +++ /dev/null @@ -1,14 +0,0 @@ -num_nodes: 1 -cluster_name: k8s-cluster -gpu_type: nvidia-tesla-v100 -gpu_count: 1 -gpu_driver_version: default -machine_type: n1-standard-2 -min_nodes: 1 -max_nodes: 1 -storage_config: conf/deploy/sclass.yaml -persistent_volume_claim: conf/deploy/pvc.yaml -pod : conf/deploy/pod.yaml -image_project: ubuntu-os-cloud -image_family: ubuntu-2204-lts -mount_directory: /mnt/filestore diff --git a/examples/power/conf/deploy-nvidia-tesla-p100/default.yaml b/examples/power/conf/deploy-nvidia-tesla-p100/default.yaml deleted file mode 100644 index 134da65f..00000000 --- a/examples/power/conf/deploy-nvidia-tesla-p100/default.yaml +++ /dev/null @@ -1,14 +0,0 @@ -num_nodes: 1 -cluster_name: k8s-cluster -gpu_type: nvidia-tesla-v100 -gpu_count: 1 -gpu_driver_version: default -machine_type: n1-standard-2 -min_nodes: 1 -max_nodes: 1 -storage_config: conf/deploy/sclass.yaml -persistent_volume_claim: conf/deploy/pvc.yaml -pod : conf/deploy/pod.yaml -image_project: ubuntu-os-cloud -image_family: ubuntu-2204-lts -mount_directory: /mnt/filestore diff --git a/examples/power/conf/deploy-nvidia-tesla-v100/default.yaml b/examples/power/conf/deploy-nvidia-tesla-v100/default.yaml deleted file mode 100644 index 134da65f..00000000 --- a/examples/power/conf/deploy-nvidia-tesla-v100/default.yaml +++ /dev/null @@ -1,14 +0,0 @@ -num_nodes: 1 -cluster_name: k8s-cluster -gpu_type: nvidia-tesla-v100 -gpu_count: 1 -gpu_driver_version: default -machine_type: n1-standard-2 -min_nodes: 1 -max_nodes: 1 -storage_config: conf/deploy/sclass.yaml -persistent_volume_claim: conf/deploy/pvc.yaml -pod : conf/deploy/pod.yaml -image_project: ubuntu-os-cloud -image_family: ubuntu-2204-lts -mount_directory: /mnt/filestore diff --git a/examples/power/dvc.yaml b/examples/power/dvc.yaml index 51d1cbff..b20ecef7 100644 --- a/examples/power/dvc.yaml +++ b/examples/power/dvc.yaml @@ -56,10 +56,10 @@ stages: cache : false - ${files.directory}/${files.data_dir}/${files.data_file}${files.data_type}: cache : false - - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type}: - cache : false - - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type}: - cache : false + # - ${files.directory}/${files.model_dir}/${files.model_file}${files.model_type}: + # cache : false + # - ${files.directory}/${files.model_dir}/${files.model_file}.optimizer${files.model_type}: + # cache : false - ${files.directory}/${files.reports}/attack/${files.name}/${files.predictions_file}: # logit outputs for our model cache : false deps: diff --git a/examples/pytorch/cifar100/conf/cifar100.yaml b/examples/pytorch/cifar100/conf/cifar100.yaml index 541b92dd..01df936a 100644 --- a/examples/pytorch/cifar100/conf/cifar100.yaml +++ b/examples/pytorch/cifar100/conf/cifar100.yaml @@ -33,7 +33,7 @@ hydra: _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper launcher: _target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - n_jobs: 6 + n_jobs: 4 prefer : processes verbose: 10 timeout: null diff --git a/examples/pytorch/cifar100/conf/data/torch_mnist.yaml b/examples/pytorch/cifar100/conf/data/torch_mnist.yaml index 9d79c036..56e9c38b 100644 --- a/examples/pytorch/cifar100/conf/data/torch_mnist.yaml +++ b/examples/pytorch/cifar100/conf/data/torch_mnist.yaml @@ -9,7 +9,6 @@ sample: sklearn_pipeline: _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline preprocessor: - name: sklearn.preprocessing.StandardScaler with_mean: True with_std: True diff --git a/examples/pytorch/cifar100/conf/model/torch_mnist.yaml b/examples/pytorch/cifar100/conf/model/torch_mnist.yaml index 9c18c54b..7c98df88 100644 --- a/examples/pytorch/cifar100/conf/model/torch_mnist.yaml +++ b/examples/pytorch/cifar100/conf/model/torch_mnist.yaml @@ -8,6 +8,6 @@ init: name : torch_example.ResNet18 _target_: deckard.base.model.Model trainer: - nb_epoch: 100 + nb_epoch: 10 batch_size: 1024 library : pytorch diff --git a/examples/pytorch/cifar100/dvc.lock b/examples/pytorch/cifar100/dvc.lock index d9f081aa..a7d3feef 100644 --- a/examples/pytorch/cifar100/dvc.lock +++ b/examples/pytorch/cifar100/dvc.lock @@ -100,17 +100,17 @@ stages: md5: 1070854e6c00fc787bc0fdfc82792fd6 size: 761280311 - path: cifar100/models/model.optimizer.pt - md5: dead7ac200960668972932049a2695cb + md5: c13297479b0da6a736dfd1221ba175ac size: 44989261 - path: cifar100/models/model.pt - md5: ac30436350a0765a65ab535299e93103 + md5: d2e61603d2c2e3de37a441351b168efa size: 44998157 - path: cifar100/reports/train/default/predictions.json - md5: 960c0bcc9c969d9218ae3a5f4256d337 - size: 24436522 + md5: d3714b8d6a66fb803936da52650f88ec + size: 24412355 - path: cifar100/reports/train/default/score_dict.json - md5: d5d237be531584dab39c38b994c9ed1f - size: 411 + md5: 07a51c660e5d2a980e6c1fbc3dce0c16 + size: 842 attack: cmd: python -m deckard.layers.experiment attack --config_file cifar100.yaml deps: @@ -118,7 +118,7 @@ stages: md5: 1070854e6c00fc787bc0fdfc82792fd6 size: 761280311 - path: cifar100/models/model.pt - md5: ac30436350a0765a65ab535299e93103 + md5: d2e61603d2c2e3de37a441351b168efa size: 44998157 params: params.yaml: @@ -335,14 +335,14 @@ stages: name: sklearn.metrics.log_loss outs: - path: cifar100/attacks/attack.pkl - md5: 18be96acfa0eae64fac63405bd53cac1 + md5: b5d92a276b64215cbfbd6dbfb91c0d00 size: 123046 - path: cifar100/reports/attack/default/adv_predictions.json - md5: 98ceb6604311d2273da8b11e49e4150d - size: 21402 + md5: 36f3eb805d4c1d45846c9e6684e2adfe + size: 21009 - path: cifar100/reports/attack/default/score_dict.json - md5: f21ab891918c857171941e84dcc1b09a - size: 561 + md5: ddbabb9810f501b7bd027f511c11d526 + size: 1111 attacks@ResNet18: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 stage=attack ++hydra.sweeper.storage=sqlite:///cifar100/reports/attack/ResNet18.db @@ -395,8 +395,8 @@ stages: size: 1380 outs: - path: cifar100/reports/attack/ResNet34.db - md5: 5cd8e5a0e50fe7ef759c6c04fc430863 - size: 475136 + md5: 95cd20456616d5eb65d47a74c8ea1fdd + size: 487424 attacks@ResNet50: cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 stage=attack ++hydra.sweeper.storage=sqlite:///cifar100/reports/attack/ResNet50.db @@ -415,3 +415,21 @@ stages: - path: cifar100/reports/attack/ResNet50.db md5: 5556f78590f52e7879cb434450ab78c8 size: 745472 + attacks@ResNet101: + cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet101 + stage=attack ++hydra.sweeper.storage=sqlite:///cifar100/reports/attack/ResNet101.db + --config-name cifar100.yaml + deps: + - path: attacks.sh + md5: 29a1990d363aa5e09675957c1beb3f61 + size: 2929 + - path: cifar100/reports/attack/default/score_dict.json + md5: b14eb1d736510305310573ea477f1d20 + size: 1107 + - path: models.sh + md5: 1937e58bedac027034aea7d4a5712407 + size: 1380 + outs: + - path: cifar100/reports/attack/ResNet101.db + md5: bb029a105080100e8ea5ee180fe5cfbc + size: 544768 diff --git a/examples/pytorch/cifar100/dvc.yaml b/examples/pytorch/cifar100/dvc.yaml index 254a637a..2bd4ac3e 100644 --- a/examples/pytorch/cifar100/dvc.yaml +++ b/examples/pytorch/cifar100/dvc.yaml @@ -54,9 +54,9 @@ stages: # persist: True attacks: foreach: # This is a loop over the ResNet models - - ResNet18 - - ResNet34 - - ResNet50 + # - ResNet18 + # - ResNet34 + # - ResNet50 - ResNet101 - ResNet152 do: diff --git a/examples/pytorch/mnist/dvc.lock b/examples/pytorch/mnist/dvc.lock deleted file mode 100644 index c687d447..00000000 --- a/examples/pytorch/mnist/dvc.lock +++ /dev/null @@ -1,790 +0,0 @@ -schema: '2.0' -stages: - train: - cmd: python -m deckard.layers.experiment train --config_file mnist.yaml - params: - params.yaml: - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: mnist - model_dir: models - model_file: model - model_type: .pt - name: default - params_file: params.yaml - predictions_file: predictions.json - reports: reports - score_dict_file: score_dict.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss - outs: - - path: mnist/data/data.pkl - md5: de934a5f5157970e5f30b8f3f1856a68 - size: 222320311 - - path: mnist/models/model.optimizer.pt -<<<<<<< HEAD:examples/pytorch/mnist/dvc.lock - md5: 7cc094a1d1c432411977b26f03b17086 - size: 44780845 - - path: mnist/models/model.pt - md5: 8e3eb9a35ce142da8a2c1b70163a5dcf - size: 44785941 - - path: mnist/reports/train/default/predictions.json - md5: df8bbdb790d3af78ca4214a8558620ae - size: 2883117 - - path: mnist/reports/train/default/score_dict.json - md5: a9efba8e9e862c7fe28fa1c4d2b1819f - size: 404 -======= - hash: md5 - md5: 1316143c68da44c95cc0294482ba8a50 - size: 44780845 - - path: mnist/models/model.pt - hash: md5 - md5: 0c750c2da34962313084b05ceb424aa2 - size: 44785941 - - path: mnist/reports/train/default/predictions.json - hash: md5 - md5: 1396b6032cbd1e76ace82789dcf54861 - size: 2883588 - - path: mnist/reports/train/default/score_dict.json - hash: md5 - md5: 45631341a862aa88bfffbde78d099c6d - size: 411 ->>>>>>> 6cb0fceb656c2739de3277e4f1d6ce4c521331ee:examples/pytorch/dvc.lock - attack: - cmd: python -m deckard.layers.experiment attack --config_file mnist.yaml - deps: - - path: mnist/data/data.pkl - md5: de934a5f5157970e5f30b8f3f1856a68 - size: 222320311 - - path: mnist/models/model.pt -<<<<<<< HEAD:examples/pytorch/mnist/dvc.lock - md5: 8e3eb9a35ce142da8a2c1b70163a5dcf -======= - hash: md5 - md5: 0c750c2da34962313084b05ceb424aa2 ->>>>>>> 6cb0fceb656c2739de3277e4f1d6ce4c521331ee:examples/pytorch/dvc.lock - size: 44785941 - params: - params.yaml: - attack: - _target_: deckard.base.attack.Attack - attack_size: 10 - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.attack.AttackInitializer - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - name: art.attacks.evasion.HopSkipJump - method: evasion - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - files: - _target_: deckard.base.files.FileConfig - adv_predictions_file: adv_predictions.json - attack_dir: attacks - attack_file: attack - attack_type: .pkl - data_dir: data - data_file: data - data_type: .pkl - directory: mnist - model_dir: models - model_file: model - model_type: .pt - name: default - params_file: params.yaml - predictions_file: predictions.json - reports: reports - score_dict_file: score_dict.json - model: - _target_: deckard.base.model.Model - art: - _target_: deckard.base.model.art_pipeline.ArtPipeline - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - initialize: - clip_values: - - 0 - - 255 - criterion: - name: torch.nn.CrossEntropyLoss - optimizer: - lr: 0.01 - momentum: 0.9 - name: torch.optim.SGD - library: pytorch - data: - _target_: deckard.base.data.Data - generate: - _target_: deckard.base.data.generator.DataGenerator - name: torch_mnist - sample: - _target_: deckard.base.data.sampler.SklearnDataSampler - random_state: 0 - stratify: true - sklearn_pipeline: - _target_: deckard.base.data.sklearn_pipeline.SklearnDataPipeline - preprocessor: - name: sklearn.preprocessing.StandardScaler - with_mean: true - with_std: true - init: - _target_: deckard.base.model.ModelInitializer - name: torch_example.ResNet18 - num_channels: 1 - library: pytorch - trainer: - batch_size: 1024 - nb_epoch: 100 - scorers: - _target_: deckard.base.scorer.ScorerDict - accuracy: - _target_: deckard.base.scorer.ScorerConfig - direction: maximize - name: sklearn.metrics.accuracy_score - log_loss: - _target_: deckard.base.scorer.ScorerConfig - direction: minimize - name: sklearn.metrics.log_loss - outs: - - path: mnist/attacks/attack.pkl -<<<<<<< HEAD:examples/pytorch/mnist/dvc.lock - md5: 1a18d2941654a34e7f45f129826065e0 - size: 31517 - - path: mnist/reports/attack/default/adv_predictions.json - md5: 5b369c0249f7e0e6209fc3d502b51bca - size: 2167 - - path: mnist/reports/attack/default/score_dict.json - md5: fe4ddf5680ad4e6856132f01b8123bd8 - size: 544 -======= - hash: md5 - md5: e1047df78f629f7267cfa7ef869f19f5 - size: 31517 - - path: mnist/reports/attack/default/adv_predictions.json - hash: md5 - md5: 2ab54c7713532e6e1303e5653c9e14f3 - size: 2194 - - path: mnist/reports/attack/default/score_dict.json - hash: md5 - md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 - size: 472 - models: - cmd: bash models.sh - ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 - --config-name grid.yaml - deps: - - path: output/data/data.pkl - md5: de934a5f5157970e5f30b8f3f1856a68 - size: 222320311 - - path: output/models/model.pt - md5: 38451da384fb8f787707a2b39b8418de - size: 44786389 - outs: - - path: model.db - md5: d1eac324650402da6e3de1aebe0e3b3c - size: 237568 - attacks: - cmd: bash attacks.sh - ++model.init.name=torch_example.ResNet18,torch_example.ResNet34,torch_example.ResNet50 - ++stage=attack --config-name grid.yaml - deps: - - path: model.db - md5: d1eac324650402da6e3de1aebe0e3b3c - size: 237568 - - path: output/data/data.pkl - md5: de934a5f5157970e5f30b8f3f1856a68 - size: 222320311 - - path: output/models/model.pt - md5: 38451da384fb8f787707a2b39b8418de - size: 44786389 - outs: - - path: attack.db - md5: c9f920c7233802e9c46e4051c2da78e6 - size: 307200 - compile_train: - cmd: python -m deckard.layers.compile --report_folder output/reports/train/ --results_file - output/reports/train.csv - deps: - - path: model.db - md5: e5dc2d529f4841baf9cccedd7b417988 - size: 110592 - - path: output/data/ - md5: 0078b738d3ac5d26c4c487d9c43903da.dir - size: 1111601555 - nfiles: 5 - - path: output/models/ - md5: 2dc57f423c263fa18830ef6d532f592f.dir - size: 1074846 - nfiles: 14 - outs: - - path: output/reports/train.csv - md5: 54707302f1ee42d25231d73ee2c03cf3 - size: 12177 - compile_attack: - cmd: python -m deckard.layers.compile --report_folder output/reports/attack/ --results_file - output/reports/attack.csv - deps: - - path: attack.db - md5: 576e07b1a496218659b7a425a812412b - size: 319488 - - path: output/attacks/ - md5: f6967d943832917c2b1e0db449d514f7.dir - size: 336979704 - nfiles: 1044 - - path: output/data/ - md5: 837a1c3acf188d7955e48419b38d8135.dir - size: 2445523421 - nfiles: 11 - - path: output/models/ - md5: 33fa241d9672dfc7f7f27927869d4948.dir - size: 160466396 - nfiles: 2088 - outs: - - path: output/reports/attack.csv - md5: 36ffafc8cb80eb6fbed190be9d420ef7 - size: 3674355 - compile@attack: - cmd: python -m deckard.layers.compile --report_folder output/reports/attack --results_file - output/reports/attack.csv - deps: - - path: ResNet101.db - md5: c47414423a51a4b866be6ce8312cb97e - size: 561152 - - path: ResNet18.db - md5: 2ae52c7b342b46ac01c79f06bfdc6c38 - size: 2768896 - - path: ResNet34.db - md5: 6a5795cbb9d977cdaadf365a6ac76a0a - size: 983040 - - path: ResNet50.db - md5: 7e215d670119b0703c3d97d86e28e117 - size: 561152 - - path: output/reports/attack/ - md5: a76eb881be1813685bf988cf8dbe7a52.dir - size: 5109780242 - nfiles: 10514 - outs: - - path: output/reports/attack.csv - md5: 69c87ac633b8abae15d65852c79ea89c - size: 6309975 - compile@train: - cmd: python -m deckard.layers.compile --report_folder output/reports/train --results_file - output/reports/train.csv - deps: - - path: ResNet101.db - md5: 744f187243001db70f63c8161d7f913f - size: 1314816 - - path: ResNet152.db - md5: e0fcb3876ec636b12d7dc9e71b9d1e1c - size: 1314816 - - path: ResNet18.db - md5: aa69b1818219c139f8e33ac205771ec1 - size: 110592 - - path: ResNet34.db - md5: e4d151b9800a1255a0143368057cb146 - size: 1339392 - - path: ResNet50.db - md5: 9c9bc0aab00251ca5e9bd210366bc055 - size: 1339392 - - path: output/reports/train/ - md5: 098781f04bdba3498c00a157e3e45826.dir - size: 403646689 - nfiles: 640 - outs: - - path: output/reports/train.csv - md5: 152c61d1ae1a58e89c03c1351d5bf406 - size: 411488 - attacks@ResNet152: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet152 - stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet152.db - --config-name mnist.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: mnist/reports/attack/default/score_dict.json - hash: md5 - md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 - size: 472 - - path: models.sh - hash: md5 - md5: 1937e58bedac027034aea7d4a5712407 - size: 1380 - outs: - - path: mnist/reports/attack/ResNet152.db - hash: md5 - md5: afb6f3d2616446226012b3ff1143086a - size: 462848 - plot: - cmd: python -m deckard.layers.plots --path output/plots/ --file output/reports/attack.csv - deps: - - path: ResNet101.db - md5: c47414423a51a4b866be6ce8312cb97e - size: 561152 - - path: ResNet152.db - md5: 690f931bf696f8e5f1e044fa7b335411 - size: 425984 - - path: ResNet18.db - md5: 2ae52c7b342b46ac01c79f06bfdc6c38 - size: 2768896 - - path: ResNet34.db - md5: 6a5795cbb9d977cdaadf365a6ac76a0a - size: 983040 - - path: ResNet50.db - md5: 7e215d670119b0703c3d97d86e28e117 - size: 561152 - - path: output/reports/attack.csv - md5: 69c87ac633b8abae15d65852c79ea89c - size: 6309975 - outs: - - path: output/plots/data.csv - md5: 8283c78e358db5d4ce19e5d44b6e735e - size: 2185730 - attacks@ResNet101: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet101 - stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet101.db - --config-name mnist.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: mnist/reports/attack/default/score_dict.json - hash: md5 - md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 - size: 472 - - path: models.sh - hash: md5 - md5: 1937e58bedac027034aea7d4a5712407 - size: 1380 - outs: - - path: mnist/reports/attack/ResNet101.db - hash: md5 - md5: 600452804d96c8b8483c3f8da01130c4 - size: 462848 - attacks@ResNet34: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet34 - stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet34.db - --config-name mnist.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: mnist/reports/attack/default/score_dict.json - hash: md5 - md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 - size: 472 - - path: models.sh - hash: md5 - md5: 1937e58bedac027034aea7d4a5712407 - size: 1380 - outs: - - path: mnist/reports/attack/ResNet34.db - hash: md5 - md5: 9244c6545aa2d39680b601311b446b7d - size: 1593344 - attacks@ResNet50: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet50 - stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet50.db - --config-name mnist.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: mnist/reports/attack/default/score_dict.json - hash: md5 - md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 - size: 472 - - path: models.sh - hash: md5 - md5: 1937e58bedac027034aea7d4a5712407 - size: 1380 - outs: - - path: mnist/reports/attack/ResNet50.db - hash: md5 - md5: d9ee221b942b56d9bb720e022e05bf4b - size: 462848 - afr: - cmd: python -m deckard.layers.afr --dataset mnist - deps: - - path: output/plots/data.csv - md5: 8283c78e358db5d4ce19e5d44b6e735e - size: 2185730 - outs: - - path: output/plots/aft_comparison.csv - md5: a72d5fbf5c21b5055e6bc20a0e48c6aa - size: 178 - - path: output/plots/aft_comparison.tex - md5: e53edcb94e353f2e03324d1c02673332 - size: 401 - - path: output/plots/cox_aft.pdf - md5: 6b5fd449d724cf097eea5d8569dda3f0 - size: 30712 - - path: output/plots/cox_partial_effects.pdf - md5: a434a352ea874a5c188f76ef8456dbdf - size: 28132 - - path: output/plots/log_logistic_aft.pdf - md5: 6b1e524a7b7c2da913e4c9346115167c - size: 33524 - - path: output/plots/log_logistic_partial_effects.pdf - md5: f3c5ccbbb5129cbffe6b38f70d655245 - size: 29147 - - path: output/plots/log_normal_aft.pdf - md5: c0b1f808746c28b5b352ca425c6d2d33 - size: 34017 - - path: output/plots/log_normal_partial_effects.pdf - md5: 1c67a22e51019ceeb39573ea21b4a7ba - size: 29844 - - path: output/plots/weibull_aft.pdf - md5: 4b8119d87bd201bea0fa9955e3f3481d - size: 32175 - - path: output/plots/weibull_partial_effects.pdf - md5: 4b1433467ad94132bde9003c9faaed10 - size: 29359 - copy_results: - cmd: cp -r output/plots/* ~/ml_afr/mnist/ - deps: - - path: output/plots/aft_comparison.csv - md5: a72d5fbf5c21b5055e6bc20a0e48c6aa - size: 178 - - path: output/plots/data.csv - md5: 8283c78e358db5d4ce19e5d44b6e735e - size: 2185730 - attacks@ResNet18: - cmd: bash attacks.sh ++attack.attack_size=100 ++model.init.name=torch_example.ResNet18 - stage=attack ++hydra.sweeper.storage=sqlite:///mnist/reports/attack/ResNet18.db - --config-name mnist.yaml - deps: - - path: attacks.sh - hash: md5 - md5: 963c858a322d7a4990a92a25d5684c57 - size: 2907 - - path: mnist/reports/attack/default/score_dict.json - hash: md5 - md5: a7b1ec550fe1eeb7c5b16c0f36b28ff8 - size: 472 - - path: models.sh - hash: md5 - md5: 1937e58bedac027034aea7d4a5712407 - size: 1380 - outs: - - path: mnist/reports/attack/ResNet18.db - hash: md5 - md5: 2b4e2a2ac1cdd015cf469496a9861088 - size: 999424 - models@ResNet50: - cmd: bash models.sh ++model.init.name=torch_example.ResNet50 stage=train ++hydra.sweeper.storage=sqlite:///mnist/reports/train/ResNet50.db - --config-name mnist.yaml - deps: - - path: mnist/models/model.optimizer.pt - hash: md5 - md5: cc914518d5c8fdab1e7ec43c7db63a3b - size: 44780845 - - path: mnist/models/model.pt - hash: md5 - md5: 0508fa68d600b39cd8ce1dad0152fba3 - size: 44785941 - - path: models.sh - md5: e5079ede85d4f9b286984e507a157af4 - size: 1371 - outs: - - path: mnist/reports/train/ResNet50.db - hash: md5 - md5: 957e39666f06c9e8e207a9f98bc569b5 - size: 913408 - models@ResNet18: - cmd: bash models.sh ++model.init.name=torch_example.ResNet18 stage=train ++hydra.sweeper.storage=sqlite:///mnist/reports/train/ResNet18.db - --config-name mnist.yaml - deps: - - path: mnist/models/model.optimizer.pt - hash: md5 - md5: f4e28adc9c0d30180eca422da2dd00e3 - size: 44780845 - - path: mnist/models/model.pt - hash: md5 - md5: 53a2d89abb350e2601f35f36b95f6095 - size: 44785941 - - path: models.sh - hash: md5 - md5: cd5b2760310df71a36a2ea26021477b4 - size: 1366 - outs: - - path: mnist/reports/train/ResNet18.db - hash: md5 - md5: 225273e0494668fa42bc6f69fb29d392 - size: 901120 - models@ResNet34: - cmd: bash models.sh ++model.init.name=torch_example.ResNet34 stage=train ++hydra.sweeper.storage=sqlite:///mnist/reports/train/ResNet34.db - --config-name mnist.yaml - deps: - - path: mnist/models/model.optimizer.pt - hash: md5 - md5: cc914518d5c8fdab1e7ec43c7db63a3b - size: 44780845 - - path: mnist/models/model.pt - hash: md5 - md5: 0508fa68d600b39cd8ce1dad0152fba3 - size: 44785941 - - path: models.sh - hash: md5 - md5: e5079ede85d4f9b286984e507a157af4 - size: 1371 - outs: - - path: mnist/reports/train/ResNet34.db - hash: md5 - md5: af60ce478f27cda303ebd34c63cf05d3 - size: 913408 - models@ResNet101: - cmd: bash models.sh ++model.init.name=torch_example.ResNet101 stage=train ++hydra.sweeper.storage=sqlite:///mnist/reports/train/ResNet101.db - --config-name mnist.yaml - deps: - - path: mnist/models/model.optimizer.pt - hash: md5 - md5: cc914518d5c8fdab1e7ec43c7db63a3b - size: 44780845 - - path: mnist/models/model.pt - hash: md5 - md5: 0508fa68d600b39cd8ce1dad0152fba3 - size: 44785941 - - path: models.sh - hash: md5 - md5: cd5b2760310df71a36a2ea26021477b4 - size: 1366 - outs: - - path: mnist/reports/train/ResNet101.db - hash: md5 - md5: a8e178cba49addf77bac3b27e974ce8c - size: 917504 ->>>>>>> 6cb0fceb656c2739de3277e4f1d6ce4c521331ee:examples/pytorch/dvc.lock From c248fb4293c7d14a83ef9bef2c1f1faa178db2ff Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 22:39:05 +0000 Subject: [PATCH 139/148] removed scorer logic from exp --- deckard/layers/optimise.py | 6 +++--- examples/pytorch/cifar100/conf/model/torch_cifar100.yaml | 2 +- examples/pytorch/cifar100/conf/model/torch_mnist.yaml | 2 +- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index 9594d0b6..c4523aa6 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -267,6 +267,9 @@ def optimise(cfg: DictConfig) -> None: cfg = OmegaConf.to_container(OmegaConf.create(cfg), resolve=True) raise_exception = cfg.pop("raise_exception", False) working_dir = Path(config_path).parent + direction = cfg.get("direction", "minimize") + direction = [direction] if not isinstance(direction, list) else direction + optimizers = cfg.get("optimizers") stage = cfg.pop("stage", None) cfg = parse_stage(params=cfg, stage=stage, path=working_dir) exp = instantiate(cfg) @@ -275,9 +278,6 @@ def optimise(cfg: DictConfig) -> None: Path(folder).mkdir(exist_ok=True, parents=True) write_stage(cfg, stage, path=folder, working_dir=working_dir) id_ = Path(files["score_dict_file"]).parent.name - direction = cfg.get("direction", "minimize") - direction = [direction] if not isinstance(direction, list) else direction - optimizers = cfg.get("optimizers") optimizers = [optimizers] if not isinstance(optimizers, list) else optimizers try: scores = exp() diff --git a/examples/pytorch/cifar100/conf/model/torch_cifar100.yaml b/examples/pytorch/cifar100/conf/model/torch_cifar100.yaml index ea5628ae..d7d0f91d 100644 --- a/examples/pytorch/cifar100/conf/model/torch_cifar100.yaml +++ b/examples/pytorch/cifar100/conf/model/torch_cifar100.yaml @@ -8,6 +8,6 @@ init: num_classes: 100 _target_: deckard.base.model.Model trainer: - nb_epoch: 10 + nb_epoch: 1 batch_size: 1024 library : pytorch diff --git a/examples/pytorch/cifar100/conf/model/torch_mnist.yaml b/examples/pytorch/cifar100/conf/model/torch_mnist.yaml index 7c98df88..26c462f6 100644 --- a/examples/pytorch/cifar100/conf/model/torch_mnist.yaml +++ b/examples/pytorch/cifar100/conf/model/torch_mnist.yaml @@ -8,6 +8,6 @@ init: name : torch_example.ResNet18 _target_: deckard.base.model.Model trainer: - nb_epoch: 10 + nb_epoch: 1 batch_size: 1024 library : pytorch From 93950900ad9c01e03de094c2652aab7a95462493 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 22:42:04 +0000 Subject: [PATCH 140/148] linting --- deckard/base/experiment/experiment.py | 2 +- deckard/layers/compile.py | 12 +++++++----- deckard/layers/optimise.py | 2 +- 3 files changed, 9 insertions(+), 7 deletions(-) diff --git a/deckard/base/experiment/experiment.py b/deckard/base/experiment/experiment.py index c04df0b2..a69f5065 100644 --- a/deckard/base/experiment/experiment.py +++ b/deckard/base/experiment/experiment.py @@ -6,7 +6,7 @@ import numpy as np from hydra.utils import instantiate -from omegaconf import DictConfig, OmegaConf, ListConfig +from omegaconf import DictConfig, OmegaConf from ..attack import Attack from ..data import Data diff --git a/deckard/layers/compile.py b/deckard/layers/compile.py index 21a1cbbc..170bace7 100644 --- a/deckard/layers/compile.py +++ b/deckard/layers/compile.py @@ -42,7 +42,7 @@ def parse_folder(folder, files=["params.yaml", "score_dict.json"]) -> pd.DataFra :return: Pandas dataframe with the results """ folder = Path(folder) - + logger.debug(f"Parsing folder {folder}...") path_gen = [] for file in files: @@ -58,6 +58,7 @@ def parse_folder(folder, files=["params.yaml", "score_dict.json"]) -> pd.DataFra df = pd.DataFrame(results).T return df + def add_file(folder, path_gen, results): all_files = Path(folder).glob("**/*") for file in all_files: @@ -67,6 +68,7 @@ def add_file(folder, path_gen, results): results[file.parent.name][file.stem] = file return results + def read_file(file, results): suffix = file.suffix folder = file.parent.name @@ -164,7 +166,7 @@ def parse_results(folder, files=["score_dict.json", "params.yaml"], default_epoc return df -def format_control_parameter(data, control_dict, min_max = True): +def format_control_parameter(data, control_dict, min_max=True): logger.info("Formatting control parameters...") if hasattr(data, "def_gen"): defences = data.def_gen.unique() @@ -186,7 +188,7 @@ def format_control_parameter(data, control_dict, min_max = True): value = np.nan data.loc[data.def_gen == defence, "def_value"] = value control_dict.pop(defence) - + else: logger.warning(f"Defence {defence} not in control_dict. Deleting rows.") data = data[data.def_gen != defence] @@ -211,7 +213,7 @@ def format_control_parameter(data, control_dict, min_max = True): else: logger.warning(f"Attack {attack} not in control_dict. Deleting rows.") data = data[data.atk_gen != attack] - + # if min_max is True: # atk_min = data[data.atk_gen == attack].atk_value.min() # atk_max = data[data.atk_gen == attack].atk_value.max() @@ -308,7 +310,7 @@ def save_results(results, results_file, results_folder) -> str: atk_gen_dict, control_dict, ) - report_file = save_results(results, results_file, results_folder,) + report_file = save_results(results, results_file, results_folder) assert Path( report_file, ).exists(), f"Results file {report_file} does not exist. Something went wrong." diff --git a/deckard/layers/optimise.py b/deckard/layers/optimise.py index c4523aa6..691b97c8 100644 --- a/deckard/layers/optimise.py +++ b/deckard/layers/optimise.py @@ -281,7 +281,7 @@ def optimise(cfg: DictConfig) -> None: optimizers = [optimizers] if not isinstance(optimizers, list) else optimizers try: scores = exp() - score = [v for k,v in scores.items() if k in optimizers] + score = [v for k, v in scores.items() if k in optimizers] logger.info(f"Score is : {score}") except Exception as e: logger.warning( From 1383c5f391cce8764886fdb0cd2d1696728e3513 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Tue, 28 Nov 2023 22:42:20 +0000 Subject: [PATCH 141/148] linting --- examples/pytorch/cifar100/torch_example.py | 35 ++++------------------ 1 file changed, 5 insertions(+), 30 deletions(-) diff --git a/examples/pytorch/cifar100/torch_example.py b/examples/pytorch/cifar100/torch_example.py index 2007d9d0..d1f8e038 100644 --- a/examples/pytorch/cifar100/torch_example.py +++ b/examples/pytorch/cifar100/torch_example.py @@ -12,12 +12,7 @@ def ResNet18(num_channels=1, num_classes=10): model = models.resnet18() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -26,12 +21,7 @@ def ResNet18(num_channels=1, num_classes=10): def ResNet34(num_channels=1, num_classes=10): model = models.resnet34() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -40,12 +30,7 @@ def ResNet34(num_channels=1, num_classes=10): def ResNet50(num_channels=1, num_classes=10): model = models.resnet50() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -54,12 +39,7 @@ def ResNet50(num_channels=1, num_classes=10): def ResNet101(num_channels=1, num_classes=10): model = models.resnet101() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -68,12 +48,7 @@ def ResNet101(num_channels=1, num_classes=10): def ResNet152(num_channels=1, num_classes=10): model = models.resnet152() model.conv1 = nn.Conv2d( - num_channels, - 64, - kernel_size=7, - stride=2, - padding=3, - bias=False, + num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, ) model.fc = nn.Linear(2048, num_classes) return model From 6c3de3c0779bda6940d1b5f3f4ee89d09c7b97df Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Wed, 29 Nov 2023 00:32:58 +0000 Subject: [PATCH 142/148] afr (broken) --- deckard/base/model/art_pipeline.py | 6 +- deckard/layers/afr.py | 178 ++++++----- deckard/layers/old_afr.py | 464 +++++++++++++++++++++++++++++ deckard/layers/plots.py | 29 +- examples/pytorch/mnist/attacks.sh | 20 +- examples/pytorch/mnist/dvc.lock | 52 ++++ examples/pytorch/mnist/dvc.yaml | 2 +- setup.py | 2 + 8 files changed, 644 insertions(+), 109 deletions(-) create mode 100644 deckard/layers/old_afr.py create mode 100644 examples/pytorch/mnist/dvc.lock diff --git a/deckard/base/model/art_pipeline.py b/deckard/base/model/art_pipeline.py index 679a089f..a7ba5590 100644 --- a/deckard/base/model/art_pipeline.py +++ b/deckard/base/model/art_pipeline.py @@ -62,9 +62,11 @@ def __call__(self): if "torch" in str(library) and not isinstance( model, tuple(torch_dict.values()), - ): + ): + import torch + device = "gpu" if torch.cuda.is_available() else "cpu" model = TorchInitializer( - data=data, model=model, library=library, **kwargs + data=data, model=model, library=library, device, **kwargs )() elif "keras" in str(library) and not isinstance( model, diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index ad085550..08ba9c7b 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -1,5 +1,6 @@ import pandas as pd import numpy as np +from pathlib import Path import matplotlib.pyplot as plt @@ -10,23 +11,21 @@ LogLogisticAFTFitter, CoxPHFitter, ) +from .plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling import matplotlib from pathlib import Path import logging import yaml import argparse -from .plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling - logger = logging.getLogger(__name__) if "__main__" == __name__: + afr_parser = argparse.ArgumentParser() afr_parser.add_argument("--target", type=str, default="adv_failures") afr_parser.add_argument("--duration_col", type=str, default="adv_fit_time") afr_parser.add_argument("--dataset", type=str, default="mnist") - afr_parser.add_argument("--plots_folder", type=str, default="mnist/plots") - afr_parser.add_argument("--file", type=str, default="mnist/plots/data.csv") afr_args = afr_parser.parse_args() target = afr_args.target duration_col = afr_args.duration_col @@ -39,8 +38,10 @@ } matplotlib.rc("font", **font) - FOLDER = Path(afr_args.plots_folder) - data = pd.read_csv(afr_args.file, index_col=0) + + FOLDER = Path(f"{afr_args.dataset}/plots/") + csv_file = FOLDER / "data.csv" + data = pd.read_csv(csv_file, index_col=0) data.columns = data.columns.str.strip() data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x) data.def_value.replace("", 0, inplace=True) @@ -51,14 +52,35 @@ data.dropna(axis=0, subset=["atk_value", "atk_param"], inplace=True) data.dropna(axis=0, subset=["def_value", "def_param"], inplace=True) data.loc[:, "adv_failures"] = (1 - data.loc[:, "adv_accuracy"]) * data.loc[ - :, - "attack.attack_size", + :, "attack.attack_size" ] data.loc[:, "ben_failures"] = (1 - data.loc[:, "accuracy"]) * data.loc[ - :, - "attack.attack_size", + :, "attack.attack_size" ] + # data=data[data['def_gen'] == 'Gauss-in'] + # data=data[data['atk_gen'] == 'HSJ'] + + print( + "Adversarial Accuracy:", + "\n", + "ResNet152:", + data[data["model_layers"] == 152].adv_accuracy.mean(skipna=True), + "\n", + "Resnet101:", + data[data["model_layers"] == 101].adv_accuracy.mean(skipna=True), + "\n", + "Resnet50:", + data[data["model_layers"] == 50].adv_accuracy.mean(skipna=True), + "\n", + "Resnet34:", + data[data["model_layers"] == 34].adv_accuracy.mean(skipna=True), + "\n", + "Resnet18:", + data[data["model_layers"] == 18].adv_accuracy.mean(skipna=True), + "\n", + ) + def plot_aft( df, file, @@ -146,7 +168,7 @@ def score_model(aft, train, test): def make_afr_table(score_list, aft_dict, dataset): assert len(score_list) == len( - aft_dict, + aft_dict ), "Length of score list and aft dict must be equal" aft_data = pd.DataFrame() aft_data.index.name = "Model" @@ -207,19 +229,13 @@ def clean_data_for_aft( cleaned = cleaned[cleaned["adv_accuracy"] != -1e10] cleaned.dropna(inplace=True, how="any", axis=0) y = cleaned[target] - del cleaned[target] assert ( target in cleaned ), f"Target {target} not in dataframe with columns {cleaned.columns}" return cleaned, y, data def split_data_for_aft( - data, - target, - duration_col, - kwarg_list, - test_size=0.2, - random_state=42, + data, target, duration_col, kwarg_list, test_size=0.2, random_state=42 ): cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) X_train, X_test, y_train, y_test = train_test_split( @@ -246,28 +262,23 @@ def split_data_for_aft( "adv_failure_rate", "model_layers", "adv_fit_time", - "model.trainer.nb_epoch", + # "model.art.pipeline.initialize.kwargs.optimizer.lr", ] X_train, X_test, y_train, y_test = split_data_for_aft( - data, - target, - duration_col, - kwarg_list, - test_size=0.2, - random_state=42, + data, target, duration_col, kwarg_list, test_size=0.2, random_state=42 ) weibull_dict = { "Intercept: rho_": "$\\rho$", - "Intercept: lambda_": "$\lambda$", # noqa W605 + "Intercept: lambda_": "$\lambda$", "data.sample.random_state: lambda_": "Random State", "def_value: lambda_": "Defence Strength", "atk_value: lambda_": "Attack Strength", "train_time: lambda_": "$t_{train}$", "predict_time: lambda_": "$t_{predict}$", - "adv_accuracy: lambda_": "$\lambda_{adv.}$", # noqa W605 - "accuracy: lambda_": "$\lambda_{ben.}$", # noqa W605 + "adv_accuracy: lambda_": "$\lambda_{adv.}$", + "accuracy: lambda_": "$\lambda_{ben.}$", "adv_fit_time: lambda_": "$t_{attack}$", "adv_log_loss: lambda_": "Adv. Log Loss", "adv_failure_rate: lambda_": "$h_{adv.}(t,;\\theta)$", @@ -304,58 +315,61 @@ def split_data_for_aft( }, } - weibull_layers = plot_partial_effects(aft=wft, **weibull_partial_dict_layers) + weibull_layers = plot_partial_effects( + aft=wft, + **weibull_partial_dict_layers, + ) wft_scores = score_model(wft, X_train, X_test) - # cox_replacement_dict = { - # "adv_failure_rate": "$h_{adv}(t,;\\theta)$", - # "def_value": "Defence Strength", - # "data.sample.random_state": "Random State", - # "train_time": "$t_{train}$", - # "model_layers": "No. of Layers", - # "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", - # "adv_accuracy": "$\lambda_{adv.}$", # noqa W605 - # "adv_fit_time": "$t_{attack}$", - # "adv_log_loss": "Adv. Log Loss", - # "predict_time": "$t_{predict}$", - # "accuracy": "$\lambda_{ben.}$", # noqa W605 - # "failure_rate": "$h_{ben.}(t,;\\theta)$", - # "atk_value": "Attack Strength", - # } - # cox_partial_dict = { - # "file": "cox_partial_effects.pdf", - # "covariate_array": "model_layers", - # "values_array": [18, 34, 50, 101, 152], - # "replacement_dict": cox_replacement_dict, - # "title": "$S(t)$ for Cox AFR", - # "ylabel": "Expectation of $S(t)$", - # "xlabel": "Time $T$ (seconds)", - # "legend_kwargs": { - # "title": "No. of Layers", - # "labels": ["18", "34", "50", "101", "152"], - # }, - # } - # cox_plot_dict = { - # "file": "cox_aft.pdf", - # "duration_col": duration_col, - # "title": "Cox AFR Model", - # "mtype": "cox", - # "replacement_dict": cox_replacement_dict, - # } - # cox_afr, cft = plot_aft( - # df=X_train, - # event_col=target, - # **cox_plot_dict, - # ) - # cox_scores = score_model(cft, X_train, X_test) - # cox_partial = plot_partial_effects( - # aft=cft, - # **cox_partial_dict, - # ) + cox_replacement_dict = { + "adv_failure_rate": "$h_{adv}(t,;\\theta)$", + "def_value": "Defence Strength", + "data.sample.random_state": "Random State", + "train_time": "$t_{train}$", + "model_layers": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", + "adv_accuracy": "$\lambda_{adv.}$", + "adv_fit_time": "$t_{attack}$", + "adv_log_loss": "Adv. Log Loss", + "predict_time": "$t_{predict}$", + "accuracy": "$\lambda_{ben.}$", + "failure_rate": "$h_{ben.}(t,;\\theta)$", + "atk_value": "Attack Strength", + } + cox_partial_dict = { + "file": "cox_partial_effects.pdf", + "covariate_array": "model_layers", + "values_array": [18, 34, 50, 101, 152], + "replacement_dict": cox_replacement_dict, + "title": "$S(t)$ for Cox AFR", + "ylabel": "Expectation of $S(t)$", + "xlabel": "Time $T$ (seconds)", + "legend_kwargs": { + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, + } + cox_plot_dict = { + "file": "cox_aft.pdf", + "duration_col": duration_col, + "title": "Cox AFR Model", + "mtype": "cox", + "replacement_dict": cox_replacement_dict, + } + cox_afr, cft = plot_aft( + df=X_train, + event_col=target, + **cox_plot_dict, + ) + cox_scores = score_model(cft, X_train, X_test) + cox_partial = plot_partial_effects( + aft=cft, + **cox_partial_dict, + ) log_normal_dict = { - "Intercept: sigma_": "$\sigma$", # noqa W605 - "Intercept: mu_": "$\mu$", # noqa W605 + "Intercept: sigma_": "$\sigma$", + "Intercept: mu_": "$\mu$", "def_value: mu_": "Defence Strength", "atk_value: mu_": "Attack Strength", "train_time: mu_": "$t_{train}$", @@ -365,8 +379,8 @@ def split_data_for_aft( "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_": "Learning Rate", "data.sample.random_state: mu_": "Random State", "adv_log_loss: mu_": "Adv. Log Loss", - "adv_accuracy: mu_": "$\lambda_{adv.}$", # noqa W605 - "accuracy: mu_": "$\lambda_{ben.}$", # noqa W605 + "adv_accuracy: mu_": "$\lambda_{adv.}$", + "accuracy: mu_": "$\lambda_{ben.}$", "adv_failure_rate: mu_": "$h_{adv}(t,;\\theta)$", "def_gen": "Defence", "learning_rate: mu_": "Learning Rate", @@ -404,12 +418,12 @@ def split_data_for_aft( "atk_value: alpha_": "Attack Strength", "train_time: alpha_": "$t_{train}$", "predict_time: alpha_": "$t_{predict}$", - "adv_accuracy: alpha_": "$\lambda_{adv.}$", # noqa W605 - "accuracy: alpha_": "$\lambda_{ben.}$", # noqa W605 + "adv_accuracy: alpha_": "$\lambda_{adv.}$", + "accuracy: alpha_": "$\lambda_{ben.}$", "adv_fit_time: alpha_": "$t_{attack}$", "model_layers: alpha_": "No. of Layers", - "model.art.pipeline.initialize.kwargs.optimizer.lr:": "Learning Rate", - "adv_failure_rate: alpha_": "$h_{adv.}(t,\\theta)$", # noqa W605 + "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", + "adv_failure_rate: alpha_": "$h_{adv.}(t,\\theta)$", "alpha_": "", } diff --git a/deckard/layers/old_afr.py b/deckard/layers/old_afr.py new file mode 100644 index 00000000..61451422 --- /dev/null +++ b/deckard/layers/old_afr.py @@ -0,0 +1,464 @@ +import pandas as pd +import numpy as np + +import matplotlib.pyplot as plt + +from sklearn.model_selection import train_test_split +from lifelines import ( + WeibullAFTFitter, + LogNormalAFTFitter, + LogLogisticAFTFitter, + CoxPHFitter, +) +import matplotlib +from pathlib import Path +import logging +import yaml +import argparse +from .plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling + + +def plot_aft( + df, + file, + event_col, + duration_col, + title, + mtype, + xlabel=None, + ylabel=None, + replacement_dict={}, + **kwargs, +): + if mtype == "weibull": + aft = WeibullAFTFitter(**kwargs) + elif mtype == "log_normal": + aft = LogNormalAFTFitter(**kwargs) + elif mtype == "log_logistic": + aft = LogLogisticAFTFitter(**kwargs) + elif mtype == "cox": + aft = CoxPHFitter(**kwargs) + assert ( + duration_col in df.columns + ), f"Column {duration_col} not in dataframe with columns {df.columns}" + if event_col is not None: + assert ( + event_col in df.columns + ), f"Column {event_col} not in dataframe with columns {df.columns}" + plt.gcf().clear() + # enforce numerical values + df = df.apply(pd.to_numeric, errors="coerce") + print(df.columns) + input("Press Enter to continue...") + aft.fit(df, duration_col=duration_col, event_col=event_col, **kwargs) + ax = aft.plot() + labels = ax.get_yticklabels() + labels = [label.get_text() for label in labels] + for k, v in replacement_dict.items(): + labels = [label.replace(k, v) for label in labels] + ax.set_yticklabels(labels) + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_title(title) + ax.get_figure().tight_layout() + ax.get_figure().savefig(FOLDER / file) + logger.info(f"Saved graph to {FOLDER / file}") + plt.show() + plt.gcf().clear() + return ax, aft + +def plot_partial_effects( + aft, + covariate_array, + values_array, + title=None, + file="partial_effects.pdf", + xlabel="Covariate", + ylabel="Failure rate", + legend_kwargs={"loc": "upper left"}, + replacement_dict={}, + cmap="coolwarm", + **kwargs, +): + plt.gcf().clear() + # kwargs.pop("replacement_dict") + pareto = aft.plot_partial_effects_on_outcome( + covariate_array, values_array, cmap=cmap, **kwargs + ) + labels = pareto.get_yticklabels() + labels = [label.get_text() for label in labels] + for k, v in replacement_dict.items(): + labels = [label.replace(k, v) for label in labels] + pareto.set_yticklabels(labels) + pareto.legend(**legend_kwargs) + pareto.set_ylabel(ylabel) + pareto.set_xlabel(xlabel) + pareto.set_title(title) + pareto.get_figure().tight_layout() + pareto.get_figure().savefig(FOLDER / file) + logger.info(f"Saved graph to {FOLDER / file}") + plt.gcf().clear() + return pareto + +def score_model(aft, train, test): + train_score = aft.score(train) + test_score = aft.score(test) + scores = {"train_score": train_score, "test_score": test_score} + plt.show() + return scores + +def make_afr_table(score_list, aft_dict, dataset): + assert len(score_list) == len( + aft_dict, + ), "Length of score list and aft dict must be equal" + aft_data = pd.DataFrame() + aft_data.index.name = "Model" + aft_data.index = aft_dict.keys() + aft_data["AIC"] = [ + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan + for x in aft_dict.values() + ] + aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] + aft_data["BIC"] = [ + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan + for x in aft_dict.values() + ] + # aft_data["Train LL"] = [x["train_score"] for x in score_list] + # aft_data["Test LL"] = [x["test_score"] for x in score_list] + aft_data["Mean $S(t;\\theta)$"] = [ + x.predict_expectation(X_train).mean() for x in aft_dict.values() + ] + aft_data["Median $S(t;\\theta)$"] = [ + x.predict_median(X_train).median() for x in aft_dict.values() + ] + aft_data = aft_data.round(2) + aft_data.to_csv(FOLDER / "aft_comparison.csv") + logger.info(f"Saved AFT comparison to {FOLDER / 'aft_comparison.csv'}") + aft_data = aft_data.round(2) + aft_data.fillna("--", inplace=True) + aft_data.to_latex( + FOLDER / "aft_comparison.tex", + float_format="%.2f", + label=f"tab:{dataset}", + caption=f"Comparison of AFR Models on the {dataset.upper()} dataset.", + ) + + print(yaml.dump(aft_data.to_dict(), default_flow_style=False, indent=4)) + return aft_data + +def clean_data_for_aft( + data, + kwarg_list, + target="adv_failure_rate", +): + subset = data.copy() + assert ( + target in subset + ), f"Target {target} not in dataframe with columns {subset.columns}" + + cleaned = pd.DataFrame() + kwarg_list.append(target) + for kwarg in kwarg_list: + cleaned = pd.concat([cleaned, subset[kwarg]], axis=1) + cols = cleaned.columns + cleaned = pd.DataFrame(subset, columns=cols) + if "accuracy" in cleaned.columns: + cleaned = cleaned[cleaned["accuracy"] != 1e10] + cleaned = cleaned[cleaned["accuracy"] != -1e10] + if "adv_accuracy" in cleaned.columns: + cleaned = cleaned[cleaned["adv_accuracy"] != 1e10] + cleaned = cleaned[cleaned["adv_accuracy"] != -1e10] + cleaned.dropna(inplace=True, how="any", axis=0) + y = cleaned[target].copy() + return cleaned, y, data + +def split_data_for_aft( + data, + target, + duration_col, + kwarg_list, + test_size=0.2, + random_state=42, +): + cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) + X_train, X_test, y_train, y_test = train_test_split( + cleaned, + y, + test_size=test_size, + random_state=random_state, + ) + assert ( + duration_col in cleaned + ), f"Duration {duration_col} not in dataframe with columns {cleaned.columns}" + return X_train, X_test, y_train, y_test + + + +logger = logging.getLogger(__name__) + +if "__main__" == __name__: + afr_parser = argparse.ArgumentParser() + afr_parser.add_argument("--target", type=str, default="adv_failures") + afr_parser.add_argument("--duration_col", type=str, default="adv_fit_time") + afr_parser.add_argument("--dataset", type=str, default="mnist") + afr_parser.add_argument("--plots_folder", type=str, default="mnist/plots") + afr_parser.add_argument("--file", type=str, default="mnist/plots/data.csv") + afr_args = afr_parser.parse_args() + target = afr_args.target + duration_col = afr_args.duration_col + dataset = afr_args.dataset + + font = { + "family": "Times New Roman", + "weight": "bold", + "size": 22, + } + + matplotlib.rc("font", **font) + FOLDER = Path(afr_args.plots_folder) + data = pd.read_csv(afr_args.file, index_col=0) + data.columns = data.columns.str.strip() + + data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x) + data = data.applymap(lambda x: pd.to_numeric(x, errors="coerce")) + data.def_value.replace("", 0, inplace=True) + data.atk_value.replace("", 0, inplace=True) + data.dropna(axis=0, subset=["atk_value", "atk_param"], inplace=True) + data.dropna(axis=0, subset=["def_value", "def_param"], inplace=True) + adv_failures = (1 - data.loc[:, "adv_accuracy"]) * data.loc[ + :, + "attack.attack_size", + ] + ben_failures = (1 - data.loc[:, "accuracy"]) * data.loc[ + :, + "attack.attack_size", + ] + data['adv_failures'] = adv_failures + data['ben_failures'] = ben_failures + data = drop_frames_without_results(data) + data = calculate_failure_rate(data) + data = min_max_scaling(data) + + kwarg_list = [ + "accuracy", + "train_time", + "predict_time", + "atk_value", + "def_value", + "data.sample.random_state", + "adv_failure_rate", + "model_layers", + "adv_fit_time", + "model.trainer.nb_epoch", + "adv_failures", + "ben_failures", + ] + + X_train, X_test, y_train, y_test = split_data_for_aft( + data, + target, + duration_col, + kwarg_list, + test_size=0.2, + random_state=42, + ) + + + + + weibull_dict = { + "Intercept: rho_": "$\\rho$", + "Intercept: lambda_": "$\lambda$", # noqa W605 + "data.sample.random_state: lambda_": "Random State", + "def_value: lambda_": "Defence Strength", + "atk_value: lambda_": "Attack Strength", + "train_time: lambda_": "$t_{train}$", + "predict_time: lambda_": "$t_{predict}$", + "adv_accuracy: lambda_": "$\lambda_{adv.}$", # noqa W605 + "accuracy: lambda_": "$\lambda_{ben.}$", # noqa W605 + "adv_fit_time: lambda_": "$t_{attack}$", + "adv_log_loss: lambda_": "Adv. Log Loss", + "adv_failure_rate: lambda_": "$h_{adv.}(t,;\\theta)$", + "failure_rate: lambda_": "$h_{ben.}(t,;\\theta)$", + "model_layers: lambda_": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_": "Learning Rate", + "def_gen": "Defence", + } + + weibull_plot_dict = { + "file": "weibull_aft.pdf", + "title": "Weibull AFR Model", + "mtype": "weibull", + } + + + + weibull_partial_dict_layers = { + "file": "weibull_partial_effects.pdf", + "covariate_array": "model_layers", + "values_array": [18, 34, 50, 101, 152], + "title": "$S(t)$ for Weibull AFR", + "ylabel": "Expectation of $S(t)$", + "xlabel": "Time $T$ (seconds)", + "legend_kwargs": { + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, + } + + + + + weibull_afr, wft = plot_aft( + X_train, + event_col=target, + duration_col=duration_col, + **weibull_plot_dict, + replacement_dict=weibull_dict, + ) + weibull_layers = plot_partial_effects(aft=wft, **weibull_partial_dict_layers) + wft_scores = score_model(wft, X_train, X_test) + + # cox_replacement_dict = { + # "adv_failure_rate": "$h_{adv}(t,;\\theta)$", + # "def_value": "Defence Strength", + # "data.sample.random_state": "Random State", + # "train_time": "$t_{train}$", + # "model_layers": "No. of Layers", + # "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", + # "adv_accuracy": "$\lambda_{adv.}$", # noqa W605 + # "adv_fit_time": "$t_{attack}$", + # "adv_log_loss": "Adv. Log Loss", + # "predict_time": "$t_{predict}$", + # "accuracy": "$\lambda_{ben.}$", # noqa W605 + # "failure_rate": "$h_{ben.}(t,;\\theta)$", + # "atk_value": "Attack Strength", + # } + # cox_partial_dict = { + # "file": "cox_partial_effects.pdf", + # "covariate_array": "model_layers", + # "values_array": [18, 34, 50, 101, 152], + # "replacement_dict": cox_replacement_dict, + # "title": "$S(t)$ for Cox AFR", + # "ylabel": "Expectation of $S(t)$", + # "xlabel": "Time $T$ (seconds)", + # "legend_kwargs": { + # "title": "No. of Layers", + # "labels": ["18", "34", "50", "101", "152"], + # }, + # } + # cox_plot_dict = { + # "file": "cox_aft.pdf", + # "duration_col": duration_col, + # "title": "Cox AFR Model", + # "mtype": "cox", + # "replacement_dict": cox_replacement_dict, + # } + # cox_afr, cft = plot_aft( + # df=X_train, + # event_col=target, + # **cox_plot_dict, + # ) + # cox_scores = score_model(cft, X_train, X_test) + # cox_partial = plot_partial_effects( + # aft=cft, + # **cox_partial_dict, + # ) + + log_normal_dict = { + "Intercept: sigma_": "$\sigma$", # noqa W605 + "Intercept: mu_": "$\mu$", # noqa W605 + "def_value: mu_": "Defence Strength", + "atk_value: mu_": "Attack Strength", + "train_time: mu_": "$t_{train}$", + "predict_time: mu_": "$t_{predict}$", + "adv_fit_time: mu_": "$t_{attack}$", + "model_layers: mu_": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_": "Learning Rate", + "data.sample.random_state: mu_": "Random State", + "adv_log_loss: mu_": "Adv. Log Loss", + "adv_accuracy: mu_": "$\lambda_{adv.}$", # noqa W605 + "accuracy: mu_": "$\lambda_{ben.}$", # noqa W605 + "adv_failure_rate: mu_": "$h_{adv}(t,;\\theta)$", + "def_gen": "Defence", + "learning_rate: mu_": "Learning Rate", + } + + log_normal_graph, lnt = plot_aft( + X_train, + "log_normal_aft.pdf", + target, + duration_col, + "Log Normal AFR Model", + "log_normal", + replacement_dict=log_normal_dict, + ) + lnt_scores = score_model(lnt, X_train, X_test) + lnt_partial = plot_partial_effects( + file="log_normal_partial_effects.pdf", + aft=lnt, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], + replacement_dict=log_normal_dict, + title="$S(t)$ for Log-Normal AFR", + ylabel="Expectation of $S(t)$", + xlabel="Time $T$ (seconds)", + legend_kwargs={ + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, + ) + log_logistic_dict = { + "Intercept: beta_": "$\\beta$", + "Intercept: alpha_": "$\\alpha$", + "data.sample.random_state: alpha_": "Random State", + "def_value: alpha_": "Defence Strength", + "atk_value: alpha_": "Attack Strength", + "train_time: alpha_": "$t_{train}$", + "predict_time: alpha_": "$t_{predict}$", + "adv_accuracy: alpha_": "$\lambda_{adv.}$", # noqa W605 + "accuracy: alpha_": "$\lambda_{ben.}$", # noqa W605 + "adv_fit_time: alpha_": "$t_{attack}$", + "model_layers: alpha_": "No. of Layers", + "model.art.pipeline.initialize.kwargs.optimizer.lr:": "Learning Rate", + "adv_failure_rate: alpha_": "$h_{adv.}(t,\\theta)$", # noqa W605 + "alpha_": "", + } + + log_logistic_graph, llt = plot_aft( + X_train, + "log_logistic_aft.pdf", + target, + duration_col, + "Log Logistic AFR Model", + "log_logistic", + replacement_dict=log_logistic_dict, + ) + llt_scores = score_model(llt, X_train, X_test) + llt_partial = plot_partial_effects( + file="log_logistic_partial_effects.pdf", + aft=llt, + covariate_array="model_layers", + values_array=[18, 34, 50, 101, 152], + replacement_dict=log_logistic_dict, + title="$S(t)$ for Log-Logistic AFR", + ylabel="Expectation of $S(t)$", + xlabel="Time $T$ (seconds)", + legend_kwargs={ + "title": "No. of Layers", + "labels": ["18", "34", "50", "101", "152"], + }, + ) + aft_dict = { + "Weibull": wft, + "LogNormal": lnt, + "LogLogistic": llt, + # "Cox": cft, + } + score_list = [ + wft_scores, + lnt_scores, + llt_scores, + # cft_scores, + ] + aft_data = make_afr_table(score_list, aft_dict, dataset) diff --git a/deckard/layers/plots.py b/deckard/layers/plots.py index 888c8527..db5427f7 100644 --- a/deckard/layers/plots.py +++ b/deckard/layers/plots.py @@ -153,7 +153,7 @@ def calculate_failure_rate(data): / data.loc[:, "predict_time"] ) data.loc[:, "adv_failure_rate"] = ( - (1 - data.loc[:, "adv_success"]) + (1 - data.loc[:, "adv_accuracy"]) * data.loc[:, "attack.attack_size"] / data.loc[:, "adv_fit_time"] ) @@ -264,24 +264,25 @@ def min_max_scaling(data, **kwargs): data, subset=[ "accuracy", - "adv_success", + "adv_accuracy", "train_time", "adv_fit_time", "predict_time", + "adv_success", ], ) - sense_dict = { - "accuracy": "max", - "adv_accuracy": "max", - "adv_success": "min", - "model_layers": "diff", - # "atk_param": "diff", - # "def_param": "diff", - "atk_gen": "diff", - "def_gen": "diff", - "data.sample.random_state": "diff", - } - data = pareto_set(data, sense_dict) + # sense_dict = { + # "accuracy": "max", + # "adv_accuracy": "max", + # "adv_success": "min", + # "model_layers": "diff", + # "atk_param": "diff", + # "def_param": "diff", + # "atk_gen": "diff", + # "def_gen": "diff", + # "data.sample.random_state": "diff", + # } + # data = pareto_set(data, sense_dict) data = calculate_failure_rate(data) data = min_max_scaling(data) if "Unnamed: 0" in data.columns: diff --git a/examples/pytorch/mnist/attacks.sh b/examples/pytorch/mnist/attacks.sh index 8ec1f079..717f8dc4 100644 --- a/examples/pytorch/mnist/attacks.sh +++ b/examples/pytorch/mnist/attacks.sh @@ -3,20 +3,20 @@ # # This script is used to generate the attacks for the example. # Fast Gradient Method -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 stage=attack ++hydra.sweeper.study_name=fgm ++direction=maximize $@ +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.FastGradientMethod ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 stage=attack ++hydra.sweeper.study_name=fgm ++direction=maximize $@ -# Projected Gradient Descent -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 stage=attack ++hydra.sweeper.study_name=pgd ++direction=maximize $@ +# # Projected Gradient Descent +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ProjectedGradientDescent ++attack.init.eps=.001,.01,.1,.5,1 ++attack.init.norm=inf,1,2 ++attack.init.eps_step=.001,.003,.01 ++attack.init.batch_size=1024 ++attack.init.max_iter=10 stage=attack ++hydra.sweeper.study_name=pgd ++direction=maximize $@ -# DeepFool -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=1,3,5,10 stage=attack ++hydra.sweeper.study_name=deep ++direction=maximize $@ +# # DeepFool +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.DeepFool ++attack.init.max_iter=10 ++attack.init.batch_size=1024 ++attack.init.nb_grads=1,3,5,10 stage=attack ++hydra.sweeper.study_name=deep ++direction=maximize $@ -# HopSkipJump -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 stage=attack ++hydra.sweeper.study_name=hsj ++direction=maximize $@ +# # HopSkipJump +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.HopSkipJump ++attack.init.max_iter=1,3,5,10 ++attack.init.init_eval=10 ++attack.init.norm=inf,2 stage=attack ++hydra.sweeper.study_name=hsj ++direction=maximize $@ -# ##################################################### -# PixelAttack -bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=pixel ++direction=maximize $@ +# # ##################################################### +# # PixelAttack +# bash models.sh attack=default ++attack.init.name=art.attacks.evasion.PixelAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=pixel ++direction=maximize $@ # ThresholdAttack bash models.sh attack=default ++attack.init.name=art.attacks.evasion.ThresholdAttack ++attack.init.max_iter=10 ++attack.init.th=1,4,16,64,256 stage=attack ++hydra.sweeper.study_name=thresh ++direction=maximize $@ diff --git a/examples/pytorch/mnist/dvc.lock b/examples/pytorch/mnist/dvc.lock new file mode 100644 index 00000000..36e3cd85 --- /dev/null +++ b/examples/pytorch/mnist/dvc.lock @@ -0,0 +1,52 @@ +schema: '2.0' +stages: + compile@attack: + cmd: python -m deckard.layers.compile --report_folder mnist/reports/attack --results_file + mnist/reports/attack.csv + deps: + - path: mnist/reports/attack/ + md5: c1191d4aed87840a6035d44b5edfed67.dir + size: 63191255 + nfiles: 8010 + - path: mnist/reports/attack/ResNet101.db + md5: 600452804d96c8b8483c3f8da01130c4 + size: 462848 + - path: mnist/reports/attack/ResNet18.db + md5: 920b0ed178ec504c0d7990777862283f + size: 1363968 + - path: mnist/reports/attack/ResNet34.db + md5: 3f56dd2ea0783a56a2a9e3eaaad88c21 + size: 1945600 + - path: mnist/reports/attack/ResNet50.db + md5: d9ee221b942b56d9bb720e022e05bf4b + size: 462848 + outs: + - path: mnist/reports/attack.csv + md5: b6d03ebcc65d652ca2392c7510983fa5 + size: 9063722 + plot: + cmd: python -m deckard.layers.plots --path mnist/plots/ --file mnist/reports/attack.csv + -o data.csv + deps: + - path: mnist/reports/attack.csv + md5: b6d03ebcc65d652ca2392c7510983fa5 + size: 9063722 + - path: mnist/reports/attack/ResNet101.db + md5: 600452804d96c8b8483c3f8da01130c4 + size: 462848 + - path: mnist/reports/attack/ResNet152.db + md5: afb6f3d2616446226012b3ff1143086a + size: 462848 + - path: mnist/reports/attack/ResNet18.db + md5: 920b0ed178ec504c0d7990777862283f + size: 1363968 + - path: mnist/reports/attack/ResNet34.db + md5: 3f56dd2ea0783a56a2a9e3eaaad88c21 + size: 1945600 + - path: mnist/reports/attack/ResNet50.db + md5: d9ee221b942b56d9bb720e022e05bf4b + size: 462848 + outs: + - path: mnist/plots/data.csv + md5: 73aac3fbd8615cc932c5537c7a9b74b4 + size: 2353615 diff --git a/examples/pytorch/mnist/dvc.yaml b/examples/pytorch/mnist/dvc.yaml index 21d8c792..a66b36dc 100644 --- a/examples/pytorch/mnist/dvc.yaml +++ b/examples/pytorch/mnist/dvc.yaml @@ -97,7 +97,7 @@ stages: outs: - ${files.directory}/plots/data.csv afr: - cmd: python -m deckard.layers.afr --dataset ${files.directory} --file ${files.directory}/plots/data.csv + cmd: python -m deckard.layers.afr --dataset ${files.directory} --data_file ${files.directory}/plots/data.csv --target adv_accuracy --duration_col adv_fit_time --dataset mnist deps: - ${files.directory}/plots/data.csv plots: diff --git a/setup.py b/setup.py index c5556dbd..65317ddb 100644 --- a/setup.py +++ b/setup.py @@ -29,6 +29,8 @@ "hydra-rq-launcher", "sqlalchemy<=1.4.46", "dvc", + "paretoset", + "lifelines", ] test_requires = [ "pytest", From 7b4b128455b148e9ad06b6ad7eaecec53d59da95 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Wed, 29 Nov 2023 00:42:38 +0000 Subject: [PATCH 143/148] update files config to accept "None" folders --- deckard/base/files/files.py | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/deckard/base/files/files.py b/deckard/base/files/files.py index 9946cf34..ddbf01fd 100644 --- a/deckard/base/files/files.py +++ b/deckard/base/files/files.py @@ -54,21 +54,21 @@ def __init__( for need in needs: assert need is not None, f"Need to specify {need}" files.update(kwargs) - self.reports = str(Path(reports).as_posix()) - self.data_dir = str(Path(data_dir).as_posix()) - self.model_dir = str(Path(model_dir).as_posix()) - self.attack_dir = str(Path(attack_dir).as_posix()) - self.data_type = data_type - self.model_type = model_type - self.attack_type = attack_type + self.reports = str(Path(reports).as_posix()) if reports else None + self.data_dir = str(Path(data_dir).as_posix()) if data_dir else None + self.model_dir = str(Path(model_dir).as_posix()) if model_dir else None + self.attack_dir = str(Path(attack_dir).as_posix()) if attack_dir else None + self.data_type = data_type if data_type else None + self.model_type = model_type if model_type else None + self.attack_type = attack_type if attack_type else None self.directory = ( Path(directory).as_posix() if Path(directory).is_absolute() else Path(Path(), directory).as_posix() - ) - self.name = name - self.stage = stage - self.files = files + ) if directory else None + self.name = name if name else None + self.stage = stage if stage else None + self.files = files if files else {} logger.debug(f"FileConfig init: {self.files}") def __call__(self): @@ -95,10 +95,10 @@ def _set_filenames(self, **kwargs): data_type = self.data_type model_type = self.model_type attack_type = self.attack_type - reports = str(Path(directory, reports).as_posix()) - data_dir = str(Path(directory, data_dir).as_posix()) - model_dir = str(Path(directory, model_dir).as_posix()) - attack_dir = str(Path(directory, attack_dir).as_posix()) + reports = str(Path(directory, reports).as_posix()) if reports else None + data_dir = str(Path(directory, data_dir).as_posix()) if data_dir else None + model_dir = str(Path(directory, model_dir).as_posix()) if model_dir else None + attack_dir = str(Path(directory, attack_dir).as_posix()) if attack_dir else None if name is None and stage is None: path = Path(reports) elif name is not None and stage is None: @@ -109,17 +109,17 @@ def _set_filenames(self, **kwargs): path = Path(reports, stage, name) for kwarg in files: name = files.get(kwarg) - if "data_file" == kwarg: + if "data_file" == kwarg and data_dir is not None: new_path = Path(data_dir, name) if new_path.suffix != data_type: new_path = Path(data_dir, Path(name).stem + data_type) new_files[kwarg] = str(new_path.as_posix()) - elif "model_file" == kwarg: + elif "model_file" == kwarg and model_dir is not None: new_path = Path(model_dir, name) if new_path.suffix != model_type: new_path = Path(model_dir, Path(name).stem + model_type) new_files[kwarg] = str(new_path.as_posix()) - elif "attack_file" == kwarg: + elif "attack_file" == kwarg and attack_dir is not None: new_path = Path(attack_dir, name) if new_path.suffix != attack_type: new_path = Path(attack_dir, Path(name).stem + attack_type) From e1c9ac2bdc103fe74449d0a5fc8faf9bc7726cfe Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Wed, 29 Nov 2023 00:43:12 +0000 Subject: [PATCH 144/148] update torch initializer to accept device --- deckard/base/model/art_pipeline.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deckard/base/model/art_pipeline.py b/deckard/base/model/art_pipeline.py index a7ba5590..65e2c866 100644 --- a/deckard/base/model/art_pipeline.py +++ b/deckard/base/model/art_pipeline.py @@ -64,9 +64,9 @@ def __call__(self): tuple(torch_dict.values()), ): import torch - device = "gpu" if torch.cuda.is_available() else "cpu" + device_type = "gpu" if torch.cuda.is_available() else "cpu" model = TorchInitializer( - data=data, model=model, library=library, device, **kwargs + data=data, model=model, library=library, device_type=device_type, **kwargs )() elif "keras" in str(library) and not isinstance( model, From e48c77257fad85ace38f63bba8b87b7de6f68cd3 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Wed, 29 Nov 2023 00:43:48 +0000 Subject: [PATCH 145/148] update confs --- examples/power/conf/files/cifar.yaml | 6 +++--- examples/power/conf/files/cifar100.yaml | 6 +++--- examples/power/conf/files/mnist.yaml | 6 +++--- examples/power/conf/model/art/initialize/default.yaml | 3 +-- examples/power/dvc.lock | 4 ++-- 5 files changed, 12 insertions(+), 13 deletions(-) diff --git a/examples/power/conf/files/cifar.yaml b/examples/power/conf/files/cifar.yaml index 2a6598ec..2c7636a6 100644 --- a/examples/power/conf/files/cifar.yaml +++ b/examples/power/conf/files/cifar.yaml @@ -1,16 +1,16 @@ _target_: deckard.base.files.FileConfig reports: reports data_dir: data -model_dir: models +model_dir: null attack_dir: attacks directory: /result/cifar/ score_dict_file: score_dict.json data_file : data -model_file : model +model_file : null attack_file : attack attack_type : .pkl data_type : .pkl -model_type : .pt +model_type : null params_file : params.yaml # train_labels_file : train_labels.json # test_labels_file : test_labels.json diff --git a/examples/power/conf/files/cifar100.yaml b/examples/power/conf/files/cifar100.yaml index e9a11634..5674932d 100644 --- a/examples/power/conf/files/cifar100.yaml +++ b/examples/power/conf/files/cifar100.yaml @@ -1,16 +1,16 @@ _target_: deckard.base.files.FileConfig reports: reports data_dir: data -model_dir: models +model_dir: null attack_dir: attacks directory: /result/cifar100/ score_dict_file: score_dict.json data_file : data -model_file : model +model_file : null attack_file : attack attack_type : .pkl data_type : .pkl -model_type : .pt +model_type : null params_file : params.yaml # train_labels_file : train_labels.json # test_labels_file : test_labels.json diff --git a/examples/power/conf/files/mnist.yaml b/examples/power/conf/files/mnist.yaml index 09d39bb7..d45b1926 100644 --- a/examples/power/conf/files/mnist.yaml +++ b/examples/power/conf/files/mnist.yaml @@ -1,16 +1,16 @@ _target_: deckard.base.files.FileConfig reports: reports data_dir: data -model_dir: models +model_dir: null attack_dir: attacks directory: /result/mnist/ score_dict_file: score_dict.json data_file : data -model_file : model +model_file : null attack_file : attack attack_type : .pkl data_type : .pkl -model_type : .pt +model_type : null params_file : params.yaml # train_labels_file : train_labels.json # test_labels_file : test_labels.json diff --git a/examples/power/conf/model/art/initialize/default.yaml b/examples/power/conf/model/art/initialize/default.yaml index becd0f9a..6cd286df 100644 --- a/examples/power/conf/model/art/initialize/default.yaml +++ b/examples/power/conf/model/art/initialize/default.yaml @@ -4,5 +4,4 @@ optimizer: name : torch.optim.SGD lr : 0.01 momentum : 0.9 -clip_values : [0, 255] -device_type : ${oc.env:DECKARD_DEVICE_TYPE,cpu} +clip_values : [0, 255] \ No newline at end of file diff --git a/examples/power/dvc.lock b/examples/power/dvc.lock index 49bf8b09..9e991ad0 100644 --- a/examples/power/dvc.lock +++ b/examples/power/dvc.lock @@ -32,7 +32,7 @@ stages: data_file: data data_type: .pkl directory: mnist - model_dir: models + model_dir: null model_file: model model_type: .pt name: default @@ -292,7 +292,7 @@ stages: data_file: data data_type: .pkl directory: mnist - model_dir: models + model_dir: null model_file: model model_type: .pt name: default From 41e994661f52f5b1bdefd13061b74864562f3038 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Wed, 29 Nov 2023 00:45:18 +0000 Subject: [PATCH 146/148] linting --- deckard/base/files/files.py | 12 ++++--- deckard/base/model/art_pipeline.py | 9 +++-- deckard/layers/afr.py | 16 +++++++-- deckard/layers/old_afr.py | 28 ++++++--------- .../conf/model/art/initialize/default.yaml | 2 +- examples/pytorch/cifar100/torch_example.py | 35 ++++++++++++++++--- 6 files changed, 70 insertions(+), 32 deletions(-) diff --git a/deckard/base/files/files.py b/deckard/base/files/files.py index ddbf01fd..439782ab 100644 --- a/deckard/base/files/files.py +++ b/deckard/base/files/files.py @@ -62,10 +62,14 @@ def __init__( self.model_type = model_type if model_type else None self.attack_type = attack_type if attack_type else None self.directory = ( - Path(directory).as_posix() - if Path(directory).is_absolute() - else Path(Path(), directory).as_posix() - ) if directory else None + ( + Path(directory).as_posix() + if Path(directory).is_absolute() + else Path(Path(), directory).as_posix() + ) + if directory + else None + ) self.name = name if name else None self.stage = stage if stage else None self.files = files if files else {} diff --git a/deckard/base/model/art_pipeline.py b/deckard/base/model/art_pipeline.py index 65e2c866..6b540f2d 100644 --- a/deckard/base/model/art_pipeline.py +++ b/deckard/base/model/art_pipeline.py @@ -62,11 +62,16 @@ def __call__(self): if "torch" in str(library) and not isinstance( model, tuple(torch_dict.values()), - ): + ): import torch + device_type = "gpu" if torch.cuda.is_available() else "cpu" model = TorchInitializer( - data=data, model=model, library=library, device_type=device_type, **kwargs + data=data, + model=model, + library=library, + device_type=device_type, + **kwargs, )() elif "keras" in str(library) and not isinstance( model, diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index 08ba9c7b..20acb4f8 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -168,7 +168,7 @@ def score_model(aft, train, test): def make_afr_table(score_list, aft_dict, dataset): assert len(score_list) == len( - aft_dict + aft_dict, ), "Length of score list and aft dict must be equal" aft_data = pd.DataFrame() aft_data.index.name = "Model" @@ -235,7 +235,12 @@ def clean_data_for_aft( return cleaned, y, data def split_data_for_aft( - data, target, duration_col, kwarg_list, test_size=0.2, random_state=42 + data, + target, + duration_col, + kwarg_list, + test_size=0.2, + random_state=42, ): cleaned, y, data = clean_data_for_aft(data, kwarg_list, target=target) X_train, X_test, y_train, y_test = train_test_split( @@ -266,7 +271,12 @@ def split_data_for_aft( ] X_train, X_test, y_train, y_test = split_data_for_aft( - data, target, duration_col, kwarg_list, test_size=0.2, random_state=42 + data, + target, + duration_col, + kwarg_list, + test_size=0.2, + random_state=42, ) weibull_dict = { diff --git a/deckard/layers/old_afr.py b/deckard/layers/old_afr.py index 61451422..09072cb7 100644 --- a/deckard/layers/old_afr.py +++ b/deckard/layers/old_afr.py @@ -67,6 +67,7 @@ def plot_aft( plt.gcf().clear() return ax, aft + def plot_partial_effects( aft, covariate_array, @@ -100,6 +101,7 @@ def plot_partial_effects( plt.gcf().clear() return pareto + def score_model(aft, train, test): train_score = aft.score(train) test_score = aft.score(test) @@ -107,6 +109,7 @@ def score_model(aft, train, test): plt.show() return scores + def make_afr_table(score_list, aft_dict, dataset): assert len(score_list) == len( aft_dict, @@ -115,13 +118,11 @@ def make_afr_table(score_list, aft_dict, dataset): aft_data.index.name = "Model" aft_data.index = aft_dict.keys() aft_data["AIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan - for x in aft_dict.values() + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() ] aft_data["Concordance"] = [x.concordance_index_ for x in aft_dict.values()] aft_data["BIC"] = [ - x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan - for x in aft_dict.values() + x.AIC_ if not isinstance(x, CoxPHFitter) else np.nan for x in aft_dict.values() ] # aft_data["Train LL"] = [x["train_score"] for x in score_list] # aft_data["Test LL"] = [x["test_score"] for x in score_list] @@ -146,6 +147,7 @@ def make_afr_table(score_list, aft_dict, dataset): print(yaml.dump(aft_data.to_dict(), default_flow_style=False, indent=4)) return aft_data + def clean_data_for_aft( data, kwarg_list, @@ -172,6 +174,7 @@ def clean_data_for_aft( y = cleaned[target].copy() return cleaned, y, data + def split_data_for_aft( data, target, @@ -193,7 +196,6 @@ def split_data_for_aft( return X_train, X_test, y_train, y_test - logger = logging.getLogger(__name__) if "__main__" == __name__: @@ -218,7 +220,7 @@ def split_data_for_aft( FOLDER = Path(afr_args.plots_folder) data = pd.read_csv(afr_args.file, index_col=0) data.columns = data.columns.str.strip() - + data = data.applymap(lambda x: x.strip() if isinstance(x, str) else x) data = data.applymap(lambda x: pd.to_numeric(x, errors="coerce")) data.def_value.replace("", 0, inplace=True) @@ -233,12 +235,12 @@ def split_data_for_aft( :, "attack.attack_size", ] - data['adv_failures'] = adv_failures - data['ben_failures'] = ben_failures + data["adv_failures"] = adv_failures + data["ben_failures"] = ben_failures data = drop_frames_without_results(data) data = calculate_failure_rate(data) data = min_max_scaling(data) - + kwarg_list = [ "accuracy", "train_time", @@ -263,9 +265,6 @@ def split_data_for_aft( random_state=42, ) - - - weibull_dict = { "Intercept: rho_": "$\\rho$", "Intercept: lambda_": "$\lambda$", # noqa W605 @@ -290,8 +289,6 @@ def split_data_for_aft( "title": "Weibull AFR Model", "mtype": "weibull", } - - weibull_partial_dict_layers = { "file": "weibull_partial_effects.pdf", @@ -306,9 +303,6 @@ def split_data_for_aft( }, } - - - weibull_afr, wft = plot_aft( X_train, event_col=target, diff --git a/examples/power/conf/model/art/initialize/default.yaml b/examples/power/conf/model/art/initialize/default.yaml index 6cd286df..b694473b 100644 --- a/examples/power/conf/model/art/initialize/default.yaml +++ b/examples/power/conf/model/art/initialize/default.yaml @@ -4,4 +4,4 @@ optimizer: name : torch.optim.SGD lr : 0.01 momentum : 0.9 -clip_values : [0, 255] \ No newline at end of file +clip_values : [0, 255] diff --git a/examples/pytorch/cifar100/torch_example.py b/examples/pytorch/cifar100/torch_example.py index d1f8e038..2007d9d0 100644 --- a/examples/pytorch/cifar100/torch_example.py +++ b/examples/pytorch/cifar100/torch_example.py @@ -12,7 +12,12 @@ def ResNet18(num_channels=1, num_classes=10): model = models.resnet18() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -21,7 +26,12 @@ def ResNet18(num_channels=1, num_classes=10): def ResNet34(num_channels=1, num_classes=10): model = models.resnet34() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(512, num_classes) return model @@ -30,7 +40,12 @@ def ResNet34(num_channels=1, num_classes=10): def ResNet50(num_channels=1, num_classes=10): model = models.resnet50() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -39,7 +54,12 @@ def ResNet50(num_channels=1, num_classes=10): def ResNet101(num_channels=1, num_classes=10): model = models.resnet101() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model @@ -48,7 +68,12 @@ def ResNet101(num_channels=1, num_classes=10): def ResNet152(num_channels=1, num_classes=10): model = models.resnet152() model.conv1 = nn.Conv2d( - num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False, + num_channels, + 64, + kernel_size=7, + stride=2, + padding=3, + bias=False, ) model.fc = nn.Linear(2048, num_classes) return model From 5f9b14cae6447b449fb228162257b9e25d94f920 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Wed, 29 Nov 2023 00:45:26 +0000 Subject: [PATCH 147/148] linting --- deckard/layers/afr.py | 1 - 1 file changed, 1 deletion(-) diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index 20acb4f8..7d947a60 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -21,7 +21,6 @@ logger = logging.getLogger(__name__) if "__main__" == __name__: - afr_parser = argparse.ArgumentParser() afr_parser.add_argument("--target", type=str, default="adv_failures") afr_parser.add_argument("--duration_col", type=str, default="adv_fit_time") From 317de35d975a34e5bdf356abb0e70b795431ed51 Mon Sep 17 00:00:00 2001 From: Charlie Meyers Date: Wed, 29 Nov 2023 00:49:34 +0000 Subject: [PATCH 148/148] linting --- deckard/layers/afr.py | 37 ++++++++++++++++++------------------- 1 file changed, 18 insertions(+), 19 deletions(-) diff --git a/deckard/layers/afr.py b/deckard/layers/afr.py index 7d947a60..67e567e6 100644 --- a/deckard/layers/afr.py +++ b/deckard/layers/afr.py @@ -13,7 +13,6 @@ ) from .plots import calculate_failure_rate, drop_frames_without_results, min_max_scaling import matplotlib -from pathlib import Path import logging import yaml import argparse @@ -278,16 +277,16 @@ def split_data_for_aft( random_state=42, ) - weibull_dict = { + weibull_dict = { # noqa w605 "Intercept: rho_": "$\\rho$", - "Intercept: lambda_": "$\lambda$", - "data.sample.random_state: lambda_": "Random State", + "Intercept: lambda_": "$\lambda$", # noqa w605 + "data.sample.random_state: lambda_": "Random State", # noqa w605 "def_value: lambda_": "Defence Strength", "atk_value: lambda_": "Attack Strength", "train_time: lambda_": "$t_{train}$", "predict_time: lambda_": "$t_{predict}$", - "adv_accuracy: lambda_": "$\lambda_{adv.}$", - "accuracy: lambda_": "$\lambda_{ben.}$", + "adv_accuracy: lambda_": "$\lambda_{adv.}$", # noqa w605 + "accuracy: lambda_": "$\lambda_{ben.}$", # noqa w605 "adv_fit_time: lambda_": "$t_{attack}$", "adv_log_loss: lambda_": "Adv. Log Loss", "adv_failure_rate: lambda_": "$h_{adv.}(t,;\\theta)$", @@ -295,7 +294,7 @@ def split_data_for_aft( "model_layers: lambda_": "No. of Layers", "model.art.pipeline.initialize.kwargs.optimizer.lr: lambda_": "Learning Rate", "def_gen": "Defence", - } + } # noqa w605 weibull_plot_dict = { "file": "weibull_aft.pdf", @@ -337,14 +336,14 @@ def split_data_for_aft( "train_time": "$t_{train}$", "model_layers": "No. of Layers", "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate", - "adv_accuracy": "$\lambda_{adv.}$", + "adv_accuracy": "$\lambda_{adv.}$", # noqa w605 "adv_fit_time": "$t_{attack}$", "adv_log_loss": "Adv. Log Loss", "predict_time": "$t_{predict}$", - "accuracy": "$\lambda_{ben.}$", + "accuracy": "$\lambda_{ben.}$", # noqa w605 "failure_rate": "$h_{ben.}(t,;\\theta)$", "atk_value": "Attack Strength", - } + } # noqa w605 cox_partial_dict = { "file": "cox_partial_effects.pdf", "covariate_array": "model_layers", @@ -377,8 +376,8 @@ def split_data_for_aft( ) log_normal_dict = { - "Intercept: sigma_": "$\sigma$", - "Intercept: mu_": "$\mu$", + "Intercept: sigma_": "$\sigma$", # noqa w605 + "Intercept: mu_": "$\mu$", # noqa w605 "def_value: mu_": "Defence Strength", "atk_value: mu_": "Attack Strength", "train_time: mu_": "$t_{train}$", @@ -388,12 +387,12 @@ def split_data_for_aft( "model.art.pipeline.initialize.kwargs.optimizer.lr: mu_": "Learning Rate", "data.sample.random_state: mu_": "Random State", "adv_log_loss: mu_": "Adv. Log Loss", - "adv_accuracy: mu_": "$\lambda_{adv.}$", - "accuracy: mu_": "$\lambda_{ben.}$", + "adv_accuracy: mu_": "$\lambda_{adv.}$", # noqa w605 + "accuracy: mu_": "$\lambda_{ben.}$", # noqa w605 "adv_failure_rate: mu_": "$h_{adv}(t,;\\theta)$", "def_gen": "Defence", "learning_rate: mu_": "Learning Rate", - } + } # noqa w605 log_normal_graph, lnt = plot_aft( X_train, @@ -419,16 +418,16 @@ def split_data_for_aft( "labels": ["18", "34", "50", "101", "152"], }, ) - log_logistic_dict = { - "Intercept: beta_": "$\\beta$", + log_logistic_dict = { # noqa w605 + "Intercept: beta_": "$\\beta$", # noqa w605 "Intercept: alpha_": "$\\alpha$", "data.sample.random_state: alpha_": "Random State", "def_value: alpha_": "Defence Strength", "atk_value: alpha_": "Attack Strength", "train_time: alpha_": "$t_{train}$", "predict_time: alpha_": "$t_{predict}$", - "adv_accuracy: alpha_": "$\lambda_{adv.}$", - "accuracy: alpha_": "$\lambda_{ben.}$", + "adv_accuracy: alpha_": "$\lambda_{adv.}$", # noqa w605 + "accuracy: alpha_": "$\lambda_{ben.}$", # noqa w605 "adv_fit_time: alpha_": "$t_{attack}$", "model_layers: alpha_": "No. of Layers", "model.art.pipeline.initialize.kwargs.optimizer.lr": "Learning Rate",